PyBer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e9bd23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.Import your data into a Pandas DataFrame.\n",
    "# 2.Merge your DataFrames.\n",
    "# 3.Create a bubble chart that showcases the average fare versus the total number of rides with bubble size based on the total number of drivers for each city type, including urban, suburban, and rural.\n",
    "# 4.Determine the mean, median, and mode for the following:\n",
    "## The total number of rides for each city type.\n",
    "## The average fares for each city type.\n",
    "## The total number of drivers for each city type.\n",
    "# 5.Create box-and-whisker plots that visualize each of the following to determine if there are any outliers:\n",
    "## The number of rides for each city type.\n",
    "## The fares for each city type.\n",
    "## The number of drivers for each city type.\n",
    "# 6.Create a pie chart that visualizes each of the following data for each city type:\n",
    "## The percent of total fares.\n",
    "## The percent of total rides.\n",
    "## The percent of total drivers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f5de104",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.Import your data into a Pandas DataFrame.\n",
    "# Add Matplotlib inline magic command\n",
    "%matplotlib inline\n",
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statistics\n",
    "import scipy.stats as sts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "766a1ed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Files to load\n",
    "city_data_to_load = \"Resources/city_data.csv\"\n",
    "ride_data_to_load = \"Resources/ride_data.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ceadb90b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Richardfort</td>\n",
       "      <td>38</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Williamsstad</td>\n",
       "      <td>59</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Angela</td>\n",
       "      <td>67</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>West Robert</td>\n",
       "      <td>39</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>West Anthony</td>\n",
       "      <td>70</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>West Angela</td>\n",
       "      <td>48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Martinezhaven</td>\n",
       "      <td>25</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Karenberg</td>\n",
       "      <td>22</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Barajasview</td>\n",
       "      <td>26</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  driver_count   type\n",
       "0    Richardfort            38  Urban\n",
       "1   Williamsstad            59  Urban\n",
       "2    Port Angela            67  Urban\n",
       "3     Rodneyfort            34  Urban\n",
       "4    West Robert            39  Urban\n",
       "5   West Anthony            70  Urban\n",
       "6    West Angela            48  Urban\n",
       "7  Martinezhaven            25  Urban\n",
       "8      Karenberg            22  Urban\n",
       "9    Barajasview            26  Urban"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the city data file and store it in a pandas DataFrame.\n",
    "city_data_df = pd.read_csv(city_data_to_load)\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84121f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South Latoya</td>\n",
       "      <td>2019-03-11 12:26:48</td>\n",
       "      <td>9.52</td>\n",
       "      <td>1994999424437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Paulville</td>\n",
       "      <td>2019-02-27 11:17:56</td>\n",
       "      <td>43.25</td>\n",
       "      <td>793208410091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Simpsonburgh</td>\n",
       "      <td>2019-04-26 00:43:24</td>\n",
       "      <td>35.98</td>\n",
       "      <td>111953927754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Karenland</td>\n",
       "      <td>2019-01-08 03:28:48</td>\n",
       "      <td>35.09</td>\n",
       "      <td>7995623208694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>North Jasmine</td>\n",
       "      <td>2019-03-09 06:26:29</td>\n",
       "      <td>42.81</td>\n",
       "      <td>5327642267789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873\n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577\n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003\n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178\n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344\n",
       "5        South Latoya  2019-03-11 12:26:48   9.52  1994999424437\n",
       "6       New Paulville  2019-02-27 11:17:56  43.25   793208410091\n",
       "7        Simpsonburgh  2019-04-26 00:43:24  35.98   111953927754\n",
       "8     South Karenland  2019-01-08 03:28:48  35.09  7995623208694\n",
       "9       North Jasmine  2019-03-09 06:26:29  42.81  5327642267789"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the ride data file and store it in a pandas DataFrame.\n",
    "ride_data_df=pd.read_csv(ride_data_to_load)\n",
    "ride_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d944484b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            120\n",
       "driver_count    120\n",
       "type            120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect City Data DataFrame\n",
    "# Get the columns and the rows that are not null.\n",
    "city_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87156b74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            0\n",
       "driver_count    0\n",
       "type            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are null.\n",
    "city_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5aa80c90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            object\n",
       "driver_count     int64\n",
       "type            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "city_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "24ee705e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Suburban', 'Rural'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the unique values of the type of city.\n",
    "city_data_df[\"type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3a649c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of data points from the Urban cities.\n",
    "sum(city_data_df[\"type\"]==\"Urban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a005c582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of data points from the Suburban cities.\n",
    "sum(city_data_df[\"type\"]==\"Suburban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "df2e0e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of data points from the Rural cities.\n",
    "sum(city_data_df[\"type\"]==\"Rural\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75e8887f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       2375\n",
       "date       2375\n",
       "fare       2375\n",
       "ride_id    2375\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect Ride Data DataFrame\n",
    "# Get the columns and the rows that are not null.\n",
    "ride_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa8dd69a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       0\n",
       "date       0\n",
       "fare       0\n",
       "ride_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are null.\n",
    "ride_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "19485384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        object\n",
       "date        object\n",
       "fare       float64\n",
       "ride_id      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "ride_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "48a54726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.Merge DataFrames\n",
    "# Combine the data into a single dataset\n",
    "pyber_data_df= pd.merge(ride_data_df,city_data_df, how=\"left\", on=[\"city\", \"city\"])\n",
    "# Display the DataFrame\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d8df3e96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.Create a bubble chart that showcases the average fare versus the total number of rides with bubble size based on the total number of drivers for each city type, including urban, suburban, and rural.\n",
    "# Create the Urban city DataFrame.\n",
    "urban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Urban\"]\n",
    "urban_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "54488200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Suburban and Rural city DataFrames.\n",
    "suburban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Suburban\"]\n",
    "rural_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Rural\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb9d8fa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>Barronchester</td>\n",
       "      <td>2019-01-27 03:08:01</td>\n",
       "      <td>27.79</td>\n",
       "      <td>6653622887913</td>\n",
       "      <td>11</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>East Kentstad</td>\n",
       "      <td>2019-04-07 19:44:19</td>\n",
       "      <td>18.75</td>\n",
       "      <td>6575961095852</td>\n",
       "      <td>20</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>Lake Omar</td>\n",
       "      <td>2019-01-17 21:33:35</td>\n",
       "      <td>21.71</td>\n",
       "      <td>966911700371</td>\n",
       "      <td>22</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>Myersshire</td>\n",
       "      <td>2019-02-27 17:38:39</td>\n",
       "      <td>17.10</td>\n",
       "      <td>5706770909868</td>\n",
       "      <td>19</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>West Hannah</td>\n",
       "      <td>2019-04-19 01:06:59</td>\n",
       "      <td>37.78</td>\n",
       "      <td>2273047151891</td>\n",
       "      <td>12</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               city                 date   fare        ride_id  driver_count  \\\n",
       "1625  Barronchester  2019-01-27 03:08:01  27.79  6653622887913            11   \n",
       "1626  East Kentstad  2019-04-07 19:44:19  18.75  6575961095852            20   \n",
       "1627      Lake Omar  2019-01-17 21:33:35  21.71   966911700371            22   \n",
       "1628     Myersshire  2019-02-27 17:38:39  17.10  5706770909868            19   \n",
       "1629    West Hannah  2019-04-19 01:06:59  37.78  2273047151891            12   \n",
       "\n",
       "          type  \n",
       "1625  Suburban  \n",
       "1626  Suburban  \n",
       "1627  Suburban  \n",
       "1628  Suburban  \n",
       "1629  Suburban  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d10d4d30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>Randallchester</td>\n",
       "      <td>2019-04-13 11:13:31</td>\n",
       "      <td>43.22</td>\n",
       "      <td>1076079536213</td>\n",
       "      <td>9</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>North Holly</td>\n",
       "      <td>2019-02-02 14:54:00</td>\n",
       "      <td>12.42</td>\n",
       "      <td>1985256326182</td>\n",
       "      <td>8</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>Michaelberg</td>\n",
       "      <td>2019-03-27 18:27:34</td>\n",
       "      <td>54.85</td>\n",
       "      <td>4421836952718</td>\n",
       "      <td>6</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-02-23 21:12:24</td>\n",
       "      <td>47.90</td>\n",
       "      <td>3269652929887</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-05-06 08:57:56</td>\n",
       "      <td>51.80</td>\n",
       "      <td>4018025271936</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "2250   Randallchester  2019-04-13 11:13:31  43.22  1076079536213   \n",
       "2251      North Holly  2019-02-02 14:54:00  12.42  1985256326182   \n",
       "2252      Michaelberg  2019-03-27 18:27:34  54.85  4421836952718   \n",
       "2253  Lake Latoyabury  2019-02-23 21:12:24  47.90  3269652929887   \n",
       "2254  Lake Latoyabury  2019-05-06 08:57:56  51.80  4018025271936   \n",
       "\n",
       "      driver_count   type  \n",
       "2250             9  Rural  \n",
       "2251             8  Rural  \n",
       "2252             6  Rural  \n",
       "2253             2  Rural  \n",
       "2254             2  Rural  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "13d69c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh        18\n",
       "Barajasview        22\n",
       "Carriemouth        27\n",
       "Christopherfurt    27\n",
       "Deanville          19\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4.Determine the mean, median, and mode for the following:\n",
    "## The total number of rides for each city type.\n",
    "### Get the number of rides for urban cities\n",
    "urban_ride_count = urban_cities_df.groupby([\"city\"]).count()[\"ride_id\"]\n",
    "urban_ride_count.head()\n",
    "## The average fares for each city type.\n",
    "## The total number of drivers for each city type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bb79abf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Barronchester    16\n",
       "Bethanyland      18\n",
       "Brandonfort      19\n",
       "Colemanland      22\n",
       "Davidfurt        17\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Get the number of rides for suburban cities\n",
    "suburban_ride_count = suburban_cities_df.groupby([\"city\"]).count()[\"ride_id\"]\n",
    "suburban_ride_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cdb89e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bradshawfurt      10\n",
       "Garzaport          3\n",
       "Harringtonfort     6\n",
       "Jessicaport        6\n",
       "Lake Jamie         6\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Get the number of rides for rural cities\n",
    "rural_ride_count = rural_cities_df.groupby([\"city\"]).count()[\"ride_id\"]\n",
    "rural_ride_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc61adc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh        24.641667\n",
       "Barajasview        25.332273\n",
       "Carriemouth        28.314444\n",
       "Christopherfurt    24.501852\n",
       "Deanville          25.842632\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## The average fares for each city type.\n",
    "### Get average fare for each city in the urban cities.\n",
    "urban_avg_fare = urban_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "urban_avg_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f1defa0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Barronchester    36.422500\n",
       "Bethanyland      32.956111\n",
       "Brandonfort      35.437368\n",
       "Colemanland      30.894545\n",
       "Davidfurt        31.995882\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Get average fare for each city in the suburban cities.\n",
    "suburban_avg_fare = suburban_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "suburban_avg_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "62b8d5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bradshawfurt      40.064000\n",
       "Garzaport         24.123333\n",
       "Harringtonfort    33.470000\n",
       "Jessicaport       36.013333\n",
       "Lake Jamie        34.358333\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Get average fare for each city in the rural cities.\n",
