face_meteor_pilot_ratings_Chloe_v4plusv5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "# from pymer4.models import Lmer\n",
    "from natsort import natsorted, ns\n",
    "import glob\n",
    "import networkx as nx\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "fpath = 'C:/Users/becha/Desktop/Face_meteor_analysis'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 102 groups for v4+v5.\n"
     ]
    }
   ],
   "source": [
    "datalist = natsorted(glob.glob(os.path.join(fpath,'Data', 'preprocessed_data', 'preprocessed_data', \"v4+v5\",\"*\")))\n",
    "len(datalist)\n",
    "print('There are',len(datalist),'groups for v4+v5.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = []\n",
    "\n",
    "def loop_directory(directory: str):\n",
    "    '''Loop files in the directory'''\n",
    "\n",
    "    for filename in os.listdir(directory):\n",
    "            if filename.endswith(\".csv\"):\n",
    "                file_directory = os.path.join(directory, filename)\n",
    "                lst.append(file_directory)\n",
    "                pd.read_csv(file_directory)\n",
    "                \n",
    "                \n",
    "            else:\n",
    "                continue\n",
    "#     print(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data of games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "loop_directory('C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_100_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_101_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_103_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_104_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_105_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_106_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_107_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_108_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_109_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_110_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_111_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_112_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_113_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_114_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_116_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_117_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_118_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_119_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_129_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_130_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_131_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_132_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_134_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_135_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_159_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_160_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_161_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_162_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_189_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_190_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_191_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_192_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_193_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_194_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_196_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_197_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_198_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_199_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_200_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_202_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_203_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_204_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_205_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_206_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_215_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_216_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_218_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_37_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_38_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_39_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_40_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_41_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_42_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_43_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_44_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_45_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_46_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_47_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_48_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_49_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_50_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_51_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_52_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_53_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_54_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_55_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_57_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_58_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_60_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_62_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_63_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_66_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_67_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_69_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_71_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_72_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_73_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_74_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_75_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_76_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_77_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_79_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_80_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_81_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_82_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_83_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_84_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_85_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_86_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_87_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_88_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_89_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_90_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_91_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_92_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_93_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_94_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_95_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_96_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_97_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_98_v5.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4+v5\\\\group_99_v5.csv']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst\n",
    "# print(len(lst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df0 = pd.read_csv(lst[0])\n",
    "# df1 = pd.read_csv(lst[1])\n",
    "# df2 = pd.read_csv(lst[2])\n",
    "# df3 = pd.read_csv(lst[3])\n",
    "# df4 = pd.read_csv(lst[4])\n",
    "# df5 = pd.read_csv(lst[5])\n",
    "\n",
    "datlst = []\n",
    "for i in range(0, len(lst)):\n",
    "    datlst.append(\"df\" + str(i))\n",
    "    globals()[\"df\" + str(i)] = pd.read_csv(lst[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['df0',\n",
       " 'df1',\n",
       " 'df2',\n",
       " 'df3',\n",
       " 'df4',\n",
       " 'df5',\n",
       " 'df6',\n",
       " 'df7',\n",
       " 'df8',\n",
       " 'df9',\n",
       " 'df10',\n",
       " 'df11',\n",
       " 'df12',\n",
       " 'df13',\n",
       " 'df14',\n",
       " 'df15',\n",
       " 'df16',\n",
       " 'df17',\n",
       " 'df18',\n",
       " 'df19',\n",
       " 'df20',\n",
       " 'df21',\n",
       " 'df22',\n",
       " 'df23',\n",
       " 'df24',\n",
       " 'df25',\n",
       " 'df26',\n",
       " 'df27',\n",
       " 'df28',\n",
       " 'df29',\n",
       " 'df30',\n",
       " 'df31',\n",
       " 'df32',\n",
       " 'df33',\n",
       " 'df34',\n",
       " 'df35',\n",
       " 'df36',\n",
       " 'df37',\n",
       " 'df38',\n",
       " 'df39',\n",
       " 'df40',\n",
       " 'df41',\n",
       " 'df42',\n",
       " 'df43',\n",
       " 'df44',\n",
       " 'df45',\n",
       " 'df46',\n",
       " 'df47',\n",
       " 'df48',\n",
       " 'df49',\n",
       " 'df50',\n",
       " 'df51',\n",
       " 'df52',\n",
       " 'df53',\n",
       " 'df54',\n",
       " 'df55',\n",
       " 'df56',\n",
       " 'df57',\n",
       " 'df58',\n",
       " 'df59',\n",
       " 'df60',\n",
       " 'df61',\n",
       " 'df62',\n",
       " 'df63',\n",
       " 'df64',\n",
       " 'df65',\n",
       " 'df66',\n",
       " 'df67',\n",
       " 'df68',\n",
       " 'df69',\n",
       " 'df70',\n",
       " 'df71',\n",
       " 'df72',\n",
       " 'df73',\n",
       " 'df74',\n",
       " 'df75',\n",
       " 'df76',\n",
       " 'df77',\n",
       " 'df78',\n",
       " 'df79',\n",
       " 'df80',\n",
       " 'df81',\n",
       " 'df82',\n",
       " 'df83',\n",
       " 'df84',\n",
       " 'df85',\n",
       " 'df86',\n",
       " 'df87',\n",
       " 'df88',\n",
       " 'df89',\n",
       " 'df90',\n",
       " 'df91',\n",
       " 'df92',\n",
       " 'df93',\n",
       " 'df94',\n",
       " 'df95',\n",
       " 'df96',\n",
       " 'df97',\n",
       " 'df98',\n",
       " 'df99',\n",
       " 'df100',\n",
       " 'df101']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datlst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data of ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_dislike = []\n",
    "\n",
    "def loop_directory(directory: str):\n",
    "    '''Loop files in the directory'''\n",
    "\n",
    "    for filename in os.listdir(directory):\n",
    "            if filename.endswith(\".csv\"):\n",
    "                file_directory = os.path.join(directory, filename)\n",
    "                lst_dislike.append(file_directory)\n",
    "                pd.read_csv(file_directory)\n",
    "                \n",
    "                \n",
    "            else:\n",
    "                continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "loop_directory('C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID100.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID101.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID103.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID104.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID105.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID106.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID107.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID108.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID109.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID110.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID111.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID112.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID113.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID114.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID116.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID117.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID118.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID119.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID129.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID130.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID131.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID132.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID134.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID135.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID159.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID160.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID161.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID162.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID189.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID190.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID191.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID192.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID193.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID194.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID196.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID197.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID198.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID199.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID200.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID202.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID203.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID204.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID205.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID206.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID215.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID216.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID218.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID37.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID38.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID39.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID40.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID41.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID42.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID43.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID44.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID45.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID46.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID47.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID48.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID49.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID50.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID51.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID52.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID53.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID54.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID55.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID57.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID58.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID60.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID62.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID63.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID66.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID67.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID69.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID71.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID72.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID73.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID74.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID75.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID76.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID77.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID79.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID80.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID81.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID82.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID83.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID84.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID85.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID86.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID87.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID88.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID89.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID90.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID91.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID92.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID93.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID94.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID95.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID96.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID97.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID98.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4+v5\\\\rating_GroupID99.csv']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst_dislike\n",
