final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sue6GMKsozmN"
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "import numpy as np\n",
    "#from collections import Counter\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "#import statistics\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "import os.path\n",
    "import seaborn as sns; sns.set()\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZoJtJd7LpupG"
   },
   "outputs": [],
   "source": [
    "fn = \"chessDB.db\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NjH8XAfjreAV"
   },
   "outputs": [],
   "source": [
    "conn = sqlite3.connect(fn)\n",
    "c = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NKDszKwCuYGK"
   },
   "outputs": [],
   "source": [
    "user_date_df = pd.read_sql_query(\"SELECT users.uid, min(user_events.date) FROM users INNER JOIN user_events ON users.uid = user_events.user_id GROUP BY users.uid;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_date_max = pd.read_sql_query(\"SELECT users.uid, max(user_events.date) FROM users INNER JOIN user_events ON users.uid = user_events.user_id GROUP BY users.uid;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0     1992-02-02\n",
       " 1     1991-11-24\n",
       " 2     2017-05-20\n",
       " 3     1994-11-20\n",
       " 4     1992-09-12\n",
       " 5     1999-08-18\n",
       " 6     1993-02-23\n",
       " 7     1991-11-10\n",
       " 8     1995-07-15\n",
       " 9     2008-04-26\n",
       " 10    1995-12-17\n",
       " 11    1991-12-01\n",
       " 12    1991-11-17\n",
       " 13    1991-11-10\n",
       " 14    1993-05-09\n",
       " 15    2000-03-12\n",
       " 16    2018-04-21\n",
       " 17    1995-05-21\n",
       " 18    2000-08-13\n",
       " 19    1996-12-22\n",
       " Name: min(user_events.date), dtype: object, 0     2018-12-01\n",
       " 1     2018-12-29\n",
       " 2     2017-05-20\n",
       " 3     1995-09-10\n",
       " 4     2012-01-14\n",
       " 5     2015-04-13\n",
       " 6     1993-02-23\n",
       " 7     2015-11-08\n",
       " 8     1998-03-07\n",
       " 9     2011-05-07\n",
       " 10    1995-12-17\n",
       " 11    1997-06-15\n",
       " 12    2003-12-02\n",
       " 13    2016-10-04\n",
       " 14    1994-08-24\n",
       " 15    2006-04-30\n",
       " 16    2018-09-30\n",
       " 17    1995-05-21\n",
       " 18    2000-08-13\n",
       " 19    1996-12-22\n",
       " Name: max(user_events.date), dtype: object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_date_df.head(20)['min(user_events.date)'], user_date_max.head(20)['max(user_events.date)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>uid</th>\n",
       "      <th>max(user_events.date)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12435050</td>\n",
       "      <td>2018-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12435066</td>\n",
       "      <td>2018-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12435088</td>\n",
       "      <td>2017-05-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12435092</td>\n",
       "      <td>1995-09-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12435105</td>\n",
       "      <td>2012-01-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12435108</td>\n",
       "      <td>2015-04-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12435118</td>\n",
       "      <td>1993-02-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12435133</td>\n",
       "      <td>2015-11-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12435144</td>\n",
       "      <td>1998-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12435145</td>\n",
       "      <td>2011-05-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12435147</td>\n",
       "      <td>1995-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12435149</td>\n",
       "      <td>1997-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12435157</td>\n",
       "      <td>2003-12-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12435176</td>\n",
       "      <td>2016-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12435177</td>\n",
       "      <td>1994-08-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12435180</td>\n",
       "      <td>2006-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12435188</td>\n",
       "      <td>2018-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12435197</td>\n",
       "      <td>1995-05-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12435199</td>\n",
       "      <td>2000-08-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12435203</td>\n",
       "      <td>1996-12-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         uid max(user_events.date)\n",
       "0   12435050            2018-12-01\n",
       "1   12435066            2018-12-29\n",
       "2   12435088            2017-05-20\n",
       "3   12435092            1995-09-10\n",
       "4   12435105            2012-01-14\n",
       "5   12435108            2015-04-13\n",
       "6   12435118            1993-02-23\n",
       "7   12435133            2015-11-08\n",
       "8   12435144            1998-03-07\n",
       "9   12435145            2011-05-07\n",
       "10  12435147            1995-12-17\n",
       "11  12435149            1997-06-15\n",
       "12  12435157            2003-12-02\n",
       "13  12435176            2016-10-04\n",
       "14  12435177            1994-08-24\n",
       "15  12435180            2006-04-30\n",
       "16  12435188            2018-09-30\n",
       "17  12435197            1995-05-21\n",
       "18  12435199            2000-08-13\n",
       "19  12435203            1996-12-22"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290434"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(user_date_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_date_df = pd.read_sql_query(\"SELECT users.uid, min(user_events.date) FROM users INNER JOIN user_events ON users.uid = user_events.user_id WHERE users.gender = 'M' GROUP BY users.uid;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "234227"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(male_date_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_date_df = pd.read_sql_query(\"SELECT users.uid, min(user_events.date) FROM users INNER JOIN user_events ON users.uid = user_events.user_id WHERE users.gender = 'F' GROUP BY users.uid;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30755"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(female_date_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "unknown_date_df = pd.read_sql_query(\"SELECT users.uid, min(user_events.date) FROM users INNER JOIN user_events ON users.uid = user_events.user_id WHERE users.gender = 'U' GROUP BY users.uid;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25452"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unknown_date_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "colab_type": "code",
    "id": "J1AWj3Rxucj4",
    "outputId": "fe7cae6d-2a4e-4c7d-d2d3-7ee463536f1a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>uid</th>\n",
       "      <th>min(user_events.date)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12435050</td>\n",
       "      <td>1992-02-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12435066</td>\n",
       "      <td>1991-11-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12435088</td>\n",
       "      <td>2017-05-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12435092</td>\n",
       "      <td>1994-11-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12435105</td>\n",
       "      <td>1992-09-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12435108</td>\n",
       "      <td>1999-08-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12435118</td>\n",
       "      <td>1993-02-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12435133</td>\n",
       "      <td>1991-11-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12435144</td>\n",
       "      <td>1995-07-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12435145</td>\n",
       "      <td>2008-04-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12435147</td>\n",
       "      <td>1995-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12435149</td>\n",
       "      <td>1991-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12435157</td>\n",
       "      <td>1991-11-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12435176</td>\n",
       "      <td>1991-11-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12435177</td>\n",
       "      <td>1993-05-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12435180</td>\n",
       "      <td>2000-03-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12435188</td>\n",
       "      <td>2018-04-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12435197</td>\n",
       "      <td>1995-05-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12435199</td>\n",
       "      <td>2000-08-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12435203</td>\n",
       "      <td>1996-12-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         uid min(user_events.date)\n",
       "0   12435050            1992-02-02\n",
       "1   12435066            1991-11-24\n",
       "2   12435088            2017-05-20\n",
       "3   12435092            1994-11-20\n",
       "4   12435105            1992-09-12\n",
       "5   12435108            1999-08-18\n",
       "6   12435118            1993-02-23\n",
       "7   12435133            1991-11-10\n",
       "8   12435144            1995-07-15\n",
       "9   12435145            2008-04-26\n",
       "10  12435147            1995-12-17\n",
       "11  12435149            1991-12-01\n",
       "12  12435157            1991-11-17\n",
       "13  12435176            1991-11-10\n",
       "14  12435177            1993-05-09\n",
       "15  12435180            2000-03-12\n",
       "16  12435188            2018-04-21\n",
       "17  12435197            1995-05-21\n",
       "18  12435199            2000-08-13\n",
       "19  12435203            1996-12-22"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_date_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RJQgYng8yjub"
   },
   "outputs": [],
   "source": [
    "year_count = user_date_df.groupby('min(user_events.date)').count()\n",
    "year_count_m = male_date_df.groupby('min(user_events.date)').count()\n",
    "year_count_f = female_date_df.groupby('min(user_events.date)').count()\n",
    "year_count_u = unknown_date_df.groupby('min(user_events.date)').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>uid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min(user_events.date)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991-01-28</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-31</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-18</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-26</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-17</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       uid\n",
       "min(user_events.date)     \n",
       "1991-01-28              10\n",
       "1991-01-31              20\n",
       "1991-02-18               3\n",
       "1991-02-26               1\n",
       "1991-03-17              65"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aJTRGIqwzuD6"
   },
   "outputs": [],
   "source": [
    "year_count['cumsum'] = year_count['uid'].cumsum()\n",
    "year_count_m['cumsum'] = year_count_m['uid'].cumsum()\n",
    "year_count_f['cumsum'] = year_count_f['uid'].cumsum()\n",
    "year_count_u['cumsum'] = year_count_u['uid'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uid</th>\n",
       "      <th>cumsum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min(user_events.date)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991-01-28</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-31</th>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-18</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-26</th>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-17</th>\n",
       "      <td>65</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       uid  cumsum\n",
       "min(user_events.date)             \n",
       "1991-01-28              10      10\n",
       "1991-01-31              20      30\n",
       "1991-02-18               3      33\n",
       "1991-02-26               1      34\n",
       "1991-03-17              65      99"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_count.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0qXbtD37tVd-"
   },
   "source": [
    "# Number of players who register every day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sObxZHF4z6IC"
   },
   "outputs": [],
   "source": [
    "year_count.index = pd.to_datetime(year_count.index)\n",
    "year_count_m.index = pd.to_datetime(year_count_m.index)\n",
    "year_count_f.index = pd.to_datetime(year_count_f.index)\n",
    "year_count_u.index = pd.to_datetime(year_count_u.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_percents_gender = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6530, 6470, 2647, 3322)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(year_count.index), len(year_count_m.index), len(year_count_f.index), len(year_count_u.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_count_y = year_count.asfreq(freq='Y', method='pad')\n",
    "year_count_m_y = year_count_m.asfreq(freq='Y', method='pad')\n",
    "year_count_f_y = year_count_f.asfreq(freq='Y', method='pad')\n",
    "year_count_u_y = year_count_u.asfreq(freq='Y', method='pad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28, 28, 25)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(year_count_y), len(year_count_m_y), len(year_count_f_y), len(year_count_u_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_percents_gender['m_percent'] = [m / a for m, a in zip(year_count_m_y['cumsum'], year_count_y['cumsum'])]\n",
    "year_percents_gender['f_percent'] = [m / a for m, a in zip(year_count_f_y['cumsum'], year_count_y['cumsum'])]\n",
    "#year_percents_gender['u_percent'] = [m / a for m, a in zip(year_count_u_y['cumsum'], year_count_y['cumsum'])]\n",
    "year_percents_gender['u_percent'] = [1 - x - y for x, y in zip(year_percents_gender['m_percent'], year_percents_gender['f_percent'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_percents_gender.index = year_count_y.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2023
    },
    "colab_type": "code",
    "id": "MXtnrs9vs1zL",
    "outputId": "f3a39a2c-6163-4077-a7a2-17c92be9b58e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uid</th>\n",
       "      <th>cumsum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min(user_events.date)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-10</th>\n",
       "      <td>1</td>\n",
       "      <td>290301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
       "      <td>1</td>\n",
       "      <td>290302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-30</th>\n",
       "      <td>1</td>\n",
       "      <td>290303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>1</td>\n",
       "      <td>290304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-05</th>\n",
       "      <td>1</td>\n",
       "      <td>290305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-07</th>\n",
       "      <td>1</td>\n",
       "      <td>290306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-08</th>\n",
       "      <td>1</td>\n",
       "      <td>290307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-10</th>\n",
       "      <td>1</td>\n",
       "      <td>290308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-13</th>\n",
       "      <td>1</td>\n",
       "      <td>290309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-14</th>\n",
       "      <td>2</td>\n",
       "      <td>290311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-15</th>\n",
       "      <td>1</td>\n",
       "      <td>290312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-21</th>\n",
       "      <td>5</td>\n",
       "      <td>290317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-22</th>\n",
       "      <td>1</td>\n",
       "      <td>290318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-24</th>\n",
       "      <td>1</td>\n",
       "      <td>290319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-27</th>\n",
       "      <td>1</td>\n",
       "      <td>290320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-28</th>\n",
       "      <td>1</td>\n",
       "      <td>290321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-29</th>\n",
       "      <td>5</td>\n",
       "      <td>290326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-05</th>\n",
       "      <td>2</td>\n",
       "      <td>290328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-06</th>\n",
       "      <td>1</td>\n",
       "      <td>290329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-08</th>\n",
       "      <td>1</td>\n",
       "      <td>290330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-12</th>\n",
       "      <td>4</td>\n",
       "      <td>290334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-13</th>\n",
       "      <td>1</td>\n",
       "      <td>290335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-19</th>\n",
       "      <td>1</td>\n",
       "      <td>290336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-20</th>\n",
       "      <td>2</td>\n",
       "      <td>290338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-25</th>\n",
       "      <td>1</td>\n",
       "      <td>290339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-26</th>\n",
       "      <td>1</td>\n",
       "      <td>290340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-28</th>\n",
       "      <td>2</td>\n",
       "      <td>290342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-07</th>\n",
       "      <td>1</td>\n",
       "      <td>290343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-12</th>\n",
       "      <td>1</td>\n",
       "      <td>290344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-19</th>\n",
       "      <td>1</td>\n",
       "      <td>290345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-29</th>\n",
       "      <td>2</td>\n",
       "      <td>290396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>1</td>\n",
       "      <td>290397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>1</td>\n",
       "      <td>290398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>1</td>\n",
       "      <td>290399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-18</th>\n",
       "      <td>1</td>\n",
       "      <td>290400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>1</td>\n",
       "      <td>290401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-26</th>\n",
       "      <td>2</td>\n",
       "      <td>290403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-29</th>\n",
       "      <td>1</td>\n",
       "      <td>290404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>1</td>\n",
       "      <td>290405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-02</th>\n",
       "      <td>2</td>\n",
       "      <td>290407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-03</th>\n",
       "      <td>1</td>\n",
       "      <td>290408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-06</th>\n",
       "      <td>1</td>\n",
       "      <td>290409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-07</th>\n",
       "      <td>1</td>\n",
       "      <td>290410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-09</th>\n",
       "      <td>2</td>\n",
       "      <td>290412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-10</th>\n",
       "      <td>2</td>\n",
       "      <td>290414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-16</th>\n",
       "      <td>1</td>\n",
       "      <td>290415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-17</th>\n",
       "      <td>3</td>\n",
       "      <td>290418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-18</th>\n",
       "      <td>1</td>\n",
       "      <td>290419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-23</th>\n",
       "      <td>1</td>\n",
       "      <td>290420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-24</th>\n",
       "      <td>3</td>\n",
       "      <td>290423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-27</th>\n",
       "      <td>1</td>\n",
       "      <td>290424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>1</td>\n",
       "      <td>290425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-03</th>\n",
       "      <td>1</td>\n",
       "      <td>290426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>1</td>\n",
       "      <td>290427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-10</th>\n",
       "      <td>1</td>\n",
       "      <td>290428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-17</th>\n",
       "      <td>1</td>\n",
       "      <td>290429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-21</th>\n",
       "      <td>1</td>\n",
       "      <td>290430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-23</th>\n",
       "      <td>1</td>\n",
       "      <td>290431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-24</th>\n",
       "      <td>1</td>\n",
       "      <td>290432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-30</th>\n",
       "      <td>2</td>\n",
       "      <td>290434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       uid  cumsum\n",
       "min(user_events.date)             \n",
       "2018-03-10               1  290301\n",
       "2018-03-29               1  290302\n",
       "2018-03-30               1  290303\n",
       "2018-03-31               1  290304\n",
       "2018-04-05               1  290305\n",
       "2018-04-07               1  290306\n",
       "2018-04-08               1  290307\n",
       "2018-04-10               1  290308\n",
       "2018-04-13               1  290309\n",
       "2018-04-14               2  290311\n",
       "2018-04-15               1  290312\n",
       "2018-04-21               5  290317\n",
       "2018-04-22               1  290318\n",
       "2018-04-24               1  290319\n",
       "2018-04-27               1  290320\n",
       "2018-04-28               1  290321\n",
       "2018-04-29               5  290326\n",
       "2018-05-05               2  290328\n",
       "2018-05-06               1  290329\n",
       "2018-05-08               1  290330\n",
       "2018-05-12               4  290334\n",
       "2018-05-13               1  290335\n",
       "2018-05-19               1  290336\n",
       "2018-05-20               2  290338\n",
       "2018-05-25               1  290339\n",
       "2018-05-26               1  290340\n",
       "2018-05-28               2  290342\n",
       "2018-06-07               1  290343\n",
       "2018-06-12               1  290344\n",
       "2018-06-19               1  290345\n",
       "...                    ...     ...\n",
       "2018-12-29               2  290396\n",
       "2019-01-04               1  290397\n",
       "2019-01-05               1  290398\n",
       "2019-01-06               1  290399\n",
       "2019-01-18               1  290400\n",
       "2019-01-20               1  290401\n",
       "2019-01-26               2  290403\n",
       "2019-01-29               1  290404\n",
       "2019-01-31               1  290405\n",
       "2019-02-02               2  290407\n",
       "2019-02-03               1  290408\n",
       "2019-02-06               1  290409\n",
       "2019-02-07               1  290410\n",
       "2019-02-09               2  290412\n",
       "2019-02-10               2  290414\n",
       "2019-02-16               1  290415\n",
       "2019-02-17               3  290418\n",
       "2019-02-18               1  290419\n",
       "2019-02-23               1  290420\n",
       "2019-02-24               3  290423\n",
       "2019-02-27               1  290424\n",
       "2019-03-01               1  290425\n",
       "2019-03-03               1  290426\n",
       "2019-03-05               1  290427\n",
       "2019-03-10               1  290428\n",
       "2019-03-17               1  290429\n",
       "2019-03-21               1  290430\n",
       "2019-03-23               1  290431\n",
       "2019-03-24               1  290432\n",
       "2019-03-30               2  290434\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_count[-100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "colab_type": "code",
    "id": "ewh139VTtc4q",
    "outputId": "eebd8fc6-bc08-4f37-d9db-710cb35a4f36"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(year_count.index, year_count['cumsum'].values, label=\"all\")\n",
    "plt.plot(year_count_m.index, year_count_m['cumsum'].values, label=\"male\")\n",
    "plt.plot(year_count_f.index, year_count_f['cumsum'].values, label=\"female\")\n",
    "plt.plot(year_count_u.index, year_count_u['cumsum'].values, label=\"unknown\")\n",
    "plt.title(\"Number of chess players registered in the US by year who played in a tournament by gender\", fontsize=20)\n",
    "plt.xlabel(\"Year\", fontsize=15)\n",