    "rural_avg_fare = rural_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "rural_avg_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6fdde27c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh        12.0\n",
       "Barajasview        26.0\n",
       "Carriemouth        52.0\n",
       "Christopherfurt    41.0\n",
       "Deanville          49.0\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## The total number of drivers for each city type.\n",
    "### Get the average number of drivers in the urban cities.\n",
    "urban_driver_count = urban_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]\n",
    "urban_driver_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8b8f4532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Barronchester    11.0\n",
       "Bethanyland      22.0\n",
       "Brandonfort      10.0\n",
       "Colemanland      23.0\n",
       "Davidfurt        23.0\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Get the average number of drivers in the suburban cities.\n",
    "suburban_driver_count = suburban_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]\n",
    "suburban_driver_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "be8831ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bradshawfurt      7.0\n",
       "Garzaport         7.0\n",
       "Harringtonfort    4.0\n",
       "Jessicaport       1.0\n",
       "Lake Jamie        4.0\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Get the average number of drivers in the rural cities.\n",
    "rural_driver_count = rural_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]\n",
    "rural_driver_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7314cc42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5.Create box-and-whisker plots that visualize each of the following to determine if there are any outliers:\n",
    "## The number of rides for each city type. (x_axis)\n",
    "## The fares for each city type. (y_axis)\n",
    "## The number of drivers for each city type. (size of marker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe3021e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7faa209061f0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Build the scatterplots for urban cities\n",
    "plt.scatter(urban_ride_count,\n",
    "            urban_avg_fare,\n",
    "            s=10*urban_driver_count, c=\"coral\",\n",
    "            edgecolor=\"black\", linewidths=1,\n",
    "            alpha=0.8, label=\"Urban\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "#Add the legend\n",
    "plt.legend()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "57e2c968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7faa438a2a30>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Build the scatterplots for urban cities\n",
    "# Build the scatter plots for suburban cities.\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b93f609a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7faa607964f0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Build the scatterplots for rural cities\n",
    "# Build the scatter plots for rural cities.\n",
    "plt.scatter(rural_ride_count,\n",
    "      rural_avg_fare,\n",
    "      s=10*rural_driver_count, c=\"gold\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Rural\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e73654d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAssAAAEeCAYAAAB8E1jaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAADrhElEQVR4nOzdd3gUVRfA4d/dTe+VhBBCk957lSaIgiC9Vws2REVFRURAwIKIHez03osgKgQBRcCP3juEmt7r7v3+mElI2Q0bSAjlvs+zz5KpZ2Yn5OydM/cKKSWKoiiKoiiKouRlKO4AFEVRFEVRFOVupZJlRVEURVEURbFCJcuKoiiKoiiKYoVKlhVFURRFURTFCpUsK4qiKIqiKIoVKllWFEVRFEVRFCtUsqxkEUKMF0JIIUTrAqwTKoS46/ofFEKcE0KcK+YYhurnc2hxxvGgEUJUEkKkCSHeLO5YioLQ7BNCbCvuWBRFUR4E92WyrCco2V8mIUSEEGKzEGLAbW67rIXtSyFEshDipBDiGyFE6cI6lluMsbWF+NKFEJeFECuEEC2LM75bIYSoLIT4QQhxSj/XiUKIs0KITUKIcUKIgOKO8V6hf5HIfW1ECiEOCiHmCiF6CSEcCmlfmdfi+MLYno0+AyKBb7LFIYQQjwkhvtITzWghRIoQ4rgQ4vP8rh8hhI++zDkhRKr+e/SzECLYyvJPCyG+E0L8K4RI0o9/Un4BCyFKCCG+EEKc1vcRIYRYK4RokntZqXWO/z7QQgjR09aToiiKotwacT8OSpKtpXOC/m4PVAa6AkZgupRy1C1uuyxwFogFPs82yxdoBdQErgP1pJSXbmUft0tvGd4CnAdm6ZNdgPpAW0ACfaSUS3Ot5wf4AReklEk27isUaCWlFIUQurV9tAXWA07AP8D/gCSgLFAHqAi0l1L+kW2dcwBSyrJFFdfNCCE8gZLAFSllbHHFkZt+bsoAXwAxaF+aPdB+Rx4GXIGTwCAp5b+3ua/WaNfiBCnl+NvZlo37awbsAN6VUk7JNt0JSAbSgL+A/Wj/F7QFagHXgIellCdzbc8X+BuoBGwGdgNVgCfRfs+bSinP5FonBvAEooEooAIwWUo51krMZfSYSwG7gO1ov4fdAWegl5RypYX1jujHUEXej/+RK4qi3C2klPfdCy0ZlBamPwKY9VfZW9x2WX3756zMX6PPH1+Mx99ajyHUwry39XlnC2lfoZbOdSEfz0k95iFW5tcCSueads7aZ/Sgv/RzIy39DqAleV/q82PQErHb2VfmtTj+Dh3bfMBk4XqwB94FvHNNNwAz9RjXWtjed/q8z3JNH6lP32hhnceAMvq/h+rLTcon5lX6Ml+gN2Do0x9C+1IeCfhYWO8tfb12xX1NqZd6qZd63c+v+7IMwxop5Z/AMUAADYUQVfRbpJutraPfmk4XQgTauJvf9Xd/C9uyE0K8KITYKYSI02/R7hVCjBBCGHItm1nuMUuvwVwshLguhDCLAtQUW/CT/l5Wb0nOvk+rNctCiL5CiP/0Eojr+u36oPx2JIToIIT4Vb+lnKrfYp4qhPCyNVghRAn0pEFKOdvSMlLKA1LKi1bWd9H3eUGP4ZQQ4i0hRJ6WcKHVGC8XQpzRjzNOCLFDCDHQyrZD9fPloJeCHNf3MSvb9vLULOu3888VMDYhhHhFCHFELx+4JIT4WgjhKQqxPltKGSulHAnMQUucP8oVRyUhxEdCiD1CiHA97vNCiO9zlyXo52GL/uP7ImfpR2t9GU8hxJtCK5EKE1qtcbgQYo2lEoT8CCE8gJ7A37mvByllupRyspQyOtd0MzBR/7F1ru25AoOARLSyh+y+RvvS0UEIUT7XNjdKKc/bGLMT0BHtC/xYKWVWC7GU8hTwA+ADWCofW6S/P23LvhRFUZRbY1fcARSDzERESimPCSG2AG2EEJWklCdyLKjd0q0BLJdSXrVx+4/o73tybcseWAt0AI4DC4AUoA3wFdAY7Q9zbhWAf4ETaK1mzkCcjbHcTIYtCwkhXkOrA41BS6Ji0I7jb7SWL0vrjEMrg4kC1qHdsq4FvAF0FEI0lVLachyxepxuQoiSUsortsSsswc2AUHABn07XdESQCdulOlkmgEcQbtNfwWttKYjMFcIUVlK+Z6V/SwHGur7WIV2rIUd2zfAC8Bl4Hu0coIuQCN9W+k27LMgJgCDgSeEEB7ZPqvuwPNoSfDfehzVgWeAzkKIBvJG+dEq/X0IsBXtLkSmc/p7VWAy2jlfj1a6EIJ2bI8LITpLKTfaGHNLwAGtjKEg0vT33L8PTdF+3zZJKeOzz5BSmoUQm4DhaL/DZ7g1Pmif3/Xc+9BlbvcRtP8nssdwXghxCWgnhBDZE21FURSlEBV303ZRvLBehtGOG2UYZfRpPfXlP7Ww/Cx9Xvts08py4xb1+GyvL4C9aH9wfwHscm1rvL7eV4Ax23QjWmuvBJ60sB8JTCng8bfGehnGWH3eQQvzMmNsnSuOVLSkt2y26Qa0JDHPuUZLHiRaMuWVa95Qfd70AhzPMn2d02jJdmPA5SbrnNPX+RVwzja9hP7ZxQD2udapYGE7DsCfaMloqVzzQvV9HAD8LKybeaxDbyc2tDpiifYlyytXbH+RT1nQTc5N2Zssd1Ffrk22aaUARwvLPopW/jDDyrU43so+PK2cu2C0LwZHC3BcH+n76lHA35fMcoaFuaa/pE//ysp6b+jzP85n25nXgMUyDLRkPEM/d24W5n+qr3/Myvor9fnVCnLM6qVe6qVe6mX7674uw9DLCsYLISYLIZYBG9Falj+XN26TrkL7ozxUCOGYbV0voDdagvYHeXmi3ZrNfI1Ee9hsN7BYSpnVSqWXWIwArgKvSSlNmfP0f7+O9gfP0q3Wa+RtZbRV2Wzn4BOhlZt8gNYy/ZyN2xiAlpR9JaU8ly1uM/Am2heP3Ebq789KKWOyz5BSzgL2YflYrXkWWAGUA6YCO4F4IcR+IcQkkX9PGCOllMnZ9n8dWI32+VXOFdvp3CtLKdPQWnXtuHHXILf3pJQRBTiegsY2RH+fnP186rG9cwv7tVVmC3FWSZGU8pKUMjX3glLKTcBhtDsONpNa2UeecyelDEP7klRFCBFi4+Yyl7P57oMQoiHa72882hfJ7Dz1d2sPZ2ZO97J1f7npn/9mtC+fE7PP08s7ntF/9Layicw7XraeI0VRFKWA7vcyjMw6w8yW4G3AT1LKeZkLSCkzhBA/AuOAHmjlEaCVRDgD30spLd3ePC+z9bQgtJ4P6qL1kPGrEOJ5KeX3+uxKaLf0TwJjLZSkgvakflUL0/dbSk5sVIa8tZbRQFsp5T4bt1FPf9+ae4aU8owQ4qK+n+yaorXE9hJC9LKwTQfAXwjhK6WM1Gt6y+ZaJlRKGarvJxroIbSeSDoADdDKHmrprxeEEI9JKXfn2kas1Oo+c8usZ82RgOhJ2VtoSXEI2uefXSkL2wKtB4OCKkhsdfV3S+UFO7GxnOYWZJUsZU3QLt4BaC2mtdHiNGZbJ40CEkI0B15Bu25KoF0f2ZUCLtiwKV/9PTrfpW7stxJaaZQ90NfSl6WbbUJ/v93yh1fRPtvXhBBN0e7I+KKVvJxFu8ZNVtaN0t/9rMxXFEVRbtN9nSxL27sz+x4Yg9bampksD0f7w/+LjfuKBUKF1u/pSeBjIcRcveUo8494RfImr9m5WZhma620JVullK1B6ysW7cvA18BaIURDaVsddmbr2jUr86+SN1n2Rbu28jtW0I43Ei3xamVhfmj2H/SW7e/0F/oDZd8CndEehKqTa/0YK/vNTC6zkjy9FW8XWvK3Da2eOBYtSSmL1rrriGW38hnZHBv5fAZSSpMQIvIW9m+LzAc4w7NN+wwtubsC/IbW+pzZOj6UvNdCvoQQ3dBakFPQHo49jfZAnRmthKMV1s97bplxONmw34poddc+aInyGguLZbYce1qYB1p3e9mXuyVSyiNCiPrAe2jlLC+j1b3/CCxEuy6t1cFnfqFLtjJfURRFuU33dbJsKynlJSHEWqCbEKIqWsJUA62cIjz/tfNs65QQIgrtj3AltP5cM/+YrpRSdi9oeAVc3lpcUcAPQhts4mu0JNOWWDJjD0C7zZ6bpV5CYgGDlNLHxtha27KchfXChBB90VoSa2e2VN/KtoBRaEn+ML1UJIsQoh83SiEsxVHUD1ZlPlwXQK4HyYQQRrS4C7VPbyHEQ2h1wxnAf/q0EmglNoeAZjLXA2n6eSqoD9C+lDaQUh7Ntb3vsPwlyprMhNI3v4X03/E/9eV6SSlXW1n0uP5eycr8ivr7CSvzbSalPAs8lXu6EGKY/s/cd00yZR6rLQ+VKoqiKLfgvq5ZLqBv9ffh+gv0FsyCEELYAe76j5nn9xhaS2ITvVeM4jQTLentpt/+vpn/6e95kha9NdbSaIU7AW8hRPVbjtJ2qdzCrX8LHtLfl1uYV5CErSjs1d9bWJjXhKL50jtOf1+bLSkuj3ZN5+kdQm/lz9GFmi6zfMBoYR5o5/2IhUTZgOXjzc8B/b2KtQWEEDXR7lj4oD0IaC1RBu06TgaaCyHcs8/Q43tU/3FLAeMsiMya5flW5ldBa4U/WIQxKIqiPNBUsnzDn2gtREPQHuw7IaW8lT+CI9BqICPRWuDQH/b7Cm00ty+FELlrYRFClBRCVLvF2G2mP1CYWR4xJb9ldfPR6o9f1muGgaxkYSqWr6Hp+vsPwkJfzEIIV1v70NWXfS+fh/heRSvnOHIbrcpwoyuz1rn234EbCUtxmaO/v6vXxgOg3yWw5TO0mRDCQwjxJVrNfgzaIDaZzunvLfQW7cx13NDKYCwl7ZmfibUH0M4BFbNfJ3pd9PtAQX8fQvV3i9eWEKIOWmLrjtbzzLr8NialTADmoo1oOD7X7BFo5Tm/yVwj+BWUEMIx+8PF+jQhhJgINAPWZ9bv514PrfRob+4HaRVFUZTCo8owdFJKKYSYiVaTCTdvVfYSQozP9rMH2sNwrdBael6UUmbv+/YDtAeinkfrj3Yz2q3zEmi3c5ujjTB25DYPxRYr0HqkaCmE6CCl/M3aglLKc0KIt4FpwF4hxGK0MosOaL0AHEB7ACn7On/q63wInBRC/Ir2oJIbWk1rK7QHmh6zIVZ7tF4C3hdC7NLjjkZrGWyONrx4Itp5vR3fAsOApUKI5WifTQ09xiVAn9vc/i2TUm4VQnyPdsfjsB5fOlqtdixaby6WeiW5mVeFNjSz4MZw1y3RksMTwECZre9xKeVVIcQioC+wT+9n2BNoj1ZzvI+8dePH0c5lXyFEGtqDehKYq/dIMx3tbsfebMfVHC1RXqsfo02klIeEEMeBR4QQxuy9zgghvNG+EPvo7031h+ly+zxX4jkG7QvUKD3Z3oX2IO6TaKUPL+XegBDiGW60imfesegsbgzackxKmX2wl4rANiHE72hfHhzQzmk1tPKLwVYOubW+rKW7IYqiKEphKe6+64rihZV+lm1YzxvttnEK4GtlmbKZ28/1SkPryWAh0NDKugKtxe5PtKfY09ASie1of5RLW9jPrFs4jtZY6Wc52zKd9WV2Z5s2nlz9LGeb1w+tJCMF7YGveWgPgIVaO9doCcMStGQuTV9vH9oXkgY2HosBLWH9DG1wlstoCVU8WqL+OZaHbT6H9SHJLR4nWiveZrRkPF7/XLpipa/g/I5dnz8U6/0sFzQ2A/AaWklPqn4evkFLVuOBfQW4Ps7lunbT9evxIFpLak/Awcq6LmiDiJzSr4WLehy+1s4HWs8lf6Il9ubcx6efp31oX3oi0PoOrpnf9ZjPsb2ir/O4jb+3uV+WriUftH7Uz6Ndx1eAn4FgKzHMusk+QnMt7492B+csWtlHHFpS/qq1z0Ffb4F+LZQo6P8R6qVe6qVe6mX7S0hZ1M8m3TuENgTvFmCelNLSaHqKclfRe3U4ASySUt7KA3b3FaENeX0abcjrJ4s7nqKiP2x5DlggpSzuMiFFUZT7mqpZzmm0/v51sUahKLkIIQL1OvHs01zQWtZBa4194EltWO73gS5CiAbFHU8RGoN2F8zaEOyKoihKIXnga5b1p+OfAOoDjwPrpJT/Fm9UipLHq0A/IUQoWhlAINrgKcHABmBpsUV29/kOrZ4+v5Ed71n6A5BXgEFSSptHK1QURVFuzQNfhqGPHvcLWp3gb2gP5t3K0MWKUmSEEI8Ab6A9QOeD1v/xCbS61c9lzodJFUVRFEUpJA98sqwoiqIoiqIo1txzZRh+fn6ybNmyxbb/xMREXF1di23/trpX4oR7J1YVZ+G6V+KEeyfW/OL877//IqSU/nc4JEVRlHvePZcsly1blj179hTb/kNDQ2ndunWx7d9W90qccO/EquIsXPdKnHDvxJpfnEKI83c2GkVRlPuD6g1DURRFURRFUaxQybKiKIqiKIqiWKGSZUVRFEVRFEWxQiXLiqIoiqIoimLFPfeAn6Iod4+4uDiuX79OenrBu3n29PTk6NGjRRBV4bsXYrW3t8doNBZ3GIqiKPcdlSwXEiklO3bs4PTp04SEhNC6dWu0gbYU5f4UFxfHtWvXKFWqFM7OzgW+3uPj43F3dy+i6ArX3R6rlJLk5GQSExOJi4vDw8OjuENSFEW5b6hkuZB8/dU0/vp9Js3rmvltlYF/dvRjzNgPijssRSky169fp1SpUri4uBR3KA88IQQuLi6UKlWK69evq2RZURSlEKlkuRCEh4ezcul3rPrWHQ93I0nJZnqMWMTZAU9Rrly54g5PUYpEeno6zs7OxR2Gko2Dg8MtlcQoiqIo1qkH/ApBfHw8Xh4GPNy1ekEXZwMlfI3ExcUVc2SKUrRUqdHdRX0eiqIohU8ly4UgODiYDPxYsi6G+AQT6zfHcj3GnYceeqi4Q1MURVEURVFug0qWC4GDgwNfz1jIhl016Dg8iQWbHuKLr+fj6upa3KEpyn1lypQpPPPMM8UdhqIoivIAUclyIQkJCeGXOavY9vdx5i/aQKVKlYo7JEW5Jy1YsIAGDRrg5uZGyZIlefzxx9m+fTsAY8aM4ccffwTg3LlzCCHIyMgo8D6mTJmCm5sbbm5uODk5YTQas36uXr16oR6PoiiKcm9TybKiKHeNzz77jFdffZUxY8Zw7do1Lly4wIsvvsjq1asLdT9jxowhISGBhIQEZs6cSdOmTbN+Pnz4cKHuS1EURbm3qWRZUZS7QmxsLOPGjeObb76he/fuuLq6Ym9vT+fOnZk6dSoA48ePZ+DAgQC0bNkSAC8vL9zc3Ni6dSs+Pj4cPHgwa5vXr1/H2dmZ8PBwm2KYOnUqPXr0yDHt5Zdf5q233gKgdevWvPPOOzRq1AhPT0+efPJJoqKispbduXMnzZo1w8vLi9q1axMaGpo1b9asWZQvXx53d3fKlSvH/PnzC36SFEVRlDtOJcuFKC0tjXPnzpGSklLcoSjKPeeff/4hJSWFbt262bT8X3/9BUBMTAwJCQm0atWKvn37Mm/evKxlFi5cSLt27fD397dpmwMHDmTjxo3ExMQAkJGRweLFi+nbt2/WMnPmzOHnn3/m8uXL2NnZMXLkSAAuXbpEp06dGDt2LFFRUXz66af06NGD8PBwEhMTGTlyJBs2bCA+Pp6///6bOnXq2BSToiiKUrxUslxIIiIi6NOzPa+88Cg9urbm0qVLxR2SotxTIiMj8fPzw87u1rt/HzJkCAsWLMBsNgMwd+5cBg0aZPP6JUuWpGXLlixduhSAjRs34ufnR926dbOWGTRoEDVq1MDV1ZUPPviAJUuWYDKZmDdvHh07dqRjx44YDAbat29PgwYN+PXXXwEwGAwcOnSI5ORkSpYsqWqjFUVR7hEqWS4kK1csp1GV86ye4UHHZldZvGhucYekKPcUX19fIiIibumBvUyNGzfG1dWVrVu3cuzYMU6dOkWXLl0KtI0hQ4ZktU7PmzcvT7JdunTprH+XKVOG9PR0IiIiOH/+PEuXLsXLyyvrtX37dq5cuYKrqyuLFy9m5syZlCxZkk6dOnHs2LFbPk5FURTlzlHJciFxcXElPFoQF2/iSji4uLgVd0iKck9p2rQpTk5OrFq1yqblrQ3AkZnszp07l549e+Lk5FSgOLp27cqBAwc4dOgQ69atY8CAATnmX7x4MevfFy5cwN7eHj8/P0qXLs2gQYOIiYnJeiUmJvL2228D0KFDB37//XeuXLlClSpVePbZZwsUl6IoilI8VLJcSHr26oXRowNPPJdMrKkVAwcNLfA20tLS+GjKOAb2fZRvv5medStZUR4Enp6eTJw4kZdeeolVq1aRlJREeno6GzZsYPTo0XmW9/f3x2AwcObMmRzTBw0axMqVK5k3bx6DBw8ucBxOTk707NmT/v3706hRI0JCQnLMnzdvHkeOHCEpKYlx48bRs2dPjEYjAwcOZO3atfz222+YTCZSUlIIDQ0lLCyMa9eusWbNGhITE3F0dMTNzQ2j0Vjg2BRFUZQ7TyXLhcTR0ZGp02by19/H+Orb2bi5Fbxlee6cX7hyai5vD7vEzi1fs3HjxiKIVFHuXqNGjeKzzz5j0qRJ+Pv7U7p0ab7++mu6du2aZ1kXFxfeffddmjdvjpeXFzt37gS0ETXr1auHEIKHH374luIYMmQIBw8etFjvPGjQIIYOHUpgYCApKSl8+eWXgFaesXr1aqZMmZIV+9SpUzGbzZjNZqZNm0ZQUBA+Pj5s3bqVb7/99pZiUxRFUe6sW3+SRil0Vy6fp3FtQY3KztSuksiVy5eLOyRFueMGDBiQp/Qh0/jx43P8PHHiRCZOnJhnuZCQEJo0aWK1VCO7oUOHMnTo0DzrOzs75+lGDqBChQp8+OGHFrfVuHFjtm7danGetemKoijK3U0ly3eRLl37MmrkanYeSODYWW9+eq5DcYekKPecc+fOsWLFCvbu3XtL65vNZj777DP69u2Lh4dHIUenKIqi3GtUsnwXqVWrFnMW/M6pU6eoXr06vr6+xR2SotxT3nvvPaZPn84777xDuXLlCrx+YmIiAQEBlClTRpVBKYqiKIBKlu86QUFBBAUFFXcYinJP+uCDD/jggw9ueX1XV1cSEhKszs8+Ip+iKIryYFAP+CmKoiiKoiiKFSpZVhRFURRFURQrVLKsKIqiKIqiKFaoZFlRFEVRFEVRrFDJsqIoiqIoiqJYoZJlRVEUXWhoKMHBwcUdhqIoinIXUcnyfUhKiZSyuMNQFKuklBw+fJgtW7Zw8ODBIrlehRCcOnUqx7Tx48czcODAQt+XoiiKcv9SyfJ95NKlS0yZ9B6tWlTj+LFDdO7YhF9+/pHU1NTiDk1RsuzYsYNenTow9fWXWPfZeCa8MIxenTqwY8eOYo0rIyOjWPevKIqi3J1UsnyfOH36NMMGdcLXsIAVXzlSpYIdU19P4PC/Uxjx4mCVMCt3hR07djDh1Rd5o6Yfiwc8zLQnGrJ0YCveqOnHhFdfvKMJc2bJxccff0xgYCDDhg3LmjdlyhT8/PwoW7Ys8+fPz5q+fv166tati4eHB6VLl2b8+PFZ886dO4cQgtmzZxMSEoKfnx+TJ0++Y8ejKIqiFI07miwLIYxCiL1CiHX6zz5CiN+FECf1d+87Gc/9ZPLEN3ixbwLP9ffGz0cbmLFKBSc+ecsbD+NuFsyfW8wRKg86KSWfTRrPxLbVaVI+CCEEoJVLNCkfxMS21Zk+ecIdLSG6evUqUVFRnD9/nu+//z5rWkREBJcuXWL27NkMHz6ckydPAtoIf3PmzCEmJob169czY8YMVq1alWOb27dv5/jx4/z5559MnDiRo0eP3rHjURRFUQrfnW5ZfgXI/pfjbeBPKWVF4E/9Z6WATp8+zdVLh+j8iGeeeQaD4Jnezqxc/lMxRKYoNxw6dAhDQjSNy5W0OL9xuZIQH8Xhw4fvWEwGg4EJEybg6OiIs7Nz1vQPPvgAR0dHWrVqRadOnVixYgUArVu3pmbNmhgMBmrVqkW/fv3YunVrjm2+//77ODs7U7t2bWrXrs3+/fvv2PEoiqIohe+OJctCiGCgE/BjtslPArP1f88Gut6peO4n58+fp2oFI0ajsDi/SgUnrl69gslkusORKcoNERERhHi5ZrUo5yaEIMTTlfDw8ELZn9FoJD09Pce09PR07O3ts3729/fHyckpxzLe3t64urpm/VymTBmuXr0KwL///kubNm3w9/fH09OTmTNnEhERkWP9wMDArH+7uLiQkJBQKMejKIqiFA+7O7ivz4HRgHu2aQFSyisAUsorQogSllYUQgwHhgMEBAQQGhpatJHmIyEhoVj3b0lKSgo1Gr9M6Ikb330SUvwIPfE0ABkZkqeHS7Zt21ZcIebrbjynlqg4c/L09CQ+Pt7m5Z2dnTkbFU+GKQMhBFKCyXzjC5yUknPR8bi4uBRou9aULl2aI0eO5OgK7sSJEzz00EPEx8eTlJQEkGNfSUlJREdHc/Xq1ayE+cyZM1SpUoX4+Hj69u3L8OHDWbJkCU5OTrz11ltERkYSHx+flRTHx8djZ6f912oymUhJSSmU47FF5v7uhetUURTlXnFHkmUhxBPAdSnlf0KI1gVdX0r5PfA9QIMGDWTr1gXeRKEJDQ2lOPdviclkonPHJnz6RhLVKmq3kkNPPE3rSlrpxYx5UUQzgNbPvVScYVp1N55TS1ScOR09ehR3d/ebL6hr3LgxwsOXPeev06R8ECazCaPBmDV/55nLGDz9aNSokdXW54Lo27cvn332GY0bNyYoKIjNmzezceNG/vnnH9zd3XFxcUEIkeMYXFxcAPj000+ZMmUK//77Lxs3bmTMmDG4u7uTmJhIUFAQ/v7+7Nq1i2XLlvHoo4/i7u6Om5sbAO7u7lnJstFoxMnJqUDn6XbEx8fj5ORE3bp178j+FEVRHgR3qgyjOdBFCHEOWAS0FULMA64JIUoC6O/X71A89xWj0cjLr4xn9CcpHDmZnDXdZJKs2hTLys0eDBn2fDFGqChamcWoseMZt/kwO89cznqQT0rJzjOXGbf5MK+9+36hJMoA48aNo1mzZrRo0QJvb29Gjx7N/PnzqVGjRr7rBQYG4u3tTVBQEAMGDGDmzJlUqlQJgG+//ZZx48bh7u7OxIkT6d27d6HEqiiKoty9xJ0evEJvWX5DSvmEEGIqECml/EgI8TbgI6Ucnd/6DRo0kHv27LkDkVp2N7cubvh1PV9/MQEf91g6dBnJgjmfUzK4Ju+O+5Ty5csXd3hW3c3nNDsVZ05Hjx6latWqBV5vx44dTJ88AXNsBGW93bkQmwjuPrz27vs0b968CCK9ffHx8Xesdfh2xMfHExYWZvFzEUL8J6VsUAxhKYqi3NPuZM2yJR8BS4QQTwMXgF7FHM897fGOnXi0w2Ps37+fS5cu8dV3G6lQoUJxh6UoOTRv3pxm639j165dJCUl4e/vT/Xq1QutRVlRFEVRCtMdT5allKFAqP7vSOCROx3D/cxoNFKvXj3i4uJUoqzctYQQVKtW7Z5orVUURVEebGoEP0VRFEVRFEWxQiXLiqIoiqIoimKFSpYVRVEURVEUxQqVLCuKoiiKoiiKFSpZVhRFURRFURQrVLKsKIqiKIqiKFaoZFlRFCWX1q1b8+OPPxba9saPH8/AgQMLbXuKoijKnaOSZUVR7jgpJYcPH2bLli0cPHiQohpJdPv27TRr1gxPT098fHxo3rw5u3fvLpJ9KYqiKPen4h7BT1GUB8yOHTuYPHUa8WkmfIJCiL5yETd7A++++XqhDncdFxfHE088wYwZM+jduzdpaWls27YNR0fHQtuHLTIyMu7o/hRFUZTCpVqWFUW5Y3bs2MFrY8ZRv+8LDJn6E93e+IBhn/5C/b4v8NqYcezYsaPQ9nXixAkA+vXrh9FoxNnZmUcffZRatWrlKYs4d+4cQogcie3p06dp1KgRnp6e9O3bl6ioKABCQ0MJDg7Osa+yZcvyxx9/AFrJRc+ePRk4cCAeHh7MmjULgJSUFPr06YO7uzv16tVj//79Wet/9NFHVKhQAXd3d6pVq8bKlSuz5s2aNYsWLVrwxhtv4O3tTbly5diwYUOhnSdFURQlfypZVhTljpBSMvmTT+nwwttUqNMQIQSgDX1doU5DOrzwNpOnTiu0koxKlSphNBoZMmQIGzZsIDo6ukDrz5kzh59//pnLly9jZ2fHyJEjbV539erV9OzZk5iYGAYMGJA1rVevXkRFRdG/f3+6du1Keno6ABUqVGDbtm3Exsby/vvvM3DgQK5cuZK1vX///ZfKlSsTERHB6NGjefrpp4usdEVRFEXJSSXLiqLcEYcOHSLRBOVrN7A4v3ztBiSkmzl8+HCh7M/Dw4Pt27cjhODZZ5/F39+fLl26cO3aNZvWHzRoEDVq1MDV1ZWxY8eyZMkSTCaTTes2bdqUrl27YjAYcHZ2BqB+/fr07NkTe3t7Ro0aRUpKCjt37gSgV69eBAUFYTAY6NOnDxUrVmTXrl1Z2ytTpgzPPvtsVvJ/5coVm49DURRFuT0qWVYU5Y6IiIjAKzA4q0U5NyEE3iWDCQ8PL7R9Vq1alVmzZhEWFsahQ4e4fPkyr776qk3rli5dOse/09PTiYiIKPC6lqYZDAaCg4O5fPkyoLVi16lTBy8vL7y8vDh06FCOfQUGBmb928XFBYCEhASbYlEURVFuj0qWFUW5I/z8/Ii+ctFq+YCUkugrYfj7+xfJ/qtUqcLQoUM5dOgQrq6uJCUlZc27evVqnuUvXryY9e+wsDDs7e3x8/PLs67JZMqT4Fv6QpB9e2azmbCwMIKCgjh//jzPPvssX3/9NZGRkcTExFCjRg1VZqEoinKXUMmyoih3RI0aNXCzE5zZv8fi/DP79+Bmb6B69eqFsr9jx44xbdo0wsLCAC1ZXbhwIU2aNKFOnTr89ddfXLhwgdjYWD788MM868+bN48jR46QlJTE5MmT6dmzJ0ajkUqVKpGSksL69etJT09n0qRJpKam3jSe//77jxUrVpCRkcHnn3+Oo6MjTZo0ITExESFE1peEX375hUOHDhXKOVAURVFun0qWFUW5I4QQvDv6DX6b8RGn9+3OajmVUnJ6325+m/ER7775utUyjYJyd3fn33//pXHjxri6utKkSRNq1KjBtGnTaN++PX369KFWrVrUr1+fJ554Is/6gwYNYujQoQQGBpKSksKXX34JgKenJ99++y3PPPMMpUqVwtXVNU/vGJY8+eSTLF68GG9vb+bOncuKFSuwt7enWrVqvP766zRt2pSAgAAOHjxYqF3oKYqiKLdH3Gu3+ho0aCD37LHcMnUnhIaG0rp162Lbv63ulTjh3olVxZnT0aNHqVq1aoHXy+xnOS41A99SIURfCSuSfpYLU3x8PO7u7sUdxk3Fx8cTFhZm8XMRQvwnpbT8dKWiKIpilRqURFGUO6p58+asb9aMXbt2kZSUhL+/P9WrVy+0FmVFURRFKUw2JctCiNJAbcALiAH2Sykv5reOohSGjIwMpJTY29sXdyhKIRJCUK1atXuitVZRFEV5sFlNloUQ9sBz+qs8cAqIB9yBh4QQZ4GZwPdSyrQ7EGuRMpvNWtdWXl44ODgUdzgPtLS0NDZs2MDcRUs4dvw4CEGpoCAG9u5Jt27dcHNzK+4QFUVRFEV5QOTXsrwf2IyWLP8rpczqjV8IYQQaAQOAvUDhPL5eTJKSkhjx4iAuntuPvaMvX89YSPny5Ys7rAdSYmIiz730MpEZBup1GUzH2tpIb2EnDrNi40oWLF3Oz9/NoGTJksUdqqIoiqIoD4D8esNoLaUcIaX8O3uiDCClNEkp/5FSjgBaF2mEd8Bvv/2Gp90+fvvZk76PhvPLT18Vd0gPrLffHUu6TzC9xnxCxbqNMRgMCCEoXbkGT77yHmVaduS5ESNtHklNURRFURTldlhNlqWU123ZgJSy8IbbKibXrl3j331RvPDuaRavj+DS5euYzebiDuuBc+bMGf7dd5D2w17GYLB8aTZ5ojfJRie2bdt2h6NTFEVRFOVBdNN+loUQZYQQ/tl+7iyEWCOE+EEIEZjfunc7KSVffjGV1Us/pUd7M20aJtKldQYpMf/w0vODSE5OLu4QHyir166laqvHsLvJw3w12j7B0pWr7kxQiqIoiqI80GwZlGQpUA60xBmYB/wNOACziiyyO2DDhg38vXkmiz935/XnKtKnW22eG1yNedO88Xf+h88+nVTcIT5Qwi5fwbdUmZsu5xdchstXr92BiBRFURRFedBZTZaFEC2FEK2ASoC7EKIlMAL4H1qyPBtooC/X8o5EW8gWzP2KkYPt8XA3ApDZzavBIBj1tAd/bFpGbGxsMUb4YHFxdiY1KfGmy6UmJeLk5HQHIlIU2wghOHXqVHGHoSiKohSB/FqWywFlAaG/lwPaAEf0n0sD5mzz7impqamcPnWMJnVdLc738rCjagUDR44csWl7GRkZTJk0li4dGzNu7OukpqYWZrgPhDYtH+bkzi03Xe7Y35tp1+rhOxCRUlSklBw+fJgtW7Zw8OBBimIk0bJly+Ls7IybmxuBgYEMHTqUhISEQt+PoiiKcn/L7wG/2VLK2cAeoAaQjpYUT5ZSzgGWA3FSyjn6cvcUIQRSQn6dKqSnS4xGo03bW7FiBRePL+Drd5NJuLaC+fPmFFKkD45WrVqRHnWN0/t2W10mPOw85/7bQbeuXQtln0lJSfz555+sXLmSv/76i7S0e77L8Lvejh076NW9DVM/GMi6RS8w4Z3u9Orehh07dhT6vtauXUtCQgL79u1j7969fPjhhwXeRkZGRqHHpSiKotw7bKlZfgGoCrwBvCqlvKxP7w4sLqrAipqDgwN16jZmyz/xFudfC0/n9EUjNWvWtGl70VGRVCxjJqSUA1XLQ2SEqqktKKPRyLSPprBpxocc3PYH5mzfZKSUnDnwH8s/HM24t97Ax8fntvaVnp7OZ59/QZsOHZk+dykLt+7mw5mzaPNYR37+ZZbqDaWI7Nixgwljh/LG4Kss/sKdae+4sfQrD94YfJUJY4cWScIMEBgYSIcOHdi3bx+hoaEEBwfnmF+2bFn++OMPAMaPH0/Pnj0ZOHAgHh4ezJo1iz179tC0aVO8vLwoWbIkI0aMUF+s7iJCiAFCiE23uG6oEOKZQo5ngxBiSGFus7gIIVoLIcKKOw5bCCFChBAJ+lgQ1paRQoiHbnH7Nz0XQojDQojWt7J95dbZ8tnfjpsOdy2lPAE8ZmH6Pd90OnjYSCa/P5jqldIoFXhj1L7kFDMTvoqnR+8RODs727Stzl2e5OmhP3PgZDyXw72Y+WP/PMtIKYmJiQHAy8sLkVkkrWSpV68eP379BVOmTmPHoh8IqdkAg9HI5eMHcbc38NF779C6devb2ofJZOK1N9/kXFwGAz76AQ/frM5eiLh0gQXfTeXSlSuMfedt9RkVIikln00dw8SRRprUdcNk0r6QCCFoUteNiSMT+PTTd2nWbEuhn/ewsDA2bNhA27ZtbVp+9erVLF26lDlz5pCamsru3buZPn06DRo0ICwsjMcff5xvv/2WV199tVDjvFsIIc4BzkB5KWWiPu0ZYKCUsrUN64cC86SUPxZiTB2Ad4G6QApaSeA0KeUaKeV8YH5h7et2SSkfL+4YioMQYijwjJSyRXHsX0p5Acga4rUorkMbYrinB2nLjxCiLHAWsJdS3lW33Ir6s89vuGsHW4axFkI4SinvyQLdZs2a8dRzHzLwzbG0bZREtYckVyMka7cYaNayD8+/8AqgDYW9fft2/grdSIWK1Vm/fj3t27fPMSx2UFAQi5f9yenTpylXrhxeXl5Z86SUrF2zhnlzviTi+nmklAQGVWDQkFfo2KnTnT7su17NmjVZOGcWJ06c4NChQ5jNZsoP6ErdunULJYnasGEDxy5H0m/cdIx2OX8F/EqF0Ovtj5g75nk6dvgf9evXv+39KZpDhw5hMF2hcR13i/Mb13GFny5z+PBhatSoUSj77Nq1K0IIEhISaNu2LRMmTGDfvn03Xa9p06Z01Ut9nJ2dqVu3Lu7uWtxly5blueeeY+vWrfdtsqyzA14BphR3IEKInsDPwCigMxAPPAwMBNbcZF27u+0Pe3GxdC7U+SkcRXkeH9TP6G467vzKMA4IIUYLIYIszRRClBRCjEYb7vqe1aNnL5av/ocytcZyLKIPeL/Ctz9uYtz4DzEajcTFxfHUkB78+NVTVPRZhLNdNBuXvUqv7o8QFpbzboyHhwd169bNkSgDfP3VNBbNGsXoIZf5c44nf87x4rX+F5n13Ui+m6FGC7SmUqVKdO/enZ49e1KvXr1Ca22cs3Axjbr0y5MoZ3J0caX2Y92Zv+ierTK6K0VERBASZLD6OQohCAkyEB5eeOMcrVq1ivj4eEJDQzl27BgRERE2rVe6dOkcP588eZInnniCwMBAPDw8GDNmjM3buodNBd4QQnhZmimEaCaE2C2EiNXfm+nTJ6Mlsl/rt0W/1qdXEUL8LoSIEkIcF0L0tiUIoV0wnwEfSCl/lFLGSinNUsqtUspn9WWGCiG2Z1tHCiFeEkKcBE7q054UQuwTQsQJIU4LIfLcMdWXe0oIcVQIES2E+E3vMtXSck5CiHlCiEghRIx+DgL0eVmlHUKI/fp5yHzJzNv0QogmQoi/9fX353f7XghRWgixQggRru8z87wahBBjhRDnhRDXhRBzhBCe+ryy+v6eFkJcADbr52qHEGK6ECIKGC+EcBRCfCqEuCCEuCaEmCmEsHhbVQjxtn7+4oUQR4QQ3fTpVYGZQFP9OGP06Va3LYTwE0Ks048/SgixTQiRJy8RQkwQQnyl/9teCJEohPhE/9lZCJEihPDOdrx21q5DXTshxEn9M/5GWPlPSd/2LH25I0DDXPPPCSHeEkIcABL1/Z4TQrQTQgQJIZKFED7Zlq8rhIgQQtjrP1u91ixdwxbia5Ht+rkotJZ9hBCe+nUQrl8XYzPPqxBivBBiXrZtZJ0z/edQIcQH+jUSL4TYJITw0xf/S3+P0c9pUwsxjRdCLBNCLNbX/58Qona2+UFCiOV6bGeFECMtrDtPCBEHDLXymUzTjytWCLFdn5bvZ69/ztNybWutEOJVS+c2t/yS5RZACWC/EOKEvtEF+vtxYB/gC9yT3cZl5+Pjw+AhQ3n3vSm8NOIVypcvnzVvygdjqBa8n9mfeNLnCR+8PI189b4n/R+7wujXn73pU/ynTp1i3cqZzJjgQYNargghMBgEjeq4MnOiO8sWf8mFCxeK+hAL5MqVK8yZM4cZ337Dli1b7quhpdPT0zly7BgP1WuS73KVG7Zg1//u6e+Bdx0/Pz/OXzJZ/Z2RUnLhshl/f3+L829Hq1atGDp0KG+88Qaurq4kJSVlzTOZTHkS9Nx/O0eNGkWVKlU4efIkcXFxTJkypUh68LjL7AFC0Z5XyUFPANYDX6L9HfgMWC+E8JVSvgtsA0ZIKd2klCOEEK7A78ACtL8r/YBvhRDV9e311xMOSyqj9b60rIDxdwUaA9WEEI2AOcCbgBfa361zFo6rKzAG7Zkcf/04FlrZ/hDAU4/NF3geyDOSlZSytn4e3NBaxo8D/xNClEI7h5MAH7TzvFxkGwQsW1xGYB1wHq0HqlLAIn32UP3VBiiPdiv661ybaIX27FEH/efGwBm0z2Iy8DFaN7F1gIf07Y+zctyn0RIRT2ACME8IUVJKeVQ/B//ox+ulL5/ftl8HwtDOdQDaubf0i7UVaK3/uyFwVT8mgKbAcSlldPYVLF2H2WY/oW+nNtA723nJ7X2ggv7qgPaZ59YP6AR4ZW8F1Z/v+gfokW3Z/sAyKWW6jddaV/RrOPdOhRAhwAbgK339Omh5Gfo0T7TroRUwGBhm5Rgt6a8vXwJtTI3M/wMy8z0v/Zz+Y2X9J9HG6PBB+51fpX/JMQBrgf1o18EjwKtCK7HKvu4ytN9TS6VVnwL1gWb69kej9cyWxcpnPxvol+1Lg5++f2u/3znk1xtGhJTyDf2Ankb7UA4BvwJPAcFSyreklPdt88r169fZtfM3RgzOW1/cu5MnGcmn2bs3/4Rq9coldG8v8fTIW3Pu42VH59ZmVq8q6N+AoiGl5KsvP2VA74e5dHg8xphPmP/Dc3Tr0orTp08Xd3iFwmw2YxDWWzczGYx299WXhLtBjRo1kHZB/LvPcl/a/+5LBPsgqlcvmpK/V199ld9//52kpCRSUlJYv3496enpTJo06aZdPcbHx+Ph4YGbmxvHjh1jxowZRRLjXWgc8LKFBK4TcFJKOVdKmSGlXAgcQyuRsOQJ4JyU8hd9+f+h9ajUE0BKuUBKWcvKur76+5UCxv6hlDJKSpmM9jfsZynl73qr9CUp5TEL6zynr3dUT3ymAHWE5dbldD22h6SUJinlf1LKOGvBCCFaoCXGXfTlBgK/Sil/1WP6He0LSkcLqzcCgoA3pZSJUsoUKWVmS/oA4DMp5RkpZQLwDtA3s6VQN15fLzOZvyyl/Eo/xhTgWeA1/XzF68fd19JxSCmXSikv6zEvRmv1bGTlmMVNtp0OlATKSCnTpZTbpOVvof8AFYUQmQ10PwGlhBBuaMngVkv7z8dHUsoYvc51C1qiaUlvtB7AoqSUF9G+HOb2pZTyYrZzm90CtGQ681z01aeBbdda9ms4twHAH1LKhfq5i5RS7tO/WPUB3pFSxkspzwHTgEFWz0Zev0gpT+j7XYL182PNf1LKZVLKdLQv0k5AE7QvKP5SyolSyjQp5RngB3Jea/9IKVfp11eO49YT3aeAV/TfYZOU8m9bSoGllLuAWLQEGX2foVJKm3pjuGlvGPoBbZNSfiulnCKlnCGl3KGfBJsI7XbVLqHdZjoshJigTx8vhLgktFtj+4QQlv6TKDanT5+mSgUjLs55T5MQggY1TBw/fjzfbVy5fIaKZa0/nFmpnIErl87cdqyFYfmypfy7dQarvnXnnRd8GN7fnx+nePB8z2uMfGkAKSkpxR3ibXNwcKBkyUDCTubff/a5w3upUqniHYrqwSCEYNSbUxj3pYmdexOyWmallOzcm8C4L0289sbkInuo0t/fn8GDB/Pll1/y7bff8swzz1CqVClcXV3z9I6R2+TJk1mwYAHu7u48++yz9OnTp0hivNtIKQ+htWi+nWtWEForZ3bn0RpXLCkDNNZvF8cI7Rb9ACDQhjAi9feSNgV9w8Vs/y6N1iJ6M2WAL7LFGIU21oCl45oL/AYsEkJcFkJ8IvTb67kJIUqjJR1DpPbQfOa+euU6Jy2wfJylgfPScv1m7s/iPFq9eUC2aRfJKfvP/oAL8F+2ODbq0y0dy2D973XmsjUAP0vL2rDtqcApYJMQ4owQIvd1BoCeNO1BS4xboiXHfwPNubVk+Wq2fyeR7cGwXILIea5yX/OQ99xmtwytLCUILW6J1uIJtl1r+W3b2jXth9YanPuasPa7aYmt58earLillGa0uwdBaMcclOuaH0P+12p2fmiJ96223s1G+5KK/j7X1hVv2htGIUkF2kopE/T/TLYLITbo86ZLKT+9Q3EUiJubGxFRZqSUFv+AR0QbqeZu+WGlTD6+JQm7Yr0+PeyKGR/fgv4NKHxms5l5c75g0ginrBENM3Vs48mmHZFs2rSJLl26FFOEhUMIwcDePVm2finBFatZ/FzNZjP7Nq7gnecLctdKsUXz5s15f9IsPv30XcxpYZQtZceFy2awD+L9SZNp3rx5oe3r3LlzeaZlbxEeOnRo1r/feONGpcH48eMtxn3sWM6GyIkTJ2b9+z4vyXgfbeTW7PV+l9H+8GUXgpYIQd5b6ReBrVLK9rew/+P6+j3QbsHaKnsMF9Fupd/MRbSWxJv2rKE3GE0AJgitl4Bf9Vh/yr6c0OpzVwGfSyk3ZJt1EZgr9bprG+IKEZYfeMr9WYQAGcA1IPNbYO7PI/vPEWjlI9WllJfyC0Jv9fwBrXXuHymlSQixDy3Js7SffLettzS/Dryul+RsEULsllL+aWH3W4G2aL2h7NZ/7oDWqv2XheUtxVNQV9CS0sP6zyEF2YeUMkZoXRr2RiuDWZit5dyWay2/+C9iuUU/Aq3FvgxajzGZcWee/0S0LzCZbPnCaks82WU99KG3BgejXacZwFkpZX4tUfntIwLtTkgFtFKO/FjazjzgkNBqqKui/V7axJZ+lm+b1GQOnWWvv+76vy7Vq1cnxeTH/w4l5Zl35Xo6uw8ZaNWqlYU1b+jUuScr/jCSlpa3396UVDOrNgs6de5eaDHfqujoaBLirlG9kuVhpFs2MLN/7993OKr8paSksHbtWj7/4gtWrlyZow41Pz169EBEXSF00Y+Ycg04kZ6WyvoZH1PGx/22u6hTLGvevDlLV2zhrXHz6dxvBuM/WsHSFVsKNVFWCo+U8hRan/ojs03+Faik1xrbCSH6oNVVrtPnX0Orl8y0Tl9+kF67aC+EaCi0h8Jutn+JVuv7nhBimBDCQ2gPtbUQQnxv42H8BAwTQjyir1tKCFHFwnIzgXey1VJ7CiF6WdqgEKKNEKKmfts7Di1BsVS79TNwTEr5Sa7p84DOQogOQgijfge2tRDC0m2OXWiJ20dCCFd92cxfmIXAa0KIcnpZwhRgsZVW6Dz0lr8fgOlCiBL6sZXKVUeayRXtb3e4vtwwtJblTNeAYCGEgy3bFkI8IYR4SC9RiEM7f9bq37ai1d4ekVpPXaHAM2jJl7WngnNfhwW1BO168NY/l5dvYRsL0OLuwY0SDCjAtWbFfLQHFXvrv4O+Qog6UkqTHvdkIYS7/gVnFNr1Blpdc0uh9UvsiVa2Y6twtPrgm53T+kKI7kIrBXoVrcF0J9p1HCe0hyKd9eu+hhCiYX4by6RfTz8DnwntQUGjEKKpEMLRwuJ5PnspZRjaF625wHIr5S0W3amW5cwHFP5DK/D/Rkr5rxDicWCEEGIw2i2W12WuIn193eHAcICAgABCQ0PvVNgMf2EM/7t8jktp4OFmJCHVj/X7hnE1QvLyayX577//brqNXgPHsfifWAL9DNjba1/A09MlV8LNDBjqzbVr17h2rXAHMUlISCjQeTKbzfTq9yqhJ4xYugsuvU1U8vItknNf0FgB0tLSOHX6NFIYcHB25fK168yaNZvy5cvh5GQ54c/u5eeHE3bpEsl/r8To5qHVKGekkZEQT7cmtSgVFMS2bdtyrHMrcRaHOxWnp6cn8fGWB/WxReXKlbNGyLzbh6E2mUy3dax3islkIiUlpSg+/4lkq3mUUkYKIZ4AvgBmoN1Kf0LeeIblC2C2EOIFtNbTkUKIR9HqFz9Da6jZj/ZHHCHEAGCMtNJHrZRymRAiAa2f5a/QWisPo93Gvykp5S49sZuONhLtNeAltDrr7Mut1BPORXqSEYv2YOJSC5sNREt4goEEtC8U8yws1xdI1uPP9LiUcpsQ4kngE7SE14SWTLxgIX6TEKIzWs3sBbSEdQGwAy15CEJrXXVCKw0paFL3Flp9+k6hPfh0Ce1z/S1XHEeE1qPAP2hJ0xw9hkyb0T6Xq0IIs5TS7ybbroj2MKI/EA18K6UMtRLj32h9f2e2Ih9Ba2W01qoMFq7Dm5yH3CagfcZn0VpGf0HrTrEg1gA/AheklFmtoQW81vKQUl4QWunqp/r2Y4GxaMnwy2i/J2fQztEPaNcJUsrfhRCLgQNoLbUfAzbdMpZSJgmtp4kdepXAY1LKnRYWXY1WNz0b7f+G7pmlu/p1PA3tnDqi3Y0Za8v+dW8AH6IlvW5o/49Y+mJn7bOfjZYsv6LHMwZ4WN6kb3Rxp28fCq0bopVoH2Y42oclgQ+AklLKp/Jbv0GDBnLPnj1FHWYOu3fv5oeZn3D0yP8YOOQ1QjevYehTo+jwWM6eh44dO8bZs2cpXbo01atXz7rFn5GRwffffc3yJT8RVCIdKeFqhAO9+w7nmeEvYjAUfgN/aGhogVtGXxjej85N99CxjWeO6SaTpO+rcbz1/kIaNGhQiFFqChJreno6M7//nqmff0VAtbpUa9kBR1c30pKTOL7jTy78729efekFRo54CUdHS182czp58iS/bdpEbHwC/r4+dHz8cav1q7dyTovDnYrz6NGjVK1604ZBq+Lj47P6Lr7b3SuxxsfHExYWZvFzEUL8J6Us/F9gRVEUnRBiPNpDrwNvtmxxEEK0RPtSW1ZvqbaJTS3LehP3OLSnOn2llJ56K0ElKWXuLmrypdfwhKJ9I8mqPxNC/MCNW3h3lYYNG9Kw4VISEhLYtWsXw597KUeta0JCAqNff56L5/6lZkUDR8+Y8favxbTPf8Tb2xs7OztefOlVnnr6eU6cOIEQgkqVKtmUzN1JL4x4h9dH9sbJMY7WTdwxGAQRURl89nMcJcu0KvYBOtLS0nhp5KtcSjHT6a1PqFC/KdmbwWu06cjZ/bv4a/tv7Bv+PD/M+AYXF5d8tggVK1akYkX1IJ+iKIqi3M/01vBXgB8LkiiD7TXL09HqkgZwo9b4MBZuF1kJ0F9vUc582KEdcEwIkf3Jtm5oXdPdtdzc3DAY8nY79slH71PK429Wz/BgyhvuLP/ag9pl9/LBhDdzLOfk5EStWrWoWbPmXZcoA9SqVYuPps3nl3UP0enZOPqPSqDXK6l4BQ9h6rSZRdZLga3Gvj+BCOFM11ETcPcPwFK9iLuPPx1fepsM32Bef+ud+/3Bq2JnNhfo/xuliKnPQ1EUJS/9+YgYtN5mPi/o+rbWLHdDa1ZPFEKYAaSUl4TWqbotSqLVjhjREvQlUsp1Qoi5Qog6aAn4ObR+B+8pSUlJhG5ew7rvPDEYtOTNYBA839+Lx58JJSIiAj8/a73q3H3q16/PnPnruXjxIklJSZQuXRpXV9fiDoszZ86wdecunv1iPnYO9jjY25GSlICTy40ebdJSkjEI7UtJh6df5efXh3Lw4EFq1bLWfatyO1xdXbl06RIBAQHY29sX+5epB5mUkvT0dK5du3ZX/L4qivJgklKOL+4YLJHaoDm3/J+jrclyWu5lhdZJfaTlxXOSUh5A6+4l9/SCdJJ9V4qPj8fFiTzdrTk6GvD1NhITE1OoyXJUVBS7d+/GwcGBpk2b2vQwW0EJIQgJsdRDTvFZtGQp1dp0xM7BAYCSgYFcCAsjwyMNBydn0lNTSIqNIjgoCIFAGI1Ub9eZ+YuWqGS5iAQHBxMREcH58+fJyLDpwfscUlJSiuT6LQr3Qqx2dnZERETQpEn+o1MqiqIoBWNrsrwUrWX4NQC9fOJzbgy3+cDy9/fHzsGHIycTqFbROWv6hUtpxMQ7FmrSOWfWT/z848c0rCFITIZJExyZMOlbWrRoUWj7uFut+XUD/SZ/l/Wzq4sr5cuWJTIykpSYSBwdHCgXEoKT043PoE7rx/j+pT5MMU3I6nXhXhYeHs62bdsIDg6mUSOLA2bdUQaDgRIlSlCiRIlbWj80NJS6dfN8h74r3SuxXrp0qUgeGFYURXmQ2Zosj0Hr3uYgWmfWJ9G6IplQRHHdMwwGA8+/9C5vfzqKN5/OoE41F46cTGHqT2k8PXwCDnpL6O3auXMnyxd9yJLPXSnhpw0SdfBYMq+MeZYlK7bdU6UeBZWRkUFCQgIefjmTMkcHR4JKBlldz9nNA6O9A/Hx8Xh5eRVxlEUrKiqKXgMG4VOxJtdOHeXlpwbR9wEZRU5RFEVRitNNmyD0OuOxwFtSSje0YQndpZSv6R2DP/Ce6NyFV9/6np/WVqLT8GS+WlKWp1/8ir79Cq/nlFXL5zC0m8xKlAFqVnHmkcZpbPh1faHt526U2VJ2Kw/rmc3m+6JVec+ePXiUfoguL7/LI0+/xpoNv918JUVRFEVRbttNW5b1ztBfAsbrP1sbKeeBFhwcTLnyVQEzpUMqEVIm90iwtycq6iqlAvK2UgeVMBEdbVPp+D3LYDAQGBjItfOnKVnO9m7erl44S1xMDB98+BFCCAJL+PNEx473ZFdxZcuW5drpoxzasZmTu7dT7yFbRu4tGCklJ06cIDIyEpPJhJeXF9WqVbsvvmwoiqIoyq2ytQxjNvA88G0RxnLP+vfffxn71hAGdU6nW39Hjpw6xqiX1/P22Jm0adu2UPZRu+7DbN21n0Z1bjzMKaXkrz32DHmhXqHs427Wp1tX/vxjLSWfHZU1zZSRQeSVMFKTE3F0csEnsBR2Dg4kJCRwPTyc7Utn41P2IdJDaiGlZN/VMBY/P4IKpUvx8vPD76kHoSpVqsSH495l2ao1NH+oNCNetKnXRpskJCSwbt065ixaQlxKOt6BQQhhID4qApkcT78e3enRo/t9XeqjKIqiKNbYmiw3Al4WQowGLnKjr2WklC2LIrB7hZSSz6aO4f2XoEVDbwBqVXWhaoUkxn46hlat/y6UB2569xnIkIEL8VoYwZPt3UlKMfPLskRwrMPDDz9829u/23Xv3o3vnuxOXI/BICX//b6OPX+sw87JGUdnV9KSk0hNjKdyk1YE1WqET1BpLh/dx7NTvsa35I0R+Vr2Gsrx3Tt4dcw4Rr3wLL179SrGoyqYtm3b0raQvnxlOnDgAC+99jr+lWrRaNArlKlWK0cXcFfPnmLLH2v4qVtPprw/lnbt2hXq/hVFURTlbmdrsvyD/lJyiYiIIDL8PM0b5BwiunY1F4wyinPnzlG+fPnb3o+/vz8//LyS72dOp9/rv+Ho5ESHxwczevKLD8Rtcl9fX154aihfvfkMSWkZVGr+CF3e/hjf4LJZy0ReOs/+TWv47Zsp2Nk70KDt4zkSZQCjnR3VmraiZIVKfDbhVby9vGjfvv0dPpq7w4EDBxj+8qs88txoKtaz3MoeWO4hHnt2FFfadebdKe9iMpno0KHDHY5UURRFUYqPTcmylHJ2UQdyr3J0dCQ9A9LSJI6ON1rkzGZJUoq5UPtmLVWqFBM++BT49KbL3o8qV6pIcmICLZ99kzK1G+Ls6p5jvqtvAI16DiG4Rl02fT2ZgDLW63q9S5Sk0yvjmDz1A9q2bftAfOHILj4+npdee51Hnn+LinUb33T5kuUq0v2tj3h/yptUqlSJcuXK3YEoFUVRFKX42VwfIIQIEEJ0FkIME0I8lfkqyuDuBR4eHtSu05yFa2NzTF/5WyzBITUJCrLetZliu+TkZEaPHcfAiV9Qp3EzTInxhF84Q2z4VeIirxN97TJRl86TGh9LSLU69Jn4Net/+oKk+Fir2yxdqTr2Xv5s27btDh7J3WHdunX4VaplU6KcKaBMeaq168yCRYuLMDJFURRFubvYlCwLIboCp4GJwHfAy/r7PT8CX2F4Z+xHrNpaihHj4/hhYTijpsTy82o/xk2YXtyh3Tc2btyIb4VqlK5UHTc3d8qWKUOFcmXxdXfF09kRc1oKHr7++AaVxsnVDf8yFShTpzF7N2/Id7s1HunMwqXL79BR3B2klMxZtIQ67Z8s8Lp12nZiza8bSUpKKoLIFEVRFOXuY2vL8iRgmJSyLpCovw8H/iuyyO4hJUuWZMnyP+jU5wvSPUbTpvNnLF8VStmyZYs7tPvGvMVLqd2uS45pDg6OeHl54+vjC4CzW86yjJrturDrt9X59s9c6qEqnL1wofADvosdP36chDQTIVVrFnhdD19/AirVeCBb4xVFUZQHk60P+IVIKZfmmjYbuAq8Ubgh3ZscHBx4/PHHizuM+5KUkuMnTtCxZr1s08zExcWRlJyMyWQmLTWN5IQEXD09EUL7Dhj4UFUSYqJJS0nG0dnF4rbt7B1IS3uwxtaJiIjAKyAoR68XBeFeoiQRERGFHJWiKIqi3J1sTZavCyECpJTXgHNCiKZABPBgPRWlFIv09HQQAoPRiMlsIiIiguiYGIwOTjg4u2JwMODsbSAlOZGEmEic3Txw9/YlIz2NtJRkFk8dh72jE+7evtR6uB2lq9TIShQTY6Px9PAo5iO8s8xmM4bbeaBRCEwmU+EFpCiKoih3sYJ0HdcCWA5MB7YAZmBaEcWlKFkcHBywt7MjITaaa1HRCHtHvEuGYLS/MfS3fUY6Zql1AB53/QpXzpxAGIykp6VRocWjGO3sib12meVff4yDgwMtewygZotHOLLjT9q1frC6Cvfy8iI+6tZbhpNjIvHyqlGIESmKoijK3SvfZFkI8YaU8lMp5cf6z4FSyjlCiFDAVUp59E4EqRSOtLQ0/vjjDxYsXc7Z8+ext7fn4aZNGNC3D1WqVCnu8PLVrHFjQlcvpWaHrrh5+eaZbzTakZGaSkpCPGaTCc/AYM7u2UG5uo2p3OzGQB51O/Ui7PBefv/lS66cOcnxbZv4dNmD1btDtWrVMCXEcP3CWUqEFKwLuOTEeC4c2EOLSe8WUXSKoiiKcne52QN+43L9fARASnlBJcr3ltjYWAYNe4qv5i8nuE13+k7+ji7vfMpVR3+GvjiSH378qbhDzJeXuysHN/+Kq6cPAGaTiYToSKKuXCLi8kWir14mLvwqqUkJeAeF4OTmzqHN66jYpHWO7RgMBkJq1qfn+C/4b8tvBHh7UbJkyWI4ouJjZ2dHvx7d2fv7mgKve2DrJtq0aI6Pj08RRKYoiqIod5+bJcu5nwC6tSeClGIlpeSV19/Evkx1eo/9lCqNmuPu7YtPYCmad+vPwMkzmb18Nb/++mtxh2qRyWRi+649uHl4sGfNQqKvXeH6xbOkp2fg6O6Jq5cvDq5upKem4ubrj9lk4uCfa0lJjMevbEWLvWGYMtJ57IW3uBYVTVxcXDEcVfHq3r0bp3du4eq50zavEx8dyb5flzKof98ijExRFEVR7i43S5ZzZxnW++BS7loHDx7kVNgV2g58zmIPCG7ePjzy9GvM+PHnfLtZKwgpJSdPnuTAgQMkJyff1ra2b9+Og3cJuo54i80/f8G+31biXTIEd/8AHF3dsHd2wWzKwNnDEztHZw78vop/Fv/E46+8j4OLKykJ8XpMZpLiYogMOw9pqdSq35Cy9Zqwdu3awjjke0qJEiX4YOw7rJo6husXz910+fjoSJZ9+BZP9e1FrVq1ij5ARVEURblL3OwBPychxJxsP7vm+hkp5eDCD0spTKvXrqNam04YDNa/G5WtXofNyWkcP378tuuXzWYzb749hn/2HsDFwwuRHMfP382gdOnSt7S948eP4/9QVVZ/O5WWg17k0rGDzH19MNXbdqJ8gxY4urpy9dRxoi+d4+jW33Dx9KbrO5+AlBiMdsRcv0JiTCRmkwk3N1eCgwJxdXEBBHXaP8m8n6cxYMCA2zpma8xmMzt37mTTn5uJjI7Gwd6esiGl6d61K6VKlSqSfdqqQ4cOZGRkMHHS61Rv34W6jzyBu3fOevDkxHgObN3EvvVLGda3J888/cAP2qkoiqI8YG6WLE/O9fOUogpEKTrXIiLwqp1/7wVCCLwCg4iMjLzt/a1evZqDF6/y1LRZ2Dk4sHPdUsZP/pCfZn57S9tLSk7m6tnTeJYMoWnPIQCEnz/Ngd/XsPGLCaQmJRAZdp66nXrTYeR7BJSvDEB8+FXMpgy8PD0JCCiBndEuzxeGUhWrcfHiRUwmE8bb6U4tFykly1esYOZPv2B2dKNSi3Z4lK6FKSOdf88cZ3bfAdSvVZPRo16lQoUKhbbfgurUqRNVq1Zl3oKFzH3zKQKr1MLdPxCDwUhidAQXDuymdfNmzPzsY2rXrl1scSqKoihKcck3WZZSTrhTgShFx9vDk8jomyfBidFRuLu733S5mzl/4QKlazbEzsEBgEoNmrHuj1W3vD03V1fOHTlAl7c/yprmX6YCjzzzGgBJcTHMeX0Y7Z57M8d6zh5eRF2+gKeLDw72Dha3bTAYsHNwJCUlBVdX11uOMTspJR99MpWNf++i/fNjCKpYhfT0dEwmE0IIqjZpRau+T7N3868MfHo4307/lLp16xbKvm9F+fLlGTf2XUa9+grbtm0jMjISs9mMl1c1mn/wDr6+eXsfURRFUZQHha39LCv3sMc7tGfs1C9p+FhXq6O2XTlzAlISqFHj9vvPrVSxImt+mkejx7vh4OTMoW2/U6XSQ1nz09LSWLp0KfOXLicsLIyXhj/L//btY/DAgfj5+eXZnrOzMxiNlKpiuVbWwdmFtJRkbbCNbC3Hdo5OGIxGDFaOOTU5ib1bNnL96hXeGjsODzc3WjRtTLt27XBwsJxc2+Knn39h07//o8/Yz0hOS+fU6dOYJRiMdoAkIz0dVxdnqrVoj0/JYEaMepOFs38mJCTklvdZGNzc3NQolIqiKIqSy80e8FPuA02aNMHNYGLPptUW56enprB59tc8NWhAvnXNtnrsscdoXbc6P4zsz0+vDSZy/9+MH6v1y5uamsrwF0fw06qNeFSuR4Nug3Fw8+D3/Sfp1X8gly5dyrM9Pz8//ILLkJ6aYnF/dvYOBJSryPm9O3POMJuxs3fEPtvgJQAZaWlsnPUNnz3fhwP//EXlVo9zMc2O4wmSr+Yvp81jHfnhx58wm80FPvaEhAS+/2U27Z9/iwtXrhCTlIy7f0n8SpfDJ6g0PkEh+IeURzi5cunadYye/lRq25kffv6lwPvKLjk5melffEHL9h04euw4I0e9zunTtvd0oSiKoiiKZSpZfgAYDAa+/WI6xzcuZcP307h+4SwApowMDv8dyvxxL9OkagX69+tXaPsbN/Zd1i9bzNwZX7J80QJKlCgBwMSJE9n+3z4uXThPdGwckTExmEwmLl04x+WYBIY9/WyeJNVkMuHl5UVirlKSpLgYjm7bxL6NK/EKLMX/1i/JtV4GRqMxRxcu6akpzPngTa5dvUr9Lv25euYEKYkJpAs7ws6c4uLFC9TrNoSlf27jrXfeLXDCvG7dOvwq1iA2JQ2PEkF4+gdi7+iUYxkhBC7unviWCgF7R3wr1ebXTX/cchd2UkpeeuU1th67QJe3p+IXUo60UlUZ/MxzXLx48Za2qSiKoiiKRpVhPCCCg4NZumAeixcvYcEnbxEXn4DJlEHdWrUY+/JzPPLII1ZLNG5VQEBAjp9nzZ7N3BVrafPMKKo+3B47B0cA3K/sY+DUX7h48D9+//5Tnn9pBN98+UVWi7CXlxeG9FTsDYLY8GsYDIIdi3/m7N6dlK5eDxdPbzIy0jn3v39YNmEk7Z57C3c/LTkX0pzjwb1V307F0dufMnUbs33+9zz5zieUeuhG7x9hR/fz21eT6P3aOHaunMsXX33Na6+MtPmYFyxbgX/Tx/EsEYSDk/NNl3f39UdKM26lH+L333+nR48eNu8r0549ezh9JZwhH0/BYDBgOBdOk049SUtK4JfZcxg3Vo22pyiKoii3yqZkWWhZ1DNAP8BPSllLCNESCJRSLsl/beVu4e3tzfPPP8dzzw0nOTkZOzs7i7W5ZrOZPXv2EBkZSf369bNahW/H8hUr+H7BUp5460PK12mcZ77BYKBM7YZ0e/tjdi+aybgJE5nywUSEENSrV4+Yy+dxdzASFnaRdV9PofojnRn02Vyc3G48kNis/3PsWTWfRWOeo9u7UwksV5H01GTc3IIBiLh0gTMH/8fgz+cx/+3htHnmNUqWr5QjjuCqtWna91m2rVpI95fHMvvNYTzz1DCbH3w8eeoUHftVtClRzuTu44+zf0lO3WLZxMGDBwmp3ThPCc1D9Zry76zpNm8nIyODLVu2sPGPP0nPyKBVs6Z07NhRqxlXFEVRlAeUrWUYE4Gnge+BzKeQwoC3iiIopWgJIXBxcbGYKEdERNCjT3/e+Wg636/eRKfuvZk7d95t7S8pKYlPpn9Jt9GTcfPyQZpN1hc2CDq/PIa/9uzjwIEDADg5OdHzyS787/e1bJ3zDU16DqXhk/2xc3DAlJGOOSMDU0Y6zu6eNOk5lPpd+rJ03MtcOXUMTw8PjAatZXn3pjVUbtGey8cPkZGWStma9S3WaFdq2poLxw5hdLCndK2GNg9aYjabiY6JxcXds0DnRxgM2Ds6sW/f/gKtl8nX15f463lrvaOvXsbf17ZhqTNryT/+YQ4ZZWphX7Ups37dQu8BA4mKirqluBRFURTlfmBrGcZQoK6UMkIIMUOfdhYoXyRRPcD27dvHkSNHaNCgAZUqVbr5CoXsk2nTcatSjycHDAcgLjKcr8Y8T/PmzShf/tY+7g0bNhBYuSb+pcqQeukSSXGxuHrlTeLSU1MQZjMeXt5Ubd2Rt8a8S+06dTCZTLg6O7FzxSK8KtSgdvsuAJilGWk2IyUIBMLeHidHbxo90Zszu7bxy4g+NHn0CXyCQkBKdqxZQqfXJ2FIT8HDx89qv8p29g44ODmTnpJC1Yfbs/GPZfTv3/+mx3nq1CmM9vYkRkfiWaJkgc5RUkw018+fL9A6mdq1a8cnn3/Jyb3/UrGu1mqfEBPNvyvmMP71l23axtx58wg32dF//BdZXyBqtniEP+fO5JNp0/lo8ge3FJuiKIqi3OtsbVk2Agn6vzOfl3LLNk0pBNu2bePtUb0JOziWF4c/ydGjR+94DJu3bqXJE72zfvbw9adCk9Zs27btlre5eMUqarXTElx/fz+S46JJSYzPsUxGWiqx16/g4+PNhQsXcS1TmYMnz5JYoiKmcnU5jzsmowOx4Vc5f/A/AAzCgNFoh52dHUa7GwOOmM1mqrZ8lLp16vLmsH50bVCFbo2q4enmQq169ahcow4xVy9lDYOdW/TliyDNuHp64+blQ2ysbQ/excbG4utXgiNbNxbo/KQmJXBu7z84OrkUaL1Mrq6ufDN9Gtt/msaCcS8Tc+0ys14fQv8uj9OmTRubtrFk5Wqa9Ricp6W9Wbf+/L55y20PWa4oiqIo9ypbW5Z/BT4TQrwGWTXMHwC23Z9WbLLlz3U809NEz44+ODuGs337dqpWrXpHY3BxcSE5IQ5XT6+saelJ8bdVt3rp8hValtX6WXZ0cKRMSAiXLl0mIToSewdHSqanE3P1Ej7eXkRERuLs6UNwlZr4hZSjbI26+JXSKn8C6zTn2vnTrP98As36PEWV5u1wcnXL2k9qUiJJcTGYUlOo1agZ535bQrt27bLmf/ndD5jS0vHw9ady/cbsXj2fhwc8nyNWs9nMzuWzqd+uE0Y7OzLSUnF0crTpOO3s7PALDOTM7u0k93sWZxvLMY5u3URI5Ro4Jt766Il16tThjw3r2b17N5GRkWxauwofH9tKMACioqLwCsjbGu7s5oHRwZHExERVu6woiqI8kGxtWR4FBAGxgCdai3IZVM1yoapStS5rNsOG0Fj+/NeeypUr3/EY+nTvxp+zviIhJhqz2cyRnX9x6eAe2rdvD2jdlB04cICvvv6aCZMm8/HUT1m1ahVJSUlWt2kym3L0tOHs5MxDFcpTplQQfp4eONjZUaF8OaJjYnDzDcDV0xshBEIYcnTdZjAaqdy0NT3e/ZRdy2Zz5eg+rp09wfVzJ7l29gQpsZH4e3tSqVJF3FzyjsZXrXJlzh7aC8BjQ0cQdmA3G7+ewtXTx0iOj+Pi4X2s/XQs6bFRtOwxEIDzh/ZSo4ptn0PJkiWJjwynbpvH2PjVJDLS0266zrXTx9mzej7latQhOKhgpRu52dvb06xZM9zd3QuUKANUrVyZswf35o3v/BmcHezw9va+rdgURVEU5V5lU8uylDIO6CqECEB7wO+ilPJqkUb2AOrZqzfp6als3b+Tp194jJYtW97xGJ4b/iyJSV8w+/UhmBEElwzguy8/x9vbm02bNjHjx58Jj0vkoaZtcfUKJiMtlb/X/cmH0z6nW+dOvPTC83l6jvDz9SX62uUcrdUgcHZ2wdkZDNEGYqJjsHd2zWopzkhPIzEmCjfPG0mak6MjaSnJBJSvRMNuAzm3exs9XxuLNEuEQSC4kZBfOH6Ih8qXyxHHgD69mPzNj9Ru9Siunl48O+Ubdv+2ms3ffUJCTDRe/gHUf6Qj9ds9gZ2DA2aTiUOb1zF6xlc2nbvAwEBqVq2M10OVSYiJYvXHb9Nu+JsW65fNZjNn9uwg9Jcv6PbSaPZuWM6ooX1t2k9ReHbYEN6e9DGBZSvgE1gKgKT4WH7/aTrDBvS3Wt+tKIqiKPc7W7uOy2yBDtdfCCEMUsqCD3GmWGUwGBgwcAgMHFJsMdjZ2TH6jdd55eURJCUl4eXlhRCCb2fMZP7q9bQZ8jLlazfIWdvauTexEdfZsWIuA4Y+xc/fzcgxbHXXTo/ze+gGgitVs7rfqJhoPAOCs34+uXMrpStWxcXjRimDj483l65ex8Xdk6otOzBn5TwSY2Jw88rb6nnwjzW8OqhXjmktWrRAfjyVY7u2U6VRC5xc3Xi4+wAe7j7AYky7N66kQumgAj1oObBPbz6c+Qt93/+cbcvnsnTcS5SoUJXKLdrh5uOHOSOD62dOcGjzOlxc3ej35kScXN1IvHbR5vriotCyZUtGPn2F6e+9hH/5ytg5OBB29AD9e3Zn8OBBxRaXoiiKohQ3W2uWMyDHQGgACCEygMvACuB9KaXFB/6EEE7AX4Cjvs9lUsr3hRA+wGKgLHAO6C2ljC7gMRQ5k8nE9u3b+W/PP5QKLsv69etp164djo621bLeixwdHbOOb8nSpSxcu5H+E77K1Tp8g6dfCToOf52/ls7iuREvs3DO7Kyu6bp17crMX7qT1O8Zi92qmaUZDEbs9OWllBz8Yw1t9VKITK6urhgFJMZG4+rpTUjNBpz83z/Ubdsxx3J7N/+KOeZ6nuTTaDTyxaef8NQLIzAYjVSq39Tq8e/b8iuHNyxh4exfkFISHR1t02h+rVq14ruff2Hb0lm07vs0zbv24/COzRzeuYWk+FiMdvb4BgbR57X3KFWxKimJ8Sx4/xVGvvBcnmG577S+ffrQpXNndu7cSUZGBg0aTChwOYeiKIqi3G9sTZZfBroCHwEX0UoxRgPrgePA+8DnaAOXWJIKtJVSJggh7IHtQogNQHfgTynlR0KIt4G3ucvqoA8dOsQ7o4fj7xFB64bp2IvX+W35e3w+zYUx731erK2Bd0JqairTv5lBj/c+t5ooZ/dwzyEsOXGY3377jc6dOwNaP8D9enRj9fQJ9Hz7Q+wdcn7JkBKMRjv935K/F/+IvUFQqX6THMsJBGVKB3P2/HlMGek4eXiSHH+jp4qEmGh2b1jG+X/+ZPYP31nsR7patWr88PUXvPTa6xz8cx212nehQu2GGAwGTBkZHNu1nYN/rIH4SOb+9APXr1/nlTdGc/ZCGM8M6s+SFasYN+ZtgoKCAO2L1NatWwndtp3EpCQC/P15bcRLTP5kKr+nJNOq79PUafM4ddo8nieW8LDzrJk+nu6PtqFXz543Pbcmk4nExEQcHBxwdHQs9BEXQXvAs23btoW+XUVRFEW5V9maLI8C6kkpY/WfTwgh9gD/SSkrCCEOAv9ZW1lKKbnRzZy9/pLAk0BrffpsIJS7KFk+c+YMr73cj7HPpdGqiQcAoSeMfDnOkyMnk3lt4nCcnefSpEmTm2zp3vXHH3/gW7ZSVo8UNyOEoO5j3ZmzcH5Wsgzw2isjCR87jsWT3qDdsFcILPfQjXXQkuSEqAj+XT6byLMnGPL+NAwW6mTt7R0oX64c4eHhxFwN4/J/24gKO0tKfCzXTh2l46Pt+GTu7DxDbWdXs2ZNflu7mk2bNjFn4WzWTHsPRydnUlOSqVurFq8P60fr1q0JCwvjhVdfp+WwV+nYqAUe5/+HOcbMkGeGs3bFMvbv389b772Pg08ADzVujXOQO8euXmLl2AmUCwrAPf4q34/oR+UW7ajW/BFcPb0xmTK4dv4Mh/5cQ+ylc4x8fjh9eve2GmtMTAxrVq1i5YI5XLp0CVdHB1LTMzDY2dO2w2P07D+QmjVrFknirCiKoigKCC2PvclCQoQDtaSUV7JNCwIOSCn9hBBGIEpKabWvLH2Z/4CHgG+klG8JIWKklF7ZlomWUuYpQBVCDAeGAwQEBNRftGiRzQd4Oy5fCsPRLgZf7xtJW0KKH25OEdq/E81ExDhSNlvid7dISEjAzc3t5gvexPkLF7Bz9czRRZstwi+coXzZMnladyMjo4iMikQY7XB0dcdgNOJsFFyPjMKUno6LuwcePn4ICyPr5RYRdg5fL0/s7e0xGAy4urpaHJHvZqSUmM1mDAZDjqTzytWrpEkDbt6+ABjTkjA5uBB99RLuzo5Ex8biWSLI4tDWCTFRpCXEUjo4mPj4eOLi48kwab2CONg74OPthbu7u9Uk12Qyce3qVRLiYnF3sMPb2QEnezsyn2E0mSWxyalEJ6djsLfHPyAw6/MurM++qN0rccK9E2t+cbZp0+Y/KWWDOxySoijKPc/WluU5wO9CiC/QyjCCgVfQWoMBHkUrx7BKSmkC6gghvICVQogatgYppfwebahtGjRoIFu3bm3rqrcsISGBTu8OZ+1MVzzcbyTLoSeepnWlnwAwmyVdX4zj0y/XF8toe/kJDQ2lMM5Tp+49afncGALKaKP3mU0mju3ewd7NG4iJuI69oyOV6jaiXrsn8PC58VDf6jnzmPhaHRo2bJhnmyaTib/++ottO/4mNj6e+nVqs2blCkLadKVpm8dtGunm7KG9/O/XDaxZvrTIWlUHPvUMFToOoFzZugC4n/uP+LL12bnvONvmTqXP+5/jVKU+qVbW3/zLV9QIj+Ddd94u0H4vXLjAy08P5RF/I0MaVcHT4AiWx0/BLCU7j19m0vdHGPDSKAYMHlJon31Ru1fihHsn1nslTkVRlHuJrcnym8BJoC9af8tXgG+AH/T5W9BKKG5KShkjhAgFHgOuCSFKSimvCCFKAtdtD71oRUZG4uMpciTKuRkMggql7bh69eodS5bNZjNxcXEYjcY8XbQVBSEEmXcfYsOvMXfyW9g5u1G9bSfqhZQnNSmBkzu38s1rw3h04HPUb/9E1rrWWnmNRiNt2rTJqvcODQ1l4vj3eXXsBOq0fhRnN498Y8pIT+efZbN4pm/vIi0/KFe6NFfOHKdcjbo5pocdPYCjhxfla9XPd/2mXfoy/+1nee2Vkbi4WB+dLyEhgb///puoqCgiIiL4Zea3DKoeyMsta930+AwGQbMKpfjZz4sXZ36Og6MT/vmUoNyM2WwmMTERKaX2QOUd7jIuIyODrVu3cvTwYeKjozDa2eHh7UOjJk2oW7euKjdRFEVR7jhb+1k2AzP1l6X5KfmtL4TwB9L1RNkZaAd8DKwBhqA9ODgEWG176EXL1dWV2PgMTCaJ0Wj9D3RUrBlX17wDYBS29PR0Fi6Yz7Il35EQF06GSVKufFUGDB5J+/btiyyJCAoIJDzsHN4lApk18Q2qtnqcek/krLENrlqbOo91Z/VHo3F0caVq44eJunwx37rh3Bo2bEj3x9qx9MO36TF6itWHCdPTUln71WQql/Slpw0Pxd2OAf36MOT5EQRXqk5IlZoA7AvdyJn//qZ5X2vPst7g4euPd+lyHDhwwGJd+4kTJ1i2cAG/r1tFnRIeBLraExtxnTbeZrYePsWu02H0alCFx6qXw9kh/54yAj1d+apLI57+7ENeGf9hgY4zLCyMVcuXsfefHZw8eQJhNiEEpJsl5ctXoGb9RjzZs1eRDpJz/fp1VixdyqpF8whxEjQu6U6AkwNmKYk+ksqHC39GePrRc+BQOnXufEd+5xRFURQFbG9ZRh+QpBHgBzdGf5BS/mzD6iWB2XrdsgFYIqVcJ4T4B1gihHgauAD0ym8jd5Kfnx9ly9dk2+6jtG5iuQX3zIVUrkW5Urt27SKNJT09nddfG45M3MpHrzpTraIXZrPk7/9O8eXXL3DyxEu8NGJUkey7V7cufPrzAhJiovEOLpcnUc7kXTKYds+/xR8/foYQgorlyhAcHGxxWWtGvfoKDg7fMnv0U1Rt9Rh12nXGWx/QIykuln1bNnD4z7W0qF+HDya8f0v1yQVRpUoVPnr/XSZ+NAWT0YH+XZ/gwpbNtGvdGqOnbV2qOTi7kJqas1AjIyODjyd9wI4Nq+lepSRL+zbDz82FpOQkLl9wpIKPO1LCrkuRLD18kpmhe5naqy21gv3z3Vcpb3eG1ipNVKRtw2Zfu3aNTz6YwP6d23miYgDPlwugStNWuDtpdebJaemcvB7DrhPbGTV4GYEPVeHt8R9QsWJFm7Zvq1/XrePTCWN5rJwv33aoTnl/rzzLPCcl/7twnaULZvDTN18wbcYP1KhhcyWXoiiKotwyWwcl6QrMQyvFqA4cBmoA24GbJstSygNAXQvTI4FHbA/3zho87FWmf/Q01Sum4++bs2UvKdnMlBlJ9Bs4Bjs7m79z3JKlSxZjTtjKF+95Z7VyGwyCFg3dqFnZxMA3Z9Di4UeKJGlv3bo1Ez/6hL/XLKbdi+/ku2ypKrUwOjjx14If+GD0KwXelxCCl196iS5PPMHipctYPPYFMswSYTBgSk+j46PtefXzqVSrZn1wk8LWpk0bWrVqxenTpzl79izLFy1gxoyZ/B128abrSimJuRKGv/+NJDcjI4M3XxmB+fQBlg1shUu2FuPoyCi8nez1ob6hSWk/mpT2Y8f5cEYt/oPJ3VvTuFz+Q2J3qlGOhfFxREZG4uvra3W5jb/+yqcT36Nf5RJMGdoWR/u817Czgz21gv2pFezPsCbVWHfwLC/278WA519myFNPFcrdjAVz57Dom+n8+GQDi0lyJiEE9csEUL9MANtOhvHqU4OY8vV3NGrU6LZjUBRFUZT82No0NwkYJqWsCyTq78PJp7u4+0Hr1q3p0XcMg95M4ucl0ZwLSyU9XbLs12gGvhFP2ap9GTR4WJHGIKVk6aKZDO/jZLEcxNPDSP9OZpYu/qVI9m9vb89br73C5VPHKVE+/9vwQgg8g0rjaEq+rb56y5Qpw+g3Xufv0M38uX4Nv61azq5tW5k0YXyORDk5OZkDBw7wzz//sH//fhITE295n/kxGAxUrFgRBwcHhBA88UQnju/4nYy0tHzXO3/kAO6OdlStWjVr2ieTP8B0+gCfPdkkR6KcYcogIT4OL+e8A900L+PP1PY1GLtyK6fDY/Ldp7uTAx6OdqxcvszqMksWLeSbiWOY0bEOTzerbjFRzs1oMPBk7QrM792MP+fO5NMPp2BLTzr5iY+PZ95X0/ihR5N8E+XcHq4YzCfta/DuyBc5derUbcWgKIqiKDdja5NoiJRyaa5ps4GrwBuFG9LdZfDQp2nctAVLFs3m1Y9CeaKrgZNnOvDO+GE0aNCgyB84SkpKIiLiMjWr5B3SOVPjOi4sm7anyGLo2LEjHu6uRF06j4d/IM6u7pDruDPSUkmIjkSmpfDUkMG31dqelJTEhl9/ZePKpURcv47ZbMbTy5s2HTvzZLduxMfHs3DxElauXY97iSCcXF1JS04i5koYnR/vQL8+vXnooaLrzi8kJIRGdWqxZeEPtBv8osVrIDU5idC53/DKkIFZ80+dOsW2X1ezfGArjLlKSJKTk3G2N+aZnqlukDdP1wnh2y3/Y1rv/L+IuDva89dfm2H4c3nmbdu2jdnTP+bHHk0o6VnwrtBKeLgwo1tTnl+xigWlSzNg0OACbwMgLS2Nq5cv8enj9QjwKHj9cb2QAJ6vE8f0DyfxzU+zbikGRVEURbGFrRnNdSFEgJTyGnBOCNEUiADu7KPyxaRy5cq89/4UQOu54Zlnnr9j+7azs8NkApMJrOWfqWlm7OyKbqhkg8FAk8aNSQ+/jMnVlfDI8Kw+kqWUpKckI03peHl6knLlPI0bayUYJpMJk8lkcSQ9S9LT0/n2yy9YvXgBDQLcGFa1FME1KiKA8IRk1v86j9YfTSbR3pXmPQfTf8p3ePrfeIgwLiqCfZvXM+CZ5xj14nP5DvZx4sQJNqxby+H/7eHc2dOkpKTi4uJChYoVqVGvEZ26dCEkxPpALB9+MJGhzz7Hum8+pFn3QfgGlQa03iTOHviPbQu/p33TBnTt2jVrnWULF9CtcmCOFuVMJpOZfJ4jBeDJqkF8P+9vrsYmEuhpPcE0CkF8XGye6XFxcUx59y0mt6t5S4lyJjcnBz7pWJ9BX35G84dbUrZs2QJvY/PmzTgaBNWCrJeK3EznmuWZ+cufXLhwId/PSlEURVFuh63J8g9AC2A5MB2tqzgzMK2I4lJ0jo6O1KhZn9Cd+2nXwnKXapu2pdC8ZacijWNQ3958PmcJfcd9RkZGOvEJCZhMJgwGI44efri5uXF4xxYCvD3YuvUvXnx1FOHh4QiDAQd7e9q3bUv/Pr2sjjaXmprKay8+j9OVkyzq1YQSHjm7Wivt40FEQjJrz8Xy2ItjcQwojatPzkTLw8ePlj2HUPPh9nwx+U0c7O3p1q1bjmVOnDjBx+PHcfX0MTpXLMFTpfx4qG4DnOzsSExL58S1aP63YzXPzP6BqvUaMXrceEqVKpUnXg8PD+b+/CM//fILiye9hrO3P44ursRcu0IJL3dGPT2Izp07Zx1rYmIiv61ZyeLeTa2c4ZuXNDjb2/F4xRKs2HuCF1vneQQgB7PZnGfajzNn0DrAmXoht961XKYgLzeerVOaz6ZM4svvfyzw+kvn/ELXjh3gNipnHOyMdKkcyLJFCxk1+q4Z+FNRFEW5z9iaLE/Vu49DSjlH7yfZVUp5tMgiU7IMGDyC6R89Rd3qGfh65/zIjpxMZu1WB2bP71ekMbRv356lK1fz63dTeezp1/DNlaie2reL1Z9PxN5oxLlMVTq8NokSIeUQQpAcH8f+v37jxdHvUjbQjy+mfZrn4bP33noT7/DTfNC5MQZD3mQ6MTWd9/48TPcxnxBYuizhCclcPH+eMuXKYRA5Sxe8A4Lo8fZHTBk3glatWuHj44OUklk//ciC777h5Ubl6DS0bZ6SBzcnBwI8XHm4YjAvtDCxZO9JhnTvzMh3xtElWwtxJldXV0aOGMHzw4dz7NgxkpOT8fX1pUKFCnm+EGzatIkQVztSMjJITc/IUydsNBgx2VAC3LlyEGO2HMs3WTZJiaeXV45pycnJrFu2mPk9C++BuG61K/DjrM2EhYUVqOeT8+fPc/n0cdwdO99WsgzQo1Z5Bi5fwqtvvFnkvaMoiqIoD6abJst6d28JQggvKWUqgJTyQpFHpmRp1aoVp0+OZuAbn9L7MTPN6jmTmmbmj79TWf+XA+9/8J3F1s/CZG9vzzdfTGfs++P5/uV+VH74UfyCy5KanMSpnVs4e2gvPqXKMGTyN7i45xz13MXDk6ZP9KZxx57sWDmPfoOHsmD2L/j5aSP+JSUlcWbP3yzo97DFRBlg/aEzlKzZgMCQsgD4uzmTHJNAbGws3l5567l9g0pTruHDrFi5kqefeoovP5vGrjWLmN+7WZ5Wa0sc7IwMbFiF5uVK8spH40lOTiKgZJDlZR0cqFWrVp7pZrOZv/76i7mLlrD9n504GA0MWL2ftIQ4ulUNol/dCpTx1e4WODk7k5yegcksMVo5BwCBbk7EJFkbM1CTkJpOzQY5+3X+66+/qOnnelvlF7k52tvRsUIJfl23luHPv2DzepcuXaKCr0e2DihvXZCXG+b0VBISEvDwyH8wG0VRFEW5FTdNlqWUJiHECcAXuFz0ISmWPPXMczRt3pKli+cwfuYujEY7mrZ4nHmL+lCyZP7diRUWFxcXPpv6CRcuXGDVmjWEXTiAp7MznuWDychIp9+46dg7OiGlJOzEYa6dP4spIx13Hz8q1muMvYMjD/cYzHYEL496nQWzZyGEIDoqkt7VSuJgZ70Eft7BMOo+PeDGBAG+Lo5ci4zA28sLS5lX3UefZP7n4wgsUYLtKxfyU89meFjobSI/5fw8+a57E576/BNe1uvWbZGcnMzro9/m2KVr1HmsB0/1exkS4wjwcCEmIpx9W3+n1+K1vNeiIp1rVcDezg5XN3dik1PxcXWyul0Ho4E0k8nq/KS0dGLNGfTIVa99aN9e6gcU/oiP9Ur5suK/XYDtyXJCQgKu9oX3uIObo4NKlhVFUZQiY2sZxnxgnRDiCyCMbAWWUsrNRRGYklfVqlUZV8DR2YpCSEgII0eMALR+g9t1fILHRk3GzsGRvVs28s/apaSmphJUpRYGOztidm5nzcxp1Gv7GC17DKJ594H8/Pof7Nu3j5CQEBLj4xlQo7zV/ZnNkjMRsXStWCXHdBcHe2RCCknJybg4520tLlmuIpFR0Xz2wfvMfKJOgRPlrO14uvFuyyocCwsjKSkp36GrtXjNvD76ba6aHRgw8WuMdnbExMaQlBAHgJefP6179KdGs1ZM/vhdnB3saFelDN6+vly9eAFvF0ervawkpGXg7mj9gcmNh8/h0rwBJUqUyDH9+MF9PBxsvUeVW1Ul0Ifju/YWaB0nJydSMvLWVN+q5LQMnJysf8FQFEVRlNtha7Kc2Ww0Ptd0CVjPcpT73l9//YWjTwABZcqz4acvOXloH037PktgpRpZNcESiA+/wt71S/nx3REMHT+dGu26MH/REnr36IaTnRFXg/XePEzSDMKAyFWTKgS42BtJTUmxmCwDJKek0raCB5UCbBtxz5oWDwVzziBZt2YNvfv2zXfZ0NBQjl2+zoAJX2HUuzBxcnQkIt2ElDd63fMrWYrOr7zH+KljaFUxGBcXV4SdPdFJ1luXd1+KoqKVY7kel8RP+y7wUpe8PUzEx8bhVSmwAEdsG28XR+Lj4wu0jq+vL1fikwpl/4mp6SRnmFSrsqIoilJkbHoiRkpZzspLJcoPuA2//0HlFo+yY80iTh7aT5e3PqZMtdq4ONjhaGfA0c6Ak50B38AgWg19maCaDZn/4TvUeLgdf2zZQnx8PIab9FVtbzTiaDSQnJA3KTMKMFno+QEgLTWZmMhwBjSsVCjH6uPswLJ5s2663NxFi6nXsVdWogzg5OSM0cGBhNT0HMuWKlcB9zIV2XL8IgIIDgkhIiWDuGTLdclLD1+iZ4MqeaZHJSbzytp/6T18hMWWb4PRgMl8e4OIWGKSssAP1lWtWpUUexdS0jJue//rD52hZdt2RT6KpqIoivLgsvmvnBDCXgjxsBCij/6zqxCi4KMJKPeVyKhoXDw8CF02j/bPj8bd3c3iA2oGIXC0M9Ks5xASExO5cPwwCAOXL18mw0qym13bCgEc3Lk9z3SzxGKylp6exj8bVuNpBw4pcVy6FMa1a9eIjYsjPSM9z/K2cHGwJ+rqZcLDw60uEx4ezsEjx6jS6OE887x9/YhOzjvqX/U2HVlxVHscwMHegZCyZbmWnE5EQjLmbAnu0etxRKSYaFHhxsOcUkoOhIUzbOnftO47jMHDnrIYV0DJUlyKSbD5WG0VFh1PQEDBuqIzGAz0HDTM4rkoCCkly45eodfAnAOjpKSksGbNGt4Y8QLDB/blxaGDmDx+HAcPHrztUQcVRVGUB49NybIQoiZwAq2/5Z/0ya2An4soLuUeYTQaOfjPNnyDy1Ei+OYDQ9gZDdR+9Em2LJvHpcuXmTlvCXGp6cz+N/9eCAfUKcehP1bn7D9YQmK6KUe9akJiAhfOn+PMyRPsXD6HJt5G3MxpuJlSMSbHEX/9CmdOnuDC+XMFHx5bQNUSXhw9aj3WyMhIPPwCsLPPW1bi4eFBqoT4lJxJord/ANcSb7QkOzk6UbZ8BZINDpyMjONqXBLxKel89s8J+jaqisEgSE5LZ9W+kwxctI33dl7g+bGTeO7FEVZrnavWqcfR6zEFO14bHLsaRbVadQq8XucnnyQ+LYPoxJRb3vee89eQHr7Uq1cP0EYF/Gr6Z3Rq3Zwt302lvSGc4SF2DA4wUfrCHt57bggDu3dhx44dt7xPRVEU5cFj673LGcA4KeVcIUS0Pm0rWvKsPMCCAgLY/nsoDTpZHy0vt0oNm7Nl9je4evnyzOdzcDi2nSkzztGifEkq+HtZXKd2sD/l7I7w18pFtO7RH4DEtHQw2uHi7IzJZOLqlcskJ8Tj7+JA2H+huMRepU31Eni65HywzywlcclpXL54Hld3TwJKBmI05OydQUrJ3ovX2XvhGkcvRxCTlMLDQ8px4vxFli9fTnBwMOXL561CsrOzw2yyXF5gEAaCQ8pw8dxZDELg6qgl1GaTCXtjzu+t9nb2lA4pQ3pGOteuR/DCmn/491oCVwzXmHPoMgmp6TRp0ZKXprxFkyZNbloK0aBRI6bM+4ERUt7WEO1/nQxj6e6jXIyKo6SXG+FpMPCdoQXejre3N14+vrz9wx6+6tY0355QLAmPT2LC5kO8NvlTrS/v5GRee/E5XK+dYW63hgR55ewir0n5IAY2rMrfZy4xadRLPPvmWLr37FnguBVFUZQHj63JcnVgnv5vCSClTBRCOBdJVMo9w9/Xm6gLp3G4SQ8R2dk7OZMSF0PDJ3ohhMDB0QnX0hU4dT3aarIshOCLJ5swcOE6/kxPp2W3fkQlp+PjW4L09HTOnzuLmxFCPBzZ+esqLm5ZSfcqJTEa8iauBiHwcnHE3cmB6wkJnD19mjLlymFvZ4+UkrX7TzP774MYpYkWIb50CPHA18WPqy4O9Kvsz+mT//JSv+6UeqgKTdq048SBfYSdP0tKcgpOzk5cPHWeyOtX8C2Rt0s/ZydngkPKEHbhAr7pJrxdHLl44ghVfCyfv5PX4/hs+ylKtenCP2PGkpqaiqOjIx4eHjYPIw5Qp04dhKcf/124RoMyt/ag3887DrBu73GerVeW6o1COHI9lkl/Hef08WO3tL0SAQF41mjEW+t2MaVjA5wtDANuydXYREau2UWPZ0fwSLt2SCl5983XCYg+z/udGyMMgrT0dFJTUzCbJQIw2tnh5OREi4eC+dHXk2enTsLH15fWbdrcUuyKoijKg8PWZPkcUB/YkzlBCNEIOFUEMSn3kLBTx3Eng5hrV21eJyEqkoz0NFy9/ZFIpDQTefkC68wOtK9a1urAJN6uTiwY0JqxG//m0xfXUqbFYzR5vCunoqJwNqVx8cR+joZuoI6vMwv7t2TTkfNcvHTJahxGg6CkhysRCcmcP3sW1xIl+WDdPyQkJPBui4eoW9I7RytsqIORthUC6B4QRGpqGuv2HuWbCW9TPcif0e0b4OnsRHxKGmMvnePPRbNo3KUPJQICcc7VU4eLiytlypcn/No1wsNj2bZ2OR8/XJbIhGQc7IzEp6Tx34VrLD1ymRiDE32HvUzf/v1va4Q6IQQDn32BL6dP5pfSJfKMXngzV2MTmffPIZb2boyv3lJvAGb3b82LyxfSrVdviy3tNzN56jQmjx/H00t/Z3iDCjxcsZTV2JLS0tl4+Bw/7r3AwJdH0X/gIAB2795N2P5d/PBkfS5fDiMxIR4BONkZybyUMsySlHQTdvb2eHp5M75NNSZNnkDLVq3UyH+KoihKvmxNlt8D1gshZgIOQoh3gOeBZ4ssMuWeEBcTQ78apZi/fjENO/fCzv4mrZ1S8t+GZVTxdGD7nC+