diabesties_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diabesties Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "from sqlalchemy import create_engine\n",
    "import MySQLdb\n",
    "import src.data_processing as dp\n",
    "import src.join_tables as jt\n",
    "import src.add_dates as ad\n",
    "import src.feature_engineering_entries as fee\n",
    "import src.feature_engineering_usage as feu\n",
    "import src.feature_engineering_demo as fed\n",
    "import src.data_cleaning as dc\n",
    "import src.run_models as rm\n",
    "from statsmodels.discrete.discrete_model import Logit\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, auc, confusion_matrix, precision_score, recall_score \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option('display.max_columns', 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import and Process Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process Demographics Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import demographics data\n",
    "engine1 = create_engine('mysql://root:hello@localhost/prod_diabesties_user_space')\n",
    "df_demo = pd.read_sql_table('user_metadata_transposed', engine1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create dataframe with column names\n",
    "df_demo_cols = pd.DataFrame(df_demo.columns.values) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This is commented out to prevent overwriting the excel worksheet once column actions have been specified. \n",
    "# writer = pd.ExcelWriter('excel/demographics.xlsx')\n",
    "# df_demo_cols.to_excel(writer,'demographics')\n",
    "# writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import dataframe from excel with data processing instructions for each feature\n",
    "df_demo_cols = pd.read_excel('excel/demographics.xlsx', sheetname='demographics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#run processing function - drop columns, convert data types\n",
    "df_demo_1 = dp.process_data(df_demo, df_demo_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3085 entries, 0 to 3084\n",
      "Data columns (total 7 columns):\n",
      "user_id          3085 non-null object\n",
      "birthdate        3085 non-null datetime64[ns]\n",
      "college_type     3085 non-null category\n",
      "diabetes_type    3085 non-null category\n",
      "ethnicity        3085 non-null category\n",
      "gender           3085 non-null category\n",
      "diabestie_id     3085 non-null int64\n",
      "dtypes: category(4), datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 85.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#check output\n",
    "df_demo_1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process Glucose Log Entries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import glucose log entries\n",
    "engine2 = create_engine('mysql://root:hello@localhost/project')\n",
    "df_entries = pd.read_sql_table('entries', engine2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create dataframe with column names\n",
    "df_entries_cols = pd.DataFrame(df_entries.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This is commented out to prevent overwriting the excel worksheet once column actions have been specified. \n",
    "#write to excel\n",
    "# writer = pd.ExcelWriter('excel/entries.xlsx')\n",
    "# df_entries_cols.to_excel(writer,'entries')\n",
    "# writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import dataframe from excel with data processing instructions for each feature\n",
    "df_entries_cols = pd.read_excel('excel/entries.xlsx', sheetname='entries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#run processing function - drop columns, convert data types\n",
    "df_entries_1 = dp.process_data(df_entries, df_entries_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51182 entries, 0 to 51181\n",
      "Data columns (total 13 columns):\n",
      "id                51182 non-null int64\n",
      "user_id           51182 non-null int64\n",
      "glucose           49452 non-null float64\n",
      "carbs             5921 non-null float64\n",
      "insulin           15850 non-null float64\n",
      "insulin_type      51182 non-null category\n",
      "tag               51182 non-null category\n",
      "entry_time        51182 non-null datetime64[ns]\n",
      "note              51182 non-null int64\n",
      "created_at        51182 non-null datetime64[ns]\n",
      "mood              15049 non-null category\n",
      "diabestie_mood    119 non-null category\n",
      "private           51182 non-null category\n",
      "dtypes: category(5), datetime64[ns](2), float64(3), int64(3)\n",
      "memory usage: 3.4 MB\n"
     ]
    }
   ],
   "source": [
    "#check output\n",
    "df_entries_1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process App Usage Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import app usage entries\n",
    "engine3 = create_engine('mysql://root:hello@localhost/prod_diabesties_analytics')\n",
    "df_usage = pd.read_sql_table('events', engine3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create dataframe with column names\n",
    "df_usage_cols = pd.DataFrame(df_usage.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This is commented out to prevent overwriting the excel worksheet once column actions have been specified. \n",
    "#write to excel\n",
    "# writer = pd.ExcelWriter('excel/usage.xlsx')\n",
    "# df_usage_cols.to_excel(writer,'usage')\n",
    "# writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import dataframe from excel with data processing instructions for each feature\n",
    "df_usage_cols = pd.read_excel('excel/usage.xlsx', sheetname='usage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#run processing function - drop columns, convert data types\n",
    "df_usage_1 = dp.process_data(df_usage, df_usage_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 388389 entries, 0 to 388388\n",
      "Data columns (total 5 columns):\n",
      "id             388389 non-null int64\n",
      "user_uuid      388389 non-null object\n",
      "action         388389 non-null category\n",
      "object_name    381868 non-null category\n",
      "time           388389 non-null datetime64[ns]\n",
      "dtypes: category(2), datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#check output\n",
    "df_usage_1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the first few rows of the demographics dataframe\n",
    "df_usage_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create User Id Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import user id table\n",
    "df_users = pd.read_sql_table('app_users', engine1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create dataframe with column names\n",
    "df_users_cols = pd.DataFrame(df_users.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This is commented out to prevent overwriting the excel worksheet once column actions have been specified. \n",
    "# write to excel\n",
    "# writer = pd.ExcelWriter('excel/users.xlsx')\n",
    "# df_users_cols.to_excel(writer,'users')\n",
    "# writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import dataframe from excel with data processing instructions for each feature\n",
    "df_users_cols = pd.read_excel('excel/users.xlsx', sheetname='users')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#run processing function - drop columns, convert data types\n",
    "df_users_1 = dp.process_data(df_users, df_users_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3085 entries, 0 to 3084\n",
      "Data columns (total 2 columns):\n",
      "id      3085 non-null int64\n",
      "uuid    3085 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 48.3+ KB\n"
     ]
    }
   ],
   "source": [
    "#check output\n",
    "df_users_1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   id                                  uuid\n",
       "0   1  850d6baa-1257-11e2-a9e8-1231380da217\n",
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     "execution_count": 104,
     "metadata": {},
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    }
   ],
   "source": [
    "#view the first few rows of the demographics dataframe\n",
    "df_users_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add user_id to demographics, entries, and usage dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#rename columns in order to facilitate the join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_1.rename(columns={'user_id' : 'uuid'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_usage_1.rename(columns={'user_uuid' : 'uuid'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_users_1.rename(columns={'id': 'user_id'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#join on uuid so dataframe has user_id feature\n",
    "df_demo_1 = jt.add_user_id(df_demo_1, df_users_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#join on uuid so dataframe has user_id feature\n",
    "df_usage_1 = jt.add_user_id(df_usage_1, df_users_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Test Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#First five users in the app are test users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_1 = dc.remove_test_users(df_demo_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_entries_1 = dc.remove_test_users(df_entries_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_usage_1 = dc.remove_test_users(df_usage_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a table with dates of usage to build additional time based features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_dates = ad.create_dates(df_usage_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_use</th>\n",
       "      <th>last_use</th>\n",
       "      <th>one_week</th>\n",
       "      <th>two_weeks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>2012-10-31</td>\n",
       "      <td>2012-11-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>2012-10-31</td>\n",
       "      <td>2012-11-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-10-25</td>\n",
       "      <td>2012-10-26</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>2012-11-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012-10-25</td>\n",
       "      <td>2012-10-25</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>2012-11-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012-10-26</td>\n",
       "      <td>2014-04-28</td>\n",
       "      <td>2012-11-02</td>\n",
       "      <td>2012-11-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          first_use    last_use    one_week   two_weeks\n",
       "user_id                                                \n",
       "6        2012-10-24  2012-10-24  2012-10-31  2012-11-07\n",
       "7        2012-10-24  2012-10-24  2012-10-31  2012-11-07\n",
       "8        2012-10-25  2012-10-26  2012-11-01  2012-11-08\n",
       "9        2012-10-25  2012-10-25  2012-11-01  2012-11-08\n",
       "10       2012-10-26  2014-04-28  2012-11-02  2012-11-09"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add dates to demographics, entries, and usage dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_1 = ad.add_dates(df_demo_1, df_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_entries_1 = ad.add_dates(df_entries_1, df_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_usage_1 = ad.add_dates(df_usage_1, df_dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create features for entries by time period and join to demographics table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#add features to entries table\n",
    "df_entries_1 = fee.add_entrytime_features(df_entries_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create new table grouped by user to join to demographics table\n",
    "user_entry_counts = fee.user_entry_counts(df_entries_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#join entry features to demographics table\n",
    "df_demo_2 = jt.join_tables_index(df_demo_1, user_entry_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3080 entries, 0 to 3084\n",
      "Data columns (total 23 columns):\n",
      "uuid               3080 non-null object\n",
      "birthdate          3080 non-null datetime64[ns]\n",
      "college_type       3080 non-null category\n",
      "diabetes_type      3080 non-null category\n",
      "ethnicity          3080 non-null category\n",
      "gender             3080 non-null category\n",
      "diabestie_id       3080 non-null int64\n",
      "user_id            3080 non-null int64\n",
      "first_use          2940 non-null object\n",
      "last_use           2940 non-null object\n",
      "one_week           2940 non-null object\n",
      "two_weeks          2940 non-null object\n",
      "total_entries      1874 non-null float64\n",
      "entries_w1         1874 non-null float64\n",
      "entries_post_w1    1874 non-null float64\n",
      "entries_w1w2       1874 non-null float64\n",
      "entries_post_w2    1874 non-null float64\n",
      "moods_w1           1874 non-null float64\n",
      "moods_w1w2         1874 non-null float64\n",
      "moods_total        1874 non-null float64\n",
      "notes_w1           1874 non-null float64\n",
      "notes_w1w2         1874 non-null float64\n",
      "notes_total        1874 non-null float64\n",
      "dtypes: category(4), datetime64[ns](1), float64(11), int64(2), object(5)\n",
      "memory usage: 494.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_demo_2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create features for usage by time period and join to demographics table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_usage_2 = df_usage_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "page_view      295155\n",
       "chat_pause      47827\n",
       "chat_resume     31974\n",
       "a1c_update       6517\n",
       "chat_online      5715\n",
       "Name: action, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#chat_pause: user pushed home button, chat_resume: user went back into the app, chat_online: user had lost internet\n",
    "#connection but now internet connection has been re-established, a1c_update: user updated their a1c score \n",
    "#a1c is a blood test that measures blood sugar levels. \n",
    "df_usage_2.action.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#remove chat_pause and chat_resume as these are not important for analysis of usage patterns\n",
    "df_usage_2 = feu.remove_actions(df_usage_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "page_view      295155\n",
       "chat_resume     31974\n",
       "a1c_update       6517\n",
       "chat_pause          0\n",
       "chat_online         0\n",
       "Name: action, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usage_2.action.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#rename chat_resume as page_view for ease of analysis. \n",
    "df_usage_2 = feu.consolidate_actions(df_usage_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "page_view     327129\n",
       "a1c_update      6517\n",
       "Name: action, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usage_2.action.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create dummy variables for page_view and a1c_update\n",
    "df_usage_2 = feu.create_dummies(df_usage_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 333646 entries, 572 to 388387\n",
      "Data columns (total 11 columns):\n",
      "id                   333646 non-null int64\n",
      "uuid                 333646 non-null object\n",
      "object_name          327129 non-null category\n",
      "time                 333646 non-null datetime64[ns]\n",
      "user_id              333646 non-null int64\n",
      "first_use            333646 non-null object\n",
      "last_use             333646 non-null object\n",
      "one_week             333646 non-null object\n",
      "two_weeks            333646 non-null object\n",
      "action_a1c_update    333646 non-null uint8\n",
      "action_page_view     333646 non-null uint8\n",
      "dtypes: category(1), datetime64[ns](1), int64(2), object(5), uint8(2)\n",
      "memory usage: 23.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_usage_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#add features for usage in week 1, week 1+ week 2, and total\n",
    "df_usage_2 = feu.add_usage_time_features(df_usage_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 333646 entries, 572 to 388387\n",
      "Data columns (total 17 columns):\n",
      "id                   333646 non-null int64\n",
      "uuid                 333646 non-null object\n",
      "object_name          327129 non-null category\n",
      "time                 333646 non-null datetime64[ns]\n",
      "user_id              333646 non-null int64\n",
      "first_use            333646 non-null object\n",
      "last_use             333646 non-null object\n",
      "one_week             333646 non-null object\n",
      "two_weeks            333646 non-null object\n",
      "action_a1c_update    333646 non-null uint8\n",
      "action_page_view     333646 non-null uint8\n",
      "a1c_w1               333646 non-null bool\n",
      "a1c_w1w2             333646 non-null bool\n",
      "a1c_total            333646 non-null bool\n",
      "page_view_w1         333646 non-null bool\n",
      "page_view_w1w2       333646 non-null bool\n",
      "page_view_total      333646 non-null bool\n",
      "dtypes: bool(6), category(1), datetime64[ns](1), int64(2), object(5), uint8(2)\n",
      "memory usage: 25.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_usage_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create dataframe that summarizes usage of app in given time buckets on a per user basis\n",
    "usage_counts = feu.user_usage_counts(df_usage_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_usage_counts</th>\n",
       "      <th>a1c_w1</th>\n",
       "      <th>a1c_w1w2</th>\n",
       "      <th>a1c_total</th>\n",
       "      <th>page_view_w1</th>\n",
       "      <th>page_view_w1w2</th>\n",
       "      <th>page_view_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1285</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>244</td>\n",
       "      <td>375</td>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total_usage_counts  a1c_w1  a1c_w1w2  a1c_total  page_view_w1  \\\n",
       "user_id                                                                  \n",
       "6                        10       1         1          1             9   \n",
       "7                         8       1         1          1             7   \n",
       "8                        11       1         1          1            10   \n",
       "9                        16       2         2          2            14   \n",
       "10                     1285       0         0          5           244   \n",
       "\n",
       "         page_view_w1w2  page_view_total  \n",
       "user_id                                   \n",
       "6                     9                9  \n",
       "7                     7                7  \n",
       "8                    10               10  \n",
       "9                    14               14  \n",
       "10                  375             1280  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usage_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_2 = jt.join_tables_index(df_demo_2, usage_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering on demographics table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Age at First Use Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_2 = fed.create_age(df_demo_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_2 = dc.srub_age(df_demo_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3080.000000\n",
       "mean       37.485714\n",
       "std        16.451325\n",
       "min        13.000000\n",
       "25%        22.000000\n",
       "50%        35.000000\n",
       "75%        50.000000\n",
       "max        84.000000\n",
       "Name: age2, dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo_2.age2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Consolidate College Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#consolidate college types by equating university to college\n",
    "df_demo_2 = fed.consolidate_college_type(df_demo_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College        1647\n",
       "Other          1124\n",
       "High School     309\n",
       "Name: college_type, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo_2.college_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate total duration as time of first_use - time of last_use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_2['duration'] = (df_demo_2['last_use'] - df_demo_2['first_use']) + datetime.timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_2.duration.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                        1508\n",
       "mean      94 days 07:48:51.564986\n",
       "std      153 days 13:26:52.751460\n",
       "min               2 days 00:00:00\n",
       "25%               5 days 00:00:00\n",
       "50%              24 days 00:00:00\n",
       "75%             112 days 00:00:00\n",
       "max             886 days 00:00:00\n",
       "Name: duration, dtype: object"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo_2['duration'][df_demo_2['duration'] > datetime.timedelta(days=1)].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert null values on usage and entry features to zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_2 = dc.convert_null_time_entries(df_demo_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Y labels for churn after week 1 and churn after week2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     550.000000\n",
       "mean       61.547273\n",
       "std       165.298892\n",
       "min         1.000000\n",
       "25%         2.000000\n",
       "50%         9.000000\n",
       "75%        43.000000\n",
       "max      2513.000000\n",
       "Name: entries_post_w1, dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore different ways to define Y for week 1\n",
    "df_demo_2.entries_post_w1[df_demo_2.entries_post_w1 > 0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     449.000000\n",
       "mean       66.966592\n",
       "std       176.209003\n",
       "min         1.000000\n",
       "25%         2.000000\n",
       "50%         8.000000\n",
       "75%        47.000000\n",
       "max      2470.000000\n",
       "Name: entries_post_w2, dtype: float64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore different ways to define Y for week 2\n",
    "df_demo_2.entries_post_w2[df_demo_2.entries_post_w2 > 0].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create 8 different definitions of churn. One set uses data from only week1 and the other uses week 1 and week 2 data. Define churn as zero entries after the time period, as less than or equal to the 25th percentile or less than or equal to the median value. \n",
    "Finally, we will try to predict whether a user will be short term - less than or equal to the median value of 24 days duration, or a long term user, greater than 24 days. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_w1_0 = np.asarray(df_demo_2['entries_post_w1'] == 0) \n",
    "y_w1_2 = np.asarray(df_demo_2['entries_post_w1'] <= 2) \n",
    "y_w1_9 = np.asarray(df_demo_2['entries_post_w1'] <= 9)\n",
    "y_w1_length = np.asarray(df_demo_2['duration'] <= datetime.timedelta(days=24))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_w2_0 = np.asarray(df_demo_2['entries_post_w2'] == 0) \n",