    "# print(len(lst_dislike))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['df_dislike0',\n",
       " 'df_dislike1',\n",
       " 'df_dislike2',\n",
       " 'df_dislike3',\n",
       " 'df_dislike4',\n",
       " 'df_dislike5',\n",
       " 'df_dislike6',\n",
       " 'df_dislike7',\n",
       " 'df_dislike8',\n",
       " 'df_dislike9',\n",
       " 'df_dislike10',\n",
       " 'df_dislike11',\n",
       " 'df_dislike12',\n",
       " 'df_dislike13',\n",
       " 'df_dislike14',\n",
       " 'df_dislike15',\n",
       " 'df_dislike16',\n",
       " 'df_dislike17',\n",
       " 'df_dislike18',\n",
       " 'df_dislike19',\n",
       " 'df_dislike20',\n",
       " 'df_dislike21',\n",
       " 'df_dislike22',\n",
       " 'df_dislike23',\n",
       " 'df_dislike24',\n",
       " 'df_dislike25',\n",
       " 'df_dislike26',\n",
       " 'df_dislike27',\n",
       " 'df_dislike28',\n",
       " 'df_dislike29',\n",
       " 'df_dislike30',\n",
       " 'df_dislike31',\n",
       " 'df_dislike32',\n",
       " 'df_dislike33',\n",
       " 'df_dislike34',\n",
       " 'df_dislike35',\n",
       " 'df_dislike36',\n",
       " 'df_dislike37',\n",
       " 'df_dislike38',\n",
       " 'df_dislike39',\n",
       " 'df_dislike40',\n",
       " 'df_dislike41',\n",
       " 'df_dislike42',\n",
       " 'df_dislike43',\n",
       " 'df_dislike44',\n",
       " 'df_dislike45',\n",
       " 'df_dislike46',\n",
       " 'df_dislike47',\n",
       " 'df_dislike48',\n",
       " 'df_dislike49',\n",
       " 'df_dislike50',\n",
       " 'df_dislike51',\n",
       " 'df_dislike52',\n",
       " 'df_dislike53',\n",
       " 'df_dislike54',\n",
       " 'df_dislike55',\n",
       " 'df_dislike56',\n",
       " 'df_dislike57',\n",
       " 'df_dislike58',\n",
       " 'df_dislike59',\n",
       " 'df_dislike60',\n",
       " 'df_dislike61',\n",
       " 'df_dislike62',\n",
       " 'df_dislike63',\n",
       " 'df_dislike64',\n",
       " 'df_dislike65',\n",
       " 'df_dislike66',\n",
       " 'df_dislike67',\n",
       " 'df_dislike68',\n",
       " 'df_dislike69',\n",
       " 'df_dislike70',\n",
       " 'df_dislike71',\n",
       " 'df_dislike72',\n",
       " 'df_dislike73',\n",
       " 'df_dislike74',\n",
       " 'df_dislike75',\n",
       " 'df_dislike76',\n",
       " 'df_dislike77',\n",
       " 'df_dislike78',\n",
       " 'df_dislike79',\n",
       " 'df_dislike80',\n",
       " 'df_dislike81',\n",
       " 'df_dislike82',\n",
       " 'df_dislike83',\n",
       " 'df_dislike84',\n",
       " 'df_dislike85',\n",
       " 'df_dislike86',\n",
       " 'df_dislike87',\n",
       " 'df_dislike88',\n",
       " 'df_dislike89',\n",
       " 'df_dislike90',\n",
       " 'df_dislike91',\n",
       " 'df_dislike92',\n",
       " 'df_dislike93',\n",
       " 'df_dislike94',\n",
       " 'df_dislike95',\n",
       " 'df_dislike96',\n",
       " 'df_dislike97',\n",
       " 'df_dislike98',\n",
       " 'df_dislike99',\n",
       " 'df_dislike100',\n",
       " 'df_dislike101']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datlst_dislike = []\n",
    "for i in range(0, len(lst)):\n",
    "    datlst_dislike.append(\"df_dislike\" + str(i))\n",
    "    globals()[\"df_dislike\" + str(i)] = pd.read_csv(lst_dislike[i])\n",
    "datlst_dislike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 10 and dislike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 取round 10的島主的pgg_receive\n",
    "dictator_pgg = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[9]['pgg_receive'])\n",
    "\n",
    "# print(dictator_pgg)\n",
    "# print(len(dictator_pgg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 取round 10的所有人的pgg_receive\n",
    "all_pgg = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 10]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg)\n",
    "# print(len(all_pgg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：v5中島主最後一round pgg表現的顯著程度\n",
    "ratio_10 = [x/y for x, y in zip(dictator_pgg, all_pgg)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratio = pd.DataFrame({\"group\": datlst, \"dictator's pgg receive\": dictator_pgg, \"all player's pgg receive\": all_pgg, \"ratio\": ratio_10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>dictator's pgg receive</th>\n",
       "      <th>all player's pgg receive</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>df0</td>\n",
       "      <td>207</td>\n",
       "      <td>530</td>\n",
       "      <td>0.390566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>df1</td>\n",
       "      <td>152</td>\n",
       "      <td>656</td>\n",
       "      <td>0.231707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>df2</td>\n",
       "      <td>224</td>\n",
       "      <td>360</td>\n",
       "      <td>0.622222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>df3</td>\n",
       "      <td>198</td>\n",
       "      <td>500</td>\n",
       "      <td>0.396000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>df4</td>\n",
       "      <td>320</td>\n",
       "      <td>320</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>df97</td>\n",
       "      <td>200</td>\n",
       "      <td>700</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>df98</td>\n",
       "      <td>118</td>\n",
       "      <td>528</td>\n",
       "      <td>0.223485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>df99</td>\n",
       "      <td>250</td>\n",
       "      <td>760</td>\n",
       "      <td>0.328947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>df100</td>\n",
       "      <td>200</td>\n",
       "      <td>800</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>df101</td>\n",
       "      <td>250</td>\n",
       "      <td>700</td>\n",
       "      <td>0.357143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     group  dictator's pgg receive  all player's pgg receive     ratio\n",
       "0      df0                     207                       530  0.390566\n",
       "1      df1                     152                       656  0.231707\n",
       "2      df2                     224                       360  0.622222\n",
       "3      df3                     198                       500  0.396000\n",
       "4      df4                     320                       320  1.000000\n",
       "..     ...                     ...                       ...       ...\n",
       "97    df97                     200                       700  0.285714\n",
       "98    df98                     118                       528  0.223485\n",
       "99    df99                     250                       760  0.328947\n",
       "100  df100                     200                       800  0.250000\n",
       "101  df101                     250                       700  0.357143\n",
       "\n",
       "[102 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The degree of dislike of the dictator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先以df_dislike0為例\n",
    "dislike = []\n",
    "for i in range(0,len(lst)):\n",
    "    dislike.append(np.mean(globals()[\"df_dislike\" + str(i)]['liking_p1'][1:]))\n",
    "\n",
    "# print(dislike)\n",
    "# len(dislike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratio['dislike'] = dislike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>dictator's pgg receive</th>\n",
       "      <th>all player's pgg receive</th>\n",
       "      <th>ratio</th>\n",
       "      <th>dislike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>df0</td>\n",
       "      <td>207</td>\n",
       "      <td>530</td>\n",
       "      <td>0.390566</td>\n",
       "      <td>73.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>df1</td>\n",
       "      <td>152</td>\n",
       "      <td>656</td>\n",
       "      <td>0.231707</td>\n",
       "      <td>31.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>df2</td>\n",
       "      <td>224</td>\n",
       "      <td>360</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>df3</td>\n",
       "      <td>198</td>\n",
       "      <td>500</td>\n",
       "      <td>0.396000</td>\n",
       "      <td>53.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>df4</td>\n",
       "      <td>320</td>\n",
       "      <td>320</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>df97</td>\n",
       "      <td>200</td>\n",
       "      <td>700</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>85.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>df98</td>\n",
       "      <td>118</td>\n",
       "      <td>528</td>\n",
       "      <td>0.223485</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>df99</td>\n",
       "      <td>250</td>\n",
       "      <td>760</td>\n",
       "      <td>0.328947</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>df100</td>\n",
       "      <td>200</td>\n",
       "      <td>800</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>df101</td>\n",
       "      <td>250</td>\n",
       "      <td>700</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>53.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     group  dictator's pgg receive  all player's pgg receive     ratio  \\\n",
       "0      df0                     207                       530  0.390566   \n",
       "1      df1                     152                       656  0.231707   \n",
       "2      df2                     224                       360  0.622222   \n",
       "3      df3                     198                       500  0.396000   \n",
       "4      df4                     320                       320  1.000000   \n",
       "..     ...                     ...                       ...       ...   \n",
       "97    df97                     200                       700  0.285714   \n",
       "98    df98                     118                       528  0.223485   \n",
       "99    df99                     250                       760  0.328947   \n",
       "100  df100                     200                       800  0.250000   \n",
       "101  df101                     250                       700  0.357143   \n",
       "\n",
       "       dislike  \n",
       "0    73.333333  \n",
       "1    31.333333  \n",
       "2    66.666667  \n",
       "3    53.666667  \n",
       "4     1.666667  \n",
       "..         ...  \n",
       "97   85.666667  \n",
       "98   50.000000  \n",
       "99   63.000000  \n",
       "100  51.000000  \n",
       "101  53.666667  \n",
       "\n",
       "[102 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.6560914697725831 .\n"
     ]
    }
   ],
   "source": [
    "my_rho = np.corrcoef(ratio_10, dislike)\n",
    "\n",
    "# print(my_rho)\n",
    "print(\"The value of correlation is\",my_rho[0][1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 442
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(x= ratio_10, y=dislike, data=df_ratio, ci=None)\n",
    "ax.set_title(\"The score of being liked of the dictator vs. The pgg_receive in round 10\")\n",
    "ax.set_xlabel(\"The ratio of pgg_receive of the dictator in round 10\")\n",
    "ax.set_ylabel(\"The score of being liked of the dictator\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 9 and dislike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 取round 9的島主的pgg_receive\n",
    "dictator_pgg_9 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_9.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[8]['pgg_receive'])\n",
    "\n",
    "# print(dictator_pgg_9)\n",
    "# print(len(dictator_pgg_9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 取round 9的所有人的pgg_receive\n",
    "all_pgg_9 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_9.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 9]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_9)\n",
    "# print(len(all_pgg_9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：v5中島主round 9 pgg表現的顯著程度\n",
    "ratio_9 = [x/y for x, y in zip(dictator_pgg_9, all_pgg_9)]\n",
    "# type(ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratio_9 = pd.DataFrame({\"group\": datlst, \"dictator's pgg receive\": dictator_pgg_9, \"all player's pgg receive\": all_pgg_9, \"ratio\": ratio_9})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>dictator's pgg receive</th>\n",
       "      <th>all player's pgg receive</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>df0</td>\n",
       "      <td>220</td>\n",
       "      <td>710</td>\n",
       "      <td>0.309859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>df1</td>\n",
       "      <td>135</td>\n",
       "      <td>508</td>\n",
       "      <td>0.265748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>df2</td>\n",
       "      <td>206</td>\n",
       "      <td>430</td>\n",
       "      <td>0.479070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>df3</td>\n",
       "      <td>174</td>\n",
