face_meteor_pilot_ratings_Chloe_v4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 50 groups for v4.\n"
     ]
    }
   ],
   "source": [
    "datalist = natsorted(glob.glob(os.path.join(fpath,'Data', 'preprocessed_data', 'preprocessed_data', \"v4\",\"*\")))\n",
    "len(datalist)\n",
    "print('There are',len(datalist),'groups for v4.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "loop_directory('C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_129_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_130_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_131_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_132_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_134_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_135_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_159_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_160_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_161_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_162_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_189_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_190_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_191_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_192_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_193_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_194_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_196_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_197_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_215_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_216_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_218_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_37_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_38_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_39_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_40_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_41_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_42_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_43_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_44_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_45_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_46_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_47_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_48_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_49_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_50_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_51_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_76_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_77_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_79_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_80_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_81_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_82_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_83_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_84_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_85_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_86_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_87_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_88_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_89_v4.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v4\\\\group_90_v4.csv']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst\n",
    "# print(len(lst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": 82,
   "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']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datlst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data of ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "loop_directory('C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID129.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID130.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID131.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID132.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID134.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID135.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID159.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID160.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID161.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID162.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID189.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID190.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID191.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID192.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID193.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID194.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID196.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID197.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID215.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID216.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID218.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID37.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID38.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID39.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID40.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID41.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID42.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID43.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID44.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID45.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID46.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID47.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID48.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID49.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID50.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID51.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID76.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID77.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID79.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID80.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID81.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID82.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID83.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID84.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID85.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID86.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID87.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID88.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID89.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v4\\\\rating_GroupID90.csv']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst_dislike\n",
    "# print(len(lst_dislike))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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']"
      ]
     },
     "execution_count": 86,
     "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": 87,
   "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": 88,
   "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": 89,
   "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": 90,
   "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": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>293</td>\n",
       "      <td>293</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>df1</td>\n",
       "      <td>173</td>\n",
       "      <td>240</td>\n",
       "      <td>0.720833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>df2</td>\n",
       "      <td>31</td>\n",
       "      <td>53</td>\n",
       "      <td>0.584906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>df3</td>\n",
       "      <td>63</td>\n",
       "      <td>275</td>\n",
       "      <td>0.229091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>df4</td>\n",
       "      <td>99</td>\n",
       "      <td>432</td>\n",
       "      <td>0.229167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>df5</td>\n",
       "      <td>0</td>\n",
       "      <td>390</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>df6</td>\n",
       "      <td>161</td>\n",
       "      <td>278</td>\n",
       "      <td>0.579137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>df7</td>\n",
       "      <td>170</td>\n",
       "      <td>390</td>\n",
       "      <td>0.435897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>df8</td>\n",
       "      <td>315</td>\n",
       "      <td>315</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>df9</td>\n",
       "      <td>150</td>\n",
       "      <td>308</td>\n",
       "      <td>0.487013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>df10</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>df11</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>df12</td>\n",
       "      <td>165</td>\n",
       "      <td>473</td>\n",
       "      <td>0.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>df13</td>\n",
       "      <td>300</td>\n",
       "      <td>315</td>\n",
       "      <td>0.952381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>df14</td>\n",
       "      <td>160</td>\n",
       "      <td>165</td>\n",
       "      <td>0.969697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>df15</td>\n",
       "      <td>150</td>\n",
       "      <td>165</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>df16</td>\n",
       "      <td>150</td>\n",
       "      <td>420</td>\n",
       "      <td>0.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>df17</td>\n",
       "      <td>222</td>\n",
       "      <td>462</td>\n",
       "      <td>0.480519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>df18</td>\n",
       "      <td>150</td>\n",
       "      <td>302</td>\n",
       "      <td>0.496689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>df19</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>df20</td>\n",
       "      <td>155</td>\n",
       "      <td>480</td>\n",
       "      <td>0.322917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>df21</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>df22</td>\n",
       "      <td>150</td>\n",
       "      <td>573</td>\n",
       "      <td>0.261780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>df23</td>\n",
       "      <td>165</td>\n",
       "      <td>600</td>\n",
       "      <td>0.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>df24</td>\n",
       "      <td>150</td>\n",
       "      <td>450</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>df25</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>df26</td>\n",
       "      <td>232</td>\n",
       "      <td>320</td>\n",
       "      <td>0.725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>df27</td>\n",
       "      <td>75</td>\n",
       "      <td>323</td>\n",
       "      <td>0.232198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>df28</td>\n",
       "      <td>150</td>\n",
       "      <td>551</td>\n",
       "      <td>0.272232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>df29</td>\n",
       "      <td>317</td>\n",
       "      <td>593</td>\n",
       "      <td>0.534570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>df30</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>df31</td>\n",
       "      <td>150</td>\n",
       "      <td>578</td>\n",
       "      <td>0.259516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>df32</td>\n",
       "      <td>366</td>\n",
       "      <td>366</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>df33</td>\n",
       "      <td>400</td>\n",
       "      <td>450</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>df34</td>\n",
       "      <td>100</td>\n",
       "      <td>195</td>\n",
       "      <td>0.512821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>df35</td>\n",
       "      <td>223</td>\n",
       "      <td>374</td>\n",
       "      <td>0.596257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>df36</td>\n",
       "      <td>150</td>\n",
       "      <td>570</td>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>df37</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>df38</td>\n",
       "      <td>59</td>\n",
       "      <td>186</td>\n",
       "      <td>0.317204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>df39</td>\n",
       "      <td>200</td>\n",
       "      <td>428</td>\n",
       "      <td>0.467290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>df40</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>df41</td>\n",
       "      <td>95</td>\n",
       "      <td>434</td>\n",
       "      <td>0.218894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>df42</td>\n",
       "      <td>300</td>\n",
       "      <td>600</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>df43</td>\n",
       "      <td>170</td>\n",
       "      <td>255</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>df44</td>\n",
       "      <td>150</td>\n",
       "      <td>450</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>df45</td>\n",
       "      <td>25</td>\n",
       "      <td>315</td>\n",
       "      <td>0.079365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>df46</td>\n",
       "      <td>150</td>\n",
       "      <td>293</td>\n",
       "      <td>0.511945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>df47</td>\n",
       "      <td>156</td>\n",
       "      <td>524</td>\n",
       "      <td>0.297710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>df48</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>df49</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group  dictator's pgg receive  all player's pgg receive     ratio\n",
       "0    df0                     293                       293  1.000000\n",
       "1    df1                     173                       240  0.720833\n",
       "2    df2                      31                        53  0.584906\n",
       "3    df3                      63                       275  0.229091\n",
       "4    df4                      99                       432  0.229167\n",
       "5    df5                       0                       390  0.000000\n",