    "y_w2_2 = np.asarray(df_demo_2['entries_post_w1'] <= 2) \n",
    "y_w2_8 = np.asarray(df_demo_2['entries_post_w1'] <= 8)\n",
    "y_w2_length = np.asarray(df_demo_2['duration'] <= datetime.timedelta(days=24))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dummies and drop any remaining unecessary features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#use this dataframe for logistic regression, which requires that one dummy variable be dropped in each set\n",
    "df_demo_3_dropdummies = fed.dummies_logistic(df_demo_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#use this dataframe for decision trees etc. which do not require dropping dummy variables\n",
    "df_demo_3_alldummies = fed.dummies_other(df_demo_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns_drop = ['uuid', 'birthdate', 'user_id', 'first_use', 'last_use', 'one_week',\\\n",
    "               'two_weeks', 'total_entries', 'entries_post_w1', 'entries_post_w2', \\\n",
    "               'moods_total', 'notes_total', 'total_usage_counts', 'a1c_total', \\\n",
    "               'page_view_total', 'age', ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_3_alldummies.drop(columns_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_3_dropdummies.drop(columns_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# add a column with a constant for logistic regression\n",
    "df_demo_3_dropdummies['constant'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diabestie_id</th>\n",
       "      <th>entries_w1</th>\n",
       "      <th>entries_w1w2</th>\n",
       "      <th>moods_w1</th>\n",
       "      <th>moods_w1w2</th>\n",
       "      <th>notes_w1</th>\n",
       "      <th>notes_w1w2</th>\n",
       "      <th>a1c_w1</th>\n",
       "      <th>a1c_w1w2</th>\n",
       "      <th>page_view_w1</th>\n",
       "      <th>page_view_w1w2</th>\n",
       "      <th>age2</th>\n",
       "      <th>duration</th>\n",
       "      <th>college_type_High School</th>\n",
       "      <th>college_type_Other</th>\n",
       "      <th>diabetes_type_2</th>\n",
       "      <th>diabetes_type_Other</th>\n",
       "      <th>ethnicity_Asian</th>\n",
       "      <th>ethnicity_Caucasian</th>\n",
       "      <th>ethnicity_Latin American</th>\n",
       "      <th>ethnicity_Native American</th>\n",
       "      <th>ethnicity_Other</th>\n",
       "      <th>ethnicity_Prefer not to say</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>constant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1 days</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4 days</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>57 days</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1 days</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38 days</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   diabestie_id  entries_w1  entries_w1w2  moods_w1  moods_w1w2  notes_w1  \\\n",
       "0             0         1.0           1.0       0.0         0.0       1.0   \n",
       "1             0        15.0          15.0       1.0         1.0      15.0   \n",
       "2             0         0.0           0.0       0.0         0.0       0.0   \n",
       "3             0         0.0           0.0       0.0         0.0       0.0   \n",
       "4             0         0.0           0.0       0.0         0.0       0.0   \n",
       "\n",
       "   notes_w1w2  a1c_w1  a1c_w1w2  page_view_w1  page_view_w1w2  age2 duration  \\\n",
       "0         1.0     0.0       0.0          22.0            22.0  22.0   1 days   \n",
       "1        15.0     0.0       0.0          94.0            94.0  46.0   4 days   \n",
       "2         0.0     0.0       0.0          10.0            24.0  25.0  57 days   \n",
       "3         0.0     1.0       1.0           9.0             9.0  26.0   1 days   \n",
       "4         0.0     0.0       0.0          11.0            11.0  19.0  38 days   \n",
       "\n",
       "   college_type_High School  college_type_Other  diabetes_type_2  \\\n",
       "0                         0                   0                0   \n",
       "1                         0                   0                0   \n",
       "2                         0                   0                0   \n",
       "3                         0                   0                0   \n",
       "4                         0                   1                0   \n",
       "\n",
       "   diabetes_type_Other  ethnicity_Asian  ethnicity_Caucasian  \\\n",
       "0                    0                0                    0   \n",
       "1                    0                0                    0   \n",
       "2                    0                0                    1   \n",
       "3                    0                0                    0   \n",
       "4                    0                0                    0   \n",
       "\n",
       "   ethnicity_Latin American  ethnicity_Native American  ethnicity_Other  \\\n",
       "0                         0                          0                0   \n",
       "1                         0                          0                0   \n",
       "2                         0                          0                0   \n",
       "3                         0                          0                0   \n",
       "4                         0                          0                0   \n",
       "\n",
       "   ethnicity_Prefer not to say  gender_Male  gender_Other  constant  \n",
       "0                            1            1             0         1  \n",
       "1                            1            1             0         1  \n",
       "2                            0            0             0         1  \n",
       "3                            1            0             0         1  \n",
       "4                            1            1             0         1  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo_3_dropdummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis and Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_demo_3 = df_demo_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create variable for mean of age\n",
    "mean = df_demo_3.age2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create variable for median of age\n",
    "median = df_demo_3.age2.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create variable for mode of age\n",
    "mode = df_demo_3.age2.mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create strings for mean, median, and mode\n",
    "mean_label = \"Mean: {:0.0f}\".format(mean)\n",
    "median_label = \"Median: {:0.0f}\".format(median)\n",
    "mode_label = \"Mode: {:0.0f}\".format(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2122\n",
       "True      958\n",
       "Name: age2, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate number of users under the age of 26\n",
    "(df_demo_3.age2 < 26).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31103896103896106"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate percent of users uder 26\n",
    "958/3080"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CQkL47bffjJFNmLhTp05x6tQppWOIQu6dd97hwYMHbNq0iXfffVdnO39/f+7d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a36fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot histogram of users\n",
    "sns.plt.hist(df_demo_3.age2, bins=25, range=(0, 115), color='purple')\n",
    "sns.plt.xlabel('Age')\n",
    "sns.plt.ylabel('Number of Participants')\n",
    "sns.plt.vlines(x=mode, ymin=0, ymax=450, linestyles='dashdot', label= mode_label)\n",
    "sns.plt.vlines(x=mean, ymin=0, ymax=450, linestyles='dashed', label= mean_label)\n",
    "sns.plt.legend(loc=7, fontsize='xx-large' )\n",
    "sns.set_style('white', {'axes.grid' : False})\n",
    "sns.plt.title('Age of Participants', fontsize='xx-large')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar chart of type of diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116752cd0>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YGIg9e/YAAIqKijBx4kSoVCqMHTsWd+7cqckyiYjIiBoLiHXr1iE8PBw6nU7f9v/+3//D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\nYkAQEZFR/x/cDWYjGj88TgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1164c1b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create bar chart for diabetes type\n",
    "sns.plt.bar((1,2,3), df_demo_3.diabetes_type.value_counts(), width=0.6, color=('purple', 'darkblue', 'grey'), tick_label=['Type 1 Diabetes','Type 2 Diabetes', 'Other'], align='center')\n",
    "sns.plt.ylabel('Number of Participants')\n",
    "sns.despine()\n",
    "sns.plt.title('Type of Diabetes', fontsize='xx-large')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        1560\n",
       "2        1289\n",
       "Other     231\n",
       "Name: diabetes_type, dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate number of participant with each type\n",
    "df_demo_3.diabetes_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50649350649350644"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of type I diabetics\n",
    "df_demo_3.diabetes_type.value_counts()[0]/(1289+231+1560)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41850649350649349"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# of Type II diabetics\n",
    "df_demo_3.diabetes_type.value_counts()[1]/(1289+231+1560)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QyTUgIiIiABg/fryjahERkUIk13MQN27cYPjw4XzxxRfWZzr8lq5iEhF5sOUa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b775a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.plt.bar((1,2,3), df_demo_3.gender.value_counts(), width=0.6, color=('purple', 'darkblue', 'grey'), tick_label=['Female','Male', 'Other'], align='center')\n",
    "sns.plt.title('Gender of Participants', fontsize='xx-large')\n",
    "sns.plt.ylabel('Number of Participants')\n",
    "sns.despine()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    1777\n",
       "Male      1295\n",
       "Other        8\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo_3.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.576948051948052"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of female users\n",
    "1777/3080"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ethnicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TZVAsX76ciIgIIiMj823GU9GiRYmJiaFt27bEx8ezePFiTpw4oT9mw8rKisTE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\n/wfCAB4MrihYfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1362cfb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.plt.bar((1,2,3, 4 ,5, 6, 7), df_demo_2.ethnicity.value_counts(), \\\n",
    "            width=0.6, color=['grey', 'purple', 'purple', 'purple', 'purple', 'purple', 'purple'], \\\n",
    "            tick_label=['Not Specified', 'Caucasian', 'Latin Amerian', 'Other', 'African Amercian', 'Asian', 'Native American'], \\\n",
    "           align='center', )\n",
    "sns.plt.title('Ethnicity of Participants', fontsize='xx-large')\n",
    "sns.plt.ylabel('Number of Participants')\n",
    "sns.plt.xticks(rotation=40, fontsize='medium')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prefer not to say    1632\n",
       "Caucasian             945\n",
       "Latin American        154\n",
       "Other                 139\n",
       "African American       99\n",
       "Asian                  86\n",
       "Native American        25\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo_2.ethnicity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5298701298701298"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# of users who did not specify ehtnicity\n",
    "1632/3080"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of # of log entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96655256723716376"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of entries that included glucose\n",
    "glucose = df_entries_1.glucose.count() / df_entries_1.id.count()\n",
    "glucose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11522738386308068"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of entries that included carbs\n",
    "carbs = df_entries_1.carbs.count() / df_entries_1.id.count()\n",
    "carbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30975061124694375"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of entries that included insulin\n",
    "insulin = df_entries_1.insulin.count() / df_entries_1.id.count()\n",
    "insulin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09646943765281174"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of entries that included notes\n",
    "notes = df_entries_1.note.value_counts()[1] / df_entries_1.id.count()\n",
    "notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29390709046454766"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of entries that included mood\n",
    "mood = df_entries_1.mood.count() / df_entries_1.id.count()\n",
    "mood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0022493887530562347"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# of entries with a diabestie mood response\n",
    "df_entries_1.diabestie_mood.count() / df_entries_1.id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create a tuple consisting of % of entries with glucose, insulin, carbs, mood, and notes\n",
    "stats_entries=(glucose, insulin, carbs, mood, notes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8FTZDCCHE62IwMXh4ePDLL79w/fp1srOzqVKlygu1GOzt7UlNTVWndTqdmhQu\nXrzIvn37iIiIwM7OjnHjxrFjxw5atWr1CpsihBDidcg3MSxevJiRI0fyP//zP3nODwoKem7FderU\nYe/evbRu3ZrTp0+j1WrVeQ4ODtjY2GBtbY25uTmlSpXi4cOHBdwEIYQQr1O+iaFmzZoA1K9fv0AV\n+/v7c/jwYbp3746iKMycOZMtW7aQlpZGQEAAAQEB9OzZE0tLSypUqEDHjh0LtgVCCCFeK42iKMrz\nFvj0009ZuXKlqeLJU3R0NH5+fkRERODi4vLS79dovjJCVK+Poowt7BCEEEJl8HTVx48fc+fOHVPE\nIoQQoggwOPj84MEDfH19eeedd7C2tlZPLY2IiDBFfEIIIUzMYGL4/vvvTRGHEEKIIsJgV9KsWbNw\ndnbW+5s4caIpYhNCCFEI8m0xDB8+nIsXLxIXF4efn59anp2dzXvvvWeS4IQQQphevolh9uzZJCYm\nMmPGDCZNmvR/b7Cw4J133jFJcEIIIUwv38Rgb2+Pvb09S5cu5cqVKyQlJZFzZuvNmzf54IMPTBak\nEEII0zE4+Dxt2jT27NlD+fLl1TKNRsMPP/xg1MCEEEIUDoOJ4dChQ+zcuRMbGxtTxCOEEKKQGTwr\nqXz58hi4OFoIIcRbxGCLoUSJErRp0wYvLy+9u6oauomeEEKIN5PBxODt7a0+cEcIIcTbL9/EEBsb\ni5OTU553PT169KhRgxJCCFF48h1jGDp0qPp65MiRevPmzJljvIiEEEIUqnwTw9MDzrdu3cp3nhBC\niLdLvolBo9Hk+TqvaSGEEG8Pg6erCiGE+O+S7+DzvXv3CA4OzvU6Z1oIIcTbKd8WQ/fu3fN8nde0\nEEKIt0e+LYYRI0aYMg4hhBBFhIwxCCGE0COJQQghhB5JDEIIIfQYvFdSixYtyM7OVqc1Gg02NjZU\nqVKF8ePH4+zsbNQAhRBCmJbBxNCkSRNcXFzo0qULAJs3b+avv/7C19eXL774glWrVhk7RiGEECZk\nsCvp5MmT9O/fX33UZ8+ePbl06RL+/v4kJSWZIkYhhBAmZDAxmJmZcfDgQXX64MGDWFlZcf/+fbKy\nsowanBBCCNMz2JU0a9Ysxo8fz9ixYwGoWLEiQUFBhIWF8emnnxo9QCGEEKZlMDFUq1aNDRs2EBMT\ng0ajoWzZsgAMHz7c6MEJIYQwPYOJ4ebNm4wZM4Zbt26hKArlypVj/vz5VK5c2RTxCSGEMDGDYwxT\npkxh0KCr2xu5AAAgAElEQVRBREZGcuzYMQYPHszkyZNNEZsQQohCYDAxJCQk0LJlS3W6devWJCYm\nGjUoIYQQhcdgYrCysuLcuXPq9NmzZ7G1tTVYsU6nY/LkyQQEBNCnTx9u3LihN////b//R8+ePenR\nowejRo3i8ePHBQhfCCHE62ZwjGHixImMHDkSR0dHFEUhKSmJr7/+2mDFu3fvJiMjg7CwME6fPs2s\nWbNYunQp8OTRoF9++SWLFi2iYsWK/Pzzz9y+fZsqVaq8+hYJIYR4JQYTg6enJ+Hh4Vy/fh2dTkfl\nypWxsrIyWPHJkyfx9vZW6zh79qw6LyoqCkdHR1atWsWVK1fw8fGRpCCEEEXEC91Ez9LSkmrVquHm\n5oaVlRV16tQx+J6UlBTs7e3VaXNzc/WCuISEBP7880969+5NSEgIf/zxB0ePHi3gJgghhHidCnR3\nVUVRDC5jb29PamqqOq3T6bCweNJAcXR0pGLFiri6umJpaYm3t7dei0IIIUThKVBi0Gg0BpepU6cO\nBw4cAOD06dNotVp1Xvny5UlNTVUHpE+cOEG1atUKEooQQojXLN8xhpiYmDzLFUV5oRaDv78/hw8f\npnv37iiKwsyZM9myZQtpaWkEBAQwY8YMAgMDURQFLy8vmjZtWuCNEEII8fpolHy+5X19fdFoNPkm\ngT179hg1sKdFR0fj5+dHREQELi4uL/1+jeYrI0T1+ijK2MIOQQghVPm2GEz5xS+EEKLokEd7CiGE\n0COJQQghhJ58E8PcuXMB1DOLhBBC/HfId4xhx44dNGrUiBkzZmBnZ5drEPqDDz4wenBCCCFML9/E\nMHToUJYvX05cXBwLFy7Um6fRaPjhhx+MHpwQQgjTyzcxdOvWjW7duvHNN9/I09qEEOK/iMGb6A0Y\nMIC5c+dy9OhRsrOzadCgAZ9//jl2dnamiE8IIYSJGTwrafr06aSnpzNz5kxmz55NZmYmU6ZMMUVs\nQgghCoHBFsO5c+fYvHmzOj158mRat25t1KCEEEIUHoMtBkVRePjwoTr98OFDzM3NjRqUEEKIwmOw\nxdC/f3+6du1Ks2bNgCe3yhg8eLDRAxNCCFE4DCaGzp074+7uzvHjx9HpdCxevBg3NzdTxCaEEKIQ\nGEwMAFqtVu95CkIIId5ecq8kIYQQeiQxCCGE0GOwKykrK4tDhw6RmJioV96hQwejBSWEEKLwGEwM\ngYGBxMTE4OrqqvesZ0kMQgjxdjKYGC5dusTOnTtNEYsQQogiwOAYg6urK3FxcaaIRQghRBFgsMXw\n6NEjWrZsiVarxcrKSi2X224LIcTbyWBiGDJkiCniEEIIUUQY7EqqX78+6enp7N27l127dvHw4UPq\n169vitiEEEIUAoOJYcWKFQQHB1O2bFlcXFxYtmwZy5YtM0VsQgghCoHBrqTNmzfz888/Y2NjAzx5\nslunTp0YOnSo0YMTQghhei902+2cpABgbW2NhcUL3WJJCCHEG8jgN3yDBg0YOXIkHTt2BGDTpk18\n+OGHRg9MCCFE4TCYGL744gt++uknNm3ahKIoNGjQgICAAFPEJoQQohDkmxju3btHmTJluHPnDk2b\nNqVp06bqvLi4OMqVK2eK+IQQQphYvolh0qRJLF++nN69e+vdI0lRFDQaDRERESYJUAghhGnlmxiW\nL18OwIYNG3B0dNSbFx0dbdyohBBCFJp8z0q6c+cOMTEx9O7dW30dExPDrVu3GDRokMGKdTodkydP\nJiAggD59+nDjxo08l/vyyy/56quvCr4FQgghXqt8WwyLFi0iMjKSuLg4evXq9X9vsLDQG2/Iz+7d\nu8nIyCAsLIzTp08za9Ysli5dqrdMaGgoly9f5oMPPij4FgghhHit8k0MQUFBAHz77bcMHjz4pSs+\nefIk3t7eAHh6enL27Fm9+adOneLMmTMEBARw7dq1l65fCCGEcRi8wG3jxo0FqjglJQV7e3t12tzc\nnKysLODJWU3ffPMNkydPLlDdQgghjMfgdQxVq1YlODiY2rVr610Bbaj7x97entTUVHVap9OpV0zv\n3LmThIQEBg8ezL1793j06BFVqlShU6dOBd0OIYQQr4nBxJCYmEhkZCSRkZFqmUajMfg8hjp16rB3\n715at27N6dOn0Wq16ry+ffvSt29f4MlZT9euXZOkIIQQRYTBxLBmzRrgSdeQTqejePHiL1Sxv78/\nhw8fpnv37iiKwsyZM9myZQtpaWly5fQb6MGDB+zYsYNbt25hZWVF7dq1adasGebm5uoyiqLw448/\nUq1aNb1bs1+6dInt27eTnZ1N8+bN8fT0VOeFh4djY2ODj4+PSbdHvLqHDx8SHh5OVFQUZmZmVKtW\njRYtWmBra0t6ejo7duzg6tWrmJubU6dOHZo2bapeEyXHRNFmMDHcunWL0aNHc+vWLRRFoVy5cixY\nsIBKlSo9931mZmZMmzZNr8zV1TXXctJSKPqys7NZu3YtLi4uDB48mOTkZDZu3IiZmRm+vr7Ak67C\n7du3c/XqVapVq6a+V1EUNm/ejL+/Pw4ODoSGhqLVarGzsyM5OZnz588zbNiwwto0UUA6nY7Q0FDs\n7Ozo168fWVlZbNu2jU2bNtGjRw/CwsJIS0ujR48eaDQatmzZQlZWFv7+/nJMvAEMDj5PnjyZQYMG\nERkZybFjxxg8eDBffvmlKWITRURycjLOzs588sknvPPOO1SqVIkaNWqo16YkJCSwatUqrl69qjcO\nBZCamkpaWhru7u64urpiY2NDQkICAAcPHqR+/fpYW1ubfJvEq7l79y537tyhQ4cOODk54ezsTMuW\nLbl8+TJ37tzhxo0bdOzYkfLly+Pi4kLbtm2JjIwkIyNDjok3gMHEkJCQQMuWLdXp1q1bk5iYaNSg\nRNHi6OhIly5d1A/r3bt3uXjxIlWqVAGetCpLly7NkCFDcn2g7ezssLS05Pbt28THx5Oenk7x4sVJ\nSkri8uXL8jTAN5SjoyO9evXSO/Mwp5vo2rVrWFhYULZsWXWek5MT2dnZxMTEyDHxBjDYlWRlZcW5\nc+eoWbMmAGfPnsXW1tbogYmiacmSJdy7d4+yZcvSoEEDADw8PPDw8MhzeTMzM1q0aMHq1atRFAUf\nHx8cHBzYvHkzDRs2xNLS0pThi9fEzs6OqlWr6pX98ccflCpVChcXF7KyskhLS8POzg6ApKQkANLS\n0uSYeAMYTAwTJ05k5MiRODo6oigKSUlJzJ8/3xSxiSKoY8eOpKens3PnTsLCwtSzy56nXr16eHh4\noCgK1tbWxMfHExUVRevWrTlw4ACnTp2iZMmSdOjQgRIlSphgK8TrdujQIS5cuECPHj1wcXGhVKlS\nbN26lbZt26IoCuHh4ZiZmZGdnQ3IMVHUGUwMnp6ehIeHc/36dRRFoVKlSlhZWZkiNlEE5XQPtG/f\nnu+++464uDjeffddg+97+pg5cOAAjRs3JjY2lpMnTzJ06FBOnTrFjh076N69u9FiF8axf/9+9u3b\nR6tWrdQTD7p168avv/7KnDlzsLKywsfHhzt37uh1NcoxUXQZTAwxMTFMnz6dP/74A0tLS5o0acLE\niRMpVaqUKeITRUBycjLR0dFUr15dLctJBmlpaS9V1/3794mOjqZdu3ZERkZSvnx5bG1t0Wq1HDp0\n6LXGLYxv586dREZG0qZNG+rVq6eWOzk5MWzYMFJTU7G2tkan07Fr1y5KliyZqw45Jooeg4PPY8eO\npVGjRhw8eJCIiAhq1arF+PHjTRGbKCLu3bvH+vXr1X5igNu3b6PRaChduvRL1bVv3z68vb0xMzND\no9GgKArw5PRH8WbZu3cvx44do3379npJIT09nZCQEBITEylWrBgWFhZcvnwZe3v7PI8XOSaKHoOJ\nISUlhd69e2Nvb4+DgwP9+/cnNjbWFLGJIqJixYq89957bNq0ibi4OKKiotiyZQt169bVOyvFkNjY\nWOLi4nB3dwfA2dmZqKgoYmJiOHXqFC4uLsbaBPGa3blzh4MHD9KwYUOqVq1KSkqK+mdtbU1WVhbh\n4eE8ePCAq1evsn37dnx8fPQe+gVyTBRVBruSatasyW+//Ub79u2BJ9m9Ro0aRg9MFB3m5ub06NGD\nnTt3EhISgpmZGR4eHjRv3vyl6tm3bx8+Pj6YmT35PVK+fHnq1q3LmjVrKFWqFJ07dzZG+MIILly4\ngKIoHDlyhCNHjujN++yzz+jSpQvbtm1j+fLlFCtWjKZNm+q1KnLIMVE0aZScdls+GjZsSEJCAtbW\n1piZmZGenv5/b9ZouHDhgtGDjI6Oxs/Pj4iIiAL9gtBoivaDgBRlbGGHIIQQKoMthqNHj5oiDiGE\nEEWEwcSQnp5OcHAwR48eJTs7mwYNGvD555+rF64IIYR4uxgcfJ42bRrp6enMnDmT2bNnk5mZyZQp\nU0wRmxBCiEJgsMVw7tw5Nm/erE5PnjyZ1q1bGzUoYTxFebxFxlqEKBoMthgUReHhw4fq9MOHD/Xu\nwS+EEOLtYrDF0L9/f7p27UqzZs0A2LNnD4MHDzZ6YEIIIQqHwcTQrFkz3N3dOX78ODqdjsWLF+Pm\n5maK2IQQRlaUuxZBuhcLi8HE0KtXL3bs2KH3zGYhhBBvL4OJ4f3332fTpk14eHjoPZ2rXLlyRg1M\nCCFE4TCYGM6cOcOZM2f0yjQaDREREUYLSgghROExmBj27NljijiEEEIUEfmerhobG8uIESNo27Yt\nU6ZM0TtlVQghxNsr38QwceJEqlSpwrhx48jIyCAoKMiUcQkhhCgk+XYlxcbG8v333wNP7rDaoUMH\nkwUlhBCi8OTbYrC0tNR7/fS0EEKIt5fBW2LkePbJS0IIId5O+XYlXblyBT8/P3U6NjYWPz8/FEWR\n01WFEOItlm9iCA8PN2UcQgghioh8E4Ozs7Mp4xBCCFFEvPAYgxBCiP8OkhiEEELokcQghBBCj8F7\nJRWUTqdj6tSpXLp0CSsrK/7zn/9QsWJFdf7WrVtZvXo15ubmaLVapk6dipmZ5CkhhChsRvsm3r17\nNxkZGYSFhREYGMisWbPUeY8ePWLBggX88MMPhIaGkpKSwt69e40VihBCiJdgtBbDyZMn8fb2BsDT\n05OzZ8+q86ysrAgNDcXW1haArKwsrK2tjRWKEEahKAo//vgj1apVo379+sCTZ6KHh4cTFRWFmZkZ\n1apVo0WLFuqxfunSJbZv3052djbNmzfH09NTrS88PBwbGxt8fHwKZXuEyGG0FkNKSgr29vbqtLm5\nOVlZWU9WamZG6dKlAVizZg1paWk0atTIWKEI8drpdDq2bdvG1atX9cpCQ0N5/Pgx/fr1o0ePHsTG\nxrJp0ybgSSLZvHkzzZo1o2PHjmzbto20tDQAkpOTOX/+PA0aNCiU7RHiaUZrMdjb25OamqpO63Q6\nLCws9Kbnzp1LVFQUixcvlltuiDdGQkICGzdu5OHDh3pPNbx79y537twhMDBQ/VHUsmVLQkJCePTo\nEVlZWaSlpeHu7o65uTk2NjYkJCRgZ2fHwYMHqV+/vrScRZFgtBZDnTp1OHDgAACnT5/O9czoyZMn\n8/jxY5YsWaI2s4V4E9y6dYvSpUszZMgQvS9yR0dHevXqpddSzvnB8+jRI+zs7LC0tOT27dvEx8eT\nnp5O8eLFSUpK4vLly2p3lBCFzWgtBn9/fw4fPkz37t1RFIWZM2eyZcsW0tLSqFWrFr/88gv16tWj\nX79+APTt2xd/f39jhSPEa+Ph4YGHh0eucjs7O6pWrapX9scff1CqVCkcHR0BaNGiBatXr0ZRFHx8\nfHBwcGDz5s00bNhQ7mD8hstrzCk7O5vw8HB1jNXLyws/Pz/1DMyiOuZktMRgZmbGtGnT9MpcXV3V\n1xcvXjTWqoUoEg4dOsSFCxfo0aOHWlavXj08PDxQFAVra2vi4+OJioqidevWHDhwgFOnTlGyZEk6\ndOhAiRIlCjF68TJ0Oh3bt2/n6tWrVKtWTS2PiIjg77//pmfPnmRkZLBx40asra1p0qSJOubk7++P\ng4MDoaGhaLVa7Ozs1DGnYcOGFcr2yIUDQhjB/v37iYiIoGXLlnpfFPDkrLycLqgDBw7QuHFjYmNj\nOXnyJEOGDKFq1ars2LGjMMIWBZCQkMCqVau4evWq3phTVlYWJ06coEWLFri4uFClShX8/Pw4duwY\niqKQmpqqjjm5urqqY05AoY85SWIQ4jXbuXMn+/bto02bNs8dN7h//z7R0dF4eXlx8+ZNypcvj62t\nLVqtlhs3bpgwYvEq8htzunv3LpmZmXoX9lasWJHU1FTi4+OL9JiT0bqShPhvtHfvXo4dO0b79u31\n+ovzsm/fPry9vTEzM0Oj0aAoCvCkW0K8OfIbc3r48CGWlpZ6rYicExMePnzIO++8U2THnCQxCPGa\n3Llzh4MHD9KwYUOqVq1KSkqKOs/Ozk7vli+xsbHExcXRqVMn4Mlt7g8cOEBMTAxnzpzBxcXF5PGL\n1yszM1PvFH1Anc7OzgaK7piTJAYhXpMLFy6gKApHjhzhyJEjevM+++wz3n33XXV63759+Pj4qMmi\nfPny1K1blzVr1lCqVCk6d+5s0tjF62dpaale1JsjZ/rp1oCVlZX6+tkxp6FDh3Lq1Cl27NhB9+7d\nTRM4khiEeCX//Oc/1de+vr74+vq+0PsCAgJylfn5+ek9Tle82YoXL05mZiaPHz9Wxx5yWpEODg65\nls8Zc2rXrh2RkZF6Y06HDh0yaewy+CyEEEbg5OSEpaUlN2/eVMtu3LhBsWLFKFWqVK7li9KYkyQG\nIYQwAktLS7y8vNi+fTs3b97k2rVrRERE8OGHH+ZaNmfMyd3dHXgy5hQVFUVMTAynTp0y+ZiTdCUJ\nIYSR+Pv7k5WVxY8//oi5uTmenp40btw413JFbcxJo+S0V4qw6Oho/Pz8iIiIKFDm1Gi+MkJUr4+i\njDXZuoryvjDlfhBPFOXjAeSYKCzSlSSEEEKPdCWJ/0pF+Zey/EoWhU1aDEIIIfRIYhBCCKFHEoMQ\nQgg9MsYghPivV5THnMD0407SYhBCCKFHEoMQQgg9khiEEELokcQghBBCjyQGIYQQeiQxCCGE0COJ\nQQghhB5JDEIIIfRIYhBCCKFHEoMQQgg9khiEEELokcQghBBCjyQGIYQQeiQxCCGE0COJQQghhB6j\nJQadTsfkyZMJCAigT58+3LhxQ2/+nj176Ny5MwEBAaxfv95YYQghhHhJRksMu3fvJiMjg7CwMAID\nA5k1a5Y6LzMzk6CgIFauXMmaNWsICwvj/v37xgpFCCHESzDaE9xOnjyJt7c3AJ6enpw9e1ad9/ff\nf1OhQgVKlCgBQN26dTl+/DitWrXKs67s7GwA7t69W6BYLCySC/Q+U4mOjjbZuoryvpD98ITsh/9j\nqn3xtu+H9957DwuLF/+6N1piSElJwd7eXp02NzcnKysLCwsLUlJScHBwUOcVK1aMlJSUfOu6d+8e\nAL169SpQLFWqFOhtJuPnF2qydRXlfSH74QnZD//HVPvibd8PERERuLi4vPDyRksM9vb2pKamqtM6\nnU7NWM/OS01N1UsUz6pVqxbr1q2jTJkymJubGytkIYR4K7333nsvtbzREkOdOnXYu3cvrVu35vTp\n02i1WnWeq6srN27cIDExETs7O06cOMHAgQPzrcvGxoZ69eoZK1QhhBBP0SiKohijYp1Ox9SpU7l8\n+TKKojBz5kzOnz9PWloaAQEB7Nmzh2+++QZFUejcuXOBu4mEEEK8XkZLDEIIId5McoGbEEIIPZIY\nhBBC6JHEIIQQQs9bmxgyMjJYsWIF7du3x8vLCy8vL3r27MmOHTvUZdzc3Ni7d28hRml6EyZMYNSo\nUUZfz9P7tk+fPsyePdvo6yyolJQU5s+fz8cff4yHhwe+vr7Mnj2b5OSCX/Tk6+vL2rVrX2OUxufr\n64ubmxu7d+/ONS8xMZGaNWvy4YcfGmXde/fuxc3NzSh1vwpfX1/at29PVlaWXnlqaipubm5ERkYa\nrCM1NZWff/7ZWCEahdFOVy1MGRkZ9O3bl4SEBEaOHEnt2rXJyMhgz549jB8/nsTERHr06FHYYf7X\nWLx48UtddWlKycnJdO/eHXt7eyZNmkSlSpWIiopizpw5nDx5kjVr1mBtbV3YYZqMpaUlu3fvpnnz\n5nrle/bsUe9A8N/m4sWLrFq1ikGDBhXo/SEhIezZs4euXbu+5siMp2h+Wl/RsmXLuH79Otu3b6dU\nqVJquaurKzY2NixevJhu3boVYoT/XRwdHQs7hHx99dVXKIrCqlWrsLW1BaB8+fJUrVqVFi1asHHj\nRrp3717IUZpO/fr12bt3L9nZ2XoXk/7+++94enoSFRVViNEVDmdnZ4KDg2nZsuVLXT2c40088fOt\n60rS6XSsX7+eTz/9VC8p5OjatSsbN27MdQX1s03/6Oho3NzcuHz5MgCPHz9m5syZNGrUiDp16jBs\n2DBiY2OBJ//4tWvX8vHHH+Pu7k779u3Zv3+/WtfVq1fp06cPXl5eNGjQgC+++IK0tDR1/qZNm/j4\n44+pXbs2HTt2ZN++fa9zl+TLzc2NDRs20KlTJ9zd3WnXrh1//vmnOj8sLAx/f39q1apFy5Yt2bRp\nk957n+6Gi4yMxM3NTe+K9hxPdyUtXryYESNGMGvWLOrXr0+9evWYPn16ofwazcjIYMuWLfTu3VtN\nCjnKlSvH6tWradmyJWlpaUydOpXGjRtTs2ZNfHx8WLZsmbpsnz59mDp1Kq1ataJhw4b8/fffANy4\ncYPu3bvj7u5Op06d9PbtiRMn6NKlCx4eHjRu3Jg5c+YUiV/kjRo1IiMjg+PHj6tlqampREZG6rUi\n7t+/z/jx42nYsCF16tTh888/Jy4u7oXn37hxg/79+1O7dm3atm3LlStXTLOBBdCnTx/KlSvH1KlT\n85z/vM//hg0bCA4O5ty5c7i5uan3PPr+++9p1qwZXl5e9OjRg9OnT5tqc17IW5cYbt26xb1796hf\nv36e821sbHBycnrpeidPnkxERARz587l559/Ji0tjTFjxgDw7bffsnDhQkaNGsXmzZtp3rw5n332\nGRcvXgQgMDCQcuXK8dtvv7F8+XKOHj3Kt99+C8DBgweZMWMGn3/+OVu2bCEgIIBRo0bpfYkY06JF\ni/j888/57bffsLe3Z8qUKQCcP3+eqVOnEhgYSHh4OH379mXChAlcv379lde5b98+UlNTCQsLY9Kk\nSfz444/s2bPnlet9Wbdu3SI1NRUPD48859etWxdHR0eCgoI4ffo0S5YsYefOnfTp04f58+dz/vx5\nddlffvmFSZMmsXz5clxdXQH46aef6NixI5s2baJGjRoMHTqUlJQUsrOzGT58OA0aNGD79u3qMbVh\nwwaTbPfzWFtb4+3trTfOsH//fmrVqqX+0MrKyqJ///7cvn2bFStWsHr1amJjYxk+fDiKohicn5mZ\nyeDBg7Gzs+PXX39l5MiRfPfdd4W1yQZZWloybdo0Dh06xLZt23LNf97nv3Xr1nz66ae8//77HDp0\niLJlyxIaGsratWuZOnUqGzduxMfHh379+pn05omGvHVdSfHx8YB+98WDBw9y9ZmuWLHihetMTk5m\n69atLFq0iI8++giAadOmsX79ejIyMggJCWHo0KG0adMGgJEjR3LmzBlWrFjBvHnziI6OpkGDBpQr\nV44KFSqwdOlSLC0tAVi+fDkDBw6kdevWAFSoUIFz584REhKCl5dXwXfEC+rZsyc+Pj4ADBw4kGHD\nhpGRkcHt27fRaDSULVsWZ2dnevbsScWKFfNshb0sGxsbvvzyS6ysrKhcuTKrV6/mr7/+wt/f/5Xr\nfhlJSUkAz71PFzy5vUv37t2pWbMmAIMGDeKbb77h8uXL1KhRA4AGDRrQqFEjvfd16tSJgIAA4MkP\ni3379rFjxw78/f1JSkqidOnSODs74+Liwvfff88777zzujexQFq0aMG8efOYNGkS8KQb6eOPP1bn\nHzx4kOvXrxMREaH+yFqwYAF+fn4cOXKEjIyM587PysoiJiaGsLAwHB0dqVq1KtevX2fevHmm39gX\nVK9ePbp27crMmTPx9vZWexwURTH4+bezs8Pc3JwyZcoATz7zY8aMUT93Q4cO5dixY6xbt47x48cX\nzgY+461rMeTcyvvhw4dqmaOjI5s2bWLTpk389NNPpKWlvVSzPSoqiqysLNzd3dWyChUqMHbsWJKT\nk0lISMDT01PvPXXr1uXq1avAkwPlhx9+oGHDhowePZq///6bypUrA3DlyhW++eYb9cwpLy8vNm7c\naLK+3EqVKqmvc+6Gm5WVhbe3Nx4eHnTr1o1WrVoxe/ZsypQpQ/HixV95nWXLlsXKykpvvZmZma9c\n78sqWbIk8H8JIj8dOnQgNjaWoKAgBg8ejLe3N2lpaeh0OnWZ8uXL53pf7dq11ddWVlZotVouX76M\no6Mjffv2JSgoiMaNG/M///M/JCUl4ezs/Jq27NU0bdqUe/fucfbsWR4/fsyBAwf0kvbVq1cpV66c\nXsv7vffew9nZmStXrhicf+XKFcqVK6f34+3pz1ZRNXbsWADmzp2rlsXHxxv8/D8tNTWVmJgYJk2a\npPeZj4yMLFLjN29di6FChQqULFmSU6dOqV0E5ubmVKxYESDPPvC8PJ04cr7E8hpEyu+MFZ1Op35x\n9O/fn5YtWxIREcHBgwf517/+xcGDBwkKCiI7O5vAwECaNWum935TncWT03J5mqIo2NjY8NNPP/Hn\nn3+yf/9+9uzZw9q1a1m2bFmuX8bASyXavNZZGCpUqICjoyN//fVXnt1JQUFBlC1blkuXLnH48GE6\ndOhA+/btmTJlCh06dNBb1sbGJtf7nx3H0ul06rZPnDiRnj17snfvXvbt28fgwYMZPnw4I0aMeI1b\nWDD29vY0bNiQ3bt3ExcXh1ar1fuSN3TMG5qv0WhyzSsqx8TzlChRgokTJxIYGKgmyhf5/D8t53My\nazY4tPkAAAT+SURBVNYstbWZI69jqLC8dS0GCwsLAgICWLlyJQkJCbnm5/ewH0tLS72kcevWLfW1\ni4sL5ubmen3Kt27dokGDBqSmpvLuu+/mGhP4888/qVKlCo8fP2bGjBlkZmbSq1cvli1bxuTJk9W+\nSldXV27fvk3FihXVv61bt+bZl2lKf/75J4sXL6ZOnTqMHj2aLVu2ULNmTX7//Xfg+fvrTWFubs4n\nn3zCmjVrePTokd68Gzdu8NNPP5Gens6GDRuYPXs2Y8aMoU2bNlhZWZGcnGzwbJOcMSaAR48ecfHi\nRapWrcr9+/f597//TenSpRkwYACrV69m8ODBhf4/f1qLFi3Ys2cPu3btokWLFnrzXF1diYmJUU++\ngCefqzt37uDq6mpwvlar5fbt23pPbXz6s1WUtWnThsaNG/Pvf/8beJJEn/f5B/QSYfHixSlTpgyx\nsbF6n/nVq1dz8OBB022IAW9diwFg+PDhnD59mq5duzJixAi8vLzIzMxkz549rFixAmdn51zNdnd3\ndzZt2kSTJk3IyMhgwYIF6j/U3t6eLl26MGvWLOzt7XF0dGTWrFlUq1YNJycnBg8ezKJFiyhXrhw1\natRg69atHD58WD0H/tSpU1y/fp1x48Zhbm7Orl271F+ogwYNYsyYMVSpUoVGjRpx+PBhvvnmG776\n6iuT77en2drasnz5chwdHfH19eX69ev8/fff6rnY7u7u/Pjjj1SvXp24uDhWrlxZqPEW1PDhwzlw\n4AD9+/dn5MiRVKhQgQsXLjBnzhxq1qzJwIEDWbFiBbt27cLFxYW4uDjmzp2LoihkZGQ8t+6ffvqJ\n999/H3d3d5YsWYKtrS2ffPIJGo2G3bt38+jRIwYPHkx6ejqHDh3KdxC8MPj6+jJlyhSio6MZNmyY\n3ryPPvoINzc3/n9798/SOhSHcfxraDNopwpWwaGIQxGnLqU4OBW1UQq1CIp/iFRcdJBCW5CCOFg0\niKVDEQsSBxEd7SIubr4DfQMKOvgKXLyDkEsQlOtSrzyfLZxwSAInD+f8DkmhUKBcLvP29ka1WiUa\njZJMJgkGg5+2G4bBwMAApVKJYrHI8/Oztxnjf7C1tcXk5KR3/Nn4B+js7OTl5YWHhwf6+vrI5/M0\nGg16enoYHh7m8vKS8/Nz7/yf4NfNGOB96ef4+Bjbtjk9PSWbzZLL5bi6umJ1dZVWq/VhP/LGxgaR\nSISZmRmKxSLr6+sYxt/HUy6XSSQSrK2tMTs7SygU4uDgAID5+Xny+Tx7e3tMTU1xc3PD4eGh9w+J\nWq2GYRjMzc2Ry+UwTdN78adSKSqVCq7rkk6ncV2X7e1trxjdLrFYDMdxuLi4YGJigs3NTWzbZnp6\nGngvpr6+vpLJZNjd3aVQKLT1er8rHA5zdnbG0NAQlUoFy7JwHIfx8XGazSamabK/v8/t7S2WZVEq\nlRgZGWF0dJS7u7tP+15ZWeHk5IRMJsPT05PXXzAY5OjoiMfHR7LZLEtLSwwODnrF3p+gu7ubeDxO\nNBr9UD/p6Oig0WgQDodZWFjAtm16e3txXRfTNL9sDwQCNJtNb3a/s7ODbdttutN/19/f71vy+2r8\nj42N0dXVRTqd5v7+nsXFRZaXl3EcB8uyuL6+pl6vE4/H23VLH+iz2yIi4vMrZwwiIvJ9CgYREfFR\nMIiIiI+CQUREfBQMIiLio2AQEREfBYOIiPgoGERExOcPgSsqwnhSayQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11797e950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.plt.bar((1,2,3,4,5), stats_entries, width=0.6, color=('darkblue'), align='center', \\\n",
    "           tick_label=['Glucose', 'Insulin', 'Carbs', 'Mood', 'Note'])\n",
    "sns.plt.title('Log Elements as Percent of Total Log Entries', fontsize='xx-large')\n",
    "sns.plt.ylabel('Proportion of Log Entries')\n",
    "sns.plt.xlabel('')\n",
    "sns.plt.xticks(fontsize='x-large')\n",
    "sns.plt.ylim(ymin=0, ymax=1.1)\n",
    "sns.plt.annotate(\"{:.0%}\".format(glucose), (0.9,1), fontsize='x-large', color='grey')\n",
    "sns.plt.annotate(\"{:.0%}\".format(insulin), (1.9,.33), fontsize='x-large', color='grey')\n",
    "sns.plt.annotate(\"{:.0%}\".format(carbs), (2.9,.15), fontsize='x-large', color='grey')\n",
    "sns.plt.annotate(\"{:.0%}\".format(mood), (3.9,.33), fontsize='x-large', color='grey')\n",
    "sns.plt.annotate(\"{:.0%}\".format(notes), (4.9,.13), fontsize='x-large', color='grey')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize distribution of entry data by user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3080.000000\n",
       "mean       16.599026\n",
       "std        79.968185\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         1.000000\n",
       "75%         4.000000\n",
       "max      2545.000000\n",
       "Name: total_entries, dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo_2.total_entries.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3080"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of total users\n",
    "total_users = df_demo_2.user_id.count()\n",
    "total_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39155844155844155"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% of users that did not make any entries\n",
    "zero_entries = (df_demo_2.total_entries[df_demo_2.total_entries == 0].count()) / total_users\n",
    "zero_entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39805194805194805"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% of users that made 1-5 entries\n",
    "less5_entries = df_demo_2.total_entries[(df_demo_2.total_entries <= 5) & (df_demo_2.total_entries >0)].count() / total_users\n",
    "less5_entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.052922077922077919"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% of users that made 6-10 entries\n",
    "less10_entries = df_demo_2.total_entries[(df_demo_2.total_entries <=10) & (df_demo_2.total_entries > 5)].count() / total_users\n",
    "less10_entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.092207792207792211"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% of users that made 11-50 entries\n",
    "less50_entries = (df_demo_2.total_entries[(df_demo_2.total_entries <= 50) & (df_demo_2.total_entries > 10)].count()) / total_users\n",
    "less50_entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.030519480519480519"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% of users that made 51-100 entries\n",
    "less100_entries = (df_demo_2.total_entries[(df_demo_2.total_entries <= 100) & (df_demo_2.total_entries > 50)].count()) / total_users\n",
    "less100_entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.034740259740259793"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of users that made more than 100 entries\n",
    "more100_entries = (1 - zero_entries - less10_entries - less100_entries - less50_entries - less5_entries) \n",
    "more100_entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2545.0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#highest number of entries\n",
    "df_demo_2.total_entries.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stats_user_entries = [zero_entries, less5_entries, less10_entries, less50_entries, less100_entries, more100_entries]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IgQMHMmTIEKpXr056ejpbtmzh888/Z+jQodStWxe4ei6So6Mjr732Gi+++CKVK1dm7969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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x138f3d510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.plt.figure(figsize=(10,5))\n",
    "sns.plt.bar((1,2,3,4,5,6), stats_user_entries, width=0.6, \n",
    "            color=('darkblue','darkblue', 'darkblue', 'purple', 'purple', 'purple'), \n",
    "            align='center', \n",
    "           tick_label=['Zero', '1-5','6-10', \"11-50\", \"51-100\", '100+'])\n",
    "sns.plt.title('Percent of Users by Number of Entries', fontsize='xx-large')\n",
    "sns.plt.ylabel('Proportion of users')\n",
    "sns.plt.xlabel('Number of Entries')\n",
    "sns.plt.xticks(fontsize='large', rotation=10)\n",
    "sns.plt.ylim(ymin=0, ymax=1.1)\n",
    "sns.plt.annotate(\"{:.0%}\".format(zero_entries), (0.8,0.44), fontsize='x-large', color='grey')\n",
    "sns.plt.annotate(\"{:.0%}\".format(less5_entries), (1.9,.43), fontsize='x-large', color='grey')\n",
    "sns.plt.annotate(\"{:.0%}\".format(less10_entries), (2.9,.08), fontsize='x-large', color='grey')\n",
    "sns.plt.annotate(\"{:.0%}\".format(less50_entries), (3.9,.12), fontsize='x-large', color='grey')\n",
    "sns.plt.annotate(\"{:.0%}\".format(less100_entries), (4.9,.06), fontsize='x-large', color='grey')\n",
    "sns.plt.annotate(\"{:.0%}\".format(more100_entries), (5.9,.06), fontsize='x-large', color='grey')\n",
    "\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize A1C updates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118ca6250>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD3CAYAAADi8sSvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntT4b7Jg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1398cd410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of users that made 1 or more A1C update\n",
    "df_demo_2[df_demo_2.a1c_total > 0].a1c_total.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    534.000000\n",
       "mean      11.449438\n",
       "std       39.181258\n",
       "min        2.000000\n",
       "25%        2.000000\n",
       "50%        3.000000\n",
       "75%        8.000000\n",
       "max      789.000000\n",
       "Name: a1c_total, dtype: float64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo_2.a1c_total[df_demo_2.a1c_total >1].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Duration of Use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77337662337662338"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Users who used the app for less than thirty days\n",
    "less30_days = ((df_demo_3.duration > datetime.timedelta(days=30)).value_counts()[0])/3080\n",
    "less30_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22662337662337662"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Users who used the app for more than thirty days\n",
    "more30_days = ((df_demo_3.duration > datetime.timedelta(days=30)).value_counts()[1])/3080\n",
    "more30_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GDRrc8rlMvr6+at++vRYsWCDDMFS3bl0dOHBACxYsUK9eveTl5fWv+r+ZggUL6sCBA/rp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x137d64c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.plt.figure(figsize=(10,5))\n",
    "sns.plt.bar((1,2), [less30_days, more30_days], width=0.6,\n",
    "            color=('darkblue','purple'), align='center', \\\n",
    "           tick_label=['Less than 30 days', 'More than 30 days'])\n",
    "sns.plt.title('Percent of Users by Duration of Use', fontsize='xx-large')\n",
    "sns.plt.ylabel('Proportion of users')\n",
    "sns.plt.xlabel('Duration of Use')\n",
    "sns.plt.xticks(fontsize='large', rotation=0)\n",
    "sns.plt.ylim(ymin=0, ymax=1.1)\n",
    "sns.plt.annotate(\"{:.0%}\".format(less30_days), (0.95, 0.80), fontsize='x-large', color='grey')\n",
    "sns.plt.annotate(\"{:.0%}\".format(more30_days), (1.95,.26), fontsize='x-large', color='grey')\n",
    "\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create X variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create X variables for logistic regression based on one week and two weeks of data\n",
    "X_w1_lr = np.asarray(df_demo_3_dropdummies.drop(['entries_w1w2', 'moods_w1w2', 'notes_w1w2', \\\n",
    "                                     'a1c_w1w2', 'page_view_w1w2', 'duration'], axis=1))\n",
    "X_w2_lr = np.asarray(df_demo_3_dropdummies.drop(['entries_w1', 'moods_w1', 'notes_w1', \\\n",
    "                                     'a1c_w1', 'page_view_w1', 'duration'], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create X varaiables for decision tree models based on one week and two weeks of data\n",
    "X_w1 = np.asarray(df_demo_3_alldummies.drop(['entries_w1w2', 'moods_w1w2', 'notes_w1w2', \\\n",
    "                                     'a1c_w1w2', 'page_view_w1w2', 'duration'], axis=1))\n",
    "X_w2 = np.asarray(df_demo_3_alldummies.drop(['entries_w1', 'moods_w1', 'notes_w1', \\\n",
    "                                     'a1c_w1', 'page_view_w1', 'duration'], axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_X_w2 = df_demo_3_alldummies.drop(['entries_w1', 'moods_w1', 'notes_w1', \\\n",
    "                                     'a1c_w1', 'page_view_w1', 'duration'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_X_w2_lr = df_demo_3_dropdummies.drop(['entries_w1', 'moods_w1', 'notes_w1', \\\n",
    "                                     'a1c_w1', 'page_view_w1', 'duration'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diabestie_id</th>\n",
       "      <th>entries_w1w2</th>\n",
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       "      <th>ethnicity_Native American</th>\n",
       "      <th>ethnicity_Other</th>\n",
       "      <th>ethnicity_Prefer not to say</th>\n",
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       "      <th>3</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   diabestie_id  entries_w1w2  moods_w1w2  notes_w1w2  a1c_w1w2  \\\n",
       "0             0           1.0         0.0         1.0       0.0   \n",
       "1             0          15.0         1.0        15.0       0.0   \n",
       "2             0           0.0         0.0         0.0       0.0   \n",
       "3             0           0.0         0.0         0.0       1.0   \n",
       "4             0           0.0         0.0         0.0       0.0   \n",
       "\n",
       "   page_view_w1w2  age2  college_type_High School  college_type_Other  \\\n",
       "0            22.0  22.0                         0                   0   \n",
       "1            94.0  46.0                         0                   0   \n",
       "2            24.0  25.0                         0                   0   \n",
       "3             9.0  26.0                         0                   0   \n",
       "4            11.0  19.0                         0                   1   \n",
       "\n",
       "   diabetes_type_2  diabetes_type_Other  ethnicity_Asian  ethnicity_Caucasian  \\\n",
       "0                0                    0                0                    0   \n",
       "1                0                    0                0                    0   \n",
       "2                0                    0                0                    1   \n",
       "3                0                    0                0                    0   \n",
       "4                0                    0                0                    0   \n",
       "\n",
       "   ethnicity_Latin American  ethnicity_Native American  ethnicity_Other  \\\n",
       "0                         0                          0                0   \n",
       "1                         0                          0                0   \n",
       "2                         0                          0                0   \n",
       "3                         0                          0                0   \n",
       "4                         0                          0                0   \n",
       "\n",
       "   ethnicity_Prefer not to say  gender_Male  gender_Other  constant  \n",
       "0                            1            1             0         1  \n",
       "1                            1            1             0         1  \n",
       "2                            0            0             0         1  \n",
       "3                            1            0             0         1  \n",
       "4                            1            1             0         1  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X_w2_lr.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_w1_lr = df_demo_3_dropdummies.drop(['entries_w1w2', 'moods_w1w2', 'notes_w1w2', \\\n",
    "                                     'a1c_w1w2', 'page_view_w1w2', 'duration'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   diabestie_id  entries_w1  moods_w1  notes_w1  a1c_w1  page_view_w1  age2  \\\n",
       "0             0         1.0       0.0       1.0     0.0          22.0  22.0   \n",
       "1             0        15.0       1.0      15.0     0.0          94.0  46.0   \n",
       "2             0         0.0       0.0       0.0     0.0          10.0  25.0   \n",
       "3             0         0.0       0.0       0.0     1.0           9.0  26.0   \n",
       "4             0         0.0       0.0       0.0     0.0          11.0  19.0   \n",
       "\n",
       "   college_type_High School  college_type_Other  diabetes_type_2  \\\n",
       "0                         0                   0                0   \n",
       "1                         0                   0                0   \n",
       "2                         0                   0                0   \n",
       "3                         0                   0                0   \n",
       "4                         0                   1                0   \n",
       "\n",
       "   diabetes_type_Other  ethnicity_Asian  ethnicity_Caucasian  \\\n",
       "0                    0                0                    0   \n",
       "1                    0                0                    0   \n",
       "2                    0                0                    1   \n",
       "3                    0                0                    0   \n",
       "4                    0                0                    0   \n",
       "\n",
       "   ethnicity_Latin American  ethnicity_Native American  ethnicity_Other  \\\n",
       "0                         0                          0                0   \n",
       "1                         0                          0                0   \n",
       "2                         0                          0                0   \n",
       "3                         0                          0                0   \n",
       "4                         0                          0                0   \n",
       "\n",
       "   ethnicity_Prefer not to say  gender_Male  gender_Other  constant  \n",
       "0                            1            1             0         1  \n",
       "1                            1            1             0         1  \n",
       "2                            0            0             0         1  \n",
       "3                            1            0             0         1  \n",
       "4                            1            1             0         1  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_w1_lr.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_w1 = df_demo_3_alldummies.drop(['entries_w1w2', 'moods_w1w2', 'notes_w1w2', \\\n",
    "                                     'a1c_w1w2', 'page_view_w1w2', 'duration'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   diabestie_id  entries_w1  moods_w1  notes_w1  a1c_w1  page_view_w1  age2  \\\n",
       "0             0         1.0       0.0       1.0     0.0          22.0  22.0   \n",
       "1             0        15.0       1.0      15.0     0.0          94.0  46.0   \n",
       "2             0         0.0       0.0       0.0     0.0          10.0  25.0   \n",
       "3             0         0.0       0.0       0.0     1.0           9.0  26.0   \n",
       "4             0         0.0       0.0       0.0     0.0          11.0  19.0   \n",
       "\n",
       "   college_type_College  college_type_High School  college_type_Other  \\\n",
       "0                     1                         0                   0   \n",
       "1                     1                         0                   0   \n",
       "2                     1                         0                   0   \n",
       "3                     1                         0                   0   \n",
       "4                     0                         0                   1   \n",
       "\n",
       "   diabetes_type_1  diabetes_type_2  diabetes_type_Other  \\\n",
       "0                1                0                    0   \n",
       "1                1                0                    0   \n",
       "2                1                0                    0   \n",
       "3                1                0                    0   \n",
       "4                1                0                    0   \n",
       "\n",
       "   ethnicity_African American  ethnicity_Asian  ethnicity_Caucasian  \\\n",
       "0                           0                0                    0   \n",
       "1                           0                0                    0   \n",
       "2                           0                0                    1   \n",
       "3                           0                0                    0   \n",
       "4                           0                0                    0   \n",
       "\n",
       "   ethnicity_Latin American  ethnicity_Native American  ethnicity_Other  \\\n",
       "0                         0                          0                0   \n",
       "1                         0                          0                0   \n",
       "2                         0                          0                0   \n",
       "3                         0                          0                0   \n",
       "4                         0                          0                0   \n",
       "\n",
       "   ethnicity_Prefer not to say  gender_Female  gender_Male  gender_Other  \n",
       "0                            1              0            1             0  \n",
       "1                            1              0            1             0  \n",
       "2                            0              1            0             0  \n",
       "3                            1              1            0             0  \n",
       "4                            1              0            1             0  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_w1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Classifiers with sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_w1_lr, y_w1_9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(X_w1, y_w1_9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifiers = [RandomForestClassifier(), GradientBoostingClassifier(), AdaBoostClassifier()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Scores \n",
      "                    Accuracy:   0.90 \n",
      "                    Precision:  0.91 \n",
      "                    Recall:     0.98 \n",
      "                    AUC:        0.88\n",
      "  Confusion Matrix: \n",
      "                    True Negatives:   9 \n",
      "                    False Negatives:  12 \n",
      "                    True Positives:   684 \n",
      "                    False Positives:   65\n",
      "RandomForestClassifier: 0.82 \n",
      "                        Accuracy:   0.93 \n",
      "                        Precision:  0.95 \n",
      "                        Recall:     0.97 \n",
      "                        AUC:        0.82\n",
      "  Confusion Matrix: \n",
      "                        True Negatives:   32 \n",
      "                        False Negatives:  20 \n",
      "                        True Positives:   683 \n",
      "                        False Positives:   35\n",
      "GradientBoostingClassifier: 0.92 \n",
      "                        Accuracy:   0.93 \n",
      "                        Precision:  0.95 \n",
      "                        Recall:     0.98 \n",
      "                        AUC:        0.92\n",
      "  Confusion Matrix: \n",
      "                        True Negatives:   28 \n",
      "                        False Negatives:  13 \n",
      "                        True Positives:   690 \n",
      "                        False Positives:   39\n",
      "AdaBoostClassifier: 0.87 \n",
      "                        Accuracy:   0.92 \n",
      "                        Precision:  0.95 \n",
      "                        Recall:     0.97 \n",
      "                        AUC:        0.87\n",
      "  Confusion Matrix: \n",
      "                        True Negatives:   30 \n",
      "                        False Negatives:  21 \n",
      "                        True Positives:   682 \n",
      "                        False Positives:   37\n"
     ]
    },
    {
     "data": {
      "image/png": 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AFEVcunTJKf0XKBgVtee1114r8W85duxYKUm6ePFilcRUkIDeuHEDFy5cKLZ/z549MJvN\nCAoKsjsye/78eRw5cgQA0K9fvxJfcyD/ffLUU08ByH8eBw4ckPb9+uuvyM7OBpD/OhRO/AoMHjy4\nzGqwy5cvhyAI8PX1xYIFC0osUKPX67Fo0SIoFApp/VVHOPM9SUTOxeSPiFzCzc0N7du3x8yZMxEV\nFVXq1EGj0SjdLmn9P0eUNiWr8HZXrs8lV7/lVdYISkFCJ4oifv3112L7f/nlFwD5HyYHDRokbb94\n8aKULPbs2bPM/vv06SPdLrwESOHrQmfOnInTp08Xe2yrVq0wf/58TJ061eHqsABw9uxZ6QuHJ554\nAh4eHqW2HT58uDTCV/iDelVwxTlS+PV9UOPGjaXbmZmZTukfAOrXr48GDRrYbadWq0st6OLj4yMl\nE4WT5soYMGCAlPSXVKio4JwfMmRIiV+QFFZQFAbI/0KlLIUT4cKPK7yW4ODBg0t9fMH04weJoigd\nr3PnzsVmWBQWGhqKhx56CACkEXp7nPmeJCLn4rRPIqoSCxcuLDKtx2Aw4MyZM/jmm2+QkpICd3d3\nDB8+HJMmTSrz2o/C0+eysrLKrP5YmsKjfYVHDwt/+1x4hNHZ5Oq3vIKDg0vd16FDBzRt2hTXrl3D\ntm3bMHXqVGmf0WiURuoGDBhQJIEqfC3b+++/b/capQIFSx4A+aMnERERuHjxImJjYzFmzBgEBQWh\nZ8+e6N69O3r27OnwtXUPKiiyUvAcy6LRaNCmTRscO3YMly9fhiAIdhMBR7niHCkr6Sr8RcuDhUGq\nUlnnWGH16tUr88sfvV6PjIwMWK3WKonLx8cHjz76KA4ePIidO3cWKSSTlZUljeQ5MuWz8MipvSmp\nrVu3lq43LDyKefXqVQBA06ZNy3wd2rZtK43aFZaYmCgl8Xv37pWqrdqTmJjoUDtnvieJyLk48kdE\nVSIsLAytWrWSfrp06YIpU6Zg8+bNaNq0KbKzs7Fo0SLMmzfP7nEKPFiV0FHXr1+XbheeklT42Hfu\n3KnQsStCrn7LQ6PR2C0bXzD6Fx8fX6SC4d69e2EwGAAUr/KZnp5eoXgKJ/BqtRpff/11kZGru3fv\nYsuWLZg9ezZ69eqFsWPHYs2aNeUeNSucaDlynVvBB1pBEKRpeVXBFedIWaOahT2YSFQlR6dy6/X6\nMvcXfIFUlbEWTP28cuVKkQRu165dsFgsaNiwoUPXFxacU0ql0u4UXrVaDV9fXwBFR1wLplAX7CuN\nXq8v8bWq6PvOarUWq95ZEme+J4nIuTjyR0ROFRQUhC+//BJPPfUUcnNzsWHDBoSEhEiVER/UpUsX\nqXDIiRMnKlQkoKB4B1B0NKddu3ZQqVSw2Ww4fvx4uY+7detWXLt2Dd26dUOnTp0cXmNLrn4LFF6A\nvDSOVOIbMWIEFi9eDEEQsG3bNsycORMA8PPPPwPIn9L3YPXWwqNI8+bNQ6dOnRyK+cHRjvr16+Pr\nr79GQkICoqKisG/fPpw7dw6CIEAURcTFxSEuLg4bNmzAihUrHB4xLm/yUPj5VNWoH+Cac8TZ1RYd\nOc+qswEDBmDevHmwWq2IiopCixYtABSd8unIa1jec6rgdSt8PpUnuS3pGtXC5+no0aMxceJEh+Mp\na4poYc56TxKRc3Hkj4icrkmTJpg7d650///+7/9KXSw6MjJS+hD0448/lruv27dvS9ethISEFJnu\n5OnpKRVruHHjRpGphY5Ys2YNli5dikmTJhVZksIeZ/Vb+INoWR8Sq2qEKjg4GN26dQMAbNu2DUD+\nKMehQ4cA5CeHDyZEhaczenl5FRkdLuvnwfUfC4SHh+Pll1/Gpk2bcOzYMSxZsgRjxoyRpgtfvHhR\nqgTpiMIjKwWjLWUpaKPRaBweSXOEXOdmedhLRKpyJFQOvr6+0nWGUVFRAPLP74J/Txxdd7TgnBcE\nwe4InMVikUa5C79XAgMDAdgfwbPZbCWO1BU+lkqlcvh916pVK7vLWDyoqt+TRORcTP6IyCVGjRqF\nxx57DED+B54333wTFoulWLuGDRsiMjISQH7VvIJRQEd99tln0nVAzz77bLH9hStROlrZDsgvDFIw\nohgYGIhHHnmkXHE5o9/CH9IKF8p5UEnVOSuqYFrn1atXceXKFWlKXOF9hRWMngBAXFxcmcdOS0vD\nZ599hi1bthSpuGg2m3Hp0iVp7cYCPj4+GDRoEBYuXIiffvoJ3t7eAIB9+/Y5/HwKfzlQUtGKwsxm\ns3QNY+PGjat05A+Q79y0p6BKpMViKfV6QKPRWOGphtVJwdTPhIQE3LhxA7t374bVakVoaGip6/89\nqPA5Ze+cj4+Pl94/hb/wKFhu4dq1a2VOw7x06VKJ1z2GhoZKI3inTp2yG/NXX32F9evXS9c22uPM\n9yQROReTPyJymQULFkjX/CQkJODbb78tsd2rr74qTWWaO3curly54tDxf/zxR/zwww8A8kf9Sqoo\n+sQTT0gLSq9ZswYnTpywe1yTyVTkWsUXXnih3N+OO6Pfgg9WQOmFGtLS0uwmNeUxaNAg6RqjPXv2\nSJUR27RpU+L6YO3atZNG17Zu3Vrm6NB3332HJUuWYM6cOdi9e7e0fdiwYRg+fHiZC1A3atRI6t9k\nMjn8fFq3bl0kvoJrF0uydetW6YN4jx49HO7DUXKdm/YULsJUeD3Cwo4cOVLilzk1zYABA6Rkd9eu\nXdi5cycAx0f9gD/XxwSADRs2lNl23bp10u3C1XALKuZardYyZ0CUtk+j0Uij9AkJCWWeS0ePHsUn\nn3yCefPm4csvvywz3gLOfE8SkXMx+SMil6lfv36RRYk///zzEpOWZs2aSdNEs7Ky8Mwzz2Dv3r2l\nHtdiseDLL7+U1mFzc3PDf/7zH2i12mJtNRoNFi5cCKVSCavVipdeeqnE0u4F0tLSMGPGDOkb7nbt\n2pW6TEVZnNFv4RGGDRs2FPvwbbVaMX/+/Cr9UK7X66XS81u3bsVvv/0GACWu2Qjk/y0KRmAzMjLw\n2muvlVgAIiYmBsuWLQMAaLVajB07VtrXr18/APkFgEr7wuDKlSvSqJyjIzRA/t+l4HqolJQUvPnm\nmyWOpFy4cAEffvihFF9Jo8qVJde5aU/h86ykEcnU1FR89NFHVd6vHPz8/KSpn1u3bi1Xlc8CrVu3\nlkZfo6OjS10T8scff5SSt2bNmknnOZCf/DVs2BAAsHjx4hLXHjx+/DhWr15dahxTpkyRbr/xxhsl\nzgC4d+9ekSn5kyZNKv2JFeLM9yQRORcLvhCRSz377LPYsmULzp07h7y8PLzzzjv46quvirUbO3Ys\n8vLy8MEHHyAjIwMzZsxA586dMWTIELRo0QKenp5IS0vDqVOn8Msvv0jXSOn1evzvf/8rs7BI9+7d\nMX/+fMybNw85OTl45ZVX0KFDBwwbNgytWrWCVqtFamoqfvvtN2zevFm6JqdJkyZYsmRJuQuuOKvf\nTp06ISwsDDdv3sSZM2cwZcoUTJ48GYGBgbh69SrWrFmD+Ph4qU1VGTVqFLZs2SItk6DRaDBs2LBS\n27/00kvYt28f4uPjER0djZEjR2Ly5Mlo2bIlsrKycPToUaxdu1YaHfjnP/+JoKAg6fFTp07Fli1b\nkJ2djQ8//BCxsbEYOnQoGjZsiOzsbJw5cwarVq2C0WiEUqnEjBkzyvV8XnzxRURHR+Ps2bPYvn07\nrl69iokTJyI8PBwGgwEHDx7EmjVrpKm1b775ZqkLW1eWXOdmWYYPH47PP/8cVqsVq1atQm5uLoYN\nGwZ3d3ecOnUKK1euxN27d6v8PJPLkCFDcOjQISnhatq0KVq2bFmuY7z33nsYPXo0srKy8N577+Ho\n0aMYNWoUgoODkZKSgm3btkmFZNzd3fHpp58WWYRdr9dj/vz5mD59OrKzszFhwgRMmTIFPXv2hCAI\niI6OxurVq6FQKKRCQQ/q3r07JkyYgHXr1uHmzZsYOXIkJk2aJCWmZ8+exbfffou7d+8CAAYOHIgB\nAwY49Pyc/Z4kIudh8kdELqVSqbBgwQKMGzcOgiBg//792LFjh3StTWGTJ09GmzZtsGDBAiQkJCA2\nNhaxsbGlHrtv3754++23HVpQeNy4cQgODsb8+fORlJQkVaYrzeDBgzF//vxKV6yryn5VKhU+/vhj\nvPDCC8jOzsaJEyeKTe+KjIzEqFGjypyeVV7dunVDo0aNpCmAvXv3LvN1cXd3x7JlyzBr1iwcO3YM\nV69eLXHJD5VKhb///e/FRh8aNGiAJUuW4O9//zuysrKwc+dOaTpeYTqdDvPmzZOmuznKzc0Ny5Yt\nw6uvvorDhw/j4sWL+Pe//12snV6vx7x580q8trEqyXVulqZx48Z48803sXDhQgiCgE2bNmHTpk3S\nfqVSiVdffRUZGRlYvny5U2JwpQEDBmD+/PnSCHB5Rv0KhIWF4bvvvsPf/vY3JCYmYu/evSXOXggN\nDcWnn35aYnLZt29fLF68GK+99hoMBgM+//xzfP7559J+lUqFRYsWYcGCBaVOV3777bfh7u6OlStX\nIjMzE0uWLCmx3aBBg/Dxxx87/Pyc/Z4kIudh8kdELte+fXuMHTsW69evB5D/LXmvXr1KXAOsa9eu\n+Pnnn3HgwAHs2LEDZ86cQVJSEkwmE7y8vNCkSRM8/PDDGD58eLm/ne/Tpw927NiBnTt3Ijo6GufO\nncOdO3dgNBqh1+vRqFEjdO3aFU899RTatGlTJc+9qvvt2LEjtm/fjuXLlyM6Ohq3bt2CTqdDREQE\nxowZgyeeeALR0dFVFjuQX2V0xIgR+OKLLwCUXOjlQX5+fli5ciX27t2Ln3/+GXFxcVLlzIIqos8+\n+2ypi1F3794d27dvx9q1a3H48GFcu3YNubm58PT0RKNGjdC7d2+MHz/e4UXEH+Tr64vly5dj7969\n+PHHH6X4PD09ERISgsjISIwePbrIiKQzyXVulubZZ59Fx44dsWLFChw/fhz37t2Dr68vunbtismT\nJ6NTp07StNiazs/PD926dcPhw4cBAEOHDq3QcVq2bInt27dj48aN2LVrFy5evIjs7GzUq1cPTZs2\nxfDhwzFs2LAyl1YYPHgw2rVrhxUrVuDw4cNITEyEXq9Hp06dMG3aNHTq1AkLFiwo9fEqlQpvvPEG\nnnzySaxbtw7Hjx9HcnIyLBYL/P390bFjR4wePRp9+/Yt9/Nz9nuSiJxDITpzNVciIiIiIiKqFljw\nhYiIiIiIqA5g8kdERERERFQHMPkjIiIiIiKqA5j8ERERERER1QG1JvmzWq1ITEwscXFeIiIiIiKi\nuq7WJH/JycmIjIxEcnKy3KEQERERERFVO7Um+SMiIiIiIqLSMfkjIiIiIiKqA5j8ERERERER1QFM\n/oiIiIiIiOoAJn9ERERERER1AJM/IiIiIiKiOoDJHxERERERUR3A5I+IiIiIiKgOYPJHRERERERU\nBzD5IyIiIiIiqgOY/BEREREREdUBTP6IiIiIiIjqACZ/REREREREdQCTPyIiIiIiojqAyR8RERER\nEVEdwOSPiIiIiIioDmDyR0REREREVAcw+SMiIiIiIqoDmPwRERERERHVAUz+iIiIiIiI6gDZk7+4\nuDhMnDix2Pa9e/di9OjRGDduHL7//nsZIiMiIiIiIqo91HJ2/vXXX+Pnn3+GTqcrst1iseD999/H\nDz/8AJ1OhwkTJqB///4ICAiQKVIiIiIioupLsFiQsv8gcq5cAQSh0scTIcImCBBE4f5vG2zin/dt\noq3Qvvz7f7a3QUDlY3iQRdAg3RIEk6Cv8mNXhJtOga4DOqJjj0fkDsVhsiZ/YWFhWLJkCWbPnl1k\n+5UrVxAWFgYfHx8AQJcuXfD7779j6NChcoRJRERERFQlRFGEMTkZgtFkt23qocNIjtoJpZv7n4+H\nKB2n8G0hPdM5Aduhuv+jqcBjRQDX/drjrmcTWJTuZba1KTWwqtwq0IsTmYFtG/9Ado4RvQf1kTsa\nh8ia/A0ePBiJiYnFtufk5MDLy0u67+HhgZycHFeGRkRERETVmGCxID32JAw3bsoaR8EIWf7Ily3/\nt80GqyjAJlhhEwVYhfx9QkoaNL+fq0AvVf852KDxxj19Q1iV8iVUf/i0gkWts9+wmskxZMBT7wsA\nsCnd8FuXcy5nAAAgAElEQVTUaSZ/leHp6Ync3Fzpfm5ubpFkkIiIiKi2sJlMEExmucNwCsFiRuL3\nPyDnyjUolIoqO64oCMi5dLnKjldZInA/iSr7OYoALvt3Rrq+gSvCKlWWNkjW/msqm2DFpl//i/iE\nQxgzbDbaRvQCAJhtNpkjc1y1TP4eeugh3LhxAxkZGdDr9Thx4gSmTp0qd1hEREREdhn+SETO5csQ\n7Vx3ZTMYcO2bb10UFZWHTaFChq4+TCr715bd8wjBXc8mzg+KZHXpWgw2bvsYJrMBAHAn9TraRvSC\nmzUbF939ZI7OcdUq+du6dSsMBgPGjRuHOXPmYOrUqRBFEaNHj0b9+vXlDo+IiIjqoNwbN3F39x6Y\nUlLttr139JgLIqLyEgHc9mqOdF0wbEpVmW0tKi0ydPKOzJF9oihCFK86vR+zOQ/LNyzFnXtJAAB3\njRbjR0xFk9DmMOAazqiC0KZFC6fHUVUUoiiKcgdRFRITExEZGYk9e/YgJCRE7nCIiIioGhMsFuRc\nuQrBaCyzXd7t27j65dcuiqpmSPEIxVX/TjBofOQOxWGCnYSPqocsiLgG+6mJBXCgVdXYsfRZWE35\nl6N5Bz2EPs99Iu3TqJVo3zwAs8Z3hq9X2QVrqotqNfJHREREVBGC2YzUQ0eQc/Ua7H0sNNy4iczT\nZ1wTWC2Tp/bEmQaPQVTIvlQ0VaE8iEiXOYZciMiowOOckYAlJycjICAAarUah57cgYEDB2Lbtm3o\n379/lRxfTkz+iIiIyGVEUYQpJQWCuQoLnIjAuXffg+nO3ao7ZiEpHmH4w6cVjGoPpxy/iEJFUUpM\nYWWesGXUeMvaf02h05e98IHZZIPeww1Dn2oLjypIWJb9fBYXblQsfTOg6kbRurQMwvxp3avoaPJ4\n9tlnsXbtWrRt2xZnzpxBr169kJeXJ3dYVYbJHxEREVWYYLUi8/QZh8rt5yXdwp1du10QlX0GjRfS\ndA1hU5b9USjLPQB3vZq6KCqqrtp3CYHCTrFSnYcbWndoiJDGri/+cTopExaX9/qnwqNvNdXp06fR\nrVs3GO9PBb95U94lRJyFyR8REREVI9psdkfnRFHEhfc/qnFTKFP1jXA6uD9EBa8Dq6sGjWwDbx+t\n3XaBDbwQEOQJhb3Mrxwysk1YvD4Wpy+nwmItuyJsdVEbRvTKMnjwYOzcuVO6P378eKxbt07GiJyH\nyR8RERFJRJsNV79ehrvR++0WQ5GTCECEAqJCCVGhhID7v+/fF1Ho9gP74xoOlDv8WuWJsR3QrnMj\nucNwiEKpgErl+PWKGdkmLPjmWI1K1KpSbRjRs+fxxx+XEj9PT0+cOXMGTZo0kTcoJ2LyR0RERJLb\n26OQvD3KKccWAdzxbIo0fTBsCg0EhQqiQlEkOZMSuFKStz8TPI7ayU2hVKDjw6Ho8HAolFW4gLsr\n1MTRt/LQqJXY/OETcodRreXk5MDT0xObN2+Gj48Ppk+fjv/9739yh+V0TP6IiIhqoPTYk7i5bgPy\nkpL+3CgWvikW2SyICmRrfCEolFJDsdBjClorzRZAG1TueASFClalG6xKN1hUbvdva/J/379fG9ZO\nExQ2XG19FILKWuljKRVK6DRa6NT3f9zcoVPr8m9rtH/uu/9b7/bnfa1aC3e1G5QyVt308HSHu7Z6\nfZSs7Umdo9o3D5A7hGrrhx9+wLhx46DT6ZCTkwOtVguTySR3WC5Tvd6xREREdYzZakaqIQ13c9OQ\nariHVEMajFYzLDYLLDYrzILl/m0LLIIVZpsF6oxcRK6Jh0pwrEZfurY+4oIjYVO5OfnZ1Fz3gq5D\nVJadLJi0ucjyT4ZNbYG72h06tXuRBE2rdi+SuOk1Onho9PBw00Ev/c7fpnfTwV3lVu5ryZjcUFnq\nwjTNirJarWjTpg0SEhIAAAaDARkZGfD19ZU5Mtdi8kdEROREJqsZKYZ7SMkt+EmTbt81pCHTmFXu\nY7a8mgeVIEKAAiYHlh842WgQp0mWwb2pCR16BBVJ4vQaXX4ydz+502rcoVfroNW4Q6tyh1Ipz4jb\n4vWxiLngnCUtyLVq4gLhNdXSpUvx8ssvQ7y/VEpoaCgSEhKg1dov+lPbMPkjIiKqhDyLMT+ZM6QV\nTfDuJ3xZppwq71NjA276tMbVep1gU5a9nlitphChUCqgUAJKpQJKlQJKpQIqtRIqVf6PWqWCRq2C\nSq2CSpVf7EOpUkKlUkCtViGsmT86dwuDshxFQJyBI3rVGxO1muunn37CzJkzAQAKhQKff/45pk+f\nLnNU8mHyR0REVAaDOU9K5O7m3kNqbhru3r+fmpuGbHOu3WMoHJye+VCiCc1vmuBmZ8EutckLCYGP\nOHTM6qxZTz186mmhdXOHVuMGrZs79G5aaN3doXdzh0ajgUqlgFKZn6yp1ErptlKldLjISJmJ1ck/\ngE1xTnh2VBMwqau9rFYr1Go1Ro4cCU9PT4SGhuL06dNQq+t2+lO3nz0REdVpoigi12LA7YxUXL58\nG2nZmcg0ZiPTlIMsUzayjNkw2swPPgheBgEeeQIawB0NUPoHRk+DDa2uO1ZIQFAocTEwEoluWsDe\npXn2Z3rapfIzQQEFFApAoVBCAQWUCsX9bfd/lEooFMoi24u0KbRNpVRBp3eDTqeBVqeB+/3fWq0G\nWp06/742f5tOn/+7MjhSRmVhUld3paamIiIiAunp6TCbzVCr1cjOzpY7rGqDyR8REVULVosNxjxL\n/o/RCmOeBaY8C4xGC4x5VmmfYHNsFA3Ir3hpEawwWowwWk0wWk3Is5pgtBphtOTfVxm08MjxL/Qo\ndwDu0KEedJV8ThYAp4MreRAn6PxoGIaP6eDSPpmsOUdtX3ybqDz++te/4osvvpDuL1++HC+++KKM\nEVU/TP6IiMhlrFYbUpKzcedWFpJvZeHOrSzcu5uDvDwLbC5LCNQAPOEGT7sDbDWNj1/Z6arGTYWH\nwgMROaxVlfTHhE4+rOpI9KfLly+jY8eOyM3Nn4av0Wiwe/du9OnTR+bIqh8mf0RE5BS5Oab8JC8p\nC3duZ+JOUhZS7+ZAcPD6NyqfsRM7omXHUJf2ycqTVY/TFYnKx2q1okWLFtL9oUOH4tdff5UxouqN\nyR8RUS0hCiJycuRdqDY9NRf7dybgzu0sGHLM9h9AdogQ7eTKVgi4DQVe+y4W+C7WNWHVUEysiGof\ntVqNZs2a4datWzhx4gTatGkjd0jVGpM/IqJa4ExMIqJ+jmfCVUUCcm463Fbj4w2FnTXfFCoV3Px8\n4ebvX2Y7AFAoFQgO8cGeyymIuZQK2C1oWb5FwmsbJnREdc+AAQOwZ88exMXFoX379rhy5YrcIdUY\nTP6IiGq47Cwjtqw7CdTw2ZQKUYDaZoJasEAtmKERzFALZqht93/f/1GKNif1L8LbmIJmfbrAq2WE\n3fba+kHwbt0KSo1z1tn7dNdFpxxXbkzWiKiidu3ahccffxxWqxUA8Pe//x379u2TN6gahskfEVEN\nl3QjvUYlfu7WXHia0uBpSoeXOQ2epjRorblQirZyjWHFtNIjtqUObmp3BOh84a/3Qz29HwL0fvDX\n5f+up/eDl5sHFAr7R1aoVNB4e1f8iVWxmlhAhZUnicgZrFYrunXrhtjYP6e2v/rqq/jkk09kjKpm\nYvJHRFTDVfcCKg2yrsDTnAYvU36i5yZUzXWJ/fuOxpT+AxxO7iqC1Swdw8qTRORMPj4+MBgMAIDA\nwEAkJCTA19dX5qhqJiZ/RES1kEqthK6URbRtJhNs9/8TdVr/ghUNcq6iadopp1yRpm1QHy17D4Da\n3dMJR/9TdatmufWTkXKHQETkck8++STWrl2LRYsWYc6cOXKHU6Mx+SMiqoVCva3oH1HyCNuN79a4\nOBrHqDz0COjRw247bcNgBPXrC7WncxM/ADh9OdXpfThKoy67qAwRUW2xZMkSvPLKK/jwww/x2muv\nYfXq1VixYgXUaqYulcVXkIjIxcwmK2w2+1MI007EIPXAIeRl5cICNSxQwSLe/13ofpaoB1B0+ovh\n5k3cOL7POU/AQSo/H4RNnQJ3N22Z7RQqFbzCW1TJ9Xa1eZpm++YBcodARORUOTk5CA8Px+3btwEA\nCxcuxGuvvQYATPyqCF9FIiIXSUvNxabvYnA7MbMcj2rptHgqwhDeCOLUkQjwCkSgvh4C9P7Qqt2K\nN1Qqq/Q6vNqc1NnD6+mIqC546623sGjRIul+mzZtcOrUKRkjqp2Y/BERucjmNbHlTPwqwd7K4A+I\n7uJZZLk4TzcPNPAMRLBXEBp4BsHL3QMeYWHwatUSSjvfvtbmRE2jVmLzh0/IHQYRUa3y8MMP48SJ\nEwAAlUqFTZs2YeRIXuPsDEz+iIhcwGqx4dYfGS7rz8uU5nDb/Z09kdw5FK2DwtE6sAVaB4UjyKNe\nhfuubkVSqhKnXhIRVb3XX38dY8aMQY8ePXD48GG5w6nVmPwREbmAIIguW4vPNy8ZGQHX8XtDfalt\nPNz0qOdVDwEdOuHFnpEI0PtXWf/VqUhKVeHUSyKiqnPx4kV06dIFXbt2xb59+/D000/DYrHwuj4X\n4CtMRDWWyWhFelouTHlWGPMsMBot+b/v3zcVum8yWmA2WWWLtbRZmGqb/TXvVKIVapsZBp0NJncr\nRKUFotIC4f7vP3+syPUw4qx/HqDUFTlGsFcQ2gSGo3VQC7QODIe/vvzrI9Wk6ZyFkzVfL3e5wyEi\novueeuopbNmyBQCwf/9+aTsTP9fgq0xENY4oitj76wUc23/VoaqZ1ZVKsKDvtXUOt9/axwdXQxxJ\nZBRo5N0ArQNboE1QOFoFtoCfzqfEljUpoQOY1BER1VS///47evXqBbPZDADQ6XQ4efKkzFHVPUz+\niKjGuXHlHg7vvSx3GJWmKGdRlnQvVan7BIMnhGx/2LL8IWT74bLVHZcB/IwkAEmVC7SSWCSFiKhu\ne/fddzF37lzp/uTJk7FixQr5AqrDmPwRUY3jysIpzuRtSnG4bUKYO9K9/0z+BIMXhCw/2LL9IWT7\nA9YSlluoJlgkhYiobps2bRrmzp0LHx8fXLhwAQ0aNJA7pDqLyR8R1TiC4KLKKU6kN2egReoxHOjk\nabdtqp8a6vCmeLxBBLZuz4A507daJ3sFWCSFiKhuslqt6NKlC1JTU5GUlIQGDRogPT0dvr7lv96c\nqhaTPyKqFfwDPNAozBdanQbuWjW0Oo30465Vw00N2FLvAoKd69pEEbe3/YrsiwlQ60uvlgkAVoOh\nQrGqRCvcrQZY1AqcbBVYUggQDd73p3D6Q0jyA25qcA4AEFShPqsSr7sjIqLSrFq1ClOmTIF4/9KG\n2NhYdO7cmYlfNcHkj4iqLVEQYTLdr+R5v5qnKc+K5KSsYm1btQ9G5LBWJR4n7UQMLn70CQST/cqa\nBTwAILN4P4VVNu0xaJUAAKVCCTerH7LvekPI8oOQ4wfYNJU8evkwoSMiosqwWq0IDw/HtWvXAAAK\nhQIff/wxOnfm7I/qhMkfEVULoiji2IGriI9NRE6WESaTDSaTzeHH2wwGmFKKX0MnCiLOv7uoKkOt\nMqcDg2G62A5Cth9yhYr9c8xiKkREJLfr16+jWbNm0mhf06ZNkZCQwOUbqiH+RYioWji88zz27rxS\n4cff3r4DJ1bHVmFEFXcptPSRM1FUwGz0xVXVQzinbAZkKirVF4upEBGR3Jo0aQKlUglBELBixQpM\nmjRJ7pCoFEz+iKhaOL3/PICKFzHRWbKrLpgKMKmVONtCi0MdPQBFyQmd9W4ILInhgLbyxVpYTIWI\niOT0+uuv48svv0RmZiYA4PLlywgJCeFoXzXHvw4RVQtGowVQVCwp0pszEJRzo1yPSdQWL7RSmEIU\nkaHxwhH/dshV6UppJEAddBOqBtdg1YoQlCUnfYLBE5brrSHk+DscX5eWQZg/rbvD7YmIiFwhOTkZ\nrVq1QkZG/rJLf/nLX7B8+XI0adJE3sDIIUz+iKh6EEWghNxJJZihtlmgFsxQC2ZoBDPUNrN0X2/J\nQmDuTagFi8NdHfNtg30BXSoVrtIzHZom8bDpc5B/ZWLx4EWbEtZbzWFNbgKISoeOyxE9IiKqrl54\n4QUsW7ZMut+nTx8sX75cxoiovJj8EVG11S5xO9yt9qdz5io1gNJ+dUyLQoPLHiE4UK9TxYNSm6EJ\nSYA6KLHMZraMQFiut4Jo1rOSJhER1Xg+Pj7Iysqvgu3m5oZDhw7h4YcfljkqKi8mf0TkEqIowpBr\nRkZaHjLTDYV+G5CZnodcRfE19bYH9cBtNy8Zos1XeOqlKIo4cP03rIrbhGxTTqmP8dP54PlOY9Et\npBMUpVz7R0REVNO4u+d/efnkk09i8+bNMkdDFcXkj4icJjvLiIO7LuHGlVSkpxlgtZSxwLrCsWmR\nrvDg1MukrGR8E7MO8XcTSn2MQqHA0Ob9MLbdE9BrSrlGkIiIqIY4evQohgwZgvj4eISEhCAhIQF3\n7txBRESE3KFRJTD5I6JysVptuHk1DZnpeXbbbv0+rlJ9CWJ+srj1k5GVOk5Fma1mrD/zM366sBM2\nofQ1Bx/ya4xpXSegmX9jF0ZHRETkHL1798ahQ4cAAN26dUNSUhJ8fX3h6+src2RUWUz+iMhhJqMF\n3/2/Y7h1M8PpfXkbU2BTqKBRyzMiGJd8Dt/ErMednOILxxfQqbWY0H4kBj3UB0pl9Rm5JCIiqohf\nfvkFo0aNgs2W/4Wnn58fYmJiZI6KqhKTPyJy2Lm4205N/JSCFVprLvzybqNpWhziGg50+SLm6XmZ\nWHlyI478UfZ/dj1Cu2BSp6fhr+O3oEREVPP16NEDR48ele6/+eabeO+992SMiJyByR8ROSz1bumF\nThzlYUqH1poLrTUHOksOtNYcaO//drMZiyyY0LqZP6a5aMkDQRCw88oBrDvzE/IsxlLb1fcIwNQu\n49ExuI1L4iIiInIFDw8PAEBwcDASEhLg6ekpc0TkDEz+iAiiKCIzPQ8Wc+nXtQGAIddcqX7a396D\nwNw/HGqrdHfHP18eAqXG/hIOlXU17Sa+jlmLK2mlLxSvUqowsuVAPNVqKNzUFVuMnoiIqLowGo1o\n2bIl3nnnHUyaNAm7du3Cjh07MGTIELlDIydSiKIoyh1EVUhMTERkZCT27NmDkJAQucMhkpUgiLh+\nORXJSZmw9w7PTM/DiSPXK9xXLkQYStius5nQ4n6ipxbMCMy5CT/jHYeP22z6iwgeOrjCcTkiz2LE\nhrNbsf1SNMr6p7B1YAu80HUCQryDnRoPERGRK3zwwQd44403AAAqlQpWq1XmiMhVOPJHVAtt/T4O\ncb87NsJWWY0zLsA/51qx7W6CBfXN6UW2Zao9sK/+I2jSwAvDezWDh66EUT2lEp7Nm8O9nr+zQoYo\nijiedArfxn6PtLzSr2H0cvPAxI6j0bfJo1yzj4iIaryMjAyEh4cjJSW/mJlSqcSqVatkjopcickf\nUS2TmW5wWeIHAAHmdDQy3nWobcPQQHy0eKaTIyrb3dx7WB6zHrG3z5bZrn/THni2w5Pwcuc1D0RE\nVPO9++67mDt3rnS/c+fO+O2336BWMx2oS/jXJqpl0lJLmoTpHErBCn/DLccfIOPomVWw4ZeLu/FD\n/DaYbZZS24V4B2Na1wloFdjChdERERE5V8EafWq1Gr/++isGDhwoc0QkByZ/RLWMJTu70sfQm+0v\n56A3Z6FxxlnorI5XAPVo1qwyYVXYhZQr+PrEGvyRdbvUNm4qDZ5uMwzDwyOhVvGfRiIiqvkmTZoE\npVKJFStW4OWXX4Zer8fUqVPlDotkxE84RLWMKaX4ouTulhzUL+G6vMKUogAf413UMyTBGeNz2oYN\nETZhnBOOXLpsUw7WnP4Re68eLrNdp+C2mNp5HII8XbumIBERkTPEx8fj4YcfRl5eHgDgs88+g6en\nJxM/YvJHVJOYjBbk5pS93EKuofhyDXpLNlrcK3vR8sKUOh1MFhsEQURJNTAVIuAuWnDcpxUSPMOK\n7Pvgb72KtVd7eEAfFgqFUulwDJUhiiIOXP8Nq+I2IdtU+sikn84Hz3cai24hnVjQhYiIaoXHH38c\n27dvl+4//fTTXLOPJEz+iGoAwSZg68bTOB2TCFEo/+osCrUaAX16l9lGqVbBq2UE6g+IxILlxxFz\nwbEiLg/yadO6Qo+rKklZyfgmZh3i7yaU2kahUGBo834Y2+4J6DU6F0ZHRETkHL///jt69uwJiyX/\nunZPT0+cOXMGTZo0kTcwqlaY/BHVAHEnEitVwVOldUfEP2c53P705dQK9aNRu2ZkryRmqxmbz+/A\nTxd2wiaUvlj9Q36NMa3rM2jmH1ZqGyIioprG29tbSvxmzJiBzz//XOaIqDpi8kdUAyTeSLffqAxq\nlJ4MlcRiFSrUT/vm8lwzF5d8Dt/ErMednOLXOxbQabSY0G4kBj3UB0oXTT8lIiJyps2bN+OLL77A\nrl27EBERgU8//RTPPfccAgJ4DTuVjMkfUQ2Qdzu5Uo8PVtmv3lkZGrUS7ZsHYNb4zk7t50HpeZlY\neXIjjvxR9vWMPUK7YFKnp+Gv83VRZERERM5jtVrRvn17nD9/HgCwYsUKTJkyBbNmOT7Lh+omJn9E\nNYA5PQOAvsg2jc0ItVB28ReNzYT62VfR9KHKX9e29ZORlT5GVTpy8wT+34k1yLMYS21T3yMAU7uM\nR8fgNi6MjIiIyHm++uorTJ8+HaKYXwOgYcOGePrpp2WOimoKJn9EMkq8kY5rl1JhtZQ9LTPNWPyt\n2jz1BBpmX3aoH79OzwMAMrJNWLw+Fqcvp1Z4amd1cDr5PP53dDlKrkUKqJQqjGw5EE+1Ggo3tZuL\noyMiIqp6VqsVzZo1wx9/5NcAUCgUWLJkCf72t7/JHBnVJEz+iFxAFEUYcs3ISMtDZroBGWkG7Iu6\nCKvF0QSshARGqYRCXfZbWOPtjYA+vRA8bCgAYPH62ApX8awuDOY8fPH7d6Umfq0DW+CFrhMQ4h3s\n4siIiIicKzExEQAQHh6O+Ph4qO18DiB6EM8YoiokCiJuXLuHpBsZyEgzIDM9Dxn3kz3HEz3HBD3W\nDz2ef79cj6mJVTwftOrUD7hnKF4Ax8vNAxM7jkbfJo9yzT4iIqoVUlNTMXbsWOzduxdqtRqrV6+G\nm5sbp3lShTH5I6oioiBi85pYxJ+65ZL+vPXlT3BqWhXPB528fRZ7rx0ptj0i4CHM7jUdXu5cxJaI\niGqHl19+GZ999hkAYOTIkfjpp5/wzDPPyBwV1XRM/oiqyJ3kLJclfr6G26jv18zp/chVxbMkOeZc\nfPn76mLbtWp3vNxtChM/IiKqFa5fv4527dohJycHAKBWq/HKK6/IHBXVFkz+iKpIWkpupR4fkPsH\nvI2lr1NXwMOcgXqGJOgC3qhUfwWqWxXP0qw4uRHpeZnFtk/sMBpBntVjZJKIiKgyJkyYgPXr10v3\nBw0ahKioKBkjotqGyR+Ri2jcVPD118PHTwdfPz0yftkMrTkLWksOvEz3oCylgElJ9I3D4N2mtROj\nrV5OJMXhwPXfim1vX78VBjzUS4aIiIiIqt4vv/wCANBqtfjtt9/Qvn17mSOi2obJH5EThTbxw+BR\nbeHrp4POw61IIZIjaxdCtFqLtA8aEAmFquziKvrQUAT27QOVu7tTYq5usk05+OrE2mLbdRotpj/y\nHIu7EBFRjTZu3DgsW7YMnp6eOHPmDObOnYtVq1bJHRbVUkz+iJzI01uLhqG+Drd/aPo0KDUaJ0ZU\n8yyP3YAMY1ax7ZM7jkGA3l+GiIiIiCpv7969GDx4MKxWK/bv34/k5GQ0adKEiR85FZM/Iqq2jv0R\ni8M3TxTb3im4LR5r2l2GiIiIiCrv4YcfxokTf/7/Nm7cOBmjobqEyR+RE2VfuIgLH0SXuE+02Vwc\nTc2SZczGNzHrim330OjwUtdnOd2TiIhqnPXr1+PZZ5+FIOQvvRQQEIBLly7B19fxWUJElcHkj6iK\nlJTMmdLScO/cMaf2m5FtwuL1sTh9ObXC6/hVN6Io4puY9cgy5RTb93zncfDX8z9JIiKqeT7++GMp\n8XvnnXfw9ttvyxwR1TVM/ojsMCQm4d7RY7BkFl9moLAb51IAhesrcC5eH4uYC3dd3q8zHf0jBscS\nY4tt79qwPXo3fkSGiIiIiCpm6dKlCA8Px8CBA/Hbb78hMjISUVFR0Gq1codGdRCTP6Iy5Fy9hrNv\nvg1bXp7dtkaPxkBwxZM/bcOGFSr2cvpyaoX71KjLriwqh4y8THwTs77Ydk83D7zY9RlO9yQiohrB\naDTioYcewq1bt6BWq2GxWKBWq7F//365Q6M6rPp98iOqRu7u3utQ4ldZCrUaTf8yuUKPrcxUz/bN\nq9fi6KIo4quYdcgx5xbbN7XLOPjqfGSIioiIqHzmzp0LnU6HW7duAQBatGghc0RE+TjyR1QGU2pK\npR6v0uoQMftfZbZRatTwahkBjbd3ke3OvJZPo1aiffMAzBrfuUqPW1kHbxzHiaS4Ytu7hXRCj9Cu\nMkRERETkuNTUVERERCAtLQ0AoFKp8P333+Opp56SOTKifEz+iJzIu3VLBPSs2JIElbmWb+snIyv0\nODml5WXg29gNxbZ7u3tiWpcJnO5JRETV3tKlS6XE79FHH8XRo0dljoioKCZ/ROUUNKA/PBo3Lr4j\nRcTZc0U3qSpxMXdFr+Wrjtfx2SOKIv7f72uQayk+xfaFLhPgrfWSISoiIiL7Ll++jL179+LFF1/E\nvHnzsHv3brz33nvo06eP3KERFcPkj6ic/B/uinqPdiu2PSPuFnAupsr6qehUz+p2HZ8j9l07ipO3\nzxbb3iOsKx4NrV5TU4mIiAqMGTMGP/zwAwBg7Nix8PX1xcGDB2WOiqh0TP6Iaonqeh2fPamGNKw4\ntbHYdh+tN6Z2HidDRERERGWLjY1Fjx49YDKZAAA6nQ6pqalcrJ2qPSZ/RDVITbyWryz50z1XI89i\nLPZW3kIAACAASURBVLbvxa7PwMvdU4aoiIiISte/f39ER0dL9ydOnIhVq1bJGBGR45j8EZFs9lw9\nhLjk88W292ncDQ836iBDRERERKW7fPmylPh5e3sjPj4eISEhMkdF5LiaVxmCiGqFu7n3sOrUpmLb\n/XQ+mNJ5jAwRERERFWe1WrFs2TIAQPPmzTFixAi8+uqryMzMZOJHNQ5H/ojI5QRRwBfHV8FoNRXb\n91LX5+Dp5iFDVEREREWtXbsWEydOhCAICAgIwMiRI/HTTz/JHRZRhTH5IyKX23n5AOLvJhTb/ljT\nHujcsK0MEREREf3JarWiZcuWuHLlirTt9u3bMkZEVDU47ZOIXCo5JwVr4rYU215P74fJHZ+WISIi\nIqI/LV68GG5ublLi17hxY+Tl5WH69OkyR0ZUeUz+iMhlCqZ7mmzmYvtmPDwRejedDFERERH96dVX\nX4UoilAoFPj2229x/fp1aLVaucMiqhJM/ojIZbYnRON8yuVi2wc81BvtG7SSISIiIiJgx44d0u0F\nCxagTZs2MJvNmDJlinxBETkBkz8icolb2Xew9kzxi+QDPephYoenZIiIiIjquuTkZPj5+WHo0KH4\ny1/+AgB4++23cfbsWajVLI1BtQ+TPyJyOkEQ8Plvq2CxWYrtm/HwROg0nE5DRESu9cILLyA4OBgZ\nGRkAgJSUFJkjInI+fqVBRE73S8JuJNy7Wmz7kOb90LZ+hAwRERFRXXXx4kV06dIFubm5AAA3Nzfs\n27cP3bt3lzkyIudj8kdkR46bL+56NIZZlT86dSc2G9qkM8XapaXmujq0GiEx8zY2nNlabHt9z0A8\n02GUDBEREVFd9sgjj0iJ34gRI7huH9UpsiZ/giBg/vz5uHjxItzc3LBw4UI0btxY2v/zzz/j22+/\nhVKpxOjRo/HMM8/IGC3VRelWLU6EDINNqZG2JV3JA65cly+oGsQm2LD0+EpYBGuR7Qoo8LdHJkGr\ndpcpMiIiqktOnz6N1q1bQ61WIzo6GpGRkTh27BgiIjj7hOoWWZO/3bt3w2w2Y8OGDTh16hQ++OAD\nfPHFF9L+jz76CL/88gv0ej2GDRuGYcOGwcfHR8aIqa65ZvQtkvhVhYxsExavj8Xpy6mwWIUqPXZ1\n89OFnbiSdqPY9mHh/dEysLkMERERUV3Tt29fHDhwAM2bN8elS5fQuXNnpKenyx0WkSxkLfgSExOD\n3r17AwA6duyIs2fPFtkfERGB7OxsmM1mab0VIlfKEyqX+NUL8iy2bfH6WMRcuFvrE7+bGUnYGL+t\n2PZgryCMbzdChoiIiKgu2bFjB9RqNQ4cOAAAyMrKkjkiIvnJmvzl5OTA0/PPD8cqlQpW65/Tw1q0\naIHRo0dj2LBh6NevH7y9veUIk6hCfPx06PJoWLHtpy+nVuh4GnXNKc5rFWxY+ttK2ARbke0KhQJ/\ne2Qy3NRuMkVGRES1ndVqRYcOHTB06FDYbPn/D82ePRt37tyROTIi+ck67dPT01O64BbIvwawYE2V\nCxcuYN++fdizZw/0ej1ee+01bN++HUOHDpUrXCIAQOumWjTuWPaURQ8vdzQLD4RWV3zksKIjfu2b\nB1TocXLYcm47rmX8UWz7iIiBCA9oJkNERERUVwwaNAinT58GADRo0ACXLl0qMthAVJfJmvx17twZ\n0dHRePzxx3Hq1CmEh4dL+7y8vKDVauHu7g6VSgV/f38O11O10DjYHQ/3auqy/jRqJdo3D8Cs8Z1d\n1mdlXEv/A5vPbS+2PcQ7GGPaDpchIiIiqu2sVuv/Z+/Ow6Ks9zaA37PBsAmyiQuCggIuaBohqbiB\nOwpmuZunjh1LPVnW6ZSnzUptO6/ZotniUpktIm64m+KWuGTIDu7iwiY7AzPM8/5BzYlGaIAZnpnh\n/lyX18vznWeG2/OGzHd+G27fvo1OnTph3759cHFxweuvv47FixeLHY3IrIja/EVGRuL48eOYOnUq\nBEHAsmXLsGPHDlRUVGDKlCmYMmUKpk+fDoVCgc6dOyMmJkbMuEQms+P9iWJHMAp1jbp2uqdQd3RT\nKpFifuijsJEZd/McIiKid999Fy+88AJsbW1RWVkJuVyOsrIysWMRmSVRmz+pVIqlS5fWqfn5+em+\nnjZtGqZNm9bSschCaaurUZX31+vpaiorcWXj16jMuQmpTcPNiBo9AXuuNTXUltR4XCvO0atHB42C\nn6vPPZ5BRETUNGVlZfDz80Nubi4AoLq6Wm8/CSKqi4e8k1W4ERuH699+B211tVFfV/AKMurrWbPs\ngiuIS9unV/dx7ojJPcaKkIiIiKzVCy+8gHfeeUd33adPH5w5c0a3dwQR3ZvlbB9IVI/yq9dwdcNX\nRm/86iOR8sfmz6pr1Pg4cQO0f5ruKfttuqdcxl/GRERkHD/++KOu8ZPJZNi9ezfOnz/Pxo/IAPwp\nIbNVU1WF0rR0aP5i3v6dA4daKFEtZXuvFv1+luD75J3IKbmtV3+o51j4tvUWIREREVmboqIiuLi4\nYPLkyXB1dUWvXr1w5MgRsWMRWRQ2f2SWVHfuIHnJKwat4WtpcgcHsSOYlcz8S9iRsV+v3sXFG9FB\no0VIRERE1iQlJQUhISFQqVSoqKiAUqlEQUGB2LGILBKbPzJLOVu3N7vxU3Zo3+Dj2mo1HLt2QecZ\n0yBV3Hvjl6vbspF/sbhZOaxZlaYaHydugCAIdeoyqax2uqdUJlIyIiKyBlFRUdi5c6fu+vvvv8fs\n2bNFTERk2dj8kVmquHq1Wc/v9Mhk+Mxo/k6xMuUNAGz+6rP5wnbcKs3Vqz/Sczw6u3QUIREREVmD\nY8eOYfjw4VCr1QAABwcHnD9/Hv7+/iInI7Js3LmCrI5UqYTH4IFix7B6aXlZiM/UX2/p5+qDCYGR\nIiQiIiJroFKpMHjwYF3jN3fuXJSVlbHxIzICjvyRxWjTqycUzg2fuWfj6gbP4UNh37lzy4RqpVSa\nKnyS+BUE1J3uqZDKMT/0Ucg43ZOIiBpJo9FALpdDqVSiZ8+eyMnJQVpaGry8uNEakbGw+SOL0Xn6\nFDj37Cl2DAKw6dc43CnL06tP6T0Bndo0vNaSiIjojzQaDfr06YPU1FScOHECYWFhSE5OFjsWkVVq\nVPNXXFyM06dPIzk5GYWFhSgpKYFSqYSXlxcCAwMRGhqKtm3bmiorEZmB5DsZ2JN9WK/e3a0rxncf\n0fKBiIjIYn3xxReYO3eubuOwN998E7t27RI5FZH1Mqj5O3HiBL788kucOHECgiDo7ewHABKJBDKZ\nDAMHDsRjjz2G0NBQo4clInGp1CqsPv2VXt1GpsBTobMhlXIZMRER/TWVSoWAgABcu3YNQO37yA8+\n+AALFy4UORmRdWuw+UtLS8PLL7+M5ORkuLu7IyoqCr169YK/vz/atm0Le3t7lJSU4O7du8jKysLZ\ns2dx5swZzJkzB3369MFLL72E4ODglvq7EImqqLQKKzefQ1J2PtQardhxTGJn5iHkleufrTQ9OBod\nnNqJkIiIiCyRi4sLqqqqAAD+/v5IS0uDXM7VSESmVu9P2bvvvosNGzZgyJAhWLx4MQYMGACJRFLv\nCw0ePBiPPfYY1Go19u/fjx9++AHTpk3DnDlz8Pzzz5skPJE5Wbn5HM6m6x97YC1UatU9d/cM8vDH\n6G5DWz4QERFZlN83dAFqd/D85JNP8NVXX2H69OkiJyNqPeqdo3X+/Hls2rQJH3/8McLCwhps/P5I\noVBg7NixWLduHdavX4+zZ88aLSyROUvKbtqh9Aq5ZUyV3H/xGMqqy+vUZBIpngyZBanEMv4OREQk\njkWLFkGhUGDJkiUAgA8//BA1NTVs/IhaWL3v2L755ptmT9kMCQnB5s2bm/UaRJaiqVM9g/3djZzE\n+Kpr1NiRsV+vPtgnFF5OniIkIiIiS3Djxg04Ozvjgw8+AAB8+umnIiciat0M/rj+hx9+QFlZmSmz\nELUqCrkU/QM9sWhqP7Gj/KXDl0+iSFVSpyaBBNFBI0VKRERE5m7mzJnw9vZGSUnt74+IiAjk5zdt\nlgwRGYfBK2tffvllvPXWWxg+fDgmTpyIQYMGQSbjQc5EDdnx/kSxIzSbRluDben79OoDvPuhQxse\nvEtERPp69eqFlJQUAIBSqcTx48fRr5/5f9hJZO0MHvlbsWIF+vXrh71792LevHkYPHgw3nrrLR7C\nSWTljl89fc8dPmOCRomQhoiILMF7770HAHjkkUdQWVnJxo/ITBg88hcdHY3o6Gjk5+dj586d2L59\nO7766it8/fXX6NKlC6KjoxEVFYX27dubMi8RtSCtoEVc2l69er/2veDb1luEREREZI4SEhIQERGB\nvn37IjExEaNHj77nudBEJK5Gb9Hn7u6OOXPmIDY2Frt27cL8+fPh4OCAlStXYsSIEZg9ezZiY2NR\nWVlpirxE1IISb5xHTultvfqkHmNESENEROYoNDQUQ4YMgVqtxpkzZ8SOQ0QNaNb+7H5+fhg0aBDC\nwsLQrl07aLVaJCYm4qWXXkJ4eDjWrFkDrdY6D7smsnaCIGBr6h