       "      <td>500</td>\n",
       "      <td>0.348000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>df4</td>\n",
       "      <td>60</td>\n",
       "      <td>210</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>df97</td>\n",
       "      <td>200</td>\n",
       "      <td>680</td>\n",
       "      <td>0.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>df98</td>\n",
       "      <td>161</td>\n",
       "      <td>680</td>\n",
       "      <td>0.236765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>df99</td>\n",
       "      <td>207</td>\n",
       "      <td>800</td>\n",
       "      <td>0.258750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>df100</td>\n",
       "      <td>200</td>\n",
       "      <td>800</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>df101</td>\n",
       "      <td>249</td>\n",
       "      <td>790</td>\n",
       "      <td>0.315190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     group  dictator's pgg receive  all player's pgg receive     ratio\n",
       "0      df0                     220                       710  0.309859\n",
       "1      df1                     135                       508  0.265748\n",
       "2      df2                     206                       430  0.479070\n",
       "3      df3                     174                       500  0.348000\n",
       "4      df4                      60                       210  0.285714\n",
       "..     ...                     ...                       ...       ...\n",
       "97    df97                     200                       680  0.294118\n",
       "98    df98                     161                       680  0.236765\n",
       "99    df99                     207                       800  0.258750\n",
       "100  df100                     200                       800  0.250000\n",
       "101  df101                     249                       790  0.315190\n",
       "\n",
       "[102 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio_9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.46538895571131983 .\n"
     ]
    }
   ],
   "source": [
    "my_rho_9 = np.corrcoef(ratio_9, dislike)\n",
    "\n",
    "# print(my_rho)\n",
    "print(\"The value of correlation is\",my_rho_9[0][1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 435
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_9 = sns.regplot(x= ratio_9, y=dislike, data=df_ratio_9, ci=None)\n",
    "ax_9.set_title(\"The score of being liked of the dictator vs. The pgg_receive in round 9\")\n",
    "ax_9.set_xlabel(\"The ratio of pgg_receive of the dictator in round 9\")\n",
    "ax_9.set_ylabel(\"The score of being liked of the dictator\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 9 & 10 and dislike "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 9 & 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_910 = [(x+y)/2 for x, y in zip(ratio_9, ratio_10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.6805734904263204 .\n"
     ]
    }
   ],
   "source": [
    "my_rho_910 = np.corrcoef(ratio_910, dislike)\n",
    "\n",
    "print(\"The value of correlation is\",my_rho_910[0][1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 1-8 and dislike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 1的島主的pgg_receive\n",
    "dictator_pgg_1 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_1.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[0]['pgg_receive'])\n",
    "\n",
    "# print(dictator_pgg_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[370, 270, 262, 230, 420, 328, 400, 400, 540, 464, 460, 560, 422, 430, 490, 444, 310, 490, 278, 188, 315, 180, 405, 282, 285, 300, 198, 263, 300, 210, 218, 345, 375, 368, 347, 375, 480, 200, 540, 390, 500, 340, 440, 720, 240, 293, 263, 263, 360, 405, 345, 300, 132, 255, 248, 300, 233, 320, 353, 285, 308, 285, 470, 240, 520, 470, 382, 330, 330, 450, 450, 500, 410, 364, 440, 360, 436, 410, 320, 233, 300, 150, 330, 180, 285, 375, 233, 212, 330, 225, 263, 338, 458, 488, 290, 494, 500, 472, 480, 660, 440, 388]\n"
     ]
    }
   ],
   "source": [
    "# 取round 1的所有人的pgg_receive\n",
    "all_pgg_1 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_1.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 1]['pgg_receive']))\n",
    "\n",
    "print(all_pgg_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 1中pgg表現的顯著程度\n",
    "ratio_1 = [x/y for x, y in zip(dictator_pgg_1, all_pgg_1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 2的島主的pgg_receive\n",
    "dictator_pgg_2 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_2.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[1]['pgg_receive'])\n",
    "# print(dictator_pgg_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 2的所有人的pgg_receive\n",
    "all_pgg_2 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_2.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 2]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 2中pgg表現的顯著程度\n",
    "ratio_2 = [x/y for x, y in zip(dictator_pgg_2, all_pgg_2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 3的島主的pgg_receive\n",
    "dictator_pgg_3 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_3.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[2]['pgg_receive'])\n",
    "# print(dictator_pgg_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 3的所有人的pgg_receive\n",
    "all_pgg_3 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_3.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 3]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 3中pgg表現的顯著程度\n",
    "ratio_3 = [x/y for x, y in zip(dictator_pgg_3, all_pgg_3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 4的島主的pgg_receive\n",
    "dictator_pgg_4 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_4.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[3]['pgg_receive'])\n",
    "# print(dictator_pgg_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 4的所有人的pgg_receive\n",
    "all_pgg_4 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_4.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 4]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 4中pgg表現的顯著程度\n",
    "ratio_4 = [x/y for x, y in zip(dictator_pgg_4, all_pgg_4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 5的島主的pgg_receive\n",
    "dictator_pgg_5 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_5.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[4]['pgg_receive'])\n",
    "# print(dictator_pgg_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 5的所有人的pgg_receive\n",
    "all_pgg_5 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_5.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 5]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 5中pgg表現的顯著程度\n",
    "ratio_5 = [x/y for x, y in zip(dictator_pgg_5, all_pgg_5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 6的島主的pgg_receive\n",
    "dictator_pgg_6 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_6.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[5]['pgg_receive'])\n",
    "# print(dictator_pgg_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 6的所有人的pgg_receive\n",
    "all_pgg_6 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_6.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 6]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 6中pgg表現的顯著程度\n",
    "ratio_6 = [x/y for x, y in zip(dictator_pgg_6, all_pgg_6)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 7的島主的pgg_receive\n",
    "dictator_pgg_7 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_7.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[6]['pgg_receive'])\n",
    "# print(dictator_pgg_7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 7的所有人的pgg_receive\n",
    "all_pgg_7 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_7.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 7]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 7中pgg表現的顯著程度\n",
    "ratio_7 = [x/y for x, y in zip(dictator_pgg_7, all_pgg_7)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 8的島主的pgg_receive\n",
    "dictator_pgg_8 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_8.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[7]['pgg_receive'])\n",
    "# print(dictator_pgg_8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 8的所有人的pgg_receive\n",
    "all_pgg_8 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_8.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 8]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 8中pgg表現的顯著程度\n",
    "ratio_8 = [x/y for x, y in zip(dictator_pgg_8, all_pgg_8)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 1-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_RD = pd.DataFrame({\"group\": datlst, \"round 1\": ratio_1, \"round 2\": ratio_2, \"round 3\": ratio_3, \"round 4\": ratio_4, \"round 5\": ratio_5, \"round 6\": ratio_6, \"round 7\": ratio_7, \"round 8\": ratio_8})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_RD = []\n",
    "mean_RD = df_RD[['round 1', 'round 2', 'round 3', 'round 4', 'round 5', 'round 6', 'round 7', 'round 8']].mean(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.344635\n",
       "1      0.264007\n",
       "2      0.367081\n",
       "3      0.319701\n",
       "4      0.255579\n",
       "         ...   \n",
       "97     0.238796\n",
       "98     0.242446\n",
       "99     0.317131\n",
       "100    0.254086\n",
       "101    0.334888\n",
       "Length: 102, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_RD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cor = pd.DataFrame({\"ratio of round 1-8\":mean_RD,\"dislike\":dislike})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.4365234576960349 .\n"
     ]
    }
   ],
   "source": [
    "my_rho_1to8 = np.corrcoef(mean_RD, dislike)\n",
    "\n",
    "# print(my_rho)\n",
    "print(\"The value of correlation is\",my_rho_1to8[0][1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 447
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_1to8 = sns.regplot(x= mean_RD, y=dislike, data=df_cor, ci=None)\n",
    "ax_1to8.set_title(\"The score of being liked of the dictator vs. The pgg_receive in round 1-8\")\n",
    "ax_1to8.set_xlabel(\"The ratio of pgg_receive of the dictator in round 1-8\")\n",
    "ax_1to8.set_ylabel(\"The score of being liked of the dictator\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 1-10 and dislike "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_1to10 = [(x*8+y*2)/10 for x, y in zip(mean_RD, ratio_910)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.34575033465145727,\n",
       " 0.26095135481361853,\n",
       " 0.40379372236625927,\n",
       " 0.3301610057444275,\n",
       " 0.3330347543272628,\n",
       " 0.4611684784096644,\n",
       " 0.4133126061899062,\n",
       " 0.25766628024692545,\n",
       " 0.31806407544125054,\n",
       " 0.25099507804657534,\n",
       " 0.3386799013216212,\n",
       " 0.2893457547320099,\n",
       " 0.49068852043499345,\n",
       " 0.3066294730248219,\n",
       " 0.39349434038074876,\n",
       " 0.34647210768950654,\n",
       " 0.29307267100388545,\n",
       " 0.25135745207173776,\n",
       " 0.44796322116018084,\n",
       " 0.41455998265737204,\n",
       " 0.7630208456457468,\n",
       " 0.24640409246700162,\n",
       " 0.26766155605348463,\n",
       " 0.3571047719694599,\n",
       " 0.3875393912311673,\n",
       " 0.33408411819536055,\n",
       " 0.37630975006711737,\n",
       " 0.3851778419012765,\n",
       " 0.2878668593000686,\n",
       " 0.30073077752397126,\n",
       " 0.31030630799302567,\n",
       " 0.6288439425589271,\n",
       " 0.7471636363636364,\n",
       " 0.5732558971143952,\n",
       " 0.35607040170868093,\n",
       " 0.3878895124442299,\n",
       " 0.3120374302904353,\n",
       " 0.3571652362274257,\n",
       " 0.2533232300804886,\n",
       " 0.3015391681111491,\n",
       " 0.593683405292926,\n",
       " 0.2658152734778122,\n",
       " 0.32710204694263595,\n",
       " 0.24166666666666664,\n",
       " 0.40186985756751437,\n",
       " 0.48778121181219347,\n",
       " 0.27467764427850916,\n",
       " 0.32777209354731635,\n",
       " 0.2937299866515116,\n",
       " 0.30930852637457606,\n",
       " 0.32333188949104674,\n",
       " 0.2898409217488165,\n",
       " 0.6582594200067259,\n",
       " 0.323072019109724,\n",
       " 0.35730943072436006,\n",
       " 0.36374121000550697,\n",
       " 0.304062907820945,\n",
       " 0.2984184067150748,\n",
       " 0.3537216317423481,\n",
       " 0.3445632875238109,\n",
       " 0.29522725044950343,\n",
       " 0.45532850598846675,\n",
       " 0.42738165287925317,\n",
       " 0.2930744281439746,\n",
       " 0.3626323472224841,\n",
       " 0.24108717878379676,\n",
       " 0.3819142914616891,\n",
       " 0.5233878931505952,\n",
       " 0.2676005580097652,\n",
       " 0.5480453715871482,\n",
       " 0.267331793466686,\n",
       " 0.4245979210419197,\n",
       " 0.34415085817524843,\n",
       " 0.2558779403385919,\n",
       " 0.4450282100631724,\n",
       " 0.29176024581617915,\n",
       " 0.4596891788786661,\n",
       " 0.3574835746039701,\n",
       " 0.3414281602897642,\n",
       " 0.29087287900080816,\n",
       " 0.4451344645137748,\n",
       " 0.30990957436610694,\n",
       " 0.425479939832336,\n",
       " 0.4034678430517162,\n",
       " 0.24223262256943726,\n",
       " 0.31085536304494993,\n",
       " 0.5241841346918434,\n",
       " 0.33699917763928705,\n",
       " 0.2269066592293895,\n",
       " 0.3415310852850888,\n",
       " 0.2759967165497525,\n",
       " 0.29890459085927085,\n",
       " 0.35480896420690644,\n",
       " 0.2837131867486533,\n",
       " 0.32890201623931936,\n",
       " 0.4011349887964313,\n",
       " 0.329481072779542,\n",
       " 0.24902009928732943,\n",
       " 0.2399818921070634,\n",
       " 0.3124742327837756,\n",
       " 0.2532689393939394,\n",
       " 0.3351435165543867]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_1to10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.5829590084348533 .\n"
     ]
    }
   ],
   "source": [
    "my_rho_1to10 = np.corrcoef(ratio_1to10, dislike)\n",
    "\n",
    "# print(my_rho)\n",
