Multiple linear regression model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtNBZFHO3M7n"
   },
   "source": [
    "# **Automatidata project**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7KFOyc3JPSiN"
   },
   "source": [
    "# Build a multiple linear regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F5O5cx_qQJmX"
   },
   "source": [
    "## PACE: **Plan**\n",
    "\n",
    "Consider the questions in your PACE Strategy Document to reflect on the Plan stage.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D8qYlvkLQsf2"
   },
   "source": [
    "### 1. Imports and loading\n",
    "Import the packages that you've learned are needed for building linear regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "ccfeg6X6eOVZ"
   },
   "outputs": [],
   "source": [
    "# Import packages\n",
    "# Packages for numerics + dataframes\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Packages for visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Packages for date conversions for calculating trip durations\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Packages for OLS, MLR, confusion matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import mean_absolute_error,r2_score,mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dhSYPrzQ2lpH"
   },
   "source": [
    "**Note:** `Pandas` is used to load the NYC TLC dataset. As shown in this cell, the dataset has been automatically loaded in for you. You do not need to download the .csv file, or provide more code, in order to access the dataset and proceed with this lab. Please continue with this activity by completing the following instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "TyR3sBUYJBO8"
   },
   "outputs": [],
   "source": [
    "# Load dataset into dataframe \n",
    "df0=pd.read_csv(\"2017_Yellow_Taxi_Trip_Data.csv\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OnrvCSfHUWPv"
   },
   "source": [
    "\n",
    "## PACE: **Analyze**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rIcDG2e66wt9"
   },
   "source": [
    "### 2a. Explore data with EDA\n",
    "\n",
    "Analyze and discover data, looking for correlations, missing data, outliers, and duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "T4Ag-sZhWg6K"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 18 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Unnamed: 0             22699 non-null  int64  \n",
      " 1   VendorID               22699 non-null  int64  \n",
      " 2   tpep_pickup_datetime   22699 non-null  object \n",
      " 3   tpep_dropoff_datetime  22699 non-null  object \n",
      " 4   passenger_count        22699 non-null  int64  \n",
      " 5   trip_distance          22699 non-null  float64\n",
      " 6   RatecodeID             22699 non-null  int64  \n",
      " 7   store_and_fwd_flag     22699 non-null  object \n",
      " 8   PULocationID           22699 non-null  int64  \n",
      " 9   DOLocationID           22699 non-null  int64  \n",
      " 10  payment_type           22699 non-null  int64  \n",
      " 11  fare_amount            22699 non-null  float64\n",
      " 12  extra                  22699 non-null  float64\n",
      " 13  mta_tax                22699 non-null  float64\n",
      " 14  tip_amount             22699 non-null  float64\n",
      " 15  tolls_amount           22699 non-null  float64\n",
      " 16  improvement_surcharge  22699 non-null  float64\n",
      " 17  total_amount           22699 non-null  float64\n",
      "dtypes: float64(8), int64(7), object(3)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Start with `.shape` and `.info()`\n",
    "df = df0.copy()\n",
    "df.shape\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SWLHv_h_3Hcf"
   },
   "source": [
    "Check for missing data and duplicates using `.isna()` and `.drop_duplicates()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "3QZZIxxi3OV3",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total counts of missing values: 0\n",
      "shape of data: (22699, 18)\n",
      "shape of data without duplicates: (22699, 18)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Unnamed: 0               0\n",
       "VendorID                 0\n",
       "tpep_pickup_datetime     0\n",
       "tpep_dropoff_datetime    0\n",
       "passenger_count          0\n",
       "trip_distance            0\n",
       "RatecodeID               0\n",
       "store_and_fwd_flag       0\n",
       "PULocationID             0\n",
       "DOLocationID             0\n",
       "payment_type             0\n",
       "fare_amount              0\n",
       "extra                    0\n",
       "mta_tax                  0\n",
       "tip_amount               0\n",
       "tolls_amount             0\n",
       "improvement_surcharge    0\n",
       "total_amount             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing data and duplicates using .isna() and .drop_duplicates()\n",
    "#total missing values\n",
    "print('Total counts of missing values:', df.isna().sum().sum())\n",
    "\n",
    "print('shape of data:', df.shape)\n",
    "print('shape of data without duplicates:', df.drop_duplicates().shape)\n",
    "\n",
    "# number of missing values in each column\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WXWAlPTY9iLK"
   },
   "source": [
    "Use `.describe()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "2O3b9H9B9nwk"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
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       "      <th>RatecodeID</th>\n",
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       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.269900e+04</td>\n",
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       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
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       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22687</td>\n",
       "      <td>22688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/03/2017 3:45:19 PM</td>\n",
       "      <td>10/18/2017 8:07:45 PM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.675849e+07</td>\n",
       "      <td>1.556236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.642319</td>\n",
       "      <td>2.913313</td>\n",
       "      <td>1.043394</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.412353</td>\n",
       "      <td>161.527997</td>\n",
       "      <td>1.336887</td>\n",
       "      <td>13.026629</td>\n",
       "      <td>0.333275</td>\n",
       "      <td>0.497445</td>\n",
       "      <td>1.835781</td>\n",
       "      <td>0.312542</td>\n",
       "      <td>0.299551</td>\n",
       "      <td>16.310502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.274493e+07</td>\n",
       "      <td>0.496838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.285231</td>\n",
       "      <td>3.653171</td>\n",
       "      <td>0.708391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.633373</td>\n",
       "      <td>70.139691</td>\n",
       "      <td>0.496211</td>\n",
       "      <td>13.243791</td>\n",
       "      <td>0.463097</td>\n",
       "      <td>0.039465</td>\n",
       "      <td>2.800626</td>\n",
       "      <td>1.399212</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>16.097295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.212700e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-120.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>-120.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.852056e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>8.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.673150e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.610000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>11.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.537452e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.060000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.134863e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>33.960000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>999.990000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1200.290000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Unnamed: 0      VendorID   tpep_pickup_datetime  \\\n",
       "count   2.269900e+04  22699.000000                  22699   \n",
       "unique           NaN           NaN                  22687   \n",
       "top              NaN           NaN  07/03/2017 3:45:19 PM   \n",
       "freq             NaN           NaN                      2   \n",
       "mean    5.675849e+07      1.556236                    NaN   \n",
       "std     3.274493e+07      0.496838                    NaN   \n",
       "min     1.212700e+04      1.000000                    NaN   \n",
       "25%     2.852056e+07      1.000000                    NaN   \n",
       "50%     5.673150e+07      2.000000                    NaN   \n",
       "75%     8.537452e+07      2.000000                    NaN   \n",
       "max     1.134863e+08      2.000000                    NaN   \n",
       "\n",
       "        tpep_dropoff_datetime  passenger_count  trip_distance    RatecodeID  \\\n",
       "count                   22699     22699.000000   22699.000000  22699.000000   \n",
       "unique                  22688              NaN            NaN           NaN   \n",
       "top     10/18/2017 8:07:45 PM              NaN            NaN           NaN   \n",
       "freq                        2              NaN            NaN           NaN   \n",
       "mean                      NaN         1.642319       2.913313      1.043394   \n",
       "std                       NaN         1.285231       3.653171      0.708391   \n",
       "min                       NaN         0.000000       0.000000      1.000000   \n",
       "25%                       NaN         1.000000       0.990000      1.000000   \n",
       "50%                       NaN         1.000000       1.610000      1.000000   \n",