JPvYfPTu0ZfTIl9izLZR3ft3FW21q4eNq+aaFvdFAeW9Xyqfa0axcCZZ9+CYkxlLR34uGge5M7NU4a1S8KoE+bNp//KbH4OfmzPlLEbz441p6Vy/FsPZVLCdsElLSTcTHxZKaEE/f6sH0rBrEh9uO8/Xm//Flv3aU8/NkWpcmDFixAfFway6mpFCyVDDu7jm7NnN0cCS4dAh/r1mEl5cH884n89XeXaSlp+Pu5sZDVaoy/IPXadasWaElc527dGHjmpXM/vcYTzWtVqB1Nx+/QLty/lmJclxyKilSUL50MJ0qRPH7b7/x3Au2D0ySyd7envcnTWHTptbMmfUTn+zYTPcqJWlcpgQeTo6YzGZiklP54+RlNp4Op36zh5k04/2sOmWA7776nFY+BsIvX8THyYEAb7c8ZS2gldakpJuIiY/GIyWNjOthbNmyhUceeaTAcSuKoigPDpuSZSnlOiHE48AzaLXKZYDuUsr/ijI45e7n6ORIu6pl2XR+H4e3/kaV5o/gaKXPY1NGBpFhFzj55xrW9mvN4CXb+eTdN4mKiqJ9+/YMGzKEz6d+Qo8Fy2ge7EXXasGU8nLDKAxcj09i/bGL/HYmnMYt27Jo5gQOHDjA3l+Xs3BYJxzt817K1Ur6ciE2mWsJKQS4WR+0Iiktgw+2HGVozVL0qV3WasumWUpMZjNpifGU8b7R68ekR2owaetRxq7axrRebagY4M3rjcryxefjeXzkWK5ckhhCyuLqcqPzGLPZzO4NKzjxxyqWL15EUJDlobQLkxCCcZM/4qk+PQl0P0vHGuVsXjc5PQN3B62uODE1nSuJqYSULYdBCNwdjMQl33q/yUIIOnToQIcOHThx4gTLFs5n/D87iIyMxGg0EhAYSItHurLwq945et4wmUx8+vHHbPvtVyYNe5hAd5d867GFEDg72OHsYEcJNyd6VfDmlWeHMnvpKurXr3/L8SuKoij3N5uSZSGEn5Tyf8CLRRyPco8JqVCJw/v/ZHqnejw//ys8PL1xL1kaZw8v7BwcEAhMpgxSEuJIjY9l5+wvGFDJD6NBUKJEAPXr1yc0NBQABwcHRr87ludfHsm6NWv4ctUyIsPPYjab8fL2pvVjPVj0dc+s0elmf/cNw2oHk5GehtmUgYODQ45E18nejsdrVmDZoTBeavKQ1WP44u8T1Av0pHf1YDIyMjAaLT9sZjJLzGYzpb08cnSPZzAIxrSswuAVu1l34DSdaz9En/qVcLE3Mvmj0biXq8rFJu2oXq8RpowMLhzdz+E/11HKz4sFs36+I4lypsDAQL7+ZQ4jnhrMhZhEnm5aFXsrx5td/ZAAPth7jP61kolMTic4pAzOTs5IKdlyIZpnn2lUKPFFRUVx7OAB0mOjaB7sRYZZsufiBU4cPkhiYqes5TIyMnj3zdc5+88WmgT7UtKjYL1YGg0GWpYLYPHRq7zzwtO8/eE02t5lLczR0dFs2bKFowf2cfroYVJSUnB0dKJcpf+3d9/xUVVpA8d/506fTCa9NwghJDTpAiJSRGwUFRtiQ1fX7qqvbe1l1+7ae2/YsIC9IYiNXkMNPb2X6XPP+8cMIYGEGpqe736yJDN37n3uSZBnTp7znC7kH9Gb4cOHEx8ff7DDVBRF+cvb3TKMjUKIGYS2vf5USrmHPbeUv6pTTj+DSz54h38M7srDI/zc8fy9dBp2IlkDhmGOjA6tBg34KV86jxXff8rRUTq3j+7PQz8t4tSJ57V6TqfTycRJk5g4aVKrz2/cuJGXnnuO3374lhvOPJLyLRvRpcSvS5xRMcTExmK1hMoFzjmyK+e9Mo2Tu6SQFbMtoZIylPiurqznp8JSPjy9P5oAfzBIIBjEYNAQbEuIG71+glIS77BibGXm2WjQuGt4V674ci7Hde2AxWRkTM9OjMrP4ptl67n51UdZ/2Mn4uPiyOvcmece/g/du3ffp84UW1VVVbF8+XJWLF9OZVkJWZ1yePLxx8jpkkd+fj4dOnRocZ3s7Gze+PAT/nPn7Zw3ZRaX9MtmaOf0NmfUpZQEgjrr633c+fMq7hw3lAh7BI1eP8/NXgoJ6Rx11FH7fB/ffvMNj91+E7cOzWPIyOFNtesef4DPFq3ln5PO4unX36Zz587ce/tteArmcM3R3Xhr1t79gsti1DAbDTx8Uh+uuuV6HE+/yIAB7ZP074uSkhKe/d9jzPzua47KiOWIhEhOyInGbnbi9gVYW7aUJe/+xrMP3MuRQ4dz+b+uJzNz120bFUVRlL2zu8lyJnAGoW2vnxdCTAfeBb6SUu77NlzKYatDhw506taTr5auY+wROXRJjOHd+XP47IfPsUbFoBkM1FVX0S/Zyf0DshiSk0ZVo4fvCsv56LTT9uhaUkree+dtXnviUbLtgnFdUshLiGp63h/UqXE3sLGwmpj4BOITEkiNdnDJMb25/cdlvDC2D1ajgUAgQDAQQAiYunQTp+Wn4rSYAIkGBHw+AgIMBiNGo5GgLimqdwMQa2+7nCM3PpL8OAffr9jIST1Ci92sJiPjeoVmtX8WyTz27PN7NsBt0HWd2bNn88GbrzN75gycRjDrfgKBIGddcxOfP/8YGM3UBQXJGZmcdf5FjBk3jsjISAASEhJ47Jnn+OGHH3jrlRd4ePaPDEyPJS82guSoCDQBlQ0eCirqmFtShxYVz9V3/ZcZ33/L8S9/S7zdTKXLR16vvjz61DP7XFddVVXFA7fdzItj+5KTGNPiOavJyJn9uhBtt3DLtVdy8ZXXUjDrO94662gKK2qp8ezdJjO1Hj8Oi5kuybHcN6Ibd/3fdbw//aumMToYPvvkE5564D7O6JLA5+ceg9Nm2eGY3plJTAAaPD6mLlrDhaeN4+Jrb+CsiRMPfMCKoih/A7tbs1wBPAs8K4TIBCYC9xPalCRh/4WnHA6uvulWrrrgHDJjnfTKSOTW4/pw3fAAxbWN+INB4h22pgV7dW4v1077k3MuuZy4uLg9us5br7/GtFee4a3TB/H6r0vINLdMXE0GjQSHjRibhY1VFUgpSUxM5PS+XVhZUsVV0xfwnxFdiLGaMRs1BPDDujLePqUfoYlXEdp6WwgMmiAQDNLo91PS6McWGYUmRKsLx5obl5fCZ0vXNiXLW43qmsUDL32H3Mc+xwCbNm3izptuYMWCOWgeF91jIhicEUN+gpOUSBvrou08OLIrKyvqWVhay6zVy3nlP7fxwhOPceeDjzBixAggVMN77LHHcuyxx7J27VoWLlxIwaKFzC7egpSS6LgM8o7uzYDkZJYuWsjbLz5LZ6eZU7pn4ff5MJpMlDeUcvaYEzh5wplMOOvsvZ7h/HTqx4zIjNkhUW7uuPwsXpk3g7tuuoHXTj0Si8lIp4RoqjwBNlQ3tvjNwe74vrCUIzuFSmAGdEzhmMISHn/oAe649/69uoe9IaXkt99+4/svp/PnH3+gV27h+VOPIieh7XHYymE1c96R+QzvnMbNLz7OpvXrGHDUkAMQtaIoyt/L7s4sN5cU/ogHato1GuWwlJ+fz31PPMv/XXsFF/eoZkzPbOxmEx3jt8366rpk7oYSHvllBQPHnsFFl1y6R9dYvnw57z3/FG9MGESi005lg5sBmc5WjzUaNDKjI1hXVUFERAT2iAgu6d+JJ3+o5vxP5nPLkFyOzoqjuN6D2aCRErkt6RZCoEuJQMPl91PS4CUoBDZNsDs5bo+kKP77y+odkmK72YRGaCtmm23v25N/89VX3HnjdZhdtQxMieKSfnnkJbQchyKjRt+0WPqmxTIRqHJ5+WjZZt5cvIlbLruI4087k9vvuQ+jcdtf/06dOtGpUydoNtsvpeTVl17i/huv5eTOSbwypheZsTuOeVFNA1MX/cDFH77LyWefx5XX/muPZ5p//voLrslL2+kxQgiGJln5pr6WrqmhN1pmo4HxvXP5ePlmrjuqy25fzxsIMm1VCa9P7t/02FVHd2fsG9PZ8s/LSUvbeSztQUrJQ/ffx5yvPyXDAnJLCf8d2Q2zqw5dRofeuO2GjFgnL5w6mMs/+YxOXfL2c9SKoih/P7u7wK8rcDahGWUr8AEwXkr5536MTTmMDBw4kGffmsJLTz/JC6//xHHZ8eTGRWLUBGUNbr5cW4ElPpkLbrmHE086adcn3M77b73BxG6pJDpDnTaklDs93mjQiLebqaqswONx01BTyd0je7CopIb//rycZ+euIy/OQbrThi4lWjhJ9gSCNPiCNPjcGAwaHeJDM8prK6vYxSUBSIiwoOs6tW4v0c1KNqSUBPRgm4sHd8f0zz/nnhv/hd3v4tajuzAqJ6kpuZe6jq7rSCmRusTn96EJDU3TiLFbuKR/J47LSeb2H5fx40fv0lBXz0NPPNlmPFJKHv7P/Sz+9lM+OPto4hxtJ/ip0Q6uHNqTc/t7uX7aB9xVVsZd//nvHiXMLpeLKNvOF6vpUmIIeOkc1zJhP6V3Zya9NI3Tuqbv9uzymwvW0y0tkfSYbSUXdrOJkzsn8vH773P1ddftdux7a8mSJcz+YioPH9+by9/5htfG9SEjys6G6gbq6mqJjore7XM5rGYeOak/n5WXsWrVKnJzc/df4IqiKH8zu/uv2WwgBbgESJdS/ktK+acQQnXzV5p07tyZh554iilffU/i6IkUJPVkflQXGnqO4s5nX+XdT6fvVaJcU1PDzO++ZmzPbaUNsQ4b5S7vTl8XZbXQ2FBPeWkpmdERGA2hGdcPzz6K64bksb7WTaMvyMqKBgrK61lZ0cCWOg/1/iBpMU46xEVhNRkxGw2kRIaSan0XGbMQAotRwxsItni8tM5FRIRjj3bda27hwoXcc9P1RARcvDi2D6M6J6PrOl6vF5/HQ9DvBz2IJkM71mm6jh4I4PN58Xo8+AMBsqLtvDi2D91iLMz5bjpPPPpIm9d77eWXWfTtp7w44aidJsrNRdksPHPKIIrmzODp/z2+R/eXkJTEhsq6nR7T2NhISb2bTtuVaqREObjm2P5c9eVCNtbseu3xh0s38dnqcm47afAOz53SoyNffvLhLt+MtYefvv+OMTmJvDBzARcekUFmdARCCGJtZupra/f4fIlOO4kRFh6+9879EK2iKMrf1+6WYSRJKX1bvxBC9ADOJzTTfOD6XimHhcTERCZffHG7nW/RokX0SIoiqtlip7yUeBavLtzp64QmsGpgMhpatEcTQjAgPY7AAMnb8wtblDFsqXfjsFqxmVv+1dh67QaXB2cbG6ZAaEbW5Q9i267v86dL1nH82FN2fbOt8Hg8/Pu6azC66/jfyb3IiYvA5/WC1DFoGgajoWWJiAjNrG+l65JAIIA34MdsMvPY8Ucw6eM/efeVFxk+6jh69+7d4nplZWW8/cLTfHD2Udh3cwvqrSwmI4+NGcCEd95k3GkTyMrK2q3XnTThLD59+r+MyGu75rmuoYGv15QxZeSOSe7YXjkEpc5Fn83j9K6pnNI1jYSIljP7C4qrmbJkM2tqvTx/7mgSInfsB54ZG4nucVFeXt7UonB/EUJQ7fKwaGMp/zl6WzeRfUnTo2wWtqwqYPXq1XTu3Hnfg1QURVF2b2ZZSukTQiQIIa4RQswHFgL9gGv2Z3BK+5NS8sX0adx0w6Xcc9fNbNy48WCHtEv19fVEW1omn73SE/izqJqgrrf5Ol3XMQiwmlovNciJdbCqqgGQCAESicsXxG7e8T2kEAKjJqhye3Y6u1xc78FmNrXoYjB77WYe/2Een304hdFDBnLv3XdSVla2i7ve5o1XX6FxyzrO6p5O90QnPo8XAxKzwYBxN2qpNU1gNmqYNC20ME/o3D2iK2ZvA3fdfAP6dmP46ccfM7pjPPGO1jeX2RWnzcK43CQ+fn/KDs81Njby008/8dxzz1FaWsqLL77Ir7/+ytFHH816v4kvlhbS6PUzf2Mp3y1fz/cFG1heVInXH+DJnxfTISG6RS18c6f0zuWF806gUpo544M/uWL6Au74YRm3fr+UMz74g//MXkvf3GzeuujkFuUXzQkhyEuMoqCgYK/ufU8MP3YU7y1cz6jshKafUSkl1W4fkVGt3+MuCRjTOYmvpk9rx0gVRVH+3nY6syyEMAFjgQuA0cAa4D1CO/idIaXc/X/xlXbhcrkoLCzEaDTSuXPnPa6Bfe/dt/hkyt1cPEGjqCzAxRd8wxvvfElKSkqL4wKBAD6fD5vN1i69gPeF1WrFE2iZ0GUnRJMc7WTm+nKGZye1+rpgINTVsK2FUgkRFoyaxsZaN1nRduq9ASwmA2Zj62Nq1DQafUEa3V4i22ght7C4mq4pocVnPr+PJ7/5g08XreWynmkM6pBAg9fPF99OYeiLz3PNbXdx4YUXYrW23Y7O7/fz9qsvYQz6ubhPqAuF2aA19SDeEwZNoAkDvmCQLjF2Ruck8fX6QubOndvUXzgQCDD13Td5ZnS3HV4vpWRZUSVLiypw+fzYzEbykmLplZG4w8/IqUdkc85H73PZVVfj9/v57LNPeefF59hQuJZUm4HsmAiGXnQtf7zyP96urKfMK4mIieOqxYuxazq9UmJIc9rQkayuaKCgvA5PEO4ZM3CnHUWyE6K55cRBXD2yH/M2llLr9mI2GDgrKoIeafG79bOcYjdRXl6+x+O7p3r06EFCSirJEUEqG90IBLVeP0arnai9TZaB3mlxvDpvTjtGqiiK8ve2qzKMUkAHXgfuDO/ihxBC7eR3gPl8Pv735FN8/Nk0IhNTCPi84HVxyYXnc/ZZZ+12Qvv+u8/x8PU2crNDCVp5VSVff/UVF06eTGVlJR9Pncrb739IaWkpBqMJqescfdRgLpg0kYEDB+5zP929kZ2dzZLSGgJBvUV5wTkDu/HCj3MYkpXQaks3XdfxBHTi2kh+hRCckJvCpyuKuOrITlS4vCQ6HW0HIiAh0s6m2kY6Go07lGoAfLqymDMG96KisoKfl6xm2pJCHj++B73TE5piH9EpmaHLN3PH/Xcw/YP3+N+Lr7T5K/MZM2YgGms5NS8FTQ/udaK87Z7BbAglzKflJfP1mlI+fPvNpmT5zz//JNUCnRKim14TCOpMX7KWD+cU0OjxMDg9lgiTgQp/kI//XIZmMDKhXx7je3VueqOREuUgP8bGLTfdyJ8zviffGuDKLskMGjKImAgrmhDMiLbzyvh+rKqo49/fLcZAPWOOzqZ7UhRGg6HpTY7RbMIWGcNn8wv46I+lLNtSwc0nHNnUjrA1ERYTQzun79UYGUToTcOBYDWbGNi9Ez5NR+qSuNgkIiMdLTbE2VO5STGsmfF7O0apKIry97arZHkxMAQ4ElgthFgnpaze/2Epzem6zpXXXkdZ0MQ5D7yEMy7U2rpk3RpeevFhKiorufrKK3frXFLqGI3b/iE2GiVBXefRxx/n5TfeJrlrH/LHX8gxGVloQuD1uFm94E8uv/kOoi0GXn7uGXJy2t46en/Izs4mLSePmas3t6hpHdElk+mL1vDy3EIuO7JlTFJKXL4AmqbtUD/c3GndMjj/o985rlMikVYLkdadL8CLsVupcPnYWOci1moi2mZpStQXFFWzud5HvtPM3BVreeK3VZzRLY0j0uJbJPlCCMZ1TefDgmL6Jxq5/LyJPPPGO612MJjz6y/oXjcjO3TGqIl9SpS3XT/Ukzon2kZShIVZM35qmq0tLS2lQ9S2JNTl83PTxz/jdTVyZd8sjkyPaxGDlJJ5RdW8vmA13y1fz6OnD8dps1BYXsP8VYXk1FTxyKBUeiRG4bCYdnhT9/umCm77bgmX9+vImNwkglLiDeiUNnrxBSVmk4mgP4C7rprhWXGh8o6CIs575Quem3QcGa20sttX7qDcp/Z+e8LldpEYHbXTxH9POSxmGl2udjufoijK391Ok2Up5TAhRBZwHnAD8KQQ4lsgAtizlT/KXps5cyZrSys5556n0ZqVXSR3zOH0Wx7i1evO58zTTycpqfVyhObGnXIht//vYS47209RqZ8vZznQ/vyILQ0+Trn5v6TGxxNhMTWrg3WQk3QC1UOHM/fXWYwYfQKvvfg8I0eO3D8324Yzzp/MWw/czjG527ZlFkLw75MGcf6rX5DmtDE2f1tvXCklVW4fcQ77Tmfd06Ps5MRFct/MVbx+2pG7jMMYThQ7ZHeiqrKKwupqIowGpNT5v28W0SU1kfPe+RGL2YxHAkLjrh+XsrKinlqPH01AutNOXoKTJLsJp8XEjUd24Op/TOaFN9/BYrFgt9txOkNJ4OK5c/D5A3SMtra5HfXe0MIbr+TH2dlY4qK4uJjU1FTcbjcRxtB1AkGdmz6eQZxB5/YxvVu9vhCCfmmx9EmJ4ZHZK/nXBz9y9Yi+/N+HP3FRz1QGZcSRHedsdRbe49e577slPDAyn+6JTiRg0jTMFg2H2UiVx0+NJ0DHcKu4ejfUeXyc0DEep8nAZe98y7sXj2l1l7t9sbraxanZ2bs+sB2YTCZ8gbbr7veGP6hjNqn/PCuKorSXXXbDkFJuAO4F7hVCDCGUOOvAIiHEq1LKG/dzjH97H3/2OT2OHdciUd7K7oyi86DhfPHFF0yePHmX55p88aVEOp1M+WEadkcUwrCK4joXl9/9CBH21me3jAaNhEg7xx83mrjICM694EI+en8Kgwfv2JVgfxk5ciTTP/6Q+7+bz22j+jbNbsY77Dwz8Tguf+dbttS5ubhfNkZNUNrgxhPUibK3nUj5AkGen1PI5sYAVrOJV+av59L+2bssaRGA2WwhJSWFxKQkqqpruPnLOVg7dmNuRTmT+ubj9vl5edYi1lfVM6xDPJOPyCDWZiYoJRtrXSwrq2f6ii1MX1VMTkIM6ytrmXjCSKKckTR4fDicUeR178HchQvpFWnFbDDsciHfnjJqGp1j7Mwq9bBhwwaSk5Opr6+nosFFQ2MDny5eh9/t5vaTe+0yUdc0wQ1HdeGfn8/joje+5NHRPUm0GkiK3LGzCITGfkudi6sHdOSIpCiMmtbi/nQkcTYzBk2wsaqOjnFRRNssOE0GJGAzaSwrq+XmqT/z7DnHtduY+AJB1lXWH7A+xZlZHVlXWUty1J7tPrgzhRU1e72ToqIoirKjPZqqklL+IqW8BEgGrgJ67JeolBbKK6uITW57RzFnUiplFZW7dS4hBGecOZGnnnuX6MhkVq1ey6Rrb24zUW75WhgweAi9jxrGpRecS2Hhzlu3tSeDwcADjz9BSUxHrvn0N5ZsKW/qhdshPoo3Jp/EyoYgZ334Ox8t3UijbsRgaP29YFDX+XFtKZM+nsNGD7x18cm8dN7x/Liphv/OLMDtb7te1RfUMZqMTRWllQ0e7vipgJg+x3Dfw49iC/r5Zlkh3vpapp8zmOsHd+bkLinkxDmItZtJiLAQazPz2+YqDJrg0j5ZXHpEClNPH8CbJ3XliwtH8vM/j+elE3sw2lhJN4dgaVktby7aQFDf+6Zibn+Qojo3qyrqKSivY2V5PZtqXRg0gc/j5pnHH2NY/96888zjLFy7gcqizUz5bTHndU8h4PcTDAZ32dJMIql0eZjUPZX+qTEIwGnd8c2KlJI35hdi0jRO7JyEyaDt8EYg9IZFEmM14zQbKKlrRNM0dCkxGzTSnXZuGJTD72s28cqsxXs9LtubvXYL+d27Y7G072x1W/J79WFZ8e793d1dy4oqyT+i964PVBRFUXbL3mx3jZTSQ6grxnvtG47SmvSUZEo3FpKZ3/p7k+pN6xg6YMfuBTvz008/8dUH75DX4wiS0jL26LUDjx9H+eI/uPGqy/lg2pcHbNGf3W7niedf5P333uP2t14nwl/A0WmhWliXL0ANZjZrUTy/IYhl3To6GDwMTosiIyoCKaGs0UNBRQN/bKkiNSaKy44dwLDcjKaZ5JfPO55Hvp3DWR/+yeX9OzIiO2mHhYMefwCrzU69x8e0JYW8umgTZ0z+JxddcglXX34ZjdXl3DO6J0M7JhII6hRW1GDz+ImymADJ24s28vrCjUzuk8Ujo7oRYTGhS0lJvYcqlwu3y4XdbictJjL0oddR43Lz7pIt/LSugntHdCUrevdbugV1yeY6F76gTkWjjwqXF1cgiMWgEWk2UeXysrmqloEVa3l60tFEWs2Meeoj5hXVYDcZGJgeiy4h4PdBQMNkMrX5/X5n4QYS7WbG5iZTXNdItNWEoZWFl+X1Lj4t2MIVp5oxtnGuUKocSsATIiysq3ZR7/VjDSfMBk2QHGnliv4defS7P+icHLvXC/qa+3DZFib86/Z9Ps/uGn7sKO6e+h6TB8l2qUcH+HJtOf84v/1m2xVFUf7u9ipZVg6s0089hRvu+S+9ho3GZGnZZqy6rJh182Zz4l3/t0fnnPLay8Q5bHQaccIex5PeqTO2uEQaSza3aDt2IJhMJiaddx4TJ03i999/Z8miRZTV12F3RDK5e3eOOuooDAYDa9eu5b677+ajxb+SHRvaTyfOYadHTkfOHTaA7IRoqhs9/FZYxObqBgK6jtlgYMwROQzNzeDjeSt49Nc1HJkeQ358JPF2C540Py/NLWSVx8Dq+hV0zO/OkBPGsnTen4wZ8S7LFi/g0eO6kR1to6LBTaTVTGasky019VQ0enl94QbWVjfy0tjepDttTRulaEKQEmkjoEtKSkrIblYva9A04mwWnj6xJx8tL+KSaQt46oSe5Ma33ie4uaAuWVlRx9wtNXy/rpwaj58eiU4izAbcAZ0NNY0sKasjNzaCeeu2UFbvItpu5bS+ebw9v4DhWaHFfBpgkAaCuo7P68VkNu/QstAbCPLGgnW8Nq4PNpOBknoPac6oHUpaGjw+lpfWICXY2+h/vZUQAiklmqaR7LBQUu8iK85JwO9DE6GylPF5KbyyYAN3fzaLt/8xhpSonXQz2YXFm8spdElGjBix1+fYUz179sQSn8KvhVsYkrPvyb7bF6BGs3HUUUft+mBFURRltxyQZFkIkQG8Sah8QwdelFI+IYS4C/gHsLWp6a1Syi8PREyHkwEDBjCkdw8+fOBmjjnnn6Tl5KHrOmsW/MGM15/iuisvIyYmZtcnCissLGTDimVEWMzEJibvcTxCCKITU+jr11u0HTuQNE1j8ODBbdZNZ2dnc+bEifx71g88MDIfs0HDarUhrRF8t3IzN3z4I1UNbvISIsl02jAbNLwBna8WuFhdVU9atJNT+uYTH2ljXXkNS4oa6ObTeamggpNOOxPb/Dl4Nq0hmQqGJUbzureCEwZ34sScJHy6xOP3s6HBjcVkIMkZwctzC9lY6+aFk3vhsJh26P0sBCQ6LGysd1FXX48zMpQMawYDejCAX5eclp9KtNXEFV8s4uVxvcmMithpHfOsDRXcP2sl+fGRXNK3A4MzYtGECJVTSAkIvi8s4/t15fy0voJzX/mC2Tedw/henXn4mz84KSehRXxGg4YQEr/PBxZLizrmb1aX0C3RSUaUjYrwNuSaAH/AjyY0hKYhpaS4toEaj5/uiZHsSXc0u8kIePEGdIxCI6DrmAwacXYL0VYTIzslcN/0X3l64qi96gvu9Qe464cl3HjfI3u9JfneEEJw5Y238OD1V9InM2mPd0xszhcIUlzv5vIbbjooLR4VRVH+qg7UzHIAuF5KOV8IEQnME0J8F37ucSnlIwcojsOSEIL777mbd959lzeevY+6RjfBQIDsrAzuu+WGPZ4J++i9dzklL4Vv11XsQ1AwoEMyD82eSVlZ2X7fGnhPNDY2cst117J56XwynVa+XrGZi/p25LV5K3l/yUaGd0zk7qG5dEuMavVX37oumVdUxUfLt/BRcS1XDO/LKb0H8BaC+Ng4vMt+48GhuXRPDW1yMX9jKXWNLi4alYtZ07BrAqwmEh1Q7/Xz05pipq0o4sPTB+DcSWs6AaQ4bJSWFBMZGYnP5yMYDBLQdardPmKsJo7JimNddSO3/bicW4fkEmE2EW0zEWk2tUic52yp5vafCrj7mC6MyN72vZFN/wd+XScjysYdQ7twad+OnDN1LndNm81944/mqM4ZbK5zE9RDJQ9bhT4P7QSohet6g7rOtILNnN09DSklvoCOJkCTOjIIARlASqjy+IkwGajx+EmL3P1WaZJQsh5jNVHr9pIcFYHP40ETodjSI20MyYpn/h9rmbFqE8O77NniNiklD/24kLzBww7orPJWRx11FH2PPYH7v/uFe0/ov1flGFJKHv1pERnj+jD6+OP3Q5SKoih/XwckWZZSFgPF4c/rhRAFQNsr1pQdaJrGuZMmcc7EiVRUVGA0GomNjd2rc61YvICrchJYUFpHdXkZaR33vG9yXXkpqfkd6JYUzZo1aw6ZZDkYDHLDVZeTWrOBxycdQ43LyynPTOXHtaVkRdn4+IwjibWZkUIg2khKNE3QPz2O/ulxFFY1cNdPS3nmp/mccsUNXD+wI6cckdNi9vKjuSs4s1saZqMJPRAITakS+sNhNvLagg3cODgHu0lDl01PtyBl6MNhNVPmqmf9ukL8Hg/RZg2jxUqN24fVaECXoRnmH9eVs6iklhM6J1Pl8lHa4CEl0obDbMQf1Lnpu6XcPyKfoVnxrdyhBCGo8fiINBvRhCA7NoI3xvfhnE/mcWx+FucMyOfZb39nfY2LzChbi9ptgyYI6hK/34+u60ips7Kinj7JUfiDOp6gHtoevNlrdCmp9/jJiraFtgvfg3xw66F2k4HqBg+aEJjM5tAMd3iNsiYE5/bM5MO5K/YoWdZ1yaMzFrHGFM9z99y3+0G1s5tuu4OrL72Yu7+Zy79H9WlzF8nWBII6j81YxDKiODIt7aDvuKkoivJXI7Z2FDhgFxSiAzAT6A5cR2gr7TpgLqHZ5x02PRFCXAJcApCUlNR3ypQpByrcHTQ0NOBw7H1d5IGyszgLV68m3WHGEwhQ5oOYpNQ9Orff56WutIjceCdb6lxEJiQ39QVurq6ujkAgQFRU1E635W7PMa2vr6eyeAsdYhwgwq3AKmqJthhJiDA3lT9IGe64sBt5RSCoU9rgwRKbSLTuaZEEImFlaRU5sREYwjW2zXOVBm+ACrePDtH2UOUDrW/VvPWvoRACf1APdX0wGggEdYJSBxlK4re+0uULsqnOTaTFSIzVTITZQECXaALqrdEEa8pJsFt2vD+57Q9fMIhJ0xBCEG4+QbXHT5UnQHZCNGvKqkmLtGAQAk0IjJpoureme0HgDepsqnXTKdaOBPzB0EXMzbpcBHVJMNzJotbjp8EXICohGYe3tu2B3/qfpmb34AnoWI2GphWAUkrWVjWSGW3HZNBYU9lAVnzUbiWbgaBOcZ0L3WghIytrp6ULB+LvvZSSos2b8boaSI20YW2l5d72vP4gRXUujDY7aekZuFyuNuMcPnz4PCllv/aOW1EU5a/ugCbLQggH8DNwv5RyqhAiCagg9M/ivUCKlHKnzYL79esn586du/+DbcOMGTMYNmzYQbv+7tpZnGeefDz3DkilQ1wUw1/4hlPv/B9xSSm7fe4vXn6Kk8RmLhjYlZu+nMexV97KqFGjWhzznwcf4oc5i4hOSadxfQFT35+C3d56F4f2HNPLJ5/POEcjo7t1oKrRzbmvTOeyPlkM7RBPcU0DiRFmoq1mgrqOjsC8kxZhUkrKG9yU1rnIio3k246j+ejZx3jjwhOxhHcFXF9Ry9XvfsPnE0O10z6/DxGupwW46ouFnNA5iRM7h2rD/UEdCS2SOSlDC+QMJhNBvx+/rlNU70UA0VYjUVYTRhFKQs1GLVR3LCWnffAnx3VKZM6WarxBnduHdiEoJbPyxjJ2/VckRlhblFBsq1WGjbVuzEaNOJsZgcBk1JASlpfXct/sQv51/GAWbipjU1Extw/Lp9btpcblwaiFk2ZCpRH+oGRuUQ3frC3lhTG9KWvwgsGAQQgCgQApkaEFqUV1bixGjRiriXXVjfzrm6Vcd+d9HLNyWji6bW9ctkash3cVbP7mYk1lAxlxUVjC41fp8jLmrVm8NWEAiRFWHpq9ir5dOjG+V+vbhwPUur18umgtby0tYuIlV3DehRdiNO48MT1Qf++llHz37bc8fv89ZFgkY3OTOCI9gfSYyKYFj8W1jSzeUs60VSWsrg9wxQ03M3b8eIQQO41TCKGSZUVRlL1wwLphCCFMwMfAO1LKqQBSytJmz78ETN9f16+pqcHn8xEXF7fTWc6/g6joaKoaPeQmxXJB7yw+ePlJzrzxHoy7sevX6sULKFnwC+MvCNV2Vnt8rc4qf/PDj4y79VFik9N469Z/snbtWnr02P9tuZcsXMBD5w8D4MGv/2B0x3hOzgvNnFvinBTVNlDr8RNjM2Nupa0ZhJK0OreXSrcfvy6Jd9hw2izE2810jDTx3M8LufbYUM5RVNtAZtS2NwEmowmv14sIJ8XzS2p5aFT3pueNBg1vINhUCyxlaIZXMxgI+v2YDKHFa4FgkC7xTgwGgdR1BGDQQn2eLYbQbPCo7ESkhJfH9uazlcVc8eVi+qdG072ngRirmXC17w73V9LgJSglcVYzupRNiX1QSgyaxuld0/ho3kruOHkwk99Yz3uLN3J+n47EO2wU1zZS5/YSZzcjgAiLgeRoBxZDRWjG2B+kg9OBBArLq0mMCL0ZcfkDRFmsCKBDdASaAHcgGI6oxXQ1ssXM+w4NmLdNwwPfri7hmPyOxCenUVFZQazVyGu/LcNmMtE5MRqHJXSPZfUuCkoqWVTWwG9bqhk66nhevP3pFp1HDgVCCI4bPZoRI0cyY8YMvv38U57/dhE1VZVYTUa8gSAOZxT53Xtw8jUXM3LkyAO6IFFRFOXv6EB1wxDAK0CBlPKxZo+nhOuZAU4Blrb3tX/99VdefuEh1q5egtWiYTBHMeGMSzjv/Mm7nE36qxo4bCTfffUuA7NTuWhgV1ZN/4OPHr+XcVfehM3e9k5iBfP/5OeXH+Ol8f2Jtlspq3OxptrdahLcPT+P2VPfJrFDZ9xV5aSn73tbrN0RDAYxGjR+XLGRtcXl3DthW6cOi8lIh7go6j0+qho91Ht9RNpDPYc1AboM/ZrfGwwSEeEgLime0uItJEaGk2EBNw7pwlkf/sGorh3olhqPLmWLGmQhBGazGZ/Px+rKetIirdiatUgThLZ0DgR1BBp+XUdoBqSuY9QEgaCOQQhMBg2jIVwyEp5RNGoCqYM3qGM2aOQnOJhaUIwQgvF5qXRNcHLK+38wyGxEAgFd0vz9gK5LSho8eAI6qZGhWWddl02lKXUePw6LmeNynDzy22wcVjNPnX0sV7zzHVvq3ZzTMwuP10d2TARmo0ZAl+gIvIEgW+rdVLj9ZMY6m8pUHBYzxfVu4u1m/EGJzWhACIFBwISuaVS5fduSXyFCoyMBKUOz4EK0KGkBwmUuoQd9gSDvL9/CneOOISoqiqioKPoHjHxVu5EfSOKFmStwuRvRhEZsXDxde/Wl/4m9uGnECKKiovb9h20/MhqNHHvssRx77LEAuFwuPB4PFouFiIj22+1PURRF2bUDlS0eBZwLLBFCLAw/ditwthCiF6F/ItcDl7bnRadP+5xnn7iOGyZrHHNHNAaDYFWhh6feeoBlS+fx8KPP/S1bLI0/9TROfe4prnF7cdosPHjykTzy0yJeveEf5B49ij4jjm8qywgE/BTM+5Ml308jWLKe104dQH5KHABTFxdy/LhTWy2veOC+e3nuhReprCjkpWee3KPWdvsiIyOT5cWVvD57MVcf2WmH2lUhBE6bBU0TBI1m4lNS8fm86LrEpGk4LRasVisGTaOsrJQoi6lFKUOMzcxFvbN487elPHjaMKJsFqrc/hbX0DQNi8XChtpyOkZHhMoJms2QhhJzGd4NMDSbH9SD6FtzRgmGZlmiQCDDi/JMGgR08AV0Mpw21lW7mo7LjXOQGxua1dVEaKY4dG1o8AUoafBiNWqkRVqxGA14g0GMmqEpX632+EmOcmA3G5FS4vUHSHJG8OoFJ/DyL4s5/s2ZDMmI4ficJCJMRhp8AX7bXM2fJfXUe7x0iHViajbeZqOB0joPJoPW1NFiqwldU3knoDNjXTnDOsRvXXOI3DpxLNkhUd5a97x1JvyFuYXkpiTSK2Pb4tJYh4PExEQeevKZ3fhpOXzY7fY2y5gURVGU/etAdcP4hdaXUu23nsoul4vHHr6Fl++zkZ25rS41N9vKY7dauOjWH/jpp58YOXLk/grhkBUbG8tRI0YxfelKJvbPQ9MEN47sxbn9Gvho4VLev/trGoMSg9GI1+2mf0YcN/fMYuiY0U2zhv5gkE9WFPPc3RNbvUZkZCQ33nD9gbwtAE6ZeC7Pv/wYNQ0uhmQltHlctdtPbHwijogIaGOmrq62lnTHjr/iPqlLKs+/+yvl9S46J8awrroBf1Bv0TFCCIEuNGxmQ7jWFJqvWDMaNNAMoTH2eDAKQUDXsRgMNPp8WJvPRovQa7bOqho1gSZCi+XqfX6K6z04zAasRgORllC9cFmjF4fZSK0nQGN4++5YmxmnxYjREJrZ1oRoeiNQ4fKiaQK72RiqE252v06bhXG9OvP14jWc0CWNgrI6Gv0BNCA3NZ57zjqB05//hNJGL+nhkhS3L0B1o4dOsQ6K60Mt6GSzBNhqNJAaaeW/v6wiOdJKXpwj1BGk2fjJcC/ora/xBIJYjaH4vlpVzFdrK3j74pNb1DTrUv7ty6wURVGU9vWXrUP4/vvv6ZPvIztzx1+3mkyCSWM0Ppv6xt8yWQaYNPlirjn/bAZ1TKFjfGiMUqIcXHVMT64c2gOXL4A3ECDSam7aaW4rKSWPz1hMtwGDDrmaz5PHjuX+O2/nwpzYNvvV1ri8lLp8zCksZ0XJCtaV1+ANBLGZjXRKiKF7WgJDctII+P1YjDv2A44wGzk6M46ZqzdzWp9cOiXEMHdLFYMyW7ZpMxkE3oDOjotoQ7O9gWAQzaAhkOiSpm4Tjf4gEc06IUgZeqze628qmzAbQmUQjvBxlW4/3oAHKUNJZq03QKM/iDegYzMZyIiyhRbFSUlQ1wlKicVoRCIpb/BR5wuQFRfaca/RFwAhmhYxAnw8byUTuqZyarcMCO+sXlznwhwdT4TFRL8OKcxcX87EI7KQUlJS10CSw4LNZCArOoKVFXW4/EFMhq1dNQQ2o4Gbh+Ry1ZeLuGFwZ0ZlJzQt6BNsbae3reyi3hfAbDLw8txCPl5RwjPnHEdsRMvvT2l9I3EJh0YbQ0VRFOWv4S+bLBcXFdE5y9/m8zkdLBRNXX/gAjrE5Ofnc83t93L1/bfz9NgBZMVtW6QnhCDCYiLCsuOCPyklz/+ylHkeK688eOjtJeNwOOh9RE9SjKUU1zUSY7NgDSd9Hn+AFWW1vLJgA0vK6jkqM46eSU6O65WG1ajh8gdZVVHPD4sKePCr3xiUGs0tw/KJamUjke6JTgqKK4BcTumTy4dLV7dIlnUpSbYZKaxqDJVgNOtSJwENiR7UCfgDCAS61DEZDPiDOo2+ICmRNqSEGo+PSpcPTdA0K6wDbn+Q2ZsqqfcF0DRBekSof/GI7HjqfUHi7WaCEiQBnDYLG2vdRJiNRBi18Oy0Rm2jhxqPH7vZRIe4qKbfGny3poSBHVt2R/lt7WYu7JnO7d8tZm1VA42+AEJA59QkTu/flVN653Lv5zM5q0cmnkCAYFDHGf75MWgCh9mEjsSgGQjoOjL8v8EZsTwyqgf3z1rJF6tLmNQjg/5pMU1Js5QSKQXeYJBpK4v5bn01SbFRvD75JJKcO/5GoKC0hryhf883wIqiKMr+8ZdNluPi41m4vO3b21TkIy4+6QBGdOg5acwYgsEg/7j/Tq45shOj8rN22p92Q2UdL/xWwGZbIs+9/tIh22+6aMtmhp/SB4PPw8aqSqQe6rrw07oKpizdxNndM7j/2B44rTu+GRiQHsckoKTOxWvz1nLmlF+59ZiuDO3YcrYyLz6Sz9YUAnB8t468NHMR87ZU0TctNrSLnddLbpyDzfVuvMFgqDdwWKiqIjx7qgfDNcahr8savUTbQtthl9S7cQeCpIYXCYbLmQGIsUrqPAGSHRYu+mw+z5zUi6xoO6fmp/G+LxDaNESXGIQgNsJGhMXMltpGqj0BzJrAajJgNRnpGB+xXRs7yQfLt3DVcYOaHpu+aA1LtlTwesDH6V1TmdQjDYtBo8EXZEWNm8e++hU/gipPgBnryuga7yDK2nJHQZvJgNuv47QIjFroekIIrEYDvVKiePu0fkxbWcwTfxRS0uAhLz6SZIeFgC7ZVOdmYUktFouZB08bztDO6W1uvLG4wsV53bu3+pyiKIqi7I2/bLI8atQonnni35RV+EmMb5kU6bpkypdBxpxx/kGK7tAxdvx40jMzeeXpJ/nfaz8wtksyJ+VnkhBpx2I0UO/xsXhLOR8u28KahgDjzpzI7f+4BJtt97crPtDq6utJiIzAoEUSn5BAMBjg5V+W8O3aMl4Z149OcbtO8hMiLFw3KIcTcpK45Yfl1Lh9ODttez7GZqbO7QPAZjZxy4mDuPvL2bx92gBshtC+chaTkSOSopi5oZLjOrVeGmAyaHgDOgYBdV4/7oBOttNGeaMHb1AnK8oeajHHtkSZ8OczNlRww+AcNtd5uPLLRbxxSl9ibWYiLUa+XFPK0ZlxGDWN9dUNaEYTKemZmC1mNq1fR1q0o+XmKmGLSmpwBeHIDqGZ5Vd+WcyHfy7j4VHdGJ+X0lR/7Q0GMZrMdE+L57Su6SwsruH2H5dxzZcLefuUPuTEtpz1dVqMbKhxtdgYpjmLQePU/FRO65pOldtLQXk9lW4fui7pmxpDYZ2Pty4eQ2bsjm0Kt9pYVce6Bj8DBgxo8xhFURRF2VN/2VYQUVFRXHjxTVx+VwPLVrmbHq+sDnDv09V4tSM47rjjDmKEh44+ffrwzKuv88rU6fiPGMm1P63mpDd/ZujzX3PWR3/ybpmBMdfexvSfZnHF1dcc0ony9gTw5ZL1fLt4DS+P67NbiTKEF5gBvVKieeL4HjwyewXlDV6K692tHn9053RGdOvEVV8upNrlxmgI1R9P6JrGh8u27HD81p3+BKHOFS5/kJIGL2lOG0FdUu32k+60NiXKTfthhz8WFNegS+iXGsv4/FSOTIvh3cWbcPuDxNrMTFm6mSWldUihEZuYTKfOuURFRWGz2oiKiaO43rVDLXV5o4fbfljOlcP7ommC9+es4KuFK3lxTG96p0Q3zeYGdB1NaE0L6YQQ9E6N4d0JR5IZbePy6QsxbtdlxmI0YDFo1HsDuxz7OLuFIVnxjMtLZXROEh8u38Klx/TeaaIM8NGiQsaefrbqO6woiqK0q79ssgxw3vmTOffiR7jlCSenXVnHef/XwISrvVjiz+XpZ99S/6huJzMzk+tuvInPf/iZmfMW8fuSAr77bQ4vvf0eJ5xwwmEzXs7ISKpdHgBKaht58oe5PHxcD2Ltbe/Wtz2haTT6AhRWNSCAGwblUN7o4awpv3LtF/OZX1SN09byfNeM7Eu3jCTO/2Q+84tqABiaFUe5y8vMDRUtjtUl4Z34oMrtY0u9m3SnDZvJQI3HH6pP3ppwbrc+UJeSp+es4+weaYjwVtQTe2QwfXUpRpMZi9HAA8f35tYfl7OgpBaHI6JFd4uExER0k5WiOhd6OGHeVOPiks/nc8aR3Tm2awfK6ly88PN8njyxF/F2S3hr6dAOhEEJJrN5h/Y2TquJ104ZQLnLyw3fLEHXWwYeazdT0uDF7Q/iCQSRUuIJBPEGggT07RdBgscf5KbvlxETYeOs/vk7/X4VltfwRWEVZ0xsvTuLoiiKouytv2wZxlbjxp/CmLHjWLNmDV6vl44dOx6ytbZK+8jtkseKkiqG5Nh5bsYCTs9PbnNGWYZ7EW9ta6aFF5ZVN7qpdPlIdliINJvoHOfgPZuZCfmppERauW/GMvplt9xoRQjBJYO70sUuuGPGCnJjI5jQNZVbhnTh7p9X0Cs5qmnRmy+o0+ALUOv1o+sQaTZiCy9ErHH7yIgKzd6Hyi9ki8343lmyGQ04MSeJoK5j0DSyYyNId9qYvbECcqFDlJ3oCBs3fr+cOY1GTu+eSf8OyU3JdUZmFkVbNvNzYSm/bKzkpw2VXDmiH6f2yQVg6oJVHN8pkVSnDV8wiDcYpM7ro94bRAf0hlCrOaOmEWWzNLWcS3RYOat7Ot+uLeeqrxZzxzFdiLdbKG/0UuvxI5FUeXykOKwIQl09pJQEdB2/BKMBjJrGuupG7pxRQKrTziWD89vsbAIQ1HXu/mExl91wM0lJf+91CIqiKEr7+8snyxDaJCI3N/dgh6EcIF1792PJnC/onhrPzFUb+fTsgS2e9wWC1Lq9uHx+PP4gEomGCO/GJ8JbUgfIjonApG3r8xtnM/P66hKmTxyE1ahx9y9rcPlC3SS2EfRLj2V0birfrinhlYWbWFVRT0APcv6n83lgZFeEEHgCQWIjbCQ7Hfj8PqpcofpnKUM771kMhqZZXwgl8kIIZm2o4O3Fm3h1bG9MBo2grhPQdeq9ATQk7y4sZNQgD4/PWo7H68MRFUXayFN59MdvCfyygiOSo3EYBe6gZF2Nm2VltTS6PXRLTiEoJcuLKkly2vho3gqeOr471Y0eKlwevIEgZY0+Ymym8I6HAl2Gkv6S2gYQEGO3EmWzMr5rOl+tKaN7cjRnfTSH8V1SODk3mewYO5oQFFY3UuvxQ/jNCUKgaSB1SaXbx4fLi5haUMTlR3ZmSFYsMqLtN7e6Lvnv9wuIyj2CUydMaLefIUVRFEXZ6m+RLCt/LyOPO47/e+816l1eNILc99NyILTIrHdyFDkxNqJtZuKsJqwO67ZtpWWoHndLnZtYqwkpJf4gmAyhpM5s1EiNtDK3qIbOcQ6GZsbz5ZJCJvTt0nRts9lEZUDHYtQYm5/G2Pw05m2p4savF7KktJarvlrMXcfkkxXrIC0mEiklVX4fnmCo9/HWnfvCGz63aDc3bWUxT/9ZyOOje5AWZWdzrYsPlxfx47pyaj1+EiMspERa8QclG2rdmDRBZVUFj993J4OOGclV9z1KbW0tDQ0NWK1WhiYmMnDgQDRNY9asWcz8/lumLljImsJCrP4ARqOJ4gYPdqNGQnQEDpOh1UWBsTYzLn+AKreXapeXzGg7GU4r2TEOrEYj3xeW8dnKYkZ3SqRfagydYiMob/SGduTTdTbXeVheXsdvm6uZtbGSPinR3H9sN/pnJrK+qoHExNaTZV8gyAM/LGS9NYmnH39iv+zGKaXE6/W2+3kVRVGUw4dKlpW/FF3XWbZkCeuKynCVFXNG11RyYiMISsnmOhevLViHEIJzuqczPj+1RQsyIUIlAJ5AMLS4TmgEdB1vQMcUTqh7JDqZX1zDuPw0JnRL46XFa1skyzabHQwGXL4AERYTDV4/N3+ziDuOyWNIZixP/VHIpdMX8K/BuVzQ14GmCRr8OkZNo8btIz7CgiYE/qCO2RDairq43sNDs1dT3ODhuZN6EWkxcuO3S5lfUsPY3GSeOL4HHaLtbKn3EGUxsSDSyh3HdCHdacMV0PmkoIi35//CxWfN4r7/Pc0555yzw7gNGzaMYcOGATBz5kw+euBWhBAkRFiIj7Ci6zoBv6/V/2AIEdqoxW4yUuX2UdnowWkx8fTvqxnZMZ4bjupMWYOX6auK+WJ1CQUV9ZQ3+rig80lc+vT3dI5z0D3BSa+UKK4blEOU1czmOjdFNQ3omoGIVnZYLCiu5K7vF5PRexBPP/hQu28Fres6H77/PlNef5nSoiLOv/wqZnz7DZdceRWpqantei1FURTl0KaSZeUvIxAIcNett7Bl7s+8cNogor21dIi2YzaEFtKZDILJvbJYUFLL/35fy+LSOm47Jq9py2cILZ4T0LS4zmTQMGgSX1BHSugUE8H3heVM7mdDFxpVDS27YwggJjaeisoy7GYjM9aVke60srKinj83V1HnC3BK1zTum7mCj1eWMjY3iXSnjXSnjUq3jyiriQizgcJqF2WNXr5YXcLcohrO6JbGg8d25fvCch7/fS2ndU3lnmF5WE2h7bT9QR23P0hapBW/ruMwGzEbDERYTPyjb0dGd0pkXkkd91x/JX/MnsXd9/8Xp7P17hJ+v5+S4mKijogjwRGqndYMGtIPQV22GK8W9y4gzh5aBKojWVhSw5QJ/QBIdFiY3KfDtmsEdWbGR3LdwBw+LigiNdLK0KwEom2h1yc5LKysqCcjq+O22XUpWVpUwYdLNvBbSSP/uu0eTjjxxDZ7Lu8tKSX33Xk7G2Z/zz1D8uie2osfYyMRv87norNO5+X3PiAtLa1dr6koiqIculSyrPwlSCn5z913Ubd4Ns+fOhiLyUjBsqUE9NAMrdmoNSVdfVKieeHkXlz/7VIenr2Km4bkNiVcQoQ6VWytXw49JrAYNECiCTAYNGxmI7VVDdhb2eUwOiaG2rpaXp9fyLuLNuLyB+idHEV8hJlYm4l1NS4AYhx2Hp+7ifTMTMpK1mPwNmIzCjSgqN7D6E6JHJudwF3D8rCZjLw8fz1frirlqRN6kBsf2RQbEO6gYaLBF0SXoT7QRoOGlOD2BxBCkBtr56WTuvPkzGkckfMeXbrk0bN3H449eSzDhw/HZArdS0FBAZX1LuIjrE33JBAYTWb8fh+aMLCz/DTWZqbW7ScjyoZru627twq9CRFcNqATo3OSmLa6jAs/m0+Kw0pevINOMRF4dZ0Vns34RTEFVS6Wldej25xMOHcy148bR1TUjlvZt4dly5Yx9/sveX/iUGzmbbsQXjKkBwZtGS889QT3PPDQfrm2oiiKcuhRybLylzBnzhwW/fgVb501BEu4q4TBaESXEqMmdmhzZjMZeHhUNyZOncvCklp6p0QDodrkCLOBem+AqPAOfwKQQiAQNPi27cb31eoShuZm7hBLIKjz5O9rKKus5qbBOSRHWshw2rGbDBg0QVmjl3FdUvi2sIzFZRpX3fRvjh01iisv/Qczv57OkLRojkyLwWkxcUp+KpoQvL14E9+uLeOlMb2Is5ubuncAuPwBqj1+4u1mihs8GMM79HkCQYrr3aHk2Woi2hoqlXhxTC8e/X0dBbWVDPZv5uNH7uKxe+/iX/++g+NGj+bPn3/Aq0vWVjWQExfZdF8GgwE9GNqu2qhpbSbMRfVuttR7iLNbqPX40aUkIVxesj1/UCcjxsG1Q2K5fGBnlpXV8fumCuaU1lPhg4IV9Uw451z6je3Bufn55OTk7Jfa5Oa+/OxTxucmNyXKzZ3eO4eT3vgK3z33HTatFBVFUZR9o5Jl5S/hw7fe4Jwe6U2dKbw+Hz6fj6Ax9LUMZ5fN07UIs5GzuqXz0bItTckyhGZlyxq9RFqMoV7IW18PrK9xEW0xMmdTBZ+uKOLjy1p22ggEda5792uMup9HjutGoy+IOxBkQ60Lm9FAQJcEpCQjJpJrBsdyTHY1l19xMVv+fQ/Pv/IaIwf1p9ZdQYM/iCbc3PFTAWO7JPPmoo28dWq/pjKHrTdS5/VTXO/BZjJQ4fKRHGGhRAiqXD6q3D6SIiw4rSYEocRUAmaDgesGZvPvGSvZUl3HC6cNZllRBXff928+fv89yjcUMmlgdz5atoWbh+Y13ZsATCZTaMHbThLmTwqKOTY7gcIaFxVeHYsJ1lW7SIww4zCHtsFu2l8FMJtM6FLi9gVwmA2M692F5JRk3L4gJ7wxgxv/fdte/1zsjbqaKrpHtr7xjtNmwSjA4/GoZFlRFOVvQiXLymGvrKyMeb/O4u4Lhjc9VlFWRpTVhDsQRAhLKNltJWE+OTeJF+evp8rlIzaciDrMJuq8ATbVukmNtIZrdEOvWlRWx/Gdk7nmuwK0hHTOfG8WnROiyHJa0YTk5yWrMQT9PDiqO34pSI5yEGExEdQlDV4/Bk3gsJiayieGdEji1bFGzrzzFiIjIhg85GgG1K+iqqqadxatp7zRyztLNnPf8HwiTAa8AR0dicsfpNrtJxjuEa0JQcdoO0EpCeiSOq+fjtF2TM27V4RvXBOhUo9bj+7COVPnMrxLFt1S43n77KFc/+GP+OuqObnHQM57dTmTjsgkPWrb4jkhBBaLBZ/Xiz+oYzK0TJjLGrx8urKY247OpbjBw7drSrh9eDfqPX4qG92UNHixGQ0YNUFQl9T5dbweFy5/EKvdTmpGVtOCPrtZ4HZ70HV9v88mN9cpryvzv1zIiT12fG5VaRW2SKfq1a4oivI38pfewU/5e1i4cCH90mKbZpUDwQAN9XUkO+24/aE+xEIL9fNtvgmJBCItJo5IcrK4tLbFOZMdVkwGjbXVjRTVe6j2+GnwB1hWVsdrizbjNkfw4+zf+WLW7/zzP0/Q/Zwr+aVaUFzn5pZjutIjLZ7MWCcOqxkR7t0cbbcQGf66uX7pcYzvksr/7ruTiupafJqZ848dzKsTh5Md66RfajQ9EiPZUONibXUj66pdbKnzENQlTouRDtF20iJtGA0aDb4AQSnJjLK1TJShaSfArZuvxNhMXNk/myd/mAuA2Wjg/4Z0IS/awpQ5BVw+vC9Xf7mI8kZPi9MIITBbLAiDIbT7XnjxY43bx7VfL+bsHpmkRFrJcNqocHkRQuC0mcmIdRJlt1Ln12nUzEiDAT0iisjEFDrkdCYzq0OLzhfeQBCTyXhAE2WAcaecyo+bqllW1HLXRV8gyJOzC5gw6YIDHpOiKIpy8Kj/4iuHvYaGBqIshqava2vriDQbMBkMRNksVLh8CLbuzqeFkmZkU+IcaTZS7fHjC+h4A6GtmAO6JNEZQU5SLHarBZ8OFS4fJ/XK5fvrz2JgZgKzZ88mMjKS/v37E/T7sdSXkxMXwdEdElutz92Zs3tmkGyWrFg0n6krijCbTGRnZtKIxr+OyqNLYjTpThtp4c4ZNpOBjCgbKZGhz4UAX0Cn0uXDZBDbtsoOkxDq46xtW8godckJuSkUllWxriL8ZkHXuWVoHj8sL6RTQjTj+uZz4Sfz+HVjRYvtq4UQmEwmTBYLQQmzN1Zw3qfz6J0aw9k9M9ElmA0adR4/NS4PRbWNrKmsw2u00jEnh06dcjCZTCQlJhHldGI27VgfvLGqjoSEhD0ax/YQGxvL3Y8+ydVfLeaB7xfwfcEGqho9nPPeLCK6DuD8yZMPeEyKoijKwaPKMJTDntlsxhvclsi5GxuJDC/yS4i0UVheQ6MvQITZGNoJj62zzAASb1BiNZnQjEaEpjVteb1VbISNL1cWoWcL7h03BJPBQL+kSJYsXMDw4cOpr6/nuUcfZESyk1jMO92auS19U2MIBIKc2CWBDxZvZHlxJVKG2rgNyowPl5HoWMKLCwMSqj3+Fp0mqty+pjrr7em6RCB2eM5k0Bifl8KnC1bzr1H90AwGHJqRK/pn8/KsRTxzznFkxUXxzMwFPPjLKibkp9I7NRq7yYjbH2RpWS0fLi8CzcCkgd05KiOWYrebIIKieg9C02g0WLE67CQ4IzEZd0yK2/LJ0g2cdNqZezyW7eHoo49myhff8NnUqXy7bDG9TXZufPw5+vXr1+6t6hRFUZRDm0qWlcNeeno6b5bXI6UMbSXtcZHgsABg0DRSohxsqWkgK9rWlGxCuE2cDqurGrmgnwOjsfW/DguLq3ns97X83wmRmOpCr89PjuGthfMB+GLaNAamRGLSgsRZLHt1D0IIYm0mRndJ55OlG3l05lKOyUpgYFoMommR4bbjo60mqlxeaj1+oqwm/EGdao+fDKctlGQHdQyaCG9LLfHrOiaDYYdrAgzMiOWJuRsBMJnMeF1ujstJ4ok/1rKxqo5hXTI4JjedpUUVfDxvJd/9vp5Gr48Ii4nMuChuHXM0vTMSWySRbo+br9b8QnZqMmlp6U2PSynZXF3PlpoGGiK68ue6YrLinCRG2lu83uXz801hOVOePm2vxrM9JCQkcPGllwIwY8YM+vfvf9BiURRFUQ4elSwrh70jjjiCoCOahZvK6J2ZhN/vx2TctijNYTWT5IxgQ00jqZFWHJZtP/ZziqqxmgzkJ+y4QYeUki9XFfP472u5/9RhuE3bks30mEhKFqxm8eLFPPnAfVzfPZY5WypJjLHj9/sxGFuf4d0Zvy6Ji7AxKi+TZX4br/++nGv7hVrTbU0kdUmo17MmyIiys7HWRZ03QL0vgABsplA/aUmoLEOES06MmrbD5ivG8Nd58U7WltcQCOpERUexqbqSBIeNozPjmLO+hMxYJ0IIeqQl0CNt98oiLBYrn64u56z+8ei65Je1W/hk/koWbirDbtTIirYzKPsops+YQ2FVI1II+ndI4bS+efTJTOS9uavod9QxJCYm7tEYKoqiKEp7U8myctgTQnD6uRfywZQX6J2ZFGp6sd0xUXYLRoNGUW0Ddq+fOLsZq9HA+0u3cHq3jBazmlJKlpTW8sq89ZR5dZ6dNJrcpFhmND+h1Fm9aiW3//MCGitKGNGxC/VePxur6kEP4vMEMBiNGE2mHWJpjcsXoMLlJd5h44hEBxGpffh8/RqsBkGd20ek1YRmMIQ6Q4QX7gkRqk2u9fqJtZkISjCE78OkCXQh8Os6AoGhWQ2zHm4MoonQY3azkRirmZK6RtJjIjFZrNR7/OTFOygorgBy9/h7Mnd9CbEdc3lj0SY+nb+CjEgrE7qmcfugDsTaQ7PvM6JsPD+mD1JKyhu9zFhXxgPTZ1HrC+J2xPPBtFf3+LqKoiiK0t5Usqz8JZw8dizvvvoS0xYX0tkk0CUYtstSIywmsuOjqWp0s6nWzXdry5i5sZIBmfF8ubIIlz/IqsoGFpbW4pcap/fL4/R+XXYoXwgEg6wuXEe0QfLESX255t0KYuwWTuqSwhnvreP6wTnYTUZ8wQB+KTGZzW0mzGUNHhaV1PD+ko1I4P4vfqPO42NlQRXx8QkkJadQFfBTWllPhFEDPYhBE3gCOp6ATlDX6RgTgcsfRNP1rQ0vkIQWNFqMoY4VupRNiXRQ1zEYjS3eIFiNGr5AEICYuHiqSraQEmnll5LKPf5eSCl5c34hkTFpeCtL+UdeFsd1SsLZSicQCCX9iQ4rp3fPYGTHBD5bVcJba+v5aMoUrrjmGtV5QlEURTmoVLKs/CU4HA6efPk1/nnu2ZwYHSDDaSPCsuOmEQZNEO+w8e3act5fWcako3qyqtGLr86DzWSiU2YGJx/Zi+6p8W0u1CspLqK8rpHBnVLxB/WmHf0SIqwcmRHHF6tKOKN7OmaDAV8wSDAYxLhdwj1ncyXvLd7IwuJqeiVH0Snaxkm5KTgsJipdHmzFdXxfWE5pJwtpOVnYk5LxeDxUlJdhCerERtiQQHWDmwhzaLGdEOEFfOHWcFuFehrrGDQDupQEdYl5uy2oA7rEGJ6xjoyMpLrSQo27ZTu93fXC7KX8XFjMSfkGXp58HEIPUrR5ExWuBmKsJqJslhYlIYGgTo3bS7XHj9FsYdLwIxl/VJB/f/k+t27awP0PP4phu/FTFEVRlANFJcvKX0aHDh14ZcpHnHr8KL5c9TsX9u7QlIAC1Hn8TF9ZxEcFRThsNqZcMpaUqD3bXMIfCNBYX09Rg5e8lETsZiMNvkDT8+f26sC/vpzPkemxZEXbMWoagUCgKVmu8/h55JcVLCqu5sJemdw/PI9GX4A6f5COcVEIIWjw+hiULdANJpA6/roqtlRVkpyaRnanTqxbswZNEzR6/TjMofMaNYHXF2w1Zk0IAnqoF7IvqGM0mVok04GgTnljqARk6/EZWVl8WbCJddUN6LrcrQ4fUkpe/HUZT85axum9srlzdP/w60xk53TG5XJRXVlJWWUdRk3Dl6WzprKeoC6JjIoiPTkWmzUUQ6wJnhg/iOs//4MH77uHW++8e4++T4qiKIrSXlSyrPylpKWlcc8jj/PSXTeyqNrPE2/9gsWgAQJvMMiIvCzuHH8MPdMS9qoFWG1NNQ6zxqyNldzTrycpUQ7qfQHqPH6cVhPdkqK4amAul32xkP+N7kHnuEhkMIiu62ysdXPltHkM7xDHlAn9sRoNVLq8VHsDdAgnygAefxCLLZK8lHjWVlczOjcVl89P0ZZNOGJiScvMYsvGDZiFjsMUmg0Oncu309i9wSAGg3GHrh+F1Y0kR0U0beoCYNAMVBoiaHRauPTj2ZzdI5NjctNb1D5vpeuSXwu38N7ijSypDdC/QzJ3jO7XIsEWQITdToTdTlAPEggE2GQ2k9GxE0ajsdXzmo0GHjy5P2e/+zkzh49k6NChu/19UhRFUZT2opJl5S9nxIgRPHqPlX8M7cm944+mwetHAA7L3vVAbs7r9bK8rB6b1Ur31HiEEHRLjWdeURXDs5MAGJOfhsWocen0RRydGce4LsnEOWxcNm0+V/TvyIm5ydR5/BQ3eABBh7ioFrvtufxBomLtdEuN561ZGwCwm010jDWwsaYagMyOHVm/bh26N0CM1YzFqBGUEm+g5eyyhKYdC00mU6vt8RYWV9M1Nb7FY4Ggzq9banj2jSls2LCBd998lUd+/ZGxnZPoEOsgwmzC5QuwqaaBz1eXEZ2WxbHn/JOCpx7nnuP6t5r8bmXQDBjMBoSmYTHvWCrTnN1s4o7h3bnttlvo9/V32O32nR6vKIqiKO1NrZxR/nJMJhOnnXMeL/+5EoOmEWWz4LRZ9jlRBtB1ndcWbODM/vlNM8EnH9GZTwqKWxx3XOcUPp00hNyEKG77qYDj3pzF6E6J5Cc4WV3ZQL1fJzEygo7xLRNlXyCIO6gTGengyI4prK/1sKG6EQj1jM6MjqChugqfz0/n3Fw8wVBy7Q3qRFqMVLn9IEMJcihJlgR0MJnNrSbKUko+WVHMST1zWjz+8+pNpOXkkZeXx+jRo3n5nff531sf4Ok1ipnGDD6ssTHDkEptt2E88MrbvPnRJ9RUVDAmJ56suB3b8O2LvllJ9Igy8MX06e16XkVRFEXZHWpmWflLOu/CyZz96VR+XrWJY3Iz2u28X6wqpsgVYEyz5HJUfhZP/zCXBUXV9E6NaXo8ymrmnF5ZbKlzkRbdyCUDOqNpoXZvRkPr71MrGj1Ex8ShCQ2zEcb16swHyzbxf0PygFDCnOq0sbloM9k5uSSlpFBWUUZGlJ2YCAMbKuuRSBACQWjhntA0DMbWF8gtKqnBKwX9s5KbHnP5/Dzx2xpufuyZFsfm5uaSe8MNrZ7H5/Mx7aMpvDqm1+4M4x47o0cmD735GhNOP13toKcoiqIcUGpmWflLstls3PngI/xn1ko2VdW1yzk9vgDvrK6GCCd6s+30zEYDN50wiHt+LsDVbLEfQKXLw7SVRdw1oht2iwmrydhmolzv8dEYhPiEbRt/nNEvj28LK1hVUd/0mN1sItKoUV1VRWxcPM7YeNZVu6j1BoiOsOILSgJBHV9QR0dgNptDW3xvJxDUeeiXVUweckTTrLuUkkd+WkSfkcczePDg3R6bhQsXkmE3khHbvrPKW/XNTKKhvITNmzfvl/MriqIoSltUsqz8ZfXu3ZtLb7yNyz79k437mDAv3VLBpjoX/3nyOXJ79GLaksIWzw/rkkH/Thlc/81i3P5QwiylZOqyzfRPiyXdufNaW5cvVMOclpHRot43IdLOVSP6cddPy/EH9abHY+wWqqsqAUlCQiIdc3LAHkWjNCCBjXUeNKMJs8XS5kzsq/PXER8dxUk9soHQQr1Hf1rEakMs/3frbXs0PisKCugWt//qiYUQdE10UlBQsN+uoSiKoiitUcmy8pd26oQJXHLLXVz0yZ98vngtstmMMMDa8hpemLmIh7/5k1dnL6GopqHF87ouefOPAq75ZjGpGVkcffTR3HjnPTy7cDOzVrec5bz5+IEkxcVy0afzWF1RR2m9m182VTMuP63N+KSUVDV62FTnJjUjE7ttx4RzzBGdSIuP5e6flqProfitJiMmIXG53ACYTWaSkpLI7pSD1WbDEZvA+hoXjd7WO2R8v6aEqStLue2kwQgh2FhVxz8+/IU19lSeffUNIiIidj24zaxaupjc+Mg9es2e6hJtZfXKFfv1GoqiKIqyPZUsK395Y8eP55m3P2BKcZArpv7Gr2u3UNng5qp3v+OKt77GW1tJmtFPRVkp5778Obd9OpN6t48fVmzggg9m8auM461PpjclkDk5Ofzv5Te4/4+N/PvLOSzcVIaUoV7Et5wwkE6pSYx57zce+G0ta6ob6ZkcvUNMupTUub2sr26gVtfokN0JR0TrPZ+FENw3/mgqAxq3/bAUjz/U8cJmNODxuFt9TXJyMikZWRQ3+kPXcHnxB4NIKfm8YAsP/baW/5wylDXl1dz8xRwu+GQuoy6+hmdfeZ3IyD1Pehvra3Fad97ZYl85rSYa69qnpEZRFEVRdpda4Kf8LeTm5vLmB1OZPn06T7zyIgvf+paJXVO449Q+2E0mhAApYVLPdP47cyX9HprC0cNHcO6t9zNy5Eg0TWPVqlVN5+vWrRsffvkNX0ybxr1vvUblF/OxmU3Uebz0GTCQh/55KwXLltHw9KNUurw0+gJNu9b5AjqeQBCrzUZcchqRTmeb22FvZTEZeeKskdz3xa9M/OhP7hiWR4coG43u1pNlAEdEBDm5udTXN1BbXcXKkhqe+m0NfxTXkZKUxLXfLKFLXh6jJl7G7WPG7PFscnOaZmC7Sft2p0uJpnbyUxRFUQ4wlSwrfxtGo5Hx48fTUF9PireSK4d3w+t2U+7zIqVECIHF7uTOcUOo+nIREy+6hBEjRrR5vsjISM6aOJEzzz6b+vp6PB4PDoejqRdwt27dmPfVJ2R1ysHj8RAMBhEIoswmrFYrBm3PEj+z0cA9445mxspN3PzVb+REWxmdl0FqalqbdckCQX0APllXy+eryhg5YTJ3nTURh8NBQkIC2k76Ie+JuKRkSjdvaZdztaWs0UdsfMKuD1QURVGUdnRAkmUhRAbwJpAM6MCLUsonhBCxwPtAB2A9cIaUsvpAxKT8fU19+w3uOjKPuNjYNo85v082H731+k6T5a2EEDidTpzOlp0gDAYDAV1iNpkxm9qvRGFYlwwGdEzmwa//5K5f1/Hc8kq6J0WTF2MlPsKKHJTP+3NXsKrKRUFlI2WeICdPOJPXHjyLjIz2a6PXXF6PI1i8aOZ+OfdWy6vcXNCt2369hqIoiqJs70DNLAeA66WU84UQkcA8IcR3wAXAD1LKB4QQNwM3AzcdoJiUvyGv10tx0WZ6pB2x0+P6ZSXx8Id/7tO1