    "print(\"The value of correlation is\",my_rho_1to10[0][1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualiztion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1to10 = pd.DataFrame({\"round 1-10\": ratio_1to10, \"dislike\":dislike})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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umCNd/KDpdfXka7HUaXyPR2tV4MvR54MLhj+HcNP9fQ+tQc7i7j8GbicER2dWmI9D0ecJZtZV4TQvJlwkfuXu/17hNE8BVhNuXP+pcKSHMsnvjL4+vlTZceBf3D1XONDM7g88lvD08KOzpgrLOEB4kgThyV2lzo8+P+Hue4vM9wpCgAEhCJ8PdxL60Stc9n7gY9HX8wtGvzT6/LC7T5WYbzzParZH7P0l5ltqH/5DwsODo4SnZ8dx92nCDWNTRPVBvk/I91XAudE+k7SV8FZ0t7t/qdh83P0OwpvKdJQ+Fv8+P3b3a4pM9xuKtMpYoXjeX3b3/ysy75sJb+hh/vbRSlW732yl9m1eUlTP4dnR1w+4+9EiyS4hvF2pmJn1AM+Kvv69ux+uZvp6RNv1O9HXx1Y7vZmtJbwVhfBwr1idk/cSjuEe8vVKCn3a3Ut1UTWX+Jx0joXWpws9ryBdLL6W3bvG5TbDEMXP63cRgkKYfTzM93kd4KPuXqyrsE8DewhvhJ+VGP5wQtAMRa7vkfcWG2ih3vCfRF8vcfdiXZV8iPAmptH+ucTwrdR+zqnl/qie5ZUT7wfnF9aHNLOV5LtTKNwH431sR4n9IDnNE82srcL8VOrdJYZ/JfosPCYqNUIokVBMfA293t2/VTgyOp/F95bzdQ2tdr3n855iMYvbJpmrC6P42t1Tz8IWS+upu0oMj/uSW1MwPG5V6JFmNitwSVgbffaRD2TK+Qnh6cm9gR+Z2YcJxSzvKDPNo6LP/61g/rG4kZufF7kRj11HKM6RjtIXm3+pdYq3TwehkYdS+Yh3nqKV0AtZaJQoPlBnHZQJVwOPJr+ejfbdEg8ZIBRR+lvgwWbW4e4ZM0sT6mECvN/Mil6wCU84ocLtUaDaffhh0eegl25J9ns15KMRTiO8wTiJcILeVuKGLN7P7jPHcRg3OpPcrvG+8d0y032XUAS5WvG859pHn8/87aOVqvXcV8s2L+d+hBIcUOI3cfdxM7uB8ES3UmeR7zdq1s1HI5jZgwj1n55AuFnvIdyoJs1qEKcCD4vm45TeJgejbfJYwr5UrCPmSq49Rbn79WZ2K7CZcHN2rOufqPGN+xPethQ2lvO/hGoI77XQaNXlhJupxdw34vVlzuuzjocFOq9DeBs0i7vnzOx7zD6PxOf2vdGNajE/JvxuhY253I/wQBnCQ7tiy52M9rknzJ31ih0hFL0spp5zTi33R/N1jvs64WHKWsJD268nxj2TcN64m/CQtFh+Xm9mr55jGV3AOkK1qUYYdffbS4wrdY2o1PVeukupSq+hbwE2m1m3uzfyQUYt6z2f9xSLWXytK3XubKhFETSWefocP+0uPLHGT09XkujGoYyK3hq6+wEzezGhn5OHEr21ik5cVxCKyRbukBujz6FKlhGJ+yos2cG6ux81s5Fo/utLJCvV30q8fdoS+Sun0reqa8m3uFuuc/g90WepfNer3LLjcW2Ek8rdhHzHrfCuLTZRgUr2qUJz7cOFx1rc8fVdZeb5+xry0Qhvij5vJZR/L3VhifezDqrfz+J9o9w6lvudy5nz+CK/j64zMytzszrfqt1v6tnm5SSP1Ub+JnEeD7p7wzuwNrPnEd74xNeIuIGK+CFHD+FJbC0tRcfb5GCZBzsw9/murn6xCA26vY0QnCT7i31+9PmtIm9A3ksoYfNM4C+jv6yZ7SLUz/q4u4/Vma9GK/cWuti9wEKc16Gy603yt5/z3B49zBwhVAFIulfi/4W8NowUK7UUqeecU8v90byc46J7qi8R3hw+n+ODxvhY+kKREgVxfk4gH6jWnZ8KVXtMVKPceamaa6gR9ttGBo21rPd83lPUrMyDjws99HNbr/jaNNd+F4+vqyuxxVI8tVpxvj/g7lbB345KZ+zu/0t4Wv0KQl2c3xNO7C8BrjWzwiIW9TRfW7a57rmUKC4F+e3zswq3zwU1LL6uvM+jYr9Hcj//g0q2SZPyWahZgcwXCW+5NxPeOJYSb9cvVbifba8yH/X+Dot1H63HfG/zuVT7m8zbsWRm6wktVLYTih2dRaiLucbdT3T3Ewn1PerNR737Ub1NtX82+nx03I1AVIwxLhZWrEjnlLufRyjx8U/kG7OKv99qZn9QZ76abTGc14vNs57lLMS1p5hy+2g955xa1mc+z3HxsXJeXAUp6rLgqQXji+XnvArzs7vK9W2WSs5LS+0a2ozja2OJv1ofYhWKg+SSpWmi4tdxSaJyD6Pm1KpBY1w/5PT5mLm7H3T3j7v7Nnc/iVCZ++PR6L8ws2ckksdPEU6pYhHxE56S05jZCkIxh2T6SsXb59SoCE+jxE22Q/n1jfvDqfcJeynliprFTwXjJs4hlN2PT5Dzss/UIN425eoc1VKkrhG+TnjyOgO82czeXiJdPcdhvP6V/Ja1zruSfXSkiW8ZazFf577ksdrI3yQ+P55gBX1jNsDTCG8SbwFe4O43eKgLnFTJm4pS4m2yMgpQS5nX852730ZoGdrI12HcSvgtJoCvlpn2x+7+Znd/NKHkxfMJb33WE1rxbGULdV6v5HhI/vZzntujqh7rioxKzqfcsbaQdVXrOefUcn80n/d3V0Xz7ya8hYdQl7sTuN1DWxgLmZ/FqpprqBMaIIzFJZNWlJm20dcCmN97ipo14mXWHG6JPk80s2LnFDh+372lRJqKtGrQGNcRObvMRmoYd7/F3V9BeFoLcHZidDzsaVXMMu6E+VQzO6lEmieQL5p2Y4k0pcTbp4fQ6E5DeKiUHzcs8sQySZ8UfVab70qdXcG4/4vyGzcqc300/NlFp1p4P4s+B6LGQoqppu5YQ7n75YS36zng783sb4ski/ezB5rZGVUuIt43ytXLKfc7VzLvZuyj8UOV+XqiWc82L+d2QnPkUOI3MbNuwtu8alxPuIkwqjtHVrId45uWm4oVrYsa93lS4fCE+GFBqWX8LJGm6L4UBcJxQ2vzdb6D/BuQF0SfcXG6L7t7RY0TufuEu19GKEUDcGb0m7akBTyvFz0PRftXfI5O/vbxuf3EqFG6Yh5J8WKFt5NvxOhxJZa7ksob92uEes45tdwfzdc5Li6d9YXoa+Gx9Pk58vOcRuZlkYv357PLdAYfn1tv9ePrM8bXkZMpIprffOy/83lPsZh9n1A/GkJLv8XEccBdwC/rWVirBo3/TXjCuoLSLX4BYGYVVxKOnv6VEzcikHxlH3da+yAze2WFi7qCcGFoB/66SD7aCF1bAHzPi7RSWo67/4r8yfq95W4MzGylmVVTBCFucOECM5v11MbMnkIoAgX5k3Oj9ZvZ8wsHRq0dxjdE/10wekf0+RwzKxdMVLXP1OEK8vvwa4rkIU3og65p3P1z5DtFfpeZvbEgyVXk66p8oFyrcUW2afz7PNrMZp3kzex+wLaaMp7fR59mZg8rHBndiMQtrTV6H41v+HrLpqpdPdu8pOht6xejrxeVOCf8P6qsr+OhLmDcAuLbzWxVhZMea8m6TJq4juSDS9zY/AWhoZi5llH0t/JQTzBuCOLNUZHQQm8mHMPjVNfYR7UuIwTSD4n26fgGtlhxurmuZfF1zMjXCWxVO6LP+TyvvzoqwljoRYRGWHKE7hxiNxJa+IbQB2Exbyo2MHr4EbcOeWFhK5+Rv6TOFhCrVM85p5b7o3k5xyXEx8wfmtlp5B8IFT2WyO9jZ5lZ2UZUFujeYSHE19AzyLcqe4yZbST0pwqzr6G/iD63FLtHBF5I7Y1SlTOf9xSLloe2AuJrzxsKr1PR/X/8W32u7pJV3vi+Ra5ljj5aonT9lOjXK5FmKyX6VCF0chv3yfIFEv2xES7ijyN0KntLFXm/kNCp9Qs4vq+hXo7vyPnpBdP9WzQ87rw27oi2jdB57XbgVQXTvDmaJge8lXzH1CeR79dshtn9Us253aJ0Z5Hvk+ZGwhOIdDQuRTgZXEwoD91fxTZaE03jhNbWzkqs63PI993znVp+8zmWvSOafoxwk/aixDo9lNASpROKkxT2KdhOeGLohGDtQhJ9LhE6tX1+tP9uL5h2e6l9OrEPFt2G5daZ0K2KE54SvY58h8GbCF0uHJhr/mW21W6K9wG2ldLH1LUU7zPqL8jv+68rGPfMxLirCE/QLRqXJjxRfA9woMjy4o549wF/RL4j3scSGuGpqSNewo3wz6Np74z2/ThP50TDnNJ9JNa0zaNpT038po8sk66e/abmbT5H3u9PCCic0B3PfaPhK4GLCOe3+DcpPEbK5fcBhADNCW9hnpj4rXsJnWp/o2CabvJ9aT6nRH4fmNgOHwR6o+GrCQ/jpsn321rs2P1BNO4DFOmDMUrzGMJ5OL7OxJ2d93D8NeGtlR6Dtf4RWit0QlFVJxTHSpdIeyuhD8kt5Du7N0Jro/Gx8dNSvyEF54Aa8pqcV9n1p75+3Wo+r1ewDnH+xwgPYR+cWOZLo+U5RfpiBP4sMf07yXeYvh7492jfjqffWjDtaeT7pfwqUR+Q5B8wZshfG6papyL53EoF51jqO8/Xcn9Uz/LmPH8TOnpPHkuDc6z/FxPr8HaOvz9cQwisvgJ8ssrtfy3Fr7lz/i5U2CdwLcdblO6bUbpRwkPWtmj4meTPIXuBdQXTpcn3AX4t+etIF/BKwjUmvk8sPJ7rWm/m6Z4imkcnocGf+O+fonldXzD8hBqPxa6C+Xw+mv//FAzvKTLtH5C/Xn4auFc0fBMhpnHCOWN9LXk7bln1zqDMQTDXDtlPHUFjNP5i8ieW+KIxSv4i78AdVeT9osR0TghMDhQMm9UxbLQz7SxId4Bw0xJ/Lzw42sh34hufjOI6gx6tw1/Wst0SaZ+WOEiccCHan9i54r9TqvyNH0H+oHfCDeGRxPefE10Yas17ieXuiKZ/N/kbhaOENw7JfeAJJabfQHiVH6fNRetxuGB7vK1guu2l9unENP3V7ueE4ObbiXlkEts1Q+j7Kx5XcSft0bx306CgMRr3msQ2e0XBuJeRv8nxaF/YH+3Tx7ZrkXneG7gtkWYy8VvsI3/jVfT4n2P9H5DYBvF+MZH4fiewucS0ZX/TCpb93cQ8RqJ87AYe1Yj9pp5tXkHen1cwj+R57Ivkz1nbq8zvEzn+XHqU489NxfaP5PlxLLEdz0+keX9yHoTjJ87/twgNOZU6dl9WsP3ujOb/voJ0ryR/TckVLMMJnYLPCjppfND45xy/rh8pkza5bbPRfpg8798DPLTUb0iLBI3RuJrO6xWsQzzdC8ifO8Y4/rj7EcVv4ozQB17yN4iv7zlCFzHxw6tHl9g3k/c2o4nf7wvkj4231Pk7baXCcyy1n+ervj+qc3lznr/JnxfivzfNse7dhBITyWnGOP7ew1laQeN68kX04+1/KPF9tNi+G037LI6/Dz+Y+L3/g9IPgepab+b3nuICjv+tS/1V9XsU+V3m+iv6uxEeZMXbOMfx14Bx4Em15Kvwr1WLpwLg7v9AiLD/nbCjGOHgvovwlOTVhCdUlfoc4a3KTkK532nCE+W7CE/8znP3WUUsPLRUt43wtOlr5Cta7yc8oXwr+YZ04mlm3P2lhCc4VxB+4HhZnwce4e4fqSLvs7j7N8m3gHkj4Uatl3Dg/xD4e+A0d7+z5EyKz/enhIq1HyA8vWknnMivJzzhf6S7N6qfomKmCDeh7yBceDsIN0GXAQ939+tK5HsfoUz7Cwmv8/eR79PtV4ST2dMJT+jnnYc6l88A3kh465UjnGi/RiiXf00ieVObx3f3DwNvIGyrj5rZyxLjPkl463MJcDNhXziBcKN6DaGIVn+Red5FeBPyfsLv2Ea4uPwHoc+l39aR398Qzg3vIF8Pl+j/dxJumG+tdf5zeDbwEeAOwv51SvRXrmGAqtS6zSuY72WEp7LfIOxzHYSK8xcBzyVcgGrJ7zVRft9L+A3iPmhvJZzvnllkslcRHhD9mnDjGW/HY0Xz3P0NhCLpPyOcF9LAYJTfZ5BvlKFYnj5JON//NErXF83/XgXpPkbYTz9HOD/3EPbT7wDPdfcXeemWrBvpcvJdiUDp4nQQrkXvJrxN/T0hzxngJsIbmjPc/aZ5yueCWoDz+g8J9xFfIB/E/Jpw/dzqRbpj8eDlhOL9u6LpjBAkPMPdP0S+P8ZZ5/Xdg4cAACAASURBVPZo33wC4cHHQcL+fwvhTerzyBfbXrDrQh3n+arvj+pZXoU+m/jfKd6/ajIvE+7+LMLbq/8hvElbSTg//oZwLJ5PKDq8JLj7PYSqRm8k3NtNE9b3NsJvcoa7F+2D1t2/RKhHdw0haGsjnJf/3N3/bB7zPG/3FIudu3+K8Ht9gXCMrQSGCQ+vBtz96kYsJ37dLyKLjJmdA1wJ3Onu/U3OjojIsmFm8c3Rfb3B3ShEDeT8hhDIr4oeIFY6rRFuiPuAJ7r7tY3Mm4hIKS39plFkiYsbSfpOU3MhIiKNFDeEc101AWPkeYSA8RDhLbmIyIJQ0CjSJGbWZmaXm9lTk33YmdkZZnY58IeEIiH/2rRMiohI1czsk2Z2frJbMDO7r5l9hHwr3/9SYtq/NbPXmVlf3Bqima0xswsJRe0g1GmtqLsVEZFGUPFUkSaJutVIdkZ+iFAnK+7WIAe82t3/faHzJiKynNVbPNXM9hBaQ4fQkE4OSHY58y53v7jEtP9FqKMJ+ZZWe8n3KXol8MfufrTafImI1Co9dxIRmSczhIrzfwg8hNAKYBuhvsp1wCXuPp8dhouIyPz4a0LjLw8DNhIeBv6e0OLqR+ZomOIjhIeIjyO0CNlLaK3yJkJrvZ9292ONPJnZpVTXB92wu2+pIr2IiN40ioiIiLQqM9tBaHK/UmpcTUSqpqBRRERERERESlJDOCIiIiIiIlKSgkYREREREREpSUGjiIiIiIiIlKSgUUREREREREpSlxtLmJndAawGdjc5KyIiIiKytPUDh9z9vs3OiDSegsalbfXKlSvXnnbaaWubnRERERERWbp++ctfcuTIkWZnQ+aJgsalbfdpp5229oYbbmh2PkRERERkCTvzzDO58cYbdzc7HzI/VKdRRERERERESlLQKCIiIiIiIiUpaBQREREREZGSFDSKiIiIiIhISQoaRUREREREpCQFjSIiIiIiIlKSgkYREREREREpSUGjiIiIiIiIlKSgUUREREREREpS0CgiIiIiIiIlpZudAVn8hkYmGdwzxpFMlpUdaQZO7mXTuq6GpV8OtE1EREREpFUpaJSSBofH2LlriMHhMSamZpjJOW0po7uzjYG+XrZt2cRAX2/N6ZcDbRMRERERaXUKGksws/OBs4EB4A+AVcBn3f1FZaZ5DHAx8ChgBfAb4D+BD7r7TIlpXgq8BjgdmAF+BrzP3b/euLWp3hU37+XSq25j36EpJqezdHekSaeMo9M5Riam2H84w017DnLRuZt58ukbq06/HGibiIiIiMhSoKCxtIsJweI4sAd4ULnEZnYe8EXgKLATGAX+GPgA8FjguUWmeR/wxmj+Hwc6gOcBXzOz17n7hxq1MtUYHB7j0qtuY3h0kp7ONP2rukml7Nj4XM45MJlheHSSS668lQOTGT71w90Vp1+/qnPJv12rdhsuh20iIiIiIq1JDeGU9npgM7AaeHW5hGa2mhD0zQBb3f3P3P2vCW8pfwScb2bPK5jmMYSA8bfAQ9399e7+GuBMQsD5PjPrb+gaVWjnriH2HZqipzPNup7O44IdgFTKWNfTSU9nmn2HpvjYd39bVfqdu4YWcnWaotptuBy2iYiIiIi0JgWNJbj7Ne5+m7t7BcnPB9YDl7n79Yl5HCW8sYTZgeeros93ufuBxDS7gQ8DncDLasx+zYZGJhkcHmNyOsuaro6yadd0dTCeybLnwBHGM9MVpZ+czjI4PMbQyGQjs72oVLsNl8M2EREREZHWpaCxMZ4UfX6ryLjrgEngMWbWWeE03yxIU5aZ3VDsjzmK1BYzuCc02NLdkZ71dqxQKmW0p4yZnNOeSlWUvrsjzcTUDIN7xqrNWsuodhsuh20iIiIiIq1LQWNjPDD6vLVwhLtngTsI9UfvB2Bm3cBJwLi731VkfrdFn5sbn9XyjmSyzOSc9BzBTszMcAerLDnpKMg8ksnWkcvFrdptuBy2iYiIiIi0LjWE0xgnRJ8HS4yPh8ctnVSbvix3P7PY8Oht48MrmUdsZUeatqiFzwqXjRlUVIgXyOacFe0pVnYs3V2v2m24HLaJiIiIiLQuvWlcGPErpwpDq2OqTV+3gZN76e5sYyKTJZcrv/hczpmO+h2czuUqSj+RyYY+Ck9eui2FVrsNl8M2EREREZHWpaCxMeI3gyeUGL+6IN1c6ed6EzlvNq3rYqCvl672NAcmM2XTHpjM0NOR5uQ1K+npaK8ofVd7moG+Xjat62pktheVarfhctgmIiIiItK6FDQ2xq+jz1l1EM0sDdwXyAK3A7j7BPA7oMfM7l1kfqdGn7PqSC6EbVs2sWF1J+NTWUbGp2a9LcvlnJHxKcansmxY3ckrz75/Vem3bdm0kKvTFNVuw+WwTURERESkNbVc0GhmV5vZO5udjwJXR59PLTLuCUAX8EN3n6pwmqcVpFlQA329XHjOqfSt7WJ6xtk9OsHdh44yMj7F3YeOsnt0gukZp29tFxedu5k/PauvqvSVdmI/NDLJV3/+e3buGuKrP/99S3VJUe02rHSbiIiIiIgstFZseeNRwI+bnYkClwPvBZ5nZh+M+2o0sxXAP0Rp/q1gmo8CLwbeamZfjvtqNLN+4DXAFPDJ+c96cU8540Q2rF7Bzl1DDA6HLiRmogZb1vV0MNDXy7Ytm44FO9WmL2dweGzWfNpSFur9VTGfZmvkNhERERERaZZWDBpvA/rmeyFm9ifAn0RfT4w+H21mO6L/97v7XwG4+yEz+wtC8HitmV0GjALPJHTHcTmwMzl/d/+hmb0feANwk5ldDnQA24C1wOvcffc8rV5FBvp6GejrDZ3V7xnjSCbLyo40AycXr39Xbfpirrh5L5dedRv7Dk0xOZ2luyNNOmqJdGRiiv2HM9y05yAXnbuZJ5++sdGr3HCN2CYiIiIiIs3UikHjJ4C3m9kmdx+ax+UMAC8tGHa/6A/gTuCv4hHu/mUzOxt4K/AcYAXwG0JQ+K/uszulcPc3mtlNwGuBVwA54Ebgn939641dndptWtdVVYBTbfrY4PAYl151G8Ojk/R0pulf1U0q0ddhLuccmMwwPDrJJVfeyvpVnS3zlq7WbSIiIiIi0mytGDR+DXgy8AMzey+wC9hLke4p6gkq3X07sL3KaX4APL3KaT4FfKqaaZaqnbuG2Hdoip7ONOt6OmeNT6Xs2PB9h6bYuWuoZYLGxUpvQEVERERkLq0YNN5OCBANuLRMOqc1129ZGhqZZHB4jMnpLP2rusumXdPVwe7RCQaHxxgamVSQU4OlUm9UREREROZfKwZVn6YJnd7L/BrcE4KX7o70cUVSi0mljO6ONBNTMwzuGVPQWKWlVm9UREREROZXywWN7n5Bs/MgjXckk2Um56TnCBhj6ZQxk3OOZLLznLOlZSnXGxURERGR+dFy/TTK0rSyI01bysjmKnuJnI2KU67saLnnHk1VWG+08K1uXG+0pzN9rN6oiIiIiCxvLR00mtnJZvbHZvZiM3ummZ3c7DxJbQZO7qW7s42JTJbcHIFjLudMZLKh/t3JegtWqWS90TVdHWXTrunqYHI6e6zeqIiIiIgsXy35msbMNgH/TmhFtXDcd4BXNbuPQ6nOpnVdDPT1sv9whgOTmaKtp8YOTGboak8z0KeWPquheqMiIiIiUouWCxrN7ETgB8BJwG7gOuAu4N7A44CnAN83s7PcfW+z8inV27ZlEzftOcjwaHiztaaro2h9u/GpLH1ru9i2ZVOzstqSVG9URERERGrRisVT/44QML4ZONXdL3D3t0QN5DwQeBNwH+Di5mVRajHQ18uF55xK39oupmec3aMT3H3oKCPjU9x96Ci7RyeYnnH61nZx0bmb1UBLlVRvVERERERq0Yp3g88ArnD3fy4c4e4zwPvM7Fzgj4DXLnTmZG7lOpR/yhknsmH1ill9CK5oT7Gup0N9CNYhrjc6MjFFLudli6jG9UbX9XSo3qiIiIjIMteKQeOJwGfnSHMDsHX+syLVqLRD+fivXHAp1VO9URERERGpRSsGjQeBU+ZIsylKJ4tELR3Kb1rXpYClwVRvVERERESq1Yp1Gr8PnG9mjyk20sweCTw3SieLQLJD+fY2o39tNxtXr2BdTycbV6+gf2037W12rEP5weGxZmd5yVK9URERERGpViu+aXwXoV7jd83sMuAaQuupJxKKpD4fyAH/2KwMyvEKO5QvFHcoDxzrUF7ByvxRvVERERERqUbLBY3ufqOZnQ/sAF4IvCAx2oBR4OXufkMTsicFkh3K96/qLpt2TVcHu0cnjnUor6Kp80f1RkVERESkUi0XNAK4+9fN7BTgPODhwAmEOow/A77s7hPNzJ/kqUP5xU31RkVERERkLi0ZNAJEgeHnoj9ZpNShvIiIiIhIa2u5hnDM7Goze8kcaV5kZlcvVJ6kNHUoLyIiIiLS2louaCQ0dtM/R5pTgLPnPScyp7hD+YlMltwcgWPcoXx3Z5s6lBcRERERWSRaMWisxEpA5RsXgbhD+a72NAcmM2XTqkN5EREREZHFp1WDxqKvrCw4BXg6MLywWZJStm3ZxIbVnYxPZRkZn5r1xjGXc0bGpxifyrJhdac6lBcRERERWURaImg0s5yZzZjZTDRoe/w9+Ud4u3g7MABc1rQMy3HUobyIiIiISOtqldZGriP/dvEJwBCwu0i6GWAEuAr4xILkTCqiDuVFRERERFpTSwSN7r41/t/McsAn3f0dzcuR1EIdyouIiIiItJ6WCBoL3BcYa3YmpHbqUF5EREREpHW0XNDo7nc2Ow8iIiIiIiLLRcsFjTEzuzdwDnAS0Fkkibv7Oxc2VyIiIiIiIktLSwaNZvZ24G84Pv9GvrGc+H8FjSIiIiIiInVouaDRzF4I/B1wNfBh4IvADuAKYCvwZ8B/Ax9rTg5luVCDPiIiIiKyHLRc0Ai8GtgDPNXds2YGsNvdLwMuM7MvAd8APt/EPMoSNjg8NqvrkLaU0d3Zpq5DRERERGTJacWg8SHA5909mxjWFv/j7t82s28Dfw18baEzJ0vbFTfv5dKrbmPfoSkmp7N0d6RJp4yj0zlGJqbYfzjDTXsOctG5m3ny6RubnV0RERERkbq1YtDYDowkvh8BTihI83/AqxYsR7IsDA6PcelVtzE8OklPZ5r+Vd2kUnZsfC7nHJjMMDw6ySVX3sr6VZ164ygiIiIiLS/V7AzU4C7g3onvQ8BDC9KcBGQRaaCdu4bYd2iKns4063o6jwsYAVIpY11PJz2dafYdmmLnrqEm5VREREREpHFaMWj8GaGIauxq4PFm9mIz6zazZwDPidKJNMTQyCSDw2NMTmdZ09Uxa3wmm2PsSIbRiQyWgvFMlsHhMYZGJpuQWxERERGRxmnFoPHrwBlmdt/o+3uAg4QWVA8BXyV0uXFxU3InS9LgntDoTXdH+rg3jJOZLL87MMmdoxPcfXCKfYePcs+hDJnsDEOjk3zl579rYq5FREREROrXcnUa3X0HIUCMvw+b2RbgjcD9gd3AR9z9F83I33K11LufOJLJMpNz0omA8dCRae4+fJTsjJNzJ2WGGeTcyeXgSGaGz/9kiAeduFqN4oiIiIhIy2q5oLEYd78DeG2z87EcLZfuJ1Z2pGmLWkmF8Ibx7sNHyWRztKWMjrYURj6gdGYA48BkRo3iiIiIiEhLa7niqWb292b2hDnSPN7M/n6h8rRcXXHzXt76pV9w5S37GBqd5Oj0DO7O0elQNPPKW/bx1i/9gu/ccnezs1q3gZN76e5sYyKTDa2kTmTIzoQAOZ0qCBjdcYf2lLFqhRrFEREREZHW1opvGrdHf9eVSfME4G3AOxYgP8vSUu5+olRR24G+XvYfznDP+BST0zPk3Olom/3cZSYXiqqu7Ghjfc8Kdo9OHGsUZz6L7C71IsIiIiIi0hytGDRWIg3kmp2Jpayw+4lCcfcTwLE3bYs9aJyrqO3DNq3hpj0Huf2eCaazjsGsN4wzOWfGnY50irXdHaRSRndHmompGQb3jM1LELdcigiLiIiISHMs1aDxTGB/szOxVCW7n+hf1V027ZqujgV701aPK27ey6VX3ca+Q1NMTmfp7kiTjuowjkxMsf9whpv2HGTr5vUcmMyw9+BR3GF6JocBDscaw+lIp9i4agVdHeHwSqeMmZxzJNP4rkMrzfdF525WYzwiIiIiUpOWCBrN7OqCQReY2dYiSduAPuAU4PPzna/lqlT3E8UsxJu2elVT1PbaW+9h6wPX85XB3zM1nSNlhuOkMNKpUCR1bXfHsYARIJtzVrSnWNnR2MOtWL6zOWcyk8XMWLOyg8zMTEsWERYRERGRxaMlgkZga+J/B/qjv0I5YATYCbx+vjO1XBXrfqKc+XzT1gjVFrU9fCTLprVd7B6ZYF1XJw6kDLo60nSkj6/jmMs5E5ks63o6GDi5sQFbMt8rO9r4/cEjHMnMkHNwHMNIGaTM+N2BIy1RRFhEREREFp+WCBrd/diduJnlgO3urkZumqSw+4m51PumbT4beKmlqO1v7hnnAet72H84Q869aKAZOzCZoas9zUBfYxulSeZ7bXsHvxs7cnx/kUAOJxs1ynM0O8P3btu/qIsIi4iIiMji1BJBY4GXAYPNzsRyFnc/MTIxRS7nZYuo1vOmbSEaeKm1qO3mE1dx+/4JhkcngRBQFivSOj6VpW9tF9u2bKorn6Xy3dGW4p7xqdBfpEX9Rdrsxnnc4e5DR/nKz3/H6550akPzIiIiIiJLW8v10+jun3L3nzc7H8tZ3P1EV3uaA5OZsmlrfdO2UH1A1lrUdsOqTi4851T61nYxPePsHp3g7kNHGRmf4u5DR9k9OsH0jNO3touLzt3c8GKhcb6npnOhv0gz0gUBI4AdGx66Avnerfc0NB8iIiIisvS1XNBoZq8ys9+a2X1KjD8pGv9nC5235WTblk1sWN3J+FSWkfHwxjEpl3NGxqcYn8qyYXVnVW/akg28tLcZ/Wu72bh6Bet6Otm4egX9a7tpb7NjDbwMDo/VvB5xUdtsQf5LyUZvO1d2pHnKGSfyrmc9hHNP38CmtV2saG/DzFjR3samtV2ce/oG3vWsh8xLq6UrO9K4O5mZHDkPeSrHAHf43dhRhkYmG54fEREREVm6WrF46guAu9z998VGuvvvzGwP8CLgPxY0Z8vIQF8vF55z6rHuHnaPThzr7iEbFUntak/X9KZtIfuArLeo7UBfLwN9vfNa77JUvoneHralbNYbxiTHQ2M9qVBcdbG2YisiIiIii1MrBo0PBC6fI81NwPkLkJdl7SlnnMiG1Stm1Ttc0Z4KgVUN9Q4Xug/IuKjt/sMZDkxmam7UZtO6rgUNxDat6+LkNSvZe/AoOS//lnQmagynoy0El4u1FVsRERERWZxaMWg8AZirPOIhYM0C5GXZa/Sbtmb0AbltyyZu2nOwaY3a1Orxp67nZ0NjZGecbC4X3jiSaASH0AjOTM7pSKfobG87VrRWRERERKRSrXj3eBfw0DnSPBRQix8LqFFv2prRB+R8FrWdT+f9wUns3DXM78eOkIuCw5QZZqH+Ytz9Rkc6xfpVnYxOZELLsw3uL1JERERElrZWDBqvAV5sZo9z9+8XjjSzxwNPA/5rwXMmdVvoPiBj81HUdr5tWtfF40+9F9/8v71kpmfIAblceMOYMiPdZnS1t7Gmu4MjmZl56S9SRERERJa+Vgwa3wtsA640s48A3wJ+B5xECBZfDUxF6aTFLFQfkEWX3aRGbeqRLFq7or2N9rSBG6kUdLWHt6WLsWitiIiIiLSOlgsa3f3XZvanwOeAi4ALE6ONUJ/xBe7+y2bkT+rTqIZp6s3DYg0SCxUWrR2bnA5Fa904MJ1ZtEVrRURERKR1tFzQCODu3zCz+wEXAI8EegmN4/wY+JS7jzQxe1KnVmqYZjG8lWzForUiIiIi0jpaMmgEiALDf2l2PqTxWqFhmsHhsVlBWlvKQkMzCxSkFQasrz77ASFvLVK0VkRERERaQ8sGjYuVmT2DUGT2dGAdobXXG4D3u/uPiqR/DHAx8ChgBfAb4D+BD7r7zELle7FZzG/Prrh577GAdnI6eyygPTqdY2Riiv2HM9y05yAXnbuZJ5++seHLXwwBq4iIiIgsH4s+aDSzJ0T//tTdjya+z8ndr5unbBVlZu8F3gSMAF8G9gMPAM4DnmNmL3H3/0qkPw/4InAU2AmMAn8MfAB4LPDchcz/YrMYG6YZHB7j0qtuY3h0kp7ONP2ruosWnR0eneSSK29l/arOhgZwzQ5YRURERGT5WfRBI3At4MBpwK2J75Vom58szWZmJwJ/BdwNPNTd9yXGPRG4GngHUVcgZrYa+DgwA2x19+uj4X8XpT3fzJ7n7pct1DosVoupYZqdu4bYd2iKns500UZ6Uik7NnzfoSl27hpqWNDY7IBVRERERJanVgga30EIEvcXfF9sTgFSwE+SASOAu19jZoeB9YnB50ffPx0HjFHao2Z2MXAVofuQZR80LhZDI5MMDo8xOZ2lf1V32bRrujrYPTrB4PAYQyOTDQl6mxmwioiIiMjyteiDRnffXu77InIbkAEeYWb3cvc4yI2L2K4iFFmNPSn6/FaReV0HTAKPMbNOd5+apzxLFQb3hDqE3R3psv1HQgjgujvSTEzNMLhnrO6gsdkBq4iIiIgsX4s+aGwV7j5qZm8G3g/cYmZfJtRtvD/wTOA7wCsTkzww+ry1yLyyZnYHcAZwP6Bsn5NmdkOJUQ+qaiWkrCOZLDM5Jz1HwBhLp4yZnHMkk6172c0MWEVERERkeVPQ2EDufomZ7Sa0fvoXiVG/AXYUFFs9Ifo8WGJ28XCVL1wkVnakaYsanalENmrtdWVH/YdZMwNWEREREVneFn3QaGZX1zipu/s5Dc3MHMzsTcA/Av8KfAjYS3jb927gs2Y24O5vqnR20eec9Tfd/cwS+bkBeHiFy2uIxdTSaaMNnNxLd2cbIxNT5HJe9o1fLupPcl1PBwMn1x/3NzNgFREREZHlrRXuKLeWGO7kA6