       "75%                       NaN         2.000000       3.060000      1.000000   \n",
       "max                       NaN         6.000000      33.960000     99.000000   \n",
       "\n",
       "       store_and_fwd_flag  PULocationID  DOLocationID  payment_type  \\\n",
       "count               22699  22699.000000  22699.000000  22699.000000   \n",
       "unique                  2           NaN           NaN           NaN   \n",
       "top                     N           NaN           NaN           NaN   \n",
       "freq                22600           NaN           NaN           NaN   \n",
       "mean                  NaN    162.412353    161.527997      1.336887   \n",
       "std                   NaN     66.633373     70.139691      0.496211   \n",
       "min                   NaN      1.000000      1.000000      1.000000   \n",
       "25%                   NaN    114.000000    112.000000      1.000000   \n",
       "50%                   NaN    162.000000    162.000000      1.000000   \n",
       "75%                   NaN    233.000000    233.000000      2.000000   \n",
       "max                   NaN    265.000000    265.000000      4.000000   \n",
       "\n",
       "         fare_amount         extra       mta_tax    tip_amount  tolls_amount  \\\n",
       "count   22699.000000  22699.000000  22699.000000  22699.000000  22699.000000   \n",
       "unique           NaN           NaN           NaN           NaN           NaN   \n",
       "top              NaN           NaN           NaN           NaN           NaN   \n",
       "freq             NaN           NaN           NaN           NaN           NaN   \n",
       "mean       13.026629      0.333275      0.497445      1.835781      0.312542   \n",
       "std        13.243791      0.463097      0.039465      2.800626      1.399212   \n",
       "min      -120.000000     -1.000000     -0.500000      0.000000      0.000000   \n",
       "25%         6.500000      0.000000      0.500000      0.000000      0.000000   \n",
       "50%         9.500000      0.000000      0.500000      1.350000      0.000000   \n",
       "75%        14.500000      0.500000      0.500000      2.450000      0.000000   \n",
       "max       999.990000      4.500000      0.500000    200.000000     19.100000   \n",
       "\n",
       "        improvement_surcharge  total_amount  \n",
       "count            22699.000000  22699.000000  \n",
       "unique                    NaN           NaN  \n",
       "top                       NaN           NaN  \n",
       "freq                      NaN           NaN  \n",
       "mean                 0.299551     16.310502  \n",
       "std                  0.015673     16.097295  \n",
       "min                 -0.300000   -120.300000  \n",
       "25%                  0.300000      8.750000  \n",
       "50%                  0.300000     11.800000  \n",
       "75%                  0.300000     17.800000  \n",
       "max                  0.300000   1200.290000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .describe()\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iXhaBfP_WOSR"
   },
   "source": [
    "### 2b. Convert pickup & dropoff columns to datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "TbHu-SSInJCX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                 int64\n",
       "VendorID                   int64\n",
       "tpep_pickup_datetime      object\n",
       "tpep_dropoff_datetime     object\n",
       "passenger_count            int64\n",
       "trip_distance            float64\n",
       "RatecodeID                 int64\n",
       "store_and_fwd_flag        object\n",
       "PULocationID               int64\n",
       "DOLocationID               int64\n",
       "payment_type               int64\n",
       "fare_amount              float64\n",
       "extra                    float64\n",
       "mta_tax                  float64\n",
       "tip_amount               float64\n",
       "tolls_amount             float64\n",
       "improvement_surcharge    float64\n",
       "total_amount             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the format of the data\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "h5L6OdYPqV0N"
   },
   "outputs": [],
   "source": [
    "# Convert datetime columns to datetime\n",
    "import datetime as dt\n",
    "\n",
    "df['tpep_pickup_datetime'] = pd.to_datetime(df['tpep_pickup_datetime'])\n",
    "df['tpep_dropoff_datetime'] = pd.to_datetime(df['tpep_dropoff_datetime'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KlF7ZNSyW0yV"
   },
   "source": [
    "### 2c. Create duration column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w1v_Y1uunbsx"
   },
   "source": [
    "Create a new column called `duration` that represents the total number of minutes that each taxi ride took."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "suC4LJFPMPCo"
   },
   "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>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 08:55:43</td>\n",
       "      <td>2017-03-25 09:09:47</td>\n",
       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "      <td>14.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-11 14:53:28</td>\n",
       "      <td>2017-04-11 15:19:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "      <td>26.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-15 07:26:56</td>\n",
       "      <td>2017-12-15 07:34:08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-05-07 13:17:59</td>\n",
       "      <td>2017-05-07 13:48:14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30841670</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-04-15 23:32:20</td>\n",
       "      <td>2017-04-15 23:49:03</td>\n",
       "      <td>1</td>\n",
       "      <td>4.37</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "      <td>16.716667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
       "4    30841670         2  2017-04-15 23:32:20   2017-04-15 23:49:03   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "4                1           4.37           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "4             4           112             2         16.5    0.5      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount   duration  \n",
       "0        2.76           0.0                    0.3         16.56  14.066667  \n",
       "1        4.00           0.0                    0.3         20.80  26.500000  \n",
       "2        1.45           0.0                    0.3          8.75   7.200000  \n",
       "3        6.39           0.0                    0.3         27.69  30.250000  \n",
       "4        0.00           0.0                    0.3         17.80  16.716667  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create `duration` column\n",
    "df['duration'] = (df['tpep_dropoff_datetime'] - df['tpep_pickup_datetime'])/pd.Timedelta(minutes=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7dcytBKhiGAr"
   },
   "source": [
    "### Outliers\n",
    "\n",
    "Call `df.info()` to inspect the columns and decide which ones to check for outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "W5bCdL5SSfg1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 19 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   Unnamed: 0             22699 non-null  int64         \n",
      " 1   VendorID               22699 non-null  int64         \n",
      " 2   tpep_pickup_datetime   22699 non-null  datetime64[ns]\n",
      " 3   tpep_dropoff_datetime  22699 non-null  datetime64[ns]\n",
      " 4   passenger_count        22699 non-null  int64         \n",
      " 5   trip_distance          22699 non-null  float64       \n",
      " 6   RatecodeID             22699 non-null  int64         \n",
      " 7   store_and_fwd_flag     22699 non-null  object        \n",
      " 8   PULocationID           22699 non-null  int64         \n",
      " 9   DOLocationID           22699 non-null  int64         \n",
      " 10  payment_type           22699 non-null  int64         \n",
      " 11  fare_amount            22699 non-null  float64       \n",
      " 12  extra                  22699 non-null  float64       \n",
      " 13  mta_tax                22699 non-null  float64       \n",
      " 14  tip_amount             22699 non-null  float64       \n",
      " 15  tolls_amount           22699 non-null  float64       \n",
      " 16  improvement_surcharge  22699 non-null  float64       \n",
      " 17  total_amount           22699 non-null  float64       \n",
      " 18  duration               22699 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(9), int64(7), object(1)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# inspect columns\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vtj4iAJMk9Vc"
   },
   "source": [
    "### 2d. Box plots\n",
    "\n",
    "Plot a box plot for each feature: `trip_distance`, `fare_amount`, `duration`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "KCEzE-gwL5gq"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a box plot for each feature to check the data distribution\n",
    "fig, axes = plt.subplots(1, 3, figsize=(8,4))\n",
    "\n",
    "sns.boxplot(y=df['trip_distance'], fliersize=1, ax = axes[0])\n",
    "sns.boxplot(y=df['fare_amount'], fliersize=1, ax = axes[1])\n",
    "sns.boxplot(y=df['duration'], fliersize=1, ax = axes[2])\n",
    "\n",
    "axes[0].set_title('trip_distance')\n",
    "axes[1].set_title('fare_amount')\n",
    "axes[2].set_title('duration')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2e. Imputations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `trip_distance` outliers\n",
    "\n",
    "Sort the column values, eliminate duplicates, and inspect the least 10 values. Are they rounded values or precise values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22026    0.00\n",
       "5501     0.01\n",
       "16827    0.02\n",
       "21088    0.03\n",