EhMTKat3EQjqbbaL21t2s4nsxGgmDh3PhLPOYsWKFaxYtpSN5WXkahY2ZPQj77hujM/Lo0uXLph3sVPevurevTtvlNQ2zdC3N48/wMryWvLz89v93IqiKIqyMwckWZZSFgPF4c/rhRAFQBowDhgWPuwNYAYqWVb2IyFE0/bPO8vpAkGJ2Mcd/+x2O0kpKRRW1JCb1PYs9t5aXulmWM+eZGVlkZWVxejRowGYMWMGE8+Z1O7X25nOnTtjjUtizvoSBnRMaffzf7t8Pb2PHEx0dHS7n1tRFEVRdkZs30prv19QiA7ATKA7sFFKGd3suWopZUwrr7kEuAQgKSmp75QpUw5MsK1oaGjA4Wi9a8Gh5HCJEw58rOsL1xJvFjh20r2h2uXBJSykNStb2Js4S4qLMHldxDmsex1va6SE1RV1dMzpjMlkavHcwfreV1dX01hZRnr07i0UbDDYcQRduz5QwrqqehLTM/dpEeK+OFz+Pu0szuHDh8+TUvY7wCEpiqIc9g7oAj8hhAP4GLhWSlm3u7+ulVK+CLwI0K9fPzls2LD9FuOuzJgxg4N5/d11uMQJBz7WafX1fPL4fbww4ahWyyNcPj+TpvzCrU+8QL9+23KLvYmzoKCAGydP5LPzRjTtktcevlhSyFJfDJdcdvkOzx2s773X6+Xs8SdzZV4MI/Iyd3n8jLheDKtcuMvj3pmzgt980Tz/+qXttiBxTx0uf58OlzgVRVEOJwfsXx4hhIlQovyOlHJq+OFSIURK+PkUoOxAxaP8fZ144ok483tz0/Q/KatrObO5saqOKz/5jX7HjaFv3777fK38/HwSOuTy+ZK1+3yurXyBIK8u2MBZF1zUbudsDxaLhbsefJQHZ6/aYVz31pqyal5dvIU7/vPAQUuUFUVRlL+3A/KvjwhNIb8CFEgpH2v21OfA+eHPzwc+OxDxKH9vBoOBh/73FOnDxnDmB79xzWd/cN93C/jn1N+4aNpChpx9MTfffke7LVS79d77eXrOOkrrGtvlfM/PXkbOgCEMHjy4Xc7Xnnr27Mk5l13NFZ/9QUXDviXM6ytquXr6PG6657+kp6e3U4SKoiiKsmcOVBnGUcC5wBIhxMLwY7cCDwAfCCEuAjYCpx+geJS/OZPJxL9uvIlLr7yK2bNnU19fz9FxcQwaNKjdO0fk5OQw6Z9Xc8Nbz/P8qYOJsJh2/aI2fFuwga+2NPLO03e2Y4Tt67wLJxMIBJn86rPcPaI7vTOT9vgcM1Zu4j+zVnD17fdy3PHH74coFUVRFGX3HKhuGL9Am5uUjTwQMShKa+x2O6NGjdrv1zl/8mTKSoq5bOpnPHJSfxKd9j16vZSSTxau4YWlpTzz+tvE7qRH9KFg8j/+QU6XLtx6280MTy7i/P65JDl3vThvQ2UdL/6xguUeMw+99Aa9evXa/8EqiqIoyk6oHfwU5QAQQvB/t/6b15OSmfjCU1zVP5sxPbJ3a9FfaV0j//1xMWXWOF58532ysrIOQMT7bujQofSa/jUvPvM0Z3/4Eb0TIjgqI5b85Fiy4pwgodHrZ215DStKqpixqYo19QFOnXgut190MVZr+3YQURRFUZS9oZJlRTlAhBBcePHFDDnmGP5zx7955c0fmZCfwpDsVDrERbVInOvcXpYWVTBtRRG/F9dy9uRLePjif+zQJu5Q53Q6ueGWW7n8mmv57rvvWPDHb7w/dyGbNv3JeZfmcttrP9KhQwfyj+jNuAlHMXz48P2+gYqiKIqi7AmVLCvKAda5c2dee+8Dli9fzsdT3uWTmb9SUVZKSlQEBiGo9/qp8wXpkpfPqImX8e+TTz4sevzujN1uZ9y4cYwbN67psRkzZvDLgiUHMSpFURRF2bUDvinJvhJClAMbDmII8UDFQbz+7jpc4oTDJ1YVZ/s6XOKEwyfWncWZJaVMOJDBKIqi/BUcdsnywSaEmHs47IJ1uMQJh0+sKs72dbjECYdPrIdLnIqiKIcT1eVfURRFURRFUdqgkmVFURRFURRFaYNKlvfciwc7gN10uMQJh0+sKs72dbjECYdPrIdLnIqiKIcNVbOsKIqiKIqiKG1QM8uKoiiKoiiK0gaVLCuKoiiKoihKG1SyvAeEEOuFEEuEEAuFEHMPdjxbCSFeFUKUCSGWNnssVgjxnRBidfjPmIMZYzim1uK8SwixJTymC4UQJx7MGMMxZQghfhJCFAghlgkhrgk/fiiOaVuxHlLjKoSwCiH+FEIsCsd5d/jxQ2pMdxLnITWeWwkhDEKIBUKI6eGvD6nxVBRF+StQNct7QAixHugnpTykNicQQgwFGoA3pZTdw489BFRJKR8QQtwMxEgpbzoE47wLaJBSPnIwY2tOCJECpEgp5wshIoF5wHjgAg69MW0r1jM4hMZVCCGACCllgxDCBPwCXAOcyiE0pjuJ83gOofHcSghxHdAPcEopTz4U/94riqIc7tTM8l+AlHImULXdw+OAN8Kfv0EogTqo2ojzkCOlLJZSzg9/Xg8UAGkcmmPaVqyHFBnSEP7SFP6QHGJjupM4DzlCiHTgJODlZg8fUuOpKIryV6CS5T0jgW+FEPOEEJcc7GB2IUlKWQyhhApIPMjx7MyVQojF4TKNQ+rXxkKIDkBv4A8O8THdLlY4xMY1XDKwECgDvpNSHpJj2kaccIiNJ/A/4EZAb/bYITeeiqIohzuVLO+Zo6SUfYATgCvCZQXKvnkO6AT0AoqBRw9qNM0IIRzAx8C1Usq6gx3PzrQS6yE3rlLKoJSyF5AODBBCdD/IIbWqjTgPqfEUQpwMlEkp5x3MOBRFUf4OVLK8B6SUReE/y4BPgAEHN6KdKg3Xs26tay07yPG0SkpZGk5OdOAlDpExDderfgy8I6WcGn74kBzT1mI9VMcVQEpZA8wgVAd8SI4ptIzzEBzPo4Cx4XUUU4ARQoi3OYTHU1EU5XClkuXdJISICC+gQggRARwHLN35qw6qz4Hzw5+fD3x2EGNp09Z/2MNO4RAY0/Air1eAAinlY82eOuTGtK1YD7VxFUIkCCGiw5/bgGOBFRxiY9pWnIfaeEopb5FSpkspOwBnAT9KKSdxiI2noijKX4HqhrGbhBDZhGaTAYzAu1LK+w9iSE2EEO8Bw4B4oBS4E/gU+ADIBDYCp0spD+riujbiHEboV9sSWA9curXm8mARQgwBZgFL2FYPeiuhWuBDbUzbivVsDqFxFUL0JLTgzEDoTfoHUsp7hBBxHEJjupM43+IQGs/mhBDDgBvC3TAOqfFUFEX5K1DJsqIoiqIoiqK0QZVhKIqiKIqiKEobVLKsKIqiKIqiKG1QybKiKIqiKIqitEEly4qiKIqiKIrSBpUsK4qiKIqiKEobVLKs7DEhhBRC5BzsOACEEHeFN2M4WNe/TwhRIYQo2cvXN4TbErb23AVCiF/2LcJWz5sghFgphLC297n3IpZzhBDf7uVrk4QQBUIIS3vHpSiKoihbqWT5LySceG390IUQ7mZfn9PGa4YJITa3YwwzhBAeIURGs8eODe809pcSvsfrga5SyuRWnh8W/j40CCHqwwnqhc2PkVI6pJSFByrmsJuB16SUnnCcW79nDeHEf+p2m3DsEyHEaCHEzPAYlAshfhZCjAWQUr4jpTyu2bG7/UZMSlkK/ARc0l6xKoqiKMr2VLL8FxJOvBxSSgehDQnGNHvsnQMYSiNw+wG8XrsQQhj38CVZQGV4+/O2FIW/H07gX8BLQoguexvjvgrPwp4PbD8bf2U4zlwgGnh8L85taOWxCcCHwJtAOpAE3AGM2dPzt+Ed4NJ2OpeiKIqi7EAly38DQgiLEOJ/Qoii8Mf/wo9FAF8Bqc1moFOFEAOEEL8JIWqEEMVCiKeFEOY9uOSTwNltzRBuP3sohHhdCHFf+PNhQojNQogbhRBl4euPF0KcKIRYJYSoEkLcut0prUKI98Mzl/OFEEc0O3eqEOLj8IzmOiHE1c2eu0sI8ZEQ4m0hRB1wQSuxRgkh3gy/foMQ4jYhhCaEOBb4rtnYvb6zAZEhXwJVQM/WxkIIESeE+FwIUSeE+BPotF0seUKI78JjsFIIcUaz504UQiwPj8EWIcQNbYRyJFAjpWz1twnh3d4+BrrvxjVfF0I8J4T4UgjRCAzfLl4BPAbcK6V8WUpZK6XUpZQ/Syn/ET6mqdRECDEz/NJF4TE9UwixVAgxptk5TeHZ717hh/4AsoUQWW3cr6IoiqLsE5Us/z38GxhIaLveI4ABwG1SykbgBMKzn+GPIiBIaBY0HhgEjAQu34PrbQFeAu7ay3iTASuQRmgW8iVgEtAXOBq4Q7Ss8x1HaPYyFngX+DScVGnANGBR+FwjgWuFEKO3e+1HhGZTW5t9fwqIArKBY4DzgAullN/Tcuwu2NkNhRPssYTGdE0bhz0DeIAUYHL4Y+vrIwgl5+8CiYS2s35WCNEtfMgrhLZgjiSU6P7YxjV6ACt3Emc8cBqwYDeuCTARuB+IBLavr+4CZBAa312SUg4Nf3pEeEzfJzQjPanZYScCxVLKheHXBAiN5xEoiqIoyn6gkuW/h3OAe6SUZVLKcuBu4Ny2DpZSzpNS/i6lDEgp1wMvEEoU98R/gTHbJVa7yw/cL6X0A1MIJZhPSCnrpZTLgGU0m50F5kkpPwof/xihRHsg0B9IkFLeI6X0hWuDXwLOavba36SUn4ZnPN3NgwiXFZwJ3BK+9nrgUXYydq1IFULUAG7gE+A6KeWC7Q8KX+s04A4pZaOUcinwRrNDTgbWSylfC39f5hOaAZ7QbMy6CiGcUsrq8POtiQbqW3n8yXCci4Bi4LrduCbAZ1LK2eHx82x3zrjwn8VtxLI73gZOFEI4w1+fC7y13TH1hO5LURRFUdqdSpb/HlKBDc2+3hB+rFVCiFwhxHQhREm4POE/hBLW3RZOyp8G7tmLeCullMHw51sT2NJmz7sBR7OvNzW7rg5sJnR/WYST1a0fwK2E6mZ3eG0r4gEzO45d2u7fCkVSymhCNctPAiPaOC4BMG4XT/PrZgFHbncv5xCahYdQon0isEGEFtANauM61YRmgbd3tZQyWkqZJqU8J/z929U1YefjVxn+c68XC4Z/0zEbOE0IEU1oNn/73wBEAjV7ew1FURRF2RmVLP89FBFKfLbKDD8GIFs5/jlgBdBZSukklGCKvbjuw4TqWPtu97gLsDf7eodOEnuoeecNjdBCsiJCidy6cBK49SNSSnlis9e2dv9bVRCasd1+7LbsaYBSSi9wE9BDCDG+lUPKgUDzewlfa6tNwM/b3YtDSnlZ+PxzpJTjCJVLfAp80EYoiwkt4tsdO73m1lvbyetXhs9x2m5ery1vECrFOJ3QbwKaxl+EFmXmEJoRVxRFUZR2p5Llv4f3gNtEqL9uPKE64K3dEEqBOCFEVLPjI4E6oEEIkQdcxl6QUtYQKlu4cbunFgIThRAGIcTx7HmJx/b6CiFODSdO1wJe4HfgT6BOCHGTEMIWvl53IUT/3Yw/SCjpvF8IERleRHYdO3aS2C1SSh+h8bijjWtNBe4SQtiFEF0Jda3YajqQK4Q4N1yPbRJC9BdC5AshzCLUrzgqXIpSR6juvDV/AtFCiN2ZHW/zmrt5v5LQeN0uhLhQCOEM124PEUK82MbLSgnVhzf3KdAHuIZQDXNzAwiVimxAURRFUfYDlSz/PdwHzCU0q7gEmB9+DCnlCkLJdGH4V+2pwA2EFm7VE6rxfX8frv0EOyZu1xBqHVZD6Nf6n+7D+QE+I1RbXE2opvVUKaU/nICOIbSwcR2hmeKXCS3Y211XEWqFV0hoAdu7wKv7EOurQGbzDg/NXEmovKQEeB14besTUsp64DhC9dZF4WMeBLZuyHEusD5cNvNPWi6Ko9l5fOFzt/r8dsfu6pq7JKX8iND3ZnL4HKWEfvY+a+MldwFvhH8Wzwifw02oVrojoTcUzZ0DPL+78SiKoijKnhKhyR9FUf4uhBAJwCyg9/aLGg9VQog7gFwp5aRmjyUCPxO6j+0XFyqKoihKu1DJsqIohzQhRCywADhXSjlzV8criqIoSntSZRiKohyyhBD/ILRI8CuVKCuKoigHg5pZVhRFURRFUZQ2qJllRVEURVEURWmDSpYVRVEURVEUpQ0qWVYURVEURVGUNqhkWVEURVEURVHaoJJlRVEURVEURWnD/wNySR8dSCjLDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add the scatter charts for each type of city.\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=10*urban_driver_count, c=\"coral\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Urban\")\n",
    "\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "\n",
    "plt.scatter(rural_ride_count,\n",
    "      rural_avg_fare,\n",
    "      s=10*rural_driver_count, c=\"gold\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Rural\")\n",
    "\n",
    "# Incorporate the other graph properties\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\", fontsize=20)\n",
    "plt.ylabel(\"Average Fare ($)\", fontsize=12)\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\", fontsize=12)\n",
    "plt.grid(True)\n",
    "\n",
    "# Add the legend.\n",
    "# Create a legend\n",
    "lgnd = plt.legend(fontsize=\"12\", mode=\"Expanded\",\n",
    "         scatterpoints=1, loc=\"best\", title=\"City Types\")\n",
    "lgnd.legendHandles[0]._sizes = [75]\n",
    "lgnd.legendHandles[1]._sizes = [75]\n",
    "lgnd.legendHandles[2]._sizes = [75]\n",
    "lgnd.get_title().set_fontsize(12)\n",
    "\n",
    "# Incorporate a text label about circle size.\n",
    "plt.text(42, 35, \"Note: Circle size correlates with driver count per city.\", fontsize=\"12\")\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"Analysis/Fig1.png\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a5cfe34a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1625.000000</td>\n",
       "      <td>1.625000e+03</td>\n",
       "      <td>1625.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24.525772</td>\n",
       "      <td>4.873485e+12</td>\n",
       "      <td>36.678154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.738649</td>\n",
       "      <td>2.907440e+12</td>\n",
       "      <td>20.075545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.050000</td>\n",
       "      <td>1.458810e+10</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.550000</td>\n",
       "      <td>2.400244e+12</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24.640000</td>\n",
       "      <td>4.711188e+12</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.580000</td>\n",
       "      <td>7.451579e+12</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>44.970000</td>\n",
       "      <td>9.991538e+12</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare       ride_id  driver_count\n",
       "count  1625.000000  1.625000e+03   1625.000000\n",
       "mean     24.525772  4.873485e+12     36.678154\n",
       "std      11.738649  2.907440e+12     20.075545\n",
       "min       4.050000  1.458810e+10      3.000000\n",
       "25%      14.550000  2.400244e+12     22.000000\n",
       "50%      24.640000  4.711188e+12     37.000000\n",
       "75%      34.580000  7.451579e+12     52.000000\n",
       "max      44.970000  9.991538e+12     73.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "urban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ca1f33ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    66.000000\n",
       "mean     24.621212\n",
       "std       5.408726\n",
       "min      12.000000\n",
       "25%      21.000000\n",
       "50%      24.000000\n",
       "75%      28.000000\n",
       "max      39.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics for urban cities.\n",
    "urban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "341dbdcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36.000000\n",
       "mean     17.361111\n",
       "std       4.323707\n",
       "min       9.000000\n",
       "25%      14.000000\n",
       "50%      17.000000\n",
       "75%      19.250000\n",
       "max      27.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics for suburban cities.\n",
    "suburban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "695178de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18.000000\n",
       "mean      6.944444\n",
       "std       2.508157\n",
       "min       3.000000\n",
       "25%       5.250000\n",
       "50%       6.000000\n",
       "75%       8.750000\n",
       "max      12.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics for rural cities.\n",
    "rural_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "844704bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.62, 17.36, 6.94)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean ride count for each city type.\n",
    "round (urban_ride_count.mean(), 2), round (suburban_ride_count.mean(),2), round (rural_ride_count.mean(), 2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0ea80ebb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.0, 17.0, 6.0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the median ride count for each city type.\n",
    "round (urban_ride_count.median(), 2), round (suburban_ride_count.median(),2), round (rural_ride_count.median(), 2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fc409573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the urban cities.\n",
    "urban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "19b59ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the suburban cities.\n",
    "suburban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "457ab418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the rural cities.\n",
    "rural_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6e6871f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the ride counts for urban trips is 24.62.\n",
      "The median for the ride counts for urban trips is 24.0.\n",
      "The mode for the ride counts for urban trips is ModeResult(mode=array([22]), count=array([7])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for the urban cities.\n",
    "mean_urban_ride_count = np.mean(urban_ride_count)\n",
    "print(f\"The mean for the ride counts for urban trips is {mean_urban_ride_count:.2f}.\")\n",
    "\n",
    "median_urban_ride_count = np.median(urban_ride_count)\n",
    "print(f\"The median for the ride counts for urban trips is {median_urban_ride_count}.\")\n",
    "\n",
    "mode_urban_ride_count = sts.mode(urban_ride_count)\n",
    "print(f\"The mode for the ride counts for urban trips is {mode_urban_ride_count}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "12d83992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the ride counts for suburban trips is 17.36.\n",
      "The median for the ride counts for suburban trips is 17.0.\n",
      "The mode for the ride counts for suburban trips is ModeResult(mode=array([17]), count=array([7])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for the suburban cities.\n",
    "mean_suburban_ride_count = np.mean(suburban_ride_count)\n",
    "print(f\"The mean for the ride counts for suburban trips is {mean_suburban_ride_count:.2f}.\")\n",
    "\n",
    "median_suburban_ride_count = np.median(suburban_ride_count)\n",
    "print(f\"The median for the ride counts for suburban trips is {median_suburban_ride_count}.\")\n",
    "\n",
    "mode_suburban_ride_count = sts.mode(suburban_ride_count)\n",
    "print(f\"The mode for the ride counts for suburban trips is {mode_suburban_ride_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1a4fc1ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the ride counts for rural trips is 6.94.\n",
      "The median for the ride counts for rural trips is 6.0.\n",
      "The mode for the ride counts for rural trips is ModeResult(mode=array([6]), count=array([5])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for the rural cities.\n",
    "mean_rural_ride_count = np.mean(rural_ride_count)\n",
    "print(f\"The mean for the ride counts for rural trips is {mean_rural_ride_count:.2f}.\")\n",
    "\n",
    "median_rural_ride_count = np.median(rural_ride_count)\n",
    "print(f\"The median for the ride counts for rural trips is {median_rural_ride_count}.\")\n",
    "\n",
    "mode_rural_ride_count = sts.mode(rural_ride_count)\n",
    "print(f\"The mode for the ride counts for rural trips is {mode_rural_ride_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c23175fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13.83\n",
       "1    30.24\n",
       "2    33.44\n",
       "3    23.44\n",
       "4    34.58\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the fares for the urban cities.\n",
    "urban_fares = urban_cities_df[\"fare\"]\n",
    "urban_fares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f264d0f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for urban trips is $24.53.\n",
      "The median fare price for urban trips is $24.64.\n",
      "The mode fare price for urban trips is ModeResult(mode=array([22.86]), count=array([5])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average fare for the urban cities.\n",
    "mean_urban_fares = np.mean(urban_fares)\n",
    "print(f\"The mean fare price for urban trips is ${mean_urban_fares:.2f}.\")\n",
    "\n",
    "median_urban_fares = np.median(urban_fares)\n",
    "print(f\"The median fare price for urban trips is ${median_urban_fares:.2f}.\")\n",
    "\n",
    "mode_urban_fares = sts.mode(urban_fares)\n",
    "print(f\"The mode fare price for urban trips is {mode_urban_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6184724e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1625    27.79\n",