tiwxe0sRwz2wq8F/iSu78hMepGM3sWoRjqG83so+5+O/k3iSdQ3Oros9SbyEVlOfQduGldFwN9vew/nOHAZKZoYzSxA5MZutrTDPQ1JmhuZsAqIiIiIstbqtkZmIu7p5J/wArgq8AdwMuA+wIro8+XA7cDX4nSLaQ/ij6vKRzh7pPATwnb+2HR4F9Hn5sL05tZmrA+WcL6LGpX3LyXt37pF1x5yz6GRic5Oj2Du3N0eoah0UmuvGUfb/3SL/jOLXc3O6t127ZlExtWdzI+lWVkPARwSbmcMzI+xfhUlg2rO9m2ZVNDlhsHrF3taQ5MZsqmbXTAKiIiIiLL26IPGov4O+As4Cx3/5S73+nuU9HnDuCRwCOidAspfu20vsT4eHh8xx8Xu31qkbRPALqAHy72llOTfQe2txn9a7vZuHoF63o62bh6Bf1ru2lvs2N9Bw4OjzU7y3UZ6OvlwnNOpW9tF9Mzzu7RCe4+dJSR8SnuPnSU3aMTTM84fWu7uOjczQ19u9qsgFVERERElrdWDBpfCHzR3YtGH+4+ClwOvGhBcwXfiz5fYWYnJUeY2dOAxwJHgR9Ggy8n9D35PDM7K5F2BfAP0dd/m9ccN0Bh34GFxSbjvgN7OtPH+g5sdU8540Te9ayHcO7pG9i0tosV7W2YGSva29i0totzT9/Au571EJ58+saGLreZAauIiIiILF+tUKex0H3Iv60rZRq49wLkJely4ErgXOCXZvYlQkM4pxGKrhrwN+4+AuDuh8zsL6LprjWzy4BRQvccD4yG71zgdajKcu47cKCvl4G+3gVv+OcpZ5zIhtUrZtUfXdGeCnUYl0j9URERERFZPFoxaNwDnGdmb3X3WcGjmXUC5wG/W8hMuXvOzJ4OvAZ4HvAsQhHTUeB/gX919ysKpvmymZ0NvBV4DqEe5m+AN0TpF7Qxn2qp78BQ13Ch16VZAauIiIiILE+tGDR+Cng7cLWZ/S3wA3efMbM24HHAu4D7AW9b6Iy5+zRwSfRX6TQ/AJ4+b5maR+o7sLmaEbCKiIiIyPLTikHje4AzCcU4rwFyZjYKrCXU0TRC66rvaVoOlwn1HSgiIiIisvS1XEM47j7t7n9CaOjmakI/hmujz6uAF7r7n7i7XmfNs7jvwIlMdlZLnoXivgO7O9vUd6CIiIiISAtp2Vc+7v454HPNzsdy1szO7kVEREREZGG03JtGWVzUd6CIiIiIyNKmoFHqor4DRURERESWtpYtniqLh/oOFBERERFZuhQ0SkOo70ARERERkaVJQaM0lPoOFBERERFZWhQ0yoLQG8jjteL2qDbPrbiOIiIiIjKbgkaZUz03/4PDY7PqOralLPTXuAzrOrbi9qg2z624jiIiIiJSWksHjWb2IOA0oMfdP9Ps/Cw19d78X3HzXi696jb2HZpicjpLd0eadMo4Op1jZGKK/Ycz3LTnIBedu5knn75xAdesOVpxe1Sb51ZcRxEREREpryWDRjMbAD4BPCwx+DPRuLOBbwLb3P1rTcjeklDvzf/g8BiXXnUbw6OT9HSm6V/VTSplx8bncs6ByQzDo5NccuWtrF/VuaTfPrXi9qg2zwcmM3zqh7tbah1FREREZG4t10+jmW0GrgUeCFxKCBCTrgNGgfMXNmdLRzJYaG8z+td2s3H1Ctb1dLJx9Qr613bT3mbHbv4Hh8dmzWPnriH2HZqipzPNup7O44IHgFTKWNfTSU9nmn2Hpti5a2ihVq8pWnF7VJvnj333ty23jiIiIiIyt5YLGoG3AR3AI9z9DcCu5Eh3d+BHwJYm5G1JqDfAGRqZZHB4jMnpLGu6Osoua01XB5PTWQaHxxgamWz4uiwGrbg9qs3zeGaaPQeOMJ5pnXUUERERkcq0YtB4DvA/7v7LMmmGgPssUH6WlEYEZRgAjAAAIABJREFUOIN7Qh3I7o70rICzUCpldHekmZiaYXDP7DeWS0Erbo9q89yeSjGTc9rbrGXWUUREREQq04pBYy+wZ440KcLbSKlSIwKcI5ksMzknPcf0sXTKmMk5RzLZuvK+WLXi9qg2z2bgDkbrrKOIiIiIVKYVg8Z9wAPmSHMGMLwAeVlyGhHgrOxI05YysjmvaB7ZqFXWlR0t2S7TnFpxe1SbZ/cocKR11lFEREREKtOKQePVwB+b2QOLjTSzLYQirN9e0FwtEY0IcAZO7qW7s42JTJbcHPPJ5ZyJTDZ043Hy0mxJsxW3R7V5ns7laEsZ0zPeMusoIiIiIpVpxaDx3UAWuM7MXk1Ud9HMzoi+fw04DLyveVlsXY0IcDat62Kgr5eu9jQHJjNl53FgMkNXe5qBvl42retqyDosNq24ParNc09HOyevWUlPR+uso4iIiIhUpuWCRnf/NfAcQp3FDwF/DhhwE/DhaPiz3V3t+degUQHOti2b2LC6k/GpLCPjU7MC0FzOGRmfYnwqy4bVnWzbsqnh67KYtOL2qDbPrzz7/i23jiIiIiIyt5asUOTu3zKz+wIvBR4FrAMOAj8GPunuo83MX6vbtmUTN+05yPBoaBF1TVdH0U7ax6ey9K3tKnrzP9DXy4XnnMqlV93GvkNT7B6doLsjTToq+jqRydLVnqZvbRcXnbt5yXfy3orbo9o8P/n0jfSubG+pdRQRERGRuVno1lCWIjO74eEPf/jDb7jhhqqnveLmvcdu/iens0Vv/jes7jwWLJQyODzGzl1DDA6HVllnojqQ3Z1tDPT1sm3LpmUVPLTi9qg2z624jiIiIlKfM888kxtvvPFGdz+z2XmRxlPQuITVEzRCY2/+h0YmGdwzxpFMlpUdaQZOXt712Vpxe1Sb51ZcRxEREamNgsalrSWLpwKY2QbgLGAN0FYsjbt/ekEztcQM9PUy0NfbkJv/Teu6FDAktOL2qDbPrbiOIiIiIjJbywWNZtYOfBR4CaUb8jHAAQWNDaCbfxERERGR5avlgkbgncDLgN8CnwWGCV1wiIiIiIiISIO1YtD4AuBW4GHufqTZmZH51+y6cc1evoiIiIhIM7Vi0LgB+IgCxqWvmoZ45iOwUyugIiIiIiKtGTQOAaubnQmZX6W6/Dg6nWNkYor9hzPctOcg5w3chzv2TzQ8sKt0+XN1OSIiIiIi0upaMWjcAbzGzE5w94PNzow03uDwGJdedRvDo5P0dKbpX9VNKmXHxudyzoHJDLffM877v3MrK9rbmJ7JNSywq3T5w6OTXHLlraxf1ak3jiIiIiKyZJVqfXQxew/wfeBKM3uimemt4xKzc9cQ+w5N0dOZZl1P53EBG0AqZazsaCM740xN58hMz9C/tpuNq1ewrqeTjatX0L+2m/Y2OxbYDQ6PNXT563o66elMs+/QFDt3DTVkvUVEREREFqNF/6bRzHKE7jNmjQKujNIUm9TdfdGvnxxvaGSSweExJqez9K/qLpludCJzbKfIAdmc05EI7uLADjgW2FXyNrDS5QOs6epg92goGjs0MqnGcVpMo+vBqsEkERERWapaIai6juJBoyxBg3tC3cTujvSsN3yxTDbHkcwMOXdSBrkcTE5n6Uh3zEpbbWBXyfJjqZTR3ZFmYmqGwT1jChBaRKMbOFKDSSIiIrLULfqg0d23NjsPrWwm11rx9pFMlpmcky4TsE1msuQcUmaYgePkcsXTVhvYVbL8pHTKmMk5RzLqKrQVNLqBIzWYJCIiIsvBog8apT6/2nuYt/zPL3jxo07h9Pss/uqfKzvStEU33aXkPASKBngUPKbK1M6tJrCrZPlJ2Zyzoj3Fyg4dSotdoxs4UoNJIiIisly0XEM4Zna7mf2/OdK8xsxuX6g8LWbuzud/OsTT//V7nP9vP+Qrg78jk60sIGqGgZN76e5sYyKTJVfiLWnKwDAcQhHVFHS1lw7aslFxwUoCu0qWH8vlnIlMNhRDPFnBwGLX6AaO1GCSiIiILBctFzQC/cBcd+i9wCnzn5XWcv2dB7jwskEe856r+Zcrfs1dB480O0uzbFrXxUBfL13taQ5MZoqm6epIk7JQ9NaArvY2OtLFd+VqA7tKlh87MJmhqz3NQJ8aPFnskg0cremaXfc1aU1XB5PT2WP1YBdifiIiIiKLWSsGjZXoAcrf8S9j+8en+ODVv+Fx772GV37men7wm/24L566j9u2bGLD6k7Gp7KMjE/NeuOXThmpqMVcM2NNd+mb9loCu7mWn8s5I+NTjE9l2bC6k21bNlW5hrLQ6mngaCHmJyIiIrKYtURFLDMrvCvvLTIMoA3YBJwPqHgqlHwDB+FN3bdvvptv33w391/fzYsfdQrPPvNkVq9oX8AczjbQ18uF55x6rIGR3aMTxxoYyUZvDjvSKXI4hnEkM8OKdFvR+mTjU1n61nZVFdiVWj7A0ekZprI5OtIpTly9govO3ax6ai2g0Q0cqcEkERERWU5aImgEdnN8txsXRn+lGPCG+cxQq9i8cRXvf/kj+MyPdnPVr/ZR6oXib++ZYPvXbuGfvv1rnvWwk3jJo/t54ImrFjSvSU8540Q2rF4xqyuDFe0p1vV0MNDXS/+6br7689+XDCy72tP0re2qKbBLLv9Hvx3hnvEpMtncse2Xc2d6JsfVv7pbDZy0gEY3cKQGk0RERGQ5aZU7mE8TgkYDXgLcBAwWSTcDjABXufsVC5e9xe3szes5e/N6hkcn+exPhti5a4gDk9NF005mZvjsT4b47E+GeMR91/LiR53CUx98Iu1tC1+SeaCvl4G+3rKdpj/yfuvKBpb19JE30NfLvkNHuX73AVKE7j1Wtrexoj0FGPeMT3HlLfvUpUILiBs4GpkIxY3LFSmN68Gu6+koWQ+20fMTERERWcxaImh09wvi/83sJcCX3P0dzctRa+pb28XfPO1BXHTuqXzjprv4zI/vZHC4dB2rn94xyk/vGGX9qk6e/4hNvOARmzjxhBULmONg07qukvURKwksaxV3qXD3oaOsWpFmU1eXulRoUXEDR/sPZzgwmWFdT2fJtJXUg230/EREREQWs5YIGpP8/7N33/FxV2e+xz9nmqRRl23JTQU3XMG4YIleDJgWAgRIAPnuTdlskpuEJZtsQtlNQtjUTd+74WbDZmWaIYQWTCCmF9kYGxfcm0aSZUtWLyNNPfeP34wQYkYaydLM/EbP+/XiJaT5eXwkz9jzzHnO99E6VcN74ibdbuXG5TO5cflMdtV3UFVdw7M7GvBEGcVxssvDr18+yH+8eogrFhVRWV5G+awClIrtPFc8DFVYjtbgkQqDhUcqAP0jFaRoTF63rCxhZ30Hda1Ggmm+03FK52DH+v6EEEIIIZKVFGAT3JKZufz0pjPZ9J1Lueuq+ZQURC+8AkHNhl0n+MzvN3HFL99gXXUN3Z7UDPaQkQqpJxxwVFzgxBfQ1LT20NjZR0u3h8bOPmpae/AFdMznYMf6/oQQQgghkpXpdhrF+MjPdPD3F8zm8+fN4vUDJ6mqruG1AyejBuccaOzm3md28+O/7ueGZTOoLC9lblHignPG2qmMVBjpjud4tNaOpWRf30jEErA0knOwY31/QgghhBDJSIpG8REWi+Li+YVcPL+Q2hY3D2128fh7dbRHCc7p9vipqnZRVe2iYtYkKitKuWxhUUKCc8ZSPEYqbK9r/1ixYbUoMtOsSVFsJPv6Rmusz8GO57laIYQQQohkIEWjiKpkkpO7rlrAnZfN49kdDayrdrHrWEfU66uPtFB9pIWinDRuPbuUz5xdTGFO/INzxsJ4j1R4afeJ/jmQbp+/f1xIny9IS4+H5i5vQlNZk319Y2Gsz8GOx7laIYQQQohkIEWjGFa63crNK4q5aflMnthaz/999RCuFjdROldp7PTwi40H+M0rB1mzeCprK8pYWZafVME5wxnPkQrhVNa6VjdZaTbKsjOTKpU12dcnhBBCCCHiK+l7CJVSX1NKnZ3odQj4255G/uedGno8ATIcFjIdVqxD1IH+oOYvO49z8wPVXPmrN3l4s4sekwTnhEcqOO022tzeIa8d6UiFwamsgwvScCprVpqtP5U1npJ9fUIIIYQQIr6SvmgEfgmsCX+ilAoope5N4HompIG7T3ar4rRJWcyaksWCaTmUFjjJSrMO+ev3neji7qc+oPzfXua7z+7mUFN3nFY+eresLKEwJ41uj5+WbmPHcaBgUNPS7aHb46cwJ40L5xXy7I4G1m+p5dkdDRGTVJM9lTXZ1yeEEEIIIeLPDO2pfcDAIXkq9J+Io2gzC5VS5GTYycmw4/EHONbWi9sbiNq62uXx88d3avjjOzWcO2cSleVlrF5QiC0Jg3PCIxXCZ/tqWnv6z/b5Qy2pTruNyVlpFGQ6+NXLB4YNjIlnKutoJPv6hBBCCCFE/JmhaDwKXKGU+rXWujH0tWg1iRgHA3efyrIzo16XZrNSNimToy3d5GbYyUqzc3CIHcW3D7Xw9qEWpuemc+uqEm5ZWcKU7LSo1yfCcCMVJmemUd/ey77jXTEFxvR6/Xj8AbyBIK09XiwWcNptOGyRi+bRpLKeinikxgohhBBCCHMxQ9H4AEaLasOAIJXvKqW+O8yv01prM3x/SW+ku09ZaXbsViv/55I5zMx3sq66hg27TuANRE4ibejo42cvHeBXLx/kqiXTWFtRyrKS5AnOiTZSwWG18JtXDtLS7YkpMKbN7eWZ7Q209ngJBqHH40ehsFg8OO1W8jMdOAelr440lfVUjXdqrBBCCCGEMJ+kf6Wntf61UqoJuBqYDlwM1AI1iVzXRDLa3ac+X4DlpfksL83nnms8rN9Sx8ObXDR09EX8db6A5pntDTyzvYGF03KorCjluqXTP1ZIJcrgkQrf+fPOiC27YeHAGIC6Vjc/3LAXhfGzCWpQ2tgy9/s1/oDG7QtQlJNOTrodGHkq61gYz9RYIYQQQghhTsnxanwYWuvHgMcAlFJB4L+11t9P7KomjrHYfZqclcZXLp7DFy+Yxcv7mlhX7eKtQ81R72PP8U6+8+dd/NuGvdy0vJjKilJOmxy9NTbeYm3ZBaNt90SnUShPzkojN91Ol8ePAmxWCxpNIKjx+oM0dvZhsyicDtuIU1nHQjg1trnLS5vbG7EYDhur9Q3ewV06M37frxBCCCGEGJ4pisZBvge8luhFTCRjuftks1q4YtFUrlg0lcMnu3lok4s/ba2nqy/ymbiuPj8Pvn2UB98+yvlzJ7O2ooxL5hdijXHXM5KxKFJG0rLb3muM7FAo0uwWstNt9PoDeP1BCASxWhQ2iwUI4g9oWrs99DoCdHv8FBc4uWVlyWi/1VG5ZWUJO+s7qGs1ElHznQ4sFoXXH8Tt8+MPaHp9AfwBTemk0a9ve137x86KRgsQEkIIIYQQiWO6olFr/b1Er2E4SqnzgTuAc4ACoBXYBfxSa71h0LXnAPcA5UA6cAh4EPiN1joQz3VHM167T7OnZPGv1y7im1ecztPvN1BVXcO+E11Rr3/zYDNvHmxmRl4Gt64q4dMri4dcy2BjWaTE2rLr9Qfp9QbQGiwWTTAIzgwbRdnpNHb14Q9ovIEgltD5TX9Q097nR1ksFBc4uWP1vLgXToNTYw83d4M21hbUGq1BKbBbLRRkDj2WI5qXdp/ov/9YAoSEEEIIIUTimK5oDFNKlQOfB84C8oAOYCtG6+o7CVzXPcB9QDPwF+A4MDm0zouADQOuvQ54EmOsyHqM4vJa4BfAucBNcVz6kKLtPoWFQ19GszvmdNi4dVUJnzm7mPdcbVRVu3hh13H8wcghucfae/npi/v51caDXH3GNCorSjmrOG/I4JyxLlJibdl1e/3G+UUFFmXBEgpJzcmwY7MqWnu89HoDBDVoNBYFVoti/tRsvrVmfsJ22sKpsf/+4n7erWnFFwj2F4tWi8JmNX7W+453cfdTu0ZU3A2c+RlLgNCU7DTZcRRCCCGESCBTFo1KqR8A3+Hj8xqXAp9VSv1Ya31XAtZ1E0bBuBG4QWvdNeh2+4D/zwF+DwSAi7TW74W+fi/wCvAppdSnQ+c5Ey7WmYWnsjumlGJlWQErywpoumYB69+t4+HNtf3nAQfzBoI89f4xnnr/GItn5LC2vIxrz5xOhsP6kevGo0iJtWU3XAwaO43GeI0wp8OG02Ez2j5DxaXb48dhs3Dd0ulJUSi1ur3YLQqn3U6Gw4o1dN7SYbOMuriLNvMzbGCAUFOnh/VbapPiZyGEEEIIMVEl30T1YYQKs7swElQ/D8wCMkIfPx/6+j8rpW6O87oswI8BN3Dr4IIRQGvtG/Dpp4ApwGPhgjF0TR9GuyrAl8ZvxSN3+aKp3H/9ElYvLKSkwEm63YpSinS7lZICJ6sXFnL/9UvGpJ2wMDudr146l7f++WJ+d/syzpk9acjrPzjWybee3En5D1/m/uf3UNPc03/b4CJlcIEXLlKy0mz9Rcpwwi27TrsRWBONRdG/0+i0WyPOY3TYLOQ5HRRkOrDbLKTZrUkxwiL8c8vJsDOzwMmkrDTynI7+72E0P7eBAUL5zqFbW/OdDtw+P9vr2qltcY/J9ySEEEIIIUYu8a9MR+6rQCOwUms9MH6zBnhQKfUs8AHwFeDxOK7rHOA04E9Am1LqamAxRuvpu1rr6kHXXxL6+NcI9/UGRvF5jlIqTWvtGac1j1i0mYXjlXhps1pYs3gaaxZP41BTF+uqXTy57RjdnsjBOR29Pn7/5lF+/+ZRLpw3hSsXT+X92raYUk7znQ5qWnv6i5Thvp9YWnY9/gBaG222uU57xPsZeH2yjLAYSTrscD+3gY+VPce76HD7Yp75memw0eMJsL2+XRJVhRBCCCESxIxF45lA1aCCsZ/Wulkp9QSwNr7LYmXoYyOwDVgy8Eal1BvAp7TWJ0NfOj308cDgO9Ja+5VSR4FFGDuoe4f6jZVSW6PcND+2pY/c4JmF8TCnMJvvXbeYb62Zz1PvH2NdtYv9jdGDc14/cJLXD5zEalGk2ywEtcbysY7mD420SIm1ZTc3w04gqPH4ggyV25OIERvRjCQdNtrPLVLwUK8vQFefD7tFkZ1uG3YGZ3jmZ6838psEQgghhBBi/JmxaLRh7MINxU38v7fC0Md/AI4Cq4HNQCnw78AVwBMYYTgAuaGPHVHuL/x1Ocw1SGaajdvLS7ltVQnvHm2lapOLFz84ETU4JxDU9HgD7DvRRW6GnUlZjqjFykiLlHBgzODiKN1uMXYMi/M4qySf/3mnZlxChMZLrOmwYYN/btGCh/wBY6xIIKg51t5LUU46OenRd2AjzfwUQgghhBDxZcZXYoeAa5RS39Fafyy6MnS28CrgcJzXFU5fURg7ijtCn+9WSl2PsaN4oVKqIkKraiThV+uRK6EBtNbLI96BsQO5LIbfy5SUUqyaNYlVsybR1NnHI+/W8sjmWpq6InfzaqC910d7r48Mu5VJmQ5ynfb+cRcwuiIllpbdvAz7uIYIjbVY02HDBv7chgoeyk634/H34PUH8fiCNHb2YQuF6wyWTO26QgghhBATmRmLxkeBfwOeUUrdqbU+GL5BKTUb+CmwELg7zutqC308MqBgBEBr3auUehH4HHA2UM2HO4m5RJYT+hhtJ1IMUJiTzh2r5/GVi+fw0u5Gqqpr2Hy0Ner1vb4A9e29HO/oIz/TzqTMNGwWdUpFylAtu7HsSMZjmH2sZ1FjTYeFjxd3//n6oajpqA6bBafDSiA089Ef0LT1eCMWjcnUriuEEEIIMZGZsWj8ObAGuBq4UinVgDELcSowAyMR9q3QdfG0P/SxPcrt4aIyY8D1K4B5GPMl+ymlbBihOn7gyNguM7XZrRauPmMaV58xjQONRnDOo+/WRm9d1Zrmbi/N3V7SbBbSbBbOnJk7LkVKvEOEBop0vtBqUWSmWSMWrOF02OYuL21ub8TRGGEDi7vw7zVUgE5BpoNeXwCvX4dah/14/cH+VNZkbNcVQgghhJjITFc0aq29SqnLgH8CPgvMBmaGbj4MPAj8bNB4i3h4A6PIm6uUcmitB89hWBz6WBP6+ApwG0YB/Oigay8AnMAbyZScajbzirK575OLuXLJVO58fAdNnX1EqR0B8PiDePxBXt3fzP974zA3rygmb5ixEKMR7xChaOcL+3xBWno8NHd52VnfwR2r531kXEos6bCDi7tYAnScDhtF2ek0dvXh9Qfx+jXHO3rJSrMlbbuuEEIIIcREpsLjAMxKKZWF0eLZobXuTvBaHsIoBO/XWt8z4OuXAS8CnUCZ1rpdKZWDUeTmAOeGZzUqpdIxCsoK4DNa68dOYT1bly1btmzr1mjhqhPHS7tP8MuNBzjW1ke310cghqN6aTYLnzhzOmsrylgyM1oXcXLbXtfO3U/t6j9fOFzhd//1Sz5SpEUNtBlQ3BXmpPUXnOu31PLrlw+htR5ydxLA7fVzvL0PbyBIToadDLt1yN1PEV+J2BEXQghhXsuXL2fbtm3bomVtCHMz3U7jYKFCMaHF4gB3AquAu5VSFwDvYqSnXg8EgC9ordsBtNadSqkvYMx1fE0p9RjQCnwCYxzHn4D18f8WUtPgM4UdvT66ev30eP1Rdx89/iBPbK3nia31LC3OY21FKVctmUa63Rr5FySh9Vtqo54vBGNcRvjrTZ0e1m+ppcDp+Eix8NVL5vL6gaaYzmKOJEDH6bCRlW4UoZctmsrCadlSnCSBkbYyCyGEECL1mb5oTCZa6yal1CrgHoxCsRzoAp4Hfqi13jTo+qeVUhdihPbcCKRjpMPeCfxam30bOMlEOlNot1ro6PXxwq4TvFsTPThne1072+va+cHze7llZTG3nl1CcUFyFza1Le5hzxeG5TsdHG7u5oVdJ9hytBVvaCzGwGLh65fOwxsIDrnzNJoAnZICJ5879zQpFJPAaFuZhRBCCJHapGgcY1rrVoyi784Yr38bY0SIiJNIZwovnV/Ehg+O8+q+Jt6va8frj7xT1trj5T9fO8wDrx/mkvmFVFaUcf6cyUMWR4kSy/nCsG6PH39A0+n30esLkJthj1osfOLM6VHvZ7QBOlIwJt5Qo1Lgw1bmulY3v9x4gCnZabLjKIQQQkwQUjSKCS1SK15hVhq+YBC3N0BXnz/irwtq2Li3iY17myib5OT28lJuWl5MrjP6oPp46/X6CQQ1tmEKRrfXT2NXH4GgRikj3XTygGJvpMXCaAJ0ROKNppVZikYhhBBiYrAkegFCJMpLu09w91O72LinidpWN32+AFprvIEg3R4/aVYLpZOcnFWSx1B1V02Lmx88v5dVP9zIt5/cye6G5BitGT5fGG3cSFhrjxd/wCgYbRYLNutHv9lwsZCVZusvFoZitLLOpbjAiS+gqWntobGzj5ZuD42dfdS09uALaElHTSIDW5nzh0kMznc6cPv8bK9rp7bFHacVCiGEECKRZKdRTEixtuK19XjJSrPxg08u4bUDTbxzqIVuT+Tdxz5fkMe21PHYljqWl+ZTWV7KlUumkmZLTHBOLOcLvf4gvd4AQa3RGiwWcNoj/7WQ73RQ09rTXywM1VI6OHhouAAdkVgjaWW2WBSZDhs9ngDb69ultVgIIYSYAKRoFBNSrK14Xn+Qw03d/OSv+8jJsJOTbiPNZsHt9dM7RELoVlcbW11t/OB5hxGcs6qUGXkZ4/ktfUws5wvdA9JjrRaF027FYYvcgDDSYiFS8JCkoyanWFuZw2wWRSCo6fVGfgNFCCGEEKlFikYx4cSaKtrZ66Pb48fjD+IJBLFaFGk2C1aLwmJR5GfYQUGPNxA1OKe528t/vHqY/3ztMKsXFLG2ooxz50xCqfgE5wx3vjAQ1ASCQbQGm02Rnzl0a+JoioVIwUMiuYxkVAqAP7RrnOGQf0KEEEKIiSDp/8VXSv3LKH+p1lrfN6aLESkhlla8cDiMNxBEKbAqhTPNSl6GUVQNDHOZNTmTc+ZM5tV9TRxt7ol4f0ENL+1p5KU9jcyaksntq0r51IqZ5KSPb3BO+HxheIxCTWtP/xgFf1DT0etFY+wyFuWk4xymCJBiITWNZlTKpCwHS2dKe7EQQggxEZjhld93I3xtYLKHivB1Ffp/KRrFx8TSihcOh7Eq1f8ICw7YhBmYJNnS7aXX6+flOy/krUPNVFW7eGVfI9HyZ46c7OH7f9nDT1/cz+oFRSycnkNBpn3MWzcHtoWuWTyVAye6OHSymw63MVZDKUVuhoNebwBfMEjWMIWgFAupS0alCCGEEGIoZigaL47wtX/EmG34MPAacAKYGrr2VuB54JdxWp8wmeFa8QaGwzisFvxBjUUpLBGO+g0Mh6lv6+WCeVO4YN4U6tvcPLK5lse21NHa4434+/T6Ajy3s4HndjbgsFrITrcxJdvBWSX5pxQSE2mMiNWisCgIao0vEDTaY7XRbmq1KHwBaOzsY9oQ5y6lWEhtMipFCCGEENEkfdGotX594OdKqbXAZUC51nrboMv/Ryn1W+AN4M9xWqIwmeFa8dyhnUgAv9YEghqLLXKqaLRwmJn5Tr61Zj5fXz2XDbuOU1Xt4v3a9qhr8gaCtPR4ae3xUtvSy1ZXG9+8Yj6XLSwa0ff20u4T/a2obp+/vxW1w+2jy+NHY2ycZqfbyXRY8Qc1ff4A/qCm1e0lqDXTcjNStliQUJ7ohmtl7vH6cdptMipFCCGEmICSvmiM4B+B9REKRgC01u8ppR4PXbcurisTpjBUK57b66elx4svVDQGA8bHQEDT1NVHQabjY+f+hgqHSbNZuf6smVx/1kw+ONbBLzYe4JW9TUSbnKgBty/AgcZuvvH4dr55xencXl4aU3BOtDEibq+fVrex26kArcHjDzA5y/hepmSlcbyjl/ZeHx19fty+bnLS7SlVLETbfc1Ms8r4jwFkVIoQQgghIjFj0Xg6sGGYaxqAm+KwFmFSkVpcNJfjAAAgAElEQVTxuj2h8JsISahBDV19fnp9AYqy08nJ+DDAJtZwmMUzcinMTqMg04EvEKTPF8QbiJ5W2dnn595ndvM/1S4qy0u5YdkMsocIzok2RmTg+Uyb1YI/GMQf0LT1eHGGwoBm5DuxWHrp9vhJt1lJt1tTpliItvva5wvS0uOhucvLzvoO7lg9b8Q7u6lIRqUIIYQQYjAzFo2dwLnDXHMe0B2HtQiTGtyKd7i5G3/AaEUdGK2kALvVgkUZ4ym8/iCNXX3YrAqnwzaicJjwqI8+f4CygkyUgm6Pn5ZuL12e6CMsDjV186/P7uYnf93H9ctmsLaijHlF2RHve/AYkcHnM8FISvX6g7h9xqiQ8FzGoux0en09TM5ycGt5KZkOq+mLhWi7r2Hh1tu6Vje/3HiAKdlppiyMx4OMShFCCCFEmBmLxueBv1NK/Qz4nta6K3yDUiobI231XOC/E7M8YRYDW/Fe2HWCTr8PpcBmsRAIaoIYu3PWUJFhsyoIGLt0raFdupGEw0Qa9ZGdbic73Y7Xb+x6tbl9/ecpB+vxBnhoUy0Pbapl1WkFrK0o4/JFRditlqhjRNxeP0ENFqX6W1wVCotSBIPg9vlx2IwxIuHzmd6AZnJWGp84c/op/4wTLdrua9jAFNymTg/rt9RK0SiEEEIIMYgZi8bvABdhnFn8vFJqO9AIFAFLgRzgCHBXohYozGNpcR4FTgdbjrbS6wtQkOnAZlUoFE2hVlVfaFYjgEYTCILbG6Cxs5c+XzDmcJiBoz6MnT4/wSBYLEbIzrTcDIpy0ulw+2jp8dLrC0S9r81HW9l8tJWinDQ+c3YJmQ5rxDEiQW2sefCJSCM8VX9kjAgMfT7TbKLtvkYyMAW3tsUtO2xCCCGEEAOYrmjUWjcppVYCP8IYr3HBgJvdwO+Bu7TWLYlYnzCf7fXteAOa3Aw7kwfsRnn9QU52e/BH2Pnz+IN0ewKcNjkz5nCYDIcNfzBIZ6/PSCoNhgs6hcXiwWm3kp/p6P+vvs2N1x/E4w9GXANAY6eHX248iEUZoTtpNgsFWvfvKlqUsbMYHBS9o0O7j4PHiMR6PtMMou2+RhItBVcIIYQQQpiwaATQWrcCf6+U+jIwH8gFOoB9Wmvzb5GIuBq4AxjW2eujvddL1JjTkOuWTo85PKWr10e724fHH0RhnC1UodmJfr/GH9C4fQGKctLJctjwBoKUTHLy4xvO4J0jLTy0yUV9W2/E+w5qY+5j+L9JmQ7ynEY6qkUZha8OFZMaTVBr41zmgDEiIzmfaQaR/lyHkkq7rEIIIYQQY8mURWNYqED8INHrEJGZJX0xw2HDGkrTBOMcYDhF1WpR2C0WtDaKO6D/zKHWmme2N3D2aZOG3WncXtfOI+/WEtQaizJ2+ezWD7f5jLbXUNBOZx89abb+s5JnhP77wvmzeP1AE1XVLl4/cBIdpaD1+IM0dPRxorOPfKcDh82CP2jcv81qFEYWpXDarf0hOMCIzmeaweA/1+Gk0i6rEEIIIcRYMu2rI6WUHbgUWABkaa3vC309HeNcY7PWOrZXi2JMmW0m3tKZeWSmWWnp8RhpmuERFRaFLdS/qUKFntaaAEYSaXa6LebwlHAgS266nW6vH68/iD8QDO02GmcobRaF1kaiaUfQx7yi7I+clbRaFJfML+KS+UW4Wnp4aJOLx9+rp6PXF/H3DGpo6THmM1qU0ZLqDxrnJNPsFvIzjQCccIJot8cf8/nMaJLpjYLBf65Dtaim2i6rEEIIIcRYMmXRqJRaA/wBmEpoXjlwX+jmpcDbwO3AowlZ4ARmxpl4JZOcLC3Oo7nLy8luD27fR0dUDBTepctwWJmSlR5TeMpHAlkKMskMzYP0BzTeQNBINsV4EAf1h+cNb1tVGrUYLZ2Uyd1XL+TOy07nuR0NVG2q4YNjnVG/x4FHIo0xIopeb4CuPj89Xj9Ou43iAmfM5zMHS8Y3Cgb+uba5vRHTU8NSbZdVCCGEEGIsma5oVEqtAJ4GmjESVM8GPhO+XWu9SSl1FLgeKRpHbTQ7RmaeiXfLyhJ21ndw5GQPPr+RNqoGZI5qbbR3BrTGYbNQkOmIOTxlcCBLToYdm1XR2uMNzVA02lMtod1GFORm2MlKH/7pmeGwcvPKYm5aMZPtde2sq3bx7I6GqME5YBSnXX0B/AHITrdRnJ/BWSX5oy7skvmNgvCfa12rGzBSUiM9JsdilzVekmk3VwghhBATg+mKRuBejJTUFVrrE0qpf41wzRZgWXyXlRpOZcfIzDPxlhbn8fVL5/Ld53ZzoqMPrTFGbRDeATR2GB02C0XZ6ThD595iCU+JFMjidNhwOmzG6I3+WYrG17v6fCilRhTIopTirJJ8zirJ5+6rF7D+vTr+550aGjs90dcVCs3JTrexZEYecwuzYv79wpL9jYLwn2u4qK1p7ekvav2hltRT3WWNl2TczRVCCCHExGDGovFc4Gmt9YkhrqkDro7TelLGqewYpcJMvMsXTWV/Yxe/e/0wHp/RNjpwBzDDYaUg09FfMEJs4SlDBbI4bBYcNsdHvnaqgSyTstL48kVz+OIFs3llXxNV1TW8ebA56vWHT/Zw11O7+OGGvdy4fCaVFaXMnhJbAWmGNwouXzSVwpz0jxVc6XaLcYbRBAVXMu/mCiGEECL1mbFozMJoTR2KE/j4gTQR1anuGKXKTLzrzpzBhl3HqWnpYZIzDc2HO4ADk0Yh9vCURASyDGxhvGlFMf9wwWxe3tfEE1vr6OqLvIPZ5fHzx3dq+OM7NZw3ZzKVFaVcOr8QW4Sznf2/h0neKFhanMfS4jxTtnYm+26uEEIIIVKfGYvGY8CiYa5ZChyJw1pSxqnuGCXDTLyxKAgGhqcEtR6T8JR4BrJsr2vnD28eYWttm7GzqSHdbiHXaWdpcR6/X7uCo809VFW72Hs8enDOW4eaeetQM9Nz07mtvJRbVhYzedC6zfhGQckkZ9IXiYOZYTdXCCGEEKnNjEXjC8A/KKXO01q/NfhGpdSVwDnAj+K+MpMaix2jRM7EG+uzXuMRnhKPQJYHXj/M714/TGefn2BQo5QR5tPZB01dHo539PW3MG742nlsdbWxbpOLDbuO4wtEDs5p6Ojjpy/u51cbD3LVkqlUVpSxrCSv/8xlot8oSHVm2s0VQgghROoyY9H4Q+DTwEtKqd8AZQBKqauBC4CvAMeBnydqgWYzFjtGiZqJNx5nvcYjPGW8A1keeP0wv9h4AE+oaLco489KayPERwHdfX6OnOzpb2FcUVbAirIC7rl6Ieu31PLw5lqOd/RFvH9vIMjT2xt4ensDi6bnUFleis1qSdgbBROFGXdzhRBCCJF6TPfqTWt9TCl1OfA48M0BNz2LMYLuMHCD1nq4c48iZCx2jBIxE288z3qNR3jKeAWybK9r7w/vUcoI1/nIuBBC40KCGl8gyLH23o+0ME7JTuP/XDKXf7hwNhv3NrFuUw1vH2qJ+vvtbujk23/eRVaaDYfNmPc4JSstbm8UTCSymyuEEEKIZGC6ohFAa71NKXU6RkJqBTAJ6AA2Ac9oreUV0wiMVWtpvGfijfdZr/EITxmP+/zDm0foDIXbOKwfLRjBaFE1io4gwaCmu88fsYXRZrWwZvFU1iyeyuGT3ayrdvHk1nq6PJGfTt0eP4Qmehw62c3UnHSy020o9fECZ6zeKJhoEtn2LYQQQggRZrpXFkqpM7TWO7XWAYzdxWejXPc5rfUf4rs6cxqr1tJ4zsSLx1mvwYVdxazJY1bwjFUgS22Lm621bQRD5zgjFWxh1tAuFGg63L4hWxhnT8niu59YxDevOJ2ntx9jXbWLfSe6ot63xx/E1erGblUUZDoocDqwWS1j/kbBRJOotm8hhBBCiIFMVzQCG5RS5Vrr+mgXKKUqgd8BUjTGYCxbS+M1E288z3qZaYj69vp2+kJtqcM1MCqUMXtSQ58vGFMLY2aajdtWlXLr2SW852qjqtrFC7uO4w9GDs7xBTSNnR4aOz2k2yygIDvNPiZvFExEiWj7FkIIIYQYzIxFYzbw11B6avvgG5VSNwEPAgfjvjITG8vW0njMxBuvs15mG6Le6/WjtUYpReQy7qOUMoJxUIyohVEpxcqyAlaWFdB0zQIee7eORzbXcqIzcnAOQJ/faKl0OoJcMr+Q8+dOjvn3Ex+Kd9u3EEIIIcRgZiwarwc2AM8opS7TWnvDNyilrgMeBmqASxOzPHMaj9bS8ZyJNx5nvcw4RD3DYSPDbqWrz09Q6/4CMppgUKM1ZNito25hLMxO52uXzuVLF81m455GqqpdVB+JHpzT2uPjN68cYt0mFzevKOb2VaWyEzYC8Wz7FkIIIYSIxHRFo9b6FaXU54Aq4CHgZgCl1FXAeqABuERrfTxxqzSneLWWjoXxOOtlxiHqS2fmkeu009TlwaKM3VSbNfLPQqMJauP7WFZy6ru+dquFK5dM48ol0zjY2MW6TUZwTo83EPH6dreP//fGEX7/5hEumjeFtRVlXDhvyrDtxcJcz00hhBBCpB7TFY0AWuuHlVIzgB8ppX4B/AV4EmjGKBjrErpAE4tHa+lYGOuzXmYdoh7+ORxv76PH4yegNQSCHwvF0VrjDRi7sjnpNj53/qwxXcfcomy+f91ivrVmPk9tq6eq2sXBpu6I12oNr+4/yav7T1JckMHtq0q5eUUx+ZmOMV1TqjHLc1MIIYQQqceURSOA1vonSqkS4OvAl4F2YLXW+khiV5YaxrO1dKyM5VkvMw9RD/8cjpzshgBowBsIYlHG8I0gmmCoizfNbuFLF80etx2prDQblRVl3F5eyqYjrazbVMOLuxtDqa0fV9fayw9f2MfP/3aAa8+cztqKUs6Q5M8hmeG5KYQQQojUYtqiMeSrwHTgfOBSrfW+BK9HxNFYnvUy8xD1gT+HY229dHv8RkoqhM45GoVuTrqNL100m7+/YPa4r0kpRcXsSVTMnsSJjj4eebeWR9+t5WSXJ+L1Hn+QP22t509b6zmzOI+15aVcfcY00u3WcV+rEEIIIYQYWtIXjUqpIMQUDLljUACI1lon/fcnTs1YnfUy+xD1wT+HDrePXl8ApRTpdgvLS/L53PmzEnLmbWpuOndeNo+vXjKHF3efoKraxbtHW6Nev6OunW/UtXP/hr3cvKKY21aVUFwgO2tCCCGEEImSHK94h/YGsRWNYoIai7NeqTBEPdnPvNmtFq45YzrXnDGdfSc6WVft4qn3j+GOEpzT2uPld68f5oE3DnPp/EJuLy/lgrkSnJOskvVxJ4QQQohTl/RFo9b6okSvQZjDqZz1SqUh6mY48zZ/ag73X7+Ef75yPn/eWs+6TS4On+yJeK3WsHFvExv3NlE2ycnt5aXctLyYXKc9zqsWkWyva//YTr/VoshMs0qqqxBCCJEikr5oFCJeZIh6/OWk2/m7c0/jf51TxjuHW6iqruFvexqJkptDTYubHzy/l5+9tJ/rzpxBZUUpi2fkxnXN4kMv7T7Rf6bY7fP3nynu8wVp6fHQ3OVlZ30Hd6yex2ULixK9XCGEEEKMkhSNQoTIEPXEUUpx7pzJnDtnMg3tvTwaCs5p7vZGvL7PF2T9e3Wsf6+OZSV5rK0o48olU0mzSXBOvGyva+dXLx+krtVNVpqNsuzMiG+y1LW6+eXGA0zJTpPnjBBCCGFSSV80KqX+BeNM439orVtDn8dCa63vG8eliRQ0f2oOaxZP5c0DJ6lv7wVNf5iMDFGPj+l5GXzj8tP56iVzeeGD46yrdvGeqy3q9dtq29lWu537/uLg02cXc+uqUmbkZcRxxRPT+i21NHV6yEqzRWzntlhU/9ebOj2s31IrzxshhBDCpJTWyZ0xMyA9dYHW+kDo81horfWE3nZQSm1dtmzZsq1btyZ6KUkv0rksrTUomJmfwflzp3DdmTOS/qxgqtrT0Mm6TTU8/X4Dvb7IwTlhFgWXLihibUUp586eLME546C2xc0XH3qP2lY3ZQWZwwZH1bT2UFLg5IHbV8hzSAghUtTy5cvZtm3bNq318kSvRYy9pN9pBC4Ofawd9LmYIMY7lTHauaxwS6rPr+nxnGD+1Bx5wZsgC6fn8MMbzuDbVy7gya31PLTJxZHmyME5QQ1/29PI3/Y0MmtyJreXl3Lj8pnkZkhwzljZXm+8uZLpsA1blFssikyHjR5PgO317fIcEkIIIUwo6YtGrfXrQ30uUlc8UhnlXJa55GbY+ex5p/F355Tx9uFmqqpdvLw3enDOkeYevv+XPfz0xf188qzpVJaXsXB6TnwXnYJ6vX4CQY0txl1cm0URCGp6vf5xXpkQQgghxkPSF41iYopXKmMiz2XJXLvRs1gU58+dwvlzp1Df5uaRzbWs31JHS0/k4JxeX4BH363j0XfrWF6az9qKUq5cPA2HzRLnlaeGDIcNa+j5GAt/UJNut5DhkH9yhBBCCDOSf8FF0onX7l9ti5vtde24fX7KsjOHvDbf6aCmtYftde3UtrhPqbiTuXZja2a+k2+tmc/XV89lw67jVFW7eL+2Per1W11tbHW1cV/WHj69soRbV5UwXYJzRmTpzDwy06y09HgIBvWwZxp7vH4jSGqmPK6FEEIIM0r6onFAEM5Iaa110n9/4uPitfuXiHNZMtdu/KTZrFx/1kyuP2smHxzroKq6hme2N+DxR94Na+728ttXD/Gfrx9m9YJC1laUcc7sSSglwTnDKZnkZGlxHs1dXtrc3ojP07A2txen3cbSYtlJF0IIIczKDEXVG4yuaBQmFM/dv3ify5Lzk/GzeEYuP/nUmdx11QL+tLWedZtcuFrcEa8NBDUv7m7kxd2NzJ6SSWUoOCc7XYJzhnLLyhJ21ndQ12r8XPOdjoiP526Pn+ICJ7esLEnUUoUQQghxipK+aNRaX5ToNYjRGc2ZvXju/sX7XJbMtYu/PKeDz58/i8+eexpvHDzJumoXr+xvItqkocMne/juc3v4yYv7uf6sGaytKOP0qdnxXbRJLC3O4+uXzu3fOa9p7flY8rDTbqO4wMkdq+fJY1kIIYQwsaQvGoX5nMqZvXju/sXzXFaizk8Kg8WiuOj0Qi46vZC6VjcPb65l/ZZa2ty+iNe7vQEe3lzLw5trOfu0AtZWlHLFoqnYrRKcM9Dli6ZSmJP+sed7ut1iPFfkjK4QQgiREqRoFGPqVM/sxXP3L57nsmSuXfIoLnDy7Svnc8fquTy/8zhVm1zsqIsenPPu0VbePdpKYXYanznbCM4pykmP44qT29LiPJYW50kasBBCCJHCpGgUY2YszuzFO5UxXueyJtpcOzMUEOl2Kzcun8mNy2eys76dqmoXz+2IHpzT1OXhVy8f5LevHuKKRUVUlpdRPqtAgnNCSiY5k+7PeDAzPC6FEEKIZCRFoxgzY3FmL96pjPE6lzVR5tqZdZzIGTPz+NlNedx91QIef6+OhzfXUtsaPThnw64TbNh1gnlFWVSWl3L9splkpZnrz2oiMevjUgghhEgW8ipHjImxPLMX71TGeJzLmghz7VJhnEh+poMvXjibL5w/i9cPnKSquobXDpyMGpxzoLGbe5/ZzY//up8bls2gsryUuUUSnJNMUuFxKYQQQiSaFI1iTIzlmb1EpDKO97msVJ9rl2rjRCwWxcXzC7l4fiGulh4e3lzL4+/V0R4lOKfb46eq2kVVtYuKWZOorCjlsoVFEpyTYKn2uBRCCCESRYrGcaaUqgSqQp9+QWv9XxGuOQe4BygH0oFDwIPAb7TWgXit9VSM9Zm9RKUyjue5rFSea5fK40RKJ2Vy11ULuPOyeTy7o4GHNrnYWd8R9frqIy1UH2mhKCeNW88u5TNnF1MowTkJkcqPSyGEECKepGgcR0qpYuA3QDeQFeWa64AngT5gPdAKXAv8AjgXuCkuiz1F43FmL9VSGVN1rl0yjxMZy8dOut3KzSuKuXlFMdvr2qmqruEvO4/jjRKc09jp4RcbD/CbVw6yZvFUKstLOfs0Cc6Jl2R+XAohhBBmY7qiUSkVy/ZLEOjUWneO93qiUcYrw/8GWoA/A/8U4Zoc4PdAALhIa/1e6Ov3Aq8An1JKfVpr/VjcFj5K43lmzwypjLFKxbl2yThOZLyDT4w3NJZyz9ULWb+ljoc2uTjW3hvxWn9Q85edx/nLzuPMn5rN7eWlXH/WDDIlOGdcJePjUgghhDArM75qqQGixFJ8lFKqEWMX73ta6+bxXFQEXwMuAS4KfYzkU8AUoCpcMAJorfuUUvcALwNfApK+aEz1M3tjKdV2UJNtnEg8g08KMh186aLZ/P0Fs3h1XxNVm1y8ceBk1Ov3nejinqc/4Mcv7OPG5TO5vbyUOYURmxDEKUq2x6UQQghhZmYsGquAUuBCoB3YDjQCRcBSIA94DegBlgBfAa5RSp2ttY7+am4MKaUWAD8CfqW1fkMpFa1oDH/9rxFuewNwA+copdK01p5xWOqYSuUze+MhVXZQk2mcSKKCT6wWxeqFRaxeWERNcw8PbXLx+Ht1dPZFLkC6PH7++E4Nf3ynhnPnTKKyvIzVCwqxSXDOmEmmx6UQQghhdmb81/GHQDVGUXa/1ronfINSKhO4F/gCUIERKHMv8K/Ad4A7x3txSikbsA6oBe4a5vLTQx8PDL5Ba+1XSh0FFgGzgL1D/J5bo9w0f9gFj6FUPbMnhpZM40SSIfikbHIm91yzkG9cfjrP7jhGVbWL3Q3RO+XfPtTC24damJ6bzq2rSrhlZQlTsqPv1IvYJNPjUgghhDA7M76t/SNgh9b6roEFI4DWukdr/W1gJ/AjrXVQa/09jN3Ia+O0vn8BzgL+Tmsd+ZDTh3JDH6NFMYa/bppXMZcvmsr91y9h9cJCSgqcpNutKKVIt1spKXCyemEh91+/ROahpZBwa7LTbqPN7R3y2vFsTR4YfJLvdAx5bb7Tgdvn7w8+GQ8ZDiu3rCzhL189jye/dA6fXDodxxA7iQ0dffzspQOc86OX+dqj7/NeTSs62oBIMaxkeVwKIYQQqcCMO40XAL8b5pp3gH8Y8Pkm4H+P24pClFJnY+wu/rvWunos7jL0cchXjlrr5VHWsxVYNgbrGJFUO7MnhpcMrcnJGnyilGJ5aT7LS/O55xoP67fU8fAmFw0dfRGv9wU0z+5o4NkdDSyclkNlRSnXLZ2OU9omRywZHpdCCCFEKjDjq5A0YOow10wLXRfWDYxrusGAttQDGC2xsQjvJOZGuT1n0HUj1ub28uyOhoQUbKlyZk8MLxlak80QfDI5K42vXDyHL14wi1f2NbFuk4s3D0bP6NpzvJPv/HkX/7ZhLzctL6ayopTTJg89PkJ8KBkel0IIIUQqMGPRuAO4RSn1C631B4NvVEqdAdyM0ZIaVgaMdwhOFjAv9P99UWax/V4p9XuMgJw7gP3AitCv+8i5xFARehpGsXtktItq6vTwsxf3j3rUgOwWilglepyImYJPbFYL86fmcNOKYladVsCWmja2utro9kQJzunz8+DbR3nw7aOcP3cyayvKuGR+IdYYC+SJLNGPSyGEECIVmLFo/D7wPLBFKfUQ8DYfpqeeB9wG2IH7AJRSGcDlwHPjvC4P8Icoty3DOOf4FkahGG5dfQVjvWuARwf9mgsAJ/DGqSan9vkCIx41MN5z7kRqSmRrslmCT6I9t2bkpZOTYedkl4eaIc5ZvnmwmTcPNjMjL4NbV5Xw6ZXFQ464EdIyL4QQQpwqZcagBaXULRjnGnP56Hk/hdHK+WWt9aOha3OBcmC/1romzksltIbvYiS4fkFr/V8Dvp4DHMZoQz03PKtRKZWOUVBWAJ/RWo9qTqNSamvuzHnLzv/WHz52duf+65dELfqizbkb2M5VmJM2JnPuhBhL3/nzTjbuacJuVUMWUi3dHnwBzeqFhfzwhjPitr5YnltTsh1cc8Z09p7o4q8fHMcXGPrvaIfVwtVnTKOyopSzivOI0uUghBBCjJvaFjeXnF/OUVetK9DTXpbo9YixZ8adRrTW65VSzwPXYezg5QKdwPvAM1rrrgHXdgAvJmShw9BadyqlvgD8CXhNKfUY0Ap8AmMcx5+A9WPxe8U6aiBRc+5EZLIzMjLJHHwS63Orvq2X53cd5/7rl3DvNQtY/24dD2+u5URn5OAcbyDIU+8f46n3j7F4Rg5ry8u49szpZDis8frWhBBCTFADu2caO/uwZuZPS/SaxPgwZdEIoLXuBh4O/WdaWuunlVIXAncDNwLpGPMl7wR+rcd4Kzjf6aCmtad/1MDgAiQZ5twJaQ8erWQOPhnNc+uHN5zBVy+dy5cums3GvY1UVbt453BL1N/jg2OdfOvJndy/YS83LZ/J7eWllElwjhBCiHEwuHsmaL7mRTECpi0azURr/V3gu0Pc/jZwVTzWMtSogYFz7sqyh36hOVzxKUYvWgtjny844rOpyW48dlKTMfjkVJ9bNquFNYunsWbxNA41dbGu2sWT245FDc7p6PXxX28d5b/eOsqF86awtqKUi06X4BwhhBBjI1L3zDGLwhcMxC+SXMSVaYtGpdQK4GwgH4jUh6W11vfFd1XmEG3UQLLOuZtIJkp78HjvpCZb8MlYPrfmFGbzvesW880183nq/WOsq67hQGN31Pt7/cBJXj9wkuKCDG5bVcotK4rJz3SMxbclhBBighque0akHtMVjaHwmD8DF2ME30SjCSWoio+KNmrADHPuUt1EaA+O505qsswKHY/nVlaajcryUm5fVcLmo62sq3bx4u4T+KP0B9W19vKjF/bx878d4NozprO2opQzTfbYEUIIkXgj6Z4RqcN0RSPwU+AS4E3gv4E6jFmGIgZDjRow05y7VDQR2oMnyk7qYOP53FJKUT5rEuWzJtHY2cej79byyOZamroiT+rx+oM8ua2eJ7fVc+bMXCoryrjmjGmk2yU4RwghxPBG0j0jUocZX+1fB2wDLlho2OAAACAASURBVNZax/YKTPRrc3tx2m0sLf54m55Z5tylqonQHjwRdlIjiddzqygnnTtWz+MrF8/hpd2NVFXXsPloa9Trd9R3sOOJHdz//B5uXlHM7eWlFBeY47EkhBAiMUbaPSNSgyXRCxiFXOBVKRhHJhjUtHR76Pb4KcxJizhqoGSSk6XFeTjtNtrc3iHvb6jiU4xOqrcHD9xJzXcOfaYu3+nA7fP376SaXbyfW/bQ7Mb1X6zgxTsuoLK8lMwhRnC0uX088MYRLvjpq3z2j1t4dX8TQYnBE0IIEUG4eybacQiRmsxYNB4EzB0ZGUf+oKaxs4+a1h58AT3sqIFbVpZQmJNGt8dPS7fnYy8cYyk+xeiM5C9hrz9It8dPry/AnuNdpiisTmUnNRUk6rl1+tRs7vvkYjbddSnfv24Rcwqzol6rNbyyr4n//d9buPjfX+P3bxyhfZgiVwghxMQS7p7p8frlDcYJxIztqf8B/EgpNUNrfSzRizGDdLs15lEDyTznLtXF0sLo9vpp7fHi9gbw+oPYrIq/7T7Bu0dbkn5+Y6rvpA4n0c+t7HQ7ayvKqCwvpfpIC+uqXby0p5FAlH/wXS1u7t+wl5+9tJ9PnDmdtRVlLJmZO6ZrEkIIYT7h7pnmLi9tbq+kp04QZiwaX8AIwnlbKfU9YCsQcStCa10bz4Ulo8KcNP7pitNHNGogGefcTQTD/SXc2eujsasPf0ATCGqUAnuo4KhtdSf9/EYJWkqO55ZSinNmT+ac2ZM53tHLo5treeTdOpq7IwfnePxBnthazxNb61lanMfailKuWiLBOUIIMZHdsrKEnfUd1LUanU7DHTsR5qe0Nte2slIqiDFOQ4U+RqO11qnzanMUlFJbly1btmzr1q2jvo9kmXM3UWyva+fup3b1p4vmOx1YLAq318+x9l68fqPg0hrS7BZm5GXgdNj6U0e7PX6KC5zcf/2SpCvqa1vcfPGh96htdVNWkDlsGExNaw8lBU4euH1FSj7mkum55fUHeXH3CdZVu3i3JnpwTlhBpoNbVhZz26oSZuan3p+NEEKI4Q0eoVXzX1/D5/N5vU1HZesxBZmxqKpi6GJRjKFkmXM3UURrYezu8+P1B9EarBaFzaYoyknHGdqFM0Pq6EjaWSZC0FIyPbccNgvXnjmda8+czt7jnazb5OLp94/h9gYiXt/a4+U/XzvMA68f5pL5RaytKOW8OZMlel0IISaQwd0ztfJPQEoz3U6jiN1Y7DSKxNhe197/l3CH20dTl4dAUOOwGQEx+ZmO/oJxoGTfoYu2kxpmhh3TiaKzz8eTW+tZt8nFkZM9w15/2uRMbltVwk0risnNsMdhhUIIIZJFbYubS84v56ir1hXoaS9L9HrE2JOiMYVJ0Wh+tS1u/vD2UZ7b0YA/GGRaTgYO29Chx42dfaTbrfzTFafziTOnx2mlsRvczhIpDKYwJy1pz2ZONFpr3j7UQlV1DRv3NjJcUF663cInl86gsqKURdMlOEeIVJZMbfYi8ZYvX862bdu2aa2XJ3otYuyZsT1ViAmjZJKThdOy2bjHitaWYQtGSP7U0WQIgxGxU0px3tzJnDd3Mg3tvTyyuZbHttTS3B15FEefL8hjW+p4bEsdy0vzWVtRypWLp8X02BVCmMPAbpjw3+FWiyIzzSp/hwuRopK+aFRKPYhxhvEurXVj6PNYaK3158ZxaULERSqmji4tzmNpcZ68S20y0/My+KcrTuerl87hrx+coKraxVZXW9Trt7ra2Opq476sPXx6ZQm3riphel5GHFcshBhr0bpF+nxBWno8SZ/kLYQYneR9Vfmhv8MoGn8MNIY+j4UGpGg0ASkchhbL/MawYKjFc1KWg6Uzk/9d3mQKgxGxS7NZuW7pDK5bOoPdDR2sq3bx9PZjUd/YaO728ttXD/F/XzvE6gVFrK0o49w5k1BKUhOEMJPtde386uWD/efSy7IzI55Lr2t188uNB5iSnSY7jkKkCDMUjaeFPh4b9LkwOWlviY2kjopktmh6Lj+68Qy+c+UCnthax0ObXNS0uCNeG9Tw0p5GXtrTyKwpmVSWl3Lj8pnkpEtwjhBmsH5LLU2dHrLSbBH/LTJDkrcQYnSSvmjUWruG+lyYk7S3jEykIbpDpY7esrIkUUsVE1Su087nz5/FZ889jTcPNbOu2sUr+6IH5xw52cP3ntvDT1/czyfPmkFleSkLpuXEd9FCiJjVtrjZXteO2+enLDtzyGvznQ5qWnuMMQwtbnkTU4gUkPRFo0g90t4yctHmNw5OHS0ucHLH6nkT/ueV6pK5pdtiUVw4bwoXzptCXaubR96tZf2WOlp7IgfnuL0BHtlcyyOba1lZlk9lRRlrFk2V4Bwhksz2eqMrKNNhG3Ymq8VijIfq8QTYXt+eNH8/CSFGz7RFo1LqWuA2YAGQqbWeE/r6AuBa4GGt9bEh7kIkiLS3jI6kjgqztXQXFzj55zXz+fqlc9mw6zhV1S6217VHvX5LTRtbatqYkp3GZ1YWc+uqUqbmpsdxxUKIaHq9fgJBjW2YgjEs2ZO8hRAjY7qiURnJCX8Ebg99qRcYGMfXBvwboDDCc0QSkfaWUyOpoxOXmVu60+1Wblg2kxuWzWRXfQfrNtXwzPYGPP7IwTknuzz8+pVD/Mdrh7l8YRGVFaVUzJLgHCESKRWTvIUQsTPjM/nLQCXwIPAN4B+Be8M3aq1PKKXeBq5GisakI+0tY0NSR5NHPAr4VGrpXjIzl5986kzuumoBT7xXz7pNLmpbIwfnBIKaFz44wQsfnGBuYRaVFaVcf9YMsiU4R4i4S+UkbyHE8MxYNH4O2AF8QWutlVKRYhYOAlfEd1kiFtLeIlJFPFtFU7GlO8/p4AsXzOJz553G6wdPsq7axav7m9BRgnMONnXzL8/s5scv7OOGZTOprChlXlF2fBctxAQmSd5CTGxmLBpPBx7QOtpLCwCagClxWo8YAWlvEakgnq2iqd7SbbEoLj69kItPL6S2xc3Dm12sf6+Odrcv4vU93gDrNrlYt8lF+awCKsvLuHxREXarBOcIMd4kyVuIicuMr8T9wHDJCDOA7jisRYyQtLcIs4t3q+hEaukumeTkO1ct4B8vm8dzOxpYt8nFzvqOqNdvOtLKpiOtFOWk8ZmzS7j17BIKcyQ4R4jxIkneQkxcZiwa9wAXKaVUpN1GpVQ6cAnwftxXJoYl7S3C7OLdKjoRW7rT7VZuWlHMTSuK2V7XTlV1DX/ZeRxvlOCcxk4Pv9x4kN++cogrFk2lsqKUVacVSHCOEONAkryFmJjMWDSuA34L/EIpdefAG5RSVuDnwHTg2wlYm4iBtLcIs0pEq+hEb+k2EoOXcs/VC1m/pY6HNrk41t4b8Vp/UPP8ruM8v+s484qyqKwo4/qzZpCVlho/CyGShSR5CzHxmPFf0geATwBfA24CugCUUn8CyjEKxme01g8nbIViSNLeIswqEa2i0tJtKMh08KWLZvP3F8zitf1NVFW7eP3AyajXH2js5t6nPwgF58xgbUUpcwolOEeIsSRJ3kJMHKYrGrXWAaXUNcA9wFeAeaGbbgDagftC/4kkJu0t5iHvJH8oEa2i0tL9UVaL4tIFRVy6oIia5h4e2uTi8ffq6OyL/DPu9vipqnZRVe2iYtYk1laUctnCImwSnCOEEELEzHRFI4DW2g98Vyn1PYyicRLQAezTWgcSujgRM2lvSW7xHClhFolqFZWW7sjKJmdyzzUL+cblp/PsjmNUVbvY3dAZ9frqIy1UH2lhak46t64q4dNnF1OYLcE5QgghxHBMWTSGhYJw9id6HeLUSHtL8onnSAkzSVSrqLR0Dy3DYeWWlSXcvKKY9+vaWVft4vmdx/EGIhf3Jzr7+PnfDvCbVw6yZvE01laUsqI0X4JzhBBCiChMXTQqpc4HzgJyMXYa39dav5nYVQlhbvEeKWEmiWwVlZbu4SmlWFaSz7KSfO6+egGPv1fHw5tqowbn+AKa53Y08NyOBuZPzaayopRPLp1BpgTnCCGEEB9hyn8ZlVLnAg8Cc8JfAnTotoPA57TWbydoeUKYWrxHSphNIltFpaU7dpOz0vjyRXP44gWzeWVfE1XVNbx5sDnq9ftOdHH3Ux/wow37uHH5TCorSpk9JSt+CxZCCCGSmOmKRqXUcuBvQDrwOvAacAKYClwMXAC8pJQ6X2u9LVHrFMKMEjFSwmySoVVUWrpjZ7UoLltYxGULizhyspuHN9fyxBDBOV0eP398p4Y/vlPDeXMmU1lRyqXzCyU4RwghxIRmuqIRuB9j3ddprZ8bdNv3lFLXAX8KXXdlvBcnhJklYqREopzKbp20iprTrClZ3HvNQr5x+Tye3d5AVbWLPcejB+e8daiZtw41Mz03ndvKS7llZTGTh2hJFkIIIVKVGYvGc4A/RygYAdBaP6OUegq4Ir7LEsL8EjFSIt7GKhVWWkXNy+mw8emzS7hlZTHbatuoqnaxYddxfAEd8fqGjj5++uJ+frXxIFctmUplRSnLSiQ4RwghxMRhxqIxCBwa5pqDwOVxWIsQKSVRIyXiZTxSYaVV1LyUUiwvLWB5aQH3XL2Q9VtqeXhzLcc7+iJe7w0EeXp7A09vb2DhtBzWVpRy3dIZ/H/27js+quvM//jnqKOGECCqNKJ3g6mSGy7YxiVuiY2bSDbJ5pdsmtMcb5xsysZOspvmZDc9Gwdwj40dOy4Y3G0JbIEMpjdVikBCCHWN5vz+mBGRYUYSYjQzd/R9v17zGjT3aPTMXO7Mfe455zmDEmJDHLmIiEhoOeNM78PeA2b30GY2sCEEsYhElf5YUiJSeuJUFVa6MzwtkS9cOonPLp7A2u3VrCoq4609gQvnbDtYzz1PbeH+57dz8/xs7sxzMW5Y9/OARUREnMqJSeO3gdeMMZ+z1v721I3GmM8DlwEXhzowEacL5pISwRoGGiyqChsekXLRoLfiYmNYOnMkS2eOZE91A6uKyniyuJITrf6HYNe3uPnzW/v581v7uWjycJbnubhkahaxvRzi3R2nvXciIhK9Ij5pNMb8h5+HXwH+xxhzF/AmcBgYAVwATAJexDs8dX2o4hSJFsFYUqI/hoGeDVWFDb1Iu2jQFxOzUvnedTP4xpVTeLqkipWFZew4dCJg+zd2HeGNXUcYkzGIO/JyWDY/u9sLL4FEw3snIiLRxVjrf+J/pDDG9G5y1emstXZATzQxxhTPnTt3bnFxcbhDEYcJlPR1XVIiKz3Rb9JXUlHHvau3nBwG2lPSed+Ns/r9BPjv7x/gpy/tpKW9gxHpST22P1zfQlJ8LF+/cgrXzR7dr7FFo7P5/xPJrLW8W3qMlUVlvLDlIG5P99+fCXExXDtrFAX5LuZkZ/SqcE60vnciEv3mzZvHxo0bN1pr54U7Fgm+iO9pxLv2ooiE0NksKRGJw0AHQlXYSBHNc0eNMSwcl8nCcZlUXzuNRzdU8PD6cg7VByic4/bw1KYqntpUxawxgynId3Hd7NEkxfu/nunvvXN7LE1tbowxDBmUQFtHhyPfOxERcbaITxqtta+HOwaRgagvS0pE6jDQaK8KG0ki8aJBf8hKS+JLl03icxdPYO22w6woLKNwX03A9luqjnP33zZz//PbuWV+NncsysE19MPHSNf3blBCLAeON9Pc1oHHgsViMMQYiDGGqmPNjn3vRETEeXRGJCLdOpMlJUoqvb2SKQlx3VZeBW/ykJIQR2NrByWVdf2aNPZHVVg5XaReNOhP8bExXDVrFFfNGsXuwydY6Suc09jW4bd9XVM7f3hjH398cx8XTx7O8vxcFk8eTuWx5pPvXWZ8AlV1zbg7LB5riTEGA3iwuD3en1vcHby5+6ij3zsREXEOJY0iEjSROgw0mFVhJbBIvWgQKpNGpPGD62dy99KprN5YyYrCMnZXN/htay