data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib as mplstyle\n",
    "import matplotlib.pyplot as plt\n",
    "import locale\n",
    "from locale import atof\n",
    "from dateutil import parser\n",
    "from datetime import datetime\n",
    "\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Imported data folder\n",
    "df_incomeLimits = pd.read_csv('school_choice_data/imported_data/income_limits.csv')\n",
    "df_studentByGrade = pd.read_csv('school_choice_data/imported_data/student_by_grade.csv')\n",
    "df_studentParticipation = pd.read_csv('school_choice_data/imported_data/student_participation_num.csv')\n",
    "df_schoolParticipation = pd.read_csv('school_choice_data/imported_data/school_participation.csv')\n",
    "df_enrollment = pd.read_csv('school_choice_data/imported_data/enrollment.csv')\n",
    "df_previouslyPublicSchool = pd.read_csv('school_choice_data/imported_data/previous_public_school.csv')\n",
    "df_studentPathway = pd.read_csv('school_choice_data/imported_data/student_by_pathway.csv')\n",
    "df_studentEthnicity = pd.read_csv('school_choice_data/imported_data/student_by_ethnicity.csv')\n",
    "df_studentGender = pd.read_csv('school_choice_data/imported_data/student_by_gender.csv')\n",
    "df_studentGeography = pd.read_csv('school_choice_data/imported_data/student_by_geography.csv')\n",
    "df_schoolAwardCount = pd.read_csv('school_choice_data/imported_data/awards_by_school_16_clean.csv')\n",
    "df_schoolAwardAmount = pd.read_csv('school_choice_data/imported_data/amount_by_school_16_clean.csv')\n",
    "df_vouchersByOgSchoolDistrict = pd.read_csv('school_choice_data/imported_data/voucher-students-by-original-school-district.csv')\n",
    "\n",
    "# Gov data folder\n",
    "df_schoolEnrollment = pd.read_csv('school_choice_data/gov_data/EnrollmentGradeSchls.csv')\n",
    "df_schoolSpecEdEnrollment = pd.read_csv('school_choice_data/gov_data/EnrollmentEnglishLearnerSpecEdSchls.csv')\n",
    "df_publicSpecEd = pd.read_csv('school_choice_data/gov_data/PublicEnrollmentEnglishLearnerSpecEd.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Functions\n",
    "\n",
    "# normalize numbers\n",
    "def normalizeNums(num):\n",
    "    if (math.isnan(num)):\n",
    "        return None\n",
    "    else:\n",
    "        num = int(math.floor(num))\n",
    "        return num\n",
    "    \n",
    "# multiply by 100 to convert from percent\n",
    "def convertPercent(num):\n",
    "    return num * 100\n",
    "\n",
    "# normalize percent from x%\n",
    "def normalizePerc(num):\n",
    "    return float(num.strip('%'))\n",
    "    \n",
    "\n",
    "# get average award\n",
    "def averageAward(awardCount, awardAmount):\n",
    "    if math.isnan(awardAmount) or math.isnan(awardCount):\n",
    "        return None\n",
    "    return round((awardAmount/awardCount),2)\n",
    "\n",
    "# Convert NaN to zeros\n",
    "# the zeros do not always indicate that no students \n",
    "# were in the program. schools with less than ten \n",
    "# students do not have to report \n",
    "def convertInt(num):\n",
    "    if math.isnan(num):\n",
    "        return 0\n",
    "    else:\n",
    "        return int(num)\n",
    "    \n",
    "# calculate percent\n",
    "def calcPerc(num, denom):\n",
    "    if num != 0:\n",
    "        percent = (float(num/denom))*100\n",
    "        return round(percent,2)\n",
    "    else:\n",
    "        return None\n",
    "    \n",
    "# calculate percent change\n",
    "def percentChange(oldVal, newVal):\n",
    "    val1 = float(oldVal)\n",
    "    val2 = float(newVal)\n",
    "    change = (val2-val1)/val1\n",
    "    return float(change)\n",
    "\n",
    "# fill in missing values\n",
    "def fillInfo(choiceCell, backupCell):\n",
    "    if pd.isnull(choiceCell):\n",
    "        return backupCell\n",
    "    else:\n",
    "        return choiceCell\n",
    "    \n",
    "# replace with None\n",
    "def fillNone(num):\n",
    "    if num == 1:\n",
    "        return None\n",
    "    else:\n",
    "        return num \n",
    "    \n",
    "def convertToStr(item):\n",
    "    if item == 'nan':\n",
    "        return None\n",
    "    else:\n",
    "        strID = item.split('.', 1)[0]\n",
    "        return strID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Drop total row in dataframe\n",
    "df_studenByGradeNoTotal = df_studentByGrade.drop(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 4500)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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VN998o6lTp17yyMyWWLRoUb3H169fr+TkZFksFknSM8880+pY3+dwOPT666/r\n888/V1hYmO69915dd911Hjn33r17FRISoujoaEnfzWX9+vU6fvy4IiIi9MADD+j+++/3SCxvqK2t\n1fLly3Xw4EFNmDBBKSkpevXVV7VixQrV1tbqjjvu0NKlS9W5c+dWxfnggw+UmJiovn37KjAwUHa7\nXcnJyfr222/1v//7vxo0aJBsNpu6devmoZkB/oU8Uz/yjO8jz6COXxYkp0+f1muvvaYHH3xQknTP\nPfeourra1d6pUyetX79ewcHBbT6Wjz76SD/96U91+vTpVp3nyJEjuuuuu3Ty5EnV1tbqhhtu0O9+\n9zvXVb2ysjINGjSo1XHeffddTZ8+XUFBQTp37pxeffVV/eIXv9CQIUNUW1urPXv2aNOmTbrlllta\nFefqq6/Wv/3bv6lHjx5ux/fs2aObbrpJXbt2lclk0pYtW1oVZ9CgQdq7d6+uueYa/eMf/9CECRNU\nW1urQYMGqbi4WOfOndPOnTs1YMCAVsWRpBEjRig7O1ujR4/WunXr9MQTT2j27NkaMGCAiouLtXbt\nWj366KN64IEHWhVn6tSpmjx5su666y516dKl1eO+nP/5n//R+vXrFR8fr127dunee+/V+vXrNXfu\nXHXo0EG5ubn6+c9/rqVLl7Yqzu23365bbrlFS5YskST94Q9/0Lp167Rz5045HA7dcccdGjlypLKz\nsz0xLVVVVclms+nAgQOuR72GhoZq+PDhmjJlisxms0fiNKasrEwvvfSSR365q62t1dGjR2WxWNSr\nVy+3tpqaGm3evFn33ntvq+Pg/5BnWo4803LkmZYhz/h+nvHLgmT58uX69NNPlZubK0m67rrrlJCQ\noO7du0v67sNo4sSJrf4LLklvvPFGg+11V65a+wE+bdo0derUSS+88ILOnj2rJUuWyG63a8uWLerX\nr5/HEsVtt92m0aNHa+nSpdq0aZMeeughzZw5U48++qgk6YknntCHH36ozZs3tyrO8uXL9fLLL2vN\nmjX6j//4D9fxnj17avfu3Ro0aFCrzl/n6quv1rFjxxQSEqKZM2eqtLRUf/jDH2Q2m1VTU6MZM2Yo\nICBAL7/8cqtjhYeHy263KzIyUqNGjdKcOXP0s5/9zNX+1ltv6amnntL777/fqjhXX321TCaTunfv\nrqlTp2rGjBmKiYlp7fAvccMNNyg7O1u33367PvnkE40cOVLPP/+87rnnHknSli1b9Ktf/UoffPBB\nq+L06tVTAYaPAAAgAElEQVRL+/btU58+fSR996EXFhamQ4cOKTQ0VIWFhUpLS9ORI0daOyV98skn\nSkpKUmVlpUaOHOl6H0V5ebn27dunoKAg5eXleezvX0M89Uvk8ePHdffdd+uTTz6RyWRSfHy81qxZ\n47r666nPBrgjz7QceablyDMtQ57x/TzjU3tIPGXLli164okn3I5lZma6/iK+8847evrppz2SKNLS\n0lxXWepTW1vb6hiSdPDgQW3ZskVms1lms1kbNmzQww8/rEmTJmnLli2uJNhan3zyiZ5//nlJUlJS\nkmbPnq0777zT1X733Xfrtddea3WcX/7ylxo1apRSU1OVlJSkpUuXqkOHtn3o28GDB/Wb3/zGdWUi\nICBACxcu1IwZMzxy/qCgIJ0+fVqRkZH66quvNGTIELf2G264QcePH/dIrD//+c9699139eqrr2rd\nunX68Y9/rBkzZmjKlCnq2rWrR2KUlpYqNjZW0ndXADt27Og2pxtuuEGlpaWtjtOzZ0/961//cv37\nLC0t1YULF1xL53379tXXX3/d6jiSlJGRoeHDh8tqtSogIMCtraamRmlpacrIyNDWrVtbHWvPnj0N\ntn/22WetjiF998ubxWLR/v37VVFRoaVLl2rixInKz89Xz549PRIDlyLPtBx5puXIMy1Dnmkdb+QZ\nvyxISkpK1LdvX9fXsbGxbvcfxsTE6PPPP/dIrF69emnZsmVuH6bfV1RU1OplZ0k6f/78Jcno6aef\nltPp1KRJk7Ru3bpWx6hT94HdoUMHBQQEuC13BwUFqaKiwiNxfvKTn+jPf/6zFixYoHHjxmn9+vUe\nOe8P1f3czp8/X+8bmk+ePOmROLfeeqvWrl2r3NxcjRo1Sm+99ZbbB2teXp769evnkVi9evVSRkaG\nMjIyVFBQoJdfflkPPfSQHnnkEU2ZMkUzZszQjTfe2KoYYWFhOnLkiCIiIlRcXKyLFy/q6NGjGjx4\nsKTvfqnwxAdRQkKCfvnLX+rxxx9Xly5d9Oyzz+rmm29WYGCgJKm4uPiS5eGW+utf/6rCwsJLkoT0\n3S8OGRkZGjdunEdiTZo0SSaTSU7n5RehL/cLZnPs2rVLb7zxhmuD7tatW/XAAw8oISHBIwkP9SPP\ntA55pmXIMy1Dnmkdb+QZvyxIampqdObMGdcmsh07dri1V1ZWeizWDTfcoKKiossmisb+ojRV//79\n9cEHH1yyxJeVlaXa2lpNnz691TEkKSIiQp999pnrKsK7777rthnvyy+/9OgLxHr06KGXXnpJL7/8\nsuu+W09LSEhQx44dVVFRoeLiYrdl5+PHj3vsHu/HH39cEyZMUHx8vIYOHarc3Fzt3bvXdW/vwYMH\nPXLV74fGjh2rsWPH6uTJk3rttdf0yiuv6OWXX271Eu3dd9+tX/ziF7r99tu1a9cu/fKXv9TSpUtV\nVlamDh06KCcn57J/75tj6dKlKi0t1X/+53/q4sWLiouLc90GI333C8tjjz3W6jiSZLFY9Omnn152\nqfyzzz5zLUG3VnBwsJ5++mmNHz++3vZDhw4pMTGx1XHOnj3rduX6qquu0osvvqj7779fCQkJbfYL\nWHtHnmk58kzLkWdahjzTOt7IM35ZkPTp00cffvihaxnwh/72t78pKirKI7EefPBBVVVVXba9b9++\nrd4wJ31XBW/atKneDUPZ2dm6ePGiXnzxxVbHSUlJ0fnz511f//Ce0XfffdftXlxPmTFjhm6++WbZ\n7Xb17t3bY+f94UauoKAgt6+3b9+uESNGeCRWWFiY3nvvPa1cuVLvvPOOnE6n/vrXv+r48eMaPny4\nnnrqKd10000eiVWfnj17av78+Zo/f7527drV6vM9/PDDrqeRzJw5UwsWLNDgwYP12GOP6dy5c7r9\n9tv1yCOPtDpOUFCQXnrpJdXU1OjChQuX/D/yxNN26tx3331KS0tTcXGxxowZ43Zvb2FhoXJycpSW\nluaRWDfccIP+8Y9/6Jprrqm33WKxeOSXyD59+ujjjz92uyrasWNH/fa3v9XPf/5zNrO3EfJMy5Fn\nWo480zLkmdbxRp7xy03tTz/9tF5//XUVFBRccpXlX//6l2699Vb97Gc/88hfcsDbfvSjH+nPf/7z\nZT+A0LCcnBw9//zzKi0tdS1lO51OhYWFac6cOZo/f75H4mzZskXnzp3T1KlT6213OBx655133Dak\ntsRjjz2mjz76SHl5eZe0XbhwQffdd5+2bdvmsfuj8R3yDPwZeaZ1yDPN55cFSWVlpW699VYdP35c\nU6dOdVV0xcXFevPNN3Xddddp586dl1TIANqPf/zjH26PY6y7feRKc+HCBZ07d+6yG44vXryoL7/8\nkhf/eRh5BkBjyDNN55cFifRdVfjkk08qLy9PZ86ckfTdfaSTJ0/Wr371K128eNErz4cHcOXw5Avm\nfCWWN+fU3pBnADQXeaZ+flmQPPXUU65lcqfT6Xq6Rc+ePWUymXT69GndcccdjT4uDUD74qlntvtS\nLG/OqT0hzwBoCfJM/fxyU/uaNWsUEhKi1NRUmUwmt0fwORwO3XnnnW3+LHIAvqcpL5i70mJ5c074\nP+QZAPUhz7SMX66QbN++XTNmzNCaNWuUnJzsOu5wOJSYmKhvv/1WW7duZbMW0M5cffXVjb5grqam\nxiNXk7wVy5tzwv8hzwCoD3mmZfxyheT222/XypUrNW/ePF199dUaN26czpw5o6SkJNXU1OiPf/wj\nSQJoh7z1gjlvxvLmnPB/yDMA6kOeaRm/XU+eNm2afvWrX+m+++7Tn/70J02ePFmVlZUefc09gCtL\n3QvmLsdTL5jzZixvzgnuyDMAfog80zIdlyxZ8nirzuDDfvKTn6i6uloPPfSQAgMDtXXrVoWHhxs9\nLAAG6d27t0JDQ91e7vR9QUFBGjNmjEcekeutWN6cEy5FngHwfeSZlvHLPSTTpk1z+7qwsFAxMTEK\nCwtzO/773//em8MCAPgJ8gwAeI5f7iH54X27U6ZMMWgkAAB/RJ4BAM/xyxUSAAAAAFcGv93UDgAA\nAMD3UZAAAAAAMAwFCeBDdu3aJYvFol27dhk9FACAHyLPwBf55aZ2oLlOnjyp3Nxcbd++XV988YW+\n/fZbhYeHa/jw4Zo+fbpGjx5t9BABAFcw8gxweRQkaPc++OAD3XPPPaqoqFBSUpJSUlLUpUsXffHF\nF3rnnXd05513auPGjbr11luNHioA4ApEngEaRkGCds3hcGj69Onq0KGDdu3apQEDBri1L126VPn5\n+TKbzQ2ep6qqqtE+AID2hzwDNI49JGjXXnrpJf3rX/9SVlbWJUmizp133qmRI0e6vs7KypLFYtHh\nw4eVmpqqPn36aMSIEZKkL774QhkZGYqLi1OvXr0UGRmpqVOn6tChQ5ec98svv9TPfvYz9e7dW/37\n91dmZqbOnz9f7xj+9re/6e6771ZkZKTCw8N1++236y9/+YsHfgIAgLZEngEaxwoJ2rXt27crMDBQ\nd9xxR7O/9/7771dUVJSWLl3q+oD/4IMPtHfvXt15552KiIjQiRMntGHDBiUkJGj//v0KDw+XJFVX\nVysxMVHHjx/X7NmzFR4ero0bN9b74b97925NmTJFQ4YM0cKFC3XVVVfpD3/4gyZPnqzNmzdr1KhR\nrfshAADaDHkGaBwFCdq1o0ePqn///rrqqqvcjp89e9btKlKXLl0UFBTk1ic6OlqvvPKK27HbbrtN\niYmJbsemTZumYcOG6ZVXXtHChQslSRs2bNCnn36ql156SUlJSZKkn//855dsanQ6nUpPT9fw4cP1\n1ltvyWQySfouSY0ePVr//d//rXfffbcVPwEAQFsizwCN45YttGtnz569JAFI0vz589WvXz/Xn7oP\n+O+bOXPmJccCAwNd/33u3DmdPn1a3bp1U//+/fXhhx+62v70pz8pNDTULakEBgbqvvvuczvfRx99\npOLiYiUnJ+v06dM6deqUTp06pbNnz+qWW27RwYMHde7cuRbNHQDQ9sgzQONYIUG7FhQUpLNnz15y\nPCMjw/WhPW3atHq/t0+fPpccq6mp0dNPP60333xTX331lVvbNddc4/rvf/7zn7r++uvVoYP7NYF+\n/fq5ff3ZZ59Jkh588EE9+OCD9Y7j9OnT6tq1a71tAABjkWeAxlGQoF0bOHCgPvroI3377bduy+kx\nMTGKiYmRJHXs2LHe7/3+Vao6ixYt0quvvqrU1FQNGzZMPXr0UIcOHZSZmana2tpmj6/uex5//HHd\neOON9fbp2bNns88LAPAO8gzQOAoStGu333677Ha73n77bSUnJ7f6fG+99ZamTZumZcuWuR13OBxu\nV64iIiL08ccfq7a21u3qVd2VqjrXX3+9pO+usN1yyy2tHh8AwLvIM0Dj2EOCdu3+++9XeHi4Hnnk\nER07dqzV5+vYsaOcTqfbMZvNphMnTrgdu/XWW1VWVqa3337bday6ulq/+93v3PrdeOON6tu3r9as\nWVPvkv/JkydbPWYAQNshzwCNY4UE7ZrFYtFrr72me+65R6NGjdLkyZM1dOhQde7cWcePH9fWrVtV\nVVWl6667rknni4+P1+9//3t169ZNMTEx+uijj5SXl3fJfcAzZszQunXrNGfOHH344Yfq1auX3nzz\nTXXu3NmtX4cOHbRq1SolJydr+PDhmj59uq699lqdOHFCe/bskdPp1NatWz314wAAeBh5BmicyeFw\nOBvvBgAAAACexy1bAAAAAAxDQQIAAADAMO2qICkuLvarON6MRRzfj+VvcbwZizjwFH/8f+xvc/K3\nON6M5W9xvBmLOA1rVwUJAAAAAN9CQQIAAADAMBQkAAAAAAxDQQIAAADAMLwYEYDPMplMOnPmTJvH\nCQgI8Ps4HTp0UFBQkEwmU5vHB4ArBXnGs3ECAgLUpUuXZp+PggSAT6qqqpLFYlH37t3b/JfoLl26\nKCAgoE1jGB3n/PnzqqysVLdu3do8PgBcCcgzno3jdDp17tw5XbhwQWazuVnn45YtAD7pwoULXNH3\noM6dO6u2ttboYQCAzyDPeJbJZJLZbNaFCxea/b0UJAAAAAAMQ0ECAAAAwDAUJAAAAAAMw6Z2AFcM\ny0tfejWeI+Var8YDABiLPGMMVkgAwIOWL1+uMWPGKCIiQv369dPUqVN1+PBhtz5Op1NZWVkaNGiQ\nwsPDlZCQoCNHjrj1ee655zRhwgT17t1bFovlkjgnT57U5MmTNWjQIIWGhio2NlYZGRleedwjAMA4\n3soz33fq1CkNHjxYFotFp06d8vicKEgAwIN2796tmTNnaseOHcrPz1enTp1011136euvv3b1Wbly\npdasWaPs7GwVFBQoJCRESUlJOnv2rKvPN998o0mTJmnOnDn1xunQoYMmTZqk3//+9zp48KByc3P1\n3nvvaf78+W0+RwCAcbyVZ74vLS1NQ4YMaZP5SNyyBQAelZeX5/b1Cy+8oMjISO3fv19jxoyR0+mU\n1WrVggULlJiYKEmyWq2Kjo6WzWZTSkqKJOmRRx6RJL399tv1xrnmmmt0//33u76OjIzUzJkztWLF\niraYFgDAR3grz9SxWq2qrq7WQw89pHfffbcNZsQKCQC0qcrKStXW1rqWw0tKSlRaWqqxY8e6+gQG\nBmrkyJE6cOBAi+OcOHFCW7Zs0c0339zqMQMArhxtmWf+/ve/a+XKlXr++efVoUPblQ0UJADQhpYs\nWaIhQ4YoLi5OklRaWipJCgkJcesXEhKisrKyZp9/5syZ6tWrlwYPHqygoCCtWbOm9YMGAFwx2irP\nVFVVaebMmcrOzlbv3r09N+B6NLsgWb58uSwWixYuXOg61pSNMw6HQ6mpqYqMjFRkZKRSU1PlcDjc\n+hw6dEgTJ05UeHi4Bg8erOzsbDmdzhZODQCM9fDDD2v//v165ZVX1LFjxzaJ8fTTT+u9997T66+/\nrpKSEmVmZrZJHG8izwBA07Rlnlm8eLGGDx/uuu2rLTWrIHn//fe1YcMGxcbGuh1vysaZWbNmqaio\nSDabTTabTUVFRZo9e7arvaKiQklJSQoNDVVBQYGWLVumVatWafXq1a2cIgB4X2ZmpjZt2qT8/Hz1\n6dPHdTwsLEySVF5e7ta/vLxcoaGhzY4TFhamAQMGaOLEiVqxYoU2bNigL7/07mMrPYk8AwBN09Z5\npu5iV3BwsIKDg12FyYABA5SVldX6CXxPkwuSM2fO6IEHHtDq1avdHg32w40zMTExslqtqqyslM1m\nkyQdPXpUO3fuVE5OjuLi4hQXF6cVK1Zox44dKi4uliRt3LhR1dXVslqtiomJUWJioubPn6/c3Fyu\nXgG4oixevNiVJAYMGODWFhUVpbCwMBUWFrqO1dTUaN++fRo2bFir4tbW1kqSzp8/36rzGIU8AwBN\n4408s3nzZu3evVu7du3Srl279Jvf/EaStHXrVs2aNcszE/n/NfkpW3WJYPTo0crOznYdb2zjTEpK\niux2u4KCgtx+CMOHD5fZbNaBAwcUHR0tu92uESNGKDAw0NVn3Lhxeuqpp1RSUuJW+X1fXaJpqub2\nbylvxfFmLOL4fix/ihMQEKCQkBDV1NS0eazLaUnsJUuWyGazacOGDQoMDNQXX3whSTKbzTKbzfrm\nm280a9Ys5eTkqE+fPurbt69ycnLUtWtX3XHHHa6Yx48fl8Ph0GeffSZJOnjwoCTp+uuvl9ls1rvv\nvquvv/5aP/rRj2Q2m3X06FE9+eSTGjp0qK6//vp6x15RUVHv/cPR0dHNnmdbIM80D59hxDEilj/F\nIc80nGeuu+46t7gnTpyQ9N1THYODgy879pbkmiYVJC+//LI+//xzrV279pK2hjbO1A28rKxMwcHB\nMplMrnaTyaSePXu6BlxWVnbJhpm6c5aVlV02UTQnkRYXF3sl8XorjjdjEcf3Y/lbnLoX/AUEBLiO\ntdUbbWtqatzitMaGDRskScnJyW7HFy9erPT0dAUEBCgjI0MXLlzQww8/LIfDoaFDh2rz5s3q2bOn\nq/+vf/1rvfHGG66vx48fL0nasmWLRo0ape7du2vVqlU6evSozp8/r2uvvVaTJk1Senq6JNU7n+7d\nuysiIsIj8/Q08kzz8BlGHCNi+Vsc8kzDeeaHOnfuLOn/fl6Xm09Lck2jBUlxcbGefPJJbd++XVdd\ndVWzTg4A7c0PN1F/X93VJJPJpMzMzAY3oFutVlmt1su233LLLbrlllsajHOlIM8AQNN5K8/80KhR\no1yxPZ1nGt1DYrfbderUKQ0fPty1qWXPnj1av369goODdc0110hqeONMaGioTp065XaPrtPp1MmT\nJ9361HeOujYAgH8izwBA+9ZoQZKQkKC9e/e6NrTs2rVLN910k6ZMmaJdu3apf//+jW6ciYuLU2Vl\npex2u6uP3W5XVVWVW599+/a5VVyFhYXq1auXoqKiPDZhAIBvIc8AQPvW6C1bFovF7WknktS1a1dd\nffXViomJkSTNmTNHy5cvV3R0tPr376/nnntOZrPZdW/bwIEDNX78eKWnpysnJ0eSlJ6ergkTJrju\nEUxOTlZ2drbS0tKUkZGhTz/9VDk5OVq0aJHbPcEAAP9CngGA9q3JT9lqyPz581VdXa2FCxe6Ns7k\n5eWpW7durj7r16/XokWLNGXKFElSfHy8nnnmGVd7jx49tHnzZmVkZGjMmDGyWCyaO3eu5s2b54kh\nAgCuYOQZAPBfLSpI/vjHP7p93ZSNMxaLpd6np3xfbGystm3b1pIhAQD8CHkGANqPZr2pHQC8iZfV\neQ4/SwC4FJ+NntXSnycFCQCfZDabdfr0aZKFh5w7d85jz8AHAH9AnvEsp9Mph8Mhs9nc7O/1yB4S\nAPC0Tp066dy5c6qoqGjzWBUVFerevbtfx+nUqZO6dOnS5rEB4EpBnvF8nG7duqlTp+aXFxQkAHxa\njx492jxGWVmZV95g7m9xAMAfkGeMj8MtWwAAAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAAwDAUJAAA\nAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAAwDAU\nJAAAAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAA\nwDAUJAAAAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAAwDCdjB4AAAAwluWlLxto7SrtvrTdkXJt2w0I\nQLvCCgkAAAAAw1CQAAAAADAMBQkAAAAAw1CQAAAAADAMm9oBAPBRl99sXv9Gc4nN5gCuPI2ukKxb\nt04jR45URESEIiIidOutt2rHjh2udqfTqaysLA0aNEjh4eFKSEjQkSNH3M7hcDiUmpqqyMhIRUZG\nKjU1VQ6Hw63PoUOHNHHiRIWHh2vw4MHKzs6W0+n00DQBAL6KPAMA7VujBUnv3r31xBNP6L333lNh\nYaFGjx6t6dOn6+OPP5YkrVy5UmvWrFF2drYKCgoUEhKipKQknT171nWOWbNmqaioSDabTTabTUVF\nRZo9e7arvaKiQklJSQoNDVVBQYGWLVumVatWafXq1W0wZQCALyHPAED71ugtWwkJCW5fP/roo3rx\nxRf1/vvvKzY2VlarVQsWLFBiYqIkyWq1Kjo6WjabTSkpKTp69Kh27typ7du3Ky4uTpK0YsUKxcfH\nq7i4WNHR0dq4caOqq6tltVoVGBiomJgYHTt2TLm5uZo3b55MJlMbTB0A4AvIMwDQvjVrU/vFixe1\nadMmVVVVKS4uTiUlJSotLdXYsWNdfQIDAzVy5EgdOHBAkmS32xUUFKRhw4a5+gwfPlxms9mtz4gR\nIxQYGOjqM27cOJ04cUIlJSWtmiAA4MpBngGA9qdJm9oPHTqk2267TTU1NTKbzXr11VcVGxvr+qAP\nCQlx6x8SEqITJ05IksrKyhQcHOx29clkMqlnz54qKytz9endu/cl56hr69Onz2XHVlxc3JQptLh/\nS3krjjdjEcf3Y/lbHG/Gas9xoqOj23AkTUOeuZyuzf6OlsX3Tpyf7G4oTv0b9d//j3PNjtMYX/x3\neKXE8rc43ozV3uM0lGuaVJBER0dr165dqqio0Ntvv605c+Zo69atzRpEW2lOIq1bum9r3orjzVjE\n8f1Y/hbHm7GIYzzyzGVc5klaDWlRfH+L0wB//Hfob3PiZ9f+4jTplq3OnTurb9++uvHGG/XYY49p\nyJAhys3NVVhYmCSpvLzcrX95eblCQ0MlSaGhoTp16pTbk0ycTqdOnjzp1qe+c9S1AQD8G3kGANqv\nFr0Ysba2VufPn1dUVJTCwsJUWFjoaqupqdG+fftc9/LGxcWpsrJSdrvd1cdut6uqqsqtz759+1RT\nU+PqU1hYqF69eikqKqpFEwMAXLnIMwDQfjRakDz++OPau3evSkpKdOjQIT3xxBPavXu37r77bplM\nJs2ZM0crV65Ufn6+Dh8+rLS0NJnNZiUnJ0uSBg4cqPHjxys9PV12u112u13p6emaMGGCa6knOTlZ\ngYGBSktL0+HDh5Wfn6+cnBylpaXx5BMA8HPkGQBo3xrdQ1JaWqrU1FSVlZWpe/fuio2Nlc1m07hx\n4yRJ8+fPV3V1tRYuXCiHw6GhQ4cqLy9P3bp1c51j/fr1WrRokaZMmSJJio+P1zPPPONq79GjhzZv\n3qyMjAyNGTNGFotFc+fO1bx58zw9XwCAjyHPAED71mhBYrVaG2w3mUzKzMxUZmbmZftYLBatXbu2\nwfPExsZq27ZtjQ0HAOBnyDMA0L61aA8JAAAAAHgCBQkAAAAAw1CQAAAAADAMBQkAAAAAwzTpTe2o\nn+Wly71xtutl30brSLm27QYEAAAkkaOBKwkrJAAAAAAMQ0ECAAAAwDDcsgUAANBC3BoGtB4rJAAA\nAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAAwDAUJAAAAAAMQ0ECAAAAwDAU\nJAAAAAAMQ0ECAAAAwDCdjB4AAAAAGmZ56csGWrtKuy9td6Rc23YDAjyIFRIAAAAAhmGF5ArAVREA\nAAD4K1ZIAAAAABiGggQAAACAYShIAAAAABiGggQAAACAYShIAAAAABiGggQAAACAYShIAAAAABiG\nggQAAACAYXgxIgAAACS17GXMEi9kRuuwQgIAAADAMKyQAAAAwOsuvxrDSkx7wwoJAAAAAMNQkAAA\nAAAwDAUJAAAAAMM0WpAsX75cY8aMUUREhPr166epU6fq8OHDbn2cTqeysrI0aNAghYeHKyEhQUeO\nHHHr43A4lJqaqsjISEVGRio1NVUOh8Otz6FDhzRx4kSFh4dr8ODBys7OltPp9MA0AQC+ijwDAO1b\nowXJ7t27NXPmTO3YsUP5+fnq1KmT7rrrLn399deuPitXrtSaNWuUnZ2tgoIChYSEKCkpSWfPnnX1\nmTVrloqKimSz2WSz2VRUVKTZs2e72isqKpSUlKTQ0FAVFBRo2bJlWrVqlVavXu3hKQMAfAl5BgDa\nt0afspWXl+f29QsvvKDIyEjt379f8fHxcjqdslqtWrBggRITEyVJVqtV0dHRstlsSklJ0dGjR7Vz\n505t375dcXFxkqQVK1YoPj5excXFio6O1saNG1VdXS2r1arAwEDFxMTo2LFjys3N1bx582Qymdpg\n+gAAo5FnAKB9a/YeksrKStXW1spisUiSSkpKVFpaqrFjx7r6BAYGauTIkTpw4IAkyW63KygoSMOG\nDXP1+f/Yu/uoKOv8/+OvUVMR1DHuTcHV8AbWrDwhuqvlvUhmKKbmnswsXNGOWniD2d3+NhVrTUuh\nlDI3ra0QC9wy15V2vUEnd+3QehfVyaIMUJsQhLxhfn90mG+ToMAMc43D83GO58j1+XC9Px/E6z3v\n63PdxMTEyNfX16FP//795ePjY+8zdOhQnTx5UidOnGjY7AAA1xzyDAA0LfV+D8miRYvUu3dv+xmo\nokQOwD0AACAASURBVKIiSVJgYKBDv8DAQJ08eVKSVFxcLH9/f4ezTyaTSQEBASouLrb36dix42X7\nqG7r0qVLjeMpKCio1/jr2//K2tT7OxoW311x3LfP2/bUNqfanz3+8e/PuSy+1Dg/I6NjeVscd8Zq\nynEiIiIacST1R575JW/LM+7MZ942J8+N485Ynv75hji1u1KuqVdBsnjxYu3fv1/bt29X8+bN6zWI\nxlKfRFq9bO8ytXxwvpIGxXdTnNpfUFS7Br+gyF0/u1q4/HfBA2J5Wxx3xiKO5yDP/IqX5Rm3Hvu9\nbU4eHMedsVx9bPO24/K1GqfOl2ylpKRoy5Ytys7OdjiLFBwcLEkqKSlx6F9SUqKgoCBJUlBQkE6f\nPu3wJBObzaZTp0459KlpH9VtAADvRp4BgKapTgXJwoUL7Umie/fuDm3h4eEKDg5Wbm6ufVtlZaXy\n8vLs1/JGR0errKxMFovF3sdisai8vNyhT15eniorK+19cnNzFRoaqvDw8IbPEADg8cgzANB0XbUg\nSU5O1htvvKH169fLbDarqKhIRUVFKisrk/TzNbozZ87U6tWrlZ2drSNHjigpKUm+vr5KSEiQJPXo\n0UPDhg3TvHnzZLFYZLFYNG/ePI0cOdK+3JOQkCAfHx8lJSXpyJEjys7O1qpVq5SUlMSTTwDAi5Fn\nAKBpu+o9JBkZGZJkf9RitYULFyolJUWSNGfOHFVUVGj+/PmyWq3q27evsrKy1LZtW4f9LFiwQOPH\nj5ckxcbGasWKFfb29u3ba+vWrUpOTtbgwYNlNps1a9YszZ492/lZAgA8FnkGAJq2qxYkv37LbU1M\nJpNSUlLsiaMmZrNZ69atu+J+oqKi9MEHH1w1HgDAe5BnAKBpq/d7SAAAAADAVShIAAAAABim3i9G\nBACgKbvyO5tqf7lrg9/bBMApDfk/y/9X96IggVer/SDEhwYAAABPwCVbAAAAAAxDQQIAAADAMBQk\nAAAAAAxDQQIAAADAMBQkAAAAAAzDU7YAF+CRggAAAA1DQQJcQ3j/AQAA8DZcsgUAAADAMBQkAAAA\nAAzDJVsAAACAk7iftOFYIQEAAABgGAoSAAAAAIahIAEAAABgGAoSAAAAAIahIAEAAABgGAoSAAAA\nAIahIAEAAABgGAoSAAAAAIahIAEAAABgGAoSAAAAAIahIAEAAABgmBZGDwAAAABA3Zk3fFtLSxtp\nT81t1mk3NN6AnOR1BUnt/0DStfqPBAAAAHgrrytIALiGt519AQAAnol7SAAAAAAYhoIEAAAAgGEo\nSAAAAAAYhntIABiqIQ+i4F4VAAC8ByskAAAAAAxDQQIAAADAMBQkAAAAAAxTp4Jk7969mjRpknr1\n6iWz2azNmzc7tNtsNi1btkw9e/ZUSEiI4uLidPToUYc+VqtViYmJCgsLU1hYmBITE2W1Wh36HD58\nWKNHj1ZISIh69eql1NRU2Ww2J6cIAPB05BkAaLrqVJCUl5crMjJSy5cvl4+Pz2Xtq1ev1tq1a5Wa\nmqpdu3YpMDBQ8fHxOnv2rL3Pgw8+qPz8fGVmZiozM1P5+fmaMWOGvb20tFTx8fEKCgrSrl27tHz5\ncr344otas2aNC6YJAPBk5BkAaLrq9JStESNGaMSIEZKkpKQkhzabzab09HTNnTtXY8eOlSSlp6cr\nIiJCmZmZmjZtmo4fP66dO3dq+/btio6OliQ9//zzio2NVUFBgSIiIvTOO++ooqJC6enp8vHxUWRk\npD777DOlpaVp9uzZMplMrpw3AMCDkGcAwPPU/iTMmp+CKTXsSZhO30Ny4sQJFRUVaciQIfZtPj4+\nGjBggA4cOCBJslgs8vPzU79+/ex9YmJi5Ovr69Cnf//+DmfGhg4dqpMnT+rEiRPODhMAcI0izwCA\nd3P6PSRFRUWSpMDAQIftgYGBOnnypCSpuLhY/v7+DmefTCaTAgICVFxcbO/TsWPHy/ZR3dalS5ca\n4xcUFPxqS5sGzePy/dRF/WMRx92xiOPOWJ4dx/37vFbiRERENOJInEeeIU7DeNucPDeOO2MRx92x\nXBfnSrnmmn8x4mWTq2X5qN77qYsGxCKOm2MRx62xPDrOFVRf0tPYvC1OU0GeuQbjuDMWcdwaizhu\njuWmOE5fshUcHCxJKikpcdheUlKioKAgSVJQUJBOnz7t8CQTm82mU6dOOfSpaR/VbQCApok8AwDe\nzemCJDw8XMHBwcrNzbVvq6ysVF5env1a3ujoaJWVlclisdj7WCwWlZeXO/TJy8tTZWWlvU9ubq5C\nQ0MVHh7u7DABANco8gwAeLc6FSRlZWXKz89Xfn6+qqqqVFhYqPz8fH3zzTcymUyaOXOmVq9erezs\nbB05ckRJSUny9fVVQkKCJKlHjx4aNmyY5s2bJ4vFIovFonnz5mnkyJH2ZZ2EhAT5+PgoKSlJR44c\nUXZ2tlatWqWkpCSefAIAXo48AwBNV53uITl06JDGjBlj/3rZsmVatmyZJk+erPT0dM2ZM0cVFRWa\nP3++rFar+vbtq6ysLLVt29b+PRkZGVqwYIHGjx8vSYqNjdWKFSvs7e3bt9fWrVuVnJyswYMHy2w2\na9asWZo9e7ar5goA8FDkGQBouupUkAwcOPCyt93+kslkUkpKilJSUmrtYzabtW7duivGiYqK0gcf\nfFCXIQFAvdT+LHWptuepN+RZ6mgY8gwANF1O30MCAAAAAA1FQQIAAADAMBQkAAAAAAxDQQIAAADA\nMBQkAAAAAAxDQQIAAADAMBQkAAAAAAxDQQIAAADAMBQkAAAAAAxDQQIAAADAMBQkAAAAAAxDQQIA\nAADAMC2MHgAAeBvzhm9raWkj7am5zTrthsYbEAAAHowVEgAAAACGoSABAAAAYBgKEgAAAACGoSAB\nAAAAYBgKEgAAAACGoSABAAAAYBge+wsA1ygeLwwA8AaskAAAAAAwDAUJAAAAAMNQkAAAAAAwDAUJ\nAAAAAMNQkAAAAAAwDAUJAAAAAMNQkAAAAAAwDAUJAAAAAMNQkAAAAAAwDAUJAAAAAMNQkAAAAAAw\nDAUJAAAAAMNQkAAAAAAwDAUJAAAAAMNQkAAAAAAwjMcVJBkZGbrpppsUHBys22+/Xfv27TN6SAAA\nL0KeAQDP4lEFSVZWlhYtWqRHH31U//73vxUdHa0JEybom2++MXpoAAAvQJ4BAM9jslqtNqMHUW3o\n0KGKiorSCy+8YN926623auzYsXryyScNHBkAwBuQZwDA83jMCsn58+f1ySefaMiQIQ7bhwwZogMH\nDhg0KgCAtyDPAIBn8piC5PTp07p06ZICAwMdtgcGBqq4uNigUQEAvAV5BgA8k8cUJAAAAACaHo8p\nSPz9/dW8eXOVlJQ4bC8pKVFQUJBBowIAeAvyDAB4Jo8pSFq2bKmbb75Zubm5Dttzc3PVr18/g0YF\nAPAW5BkA8EwtjB7AL82aNUszZsxQ37591a9fP7366qv6/vvvNW3aNKOHBje5cOGCrrvuOqOHAcBL\nkWdAngE8j0cVJOPGjdOZM2f07LPPqqioSL169dLbb7+tsLAwo4cGN+nYsaP27NmjHj16GD0UuNH3\n33+vV155Rfv371dRUZGaNWum8PBwxcXFacqUKWrevLnRQ4SXIM+APNM0kWc8m0e9h8QbFRYWatmy\nZVq7dq3T+7JarTpw4IDMZrOio6NlMpnsbeXl5VqzZo0WLlzodJwjR47o448/VnR0tHr16qVjx44p\nLS1NP/30kyZOnHjZIzMbYsGCBTVuz8jIUEJCgsxmsyRpxYoVTsf6JavVqjfeeENffvmlgoODNXny\nZHXq1Mkl+963b58CAwMVEREh6ee5ZGRkqLCwUJ07d9ZDDz2kBx54wCWx3KGqqkorV67UwYMHNXLk\nSE2bNk2bNm3S888/r6qqKo0ZM0ZLlixRy5YtnYpz6NAhjR07Vl27dpWPj48sFosSEhJ04cIF/fOf\n/1TPnj2VmZmptm3bumhmgHchz9SMPOP5yDOo5pUFyZkzZ7R582Y9/PDDkqR77rlHFRUV9vYWLVoo\nIyND/v7+jT6WTz/9VLfffrvOnDnj1H6OHj2qu+++W6dOnVJVVZX69Omjv/71r/azesXFxerZs6fT\ncXbs2KEpU6bIz89P586d06ZNm/THP/5RvXv3VlVVlfbu3astW7bojjvucCpOhw4d9Nvf/lbt27d3\n2L53717dcsstatOmjUwmk3JycpyK07NnT+3bt0/XX3+9vvrqK40cOVJVVVXq2bOnCgoKdO7cOe3c\nuVPdu3d3Ko4k9e/fX6mpqRo0aJDWr1+vp59+WjNmzFD37t1VUFCgdevW6fHHH9dDDz3kVJyJEydq\n3Lhxuvvuu9WqVSunx12bP//5z8rIyFBsbKx2796tyZMnKyMjQ7NmzVKzZs2Ulpam+++/X0uWLHEq\nzqhRo3THHXdo0aJFkqS33npL69ev186dO2W1WjVmzBgNGDBAqamprpiWysvLlZmZqQMHDtgf9RoU\nFKSYmBiNHz9evr6+LolzNcXFxdqwYYNLPtxVVVXp+PHjMpvNCg0NdWirrKzU1q1bNXnyZKfj4P+Q\nZxqOPNNw5JmGIc94fp7xyoJk5cqV+vzzz5WWliZJ6tSpk+Li4tSuXTtJPx+MRo8e7fQvuCS9+eab\nV2yvPnPl7AF80qRJatGihV5++WWdPXtWixYtksViUU5Ojrp16+ayRDFixAgNGjRIS5Ys0ZYtW/To\no49q+vTpevzxxyVJTz/9tD755BNt3brVqTgrV67Uxo0btXbtWv3+97+3bw8ICNCePXvUs2dPp/Zf\nrUOHDvrss88UGBio6dOnq6ioSG+99ZZ8fX1VWVmpqVOnqnXr1tq4caPTsUJCQmSxWBQWFqaBAwdq\n5syZuvfee+3t7777rp555hl9/PHHTsXp0KGDTCaT2rVrp4kTJ2rq1KmKjIx0dviX6dOnj1JTUzVq\n1CgdO3ZMAwYM0EsvvaR77rlHkpSTk6MnnnhChw4dcipOaGio8vLy1KVLF0k/H/SCg4N1+PBhBQUF\nKTc3V0lJSTp69KizU9KxY8cUHx+vsrIyDRgwwP4+ipKSEuXl5cnPz09ZWVku+/27Eld9iCwsLNSE\nCRN07NgxmUwmxcbGau3atfazv646NsAReabhyDMNR55pGPKM5+cZj7qHxFVycnL09NNPO2xLSUmx\n/yK+//77Wrp0qUsSRVJSkv0sS02qqqqcjiFJBw8eVE5Ojnx9feXr66vXXntNixcv1p133qmcnBx7\nEnTWsWPH9NJLL0mS4uPjNWPGDN1111329gkTJmjz5s1Ox3nkkUc0cOBAJSYmKj4+XkuWLFGzZo37\n0LeDBw/qhRdesJ+ZaN26tebPn6+pU6e6ZP9+fn46c+aMwsLC9P3336t3794O7X369FFhYaFLYn30\n0UfasWOHNm3apPXr1+vWW2/V1KlTNX78eLVp08YlMYqKihQVFSXp5zOAzZs3d5hTnz59VFRU5HSc\ngIAAfffdd/b/n0VFRbp48aJ96bxr16764YcfnI4jScnJyYqJiVF6erpat27t0FZZWamkpCQlJydr\n27ZtTsfau3fvFdu/+OILp2NIP394M5vN2r9/v0pLS7VkyRKNHj1a2dnZCggIcEkMXI4803DkmYYj\nzzQMecY57sgzXlmQnDhxQl27drV/HRUV5XD9YWRkpL788kuXxAoNDdXy5csdDqa/lJ+f7/SysySd\nP3/+smS0dOlS2Ww23XnnnVq/fr3TMapVH7CbNWum1q1bOyx3+/n5qbS01CVxbrvtNn300UeaO3eu\nhg4dqoyMDJfs99eqf27nz5+v8Q3Np06dckmc4cOHa926dUpLS9PAgQP17rvvOhxYs7Ky1K1bN5fE\nCg0NVXJyspKTk7Vr1y5t3LhRjz76qB577DGNHz9eU6dO1c033+xUjODgYB09elSdO3dWQUGBLl26\npOPHj6tXr16Sfv5Q4YoDUVxcnB555BE99dRTatWqlZ599ln97ne/k4+PjySpoKDgsuXhhvrPf/6j\n3Nzcy5KE9PMHh+TkZA0dOtQlse68806ZTCbZbLUvQtf2AbM+du/erTfffNN+g+62bdv00EMPKS4u\nziUJDzUjzziHPNMw5JmGIc84xx15xisLksrKSv3444/2m8g+/PBDh/aysjKXxerTp4/y8/NrTRRX\n+0WpqxtvvFGHDh26bIlv2bJlqqqq0pQpU5yOIUmdO3fWF198YT+LsGPHDoeb8b799luXvkCsffv2\n2rBhgzZu3Gi/7tbV4uLi1Lx5c5WWlqqgoMBh2bmwsNBl13g/9dRTGjlypGJjY9W3b1+lpaVp3759\n9mt7Dx486JKzfr82ZMgQDRkyRKdOndLmzZv1+uuva+PGjU4v0U6YMEF//OMfNWrUKO3evVuPPPKI\nlixZouLiYjVr1kyrVq2q9fe+PpYsWaKioiL94Q9/0KVLlxQdHW2/DEb6+QPLk08+6XQcSTKbzfr8\n889rXSr/4osv7EvQzvL399fSpUs1bNiwGtsPHz6ssWPHOh3n7NmzDmeur7vuOr3yyit64IEHFBcX\n12gfwJo68kzDkWcajjzTMOQZ57gjz3hlQdKlSxd98skn9mXAX/vvf/+r8PBwl8R6+OGHVV5eXmt7\n165dnb5hTvq5Ct6yZUuNNwylpqbq0qVLeuWVV5yOM23aNJ0/f97+9a+vGd2xY4fDtbiuMnXqVP3u\nd7+TxWJRx44dXbbfX9/I5efn5/D19u3b1b9/f5fECg4O1r/+9S+tXr1a77//vmw2m/7zn/+osLBQ\nMTExeuaZZ3TLLbe4JFZNAgICNGfOHM2ZM0e7d+92en+LFy+2P41k+vTpmjt3rnr16qUnn3xS586d\n06hRo/TYY485HcfPz08bNmxQZWWlLl68eNm/kSuetlPtvvvuU1JSkgoKCjR48GCHa3tzc3O1atUq\nJSUluSRWnz599NVXX+n666+vsd1sNrvkQ2SXLl30v//9z+GsaPPmzfXqq6/q/vvv52b2RkKeaTjy\nTMORZxqGPOMcd+QZr7ypfenSpXrjjTe0a9euy86yfPfddxo+fLjuvfdel/ySA+5200036aOPPqr1\nAIQrW7VqlV566SUVFRXZl7JtNpuCg4M1c+ZMzZkzxyVxcnJydO7cOU2cOLHGdqvVqvfff9/hhtSG\nePLJJ/Xpp58qKyvrsraLFy/qvvvu0wcffOCy66PxM/IMvBl5xjnkmfrzyoKkrKxMw4cPV2FhoSZO\nnGiv6AoKCvT222+rU6dO2rlz52UVMoCm46uvvnJ4HGP15SPXmosXL+rcuXO13nB86dIlffvtt7z4\nz8XIMwCuhjxTd15ZkEg/V4V/+tOflJWVpR9//FHSz9eRjhs3Tk888YQuXbrklufDA7h2uPIFc54S\ny51zamrIMwDqizxTM68sSJ555hn7MrnNZrM/3SIgIEAmk0lnzpzRmDFjrvq4NABNi6ue2e5Jsdw5\np6aEPAOgIcgzNfPKm9rXrl2rwMBAJSYmymQyOTyCz2q16q677mr0Z5ED8Dx1ecHctRbLnXPC/yHP\nAKgJeaZhvHKFZPv27Zo6darWrl2rhIQE+3ar1aqxY8fqwoUL2rZtGzdrAU1Mhw4drvqCucrKSpec\nTXJXLHfOCf+HPAOgJuSZhvHKFZJRo0Zp9erVmj17tjp06KChQ4fqxx9/VHx8vCorK/X3v/+dJAE0\nQe56wZw7Y7lzTvg/5BkANSHPNIzXridPmjRJTzzxhO677z794x//0Lhx41RWVubS19wDuLZUv2Cu\nNq56wZw7Y7lzTnBEngHwa+SZhmm+aNGip5zagwe77bbbVFFRoUcffVQ+Pj7atm2bQkJCjB4WAIN0\n7NhRQUFBDi93+iU/Pz8NHjzYJY/IdVcsd84JlyPPAPgl8kzDeOU9JJMmTXL4Ojc3V5GRkQoODnbY\n/re//c2dwwIAeAnyDAC4jlfeQ/Lr63bHjx9v0EgAAN6IPAMAruOVKyQAAAAArg1ee1M7AAAAAM9H\nQQIAAADAMBQkgAfZvXu3zGazdu/ebfRQAABeiDwDT+SVN7UD9XXq1CmlpaVp+/bt+vrrr3XhwgWF\nhIQoJiZGU6ZM0aBBg4weIgDgGkaeAWpHQYIm79ChQ7rnnntUWlqq+Ph4TZs2Ta1atdLXX3+t999/\nX3fddZfeeecdDR8+3OihAgCuQeQZ4MooSNCkWa1WTZkyRc2aNdPu3bvVvXt3h/YlS5YoOztbvr6+\nV9xPeXn5VfsAAJoe8gxwddxDgiZtw4YN+u6777Rs2bLLkkS1u+66SwMGDLB/vWzZMpnNZh05ckSJ\niYnq0qWL+vfvL0n6+uuvlZycrOjoaIWGhiosLEwTJ07U4cOHL9vvt99+q3vvvVcdO3bUjTfeqJSU\nFJ0/f77GMfz3v//VhAkTFBYWppCQEI0aNUr//ve/XfATAAA0JvIMcHWskKBJ2759u3x8fDRmzJh6\nf+8DDzyg8PBwLVmyxH6AP3TokPbt26e77rpLnTt31smTJ/Xaa68pLi5O+/fvV0hIiCSpoqJCY8eO\nVWFhoWbMmKGQkBC98847NR789+zZo/Hjx6t3796aP3++rrvuOr311lsaN26ctm7dqoEDBzr3QwAA\nNBryDHB1FCRo0o4fP64bb7xR1113ncP2s2fPOpxFatWqlfz8/Bz6RERE6PXXX3fYNmLECI0dO9Zh\n26RJk9SvXz+9/vrrmj9/viTptdde0+eff64NGzYoPj5eknT//fdfdlOjzWbTvHnzFBMTo3fffVcm\nk0nSz0lq0KBB+n//7/9px44dTvwEAACNiTwDXB2XbKFJO3v27GUJQJLmzJmjbt262f9UH+B/afr0\n6Zdt8/Hxsf/93LlzOnPmjNq2basbb7xRn3zyib3tH//4h4KCghySio+Pj+677z6H/X366acqKChQ\nQkKCzpw5o9OnT+v06dM6e/as7rjjDh08eFDnzp1r0NwBAI2PPANcHSskaNL8/Px09uzZy7YnJyfb\nD9qTJk2q8Xu7dOly2bbKykotXbpUb7/9tr7//nuHtuuvv97+92+++Ua/+c1v1KyZ4zmBbt26OXz9\nxRdfSJIefvhhPfzwwzWO48yZM2rTpk2NbQAAY5FngKujIEGT1qNHD3366ae6cOGCw3J6ZGSkIiMj\nJUnNmzev8Xt/eZaq2oIFC7Rp0yYlJiaqX79+at++vZo1a6aUlBRVVVXVe3zV3/PUU0/p5ptvrrFP\nQEBAvfcLAHAP8gxwdRQkaNJGjRoli8Wi9957TwkJCU7v791339WkSZO0fPlyh+1Wq9XhzFXnzp31\nv//9T1VVVQ5nr6rPVFX7zW9+I+nnM2x33HGH0+MDALgXeQa4Ou4hQZP2wAMPKCQkRI899pg+++wz\np/fXvHlz2Ww2h22ZmZk6efKkw7bhw4eruLhY7733nn1bRUWF/vrXvzr0u/nmm9W1a1etXbu2xiX/\nU6dOOT1mAEDjIc8AV8cKCZo0s9mszZs365577tHAgQM1btw49e3bVy1btlRhYaG2bdum8vJyderU\nqU77i42N1d/+9je1bdtWkZGR+vTTT5WVlXXZdcBTp07V+vXrNXPmTH3yyScKDQ3V22+/rZYtWzr0\na9asmV588UUlJCQoJiZGU6ZM0Q033KCTJ09q7969stls2rZtm6t+HAAAFyPPAFdnslqttqt3AwAA\nAADX45ItAAAAAIahIAEAAABgmCZVkBQUFHhVHHfGIo7nx/K2OO6MRRy4ijf+G3vbnLwtjjtjeVsc\nd8YizpU1qYIEAAAAgGehIAEAAABgGAoSAAAAAIahIAEAAABgGF6MCMBjmUwm/fjjj40ep3Xr1l4f\np1mzZvLz85PJZGr0+ABwrSDPuDZO69at1apVq3rvj4IEgEcqLy+X2WxWu3btGv1DdKtWrdS6detG\njWF0nPPnz6usrExt27Zt9PgAcC0gz7g2js1m07lz53Tx4kX5+vrWa39csgXAI128eJEz+i7UsmVL\nVVVVGT0MAPAY5BnXMplM8vX11cWLF+v9vRQkAAAAAAxDQQIAAADAMBQkAAAAAAzDTe0ArhnmDd+6\nNZ512g1ujQcAMBZ5xhiskACAC61cuVKDBw9W586d1a1bN02cOFFHjhxx6GOz2bRs2TL17NlTISEh\niouL09GjRx36PPfccxo5cqQ6duwos9lcYyyz2XzZn1dffbXR5gYAMJ4784wkvfXWW/r973+v4OBg\nde3aVTNmzHD5nChIAMCF9uzZo+nTp+vDDz9Udna2WrRoobvvvls//PCDvc/q1au1du1apaamateu\nXQoMDFR8fLzOnj1r7/PTTz/pzjvv1MyZM68Y74UXXtDx48ftfyZPntxocwMAGM+deeall17SE088\noYcfflh5eXnKycnR6NGjXT4nLtkCABfKyspy+Prll19WWFiY9u/fr8GDB8tmsyk9PV1z587V2LFj\nJUnp6emKiIhQZmampk2bJkl67LHHJEnvvffeFeO1b99ewcHBDtsqKytdNR0AgIdxV56xWq3605/+\npM2bN2vw4MH27VFRUS7PM6yQAEAjKisrU1VVlX05/MSJEyoqKtKQIUPsfXx8fDRgwAAdOHCg3vtf\ntGiRunbtqsGDB+vVV1/lXSMA0MQ0Vp7Jzc3VpUuXVFxcrH79+qlXr16aMmWKvvrqK1dPgYIEABrT\nokWL1Lt3b0VHR0uSioqKJEmBgYEO/QIDA1VcXFyvfS9evFivvvqq3n33XY0bN05LlizRX/7yF9cM\nHABwTWisPPPVV1+pqqpKzz33nJ555hlt2rRJFy9e1J133qlz5865bgJqQEGycuVKmc1mzZ8/376t\nLjfOWK1WJSYmKiwsTGFhYUpMTJTVanXoc/jwYY0ePVohISHq1auXUlNTZbPZGjg1ADDW4sWLtX//\nfr3++utq3ry5y/e/YMEC9e/fXzfddJMefvhhLVy4UC+++KLL47gbeQYA6qYx80xVVZUuXLigiohi\nzgAAIABJREFU1NRUDRs2TH379tW6det06tQp7dixw6Wx6lWQfPzxx3rttdcUFRXlsL0uN848+OCD\nys/PV2ZmpjIzM5Wfn+9wl35paani4+MVFBSkXbt2afny5XrxxRe1Zs0aJ6cIAO6XkpKiLVu2KDs7\nW126dLFvr77fo6SkxKF/SUmJgoKCnIrZt29flZaWXrbvawl5BgDqprHzTPV+evToYd/Wvn17hYSE\n6NtvXft45DoXJD/++KMeeughrVmzxuHRYL++cSYyMlLp6ekqKytTZmamJOn48ePauXOnVq1apejo\naEVHR+v555/Xhx9+qIKCAknSO++8o4qKCqWnpysyMlJjx47VnDlzlJaWxtkrANeUhQsX2pNE9+7d\nHdrCw8MVHBys3Nxc+7bKykrl5eWpX79+TsX99NNP1bp1a7Vr186p/RiFPAMAdeOOPBMTEyNJ+vzz\nz+3bysrKVFRUpE6dOjk5A0d1fspWdSIYNGiQUlNT7duvduPMtGnTZLFY5Ofn5/BDiImJka+vrw4c\nOKCIiAhZLBb1799fPj4+9j5Dhw7VM888oxMnTjhUfr9UnWjqqr79G8pdcdwZizieH8ub4rRu3VqB\ngYGGPjGqIbEXLVqkzMxMvfbaa/Lx8dHXX38tSfL19ZWvr69++uknPfjgg1q1apW6dOmirl27atWq\nVWrTpo3GjBljj1lYWCir1aovvvhCknTw4EFJ0m9+8xv5+vpqx44dKi4uVt++feXj46O9e/dq6dKl\n+sMf/qBWrVrVOPbS0tIarx+OiIio9zwbA3mmfjiGEceIWN4Uhzxz5TzTqVMnjRo1SgsWLNCzzz6r\n9u3b69lnn5W/v7+GDx9e69gbkmvqVJBs3LhRX375pdatW3dZ25VunDl58qQkqbi4WP7+/jKZTPZ2\nk8mkgIAA+4CLi4vVsWPHy/ZR3VZboqhPIi0oKHBL4nVXHHfGIo7nx/K2OD/++KOknxNGtcZ6o21l\nZaVDHGe89tprkqSEhASH7QsXLtS8efPUunVrJScn6+LFi1q8eLGsVqv69u2rrVu3KiAgwN7/L3/5\ni958803718OGDZMk5eTkaODAgWrTpo02btyop556SlVVVerSpYsWL16shx56SBcvXqxxPu3atVPn\nzp1dMk9XI8/UD8cw4hgRy9vikGeunGckaf369Xrsscd03333yWazKSYmRjk5OWrTpk2t82lIrrlq\nQVJQUKA//elP2r59u6677rp67RwAmppf30T9S9Vnk0wmk1JSUpSSklJr3/T0dKWnp9faPmzYMHvy\n+LWLFy/WcbSegTwDAHXnrjwjSW3bttULL7ygF154ocY4rnLVe0gsFotOnz6tmJgY+fv7y9/fX3v3\n7lVGRob8/f11/fXXS7ryjTNBQUE6ffq0wzW6NptNp06dcuhT0z6q2wAA3ok8AwBN21ULkri4OO3b\nt0+7d++2/7nllls0fvx47d69WzfeeONVb5yJjo5WWVmZLBaLvY/FYlF5eblDn7y8PIeKKzc3V6Gh\noQoPD3fZhAEAnoU8AwBN21Uv2TKbzQ5PO5GkNm3aqEOHDoqMjJQkzZw5UytXrlRERIRuvPFGPffc\nc/L19bVf29ajRw8NGzZM8+bN06pVqyRJ8+bN08iRI+3XCCYkJCg1NVVJSUlKTk7W559/rlWrVmnB\nggUO1wQDALwLeQYAmrY6P2XrSubMmaOKigrNnz/ffuNMVlaW2rZta++TkZGhBQsWaPz48ZKk2NhY\nrVixwt7evn17bd26VcnJyRo8eLDMZrNmzZql2bNnu2KIAIBrGHkGALxXgwqSv//97w5f1+XGGbPZ\nXOPTU34pKipKH3zwQUOGBADwIuQZAGg66vWmdgBwJ15W5zr8LAHgchwbXauhP08KEgAeydfXV2fO\nnCFZuMi5c+dc9gx8APAG5BnXstlsslqt8vX1rff3uuQeEgBwtRYtWujcuXMqLS1t9FilpaVq166d\nV8dp0aKFWrVq1eixAeBaQZ5xfZy2bduqRYv6lxcUJAA8Wvv27Rs9RnFxsVveYO5tcQDAG5BnjI/D\nJVsAAAAADENBAgAAAMAwFCQAAAAADENBAgAAAMAwFCQAAAAADENBAgAAAMAwFCQAAAAADENBAgAA\nAMAwFCQAAAAADENBAgAAAMAwLYweAAAAaBrMG769Qmsbac/l7dZpNzTegAB4BFZIAAAAABiGggQA\nAACAYShIAAAAABiGggQAAACAYbipHXbcbAgA8Ba157Sa85lETgOMQkFyDaBQAAAAgLeiIAEAoInj\nxBcAI3EPCQAAAADDUJAAAAAAMAwFCQAAAADDUJAAAAAAMAwFCQAAAADDUJAAAAAAMAyP/XUCL10C\nAKBp47MA4DxWSAAAAAAYhoIEAAAAgGG4ZAuGYIkbAAAAEiskAAAAAAzECgkAAB6K1WQATcFVV0jW\nr1+vAQMGqHPnzurcubOGDx+uDz/80N5us9m0bNky9ezZUyEhIYqLi9PRo0cd9mG1WpWYmKiwsDCF\nhYUpMTFRVqvVoc/hw4c1evRohYSEqFevXkpNTZXNZnPRNAEAnoo8AwBN21ULko4dO+rpp5/Wv/71\nL+Xm5mrQoEGaMmWK/ve//0mSVq9erbVr1yo1NVW7du1SYGCg4uPjdfbsWfs+HnzwQeXn5yszM1OZ\nmZnKz8/XjBkz7O2lpaWKj49XUFCQdu3apeXLl+vFF1/UmjVrGmHKAABPQp4BgKbtqpdsxcXFOXz9\n+OOP65VXXtHHH3+sqKgopaena+7cuRo7dqwkKT09XREREcrMzNS0adN0/Phx7dy5U9u3b1d0dLQk\n6fnnn1dsbKwKCgoUERGhd955RxUVFUpPT5ePj48iIyP12WefKS0tTbNnz5bJZGqEqQMAPAF5BgCa\ntnrd1H7p0iVt2bJF5eXlio6O1okTJ1RUVKQhQ4bY+/j4+GjAgAE6cOCAJMliscjPz0/9+vWz94mJ\niZGvr69Dn/79+8vHx8feZ+jQoTp58qROnDjh1AQBANcO8gwAND11uqn98OHDGjFihCorK+Xr66tN\nmzYpKirKfqAPDAx06B8YGKiTJ09KkoqLi+Xv7+9w9slkMikgIEDFxcX2Ph07drxsH9VtXbp0qXVs\nBQUFdZlCg/tfWZt6f0fD4ntbHHfHcs/+PCGWt8VxZ6ymHCciIqIRR1I35JnaeNvx3xvzjPH5rLH2\n2RTiuDNWU49zpVxTp4IkIiJCu3fvVmlpqd577z3NnDlT27Ztq9cgGkt9Emn10r3L1PKEkytpUHxv\ni+PGWLU/oaZ2rn5Cjct/75pIHHfGIo7xyDO18LbjvxfmGbfOqRbedmwhzzS9OHUqSFq2bKmuXbtK\nkm6++Wb997//VVpampKTkyVJJSUl6ty5s71/SUmJgoKCJElBQUE6ffq0bDab/eyVzWbTqVOnHPqU\nlJQ4xKz+uroPAMB7kWeAK7vyCbaaHwPNI6BxrWjQixGrqqp0/vx5hYeHKzg4WLm5ufa2yspK5eXl\n2a/ljY6OVllZmSwWi72PxWJReXm5Q5+8vDxVVlba++Tm5io0NFTh4eENmhgA4NpFngGApuOqKyRP\nPfWURowYoRtuuEFlZWXKzMzUnj179Pbbb8tkMmnmzJlauXKlIiIidOONN+q5556Tr6+vEhISJEk9\nevTQsGHDNG/ePK1atUqSNG/ePI0cOdK+1JOQkKDU1FQlJSUpOTlZn3/+uVatWqUFCxbw5BNcEzhz\nBTQceQYAmrarFiRFRUVKTExUcXGx2rVrp6ioKGVmZmro0KGSpDlz5qiiokLz58+X1WpV3759lZWV\npbZt29r3kZGRoQULFmj8+PGSpNjYWK1YscLe3r59e23dulXJyckaPHiwzGazZs2apdmzZ7t6vgAA\nD0OeAYCm7aoFSXp6+hXbTSaTUlJSlJKSUmsfs9msdevWXXE/UVFR+uCDD642HACAlyHPAEDTVqeb\n2gEAAOD9GnIJssRlyHBOg25qBwAAAABXoCABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSAB\nAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACG\noSABAAAAYJgWRg8AQN2ZN3x7hdY20p6a263TbmicAQEAADiJFRIAAAAAhmGFBAAAAG5X+6o/K/5N\nDSskAAAAAAxDQQIAAADAMBQkAAAAAAxDQQIAAADAMBQkAAAAAAxDQQIAAADAMBQkAAAAAAzDe0gA\n1IjnwwMAAHdghQQAAACAYShIAAAAABiGggQAAACAYShIAAAAABiGggQAAACAYShIAAAAABiGggQA\nAACAYShIAAAAABiGFyMCMFTtL2CUansJIy9gBADAe1x1hWTlypUaPHiwOnfurG7dumnixIk6cuSI\nQx+bzaZly5apZ8+eCgkJUVxcnI4ePerQx2q1KjExUWFhYQoLC1NiYqKsVqtDn8OHD2v06NEKCQlR\nr169lJqaKpvN5oJpAgA8FXkGQGMyb/i21j+37WlT43ZPjuONrlqQ7NmzR9OnT9eHH36o7OxstWjR\nQnfffbd++OEHe5/Vq1dr7dq1Sk1N1a5duxQYGKj4+HidPXvW3ufBBx9Ufn6+MjMzlZmZqfz8fM2Y\nMcPeXlpaqvj4eAUFBWnXrl1avny5XnzxRa1Zs8bFUwYAeBLyDAA0bVe9ZCsrK8vh65dffllhYWHa\nv3+/YmNjZbPZlJ6errlz52rs2LGSpPT0dEVERCgzM1PTpk3T8ePHtXPnTm3fvl3R0dGSpOeff16x\nsbEqKChQRESE3nnnHVVUVCg9PV0+Pj6KjIzUZ599prS0NM2ePVsmk6kRpg8AMBp5BgCatnrf1F5W\nVqaqqiqZzWZJ0okTJ1RUVKQhQ4bY+/j4+GjAgAE6cOCAJMliscjPz0/9+vWz94mJiZGvr69Dn/79\n+8vHx8feZ+jQoTp58qROnDjRsNkBAK455BkAaFrqfVP7okWL1Lt3b/sZqKKiIklSYGCgQ7/AwECd\nPHlSklRcXCx/f3+Hs08mk0kBAQEqLi629+nYseNl+6hu69KlS43jKSgoqNf469v/ytrU+zsaFt/b\n4rgzFnHcGcuz47h/n9dKnIiIiEYcSf2RZ37J2/4fkme8MY47YxHHmH26Is6Vck29CpLFixdr//79\n2r59u5o3b16vQTSW+iTS6mV7l6nh6T9X06D43hbHnbGI49ZYHh3nClx+bGgicRoDeeZXvO3/IXnG\nK+O4MxZxruxazTN1vmQrJSVFW7ZsUXZ2tsNZpODgYElSSUmJQ/+SkhIFBQVJkoKCgnT69GmHJ5nY\nbDadOnXKoU9N+6huAwB4N/IMADRNdSpIFi5caE8S3bt3d2gLDw9XcHCwcnNz7dsqKyuVl5dnv5Y3\nOjpaZWVlslgs9j4Wi0Xl5eUOffLy8lRZWWnvk5ubq9DQUIWHhzd8hgAgHsfo6cgzANB0XbUgSU5O\n1htvvKH169fLbDarqKhIRUVFKisrk/TzNbozZ87U6tWrlZ2drSNHjigpKUm+vr5KSEiQJPXo0UPD\nhg3TvHnzZLFYZLFYNG/ePI0cOdK+3JOQkCAfHx8lJSXpyJEjys7O1qpVq5SUlMSTTwDAi5FnAKBp\nu+o9JBkZGZJkf9RitYULFyolJUWSNGfOHFVUVGj+/PmyWq3q27evsrKy1LZtW4f9LFiwQOPHj5ck\nxcbGasWKFfb29u3ba+vWrUpOTtbgwYNlNps1a9YszZ492/lZAgA8FnkGAJq2qxYkv37LbU1MJpNS\nUlLsiaMmZrNZ69atu+J+oqKi9MEHH1w1HgDAe5BnAKBpq/djfwEAaMqufH9Rm1qftGOddkPjDAgA\nrnH1fjEiAAAAALgKKyQAAADANaT2ldprc5XW6woSltIBAACAaweXbAEAAAAwDAUJAAAAAMNQkAAA\nAAAwDAUJAAAAAMNQkAAAAAAwDAUJAAAAAMNQkAAAAAAwDAUJAAAAAMN43YsRAcBo3vYGXQAAGhMr\nJAAAAAAMwwoJAAAAgMu4a8WfFRIAAAAAhqEgAQAAAGAYChIAAAAAhqEgAQAAAGAYChIAAAAAhqEg\nAQAAAGAYHvsLANcoXsAIAPAGrJAAAAAAMAwFCQAAAADDUJAAAAAAMAwFCQAAAADDUJAAAAAAMAwF\nCQAAAADDUJAAAAAAMAwFCQAAAADDUJAAAAAAMAwFCQAAAADDUJAAAAAAMAwFCQAAAADDUJAAAAAA\nMEydCpK9e/dq0qRJ6tWrl8xmszZv3uzQbrPZtGzZMvXs2VMhISGKi4vT0aNHHfpYrVYlJiYqLCxM\nYWFhSkxMlNVqdehz+PBhjR49WiEhIerVq5dSU1Nls9mcnCIAwNORZwCg6apTQVJeXq7IyEgtX75c\nPj4+l7WvXr1aa9euVWpqqnbt2qXAwEDFx8fr7Nmz9j4PPvig8vPzlZmZqczMTOXn52vGjBn29tLS\nUsXHxysoKEi7du3S8uXL9eKLL2rNmjUumCYAwJORZwCg6WpRl04jRozQiBEjJElJSUkObTabTenp\n6Zo7d67Gjh0rSUpPT1dERIQyMzM1bdo0HT9+XDt37tT27dsVHR0tSXr++ecVGxurgoICRURE6J13\n3lFFRYXS09Pl4+OjyMhIffbZZ0pLS9Ps2bNlMplcOW8AgAchzwBA0+X0PSQnTpxQUVGRhgwZYt/m\n4+OjAQMG6MCBA5Iki8UiPz8/9evXz94nJiZGvr6+Dn369+/vcGZs6NChOnnypE6cOOHsMAEA1yjy\nDAB4tzqtkFxJUVGRJCkwMNBhe2BgoE6ePClJKi4ulr+/v8PZJ5PJpICAABUXF9v7dOzY8bJ9VLd1\n6dKlxvgFBQW/2tKmQfO4fD91Uf9YxHF3LOK4MxZx3B3LNXEiIiIaENt9yDPEaRhvm5PnxnFnLOK4\nO5br4lwp1zhdkBjtssnt+dY1+6mLBsQijptjEcetsYjj5ljunFMTRp65BuO4MxZx3BqLOG6O5aY4\nTl+yFRwcLEkqKSlx2F5SUqKgoCBJUlBQkE6fPu3wJBObzaZTp0459KlpH9VtAICmiTwDAN7N6YIk\nPDxcwcHBys3NtW+rrKxUXl6e/Vre6OholZWVyWKx2PtYLBaVl5c79MnLy1NlZaW9T25urkJDQxUe\nHu7sMAEA1yjyDAB4tzoVJGVlZcrPz1d+fr6qqqpUWFio/Px8ffPNNzKZTJo5c6ZWr16t7OxsHTly\nRElJSfL19VVCQoIkqUePHho2bJjmzZsni8Uii8WiefPmaeTIkfZlnYSEBPn4+CgpKUlHjhxRdna2\nVq1apaSkJJ58AgBejjwDAE1Xne4hOXTokMaMGWP/etmyZVq2bJkmT56s9PR0zZkzRxUVFZo/f76s\nVqv69u2rrKwstW3b1v49GRkZWrBggcaPHy9Jio2N1YoVK+zt7du319atW5WcnKzBgwfLbDZr1qxZ\nmj17tqvmCgDwUOQZAGi66lSQDBw48LK33f6SyWRSSkqKUlJSau1jNpu1bt26K8aJiorSBx98UJch\nAQC8CHkGAJoup+8hAQAAAICGoiABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAAYBgK\nEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAA\nYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSAB\nAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACG\noSABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACGoSABAAAAYBgKEgAAAACG8biCJCMjQzfddJOC\ng4N1++23a9++fUYPCQDgRcgzAOBZPKogycrK0qJFi/Too4/q3//+t6KjozVhwgR98803Rg8NAOAF\nyDMA4HlMVqvVZvQgqg0dOlRRUVF64YUX7NtuvfVWjR07Vk8++aSBIwMAeAPyDAB4Ho9ZITl//rw+\n+eQTDRkyxGH7kCFDdODAAYNGBQDwFuQZAPBMHlOQnD59WpcuXVJgYKDD9sDAQBUXFxs0KgCAtyDP\nAIBn8piCBAAAAEDT4zEFib+/v5o3b66SkhKH7SUlJQoKCjJoVAAAb0GeAQDP5DEFScuWLXXzzTcr\nNzfXYXtubq769etn0KgAAN6CPAMAnqmF0QP4pVmzZmnGjBnq27ev+vXrp1dffVXff/+9pk2bZvTQ\n4CYXLlzQddddZ/QwAHgp8gzIM4Dn8aiCZNy4cTpz5oyeffZZFRUVqVevXnr77bcVFhZm9NDgJh07\ndtSePXvUo0cPo4cCN/r+++/1yiuvaP/+/SoqKlKzZs0UHh6uuLg4TZkyRc2bNzd6iPAS5BmQZ5om\n8oxn86j3kHijwsJCLVu2TGvXrnV6X1arVQcOHJDZbFZ0dLRMJpO9rby8XGvWrNHChQudjnPkyBF9\n/PHHio6OVq9evXTs2DGlpaXpp59+0sSJEy97ZGZDLFiwoMbtGRkZSkhIkNlsliStWLHC6Vi/ZLVa\n9cYbb+jLL79UcHCwJk+erE6dOrlk3/v27VNgYKAiIiIk/TyXjIwMFRYWqnPnznrooYf0wAMPuCSW\nO1RVVWnlypU6ePCgRo4cqWnTpmnTpk16/vnnVVVVpTFjxmjJkiVq2bKlU3EOHTqksWPHqmvXrvLx\n8ZHFYlFCQoIuXLigf/7zn+rZs6cyMzPVtm1bF80M8C7kmZqRZzwfeQbVvLIgOXPmjDZv3qyHH35Y\nknTPPfeooqLC3t6iRQtlZGTI39+/0cfy6aef6vbbb9eZM2ec2s/Ro0d1991369SpU6qqqlKfPn30\n17/+1X5Wr7i4WD179nQ6zo4dOzRlyhT5+fnp3Llz2rRpk/74xz+qd+/eqqqq0t69e7Vlyxbdcccd\nTsXp0KGDfvvb36p9+/YO2/fu3atbbrlFbdq0kclkUk5OjlNxevbsqX379un666/XV199pZEjR6qq\nqko9e/ZUQUGBzp07p507d6p79+5OxZGk/v37KzU1VYMGDdL69ev19NNPa8aMGerevbsKCgq0bt06\nPf7443rooYecijNx4kSNGzdOd999t1q1auX0uGvz5z//WRkZGYqNjdXu3bs1efJkZWRkaNasWWrW\nrJnS0tJ0//33a8mSJU7FGTVqlO644w4tWrRIkvTWW29p/fr12rlzp6xWq8aMGaMBAwYoNTXVFdNS\neXm5MjMzdeDAAfujXoOCghQTE6Px48fL19fXJXGupri4WBs2bHDJh7uqqiodP35cZrNZoaGhDm2V\nlZXaunWrJk+e7HQc/B/yTMORZxqOPNMw5BnPzzNeWZCsXLlSn3/+udLS0iRJnTp1UlxcnNq1ayfp\n54PR6NGjnf4Fl6Q333zziu3VZ66cPYBPmjRJLVq00Msvv6yzZ89q0aJFslgsysnJUbdu3VyWKEaM\nGKFBgwZpyZIl2rJlix599FFNnz5djz/+uCTp6aef1ieffKKtW7c6FWflypXauHGj1q5dq9///vf2\n7QEBAdqzZ4969uzp1P6rdejQQZ999pkCAwM1ffp0FRUV6a233pKvr68qKys1depUtW7dWhs3bnQ6\nVkhIiCwWi8LCwjRw4EDNnDlT9957r7393Xff1TPPPKOPP/7YqTgdOnSQyWRSu3btNHHiRE2dOlWR\nkZHODv8yffr0UWpqqkaNGqVjx45pwIABeumll3TPPfdIknJycvTEE0/o0KFDTsUJDQ1VXl6eunTp\nIunng15wcLAOHz6soKAg5ebmKikpSUePHnV2Sjp27Jji4+NVVlamAQMG2N9HUVJSory8PPn5+Skr\nK8tlv39X4qoPkYWFhZowYYKOHTsmk8mk2NhYrV271n7211XHBjgizzQceabhyDMNQ57x/DzjUfeQ\nuEpOTo6efvpph20pKSn2X8T3339fS5cudUmiSEpKsp9lqUlVVZXTMSTp4MGDysnJka+vr3x9ffXa\na69p8eLFuvPOO5WTk2NPgs46duyYXnrpJUlSfHy8ZsyYobvuusvePmHCBG3evNnpOI888ogGDhyo\nxMRExcfHa8mSJWrWrHEf+nbw4EG98MIL9jMTrVu31vz58zV16lSX7N/Pz09nzpxRWFiYvv/+e/Xu\n3duhvU+fPiosLHRJrI8++kg7duzQpk2btH79et16662aOnWqxo8frzZt2rgkRlFRkaKioiT9fAaw\nefPmDnPq06ePioqKnI4TEBCg7777zv7/s6ioSBcvXrQvnXft2lU//PCD03EkKTk5WTExMUpPT1fr\n1q0d2iorK5WUlKTk5GRt27bN6Vh79+69YvsXX3zhdAzp5w9vZrNZ+/fvV2lpqZYsWaLRo0crOztb\nAQEBLomBy5FnGo4803DkmYYhzzjHHXnGKwuSEydOqGvXrvavo6KiHK4/jIyM1JdffumSWKGhoVq+\nfLnDwfSX8vPznV52lqTz589floyWLl0qm82mO++8U+vXr3c6RrXqA3azZs3UunVrh+VuPz8/lZaW\nuiTObbfdpo8++khz587V0KFDlZGR4ZL9/lr1z+38+fM1vqH51KlTLokzfPhwrVu3TmlpaRo4cKDe\nffddhwNrVlaWunXr5pJYoaGhSk5OVnJysnbt2qWNGzfq0Ucf1WOPPabx48dr6tSpuvnmm52KERwc\nrKNHj6pz584qKCjQpUuX/n97dx8TZR3AAfzLMWRn5zyN4CRRVKrJVlIswZjmS1fnLmG8yU0aKC5K\nF6MMXSYJqwk2/8E1XJsjNLQME6UuQmhRXTnnVFhom51sjg5Jh+zw4LgJx/UH4/LxDk/uHu7g7vvZ\n3Ljnee75Pc95e773e3seXLt2DUuXLgUw+qNCjAuRWq3Gjh07UFpaitDQUBw4cABJSUkiWJIyAAAL\nM0lEQVSQSqUAAL1e79A97K5Lly6hpaXFISSA0R8ORUVFWLdunShlvf766wgKCoLNNn4n9Hg/MCdC\np9Ph66+/tk/Q1Wq1ePPNN6FWq0UJPHKOOeMZ5ox7mDPuYc54xhs545cVEovFgr6+PvsksrNnzwrW\n9/f3i1bWsmXL8Oeff44bFK6+KI8qJiYGra2tDl185eXlGBkZQXZ2tsdlAEBUVBQ6OjrsrQhNTU2C\nyXhdXV2iPkBs9uzZqK6uxtGjR+3jbsWmVqsRHByMu3fvQq/XC7qdDQaDaGO8S0tL8dprr2H9+vWI\nj4/HoUOHcO7cOfvY3osXL4rS6vegtWvXYu3atejp6cHx48dRU1ODo0ePetxFm5mZibfffhsqlQo6\nnQ47duxAcXExbt++DYlEgoqKinG/9xNRXFyMW7du4Y033oDVasXy5cvtw2CA0R8sJSUlHpcDAHK5\nHNevXx+3q7yjo8PeBe2pxx9/HGVlZXjllVecrr969SpSUlI8LsdkMglarkNCQlBVVYW8vDyo1epJ\n+wEW6Jgz7mPOuI854x7mjGe8kTN+WSGJjo5GW1ubvRvwQZcvX8bChQtFKaugoAADAwPjrl+8eLHH\nE+aA0VrwqVOnnE4Y+vTTT2G1WlFVVeVxOVu2bMG9e/fsrx8cM9rU1CQYiyuW3NxcJCUl4cKFC4iM\njBRtvw9O5JLJZILXjY2NWLFihShlRURE4Ndff8XBgwfR0NAAm82GS5cuwWAwIDExEfv27cPzzz8v\nSlnOhIWFobCwEIWFhdDpdB7v78MPP7TfjWTr1q149913sXTpUpSUlMBsNkOlUmHPnj0elyOTyVBd\nXQ2LxYLh4WGH/yMx7rYzJicnB9u3b4der8eaNWsEY3tbWlpQUVGB7du3i1LWsmXLcOPGDcydO9fp\nerlcLsqPyOjoaFy5ckXQKhocHIwvvvgCmzdv5mT2ScKccR9zxn3MGfcwZzzjjZzxy0ntZWVl+Oqr\nr/Dzzz87tLLcvHkTSqUSmzZtEuVLTuRtzz33HH755ZdxL0D0cBUVFfj8889x69Yte1e2zWZDREQE\ntm3bhsLCQlHK+f7772E2m5GVleV0vdFoRENDg2BCqjtKSkrQ3t6Ouro6h3XDw8PIycnBjz/+KNr4\naBrFnCF/xpzxDHNm4vyyQtLf3w+lUgmDwYCsrCx7jU6v16O2thbz58/HTz/95FBDJqLAcePGDcHt\nGMeGj0w3w8PDMJvN4044tlqt6Orq4oP/RMacISJXmDOPzi8rJMBorfDjjz9GXV0d+vr6AIyOI01L\nS8PevXthtVq9cn94Ipo+xHzA3FQpy5vnFGiYM0Q0UcwZ5/yyQrJv3z57N7nNZrPf3SIsLAxBQUHo\n7e3Fhg0bXN4ujYgCi1j3bJ9KZXnznAIJc4aI3MGccc4vJ7VXVlbiiSeeQH5+PoKCggS34DMajUhO\nTp70e5ET0dTzKA+Ym25lefOc6H/MGSJyhjnjHr/sIWlsbERubi4qKyuRkZFhX240GpGSkoKhoSFo\ntVpO1iIKMHPmzHH5gDmLxSJKa5K3yvLmOdH/mDNE5Axzxj1+2UOiUqlw8OBBvPPOO5gzZw7WrVuH\nvr4+pKamwmKx4IcffmBIEAUgbz1gzptlefOc6H/MGSJyhjnjHr/tT9ZoNNi7dy9ycnLQ3NyMtLQ0\n9Pf3i/qYeyKaXsYeMDcesR4w582yvHlOJMScIaIHMWfcE/zBBx+UerSHKezFF1/E4OAg3n//fUil\nUmi1WigUCl8fFhH5SGRkJMLDwwUPd7qfTCbDmjVrRLlFrrfK8uY5kSPmDBHdjznjHr+cQ6LRaASv\nW1paEBsbi4iICMHyEydOePOwiIjITzBniIjE45dzSB4ct5uenu6jIyEiIn/EnCEiEo9f9pAQERER\nEdH04LeT2omIiIiIaOpjhYSIiIiIiHyGFRKiKUSn00Eul0On0/n6UIiIyA8xZ2gq8stJ7UQT1dPT\ng0OHDqGxsRGdnZ0YGhqCQqFAYmIisrOzsWrVKl8fIhERTWPMGaLxsUJCAa+1tRUbN27E3bt3kZqa\nii1btiA0NBSdnZ1oaGhAcnIyTp48CaVS6etDJSKiaYg5Q/RwrJBQQDMajcjOzoZEIoFOp8PTTz8t\nWF9cXIzvvvsOjz322EP3MzAw4HIbIiIKPMwZItc4h4QCWnV1NW7evIny8nKHkBiTnJyMl156yf66\nvLwccrkcf/31F/Lz8xEdHY0VK1YAADo7O1FUVITly5dj3rx5WLBgAbKysnD16lWH/XZ1dWHTpk2I\njIxETEwMdu/ejXv37jk9hsuXLyMzMxMLFiyAQqGASqXCb7/9JsInQEREk4k5Q+Qae0gooDU2NkIq\nlWLDhg0Tfm9eXh4WLlyI4uJi+wW+tbUV586dQ3JyMqKiotDd3Y0jR45ArVbj/PnzUCgUAIDBwUGk\npKTAYDDgrbfegkKhwMmTJ51e/H///Xekp6fj2Wefxc6dOxESEoJvvvkGaWlpOH36NFauXOnZh0BE\nRJOGOUPkGiskFNCuXbuGmJgYhISECJabTCZBK1JoaChkMplgm6eeego1NTWCZa+++ipSUlIEyzQa\nDRISElBTU4OdO3cCAI4cOYLr16+juroaqampAIDNmzc7TGq02Wx47733kJiYiDNnziAoKAjAaEit\nWrUKn3zyCZqamjz4BIiIaDIxZ4hc45AtCmgmk8khAACgsLAQS5Yssf8bu8Dfb+vWrQ7LpFKp/W+z\n2Yze3l7MmjULMTExaGtrs69rbm5GeHi4IFSkUilycnIE+2tvb4der0dGRgZ6e3tx584d3LlzByaT\nCatXr8bFixdhNpvdOnciIpp8zBki19hDQgFNJpPBZDI5LC8qKrJftDUajdP3RkdHOyyzWCwoKytD\nbW0t/v33X8G6uXPn2v/+559/sGjRIkgkwjaBJUuWCF53dHQAAAoKClBQUOD0OHp7ezFz5kyn64iI\nyLeYM0SusUJCAe2ZZ55Be3s7hoaGBN3psbGxiI2NBQAEBwc7fe/9rVRjdu3ahWPHjiE/Px8JCQmY\nPXs2JBIJdu/ejZGRkQkf39h7SktLERcX53SbsLCwCe+XiIi8gzlD5BorJBTQVCoVLly4gPr6emRk\nZHi8vzNnzkCj0WD//v2C5UajUdByFRUVhStXrmBkZETQejXWUjVm0aJFAEZb2FavXu3x8RERkXcx\nZ4hc4xwSCmh5eXlQKBTYs2cP/v77b4/3FxwcDJvNJlj27bfforu7W7BMqVTi9u3bqK+vty8bHBzE\nl19+KdguLi4OixcvRmVlpdMu/56eHo+PmYiIJg9zhsg19pBQQJPL5Th+/Dg2btyIlStXIi0tDfHx\n8ZgxYwYMBgO0Wi0GBgYwf/78R9rf+vXrceLECcyaNQuxsbFob29HXV2dwzjg3NxcHD58GNu2bUNb\nWxvmzZuH2tpazJgxQ7CdRCLBZ599hoyMDCQmJiI7OxtPPvkkuru78ccff8Bms0Gr1Yr1cRARkciY\nM0SusUJCAS8+Ph7nz59HZWUlzp49i/r6elitVigUCiQkJKC8vBwvv/zyI+1r//79CAkJwenTp3Hs\n2DHExcXh1KlT+OijjwTbzZw5E/X19di1axcOHz4MqVSKzMxMKJVKpKenC7ZNSkpCc3MzDhw4gKqq\nKphMJoSHh+OFF15wuFsKERFNPcwZoocLMhqNNtebERERERERiY9zSIiIiIiIyGdYISEiIiIiIp9h\nhYSIiIiIiHyGFRIiIiIiIvIZVkiIiIiIiMhnWCEhIiIiIiKfYYWEiIiIiIh8hhUSIiIiIiLyGVZI\niIiIiIjIZ1ghISIiIiIin/kPJK6RqAeJMr8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107c5da10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Students by grade, by year\n",
    "studentsByGrade = fig, ((ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(nrows=3, ncols=2, figsize=(12,12), sharex=False, sharey=False)\n",
    "\n",
    "df_studenByGradeNoTotal.plot(kind='bar',x='Grade',y='2011',ax=ax1).set_ylim([0,4500])\n",
    "df_studenByGradeNoTotal.plot(kind='bar',x='Grade',y='2012',ax=ax2).set_ylim([0,4500])\n",
    "df_studenByGradeNoTotal.plot(kind='bar',x='Grade',y='2013',ax=ax3).set_ylim([0,4500])\n",
    "df_studenByGradeNoTotal.plot(kind='bar',x='Grade',y='2014',ax=ax4).set_ylim([0,4500])\n",
    "df_studenByGradeNoTotal.plot(kind='bar',x='Grade',y='2015',ax=ax5).set_ylim([0,4500])\n",
    "df_studenByGradeNoTotal.plot(kind='bar',x='Grade',y='2016',ax=ax6).set_ylim([0,4500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a8e3d90>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x107c588d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Student count and school count by year\n",
    "fig, ((ax1,ax2)) = plt.subplots(nrows=1, ncols=2, figsize=(16,6), sharex=False, sharey=False)\n",
    "\n",
    "df_studentParticipation.plot(kind='bar',x='Year',y='Student Count',ax=ax1)\n",
    "df_schoolParticipation.plot(kind='bar',x='Year',y='School Count',ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Since the start of the program, public schools lost 8630 kids.\n",
      "Since the start of the program, non-public schools lost 18718 kids.\n",
      "Since the start of the program, choice schools gained 30388 kids.\n",
      "The school choice program started with 3911 kids and grew to 34299 kids in the 2016-17 school year.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the change in enrollment from public and private schools\n",
    "public_school_change = (df_enrollment['Traditional Public Num'][5] - df_enrollment['Traditional Public Num'][0])*-1\n",
    "choice_school_change = (df_enrollment['Choice Num'][5] - df_enrollment['Choice Num'][0])\n",
    "non_public_school_change = (df_enrollment['Non-Public (Excluding Choice Students)* Num'][5] - df_enrollment['Non-Public (Excluding Choice Students)* Num'][0])*-1\n",
    "\n",
    "starting_count = df_studentParticipation['Student Count'][0]\n",
    "current_count = df_studentParticipation['Student Count'][5]\n",
    "count_change = current_count - starting_count\n",
    "\n",
    "\n",
    "print(\"Since the start of the program, public schools lost %s kids.\") %  public_school_change\n",
    "print(\"Since the start of the program, non-public schools lost %s kids.\") %  non_public_school_change\n",
    "print(\"Since the start of the program, choice schools gained %s kids.\") %  choice_school_change\n",
    "print(\"The school choice program started with %s kids and grew to %s kids in the 2016-17 school year.\") % (starting_count, current_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WLTZs2MCyZcvw8/NDXV2dtm3bMnv2bCVfzJu2l9/7KHfmVX7vo+J+F7yv7w4hhBCFk8CH\nEEKIf6T8cm5UrlwZyFmasHHjxmLX1aRJE+rVq0dkZCSpqano6OiwadMmtLW1i5UYMrfdlJSUfM/f\nuXNHpRyg/Nr86k3Tq9LT0/PcbJUvXx4fHx98fHxITk4mJiaGLVu2sGPHDs6fP8/Ro0fR1NQstK9d\nunRBT0+PoKAgJk6cyIYNG3j27BmDBw8uMo9JruXLl3P//n2WL1+eJ2FraGhonp1d3lbuuB06dCjf\nX97fZRt3797N93x+r23ucwIDAwtNLPs2bRRHQc/Lbae477uigo8laae4ct/jbztDqEyZMnh4eODh\n4cH9+/eJjY1l+/btbNq0ie7duxMXF0fVqlXfWXsFKel3wZt8dwghhHj3JMeHEEKIf406depQpUoV\nTp48qbJtbXH06dOH58+fs2XLFg4dOkRycjJdu3Yt1vT73B0ajh07lm+7ubknXr1pz9014saNG3nK\nX7p0qchfz/X19enRowchISE0b96cP//8k/PnzxfZV01NTQYMGMDt27fZtWsXP/30E1paWiXacSZ3\nCUd+N/sF7UyS36/jr1JXVy9wG91mzZoBOQlV/y6vvoYvXrzIcz6/6yppvypVqkStWrW4c+eOsjyn\nqDaKIz4+nocPHxZY36s7iBT2vjt16lSh7eTXvxcvXihLfl5tp7iaNGmCuro6sbGxPHr0qMTPz0/V\nqlXp3Lkzq1at4vPPP+fevXvKa5T7mh08eLBY2zaXlI2NDUCB2wXn5r7J/S6oU6cOFSpU4OzZs/km\nKM7vu0MIIcS7J4EPIYQQ/xoaGhp4eHiQkpLC+PHjefz4cZ4yqampSj6HV7m7u1OmTBlCQkJKtMwF\ncpIQOjg4cPPmTfz8/FTOnTt3jjVr1qClpaWSZ6FRo0aoq6uzefNmlRu+jIwMJkyYkKeNe/fu8ccf\nf+Q5/uzZM2UafIUKFYrV3yFDhlCmTBkmTZrEpUuXcHV1pWrVqsV6LuQk4gSUXXdyHThwoMAkjLn1\nX79+vcDz9+7dy5MrBKB///7o6OiwePFi4uLi8pzPzs4mJiamxMGuVxkaGtKuXTuuX7/OypUrVc5F\nRETkeyPbuXNnatWqxdq1awtMIBofH68siYGcZVXZ2dlMnz5d5cb72rVrfP/992/U9wcPHihLn3Id\nP36cn3/+GR0dHZUdfHJ3SAoKClJJ5puamsq0adMKbefw4cPs3btX5djKlSu5ceMG7dq1K3F+D4Bq\n1arx+eefk5KSwpQpU/IEI548ecJff/1VaB3Pnj3LN/iUnZ2tzEbJ/Ww0bNiQFi1acPbsWZYsWZLn\nOenp6W8VgHF2dqZq1aqEh4fnCRQFBwdz6tQpPvnkEyUAo6mpibu7O48fP2bWrFkqW3gnJyfz7bff\noqamRv/+/d+4T0IIIYomS12EEEL8q0yYMIGzZ88SGBhIZGQkbdq0wdDQkHv37nHlyhViY2P58ssv\n8/w6raenh4ODA5GRkZw9exZTU9M8O3UUZunSpTg6OjJv3jwOHz5Ms2bNuH37NmFhYTx9+pTvvvtO\nSYaY217v3r3ZsGEDrVu3pmPHjjx9+pQDBw5gYmKSJwfBrVu3aNOmDZaWltSvXx9DQ0MyMjI4ePAg\nly5domvXrpibmxerrwYGBjg5ObFz504gJxBSEkOHDiU4OJjBgwfj6uqKnp4e586dY//+/XTv3j3f\nbU3btWvHyZMnGTBgAB07dqRcuXIYGxsr28S2a9eOLVu28Pnnn2NnZ4eWlhZWVlY4OTnx0UcfERgY\nSP/+/enYsSNt2rShXr16aGpqcvPmTY4fP86NGzdISkrKN7lscS1ZsoTPPvuMadOmERUVRYMGDbhy\n5Qo7duxQEsu+SlNTk6CgIHr06EHfvn1p2rQpNjY2aGtrc/PmTRISEvjzzz85fPiwEvgZNWoUu3bt\nYvfu3bRu3ZoOHTrw4MEDfv75Z2xtbdmzZ0+J+21ra8u6des4ceIELVu25NatW/z8889kZ2fj5+en\nMmupSZMmtG7dmujoaOzt7bG3t+evv/4iMjKStm3b5htcy9WpUyf69etH165dqVmzJgkJCezfv5+q\nVavmG0QorsWLF3Pu3DnWrVvHkSNHcHBwoFy5cly7do2DBw+yfPlyJfdOfp48