NYC_Trip_Duration_Univariate_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from datetime import datetime \n",
    "from haversine import haversine\n",
    "\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3063: DtypeWarning: Columns (12) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id1080784</td>\n",
       "      <td>2</td>\n",
       "      <td>29-02-2016 16:40</td>\n",
       "      <td>29-02-2016 16:47</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.953918</td>\n",
       "      <td>40.778873</td>\n",
       "      <td>-73.963875</td>\n",
       "      <td>40.771164</td>\n",
       "      <td>N</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id0889885</td>\n",
       "      <td>1</td>\n",
       "      <td>11-03-2016 23:35</td>\n",
       "      <td>11-03-2016 23:53</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.988312</td>\n",
       "      <td>40.731743</td>\n",
       "      <td>-73.994751</td>\n",
       "      <td>40.694931</td>\n",
       "      <td>N</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id0857912</td>\n",
       "      <td>2</td>\n",
       "      <td>21-02-2016 17:59</td>\n",
       "      <td>21-02-2016 18:26</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.997314</td>\n",
       "      <td>40.721458</td>\n",
       "      <td>-73.948029</td>\n",
       "      <td>40.774918</td>\n",
       "      <td>N</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3744273</td>\n",
       "      <td>2</td>\n",
       "      <td>05-01-2016 9:44</td>\n",
       "      <td>05-01-2016 10:03</td>\n",
       "      <td>6</td>\n",
       "      <td>-73.961670</td>\n",
       "      <td>40.759720</td>\n",
       "      <td>-73.956779</td>\n",
       "      <td>40.780628</td>\n",
       "      <td>N</td>\n",
       "      <td>1141</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id0232939</td>\n",
       "      <td>1</td>\n",
       "      <td>17-02-2016 6:42</td>\n",
       "      <td>17-02-2016 6:56</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.017120</td>\n",
       "      <td>40.708469</td>\n",
       "      <td>-73.988182</td>\n",
       "      <td>40.740631</td>\n",
       "      <td>N</td>\n",
       "      <td>848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id   pickup_datetime  dropoff_datetime  passenger_count  \\\n",
       "0  id1080784          2  29-02-2016 16:40  29-02-2016 16:47                1   \n",
       "1  id0889885          1  11-03-2016 23:35  11-03-2016 23:53                2   \n",
       "2  id0857912          2  21-02-2016 17:59  21-02-2016 18:26                2   \n",
       "3  id3744273          2   05-01-2016 9:44  05-01-2016 10:03                6   \n",
       "4  id0232939          1   17-02-2016 6:42   17-02-2016 6:56                1   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.953918        40.778873         -73.963875         40.771164   \n",
       "1        -73.988312        40.731743         -73.994751         40.694931   \n",
       "2        -73.997314        40.721458         -73.948029         40.774918   \n",
       "3        -73.961670        40.759720         -73.956779         40.780628   \n",
       "4        -74.017120        40.708469         -73.988182         40.740631   \n",
       "\n",
       "  store_and_fwd_flag  trip_duration  \n",
       "0                  N            400  \n",
       "1                  N           1100  \n",
       "2                  N           1635  \n",
       "3                  N           1141  \n",
       "4                  N            848  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the Dataset\n",
    "\n",
    "dataset=pd.read_csv(r\"C:\\Users\\Sarthak Arora\\Analytics Vidhya\\BOOTCAMP CLASSES\\NYC Trip Data Duration\\nyc_taxi_trip_duration.csv\")\n",
    "dataset.drop(columns=[\"Unnamed: 11\",\"Unnamed: 12\"],inplace=True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(729322, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our Dataset contains 729322 rows and 11 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>729322.000000</td>\n",
       "      <td>7.293220e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.662055</td>\n",
       "      <td>9.522291e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.312446</td>\n",
       "      <td>3.864626e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.970000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.630000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.939736e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       passenger_count  trip_duration\n",
       "count    729322.000000   7.293220e+05\n",
       "mean          1.662055   9.522291e+02\n",
       "std           1.312446   3.864626e+03\n",
       "min           0.000000   1.000000e+00\n",
       "25%           1.000000   3.970000e+02\n",
       "50%           1.000000   6.630000e+02\n",
       "75%           2.000000   1.075000e+03\n",
       "max           9.000000   1.939736e+06"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[[\"passenger_count\",\"trip_duration\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                    0\n",
       "vendor_id             0\n",
       "pickup_datetime       0\n",
       "dropoff_datetime      0\n",
       "passenger_count       0\n",
       "pickup_longitude      0\n",
       "pickup_latitude       0\n",
       "dropoff_longitude     0\n",
       "dropoff_latitude      0\n",
       "store_and_fwd_flag    0\n",
       "trip_duration         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our Data Doesn't have any missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     object\n",
       "vendor_id               int64\n",
       "pickup_datetime        object\n",
       "dropoff_datetime       object\n",
       "passenger_count         int64\n",
       "pickup_longitude      float64\n",
       "pickup_latitude       float64\n",
       "dropoff_longitude     float64\n",
       "dropoff_latitude      float64\n",
       "store_and_fwd_flag     object\n",
       "trip_duration           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some Datatypes need to be changed-\n",
    "1. vendor_id, store_and_fwd_flag -> categorical\n",
    "2. pickup_datetime, dropoff_datetime -> datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.pickup_datetime = pd.to_datetime(dataset.pickup_datetime, format=\"%d-%m-%Y %H:%M\")\n",
    "dataset.dropoff_datetime = pd.to_datetime(dataset.dropoff_datetime, format=\"%d-%m-%Y %H:%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.vendor_id=dataset[\"vendor_id\"].astype(\"category\")\n",
    "dataset.store_and_fwd_flag=dataset[\"store_and_fwd_flag\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                            object\n",
       "vendor_id                   category\n",
       "pickup_datetime       datetime64[ns]\n",
       "dropoff_datetime      datetime64[ns]\n",
       "passenger_count                int64\n",
       "pickup_longitude             float64\n",
       "pickup_latitude              float64\n",
       "dropoff_longitude            float64\n",
       "dropoff_latitude             float64\n",
       "store_and_fwd_flag          category\n",
       "trip_duration                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We are creating a new variable which measures the Manhattan distance travelled by the taxi in each trip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id1080784</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-29 16:40:00</td>\n",
       "      <td>2016-02-29 16:47:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.953918</td>\n",
       "      <td>40.778873</td>\n",
       "      <td>-73.963875</td>\n",
       "      <td>40.771164</td>\n",
       "      <td>N</td>\n",
       "      <td>400</td>\n",
       "      <td>1106.547604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id0889885</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-11 23:35:00</td>\n",
       "      <td>2016-03-11 23:53:00</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.988312</td>\n",
       "      <td>40.731743</td>\n",
       "      <td>-73.994751</td>\n",
       "      <td>40.694931</td>\n",
       "      <td>N</td>\n",
       "      <td>1100</td>\n",
       "      <td>715.684172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id0857912</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-21 17:59:00</td>\n",
       "      <td>2016-02-21 18:26:00</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.997314</td>\n",
       "      <td>40.721458</td>\n",
       "      <td>-73.948029</td>\n",
       "      <td>40.774918</td>\n",
       "      <td>N</td>\n",
       "      <td>1635</td>\n",
       "      <td>5477.637988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3744273</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-05 09:44:00</td>\n",
       "      <td>2016-01-05 10:03:00</td>\n",
       "      <td>6</td>\n",
       "      <td>-73.961670</td>\n",
       "      <td>40.759720</td>\n",
       "      <td>-73.956779</td>\n",
       "      <td>40.780628</td>\n",
       "      <td>N</td>\n",
       "      <td>1141</td>\n",
       "      <td>543.539520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id0232939</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-02-17 06:42:00</td>\n",
       "      <td>2016-02-17 06:56:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.017120</td>\n",
       "      <td>40.708469</td>\n",
       "      <td>-73.988182</td>\n",
       "      <td>40.740631</td>\n",
       "      <td>N</td>\n",
       "      <td>848</td>\n",
       "      <td>3216.204774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id1080784         2 2016-02-29 16:40:00 2016-02-29 16:47:00   \n",
       "1  id0889885         1 2016-03-11 23:35:00 2016-03-11 23:53:00   \n",
       "2  id0857912         2 2016-02-21 17:59:00 2016-02-21 18:26:00   \n",
       "3  id3744273         2 2016-01-05 09:44:00 2016-01-05 10:03:00   \n",
       "4  id0232939         1 2016-02-17 06:42:00 2016-02-17 06:56:00   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.953918        40.778873         -73.963875   \n",
       "1                2        -73.988312        40.731743         -73.994751   \n",
       "2                2        -73.997314        40.721458         -73.948029   \n",
       "3                6        -73.961670        40.759720         -73.956779   \n",
       "4                1        -74.017120        40.708469         -73.988182   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  trip_duration     distance  \n",
       "0         40.771164                  N            400  1106.547604  \n",
       "1         40.694931                  N           1100   715.684172  \n",
       "2         40.774918                  N           1635  5477.637988  \n",
       "3         40.780628                  N           1141   543.539520  \n",
       "4         40.740631                  N            848  3216.204774  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"distance\"]=111139*abs(dataset.pickup_longitude-dataset.dropoff_longitude)+abs(dataset.pickup_latitude-dataset.dropoff_latitude)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          9.958928\n",
       "1          2.342239\n",
       "2         12.060854\n",
       "3          1.714936\n",
       "4         13.653700\n",
       "            ...    \n",
       "729317    17.954202\n",
       "729318     5.911312\n",
       "729319     2.163636\n",
       "729320     6.125573\n",
       "729321    18.794059\n",
       "Name: avg_speed, Length: 729322, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"avg_speed\"]=dataset[\"distance\"]*60*60/(1000*dataset[\"trip_duration\"])\n",
    "dataset[\"avg_speed\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also, we are creating variables derived from Datetimes given to us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['pickup_day_name'] = dataset['pickup_datetime'].dt.day_name()\n",
    "dataset['dropoff_day_name']= dataset['dropoff_datetime'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['pickup_time'] = dataset['pickup_datetime'].dt.hour\n",
    "dataset['dropoff_time'] = dataset['dropoff_datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>avg_speed</th>\n",
       "      <th>pickup_day_name</th>\n",
       "      <th>dropoff_day_name</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>dropoff_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, vendor_id, pickup_datetime, dropoff_datetime, passenger_count, pickup_longitude, pickup_latitude, dropoff_longitude, dropoff_latitude, store_and_fwd_flag, trip_duration, distance, avg_speed, pickup_day_name, dropoff_day_name, pickup_time, dropoff_time]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset.pickup_datetime>dataset.dropoff_datetime]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>avg_speed</th>\n",
       "      <th>pickup_day_name</th>\n",
       "      <th>dropoff_day_name</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>dropoff_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id1080784</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-29 16:40:00</td>\n",
       "      <td>2016-02-29 16:47:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.953918</td>\n",
       "      <td>40.778873</td>\n",
       "      <td>-73.963875</td>\n",
       "      <td>40.771164</td>\n",
       "      <td>N</td>\n",
       "      <td>400</td>\n",
       "      <td>1106.547604</td>\n",
       "      <td>9.958928</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id0889885</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-11 23:35:00</td>\n",
       "      <td>2016-03-11 23:53:00</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.988312</td>\n",
       "      <td>40.731743</td>\n",
       "      <td>-73.994751</td>\n",
       "      <td>40.694931</td>\n",
       "      <td>N</td>\n",
       "      <td>1100</td>\n",
       "      <td>715.684172</td>\n",
       "      <td>2.342239</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Friday</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id0857912</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-21 17:59:00</td>\n",
       "      <td>2016-02-21 18:26:00</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.997314</td>\n",
       "      <td>40.721458</td>\n",
       "      <td>-73.948029</td>\n",
       "      <td>40.774918</td>\n",
       "      <td>N</td>\n",
       "      <td>1635</td>\n",
       "      <td>5477.637988</td>\n",
       "      <td>12.060854</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3744273</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-05 09:44:00</td>\n",
       "      <td>2016-01-05 10:03:00</td>\n",
       "      <td>6</td>\n",
       "      <td>-73.961670</td>\n",
       "      <td>40.759720</td>\n",
       "      <td>-73.956779</td>\n",
       "      <td>40.780628</td>\n",
       "      <td>N</td>\n",
       "      <td>1141</td>\n",
       "      <td>543.539520</td>\n",
       "      <td>1.714936</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id0232939</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-02-17 06:42:00</td>\n",
       "      <td>2016-02-17 06:56:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.017120</td>\n",
       "      <td>40.708469</td>\n",
       "      <td>-73.988182</td>\n",
       "      <td>40.740631</td>\n",
       "      <td>N</td>\n",
       "      <td>848</td>\n",
       "      <td>3216.204774</td>\n",
       "      <td>13.653700</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id1080784         2 2016-02-29 16:40:00 2016-02-29 16:47:00   \n",
       "1  id0889885         1 2016-03-11 23:35:00 2016-03-11 23:53:00   \n",
       "2  id0857912         2 2016-02-21 17:59:00 2016-02-21 18:26:00   \n",
       "3  id3744273         2 2016-01-05 09:44:00 2016-01-05 10:03:00   \n",
       "4  id0232939         1 2016-02-17 06:42:00 2016-02-17 06:56:00   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.953918        40.778873         -73.963875   \n",
       "1                2        -73.988312        40.731743         -73.994751   \n",
       "2                2        -73.997314        40.721458         -73.948029   \n",
       "3                6        -73.961670        40.759720         -73.956779   \n",
       "4                1        -74.017120        40.708469         -73.988182   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  trip_duration     distance  avg_speed  \\\n",
       "0         40.771164                  N            400  1106.547604   9.958928   \n",
       "1         40.694931                  N           1100   715.684172   2.342239   \n",
       "2         40.774918                  N           1635  5477.637988  12.060854   \n",
       "3         40.780628                  N           1141   543.539520   1.714936   \n",
       "4         40.740631                  N            848  3216.204774  13.653700   \n",
       "\n",
       "  pickup_day_name dropoff_day_name  pickup_time  dropoff_time  \n",
       "0          Monday           Monday           16            16  \n",
       "1          Friday           Friday           23            23  \n",
       "2          Sunday           Sunday           17            18  \n",
       "3         Tuesday          Tuesday            9            10  \n",
       "4       Wednesday        Wednesday            6             6  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>avg_speed</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>dropoff_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>729322.000000</td>\n",
       "      <td>729322.000000</td>\n",
       "      <td>729322.000000</td>\n",
       "      <td>729322.000000</td>\n",