69ek/P7uju3lWEREREZE5iY2Mh\nl8uRmJgIAHB1dUVurvWed0tkDQye9vlH6enp2LFjB3bv3o1bt25BEAT06tULf//73zFmzBikpqbi\n008/xQcffICCggLdmS5EZDnO307B5aLrevWYoNEipCEiInPy0ksvYfny5brr1157Da+++qqIiYjI\nEAY3f9evX8eOHTuwa9cuXLp0CYIgwMvLC3PnzsXEiRPh5+enu3fQoEEIDQ1FZGQktm7dyuaP9GjV\namjKy1FTXgFNeflvX5f/9nUFqvLyxI7YqgmCgNh7jPr5u/qid7tAERIREZE5+de//oUVK1agQ4cO\nyM7OhlKpFDsSERnA4OYvMjISAGBnZ4cJEyYgOjoaAwYMgEQiuef9CoUCzs7OkEqbNbOUrIjqTi4u\nfboWxcmp0FZViR3HIPc6uP2t9YkoEiFLS0rLy0JG/kW9+qQeo+v9mSciIuulUqnQrVs3VFZWIj8/\nHy4uLqiuroZc3qRJZEQkEoN/YgcMGIDo6GiMHDkS9vb2Bj3no48+gpubW5PDkXVJX/EOyi9dFjtG\no1zMKUZrbHW2pumP+nV27oh+HXqLkIaIiMT0xhtv4JVXXtFdJyUlITg4mI0fkQVq1FEPgYGBDTZ+\nZ8+exc8//4z58+cDALy9uRU81VKXlDS78bPv1MlIaQxXUl4N5yY+VyG3zFHv7IIr+PV2ml49psco\nSCWW+XciIqLGy8/PR2BgIAoKas96lUql+O677xAcHCxyMiJqKoPfyb344os4ePBgg/fs378fa9eu\nbXYosj7aqupmPb/Tww9B4dzUNqzptM04oyjY392ISVrOvUb9vBw9ENapvwhpiIhIDBkZGfDw8NA1\nfvfffz9qamowefJkkZMRUXPUO/IXGxuLQ4cO6a4FQcCuXbuQlqY/IgAAarUap06dgouLi/FTknWS\nSKBs5wmZgwPkDg6QO9jrvpbZ2/9Wc4BTQHfYdewgdlqDKeRSBPu7Y9HUfmJHabRrRTk4nfOrXj06\naBTX7xIRtSIBAQFQKBQAgD179mD48OEiJyIiY6i3+Rs8eDDefPNNVFRUAAAkEgkuXbqES5cu1fti\nNjY2+Oc//2n8lGSVbFxd0f/TT8SO0WhL5jyAwN7txY5hEnHp+/RqbvZtEe4TKkIaIiJqSdOmTUNc\nXBxKS0shl8tRWFgIR0dHsWMRkRHV2/x5eHjgwIEDqKyshCAIiIiIwKOPPorZs2fr3SuRSCCXy9G2\nbVvdp0REZFlul+Xh+LXTevUJAZGQy7ion4jIWiUlJSE0NBQqlQoAsHDhQqxevZqNH5EVavAdnaur\nq+7r5cuXIygoCB07djR5KLJsgiBAXVyMqju5qMrLgyo3D5XX9Q8LJ/OyLW0fhD+tcWxj64jhXQeK\nlIiIiEwtMjISBw4c0F1Pnz4dq1evFjEREZmSwR/nx8TEmDIHWShNWTnyjx1H2aXLqMrNrf2Tlw9t\ndfM2eKGWVVBxF4evnNSrjw+IgK3cRoRERERkakqlElW/nbvr5OSEpKQk+Pr6ihuKiEyq3ubvgQce\nwBNPPIG///3vumtDSCQSnDp1yjjpyKxpKiqQ9MJLqLxxo0nPl8hlRk5kuKLSKqzcfA5J2fn3PMj9\nd/6t5JS/Hen7UaOtqVNzUNhhpH+4SImIiMjU3NzccPPmTfzzn//EBx98IHYcImoB9TZ/jo6OsLGx\nqXNN9Ed3T59tcuMHAG0CA42YpnFWbj6Hs+m5on1/c1KiKsWBS8f06qO7DYO9wk6EREREZAqbN2/G\n3/72N1y4cAH+/v7IyMiARqPhTu1ErUi9zd8fj3m41zVRVV5ek5/r3LsXuvz9b0ZM0zhJ2fmifW9z\nsyvzEKpr1HVqtjIbjOk+TKRERERkTBqNBj169EBWVhYAYNiwYbh+/To/2CdqhQxe8/f7GS9/HA0k\nqo9UqYTS0wO2np6w9fSArYcHlO08YevpCWU7TyjatBE1X0NTPVuT8uoK7Mk+rFeP9BuMNrZ8U0BE\nZOk+/vhjLFy4ULehl7e3NzIzM0VORURiMbj5W7RoEZycnDBy5EiMHz8eYWFhpsxFFsozYgR8H50F\nuZMjJBLLWC8nASD77Y/8D1/LANiKmKsl7M0+gkq1qk5NLpVjfGCESImIiMhYevXqhZSUFAC1ezKs\nWbMGTzzxhMipiEhMBjd/zz77LOLj47FlyxbExsbC09MT48aNQ1RUFIKCgkyZkSyIoo0TFG2cxI7R\noMqKavhCAmfU/gBIW8mmLn+m0lRhV6b+dO5hXcLgasf1H0RElq579+5ISUlBjx498Ouvv0Iu55mt\nRK2d1NAbn3jiCcTFxSE+Ph7z58+Ho6MjvvzyS0yaNAnjx4/H2rVrkZOTY8qsREYRt+kXeEACG0ha\nbeMHAAcvHkNpVVmdmlQixcTAkSIlIiKi5sjPz4ebmxvWrFkDAIiNjUVycjJSUlLY+BERgEY0f7/r\n2rUrFixYgF27diEuLg5z586FWq3G//3f/yEiIgIzZswwRU4io6jRaHExs+kb1QCAm6flr4VT16ix\nPWO/Xn1Q5xB4OrqLkIiIiJrjySefhIeHBwoLC7FgwQJdvWfPniKmIiJz06yPgQIDA9G+fXv4+flh\n3bp1SE9Px7lz54yVjcjoamq00NYITX5+cP9OcLeC5u/IlZ9xt7K4Tk0CCaJ7jBIpERERNUV2djb6\n9u2L8vJyAIBCoeAO7URUryY1f6Wlpdi/fz/i4+Px888/o6amBs7Ozpg2bRomTJhg7IxEJqeBAA83\nByjtFLBVKqC0k0OpVMDWTgGlnQJKpRztvV3g7dPWYjayqU+Ntgbb0vbp1R/o1Bed2rQXIRERETXF\n008/jVWrVumux44di127domYiIjMncHNX3l5OQ4ePIj4+HgcP34cGo0GNjY2iIiIwIQJExAeHs75\n5GSRaiDgFwjY8dIIsaO0iBPXzuJOuf45hzFBo0VIQ0RETXXfffcBAOzs7HD69GlO8SSiv2RwtxYW\nFga1Wg2JRIKQkBBMmDABo0aN4gGhRBZEK2ixNW2PXr2vVw90de0sQiIiImqM4cOHw8XFBbGxsZgz\nZw46d+6M4cOHix2LiCyEwc2fj48PJk6ciKioKLRr186UmYjIRM7kJOFGyS29+qQeY0RIQ0REhtqz\nZw/Gjx+PmpoaAEBZWRkcHR3Z+BFRoxjc/O3YscOUOYjIxARBQGzqbr16kEc3BHr4i5CIiIj+ikaj\nwQMPPIBffvlFV3v22Wc584qImqTe5m/jxo3o27cvgoODddeGmj17dvOTEZFRJd1Jw6W71/Tqk3pw\nrR8RkTk6dOgQIiMjodVqAQAeHh7IzMyEi4uLyMmIyFLV2/wtW7YMCxYs0DV/y5Ytg0QigSA0vE2+\nRCJh80dkhmJT9df6+bX1QXC7IBHSEBHRX+nevbuu8XvnnXfw/PPPi5yIiCxdvc3f8uXLERT0vzeF\nvzd/RGR50vOykZaXpVeP6TGaP9dERGZk5cqV2LRpExITE9GpUyd8++23iI6OhlKpFDsaEVmBepu/\nmJiYOteTJk0y6AUrKyubl4jIRAStgKoqjdgxRHGvtX7ebdrj/o7BIqQhIqI/KysrQ7du3XD79m0A\ntcttZs+ejalTp4qcjIisicEbvowYMQJz5szBrFmz6r3no48+wjfffIOTJ08aJRyRoapUaqT+egu5\nt0qgqlTX/lFp/vd1pbq28Wt41rJVulR4Dedvp+rVo4NGQyqRipCIiIj+6KWXXsLy5ct1171798b0\n6dNFTERE1qre5u/GjRsoKyvTXefk5ODixYtIT0+/5/1qtRonT57kyB+1OLW6Bl+tOYmb14vFjmKW\n7nWuXzsHdzzYub8IaYiI6HcqlQrt27dHUVERAEAmkyEuLg7jx48XORkRWat6m79ff/0Vixcv1q0H\nkkgk+O677/Ddd9/V+2KCIGDgwIHGT0nUgKsXC5rV+NUYMYu5uVFyC4k3zuvVJwaNhEwqEyERERH9\nTqlUoqSkBAAwaNAgHD16VORERGTt6m3+xo0bh9TUVBQWFkIQBMTFxSEwMLDOJjB/pFAo4OnpiRkz\nZpgsLNG9FN9t3mhzoZFymKO41L0Q/jTX1dXOBUN8B4iUiIiodcvIyMDDDz+MpKQkAMCuXbvg7OyM\nsLAwkZMRUWvQ4Jq/P24pnJiYiEmTJvEYh1agpqoKpRmZqCmvaPC+imvXWyiR8djYyqBUKnC7uBIa\nAMUQcFvsUCaSW5aPY9dO69WjAiKgkClESERE1LrFxMQgLi4OADBq1Cjs3bsXo0fzrFUiajkGb/hy\n6NAhU+YgM1Fx4waS//Mq1HeLxI7SLF27e2DgcH8o7eSwVSqgtFNAqZRDKqvd4CRq8TaRE5retvR9\n0AraOjUnW0eM8BskUiIiotbp5MmTGDp0KKqrqwEA9vb2WLVqlcipiKg1qrf527hxI/r27as75H3j\nxo0GvyhHBy3X9e9+sPjGDwBcXO3QpZu72DFEU1hZhJ8u6++6O677cCjltiIkIiJqnYYOHYojR47o\nrv/2t7/hyy+/FDEREbVm9TZ/y5Ytw4IFC3TN3++HvAtCw3vlSyQSNn8WrOLqtWY9X+HiYqQk1Bw7\n0w9Ao617pqGdQolR/kNESkRE1Dr9+uuvAAAXFxekpaXBy8tL5ERE1JrV2/wtX768zuYufzx/hqzY\nXzT3DZE5OMBtwANGDENNUVJVhv0X9XeMG+0/FA429iIkIiJqPTQaDYYMGYJt27bB3d0daWlp+OST\nT7B06VKxoxER1d/8xcTENHhNrYdzcG/I7OwavMfW0xPtIkdA2a5dC6Wi+uzO/AlVNdV1arYyG4zr\nPlykRERErcP69evx2GOPQRAEBAYGIj8/H15eXmz8iMhsGLzhS32ys7Pxyy+/oEOHDjzjz0p1+ftj\ncPDpLHYMMkCFuhJ7sn7Sq4/wG4Q2SicREhERWT+VSoWgoCBcuXIFQO0SmBdffFHcUERE99Co5u+T\nTz7Bpk2bcODAASiVSuzevRvPPfcctNraHQUfeOABrF27Fra23FCCSAz7shNQrq577qFMKkNUQIRI\niYiIrNv777+P559/XrcnQteuXZGRkQG5vNmfrxMRGZ3U0Bu//fZbrFq1ChKJBMXFxRAEAStWrIBM\nJsPChQsRExODU6dO4bPPPjNlXiKqR5WmGjszDujVh/qGwc2+rQiJiIisX1xcHARBgEQiwYYNG3Dx\n4kU2fkRktgxu/rZs2QIfHx/s2bMH7dq1w9mzZ3Hnzh2MHz8eTz31FJYtW4YBAwZg165dpsxLRPU4\ndOk4SqrK6tQkEgkmBo0UKRERkXV6/vnnERsbCwA4evQopk6diurqau52TkRmz+CPpi5evIipU6fC\nwcEBAHDkyBFIJBIMH/6/TSR69eqFX375xfgpiahBmhoNtqfv16sP7BwCL0cPERIREVmfGzduoGfP\nnigpKYFUKkVNTQ2A2tlRRESWwOCRP4VCUeeMv4SEBMhkMgwYMEBXKy4uhpMTN5UgamkJV0+hoPKu\nXj06kKN+RETGMGfOHHh7e6OkpAQAMHjwYJETERE1nsHNn5+fHw4fPozKykqcP38eGRkZ6N+/Pxwd\nHQEA169fx549e9C9e3eThSUifTXaGsSl7dWrh3Tsg84uHUVIRERkPVJSUmBvb48NGzYAAGxtbZGY\nmIjDhw+LG4yIqAkMbv5mzJiBK1euYODAgZgxYwYkEglmzZoFoPZcm+joaJSXl2Pu3LkmC0tE+n6+\ncQ63y/L06pN6jBEhDRGRdYmLi0NlZe0uyg899BBUKhVCQkJETkVE1DQGr/kbP348JBIJvvjiCwDA\nlClTEBFRu328SqVC+/bt8eyzzyIsLMw0SYlIj1bQYmuq/qhfH68g+Ln6iJCIiMjyHTt2DEeOHMGS\nJUuwZMkSnDlzBu+++y78/f3FjkZE1CyN2ot43LhxGDdunF798ccfx7x584wWiogMc+7mBVwrztGr\nxwSNFiENEZHlGzhwIE6cOAGg9v2Nl5cXtm7dKnIqIiLjMMpBNAqFwhgvQ2Q0+05dw6c/XxE7hkkJ\ngoDY1D169QB3PwR5dBMhERGR5dq2bRseeugh3Q6erq6uPK+PiKxOo/5V++GHH7BlyxbcuHEDarW6\nzu6fv5NIJDh16pTRAhI1hfYe/21amwt30pFdeEWvPqnHaEgkkpYPRERkgTQaDfr27YuUlBRd7eWX\nX8bSpUtFTEVEZBoGN3+bN2/G66+/DkEQ0KZNG90un0TWQCE3eO8js7E1TX/Ur0tbb/T16ilCGiIi\ny3Tx4kVd49ehQwdkZGTwPQ4RWS2Dm7+vv/4aTk5O+PTTT3HfffeZMhNRiwv2dxc7QqNk5l9CSm6m\nXj0miKN+RER/RaVS4e2338arr76KgIAAzJo1CyEhIVi4cKHY0YiITMrg5u/q1auYMmUKGz+yxV3S\nSwAAIABJREFUKgq5FMH+7lg0tZ/YURolNnW3Xq2jkxce6NRXhDRERJZjxYoVePHFFwEA3bp1w/Tp\n07Fx40aRUxERtQyDmz93d3doNBpTZqEWpikrhyo3F1W5uajKzYMqNw9VBQVixzK6He9PFDuCUV25\nex3nbiXr1aODRkEqsbzpq0RELaGoqAjdunVDfn4+AEAqlUKpVIqcioioZRnc/EVFReHHH3/EokWL\n4OLiYspMZGRCTQ0KT59B8YXk35q82mavpqJC7GjUBFvT9M/183Bww0AfHjpMRHQvixYtwgcffKC7\n7tevH06dOsXdPImo1TH4X73o6GgcP34ckydPxuTJk+Hj4wMbG5t73jtixAijBaTmy/7kU+QeONjk\n50v5y9Fs3Cy5jZ+vn9OrTwwcCblUJkIiIiLzt2rVKgCAXC5HfHw8IiMjRU5ERCQOg9/Vjx07FhKJ\nBIIg1Pn07I8EQYBEIkFaWprRAlLzqEtKm9X4KZzbQOnVzoiJmqaotAorN59DUnY+1Bptncc8APii\ndUx3jEvbBwF1j7Foq3TG0C5hIiUiIjJPa9aswbx58wAA//3vf7F9+3YcOnRI5FREROIyuPmbP38+\ndxG0QNXNWMOn7NAe/guegkQm/ojSys3ncDY9V+wYosorL8DRq/pnaI4PiICNTCFCIiIi85OUlITQ\n0FCoVCocPHgQP/zwAxYtWoRFixaJHY2ISHQGN3/c/tj6SORy2Hp6wNbDA0pPz9qvPT2h/K1m4+5m\nNg1/UnZ+k55nief31Wd7+n7UCHVHPR1tHBDpN0ikRERE5mX06NHYu/d/66IdHBxETENEZH6atJir\nvLwcmZmZKC4uxtChQ1FcXAxnZ2djZyMTUXZoj15vLoVNWxdIpJbRHP15qqehLO38vvoUVRbj0KXj\nevWx3YdDqeBudUTUuiUkJCAiIgJqtRoA4OjoiAsXLsDX11fcYEREZqZR7/zz8/PxzDPPIDQ0FNOn\nT8dTTz0FANi0aRMiIyNx5swZk4Qk45La2MDWzdViGr+mkEok6B/oaXHn99VnZ+ZBqLV1j1qxkysx\nutsQkRIREZmPqKgoXeP35JNPorS0lI0fEdE9GPzuv7CwEFOmTMHu3bsRHByMHj16QBBqN56ws7PD\nzZs3MXfuXGRkZJgsLNEf7Xh/Ina8PxHzJ+sfbD4ytDNemxsGFydbEZIZV1lVOfZlJ+jVR/qHw9GG\nU5qIqHXauXMnVCoVACAxMREdOnRAXl4ePvnkE5GTERGZL4Obv1WrVuHWrVtYvXo1Nm3ahGHDhuke\nmzNnDr788ktoNBqsXr3aJEGJWqv4rJ+g0lTVqSlkCowL4JEqRNT6aDQaBAYGIioqCgEBAQCAgIAA\n5OTkwN3dOqb6ExGZisHN36FDhxAZGVmn6fuj0NBQjBw5EufPnzdaOKLWrlKtwu6sn/TqI7oOhIuy\njQiJiIjEs2bNGtjY2OhmGfGQdiKixjH4X827d+/C29u7wXvatWuHwsLCZociolr7LyagvLqiTk0m\nkWJCIA8oJqLWQ6VSwd/fHzk5OQAAiUSCTz75RHeOHxERGcbg5s/LywupqakN3pOUlAQvL69mhyIi\noFpTjR0ZB/Xq4b4D4G7vKkIiIiJxjBo1Stf4BQQEIDk5maN+RERNYPC0z1GjRuHkyZPYvHnzPR9f\nt24dzp49i4iICKOFI2rNDl0+gWJVSZ2aRCJBdNAokRIREbWc/Px8pKSkAAAOHjwINzc3bNmyBenp\n6Wz8iIiayOB/PefNm4cjR47g9ddfxzfffAOttvbctX//+99ISUlBdnY2OnfuzCkYREag0dZge/p+\nvXqYd3+0d/IUIRERUctZuHAhPvroIygUClRXV0MulyM/P1/sWEREFs/gkT9HR0d8++23mDp1KnJy\ncnDx4kUIgoC4uDhcvXoVEydOxLfffos2bbgJBVFzHbuaiPwK/fWzMRz1IyIrduXKFTg5OeGjjz7S\n1X4/zoGIiJqvUfMmHB0d8eqrr+I///kPLl++jJKSEtjb26Nr166wsbFp9DfXarV47bXXkJGRARsb\nG7z55pvw8fHRPZ6UlIQVK1ZAEAR4eHjg3Xffha2t5Z/bRtQQrVaLrWl79Or9O/SGj0snERIREZne\ntGnT6iwtGTNmDOLj40VMRERkfZo0aV4mk8Hf37/Z3/zAgQOorq7Gd999h/Pnz2PFihW6cwIFQcDL\nL7+MVatWwcfHBz/88ANycnLQtWvXZn9fMg9FpVVYufkckrLzodZoxY5jNn6+8Qtulebq1Sf1GCNC\nGiIi09u0aZOu8VMqlTh16hSCg4NFTkVEZH3+ctpnYWEhtm7ditu3b9epJyQkYNasWQgPD8fkyZOx\nadOmRn/zs2fPYvDgwQCAvn37Ijk5WffY5cuX4eLigvXr12PmzJkoKipi42dlVm4+h7PpuWz8/kAQ\nhHuO+vVuF4Bubl1ESEREZDrZ2dkAgOnTp6Njx46YPn06Kisr2fgREZlIg81fXFwchg8fjpdeekm3\n4xYAxMfHY968eTh9+jTKyspw6dIlvPHGG3j22Wcb9c3Lysrg6Oiou5bJZNBoNABqzxX85ZdfMHPm\nTKxbtw4///wzTp482ajXJ/OWlN30xfsKucHLVS3KL7eScbXohl49JoijfkRkPQ4dOgSFQoFu3bqh\nrKwMAHDjxg188803IicjIrJu9b6DzsjIwJIlSyCXy/HEE08gKCgIAFBRUYGlS5dCEAQ88sgjOHXq\nFBITE/HYY49h9+7djZqf7+joiPLyct21VqvVbd/s4uICHx8f+Pn5QaFQYPDgwXVGBsnyNWfEL9jf\n3YhJzIMgCIhN1R/16+7WFT09u4uQiIjI+Pr3748RI0boPuzds0f/3z0iIjKNepu/DRs2QCKR4Lvv\nvsMzzzyDDh06AKhdp1dUVAQXFxcsWbIECoUCcrkczz33HDp37owtW7YY/M379euHhIQEAMD58+fR\nvfv/3uB6e3ujvLwcV69eBQCcOXMG3bp1a9JfkqyHQi5F/0BPLJraT+woRpeSm4nMgkt69ZgeoyGR\nSERIRERkPJs3b4ZMJsO5c+cAAO7u7rh79y4mT54scjIiotaj3g1fEhMTMWzYMPj5+dWpJyQkQCKR\nYNiwYXV23pRIJHjwwQexd+9eg795ZGQkjh8/jqlTp0IQBCxbtgw7duxARUUFpkyZgrfeeguLFy+G\nIAi47777MHTo0Mb/Dcmi7Hh/otgRRHOvtX4+Lp3Qr30vEdIQERmPSqXCtGnTdNdvvvkmlixZImIi\nIqLWqd7mLy8vD6NHj9arJyYmAgAGDhyo91ibNm1QWlpq8DeXSqVYunRpndofm82wsDD8+OOPBr8e\nkaXKLriCC3fS9eoxQRz1IyLLlZ+fD3d3dyiVSoSEhOD27dvIzMyEUqkUOxoRUatU77RPGxsbvYNV\nL1++jNzc2i3oBwwYoPec3NxcODs7GzkikfWLTd2tV2vv5IkBne4TIQ0RUfOUlZWhQ4cO8PDwwKFD\nhwDUfnh87do1Nn5ERCKqt/nz8/PT22Bl3759AICePXvCzc2tzmPV1dU4evSo3jRRImrYtaIcnLmZ\npFePDhwFqdQ6dzUlIuu1ZMkSODk54datWwCAzz77TORERET0u3rfWY4dOxa//vorNm7ciJqaGmRl\nZWHjxo2QSCSYNGmS3v1vv/02CgoKEBkZadLARABQXaVBWtItxH37Cw7Fp4kdp1li77HWz93eFYN9\nQ0VIQ0TUNPn5+XB1dcWyZcsA1B7fFBcXh2+//VbkZERE9Lt61/xNnz4dO3fuxPLly/HOO++gpqYG\ngiCgT58+mDJliu6+tWvXYs+ePUhNTYWfn1+dx4iMqay0Cpkpt5GRcgeXM/OgsYLD4W+V5uLk9bN6\n9QmBkZBLZSIkIiJqmvbt2+uObwgLC8OJEydETkRERH9Wb/Mnl8uxYcMGfP755zh8+DA0Gg0efPBB\nLFiwADLZ/96Ubt68GTdv3kRoaCjef/99KBSKFglOrUN+bhkykm8jI/k2bly7CwiGPa+tm4NpgxnJ\ntrS9EIS6fylnZRsM7/KgSImIiAynUql0a/ieffZZrFy5EgcPHsSgQYNETkZERPdSb/MHAHZ2dli4\ncCEWLlxY7z1vvfUWPD09udaPjKpKpcGWr84iOz230c9183BAvwGdTZDKuPIrCnHk6im9+vjuI2Aj\ntxEhERGR4SZPnowtW7bg6aefxsqVK/H222/j7bffFjsWERE1oMHmzxBhYWHGyEFUx9EDmY1q/BQ2\nMvgFeCCglxd69u0Audz8p0xuT9+PGm1NnZqDjT1G+oeLlIiI6K+dPn0agwcPRlVVFQDghx9+wMqV\nK0VORUREhqh3w5cnn3wSV65cadaLZ2Zm4h//+EezXoNap5vXi/7yHgdHG9wX2hlTH38Azy0dhUfm\nhKDP/d4W0fgVq0pw8NJxvfqYbsNgp+A26ERknoYOHYoHHnhA1/g9+uijyMnJETkVEREZqt6RPzc3\nN4wfPx5RUVGYPXs2goKCDH7RkydP4vvvv8e+ffsQExNjlKDUumi1917c5+bhgIBeXgjo6YWOPm0h\nlVrmAei7Mg9BXaOuU1PKbTG22zCREhERNczf3x8XL14EADg7OyM5ORmdOnUSORURETVGvc3fm2++\nifHjx+PVV19FXFwcunbtikGDBqFXr17w9/dH27ZtoVQqUVpairt37yI7Oxtnz57FyZMncevWLfj4\n+GD16tUID+cUNjKOybP7o0efDmLHaLay6nLszTqiVx/pHw5HW8vYqIaIWoffd++Uy+VYv349wsPD\nsXjxYrz77rsiJyMioqZocM3fgAEDEB8fjz179mDdunXYsGEDJJJ7j7QIggCJRILevXvjueeew5gx\nY+q9l6gpHBxtxY5gFHuyjqBSo6pTU0jlGNd9hEiJiIj0bdq0CbNmzUKPHj1w4cIFDBo0CFqt5R+x\nQ0TUmv3lhi8ymQzjxo3DuHHjcP36dZw6dQqpqakoKChAWVkZnJ2d4eHhgW7dumHIkCHw8PBoidyt\nXnFKCm7t2oOqvLwG79P+ti6DzINKrUJ85iG9+rCuD6KtnbMIiYiI6tJoNAgMDNRN8UxNTRU5ERER\nGUujdvv09vaGt7e3qbKQgcqvXkPKq29AUKv/+mYyKwcuHUNZdXmdmkwixcTAkSIlIiL6n5UrV+LZ\nZ5/VnT/q6+uLtLQ0kVMREZGx1LvbJ5mvu6fPNKvxk0j4/3YxVNeosSP9gF59sE8oPBzcREhERPQ/\nixYtwjPPPKNbxrFu3TpcvnxZd4g7ERFZPnYBFkhTVtas5zv6+xkpCTXG4csncVdVXKcmgQTRQRz1\nIyLxrVixAlKpFL1790Z1dTXmzJkjdiQiIjKyZh/yTpbFzrsTvKdNETtGq6PR1mBb+j69eqj3fejQ\nxkuERETU2t2+fRtBQUGoqalBSUkJlEolampqxI5FREQmxObPSniNGQ3PYUMavEfmYA+7jh25C6sI\nTlw7g7zyAr36pKDRIqQhotbu73//O7744gvddUpKCnr27CliIiIiagls/qyEracHnAK6ix0DAFBU\nWoWVm88hKTsfag23BdcKWmxN26NX79e+F3zbcgMlImo5GRkZ6NevHyoqKgAANjY2OHz4MBs/IqJW\ngmv+yOhWbj6Hs+m5bPx+k3jjPHJKbuvVY3pw1I+IWk5KSgoCAwN1jd+ECRNQVVWFsLAwkZMREVFL\nafTI3927d7F7926kp6ejuLgYH3zwAc6ePQutVouQkBBTZCQLk5Sd36TnKeTW91mEIAjYmqo/6tfT\nszsC3LnxDhG1nJ49e0KpVEIqleLcuXMICAgQOxIREbWwRjV/O3bswCuvvAKVSqXbChoADh8+jM8/\n/xzTpk3DK6+8YpKgZDmaOuIX7O9u5CTi+/V2Ki4XXderx3CtHxG1gMGDB+Pnn39GZWUl5HI5SktL\nIZdzxQcRUWtl8FDLqVOn8MILL8DT0xNLly7F5MmTdY9FREQgICAA3377LeLi4kwSlKyXQi5F/0BP\nLJraT+woRhebuluv5u/qi97tAkVIQ0Stxc6dOyGXy3Hs2DFoNBo899xzAMDGj4iolTP4t8Dq1avh\n6uqK77//Hs7Ozrhz547usT59+uDrr79GVFQUNm3ahOjoaJOEJcu14/2JYkdocam5WUjPv6hXn9Rj\nNHdcJSKT0Gg0uO+++5CcnKyrvfjii1i2bJmIqYiIyFwY3PxduHABMTExcHZ2vufjjo6OiIiI4Mhf\nM1UVFEBdXAxNWTlqyiugqfjt/5aXQ1Ne+3VJeobYMckAW9P0R/28nTugX4feIqQhotbgj2f1eXl5\nISsrC46OjiKnIiIic2Fw86fV/vU6rurqamg0mmYFaq2qCgqRvmwFyrL1R4rI8lwsvIpfb6fp1WOC\nRkMqsb6NbYjIPHh7e+Pq1av473//i0WLFokdh4iIzIzB70IDAgJw+PBhVFdX3/PxsrIyHDlyBIGB\nXMvUFJc//7JZjZ9EyobCnNxrh08vRw886N1fhDREZK3effddyOVyJCUlAQCysrJQXV3Nxo+IiO7J\n4I7h0UcfxY0bN/DEE08gJSVF1wRqtVpcuHABTzzxBO7cuYPp06ebLKw1K83MatbzHbv5GykJNdf1\n4ptIzDmvV48OGgUpm3QiMoKioiJ4enriX//6F2pqanRr7eVyOTd1ISKiehn8G2LMmDHIzMzEmjVr\n6uz0GRwcjJqaGgiCgFmzZiEqKsokQa2d8NsajcaSyOXoMGE82vQIMnIi8QiCAK1WEDtGk21N26tX\nc7Nri3CfUBHSEJG1Wbx4Mf773//qrvv164dTp06JmIiIiCxFoz4efPrppzFs2DD8+OOPSE1NRWlp\nKezt7REQEICYmBiEhvLNrTG59O0DhYsL5A72kDk4QO7g8KevHaD08oLc0UHsqM0iCAIK88tx9WIB\nrmQX4OqlApQWq8SO1SS3y/Jw/NppvXpUYATkMn4aT0TN4+vri6tXrwKoHeWLj49HZGSkyKmIiMhS\nNPrdaHBwMIKDg+/5WHV1NW7evAlfX9/m5iIA/v9cAFs3V7FjGJ0gCMjPLcPViwW1fy4VoKykSuxY\nRrE9bR8Eoe6oZRtbR4zoOkikRERkTcLDw/HVV19h2LBhOHTokNhxiIjIwhi8ACkoKAgff/xxg/d8\n9NFHePjhh5sdiqxTeVkV4rck4b+v7cPqdw4jfssFpJy/aXDj19bd3sQJm6ewogiHr/ysVx8fEAFb\nuY0IiYjI0qWkpMDBwQErVqwAAGzcuBF5eXls/IiIqEnqHflLTk6uc5C7IAi4dOkSDh48eM/71Wo1\nDh8+zKMeqF6xX5/D5az8Jj03NLwL2jjbGTmRcW3P2A+Ntu5///YKO4z0CxcpERFZsnHjxiE+Ph4A\n8PLLL+Pf//43AMDd3V3MWEREZMHqbf6Ki4sxf/58SCQSAIBEIkF8fLzuF9G9CIKAsWPHGj8lWbzK\niupGNX5SmQQdvF3g6+eG7j290MmnrQnTNV+JqhQHLh7Vq4/uNhT2NubdtBKReTl27BiGDx8OtVoN\nAHBwcMD58/o7CBMRETVWvc3fwIED8corr6CwsBCCIODjjz9GSEhIvZu6KBQKtGvXjs0f6ZECKMwv\nb/AemUyKjj4u8OnqBh8/N3j7toXCxnI2SInPOoTqGnWdmq3MBmO7DxcpERFZojlz5mDDhg2663nz\n5mH16tUiJiIiImvS4LvrP57Zl5iYiIceekh3lhARAJSWqHDh7A3k3ymDSqWGqlKNHpBAhtr/uGQA\nJJDgiw+O6T1XrpDiwaH+8PF3QyeftlAoZC0d3ygqqiuxJ+uIXj3SbzDa2DqKkIiILNX48eOxYcMG\nuLq6IiMjg1M8iYjIqAweWvnqq69MmYMsUEV5NdZ/dBx3Cyrq1B0gMej59vY2GDo6wBTRWtSe7MOo\nUFfWqcmlcowPjBApERFZCo1Gg+DgYHTs2BH79+/H5MmTkZWVBX9/f7GjERGRFWrUvLr8/Hz89NNP\nKCgo0B3s/ju1Wo2ioiIcO3as3k1hyLpkp93Ra/waw97B8nfAVGmqsCtTf9e9oV3C4GrnIkIiIrIU\na9euxbx58yAIAtLS0qBSqaBUKtn4ERGRyRjc/KWnp2PmzJkoLy+HIAi6jWB+bwAlEgkEQYCLC9/w\nWqui0iqs3HwOSdn5UGu08ALgbfhpIXqC7+9kvHAiOXTpOEqryurUpBIpogNHipSIiMydSqVC9+7d\ncf36dQC1vz8//PBDKJVKkZMREZG1M7j5+/DDD1FWVoZp06bhgQcewDvvvINevXphzJgxuHjxIr76\n6ivY2Nhg9+7dpsxLIlq5+RzOpuc2+nk1EODSxg5KOzls7RRwcLRFYC8vi2/+1DVqbE/fr1cf1DkE\nno5cp0NE+mJjY/HQQw/prrt3746UlBTI5ZazwRUREVkug3/bnDt3DiEhIXj11VcBAAkJCbh8+bJu\nd8/IyEg88sgjWLt2LRYvXmyatGR0fx7Na64CCLgNATWA7o9cLkXsq5HNfm1zc+TKKRRWFtWpSSBB\ndI9RIiUiInMXHl577qdUKsU333yDqVOnipyIiIhaE4Pn7JWWliI4OFh33b17d6Snp+umfQYGBmLo\n0KFISEgwfkoymd9H84zR+AFANYAKAFUANAAEAMH+1jcKVqOtwba0vXr1kE590KlNexESEZG5evrp\npxEQULu5lbu7O44ePYqamho2fkRE1OIMbv6cnJxQXV2tu/b29kZVVRUuX76sq/n6+uLmzZvGTUgm\nlZRt+MHrjaWQS9E/0BOLpvYz2fcQy4lrZ3GnXP9/u0lBo0VIQ0Tm6MqVK3BycsKqVauQmZmJzZs3\nAwAGDRokcjIiImqtDJ722bNnTyQkJOC5556Dra0t/P39IQgCzp07h65duwIArl27BpnMMs9qa62a\nM+LX0cMRyKu72+dDw7ohYnxQc2OZNa2gRVzaHr16X68e6OrqI0IiIjI3M2bMwKZNm3TXERERHOkj\nIiLRGdz8zZgxA08++SRiYmLwxhtvoH///ujRowfee+89qNVq5Ofn48CBAwgNDTVlXjIDCrkUwf7u\nGNSxLU4czBI7Tos7k5OE6yW39OqTeowRIQ0RmZOysjJ4eHhApVIBAJRKJY4fP45+/axvBgQREVke\ng5u/YcOG4T//+Q9WrlyJvLw8AMCLL76IuXPnYunSpRAEAW3atOFmL1Zgx/sTDbrvWCts/ARBwNZU\n/VG/IA9/BHrwbC6i1s7R0RFqtRoA8Mgjj+C7774TOREREdH/NGpv6ZkzZ+KRRx6BVls7VTAkJATx\n8fE4cOAAbG1tMXToULRr184kQYnMQdKdNFy8e1WvHhPEUT+i1iohIQGzZs3C1au1/zYcPnwYnTp1\ngq+vr7jBiIiI/qTRBwvZ2NjUue7QoQNmz56tuz537hynt5DVuteoX9e2ndHHy7rXORLRvYWGhiIx\nMRFA7bq+AwcOcEMXIiIyW3+522dZWRmOHj2KAwcO4Pr16w3e98orr2DmzJlGDUhkLtLzLiI1T3+q\n66QeYyCRSERIRERi+fHHHyGTyXSNn5ubm243TyIiInPV4Mjf559/jo8//li3cB0AoqOj8dprr8HW\n1lZX27NnD9566y3k5eVBLm/0YCKRRdiatluv1qlNe9zfMfgedxORterduzeSk5N110uXLsXLL78s\nYiIiIiLD1Nupbdu2De+99x4kEgnuv/9+uLq64syZM4iLi4NCocDSpUtRWVmJ//znP4iPj4cgCOjd\nuzfeeOONlsxP1CIuFV7DL7dS9OrRQaMglRh8XCYRWYE7d+4AADp16oSsrCwolUqRExERERmm3ubv\n9yktn3/+OcLCwgAAKpUK8+fPR2xsLJ566iksWrQI58+fh4ODA5555hnMmDGD09/IKsWl7dWrtXNw\nx8DO94uQhohakkqlQp8+fXDw4EF06tQJmZmZ2LJlCx5//HGxoxERETVKvUMW2dnZGDp0qK7xA2rP\nK1q0aBE0Gg2eeuopnD9/HgMGDMDOnTsxc+ZMNn5klW6U3MKpG7/o1ScGjYRMKhMhERG1lNdffx12\ndnbIzMxE7969AQAuLi5s/IiIyCLVO/JXWloKPz8/vXq3bt0AAGlpaXjsscfw/PPPs+kjqxaXthcC\nhDq1tnbOGOI7QKRERGRq+fn5CAgIQGFhIQBAKpXis88+EzkVERFR89Q78qfRaPSOdQCgW9vQq1cv\n/Otf/2LjR1Yttywfx66e1qtHBURCIVOIkIiITO3pp5+Gh4eHrvELCQlBTU0NJk+eLHIyIiKi5mny\nThUPPvigMXMQmaVt6fugFbR1ak62jojw4zleRNYqPT0dAKBQKHDkyBHdcQ5ERESWrsnNn0LBUQ+y\nbncri/HT5ZN69XHdh0Mpt73HM4jIUk2ZMgUbN24EAOzduxcvvvgiqqurER4eLnIyIiIi4+Ee9UT1\n2JFxABqtpk7NTqHEKP8hIiUiImM7d+4clEolvv/+e/ztb3/T1ZctWyZiKiIiItNo8ET2rVu33nO6\ni0QiafCxDRs2GC8hkQhKq8qw/+JRvfpo/6FwsLEXIRERGVtERAQOHjyou542bZqIaYiIiEyvweYv\nJycHOTk5jXqMG8CQNYjP/AlVmqo6NRuZAuO6DxcpEREZy6FDhzBq1ChoNLUj+23atEFKSgo6deok\ncjIiIiLTqrf5++OnoUStSYW6EnuyftKrR3QdhDZKJxESEZExpaSk6Bq/p59+GitXrhQ5ERERUcuo\nt/nr2LFjS+YgMhv7shNQrq6sU5NJZYgKjBQpERE11+bNm3H69Gm8//77WLhwIVJTU7F8+XK4uLiI\nHY2IiKjFNDjtk6i1qdZUY1eG/qj3UN8wuNm3FSERETWHRqNBjx49kJWVBQB48skn4e/vj9WrV4uc\njIiIqOVxt0+iPzh46TiKq0rr1CQSCSZy1I/I4nz44YewsbHRNX6dO3fmuj4iImrVOPLS+yayAAAg\nAElEQVRH9BtNjQbbM/br1Qd63w8vJ08REhFRU5SVlaF79+64desWgNoPcD777DM8/vjjIicjIiIS\nF5s/0qOqVOPa5UJUVaobvO92TkkLJWoZCVcTUVBxV68eHTRKhDRE1FS3b9/WNX49e/bE+fPnIZfz\n1x0RERF/G1IddgA+XHYQlRUNN37WRqvVYlvaXr16SMc+6OzCzY+IzF1+fj5eeuklrF27Fv7+/nj6\n6acxbNgwTJw4UexoREREZoPNH9XRAZJW1/gBwMkbZ3GrLFevHhM0WoQ0RNQYTz75JNasWQMA6Nev\nH+bNm8fjG4iIiO6h0c1fQkICYmNjkZaWhpKSEpw8eRLbt2/HtWvX8Pjjj8POzs4UOamFKJv5fKc2\ntkbJ0ZK0ghZbU/VH/YLbBcHfzbflAxGRQbKzs9G3b1+Ul5cDABQKBXr16iVyKiIiIvPVqObvlVde\nwQ8//ABBECCTyaDVagEAycnJ2LhxI44ePYovv/wSDg4OJglL5s3ewQaBvb3EjtFo524m41pxjl59\nUg+O+hGZq8mTJ2PLli2667Fjx2LXrl0iJiIiIjJ/Bjd/mzdvxvfff49Ro0Zh8eLF2LZtGz755BMA\nwPz581FeXo4tW7Zg3bp1WLBggckCU8vzD/KEUqlo8B4XVzv0CfGGc1v7FkplHIIgYGvqbr16gLsf\ngjy6iZCIiAwRGxsLALCzs8Pp06fRs2dPkRMRERGZv0Y1fwEBAfjggw8A1G6d/TtnZ2e89dZbyMrK\nwu7du9n8WZmIcUHwbN9G7BgmkZybgazCK3r1ST1G1/lvnIjE9/zzz+Pdd98FAHz55Zc4fPgw1q9f\nL24oIiIiC2LwIe+XL1/G4MGDG7wnJCQEOTn60+eIzFXsPUb9urh4o68XRxGIzMWePXsgl8vx3nvv\nYdy4cQCAOXPmsPEjIiJqJINH/pRKJQoKChq8Jzc3F0plc7cMIWoZmfmXkJKbqVeP4agfkVnQaDTo\n378/kpKSdDVu6EJERNR0Bo/89e/fH/v379cdnPtnV65cwYEDB9CvXz+jhSMypdi0PXq1jk5eeKBT\nXxHSENEfbdy4Eba2trrGz9PTE6WlpXj77bdFTkZERGS5DG7+5s+fj+rqajz88MNYt24dLl++DABI\nTEzEF198galTp0KtVuMf//iHycISGcuVuzdw7uYFvXp00ChIJQb/WBCRiSxcuBBarRYSiQTvvPMO\n7ty5A0dHR7FjERERWTSDp3327NkTH374If7973/X+eT10UcfhSAIcHR0xHvvvYc+ffqYJCiRMW29\nx6ifh4MbBvqEiJCGiABgzZo1mDlzJhwdHfHrr79i3LhxOHv2LJcTEBERGUmjzvkbMmQIfvrpJxw8\neBApKSkoLS2Fvb09AgICEBkZCScnJ1PlJDKam6V38PP1c3r1iYGRkEtlIiQiat3Kysrg7++PO3fu\n4PXXX8etW7fg6+uLlJQUsaMRERFZFYObvwsXLqB3795QKpUYN26cbsc1IksTl7YXAoQ6tbZKZwzt\n8qBIiYharxdeeAHvvPOO7rpDhw4ipiEiIrJuBjd/Dz/8MLp06YKoqChERUXB29vblLmITCKvvABH\nr5zSq48PiICNrOGD7InIeG7fvo2goCAUFRUBAGQyGXbu3InRo0eLnIyIiMh6GbyzxSOPPIKioiKs\nWrUKI0eOxNSpU/HNN9+gsLDQlPmIjGp7+n7UCNo6NUcbB0T6DRIpEVHrNGXKFF3jN3jwYGg0GjZ+\nREREJmZw87d06VIcO3YMa9euRXR0NC5duoQ33ngD4eHh+Mc//oFdu3ZBpVKZMitRsxRVFuPQpeN6\n9bHdh0Gp4IYSRKaWkZGBkydPAgAOHjyIzp0748SJE0hISBA5GRERUevQqA1fZDIZwsPDER4eDrVa\njSNHjmD37t346aefkJCQADs7O0RERNRZv0FkLnZmHoJaq6lTs5MrMbrbUHECEbUiEydOxPbt2yGX\ny6FWqyGXy3H16lWxYxEREbUqTT7QTKFQICIiAu+99x4++ugjBAQEoKKiAjt27DBmPiKjKKsqx77s\nI3r1kf7hcLRxECERUetw8uRJ2NraYvv27QAAW1tbzhIhIiISSaNG/n4nCAISExMRHx+Pffv2oaio\nSDcqOGHCBGNnJGq23Vk/QaWpqlNTyBQYFzBCpERE1m/w4ME4duyY7nru3LlYu3atiImIiIhat0Y1\nf2fPnsXu3buxZ88eFBQUQBAE9O7dG0899RTGjRsHV1dXU+UkarJKtQrxWT/p1Ud0GQgXZRsREhFZ\nv/Xr1+saPxcXF6SlpcHLy0vkVERERK2bwc3fkCFDkJubC0EQ4O3tjSeffBITJkyAr6+vCeMRNd/+\ni0dRXl1RpyaTSDEhMFKkRETWSaPR4MSJEwgPD8ecOXOwfPlyPPTQQ1i2bJnY0YiIiAiNaP5UKhWm\nTp2KCRMm4L777jNlJiKjqa5RY2fGAb16uO8AuDtwpJrIWNavX4/HHnsMgiAgLy8P7u7uyMjIEDsW\nERER/YHBzd/x48chlzdpiSCRaH66dAJFqpI6NYlEguigUSIlIrIuKpUKgYGBup07JRIJzpw5wzP7\niIiIzFC93dzBgwfRtWtXdOnSBQBw5Ij+Ton1GTGCm2iQ+DTaGmxP36dXD+vUD+2dPEVIRGRdVqxY\ngRdffFF37efnh/T0dH5QSEREZKbq/Q09f/58LFiwAAsWLNBdSySSBl9MEARIJBKkpaUZNyVRExy7\nmoi8ikK9ekwPjkj8P3t3HhZl9T5+/D0sIyoqKIqgiCCC+4JbbrlluOQnRc0tzfKTmktZuaVZaZal\nH8tvppblWpm5EG65m0vuSqmA7KiIKIugLOKwzO8PfvPkOAMOCg7g/bqurss5z5ln7nlm0ueec859\nhHhSaWlpSuJnYWHBTz/9xPDhw80clRBCCCEKkm/yN2nSJNq2bas8NiX5E6KkyM3Nxf/yXoP2Vs5N\ncbWrbYaIhCgbgoKCaNy4Mba2tnTr1o07d+5w+vRpGe0TQgghSoECk78HTZ48uchfPDc3l08++YTQ\n0FDUajXz58/H1dXVoN+cOXOoUqUKU6dOLfIYRNl0OvZvbqTeMmgf0FBG/YR4HNevX6dx48bcvXsX\nf39/Xn75ZQ4dOmTusIQQQghRCBamdhw1ahT+/v4F9vnpp5/o3bu3yS9+4MABNBoNv/32G++//z5f\nfPGFQZ+NGzcSFhZm8jmF0Gq1+AXvMWhvUsMLTwd3M0QkROk2atQoXFxcuHs3r3jSrl27zByREEII\nIR5HviN/mZmZZGdnA3k302fOnKFly5akpaUZ7a/RaDh+/DixsbEmv/j58+fp3LkzAC1atCAwMFDv\neEBAABcuXGDIkCFERUWZfF7xbPs7LpCrKdcN2n1lrZ8QhRIUFESbNm24d+8eAOXKlePYsWO0adPG\nzJEJIYQQ4nHkm/xt3bqV+fPn67WtXLmSlStXFnjC5s2bm/ziaWlp2NraKo8tLS3Jzs7GysqK+Ph4\nli1bxrfffsvu3btNPqd4tuU36le/mhuNa3iZISIhSq/mzZuTk5MDwMCBA9myZYuZIxJCCCHEk8g3\n+Rs2bBhnz54lKSkJgHPnzuHk5EStWrUM+qpUKqytralRowZvvfWWyS9ua2tLenq68jg3N1cpGrBn\nzx6Sk5MZO3YsCQkJZGZm4u7ujq+vr8nnF8+e4IRwwpIMR4l9G/WWgkVCmODmzZvUrFkTgI8//piF\nCxfy999/4+HhYebIhBBCCPGk8k3+LCwsWLJkifK4QYMG+Pr6GhSCeRLe3t78+eef9OnTh3/++QdP\nT0/l2KhRoxg1ahQAfn5+REVFSeInHskv2HCU2LVKLbydmpghGiFKl/bt23Pq1Clef/11Vq9ezZw5\nc5gzZ465wxJCCCFEETG5NndISEiRv3jPnj05fvw4Q4cORavV8vnnn7Njxw4yMjIYMmRIkb+eKNsi\nkq5w6Zbh93RAo14y6idEAfz8/HjllVeUKZ7Hjh0zc0RCCCGEKA75Jn8HDx7E3d0dNzc35bGpevTo\nYVI/CwsL5s2bp9dWr149g34y4idM4XfZcK2fU6UaPFfb2wzRCFHyZWdn06xZMy5fvqy0zZkzx+Dv\nZSGEEEKUDfkmfxMnTmTSpEnKNE9TNnnXarWoVCq9GwkhnoZrKbGci71g0N6/gQ8WFibvaCLEM8Xd\n3Z2YmBgAnJ2dCQ0N1SvCJYQQQoiypcBN3tu2bas8NiX5E8Jcfjcy6udQoSqd67YzQzRClFyZmZlY\nWVlhZWWFv78/7dq1Y8mSJUycONHcoQkhhBCimBWY/D1o8uTJxR6MEI/jZmo8J2LOG7T/p0FPrCws\nzRCRECXTZ599xocffoiHhwfh4eF4e3uTlZVl7rCEEEII8ZSYXPAlPykpKQQHB+Ps7EzdunWLIKRn\nT0bMdXL+/ybKovD8Q/ah1Wr12qrYVKa7WwczRSREyZKSkkL9+vVJTEwE4OrVq2aOSAghhBDmUKjF\nUNu2bWPw4MFoNBoATp06Rbdu3RgzZgy9e/fmgw8+IDc3t1gCLaviD/3Jhfenk5uZaXBMZSHTbB8l\nMeM2R66cMmh/ybMHaiu1GSISomSZMmUK9vb2SuLXunVr5e9wIYQQQjxbTB7527NnDzNmzKBcuXIk\nJibi7OzM3LlzyczMxNfXl9jYWPz9/WnYsKGyP5/IX869e0R+9wMJh48YPW5tb4e1nd1Tjqr02RFy\ngJzcHL22iuoKvOjxvJkiEqLkeOutt/juu+8AsLKyYu/evXTv3t3MUQkhhBDCXEwe+fvpp5+oXr06\n+/btw9nZmcDAQKKjo/Hx8eGzzz5j7dq1NGnSBD8/v+KMt0xIv3KFC+9Pzzfxs1Crqf/2JCmw8wh3\nMu9yMOovg/be9btR3trGDBEJUTJkZ2cDsHTpUqysrHjhhRfIysqSxE8IIYR4xhVqk3dfX18cHR0B\nOHz4MCqVip49eyp92rRpw4YNG4o+yjJCq9Vya+9+on5cjTafIgvlaznjNf19Ksr6yUfaFXYITY7+\ndSxnVY7e9buaJyAhzOzixYu0a9cOlUpFRkYGVlZWUtBFCCGEEAqTkz+tVou1tbXy+NixY6hUKtq3\nb6+0ZWZmUr58+aKNsIzITk8nYtl3JB0/kW+f6t26Um/cf7GUa/hI6ZoM9oYbjpy+WK8zlcrJPmXi\n2ePj48O+ffuUxxEREXh4eJgxIiGEEEKUNCYnf25ubpw+fRqtVsvVq1e5dOkSTZo0oWrVqgDcuXOH\n/fv34+bmVmzBllap4RGE/e8rMm/eMnrcolw56o1/kxrduz3lyEqvPeGHuZetXyTH2sKKl7xeMFNE\nQpjHoUOH8PHxUaZ62tracunSJam+LIQQQggDJq/569+/P0FBQfTq1YvBgwej1WoZOnQoAP7+/rz8\n8sskJiYycuTIYgu2tNFqtdzYsZNLM2fnm/hVcK1D88ULJfErhMysTP4IO2TQ3s29A/blq5ghIiHM\nIyAggB49eiiJ39tvv01qaqokfkIIIYQwyuSRv5EjR3Lv3j1Wr16NhYUFb775Jr6+vgBcv36djIwM\nPvzwQ3r37l1swZY20avWErdjZ77HHX164jbmdSzLlXuKUZV+B6L+IlWTrtdmobLgPw1eNFNEQpiH\nt7c3tra22NjYEB4ejp1UCBZCCCFEAQq1yfvYsWMZO3asQfurr77KuHHj9NYEPuvuJyXlm/hZ2Njg\nMfEtqj/f6SlHVfpl5WSxI+SAQXtn17bUqFjNDBEJ8fRkZ2fTuHFjIiIiSE9Px8bGhtTUVHOHJYQQ\nQohSolCbvOvcuHGDQ4cO8ccff3DixAnu378vid9D7sXeMNpe0d2NFl8vksTvMR2OPkVy5h29NhUq\nBjT0MVNEQjwdy5YtQ61WExYWRm5uLvPmzTN3SEIIIYQoZQo18nf9+nXmzJnDqVOn9NpVKhXPPfcc\nc+fOxcXFpUgDLEsquNah2cIFWEii/FhycnPYFrLXoL2dS0ucK9c0Q0RCFL/MzEzq1avHjRt5Pyip\nVCqWL1/O+PHjzRyZEEIIIUobk5O/hIQEhg0bRkJCAk2bNsXb25saNWpw9+5dzpw5w4kTJxg5ciR+\nfn5KBVChz6pSJUn8nsDxa+eIT08yaB/QsJcZohHi6ahQoQJarRaAhg0bcvHiRaysCvW7nRBCCCEE\nUIjk79tvvyUhIYFPPvlEqfL5oM2bNzNnzhy+//57PvjggyINUohcbS6/X95j0N7SqQlu9jLaLMou\nT09PIiIi2LRpk1JkSwghhBDicZi85u/IkSN07NjRaOIHMHjwYDp27MjBgweLLDghdM7GXiD27k2D\ndt9GMuonypYJEyZgYWHByZMnAQgMDCQ7O1sSPyGEEEI8MZOTv8TERDw9PQvs4+npSXx8/BMHJcSD\ntFotfsG7Ddob1/DEy6GeGSISouhFRERQqVIlVqxYgVarZcyYMQAyxVMIIYQQRcbk5M/BwYGwsLAC\n+4SGhmJvb//EQQnxoAs3g4lOjjFol7V+oqwYPHgw9evXJy0tDYDevXsTHBxs5qiEEEIIUdaYnPw9\n//zznDhxgq1btxo9/uuvv3Ly5Em6dOlSZMEJARgd9atX1ZWmjg3MEI0QRcvR0ZEtW7YAUL58eQID\nA/njjz/MHJUQQgghyiKT5xNNnjyZgwcP8uGHH+Lv70/r1q2pVKkSt27dIiAggMDAQKpVq8bEiROL\nM17xjLmcEE5IYqRBu2+j3qhUKjNEJETRGjhwICtWrGDEiBH8/PPP5g5HCCGEEGWYySN/1atXZ+PG\njbRt25azZ8+yYsUKFi5cyLp167h06RJt27bll19+wdHRsTjjFc8Yv2DDCp8uVZxp5dzUDNEI8eT2\n79+PWq1m9uzZACxfvpx79+5J4ieEEEKIYleoSgIuLi6sW7eOmzdvcvnyZdLS0qhYsSINGzbEycmp\nuGIUz6jI21e5cNNw3dOAhr2wUJn8u4UQJUJ2djbt2rUjICAAgEWLFvHZZ58BYGNjY87QhBBCCPGM\nMCn502g03L17Fzs7O6ysrKhZsyY1a9Ys7tjEM+53I6N+jrbVae/ibYZohHh8GzduZMSIEeTm5gJ5\nMykeVUBLCCGEEKKoFTh8EhISwhtvvEHLli3p3LkzrVq1YsqUKdy4ceNpxSeeUdfvxHEm9h+D9v4N\nXsTSwtIMEQnxeAYMGMCwYcOUxG/BggXEx8djZ2dn5siEEEII8azJN/mLjIxkxIgRnDhxApVKRdWq\nVdFoNOzZs4dXXnmFW7duPc04xTPm98uGo37VytvTpe5zZohGiMc3btw4AFxdXbl37x4zZ840c0RC\nCCGEeFblm/x9//33pKen8+6773Lu3DmOHz/O2bNnGTlyJImJiaxevfppximeIbfSEjh+7ZxBe78G\nL2BlKRtei5ItLS0NZ2dnOnToAECvXr1ITk7mypUrsrZPCCGEEGaV7530uXPn6NKli/KrNYCtrS2z\nZ8/mn3/+4fjx408lQPF4UlLvs2RjABcjEsnKzjV3OIWy7fI+crX6MVcuZ0sP905mikgI08yePZvP\nP/8cgLi4ODIzM7GxsZEpnkIIIYQoEfId+UtMTMTLy8vosVatWsm6vxJuycYAzofEl7rE73ZGCoev\nnDJo7+vZg3JWajNEJMSj3bx5E3t7eyXxs7S0xN/fX0b6hBBCCFGi5Jv8aTQa1GrjN9u2trbcu3ev\n2IIST+5iROJjPU+FeTdO3xF6gOzcbL22Ctbl8fHoYqaIhCjYhg0bcHJyIiUlBYAOHTqQnZ3Nyy+/\nbObIhBBCCCH0yWZpZdTjjvjZqM1XSfPu/TQORB4zaO9VvysV1OXNEJEQj9anTx8ArK2tOXbsmEyJ\nF0IIIUSJJcmfAMDayoJWDWpQrYr5pqn9EXaQ+zkavbZylmr6eHY3U0RCGOfr60vt2rUBsLOzIzo6\nGo1GQ6dOsi5VCFF65OTkmDsEIcRTVmDpRJXKvFMARdHasfjR09BWLDpc/IEYkaG5x57wIwbtL9Tr\nTOVytmaISAhDZ8+epVOnTmg0eT9S+Pn54evrS926dc0bmBBlzDfffENycjKWlpZ8+OGH5g4HgDVr\n1nDt2jV69Ohh9IeeP/74g7Nnz9KgQQOGDBnyxK83b948tFotH3/88ROfy5ijR49y+PBhPvroI6Vt\n7ty5VKhQgWnTphXLa+ocOHCAM2fOkJWVpbRZW1vTtWtXpVJyaZCTk8Pnn3+Ok5MT//3vf/WOhYSE\n8NtvvwEwatQo3NzcDJ7/qM94wYIFaDQag+PJycls2LCBpKQktFotkHfP7ujoyIgRI7C1zbtvio+P\nZ8WKFfl+Z8WzqcDk79tvv+Xbb7/N93jDhg0N2lQqFcHBwU8emXim7I04QkaW/jpSKwsr+jV4wUwR\nCaGvS5cuHD16VHn82muv4evra8aIhCibcnJySE5OVv4cHBxMo0aNzBxV2XP69GklcXiaHkzs3d3d\nqVatGjdv3iQmJob9+/cTGxvL4MGDn3pcj+P7779Hq9Xy+uuvGxzbs+ff/Yq3b9/OO++8UySveenS\nJfz8/ACwt7fHxcWF3NxcoqKiuHnzJl9//TVvv/02VapUoUaNGri6unLw4EFatGihJIXi2ZZv8ufs\n7Pw04xDPsPvZGnaFHTRo7+rWnqrlpUS+MK+0tDTs7OyU6VFVqlQhJCSEmjVrmjkyIcqmnTt3AvDc\nc89x6tQpdu/eLclfGbFp0yaSk5Oxt7fn7bff1juWnJzMN998Q3BwMImJiTg4OJgpStPcuHGDhIQE\nGjVqhKWlfr2EnJwc7ty5Q4UKFcjKyiIlJaXAQoqm0mg0/P777wAMHz6c+vXr6x1ft24dV65cYe3a\ntUqyOXLkSObPn8+6deuYOHHiE72+KBvyTf4OHTr0NOMQz7CDUX9x936aXpuFyoKXG/Q0U0RC/MvW\n1hYLCwtycnKYOnUqixYtMndIQpRpgYGBAPj4+BAQEEBaWhppaWnKqIVu+mWdOnW4du0aAF5eXgwd\nOpQjR45w9OhRcnPzip5ZWFjQrVs3vSlvcXFx/Pzzz2RkZChtKpWKtm3b0qtXryJ7H0FBQWzZsgV3\nd3euX7+uTBe3sLCgd+/etG7dWul78OBBTpw4ocRdtWpVo+f8+eefiYyMVB6r1WpGjBhBnTp1gH+n\nnjo7Oytbcjk6OjJ+/Hi98+imE0LeVM8qVaowZcoUAHJzc1m0aJFyfVQqFZ06daJ793/X30dHR7Nx\n40blHAD169dn+PDhBV6TkJAQAKNJiL29Pa1atSI0NJSEhAQcHBzynfY4d+5c1Go1H3zwgfK4cuXK\npKamotVqsbKyIjs7W6+Pzvz588nJyVHOacp3xhjdlE5jlZ11CVrjxo2BvCUDW7duZdiwYQWe81G2\nb9+OVqvFzc3NIPGDvBkp//vf//S+P5aWllSpUoXExETu3LlDlSpVnigGUfpJwRdhVlk5WWwP2W/Q\n3rFOaxxtq5shIiFg/fr1VKtWjezsvG1H/v77b1JTUyXxE6KYXblyhezsbOzs8mZ9tGzZEoDNmzcb\n9L127RpeXl7UrVuXbt26cfDgQQ4fPoxWq8XLywtPT0+0Wq2SWOn88MMPZGRkUKdOHdq0aUOdOnXQ\narWcPn2aO3fuFPl7ioqKQqVS0bx5c+rUqUNubi67du1Sjp86dYq//voLrVZLw4YNcXd35/bt2wZT\nMlesWEFkZCRqtZpmzZpRu3ZtNBoNa9as4fbt23p9b9y4gaurK15eXnTs2NEgpr59+2Jllff7f6dO\nnZSqxQCZmZncv3+fZs2aUa9ePbRaLceOHVOuTVxcHOvXr0ej0VCrVi2aNm2KWq0mPDycVatW5Xsd\noqOj0Wq1VKxY0WCkTOell17i/fffN7qs6FHu3r1LpUqVaNGiBd7e3tjY2KDRaPQ+05iYGHJycrC3\ntwcw+TuT3+vZ2NgYHc3TJbk+Pj74+PgAEB4eXuj39DBd4v/g5/WwqVOnMnLkSL02XSL74FRU8ewq\ncM2fEMXt6JXT3L6XYtA+oGHR/foqhKmys7Px9PQkOjoagN69e7N//37l11shRPHSTfnU3dz26tWL\n06dPExMTY9DX0dGRoUOHKo+///57AN555x1ldCMtLY3Fixdz8OBBOnToQGRkJCqVikaNGjFo0CDl\nuatXryYmJoZz587Ro0ePIn1PFhYWzJw5U3m8ZMkS7ty5w6VLl2jatKky02r8+PHUqFEDgODgYL2E\nNzExkfj4eKysrPRGss6cOcPu3bvZsGEDkyZNUtorVqzI6NGj842pWbNm7N27l+zsbKPvd/r06UpS\n8+OPPxIbG8upU6fw8fFh48aNAPTs2VOvOMvnn3/O9evX8x1dunr1KkCxjjy9++67yp/LlSvHsWPH\n2LlzJyNGjADyRkYBXnzxRQBla56CvjPGnD9/HkD5vB4UGRlJTk4OVatWVZJce3t7kpOTOXXqFM89\n99xjvz/dSGthp8S2bt2aXbt2ceXKlcd+bVF2yMifMJuc3Bz8Q/YZtLet3YLaVZzMEJF4li1evBi1\nWq0kfm5ubuzevdvMUQnxbElKSsLCwoLKlStz69Ytbt26RZUqVZTRmAc9WD3xzp07aLVabGxsKF++\nPBqNRlljVb58eXJzc7l37x716tVjzpw5DBo0iJCQEHbt2qUkNwD3799/4vfwcKX0h5OdypUrA5Ca\nmgpAVlYWVlZWeolEo0aN9M5z7FjeHrh16tRR3ptGo6FFixYASoEcnerVH3/mTLly5fRGs3Trm+/e\nvasXd+vWrfVi0VU9zm+vU10ipJteWdTKlSun91g3TVX3dzrArVu3sLCwoEGDBiZ/Z4zRJbKOjo4G\nx3Sjuu3bt1e+w7ok8sgR/armha2q/6RV+B+cpiueXTLyJ8zmZMx5bqUlGLT7yqifeMrq1KmjjCyo\nVCrWrl3LqFGjzByVEM8W3ahMbm4u3333ncHx06dP641SVaxYUflzfHw8kDdlccLGlg8AACAASURB\nVMGCBUbPHxsbi4eHh8G6OZVKhYXFo38Lt7HJ2wc3vwRR116+fHm9dt30ygdfD/SToIf7QF6ypJt6\nrku4oqKijL6/hxMqXayP4+Epmbpro3sN3XTU/K5zUlKS0XZdsq57L8ZoNBquXbuGh4dH4YLGMG7I\nGyFLTEzkxo0b3Lp1S1kvB4X7zjwsPT0dgAoVKui1P1ip9sGpvTqZmZnEx8cbHTE05uGpv2q1mnv3\n7pGQkJBvgh8ZGYmzs7PB91ClUpmluqsoeST5E2aRq83l92DDuefNazbCvaqrGSISzzLdTU2zZs04\nf/680RsxIUTx+ueff4C8EaWHRzjOnz9PVlaWUuDlYdWqVQPyRn/ym1ZXq1Yt9u3bp6ybe+mll2ja\ntCkAP/30E1FRUQXGpyuikZBg+KMl/Jv0GBsNepQH97vTeTCh0yUZzs7ORgt9PDzqVZx0SUSXLl2M\nHs8vcatduzYqlYr09HRycnKMJmvbtm0jODiYWrVqGeybp1OYjen79OnD+vXr+eOPP5Skc8CAAYDp\n3xljKlWqBGCwRlQ3bdne3t7gOly9epX4+Hi2bt3KW2+9BeTtbXj//v18r0d2drbe/wv169fn4sWL\n7N27l1dffdVobD///DMAH3zwgd4IrlarNelHDlH2yR2OMIvzNy4RczfOoN23kYz6ieJ38+ZNWrZs\nybFjx/Dw8CAiIoJjx47Rs6dUmBXCHG7cuEFWVhblypWjb9++Bsfv3btHYGAg/v7+yo33g6pWrYpK\npUKj0dC5c2e9G+mFCxeSk5ND586dCQsLA/IqND64fYRu5L+gKYnPP/88p06d0hs1fFBcXN6/aa1a\ntTLhHf9LrVYrI166qp3R0dF6sXTo0IGgoCDu3r1L165dlfbk5GS+/fZbqlatSvv27Qv1uo87hbBC\nhQqkp6dTt25dZaon5K0NjIuLK3AbHC8vL0JCQli+fDmTJ0/WO3b79m1ln2jdmk/d5xgXF4eTU95y\nkAMHDpgcq5ubG5aWlsTFxZGbm4uNjY1SNdbU74wxderU4cKFCyQmJuq16yrVvvbaawbTfTUaDQsW\nLCA+Pl5J9mrVqkVUVBT+/v4MHDhQr39wcDBarVZvb76+ffty8eJFIiMjiY6ONtg4fu3atUBecvpg\n4qdLmJ/mjwSi5Hqs5C89PZ2wsDDu3LlD165dpXSsKBStVotfsOFaqobVPWhY3fAXTSGK0htvvMGa\nNWsAaNOmDcnJydjY2EjiJ4QZ+fv7A9C8eXOjx/v160dgYCDJycl60z0f1KpVK86dO8eCBQvw9PSk\nXLlyBAcHo9FosLe3VzYVT0pKws/Pj4iICDQaDWFhYcrIW35rvCBvOqeTkxNxcXHMnz8fDw8P7O3t\nuX37NpGRkeTm5uLl5ZVvJcv8vPTSS/j5+bFmzRo8PT0BlCRVx9nZGTs7O1JSUli0aBH169cnMzOT\nsLAwvamMhWFtbQ3kjXq2aNFCGQV9lIEDB7J+/XrWrVuHq6srNWrUIDw8nJSUFGU9XX6GDBnCokWL\nuH37NvPnz8fNzQ07OztiY2OV5LlZs2bKXtMNGzYkICCAVatW0bx5cxITE/Md/c2Ph4cHoaGhAHrb\na4Bp3xljvL292bFjhzJ1FP6tVKtWq43eE6vVamxtbUlLS2PHjh3079+fIUOG8MUXXxAYGMj169dx\nd3fH0tKSmJgYbt68CaC3fYZaraZXr17s2bOH9evXY29vj5ubGxkZGURHR3P//n0sLCwYO3as3mvr\nKpfWq1evUNdOlE2FGv9NTEzk3XffpV27dgwfPpwJEyYAsGHDBnr27Mm5c+eKJUhRtly6FULk7asG\n7QMa9jZDNOJZERoaSsWKFZXET61Ws2+fYcEhIcTTp5tKqavC+DC1Wq2M+OW35q5v374899xzaLVa\nLl++zD///INGo8HFxUXZULxPnz7UqlWLnJwc/v77b4KCgsjNzVVK4esKeeRn7NixNG7cGK1WS2ho\nKKdOnVIStfbt2+tVHzVV06ZN6dWrFxYWFoSFhREWFkblypUNpp+/8847uLi4kJGRwYULFwgNDUWl\nUtGuXbsCS//nR1cQJSoqSllvaQo3NzeGDBmCtbU1V69e5ezZs6SkpFC5cmWDjduNmTZtGl5eXmi1\nWiIiIjh37hxxcXFYW1vTq1cvZVom5CX9jo6O5OTkEBAQwLVr1wyK4TzKg/vwPVzZ1JTvTH4qVaqE\nRqNRfjDQTfksKInu1q0b8O8IoVqt5r333lMS+4CAAM6ePcvNmzepWLEib775pjLiqdOuXTteffVV\nbG1tSU5OJiAggJCQEDQaDU5OTkydOlVvtBAgICAAKHiLCPHsUGlNXP15+/ZtBg8eTGxsLN7e3ty/\nf5/g4GAuX77M2rVrWbRoEWq1mo0bN+Ll5VXccRu4fv06PXr04ODBg9SuXfupv/7DUi5eImjOJ3pt\nlZs0puln857svKn3WbIxgIsRiWRlF65i1o7FhhuRPmzFosMk3NRfjD1+ahdqOFUu1GsV5JNDXxGc\noL/fjbt9HRb0nPnElayEMGbYsGFKeXLIW/Ph5+dnxoiEEEKUZjExMaxevRoPDw9lK4mSSDfdtHr1\n6sqgjXi2mTzy98033xAXF8eKFSvYsGGD8usFwOjRo1m9ejXZ2dmsWLGiWAIVeZZsDOB8SHyhE7+S\nIiQh0iDxAxjQqJckfqLY6EYKKlasSHh4uCR+QgghnoiLiwtVq1YlIiKiUEVonraffvoJQCpYC4XJ\nyd+hQ4fo2bOnXtL3oHbt2vHiiy8q1bpE8bgYkfjoTkZYW5WMCk+/Xzas8Fm7shNtahlf5yHE4+rc\nuTPLli0DwM/Pjx9//JG0tLTHKiEuhBBCPGzChAlYWFiwevVqc4diVHx8PNevX6dr164GU0HFs8vk\ngi/Jycm4uLgU2MfR0ZHbt28/cVAif4874tfMw6GIIym86OQY/o4LNGjv39AHC1XJSE5F6bdz5076\n9+9PTk4Ox48fZ+LEiQCMGTPGzJEJIYQoSywtLZkzZ465w8hXjRo1+Pjjj80dhihhTE7+atasqZTg\nzc/FixcLLPErnj5rKwuaeTgwZai3uUMxuq9fjYrV6FintZHeQhROdnY2LVu2VBbSQ94+R0IIIYQQ\nIo/JyZ+Pjw+rVq1i48aNRqtZrVmzhvPnz/P6668XaYBlyaXIRGa9v63Iz2tKIRdzu343jtPX/zZo\nf7mBD5YWhSuLLcTDNmzYwKuvvoqufpWTkxNhYWEyzUUIIYQQ4gEmJ3/jx4/nyJEjzJ07l19++UXZ\nfHTmzJkEBQURERFBnTp1GD9+fLEFK0qvbZf3oUW/sKx9+Sp0dXvOTBGJskSj0aDValGpVHz11VdM\nmTLF3CEJIYQQQpQ4Ji+0srW15ddff2Xo0KHExsYSGRmJVqvF39+fq1ev8vLLL/Prr79SuXLRbQkg\nHq2kFHIpSHx6EseunjFo7+fVE2tLazNEJMqCL774gtGjRwN5FYfnzZuHRqORxE8IIYQQIh8mj/xB\nXgL48ccf8+GHHxIdHc3du3epUKEC7u7uqNXq4opRFKAkFHJ5lO2X95Gr1S9UU6mcLS/U62SmiERp\nlpKSgqenp7Ip9IcffoiHh0eJXnQvhBBCCFESFCr507G0tJRy6WZWkgq5FCT53h3+jD5h0N7Xszs2\nVuXMEJEozd5//32++uor5bG3tzd169Y1X0BCCCGEEKWIycnfggULTOqnUqmYOXPmYwdU1pWG4ixF\naWfoAbJys/Xaylvb4OPRxUwRidLo5s2beHl5cffuXQCsrKz4448/6Nmzp5kjE0IIIYQoPUxO/tat\nW1fgcZVKpRRckORPAKTeT2Nf5DGDdh+PLlRUVzBDRKK0ys7OVhK/bt26cejQITNHJIQQQghR+pic\n/K1fv95o+71797h27RobNmzAwcGBJUuWFFlwonTbHf4n97Pv67WpLa15ybOHmSISpUlQUBCzZs1i\n27Zt1K5dm/nz59O7d2+8vUv2VGchhBBCiJLK5OSvbdu2BR7v168f/fr1Y/369bz77rtPHJgo3TKy\n7rE77E+D9hfcO1HZppIZIhKlSZ8+fdi9ezcAixYtYtq0acyePdvMUQkhhBBClG5Ftk+AnZ0dPj4+\nbN++vahOKUqxfRFHSc+6p9dmaWFJvwayRkvk7+jRo6jVaiXxs7W1ZfDgwWaOSgghhBCibCjSTeKy\nsrK4fft2UZ5SlEKabA27Qg8atHep+xzVKtibISJRGrRr144uXbqQlZUFwFtvvUVqaqpU8xRCCCGE\nKCKPtdWDMceOHWPHjh24u7sX1SlFKXUo+gR37qfqtalUKvo3eNFMEYnS4Ny5cwBUrVqV0NBQHBxK\n/h6WQgghhBClyROv+cvNzSUzM5OcnBy0Wi3//e9/iyw4Ufpk52SzLWSfQXtHl9bUrFTDDBGJkio7\nO5thw4axefNmAH777TeCgoL4+OOPzRyZEEIIIUTZZHLyZ2tra7RdpVLh4OCAu7s7r7zyCl27di2q\n2EQpdPTqGZIykg3a+zf0MUM0oqRauXIl48ePR6vV0rVrVw4fPsygQYMYNGiQuUMTQgghhCizTE7+\ndu7cSYUKsjdbWZadnYPmfvajO+YjNzeXbZf3GrS3rtWcOna1niQ0UUZkZmZSv359rl+/DuT9eCQF\nXYQQQgghng6TC74MHDiQTz75pBhDEeaUeieTdctOcCf53qM75+PU9QDi0uIN2n0b9nqS0EQZ8emn\nn1K+fHkl8fP09ESj0TBx4kQzRyaEEEII8WwwOfm7fv26jPyVUTFXbvPD10eJvZZicExlocKu6qM/\nd61Wy+/Bewzamzk2xKNa3aIIU5RyX3/9NQAWFhZs3ryZ0NBQrKyKrOaUEEIIIYR4BJOTvwYNGhAY\nGFicsQgzCDh1lXXLT5CWet/o8W69vFCXe/QN+vkbl7h6J9ag3beRjPo9y2bMmEFiYiIAYWFhdOvW\njZycHFnbJ4QQQghhBib/7P7ee+8xbdo0XnnlFV544QVq165NuXLljPbt0aNHkQUoikdOdi57twVy\n7sRVo8ctLFX08W2K93OujzxX3qjfboN2r2ruNKxe/4ljFaXPlStXaNq0KWlpafz4448kJSXh4ODA\noUOHzB2aEOIxnD59mlGjRgEQGhpq5mgM+fn58cEHH9C4cWP8/Pye6FwajYZbt27h4uJSLOc35vr1\n64+8d7KxscHBwYGmTZsyZswYmjZtWuRxlHXdu3cnNjaW7777jm7dupk7nEIJDw/n22+/5fTp06Sn\np+Ps7Ezfvn158803KV++fKHP988//7By5UoCAgJIS0vDwcGBTp068dZbb1GrlvE6DSkpKaxcuZID\nBw5w48YNbGxsaNKkCa+99lqpu57PMpOTv9dffx2AxMRELl26ZLSPVqtFpVJx+fLloolOFIu01Pts\nXneOmOjbRo/bVirH4Nda4+JW1aTzBcWHEn77ikH7gEa9UalUTxKqKIWGDRvGxo0blcetW7c2YzRC\nCGG648ePM3fuXEaNGsWrr75qlhiaNGmCWq3Wa9NqtSQnJ3Pt2jWuX7/O3r17Wbx4MX369DFLjOLp\nCgwMZOTIkWRkZFC9enXq169PWFgYy5Yt4+DBg/zyyy/5VuU3Zvv27cycOZOcnByqVKlCvXr1uHr1\nKps3b2bfvn2sXbuWRo0a6T0nPj6e4cOHExMTg7W1NW5ubiQnJ3Py5ElOnjzJ5MmTmTRpUlG/dVEM\nTE7+Jk6cKDfyZUDstRQ2rT1L6p1Mo8dr1bHjldFtqFTFxuRz+hlZ6+dm50JLp8aPHacofS5evEi7\ndu3IzMz7btnY2HD69GmaNWtm5siEEGVdz549ad68OTY2pv/bZcx3333H1auGM2KK6vym+L//+z9q\n165t9FhMTAzvvvsuly5dYvbs2XTs2JEqVaoUe0xlxdq1a8nKysLJycncoZjs/v37TJgwgYyMDCZN\nmsTEiROxsLDg1q1bvPXWWwQFBfHll1/y6aefmnS+hIQEPvroI3Jychg9ejRTp07F2tqaO3fuMHXq\nVI4ePcp7773Hrl27sLS0VJ736aefEhMTg6enJ8uXL1dGxn/99Vc++eQTli5dSvv27WnVqlWxXAdR\ndExO/iZPnlyccYin4MK5GHZuvkhOdq7R4y3autBnYFOsrCyNHjcmLDGKwHjDKUADGvWSHwueMVOm\nTFESv6FDh/Lrr7+aOSIhxLOiUqVKVKpUqdSe31QuLi58/fXX9OrVi4yMDHbt2sXw4cPNHVapUadO\nHXOHUGj+/v7cunWLli1b6t2LOzo6snTpUnx8fPDz82Py5MnUqFHjkefbt28f9+7do169esyYMQML\ni7zyH1WqVGHRokV07tyZ6OhoLly4gLe3NwBpaWkcPHgQyEsCH5wSPWzYME6ePMnevXvx9/eX5K8U\nyLfgS48ePVi/fv3TjEUUk5ycXPb6B7Lt13+MJn4WFip6D2hCv1eaFyrxA/C7bDjq51zJkba1Wjx2\nvKL0OHToEHv27FH+3KxZM6KjoyXxE0KIYuLi4oKbmxsAUVFRZo5GFDd/f38ABgwYYHCsVq1adOjQ\ngezsbPbv32/S+eLj87bk8vDwUBI/HTs7O2W9X1xcnNKemJhITk4OkLdN08OaNGkCwI0bN0yKQZhX\nvslfbGwsd+/efZqxiGLyx5ZLnD4WbfRYBVs1r45/jjad3Ao9Uncl+ToBNwzXf/Zv6GPwF4ooe9q0\naUOPHj146aWXlLYLFy5Qt25d8wUlhChx7t69y9KlS+nXrx/NmzenZcuW+Pr68uOPPyqzBR6WmZnJ\n6tWr+c9//kOLFi1o3749M2bMIC4ujpkzZ+Ll5aVXeMXPzw8vLy98fX31zqPRaFizZg0DBw6kRYsW\nNGvWjBdeeIEPP/yQyMhIpd/p06fx8vLizJkzQN7ohpeXF0uXLi3w/JA3KrJy5UoGDBiAt7c3LVq0\nYODAgWzYsIHcXOMzbZ6U7t9rrVZrcEyj0bB27VoGDhxIy5YtadGiBQMGDGDVqlXcv2+8sndOTg6b\nN29m0KBBeHt707ZtWyZNmkR4eDhLly7Vuxbw7/WYN28e+/bto2fPnjRt2pQXX3yR8+fPK/2SkpL4\n8ssv8fHxoVmzZrRp04bXXntN+dHwYVqtls2bNzN8+HC8vb1p0qQJXbt25f333+fixYtP3L979+54\neXnx559/Ghw7deoUEydOpEOHDjRp0oROnTopU2wfpvu+jBs3joyMDL766ivlGnTs2JGpU6cSHW14\n36W7bl5eXkbf/8NycnIICgoCoGXLlkb7NG/eHIBz586ZdE5HR0cgrwL3w9/PtLQ0Jel7cGps9erV\nlSmgxup6hIeHA+Ds7GxSDMK8ZJOtMi4j7T5/n7lm9JhT7Sq8Mro1Vewfb/9GfyOjftUrVqOTa9vH\nOp8oHTZu3MiIESOUfzTs7e3Jzs6WPfuEEAauXLnC66+/zo0bN7C0tKR+/frk5uYSHBxMUFAQ27dv\nZ9WqVVSvXl15TlpaGmPHjuX8+fNYWFhQv3597t+/j7+/P0ePHsXV9dFVqCEvMZg0aRJHjhzBysoK\nV1dXypUrx5UrV9i8eTM7d+5k3bp1NG/enEqVKuHt7U1YWBhpaWm4uLhQvXr1R64Ni42N5c033yQy\nMhJLS0vq1atHVlYWgYGBBAYGcuHCBb788ssnuoYPi4qKUm62H674mZKSwptvvsnFixexsLDAxcUF\nGxsbQkNDCQ4OZteuXaxatQp7e3vlOVlZWbz//vvs3bsXAHd3d6ysrDhw4ADHjh0rcBrfP//8w2+/\n/YadnR1169YlNjZWSWyCgoJ48803SUpKQq1W4+bmRkZGBqdOneLUqVP4+vry+eef6/3w/PHHH/Pb\nb7+hUqlwdXWlYsWKXL9+nZ07d7J7926++eYbXnjhhcfun5/FixezcuVKABwcHGjQoAExMTH88ccf\n7Nmzh9mzZxstAJSens7w4cO5fPkyNWvWpF69eoSFhbFjxw6OHj3K1q1b9aZIFtbNmzeVhD2/daC6\nkTpja1WN6dWrF4sXLyY6OppFixbx/vvvY2VlRXp6OrNmzSIzM5NGjRrpJZsVK1akd+/e7Ny5k7lz\n57JixQrldXfs2MHOnTuxsrLilVdeeez3Kp4euVsr4+7eNf6ratNWtXhpcHOsrQs3zVPnRuotTsYE\nGLS/3KAnVhaPd05RsmVnZ9O4cWPCwsKUtnnz5jFnzhwzRiWEKKmysrIYP348N27coG3btixatIia\nNWsCEB0dzTvvvENoaChTpkzhl19+UZ731Vdfcf78eerUqcOKFSvw8PAA8kY2Jk2axN9//23S6x85\ncoQjR45Qt25d1q1bp7x2WloaM2bM4MCBA3z11VesW7eORo0a8euvvzJy5EjOnDnD6NGjTar2OXPm\nTCIjI2nRogVfffWVckN85swZxo0bh7+/Px07duQ///lPoa5dfi5fvsy0adPQarXUrl2b3r17G8Rz\n8eJFWrZsycKFC5U1bnFxcUydOpVz584xe/Zsli9frjxn/fr17N27F3t7e5YtW6Yke2FhYUycOJHj\nx4/nG09QUBA+Pj4sXrwYa2trbt++ja2tLampqUycOJGkpCQGDx7MzJkzlWqU58+fZ8qUKcoo2OjR\no4G80aPffvuNqlWrsn79eurXz9sqSqPRsGDBAjZs2MAXX3yhJHOF7Z8ff39/Vq5cibW1NZ988gkD\nBw5EpVKRk5PD6tWrWbx4MfPnz8fNzY2OHTvqPffs2bNUr16dtWvX0r59ewAiIyN57bXXSEhIYNWq\nVXzyySdKf13hIFPdvp1XlV2tVlOhgvEf6nUFf1JSUkw6Z9WqVVm1ahUzZsxg9erV+Pn54ezszNWr\nV0lPT6dz584sWLDAYDbYp59+Sm5uLrt378bHxwc3NzdSU1OJi4vDycmJjz76SLYfKSUKTP5SU1Mf\na/6uDPuWbA41bOk/rOUTFWTxv7wXLfrTTexsKtPVrcOThidKqNWrVyuJn4uLC2FhYU+l8p0QT1NK\n6n2WbAzgYkQiWfkUxyoNrK0saObhwJSh3thVMr4nb3HbtWsX0dHRODg4sHz5cr2CKW5ubqxcuZLe\nvXtz7tw5jhw5QpcuXbh9+zYbN25EpVKxdOlSJfGDvG1jFixYwPjx4016fd3fV88//7yS+AHY2try\nwQcfkJmZqaydexwBAQGcOXOGChUqsHz5cqpVq6Yc002bXLhwIdu2bStU8vfOO+8YbPWg0WiIj49X\n1mvVqVOH7777Tm+/5UuXLvHnn39ib2/P8uXLqVr13+2anJyclFGwgwcPEhISQoMGDcjOzlZGvD77\n7DO9UT5PT0+WLl1K//79jU4v1Xn33XextrYGUF5z06ZNxMXF0bZtWz799FO9+41WrVoxf/58xo4d\ny8qVKxkxYgTW1tbK59WyZUslkYO8xGf69OlcvXoVZ2dn0tPTqVixYqH75+fbb78F4O2332bQoEFK\nu6WlJW+++Sbx8fGsX7+eJUuWGCR/ALNmzVISP4B69eoxfPhw/u///o8LFy7o9S1s4SDdqF9B/9bq\njuU3hdqYKlWq0KxZM65cuUJKSoqSOFpaWuLk5GT0/tDKyorGjRtz4sQJUlJS9H4Irlq16mPtNSjM\no8Dkb/369YUu+qJSqQgODn6ioETxsrS0eKLELzH9NseunDZof8nrBdSW1k8SmihhMjMz8ff3Z+jQ\noYwdO5YffviBMWPGmHzzJURps2RjAOdD4s0dxhPLys7lfEg8SzYG8Mmb7R/9hGJw5MgRAPr162f0\nhrdmzZr07NmTbdu2cfjwYbp06cLRo0fJycmhefPmNGjQwOA53bp1w9nZ2aQfpnXT7bZu3Yqnpycv\nvPCCMt2xdu3arFq16knenvL+unXrppf46QwZMoQuXboUusJkYGCg0XZra2t8fHzo0qUL/fr1M0gQ\nddUY27dvr5f46VSrVo327dtz8OBBjh49SoMGDfj7779JSUmhevXqdO/e3eA5DRo0oFWrVvmuJ7Oz\nszOaQOti6dOnj9H7jeeff54qVaqQlJREUFAQLVq0UD6vI0eO8OOPP9KvXz9lfVr58uVZvXq13jkK\n29+YyMhIYmJisLCwYOjQoUb7jBo1ivXr13Px4kWSkpL0PmuVSsXzzz9v8BzdNUlLS3tkDAUxpX6C\nbgmGqfd1wcHBjB49mjt37jB69GhGjRpF9erVCQsLY/HixWzatImzZ8+yYcMG5XukG8U/fvw4TZo0\nYdmyZTRr1ozk5GS2bNnC8uXLGTNmDEuWLOHFF198/DcsnooCkz8nJydlCoMQOttD9pOj1f9F3FZd\nkRfrdTZTRKI4fPTRR8q+QV27dqVmzZqcPXvWzFEJUbxCriabO4QiZc73c+XKFQAaNmyYb59GjRqx\nbds2pa+uCEtBBTEaNGhgUvLXo0cPmjdvzoULF/jwww+VaWmdOnWiW7duTzxF7dq1vPX0D446PcjW\n1lZv5NJUBw8eVNZ3aTQajh8/zueff861a9dIT0+ne/fuBokf/Hvtzp07x7Bhw4ye+/r16wBKMRLd\nczw9PfNNHho2bJhv8vfgWk1jsfz0009s377daJ+srCwlFl0xnp49e7J//34WLVrEokWL8PLyUj6v\nVq1a6SVDhe1vjO57V7t2bSpXrmy0j4uLC5UqVSI1NZUrV67oJX8VKlQwurm6bjROVyHzcemmeuZX\nqAfyviOA3ihwQebNm8edO3cYOnQoH3zwgdLepEkTfvjhB1599VX+/vtvvvvuO2bNmgXkFao5fvw4\n1atXZ82aNcq1cnR0ZOLEidjZ2TFv3jzmzZtH165djX4/RclRYPLn6+vLpEmTnlYsohRIybzLwWjD\n+f99PLthYy1TAMuCxMREvLy8lLUGlpaWRERE6E2bEqKsauBqXyZG/nQauNo/ulMxSU9PByhwyp3u\nmK6vbvpZQVPICjrfg9RqNevXr2f16tX4+/tz9epVLly4wIULF1i2bBn1eOrDswAAIABJREFU69dn\n7ty5j70vmS7W/NZiFQW1Wk23bt1o1KgRAwcO5K+//mLcuHGsX7/eYCqgbpTpwemh+UlNTQWe/Hrn\nd5Ovi+XBiqqPigXyNrj/9ddf2bx5MyEhIYSGhhIaGsqqVauoVasWs2bN0lvDV9j+DzPlOwp5n3Fq\naqrSX0c33bW42NnZAXnJX2ZmptHpn7rP0Nho78Pi4+OVNbPGZvBYWVkxduxY3nrrLfbs2aMkf/v2\n7QPy9vQzliQPGzaMpUuXkpCQwLlz5+jQQZYAlWRS8EUUys7Qg2TlZOm12ViVo1f9ruYJSBSpCRMm\nsGLFCuXxc889x8mTJ80YkRBP15Sh3mVuzZ+56JKigqa+6W78dX11ScjDN9kPKujYw2xsbJgwYQIT\nJkwgOjqakydPcvz4cY4dO0Z4eDj//e9/2bNnjzJdsDB0N+L37t0r9HMLy9HRkYULF/LGG29w4cIF\nFixYwNy5c/X66K7d9OnTGTNmjEnnLerr/eB5U1NT2bJlS6FGWC0tLXn11Vd59dVXiYuLUz6vw4cP\nExsby9tvv82mTZuUfeUK2/9hpnxHwfB7+rQ4OjpSoUIFMjIyiI2NpV69egZ9YmNjAUyqgqsbMS9f\nvny+lWx12zXFx8crlbx1r+Hu7m70ORYWFri6upKcnKz0FSWXJH/CZGmadPZFHDFof9GjC7Zq036J\nFSVXSkqKkvhZW1tz6NAhOnXqZOaohHi67CqVM9saubLGzc2Ny5cvc/nyZfr372+0j24PM926ON00\nyQeLSTxMt83BoyQnJxMVFYWbmxtVq1bFzc0NNzc3hg8fzq1btxg4cCAJCQkcOHCAESNGFOatAf/e\nJEdERBg9npSUxLhx46hTpw4LFy584u1wOnTowCuvvMJvv/3Gxo0b6dWrl16hEd3Nf0EbvwcHB6NS\nqXBxcdGblhoeHo5WqzU69dPU6/0gV1dXAgMDiYqKyjf5O336NA4ODri4uKBWq0lLSyMqKopq1apR\nq1YtnJyc8PX1xdfXl9TUVIYPH05YWBi7du2iSZMmhe5vjG5tnm5va2OjWtHR0WRkZCjv62lSqVQ0\nadKEM2fO8M8//xhN/nRFZZo1a/bI8+mmqGZmZpKammp0La4uQbSxsVG+s7rnJSQk5HtuXdJnbBqs\nKFlkJ25hsj3hh8nM1p93bm1pzUtePcwUkSgK+/fvB/Kml/Tv358+ffqg0Wgk8RNCPJEuXboAsHPn\nTr2pfTo3b95UCoPo/r7p0qULVlZWXLp0yWgCePr0aWJiYkx6/alTpzJ8+HC2bNlicMzR0VEZxXhw\nXVZBm6c/rHPnvHXuhw8f5s6dOwbH9+/fz6VLl4iMjCyyfVCnTZuGg4MDAHPnzlXWe0He2mzIm6Kn\nm7b/oNTUVEaPHk3//v3ZvXs3kFdB1c7OjsTERI4ePWrwnGvXrj3WWm9dLJs2bTJ6Lc+ePcuoUaPo\n27evkmwsXLiQwYMH8/333xv0r1SpkpLA6QqcFLa/MfXq1aNOnTrk5uayceNGo31025B4enrmu8ax\nOPXs2RPA6Pc4NjaWkydPYm1tbbDthzHu7u44ODig1WrZunWr0T7+/v4AtGnTRmlr27atcszYOsYT\nJ06QkJCApaXlY0+jFk9PvsnfpEmTaNeu3dOMRZRgmVmZ/BH2p0F7D7eO2NkYXyQtSraAgABsbGx4\n8cUX2bBhAwC///47u3btMnNkQoiS7O7duwX+pytO0adPH9zc3EhMTGTChAncvHlTOUd0dDRjx47l\n3r17tGzZkh498n5EdHR0ZODAgWi1Wt5++229UaygoCCmT59ucpz9+vUDYMWKFfz11196x3bv3q1s\nIv/gD126aX2mFJRp3749zZs3JzU1lcmTJ+uNipw5c4bFixcD8Prrr5sc86NUqlRJuQbR0dH88MMP\nyrF27drRpk0b7t69y7hx4/Q2/b516xYTJkzgzp07VK9eXbk25cqVU6aIzp49W29rgmvXrjFx4sTH\nKloyfPhw7O3tOXfuHLNmzdJL/i9dusR7770H5BXl0Y2g6mLasmUL27Zt00saz5w5w549ewCU6pqF\n7Z+fiRMnAvDNN9+wdetW5Tw5OTn8+OOP/PzzzwC8//77hb4OD0tNTSUyMtKktZA6vr6+VK9enYCA\nAL744guys7OBvM908uTJZGVl0b9/f2rUqKH3vBs3bhAZGam3/tPCwoJx48YB8PXXX7Nt2zYlOc7O\nzmbp0qXs2LEDCwsLvTWBo0aNomLFigQFBTFz5ky9PQVPnz7NjBkzABg0aJBBHKLkyfenKCn0Ih60\nL/IYaRr9ef+WKgv+06CnmSIST6J79+78+ee/yfyFCxcYPny4GSMSQpQWD44IGDNp0iQmT56MWq1m\n2bJl/Pe//+XMmTP06NEDDw8PcnNzlWmGXl5efP3111haWirPnz59OoGBgQQFBdG3b1/q169PTk6O\nUnjKwcGBxMREvecY8/LLL3Po0CH27t3LmDFjlOc+WBDlvffe01vH5OXlxZ9//sm6des4efIkvXv3\nVm6WH6ZSqfj6668ZPXo0p0+fplu3btSvX5/U1FRldHLQoEH5Tnl9XC+//DJbtmzhzJkzfP/99/Tr\n10+ZNrt48WLGjBnDxYsX8fHxwcPDAwsLC6KiosjKysLW1pYffvhBr3DIG2+8oey1+Morr+Du7o5a\nrSY8PJwKFSrg6urK1atXH3m9H1StWjWWLl3KhAkT8PPzY9euXXh4eJCWlqYkpV5eXixYsEB5Tps2\nbXj99ddZs2YN06dP58svv8TJyUlvHdmQIUOUvfYK2z8//fv3JywsjFWrVjFr1iy+/vpratasSUxM\nDCkpKVhaWjJt2jRlNPNJ7N+/X6mwGRoaatJzbG1t+fLLLxk/fjxr1qxh+/bt1KxZk/DwcDQaDY0a\nNVIKszxoxowZnDlzhgEDBvDFF18o7SNHjiQ8PJxNmzYxffp0Fi5cSI0aNbhy5QoZGRlYWlry8ccf\n643g1axZk//7v//j7bffZvv27ezduxd3d3du377NrVu3gLzR+9mzZz/J5RFPiUz7FI+kycliZ+gB\ng/bOddvhUPHR1aVEybF//36sra2VxK9y5crExMTw5ZdfmjkyIURZVK9ePfz9/Xnrrbdwc3PjypUr\nxMXF0bRpU2bNmsXmzZsNCk/Y2tryyy+/MGnSJFxdXYmOjiY5OZlBgwaxefNmZU1RQRtfQ15ytnjx\nYmbPnk2LFi1IS0sjJCQErVZLz549Wbt2rUFiN3bsWAYMGICtrS1RUVEFrj0EqFWrFn5+fkycOBFX\nV1ciIyNJSkrC29ub//3vf3z22WePcdUe7aOPPsLa2pr79+/rFX5xdHRk8+bNTJs2jcaNGxMbG0tU\nVBQ1atRgyJAhbNu2zWDrDSsrK5YvX84HH3yAl5cXN27cIC4ujp49e7JlyxZlP71HXe+HtWnThh07\ndjBq1CicnJyIiIjg5s2beHp68vbbb/Prr78arLGbMWMGX3zxBW3btiUnJ4eQkBAyMjLo3Lkz33zz\nDfPmzXui/vmZPn06a9asoVu3bmRnZxMSEkLFihXp378/mzZtKtLR28fRsWNHtm7dSq9evdBqtYSF\nhVGjRg3eeOMNfvrpp0IVolGpVHz66acsW7aMTp06kZ2dTXh4OBUrVqRv375s2rSJIUOGGDyvc+fO\nbN++nSFDhuDg4EBERATp6el4e3szf/58Vq5cafJ2E8K8VFpTJraXAtevX6dHjx56++OYU8rFSwTN\n+USv7Wp5R4ZvXP5U47h54w4rF+vP43d0qsy4qV1MPse+iCP8eF5/LrxKpeLr3h/jXKnwFdKE+VhZ\nWSlTeN577z1lWpIQQpQWHTp0ICkpiQ0bNsj6oqdg8ODBXLx4kUWLFvGf//zH3OEIIZ6QjPyJAmXn\n5rDt8j6D9va1vSXxKyUCAgKUP//vf/+jevXqJCcnS+InhChxwsPD6d69u7IO62HBwcEkJSVhZWVV\n4EbwwjTp6el07tyZkSNHGt2yIiEhgcuXLwPQqFGjpx2eEKIYSPInCvTX1TMkZBhWDRvQqJcZohGF\nkZ2djYeHB61atWLw4MEATJkyhfj4eGXjWCGEKElcXV1JS0vjwIEDrF69Wq/YSFRUFNOmTQOgb9++\nUlK+CFSsWJEqVapw5swZlixZohTrgbxqrO+++y5ZWVm0bt1a2RZCCFG6yT5/Il+5ubn4X95r0O7t\n3BRXO/NPrRX5W7JkCe+9955Stexx9mkSQoinTa1WM2vWLGbOnMmXX37JypUrqVWrFqmpqVy7dg2t\nVkuTJk2ksEQRmjVrFuPGjWPt2rVs3bqVOnXqcO/ePa5du0Z2djaurq6yLlyIMkSSP5GvM7H/cCP1\nlkG7b0MZ9Sup0tLSqF+/vlJSXaVS8cMPPyilvIUQoqTr378/DRo0YPXq1Vy4cIGIiAhsbGxo1qwZ\nffv2ZdiwYajVanOHWWZ06NCBXbt2sXr1av4fe/cdFsXVNnD4RwcFKYoKBBVQQCUajSX2aCyxx5rE\nqLH36GuJRo2vJVGjiRrssUdUrIgCVuwoKhEbHSlSbCAoUpf2/cG387LuIhDRFT33dXmps2eGZ2YH\nmGfPOc/x8/MjKioKLS0t6tSpQ+fOnRkyZIjKxcAFQSif1Jr85eXlsWDBAkJDQ9HV1eXXX3+lZs2a\n0uuenp78/fffaGlpYW9vz4IFC9DUFCNV34b8/HwOB51Q2u5U1QH7KrYq9hDeBfb29lLi5+TkxM2b\nN8tscWFBEIS3xdHRkeXLl6s7jA9GjRo1WLBggbrDEAThLVBrJuXt7Y1MJmPfvn1Mnz5dYR2SzMxM\n/vzzT3bu3MnevXtJTU1VWJdMeLNuPgwk6lms0va+Yq7fO+fRo0dkZmYCcO7cOfT19XF3d+fu3bsi\n8RMEQRAEQRAkak3+bty4QZs2bQD45JNPCAgIkF7T1dVl7969GBgYAAXFK8T6IW9HQa/fcaXtdSrb\nUL+qqK72Lhk1ahQWFhbY29sDBYvmZmRk0Lt3bzVHJgiCIAiCILxr1NotkJqaqlCtS0tLi5ycHLS1\ntdHU1KRKlSoAuLi4kJ6eTqtWrdQV6gclOCGc0KeRStv71P0SDQ0NNUQkvCw0NJTGjRuTnp4OQGJi\nopojEgRBEARBEN51au35MzQ0JC0tTfp/Xl6ewjC1vLw8li1bxuXLl1mzZo1IPN4SNxVz/WoaW/Gp\n5cdqiEZ4WZ8+fXB0dJQSv169ekn/FgRBEARBEISiqDX5a9y4MRcvXgTg1q1b0tA1uf/+979kZWWx\nfv16afin8GbdexrNncfBStv71BO9fu+CYcOG4e7uDkCFChUICQnhyJEjao5KEARBEARBKA/Umvx1\n6tQJXV1dvvnmG5YuXcrs2bPx8PBg3759BAYGcvDgQcLCwvj+++8ZMmQIp0+fVme4HwS3YOVePwvD\nqnz2UWM1RCPIpaamArBlyxb09fUZPnw4aWlpODiIOZiCIAiCIAhCyah1zp+mpiaLFi1S2GZnZyf9\nOyQk5G2H9EGLeRbPP/G3lbb3rttFLLGhJp6ennz11Vdoamoik8nQ1tYmIyND3WEJgiAIgiAI5ZB4\nohck7sEnlbZVrmBK25rN1BDNhy0nJ4eGDRvSs2dPcnNzyc7OJjo6Wt1hCYIgCIIgCOWYSP4EAB69\neMLl2H+Utvd27Iy2llgr7m3auXMnurq63LlzB4Dq1avz4sULatWqpd7ABEEQBEEQhHJNJH8CAO4h\np8jPz1fYZqxnRAeblmqK6MPk5+fH999/T35+PhoaGvzxxx88fPhQYUkUQRAEQRAEQfg3RJeOQGJ6\nEheirypt7+7wBbraumqI6MMjX9+yadOmmJmZYWJiQmhoqMLSJ4IgCIIgCILwOsSTpYBniDe5ebkK\n2yrqGNC5dls1RfThSE1Nxc7OjoSEBFJSUjA0NOTp06fqDksQBEFBXFwcX3zxxSvb6OvrU6VKFT7+\n+GNGjhzJxx+/u2vD7tq1i19++YVmzZrh4uKi7nAUlORaFzZ79myGDRv25gJ6ixITE9HV1aVSpUpF\ntgkICODgwYNcv36d+Ph4cnNzqVq1Ko0aNWLgwIE0b95caR83Nzdmz55N/fr1cXNze5On8NrWrFnD\n2rVr6dKlC6tXr1Z47dixY/z1119ER0ejp6dHt27daNCgQbk4t+fPn7N27Vq8vb1JSEjA1NSUNm3a\nMHHiRKysrEp9vEePHrF+/XouXbpEQkICFStWpFGjRowePZpPP/1U5T6ZmZns3LmTEydOEBUVRW5u\nLpaWlnTo0IFRo0ZhZmb2uqdZLojk7wP3PDMF70gfpe1d7dtTQUesrfgmzZo1i+XLl0v/X7FiBfPn\nz1djRIIgCMVzcnJCV1dxVEh+fj7JycnExMQQFxfHyZMnWbFiBd26dVNTlO8HVdf6ZdWqVXtL0bxZ\nO3bsYM2aNbi6uqpM/mQyGUuWLMHV1RUAHR0drKys0NXVJTY2Fk9PT6lC9i+//FLsdStv/Pz8mDp1\nKlBQC6By5cpYWlqqOaqSef78Od988w2RkZFUrFgRe3t74uLiOHToEKdPn8bFxQVHR8cSHy88PJwh\nQ4aQnJyMnp4etra2PH78mHPnznHx4kUWLVpE//79FfZ59uwZQ4cOJTQ0FA0NDSwtLdHT0yMmJoat\nW7dy7Ngx/v77b2rWrFnWp//OEcnfB84r7Cyy3GyFbXraenSt015NEb3/Hj16hKOjI8+fPwdAS0sL\nT09PvvzySzVHJgiCUDxnZ2c++ugjla/FxsYydepU7t69y9y5c2nVqhXGxsZvOcL3x6uu9ftm6dKl\nRb6WnZ3NsGHDuHHjBlWqVGHSpEn069dPSvBkMhn79+9nxYoVuLu7k5WVxZ9//vm2Qi9T3333Hd26\ndVOa63/iRME6zE2bNuXvv/9GS0sLgBcvXtCwYUP09fXfeqwlNW/ePCIjI2nXrh0rV67E0NCQrKws\nFixYgJubG9OmTcPDw0M6p+LMmjWL5ORkmjdvzp9//omZmRm5ubmsXr2ajRs3smDBApo3b461tbW0\nz4IFCwgNDcXW1pY///xTWif5wYMHTJ8+HX9/f6ZOncqhQ4fQ0NB4I9fhXSEKvnzA0mTpnLx3QWl7\nZ7s2GOmJAiNviqWlpZT4tW3blpycHJH4CYLwXrC2tmbVqlVoa2uTnp6Ol5eXukMS3gNr1qyREj8X\nFxe+/fZbhZ49XV1dBg8eLA2TPH78ON7e3uoK97WYmZlhZ2en1KOblJQEQKNGjRSSJCMjI+zs7P7V\n0Mm3ISIiglOnTlGhQgWWL18uJbV6enr8+uuv2NnZERERwenTp0t0vHv37hEYGIiGhga///67NFRT\nS0uLqVOn8vHHH5Odna3ws+fRo0ecOHECTU1N/vjjDynxg4JnstWrV1OhQgUCAwPx8/Mrw7N/N4nk\n7wN28t4FMrIzFbbpaGrTw6GjmiL6MDRq1AhdXV2uX7/OhQvKybcgCEJ5Zm1tjY2NDQCRkZFqjkYo\n72JjY9myZQsAM2fOxNbWtsi2bdq0oX37gpFLO3fufCvxvS25uQW1GcrbcNajR4+Sn59Phw4dMDEx\nUXhNS0uLvn37AgXzGUvi8ePHAJiYmKgc8lyvXj2goEdP7vr16+Tn52NtbU39+vWV9jE3N8fJyQmA\noKCgEsVRnolhnx+ozJwsvELPKG1vb9MSUwMxRKcs9ezZE09PT06dOkWnTp24ceOGukMSBEF4o+TD\npl5eQggKHuZ37tzJ1atXiY+PRyaTYWJiQsOGDRkyZAifffaZQvuffvqJw4cP4+zsTK1atVi3bh1+\nfn6kpaVhbW1Nr169GDFihMqH4uDgYDZs2IC/vz+pqanUrVuX8ePHvzL2rKws9uzZg5eXFxEREeTl\n5WFtbU3Hjh0ZPny40jBWeXzr1q2jWrVqrFu3Dn9/f7Kzs3F0dGTixIm0bt2a9PR01q1bx/Hjx3ny\n5AlVq1alZ8+eTJo0CR0dndJe4jKLf8iQIVy/fh1XV1c8PDw4cuQIAA0aNGDbtm1oahb0E/j5+bFj\nxw5u3rxJSkoKlStXplWrVowdO1blPKmnT5+yadMmzp8/T3x8PLq6ulhbW9O+fXuGDRsmJQLyAidy\nPXv2BAqSt+bNm3P48GGpqEuPHj2KPf8pU6YwcOBAmjRpUqLrlZiYiIuLC5cuXSI2NpaMjAyMjIyo\nV68eAwcOpEuXLkr7BAcHs2XLFq5fv05SUpI0j61nz57069dPqVJ3adq/XPDl5euzdu1a1q5di5WV\nFWfPnn1lMRuZTMaePXvw8PAgMjKS/Px8bGxs6NGjB4MHD0ZPT0+hfXH3woMHD6RiRPL3pzjyNYsb\nNWqk8vVPPvkEoMTPRtWrVwcgOTmZx48fKyWA9+7dA1CYD9msWTOcnZ2le1mVjIwMoKD6+vtOJH8f\nKO8IH17I0hS2aWpo0qtuZzVF9P7x8fGhQ4cOZGcXzKmcNWsWnTp1UnNUgiAIb1ZkZCTh4eEAShU/\nfXx8mDhxIpmZmRgZGVGjRg2ysrKIjY3F29ubM2fO8Mcff6h8yL969SozZswAwMbGBgMDAyIiIli1\nahW3b99mw4YNCu1Pnz7NtGnTkMlkmJqaYmdnR0hIyCurASYlJTFs2DCpKIStrS26urqEh4ezYcMG\njhw5wpYtW7Czs1Pa9/z587i7u6Ojo0OtWrWIjY3F39+fMWPGsGHDBlasWEF4eDg1atTAwsKCmJgY\nNm7cSGJiIosXL/5X17os41+2bBm3bt3C3t6eZ8+eYW5uLj0sr1+/HmdnZwBMTU2xt7cnNjaWQ4cO\ncezYMZydnWnXrp10rKdPn9K/f38ePHhAxYoVqV27Njk5Ody7d4+QkBCOHTvGgQMHMDY2xsLCgsaN\nG+Pv7w8U9Nzo6+tjZGQEFLzvUPAAX5I5YXXr1qVu3bolul7BwcEMHz6c5ORkKlSoIM2vjI2NxcfH\nBx8fH6ZPn86YMWOkfa5fv87IkSORyWRUrlxZmsPv5+eHn58fly9fVqjSWdr2L5Nfn8jISJ49e4aF\nhQUWFhaYm5u/8tyePXvG6NGjuXPnDpqamlhbW6Ovr09oaChBQUF4eXmxdetWTE1NlfZ91b1QWvfv\n3wcocu6qPElLTEwkLS2NihUrvvJ4dnZ2NGrUiJs3bzJr1ixWrlyJmZkZ+fn5bN68mRs3blChQgW+\n+uoraZ/q1au/cnpNfHw8ISEhANSuXbtU51ceiWGfH6B88vEIVR5b3aZmM6pWrKyGiN4/rVq1ok2b\nNlLiN3r0aOkXmyAIwvsqODiYyZMnk5+fz0cffUTXrl2l12QyGbNnzyYzM5Nhw4Zx5coV3N3dOX78\nOOfPn6dly5bk5+ezfv16lcd2dXWlVatWnD9/Hg8PD86dO8fcuXMBOHv2rNTDAAXJx08//YRMJmP0\n6NH4+Phw6NAhfHx86N69e5G9DP/5z38IDQ3F3t4eLy8vjh07hru7O2fPnuWzzz7jwYMHTJgwgays\nLKV9Dxw4QIcOHbh06RKHDx/m/Pnz1KtXj9zcXMaNG0dqaiqHDh3i5MmTnD59mlmzZgFw+PBhnj17\n9q+veVnFf+vWLdauXYuHhwcXLlyQru2pU6dwdnbG0NCQVatWcfXqVdzc3Lhy5QpTpkwhIyODadOm\nKQyz27JlCw8ePODLL7/Ex8cHd3d3PD09OXHiBDVr1uT+/fvs2rULgP79+0sVPKEg8XB1dZWG78mH\nDtepU6dMrlFhc+fOJTk5mW7dunHp0iU8PDzw8PDAx8dH6oHctGmT9LtcHp9MJmPWrFnSfeXt7c22\nbdvQ09Pj5MmT/PPPP/+6/cvk10fey9avXz9cXV1fmTBCQY/0nTt3aNSoESdPnuTUqVMcPXqUM2fO\n0KRJEwIDA6X3+GVF3QvVqlXj2LFjHDt2jAYNGpToGicnJwMoDfmUK9wTLW9bnHXr1tGyZUt8fX1p\n3749vXv3pnXr1qxYsQI7Ozu2bdsm9RCWxB9//EF2djZVqlShRYsWJd6vvBI9fx+gVFkayRnPFbZp\noMFXdZWHNgilZ2pqKv0iNzU1JSgoqFQ/hARBUJ/nmSmsv76TgMehZOeV3+E/OpraOFVzYEKzoRjr\nF71m2r8xZcoUpSGWMpmMJ0+e8OTJEwBq1KjBxo0bFYaVBQQEkJ6eTrVq1Zg5c6ZCL06VKlWYOHEi\nV65cISoqiry8PKWeBhMTE5ydnRWqGg4dOhQXFxdiYmK4deuW9EDq6upKamoqzZo1k3oLASpWrMhv\nv/1GQECA1CMh988//3Dt2jX09PTYuHGjQgEN+XDO7t27Ex0dzaFDhxg0aJDC/sbGxixZskQqaGFo\naMi3337LvHnzyMvLY8GCBVJCI4991apVyGQywsPDadq0qdK1Lm69P3d3d6mX63Xjb9SokTQ6RVNT\nU3pYlycZc+bMUVi6Q0dHhwkTJhAWFsbx48fZsWMHc+bMASAsLAwoGMJZoUIFaR9ra2tmzJjB4cOH\nVfY4qfLixQug6OTh33rw4AHx8fHo6+uzcOFCheqahoaGzJgxAw8PD168eMHjx4+lniv5ufXv31/h\nHm3VqhWjRo0iKipKIVksbfuycPfuXc6dO4epqSnr169XWL/OwsKC1atX07FjR86cOUNISIjSMgtF\n3Qs6Ojoqe41fJTOzoLZEUdVIC29X9aGEKrq6ujRs2BB/f38yMzOlXjuAqlWrlmpepHyZB4CpU6cq\nDYV9H4mevw/Q88wXStuaf9QIq0oiQSkL8uEhc+bMISkpSSR+glCOrL++k5sPA8t14geQnZfDzYeB\nrL9e9kUvAgIC8Pf3V/gTEBBAcnIyXbp0YcmSJXh5eSk9JDZu3JgbN25w6tQplcP3DAwK1pbNy8tT\n+RDYrFkzlQ+Q8uIyqamp0jYfn4L1a3v16qXUXldXV+X28+fPA9CuXTuVlRMNDQ2l4hTytoU1btxY\nqTy/fEibtra20lxGbW1t6aE6LU1xGoack5MTjRs3LvJP4cTqdeMgb5oRAAAgAElEQVSXz70qLCYm\nhvDwcDQ1NYtcs1E+RPfixYvStho1agAFPSoXLlxQeD87d+7Mhg0blJLPosjfc3nBk7JiaWnJtWvX\nuHbtmsp1BQvfa/IEBv53bjNnzuTu3bsK81onT57MqlWrFHqPStu+LJw5U1DToUWLFioXLq9cubL0\nNQu/b3Kq7oV/q7ihunl5eaU6XkpKCkOGDGHDhg00adIEd3d37t69i7e3N0OGDMHX15fBgwdz8+bN\nYo/l4uIirbfcq1cvpbUB31ei5+8DlJOn/AO0Tz2x1MC/tXXrVsaMGcOkSZNwdnZm2bJlLFu2TN1h\nCYLwL4Qlvl/VKd/E+Zw5c0bqBZHJZFy+fJklS5YQExNDWloaHTp0eOUn7/r6+gQGBhIUFERMTAwx\nMTGEhYURFRUltVH1QFjUYubyh/TC+0RHRwMU2UuhakFp+T6vmi8mrxQob1tcfPJCLoaGhiqvifx1\nVYVxoHTr/L1u/KrmkMmLZ2hqajJixAiVx5QnRvfv3yc/Px8NDQ1GjBjBsWPHiIqKYsyYMRgYGNCk\nSRPatGnDF198Uaq1C6tUqUJqamqJhwSWlr6+PhEREdy5c4f79+8TGxtLeHi4dO6geG9NnTqVH374\ngXPnznHu3DnMzMxo2bIlbdu2pX379kqJZGnbl4WIiAigoDf422+/VdkmLi4OQOH7Tq64+YSlYWBg\nQHZ2dpG9ejKZTPp3SdYq3LJlC8HBwdjb27Nx40bpe8ja2pqff/4ZXV1dtm7dyqJFizh8+HCRx1m7\ndi1r1qwB4PPPPy+zebflgUj+BBpZOGFjal18Q0FBZmYmDg4OxMTEAAXzAuQT4gVBKJ/sq9hy82Gg\nusMoM/ZVii6LXxZ0dXVp37499erVo1+/fvj4+DB27Fh27typ8kHOz8+PpUuXEhj4v2usoaFBzZo1\n6dmzJ0ePHi3yaxVXEbNwAiXvBSzcM1aYvJhIYfLet1cVnJAfT1VPXVFf62153fhVDXeTX8ecnJxi\n563n5eWRlpaGoaEh1tbWHDlyhPXr13Pq1CmSk5O5dOkSly5dYsmSJbRr145ffvmlyIS+MBsbG6Kj\no6UiQsWRyWTExMSUqHBHaGgoS5YskYrKyFlZWdG3b18OHDigtE/Hjh1xdXVl06ZN+Pj4kJSUhKen\nJ56enujq6vL1118za9Ys6X4tbfuyIH/fCg/FLop8WG1hZTn00cTEhJSUlCLntRberqqX8mUnT54E\nYOTIkSqv2dixY9mxYwdBQUHcv39fqRJtbm4u8+fPl97bTp06sXLlynK3hMbrEMmfQF/R61dqv/32\nG7Nnz5b+X6dOnQ9ibRhBeN9NaDb0vZvz9zZUq1aN5cuXM2LECG7fvs3SpUtZuHChQpuwsDBGjBiB\nTCajSZMm9O7dGwcHB+zs7DA0NCQqKuqVyV9pVKpUiadPnxY5nLLwMD45eWJUePjoy+QPyupO9FR5\nE/HL29WpUwdPT89SxVO9enUWLVrEggULuHv3Lr6+vly8eBF/f38uXLjAuHHjcHNzk5YFKUq7du04\nd+4c165dUzkX9GXy6prm5uYcPXq0yIQiISGBoUOH8uzZMxwdHenfvz9169bFzs4OU1NTZDKZyuQP\nCoZFrl+/noyMDPz8/PD19eXcuXNERUXh4uKCpqamNP/x37R/XfIh1DNnzmTkyJFldtx/w9bWlpiY\nGOLj41W+Li8UZG5uLsX9KvL2Ra33aGxsjJmZGQkJCTx48EAh+ZPJZEydOhVvb2+goHjOL7/8UqIq\nsu8Tkfx94OqZ18GhSukm737oOnXqJP3g0NTUZPfu3XzzzTdqjkoQhLJgrF+J2W0nqTuMcqlly5YM\nHDiQffv2sXfvXr788kuFuUwuLi7IZDJatGjB1q1blR64Hj16VGax2NjY8PTpU0JCQlQu66Bq8Xn5\n3MHg4OAijyvvsZTP43qXvIn45Q/OcXFxyGQylb0jiYmJREdHY21tLfXkPXz4kKioKFq0aIGmpiYN\nGzakYcOGjBs3Dl9fX4YNG0ZQUBD37t0rtopnhw4dWLx4McnJyRw5coQ+ffq8sv3+/fuBgsIfr+pJ\nOnToEM+ePcPOzo59+/Yp9VTLFxMvLCcnh5iYGFJSUvjkk08wMDCgbdu2tG3bllmzZuHs7Mz69es5\nevQoc+bMKXX7siJ/31Td53JBQUFoaGhgbW2tNFe1LDk5OXH+/Hlu376tcp7nrVu3AGjYsGGJjmdo\naEhSUhIJCQkqX8/KyuLp06eAYi94bm4uM2bMkJ7fRo8erVAM6kMiCr584PrW61p8I0HB/PnzgYLJ\n/VlZWSLxEwRB+H8//vgjVapUAWDhwoUK83nkn/w7ODio/KT94MGD0r9ft7iHvErmoUOHlOYP5uXl\nSYtXF/b5558DcOHCBZW9FKmpqbi7uwPQunXr14rvTXgT8deuXRsrKysyMjJUXjOAFStW8N133zF1\n6lSgoHelR48eDB8+nNu3byu1b9KkiTRcr/D7LO8BfHn+Y7Vq1ejXr5/0tVTNV5Tz9vaWhgWOHTv2\nlecmv0a2trYqhyiruh8DAgLo2rUro0ePVjmHrWXLlsD/5giWtn1Zkd8Lp06dIikpSen1Fy9eMGzY\nML766iuOHz9epl/7ZfKqod7e3kpDP3Nzc6V5eaqKMKnSrFkzoOB7W5WjR4+Sl5eHkZGRwtzeNWvW\nSPfG1KlTP9jED0Ty90GzM6vJx9WUJ70LiuLi4jA2NpY+lWrdujXZ2dncuHEDbW3ReS4IgiBnZGTE\nzJkzgYJCEps3b5Zeq1WrFgDHjh1TWGbh+fPnLFmyRGFYYUlLvhdl4MCBVKtWjcDAQObNmycN88zK\nymLBggUqe8eaNGlC8+bNkclkjBs3TiqaAQW9QBMnTuTx48dYW1szYMCA14rvTXgT8WtoaDBhwgQA\nqYqrXE5ODlu2bMHNzQ1AKgijq6tL586dAfj5558VCorIZDJWrlxJdnY2VlZWCvPy5ENMC68XKPfj\njz9ia2tLQkIC3377LQcPHlT4YCE9PZ3NmzdLCWiPHj3o0uXVy1fJ78fLly8rrBGZkZHBpk2bFO5d\n+f3YsGFDrK2tSUlJ4aeffuL58/8tm5WUlMS6desAaNOmzb9qX1aaN29O06ZNSUlJYezYsQrfb48f\nP2bChAk8f/4cc3NzaT3DksjOziYiIoKIiAgyMjJKtI+joyOff/45qampTJ48WSrck5WVxc8//0xE\nRAQ2NjZSkiiXlJRERESEVFdBbvTo0Whra3PmzBmWL19Oenq69NqJEyf47bffgILK6/Ke6oiICDZt\n2gQULLkxbty4Ep/z+0g8uX7A+tbrWuxY+w/d4MGD2b17NwB37twhJycHbW1tkfQJgiAUoXfv3hw8\neJDr16/z119/0bNnT2rUqMHw4cPx8PDgyZMndOvWDRsbGzQ0NIiOjkYmk+Ho6MijR4949uwZT548\nea2Kg/IFyceOHcvBgwc5deqUtLh4SkoK7du359y5c0r7rVixghEjRhAWFkb37t2pXbs22trahIeH\nk5OTg5WVFWvXrn2jw+Rex5uIv3///oSHh7Njxw6mTZvG0qVLqVatGnFxcVJPzsSJE+nYsaO0z6xZ\ns7hx4wbh4eF0794da2trKlasSGxsLCkpKejp6bFkyRKF36UODg74+/szbdo0bG1tmTp1qtRDaWho\nyI4dO5g8eTK3bt1i7ty5/Prrr1hbW6OlpUVERISUDPbt25dFixYVe14DBgxg9+7dxMfHM3DgQGrV\nqoW+vj73798nPT0dKysrNDU1iY2NlYqmaGhosHLlSgYPHsyxY8c4e/asNIT2/v37ZGVlYWVlJX0A\nUtr2ZWnFihWMHDmSO3fu0KVLF2rXro2mpiaRkZFkZ2djaGjI5s2bS1RhU+7x48fSkh87d+6UFp4v\nzsKFCxk0aBDXrl2jffv22NraEhcXx/PnzzEyMmLdunVKczl3797N2rVrsbKy4uzZs9J2Jycnfvnl\nF+bNm8fWrVtxdXXFxsaGhw8fSr2cffr0YfTo0dI+f//9t9R7GxwcXGQFVCiYB/i+L/kgev4+UNaV\nLPjU8mN1h/HOCgwMxMDAQEr89PT0RE+fIAhCCf33v/9FR0eHrKwsqfCLvAJknz59sLCwIDo6mocP\nH+Lo6Mjs2bM5cOAArVq1AlCZmJXWp59+ipubG3379qVChQqEhYVhZWXFH3/8UeQac+bm5uzfv58f\nf/yRevXqER8fz/3797Gzs2PKlCkcPnxY5TIR74o3Ff/s2bPZunUrHTp0IC8vT1pUu3Xr1qxfv57J\nkycrtDcxMWHv3r2MGDECGxsbHj16RHh4OJUqVWLgwIF4eHgorXu4ZMkSmjdvTn5+PtHR0Qq9VVAw\n/NPV1ZU///yTzp07Y2pqSnR0NBEREZibm9OrVy92797N0qVLS1Q5s1KlShw8eJAhQ4ZQq1Yt6VrV\nrFmTSZMmceTIEbp2LZgaU/h+bNCgAfv376dXr16YmZkRFRVFXFwctWrVYuLEiRw9elShimlp25eV\natWqceDAAX788Ufq169PfHw8kZGRVK1ala+//pojR468clmQslS9enUOHTrEkCFDMDMzIywsDC0t\nLXr06MHBgwdLvXB83759OXToEL169cLIyIiwsDByc3Np2bIlzs7O/PbbbwqdG4Ur1QYGBiqtU1r4\nz8OHD8vsvN9VGvlFLS5TzsTFxfHFF18orD+kTs/u3CVw3gKFbfcNqjFo7/q3GseN4BC8tiiWR84w\nSKH76Dq0rtnsrcZSXmzatElhrkD//v2LrPglCIIgCIIgCOWF6Pl7z52JvKy0TUdLmxbWytXPhAJD\nhw5FQ0NDKj0uEj9BEARBEAThfSCSv/dY3POHBDwJVdpeSc8ILc0Pa02T4rRo0UKqUKevr09KSgov\nXryQJoQLgiAIgiAIQnknkr/32OHgEyq3G+q+e4vTqoubmxva2tpcvXqVp0+fStXm3tXJ/IIgCIIg\nCILwb4nqFe+px6kJXI75B10qKr2mgajwmZOTg5OTE6Gh/+sZXbBgAT169FBjVIIgCIIgCILw5ojk\n7z11JOQ0efllu2jo++LZs2eYmZlJC8laWloSERFRqnLHgiAIgiAIglDeiGGf76Gk9Gecj/JVdxjv\nLBMTE3R1ddHQ0GDt2rXEx8eLxE8QBEEQBEF474nk7z3kEepNTl6OusN4p/zyyy8YGhqSk1NwXe7d\nu4dMJmPixIlqjkwQBEEQBEEQ3g4x7PM9k5KVinfEJXWH8c5ITEzEwcGBpKQkAHr37o2Xl9c7sRak\nIAiCIAiCILxNoufvPXMs7CxZuTJ1h/FO+OGHHzA3N5cSv6ZNm+Ll5aXmqARBEARBEARBPUTP33sk\nXZbBifDz6g7jnVC5cmUp6dPR0cHb25u2bduqOSpBEARBEARBUB/R8/ceOXnvAunZGQrbtDQ+zMXc\njY2NAejcuTMymUwkfoIgCIIgCMIHTyR/74msHBleYWeUtjexbKCGaN6+O3fuYGxszJ07dwAICwsj\nICCAkydPqjkyQRAEQRAEQXg3iOTvPXEm0oeUrFSFbZoamrS3baGmiN6eTp060bBhQ1JSUvj8888B\n0NbWpn79+uoNTBAEQRAEQRDeISL5ew/k5ObgEeKttL1VjSaYVTBVQ0Rvx9mzZ6X5fABGRkb4+/ur\nOSpBEARBEARBeDeJ5O89cCH6Kk8zkpW296n7pRqieTs6derEF198Ia3bN3nyZFJSUqhVq5Z6AxME\nQRAEQRCEd5So9lnO5ebl4h5ySml7M6tP+MjYgkdpz9UQ1ZtnYWEBQJUqVQgPD8fExETNEQmCIAiC\nIAjCu030/JVzvrH+PE5NUNret9771euXk5ODg4MDv/32GwA7d+7k1KlTJCQkiMRPEAThLXvx4gU7\nd+5kxIgRtG7dGicnJz799FO6du3K/PnzuXnzprpDVLBs2TIcHBz46aefFLY7ODjg4OBAWFiYmiKD\nvLw8oqKiFLbFxcVJsb38p379+rRu3Zrhw4dz+PBh8vPz1RT564mIiFD4f+FzTktLU1NU/5Odnc2R\nI0cYN26cdI83btyYPn36sGrVKh4/fqxyvyFDhuDg4MCuXbvecsSl16FDBxwcHDh37pzC9szMTH79\n9VfatGmDk5MTbdq0wdfXt9ycm7+/P6NHj6ZJkyY0bNiQnj17smPHDnJzc0t9rPz8fA4fPsw333xD\no0aNaN68OUOGDOHkyZOv/N6LiIhg9uzZdOjQAScnJ5o0acLgwYNxd3d/nVMrEyL5K8fy8vM4HHxC\naXvD6vWwNauphojejHXr1qGrq0tYWBhz5syRtnfq1EmNUQmCIHyYDh8+TIcOHVi8eDGXL18mPz8f\ne3t7LC0tefDgAXv37uWbb75hxowZyGQydYf7Trt79y79+/dn//79RbaRJx3yP46OjmhpaXHlyhV+\n+uknpkyZUq4SwOjoaIYNG4azs7O6QylSREQEffr0YebMmZw7d46srCzq1KmDsbExISEhbNy4ka5d\nu3L8+HF1h/pGLFq0CBcXFxITE7Gzs6NSpUpYWVmpO6wSOX/+PIMHD+bixYtUqlQJGxsb7t27x9Kl\nSxkzZow0Xagk8vLymDFjBj/99BM3b96UrsPNmzeZPHkyM2fOVHm8s2fP0qdPH9zc3EhMTMTW1hY9\nPT38/PyYNWsW06dPV+v3rBj2WY7deHCX2OcPlLa/L71+qampODg48OBBwTlqaGiwceNGNUclCILw\n4dqyZQu///47AF9++SXjx4/H0dFRej0jIwMPDw9WrFiBh4cHeXl5rFy5Ul3hFuvYsWMAWFtbq+Xr\n7969m8DAQJo3b15kG2dnZz766COl7bdu3WL06NGcPHmSAwcOMHDgwDcZapnx8PDA19eXLl26KGyv\nVq2a9H5UqFBBHaEBBUtHff/996Snp9O8eXOmT59Ow4YNpdfj4+NZsWIFXl5eTJs2DQMDA6nSeHmz\nY8cOsrOzpak0cvKkduHChQr31bJly8jIyMDc3PytxllSCQkJTJ06lby8PH799VcGDBgAFCTzY8aM\nwcfHhy1btjBu3LgSHW/r1q14enqira3NggULpOM9fvyYiRMncvToUSwsLJg2bZq0T2JiIjNmzCAr\nK4uBAwcyZ84cDAwMAPD29mbmzJl4enrSsGFDhg4dWsZXoGREz185lZ+fj1uQ8idOdc1rU9e8jhoi\nKltr1qzByMhISvzq1auHTCZjzJgxao5MEAThw+Tv78+KFSsAmDp1Ks7OzgqJH4CBgQEDBw5k+/bt\n6Ojo4OXlha+vrzrCLRE7Ozvs7OzQ1dVVdyil9sknn0gPsfv27VNzNK9PR0dHej80NDTUEkN6ejo/\n/vgj6enpdOnShW3btikkfgBWVlasXLmSHj16kJeXx7x588jIyFBLvK+rRo0a2NnZKSTbWVlZpKen\nA9CkSROF9paWllJP4LvIxcWF9PR0unfvLiVqUPB9Lv/Qavv27SUakZCdnc2WLVsAmDhxosLxqlWr\nxooVK9DR0WH79u08fPhQeu3AgQOkpaVRv359Fi5cKCV+AB07dmT69OlAQeKtLiL5K6fuPg4hIum+\n0vb3pcKn/FMoLS0t3N3dCQwMRFtbdFQLgiCoy4oVK8jLy6NVq1bFfnJer149qcfgwIEDbyO8D9Kn\nn34KQGRkpJojeT9s27aN6OhojI2NWbJkySufO2bNmoWOjg5Pnjzh5MmTbzHKN6vwMMby9qHIkSNH\nAOjTp4/Sa40bN6Z27do8e/aMK1euFHusgIAAnj17ho6Ojsoeupo1a9KqVStkMpnC+3/9+nWgYGqS\npqZymiXvJY6Pj+f5c/UUZRRP0+WUqrl+tqY1aFi9nhqiKRvjx4/n0aNHHD58mP79+/P333+rrUtc\nEARB+J+IiAj++ecfgBKPwBg4cCB6enp07txZYXuHDh2Ij4/n9OnTrFq1inPnzqGjo0P79u1Zvnw5\nUDDsf8+ePZw9e5aoqChSU1OpWLEi9vb29O7dm379+ql8sLp27RqbN28mICAAmUxGo0aNmDJlSpEx\nOjg4AAVDEe3t7RVe8/PzY8eOHdy8eZOUlBQqV65Mq1atGDt2LDVrKs6rv3btGkOHDuXzzz9n1apV\nbNy4kePHj/Po0SMqVapEixYtmDhxIjY2NkBBcZMvvvhC2n/btm1s27aNPn36SIXNSkL+oF7UMMno\n6Gi2bduGj48PT548oUKFCtSvX5+BAwfStWtXlfukpKTw999/c+rUKWJiYtDU1MTGxoZu3boxePBg\n9PX1lfY5ffo0rq6uBAQEkJ6ejomJCQ0aNGDQoEG0bt1aaie/3gAnT57EwcGBZs2a4eLionBN/P39\nqVixIlBQQOX69escPHiQ9PR0Nm3axN27d8nKysLW1pb+/fvz7bffqrwffH192bp1K0FBQWRkZODo\n6MioUaMwNDRk6NCh0teWO3ToEAADBgzA0NDwlde+atWq/PHHH1haWlKvXsmevf755x/27t2Lv78/\nT58+BcDc3JwWLVowatQopfsqPz+fgwcPcvjwYUJCQpDJZFSpUoVPP/2U77//ngYNGrxWe/n34saN\nG2nfvr30fzn5+zFp0iR++OEH6b2YN28egwcPVjhWbGwsmzdvlu61ihUr8sknnzBs2DBatGih0Fb+\nXtepU4c///yTuXPnEhQUhLGxMePGjWPw4MGsWbOGtWvXYmVlxdmzZ4u9to8fP+bRo0dAQa+4Kg0b\nNuTevXv8888/xQ7VlffmWVtbF3kvyN+vO3fuSNumTJlCr169cHJyUrlP4V7if1OApiyI5K8cCk2M\nIPCJcmWyPvW+VNtQiddx7949PvnkE6m6V3R0NLVq1RKJnyAIwjvi4sWLABgZGSkNBSuKo6Oj0rDQ\nwn788Ufu3r2Lvb09jx49wtLSEoAHDx4wdOhQYmNj0dXVpUaNGlhYWBAbG4ufnx9+fn7cvXuXRYsW\nKRxv9+7d/PLLL+Tn51O1alUsLS25fv06gwYNKvHDudz69eulgiSmpqbY29sTGxvLoUOHOHbsGM7O\nzrRr105pv7S0NAYNGkRwcDDVq1fHzs6OsLAwPDw8uHjxIocOHcLa2ho9PT0aN27M/fv3efr0KdWq\nVcPKyqrUa9WeOXMGQCHBkvP29mb69OlkZmZSoUIFHBwcSE5O5sqVK1y5cgVvb2+WL1+OlpaWtE90\ndDTDhw/nwYMHaGlpUadOHfLy8ggKCiIwMJCjR4+ydetWhTlfmzZtkoYDf/TRR1hbW/Pw4UPOnDnD\nmTNnFBKFxo0b8/DhQx4+fIiJiQm2trZKSXdRjhw5wq5du9DT06NWrVo8ffqUoKAgFi1aRFRUFD//\n/LNC+23btrFs2TKgIFGzsbEhMDCQCRMmKCTecjExMdJUk5eTlaJ8+WXJR1sVvqfMzc2pXbs2ycnJ\nxMXFsX//fo4dO8aBAwewtbWV9pk/fz779u1DQ0ODmjVrUrFiReLi4vD09OT48eOsXr2ajh07/uv2\nL3NycqJq1apStV4nJyd0dXWV5gS+7NKlS0yePJn09HQMDAyoU6cOSUlJnD9/nvPnz/PDDz8wadIk\npf1evHjByJEjSUlJoXbt2kRGRmJnZ1fia1rY/fsFo+FMTU2LTNbkP2NiYmJKfNxXJWjyD1/k9w0U\nJJ5FJZ/wv+9ZMzMzTE1NSxxHWRLDPsshtyDlXj+rStVpatVQRet3W//+/alTp46U+HXr1k0s1C4I\ngvCOCQkJAaBu3bplNgQ/KCiI3bt34+7uzqVLlxg1ahQAS5cuJTY2lqZNm3LhwgW8vLxwd3fH19eX\nESNGALB//34SEv63zFF4eDiLFy8GYN68eVy8eBE3NzfOnz9P48aNuX37donjOnXqFM7OzhgaGrJq\n1SquXr2Km5sbV65cYcqUKWRkZDBt2jSFBz45Pz8/EhMT2bFjBxcuXMDd3R0PDw/Mzc15/vw5W7du\nBQoe/l1dXWnbti0A3bt3x9XVtUSFKGQymdRbs2PHDoyNjZUerKOiopg2bRqZmZkMGTKEK1eucOjQ\nIc6ePcvmzZupVKkSnp6erFmzRtonOzubcePG8eDBA5o1a8bZs2c5cuQIHh4eHD9+HAcHB0JDQ/nP\nf/4j7fPs2TNWr16Nnp4eu3bt4syZMxw6dIhLly4xdepUoGC4sLy3w9XVlX79+gHQvHlzXF1dmTdv\nXoneFxcXF/r27cvly5c5cuQIFy5ckO6HPXv28OTJE6ntnTt3WL58OZqamixYsEC6Hy5cuEC7du2k\nB/DCCg+drVOnbGsnhISEsGbNGrS0tFixYgU+Pj7S++Hu7k716tVJTU1l586d0j7h4eHs27cPMzMz\nPDw8OHnyJG5ubvj4+DBo0CByc3MVeolL216V1atXS/coFBQbcnV1pX///kXuExcXx3/+8x/S09OZ\nMGEC169f5/Dhw1y4cIH169djaGjImjVr8Pb2Vtr30aNH6Ovr4+3tLe3z2WefAfDdd99x7NixEs+N\nS0pKAnjl8l/y15KTk4s9nrzIUlxcnDQH8mX37t0DKPHwzYSEBGkeYY8ePdTWYSN6/sqZ6ORYbj4M\nUNrep+6XaGqUn1w+NDSUhg0bkpWVBRQUCfDz86N+/fpqjkwQhA+Z7Nlz7q1ew7M7AeRnZ6s7nH9N\nQ0cHkwZO1J78A7omxq99vMTERKDg0+qy0rlzZxo1agQUFPvQ0dEhKyuL27dvo6GhwcKFCxW+nq6u\nLjNmzGDXrl3IZDIiIyOlHqht27aRm5tLnz59FIajVa5cmdWrV9OpUydSUlJKFNfq1asBmDNnDt26\ndZO26+joMGHCBMLCwjh+/Dg7duxQWH5Ibs6cOQo9R3Z2dgwaNAhnZ+dSJaFyqnqp5CpVqoSLi4tS\ntdLNmzeTlZVFmzZtlHrE2rZty9KlS5k4cSLbt2/n+++/x9TUFC8vL6KioqhSpQrr16/HyMhI2sfG\nxoZNmzbRtWtX/vnnHymJio6OJjs7G0dHR5o2bSq119LSYkFtiHEAACAASURBVNy4cQQEBGBkZMSz\nZ88UCl/8G7Vr12bx4sXSA7OWlhZTp05lz549ZGZmcvfuXelabdiwgfz8fL7//nu+/fZb6RimpqY4\nOzvTrVs3peS98P1R1j0yV65cQVtbm06dOtGjRw+F1xwdHfn6669xdnZWWPtQvvZko0aNFJJRXV1d\nZs6cyf3797G0tCQtLY2KFSuWun1Z2bZtG6mpqXz11VdKQ6y/+OILpk+fzsKFC1m7dq3KXscRI0ZQ\nuXJlQDFxMzMzK9XPm8zMTACVw5Ll9PT0FNq+Sr169TA3NychIYG//vpL+jBD7s6dO1y7dg0o+OCk\nOOnp6UycOJGUlBRMTU0ZO3Zssfu8KeUnWxAAcFMx169qxcq0qlGyYTjvisqVK0uJ35AhQ0hPTxeJ\nnyAIandv9RqSb9ws14kfQH52Nsk3bnJv9ZriG5fkeP+/JlVRa1MlJCQUuSi5g4MDcXFxSvuoGhql\np6fHxYsXuXXrlsrhX1lZWRgbFySzhR/gLl26BECvXr2U9jExMXnlULfCYmJiCA8PR1NTUyHxK0z+\n8C4fCluYhoaG1JtXmHyuX2pqaoniKOzldf4aNGhAzZo10dTUJCUlhfHjx+Pn56ewjzy2QYMGqTxm\nx44dsbS0JDMzk6tXrwJw4cIFAHr27KmQ+MlVr15dWl/3/PnzQEHlSy0tLUJCQvjjjz+IjY1V2Gft\n2rUsXbq02GGDJdGmTRulnhJdXV2ph0Z+bbOysqSCHqqWvzAwMOCrr75SuV2uNGvBlcSIESO4c+dO\nkT1v8q9deD6YPKG/cOECW7ZsUVhU3sDAgG3btvHrr79KiVxp25cV+Xy87t27q3y9e/fuaGhoEBwc\nrNBbL/eqIZKlUXj4clHkP79K0uOmra3N5MmTgYKhzWvXriUxMZGsrCzOnTvHpEmTpGS1uNEQaWlp\njB07ltu3b6OlpcXvv/9OlSpVio3hTRE9f+VIfMojrsXeVNre27ELWprF3/TqduLECX755RcuX75M\nlSpV+Ouvv+jWrZvK9YsEQRDUISVEeT51eVZW5yN/yJEPrXqZjo4OjRs3VtiWn58vzR1S5VVrhenr\n6xMXF8fNmze5f/8+cXFxhIeHExoaKn3KnpeXBxR8oi5/qCxqvlDhQiOvIh/GpampKQ0pfJk86bx/\n/z75+fkKD5IVKlRQOd9I3hvxbwo8FLXOX3JyMkuWLOHo0aOMGjWKw4cPY2trS2pqqnQ9XjXXsW7d\nujx48IDo6GgA6e+6desWuU+9evU4cuSI1Nbc3JwhQ4awY8cONm/ezObNm6lVqxatW7emXbt2tGjR\nAh0dnVKfsyrVqlVTuV3emyO/H+Lj48nMzERHR0dh/tzL5/Gywg/jycnJZb7WoPw+8fX15d69e8TE\nxBATE0NQUJA0ZFV+DgANGjSgU6dOnD59mt9//53ff/8dBwcHWrduTfv27fn0008VityUtn1ZSE1N\nlQqjrFq1ig0bNqhsp6WlRU5ODlFRUUrf92W1ZqA8gZZ3LKgiX+JBfs8UZ+DAgcTExLB582bWrFmj\nMEy6fv36DBo0iLlz576yOFBSUhJjx47lzp07aGpqsmTJEtq0aVOir/+miOSvHHEPPkk+ip+6muob\n87nNZ2qKqGRycnJo1qyZ9BCwePFi5s6dK9bsEwThnVPJ0Z7kG0UnLOVNJceSFdMojnwYWWRkJHl5\neUoPkSYmJri6uipsS0tLU0oICyvqAezBgwcsXryYM2fOKPQ0mpub06VLFy5duqQwx+bFixfSv4t6\nYC/pumTy3qOcnBz8/f1f2TYvL4+0tDSFB7+ySnRKwtTUlN9++407d+4QHR3N1q1bWbx4sTSHHnhl\nL4/8Wsnby/9+1T7y1wp/jdmzZ1O/fn327NnDrVu3iI6OJjo6ml27dmFqasq0adPKZAH64q6t/F55\n9uwZUJAMFNXDo+oca9asiYaGBvn5+dy7dw8rK6tiY4qPj6dChQolGia6d+9e1q9fr9Ajp6OjQ926\ndXFwcJB6rwuTz7k7cOAAISEhhIaGEhoaytatW7GysmLOnDkKvdqlbf+6Ct8HQUFBxbYv/L0qV9JE\nrDjyD6heNf9Ofm+UZjjpjBkzaN++Pe7u7sTGxmJiYkLbtm3p2bOntLREUQlsbGwsI0aMICYmBm1t\nbZYtW6Y07FcdRPJXTjxJe8ql+9eVtvd07IiO1tv7ZVNae/bsYciQIdKnWVWrVmXixIlqjkoQBEG1\n2pN/eO/m/JWF1q1b8+eff/L06VP8/f1LXPGztDIyMhg2bBj379/no48+4ttvv8XJyQk7OzvpAevl\nypbyYaBQ0AuoashiSeb4wP8Sojp16uDp6flvT+Ot0dLSom3btkRHR0sP34UT4NTUVJXXQ/5a4fby\nv181NFX+8P5ykt2rVy969erF06dPuXr1KpcvX+bcuXMkJSUxb948zMzMyjTpeBV5bBkZGSo/qADF\npEXOzMyMBg0acPv2bXx9fVVWc33ZypUr8fLyol+/flLBIVX279/P/PnzAejduzft2rWjTp061KpV\nC11dXfbt26cy+dPS0mLw4MEMHjyYhw8f4uvry+XLlzl//jzx8fFMnjyZ/fv3S8sKlLb96yo8VNbX\n17dM5wSXlryXNykpiYyMDJVzTOXLWLy8pEZxPv30U2lNzcKCg4MBVFasDQkJYdSoUSQkJGBgYFBk\nhWB1EMlfOXE05BR5+XkK24x0K9LRTr1dx0XJycnBwcFBoXrW8uXL+fHHH9UYlSAIwqvpmhhT778/\nF9/wA/Pxxx/j5OREQEAAGzZsUKgIWJa8vb25f/8+JiYmHDx4UKlHJSsrS/r0Xk5fXx8LCwsePnxI\ncHCwyuGBJV0EXf5QGBcXh0wmU7nIdWJiItHR0VhbWxc5FPFtks91kn/IamRkJBWqCAoKUjnfLj8/\nX0oW5edsY2NDcHAwwcHBKufEAQQGBgJQo0YNoCDBioqKQk9PDzs7OypXrkz37t3p3r07MpmMsWPH\ncuXKFY4ePfrWkr+aNWuira1NdnY2UVFRKocCy4ujvKxz587cvn0bNzc3xo8fr/DBwsuSkpLw9vYm\nPz//lUuaANL3S1FLHsjXpyssNTWVyMhIKleujJWVFRYWFvTt25e+ffvy4sULBg0aRFhYGF5eXjg5\nOZW6fVmoVKkSZmZmJCUlERkZqTL5y83N5dq1a1hZWfHRRx+VaG7ev2FmZoaVlRXx8fHcvn1bqhpa\nmLzg0svrHaoiXzMxISGBUaNGKf0syM/Pl+bJNmvWTOG16OhoRowYwdOnTzE2Nuavv/6Silu9C0TB\nl3IgOeM55yKvKG3vZt8Bfe2y6S4va9ra2tKaK7Vq1SIjI0MkfoIgCOXY4sWL0dXVxcfHB2dn5yKL\nv8gdP3681F9D/sm8paWlyqF0R44ckeb8FZ4/J6/yeODAAaV90tPTOXFCuViaKrVr18bKyoqMjAxp\nSNfLVqxYwXfffadU/e/fkA9LLO5aFiUvL0/qMSr8QC8vOvPyUFw5b29vHj9+jI6ODs2bNweQeiU8\nPT1VDs979OiR0rqCLi4u9OnThyVLlii119XVlXpLCs9le91zLo6BgQEtW7YE/rdoe2HZ2dkcPXpU\n5b7ffPMNlStX5vnz5yxcuLDIwi95eXnMnz+fzMxMqlSpIi1fURT5fa1qPmVmZiZeXl6A4j29fPly\nBgwYwF9//aW0j5GRkfR+y69taduXFfl9s3fvXpWve3h4MHz4cL766qsil0woK/KCRAcPHlR6zd/f\nn4iICMzMzFSui/kyDQ0N/v77b5ydnaWiSIUdP36c2NhYbGxsFBLNjIwMxo0bx9OnTzE1NWXnzp3v\nVOIHIvkrFzxDvcnOU/wBZKCjz5d1PldPQEVITU1VmMTq6enJ9u3biYqKemXpXUEQBOHd5+joyH//\n+1+0tLRYv349o0aN4tq1awoP8dnZ2Vy8eJGhQ4cyd+5coKBK5KsKIhQmX+c1NDRUqiIoP+6BAwcU\nkozChR1GjhxJhQoV8Pb2ZvXq1dJDe0pKCtOmTVNZZVAVDQ0NJkyYAMCSJUukh3IoGNGyZcsW3Nzc\nAIosCFMa8rlnqtYMLE5qaioLFizg3r17aGpq8t1330mvjRw5En19fS5dusSvv/6qUEXy4sWL0nsz\ndOhQqdBJt27dsLGxITExkQkTJij0RkVFRTFmzBgyMjJo1KiRlGx37doVLS0tfHx82LJli0KyFBYW\nxr59+wAUKqC+zjmX1Pjx46WH98KJQGpqKrNmzZIK1rzM0NCQpUuXoqWlhZeXF6NGjeLOnTsKbaKi\nohg/fjynTp1CS0uLJUuWFFscRn5f79q1S2FYbUxMDOPGjZM+LC98T/fs2RMoSGSOHDmi8H12/fp1\n6QMN+bUtbfuyMmrUKPT09PDw8GDVqlUK53Dp0iUWLVoEwIABA4ocgqxKUlISERERpVqQ/fvvv0df\nXx8PDw927NghXYOIiAipA2Lo0KFKz6QxMTFEREQoFbTq2rUrUPCzQJ7Ay89LPox38uTJCr2ZGzdu\nJCoqCk1NTZydnYvtFVYHMezzHfciK5VTEcrjwLvUbkdF3bKtRPU65syZw9KlS4GChVuvXbvGl19+\nqeaoBEEQhLI0YMAAbGxsmDFjBj4+Pvj4+GBkZISlpSX5+fkKCyJXqFCBr7/+mkmTJpU4+evYsSP1\n69cnMDCQ8ePHY21tTaVKlYiNjSUlJQUTExNq1KhBaGiowqLelpaWLFu2jOnTp7Nu3Tr27t2LhYUF\nkZGRZGRk8Pnnn0vLExSnf//+hIeHs2PHDqZNm8bSpUupVq0acXFx0pDTiRMnlskwRnkV0pMnT9Kz\nZ0+aNWumtOj5lClTlIacZWRkEBERgUwmQ1NTk1mzZilUsLSzs+P3339nxowZuLi4cOjQIezs7EhK\nSpIeYrt27arQe6mrq8u6desYNWoU169f54svvqB27drk5eURHh5Ofn4+Dg4OrFq1SnrYtba2Zvbs\n2fz666/8/vvvbNq0iY8++oi0tDSpGurnn39O3759lc45ICCALl264ODgIK2tWFYaN27M1KlTWbly\nJXPnzmX16tWYm5tL94P8HlM1BLFdu3asXr2aWbNm4evry4ABA6hcuTIWFhY8f/5cWs7CyMiIJUuW\nlGge1+TJk5k8eTJXrlyhTZs22NjYKFyjzz77jKtXr5KYmChVkG3atCnDhw9n+/btzJw5k2XLlmFh\nYUFycrL0Hn799de0atUKoNTty0rt2rVZtmwZM2fOZOPGjbi4uGBjY6PwdVu2bMmMGTNKddzdu3ez\ndu1arKysFD4IehVLS0sWLFjA7NmzWbp0Kdu3b8fU1JSwsDByc3Np27atymKDw4YNIz4+nkmTJvHD\nD/+bJz1mzBguXLjA7du36dKlC7a2tqSlpUlL10ydOlVhSRiZTMbu3buBguHof/755yvjld+Xb5tI\n/t5xx8PPk5WjWLZWV0uH7vYd1BSRokePHlG3bl3pF6KWlpbSLy5BEATh/dGkSRNOnjyJl5cXZ86c\nISgoiMjISLS0tKhcuTItWrSgZcuW9OzZ85VzplTR1tZm165dbNmyhVOnThEXF0diYiKWlpb079+f\n4cOHc/bsWebPn8/Zs2cZPny4tG/nzp3Zt28fGzdu5MaNG0RERODo6MjEiROJi4srcfIHBRUs27Rp\nw+7du7l9+zYhISEYGhrSunVrBg0a9MqF10ujT58+3Lt3Dw8PD6Kjo1VWJQ0ICFD4v4aGBhUqVKBm\nzZo0btyYb775RuXSBZ07d8bd3Z2tW7dy5coVQkJCMDY2pk2bNgwcOJDOnTsr7WNnZ4e7uzvbt2/H\n29ub6OhodHR0+Pjjj+nRowfffPONUnXGIUOGYGlpyd69ewkICCA0NJSKFSvSpEkTevfuTb9+/RSK\nrrRs2ZJp06axZ88e4uPjycnJKfOhiABjx46lTp06bN++naCgIFJSUqhfvz7jx48nMDCQwMDAIkcl\ndezYkdOnT7Nv3z4uXrxIZGQkwcHB6OvrU79+fdq1a8egQYNK/ODeuXNn9uzZw7p166QKnMbGxrRq\n1Yqvv/6azp0707p1axISEvDz85PmkM2aNQsHBwfc3NwICwsjJCQEIyMj2rRpw4ABA+jSpYvC1ylt\n+7LStWtX7O3t2bZtG76+voSGhkr3Tc+ePRk0aNBbq4Tbp08frK2t+euvv7h9+zZPnz6lZs2a9OrV\ni5EjR5ZqzqGOjg7bt29n06ZNnDhxgsj/a+/Ow2O6/geOv7OKJIitKIklTFJrbLHv1F5LtEHEWrvy\nVVp8fauWtoRaSmsJokVri9gitlga+1axBEkREWsQUUnINvf3R565v4yZbIRIfV7P43k6955755yZ\no+5nzjmfc+MGVlZWNG7cmL59+xoE/qGhoeqU6fj4+EwzBme0LcWbZKK8qUnXb9nt27dp1aoV+/fv\nfyf2jYu5cJGQb6bqHYvIX4Le6xdn+R7Pk14wwn8ycYn6c6TbV2rBgFpZS5t8/+5TvOfqb0RbolRB\nho5//YxDQ4YMYfny5errxo0bG81WJYQQQgjxrpgzZw4rVqzg008/5bvvvsvt6gjxVsmav3fY3mtB\nBoGfmakZnzi3yaUa6du6dSuQOlXk2LFjEvgJIYQQItf179+fTz/91GC9ns6RI0eAjDe0F+LfSoK/\nd1RiciL+YfsNjjcrV5+i1plvJvqm9OrVS53rHBYWhoeHBwkJCTRo0CDX6iSEEEIIoVOhQgUuXLjA\nnDlzePz4sXo8Pj6e7777jqtXr2JnZ6cm9BDifSJr/t5RB8KP8fTFP3rHTExM6OpsOEf/bTh+/DjN\nmzcnMTGRgIAAnj59ip2dHWvXrs2V+gghhBBCGDN06FAOHDjAqVOnaNasGeXKlcPMzIxbt24RHx+P\ntbU1s2fPztVNyYXILRL8vYOStSlsv7rP4HhD+9qULPDBW69Ps2bNCAr6/3WDme1nI4QQQgiRW0qU\nKMH27dtZt24de/bs4c6dOyQkJFCiRAkaNWpE37591e0XhHjfSPD3jkhOSmHPthD+vvyA+BcJFE+u\nzcs5pJ5fsGL2zqxtVKvzOhm0/P396dq1q7rpqJ2dHVeuXKFkyZKvfE8hhBBCiDetYMGCDB06lKFD\nh+Z2VYR4p0jw947Yt+MyZ49HqK/NMEyJm5iSAqS8tTotXrxYDfx0e8YIIYQQQggh8iYJ/t4R165G\nZV4oh5iZm6R77tdff8XKyoqePXsSEBBA69at2bp1a5Y36BVCCCGEEEK8myT4e0ckJb29ET378kUN\njr148QJnZ2ciIiIwMTGhR48emJubExgY+NbqJYQQQgghhHhzZKuH94ipmQnO1UrSop2T3vE5c+Zg\nbW1NRETqtNPy5cvnRvWEEEIIIYQQb5CM/L1hL8yteWZZFEwg1tKO0Ev3jZZLTjJMzPJ3tSDKlSjJ\npKajMDFJf6pmVllYmGFuYaa+jomJQaPR8PDhQwBMTU1ZtWoVffv2fe33EkIIIYQQQrxbJPh7g8IL\nV+dG0Vp6xzasOp3l61PMkuhWoy3WNvlyumoAbNy4UQ38atSowZkzZzA3ly4hhBBCCCHEv5FM+3xD\nnr9IMQj8ssu+YClqlqqSQzVKdfv2bebOnQvAkCFDaN26Nbt27SI4OFgCPyGEEEIIIf7F5Gn/DXkc\nk/ha12tNtHSu1jJHpnvq9O3blzVr1gDQpUsXKlasyL59hpvJCyGEEEIIIf59JPh7QxTl9a5PKvGE\nhuU650hdQkJCqFu3Ls+fPwcgX758xMfH58i9hRBCCCGEEHmDBH9vkWlKIhWr2+sde570gpCoMPW1\nYqIlvsATPu3YBFPT15+V27FjRwICAtTXbm5u+Pr6vvZ9hRBCCCGEEHmLBH9vUb7kZ/Qc6Kp3bMHx\nldy6dVbvWHHrIjStUO+13y8mJkYN/GxsbAgODqZixYqvfV8hhBBCCCFE3iPBXy669yyK45FnDY5/\n4vwx5qZmRq7IGm9vb4YMGYKdnR2enp5YWVnh7e39OlUVQgghDEyaNAk/Pz8ANm3aRPXq1V/pPl5e\nXvj4+NCtWzdmzZr1WnVatGgRP//8s9Fz1tbWFC1alLp169KvXz+cnZ1f671yQ2xsLHFxcZQoUSLd\nMuHh4WzcuJFjx45x69YtkpKSKFy4MNWrV6dbt260bt3a4JqTJ0/St29f7OzsOHny5Jtswmvz8/Nj\n0qRJVKlSRe1/OsePH2fBggWEhYVhampK3bp1GTBgQJ5o24sXL/D29mbnzp3cuXOHggUL4urqyvDh\nw3Fycsr8Bi+JiYnB29ubwMBA7t69i5WVFVWrVqVfv360aNFCr6zuM80KV1dXNYeEyHsk+MtFW6/s\nQXlpcaCdVUFaVGj4Svfbtm0bbm5upKSkoNVqGTZsGKtXr86JqgohhBB6nj9/zp49e9TXrxP8vQm2\ntrZoNBr1taIoPH/+nMjISPz8