       "6    df6                     161                       278  0.579137\n",
       "7    df7                     170                       390  0.435897\n",
       "8    df8                     315                       315  1.000000\n",
       "9    df9                     150                       308  0.487013\n",
       "10  df10                     150                       600  0.250000\n",
       "11  df11                     150                       600  0.250000\n",
       "12  df12                     165                       473  0.348837\n",
       "13  df13                     300                       315  0.952381\n",
       "14  df14                     160                       165  0.969697\n",
       "15  df15                     150                       165  0.909091\n",
       "16  df16                     150                       420  0.357143\n",
       "17  df17                     222                       462  0.480519\n",
       "18  df18                     150                       302  0.496689\n",
       "19  df19                     330                       330  1.000000\n",
       "20  df20                     155                       480  0.322917\n",
       "21  df21                     150                       600  0.250000\n",
       "22  df22                     150                       573  0.261780\n",
       "23  df23                     165                       600  0.275000\n",
       "24  df24                     150                       450  0.333333\n",
       "25  df25                     150                       600  0.250000\n",
       "26  df26                     232                       320  0.725000\n",
       "27  df27                      75                       323  0.232198\n",
       "28  df28                     150                       551  0.272232\n",
       "29  df29                     317                       593  0.534570\n",
       "30  df30                     100                       300  0.333333\n",
       "31  df31                     150                       578  0.259516\n",
       "32  df32                     366                       366  1.000000\n",
       "33  df33                     400                       450  0.888889\n",
       "34  df34                     100                       195  0.512821\n",
       "35  df35                     223                       374  0.596257\n",
       "36  df36                     150                       570  0.263158\n",
       "37  df37                     165                       165  1.000000\n",
       "38  df38                      59                       186  0.317204\n",
       "39  df39                     200                       428  0.467290\n",
       "40  df40                     564                       564  1.000000\n",
       "41  df41                      95                       434  0.218894\n",
       "42  df42                     300                       600  0.500000\n",
       "43  df43                     170                       255  0.666667\n",
       "44  df44                     150                       450  0.333333\n",
       "45  df45                      25                       315  0.079365\n",
       "46  df46                     150                       293  0.511945\n",
       "47  df47                     156                       524  0.297710\n",
       "48  df48                     150                       600  0.250000\n",
       "49  df49                     557                       557  1.000000"
      ]
     },
     "execution_count": 91,
     "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": 92,
   "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": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratio['dislike'] = dislike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\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>293</td>\n",
       "      <td>293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>df1</td>\n",
       "      <td>173</td>\n",
       "      <td>240</td>\n",
       "      <td>0.720833</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>df2</td>\n",
       "      <td>31</td>\n",
       "      <td>53</td>\n",
       "      <td>0.584906</td>\n",
       "      <td>36.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>df3</td>\n",
       "      <td>63</td>\n",
       "      <td>275</td>\n",
       "      <td>0.229091</td>\n",
       "      <td>62.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>df4</td>\n",
       "      <td>99</td>\n",
       "      <td>432</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>68.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>df5</td>\n",
       "      <td>0</td>\n",
       "      <td>390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>df6</td>\n",
       "      <td>161</td>\n",
       "      <td>278</td>\n",
       "      <td>0.579137</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>df7</td>\n",
       "      <td>170</td>\n",
       "      <td>390</td>\n",
       "      <td>0.435897</td>\n",
       "      <td>57.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>df8</td>\n",
       "      <td>315</td>\n",
       "      <td>315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>df9</td>\n",
       "      <td>150</td>\n",
       "      <td>308</td>\n",
       "      <td>0.487013</td>\n",
       "      <td>53.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>df10</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>55.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>df11</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>86.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>df12</td>\n",
       "      <td>165</td>\n",
       "      <td>473</td>\n",
       "      <td>0.348837</td>\n",
       "      <td>51.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>df13</td>\n",
       "      <td>300</td>\n",
       "      <td>315</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>df14</td>\n",
       "      <td>160</td>\n",
       "      <td>165</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>df15</td>\n",
       "      <td>150</td>\n",
       "      <td>165</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>df16</td>\n",
       "      <td>150</td>\n",
       "      <td>420</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>73.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>df17</td>\n",
       "      <td>222</td>\n",
       "      <td>462</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>df18</td>\n",
       "      <td>150</td>\n",
       "      <td>302</td>\n",
       "      <td>0.496689</td>\n",
       "      <td>29.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>df19</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>df20</td>\n",
       "      <td>155</td>\n",
       "      <td>480</td>\n",
       "      <td>0.322917</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>df21</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>df22</td>\n",
       "      <td>150</td>\n",
       "      <td>573</td>\n",
       "      <td>0.261780</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>df23</td>\n",
       "      <td>165</td>\n",
       "      <td>600</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>df24</td>\n",
       "      <td>150</td>\n",
       "      <td>450</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>df25</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>df26</td>\n",
       "      <td>232</td>\n",
       "      <td>320</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>32.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>df27</td>\n",
       "      <td>75</td>\n",
       "      <td>323</td>\n",
       "      <td>0.232198</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>df28</td>\n",
       "      <td>150</td>\n",
       "      <td>551</td>\n",
       "      <td>0.272232</td>\n",
       "      <td>58.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>df29</td>\n",
       "      <td>317</td>\n",
       "      <td>593</td>\n",
       "      <td>0.534570</td>\n",
       "      <td>60.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>df30</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>df31</td>\n",
       "      <td>150</td>\n",
       "      <td>578</td>\n",
       "      <td>0.259516</td>\n",
       "      <td>87.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>df32</td>\n",
       "      <td>366</td>\n",
       "      <td>366</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>df33</td>\n",
       "      <td>400</td>\n",
       "      <td>450</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>27.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>df34</td>\n",
       "      <td>100</td>\n",
       "      <td>195</td>\n",
       "      <td>0.512821</td>\n",
       "      <td>64.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>df35</td>\n",
       "      <td>223</td>\n",
       "      <td>374</td>\n",
       "      <td>0.596257</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>df36</td>\n",
       "      <td>150</td>\n",
       "      <td>570</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>53.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>df37</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>df38</td>\n",
       "      <td>59</td>\n",
       "      <td>186</td>\n",
       "      <td>0.317204</td>\n",
       "      <td>73.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>df39</td>\n",
       "      <td>200</td>\n",
       "      <td>428</td>\n",
       "      <td>0.467290</td>\n",
       "      <td>30.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>df40</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>df41</td>\n",
       "      <td>95</td>\n",
       "      <td>434</td>\n",
       "      <td>0.218894</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>df42</td>\n",
       "      <td>300</td>\n",
       "      <td>600</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>42.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>df43</td>\n",
       "      <td>170</td>\n",
       "      <td>255</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>df44</td>\n",
       "      <td>150</td>\n",
       "      <td>450</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>95.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>df45</td>\n",
       "      <td>25</td>\n",
       "      <td>315</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>37.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>df46</td>\n",
       "      <td>150</td>\n",
       "      <td>293</td>\n",
       "      <td>0.511945</td>\n",
       "      <td>56.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>df47</td>\n",
       "      <td>156</td>\n",
       "      <td>524</td>\n",
       "      <td>0.297710</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>df48</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>80.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>df49</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group  dictator's pgg receive  all player's pgg receive     ratio  \\\n",
       "0    df0                     293                       293  1.000000   \n",
       "1    df1                     173                       240  0.720833   \n",
       "2    df2                      31                        53  0.584906   \n",
       "3    df3                      63                       275  0.229091   \n",
       "4    df4                      99                       432  0.229167   \n",
       "5    df5                       0                       390  0.000000   \n",
       "6    df6                     161                       278  0.579137   \n",
       "7    df7                     170                       390  0.435897   \n",
       "8    df8                     315                       315  1.000000   \n",
       "9    df9                     150                       308  0.487013   \n",
       "10  df10                     150                       600  0.250000   \n",
       "11  df11                     150                       600  0.250000   \n",
       "12  df12                     165                       473  0.348837   \n",
       "13  df13                     300                       315  0.952381   \n",
       "14  df14                     160                       165  0.969697   \n",
       "15  df15                     150                       165  0.909091   \n",
       "16  df16                     150                       420  0.357143   \n",
       "17  df17                     222                       462  0.480519   \n",
       "18  df18                     150                       302  0.496689   \n",
       "19  df19                     330                       330  1.000000   \n",
       "20  df20                     155                       480  0.322917   \n",
       "21  df21                     150                       600  0.250000   \n",
       "22  df22                     150                       573  0.261780   \n",
       "23  df23                     165                       600  0.275000   \n",
       "24  df24                     150                       450  0.333333   \n",
       "25  df25                     150                       600  0.250000   \n",
       "26  df26                     232                       320  0.725000   \n",
       "27  df27                      75                       323  0.232198   \n",
       "28  df28                     150                       551  0.272232   \n",
       "29  df29                     317                       593  0.534570   \n",
       "30  df30                     100                       300  0.333333   \n",
       "31  df31                     150                       578  0.259516   \n",
       "32  df32                     366                       366  1.000000   \n",
       "33  df33                     400                       450  0.888889   \n",
       "34  df34                     100                       195  0.512821   \n",
       "35  df35                     223                       374  0.596257   \n",
       "36  df36                     150                       570  0.263158   \n",