28uvMIr+48QnbmIObmDKG+2U1CbAxHGlppc3uINYaE2JgPzYW01tLhsVjrnXv7zPtVfPHSSaF6iSIiMkDFhDsAEYkencNAeyoQ0sntqwoZimGgyxbkkJWeSEOrm5oGb49jVx6PpaahlYZWN1npiX6rwkr3IvWiQailJsZRkJ/Lmq9cxKOfyeOaWaO6XYKjoraZZ0oOUFXXTH2zm3Zfwhh3SsII3nmV3sehw2N5c9eR/n45IiIi6mkUkeCJ5GGgc7Iz+PJlk05WpiytbfRbmTI7M5m7lkzWXLE+0NzRDzPGkDd+KHnjh3K4voWH15fzyIZyqk+0BvydDl9Fc4+1YLzJo7+qqwbwWKiqa9EQVRER6XfR+U0tImHhbxhom9tDU7sbjwdiYiA5Po6EuJiwDAM9m6qw0rNIvmgQbiPSk/jK5ZP5wqUTeWnrIVYUlrFhf23A9hZo77C04+25jY0xxPiSR4vF4j2erLVRM7xXREQil5JGEQmqZQty2Fx5nNKjjdQ1tdFhLdaak9UfjWk52XuSOywl5MNA+1IVVnpHc0d7Fh8bw7XnjObac0az41A9KwvLWL2piqYAhXPA2yPrLYADcTExeKyHGGNIiPUeR9E2vFdERCJPxCeNxpiNwO+stX/w/bwcKLHWbg5vZCLiz5zsDBZPHs7eIw20uj1YCwaLMd5eEQsYY0mMi+HiKVlh69U7k6qw0nudFw0qapsAb5VUf+s0NrS6yc5MHtBzR6eOTOe+G2fxzaum8lRxJb96ZQ+1jW0B23sstHV4h/7GxUB8XEzI5gSLiMjA5oRCOHOAkV1+fhC4ITyhiEhPSirqeH3XEWKNYVBcLIlxMcTHek9u42NjSIyLYVBcLLHG8NrOakoq6sIdsgRR59zR7Mxk2jsspbWNHK5voaahlcP1LZTWNtLeYTV3tIv0pHg+cf44Vn/uPIalJvTqd9wey/FmN8eb20mOj+3nCEVEZKBzwuXJGmBYuIMQkd7pXKB88KD4f85pbHPjsRBjIDnBO6expqHV0Yu7S2CaO9o3rmEpXD59BC98cIiWNjftHktHDzWFjje38+kV7zE3J4OCfBdXzxpFYpySSBERCS4nJI0lQIExpgo46Htsjm+YarestSv6NTIR+RB/i7snxMWQEHd670kkL+6u+Y5nT3NH+6br8N70+FjiYqG5zUNjWwdt7sAZ5MbyOjaW1/HD57azbEE2d+S5GJMxKISRi4hINHNC0ngP8DzwI7wF5QCu990CMb62ShpFQsjpi7uXVNSd1jsWG2NISYxV71gfae7omTl1aZjjzW5SEuIYlhJLc3sHJ1rc3a6DWtPYxm9e28vvXt/LZdNGsDzfxfkThvV4PIqIiHQn4pNGa22xMWYisBAYg3dO4zO+m4hEECcv7r5m66GTJ+pN7e6Tazi2tHuoaWzl6Ik2Nlce564lk7l8+ohwh+s46nHsvUDDe9OS4hk5OInpo9LJSE7g1R3V7Dva6Pc5PBZe3naYl7cdZvywFO7Mc/HReWMZPCg+xK9GRESiQcQnjQDW2hPAOgBjzIN4q6f+NaxBiXRjoJ4gO3Vx95KKOh5Yt5uK2iZSE+PITUvxW/GzoraJX67dxfC0RPU49pJ6b/umN8N77716Gm/vPcqKwjLWbT9MoA7IfUcb+cFz2/jvl3Zyw7ljWJ7vYtqo9BC+GhERcTpHJI2nGAeo3KJEpIF+guzUxd07i/ekJsb5XVswJsacfFzFe3pPvbdnr7vhvTExhgsnDefCScOpqmvmoaIyHnu3gpoAy3Y0t3fwyIZyHtlQznzXEAryXVw1cxQJcU4opC4iIuHkuKTRWlvW+W9jTDwwFcgAjgPbrbXt4YjLGDMUuBG4BpiFdyhtG7AF+AvwF2vtad0vxpjzgG8DeUASsAf4P+DX1trAqz1LxNEJsjMXd/dXvCeQSC7eE2nUextaYzIGcffSqXx5ySRe2HKIFYWlbCwPfH31vbJjvFd2jP9M3c5tC7O5fVEOowarcI6IiPjnyMuLxph0Y8zv8PY4lgCvAZuAOmPM74wx4TjzuBn4I7AIWA/8EngSmAn8CXjcGPOhbhdjzPXAG8BFwGrgf4EE4BfAoyGLXM5a1xPk+FhDbmYKI9KTGJqayIj0JHIzU4iPNSdPkKN5bcJlC3LISk+kodVNTYO3x7Erj8dS09BKQ6ubrPTEsC/ufjbFeySwU3tvT31vO3tvUxPjTvbeytlLjIvlhnPH8NS/nc9zX7yAZfOzSYoP/FV/tKGVX7+yhwt+8iqfXVnMO3uOYm3gQjsiIjIwOS5pNMakA28DnwHcwJvA4777dt/jb/nahdIu4DpgrLX2Dmvtv1trP4m3J7QC+ChwU2djX3x/BDqAi621n7LWfgOYAxQCHzPG3Bri1yB9pBPkf3La4u5OLt4Tqbr23g5J7n6x+iHJCTS1u0/23krwzBwzmJ987BzW//sSvn3NNFzd9Ix3eCwvbj3E7X9az+W/eIO/vlPKiZawDNwREZEI5LikEfh3YAbwW8Blrb3YWnubtfZiwIW3t266r13IWGtfsdY+e+oQVGvtIeB3vh8v7rLpY8Bw4FFr7Xtd2rfgHa4K8Ln+i1iCRSfIp7tixkjuu3EWS6ZnkZOZTFJ8LMYYkuJjyclMZsn0LO67cVZEDNPtLN7T3TIGXbl981TDXbwnkqn3NrIMTo7n0xeO59WvXcyD/7KAy6ZmYbrZLXuqG/ju37eSd/86vv30FnYeOhG6YEVEJCI58aznJqDIWvv5UzdYa48DXzTGzMXbsxfSxLEbnZdru3ZNXOq7f9FP+zeAJuA8Y0yitba1uyc3xhQH2DT1jKKUPnH62oT9xSmLuzu1eE8kU+9tZIqJMVw8JYuLp2RRUdvEQ+vLeezdco41+e9RbGzrYFVROauKylk4LpPl+S6unDGS+FgnXm8WEZGz4cSkMQfvXMHuvA58JQSx9MgYEwcs9/3YNUGc4rvfdervWGvdxpj9eHtUxwPb+zVIOSs6Qe5epC/u7sTiPZHOqUuvDCTZmcncc9VU7loyiX9sPsiKojLe72au9Yb9tWzYX0tWWiK3Lczh9kU5jEhPCmHEIiISTk78hm4CsnpoM9zXLhL8GG8xnOettS91eXyw7/54gN/rfLzH7gxr7Tx/j/t6IOf2Mk7pI50gO9+yBTlsrjxORa33Y2NIcoLfSp8NrW6yM5PDXrwn0qn31jmS4mP56LyxfHTeWDZX1rGisIxn3z9Aq9v/51n1iVYeWLeb/3l1D1fOGEFBXi554zMx3Y13FRERx3PiWeu7wM3GmJ9Ya3efutEYMwG4BW8xmbAyxnwJ+BqwAyg401/33auMXYSLthPkSB9OeqpgxNtZvKdzyZTS2saTS6a4ffssOT7utOI9TnuvQkW9t850ztgMfnpzBvdePY0niitYVVROea3/668dHsvzWw7x/JZDTB6RSkGeixvnjiU10YmnFSIi0hMnfrr/N7AGeNcY82vgVeAgMBJvoZkvAqnAT8MVIIAx5vPAA8A24DJrbe0pTTp7EgfjX/op7SRCRcsJcklFHY+9W05JhXeOZoev4EtKYixzsjNYtiAn7JVOuwp2vFfMGElWetJpz5kUH+NN8rs8p9Peq3BQ761zDUlJ4DMXTeDTF4zn9V1H+GthKa/vOkKglTh2HW7gO89s5Scv7uSmuWMoyHMxaURaSGMWEZH+ZZy4HpMx5v/hTcjiT92Et+jMXdba34Y8sM4gjLkL71qLH+BNGKv9tFkF3AHcbq195JRtcXiTxQQgtadCON3EUTx37ty5xcWB6uRIsJRU1HHv6i0nFzLv6QT5vhtnRVRSsWbroZO9bE3tbr+9bFnpidy1ZHJEVDzt73i760F02nsVTnqvokdZTaOvcE4Fx5t7Xoojf/xQlue7uHz6COJUOEdkQJg3bx4bN27cGGjalDibI5NGAGNMDt4hn+fi7a07DmwCVllry8IY1zfxzmMsAS631h4N0O6TwJ+BFdbaj5+y7VJgHfCGtXbxWcSipDGEnHqC7LSEN5zxOu29igTqlY0uLe0d/P39A6wsLGNLVc8DYUamJ3H7ohxuXZhNVpoK54hEMyWN0c2Jw1MBsNaWA/eFO46ujDHfAX4AFANX+BmS2tXfgJ8Atxpjft25VqMxJgn4oa9N2HpL5cydyfDGSPLYu+VU17eSmhjnd2htTIw5+Xh1fSuPvVse1tcQznid9l5FAqcsvSK9kxQfyy3zs7l53lhKKupYWVjGc5sP0tbhv3DOofoWfv7yLn61bjdLZ45keX4uC3KHqHCOiIjDODZpjDTGmI/jTRg7gDeBL/n5Uiy11j4IYK2tN8b8K97k8TVjzKNALXAd3uU4/gY8FproJVicdoJcXtNESUUdTe1uctNSum07JDmB0tpGSirqKK9pCsvrCWe8TnuvIk2kL70iZ8YYw7k5Qzg3Zwj3XjONx9+rZFVRGVV1zX7buz2W5zYf5LnNB5k6Mo2CfBc3zBlDigrniIg4gj6tg2ec7z4WuCtAm9eBBzt/sNY+bYxZDNwLfBRIAvYAXwV+ZZ06dlgcc4JcUuntEU1JiOu26it4e9FSEuJobO2gpLIuLK8vnPH29Lfb3B6a2t14PBATA0lxsWF9r0RCZWhqIp+7eAKfuWg8r+6oZkVRGW/sOhKw/Y5DJ7h39Qf8+PkdfHTeWAryXUwYnhrCiEVE5EwpaQwSa+33gO/14ffeBq4OdjwivdHc5qbDY4nrIQHrFBdj6PBYmtvc/RyZf+GMN9Dfbmpzc6yxjab2DjwesFgMBo+1NLd3sPPQCZh91n9eJOLFxhiWTB/Bkukj2H+0kVVFZTzxXgX1Lf6PvxOtbh58p5QH3ynlgonDKMh3cdnULBXOERGJQEoaRQawQQlxxMYYWtr9z0c6lds3R3NQQng+OsIZr7+/Xd/czuETLbg7LB5riTEGY8BjLW6PxdPewTMlVczJzoio4kci/W3csBS+c+10vn7FFJ4pqWJFYRnbDtYHbP/WnqO8tecomgOC0wAAIABJREFUowd7C+csW5DD8LTAyxeJiEhoKWkUGcDmjM0gJTGWmsZWPB7b7ZBPj68K7NDUBOaMDU9xl3DGe+rfbnF3cPhEC21uD7ExhoTYGAzeeKz9ZxJZ19jGL9fuYnha4mlFcZwy91WkrwYlxHLrwhyWLchmY/kxVhaW8Y8tB2nv8D/74sDxFn66ZhcPrNvN1bNGsTzfxdwcFc4REQk3JY0iA1jO0GTmZGdw9EQbx5ra/FYE7XSsqY3k+DjmZIcvsQlnvKf+7Zb2Dtwd3uUj4mI+PJyuw+NNGNOS4hgUH3taJVUtQyEDjTGGea5M5rky+fa103ns3QoeKirjwPEWv+3bOyzPlBzgmZIDTB+VTkG+i+vnjCY5TKMcREQGOk0cEBngli3IISs9kYZWNzUN3l60rjweS01DKw2tbrLSE1m2ICdMkXqFM97Ov13f4qa+xX0y2etkrcXd4aHDWuJiDZkpCQxJTqCp3X2ykuqarYe4d/UW1m6rpry2iZb2Dqy1tLR3UF7bxNpt1dy7egsvbzsctLhFIsmw1EQ+f8lE3rj7En5fMI8LJg7rtv22g/X8+1NbWHT/On7w7Db2H20MUaQiItLJOLVApzFmON6Ko9OAFGvtp7s8Pg7YYq31X/t7gDDGFM+dO3ducXFxuEORCLdm6yEeWLeb6vpWmtrdpCTEERdjcPuGeCbHx5GVnshdSyZHxNy8cMa7ZushfvDcNg7UNeOx3mI7BrBwckhqXKxhRFoS6YPiAThc30JSfCw3zx/Lix8coqK2idTEOIYkJ3xoiK3HYznW1EZDq5vszGTuu3GWehxlQNh7pIGVhWU8WVzJidaeC1ddOGkYBXkuLps24kMXbkQkfObNm8fGjRs3WmvnhTsWCT5HJo3GmE8Bv8K7RIUBrLU21rdtJvA+8Blr7Z/DF2X4KWmUM+G0IZPhjPenL+3g/94upc3tIcaYkxVTY4x3DldmSsKHhtHVNLRijGHMkCT2H2kiPtZ0O7S2pqGV9g7LkulZ/Oimc/rlNYhEosZWN0+XVLGysIwdh0702H5MxiBuX5TDrQuyuz2mRKT/KWmMbo5LGo0xlwMvApuB7wJXAp/tTBp9bTYDZdbaj4QnysigpFH6wmnFWcIR79/fP8BPX9pJY6ub5IRYPBZiDCQnxJEQd/qo/8P1Ld6lOgwcb24nNzOlxyI+pbWN5GQm8/s750f0+y/SH6y1vFd2jBWFZbyw5SBuT/fnKgmxMVxzzigK8l2cm52hwjkiYaCkMbo5cUb5N4GDwGJrbb0x5lw/bTYD+aENS8S5nJYodpUzNDnksXatpDoyPalXVVwzBiVgrSUlIa7b9gAxMYaUhDgaWzsoqaxzzL6Q6BHuzwRjDAtyM1mQm0n1tdN4dEMFD68v51C9/8I5bR0eVm+qYvWmKmaMTmd5vovrZo9hUEKs3/YiInJmnJg0zgcetdYGXvAJKoGRIYpHxLGcNiQ1UvSliuuYjCSq6lrw0xHpV1yMocNjaW7reX6XSLBE4mdCVloSX7psEv928QTWbj/MisIy3tlbE7D91gP1fPPJLdz//A5unjeWO/Nc5A5LCWHEIiLRx4lJYwLQU+m0DKAjBLGIOFagYjIt7R5qGls5eqKNzZXHI6b4TaRZtiCHzZXHqahtAuixqM2Fk4fzxHuVtLR7evX8bo8lKT6GQVpiQEIk0j8T4mJjWDpzFEtnjmJP9Qlv4ZyNVTQEKJxzvLmdP721nz+9tZ/Fk4ezPN/FxVOyVDhHRKQPnHg2Ugr0NFZ6EbCz/0MRcaaSijoeWLf7ZBXP3LQUvwlPRW1TwIXpB7o52Rl8+bJJJ0+yS2sb/VZxzc5M5q4lk5kyIo3ntxykptG7TEhvhrQOTU1gzli979L/nPaZMDErje9fP5NvLJ3K6k1VrCwsZdfhhoDtX991hNd3HSE7cxB3LHJxy/xsMlMSQhewiIjDOXGdxmeAC40xN/vbaIz5F+Ac4MmQRiXiII+9W051fSupiXEMTU08LYGJifFW90xNjDu5ML2c7ooZI7nvxlksmZ5FTmYySfGxGGNIio8lJzOZJdOzuO/GWVw+fcTJIa3J8XEca2rr9nk7h7TOyXbO3FJxNqd+JqQmxlGQ5+Kluy7i0c/kcc2sUd6iUwFU1Dbz4xd2kPejdXzt8fcpqagLYbQiIs7lxJ7G/wJuBR4xxnwMGAxgjPkCcCFwE7Ab+HXYIhSJYOU1TZRU1NHU7iY3rft5PkOSEyitbTy5ML0SmNPNyc5gTnZGrwqHnOmQ1mULckL6WmRgiobPBGMMeeOHkjd+KIfrW3hkQzkPry+n+kSr3/Ztbg9PbqzkyY2VnDN2MAV5Lj4yezRJ8SqcIyLij+OSRmvtMWPMxcBfga69jb/y3b8J3G6t7Wneo8iAVFLpLXChKp7B1Zsqrmc6pFVDgiUUou0zYUR6EnctmcznL5nImq2HWVFYyvr9tQHbb648zjf+tpn7nt/OsvnZ3JnnIjsz8l6XiEg4OS5pBLDWlgEXG2POwbu0xlDgOFBkrdWihCLdaG5z0+Gx3Q7h6upsq3iGu3R/pLlixkiy0pNOq1CZFB/jncOoqrUSYqH+TAiVeN/ajdecM4pdh72Fc57aWEljm/86eXVN7fz+jX384c19XDIli4J8F4snDe8xkRYRGQgclzQaY14B3rbWfsdauxnvmowi0kuDEuKI9VVE7I2+VvGMxNL9keJMhrSK9LdQfSaE0+QRafznDTO5e+kUVm+qYkVhGXuq/RfOsRZe2VHNKzuqcQ1N5s5FLm6eP5aMZBXOEZGByzmf+P+UBxSFOwgRp+q6MH1/VfGM9NL9kaI3Q1ojgZLb6BaKz4RIkZYUz/L8XAryXBTuq2FlYRlrth2mw2P9ti+raeK+57fz0zU7uW72aJbn5zJr7OAQRy0iEn5OTBp3A9nhDkLEqfqyMP2ZVPF0Wul+CUy9xQNDf38mRCJjDOdNGMZ5E4Zx6HgLD28o55EN5RwJUDin1e3hieJKniiuZE52BsvzXVw9a5QK54jIgOHEJTf+BFxjjFFZQZE+WrYgh6z0RBpa3dQ0eHsXuvJ4LDUNrTS0uslKTzyjKp5OLd0vH7Zm6yHuXb2FtduqKa9toqW9A2stLe0dlNc2sXZbNfeu3sLL2w6HO1QJgv78TIh0Iwcn8dXLJ/P2Ny/l17edy8LczG7bl1TU8dXH3+e8H7/CT17cQeWxphBFKiISPk7saXwWuBx42xjzE+Bd4BBw2tgSa63ORkX86K8qntFQul/UWzwQqbIvJMTF8JHZo/nI7NFsP1jPyqIynt5URVOAwjm1jW389rW9/P71vVw6NYuC/FwunDhMhXNEJCo5MWnchzdBNMAD3bSzOPP1iYREf1TxjLbS/QPVqb3Fp+rsLQZO9hZHYxIx0Kiy7z9NG5XO/TfO4p6rpvJUcSUrisrYd8T/Sl4eC2u3V7N2ezXjhqVwx6Icbp6XzeDk+BBHLSLSf5yYVK3AT6+iiJy5YFfxjNbS/QOJeosHNlX2/bD0pHg+cf44Pn5eLm/vqWFFYSlrtx8mQN0c9h9t5If/8BbOuWHOGAryXcwYrcI5IuJ8jksarbWfCHcMItEmWFU8B0Lp/min3mIB51T2DRVjDBdMGsYFk4ZxoK6Zh9eX8+i75RxtaPPbvqXdw6PvVvDouxXMcw1heb6LpTNHkhinwjki4kw6UxORoBlIpfujlXqLRbo3OmMQX79yCl+8bCIvfnCIFYVlFJcdC9i+uOwYxWXHGJaawLIF2dyxyMXojEEhjFhE5Ow5Omk0xowFzgUygOPARmttZXijEhm4BmLp/mij3mKR3kmMi+X6OWO4fs4Yth44zqqiMp7edIDmdv+Fc442tPG/r+7lt6/tZcm0ESzPz+X8iUMxRoVzRCTyOfJb3rfcxh/wVlE9ddvLwGettaWhjktEvKX7N1cep6LWW4Z+SHKC38qbDa1usjOTo6p0fzRQb7HImZsxejA/uukc7rlqGn8rrmRVURn7jwYunLNm22HWbDvM+OEpFOS5+Oi8saQnqXCOiEQux63TaIwZCbwNXAGUASuB//Ld7/c9/pavnYiEWGfp/uzMZNo7LKW1jRyub6GmoZXD9S2U1jbS3mGjunS/k3X2FifHx3Gsyf98rU7qLRb5sMGD4vnUBeNY99XFrPjkQpZMG0F3I733HWnk+89uI+/+dXxr9RZ2HKoPXbAiImfAiT2N3wHGAN8Efm6tPTkOxBgTC3wFbxL5beALYYlQZIBT6X5nU2+xyNmJiTFcNHk4F00eTuWxJh5aX85j71ZQ2+j/QkxTWwcPry/n4fXlLMzN5M58F0tnjCQhznHX9kUkShlrnbV6hTGmFNhhrV3aTZsXganW2txQxRWJjDHFc+fOnVtcXBzuUGQAU+l+Z1qz9dDJhd6b2t1+F3rPSk/kriWTuXz6iHCHKxLxWto7eH7LQVYUllFSUddj++Fpidy2IJvbF7kYOTgpBBGKnJ158+axcePGjdbaeeGORYLPiT2NI4GHemhTDFzc/6GISE9Uut+Z1FssElxJ8bHcNHcsN80dy5bK46wsKuWZkgO0uv0XnTpyopVfvbKH/31tL1dMH0FBvov88SqcIyLh4cSk8Tjg6qFNjq+diIj0kRZ6F+kfs8YO5r8+NptvXT2NJ96rZGVRGeW+4eCn6vBYXvjgEC98cIiJWakU5Lm4ae4Y0lQ4R0RCyInDU58ErgEutda+42f7IuB14B/W2o+GOr5IouGpIiIikc/jsby++wgrC8t4dWc1PZ2apSTEcuPcMSzPz2XyiLTQBCnSAw1PjW5O7Gm8D2/S+Lox5lHgVeAg3mGrFwO3AR7g/nAFKCLidOpdFAmdmBjDJVOyuGRKFhW1TawqKuOx9yqoa2r3276xrYNVReWsKipn0bhMlufncsWMEcTHqnCOiPQPx/U0AhhjrgUeBDKBri/AALXAJ621fw9DaBFFPY0icqZKKupOm8cYG2NISYzVPEaREGpp7+DZ9w+wsqiMzZU9z7gZkZ7IbQtzuH1hDlnpKpwjoaeexujmyKQRwBiTAlwPzAUG453DuAl42lrrf0XdAUZJo4icCVVMFYlMJRV1rCws49nNB2gLUDinU1yM4cqZI1me52LhuEwVzpGQUdIY3RybNErPlDSKSG+VVNRx7+otVNQ2kZoY1+PajPfdOEs9jiIhVtvYxuPvVbCqqIzKY809tp8yIo07813cdO4YUhKdOCNJnERJY3TT4HcREeGxd8uprm8lNTGOoamJH0oYwTvnamhqIqmJcVTXt/LYu+VhilRk4MpMSeCziyfw+jcu4c8fn8/iycO7bb/z8Am+8/QHLLp/Hd995gP2VJ8IUaQiEm0clzQaY75tjGk3xowJsH20MabNGHNPqGMTEXGi8pomSirqaGp3MyQ5odu2Q5ITaGp3U1JRR3mN/yUCRKR/xcYYLps2gr9+ciGvff1iPn3BONKTAvckNrS6+WthGUt+/ga3/7GIFz84iLuj+2GuIiJdOXGswkeA16y1Vf42WmsPGGNexTvf8cchjUxExIFKKr1Fb1IS4k7rYTxVTIwhJSGOxtYOSirrVFFVJMxyh6Xw7Wun87UrpvDs+wdYUVTKB1X1Adu/s7eGd/bWMGpwErcvzOHWhTkMT0sMYcQSCVQhW86UE5PGicCqHtpsA+4MQSwiIo7X3Oamw2OJ6yFh7BQXY+jwWJrb3P0cmYj01qCEWG5ZkM3N88eyyVc45x+bD9IWoEfx4PEWfvbyLn71ym6umjmK5fku5rmGqHBOlFOFbOkrJyaNyUBPY6JaAK12KyLSC4MS4oiNMbS09264mttjSYqPYVCCE79CRKKbMYa5OUOYmzOEe6+ZxmPvVvDw+nKq6vwXzmnvsPz9/QP8/f0DTBuVTkGeixvOHU2yju+oE6hCdku7h5rGVo6eaGNz5XFVyBa/HDenEagA8npokwf4Hb4qIiIfNmdsBimJsTS2ufF4uq+o7fEtv5GSGMucsboaLRLJhqUm8vlLJvLG3Zfwx+XzuXDSsG7bbz9Yz7dWb2HR/ev4/rNb2XekIUSRSn8rqajjgXW7qahtIj7WkJuZwoj0JIamJjIiPYnczBTiYw0VtU38cu0uSirqwh2yRBgnJo0vAhcZY5b522iMuRVYDLwQ0qhERBwqZ2gyc7IzSI6P41hTW7dtjzW1kRwfx5xszX8RcYrYGMPl00ew8lOLeOVri/nk+eNI66ZwzokWN395u5RLf/Y6BX9ez5qth1Q4x+FUIVvOlhPHHvwEuAN42Jc4voi3V3EMcBVwHVCLiuCIiPTasgU5bK48TkWtd/R/T+s0LluQE65QReQsjB+eyn98ZDpfv3Iyz5QcYEVhGdsPBi6c8+buo7y5+yhjMgZx+6Icli3IZliqCuc4SdcK2blpKd22HZKcQGlt48kK2bo4KJ0clzRaa6uMMVcCTwA34K2S2skApcDN1trKMIQnIuJIc7Iz+PJlk07OdymtbTw538XtG5KaHB9HdmYydy2ZHBWFElQ9UAay5IQ4bluYw60LsikuO8aKwjJe+OAg7R3+h6hX1TXz3y/t5IG1u7l61kgK8nOZm5OhwjkOoArZEgyOSxoBrLXvGWMm411+Iw/IAOqAIuBZa217OOMTEXGiK2aMJCs96bTKeknxMQxNTYiaynqqHijyT8YY5udmMj83kyMnpvPohnIe3lDOweMtftu3dXh4uuQAT5ccYMbodJbnu7hu9hgGJcSGOHLpLVXIlmBwZNII4EsMn/LdREQkCOZkZzAnOyNqe+FUPVAksOFpiXzxskl87uIJrN1ezcqiUt7eUxOw/dYD9XzzyS3c//wObp43ljvzXOQO6374o4SeKmRLMETN/wZjTDwwE2iy1u4MdzwiIk6WMzQ5KpLErrpWD0xNjCM3LcXvvM3O6oHD0xLV4ygDUlxsDEtnjmTpzJHsqW5gVVEZTxZXcqLVf8/T8eZ2/vTWfv701n4umjyc5XkuLpmaRWwve7akf3VWyK5pbMXjsd0OUe2skD00NUEVsuVDHJc0GmNuAT4GfNZaW+t7bALeaqkTfD8/A9xirVW/uohPtPYcifTWqdUDT9VZPRA4WT1QSaMMdBOzUvnedTP4xpVTWL2pipWFZew8fCJg+zd2HeGNXUcYO2QQdyxysWxBNpkpCSGMWE7VWSH76Ik2jjW1+f3866QK2RKI45JG4JPA6M6E0ednwETgFWAo3uI4/wL8MfThiUQWzd8SUfVAkbOVkhjHnXku7liUw4b9tawsKuPFDw7hDrC2a+WxZn7y4g5+sXYX154ziuX5ufquCSNVyJaz5cSkcTrwcucPxph04GrgcWvtrb5hqiUoaRTR/C0RH1UPFAkOYwyLxg9l0fihVNe38MiGCh7eUMbh+la/7dvcHp7aWMVTG6s4Z+xgCvJcfGT2aJLiVTgnlAZihWwJLicmjcOBg11+zsf7Oh4Fb4EcY8zLwG1hiE0kYmj+lsg/qXqgSPBlpSfx5SWT+LdLJvDytsOsKCylaF9twPabK4/zjb9t5r7nt7NsfjZ3LHLpokwIDZQK2dI/nJg0ngAGd/l5MWCBt7o81gKkhTIokUij+Vsi/6Tqgc6iOdjOEh8bw9WzRnH1rFHsOnyClYVlPLWxksa2Dr/t65ra+f0b+/jDm/u4ePJwlufnsnjy8B5HAcjZi/YK2dJ/nPhtuBu4yhiTiDdZvBnYbK092qWNC6gOR3AikUDzt0Q+TNUDnUFzsJ1v8og0/vOGmdy9dApPb6piRWEZu6sb/La1Fl7deYRXdx4hJzOZO/NyuGV+NhnJKpzT36KxQrb0LycmjX8A/oI3eWwHcoG7TmmzCNga2rBEIofmbw0MulLce6oeGPlCPQfbaceP0+JNS4qnID+XO/NcFO2rZWVRKS9tPUxHgMI55bVN3P/8Dn62ZhfXzR7N8vxcZo0d7LetiISe45JGa+1fjTFTgM/4Hvof3w0AY8yleBPJ34Q+ur4xxowFfgAsxVv99SDwNPB9a+2xcMYmzqT5W9FNvTF9o+qBkSuUc7Cddvw4Ld5TGWPInzCU/AlDOXS8hYc3lPPIhnKOnPBfOKfV7eGJ4kqeKK5kdnYGy/NcXHPOKBXOEQkzxyWNANbabwHfCrD5LWAI0Bi6iPrOt8bkO0AW8AywA1gIfBlYaow531pbE8YQxYE0fyt6qSJu36l6YOQK1Rxspx0/Tou3JyMHJ/HVyyfzhUsm8tLWQ6wsLGNDaeDCOe9X1PG1ijrue347t8zP5o5FOWRnRm7vqkg0i7ozRGttG9AW7jjOwG/wJoxfstb+uvNBY8zPga8A9wGfDVNs4lCavxWdVBH37Kl6YOQJ1Rxspx0/Tov3TCTExfCR2aP5yOzR7DhUz4rCMp7eVEVTgMI5tY1t/O71vfz+jb1cNjWLO/NcXDRJhXNEQinqkkYnMcaMB64ASoH/PWXzd/EOwS0wxnzNWuuInlOJDJq/FZ1UETc4VD0wsoRqDrbTjh+nxdtXU0emc/+Ns7jnqqk8VVzJiqIy9h3xf8pjLazdXs3a7dW4hiZz5yIXN88fq8I5IiEQE+4ABrhLffdrrLUfGkdorT0BvA0kA3mhDkycb9mCHLLSE2lodVPT4O1x7MrjsdQ0tNLQ6iYrPVHztyJc196YIT2cIA1JTqCp3X2yN0b8yxmazHWzR7NsQQ7XzR6thDFMQjEH22nHj9PiDYb0pHg+cf441n11MQ99ehFXzhhBd/8lymqauO/57eT9aB13/+19Pqg6HrpgRQYg9TSG1xTf/a4A23fj7YmcDKwL9CTGmOIAm6b2PTRxOs3fii6qiCvRKhRzsJ12/Dgt3mAyxnD+xGGcP3EYB+qaeXh9OY++W87RBv8zj1raPTz+XiWPv1fJuTkZLM93cfWsUSTGqXCOSDApaQyvzlrSgS6PdT6us3npE83fih6qiCvRKhRzsJ12/Dgt3v4yOmMQX79yCl+8bCIvfuAtnPNeWeCi8pvK69hUXscPn9vOsgXZ3JHnYkzGoBBGLBK9lDRGts5vC/+LGnVutHae31/29kDODXZQ4iyavxUdVBFXolUo5mA77fhxWrz9LTEuluvnjOH6OWPYeuA4q4rKeHrTAZrb/RfOqWls4zev7eV3r+/lsmkjWJ7v4vwJw1Q4R+QsROeni3N09iQGWr02/ZR2In2WMzRZSaKDqSKuRLP+XkPTaceP0+INpRmjB/Ojm87hnqum8bfiSlYVlbH/qP/COR4LL287zMvbDjN+WAp35Ln42LyxDB4UH+KoRZzP0YVwjDFTjTE3GmMKwh1LH+303U8OsH2S7z7QnEcRGSA6e2OS4+M41tT9qkKqiCtO0zkHOzszmfYOS2ltI4frW6hpaOVwfQultY20d9g+z8F22vHjtHjDYfCgeD51gbdwzspPLeTy6d0Xztl3tJH/fG4befev49+f2sy2A/WhC1YkCjiyp9EYMwf4E3Bul4dX+rYtBl4Alllrnw1DeGfiVd/9FcaYmK4VVI0xacD5QDNQFI7gRCSy9HdvjEg49fccbKcdP06LN1xiYgwXThrOhZOGU1XXzENFZTz2bgU1jf6T7eb2Dh7ZUMEjGyqY7xpCQb6Lq2aOIiHO0f0oIv3OWNvtdLmIY4yZDGwAYoE/4u2lu8paG+vbboAKYJ219uNhC7SXjDEv4a2Q+iVr7a+7PP5z4CvA7621n+3jcxfPnTt3bnFxoOKqIuI0a7YeOlkRt6nd7bciblZ6Inctmczl00eEO1yRPumvOdhOO36cFm+kaHV38MKWQ6woLGVjeV2P7YelJnLbwmxuX5TDqMEqnNNX8+bNY+PGjRsD1doQZ3Ni0vgQcCMwz1q73