       "13517    0.04\n",
       "10146    0.05\n",
       "14373    0.06\n",
       "922      0.07\n",
       "22523    0.08\n",
       "22035    0.09\n",
       "3288     0.10\n",
       "16467    0.11\n",
       "314      0.12\n",
       "1798     0.13\n",
       "7263     0.14\n",
       "8861     0.15\n",
       "22269    0.16\n",
       "12944    0.17\n",
       "22049    0.18\n",
       "17240    0.19\n",
       "Name: trip_distance, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are trip distances of 0 bad data or very short trips rounded down?\n",
    "df['trip_distance'].sort_values().drop_duplicates().head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distances are captured with a high degree of precision. However, it might be possible for trips to have distances of zero if a passenger summoned a taxi and then changed their mind. Besides, are there enough zero values in the data to pose a problem?\n",
    "\n",
    "Calculate the count of rides where the `trip_distance` is zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the count of rides where the trip_distance is zero.\n",
    "sum(df['trip_distance']==0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `fare_amount` outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22699.000000\n",
       "mean        13.026629\n",
       "std         13.243791\n",
       "min       -120.000000\n",
       "25%          6.500000\n",
       "50%          9.500000\n",
       "75%         14.500000\n",
       "max        999.990000\n",
       "Name: fare_amount, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fare_amount descriptive analysis\n",
    "df['fare_amount'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** What do you notice about the values in the `fare_amount` column?\n",
    "\n",
    "Impute values less than $0 with `0`.\n",
    "\n",
    "There are negative values for the fare_amount column. The negative values are problematic.\n",
    "The highest value is 1000, which is unlikely in real life. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute values less than $0 with 0\n",
    "df.loc[df['fare_amount']<0, 'fare_amount'] = 0\n",
    "df['fare_amount'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now impute the maximum value as `Q3 + (6 * IQR)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# built a method to impute the outliers\n",
    "def outlier_impute(column_list, iqr_factor):\n",
    "    '''\n",
    "    Impute upper-limit values in specified columns based on their interquartile range.\n",
    "\n",
    "    Arguments:\n",
    "        column_list: A list of columns to iterate over\n",
    "        iqr_factor: A number representing x in the formula:\n",
    "                    Q3 + (x * IQR). Used to determine maximum threshold,\n",
    "                    beyond which a point is considered an outlier.\n",
    "\n",
    "    The IQR is computed for each column in column_list and values exceeding\n",
    "    the upper threshold for each column are imputed with the upper threshold value.\n",
    "    '''\n",
    "    for col in column_list:\n",
    "        # Reassign minimum to zero\n",
    "        df.loc[df[col]<0, col] = 0\n",
    "        \n",
    "        # Calculate upper threshold\n",
    "        q1 = df[col].quantile(0.25)\n",
    "        q3 = df[col].quantile(0.75)\n",
    "        iqr = q3 - q1\n",
    "        print(col)\n",
    "        print(f\"iqr: {iqr}\")\n",
    "        upper_threshold = q3 + iqr*iqr_factor\n",
    "        print(f'upper_threshold: {upper_threshold}')\n",
    "        \n",
    "        # Reassign values > threshold to threshold\n",
    "        df.loc[df[col] > upper_threshold, col] = upper_threshold\n",
    "        print(df[col].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fare_amount\n",
      "iqr: 8.0\n",
      "upper_threshold: 62.5\n",
      "count    22699.000000\n",
      "mean        12.897913\n",
      "std         10.541137\n",
      "min          0.000000\n",
      "25%          6.500000\n",
      "50%          9.500000\n",
      "75%         14.500000\n",
      "max         62.500000\n",
      "Name: fare_amount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "outlier_impute(['fare_amount'], 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `duration` outliers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22699.000000\n",
       "mean        17.013777\n",
       "std         61.996482\n",
       "min        -16.983333\n",
       "25%          6.650000\n",
       "50%         11.183333\n",
       "75%         18.383333\n",
       "max       1439.550000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call .describe() for duration outliers\n",
    "df['duration'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `duration` column has problematic values at both the lower and upper extremities.\n",
    "\n",
    "* **Low values:** There should be no values that represent negative time. Impute all negative durations with `0`.\n",
    "\n",
    "* **High values:** Impute high values the same way you imputed the high-end outliers for fares: `Q3 + (6 * IQR)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute a 0 for any negative values\n",
    "df.loc[df['duration'] <0, 'duration'] = 0\n",
    "df['duration'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duration\n",
      "iqr: 11.733333333333333\n",
      "upper_threshold: 88.78333333333333\n",
      "count    22699.000000\n",
      "mean        14.460555\n",
      "std         11.947043\n",
      "min          0.000000\n",
      "25%          6.650000\n",
      "50%         11.183333\n",
      "75%         18.383333\n",
      "max         88.783333\n",
      "Name: duration, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Impute the high outliers\n",
    "outlier_impute(['duration'], 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3a. Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `mean_distance` column\n",
    "\n",
    "When deployed, the model will not know the duration of a trip until after the trip occurs, so we cannot train a model that uses this feature. However, we can use the statistics of trips we *do* know to generalize about ones we do not know.\n",
    "\n",
    "In this step, create a column called `mean_distance` that captures the mean distance for each group of trips that share pickup and dropoff points.\n",
    "\n",
    "For example, if your data were:\n",
    "\n",
    "|Trip|Start|End|Distance|\n",
    "|--: |:---:|:-:|    |\n",
    "| 1  | A   | B | 1  |\n",
    "| 2  | C   | D | 2  |\n",
    "| 3  | A   | B |1.5 |\n",
    "| 4  | D   | C | 3  |\n",
    "\n",
    "The results should be:\n",
    "```\n",
    "A -> B: 1.25 miles\n",
    "C -> D: 2 miles\n",
    "D -> C: 3 miles\n",
    "```\n",
    "\n",
    "Notice that C -> D is not the same as D -> C. All trips that share a unique pair of start and end points get grouped and averaged.\n",
    "\n",
    "Then, a new column `mean_distance` will be added where the value at each row is the average for all trips with those pickup and dropoff locations:\n",
    "\n",
    "|Trip|Start|End|Distance|mean_distance|\n",
    "|--: |:---:|:-:|  :--   |:--   |\n",
    "| 1  | A   | B | 1      | 1.25 |\n",
    "| 2  | C   | D | 2      | 2    |\n",
    "| 3  | A   | B |1.5     | 1.25 |\n",
    "| 4  | D   | C | 3      | 3    |\n",
    "\n",
    "\n",
    "Begin by creating a helper column called `pickup_dropoff`, which contains the unique combination of pickup and dropoff location IDs for each row.\n",
    "\n",
    "One way to do this is to convert the pickup and dropoff location IDs to strings and join them, separated by a space. The space is to ensure that, for example, a trip with pickup/dropoff points of 12 & 151 gets encoded differently than a trip with points 121 & 51.\n",
    "\n",
    "So, the new column would look like this:\n",
    "\n",
    "|Trip|Start|End|pickup_dropoff|\n",
    "|--: |:---:|:-:|  :--         |\n",
    "| 1  | A   | B | 'A B'        |\n",
    "| 2  | C   | D | 'C D'        |\n",
    "| 3  | A   | B | 'A B'        |\n",
    "| 4  | D   | C | 'D C'        |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "264 264    277\n",
       "237 236    124\n",
       "236 236    110\n",
       "236 237    102\n",
       "237 237     96\n",
       "          ... \n",
       "66 162       1\n",
       "79 116       1\n",
       "166 1        1\n",
       "164 224      1\n",
       "249 17       1\n",
       "Name: pickup_dropoff, Length: 4172, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create `pickup_dropoff` column\n",
    "df['pickup_dropoff'] = df['PULocationID'].astype('str') + \" \" + df['DOLocationID'].astype('str')\n",
    "df['pickup_dropoff'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, use a `groupby()` statement to group each row by the new `pickup_dropoff` column, compute the mean, and capture the values only in the `trip_distance` column. Assign the results to a variable named `grouped`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>trip_distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_dropoff</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 1</th>\n",
       "      <td>2.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 148</th>\n",
       "      <td>15.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100 1</th>\n",
       "      <td>16.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100 100</th>\n",
       "      <td>0.253333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100 107</th>\n",
       "      <td>1.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97 65</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97 66</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97 80</th>\n",
       "      <td>3.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97 90</th>\n",
       "      <td>4.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97 97</th>\n",