       "37  df37                     165                       165  1.000000   \n",
       "38  df38                      59                       186  0.317204   \n",
       "39  df39                     200                       428  0.467290   \n",
       "40  df40                     564                       564  1.000000   \n",
       "41  df41                      95                       434  0.218894   \n",
       "42  df42                     300                       600  0.500000   \n",
       "43  df43                     170                       255  0.666667   \n",
       "44  df44                     150                       450  0.333333   \n",
       "45  df45                      25                       315  0.079365   \n",
       "46  df46                     150                       293  0.511945   \n",
       "47  df47                     156                       524  0.297710   \n",
       "48  df48                     150                       600  0.250000   \n",
       "49  df49                     557                       557  1.000000   \n",
       "\n",
       "      dislike  \n",
       "0   10.666667  \n",
       "1   15.000000  \n",
       "2   36.666667  \n",
       "3   62.666667  \n",
       "4   68.333333  \n",
       "5   22.333333  \n",
       "6   66.666667  \n",
       "7   57.333333  \n",
       "8   17.666667  \n",
       "9   53.333333  \n",
       "10  55.666667  \n",
       "11  86.333333  \n",
       "12  51.666667  \n",
       "13   0.000000  \n",
       "14   4.666667  \n",
       "15   0.666667  \n",
       "16  73.333333  \n",
       "17  26.000000  \n",
       "18  29.666667  \n",
       "19  40.666667  \n",
       "20  68.000000  \n",
       "21  84.000000  \n",
       "22  55.000000  \n",
       "23  61.000000  \n",
       "24  80.000000  \n",
       "25  85.000000  \n",
       "26  32.333333  \n",
       "27  34.000000  \n",
       "28  58.666667  \n",
       "29  60.333333  \n",
       "30  20.000000  \n",
       "31  87.666667  \n",
       "32  50.666667  \n",
       "33  27.666667  \n",
       "34  64.666667  \n",
       "35  45.000000  \n",
       "36  53.333333  \n",
       "37   6.333333  \n",
       "38  73.333333  \n",
       "39  30.666667  \n",
       "40  16.000000  \n",
       "41  73.000000  \n",
       "42  42.666667  \n",
       "43  24.000000  \n",
       "44  95.666667  \n",
       "45  37.333333  \n",
       "46  56.333333  \n",
       "47  49.000000  \n",
       "48  80.333333  \n",
       "49  40.666667  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.6388979720956334 .\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": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nbL8SfH1ttmeX8Ss/mT7QzI4ETsdffftxpozOuZ34KzHgr3wV6uLg82fOua0Zpns7PgAAHyQPhxfx/Yilz3sH8B/B14vTRr83+PyBc647y3TDaRazPEJXZZlutjr8Onxwvw9/9ekAzrleUs9Zj7jgfYQ/4ct9F7AsqDNRS/F3FTc6536faTrOuRfwd/riQfpQuH7+6py7J0O+Z8nQql6Bwmnf6Jx7LMO0H8ff4Ybhq6OFKrbeLGXgyzyr4B2FtwRfv+Oc25ch2dX4uxMFM7MJwJuDr19wzu0uJv9gBMv1juDr6cXmN7Op+LuK4C++ZXqn4kr8NjwBfwU/k/9yzmXrAiifcJ90tvmW0tK9LS1dKDyWzR7gfEuhmcz79S34oA36bw/DvV8H+LFzLlNXTP8FbMLfUX1zZPjJ+KAWMhzfA1dmGhi8f/Om4OvVzrlMXUF8H38nY6j9e5bhSxn4Pmcg50eDmV8uYT24OP19PDMbT6q5+vQ6GNaxVVnqQTTPmWZWUWB5CvWNLMP/EHymbxOFasXf0c8kPIY+5Jy7LX1ksD8Lzy2H6xha7O8eznOKwQjbrsjXhUt4bJ0wmJmVS+uX67IMD/vSmpI2/NXB56lm1i+wiJgafNaTCjRy+T/81YfZwANm9gP8Y4Qv5MhzWvB5SwHTD4WNoDyS4UQ5dB/+cYV4kD7T9LP9pnD5VOEbAchWjrDyZHxJOZ35RmvCDanfRhNxN/AqUr9zqN2b5SIA+Edw/hU4wcyqnHM9ZhbHvwcIcJWZZTyg4q8QQoHLI02xdfgVwWeTy94S6P0DKMdQOA5/B+BQ/A50RZYTprCezcmzHYaNkkSXa1g37s2R7178I7bFCqedr46+neGro4Ua6L5vIMs8lyPwT0BAlnXinNtjZuvxV0QLdQp+H+bwj0MOOTM7Fv/+zRL8yfQE/IlkVL8GUwrwimA6juzLZFewTE7H16XrMyQr5NiTkXPuITN7GpiPP3na37VK0DjDkfi7FemNqdyCf8z+SvONGt2AP9kp577hHsqxX++3PYzQfh383ZR+nHNJM7uf/vuRcN++NTiRzOSv+PWW3tjHEfgLvuAvqmWab1dQ55bkL3rB9uIfLcxkMPucgZwfDdc+bg3+YsdU/EXVNZFxb8TvN7bhL2JmKs9HzexDeeZRA0zDvxY0FNqcc89nGZftGFGoh1z2LnsKPYZ+FphvZrXOuaG80DCQ3z2c5xSDER6Lsu3bhlRZBHU5rt6GV4vTd3zh1cfxRJrJz6Ggu27OuZ1m9m7gV8BCgrs+wY7ldvxjoOkVZmbw2VzIPAJhX21ZO4B2zu0zs9Zg+tOzJHs5y/Bw+VREypdLoXclp5JqMTVX59Wbgs9s5R6sXPMOx1XgN/pt+HKHrahOzZQpTSF1Kl2+Opy+rYUd8+bq0HzzAMoxFD4VfD6Nf2E8244/rGdVFF/PwrqR6zcOtIP0vNsXqTo6zcwsx8nkcCu23gxmmecS3VaHcp2EZdzlnBvyDnbN7G34OybhMSJswCC8CDEBf6V0IC39hstkV44LL5B/f5dtP12o6/Adpb+dA/vLfHvweVuGOwhX4p9QeSPwz8FfwszW4d8P+qlzrtw6XM91FzfTucBI7NehsONNdN3n3bcHFxtb8Y+4Rx0S+X8kjw2tmZ76CQxmnzOQ86Nh2ccF51S/x995ezsHBnXhtvSbDHfkw/JMJhVIDro8BSp2myhGrv1SMcdQw9fboQzqBvK7h/OcYjDCY0e+ehGOH1RXSuXy+GWxwnJ/xzlnBfytKnTCzrlb8Fd7L8W/C7IZv+N9D7DWzNIfIRhQtwmBnM0h55PlcSBILZ+/Fbh8LhnA7AdV9mGUaX1E6/lJhSyTEpUzXakCjd/i7xLPx9+xyyZcrr8vsJ6tLLIcg10P5VpHB2O4l3k+xa6TYduWzGw6voXBSvxjNafg3wWc4pyb5ZybBXxnCMox2Ho0oKawI34VfL4qbKY9eEwvfOwp0yOL3c65C/FPTPwbqcaOwu9Pm9lJgyxXqZXDfj3TNAczn5E49mSSq44OZp8zkN8znPu4cFu5MHzFxszqgPPSxmcqz4UFlmdjkb+3VArZL421Y2gptq8wyJxqZuNypAufJsl1MSev0RrUhe8nHD8cE3fO7XLO/dQ5t8I5dyj+Zd+fBqM/YGbnR5KHjwccVsQswiskWfMEK39aWvpChcvn6OARlaESNokNuX9v2B/IYK9QZ5PrUarwqlrYhDT4Z8fDHdiw1JkBCJdNrndeBvLI2FBYg79y2Qd82sy+lCXdYLbD8PcXsi4HOu1C6mhrCe/SDcRw7fui2+pQrpNw/zjZ0voGHAKvx9+JewJ4h3NuvfPvokYVcqU/m3CZjA8CyGyGdX/nnHsG37KvkXqHbil+XXQC/5sj71+dc592zr0K/+TC2/F3TabjW2EczUZqv17I9hBd93n37cGrDNMyjIpOJ9e2NpLvSg5mnzOQ86PhPL+7K5h+Lf4uNvh3iauB551vi2Eky1OuijmGOnwDdaHwyZ5cAcxQHwtgeM8pBuOJyP8Z65CZHYJveTQ9fdFGa1AXvqNwhpll2jEOKefcE865S/FXOwHOiIwOh72+iEmGncQebWaHZkmzhNSjVw9nSZNNuHwm4BtlGRLOv7QdNjxxZo6kZwWfxZa7UGcUMO6xoLxhoyMPBcPfkjHXyPtb8NkYNCaRSTHvLg0p59wN+LvTSeALZvavGZKF9ewYM1tQ5CzCupHrvZBc67mQaZeijoYXPYbriuBglnkuz+Obe4Ys68TMavF3w4rxEP4gbxS3jyxkOYYnFRsyPToWNP5yVvrwiDCYzzaPv0XSZKxLQaAaNsQ1XPs7SN1BeEfwGT4udqNzrqDGa5xznc656/FPoQAsCtbpqDSC+/WM+6GgfoX76Oi6D/fts4JGyzI5lcyPzT1PqpGb12SZ73gKb/xtKAxmnzOQ86Ph2seFTzf9Jviavi1dl6c8Fw1lWcpcWJ/PyNHRd7hvfdod+D5deBzJ2Nl3ML3hqL/DeU4xGE+SujCQrbGmcHgPWd6lLdRoDer+B3+FchzZW2wCwMwKfok0uHqWS/iSefSWdNip5rFm9k8Fzup2/I67EvhkhnJU4LsOALjfZWhlMhfn3N9J7UyvzHXgNrPxZlbMLfbwhfxLzKzfVQ8zOxf/iA+kdp5DbZ6ZvT19YNBaXXjC8j9po1cFnxeZWa6T/aLqzCDcTqoOfzhDGeL4PrhKxjn3a1Kdtn7NzD6eluQuUu9KfCdXq18Zlmm4fl5lZv12wmZ2BLBiQAVP1dHXm9kr0kcGJwphS1lDXUfDE7K6nKkGbjDLPKvgbuVvg6+XZ9kn/H8U+b6I8++ihS3YfcnMJhaYdX9LxDnShO/onZDlxOMD+IZE8s0j47py/j21sKGATwePPKb7NH4b3kNxjUEU63p8oHtiUKfDE8xMj4vlO5aFxzEj9U7aaLUq+BzO/fqHgkf00r0L30hHEt9cfuhhfAvN4Ptgy+RTmQYGFyfC1v0uS2+lMfDPDLKFvCINZp8zkPOjYdnHRYTbzOvM7DhSF2wybkuk6tgpZpazkY0ROncYCeExdAGpVkH3M7OZ+P4kof8x9NHgc3Gmc0TgnQy80aJchvOcYsCCbTpsQOuf08/Hg+NKeK53k3Oug8Fwg+yrIf2PAfRTlyPNUrL0KYHvhDPsk+I3RPqjwh9kX4Pv9PKJIsp+Gb7T3XdwYF8rdRzY0ewb0vL9KBgedq4ZdpRZge9ccyXwwbQ8nw7yJIHPkeo4t+DOx/P8llNI9cnxML6vlXgwLobfWK/AP+87r4hlFO18/BGCPu6C33oRI9f5+B78QTX8TQvxLQk6/FWRTJ2PPxCM7wzW9dTI+Bn4K3Zr6d93yspsdTpSBzMuw1y/Gd9thcO3gvYvpDo0bcA3aV/yzseD4R8gVff/JW3cGyPj7sJfgbZgXBx/Re6bwM4M8ws7Ct0OLCfVUejp+EZahqLz8ReDuh+Wabg7Hz86sk5PzZFuMPVmwMs8T9mjnY/fChweDB8PXM7AOx8/Ch9AOVKdj4frug7f6e/NaXlqSfUleFGW8h4TWQ7fA+qC4ZPwF8t6SfVbmWnb/XMw7jtk6IMuSBPtfPw3ZO98/HOFboMD/cO3Nufwj2I6/ONG8Sxpn8b3obeYVGfchm8tMtw2Hsy2DknbBwygrNFp5fz9DK5fqwHv1wv4DWH52/EXSU+IzPO9pDoPz9T5+D9G8n+FVIfO04GfkLvz8eNI9cv3vwR94JG6ANjD0Hc+vjFPusHs5wdyfjSY+eXdf+M7oo5uS015fv9vI7/hSxx4fjgFH/j8AfhFkct/LZmPuXnXCwX2iTqQ7S1IF+18/GJSnY8vIrUP2QpMS8sXJ9UH8lpSx5Ea4J/wx5jwPDF9ex7U72aYzimCaVTjG4QJ//4tmNZDacMnZ8g7i1Tf1H8EGoLh0/ENfTn8Nl9UR/IZyznYCeSopPkqzLxw48uRJucKxgcl4YYf7tTbSB2EHfBCEWW/PJLP4QOHnWnD+nVcGazs1WnpduJPKsLv6ZW3glQno+HOInxnzQW/4Z8HstwiaV8fqcRhpdlB6mQp/DusyHX8yshG6fAnbHsj3x8h2HEPtOxZ5rsqyP8NUgfyfZGNJawDS7Lkn0GqU3ZHqnP63WnL44tp+VZmq9ORPPOKref44OOPkWn0RJZrD77vo3BcwZ1IB9PeyBAFdcG4D0eW2aVp495H6iTEBXVhR1Cn9y/XDNOcDTwTSdMVWRfbSZ0YZdz+8/z+oyLLIKwXnZHvLwLzs+TNuU4LmPe9kWm0BuXYCJw2FPVmMMu8gLK/LW0a0f3Yb0nts1YWWd4zOXBfuo8D902Z6kd0/9geWY4XR9JcFZ0GfvsJy38bqc65M22770tbfi8G0/9WWrp/InVMSabNw+E7Lc7UMXlY/5ZmWiYDWDf/jwN/6w9zpI0u20RQD6P7/ZeBhdnWIaMkqAvGDWi/XsBvCPO9g9S+o50Dt7sHCC7IpuU1fB9g0XUQHt+T+C44wotLr8pSN6PnNm2R9fcbUtvGZwe5npZS4D6Wge/niz4/GuT88u6/Se0Xwr9P5fnttfgnDqJ52jnw3MMxtoK66aQeQQ+Xf0fke1umuhvkfTMHnofviqzvn5O/8/EB/W6G95ziEg5c19n+Mq4P/COW0XOQdlLbeC/wrsFsy+HfaH38EgDn3FeBk/BXvp7B70hr8a3H3Ap8CH+Fp1C/xt+VWI1/DrYXf0V2C/6K2YXOuX6PEDjf0tgK/NWam0i9iLsDf4Xvc6QaWgnz9Dnn3ou/AnI7fgWH87oOeKVz7odFlL0f59ytpFowfBh/IlWH3zD/AnwBOM4592LWiWSe7oP4Fz6/g7/6UYnf0T6Ev0J+qnNuqPppyaQbf5L4ZfyBsQp/knI9cLJz7r4s5d6Of6b6nfhHpbaT6tPq7/idzRvwV7iHnfPv/J0PfBx/1yiJ3xHehH8u/J5I8pI2P+6c+wHwMfyy+rGZvS8y7hf4uyZXA4/j68Jk/InkPfhHkOZlmOYW/J2Eq/DrsQK/8/85vs+Z5wZR3mfx+4Yvk3oPlOD/r+BPaJ8e6PTzeAvwQ+AFfP06LPjL9eJ4UQa6zAuY7vX4q5o34+tcFf7F7cuBt+IPQAMp7z1Bea/Er4OwD86n8fu7N2bI9kH8BZyn8CeG4XLc/+iZc+5j+Eeu/4bfL8SBpqC855N6aT9TmX6B398/GKSrD6Z/SFq6/8DX01/j988T8PX0DuCtzrl3uewtEQ+lG0h11QDZHxcDfyz6Bv5u5GZ8mXuADfg7HAuccxuGqZwjagT263/Bn0f8hlSQ8RT++LnUZejuwnnvxz++vi7IZ/iT+POdc98n1R9dv317UDeX4C9M7MLX/yfwdyLfRuqx5BE7LgxiP1/0+dFg5legX0X+d2TuXzJalk7n3Jvxd39+h78TNR6/f3wWvy1ejH80dkxwzr2Mf5Xm4/hzu178730Gv04WOOcy9sHpfKfx5+LX0278sb0J+H/OuX8cxjIP2znFEJTtDqAR+AW+O4jx+G3hN/gLvr8civmEt7NFpMyY2dnAncCLzrl5JS6OiMhBw8zCk6PD3RA3Ux80oPIsPtCeGFzgKzSv4U9Y6ykbdIIAACAASURBVIEznXNrh7JsIjJ6jeo7dSJjXNiIzh0lLYWIiAylsKGU+4oJ6AJvwwd0Hfi7zCIigII6kZIxswozu8HMzov24WVmC8zsBuB1+EcevluyQoqISNHM7BdmdnG02yUzO9zMfkiqleZvZ8n7r2b2L2ZWH7a6amZTzOwy/KNk4N+pLKg7CxE5OOjxS5ESCbotiHaW3IF/JyhsNj4JfMg595ORLpuIyMFssI9fmtkmfGvW4BtISALRLj2+5py7IkveX+LfEYRUS5l1pPpUvBO4wDm3r9hyicjYFc+fRESGSR/+xerXASfiW3GrwL8vcR9wtXNuODs0FhGR4fFJfOMgrwBm4i/Wbca3mPlD59zdOfL+EH+R7zX4Fv3q8K0NbsC3tvpfzrn9jQCZ2TUU1wdXi3NucRHpRWQU0J06ERERkVHKzFbh+84rlBrfEhmDFNSJiIiIiIiMYmooRUREREREZBQ7aIK6oBWq75nZ/WbWYWYueBk5V55Xm9ktZtZmZl1mtsHMLjezihx53mtmD5rZHjPbZWZrzWz50P8iERERERGRgyioA64APoLv0f2lfInN7EJ8YxVLgN8DPwCqgO8A12fJ8y1gFf7F5p/iX2g+EbjJzD4y6F8gIiIiIiKS5qB5p87MzgQ2Ac8CZwD3AL9yzr0rQ9pJQbrJwOnOuYeC4eOAu4FXAW93zl0fyfNq4M/Ac8Bi59zOYPg8YD1QCxw7kKaRRUREREREsjloujRwzt0T/m9muZICXAxMxzcb/FBkGvvM7ArgLuBDHHjH7oPB59fCgC7Is9HMfgB8Hngf8MWB/gYzewGYBGwc6DRERERERAowD+hwzh1e6oJIfgdNUFeks4LP2zKMuw/oAl5tZtXOue4C8tyKD+rOYhBBHTBp/PjxU4877ripg5iGiIiIiEhOTz75JHv37i11MaRACuoyOyb4fDp9hHMuEdwxWwAcATxpZrXAocAe59yWDNN7JvicX8jMzWx9llHjjjvuONavzzZaRERERGTwFi1axMMPP7yx1OWQwhxMDaUUY3LwuSvL+HB43QDTi4iIiIiIDAndqRuY8KW8YluZKSi9c25Rxpn6O3gnFzlPEREREREZw3SnLrPwztrkLOMnpaXLlz7fnTwREREREZEBUVCX2VPBZ7934MwsDhwOJIDnAZxznfi+7yaY2ewM0zs6+Oz3jp6IiIiIiMhgKKjL7O7g87wM45YANcBfIi1f5svz+rQ0IiIiIiIiQ0JBXWY3ADuAt5nZKeHAoPPxrwZff5SW58fB5+fMbEokzzzgw0A38IthKq+IiIiIiBykDpqGUszsTcCbgq+zgs9Xmdmq4P8dzrlPADjnOszsA/jgbq2ZXQ+0AW/Ed3dwA7A6On3n3F/M7CrgY8AGM7sBqAJWAFOBf3HObRymnyciIiIiIgepgyaoAxqB96YNOyL4A3gR+EQ4wjl3o5mdAXwOuAgYBzyLD9q+65zr15Klc+7jZrYB+AhwKZAEHgb+3Tm3Zmh/joiIiIiIyEEU1DnnVgIri8zzZ+ANRea5Fri2mDwiIiIiIiIDpXfqRERERERERrGD5k6diMhANLd20bSpnb09CcZXxWmcW0fDtJpSF0tERERkPwV1IiIZNLW0s3pdM00t7XR299GXdFTEjNrqChrr61ixuIHG+rpSF1NEREREQZ2ISLrbH9/KNXc9w/aObrp6E9RWxYnHjH29SVo7u9mxu4cNm3Zx+bL5nHP8zFIXV0RERA5yCupERCKaWtq55q5naGnrYkJ1nHkTa4nFbP/4ZNKxs6uHlrYurr7zaaZPrNYdOxERESkpNZQiIhKxel0z2zu6mVAdZ9qE6gMCOoBYzJg2oZoJ1XG2d3Szel1ziUoqIiIi4imoExEJNLd20dTSTldvgik1VTnTTqmpoqs3QVNLO82tXSNUQhEREZH+FNSJiASaNvlGUWqr4v3u0KWLxYzaqjid3X00bWofoRKKiIiI9KegTkQksLcnQV/SEc8T0IXiMaMv6djbkxjmkomIiIhkp6BORCQwvipORcxIJF1B6RNBNwfjq9TmlIiIiJSOgjoRkUDj3Dpqqyvo7EmQzBPYJZOOzp6E77durlq/FBERkdJRUCciEmiYVkNjfR01lXF2dvXkTLuzq4eayjiN9XU0TKsZoRKKiIiI9KegTkQkYsXiBmZMqmZPd4LWPd397tglk47WPd3s6U4wY1I1KxY3lKikIiIiIl7Jgzozu9vMvlLqcoiIADTW13HZ2UdTP7WG3j7HxrZOtnXso3VPN9s69rGxrZPePkf91BouXzZfHY+LiIhIyZXD2/2nAX8tdSFERELnLpjFjEnjWL2umaYW381BX9IxrjLGtAlVNNbXsWJxgwI6ERERKQvlENQ9A9SXuhAiIlGN9XU01tf5Dsk3tbO3J8H4qjiNc/UOnYiIiJSXcgjqfgZ8ycwanHPNpS6MiEhUw7QaBXEiIiJS1sohqLsJOAf4s5ldCawDtgL92hNX0CciIiIiInKgcgjqnscHcAZckyOdozzKKyIiIiIiUjbKIUj6LzLclRMREREREZH8Sh7UOecuKXUZRERERERERquS91MnIiIiIiIiA1fyO3VRZjYXeAVQB+wCHnbObSptqURERERERMpXWQR1ZtYA/ATfCmb6uDuADzrnNo50uURERERERMpdyYM6M5sF/Bk4FNgI3AdsAWYDrwHOBf5kZqc457aWqpwiIiIiIiLlqORBHfB5fED3aeAq51xfOMLMKoCPAv8GXAF8pCQlFBERERERKVPl0FDK+cDtzrl/jwZ0AM65Pufct4DbgeUlKZ2IiIiIiEgZK4egbhawPk+a9UE6ERERERERiSiHoG4XcFieNA1BOhEREREREYkoh6DuT8DFZvbqTCPN7FTgrUE6ERERERERiSiHhlK+hn+v7l4zux64B9/65SxgKfB2IAl8vVQFFBERERERKVclD+qccw+b2cXAKuCdwDsiow1oA97vnMv33p2IiIiIiMhBp+RBHYBzbo2ZHQZcCJwMTMa/Q/c34EbnXGcpyyciIiIiIlKuyiKoAwgCt18HfyIiIiIiIlKAkjeUYmZ3m9l78qR5l5ndPVJlEhERERERGS3K4U7dUmBtnjSHAWcMe0lEZEg1t3bRtKmdvT0JxlfFaZxbR8O0mlIXS0RERGRMKYegrhDjgUSpCyEihWlqaWf1umaaWtrp7O6jL+moiBm11RU01texYnEDjfV1pS6miIiIyJhQLkGdyzTQzAzf8fgbgJYRLZGIDMjtj2/lmrueYXtHN129CWqr4sRjxr7eJK2d3ezY3cOGTbu4fNl8zjl+ZqmLKyIiIjLqlSSoM7MkBwZyK81sZa4sqJ86kbLX1NLONXc9Q0tbFxOq48ybWEssZvvHJ5OOnV09tLR1cfWdTzN9YrXu2ImIiIgMUqnu1N1HKqhbAjQDGzOk6wNagbuAn41IyURkwFava2Z7RzcTquNMm1Ddb3wsZvuHb+/oZvW6ZgV1IiIiIoNUkqDOObc0/D+4a/cL59yXS1EWERm85tYu7vz7NtY+9TId+3ppmJq7MZQpNVVsbOukqaWd5tYuNZ4iIiIiMgjl8E7d4UB7qQshIsWLNoiyvaOb9r294KBlZxc1lRVMqa2ipqr/biYWM2qr4nR299G0qV1BnYiIiMgglDyoc869WOoyiEjx0htEiWHgHA7oSTgSfY6u3j5mThrHpHGV/fLHY0Zf0rG3Rw3bioiIiAxGyYO6kJnNBs4GDgX6v4wDzjn3lZEtlYhkkqlBlI59vXQnkiSdI17hA7aeRJJtHfuIx6zfHbtE0jGuMsb4DHfyRERERKRwZXE2ZWZfAj7DgeUxUo2phP8rqBMpA5kaRKmpihOzbhJJB86Ix2JAkkSfY2dnzwFBXTLp6OxJMG1CFY1z1VCKiIiIyGDESl0AM3sn8HngfuBifAB3LfAO4KdAErgeOKtUZRSRlObWLppa2unqTTClpmr/8Kp4jPFVFcTM36UDqIgZSecfw+xJJPen3dnVQ01lnMb6Or1PJyIiIjJI5XCn7kPAJuA851zC9zfORufc9cD1ZvZ74GbguhKWUUQCTZva6ezuo7Yqvr8Pup5Ekq6eBPFYDDN8UNeXpCJmxMxIJqGrN0E8VsnOrh72dCeon1rDisUNJf41IiIiIqNfOQR1JwLXOeeirSVUhP845/5oZn8EPgncNNKFE5ED7e1J0Jd0xGNGV0+Cts4e9vb0kXTgcDgHzkHCORJJhxkknaO9s5e2Tn+Hrn5qDZcvm68+6kRERESGQDkEdZX4DsZDe4HJaWkeAz44YiUSkazGV8WpiBm7unpp6+oh0edIOkfMDMuSx8yoiseYMamaxvo6VixuUEAnIiIiMkTKIajbAsyOfG8GFqalORRQu+ciZaBxbh0xg93dfpOsMKOqIkbw6DQA8ZijL+nv1DkHU2oq+ciZR3H2cTP1Dp2IiIjIECt5QynA3/CPYIbuBl5rZu82s1ozOx+4KEgnIiXWMK2Gipjtb5o2XhHDAYlkcv+fC4abAQZ1NZW87zWHK6ATERERGQblENStARaY2eHB928Cu4BVQAfwv/gWMa8oSelE5ADNrV30JZ3vZ8RBd8K3bJnoc/v/ehJJuhN9OOc33r6ko7m1q9RFFxERERmTSh7UOedWOedqnHMvBN9bgMXAj4DbgZ8Ai51zfy1hMUUk0LSpnaSDcZUVYAQNpPT/SzrAfLqk8/lEREREZOiVwzt1/QQB3kdKXQ4R6W9vT4K9Qb9zBpj5O3bpwlfsehJJ9vb2sbdHr8WKiIiIDIeS36kzsy+Y2ZI8aV5rZl8YqTKJSHbjq+J09SRIOkdFzKiOV1AVjxGvsP1/VfEY1fEKKmK+I/KungTjq8ryGpKIiIjIqFfyoA5YCSzNk2YJ8MVhL4mI5DVjQnXQjQEEfY8TMyMei+3/iwW36WLmH8VM9DlmTKguXaFFRERExrByCOoKEQeSpS6EiMD2Pd3EK4yYQTLPVplM+sAuXmFs39M9MgUUEREROciMlqBuEbCj1IUQEf9OXU1lnAoz+pwj0ZfEpb1U54Lhfc5RYUZNZVzv1ImIiIgMk5K85GJmd6cNusTMlmZIWgHUA4cB1w13uUQkv/FVccZXVdCTqKC7z3dl0NOXJGbmuzkAks4Rs/Dduhjjqyr0Tp2IiIjIMCnVWdbSyP8OmBf8pUsCrcBq4KPDXSgRya9xbh211RW0djpmTxpP+94e9vb0BV0bOGIY8ZgxvqqCuvFVbN+zj9rqChrn1pW66CIiIiJjUkmCOufc/sc+zSwJrHTOfbkUZRGR4jRMq6Gxvo4du3voTvQxd0oNPYlk0CKmf4eupipOVTxG655uairjNNbX0TCtptRFFxERERmTyuF5qPcBTaUuhIgUbsXiBjZs2kVLWxcAU2qqqKup2j8+mXS07ulmT3eC+qk1rFjcUKqiioiIiIx5JW8oxTl3rXPukVKXQ0QK11hfx2VnH0391Bp6+xwb2zrZ1rGP1j3dbOvYx8a2Tnr7HPVTa7h82Xwa6/XopYiIiMhwKfmdOjP7IPBJ4LXOuc0Zxh8K3Ad83Tn385Eun4hkdu6CWcyYNI7V65ppammns7uPvqRjXGWMaROqaKyvY8XiBgV0IiIiIsOs5EEd8A5gS6aADsA595KZbQLeBSioEykjjfV1NNbX0dzaRdOmdvb2JBhfFadxrt6hExERERkpJX/8EjgGyPf45Qbg2BEoi4iIiIiIyKhSDnfqJgPtedJ0AFNGoCwiUoSmlvZ+j19WxMx3YTBGHr/UXUgREREpd+UQ1G0BFuZJsxB4eQTK0o+ZnQ9cBhwPTMOXdz1wlXPugQzpXw1cAZwGjAOeBf4T+J5zrm+kyi0y3G5/fCvX3PUM2zu66epNUFsVJx4z9vUmae3sZsfuHjZs2sXly+ZzzvEzS13coh0MAauIiIiMDeUQ1N0DvNvMXuOc+1P6SDN7LfB64JcjXTAzuxL4FL4D9BuBHcBRwIXARWb2HufcLyPpLwR+C+zDd5jeBlwAfAc4HXjriP4AkWHS1NLONXc9Q0tbFxOq48ybWEssZvvHJ5OOnV09tLR1cfWdTzN9YvWoCoDGesAqIiIiY0s5BHVXAiuAO83sh8BtwEvAofhg7kNAd5BuxJjZLOATwDZgoXNue2TcmcDdwJcJgk0zmwT8FOgDljrnHgqGfz5Ie7GZvc05d/1I/g6R4bB6XTPbO7qZUB1n2oTqfuNjMds/fHtHN6vXNY+aoG6sB6wiIiIy9pS8oRTn3FPAP+ADt8uBW/ENo9yKf+xxH/BW59yTI1y0w/DL5/+iAR2Ac+4eYDcwPTL44uD79WFAF6Tdh38cE3yAKjKqNbd20dTSTldvgimRDsczmVJTRVdvgqaWdppbu0aohIOTHrBGAzpIBawTquP7A1YRERGRUip5UAfgnLsZOALfX91vgbuCz08ARzrnbilBsZ4BeoBXmtkh0RFmtgSYCNwZGXxW8HlbhmndB3QBrzaz/rc1REaRpk3+HbPaqni/gCddLGbUVsXp7O6jaVO+9pBKb6wHrCIiIjI2lcPjlwA451qBb5e6HCHnXJuZfRq4CnjCzG7Ev1t3JPBG4A7gnyJZjgk+n84wrYSZvQAswAevOe86mtn6LKPUrYOU3N6eBH1JRzxPQBeKx4y+pGNvT2KYSzZ4gwlY1SKmiIiIlErZBHXlyDl3tZltxLde+YHIqGeBVWmPZU4OPndlmVw4XC/fyKg2vipORdBoSCESSce4yhjjq8p/dzOWA1YREREZu0b8LCt4dBHgQefcvsj3vJxz9w1TsTIys08BXwe+C3wf2Iq/W/YN4Fdm1uic+1Shkws+Xb6EzrlFWcqzHji5wPmJDIvGuXXUVlfQ2tlNMuly3tFKJh2dPQmmTaiicW75X88YywGriIiIjF2lOBNZiw9sjsM/qhh+L0TF8BSpPzNbim9x8/fOuY9FRj1sZm/Gl/3jZvZj59zzpO7ETSazScFntjt5IqNCw7QaGuvr2LG7h51dPRlbvwzt7OqhpjJOY/3o6LB7LAesIiIiMnaVIqj7Mj6I25H2vdwsDz7vSR/hnOsysweBNwOvAJ4HngJOAebjOyffz8ziwOFAIkgrMqqtWNzAhk27aGnzDYRMqanK2Oz/nu4E9VNrWLG4oVRFLcpYDlhFRERk7BrxoM45tzLX9zISns1NzzI+HN4TfN4NvBM4D7guLe0SoAa4zznXPZSFFCmFxvo6Ljv76P0ddG9s69zfQXciuINVUxmnfmoNly+bP6r6cRurAauIiIiMXWXRpUGZuj/4vNTMDo2OMLPXA6fj+9D7SzD4Bvzdx7eZ2SmRtOOArwZffzSsJRYZQecumMXX3nwiy46fQcPUGsZVVmBmjKusoGFqDcuOn8HX3nwi5xw/s9RFLUoYsNZPraG3z7GxrZNtHfto3dPNto59bGzrpLfPjcqAVURERMYmvd2f3Q34fuiWAU+a2e/xDaUch38004DPBF0x4JzrMLMPBPnWmtn1QBu++4NjguGrR/xXiAyjxvo6GuvrfP9um9rZ25NgfFWcxrmj+5HEcxfMYsakcaxe10xTi+/moC9oFGXahCoa6+tYsbhBAZ2IiIiUhVK0fnn3ALM659zZQ1qY3DNLmtkbgA8Db8O/P1eDD9RuAb7rnLs9Lc+NZnYG8DngImAcvvuDjwXpy/HdQZFBa5hWM6qDuEzGasAqIiIiY08p7tQtzTLckWr2P9PwEQ+InHO9wNXBX6F5/gy8YdgKJSIjaiwGrCIiIjK2jPg7dc65WPQPfzfrf4EXgPfhW4kcH3y+H99a5B+CdCIiIiIiIhJRDg2lfB7fFcApzrlrnXMvOue6g89VwKnAK4N0IiIiIiIiElEOQd07gd8659ozjXTOteEbGXnXiJZKRERERERkFCiHoG4Oqb7esukFZo9AWUREREREREaVcgjqNgEXmllVppFmVg1cCLw0oqUSEREREREZBcohqLsWOAq428yWmFkFgJlVBN0D3AUcAawqXRFFRERERETKUzl0Pv5NYBG+k+57gKSZtQFT8UGn4VvH/GbJSigiIiIiIlKmSn6nzjnX65x7E74hlLuBXfiAbhf+Lt07nXNvcs4lSlhMERERERGRslQOd+oAcM79Gvh1qctR7l5s7eIPTS+x7LiZ1FaXzeoTEREREZESUVQwyuze18tl1zdRHY9x9nEzWL5wDmcdO4NxlRWlLpqIiIiIiJSAgrpRqjuR5JZHt3LLo1uprapg2fEzWb5wDkvmH0J1XAGeiIiIiMjBQkHdGNDZ08cfmjbzh6bNTBwX53ULZrF84WxOP+oQKitK/tqkiIiIiIgMIwV1o43lHr17X4Ib1m/ihvWbmFJTyXknzOKChXM49YhpVMTyZBY5iDS3dtG0qZ29PQnGV8VpnFtHw7SaUTsfEREROXgpqBtljp01iU++6QTWbNjM/73QhnPZ0+7s6uW6B1u47sEWDplQzRtOnMXyhXM45bApxBTgHbQO9iCjqaWd1euaaWppp7O7j76koyJm1FZX0Fhfx4rFDTTW142a+YiIiIiYyxUVSFkxs/Unn3zyyevXrwdgW8c+bnl0Czc9spmHm9sLns6sSeM4f+FsLjhpDifNnYyZAryDgYIMuP3xrVxz1zNs7+imqzdBbVWceMxIJB2dPQlqKuPMmFTN5cvmc87xM8t+PiIiIsNl0aJFPPzwww875xaVuiySn4K6USQ9qIvatLMrCPC28OhLuwqeZv3U8Zx/4hyWL5zNgjmTFOCNUQoyfFD7ud8/SktbFxOq40ypqTrgjnUy6djZ1cOe7gT1U2v42ptPHFCQO1LzERERGU4K6kaXsnr80syOBY4DJjjn/rvU5RlN5k6p4dIlR3LpkiPZuKOTm4M7eH/fujtnvpa2vfz43uf48b3PccQhtSxfOJvlJ81h/syJI1RyGW5NLe1cc9cz+4OMeRNrMwYZLW1dXH3n00yfWD0mg4zV65rZ3tHNhOo40yZU9xsfi9n+4ds7ulm9rnlAy2Gk5iMiIiISKoumEc2s0cweAh4HbgBWRcadYWZdZnZBqco32sw7pJYPn3kUt12+hDs/toTLzj6aI6fX5s33/I5Ovnv3s5z7nfs49zv38r27nuGFHZ0jUGIZTulBRvr7lGGQMaE6vj/IGGuaW7toammnqzfBlJqqnGmn1FTR1ZugqaWd5tauspyPiIiISFTJgzozmw+sBY4BrgFuTUtyH9AGXDyyJRsbjpoxkY+eM587P3YGt172Wj585pE0TM3fKMbT2/bw7Tue5sxvreX8797Pj9Y+R0ubTjxHGwUZXtMm/x5hbVU8byNBsZhRWxWns7uPpk2Fv6s6kvMRERERiSqHxy+/CFQBi5xzT5rZF4HXhyOdc87MHgAWl6qAY4GZcdzsSRw3exKfOPcYHn1pF2s2bGHNI5vZvGtfzryPb+7g8c0dXHnb32msr2P5wtmcv3A2syePH6HSy0ANJsgYSy1i7u1J0Jd0xAts9TUeM/qSjr09ibKcj4iIiEhUOQR1ZwO/c849mSNNM3DOCJVnzDMzFs6tY+HcOj5z3rH8rWUnNz2yhVse3cL23d058za1tNPU0s5Xb36SV86byvKTZvP6E2YzfWL/d4ek9BRkeOOr4lTEjH29yYLSJ5KOcZUxxlcVt4scqfmIiIiIRJXDmUQdsClPmhj+bp4MsVjMWHTYVBYdNpXPLz+edRvbWLNhM7c8upW2zp6ceR/c2MaDG9tY+b+P86ojp7F84RzOWzCLKbVaVeVCQYbXOLeO2uoKWju7SSZdzruWyaBF0GkTqmicW1wDJiM1HxEREZGocjhz2w4clSfNAqBlBMpyUKuIGacdMY3TjpjGygsW8MDzrax5ZAu3PraFjn3Z79wkHfz52Vb+/Gwrn7/xMV5z9CEsXziHcxfMZNK4yhH8BZJOQYbXMK2Gxvo6duzuYWdXT8ZWKUM7u3qoqYzTWF98p+wjNR8RERGRqHII6u4G3m5mxzjnnkofaWaL8Y9o/mDES3YQi1fEeO3R03nt0dP5yptO4E/PvsxNj2zhjie2sac7e4CXSDrWPvUya596marfxVgyfzoXnDSbZcfNpLa6HKrbwUVBRsqKxQ1s2LRrf4M/+fqPW7G4oaznIyIiIhIqh7PsbwBvBe4zs5XAHAAzWwAswTekshv4VqkKeLCrisc469iZnHXsTPb19rH2qe3ctGELdz+5nb29fVnz9fQlufPJbdz55DbGVcY469gZLF84h7OOncG4yooR/AUHNwUZXmN9HZedffT+Ttg3tnVm7IS9fmoNly+bP+C+40ZqPiIiIiIhc86VugyY2XnAdcCkcBDggs924GLn3N0lKl7ZMLP1J5988snr168vdVEA6OpJcNeT21mzYTP3PPUyPYnC3tuqrapg2fEzWb5wDkvmH0J1XAHecLv98a37g4yu3kTGIGPGpGouXzafc46fWeriDqumlnZWr2umqcW3DNqXdFTEjNrqChrr61ixuGFIAq2Rmo+IiMhwWLRoEQ8//PDDzrlFpS6L5FcWQR2AmdUB7wVOA6YBu4C/Ar9wzrWVsmzlotyCuqjd+3q544ltrNmwhfufeZnevsLq1cRxcV63YBbLF87m9KMOobKi5F0njlkKMg7U3NpF06Z29vYkGF8Vp3Hu8Dx2OlLzERERGUoK6kaXsgnqJL9yDuqidnX18sfHt3LThs385blW+pKF1bEpNZWcd8JsLlg4m1OPmEZFgc3wS3EUZIiIiEg+CupGFwV1o8hoCeqiWvd0c8tjW1nzyGYe3NhGodVt+sRq3nDCLJafNIdFDVPydpwtIiIiIkNHQd3oUg4NpQBgZjOAU4ApQMaXrJxz/zWihZJBmzahmnefdhjvPu0wtnXs45ZHt3DTI5t5uLk9Z76Xd3dz7QMvcu0DLzJ7Rj/mWAAAIABJREFU8jjOP3E2y0+aw0lzJ2OmAE9EREREJFTyO3VmVgn8GHgPvpPxjMkA55w7qFvUGI136rJ5qX0vN2/YzE2PbOHRl3YVnK9+6njOP3EOF5w0m+NnT1KAJyIiIjIMdKdudCmHO3VfAd4HPAf8Ct/JePaO0GRMOLRuPJcuOZJLlxzJxh2d3Bzcwfv71t0587W07eXH9z7Hj+99jiMOqWX5wtlccNIcjp45cYRKLiIiIiJSXsrhTl0z0AW8wjm3t6SFKXNj6U5dNs9u381Nj2xhzYbNPPdyZ8H5jpk5keUL/SOahx9SO4wlHNvUiIqIiIiA7tSNNuVwp24G8EMFdAJw1IyJfPSciVy+7Gie3LKbNRs2s2bDFpqDjrOzeWrbbp66YzffvuNpFsyZxPKFc1i+cDb1UxWQFELdHYiIiIiMXuUQ1DWT6nRcBAAz4/g5kzh+ziQ++bpjePSlXazZsIU1j2xm8659OfM+vrmDxzd3cOVtf6exvo4LTprD+SfOZtbkcSNU+tElW8fk+3qTtHZ2s2N3Dxs27RrTHZPrDqWIiIiMZuUQ1K0CPmxmk51zhbeYIQcNM2Ph3DoWzq3jM+cdy99adnLTI1u4+dEtvLy7O2feppZ2mlra+erNT7D4sKlccNJsXn/ibA6ZUD1CpS9vTS3tXHPXM7S0dTGhOs68ibUHdB+RTDp2dvXQ0tbF1Xc+zfSJ1WPqjp3uUIqIiMhYUA7v1MWA64AjgE8B651zHSUtVJk6GN6pK0Zf0vHgC22s2bCZWx/bSltnT0H5YgavOnIaFyycw3knzKKupmqYS1q+Pvu7Ddz5xHYqK4xpOQLd1j3d9PY5lh0/g2+8ZeEIlnD4ZLtDmUg6OnsS1FTGmTGpekzfoRQREclG79SNLiN+p87MkkCmSNKAO4M0mbI651w53FmUMlERM1515DRedeQ0vvTGBfzluVbWbNjMbY9tpWNf9gZUkw7+/Gwrf362lStufIzXHH0IyxfO4dwFM5k0rnIEf0FpNbd20dTSTldvgnkTczcuM6Wmio1tnTS1tNPc2jXqH0082O9QioiIyNhSiiDpPjIHdSIDFq+IsWT+dJbMn85X33Qi9z/zMms2bOGOJ7axpzt7gJdIOtY+9TJrn3qZqt/FOOOY6SxfOJtlx82ktnpsX0No2uQfOaytih8Q0GQSixm1VXE6u/to2tQ+6oO61eua2d7RzYTqeMY7lLFY6s7l9o5uVq9rVlAnIiIiZWvEz1qdc0tHep5ycKmKxzj7uJmcfdxM9vX2sfap7dy0YQt3P7mdvb19WfP19CW544lt3PHENsZVxjjr2BlcsHAOZx47g3GVY6/f+709CfqSjniegC4Ujxl9ScfentHdjeTBfIdSxgY17CMiIunG9q0IOeiNq6zgvBNmc94Js+nqSXDXk9tZs2Ez9zz1Mj2JZNZ8+3qT3PLoVm55dCu1VRUsO34myxfOYcn8Q6iOj40Ab3xVnIqglctCJJKOcZUxxleN7t3GwXyHUkY3NewjIiLZlPzszMyeB652zn03R5oPAx93zh0xciWTsaamKs4FJ83hgpPmsHtfL3c8sY01G7Zw/zMv09uX/Yngzp4+/tC0mT80bWbiuDivWzCLC06aw6uPnEZlRWwEf8HQapxbR211Ba2d3SSTLmeAkwwaD5k2oYrGuaP7pPFgvUMpo5u6HhERkVxKHtQB84B8Z4l1wGHDXxQ5WEwcV8lbTp7LW06eS3tXD398fCtrNmzhL8+10pfMHuDt3pfghvWbuGH9JqbUVHLeCbO54KTZnHr4NCoKDBLKRcO0Ghrr69ixu4edXT05W7/c2dVDTWWcxvrR/5jXwXqHUkYvNewjIiL5jJazlAlAYe3VixSprqaKFYsbWLG4gR17urn1sa2seWQzD25sI1ePHzu7ernuwWaue7CZ6ROrecMJs1h+0hwWNUzJ+1hfuVixuIENm3bR0tYF+HfIMp0s7ulOUD+1hhWLG0pV1CFzsN6hlNFLDfuIiEg+JQnqzCz9zLAuwzCACqABuBh4ftgLJge9QyZU8+7TDuPdpx3Gto593LxhC2s2bObh5vac+V7e3c21D7zItQ+8yOzJ4zj/xNksP2kOJ82dnK2LjrLQWF/HZWcfvf+xro1tnRn7a6ufWsPly+aPiRPFg/UOpYxOathHREQKUao7dRs5sFuDy4K/bAz42HAWSCTdzEnjeP9rDuf9rzmcTTu7ggBvC4++tCtnvi279vGzP73Az/5/9u49vu3yPPz+59bJsh0f4rMU23UITshJNglJIQ0NJQkkxIYXPYx2G332lPUp28pKeX6/7WlLS1t62tp1QLf21/Uw1o2uabtCa6dAQiAlkDaEJLZywDk6yI7k8zGSLVnS/fwhOTXBlp0Q6+Tr/XrxciR9b/sSmEiX7uu+rldaqSjIpM5hp85hY5ktNykTvNuWl1GSa31bAwar2RDZoUrDBgxzcYdSpCZp7COEEGImEpXU/YRIUqeAjwJOoGmS60JAL7Bba70zfuEJ8Vbl87P4xIZFfGLDIs71eGl0uml0emjpGI65rq1vhO/tOcP39pzhmqJs6hw26mvsVJfmxCnymamtyKe2In/OtEqfizuUIjVJYx8hhBAzkZCkTmv9F+N/Vkp9FHhaa/3lRMQixOWqKsrmk7dW88lbqznVOUxjtETzTLc35rqzPV6eePE0T7x4miWlOdQ5IiWaC4til1TFU2VhVlomcZOZizuUIvVIYx8hhBAzkfC/9bXWqdsTXsx51aU5fHpzDg9uquYNz/DFHTxXtKxvKic6hzmxa5h/2nWSFQtyqXPY2bbSRkXB3EioksVc26EUk0vm//7S2EcIIcRMJDypEyIdKKVYZs9lmT2X/337Eo6cH6Sh2c0Opwf34GjMtUfPD3H0/BDfeLaF6yvzLyZ4ZXnWOEUv5tIOpfijVBjmLY19hBBCzIQkdUJcZUopHOX5OMrz+czWpRxu66eh2cOOIx66h/0x1x52DXDYNcBXdhxnTVUB9Q4bW1faKIrxRk4IcflSaZi3NPYRQggxHaVjDeISSUUpdXDVqlWrDh48mOhQxBUIhTWvtfbR4HTz3NEO+rwzG71oULBuURF1DhtbVpSRn2WZ5UiFSG9NbQN87ukjF4d5T5ckffXulQnfsZsqCZ3Y2KckNyMpklAhRHpYvXo1hw4dOqS1Xp3oWMT0JKlLIZLUpY+xUJjfn+mlodnN88c6GBqdWac6k0GxvrqIOoed25aXkms1z3KkQqSfz/zKyQvHuzAbVcxyxt4LfsZCmk3LSvj6+x1xjHByqVAuKoRIH5LUpRYpvxQiAcxGA+9dXMx7Fxfz1btXsvdUN41OD7uOd3LBP3WCFwxr9pzoZs+Jbiy/MrBhSTF1DhublpaSnSH/OwsxnVQe5i2NfYQQQkwl7u8ClVJ/C/xBa/1avH+2EMnIYjKwcWkpG5eWMjoWYs+JLhqcHna/0RmzjXkgFGbX8U52He/Eajaw8bpS6hw23nddCVazMY7PQIjUkQ7DvKWxjxBCiEsl4qP9x4AvAq8BKKVCwBe11o8mIBYhkorVbGTLChtbVtjwBYLsfqOLhmY3e052EwhOneCNjoXZcSTSjCXbYmTzslLqHHZuXlxEhkkSPCHGyTBvIYQQ6SgRSd0oMPEQg4r+I4SYIMtior7GTn2NneHRMXYd76TR6eHlk90Ew1OfhfUGQjzT5OaZJjc5VhNblpdRV2Nn3aJCzMbYYyGlrEukOxnmLYQQIh0l4lWqFbhdKfWE1rozep90axEihhyrmfevKuf9q8oZ8AV4/lgHDc0e9p3pIUZ+x/BokF8cbOcXB9spyLawZUUZdQ4b715YiHHCToU0YBBzhQzzFkIIkY4SkdR9n0gJplupiy+mX1RKfXGadVprLR+VijkvP8vCPWsquWdNJT0X/Dx7tIOGZjcHzvURq5ltnzfAT/e7+Ol+F8U5GWxbaaPOYaPngp/vvHg6JeZ1CfFOyTBvIYQQ6SjuSZLW+gmlVBewDbAD7wNcwLl4xyJEqiual8G9N76Le298F51Do+xwemh0ujnkGoi5rnvYz5P7zvHkvnOYDAqDglyrmXfNz8I4oURzfF5XW5+Px144SXFOhuzYiZQnw7yFEEKkm4TsfGmtfwb8DEApFQb+XWv95UTEIkS6KM218rH1C/nY+oW09/uiCZ6HI+cHY64bP5/X4w0wODpGfqaFvEwzVrMBg+GPc7y6hvxsP+CSpE6kvNqKfD61sfriMO9zfd5Jh3lXFGTx4KbF8jsvhBAi6SVDOeOXgD2JDkKIdFI+P4tPbFjEJzYs4lyPl0anm0anh5aO4ZjrxkKa7gt+ui/4sZgM5Geaycs0J928LiHeqduWl1GSa33bWVKr2RA5QydnSYUQQqSQhCd1WusvJToGIdJZVVE2n7y1mk/eWs3prmEamiMlmme6vTHXBYJhuob9dA37yTAZMBkUA76xpJrXJcQ7IcO8hRBCpIuEJ3XjlFI3An8JXA/kA4PAQSKlmfsSGZsQ6eLakhw+vTmHBzdV89gLJ/nRK+cYGYvsUMTiD4bxExmX8A/PvoF7YIRtK21UFFydN77yplokkgzzFkIIkeqSIqlTSn0F+Axvn1dXC3xMKfUPWuvPxj8yIdKTUopFJTkUZFsYCQTJzTQzODLG4MgYY6HYCd75gVG+8WwL33i2hesr86lz2Nm20kZZnvWy45BRCkIIIYQQ71zCkzql1IeAzwJvAo8CLwIewAbcCnwe+HulVJPW+ucJC1SINDNxXldJjpWsPBNluVZ8gdDFBC/WkHOAw64BDrsG+MqO46ypKqDeYWPrShtFMdrEj9t5rONiowoZpSCEEEIIceUSntQBDwCdwBqtdc+E+88BP1ZK/QY4CvwNIEmdEFfJZPO6lFJkZ5jIzjBhy7PiDYQY9I0xMBKIOeRca3ittY/XWvt45DfHWLeoiDqHjS0rysjPsrzt+qa2AR7ffYq2Ph/zMkxU5WRP2lJeRimIZCZlw0IIIZJFMiR1NcBPLknoLtJa9yilfgF8NL5hCZH+Ys3rUkqRZTbiN4fIDhkpyM7g2pJ5HDjXx9BocMrvGdbwyukeXjndw8PPHGV9dRF1Dju3LS8l12oGYPsBF11DfuZlmCYd/iyjFEQyk7JhIYQQySYZkjoT4JvmGh8JjFUpdTPwILAOKAD6gCPAY1rr315y7TrgYeBGwAqcBn4MfEdrHYpn3EJMZ6bzuioLsy+WQQaCYfae6qbR6WHnsQ68gal/rYNhzZ4T3ew50Y3lVwY2LCnmpmsKOfRmP76xIFU52THjk1EKItlI2bAQQohklAxJ3WmgTin1Ga11+NIHlVIG4A7gTNwji/z8h4mc9esBGomc9ysi0qXzFuC3E669C/gfYBTYTiT5qwf+GXgP8KE4hi7EjFzuvC6LycDGpaVsXFrK6FiIPSe6aHB62P1GJ6Njb/tf+KJAKMyu453sOt6Jin6fYX+QHKsJg7q0R1KEwaDItpjw+kMySkEknJQNCyGESFbJkNT9N/A14NdKqYe01qfGH1BKLQK+CSwDPhfvwKJNXB4FXgDer7UevuRx84Q/5wI/AELALVrr16P3f55I85cPKqU+rLX+WbziF2KmrnRel9VsZMsKG1tW2PD6g+xu6aKx2c2ek90EglMneJrImARXnw+DghyrmfxMM/MmSfBMBkUorBkJTF3yKUQ8SNmwEEKIZJUMSd23gS3ANmCrUspNZDesDFgAGIBXotfFTXSH8B+IlH7+6aUJHYDWemzCzQ8CxUTOB74+4ZrR6G7fbuCvAEnqRNJ6J/O6sjNM3Flj584aO8OjY+w63klDs5u9p3pidtEMay522zQoyMs0k5dpZl6GCaUiZaBWs4FMSzL8dSXmKlevj6a2ASkbFkIIkZQS/i5Jax1QSm0G/hfwMWARUB59+AyR82jfuiSBiod1wELgl0C/UmobsIJIaeVrWuvfX3L9rdGvz03yvV4mkhyuU0plaK39sxSzEEkhx2rm/avKef+qcgZ8AZ4/1kFDs4d9Z3pidtEMa+j3jdHvG8NoUORaTYyMhSjINlNbLjseInGa2iOlydkW01tKLicjZcNCCCHiLeFJHVzc8fo68HWl1DwgDxjUWl9IYFhrol87gUPAyokPKqVeBj6ote6O3rUk+vXkpd9Iax1USrUCy4FrgDdi/WCl1MEpHrpuZqELkTzysyzcs6aSe9ZU0nPBz7NHO/j2zhP0+2J/ThMK64vXnOv18aNXzlJXY2d15fxp31QLcbWNBIKEwhrTDH/3pGxYCCFEPCVFUjdRNJFLZDI3riT69X6gFdgE7AfeBfwTcDvwCyLNUiCSiAIMTvH9xu+X7QYxZxXNy6A0J4P5WRaGRsYIxZ5tftHoWJj/+P2b/Mfv38SWZ2XbSht1NXZqyvNQUzRZEeJqyrSYMEa7XM6ElA0LIYSIJ3m1mZox+lUR2ZFrjt4+ppS6m8iO3Aal1E2TlGJOZvyd57RvY7XWqyf9BpEdvFUz+FlCJKXx7oE9F/zkZZrxBoKEwpFdOaWIWZo5zjM4yg9faeWHr7RSUZBJncNOncPGMluuJHhi1tSW55OdYaTX6ycc1jF3i8PRcSCF8yxSNiyEECIuDIkOIIn1R7+enZDQAaC1HgGej95cG/06vhOXx+RyL7lOiDlnYvfABfOzKJ+fRa7VRIbZgMlgwGxUmA0Kg/rjpyCxtPWN8L09Z9j2xCts/Pbv+Pauk5zueltPIyHescrCLGor8skym+j3BWJe2+8LkGU2UVsRu3usEEIIcbXITt3UTkS/Dkzx+HjSlznh+huAxcBbzsQppUxEmq4EgbNXN0whUsNk3QOzLCayLCYCwTC+sSDhMBgMYDUZcQ+OUJqTwXurS3j5dDdnu70xv//Zbi9P7D7FE7tPcV1ZzsUSzYVFsTsVCjFT96ypxNk+SFufD4h0uZxsTt0Ff5CKgizuWVOZqFCFEELMMZLUTe1lIklYtVLKorW+9KPZFdGv56JfXwT+jMh4hv++5Nr3AlnAy9L5UsxVsboHWkwGLCbLW+7LtpgIhmFV1XweuXMZb3iGaXC6aXS6aesbifmzWjqGaekY5p92nWTFgtyLJZrl82XX5Gq43HmG6aK2Ip9Pbazm8d2n6Bryc67PS7bFhMkQGb3hDQTJMpuoKMjiwU2LZUadEEKIuJGkbgpa6x6l1HYiidoXgIfHH4uOYLidSCnl+AiDXxKZa/dhpdR3JgwftwJfiV7zvTiFL0TSeSfdA5VSLLPnssyey9/dvgRn+yCNTjeNTg+ewdGY3+fo+SGOnh/iG8+2cH1lPnUOO9tW2ijLs16NpzWnNLUNsP2Ai6a2SIIeCmuMBkV2hpHainzuWVOZ9onMbcvLKMm1vu3fg9VsiJyhmyP/HoQQQiQXSepiewh4N/A5pdR7gdeIdL+8GwgBH9daDwBorYeUUh8nktztUUr9DOgD7iQy7uCXwPb4PwUhksPV6h6olKKmIp+ainw+s3Uph1z9NDo9NDo99FyIvRF+2DXAYdcAX9lxnDVVBdQ7bGxdaaNoXsYVP6+5Yuexjos7VL6x4MUdqtGxML1ePz3DAZztgzy4aTGbl5UmOtxZVVuRT21F/pzdsRRCCJF8JKmLQWvdpZR6N5FduruBG4FhYAfwda31Hy65/hml1Abgc8AHACtwmkhy+ITWeoYN3IVIP7PRPdBgUNxQVcANVQV8vm4Z+1t7aXR6ePaIJ+YcPK3htdY+Xmvt45HfHGPdoiLqHDa2rCgjP8sy5bq5arxraVufj3kZJqpysic9S9bW5+OxF05SnJMxJ3aqKguzJIkTQgiRFOKe1CmlvnCFS7XW+tGrGszMfmgfkaTsoRle/ypwx6wGJUQKGu8e2DMcoN8XoDDG7tiVdA80GhTrFhWxblERX7pzOfvO9NLY7Ob5Yx0MjU49ADqs4ZXTPbxyuoeHnznKzdVF1DnsbF5eSq7VfNnPMx1N7Fo62X83g0FdvL9ryM/2A66USepkt00IIUQ6SMRO3RcnuW/iDpaa5H4V/XPckzohxNUTr+6BZqOBDYuL2bC4mK/cvYK9J3todLrZdbwTbyA05bpgWPPSiW5eOtGN5WkDtywupq7GzqalJWTN0SHSk3Utncr8LAvn+rw0tQ3g6vUldXIk5wOFEEKkk0S8S3nfJPd9msju1lPAHqADKIte+6dEyh0fi1N8QohZkojugRkmI5uWlbJpWSmjYyH2nOiiodnD7pbOmOf7AsEwO493svN4J1azgY3XlVJfY+OWJSVYzcZ3HFeqiNW19FIGgyLbYsLrD9HUPpC0SZ2cDxRCCJFu4p7Uaa1/N/G2UuqjwGbgRq31oUsu/w+l1L8QGS/wqziFKIS4TJdTwpbI7oFWs5EtK2xsWWHD6w+yu6WLxmY3e052EwhOneCNjoXZccTDjiMesi1GNi8rpb7Gzs3VxVhMhqseZzJ5J11Lk5GcDxRCCJGOkqGe6NPA9kkSOgC01q8rpX4eve4/4xqZECKmKy1hS4bugdkZJu6ssXNnjZ2h0TF2Heuk0elm76keguGpexp5AyGeaXLzTJObXKuJ25eXUV9jZ92iQkzG9EvwrlbX0mSRzucDhRBCzF3J8Kq7BPjtNNe4gQ/FIRYhxAxdjRK2ZOkemGs184HV5XxgdTn93gDPH+ug0elh35keYuR3DI0G+cXBdn5xsJ2CbAtbVpRR57Dx7oWFGGe4s5XsZqNraaKk6/lAIYQQIhmSuiHgPdNcsx64EIdYhBAzcDklbP/4XAstHUOU5GSkRHfB+dkWPry2kg+vraR72M9zRz00OD0cONdHrKEkfd4AP93v4qf7XRTnZLBtpY06h41VlfOnPYuWzGa7a2k8peP5QCGEEAKSI6nbAfyFUupbwJe01sPjDyilcoh0y3wP8O+JCU8IcamZlLBlWowM+MY42+Pl//zuDPmZlpTrLlick8G9N1Vx701VdAyOsuOIh0anm8OugZjruof9PLnvHE/uO4ctzxpJ8Grs1JTnoVTqJXjx6lo629LtfKAQQggxLhmSus8AtxA5M/eXSqkmoBMoBWqBXOAs8NlEBSiE+KOZlLANjYzROTxKMKQJhTX+sTBjljBjIVK2u2BZnpX71i/kvvULae/3scPpocHp5uj5oZjrPIOj/PCVVn74SisVBZnUOezUOWwss+WmTIKXiK6lsyHdzgcKIYQQ4xL+SqW17lJKrQG+QWR8wXsnPOwDfgB8Vmvdm4j4hBBvNV0Jmy8QpHN4lEAwjFEpjAoMSpFhNpCfaUmL7oLl87P4xIZFfGLDIlp7vOxwuml0emjpGI65rq1vhO/tOcP39pzhmqJs6mrs1DtsVJfmxCnyK5fIrqVXSzqdDxRCCCEmSnhSB6C17gP+H6XUXwPXAXnAINCitZa6FyGSyHQlbH3eAMGQxqgUJqOBYDiMRhOObo6kW3fBhUXZfPLWaj55azWnOodpcEZKNM92e2OuO9vj5Yndp3hi9ymuK8uhzmGjzmGnqih2A49ESoaupe9EOp0PFEIIISZKiqRuXDSBO5roOIQQU4tVwhYIhhkJhAhrjSXa3l/ryE6d4ZJu/+nYXbC6NIeHNufw6U3VvOEZpsHpptHppq1vJOa6lo5hWjqG+dbOk6xckEedw8Y2h43y+cn57yRZupZeiXQ5HyiEEEJMlDRJnVLKDGwElgLztNaPRu+3EjlX16O1ntlBCCHErIlVwuYLBAlHkzilVGSHTmsMBgiGNH3eAAYFWRYTFpMhbbsLKqVYZs9lmT2Xv7t9Cc72QRqjJZqewdGYa4+cH+TI+UG+/mwL11fmU++ws81hozTXGqfoU8eV7Bimy/lAIYQQYqKkSOqUUluAHwFlgAI08Gj04VrgVeDPgf9OSIBCiItilbCFNWg042neWCiM1hAKaXovBKKPKQzKT6bFiFGlf3dBpRQ1FfnUVOTzma1LOeTqp9HpodHpoeeCP+baw64BDrsGeHTHcdZUFVDvsLF1pY2iGGWDc8GVDr0flw7nA4UQQoiJEp7UKaVuAJ4Beoh0wFwLfGT8ca31H5RSrcDdSFInRFKYqoTNoEChCKMJhEKEonvrYQ1KR5K9MJpgWBMcjQx9y1fMme6CBoPihqoCbqgq4PN1y9jf2kuj08OzRzz0+8amXKc1vNbax2utfTzym2OsW1REfY2N25eXkZ9lieMzSLyrMfQeUv98oBBCCDFRMryT+jyRLpc3aK07lFKPTHLNAWBVfMMSQkxlqhI2ouWWwfAfp3QblcJsVG9p36+j1wTDmuHR4MXzd3OJ0aBYt6iIdYuK+NKdy9l3ppfGZjfPH+tgaHTqncuwhldO9/DK6R4+9/RRbq4uos5hZ/PyUnKt5jg+g/i7nKH3M+2smsrnA4UQQohxyZDUvQd4RmvdEeOaNmBbnOIRQszAVCVsGeYQIX8ITWR4s3mShE0phVI6srOn4Hcnu9iyoiz+TyJJmI0GNiwuZsPiYr5y9wpeOdVDQ7ObXcc78QZCU64LhjUvnejmpRPdWJ42cMviYupq7GxaWkJWGu5+zmTofTp1VhVCCCFmKhle9ecRKb2MJQuYex/lC5HkJithO9k5zH/+wUUgGCZywi5yjm6cJjKQPBTWmI2R+9OpA+Y7lWEysnFpKRuXljI6FmLPiS4amj3sbumMOTQ7EAyz83gnO493YjUb2Li0lHqHjVuWlGA1G+P4DGbHTIbej0vHzqpCCCFELMmQ1J0Hlk9zTS1wNg6xCCGuwMQStt80u/l1k5uB6BmxQDAc7YYZORsW1hqDUlhMBkpzrYwEQmnZAfNqsJqNbFlhY8sKG15/kN0tXTQ2u9lzoptAaOoEb3QszA6nhx1OD9kWI7ctL6POYePm6mIsptT8fGy6ofcTGQwqbTurCiGEEJNJhqTuWeB+pdR6rfUrlz6olNoKrAO+EffIhBB9B8EqAAAgAElEQVSXbSQQJMNkJD8rcsbJNxYiHI7s0BmUwmRUZJmNzM+2kGUxMRYMp00HzNlsupGdYeLOGjt31tgZGh1j17FOGp1u9p7qecsZxkt5AyGePnyepw+fJ9dq4vblZdTX2Fm3qBBTCp1lnG7o/aVMhvTvrCqEEEKMS4ak7uvAh4GdSqnvAFUASqltwHuBvwE8wLcTFaAQYubGh5MblMI2P5NAMIxvLEg4DAYDZJlNb9ktCkZbyadyB8x32mL/cuVazXxgdTkfWF1OvzfA88c6aHR62Hemhxj5HUOjQX5xsJ1fHGynINvClhWRHbx3LyzEOMNkKVFiDb2fTDr8XgkhhBAzlfBXO631eaXUbcDPgf894aHfEJlZdwZ4v9Z6unN3QogkcOlwcovJgMU0edv9cHTYc+E8C7XlqdnQ4mq12L9S87MtfHhtJR9eW0n3sJ/njnpocHo4cK4PHSPB6/MG+Ol+Fz/d76I4J4NtK23UOWysqpw/bXljIsQaen+pdPi9EkIIIS5HwpM6AK31IaXUEiIdLm8CCoFB4A/Ar7XWUj8jRIqINZz8Uv2+AFlmE7UVqTkbbDZa7L8TxTkZ3HtTFffeVEXH4Cg7jnhodLo57BqIua572M+T+87x5L5z2POsbHPYqHPYcZTnvWUURSLNpd8rIYQQ4nIlPKlTSjm01k6tdYjI7txvprjuPq31j+IbnRDiSkw1nHzceLJzwR+koiCLe9ZUJirUdySZW+yX5Vm5b/1C7lu/kPZ+HzucHhqcbo6eH4q5zj04yg/2tvKDva1UFmSxzWGj3mFnqS0n4QneXPm9EkKIRHP1+hgcGcOQlVeY6FjEzCgdqz4nHgEo1Q7cqLVuj3HNvcCPtdbpPVl3Gkqpg6tWrVp18ODBRIcixLSmKksMRkvjsswmSnIzZq0scba5en184r9ex9Xno6oge9pywHN9XioLsvj+n9+Q0N2j1h4vO5xuGpo9nOgcnvG6a4qzqXPYqXfYqC7NmcUIY0v33yshhEikiWfEX/nmX+IPjAUCXa1Tl0aIpJHwnTogB3gu2v3ybTVCSqkPAT8GTsU9MiHEFZtqOLnVbIicdZqFBiLxlKot9hcWZfPJW6v55K3VnOocpsHpobHZzdkeb8x1Z7u9PLH7FE/sPsV1ZTnURUs0q4piz4y72tL990oIIRLl0g/NYjXeEsknGZK6u4HfAr9WSm3WWgfGH1BK3QU8BZwDNiYmPCHElZpsOPnVbvWfKOnQYr+6NIeHNufw6U3VHPcM0ej00NDspr1/JOa6lo5hWjqG+dbOk6xckBdJ8GrsLMjPjEvc6fx7JYQQiTDZGfHzBsVYOJQ8L1oipoQndVrrF5VS9wE/Af4L+BMApdQdwHbADdyqtfYkLkohxDsxcTh5ukinFvtKKZbb81huz+Pvbl9Cc/sgjc1udhzx4Bkcjbn2yPlBjpwf5OvPtrCqMp86h51tDhuludZZjzsdf6+EECIRpjsjLpJfUry70Fo/pZRaAHxDKfXPQCPwP0APkYSuLaEBCiHEJdK1xb5S6uJO2GfvWMpBV380weug54I/5tpDrgEOuQZ4dMdx1lYVUFdjZ+uKMorkDYIQQiQtV6+PprYBfGNBqnLiW1Ivrp6kSOoAtNb/qJSqBD4F/DUwAGzSWp9NbGRCCPF2c6HFvsGgWFNVwJqqAr5Qv5z9rb00NHt47qiHft/YlOu0hv2tfexv7eORXx9l3aIi6hw2tqwoIz9r8pmFQgghEuNyzoiL5JU0SV3UA4AduBnYqLVuSXA8QogUkKizVXOpxb7RoFi3qIh1i4r48l3L2Xeml8ZmN88d62B4dOojF2ENr5zu4ZXTPTz8zFFuri6izmFn8/JScq1zuqGxEEIkhcs9Iy6SU9yTOqVUGJhJP53mS2Yiaa11siWhQogEmth6ebwLotGgyM4wxqULYm1FPp/aWH2xW9i5Pu+kLfYrCrJ4cNPitOnIaDYa2LC4mA2Li/nK3SvYe7KHRqebXcc78QZCU64LhjUvnejmpRPdWJ42cMviYupq7GxaWkJWEp41FEKIueByz4iL5JSIV9GXmVlSJ4QQU5pqXtnoWJher5+e4QDO9sFZn1c211vsZ5iMbFpWyqZlpYyOhXippYtGp4fdLZ0x3yAEgmF2Hu9k5/FOrGYDG5eWUu+wccuSEqxmYxyfgRBCzG2Xc0ZcJK+4J3Va61vi/TOFEOllstbLk5U9tvX5eOyFkxTnZMz6jp202Aer2cjWlTa2rrTh9Qd54Y1OGp0efneim0Bo6gRvdCzMDqeHHU4P2RYjty0vo85h4+bqYiwmQxyfgRBCzD2Xc0ZcJC+pdxFCpJzpWi8bDOri/V1DfrYfcMVlp0xa7P9RdoaJu2oXcFftAoZGx9h5rJNGp5tXTvUQjDHR1hsI8fTh8zx9+Dy5VhO3Ly+jvsbOukWFmIyS4AkhxGyY7Iy4SC2S1AkhUsrltF6en2XhXJ+XprYBXL0+SbgSJNdq5oOry/ng6nL6vQGeP9ZBg9PN78/0EiO/Y2g0yC8OtvOLg+0UZFvYsiKyg/fuhYUYpTxICCGumsnOiI+FNRiMkiukiEQ0SvkCkTN1/6q17ovengmttX50FkMTQqSAy2m9bDAosi0mvP4QTe0DktQlgfnZFj68tpIPr62ke9jPc0c9NDg9HDjXh46R4PV5A/x0v4uf7ndRnJPBtpU26hw2VlXOl/MfQghxFVx6Rtwlf7WmFKVjvYrOxg/8Y/fLpVrrk9HbM6G11nP69LxS6uCqVatWHTx4MNGhCJEw2w+4eGL3abTWM6r7773gRynF3268NqVHCqS7jsFRdhzx0Oh0c9g1MON19jwr2xw26hx2HOV5XNI1WQghxBVw9fq49eYbaX3T9WbIO1CV6HjE9BKxpfq+6FfXJbeFEGJal9t6ORjtRJkpLfOTWlmelfvWL+S+9Qtp6/NdTPCOnh+Kuc49OMoP9rbyg72tVBZkURdN8JbaciTBE0KIK1RZmEVeppmwb7A30bGImUlE98vfxbothBCxXE7r5XB0VlzhPAu15ek5UiAdVRRkcf+GRdy/YRGtPV52ON00NHs40Tkcc52rz8d395zhu3vOcE1xNnUOO3fW2Li2JCdOkQshhBCJIR9dCyFSyuW0Xu73Bcgym6itmFujBdLJwqJsPnlrNZ+8tZpTncM0OCM7eGe7vTHXne328sTuUzyx+xTXleVc3MGrKordXEcIIYRIRZLUCSFSzmStlyebU3fBH6SiIEvO0qWJ6tIcHtqcw6c3VXPcM0RjNMFr6xuJua6lY5iWjmG+tfMkKxfkUeewsc1ho3y+JPpCCCHSQyIbpVwurbWe00moNEoR4o92Huu42HrZNxYk22LCZFAEoyWXWWYTJbkZPLhpMZuXlSY6XDFLtNY0tw/S2OxmxxEPnsHRGa9dVZlPncPONoeN0lzrLEYphBCpZ/Xq1Rw6dOiQ1np1omMR00tEUreHK0vq0FrP6aYqktQJ8VZNbQMXWy97/SFCYY3RoMjOMFJbkc89ayrjMnRcJIdwWHPQ1R9N8DroueCf0TqlYG1VAXU1drauKKNoBl1VhRAi3UlSl1rintSJKydJnRCTc/X6aGofYCQQJNNiorZcztDNdaGwZn9rLw3NHp476qHfNzajdQYF6xYVUV9j4/blZeRnWWY5UiGESE6S1KUWSepSiCR1Qghx+cZCYfad6aWh2c3zxzoYHg3OaJ3JoLi5uoj6Gjubl5WSYzXPcqRCCJE8JKlLLXP6jJoQQoj0ZzYa2LC4mA2Li/nq3SvYe7KHRqebXcc78QZCU64LhjUvnejmpRPdWEwGbllcTH2NnY1LS8iSuYdCCCGSiLwqCSGEmDMyTEY2LStl07JSRsdCvNTSRaPTw+6WzpgD7QPBMDuPd7LzeCeZZiO3Li2h3mHnliXFWM3GOD6DyyOlyUIIMTdIUieEEGJOspqNbF1pY+tKG15/kBfe6KTR6eF3J7oJhKZO8EbGQuxwetjh9DAvw8TmZaXU19hYf20xFpMhjs9gatJESAgh5hZJ6oQQQsx52Rkm7qpdwF21CxgcGWPX8U4amt28erqHYHjqs+cX/EGePnyepw+fJy/TzO3LS6lz2Fm3qBCTMTEJ3lTjPkbHwvR6/fQMB3C2D8q4DyGESCOS1AkhhBAT5GWa+eDqcj64upx+b4DnjnXQ6HTz+zO9xMjvGBwZ4+evt/Pz19spzLawZUUZdQ47axcWYDSouMTe1DbA47tP0dbnY16GiaqcbAwTfnY4rOn3BWjr8/HYCycpzsmQHTshhEgDktQJIYQQU5ifbeEjayv5yNpKuof9PHfUQ0OzhwNv9hGreXSvN8BT+108td9FSU4Gd6y0UV9j4/qK+W9Jsq627QdcdA35mZdhonCSeXsGg7p4f9eQn+0HXJLUCSFEGpCkTgghhJiB4pwM7r2pintvqqJjcJQdRzw0Ot0cdg3EXNc17OfJfed4ct857HlWtjls1NfYWbkgD6WuXoLn6vXR1DaAbyxIVU52zGvnZ1k41+elqW0AV69PmqcIIUSKS3hSp5SqnMFlYWBIaz002/EIIYQQ0ynLs3Lf+oXct34hbX2+iwne0fOxX6bcg6P8YG8rP9jbSmVBFnXRBO+6spx3nOA1tUeaomRbTNPuBhoMimyLCa8/RFP7gCR1QgiR4hKe1AHngBlNQFdKdQL/A3xJa90zm0EJIYQQM1FRkMX9GxZx/4ZFtPZ4aWx20+B0c7LzQsx1rj4f391zhu/uOcOi4mzqHHbqa2xcW5JzRXGMBIKEwhrTDMs7TQZFKKwZCcxsGLsQQojklQxJ3U+AdwEbgAGgCegESoFaIB/YA3iBlcDfAHVKqbVa6+5EBCyEEEJMZmFRNg9srOaBjdWc7BymsdlNo9PD2R5vzHVnur08vvsUj+8+xXVlOdTX2Klz2HhXYewyyokyLSaM0S6XMxEMa6xmA5kySF0IIVJeMvxN/nXg98A3gK9qrS++8imlsoHPAx8HbgJOR28/AnwGeCju0QohhBAzsLg0h4duW8KnNy/muGeIRmekRLOtbyTmupaOYVo6TvDN50/gKM+jzmFjm8POgvzMmOtqy/PJzjDS6/UTDuuYJZjhsMYbCFI4z0JtuTRKEUKIVKd0rPZd8QhAqaeBfK31+2Jc8xLQr7V+f/T2ISBHa10dpzCTglLq4KpVq1YdPHgw0aEIIURKcPX6aGofYCQQJNNiorY8P6Hnx7TWNLcP0tjsZscRD57B0RmvXVWZT32NnW0rbZTkWie95jO/cvLC8S7MRjVp98txvRf8jIU0m5aV8PX3Oy77eQgh0t/q1as5dOjQIa316kTHIqaXDDt17wX+zzTX7APun3D7D8D/PWsRCSGESGlNbQNsP+CiqS3SPCQU1hgNiuwMI7UV+dyzpjIhrfyVUtRW5FNbkc9n71jKQVd/NMHroOeCP+baQ64BDrkG+HLjcdZWFVBfY2frirK3JG/3rKnE2T5IW58PiHS5nGxO3QV/kIqCLO5ZM5NeZUIIIZJdMiR1GUDZNNfYoteNuwDIyW4hhBBvs/NYB4/vPkXXkB/fWJBsiwlT9KxZr9dPz3AAZ/sgD25azOZlpQmL02BQrKkqYE1VAV+oX87+1l4amj08d9RDv29synVaw/7WPva39vHIb46xblEhdQ4bty8vo7Yin09trL74/M/1eS8+/2C05DLLbKKiIIsHNy2WGXVCCJEmkqH88lWgBrhRa310kscdRHbqmrTW66P3/Ry4QWt9TVyDTTApvxRCiNia2gb43NNHaOvzMS/DNO1O1VfvXpl0ic1YKMy+M700Nrt57lgHw6Mz+wzTbFSsv7aI+ho7ZXlWGprdSbdTKYRIHVJ+mVqSYafuy8AO4IBS6r+AV/lj98v1wJ8BZuBRAKVUJnAb0JCQaIUQQiSt7QdcdA35mZdhmvRMmcHwx7NmXUN+th9wJV1yYzYa2LC4mA2Li/nK3SvYe7KHRqebXcc78QZCU64bC2leOtHNSye6sZgM3LK4mD+5oYJ5VhPhsE6KM4VCCCFmR8KTOq3180qpPyNyru4+4GMTHlbAIPAxrfXz0fsswD3AibgGKoQQIqm5en00tQ3gGwtSlRN7FMD8LAvn+rw0tQ3g6vUlbaKTYTKyaVkpm5aVMjoW4qWWLhqdHl54oxN/cOrRBYFgmJ3HO9l5vJNMs5GNS0uoc9gpyZ26eYoQQojUlfCkDkBrvV0ptQO4C7geyAOGgMPAr7XWwxOuHQSen/QbCSGEmLOa2iOlhtkWU8x2/hDZscu2mPD6QzS1DyRtUjeR1Wxk60obW1fauOAPsvuNThqaPbx8sptAaOoEb2QsFB2n4GFehonNy0qpr7Gx/tpiLCZDHJ+BEEKI2ZIUSR2A1voC8FT0HyGEEOKyjASChMIa0zQJ3TiTQREKa0YCqdd3a16GibtqF3BX7QIGR8bYdbyThmY3r57uIRie+qz8BX+Qpw+f5+nD58nLNHP78lLqHHbWLSrEZJQETwghUlXSJHVCCCHEO5FpMWGMdrmciWBYYzUbyLSk9kthXqaZD64u54Ory+n3BnjuWAeNTje/P9NLjPyOwZExfv56Oz9/vZ3CbAtbVpRR57CzdmEBxhkmxkKI9PSHM714Bkcx5ZW9K9GxiJlJmlcypdQNwFpgPmCc5BKttX40vlEJIYRIFbXl+WRnGOn1+gmHdcwSzHC0vX/hPAu15cnVKOWdmJ9t4SNrK/nI2kq6h/08d9RDQ7OHA2/2EavZda83wFP7XTy130VJTgZ3rLRRX2Pj+or505ayCiHSx89fb+P7vztDe/8IvV4/Bmt2YaJjEjOT8KROKZUL/Ap4H5HGKFPRRDtgCiGEEJeqLMyitiKfnuEA/b7ApN0vx/X7AmSZTdRWpG83yOKcDO69qYp7b6qiY3CUHUc8F8ccxNI17OfJfed4ct857HlWtjls1NfYWbkgD6UkwRMiXf3jcy38+NVW/MFw5EOgxE49E5cp4Ukd8E3gVmAv8O9AGzJYXAghxBW4Z00lzvZB2vp8ANPOqbtnTWWiQo2rsjwr961fyH3rF9LW52PHEQ+NTjdHzw/FXOceHOUHe1v5wd5WKguyqIsmeNeV5UiCJ0Qa+fnrbfz41VZGx8IYFFhMCqUU4XB4ZvXsIuGSYfh4B9AOrNVaJ/0vjlLqXuAn0Zsf11r/cJJr1gEPAzcCVuA08GPgO1rrqYcMTf+zZfi4EEJMY+exDh7ffYquIT++sSDZFhMmgyIYLbnMMpsoyc3gwU2L2bysNNHhJlRrj5fGZjeNTg8nOoenXxC1qDibOoed+hob15bkzGKEQoh42PhPezjb40URGaUC0PrDBwiHw+FA55nJjkWJJJMMO3V5wH+mSEJXAXwHuADMm+Kau4D/AUaB7UAfUA/8M/Ae4ENxCVYIIeao25aXUZJrZfsBF01tkTEHoWhTlMJ5Fmor8rlnTWXSDR1PhIVF2TywsZoHNlZzsnP4YoJ3tscbc92Zbi+P7z7F47tPcV1ZDvU1duocNt5VGHs+oBAi+fzhTC/t/SNoHdmhE6kpGZK6U0DSf1SqInUm/w70EjkD+L8muSYX+AEQAm7RWr8evf/zwIvAB5VSH9Za/yxugQshxBxUW5FPbUV+ZCB5+wAjgSCZFhO15el7hu6dWlyaw0O3LeHTmxdzzD0UnW3npr1/JOa6lo5hWjpO8M3nT+Aoz6POYWObw86C/Mw4RS6EeCeePdZBKKwxKDAoGW2SqpIhqftX4BtKqQVa6/OJDiaGvyVy9u+W6NfJfBAoBn4yntABaK1HlVIPA7uBvwIkqRNCiDioLMySJO4yKaVYsSCPFQvy+PstS2huH6Sh2c0Op4eOodGYa53tgzjbB/nab1tYVZlPfY2dbSttlORa4xS9EOJyXRgdi9kdV6SGZEjqniWSJL2qlPoScBCYtDWX1toVz8DGKaWWAt8AHtdav6yUmiqpG7//uUkeexnwAeuUUhlaa/8shCqEEEJcNUqpi7uen7tjKQdd/TQ0u/ntkQ56LsR+GTvkGuCQa4AvNx5nbVUBdTV2tq4ooyhGV1IhRPzNs5pRCknsUlwyJHXniDRNVcDbmo5MoElAvEopE/CfgAv47DSXL4l+PXnpA1rroFKqFVgOXAO8EeNnTtUJ5bppAxZCCCFmgcGgWFNVwJqqAh6pX87+s700OD08d9RDv29synVaw/7WPva39vHF3xxj3aJC6hw2bl9eRn6WJY7PQAgxma3Ly/jZay6CQU1Yh6UEM0UlQ1L3E5J7EsYXgOuB9Vrr2AcLIk1fAAaneHz8fjmdL4QQImUZDYp11xax7toivnzXcvad6aWh2c3zxzoYHp16KlEorNl7qoe9p3p4+Jmj3FxdTJ3DxuZlpeRYzXF8BkKIcTcuKqR8fiZne7yMhTQZyZAdiMuW8P9sWuu/SHQMU1FKrSWyO/dPWuvfX41vGf0aM4nVWq+eIp6DwKqrEIcQQghxVZiNBjYsLmbD4mK+evcK9p7socHp5oXjnXgDU0/xGQtpXmzp4sWWLiwmA+9bUkx9jZ1bryshy5LwtydCzCmf2LCIL/z6KKNjYfzBEGajdMFMNfK35hQmlF2eBD4/w2XjO3F5Uzyee8l1QgghRNrIMBnZtKyUTctKGR0L8VJLFw1ON7vf6MIfnHpyUSAY5vljnTx/rJNMs5GNS0uoc9i5ZUkxVrOMyBJitv3JDRWc6/Hy41db8QfD+IMarTVKSS1mqkj48PFkpZTKB/pnePnjWusHlVL/BfwZ8Kda6/++5PuZiCRzFmDelTRKkeHjQgghUtEFf5Ddb3TS0Ozh5ZPdBEIzG007L8PE5mWl1NfYWH9tMRaTvL8UYjb9/PU2vv+7M7T3j3DuRw+ABn/Hadm2SwFxT+qUUj8mUn74Wa11Z/T2TGit9X2zGNpbKKUyiQwan8wqIufsXgFOALu01tuVUh8DfkRkpMH/dcn3u5XISIOXtdYbrjCmtErqZH6UEELMPYMjY+w63klDs5tXT/cQDM/sfUheppnbl5dSX2PnpmsKMRklwRNitvzhTC/vv/29dPf09Y4NeIoSHY+YXiKSujCRpG6p1vpk9PZMaK11UtRgKKW+CDwCfFxr/cMJ9+cCZ4iUWb5nwvBxK5Hh4zcBH7nS4ePpktQ1tQ2w/YCLprYBvP4QobDGaFBkZxiprcjnnjWV1FZILxkhhEh3/d4Azx3roNHp5vdneplhfkdhtoUtK8qoc9hZu7AAo0E2EoS42lavXs2hQ4cOTdXrQSSXRJypWxj9ev6S2ylPaz2klPo48Etgj1LqZ0AfcCeRcQe/BLYnMMSE23msg8d3n6JryI9vLEi2xYTJoBgdC9Pr9dMzHMDZPsiDmxazeVlposMVQoikl8pVD/OzLXxkbSUfWVtJ97CfZ496aGz2cODNvpgzs3q9AZ7a7+Kp/S5KcjK4Y6WN+hob11fMxyAJnhBiDop7Uqe1fjPW7VSntX5GKbUB+BzwAcAKnAYeAp7Qc/gQY1PbAI/vPkVbn495GSaqcrLf8uIbDmv6fQHa+nw89sJJinMyZMdOCCGmkG5VD8U5GXz0pio+elMVHYOj7DjioaHZTVPbQMx1XcN+ntx3jif3ncOeZ2Wbw0adw46jPA+lJMETQswN0iglhaR6+eVnfuXkheNdmI2KwnkZU17Xe8HPWEizaVkJX3+/I44RCiFEapiq6iEY1ngDQbLMJkpyM9Ki6qGtz3cxwTvmHprxusqCLOqiCd5SW44keEJcJim/TC1JM9JAKVVPpHPkUiBba31t9P6lQD3wlNb6fIxvIZKYq9dHU9sAvrEgVTnZMa+dn2XhXJ+XprYBXL2+lCkjEkKIeJhrVQ8VBVncv2ER929YxNnuC+xwemhwujnZeSHmOlefj+/uOcN395xhUXE2dQ479TU2ri3JiVPkQggRPwlP6lTko7MngT+P3jUCZE64pB/4GpHB3f8Q1+DEVdPUHikPyraYpj3vYDAosi0mvP4QTe0DktQJIcQE2w+46BryMy/DNGnVg8Hwx2qIriE/2w+4Ujqpm+ia4nk8sLGaBzZWc6JjmB1ONw1OD6093pjrznR7eXz3KR7ffYrrynKor7FT57DxrsLYHzIKIUSqSIZ+wH8N3Av8O1AAfGvig1rrDuBVYFv8QxNXy0ggSCisMc3wALvJoAiFNSOB4CxHJoQQqWNi1cP8LEvMa+dnWfCNBS9WPaSbJWU5PHTbEl78fzfQ+MB67t+wiPL5mdOua+kY5pvPn2DDN/dw57+8wr+9fIbzAyNxiFgIIWZPwnfqgPuAZiLjAbRSarJDfqeA2+MblriaMi0mjNEulzMRDGusZgOZlmT4FRVCiOQgVQ9vp5RixYI8VizI4++3LKGpbYBGp4cdTg8dQ6Mx1zrbB3G2D/K137awqjKf+ho721baKMm1xil6IYS4OpLhHfMS4PvTdIXsAorjFI+YBbXl+WRnGOn1+gmHdcw3I+HoQf/CeRZqy9OjZEgIIa4GqXqITSnF9ZXzub5yPp+7Yymvv9lPo9PNb4900HPBH3PtIdcAh1wDfLnxOGurCqivsbN1RVnMxl5CCJEskiGpCxJp+x/LAiD2iWiR1CoLs6ityKdnOEC/LxDzRbLfFyDLbKK2InVmLQkhRDxI1cPMGQyKtQsLWLuwgEfql7P/bC8NTg/PHfXQ7xubcp3WsL+1j/2tfTzym2OsW1RIncPG7cvLyJ+m5FUIIRIlGf6WPw7copRSk+3WKaWswK3A4bhHJq6qe9ZU4mwfpK0vcrZjfpZl0o5tF/xBKgqyuGdNZaJCFUKIpCRVD1fGaFCsu7aIddcW8eW7lvPq6R4anR6eP9bB8OjUu5ihsGbvqR72nurh4WeOcnN1MXUOG5uXlZJjNcfxGQghRGzJkNT9J/AvwD8rpR6a+IBSygh8G7AD/zU9DY4AACAASURBVF8CYhNXUW1FPp/aWH1xttK5Pu+ks5UqCrJ4cNPitOnWJoQQV4tUPbxzZqOBW5aUcMuSEr569wpePtlDo9PNruOd+AKhKdeNhTQvtnTxYksXFpOB9y0pps5hZ+PSErLm4E6oECK5JMPfQt8H7gT+FvgQMAyglPolcCORhO7XWuunEhahuGpuW15GSa6V7QdcNLVFDvyHouVBhfMs1Fbkc8+aSknoEszV66OpfYCRQJBMi4nacnlTKESykKqHqyfDZGTzslI2LytlJBDipRNdNDrd7H6jC39w6hLXQDDM88c6ef5YJ5lmIxuXllDnsHPLkmKsZmMcn4EQQkSo2P1J4hSEUibgYeBvgMIJDw0A3wEe1VrPjVPeMSilDq5atWrVwYMHEx3KVSGJQ/Jpaht4W8JtNCiyM4yScAuRRHYe67hY9eAbC05a9VCSm8GDmxazeVlposNNORf8QXa/0UlDs4eXT3YTCM3sDOO8DBO3LSulrsbG+muLsZiSYXKUEFdm9erVHDp06JDWenWiYxHTS4qkblx0EPliIondINCitZ66FmKOSbekTiQXeZMoRGqRD2HiY3BkjJ3HOmh0enj1dA/B8MzeN+Vlmrl9eSn1NXZuuqYQk1ESPJFaJKlLLUmV1InYJKkTs6WpbYDPPX2Etj4f8zJM05ZzffXulfJmUYgkIVUP8dPvDfDcsQ4anW5+f6aXGeZ3FGZb2LKijDqHnbULCzDOcCSFEIkkSV1qSYYzdRcppW4GrgfyiOzUHdZa701sVEKkv+0HXHQN+ZmXYZq08YLBoC7e3zXkZ/sBlyR1QiSJysIsSeLiZH62hY+sreQjayvpHvbz7FEPjc0eXjvXF3NdrzfAU/tdPLXfRUlOBnestFFfY+P6ivnTDpEXQoiZSIqkTin1HuDHwLXjdwE6+tgp4D6t9asJCk+ItObq9dHUNoBvLEhVTnbMa+dnWTjX56WpbQBXr0/eSAoh5qzinAw+elMVH72pCs/gCDucHhqdHpraBmKu6xr28+S+czy57xz2PCt1NXbqHDZWLsgjcgpFCCEuX8KTOqXUamAXkQHkvwP2AB1AGfA+4L3ATqXUzVrrQ4mKU4h01dQeOY+TbTFN+4mxwaDItpjw+kM0tQ9IUieEEIAtL5O/vPka/vLma2jr87HjiIdGp5uj54dirnMPjvJvL5/l314+y7sKs6hz2Khz2LmuLEcSPCHEZUl4Ugd8lUgcd2mtGy557EtKqbuAX0av2xrv4IRIdyOBIKGwxjTDEiCTQREKa0YCc74hrRBiDpvqLGNFQRb3b1jE/RsW0drjpbHZTaPTw4nO4Zjf781eH//60hn+9aUzXFsy72KCd23JvDg9IyFEKkuGpG4d8KtJEjoAtNa/Vko9Ddwe37CEmBsyLSaMBsXo2MxadgejcwUzZdiuEGIOupyuowuLsnlgYzUPbKzmZOcwjc1uGpweWnu8MX/G6a4LPPbCKR574RTXleVQX2On3mGX6gghxJSS4V1ZGDg9zTWngNviEIsQc05teT7ZGUZ6vX7CYR2zBDMcHW9QOM9Cbbk0ShFCzC1TjX4ZHQvT6/XTMxzA2T446eiXxaU5PHTbEj69eTHH3EM0OiMlmu39IzF/ZkvHMC0dJ/jm8ydwlOdR57CxzWFnQX7mbD5VIUSKSYak7nWgZppraoDX4hCLEHNOZWEWtRX59AwH6PcFJu1+Oa7fFyDLbKK2QlqmCyHmlqa2AR7fferi6JeqnOxJR7+09fl47IWTFOdkTNolWCnFigV5rFiQx99vWUJT2wCNTg87nB46hkZjxuBsH8TZPsjXftvC6nfNjyR4K22U5Fqv+vMVQqSWZEjqHgb2KKX+Smv9vUsfVEr9DbARuCXegQkxV9yzphJn+yBtfT6AaefU3bOmMlGhCiFEQszG6BelFNdXzuf6yvl87o6lvP5mP41ON7890kHPBX/MtQff7Ofgm/18ufE4a6sKqK+xs3VFWcwP5oQQ6Svuw8eVUl+Y5O4biZyZOw3sBTqBUmA9UM3/z96dh8dVnncf/97SaLctWcLyhoQBL4CNLRazhmDAGAh2yEZoSANJX9I2SRNo0mYlhZCmWZtASJqSNglJkzRuE7JYIeBiIGyFmkUWGAwmRpaMZQtLlmRrZG3zvH+cM2YYz4y20ZwZ6fe5rrnGPuc5Z+6ZeWZ07nk2uAd43Dn3xUzFmY20+LhMpGTdigb9LpelBSGqZxQl7FYkIjKZNbeH+aufPklzR5gFlWXDdlNv6uihtrKUO/789DH1ahiKOJ7Y0c6Gxlb+8FwrneGBER2Xn2ecc3wV65bP45KlcygvLRj1Y4tEafHx3BJEUjey2RiO5Jxz+WkNJscoqZOJNpoJAEREporfbdnNN+59kUMDQ8weQVfHvd2HKC7I5+8uWcJbV8wb12MPDEV49OV91De2cu/WPRw4NLKZhwvyjfMWzWLdirmsPnE204uV4MnoKKnLLUF0v7wggMcUkRGoq6mgrqYi6VTdIhNFdU6yWZBLvxTk57FqSTWrllTzpbcv46GX9lHfuJv/eX4v4f6hpMcNDDnu39bG/dvaKAzlccGSWaxbMY8LT6imVLMXi0w6Gf9UO+f+mOnHFJHRqa0q1QW1ZIRahyUXZMvSL0WhfC4+aTYXnzSb3v4hHnixjfrG3Wx6oY2+weSx9Q9GuHfrXu7dupeSgnwuOrGatcvnsWrJLIoLpnQnKJFJQz/ViIhIIMYzPbxIJmXj0i8lhfm85eS5vOXkuRzsG2TTC3vZsKWVh156jf6h5Ale78CQv5xCK9OKQqw5aTZrV8zlTQtnURjKm7B4RWRiKakTEZGMS9f08CKZkO1Lv0wrCnFF3XyuqJtPV+8AG7fuob6xlUdf3sdgJPncCQf7BrnrmVe565lXKS8p4NKlc1i7Yi5nH1dFKF8JnkguUVInIiIZNxHTw8fSGD1Jt1xZ+qW8pIArT6/hytNr2N/Tzz1b97Bhy24e39FOivyOrt4B1j/ZwvonW6gqK+TSZXNYu3weZxxbSf4IxxKKSHCU1ImISEY1t4dpaOkkPDDIgullKcvOLC2kqaOHhpZOmtvDwyZmGqMnE6WupoLrL1p0uMtwU0dPwqVfaipLuWH14qyoZzPLCnnPGbW854xa2g4c4p7n9lC/pZX/a+pIeVx7Tz8/e6KZnz3RTPX0It5y8lzWrZjHqbUVmCnBE8lGSupERCSjGnZ5CVdZYSjl2CTwWuzKCkP09A3RsKszZVKnMXoy0dYsnUP1jOIjfjgoLsjzxtBl8Q8H1dOLuebsBVxz9gJau3r5vT+urqGlM+VxbQf6uPOxJu58rIn5FSVcvnwu65bPY9n8GUrwRLKIkjoREcmoiZgeXmP0JFMmw9Ivc8tLuO6847juvONo6Qj7E6fsZuvu7pTHvdrZy/cf2sH3H9rBgqpSLl8+l7XL53HCnOlK8EQClvGkzsyeBv7VOfd9///XAA3OucZMxyIiIpk3EdPDT/QYPZF4k2Xpl5rKUj606ng+tOp4drx28HCC99LegymPa2oP890H/sR3H/gTC6unsdZP8BZWT8tQ5CISK4ipjeqAOTH/vxN4WwBxiIhIAKLTw/f0DxJJNXMDr08PX1aUn3R6+NgxejNLC1Oeb2ZpIeGBwcNj9ETkdcfNmsbHLlrExr89n3tveDMfvXAhxx6VetwrwMttB7n1vu2s/uYfufTWh/juAy+zs70nAxGLSFQQ3S/bgaMCeFwREckC6Z4efqLG6IlMZUvmTGfJnCV8/OLFbN3dzYbG3dRvaeXVzt6Ux23bc4Bte17k6/e+yPKjy1m7fC6XL5/H/IqSDEUuMjUFkdQ1AO8zs1eBVn9bnd8NMyXn3E8mNDIREcmIdE4PPxFj9ETEY2Ysm1/OsvnlfPrSE2ho6WTDllbufraVPd2HUh7buKuLxl1d/NPd2zjtmJlegnfyXKpnFGcoepGpI4ik7tPA3cCXgWi/myv8WzLml1VSJyIyCaRzeviJGKMnIkcyM06pnckptTO58fITeXLnfuobd3P3s63sO9if8tindu7nqZ37uaX+ec48tpK1y+dx2bI5KVvq0y2XJ7cRGU7G/6I5554ys4XAGcB8vDF1v/VvIiIyRaRrevjoGL32nj4iEZeyC2Z0jF7VtMKkY/REZHh5ecYZx1ZyxrGV3LRuKU/saGdD427+8NweOsMDSY9zDh7f0cHjOzq46XdbOef4KtYtn8clS+dQXlowIbFq/UqZCsy51IPUJzwAswhws3PulkADyQFm9tSpp5566lNPPRV0KCIiaTXeX9A/c1cj9z3fRkG+pfzlv/1gHwNDjtUnVfPldyxPR+giEmNgKMKjL+9jw5ZWNm7dw4G+kXVzLsg33rxoFmtXzGX1ibOZXpyeBC/Z+pWxPQKqZxRp/coETjvtNJ5++umnnXOnBR2LDC8b+p4cC6Re+VJERCa18U4Pn84xeiIydgX5eaxaUs2qJdUcGljGQy+9Rn1jK/e9sJdw/1DS4waGHJu2tbFpWxuFoTwuXFLN2hVzufCEakrH2FVa61fKVBJ4Uuec2xn9t5kVACcAFUAX8IJzLnkbvoiICOkdoyci6VFckM+apXNYs3QOvf1D3L+tjfrG3dy/rY2+weRjYPsHI9yzdQ/3bN1DSUE+F51YzboV8zh/8SyKC/JH/Phav1KmksCTOgAzmwF8DXgfEDsl0iEz+w/g0845teaJiEhS6RqjJyLpV1KYz+XL53L58rkc7Btk0wt72bCllT++1MbAUPKhQL0DQ/6C6K1MLwpx8UmzWbtiLm9aOIvCUPLllmPXr1wwPfVaezNLC2nq6Dm8fqUmT5FcFHhS5yd0jwJLgQPAw3hLHczFW6j8L4E3mdk5zrnuwAIVEZGsV1dTQV1NhWa5E8li04pCXFE3nyvq5tPVO8DGrXuob2zlkZf3MRRJnuAd6Bvkrmde5a5nXqW8pIBLls5m3Yp5nH1cFaH8NyZ4Wr9SpprAkzrgM3gJ3feAz8W2yJlZOfCPwEf8cp8JJEIREQncaBK18Y7RE5HMKC8p4MrTa7jy9Bo6evq557k91Dfu5vEd7aTI7+jqHeC/ntzFfz25i6qyQi47eQ5rl89j5YJK8vNM61fKlJMNSd07gMedcx+J3+Gc6wI+amanAu9ESZ2IyJSj6chFpobKskKuPrOWq8+spe3AIe55bg8btuxmc9P+lMe19/Tz08eb+enjzVRPL+Ly5XOpKC0kz6AvRdfOWFq/UnJdNtTcWuBXw5T5I/C3GYhFRESySLLpyA8NRGjv6WPfgX4ad3VpOnKRSaZ6ejHXnL2Aa85eQGtXL79vbGVDYytbWlJPsdB2oI8fPdoEQCjfMLzunqWF+ZglbrXT+pUyGWRDUhcGqocpM8svJyIiU4SmIxcRgLnlJVx33nFcd95xtHSEqW9sZcOW3TzfmnqqhUG/lW7Hvh4K8/MoLymgvLSA4lDeGxK8/eF+SgtC1NVo7K3krmxI6jYDV5rZV51z2+N3mtnxwLuB/814ZCIiEhhNRy4i8WoqS/nQquP50Krj2fHawcMJ3va2gymP6x+K8NrBPl472EdRyEvwZhSHCPcPaf1KmRSyIan7OrAR2GxmtwMP4M1+OQdYBXwUmAZ8I6gARUQkszQduYgM57hZ0/jYRYv42EWLeHHPAeobd1Pf2Mor+3pSHtc3GKHtQB9tB/rIzzOqygp575nH6EchyWmBJ3XOuU1m9mHgNuCz/i3KgAHgb5xz9wURn4iIZJ6mIxeR0VgyZzpL5izh4xcvZuvubjY07qZ+SyuvdvamPG4o4mg70Mdnf/0sv9jczNrlc7l8+TzmV5RkKHKR9Ag8qQNwzt1hZn/AW3z8FKAc6AKeAX7qnNsZZHwiIpJZmo5cRMbCzFg2v5xl88v59KUn0NDSyYYtrdz9bCt7ug+lPLZxVxeNu7r4p7u3cdoxM70E7+S5VM8ozlD0ImOXFUkdgHOuGfhS0HGIiEjwSgpD5PuzXI6EpiMXkXhmxim1MzmldiY3Xn4iT+7cT33jbu5+tpV9B/tTHvvUzv08tXM/t9Q/zxkLKlm3Yh6XLZuTcHyvSDbQXz8REck6dUdXUFaUT3tPH5GIS9kFU9ORT16jWXBeJJW8POOMYys549hK/mHtSTzxSgf1jbv5w3N76AwPJD3OOXjilQ6eeKWDm363lXOOr2Ld8nlcsnQO5aUFGXwGIqkpqRMRkaxTW1VKXU0F+w70sz/cn/LXcU1HPvlowXmZSKH8PM5deBTnLjyKW65YxiMv76N+Sysbt+7hQF/yLtxDEcfD2/fx8PZ9fO43z/LmRbNYu2Iuq0+czfRiJXgSLCV1IiKSla5aWUvjri5aOrxlSmeWFiZcp07TkU8uWnBeMqkgP48LllRzwZJqDg0s46GXXqO+sZX7XthLuH8o6XEDQ45N29rYtK2NwlAeFy6pZu2KuVx4QjWl6gYuAVCtExGRrFRXU8H1Fy06fIHf1NFz+AJ/0O9yWVoQoqaylBtWL1bLzSSgBeclSMUF+axZOoc1S+fQ2z/E/dvaqG/czf3b2ugbTD6+t38wwj1b93DP1j2UFORz0YnVrFsxj/MXz6K4ID+Dz0CmMiV1IiKStdYsnUP1jOIjuuIVF+R5Y+jUFW9S0YLzki1KCvO5fPlcLl8+l4N9g2x6YS8btrTyx5faGBhySY/rHRiivrGV+sZWpheFuPik2axdMZc3LZxFYSgvg89ApholdSIiktXqaiqoq6nQpBmTnBacl2w1rSjEFXXzuaJuPl29A2zcuof6xlYeeXkfQ5HkCd6BvkHueuZV7nrmVcpLCrh06RzWrpjL2cdVEcpXgifppaRORERyQm1VqS7eJzEtOC+5oLykgCtPr+HK02vo6Onnnuf2UN+4m8d3tJMiv6Ord4D1T7aw/skWqsoKuXTZHNatmMfKBZXkj3A9TpFUsiapM7NZwDuBE4Ey59x1MduPBZ51zvUGGGJW2B/u53dbdusXahERmVS04LzkmsqyQq4+s5arz6yl7cAh/vCsl+Btbtqf8rj2nn5+9kQzP3uimerpRbzl5LmsWzGPU2srMFOCJ2OTFUmdmf0/4NtAMWCAA67zd88G/hf4S+AHgQSYRdq6+/jGvS9qWmcREZlUtOC85LLq6cVce84Crj1nAbs7e7n72VY2bNnNll1dKY9rO9DHnY81cedjTcyvKGHt8rmsXT6PZfNnKMGTUQn8m9DMLga+DzQCNwGXAH8d3e+ce87MtgJvQ0kdAIcGhjSts4iITCpacF4mi3kVJVx33nFcd95xNLeHqX92N/VbWnm+tTvlca929nLHQzu446EdLKgq5fLlXgvektnTleDJsAJP6oBPAa3A+c65bjM7JUGZRuDszIaVnUJ5xuwZxZrWWUREJhUtOC+TUW1VKR9etZAPr1rIn147SP2WVuobd7O97WDK45raw3z3gT/x3Qf+xMLqaYdb8BZWT8tQ5JJrsiGpOx34hXMu1c8Xu4A5GYonJ2haZxERmWy04LxMZsfPmsb1qxdx/epFvLjnAPWNu6lvbOWVfT0pj3u57SC33redW+/bzolzZ7B2+VzWLZ+nHzTkDbIhqSsEUtdmqACGMhBLztG0ziIiMllowXmZKpbMmc6SOUv4+MWL2bq7mw2NXhfNVztTzwn4Qms3L7R28/V7X2TF0eWsXT6Py5fPZV5FSYYil2yVDUldE3DaMGXOBF6c+FByj6Z1FhGRyUQLzstUYmYsm1/OsvnlfPrSE2ho6WTDllZ+/+xu9nb3pTx2y64utuzq4kt3v8Bpx8xk7fK5XH7yXKpnFGcoeskm2ZDU/Rb4pJld6Zz77/idZvYBYDnwuYxHliM0rbOIiEwmWnBepiIz45TamZxSO5MbLz+RJ3fup75xN3c/28q+g/0pj31q536e2rmfW+qf58xjK1m7fB6XLZuTcmyqTC7ZkNR9Dfgz4D/N7F1AOYCZ/Q1wHvAOYDtwe2ARZjlN6ywiIpORFpyXqSovzzjj2ErOOLaSf1h7Ek+80kF9427+8NweOsMDSY9zDh7f0cHjOzq46XdbOef4KtatmMclJ82hvLQgg89AMi3wLMA5t9/MVgE/Bq6M2fVt//5h4Grn3HDj7qYkTessMrHUUiAiIkEK5edx7sKjOHfhUdxyxTIeeXkf9Vta2bh1Dwf6kvfSGoo4Ht6+j4e37+Nz+c/y5kWzWLtiLhefNIdpRYGnAJJmWfGOOud2AqvMbDne0gVVQBfwuHPuqSBiMrMq4O3A5cDJwHygH3gW+BHwI+fcESukmtk5wI3AWXiLqb8M/BC43TmX9sleNK2zyMRoaOk8YkxPfp5RVpSvMT0iIhKIgvw8LlhSzQVLqjk0sIyHXnqN+sZW7nthL+H+5JeZA0OOTdva2LStjaLQs1ywpJq1K+Zy4QnVlKqn16QQ+LtoZvcDjzrnPu+ca8Rbky4bXAl8D28NvQeAZmA2XnfQfwcu88cBuugBZnYF8CvgELAe6ADWAd8CzuWNLZHjommdRSbOxq17Ds++Fx4YPDz73qGBCO09few70E/jri5uWL2Yi0+aHXS4IiIyBRUX5LNm6RzWLJ1Db/8Q929ro75xN/dva6Nv8Ih2h8P6BiPcs3UP92zdQ0lBPhedWM26FfM4f/EsigvyM/gMJJ0sJicJJgCzMHCrc+6zgQYSx8wuBMqA38e2yJnZHOD/gBrgXc65X/nbZ+C1ypUD5zrnnvS3FwP347VAvsc594txxPRU2bxFp5704X85PK1z9YwiXViKpFFDSyef+/WztHSEmVYUGnadrC+9/WS12ImISNY42DfIfc/vpb5xN3986TUGhkZ2rT+9KMTFS2ezbvk8zl14FGefuZKnn376aefccLPUSxYIvKUObxKUmqCDiOecuz/J9j1m9q/Al4BVeC1zAO8CZgE/iSZ0fvlDZnYjsAn4EDDmpC6quCBf0zqLTJD1m5tp6+5jWlEo4axheXl2eHtbdx/rNzfrMygiIlljWlGIt50yn7edMp+u3gE2bt1DfWMrj7y8j6FI8gTvQN8gdz39Knc9/SrlJQXsHWbNPMku2ZDU/TvwBTOrdc41Bx3MCEWnHYodnXqhf39PgvIPAWHgHDMrcs6lXngkheoZRfzdJUs0WYPIBGhuD9PQ0kl4YJAF08tSlp1ZWkhTRw8NLZ00t4f1eRQRkaxTXlLAlafXcOXpNXT09HPPc3uob9zN4zvaSZHf0dU7wP6e1MsoSHbJhqRuA3Ax8KiZfRXYDOwBjqhq2ZD0mVkIuMb/b2wCt8S/fyn+GOfcoJm9AiwFjgNeGOYxkk0Oc8LM0kLeumLe6IIWkRFp2OVNilJWGHpDl8tE8vKMssIQPX1DNOzqVFInIiJZrbKskKvPrOXqM2tpO3CIPzzrJXibm/YHHZqkQTYkdTvwEjgDbktRzpEd8X4FWAbc7Zy7N2Z7uX/fleS46Hb10xLJUr39gwxFHKFhErqoUJ4xFHH09iefUlpERCTbVE8v5tpzFnDtOQto7erl942tbGhsZUtLZ9ChyRhlQ5L0ExK0ymUjM/sY8AlgG/C+0R7u3w/7XJMNSPVb8E4d5eOKyAiVFIbI92e5HInBiKO4II8STQctIiI5am55CdeddxzXnXccze1h6p/dTf2WVlqDDkxGJfArEefc+4OOYSTM7CN4LYnPAxc55zriikRb4spJbEZcORHJMnVHV1BWlE97Tx+RiEvZBTMScfT0D3qTFh2tBngREcl9tVWlfHjVQj68aiHLfjidrXuDjkhGKi/oAHKBmd0AfAd4DrjAObcnQbEX/fvFCY4PAcfiTayyY6LiFJHxqa0qpa6mgtKCEPvDqQeI7w/3U1oQoq5GkxaJiMjk0twe5tDAEHml5VVBxyIjE3hLXSwzOxo4BW/cWRfwtHNuV8AxfQpvHF0DcLFzbl+SovcD7wUuBf4zbt+bgVLgofHMfCkiE++qlbU07uqipSMMMOw6dVetrA0qVBERkbRqaOlk/eZmGlo62dt9iPyymXODjklGJita6sys1szuAXYCvwHuBH4N7DSze8xsQUBxfR4voXsKr8tlsoQO4JfAPuDPzOz0mHMUA//o//d7ExWriKRHXU0F11+0iJrKUgaGHE0dPeztPkT7wT72dh+iqaOHgSFHTWUpN6xerDXqRERkUti4dQ+f+/Wz3Pd8G80d4ZRLHkj2CbylzszmAI8C84EmvDXdWoG5wJuANcAjZnZ6km6PExXXtcAtwBDwMPAxsyPG1zQ55+4EcM51m9kH8ZK7B83sF0AH8Fa85Q5+CazPTPQiMh5rls6hekbx4V8re/qGGPInRamaVkhdTQVXraxVQiciIpNCQ0snt23aTktHmGlFIRZML+PVPGMgMqTpnXNE4Ekd8Hm8hO5TwDedc0PRHWaWD/wt8DXgRuBvMhjXsf59PnBDkjJ/xGtVBMA59xszOx/4HPBOoBh4Gfg48G3nnH7zEMkRdTUV1NVUeAuS7+qkt3+QksIQdUdrDJ2IiEwu6zc309bdx7SiEFXTioIOR8YgG5K6y4GNzrmvx+/wE7xvmNlqYC0ZTOqcczcDN4/huEeBt6Q7HhEJRm1VqZI4ERGZtJrbwzS0dBIeGGTB9LKgw5ExyoYxdXPwxqyl8pRfTkRERERE0qRhlzfMoKwwlHIpH8lu2ZDUdQHHDFOmFq3vJiIiIiKSVr39gwxFHKGYhK5/MMKgc5CXnw29+mQEsiGpewR4l5mdk2inmZ0JXOmXExERERGRNCkpDJGfZwxGHOH+QXbtD7OzvYfBIYfl5RcEHZ+MTDZk31/CG1f3R3/GyAfwZr+cA6wC3gNEgH8KKkARERERkcmo7ugKyory2dt9iO7eAQYjjohzODS/Xy4JPKlzzj1tZu/Cm0XyvcDVMbsNb1mAv3DODTfuTkRERERERqG2qpSamSW8tPcgEb8beYjEhgAAIABJREFUZmF+HoYRcVqtLlcEntQBOOfqzewY4ArgVKAcbwzdM8BvnHM9QcYnIiIioiVOZLJyGIdH1Jl/k5ySFUkdgJ+4/dy/iYiIiGSFhpZO1m9upqHFmyVwKOLIzzPKivKpq6ngqpW11NVUBB2myJg0t4fZtT9Mfp6RnwdDEW+iFIfDzJTe5YisSepEREREss3GrXu4bdN22rr7CA8MUlYYIpRnHBqI0N7Tx74D/TTu6uKG1Yu5+KTZQYcrMmrRJQ3KSwqYXhxif08/4YEhDNO4uhwS+OyXZnajmQ2Y2fwk++eZWb+ZfTrTsYmIiMjU1dDSyW2bttPSEaYg31hQWcbsGcVUTSti9oxiFlSWUZBvtHSEufW+l2ho6Qw6ZJFRi13SoLQwxPyZpRxTWUYo33CRoYGg45ORCTypA9YBDzrnXk200zm3G29GzCsyGpWIiIhMaes3N9PW3ce0ohBV04qOWJg5L8+omlbEtKIQbd19rN/cHFCkImMXu6RBVGEoj5AZRIYGAwxNRiEbkrqFwPPDlHneLyciIiIy4ZrbwzS0dBIeGGRmaWHKsjNLCwkPDNLQ0klzezhDEYqkR3RJg57+QSKa7DJnZUNSVwoM9w14CJiegVhEREREDo8zKisMHdFCFy8vzygrDNHTN0TDLnXBlNxSW1VKXU0FpQUh9of7gw5HxigbkroW4KxhypwFJOyeKSIiIpJuseOMRiKUZwxFHL396q0mueeqlbVUzyjiYN8g7Qf71GKXg7IhqbsHeLOZXZVop5n9GXA+8IeMRiUiIiJTVqJxRqkM+ssclBRqYnHJPXU1FVx/0SJqKksZGHI0dfQwEHGQl68KnSOy4Y36KvBe4Od+YncPXqvcfOAy4K1AB/CVwCIUERGRKSU6zqi9x2u1SNUFMxJx9PQPUjWtkLqjtV6d5KY1S+dQPaP48JqMzVqhLqcEntQ55141s0uA/wbexhtnuTSgCbjSObcrgPBERERkCoqOM9p3oJ/94X6qphUlLbs/3E9pQYi6mgpqq0ozGKVIetXVVFBXU0Fze5gL7yjmlZ1trUHHJCMTeFIH4Jx70swW4y1vcBZQAXQCjwMbnHNaI0NEREQy6qqVtTTu6qKlw5vPbWZp4Rta7CIRx/5wPwf7BqmpLOWqlbVBhSqSVrVVpZSXFBAJd7UHHYuMTFYkdQB+4naXfxMREREJVHSc0W2bttPW3UdTRw9lhSFC/li7nv5BSgtC1FSWcsPqxdTVqOuliAQja5K6eGZWACwDws65F4OOR0RERKae+HFGPX1DDEUcxQV53hi6mgquWlmrhE5EAhV4Umdm7wbeBfy1c67D33Y83myXx/v//y3wbuec5gkWERGRjIodZ9Swq5Pe/kFKCkPUHa0xdCKSHQJP6oC/AOZFEzrfPwMLgfuBKrzJUz4A/FvmwxMRERHxxhkpiRORbJQN69SdBGyO/sfMZgBvAf7LObcaOAPYhpfUiYiIiIiISIxsSOpmAbHTpZ6N14L4Czg8gcr/4HfFFBERERERkddlQ1J3ACiP+f/5gAMeidl2CJieyaBERERERKai5vYwXb0D5JWWVwUdi4xMNoyp2w5cZmZFeMnclUCjc25fTJljgLYgghMRERERmQoaWjoPz/S6t/sQ+WUz5wYdk4xMNiR13wd+hJfcDQALgBviypwJbM1sWCIiIiIiU8PGrXsOr8kYHhgk4oKOSEYj8O6XzrkfA18BSvG6YX7HvwFgZhfiJXoPBBGfiIiIiMhk1tDSyW2bttPSEaYg31hQWUZBnkFkSMuJ5YhsaKnDOfdZ4LNJdj8CzAR6MheRiIiIiMjUsH5zM23dfUwrClE1rSjocGQMAm+pG45zrt8516WFx0VERERE0qu5PUxDSyfhgUFmlhYGHY6MUdYndSIiIiIiMjEadnXS0zdEWWGIvDwLOhwZIyV1IiIiIiJTVG//IEMRR0gJXU5TUiciIiIiMkWVFIbIzzMGNd1lTlNSJyIiIiIyRdUdXUFZUT49/YNElNjlLCV1IiIiIiJTVG1VKXU1FZQWhNgf7g86HBkjJXUiIiIiIlPYVStrqZ5RxMG+QdoP9qnFLgdlVVJnZieY2dvN7H1BxyIiIiIiMhXU1VRw/UWLqKksZWDI0dTRw0DEQV5+VqxpLcPLiqTOzOrM7ElgK/BL4M6YfeebWdjM1gUVn4iIiIjIZLZm6Ry+9PaTWX1SNbWVpWgyzNwSePZtZouBB4F84DZgMXBZTJGHgA7gXcCGTMcnIiIiIjIV1NVUUFdTQXN7mAvvKOaVnW2tQcckI5MNLXU3AYXAGc65jwObY3c65xzwv8DKAGITEREREZlSaqtKKS8pIBLuag86FhmZbEjqLgLucs69kKJMMzAvQ/GIiIiIiIjkjGxI6iqAXcOUycNrzRMREREREZEY2ZDUtQELhymzFGjJQCwiIiIiIlNac3uYrt4B8krLq4KORUYm8IlSgPuB95jZEufci/E7zWwlXhfN72Y8MhERERGRKaKhpZP1m5tpaOlkb/ch8stmzg06JhmZbEjqvgxcCTxkZjfjj50zs6XAm/EmUjkAfCOoAEVEREREJrONW/dw26bttHX3ER4YROuP55bAkzrn3Itm9k7gP4Hv+JsNaPTvO4F3OOeaAwpRRERERGTSamjp5LZN22npCDOtKMSC6WW8mmcMRIYGg45NRibwpA7AOXePmR0LXAucBVQBXcDjwI+ccx1BxiciIiIiMlmt39xMW3cf04pCVE0rCjocGYOsSOoAnHOdeIuP3xZ0LCIiIiIiU0Fze5iGlk7CA4MsmF4WdDgyRtkw+6WIiIiIiASgYVcnPX1DlBWGyMuzoMORMcqaljozqwZOB2YC+YnKOOd+ktGgRERERESyWHN7mIZdnfT2D1JSGKLu6Apqq0pHfHxv/yBDEUdICV1OCzypM7MC4F+Ba0jecmiAA5TUiYiIiMiUF7v8QE/fEEMRR36eUVaUT11NBVetrKWupmLY85QUhsjPMw4NRDIQtUyUwJM64IvAB4A/AT/DW2RcM+2IiIiIiCQQv/xAWWGIkJ+Ytff0se9AP427urhh9WIuPml2ynPVHV1BWVE+7T19RCJOXTBzVDYkdVcDLwGnOOd6gw5GRERERCRbJVp+IDYRi0Qc+8P9tHSEufW+l5g1vShli11tVSl1NRXsO9DP/nC/Zr/MUdkwUUo1cLcSOhERERGR1OKXH4hvWcvLM6qmFTGtKERbdx/rNw+/1PNVK2upnlHEwb5B2g96LXaSW7IhqWsGZgQdhIiIiIhINotdfmBmaWHKsjNLCwkPDNLQ0klzezhl2bqaCq6/aBE1laUMDDmaOnoYiDjIy8+GXn0yAtmQ1N0JXGZm5UEHIiIiIiKSrUaz/EBenlFWGKKnb4iGXZ3DnnvN0jl86e0ns/qkamorS9HQutySDdn3V4AVwH1m9kngKedcd8AxiYiIiIhkldEuPxDKM4Yijt7+kc1BWFdTQV1NBc3tYS68o5hXdra1jideyZyMJ3VmFsFbnuCIXcB9fplEhzrnXDYkoSIiIiIiGTfa5QcGI47igjxKCkd3CV1bVUp5SQGRcFf7WOKUzAsiSXqIxEmdiIiIiIgkMZrlByIRR0//IFXTCqk7evj16iS3ZTypc86tyvRjioiIiIjkutEsP7A/3E9pQYi6mgpqq0ozGKUEIZCJUszsGjNbHsRji4iIiIjkquGWH4hEHO0H+zjYN0j1jCKuWlkbUKSSSUHNfnkn8LaAHltEREREJCclWn5gb/ch2g/2sbf7kLccwZCjprKUG1YvTrnwuEwemnhERERERCSHrFk6h+oZxazf3ExDi7fMwZA/KUrVtELqaiq4amWtEropREmdiIiIiEiOiV1+oGFXJ739g5QUhqg7WmPopiIldSIiIiIiOaq2qlRJnASa1FWY2ahGbjrnmicqGBERERGReGoJk1wQZFJ3vX8bKYdaFkVEREQkAxpaOo8Ys5afZ5QV5WvMmmSdIJOkbqAzwMcXERERETnCxq17uG3Tdtq6+wgPDFJWGCKUZxwaiNDe08e+A/007urihtWLufik2UGHKxJoUvct59wtAT6+iIiIiMgbNLR0ctum7bR0hJlWFGLB9DLy8uzw/kjEsT/cT0tHmFvve4lZ04vS3mKnLp8yWurOOAHM7GjgFuBSoApoBX4DfME5tz/I2EREREQkufWbm2nr7mNaUYiqaUVH7M/Ls8Pb27r7WL+5OW1Jnbp8ylgpqUszMzseeAyoBn4LbAPOwBs/eKmZneucaw8wRBERERFJoLk9TENLJ+GBQRZML0tZdmZpIU0dPTS0dNLcHh53S5q6fMp4KKlLv3/BS+g+5py7PbrRzL4J/C3wJeCvA4pNRERERJJo2OW1kJUVht7Q5TKRvDyjrDBET98QDbs6x5XUZUOXT8lteUEHMJmY2XHAGqAJ+G7c7puAHuB9Zpb6px8RERERybje/kGGIo7QMAldVCjPGIo4evsHx/W48V0+4xPKaJfPaUWhw10+RWIFktQ55/Im6SQpF/r3G51zkdgdzrkDwKNAKXBWpgMTERERkdRKCkPk5xmDETei8oP+mLeSwrF3fovt8jmztDBl2ZmlhYQHBg93+RSJUvfL9Fri37+UZP92vJa8xcCmZCcxs6eS7Dph7KGJiIiISCp1R1dQVpRPe08fkYhL2QUzEnH09A9SNa2QuqPH3hUyqC6fMrmo+2V6lfv3XUn2R7erE7SIiIhIlqmtKqWupoLSghD7w/0py+4P91NaEKKuZnzLDQTV5VMmF7XUZVb005qyTd85d1rCg70WvFPTHZSIiIiIeK5aWUvjri5aOrzujTNLCxNOWnKwb5CaylKuWlk7rseLdvk8NBAZvjBel8/igrxxdfmUyUctdekVbYkrT7J/Rlw5EREREckidTUVXH/RImoqSxkYcjR19LC3+xDtB/vY232Ipo4eBoYcNZWl3LB68bhnoYx2+ezpHyQyzFi+aJfPsqL8cXX5lMlHKX56vejfL06yf5F/n2zMnYiIiIgEbM3SOVTPKD5iIfDigjxvDF0aFwKPdvncd6Cf/eH+hAueR6Wry6dMPkrq0usB/36NmeXFzoBpZtOBc4Fe4PEgghMRERGRkamrqaCupsKbnXJXJ739g5QUhqg7Ov0JVaa7fMrko6QujZxzfzKzjXgzXH4EuD1m9xeAMuAO51xPEPGJiIiIyOjUVpVOeKtYtMvnbZu209bdR1NHD2WFIUL+8go9/YOUFoTS1uVTJh8lden3YeAx4NtmdhHwAnAmcAFet8vPBRibiIiIiGShTHb5lMlHSV2a+a11pwO3AJcCbwFagW8DX3DOdQQZn4iIiIhkp0x2+ZTJRUndBHDOtQAfCDoOEREREck9mejyKZOLljQQERERERHJYUrqREREREREcpiSOhERERERkRympE5ERERERCSHKakTERERERHJYUrqREREREREcpiSOhERERERkRympE5ERERERCSHKakTERERERHJYUrqREREREREcpg554KOQUbIzNpLSkoqTzzxxKBDEREREZFJ7IUXXqC3t7fDOVcVdCwyPCV1OcTM+oB8YEvQsUhWOcG/3xZoFJJtVC8kEdULSUT1QhJZAQw554qCDkSGFwo6ABmV5wCcc6cFHYhkDzN7ClQv5I1ULyQR1QtJRPVCEonWC8kNGlMnIiIiIiKSw5TUiYiIiIiI5DAldSIiIiIiIjlMSZ2IiIiIiEgOU1InIiIiIiKSw7SkgYiIiIiISA5TS52IiIiIiEgOU1InIiIiIiKSw5TUiYiIiIiI5DAldSIiIiIiIjlMSZ2IiIiIiEgOU1InIiIiIiKSw5TUiYiIiIiI5DAldQEzs6PN7IdmttvM+sysycxuNbOZQZxHssN4308zqzKz68zs12b2spn1mlmXmT1iZv/PzPTZz0ET8Tk3s/eZmfNv16UzXpl46awTZnaemf3KzFr9c7Wa2UYze8tExC4TJ43XFpf7dWCX/3dkh5n9t5mdPVGxy8Qws3eZ2e1m9rCZdfvf+T8d47l0zZmFtPh4gMzseOAxoBr4LbANOAO4AHgRONc5156p80h2SMf7aWZ/DXwPaAUeAJqB2cA7gHLgV8CVTl8AOWMiPudmVgM8C+QD04APOuf+PZ1xy8RJZ50wsxuBLwL7gHq8746jgFOAB5xzn0z7E5AJkcZri68CnwTagd/g1Y2FwFuBEHCNc25MSYFknpk1ACuAg8Au4ATgZ865Px/leXTNma2cc7oFdAPuBRzw0bjt3/S3/2smz6NbdtzS8X4CFwLrgLy47XPwEjwHvDPo56pbZutF3HEG3Af8Cfi6f47rgn6eumW+TgBX+uX/B5ieYH9B0M9Vt8zWC/9vxRCwB6iO23eBf54dQT9X3UZVLy4AFvnf/av89/CnYziPrjmz9KaWuoCY2XF4F1NNwPHOuUjMvul4v5Ia3pdpz0SfR7JDJt5PM/ss8CXgO865j447aJlwE1EvzOx64Ft4f9wvBG5CLXU5I41/Q/KAl/Fa8hc4516byLhlYqWxXpwJPA78zjl3RYL93Xi9vaan9xlIJpjZKrxePKNqqdM1Z3bTuJrgXOjfb4z9UAA45w4AjwKlwFkZOo9kh0y8nwP+/eA4ziGZldZ6YWYnAl8BbnPOPZTOQCVj0lUnzgGOBe4G9vtjqD5lZtdr3FROSle92A70A2eY2VGxO8zszcB0vJZ+mVp0zZnFlNQFZ4l//1KS/dv9+8UZOo9khwl9P80sBFzj//eesZxDApG2euHXgf/A64b72fGHJgFJV51Y6d/vBZ7GG0/3FeBW4DEz+6OZzRpPoJJRaakXzrkO4FN4LbjPm9n3zezLZvZfwEa8rrp/lYZ4JbfomjOLhYIOYAor9++7kuyPbq/I0HkkO0z0+/kVYBlwt3Pu3jGeQzIvnfXiH/Amv3iTc653vIFJYNJVJ6r9+78GXgFWA08AxwD/DFwC/DdeN13Jfmn7rnDO3WpmTcAPgQ/G7HoZuNM51zbWICVn6Zozi6mlLnuZfz/eQY/pOo9khzG/n2b2MeATeDNVvS+dQUngRlQvzOwMvNa5f3bO/e+ERyVBGul3RX5M+Xc55zY55w4657YCb8ebJe98dcWcNEb8N8TMPgn8ErgTOB4oA04DdgA/M7OvTVCMkrt0zRkgJXXBif6aUZ5k/4y4chN9HskOE/J+mtlHgNuA54EL/K41kjvGXS9iul2+BHw+faFJQNL1XbHfv9/hnNsSu8NvyY226J8x6gglCGmpF/5EGl/Fmyjl4865Hc65sHPuabxk/1XgE/7EGTJ16JoziympC86L/n2yfseL/Ptk/ZbTfR7JDml/P83sBuA7wHN4Cd2esYcnAUlHvZjmH38icChmwXGHN/MlwL/5224dd8Qy0dL9N6Qzyf5o0lcywrgkWOmqF2v9+wfidzjnwsD/4V1DnjLaACWn6Zozi2lMXXCiX5RrzCwvwbSw5wK9eFMKZ+I8kh3S+n6a2afwxtE1ABc75/alOV7JjHTUiz7gB0n2nYp3cfYI3h9tdc3Mfun6rngIbybcRWZW6Jzrj9u/zL9vGn/IkgHpqhdF/n2ySXKi2+Pri0xuuubMYmqpC4hz7k94M0gtAD4St/sLeH3XfxJd58PMCszsBDM7fjznkeyWrnrh7/s8XkL3FHCRErrclY564Zzrdc5dl+gG/M4v9mN/2/oJf1IyLmn8G7IPWI/XneofYveZ2cV4E6V0odlyc0Ia/4Y87N//pZnNj91hZpfhXbwfAh5L7zOQbKBrztykxccD5H9YHsObfey3wAvAmcAFeE3X5zjn2v2yC/BmJtvpnFsw1vNI9ktHvTCza/EGtw8Bt5O4f3uTc+7OiXkWkm7p+r5Icu6b0eLjOSeNf0Oq8daXWoh3Mf9/eLNfvh1vwoOrnXP/PeFPSNIiTX9D8vDGU64GDgC/Bvbgdd9eizchxg3Oudsy8Zxk/MzsbcDb/P/OwfvBZgevJ/D7nHN/55ddgK45c49zTrcAb0AN8COgFa8bw068CS0q48otwPvj2jSe8+iWG7fx1gvgZn97qtuDQT9P3TJbL1KcN1pfrgv6OeoWTJ0AKoFv4l3I9QPteBdsZwX9HHULpl4ABcANeF3puvG66bbhrWW4JujnqNuo68Rw1wVNMWV1zZmDN7XUiYiIiIiI5DCNqRMREREREclhSupERERERERymJI6ERERERGRHKakTkREREREJIcpqRMREREREclhSupERERERERymJI6ERERERGRHKakTkREREREJIcpqRMREREREclhSupERERERERymJI6ERERERGRHKakTiQFM3vQzFzQcWSKmd1pZs7MFgQdS5SZFZjZF8xsu5n1+fG9Lei45HWT4XNiZjPM7Ntm1mRmg349qxvnOZ2ZPZimENPGzFb5sd0ctz3n38eJYmYL/NfszqBjySXZ+DdFZLJSUidTgv9HZTS39wcd80Qws5v957cq6FhG4RPAPwC7gW8AXwC2BRqRTEZfAz4KPAt8Ga+e7Ul1gJ8ANk18aLkhWbI4znNmZWIsE8PMTvR/xPutmTXH/E0ODXNcpZnd6n8m+8xst5n90MyOzlTsIkFL+SERmUS+kGDbDUA5cBvQGbevYcIjyk6fAb4CvBp0IDHWAgeBi51z/UEHIwldA5QGHcQ4rQVecs6tCzqQAE2G93GivAqcCHQFHcgkdwnej3hDwHbgEFCc6gAzqwIeAxYD9wO/AE4APgBcbmZnO+d2TGTQItlASZ1MCc65m+O3+a1x5cCtzrmmDIeUlZxzrUBr0HHEmQe0K6HLXs655qBjSIN5wENBBxGkSfI+Tgjn3ADqIZAJfwD+F2h0zvX6LeHHDHPMP+EldN9yzn08utHMPob3o+2/AJdOTLgi2UPdL0VGwMxCZvbZmHFdLWb2VTMrTFL+BH8sQYtffq+Z/dzMloziMQ93ZTKzM8zs92bWETs+wcwuMLPvm9nzZtZtZr1m9pyZ3WRmxXHnawJu8v/7QGx305gyScc/mNm7zewhM+vyH+dZM/uMmRWN9Dn55yk3sy+b2YtmdsjM9pvZvWa2Oq7cnX5sxwLHxMTbNILHeNAvW2Rm/2hmr/jvw5/81ybZ+/ZeM3vaf35tZvYfZjYv2Vgj//w3m9kO//yv+I9XNJ5uY2b2/mg3YDO71H/8rrj3KmRmHzazx/33Pmxmz5jZ35hZwu92vx6tN7NX/XhbzWyjmb07QdkzzeyXZrbHzPr9unyHmc1LUPbBuNje48f/zSRxFPnv+x6L61blH/uAv/+Qmb1gZjeOoZ7NNbPvmtcdq9/MXjOzu8zstCSxG3B+TD17MMW5V/nHHMMb62bCMVdmdpT/OW31X/etZvaBFOe/xMzuNrN9MfX262ZWMcrXYLaZ/cC8759eM2sws2tTlE86ps7M1pjZBv9zEf0O/G30c+s/7wf84jfFvSar/DLlZvb3Zna/me2KeV9+Z2ZnxT3e+2NiOT/ufDfHlR3xd5NfH5rMG0P5Tf/fA/HnTHBcwjF1FvOdaWZ/5T/2If81/76Zlac6b9y5DnePN7OrzewJMztoMd95I63X8eeb6OdjZqvN7GEz6zHv79RvzOyEkT73KOfci865J5xzvSMpb2ZlwPuAHl7/+xb1HaAJuMTMjhttLCK5Ri11IiPzc+A8vF8Ru4G3AJ8EqvG6eBxmZpcCdwEFwAbgZeBo4B14XUEucM49PYrHPhuvW+QjwA+Bo4Boq9Wn8LqZPAb8Hq+byrnAzcAqM1vtnBvyy94KvA04H/gx3h+7ETGzf/Jj2If3WhwELsP7hfQSM7vY/yV7uPNUAI8CJwGb/ZiOAt4NbDSzDznn7vCL/8aP8YaY+OHIrrKp/BewEvglMABcgffanG5mb3XOxSYif483rmo/3uvTBVzsx3tElyszM+BXwOV43YS+g/eevx9YOooYU3kX3i/MfwD+FVjgP3a0bl0CvIj3nhwCLgBuB87Eu9CJjfeDwPfwujX9zo+5Gjgd+DDeaxUt+wHg34A+v2wLsAi4DlhnZmcN06rza7zX7L1m9knn3GDc/iuACuCfY/eZ2Q+AvwB24X2GOoGzgC8CF/n1LP5cRzCzY/E+L/PwumP9J1ADXIn3GXync67eL34n8CDeBeFO//+Q+vPRhNelO75uwpFdt6N1vh+vHhbjva8/NLOIc+7HcbH/g3/uDqAeaAOWA38HvMW8rmTdKWKLnifaJe04vNfiEWAuXj3aONzxcef6Al6XuIN4n8sWvNf2HODPgfv87QDXAn/Ee02jmvz7E4Ev4bWI/h7vs1YLvBW4zMzWOefu8cs24L0O8e8Lsece43dTIV69qMR7LbqBV0b6eiTxNbzP4wb/nBcAHwQWAheO8lyfwPvu2YCXKJfDqOv1eI34+ZjZu4D1eHV8PV5vjzfht7ilKZ5kzgZKgI3OuQOxO5xzETPbCPylH7+6YMrk5pzTTbcpecO70HDAghRlHvTLPAVUxmwvw0vWhoA5Mdtn4l2o7ANOijvXUrwLjqdHGN8q/7Ed8FdJyhwHWILtX/SPuypu+83+9lVJzndn/GuC90fTAc1xzzWE9wffAZ8d4XO6wy9/R2zceAlDF14SsSDumCagaZTvbfR9ewmYGbO9GO9CwwHvi3sdB4DXgJqY7YZ34eS8r8s3PMb7/O0PAYUx2yvwumk54MEx1s33+8dHgEsT7I++j7cD+THb84Ef+PuuiNl+kv/8OoClCc53dMy/F+NdnL0MzI8rd6Ff53+d6PVO8l6vTfB4v/f3nZzgOd8FlCR5vteP8PW71y//ubjt5wCDQDswLW7fqN+v4eomr39+/z3ufTrJj+P5uPIX+OUfAyqS1IlvjTC27ycqj5fED/j7bh7B+7jGL7sjvj4kqDurEp03Zn85cFSic+BNhPRCktcw4fvCGL6beP2H8AWEAAANLklEQVR7/z6gbBTv9QL/uDvjtt8ZE0NtXAwP+fvOGOFjROt5D3DKeOs1Kb7v0/V8gGn+4w4Ap8ed61sxn4EFo/lsJXi/Qkn2f8Tff3uS/X/n7//qWB5fN91y6abulyIj8ynnXEf0P865HuBneF2YT48pdw3eRf1NzrnnY0/gnNuK1/pxipmdNIrHbnCvt169gXNuh3POJdgVbTm4ZBSPk8xf+Pf/6Jw7PBug81pMPoGXeFw33En81qU/x0tsPxMbt3NuO/BtvF/Qr0lDzFFfdM7tj3mcQ3i/6sPrzwvgaryLltudcy0x5R3wabxEJt61/v2NLma8n3OuEy+pToffutdbLgAwr2vl3+DNzPi37vWWWPx/fwLvIua9MYd9CO/5fdGvh2/gnNsVV7YAL4F6Na7c/Xgtd+vMbPowsUdboK6N3Whmc/Dq5TPOuWdjdl2Pd2H6F+7IrldfxLtwfC/DMG+2uzV4F6Vfi4v/MbwkvRKv5TwTwsDH496n5/Fa706Mex0/5t9/0K9HxBxzJ17r1UhegwK/3AG8C/vY8zyJ9901Uh/17z8RXx/88+2K35aMc67LObcvyTl+CZxgZrWjiG08302f8L/H0+UWF9N67cfwI/+/Z4zyXN93zj0TuyGAej3S53OF/7g/9+tWrJuZ+Illot1Bkz1OdPuoui6L5CJ1vxQZmfg/VuB1QQKvdS7qbP9+RZIxGov9+xOB5xPsT+T/ku3wxxNcD7zdP/d0vNalqPkjfIxUTvXv74/f4Zx7ycx2AceaWUX8hWicE/Bm1ns0NkGOcT9wI3DKeAOO8ccE2x7GSx5iHyf670fiCzvndppZC37Xx7hjInitKvGOOM8YJXrvFwNVeN0nb/R6gR6hF6+ORUXHK/1hBI8ZrcPnm9nKBPur8VoEF+O1YCfknHvMzF7CSwBnxiTX7/WPvzNa1sxKgRV4Ldw3JHlOfbzxOSUTfS8fdom7BN+P9+PCKcBPRnC+8druEneXjH5/VOAlX+C99gPAlWZ2ZYJjCoFZZlblnGtP8ZjRz9rDzrlEF7sPEpdsp3AW3o8E9wxXcCTM7Fy876yz8epS/PjW+XiJy0iM9bvpEOnvFjjSvxEjkehzn+l6PdLnE30Pjviudc51mVkDXpf/oES/TBL9+CkyqSipExmBJMlKdGxPfsy2Kv/+g8OcctooHj7hWln+r/H34/1q+hzeWIbX8C4KwRuLMqrJJZKI/hKabFbMVrxxMeWkHu82kvNAen9R3Ru/wTk3ZGbteBeU8bEdUT5m+4K4beVAh0s8xivZeUYr0XsfrWOLOHJigFixdSz6mo5kqYro+f9+mHIjqcM/xhtD9Wd44/nASyYG8FoWombiXXzNIvVzGokg6lkqyT4Tyb4/Qgz/GkS7vCUzXH1Ouf5enApgf4LW01Ezs7fjtcgdAv4H+BNeV8MIXtfN8xndd9ZYv5vakvRwGI+R/o0YiUTvT6br9UifTzrr2lhEf7RINiHNjLhyIpOWkjqR9Ir+4VjhnEvXL8HJLj6uwEvofuyce3/sDjOby/gvjqOiz2kO3kVYvLlx5UZynkRGep7RmE3cr/5mlo938RzbetIdU/6I7on+9njdQKWZhRIkdonKj0Wi9z76+vzaOTfSrlbRC7T5DD8t++GLpCQtTKPxH3hdJ68FvmdmpwAn43UrfS3BYz7jnDuV8QminqVLF5DnnKtMw3kgeT1M9tok0glUmVlJGhK7L+KN1zzdOfdC7A4zu4PRt+iM9bsp21ttUn3uR1OvI/59omu9dCV/6axrY/Gif784yf5F/v1LExyHSOA0pk4kvR7378/LwGMt9O9/lWBfsouj6Lie0fxyHB3bsSp+h5ktxJvk4JVhul6C98c3DNSZWaLuSBf496OZGXQ4iV6H8/AucmLHrET//ab4wmZ2DN4Mc/GewfsOPSfBviPOk0bb8GeF9FtrRyJaLy8bRdlx12F/fOL9wJnmLecR7fL347hyB/GS6aVmNt6E5vB7aXHLJfjSWc+GGH0rTCqPAzPNbLyzp27j9c9aohaMVaOMyRjZOl/Dfb8sxJscJj6hyyP5ZyaS4nzp+m7KBWOp19Euz4m+v05PsG0soo93xHetX/fq0vQ4yTyO19383Phxvn69WuP/94EJjkMkcErqRNLrR3gX3DeZ2RGD480sL9GaQWPU5N+/4Xz+ejxfTXJMtMvWaCYj+KF/f6OZzYp5nHzgG3jfIz8Y7iT+ZCI/w+s6dktczMfjTRIxgNe6ky6fj00gzVu778v+f38UU+7neF2LPmpmNTHlzS+f6KIyOm7lHy1m3Tv/Qubz6Qn/SH6r4O14v8x/+/+3d2+hUtVRHMd/C7oYVph2AyuEqAyUgowQy6RIMgqCgiwIsiAkfAifzKgkpZtohUFEJZEVBN0F0epkWIT1koJWIJFSGXgpuzyUlKuH33+acc6eOfscJ2hO3w8MB509+/rfm73+e//Xiojj2qcJ17JqTcbztLx991Ul6SlJGBqeko/D4xExqPc7Io6JiOEEfC+Uv3dIullug1Vp11fK46tWR0U9tog4KSKGfIpXkm68J78ue3frdxFxiZwU5ye57MKR2i+PcRt0DEbo8fL32aiuBzg22uq5VSljrl6Wx9guaZvHNNVIttJiVfm7IiIGjdFt+7+hri87JZ3Tum3lHHtAzghaZb+qgxKpR9emfjDCdt0YmzevNRAs17j7e7Rqb5fl3lLaVqsl6vxaZE+UDqE1ckbqJW1fL5D314bMpJwBRj1evwR6KDP3l5o9b0raHBED8hOIQ/KNznT51b8xnedSW6MG3sKImCr35J4l6Vo5ZXzVjdXGsi4PR8QUlZ7czFzWZZs+iYjH5Lp82yLiNXkczBxJU+SkIMtrrvMi+QnQgpKEY6OadepOkLQgM7+pOa86vpS0vaxzo07d2fL++Sd4zMyvS32whyRtjYhX1axTN17SVrlWWKsX5bFiV8v75R05a+QNcpKB89R8/anXlsqJRebLiUg+kMfLnSq/bjRD0r0qyXgy84uIuEuuUfZ5RLwtJ1qZIPfY/6rS05+ZX0XE7fIN8/aIWC+/unS03KYuk8du1i0s/Ib8qurdZR6rqhI9ZObqcAHluyR9HREb5Fdnx8sF6GfKgfj8GsucL2eXXB4Rs+Xj0ajndUjSvGyraTVCA3IdxPURsUlO5rI1M9eOZGaZORARi+SOhB0RsU6un3a8XOj8cvl8q/PUbLGkK+XEM9PUrFN3k6R1cm24Ouv0bkQslTsqvoyIRp260+Sna5vlcguSn8Z/L2luRByUj19KWpOZu+SgtdEGX5fPyRlyQLdW0nUVqzBQ5rdWTszzp6RNmbmpx9emfjCsdp2Zn5Z2OVPSZ+U6cZq8nzeoc7BcW2b+FhF3ymO6PyrXzkaduilyGYSZdecXESfLAXnDyeXv89EsRv9IZra+Rr5Y7txcGBEXysHs+fL1fo9c9gAY/UZSB4EPn9Hw0TDq1HX47rby+9sqvpskP/HYIScF+EV+JWqNpOtrrt8sdan5VKY5U+6R/15+BWW7fINzlDrUd5IzpG0p02fr9qmiTl3Ld3Plm6RfyzZtlwOHMcPc7+PkJ4k75JvgA3IP9Owux2nnMJfxYdmOYyUtk2+M/5BrbT0g6dgOv7tVDo5/lwOXl+RCv9skHaiYfoz81LEx/51yYpCJZflvjbBtdmxbLdNEWd8Buf7cwdIOPpZvcs6s+M10+XXdPWX63XJWwxsrpp1a2sOusm0/lv3wjKQr6p4n5fvnGm1N0kVDbPu1ahbdPignWvisHMfJw9iHE+UnlLvKfPbJBbIv7jB9x3poXZYxtizjOznYOKzuV7d5DnGuXSoXg99d1n2vfM6uVFstsCHW73Q5ON8rn+9bStuapZp16lq+u6a0lR9Le/hW7rxqbwsXlzb5sxxoHFYnrSx/ixx87SvzmKoONdXkjopX5EQcf3VY79rXJo3gelJ+N6n9+NY4jpX7ucsyKvfBEbbrcXIpnT3luG2Ti3H3dHvkDrCP5dd+f5Kf4E3uNr8h9nO3z6D9I3f+PNmyX36Q2/4ZdZbLh89o+ETmf328MAAMT0R8KOnyzKzMjT/MeZ0o31BuyczpQ01ffnOVpHflHuV7hpoeAADgSDCmDgAkRcQp7YlHyjiUFfITuUFjsDqMe5og6ZHyz16M2wIAAOiKMXUAYDdIejAi3pdfLRsvjwU5V35dbFXFb1ZGxAVyAfK9cra9OeW3z2Rmx8LxAAAAvUJQBwD2qTwmZKaaBbi/kcfIPZrVNbreUDPxwDg1x/OslseRSZJKxtNZNdbhQGY+MbLVBwAA/1eMqQOAf1lELFG9YvC7MnPSv7s2AABgtCGoAwAAAIA+RqIUAAAAAOhjBHUAAAAA0McI6gAAAACgjxHUAQAAAEAfI6gDAAAAgD5GUAcAAAAAfYygDgAAAAD6GEEdAAAAAPQxgjoAAAAA6GMEdQAAAADQxwjqAAAAAKCPEdQBAAAAQB8jqAMAAACAPvY3/IgF4/IyJ7EAAAAASUVORK5CYII=\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": 97,
   "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": 98,
   "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": 99,
   "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": 100,
   "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": 101,
   "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>255</td>\n",
       "      <td>384</td>\n",
       "      <td>0.664062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>df1</td>\n",
       "      <td>83</td>\n",
       "      <td>200</td>\n",
       "      <td>0.415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>df2</td>\n",
       "      <td>106</td>\n",
       "      <td>206</td>\n",
       "      <td>0.514563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>df3</td>\n",
       "      <td>60</td>\n",
       "      <td>242</td>\n",
       "      <td>0.247934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>df4</td>\n",
       "      <td>90</td>\n",
       "      <td>417</td>\n",
       "      <td>0.215827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>df5</td>\n",
       "      <td>150</td>\n",
       "      <td>450</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>df6</td>\n",
       "      <td>172</td>\n",
       "      <td>540</td>\n",
       "      <td>0.318519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>df7</td>\n",
       "      <td>80</td>\n",
       "      <td>390</td>\n",
       "      <td>0.205128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>df8</td>\n",
       "      <td>35</td>\n",
       "      <td>188</td>\n",
       "      <td>0.186170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>df9</td>\n",
       "      <td>150</td>\n",
       "      <td>525</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>df10</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>df11</td>\n",
       "      <td>150</td>\n",
       "      <td>480</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>df12</td>\n",
       "      <td>161</td>\n",
       "      <td>465</td>\n",
       "      <td>0.346237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>df13</td>\n",
       "      <td>193</td>\n",
       "      <td>315</td>\n",
       "      <td>0.612698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>df14</td>\n",
       "      <td>149</td>\n",
       "      <td>150</td>\n",
       "      <td>0.993333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>df15</td>\n",
       "      <td>555</td>\n",
       "      <td>555</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>df16</td>\n",
       "      <td>150</td>\n",
       "      <td>512</td>\n",
       "      <td>0.292969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>df17</td>\n",
       "      <td>248</td>\n",
       "      <td>528</td>\n",
       "      <td>0.469697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>df18</td>\n",
       "      <td>150</td>\n",
       "      <td>296</td>\n",
       "      <td>0.506757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>df19</td>\n",
       "      <td>525</td>\n",
       "      <td>525</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>df20</td>\n",