RjzXeA/OpNGX5sngBnW2unhirO3jDETgHeALOAZYDuwCLgE77DU86y1NX18biWNIlGopKLutN6Y2BhDSmKsKuKK9MBpx4/T4o00H1QdZ2VhGc+8X9VjYaHYGMPl00ZQkO/ivAlD8fZDSG8paYxuTkwaDwFrrbV3+n72lzT+DPiUtdYRn6LGmGzgB8BSYChwEHga+L61tvYsnldJo0gUU0Vckb5z2vHjtHgjzfGmdp4ormBVURmlNU09tp8wPIWCPBcfnTeWtCQVzukNJY3RzYlzGjOAyh7axAAJIYglKKy1FcC/hDsOEXEWVcQV6TunHT9OizfSDE6O59MXjueT54/jzT1HWVlYyrod1QTqO9l7pJHvPbuN/3ppJzeeO4bl+blMGZkW0phFIokTk8ZqYGIPbWbgndcoIiIiIgJ4C+csnjycxZOHU1HbxEPry3ns3XKONbX7bd/U1sFD68t5aH05C8dlUpDnYunMkcTHqnCODCxOTBpfAW4zxkyx1u48daMxZgFwGfC/IY9MRERERBwhOzOZe66ayl1LJvGPzQdZUVTG+xWBC+ds2F/Lhv21DE9L5LaFOdyxKIcR6UkhjFgkfJx4meRHgBt4wxjzOWA0gDFmhu/nZ4ETwE/DF6KIiIiIOEFSfCwfnTeWZz5/Pn//wvl8bN5YErtZguPIiVZ+tW435/34Ff7toWIK99bgtBohImfKcYVwAIwxS4FHgPTOhwDru68DPmatfSVM4UUMFcIREREROXPHGtt4oriClUVlVNQ299h+8ohUCvJc3Dh3LKmJThzId/ZUCCe6OTJpBDDGZAAfB/LwVhw9DhQBfzmbiqPRREmjiIiISN95PJbXdx1hRWEpr+06ErBwTqfUxDhumjuGgjwXk0YMrMI5Shqjm2OTRumZkkYRERGR4CiraeSh9eU8/l4FdQEK53SVNz6T5fm5XD59xIAonKOkMboNzP5zEREREZEz4BqawreunsZXL5/M398/wMrCMrZUHQ/YvmhfLUX7ahmRnsjtC13ctjCbLBXOEYdybNJojMkC5gNDgFh/bay1K0IalIiIiIhEtaT4WG6Zn83N88bzqU8AAAAgAElEQVRSUlHHysIyntt8kLYOj9/2h+tb+cXaXfz6ld0snTmS5fm5LMgdgjEmxJGL9J3jkkZjTDzwO2A5gau/dhbGUdIoIiIiIkFnjOHcnCGcmzOEe6+ZxuPvVbKqqIyqOv+Fc9wey3ObD/Lc5oNMHZlGQb6LG+aMIWWAFs4RZ3HcnEZjzI+Bu4G9wENABd4lOE5jrf1rCEOLOJrTKCIiIhI6HR7LqzuqWVFUxhu7jvTYPi0xjo/OG8udeS4mZqWGIML+ozmN0c2JlzZuB3YB51pre66BLCIiIiISArExhiXTR7Bk+gj2H21kVVEZT7xXQX2L3/4NTrS6efCdUh58p5TzJw6lIC+XJdOyiBsAhXPEWZyYNGYBv1HCKCIiIiKRatywFL5z7XS+fsUU/v5+FSsKy9h6oD5g+7f31PD2nhpGD07i9kU5LFuQw/C0xBBGLBKYE5PGciA93EGIiIiIiPRkUEIsyxbkcMv8bDaW17GysJTntxwKWDjnwPEWfrpmFw+s283Vs0ZRkOdinkuFcyS8nJg0Pgh83hgz2FobuM6xiIiIiEiEMMYwzzWEea4hfPvaVh57t4KHiso4cLzFb/v2DsszJQd4puQA00elU5Dv4vo5o0lOcOLpuzidEwvhxACPAOPxFsQpttYG7usfwFQIR0RERCRyuTs8rNtRzcrCMt7ac7TH9ulJcXxsXjYF+S7GDUsJQYS9p0I40S3iL1UYYzx4l884bROw1tfG369aa23Evz4RERERGZjiYmO4csZIrpwxkr1HGlhVVMbfiis5EaBwTn2Lm/97ez//9/Z+Lpw0jOX5uVw6NYvYGA1dlf7lhKTqDfwnjSIiIiIiUWHC8FS++5EZfOPKKTy96QArCkvZcehEwPZv7j7Km7uPMiZjELcvyuHWBdkMTVXhHOkfjhueKr2n4akiIiIizmSt5b2yY6woLOOFLQdxe7o/Z0+IjeGac0ZRkO/i3OyMkBfO0fDU6OaEnkaMMcuBEmvt5nDHIiIiIiLS34wxLMjNZEFuJtXXTuPRDRU8vL6cQ/X+C+e0dXhYvamK1ZuqmDkmneV5uVw3ZzRJ8bEhjlyikVNWDn0QuCHcQYiIiIiIhFpWWhJfumwSb33zEn5351zOmzC02/YfVNVz95ObWXT/Ou77xzbKahpDFKlEK0f0NIqIiIiIDHRxsTEsnTmKpTNHsaf6BCsLy3hyYxUNrf4L5xxvbuePb+7nT2/tZ/Hk4SzPd7F4sgrnyJlT0igiIiIi4jATs9L4/vUz+cbSqazeVMXKwlJ2HW7w29ZaeG3nEV7beYTszEHcscjFsvnZDElJCG3Q4lhOGZ4qIiIiIiKnSE2MoyDPxUt3XcSjn8njmlmjiOumJ7Gitpkfv7CDRT9ax9cef5/3K+pCGK04lZN6GjOMMTln8gvW2vL+CkZEREREJFIYY8gbP5S88UM5XN/CIxvKeXh9OdUnWv22b3N7eHJjJU9urGT22MEU5Ody7TmjVDhH/HLEkhvGGA9nvlajtdY6KSkOOi25ISIiIjJwtXd4WLP1MCsKS1m/v7bH9kOS47llQTZ3LnKRnZl8Rn9LS25ENyclVfWA+s9FRERERHoh3rd24zXnjGLnoROsLCpl9cYqGts6/LY/1tTO71/fxx/e2MclU7IoyHexeNJwYlQ4Z8BzUtL4C2vtD8IdhIiIiIiI00wZmcYPb5jFN5dO5amNVawsKmNPdeDCOa/sqOaVHdW4hiZz5yIXN88fS0ayCucMVCqEIyIiIiIyQKQlxfPx83J5+SsX8fCnF3HVzJHdLsFRVtPEfc9vZ9H967j7b+/zQdXxEEYrkcJJPY0iIiIiIhIExhjOmziM8yYO4+DxZh5ZX87DGyo42uC/cE6r28Pj71Xy+HuVnJuTQUGei6tnqXDOQKGkUURERERkABs1eBBfvWIKX7h0Ei9uPcTKwlLeLT0WsP2m8jo2ldfxw39sZ9mCbO5YdEYLHIgDKWkUERERERES4mK4bvZorps9mm0H6lm1vozVG6tobvdfOKe2sY3fvraX37++l/qaphBHK6HkiKTRWqu5lyIiIiIiITJ9dDr33ziLe66aypPFlawsKmPfkUa/bT0WTrS0hzhCCSVHJI0iIiIiIhJ66Unx/Mv54/jEebm8vaeGFYWlrN1+GE/kL/UuQaSkUUREREREumWM4YJJw7hg0jCq6ryFcx59t5yjDW3hDk1CQMM+RURERESk18ZkDOLrV07h7Xsu5YFb5zDPNSTcIUk/U9IoIiIiIiJnLDEuluvnjOHJz53HxKzUcIcj/UhJo4iIiIiInBWt1xjdlDSKiIiIiIhIQEoaRUREREREJCAljSIiIiIiIhKQkkYREREREREJSEmjiIiIiIiIBKSkUURERERERAJS0igiIiIiIiIBKWkUERERERGRgJQ0ioiIiIiISEBKGkVERERERCQgJY0iIiIiIiISkJJGERERERERCUhJo4iIiIiIiASkpFFEREREREQCUtIoIiIiIiIiARlrbbhjkH5ijKkZNGhQ5rRp08IdioiIiIhEse3bt9Pc3FxrrR0a7lgk+JQ0RjFjzH4gHSgNcygDwVTf/Y6wRiGhpH0+8GifDzza5wOP9nnf5QL11tpx4Q5Egk9Jo0gQGGOKAay188Idi4SG9vnAo30+8GifDzza5yL+aU6jiIiIiIiIBKSkUURERERERAJS0igiIiIiIiIBKWkUERERERGRgJQ0ioiIiIiISECqnioiIiIiIiIBqadRREREREREAlLSKCIiIiIiIgEpaRQREREREZGAlDSKiIiIiIhIQEoaRUREREREJCAljSIiIiIiIhKQkkYREREREREJSEmjSADGmLHGmP8zxhwwxrQaY0qNMb80xgw5g+f4iTFmnTGmwhjTbIypNcZsMsZ81xgztD/jlzMXjH3u5zkLjDHWd/t0MOOVsxek47y0yz4+9XaoP+OXMxfM49wYc6Ex5kljzEHfcx00xqwxxlzdH7HLmTvb/W2M+UQ3x3fnraO/X4dIuBlrbbhjEIk4xpgJwDtAFvAMsANYCFwC7ATOt9bW9OJ52oCNwDagGkgB8oD5wAEgz1pb0R+vQc5MsPb5Kc+ZDWwBYoFU4F+ttX8KZtzSd0E8zkuBDOCXfjY3WGt/GqyY5ewE8zg3xnwb+E/gKPAccBAYBpwLvGqtvTvoL0DOSDD2tzFmDnBDgM0XApcC/7DWXhusuEUikrVWN910O+UGvARY4IunPP5z3+O/6+XzJAV4/D7f8/wm3K9Vt+Du8y6/Z4C1wF7gv33P8elwv07dgr/PgVKgNNyvR7eQ7vObfe1fBtL8bI8P92vVLfif636ev9D3PNeF+7Xqplt/39TTKHIKY8x4vCf6pcAEa62ny7Y0vFeTDZBlrW3s49+YDZQAa621l5910HJW+mOfG2O+DPwCuBjvlejvop7GiBHMfe7racRam9tP4UoQBGufG2NigD3ACCDXWnukP+OWvunv73JjzEy8I0mqAJe1VkNUJappTqPI6S713a/p+iUDYK09AbwNJOMdZtpXH/Hdbz6L55DgCeo+N8ZMA34MPGCtfSOYgUrQBPs4TzTG3GmM+ZYx5svGmEuMMbFBjFfOXrD2+XnAOOB54Jgx5hpjzDd9+z0/2EFLn/X3d/n/893/WQmjDARx4Q5AJAJN8d3vCrB9N3AFMBlY15snNMZ8He+ctsF45zNegDdh/PFZRSrBErR9boyJA1YC5cC3ghWgBF2wj/ORePd7V/uNMf9irX29byFKkAVrny/w3R/GO2d9VteNxpg3gI+pBzLsgv5d3skYMwi4E/AAGj0iA4J6GkVON9h3fzzA9s7HM87gOb+Od3jiXXgTxheBK3RSETGCuc//A28hjE9Ya5vPNjDpN8Hc538BLsObOKbgTSJ+D+QCL/iGo0v4BWufZ/nuPwsMApYAacBMvHPoLgKe6HuYEiT98V3e6Rbf771gVcxOBggljSJnzvjuez0h2Fo70lpr8J5U3gSMBzYZY+b2Q3wSfL3a58aYhXh7F39mrS3s96ikP/X6OLfWft9a+4q19rC1tsla+4G19rN4i20MAr7Xj3FK8PR2n3cOOzZ4exTXWWsbrLVbgRuBSmCxhqpGvDP+Lu/iM7773wcpFpGIp6RR5HSdVx8HB9iefkq7XvOdVK7GOyRmKLDizMOTfnDW+7zLsNRdwHeCF5r0k347zrv4ne/+orN4DgmeYO3zY777fdba97tu8I0ueMn348IzjlCCqV+OcWPMdLzzWivxzmsVGRCUNIqcbqfvfnKA7ZN894HmSfTIWluGd+3GGcaYYX19HgmaYOzzVN/vTwNaui78jHdoMsAffY/5W89PQqvfj3O8a7OCd8iqhF+w9nnn89QF2N6ZVA7qZVzSP/rrGFcBHBmQVAhH5HSv+u6vMMbE+CnTfT7QDBSd5d8Z7bvXl074BWOftwJ/DrBtLt55jm/hPZHR0NXwC8Vx3jk8cd9ZPIcET7D2+RuAG5hkjEmw1radsn2m77707EOWsxD0Y9wYkwQU4C2AE+jzXiQqqadR5BTW2r3AGrxFLD5/yubv4+01WNG5rpMxJt4YM9UYM6FrQ99jI099fmNMjDHmPrzFFN6x1h47tY2EVjD2ubW22Vr7aX834O++Zn/1PfZYv78o6VYQj/MZxpjMU5/fGOMC/sf346oghy99EKx9bq09CjyGd9jjf3TdZoy5HLgS75DHF/vhZUgvBWt/n+JmYAjwvArgyEBjrO3L/F+R6Ob70ngHb2L3DLAdWARcgncoy3nW2hpf21xgP1DWdXFvY8xdwH/jvSq9F6jBuxj0YryFcA4Bl1lrt4XiNUn3grHPu3nu7+Edovqv1lqVZ48QQTrOvwfcg7dXYz9wApgAXAMk4Z3zdKOf3igJg2Ad58aYLLzr/E0E3gQ2AC68hXAscLu1VhVUwyzYn+vGmDfxVkC/zlr7bD+HLxJRNDxVxA9r7V5jzHzgB8BS4GrgIPAr4PvW2tpePM1a4A94h8DMxlueuxHvF9VK4Fe9fB4JgSDtc3GQIO3zV/GuB3cu3uGoKXjnur2F9zhfaXV1NmIE6zi31lYbYxYB38abKObhvWDwD+BH1tqznb4gQRDMz3VjzDS8CaMK4MiApJ5GERERERERCUhzGkVERERERCQgJY0iIiIiIiISkJJGERERERERCUhJo4iIiIiIiASkpFFERERERP5/e+ceZ0dR5fHvTwIEQRMS3s+gEAPIAhqEiJBB5aUguL5FJLDig0XNgqggmoGwIqiAxl03KDHIwi5+kIcRhSghBsyCuytx5R2BwYAgkJAEkJBAzv5xqjOdnu57+87czGSS8/18+tMzXdVVp6tO163TdaoqCCoJozEIgiAIgiAIgiCoJIzGIAiCIAiCIAiCoJIwGoMgCIIgCIIgCIJKwmgMgiAIgiAIgiAIKgmjMQiCIAiCIAiCIKgkjMYgCIIgCIIgCIKgkjAag6AJkmZLsoGWo7+QNF2SSRo10LJkSNpQ0jmS5kt6Kcl37EDLFXSzLrwnkl4r6buSuiS9nPRsnz6maZJmt0nEtiGpI8nWWbg+6OtxTSFpVCqz6QMty2BibfxNCYKgdcJoDNYb0o9WK8eEgZZ5TSCpMz1fx0DL0gKnA18D/gJ8CzgHuH9AJQrWRS4EPgv8ETgf17MnG92QDMyuNS/a4KDKGO1jmmul4R2sGSTtnj4S3iDpz7nf5CH9mZ6kEZIuSe/4S5L+ImmapB1692RBMLjp1QsYBIOUc0quTQSGAd8BFhfC5q1xidZOzgS+ATw+0ILkOAp4HjjUzJYPtDBBKR8HXj3QQvSRo4AHzezogRZkAFkX6nFN8TiwO7BkoAVZxzkc/0j4CjAfWAYM7c/0JI0E5gKjgVnAfwJjgBOBd0saZ2YP90GmIBh0hNEYrDeYWWfxWhpNHAZcYmZd/SzSWomZPQE8MdByFNgOWBgG49qLmf15oGVoA9sBcwZaiIFkHanHNYKZrSA8HPqDXwL/Bfyfmb2YRvJ37uf0vo4bjBeb2WnZRUmfwz8y/ytwRB9kCoJBR7inBkFNJA2RdFZuXt0CSRdI2qgi/pg0l2NBiv9XSVdJekMLea5y9ZL0Fkk3SlqUnx8i6RBJl0q6V9JSSS9KulvSJElDC+l1AZPSv7fm3XFzcSrnn0j6oKQ5kpakfP4o6UxJG9d9ppTOMEnnS3pA0jJJz0q6WdI7C/GmJ9l2AXbOydtVI4/ZKe7Gks6T9Eiqh4dS2VTV23GSfp+e7ylJV0jaThVzvVL6nZIeTuk/kvLbuC9udZImZG7Sko5I+S8p1NUQSadIuiPV/d8k3SXpVEml7XvSo6slPZ7kfULSTEkfLIm7v6RrJD0paXnS5amStiuJO7sg20eS/BdVyLFxqvcnVXATS/femsKXSbpP0tm90LNtJf2L3L1suaSnJV0r6c0VsgsYn9Oz2Q3S7kj37Mzqulk6503SFuk9fSKV+z2STmyQ/uGSfiHpmZzeflPS8BbLYGtJl8nbnxclzZN0QoP4lXMaJR0maUZ6L7I28IbsvU3PfWuKPqlQJh0pzjBJZ0iaJemxXL38TNIBhfwm5GQZX0ivsxC3dtuU9KFLPof1ovT3imKaJfeVzmlUrs2U9KmU97JU5pdKGtYo3UJaq6YPSPqopDslPa9cm1dXr4vprennkfROSbdJekH+O3W9pDF1nz3DzB4wszvN7MVW721HepI2BY4HXqD79zLje0AXcLik17VDviAYLMRIYxDU5yrgIPyr5VLgXcAXga1wl5VVSDoCuBbYEJgB/AnYAfh73LXlEDP7fQt5j8PdRm8HpgFbANmo25dwt5m5wI24282BQCfQIemdZvZKinsJcCwwHrgc//GrhaSvJxmewcvieeBI/Ivs4ZIOTV/im6UzHPgtsAfw30mmLYAPAjMlfcbMpqbo1ycZJ+bkh56uxI34CbAfcA2wAjgGL5uxkt5jZnlD5wx8XtuzePksAQ5N8vZwSZMk4KfAu3G3p+/hdT4B2LMFGRvxfvyL9i+BfwNGpbwz3ToceACvk2XAIcAUYH+845OX92Tg+7ib1s+SzFsBY4FT8LLK4p4I/AB4KcVdAOwGfAI4WtIBTUalrsPL7DhJXzSzlwvhxwDDgW/nwyRdBpwEPIa/Q4uBA4DJwDuSnhXT6oGkXfD3ZTvcvew/gB2BD+Dv4PvM7Ocp+nRgNt5BfDT9D43fjy7c5b2om9DTtT3T+eW4Hg7F63WapJVmdnlB9q+ltBcBPweeAv4O+ALwLrlr3NIGsmXpZC52r8PL4nZgW1yPZja7v5DWObiL3/P4e7kAL9u3Ah8Dfp2uA5wA/AYv04yudN4d+Gd8RPdG/F3bCXgPcKSko83sphR3Hl4OxXohn3Yv26aNcL0YgZfFUuCRuuVRwYX4+zgjpXkIcDKwK/D2FtM6HW97ZuCG+DBoWa/7Su3nkfR+4Gpcx6/GvVXeRhrha5M8/cU4YBNgppk9lw8ws5WSZgKfxMsjXFSD9QcziyOO9fbAOzIGjGoQZ3aK87/AiNz1TXFj8BVgm9z1zfGO0DPAHoW09sQ7NL+vKV9HytuAT1XEeR2gkuuT030fKlzvTNc7KtKbXiwT/EfUgD8XnnUI3qEw4KyazzQ1xZ+alxs3SJbgRsqowj1dQFeLdZvV24PA5rnrQ/GOjAHHF8pxBfA0sGPuuvCOmXmTuVoex6frc4CNcteH425sBszupW5OSPevBI4oCc/qcQqwQe76BsBlKeyY3PU90vMtAvYsSW+H3N+j8c7fn4DtC/HennT+urLyrqjro0ryuzGF7VXyzNcCm1Q87+drlt/NKf5XCtffCrwMLAQ2K4S1XF/NdJPu9/eHhXraI8lxbyH+ISn+XGB4hU5cXFO2S8vi4x8JVqSwzhr1eFiK+3BRH0p0p6Ms3Vz4MGCLsjTwha7uqyjD0nqhF20T3e3+r4FNW6jrUem+6YXr03My7FSQYU4Ke0vNPDI9fwHYt696TYP2vl3PA2yW8l0BjC2kdXHuHRjVyrtVUl9DenN/q+kB/5jCp1SEfyGFX9AOeeKIY7Ac4Z4aBPX5kpktyv4xsxeAK3E377G5eB/HjYZJZnZvPgEzuwcfvdlX0h4t5D3PukffVsPMHjYzKwnKRj4ObyGfKk5K5/PMbNVqkuYjPqfjhs0nmiWSRsc+hhvOZ+blNrP5wHfxEYCPt0HmjMlm9mwun2X4qAR0PxfAR/FO0RQzW5CLb8CXcUOpyAnpfLbl5lua2WLcaG8HN1j3yAsActfTU/GVPf/JukeSSX+fjndqjsvd9hn8+SYnPVwNM3usEHdD3EB7vBBvFj7yeLSk1zSRPRtBOyF/UdI2uF7eZWZ/zAV9Hu/4nmQ9Xckm4x3T42iCfHXDw/BO74UF+efiHwFG4CP//cHfgNMK9XQvPvq4e6EcP5fOJyc9InfPdHz0rU4ZbJjiPYcbDvl0/gdvu+ry2XQ+vagPKb3HiteqMLMlZvZMRRrXAGMk7dSCbH1pm05P7Xi7ONdyo+9Jhh+lf9/SYlqXmtld+QsDoNd1n+eYlO9VSbfydDL4Fg7K3G+r5M6ut+QqHgSDnXBPDYL6FH8MwV20wEcXM8al894Vc2RGp/PuwL0l4WX8riogzb/4PPDelPZr8NGxjO1r5tGIN6XzrGKAmT0o6TFgF0nDix3dAmPwlRl/mzfAc8wCzgb27avAOX5Tcu023DjJ55P9fXsxspk9KmkByTW0cM9KfFSoSI90eklZ3Y8GRuLupWe7l2wPXsR1LCObL/bLGnlmOjxe0n4l4VvhI5qj8RH4UsxsrqQHcQNz85zxfly6f3oWV9Krgb3xEfqJFc/0Eqs/UxVZXd5m5S7Ts/CPF/sCP66RXl+Zb+XupFn7MRw37sDLfgXwAUkfKLlnI2BLSSPNbGGDPLN37TYzK+v8zqZgzDfgAPwjxE3NItZB0oF4mzUO16Xi/OLtccOoDr1tm5bRfrfJur8RdSh77/tbr+s+T1YHPdpaM1siaR4+JWKNIGkiPQ24681sTa2AnjVOZR9rg2CdJYzGIKhJhTGUza3aIHdtZDqf3CTJzVrIvnSvuDSaMAv/6ns3PpfkabzTCT4XqKXFQyrIvrxWrar6BD4vaRiN5xvWSQfa+wX3r8ULZvaKpIV4h7UoW4/4ueujCteGAYusfI5dVTqtUlb3mY7tRs+FGvLkdSwr0zpbqWTpn9EkXh0dvhyfw/ZhfD4luLGyAh8Zydgc74xtSeNnqsNA6Fkjqt6JqvZjCM3LIHMJrKKZPjfcf7LAcODZktHflpH0XnxEcRnwK+Ah3BVzJe7aOp7W2qzetk1PVXho9IW6vxF1KKuf/tbrus/TTl3rDRPpuRpqF73fNiv7yFK1gNFrC/GCYL0gjMYgaD/ZD8neZtauL9lVnZtjcIPxcjObkA+QtC1973xnZM+0Dd7JK7JtIV6ddMqom04rbE1h1ELSBnjnPD/6szQXv4f7ZrpeZCkwQtKQEsOxLH5vKKv7rHyuM7O6rmhZB3B7mm8bsKrTVDFC1gpX4K6lJwDfl7QvsBfudvt0SZ53mdmb6BsDoWftYgnwKjMb0YZ0oFoPq8qmjMXASEmbtMFwnIzPlx1rZvflAyRNpfURqd62TWv7KFGj974VvV6ZzmX9vXYZl+3UtZYxs1FtTvKBdB5dEb5bOj/Y5nyDYK0m5jQGQfu5I50P6oe8dk3nn5aEVXW+snlVrXz5zubWdBQDJO2KL2LxSBPXVPAf478B+0gqc9c6JJ1bWVm2GWXlcBDeicrPGcr+flsxsqSd8RUKi9yFt6NvLQnrkU4buZ+0qmgaba5DppdHthC3zzqc5ofOAvaXbzeTuUReXoj3PG6s7ymprwbTqrpUYTuPRDv17BVaH0VqxB3A5pL6uvru/XS/a2UjJh0tyiTq7UvXrH3ZFV/8p2gwvorqd2Zlg/Ta1TYNBnqj15lLeFn7NbbkWm/I8uvR1ibd26dN+fQXd+Du/QcW520nPT0s/XtrfwsWBANJGI1B0H5+hHfoJ0nqsfiBpFeV7ZnVS7rSebX00v5RF1Tck7m0tbLYxLR0PlvSlrl8NgC+hbcllzVLJC0WcyXuWnduQebX44uArMBHp9rFV/MGqnzvyvPTvz/KxbsKd736rKQdc/GV4pd1WrN5Q+cpt+9j6ih9tT3i9ySNak7BRxa+K2mTYhz5Xm75xZa+jz/fV8sWYUqLbGR8D6+HiyX1+NouaSNJrRiU09P5H4CP4DpYti3ARfj8tmkq2Y9Q0uaSmo5CpkVVfoW7E0/Mh0naH1/06Fl8W5C+shCfY9ijDnrJxen8A5Xvh7mpCvsZlpHmvF2Jz3HuLKQxlhqL6eSYks7fltRjjnThWrP2pQvYLf9s6R2bhK8oW8ZCyo0eaFPbNBjopV5ncyNPzBuaqY37WptEuyHl+9GkW3k6qXbzXCtJH7CuwFdI7ywEn4qX/81mFtttBOsV4Z4aBG3GzBamPauuA+6QdAs+grIS70iNw10jh7Yhu2wPyNMk7YV/id4JOArf0qCs43ZrkuV8SW8kfYk2s/MaPNNcSRfi+1LeLekafB7SkcAb8UVfvllT5i/jI1inpkVWbqV7n8bXAKea2SM106rDfcA9SeZsn8bX4+Wzyjg1s4fS/nhfB/4g6Wq692kcAfwB3ysvz4/xuXpH4OXyM3zV0ffhi0i8gW73sHYzGV845tP4QjOz8PmKW+HuUwcCXyEttmRm90o6Bd+j7y5JN+AL6YzERxyeI41UmNn9kk7CO+T3SLoJd8XaENepg/C5s3U37r4Wd+WdmNKYUraQh5lNk29QfgrwkKSbcdfiEcAuwMG4of/pGnl+Gl+d9JuSDsPrI9vPbiVwohX2YOslt+D7gN4kaQ6+WM8fzGxGbxIzs1skfRn/UDFf0i/w/QM3w+dtjcfftzqjfmcB705+x7MAAAQTSURBVMAXFhpL9z6NHwJ+ge+NWEemmZIm4x9C7pOU7dO4NT46eAe+HQi4N8HjwIclLcfrz4ArzOxR3CjOdPCn+Dt5IG4wzgCOLhHhlpTeDHzhpZeBOWY2p81t02CgJb02szuTXh4M/C61E1vj5Xwz1cZ4bczseUmfxOfU35bazmyfxjfi23QcXDc9SVvgBn/GFul8maTMbfcbZtbMzb4v6Z2Ff4w9TdI+uPG9O/778RS+LUcQrF/U3ZsjjjjWxYMW9mmsCJuQ7p9QEjYKH7GZjy/6sBR3GbsCOLamfB002PMsxdkRH1F4HHepuQfvQA2hYn8zfIW9eSm+5Z+Pkn0ac2Efxjthz6Vnugc3TIa2WO7D8ZHQ+XgnezH+Bf2wBvXU1WIes9NzbAych3e8X8L3mpsEbFxx3/G48b0MN4z+Hd9I+25gcUn8ofioaZZ+F77wy/Yp/+t7qZuVupWLoyTvLfj+i8uTHtyOd3p2LLlnHO7O/FSK/xd8Vcz3l8TdK+nDo+nZFqVymAq8ve57ksJ/mOka8OYmz34U3ZvaL8cX0vhdqscxLZTh9vgI66MpnWfwDej3q4hfuR9ggzw2TXk8hhszq+171yjNJu/a24CfpPpZnnRxHj4aO7YF+bbBjf+n8fd9XtKtDmru05gLe1fSlUVJHxbgH8eKurBf0skluCGz2j6BKf95uHH3TEpjLyr2FMQ/hFyFL7TySoXctdsmetGepPtGFeu3Rj2WlnODPErLoI96PRzf6umpVG9345vTt/V58A9st+Nu0c/iI5BjGqXXpJwbHZXl06708I9V38mV8xP4u7RD3bzjiGNdOmS2ts8FD4IgaB1Js4HxZla6d0OLab0W77DOM7NxzeKnew4FZuJfsM9sFj8IgiAIgmBtJeY0BkEQJCRtWVxYJs0D+jY+othjDlzFvLORwDfSv+2YNxcEQRAEQTBgxJzGIAiCbt4HnCvp17jr3Qh8Ls5o3J1uSsk9F0naG5iLuwDugM+nGgFMNbOyTbqDIAiCIAgGDWE0BkEQdHMnPifnYLo3uH8En6N4gZXvUXct3QtLDKd7PtU0fB4fAGnF3I4aMiw2s0t6J34QBEEQBEH7iTmNQRAE/YCkTnwBnmY8au3frDoIgiAIgqDXhNEYBEEQBEEQBEEQVBIL4QRBEARBEARBEASVhNEYBEEQBEEQBEEQVBJGYxAEQRAEQRAEQVBJGI1BEARBEARBEARBJWE0BkEQBEEQBEEQBJWE0RgEQRAEQRAEQRBUEkZjEARBEARBEARBUEkYjUEQBEEQBEEQBEElYTQGQRAEQRAEQRAElYTRGARBEARBEARBEFQSRmMQBEEQBEEQBEFQSRiNQRAEQRAEQRAEQSVhNAZBEARBEARBEASV/D/H9OZQX583QAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 454
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_1to10 = sns.regplot(x= ratio_1to10, y=dislike, data=df_1to10, ci=None)\n",
    "ax_1to10.set_title(\"The score of being liked of the dictator vs. The pgg_receive in round 1-10\")\n",
    "ax_1to10.set_xlabel(\"The ratio of pgg_receive of the dictator in round 1-10\")\n",
    "ax_1to10.set_ylabel(\"The score of being liked of the dictator\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 10 and round 1-8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is 0.393907642480328 .\n"
     ]
    }
   ],
   "source": [
    "rho = np.corrcoef(ratio_10, mean_RD)\n",
    "\n",
    "# print(my_rho)\n",
    "print(\"The value of correlation is\",rho[0][1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.DataFrame({'round 1-8': mean_RD, 'round 10': ratio_10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The ratio of pgg_receive of the dictator in round 10')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 386
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt = sns.regplot(x = mean_RD, y = ratio_10, data=dat, ci=None)\n",
    "plt.set_xlabel(\"The ratio of pgg_receive of the dictator in round 1-8\")\n",
    "plt.set_ylabel(\"The ratio of pgg_receive of the dictator in round 10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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