eYKTkxM1a9akUaNG\nGBsb8/z5c3799VdOnz5Ns2bNVHbl+f777+nSpQvz589n165dyrkrV65w8OBB9u7d+0azVwC0tbVZ\nsWIFAwcOpFu3bspY/fHHH0RGRlKlShVWrlypsqxsxowZxMTEEBgYSEJCAvb29qSnpxMWFsZff/2F\nt7d3vlt6CyGEeHck8CGEEOJfRUNDg8DAQLZu3UpwcDD79u3j0aNHVK1aFWNjY8aOHYu7u3u+z+3X\nrx+RkZE8f/6c3r17FzvnBYCpqSmHDh1iyZIlREREEBsbi7a2Nq1ateKrr77KdzvU7777Dl1dXbZs\n2cKaNWvQ1dXFzc0Nb29vmjdvrlLWxMSEyZMnEx0dzZEjR7h37x5VqlShVq1ajB49utizU3INGDCA\nnTt3Ymlpme92wYWxsrJix44dzJ07l7179/Ly5UusrKxYv349VapUyTfwMW7cOB48eMCePXvw8/Pj\nxYsXtGrVSgl8+Pr6oq6uzqFDh4iNjSUrK4s+ffrg5OQE5OySceTIEQICAjhw4ABxcXFoaGigq6tL\n8+bNmTlz5lvnQTA3N2f//v3MnDmTQ4cOcfToUerXr09wcDD37t3LE/gAsLS05MiRI6xcuZLdu3cT\nEhJCdnY2urq61KtXDy8vL5UdXLS0tAgLC2PBggX8/PPPrFq1ChMTE8aNG4eLi8sbBT5q1qzJt99+\ny8yZM/nxxx/JzMykcePG+Pj4KLuCvCooKIhZs2axa9cuVq9ejYmJCV5eXowaNYqtW7cW2E7Xrl0Z\nNGgQS5cuJSIiAk1NTVxdXZkxYwa1atUqcb9z6ejoEBkZyapVq9i2bRuBgYGoq6tjYGCAm5tbkcs8\ntLW1mT17NtHR0fz222/s2bOH8uXLY2pqyty5cxkyZIjKTj01a9bk8OHD+Pv7s3PnTlavXo2WlhZG\nRkYMGzZMmdH0phwdHYmMjGTp0qVERUURHh5O9erV6dOnD97e3nl2idLR0WHv3r34+fmxfft2VqxY\ngZaWFg0aNGDEiBHFyh8jhBDi7ailpaVlF11MCCGEEP8mS5cuZfbs2SxatIjhw4eXdnfEG4iOjsbF\nxYU+ffrkWZ4jhBBCiOKTHB9CCCHEByYjI4PVq1dTuXJl+vTpU9rdEUIIIYQoVaUa+Fi6dCnt2rXD\n2NgYc3Nz3N3dOXv2rEqZ7OxsfH19qVevHnp6ejg7O3Pu3DmVMmlpaQwfPhwTExNMTEwYPnx4ni3b\nzpw5Q+fOndHT0+OTTz5h4cKFKgmmAMLDw2nRogU1atSgRYsW7Nix4++5cCGEEOJvsHfvXhYtWoSr\nqyvJycl89dVXVKpUqbS7JYQQQghRqko18PHrr78ydOhQ9u7dy/bt29HQ0KBbt24q2b39/PxYvnw5\nCxcu5ODBg1SvXp3u3burbOv25ZdfkpCQQGhoKKGhoSQkJDBixAjl/IMHD+jevTs1atTg4MGDLFiw\nAH9/fwICApQycXFxfPHFF7i5uREdHY2bmxuDBw/m+PHj72cwhBBCiLcUFhbG/PnzuXr1Kl5eXnz9\n9del3SUhhBBCiFL3j8rx8ejRI0xMTAgODsbJyYns7Gzq1avHsGHDGD9+PJCT2bt27drMmTOHIUOG\nkJiYSIsWLYiIiKBly5YAxMTE4OTkxG+//Ubt2rX58ccfmTlzJhcuXKB8+fJATobxNWvWcPbsWdTU\n1BgyZAh//fUXYWFhSn9cXV2pVq0aP/744/sfDCGEEEIIIYQQQry1f1SOj0ePHpGVlYWOjg4AV69e\n5c6dO7Rv314pU758eezs7Dh27BiQM1OjYsWKtGjRQinTsmVLtLW1VcrY2toqQQ8ABwcHkpOTuXr1\nKgC//fabSju5ZXLrEEIIIYQQQgghxL/PPyrwMXHiRKytrZUt/u7cuQNA9erVVcpVr16dlJQUAFJS\nUvj4449VtiRUU1OjWrVqKmXyqyP3XG5bhbUjhBBCCCGEEEKIfx+Noou8H5MnTyY2NpaIiAjKlClT\n2t0RQgghhBBCCCHEB+AfMeNj0qRJbN26le3bt1OzZk3luK6uLgB3795VKX/37l1q1KgBQI0aNUhN\nTVXZoSU7O5t79+6plMmvjtxzuW0V1o4QQgghhBBCCCH+fUo98OHj46MEPerUqaNyztTUFF1dXX75\n5Rfl2NOnT4mJiVFyejRv3pxHjx4RFxenlImLiyMjI0OlTExMDE+fPlXK/PLLL+jr62NqagpAs2bN\nVNrJLfNq7hAhhBBCCCGEEEL8u5SZOHHizNJqfPz48WzcuJGffvoJIyMjMjIyyMjIAKBs2bKoqanx\n8uVLvvvuO8zNzXn58iVTpkzhzp07fPfdd2hpaVGtWjWOHz9OaGgo1tbW3Lx5kzFjxtC4cWNlS1tz\nc3PWrl3L6dOnqV27NjExMUyfPp2vv/5aCWzo6+szf/58ypYty8cff8y6desIDg7Gz88PAwOD0hoi\nIYQQQgghhBBCvIVS3c42d/eW1/n4+DBp0iQgZ9nKggUL+Omnn0hLS6NJkyYsWbIES0tLpXxaWhre\n3t7s2bMHACcnJxYtWqRS/5kzZxg/fjwnT55ER0eHIUOG4OPjo5IUNTw8nLlz55KUlISZmRlTp06l\na9euf8elCyGEEEIIIYQQ4j0o1cCHEEIIIYQQQgghxN/pH7Ory4fkxYsXypIdIYQQ/7u0tbXR0JB/\naoUQQgghSpP839g79uLFCx4+fIiOjo7KMhohhBD/W7Kzs0lLS6NSpUoS/BBCCCGEKEWlvqvLhyYj\nI0OCHkIIIVBTU0NHR0dmAAohhBBClDIJfPwNJOghhBAC5N8DIYQQQoh/Agl8CCGEEEIIIYQQ4oMl\ngQ8hhBBCCCGEEEJ8sCTwIUqdr68vtra277VNT09P3N3d32ubpcnW1hZfX9+3qiM8PBwdHZ131KPS\nYWhoSHBwsPJYR0eH8PDwUuzRu1Wc63m9TGmPwfv6/JfG94wQQgghhPhnkDTz74l2yv732l5GjQ4l\nKu/p6UlISAgAGhoaGBoa4uLiwqRJk9DW1v47uqjw8vJixIgRf2sbf6eAgACmT5/OmDFjmDZtmsq5\n4OBgvL29uXnzpspxa2trhg8fjpeX1/vs6r/W3zVeiYmJ/4hgTnBwMP/5z3+Ux7q6utja2jJz5kxq\n1qz5t7b9tmNw+vRp5s+fz4kTJ0hPT6d69eo0atSIefPmYWJi8g57KoQQQgghxJuRGR9CYW9vT2Ji\nIr///jtTp07lxx9/zHMj/6rnz5+/k3YrVqxI1apV30ldpSEoKIgxY8awYcMGXr58WdrdESWgq6uL\nlpZWaXcDgAoVKpCYmMj58+dZvXo1p0+fpm/fvn/7e+ptxuDevXu4urpSsWJFNm/eTFxcHKtWrcLM\nzIyHDx++454KIYQQQgjxZiTwIRRaWlro6upiZGSEm5sbbm5u7Nq1C4Do6Gh0dHSIjIykffv2VK9e\nnQMHDgCwZ88e2rZti66uLg0aNGDOnDlkZmYCMHv2bNq2bZunrY4dO+Lt7Q3knYKelZXFokWLqF+/\nPjVq1MDOzk7pB8DVq1fR0dHh1KlTKnW+PmV/4cKFWFlZUaNGDerUqVPgrJKQkBDMzMx49uyZyvFh\nw4bRu3fvQscsLi6O1NRUJk6cSLly5di3b59yLjo6mv/85z/KFsc6Ojr4+vri7OzM9evXmTZtmnI8\n17Fjx+jcuTP6+vp88sknjB07lgcPHijnnZ2dGTduHLNnz6ZWrVpYWFgwdepUsrKylDJ3796lT58+\n6OnpYWVlxfr16/P0Oz09ndGjR2NhYYGRkRGdO3fOM54hISFYWVmhr6+Pu7s7KSkphY4F5Mx+sbOz\nw8DAgE8++QQvLy/S0tKU88HBwRgaGhIVFYWtrS0GBgZ06dKFpKSkIut+lbW1NYsXL+brr7/G2NgY\nS0tLli1bplLm8uXLODs7o6urS9OmTYmIiMhTz+vvmZkzZ9K0aVP09PSwtrZm+vTpPH36VDmf+17d\nunUrDRu/VQjOAAAgAElEQVQ2xMjIiL59+5KamqqUOXnyJN27d6dWrVoYGxvj6OhIXFxckdekpqaG\nrq4uenp6tGnTBh8fH86ePcvly5fz7WvuOPj7+6scu3PnDr169UJfXx8rKys2bdpUaLuv15ucnMyw\nYcMwMzNDX1+fTz/9lMOHD+f73NjYWNLS0li+fDkNGzbE1NSUTz/9lNmzZ1O/fv0S1VnYmBb1nQBw\n5swZXF1d0dPTo2bNmnh6epKenl7otQshhBBCiP8NEvgQBSpXrlyeWR0zZ85k6tSp/PbbbzRt2pQD\nBw4wfPhwhg0bRmxsLAEBAYSHhzN79mwAevXqRXx8PBcuXFDqSEpKIi4ursAcGytXrsTf35+ZM2dy\n9OhRnJ2dGTBgAAkJCcXue3h4OAEBAXzzzTecOHGCTZs20aRJk3zLduvWjaysLHbv3q0cS09PZ+fO\nnQwYMKDQdgIDA+nRoweampr06tWLwMBA5VyLFi3w9fVVfslPTEzEy8uLoKAgDA0N8fb2Vo5Dzo1b\njx49cHJy4tdff2X9+vWcPn2aUaNGqbS5ZcsWypQpQ2RkJIsXL2blypVs27ZNOT9y5EiuXLlCWFgY\nwcHBbNy4kWvXrinns7OzcXd3Jzk5mU2bNnH48GHs7Ozo2rUrt2/fBuD48eOMHDmSwYMHEx0djaOj\nI/Pnzy9y3NXV1fH19SUmJobVq1dz4sQJJcCV69mzZyxdupSAgAAiIyNJT09n7NixRdb9uhUrVmBp\naUlUVBSjR49m+vTpSoAhKyuL/v37k5WVRWRkJAEBASxYsCBPcOt1FSpUICAggGPHjvHNN9+wbds2\nlixZolLm2rVrbNu2jaCgILZt20ZCQgJz5sxRzj98+BB3d3f27NnDgQMHsLa2xs3Njfv375fo+sqV\nKweUfGaVr68vTk5OREdHM3jwYDw8PPIEtQqSkZGBs7Mz165dIzg4mKNHj+Z5/V6lq6tLVlYW4eHh\nZGdnv3GdRY1pUd8JGRkZfP7552hra3PgwAGCgoKIi4vL89kRQgghhBD/myTHh8jXiRMnCA0NzTNb\nw8fHh/bt2yuPlyxZgpeXF/379wfAzMyMmTNnMmLECObMmUO9evVo0KABmzdvZurUqUDOjbuFhUWB\ngYiAgABGjRqFm5sbAFOmTOHo0aMEBATwww8/FKv/169fR1dXl/bt26OpqYmxsTGNGjXKt2z58uXp\n1asXQUFBdO/eHYDQ0FAqVapEp06dCmzj0aNHhIWFsWPHDgB69+7N0qVLuXPnDrq6upQtW5bKlSsr\nv+S/Sl1dnUqVKqkcX7ZsGd27d1fJY/HNN9/Qpk0b7t69S/Xq1QGoW7cuU6ZMAcDCwoJ169YRFRVF\nz549uXjxIvv27SMiIoKWLVsCOTeNDRs2VOo8fPgwp0+f5uLFi5QvXx6AqVOnEhERwaZNmxg9ejSr\nVq2ibdu2jB8/Xmnn5MmT+c4eedXIkSOV/zY1NWX27Nn07duXVatWoa6eE2d98eIFS5YsoXbt2kBO\njpdRo0aRnZ2NmppaofW/qn379gwfPhyAESNG8P333xMVFUXz5s05dOgQ58+fJz4+HmNjY+D/AwKF\nefWG3NTUlLFjx+Lv76+8d3P7v2LFCqpUqQLA4MGDVRKmvv6ZWbRoEdu3b2ffvn3FTqh78+ZN/P39\nMTQ0xMLColjPyeXi4sKQIUMAGD9+PNHR0axcubJYn53Q0FBSUlLYt28fH3/8MZDzmS5Is2bNGDdu\nHJ6enowfP57GjRvz6aef4ubmpuT3KE6dRY1pUd8JoaGhPH78mO+//55KlSoB8N133+Hi4sLly5ep\nVatWscZOCCGEEEJ8mGTGh1Ds378fQ0NDdHV1+eyzz7Czs2PRokUqZV4PHsTHx/PNN99gaGio/A0b\nNoyMjAzu3LkD5Mz6CA0NVZ6zZcsW5QbmdQ8ePCA5OVm5ac9la2vL+fPni30t3bp14+nTp9jY2DBq\n1CjCwsIK/bV/4MCB/PLLL0oS0qCgIPr06YOGRsGxwW3btmFgYKCMiZmZGY0bN1aSxJZUfHw8mzdv\nVhlLR0dHAK5cuaKUe3UJAYCenh53794FchJVqqurqwSVTExM0NfXV2nn8ePHWFhYqLR17tw5pZ3E\nxESaNWum0s7rj/MTFRVFt27dsLS0xMjIiAEDBpCZmam8FyBnSVVu0CO3/5mZmSpLYoqjqHEwMDBQ\ngh4ATZs2VYIvBQkPD8fR0ZE6depgaGjI5MmTuXHjhkoZY2Nj5QY9t9179+4pj+/evcvXX39NkyZN\nMDExwcjIiLt37+ap53UZGRkYGhpiYGBA/fr1yczMZP369ZQtW7bwgXhNfq9bcT87CQkJ1K9fXwlQ\nFMe0adO4cOECfn5+WFpasn79elq2bElUVFSx6yxsTIvznZCYmEj9+vWVoAfkzLhSV1cv0feGEEII\nIYT4MMmMD6Gws7PDz88PDQ0N9PX10dTUzFPm9R1esrKy8PHxoVu3bnnKVqtWDYCePXsyY8YM4uLi\nKFu2LBcuXHijrWRzZwPk3ry+OrX+9eUARkZGHD9+nKioKA4dOsTUqVNZuHAh+/fvz3eXGmtra2xs\nbNiwYQPOzs6cOnWqyF/IAwMD+fPPP1Vu6LKysrh37x5ff/11ia8vKyuLgQMHqsyayPVq4OL110VN\nTS3PMoPCZk5kZWVRo0YN9uzZk+fcqzeOJXXt2jXc3d0ZOHAgkydPpmrVqsTHxzN06FAl5wuQJ5iU\n29dX85QUR3HGoSR+++03vvjiC3x8fJg/fz5VqlRh9+7deRL85tfuq3339PQkJSWF+fPnY2JigpaW\nFl27dlUZg/xUqFCB6Oho1NXVqV69ep73aX7X9+LFize51HeuatWqdOvWjW7dujFjxgzatGnDokWL\n8s3vk5+ixrQgxZkhVJJZREIIIYQQ4sMkgQ+hqFChQomnhNvY2HDhwoVCn5ebrHHLli2ULVuW5s2b\nF7hFZ+XKldHX1yc2NlblpikmJoa6desC/x9Qyc1HATlbar6uXLlydOrUiU6dOjFmzBjq1KnDsWPH\nVJbqvGrQoEH4+fmRmppKy5YtVWYlvO7cuXMcP36csLAwatSooRx/+vQpnTp14siRI7Rq1YqyZcvm\nuytHfsdtbGw4d+7cW03Lr1OnDllZWZw4cYIWLVoAOct+kpOTVdpJSUlBXV29wNehbt26HD9+XOXY\n649fd+rUKTIzM/H19aVMmTIA+SYUfR/q1q3LrVu3uHHjBkZGRkDO8q3CbqZjY2PR19dXWe5y/fr1\nErcdGxvLggULlGVSKSkpKjNeCqKmplboa1+tWjWV93xKSorK41zHjx9XyU1z/Phx5bNTlAYNGrBp\n0yZSU1NLNOvjVWXLlqVmzZpK3962zuJ8J9StW5egoCAePnyoBO+OHTtGVlZWsa9dCCGEEEJ8uGSp\ni3gr3t7ehIaGMm/ePM6ePcuFCxcIDw9n+vTpKuV69erFtm3b2LZtG7169Sq0Ti8vLwICAggNDeXi\nxYvMmzePmJgYJfdF+fLladasGX5+fpw7d45jx46p5GCAnN1DAgMDOXPmDElJSQQHB6OpqVnojeXn\nn39OSkoKa9asUXKWFCQwMJAGDRpgb2+PpaWl8te4cWPatm2rJDk1MTHh6dOn/PLLL6SmpvL48WPl\neExMDLdu3VJ2rxg9ejQnT55kzJgxxMfHc/nyZSIiIko0e6R27dp06NCBMWPGEBcXR0JCAiNHjlRy\neUDOtsUtW7akb9++7Nu3T0k2O3/+fI4ePQrk5Mw4dOgQS5cu5dKlS6xbt46dO3cW2ra5uTlZWVms\nWLGCpKQkQkNDWbVqVbH7/i7Z29tTp04dPDw8SEhIIC4ujsmTJxe6dMnCwoLk5GQ2b95MUlISP/74\nI1u3bi1x2+bm5mzevJnz589z8uRJvvjiixIvV8lPmzZt+O9//8upU6eIj49n5MiRSgLUV+3YsYN1\n69Zx6dIlli5dSlRUFJ6ensVqo2fPnlSrVo2+ffty9OhRkpKS2L17d4G7ukRERDB8+HAiIiK4ePEi\nf/75J/7+/uzbt48uXbq8UZ35Keo7wc3NjQoVKuDh4cGZM2c4cuQIY8aMwcXFRfJ7CCGEEEIICXy8\nLxk1OrzXv/fFwcGBzZs38+uvv+Lg4ICDgwPffvut8it7LhcXF548ecK9e/fo0aNHoXV6eHjg5eXF\njBkzsLW1ZdeuXQQGBmJtba2UCQgIAHISXI4ZMyZP4KNKlSqsX78eJycn7Ozs2L59O+vXry9whgPk\nLPPo1q0bWlpaSpLT/GRmZrJ582ZcXV3zPe/q6sr27dtJT0+nRYsWfPHFFwwdOhRzc3P8/PwAlNwR\njRo1wtzcHAArKyt2797NtWvX6NKli7ItaG5S0+JasWIFJiYmdO3alT59+qgkmoScmQWbN2+mdevW\njB49mmbNmjFkyBAuXryoLKlp1qwZ/v7+rFmzhlatWrFjxw4mTpxYaLtWVlYsWLCAFStW0LJlSwID\nA1V25nif1NXVCQoKIisriw4dOuDh4cH48ePR0tIq8DlOTk589dVXTJo0iVatWvHLL78wefLkErcd\nEBBARkYG9vb2fPHFF/Tv319l/N/U3LlzqVmzJl26dGHQoEEMGDBAmf30qokTJ7J9+3ZatWrFmjVr\nWL58OY0bNy5WG9ra2uzatQsDAwN69+6Nra0tvr6+BS4XqVevHtra2kybNo02bdrg4ODApk2bmDNn\nDuPGjXujOvNT1HdChQoV2Lp1Kw8fPsTBwYG+ffvSrFkz5XtCCCGEEEL8b1NLS0t780XxIo/09HSV\nJH3i36Vnz54YGBiwbNmy0u6KEOIDIf8uCCGEEEKULsnxIQSQlpbG0aNHOXjwIL/++mtpd0cIIYQQ\nQgghxDsigQ8hgNatW5OWlsb06dOxtLQs7e4IIYQQQgghhHhHJPAhBPnvCiOEEEIIIYQQ4t9PkpsK\nIYQQQgghhBDigyUzPoQQQgghhBBC/ONoR/9Y2l0oVEbroaXdBVFMMuNDCCGEEEIIIYQQHywJfAgh\nhBBCCCGEEOKDJYEPIYQQQgghhBBCfLAk8CGEEEIIIYQQQogPlgQ+hCiAoaEhwcHBb1XHhQsX+Oyz\nz9DV1cXa2vod9az0+Pv7q1yHr68vtra2pdijf4bw8HB0dHSUx8HBwRgaGpZij96t4lzP62X+CWNg\nbW2Nv7//B9OOEEIIIYR4M7Kry3vyvjMSlzTDsKenJyEhIUyePBlvb2/leHR0NC4uLly6dImPP/74\njfpy9epVbGxslMeVK1emTp06jBs3Dicnpzeq899i7ty5lC9fnri4OLS1tQstm5KSgrW1NdWrVych\nIQF1ddW4pI6ODuvWrcPV1VU55uvry/bt24mJiflb+l8ULy8vRowYUSptvwvv4v2dnx49etCxY8d3\nVt/bcHZ25siRIwCULVsWY2Nj+vbty9dff02ZMmX+tnbfxRgEBgayevVqLl++TJkyZTAyMqJz585M\nnTr1HfVSCCGEEEL8L5AZH0JRrlw5/P39uXfv3t9S/9atW0lMTGT//v00btyYgQMHcvbs2b+lreLK\nzMz8W+u/fPkyLVu2xNTUlGrVqhVaNiQkBEdHR7S0tDhw4MDf2q93pWLFilStWrW0u/GPU758eapX\nr17a3VD069ePxMREfvvtN0aMGMHcuXP/9hkKbzsG69evx8fHhy+++ILo6Gj27duHt7c3jx8/foe9\nFEIIIYQQ/wsk8CEUrVu3xtjYmEWLFhVa7siRIzg4OKCrq0vt2rWZNGlSsQIIVatWRVdXlzp16jBt\n2jSeP39OdHS0Spk9e/bQtm1bdHV1adCgAXPmzFGpOzMzk9mzZ2NlZUWNGjWwsbFh1apVxe6bs7Mz\nY8eOZerUqZibm9OpUycgJ0Dh7OyMrq4uTZs2JSIiosjrycrKYtGiRdSvX58aNWpgZ2fHrl27lPM6\nOjr88ccfLFq0CB0dHXx9fQutLygoiN69e+Pu7s769etVzuUuLxk0aBA6OjpYW1sTHBzMwoULOXfu\nHDo6Oujo6ChLc9LT0xk9ejQWFhbKr+SnTp1S6stdhhAVFYWtrS0GBgZ06dKFpKQklXb9/PyoU6cO\nhoaGjBgxgkePHqmcf32pi6enJ+7u7qxcuZJPPvkEU1NTRo4cqXKzmpGRwYgRIzA0NKR27dosXboU\nd3d3PD09Cxyb+/fvM3ToUCwtLdHT06Nly5YEBQWplHF2dmbcuHHMnj2bWrVqYWFhwdSpU8nKyip0\n3F8VHR2Njo4OUVFRODg4oK+vj729Pb///rtKuZCQEKysrNDX18fd3Z2UlBSV868v87hy5Qp9+vSh\nTp06GBgY0KZNmzzvMWtraxYvXszXX3+NsbExlpaWLFu2TKVMQEAAdnZ2GBgY8Mknn+Dl5UVaWlqR\n11WhQgV0dXUxNTVl+PDhtG3bVnmv5rckJXccUlNTVY7v2bOHJk2aoKurm+/7pbAxAIiMjMTBwQE9\nPT3MzMxwd3fn6dOn+T5/z549uLi4MGTIEGrVqkXdunXp1q0b8+fPL1GdT58+LXRMr1+/Tr9+/TAy\nMsLIyIj+/ftz8+ZNlTJr166lUaNGVK9enUaNGrFu3boCr1sIIYQQQvzzSOBDKNTV1Zk5cyZr167l\nypUr+Za5desWbm5uNGjQgMOHD+Pv78/WrVuZNWtWsdt5/vy5cuOgqampHD9w4ADDhw9n2LBhxMbG\nEhAQQHh4OLNnz1bKeHp6snHjRubNm0dcXBz+/v5UqVKlRH3bvHkz2dnZ7Nmzh1WrVpGVlUX//v3J\nysoiMjKSgIAAFixYwLNnzwq9jpUrV+Lv78/MmTM5evQozs7ODBgwgISEBAASExOpXbs2o0aNIjEx\nES8vrwLrOnr0KPfv36dDhw706tWLvXv3qsy8+eWXXwBYtmwZiYmJ/PLLL/To0YNRo0ZRu3ZtEhMT\nSUxMpEePHmRnZ+Pu7k5ycjKbNm3i8OHD2NnZ0bVrV27fvq3U+ezZM5YuXUpAQACRkZGkp6czduxY\n5fzPP//M3LlzmTRpElFRUdSuXZsVK1YUOiYAMTExnDt3jrCwMNauXcvOnTtVglNTp07lyJEjBAUF\nsX37dv74448il+o8ffoUGxsbNm7cSGxsLB4eHowZM4aoqCiVclu2bKFMmTJERkayePFiVq5cybZt\n24rs8+tmzZrFjBkziIqKomrVqgwfPpzs7GwAjh8/zsiRIxk8eDDR0dE4OjrmuRl/3aNHj/jss8/4\n+eef+fXXX+natSsDBgzgwoULKuVWrFiBpaUlUVFRjB49munTpxMXF6ecV1dXx9fXl5iYGFavXs2J\nEydUlqYVV7ly5Xj+/HmJnvPs2TMWLlzI8uXLiYyM5OXLl/Tv318Zl6Ls37+fPn360K5dOw4dOsSO\nHTv49NNPCwxM6erqcuLEiUKDK8Wps7AxzcrKom/fvty9e5cdO3awY8cObt++Tb9+/ZTr2rFjBxMm\nTMDT05OYmBg8PDwYN24ce/bsKebICSGEEEKI0iY5PoSKjh070qJFC+bMmcOaNWvynP/xxx/R09Pj\nm2++QV1dnbp16zJjxgzGjBnDlClTqFChQoF1d+7cGXV1dZ48eUJWVhampqZ0795dOb9kyRK8vLzo\n378/AGZmZsycOZMRI0YwZ84cLl++zNatWwkNDaVDhw4A1KxZs8R9MzExYd68ecrzDh48yPnz54mP\nj8fY2BjImclQVP6RgIAARo0ahZubGwBTpkzh6NGjBAQE8MMPP6Crq4uGhgba2tro6uoWWtf69evp\n0aMHmpqa1KxZk8aNGxMSEqIES3KXyVSpUkWlLm1tbTQ0NFSORUVFcfr0aS5evEj58uWBnGBDREQE\nmzZtYvTo0QC8ePGCJUuWULt2bSAnX8eoUaPIzs5GTU2NlStX0qdPH4YMGQLA+PHjiY6O5vLly4Ve\nS6VKlfj2228pU6aM8it9VFQUY8eO5dGjRwQFBbFq1SratWsH5CRMtbS0LLROAwMDvvrqK+Xx4MGD\nOXz4MKGhobRt21Y5XrduXaZMmQKAhYUF69atIyoqip49exZa/+umTJlCmzZtAPD29sbR0ZFbt25h\naGjIqlWraNu2LePHj1faOXnyZJ5ZOq+ytrZWSQo7fvx4IiIiCA8PZ8KECcrx9u3bM3z4cABGjBjB\n999/T1RUFM2bNwdg5MiRSllTU1Nmz55N3759WbVqVZ6cMPnJysri4MGDHDx4sNAZNvl58eIFCxYs\noGXLlgB8//33NGzYkKioKOzt7Yt8/uLFi3F1dVXJz2FlZVVgeR8fH/744w8aNmxIrVq1aNq0Ke3a\ntaNnz55KwLQ4dRY2plFRUZw5c4ZTp05hamoKwH//+18aNWqkXFdAQADu7u5KHRYWFvz+++/4+fl9\n8DmKhBBCCCE+FDLjQ+Qxa9YswsLC8kzvh5xZDE2bNlW5ybK1tSUzM7PIG+LVq1dz+PBhQkJCMDc3\nJyAggI8++kg5Hx8fzzfffIOhoaHyN2zYMDIyMrhz546S8LN169b51l/cvjVs2DDP8wwMDJSgB5Cn\nntc9ePCA5ORk5Sbw1fbOnz9f6DjkV1d4eDju7u7Ksd69exd6I12Y+Ph4Hj9+jIWFhcpYnjt3TmUm\nj5aWlhL0ANDT0yMzM1NZOpGYmEizZs1U6n79cX7q1q2rkjRTT0+Pu3fvAjlLPp4/f06TJk2U89ra\n2kUGPl6+fMmSJUuws7PDzMwMQ0NDduzYwY0bN1TK1a9fX+Xxq22XxKv16OnpASj1vMm4ZGRkMH36\ndFq0aIGpqSmGhoacOnWqxP2PioqiW7duWFpaYmRkxIABA8jMzOTOnTuFtv/TTz9haGiIrq4uffr0\noVevXvj4+BT6nNepq6urvG4mJibo6+sX+/2ekJCgEqQqip6eHvv27ePo0aN4enqSnZ3NmDFjaN++\nvbJ0qjh1FjamiYmJ6OvrK0EPyAmmvnpdiYmJtGjRQqWON/mcCyGEEEKI0iMzPkQeTZo0oWvXrkyf\nPl3l1+iiqKmpFXre0NAQc3NzzM3N0dbWZvDgwRw7dkzZTSMrKwsfHx+6deuW57lFJQYtSd+K2l3l\nbRU1Dq8LDQ3l8ePHSr6RXC9fviQ2NjZPcKUoWVlZ1KhRI9+p+JUqVVL+W0ND9eOf2++S5MTIz6vL\nl3LrLe5yiIL4+/srS5AsLS2pWLEis2fPzhPUeFdtv1pP7ri8zTVMmzaN/fv3M2fOHMzNzalQoQIe\nHh55cuMU1v9r167h7u7OwIEDmTx5MlWrViU+Pp6hQ4cWmWOne/fuTJw4kbJly6Kvr68SmFJXV89z\nbS9evMi3npK+t98FS0tLLC0tGTZsGDExMTg5OfHzzz/Tr1+/Yj3/Td8TRV1raYyFEEIIIYR4MzLj\nQ+Rr+vTpxMTE5NldpG7duhw/flzl5jgmJoayZctiZmZW7Po//fRT6taty8KFC5VjNjY2XLhwgVq1\nauX509DQwNramqysrDwJUd+2b3Xr1uXWrVsqv76fOHGi0ABA5cqV0dfXJzY2VuV4TEwMdevWLfL6\nX7V+/XqGDRtGdHS0yl+nTp1UZn1oamry8uVLleeWLVs2zzEbGxtSUlJQV1fPM44l2WUjdzxf9frj\nkjIzM0NTU5OTJ08qxx4/flzk7j4xMTE4OjrSu3dvGjRogJmZGRcvXnyrvrypNxmX2NhYevfujaur\nK1ZWVhgYGBSYR6cgp06dIjMzE19fX5o3b46FhQXJycnFem6VKlWoVasWRkZGebawrVatGo8fP+bB\ngwfKsdOnT+epIysrixMnTiiPr1+/TnJycrHf7w0aNMiTk6Wk6tWrB+TMoHkXddatW5fk5GSuXr2q\nHEtKSiI5OVlpq27duhw7dkzleTExMcp5IYQQQgjxzyeBD5GvWrVqMXjwYJWklABDhw7l9u3bjBs3\njsTERPbu3cusWbMYNmxYofk98jNq1CjWrVunBBy8vb0JDQ1l3rx5nD17lgsXLhAeHs706dOBnLX1\n3bt356uvviI8PJykpCSOHj3Kxo0b36pv9vb21KlTBw8PDxISEoiLi2Py5Ml5ZkS8zsvLi4CAAEJD\nQ7l48SLz5s0jJiam0CSmr/vjjz84deoUgwYNUn7Zzv1zd3cnLCyMhw8fAjlLC6Kiorhz546yHMXE\nxITr16/z+++/k5qayrNnz7C3t6dly5b07duXffv2kZSURFxcHPPnz+fo0aPF7puHhwchISGsW7eO\nS5cusXTpUpUb3zdRsWJF+vfvryQOPX/+PF999ZWSV6QgFhYWHD58mJiYGC5cuMCECRO4du3aW/Xl\nTY0YMYJDhw6xdOlSLl26xLp169i5c2ehzzE3N2fnzp38/vvvnDlzhuHDhxeZPDe/OrKyslixYgVJ\nSUmEhobm+Xy+iaZNm6Ktrc3s2bO5fPky4eHh/Pe//81TTkNDg0mTJhEXF0dCQgKenp7Uq1evWPk9\nAMaNG0dYWBhz587l/PnznDt3juXLlxe4Pe3YsWNZtGgRsbGxXLt2jd9++w0PDw8qVKhA+/bt36jO\n19nb21O/fn2GDx/OqVOnOHXqFMOGDcPGxkbJ8eLl5cWmTZtYvXo1ly5d4vvvv2fLli0qOWeEEEII\nIcQ/myx1eU8yWg8t7S6UmLe3NyEhISrHDAwM2LJlC9OnT6d169ZUqVKFnj17KsGJknB0dMTExITF\nixfj5+eHg4MDmzdvZvHixQQEBKChoYG5uTl9+/ZVnrNq1SrmzZvHxIkTSU1NxcDAQEn4+KZ9U1dX\nJygoiK+++ooOHTpgZGTE3LlzGTZsWKHP8/Dw4NGjR8yYMYOUlBRq165NYGCgShLLogQGBmJubp5v\nksdOnTqRlZXF1q1bGTx4MHPnzmXKlCnUr18ffX19Tp8+TdeuXdmxYweurq6kp6ezfPly+vXrx+bN\nm5k7dy6jR4/m7t271KhRgxYtWtCnT59i961Hjx4kJSUxZ84cnjx5gpOTEyNHjmTDhg3FriM/c+bM\nIT6YLKMAACAASURBVCMjgz59+qCtrc3IkSNJSUmhXLlyBT5nwoQJXL16FTc3N8qVK0ffvn1xc3Mr\nlTwLzZo1w9/fnwULFrBo0SI+/fRTJk6cWOjuKvPmzcPLy4vOnTujo6ODp6dniQMfVlZWLFiwAD8/\nP+bNm0fz5s2ZM2eOknz2TX300Uf88MMPTJ8+naCgIOzs7JgyZQojRoxQKaelpcW4cePw8PDgxo0b\nNG3alKCgoGIv+ejYsSNBQUEsXLiQZcuWUbFiRZo3b87Qofl/N9rb2xMcHMzatWtJTU3lo48+omHD\nhvz8889YWFi8UZ2vU1NTY8OGDfj4+ODi4gJA27ZtWbRokXJdXbp0YdGiRfj7+zNp0iSMjY355ptv\nJLGpEEIIIcS/iFpaWtrbLb4XKtLT05XtVYUQRXv27BnW1tZ4eXmVaLaMEP8W8u+CEEII8Wa0o38s\n7S4U6t/44/b/KpnxIYR4r+Lj47lw4QJNmjTh4cOH+Pn58ejRI3r06FHaXRNCCCGEEEJ8gCTwIYR4\n75YvX87FixcpU6YM1tbW7N69G0NDw9LulhBCCCGEEOIDJIEPIcR7ZWNjw6FDh0q7G0IIIYQQ/8fe\nvUdZVZ554v9WigSZwlg2VBUSBYcWAQmGbhGIHYxc2iuKeGlkXIlBaBQ0Gjp4QY3pRDuIeCMGLxnB\nToy3kWjEjMFpWyRo0IozKi4xpYkjoiEUoqWC4AXq90fG80tFQ1Co2/bzWeusVWe/z9n7eV2vp8qv\n+wJ8QniqCwAAAFBYgg8AAACgsAQfzaCx0YNyAPD7AACgLRB87GAVFRVpaGjwxy7AJ1xjY2MaGhpS\nUVHR2q0AAHyiubnpDtahQ4fsvPPOeeONN1q7FQBa2c4775wOHfyqBQBoTa3619jDDz+cq6++Ok8+\n+WRWr16duXPn5sQTTyyNV1ZWfujnJk2alMsuuyxJMmXKlNx6661NxgcNGpT777+/9P7tt9/OBRdc\nkJ/+9KfZtGlTDjzwwFx++eVNHp+5atWqTJ8+PUuXLs1OO+2U4447LhdffHE+85nPfOR5dejQIbvs\nsstH/hwAAACwY7Vq8LFhw4bss88+GT9+fE499dQPjNfV1TV5//jjj+eEE07I0Ucf3WT7QQcdlOuv\nv770/s/DihkzZuTee+/NvHnzsuuuu+b888/PuHHjsmTJkpSXl2fz5s0ZN25cdt1119x777157bXX\nMmXKlDQ2Nmb27Nk7cMYAAABAS2rV4OPggw/OwQcfnCSZOnXqB8ZramqavL/33nuz11575Utf+lKT\n7R07dvxA7ftef/313HTTTZk7d26GDx+eJLn++uszYMCAPPjggxk5cmQeeOCBPPPMM3nqqaey++67\nJ0m+853v5Iwzzsi3vvWtfPazn93uuQIAAAAtr93c3HT9+vW58847c9JJJ31gbNmyZdlrr72y3377\n5YwzzsjatWtLY0888UTefffdjBgxorRt9913T58+ffLoo48mSWpra9OnT59S6JEkI0eOzNtvv50n\nnniiGWcFAAAANKd2c8e1BQsW5J133sn48eObbB81alSOPPLI9OzZMy+++GIuvvjiHHXUUXnwwQfT\nsWPH1NfXp7y8PF26dGnyuaqqqtTX1ydJ6uvrU1VV1WS8S5cuKS8vL9UAAAAA7U+7CT5+9KMf5fDD\nD0/Xrl2bbD/22GNLP/fv3z8DBw7MgAEDct999+Woo45q6TYBAACANqRdXOqyfPnyPP744x96mcuf\n22233dK9e/c8//zzSZLq6ups3rw569ata1K3du3aVFdXl2r+9PKYJFm3bl02b95cqgEAAADan3YR\nfPzoRz9Kz549c9BBB/3V2nXr1mX16tWlm50OHDgwn/70p7N48eJSzcsvv5y6uroMGTIkSTJ48ODU\n1dXl5ZdfLtUsXrw4HTt2zMCBA3fsZAAAAIAW06qXuqxfv750ZsaWLVvy0ksvZfny5dl1112zxx57\nJEneeuut3HHHHTnjjDNSVlb2gc9fcsklOeqoo1JTU5MXX3wx3/3ud1NVVZXRo0cnSXbZZZd85Stf\nybe//e1UVVWVHmfbv3//UpAyYsSI9OvXL6eeemouvvjivPbaa7nwwgvz1a9+1RNdAAAAoB1r1eDj\n8ccfz5FHHll6P3PmzMycOTPjx4/PtddemyS58847s2HDhpx44okf+Hx5eXlWrFiR2267La+//npq\namoybNiw3Hjjjdl5552b7Le8vDwTJkzIpk2bcuCBB+a6665LeXl5aT+33357pk+fnkMPPTQ77bRT\njj/++Fx00UXN/E8AAAAAaE5lDQ0Nja3dBAAAAPypiqXzWruFrdowbGJrt8A2ahf3+AAAAAD4OAQf\nAAAAQGG16j0+AACAtsulBkAROOMDAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwA\nAAAAhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwA\nAAAAhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwA\nAAAAhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwA\nAAAAhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsDq0dgMAAPBJVFF/f2u3wCec\nNcgnhTM+AAAAgMISfAAAAACFJfgAAAAACkvwAQAAABSW4AMAAAAoLMEHAAAAUFiCDwAAAKCwBB8A\nAABAYbVq8PHwww/nhBNOSL9+/VJZWZmbb765yfiUKVNSWVnZ5DVq1KgmNW+//XbOOuus9OrVK927\nd88JJ5yQl19+uUnNqlWrMm7cuHTv3j29evXK2WefnXfeeadJzUMPPZQvf/nLqampyRe+8IXMnz+/\neSYNAAAAtJhWDT42bNiQffbZJ5dcckk6der0oTUHHXRQ6urqSq877rijyfiMGTNyzz33ZN68ebn3\n3nvz5ptvZty4cdm8eXOSZPPmzRk3blzWr1+fe++9N/PmzcvChQtz/vnnl/bxwgsv5J/+6Z8yePDg\n/PKXv8y//Mu/5Oyzz87dd9/dfJMHAAAAml2H1jz4wQcfnIMPPjhJMnXq1A+t6dixY2pqaj507PXX\nX89NN92UuXPnZvjw4UmS66+/PgMGDMiDDz6YkSNH5oEHHsgzzzyTp556KrvvvnuS5Dvf+U7OOOOM\nfOtb38pnP/vZ3HjjjenWrVtmz56dJOnTp08ee+yx/OAHP8iYMWN29LQBAACAFtLm7/GxbNmy7LXX\nXtlvv/1yxhlnZO3ataWxJ554Iu+++25GjBhR2rb77runT58+efTRR5MktbW16dOnTyn0SJKRI0fm\n7bffzhNPPFGq+dN9vF/z+OOP5913323O6QEAAADNqE0HH6NGjcp1112Xu+++OxdffHH+9//+3znq\nqKPy9ttvJ0nq6+tTXl6eLl26NPlcVVVV6uvrSzVVVVVNxrt06ZLy8vKt1lRVVeW9997LunXrmmt6\nAAAAQDNr1Utd/ppjjz229HP//v0zcODADBgwIPfdd1+OOuqoVuwMAAAAaA/a9Bkff2633XZL9+7d\n8/zzzydJqqurs3nz5g+clbF27dpUV1eXav708pgkWbduXTZv3rzVmrVr16ZDhw4fOJsEAAAAaD/a\nVfCxbt26rF69unSz04EDB+bTn/50Fi9eXKp5+eWXU1dXlyFDhiRJBg8enLq6uiaPuF28eHE6duyY\ngQMHlmr+dB/v1/zd3/1dPv3pTzf3tAAAAIBm0qrBx/r167N8+fIsX748W7ZsyUsvvZTly5dn1apV\nWb9+fS644ILU1tZm5cqVWbp0aU444YRUVVVl9OjRSZJddtklX/nKV/Ltb387Dz74YJ588smccsop\n6d+/fw466KAkyYgRI9KvX7+ceuqpefLJJ/Pggw/mwgsvzFe/+tV89rOfTZJMmDAhq1evzrnnnpu6\nurr8+Mc/zi233JLTTz+9tf7RAAAAADtAWUNDQ2NrHXzp0qU58sgjP7B9/PjxueKKK3LiiSdm+fLl\nef3111NTU5Nhw4bl/PPPb/KElrfffjsXXHBBFixYkE2bNuXAAw/M5Zdf3qRm1apVmT59en75y19m\np512yvHHH5+LLrooHTt2LNU89NBDOe+88/Kb3/wm3bp1yze+8Y2cfPLJzfsPAACAT6yK+vtbu4W/\nrm5la3ewVRuGTWztFtq1Nr8GrT92kFYNPgAA4JOqzf9HZ+I/PAuuza9B648dpE0/1QUA4JOsYum8\n1m5hq/zRD0B70K5ubgoAAADwUQg+AAAAgMISfAAAAACFJfgAAAAACkvwAQAAABSWp7oAAJ9Ybf5R\njgDAdnPGBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAAhSX4AAAAAApL8AEAAAAUluAD\nAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAAhSX4AAAAAApL8AEAAAAUluAD\nAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAAhSX4AAAAAApL8AEAAAAUluAD\nAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAAhSX4AAAAAApL8AEAAAAUluAD\nAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAAhSX4AAAAAApL8AEAAAAUluAD\nAAAAKCzBBwAAAFBYrRp8PPzwwznhhBPSr1+/VFZW5uabby6Nvfvuu/n2t7+dAw44IN27d0+fPn0y\nadKkrFq1qsk+jjjiiFRWVjZ5nXzyyU1qGhoaMnny5PTo0SM9evTI5MmT09DQ0KTm6aefzuGHH55u\n3bqlX79+mTVrVhobG5tv8gAAAECza9XgY8OGDdlnn31yySWXpFOnTk3G3nrrrTz55JOZPn16lixZ\nkltuuSUvv/xyjjvuuLz33ntNak888cTU1dWVXldeeWWT8UmTJmX58uVZsGBBFixYkOXLl+eUU04p\njb/xxhsZO3Zsqqur88ADD+SSSy7J1VdfnR/84AfNN3kAAACg2XVozYMffPDBOfjgg5MkU6dObTK2\nyy675Gc/+1mTbVdeeWWGDh2aurq69O/fv7T9v/yX/5KampoPPUZdXV3uv//+LFq0KIMHDy7t57DD\nDstzzz2X3r1754477sjGjRtz7bXXplOnTtlnn33y7LPP5pprrsnpp5+esrKyHTltAAAAoIW0q3t8\nvPnmm0mSysrKJtt/+tOfplevXhk6dGguuOCCUl2S1NbWpnPnzhkyZEhp29ChQ1NRUZFHH320VPPF\nL36xyVknI0eOzOrVq7Ny5crmnBIAAADQjFr1jI+P4p133skFF1yQQw89NJ/73OdK248//vjsscce\n6datW37zm9/kO9/5Tp5++uncddddSZL6+vp06dKlyVkbZWVl6dq1a+rr60s13bt3b3K8qqqq0tie\ne+7ZzLMDAAAAmkO7CD7ee++9TJ48Oa+//npuvfXWJmNf+9rXSj/3798/e+65Z0aOHJknnngiAwcO\nbOFOAQAAgLakzV/q8t5772XixIl5+umnc/fdd+dv/uZvtlr/d3/3dykvL8/zzz+fJKmurs66deua\nPKGlsbExr7zySqqrq0s1a9eubbKf99+/XwMAAAC0P206+Hj33XczYcKEPP3007nnnnv+4g1M/9TT\nTz+dzZs3l2oHDx6c9evXp7a2tlRTW1ubDRs2lO77MXjw4CxbtiybNm0q1SxevDi77bZbevbsuYNn\nBQAAALSUVg0+1q9fn+XLl2f58uXZsmVLXnrppSxfvjyrVq3Ke++9l5NOOimPPfZYbrjhhpSVlWXN\nmjVZs2ZNNm7cmCT5v//3/2bWrFl5/PHHs3Llyvyv//W/MnHixOy7774ZOnRokqRPnz4ZNWpUpk2b\nltra2tTW1mbatGk55JBD0rt37yTJcccdl06dOmXq1KlZsWJFFi5cmKuuuipTp071RBcAAABox8oa\nGhoa/3pZ81i6dGmOPPLID2wfP358zj333HzhC1/40M/NnTs3J554Yl566aVMnjw5zzzzTDZs2JDP\nfe5zOfjgg3Puuedm1113LdU3NDTk7LPPzi9+8YskyWGHHZZLL720ydNhnn766UyfPj3/5//8n1RW\nVmbChAk555xzBB8AUGAV9fe3dgtbV9e2ny63YdjE1m6hXWvz6y+xBguuza9B648dpFWDDwCA1uSP\n/u3jj/7t0+bXX2INFlybX4PWHztIm77HBwAAAMD2EHwAAAAAhSX4AAAAAApL8AEAAAAUVofWbgCA\nT7a2fmO1DdWjWrsFAAC2gzM+AAAAgMISfAAAAACFJfgAAAAACkvwAQAAABSW4AMAAAAoLMEHAAAA\nUFiCDwAAAKCwBB8AAABAYQk+AAAAgMISfAAAAACFJfgAAAAACkvwAQAAABSW4AMAAAAoLMEHAAAA\nUFiCDwAAAKCwBB8AAABAYQk+AAAAgMISfAAAAACFJfgAAAAACkvwAQAAABSW4AMAAAAoLMEHAAAA\nUFiCDwAAAKCwBB8AAABAYQk+AAAAgMISfAAAAACFJfgAAAAACkvwAQAAABTWNgUfGzduzJw5c7Jk\nyZLm7gcAAABgh9mm4KNTp06ZOXNmVq5c2dz9AAAAAOww23ypS79+/QQfAAAAQLuyzcHHBRdckPnz\n57vcBQAAAGg3Omxr4Y033pguXbpk7Nix6dWrV/bcc8/stNNOTWrKyspy00037fAmAQAAAD6ObQ4+\namtrU1ZWlqqqqrz55pt56qmnPlBTVla2Q5sDAAAA2B7bHHw8++yzzdkHAAAAwA63zff4AAAAAGhv\ntvmMjz/1zjvv5M0338yWLVs+MFZVVbXdTQEAAADsCB8p+Lj11ltz9dVXp66uLo2NjR9a8+qrr+6Q\nxgAAAAC21zZf6nLrrbdm6tSp2XXXXTN9+vQ0NjZm0qRJmTJlSv7mb/4mAwYMyOWXX96cvQIAAAB8\nJNscfMydOzdf+tKX8j//5//MKaeckiQ54ogj8m//9m955JFHsm7dumzevLnZGgUAAAD4qLY5+Pjt\nb3+b0aNH//FDn/rjx957770kSdeuXfO1r30t119/fTO0CAAAAPDxbHPwUVFRkfLy8iRJ586dU15e\nnj/84Q+l8S5duuSll17a8R0CAAAAfEzbHHzstddeqaurS5J06NAh/fv3zx133JEtW7bknXfeyYIF\nC7LHHnt8pIM//PDDOeGEE9KvX79UVlbm5ptvbjLe2NiYmTNnpm/fvunWrVuOOOKIPPPMM01qGhoa\nMnny5PTo0SM9evTI5MmT09DQ0KTm6aefzuGHH55u3bqlX79+mTVr1gduznr33XdnyJAhqa6uzpAh\nQ3LPPfd8pLkAAAAAbc82Bx+HHHJI7rrrrmzatClJMm3atCxZsiT/9b/+1+y999751a9+la9//esf\n6eAbNmzIPvvsk0suuSSdOnX6wPicOXMyd+7czJo1Kw888ECqqqoyduzYvPnmm6WaSZMmZfny5Vmw\nYEEWLFiQ5cuXl+5BkiRvvPFGxo4dm+rq6jzwwAO55JJLcvXVV+cHP/hBqaa2tjYnn3xyjj/++Cxd\nujTHH398vva1r+Wxxx77SPMBAAAA2payhoaGD38u7TZYsmRJ7r777pSXl+fQQw/NyJEjP3Yjn/vc\n53LppZfmxBNPTPLHsz369u2bf/7nf8706dOTJBs3bkzv3r1z0UUXZcKECamrq8uQIUOyaNGiDB06\nNEmybNmyHHbYYfn1r3+d3r17Z968efnXf/3XPPvss6VwZfbs2Zk/f35WrFiRsrKyTJgwIa+99lp+\n9rOflfoZM2ZMunbtmnnz5n3sOQHw11XU39/aLWzVhupRrd0Czaitr7/UrWztDrZqw7CJrd1Cu9bm\n119iDRZcm1+D1h87yDaf8fFhvvzlL+eKK67I7Nmztyv0+DArV67MmjVrMmLEiNK2Tp065YADDsij\njz6a5I9nanTu3DlDhgwp1QwdOjQVFRVNar74xS82OaNk5MiRWb16dVau/OO/SL/+9a+bHOf9mvf3\nAQAAALRPHzn4WLVqVW677bZcffXVpZuZbt68OWvXri095WVHWLNmTZKkqqqqyfaqqqrU19cnSerr\n69OlS5eUlZWVxsvKytK1a9cmNR+2j/fH3j/W1o4DAAAAtE8dtrWwsbEx55xzTubPn5/NmzenrKws\n++67b3bfffds2LAhf//3f59zzz03p512WnP2CwAAALDNtvmMjzlz5uSGG27ItGnTcs899zR5Kspn\nP/vZjB49Oj//+c93WGM1NTVJkrVr1zbZvnbt2lRXVydJqqurs27duia9NDY25pVXXmlS82H7eH/s\n/WNt7TgAAABA+7TNwcePf/zjjB8/Pueff3722WefD4z3798/v/3tb3dYYz179kxNTU0WL15c2rZp\n06YsW7asdE+PwYMHZ/369amtrS3V1NbWZsOGDU1qli1bVnoaTZIsXrw4u+22W3r27Jkk2X///Zsc\n5/2aP713CAAAAND+bHPw8fLLL2fw4MF/cbyioqLJY2a3xfr167N8+fIsX748W7ZsyUsvvZTly5dn\n1apVKSsry5QpUzJnzpwsXLgwK1asyNSpU1NRUZHjjjsuSdKnT5+MGjUq06ZNS21tbWprazNt2rQc\ncsgh6d27d5LkuOOOS6dOnTJ16tSsWLEiCxcuzFVXXZWpU6eW7g1y6qmn5pe//GWuvPLKPPvss7ni\niiuydOnSTJky5SPNBwAAAGhbtjn4qKqqyssvv/wXx5988sl87nOf+0gHf/zxx3PggQfmwAMPzMaN\nGzNz5swceOCB+d73vpckOfPMMzNlypScddZZGT58eP7whz/kzjvvzM4771zaxw033JDPf/7zOfbY\nY3Psscfm85//fK6//vrS+C677JK77rorq1evzvDhw3PWWWfltNNOy+mnn16qGTJkSObPn59bbrkl\n//AP/5Dbbrst8+fPz6BBgz7SfAAAAIC2ZZtvbnr44YfnxhtvzIknntgkeEiSJUuW5JZbbsnXv/71\nj3TwYcOGpaGh4S+Ol5WVZcaMGZkxY8ZfrKmsrMwPf/jDrR6nf//++cUvfrHVmjFjxmTMmDFbbxgA\nAABoV7b5jI/zzjsvXbt2zbBhw3LaaaelrKwsc+fOzRFHHJGxY8emT58++Zd/+Zfm7BUAAADgI9lq\n8LFly5bSz5WVlbn//vtzyimn5He/+13Kysryn//5n/n973+fadOmZdGiRamoqGj2hgEAAAC21VYv\ndfnyl7+cq6++OgMHDkzyxxuYnn/++Tn//POT/PHRse/fIBQAAACgrdnqGR+vvfZaRo0alRkzZmTD\nhg0fGBd6AAAAAG3ZVoOPRx99NJMnT85//+//PUOGDMl9993XUn0BAAAAbLetBh8VFRX53ve+l//8\nz/9MTU1Nxo8fnwkTJqS+vr6l+gMAAAD42LbpcbZf+MIXcv/992fevHn57ne/m0GDBmW33Xb7QF1Z\nWVkeeeSRHd4kAAAAwMexTcFHkrzzzjv5/e9/n40bN6ZLly6pqqpqzr4AAAAAtts2BR9LlizJtGnT\nsnLlypx88sm58MILs/POOzd3bwAAAADbZavBx6uvvpoZM2bkjjvuSN++fbNo0aLsv//+LdUbAAAA\nwHbZavAxaNCgbNy4Meeff37OPPPMdOiwzVfGAAAAALS6rSYZn//853PVVVelV69eLdUPAAAAwA6z\n1eBj4cKFLdUHAAAAwA73qdZuAAAAAKC5CD4AAACAwhJ8AAAAAIUl+AAAAAAKS/ABAAAAFJbgAwAA\nACgswQcAAABQWIIPAAAAoLAEHwAAAEBhCT4AAACAwhJ8AAAAAIUl+AAAAAAKS/ABAAAAFJbgAwAA\nACgswQcAAABQWIIPAAAAoLAEHwAAAEBhCT4AAACAwhJ8AAAAAIUl+AAAAAAKS/ABAAAAFJbgAwAA\nACgswQcAAABQWIIPAAAAoLAEHwAAAEBhCT4AAACAwhJ8AAAAAIUl+AAAAAAKS/ABAAAAFJbgAwAA\nACgswQcAAABQWIIPAAAAoLA6tHYDANCWVSyd19otbNWGYRNbuwUAgDbNGR8AAABAYbXp4GPAgAGp\nrKz8wOuf/umfkiQzZ878wNjee+/dZB+NjY2ZOXNm+vbtm27duuWII47IM88806SmoaEhkydPTo8e\nPdKjR49Mnjw5DQ0NLTZPAAAAoHm06UtdFi9enM2bN5fe/+EPf8hBBx2Uo48+urStd+/e+fnPf156\nX15e3mQfc+bMydy5czN37tz07t07l156acaOHZtf//rX2XnnnZMkkyZNyksvvZQFCxYkSc4444yc\ncsopuf3225tzegAAAEAza9PBR9euXZu8v+mmm7Lzzjtn7NixpW0dOnRITU3Nh36+sbEx1157bb7x\njW9kzJgxSZJrr702vXv3zoIFCzJhwoTU1dXl/vvvz6JFizJ48OAkyZVXXpnDDjsszz33XHr37t1M\nswMAAACaW5u+1OVPNTY25qabbsq4cePSqVOn0vYXXnghffv2zb777puTTz45L7zwQmls5cqVWbNm\nTUaMGFHa1qlTpxxwwAF59NFHkyS1tbXp3LlzhgwZUqoZOnRoKioqSjUAAABA+9Rugo/Fixdn5cqV\n+epXv1raNmjQoFxzzTVZsGBBvv/972fNmjU5+OCD8+qrryZJ1qxZkySpqqpqsq+qqqrU19cnSerr\n69OlS5eUlZWVxsvKytK1a9dSDQAAANA+telLXf7Uj370o/z93/99BgwYUNr2j//4j01qBg0alIED\nB+aWW27J6aef3tItAgAAAG1MuzjjY+3atbn33ntz0kknbbWuc+fO6du3b55//vkkKd37Y+3axlvj\ndAAAF0lJREFUtR/YX3V1dZKkuro669atS2NjY2m8sbExr7zySqkGAAAAaJ/aRfBxyy23pGPHjjn2\n2GO3Wrdp06Y899xzpcCjZ8+eqampyeLFi5vULFu2rHRPj8GDB2f9+vWpra0t1dTW1mbDhg1N7vsB\nAAAAtD9t/lKXxsbG/PjHP84xxxyTzp07Nxm74IILcuihh2b33XfPK6+8ktmzZ+ett97K+PHjk/zx\nXh1TpkzJFVdckd69e2evvfbKZZddloqKihx33HFJkj59+mTUqFGZNm1arrrqqiTJtGnTcsghh3ii\nCwAAALRzbT74WLp0aX73u9/lhz/84QfGfv/732fSpElZt25dunbtmkGDBuU//uM/0qNHj1LNmWee\nmY0bN+ass85KQ0ND9ttvv9x5553ZeeedSzU33HBDzj777NIZJYcddlguvfTS5p8cAAAA0KzKGhoa\nGv96GQA0j4r6+1u7ha2rW9naHWzVhmETW7uFds362z7W3/Zp8+svsQYLrs2vQeuPHaTNn/EBNK+2\n/gtvQ/Wo1m4BAABox9rFzU0BAAAAPg7BBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAA\nhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAA\nhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAA\nhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAA\nhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAA\nhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAAQGEJPgAAAIDCEnwAAAAA\nhSX4AAAAAAqrTQcfM2fOTGVlZZPX3nvvXRpvbGzMzJkz07dv33Tr1i1HHHFEnnnmmSb7aGhoyOTJ\nk9OjR4/06NEjkydPTkNDQ5Oap59+Oocffni6deuWfv36ZdasWWlsbGyROQIAAADNp00HH0nSu3fv\n1NXVlV6/+tWvSmNz5szJ3LlzM2vWrDzwwAOpqqrK2LFj8+abb5ZqJk2alOXLl2fBggVZsGBBli9f\nnlNOOaU0/sYbb2Ts2LGprq7OAw88kEsuuSRXX311fvCDH7ToPAEAAIAdr0NrN/DXdOjQITU1NR/Y\n3tjYmGuvvTbf+MY3MmbMmCTJtddem969e2fBggWZMGFC6urqcv/992fRokUZPHhwkuTKK6/MYYcd\nlueeey69e/fOHXfckY0bN+baa69Np06dss8+++TZZ5/NNddck9NPPz1lZWUtOl8AAABgx2nzZ3y8\n8MIL6du3b/bdd9+cfPLJeeGFF5IkK1euzJo1azJixIhSbadOnXLAAQfk0UcfTZLU1tamc+fOGTJk\nSKlm6NChqaioaFLzxS9+MZ06dSrVjBw5MqtXr87KlStbYIYAAABAc2nTwcegQYNyzTXXZMGCBfn+\n97+fNWvW5OCDD86rr76aNWvWJEmqqqqafKaqqir19fVJkvr6+nTp0qXJWRtlZWXp2rVrk5oP28f7\nYwAAAED71aYvdfnHf/zHJu8HDRqUgQMH5pZbbsn+++/fSl0BAAAA7UWbPuPjz3Xu3Dl9+/bN888/\nX7rvx9q1a5vUrF27NtXV1UmS6urqrFu3rskTWhobG/PKK680qfmwfbw/BgAAALRf7Sr42LRpU557\n7rnU1NSkZ8+eqampyeLFi5uML1u2rHRPj8GDB2f9+vWpra0t1dTW1mbDhg1NapYtW5ZNmzaVahYv\nXpzddtstPXv2bKGZAQAAAM2hTQcfF1xwQR566KG88MILeeyxx3LSSSflrbfeyvjx41NWVpYpU6Zk\nzpw5WbhwYVasWJGpU6emoqIixx13XJKkT58+GTVqVKZNm5ba2trU1tZm2rRpOeSQQ9K7d+8kyXHH\nHZdOnTpl6tSpWbFiRRYuXJirrroqU6dO9UQXAAAAaOfa9D0+fv/732fSpElZt25dunbtmkGDBuU/\n/uM/0qNHjyTJmWeemY0bN+ass85KQ0ND9ttvv9x5553ZeeedS/u44YYbcvbZZ+fYY49Nkhx22GG5\n9NJLS+O77LJL7rrrrkyfPj3Dhw9PZWVlTjvttJx++uktO1kAAABghytraGho/OtlQFFV1N/f2i1s\n1YbqUa3dAs2sra/B1LXtR5tvGDaxtVto16y/7WP9bZ82v/4Sa7Dg2vwatP7YQdr0pS4AAAAA20Pw\nAQAAABSW4AMAAAAoLMEHAAAAUFiCDwAAAKCwBB8AAABAYQk+AAAAgMISfAAAAACFJfgAAAAACkvw\nAQAAABSW4AMAAAAoLMEHAAAAUFiCDwAAAKCwBB8AAABAYQk+AAAAgMISfAAAAACFJfgAAAAACkvw\nAQAAABSW4AMAAAAoLMEHAAAAUFiCDwAAAKCwBB8AAABAYQk+AAAAgMISfAAAAACFJfgAAAAACkvw\nAQAAABSW4AMAAAAorA6t3QBJRf39rd3CVm2oHtXaLQAAAMDH4owPAAAAoLAEHwAAAEBhCT4AAACA\nwhJ8AAAAAIUl+AAAAAAKS/ABAAAAFJbgAwAAACgswQcAAABQWIIPAAAAoLAEHwAAAEBhCT4AAACA\nwhJ8AAAAAIUl+AAAAAAKS/ABAAAAFJbgAwAAACgswQcAAABQWIIPAAAAoLAEHwAAAEBhCT4AAACA\nwmrTwccVV1yR4cOHZ4899sjf/u3fZty4cVmxYkWTmilTpqSysrLJa9SoUU1q3n777Zx11lnp1atX\nunfvnhNOOCEvv/xyk5pVq1Zl3Lhx6d69e3r16pWzzz4777zzTrPPEQAAAGg+bTr4eOihhzJx4sTc\nd999WbhwYTp06JCjjz46r732WpO6gw46KHV1daXXHXfc0WR8xowZueeeezJv3rzce++9efPNNzNu\n3Lhs3rw5SbJ58+aMGzcu69evz7333pt58+Zl4cKFOf/881tsrgAAAMCO16G1G9iaO++8s8n766+/\nPj169MgjjzySww47rLS9Y8eOqamp+dB9vP7667npppsyd+7cDB8+vLSfAQMG5MEHH8zIkSPzwAMP\n5JlnnslTTz2V3XffPUnyne98J2eccUa+9a1v5bOf/WwzzRAAAABoTm36jI8/t379+mzZsiWVlZVN\nti9btix77bVX9ttvv5xxxhlZu3ZtaeyJJ57Iu+++mxEjRpS27b777unTp08effTRJEltbW369OlT\nCj2SZOTIkXn77bfzxBNPNPOsAAAAgObSps/4+HPnnntuBgwYkMGDB5e2jRo1KkceeWR69uyZF198\nMRdffHGOOuqoPPjgg+nYsWPq6+tTXl6eLl26NNlXVVVV6uvrkyT19fWpqqpqMt6lS5eUl5eXagAA\nAID2p90EH+edd14eeeSRLFq0KOXl5aXtxx57bOnn/v37Z+DAgRkwYEDuu+++HHXUUa3RKgAAANBG\ntItLXWbMmJGf/vSnWbhwYfbcc8+t1u62227p3r17nn/++SRJdXV1Nm/enHXr1jWpW7t2baqrq0s1\nf3p5TJKsW7cumzdvLtUAAAAA7U+bDz7OOeecUuix9957/9X6devWZfXq1aWbnQ4cODCf/vSns3jx\n4lLNyy+/nLq6ugwZMiRJMnjw4NTV1TV5xO3ixYvTsWPHDBw4cAfPCAAAAGgpbfpSl+nTp+f222/P\nT37yk1RWVmbNmjVJkoqKinTu3Dnr16/PJZdckqOOOio1NTV58cUX893vfjdVVVUZPXp0kmSXXXbJ\nV77ylXz7299OVVVVdt1115x//vnp379/DjrooCTJiBEj0q9fv5x66qm5+OKL89prr+XCCy/MV7/6\nVU90AQAAgHasTQcfN9xwQ5JkzJgxTbafc845mTFjRsrLy7NixYrcdtttef3111NTU5Nhw4blxhtv\nzM4771yqnzlzZsrLyzNhwoRs2rQpBx54YK677rrSvULKy8tz++23Z/r06Tn00EOz00475fjjj89F\nF13UcpMFAAAAdrg2HXw0NDRsdbxTp0658847/+p+OnbsmNmzZ2f27Nl/sWaPPfbI7bff/pF7BAAA\nANquNn+PDwAAAICPS/ABAAAAFJbgAwAAACgswQcAAABQWIIPAAAAoLAEHwAAAEBhCT4AAACAwurQ\n2g0AbE3F0nmt3cJftWHYxNZuAQAA+Auc8QEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAA\nQGEJPgAAAIDCEnwAAAAAhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAA\nQGEJPgAAAIDCEnwAAAAAhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAA\nQGEJPgAAAIDCEnwAAAAAhSX4AAAAAApL8AEAAAAUluADAAAAKCzBBwAAAFBYgg8AAACgsAQfAAAA\nQGEJPgAAAIDCEnwAAAAAhSX4AAAAAApL8AEAAAAUluADAAAAKKwOrd0AbV/F0nmt3cJWbRg2sbVb\nAAAAoI1yxgcAAABQWIIPAAAAoLAEHwAAAEBhCT4AAACAwhJ8/Jkbbrgh++67b2pqavLlL385v/rV\nr1q7JQAAAOBjEnz8iTvvvDPnnntuvvnNb+aXv/xlBg8enOOPPz6rVq1q7dYAAACAj0Hw8Sfmzp2b\n//bf/ltOOumk9OnTJ7Nnz05NTU3mz5/f2q0BAAAAH0NZQ0NDY2s30Ra888472W233TJv3rwcffTR\npe3Tp0/PihUrcu+997ZidwAAAMDH4YyP/2fdunXZvHlzqqqqmmyvqqpKfX19K3UFAAAAbA/BBwAA\nAFBYgo//p0uXLikvL8/atWubbF+7dm2qq6tbqSsAAABgewg+/p/PfOYzGThwYBYvXtxk++LFizNk\nyJBW6goAAADYHh1au4G25LTTTsspp5yS/fbbL0OGDMn8+fPzhz/8IRMmTGjt1gAAAICPwRkff+KY\nY47JzJkzM3v27AwbNiyPPPJI/sf/+B/p0aNHa7f2sV1xxRUZPnx49thjj/zt3/5txo0blxUrVjSp\naWxszMyZM9O3b99069YtRxxxRJ555pkmNZdddlkOOeSQdO/ePZWVlR96rHPOOScHHXRQampqMmDA\ngGabE+1LS63Bp556KhMnTkz//v3TrVu3DBo0KHPmzMmWLVuadX60bS21/l555ZUcc8wx6du3b6qr\nq9O/f/9Mnz49r7/+erPOj7atJX8Hv2/dunXp169fKisrs27duh0+J9qXllyDlZWVH3jNnz+/2eZG\n29fS34G33357vvSlL6Wmpia9evXKKaec0izzon0SfPyZSZMm5amnnkp9fX2WLFmSf/iHf2jtlrbL\nQw89lIkTJ+a+++7LwoUL06FDhxx99NF57bXXSjVz5szJ3LlzM2vWrDzwwAOpqqrK2LFj8+abb5Zq\n3n777YwePTpTpkz5i8fasmVLxo8fnxNOOKFZ50T70lJr8IknnkiXLl1y3XXX5ZFHHsmMGTMye/bs\nXHnllc0+R9qullp/n/rUpzJ69Ojcdttteeyxx3LNNddkyZIlOfPMM5t9jrRdLfk7+H1Tp071Px8o\naek1+P3vfz91dXWl1/jx45ttbrR9Lbn+rrvuulx44YX5+te/nmXLluWee+7J4Ycf3qzzo30pa2ho\naGztJmg569evT48ePXLzzTfnsMMOS2NjY/r27Zt//ud/zvTp05MkGzduTO/evXPRRRd94DKfu+++\nOyeddFIaGhr+4jGuvvrq/PCHP8xTTz3VrHOhfWqJNfi+Cy+8MEuWLMmSJUuaZS60Py25/q677rpc\neeWVqaura5a50P409/q79tpr84tf/CLf/OY3M2bMmPzud79Lly5dmn1etB/NuQYrKyvzox/9KGPG\njGmRudD+NNf6a2hoyD777JObb745w4cPb7H50L444+MTZv369dmyZUvpNLGVK1dmzZo1GTFiRKmm\nU6dOOeCAA/Loo4+2VpsUWEuuwTfffPOvnhbOJ0tLrb/Vq1fnnnvuafdnDbJjNef6e/LJJzNnzpxc\nd911+dSn/HnHh2vu78Bzzz03vXr1yvDhwzN//nyXm9JEc62/xYsXZ/Pmzamvr8+QIUPSr1+/nHji\niXnhhRd29BRox/xm/IQ599xzM2DAgAwePDhJsmbNmiRJVVVVk7qqqqrU19e3eH8UX0utwSeeeCK3\n3HJLTj755I/fLIXT3Otv4sSJ2W233dKvX7907tw5c+fO3f6mKYzmWn8bNmzIxIkTM2vWrHTv3n3H\nNUzhNOd34HnnnZf58+fnZz/7WY455phccMEFufzyy3dM4xRCc62/F154IVu2bMlll12Wf/u3f8tP\nfvKTvPfeexk9enTeeuutHTcB2jVPdfkEOe+88/LII49k0aJFKS8vb+12+ARqqTX43HPPZdy4cZky\nZYpTbilpifX3ve99L+ecc05++9vf5rvf/W5mzJiRq666qlmORfvSnOvvnHPOydChQ33fsVXN/R14\n9tlnl37ed999s2XLllx++eU566yzdvixaH+ac/1t2bIl7777bmbNmlU6e+SHP/xh+vTpk0WLFuWY\nY47ZocejfXLGxyfEjBkz8tOf/jQLFy7MnnvuWdpeU1OTJFm7dm2T+rVr16a6urolW6TgWmoNPvvs\nsxk9enSOOeaY/Ou//uv2tEyBtNT6q6mpyd57753DDz88V155Zf793/89L7300nb1TvvX3OtvyZIl\nueWWW9KlS5d06dKlFIDsvffeueiii7Z/ArR7rfF34H777Zc33njDGcQ0+/p7fz99+vQpbdtll13S\nrVs3v4MpEXx8ApxzzjmlL5u99967yVjPnj1TU1OTxYsXl7Zt2rQpy5Yty5AhQ1q6VQqqpdbgb37z\nm4wePTpjxozJzJkzd0jvtH+t9R34/rXt77zzznbth/atJdbfXXfdlYceeihLly7N0qVL8/3vfz9J\n8vOf/9zjHGm178CnnnoqO+20U3bZZZft2g/tW0usv6FDhyZJfvvb35a2rV+/PmvWrMkee+yxnTOg\nKFzqUnDTp0/P7bffnp/85CeprKwsXUtXUVGRzp07p6ysLFOmTMkVV1yR3r17Z6+99spll12WioqK\nHHfccaX9rFq1Kq+99lpefPHFJMny5cuTJL169Urnzp2TJM8//3zWr1+f1atX59133y3V9O3bN5/5\nzGdactq0IS21Bp955pkcddRRGTZsWL75zW+WjpP8//8ngE+ellp/ixYtyquvvpqBAwemoqIiv/nN\nb3LhhRdm//33T69evVp+4rQJLbX+9tprrybHXbduXZI/nvHhqS6fbC21Bn/xi1+kvr4++++/fzp1\n6pSlS5dm5syZOemkk9KxY8eWnzhtQkt+Bx5++OE599xzc+WVV6aysjIzZ85M165dc8ghh7T8xGmT\nPM624P7SEy3OOeeczJgxI0nS2NiYSy65JP/+7/+ehoaG7Lfffrnsssuyzz77lOqnTJmSW2+99QP7\nueeeezJs2LAkyRFHHJGHH374AzVPPvlkevbsuSOmQzvUUmtw5syZmTVr1ocea1sePUoxtdT6e/D/\na++OTSUEogCKvmC7MNEuLEJLMLQAYyMDwXqMDRaswsgizAx0023gC3/2nA4Ghhm4A/Pe7xjHMbZt\ni/M8I8uyqKoquq4zWeiHPXkHf1vXNeq6Ns6Wx/bgsiwxDEPs+x7XdUWe59E0TbRtG6+Xd9Zf9eQZ\neBxH9H0f8zzHfd9RlmVM0xRFUfzByviPhA8AAAAgWf74AAAAAJIlfAAAAADJEj4AAACAZAkfAAAA\nQLKEDwAAACBZwgcAAACQLOEDAAAASJbwAQAAACRL+AAAAACS9QGW0z52RQ1zHwAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a90e910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph the number who have or have not previously attended public school\n",
    "# Setting the positions and width for the bars\n",
    "pos = list(range(len(df_previouslyPublicSchool['Previously Attended an Indiana Public School'])))\n",
    "width = 0.25\n",
    "\n",
    "# Plotting the bars\n",
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "\n",
    "# Create a bar with previous data\n",
    "plt.bar(pos, \n",
    "        df_previouslyPublicSchool['Previously Attended an Indiana Public School'], \n",
    "        width, \n",
    "        alpha=0.8, \n",
    "        color='#ffd59b', \n",
    "        label=df_previouslyPublicSchool['Year'][0])\n",
    "\n",
    "# Create a bar with no record data\n",
    "plt.bar([p + width for p in pos], \n",
    "        df_previouslyPublicSchool['No Record of Attending an Indiana Public School'], \n",
    "        width, \n",
    "        alpha=0.8, \n",
    "        color='#ffa474', \n",
    "        label=df_previouslyPublicSchool['Year'][1])\n",
    "\n",
    "# Set the y axis label\n",
    "ax.set_ylabel('Year')\n",
    "\n",
    "# Set the chart's title\n",
    "ax.set_title('Previously attended public school')\n",
    "\n",
    "# Set the position of the x ticks\n",
    "ax.set_xticks([p + .5 * width for p in pos])\n",
    "\n",
    "# Set the labels for the x ticks\n",
    "ax.set_xticklabels(df_previouslyPublicSchool['Year'])\n",
    "\n",
    "# Setting the x-axis and y-axis limits\n",
    "plt.xlim(min(pos)-width, max(pos)+width*4)\n",
    "plt.ylim([0, 20000])\n",
    "\n",
    "# Adding the legend and showing the plot\n",
    "plt.legend(['Previously Attended an Indiana Public School', 'No Record of Attending an Indiana Public School'], loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Drop totals in student pathway dataframe\n",