    "plt.ylabel(\"Number of chess players\", fontsize=15)\n",
    "plt.legend()\n",
    "plt.savefig('number_of_chess_players.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = year_percents_gender.index\n",
    "#y = np.vstack([year_percents_gender['m_percent'], year_percents_gender['f_percent'], year_percents_gender['u_percent']])\n",
    "labels = [\"male_percent\", \"female_percent\", \"unknown_percent\"]\n",
    "colors = [\"sandybrown\", \"tomato\", \"skyblue\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "ax.stackplot(x, year_percents_gender['m_percent'], year_percents_gender['f_percent'], year_percents_gender['u_percent'], labels=labels)\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=15)\n",
    "plt.title(\"Gender Percentages from 1992 - 2019\", fontsize=20)\n",
    "plt.xlabel(\"year\", fontsize=15)\n",
    "plt.ylabel(\"percentage\", fontsize=15)\n",
    "plt.savefig('gender_ratios.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slope from 1992 to 2004 and 2004 to 2019. The graph looks a bit suspect: is this real or some inconsistencies on my end or the websites/database end. It will have to do for now, because the answer may take some time. I may investigate more later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('1992-01-04 00:00:00'), Timestamp('2004-04-02 00:00:00'))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_count.index[110], year_count.index[4500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6530"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(year_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.515034168564924"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope1 = (year_count['cumsum'].values[4500] - year_count['cumsum'].values[110]) / (4500 - 110)\n",
    "slope1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.9310004928536224"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope2 = (year_count['cumsum'].values[6529] - year_count['cumsum'].values[4500]) / (6529 - 4500)\n",
    "slope2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From 1992 to 2004, we see about 63 people new people registering and playing in a tournament every year. This has droped significantly down to 4 from 2004 - present"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LH0OPNSzwDGz"
   },
   "source": [
    "# Average rating and standard deviation by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 988
    },
    "colab_type": "code",
    "id": "b6ws0ptTwX0u",
    "outputId": "b938bdf5-e82e-4a9d-deb3-982c987124b1"
   },
   "outputs": [],
   "source": [
    "ratings_df = pd.read_sql_query(\"SELECT date, user_events.reg_after, users.uid FROM users INNER JOIN user_events ON users.uid = user_events.user_id;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>date</th>\n",
       "      <th>reg_after</th>\n",
       "      <th>uid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996-08-25</td>\n",
       "      <td>1385 (P5)</td>\n",
       "      <td>12683802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994-12-10</td>\n",
       "      <td>735 (P8)</td>\n",
       "      <td>12632612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1994-11-06</td>\n",
       "      <td>711 (P4)</td>\n",
       "      <td>12632612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-02-10</td>\n",
       "      <td>576 (P5)</td>\n",
       "      <td>12831758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1993-04-15</td>\n",
       "      <td>None</td>\n",
       "      <td>12582004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  reg_after       uid\n",
       "0  1996-08-25  1385 (P5)  12683802\n",
       "1  1994-12-10   735 (P8)  12632612\n",
       "2  1994-11-06   711 (P4)  12632612\n",
       "3  2001-02-10   576 (P5)  12831758\n",
       "4  1993-04-15       None  12582004"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_date_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows are dates (one date column), columns is the rating for each uid (300k uid columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = list(sorted(set(ratings_df['date'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10057"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1991-01-28', '2019-04-09')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates[0], dates[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_date_df['dates'] = dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>dates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1991-01-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1991-02-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1991-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1991-03-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dates\n",
       "0  1991-01-28\n",
       "1  1991-01-31\n",
       "2  1991-02-18\n",
       "3  1991-02-26\n",
       "4  1991-03-17"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_date_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3283074"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's just keep track of rating by year, and the latest rating for that year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n",
      "1300000\n",
      "1400000\n",
      "1500000\n",
      "1600000\n",
      "1700000\n",
      "1800000\n",
      "1900000\n",
      "2000000\n",
      "2100000\n",
      "2200000\n",
      "2300000\n",
      "2400000\n",
      "2500000\n",
      "2600000\n",
      "2700000\n",
      "2800000\n",
      "2900000\n",
      "3000000\n",
      "3100000\n",
      "3200000\n",
      "950.0583763122559  seconds\n"
     ]
    }
   ],
   "source": [
    "uid_date_d = {}\n",
    "start = time.time()\n",
    "for index, row in ratings_df.iterrows():\n",
    "    if index % 100000 == 0:\n",
    "        print(index)\n",
    "    if not row.uid in uid_date_d.keys():\n",
    "        uid_date_d[row.uid] = {}\n",
    "    year = row.date[:4]\n",
    "    #uid_date_d[row.uid][row.date] = row.reg_after\n",
    "    # get rid of provisional part\n",
    "    uid_date_d[row.uid][year] = int(row.reg_after.split(\" \")[0]) if row.reg_after else None\n",
    "end = time.time()\n",
    "print(end - start, \" seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290434"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(uid_date_d.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1996': 1385,\n",
       " '1991': 'NA',\n",
       " '1992': 'NA',\n",
       " '1993': 'NA',\n",
       " '1994': 'NA',\n",
       " '1995': 'NA',\n",
       " '1997': 1385,\n",
       " '1998': 1385,\n",
       " '1999': 1385,\n",
       " '2000': 1385,\n",
       " '2001': 1385,\n",
       " '2002': 1385,\n",
       " '2003': 1385,\n",
       " '2004': 1385,\n",
       " '2005': 1385,\n",
       " '2006': 1385,\n",
       " '2007': 1385,\n",
       " '2008': 1385,\n",
       " '2009': 1385,\n",
       " '2010': 1385,\n",
       " '2011': 1385,\n",
       " '2012': 1385,\n",
       " '2013': 1385,\n",
       " '2014': 1385,\n",
       " '2015': 1385,\n",
       " '2016': 1385,\n",
       " '2017': 1385,\n",
       " '2018': 1385,\n",
       " '2019': 1385}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uid_date_d[12683802]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1385"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#uid_date_d[12683802]['1996'].split(\" \")[0]\n",
    "int(\"1385\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1385'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"1385\".split(\" \")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "uid_set = set(ratings_df['uid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "uids = list(uid_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290434"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(uids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1991',\n",
       " '1992',\n",
       " '1993',\n",
       " '1994',\n",
       " '1995',\n",
       " '1996',\n",
       " '1997',\n",
       " '1998',\n",
       " '1999',\n",
       " '2000',\n",
       " '2001',\n",
       " '2002',\n",
       " '2003',\n",
       " '2004',\n",
       " '2005',\n",
       " '2006',\n",
       " '2007',\n",
       " '2008',\n",
       " '2009',\n",
       " '2010',\n",
       " '2011',\n",
       " '2012',\n",
       " '2013',\n",
       " '2014',\n",
       " '2015',\n",
       " '2016',\n",
       " '2017',\n",
       " '2018',\n",
       " '2019']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# years: 1991 - 2019\n",
    "years = [str(x) for x in range(1991, 2020)]\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for num, uid in enumerate(uids):\n",
    "    for index, year in enumerate(years):\n",
    "        if year not in uid_date_d[uid]:\n",
    "            try:\n",
    "                uid_date_d[uid][year] = uid_date_d[uid][years[index - 1]]\n",
    "            except:\n",
    "                uid_date_d[uid][year] = \"NA\"\n",
    "        elif uid_date_d[uid][year] == None:\n",
    "            try:\n",
    "                uid_date_d[uid][year] = uid_date_d[uid][years[index - 1]]\n",
    "            except:\n",
    "                uid_date_d[uid][year] = \"NA\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(uid_date_d, open( \"uid_date_d.pickle\", \"wb\" ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "uid_date_d = pickle.load( open( \"uid_date_d.pickle\", \"rb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "10000\n",
      "20000\n",
      "30000\n",
      "40000\n",
      "50000\n",
      "60000\n",
      "70000\n",
      "80000\n",
      "90000\n",
      "100000\n",
      "110000\n",
      "120000\n",
      "130000\n",
      "140000\n",
      "150000\n",
      "160000\n",
      "170000\n",
      "180000\n",
      "190000\n",
      "200000\n",
      "210000\n",
      "220000\n",
      "230000\n",
      "240000\n",
      "250000\n",
      "260000\n",
      "270000\n",
      "280000\n",
      "290000\n",
      "6132.803323030472 seconds\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(index=years)\n",
    "start = time.time()\n",
    "for num, uid in enumerate(uids):\n",
    "    if num % 10000 == 0:\n",
    "        print(num)\n",
    "    u = []\n",
    "    for year in years:\n",
    "        u.append(uid_date_d[uid][year])\n",
    "    df[uid] = u\n",
    "end = time.time()\n",
    "print(end - start, \"seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1991    1469.460505\n",
       "1992    1469.567591\n",
       "1993    1476.286349\n",
       "1994    1479.513102\n",
       "1995    1478.230063\n",
       "1996    1475.899347\n",
       "1997    1471.365812\n",
       "1998    1465.221455\n",
       "1999    1457.432788\n",
       "2000    1450.476871\n",
       "2001    1443.122789\n",
       "2002    1440.520764\n",
       "2003    1436.994135\n",
       "2004    1437.610538\n",
       "2005    1445.361082\n",
       "2006    1451.171223\n",
       "2007    1455.811560\n",
       "2008    1460.367420\n",
       "2009    1464.976540\n",
       "2010    1469.812222\n",
       "2011    1473.800965\n",
       "2012    1478.176615\n",
       "2013    1481.488033\n",
       "2014    1484.912118\n",
       "2015    1487.496926\n",
       "2016    1489.286444\n",
       "2017    1491.377069\n",
       "2018    1492.500993\n",
       "2019    1494.856873\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aggs = df.copy(deep=True)\n",
    "df_aggs['mean'] = df.mean(axis=1)\n",
    "df_aggs['std'] = df.std(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>12582912</th>\n",
       "      <th>12582913</th>\n",
       "      <th>12582914</th>\n",
       "      <th>12582916</th>\n",
       "      <th>12582917</th>\n",
       "      <th>12582918</th>\n",
       "      <th>12582919</th>\n",
       "      <th>12582920</th>\n",
       "      <th>12582921</th>\n",
       "      <th>12582922</th>\n",
       "      <th>...</th>\n",
       "      <th>12582904</th>\n",
       "      <th>12582905</th>\n",
       "      <th>12582906</th>\n",
       "      <th>12582907</th>\n",
       "      <th>12582908</th>\n",
       "      <th>12582909</th>\n",
       "      <th>12582910</th>\n",
       "      <th>12582911</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>...</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>1469.460505</td>\n",
       "      <td>404.693575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1451</td>\n",
       "      <td>2356</td>\n",
       "      <td>NA</td>\n",
       "      <td>1125</td>\n",
       "      <td>1535</td>\n",
       "      <td>1203</td>\n",
       "      <td>1237</td>\n",
       "      <td>1522</td>\n",
       "      <td>882</td>\n",
       "      <td>792</td>\n",
       "      <td>...</td>\n",
       "      <td>417</td>\n",
       "      <td>619</td>\n",
       "      <td>1528</td>\n",
       "      <td>1377</td>\n",
       "      <td>1765</td>\n",
       "      <td>1056</td>\n",
       "      <td>870</td>\n",
       "      <td>607</td>\n",
       "      <td>1469.567591</td>\n",
       "      <td>401.143367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>1434</td>\n",
       "      <td>2259</td>\n",
       "      <td>1213</td>\n",
       "      <td>1125</td>\n",
       "      <td>1535</td>\n",
       "      <td>1203</td>\n",
       "      <td>1237</td>\n",
       "      <td>1574</td>\n",
       "      <td>882</td>\n",
       "      <td>792</td>\n",
       "      <td>...</td>\n",
       "      <td>545</td>\n",
       "      <td>619</td>\n",
       "      <td>1501</td>\n",
       "      <td>1291</td>\n",
       "      <td>1355</td>\n",
       "      <td>756</td>\n",
       "      <td>1017</td>\n",
       "      <td>607</td>\n",
       "      <td>1476.286349</td>\n",
       "      <td>399.984928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>1485</td>\n",
       "      <td>2259</td>\n",
       "      <td>1125</td>\n",
       "      <td>1125</td>\n",
       "      <td>1535</td>\n",
       "      <td>1203</td>\n",
       "      <td>1237</td>\n",
       "      <td>1574</td>\n",
       "      <td>882</td>\n",
       "      <td>792</td>\n",
       "      <td>...</td>\n",
       "      <td>618</td>\n",
       "      <td>619</td>\n",
       "      <td>1477</td>\n",
       "      <td>1646</td>\n",
       "      <td>1355</td>\n",
       "      <td>756</td>\n",
       "      <td>1017</td>\n",
       "      <td>607</td>\n",
       "      <td>1479.513102</td>\n",
       "      <td>400.431339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1485</td>\n",
       "      <td>2259</td>\n",
       "      <td>1125</td>\n",
       "      <td>1192</td>\n",
       "      <td>1535</td>\n",
       "      <td>1203</td>\n",
       "      <td>1237</td>\n",
       "      <td>1574</td>\n",
       "      <td>882</td>\n",
       "      <td>792</td>\n",
       "      <td>...</td>\n",
       "      <td>618</td>\n",
       "      <td>619</td>\n",
       "      <td>1481</td>\n",
       "      <td>1760</td>\n",
       "      <td>1355</td>\n",
       "      <td>781</td>\n",
       "      <td>1017</td>\n",
       "      <td>607</td>\n",
       "      <td>1478.230063</td>\n",
       "      <td>401.704975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 290436 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     12582912 12582913 12582914 12582916 12582917 12582918 12582919 12582920  \\\n",
       "1991       NA       NA       NA       NA       NA       NA       NA       NA   \n",
       "1992     1451     2356       NA     1125     1535     1203     1237     1522   \n",
       "1993     1434     2259     1213     1125     1535     1203     1237     1574   \n",
       "1994     1485     2259     1125     1125     1535     1203     1237     1574   \n",
       "1995     1485     2259     1125     1192     1535     1203     1237     1574   \n",
       "\n",
       "     12582921 12582922  ... 12582904 12582905 12582906 12582907 12582908  \\\n",
       "1991       NA       NA  ...       NA       NA       NA       NA       NA   \n",
       "1992      882      792  ...      417      619     1528     1377     1765   \n",
       "1993      882      792  ...      545      619     1501     1291     1355   \n",
       "1994      882      792  ...      618      619     1477     1646     1355   \n",
       "1995      882      792  ...      618      619     1481     1760     1355   \n",
       "\n",
       "     12582909 12582910 12582911         mean         std  \n",
       "1991       NA       NA       NA  1469.460505  404.693575  \n",
       "1992     1056      870      607  1469.567591  401.143367  \n",
       "1993      756     1017      607  1476.286349  399.984928  \n",
       "1994      756     1017      607  1479.513102  400.431339  \n",
       "1995      781     1017      607  1478.230063  401.704975  \n",
       "\n",
       "[5 rows x 290436 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aggs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average rating')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "plt.plot(df_aggs.index, df_aggs['mean'].values)\n",
    "plt.title(\"Average rating per year\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Average rating\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rating average appears to peak around 1994, dip down near 2003 and has been on the increase ever since."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aggs.to_csv(\"df_aggs.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle(\"df_aggs.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aggs = pickle.load(open(\"df_aggs.pkl\",\"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aggs.replace(\"NA\", float('nan'), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aggs = pd.read_csv(\"df_aggs.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_original = df_aggs.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aggs = df_aggs.iloc[:, 0:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2206760, (29, 290434))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aggs.isnull().sum().sum(), df_aggs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1991    1469.460505\n",
       "1992    1286.806318\n",
       "1993    1191.595125\n",
       "1994    1125.614566\n",
       "1995    1077.378530\n",
       "1996    1037.228633\n",
       "1997    1006.460670\n",
       "1998     976.385157\n",
       "1999     947.492651\n",
       "2000     918.462384\n",
       "2001     888.196273\n",
       "2002     861.308821\n",
       "2003     842.029773\n",
       "2004     836.162595\n",
       "2005     843.312802\n",
       "2006     847.765835\n",
       "2007     850.684329\n",
       "2008     852.789474\n",
       "2009     854.638472\n",
       "2010     856.243988\n",
       "2011     857.458140\n",
       "2012     858.545612\n",
       "2013     859.410848\n",
       "2014     860.268129\n",
       "2015     860.849392\n",
       "2016     861.306603\n",
       "2017     861.690709\n",
       "2018     861.951350\n",
       "2019     862.038904\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aggs.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rating by gender per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_df = pd.read_sql_query(\"SELECT users.uid FROM users WHERE users.gender = 'F';\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46942"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(female_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_df = pd.read_sql_query(\"SELECT users.uid FROM users WHERE users.gender = 'M';\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401926"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(male_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "unknown_df = pd.read_sql_query(\"SELECT users.uid FROM users WHERE users.gender = 'U';\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39718"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unknown_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_df = pd.read_sql_query(\"SELECT users.uid FROM users;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(488586, 290434)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(users_df), len(df_aggs.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_uids = female_df['uid']\n",
    "male_uids = male_df['uid']\n",
    "unknown_uids = unknown_df['uid']\n",
    "#female_uids = [str(x) for x in female_uids]\n",
    "#male_uids = [str(x) for x in male_uids]\n",
    "#unknown_uids = [str(x) for x in unknown_uids]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_df = df_aggs[df_aggs.columns.intersection(female_uids)]\n",
    "male_df = df_aggs[df_aggs.columns.intersection(male_uids)]\n",
    "unknown_df = df_aggs[df_aggs.columns.intersection(unknown_uids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(290434, 290434)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(female_df.columns) + len(male_df.columns) + len(unknown_df.columns), len(df_aggs.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_df_aggs = male_df.copy(deep=True)\n",
    "#male_df_aggs['mean'] = male_df.mean(axis=1)\n",
    "male_df_aggs_mean = male_df.mean(axis=1)\n",
    "male_df_aggs_std = male_df.std(axis=1)\n",
    "#male_df_aggs['std'] = male_df.std(axis=1)\n",
    "#male_df_aggs['median'] = male_df.median(axis=1)\n",
    "male_df_aggs_med = male_df.median(axis=1)\n",
    "#male_df_aggs['max'] = male_df.max(axis=1)\n",
    "male_df_aggs_max = male_df.max(axis=1)\n",
    "#male_df_aggs['min'] = male_df.min(axis=1)\n",
    "male_df_aggs_mode = male_df.mode(axis=1)\n",
    "male_df_aggs['mean'] = male_df_aggs_mean\n",
    "male_df_aggs['std'] = male_df_aggs_std\n",
    "male_df_aggs['median'] = male_df_aggs_med\n",
    "male_df_aggs['max'] = male_df_aggs_max\n",
    "male_df_aggs['mode'] = male_df_aggs_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_df_aggs = female_df.copy(deep=True)\n",
    "female_df_aggs_mean = female_df.mean(axis=1)\n",
    "female_df_aggs_std = female_df.std(axis=1)\n",
    "female_df_aggs_med = female_df.median(axis=1)\n",
    "female_df_aggs_max = female_df.max(axis=1)\n",
    "#female_df_aggs_mode = female_df.mode(axis=1)\n",
    "female_df_aggs['mean'] = female_df_aggs_mean\n",
    "female_df_aggs['std'] = female_df_aggs_std\n",
    "female_df_aggs['median'] = female_df_aggs_med\n",
    "female_df_aggs['max'] = female_df_aggs_max\n",
    "#female_df_aggs['mode'] = female_df_aggs_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "unknown_df_aggs = unknown_df.copy(deep=True)\n",
    "unknown_df_aggs_mean = unknown_df.mean(axis=1)\n",
    "unknown_df_aggs_std = unknown_df.std(axis=1)\n",
    "unknown_df_aggs_med = unknown_df.median(axis=1)\n",
    "unknown_df_aggs_max = unknown_df.max(axis=1)\n",
    "#unknown_df_aggs_mode = unknown_df.mode(axis=1)\n",
    "unknown_df_aggs['mean'] = unknown_df_aggs_mean\n",
    "unknown_df_aggs['std'] = unknown_df_aggs_std\n",
    "unknown_df_aggs['median'] = unknown_df_aggs_med\n",
    "unknown_df_aggs['max'] = unknown_df_aggs_max\n",
    "#unknown_df_aggs['mode'] = unknown_df_aggs_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aggs_mean = df_aggs.mean(axis=1)\n",
    "df_aggs_std = df_aggs.std(axis=1)\n",
    "df_aggs_med = df_aggs.median(axis=1)\n",
    "df_aggs_max = df_aggs.max(axis=1)\n",
    "#df_aggs_mode = df_aggs.mode(axis=1)\n",
    "df_aggs['mean'] = df_aggs_mean\n",
    "df_aggs['std'] = df_aggs_std\n",
    "df_aggs['median'] = df_aggs_med\n",
    "df_aggs['max'] = df_aggs_max\n",
    "#df_aggs['mode'] = df_aggs_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(male_df_aggs.index, male_df_aggs['mean'].values, label=\"male\")\n",
    "plt.plot(female_df_aggs.index, female_df_aggs['mean'].values, label=\"female\")\n",
    "plt.plot(df_aggs.index, df_aggs['mean'].values, label=\"all\")\n",
    "plt.plot(df_aggs.index, unknown_df_aggs['mean'].values, label=\"unknown\")\n",
    "plt.title(\"Average rating per year by gender\", fontsize=20)\n",
    "plt.xlabel(\"Year\", fontsize=15)\n",
    "plt.ylabel(\"Average rating\", fontsize=15)\n",
    "plt.legend()\n",
    "plt.savefig(\"average_rating_per_year.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a significant difference between the average male and female chess ratings and the difference, about 250 remains relatively consistent over this timeframe. The average for males is about 1450 and for females is about 1200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(male_df_aggs.index, male_df_aggs['median'].values, label=\"male\")\n",
    "plt.plot(female_df_aggs.index, female_df_aggs['median'].values, label=\"female\")\n",
    "plt.plot(unknown_df_aggs.index, unknown_df_aggs['median'].values, label=\"unknown\")\n",
    "plt.plot(df_aggs.index, df_aggs['median'].values, label=\"all\")\n",
    "#plt.errorbar(df_aggs.index, df_aggs['mean'].values, df_aggs['std'].values, label=\"average\")\n",
    "plt.title(\"Median rating per year by gender\", fontsize=20)\n",
    "plt.xlabel(\"Year\", fontsize=15)\n",
    "plt.ylabel(\"Median rating\", fontsize=15)\n",
    "plt.legend()\n",
    "plt.savefig(\"Median_rating_per_year.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(male_df_aggs.index, male_df_aggs['max'].values, label=\"male\")\n",
    "plt.plot(female_df_aggs.index, female_df_aggs['max'].values, label=\"female\")\n",
    "plt.plot(unknown_df_aggs.index, unknown_df_aggs['max'].values, label=\"unknown\")\n",