       "      <td>1.006667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4172 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                trip_distance\n",
       "pickup_dropoff               \n",
       "1 1                  2.433333\n",
       "10 148              15.700000\n",
       "100 1               16.890000\n",
       "100 100              0.253333\n",
       "100 107              1.180000\n",
       "...                       ...\n",
       "97 65                0.500000\n",
       "97 66                1.400000\n",
       "97 80                3.840000\n",
       "97 90                4.420000\n",
       "97 97                1.006667\n",
       "\n",
       "[4172 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### YOUR CODE HERE ###\n",
    "grouped = df.groupby('pickup_dropoff')[['trip_distance']].mean()\n",
    "\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '142 13': 5.058333333333334,\n",
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       " '142 162': 1.6821052631578948,\n",
       " '142 163': 0.8309375,\n",
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       " '142 166': 2.6875,\n",
       " '142 17': 8.274999999999999,\n",
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       " '142 174': 12.6,\n",
       " '142 181': 8.14,\n",
       " '142 186': 1.8606666666666667,\n",
       " '142 209': 7.3,\n",
       " '142 211': 5.0,\n",
       " '142 220': 9.0,\n",
       " '142 223': 5.795,\n",
       " '142 224': 3.885,\n",
       " '142 225': 8.8,\n",
       " '142 229': 1.6320000000000001,\n",
       " '142 230': 1.051212121212121,\n",
       " '142 231': 4.8425,\n",
       " '142 233': 2.2944444444444443,\n",
       " '142 234': 2.9166666666666665,\n",
       " '142 236': 2.0282758620689654,\n",
       " '142 237': 1.3573333333333333,\n",
       " '142 238': 1.44875,\n",
       " '142 239': 0.9964999999999999,\n",
       " '142 24': 2.1580000000000004,\n",
       " '142 243': 7.75,\n",
       " '142 244': 6.058333333333334,\n",
       " '142 246': 2.075714285714286,\n",
       " '142 249': 2.982,\n",
       " '142 261': 6.45,\n",
       " '142 262': 2.686666666666667,\n",
       " '142 263': 2.29,\n",
       " '142 264': 0.4,\n",
       " '142 41': 2.9244444444444446,\n",
       " '142 42': 3.94,\n",
       " '142 43': 1.1046153846153848,\n",
       " '142 48': 0.9956756756756756,\n",
       " '142 50': 1.0758333333333334,\n",
       " '142 68': 1.8776470588235294,\n",
       " '142 74': 3.8925,\n",
       " ...}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Convert `grouped` to a dictionary\n",
    "grouped_dict = grouped.to_dict()\n",
    "\n",
    "# 2. Reassign to only contain the inner dictionary\n",
    "grouped_dict = grouped_dict['trip_distance']\n",
    "grouped_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.521667\n",
       "1    3.108889\n",
       "2    0.881429\n",
       "3    3.700000\n",
       "4    4.435000\n",
       "Name: mean_distance, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Create a mean_distance column that is a copy of the pickup_dropoff helper column\n",
    "df['mean_distance'] = df['pickup_dropoff']\n",
    "\n",
    "# 2. Map `grouped_dict` to the `mean_distance` column\n",
    "df['mean_distance'] = df['mean_distance'].map(grouped_dict)\n",
    "\n",
    "# Confirm that it worked\n",
    "df['mean_distance'].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `mean_duration` column\n",
    "\n",
    "Repeat the process used to create the `mean_distance` column to create a `mean_duration` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.847222\n",
       "1    24.470370\n",
       "2     7.250000\n",
       "3    30.250000\n",
       "4    14.616667\n",
       "Name: mean_duration, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_duration = df.groupby('pickup_dropoff').mean()[['duration']]\n",
    "\n",
    "# Create a dictionary where keys are unique pickup_dropoffs and values are\n",
    "# mean trip duration for all trips with those pickup_dropoff combos\n",
    "grouped_duration_dict = grouped_duration.to_dict()\n",
    "grouped_duration_dict = grouped_duration_dict['duration']\n",
    "\n",
    "df['mean_duration'] = df['pickup_dropoff']\n",
    "df['mean_duration'] = df['mean_duration'].map(grouped_duration_dict)\n",
    "# Confirm that it worked\n",
    "df['mean_duration'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `day` and `month` columns\n",
    "\n",
    "Create two new columns, `day` (name of day) and `month` (name of month) by extracting the relevant information from the `tpep_pickup_datetime` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 'day' col\n",
    "df['day'] = df['tpep_pickup_datetime'].dt.day_name().str.lower()\n",
    "\n",
    "# Create 'month' col\n",
    "df['month'] = df['tpep_pickup_datetime'].dt.strftime('%b').str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>...</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>duration</th>\n",
       "      <th>pickup_dropoff</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 08:55:43</td>\n",
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       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>231</td>\n",
       "      <td>...</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "      <td>14.066667</td>\n",
       "      <td>100 231</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>saturday</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-11 14:53:28</td>\n",
       "      <td>2017-04-11 15:19:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>186 43</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-15 07:26:56</td>\n",
       "      <td>2017-12-15 07:34:08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>262 236</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>friday</td>\n",
       "      <td>dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-05-07 13:17:59</td>\n",
       "      <td>2017-05-07 13:48:14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>188 97</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>sunday</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30841670</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-04-15 23:32:20</td>\n",
       "      <td>2017-04-15 23:49:03</td>\n",
       "      <td>1</td>\n",
       "      <td>4.37</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "      <td>16.716667</td>\n",
       "      <td>4 112</td>\n",
       "      <td>4.435000</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>saturday</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
       "4    30841670         2  2017-04-15 23:32:20   2017-04-15 23:49:03   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "4                1           4.37           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  ...  tip_amount  tolls_amount  \\\n",
       "0           100           231  ...        2.76           0.0   \n",
       "1           186            43  ...        4.00           0.0   \n",
       "2           262           236  ...        1.45           0.0   \n",
       "3           188            97  ...        6.39           0.0   \n",
       "4             4           112  ...        0.00           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount   duration  pickup_dropoff  \\\n",
       "0                    0.3         16.56  14.066667         100 231   \n",
       "1                    0.3         20.80  26.500000          186 43   \n",
       "2                    0.3          8.75   7.200000         262 236   \n",
       "3                    0.3         27.69  30.250000          188 97   \n",
       "4                    0.3         17.80  16.716667           4 112   \n",
       "\n",
       "   mean_distance  mean_duration       day month  \n",
       "0       3.521667      22.847222  saturday   mar  \n",
       "1       3.108889      24.470370   tuesday   apr  \n",
       "2       0.881429       7.250000    friday   dec  \n",
       "3       3.700000      30.250000    sunday   may  \n",
       "4       4.435000      14.616667  saturday   apr  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `rush_hour` column\n",
    "\n",
    "Define rush hour as:\n",
    "* Any weekday (not Saturday or Sunday) AND\n",
    "* Either from 06:00&ndash;10:00 or from 16:00&ndash;20:00\n",
    "\n",
    "Create a binary `rush_hour` column that contains a 1 if the ride was during rush hour and a 0 if it was not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 'rush_hour' col\n",
    "df['rush_hour'] = df['tpep_pickup_datetime'].dt.hour\n",
    "\n",
    "# If day is Saturday or Sunday, impute 0 in `rush_hour` column\n",
    "df.loc[df['day'].isin(['saturday', 'sunday']), 'rush_hour'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rush_hourizer(hour):\n",
    "    if 6 <= hour['rush_hour'] <= 10:\n",
    "        val = 1\n",
    "    elif 16 <= hour['rush_hour'] <= 20:\n",
    "        val = 1\n",
    "    else:\n",
    "        val = 0\n",
    "    \n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>rush_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 08:55:43</td>\n",
       "      <td>2017-03-25 09:09:47</td>\n",
       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "      <td>14.066667</td>\n",
       "      <td>100 231</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>saturday</td>\n",
       "      <td>mar</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-11 14:53:28</td>\n",
       "      <td>2017-04-11 15:19:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>186 43</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>apr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-15 07:26:56</td>\n",
       "      <td>2017-12-15 07:34:08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>262 236</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>friday</td>\n",
       "      <td>dec</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-05-07 13:17:59</td>\n",