       "      <td>729322.000000</td>\n",
       "      <td>7.293220e+05</td>\n",
       "      <td>729322.000000</td>\n",
       "      <td>729322.000000</td>\n",
       "      <td>729322.000000</td>\n",
       "      <td>729322.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.662055</td>\n",
       "      <td>-73.973513</td>\n",
       "      <td>40.750919</td>\n",
       "      <td>-73.973422</td>\n",
       "      <td>40.751775</td>\n",
       "      <td>9.522291e+02</td>\n",
       "      <td>2708.843903</td>\n",
       "      <td>10.629816</td>\n",
       "      <td>13.611698</td>\n",
       "      <td>13.60616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.312446</td>\n",
       "      <td>0.069754</td>\n",
       "      <td>0.033594</td>\n",
       "      <td>0.069588</td>\n",
       "      <td>0.036037</td>\n",
       "      <td>3.864626e+03</td>\n",
       "      <td>4355.018006</td>\n",
       "      <td>10.711357</td>\n",
       "      <td>6.402853</td>\n",
       "      <td>6.48637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-121.933342</td>\n",
       "      <td>34.712234</td>\n",
       "      <td>-121.933304</td>\n",
       "      <td>32.181141</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-73.991859</td>\n",
       "      <td>40.737335</td>\n",
       "      <td>-73.991318</td>\n",
       "      <td>40.735931</td>\n",
       "      <td>3.970000e+02</td>\n",
       "      <td>690.218132</td>\n",
       "      <td>4.802708</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-73.981758</td>\n",
       "      <td>40.754070</td>\n",
       "      <td>-73.979759</td>\n",
       "      <td>40.754509</td>\n",
       "      <td>6.630000e+02</td>\n",
       "      <td>1401.646452</td>\n",
       "      <td>9.042801</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>-73.967361</td>\n",
       "      <td>40.768314</td>\n",
       "      <td>-73.963036</td>\n",
       "      <td>40.769741</td>\n",
       "      <td>1.075000e+03</td>\n",
       "      <td>2682.035846</td>\n",
       "      <td>14.087260</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>-65.897385</td>\n",
       "      <td>51.881084</td>\n",
       "      <td>-65.897385</td>\n",
       "      <td>43.921028</td>\n",
       "      <td>1.939736e+06</td>\n",
       "      <td>660978.356561</td>\n",
       "      <td>4194.190315</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count    729322.000000     729322.000000    729322.000000      729322.000000   \n",
       "mean          1.662055        -73.973513        40.750919         -73.973422   \n",
       "std           1.312446          0.069754         0.033594           0.069588   \n",
       "min           0.000000       -121.933342        34.712234        -121.933304   \n",
       "25%           1.000000        -73.991859        40.737335         -73.991318   \n",
       "50%           1.000000        -73.981758        40.754070         -73.979759   \n",
       "75%           2.000000        -73.967361        40.768314         -73.963036   \n",
       "max           9.000000        -65.897385        51.881084         -65.897385   \n",
       "\n",
       "       dropoff_latitude  trip_duration       distance      avg_speed  \\\n",
       "count     729322.000000   7.293220e+05  729322.000000  729322.000000   \n",
       "mean          40.751775   9.522291e+02    2708.843903      10.629816   \n",
       "std            0.036037   3.864626e+03    4355.018006      10.711357   \n",
       "min           32.181141   1.000000e+00       0.000000       0.000000   \n",
       "25%           40.735931   3.970000e+02     690.218132       4.802708   \n",
       "50%           40.754509   6.630000e+02    1401.646452       9.042801   \n",
       "75%           40.769741   1.075000e+03    2682.035846      14.087260   \n",
       "max           43.921028   1.939736e+06  660978.356561    4194.190315   \n",
       "\n",
       "         pickup_time  dropoff_time  \n",
       "count  729322.000000  729322.00000  \n",
       "mean       13.611698      13.60616  \n",
       "std         6.402853       6.48637  \n",
       "min         0.000000       0.00000  \n",
       "25%         9.000000       9.00000  \n",
       "50%        14.000000      14.00000  \n",
       "75%        19.000000      19.00000  \n",
       "max        23.000000      23.00000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Discrepencies in the Data Set (Data Validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **ID**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "729322"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"id\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Each id uniquely identifies a row\n",
    "<br>\n",
    "We might want to see the patterns within these ids though"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Vendor_ID**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.535403\n",
       "1    0.464597\n",
       "Name: vendor_id, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total- 729322\n"
     ]
    }
   ],
   "source": [
    "display(dataset[\"vendor_id\"].value_counts(normalize=True))\n",
    "print(\"Total- \"+ str(dataset[\"vendor_id\"].value_counts().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Vendor ID')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(palette=\"Set2\")\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.ylim((0.0,0.8))\n",
    "\n",
    "dataset[\"vendor_id\"].value_counts(normalize=True).plot(kind='bar')\n",
    "\n",
    "plt.ylabel(\"Fraction\")\n",
    "plt.xlabel(\"Vendor ID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### vendor_id has two unique categories- 1 and 2\n",
    "Vendor 2 gets slightly more rides than Vendor 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Passenger_count**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    517415\n",
       "2    105097\n",
       "5     38926\n",
       "3     29692\n",
       "6     24107\n",
       "4     14050\n",
       "0        33\n",
       "9         1\n",
       "7         1\n",
       "Name: passenger_count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"passenger_count\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We see that there is passenger_count of 0 as well, what would that mean?\n",
    "We should inspect this "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "               id vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "3572    id3364866         1 2016-02-13 14:04:00 2016-02-13 14:21:00   \n",
       "23847   id3485118         1 2016-05-11 19:15:00 2016-05-11 19:32:00   \n",
       "45987   id2571672         1 2016-03-06 05:24:00 2016-03-06 05:25:00   \n",
       "76998   id1253169         1 2016-02-28 01:08:00 2016-02-28 01:09:00   \n",
       "101709  id1220914         2 2016-01-17 20:51:00 2016-01-17 20:52:00   \n",
       "\n",
       "        passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "3572                  0        -73.990479        40.742004         -73.989243   \n",
       "23847                 0        -73.987572        40.754566         -73.972031   \n",
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       "101709                0        -73.959999        40.766861         -73.960106   \n",
       "\n",
       "        dropoff_latitude store_and_fwd_flag  trip_duration     distance  \\\n",
       "3572           40.763679                  N           1029   137.386145   \n",
       "23847          40.762836                  N            981  1727.228360   \n",
       "45987          40.761196                  Y              5     0.000000   \n",
       "76998          40.736492                  N             13     0.000004   \n",
       "101709         40.766911                  N             48    11.871918   \n",
       "\n",
       "        avg_speed pickup_day_name dropoff_day_name  pickup_time  dropoff_time  \n",
       "3572     0.480651        Saturday         Saturday           14            14  \n",
       "23847    6.338453       Wednesday        Wednesday           19            19  \n",
       "45987    0.000000          Sunday           Sunday            5             5  \n",
       "76998    0.000001          Sunday           Sunday            1             1  \n",
       "101709   0.890394          Sunday           Sunday           20            20  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset[\"passenger_count\"]==0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many trips which have trip_duration less than 10 seconds "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "               id vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "501596  id3066152         1 2016-02-01 00:27:00 2016-02-01 00:27:00   \n",
       "592303  id2154895         1 2016-05-23 23:37:00 2016-05-23 23:37:00   \n",
       "45987   id2571672         1 2016-03-06 05:24:00 2016-03-06 05:25:00   \n",
       "568782  id3533680         1 2016-01-10 04:17:00 2016-01-10 04:17:00   \n",
       "541539  id1610720         2 2016-03-13 14:08:00 2016-03-13 14:08:00   \n",
       "\n",
       "        passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "501596                0        -74.014488        40.708229         -74.014488   \n",
       "592303                0        -73.861633        40.705029         -73.861633   \n",
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       "541539                0        -73.922081        40.636040         -73.922081   \n",
       "\n",
       "        dropoff_latitude store_and_fwd_flag  trip_duration  distance  \\\n",
       "501596         40.708229                  N             45       0.0   \n",
       "592303         40.705029                  N              8       0.0   \n",
       "45987          40.761196                  Y              5       0.0   \n",
       "568782         40.598701                  N              3       0.0   \n",
       "541539         40.636040                  N              8       0.0   \n",
       "\n",
       "        avg_speed pickup_day_name dropoff_day_name  pickup_time  dropoff_time  \n",
       "501596        0.0          Monday           Monday            0             0  \n",
       "592303        0.0          Monday           Monday           23            23  \n",
       "45987         0.0          Sunday           Sunday            5             5  \n",
       "568782        0.0          Sunday           Sunday            4             4  \n",
       "541539        0.0          Sunday           Sunday           14            14  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dataset[dataset[\"passenger_count\"]==0].sort_values(\"distance\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many which covered 0 metres, maybe trip was started by mistake and then realised by the driver later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547110</th>\n",
       "      <td>id1189877</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-04-06 12:35:00</td>\n",
       "      <td>2016-04-06 12:35:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.891350</td>\n",
       "      <td>40.646751</td>\n",
       "      <td>-73.891350</td>\n",
       "      <td>40.646751</td>\n",
       "      <td>N</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "195197  id2251007         2 2016-05-23 14:20:00 2016-05-23 14:20:00   \n",
       "543304  id3062678         1 2016-04-06 21:38:00 2016-04-06 21:38:00   \n",
       "443300  id2509635         1 2016-03-07 10:52:00 2016-03-07 10:52:00   \n",
       "301923  id1792292         1 2016-06-30 18:09:00 2016-06-30 18:09:00   \n",
       "547110  id1189877         1 2016-04-06 12:35:00 2016-04-06 12:35:00   \n",
       "\n",
       "        passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "195197                1        -73.781830        40.644699         -73.781830   \n",
       "543304                1        -73.982010        40.757099         -73.982010   \n",
       "443300                1        -73.790337        40.643955         -73.790337   \n",
       "301923                1        -73.794342        40.657082         -73.794342   \n",
       "547110                1        -73.891350        40.646751         -73.891350   \n",
       "\n",
       "        dropoff_latitude store_and_fwd_flag  trip_duration  distance  \\\n",
       "195197         40.644699                  N              8       0.0   \n",
       "543304         40.757099                  N              4       0.0   \n",
       "443300         40.643955                  N              2       0.0   \n",
       "301923         40.657082                  N              2       0.0   \n",
       "547110         40.646751                  N              5       0.0   \n",
       "\n",
       "        avg_speed pickup_day_name dropoff_day_name  pickup_time  dropoff_time  \n",
       "195197        0.0          Monday           Monday           14            14  \n",
       "543304        0.0       Wednesday        Wednesday           21            21  \n",
       "443300        0.0          Monday           Monday           10            10  \n",
       "301923        0.0        Thursday         Thursday           18            18  \n",
       "547110        0.0       Wednesday        Wednesday           12            12  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset[\"trip_duration\"]<10].sort_values(\"distance\").head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.709447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.144102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.040712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>0.019264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.053373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0.033054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count     count\n",
       "6                0  0.000045\n",
       "0                1  0.709447\n",
       "1                2  0.144102\n",
       "3                3  0.040712\n",
       "5                4  0.019264\n",
       "2                5  0.053373\n",
       "4                6  0.033054\n",
       "8                7  0.000001\n",
       "7                9  0.000001"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy=dataset[\"passenger_count\"].value_counts().to_frame().reset_index()\n",
    "xy=xy.rename(columns={\"passenger_count\":\"count\",\"index\":\"passenger_count\"})\n",
    "xy=xy.sort_values(by=\"passenger_count\")\n",
    "xy[\"count\"]=xy[\"count\"]/dataset.shape[0]\n",
    "xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29d989f6ac8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(palette=\"Set2\")\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "sns.barplot(data=xy, y=\"count\",x=\"passenger_count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Around 71% of rides have only one passenger where as 14.5% have 2 passengers.\n",
    "\n",
    "There are rides with 0,7 and 9 passengers as well.\n",
    "We still have to investigate what is up with 0 passenger list.\n",
    "\n",
    "We can also infer that people who carpool are very less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    0.994461\n",
       "Y    0.005539\n",
       "Name: store_and_fwd_flag, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(dataset[\"store_and_fwd_flag\"].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'store_and_fwd_flag')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(palette=\"Set2\")\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.ylim((0.0,1.0))\n",
    "\n",
    "dataset[\"store_and_fwd_flag\"].value_counts(normalize=True).plot(kind='bar')\n",
    "\n",
    "plt.ylabel(\"Fraction\")\n",
    "plt.xlabel(\"store_and_fwd_flag\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 99.45 % of rides did not have any internet connection problems\n",
    "Maybe the rest 0.55% of rides were intercity or to remote areas.\n",
    "We should check if the 'Y' flagged have travelled more distance and hence the average time taken for the ride is more?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Distance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1106.547604\n",
       "1          715.684172\n",
       "2         5477.637988\n",
       "3          543.539520\n",
       "4         3216.204774\n",
       "             ...     \n",
       "729317    1476.234374\n",
       "729318     517.239761\n",
       "729319     404.479760\n",
       "729320     760.591984\n",
       "729321    6389.980029\n",
       "Name: distance, Length: 729322, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"distance\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    729322.000000\n",
       "mean       2708.843903\n",
       "std        4355.018006\n",
       "min           0.000000\n",
       "25%         690.218132\n",
       "50%        1401.646452\n",
       "75%        2682.035846\n",
       "max      660978.356561\n",
       "Name: distance, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.distance.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>avg_speed</th>\n",
       "      <th>pickup_day_name</th>\n",
       "      <th>dropoff_day_name</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>dropoff_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>id3155891</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-28 11:21:00</td>\n",
       "      <td>2016-06-28 11:25:00</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.996422</td>\n",
       "      <td>40.298828</td>\n",
       "      <td>-73.996422</td>\n",
       "      <td>40.298828</td>\n",
       "      <td>N</td>\n",
       "      <td>240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>id0786923</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:34:00</td>\n",
       "      <td>2016-03-26 13:37:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.996323</td>\n",
       "      <td>40.753460</td>\n",
       "      <td>-73.996323</td>\n",
       "      <td>40.753460</td>\n",
       "      <td>N</td>\n",
       "      <td>159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>id2323213</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-13 16:49:00</td>\n",
       "      <td>2016-06-13 17:04:00</td>\n",
       "      <td>5</td>\n",
       "      <td>-73.967171</td>\n",
       "      <td>40.763500</td>\n",
       "      <td>-73.967171</td>\n",
       "      <td>40.763500</td>\n",
       "      <td>N</td>\n",
       "      <td>897</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>id3235868</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-02-29 21:39:00</td>\n",
       "      <td>2016-02-29 21:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.995232</td>\n",
       "      <td>40.744038</td>\n",
       "      <td>-73.995232</td>\n",
       "      <td>40.744038</td>\n",
       "      <td>N</td>\n",
       "      <td>256</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>id1865738</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-13 11:38:00</td>\n",
       "      <td>2016-03-13 12:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.912781</td>\n",
       "      <td>40.804428</td>\n",
       "      <td>-73.912781</td>\n",
       "      <td>40.804428</td>\n",
       "      <td>N</td>\n",
       "      <td>1330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728720</th>\n",
       "      <td>id3423342</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-05 21:32:00</td>\n",
       "      <td>2016-05-05 21:32:00</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.977898</td>\n",
       "      <td>40.766392</td>\n",
       "      <td>-73.977898</td>\n",
       "      <td>40.766392</td>\n",
       "      <td>N</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728775</th>\n",
       "      <td>id2618105</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-13 20:06:00</td>\n",
       "      <td>2016-03-13 20:14:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.974258</td>\n",
       "      <td>40.759895</td>\n",
       "      <td>-73.974258</td>\n",
       "      <td>40.759895</td>\n",
       "      <td>N</td>\n",
       "      <td>464</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728838</th>\n",
       "      <td>id1232120</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-29 04:04:00</td>\n",
       "      <td>2016-05-29 04:11:00</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.984558</td>\n",
       "      <td>40.750839</td>\n",
       "      <td>-73.984558</td>\n",
       "      <td>40.750839</td>\n",
       "      <td>N</td>\n",
       "      <td>421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729025</th>\n",
       "      <td>id3524028</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 18:03:00</td>\n",
       "      <td>2016-04-06 18:26:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.083611</td>\n",
       "      <td>40.632401</td>\n",
       "      <td>-74.083611</td>\n",
       "      <td>40.632401</td>\n",
       "      <td>N</td>\n",
       "      <td>1369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729308</th>\n",
       "      <td>id3477255</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-20 08:22:00</td>\n",
       "      <td>2016-01-20 08:47:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.977859</td>\n",
       "      <td>40.749538</td>\n",
       "      <td>-73.977859</td>\n",
       "      <td>40.749538</td>\n",
       "      <td>N</td>\n",
       "      <td>1497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2901 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "263     id3155891         2 2016-06-28 11:21:00 2016-06-28 11:25:00   \n",
       "327     id0786923         2 2016-03-26 13:34:00 2016-03-26 13:37:00   \n",
       "795     id2323213         2 2016-06-13 16:49:00 2016-06-13 17:04:00   \n",
       "1176    id3235868         1 2016-02-29 21:39:00 2016-02-29 21:44:00   \n",
       "1257    id1865738         2 2016-03-13 11:38:00 2016-03-13 12:00:00   \n",
       "...           ...       ...                 ...                 ...   \n",
       "728720  id3423342         2 2016-05-05 21:32:00 2016-05-05 21:32:00   \n",
       "728775  id2618105         2 2016-03-13 20:06:00 2016-03-13 20:14:00   \n",
       "728838  id1232120         2 2016-05-29 04:04:00 2016-05-29 04:11:00   \n",
       "729025  id3524028         2 2016-04-06 18:03:00 2016-04-06 18:26:00   \n",
       "729308  id3477255         2 2016-01-20 08:22:00 2016-01-20 08:47:00   \n",
       "\n",
       "        passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "263                   2        -73.996422        40.298828         -73.996422   \n",
       "327                   1        -73.996323        40.753460         -73.996323   \n",
       "795                   5        -73.967171        40.763500         -73.967171   \n",
       "1176                  1        -73.995232        40.744038         -73.995232   \n",
       "1257                  2        -73.912781        40.804428         -73.912781   \n",
       "...                 ...               ...              ...                ...   \n",
       "728720                2        -73.977898        40.766392         -73.977898   \n",
       "728775                1        -73.974258        40.759895         -73.974258   \n",
       "728838                2        -73.984558        40.750839         -73.984558   \n",
       "729025                1        -74.083611        40.632401         -74.083611   \n",
       "729308                1        -73.977859        40.749538         -73.977859   \n",
       "\n",
       "        dropoff_latitude store_and_fwd_flag  trip_duration  distance  \\\n",
       "263            40.298828                  N            240       0.0   \n",
       "327            40.753460                  N            159       0.0   \n",
       "795            40.763500                  N            897       0.0   \n",
       "1176           40.744038                  N            256       0.0   \n",
       "1257           40.804428                  N           1330       0.0   \n",
       "...                  ...                ...            ...       ...   \n",
       "728720         40.766392                  N             14       0.0   \n",
       "728775         40.759895                  N            464       0.0   \n",
       "728838         40.750839                  N            421       0.0   \n",
       "729025         40.632401                  N           1369       0.0   \n",
       "729308         40.749538                  N           1497       0.0   \n",
       "\n",
       "        avg_speed pickup_day_name dropoff_day_name  pickup_time  dropoff_time  \n",
       "263           0.0         Tuesday          Tuesday           11            11  \n",
       "327           0.0        Saturday         Saturday           13            13  \n",
       "795           0.0          Monday           Monday           16            17  \n",
       "1176          0.0          Monday           Monday           21            21  \n",
       "1257          0.0          Sunday           Sunday           11            12  \n",
       "...           ...             ...              ...          ...           ...  \n",
       "728720        0.0        Thursday         Thursday           21            21  \n",
       "728775        0.0          Sunday           Sunday           20            20  \n",
       "728838        0.0          Sunday           Sunday            4             4  \n",
       "729025        0.0       Wednesday        Wednesday           18            18  \n",
       "729308        0.0       Wednesday        Wednesday            8             8  \n",
       "\n",
       "[2901 rows x 17 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset.distance==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Around 3000 records has trips of zero distance, this could be due to various reasons like** :\n",
    "\n",
    "- System error\n",
    "- Rider cancelled the ride after accepting\n",
    "- passenger canncelled after accepting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distance\n",
       "(0, 10]     679797\n",
       "(10, 20]     31357\n",
       "(20, 30]     15010\n",
       "(30, 40]       131\n",
       "(40, 50]        69\n",
       "(50, 60]        18\n",
       "(60, 70]         8\n",
       "(70, 80]         7\n",
       "(80, 90]         9\n",
       "Name: distance, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ab=(dataset['distance']/1000).groupby(pd.cut(dataset['distance']/1000, np.arange(0,100,10))).count()\n",
    "display(ab)\n",
    "ab.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of our trips are between 0 and 10 kms, some between 10 and 20 also, 20 and 30.\n",
    "<br>Very few beyond 30 kms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cont_univ(x):\n",
    "    sns.set(palette=\"Set1\")\n",
    "    plt.figure(figsize=(10,8))\n",
    "    \n",
    "    figure,ax = plt.subplots(nrows=2,ncols=1,figsize=(12,8))\n",
    "\n",
    "\n",
    "    \n",
    "    sns.distplot(x,bins=25,kde=True,ax=ax[0])\n",
    "    ax[0].set_title('Distribution of the trip duration')\n",
    "    \n",
    "    # calculating descriptives of variable\n",
    "    mini = x.min()\n",
    "    maxi = x.max()\n",
    "    ran = x.max()-x.min()\n",
    "    mean = x.mean()\n",
    "    median = x.median()\n",
    "    st_dev = x.std()\n",
    "    skew = x.skew()\n",
    "    kurt = x.kurtosis()\n",
    "\n",
    "    # calculating points of standard deviation\n",
    "    points = mean-st_dev, mean+st_dev\n",
    "\n",
    "    sns.lineplot(points, [0,0], color = 'black', label = \"std_dev\",ax=ax[0])\n",
    "    sns.scatterplot([mini,maxi], [0,0], color = 'orange', label = \"min/max\",ax=ax[0])\n",
    "    sns.scatterplot([mean], [0], color = 'red', label = \"mean\",ax=ax[0])\n",
    "    sns.scatterplot([median], [0], color = 'blue', label = \"median\",ax=ax[0])\n",
    "    \n",
    "    ax[0].set_title('std_dev = {}; kurtosis = {};\\nskew = {}; range = {}\\nmean = {}; median = {}'.format((round(points[0],2),round(points[1],2)),\n",
    "                                                                                                   round(kurt,2),\n",
    "                                                                                                   round(skew,2),\n",
    "                                                                                                   (round(mini,2),round(maxi,2),round(ran,2)),\n",
    "                                                                                                   round(mean,2),\n",
    "                                                                                                   round(median,2)))\n",
    "    \n",
    "    sns.boxplot(x,ax=ax[1])\n",
    "    ax[1].set_title('box plot of trip duration')\n",
    "\n",
    "    \n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cont_univ(dataset.distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cont_univ(np.log(dataset[dataset.distance>0].distance))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we logtransform our distance variable and see its distribution, we can see that it is Leptokurtic and slightly negatively skewed\n",
    "<br>Whereas there are a lot of outliers in the original distance without log transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cont_univ(dataset[(dataset.distance<(dataset.distance.mean()+3*dataset.distance.std())) & (dataset.distance>(dataset.distance.mean()-3*dataset.distance.std()))].distance\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After removing the outliers, we can see that the distribution looks more like a normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Trip_Duration**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29d9df82248>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(dataset['trip_duration'],orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that there is one point with very large value, let's analyse that one value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>avg_speed</th>\n",
       "      <th>pickup_day_name</th>\n",
       "      <th>dropoff_day_name</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>dropoff_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21813</th>\n",
       "      <td>id1864733</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-05 00:19:00</td>\n",
       "      <td>2016-01-27 11:08:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.78965</td>\n",
       "      <td>40.643559</td>\n",
       "      <td>-73.95681</td>\n",
       "      <td>40.773087</td>\n",
       "      <td>N</td>\n",
       "      <td>1939736</td>\n",
       "      <td>18578.129214</td>\n",
       "      <td>0.03448</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "21813  id1864733         1 2016-01-05 00:19:00 2016-01-27 11:08:00   \n",
       "\n",
       "       passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "21813                1         -73.78965        40.643559          -73.95681   \n",
       "\n",
       "       dropoff_latitude store_and_fwd_flag  trip_duration      distance  \\\n",
       "21813         40.773087                  N        1939736  18578.129214   \n",
       "\n",
       "       avg_speed pickup_day_name dropoff_day_name  pickup_time  dropoff_time  \n",
       "21813    0.03448         Tuesday        Wednesday            0            11  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset.trip_duration==dataset.trip_duration.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks like a 22 day trip! We have to further investigate this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cont_univ(dataset.trip_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5OfH666/j5+fHRx99ZP7Dd+fOnQ9c/a9Vq1YMHz6cjh07EhYWxu7duwkKCqJbt268+OKL5kU3wsPDCQoKYtSoUQQEBODr68uvv/4KwK1btxg0aBAdO3bklVdeYdiwYeaV986fP0///v3p1q0b/v7+rF279r5xBAUFZbpvH374YaZyycnJLFu2zLx3V1hYGL6+vjg5OeHg4EC3bt3YuHFjpnrZLXevLVu24OHhwejRozO9duPGDaZMmWKxie/9JCcnM2rUKMaMGWNxXlVV0tLSSElJIT09ndTUVBwcHGw2JmdnZ+rWrWved00IIQojWeVPCCGyEBYWRmJiIqGhoZhMJiZNmmSx6e3PP//M+++/z/z583nuuee4ePEic+bMYfny5bi5uXH27Fn69evH9u3bqVmzJuHh4bRs2ZLw8HASEhJITEwkMjISg8FAxYoVLa49cOBABg4cmOOYo6KiLPZtsre3p0SJEkRHR1tsegtgZ2fH8ePHefPNN0lJSTGPRCQmJtKoUSPGjx9PkSJFGDlyJLNnz2b8+PG0bt2a1q1bP/D6lSpV4rPPPkPTNPr06cP06dMpV64c0dHRtGzZkj59+gAZSdukSZOoWrUqixcvZs6cOaxYsYKpU6fi7e3NN998Q0xMDN26daNSpUoYjUaGDRvGzJkzqV69OvHx8fTo0QNvb2/q1KljEcPq1auzda9+/vlnypcvb74vkZGRFpsIe3l5ER0dnaledsvd6+7S4evXr7c4bzKZePfddxk1ahQGw8O/nidOnEiPHj2oUqWKxfkmTZrg4+NDy5YtsbOzo3z58vTo0cOmY2rWrBnLly+nb9++WcYphBC2SBIqIYTIQv369ZkzZw69e/fGx8eHvn37UrZsWaKiooiKiuLNN9/klVdeMS+9feDAAWJiYnjttdfMbSiKQkREBG3btmXv3r2UKVMGT09PKleuzOHDhzl9+jTt2rXLdO3g4GB++OGHTOeXLl2aKTH6L1VVURTF4pymaej1+vuWr1mzJgcOHGDv3r0MGjSIHTt2ZEqaBg0axNChQ+87dfBedze3VRSF+fPn89NPP7F582bOnz+PpmkkJycD8PTTT1O1alUAqlWrxoYNG4CM6XR3f/bw8KBDhw5AxpSyiIgIxo0bZ75WSkoKJ0+ezJRQBQUFma9zV7169Zg0aZLFuQsXLlCmTBmL+/Tfe6dpGjpd5gkd2S2XXbNnz+b555+nadOmD32uaOXKlRgMBgIDA7ly5YrFa2vXruXKlSvs27cPe3t7xo4dy4wZM3j//fdtNqZnn32Wv//+O1fxCSGELZCESgghslC6dGnCwsIIDw/n559/pl+/fkyePJmiRYui1+sJDg7mf//7Hx06dKB27dqoqkqTJk0s9pOKjIzEw8MDV1dXevXqRbly5WjatCkuLi7s37+f48eP33c6WnZHqL777jvziEyNGjXo2rWrxSIU6enpxMbG4unpaVEvOjqaM2fO0Lx5cwBatGhBsWLFiIiIICYmBmdnZ55//nkgI2HIapTiLicnJyDjOayuXbvSpk0bGjRoQPfu3dmxY4f5OR1HR0dzHUVRzOcNBoPFszx3ExWTyYSzszOhoaHm127cuIGzs3OmGLI7QqUoisUUzlKlSlncu5iYGIvRvpyWy66NGzdSokQJwsLCSEpKIjo6Gn9/f4v3CrBhwwZSUlLw9/cnPT3d/HNwcDBhYWH4+flRrFgxAF5++WXzM3G2GpPBYMhTIiqEENYm/wUTQogsrFq1irFjx9KsWTNGjRpFs2bNOHnyJJDxDFK9evUYPXo07733HsnJyTRp0oQDBw6Yn4fas2cPXbp0ISUlBS8vL9zc3Fi9ejVNmzalWbNmbN++ndjYWPMIV2688sor5oUjpk2bRu3atYmNjTU/07Vu3Trq1KmDi4uLRb20tDTeeecdLl26BGRMfzMajVSsWJGoqChmzJhBSkoKJpOJpUuX0qlTpxzFdenSJRISEhg+fDitWrUiPDyctLS0TM+g3euFF14wPxt1+/ZtduzYgaIolC9fHkdHR/Mf9JGRkfj6+nLixIkcxfVf5cuXt5jC2bp1azZu3EhSUhJpaWmsX7+eNm3aZKqX3XLZtX//fjZu3EhoaChTp06lTJkymRIXyBjx2bx5M6GhoQQHB5vvh6enJ9WqVSMsLAyj0YimaYSFhVG7dm2bjunKlStUqFAh1zEKIYS1SUIlhBBZCAgIwGQy0alTJ7p160Z8fDy9e/e2KNO1a1fKly/P9OnT8fb2ZvLkybzzzjt06dKFzz//nK+//pqiRYsC0LZtW27dukW1atUoXbo0jo6OefpD/H7s7Oz46quv+Oijj+jcuTObNm3i448/BjCPMkRHR1O6dGmmTp3K0KFD8ff3Z+7cucyfP58iRYoQFBTE888/T9euXenYsSNOTk689dZbwMMXpfivKlWq8OKLL9KxY0c6duzI7t278fb2NidwDzJ27FguXLiAn58fw4YN4+mnn8bR0RF7e3vmzZvH2rVr8fPzo3///rz99tvmxTdyw8fHhwsXLhAXFwdkLKrRrl07XnrpJXx9falRowYBAQFAxkjg3SmP2S2XH44fP46/v3+W5d588028vLzo3LkzXbp0ITY21rxIhC3GBLBv3z7zlE4hhCiMFC0nm2YIIYQQgNFoZOTIkRbTGvPTypUrqVatGnXr1iUtLY2ePXsydOhQXnjhhQK53vz589Hr9dlKErMjISGBqVOnMn369HxpLz/YakxBQUGsW7cuW6sRCiGELZKESgghRI6dPn2a5OTkTAtB5Jfw8HBmzJiBqqqkp6fToUMHhg4dWiDXgoypj4MHD2b69Om4u7vnub0jR47g5uaWadVGa7LFmD7++GNeeOEFfHx8rB2KEELkmiRUQgghhBBCCJFL8gyVEEIIIYQQQuSSJFRCCCEeiW3bttGtWzd8fX0ZOHAgt2/ffmj5pUuX4uvra3Fu5cqV5kUyRo4cSVpaWkGGfF9bt241L0ry+eefExIS8shjiI2N5d133yUgIIAOHTo8MIbTp09Tt25d/P39zf+7cOHCI45WCCEeb5JQCSGEKHDHjx9nypQpfPHFF2zevJly5coxZ86cB5b/9ddfWbhwocW57du3s2LFCpYsWcIPP/xAamoqS5cuLeDIH+7tt982r+z3KI0ZMwYvLy9CQkJYunQp06ZNIyoqKlO5o0eP4uvra15SPzQ0VJYoF0KIfCYb+wohRCEUHh7Op59+SqlSpfj7778pUqQIAwcO5Ntvv+Xvv/+mXbt2jBs3DoBdu3bx9ddfk56ejqOjI6NHj6Zu3brcuHGDiRMncvPmTa5fv84zzzzDZ599RsmSJWnVqhVdu3bl0KFDREZG4u/vz/DhwzPFERQURHJyssW5evXqMWnSJItzGzdupHv37jz77LMADB06lNjY2Pu+txs3bjBlyhTee+89goODzedDQkLo378/rq6uAHz44Yekp6cDGUuCnzhxgmnTpmVqr2bNmvTr14+DBw+SlJTEkCFD2Lp1K2fOnMHDw4P58+fj5OTE+fPnmTZtGrGxsZhMJnr37k1gYCCQMRK1adMmXF1dKVu2rLntMWPGUKlSJQYMGMDatWv5/vvvSU9P586dO7zxxhv07NmT9evXExYWhk6n49KlSzg6OjJjxoxMi0OEhISwZMmSTPHPnDmTKlWqmI9jY2M5ePCgOSH18vJizZo1FC9ePFPdo0ePcvnyZbp27Yper