    "plt.plot(df_aggs.index, df_aggs['max'].values, label=\"all\")\n",
    "#plt.errorbar(df_aggs.index, df_aggs['mean'].values, df_aggs['std'].values, label=\"average\")\n",
    "plt.title(\"Max rating per year by gender\", fontsize=20)\n",
    "plt.xlabel(\"Year\", fontsize=15)\n",
    "plt.ylabel(\"Max rating\", fontsize=15)\n",
    "plt.legend()\n",
    "plt.savefig(\"Max_rating_per_year.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can understand why the average and median rating has not been going up because the max hasn't either. We should also consider that rating is similar to a log scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(male_df_aggs.index, male_df_aggs['std'].values, label=\"male\")\n",
    "plt.plot(female_df_aggs.index, female_df_aggs['std'].values, label=\"female\")\n",
    "plt.plot(df_aggs.index, df_aggs['std'].values, label=\"all\")\n",
    "plt.plot(unknown_df_aggs.index, unknown_df_aggs['std'].values, label=\"unknown\")\n",
    "#plt.errorbar(df_aggs.index, df_aggs['mean'].values, df_aggs['std'].values, label=\"average\")\n",
    "plt.title(\"Standard deviation of rating per year by gender\", fontsize=20)\n",
    "plt.xlabel(\"Year\", fontsize=15)\n",
    "plt.ylabel(\"Min rating\", fontsize=15)\n",
    "plt.legend()\n",
    "plt.savefig(\"std_rating_per_year.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'mode'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2655\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2656\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2657\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'mode'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-51-e6f3d43247fe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmale_df_aggs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmale_df_aggs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mode'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"male\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfemale_df_aggs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfemale_df_aggs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mode'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"female\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_aggs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf_aggs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mode'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"all\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munknown_df_aggs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munknown_df_aggs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mode'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unknown\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2925\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2926\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2927\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2928\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2656\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2657\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2658\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2659\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2660\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'mode'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(male_df_aggs.index, male_df_aggs['mode'].values, label=\"male\")\n",
    "plt.plot(female_df_aggs.index, female_df_aggs['mode'].values, label=\"female\")\n",
    "plt.plot(df_aggs.index, df_aggs['mode'].values, label=\"all\")\n",
    "plt.plot(unknown_df_aggs.index, unknown_df_aggs['mode'].values, label=\"unknown\")\n",
    "#plt.errorbar(df_aggs.index, df_aggs['mean'].values, df_aggs['std'].values, label=\"average\")\n",
    "plt.title(\"Mode of rating per year by gender\", fontsize=20)\n",
    "plt.xlabel(\"Year\", fontsize=15)\n",
    "plt.ylabel(\"Min rating\", fontsize=15)\n",
    "plt.legend()\n",
    "plt.savefig(\"mode_rating_per_year.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Michael\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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88z9/lqKiIq677rXMnj2H2tqxUccacBatJEmSJEmSBpGlS5/hqquu5vrrX0tZWRnPPruMdDoVdawB5/RASZIkSZKkQeQ1r3kDX/jCp3nwwfvJy8vn3HPPY/nyZVHHGnAWrSRJkiRJkropzM9jwZlj+uW8J3LGGdP5yU9+ecz9n/70TX2YaPCyaCVJkiRJktRNXhzyCi2bRMk1rSRJkiRJkjToWLSSJEmSJEnSoGPRSpIkSZIkSYOORStJkiRJkiQNOhatJEmSJEmSNOi4DL4kSZIkSVI3BbRDZ1vfnzi/iA4K+/68w5BFK+kkpeIdtKc6Bqy/wkQBiXTBgPUnSZIkSae1zjaa1j3b56ctmz4H8vuvaPXmN7+Gb37zO4wdO67f+hgoFq2kk9Se6mD5jlUD1t/ccedQErNoJUmSJEk6PVi0kiRJkiRJGkSWL1/KT37yQ/Lz89m5cweXXHI5xcXFPPbYo0CGf//3/+Lhhx/kvvvuoa2tlfz8fG666WYmTZpy+BypVIpvf/u/ePbZZaRSaa677gbe+tZ3RHZNJ8OF2CVJkiRJkgaZF198gU984h/5/vdv5de/vp2qqmp+8INbmT59Bg8++ACLFj3Kt771HW699XYuvvgy7rzz9iOOv+uu3wDwwx/+jO9978c89tijrFjR99Md+5MjrSRJkiRJkgaZadPOYMyYWgAqK6uYP/8CAGprx9LYeJCbbvoSDz74AFu3buHpp59kxozgiOOXLn2GtWvXsGzZUgBaW1tYv34ds2fPGdgLOQUWrSRJkiRJkgaZvLwjSzaJROLw13v27OaDH3wvb3rTW7joooupqRnB2rXhEe1TqTQf/vBHueKKqwDYv38/xcXF/R+8Dzk9UJIkSZIkaQhZvfpFJkyYyFvf+g7OPPMsFi16mHQ6dUSbefPm8/vf/5ZkMklLSwsf/vD7eOGF5yNKfHIcaSVJkiRJktRdfhFl0/thKl1+0SmfYsGCi1i3bg3vfOdfkslkOP/8uWzYsP6INq9//ZvZtm0r733v20mlUlx33WuYO3f+Kfc9kCxaSZIkSZIkddNBIeQXRtL33Lnzjygw3XHHXYe/fv/7P0QymT7msV3bfuxjf98/AQeI0wMlSZIkSZI06Fi0kiRJkiRJ0qBj0UqSJEmSJEmDjkUrSZIkSZIkDToWrSRJkiRJkjToWLSSJEmSJEnSoJMXdQBJkiRJkqTBJhXvoD3V0efnLUwUkEgXnLDdv/zLF3j22eV84AN/zTXXvLpPM9x8803MmTOP6657TZ+et69ZtJIkSZIkSeqmPdXB8h2r+vy8c8edQ0nsxEWre++9m4ceepL8/Pw+zzBUWLSSJEmSJEkaRD71qb8lk8nw/ve/h7e+9e386lc/J53OEASz+OQn/5FEIp/XvvZVXHbZFbz44ipqakZy/fWv5Y47fkFd3R7+6Z8+z5w583j22WV897vfpr29jcbGJj760b/lssuuPKKve++9+4jz/93ffYrCwsJoLrwb17SSJEmSJEkaRP7t374GwOc+90Xuuuu3/M///JBbbrmN6uoafvazWwGor9/HRRddzI9+dBsdHe0sWvQw3/729/nf//sD3H77zwG4885f8g//8Fl++MOf8Q//8Bm+973/OaKfDRvWv+z8P//5rQN7scfhSCtJkiRJkqRB6Nlnl7Jt21Y++MH3ApBMdhIEsw7vv+iiSwCorR3LeeedD8CYMbU0Nh4E4LOf/SJPPvkYDz/8IC+88Dytra0nPP/MmbMYLCxaSZIkSZIkDUKpVJqrrrqaj33s7wFoaWkhFssc3t91vatEIvGy4z/ykfczd+485syZx7x5C/jCFz5zwvOnUqn+uJST4vRASRpALZkmXmh4kU1Nm2hON9KSaerXP6l43z/tRJIkSdLAmDNnHosWPUJDQz2ZTIavfvVf+cUvftajYw8ePMDWrZt53/s+xEUXXcJjjz1KOp0+4flvv/22/riUk+JIK0nqJ+lMmt0tdWw6sIVNB7ew8eAWdjbvIp3J/stIUaKQ0SUjGV08ktElo6goKCcWi/Vphp4+mUSSJEnS4DNjxkze+97389GPfohMJsP06TN597vf26NjKyoqueGG1/Gud72FvLw85s5dQFtb2xFTBI92/ne+88Z+uprei2UymRO3Or1MATbu29dEOu1nM1SMGlVOXV3jgPbZkmnql8efHku2+FA2YP2p57ref5lMhhf2reaRbU+w8cAW2lJtABQliphSMZHxFbV0JDtpS7azp2Uve1rraE1m2xQmChldPILxZeOYUjGxTwpY3jfDXxQ//6RDvP8UJe8/Rcn7b/jZtWsztbWTj9iWinfQnur7mQuFiQIS6ZP/h+W8vDjJZPrEDSN0tM8zHo8xYkQZwFRgU0/O40grSeojGw9s4Xfr72Ht/g3UFFUzf8xsplROZmrFREaXjCIeix9R7JxeNZVMJkNTZzN7Wvdmi1gtdWxt2sGO5l1cWDuXvLg/piVJkqQoJNIF/TNrYXDXmwYVfxuSdNrqq385WbV7O79+4X5W1a2mLL+UN8y8lgvGziUv/ueFENtogQyk40cuahiLxSgvKKO8oIwzKqeQyWR4sX4NK/e+wMGORi4bdxFlBaWnnFGSJEmShhqLVpJOW+2pjlOa4tmSbGXV3pfYcGAziViCc0ecSVAzg/xYHit3vXTUY84dN/O454zFYpw9IqC6qJLFO5Zw/+aHWDhuAeNKa086pyRJkiQNRRatJKmXMpkML9WvYdW+1WQyac4ePYMZ5dMpyivqsz7GldbyqslX8diOp3h025OcN/IszqoJ+nyhdkmSJEkarCxaSVIvJNNJntq1jK2N25lQNo45o85l7IgRNDa29XlfZQWlXDPpCp7ZtZyVe1+kvm0/F42dR348v8/7kiRJkqTBJh51AEkaKpo7W/jjlkfZ2rid80edw6XjLuz39aby4nksHLuAOaPOZXvTTh7Y/AjNnS392qckSZIkDQaOtJKkHtjTspfHdzxNOpPmivEXM65s4NaYisVizKqZQXVRFYu2L+aJHU/zF5OuIBHz3x0kSZKk/pKXaifT1vczKmJFRSQThX1+3uHIopUkncC6/RtYunsFZfmlXD5hIRUF5ZHkGFMyigtr5/HEjqd5bs/zzBszO5IckiRJ0ukg09bGvqXP9vl5R8yfA6V9W7S69NL5PP740j4952Bg0UqSjiGdSbNszwrW7d/I2NIxXDx2AQWJgkgzTSofz97q6YQN6xhVPIJJFRMizSNJkiRJ/cWilSQdRTKd4tHtT7KnpY5Z1TOYPeoc4oPkyX2zR53DvtZ6nt61nKqiyshGfkmSJEnqH8uXL+WHP/wu3/rWdwG4+eabmDNnHr/61c8544zphOFqampG8MUvfpmKisrDxz3//Apuvvkm/uM/vsH999/D3r11bN26hd27d3HDDa/jPe95H+l0mm9846ssXbqEWAxe9arreOc7b+Td734r//zPX2bKlKncdNOnKSsr4xOf+EdWrVrJj3/8A972tndx660/oqioiE2bNnLGGdP5/OdvJj+//x4U5YIoktRNJpNhye7l7Gmp48LaecwZfe6gKVgBJGJxLhl3AYl4nMe3P00ynYw6kiRJkqQBsG7dWt72tndy6623U1ZWxgMP3Ht439q1a/jyl7/IV77yNSZMmHi4/de+9t9897u38NOf/pjGxkZ++9s72b17Nz/+8c/53vd+wqOPPsSTTz7OwoWXsmzZMwBs2LCOlSufA+Dppxdz8cWXAbBq1Ur+9m8/yc9+dge7d+/i6acX9+v1WrSSpG5WN6xj08GtnDvyLKZVTo46zlGV5JewcOwCDnQcZMnu58hkMlFHkiRJktTPqqtrCIJZAEybNp2DBw8e3vfxj/8f5s+/gEmTphzeNnfufPLz86murqGiooLm5iaWL1/CddfdQCKRoKioiGuuuZZly55h4cJLWLp0CRs3bmDq1GnE4wkaGup56qknWLjwUgCmTj2D0aPHEI/HmTx5Ko2NB+lPFq0kqYudzbtZUfc8E8vGc3ZNEHWc4xpbOoZzR5zJpoNbWH9gU9RxJEmSJPWRWLeZHslkdnZFQcGRa+x2/cfrz3/+Szz66MOsXbvm8Lau7WOxGJlMhnS6+z94Z0ilUpxzznmsW7eWpUufYc6ceZx//lwefvhPJJNJamtrj3m+/mTRSpJyGjuaeGLHM1QUVHDh2Hkv+w/FYHT2iFnUloxm2Z4V1Lc1RB1HkiRJUh+orKxix47ttLe3c/DgAVasOPFTDOfNW8AHP/gRvvKVL5FOp4/Tbj733vsHUqkUbW1tPPDAfcyZM5+8vDzOOuss7rjjF8yZM5958+bzk5/8kIsuuqQvL61XXIhdkoDOdCeLti8mRozLxy8kPz40fjzGYjEuHruA+zY/xOM7nubVk6+K/AmHkiRJ0nAQKypixPw5/XLeE5k27QwWLryEd73rLYwdO47Zs3uW49prb+Dee+/mjjt+ecw2r3vdm9i6dQs33vg2kskkr3zltVxxxSsAWLjwUp57bjmTJ0+hpmYEDQ31XHLJZT27sH4Qcx2Ul5kCbNy3r+koQ+Y0WI0aVU5dXeOA9tmSaWL5jlUD1t/ccedQEisbsP5OB4e+h5lMhsd2PMWOpl1cOeESaktH9+o85eVFNDa29ajtueNm8vyONSdu2Et7W+t5cMujTCwfxyXjLjy83ftm+Ivi5590iPefouT9pyh5/w0/u3ZtprZ2cK5n211eXpxk8tgjqQaDo32e8XiMESPKAKYCm3pyHqcHSjrtrdr3EtubdjJ39Hm9LlgNFiOLazhn5JlsadzOzubdUceRJEmSpFNm0UrSaW1r43ZW7VvN1IrJzKiaFnWcU3Jm9QzK8ktZtnsFqXQq6jiSJEmSdEosWkk6be1uruOpnUsZUVTDgjHnD4mF148nEU8wb8xsGjubWN2wNuo4kiRJ0pDi8kl9oy8/xwFdaTgIgrcDnwHyga+HYfjf3fafD3wfqAAWAR8KwzAZBMEk4KfAaCAE3hGGYVOX494HXBaG4Y2592OBHwG1QBr4RBiGD/Xz5UkaQjKZDL9Zcy/xeIJLx19IIp6IOlKfGFday4SycbywL2RKxaSo40iSJElDQiKRR2dnBwUFhVFHGfJSqSTxPvr9asBGWgVBMB64GbgUOB/4QBAEZ3Vr9lPgb8IwnAnEgPfntn8b+HYYhrOApcBnc+csCoLgy8DXu53n34G7wjA8H3gbcFsQBMPjN1JJfWLl3hdZv38T5444k5K84qjj9Km5o88DYPmelREnkSRJkoaGsrIq9u+vo6Oj3RFXpyCTSdPY2EBxcd88DGogR1pdDTwUhmE9QBAEdwBvBv45934yUByG4VO59rcAXwiC4PvA5cDru2x/FPhUbnsc+CTw58dlwW+AQyOr1gFFQBlwoB+uS9IQk0wn+c26uxldMpLpVVOjjtPnSvNLOGfELFbsfYHV+9Yyd2TfP6ZXkiRJGk6Ki0sBOHBgL6lUMuI0xxePx0mnB+vTA2MUFBRRVlbZJ2cbyKLVOGBnl/c7gQtOsH8CMBI4GIZhstt2wjB8AHggCIIbu3YUhuGdXd5+Ang2DMNeFaxyj2HUEDJqVPmA9lfX3EF5edGA9VdSWsCo0oG9xuHq7vBP1LXu48MXvJtkum/+g9TTeyE/PzEg98380nPY1LSV3627n1fMXEhBIr/f+1R0Bvrnn9SV95+i5P2nKHn/DUflZFcZ0mAxkEWrONB1jF2M7HpTJ9rffTvdjjumIAg+BnwQuKK3YfftayKddkjgUDFqVDl1dY0D2mdLpoPGxraB66+8g7qWgb3G4aipo5lfrfoDZ9UETC2dwvIdq075nOXlRT2+FzrLUwN238wdeR4Pb3ucXyy7m2unXj0gfWrgRfHzTzrE+09R8v5TlLz/FKWhev/F47FeDxAayKcHbgPGdnlfC+zowf49QGWXNanGdjvuqIIg+ArZNbEuD8Nw6ynkljSM/GHjH2lPtfPGGTdEHaXf1ZaOZvbos7h/80Psba2POo4kSZIk9cpAFq0eBP4iCIJRQRCUAG8C7ju0MwzDzUBbEASX5Da9C7g3DMNO4DHgrbnt7wbuPV5HuRFWrwAuCcNwW99ehqShamcFcSwUAAAgAElEQVTzbh7f8RSXjruQsaVjoo4zIG4445XEYnHuWPv7qKNIkiRJUq8MWNEqDMPtwKeBh4HngNvCMHwmCIJ7giCYn2v2DuBrQRCsJrtw+jdy2z9M9mmDLwKXAZ85Vj9BEMSAzwOjgUeCIHgu92dcv1yYpCHj1+vupjBRwPVTXxl1lAFTVVTB9VOv4fm9L/L83hejjiNJkiRJPTaQa1oRhuFtwG3dtl3X5esVHLk4+6Htm4Erj3PeW8g+VZAwDDNAdV/klTR8vLgv5MV9IW+Yfj1lBaVRxxlQr5hwKYt3LuVXa37PrOoZ5LsouyRJkqQhYCCnB0pSJFLpFHeuu5uRxSO4YsIlJz5gmEnEE7xlxuvY11bPI9ueiDqOJEmSJPWIRStJw94TO55hV/Nu3jD9evLjAzrAdNAIaqZzzogzuW/TQzR1NEcdR5IkSZJOyKKVpGGtpbOVP2x8gBlV05g98uyo40TqDdOvoyPdwT2bHow6iiRJkiSdkEUrScPa/ZsformzhTfNeA2xWCzqOJGqLR3DxeMu4LHti9ndUhd1HEmSJEk6LotWkoatxo4mHt32JBfUzmVi+fio4wwK10+9hvx4Hr9bf2/UUSRJkiTpuCxaSRq2Ht32JMl0kldOvjLqKINGRUE5r5z8ClbUrWLd/o1Rx5EkSZKkY7JoJWlYaku28+i2Jzhv5FnUlo6JOs6gctXEy6gqrOTXa+8mnUlHHUeSJEmSjsqilaRh6ckdT9OSbOUaR1m9TEGigNdOezWbG7eyfPeKqONIkiRJ0lFZtJI07CTTSf609TFmVE1jauXkqOMMSgtq5zChbBy/23AfnanOqONIkiRJ0stYtJI07CzZ/Rz72w9wzeRXRB1l0IrH4rxx+g3UtzXwyLYnoo4jSZIkSS9j0UrSsJLOpPnj5kcYXzaWs2pmRh1nUAtqpnPOiFncv/khmjqao44jSZIkSUewaCVpWHl+74vsbtnDKyddSSwWizrOoPf66dfTlmznnk0PRh1FkiRJko5g0UrSsJHJZHhg8yOMKKphzujzoo4zJIwtHcMl4y7gse2L2d1SF3UcSZIkSTrMopWkYWPd/g1sOriFqyddTiKeiDrOkHH9tFeSH8/jrg33Rx1FkiRJkg6zaCVp2Hhg8yOU5Zdy0dgFUUcZUioKyrlq4uU8u2clmw9ujTqOJEmSJAEWrSQNE9sad/BifcgrJl5GQSI/6jhDzl9MupzS/BJ+v/6+qKNIkiRJEmDRStIw8cctj1CYKODy8RdFHWVIKs4r4tWTr2J1w1pW16+NOo4kSZIkWbSSNPTtbd3Hst0ruHT8RZTkl0QdZ8i6bPxCqgur+P36+8hkMlHHkSRJknSas2glacj705ZFxGNxrpp4WdRRhrT8RD7XT72GzY1bWVG3Kuo4kiRJkk5zFq0kDWmNHU0s3rmEC2vnUlVYGXWcIe+C2rnUlozmrg33k0qnoo4jSZIk6TSWF3UASToVi7Y9SWc6yV9MuiLqKINSLAEtqaZeHACvnHYFP1n1Kx7ftZgFY8/vdZ+FiQIS6YJeHydJkiRJXVm0kjRopOIdtKc6ety+M9XJo9uf5MwRM6goKaEl04viDJCOD/+RRB2pDp7fsaZXx2QyGWqKqrl73R8hFSMRT/Tq+LnjzqEkZtFKkiRJ0qmxaCVp0GhPdbB8R8/XUlq3fyPNnS2MLant1XGHnDtuZq+POR3EYjFmjzybh7c9ztr9G5hVMyPqSJIkSZJOQ65pJWlIymQyrG5YS3VhJaOLR0YdZ9ipLR1NbcloXqgP6Ux1Rh1HkiRJ0mnIopWkIWlH8y4aO5qYVTODWCwWdZxh6bxRZ9OR6mB1w9qoo0iSJEk6DVm0kjQkrW5YR3FeEZPKJ0QdZdgaUVTNxPLxrK5fS1uyLeo4kiRJkk4zFq0kDTn1bfvZ01LHzKrpxGP+GOtP5408i1QmzQv7wqijSJIkSTrN+NuepCEnbFhLXiyP6VVToo4y7FUUlDO1chLrD2x0tJUkSZKkAWXRStKQ0tLZwuaD25hWNZmCREHUcU4LZ9bMJJVJs2b/+qijSJIkSTqNWLSSNKSs2b8ByBBUTY86ymmjoqCcCWXjWNuwgc50Muo4kiRJkk4TFq0kDRmd6U7W7d/IhPLxlBWURh3ntHJmzUw60p1sOLAp6iiSJEmSThMWrSQNGRsObKYz3cms6hlRRzntjCyuYVTxCFbXryWdSUcdR5IkSdJpwKKVpCEhnckQNqxjZFENI4troo5zWjqzZiYtyVa2NG6LOookSZKk04BFK0lDwramHTR3tjCrxlFWURlXWktFQTkv1a8lk8lEHUeSJEnSMGfRStKQsLp+LWX5pYwvGxd1lNNWLBbjzJqZ7G8/wK6WPVHHkSRJkjTMWbSSNOjtbd3HvrZ6gurpxGOxqOOc1iZXTKQ4r4iX6tdEHUWSJEnSMGfRStKgt7p+LfnxfKZWTo46ymkvEYsTVM9gd0sd+9oaoo4jSZIkaRizaCVpUGvqaGZb0w6mV00lP54XdRwB0yunkB/Pd7SVJEmSpH5l0UrSoBY2rCNGjJnVZ0QdRTn5iXymV01lW+N2Gjuaoo4jSZIkaZiyaCVp0OpIdbDhwCYmVUykJK846jjqIqg+g1gszuqGtVFHkSRJkjRMWbSSNGit27+RZCbFrOrpUUdRN8V5xUytmMTGA5tpS7ZFHUeSJEnSMGTRStKglMqkWbN/PWNKRlFdVBV1HB3FrJoZue/ThqijSJIkSRqGLFpJGpS2HNxGa7KNWdUzoo6iY6goKGdC2TjWNqynM52MOo4kSZKkYcailaRBJ5PJsLphLRUF5YwtHRN1HB3HrOrpdKQ72dq4PeookiRJkoYZi1aSBp3dLXXsbz/ArOrpxGKxqOPoOEYWj6A8v4yNBzZHHUWSJEnSMGPRStKgEzasozBRyJSKSVFH0QnEYjGmVE5iT+temjqbo44jSZIkaRixaCVpUDnQfpAdzbuYWTWNRDwRdRz1wNRccXHTgS0RJ5EkSZI0nFi0kjSohA3rSMTiTK+aFnUU9VBpfgmjS0ax8eAWMplM1HEkSZIkDRMWrSQNGk0dzWw8uIUpFZMoyiuMOo56YWrFJJo6m9nbWh91FEmSJEnDhEUrSYPGk9uXks6kmVU9I+oo6qWJ5ePJiyXYeNAF2SVJkiT1DYtWkgaFjlQnT25fwrjSWioKy6OOo17Kj+cxoXw8Wxq30ZnqjDqOJEmSpGHAopWkQWHJruU0d7Ywq8ZRVkPV1IpJdKaTvLA3jDqKJEmSpGHAopWkyGUyGR7a+hjjy2oZXTwy6jg6SWNKRlGSV8zSXSuijiJJkiRpGLBoJSlyYcM6drXs4dKJFxKLxaKOo5MUi8WYWjGJNfUb2N9+IOo4kiRJkoY4i1aSIrdo25OU5Zcye9TZUUfRKZpSOYkMGZbsejbqKJIkSZKGOItWkiJV39bAyr0vcvG4C8hP5EUdR6eooqCcyRUTeHrXMjKZTNRxJEmSJA1hFq0kReqx7U8BcNn4iyJOor4yr3Y2O5t3s7Vxe9RRJEmSJA1hFq0kRaYz1cmTO57hvJFnUVNUHXUc9ZHzR59NXjyPp3YtjTqKJEmSpCHMopWkyCzfs5KmzmYun3Bx1FHUh4rzi5g98myW7n6OZDoZdRxJkiRJQ5RFK0mReXT7k4wpGU1QPT3qKOpjF46dR3NnC6v2rY46iiRJkqQhyqKVpEhsOriFzQe3csWEi4nFYlHHUR+bVT2DioJynt65LOookiRJkoYoi1aSIrFo22IKEwVcUDs36ijqB4l4ggW1c1i17yUaO5qijiNJkiRpCLJoJWnANXY0sWzPCi6snU9xXlHUcdRPLqqdTzqTZunu56KOIkmSJGkIsmglacAt3rGEZDrJFRMWRh1F/WhcWS3jy8by7J7no44iSZIkaQiyaCVpQKXSKRZtX8zM6unUlo6JOo762eyRZ7PhwCanCEqSJEnqNYtWkgbUqn0v0dC+nysmXBx1FA2A2aPOIUOGlXtfiDqKJEmSpCHGopWkAbVo22KqC6s4d8SZUUfRABhfNpYRRTWsqLNoJUmSJKl3LFpJGjC7mnezumEtl42/iEQ8EXUcDYBYLMbsUWcT1q+lLdkWdRxJkiRJQ0he1AEknT4WbV9MXizBxeMuiDqKeiCPJKQ6e31cIt1OQTp1+P286ik8tPUxwj3PsmDUWT07SX4RHRT2um9JkiRJw8eAFq2CIHg78BkgH/h6GIb/3W3/+cD3gQpgEfChMAyTQRBMAn4KjAZC4B1hGDZ1Oe59wGVhGN6Ye18A/ACYD7QCbw/DcHU/X56k42hLtvH0zmXMHTOb8oKyqOOoJ1KdtNVt7XHzeCpJ5YG9dGzbxIGOJOQVEcsvZExeIWWxfJZseoozD7T36Fxl0+dAvkUrSZIk6XQ2YEWrIAjGAzcD84B24MkgCB4Ow/DFLs1+CvxVGIZPBUHwA+D9wP8A3wa+HYbhL4Ig+CzwWeBTQRAUATcBHwHu7HKejwLNYRieGQTB5cAtwEX9eoGSjmvp7udoS7Vz+fiFUUdRH0okO6k8sJfqhj1UHKwnnskAkOzW7sxR5awsb6c5XENBzXjyJs0mVlQ+8IElSZIkDRkDOdLqauChMAzrAYIguAN4M/DPufeTgeIwDJ/Ktb8F+EIQBN8HLgde32X7o8CnctvjwCeBC7v0dT3wOYAwDBcFQTAqCIJJYRhu6berk46hubOFZDpJZWFF1FEitXjnUsaV1jKlYlLUUXSKEslOqvbXUd1QR3ljtlDVkV9I3agJNFSPZtbMSyjYuZ1Mso1MZzt0tnFuZwNL4nVsGDWKmTs30LFnA/HameRNOo9YQUnUlyRJkiRpEBrIotU4YGeX9zuBC06wfwIwEjgYhmGy23bCMHwAeCAIght70NcEwKKVBtTuljoe2/4UBfF8XnvGq6OOE5kdTbvYdHALb5p+A7FYLOo46qE0kEqlD79PJDuZuGMDI+t3ESdDe0ERu0dNoL5yFM0l5ZD73rZm8sjklRDPL4Hi7LHTMpMo2Pcwq2pGMrN2Pmx/nvSukI7da6F2Fow7m1iX6YBpJEmSJJ3uBrJoFQcyXd7HOPL3kmPt774dTvz7zIn6OqERI1xzZ6gZNWpgpxrVNXdQXl50zP1r9m5g0bZnAGhOdpJfHKMo7+TX6CkpLWBU6dCcTnXv9gdIxOJce/blVBxnStiJPtO+lp+f6LP+enqevuyzv/trOdDOgeZOyGQY07SXoG4j+alOtlWOZUfFaBoLS0lnYhxsLmD/7iL2NxWyv7mI21r3UpwHUypTuT9pqooyTKGGVW27WdgxlXjVbBIlZ1C690WKdrxAZtcaWmpm0lI9g0w8j+p4fMD/Tg9lflaKkvefouT9pyh5/ylKp8v9N5BFq23AZV3e1wI7uu0fe5T9e4DKIAgSYRimcm26HnesvsYC64/R1wnt29dEOt29VqbBatSocurqGge0z5ZMB42NbS/bnslkeGHfap7f9xJjSkYxvXIqT+x8hm379jCmZNTJ91feQV3LwF5jX0ilUzyyYTHnjjyL9kaoazz2NRzrM+0vneWpPumvvLyox+fpqz576lT6i6VS5LW1cPbejYxu3c/+glKeGT2Lje2jqNtRyoGWIhpbCkln4gAU5CWpLG1j7tRy6nY1svlgHi/uy/5nprIgTdXY0bSMqWNNawNTEuVAIc0j5pBfPo2qhtWU7X2B4vp17Bt5PsnO5ID/nR6qovj5Jx3i/acoef8pSt5/itJQvf/i8VivBwgNZNHqQeCmIAhGAc3Am4APHNoZhuHmIAjagiC4JAzDJ4B3AfeGYdgZBMFjwFuB24B3A/eeoK97cu0eD4LgUqDN9aw0EFKZNEt2LWfjwS1MrZjEgtq5dKSyT0vb337glIpWQ9WqfS/R1NnMwrELoo6i3sikGbH5Jc7atgKAF6sn83RqOus3jKSxtZBEPE1FSRuTRu+nqrSNipI2iguSxGJw4RnjaSzaQyYD9W1xtjTmsaUxj207xhAb9SJ31Tcxr3MkC8e2E4tBZ0EldWMupKCtnpr6lYza8zQdqyeQf/5rIv4QJEmSJEVpwIpWYRhuD4Lg08DDQAHw/TAMnwmC4B7gc2EYLgXeAXwvCIIKYDnwjdzhHwZ+HATBZ8iuS/W2E3T3TeA7QRC8QPZJhe/q+yuSjtSR6uDxHU+zu6WOc0ecydkjZhGLxShKFFGYKGB/+4GoI0Zi8c4lVBaUc2bNzKijqIeKDu5l4spHKTlQx56iKv6UOJeV28bT0l5AaVE7507ZydiaRuInWJ4sFoMRxWlGFHcwZ3QH6Qz8JlVF/chdPPVswK6WBNdOaaU4LzuqtaOoht21lzKybhmxZ+4k1dZM4YVvIRaLD8BVS5IkSRpsBnKkFWEY3kZ2tFTXbdd1+XoFRy7Ofmj7ZuDK45z3FrJPFTz0vg14z6nmlXqqubOFR7Y9QVNHExfVzmdq5Z+fkBeLxagqrORA+8EIE0bjQPtBXtgXcvWkK0jEE1HHUQ+M3Pg84156kmR+EU+MvIzfbxxPW2cBFSVtnD9tB2OqmjjZtfTjMTgzVs2jiQYWTt3L05tGctvqMl4zrZnRJdllBzPxPOpGX8D0ono6V95HpnEvRa/4ALG8gj68SkmSJElDwYAWraThKJ3J8Keti+hIdXLlxEuPOgWwsrCC9fs3kc5kiJ9GT897etcy0pk0F42dH3UUnUgmw9iXFjN640q2lUzi+3ULadhVSHVZC2dP3sPIipaTLlZ1NSVTzaMZyK/ZzVsLirlrQwk/D8u4ZlIrZ43ozDaKxSi64I3EqyfQ/tQvaGnZT/Gr/i/x4yziL0mSJGn4cc6FdIraU+00d7Zw7sgzj7lmVVVhJalMiubO5gFOF51MJsPinUs4o3LqabmW11ASSyWZ/OyDjN64kmeYxX9su5yi6mKuvwouDLYxqrJvClYAJRRQSzkb4/WMLU3xjllNjC1Ncd/mEv60tYhU7jmvsViMgvNeTdHVHyG9dxMtv/sS6QO7+yaEJEmSpCHBopV0itqS2SezleQVH7NNVWElwGm1rtWGA5vZ07KXheNcgH0wS3S0MfWpu6nauZ7ftczjzpYFXH5pEa++upDa0f3T59R0DXtjLRykjdL8DG+e0cy80e2sqCvkV2tLaer4c4Usf9oCSq7/FLQ10/K7L5Hava5/QkmSJEkadCxaSaeoNVe0KsorOmabyoJyYpxeRavFO5dQmChgzqhzo46iY8hvOcjkRb+luGEPtzRdxrYJ5/Gm15ZwxtQ8Yv04jXVqpgaAjbF6ILvW1RUT2rh+Sgt1rQl+trqMnfVth9snamdQ8vrPQEExLXf/G8ntL/ZbNkmSJEmDh0Ur6RS1pbK/XBcnjl20yovnUVZQxv7TZDH2tmQ7y/asYN7o2RTlFUYdR0cR272HyY/8hkRrCz9JX8PYK2dx6cJCCgv7f821SoqoyZSwMV5/xPagppO3BU2kga/+Zj0Nje2H98Urayl53WeIV4ym9YFvkNq3pd9zSpIkSYqWRSvpFPVkpBVAVUHlaTPS6tk9K+lIdTg1cJBqfn4jwZLf056K88C4Gzj/+inUjhnYpztOzdSwi0Za6Txi+8jiNG+c3kxzW5Kv3f4cLW1/3h8vrqD42r8jVlBC673/SbqxbkAzS5IkSRpYFq2kU9SWbCc/nk9e/Pi/9FcVVtDU2UxnOjlAyaKzeOcSxpSMYmrF5KijqIt0OsPuJ9dx4eYHaIhVsGbhG5g6bzSJxMA/0XJquoZMDDbF6l+2b0xJmr+5YSo797XwjTufpzOZOrwvXjaC4ms/TibZQes9XyXd1jiQsSVJkiQNIItW0ilqTbb1aArcocXYDwzzKYK7W+pYf2ATC8cu6Nd1kdQ77e0ZXnhwK1fWP8ze/BHsuvp1FI8siyzPSEoozxQeXtequ7MnV/BXN5zFmq37+c7vXySdzhzel6gZT/GrP0a6aS+t932dTLL9qOeQJEmSNLRZtJJOUVuq7bjrWR1yujxB8KmdS4nH4lxQOzfqKMNGOgPtyfRJ/UllMuyqS/LkPbt4Q/uDtBeUsv2ya+lI5J/w2MyJo520GDGmZmrYFjtAJ6mjtrnwrDG87eoZLF9Tx60PhGQyf06UVzuToqv+mnTdBlof/DaZ9NHPIUmSJGnoyos6gDTUtSbbqCmqPmG70vwS8mJ5Q6ZolYp30J7q6N0x6TRP7VzCrJrp5BfEack09er4dNzCw9F0ptJs3nlyI/Ta6/ey+MEG/m/5H4nlxXls2uU07+uEbmtJHc2MMQUn1WdPTclUszK+k62x/UzLjDhqm2vmT+Rgcwd/WLyZytICXn/ZtMP78qfOI3PJu2h//Ce0P/ZjCi9/r6P7JEmSpGHEopV0itqSbRSfYBF2gFgsRlVhxZB5gmB7qoPlO1b16pjtTTs52NHEiMKaXh8LcO64mb0+5nRQEE8xppcz+dJpeGF1gmUbN/K3VX+iPNHBinP+grKyMnp6qkSsP8daQW2mnIJMgk2xhmMWrQDeePk0DjR18PsnNlFZWsAr5k44vK/grKvINDfQ8exdxEqrKJz/xn7NLEmSJGngWLSSTkFnupNkJkVRD6YHQnaK4ObGbWQymWE5ImTjgc0UJgoZV1YbdZRhJZ7qpHnnph63T6biPLt+LAcaE3xyzCJGdR5g6ehZ7Gs8AI09H+lXM33aiRudggRxJmeq2RxrIE2GOEf/OxGLxXjPtQGNLR389IE1lJcUMH/W6MP7C+a/kUzLfjqW/55YSRUFZ13Vr7klSZIkDQzXtJJOQVtuAeiejLSC7BMEO9OdtCZb+zNWJNpTHWxv3sWUionEY/5oiUoyFWPZunE0NBbxkdF/ZEznDlaOOoN9xZVRRzuqyZlq2mJJ9nD8qaSJeJwPvf4czhhfyXfvepGNXaZLxmIxCi+7kcSk2bQ/cSvJHS/1d2xJkiRJA8DfLKVT0JpsA+jR0wMBKg8vxj40pgj2xpbGbaQzaaZUTIw6ymkrlY6xfP14GpqK+EDto0xL7qF99tXsLBsZdbRjmpSpIp6JsSl+5FME8xNQ0HngiD/lNPF3r51MVWke/33nClr37z28rzDVROXl7yBePoq2P/0PeY3bX3Z8j/7gkwglSZKkwcLpgdIpaMsVrXoz0gqyTxAcblPoNh3YQmVBBdWFVVFHOS1lC1bjqG8s5l1jF3Nm+zY2VI5l9MwLYemfoo53TIXkMTZTwaZYAxcx+fD2WKqDxo0vHzEVBz44N8NXHsvwtdtX8rFLYuTF/zytMHHGRXQ+dzf77/s2+Wdf3etpuGXT50B+z4rQkiRJkvqXI62kU9CayhWterimVUGigJK84iHzBMGeauxoYm9bPVMqJg7LtboGu3Qantswln0HS7lu/PPMb1/HjtIRhNWToo7WI1My1TTEWjlAz6bNTqyM8Z45MdbVw+3PH7lYfLyshrwzLiDTsJ3Utt4/DECSJEnS4GHRSjoFrck24sQoSBT0+JiqwsphNz1w08EtAE4NjEA6Ays2jqXuQBkXTNjEK9qf52BBCc+PPAOGSAFxSqYagE2xhh4fM398jFdNh0Wb4LFN3QpXtQHxkVNIbVpO+uCevowqSZIkaQBZtJJOQdv/Z+++4+S4rgPf/6qqc56enDCDMGgQBAgwglmkGERRsuSV9fTWQU7P8pNWftauZMvrbO86PFu2rOC0Xj9bz7Ys7dpay5YtWaZIijmACQARGnFyTh2mY1Xd/aMHBANATOqunsb5fj79wUx1Vd3TMz3dqNPnnmsW8Ll8q6ouinkjpEsZLNuqYmS1o5RiMD1Ce6CVgDvgdDhXFKXg8LkOphbD7O6Z5D3Wi2gKXm7bia1vnpf3CD7iyr+qpBXA+3drXN0GXz2sOD13IXGlaRqugVvBF6R84jFUWfpUCSGEEEIIsRltnqsaIepQfjlptRoxbxSFIl3KVCmq2prNz5MtL9Ef2RxT0RqFUnBksJ3JhQiJ7hne5TpMrLjEkdZt5Nyre07Wg34VZ0JLU8Rc8TG6pvHj12vEA/BnBxUL+dclrlwe3LvuglIe8+STKKUufSIhhBBCCCFEXZKklRDrULCK+I3VNW2ONdgKgoPpYQzNoDfc5XQoVwyl4OhwG+PzUQa6Zrk5fIb+9CSDkQ6mgs1Oh7cmfXYTSoOhVVZbBT0aH7tJo2jCnz6vKFsXklN6uAVj6/XY8yNY429t6i6EEEIIIYSob5K0EmId1lJpFfaE0DW9IZqxW7bFUGaUnlAXbt3tdDhXjKHpGKOzMbZ1zLG3ZZy9M2dY9AY5Ed+81W7thPAr96qTVgBdEY0fu15jaBG+fEi9oarK6NqNHu/FOvcCdmZ2I0MWQgghhBBCVJnL6QCE2Kws26ZoFfGvMmmlazpRT7ghklbjS5OU7TJbo5s3WbLZzGX8JEdbaY9lSHTMsH/yFGjwSusASquPzyH8sRjsWP1xO2anSC6NEtjaTdoFpdbWFR3nsWE/M3zPLvjGCUVvFO7ZXrlP0zRcO2+n9PI/UT7xXTzXvg/NtfKFE4QQQgghhBDOkaSVEGuULS8BrLrSCiDqjTK1tPlXNTuXHsZv+GgPrCy5INYnX3LxytlOAr4Se/snuWphiGhpiRfbdpKvoz5Wlmbz3JmDqz7OJkfJKPPQ2SfZZ6QozE2s6Ljr+67HC7x7Jwwvwv86qhhohi2xygIJmtuLe9c7KB/6Fuapp3FfddeqYxNCCCGEEELUXn18LC/EJpRZbqTuN1afLIh5o+StAkVz865qVjSLTGQn6Yv0otdJhU8js2yNl890oWyN67aP05ufoS8zxdloJ9PBuNPhbYgm/OhKY07LAWBZ9sputk3JtDAtm3+/VxHyKv78BZtMwaJkVm5moBl692HPDlKcOvfa9jffbId/BkIIIYQQQogLpNJKiDXKFLPA2iqtYt4IAIulNO2uzVmlNOEoAokAACAASURBVJQZxUbRH+l1OpSGpxQcHWojnfNx3fYx2vQ0e2bPsuANcbKpcX7+Bjox/MxpOWzbZi5VWNFxqWyJzMSFhQ3u6zX4+1NB/v8Xytzfl7+wo95Lp/ss+plnGc8HUcZb+7DtHbBB2rMJIYQQQghRF6Q8Qog1SpcqSavV9rSC168guHn7Wg2mh4l5ozT5Yk6H0vCOn4Lx+Sg7OmfpiGbYP3MKW9N5pa1++lhtlBYVoKCZLC5Pv12LLWGLm9qLvDrn4eTC6z6b0XTmWvZjWEWaFo5tQLRCCCGEEEKIamqsqx0haiiznLTyGd5VH+t3+fAa3k2btEqXMswVFqTKqgYmpyyefRnaolm2d86zfWGUSCnHkdbtFFyrf+7Vu7gKADCWm1nXeW7pKtIeMHlo2E+mpL22veRtIhPZTjg7iLcgqwkKIYQQQghRz2R6oBBrlC5l8ehuDN1Y0/Exb2TTJq0G08NoQJ8krapqacnmkceLREKwd+sksWKG7alxRkOtTAeanA6vKry4CCsvo7kZorSs+TyGBg/25/mbEyG+NRjggwNL6Mu5q8XYLgK5CZpnDzHedRes8W/4YlxWEVVY2bTGjVJQeVy2jrmGBLoQQgghhBD1TJJWQqxRppRdUz+r82LeKKcXz2Irha5plz+gTiilGEyP0B5oI+DyOx1OwzItxcOPlbAsuPedkJ8wuWb2DAXDw/HmPqfDq6pmFWCwuECJGJ51vE01+Wze2Zvn20MBDk55OdBRWfhA6S7mmvfRPvUM0dRJUk1XbVToqEKBuRde3rDzrUQp7MWT2A1BSVoJIYQQQojGItMDhVijTDG7pn5W58W8USxlky1nNzCq6pvJz7FUztEf2eJ0KA3t2edLzM7Z3Hmrh1gEdi4MEyoXONK6HVNv7M8bmpenCJ5fRXA9dsfLJJpKPD3uZWLpQkVVwd9GNthDNHUKdyn9NmcQQgghhBBCOEWSVkKsUaaUxWesJ2m1vIJgcXNdMA+mh3FpBr3hLqdDaVjDIyYnT1tcc7WLvi0ugvOT9KcnGQq3M+ePOh1e1QXxEDR8G5K00jS4pzdP2KP45jk/JevCfQvxPdi6m+bZVypLNAohhBBCCCHqiiSthFgDpRTp0voqrSKeCBqbawVB07YYzozRE+7G1eDVPk7JFxRPPlsi3qRx7T43ulmm5+jT5FxekvEro7pNQ6Mn2MICeSzsdZ/P54J39+dIl3QeGbkwpdU2vCzE9+AtLRDOnFv3OEIIIYQQQoiNJUkrIdagYBUwbXNdPa1cukHYE95USavx7ARlu8xWmRpYFUopnn6uRKkEd97mxTA0Oo8/gyef5UjrdqwNbBhe73oCrdiaYoH8hpyvO2RxoKPIsXkPyXn3a9uXgj3kfW3EFo5hmBsz1ttRto21lMXKZrGWlrByOax8Djufxy4WsItFlGVd/kRCCCGEEEJcAaRUQog1SBUzAPhd62t8HPNGmC8sbkRINXEuPYzf5aMt0Op0KA3pzDmLoWGLG65zE2/SCc+M0DJ8jJm+3SzoEafDq6k2XxOG0pnVlmhRwQ05582dRQbTLh4Z9bElYuJ3KdA05puvoXP8UeJzh1DqgXWPYxcLWOkMVjZTSU5lM1iZyvd2Lnf5qYiahisex93ajrutDXdbG4Y/sO64hBBCCCGE2GwkaSXEGqRLlaTVenpaAUS9UYYzY5TtMm7dffkDHFQwC0wsTbErPrCpVjvcLLJLNs8+X6KtVWfPVS70cpGew9+lEGpicse1cPaM0yHWlKHpNKsAc1qussImq3/OtXV143pTfugHQhafezLPi/NxPrTvwt+vcuUIDD2NffoF3NsOrHgMc2mJwsgIheHhyr8jI5QXFt6wj+73Y4TCuNvaMUJhjGCw0mxLKVAKpRSgwK78axeKlGemyZ9Kkj9xrPLzCFeOd7e14+nsqpxDCCGEEEKIBidJKyHWIL3cPH09Pa3gQjP2VDFNi7953XFV01BmFIWiP9LrdCgNRynFk0+XsBXceZsHXdfoPvwU7mKOU9e/C2VcOdMCX69VBZnWs6TI08TqK41cCiaffOoN23TgBk8Hz4+207/wCltcS8v3KNp1P+r5/4U9Axiet5zPLpcx52Ypz85gzs1RnpvDXrqw+qcRDhPcvg3P1u24IlGMcAgjGEJzry0hrSwLc36O8vQ0pekpiqMjFM6cBk3DP7CTwN59GAGpwBJCCCGEEI1LklZCrMH5Sqv1J60qK8EtFlN1n7QaTA/T5I2+FrPYOMdPmoxP2tx6wE0krBOZPEd87CSTO64nH2tDK69/FT2n7eocwLeKNophTxORjn0kZx5GhTzsDycuuW+zJ0S4s/st2wPh8EX3v8U7xclyjIfyvfxIKIlLU4DGgt1Nu3Uabe4QdusNWKkU5dmZSpJqdgZzcfG1qX16KIy7pQVXYhfueDOu5ji6x0vLvt3MHjq24sf5djTDwN3ahru1jcDVe1BKYaVS5JPHyZ86Sf7MaQKJqwjcfP2GjCeEEEIIIUS9kaSVEGuQKmVw6ca6p/QFXQFcuovF5cqtepUqppkvLHJt616nQ2k4qbTNwRfLdHfpJAaWpwW++gT5SDPTA9c5Hd6G8aEz8uTjK96/qbWVhZkZ2jvhpDnCjkOzaJeYIuhq7acwPf+W7QMPXLw/lVtT3Ocf5e+WtvNssZ3bvRNQLFLOlUhZEcwTRynnk6iyCYDm8eBuaSHQuwV3SyvulhZ07/oS1muhaRquWIzwgVsI7N7D0uFXyB17leHTJ4lPzhB78HvQff7Ln0gIIYQQQohNQpJWQqxBupQh7AmhrbO3k6ZpxLzRul9BcDA9jIZGn0wN3FC2rXj8qRKGAbff4kHTNDqTB3EVc5y74QHUFbRa4KV0ZV2MhYvM+2yaCxvw81AK8gX6c/N8KD2GJ7+EXppDt20AcrqO26/ha/Xg6juAu7UVIxJd99/6RjPCYSK33UFg9x6KRw8x+81vsvD4E8QffC/Ru+9Gd791eqMQQgghhBCbjSSthFiDdLGStNoIMU+k0i9Kqbq7MIZKv6XB9AgdwbZ1T4cUb3TkqMnMrM07bvcQDOj4F6doHnqVub495GNtTodXFzqWDHQbxkLmqpNWtmnCUg4tl1++5SBfQFue4rdV15nwNJOMbGUgWoKAn669e9FyI+jTz2O1eyAcq8bD2jCupiaa3v0uLF+Q6W99m5n/+RVSTz9J98d/GnerrPIphBBCCCE2N0laCbEG6VKGJv/G9HaKeaOcTp0jZ+YJuuuvqfJ0fpacmWdf6x6nQ2koc/M2Lx8us7XPYFu/AbZN75HHMb0BJhI3Oh1e3XDbGm05g/Gwxd5ZdckpggCYJlomW7lllxh6+Qiu5QoqZRiogB/aWrAD/srXXi8z5Tjfym/hXt8o+71zaLqOiu1CLZ5EnzmIHewGvf7fKv19ffR86mfJHn6FyT//M4Z+89fp+ujHCey6yunQhBBCCCGEWLOVd8UVQrwmVUoT8Vy8yfNqvb4Zez06lxrGpbvoCXU6HUrDUErx1LMlvB645UBlWmDL4BH86TnGrr4N2+11OsS60pU1yLkVi177Ddt108K/sIR17BTGsSSuQ0cxzg6hzS2gXC6iNx/A2taHuWcX1r6rsXdux+7pQsWbwOcDTWO3e4EtRobHC51k7eXklKZjtx9AK2fR5l914BGvXeia/Wz5xV/BFY4w+tnPsPjId1DLlWVCCCGEEEJsNpK0EmKVTNtkqZwj7AluyPmi3ghAXTZjN22TkewYvaFuXJug2mSzOHXaYnbO5sbrPfi8Gu58ho6TB0m3biHVsc3p8OpOZ9aFpmA8ZIGtCM5maD82Rtcrw7ScmUaNjKNcLqyuDszEDqz9e7B3bid+952ophh4vXCJqbeaBvf5R7HReCT/uhUIA53Y4X60+SNQztTokW4MT3sHvb/wywT37GX6b/+G6b/+Eso0nQ5LCCGEEEKIVZOklRCrlCllAYh4N6bSymO4CboCdVlpNZqdwLRNtka3OB1Kw8gVTF54uUR7m872rZUeTd1Hn0JTMLrn9ksmV65kXlujMwUtoym6jowQH5wFBemuGFOJToz77sDeuR3V2Q6h4Kp/hk1GiVu8U5w0Y7y6eOFtUbXeAIA+/cKGPp5aMPx+un7qE8QffC+pxx9j5Pd+BzNVf68xQgghhBBCvB1JWgmxSulSpepioyqtAGLeSF0mrQbTwwRcftr8LU6H0jC+9cQkxRLcfGNlWmBk8hzRqUEmd15PORBxOry6YxTLxIbn+MC3Z7jhaJZ80M30QAdTu7tIdzVRCvvQ9PW/ld3gnaZFz/O1ITcFa3k6nTuEar4GLTsES+PrHqPWNF2n5QMfpPMnP0ZxeIjh3/x1CkODToclhBBCCCHEiknSSohVSi1P4wtvUE8rqPS1ypSyWLa1Yedcr3Qxy+TSNP2R3rpc1XAzmpu3eerlWXbtdNEc19HNEt1HnyQfjjOz9Rqnw6srenqJ5jPTdB4ZJTSTJtsU4MvvjvPMTc0Uo/4Nr0gzNLjfP0qqrPFvIxd6QKmmq1HuMPr0c6DstzlD/QrfdIDen/sFAEZ+57fIvvKywxEJIYQQQgixMpK0EmKVUq9VWoU27JwxbxSFeq2Kqx68Mv0qCkV/RKYGbgSlFM8eLBHwGVy3zw1Ax8kX8BSWGN17J+iGwxHWCaUITaXwP30MXzpHpj3KxN5e0v1t2D4PY6HqJXa7XDkOtFg8PgmTueXEle7CbrsJrZRCWzhetbGrzdfXz5Zf/FU8Xd2M/8kfkj18yOmQhBBCCCGEuCxJWgmxStWYHnihGXv9TBF8afIwcV/stdjE+pwdtJiatnnPnV14vRr+1Awt544wu2U3uaYOp8OrD7ZNfHCWppF5rNYo43t7SfXGsTyVRQC6si5SPpslV/Uqnt7TXcarwz+cUxdW3Qv2oILdaHOvgJmr2tjV5opG6fnkz+Dt7mHij7/I0tHNtTKiEEIIIYS48kjSSohVShfThNxBjA2sjAl7QuiaXjcrCI5nJxnLTkqV1QYplxXPv1impVnnwDVxUDY9Rx7H9PiY3HXA6fDqglEyaTsxQXAuS6ozRuG6AZTrjX9j3dnK9+NVrLYKueHBLRqn0nBobnmjpmG3HQBloc28WLWxa8EIBOn55M/i6exk/A8/T+7E5q0eE0IIIYQQjU+SVkKsUrqUJbKB/awAdE0n6qmfZuzPT76Erun0hXucDqVu2AqKpn3RW3qpdMn7iqbNC6+UyOcV11/nQmkQPXeUQGqGoV23kNPcb3ts0bRRlw9vU/NkCrQfG8NdKDO7vY10d9NFe1YFyzrRgs5Y2KxqPLe0Q3cQ/nFIUTzflN0TQTVdjZ4+A/npqo5fbUYoRPcnfxZ3axtjX/gDcieTTockhBBCCCHERbmcDkCIzSZVSm940goqKwhOLE1t+HlXy1Y2B6deJhHfjs/lczqculG2bIYmLl4J5/W5KRbKF70vtwQnkm7aO2yWylmyqQydJw8yHWzjZdUKlzjn6w20e9YVe91SitBMhtjIHKbHzWyiDdP/9o+1K2twvKVM3rDxW9X53EXXND6wFb74quKhUcV7+yoJNNV8DSp9Bn3qOey+94C2eT/3cYUj9Hzq04x+5v9l7PN/QM8nfwb/9h1OhyWEEEIIIcQbbN7/cQvhkHQxU5U+TzFvlIJVpGAWNvzcq3Fy4QyLxRTXd+xzNI5GoBScPe1C16FvW2VKm//oo3itEoc692/4Cnibim3TNDRL0/AchYifqau6LpuwAujOVj5rmajiFEGArWGNG1vhsQmYzp9vyu5Gtd6AVpxDS52q6vi14IpG6fmZT+OKRhn73O9TGDzndEhCCCGEEEK8gSSthFgFpRSZUqZKlVZRAMf7Wj078QJ+l5/dzTsdjaMRzM1qLC7o9G218HjAX1rCe+IphmJ9LAbiTofnHFvRemqK0Gylf9XsjnaUa2VvR+GSRqikMRaq7hRBgPdu0XC/qSm7Cm9F+dvRZl8Cq1j1GFbK0MBYSq365nXr9P2Hj2EEAoz+/u9STh5d8bGuOnr8QgghhBCiMcn0QCFWIWfmMZVFxFud6YFQWUGwI9i24edfiVw5zyszR7il80bchrw8rIdlwbnTLgJBm86uymp3eycPA3Ck4xonQ3OWUjSNzOHLFJjvb2GpZXV/SxoaXRkXp+JlirrCa1evWi3s0XigF74+qDgyD9c081pTdn3oG2izL6Pab67a+Kthl4rMHTq25uMjd97Fwr/9K4Nf+AJN9z+AK9Z02WOab7gWgt41jymEEEIIIcTlSKWVEKuQWq6Cqkallc/lw2d4Ha20enH6Fcq2yS2dNzoWQ6MYH9UpFjW2D1hoOsRy8/QtDlHYdTt5T9Dp8BwTmskQmsmQ7oiuOmF1XnfWQGkwWYNqq9s6oDMA/zioKJ1vyu6Lo2IJtMUkFOarHkMtGKEwsfvehabrLD78EFY263RIQgghhBBCSNJKiNVIlzIARKuQtILKFMGUgysIPjP+At2hTnrD3Y7F0AjMMoyOGMSbbaIxBUqxb+IVioaXwtV3OR2eY7zpPLHhOfJRP6nuy1fyXEqsqOMva4xVua8VgKFpfGCrxkIJHh67sI6jarkWDA/69HOV5mUNwBWOELvnfpRpsvjwv2EXnO2vJ4QQQgghhMz/EWIVzietqlFpBRD1Rji9eBZb2eg1XplsLDvBUGaEDw68D03ToDGuwzeMR7doD138PpfLwnzdq+nREwaWqXHt1SaxEMTnx2lbmubU1uvpDfgueZ5LMbQG+GUspGk+M43pczO3rW1dTeg1NHoyBqebTEp69X822yMa17UoHhmHG1oVrX4NDC+q5Xr0qafRMudQkW1Vj6MWXE1NRO++h8Xv/BuLjzxE7L4H0N1up8MSQgghhBBXKElaCbEKryWtvBEUGz81KeaNYimbbGmpKn2z3s4zEwdxaQY3dlxb03E3C90qszQxeNH7XC4D06xU/RTLBqfObqWjKY17aZJcVnHd2CGybh9nlEFnLnPJ81xKfMfmTogYJRP+7WHQYGagHWWsPyHbnXFxKm4yHjJJbECMl/O+Po2jC4qvDyp+YhdomoaK7kAtJtFmXkCFekFvjOSOp62d6B13kXrsEVLffYTYO+9FMwynwxJCCCGEEFcgmR4oxCqkimncuhufUZ3mw6+tIFiq7RRB0zZ5fvIl9rZeTch95fZb2ghnJ+NYtsaOrjkAejLThMoFkk1bUDWunqsLtqLvmVOQyjC3vQ3LuzGJnaaiTrCkMRqu/hRBgIhH4109GscX4ejC8kZNx24/gGbm0OYO1SSOWvH29hK+5TbKkxOkn3oCZdtOhySEEEIIIa5AV+AVlBBrly5liHrClelzVRD1hNHQWKxxX6sjs8dZKuekAfs6FUouRmaidDenCfnKuGyTgYVR5r1hpgNr7+G0mXUdGiIymYK7b6YY9m/YeTU0urMuZgIWOa36DdkB7uiAdn9lNcHXmrL727AjO9Dmj0GNk83V5t++g9B1N1AcGiT7wvOoBundJYQQQgghNg9JWgmxCulipqrT9gzdIOwJ1XwFwWcmDhLzRrkqPlDTcRvNmYk4Co3tnZUqq62LE3jtMiea+9bVw2mzip+dpu3kJDMDHbB354afvydTWUXwlLs2fy+GXmnKPl+ER8cvbFet14NuoE83XmIncPUeArv3kE+eIHeksarJhBBCCCFE/ZOklRCrkC5liHgiVR0j5o3WtNJqsZji2FySmzuur3nz90aSK7oZnY3S25Ii4DXxmUW2pscZDzaT8q6y83oDCMxm6HnxHOn2KGP7+6oyRrSoEyppJD21S/IORDX2N1dWEpwrLCeoXH5U8360pTHK48dqFkutBK+7Ht/2HSwdeoVc8oTT4QghhBBCiCuIXKEKsQqpUqZqKweeF/NGWCrnKFvlqo5z3nMTL6JQHOi8oSbjNarT481ommLbcpXVwMIomoKTTb0OR1Z7qlSm77nTlP0ehm4dAL06VWaVVQRdjLqWWDJq09sKKk3Zda0yTfA81XQVyhMj/8o3wK7NdMVa0TSN8M234unuIfv8sxTOnnE6JCGEEEIIcYWQpJUQK1S2yuTNPNEqr+p3oRl79atHlFI8M3GQgdg22gItVR+vUWVybsbnw/S1LeJzW4SLS3RnZxiMdpB3+5wOr+aK334Mb7bIyE3bsDzVXaS2J+NCaXAmXNiQ8xk6uKz8295aXAXe1WVydAGSc8XKdruI1rIfe2keY+6Vy57jojdqk6heC03Xid55F+6OTtJPP0lh8JzTIQkhhBBCiCtAda8mhGgg6VIGoAaVVstJq2KKVn9zVcc6kxpkJj/Hu/vvreo4jS45GsfQbbZ2zAOwY3EMUzM4E+12OLLaC8xlKT9xlNntbWTbolUfL1LSaba8nA7nuWZx/StfarZFYW70svvd6oJn3X187Sz094zg0ipVV8HOXZQmjlEqe0Bf3Sqj3tYtYGzM6orVoLlcxO56J4uPPET6yceJ7NxB4JY7nA5LCCGEEEI0MKm0EmKFapW0Crj8uHVXTfpaPTN+EJ/hZX/b3qqP1ahSOS+TCyH62xfxuGxCpRwduXmGoh2YxpX1uYBm2fQePIMWCTG+b0vNxk2UIkwEymRdtZsi6NLg/fEZZk0Pj6Vir233764kgF35wZrFUkua20307ntxNTcz+qUvkT0szdmFEEIIIUT1SNJKiBVKnU9aVXl6oKZpy83Yqzs9sGAWeGn6ENe378NreKo6ViM7NdaC22WxtX0BgG2L45iazmCkw+HIaq/t+Dj+VB7f970b2127hF2iVKnoOh3O12xMgEQgx55AlocX4yyUK49X90exvN3o5jyaWbsFFWpJ93iI3XMfvq4uJv74iywdfdXpkIQQQgghRIOSpJUQK5QuVpJW0SqvHgiVKYKpYgql1OV3XqOXpg9Tssvc0nlj1cZodAtZH7PpIDs6F3EZNoFyga6lWYYj7ZTreJpXNfgWc7QfH2N+SzOuqwZqOnbc9tJScHF6g/parcb7mmcA+Mb8hZ5wtrcLpXkx8udA2TWPqRZ0j5fej30Md0cn43/0BVlVUAghhBBCVIUkrYRYoXQpjYZGyL3+vjmXE/NGKNsmObN6lSPPTBykI9BGf6R207gaiVJwcqwFr8ukv71SUbNtcQxb0zgX6XQ4uhqzFb0Hz2C7Dcau7XckhIGMnyl/mbS7tiv3NblM7onNcyQX5mQ+UNmo6Zj+fnQ7j16aqmk8teQKBun55M/ibmlh7At/QP70KadDEkIIIYQQDUaSVkKsULqUIeQJYuhG1ceKvq4ZezVMLk1zNjXELV03omlaVcZodPMZPwvZANs65zEMhc8s0p2dZSTcRsl1ZU23bD01SXB+idHr+rF8zlSY7chUVml0otrqzsgiza4SX59rxbQq1ZHK1YTtimEURsAu1TymWnFFIvR88tO4YjHGPv9ZCufOOh2SEEIIIYRoIDXtEpxIJH4A+CXADXwumUz+0Zvu3w/8ORABHgc+mkwmzUQisQX4G6ANSAI/mEwms4lEIgZ8GdgGzAAfSiaTk4lEwgP8JXANYAE/k0wmv1OTBykaVqqYqXoT9vNiy1MQF4spukMbX7XzzMRBdE3npo7rNvzcV4ozk8143SY9LSlAZ9viOADnol3OBlZjnmyBziMjpLqaWOyt7mqXbydSdtGWd3MqnOe6+VBNx3brivc3z/AXU918+3iGGwE0DdPXjzt7CKMwjBXYUdOYaskVi9HzqZ9j9Hd/m9HPfoauj/80gV1XOR2WEEIIIYRoADWrtEokEt3AbwK3A/uBn0wkErvftNvfAD+VTCZ3AhrwkeXtfwz8cTKZ3AW8APzy8vbfAJ5IJpNXAf8d+Pzy9g8DRjKZ3Lv89Zeq8qDEFSVdytSknxWA23ATdAeq0oy9aJV4ZuIge1t21ywJ12gWsj7mMwH62+cxdIXHLNGTnWYs1ErB5XU6vNpRit6DZ1G6xuj1W8Hhqr2BjI9Zn8lijacIAlwVyLE7kOXrh9OvNWXH8GN7OjHKM2hmpuYx1ZI7Hqfn0/8ZV1MTY5/7fTLPP+d0SEIIIYQQogHUcnrgvcAjyWRyPplMLgF/D3zw/J2JRKIP8CeTyWeXN30J+D8SiYQbuHN5/9e2L3/9HiqVVgBfAd69vL8BBBOJhAEEgdouKSUaUrpUu0orYHkFwY2fHvjM+EGWyjnu3XLnhp/7SnF2Mo7bsOhtqfx++hbG0JTiTOzKqrKKn50mPJ1mbN8WygHnp0Ruz/iB9a8iaFr2mm7vjU2Bgq/NtlI2K9uK7i5szY2eP4dpWm97fPWWXagNd7yZ3k//Ar6t25j4sz9h4aFvOx2SEEIIIYTY5Go5PbALmHjd9xPATZe5vwdoAdLJZNJ80/Y3HLM8jTANtFJJbP0oMA7EgO9fbbDNzbWdXiLWr7W1egklW9lkShk6mppfG2dmqUQ47KvamG3hOOMTk/iDbly6QSDooTW4vsdo2RbfffYJEi3bObBj79vuW+3HdzFut1HTMVczXi5VxOUySC15mEmFSPTM4fPquK0yvalJJsOtlP3By76oarqGy7W6vmiatvpj1jPeSsZ15Yp0Hxom2x4lvbMT1+uqrC42pqbp6PrKPyfRNFa8v65ruF0GcQy68h5ORwrcko6h6TruVT52BaSXyqs65jyDMu+/Jszfvax4fsHPVd5KpWRJdRNTg5SXJslrLZc8PqBphMNrq9SrPJdrW+UXCHjwvfl1tzVM22/9Oic/+zlm/sdXcBeX6P+RD6Ot4ncvNp9qvv8KcTny/BNOkuefcNKV8vyrZdJKhzd8kKwB9gruf/N2Xnfcm+einD/m14BngNuAAeDhRCLxYjKZHFppsHNzWWx7s3/ufeVobQ0zM1O96TeZUhZL2bhM72vj5FSJTKZ6TZ8DWhCFYmxumriviVy4xExufY/xhcmXmcnN84Ht1SNzOAAAIABJREFU33PZn1e1H9/FlMNWTcdczXiaZWGaFqfGYrh0i57mBUzTZuv8GLqyOR3pwjSty55H2WpF+73hGLX6Y9Yz3krG7X7xHJplM3z9VkzLfuNxFxlTKRvbfuN+bz82K97fthXl5fG2p3080Z5mSi/Qb9uvbV/5uGrVx7ze3TsCPH5okoeWOunRU/g0mzIRfHqQEGNkrTD2Jd56bcsikymuesxw2Eu5vLZj18OTK5G5xOtI/Mf+b0xfiPGv/xOZiWk6fuwn0Fw1baMpaqTa779CvB15/gknyfNPOGmzPv90XVt1gVAtP/ocBV7fUbqDSiXU5e6fBqLLU/1Y3uf8cWPL+5FIJFxAGJgD3g/8ZTKZVMlk8iTwLG+s6hJiVdKlygtC1FubnlZQmR4IbFhfK6UUDw0/RnugjT0t0iR5LbIFN5MLIba0LeJ22bgsk770JFOhZpY8fqfDq5ngTJqm4Tmmd3VRqnE13uVsz/hAwSkHVhEEMHSN+wOj5JSLJwrn39I0Fu1udCyi2sTbHt8oNF2n7Qd+iJYPfJDMc88y9vnPYuVlpr4QQgghhFidWiatvgPck0gkWhOJRAD4PuBfz9+5XAVVSCQSty1v+jDwrWQyWQaeAP7P5e0/DHxr+etvLn/P8v1PLO9/CPhegEQi0QrcALxSrQcmGl+6WEla1bKnVcgdwtB0FgqLG3K+EwunGM2Oc++Wd6BrMlVnLc5OxtE1RX9b5XfSl57ErSzOxXsdjqyGbEX3S4OUAh6mrqq/Hl5By6A77+F0OI9SzlTLtht5rvPMcqjUwqgZBKCMn4xqJaTP4yXrSFy1pmka8QffS8ePf4TcySSjv/tbmIsb83omhBBCCCGuDDW7ck0mk2PALwKPUkkg/W0ymXw+kUh8M5FI3LC82w8Cf5BIJE4AIeALy9v/A5XVBo8BdwC/tLz9l4GbE4nE0eV9Pr68/T8BNy5vfxj4hWQyeaq6j1A0svOVVhFP7Xqd6ZpGZ7CDwfQwZXv9q6E9NPRdop4IN3ZcuwHRXXkyWZiYi9DbmsLjtjBsi/70JFOBJrLeoNPh1Uzz2WkCiznG9/Wh1tgrq9p2pH0sei3GSwuOxXCbb5KwVuKhfA+WqsxkT6sOyspDkz6CxsqnSm52kVtvo/v/+QSl6WmGfuPXyJ+St2MhhBBCCLEyNW0wkUwm/xb42zdte/B1Xx/iItP4lquw7rrI9nngfRfZPkVliqAQG2LJzAEQctc2OXFVfIDR7DhnU4Mc6Nm/5vMMp0dJLpzme7c/iFuXvjJrcfgEoMHW9koiZEt6Co9tciZaf9VG1WIUTTqPjJBtDbPYG3c6nEvanvXxuErz8tI5Eg7F4NFs7vWP8g+5bTxfbOUW3zQKnQW7hzbjLBFtipTqvPyJ6pihAUsrW+E0srUPzyc+wehf/AUjn/lt2t73PuJ33YWmvbk15dvTfD5Mo7YN54UQQgghhHPk6lWIFciV82ho+Fy17d/T4m+m1d9Mcv40lm3DGgtbvjP8GD7Dx+3dBzY2wCtELqc4dRa6m1P4PCa6bbM1PcGsL0rKF75iXkg7Xh3BKJuMXtdfWd6vTvktg56ch5fd59hJEO0ta3bUxnZ3hp3uRZ4ttpNwLxI3ShQJs2Q3EdamyakYZTZvLzS7VGTu0LFVHRO9534yTz/J9Ne/Tuqllwnfchu6x7Pi45tvuBaCkrQSQgghhLhSrGl64HLTcyGuGHkzj8/ldaQX1K74AEtmjsMzq7s4PG82P8dL04e5o/tm/K7Ne4HspFePl7EVbOuoVFl1Z2fwWmXOxK6cKivfYo6WM1PMbm+nEKv/6ZA7Mn7mzAzTvrKjcbzTN4YLxUP5Hs632FpUXdi4aNJHeOviuI1N93iIvONuQtffQHFkmIVvfoPy/LzTYQkhhBBCiDq1qivwRCLx4eV+U0uJRGJbIpH4k0Qi8ctVik2IupEz844lfLqDnUQ8Yb47/PSaGks/PPwEuqZzV+9tl99ZvEWhqDhx0mTbFgh4y6AU/elJUp4g877arSbpKKXofnkQy+1ick+P09GsyLaMDx2N0w6tInheSDe50zfOiBXmaLkJABsXi6oLr5YnpM06Gp8TNE0jsHsPsfseQJkmC//6L+TPSJ8rIYQQQgjxVitOWiUSiR8GPg/8FWAtbz4E/OdEIvHpKsQmRN3Im3kCDiWtNE1jV9MA49lJkgunV3VsppTlmYmD3NRxHTFvtEoRNrZjx8uYJuzbXfm+JZ8iVM4zGO2o6ylyGyk6Ok94Os3E3h4sr9vpcFbEZ+vs8ndXVhF0uJrpGs883cYS3y10kbMrc3xzKkZehYlqkxiUHI3PKZ72duLveR/ullYyTz9F+pmnUOb6F50QQgghhBCNYzWVVp8CfiqZTP4Wy0mrZDL5p8BHgJ+sQmxC1I1cuYC/xv2sXq8/0kvYE+I7w4+t6rjHRp+mbJe5d8s7qhRZYyuVFMeSJn29Bk3LOb/+9AQFw81EsNnZ4GpEMy26XxkiHw0wt63d6XBW5drgVrJum0mHpwhqGtznH6GkdB4tdJ/fyoJdqVpr0ke50qYJnqf7/cTuvZ/AnmsonD7F/Lf+GXPBuVUfhRBCCCFEfVlN0moAeOYi258Fui+yXYiG4WSlFYChG9zecxPH508ykhlf0TFFq8Tjo09zTcvVdATbqhxhYzp+0qRUgn17K9VFoVKO1nyKoUgHyoH+Zk5oPzGOJ1eqNF/XN1dl2d7gFgwbTkfyTodCi1HkZu80x8tNJMuVDKiFh5TqwK9lCGiLDkfoHE3XCV17HdF33osqFJj/5jfIHT+2punQQgghhBCisaymofowsB8496bt9yzfJ0TDypl5/G5nm5jf0nUDjww9ycPDj/GjV3//Zfd/ZvwgS2aO+/qkymotTFNx9FiZ7i6dlmYdytCfmsDSdEbCmzsJuKtzAN8KPrMI5gyWTkzCzn4G9t244vPHbBf7OxNv2NYcijOy6kjXx6d72LLk5XS4wG3TEXSHVhE874B3ijNmhO/ke+gxlgjqJlnVQkAtENPGwSqCu/6b3FeLt7sH93vfT/qZp8i+8Dyl8THCt96G4Q84HZoQQgghhHDIapJWvw/8SSKR6KBSofWORCLxo8AngZ+vQmxC1I2cw5VWAH63j9u6DvDd0af4nm0P0OxvuuS+RavEwyOPsy3az7Zof+2CbCCnzpgUirBvT6XKyijm6VqaZSzUStnYHH2dLsWHzsiTj192v87RRXRlM+mzsFaw/3nevmlGhk68YVv7A/9u1XFuhIGMn3PhIhP+Et15ryMxnGdo8KB/mL/O7uTb+R7+XWAQTdNYsHtp10/C3CFU992Oxug03e8nevc95E8myb54kPlv/BORW2/D29PrdGhCCCGEEMIBK57fkkwm/zvw68AvAH7g/wN+DPhUMpn8YnXCE8J5lm1RskqOJ60A3tl7BwCPjj5xyX2mlqb5zAtfZKGwyIP999YqtIZi24ojx0xaW3Ta2yovk82jJzGUYjDS6XB0teFN53FNLpDpiGJ5VvP5Rn3py3px2Ti+iuB5zUaRO3wTnDWjHCnHASjjJ6Pa0LJDsDTqcITO0zSNQGIX8Qe/ByPgJ/Xow2Sef1aatAshhBBCXIFWdSWSTCb/hEq1VQtQSiaT6eqEJUT9yJuVi12/w0krzQCv18X+tj08Nf4cd/XdQuBNUxYPTx/nf574R1y6i5/Y94P0xbvIqeyaxrN16/I7NajBIYtsVnHgejeapqFZJs0jSab9MZY8zicvq04pYiPz2D4PmY7NveqkR+n0ZX2cCRe4ow6mCAJc55nlTDnCo/kutriyxPQSKdVO2F1An3wau//9YDhbFVYPXLEYTe9+L9mXXyJ//CilyUlCW7pxD2zu56QQQgghhFi5FSetEonED19kG1SWPCoBo8CzyWTyyr3SFQ0pZ+YAHF09EKBklTgyfpI2Xysl6zBfO/EvXN28CwBb2RyaOcqJhVM0++Lc1nUT2UKOl8ZfXfN4e7t2blTom4pSisNHy0QjGlt6DQBi46dxlwoMdmx1OLraCM5m8eRLFPZvR+m20+Gs20DGx5lIgbFAid6c88kgTYMHAiN8KZPgX3O9fCh4Bl3TUa03oo0/gjZ9ENV5u9Nh1gXNMAjfcCPerm7STz/B4Gc/S9v3/xCRO+5E05xPQAohhBBCiOpaTaXVLwNbqUwpXAQ0IMqFdbo14GQikbg/mUxKY3bRMM5XWr25qskpTb4oncF2Ti6cYVfTACW7xFPjzzOTn2NnbDv72/ZiXCEr21XD+ITN/ILi9ls8lYtipWg9d5h8KMacL+J0eFWnWTbRsXmKQS9mRxxmZ50Oad36lny4bY3T4XxdJK0AInqZe/xjfCu/hRdLrdzonQFfHBXfiz5/GCvcByHp43Sep6uL+HveR/7IK0z91V+SO3Gc9h/+EXRffbwuCyGEEEKI6ljNle1/A44Ae5PJZDyZTDYBVwEvAJ8AOoEk8HsbHqUQDsqV84Dz0wNfb1fTAAWryEvTh/nXwUeYLyxyS+eNXN++TxJW63T4aJmAX2P71kqVVWhuDH9mntm+3ZUSmQYXmVjEMG0WtzQ3zON1KY3+rJcz4QLWa5+zOG+3e4EB1yJPFjqYsSqVnKp5H8rThD75dGU1QfEa3e+n96Mfpfl7P0Dm4HMM/ZdfozA85HRYQgghhBCiilZzdfufgI8mk8mj5zckk8kk8FPAzyeTySkq1Vj3bGyIQjgrZ1aSVvXQiP289kArcW+M06lzuA037+q7m/6IVGWs1+ycxcSkzdVXuTCMSsKm9exhyh4/i1fA1ECjWCY8lWKpOUQpWB8VSRtlIO2naChGg/WTCNI0uM8/ilez+GZuC6YN6AZ25+1gFdCmnnM6xLqj6TrN730fPT/7n1HlEiO/9V9ZfPRhlKqfZKQQQgghhNg4q0lahaj0rnozi8o0QYAM4FlvUELUk/z5pFWdTA+EyupaN7Tv56r4Tt615W6i3saftlYLh1818bghMVCZOe3NLhCZGWau72qUYTgcXfXFRudRmsZid5PToWy4LTkvHkvjVJ2sInheQLe43z/KjO3n2+PLM/Z9zajma9AzZyEjlUQXE9iZoO9X/gv+XbuZ/vJfM/Gnf4SVyzkdlhBCCCGE2GCrSVp9B/ijRCLRd35DIpHYCnweeDiRSGjAjwOHNzZEIZx1vtKqnqYHAjT74+xv3YPbcDsdSkNIpW0Ghy12JVx4PJUqq5ZzR7B1g7m+qx2Orvq8mTyBhRyZjii2Z1ULy24KhtLYlvVxLlTA1OqrKmeHO80e9xwPT7o4l6nEppqvQXnj6FPPgFlfibZ6YYTDdP/0f6Tlgx8i+/JLDP/XX6U4Nup0WEIIIYQQYgOtJmn1McANnE0kEhOJRGIKOL287ePAu4FPAr+04VEK4aC8WcDQDDy6JIca2avHTAwddu+q/J6NUoH46EkWugcwvfWVsNxwShEbnsf0GGQ6opfff5PakfFRMhTDdTRF8Ly7/eM0eRRfPqXImwo0A7vjdrBKaNPPOh1e3dJ0nfgDD9L76Z/HLpUZ+e3fIHv4kNNhCSGEEEKIDbLipFUymZwGbgLuBT4D/CZwTzKZvDmZTI4CzwHdyWTy0apEKoRDcmYev8sny6s3sFxecfqMyY7tLgL+yu+5efgYum0yu/Uah6OrvuBsFk++xGJPHKU3biP/niUvXquyimC98Wo2H95WZrEIf3dWVXo0+eKoln3omUHIDDodYl3z7xhgyy/+Cu62dsa/+DkWHvq29LkSQgghhGgAq5oDkkwmFfDo8g2ARCLhAW5MJpNPbXBsQtSFfDlfV03YxcY7dqKMZcOe3csvibZFy+CrZFp6KITjzgZXZZplEx1boBj0km8KOh1OVRlobM/4OBkpUNYUblVfiej+kM0DvRrfHFEkYnCgDVR8LyozjD71DLa/HeS16JLc8Ti9P/cLTPz5f2Pmf3yF0uQEbd//Q2iuxpvuKoQQQghxpVjx/+QSicQNwJ8Be7l4hVbjdykWV6ScmcdfR03YxcYqlRQnkib9WwyikcpLW2zyHO5ijpFr3uFwdNUXmVjEMC1mB9ory9k1uB0ZP8dieYaCBXZk6+/v+p3dcCoF/3BO0R+C9oCO3Xk7+tA30Keexe6+2+kQ65ru9dL1sZ9i9h++xsK3/oXy9DSdH/04RrCxE7JCCCGEEI1qNR8/fg7IAz8J/DHwCWDr8r8/vPGhCVEfcqZUWjWy5CmTUhmu2XPh5bDl3BGKwSiZ1i0ORlZ9RrFMeCrFUnOIUtDrdDhr5vYHoO2t23WfF1/bGyvltqPw2ynOtdrsCby1iq5o8JZjLkazFPm5hTXHfCm6pvEDA/B7hxR/dUrxH/eC29uEat6PPvtSZZpguH/Dx90sDA1YSl12v44H7scXjzHx1a8y8pu/Tu9HPoKn7SJPkhXQfD5MY/P+fQghhBBCbGarSVpdC9yZTCZfTCQSPwkkk8nknyUSiXEqTdr/vioRCuGwvJmn2dfkdBiiCkzT5uhxk84OnZbmSrGof3Ga4OIUY7tva/jKo9joPErTSHVv7ue3rdmcmxl8y/bm4rUX3d6uaZyJpDk1ew7Xm6YINtHKwszMZcfc2tq/xmgvL+rR+P4d8OcnFN8YUnxgq4aK70FlhirVVoEOMHxVG7+e2aUic4eOrWxn3U3snvtJPfYIZz/zGaLvuBtPR+eqx2y+4VrYxEldIYQQQojNbDUddzXg/P/kT1GZJgjwDWDfRgYlRD3JlwtSadWgXjy2QC6vuObqCytDtgy+imW4me9JOBhZ9XkzeQILOTIdUSzPldXzpyfrwtJhKmg5Hcol7W7SuLMTnpyEV+cVaDp2x21gFdGmDzod3qbhaW8n/u73ovv9LD78EMXhIadDEkIIIYQQq7CapNWrwIPLXx8Dblv+uh3pZyUalFJqefVASVo1GqUUjz4/TbxJo6uz8lLoKuSIjZ9mvjeB7fY4HGEV2TaxkXlMj0GmPep0NDXXktPxWDAWMp0O5W29d4tGdxC+ekaxWFxeTbB5L3r6DGRHnQ5v0zDCYZoeeBBXvJnU49+lcO6s0yEJIYQQQogVWk3S6neAP0wkEh8BvgJ8byKR+DrwVeC7VYhNCMeV7TKWsghII/aGMzpuMzVXZO/VbrTlaYDNI8fQlc1s3x6Ho6uy42fw5EqkuuMoYzVvA41BR6Mr62IyZGFpyulwLsmla3x4QMO04cunFbZSqPg+lCeGPvU0WCWnQ9w0dI+X2L33425rI/3k4+RPnXQ6JCGEEEIIsQIrvlpJJpNfA24Gnk8mk0NUqq5KwDeB/6s64QnhrJyZB5BKqwb06rEy0ZCbrX2VQlHNtmgeOka6tZdSKOZwdNWjly146iWKQS+5+JW7olp3xsDUYSpQv1MEAdr8Gt+3VeNMGh4eA3QDu+NWMHNoMy86Hd6morvdxN55H56uLjLPPk3uxHGnQxJCCCGEEJex4qRVIpH4FeBYMpk8BJBMJh9NJpMfAn4e+KUqxSeEo3LlStIq4Loymx43qrl5m4lJmztuaEHXK1VW0YmzuIs5Zvv3Xuboza39+BjkCiz2xhu+0fzbac0ZuC0YC9f3FEGAG1rhuhb49ohiMKPA34Zq2o2eSkJuwunwNhXN5SJ61z14ereQPfgcS68ecTokIYQQQgjxNt62+24ikWgBAsvf/irwz4lEYvZNu11LZfXAT258eEI4K28WAAi4ApfZU2wmR4+Xcbngln3NnJlPAdAyeIRCMEqmtdfh6KrHky3QmpyAXdsohep3WlwtnJ8iOBYysTSFoeo3gadpGh/cCucyiq+cVnzqGvC0XIfKjqBPPo1q6XY6xE1FMwyid95F+qknWHr5RZRlErxm/2vThIUQQgghRP24XKXVu4FB4Nzy9weXv3797R+Af65SfEI4KmfmAPC7pdKqUeRyNmcHLXbucBHwVfL2gYUpgovTlSqrBr5w7Tw8jNI0uO06p0OpC91ZA9OA6TqfIgjgc2n8++0aMwX41ogC3YXdcStaOUN5/AWnw9t0NF0nctsd+LYPkDt8iOxLL6DUlZ3IFUIIIYSoR29baZVMJv86kUicoZLcehx4PzD/ul0UkKGymqAQDedCpZX0tGoUx5Imtg27d114+WsZfBXL5WahZ6eDkVVXcCZN08g8E3t66Axfub2sXq9taXmKYMiic+lt3w7rwkBU47Z2xeMTsDeu2BbpxI7uxJw6gjk37HR4m46m64RvuRXN5SJ/7ChYFqEbD0jFlRBCCCFEHbns/9KTyeTTAIlEYiswnEwm5aNIccWQRuyNpVxWJE+a9G0xiIQrhaauQo7oxBnm+q7GdnkcjrBKlKL75SFKfg/TiU46nY6nTuhodGZdTIRM7CkPOs4mKwwdsPJvu8/39sCJRQ9fPa34uT0lPPGrYWmE3MG/+9/s3Xl4ZHd95/v375zaVVVSqVRauyX1errddnvB2MYL3oBgIASHQCYhISQDSYY1y+RObra5SzKTOzeTgHG4eQackAwhJCEXCASzecU2xoDx0lv1KrX2paRSqfY65/zmD6lXd7tbaklHVfq+nkfP0zpVp85HanW16lvf3/eHmboDpcxlXtyHjX9559YxpRTR194EpkHpwH6Uz0/0htd4HUsIIYQQQixaylvLQ8DPWZb1OiAA5/52n06nf3UlgwmxHpROD2KXolUjOHrcplKFq3efeepLnjyA0i7T/Vd7mGx1tQ5MEZktMHDLdrRvmUWNBtWTNznZbDMZcegsetttpVyHcmb4kvd7VyLMX41v4stHSrwjOUWwaSvO3H6cyrM4oeXNZAumesHceEUrWCxc3XAj2DbF/S+jAn6art7rdSwhhBBCCMHSilYfBz4EvARkz7tNuq9EQyraJQJmANOQF/r1TmvN/oM2bUmD9tTiOD/HJnlyP/PtvVSbmr0NuEp0uULXS0MUklGyvUmv46w77UUTnwMjMe+LVpdrW7jE7fFZnsoluCaSZ0+LD1/3HvToARx/G5hSZF8qpRTRm27BrdYo/Ph5lD9AxNrldSwhhBBCiA1vKb+h3w98JJ1Of2q1wgix3hTtknRZNYihYYfcvOauO/xnZtaceBF/pcRQ/zXehltF1ceewV+uceK2nQ09ZH65TK3oKpinlwjWi/sSGQ4Vm/jH6Q5+Pz5C8qp7qY4ewlcewI7skr/rZVBKEb/tdubsGvnnnsXw+wlt3eZ1LCGEEEKIDe1SuweeLQ58a7WCCLEelewyYZ/sHNgI9h20aWpS9Pee6ZpT+75LuamF+bZNHiZbPYFCmeqT32emr41iW8zrOOtWz7yPqglTdbCL4CkBQ/Pu1ARZ28eXJ1sxQjGc0CYMO4uyZ72OV7eUYdD8+jvxd3aRe+YpKicHvY4khBBCCLGhLaVo9RXgXasVRIj1qFSTTqtGMJ1xGZ9w2bPLh2EsdKBEZidQUycXZlk1aFdK14snQSnG9i5vztFG0VE08bkLuwjWky2hMnfEszyVjbN/rIwb6MQ1wvhKA6Dr62tZT5Tpo/mue/Al25j77hPkDx3yOpIQQgghxIa1lOWBw8AfWZb1duAwUDn7RhnELhpR0S7REmzMWUcbyb4DNfx+2Ln9zFNe28DLaH+I2U07PUy2eiKZeRJDMwTecDu1iO11nHXN1IrOvMlo1Mad93ZEo+24S7r/G+JTHCw18dAzM3w4YRAJ9hMqHUSVhqkFL79YWT8LI9eG4ffTcs8byH77Gww/9BCbmlsJb9/hdSwhhBBCiA1nKUWr1wHPLv6597zbZBC7aEglu0R3tNPrGGKRq6G2xBf1hYLmxKCDtdNEG5qKrfGXCzSPHcPdcwclfGBf+jGDyw3tBa3pfvEktaCf6J23wL6nvE607vXkfQzHHcZ9ZbzqrXS1JjNXXvJ5bw4P8bn5bXyp1sqbwkVaVQuR6hjT5Tj2Zf7khtpdkP0mzmEEg7Tc+yZyTzzKyCf+nE2/87uEevu8jiWEEEIIsaFcdtEqnU7fvZpBhFiPinaJsCwPXDdqjsvgWG5J55w4ZqK1QTRRYnCsBMBV4y+jtGZ+600MDgxf1uPs6KifXpT4WJbo1DxDr+mnNVRX5TbPdBRMTBdOBIpc5XWYJdrkL/FGq4lvHYLd/lkMXzdhlaPFGGHa3QI05vLXtWCEw/R+8IMMfPKTjHziL+j9vT/En5RdOIUQQggh1spSZlphWZbPsqyftSzrP1uW1WpZ1p2WZbWtVjghvORql7JdISKD2OuWY8P4qEFbyiW0+NdouA7bZo4yFuvGjTXg05er6XrxJOVYiMzWdq/T1A2fVnQWTAb8BXQdNg//5J4YMVXl0VIPtvYzpzsJq3nCLK3IK17J39pKz8d+G12tMPKJ/45TKHgdSQghhBBiw7jsopVlWV3Ay8CngT8EWoDfAvZZllVvb0wLcUllu4JGyyD2OjYxbuA4ip7NZ5b/bZo7SciucKStMWdZtZ6YIpwrMba3F4wlvS+x4fXM+ygbLtPhpS1BXQ8CPsVd4VGm3DAvVZPkdRs1HaLFGEFRf1/PehPs6aH7Qx+lNjnJ6F8+gFureR1JCCGEEGJDWMormj8H9gMpoLR47BeAHwJ/tsK5hPBc0V74MZflgfVJaxgZNonHXWJxffrgjunD5IJxJqMd3gZcBYbt0Ll/iEIyylxPwus4daezYGJqxUisPgfX7/TNsdmc56lKJyXXx6zbg0/ViKkJr6M1hMiu3XT88vspHU4z8TefQbtSDBRCCCGEWG1LKVrdDfxf6XT69K6B6XR6HvhdFoa0C9FQSqeKVn4pWtWjzLSiUlZ0b3ZOH2stZmgtzXKkbQeoxpvzkzo8TqBUY/Tavob8+labTys218KMRJ26XCKoFNwTHqWiTZ6qdFIhSsFtIa6m8J274a9YpvjNt9D2zncz/9z3mf6Xf/Y6jhBCCCFEw1tK0SoMXKgfPohMeRUN6FTRSpYH1qfy25VEAAAgAElEQVTRIZNgSJNsO1N82JE5TNXwM9jS712wVeIr12g/OEq2J0EhFfM6Tt3qr0ao+DSZOlwiCJAyy1wXmOalapJJJ0RWd6NRtBgjyEa/KyPx5vtovvseZr/5MLOPfsfrOEIIIYQQDW0pRatvA//JsqxTBSptWVYz8F+Bx1Y8mRAeK9ZkeWC9ms8pcjmDnk3O6YajUK3EpuwQJ1q34ph+bwOugo79wxiOszDLSizb5loEw4WRaH0uEQS4LTRBSDk8WurBkaHsK04pRfvP/QJN113P1D/8Pfkf/8jrSEIIIYQQDWspRavfAG4HRljouvoScBLYDvz2ykcTwltFuwxIp1U9GhkyME1NR+eZbpltmaMoNMeSOzxMtjoC82Xajk2S2dpOJS4/r1cigEFH0WQkVp9LBAFCyuGO0BjDTpR0reWsoeyjMpR9hSjDoOsDv05oyxbG/sdfUTp21OtIQgghhBAN6bKLVul0ehjYy8LOgX8FPMrC7oFXpdPp46sTTwjvFO0iABF/yOMkYinKJZieMujsdjF9C8cM12Fr5ihjsW4Kwai3AVdB18sncU3F+J5NXkdpCD3zPso+zUyofgs8V/tn6DCKPF7uoqpNZt1ufKpKTE15Ha1hGMEg3R/5DXwtCUY++XGqE+NeRxJCCCGEaDi+Jd7/VmAonU4/BGBZ1sdZGMIuywNFwynZZRSKoBn0OopYgtERE6Wgu+fMAPbNcycJORWOtO30MNmF7eraQegS7x/EAgmSXRdZ0jg2BUMzcPO1XL31mlfc3OL6uK7LOudYMtrK0LITN76ugrmwRDBmkyybXsdZFkPBPeER/qGwg+cq7dwecinqZmJqkoJO4BDwOmJD8MXi9PzGb3Pyv/7fjDzwcXp/7w8xm5q8jiWEEEII0TAuu2hlWdZ7gU8DvwN8a/FwK/ANy7J+IZ1OyzY6oqGU7BJhXwhDLWUVrfCSbcPEmEFbyiV4qkFOa7ZPHyYXjDMZ7fA034WEMBh66slXvU8ilWJ26gIdMlqTSo/j95mMVbPoCzxOsG+SocFD5xzrePP9V5S50fldRXvRZCTqcM2URtXpXiM9viJX+Wf4QSXF1f4ZTLrpNA7RrMaY0X1ex2sYgY4Ouj/4EYb/+39j7K8+Rc/HfhPlW+p7gkIIIYQQ4kKW8mr8d4EPptPpB04dSKfT7wU+DPzRSgcTwmvFWkmGsK8zAcOhI8pFPwozBo6juGane/rYDp2htTTLRM9OOmLqnPuHfRd/rPM/TLX+5huF5kqE8mXmulvQphRXV1LPvEnJr5mt4yWCAK8PjWGgeazcjUOAed1Ok5ElQN7raA0lstOi473vo3hwP5Nf+Dxar7/nCyGEEEKIerSUtwL7ufAywEeAT6xIGiHWkZJdIuKTeVbrieHUKIwNXPA2V8ORo1tIRIsESsMUFjZ/ZPvkEWqGyQlH4Zx3rt2z7aKPd77W7VuXmXqVaE3zyCy1oI9CW8zrNA2nq+BD6SojUZvWOl0iCBA1bF4XmuDJcjcnajG2+l2a9AwJY4QJdyfUaRfZetR82x1UR0eZ/ebDBLq6SNz7Rq8jCSGEEELUvaUUrY4CbwEePO/4G1nYRVCIhlK0S4T9Ea9jiMs0MRulXPVz1ebJ08dCdoXOQoaBeBeOUb+FhwsJZ4sESlUyW1ILA4zEigq4ivbCwi6CV0/X7xJBgBsC07xcTfJYuZte3zxZ3UWbcZImNUNBJ72Ot+6ZCijMXdZ9O978JmojQ0x94fOEmmNEd+9e1jVVKIQt8xSFEEIIIZZUtPp/gYcsy7oe+MHisRuBXwA+tNLBhPBa0S7TGZEOlnqgNQxMJIgEq6SaC6ePb85NooCT8fU3y+qKaE18dJZayE+xVYY+r5ZNeZMfRR2yQZdEpX6Lnj6luSs0wpeKW3mx2sYNQU1ZZ2hWYxR1M3rJe7JsLG61QubFA5d9/9DV11IeGWH4oYdIvPmt+FpalnzN5I3XQ5MUrYQQQgghLnsISjqd/p/ALwK7WShg/ZfFP//sqd0EhWgkpVqJiMy0qgvZQoi5Ypj+jlnUYkOM4br0zk8wGUlQ8jfWMs/wbJFAqUauq4XTX7BYcV15H0rDSMy59J3Xua2+efp88zxT6aDk+si6PRg4NKsJr6M1HMPvp/nue1GmSfax7+CWy15HEkIIIYSoW0vZPfDXgC+l0+l/XMU8QqwbRVsGsdeLgYkEftOhuzV3+lhXYZqAazMQ7/Qw2SrQmviYdFmthYCrSBVNRqI2e6b9db1EUCm4KzTK3+V38r1KB/eEHQo6SVRNU9CtXsdrOGZTlOa77mX2Ww8z98SjtLzhJ1Bm/XbrCSGEEEJ4ZSnbTf0psPQedyHqUM21qbk1KVrVgWLFz0Q2yuZUFp+5uGOX1vTlxpn3h5kJxb0NuMLCswXpslpDm+ZNCgFNNljfuwgCpMwyewMZXqi2kXGCzOlOXExajNGFNbZiRflTKeK33k5tcpL5Z5+RHQWFEEIIIZZhKUWrH7MwdF2Ihle2F5ZzRPxStFrvBidbUAp6U9nTx1oqeZqrRQbjnY1V2NGa5tGsdFmtodNLBKP1v0QQ4NbgBD5cnih34+IjpzsJqTwUR7yO1pBCW7YS2Xst5ePHKB7Y53UcIYQQQoi6s5Tpq5PAA5Zl/R5wHCidfWM6nX7TSgYTwkvFWhGAsK+xZiE1mpptMDzdTFdrjlDgTFGhPzdG1TAZjbZ5mG7lhWcL+Ms1premGqsYt44FXUWqaDASs9mTqe8lggBNhs0toQmeLHczUIvS79c06Qz+zIvo+DYwZCj7Smvaex3O3ByF53+ELxYn2NvndSQhhBBCiLqxlE6rEvB3wLeAo8DIeR9CNIziqU4rWR64rg1NN+O4Bv3ts6ePhewKHYUZhmPtOEYDzZA5q8uqlJAuq7XUk/dRCGjmAvW/RBDghsA0zUaFx8vduFqRdXtQdhE187LX0RqSUor4rbfjS7Yx99R3qc1kvI4khBBCCFE3Lvst1XQ6/curGUSI9aRoLzQSyvLA9cvVC0sDk7EC8Uj19PHNuQkUcDLW4V24VRCZOdVl1S5dVmusK+/jx+1VRmIOLZn6L4T6lObO0Bj/WuznpWqS64KgmzajZl5e6LYKNNYcuPVA+Xw033UPsw9/jbnHHiFx39swIxGvYwkhhBBCrHtL6bTCsqyEZVn/ybKsv7Esq92yrJ+xLGvXaoUTwiulxaKVDGJfv8ZnY1Rqfvo7zsyyMlyX3vlJJiIJSv4GWtqpNfGxLNWwn1JCXuiutZCjSJUWlghqGmOY9g7fHJvMPE9XOqhoA528FpSJMfGsDGVfJWYkQvPd96KrVeYefxRt215HEkIIIYRY9y67aGVZ1k7gEPArwHuAKPBO4AeWZd26OvGE8MapopUsD1yftIaBiQRNoQpt8cLp412FaQKuvTCAvYH4RjP4yzVyXQnpsvJIz7yPfECTCzRGQUcpuDs8Skn7eLbcAb4wuu0GVHEUNT/gdbyG5W9NEr/tDuzMNLlnnpIdBYUQQgghLmEpnVZ/AXwxnU5bQGXx2HuAfwb+dKWDCeGlYk06rdaz2XyYXDFEf3v2TA1Ha/pz48z7w8yEGmh5k9YEjo1SDQeky8pD3XkfaBiJNU53TIdZYo9/lh9V25guK3SLhQ62oiafA6d66QcQyxLs7aPp+tdQGRyg8NILXscRQgghhFjXllK0ugX45NkH0um0y0LB6vqVDCWE10p2GZ8y8ctOWuvSwEQCv8+mO5k7fSxRmSdeLTIQ72yobqTITAGjUCbX3dJQX1e9CTmKtpLBSLRxilYAd4TGMNF8ddgPysDteB04JVRGiimrKbLnakJbt1F86UXKJ457HUcIIYQQYt1aStFKAxdqO2nnTOeVEA2haBcJ+8MoKRKsO4Wyn8m5JnpTc5jGmaU1fXPjVA2T0Wibh+lWmNbER2dxYmFKLdJl5bWevI/5oCbXILsIAkQNm5uCk7yUNTk6pyGcQjfvRM0ehLLscrdalFLEbrkVf3sHuWeeojY15XUkIYQQQoh1aSlFq38F/tiyrOji59qyrK3Ax4F/W/FkQnioaJdlntU6NTCZwFCa3tSZAewhu0JHcYbhWDuuUf+7u50SmSngr9hUt/dIl9U60DPvQ2kYaqAlggA3BqdoCbh8eUDjaI1OvQbMoAxlX2XKNGm+826MSIS5xx/BKRQufZIQQgghxAazlKLVbwGtwAzQBDwHHAGqwH9c+WhCeKdUK0nRah0qV2BkOk536zxBv3P6eG9uAgUMxhpoALvWxMaz1EJ+nI6E12kEi7sIFk2G4o2ziyCAX2nesbnGaBGeHgfMIDp1I6o8hZo74nW8hmaEQrTc/Qa04zD32CPoWs3rSEIIIYQQ68olB/ZYltUE3MPCEsA3ATexMMOqCuxPp9OPrGpCITxQtEtE/U1exxDnOXQUXG3Q1zF7+pjhumyen2Qi0krZH/Qw3coK5UoESjUy/W0Epctq3ejNmfywy2HELNJAC1HZ2+KyqwUeHtJcm4Tm+Db03BHU1I/Q0V7whbyO2LB8LS3E77iTucceIffMU8Rff5fXkYQQQggh1o1X7bSyLGsvcBT4CvAN4AAwn06n/yydTj8gBSvRqEp2ibC8SFtXHEdz4Ai0xQvEwmd2NusuTBNwbQbjDdRlBcTG5rD9JsXW6KXvLNZMd96H6cKh4JzXUVaUUnB/v8Jx4V8HNCiF23ELuFXU9I+8jtfwgj2biN5wI5WTgxRelCH4QgghhBCnXKrT6k+BY8D9gLP4+YMsdFstmWVZPw/8AeAHPp5Op//yvNuvAz4DxIEngV9Pp9O2ZVm9wOdYGPqeBt6TTqfzlmW1AH8PbAWmgHen0+lxy7ICwJ8BdwAB4DfT6fS3lpNZbEwlu0zEL4Ov15PjAw6lMuzZfKbLCq3py42TC0SYCcW8C7fCAvkyoXyZ2U2tYEiX1Xri04quvEk6muN1BDFpnL+fVFhxbw98c1hzc1azsyWBTuzBmN2HE98OkQ6vIza08O6rsLNZii+/yNzzzxO9426vIwkhhBBCeO5SM61eB3w4nU4/m06nfwB8ALhhccngkliW1QP8CXA7cB3wq5ZlXXXe3T63eL2dgFq8HsCngE+l0+ldwA+BP1w8/sfAd9Pp9G7g08AnFo//b0AbcAPwbuBvLMtqnFcWYlVprSlKp9W6orVm38EaiWZIxoqnjyfK88SrxYUuqwZaQhcbn8M1DQqpxinENZLeeR9lw+FkU+NtnHtPDySD8C8nNLar0W3Xon1NGBPfA904uyauR0opYjffgr+9g7HPf57S8eNeRxJCCCGE8NylilYxYPzUJ+l0+jhgA8llXOsNwKPpdHomnU4XgC8CP3PqRsuy+oBwOp1+dvHQZ4F3WZblB16/eP/Txxf//FYWOq0A/gG4b/H+Pwv8aTqd1ul0ej/wRmigt8PFqqo4VVztyiD2dWRs3GV2VnO1dW5tqj83TtXwMdrUONOFfOUa4WyRfCqGNpeyV4ZYK+0Fk7Brcjhe8jrKivMbip/eopgqw+OjgOHHbb8JVc2iZvZ5Ha/hndpR0BeLMfqXD1Cbnb30SUIIIYQQDexSr4gM4Py3VmtcxgD3C+gGxs76fAzYdBm3twG5dDptX+C80+cs3p4DUsB24E7Lsr5rWdb3gI50Oi1vEYvLUrIXXohK0Wr92HewRjgE2/rOHAvZFTqKMwzF2nGNxinuxMbnQCnmO5q9jiIuwkBhVZs5ES1TNRrvv5bdCcXeVvjWsCZT1hDrw431ozIvQCXrdbyGZ4RCbPrAB3DLZUYf/ARupfE6+oQQQgghLtdyik/LZcA5e4Qrzi2IXez2849z1nnnd0+dOsfHQmHr9cA1wDcty9qVTqcve3JuMinDj+tNaoWWUpWyOQA6kolXfcypQpVYbO2WEPr9ZkNf72LXnJlxGB4pcstNYfyBKj6fCUB/dhKA0UT36WNLpQx12ecq9er3vezHOe+aShkYi0U3o2rTlMlTbItB0H/6XQWlOH2fpVJKXeDcCx270LnLu+6VXHM5177w9ZZ+zaVcd4/dwguhGQaaq+yZP7NiXhkG/uX+PCq1/HOv4LoAhmkSiZ3ZffM9V7v80dMVvjas+PD1QfTWOyjt/2f8U88Q3PVTKGUs/ntd2x07N8o1W7b3E/qd3+Tgn/wps5//LNZ//C3UOi7Or9T/v0Ish/z8CS/Jz5/w0kb5+bucotXHLMsqnHfOBy3Lmjn7Tul0+r9c4nGGWRiMfkonMHre7V0XuH0SaLYsy0yn087ifU6dN7J4v2HLsnwsLGfMsLCk8QvpdFoDL1mWNQRYwHOX+mJPyWTyuO75tTKxXqVSMaam5lfksUayGQDsonrVxyzqKvPz5RW55uWoxZyGvt7Frvn9H1YwTdjaB67jYNsOpuuwaW6c8aZW8soHtrOs62lXY1/muVpf/L4+n3n5j3PeNbV2cd2FOnxsPAtak+uInz62cB/O+XwptNYXOPdCxy507vKueyXXXM61L3y9pV9zKdftqIWIV00ONhXYOXum0Kpdl9pyfx61Xta5fp95RdeFhX9b8/NnOnr8wJs2Kb466PK9wTJXt5qo1E0w9iSFky+gW/cQrJ17zlrYKNcMFKs4/RZt73wX01/8Jw61fo62n7p/TTNcrpX8/1eIpZKfP+El+fkTXqrXnz/DUEtuELrU23YngZ9nYSD6qY9x4J3nHXv/ZVzrO8C9lmWlLMuKLD7GN07dmE6nB4GyZVm3LR76ReDhdDpdA77LwpwqgPcCDy/++euLn7N4+3cX7//VU/e3LGsr0MvCroNCXFKxtjDoWwaxe69U0hw77rB9q49Q6ExjZU9+Cr/rMBDvepWz64tyXKJT85QSEeyQ3+s44hIUip25MMORKgVz+cWi9ez1ndAZhi8NaCqORse2oJs2o6afh2rO63gbQuIn7iN+623MfPUrzP/gst93E0IIIYRoGK/aaZVOp/tX6kLpdHrEsqzfBx4DAsBn0un0c5ZlfR34o3Q6/UPgPcCnLcuKA88DDyye/kHgby3L+gMWCmk/t3j8D4HPWpa1H8gung/wu8CDi8cB3r+UpYFiYyvZC50+YZlp5bkD6RqOC1fvPuupSmv6cuNkA01kg42zjDc6NY/huMx3tngdRVwmKxfmh215jsRLXDfbOD+Lp5iG4me2woP7Nd8Z0by118DtuAVj4MsY40+j9U1eR2x4Sinaf/F9VCcnGf+bz+BPtRPq7/c6lhBCCCHEmlnLmVak0+nPA58/79hbzvrzi8Arfgte7MK66wLHZ4C3X+B4jjMdWEIsSfHUIHa/FK28VKtpDqZt+npNmpvPNIWmSlmitTIvpLafu5VgPXM10Yk5yrEQ1aa1nZ0jlq+l5qO95CfdoEUrgK1xxWtTmsdH4cY2TUekCd1+E8b401SOPQtIkXW1GX4/3R/8CCf/5P9k5MGP0/cH/xlfS8LrWEIIIYQQa2L9TvUUwiOnilZhU5YHeunQEZtqFfbuObe23j83Ttn0M97U6lGylReZyeOrOcx3yo6B9WZnLsx0yGYmUPM6yqp5W58iYMA/Hte4WqPj29GRbkovfR1q9TdLoR754nF6PvwbuKUSIw8+gFuteh1JCCGEEGJNSNFKiPOU7BIhM4hpLH8nLnFlHEez/4BNV6dBqu3M30Mwn6WtPMdgvBOtGuTpS2vi43NUwwHKcenuqzfb50MoDYfjJa+jrJqYX3H/FsXAPDw5BiiF23krAMb49xam14tVF9y8ma4P/DqVwQEmPvsQWr7vQgghhNgAGuRVnxArp1gryTwrjx074VAsafbuOXcgedvgARxlMBRr9yjZKjgxjL9cW+iyapTljhtIk2OyuRjkcKyMpnGLCK9pgz0J+PpJzWRJgz9KZO9bUcVRVO6o1/E2jOh119N2/zuZf+77zHztX72OI4QQQgix6qRoJcR5SnZZdg70kOtqXt5fI9mq6O468xRlVkokxo4zEm2jZjbQ7no/3IcdMCkmmrxOIpZpZy7EfMBhPFy/SwRNA3xO6aIffrfMv+urEDA0XzjqYNglQltuhFAKY/I5zErmVc+/6Af1+z3zSuK+txJ73a1kvvIl5n/0A6/jCCGEEEKsqjUdxC5EPSjaRRnC7qGTww5zOc1ddwRQZ3UeJU8ewHBdBuOdHqZbWZHpeRidZH5zKxjSZVWvtsyH8HXMkY6XeL3XYZZJuQ7lzPCr3icEvL01xhemOvn2sRzv3l2k6uvBX86gh5/CjlhL7hYMpnqhkYrQa0ApRcd730dtYoLxhz69sKNgb5/XsYQQQgghVoV0WglxnoVOKylaeUFrzUv7bGIxRX/vmVlWynVoG9zPfLKbfCDiYcKVlTo8BsEAhbaY11HEFQhogy35EEdjJWzteB1nVd3QNM+eSJ5vziYZnauBGcYJ9WLYsxjVSa/jbRiGP0D3hz6KGY0y+slPYGezXkcSQgghhFgVUrQS4jzFWomIFK08cfRknumMyzVX+THO6jxqHj2Gv1Jkqm+3h+lWVqBQpmV4Bq7eiTblqbje7cyFqZiaQ8URr6OsKqXgp5OTBJTmM0/P4GpwA524vmbM8gA4Ra8jbhi+5ma6P/wxnEKe0U89gFuTHQWFEEII0XjklZIQ5ynZUrTyyiPPThIOw/ZtZ+3cqDWpEy9Rbmohn+z2LtwKazs8sfCH63Z5G0SsiM2FIGHb4Nn8Ea+jrLq4z+EdySmOTVd5cq4FlMIObwdl4iseAe16HXHDCPX20fn+X6N8/DgTn/1r2VFQCCGEEA1HilZCnMVxHcpORQaxe2A645IemGfPLj8+80yXVdPsOJHcNFNb9jbM7npGzSF5YpLs5iTEZAB7IzBR7J4Ls784xLyvsZcIAlzXNM8Nm8N8M5tkouoHI4Ad3obhFjHLg17H21BiN7yG5Dt+mvnvP8vM17/mdRwhhBBCiBUlg9iFOEvZqQAQ8TfO3KR68fL+GqGAwa6d5z4ttZ14GdsfZHbTDnAbY6ex1hOTmDWHqZ1dJLwOI1bMnmyEHycLHGgucnOmseeUKQW/dEuC//1f8vzjdCcf6hrC9CdwAp2Y1XFcXwvaLz/dy2UqoDB32fdP3fV6akODZL70L4QSzcSvvXbJ11ShELYZXPJ5QgghhBCrSYpWQpylWCsBSKfVGpvLuQycdLjn5nYCgfzp44FijubxE0xuuw5t+lENULTSrkvq8Dj5thjFZNTrOGIFxW0fu8ObONAyyo2ZKCb11RloO0tb1pcKmfxkYoJ/zHTzeLaFO+Mz2P7NhOwcvuJRSpFrwAi86mO8+q0bl1utkHnxwJLOCe7cjW9gkNG/+zuKP/EW/K2tSzo/eeP10CRFKyGEEEKsL1K0EuIsJftU0UpmWq2lfQdsDAWvf02KwdyZolVyYB8oRaZvj4fpVpa9/zDBQoXR62SL+kZ0W3wXB0rDnIiW2Z6vn+cRV2syc+UlndNatdlUm2KHL8K3s0k67BlSZhUfm+kwDmMUjjDlboVXKd6F2l0wL3qzWALl89F81z3MPvw15h57hNa3vA0jXD8/g0IIIYQQFyIzrYQ4S3GxaCWD2NdOsag5csxmx3Yf8aj/9HHDrpIcOkS2ayu1cON0JNWe/D6VpiBz3bJ0qhHtDvcQq5rsa9kYu+gpBW8IjxBUDl8r9lHVBjYhsrqHkMoTU1NeR9xQzEiE5rvuxa2UyT7xKNqxvY4khBBCCHFFpGglxFlOF638UrRaK/sP1dAarr7q3MbP1qFDmHaVqS3XeJRs5UUyeZyBYaZ2doJRX0vHxOUxlMGeuQgjTVVmAvW/nPVyNBk2b42cJOMGeaTUA0BBt1LUzTSrMfxsjALeeuFPJonfdgf21BTzz35PdhQUQgghRF2TopUQZzmzPFBmWq2FSkVz6LBNf69JPHbW05HrkDr+EvnWLkotHd4FXGGpw2MQCjKzpd3rKGIV7Z4LY7iwf4N0WwH0+fLcEpxgf62VfdUEoJhxN+HgJ2kMomj8HRXXk1BfP03XXk/5+DGK+/d5HUcIIYQQYtmkaCXEWU4NYpflgWvjwCGbWg2uvcZ/zvHE6FEC5TyT267zKNnK8xcrtAxl8N90Ha5fhvg0sohjsj0f4lC8RE0tbbh5Pbs1OMFmM893Sj1MO0E0PjJuLz6qJNQIIB0/aylyzV6Cff0UfvwjKkMnvY4jhBBCCLEsUrQS4iwlu4yhDIKy7feqq1Y1+w/V6Nts0po466lIa1LHXqAUa2U+1etdwBXWdmQcgMDtr/U4iVgLV2ebqJqaI/GlDTevZ4aCt0YGCSiXrxb7qWqDKlFyupMmY1bmW60xpRTxW2/Hl2wj99ST1GYyXkcSQgghhFgyKVoJcZaiXSLsC6GUzBtabQcO2VSrcN3ec7usYpMnCednmdp63cKU5wZg1Bzajk2S3dSKkWj2Oo5YA50lP8myj5dbCugN1GEUNWzeEl6Yb/Xo4nyrnG4/Pd8qRM7jhBvLqR0FVSDA3GOP4BQ3zpJVIYQQQjQGKVoJcZaSXSIsSwNXXa2m2XewxuZNJsnWc5+G2o+/QDUUZbZ7m0fpVl7rwBRmzWFqZ5fXUcQaUSiuzkaYDtlMhDbGQPZT+v15bglOsq/Wyv7T8616qREmaQziY+N0n60HZiRC8933oqtV5h5/FG3LjoJCCCGEqB9StBLiLEW7RESGsK+6A+nFLqtrzt0xkIkBojNjTG3dC0aDzH1yNanDYxSSUYptMa/TiDW0MxfG7yj2baCB7KfcGhxnk5nn26UeMk4QjcG024/GIGWcwEAKJ2vJ35okfvvrsTPT5J55SnYUFEIIIUTdkKKVEGcp1UpEfBGvYzS0Wk2z/0CNTd0GqbZzC1PqxUex/UFmNu/2KN3Ki4/NEsxXmLSky2qjCWgDKxfmaKxE2dg4A9lhYb7V2yKD+JXLvxb7qGmFQ4BpdwsmNZLGALCxvideC27upemGG6UqS+UAACAASURBVKkMDlB46QWv4wghhBBCXBYpWglxlqJdJiydVqvq0GGbcuWVs6yC+VkY3E+mbw+uz3+Rs+tPe3qMaiTAXE+r11GEB67ORnAMONi88bqtoobNW8MnybghHiltAqBKhBm9mZAqLOwoKB0/aypy1R5C27ZTfOlFyieOex1HCCGEEOKSpGglxFlkptXqsm3NywdqdHcZtKfO7bJKHX8RTJPp/ms8SrfywjMFolPzTO3oXGg9ERtOsuqnqxhgX0txQw1kP+Xs+VY/qrQBUNQJ5tx2osYM5I54nHBjUUoRu/l1+Ns7yD3zFLWpSa8jCSGEEEK8KilaCXGWol0i4pei1aW4Giq2u+SP/YdqlMuwZ4/vnONufp7E8GHcnTdRMIMXPb/eXvKnDo/h+AwyW9u9jiI8dE02Qi7gMBSpeh3FE7cGx9nhy/JYuZtD1YXdM3O6k6JuRmVehPywxwk3FmWaNN91N2ZTE9nHH8XJz3sdSQghhBDionyXvosQG0PNqWG7tnRaXYaa4zI4trSt6x0HXtrvp7lFU7LzDI6due2asRdQWjO/4zYGB4Yu+hg7OgLLjbzm/MUqiZMZprd34AbkqXYj2zofImwbvNxSoLcY9DrOmjMUvCVyki8WtvFwqZcm4zibfQVm3M30hMEYewK3960QbPE66oZhBEM0330vsw//G9nHHiHxE2/xOpIQQgghxAVJp5UQi4p2CYCIFK1WxcSYQa2q6O13zjnuc6psyxxjqHkzbizpUbqV13Z0HNBM7ez0OorwmIliTzbCQLTCrH9j7prnV5p3RE7QbFT5cmELU04IjYnuvB2UiTH8bajlvY65ofiaW2i+826cuTlyTz2JdmUwvhBCCCHWHylaCbGodLpotbRB7MtdKrfUD0frc5fU1dFaOdeB4ZMm8WaX5pZzg2/LHMPv1kindnmUbuUZtkPy2ARzPa1UozLYX8DebASfhueTG7cwEzYc3tl0HJ9y+f8LW5h3/eCL4G56I7hVjKFvgr3xBtZ7KdDVTeymm6mODDP55S97HUcIIYQQ4hVkzYoQi4p2GYDwEmdaLWep3HLsaKudc52+rjhBX33UnSfGDapVxc7d53aZGK7Djuk0E9EOspHG2V0vMTCFr+owtbPL6yhinQg7JnuyTbyUKPDa6Shxe2P+99ts1Hhn03G+kN/OvxS28Fs2hMJJ3E1vxBj6FsbQt3B77wNz4y2j9Ep45y7suTlmnngCX28/LXfe7XUkIYQQQojT6uMVrxBroFhbeIdflgeuLNdd6LKKxV/ZZdWbHSBslzmU2u1RulWgNanD4xRamyi0Rb1OI9aR62abUBp+3FrwOoqn2s0yP9U0wIwb4q+PBbBdDeF23J57oJZbWCrobMyh9V6Jvua1NO3ezeTf/0+KBw94HUcIIYQQ4jQpWgmxqHSq00qKVitqctygUlmYZaXUWTdojTV1iNlQgsloh2f5Vlp8LEtovrzQZXXOFyw2uqhtsisX4WBzkYLpXPqEBtbny/Pm8EmOzpv8w1GNqzU0deN23w3lDMbII+BuzPlfXlCGQc/73kegs4vR/+9BquNjlz5JCCGEEGINSNFKiEWnB7EvcXmguDjXhaGTJtGYS0vi3C6rnrlh4pV5DrXvbqjiTio9RjUcILu5cZY7ipVzw0wTroIXNni3FcBVgSxv66nx4wx8dVCjtYboZnTXHVCawBh9DPTGLu6tJTMUouejv4EyTUYe+DhOfuPOXxNCCCHE+rExh2oIcQGnBrGHTRmcvVImxw0qZcW2HfYruqyumtxHLhhnuHmTZ/lWWni2QGwyx+jeXjDkPYFG5g9HoP2Vx41QkFD7xQuWIWBXtcq+RI7XGZuI6DP/DVdMLnqucjSlzOyVxl537um0yTp+nhgD0Ly9D4hvBdfGmHgGY/RJ3O47Qcm/p7Xgb0vR/aGPMvxn/w+jn/okm37rd1A++VVRCCGEEN6R30SEWFS0S/gNH37T73WUhuC6MDRoEou5JFrP7bLqzo3QUp7j+5tvaagXo6nDYzg+g8y2C1QzRENxlcuJqYFXHE9Wrr/g8bP1BFwO9msedY6zJxM4fTxBitmpqQuesyXVfwVp1y+l4B39CtA8MQa2q7l/CxgtO3HdGsbUD1DjT6M7b2+ojsz1LLx9Bx3v+xXGP/M/mPjc39LxS7+Cku+9EEIIITwiRSshFpVqJRnCvoImFmdZbd95gS6riX3MB2IMtfR6lm+l+UpVWk5myGxtxwnIU6u4uHjVoHve5HhLjZ2zfvzuxi4IGEpxfz/4lObxMbC15l1bwWjds1C4yryAq0x0x+u8jrphxG+5ler4GDNf+yqBrm5af+I+ryMJIYQQYoOSV1ZCLCraZRnCvkJcF4YHF3YMbDmvy6prfpREOctzm25GN1CXVdvRCZSrFwawC3EJ1oyf0ZjD8eYa1mzg0ic0OKUUP9kHfkPz7RGouZqf2w5m8lpc7WDMvIyLRusG2ml0nUu+/X6q4+NMf/GfCHR0Er3ueq8jCSGEEGIDapxXjEJcoZJdkqLVCpkYu/iOgVdN7CcfaOJkos+zfCtN2Q5tRyfI9SSoxmQmmri0RMWko2ByJFHDVvrSJ2wASinu6zV4y2bF89PwucMaR4NuuwE3uRdj7gjFH3wRtOt11A1BGQadv/x+gn39jH36ryifHPQ6khBCCCE2IClaCbGoUCvSJDsHXrFTOwbG4q/cMbBzfozW0gwH269qqC6rlhNT+Ko2k9JlJZZgV8ZP1QcDzbbXUdaVN2xSvL1P8eIMfPawxtagk9fjJq+lOvBD1PjTUrhaI0YwSM+HP4YZaWL0wU9gZ7NeRxJCCCHEBtM4rxqFuEILRasmr2PUhYDh0BHlgh/FGYNqRXHtbofO2Fm3NWmund5PORihvGnLBc8N+y78mKc+zPXYkaI1yUOjFBNNFFIxr9OIOpIsm7QVDQ631nDW48+2h+7qVrxzi2L/LDx0SFN1QbddT2jPGzFyx1DjT0nhao34Wlro/sjHcPJ5Rv/yAdxq1etIQgghhNhAZKaVEIsKtQJN/ojXMeqC4dQojA284rjjKg6mt5CIFmmqDVMYO3NbWzFLPJ9hX3IL+YmTF3xcu2fbBR/3lNbtW68o92qIjWcJ5UoM3rxNdjcTS2bNBHh6U5nBuE2b12HWmds6FT4D/umY5tOHNP/egrZr30hxIoMx/Tyu1uiuOxpqB9L1KtTbR9cHfo3RTz3I+F9/hq5f/XWUId93IYQQQqw++Y1DCKDm1Ki6NSlaXaHh6WYqNR/buzKvmGW1PTtMyQwwEkt5lm81tKfHqYUDZDcnvY4i6lB70SBRNjicqOEi3Vbnu7ld8Z4dihPz8KkDmlzJRif34ra9BmP+BGrsSem4WiPR619D20+/i/wPnyPz1a94HUcIIYQQG4QUrYQACnYRQIpWV8BxFcfHW0lEi7TGSufc1lrOkajkOd7SjdtAXRHhmTyxiTkyVhfabJyvS6wdhcLK+CkGNEcDea/jrEs3tCl+xVKMF+FPvnKc2YpGJ6/BTd2IMT+AMfqEFK7WSOLN9xG/7Q5mvvoVct//ntdxhBBCCLEByKssIViYZwXITKsrMHSqy6o784pVctuzw5RNP8PRdm/CrZKOg6M4fpOZnZ1eRxF1rKtg0lw2eCE0J91WF3FVQvHrVynmijaf3KeZKGl069W4qdei8oMYI4+AW/M6ZsNTStHxi79EeKfFxN88ROnYUa8jCSGEEKLBSdFKCM4UrSI+2T1wORxXcWJsocsqeX6XVSlHsjzP8eZu3AaagRLMlWgenmFqeweuX8YDiuVTKK7K+Jk3bU7GN9ZOgqYBPqd0WR87m8r8/tv6cFzNg/tcRufKmM1bIXUjqjCKefJhzGr2sh/PhxS5lkP5fHR/8CP4Eq2MPvgA5clJryMJIYQQooHJKy0hkE6rKzU01UzF9nFt99grbtu22GU1FOvwINnqaT80ijYV0zu7vI4iGkBnwaTNDnAoWaM358NgYwz1V65DOTN82ffftGs7/6FzkE+P9/DJgz7e1zHK9rAfFdmJr3gYBr9BtWk3GKFLPlYw1Qum/0riNxRTAYW5y77v5g+8n4G/+AsO/B9/zOaPfhQzdOnv+flUKIRtBpd8nhBCCCE2DilaCcHCzoEAUZlptWSnZlm1xl45y6qlPE9bOcfB1r6G6rLyFyskBqfJbG3HDvnliVRcMYXiNaUWvhmbZKDZZuucFFMuJuWv8aGuIT493sNDE928JzXO1U1gN12Fr3AIf34fdtNutClvQiyFW62QefHAks6J33oH2Ue/zcAnH6T5rntQS9xBNXnj9dAkRSshhBBCXFzjvIoU4gqc6bSSotVSnZxqpmov7Bh4vu3ZYSqGn5OxxppllUqPobRmapd0WYmV02OHaS0ZpFtrOEpmW72aZp/Df+gapitQ5e8mu3huPo72xbGjVwMKX34/yr68riGxfIHubpK3vY7q8BCFF37sdRwhhBBCNCApWgnBQtHKb/gImAGvo9QV21GceJUuq1RpjhPNXbiG6VHClWdWaiSPTzLb20a1aenLYYS4GIXiqukAJb9moHnjzLayHfeyP7Q+c/8gNv8+dZJtoSL/PN3BN2YSVHWIcvgqtPLjKxxEV6Yv/lhef+ENovmaqwlt30Fx30uUTxz3Oo4QQgghGoysahGChaKVzLNauqGpFqq2jx3do6+4bcfsYpdVvLFmWbUdmcC0XSZ3d3sdRTSgVMmgrbjQbdU/58PUjT3bytWazFz5su/fWrVfcf+fDBzlW85mHplrY6KoeGN4GL/aRptxgkDpCLO6h4Jue8VjhdpdaJx6umeUUsRuugUnlyP3vacxY3H8ba/8fgshhBBCLIcUrYQACnZBlgYuke0szLJKxgskoue+iEyUc7SV5zjY2odTR11Wu7p2ELpIA2oskCCZBI79GLZuZteu60/fppSB1u5lXaPF9XFdl3X682S0laErSi0aiUKxOxPgu5vLHG+22ZGV2VaXYip4c3iImFHl2Uonee3n7ZFBptxtJI1BWo0RAm6JrO5BS4P5qlCmSfOddzPz9a8y9/ijJN7yNsyI/J8qhBBCiCsnRSshWOy08skv2EsxONlCzTHZcYFZVjtmh6mY9TfLKoTB0FNPXvC2RCpF7eWjJMoVJgI21bPuZxgGrnt5Ratg3yRDg4dOf97x5vuvLLRoOKmSSapocLi1ypY5H74G77ZaCUrB7aEJ4kaNb5c28YX8Nn666QSafprVOHFjkoAuknH7sZHB36vBCIVoufteZr/x9YXC1ZvejPLJr5lCCCGEuDLylqMQnFoeKEWry1WtwcBEK6l4npbzuqxaS3MkyzmONXc31CwrXJfYxBzlaIhqVGZZidV11XSAig+Ot9S8jlJX9gZmuD9yghk3yOfzO8g4IeZ0F1POFkxqdBiHCatZr2M2LF+ilfhtd2Bnpsk9+wxay+QwIYQQQlwZKVoJgRStlmr/Yag5Jtu7z+uy0pod2WHKpp+hWGPNsvKNZPDVHOa7mr2OIjaAZNmkvWByuLVGTXYSXJKt/nn+XdMxbBT/UNjOsN1EmTgT7k5qhGgzTpJQw+A6XkdtSMHePpquvZ7KieMU9+/zOo4QQggh6pwUrcSG52pXBrEvQaWq2XcI2pvzNDdVzrktWc7RWp7nWEsPrtFATy9aEzgxRjUSoBwPe51GbBBXZfxUTTiWkG6rper0lfj56FEiyuafC1t5uZrAIcCku52cmyJqZFCjj0I153XUhhS5Zi/Bvn4KP/4RlSGZ2ieEEEKI5WugV5VCLE/ZLqPR0ml1mfYfrFGtccEuq+2zw5TNAMPR+ppldSnhbBGjUCbX2bwwPEeINdBaNunMmxxJ1KggXUFL1WJU+bmmo3SbBb5Z6uXh4mZq2mBOdzPlbAG7gDH4VVTuhNdRG45Sivitt+NrTZJ7+knsuazXkYQQQghRp6RoJTa8fK0IIEWry1CpaPYftOnfBPHIuV1WbaU5WivzHGvpbrguq/hYFjcSpJSQbjyxtnZn/NRMeD70yg0PxKWFDYd3NR3nluA4+2sJ/j6/g4wTpEwcvelNEGjBGHsCNfoEOOVLP6C4bMrno/mue8A0mXv8Udxq5dInCSGEEEKcR7Z1ERte4QqLVgHDoSO6kokuLOzjnOsEDIe1rjvvO1CjVoPrr4bS1Fk3aM327DAlM8Bwne0YeCnBXJlAsUr56n7pshJrLlEx6cqbPB+ZYY9qI6AbqCC8RozFnQU3mQX+rdTH5/I7eFN4mB5fBLf3PlTmZVTmBVRxDLfzVoj2eh25YZhNTTS//m6y3/4Guae+S/Nd96Aa6U0NIYQQQqw6KVqJDa9QKwAse6aV4dQojA2sXKCLsHu2nXOdluhOtOFf9eueUi5r9h+y6e8zaW1xGDmraNVWypKo5NmX3IKrGugFidbEx2ax/SZ2dxvMSLeLWHvWjJ/Ho2UOtBS5bnYNKuQNqt+f571mmq8V+/i3Uh+zgzY/tVXhb7sWHd2MMf5dzJFHcePb0O03gRn0OnJDCHR0EH3tzeSfe5bCiy8Qvf4GryMJIYQQoo400KtLIZbnSjutNoqXD9Swbbh+73mFMq3ZMTtM0RdkOJbyJtwqCebKhPIV5rtawJSnS+GN1rLJplqEFxIFHGQnwSsRM2ze3XSM1wYmeWbKxwP7NFMlDaFW3L634SavReWOYwx8BfLDXsdtGOGdFqHtOyjue4ny4IDXcYQQQghRR+RVmNjwCrYUrS6lVNIcTNts7TdJtJz7tJEqZWmpFjjW0oNusC6r5tFZ7IBJvi3mdRqxwd1cTlHwuxyOl7yOUvdMBXeGx3j/9gozZfjzlzXPTWo0Brrtety+t4ERwBz5Dmr8aXRVvudXSilF7KZb8KVS5J5+Cnt2xutIQgghhKgTDfQKU4jlKdSKKBQRX9jrKOvWywdqOA5c9ypdViPRNm/CrZLQXIlgoUKuK7EwFEcID/XZTbSVfTzfmkdLt9WK2NPi8tvXKnoi8IVjmr9Oa+arGkJJ3L6fxG3di5o7Su7bH4eyLA2+Uso0aX793RiBANnHH8WtyOB7IYQQQlyaFK3EhleoFYn4whiN1CW0goqnuqy2mLQ0n/s9ai/O0lwtcLQRu6xGZqkFfRSSMkNIeE+huGEmSjbocDwqL/ZXSmtQ8cE9irf3KdJZ+G8val7MaDBMdOoG3N770K6LcfLrqLmjXsete2YkQvOdd+MWi8w9+QTacbyOJIQQQoh1roFeZQqxPIVaQZYGvoqX99VwXbjumgt0WWWHKfhCjEYba5ZVOFskUKqS65YuK7F+bJsPEa+aPN9akG6rFWQoxV3dit/aq0gE4W8Paz53xKVoawi3/y/27js8rrw8+P73nDO9aWakmVG3Lcka27suW9llC2wBlppACDUhBAiEEh7SSN73Idf15H3ypBNYIJsQSHloISQQSiAs2xtsY9d17bEtWb1rRtPrOef9Q/J6XWRrJEsjae7Pdc11rU773bOees/9u3/4XvU/wBlCnXgcZfJJMCXRshLWUAjvy26kPDHO1Pe/X+twhBBCCLHOyeqBou5lyzlJWi0ilzM4drxCT5dGg+/sHHckl8BXynGgqRtT2USJndNVVg4rueDyVpQUYjWoC9VWDzcnGXWVaM/J6nYroamAfqZfVbsdfncn/GRc494xjb6kyTu3lbnZ5kRpuQVmD6HOHYP8NLS8HMWyzPcNzUKFtVv5dT1y9mynEo8Tf/hhbN29+G58ea1DEkIIIcQ6JUkrUfey5Rx+u6/WYaxLBw5XMEzYe6Eqq8QwGauD8U3Wy8oVz2ItlJnpCsNmSsaJTSGacvJ0U5rnghlJWq2QYugUZs9fIfB2G/S22PnmdIS/P24nxhCvKo5iVQMorl4suZMw+GPKru2Yloaqx7WHOkGr76QVgOfa61AqJSa/+i/Y29uxd3TWOiQhhBBCrEMyPVDUvflKK6moOVciVSJ2osL2bg2f9+yXiobJQbzlPCf97Zurysow8I0lKDlt5ANSfSfWH4upsDfhZthdYspernU4m1a7vcj/aB3mVl+Ch45nuWe8nUTFgmltpOzZDYqGJfsCanEMTJmquRyKqtL23veiulyM3fN59Gy21iEJIYQQYh2SpJWoe9LT6sIeeHISuFCVlUGk7wBpq5Nxd2MNIltFR/uwFisk2/xSZSXWrSvnXNh0hecaM7UOZVOzqiZvbJzh469sZLps5e7RDk7mnaC5KHt2Y1qCWAqDaPk+SVwtk8Xno/XDH6McjzPx5S9iGkatQxJCCCHEOiNJK1HXynqZklGWpNU5MhmDJw/E6e2x4PWc/TLhH+vDkU1yMtC+qRI7im7AkwcoumwUGuTxINYvm6Gye85Fn6fAnLVS63A2vWs6XXy8dRi3pvMPE208kvRjYqHi6kW3t6OVp9HyJ8CUhMtyOLt7CL/jXWQPHWT2B9+rdThCCCGEWGckaSXqWraSA7gsSatiWUM3NkcSZ/+hMiiw58pz2t4ZBpETz5L3+JlwBWsT3CoJnpqCdJZUW2BTJePE5rQn4UYz4fmgVFutREU3LnkzTQhqRT4SGeQKZ4b/iof46lSEbAWK1jZKtk608ixqNkalUrn09Wp9p9ehhlfeju/Gm4j/4HtkDu6vdThCCCGEWEekEbuoa9ny6aTVynpa6YbCY0e2oiomW8IJOkNJrJaN+at7Km1wok/npqua8LhzZ+0LjJ3EkU0ysPcVkCrWKMLLT6kYRF4YhdYwBZ+z1uEIcUkuXWNn0sUL/hwvn9NrHc6GZJgms8nCJY8LliovHvcaaz8BM8TjuRbGClZ+0TVAQAvgVnSCjFLJHGXG2IqJtuj1HGGDi+yuS4qiEP7VX6M4MszEl/+Bzk/9L2zhcK3DEkIIIcQ6IJVWoq69mLRa7tLlC1I5OxVdw2bROTEW4uFDXRwbaaJQ2nh54QOHyqgK3HlD5Owdp6usfI2kwptrlaemvkls+TLceJVUWYkN46qEGxN43p+udSh1Q1HgZfZp3uruJ2da+Wqml/6yl6zZxKzRgZ0MIfUUCpJIrJZqs9H6kd8CFMbu+TxGcfP8MCKEEEKI5VvTb9TRaPRdwKcAK/DZWCz2t+fs3wd8GfABjwK/GYvFKtFotBP4GhAGYsC7Y7FYJhqN+oGvA13ANPC2WCw28ZLreYH9wPtjsdjDq33/xMZzptJqZUmrucx8dc51vSMUyxZOTQYYnAwwOBWgNZhiWySBx1lacbyrLZkyONmvsytqocFrhZd8Fw6MHseeS3Hq2rs2VWJHreiEj46RjvjwdjTD4PFahyTEkvjKFranHRz0Zcnp8gV/LW2xZPgVz3G+l93Kf+a28TrnEDttYJoqjeogYbWPaaMLQwraq2INhWj54IcYvfszTH7lX2j+wAdRNtH7jRBCCCGqt2aVVtFotA34P8DNwD7gg9FodNc5h30N+FgsFusFFOA3FrbfA9wTi8V2AM8Cf7Sw/U+Ax2Kx2E7gS8Dd51zvC0Dgct8XsXlky/NLbK84aZV14LSVsFt1fK4ie7dNcMuVp+hommM87uXxF7by3MlWypX1Xdy4/1AZVYXdV56zYqChEznxc3INIVLhLbUJbpU0HZ/AWiwzfmVHrUMRomr74h7KqsnTmZO1DqXuNKhl3uHpo13L8sN8JwdLQfL4mTG2YaVASO1DpVzrMDcc95V7aHzTL5J+6mfMPXh/rcMRQgghRI2t5TfoO4EHY7FYPBaLZYH/AN56emc0Gt0COGOx2JMLm/4F+OVoNGoFbl04/sXtC//9euYrrQD+FXjtwvFEo9G3M18ncnC17pDY+C5HpZVpwlzWid9zdm8Ul73Crs5pXrmnn+6WWaaSHoZnGlYU72pKJg36T+nsjFpwOc/+ZTs4chx7Ps1E77WbqsrKSGeIHB1lri1Arslb63CEqFqoaKU1b+Ox1FGkxffasykGb3H3s82S5if5Dp4tNlHAx7SxDQslwmofmiSuqhZ8/Rtx793H9Le+Sf6EVL8KIYQQ9Wwt69ZbgfGX/D0OXH+J/e1AE5CKxWKVc7afdc7CNMIUEIpGoxbgE8DtwH8vJ9jGRs9yThM1FApVn3QwRitYNSttzY1VnzudLeH1OpgaK1AsWwh6i1gs53fXtVhgZ+cc8bSL8XgD29tSy8r7KKpy1vVVTcPldVR/oUU88WQGTYMbr/fgcqlYrRperwN0nea+5yg0NkNXL15FIZe88H1dqXPv43n7lYvvr1bpvsdQdZOpq7dhsWgoioqqXjiXrygsug8uvu/s6yjnHHvu39WPXd14SxtzJeOuZMzljH3h8aofs5pxL+eYSxlbVRWsizz29yU9/MgZZ9Rnoyu3vIUEFGXx6y96jqpWfc5Kx70cY1Y79qXGtAK/7Bvme5l2Hi60oSsWbnJqJOghwEnC2kkSbEdX7MDp1277Rcecf/29+DGX20rGXO55LpcNxyLv3cFP/g4Hfu+TTPzDPez7m7/GFpTCeXFhy/n8J8TlIo8/UUv18vhby6SVCmf9DKwAxhL2n7udl5x37lf/03//I/PTDPPRaHRZwc7OZjAM+dV6owiFvExPV9+MeCY5h9viWta5ObNEOl1gYmr+4ehz5qhUFm++2xJM8cJQhERaw+eqvr+VaZhnXd/QddLpS698tRSJOYPYiRK7r7Cg6yXSaSh756/fOHgEay7N8JW3ks7M981RdP2i93W5zr2P5+03L76/Go5kjvKTh5jpiZBz2aCiY5oGhnHhVR9Nk0X3qaq66L7zr2Oec+y5f1c3dvXjLW3MlYy7kjGXM/aFx6t+zGrGvZxjLmVswzApL/LY7846adBcPO9L05GyLXPsxa+/6DmGUfU5Kx33coxZ7dhLHfP1jgEsZgeP5SPkdYVXOHQqSjdNaj8BjjOtd1PBvvDaffEeZPbypY+53JY7ptdrX3astlyJ9EXefyMf+ihDf/q/Ofynk1nE+gAAIABJREFUf0n7734SxSI9wsTZlvv5T4jLQR5/opY26uNPVZWqC4TWcnrgCNDykr+bgbEl7J8CGqLR6OmfOVtect7ownEsVFd5gRCwA/jHaDS6H7gW+HI0Gr3tst4bsSlkK9kV97OamgVVMfA6L/6hvTmQRsFkLO5b0XirYf/BMhYL7N51di8rRa8QPvkc2UCEdFP7ImdvTK0HhsBhZ+KKtlqHIsSKaCjc7NvBsLtE3CZT0WpFVeAu5zD7bDM8Wwpzf6GNouli2uhGwSSsnsRKvtZhbij29g4i730f+RPHmf73f6t1OEIIIYSogbVMWt0P3BGNRkPRaNQF/BLw49M7Y7HYIFCIRqM3LWz6VeC/Y7FYGXgMePvC9vdwZsrfjxb+ZmH/Y7FY7EAsFuuIxWL7YrHYPuYbt38gFos9tJp3TmxM2XIOt2VlSavpGfC5ilxqRpDNYhBqyDIe92KuoyK+2bjBqUGdK3ZYcDjO6WU1fAxbIctE73WbqpeVd2IO3/gc9jtuQrdbL32CEOvcjd4omgGH/Llah1LXFAXucIxyvX2SA6Um/jvfQdF0MmV0YwIhtQ+K8VqHuaH4rr8B/52vZu6B+0j97Ke1DkcIIYQQa2zNklaxWGwU+J/AQ8B+4BuxWOzpaDT6o2g0eu3CYe8GPhONRo8BHuBzC9s/wvxqgy8AtwCfWtj+R8AN0Wj0yMIxH12beyM2i2w5t6JKK103mUmA37O0X89bgimKZSvx9PL6zqyGZ58vYbPBledUWVEpETn5HJlgC5nGTVSNZJi07h+i6LZjvenaSx8vxAbg0RxsTzs51pCnqC5veqK4PBQFbnVMcLN9nBfKQX6Q20LBdDJl9GCioYw9ArnJWoe5oYTe+jacvVEmv/ovFIeHah2OEEIIIdbQmjYHiMVi3wC+cc62173kvw9wdnP209sHgVdeYHsceNMlxjzvPCFOW2nSajZuYBjgdy+tt1TYn0VTdcbiPhp9tZ8mMjauMzpmcN3VVuz2cyqpjj2JtZhj8Ko7NlWVVfDUNM5kjlMv306T9EcRm8iehJtjDXmONuTYl5DFRGrtBscUNsXgwUIb381p/ILrFFNGNy22YdSR+zDabgd3a63D3BAUi4WWD32Ywf/9vxi75/N0fup/obndtQ5LCCGEEGtgLacHCrGuGKaxkLRa/gff6Zn5ioalJq001aQ5kGEi4UE3apsIMk2TZ54r43Yp7NxxdvJG0cso+x8g3dhKdhNVWallnZbDw2QaPSTbg7UOR4jLKlS00pKzcsifwzhv/RJRC1fbZ3iNc5jBiodvZ7vImQ7MltvA6kEdvR8yw7UOccOwNPhp/c2PUo7HmfjyFzGXueCBEEIIITYWSVqJulWoFDAxV1RpNTVt4HaBw1ZZ8jmtwRS6oTGdrO2vxKcGdWbjBlfvs2LRzk6gNQ2+gJJPM9l7XY2iWx3hY2NYC2XG9m3ZVNVjQpy2J+EmZdMZdK/tynNicbttcV7vHGJMd/OtbDdZHBidd4E9gDr6IMrcMdZVo8M1pCmgZZNLvnlawkTe/Gayhw6S+M63qjr3pTeLLs8PIYQQYqOQuTGibmXK8w2LV5K0mp4xCDdWd07Qm8durTA266M5kFn22Cuh6yY/f75MwK/QvU07a59aKRPqex6zrZdssGWRK2w81lyRcGycRGcjuSZvrcMRYlVsyzjwlFUOBrJsyzpqHY5YsMM2h1XR+X5uK38bgw/t0vC1vwZ1/BHUyScx8tOYkRtBra+PZUapyOyBF6o6x3S4cXR1M/PjH1MuVbC3Vb+ybeO1V4HbXvV5QgghhFh7Umkl6lZ2hUmrZKZEJmsSqjJppSjzDdmnk25Kldo8BWMnKqQzJtdebUNVz644ahw8jLVUwLzmrprEtlpaDg2DaTK+p6PWoQixajQUrphzM+IuEbeVax2OeIlua5pfcp9itqjwhSMm8YoVo+1OjMZ9KKk+1KEfQilV6zDXPUVR8L7sRiyBAKnHH0VPp2sdkhBCCCFWkSStRN3KlrMAy+5pNTA2XyUVbqr+3NZgGhOFicTaV/yUSib7D5Zpiai0t579EqBWSoT7DpAKdUBk65rHtlqciSyBgRmme5spuaX6RGxuVySdaAYcDORqHYo4R6clw4d7S2TK8IXDJtMFMJv2YbTfCeUc6uAPIC2r412KYrHQ8IrbAUg+8hBmZelT9IUQQgixsUjSStStlVZanRrLoqrQGKj+XK+ziMdRZGzWt6yxV+LwC2UKRbj2aivKOX2dmgYOYykXmOi9ds3jWjWmSev+QXS7hcmdm6epvBCLceoa29NOYr48BVWaVa83Wz0GH71CoWLAF46YDKZNcLdjbH0j2HxoYw+SO/gjMOXf7mI0rxffTbdQScRJP/0kZp32BRNCCCE2O0laibqVrawwaTWaIRhQsWiXPvZcigKtjSnmsk5yReuyxl+OXM7k8AsVtm3RCDWd08uqXCTUf4BkeAt5f2TNYlptDaMJvFMpJq5ox7DVV78YUb/2JNxUVJNjDVJttR61uRU+eqWCRZ1PXD0ybmJa3Bgdr8NoiFI89jDqyE+gkq91qOuavb0D1+69FPpOUjhxvNbhCCGEEGIVSNJK1K1sOYeCgsvirPrcim4wNJElHFr+U6glMN+HYzy+zCmCikKxYlR1e/ZACd2A3bu18/YF+g5iKRcZ7r6GYsVAN81Fr7NRfs9WKjptzw+Qb3Ay0715EnFCXEqoaKUlZ+WQP8fGecbWl4hT4Xd2K+zww/cGTP45ZpIzVMzmG3Fd/3bIT6MOfB8yw7UOdV1z79mLraWV9DNPUZ6ZqXU4QgghhLjMpOxA1K1sOYfL4kRVqk88jU5nKVcMwk3Lfwo57RUCnhxjcS9dzXHOmal3SYZhMji59Ka9uRycPGmlpc0gnskQf8nChVa9xL5TBxn1tXE0a4Nsiu1NZQbHL3z97RFbdcHWSOTYGLZciRO37QK1yv/BQmxwexJu7m2bY8BTpCsjvdzWI7dV4X1ReHQcfjBk8umDJu/ZDtfsvYb0ZB51/DG00QcwvNswwy8Di/w7nktRVXy33Er8hz8g+ehDBF/3RlSH/H8SQgghNgtJWom6lS1nlz018ORoEmBFlVYw35D9yFCEVM5Og7u4omtdymC/BU2Dji36efu2T8ewGWWORK5c1RjWki1TIHx0jHhnI9nw2vcOE+JysjpdEL7wPlVVUB12HOHgWdt3YfJTI8vhcIldrtaLXr+ocd75im6Sn02sKG5xaYqi8IpW2OqFr5ww+cIRk7f7ZrjGHsDY+gaU2UMoswdRcmOY4ZdherdR9a8cm5xqd9Bw620k7v0RqccfpeH2O1FUmUwghBBCbAaStBJ1K1vOLTtp1T+WxOe24nYrsIJFi5oDaV4YDjEW99Hgnl7+hS4hlVSYnVHp3FrBdk6RlLVSpHcmxkhDO0nnMrrKr1Ntzw9gqgpj+7bUOhQhVsxQDE5ND1xwn6qqBIr7Lrh/SwUOh7I8n+zDX1q8AV+AEInps1+DtoW2riBiUa0tXoXf3QPfPGnyjZ9OcDAA7+hRcTXtw/RuQZ14AnX8Ucz0KYzwDbDMlW83K2tTE97rbyD95E/JHjyAZ99VtQ5JCCGEEJeB/Awl6tZKklZ9Yym2tnrOW32vWlaLQbghy3jci7FKbWdMEwb6NaxWk7b281ejis7EsBiVTVVl5RtL0DA2x8QV7VScG2MqoxCrYWvSimZAX2AF2XVxWWkqWPT8BW8+pcBv9BT5lRsjHJ0z+fQBgxOzBSwWB0rbbdC4DyU7hjbwXbT4YbRKbtFrXfBGudZ3f1U5erbj6O4hd+gAxRHpBSaEEEJsBlJpJepWtpyjzdNS9XmpXImpRJ4b9zYBK5/S1xJMMznnJZ520eS7/Ct9TYyppJIqPb0VtHOe8bZKkZ6Z4ww3dJJy+C/72LWg6AZtzw1Q8DqY2d5c63CEqCmbodCZsjDoq3DFjA2HLtPKak0xdAqzIxc95lW39BAYHOab08383XEbV7lTvKlxBo/mBPduLPl+1OlnMeLHqbi6QV1aDyd7qBO0tVuxdq0pioL3+huoJOKknniM4OveiOZd5mInQgghhFgXpNJK1K3l9rTqH5tvTr6t9fJMzQg1ZLFoOmOzl7/vUiEPp/o0/AGDSMv5VVY7pl7AYlR4IXLFZR+7VsLHxrBni4xcsw1Tk5c4IXoSVgwVTjVs7iqbzabTXuS3W4e40z/LwayXvxrZwjNpH6bqpOLeRcXZhaJnsaYPoBbH58tqBYrFQsOttwGQfOQhzIpUGQohhBAbmVRaibpU1suUjPKyklZ9o0lURaEj4ubIJVbXdpYLbElNoJkGRxov3DxXU02aAxnG417KFRWr5fzk0nKYJhw/ZkFRYHu0ct7Q7mKG7bMnOBXoIu1ouCxjXk47WrbjOCev7rUFaGy5SJVAMgNHn4HtW+jZd/0lx/AbFva1RF/8u9ETRCaUiM3GW1aJZDX6/RV6E1Y0U6qtNgqravKaQJx97gzfngnzrZkIP894+aWmKUK2CIbFjyXfj6UwgFGeoeLsBm150943E83rxXfzrSQfvJ/Ukz/Fd9MtK57OL4QQQojakKSVqEvZyvw0vOVWWnWEPdhtizQ1Nk2ChRRbUxOEcwlOf0yecfqZdAcveEpnKMHojI9jIyF2b52sOqYLGRuZnxa4fUcF+wVmjlw5cRADhSPNuy/LeJebA5Xhxx89a1sgdH6z6JdqPDmJwzSYcBro55x7IfYtUwwPHnvx78hdb15+wEKsYz0JK0+0FxjxVtiS2rzTwzaKin7xHydM8+xjGrUCHwgP8Wy2gf9OhPibkU5ua5jlVl8ci70XTZvFVhzEmjlI2dZGxdoCyvmVpvXU4c/e1o5771VkDzxPvrEJ185dtQ5JCCGEEMsgSStRl7Ll00mr6qb4GYZJ/3iKl195fq8k1TBozc6wJTWBr5SjpFroa2hj2BfmuomjbE8MM+kKXLDayucqsbU5wamJIC3B9Ip7W+WyMHhKI9hoEI6c/+UomJulMznEkfAVFKzOFY21XjiSOVxzOebaAug2eWkT4qXCORVvUeGkv0JnyoKCVJ3UimGazCYLFz0mWKpc8JhuCvy6Z5YHC63clwzxXNrDa13DNGseVHoJKGO4SiNQnCFudFDm7B9mHGEDFl9EctNx7d5DOT5L5ufPYAkEsDVX38dSCCGEELUlDV9EXXoxaWWprtJqdCZLsaTT3Xqm/5Ri6GxPDHPb8HPsnukHEw41dfFQx9WcCHZQsNg54e/AW87Tmp1d9No9LbO47SUOD0aorKBZsmnMTwtUVejpPX9aIKbJnrHnKVgcxEI7lj3OumKY+IdmKdstpCPrb6qjELWmoNAzZyXpMJhxXp4pyKI23GqFN7qGeLOrn6Kp8fXMdp4oRCibNmbNLUzrW9GoEFFP0KCMoVC//96KouB7+c1oXh/JRx9Bz2ZrHZIQQgghqiRJK1GXzlRaVZe06htLAtDddiYxEhw5Qc/cKAmHl6ead/JE225GvGEM9czTa8IdJGVz0ZMYQTEv/AVCU02u3DpBoWTh+GhTtXfpRSPDKpm0Snevjs1+/v7W1Cih3AyHI1eib5JVpLyTSazFCnOdjaBKBYkQF9KZsmDT4WRAGrJvBt3WNO/1xthpTfCzYjNfz/QwrTso0MC4sYOsGcSnThNRj2MjU+twa0a12Wh45e1g6CQfeRBTl8bsQgghxEYiSStRl7Ll+V9bq01a9Y+m8DithP1nptS5E5PkNRvPRaLEnQ0XnP6HonAi0IG7UqAts3j39oCnQGdojqFpP4nM0pYwf6lsRmFoQKMppBMKn58cU0yDPeMHSNl9DAS7qr7+eqQVy/jG58j5XRQapAGxEIvRTIVtc1bG3TpZa/1W32wmDsXgda5hftF1ioxp5auZ7TxVCKObGgmzgym9CwWTiNaHXxkBoz4TlpaGBnw33UJldpb0U09iykqLQgghxIYhSStRl1ZSadXd6juzCpFp4p6bIuHwXvLcKaefObuHnsQI6iLVVgC9bTM4bRUODzSjG0uvGjIMOH5Uw2KB7u36BY/pmj2Jt5TmQMs+zAs06d1wTJPA0PyUy7mOCze5F0Kc0ZW0oAB9/vpMXmxWPdYU7/XE6LGmeKzYwr9me4jrdop4mTB6SRtNeJRZlJGfQHa01uHWhL2jE9eevRT6TpJ4/PFahyOEEEKIJdoE31qFqF62nMOqWrBpS19LKVsoMz6bo+slUwOt+TTWYn5JSSsUheOBDpx6iY7U1KKHWTSTK7ZMki3aODneuOhxmgYRz5lbfFwjm1W5dm+F9uDZ+yIeaHWU2D11mDlfGLOl5bz9596clsX3acr6+JXaOZfDmcyTag2g2zfHVEchVpOzotKe1hjwVSir6+N5LC4Pl6rzJtcgb3AOkjDsfCXTy7PFJgxTY85sY8roAUVDG7kPZfwx0Iu1DnnNuffsw9bWzuR3vkP+xPFahyOEEEKIJZAltkRdypZzVa8c2D+WAjirCbs7MQnAnH0JSStg1uFj1uGjOznKsDeEoV54GacmX462xiQDEwGaA2kaXOd/uVD0CtnxAQCSWTuxE520NiZpMCfJjp9/zd74ENZKiSOeCNmJwUvGWmnrfvH65wr21H5qoVIxCAzNUnLaSEd8lz5BCAFAT8LKsE9n0FehZ06SvZvNDtscHZYM9+bbebjQxslyA3e5hvGrYLa9CjN1EiV+CCU7ihm+HtO77cLT2jchRVHw3XwLqQfvZ+zvvkDnH/0x1kCg1mEJIYQQ4iKk0krUpWwlW/3UwNEkCrCt5aVJqwl0zULatsRrLVRb2fUyW1KTFz10R/s0NovO4YEIxiIFEZm8jReGwjx9vB27tcLO9ukLHueoFNmaGmfU00TKXl2ybr1qGI2jlnUSW5vq5guXEJdDoKjRmFc56S9jItVWm5FbrfBm1wB3OYeY0p3833Qv+4uNmIqGGboaY8sbwOJGHX8UdeiHkLv4+9FmotrstL///RjFIuP3fB6jVKp1SEIIIYS4CElaibqULedwW6pswj6Woi3kxmk/U6DoTkyQawhhVpE0mXN4mXL66UqOYTEWX8XIajHYtWWSdN7BqYkz/ZoMEyYSHn74kMrjL2xleMZHxJ/l2t4RrJYL98rqTQwDcDzQseQ41zNbpoBnOk0m7KPkvsASiUKIi+pJWMnZTMbdF+5/JzY+RYErbQne643Raslxf6Gdvz9hI1E0wdGIseUNGM03QyWHNvzfZJ74CpSStQ57TdhbWmj5wAcpDJxi8p+/LI3ZhRBCiHVMklaiLs1PD6wuaTU6k6UjfGYaoFnK40jFyflDVY9/ItCOzaiwNTlx0eMi/izNgTQnx4P0jxXoGw/y6KFt7O9vJZVR2N46zW27T7Fn2wQex4UbK/uKWdoyMwz4WihYNkGCxzAJDM6iWzWSbTKtQ4jlaMloOMsKJ4JSbbXZ+dQyb3X1c6djhIGMyl8dMHl6av5f3Wzowdj2FoymqyhPHkc99V2UyadAL9Q67FXnueoamt7yVtLPPM3s979b63CEEEIIsQhJWom6VG3SqlTWSaSLRILOMxunBlAwyQbCVY+fsnuYcAXZmhzHqi9ebQWws2MKi2ryjz+Y5sRYE25Hiau6R3n763W6WxLYrBeplDBNdsQHKakW+v2tVce5HnmnUtjyJeY6GzE1eQkTYjlUFHoTVmadBtPOxVczFZuDosA++yy/v6tIqwu+2WfyTzGTZMkE1YLZuJeG1/4BZsN2lLljqP3fRokfhotUA28Ggbteh+/lNxP/wfdIPf1krcMRQgghxAXINz5RdwzTqLoR+/RcHoBw4EzSypzsx0Qh11B9pRXMV1tZTJ1tybGLHme36uztGufWfV5uvuIU1/WOEvFnUZfw7G3JztJYSHHS305F3fjrLii5Ir6xBHm/i7y/uko5IcTZtiYtOMoKR5tKUm1VJ5ocJh+5QuEXtijE5uDPnje5f9SkbJioTi9m88sxtr4JnBHU6WcXkldHNm3ySlEUIu95L87tvUz+05fJ952sdUhCCCGEOIckrUTdKVQKmJhVVVpNJeaTVpHAmXPMiX4KviCGZXmrb2VsLsbcTWxJTWCrXLwRbJMvx2te5l90CuCFuMoFrpzpJ273MuSLLCvGdcU0sb8wAECio1GarwuxQpqpsCM+X201atn808HEPFVReEWrwif3KfQ2wI+GTP5yv8nPT6XmezvZAxjtd6J3vAbsftTpZ1D7/wNl9hAYS38P2igUi4XWj/wWlkCAsb/9HOXZ2VqHJIQQQoiXkKSVqDuZcg6gqqTVZOLsSivT0GHqFNlA84piORloRzUNeuZGV3Sdc6mmwb6pExiKwoFwT1WN4tcrZyKHZTpJsi2Abt/4VWNCrAdbUhacZYXnnHNSbVVnmhwK79uh8ps7Fawq3H3vEH9/1GQ8t/A4cLVgdLwGveO1YA+izvwcte8/UGYPgL65VtzTvF5af+sTmOUyo5//LEYhX+uQhBBCCLFAklai7mSXkbSaSuRwOyy4HfNVVUZ8GCqlFSetclYHw94IW9KTNOUSK7rWS0XjQzSUshxq6t4UzdeVik5geBbd5yIT9tU6HCE2jdPVVtOWIpMuWUlws9NUsOj5s267vAX+4Moi73l5hNGMyV8fMPjPvjLFwsIxdh9q6y3QfieKoxF15nm0/n9Hm3oGrZREz6XPu+Z5N9Z/hZa9tY2WD32E0ugI41/6IqYhvd6EEEKI9UDKFUTdyZazAFX1tJpM5Am/ZGqgPnFi/lorTFoBHAtuIVBMs3e6jyfadq84yRTOxtmammDA18yUO7ji+NYD/2gCtayTv24blHK1DkeITWVL0sKJJp2jTWUiQxoKG78yU1yYYugUZkcuuO+OW3roGunnvrlGnphq4NkZC7c1JHi5bw67ulB9Zd2C4mlEK4yiJo5gzh2j5OqgqIVB0RYd1x7qBG15U+nXkvvK3YTe+W6mv/E1Zv7jW4Te9o5ahySEEELUPam0EnVneZVWeSIvacKuT54Et5+y07PieAxV5fnw9hen9Cnm8n/ddVSK7J7pJ2lzEwt2rji29cCezuOZTpMJ+zAalp5oFEIsjYrCvoKfhMNgwi3VVvXMrRn8YuM0v902xBZ7gR8lmviz4a08NBegaMwnM03NQ8UdpezZjal5MbMDWNPPoxYnYAXvX+tF4PY7abjtDhI/+TFzjzxU63CEEEKIuidJK1F3spXqklblikE8VThr5UB94gRKpOuyNQPPWZ0cbuoiUMzQmxhe1jUU02Tv1ElU02B/uAdD2fhPb0U3CA7MULFbSLYFah2OEJvW9pIHd0nhaGNZelsJmm0l3t88xsdahmm3Fy+SvNqJ4t+DqTqwFE5hzexHLU2DubEfQ+F3vAvXlXuY+tpXSP/8mVqHI4QQQtS1jf+tVogqZcs5FBRcFuelDwZmknlMzjRhNzKzmNk4RLoua1zjniaGvGG6kuOEltHfqicxQrCY5nBTFznr0u7betcwmsBSrBDf2oSpycuVEKtFZb631ZzDYFyqrTa1im5c8Gaa5+9rs+Z4b2iED0cGabOdqbx6IOEnWzap6AZFPBQcOyk4ophoWPInsWQOYBZnqVT0+WvX+k5XSdE0Wj/8URxd3Ux86Yvkjh2tdUhCCCFE3ZJvgaLuZMs5XBYn6hIrkc6sHDhfmXW6n5VymZNWAEeDW0naXOyZPomjXFzyeY35JN3JUYY9IcY9TZc9rpoYncQzlSId9lH0bo4knBDrWUfKItVWm5xhmswmCxe8FUuVRfe5C3O8yd7Hu9wnCKk5fjwX4i9Gu7hvysP0XJHZVJHprIPRcg8zRie6rmMvHEfNvEAqmcDYgE3NVbudtt/6BNZwmLEv3E1hcKDWIQkhhBB1SZJWou5ky9mq+1nBmUorffIEWOzQ2HbZYzNUlf3hXlQTrppeWn8rm15i7/RJslYnRxu3XvaYakGp6HDfT9FtMi1QiLWiorBz1krSYTDmkWorcb5WS463uk/xLvcJmtQ8Dxba+Ie57Rwr+RdmBCrkzQATxg4SRhtW8kTU4ygzz4FeqHX4VdM8Hto+8XuoLjejn/00pcmJWockhBBC1B1JWom6ky3nqkxa5XDaLXid8ysf6RMn0cJdKOriKyWtRM7q4FCoC38xQzQ+tPiBhoG/kGbf1AksRoXnw9vRVymmtdZyaATmUjItUIg11p624CkpHG0sSbWVWFSrJcfb3P28xdWPVTH4r/wWvp7dzlDl9GIZChmziQljBxmzEVJ9qP3fQUkc3XDN2q3BIO2/83tgwshn/prKXPXT94UQQgixfPJtUNSd6pNWecIBJ4qiYJbyGPEhtObtqxghTLgbGfBF2JaaIJyNv7jdppdpTU+zd+oEOx/5NjeOHyFQyHCkcRsZ29Lv03rmmkkTOj4Ou3sp+mRaoBBrab7aykbKbnLcmqp1OGIdUxTosqZ5X8NJ7nIOkTUsfCvbw3ey25jWHQAYWJgz2zHbXw2ORtSpp1AHvg/ZsRpHXx1bcwttn/gd9HSGkc98Gj2brXVIQgghRN2QpJWoO/NJK/elD1wwlcgTOT01cKofTBMtsrpJK4BYcAtzNjd7ZvrYHh/Cef8/ccfQz9k700ewkCIV7uD58HYe6LyGUW941eNZC2a5TOfTfZRdNrjl2lqHI0Rdak9reIsKP3NOs/FaaIu1pipwpS3B+7zHuNUxxmjFzVcyvdybaydrWOYPsjVgtL8avfU2MHW0kZ+gjj6Anp6pbfBVcGzdRtvHPk55coLRz38Wo7j0vpNCCCGEWD5JWom6U01Pq4puMJMsnN3PCgUt0r2KEc4zFJX94fnkWHdyDFSN44EOnmjdzUMdVzN6xQ1MuBupaJZVj2WtlH7yGI50gaHrusBmrXU4QtQlZaHaalYrcrQhX+twxAZhVUyut0/zAe9RrrZNc6Qc5J/SUfYXGzEm7gdeAAAgAElEQVRM5kuzvFswtv4CRtPVkB0nde+nUaafBaNc6/CXxLVzF80f+BCFvpOMf/EezEql1iEJIYQQm54krURdKetlSkZ5yUmr2VQBwzQJ+xdWDpw8iRpsR1mjqXh5q4NH2/byQOc15G//Nfr8baTs7vkP/5uMazZD6ZEnmekKk2n21zocIepaW0ajveziZ6EUeU2asoulc6o6tznH+TVPjIiW5/5CO585amcwvVC1p1owG/dgdL0FW+c+1Phh1P5voyRPsNDNfV3zXnsd4V95D9mDBxj/0t9L4koIIYRYZZK0EnUlW8kBLDlp9dKVA03DQJ88uer9rM5Vstgoa5u76kjRDTqe7kPxeRjb21nrcISoewoKd+ZaKKsmPw2lax2O2IAatSK/7O7nDc5B0mWFuw+b/FufQaa8kJiyuHBf/3b0zteD1Ys68QTq4H9Bfqq2gS+B/xW3EXrbO8n8/FlJXAkhhBCrbPPMKxJiCbLl00mrpfW0Op20igScGIkRKBfQIj2rFl+9aj4ygjOVx/H+d2BkBmodjhACaDQc7Iu7ea4xy86ki9a8rdYhiQ1GUWCHbY6bdoS5d8LJo+NwKG7y+k542elWjM4QRufrUNL9KNM/Rxv6EYa3CzN0DVTRf7IamgJkkyu6RtNNN6CWC0z+538yUf4cbe99L4q2+Aq+isNBRbOvaEwhhBCiHknSStSVF5NWlqVVWk0mctitGj63jfLgCYA1acJeTzxTScJHx5jdFmLrjm54dqDWIQkhFlw76+G4r8AjkSRvG2hCY/NNTRarz6HBm7aqXBc2+c4pk3/vN3lyEt7fmscHoCiYvm5MTydK/BBK/DBKZhAzsAszuBu0y5swNUpFZg+8sPILeRrwXHs96Wef5tRnPovvllcsmrhqvPYqcEvSSgghhKiWTA8UdeVMpdXSpweGA04URUGfPIHi8qN4m1YzxLqiFSt0PtlH0etg9KqttQ5HCHEOq6lyy5SPuL3CoUC21uGIDUpTwaLn6bAX+Hi0yK92lZkrmfzxd/r4dl+ZYjGPRc9jMStogZ0ona9Dcbehxg+h9X8bbfYgWiU7f0y1N1a3ybtr5y48115PcXiI5GOPYOrSA04IIYS4nKTSStSVbHn+S9dSk1aTiTztofnpCfrECbRID8ombIJeE6ZJx7P9WIplTtx8BYZ18WkVQoja2ZaxszVj5+mmDD1pJ56KPFdFdRRDpzA78uLfe4DtrSqPu/Zy39E0+2c0Xhec5VpPCvX0W6zWhuJuQCsMos48hxl/gbKjE9MSrGoxEnuoE1a5L6Rr5y5QFDLPPEXy0YdpuPWVF50qKIQQQoilk0orUVeq6WmlGwYzc/OVVkY2gZmZXfMm7JtZsH8a/0ic8d0d5IOeWocjhFiEgsItUz4MTB4PpWodjtgknKrBu68L8InWIULWMv8+E+Ge8XZGi2em0JkWDxX3LsquHYCCNXccS/YISmX9LQ7g2rETz3UvozQyTPLRh6XiSgghhLhMJGkl6kq2nMOqWrEt4VfXeKqIbphEAi70CelndTnZU3nanh8gHfExHW2pdThCiEvwlS1cO+uhz1dgyFWsdThiE2m1l/hwywhvb5pgpmzl7rEOvjsbIqcvfERVFExrgLJnLxVnF4pRwJo9jJY7Dsb6eiy6duzEc/1LE1eyqqAQQgixUpK0EnUlW85V1c8KIOx3ok+eAIsNtalzNcOrC4pusOXJk5iaytD1PVVN8xBC1M5VCQ/+ksajkSQVxax1OGITURW41pvmD9oHudGb5KepBv5iZCuPJv1UzIX3CEXBsEUoe69Ct7ejluNY0/tRCyNgGrW9Ay/hiu7Ec/0NlEaGmXvgfoxSqdYhCSGEEBuaJK1EXclWslUkreanEkaCLvTJk2ihLhRV2sCtVPPhYVyJLEPXd1F2Xd4VoYQQq0czFW6dbCBp03k+mKl1OGKDqejGeTfTPHu7lQpvDEzyW82DtNoK/CAe4i+HO/l5yk2psnCcoVC0tlFw7UXXGrAUh7Gk92MWZ6lU9PPHqMF9dUV34L3pFspTk8zddy9GoVCDKIQQQojNQb6Bi7qSLedwW5behN1mUfHZTXIzg9j2vX6Vo9v8PBNJIsfGmekOk2oL1jocIUSVOnJ2tqcc/DyY4dVl6W8llsYwTWaT5ydugqXKBbfbKPCL9iQDqodHCq18c7aVh+ZyvMIxRqflpatYdmInQEAdxV44Tt70Mme0UsHx4hGOsAE16Inu7OpGtdlIPvowiXt/hH9XFNXdsPaBCCGEEBucVFqJulLt9MBQwIk5OwimgRbpXuXoNjetWGbLUycp+JyM7ttS63CEEMt005QP1VT415nHqU0di6gXW60ZftVznLucQ+QMC9/K9vCd7FZm9DPN2ot4mTCiJIxW7GRpVmM0KGMo1L4Rur29A/8dr8bI5xn47Gcpjo3WOiQhhBBiw5GklagrVSWt5vKE/U6MxPyHTDXYsZqhbW6mSefT/WilCgM39GBaZClwITYqt65x65SPvsIk+4PZS58gxAqoClxpS/A+7zFucYwxUvHwfzNR7su3kTdOv5coZMwQ48ZOsmYQrzJNsxrDTu1XGbRFIvhf/VpMw2D4L/6UfH9/rUMSQgghNhRJWom6YZjGQtLKvYRjTaYSeSIBF0Z8BKxOFLdMZ1uuphOTNIwlGN/TSSFw6f//Qoj1LZpyste1laea0kzby7UOR9QBq2LyMvs0H/AeZZ9tloOlRv4xs4Pnio0YCwV/BhYSZgdTRg8mKmGtH2XqGdBru8qgNRhk6yc+geZ0MfLpvyD7wpGaxiOEEEJsJJK0EnWjUClgYi6p0mouXaSiG4QD85VWarANRVa5Wxb3VIq2/YMkWwNM9zbXOhwhxGWgoPC2ppfjrKj8pCVBWVYTFGvEperc4RzlPZ7jhNU8Dxba+Uqml6HKmR9ESriZMHpJGmHIDKKe+i6kB2sYNdiamuj4w/+JNRRm9O6/If3s0zWNRwghhNgoJGkl6ka6PD+NxbOESqvJRB6AsN+BER9FC7SvamyblTVbZOtPj1P02Bm8oRsk8SfEpuHW7Nwx4WfOrvPTkDRlF2srpBX4ZXc/b3INUDJVvpXt4XvZLSQN68IRKimzBbPtDrA40cYeQhl7GCr5msVs8fvp+OQf4uzqZvyLf0fiJ/dimpLwFUIIIS5GklaibsQLCQCCDv8lj51K5AAIO8uYxQxqsG1VY9uMlIrBtieOoxomp26OYlhlsVIhNpuOnJ19cTeHAzkG3OevAifEalIU6LUm+XVvjJvs45yq+Pjn9A6eKESomAs/ktgDGFvegNF0NUpmCHXguxQHn4MaJYs0l5u23/49PFdfw/S3/pXpf/06pmHUJBYhhBBiI5Cklagb8fzppNWle1NNJfJYNIWG8gwAalAqrapimnQ8248zkWXwhh6KPmetIxJCrJIbZrw0Fiw82Jwkp9V+xTZRf6yKyY2OKd7nPUaPNcnPis18NdPLeGXhvUdRMRv3YGx5E1h95J76JuroA1CuzUICqs1Gy4c+QuBVr2HuwfsZu+fzGMXa9t0SQggh1itJWom6MVOIoyoqfrvvksdOJfKE/E7MuYWVAwNSaVWN0PEJgoMzTFzZTqo1UOtwhBCrSDMVXjXup6QaPNicxESmO4na8Kll3uAa4i2ufoqmyjey2/nhiIXK6U7tdj9G52tx7nsj5MZRB76LMherSdWVoqqE3v5OQu/6FbIH9jP8V39OJZlc8ziEEEKI9U6SVqJuxAsJAnY/mqpd8tjJRI6w34kRH0Vx+lCdl050iXmeySStBwaZaw8yuUuSfULUg8aSlRunfQx6ihzx52odjqhzXdY07/XGuMKa4P4JK58+aDKUWUhMKSqO3lswtv4COBpRJ3+GOnIvlGrTly1w+520fvTjlMZGGf6zP6E0MV6TOIQQQoj1SprMiLoxm4/T6Lh01Y9pmkwl8uzcEkRPjEiVVRVsmQJbf3qCgtfJ0PXSeF2IjczqdEF48f2qw44jfGa69fUEGC4P8kQ4zTZHmEbDfskxihovXkPRTfKziRXHLQSAQzG4yzXMjSE3/zZo53OHTG5vM3l1+8L7ks2H0f4alORxlOlnUQe+h9l0NWZgJyhr+5uuZ99VdPz+HzL6uc8y9Kd/QuvHPo6rN7qmMQghhBDrlSStRN2YLSS4onHHJY+by5QoVQwiATvG8BjW6C1rEN3Gp1Z0tj1xHMzTjdcvXdEmhFi/DMXg1PTAovsbi1edt39X3GB8q8l/2vt55bADzbx44jpAiMT0NADbQltXGLEQ59vlN/iDgMJ3B0zuH4XDcZMPt+fxAigKpj+K6W6fr7iafgYzPYDRfBPYL71oy+Xk2NZF5//7R4ze/TeM/s1fEfn1D+B72Q1rGoMQQgixHknSStSFkl4mVUovqdLq9MqBLc4ilAuLVlo5dIMuzYeiuAi1rH5Fkd+wsKNlO8fGT6z6WFUzTTqe7seRzNF/yw5KXketIxJC1IBTV7lmws7P2oo8Hy5xzaQNBam4FLXltCi8s0dhb6PJt/pM/vg7fdzeCq9uV7CoCljdGG13oKT7USafRh38PmbjXszg7jWturKGQnT8P59i7G8/x8SX/p7S+CiNb3oziirdPIQQQtQvSVqJuhAvzE85aXQubeVAgBDz52iLTQ8sFhl76kkUvcTc1OTlCfQi7FumcHRcZK7OKtnRsh3HOe3vvLYAjS3WMxueeA6GZ+Hma+i++splj+U3LOxrmZ8S0egJMrzsKwkhaqUla2HnjMHRpjL+okrPnPXSJwmxBnYFFD65D36cbOD+43Mcjpu8swc6PMp81ZWvG9PVijL1FOrM85jpwfmqK0fjmsWoud20/87vM/m1rxD/rx9QGhuj+f0fRLVferqtEEIIsRlJ0krUhdmFpFVwKZVWc3k0VcFTnKQMqMH67mnlQGX48UfP2hYInZnS451I4h+Jkwl5SeRn4Zxjq2HfMsXw4DEAIne9eflBCyFqakfcypzD4FCohK+oEs7LdGGxPrgsCh+8vZ0dapJv9ZncfcjkjjaTV52uurI4MVtfiZ4eRJ18EnXwvzCDuzEb94C6/I/NmgJkl746YOtb34Iz1Mjkd7/L8J/+f3T8xm9gDVS3Gq/icFDRJNklhBBiY5OklagLs/k4AE1LqLSaTORpanBgJo6juIMoNtdqh7dhuafT+Efi5AJuEp2N0nhdCAGAgsK143Ye7szzdGuB2waduCsyxUmsPU0F9PxZ21RDZ4+vQM9u+M6ghftGNY7Edd7dVaHDvbDKoCuM2XkXzDyPEj8I6VMQvh7F2VTF4BYqzFcaGqUiswdeqC54TwMNt91B6rFH6P/zP6fhlbdjDS294rrx2qvALUkrIYQQG5skrURdiBcSWBQNn817yWOnEjnCARdGYgQ12L4G0W1MzniWwOAMeZ+T2W0hSVgJIc5iNRVuHHPwUGeeJ1uLvGLYgeUSjdmFuNwUQ6cwO3LWNr3QQ3F6CA34ZR9cYXHz7Zkwnz5i5XZ/nDv8cSynH6pqC4rLiSXfB6MPoNua0R2doFy6etAe6gRtZdNj7W3tBF77epIPPUDiJz/Ge+NNOLu6V3RNIYQQYiORnz1FXZgpxAk6AqiXaKhqmiZTiTwRvx1jbnzRJuz1Tpueo/HUFCWPndnuMKjyRVQIcT5PWeW6cTtJu8FzkSImZq1DEuI8u1xZfrdtkH2eNPfPNXL3aCcDhTMLiphWP2XvPgxbBK00gTV9AKWy9Kl+K2Vp8BN47euxhsKkn3iMzHPPYpryXBJCCFEf1rTSKhqNvgv4FGAFPhuLxf72nP37gC8DPuBR4DdjsVglGo12Al8DwkAMeHcsFstEo1E/8HWgC5gG3haLxSai0WgL8M9AM2AAvxeLxR5ckzsp1qV4PrGkflbpXJlCSafDlQO9giaVVuexZQo4TgxSdtiY7olgapL7FkIsrjln4YoZgyOhMv5ihd6ENGYX649LM3hnaJK97gzfmQlxz3g7N3iTvDY4i1M1QNHQnV0Y1iYs+T6s2RfQrWF05xZQVv/jtGp34L/z1aSffpLckcNU5ubw3XSLNGgXQgix6a3Zt81oNNoG/B/gZmAf8MFoNLrrnMO+BnwsFov1AgrwGwvb7wHuicViO4BngT9a2P4nwGOxWGwn8CXg7oXtfwX8IBaL7QPeCXwjGo1KF9g6NluIV7VyYIs2B0gT9nNZc0VCJyYx7Vam/3/27jw+jus68P3vVlVX73tjJwiCG0hKpMRFG7VYkmVb1uItjhM78TgvnrFjZyZx3iTvzbxxnjPJ53lelokny0xiJ46dWHa8r1qsXZZEiaKohaJEEtwBEGujuwH03l3L/AGQIiUS4AKABHi+n099ulF1u84tCQ12n7r33NXNuIa8rYQQM1ud89CW13k9VWM4YF3s7ghxRusCRX5/SQ83RsbYno/yF8c6eK0Y4vjAJteIUA9twDZb0eojePKvourZeemb0jTC191A6NrrqQ30k3vofqzc/MQWQgghLpb5HCJxB/BEd3d3tru7uwh8H/jw8YNdXV0dgL+7u3v71K6vA7/c1dXlAW6Zan9i/9Tzu5kcaQXwr8B7p9r/CPjW1P6DgA8IzcE1iQWgYlUp1Iskz2Kk1XCuBEDczgAKLdY6x71bOIxKnYb9QziaonztGhyPJKyEEGdHodg85CVSU+xoqZL3OBe7S+IyYtnOKZvrvn3fyZvh2twdG+EzTb0ENYtvjLTwteEWRqvaZBtHUTXbqfqvwFUGnlI3WnE/llU9Nc4cXItSikDXGmLvvhPXssg+9ACVI4fnIJIQQghxaZjP6YGtwOBJPw8C185wfAmQAia6u7utt+w/5TVT0wgngIbu7u4fnHSe3wde6e7unr/iA+KSkq3kAM4qaTWSK6MU+ErDuJFGlGHOdfcWhMmE1eTbM726mbDfC4WL3CkhxIJiTBVmf6q9zLNLKtzS52Pmv8pCXBjHdcmMV07Zl6hZb9t3OgEqfNQ/xkt6A9vKTfxleRk3+YbYaI5OlXI0gJVE1AiR+gje+hg5t42yGwMUvkYH5uj+jtnYRPzue5l4+ikmnn2a+ugooc1bUJpM2RdCCLG4zGfSSoNTbjopJutNzXT8rfs56XVvrf58yjm7uro+B3waeMe5djaZlIFZC01Dw+lXBuytHwVgRcsSGlLTrx44Xq7TGA+g5wfxNHec8ZwA2WoWn89DrWyhzcOHRKUUSmkYU1PylFInns9JPG3q/EMZGvcNAJDpasUJelGKOblmpdRJ51VvizHbcU+Nd/qYZxv7bPt1LjHPNva5xTu7mBcS90Jink/s08c795jnEnc2Y55N7DPHu/C4Z4o9FzHDtsbNAwF+0Vbi2fYK7yu9+fdT0xSeGf6mKU2bsc3ZUmrmeLMd82zjznbMs4l9ITFnvJ4zxJ2r65wp9rnG3erJss6X5+FiK09W2thTT/Du4ABLPGVAp0wbdTdBhB5SqpcK40zQjqbrBMKTNac8Hp1weJbrT4W9RD/4PjLPb2f8tddxJ3I0vfsOjEAAgEDAxDfN55jFYrrPakLMNfn9ExfT5fL7N59Jq2PAzSf93AwMvOV4y2mOjwDRrq4uvbu7255qc/x1/VPtjnV1dRlAGMgAdHV1/RmT0wdv6e7uPnWt47OQyRRwHFmZZaFoaAiTTudPe+zwcD8AWsV3xjbH9Q1N0BQxqGcHUUs3TdvesWwqlTrKcXCcuZ/q4rourutgWfaJn48/n5N4jkugdxSe346ra6RXNWP5POBMTq2Yi2t2Xfek87pvizHbcU+Nd/qYZxNb07Sz7te5xDyb2Oce7+xiXkjcC4l5PrFPH+/cY55L3NmMeTaxzxzveLLp/OOeKfZ0MadanFfMSEVx4zEfzy6p8GBomBtHPfhsheO41Gf4m+Y6zoxtzpbrzhxvtmOebdzZjnk2sc83psfQZ76eM8Sdq+ucKfb5xA1i80H/YbqNKE9V2vjGxAqu9GS52TdIULOoY1JmJSE1SlQNkWIPTtYgrzaA0vDWbfL56mxe2gneq7YQiSSYeH4bfd/5AdF33IqnoRGzVCM/w+eehW66z39CzDX5/RMX00L9/dM0dc4DhOZzDPFjwDu7uroaurq6AsAvAT8/frC7u7sHqHR1dd04tevjwEPd3d114BngV6b2/xvgoannD079zNTxZ7q7u+tTI6xuA248n4SVWFwy5SwezSBizvzmGMmV6QyVwHXQLvOVA2vbX6bz2W5Ixhhe0zqZsBJCiFmQqOpsHfBR0Cy2tVWoaXKTSFz6lII15ji/Gd7HNeYIe+px/infxSvVJJP3ORUFt4Ehp4sqQbTMq2i9D0AlM+d983UuJ/Heu0HXyT3yc0p79+C68r4SQgix8M1b0qq7u7sf+C/Ak8CrwLe6u7t3dHV1PdjV1bVlqtmvAV/q6urax2Th9L+e2v9ZJlcb3MPkaK3PT+3/Q+D6rq6uN6ba/HZXV5cCvgA0Ak91dXW9OrVJRe3LVLaSI+FLoNRbZ5OeqlCuU6xYtBuT5c+0+GWatHJdmnf3Uf3BQ+SbY/BL75Gi60KIWZcq69xRaCRvOpOJK+Z2xI0Qs8VUDu/wD/KJUDdNepnHK0u4r7CKAWtyWp6NyajTidN4PdSLaD33U9p1Pzj1Oe2XEU+QuOsezNY2Cjt3cOyrX8UuSAFKIYQQC9t8Tg+ku7v7W7y5qt/xfXed9HwXpxZnP76/B7j1NPuzwPtOE0pqu4oTMuUsSf/ZrxyYIgeajhZtmuuuXXoch/adR0geSeO55ioOL/NytSkjrIQQc2OJ5efaQS8vtFb5kdHLXekIHnf6GwxCXCqSepVfDh6mux7lqUor3yqu4gpPllumpgwSaseJdKLSL1HtfhrNE8Jpuh6Cc3dTTPN6id56O+V9eyi88jKVP/4CLZ/+DP4VK+csphBCCDGXZIkRsehlKjmSvsSM7UZyZQDCtRG0aAtKn9ec7kWn1W2WP7uf5JE0Q1e04f3lu0FWIRJCzLHWosGWIS/HjBI/b8thK5nSJBaON6cMdnOtd4S99Tj/lF/Dy9UktgvoXtzmrYRu+y1QGvqxx9D6n4DaxBz2SRFYewXLPvc5lK7R96dfJPvQA7jzUH9TCCGEmG3yjVQsamWrTMkqk/TNPNJqJFdGAZ7CEFqibe47dwnxjpdY+cQbhIfG6N3SydCV7TNOpxRCiNnSnjd4d6mV3mCVB1tz1JR8uRYLi6kcbvFNThlsNko8UVnCX+7xcmRiMgnraViO0/F+nNRGKA6gHfkxauRFsOemODuAf+lSlv7hfyW0aQujP/ge/X/1l1gTc5csE0IIIeaCJK3EopYp5wBI+mceaTWYKdIU0XELo2jxyyRp5bo0dA/S9chuzHKNwzd1kV1xGU6LFEJcdOtrcW4ditIXrPKjpRkKhtS4EgtPUq/y4cBh7g0cpWTB37zh8q8HHcZLFmg6bvIqnOUfwo0sR+XeQDvyQ1RuH7hzk6jVAwFaPv0ZGj/+Ccrd++j5r/8vpX175ySWEEIIMRckaSUWtUwlC3BWI636RgpcmagAXBYrB5rFCiuf3EPbqz3km2Psu3MD+VYpByeEuHiuGA9wd3+ccdPmB0tHGfXObeFqIeaCUtDlGec/XVnl9lZ4eRT+72/v55lBF9t1wQjgttyE03EvmDG0ke1oR38ChblZ8FopRewdt7H0v3wBze/j2H//M0Z/+H1cy5qTeEIIIcRskqSVWNQylamRVjPUtKrVbYayJVYEJlfZ0RfzSCvXJXF4hK6f78afK9F77XKO3LQay2de7J4JIQQdRR8f6k3iAj9sz9AbmLvpU0LMJa8O93Ro/MFViuWNfn501OVLr7l0j03VbfMlcdrvxG69DVwHvf8xtL5HYOqG26z3p72djs//EZGbbib74P30fvFPqA4MzEksIYQQYrZI0kosatlyDlM3CXoC07brHy3iutCsj4FuosIN89TD+WWUa3Q+u5+lLx6mlAiy784NZDsbJ28LCyHEJSJV9fDh3hSRus79S7K8ES1d7C4Jcd4a/Yo/uHsZn1itKNvw5b0uf7/H4VjBnfz3N9yB0/kBnIZroDKK3vNT1MBTUBuf9b5oPh/Nn/hNWn/7d7CyWXr/5AuMPfEYrisLIAghhLg0XV7Lo4nLzmglS8qXmLGoeN/I5AirqJVGi7eiFtmqecp2iPeM0rqrF8226b+6g/TqZklWCSEuWSFL50O9SR5uHeOp5nEmPBYr5Iu1WEB0DbAnVybWXYfNsQobIvDsiM4jAzp/uRs2Jy3uWWKR9ALR5bihJTDWjTa2H/I9EFkG8StQnuA5BDaw8EzbJLRxE77lyxn++j8x8q37KLy2i+bf+CRGLHbe1yuEEELMBUlaiUUtW8mROJt6VsMFvKaOkR9CW3LFPPRsfmh1i+ShERr2D2GWaxQTQXqvW0k14r/YXRNCiBmZrsbd/XGebprg5WQRJ/0U1ygH011cNxbE4qQcm0pmsk6VXVlJNd0LwFYdNrZpPDUW55lsjFczJjdExnhnLEdIt4EohK5Cr/ajTRyFiaPYZhO2tw20mafyexuWgj590grAiMZo/Z3fY/ypJ0l/79sc/aPP0/Tx3yC8ecuFXLYQQggxqyRpJRYt13XJlLOsjC2fsW3vSJ5VDTpuaQw9vvCLsHtKNVIHBkkdGkGv2+QbI/Rt6STfEpPRVUKIBUVD8Y7hCNGazvONPfR0aLxnIE6yNvOXciEuVX7N4b2JDFsjYzw6lmTbRIwX8xHeER3j5ugYPs3E9ndie1vRK8fQakNotREcbwu2txXU7HyEV0oRu+12AmvXMvgPX2bw7/6W4o030/jRj6H55AaXEEKIi0+SVmLRKlllKnZ1xpUDXdflWLrA3StqUAYtsXCLsPvGSjR2DxDrzaBcl7ElSUbWtFBOhC5214QQ4rwpFBtzIa7esJWvHXuU73eMcstwlLMJAIMAACAASURBVLUT09crFOJSFzVsPpwa4eZIjodyKR4ZS/LMRIxbIjluio7j07zYgRXYdit6tW9y9FV1CNvbhuNtBqXPSj/M5haW/ufPk/nZT8g+eD/l/fto/uSn8K9cNSvnF0IIIc6XJK3EopUpT66+k/RPv3Lg6HiFctWmwzsBgLZQRlq5LmahQnA0f2Lzj5exdY3MiibSq5uphXwXu5dCCDFrVvtb+ZWjKR5tGeOJlnEGAjVuGY7icWUEqbg0WbYDgOu++fx0knqVX0/1c6zq5YmJFA+PpXh6Is6N4Rw3hnP4NC+WdyXKaMGsHcOo9uLWBql7WrE8jaDenDJ7fAKhroDiuRVzb3r3HYRXLmfgvvvo+9MvknzXu2i4806UfnbJMeXzYenec4ophBBCTEeSVmLRylRyADOOtOodnizC3kAOTD8qOHMNrHnnuriVKtGJOlcPWjAwgnrgNZKFqRW1TA+0NMD6dehXrqLB5+VC1z+MOQZXt3SRDCXou+ALEEKI2RG0dd53LMGLyQI7kwVGfHXuHIgTr8lHGnFpcVyXzHgFgETNOvF8On4q3G2Os1n383ylicfGUzwzHmOzd5RN3jQ+pQMdmKSIaoP4aj1o1QEm3CaKbgJQ+Bod0MGpVcns2nNefY/e8R4KO3eQeeQRxl96iciNt2BEozO+LrllIwQlaSWEEGL2yCc8sWhlKlMjrXzTj7TqG8mjFAQqw2jxJTOuNDgnHBdPpYanVEOv2+g1a/Lx+HPLpuYepTbV3DIN3FSUfMJPLeSj7vdM1qqqZGHnC7PSJW/HCH09+2i684Ozcj4hhJgtGorrMmFayiaPtYzx3Y5Rbh2K0pWXGjxicWjWy3wweJRhezJ59Vy1mZeqDWz2ptnkTYMKknZW4KVAVBskoR0j7KYZd5snh3VdIM00iWy9CXNJO/nnnyP7wE8Jbb4G/+qui/M5SQghxGVLklZi0cqUc/gNHwHP9F9i+kYKNMf9uGMDaJ3XzHm/lO3gKdUwS9XJx3INT7mGOukzpq1r2KaO7TGwIn5sj06iqY3g6pXs6X4FxzSINzRQTKfnvL9CCHGpWlry8pGeFI+0jPFY6xjHxqvcPBLBdGR1QbE4NOllPnCa5NUmb5rN3jSoMCNOCD8TRLUhUloP7kAet/E6YN0Fx/ct7cCTaiD//LMUdmyn1n+M8A1b0f1ST04IIcT8kKSVWLSyleyMo6xgMml1RZMGo8W5K8LuuvjGS0QHxjBLtRO7bUOjFvBSaYpSC5jU/Sa218DV3v6Fq6GjA1/XSpwju+emj0IIcRF5/AFonL6N5vPiazz177oP+NVqiudVmhciaQZCFu8ttbHECk57rqoO/mScciZ3gT0XYu4dT16N2D6erzTx/EkjrzZ7R0FFKTsRAipHwkqj9z1EwekDczWYkQuKrQcCRG9/F+XufRRe3kn2Zz8hcsONeNuXztLVCSGEEGcmSSuxaI1WcjT5U9O2KVXqjI5XWLXCgVHQErNfhN2bLxM9lsNbrFL3Goy3xqgFvNQDJrZHn5zWJ4QQlzlHORxJH522TbK68YxtWoFbfD52Nlf5Tugoq3Me1mY86Gco0h6ngZg+fWJLiEtNo17h/cEeRuzhU5JXm7xpNpujoCWIt2/BLRylPvwGmvUGbmwNbvIqMM5/cRalFIE1azGbW5jY9jTjTz2Bb+UqQluuRfN4ZvEKhRBCiFNJ0kosSq7rki1nWZuYfqnmvpHJIuytnuMrB87eSCtPsUq0P4d/oozl0cl2JCkmw6BJkkoIIeZCsqLzzh4/rzXU2J+oMxy02TLoJVqT6YJicTmevErbwzxXaWJ7tZmXp5JX73MMfMmriN5wD7mnv4Ma24eaOIib2IAbXwva+X/8N2Ix4nfeTfG1Vym9vpv68BCRG2/G0zDDMEkhhBDiPEnSSixKhXqRmlM/iyLsk0mruJ1B+SNo/gsbQg9gVOpE+3MEckVsXWNsSYJCY/i0U/6EEELMLsNVbBrx0lLUebmpypNLy1yRMVmZM1DITQOxuDScJnn12m6XW1tdPrAugNu8FTe+Di29E230JdyxfbgNm3HDnec90lvpOqGNmzHbljDx7DPkHn6IwJXrCW64epavTgghhJCklVik3lw5MD5tu96RAiG/ByM/iLrQUVauS7Q/R3hoHFdTjLfEyDdFcQ1JVgkhxHxrKRrc0TOZuNrdUGMgaLFp2Eu4Ln+TxeJzPHk1bI/wkr6CB/sUz3xzP7c1uWxtimIuuQOKg2jpF9EGn8bNvYHTcA0Ems87ptnYROKe91HYuYPS7teoDfQT7liCsTw6i1cmhBDicief3MSilClPJa38M4+0WtoYxMn1X1g9K9cl1pshMjROMRlicH07E21xSVgJIcRF5LUV1w942TxkMuF1eLyjzL5EDQd35hcLsQA16WX+3aoav3uloiPl46c9Ll98xWXbkIvlb8bpuBen+Wawyuh9P0frfxyqY+cdTzNNIltvInLLrdj5Akf+/M8Ze/JxXFfeY0IIIWaHfKMWi1KmMrka1HQjrWzHoT9dZF2sDFYVPdVxfsFcl3hvhnA6z0RTlNyyFI5HP79zCSGEmFUKRceEh3cd9dNS1NmTqvNER5kRvXKxuybEnOkIK/6vezr57DpFygc/OOLy3151eSENVmQ5TueHcFKboDSEdvQnqOHtYJXPO56vYxmJe99PYMUKRr75Dfr/6ktY4+efDBNCCCGOk6SVWJQylRxBTwDfNCvlDGVKWLbDCmMEAL2569wDuS7e148QSueZaI4yviQuqwEKIcQlyGdrXDfo44Z+L3UNfhYe4gn/IDXlXOyuCTFnVkYVv32F4tNrFWEPfOeQy5++6vJSRsdOrMfp/BBurAs11o125IeozC5w6ucVSw8EaP+t36LhY79OuXsvR7/wefIvvzTLVySEEOJyIzWtxKKUKWdnrGd1vAh7qtaHCiZQ4dQ5xXAdB99rBzD7RiYTVm2SsBJCiEtdS9Eg1aNzaInOK94sBzo13jEcZVnxzDc5hFjIlFJ0xWB1FPbk4KE+l28edHmsH+5s97G+8Tq0+NqpYu2v4Ob24iY34Ea7QDu3keNKKeK330FgzTqG/vHLDP6vv6F44800fvRjaD7/HF2hEEKIxUySVmJRylZytAanLy7aO1LA0MHMHkJvW4s6h4ST6zikv/MDzN4hqivbGI96JGElhBALhMdR3FBOclUtxcPePh5YkmNZ3stN6QjRunw0EgubrgF2Gc2xMexTp/xdFYX1EdiV03jwmM4/79doCzjc3ebjiqatEBtFZXajRnZA9nVIXAnhDpQ6u8kZOjY24G1tZen/84dkfvpjsg89QLl7H82f/BT+Vatm/4KFEEIsavLJTCw6juuQqeS4MrV22nZ9IwXWJS0oj5/T1EDXcRj+568xsX0H1dVLqa1ogfTIhXZbCCHEPGu1A3zkaIpdiSI7kwW+FUyzMRdkUyaE6UoFBbEwKcemkjmGXVlJNd172jbrgDXN8EohzKNjCb5ywGSpt8w7Y4q1vuVogSR6tRdtZAfu6G7qvnZcIzHzDTp785v9MAxSH/owwfVXMfTVr9D3Z18kcdc9JO99P8qQryBCCCHOjvyLIRadfK2A5VikfDOvHPi+pgyMgd5ydkkr13EY/vo/MfHcsyTufBdHPVWUXZuNbgshhLgIdBSbsiG6xv0835DnpWSRfZEyW9MRVuVlyqBYvDQFm8N5rg7l2VmI8PhYgq8Nt9LsqXJ7LMuGQBTDzmJU+vCU9uPoQWzvUlwjek6jy/2rVrH0C39M+tvfIvvAzyi+vpuWf/spzJbWObw6IYQQi4XcRhSLTqaSBSAxTU2r8UKViWKNZWoI5QujxVrO6tzp7/4rE889S/L9HyTx3nfPSn+FEEJcfEFb546hGB/qSRKwdR5tHeNH7RmOVTMXu2tCnDPLdnDdyceZNtdx2BwY4z+2HOaXk4M4Lnwr3cKf9S9jW2kZed8Gqt7l4NTxlPaiF17HqeZOf64z9Ef3+2n+Pz5Jy2f+PfXMKD1/8keMPfEYrnumVwghhBCTZKSVWHQy5RwASf+ZR1odL8IeL/eit3SdVT2r/MsvMfbYo8TueBfJe9+PU0zPToeFEEJcMloqJh/uSbIvWub5VJ7/PvAz1jb5uHY0TNA+t6LUQlwMjuuSGa+QqFlkxivn9NoOKnw8MMJBK8IL1UZ+lG3mkVySLd4wV5lh4tooEXcEX2UfFTfIuNNMjdCJ17fNkIQKb96Cf8VKhr7+VUa+dR+F13bR/BufxIjFzutahRBCLH6StBKLzvGRVtOtHtg3UiCuFTAqubOaGljPjDL89a/iXdZJw4d/Zdb6KoQQ4tKjoVg3HmB53seBzUmedfewP1JhUzbI1dkgHql3JRYxpWCVZ4KVxgS9dogXKo38otLKC9VGNpqjbDYbSOkjRNQITfohKm5oKnkVPKvzG7EYbb/7fzL+1BOkv/cdjv7R52n6+G8Q3rxljq9MCCHEQiRJK7HoZMo5wp4Qpm6esU3vSIGrwpPJLb159bTncy2LwS//HTgOLZ/6jBQPFUKIy4TP0fhQ8jraX02zvSHPjlSBN6IlrhsN0zXhR0NWjRWLl1LQYRToCBUYtPy8UG3i+WozO6sNbDBbuMY7RLM+PJW8OkjZDWONHoVo01mcWxG77Z0E1qxl8B+/wuDf/S3FrTfR8NFfQ/f75/7ihBBCLBjy7VssOtlKbtqpgTA50uoDgQy4frRE+7RtR3/8QyqHD9Hy6c9iNjbOZleFEEIsALG6wZ0DcQb9NbY1TPBEyzi74kVuTEdoL3kvdveEmHMtRpkPGEcZtb3sqDbyci3FK7UkV3hauc47SLsxSFiNkH/879D3PIm58V70tnUzll8wW1pZ+p8/T+b+n5B94H5K+/fR9PHfIHjFlfN0ZUIIIS51krQSi85oJUtHeMkZj9fqNoOZIkuaBtCbV6O0M0/zKL6+m9zPHyR6y62Er7l2LrorhBDiIvH4AzDDvQjN58XXOHkjpBNYVmmi257gGf8wP23Psqwe4sZyI832zKNDlC1Fp8XCltKr3BXo40ZniBerjeyuJXi9nmC1p40bvINsvbaZyv5nKT/452gNyzE33oPRcTVKnfmzljIMUh/4JYJXbmDoa/9I/5f+gvANW2n8yEfRw+F5vDohhBCXIklaiUXFcR1ylTE2NW44Y5v+0SIhygRrGfTmd56xnTWWY+irX8FsW0LDr35sLrorhBDiInKUw5H00WnbJKsb39bGC9ymPByKKfYnCnwzUqCloLM24yFWPXOx9s6GZRfcZyEuBVGtzh3+fm7wDvNSLcWr1RTd9TgvHwxz1+YtLK/uobrncSqP/DVatAXfFXfg6diI0s78/gi1NLL8D/6A0UceIfPYY5Re20XTBz9IZMuWM47YqrhlDEfD0mXEoxBCLFaStBKLynh1Atu1ScxQhH25MQKA0XL6elau4zD4j1/BqVZZ8unPoJlnro8lhBDi8qO7itU5D53jBodidQ7E6zzRYdOa11mbMYnWpFi7WPyCmsUtviGu9Y6wq5piV1rn///JYZaGfNzechfrQ0fRs7spPfcN3Bd/jBvrwo2sBMN3xnPqTa0k7rqXie3PMXDffaQfe5zw9TdghCNva1sLezG71kFQklZCCLFYyScqsahkKjkAUr4z17TqGy6w2jsChomWWnbaNtkHfkZ5314aP/ZxvK1tc9FVIYQQi4DHUazJmtx5JMDaUQ8jAZvHl5V5oaXChOlc7O4JMS98yuE63whf+tUVfLhTUazD1w8o/vRIJ9sD76PecisYfrT0TrTD30UNPA2lYXBPP2XWiMeJ33kXoWuvx8qMkv3ZTyi+sRvXkfeUEEJcbmSklVhUMuXJFQET/ulGWuW5wZtGb1qJ0t/+Fih17yPz0x8Tvu4GIjfeNGd9FUIIsXh4HMXarMmKMQ8H4nUOxev0h8o0lXQ6xwyai2eeFiXEYmEaGlubFdc1wWsZeKLf5duH4SFzKTc1d3DDkhzBwn7UxCG0/GFcMzY1+moFvGXVZ6UUga41eNvbye94geLLL1E5fJjw5mswW1sv0hUKIYSYb5K0EovKSHkUhSLhjZ32uOu6jKYzpIKj6M03v+24nc8z+A9/j6exkaaP/5sZV70RQgghTmY6iisyJitzHg7H6hyJWmxvq+KrKzZaI9zKzAXbhVjodKXYmIKrk9A9Dk/2uzzQ6/JwX4yNqWu5uWkT7c5R1Hg32sgLuOmXcMPLcMMdEGgB7c2vKHogSOzW26n29pDf+SJjjz+C2dpKaNMWCLdcxKsUQggxHyRpJRaVfdkDLA0vwaN7Tnt8dLxCizOIAvSWrlOOua7L0L98DadQoO13fg/NJ18shBBCnB/v1MirrqyHoaDNkZjF84E0L5Cmo9XLlWNB2ksmCrk5IhYPpcCwy6fsuzIMV66BwZLimRGdHaMaL6YNlgWXc3NTB1cnRzHyB1GFHpg4CMqAQDOE2iDQipoagWW0NRJovpPi/oPk39hD9v6fUlu5nLZUEjPoo4bUtRJCiMVIklZi0Zio5emZ6OPuznedsU3fSIEVxjCupqM3Lj/19duepfjKy6R++VfwLe2Y6+4KIYS4DGgoWosGrUWDeFMrh8IlXvAPciScJWBpdBS8LCv6aC+aeFwpNSoWNuW6VNO9pz2WAN4fhHf7NV7Kh9k2EeMbh01+pDVwXcRkc7CNRpVBs7JopWFU8RguCseI4BgJHE8cNC+elJ/Y1qsoHzlG4fAR9n/xz0jdcRuRuz+E7pcbjkIIsdhI0kosGm+M7sPF5crU2jO26R3Os9IzgtawHGW8WTuhnk4z8q/fxN+1hvi73jMf3RVCCHGZiTkmd7qNrDlsczhU4XCowqFwhb2xMroDbSUvnUUvKatwsbsqxHmz7OmLpXtwuD6U49pgjoOVANsLcZ4YS/D4WJJmTyvrA3nW+ydo0nPo1uRmWEegcgRHC2DrUTBieJcvwbe0BStbIv3zR8k+u53E3fcQvfkdaL4zr04ohBBiYZGklVg0Xs/sJeaNsiR05uKcg8NZbjMyeFquP7HPdRwGv/oVlKZo/s1/i9LkTrcQQoi5o7uKVXk/q/J+bFwGAzWOBqscCVX4RajKL/q+R7LDYGnJS3vRS0vZxHBlGqG49Dm4ZMYrZ90+SYW7zSw3Gx4O1KN012M8Ot7Ao+MNNGhlVnvG6PKM0ahP4FcT+NwJvPYgnvogjqtRUxH8GzYSf+ctjD7yDOnv/CuZn/2U2G23E7v9DoxodA6vVgghxHyQpJVYFOp2nb3Z/VzTtHH64unpw2i6e0o9q9zPH6Ry8ADNn/wUnmRqHnorhBDicuTxB6jq4GtMnLJ/5dTmFl2y5Rq5VSle7n+NXfESrySK6K6izQrQUQ/RYQVptH1nXQtL2S7lTG72L0aIWRTR6mz2jrLZO0re8bC/HmV/Pca2agvbqi2ktDLLPXk6jQna9AmCKo9P5fGrPPqhJ7EOPUlixXLs9e8k3z1C9sH7yT38EJGtNxJ/952YzVKwXQghFipJWolFYU/6AFW7xvrUujO2KVUsGmp9uH6F3rQSgEpvD6M/+RGhLdcQvv6G+equEEKIy5CjHHpz/eTS6WnbXbfxevQju9mo/Iz6bUaCNiOBEr2BIs8AXguaigatBZ3Gkj7tKKzOhmWzexFCzLHwKQksgwP1GPvrUXZWG9hRbcTEpsPI0+nJs8pboPPqTkJ6gXrfG6iex4n4IHjrUkppk4nntjH+zNMEr7qaxHvuwr9q1cW+PCGEEOdIklZiUXipfzcezcPq+MoztjmWLrDCGKEWbkOZfpxajaF/+DJ6KEzTr39i+hFaQgghxDwzXEVzyaC5NPlxraw7jAQmk1iDIYveqIXmQFNJp6Wg01I08Nryb5lYPMKaxSbvKJu8o1RdjV4rxJF6hCNWmANWjEfK0PySxqaupWy87j10BMvYR3ZiHd5BOHCQwHooFxIU971B36uv4O1YRvTGmwhfez16KHSxL08IIcRZkKSVWPBc1+Wlwd2sSazE1D1nbHegJ80NRhq97XYARn/4PWqDA7T93u/LBxchhBCXPL+t0ZHX6Mh7cHAZ9TsMhiwGQjaDIRvcGsmyRmvBoK2gE7CkRqNYPLzKYZVnglWeCVwXRh0fvU6UXrONh18e4cGdI8RCJhtXrWLTpq2silZwj+7Ec/hFApEs5VFFeXyYkW/dR/q73ya0cRORG28msO4KqWcqhBCXMElaiQVvsDhMupjhjiXvOGMbx3U5vPs1btEdfEvXUXzjdcYee5TY7XcQvOLKeeytEEIIceE0FI1lncayzoa0y7jXOZG82t1YY3cjJMoaOTfDUsMhZOkXu8tCzBqloEGv0Oqtc/f1K/G1d7LrmM3L+9Nse32QJ1/pJ+A1uGrlCjatv54rElW8h54jdOA5aqNlyjlF8bVXyL+4AyOeILL1RiJbb8JsarrYlyaEEOItJGklFrzXR/cCcGVq7Rnb7D2ao6HaBwFQ4TaG/va/YTa3kPrwR+arm0IIIcScUChiVZ1YVWddBgoeh2Nhi/6QzZP+IVgBLSUPW5SPpG4TtCWBJRaXoM/ghisbuOHKZqp1mz1Hsry8P82rB0d5/o1hvB6dq1ddyXXX3spacwD/wW3Uj7xMJQvl8RLZB+8n+8DP8HUuJ7R5C6HNWzAbGi/2ZQkhhECSVmIR2J3ZS2e8nZj3zMsa/+LVfq73jkCslfT3f4Cdz9P2Hz6HZprz2FMhhBBi7oXqGmuyJmuyEG1qYU9tiIORCj/TjsBKaCx7WFb0sqzgI1U1znolQiEuRcFoDE+tBrURAEzguma4rjmFdWOSfYNFXjg4zouHRnlhzzABr8Y1y29k67W30lnbT61nJ7XhAcoZRTU3wOj3v8vo97+Lb0kb0Q3riVy1Hm9DwykxNa8X5/iMQo+PGt75vWghhLiMSNJKLGiFWpEj4z380hXvPWOb8WKNXQdG+Gg8TWViOfkXd5D8wIfwdSybv44KIYQQF0HC8bIlG2ZLNox3ZTPbsgc4GqqyI1lgR6pAqK6xrOCjXjqGUu60KxEKcSnSbJvcjheoZAZPezwJ3AW8pwX2lwO8WgyzfZ/FL/ZqhPQQVwW3cE1wkPZQP8H6KHYFKllFeWyY4Qf7GX7w5+ihAGZTErMxgREKkLz+BmqlLAChlRvBI0krIYSYK5K0EgvaG5l9uLhsbt0A9unbbNs9SIvKYI/Xye0/QHD9BhJ33TO/HRVCCCEuskYCJxJYJd3maLDK0VCFfdEyrw8/irFS0VoyaS2btJVMGioedBmFJRYIy3ZmbLPKW2CVt8AHYoruSpBdxQgv5CNsm4gR1FZxhT/PNf5jLGs/RrAli1OF8phGOVenfKiP8qE+tKAfp+xgdDRgJGME5uHahBDiciZJK7GgvZ7ZS8QM0xlvJzNafNtxx3V5+tUB7giPMPYqeFJJmv/db8kqMUIIIS5rAVtn3USAdRMBLOXi3rye5/Ztoz9QY3soD4DhKJrLnhNJrMaKKSOxxCXJcV0y45Vzek0LZVrMUW73aByuhzloRXm1GGFHcT0m61jpyXG9eZSVDf2kGidw6lDKKsq5OrltL8Cz4IaDVG8YInT9zXjbl6KUvD+EEGK2SdJKLFiWY7Ens59NjRvQ1OmTUPt6cmSzeZald+OiaPsPv4cekHtiQgghLg8efwCm6klXdfA1Jk7briOxgjADUIJS2eKYUeKYUeSYt8iOQAFSoLmKBttLs+WnxQ7QbPlJ4UU/aYCLsl3Kmdw8XJkQs8NUDmvMcdaY41iuotcKcbAe5aAVZU99Czqb6NTH2OLtZXXDAKmmHK4F5ZyiPFYn++jjZB95DE9DA6HN1xDevAXvsk5JYAkhxCyRpJVYsA6NHaViV6ZdNfCpV/r5pcwzOPkqDe+8CrOldR57KIQQQlxcjnI4kj4KQJwGcun0adslqxtPtAPwAJ1AJwY1TWfUb5P1O+R8Nd7wVdilTSamDBviFY14RSde1bgyuBQvrhR3FwuSoVyWe/Is9+S5w4UBO8jBeoQeK8y3S1cDVxNUNW5uHGdN4ABtLQMYlRqVMahMTJB7+CFyP38QIx4nvOVaQluuwde5XEb4CyHEBZCklViwXs/sxdAM1iRWnfb4eLGG54Wn6BzvI7xUI3rvJ+a5h0IIIcTCZzqK1qJB69QsfBeXvOmS89nk/C5Zr82BRB1XwQscwBtSpKoeUhUPDVUDkxIOLpokssQCoilYYhRZYhSBQYqOQY8V4qgVZttYIz+vNgAu6+MlrluVZRn9RMeOUk3XqGRz5B57mNyjD6OHQ4Q3X0P4uhvwrVgpCSwhhDhHkrQSC5Lruuwe3cPq+Aq8unnaNq8++AtuGX0JXwLi73kvWuj0UyKEEEIIcfYUikhNEalpdBY0HMfBVi4TpoORijNQH2fUW+f1WBFbg8d4BW0VxOoGiapBvDb5GKrlsJWLLnWyxAIQ1CzWmWOsM8dYftd6DvVnOZC26SmG+eaxMOVaOzrXcFVigi3LRumwj6H1H6OSKTD2iycZe+pJdJ+H4KoOwps2E9pwDZrhOev4yufD0mWVQiHE5UeSVmJBGimlSZcz3N5+82mPVwYHSTz2PQh4iK314t149zz3UAghhLh86K4iXtXprCVYPTK5z8ElZ1o4HXEO5AbIeS1GfHUOhiug4OH+H6NWQaSuE57aQtbUc+vNnyWpJS41uuOgdm1nNXDbqtXYrQEGiopDEwaHJuLcNxinZK3CoyzWt4+wYcUQbRP9GJlxJl4/yMTug2jGd/A2+PG2NWK2LAV/EswInKFOa3LLRghK0koIcfmRpJVYkHZn9gKctp6VXSrR8z++BLg0rqrju+YjKFOKrwshhBBz7eTC7wABIKLFaNdMqAN1qOOQ06toazrpPrSLrF4j763T669T1MpvO6fP0Qm4On7HIOAaBBx98vH4c0tHYC9/xAAAHuZJREFUz5XwWxpeR0k9LTHvNKVYEoIlIXhHq8JxXYZKcGjCw5F8Kz8ptJJzN0HCpTU5xk3WAdrH+3GG85QHe1BGD74YeOMaZmMMFUiCN4HrTYA3DmeYVSCEEJcDSVqJBen10b20hVpI+OKn7HfqdYb+4e9xs6P4VgfwpOJ41t52kXophBBCXF5OLvx+3JkKwG+5eiv5vu1MLpGiAV5sZVI2XMqGS8njUDJcqoZLVbep6hYTukvV41LT33Ky2OSDcsFva/gtDb+tkVJ13Iby5L6p/Z7KCOMei4Cl45FRXOIC6Rpgvz3ZutQ3ud02lcQdr0FPUeNoIczLhS382HMNTsxmZekYG0sHWZIbpjxqw8EsZjSHL+bii03lq4wgxcKLGA3t6IlWjMQStFACZfqpIaOvhBCLmyStxIJTqpc4NH6Udy+99ZT9drHIwP/8a8r7uznUupxboofxXvcJlC6/5kIIIcRCoLuKUF0RqgPlt2am3uTgUtNdqjrEk42M58cp6w5lw6asO5R0h7Lh0KsK5GNV6pp74rUPDj4Ayyefe21FwNIJWhrBkx4DlkbA1vDbuozgEtNSjk1hpHfGdgawAljhAeJgx2Co7mWg5uNobTMv1Dxo4+N0FAZYVegjNlZgAqj5fRhxRazQg7/vDU7Ucdc9GKl2VKoTPdmBlupAi7fJ514hxKIjf9XEgrMn043jOqdMDaym0/T96f9HbXiY9Ds+wIbiw1jxZRidWy5iT4UQQggxFzQUPlvhs2Glp5G6P/TmQRewJreIP8bE2Bh1HMqaRUnZhNZ1cbR7F0XNoqAsClqdotdiwG9RVHWc0+SmNBcCrjE5RdHRMSo2XkdhOhpee/LRdBRee/LRdDSCVKhoDubpTigWDcd1yYxXzuu1PiosZ5zlOuAH1wcTjUGGrKs5VLIxJ8ZoyI/QNJBhYgAySicbiOFGfARjLi2eKqHRZ1D245Mn1HS0+BL01FK0VMdkMiu5FOWR0VhCiIVLklZiwdmd2UvIE6Qj0g5ApbeHI3/zP7ArFdo+9x/Z//STbNDK+G/+GErJB0UhhBBiMTvdlMTjTjc18Yqrt5LuHyfIyeW3NMDExUNVh4ruUDOgqrtUdZeKfnyaYp16QGci6FBTDlVlYymX0xuFVZPPvD334Vnl4nV1TFfDN/XodXW8Jx4nn5sn73OmHm1FJTN2wf+txKVNKYiqOlGzDiYQM4EllGstqEQLE4eO4M+OEh7sRw3C2D6NPd4U2UCMYMJLQ9ylqVwkevgl9O5njp8VLdaMluxAT3VMJbOWonyhaXoihBCXDklaiQVlf+4gr47s5rqWzWhKo/jG6wz+3d9iBIO0/6f/Qk/R4TpeYTyxjnDz6ovdXSGEEEIsIIrJ0Vs+W4fa6dus7FjDwZ59J352cKnrUNfcqQ3quosZDTFezFPXXBIrVnCs9yB1zaKuueSm2lv6ZHvn9AvGvckFT0LhcRSGM/nocU/+WcPjTj5PqV5KiQJDE3sZi5QwHIUx1VY/3aOr0Fxk+uMlzDR1mq9exUDT5MJC9VoNlc4SUSZtBw7RPnIAlZ1MnpbQOGaGmTDbUEEv0Sgk7RrxsdfxHtzO8fu5WiiBnmjDSLSiRxrRo03o0QaUxzdzhzw+qaUlhJg3krQSC8ax/ABffu1faAik+MCKuxjf9izD//I1zJZWNvzxHzLhmOTv+ysasQnf+msXu7tCCCGEuAxoKLz2ZH2sk8X9YXJjk9PGtly7hZ0v9p3xHLaaSnZpLvWpRJaludQ0F0uHYChKuVyirjmTbZR74njJcKgr60TSzNKK0ABktkPLWV6EC8ZU0mvycernU/a9/fF44sycSpx5830M+2qYU9MkfbaGLsmw2WeauG3NrLzlRjIHduNaFlZ2gnp2jPLIGG56nGh2HM9wHjU8+ZIcYKNRNTzg0TDNMj5zH17PbjQDNA2UBso0Uf4AKhBCBcKYqTb0QBQ9FEMLhFCahi/Zgq7PXdJK+XxYc3h+IcTCIkkrsSBkyjn+166v4jO8fHbDb1J++DEyP/4hgbVX0PLZf483mWToxV2sKL5KX2Qj61NtF7vLQgghhBBnRXcVug0+W0H97cdXh5dRV6XJel329OcKxaPkcjmarr2WIzufo4aDpRws5WLhTCa8lIPF5L761PP61PHj7apWdfK55lI5/nrNxVKTCTVLe/u0yIcO/wg6Tt1n2pPJq+Obf+oxaGmELJ2gpROaKoCvy2qO58RxHI4OTkz9ZEA0NblNTUutWxYqX4B8kVqxSrVQwypXoVxFr1WoFCoEbAuNk/9f1qa249NR954aVIHSFcrQUYaBMjyTm+lFGSbK45nab5w4rvl8aH7/iU2Z3mlLeCS3bISgJK2EEJMkaSUueYV6kf+56x+pORaf6/wo1W98h/yO7YRv2ErzJ34TZRi8tG+Y/gf/mVW6QeOtH7nYXRZCCCGEmDXT1e16q+N1vJrtzWSHB9523DO1TWf10nXU66Vp27i4WLjUlE1NTdb4ath4NUd27aSmbKrKoawsysqe3AyLisdmTFmUNOu0tcD81pvJrHBdJ1TXCR9/PrW64+U6jTEYjdH61p01m9aWC7tRW647jI1XGM/XmCjUyBfrFEs1yqU6VEsErAIRSoQoE3IrBNwqPqeG162jORauU8GtglsC2wHHUeCA6wDOGeq9aRqabyqJFQigh0Lo4TB6KIwejuDaM2RmhRCXFUlaiUtaza7x97u+TmE8w6fSyyl/6y8ASL7/gyTueR8AD23vYeezz/O5SA/WFfeSamm+mF0WQgghhFjQziVJdrI2b4KegQw+wAdE39ZCB3RcPFgalA2XsuFQNlx80Shj1SJFwybvsRnw16jppyY9NBdCUwmscF0n6hgMTnQTZIyyWSdc1/G4MxUIW5g022bo2W2n7POsXM7QwcMXfG4FxKY2YLIIvDn5tPNdd7Lr4ScoOinSroeC40GLNzFue6jWaiirgMcq4ncLxLUicb1AQisS14qEVAXXBqcOdn3ysVbXsSwD23Jx6iWcbIFa/zFwnBP9yf70RxjJJGZDI57GJsyWFszmFsyWFox4AqUtzv/HQojTk6SVuGTZjs3XX/0Xktv3cu++OlSfJ3zDVpLv/yCeZIpq3eafH3wD7+Fn+Gz0NfBFiV17z8XuthBCCCGEmIZC4XHAU1NEapMJiE5vI5WR7CntqppDYSqJdfzx+PP+QI39Rpkdmecmc2Gdk68xnOPTEdWJqYjHN3OqHpd+/NFV6FP1u/SpzZjad+K5q9Av40L1hq6IaHUiWh0oA7B0aQrbtqZa+AE/jpsiX4eJumK8rjhWVxRqNvV6GVeroBklPN4SfrdMRCsR00pEtRJhVUExldiqglWBWlVRr01QOTZB6cA+sN5MaCnDwNOQxGxuxmxtx7tkKWZzC56mJjTTnO//PEKIeSBJK3FJciyLx3/4V2x55g1CZYfghqtIfejDeJe0A5AZr/DT79/P7bVnaAqO4+m4EvuaD1I26uCephjEHPBypiWuhRBCCCHEufD4A9B46r7Tjtayp7YqoEFk80Z6D+5isJBlQqtT1mwqyqasWZQNm1FlU1Z1KtqFTTk7Obnl7/s+7rIyhqMIaEXsJZXJFRxPu7Ljm6s7njh+ShsNY4ElxVyrTv8ZRnhpvGXU1skHNHBdH2U3yKjj4ajloeRqOK4DWGh6HTNUwx+38TtFIqpEUJUJ2RWMqoVdAatsYZWGKe8ZpvDKrlNCKL8HIxrETMbwNjVgNjVhNjahx1No/iDK9KPMAJh+lJLRWkIsFJK0EpeU+mia4u7dHHvoxyzLTlBqS7Lk1z5FYHXXiTYH9+4n+9R9fFjvpR5K4b/ld9HXrGPbwVeglJ63vl4fWzZvsYQQQgghFrPzmZKoaRqbnI2Qy+NLj+F7e4upzYPL5KqMjgJHubS3raBSLWHhYp