       "      <td>2017-05-07 13:48:14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>188 97</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>sunday</td>\n",
       "      <td>may</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30841670</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-04-15 23:32:20</td>\n",
       "      <td>2017-04-15 23:49:03</td>\n",
       "      <td>1</td>\n",
       "      <td>4.37</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "      <td>16.716667</td>\n",
       "      <td>4 112</td>\n",
       "      <td>4.435000</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>saturday</td>\n",
       "      <td>apr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
       "4    30841670         2  2017-04-15 23:32:20   2017-04-15 23:49:03   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "4                1           4.37           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  ...  tolls_amount  improvement_surcharge  \\\n",
       "0           100           231  ...           0.0                    0.3   \n",
       "1           186            43  ...           0.0                    0.3   \n",
       "2           262           236  ...           0.0                    0.3   \n",
       "3           188            97  ...           0.0                    0.3   \n",
       "4             4           112  ...           0.0                    0.3   \n",
       "\n",
       "   total_amount   duration  pickup_dropoff  mean_distance  mean_duration  \\\n",
       "0         16.56  14.066667         100 231       3.521667      22.847222   \n",
       "1         20.80  26.500000          186 43       3.108889      24.470370   \n",
       "2          8.75   7.200000         262 236       0.881429       7.250000   \n",
       "3         27.69  30.250000          188 97       3.700000      30.250000   \n",
       "4         17.80  16.716667           4 112       4.435000      14.616667   \n",
       "\n",
       "        day  month rush_hour  \n",
       "0  saturday    mar         0  \n",
       "1   tuesday    apr         0  \n",
       "2    friday    dec         1  \n",
       "3    sunday    may         0  \n",
       "4  saturday    apr         0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the `rush_hourizer()` function to the new column\n",
    "weekday = {'monday', 'tuesday', 'wednesday', 'thursday', 'friday'}\n",
    "df.loc[df['day'].isin(weekday), 'rush_hour'] = df.apply(rush_hourizer, axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4. Scatter plot\n",
    "\n",
    "Create a scatterplot to visualize the relationship between `mean_duration` and `fare_amount`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mean duration - fare amount')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot to visualize the relationship between variables of interest\n",
    "### YOUR CODE HERE ###\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.regplot(x=df['mean_duration'], y=df['fare_amount'],\n",
    "           scatter_kws={'alpha':0.5, 's':5},\n",
    "           line_kws={'color': 'red'})\n",
    "plt.ylim(0,70)\n",
    "plt.xlim(0, 80)\n",
    "plt.title('Mean duration - fare amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You know what one of the lines represents. 62 dollars and 50 cents is the maximum that was imputed for outliers, so all former outliers will now have fare amounts of \\$62.50. 52 dollars is the fare to JFK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.0    514\n",
       "62.5     84\n",
       "59.0      9\n",
       "50.5      9\n",
       "57.5      8\n",
       "51.0      7\n",
       "60.0      6\n",
       "55.0      6\n",
       "51.5      6\n",
       "53.0      4\n",
       "52.5      4\n",
       "61.0      3\n",
       "62.0      3\n",
       "55.5      3\n",
       "56.0      3\n",
       "56.5      3\n",
       "58.5      2\n",
       "59.5      2\n",
       "61.5      2\n",
       "57.0      2\n",
       "54.0      2\n",
       "58.0      1\n",
       "54.7      1\n",
       "54.5      1\n",
       "Name: fare_amount, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### YOUR CODE HERE ###\n",
    "df[df['fare_amount']>50]['fare_amount'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine the first 30 of these trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>duration</th>\n",
       "      <th>pickup_dropoff</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>rush_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18600059</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-05 19:15:30</td>\n",
       "      <td>2017-03-05 19:52:18</td>\n",
       "      <td>2</td>\n",
       "      <td>18.90</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.58</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>72.92</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>236 132</td>\n",
       "      <td>19.211667</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>sunday</td>\n",
       "      <td>mar</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>47959795</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-03 14:24:57</td>\n",
       "      <td>2017-06-03 15:31:48</td>\n",
       "      <td>1</td>\n",
       "      <td>18.00</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>66.850000</td>\n",
       "      <td>132 163</td>\n",
       "      <td>19.229000</td>\n",
       "      <td>52.941667</td>\n",
       "      <td>saturday</td>\n",
       "      <td>jun</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>95729204</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-11 20:16:16</td>\n",
       "      <td>2017-11-11 20:17:14</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>132 132</td>\n",
       "      <td>2.255862</td>\n",
       "      <td>3.021839</td>\n",
       "      <td>saturday</td>\n",
       "      <td>nov</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>103404868</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-12-06 23:37:08</td>\n",
       "      <td>2017-12-07 00:06:19</td>\n",
       "      <td>1</td>\n",
       "      <td>18.93</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>29.183333</td>\n",
       "      <td>132 79</td>\n",
       "      <td>19.431667</td>\n",
       "      <td>47.275000</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>80479432</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-24 23:45:45</td>\n",
       "      <td>2017-09-25 00:15:14</td>\n",
       "      <td>1</td>\n",
       "      <td>17.99</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.64</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>73.20</td>\n",
       "      <td>29.483333</td>\n",
       "      <td>132 234</td>\n",
       "      <td>17.654000</td>\n",
       "      <td>49.833333</td>\n",
       "      <td>sunday</td>\n",
       "      <td>sep</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>16226157</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-02-28 18:30:05</td>\n",
       "      <td>2017-02-28 19:09:55</td>\n",
       "      <td>1</td>\n",
       "      <td>18.40</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>62.84</td>\n",
       "      <td>39.833333</td>\n",
       "      <td>132 48</td>\n",
       "      <td>18.761905</td>\n",
       "      <td>58.246032</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>feb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>55253442</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-05 12:51:58</td>\n",
       "      <td>2017-06-05 13:07:35</td>\n",
       "      <td>1</td>\n",
       "      <td>4.73</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>228</td>\n",
       "      <td>88</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>15.616667</td>\n",
       "      <td>228 88</td>\n",
       "      <td>4.730000</td>\n",
       "      <td>15.616667</td>\n",
       "      <td>monday</td>\n",
       "      <td>jun</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>65900029</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-03 22:47:14</td>\n",
       "      <td>2017-08-03 23:32:41</td>\n",
       "      <td>2</td>\n",
       "      <td>18.21</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>45.450000</td>\n",
       "      <td>132 48</td>\n",
       "      <td>18.761905</td>\n",
       "      <td>58.246032</td>\n",
       "      <td>thursday</td>\n",
       "      <td>aug</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>80904240</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-26 13:48:26</td>\n",
       "      <td>2017-09-26 14:31:17</td>\n",
       "      <td>1</td>\n",
       "      <td>17.27</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>42.850000</td>\n",
       "      <td>186 132</td>\n",
       "      <td>17.096000</td>\n",
       "      <td>42.920000</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>sep</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>33706214</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-04-23 21:34:48</td>\n",
       "      <td>2017-04-23 22:46:23</td>\n",
       "      <td>6</td>\n",
       "      <td>18.34</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>57.80</td>\n",
       "      <td>71.583333</td>\n",
       "      <td>132 148</td>\n",
       "      <td>17.994286</td>\n",
       "      <td>46.340476</td>\n",
       "      <td>sunday</td>\n",
       "      <td>apr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>99259872</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-22 21:31:32</td>\n",
       "      <td>2017-11-22 22:00:25</td>\n",
       "      <td>1</td>\n",
       "      <td>18.65</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>63.36</td>\n",
       "      <td>28.883333</td>\n",
       "      <td>132 144</td>\n",
       "      <td>18.537500</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>nov</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>61050418</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-07-18 13:29:06</td>\n",