       "      <td>153</td>\n",
       "      <td>488</td>\n",
       "      <td>0.313525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>df21</td>\n",
       "      <td>155</td>\n",
       "      <td>555</td>\n",
       "      <td>0.279279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>df22</td>\n",
       "      <td>150</td>\n",
       "      <td>569</td>\n",
       "      <td>0.263620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>df23</td>\n",
       "      <td>150</td>\n",
       "      <td>570</td>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>df24</td>\n",
       "      <td>240</td>\n",
       "      <td>600</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>df25</td>\n",
       "      <td>150</td>\n",
       "      <td>525</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>df26</td>\n",
       "      <td>76</td>\n",
       "      <td>102</td>\n",
       "      <td>0.745098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>df27</td>\n",
       "      <td>111</td>\n",
       "      <td>318</td>\n",
       "      <td>0.349057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>df28</td>\n",
       "      <td>150</td>\n",
       "      <td>533</td>\n",
       "      <td>0.281426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>df29</td>\n",
       "      <td>150</td>\n",
       "      <td>575</td>\n",
       "      <td>0.260870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>df30</td>\n",
       "      <td>68</td>\n",
       "      <td>368</td>\n",
       "      <td>0.184783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>df31</td>\n",
       "      <td>150</td>\n",
       "      <td>585</td>\n",
       "      <td>0.256410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>df32</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>df33</td>\n",
       "      <td>150</td>\n",
       "      <td>585</td>\n",
       "      <td>0.256410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>df34</td>\n",
       "      <td>120</td>\n",
       "      <td>491</td>\n",
       "      <td>0.244399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>df35</td>\n",
       "      <td>150</td>\n",
       "      <td>398</td>\n",
       "      <td>0.376884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>df36</td>\n",
       "      <td>155</td>\n",
       "      <td>405</td>\n",
       "      <td>0.382716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>df37</td>\n",
       "      <td>208</td>\n",
       "      <td>464</td>\n",
       "      <td>0.448276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>df38</td>\n",
       "      <td>85</td>\n",
       "      <td>257</td>\n",
       "      <td>0.330739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>df39</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "      <td>0.382716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>df40</td>\n",
       "      <td>150</td>\n",
       "      <td>540</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>df41</td>\n",
       "      <td>100</td>\n",
       "      <td>420</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>df42</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>df43</td>\n",
       "      <td>155</td>\n",
       "      <td>305</td>\n",
       "      <td>0.508197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>df44</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>df45</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>df46</td>\n",
       "      <td>97</td>\n",
       "      <td>248</td>\n",
       "      <td>0.391129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>df47</td>\n",
       "      <td>132</td>\n",
       "      <td>518</td>\n",
       "      <td>0.254826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>df48</td>\n",
       "      <td>150</td>\n",
       "      <td>600</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>df49</td>\n",
       "      <td>155</td>\n",
       "      <td>563</td>\n",
       "      <td>0.275311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group  dictator's pgg receive  all player's pgg receive     ratio\n",
       "0    df0                     255                       384  0.664062\n",
       "1    df1                      83                       200  0.415000\n",
       "2    df2                     106                       206  0.514563\n",
       "3    df3                      60                       242  0.247934\n",
       "4    df4                      90                       417  0.215827\n",
       "5    df5                     150                       450  0.333333\n",
       "6    df6                     172                       540  0.318519\n",
       "7    df7                      80                       390  0.205128\n",
       "8    df8                      35                       188  0.186170\n",
       "9    df9                     150                       525  0.285714\n",
       "10  df10                     150                       600  0.250000\n",
       "11  df11                     150                       480  0.312500\n",
       "12  df12                     161                       465  0.346237\n",
       "13  df13                     193                       315  0.612698\n",
       "14  df14                     149                       150  0.993333\n",
       "15  df15                     555                       555  1.000000\n",
       "16  df16                     150                       512  0.292969\n",
       "17  df17                     248                       528  0.469697\n",
       "18  df18                     150                       296  0.506757\n",
       "19  df19                     525                       525  1.000000\n",
       "20  df20                     153                       488  0.313525\n",
       "21  df21                     155                       555  0.279279\n",
       "22  df22                     150                       569  0.263620\n",
       "23  df23                     150                       570  0.263158\n",
       "24  df24                     240                       600  0.400000\n",
       "25  df25                     150                       525  0.285714\n",
       "26  df26                      76                       102  0.745098\n",
       "27  df27                     111                       318  0.349057\n",
       "28  df28                     150                       533  0.281426\n",
       "29  df29                     150                       575  0.260870\n",
       "30  df30                      68                       368  0.184783\n",
       "31  df31                     150                       585  0.256410\n",
       "32  df32                     150                       600  0.250000\n",
       "33  df33                     150                       585  0.256410\n",
       "34  df34                     120                       491  0.244399\n",
       "35  df35                     150                       398  0.376884\n",
       "36  df36                     155                       405  0.382716\n",
       "37  df37                     208                       464  0.448276\n",
       "38  df38                      85                       257  0.330739\n",
       "39  df39                      62                       162  0.382716\n",
       "40  df40                     150                       540  0.277778\n",
       "41  df41                     100                       420  0.238095\n",
       "42  df42                     150                       600  0.250000\n",
       "43  df43                     155                       305  0.508197\n",
       "44  df44                     150                       600  0.250000\n",
       "45  df45                     150                       600  0.250000\n",
       "46  df46                      97                       248  0.391129\n",
       "47  df47                     132                       518  0.254826\n",
       "48  df48                     150                       600  0.250000\n",
       "49  df49                     155                       563  0.275311"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio_9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.5244489600696609 .\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": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 103,
     "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": 104,
   "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": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.6745843154078779 .\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": 106,
   "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": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[278, 188, 315, 180, 405, 282, 285, 300, 198, 263, 300, 210, 218, 345, 375, 368, 347, 375, 240, 293, 263, 263, 360, 405, 345, 300, 132, 255, 248, 300, 233, 320, 353, 285, 308, 285, 233, 300, 150, 330, 180, 285, 375, 233, 212, 330, 225, 263, 338, 458]\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": 108,
   "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": 109,
   "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": 110,
   "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": 111,
   "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": 112,
   "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": 113,
   "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": 114,
   "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": 115,
   "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": 116,
   "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": 117,
   "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": 118,
   "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": 119,
   "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": 120,
   "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": 121,
   "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": 122,
   "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": 123,
   "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": 124,
   "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": 125,
   "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": 126,
   "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": 127,
   "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": 128,
   "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": 129,
   "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": 130,
   "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": 131,
   "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": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.351946\n",
       "1     0.376221\n",
       "2     0.816342\n",
       "3     0.248377\n",
       "4     0.278953\n",
       "5     0.404714\n",
       "6     0.372217\n",
       "7     0.337477\n",
       "8     0.322116\n",
       "9     0.384881\n",
       "10    0.297334\n",
       "11    0.305601\n",
       "12    0.300999\n",
       "13    0.590420\n",
       "14    0.688576\n",
       "15    0.477934\n",
       "16    0.363824\n",
       "17    0.366085\n",
       "18    0.376907\n",
       "19    0.359727\n",
       "20    0.263792\n",
       "21    0.343555\n",
       "22    0.301487\n",
       "23    0.319366\n",
       "24    0.312498\n",
       "25    0.295337\n",
       "26    0.639062\n",
       "27    0.331183\n",
       "28    0.377430\n",
       "29    0.355247\n",
       "30    0.315314\n",
       "31    0.308532\n",
       "32    0.285902\n",
       "33    0.287542\n",
       "34    0.274382\n",
       "35    0.447518\n",
       "36    0.282857\n",
       "37    0.375384\n",
       "38    0.306394\n",
       "39    0.425599\n",
       "40    0.344613\n",
       "41    0.245667\n",
       "42    0.294819\n",
       "43    0.508372\n",
       "44    0.348332\n",
       "45    0.242463\n",
       "46    0.314030\n",
       "47    0.275929\n",
       "48    0.311131\n",
       "49    0.284097\n",
       "dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_RD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cor = pd.DataFrame({\"ratio of round 1-8\":mean_RD,\"dislike\":dislike})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.46060535931918795 .\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": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 148,
     "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": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_1to10 = [(x*8+y*2)/10 for x, y in zip(mean_RD, ratio_910)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[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",
       " 0.40186985756751437,\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",
       " 0.29087287900080816,\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]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_1to10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.5910820136013083 .\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": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1to10 = pd.DataFrame({\"round 1-10\": ratio_1to10, \"dislike\":dislike})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 151,
     "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": 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": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is 0.3787139594858528 .\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": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.DataFrame({'round 1-8': mean_RD, 'round 10': ratio_10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The ratio of pgg_receive of the dictator in round 10')"
      ]
     },
     "execution_count": 143,
     "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": {
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   "language": "python",
   "name": "python3"
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    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "toc": {
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "293px"
   },
   "toc_section_display": true,
   "toc_window_display": true
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}