/NixYwfz5s3j448/zsVaZo+/vz+zZs1ixowZRoM/RVFYvHgxP//8\nM1qtFnNzc0qVKoWNjQ2RkZEEBgYSGBhI48aNWbBgAQUKFMiFVrw5t27dYvDgwSQlJVGkSBE+/PBD\nypQpk9vVypLExEQ+//xzTp8+jaWlJRqNhgcPHrBr1y4OHDjAkiVLaNSoUZbvFxUVRe/evYmMjMTC\nwoLy5cvz5MkTjh8/zvHjx/niiy8YNWqUWr5o0aLUqpV+lnqtVktwcDAA9vb26ZYT7z4J/nLJo7ho\ngm6eMDjeyak1lmYW2bpXcnIyLi4uhISEqMeePXv22nUUQggh0rNv3z7i4uJo3LgxR44cYefOnUya\nNAlra+vcrhoAlStXNjo6kZyczM8//8ySJUuYOHEirq6u2NnZ5UINs2/evHnq/rzGjB07ll27dmFj\nY8OwYcPw8PDAxsYGSH149/f3x8vLiyNHjjB06FB+++03LCyy98zxLmjTpg01atTAyspK73hgYCBJ\nSUk4ODjg7+9Pvnyp+yQ/f/6cgICAd3pLq/nz53P69GmqVavGkiVLKF68OCkpKSxcuJClS5cybtw4\nAgMDsbW1zdL9ZsyYQWRkJBqNhsWLF6sB27p165g6dSqLFi2iQYMG1K5dG4BmzZrRrFmzdO+3ePFi\ngoODcXR0ZPLkya/fYJFrZJ+/XLL96j5SFK3eMRtLaz52bJKt+6xcuRJLS0s18Pvwww959uwZX331\nVY7VVQghhHjZtm3bAGjXrh0fffQRcXFxegnG3lXm5ub85z//QaPREBcXx86dO3O7Sjliw4YN7Nq1\nC2tra5YvX86QIUPUwA/A1NSUTz75RH1uOHv2LH/88Ucu1vjVFShQAEdHR0qXLq13PDo6GoBq1aqp\ngR9A/vz5cXR0pGzZsm+1nlkVExPD+vXrMTU1xcvLi+LFiwNgZmbG2LFjady4MU+ePGHDhg1Zul9s\nbCz79+8HUoPAtCN1vXr1om3btgBs3bo1S/e7dOkSixcvxtzcnDlz5uj1K5H3SPCXC2Je/MP+8KMG\nxztUaoGVhZWRK9I3dOhQFEXBxMSEhQsXcufOnSz/KiSEEEK8iqioKI4fPw5A48aNadOmDUCeyiZd\ns2ZNAK5fv57LNXl9sbGxzJ49G4DBgweroznGODs789lnnwH865aGpKSkAGBpaZnLNcmeffv2ER8f\nT40aNXB0dDQ436NHD4As/7jy6NEj9bNIO/VZp2rVqgDcvXs303spisKUKVNISkqiX79+VKlSJUt1\nEO+ud3f8+19sZ+h+klKS9I5ZmeejfaUW6Vyhz9/fn06dOgGwbNkyvLy8uHz58js9nUEIIcS/x44d\nO0hJScHZ2ZlSpUrRrl07Fi5cyLlz57h27Vq6maVPnjzJ8uXLuXTpEomJidSsWZMxY8Zk+F6RkZGs\nXr2aEydOcOfOHRITE7Gzs6NGjRp4enpSv379V2pDcnIyQLrTVPfu3cuGDRu4ePEi8fHxFC9enAYN\nGjB48GDKly9v9JqQkBB8fHw4deoUT548wdbWFhcXF/r27UvDhobr+WNjY/Hx8WHfvn3cvHkTMzMz\nSpUqRePGjfn888/VdX0vJ+MYNmwYADNnzqR79+7s2bOH2NhYLCws8PT0zLTt/fv3p06dOri6umZa\nVlfPP/74gwMHDhAeHk5sbCw2NjZoNBq6dOmCm5ubwfZUkZGRLFu2jGPHjhEVFYWVlRXly5enbdu2\neHh4kD9//lcu/3LCl5c/ny1btrBlyxYAQkNDM0xmo9Vq2bp1K5s3byY0NJTExETKlCnDxx9/zKBB\ngwzWRU6cOJEtW7awYMECrl27xtq1a0lISKBChQqsXr0aW1tbNTmL7vvJzPnz54H//0HiZS4uLgBc\nvnyZ+Pj4TKdWFy9eHDMzM1JSUrhy5YrBjwF///03kDpbLDP+/v6EhIRQrFgxRo4cmWl58e6Tkb+3\nLDYxjr3XggyOf1yxGbb5Mh5Gj4mJoXjx4nTu3JmOHTsCMGjQIMLCwiTwE0II8dbopny2b98eAEdH\nRzVz5qZNm4xe8/vvv9OvXz8OHz6MhYUF5cqV49SpU/Tu3ZuzZw0zXwMcOXKETp06sXr1au7du4eD\ngwP29vbExMQQGBhI//798ff3z3b9ExISOHo0dQZO48aN9c5ptVrGjx/PF198wZEjR7C2tsbJyYmn\nT5+yefNmunTpwt69e42279NPP8Xf35+EhAScnZ0xNzfn4MGDDBgwQB2Z03nx4gWenp788ssvRERE\nUK5cOcqWLcudO3dYvXo1Xbt2JTIyEvj/ZBy6Ea2KFStSq1YtihYtCsCJE6k5BKpWrZqlJC729va0\nb99evT4jd+/epWvXrsydO1cNAipVqkRKSgqnT5/mf//7H1OnTtW75vr167i5ubFp0yZiY2PRaDQU\nK1aMixcvMmfOHPr3709SUtIrl3+Z7vPRBcu61xklMIHUJCvDhw9n0qRJnDlzhoIFC+Lo6MitW7dY\nsmQJ3bp1U7+Dl/3666/8/PPP2NnZUbRoUaysrF55HQnjIwAAIABJREFU5lVERARAuslpSpQogbm5\nOVqtltu3b2d6PxsbG/Xv5rRp07hz5456bseOHfj7+2Nubq6OAKcnKSmJ+fPnAzB8+HCZ7vkvIcHf\nW7b770M8T36hd8zCzIJOTq0yvO4///kPhQsX5tGjRwDExcW9sToKIYQQ6bly5QqhoaEA6g+Raf97\n27ZtJCYm6l3z999/8/333wPwzTffEBQUhJ+fH4cOHaJWrVrqyEdaiYmJTJo0iRcvXtC/f3+OHTvG\n1q1b2bVrF4cOHaJhw4ZqdsusUBSF+Ph4Lly4wLBhw7h//z6tW7c2GDlcvHgxO3bsoECBAixdupRD\nhw6xefNmjh07Rv/+/UlISGD8+PGEhYWp15w8eZIZM2ag1Wr58ssvOXbsGL6+vhw+fBgvLy8sLS1Z\nuXKlXmC8efNmLl++TO3atQkKCmLHjh1s27aNgwcP4uLiQnR0NEuWLAFSk3GsW7dOXQs2fvx41q1b\npybouHHjBgCVKlXK0meRHTNnziQyMpK6devy559/snPnTrZu3crx48cZOHAgABs3btRLRPPTTz/x\n9OlT+vbty9GjR/Hz82P37t34+flRuHBhgoOD9dZaZrf8y3Sfj64PNm3alHXr1rFu3boM2zZ79mwO\nHTqEo6MjW7Zs4cCBA2zZsoXDhw/z8ccfExkZyZgxY9BqtQbXBgcHM2nSJPbs2cP+/fv1thcJCAgg\nICBAnQ6dmSdPngCkm3jI1NRUDSx1ZTMzY8YMOnToQFhYGG3btqVz5840b96c8ePHU6JECRYtWkS1\natUyvMfevXu5c+cOhQsXVqeeirxPhovesoCwgwbHWpZviJ1VQaPlb9++TZUqVfjnn3+A1IXq2fkf\nihBCiKyLe5bAtvXBhF97REqy4QNfXmFmbkr5isXo0tMFmwL5Mr8gG3SjftWrV9dLJNGxY0fmzZvH\nkydPCAwMpEOHDuo5Hx8fUlJS6NatG3369FGPFy1alIULF9KmTRv13zmdS5cuER8fT4kSJfj6668x\nM/v//W91U9COHTtGeHg4Wq3WYNrhqVOnMtwbrVmzZuqohk58fDw+Pj4ATJ8+XW8vNCsrKyZNmkRk\nZCT79+9n8eLFLFiwAEgNGBVFoWfPngwdOlS9xsTEhK5duxITE8PMmTNZuHAh3bt3x8zMTA2gP/74\nY72H/qJFizJx4kSWLFlikNAkPboM3zmdtTQhIYHz589jYmLCtGnTKFKkiHrO0tKS8ePHs3btWhIT\nE7lx44YanOoC4+7du+tlE61cuTJffPEFJ0+e1EvIkt3yOeHBgwesX78eCwsLFi1apLfWrnDhwvz4\n44+0a9eOkJAQDhw4YLA3YokSJejXr5/6Ou1nY2zdXkZevEgdFMiojbrMprqymTE3N6dKlSocO3aM\nmJgYvR8rihQpYjDt1hhdttw+ffoYZFYVeZeM/L1NJlpiE/VH7MxMTOninP4eQ7Vr11b/QWzVqhVJ\nSUkS+AkhxBuybX0w165G5enADyAlWcu1q1FsWx+cs/dNSVGnWaYd9QMoXbq0ujbp5cQvhw8fBuCT\nTz4xuKednZ3RTcdr1arF2bNn2bt3r17gp6N7eNVqtSQkJBict7W1Vaf+6f589NFH6lTHP//8kzFj\nxhAbG6tec+bMGeLi4ihSpAjt2rUz+hno1tQFBQWRkpJCXFwcZ86cAaB3795Gr3F3d8fS0pKoqCg1\nO7eDgwMAK1asICAgQK8eNWvWxNvbO8trrHQP5rp1jDklX758BAUFqSn+X5aQkEChQoUA/aBE17ap\nU6dy6tQpvXp5eHiwcOFCdVriq5TPCUFBQSQlJVG5cmWjbcuXL5/aL4OCDJfr1KhRAxMTkxypi7H+\n/TLd6GNW3jMpKYlhw4YxZ84cypQpw++//87FixcJCgpi9OjRhIaGMmjQIKPTl3WuXLnCuXPnsLS0\nxMPDI+uNEe88Gfl7m0wNHyaalKtHMZsiescuXLhAhQoVsLW15eTJk9SsWZP9+/dnOnddCCHE67kd\nkbUpVXlFTrfnyJEjPHz4EFNTU6MP4506deLcuXMcP36cO3fuULp0aeLj49UpgemNiGQ0QmdlZUVI\nSAiXL1/m1q1b3Lp1i7CwMMLDw9UyxqblpbfPH6RO2fvyyy85cOAAI0eO5LfffgPg5s2ban1eHknU\n0WU7jIuL49GjRzx58oTk5GQsLCzSnXaZP39+KlSowNWrV7l58ybVq1fn008/ZcOGDdy6dYuxY8di\nYWFBzZo1adKkCS1btkw3aY4xxYsXJzQ0lJiYmCxfkx1WVlbcvn2bc+fOERERwe3bt/n7778JDQ1V\n1+Kl/Q5GjhzJiRMnCA4OxtPTkwIFClC/fn2aNm1Ky5YtKVasmN79s1s+J+iyvEZERNCrVy+jZXT9\nNm1f09GNcuYE3Q8ZL0+XTkt3LisjcH5+fhw9epTixYuzatUqChZMnV1WokQJRo4ciZ2dHdOnT2f6\n9Ok0b97caHbU3bt3A9CkSRMKFy6c7TaJd5eM/L1Vit4rE0zo+lFbvWPt2rWjRo0a6v/0y5Urx5Mn\nTyTwE0KIt6BM2X/XQ05Ot0c35VOr1dK0aVOcnJz0/syYMUM9rxv9001JhPQza+oeTl92+vRpunfv\nTvfu3fnf//6Ht7c3e/bsQavV0rlz51duh4uLi5qA5cSJE+rInW49fUaJLdK2ITY2Vr0mf/786QaM\naa/TlS9UqBC+vr4MHDiQEiVKkJSUxKlTp5g7dy4dO3akZ8+eXLt2LUvtKVeuHPD/WRyz4sqVK0aD\n5pfdvXuXkSNH0rp1a8aPH8+iRYvYsmULDx48oG3bturIX1o1atRgy5YtdO7cGRsbG549e8a+ffv4\n5ptvaNq0KePHj9frF9ktnxN0I60xMTH89ddfRv/okr2kHZXVyclpqLrpuukF7ykpKWr7004vTY9u\nRK9Xr15G/2716tWLwoUL8/DhQ7Xvv+zgwdRlSjk94ipyn4z85aL69rX4sEBqZqqgoCBat26t/oJm\nbAqLEEKIN6tLT5d/3Zq/nJJ24+giRYrorc16uVxcXBx+fn588cUXesFBfHy80WyUxtYxhYWFMXDg\nQBITE6lTpw5dunTByckJR0dHbG1tCQ8PZ/v27a/cnjp16lCgQAGePXvG5cuXqVOnjhqgGXvY10kb\nhNjY2Kj/bj9//tzo2kMd3T3TBo+FChViwoQJTJgwgatXr3LixAkOHz7MiRMnOHfuHAMGDGDv3r2Z\nrs9q1qwZa9eu5fLly/zzzz/pBtM64eHhdO3alUKFCrFmzZp0R16fP39O//79iYiIoEyZMvTq1Yuq\nVavi6Oiojny9nC1Vx9HRkR9//JHExESCg4M5fvw4f/75JyEhIezYsYPnz5/zyy+/vHL516X7TD08\nPJgyZUqO3fdVVKhQQR0tN+bBgwekpKRgamqqt842Pbr7VKhQweh5U1NTypYty5MnT4y+57179wgN\nDSVfvny0bNkyGy0ReYEEf7mo20ep6wnq1avHqVOn1OOjRo1i0aJFuVUtIYR4b9kUyEfvwfVyuxrv\npN27d/PixQssLS3ZvXu30REfgMDAQEaOHMn9+/c5fPgwzZo1o1SpUty7d48rV66o6fjT0mWrTGvN\nmjUkJibSoEEDVq5cabAu6v79+6/dJt09dSNguv37QkND0w3kdGv28ufPzwcffECBAgUwMzMjKSmJ\nsLAwdcuLtOLj49Wpg2XLlgVSpxTeuHGDatWqYW1tjbOzM87OzvTv35+///6bHj16EBUVxcmTJ2ne\nvHmG7ahfvz52dnbExMSwevVqRo0alWH5jRs3AqlJQTJKThIYGEhERAR2dnb4+voaTP9LSEgwGK3S\narXcuXOHe/fu4erqiqWlJa6urri6ujJmzBh8fX2ZPHkygYGBxMXFkT9//myVz6ntBnSjpcb6ns71\n69eJj4/HwcEh3f6eE3SbrhvLegup05QhdTpyVkYcdZlB02ZgfZku6DO2PcW5c+eA1CnOsr3Dv49M\n+8wltT6sRrnCZfDw8FADvyJFivDw4UMJ/IQQQrxzdFM+W7RokeGDcPPmzdVRId3WBq1atdJ7nVZ8\nfLy6vigt3cOpk5OT0YQYaZPKpKSkZLUZquDgYDVw0T18165dG1tbW548eWK0TpC6nx+kBlympqbY\n2NhQt25dgHS3Fti4cSNJSUnY2dmpawY9PT3p27evOr0urUqVKqnr3NK2TZfsQ1H0l5FYWloyaNAg\nAFauXJluEAGpeQV0ayE///zzDPcJ1n0HH374odF1X9u2bVNHPnX1fPjwIW3atKFfv348ePDA4Jq0\nm91rtdpsl88pTZo0wdTUlFOnThkNAJOTkxkxYgQ9evRg1apVOfa+xrRo0QJzc3POnj1rtC66vm4s\nYZIxrq6uAGzdutXo341jx47x8OFDzMzMDDaAh9QpwfD/61vFv4sEf7kgOTkZV5vUf2h+++03ChUq\nxNSpU3n8+PEbWdQshBBCvI47d+5w+vRpALp165ZhWXNzc7XMoUOHePToEYMGDcLa2prAwEAWLlyo\nZnP8559/+PLLL42OUOhGZgICAtRNsAGePn3KDz/8oLe5e3aXSoSGhjJx4kQg9QFX9wBsY2PDgAED\nAJgyZQqHDh3Se4+ZM2dy4MABLCwsGD16tHpuxIgRmJqasmHDBry9vdX2KYrC1q1bmTt3LgCjR49W\np8t26tQJSN1H7+LFi+q9tFotv/32G7dv38ba2lrv4Vw3ZfTu3bsGbRo4cCB169YlPj6e/v378+uv\nv+rtCZyUlMSmTZsYOHAgSUlJ1KlTh759+2b4Oem+g9DQUA4cOGBwrx9++EHv84HUpCKurq5otVrG\njx+vF9DFxsaqn0XNmjUpUKBAtsvnFAcHBzp37kxKSgrDhg1TR3QhtY999dVX3Lx5E2tr63QTwqTn\n+vXrXL9+PcvrFAsXLoy7uztarZYxY8aoQXdKSgoLFizg6NGj2NnZGWzK/uzZM/W90urbty82NjaE\nhIQwceJEvdHZkydPMmHCBAB69OjBBx98YFCfq1evAmQr6ZDIO2Ta51t2fd8l/lp5hB3mv5KUlIS5\nufkby84lhBBC5IRt27ahKApFixalSZMmmZbv0aMHy5cvJykpiS1btjB48GC8vLwYN24cv/zyC+vX\nr6dUqVLcuHGD58+f07x5c71AC2DAgAHs2LGDqKgoOnToQPny5TExMeHmzZskJibi7OzM/fv3iYmJ\nISoqyiD74uXLlw0e2hVF4fHjx9y6dQuADz74gDlz5uilzx8+fDg3btxg586dDB06lA8//JCiRYty\n48YNdZrid999R+XKldVr6tWrx+TJk/n++++ZO3cuK1euxMHBgXv37qmBbb9+/fRS5g8ZMoQjR45w\n7tw5evToQZkyZShUqBD37t0jOjoaU1NTpk2bprd3n5OTE2FhYcyaNQs/Pz/69OlD9+7dgdSge+nS\npYwfP56DBw8yc+ZM5s6dS5kyZcifPz/h4eHEx8cDqWsE582bl+GoH0Dr1q2pUqUKISEhDB8+HHt7\newoWLEhkZCT//PMPdnZ2ODg4EBoaSlRUlHrd999/T48ePTh16hStWrXCwcEBCwsLbt26RXx8PHZ2\ndmpyoFcpn1OmTJnC3bt31cRC5cqVw9ramvDwcJ4/f46FhQULFy40OlU5I7o9LmfOnKl+P5kZO3Ys\n586d4/Lly7Rt2xaNRsODBw949OiRWo+Xp2ju27ePSZMmAaj7RgKULFmSn376idGjR7N9+3b27NlD\nhQoViI6OVoPrxo0bM3nyZKN10fXZzNaOirwpV0f+tFotU6ZMwd3dHU9PT71f9gAOHDiAm5sb7u7u\n6vz0vCpFm4zP9sX8tfJI6uuUFG7fvp3LtRJCCCEyp5vy2alTp0wDBkhd16abeqabsvbxxx+zYcMG\n2rZti4mJCdevX8fJyYnly5cbXdNmb2/Ptm3b6NatG6VKleLmzZvcu3cPZ2dnJk2axKZNm2jUqBGA\n0amTsbGxBtkbL126xLNnz6hTpw7jxo0jICDAYM2bmZkZc+fOZf78+TRs2JC4uDhCQ0MpWrQovXv3\nZsuWLeqoXVp9+vRhw4YNdOzYEQsLC65cuYKpqSlt27bl119/5b///a9eeUtLS1auXMno0aOpXLky\n0dHRhIWFYWlpSefOnfH19TWY5jdx4kRatWqFpaUl4eHhBlsQ2NrasnTpUlasWEHnzp0pWbIkd+7c\nISwsjAIFCtC2bVuWLVuGt7e30bVeLzM3N2ft2rWMHDmSSpUq8ejRI3Uz94EDB7Jjxw51b8O0I4P2\n9vZs3ryZnj178uGHHxIZGcnNmzcpUaIE/fv3x9/fX29bjOyWzym2trasWrWK6dOnU7t2bR4/fkxY\nWBgFCxZUv4Os/NiREwoUKMDvv//OiBEjKF26NGFhYSQlJdGyZUvWrVtHvXrZW4vcpEkTtm/fjru7\nO8WKFePatWvExcVRq1YtvvvuO7y9vdNdP6gblMjJkVbx7jBRXp44/hbt3buXAwcOMGvWLIKDg1m2\nbBlLliwBUqcUdOjQAV9fX/Lnz0+vXr1YtmxZutMib9++TatWrdi/fz9lypR5m80wKmTvaTbvSV2M\n/nf4WTbtnE1C4nMg9Ze7S5cuZekfUCGEEEIIIYTICbk68nf27Fn1FxUXFxcuXbqknrt+/bqaXcnS\n0pLatWur6w3yAutippgoWmL+ecjaLdNISHxO/nw2eC2fy9WrVyXwE0IIIYQQQrxVuRqBxMbG6k07\nMDMzIzk5GXNzc2JjY/WGm21sbDLcd+ddY1m+JOWfbMTErj4fVWpIEav8tHerzFeDxuZ21YQQQggh\nhBDvoVwN/mxtbfUyUWm1WnVE7OVzcXFxeWrucenCpbnXwQ77Q75MbVSJcHuFFv2G6i0qF0IIIYQQ\nQoi3JVeDv1q1anHw4EE6dOhAcHAwGo1GPefo6EhERAQxMTFYW1tz5swZdQ+bvOI/3cfzZ60TPIp/\ngnuZmpQrbJ/bVRJCCCGEEEK8p3I1+GvTpg1Hjx6lZ8+eKIrCDz/8wI4dO4iPj8fd3Z2JEycyaNAg\nFEXBzc0t26l2c5uFmQWtHd9OlighhBBCCCGEyEiuZvvMSe9atk8hhBBCCCGEeJfkarZPIYQQQggh\nhBBvhwR/QgghhBBCCPEekOBPCCGEEEIIId4DEvwJIYQQQgghxHtAgj8hhBBCCCGEeA9I8CeEEEII\nIYQQ7wEJ/oQQQgghhBDiPSDBnxBCCCGEEEK8ByT4E0IIIYQQQoj3gAR/QgghhBBCCPEekOBPCCGE\nEEIIId4DEvwJIYQQQgghxHtAgj8hhBBCCCGEeA9I8CeEEEIIIYQQ7wEJ/oQQQgghhBDiPSDBnxBC\nCCGEEEK8ByT4E0IIIYQQQoj3gAR/QgghhBBCCPEekOBPCCGEEEIIId4D5rldgZySkpICwP3793O5\nJkIIIYQQQgiRe0qWLIm5uWGo968J/h4+fAiAh4dHLtdECCGEEEIIIXLP/v37KVOmjMFxE0VRlFyo\nT4578eIFly5donjx4piZmeV2dYQQQgghhBAiV6Q38vevCf6EEEIIIYQQQqRPEr4IIYQQQgghxHtA\ngj8hhBBCCCGEeA9I8CeEEEIIIYQQ7wEJ/oQQQgghhBDiPSDBXw7SarVMmTIFd3d3PD09iYiI0Dt/\n4MAB3NzccHd3Z+PGjblUS5FXZda//P39+fTTT+nZsydTpkxBq9XmUk1FXpRZ/9L55ptv+PHHH99y\n7URel1n/unDhAr1796ZXr16MHj2ahISEXKqpyIsy61/bt2+nW7duuLm58ccff+RSLUVed/78eTw9\nPQ2O57Xnewn+clBgYCCJiYls2LCBcePGMWvWLPVcUlISM2fOxMfHhzVr1rBhwwYePXqUi7UVeU1G\n/evFixcsWLCA1atXs379emJjYzl48GAu1lbkNRn1L53169cTFhaWC7UTeV1G/UtRFL755htmzpzJ\nunXraNKkCXfu3MnF2oq8JrP/f82ePZtVq1axbt06Vq1axdOnT3OppiKvWr58Of/73/8MfpjKi8/3\nEvzloLNnz9KkSRMAXFxcuHTpknru+vXrODg4UKhQISwtLalduzanT5/OraqKPCij/mVpacn69evJ\nnz8/AMnJyeTLly9X6inypoz6F8Bff/3F+fPncXd3z43qiTwuo/4VHh6OnZ0dv/76K3369CEmJoYK\nFSrkVlVFHpTZ/7+cnJx49uwZiYmJKIqCiYlJblRT5GEODg4sWrTI4HhefL6X4C8HxcbGYmtrq742\nMzMjOTlZPVegQAH1nI2NDbGxsW+9jiLvyqh/mZqaUqxYMQDWrFlDfHw8jRo1ypV6irwpo/4VFRXF\nL7/8wpQpU3KreiKPy6h/PXnyhHPnztGnTx9WrVrFiRMnOH78eG5VVeRBGfUvgEqVKuHm5kbHjh1p\n3rw5BQsWzI1qijysbdu2RjdMz4vP9xL85SBbW1vi4uLU11qtVu0oL5+Li4vT6yxCZCaj/qV77eXl\nxdGjR1m0aJH8simyJaP+tXv3bp48ecKQIUPw9vbG398fPz+/3KqqyIMy6l92dnaULVsWR0dHLCws\naNKkicHIjRAZyah/Xb16lUOHDrF//34OHDhAdHQ0u3btyq2qin+ZvPh8L8FfDqpVqxZBQUEABAcH\no9Fo1HOOjo5EREQQExNDYmIiZ86coWbNmrlVVZEHZdS/AKZMmUJCQgKLFy9Wp38KkVUZ9a++ffvi\n5+fHmjVrGDJkCJ06daJ79+65VVWRB2XUv+zt7YmLi1OTdJw5c4ZKlSrlSj1F3pRR/ypQoABWVlbk\ny5cPMzMzihQpwj///JNbVRX/Mnnx+d5w/FK8sjZt2nD06FF69uyJoij88MMP7Nixg/j4eNzd3Zk4\ncSKDBg1CURTc3NwoUaJEbldZ5CEZ9a+qVavi6+tLnTp16NevH5D6wN6mTZtcrrXIKzL7/5cQryOz\n/vX9998zbtw4FEWhZs2aNG/ePLerLPKQzPqXu7s7vXv3xsLCAgcHB7p165bbVRZ5XF5+vjdRFEXJ\n7UoIIYQQQgghhHizZNqnEEIIIYQQQrwHJPgTQgghhBBCiPeABH9CCCGEEEII8R6Q4E8IIYQQQggh\n3gMS/AkhhBBCCCHEe0CCPyHEe83Pzw8nJ6dM/wQGBr7yvX/99decr3gWGGuHs7Mz1atXp02bNnz7\n7bfcv3//jddj4sSJODk5ceXKFfVYQkICPj4+mZZ7W06ePJnud1+1alUaNmzI559/zp9//vna7+Xv\n709kZGQO1Nq4gQMHMmPGjHTPr127Ficnp1fa6+z27dtMmzaNNm3aUK1aNerUqYObmxtLlizR2+j4\nXeHp6WnQ1ps3b9KvXz9q1qxJrVq1WLp06Sv3vdu3b+Pk5MSIESP0jl+4cIEjR45ku77R0dHUrVs3\nR/qZEEIYI/v8CSEE4Orqiqura7rny5cv/xZrk3MKFCig7v2o8/z5c4KDg1m/fj379+/H19eXkiVL\nvrE6tG7dmtKlS1OsWDH1WJ8+fQgPD2fgwIEZlnvbnJ2dad26td6x+Ph4rl69yuHDhzl8+DDz58+n\nQ4cOr3T/OXPmsGLFCrZu3ZoT1TWwefNmzp8/z9y5c42eP336NHPmzHmle585c4bPP/+clJQUWrRo\nQZs2bYiPj+f8+fMsWLCATZs28ccff7zRvpRd3bp1w9XVlXz58qnHJkyYQHBwMG3atKFChQrUqVOH\nihUrvlLfK1iwIKNGjaJChQrqsUOHDjF8+HAmTJhA48aNs3W/IkWKMGTIEKZOnYq/vz82NjbZul4I\nITIjwZ8QQpAa/H3xxRe5XY0cV7BgwXTbNWXKFDZs2MCiRYv4/vvv31gdWrdubRBQPX78OEvl3raP\nPvoo3c/L19eXyZMnM3v2bNq2bYuZmVm272+s3Tnl6dOneHl50a9fPwoXLmxwfufOnUyePJkXL15k\n+95arZaJEyeSL18+Nm7cSNmyZfXO//LLLyxcuJAZM2bwyy+/vHIbclr37t0NjoWEhFCyZEl+/vln\nveOv0veM/f2Kjo5Gq9Vm+146np6e/Pbbb/z8889MmDDhle8jhBDGyLRPIYR4Tw0aNAhApphlUY8e\nPShdujT37t3j5s2buV0dAxs2bCAuLo5evXrpHY+OjmbkyJF8+eWXFClSxCBwy4pr164RGRlJixYt\njF4/YsQISpQowcGDB0lKSnrlNrwNSUlJ2NnZ5XY10mVlZYWbmxvr16/n6dOnuV0dIcS/jAR/QgiR\nTdHR0Xh5edG+fXtq1KhBjRo16NixI0uXLiU5OTnDa+Pi4vjhhx9o164d1apVo0GDBowaNYqQkBCD\nsg8fPmTq1Kk0bdqUqlWr0rJlS+bMmUNsbGyOtKNEiRIAxMTE6B0PDw9n/PjxNGzYkKpVq9K6dWtm\nz57Ns2fP9MolJyfz888/07lzZ1xcXHB1dWXQoEEcP35cr1za9VS6NVJ37tzh2bNnODk5MXHiRINy\njx8/pkqVKri7uxut+6BBg6hcuTJRUVHqsePHjzNgwABq166Ni4sL7u7u7N69+7U/p7R0I2oJCQl6\nx7du3Yqnpyd169alatWqNG7cmHHjxumt7WvZsiVbtmwBoGvXrrRs2VI9pygK69ato1u3blSvXp26\ndesybNgwLl++nKV6JSUlsXbtWho0aEDx4sX1zv3999/s37+f7t27s3XrVvV7zw5dv7527ZrRUS0T\nExPmzJnDkiVL9I47OTkxfvx4Tpw4QY8ePahevTotW7Zk/vz5Bp8hQEREhF7fa9++PcuWLTMaUN6+\nfZvJkyfTtGlT9e+gj4+PXtm0a/4WLVqEk5MTAFevXsXJyUn9DtJb8xcYGIinpyd16tShXr169O/f\nn9OnT+vVIe2av4kTJzJp0iQAZs6ciZOTE9euXaNWrVo0b94cRVEM2jFp0iScnJz0flD45JNPiI+P\nZ/369QblhRDidUjwJ4QQ2fDs2TM+++wzVq+5tRI7AAAUNUlEQVReTcWKFenbty+dOnXi4cOHzJ8/\nP921Vjr/+c9/+O233yhXrhz9+vWjWbNmBAUF4eHhwY0bN9Ryd+/epUePHqxfv54qVarQv39/ypcv\nz4oVK/D09CQ+Pv612xIREQGgFwycP3+e7t27s3PnTlxcXPDw8KBo0aKsXLmSzz77TC9QnDFjBosW\nLcLOzg4PDw/atWvH+fPnGTRoECdPnjT6nro1UgUKFMDS0pJRo0YZnW5XtGhRGjZsyPnz57l7967e\nuejoaE6cOEH9+vX54IMPANi0aRMDBgwgNDSUDh064O7uzuPHjxkzZgxLly597c8KICoqitDQUCwt\nLfXWeHl5eTFhwgT++ecfunXrhoeHBx988AH+/v54enqq0yz79u2Ls7MzAO7u7vTt21e9x4QJE5g6\ndSpJSUn07NmTdu3acebMGXr27GkQTBtz5swZHjx4YHSNmYODA9u2bWPmzJkULFjwldpeqVIlihcv\nzsWLF/H09CQgIMAgYUy9evVo1qwZFhYWesdDQ0P5/PPPyZ8/Px4eHhQqVIilS5cyZMgQvUAyJCQE\nNzc3du/eTf369enfvz+FChVi3rx5DB8+nJSUFLVsWFgYbm5ubN68mcqVK9O7d2+srKzw8vLif//7\nn9E2uLq6MmrUKACKFSvGqFGj9L6Dly1btoyRI0dy/fp12rZtS8eOHbl8+TL9+/fn6NGjRq9p3bo1\nrVq1AqBx48aMGjWKDz74gLZt23Lv3j3OnDmjVz4hIYG9e/fi4uJCuXLl1OOOjo6UKlUKf3//dOsn\nhBCvRBFCiPfY5s2bFY1Go/Tp00dZuHCh0T+RkZFq+WXLlikajUbZuHGj3n3u3r2rVK1aVWnUqJHB\nvVetWqUoiqKEhoYqGo1G+frrr/Wu3bVrl6LRaJRZs2apxwYPHqw4OTkpBw8e1Cv722+/KRqNRvHy\n8sq0bRqNRmnRooXRcykpKcqoUaP03jc5OVn5+OOPlcqVKyt//vmnXvk5c+YoGo1GmTRpkqIoivLs\n2TPF2dlZ8fDw0Ct34cIFRaPRKF988YV6bMKECYpGo1EuX76sHmvRooVSu3ZtvWtfLrd9+3ZFo9Eo\nK1as0Cv3xx9/KBqNRvHz81MURVHu3bunVK1aVWnfvr0SHR2tlnv+/Lni7u6uODs7K6GhoRl+VidO\nnFA0Go0yYcIEg3OxsbHKqVOnlG7duikajUZZsGCBeu7+/fvq55CcnKx33eDBgxWNRqMcPnw4w88i\nICBA0Wg0ypdffqkkJSWpx2/duqW4uroqTZo0URISEjKs//z58xWNRqOcPn06w3KKoih9+vRRNBqN\n8vTp00zLpnXs2DHFxcVF0Wg0ikajUZydnZVu3bops2bNSvd9dWWnTZumHktKSlJGjhypaDQaZfPm\nzYqiKIpWq1U6deqkVKtWTbl48aLePX744QdFo9Eoa9euVY/17t1bcXJyUvbs2aMe02q1ysCBAxWN\nRqNcunQp3bZqNBrlk08+0XuPl7+XGzduKJUrV1batWunREVFqeVu3rypuLi4KJ06dVIURVEiIyMV\njUajDB8+XC3z8t97RVGU48ePKxqNRpkyZYre++q++7Rt0xk5cqTi5OSk16eFEOJ1ScIXIYQATp06\nxalTp4yec3V1pUyZMkDqr/kFCxaka9euemVKlSqFvb19hmvBdKMc4eHhxMbGYmtrC6SOFgQGBvLh\nhx8CqSNMQUFBNGvWjObNm+vdo0+fPvj4+LBlyxa+/vrrTNulm+6moygK//zzD8eOHeP69euUK1eO\n4cOHA3Du3Dlu3rxJly5daNq0qd59Ro8ezfbt29mxYwdTp05Fq9WiKAr37t3j4cOH6lTDatWqERgY\nmCMZH1u3bo21tTUBAQHq+kSAgIAArKysaNOmDQDbt28nMTGR0aNH6yU6sbKyYvTo0QwYMIAtW7Zk\nKXnGli1b1KmZL7OysmLw4MHq6BGApaUls2fPpmLFigYJYHQp+zNL8uLr6wvA5MmTMTf//3+W7e3t\n6dmzJ0uXLuXYsWMGfSEt3fTQihUrZvher6NBgwb4+/uzcuVKdu/ezePHjwkJCSEkJAQfHx9q166N\nl5cX9vb2etdZW1szZswY9bW5uTlff/01+/btY8eOHXTv3p3z588TFhaGh4cHVatW1bt+zJgx/P77\n7/j5+eHh4cH9+/c5c+YMjRo14uOPP1bLmZiY8OWXX+Li4oKlpeVrtXX37t0kJyczYsQIvWm0ZcuW\nZcKECbx48SJbaxvr1atH6dKl2bNnD9988436PW/fvh0LCwuj2WMrVqzIvn37uHz5Mo0aNXqt9ggh\nhI4Ef0IIAYwaNSpL2T4rV65M5cqViYuL4/z580RERHDz5k0uXrxIRESE3tS0lzk5OVGzZk3OnTtH\no0aNcHV1pWnTprRo0ULvgfny5csoikJMTIxe4KZjYWHBvXv3ePDgQabrt549e6aX1dDExARra2sc\nHBwYNmwYAwcOVKcC6tY71a1b1+A+lpaWamB348YNnJ2d6dChAzt37qRFixbUrFlTbUtOBSD58+en\nTZs2bNu2jcjISOzt7YmKiuLMmTO0a9dODZ4vXboEpK75+/vvv/XuoZsee/Xq1Sy9Z9qtHl68eMH+\n/fsJDw+nUaNGzJs3zyBRSOHChencuTNarZawsDCuX79OZGQkoaGhHDt2DCDTzI8hISHky5eP33//\n3eBceHg4kPrdZBT8PX78GFNT0zeeyKR06dJMmTKFb775hitXrnDixAkOHz7MyZMnOXv2LP3798ff\n35/8+fOr1zg5OVGoUCG9+zg4OGBnZ6d+L7o1r7du3TLa521sbAgNDUVRFEJDQwFwcXExKFelShWq\nVKny2u3U1cvYe/Ts2TPb9zMxMaFz584sXbqUI0eO0Lx5c2JiYjh8+DBNmzY1mp1Vdyw6Ojrb7yeE\nEOmR4E8IIbIhISGBefPmsWHDBp4/fw6krpmrW7cuhQsX5uHDh+lea2JiwsqVK1mxYgU7duwgKCiI\noKAgvvvuOxo2bMiMGTMoU6aMupYqODiY4ODgdO8XExOTafBXunRpDhw4kKW26RLJ6IKql+nW1+na\n7eXlRdWqVfHz81NHTn/88UeqVq3Kd999x0cffZSl983IJ598wrZt2wgICGDo0KHs2rULrVZL586d\n1TK6RDQZJcfIatbEl7d6GDt2LF999RUBAQH897//ZeHChXqjcwB79+5l7ty56qivtbU1VatWxdnZ\nmWPHjhlN8pHWs2fP1OQ5r1r/2NhYrKysMmldxgIDAw0SnpQuXdrodgkmJibqDyEDBw7k+vXrDB8+\nnIiICAICAnBzc1PLptdHixUrpq471fV53V6K6YmLi1M/i/T6aU7Q1Scn36Nr164sXboUf39/mjdv\nzq5du0hKSqJLly5Gy1tbW+vVRQghcoIEf0IIkQ2zZs3ijz/+oG3btnh4eODk5KSOtrRv3z7D4A9S\nRzDGjBnDmDFjCA8P5+jRo+zYsYNjx44xduxYNm3apD70jRgxQm+63Jum21D6wYMHRs/rHkJ17bWw\nsGDgwIEMHDiQu3fvcvToUXbv3s2RI0cYOnQo+/fvN0j+kV267JW7du1Sgz87OzuaNGmiltF9XoGB\ngQZTDl+Xubk5P/zwA6Ghoezfv5+ffvqJcePGqefPnz/PmDFjKFmyJPPmzaNatWrY29tjYmKCt7e3\nOvqXEWtra2xsbDh06NAr17NQoUJERESQnJxsEJxmVWBgoMGUV1dXV7p378706dPZtWsXvr6+lC5d\n2uBaR0dHRo8ezbhx49SATsdYVk9I7U+60S3dd/j999/To0ePDOupKxsXF2dwTqvVkpiY+NqBcNr3\neHlU7sWLF1haWmJqmr2ceeXLl8fFxYX9+/eTmJjIrl27KFSoEC1atDBaXvf3Le0G9UII8bok26cQ\nQmSDv78/RYsW5aeffqJevXpqIPTixQs1K2V6Iz1Xr17Fy8tLHc0rX748ffr04Y8//qBcuXJcuHCB\nxMRENR29bjrjyxYuXIi3tzeJiYk52jbdSN1ff/1lcE6r1XL27Fmsra0pXbo0kZGRzJs3j4MHDwLw\n4Ycf8umnn7Jy5Urq16/PgwcPuH379mvXyczMjI4dO3LlyhX++usvgoODadeunV5Qqfu8Ll68aHD9\nzZs38fLyyvLopzH58+fHy8sLMzMzVqxYoTcau3PnTrRaLd9++y0dO3bEwcEBExMTADV7a9r+oDuX\nlpOTE/fv3zf6w8GhQ4eYP39+ptNWixcvrk4VflWzZs0iNDRU78+aNWuA1PWO0dHRBAYGpnu9rm26\nEWKdS5cuGUx9vXPnDlFRUdSoUQMgwz6flJTErFmz1Lroyl64cMGg7Llz53BxcTHYciK7NBpNuu/x\n3XffUaNGDb1tPNIy9h3rdOnShfj4eAIDAzl79izt2rVLd33ikydPgNT1xEIIkVMk+BNCiGzIly8f\nCQkJelOxUlJS+P7779WU/uklgkhMTMTHx4fFixfrBQSxsbE8ffqU4sWLY2lpib29PXXr1iUoKMhg\nn7qtW7fyyy+/cPjw4ddOavGy2rVrU7ZsWfbu3Wuw8fvChQu5d+8e7du3x9LSEisrK5YvX85PP/2k\nF4QmJiby8OFDLC0tDfabS8vCwiLTPRF1PvnkEwCmTZuGoijq67TnzczMWLBggV4AlZyczIwZM/Dx\n8XmtoAhSE9n07dsXrVbLN998o9ZdNyrz6NEjvfLHjx9X0/SnbaduVC5tH+nWrRuKojBjxgy9zzIq\nKopvv/0Wb29vdVQ2PZUqVQIwWPOYU9zd3bGwsGDBggUEBQUZnH/06BFLlizBysqK9u3b6517+PAh\nK1asUF/rgjlAnR5at25dypQpg6+vL+fOndO73tvbm1WrVqnrAu3t7alZsyZHjhzRmyKq1WpZvnw5\niqK8doKUTp06YWpqytKlS9UgDFLXJO7atQt7e/t0R5mNfcc6HTt2xMLCgh9//JHk5OR0p3zC/3+X\nuu1BhBAiJ8i0TyGEyIbOnTvj4+ODm5sbrVu3Jjk5mSNHjhAeHk6RIkWIjo4mJibGYPQDoHr16rRt\n25Y9e/bQrVs36tevT3JyMoGBgTx58oTvv/9eLTt9+nQ8PDwYM2YMTZs2pVKlSoSHh3Po0CHs7Oz4\n9ttvc7xtpqamzJo1i0GDBjFs2DBatGiBg4MD586dIzg4GEdHRzXDaPHixenXrx+rVq2iU6dONGvW\nDFNTUw4fPsz169cZMWJEhuulPvjgA27evMn48eNp3LixQfbUtKpUqYKjoyNXr16ldOnS1KpVS+98\nuXLl+Oqrr5g1axadOnWiZcuWFCpUiKCgIK5fv06LFi0MAsZXMXr0aPbs2UNYWBgrV65k6NChdOjQ\ngVWrVjFt2jROnz5N8eLFCQ0N5ciRIxQuXJjHjx/rBZ669W+zZs2iYcOGjBo1iu7du3PgwAH27NlD\naGgoTZo0ITk5mV27dhETE8O4ceMync7avHlzlixZwtmzZ2nQoMFrt/VlZcuWZfbs2UyYMIHBgwdT\nrVo1XFxcsLa25tatW/z5558kJSUxd+5cihYtqnettbU18+fP5+TJkzg6OnL8+HHCwsLo0qWLOuXR\nzMwMLy8vBg8eTJ8+fWjVqhX29vZcunSJEydOUKZMGb788kv1ntOmTaNPnz4MHTqU1q1bU7p0aU6c\nOMHly5fp27cv1atXf632Ojo6MmrUKBYuXKjWU1EUAgICSEhIUINXY3Tf8bp163j69Cmenp7qsUKF\nCtGyZUv27NmDvb09tWvXNnoPRVE4d+4cTk5OBp+nEEK8Dhn5E0KIbBg7dixffPEFpqam/PHHHwQG\nBlK6dGlWrlzJsGHDAAxGzdKaPXs248aNIyUlhQ0bNuDn54e9vT1LlizRW+tUoUIF/Pz8+OyzzwgN\nDWX16tWEhobSpUsXfH1931hK/1q1auHr60uHDh04d+4cv//+OzExMQwfPpxNmzbpZZP86quvmDp1\nKra2tmzZsoWNGzdiY2PDrFmzMl2r+NVXX1GpUiV2797Ntm3bMq2XLnjr3Lmz0Wl1AwYMwNvbG2dn\nZ/bu3cuGDRswNzdn4sSJRpO0vApra2umTJkCwOLFi7l16xYfffQR3t7eVKlShcDAQDZu3MijR48Y\nPXo027Ztw9TUVK8/9O7dm0aNGnHp0iXWrFlDXFwcJiYmLFy4kMmTJ5M/f342bdrErl27qFixIr/8\n8gtDhgzJtG7Vq1enePHi6W4+nhN02V11G9dv3boVHx8fLly4QIcOHdi2bRtt27Y1uM7BwYHFixfz\n8OFD1q9fT0pKCv/973/x8vLSK1enTh02bdqkbnC/evVq7t69i6enJxs2bND7QcXJyYlNmzbRvn17\nTp06xZo1a3j+/DmTJk1i0qRJOdLekSNHMn/+fEqVKsW2bdvYsWMH1atXZ+3atRkGl3Xr1sXDw4On\nT5/y+++/c/36db3z7dq1A8jwB4mLFy/y9OlTOnXqlCNtEUIIHRMlszRkQgghhHjneXt7M3fuXPbs\n2UO5cuVyuzpAapDm7OycpQD/fTFv3jyWLVvG3r17KVu2rNEyU6dOZfv27Rw8eNBgmwwhhHgdMvIn\nhBBC/Av07t2bQoUKsXHjxtyuikjH/fv38fX1pV69eukGfvHx8ezcuVP9PoUQIidJ8CeEEEL8C9ja\n2jJ27FjWrVtnkIBG5K7t27fTvXt3OnXqRHR0tN5eki/z8fHBysoqS9N9hRAiuyT4E0IIIf4levbs\nSY0aNTLcMF68fSVLluTOnTtYWVkxffp06tata7RcdHQ0Pj4+TJ8+nYIFC77lWgoh3gey5k8IIYQQ\nQggh3gMy8ieEEEIIIYQQ7wEJ/oQQQgghhBDiPSDBnxBCCCGEEEK8ByT4E0IIIYQQQoj3gAR/Qggh\nhBBCCPEekOBPCCGEEEIIId4D/wdGyEsG6lZkowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x138d84d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rm.run_logistic(X_train, X_test, y_train, y_test)\n",
    "rm.run_classifiers(X_train_c, X_test_c, y_train_c, y_test_c, classifiers)\n",
    "plt.plot([0,1], [0,1],'--', color='black')\n",
    "sns.despine()\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The above scores may differ from the scores in the presentation slide as the model scores change slightly ever time the model is run due to differences in the train/test split of the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Feature Importance with Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train_c, y_train_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_X_w1 = pd.DataFrame(X_w1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([               u'diabestie_id',                  u'entries_w1',\n",
       "                          u'moods_w1',                    u'notes_w1',\n",
       "                            u'a1c_w1',                u'page_view_w1',\n",
       "                              u'age2',        u'college_type_College',\n",
       "          u'college_type_High School',          u'college_type_Other',\n",
       "                   u'diabetes_type_1',             u'diabetes_type_2',\n",
       "               u'diabetes_type_Other',  u'ethnicity_African American',\n",
       "                   u'ethnicity_Asian',         u'ethnicity_Caucasian',\n",
       "          u'ethnicity_Latin American',   u'ethnicity_Native American',\n",
       "                   u'ethnicity_Other', u'ethnicity_Prefer not to say',\n",
       "                     u'gender_Female',                 u'gender_Male',\n",
       "                      u'gender_Other'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X_w1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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acfDgQebPn8/atWu57777avhqRERERKqPgm4t16NHD1599VU+/PBD6tevT506dZg1axbT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119520610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the feature importance\n",
    "\n",
    "feat_scores = pd.DataFrame({'Fraction of Samples Affected' : rf.feature_importances_}, \\\n",
    "                          index=df_X_w1.columns)\n",
    "feat_scores = feat_scores.sort_values(by='Fraction of Samples Affected')\n",
    "feat_scores.plot(kind='barh', figsize=(10,10), colormap='RdGy')\n",
    "sns.despine()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Notes\n",
    "# only 94 new A1C values recorded in events meta table. Hard to figure out relationship to events table - doesn't quite\n",
    "# make sense. From there would have to connect to user_id to try to see who re-entered A1C and if there was improvement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}