       "      <td>2017-07-18 13:29:19</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>230</td>\n",
       "      <td>161</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>230 161</td>\n",
       "      <td>0.685484</td>\n",
       "      <td>7.965591</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>jul</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>54444647</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-26 13:39:12</td>\n",
       "      <td>2017-06-26 14:34:54</td>\n",
       "      <td>1</td>\n",
       "      <td>17.76</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>211</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>55.700000</td>\n",
       "      <td>211 132</td>\n",
       "      <td>16.580000</td>\n",
       "      <td>61.691667</td>\n",
       "      <td>monday</td>\n",
       "      <td>jun</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>94424289</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-07 22:15:00</td>\n",
       "      <td>2017-11-07 22:45:32</td>\n",
       "      <td>2</td>\n",
       "      <td>16.97</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>30.533333</td>\n",
       "      <td>132 170</td>\n",
       "      <td>17.203000</td>\n",
       "      <td>37.113333</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>nov</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>103094220</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-06 05:19:50</td>\n",
       "      <td>2017-12-06 05:53:52</td>\n",
       "      <td>1</td>\n",
       "      <td>20.80</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.85</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>64.41</td>\n",
       "      <td>34.033333</td>\n",
       "      <td>132 239</td>\n",
       "      <td>20.901250</td>\n",
       "      <td>44.862500</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>66115834</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-08-04 17:53:34</td>\n",
       "      <td>2017-08-04 18:50:56</td>\n",
       "      <td>1</td>\n",
       "      <td>21.60</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>264</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>12.60</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>75.66</td>\n",
       "      <td>57.366667</td>\n",
       "      <td>264 264</td>\n",
       "      <td>3.191516</td>\n",
       "      <td>15.618773</td>\n",
       "      <td>friday</td>\n",
       "      <td>aug</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>55934137</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-09 09:31:25</td>\n",
       "      <td>2017-06-09 10:24:10</td>\n",
       "      <td>2</td>\n",
       "      <td>18.81</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>163</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>66.00</td>\n",
       "      <td>52.750000</td>\n",
       "      <td>163 132</td>\n",
       "      <td>17.275833</td>\n",
       "      <td>52.338889</td>\n",
       "      <td>friday</td>\n",
       "      <td>jun</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>13731926</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-21 06:11:03</td>\n",
       "      <td>2017-02-21 06:59:39</td>\n",
       "      <td>5</td>\n",
       "      <td>16.94</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>60.34</td>\n",
       "      <td>48.600000</td>\n",
       "      <td>132 170</td>\n",
       "      <td>17.203000</td>\n",
       "      <td>37.113333</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>feb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>52277743</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-20 08:15:18</td>\n",
       "      <td>2017-06-20 10:24:37</td>\n",
       "      <td>1</td>\n",
       "      <td>17.77</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.27</td>\n",
       "      <td>88.783333</td>\n",
       "      <td>132 246</td>\n",
       "      <td>18.515000</td>\n",
       "      <td>66.316667</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>jun</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>2684305</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-01-10 22:29:47</td>\n",
       "      <td>2017-01-10 23:06:46</td>\n",
       "      <td>1</td>\n",
       "      <td>18.57</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>66.00</td>\n",
       "      <td>36.983333</td>\n",
       "      <td>132 48</td>\n",
       "      <td>18.761905</td>\n",
       "      <td>58.246032</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>jan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>90860814</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-27 21:50:00</td>\n",
       "      <td>2017-10-27 22:35:04</td>\n",
       "      <td>1</td>\n",
       "      <td>22.43</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>163</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>58.56</td>\n",
       "      <td>45.066667</td>\n",
       "      <td>132 163</td>\n",
       "      <td>19.229000</td>\n",
       "      <td>52.941667</td>\n",
       "      <td>friday</td>\n",
       "      <td>oct</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>106575186</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-16 06:39:59</td>\n",
       "      <td>2017-12-16 07:07:59</td>\n",
       "      <td>2</td>\n",
       "      <td>17.80</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>75</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>64.56</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>75 132</td>\n",
       "      <td>18.442500</td>\n",
       "      <td>36.204167</td>\n",
       "      <td>saturday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>110495611</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-12-30 05:25:29</td>\n",
       "      <td>2017-12-30 06:01:29</td>\n",
       "      <td>6</td>\n",
       "      <td>18.23</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>68</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>68 132</td>\n",
       "      <td>18.785000</td>\n",
       "      <td>58.041667</td>\n",
       "      <td>saturday</td>\n",
       "      <td>dec</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>87017503</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-15 22:39:12</td>\n",
       "      <td>2017-10-15 23:14:22</td>\n",
       "      <td>1</td>\n",
       "      <td>21.80</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>261</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>35.166667</td>\n",
       "      <td>132 261</td>\n",
       "      <td>22.115000</td>\n",
       "      <td>51.493750</td>\n",
       "      <td>sunday</td>\n",
       "      <td>oct</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>12762608</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-17 20:39:42</td>\n",
       "      <td>2017-02-17 21:13:29</td>\n",
       "      <td>1</td>\n",
       "      <td>19.57</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.67</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.01</td>\n",
       "      <td>33.783333</td>\n",
       "      <td>132 140</td>\n",
       "      <td>19.293333</td>\n",
       "      <td>36.791667</td>\n",
       "      <td>friday</td>\n",
       "      <td>feb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>71264442</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-08-23 18:23:26</td>\n",
       "      <td>2017-08-23 19:18:29</td>\n",
       "      <td>1</td>\n",
       "      <td>16.70</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>132</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>42.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>99.59</td>\n",
       "      <td>55.050000</td>\n",
       "      <td>132 230</td>\n",
       "      <td>18.571200</td>\n",
       "      <td>59.598000</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>aug</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>11006300</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-07 17:20:19</td>\n",
       "      <td>2017-02-07 17:34:41</td>\n",
       "      <td>1</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>170</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>62.84</td>\n",
       "      <td>14.366667</td>\n",
       "      <td>170 48</td>\n",
       "      <td>1.265789</td>\n",
       "      <td>14.135965</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>feb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>68882036</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-14 23:01:15</td>\n",
       "      <td>2017-08-14 23:03:35</td>\n",
       "      <td>5</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>265</td>\n",
       "      <td>265</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.80</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>265 265</td>\n",
       "      <td>0.753077</td>\n",
       "      <td>3.411538</td>\n",
       "      <td>monday</td>\n",
       "      <td>aug</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>74720333</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-09-06 10:46:17</td>\n",
       "      <td>2017-09-06 11:44:41</td>\n",
       "      <td>1</td>\n",
       "      <td>19.10</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>15.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>68.60</td>\n",
       "      <td>58.400000</td>\n",
       "      <td>239 132</td>\n",
       "      <td>19.795000</td>\n",
       "      <td>50.562500</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>sep</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>51937907</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-19 06:23:13</td>\n",
       "      <td>2017-06-19 07:03:53</td>\n",
       "      <td>6</td>\n",
       "      <td>19.77</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>238</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17.57</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.3</td>\n",