2fgwIG0a9fuvvddCCFE7khCJYQQhdTx48eZNGkS1apV4/XXXyc4OJjly5eTkJBAixYtGDBgAMnJycyZM4fly5fj5ubG2bNn6devH9u3b+eHH36gTp06DBw4EE3TGDhwIKGhofTv3x+ApKQkVq1aRXR0NG3btqV79+6ULl3aIobVq1dnK9aLFy9SpUoVBg8ezNWrV6lcubI54fsvk8nEu+++y6hRozAYDJnauHnzJgMGDCAmJoYGDRowatQoIGNj4wdJS0vjqaeeYu3atQQHBzNhwgS2bNmCu7s7gYGB7Ny5k44dOzJs2DBmzpxJ9erViY+Pp0ePHnh7e3Pjxg22b99OSEgIjo6O5r24/isxMZH/+7//Izg4GDc3N37//Xf69etHz549ATh8+DCbN2/Gy8uLKVOmEBwczIwZMyzaCAgIyNZoV0REBO7u7ixZsoS9e/eSlpbGgAEDKF++fKayRYoUoXPnzgQFBXHx4kVeffVVSpUqRc2aNbO8jhBCiOyRhEoIIQqpZ599lmrVqgFQpkwZnJ2dsbe3p0SJEhQtWpQ7d+5w+PBhYmJieO2118z1FEUhIiKCvn37cuTIEZYsWcLFixc5e/YstWvXNpdr3bo1AJ6enpQsWZI7d+5kSqiyO0JlNBrZvXs3S5cupWTJknzyySdMmDCBefPmWZSbPXs2zz//PE2bNiU8PDxTGwcOHODrr7/G3t6eMWPGMGfOnGxtVtu+fXvzfapcuTKenp7me3jnzh0uXrxIRESERZKXkpLCyZMnOX/+PG3btqVYsWIAdO/enW+//dai/aJFizJ//nz27NnDxYsXOXXqFElJSebXq1evjpeXFwDVqlUjLCwsU4zZHaFKT0/nypUrFCtWjNWrV3Pp0iV69epF2bJlqVGjhkXdDz74wPxzxYoV6dSpE7t375aESggh8pEkVEIIUUjZ29tbHN87ogOgqipNmjSx2IA3MjISDw8PPvnkE44dO0b37t1p1KgRRqOR/+6k8d+NVhVF4X67bGR3hMrDw4MqVaqY93jq1q0bffv2zVRu48aNlChRgrCwMJKSkoiOjsbf35/Q0FA8PDxo166dObHp0qULc+fOzdb17ezs7vvzXSaTCWdnZ0JDQ83nbty4gbOzMzNnzrR473q9PlP9qKgoevTowcsvv0z9+vXp0KEDu3fvNr/u6Oho/vlB9zK7I1QeHh5Axj0EKFu2LPXq1ePYsWMWCZXJZCI4OJjevXub75mmaff9nAghhMg9WZRCCCEeY02aNOHAgQOcP38egD179tClSxdSUlLYv38/ffv2JSAggJIlS3Lw4EFMJlOBxNG+fXt2795tXtlv+/bt9x0l2b9/Pxs3biQ0NJSpU6dSpkwZc5LTvn17tmzZQkpKCpqmsWPHjnwbaSlfvjyOjo7ma0VGRuLr68uJEydo0aIFW7duJS4uDlVVLZKuu06cOEGJEiX43//+R7NmzczJVEHcz9KlS1O9enXzyn43btzg6NGjmUan9Ho9u3btYs2aNQBcvXqV7du3m0frhBBC5A/5ZyohhHiMeXt7M3nyZN555x3z6MTXX39N0aJFeeutt5g5cyaff/45dnZ21KtXj4iIiAKJo1WrVkRFRdG7d29UVeXpp582LyCxc+dOVq9ezYIFCx7aRs+ePblz5w7dunXDZDJRvXp1xowZAzx8UYrssLe3Z968eUybNo2FCxdiNBp5++23qV+/PpCx/Hj37t1xcXHhueeey7Tke9OmTVm7di0dOnRAURQaNmxIiRIluHTpUq7iycpXX33F5MmT+e6771BVlbfeeotatWoB8MYbbxAUFETr1q2ZNWsWkyZNYsOGDZhMJsaNG5dpMQwhhBB5o2j3m3cghBBCFCIJCQlMnTqV6dOnWzsUIYQQTxhJqIQQQhR6R44cwc3NTUZfhBBCPHKSUAkhhBBCCCFELsmiFEIIIYQQQgiRS5JQCSGEEEIIIUQuSUIlhBBCCCGEELlkE8um376diKoWnke5SpYsxs2bCdYOQ/xD+sO2SH/YFukP2yL9YVukP2yL9Idtkf74l06n4OZW9IGv20RCpapaoUqogEIX7+NO+sO2SH/YFukP2yL9YVukP2yL9Idtkf7IHpnyJ4QQQgghhBC5JAmVEEIIIYQQQuSSTUz5E0IIIYQQQuRdcnIiCQmxmEzGPLUTE6NDVdV8iqqwULC3d8TNzR1FUbJdSxIqIYQQQgghHgPJyYnEx9/G1dUdOzv7HCUF9zIYdBiNT1ZCpWkqsbE3SEi4g7Oza7bryZQ/IYQQQgghHgMJCbG4urpjb++Qp2TqSaUoOpyd3UhOztnqhpJQCSGEyBeapqHduol67A+0pERrhyOEEE8ck8mInZ29tcMo1PR6A6pqylEdmX98J+4AACAASURBVPInhBAiV9QTx9EOh6P9fQHt4kW0ixcgLi7jRTc39K8NQPfyKyhFilg1TiGEeJLIyFTe5Ob+SUIlhBAiR7SUFExffoa6cnnGCXd3lHIV0LXvhFK+PMpT7pg2rMM0Zxamb5eiHzAQXfeXUezlX02FEEI8fmTKnxBCiGxT/zqJsedLqCuXo3v5Fez2HsI+bA92C5ZgGD8Rfc/e6Np1wO7rBRgWL0cpWw7TjI9I79IB07r/Q3viVowSQgjxX4sWfcMffxzNdD4y8hqBgX45aisw0I/IyGv5FVquSUIlhBAiS5rRiGnBfIy9g9Di4zDMC8Yw7n0Ul+IPrKOr1wDDwmUY5i9E8fDENGUSpvlzH2HUQgghbM3Ro79iMuXsGSVbJ1P+hBBCPJR25TLGce+hHfsDXfuO6Me9j1I8e8vJKoqC0tgHpVETTJPGoy6Yj1q3PromPgUctRBCiO+/X8V3363IVV1FAU178OuvvPIqPXr0fGgbMTHRTJ78PsnJyeh0Cj4+zTl9+i9mzJjKRx/NwmQyMn36FAC8vStnGVNc3B0mT36fmJhoypWrQFpaGgAmk4l58z7/J1lT6dTJlx49ejFu3CjatevAiy+2BqB//1cZM2YClSs/l827kD0yQiWEEOKBNKMR4/AhaBcuoP/4EwwzZmc7mfovRVHQj30fpULFjOQsJqYAohVCCGFLNm8OxcenGYsWfcuAAW/i4OBIlSpVGT16AhUrejN16iQGDx7K4sUrefrpZ7Jsb+HC+VSu/BzLl39Pt24vcevWTQA2bdoAwOLFK1mwYBn79u3hjz+O0r59J3bs2AbA5csRpKWl5XsyBTJCJYQQ4iHUNd+hnTuL4/SZ2DVqCLev56k904T3SRo8CHXUcIrMno2if/DXkOZUlDQHpzxdTwghnmQ9evTMchTpQfJjY98GDRoyfvx7nDlzGh+fZnTv/jIHD+4DIDY2lhs3bvD8840B6NjRl82bQx/a3tGjv/LBBx8BUKdOPXMSduTIL5w9e4Zffz0CQHJyEufPn8PPL4A5c2aSlJTIjh3baN++Y57ez4NIQiWEEOK+tFs3Mc37CqWxD4aGDUnauTtf2lXadcC0MYTEDz5E92KrB5Zzat0SJKESQohCq1atOqxYsYaDB/ezc+d2fvxxk/m1jCmF/84p1D/kH9j+raPcU0cPgMmk8r//DeOFFzK+U2JjYylSpAh2dnY0bdqc/fv3smtXGJ988nl+vTULMuVPCCHEfZm+/AxSkjGMHpev+5roatVBqV0Xbf9etPPn8q1dIYQQtmXevM/Ztm0LHTv6MmLEaM6cOY1eb8BkMlG8uCteXl4cPLgfgLCwrVm216BBQ7Zt+xGAv/76k6tXrwBQv34DNm4MwWg0kpSUxP/+N4A//zwOQPv2nVi9esU/1ytVIO9TEiohhBCZqMePoW5Yh67nqyjlK+R7+0qHjuDugRq6Hu3uZsBCCCEeK9279+Cnn3by2ms9GTduJBMmfEijRk2YNetjjh//g/ffn8KSJcH069fTnBw9zIABg7h69QqvvvoyK1YsNU/5CwgIpHTp0vTr15PXX+9Np05+1KvXAMgYJUtISKBdu4KZ7gegaNrD1u/IsGnTJr7++muMRiN9+/alV69eFq9/9dVXrFu3DhcXFwBefvnlTGUe5ubNBFQ1yzBshru7M9evx1s7DPEP6Q/bIv1hW3LTH5qqYuz9Clp0JHYhP6IUK4bD7ev5NuXPfJ0b11EXBUOpp9G92hdFZ/lvfE6tW5Lq5p6v17Q2+f2wLdIftkX6I++ioi7h5VU2X9rKj2eoCqt776NOp1CyZLEHls9ysmJ0dDRz5sxh/fr12NvbExQURKNGjfD29jaXOXHiBJ9++il169bNY/hCCCGsTd24Ae3P4+inTkcp9uAvkLxSnnJHadcB7YdN8PcFqOiddSUhhBCPte+/X8mWLT9kOv/UU08xa9YXVogoa1kmVAcPHqRx48a4umYsk9u+fXu2bt3KkCFDzGVOnDjBN998w9WrV3n++ecZPXo0Dg4OBRe1EEKIAqHFxWH6fA5K7broOudsx/rcUGrWRtu1A+2PoyiSUAkhxBOvR49e9OiR/ZlutiDLZ6hiYmJwd/93yoWHhwfR0dHm48TERKpWrcqoUaPYsGEDcXFxzJs3r2CiFUIIUaBMX38FsbfRjx2frwtRPIhiMKDUqIV2+hRaclKBX08IIYTIb1mOUKmqavGlqmmaxXHRokVZsGCB+bh///6MGzeOESNGZDuIh81JtFXu7s7WDkH8h/SHbZH+sC3Z7Y/0U6eIWfMdRV/thWvzRhavGVPvoHd2LIjwMPo0IuFwOA7nTuHg42M+7+Bkj8tj+FmS3w/bIv1hW6Q/8iYmRofBkH9rzuVnW4WJTqfL0Wcxy4TKy8uLI0eOmI+vX7+Oh4eH+fjatWscPHiQwMBAICPhMhhytr2VLEoh8kL6w7ZIf9iWnPRH+rQZ4FSUtAH/y1THISmNpPiUgggRnEuApxfJ4YdJq1nPfNqUlEbqY/ZZkt8P2yL9YVukP/JOVdV8W0jiSV6UQlVVi89iVotSZJl2+vj4cOjQIW7dukVycjLbt2+nRYsW5tcdHR355JNPuHz5MpqmsXLlStq2bZvHtyGEEOJRUs+dRdu1A90rvVD+eWb2UVLq1IWoSLToqEd+bSGEECIvskyoPD09GTFiBH369CEgIABfX19q1arFG2+8wfHjxylRogSTJ09m8ODBdOjQAU3T6Nev36OIXQghRD5RFy+AIkXQ9+xtlesr1WuCXo/2x1GrXF8IIYR1vPZazyzLpKWlMXTooEcQTe5ka26en58ffn6Wqz3997mp9u3b0759+/yNTAghxCOhXY5A3fojulf7WGV0CkBxcoLKVdBOHEdr3RZFn7Op40IIIXLPoFcoZriDjjRU7EkwFn9k1166dFWWZX7//Tfq1KmXZTlrkW8sIYR4wpmWLAKDAX1v684u0NWui/rXSTh7Bp6rZtVYhBDiSWHQKxTXzqHbHQCJl9AXLUvxZiEk6CthzGPbv/12hOXLF2NnZ0dk5DWaNm1BkSJF2LdvD5qmMWvW53Tp0p79+4+waNE33LhxncuXI4iOjsLX15++fQcA8PPPB2jVqh0//riJgwf3Exd3hxs3ruPv342oqCh+++0wLi7FmTXrCxwcHPjmm7n8+uth4uLieOqpp5g8+WOuX7/OqFFvs2zZavR6Hf369WL69NlUqlQlz/fwyVy6QwghBABadBTqxg3o/Luh/GeLDKuoUBGcnVF/l2l/QgjxqBQz3EG3PyOZAiDxErr9ARTR3cmX9k+e/JORI8eycOG3rF+/BldXNxYt+hZv70rs2LHdouy5c2eZM2cuwcFLWbFiGfHxGQtD/PXXn1SrVt3888cfz+LTT7/iyy/n0LixD8uWrQbgl18OceXKZSIiLjJ//mJWr16Pp6cX27ZtoUqV5/D378a8eZ8zZ84nBAQE5ksyBTJCJYQQTzTTsiWgaehfG2DtUFB0uoyNfg8dQIuXlb6EEOJR0JH2bzJ1V+IldFpavrRfoUJFPD29AChe3JUGDRoC4OnpRXx8nEXZevUaYGdnh5tbCVxcXEhMTCAhIR5Pz1LodBnjQDVr1qZo0WIULZqx6l79+s8D4OVVivj4eJ59tjRDhoxg06YQIiIu8eefx3nmmWcB6Nt3AAMG9MbBwYH335+cL+8PZIRKCCGeWNqtm6jr/w9dJz+UZ56xdjgAKLXrgKahHf/D2qEIIcQTQcUeipa1PFm0LKpiny/t37udkl6vf2BZe/t/r6koCpqmcejQARo3/nePQjs7u4e2f+rUX4wYMQRNU2nZsjUtWryIpmVsz5SQkEBSUhK3b98mLs4ymcsLSaiEEOIJZVqxHFJT0Q94w9qhmCkln4JnS6P9cdT8BSiEEKLgJBiLozYL+TepKloWtVkIyeqjW5jiYX755RCNGjXJdvnff/+VunXrExAQSOnSZTh4cD+qmrGf1uzZ0+ne/SW6dg1k9uzp+RajJFRCCPEE0uLiUNd8h65NO5Ry5a0djgWldl24eRP15J/WDkUIIR57RpPGHcWb9JYHMPn+TXrLA9xRvDHZwJ6+6elpJCQk4OZWItt1Wrdux7lzZ+jTpwdDhw6iSpWqREZeY+fOMK5evcJLL73Cyy+/wuXLEezcGZYvcSqaDfwT4M2bCaiq1cPINtnJ27ZIf9gW6Q/b8qD+MC2Yj2nuFxhWr0P3XNUs23G4fZ2knbsLIsRMtNRU1M9mYdeuPcq0mY/kmo+K/H7YFukP2yL9kXdRUZfw8iqbdcFsMBh0GI02kFVZwb33UadTKFmy2APLywiVEEI8YbTkJEwrl6M0fyFbydSjpjg4oFStRvruXWjJSdYORwghhHgoSaiEEOIJo/6wCWJj0fe3nWen7qXUrgtJSag7d1g7FCGEEOKhJKESQognjBq6AaWiN0qdutYO5cHKlEV5+hnU0A3WjkQIIYR4KEmohBDiCaJdOI92/Bi6gG4oimLtcB5IURTsOnRAOxyOduWytcMRQgghHkgSKiGEeIKYQjeAwYCus5+1Q8mSXfsOoCiYNoZYOxQhhBDigSShEkKIJ4SWno66ORSlWQuUEiWtHU6WdB4eKI2aoG4KQVOfzJWmhBBC2D5JqIQQ4gmhHdwPN2+i9+9q7VCyTRfQDSIj0X752dqhCCGEEPclCZUQQjwhTKEboERJlGYtrB1KtulatgZnF1mcQgghhM0yWDsAIYQQBU+7dQtt70/oer6KYmdn7XCyTXFwQNehE+rGDWhxcSguLtYOSQghHjsGvUKxxDvo0tNQ7exJKFo8X9r97bcjLF++GDs7OyIjr9G0aQuKFCnCvn170DSNWbM+58yZ0yxaNB+j0UipUs8wevR4ihd3ZdeuHaxevYLU1FTS09MYO3YiNWvWZsiQgVSrVp0//vid2NjbDB8+iiZNmuZLvLklCZUQQhQy9qlJKEmJ2SprTL2DQ1Iaaev+D4xGHF98Ef3t6zm+pt6YnuM6+UUX0A31/1ajbvsR/UtBVotDCCEeRwa9QvGIc+gCAuDSJfRly1I8JISE8pUw5kP7J0/+ybfffk/x4q74+bXlrbeGs2jRt3z00YeEhKxj796f+OKL+bi4uBASso6vv/6S994bT2joOmbO/AxXV1c2bw7l22+XMnPmHADS0418880S9u/fy4IFX0tCJYQQImeUpESSdu7OVlm9syOJccmo/7cGnn6a1AsX4cLFHF/T2adRjuvkF6VadRTvSqihGyShEkKIfFYs8Y45mQLg0iV0AQEU2X+QVAfnPLdfoUJFPD29AChe3JUGDRoC4OnpxYED+4iOjmLYsDcBUFUTLi7F0el0fPTRJxw4sI+IiEscPforOt2/Tyo1atTE3HZ8fFyeY8wrSaiEEOJxFxUJMTEoHTtbO5JcURQFnX9XTLNnop47i867krVDEkKIx4YuPe3fZOquS5cyzjvkvX2DwTLd0Ov15p9V1UStWrWZMSNj5Ck1NZXk5GSSkpJ4442+tGvXkdq161Kxojfr1q0x17O3twcyvh80Tct7kHkki1IIIcRjTvvjKBgMKNVrWDuUXNN19gODAXWjLE4hhBD5SbWzh7JlLU+WLZtxvoBVq1aDP/88TkRERkK3dOlC5s79jMuXI1AUhT59+lOvXgP27NmNasPbZ8gIlRBCPMa09HS0E8dRqjyH4ljE2uHkmlKiJErzF1A3b0IbOqJQLawhhBC2LKFocYqHhPw77a9sWdSQEJKdi0NawSYxJUqUZMyYiUycOBZVNeHu7snEiZMpVswZb+/K9OwZiE6n0LBhE44d+71AY8kLRbOBcbKbNxNQVauHkW3u7s5cvx5v7TDEP6Q/bIv0R8FzuH09289QOVw4TdKq79D17I1SoWKur+ns04j4g+G5rp8bTq1bkurmbj5Wf9qFcfgQDHO+zFhOvRCS3w/bIv1hW6Q/8i4q6hJeXmWzLngP8yp/xnRUg13GKn+KgtFou6NCBene+6jTKZQsWeyB5WXKnxBCPMbSjvwKLi5Qrry1Q8kzpWlzKFkS08YQa4cihBCPFaNJI9bRhVvFShLr6ILRVHgGOmyBJFRCCPGY0u7cwXj2LEqtOii6wv+fe8XODl2b9miHDqClpFg7HCGEEAKQhEoIIR5b2tFfAVDq1LVyJPlH1+IFSElBO/KLtUMRQgghAEmohBDisaSZTGi//4ahShUUVzdrh5NvlAYNwbEI6t491g5FCCGEALKZUG3atIlOnTrRrl07Vq5c+cByP/30E61atcq34IQQQuTS6VOQkIBD48bWjiRfKQ4OKI0bo+77ySb2HhFCCCGyTKiio6OZM2cOq1atIiQkhO+//55z585lKnfjxg1mzJhRIEEKIYTIGfXXw1C8OIYqla0dSr7TNX8RIiPRzp21dihCCCFE1gnVwYMHady4Ma6urjg5OdG+fXu2bt2aqdyECRMYMmRIgQQphBAi+7Qb1+HSRZR6DR6LxSjupWvWAgBtn0z7E0IIYX1ZftPGxMTg7v7vPiAeHh5ER0dblFm+fDnVqlWjdu3a+R+hEEKIHNF+OwI63WO1GMV/KZ6eKFWryXNUQgjxhPjttyMMGTIQgOnTp3Dq1EkrR2TJkFUBVVVRFMV8rGmaxfGZM2fYvn07S5cuJSoqKldBPGyjLFvl7u5s7RDEf0h/2Bbpj4JlTL2D3tnxvq9paWnEHf8Du5o1Ker1FADODyibE3Z2+nxpJyccnOxxecBnKa5DO+I//4IS+nT0JUo80rjySn4/bIv0h22R/sibmBgdBkPOZybodDri4+1JT1ews9Nwdk5DVdVctVUQ9HodiqJgMOiYMGFSgV9Pp9Pl6LOYZULl5eXFkSNHzMfXr1/Hw8PDfLx161auX79O9+7dSU9PJyYmhp49e7Jq1apsB3HzZgKqWngeLpadvG2L9Idtkf4oeA5JaSTF338fJvX339CSUzDVqkt8fArOzo7EP6BsTjinm/KlnZwwJaWR+oDPklq/CaifcX3jFvSduzzSuPJCfj9si/SHbZH+yDtVVTEa1RzV0et1/P23AwEBOi5dgrJlISTEgfLlU0lLM+Ypnt9+O8Ly5Yuxs7MjMvIaTZu2oEiRIuzbtwdN05g163POnDnNokXzMRqNlCr1DKNHj6d4cVd++eVnvvjiU+zt7SlbthyapmE0qgwZMpD+/QdSq1YdZs+ezoUL57l16xbe3t588ME0bt26xbhxI6lQoSJnzpymRImSTJkyHReX4tmOW1VVi8+iTqc8dAAoy7TTx8eHQ4cOcevWLZKTk9m+fTstWrQwvz5s2DC2bdtGaGgowcHBeHh45CiZEkIIkX+0X4+AuzuUKWvtUAqUUq06lCyJtm+vtUMRQohCLTHx32QK4NIlCAjIGLHKDydP/snIkWNZuPBb1q9fg6urG4sWfYu3dyVCQtYxf/5XzJ79FUuWrKJhw8Z8/fWXpKWlMW3aJKZOncHixStwcHDI1O6JE8cwGOz45pslfP/9BuLj4zl06AAA586dpUePXnz77RqKFSvG9u1b8uW9PEiWI1Senp6MGDGCPn36kJ6eTmBgILVq1eKNN95g2LBh1KxZs0ADFEIIkT3atasQeQ2lfUeLqdmPI0WnQ9esBequnWjp6Sh2dtYOSQghCqX09H+TqbsuXYL0dIX75DE5VqFCRTw9vQAoXtyVBg0aAuDp6cWBA/uIjo5i2LA3AVBVEy4uxblw4RwlS7pTrlx5ADp29GXBgq8t2q1Tpx4uLsVZt24NEREXuXLlMsnJyQC4uZWgcuXn/rm+N3FxcXl/Iw+RZUIF4Ofnh5+fn8W5BQsWZCr37LPPsmvXrvyJTAghRI5ovx4GOzuUmk/GAkG6Fi+ihm5A++N3lAbPWzscIYQolOzsVMqWtUyqypYFO7v8eRzHYLBMN/R6vflnVTVRq1ZtZsyYA0BqairJyclERUUC2n3r3LV//x4WLvyGl14KolOnLsTGxpr3J7S3txxdK+h9C23jSTMhhBB5oiUno/15AqVGTRTHR7t4hLUojX3AYEDd+5O1QxFCiEKraNFUQkJUyv4zUzzjGSoVZ+e0Ar92tWo1+PPP40REZGRzS5cuZO7cz/D2rsStW7c4e/YMADt2bMtU98iRX2jVqg2dO3ehWLFiHD36K6pqKvCY7ydbI1RCCCFsm3bsdzAaUeo/OSM1StGiKA0aou7fA++MsnY4QghRKJlMKmXKJLNvnwNGow6DQaVo0VTUnK1tkSslSpRkzJiJTJw4FlU14e7uycSJkzEYDHzwwTSmTp2IXq83T9/7Lz+/rnz44Xh27NiGwWBHzZq1uHbtGvXrF3zc91K0gh4DywZZ5U/khfSHbZH+KHgOt6+TtHO3+VjTNNT5c8HRAX2/NyzK5tsqfz6NiD8Ynud2csKpdUtS3dwfWsa06ltMMz/GbtNWlNJlHlFkuSe/H7ZF+sO2SH/kXVTUJby88mdRIoNBl+MVAx8X997HPK/yJ4QQwsZFRcLNGyi161k7kkdO1/wFANR9ssmvEEII65CESgghCjntzxOg06FUrWrtUB45pXQZKF8Bda8kVEIIIaxDEiohhCjENE1FO3kCKlREKeJk7XCsQtf8BbQjv6AlJlo7FCGEEE8gSaiEEKIwu3wZ4uJQqj+5ewLqWrwIRiNa+CFrhyKEEFamoGlP5nNP+SU3y0tIQiWEEIWYdvIEGAwolatYOxSrUWrXAWcX1J9kH0QhxJPN3t6R2NgbGI3pBb730uNI0zQSE+MwGOyzLvwfsmy6EEIUUppqQvvrJEqlyij5sZ19IaXY2aFr3gJ1709oRiOKQb7ahBBPJjc3dxIS7nDrVnSe92TS6XSoj2LtdBtjMNjjlsUKs5nqFFAsQgghCtrFi5CYiFK9hrUjsTpdy9aoP25G+/0oSoMnZy8uIYT4L0VRcHZ2xdnZNc9tyTL22SdT/oQQopDS/jwB9vZQsZK1Q7E6pWkzsLdH3bXD2qEIIYR4wkhCJYQQhZBmNKKd/gulSlUUOztrh2N1ilNRlMY+qLt3ynMDQgghHilJqIQQojC6cA5SUmS633/oWrWGyGtop09ZOxQhhBBPEEmohBCiENL+PAFFikD5CtYOxWboWrQEnU6m/QkhhHikJKESQohCRktORjtzGqVqNRS93trh2AylRAmUOnXRZPl0IYQQj5AkVEIIUcgYfz4E6eko1WS63710LdugnTmNduWytUMRQgjxhJCESgghChnjrp3g7Axlylo7FJuja9UaAHX3TitHIoQQ4kkhCZUQQhQiWnw8xvBwlKrVUXTyn/B7Kc88i1K5ijxHJYQQ4pGRjX2FEKIQUXfvzJju9wSs7qdHw+H29ZxXbNKEtOXLsLtwBp2bW46qak5FSXNwyvk1hRBCPLEkoRJCiEJE3fojSqlS8PQz1g6l4CUnk3QwPMfVNDsH0DSSFi1CV7d+juo6tW4JklAJIYTIAZkvIoQQhYR28wZa+CHsWrZCURRrh2O7PL2guKvsRyWEEOKRkIRKCCEKCTVsG5hMGNq0tXYoNk1RFJQqz8HfF9BSU60djhBCiMecJFRCCFFIqD/+gFK5Cvry5a0dis1TqjwHJhOcP2ftUIQQQjzmJKESQohCQLtyGe3Y7+g6drZ2KIVD6dLg5CTT/oQQQhQ4SaiEEKIQULf+CICuQycrR1I4KDo9SqXKaOfOoJmM1g5HCCHEY0wSKiGEsHGapqH+uBmlXn2UUk9bO5xCQ6lSFVJT4eJFa4cihBDiMZathGrTpk106tSJdu3asXLlykyvh4WF4efnR+fOnRkzZgxpaWn5HqgQQjyptLNn0C6cl+l+OVW+Auj1aPIclRBCiAKUZUIVHR3NnDlzWLVqFSEhIXz//fecO/fvl1NSUhKTJ09myZIl/PDDD6SmprJhw4YCDVoIIZ4k6o+bwWBA16a9tUMpVBQ7OyhTFu3CeWuHIoQQ4jGWZUJ18OBBGjdujKurK05OTrRv356tW7eaX3dycmLXrl089dRTJCcnc/PmTVxcXAo0aCGEeFJoqoq69QeUJk1R3NysHU6ho1SoCDeuo8XFWTsUIYQQjylDVgViYmJwd3c3H3t4eHDs2DGLMnZ2duzZs4f33nsPDw8PmjVrlqMgSpYslqPytsDd3dnaIYj/kP6wLdIf+Sc1PJwbUVG4ThiH0z/31Zh6B72zY7bbcM5B2Qexs9PnSzuP+pqmGlWJ3xmGQ2QEDs80yLK8g5M9LgX8+ZXfD9si/WFbpD9si/RH9mSZUKmqiqIo5mNN0yyO73rhhRcIDw/n008/5YMPPmD27NnZDuLmzQRUVct2eWtzd3fm+vV4a4ch/iH9YVukP/KX8bv/A8ciJNRrQuI/99UhKY2k+JRs1Xd2diQ+m2Uf2k66KV/aedTX1Iq5QtGipJw8RVqVGlmWNyWlkVqAn1/5/bAt0h+2RfrDtkh//EunUx46AJTllD8vLy+uX79uPr5+/ToeHh7m49jYWPbv328+9vPz4/Tp07mNVwghxD+09DTU7VvRvdgKxamotcMplBRFh1K+ItrfF9A01drhCCGEeAxlmVD5+Phw6NAhbt26RXJyMtu3b6dFixbm1zVNY9SoUVy7dg2ArVu3Uq9evYKLWAghnhDaoUNw5w66TrK6X55UqABJSRAdbe1IhBBCPIaynPLn6enJiBEj6NOnD+np6QQGBlKrVi3eeOMNhg0bRs2aNZkyZQqDBg1CURS8vb358MMPH0XsQgjxWFO3bIbixVGa+Fg7lEJNKV8RDdAunEfxKmXtcIQQQjxmskyoIGMan5+fn8W5BQsWmH9u06YNbdq0yd/IhBDiCaYlJaLu3oXOrwuKnb21wynUFGdn8PDIWD7dJ2eLJgkhhBBZydbGvkIIIR4t9afdkJIsm/nmE6V8RbgcgZYuG88LIYTIX5JQ+S7r9gAAIABJREFUCSGEDVK3/ABeXih15JnU/KBUqAgmE0RcsnYoQgghHjOSUAkhhI3Rbt9GO3QAXYfOKDr5z3S+KFMG9Hq0CxesHYkQQojHjHxTCyGEjVF3bAOjUab75SPFzh5Kl8l4jkoIIYTIR5JQCSGEjVG3/IBSoSJK5SrWDuWxolSoCNdj0OJlo0ohhBD5RxIqIcT/s3fn4VGVh9vH72eWJATCEgirgCAIKosiiCwiuCFIFBBbxIr+XKiIRdGCqFRbQRG0L6JWK1Zt3SooaxAQN4osgthWVJaCCLJJAmHPNjPnvH9MEhJISHJIcibJ93NduTJzljn3zJPJzD3nzAwiiL13j+x/fyNPv+tkjHE7TqViWp4jSbJ/Yi8VAKD0UKgAIIJYSxZLEof7lYUGDaTYWInD/gAApYhCBQARxFq8UKZDR5mzmrodpdIxxhP+kt+ftsm2LbfjAAAqCQoVAEQIa+sW2f/bzN6pstSypXT8uJSc7HYSAEAlQaECgAhhLf5I8nrlueZat6NUWqZF9vuoOOwPAFBKKFQAEAFs2w5/ul/XbjJ167kdp9IyNWtKCQkUKgBAqaFQAUAEsNd/K+3ZzeF+5cC0OEf6eYfsQMDtKACASoBCBQARwFq8UIqOlqfPlW5HqfTMOedIoZC04ye3owAAKgEKFQC4zA4GZX28RJ5evWVq1HA7TuXX/GwpKkr2pk1uJwEAVAIUKgBwmb1mtXQwlcP9yonx+WVatZb9v02yLT4+HQBwZihUAOAya/FHUo04mZ693I5SdbQ5T0pLk3btdDsJAKCCo1ABgIvs9HRZn38qz9XXyERFuR2nyjCtWkler+zNG92OAgCo4ChUAOAia/kyKS2Nw/3KmYmOkVq0lL1pk2zbdjsOAKACo1ABgIusOR9KjRrJXNzF7ShVjmlznnT4kLTvF7ejAAAqMAoVALjE/nmH7DWr5R18k4zX63acKsec20YyRvYmDvsDADhHoQIAl4Tmzpa8XnmuH+R2lCrJVK8uNW0mezMfnw4AcI5CBQAusANZsubPlbnscpkGDdyOU2WZtudJKcmyUw+4HQUAUEFRqADABfayL6TUA/LeeJPbUao0c25bSeKwPwCAYz63AwBARRaVmSaTdrzE66XNfFemQQPFnNdG5mBKidb1BgMl3h4KZmrXlho2Ch/2172n23EAABUQhQoAzoBJO660z74o0Tp2aqqsdetkLu+j9GXLS7zNuO5dS7wOCmfatJX9ry9kHz3idhQAQAXEIX8AUM7s//5bMkam40VuR4Gy30clyf7fZpeTAAAqomIVqqSkJPXv31/XXHON3n333VPmf/rpp7rhhht0/fXX695779Xhw4dLPSgAVAZ2KCj72/9Irc+VqVnT7TiQpHoJUnxd3kcFAHCkyEK1b98+TZs2Te+9957mzZunmTNnauvWrbnzjx07pj/+8Y+aMWOGFixYoDZt2ujFF18s09AAUGH973/S8ePydLrY7STIZoyRadtW2rFd9tGjbscBAFQwRRaqVatW6dJLL1Xt2rUVGxurvn37asmSJbnzA4GAnnjiCTXI/tjfNm3aaO/evWWXGAAqMOs/30g1a0otW7kdBXmYNudJlqXg6lVuRwEAVDBFfihFcnKyEhIScs/Xr19f69evzz1fp04dXX311ZKkjIwMzZgxQ7feemuJQtStW6NEy0eChIQ4tyMgD8YjslSl8QhmHpY3LqZYy4YOHNDRbT8q5uqrFFMr1vE2/X6v4oq5TUklWra0tlkaynOb9rktdaRmTdmrVyrhvpFluq2qdP+oCBiPyMJ4RBbGo3iKLFSWZckYk3vetu1853McPXpUo0aNUtu2bTVo0KAShThw4Jgsyy7ROm5KSIhTSgqHhUQKxiOyVLXxiE7LUtrRjGIta61YLRmjrPPaK1DMdQoSFwjpaDHXj4uLKfaypbXN0lLu2zy3jTJXf6Xkn/fJVHNeeE+nqt0/Ih3jEVkYj8jCeJzg8ZjT7gAq8pC/hg0bKiXlxHekpKSkqH79+vmWSU5O1rBhw9SmTRs99dRTZxAXAConOxTK82EUtdyOgwKY8y6QMjNlLSvZx+ADAKq2IgtV9+7dtXr1aqWmpio9PV1Lly5Vr169cueHQiHdc8896tevnx577LEC914BQFVnb94Y/jCKi/gwiojVrJlMQoKsxR+5nQQAUIEUechfgwYNNGbMGA0fPlyBQEBDhgxRhw4ddPfdd2v06NH65ZdftGHDBoVCIX388ceSpHbt2rGnCgDysNd8JdWpI7XiwygilTEeea+4UoHZH8o+dEimdm23IwEAKoAiC5UkJSYmKjExMd+01157TZLUvn17bdq0qfSTAUAlYe/aKe3eJdO3n4zh+9Qjmf+qqxSY+b6sT5fKO+RXbscBAFQAPLIDQBmz166RoqNlOl7odhQUwXNOK6llSw77AwAUG4UKAMqQffiw7I0/yFzUSSYq2u04KIIxRt5+A2T/e53sX/hORQBA0ShUAFCG7G/WSpJM50tcToLi8vTtJ9m2rI8Xux0FAFABUKgAoIzYWVmy//2N1PY8mdp13I6DYjLNmsu0a89hfwCAYqFQAUAZsb/7VsrIkOeSS92OghLy9Bsge9NG2dt+dDsKACDCUagAoAzYtiV77VdSo8bSWU3djoMS8lxzreTxKLRkkdtRAAARjkIFAGXhx63SgQMyXS/lC88rIJOQINOlq6zFC2XbtttxAAARjEIFAGXAWvOVFBcnc975bkeBQ55+10k7d8r+/ju3owAAIhiFCgBKmZ2SLP20TebiLjLeYn1/OiKQ58qrpagoPpwCAHBaFCoAKGX2mq8kn0+mU2e3o+AMmLg4mcsul7V0sexQyO04AIAIRaECgFJkHz0i+/v1Mu07yMTGuh0HZ8jb7zpp/37Z69a6HQUAEKEoVABQiuwv/yVZlkz3nm5HQSkwPXtJ1atz2B8AoFAUKgAoJfaB/bL/82+ZTp1l6sS7HQelwMTEyHPVNbI+Xiz7yBG34wAAIhCFCgBKib3s8/B7p3r2cjsKSpHn5t9I6emyZn/gdhQAQASiUAFAKbD37Ja9cYNM124yNWq4HQelyNP2PJlLuir0z3dkBwJuxwEARBgKFQCUAuvzT6XYWJlu3d2OgjLgvfV2KXmfrKVL3I4CAIgwFCoAOEP2th+l7T/J9LhMJjrG7TgoA6bHZVKLlrLe/rts23Y7DgAgglCoAOAM2JYl64tPpVq1ZC7u4nYclBHj8cj7m9tkb9ooe93XbscBAEQQChUAnIHgv5ZJe/fKXN5HxudzOw7KkOe6RKlOvEJv/93tKACACEKhAgCH7EBAma//TUqoL9Oug9txUMZMTIw8vxoqe/ky2T9tczsOACBCUKgAwCFr3mzZu3fLc8WVMh7+nVYF3l/fLEVFKfTuW25HAQBECJ4BAIAD9rFjCv31L/K2by+1OtftOCgnJr6uPANukJU0X3ZqqttxAAARgEIFAA6EZrwspaYqetR9Msa4HQflyPub4VJmpqwP3nc7CgAgAlCoAKCE7G0/ynrvHXkG3ihvm7Zux0E5My3PkenZS6GZ/5Sdmel2HACAyyhUAFACtm0r+OwzUkw1eX93v9tx4BLv8Nul1AOyFi10OwoAwGXFKlRJSUnq37+/rrnmGr377ruFLjdu3DjNmTOn1MIBQKSxv/hM9uqV8t57n0x8XbfjwCWmS1eZ885XaMYrstPT3Y4DAHBRkYVq3759mjZtmt577z3NmzdPM2fO1NatW09Z5p577tHHH39cZkEBwG12RoaCz02ROaeVPDcNdTsOXGSMkfehh6W9e2T94w234wAAXFRkoVq1apUuvfRS1a5dW7Gxserbt6+WLFmSb5mkpCRdeeWV6tevX5kFBQC3WW+9Ke3ZLe/4x2T8frfjwGWezl3kuba/Qm/+Tfbu3W7HAQC4xFfUAsnJyUpISMg9X79+fa1fvz7fMnfddZck6ZtvvinleAAQGew9uxV64zV5ru4rT5eubsdBGfHKVvTBlGIv77/j/3R82eeyn5momCcnFrl8MPOwotOy8k2zY6srKzq2xFkBAJGhyEJlWVa+jwS2bbvUPyK4bt0apXp55SEhIc7tCMiD8YgslXE8Djz2vIKSEp76k3x5rl8w87C8cTHlmsXv9yquBNssybKltc3S4Mo2g1nSv/9d7OW9kmIuv1wZH38s69235G/d+rTLZ2avk1d078tVqxLeZyqKyvj/qiJjPCIL41E8RRaqhg0bat26dbnnU1JSVL9+/VINceDAMVmWXaqXWZYSEuKUknLU7RjIxnhElso4HtbarxT86CN57/2dDkbVlPJcv+i0LKUdzSjXPHGBkI4Wc5txcTHFXra0tllaKso27Ys6S2vW6vi8+fLcPVLGe3JlynP5BYxHKC1LmZXsPlNRVMb/VxUZ4xFZGI8TPB5z2h1ARb6Hqnv37lq9erVSU1OVnp6upUuXqlevXqUaEgAilX38uIITn5AaN5Fn+P+5HQcRyPj88lxzrbR/v+x1a92OAwAoZ0UWqgYNGmjMmDEaPny4Bg4cqAEDBqhDhw66++679d1335VHRgBwTWjyJGn3bvkmTZaJKd/Dz1CBtD5XOqeV7OXLZB875nYaAEA5KvKQP0lKTExUYmJivmmvvfbaKcs988wzpZMKACJAaNFCWQvnyzNipDydOrsdBxHMGCPPNdfKevVl2V98KpM40O1IAIByUqwv9gWAqsbevUuhp5+U6XiRvCNGuh0HFYCpW0+mazfZ3/5X9u5dbscBAJQTChUAnMQOBhV8ZKwkyff0VBlfsXbmAzI9e0lxcbIWzJWdlVX0CgCACo9CBQAnCb36suz138o74Y8yTZq4HQcViImOluf6QdKBA7I/WeJ2HABAOaBQAUAe1rqvZf3tVXmuHyjvtf3djoMKyLRoKdO9h+z//Fv2pg1uxwEAlDEKFQBksw8fUvCxh6WmzeQd/5jbcVCBmcv7SI0ay1q4QPaRw27HAQCUIQoVAEiyAwEFx4+VDuyXb/KzMrHV3Y6ECsx4ffIMulEKhWTNnyvbstyOBAAoIxQqAFWebdsKPf2k7NUr5Z3whDwXtHM7EioBE19X5tr+0o7tslevdDsOAKCM8NFVACqVqMw0mbTjJVon8523Zc2drahbhyv68l7SwZRir+sNBkoaEVWI6XCh9ONW2cs+l312C6ltK7cjAQBKGYUKQKVi0o4r7bMvir289d162fPnyLTroGCzFgqVYF1JiuvetaQRUYUYY6T+A2Tv2iVr7mzZY+53OxIAoJRxyB+AKsvesV120jyp+dkyideHn/wCpczEVJNn4GDp8CEd+/s/ZAf4fioAqEwoVACqJHt/iqwP3pfi4+UZ8msZLzvsUXZMs+YyA29UaPt2WbPel82hogBQaVCoAFQ59qFDsv75ruT1yjP0Fplq1dyOhCrAc0E7xd40RPppm6wPZ8kOBd2OBAAoBRQqAFWKvXmTrL/9VUpPk+fXw2Rq13E7EqqQqIsvluk3QNq6JfyeKivkdiQAwBniGBcAVYIdDMr+/BPZa9dIjRrJM2iITHxdt2OhCvJc3FlWKCh76RLZ8+fJvqKP25EAAGeAQgWg0rNTU2XN/UDau1emS1eZK6+W8fHvD+7xXHKprEBA9hefKeO5Z2Wemirj9bodCwDgAM8oAFRq9oYfZH20QDJGnpt+LdPmPLcjAZIkT4/LZAWDCi5ZLHPsuHyTn5WJi3M7FgCghHgPFYBKyT56VNbsWbLmfCDVS5DnrnsoU4g4nsv7KPqBMbK/WqXA8Jtl79judiQAQAlRqABUKrZlyfr3N7L++pLs/22W6X2FPMP/T6Z2bbejAQWKumGgfK/8TTqYqsBvhspavcrtSACAEqBQAag07J+2KX3M/bIXJUkNG8kzYqQ8PXvx3hREPE+XS+R/d5ZMgwYKjhqh0Ltvy7Ztt2MBAIqB91ABqPDs1FSF3ntb1j/ekGJiZAZcL9PxIhlj3I4GFJtpcpZ8/3hPwQnjFXp2suyNG+Qd+7BMLfauAkAkYw8VgArL2vCDgn94VIG+fWT97VV5rrxa1f/+ljwXdqJMoUIy1avL9+fp8owYKWtRkgIDr1No3mzZluV2NABAIShUACoUOxBQaMkiBW67RcFhN8n69GN5Bg2Rf06SfM88J098vNsRgTNiPB757v2dfP/8UKZ5C4X++AcFbxsma+MGt6MBAArAIX8AIp5t27I3b5SVtEDW4o+k1ANS06byjn1EnusH8lHTqJQ8bdrKvPm2rIULFHr+OQWH3STPkF/JO+p+PmQFACIIhQpAxLL37ZO1aKGsjxbI3rpF8vtlLu8j7w2DZHpcJuNhJzsqN2OMvIk3yNP7CoVeeUnW++/KSpovz5VXy3PDIJnOl3A/AACXUagARAQ7PU32//4ne+MG2Zs3Zv/eJNm2TIcL5X3scXmuuZY36KPS8cpW9MGUohe8+y6FrrpCgXnzFPjsM1kfJck0bCj/tf3k73utPA0bFnubdmx1ZUXHnkFqAEAOChWAMhOVmSaTdjzfNDsrU9bOXbJ27JC1Y3v49/afZO3cKWW/8d7UrCVP61byDr9N/quuluess8IrWwGpiCee3mCgTK4LUGbS05W2ak3xl+9wkTzntQu/8PDtf5X19zeV9fc3pWbNZc6/QKbt+TI1apz2ImKv7CNRqJCHvf0nWd+tl+eqq2Wq8bcBlESxClVSUpJeeeUVBYNB3Xbbbbrlllvyzd+4caMee+wxHT9+XJ07d9af/vQn+Xx0NaAqsjMyZO/eJXvvXgW3blLm6q+kw4dkHz4sHTooHTok5Xy/jjFS7TpSQkL4EL6GjaSGDaWatSRjFJIU2rxF2ryl2NuP6961bK4YEEGM3y/TroPUroPsQ4dkr/+v7A3fy16ySPbHi/OUq/Nkqp++XKFqs3dsV2jGK+H3p1qWQi8+L+/9D8rTfwCflgoUU5GtZ9++fZo2bZrmzJmjqKgoDR06VF27dlWrVq1ylxk7dqwmTZqkCy+8UI8++qhmzZqlYcOGlWlwN/i8RjV8h6Xjqaod7dOxYC0FQ3zxIiKLbdtSKJT9E5SCeU6HQuHzVniaHQyeumwwKAWypMxM2VlZUu5PZu5pOys8X1mZ0qHDsg/sl70/Rdq/X3uOHsnNEsw5Ub26VLu2TKPGUrsOUr0EmXr1pLp1ZXx+V24nOOf1GEVnpUuSjtk1FLC88ngtRUcFZVn8TyxvpnZtmV69pV69Zacky97wQ/hn8UeylywK38/qN5DqN8j9zZcGw96xXaHX/ipr0UIpKkqe39wmT9dLFfrLiwo99rCsmf+Ud9wj8rRr73ZUVCI5z6U9ypKlqErzXLrIQrVq1Spdeumlqp39iUJ9+/bVkiVLdN9990mSdu/erYyMDF144YWSpMGDB+uFF16osIXKtm0pOVlS/sH1eoxq2Nvl+fpOhdJ2yxPbRDW6vK4j5myFyvIJRFaW7OPHpKPHpOPHwqePZR9CVa2aTEyMVK2aFFMt/NvrLf5lBwPSwYOyDxyQUg/ITk2VDhyQHQzIJCTIJNSXEurL1EsI70HwevMta6cekA4elHy+8CdO1aol1aodfo9LXJyUniYdOiT70KHwHoqDB6Xjx6TYWCmupkxcTSkuTqZmTSkmJvvJfEB2IBDOFghkP7nPf9oOBnMPDZOkw7FRCqZlhc8U9mpaSacXJBQKl4jMjOzfmbIzM8LzfP5wMfD5JL8v/Nu2pZAVLi+WnV1iwudtywpfhzzTFLIKLUF2KHja+eHz2bdLKFT86+SUMVJUVPhDImrWlKkTL2/TpjIdOyqqYX2F4mrJ1K8vX716Or55C6WpEvF6jKqlJktJC7Xrit/o5bd8Sj0gxdf16t6RHsXHZ1GqXGQS6stcXl92r95SSrLsTRtl/7JX9p490oYfch/Zjr3+avj/e514qU4dmfi64d+1aoXv2z6/5A//GH/O/zb/qT+ePI85ef+fFvC/NXdvR0HLFbFukcvlns477aTLsE85oWBarOzU7MfUnJKZt2wWVDwLm5/3tDGSxxP+7fWeOJ/7mGBJweCJxwKvN/zhIl5veLmcdYLB8HLBYPh/fCAYzp/9eGN8vvCyPl/4sSIzU0pPlzIywo9VGRmyM8K/wz/p4WV2/izrk48lv1+eW26V9/Y7ZerWC0fv1kPWgnkKvThNwd/8Wp7rB8p72x2SMbKzMqWMPI+DMlJ0lBQdI8VEy0THSFHR4dsglJ09FMq9HjlZTU5mny98fW1bsiwFj1aTfeBo+LrkuT2L9bdz8t9N3umnLGNO/Cps/dONe0nmSSc/pSwdTnYelnCPYzCrxon7x5mybXm94efS5uu7ZaftloltouqdX9NR01yhk0tVzVpFHrocSYosVMnJyUpISMg9X79+fa1fv77Q+QkJCdq3b18pxyw/1nvvKPTs5FOmByRl5J5rJMmS9H/llqtcVKsmU6eOjM8na/WqcPk5HWNk4mqGn+wfL8YdLjZWJra67Iz08PJOXyHNeWDKlpuy0IsrZEZh2y9suscrRUfJREdL0dFSVJRMzgNHMCg7GDhRbILBEzk9nvC6nvB54/GGH0xM9mV6PJI3/ABqvN7wA06UT/JG53+QzfNj8p0PPyCZk5Y5eTmPpIyt2/Jk8oQvu6CfvA/SXp/ky3s6e3sn/WO2s3/8cTHKOJohHT6m6AsukPlxe2EDg9OY9dUqvb96RYHzvK/XVOjIkQLnnbKs16NQ6My/FDZnm17bknbuVCihkX5+broCOW9Z2y3N+ZPUtKlkTOl8CW1JrmdBhnbrqV9d2r1UslQ0xpgTe6Sy2ZmZ4ZKVvE/+mGhZBw+GDxf8eXv4kMFDh8NP9quQivts5Qx4PFL16vIPGqyom2+WJ76uJDv/+1Mvv0x2507KeudtZX34gawF88olWpUcjwhW2uNR8HPpOwteuE4dRX2xspQTlJ0iC5VlWfmeONm2ne98UfOLo27dyGmgoeFDldEg/tQn1ZkHpP8+HD4d21yq3jx8utlgyVeG+X0+eWrGSSb7CXD16jLVq0tSuJhkZMhOD78CZWekyy7BK8PG45GnTm156sTLE18nvLcrDzsrS6F9+2Tt3y8rZb9s25InPl6eOnXC69SqGX51TJIdDMo+ckTW4SOyDh+WfeyoTLVYeWrXkqlVS55atcKvcuZctmXJTkuTffSorKPHZGekhy8rOlrGeCR/9itvOa+Oen0y2Xt+TEn2whWH11s+e3UiYJs1y2mb0Xm2GZ3Yt1y2mau8t1lG24vLPCr//wr5Ildj5Klb/C8wLpUP1c7ZZjD8AopVr558Id8pDyKe2uHOXSpKeD1PFndRe9Ur6diUw99P9MkTXLqfnPw/KPx/Of3EkQKBnBeKgifOBwLhaYGg7EBWeO+7VMiemvzTTGxseA/GiYmFr5v3oayIPUd2QfPzXna+5ySn7o0wp9vrccrpgqYVcPmys48YsMJ7oWwrfFt5jIzHG36RKufFNY9Hsqx8RyLY2Uc2mOyjHozPH/7t9YX3FOWMQfZeK+vIkfDRJdExMjExMtHR4cf0mOjc0znT5fcX73labJSqjxmt4E2DFfj2Wxl/VPgxOjpaJjpKnrp15YmNzX7+kf2TmRneIyZlH7XhPfHb6wtfr2Ao/PeU97Bzj5FMzguDnvDp7BdOixzffL916vTs06dejl3weqe7bU53sxV5m5bm+9EcvCDt5EVsJ697n+42DB6Xdn4YPn18R/hHkjo9K0XXzbeor2kzRSdUnO+YLPJhr2HDhlq3bl3u+ZSUFNWvXz/f/JSUE69q7N+/P9/84jhw4FgEHSLil6667pSptaOPyB//XPbgbwz/VG+uQJ/+OpRZs9xTlotoSXEJUqsilwwvW71u+AWH4qpWR6pb9GJFSUiIU0rK0TO7EDc+Q6WSbvOU8aik17Ostzfgljs14JZCXrkrgVK5f+RRO+OI/Jf10C+PT9elY7prx44T85o3l96fE1RMTHqpbe9MHXSyUhn+/RQ6HpFyP6lWp9xjuKm07x+V0jm1pHMuKJdNMR6RpdQfP6KPyP/FxBNFSsp+Ln1Dwc+lI+hvweMxp90BVOQLl927d9fq1auVmpqq9PR0LV26VL169cqd36RJE0VHR+ubb76RJM2fPz/f/MriWLCWrJ7zTuyZqt5cVs95Ohas5W4wAChHx6rXkjVvnur/41nNfz1FzbP/JTZvLs2bZ6l69Ux3AwIAIlJlfi5d5GtiDRo00JgxYzR8+HAFAgENGTJEHTp00N13363Ro0erffv2eu655zRhwgQdO3ZMF1xwgYYPH14e2ctVMGTrsLeVavRZKb83qECIT/kDUPUEQ7YON2ulGi++pAtMUKuXZyoj5JfPFy5TpfF+LQBA5ZP3ubRHAVnyV5rn0saOgM9OjaxD/orGLunIwnhEFsYjsjAekYXxiCyMR2RhPCIL43HCGR/yBwAAAAAoGIUKAAAAAByiUAEAAACAQxQqAAAAAHCIQgUAAAAADlGoAAAAAMAhN76b/RQej3E7QolVxMyVGeMRWRiPyMJ4RBbGI7IwHpGF8YgsjEdYUbdDRHwPFQAAAABURBzyBwAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKAAAAAByiUAEAAACAQxQqAAAAAHCIQgUAAAAADlGoSiApKUn9+/fXNddco3fffdftOFXeSy+9pOuuu07XXXedpk6d6nYcZJsyZYrGjx/vdowq7/PPP9fgwYPVr18/TZo0ye04Vd78+fNz/19NmTLF7ThV1rFjxzRgwADt2rVLkrRq1SolJibqmmuu0bRp01xOV/WcPB4zZ87UgAEDlJiYqEceeURZWVkuJ6xaTh6PHO+8845uvfVWl1JVDBSqYtq3b5+mTZum9957T/PmzdPMmTO1detWt2NVWatWrdKKFSs0d+5czZs3Tz/88IN8pbekAAAgAElEQVQ++eQTt2NVeatXr9bcuXPdjlHl7dy5U0888YRefvllLViwQBs2bNC//vUvt2NVWenp6Xrqqaf09ttva/78+Vq3bp1WrVrldqwq59tvv9XNN9+s7du3S5IyMjL06KOP6uWXX9aiRYv0/fffcz8pRyePx08//aTXX39d77//vhYsWCDLsvTee++5G7IKOXk8cmzdulUzZsxwJ1QFQqEqplWrVunSSy9V7dq1FRsbq759+2rJkiVux6qyEhISNH78eEVFRcnv9+ucc87Rnj173I5VpR06dEjTpk3TPffc43aUKu+TTz5R//791bBhQ/n9fk2bNk0dO3Z0O1aVFQqFZFmW0tPTFQwGFQwGFR0d7XasKmfWrFl64oknVL9+fUnS+vXr1bx5czVt2lQ+n0+JiYk8rpejk8cjKipKTzzxhGrUqCFjjM4991we18vRyeMhSVlZWXr88cc1evRoF5NVDD63A1QUycnJSkhIyD1fv359rV+/3sVEVVvr1q1zT2/fvl2LFy/WP//5TxcT4fHHH9eYMWO0d+9et6NUeTt27JDf79c999yjvXv3qnfv3nrggQfcjlVl1ahRQ/fff7/69eunatWqqUuXLurUqZPbsaqcp556Kt/5gh7X9+3bV96xqqyTx6NJkyZq0qSJJCk1NVXvvvuuJk+e7Ea0Kunk8ZCkP//5z7rxxht11llnuZCoYmEPVTFZliVjTO5527bznYc7tmzZojvuuEPjxo3T2Wef7XacKuuDDz5Qo0aN1K1bN7ejQOE9IqtXr9bTTz+tmTNnav369RyK6aJNmzZp9uzZ+uKLL/Tll1/K4/Ho9ddfdztWlcfjemTat2+fbrvtNt14443q2rWr23GqrJUrV2rv3r268cYb3Y5SIVCoiqlhw4ZKSUnJPZ+SkpJvtyjK3zfffKPbb79dDz30kAYNGuR2nCpt0aJFWrlypW644Qa98MIL+vzzz/X000+7HavKqlevnrp166b4+HjFxMToqquuYo+6i1asWKFu3bqpbt26ioqK0uDBg7V27Vq3Y1V5PK5Hnh9//FFDhw7VoEGDNGrUKLfjVGkLFy7Uli1bdMMNN2jChAn6/vvvOdLhNDjkr5i6d++uF198UampqapWrZqWLl2qiRMnuh2rytq7d69GjRqladOmsVckArz55pu5p+fMmaO1a9fq0UcfdTFR1danTx89/PDDOnLkiKpXr64vv/xSV155pduxqqy2bdvq2WefVVpamqpVq6bPP/9c7du3dztWldexY0f99NNP2rFjh8466ywtXLiQV+NddOzYMd1555164IEHNHDgQLfjVHl5D7dcs2aNXnrpJT3//PMuJopsFKpiatCggcaMGaPhw4crEAhoyJAh6tChg9uxqqzXX39dmZmZeuaZZ3KnDR06VDfffLOLqYDI0LFjR911110aNmyYAoGAevTowRNFF/Xs2VMbNmzQ4MGD5ff71b59e40YMcLtWFVedHS0nnnmGf3ud79TZmamLr/8cl177bVux6qyPvzwQ+3fv19vvvlm7ot0V1xxhe6//36XkwFFM7Zt226HAAAAAICKiPdQAQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAJS6O+64Q6mpqadMv/vuu7V169ZS2UZqaqratGlTKpeV46WXXtKnn34qSZo+fbrmzZtXqpcPAKh8+B4qAECpW7lyZYHTX3vttXJOUjJr1qxRq1atJInvvwEAFAuFCgBQqh555BFJ0m233aatW7eqb9++2rx5sx588EFNnjxZ06dPV1pamp577jk1btxY27ZtU0xMjJ555hmdc845p73spUuXatq0aapWrZratWuXO33OnDn6+OOP9eqrr55yfvz48Tp06JB27typ3r17a8iQIXryySd1/PhxpaSkqG3btnr++ef14Ycf6vvvv9fUqVPl9Xr12WefqXXr1rrzzju1bt06TZ06Venp6fL7/XrggQfUq1cvzZkzR5988ok8Ho927NihmJgYTZkypcjrAQCoPDjkDwBQqiZPnixJ+sc//qFGjRqpdevWWrx4sa6++up8y33//fe69dZblZSUpMGDB2vs2LGnvdz9+/fr0Ucf1Ysvvqg5c+aoSZMmxc6UkZGhjz76SGPHjtWsWbM0cOBAzZo1S0uXLtWuXbu0bNky3XLLLWrXrp3GjRuXL+vBgwc1evRoPfbYY0pKStKUKVM0duxY7dy5U5L09ddf6w9/+IMWLlyojh07asaMGcXOBQCo+ChUAIAy1blz5wKnt23bNnfejTfeqI0bN+rgwYOFXs4333yjc889N/eQvF//+tfFznDxxRfnnh47dqzi4+P12muv6Y9//KOSk5OVlpZW6Lrr169Xs2bN1LFjR0lS69at1alTJ61du1aSdMEFF6hhw4aSpPPPP1+HDx8udi4AQMXHIX8AgDIVGxtb4HSv11usaXnZtp172uc78RBmjMk3LxAIFJrhwQcfVCgUUr9+/dS7d2/t3bs337onC4VCMsackiMYDMrv9ysmJqbQHACAyo89VACAUuf1ehUMBk+7zKZNm7Rp0yZJ0syZM3XRRRepZs2ahS7fpUsXbd26NXedOXPm5M6Lj4/Xli1blJmZqUAgoI8//rjQy1mxYoVGjRql/v37S5K+/fZbhUKhQnNfeOGF2rZtm9avXy9J2rJli77++mtdcsklp71+AICqgT1UAIBSd+211+rWW2/V8ePHC12mXr16ev7557V7927Fx8dr6tSpp73M+Ph4Pffcc/r9738vv9+vLl265M7r0aOHunTpon79+ikhIUFdu3bV5s2bC7ycMWPGaNSoUYqNjVWNGjXUpUsX/fzzz5KkK664Qv/v//2/fHu44uPjNX36dE2cOFEZGRkyxmjy5Mlq0aKF/vOf/5TkZgEAVELG5tgEAEA5W7NmjSZOnKiFCxe6HQUAgDPCHioAQMT429/+pqSkpALn3Xnnnbr++uvLOREAAKfHHioAAAAAcIgPpQAAAAAAhyhUAAAAAOAQhQoAAAAAHKJQAQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKAAAAAByiUAEAAACAQxQqAKjk1qxZowEDBpT7dtu0aaPU1NTTLrNs2TJNnz69RJcbCoU0cuRI9e3bV++8806+eevXr9fjjz9e4HrfffedRo8eXaJtney3v/2t5syZc0aXkVfevKWRDwBQ/nxuBwAAVF3fffedDh8+XKJ19u3bpxUrVui///2vvF5vvnlbt27Vvn37Clyvffv2euGFFxxnLQt580ZiPgBA0ShUAFAFpKWlafTo0dqxY4dq1qypJ598Ui1atNDRo0f1pz/9SZs2bZIxRpdddpkefPBB7dixQ0OHDtVbb72l8847T+PGjZPP59PTTz+d73LHjx+v6Ohobdq0SQcOHFCPHj00YcIE+f3+fMv95S9/0UcffSSv16sWLVroD3/4g/bs2aP3339foVBIcXFxGjNmTL511q1bp6lTpyo9PV1+v18PPPCAOnXqpLvuukvBYFCDBw/Wiy++qGbNmkmS9u7dqxdeeEFHjx7VI488ooEDB+qpp55SbGysjh8/rnHjxmnKlClauHBhsXPv27dP48ePV3Jysho3bqwDBw7kzmvTpo1Wr16t+Pj4fOe3bNmSb7uzZ8/W1KlT9e233+r48eOybVuTJk1S48aNT8k7ceJELVy4sNBx8fl8at++vUaMGKGVK1cqOTlZd911l4YNG1ZqfysAgJLhkD8AqAL27t2r22+/XfPnz9eAAQM0btw4SdKkSZNUu3ZtJSUlafbs2dq8ebPeeOMNnXPOORo7dqwefvhhffDBB9q0aVOhh9KtX79eb7zxhhYtWqQff/xRM2fOzDd/9uzZ+vLLL/Xhhx8qKSlJrVu31vjx49WxY0cNHTpU/fv3P6VMHTx4UKNHj9Zjjz2mpKQkTZkyRWPHjtXBgwc1Y8YMxcTEaP78+bllSpIaNWqk0aNHq3Pnzpo8ebIkacuWLfrzn/+spKQkRUVFlSi3JD355JPq2LGjPvroI02YMEE//fRTsW7vvNv94YcflJycrJkzZ2rRokUaNGiQXnvttQLz5ihsXCQpKytLderU0fvvv68XXnhBkydPVmZmZrFyAQBKH4UKAKqANm3aqFOnTpKkQYMG6fvvv9fRo0e1fPly/eY3v5ExRlFRURo6dKiWL18uSfrVr36ls88+W5MmTdILL7ygmJiYAi970KBBql69uqKionTDDTdoxYoV+eYvX75cgwcPVmxsrCRp+PDh+uqrr5SVlVVo3vXr16tZs2bq2LGjJKl169bq1KmT1q5dW6Lr3ahRIzVp0sRRbklatWqVBg8eLElq3ry5unbtWuLtXnTRRXrggQf0/vvva8qUKVqyZImOHz9+2vVPNy6SdOWVV0qSLrjgAmVlZSktLa1YuQAApY9CBQBVgMeT/9+9MUY+n0+WZckYkzvdsiwFg0FJ4T0hO3bsUFxcnDZu3FjoZed9H5Nt26ds63TbKEwoFMq3Ts5lF7XeyXJKXEGKyi2FbyfbtnPP+3wFHyl/cjnMu91ly5bpt7/9raRwEbr55puLzF3UbRYdHZ2bLyc/AMAdFCoAqAI2b96cW4pmzpypiy++WNWqVVPPnj31zjvvyLZtZWVladasWerevbskaerUqWrdurVef/11TZo0Sbt37y7wshcvXqysrCxlZmZq7ty56tOnT775l112mWbPnp27F+Xtt99Wly5dFBUVJa/XW2BJuvDCC7Vt2zatX79eUvgQuq+//lqXXHLJaa9nYZfnJHdO9pxDAffs2aM1a9bkzouPj9d3330nSVq4cGGh21m5cqX69OmjYcOGqV27dvr0008VCoVOm/d04wIAiCwUKgCoAlq2bKmXXnpJ119/vT7//HM988wzkqQJEyYoNTVViYmJSkxMVIsWLXTPPfdo2bJl+uSTT/T444+rTZs2uu222/TQQw8V+OQ/JiZGw4YNU2Jiojp37qwbb7wx3/whQ4aoW7duuummm9SvXz9t2LBBzz33nCTp0ksv1YoVKzRx4sR868THx2v69OmaOHGiEhMT9dBDD2ny5Mlq0aLFaa/nhRdeqJ07d+q+++4r8jYpKrckPfHEE/rxxx/Vr18/PfbYY2rbtm3uvAkTJujJJ5/UoEGD9OOPPyohIaHA7QwdOlRr165VYmKiBg0apKZNm2rXrl2yLKvQvIWNCwAg8hib4wQAAA6NHz9erVu31p133ul2lBKpqLkBAJGHPVQAAAAA4BB7qAAAAADAIfZQAQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwKGCv/K9nB08eFyWFTmfjVG3bg0dOHDM7Rg4CeMSmRiXyMS4RC7GJjIxLpGJcYlMVW1cPB6jOnWqFzo/IgqVZdkRVagkRVwehDEukYlxiUyMS+RibCIT4xKZGJfIxLicwCF/AAAAAOAQhQoAAAAAHKJQAQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKAAAAAByiUAEAAACAQxQqAAAAAHCIQgUAAAAADlGoAAAAAMAhChUAAAAAOEShAgAAAACHKFQAAAAA4JDP7QAAgNL13ntv6ZdfdikQCJXpdg4fPiRJqlWrdplup2nT5ho2bHiZbgMAAKcoVABQyezcuUPbN/6ghoFgmW7noD/8EBK9Y0eZbeMXPw9TAIDIxiMVAFRCDQNB3Z56sEy38ff4OpJUptvJ2QYAAJGK91ABAAAAgEMUKgAAAABwiEIFAAAAAA5RqAAAAADAIQoVAAAAADhEoQIAAAAAhyhUAAAAAOAQhQoAAAAAHKJQAQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKAAAAAByiUAEAAACAQxQqAAAAAHCIQgUAAAAADlGoAAAAAMAhChUAAAAAOEShAgAAAACHKFQAAAAA4BCFCgAAAAAcolABAAAAgEMUKgAAAABwiEIFAAAAAA5RqAAAAADAIQoVAAAAADhEoQIAAAAAhyhUAAAAAOAQhQoAAAAAHKJQAQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKQMRbuXK5Vq5c7nYMIOJxXwGA8udzOwAAFGXFin9Jknr06OVyEiCycV8BgPLHHioAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKAAAAAByiUAEAAACAQxQqAAAAAHCIQgUAAAAADlGoAAAAAMAhChUAAAAAOEShAgAAAACHKFQAAAAA4BCFCgAAAAAcolABAAAAgEMUKgAAAABwiEIFAAAAAA5RqAAAAADAIQoVAAAAADhEoQIAAAAAhyhUAAAAAOAQhQoAAAAAHKJQAQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKAAAAAByiUAEAAACAQxQqAAAAAHCIQgUAAAAADlGoAAAAAMAhChUAAAAAOEShAgAAAACHfG4HiDRPP/2ktm7dpLZtz9e4cRPcjqM77hiWe/qNN95z9fJKsm5xly1qubzzk5KSilyvJNNPnlaS83mdbh4AlKfNmzdKUpn9T2rYsLF++WVP7nljjGzbliT5fD4lJNTX/v0pCgQCqlMnXocOHVRCQn0dOXJYo0Y9qLlzZ8kY6dZb79Bbb72hYDAoY6RQKKjk5GTZtq1AIKB77hmtzz9fqsTEgXr55ekaP/5xxcXV1F//+qJGjhytWrVqS5IOHTp4yrSTpx8+fEhTpkzU+PGPq2nT5jp06KCmT39Wv/yyVw0aNNKYMeNy1/355+165pknVadOvFJTD+TO37XrZ/35z8/I7/fL4/Hod797ULNm/VN79+5WIBBQfHxdHTyYqoYNG8m2be3b94seeugRNW7cRH/964saNuw2vffeP3TFFVfp1Vf/Itu2Vb9+g+zbZYzmzv1AoVBQWVlZ2rt3j37/+0dVo0aN3NxxcTX10kvTJEnDh9+pt956XaFQSF6vV4MGDdFf/vK86tVLkGTn3o62Hb5dH3roEdWoUUOTJ/9JkvToo3/MvR2mTp2kX37ZW+S4l8bzD6C4CrtfRzIK1Um2bt0kSdq0aYPLSQAAiCx5y5Sk3DIlScFgUHv3nph/8GCqJCk5eZ8k6ZVXpist7bgkacaMv2jPnt2Fbue1116WZYX0yisvKj09Xa+++pLatDlPW7Zs1oIFc3TrrXdIkpKS5p4y7eTpmzdvzL2MSZOeVVLSXO3YsV1SuEDlXXfGjL8oIyMj93rkzF+zZrUkKRAISJJefvmF3OsiSampByQp3/V/+eXp6tq1m7Zs2awZM17S3r179OOPW3NvsxO3S/7Lylm3du3a+a77tm1bszO+lO+2e+WVF5WRkaFdu3YWeFvmXFZmZqYk5bsdilOmgPJW2P06knHIXx5PP/1kvvNTp05yKUnYya8wnukrjmdyeSVZt7jLFrXcyecTExNPu1xJppd02yW5vig73NZA4SL9/pG3NJyuTEnhPSu2beeus2fPbi1fvky2bWvFiuU6fPiQDh06qBUr/pVvmqR807/8clnutvbs2a0NG77Tl18uy7et5cuX6fDhQ9q2bVuBub744rNTCs/J5wu7vv/61+eybVt79uyWbdsKhYKnvV3yTsube/nyE5lPzlhUlryXlbP+Dz98r2XLPivyOuSI9L8tVB6F3a8jHXuo8sjZO5WDvVRA5JkyZaLbESLezz/vUKyncrxedszjUfLPOyrVuPv9XgUCIbdjVDg5ZcSyLC1YMCf7tJ1v2q233qGkpLm504PB/AXm5ZdfOGVaKBTUggVztHXr5kK2bBcyvWiWZTleN6+CitiZeOWV6fn2LgKRIu/9N+/9OtJVjkdcAABQJYRCQa1evVKrV6/MLRo50yTlm36ywvbmrF69Ujt3FnzIXGVUnD1sgBsKu19HOvZQAahQHn74D25HiHhTpkxUxvpv3Y5RKmpYluo1a16pxj0hIU4pKUdL/XKrymFZXq9P3br1kBQ+XC8UCuab1q1bj9zpJ4uNrV5gmejWrYe2bt1cZUpVYbcD4La899+89+tIxx6qPFq1apvvfNu257uUBAAA5OX1hl8D9ng8uv76wUpMHCSPx+SbJinfdJ8v/+vG9947+pRpXq9P118/WL///e8L2bJxnNlTSofe5lz30jJy5P0yxvn1AspKYffrSEehyuPRRx/Pd97tj00/+WNKz/RjS8/k8kqybnGXLWq5k8/nfGx6YcuVZHpJt12S64uyw20NFC7S7x+xsdVzTzdu3OS0y3q9Phljctdp3LiJevXqLWOMevbspVq1aqt27Trq2fPyfNMk5Zt+2WW9c7fVuHETnX9+e112We982+rVq7dq1aqtli1bFpirT58r82U/+bqc7vpefvkVMsaoceMmMsYUWIwKuqzY2Or5cvfqdSLzyRmLypL3snLWv+CCdurd+8oir0OOSP/bQuVR2P060lGoTpKzl4q9UwAA5NewYeN85/Pu5fD5fGrUqLH8fr8kqU6deBljVL9+A8XExGjkyPvVsmUrnXNOK40YMUotW7ZSs2Znq3nzs3XWWWcpKioqd927775XrVu30ciRv1O1atX029/ep8TEQWrduk2+V6wLmnby9BEjRuVeRs685s3PVnR0tJo1OzvfuiNGjFJMTIwaNWqcb/7Ikb+TJPn9fkVHR+vee0erWbOzc/PGx9eVMUaNGjVWw4aNZIzRvffen5tjxIj71Lp1G9199z25t9mJ22W0WrZspebNz1ajRuHb995778+XOzFxkFq2bKWWLVtpxIj7spdvoZYtW2nkyN8pJiZGZ53VNN/t6PP5c3OMGDFK0dHRio6Oznc7NGzY6Az/IoDSV9j9OpIZOwI+5uXAgWO5n+gRCcrq+HacGcYlMpXHuOR8wltleh9NWcp5D9XtqQfLdDt/j68jSWW6nb/H11FMh46VauzL8j7DfcU5HmMiE+MSmarauHg8RnXr1ih8fjlmAQAAAIBKhUIFAAAAAA5RqAAAAADAIQoVAAAAADhEoQIAAAAAhyhUAAAAAOAQhQoAAAAAHKJQAQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKAAAAAByiUAEAAACAQxQqAAAAAHCIQgUAAAAADlGoAAAAAMAhChUAAAAAOEShAgAAAACHKFQAAAAA4BCFCgAAAAAcolABAAAAgEMUKgAAAABwiEIFAAAAAA5RqAAAAADAIQoVAAAAADhEoQIAAAAAhyhUAAAAAOAQhQoAAAAAHKJQAQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwCGf2wEAoCg9e17udgSgQuC+AgDlj0IFIOL16NHL7QhAhcB9BQDKH4f8AQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKAAAAAByiUAEAAACAQxQqAAAAAHCIQgUAAAAADlGoAAAAAMAhChUAAAAAOEShAgAAAACHKFQAAAAA4BCFCgAAAAAcolABAAAAgEMUKgAAAABwiEIFAAAAAA5RqAAAAADAIQoVAAAAADhEoQIAAAAAhyhUAAAAAOAQhQoAAAAAHKJQAQAAAIBDFCoAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKAAAAAByiUAEAAACAQxQqAAAAAHCIQgUAAAAADlGoAAAAAMAhChUAAAAAOEShAgAAAACHKFQAAAAA4JDP7QAAgNL3i9+nv8fXKfNtSCrT7fzi9+nsMrt0AADOHIUKACqZpk2by+/3KhAIlel26hw+JEmKqVW7zLZxtsLXBwCASEWhAoBKZtiw4UpIiFNKylG3owAAUOnxHioAAAAAcIhCBQAAAAAOUagAAAAAwCEKFQAAAAA4RKECAAAAAIcoVAAAAADgEIUKAAAAAByiUAEAAACAQxQqAAAAAHCIQgUAAAAADlGoAAAAAMAhChUA4P+3d/8xVdV/HMdfF7wIxFq7iWHYrxnGSqMsMrIVWamgpIJbWiObVHNjKVgYUkmLlB9ZF5K5pTHXSgsyRCFNKKoJOFDXvCPDQSphMcShNeWHcKF/9H4Bxex+yXPzPh9/3XPuvcfn9TM9vDlHAQAATmKgAgAAAAAnMVABAAAAgJMYqAAAAADASQxUAAAAAOAkBioAAAAAcBIDFQAAAAA4aYTRAZLk4WEyOuECrtgE1sVVsS6uiXVxXayNa2JdXBPr4prcaV3+7rOa+vr6+q5QCwAAAABcVbjlDwAAAACcxEAFAAAAAE5ioAIAAAAAJzFQAQAAAICTGKgAAAAAwEkMVAAAAADgJAYqAAAAAHASAxUAAAAAOImBCgAAAACcxEA1SHFxsSIjIzVt2jRt2rTJ6Byck5ubq5kzZ2rmzJnKysoyOgf9ZGZmKjk52egM9FNeXq7o6GhFRETonXfeMToH52zbts3x91hmZqbROW7v9OnTmjVrlo4dOyZJqqqqUlRUlKZNmyar1WpwnfsavC75+fmaNWuWoqKitGLFCp09e9bgQvc0eF3O+/TTTxUbG2tQletgoOqnpaVFVqtVmzdvVlFRkfLz89XQ0GB0lturqqpSRUWFtm7dqqKiIv30008qKyszOguS9uzZo61btxqdgX6ampqUmpqqdevWafv27Tp48KB++OEHo7PcXkdHh1atWqVPPvlE27Zt0759+1RVVWV0lts6cOCAFixYoGHQUSQAAAhdSURBVKNHj0qSOjs7lZKSonXr1mnHjh2qra3lz40BBq/LkSNHlJeXp88//1zbt29Xb2+vNm/ebGykGxq8Luc1NDRo/fr1xkS5GAaqfqqqqvTggw/quuuuk6+vr6ZPn66vv/7a6Cy35+/vr+TkZHl5eclsNmvcuHH6/fffjc5ye6dOnZLVatXixYuNTkE/ZWVlioyMVEBAgMxms6xWq0JCQozOcnt2u129vb3q6OhQT0+Penp6NHLkSKOz3FZBQYFSU1M1evRoSZLNZtMtt9yim266SSNGjFBUVBTnfwMMXhcvLy+lpqbKz89PJpNJ48eP5/xvgMHrIklnz57VypUrtWTJEgPLXMcIowNcyfHjx+Xv7+/YHj16tGw2m4FFkKSgoCDH46NHj2rnzp367LPPDCyCJK1cuVKJiYlqbm42OgX9NDY2ymw2a/HixWpublZ4eLgSEhKMznJ7fn5+Wrp0qSIiIuTj46PQ0FBNmjTJ6Cy3tWrVqgHbFzv/t7S0XOkstzd4XQIDAxUYGChJamtr06ZNm5Senm5EmlsbvC6S9N577ykmJkZjx441oMj1cIWqn97eXplMJsd2X1/fgG0Yq76+XosWLdLy5ct16623Gp3j1r744guNGTNGYWFhRqdgELvdrj179mj16tXKz8+XzWbjtkwXUFdXpy+//FLfffeddu/eLQ8PD+Xl5RmdhXM4/7u2lpYWLVy4UDExMZo8ebLROW6vsrJSzc3NiomJMTrFZTBQ9RMQEKDW1lbHdmtr64DLmzDO/v379fzzz+uVV17R3Llzjc5xezt27FBlZaVmz56tDz74QOXl5Vq9erXRWZA0atQohYWFyWKxyNvbW0888QRX2l1ARUWFwsLCdP3118vLy0vR0dGqqakxOgvncP53Xb/88ovmz5+vuXPnKj4+3ugcSCopKVF9fb1mz56tN954Q7W1tW5/JwS3/PXz0EMPae3atWpra5OPj49KS0uVlpZmdJbba25uVnx8vKxWK1dEXMTGjRsdjwsLC1VTU6OUlBQDi3DeY489ptdee01//vmnrrnmGu3evVuPP/640VluLzg4WO+++67a29vl4+Oj8vJyTZw40egsnBMSEqIjR46osbFRY8eOVUlJCd99dwGnT59WXFycEhISNGfOHKNzcE7/2y6rq6uVm5ur7OxsA4uMx0DVzw033KDExEQ999xz6u7u1rx583T33XcbneX28vLy1NXVpYyMDMe++fPna8GCBQZWAa4pJCREL7zwgp555hl1d3drypQpfGHoAh5++GEdPHhQ0dHRMpvNmjhxol566SWjs3DOyJEjlZGRoZdfflldXV169NFHNWPGDKOz3N6WLVt04sQJbdy40fGNvKlTp2rp0qUGlwEDmfr6+vqMjgAAAACA/yL+DRUAAAAAOImBCgAAAACcxEAFAAAAAE5ioAIAAAAAJzFQAQAAAICTGKgAAMNu0aJFamtru2D/iy++qIaGhmH5Ndra2nTHHXcMy7HOy83N1TfffCNJysnJUVFR0bAeHwBw9eHnUAEAhl1lZeVF92/YsOEKl/wz1dXVuv322yWJn3UDALgsDFQAgGG1YsUKSdLChQvV0NCg6dOn69ChQ1q2bJnS09OVk5Oj9vZ2rVmzRjfeeKMOHz4sb29vZWRkaNy4cZc8dmlpqaxWq3x8fDRhwgTH/sLCQu3atUsffvjhBdvJyck6deqUmpqaFB4ernnz5untt9/WmTNn1NraquDgYGVnZ2vLli2qra1VVlaWPD099e233yooKEhxcXHat2+fsrKy1NHRIbPZrISEBD3yyCMqLCxUWVmZPDw81NjYKG9vb2VmZv7t5wAAXD245Q8AMKzS09MlSR9//LHGjBmjoKAg7dy5U08++eSA19XW1io2NlbFxcWKjo5WUlLSJY974sQJpaSkaO3atSosLFRgYOBlN3V2duqrr75SUlKSCgoKNGfOHBUUFKi0tFTHjh3T999/r2effVYTJkzQ8uXLB7SePHlSS5Ys0euvv67i4mJlZmYqKSlJTU1NkqS9e/fqzTffVElJiUJCQrR+/frL7gIA/PcxUAEA/lX333//RfcHBwc7nouJidHPP/+skydPDnmc/fv3a/z48Y5b8p5++unLbrjvvvscj5OSkmSxWLRhwwa99dZbOn78uNrb24d8r81m080336yQkBBJUlBQkCZNmqSamhpJ0l133aWAgABJ0p133qk//vjjsrsAAP993PIHAPhX+fr6XnS/p6fnZe3rr6+vz/F4xIj/ncJMJtOA57q7u4dsWLZsmex2uyIiIhQeHq7m5uYB7x3MbrfLZDJd0NHT0yOz2Sxvb+8hOwAAVz+uUAEAhp2np6d6enou+Zq6ujrV1dVJkvLz83Xvvffq2muvHfL1oaGhamhocLynsLDQ8ZzFYlF9fb26urrU3d2tXbt2DXmciooKxcfHKzIyUpJ04MAB2e32IbvvueceHT58WDabTZJUX1+vvXv36oEHHrjk5wMAuAeuUAEAht2MGTMUGxurM2fODPmaUaNGKTs7W7/99pssFouysrIueUyLxaI1a9bo1VdfldlsVmhoqOO5KVOmKDQ0VBEREfL399fkyZN16NChix4nMTFR8fHx8vX1lZ+fn0JDQ/Xrr79KkqZOnar3339/wBUui8WinJwcpaWlqbOzUyaTSenp6brtttv0448//pPfFgDAVcjUx70JAIArrLq6WmlpaSopKTE6BQCA/wtXqAAALuOjjz5ScXHxRZ+Li4vTU089dYWLAAC4NK5QAQAAAICT+E8pAAAAAMBJDFQAAAAA4CQGKgAAAABwEgMVAAAAADiJgQoAAAAAnMRABQAAAABO+gtIhCth4YJORwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cont_univ(np.log(dataset[dataset.trip_duration>0].trip_duration))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S3N988w0eHh54eHgwfPjwdHte5RcGg4H+/fubVjAEiImJoU2bNs+9Ct7mzZv59NNPzY6tWLGC1q1b07ZtW3r27ElYWFi6egMHDnziBsQHDhxIt5DH2rVradmyJV5eXnzxxRdER0dnOr5z587h5uZmdiwzv5VVq1Y98fcdHh5Oo0aNTKsOAly6dImPP/4YLy8vvL292b9/v6nswIEDn2sJfyGEyHVKCCHES3H48GHVunXr3A5DKaVUTEyMql+/vrpw4YJSSqmzZ8+qevXqqdjYWLNyycnJqkGDBur06dNKKaX27NmjWrRooZRSKigoSLVr104lJycro9GoPv/8c7VkyZKXeyMvydKlS9WyZctM3/fu3atatGihqlevrk6cOJGpNu7fv6/Gjh2r6tSpo/r162c6HhISotzd3U3Pft26dapLly7prv/++++riRMnmo4lJiaq//znP8rZ2dnsd3Xo0CHVqFEjFR4erpRS6ueff1aff/55hvGlpqaqlStXKhcXF1WnTh3T8cz8VkJDQ5Wrq2u63/fPP/+smjZtqipXrqzu3btnOt6tWzf1448/KqWUOn36tHJyclKpqalKKaXmz5+v1q5dm2G8QgiRV8gIlRBCZLP4+HgGDRpk+tf8MWPGpPsX99DQUJo0acJff/0FwJ49e+jQoQPe3t507tyZY8eOcf/+ferWrUtCQgIA48aNo1u3bqY2WrRoweXLl83aXbp0KV5eXun+3L9/36xcamoq48eP55133gGgUqVKKKXSlbOysuL333+nWrVqKKW4fv06JUqUMF3/hx9+wMrKivj4eKKiokwbtu7evfupiwU0a9aMIUOG4O7uTnBwML/99hudO3fG19eXJk2amBbpOHLkCJ07d8bf3x9vb288PT35888/AYiKiqJ///64u7vz8ccfM2jQINPKgJcvX6Z3796mEZXNmzc/MY7OnTune04TJ05MVy4xMZHVq1eb7fW1Zs0avv76a+zt7Z/Y9pNs374de3t706p/D73xxhtMmDDBtHFtzZo1uXXrlun8kSNH2L9/P507dzard+DAARITE9OtcHf69GlcXFxwcHAA0vppz549pKSkPDO+M2fOcP78ebOFNCDj30pkZCRfffVVus1/IyIi2LVrF8uXL093LYPBYFqiPz4+ngIFCpjOdejQgW+//TbDeIUQIs/I5YROCCHynZ9//ln17t1bKaWUXq9Xo0ePVlevXjWNUB06dEg1b95cnT17Viml1JUrV5Snp6eKiopSSil14cIF5erqquLj45Wfn5/as2ePUkqpFi1aKBcXFxUXF6cuXryo3N3dsy3m2bNnK19f36eev3v3rnJzc1PVq1dXwcHBZufWrl2r6tWrpzw8PMxGIZ6madOmasGCBUoppYxGo+rWrZu6cuWKUkqp27dvq3fffVfdu3dPHT58WL377rvqzJkzSimlli9frrp27aqUUmro0KFq5syZSimlIiIilKurq5o3b55KTU1VHh4e6tSpU0qptNEVd3d3dezYsed7II/Ys2eP6tat21PvJbMjVA/99NNPZiNUj0pOTlZ+fn5q+vTpSqm059GmTRsVERGh5s2bZzZC9dDjI59Hjx5VH3zwgbpx44ZSKq1/KleurCIiIjIV3/Xr181GqB736G9Fr9er7t27qwMHDjxzBPbxEaqzZ8+q+vXrq0aNGqnq1auroKAgs/Kenp7q0KFDmYpXCCFymyybLoQQ2axevXrMmTMHPz8/XFxc6NGjB46Ojty+fZvbt2/z6aef8vHHH5s28A0JCeHOnTtmG85qNBrCwsL46KOP+P333ylfvjylSpWicuXKHD16lPPnz9OiRYt01166dCm//vpruuOrVq0yjSw9Sq/XM336dH7//XdWrVr11Ht644032L9/P6dPn6Znz55UrFiRChUqANCtWze6du3KN998w6BBg1i3bl2Gz8jZ2dl0n0uWLGHv3r388ssvXL58GaWU6V2sMmXK8O677wJQrVo1fv75ZwD27dtn+mxvb0+rVq0AuHr1KmFhYYwaNcp0raSkJM6cOUOdOnXMYujcuXO6d76cnJwYP3682bG///6b8uXLZ3hPLyoqKopBgwZhbW3N0KFDSU1N5csvv2TkyJHPNRLm7OzMgAEDGDhwIBqNhnbt2mFjY5NuE+Xn9aTfyuzZs3nvvfdwdXXlyJEjmWonOTmZoUOHMn36dJo2bcrx48f59NNPqVmzJqVLlwagbNmyXLlyhQYNGrxQzEII8TJIQiWEENmsXLlyBAcHc+TIEQ4fPkyvXr2YNGkSRYoUQafTsXTpUj777DNatWpF7dq1MRqNNGzY0Gw/qvDwcOzt7bGxsaFr16689dZbuLq6UqxYMQ4cOMDJkyefOD2tX79+9OvXL1NxPnjwgEGDBqGUYuPGjU9MuGJjYzl8+DAfffQRANWrV6dq1apcuHCB5ORkjEYj1apVQ6PR0KFDB9asWZOpaxcuXBiAhIQEfHx8aN68Oc7OzrRr145du3ah/n9Hj4IFC5rqaDQa03ELCwvTZwCtNm0Gu8FgoGjRogQGBprORUZGUrRo0XQxbNiwIVOxajSaHF8k4dy5c3z22Wc0b96c4cOHo9PpOHbsGNevXzdN6YuMjMRgMJCcnMyUKVOe2lZcXBz169enQ4cOQNrUu3nz5pmmY2bF034r//3vf7G1tSU4OJiEhAQiIiLw8vIye/6Pu3DhAklJSTRt2hSAOnXq8M477/C///3PlFBZWlqi0+myHK8QQrxM8g6VEEJks/Xr1zNy5Ejc3Nzw9/fHzc2NM2fOAGBnZ4eTkxPDhw/n3//+N4mJiTRs2JCQkBDT+1D79u2jbdu2JCUl4eDgQIkSJdiwYQOurq64ubmxc+dOoqOjTSNcWWEwGOjXrx9ly5ZlxYoVT0ymIC1RGTVqlOndpYsXL/L3339Tu3Ztzp07x8iRI02jPAEBAc89onDt2jXi4uIYMmQIzZo148iRI6SkpGSYwHzwwQemd6Pu37/Prl270Gg0VKhQgYIFC5r+Qh8eHo6npyenTp16rrgeVaFCBa5fv57l+hm5ffs2PXr04LPPPmPUqFGmRKJu3brs27ePwMBAAgMD6dy5Mx4eHs9MpgDu3LmDn58fcXFxACxevJjWrVuj0WiyFN+zfisHDhzgv//9L4GBgUyePJny5cs/M5kCcHR0JDY21vT+YFhYGJcuXaJatWqmMjdu3ODtt9/OUrxCCPGyyQiVEEJkM29vb/744w88PDwoVKgQpUuXxs/Pj3PnzpnK+Pj4EBQUxPTp05k4cSKTJk3iiy++QCmFhYUFixcvpkiRIgB89NFHrFixgmrVqqHVailYsCDNmzd/oRi3b9/O8ePHSUhIoF27dqbjM2fOpEqVKnh5eTF58mRq1qzJwoULmTp1Knq9HisrK2bNmoWDgwPe3t6EhYXRrl07dDod77zzjukv+7t372bDhg189913z4yjSpUqNGnSBHd3d6ysrKhcuTKVKlXi2rVrWFlZPbXeyJEjGTNmDG3atMHGxoYyZcpQsGBBrKysWLRoEVOmTGHZsmXo9XoGDx5MvXr1svysXFxcGD16NDExMRQrVuyZZX/44QdOnTqVYdLzqEWLFpGYmMjatWtZu3YtkLYYyI8//pileN9++2369etHhw4dMBqN1KtXj3HjxgFw8uRJxowZk2HS86iMfivPq1ixYixYsIApU6aQkpKCTqfjq6++Mk2rjIyM5N69ezg5OT1320IIkRs06tE5E0IIIUQ20Ov1DBs2zGwaY3b6/vvvqVatGnXr1iUlJYUuXbrw+eef88EHH+TI9ZYsWYJOp3vqyoUPxcXFMXny5HQr7+Uln3/+uWlFxLxo/vz52Nra0rVr19wORQghMkWm/AkhhMh2ly9fNltkI7tVqlSJr776Cm9vb3x8fPjggw9yLJkC6N27N4cPH+bu3bvPLHfu3LkMk67cFBERYTbKlNeEh4dz+vTpdEvECyFEXiYjVEIIIYQQQgiRRTJCJYQQQgghhBBZJAmVEEKIHDF//nzc3d3x9PRk+PDhJCcnA2nT4jp37kzbtm3x9vZm3759pjrnz5/Hz88Pb29vfH19n7o6X2hoKL6+vnh5edGxY0dOnjwJQEpKCuPGjcPDwwMPDw+mT5+OwWDI+Zt9zI4dO/Dz8wNg7ty5BAQEvPQYoqOj+fLLL/H29qZVq1ZmMTztORuNRmbOnEnr1q1p06YNAwcOJCoq6ontb968GQ8PD1q0aMH48eNJTU19KfclhBB5Ti5tKCyEECIfO3z4sGrZsqVKTExURqNRffbZZ+q7775TSinl6empgoODlVJKnT9/XtWpU0clJyerhIQE5erqqvbu3auUUio4OFi1bNnyie03bdpUHTx4UCml1M6dO5WHh4dSSqkVK1aoAQMGKIPBoFJTU1XHjh3V1q1bc/p209m+fbvq1q3bS7/uo/r3769mzpyplFIqPDxcOTs7q/Dw8Gc+502bNqnu3bur5ORkpZRSM2bMUP7+/unaPn/+vGrcuLG6d++eMhgMaujQoWrp0qUv6c6EECJvkWXThRDiFXDkyBH+85//ULp0aa5cuUKhQoXo168fa9eu5cqVK7Ro0YJRo0YBsGfPHhYvXkxqaioFCxZk+PDh1K1bl8jISMaNG8e9e/e4e/cub775Jt988w0lS5akWbNm+Pj4cOjQIcLDw/Hy8mLIkCHp4ujcubNp36mHnJycGD9+vNkxo9FISkoKSUlJaLVakpOTKVCgAAA///yzaa+lsLAwihUrhk6n4/fff6dcuXKmxSU+/PBDypYt+8TnYTAYiImJASA+Pt7Udq9evejWrRtarZaoqChiYmIoXrw48OwlzWvWrEmvXr04ePAgCQkJDBw4kB07dnDhwgXs7e1ZsmQJhQsX5vLly0yZMoXo6GgMBgN+fn60b98eSBuJ2rp1KzY2Njg6OpraHjFiBO+88w59+vRh8+bNbNy4kdTUVB48eEDfvn3p0qULW7ZsITg4GK1Wy7Vr1yhYsCAzZsygYsWKZnEGBASwcuXKdPE/voR5dHQ0Bw8eZM6cOQA4ODiwadMmihcvTkhIyFOfc6VKlfj3v/9tWrK+Ro0arF+/Pt31du/eTbNmzbC1tQWgU6dOTJ48OU8vyCGEEDlFEiohhHhFnDx5kvHjx1OtWjU++eQTli5dypo1a4iLi6Nx48b06dOHxMRE5syZw5o1ayhRogQXL16kV69e7Ny5k19//ZU6derQr18/lFL069ePwMBAevfuDUBCQgLr168nIiKCjz76iHbt2lGuXDmzGDZs2JCpWBs2bIiLiwtNmzbF0tKSChUq0KlTJwAsLCxQStG8eXNu3rzJ6NGj0el0XLlyBTs7O0aNGsW5c+coVqwY/v7+T2x/6tSpDBgwgClTphAbG8uKFStM5ywtLZk1axbff/89NWrUwNnZGYCPP/74qfGmpKTwxhtvsHnzZpYuXcqYMWPYvn07dnZ2tG/fnt27d+Pu7s6gQYOYOXMm1atXJzY2lk6dOlGpUiUiIyPZuXMnAQEBFCxYkAEDBqS7Rnx8PD/++CNLly6lRIkSHD9+nF69etGlSxcAjh49yi+//IKDgwNfffUVS5cuZcaMGWZteHt74+3tneHzDwsLw87OjpUrV/L777+TkpJCnz59qFChwjOfc926dU1tPHjwgEWLFj1xxb3w8HCzZNfBwYGIiIgM4xJCiPxIEiohhHhFlC1blmrVqgFQvnx5ihYtipWVFba2thQpUoQHDx5w9OhR7ty5Y7ZkuUajISwsjB49ehAaGsrKlSu5evUqFy9epHbt2qZyH374IQClSpWiZMmSPHjwIF1CldkRqs2bN3Pjxg3279+PlZUVI0eOZMaMGYwdO9YU065du7h+/Tpdu3alYsWK6PV69u3bx5o1a6hduza7du2iX79+/Pbbb2ab/EZGRjJ27FjWrl1LzZo12bVrF4MGDSIoKIjChQsDMGzYMAYPHszYsWOZMGFCusTkSVq2bGl6tpUrV6ZUqVKm5/7gwQOuXr1KWFiYaSQQICkpiTNnznD58mU++ugjrK2tAWjXrp1pk96HihQpwpIlS9i3bx9Xr17l3LlzJCQkmM5Xr14dBwcHAKpVqx2O2xAAACAASURBVEZwcHC6GDM7QpWamsqNGzewtrZmw4YNXLt2ja5du+Lo6Jip5xwWFsaAAQNwcnJ64n5Q6rEFgpVSaLXyWrYQ4vUkCZUQQrwiHk0qIG2k53FGo5GGDRuabagbHh6Ovb09X3/9NSdOnKBdu3a8//776PV6s78YP5w2B2kJz+N/aYbMj1AFBwfTpk0bU4LRsWNHvvrqK1JSUggODsbd3R2tVku5cuVwcXHh7Nmz2NvbU7FiRVOS17x5c8aMGcP169fNpr6FhoZSpkwZatasaSo3depULl++TEpKCra2tlSoUAFLS0t8fHyYPHlypmK2tLR84ueHDAYDRYsWJTAw0HQsMjKSokWLMnPmTLPn9XBK46Nu375Np06d6NixI/Xq1aNVq1b89ttvpvMFCxY0fX7a88/sCJW9vT0Avr6+ADg6OuLk5MSJEycyfM6HDx9m6NChfPLJJ/Tp0+eJ7ZcuXZo7d+6Yvt+5c8eUDAohxOtG/jlJCCHykYYNGxISEsLly5cB2LdvH23btiUpKYkDBw7Qo0cPvL29KVmyJAcPHsyxFfAejrA8TNqCg4OpXbs2VlZWfPPNN/z6669A2kazR44c4b333qNx48bcuHHDtOLc0aNH0Wg06d6jqlKlChcvXuTKlSsA/O9//yMxMZEKFSpw+PBhpk2bhl6vx2g0snXrVt5///1suacKFSpQsGBBU0IVHh6Op6cnp06donHjxuzYsYOYmBiMRqNZ0vXQqVOnsLW15bPPPsPNzc2UTOVEH5QrV47q1aubVvaLjIzk2LFj1KhR45nP+fTp0wwcOJAZM2Y8NZkCaNasGXv27OHevXsopdi4cSPNmzfP9vsQQohXgYxQCSFEPlKpUiUmTZrEF198gVIKCwsLFi9eTJEiRRgwYAAzZ85k7ty5WFpa4uTkRFhYWI7E8emnnzJt2jRat26NlZUVVapUMU0LXLBgAZMmTWLZsmVotVr8/f1No00LFy5k4sSJJCYmYmVlxfz58ylQoAARERH069ePpUuXUqFCBSZMmMCgQYMAKFSoEPPnz8fa2pq+ffsydepUvLy80Gq1ODk58eWXXwLPXpQiM6ysrFi0aBFTpkxh2bJl6PV6Bg8eTL169YC0pcjbtWtHsWLFqFq1Kvfv3zer7+rqyubNm2nVqhUajYb69etja2vLtWvXshRPRh4+5x9++AGj0ciAAQOoVasW8PTn/J///AelFLNnz2b27NlA2pTHhQsXsnv3bjZs2MB3331H1apVGTBgAD169CA1NZXatWvLghRCiNeWRj1pToEQQgiRz8TFxTF58mSmT5+e26EIIYTIRyShEkII8VoIDQ2lRIkS6ZYiF0IIIV6EJFRCCCGEEEIIkUWyKIUQQgghhBBCZJEkVEIIIYQQQgiRRZJQCSGEEEIIIUQW5btl0+/fj8dozNuvhZUsac29e3G5HYZ4QdKP+Yf0Zf4g/Zg/SD/mH9KX+YP0I2i1GkqUKPLU8/kuoTIaVZ5PqIBXIkaRMenH/EP6Mn+QfswfpB/zD+nL/EH68dlkyp8QQgghhBBCZJEkVEIIIYQQQgiRRfluyp8QQgghhBCvq8TEeOLiojEY9NnS3p07WoxGY7a0lfdpsLIqSIkSdmg0mkzXkoRKCCGEEEKIfCAxMZ7Y2PvY2NhhaWn1XEnB01hYaNHrX4+ESikj0dGRxMU9oGhRm0zXkyl/QgghhBBC5ANxcdHY2NhhZVUgW5Kp141Go6Vo0RIkJj7fqoaSUOUTKi4OFX4rt8MQQgghhBC5xGDQY2lpldthvNJ0OguMRsNz1ZGEKp/QTxhDagcf1N27uR2KEEIIIYTIJTIy9WKy8vwkocoHVMRt1J5dEBeLYdb03A5HCCGEEEKI10amEqqtW7fi4eFBixYt+P7779OdP3v2LL6+vrRs2ZLRo0ej16etKnLr1i26du1Kq1at+Ne//kV8fLxZvR9//JERI0aYvqekpODv74+7uzs+Pj5cvnz5Re7ttWH46UdQCq1PO4xB2zEePJDbIQkhhBBCCJHO8uXf8r//HUt3PDz8Fu3bt3muttq3b0N4HnjlJcOEKiIigjlz5rB+/XoCAgLYuHEjly5dMivj7+/PuHHjCAoKQinFpk2bAJg4cSJdunRhx44d1KhRg0WLFgGQnJzMrFmzmDp1qlk7a9eupVChQmzfvp1Ro0YxcuTI7LrPfEulpmLc8iMa10boRo6FtyqgnzIJlZiY26EJIYQQQghh5tixPzEYnu8dpbwuw2XTDx48SIMGDbCxSVs6sGXLluzYsYOBAwcCcPPmTZKSkqhTpw4Avr6+zJs3jw4dOnD06FEWLlxoOt6tWzf8/f05evQoRqMRf39/Tpw4YbrW3r17GTx4MADvvfceUVFR3Lp1izJlymTvXecj6rfdEBmJruPHaKyssBg9Dn3fXhi+W4LFoKG5HZ4QQgghhMglGzeu54cf1r1QGxoNKJX++Mcfd6NTpy7PrHvnTgSTJo0lMTERrVaDi0sjzp8/y4wZk5k6dRYGg57p078CoFKlyhnGEhPzgEmTxnLnTgRvvfU2KSkpABgMBhYtmvv/yZoRDw9POnXqyqhR/rRo0YomTT4EoHfvbowYMYbKlas+51N4tgwTqjt37mBnZ2f6bm9vb5YEPX7ezs6OiIgI7t+/j7W1NRYWFmbHAdzc3HBzc2PLli3PvJadnR23b99+roSqZEnrTJfNTXZ2RbOlnbs//4iuXDnsvd3R6HTg0Zz7HdqTsGYlNt06YVmlSrZcRzxZdvWjyH3Sl/mD9GP+IP2Yf0hfvlx37mixsPhnAppOpyU71qh4Uhs6nfm1nmTbtv/i5taIbt16cOhQCH//fZmqVavxySf9qVKlMl27dmTQoC94//0GrFjxHX/9FfrMNles+JaqVd/lm28WcOzYn+zZE4xOp+XXXwPRaDSsWfMDKSkpDB48gOrVq+Ph0ZqgoO00b/4RYWFhpKamUK1atQzvV6vVPtdvN8OEymg0mq12oZQy+/6084+Xg4xXzXi8jlIKrfb51s24dy8Oo/EJaXQeYmdXlLt3Y1+4HXX5EqmHDqEb/AWRUQn/HP9sKOwM5u6X/8Zi+Ro0z/kMReZkVz+K3Cd9mT9IP+YP0o/5h/Tly2c0Gs024W3fvjPt23d+oTaftbFvRhv+Ojm9x+jR/+bcuXO4uLjh7d2B/ft/x2AwEhkZxd27d6lXrz56vZGWLVvz3/8GPLPNP/8MZcKEqej1RmrWrEuZMm9iMBj544/DXLx4gdDQowAkJiZw4cJF2rTxZtasGcTExBIUtJ0WLdwztUmx0Wg0++1qtZpnDtpkmFA5ODgQGhpq+n737l3s7e3Nzt99ZKnuyMhI7O3tsbW1JTY2FoPBgE6nS1fvSUqVKsWdO3coX768WVviyQw/bgBLS7RevmbHNSVKoBs6DMOEsRgDf0bn0y6XIhRCCCGEEK+rWrXqsG7dJg4ePMDu3TvZtm2r6VzaVMJ/BkF0ugzTEtOgzT91dAAYDEY++2wQH3zQDIDo6GgKFSqEpaUlrq6NOHDgd/bsCebrr+dm162ZyXDowsXFhUOHDhEVFUViYiI7d+6kcePGpvNvvvkmBQoU4M8//wQgMDCQxo0bY2lpibOzM9u2bQMgICDArN6TfPDBBwQGBgIQGhpKgQIF5P2pp1AJ8Ri3BqJt0QqNrW2681ovXzRO9TDM+RoVdS8XIhRCCCGEEK+zRYvmEhS0HXd3T4YOHc6FC+fR6SwwGAwUL26Dg4MDB/9/derg4B0ZtufsXJ+goLTc4uzZ09y8eQOAevWc/390S09CQgKffdaH06dPAtCypQcbNqz7/+uVzpH7zDChKlWqFEOHDqV79+54e3vj6elJrVq16Nu3LydPpgU6a9Yspk2bRqtWrUhISKB79+4AjB8/nk2bNuHh4UFoaChDhgx55rX8/PxISUmhdevWTJkyhZkzZ2bDLeZPxm2/QHw82o4fP/G8RqPBYvR4SEjAsHTxS45OCCGEEEK87tq168Tevbvp2bMLo0YNY8yYibz/fkNmzZrGyZP/Y+zYr1i5cim9enUxJUfP0qdPf27evEG3bh1Zt24VZcq8CYC3d3vKlStHr15d+OQTPzw82uDk5AykjZLFxcXRooV7jt2nRqknrdvx6nod3qFSSqHv5AsaDRYbfnrmu2n6kf4Y9/+O5a59aAoWzPI1RXoyNzz/kL7MH6Qf8wfpx/xD+vLlu337Gg4Ojtna5rPeocqvHn+OL/wOlch71PFjqAvn0Y2dmOFCH1qfdhi3/4pxdzC61s+3WZoQQgghhBAv08aN37N9+6/pjr/xxhvMmjUvFyLKmCRUryDjph/A2hqtR+sMy2qc60PZchgDtkhCJYQQQggh8rROnbrSqVPX3A7juch62q8YFXUPY3AQ2jbeaAoVzrC8RqtF5+WDOnoEdT3sJUQohBBCCCHE60MSqleM8egfoNej9Wyb6Tratj6g1WII/DkHIxNCCCGEEOL1IwnVq+bWTQA0b1XIdBVNqVJoXNwwBv6M0utzKDAhhBBCCCFeP5JQvWLUrZtgY4OmSJHnqqfzaQd376AOhuRQZEIIIYQQQrx+JKF6xahbN9H8/5r7z0PT+AMoYYsh4KcciEoIIYQQQojn17NnlwzLpKSk8Pnn/V9CNFkjCdUrRt26BVlJqCyt0LZpi/p9L+peZA5EJoQQQgghXkUWOg02BWKwLRCJTYEYLHTP3pYnO61atT7DMseP/0WdOk4vIZqskWXTXyFKKbh1E02jxlmqr/Nuh3HNKoy//Bddj97ZHJ0QQgghhHjVWOg0FFeX0P7mDfHX0BVxpLhbAA90ldAb1Au1/ddfoaxZswJLS0vCw2/h6tqYQoUKsX//PpRSzJo1l7ZtW3LgQCjLl39LZORdrl8PIyLiNp6eXvTo0QeAw4dDaNasBdu2beXgwQPExDwgMvIuXl6+3L59m7/+OkqxYsWZNWseBQoU4NtvF/Lnn0eJiYnhjTfeYNKkady9exd//8GsXr0BnU5Lr15dmT59Nu+8U+WFn6GMUL1Kou5BcnKWpvwBaN6uiKZ2XQw//5SWnAkhhBBCiNeatcUDtAfSkikA4q+hPeCNtcWDbGn/zJnTDBs2kmXL1rJlyyZsbEqwfPlaKlV6h127dpqVvXTpInPmLGTp0lWsW7ea2NhYAM6ePU21atVNn6dNm8V//rOA+fPn0KCBC6tXbwDgjz8OcePGdcLCrrJkyQo2bNhCqVIOBAVtp0qVqnh5+bJo0VzmzPkab+/22ZJMgSRUrxT1cIW/LCZUAFofX7h6BXX8WHaFJYQQQgghXlFaUv5Jph6Kv4aW1Gxp/+23K1KqlAMFCxakeHEbnJ3rA1CqlAOxsTFmZZ2cnLG0tKRECVuKFStGfHwc4eG3KFWqNFptWtpSs2ZtihSxxsGhNAD16r0HgINDaWJjYylbthwDBw5l69YA5s+fw+nTJ0lMTACgR48+XLhwnps3b9C1a/dsuT+QhOqVom7dSvvwIglVi1ZQuDBGWZxCCCGEEOK1Z8QKijiaHyziiBHLbGnfwsL8DSOdTvfUslZWVqbPGo0GpRSHDoXQoIGL6bilpXlcj7d/7txZhg4diFJGmjb9kMaNm5hmZsXFxZGQkMD9+/eJiTFP5l6EJFSvkocjVKXLZLkJTeEiaFu6Y9y5AxUfn12RCSGEEEKIV1CcvjhGt4B/kqoijhjdAojTF8/dwP7fH38c4v33G2a6/PHjf1K3bj28vdtTrlx5Dh48gNFoBGD27Om0a9cBH5/2zJ49PdtilITqFZLVPagep23rA4mJGH/fmz2BCSGEEEKIV5LeoHigqURq0xAMnldIbRrCA82LL0iRHVJTU4iLi6NECdtM1/nwwxZcunSB7t078fnn/alS5V3Cw2+xe3cwN2/eoEOHj+nY8WOuXw9j9+7gbIlTo/LZ6gT37sVhNObtW7KzK8rdu7HPXS91QD+4fx/L9T++0PWVwUBqU1e0TZphMWnqC7X1OstqP4q8R/oyf5B+zB+kH/MP6cuX7/btazg4OGZc8DlYWGjR643Z2mZe9/hz1Go1lCxp/dTyMkL1CsnqHlSP0+h0aN9viPHQQVntTwghhBBCiBcgCdUrwrQHVZmsvz/1KI2LG9y9g7p8KVvaE0IIIYQQ4nUkCdWr4gX3oHqctkHay33qUEi2tCeEEEIIIcTrSBKqV0R27EH1KE3pMlDhbYyHDmZLe0IIIYQQQryOJKF6RWTHHlSP0zZ0Rf15FJWUlG1tCiGEEEII8TqRhOpVkQ17UD1O29AFkpNRx/7KtjaFEEIIIYR4nUhC9YrIrj2oHqVxfg8sLTHKe1RCCCGEEEJkiSRUrwh162a2vT/1kKZQYTR1nWRhCiGEEEIIIbLIIrcDEJmjbt1CU7FStrerbeiGYe5s1N27aOzssr19IYQQQgiRt1noNFjHP0CbmoLR0oq4IsXRG158r9K//gplzZoVWFpaEh5+C1fXxhQqVIj9+/ehlGLWrLlcuHCe5cuXoNfrKV36TYYPH03x4jbs2bOLDRvWkZycTGpqCiNHjqNmzdoMHNiPatWq87//HSc6+j5DhvjTsKFrNjyFrJMRqldAdu9B9ShNQxcAmfYnhBBCCPEastBpKB52CctGrugqvo1lI1eKh13CQqfJlvbPnDnNsGEjWbZsLVu2bMLGpgTLl6+lUqV3CAj4iSVLFjB79gJWrlxP/foNWLx4PkajkcDAn5g58xtWr/6BLl26s3btKlObqal6vv12JZ9//gXffbc4W+J8ETJC9SrI5j2oHqWpXAVKlkyb9tfWO9vbF0IIIYQQeZd1/AO03t5w7VragWvX0Hp7Y70/hOiCxV64/bffrkipUg4AFC9ug7NzfQBKlXIgJGQ/ERG3GTToUwCMRgPFihVHq9UyderXhITsJyzsGseO/YlW+8840PvvNzS1HRsb88IxvihJqF4B2b0H1aM0Wi3aBi4YD4agjEY0Whm0FEIIIYR4XWhTU/5Jph66dg2tPjVb2rewME83dDqd6bPRaKBWrdrMmDEHgOTkZBITE0lISKBv3x60aOFO7dp1qVixEj/9tMlUz8rKCgCNRpM2kyuXZepvz1u3bsXDw4MWLVrw/fffpzt/9uxZfH19admyJaNHj0av1wNw69YtunbtSqtWrfjXv/5FfHw8ADExMfTr1w93d3e6du3K3bt3AUhJSeHLL7+kTZs2eHl5cfCgbDoLObMH1aM0Lq5wPwp1/lyOtC+EEEIIIfImo6UVODqaH3R0xGhhmePXrlatBqdPnyQsLC2hW7VqGQsXfsP162FoNBq6d++Nk5Mz+/b9htFozPF4sirDhCoiIoI5c+awfv16AgIC2LhxI5cuXTIr4+/vz7hx4wgKCkIpxaZNaRnkxIkT6dKlCzt27KBGjRosWrQIgG+++QZnZ2e2b99Ohw4dmDJlCgCBgYEYjUa2bt3KzJkzGTFiRHbf76spB/agepS2Qdp7VLLanxBCCCHE6yWuSHGMAQH/JFWOjhgDAogrUjzHr21rW5IRI8YxbtxIunfvxIUL5xk4cAiVKr1DpUqV6dKlPX5+HbGxKcHt2+E5Hk9WZTjl7+DBgzRo0AAbGxsAWrZsyY4dOxg4cCAAN2/eJCkpiTp16gDg6+vLvHnz6NChA0ePHmXhwoWm4926dcPf35+9e/eaRro8PT2ZNGkSqampGI1GEhMTMRgMJCYmUrBgwRy56VdNTuxB9ShNyTfQVKmK8WAIut59c+QaQgghhBAi79EbFA/KV8J6fwhafSpGC8tsW+XPyckZJydn0/fNm7eaPvfp09/02c2tcbq6EydONfs+ZMgwABYsWGo6Vrp0GbM2c0uGI1R37tzB7pHltO3t7YmIiHjqeTs7OyIiIrh//z7W1tameZMPjz9ex8LCAmtra6KiovDx8SE6OppGjRrRrVs3hg0blj13+YrLiT2oHqdp6Io6/hcqIT5HryOEEEIIIfIWvUERXbAYUdYliS5YLFuSqddJhiNURqMRjeafZROVUmbfn3b+8XJAuu+P1tFqtSxYsIA6derwww8/cPXqVXr27En16tV5883MJxMlS1pnumxusrMrmumyERG3sahcmZLPUed5Jbt/ROSq5RS9cIpCHzXPsevkN8/TjyJvk77MH6Qf8wfpx/xD+vLlunNHi4VF9i8wlhNt5mVarfa5frsZJlQODg6Ehoaavt+9exd7e3uz8w8XlQCIjIzE3t4eW1tbYmNjMRgM6HQ6s3r29vZERkbi4OCAXq8nPj4eGxsbdu/ezZw5c9BoNFSoUIHatWtz4sSJ50qo7t2Lw2jM21m1nV1R7t6NzVRZpRT669cxurhluk5WqApVoWBBooN2EVfn/Ry7Tn7yPP0o8jbpy/xB+jF/kH7MP6QvXz6j0Yhen72LN1hYaLO9zbzOaDSa/Xa1Ws0zB20yTDddXFw4dOgQUVFRJCYmsnPnTho3/mee45tvvkmBAgX4888/gbSFJRo3boylpSXOzs5s27YNgICAAFO9Dz74gICAAAC2bduGs7MzlpaWVK1alV27dgEQFRXFqVOnePfdd5/3GeQvObgH1aM0VlZo6r2H8aAsTCGEEEIIIURmZZhQlSpViqFDh9K9e3e8vb3x9PSkVq1a9O3bl5MnTwIwa9Yspk2bRqtWrUhISKB79+4AjB8/nk2bNuHh4UFoaChDhgwBYPDgwRw/fpzWrVuzfv16xo0bB8DIkSM5efIkrVu3pkePHnzxxRe89dZbOXTrr4ac3IPqcdpGjeHaVdS1qzl+LSGEEEIIIfIDjcoLu2Flo/w25c8QtB3D8C+x2ByIttI7ORqXunWTVI+P0H3hj657rxy9Vn4gUxnyD+nL/EH6MX+Qfsw/pC9fvtu3r+Hg4JhxwefwOk75e/w5vvCUP5HLcngPqkdpyryJpnIVjPt+y/FrCSGEEEIIkR9IQpXHqZs3cnQPqsdpmjRDHfsLFR39Uq4nhBBCCCHEs/z1VygDB/YDYPr0rzh37kwuR2ROEqo8Tt269VLen3pI+0FTMBox7t/30q4phBBCCCFyj06nJSmpELGxRUhKKoROl3dThBEjxlK1arXcDsNMhsumi9ylbt1Ek8PvTj1KU6062Nlj3PcbujZeL+26QgghhBDi5dPptISFFcLbW8u1a+DoqCUgQEv58okYDC/27tRff4WyZs0KLC0tCQ+/hatrYwoVKsT+/ftQSjFr1lwuXDjP8uVL0Ov1lC79JsOHj6Z4cRv++OMw8+b9BysrKxwd3zK1OXBgP3r37ketWnWYPXs6f/99maioKCpVqsSECVOIiopi1KhhvP12RS5cOI+tbUm++mo6xYoVf8En9XR5N/0UKKUg/BaaMjn//tRDGo0GbZOmqJD9qOTkl3ZdIYQQQgjx8sXHFzAlUwDXroG3t5b4+ALZ0v6ZM6cZNmwky5atZcuWTdjYlGD58rVUqvQOAQE/sWTJAmbPXsDKleupX78BixfPJyUlhSlTxjN58gxWrFhHgQLpYzl16gQWFpZ8++1KNm78mdjYWA4dStv+59Kli3Tq1JW1azdhbW3Nzp3bs+VenkZGqPKye5EvZQ+qx2k/aIrxx42oo0fQuDXOuIIQQgghhHglpab+k0w9dO0a6PXZM+7y9tsVKVXKAYDixW1wdq4PQKlSDoSE7Cci4jaDBn0KgNFooFix4vz99yVKlrTjrbcqAODu7sl33y02a7dOHSeKFSvOTz9tIizsKjduXCcxMRGAEiVsqVy56v9fvxIxMTHZci9PIwlVHqZu3QJezh5Uj9LUbwCFC2Pc9xtaSaiEEEIIIfItS0sjjo7mSZWjI1hYZM9S6RYW5umGTqczfTYaDdSqVZsZM+YAkJycTGJiIrdvhwPqiXUeOnBgH8uWfUuHDp3x8GhLdHQ0D3eDsrKyMiub07tESUKVhz3c1JcXTKiskhPQJMQ/37Wd38Pw226s/vUvNBrNc19TFS5CSoHCz11PCCGEEEK8PEWKJBMQoH3kHSoICDBSpEgyBkPOXrtatRrs27eHsLBrlC/vyKpVy4iMvMvw4WOIiori4sULvPNOZXbtCkpXNzT0D5o1a07r1m25efMGx479yXvv1c/ZgJ9CEqq8LJv2oNIkxJOw+/n2ljLalEBFRpKw7vssjZAV/rApSEIlhBBCCJGnGQxGypdPZP/+Auj1WiwsHiZTOb+Zr61tSUaMGMe4cSMxGg3Y2ZVi3LhJWFhYMGHCFCZPHodOpzNN33tUmzY+TJw4ml27grCwsKRmzVrcunWLevVyPOx0NCqnx8Besnv34jAa8/YtZXbncP3kCRh37cRq78EXul6B+3efO6FSCfEY58xC49oIbZNmz33Nwh82JbmE3XPXe5XIDvD5h/Rl/iD9mD9IP+Yf0pcv3+3b13BwcMzWNi0stOj1OZ9c5SWPP0etVkPJktZPLS+r/OVhL3sPqkdpCheBcuVRF8/nyvWFEEIIIYR4FUhClYepWzdf+P2pF6GpXAUiIlDR93MtBiGEEEIIIfIyeYcqFxijoylwP+qZZZRSpNy6icV771Hg/t0Xup5On5qleprKVVC7dqIuXkDz3vsvFIMQQgghhBD5kSRUucAYF5fhO00qNQVSUtBH3nvu958eV9Qla8mQxrYklHwDdeE8SEIlhBBCCJHHaVDKiEYjk9CyKivLS8jTzquSktP+W7BgroahqVIFrl1FJSXmahxCCCGEEOLZrKwKEh0diV6fmuN7L+VHSini42OwsLDKuPAjZIQqr0pOSvtvgQK5GobmnSqogyGoy5fQVK+Zq7EIIYQQQoinK1HCjri4B0RFRWA0Zs8mUlqtFqPx9Vnlz8LCihLPuVK1JFR5VXLaCJUml0eoeLMsFCkC58+BJFRCCCGEEHmWRqOhaFEbiha1ybY2Zfn7jMmUv7wq6eEIVS5P+dNq0VSuNi64swAAIABJREFUirp4AZWatcUthBBCCCGEyK8kocqjVB6Z8gegebcapKbC5Uu5HYoQQgghhBB5iiRUedXDRSlyeYQKgLfegkKFUGdP53YkQgghhBBC5CmSUOVVD0eoCuaBESqtDs3/sXfv4VFW99rH72cOOQdCICGIgqgULCjUBkFUEE/hkFSMumuh4t4qqNtukFYsFIobxVK4qKBVW7VW2woVVAw7CAGtpcihCLy1aAsIooiAOQMhyUzmsN4/JgkJJISMITOZfD/XlSuZ5/h7sjKZ3FnrWdPnUob9AQAAAKcgUIUrt1uyLCmqedM2nivWpd+Wqqqk/Qz7AwAAAGoQqMKVyyVFR4fPG7Nd2Csw7O/f/w51JQAAAEDYCJO/1nEatyssJqSoYdntsvr0ldm7R8bLsD8AAABAIlCFLeN2h8eEFHVYl/YLDPv77LNQlwIAAACEBQJVuHKFVw+VpJPD/pjtDwAAAJBEoApfbpcUE2Y9VHZ74E1+P90j4/WGuhwAAAAg5AhU4crllhVmQ/4kyfp2zWx/DPsDAAAAzipQ5ebmavTo0br55pu1ZMmS09bv2rVL2dnZysjI0MyZM+Wt7r04fPiwxo8fr5EjR+rBBx9UeXm5JOn48eOaNGmSRo0apfHjx6uwsFCSVFVVpblz52rs2LEaM2aMNm7c2FLX2fa4XWHxHlSnufAiKSaGYX8AAACAziJQ5efna9GiRVq6dKlycnK0bNky7dtX/72Ipk2bptmzZ2vt2rUyxmj58uWSpDlz5mjcuHHKy8tT//799fzzz0uSFi9erPT0dK1Zs0Z33HGHnnzySUnS7373O5WWlurtt9/W4sWLNWPGDBljWvqaw54xJvA+VOHYQ1Uz2x/D/gAAAICmA9XmzZs1ZMgQJSUlKS4uThkZGcrLy6tdf+jQIblcLg0cOFCSlJ2drby8PHk8Hm3btk0ZGRn1lkvS+vXrlZWVJUnKzMzUhg0b5PF4tGbNGk2cOFGWZal379565ZVX2mWgkqdKMib8JqWoZl3aLxD4PmfYHwAAANo3R1MbFBQUKCUlpfZxamqqdu7c2ej6lJQU5efnq7S0VAkJCXI4HPWWn7qPw+FQQkKCSkpKdODAAW3btk2PP/64fD6fpk6dqksuuaRZF9S5c0Kztg8F71fHlJjYeO+T/6hbxyXFJCUq+gzbnS2n037G8zWXuexSHV8ZI/vePYq/YkCD20THRalDSmKLnTNcpbSDa2wvaMvIQDtGBtoxctCWkYF2PLMmA5Xf75dlWbWPjTH1Hje2/tTtJJ32uO4+NptNPp9PX3/9tZYsWaI9e/bovvvu05o1a5SYePaNWFx8Qn5/ePdqdZJUVuZqdL0pOSZJchm7qs6w3dlK9PjOeL6g9O4jz7/+peOlJ2Q5Tv8x8lVUyV1Y1rLnDDMpKYkqjPBrbC9oy8hAO0YG2jFy0JaRgXaUbDbrjJ02TQ75S0tLq500QpIKCwuVmpra6PqioiKlpqYqOTlZZWVl8vl8p+2XmpqqoqIiSZLX61V5ebmSkpLUpUsXjRkzRpZlqW/fvkpLS9Pnn3/ezEuOAK5A+LHCcVKKaieH/e0PdSkAAABAyDQZqIYOHaotW7aopKRElZWVWrdunYYNG1a7vnv37oqOjtaOHTskSStXrtSwYcPkdDqVnp6u1atXS5JycnJq9xs+fLhycnIkSatXr1Z6erqcTqdGjBhRu/3Bgwd15MgR9erVq2WvuC1wuwOfw3BSiloXVc/29+9PQl0JAAAAEDJNBqquXbtq6tSpmjBhgsaOHavMzExdfvnlmjhxoj7++GNJ0sKFCzVv3jyNHDlSFRUVmjBhgiTpscce0/LlyzV69Ght375dDz/8sCRpypQp+uijjzRmzBgtXbpUs2fPliQ98sgjKigo0JgxY/TAAw9o7ty5zRruFymMu3p4Xpi9sW9dlt0hq8+lMnt2y3g9oS4HAAAACAnLRNg0em3iHir3MRXlrm10vX/HNpk178g25ceyEjt84/MlDh2sss1bv/FxTmU+2yf/n1+T7Y7vy+pzab11cTeMkLtTSiN7RgbGFEcO2jIy0I6RgXaMHLRlZKAdW+AeKoRAWxjyJ0m9eklxcTL/4k1+AQAA0D4RqMKRyyVZluR0hrqSM7Jsdll9L5XZu0emqirU5QAAAACtjkAVjtxuKSam0Wnmw4n17f6SxyOz79NQlwIAAAC0OgJVOHK7wn+4X40ePaWEBJl/M+wPAAAA7Q+BKgwZl0sK4/egqsuy2WRd+m1p316Zmnu/AAAAgHaCQBWO3O6200Ol6mF/Xq/Mp3tCXQoAAADQqghU4cjtkqLbRg+VJOn886XEDrzJLwAAANodAlU4crlltaUeKssm69v9pM/2yVRWhrocAAAAoNUQqMKR2yXFtJ1AJSkQqPx+mT27Q10KAAAA0GoIVGHGGH/1PVRtaMifJJ3XXUpKYtgfAAAA2hUCVbhxV79BblvrobKswOQUn++XqSgPdTkAAABAqyBQhRu3K/C5rfVQqXrYnzEyu3eFuhQAAACgVRCowk31ezm1pUkpanVNk5I7y/yLYX8AAABoHwhU4cZV3UPVxob8SdXD/vr1lw58IX9hYajLAQAAAM45AlW4acND/iTJuuxySZLnvXdDXAkAAABw7hGowoxxBYb8tcUeKkmykjtL518g79o8GWNCXQ4AAABwThGowk0b76GSJOvygfIfOCDzycehLgUAAAA4pwhU4aZ6Ugq1xUkpqlnf7idFRcmfuzLUpQAAAADnFIEq3Lhckt0uORyhriRoVkyMHNdcK3/eOzJVVaEuBwAAADhnCFThxu2SoqNlWVaoK/lGnCNHScePy/ztr6EuBQAAADhnCFThxuVu08P9ativuEJK7Srf/+WEuhQAAADgnCFQhRnjdkkxbXdCihqW3S5bZpbM5o0yRbwnFQAAACITgSrcuCOjh0qS7FljJZ9P/tWrQl0KAAAAcE4QqMKNy9Vm34PqVFavi2Rddrn8/5fDe1IBAAAgIhGowo3bJasNvwfVqWxZY2X27ZXZvSvUpQAAAAAtjkAVbiJoyJ8k2UaOkpxO+f/v7VCXAgAAALQ4AlUYMX6/VFUlRVAPldWho2wjbpB/zTsyHt6TCgAAAJHlrAJVbm6uRo8erZtvvllLliw5bf2uXbuUnZ2tjIwMzZw5U16vV5J0+PBhjR8/XiNHjtSDDz6o8vJySdLx48c1adIkjRo1SuPHj1dhYf1Z4E6cOKEbb7xRW7du/abX17a43YHPEXIPVQ1b1i3S0aMyH2wIdSkAAABAi2oyUOXn52vRokVaunSpcnJytGzZMu3bt6/eNtOmTdPs2bO1du1aGWO0fPlySdKcOXM0btw45eXlqX///nr++eclSYsXL1Z6errWrFmjO+64Q08++WS94z3xxBM6fvx4S11j2+FyBT5H0JA/SbKuulrq0kW+N5eHuhQAAACgRTUZqDZv3qwhQ4YoKSlJcXFxysjIUF5eXu36Q4cOyeVyaeDAgZKk7Oxs5eXlyePxaNu2bcrIyKi3XJLWr1+vrKwsSVJmZqY2bNggj8cjSVq9erXi4+PVp0+flr3StsAdCFRWBLwPVV2WwyH7uLtkNm+U/6N/hLocAAAAoMU0GagKCgqUkpJS+zg1NVX5+fmNrk9JSVF+fr5KS0uVkJAgh8NRb/mp+zgcDiUkJKikpESHDx/WH/7wBz366KMtc3VtTc2QvwjroZIk2w/GS507y/frxUyhDgAAgIjhaGoDv98vy7JqHxtj6j1ubP2p20k67XHdfSRp5syZ+vnPf66Yb3APUefOCUHv21q8Xx1TYuLp1+ix+VUuKS65gxwNrA+W02lv8HznUnRclDqkJNZZkqgTD0/RsZ/PVofdHylm2LBWredcSal3jWjLaMvIQDtGBtoxctCWkYF2PLMmA1VaWpq2b99e+7iwsFCpqan11tedVKKoqEipqalKTk5WWVmZfD6f7HZ7vf1SU1NVVFSktLQ0eb1elZeXq7S0VPv379fMmTMlSV9++aVmzZqlJ554QkOGDDnrCyouPiG/P7x7QDpJKitznbbcX1omSarwWbIaWB+sRI+vwfOdS76KKrkLy+otMxnfk57/jUrm/lKO1wY2GrDbipSURBWeco1om2jLyEA7RgbaMXLQlpGBdpRsNuuMnTZNDvkbOnSotmzZopKSElVWVmrdunUaVqd3oXv37oqOjtaOHTskSStXrtSwYcPkdDqVnp6u1atXS5JycnJq9xs+fLhycnIkBe6ZSk9PV9++ffW3v/1NK1eu1MqVK9W/f3/NnTu3WWGqzXNH5qQUNayoKNnv/2+Zf30s89e/hLocAAAA4BtrMlB17dpVU6dO1YQJEzR27FhlZmbq8ssv18SJE/Xxxx9LkhYuXKh58+Zp5MiRqqio0IQJEyRJjz32mJYvX67Ro0dr+/btevjhhyVJU6ZM0UcffaQxY8Zo6dKlmj179jm8xDakZpa/CJuUoi5b5i1Szwvle/7XMj5fqMsBAAAAvhHLRNgMAW1iyJ/7mIpy15623P/eOpntH8o+fVaLni9x6GCVbW7d9/SKu2GE3J1SGlznW7tGvp/+RPYn58s+JqtV62pJdIFHDtoyMtCOkYF2jBy0ZWSgHVtgyB9akdslRUdu71QN200Zsr7VR77fPitTPV0+AAAA0BYRqMKJ2y19gxkO2wrLZpP9R1OkgwflX/l2qMsBAAAAgkagCiPG5YrYCSlOZV07XNblA+V78TcyNe+/BQAAALQxBKpw0k6G/EmB9ySz/88UqSBf/jeWhbocAAAAICgEqnDiah9D/mrYBg2WNWiwfK/+LtA7BwAAALQxBKpw4nbJaic9VDXsDz4kFRXRSwUAAIA2iUAVTtrJpBR12a5IlzV4SKCXqrIy1OUAAAAAzUKgChPG55M8nnYzKUVd9gcekoqL5X/j9VCXAgAAADSLI9QFoJq7+h6iCBnyZ5dRdGnh2W18YQ9VfPe78v/+JcXeeIOs2Nhmn8/ExasqOq7Z+wEAAADfBIEqXLiqpw6PlB6qykpVbN561pub/gNkduxQ+a9+JdtVVzf7dHE3jJAIVAAAAGhlDPkLF9U9VFZMZPRQNZd1QQ/pootltmySqaoKdTkAAADAWSFQhQt3hPVQBcE27DqpokJm+4ehLgUAAAA4KwSqcFHzPkztbJa/uqzzLwj0Uv19s0yVO9TlAAAAAE0iUIUJE2GTUgTLNnxEoJdqG71UAAAACH8EqnDBkD9JktX9fOniSwK9VOXloS4HAAAAOCMCVbhw0UNVw3bDTVJVlfwrV8gYf6jLAQAAABpFoAoXbpfkdMqy20NdSchZqV1l3TxK2v+ZzKaNoS4HAAAAaBSBKly43O16QopTWVd8V1a/y2T+9leZLz4PdTkAAABAgwhUYcK4XQz3q8OyLFljMqXkZPlz3pI5URbqkgAAAIDTEKjChdvd7iekOJUVFS3bbf8huVzyv/2WjJ/7qQAAABBeCFThwu1iyF8DrNSuskaOkQ58IfPB30JdDgAAAFAPgSpcuFyyGPLXINvA78i6fKDMB3+T+WxfqMsBAAAAahGowgVD/s7IGjVaSkkJTKVexv1UAAAACA8EqnDhckkx9FA1xnJGyZZ9B+9PBQAAgLBCoAoDxuuVfD56qJpgpaTKyhgtffG5zOZNoS4HAAAAIFCFBbcr8JlJKZpkDfyOrG/3l1n/vszBL0NdDgAAANo5AlU4cFUHKialaJJlWbJGZ0odkwJTqVdWhrokAAAAtGMEqnDgdkuSLIb8nRUrJka27NulE2Xyv/N/MsaEuiQAAAC0U2cVqHJzczV69GjdfPPNWrJkyWnrd+3apezsbGVkZGjmzJnyer2SpMOHD2v8+PEaOXKkHnzwQZWXl0uSjh8/rkmTJmnUqFEaP368CgsLJUkFBQW69957dcstt+jWW2/Vli1bWuo6wxs9VM1mnddd1vU3Srt3yfy/7aEuBwAAAO1Uk4EqPz9fixYt0tKlS5WTk6Nly5Zp37767wU0bdo0zZ49W2vXrpUxRsuXL5ckzZkzR+PGjVNeXp769++v559/XpK0ePFipaena82aNbrjjjv05JNPSpIWLFig66+/XitXrtSvfvUrPfLII/L5fC19zeGnuoeKe6iaxxo8RLr4Epl1efLt/TTU5QAAAKAdajJQbd68WUOGDFFSUpLi4uKUkZGhvLy82vWHDh2Sy+XSwIEDJUnZ2dnKy8uTx+PRtm3blJGRUW+5JK1fv15ZWVmSpMzMTG3YsEEej0c33XSTMjMzJUk9e/aU2+1WRUVFy15xGDI1k1Iw5K9ZLMsm2/duleLiVfnoNN70FwAAAK3O0dQGBQUFSklJqX2cmpqqnTt3Nro+JSVF+fn5Ki0tVUJCghwOR73lp+7jcDiUkJCgkpKS2vAlSS+//LIuvfRSJSYmNuuCOndOaNb2oeD96pgSE0+GJ5fxySUpoUtH2WJbPlQ5nfZ652sNrXbOxBj57p+o8ldele+Be9XlzTfkvOTic3/eaikpzfv5RPiiLSMD7RgZaMfIQVtGBtrxzJoMVH6/X5Zl1T42xtR73Nj6U7eTdNrjuvvYbCc7y1599VUtW7ZMr7322tlfSbXi4hPy+8N7koJOksrKXLWP/cdPSJJOVBlZXlcjewUv0eOrd77W0KrnjElUzK+eUsXUqSq4/T/kfPkPsnr0POenTUlJVGFh2Tk/D8492jIy0I6RgXaMHLRlZKAdJZvNOmOnTZND/tLS0monjZCkwsJCpaamNrq+qKhIqampSk5OVllZWe09UHX3S01NVVFRkSTJ6/WqvLxcSUlJkgL3Ub3xxhtasmSJunXr1pxrbbvcLikqSpaNSReDZe95oRwvvSJ5quSZ9F8yh74KdUkAAABoB5r8C37o0KHasmWLSkpKVFlZqXXr1mnYsGG167t3767o6Gjt2LFDkrRy5UoNGzZMTqdT6enpWr16tSQpJyendr/hw4crJydHkrR69Wqlp6fL6XTq1Vdf1datW/XnP/9ZaWlpLX6xYcvl5v6pFmC7pLccv31ZqqiQZ+J/yRw5HOqSAAAAEOGaDFRdu3bV1KlTNWHCBI0dO1aZmZm6/PLLNXHiRH388ceSpIULF2revHkaOXKkKioqNGHCBEnSY489puXLl2v06NHavn27Hn74YUnSlClT9NFHH2nMmDFaunSpZs+eLWOMnnvuOZWUlOiuu+7SLbfcoltuuaX2vqtIZirKpdjYUJcREWx9Lw2EquPHAz1V7eDnBwAAAKFjmQh7V9Q2cQ+V+5iKctfWPvb9erGs7ucH3qz2HEgcOlhlm7eek2OHyznjbhghd6eTk6P4P/6nvA/cJ3VJCdxT1SXlDHsHhzHFkYO2jAy0Y2SgHSMHbRkZaMcWuIcK55ZxuaRjR6WuXUNdSkSxXTZAjmdfkAry5Z10j0xJcahLAgAAQAQiUIVaYYEkyUolULU023eukOPXv5U5fEje+++VKS0NdUkAAACIMASqEDMF1ff4EKjOCVv6IDmefk7mywPyPnCvzLGjoS4JAAAAEYRAFWr5+VJMjNShQ6griVi2wVfJ8dQzMvs/k/fBSTJl7XscMAAAAFoOgSrETEG+lJLa6Jseo2XYrr5Wjl89LfPpbnnvu1v+fXtDXRIAAAAiAIEqhIzxSwX5spiQolXYhl0nx+JnZQoK5L3zNvle+q2MxxPqsgAAANCGOUJdQLt29JhUVcX9Uy3ALqPo0sKmN+x3qfy/f0XuZ56W97lnZNatUcxPZ8h+8cXNOp+Ji5eUGFyxAAAAiBgEqlCqnpCCGf5aQGWlKprzvldXD5Otcxf517yjikn3ybpmmKyrr5FlP7unRNwNI4IsFAAAAJGEQBVCJ2f4Sw1tIe2U1ffbsvW4UGbtGpkN62X27JIta6ystG6hLg0AAABtBPdQhVJBvtSpk6yo6FBX0m5ZcXGy3XqbbHfcKZWXy//7l+T/219lfN5QlwYAAIA2gB6qEDIF+dw/FSasPn1l69FDZt1amQ/+drK3qtt5oS4NAAAAYYweqhAxniqppIT7p8KIFRsn2y23yvb9H0iVlYHeqr/+RcblCnVpAAAACFP0UIVKYaFkDFOmhyGrdx/Z7u8h8+5amU0fyHz4d1kDBspKv1JWl5RQlwcAAIAwQqAKkZMTUhCowpEVEysra6xM+pUy27bK/OP/yWzfJl10sWxXDpYZMTzUJQIAACAMMOQvVAryJadT6tQp1JXgDKxu58n2vVtlmzxV1vARUkG+/K8vVcV/3q2KFW/L+HyhLhEAAAAhRKAKEZOfL6WmyrJogrbAik+Q7drhsv3PVFljb5OcDpX+z2R5br9FvjXvEKwAAADaKf6aDwFjjFSQLyuF4X5tjWW3y9b/MsW99LKSX3xBlt0u34xp8txxi3x5qwlWAAAA7QyBKgT8RUVSZaXEhBRtlmWzKXbMaDmWvy3HgqdkWTb5pj8iz8gb5J3/pPz/2CHj94e6TAAAAJxjTEoRAt59+ySJKdMjgGWzybp5pKwbb5Z5/z35Vq+S/6035P/zEiklVbYbb5LtpgxZA74jy24PdbkAAABoYQSqEPDu+yzwRWpqaAtBi7FsNlk33izbjTfLlJfLv2G9/O+ulX/Fm4Fw1amTbFcPk23YcFlDr5GVkBDqkgEAANACCFQh4N23T0rsICs2LtSlIEh2GXm/+krRFVUNbzDkSmnIlTIVFfJu/bu8WzbLu+Gv8q9aKTkcsg8YIMeVg2Xv31+2S3rLiopq8pwmLl5V0fzMAAAAhBMCVQh4933G/VNtXWWl3B/+XRVlrrPY2JIGXy3boCHSV1/J7N0j395P5duxI7DabpfS0mSdd77U/XxZ3bpJnZJl2erf4hh3wwiJQAUAABBWCFStzHiq5P3iC1lXDgl1KWhlls0u9egpq0dP6YabZcqOS4cOyRz6KvDxjx3Stq0ykuRwSF1SZKWmSqldZaWkyt/vUpmOnU8LWgAAAAgdAlUrM198IXm9EhNStHtWYgepbwdZfS+VJBm/TyookMn/OvC5IF9m/2fSzn/KSCr/82tSVJSsC3oEglnPC2VdeKGsPpfKuviSsxo2CAAAgJZFoGpl5tM9kiSLIX84hWWzS2ndZKV1q7fcVJRLhYVypnaRp7BY5ssDMgc+l9m4QfJ4Ahs5HLIuulhW30sDHxf2kpI7y+rcRerUSZaDpzoAAMC5wF9Zrczs/VRyOqXkzqEuBW2EFRcv9YxX1A0jZDql1C43Pp/01UH5d++S2bNLZvdu+Td+IP1fzikHsKSkpEC46naerO7dZXW/QNb550vnXyCr23lSfLwsy2rlKwMAAGj7CFStzOzdI0evC2V4TyI0k11G0aWF9Rd2iJeuTA98SDLGyBQXy3/kiExpqUxpSeBzSYlMSYn8hw/Jv2ObVFFR/zg2mxQbJyshXlZcnKz4BKlTJ5m0brJSUqWUVFkpKYH7ujp2lOLjpbh4er4AAEC7d1Z/DeXm5uo3v/mNvF6v7r77bo0fP77e+l27dmnmzJkqLy9Xenq65syZI4fDocOHD2vatGkqLi5Wr169tHDhQsXHx+v48eN65JFHdPDgQSUnJ2vx4sVKSUlRVVWVZs6cqU8++UQxMTFauHChLr744nNy4aFiPv1UjkHflSfUhaDtqaxUxeatzdsnoWPg44ILJUmWJJsxUmWldLRUprRUOn5Mcrslt0tyu+V3u6UTJ2SrqJD/k4+l0tLGjx8TK8XHSfEJsuLjpfiEQG9XfHwgdMUnyIqNleLipNg4KS5OVlxc9ePYQIiLi5Pi4gPLnE56ygAAQJvSZKDKz8/XokWLtGLFCkVFRenOO+/U4MGDdckll9RuM23aNM2dO1cDBw7Uz372My1fvlzjxo3TnDlzNG7cOI0ZM0bPPfecnn/+eU2bNk2LFy9Wenq6XnzxReXk5OjJJ5/U4sWL9ac//UmxsbFas2aNtm3bphkzZmj58uXn9BvQmuzHj0qFBXL0Ol9Op0tuX6x8fhPqsmSzWXJXOeTz2WS3+xUd5ZW/gbrsNkvRVZWyfF4Zu0PuqPCoH81jWVYgvMTFyTqve6PbJd5wnXyyZKqqAj1cxUXyFxXLnDghVVbIlJfLVFZK5eUyFRUyFRVSRYXM0RKZikqpIrBcVY28V1dD7Pba4FUbvmqC2CnBTNW9Y5ZlBYY11nzEBcKclZhYG+oUGyvLbg8cv+bDZpccgc/+KH+gZtvJ9cymCADAueOwW0pwHJNNVfIrSie8HeX1tc2/K5sMVJs3b9aQIUOUlJQkScrIyFBeXp5+9KMfSZIOHTokl8ulgQMHSpKys7P1zDPP6I477tC2bdv03HPP1S7/4Q9/qGnTpmn9+vVasmSJJCkzM1OPP/64PB6P1q9frylTpkiSBg0apJKSEh0+fFjnnXdey1/5OWZOnJA5fKi2F8B2/KisT7dLkhzRO6U9Hyr2kv9WpS01pKHEZrNUUhKl539jqaRYSu5s138/aFNyclW9UGW3WYotKZD1m+el4hKpc7JiH/xvVSaHtn6cQ431iMXEBT46dWlwN6v6o4bx+yVPVSBY1f3wVElVHpnar6sUdV43+SsrpcpKGZcrENgqKwMzH1ZWylR/qLJS8vta7FKPNHghVnXwsjUaxGSz1X5tnbqN3SbZHdWf7fXCWmA/RyC0NbafzRb4sCzJVv1dtazqZdXf6Zr11b16Vt3HdT9stur9dXKf2uPV3bZmf9X5+pTzn6GG08Lt2dRgO3Xbs6jBVmebOjV4iuNljlbWP97ZOBe9os05ZkvX2eh2jSxvdPMGVjTnukwDrw0NLZOkOou9rniZ4vLTVzRLE3U2dRlNXWdzf2bO5nvR4KU243t4qgZrbKhNz2Lfxq63ppbaz/VWylsZL1N8ouFtGzp+3fPUfm3V+9TgtsacUsOpdZmGz39qLaee/9Rz1fzeqqmn7nKCO5+sAAAgAElEQVS/v/6HzydjTOD3ot1e/zWlZnufL7Bd9WdZ1snXElud1xCfX/J5T27r9Qb2r/kHoNN58nXEbySvR/J6ZbzewLbVx5bTITmcge0d1a8znurXYa9HqvIEJrsyRoqKCtzz73TKW9ZJpswdOGZVleRySVVuyRVYpiinFB0tRUVL0dGyoqMD397KysAtBhUVgX/EVlYGXvtiY2VPiFdsbLG0a5qM95BkdVfMJU/qxIkO8h07LpUdl9X7W7INGnz6z0sYajJQFRQUKCXl5I3wqamp2rlzZ6PrU1JSlJ+fr9LSUiUkJMhR/V/kmuWn7uNwOJSQkKCSkpIGj/X111+3yUDlyc6SCvJrH/skeSTZk7xy9uooz4ESWfueV3Sfn6rCH3Pa/sv/vlmvb9nYIrXYX+4g3/HjDa4zxqaDB09OFqdD0oo50gUXSJblP3kM41e9DQ8dkub8r3TBBfJZp/8n/0znPBf+q/huZXVm5sRwZNlsUnRM4KOh9XW+jh06WGUNhDirgW1N7QuhOfkC6jeBcOZ2n/yocgdeAPwm8AJk/LVfx3zrEvl9PjkdljyVVYHwV+dFTj6f5PcHXsD8/kCI8/kkX51lp6wztftXf/Z6AzX4fLKMCbzA+U7uZ/wnz1PvfL7qWk319TX1NVQQ6gLQIvKb3gRtBG0ZGc5FO3oluWsfdZPklzSj3jbWNcMiJ1D5/f569zTUpu0m1p+6naRG740wxshms522T83y5ujcOaFZ258r7ud/LX9xiWzJybIld5ItpkK2Dd+V5ZD8HdyyjbkrsGHv6xQX1fG0/RPdZXJ++u+WKcayZOuc3OAqr1dylJ/+g2BLqh1RdXLD8nKdJilJtoYmJjjDOc8FR4cEdcnKaLXzyW6Xrhuu6NY7o2S3K7qVr7FVzxeKc9rtgRATAeczNf+BrfufWL8/8B9Ir1cy5vRtGviot43fKBBYT35tGltnAo+tuHjZ4uKqj+Wvf3y/vzoEmkDwVN3lpv666uPVW+f316mv7rFVu52pu+7svnHN+CafdWM045gtW6dpbLtG92/G9o0ewjTeg3E2vR9n3PhM25+hnjNv8M32b/LwjXw/zup7cXa9gk3da9rgz0GD1xVkz1ndazy156juNdT0njfY03PK+eqc15y2rJGesFPrqFtDQ+eu9/Upddf8Lqn9uv45Td1lMvVrMSbQi14zWqHma5ut+veZv84/4nyBfWy2wMROdpssuyPw+mBM9T/UvPX/cVezrcMe2NZR3YPl88l4vIHtvb7AP+xslixHoAeqpifKcjgCvz+9HsnjlfF4ZDweye8PvIel0ykrOkqWM6r2PS2Npyrwz8jqkSTGUyXL4ZQVExPohYqJkRUTLcvhCBzP5ZJxuas/uyRJtvh4WfFxsuLjZYuLlxUXK+PzBW4ZOHpE/k9+K+PyypTul837haxYv2wjV8qW+i1ZHTvK1rFj4PvZBjQZqNLS0rR9+/bax4WFhUpNTa23vrDw5MxjRUVFSk1NVXJyssrKyuTz+WS32+vtl5qaqqKiIqWlpcnr9aq8vFxJSUnq2rWrCgoK1KNHj3rHao7i4hMN3v/T6i7pL528zUxJ0cdl79hTKj8gW8ES2SQpvqc8XWbpqLvDabtnjr9XmePvPedlulyxuvZahw4cOLmsZ0/p9RVexcRUnqzfdVzOa6/WqRt6VuToaMzp9YfCGaZOOCdSuiWqsLCsdU/a2pPqhWISvxBcY0pKK7YlEyOeM63ajjhnaMfIQVtGhhZvx45SUq8uij76kVR+INBBJQX+Lr60v0rdHQJDu0oqznSUVmWzWWfstGmy+2fo0KHasmWLSkpKVFlZqXXr1mnYsGG167t3767o6Gjt2LFDkrRy5UoNGzZMTqdT6enpWr16tSQpJyendr/hw4crJyfwXjmrV69Wenq6nE6nhg8frpUrV0qStm/frujo6DY53K8hJ7wd5b8mR4rvGVgQ31P+a3J0wnt671Rrio93KyfHr57VZfXsKeXk+BUf76633Yn4jvLn5Kjuhv6cHJ2ID239AAAAaFvC9e/iYFmm0fEBJ+Xm5uqFF16Qx+PR7bffrokTJ2rixImaPHmyLrvsMu3evVuzZs3SiRMn1K9fP82bN09RUVE6dOiQpk+fruLiYnXr1k1PPfWUOnbsqKNHj2r69Ok6ePCgEhMTtXDhQp1//vlyu92aPXu2PvnkE0VFRWnu3Lnq169fsy4obHqoGlAzm4nT7pXH5wib2UzsdpvKy6Pl9drkcATClM/nP207h91SQvkx2bwe+R1OnYgPj/pDhf+8RQ7aMjLQjpGBdowctGVkOFfteHKWP4/8cobN38UNaaqH6qwCVVsSzoGqBr9gIgPtGDloy8hAO0YG2jFy0JaRgXZsgSF/AAAAAICGEagAAAAAIEgEKgAAAAAIEoEKAAAAAIJEoAIAAACAIBGoAAAAACBIjlAX0NJsNivUJZyVtlInzox2jBy0ZWSgHSMD7Rg5aMvI0N7bsanrj7j3oQIAAACA1sKQPwAAAAAIEoEKAAAAAIJEoAIAAACAIBGoAAAAACBIBCoAAAAACBKBCgAAAACCRKACAAAAgCARqAAAAAAgSAQqAAAAAAgSgaoV5ebmavTo0br55pu1ZMmSUJeDBjz77LMaM2aMxowZowULFkiSNm/erKysLN18881atGhR7ba7du1Sdna2MjIyNHPmTHm9XknS4cOHNX78eI0cOVIPPvigysvLQ3ItkObPn6/p06dLan57HT9+XJMmTdKoUaM0fvx4FRYWhuw62rP3339f2dnZGjVqlObOnSuJ52RbtHLlytrfrfPnz5fEc7KtOXHihDIzM/XVV19JarnnIe3auk5tx2XLlikzM1NZWVmaMWOGqqqqJNGOzWbQKr7++mszYsQIU1paasrLy01WVpbZu3dvqMtCHZs2bTLf//73jdvtNlVVVWbChAkmNzfXDB8+3Hz55ZfG4/GYe+65x6xfv94YY8yYMWPMP/7xD2OMMTNmzDBLliwxxhgzadIks2rVKmOMMc8++6xZsGBBaC6ondu8ebMZPHiw+elPf2qMaX57zZkzx7zwwgvGGGPefvttM2XKlNa+hHbvyy+/NNdcc405cuSIqaqqMj/4wQ/M+vXreU62MRUVFWbQoEGmuLjYeDwec/vtt5tNmzbxnGxDPvroI5OZmWn69etnDh48aCorK1vseUi7tp5T23H//v3mpptuMmVlZcbv95tHH33UvPLKK8YY2rG56KFqJZs3b9aQIUOUlJSkuLg4ZWRkKC8vL9RloY6UlBRNnz5dUVFRcjqduvjii/XFF1+oZ8+euuCCC+RwOJSVlaW8vDwdOnRILpdLAwcOlCRlZ2crLy9PHo9H27ZtU0ZGRr3laF1Hjx7VokWL9MADD0hSUO21fv16ZWVlSZIyMzO1YcMGeTyeEFxN+/Xuu+9q9OjRSktLk9Pp1KJFixQbG8tzso3x+Xzy+/2qrKyU1+uV1+uVw+HgOdmGLF++XI899phSU1MlSTt37myx5yHt2npObceoqCg99thjSkhIkGVZ+ta3vqXDhw/TjkFwhLqA9qKgoEApKSm1j1NTU7Vz584QVoRT9e7du/brL774QmvWrNEPf/jD09otPz//tPZMSUlRfn6+SktLlZCQIIfDUW85Wtfs2bM1depUHTlyRNLpz7+zaa+6+zgcDiUkJKikpERdu3Zt5atpvw4cOCCn06kHHnhAR44c0XXXXafevXvznGxjEhISNGXKFI0aNUqxsbEaNGiQnE4nz8k25Mknn6z3uKG/aYJ9HtKurefUduzevbu6d+8uSSopKdGSJUs0b9482jEI9FC1Er/fL8uyah8bY+o9RvjYu3ev7rnnHj366KO64IILGmy3xtqzoXalnVvXG2+8oW7duumqq66qXdYS7WWMkc3Gr8zW5PP5tGXLFv3iF7/QsmXLtHPnTh08eJDnZBuze/duvfXWW/rrX/+qDz74QDabTZs2beI52YY19nzjd23blJ+fr7vvvlu33XabBg8eTDsGgR6qVpKWlqbt27fXPi4sLKztckX42LFjhyZPnqyf/exnGjNmjD788MN6N1bWtFtaWlq95UVFRUpNTVVycrLKysrk8/lkt9tp5xBYvXq1CgsLdcstt+jYsWOqqKiQZVnNbq/U1FQVFRUpLS1NXq9X5eXlSkpKCtVltUtdunTRVVddpeTkZEnSjTfeqLy8PNnt9tpteE6Gv40bN+qqq65S586dJQWGCb388ss8J9uwU59v3+R5SLuG1meffab77rtPd911l+655x5Jp7cv7di09hMdQ2zo0KHasmWLSkpKVFlZqXXr1mnYsGGhLgt1HDlyRA899JAWLlyoMWPGSJIGDBigzz//XAcOHJDP59OqVas0bNgwde/eXdHR0dqxY4ekwAxWw4YNk9PpVHp6ulavXi1JysnJoZ1b2SuvvKJVq1Zp5cqVmjx5sq6//nrNmzev2e01fPhw5eTkSAqEtPT0dDmdztBcVDs1YsQIbdy4UcePH5fP59MHH3ygkSNH8pxsY/r27avNmzeroqJCxhi9//77uvLKK3lOtmEt+dpIu4bOiRMndO+992rKlCm1YUoS7RgEyxhjQl1Ee5Gbm6sXXnhBHo9Ht99+uyZOnBjqklDH3Llz9dZbb6lHjx61y+68805deOGFmjdvntxut4YPH64ZM2bIsizt3r1bs2bN0okTJ9SvXz/NmzdPUVFROnTokKZPn67i4mJ169ZNTz31lDp27BjCK2u/VqxYoQ8//FC//OUvm91eR48e1fTp03Xw4EElJiZq4cKFOv/880N9Se3Om2++qVdffVUej0dXX321Zs2apa1bt/KcbGNefPFFrVixQk6nU5dddpkee+wxff755zwn25jrr79ef/zjH3X++edry5YtLfI8pF1bX007vvfee1q4cKEuvvjieuumTJlCOzYTgQoAAAAAgsSQPwAAAAAIEoEKAAAAAIJEoAIAAACAIBGoAAAAACBIBCoAAAAACBKBCgAQEvfcc49KSkpOWz5x4kTt27evRc5RUlKiPn36tMixajz77LN67733JElPP/107XuvAADaJ0eoCwAAtE+bNm1qcPlLL73UypU0z9atW3XJJZdIkqZMmRLiagAAoUagAgC0uhkzZkiS7r77bu3bt08ZGRnas2ePfvzjH2vevHl6+umnVVFRoYULF+q8887T/v37FRMTo1/+8pf13oSyIevWrdOiRYsUGxur/v371y5fsWKF1q5dqxdeeOG0x9OnT9fRo0d18OBBXXfddbr99tv1+OOPq7y8XIWFherbt68WL16sN998U5988okWLFggu92uv/zlL+rdu7fuvfdebd++XQsWLFBlZaWcTqcefvhhDRs2TCtWrNC7774rm82mAwcOKCYmRvPnz2/yOgAAbQND/gAArW7evHmSpD/84Q/q1q2bevfurTVr1uimm26qt90nn3yiu+66S7m5ucrOzta0adPOeNyioiL97Gc/069//WutWLFC3bt3P+uaXC6X3nnnHU2bNk3Lly/X2LFjtXz5cq1bt05fffWV1q9fr/Hjx6t///569NFH69VaWlqqyZMna+bMmcrNzdX8+fM1bdo0HTx4UJK0bds2/fznP9eqVas0YMAAvfjii2ddFwAgvBGoAAAhl56e3uDyvn371q677bbbtGvXLpWWljZ6nB07duhb3/pW7ZC873//+2ddw3e/+93ar6dNm6bk5GS99NJL+t///V8VFBSooqKi0X137typHj16aMCAAZKk3r1764orrtCHH34oSerXr5/S0tIkSd/+9rd17Nixs64LABDeGPIHAAi5uLi4Bpfb7fazWlaXMab2a4fj5MucZVn11nk8nkZr+PGPfyyfz6dRo0bpuuuu05EjR+rteyqfzyfLsk6rw+v1yul0KiYmptE6AABtGz1UAICQsNvt8nq9Z9xm9+7d2r17tyRp2bJl+s53vqMOHTo0uv2gQYO0b9++2n1WrFhRuy45OVl79+6V2+2Wx+PR2rVrGz3Oxo0b9dBDD2n06NGSpH/+85/y+XyN1j1w4EDt379fO3fulCTt3btX27Zt05VXXnnG6wMAtH30UAEAQmLkyJG66667VF5e3ug2Xbp00eLFi3Xo0CElJydrwYIFZzxmcnKyFi5cqEceeUROp1ODBg2qXXf11Vdr0KBBGjVqlFJSUjR48GDt2bOnweNMnTpVDz30kOLi4pSQkKBBgwbpyy+/lCRdf/31euqpp+r1cCUnJ+vpp5/WE088IZfLJcuyNG/ePPXq1Uv/+Mc/mvNtAQC0MZZh3AEAIAxt3bpVTzzxhFatWhXqUgAAaBQ9VACANuV3v/udcnNzG1x377336nvf+14rVwQAaM/ooQIAAACAIDEpBQAAAAAEiUAFAAAAAEEiUAEAAABAkAhUAAAAABAkAhUAAAAABIlABQAAAABBIlABAAAAQJAIVAAAAAAQJAIVAAAAAASJQAUAAAAAQSJQAQAAAECQCFQAAAAAECQCFQBAW7duVWZmZquft0+fPiopKTnjNuvXr9fTTz/drOP6fD49+OCDysjI0GuvvVZv3c6dOzV79uwG9/v44481efLkZp3rVPfff79WrFjxjY5RV916W6I+AEDLcoS6AAAAzuTjjz/WsWPHmrVPfn6+Nm7cqI8++kh2u73eun379ik/P7/B/S677DI988wzQdd6LtStNxzrA4D2jkAFAJAkVVRUaPLkyTpw4IA6dOigxx9/XL169VJZWZnmzJmj3bt3y7IsXXvttfrxj3+sAwcO6M4779Qf//hHXXrppXr00UflcDj0i1/8ot5xp0+frujoaO3evVvFxcW6+uqrNWvWLDmdznrbPffcc3rnnXdkt9vVq1cv/fznP9fhw4f1+uuvy+fzKTExUVOnTq23z/bt27VgwQJVVlbK6XTq4Ycf1hVXXKH77rtPXq9X2dnZ+vWvf60ePXpIko4cOaJnnnlGZWVlmjFjhsaOHasnn3xScXFxKi8v16OPPqr58+dr1apVZ113fn6+pk+froKCAp133nkqLi6uXdenTx9t2bJFycnJ9R7v3bu33nnfeustLViwQP/85z9VXl4uY4zmzp2r884777R6n3jiCa1atarRdnE4HLrssss0adIkbdq0SQUFBbrvvvs0bty4FvtZAQCcxJA/AICkQNj4z//8T61cuVKZmZl69NFHJUlz585VUlKScnNz9dZbb2nPnj36/e9/r4svvljTpk3TT3/6U73xxhvavXt3o0Ppdu7cqd///vdavXq1PvvsMy1btqze+rfeeksffPCB3nzzTeXm5qp3796aPn26BgwYoDvvvFOjR48+LUyVlpZq8uTJmjlzpnJzczV//nxNmzZNpaWlevHFFxUTE6OVK1fWhilJ6tatmyZPnqz09HTNmzdPkrR371796le/Um5urqKioppVtyQ9/vjjGjBggN555x3NmjVLn3/++Vl9v+ue91//+pcKCgq0bNkyrV69WrfeeqteeumlBuut0Vi7SFJVVZU6deqk119/Xc8884zmzZsnt9t9VnUBAJqHQAUAkBToPbniiiskSbfeeqs++eQTlZWVacOGDfrhD38oy7IUFRWlO++8Uxs2bJAk/cd//IcuvPBCzZ07V88884xiYmIaPPatt96q+Ph4RUVF6ZZbbtHGjRvrrd+wYYOys7MVFxcnSZowYYL+/ve/q6qqqtF6d+7cqR49emjAgAGSpN69e+uKK67Qhx9+2Kzr7tatm7p37x5U3ZK0efNmZWdnS5J69uypwYMHN/u83/nOd/Twww/r9ddf1/z585WXl6fy8vIz7n+mdpGkG264QZLUr18/VVVVqaKi4qzqAgA0D4EKACBJstnqvyRYliWHwyG/3y/LsmqX+/1+eb1eSYGekAMHDigxMVG7du1q9Nh172Myxpx2rjOdozE+n6/ePjXHbmq/U9WEuIY0VbcU+D4ZY2ofOxwNj6Y/NRzWPe/69et1//33SwoEoR/84AdN1t3U9yw6Orq2vpr6AQAtj0AFAJAk7dmzpzYULVu2TN/97ncVGxura665Rq+99pqMMaqqqtLy5cs1dOhQSdKCBQvUu3dvvfzyy5o7d64OHTrU4LHXrFmjqqoqud1uvf322xoxYkS99ddee63eeuut2l6UP/3pTxo0aJCioqJkt9sbDEkDBw7U/v37tXPnTkmBIXTbtm3TlVdeecbrbOx4wdRdU3vNUMDDhw9r69atteuSk5P18ccfS5JWrVrV6Hk2bdqkESNGaNy4cerfv7/ee+89+Xy+M9Z7pnYBALQeAhUAQJJ00UUX6dlnn9X3vvc9vf/++/rlL38pSZo1a5ZKSkqUlZWlrKws9erVSw888IDWr1+vd999V7Nnz1afPn1099136yc/+UmDf/zHxMRo3LhxysrKUnp6um677bZ662+//XZdddVVuuOOOzRq1Cj9+9//1sKFCyVJQ4YM0caNG/XEE0/U2yc5OVlPP/20nnjiCWVlZeknP/mJ5s2bp169ep3xOgcOHKiDBw/qRz/6UZPfk6bqlqTHHntMn332mUaNGqWZM2eqb9++tetmzZqlxx9/XLfeeqs+++wzpaSkNHieO++8Ux9++KGysrJ066236oILLtBXX30lv9/faL2NtQsAoHVZhjEAAIBzaPr06erdu7fuvffeUJfSLG21bgBA66KHCgAAAACCRA8VAAAAAASJHioAAAAACBKBCgAAAACCRKACAAAAgCARqAAAAAAgSA2/nXsbVlpaLr8/PObZ6Nw5QcXFJ0JdBloI7RlZaM/IQ5tGFtozstCekac9tanNZqlTp/hG10dcoPL7TdgEKklhVQu+OdozstCekYc2jSy0Z2ShPSMPbRrAkD8AAAAACBKBCgAAAACCRKACAAAAgCARqAAAAAAgSAQqAAAAAAgSgQoAAAAAgkSgAgAAAIAgEagAAAAAIEgEKgAAAAAIEoEKAAAAAIJEoAIAAACAIBGoAAAAACBIBCoAAAAACBKBCgAAAACCRKACAAAAgCARqAAAAAAgSAQqAAAAAAiSI9QFoPmWLv2jDh48ENS+x44dlSR17JjUkiXVc8EFPTVu3IRzdnwAAAAgXBCo2qCDBw/oi13/UprH2+x9S52BJo8+EFwga8rXTn6kAAAA0H7w128blebx6j9LSpu936vJnSQpqH2bc3wAAACgPeAeKgAAAAAIEoEKAAAAAIJEoAIAAACAIBGoAAAAACBIBCoAAAAACBKBCgAAAACCRKACAAAAgCARqAAAAAAgSAQqAAAAAAgSgQoAAAAAgkSgAgAAAIAgEagAAAAAIEgEKgAAAAAIEoEKAAAAAIJEoAIAAACAIBGoAAAAACBIBCoAAAAACBKBCgAAAACCRKACAAAAgCARqAAAAAAgSAQqAAAAAAgSgQoAAAAAgkSgAgAAAIAgEagAAAAAIEgEKgAAAAAIEoEKAAAAAIJEoAIAAACAIBGoAAAAACBIBCoAAAAACBKBCgAAAACCRKACAAAAgCARqAAAAAAgSAQqAAAAAAgSgQoAAAAAgkSgAgAAAIAgEagAAAAAIEgEKgAAAAAIEoEKAAAAAIJEoAIAAACAIDlCXUCk2rRpgzp0iNVllw0KdSk4RzZt2iBJuvrqYSGuBAAAAKFCoDpHNm78m5xOO4Eqgm3c+DdJBCoAAID2jCF/AAAAABAkAhUAAAAABIlABQAAAABBIlABAAAAQJAIVAAAAAAQJAIVAAAAAASJQAUAAAAAQSJQAQAAAECQCFQAAAAAECQCFQAAAAAEiUAFAAAAAEEiUAEAAABAkAhUAAAAABAkAhUAAAAABIlABQAAAABBIlABAAAAQJAIVAAAAAAQJAIVAAAAAASJQAUAAAAAQSJQAQAAAECQCFQAAAAAECQCFQAAAAAEiUAFAAAAAEEiUAEAAABAkAhUAAAAABAkAhUAAAAABIlABQAAAABBIlABAAAAQJAIVAAAAAAQJAIVAAAAAASJQAUAAAAAQSJQAQAAAECQCFQAAAAAECQCFQAAAAAEiUAFAAAAAEEiUAEAAABAkAhUAAAAABAkR6gLANqqPXt2SZLuuWdciCsBIsNFF/XW/v176y1zOqPk8VQFdbwOHTrq+PFjtY+joqJ0442jtHr1SmVmjtV77+Wpa9du+q//mqSXXnpOhw8f0vXX36y//GXtacdKTu6skpJiSZLDEXjp9Hq9tevj4uJVUVEuu92hpKSOKi4uVteuXeVwOFVUVKjk5GSVlJSoQ4eOKioqlN3ukM1mKTW1q7xer/Lzv1b37t11330P6fe/f0H5+UfUsWOSCgsLdP/9P9L777+rcePu1tKlf9C4cf+/vfsPjqo+9zj+2ewuQowSliQEAmivQhlRsS2RpoSfRfkZ24vMaIsBbhCuXFpQbkFQW62oIZQRGelMwXq1zOAtqFFvqCKipIRAARHN9Y5AEEiAUAzkF/lBstmc+wdm3d1sNruHDUvC+zXjQPZ8v8/3ec73nMDDbo4ztWHDq6qvv6jS0m8kSQkJvRQVZZXdblN6eobeeGOD5s1boO7dY905Fhef0AsvPCOn06n//M9liomJ0YoVz6pHD4fOnz8nSYqPT5DNZpNhSBbLpVp/9atFXnE8VVSU609/elm//OUM95qGYehPf3pZ8+YtUGVlhVaseFa9evXWtGkP6I9/fElxcfHq0uU6/frXi9xjm2vzzLmsrEzPPbdc9fUXde5cqRIT++jf/m2O3nhjg9/xzfmsXbtaFouUnj7b7xjfsZI0Y8ZsbdjwqiwWedXbXF+gGIGOBzPOM49f/7rluS4uPqGsrOVauvR3uuGGG/2er2DyDLRGW3kGW6e/8c17/NRTT6g9/trpm1uouV7ueu01J5R54ag52PPo+Xp8/A1Brx/qtdXe+xhu1meeeeaZSCcRTvVkjRQAABecSURBVHV1DTKMSGch5efvlNUapZSUEe0Su/HsWd1VdzHkuZ936yZJpuYGG9/WK1GpqaPaJX4kXX/9daqt/e4vdu+993YEswE6n/LyshavNTW5TMerr6/3+trlcqmw8LAk6ciRQ2psbFRlZYWOHDmkkpLTkqTjx7/2G6uurs4jpyY1NTV5HXc6nZIkw2hyj62pqdGFC1VyuVyqrq6Wy+VSbW2NO4bL5VJVVZWqq6slSVVVVTpy5JBOnTopl8ulmppLYw8e/Eznz5fqyJGvVFR0QkeOfKXi4iJ37OY4lZUVKi8v05Ejh1RUdEL19fUaMuQH7hz/8IfnVVFRIUn6/PPP9H//978qLy9XdfUFd5wLFy7Faf6vvLxMDQ3ecTy9+eZ/67PP9nuteeTIIX322X7V19dr27b3VV5ersrKCn3xxUHV1dWpqqrKHbd5bHNtnjlv3vyG9u/f566zea+az4G/Gt9887918OCnAc9Da2OLi4ta1NtcX6AYgY4HM84zD3/n+g9/eN6d4/nz5/yer2DyDLRGW3kGW6e/8Z7Xw2233dnm3FD55hZqrpe7XnvNCWVeOGoO9jx6vp6S8mPV1jYEtX6o11Z772OoLBaLoqO7tHqcj/wBJvCuFNB5NDdTVwN/ubhcjTIMQyUlp92/thXDMAzt2rVTlZWXGqji4hNe82pra4Kue9euv7vjeKqoKNeuXX/3ym3Xrr8rLy9XhmEoLy+3xZqe8vJy3WN9c66oKNf27dtbrc1fjRUV5crLyw14Hjxzz8v7u9dY33o962stRqDjwYzzzSMvz/tce+5bSclp7dy5o0X9xcUn2swz0Bpt5Rlsnf7jfHc9bN++vc25ofLNrbi4KKRcL3e9YOKbmRPKPLPxA8Vo7ZryHVdeXh7U+qFeW+Go6UrjI3/tpLKyQlVVlcrKWh722MXFRYqOujp74eqoKH1TXNQudUea3W6V02n+X8sB4EpqamrS//xPttLTM7R+/R9Nx2lsbHTH8ZST846amowWY5s/JeL5kcjW4koWvzkHM99zfHp6hnJy3lFjY8vv0Z5jPHN3ufzHb6730lwjYIxAx4MZ55uH77n23TeXy7u+pqYmrV//xzbzDLRGW3kGW6e/OJ7XQzBzQ+Wb2/r1a0PK9XLXCya+mTmhzDMbP1CM1q4p33F//etfVVfX0Ob6oV5b4ajpSrs6/1YOAAAui8vVqD178iVd3rtwhmG443jasye/RVNiGIakUD537z22Oec9e/K/jRWYZ42Xfm05x3OMZ+6txW+u17O+1mIEOh7MuJZ5eJ/rtvbN5WpUScnpNvMMtEZbeQZbp784ntdDY2Pbc0Plm1tb5yLc6wUT38ycUOaZjR8oRmvn0Xfcjh07glo/1GsrHDVdabxD1U66d49VXFxPLVr0RNhjZ2Ut18WCL8IeNxximpoU1/8mPf74byOdStjFx9+g0tILkvjIH4Crn9VqU0rKcElSnz5Jppsqi8XijuMpJWW4du7M9WqqLBbLt+9IBNtUWbzGeuacm/txm02V5/iUlOHasePjFmt7jvHMvbX4nvU219dajEDHgxnXMg/vc93WvlmtNvXq1Utnz54NmGegNdrKM9g6/cXxvB5strbnhso3t7bORbjXCya+mTmhzDMbP1CM1s6j77gxY8aorq6hzfVDvbbCUdOVxjtUAAB0QlFRUbrvvqmSpLlz55uOY7PZ3HE8paX9q6KiLC3G2mxW9+/bits81jfntLR/bXO+5/jmfHzj+Y7xzN1q9R+/uV7P+lqLEeh4MON88/A91777ZrW2PF9z585vM89Aa7SVZ7B1+ovjucfBzA2Vb25z5/4qpFwvd71g4puZE8o8s/EDxWjtmvId9+CDDwa1fqjXVjhqutJoqAAT/uu/3oh0CgDCpE+fpEin4OYvF6vVJovFoj59kty/thXDYrEoNXWk+3HD/fvf7DUvOvr6oOtOTR3l97HFsbE9lJo6yiu31NRRGjFitCwWi0aMGN1iTU8jRox2j/XNOTa2h8aNG9dqbf5qjI3toREjRgc8D565jxgxymusb72e9bUWI9DxYMb55jFihPe59ty3Pn2SNHLkmBb19+9/c5t5BlqjrTyDrdN/nO+uh3HjxoX98de+ufXvf1NIuV7uesHENzMnlHlm4weK0do15TuuR48eQa0f6rUVjpquNB6b3k54bDqPTQcQmn/5lwEtHp1ut3cx/ej0G2/s7vXo9C5dumj8+CkqLDysKVN+ruLiE0pK6qeHH56nwsLDqq6+oJ/+dLzfR6c7HD3dj0O32WyKioryenR6dPT1cjqdslptcjh6qK6uTr169VJsbA81NNQrISFBTqfz2zi1stnsstls6t27j6Kjr1dNTY369u2rOXN+pWPHvtbFi3Xq2TNOtbW1mjt3vqqqKjVr1lyVlJzSrFlzLz2cKDpa9fUXZbVa1bt3H8XGOhQXF6eMjH9XSclpPfTQLHXt2tWd4623DtTu3XlqamrSwoW/UUpKqv7xj3zFxyeooaFeVqtViYm95XA4vm0oYhUXF6f09AyvOJ5uuulmHT/+tWbNmuNec+DAQTp+/Gs99NAs3X77nfrHP/KVlNRPM2dm6ODBT5WY2FtxcQmaMSPDPba5Ns+c77zzNhUUfKno6Gg1NNSrb9/+evjhR1RSctrv+OZ8CguPyOFwKCPjEb9jfMf26HFpbHFxkRwOh1e9zfUFihHoeDDjPPOYMaPlub711oHau3e3Hn10se644y6/5yuYPAOt0Vaewdbpb3zzHv/Hf8xTU1P4/x3fN7dQc73c9dprTijzwlFzsOfR83WHo7tqaxuCWj/Ua6u99zFUbT023WIE81OfHcj589UtnjoUCVlZy2W3W9v1Z6hmlZWHPPd1Rw9JMjU32Phd7xzS6X+GSpL7SYadsdZrge9+ouNjTzsX9rNzYT87n2tpT6OiLOrZM6b141cwFwAAAADoVGioAAAAAMAkGioAAAAAMImGCgAAAABMoqECAAAAAJNoqAAAAADAJBoqAAAAADCJhgoAAAAATKKhAgAAAACTaKgAAAAAwCQaKgAAAAAwiYYKAAAAAEyioQIAAAAAk2ioAAAAAMAkGioAAAAAMImGCgAAAABMoqECAAAAAJNoqAAAAADAJBoqAAAAADCJhgoAAAAATKKhAgAAAACTaKgAAAAAwCQaKgAAAAAwiYYKAAAAAEyioQIAAAAAk2ioAAAAAMAkGioAAAAAMImGCgAAAABMoqECAAAAAJNoqAAAAADAJBoqAAAAADCJhgoAAAAATKKhAgAAAACTaKgAAAAAwCQaKgAAAAAwyRbpBDqr1NRRuvHGbpFOA+0oNXVUpFMAAABAhNFQtZPhw0cqPv4GlZZeiHQqaCfDh4+MdAoAAACIMD7yBwAAAAAm0VABAAAAgEk0VAAAAABgEg0VAAAAAJhEQwUAAAAAJtFQAQAAAIBJNFQAAAAAYBINFQAAAACYREMFAAAAACbRUAEAAACASTRUAAAAAGASDRUAAAAAmERDBQAAAAAm0VABAAAAgEk0VAAAAABgEg0VAAAAAJhEQwUAAAAAJtFQAQAAAIBJNFQAAAAAYBINFQAAAACYREMFAAAAACbRUAEAAACASTRUAAAAAGASDRUAAAAAmERDBQAAAAAm0VABAAAAgEk0VAAAAABgEg0VAAAAAJhEQwUAAAAAJtFQAQAAAIBJNFQAAAAAYBINFQAAAACYREMFAAAAACbRUAEAAACASTRUAAAAAGASDRUAAAAAmERDBQAAAAAm0VABAAAAgEk0VAAAAABgki3SCcCcf9ptet3Rw9Q8SabmBhv/5naJDAAAAFx9aKg6oH79bjI9t0dlhSSpa/fYcKXj5WZdXn4AAABAR0JD1QH98pczIp0CAAAAAPEzVAAAAABgGg0VAAAAAJhEQwUAAAAAJtFQAQAAAIBJNFQAAAAAYBINFQAAAACYREMFAAAAACbRUAEAAACASTRUAAAAAGASDRUAAAAAmERDBQAAAAAm0VABAAAAgEk0VAAAAABgEg0VAAAAAJhEQwUAAAAAJtFQAQAAAIBJNFQAAAAAYBINFQAAAACYZIt0AuEWFWWJdAperrZ8cHnYz86F/ex82NPOhf3sXNjPzuda2dO26rQYhmFcoVwAAAAAoFPhI38AAAAAYBINFQAAAACYREMFAAAAACbRUAEAAACASTRUAAAAAGASDRUAAAAAmERDBQAAAAAm0VABAAAAgEk0VAAAAABgEg1VO8jJydGkSZN07733auPGjZFOBwGsXbtWkydP1uTJk7Vy5UpJ0u7du5WWlqZ7771Xq1evdo/96quvNHXqVI0fP15PPvmkGhsbJUklJSWaPn26JkyYoHnz5qmmpiYiteA7WVlZWrp0qaTQ962qqkpz587VxIkTNX36dJWWlkasDkiffPKJpk6dqokTJ+q5556TxD3akb333nvu77lZWVmSuEc7ourqak2ZMkWnTp2SFL57kr2NHN893bRpk6ZMmaK0tDQtW7ZMDQ0NktjTVhkIq3/+85/GmDFjjPLycqOmpsZIS0szCgsLI50W/MjPzzceeOABo76+3mhoaDBmzJhh5OTkGKNGjTKKi4sNp9NpZGRkGLm5uYZhGMbkyZONgwcPGoZhGMuWLTM2btxoGIZhzJ0719iyZYthGIaxdu1aY+XKlZEpCIZhGMbu3buNYcOGGY8//rhhGKHv2+9//3tj3bp1hmEYxjvvvGMsXLjwSpeAbxUXFxupqanGmTNnjIaGBuMXv/iFkZubyz3aQdXW1hrJycnG+fPnDafTaUybNs3Iz8/nHu1gPv/8c2PKlCnG4MGDjZMnTxp1dXVhuyfZ28jw3dNjx44Z99xzj3HhwgWjqanJWLJkifHaa68ZhsGetoZ3qMJs9+7d+vGPf6zY2FhFR0dr/Pjx2rp1a6TTgh/x8fFaunSpunTpIrvdrltuuUUnTpzQTTfdpH79+slmsyktLU1bt27V6dOndfHiRd11112SpKlTp2rr1q1yOp3av3+/xo8f7/U6IqOiokKrV6/WI488Ikmm9i03N1dpaWmSpClTpmjnzp1yOp0RqAYfffSRJk2apMTERNntdq1evVrdunXjHu2gXC6XmpqaVFdXp8bGRjU2Nspms3GPdjCbN2/W008/rYSEBElSQUFB2O5J9jYyfPe0S5cuevrppxUTEyOLxaKBAweqpKSEPQ3AFukEOptvvvlG8fHx7q8TEhJUUFAQwYzQmgEDBrh/f+LECX3wwQd66KGHWuzf2bNnW+xrfHy8zp49q/LycsXExMhms3m9jsj43e9+p8cee0xnzpyR1PJ+DGbfPOfYbDbFxMSorKxMvXr1usLVoKioSHa7XY888ojOnDmj0aNHa8CAAdyjHVRMTIwWLlyoiRMnqlu3bkpOTpbdbuce7WCef/55r6/9/b3H7D3J3kaG754mJSUpKSlJklRWVqaNGzcqMzOTPQ2Ad6jCrKmpSRaLxf21YRheX+PqU1hYqIyMDC1ZskT9+vXzu3+t7au//WW/I+PNN99U7969lZKS4n4tHPtmGIaiovhWGQkul0t79uzRCy+8oE2bNqmgoEAnT57kHu2gDh06pLfffls7duxQXl6eoqKilJ+fzz3awbV27/H9t+M7e/asZs6cqfvvv1/Dhg1jTwPgHaowS0xM1Keffur+urS01P0WKq4+Bw4c0IIFC/TEE09o8uTJ2rdvn9cPTDbvX2Jiotfr586dU0JCghwOhy5cuCCXyyWr1cp+R9D777+v0tJS/exnP1NlZaVqa2tlsVhC3reEhASdO3dOiYmJamxsVE1NjWJjYyNV1jUtLi5OKSkpcjgckqRx48Zp69atslqt7jHcox3Hrl27lJKSop49e0q69LGgV199lXu0g/O99y7nnmRvrx5ff/21Hn74YaWnpysjI0NSy71mT7/T+VrECPvJT36iPXv2qKysTHV1ddq2bZtGjhwZ6bTgx5kzZzR//nytWrVKkydPliQNGTJEx48fV1FRkVwul7Zs2aKRI0cqKSlJ1113nQ4cOCDp0pOqRo4cKbvdrqFDh+r999+XJL377rvsd4S89tpr2rJli9577z0tWLBAY8eOVWZmZsj7NmrUKL377ruSLjVpQ4cOld1uj0xR17gxY8Zo165dqqqqksvlUl5eniZMmMA92kENGjRIu3fvVm1trQzD0CeffKK7776be7SDC+efm+zt1aG6ulqzZ8/WwoUL3c2UJPY0AIthGEakk+hscnJytG7dOjmdTk2bNk1z5syJdErw47nnntPbb7+t/v37u1978MEHdfPNNyszM1P19fUaNWqUli1bJovFokOHDumpp55SdXW1Bg8erMzMTHXp0kWnT5/W0qVLdf78efXu3VsvvviiunfvHsHKkJ2drX379mnFihUh71tFRYWWLl2qkydP6oYbbtCqVavUt2/fSJd0zXrrrbf0+uuvy+l0avjw4Xrqqae0d+9e7tEOav369crOzpbdbtcdd9yhp59+WsePH+ce7YDGjh2rDRs2qG/fvtqzZ09Y7kn2NrKa93T79u1atWqVbrnlFq9jCxcuZE9bQUMFAAAAACbxkT8AAAAAMImGCgAAAABMoqECAAAAAJNoqAAAAADAJBoqAAAAADCJhgoAEBEZGRkqKytr8fqcOXN09OjRsKxRVlam73//+2GJ1Wzt2rXavn27JGnNmjXu/8cKAODaZIt0AgCAa1N+fr7f11955ZUrnElo9u7dq1tvvVWStHDhwghnAwCINBoqAMAVt2zZMknSzJkzdfToUY0fP16HDx/WokWLlJmZqTVr1qi2tlarVq1Snz59dOzYMXXt2lUrVqzw+p9N+rNt2zatXr1a3bp10+233+5+PTs7Wx9++KHWrVvX4uulS5eqoqJCJ0+e1OjRozVt2jQ9++yzqqmpUWlpqQYNGqSXXnpJb731lr788kutXLlSVqtVH3/8sQYMGKDZs2fr008/1cqVK1VXVye73a5HH31UI0eOVHZ2tj766CNFRUWpqKhIXbt2VVZWVpt1AAA6Bj7yBwC44jIzMyVJf/nLX9S7d28NGDBAH3zwge655x6vcV9++aXS09OVk5OjqVOnavHixQHjnjt3Tk888YRefvllZWdnKykpKeicLl68qL/97W9avHixNm/erJ///OfavHmztm3bplOnTik3N1fTp0/X7bffriVLlnjlWl5ergULFujJJ59UTk6OsrKytHjxYp08eVKStH//fv32t7/Vli1bNGTIEK1fvz7ovAAAVzcaKgBAxA0dOtTv64MGDXIfu//++/XVV1+pvLy81TgHDhzQwIED3R/Je+CBB4LO4Uc/+pH794sXL5bD4dArr7yiZ555Rt98841qa2tbnVtQUKD+/ftryJAhkqQBAwbohz/8ofbt2ydJGjx4sBITEyVJt912myorK4POCwBwdeMjfwCAiIuOjvb7utVqDeo1T4ZhuH9vs333x5zFYvE65nQ6W81h0aJFcrlcmjhxokaPHq0zZ854zfXlcrlksVha5NHY2Ci73a6uXbu2mgcAoGPjHSoAQERYrVY1NjYGHHPo0CEdOnRIkrRp0yb94Ac/0I033tjq+OTkZB09etQ9Jzs7233M4XCosLBQ9fX1cjqd+vDDD1uNs2vXLs2fP1+TJk2SJH3xxRdyuVyt5n3XXXfp2LFjKigokCQVFhZq//79uvvuuwPWBwDo+HiHCgAQERMmTFB6erpqampaHRMXF6eXXnpJp0+flsPh0MqVKwPGdDgcWrVqlX7zm9/IbrcrOTnZfWz48OFKTk7WxIkTFR8fr2HDhunw4cN+4zz22GOaP3++oqOjFRMTo+TkZBUXF0uSxo4dqxdffNHrHS6Hw6E1a9Zo+fLlunjxoiwWizIzM/W9731PBw8eDOW0AAA6GIvB5w4AAFehvXv3avny5dqyZUukUwEAoFW8QwUA6FD+/Oc/Kycnx++x2bNn67777rvCGQEArmW8QwUAAAAAJvFQCgAAAAAwiYYKAAAAAEyioQIAAAAAk2ioAAAAAMAkGioAAAAAMImGCgAAAABM+n9qrZfso23YNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cont_univ(dataset[(dataset.trip_duration<(dataset.trip_duration.mean()+3*dataset.trip_duration.std())) & (dataset.trip_duration>(dataset.trip_duration.mean()-3*dataset.trip_duration.std()))].trip_duration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we log-transform, we can see that the chart looks like a normal distribution. So, we can see that our variable follows lognormal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **avg_speed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    729322.000000\n",
       "mean         10.629816\n",
       "std          10.711357\n",
       "min           0.000000\n",
       "25%           4.802708\n",
       "50%           9.042801\n",
       "75%          14.087260\n",
       "max        4194.190315\n",
       "Name: avg_speed, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.avg_speed.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29dad8f3c88>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(dataset.avg_speed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(729294, 17)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refined=dataset[dataset.avg_speed<=200]\n",
    "refined.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have removed all the speeds which were unreal, as this high speed is not permitted on road for taxis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cont_univ(refined.avg_speed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time related Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29dad87cc08>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,ax= plt.subplots(nrows=2,ncols=1,figsize=(10,7))\n",
    "\n",
    "sns.countplot(dataset.pickup_time,color='r',alpha=0.5,ax=ax[0])\n",
    "sns.countplot(dataset.dropoff_time,color='b',alpha=0.5,ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shape more or less looks the same\n",
    "<br>7-8PM,8-9PM are the busiest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_bins = [0,5,10,15,20,24]\n",
    "names = ['Early Morning','Office Hours','Noon Hours','Evening','Night']\n",
    "\n",
    "dataset['Pick_time_frame'] = pd.cut(dataset['pickup_time'],bins=cut_bins,labels=names)\n",
    "\n",
    "dataset['Drop_time_frame'] = pd.cut(dataset['dropoff_time'],bins=cut_bins,labels=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,ax = plt.subplots(nrows=1,ncols=2,figsize=(12,8))\n",
    "\n",
    "sns.countplot(dataset['Pick_time_frame'],ax=ax[0])\n",
    "\n",
    "sns.countplot(dataset['Drop_time_frame'],ax=ax[1])\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most number of trips are during the evening time and the least number of trips is between late night to early morning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Day Wise**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Friday       111744\n",
       "Saturday     110252\n",
       "Thursday     109344\n",
       "Wednesday    105074\n",
       "Tuesday      101254\n",
       "Sunday        97682\n",
       "Monday        93972\n",
       "Name: pickup_day_name, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['pickup_day_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(palette=\"Set2\")\n",
    "\n",
    "\n",
    "figure,ax = plt.subplots(nrows=2,ncols=1,figsize=(10,7))\n",
    "\n",
    "sns.countplot(x='pickup_day_name', data=dataset,ax=ax[0])\n",
    "ax[0].set_title('Day wise count of n.o pick up ')\n",
    "\n",
    "sns.countplot(x='dropoff_day_name',data=dataset,ax=ax[1])\n",
    "ax[1].set_title('Day wise count of n.o drop off ')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Highest number of rides are recorded on friday's and Saturday's.\n",
    "\n",
    "- Monday has the least number of trips. Also we can see a trend of increasing trips from monday to saturday and a decreasing trend from sunday through monday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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