9UhP5/t3fvQZKV5R3Hv+85p7vntpcBFnZd3EUxPCCJbgDFREAjamkwUYJKCV6ohIulmKQSNSRCLBJNyliCQkEqiSAxeDch5QWolIKyiAQwIiuXRxflsstlF4a9zLUv5+SPc7qnZ5jZnZ2d7ZnZ/X2qurrPe94+7zmzz7498/T7vqdBc6H6jEZrAfu8rLmv0rOC7cNPU09SCIF6JWYkpFRpUAtpvrj9FGt2TStjQlIrKe7I2H63xnu23s7wodtbI8VKWeCx8ASDy4aLkWOM39kxG0+YlYtk2kK5i2MI0BMa9NBgRTw6RY2IlSedzOYnNjPWgM21iKFaYLiaUa9VWdrfT310mHR4B9HIdroHB+gb3UnP2DCVsTHYvp3alm0M3f9IW6MQVyCp5M9xBUJXQuipQE8PoaeX0NVLqHQTV3qJu3pIunuJy11EpQokZUJShqQy4bneVSMbq+YHjuJdLi4vIrOnpJXMq7RWY+SXv2B4w31sv++npE9vAWDHspiBPzyO33/LhUTFvPWBZ5/j6du+ycotP2Z5HDP2m2/j4BNPI8QlCNX5vAwRERERWcACgXI6vtVPhY1P/XpCnaR47O6+dScccyL33HMDAP0r+nlu61by5Ni4jPE7LTai/HnVyiMYqQ5TK+7aWA0NamStRFctpFRJqUUptTjfHi3u/rhl9AnGlo217gSZ2wkzXMo1yhgf9VUkxUpTjAQrF0mz5vTI7YMPs7VvZMIUysBOni3XWnXy+vmUymgOkmO9y5Yz3fLyK484gnTSAu8j1QbbhutsH66zbajOtsEx6s88TfTsU8Q7BigNbqc8vJPK2DCVwRGSRgOoF48hMrZCEgilQFzKKJUy4hJEJYgSCDFEcfGcjL8eavsnTwmkUYk0KpPGJbKoRBaXIW4mvMrF3RUrxKUyUalCXC4epQqhmRyLi7qlLihVCKUuQlKBchch0p/ucmDqaOSb2VnAxeQ3Lfmsu181af864PPAUuA24P3uXjezNcD15IOGHTjb3QfNbDnwJeDFwFbgne7+lJmVgWuAE8gnX5/l7g915CJlWlmW0di5k9rWLYw9/hhDP9/A0AP3Q7VKIwpsOjTh0eP6aBz1IuyoE1lXNn5yx13s2Pxrsuc2cQwPc3g0yi8qx3L0W/+Y3v6DW8eujY4RdnOXm31wQYRGlbAn36SJiIiIyH4vEChlUGqEfDojsJpeNj75eOsOjj27PUpzpBic8KbTuefmPFGWkZEGWLLiYJ4ZeIY0QCNAI8poNBNlAZYvO5jhwUFqUZYnwqJswqMapYzEKbWo3qpTnzxrbuttPD+D9GxrDbHnnXGRvGqO/mqtFVYktsbXEmuuGda2fliRLNs08DOee9BbdVr10sDqpw7nmcefaK09lrQdPwJ6CBN/rn1AX1/xIjdWT6mP1mhUa2S1BtTqVJYsobp9G/HYGMngKKXqKFGWsitpiEijiDQKZCGQRQGiOoQ6IRohBIhCRggZUfuDlDhkRIF8FfyQPzVfQz4i7XntEUhDRBYSsigmC3HxnECc5Bm14jkkCSEuEeISUVIiShKiUpkoSYhLZeJSmVDpJpRKhDghlJL8OUkISUwolYnKZUKlQlSpEJUrhEolr68RZdJhHUtamdlq4JPA8eSz0O8ws1vd/YG2atcD57r7nWZ2DXAe8M/A1cDV7v5VM7sEuAT4K+ATwHp3P83M3gN8DjgT+FNgyN2PMbNTgOuAV3XkQg9QWaNBOjJCY2SYdHiYxtAQtWe2UtuyhbEtTzO25UkaW5+BsfERUUN9ZR5eG/PIqmVUX7CSNdEhvHRHxtJNW+nd+HWWhB0sK/rEWlxiqG8N29edznFHH/u8zjIZq7NisLMdaLS0wbYtT9O/YkVH2xURERGRA1cgT+JUspie52WZxh110Gpq5Ulf6qbFYxopE6dKHnb88Tz2f3fl0ySLaZOVJT3sGNz5vGmVWSmhWh8rplQW5cX7Gsn4NMsxsuI9jYlTMptfBA/cDYdNd4YDcMQ0u1pJMSaMFsuTWhOTY63yDOK0xIqXrCGrrqJETBJiEgKlakapllKqNVje08vI0wPE1TpR8SjXG2TVOqHRINRTslqdrN4grdfz142ULEvJ0ow0y2ikKWTZ+CNNydKUkGVARsiyCa/zf+tJF9ha1G1+ZABxBHEMSf4IcQKlBJISUSkhlMt5cqtcJqpUiHt6CF09xN1dxdTLbpKebpKeXqKuHkJ3N1FXN1GlK0+aKSkmk3RypNXrgVvcfQDAzL4JvB34u2J7LdDt7ncW9a8DLjWzzwOnAG9rK/8hedLqtGIfwFeAq8ysVJT/LYC732ZmK8xsjbs/NoPzjAGiaHH/Z0mHh9n5k7vJ6vUJnWPW6iTzjjDL0in2A1lKljYYHh1ix/Bz+W1oq2MwNkZab0AjI2o0SOoZca1B0ph6tFEjwM7emJ3LI3auPowdvTE7eyOGu2O6Kik2XOXkoSpLnx0ABkgz2N61hJHlR7Ot/wUsXbWG/tVrCX0HccguFkzMGg0GNmzYBz/J6cUvNHqX9dO1ZCm91fru37CXyr19JOUKvcv6AfZ5u+XePnqX9U9os2lftd1sE+hIu+3tTdfmTNoOUUSW7vrbuNm0OZO297S9mba5N+3uTZuzaXuq9mbT5p60O5dtzqTt6dqDPP72pt3p2t5Vm7B31zpVu7trb67abG+73NXZNsu9fXTFtd3G2Fy3OZPPjdm2Obn/m+rfcbp25+o6dxU7U7W9t+3OJFYnt92pNtvb3Zs296S9dnGp3NE2m/3fbD6vZnuNwKx/H5urNpt213bS18Pm7U/Oqr2mJaUe0tExIqBcPJZ2l4iHIE+pxK26a1cfyaObH551W80RZMecdCob7vg+achHjKUha40m6166hB1DO/O1ycjo7z+Uan2MRshoUKxFViTBUsgTZm2JsQb5Mauk4+8JGZvGHm0/kVxzvmh3sT3dnMXWiLikNdIsyvKfT/tflK3XxfFLXV1F/zler/kqKyplQJzE5Dms4m+11nHy7YziTzkysixvp1WvSIKR5vUCGVmWEjIIGURpRtzIiNIsT+wV20maUa5nJA1I6hmlNCWpQ6mRktTz/Ukd4gYkjYy4nuV33azXiEerxEMZoZE33UyxzeR/SRbydeeyEPKF+6P8dVqMZMsCZFEgBOjOQj4kLUQUw9ry0W4AIZC1hrGF/KcZxrcn/IOE8feMD3ljvG6zbMLzFEIgO2g59HW33tuqWry3lYqcuGN834SEXX69S7rLHHpQD/GadUQ9E29xsBhzFm3nHO+qXrswIfD3ITP7a6DX3S8uts8FXunu5xfbvwN82t1PKrZfAtwIvAa4290PL8oTYNjdy2Y2VhyzXuzbBLwSuBU4zd03FuW3Ax919ztmcKonAevn6rpFRERERERERKTlZOD2mVTs5EiriPG8NeRpxXQG+yeX0/a+yanF6d4zua1duZv8B/gk8zn2UkRERERERERk/xEDq8jzLjPSyaTVJvJkUNNK4IlJ+1dNsX8LsMzMYndvFHWa79tc1NtUjMBaAjzbdqyHJx1rJsaYYcZPRERERERERERmbI/mEU+/SNDc+x5warG+VA9wBnBzc6e7PwqMmtmri6L3ADe5e418ut6ZRfl7gZuK1zcW2xT71xf1W+VmdhIwOsP1rEREREREREREZAHoWNLK3TcDHyNfb+pe4MvufpeZ3WhmJxTVzgYuN7OHyO9LekVR/gHgfDN7gHy01sVF+SXAq8zs/qLOB4vyK4FKUX4FeQJMREREREREREQWiY4txC4iIiIiIiIiIjJTnZweKCIiIiIiIiIiMiNKWomIiIiIiIiIyIKjpJWIiIiIiIiIiCw4SlqJiIiIiIiIiMiCk8z3CYjsDTM7i/xukiXgs+5+1TyfkuyHzOxW4FCgVhRdABzJFLFnZq8HLgO6ga+5+8XPP6LI7pnZUuAO4C3u/sh0sWVm64DPA0uB24D3u3vdzNYA15PHrgNnu/vgPFyKLEJTxN8XgJOAoaLKpe5+w57GZaevQxYfM/s48M5i87vu/lH1f9Ip08Sf+j/pCDP7O+DtQAZc4+6Xqf/TSCtZxMxsNfBJ8g+RdcD5ZvbS+T0r2d+YWQCOAl7u7uvcfR2wiSliz8y6gWuBtwLHAK8wszfP06nLImZmJwK3k8ceu4mt64EL3f0oIADnFeVXA1e7+9HAPcAlnbsCWcwmx1/hBOCUZj9Y/ME2m7gUmVbxx9kbgd8m/3w93szehfo/6YBp4u901P9JB5jZa4DXAS8jj7kPmdnLUf+npJUsaq8HbnH3AXcfAr5JnpkWmUtWPP+Pmf3MzC5k+th7JfBLd/918Y3a9cA75uWsZbE7D/gg8ESxPWVsmdlaoNvd7yzqXVeUl4BTyGOzVd6hc5fFb0L8mVkPsAa41szuM7NLzSxiD+Oy0xchi9KTwF+6e9Xda8CD5MlT9X/SCVPF3xrU/0kHuPsPgd8r4ulQ8llxy1H/p+mBsqi9gPzDpelJ8g8QkbnUD3wf+BD5VMAfAF9j6tibKiYP78hZyn7F3c8FMGvmTKeNrenKDwF2tE1HUCzKjE0RfyuBW4APANuB7wB/AgyyZ3Epskvufn/ztZn9Bvk0rStR/ycdME38nQy8FvV/0gHuXjOzS4EPA99Av/8BSlrJ4haRz/dtCkA6T+ci+yl3/zHw4+a2mV1DPq/8E23VmrGnmJR9ZbrYmmk5KBZlltz9V8DpzW0zuxJ4L/k3uXsSlyIzYmbHAt8FPgLUmThVVf2f7FPt8efujvo/6SB3/7iZfQr4Nnnfd8D//qfpgbKYbQJWtW2vZHwqjcicMLOTzOzUtqIAPMLUsaeYlH1lutiarnwLsMzM4qJ8FYpFmSUz+y0zO6OtKJDfmGJP41Jkt8zs1eQjnC9y939H/Z900OT4U/8nnWJmRxeLq+Puw8B/kY/yO+D7PyWtZDH7HnCqma0o1ts4A7h5ns9J9j/LgU+bWZeZLQHeB7ybqWPvfwEzs5cUHxZnATfN14nLfmXK2HL3R4HR4pdsgPcU5TVgPXBmUf5eFIsyewH4rJn1F+tlnA/cwB7G5XycuCwuZvZC4L+Bs9z9q0Wx+j/piGniT/2fdMqLgX8zs4qZlckXX/8X1P8paSWLl7tvBj4G3ArcC3zZ3e+a37OS/Y27f4d8iPhPgZ8A17r7j5gi9tx9FDgH+E/gAeAhxhdCFJm13cTW2cDlZvYQ0AdcUZR/gPzOlg+Qr8lxcSfPWfYf7n4f8I/Aj8jj7153/8os41JkVz4MdAGXmdm9ZnYveYydg/o/2femir/fRf2fdIC738jEvznuKJKn53CA938hyyZPeRQREREREREREZlfGmklIiIiIiIiIiILjpJWIiIiIiIiIiKy4ChpJSIiIiIiIiIiC46SViIiIiIiIiIisuAoaSUiIiIiIiIiIgtOMt8nICIiInKgMLNHgLWTikeAR4F/dffLZ3icFwKvLm6HjZn9ANjo7ufO2cmKiIiIzDONtBIRERHprE8Bq9oerwC+B1xmZmfO8BjXAm9q2/4j4C/m8iRFRERE5ptGWomIiIh01qC7P9W2/RTwITN7M3Am8LUZHCO0b7j7wByen4iIiMiCoKSViIiIyMJQBWoAZvZa4FLgeKAEPAhc5O43m9l1wKlFvfe5e2ifHmhm5wAXAZcBfwMcDNwNfNDdHyzedxhwNfAGYKioewHwCXe/rgPXKiIiIrJbmh4oIiIiMo/MrMfMPgIcA3ypWK/qJmA98DLy6YOPAV80szLwZ8W+r5NPL5zKi4GzgTOAN5Kvo3Vl0V4EfAdYDbyOfGrh2cV7RERERBYMjbQSERER6axLzOyi4nUAuoD7gHe5+7fM7EjgEuAz7p4BmNnlwC3AYe7+uJlVgZFJ0wzblYD3t42suhL4ZLHvNcAJwJHu/qti/7uBDXN9oSIiIiJ7QyOtRERERDrrKmAd+dS/S4Fh4AvNOwG6+8PAF4E/N7NrzWw98O3ivfEM28iAX7ZtbwPKxevjgC3NhFXR5s+LOiIiIiILhkZaiYiIiHTWgLtvLF7/k5mlwOfMbKu7f8XMjgVuB+4Evk++MHuJ8cTVTKTuXp9U1ly8vc7UX1yGKcpERERE5o2SViIiIiLz6zPAW4GrzexW4BzgMXd/c7OCmV1QvGwmlrK9aO8+4BAzO7IY1YWZGbBsL44pIiIiMuc0PVBERERkHhXrVp0HdANXAFuBI8zsDWa2tlhv6h+K6pXieSfwIjNbO4v2biW/m+AXzex4M3sF8B/F7r1JhomIiIjMKSWtREREROaZuz9Enph6B/AQcAP5tMD7gAuBC4Ah8jsJQr4ulgEPmtnKWTR5BjBAfhfCbwHXkyesqrO/ChEREZG5FbJMX6iJiIiIHCjM7BDgROBmd28UZSuBJ4FT3H39fJ6fiIiISJPWtBIRERE5sDSAbwCXmdm1QB/w98BG8sXfRURERBYETQ8UEREROYC4+3PAHwCnAhuAH5LfUfAN7l6bz3MTERERaafpgSIiIiIiIiIisuBopJWIiIiIiIiIiCw4SlqJiIiIiIiIiMiCo6SViIiIiIiIiIgsOEpaiYiIiIiIiIjIgqOklYiIiIiIiIiILDhKWomIiIiIiIiIyILz/4siIjifbMjKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "labels = [\"all\", \"male\", \"female\", \"unknown\"]\n",
    "for index, df in enumerate([df_aggs, male_df_aggs, female_df_aggs, unknown_df_aggs]):\n",
    "    ratings_2019 = df.iloc[28, :]\n",
    "    ratings_2019.isnull().sum()\n",
    "    ratings_2019_clean = ratings_2019.dropna()\n",
    "    count = 0\n",
    "    ratings_list = []\n",
    "    for row in ratings_2019_clean:\n",
    "        if row == \"NA\":\n",
    "            count += 1\n",
    "        else:\n",
    "            ratings_list.append(row)\n",
    "    sns.distplot(ratings_list, label=labels[index], bins=30)\n",
    "    plt.title(\"Distribution of ratings\", fontsize=20)\n",
    "    plt.xlabel(\"Rating\", fontsize=15)\n",
    "    plt.ylabel(\"Percentage\", fontsize=15)\n",
    "plt.legend()\n",
    "plt.savefig(\"rating_distributions.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## df_aggs to sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aggs = pd.read_csv(\"df_aggs.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aggs.rename(index=str, columns={\"Unnamed: 0\": \"date\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>date</th>\n",
       "      <th>12582912</th>\n",
       "      <th>12582913</th>\n",
       "      <th>12582914</th>\n",
       "      <th>12582916</th>\n",
       "      <th>12582917</th>\n",
       "      <th>12582918</th>\n",
       "      <th>12582919</th>\n",
       "      <th>12582920</th>\n",
       "      <th>12582921</th>\n",
       "      <th>...</th>\n",
       "      <th>12582904</th>\n",
       "      <th>12582905</th>\n",
       "      <th>12582906</th>\n",
       "      <th>12582907</th>\n",
       "      <th>12582908</th>\n",
       "      <th>12582909</th>\n",
       "      <th>12582910</th>\n",
       "      <th>12582911</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1460.779968</td>\n",
       "      <td>401.268542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1992</td>\n",
       "      <td>1451.0</td>\n",
       "      <td>2356.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1522.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>...</td>\n",
       "      <td>417.0</td>\n",
       "      <td>619.0</td>\n",
       "      <td>1528.0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>1462.073849</td>\n",
       "      <td>397.810118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1993</td>\n",
       "      <td>1434.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>1213.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>...</td>\n",
       "      <td>545.0</td>\n",
       "      <td>619.0</td>\n",
       "      <td>1501.0</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>1469.781914</td>\n",
       "      <td>397.131193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1994</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>...</td>\n",
       "      <td>618.0</td>\n",
       "      <td>619.0</td>\n",
       "      <td>1477.0</td>\n",
       "      <td>1646.0</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>1473.684966</td>\n",
       "      <td>397.726332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>...</td>\n",
       "      <td>618.0</td>\n",
       "      <td>619.0</td>\n",
       "      <td>1481.0</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>1472.955166</td>\n",
       "      <td>399.040183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 290437 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   date  12582912  12582913  12582914  12582916  12582917  12582918  12582919  \\\n",
       "0  1991       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1  1992    1451.0    2356.0       NaN    1125.0    1535.0    1203.0    1237.0   \n",
       "2  1993    1434.0    2259.0    1213.0    1125.0    1535.0    1203.0    1237.0   \n",
       "3  1994    1485.0    2259.0    1125.0    1125.0    1535.0    1203.0    1237.0   \n",
       "4  1995    1485.0    2259.0    1125.0    1192.0    1535.0    1203.0    1237.0   \n",
       "\n",
       "   12582920  12582921  ...  12582904  12582905  12582906  12582907  12582908  \\\n",
       "0       NaN       NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "1    1522.0     882.0  ...     417.0     619.0    1528.0    1377.0    1765.0   \n",
       "2    1574.0     882.0  ...     545.0     619.0    1501.0    1291.0    1355.0   \n",
       "3    1574.0     882.0  ...     618.0     619.0    1477.0    1646.0    1355.0   \n",
       "4    1574.0     882.0  ...     618.0     619.0    1481.0    1760.0    1355.0   \n",
       "\n",
       "   12582909  12582910  12582911         mean         std  \n",
       "0       NaN       NaN       NaN  1460.779968  401.268542  \n",
       "1    1056.0     870.0     607.0  1462.073849  397.810118  \n",
       "2     756.0    1017.0     607.0  1469.781914  397.131193  \n",
       "3     756.0    1017.0     607.0  1473.684966  397.726332  \n",
       "4     781.0    1017.0     607.0  1472.955166  399.040183  \n",
       "\n",
       "[5 rows x 290437 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aggs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aggs.to_csv(\"dates_uids.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = [[] for _ in range(len(df_aggs['date']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df_aggs['date'])):\n",
    "    ratings[i] = df_aggs.iloc[i, 1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([          nan,           nan,           nan, ...,           nan,\n",
       "        1460.77996845,  401.26854195]),\n",
       " array([1451.        , 2356.        ,           nan, ...,  607.        ,\n",
       "        1462.07384857,  397.81011759]),\n",
       " array([1434.        , 2259.        , 1213.        , ...,  607.        ,\n",
       "        1469.78191437,  397.13119251]),\n",
       " array([1485.        , 2259.        , 1125.        , ...,  607.        ,\n",
       "        1473.6849663 ,  397.72633198]),\n",
       " array([1485.        , 2259.        , 1125.        , ...,  607.        ,\n",
       "        1472.95516569,  399.04018256]),\n",
       " array([1485.        , 2259.        , 1211.        , ...,  607.        ,\n",
       "        1471.15156811,  400.25859023]),\n",
       " array([1485.        , 2259.        , 1235.        , ...,  607.        ,\n",
       "        1467.1391498 ,  401.63868689]),\n",
       " array([1485.        , 2259.        , 1251.        , ...,  607.        ,\n",
       "        1461.41577372,  402.55863253]),\n",
       " array([1485.        , 2259.        , 1243.        , ...,  607.        ,\n",
       "        1454.13803061,  402.50945216]),\n",
       " array([1485.        , 2259.        , 1292.        , ...,  607.        ,\n",
       "        1447.79120246,  401.80819094]),\n",
       " array([1485.        , 2259.        , 1316.        , ...,  607.        ,\n",
       "        1440.97235243,  402.24018351]),\n",
       " array([1485.        , 2259.        , 1399.        , ...,  607.        ,\n",
       "        1439.0977587 ,  402.57214676]),\n",
       " array([1485.        , 2259.        , 1389.        , ...,  607.        ,\n",
       "        1436.01701197,  402.75299467]),\n",
       " array([1485.        , 2259.        , 1389.        , ...,  607.        ,\n",
       "        1437.28468468,  402.28687575]),\n",
       " array([1485.        , 2259.        , 1458.        , ...,  607.        ,\n",
       "        1445.23674979,  398.61107189]),\n",
       " array([1485.        , 2259.        , 1451.        , ...,  607.        ,\n",
       "        1451.08822972,  397.47237748]),\n",
       " array([1485.        , 2259.        , 1443.        , ...,  607.        ,\n",
       "        1455.75717669,  397.03387025]),\n",
       " array([1485.        , 2259.        , 1429.        , ...,  607.        ,\n",
       "        1460.37728521,  397.30520217]),\n",
       " array([1485.        , 2259.        , 1414.        , ...,  607.        ,\n",
       "        1464.97329293,  398.12485066]),\n",
       " array([1485.        , 2259.        , 1433.        , ...,  607.        ,\n",
       "        1469.79602273,  400.22975344]),\n",
       " array([1485.        , 2259.        , 1427.        , ...,  607.        ,\n",
       "        1473.78892045,  401.4899576 ]),\n",
       " array([1485.        , 2259.        , 1451.        , ...,  607.        ,\n",
       "        1478.16912879,  402.55708024]),\n",
       " array([1485.        , 2259.        , 1453.        , ...,  607.        ,\n",
       "        1481.50681689,  403.85952726]),\n",
       " array([1485.        , 2259.        , 1485.        , ...,  607.        ,\n",
       "        1485.0506389 ,  405.70052937]),\n",
       " array([1485.        , 2259.        , 1414.        , ...,  607.        ,\n",
       "        1487.63691434,  406.76020174]),\n",
       " array([1485.        , 2259.        , 1414.        , ...,  607.        ,\n",
       "        1489.39078175,  407.01243418]),\n",
       " array([1485.        , 2259.        , 1433.        , ...,  607.        ,\n",
       "        1491.45575849,  406.40606556]),\n",
       " array([1485.        , 2259.        , 1425.        , ...,  607.        ,\n",
       "        1492.50099328,  405.59684752]),\n",
       " array([1485.        , 2259.        , 1425.        , ...,  607.        ,\n",
       "        1494.85687258,  403.58740048])]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "uid_date_ratings = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "uid_date_ratings['uids'] = df_aggs.columns[1:]\n",
    "for i in range(len(df_aggs['date'])):\n",
    "    uid_date_ratings[df_aggs['date'][i]] = ratings[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uids</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12582912</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1451.0</td>\n",
       "      <td>1434.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12582913</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2356.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>2259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12582914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1213.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>1251.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>1427.0</td>\n",
       "      <td>1451.0</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1414.0</td>\n",
       "      <td>1414.0</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>1425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12582916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12582917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1518.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       uids  1991    1992    1993    1994    1995    1996    1997    1998  \\\n",
       "0  12582912   NaN  1451.0  1434.0  1485.0  1485.0  1485.0  1485.0  1485.0   \n",
       "1  12582913   NaN  2356.0  2259.0  2259.0  2259.0  2259.0  2259.0  2259.0   \n",
       "2  12582914   NaN     NaN  1213.0  1125.0  1125.0  1211.0  1235.0  1251.0   \n",
       "3  12582916   NaN  1125.0  1125.0  1125.0  1192.0  1192.0  1192.0  1192.0   \n",
       "4  12582917   NaN  1535.0  1535.0  1535.0  1535.0  1535.0  1535.0  1535.0   \n",
       "\n",
       "     1999  ...    2010    2011    2012    2013    2014    2015    2016  \\\n",
       "0  1485.0  ...  1485.0  1485.0  1485.0  1485.0  1485.0  1485.0  1485.0   \n",
       "1  2259.0  ...  2259.0  2259.0  2259.0  2259.0  2259.0  2259.0  2259.0   \n",
       "2  1243.0  ...  1433.0  1427.0  1451.0  1453.0  1485.0  1414.0  1414.0   \n",
       "3  1192.0  ...  1192.0  1192.0  1192.0  1192.0  1192.0  1192.0  1192.0   \n",
       "4  1535.0  ...  1518.0  1518.0  1518.0  1518.0  1518.0  1518.0  1518.0   \n",
       "\n",
       "     2017    2018    2019  \n",
       "0  1485.0  1485.0  1485.0  \n",
       "1  2259.0  2259.0  2259.0  \n",
       "2  1433.0  1425.0  1425.0  \n",
       "3  1192.0  1192.0  1192.0  \n",
       "4  1518.0  1518.0  1518.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uid_date_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "uid_date_ratings.to_csv(\"uid_date_ratings.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking at the number of tournaments per state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_df = pd.read_sql_query(\"SELECT state, COUNT(*) FROM events_dim GROUP BY state ORDER BY COUNT(*) DESC LIMIT 100;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>state</th>\n",
       "      <th>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NY</td>\n",
       "      <td>24598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>20329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TX</td>\n",
       "      <td>16094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PA</td>\n",
       "      <td>10730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NJ</td>\n",
       "      <td>8560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OH</td>\n",
       "      <td>8320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AZ</td>\n",
       "      <td>7180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>IL</td>\n",
       "      <td>6867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MA</td>\n",
       "      <td>6591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>6299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GA</td>\n",
       "      <td>5453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MN</td>\n",
       "      <td>4689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>VA</td>\n",
       "      <td>4395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MI</td>\n",
       "      <td>4364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NC</td>\n",
       "      <td>4239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TN</td>\n",
       "      <td>4157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MD</td>\n",
       "      <td>4115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MO</td>\n",
       "      <td>3867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KY</td>\n",
       "      <td>3817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>IN</td>\n",
       "      <td>3752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LA</td>\n",
       "      <td>3451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NV</td>\n",
       "      <td>3244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CO</td>\n",
       "      <td>3109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>WA</td>\n",
       "      <td>2989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>WI</td>\n",
       "      <td>2871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CT</td>\n",
       "      <td>2768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>OR</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>IA</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>UT</td>\n",
       "      <td>1684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>AL</td>\n",
       "      <td>1421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>NOR</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>JAPAN</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>SRB</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>BERMUDA</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>TUR</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>ISL</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>THA</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>$100 (NY</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>AUS</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>BEL</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>DEN</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>CHN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>N</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>POL</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>AZE</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>BRA</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>FIN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>UZB</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>VI</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>BUL</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>CRO</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>MEDIUM) (IL</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>ROU</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>ISD</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>LAT</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>POR</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>UAE</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>AUT</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>GAME/30) (AZ</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>HUNGARY</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           state  COUNT(*)\n",
       "0             NY     24598\n",
       "1             CA     20329\n",
       "2             TX     16094\n",
       "3             PA     10730\n",
       "4             NJ      8560\n",
       "5             OH      8320\n",
       "6             AZ      7180\n",
       "7             IL      6867\n",
       "8             MA      6591\n",
       "9             FL      6299\n",
       "10            GA      5453\n",
       "11            MN      4689\n",
       "12            VA      4395\n",
       "13            MI      4364\n",
       "14            NC      4239\n",
       "15            TN      4157\n",
       "16            MD      4115\n",
       "17            MO      3867\n",
       "18            KY      3817\n",
       "19            IN      3752\n",
       "20            LA      3451\n",
       "21            NV      3244\n",
       "22            CO      3109\n",
       "23            WA      2989\n",
       "24            WI      2871\n",
       "25            CT      2768\n",
       "26            OR      2015\n",
       "27            IA      1873\n",
       "28            UT      1684\n",
       "29            AL      1421\n",
       "..           ...       ...\n",
       "70           NOR        27\n",
       "71         JAPAN        25\n",
       "72           SRB        25\n",
       "73       BERMUDA        22\n",
       "74           TUR        19\n",
       "75           ISL        17\n",
       "76           THA        16\n",
       "77      $100 (NY        14\n",
       "78           AUS        14\n",
       "79           BEL        14\n",
       "80           DEN        14\n",
       "81           CHN        13\n",
       "82             N        13\n",
       "83           POL        13\n",
       "84           AZE        12\n",
       "85           BRA        12\n",