       "      <td>76.13</td>\n",
       "      <td>40.666667</td>\n",
       "      <td>238 132</td>\n",
       "      <td>19.470000</td>\n",
       "      <td>53.861111</td>\n",
       "      <td>monday</td>\n",
       "      <td>jun</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "11      18600059         2  2017-03-05 19:15:30   2017-03-05 19:52:18   \n",
       "110     47959795         1  2017-06-03 14:24:57   2017-06-03 15:31:48   \n",
       "161     95729204         2  2017-11-11 20:16:16   2017-11-11 20:17:14   \n",
       "247    103404868         2  2017-12-06 23:37:08   2017-12-07 00:06:19   \n",
       "379     80479432         2  2017-09-24 23:45:45   2017-09-25 00:15:14   \n",
       "388     16226157         1  2017-02-28 18:30:05   2017-02-28 19:09:55   \n",
       "406     55253442         2  2017-06-05 12:51:58   2017-06-05 13:07:35   \n",
       "449     65900029         2  2017-08-03 22:47:14   2017-08-03 23:32:41   \n",
       "468     80904240         2  2017-09-26 13:48:26   2017-09-26 14:31:17   \n",
       "520     33706214         2  2017-04-23 21:34:48   2017-04-23 22:46:23   \n",
       "569     99259872         2  2017-11-22 21:31:32   2017-11-22 22:00:25   \n",
       "572     61050418         2  2017-07-18 13:29:06   2017-07-18 13:29:19   \n",
       "586     54444647         2  2017-06-26 13:39:12   2017-06-26 14:34:54   \n",
       "692     94424289         2  2017-11-07 22:15:00   2017-11-07 22:45:32   \n",
       "717    103094220         1  2017-12-06 05:19:50   2017-12-06 05:53:52   \n",
       "719     66115834         1  2017-08-04 17:53:34   2017-08-04 18:50:56   \n",
       "782     55934137         2  2017-06-09 09:31:25   2017-06-09 10:24:10   \n",
       "816     13731926         2  2017-02-21 06:11:03   2017-02-21 06:59:39   \n",
       "818     52277743         2  2017-06-20 08:15:18   2017-06-20 10:24:37   \n",
       "835      2684305         2  2017-01-10 22:29:47   2017-01-10 23:06:46   \n",
       "840     90860814         2  2017-10-27 21:50:00   2017-10-27 22:35:04   \n",
       "861    106575186         1  2017-12-16 06:39:59   2017-12-16 07:07:59   \n",
       "881    110495611         2  2017-12-30 05:25:29   2017-12-30 06:01:29   \n",
       "958     87017503         1  2017-10-15 22:39:12   2017-10-15 23:14:22   \n",
       "970     12762608         2  2017-02-17 20:39:42   2017-02-17 21:13:29   \n",
       "984     71264442         1  2017-08-23 18:23:26   2017-08-23 19:18:29   \n",
       "1082    11006300         2  2017-02-07 17:20:19   2017-02-07 17:34:41   \n",
       "1097    68882036         2  2017-08-14 23:01:15   2017-08-14 23:03:35   \n",
       "1110    74720333         1  2017-09-06 10:46:17   2017-09-06 11:44:41   \n",
       "1179    51937907         2  2017-06-19 06:23:13   2017-06-19 07:03:53   \n",
       "\n",
       "      passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "11                  2          18.90           2                  N   \n",
       "110                 1          18.00           2                  N   \n",
       "161                 1           0.23           2                  N   \n",
       "247                 1          18.93           2                  N   \n",
       "379                 1          17.99           2                  N   \n",
       "388                 1          18.40           2                  N   \n",
       "406                 1           4.73           2                  N   \n",
       "449                 2          18.21           2                  N   \n",
       "468                 1          17.27           2                  N   \n",
       "520                 6          18.34           2                  N   \n",
       "569                 1          18.65           2                  N   \n",
       "572                 1           0.00           2                  N   \n",
       "586                 1          17.76           2                  N   \n",
       "692                 2          16.97           2                  N   \n",
       "717                 1          20.80           2                  N   \n",
       "719                 1          21.60           2                  N   \n",
       "782                 2          18.81           2                  N   \n",
       "816                 5          16.94           2                  N   \n",
       "818                 1          17.77           2                  N   \n",
       "835                 1          18.57           2                  N   \n",
       "840                 1          22.43           2                  N   \n",
       "861                 2          17.80           2                  N   \n",
       "881                 6          18.23           2                  N   \n",
       "958                 1          21.80           2                  N   \n",
       "970                 1          19.57           2                  N   \n",
       "984                 1          16.70           2                  N   \n",
       "1082                1           1.09           2                  N   \n",
       "1097                5           2.12           2                  N   \n",
       "1110                1          19.10           2                  N   \n",
       "1179                6          19.77           2                  N   \n",
       "\n",
       "      PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "11             236           132             1         52.0    0.0      0.5   \n",
       "110            132           163             1         52.0    0.0      0.5   \n",
       "161            132           132             2         52.0    0.0      0.5   \n",
       "247            132            79             2         52.0    0.0      0.5   \n",
       "379            132           234             1         52.0    0.0      0.5   \n",
       "388            132            48             2         52.0    4.5      0.5   \n",
       "406            228            88             2         52.0    0.0      0.5   \n",
       "449            132            48             2         52.0    0.0      0.5   \n",
       "468            186           132             2         52.0    0.0      0.5   \n",
       "520            132           148             1         52.0    0.0      0.5   \n",
       "569            132           144             1         52.0    0.0      0.5   \n",
       "572            230           161             1         52.0    0.0      0.5   \n",
       "586            211           132             1         52.0    0.0      0.5   \n",
       "692            132           170             1         52.0    0.0      0.5   \n",
       "717            132           239             1         52.0    0.0      0.5   \n",
       "719            264           264             1         52.0    4.5      0.5   \n",
       "782            163           132             1         52.0    0.0      0.5   \n",
       "816            132           170             1         52.0    0.0      0.5   \n",
       "818            132           246             1         52.0    0.0      0.5   \n",
       "835            132            48             1         52.0    0.0      0.5   \n",
       "840            132           163             2         52.0    0.0      0.5   \n",
       "861             75           132             1         52.0    0.0      0.5   \n",
       "881             68           132             2         52.0    0.0      0.5   \n",
       "958            132           261             2         52.0    0.0      0.5   \n",
       "970            132           140             1         52.0    0.0      0.5   \n",
       "984            132           230             1         52.0    4.5      0.5   \n",
       "1082           170            48             2         52.0    4.5      0.5   \n",
       "1097           265           265             2         52.0    0.0      0.5   \n",
       "1110           239           132             1         52.0    0.0      0.5   \n",
       "1179           238           132             1         52.0    0.0      0.5   \n",
       "\n",
       "      tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
       "11         14.58          5.54                    0.3         72.92   \n",
       "110         0.00          0.00                    0.3         52.80   \n",
       "161         0.00          0.00                    0.3         52.80   \n",
       "247         0.00          0.00                    0.3         52.80   \n",
       "379        14.64          5.76                    0.3         73.20   \n",
       "388         0.00          5.54                    0.3         62.84   \n",
       "406         0.00          5.76                    0.3         58.56   \n",
       "449         0.00          5.76                    0.3         58.56   \n",
       "468         0.00          5.76                    0.3         58.56   \n",
       "520         5.00          0.00                    0.3         57.80   \n",
       "569        10.56          0.00                    0.3         63.36   \n",
       "572        11.71          5.76                    0.3         70.27   \n",
       "586        11.71          5.76                    0.3         70.27   \n",
       "692        11.71          5.76                    0.3         70.27   \n",
       "717         5.85          5.76                    0.3         64.41   \n",
       "719        12.60          5.76                    0.3         75.66   \n",
       "782        13.20          0.00                    0.3         66.00   \n",
       "816         2.00          5.54                    0.3         60.34   \n",
       "818        11.71          5.76                    0.3         70.27   \n",
       "835        13.20          0.00                    0.3         66.00   \n",
       "840         0.00          5.76                    0.3         58.56   \n",