       "1626    18.75\n",
       "1627    21.71\n",
       "1628    17.10\n",
       "1629    37.78\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the fares for the suburban cities.\n",
    "suburban_fares = suburban_cities_df[\"fare\"]\n",
    "suburban_fares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ceaadb0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for suburban trips is $30.97.\n",
      "The median fare price for suburban trips is $30.75.\n",
      "The mode fare price for suburban trips is ModeResult(mode=array([17.99]), count=array([3])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average fare for the suburban cities.\n",
    "mean_suburban_fares = np.mean(suburban_fares)\n",
    "print(f\"The mean fare price for suburban trips is ${mean_suburban_fares:.2f}.\")\n",
    "\n",
    "median_suburban_fares = np.median(suburban_fares)\n",
    "print(f\"The median fare price for suburban trips is ${median_suburban_fares:.2f}.\")\n",
    "\n",
    "mode_suburban_fares = sts.mode(suburban_fares)\n",
    "print(f\"The mode fare price for suburban trips is {mode_suburban_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "04b0ff4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2250    43.22\n",
       "2251    12.42\n",
       "2252    54.85\n",
       "2253    47.90\n",
       "2254    51.80\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the fares for the rural cities.\n",
    "rural_fares = rural_cities_df[\"fare\"]\n",
    "rural_fares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fb5ef184",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for suburban trips is $34.62.\n",
      "The median fare price for rural trips is $37.05.\n",
      "The mode fare price for rural trips is ModeResult(mode=array([37.05]), count=array([2])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average fare for the rural cities.\n",
    "mean_rural_fares = np.mean(rural_fares)\n",
    "print(f\"The mean fare price for suburban trips is ${mean_rural_fares:.2f}.\")\n",
    "\n",
    "median_rural_fares = np.median(rural_fares)\n",
    "print(f\"The median fare price for rural trips is ${median_rural_fares:.2f}.\")\n",
    "\n",
    "mode_rural_fares = sts.mode(rural_fares)\n",
    "print(f\"The mode fare price for rural trips is {mode_rural_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "aab2baed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2250    9\n",
       "2251    8\n",
       "2252    6\n",
       "2253    2\n",
       "2254    2\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the rural cities.\n",
    "rural_drivers = rural_cities_df['driver_count']\n",
    "rural_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "60931668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of drivers for rural citites is 4.30.\n",
      "The median of drivers for rural citites is4.00.\n",
      "The mode of drivers for rural citites is ModeResult(mode=array([1]), count=array([32])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average number of drivers for the rural cities.\n",
    "mean_rural_drivers = np.mean(rural_drivers)\n",
    "print(f\"The mean of drivers for rural citites is {mean_rural_drivers:.2f}.\")\n",
    "\n",
    "median_rural_drivers = np.median(rural_drivers)\n",
    "print(f\"The median of drivers for rural citites is{median_rural_drivers:.2f}.\")\n",
    "\n",
    "mode_rural_drivers = sts.mode(rural_drivers)\n",
    "print(f\"The mode of drivers for rural citites is {mode_rural_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c5f720d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1625    11\n",
       "1626    20\n",
       "1627    22\n",
       "1628    19\n",
       "1629    12\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the suburban cities.\n",
    "suburban_drivers = suburban_cities_df['driver_count']\n",
    "suburban_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c0378ab9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of drivers for suburban citites is 13.7.\n",
      "The median of drivers for suburban citites is16.0.\n",
      "The mode of drivers for suburban citites is ModeResult(mode=array([20]), count=array([79])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average number of drivers for the suburban cities.\n",
    "mean_suburban_drivers = np.mean(suburban_drivers)\n",
    "print(f\"The mean of drivers for suburban citites is {mean_suburban_drivers:.1f}.\")\n",
    "\n",
    "median_suburban_drivers = np.median(suburban_drivers)\n",
    "print(f\"The median of drivers for suburban citites is{median_suburban_drivers:.1f}.\")\n",
    "\n",
    "mode_suburban_drivers = sts.mode(suburban_drivers)\n",
    "print(f\"The mode of drivers for suburban citites is {mode_suburban_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0f4c2a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1    72\n",
       "2    57\n",
       "3    34\n",
       "4    46\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the urban cities.\n",
    "urban_drivers = urban_cities_df['driver_count']\n",
    "urban_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a4bf2d5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of drivers for urban citites is 36.7.\n",
      "The median of drivers for urban citites is37.0.\n",
      "The mode of drivers for urban citites is ModeResult(mode=array([39]), count=array([86])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average number of drivers for the urban cities.\n",
    "mean_urban_drivers = np.mean(urban_drivers)\n",
    "print(f\"The mean of drivers for urban citites is {mean_urban_drivers:.1f}.\")\n",
    "\n",
    "median_urban_drivers = np.median(urban_drivers)\n",
    "print(f\"The median of drivers for urban citites is{median_urban_drivers:.1f}.\")\n",
    "\n",
    "mode_urban_drivers = sts.mode(urban_drivers)\n",
    "print(f\"The mode of drivers for urban citites is {mode_urban_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ceb68ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban cities ride count.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(10, 41, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e3ed68e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the suburban cities ride count.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(10, 41, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ffbbe303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the suburban cities ride count.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(0, 21, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6b5c2d27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add all ride count box-and-whisker plots to the same graph.\n",
    "x_labels = [\"Urban\", \"Suburban\",\"Rural\"]\n",
    "ride_count_data = [urban_ride_count, suburban_ride_count, rural_ride_count]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Ride Count Data (2019)',fontsize=20)\n",
    "ax.set_ylabel('Number of Rides',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(ride_count_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 45, step=3.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig2.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d5c2d728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "West Angela has the highest rider count.\n"
     ]
    }
   ],
   "source": [
    "# Get the city that matches 39.\n",
    "urban_city_outlier = urban_ride_count[urban_ride_count==39].index[0]\n",
    "print(f\"{urban_city_outlier} has the highest rider count.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "54ad7b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1625.000000\n",
       "mean       24.525772\n",
       "std        11.738649\n",
       "min         4.050000\n",
       "25%        14.550000\n",
       "50%        24.640000\n",
       "75%        34.580000\n",
       "max        44.970000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban fare data.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "urban_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "dd59cca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    625.000000\n",
       "mean      30.970128\n",
       "std       10.677508\n",
       "min       12.050000\n",
       "25%       21.970000\n",
       "50%       30.750000\n",
       "75%       39.830000\n",
       "max       49.960000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the suburban fare data.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "380eae55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.000000\n",
       "mean      34.623440\n",
       "std       14.558046\n",
       "min       10.110000\n",
       "25%       19.900000\n",
       "50%       37.050000\n",
       "75%       47.070000\n",
       "max       58.550000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the rural fare data.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 61, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "rural_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "547ef7f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add all ride fare box-and-whisker plots to the same graph.\n",
    "x_labels = [\"Urban\", \"Suburban\",\"Rural\"]\n",
    "ride_fare_data = [urban_fares, suburban_fares, rural_fares]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Ride Fare Data (2019)',fontsize=20)\n",
    "ax.set_ylabel('Fare ($USD)',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(ride_fare_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 60, step=5.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig3.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e6704d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1625.000000\n",
       "mean       36.678154\n",
       "std        20.075545\n",
       "min         3.000000\n",
       "25%        22.000000\n",
       "50%        37.000000\n",
       "75%        52.000000\n",
       "max        73.000000\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the urban driver count data.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_drivers,labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Driver Count Data (2019)')\n",
    "ax.set_ylabel('Number of Drivers)')\n",
    "ax.set_yticks(np.arange(0, 90, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "urban_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bfd23d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add all ride fare box-and-whisker plots to the same graph.\n",
    "x_labels = [\"Urban\", \"Suburban\",\"Rural\"]\n",
    "city_drivers_data = [urban_drivers, suburban_drivers, rural_drivers]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Driver Count Data (2019)',fontsize=20)\n",
    "ax.set_ylabel('Number of Drivers',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(city_drivers_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 76, step=5.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig4.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "bf923f20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        4327.93\n",
       "Suburban    19356.33\n",
       "Urban       39854.38\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of the fares for each city type.\n",
    "sum_fares_by_type = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "sum_fares_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bcdf0ebb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63538.64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of all the fares.\n",
    "total_fares = pyber_data_df[\"fare\"].sum()\n",
    "total_fares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b60d2f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63538.64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of all the fares.\n",
    "total_fares = pyber_data_df[\"fare\"].sum()\n",
    "total_fares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "bafe2125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        6.811493\n",
       "Suburban    30.463872\n",
       "Urban       62.724635\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of fare for each city type.\n",
    "type_percents = 100 * sum_fares_by_type / total_fares\n",
    "type_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4ae26fc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        6.811493\n",
       "Suburban    30.463872\n",
       "Urban       62.724635\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of fare for each city type.\n",
    "type_percents = 100 * pyber_data_df.groupby([\"type\"]).sum()[\"fare\"] / pyber_data_df[\"fare\"].sum()\n",
    "type_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e7243773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the percentage of fares by city type pie chart.\n",
    "plt.pie(type_percents, labels=[\"Rural\", \"Suburban\", \"Urban\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "effd0bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the percentage of fares by city type pie chart.\n",
    "plt.pie(type_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Fares by City Type\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "389ba575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import mpl to change the plot configurations using rcParams.\n",
    "import matplotlib as mpl\n",
    "# Build Pie Chart\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(type_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Fares by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig5.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2070ba59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d6d5961f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        5.263158\n",
       "Suburban    26.315789\n",
       "Urban       68.421053\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of rides for each city type.\n",
    "ride_percents = 100 * pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"] / pyber_data_df[\"ride_id\"].count()\n",
    "ride_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "eccf6382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build percentage of rides by city type pie chart.\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(ride_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Rides by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig6.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b9dd417f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyber_data_df.groupby([\"type\"]).count()[\"driver_count\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "38cacae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        2.623613\n",
       "Suburban    16.481668\n",
       "Urban       80.894719\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of drivers for each city type.\n",
    "driver_percents = 100 * city_data_df.groupby([\"type\"]).sum()[\"driver_count\"] / city_data_df[\"driver_count\"].sum()\n",
    "driver_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "cfd161fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build percentage of drivers by city type pie chart.\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(driver_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Drivers by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig7.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faf2374e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "VirtualEnvironment",
   "language": "python",
   "name": "virtualenvironment"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}