       "86           FIN        12\n",
       "87           UZB        12\n",
       "88            VI        12\n",
       "89           BUL        11\n",
       "90           CRO        11\n",
       "91   MEDIUM) (IL        11\n",
       "92           ROU        11\n",
       "93           ISD        10\n",
       "94           LAT        10\n",
       "95           POR        10\n",
       "96           UAE        10\n",
       "97           AUT         9\n",
       "98  GAME/30) (AZ         9\n",
       "99       HUNGARY         9\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like I messed up getting the states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_df = pd.read_sql_query(\"SELECT * FROM events_dim;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "202409"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(events_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = []\n",
    "for s in events_df['state']:\n",
    "    adding = s.split()[-1][-2:]\n",
    "    if len(s) == 2:\n",
    "        states.append(s)\n",
    "#     if adding == \"-3\":\n",
    "#         print(s)\n",
    "#     states.append(adding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199129"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016204813027088715"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len(events_df) - len(states)) / len(events_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>event_id</th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>event_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>199101282140</td>\n",
       "      <td>1991-01-28</td>\n",
       "      <td>CA</td>\n",
       "      <td>MISSION OAKS WINTER ACTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>199101310270</td>\n",
       "      <td>1991-01-31</td>\n",
       "      <td>MA</td>\n",
       "      <td>JANUARY TOURNAMENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>199102182150</td>\n",
       "      <td>1991-02-18</td>\n",
       "      <td>CA</td>\n",
       "      <td>FEB CARMICHAEL TOUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199102260280</td>\n",
       "      <td>1991-02-26</td>\n",
       "      <td>MA</td>\n",
       "      <td>FRAMINGHAM FEB 4 SEC SWISS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>199103170680</td>\n",
       "      <td>1991-03-17</td>\n",
       "      <td>NY</td>\n",
       "      <td>H.C.E.S. #40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       event_id        date state                  event_name\n",
       "0  199101282140  1991-01-28    CA  MISSION OAKS WINTER ACTION\n",
       "1  199101310270  1991-01-31    MA          JANUARY TOURNAMENT\n",
       "2  199102182150  1991-02-18    CA         FEB CARMICHAEL TOUR\n",
       "3  199102260280  1991-02-26    MA  FRAMINGHAM FEB 4 SEC SWISS\n",
       "4  199103170680  1991-03-17    NY                H.C.E.S. #40"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# events_df_cleaned = pd.DataFrame()\n",
    "# events_df_cleaned['event_id'] = events_df['event_id']\n",
    "# events_df_cleaned['date'] = events_df['date']\n",
    "# events_df_cleaned['event_name'] = events_df['event_name']\n",
    "# events_df_cleaned['state'] = events_df['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_df_cleaned = pd.read_sql_query(\"SELECT * FROM events_dim WHERE length(state) = 2;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(events_df_cleaned) == len(states)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Got the state incorrectly about 1.6% of the time, let's get rid of the incorrect ones for now, but maybe will rewrite the scraping during the summer. This happened because there is no set format for entering in the state of a tournament in the database unfortunately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_df_cleaned.to_sql('events_dim_cleaned', con=conn, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation between chess players in each state and number of tournaments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_state = pd.read_sql_query(\"SELECT COUNT(uid), state FROM users GROUP BY state ORDER BY COUNT(uid) DESC;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>COUNT(uid)</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51125</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49802</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35380</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28796</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23794</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   COUNT(uid) state\n",
       "0       51125    CA\n",
       "1       49802    NY\n",
       "2       35380    TX\n",
       "3       28796    IL\n",
       "4       23794    FL"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_state.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All users, vs. the joins in Tableau reduces the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_state = pd.read_sql_query(\"SELECT COUNT('event id'), state FROM events_dim_cleaned GROUP BY state ORDER BY COUNT('event id') DESC;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNT('event id')</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24598</td>\n",
       "      <td>NY</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20329</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16094</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>10730</td>\n",
       "      <td>PA</td>\n",
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       "      <th>4</th>\n",
       "      <td>8560</td>\n",
       "      <td>NJ</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   COUNT('event id') state\n",
       "0              24598    NY\n",
       "1              20329    CA\n",
       "2              16094    TX\n",
       "3              10730    PA\n",
       "4               8560    NJ"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>OH</th>\n",
       "      <td>8320</td>\n",
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       "      <td>7180</td>\n",
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       "      <th>FL</th>\n",
       "      <td>6299</td>\n",
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       "      <th>GA</th>\n",
       "      <td>5453</td>\n",
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       "      <th>MN</th>\n",
       "      <td>4689</td>\n",
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       "      <th>VA</th>\n",
       "      <td>4395</td>\n",
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       "      <th>MI</th>\n",
       "      <td>4364</td>\n",
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       "      <th>NC</th>\n",
       "      <td>4239</td>\n",
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       "      <th>TN</th>\n",
       "      <td>4157</td>\n",
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       "      <th>MD</th>\n",
       "      <td>4115</td>\n",
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       "      <td>3867</td>\n",
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       "      <th>IN</th>\n",
       "      <td>3752</td>\n",
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       "      <th>LA</th>\n",
       "      <td>3451</td>\n",
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       "      <th>NV</th>\n",
       "      <td>3244</td>\n",
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       "      <td>3109</td>\n",
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       "      <th>WA</th>\n",
       "      <td>2989</td>\n",
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       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>2871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>2768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>IA</th>\n",
       "      <td>1873</td>\n",
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       "      <th>UT</th>\n",
       "      <td>1684</td>\n",
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       "      <th>AL</th>\n",
       "      <td>1421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>AE</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VI</th>\n",
       "      <td>12</td>\n",
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       "      <td>8</td>\n",
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       "      <td>7</td>\n",
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       "      <th>QC</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>LO</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>NA</th>\n",
       "      <td>2</td>\n",
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       "      <th>QU</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>FM</th>\n",
       "      <td>1</td>\n",
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       "      <th>GE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NB</th>\n",
       "      <td>1</td>\n",
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       "      <th>NF</th>\n",
       "      <td>1</td>\n",
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       "      <th>NN</th>\n",
       "      <td>1</td>\n",
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       "      <th>OG</th>\n",
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       "      <th>ON</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>OS</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pa</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       COUNT('event id')\n",
       "state                   \n",
       "NY                 24598\n",
       "CA                 20329\n",
       "TX                 16094\n",
       "PA                 10730\n",
       "NJ                  8560\n",
       "OH                  8320\n",
       "AZ                  7180\n",
       "IL                  6867\n",
       "MA                  6591\n",
       "FL                  6299\n",
       "GA                  5453\n",
       "MN                  4689\n",
       "VA                  4395\n",
       "MI                  4364\n",
       "NC                  4239\n",
       "TN                  4157\n",
       "MD                  4115\n",
       "MO                  3867\n",
       "KY                  3817\n",
       "IN                  3752\n",
       "LA                  3451\n",
       "NV                  3244\n",
       "CO                  3109\n",
       "WA                  2989\n",
       "WI                  2871\n",
       "CT                  2768\n",
       "OR                  2015\n",
       "IA                  1873\n",
       "UT                  1684\n",
       "AL                  1421\n",
       "...                  ...\n",
       "AE                    40\n",
       "VI                    12\n",
       "JA                     8\n",
       "CN                     7\n",
       "GU                     7\n",
       "KO                     6\n",
       "QC                     6\n",
       "NP                     3\n",
       "NT                     3\n",
       "AS                     2\n",
       "LO                     2\n",
       "NA                     2\n",
       "QU                     2\n",
       "FM                     1\n",
       "GE                     1\n",
       "HN                     1\n",
       "IR                     1\n",
       "JP                     1\n",
       "KA                     1\n",
       "MP                     1\n",
       "NB                     1\n",
       "NF                     1\n",
       "NN                     1\n",
       "OG                     1\n",
       "ON                     1\n",
       "OS                     1\n",
       "TE                     1\n",
       "US                     1\n",
       "ca                     1\n",
       "pa                     1\n",
       "\n",
       "[82 rows x 1 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_state.set_index('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7</td>\n",
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       "      <th>KO</th>\n",
       "      <td>6</td>\n",
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       "      <th>AS</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LO</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NA</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QU</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FM</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NB</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NN</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OG</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ON</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pa</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       COUNT('event id')\n",
       "state                   \n",
       "NY                 24598\n",
       "CA                 20329\n",
       "TX                 16094\n",
       "PA                 10730\n",
       "NJ                  8560\n",
       "OH                  8320\n",
       "AZ                  7180\n",
       "IL                  6867\n",
       "MA                  6591\n",
       "FL                  6299\n",
       "GA                  5453\n",
       "MN                  4689\n",
       "VA                  4395\n",
       "MI                  4364\n",
       "NC                  4239\n",
       "TN                  4157\n",
       "MD                  4115\n",
       "MO                  3867\n",
       "KY                  3817\n",
       "IN                  3752\n",
       "LA                  3451\n",
       "NV                  3244\n",
       "CO                  3109\n",
       "WA                  2989\n",
       "WI                  2871\n",
       "CT                  2768\n",
       "OR                  2015\n",
       "IA                  1873\n",
       "UT                  1684\n",
       "AL                  1421\n",
       "...                  ...\n",
       "AE                    40\n",
       "VI                    12\n",
       "JA                     8\n",
       "CN                     7\n",
       "GU                     7\n",
       "KO                     6\n",
       "QC                     6\n",
       "NP                     3\n",
       "NT                     3\n",
       "AS                     2\n",
       "LO                     2\n",
       "NA                     2\n",
       "QU                     2\n",
       "FM                     1\n",
       "GE                     1\n",
       "HN                     1\n",
       "IR                     1\n",
       "JP                     1\n",
       "KA                     1\n",
       "MP                     1\n",
       "NB                     1\n",
       "NF                     1\n",
       "NN                     1\n",
       "OG                     1\n",
       "ON                     1\n",
       "OS                     1\n",
       "TE                     1\n",
       "US                     1\n",
       "ca                     1\n",
       "pa                     1\n",
       "\n",
       "[82 rows x 1 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_state.set_index('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_events_state = pd.merge(users_state, events_state, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>COUNT(uid)</th>\n",
       "      <th>COUNT('event id')</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>51125</td>\n",
       "      <td>20329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>49802</td>\n",
       "      <td>24598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>35380</td>\n",
       "      <td>16094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <td>28796</td>\n",
       "      <td>6867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>23794</td>\n",
       "      <td>6299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>18729</td>\n",
       "      <td>8560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>17878</td>\n",
       "      <td>10730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>17854</td>\n",
       "      <td>7180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>16111</td>\n",
       "      <td>8320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>14714</td>\n",
       "      <td>4364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>13876</td>\n",
       "      <td>4395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>12806</td>\n",
       "      <td>3752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>12000</td>\n",
       "      <td>6591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>10847</td>\n",
       "      <td>2989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <td>10496</td>\n",
       "      <td>4157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <td>10307</td>\n",
       "      <td>4239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <td>10228</td>\n",
       "      <td>4115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KY</th>\n",
       "      <td>10152</td>\n",
       "      <td>3817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>9548</td>\n",
       "      <td>5453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>6786</td>\n",
       "      <td>3867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>6658</td>\n",
       "      <td>2768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>6260</td>\n",
       "      <td>3109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN</th>\n",
       "      <td>5859</td>\n",
       "      <td>4689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NM</th>\n",
       "      <td>5698</td>\n",
       "      <td>1009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UT</th>\n",
       "      <td>5332</td>\n",
       "      <td>1684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>5229</td>\n",
       "      <td>2871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>5214</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>5018</td>\n",
       "      <td>3451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>4812</td>\n",
       "      <td>1421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NV</th>\n",
       "      <td>4366</td>\n",
       "      <td>3244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KS</th>\n",
       "      <td>3197</td>\n",
       "      <td>585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WV</th>\n",
       "      <td>3081</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <td>2910</td>\n",
       "      <td>1170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NE</th>\n",
       "      <td>2900</td>\n",
       "      <td>860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH</th>\n",
       "      <td>2642</td>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS</th>\n",
       "      <td>2365</td>\n",
       "      <td>617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>1795</td>\n",
       "      <td>737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>1712</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RI</th>\n",
       "      <td>1648</td>\n",
       "      <td>1217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>1547</td>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>1507</td>\n",
       "      <td>1396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VT</th>\n",
       "      <td>1506</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HI</th>\n",
       "      <td>1355</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>1329</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ND</th>\n",
       "      <td>691</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>674</td>\n",
       "      <td>586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <td>645</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WY</th>\n",
       "      <td>484</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AE</th>\n",
       "      <td>423</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP</th>\n",
       "      <td>350</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ON</th>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VI</th>\n",
       "      <td>140</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GU</th>\n",
       "      <td>67</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QC</th>\n",
       "      <td>67</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MP</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NB</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NA</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NP</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       COUNT(uid)  COUNT('event id')\n",
       "state                               \n",
       "CA          51125              20329\n",
       "NY          49802              24598\n",
       "TX          35380              16094\n",
       "IL          28796               6867\n",
       "FL          23794               6299\n",
       "NJ          18729               8560\n",
       "PA          17878              10730\n",
       "AZ          17854               7180\n",
       "OH          16111               8320\n",
       "MI          14714               4364\n",
       "VA          13876               4395\n",
       "IN          12806               3752\n",
       "MA          12000               6591\n",
       "WA          10847               2989\n",
       "TN          10496               4157\n",
       "NC          10307               4239\n",
       "MD          10228               4115\n",
       "KY          10152               3817\n",
       "GA           9548               5453\n",
       "MO           6786               3867\n",
       "CT           6658               2768\n",
       "CO           6260               3109\n",
       "MN           5859               4689\n",
       "NM           5698               1009\n",
       "UT           5332               1684\n",
       "WI           5229               2871\n",
       "OR           5214               2015\n",
       "LA           5018               3451\n",
       "AL           4812               1421\n",
       "NV           4366               3244\n",
       "...           ...                ...\n",
       "KS           3197                585\n",
       "WV           3081                799\n",
       "SC           2910               1170\n",
       "NE           2900                860\n",
       "NH           2642                868\n",
       "MS           2365                617\n",
       "AR           1795                737\n",
       "SD           1712                456\n",
       "RI           1648               1217\n",
       "ID           1547                492\n",
       "DC           1507               1396\n",
       "VT           1506                303\n",
       "HI           1355                380\n",
       "AK           1329                379\n",
       "ND            691                300\n",
       "DE            674                586\n",
       "MT            645                810\n",
       "WY            484                186\n",
       "AE            423                 40\n",
       "AP            350                 85\n",
       "ON            152                  1\n",
       "VI            140                 12\n",
       "GU             67                  7\n",
       "QC             67                  6\n",
       "MP             22                  1\n",
       "NB              9                  1\n",
       "AS              6                  2\n",
       "NA              5                  2\n",
       "NP              3                  3\n",
       "CN              2                  7\n",
       "\n",
       "[63 rows x 2 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_events_state.set_index('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_events_state = users_events_state.rename(index=str, columns={\"COUNT(uid)\": \"uid_count\", \"COUNT('event id')\": \"event_count\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "#users_events_state.plot.scatter('uid_count', 'event_count')\n",
    "plt.scatter(users_events_state['uid_count'], users_events_state['event_count'])\n",
    "plt.title(\"Number of Events vs. Number of Chess Players in each State\", fontsize=20)\n",
    "plt.ylabel(\"Number of Events\", fontsize=15)\n",
    "plt.xlabel(\"Number of Chess Players\", fontsize=15)\n",
    "plt.savefig(\"events_chess_players_state.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>uid_count</th>\n",
       "      <th>event_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>uid_count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.961486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event_count</th>\n",
       "      <td>0.961486</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             uid_count  event_count\n",
       "uid_count     1.000000     0.961486\n",
       "event_count   0.961486     1.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_events_state.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Qf10sjW7wY4C"
   },
   "source": [
    "# Close Connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WlxicEaCrurM"
   },
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "chess_exploration.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
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