    "df_studentPathwayWoTotal = df_studentPathway.drop([4,8,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10aac6a50>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vvvuO+Ph4zp8/z+rVq2nVqhUAbdu2VY9rO3HiBCdOnOCdd97Bz89Pfb+oONzd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107c53f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop raw pathway numbers\n",
    "df_studentPathwayPerc = df_studentPathwayWoTotal.drop(['2013','2014','2015','2016'], axis=1)\n",
    "\n",
    "# Convert percentage \n",
    "df_studentPathwayPerc['2013 %'] = df_studentPathwayPerc['2013 %'].apply(convertPercent)\n",
    "df_studentPathwayPerc['2014 %'] = df_studentPathwayPerc['2014 %'].apply(convertPercent)\n",
    "df_studentPathwayPerc['2015 %'] = df_studentPathwayPerc['2015 %'].apply(convertPercent)\n",
    "df_studentPathwayPerc['2016 %'] = df_studentPathwayPerc['2016 %'].apply(convertPercent)\n",
    "\n",
    "# Transpose pathway dataframe, export and reupload\n",
    "df_studentPathwayPercExport = df_studentPathwayPerc.transpose()\n",
    "df_studentPathwayPercExport.to_csv('school_choice_data/exported_data/df_studentPathwayPerc.csv')\n",
    "df_studentPathwayPercTransposed = pd.read_csv('school_choice_data/imported_data/df_studentPathwayPerc-transposed.csv')\n",
    "\n",
    "# Graph student pathway\n",
    "fig, ((ax1)) = plt.subplots(nrows=1, ncols=1, figsize=(6,8), sharex=False, sharey=False)\n",
    "\n",
    "df_studentPathwayPercTransposed.plot(kind='line',x='Pathway',y='Continuing Choice',ax=ax1)\n",
    "df_studentPathwayPercTransposed.plot(kind='line',x='Pathway',y='Previous Choice',ax=ax1)\n",
    "df_studentPathwayPercTransposed.plot(kind='line',x='Pathway',y='Previous SGO',ax=ax1)\n",
    "df_studentPathwayPercTransposed.plot(kind='line',x='Pathway',y='Two-Semesters',ax=ax1)\n",
    "df_studentPathwayPercTransposed.plot(kind='line',x='Pathway',y='Special Education',ax=ax1)\n",
    "df_studentPathwayPercTransposed.plot(kind='line',x='Pathway',y='F School',ax=ax1)\n",
    "df_studentPathwayPercTransposed.plot(kind='line',x='Pathway',y='Sibling',ax=ax1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108684f90>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z5+Lrr7+udJ2Y0ja/vpjKfqMmTGk/VxOmGK8pHCMCtd/Hlt9eAyUnVnbv3g1/f384OzuL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\n5+rqKl9fX2NkwsjISDVu3NiuX3ybNm3k5OSkP//809gINmzY0G7nYs+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HAAAa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\nRERERERE5Lb+HygokwtLf+N9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aa54a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph student ethnicity numbers by year\n",
    "fig, ((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(nrows=2, ncols=3, figsize=(12,8), sharex=True, sharey=True)\n",
    "\n",
    "df_studentEthnicity.plot(kind='line',x='Year',y='White',ax=ax1)\n",
    "df_studentEthnicity.plot(kind='line',x='Year',y='Hispanic Ethnicity and of Any Race',ax=ax2)\n",
    "df_studentEthnicity.plot(kind='line',x='Year',y='Black',ax=ax3)\n",
    "df_studentEthnicity.plot(kind='line',x='Year',y='Multiracial (two or more races)',ax=ax4)\n",
    "df_studentEthnicity.plot(kind='line',x='Year',y='Asian',ax=ax5)\n",
    "df_studentEthnicity.plot(kind='line',x='Year',y='American Indian/ Alaskan Native',ax=ax6)\n",
    "# df_studentEthnicity.plot(kind='line',x='Year',y='Native Hawaiian or Other Pacific Islander',ax=ax1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108bd1810>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0qswCXszR4YNjjmn7bQIl+LBvCBKCZW7oGREREd0sRVUWPL1Di8x8o0Nbp1AZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a99ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph student gender numbers by year\n",
    "fig, ((ax1,ax2)) = plt.subplots(nrows=1, ncols=2, figsize=(15,4), sharex=True, sharey=True)\n",
    "\n",
    "df_studentGender.plot(kind='line',x='Year',y='Female',ax=ax1)\n",
    "df_studentGender.plot(kind='line',x='Year',y='Male',ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108842750>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bt6/y8vIsTuc8derUUUFBgRo0aCBJ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107ccfb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph student geography numbers by year\n",
    "fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(16,8), sharex=False, sharey=True)\n",
    "\n",
    "df_studentGeography.plot(kind=\"bar\",x=\"Year\",y=\"Metropolitan\", ax=ax1)\n",
    "df_studentGeography.plot(kind=\"bar\",x=\"Year\",y=\"Suburban\", ax=ax2)\n",
    "df_studentGeography.plot(kind=\"bar\",x=\"Year\",y=\"Rural\", ax=ax3)\n",
    "df_studentGeography.plot(kind=\"bar\",x=\"Year\",y=\"Town\", ax=ax4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Duplicate df_schoolAwardAmount dataframe, find average award amount\n",
    "df_AvgAwardAmt = df_schoolAwardAmount\n",
    "\n",
    "df_AvgAwardAmt['2011'] = np.vectorize(averageAward)(df_schoolAwardCount['2011'],df_schoolAwardAmount['2011'])\n",
    "df_AvgAwardAmt['2012'] = np.vectorize(averageAward)(df_schoolAwardCount['2012'],df_schoolAwardAmount['2012'])\n",
    "df_AvgAwardAmt['2013'] = np.vectorize(averageAward)(df_schoolAwardCount['2013'],df_schoolAwardAmount['2013'])\n",
    "df_AvgAwardAmt['2014'] = np.vectorize(averageAward)(df_schoolAwardCount['2014'],df_schoolAwardAmount['2014'])\n",
    "df_AvgAwardAmt['2015'] = np.vectorize(averageAward)(df_schoolAwardCount['2015'],df_schoolAwardAmount['2015'])\n",
    "df_AvgAwardAmt['2016'] = np.vectorize(averageAward)(df_schoolAwardCount['2016'],df_schoolAwardAmount['2016'])\n",
    "\n",
    "# Export avg award amount\n",
    "df_AvgAwardAmt.to_csv('school_choice_data/exported_data/df_AvgAwardAmt.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Sort avg award amount by school number\n",
    "df_AvgAwardAmt_sorted = df_AvgAwardAmt.sort_values('School No.')\n",
    "\n",
    "# Count values by county\n",
    "df_CountySchoolsCount = pd.value_counts(df_AvgAwardAmt_sorted['County'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Merge award count and award amount\n",
    "df_schoolsCountAmount = df_schoolAwardCount.merge(df_schoolAwardAmount,on='School No.',how='outer').drop(['School Name_y', 'County_y'], axis=1)\n",
    "df_schoolsCountAmount = df_schoolsCountAmount.rename(columns = {'School Name_x':'School Name','County_x':'County','2011_x':'2011 Count','2012_x':'2012 Count','2013_x':'2013 Count','2014_x':'2014 Count','2015_x':'2015 Count','2016_x':'2016 Count','2011_y':'2011 Avg Amount','2012_y':'2012 Avg Amount','2013_y':'2013 Avg Amount','2014_y':'2014 Avg Amount','2015_y':'2015 Avg Amount','2016_y':'2016 Avg Amount'})\n",
    "df_schoolsCountAmountCols = df_schoolsCountAmount.columns.tolist()\n",
    "df_schoolsCountAmountCols = ['School No.',\n",
    "                             'School Name',\n",
    "                             'County',\n",
    "                             '2011 Count',\n",
    "                             '2011 Avg Amount',\n",
    "                             '2012 Count',\n",
    "                             '2012 Avg Amount',\n",
    "                             '2013 Count',\n",
    "                             '2013 Avg Amount',\n",
    "                             '2014 Count',\n",
    "                             '2014 Avg Amount',\n",
    "                             '2015 Count',\n",
    "                             '2015 Avg Amount',\n",
    "                             '2016 Count',\n",
    "                             '2016 Avg Amount']\n",
    "df_schoolsCountAmount = df_schoolsCountAmount[df_schoolsCountAmountCols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the zeros do not always indicate that no students were in the program.\n",
      "schools with less than ten students do not have to report\n"
     ]
    }
   ],
   "source": [
    "# Duplicate awards count df then convert to int\n",
    "df_schoolsCountAmountClean = df_schoolsCountAmount\n",
    "\n",
    "df_schoolsCountAmountClean['2011 Count'] = df_schoolsCountAmount['2011 Count'].apply(convertInt)\n",
    "df_schoolsCountAmountClean['2012 Count'] = df_schoolsCountAmount['2012 Count'].apply(convertInt)\n",
    "df_schoolsCountAmountClean['2013 Count'] = df_schoolsCountAmount['2013 Count'].apply(convertInt)\n",
    "df_schoolsCountAmountClean['2014 Count'] = df_schoolsCountAmount['2014 Count'].apply(convertInt)\n",
    "df_schoolsCountAmountClean['2015 Count'] = df_schoolsCountAmount['2015 Count'].apply(convertInt)\n",
    "df_schoolsCountAmountClean['2016 Count'] = df_schoolsCountAmount['2016 Count'].apply(convertInt)\n",
    "\n",
    "print('the zeros do not always indicate that no students were in the program.')\n",
    "print('schools with less than ten students do not have to report')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# import school ratings df\n",
    "df_schoolRatings = pd.read_csv('school_choice_data/imported_data/final-2016-af-school-grades.csv')\n",
    "\n",
    "df_schoolRatingsCond = df_schoolRatings.drop(['IDOE_CORPORATION_ID','CORPORATION_NAME'], axis=1)\n",
    "df_schoolRatingsCond = df_schoolRatingsCond.rename(columns = {'IDOE_SCHOOL_ID':'School No.','SCHOOL_NAME':'School Name','2016 Grade':'2016_grade'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Import reworked awards data - data without symbols\n",
    "df_schoolAwardsReworked = pd.read_csv('school_choice_data/imported_data/awards_by_school_16_reworked.csv')\n",
    "df_schoolAwardsReworked\n",
    "\n",
    "# dupe dataframe and apply fillNone function to all cells\n",
    "df_schoolAwardsWithNull = df_schoolAwardsReworked\n",
    "df_schoolAwardsWithNull['2011'] = df_schoolAwardsWithNull['2011'].apply(fillNone)\n",
    "df_schoolAwardsWithNull['2012'] = df_schoolAwardsWithNull['2012'].apply(fillNone)\n",
    "df_schoolAwardsWithNull['2013'] = df_schoolAwardsWithNull['2013'].apply(fillNone)\n",
    "df_schoolAwardsWithNull['2014'] = df_schoolAwardsWithNull['2014'].apply(fillNone)\n",
    "df_schoolAwardsWithNull['2015'] = df_schoolAwardsWithNull['2015'].apply(fillNone)\n",
    "df_schoolAwardsWithNull['2016'] = df_schoolAwardsWithNull['2016'].apply(fillNone)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# dupe dataframe and find the percent change in student count from the first year of SC in 2011 to current numbers\n",
    "df_schoolsPercChange = df_schoolAwardsReworked\n",
    "df_schoolsPercChange['percent_change'] = np.vectorize(percentChange)(df_schoolAwardsReworked['2011'],df_schoolAwardsReworked['2016'])\n",
    "\n",
    "# dupe dataframe and find the percent change in awards from the first year of SC in 2011 to current numbers\n",
    "df_schoolAwardsPercChange = df_schoolAwardsWithNull\n",
    "df_schoolAwardsPercChange['percent_change'] = np.vectorize(percentChange)(df_schoolAwardsPercChange['2011'],df_schoolAwardsPercChange['2016'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Merge school ratings and awards percent change; clean dataframe\n",
    "df_schoolsChangeGrade = df_schoolRatingsCond.merge(df_schoolAwardsPercChange,on='School No.', how='outer').drop(['2012','2013','2014','2015'], axis=1)\n",
    "df_schoolsChangeGrade['School Name'] = np.vectorize(fillInfo)(df_schoolsChangeGrade['School Name_y'],df_schoolsChangeGrade['School Name_x'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# organize dataframe with percent change: drop and reorder columns, then export to csv\n",
    "df_schoolsChangeGrade = df_schoolsChangeGrade.drop(['School Name_x','School Name_y'], axis=1)\n",
    "cols = list(df_schoolsChangeGrade)\n",
    "df_schoolsChangeGrade = df_schoolsChangeGrade.rename(columns = {'percent_change':'Count % Change','2016_grade':'Grade', '2011':'SC 2011', '2016':'SC 2016'})\n",
    "cols = ['School No.','School Name','County','Grade','SC 2011','SC 2016','Count % Change']\n",
    "df_schoolsChangeGrade = df_schoolsChangeGrade[cols]\n",
    "df_schoolsChangeGrade.sort_values('School Name').to_csv('school_choice_data/exported_data/df_schoolsChangeGrade.csv')"
   ]
  }
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