       "861         6.00          5.76                    0.3         64.56   \n",
       "881         0.00          0.00                    0.3         52.80   \n",
       "958         0.00          0.00                    0.3         52.80   \n",
       "970        11.67          5.54                    0.3         70.01   \n",
       "984        42.29          0.00                    0.3         99.59   \n",
       "1082        0.00          5.54                    0.3         62.84   \n",
       "1097        0.00          0.00                    0.3         52.80   \n",
       "1110       15.80          0.00                    0.3         68.60   \n",
       "1179       17.57          5.76                    0.3         76.13   \n",
       "\n",
       "       duration pickup_dropoff  mean_distance  mean_duration        day month  \\\n",
       "11    36.800000        236 132      19.211667      40.500000     sunday   mar   \n",
       "110   66.850000        132 163      19.229000      52.941667   saturday   jun   \n",
       "161    0.966667        132 132       2.255862       3.021839   saturday   nov   \n",
       "247   29.183333         132 79      19.431667      47.275000  wednesday   dec   \n",
       "379   29.483333        132 234      17.654000      49.833333     sunday   sep   \n",
       "388   39.833333         132 48      18.761905      58.246032    tuesday   feb   \n",
       "406   15.616667         228 88       4.730000      15.616667     monday   jun   \n",
       "449   45.450000         132 48      18.761905      58.246032   thursday   aug   \n",
       "468   42.850000        186 132      17.096000      42.920000    tuesday   sep   \n",
       "520   71.583333        132 148      17.994286      46.340476     sunday   apr   \n",
       "569   28.883333        132 144      18.537500      37.000000  wednesday   nov   \n",
       "572    0.216667        230 161       0.685484       7.965591    tuesday   jul   \n",
       "586   55.700000        211 132      16.580000      61.691667     monday   jun   \n",
       "692   30.533333        132 170      17.203000      37.113333    tuesday   nov   \n",
       "717   34.033333        132 239      20.901250      44.862500  wednesday   dec   \n",
       "719   57.366667        264 264       3.191516      15.618773     friday   aug   \n",
       "782   52.750000        163 132      17.275833      52.338889     friday   jun   \n",
       "816   48.600000        132 170      17.203000      37.113333    tuesday   feb   \n",
       "818   88.783333        132 246      18.515000      66.316667    tuesday   jun   \n",
       "835   36.983333         132 48      18.761905      58.246032    tuesday   jan   \n",
       "840   45.066667        132 163      19.229000      52.941667     friday   oct   \n",
       "861   28.000000         75 132      18.442500      36.204167   saturday   dec   \n",
       "881   36.000000         68 132      18.785000      58.041667   saturday   dec   \n",
       "958   35.166667        132 261      22.115000      51.493750     sunday   oct   \n",
       "970   33.783333        132 140      19.293333      36.791667     friday   feb   \n",
       "984   55.050000        132 230      18.571200      59.598000  wednesday   aug   \n",
       "1082  14.366667         170 48       1.265789      14.135965    tuesday   feb   \n",
       "1097   2.333333        265 265       0.753077       3.411538     monday   aug   \n",
       "1110  58.400000        239 132      19.795000      50.562500  wednesday   sep   \n",
       "1179  40.666667        238 132      19.470000      53.861111     monday   jun   \n",
       "\n",
       "      rush_hour  \n",
       "11            0  \n",
       "110           0  \n",
       "161           0  \n",
       "247           0  \n",
       "379           0  \n",
       "388           1  \n",
       "406           0  \n",
       "449           0  \n",
       "468           0  \n",
       "520           0  \n",
       "569           0  \n",
       "572           0  \n",
       "586           0  \n",
       "692           0  \n",
       "717           0  \n",
       "719           1  \n",
       "782           1  \n",
       "816           1  \n",
       "818           1  \n",
       "835           0  \n",
       "840           0  \n",
       "861           0  \n",
       "881           0  \n",
       "958           0  \n",
       "970           1  \n",
       "984           1  \n",
       "1082          1  \n",
       "1097          0  \n",
       "1110          1  \n",
       "1179          1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set pandas to display all columns\n",
    "pd.set_option('display.max_columns', None)\n",
    "df[df['fare_amount']==52.0].head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "All the trips are associated with locationID 132 and have the same RatecodeID 2. The RatecodeID 2 indicates the trips fro JFK.\n",
    "\n",
    "Lots of trips have the same tolls_amount 5.76 dollars and 5.54 dollars, this indicates the locatation 132 frequently visited may require tolls to get to and from.\n",
    "\n",
    "Some trips happened in rush hour have extra charge of $4.5.\n",
    "\n",
    "Mean distances for most of the trips are in the range of 17-20 miles and mean duration more than 40 min, few trips have relatively small distance and duration. So, the specific trip with RatecodeID 2 may have a flat fare.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Isolate modeling variables\n",
    "\n",
    "Drop features that are redundant, irrelevant, or that will not be available in a deployed environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 25 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   Unnamed: 0             22699 non-null  int64         \n",
      " 1   VendorID               22699 non-null  int64         \n",
      " 2   tpep_pickup_datetime   22699 non-null  datetime64[ns]\n",
      " 3   tpep_dropoff_datetime  22699 non-null  datetime64[ns]\n",
      " 4   passenger_count        22699 non-null  int64         \n",
      " 5   trip_distance          22699 non-null  float64       \n",
      " 6   RatecodeID             22699 non-null  int64         \n",
      " 7   store_and_fwd_flag     22699 non-null  object        \n",
      " 8   PULocationID           22699 non-null  int64         \n",
      " 9   DOLocationID           22699 non-null  int64         \n",
      " 10  payment_type           22699 non-null  int64         \n",
      " 11  fare_amount            22699 non-null  float64       \n",
      " 12  extra                  22699 non-null  float64       \n",
      " 13  mta_tax                22699 non-null  float64       \n",
      " 14  tip_amount             22699 non-null  float64       \n",
      " 15  tolls_amount           22699 non-null  float64       \n",
      " 16  improvement_surcharge  22699 non-null  float64       \n",
      " 17  total_amount           22699 non-null  float64       \n",
      " 18  duration               22699 non-null  float64       \n",
      " 19  pickup_dropoff         22699 non-null  object        \n",
      " 20  mean_distance          22699 non-null  float64       \n",
      " 21  mean_duration          22699 non-null  float64       \n",
      " 22  day                    22699 non-null  object        \n",
      " 23  month                  22699 non-null  object        \n",
      " 24  rush_hour              22699 non-null  int64         \n",
      "dtypes: datetime64[ns](2), float64(11), int64(8), object(4)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   VendorID         22699 non-null  int64  \n",
      " 1   passenger_count  22699 non-null  int64  \n",
      " 2   fare_amount      22699 non-null  float64\n",
      " 3   mean_distance    22699 non-null  float64\n",
      " 4   mean_duration    22699 non-null  float64\n",
      " 5   rush_hour        22699 non-null  int64  \n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "df1 = df[['VendorID', 'passenger_count', 'fare_amount', 'mean_distance', 'mean_duration', 'rush_hour']]\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Pair plot\n",
    "\n",
    "Create a pairplot to visualize pairwise relationships between `fare_amount`, `mean_duration`, and `mean_distance`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x21c42073550>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jvf/uJZvpCT3Z4pFg66EWrly5SXFsI/oHg9nRbHflzCLOy+mDRiOE7RNqRxPyUhiebuNgo/2ono/e3ItjeV8CAZHtVS1H/b0cDQ63YAlB1A9yvDkV2UA98JwgCCOALcBcIEUUxWqATsciWelgQRBmA7MBMjMzj/NQVajoGYezx0BApN3lxe7xk5scS98Yg+xUhBKejsVkoNEIZPeNUTzX4TgXA5KsrLh6FNsqW7EYtGTEWxAEcPsCJFi6xiwdN6p/PC/PPktxYtRoBCYOTWHV7LNptLt54PLh3Pl6eMlbXYebFVePCiuXWzZjJJkJFrZUNHPj2GDli0Hblc+tbnXxxEel8nW6OzVw/LQdTib01iZbHF7Meh0BMaBoG9HIg72x155s8Ujg8fuZMz43LEI1Z3wuXn/XuKLZtj/QtU1KTyxduzss4rVyQxnLZoxk4tAU2f4DYpCrMDQtjswEi3yNBItBjuQBbNhbR327+4js7XD3paeycOnHfU9N21F/L0cDjUZgUn4qeXPGHrUzdLydCh0wGrhFFMUvBEF4DLijtweLorgSWAlBL/z4DFGFit6hJ3vsXrsv5Wv5spxtVW0RpL3jiWiEq9Bre3wib31bxZVFmdz22tYwJ+CFTeVhEZasJGuPE6NUc59gMXDv1Hxmj8smIAbTKlIN/X9uGcu7IRNVZoIlolb/0c48c/dxS78pLm+ApWt3k24zcajZSUldO69srjwmNfonK47UJpdMKaAoy8bm8q4SQZNegy0K8fGHsFcJSVYjqzZXMOu8bDktt2pzBZMKUuV9otn2N4dacHoDsg1MHJpCcqyRXz77RRhHY94r37J27ljcXpGVG8rk+zLvosF8XFLH+CEppNlMEVolCy/N54+rt8qaEcdCE6KniIBU4XXj2Owf/Hv5vk7i8SZqVgKVoih+0fn6NYJORm1n2oPO/+uO8zhUqDiuCK3dh64a+BvHDVIk7R1PRCOSheZt573ybVTdiOlFGYrHKSG0vPXy0RlsrWxl+bpSVqwv5YmPSuUVcH1HcGV1dnYfsvvGUNHsiCiLfXDt7gjy3ZzxQSIaIGsiXPn059z87294akMZM8/OIsFiOGbaDqcSlGxy/ls7+P3EvLB7vPSKQv78/p4IEuPSKwp/EHuVMCDJyu2ThvLsxqCuxrMby7h90tCwMWQmWCJIonPG5/Lq5krZBiRHd0NJveIqv7bNHaHHsOyDfbKmiD9AhAZGqAaK5Jx8X02InsrCpYiMEjn6WBF5o0EieR+OsBsNxzVSIYpijSAIhwRBGCKK4l5gArCr89+1wIOd/791PMehQsXxRrTwsYDIw9NG0M9mYlRmwg+yku4ewuwbE5Tz/eJAIylxJnnCilb2lhFvZsXVoxiaGifLCkvoHrJttHfV5gtCsJY+dGWVZjMxvSgDh8dPWX2HHEpVCmOXNzpJjzfxwg1nYnf7SLQYmLPqGzlqolQdsHx9CbPOy+aJj0qPW0j4ZEU0m2x1esOiRo12N5vLW6lqcYdFCdLjTT+IvYbaVHq8maevGYNBpyUlNpLoWtHs4PH1JTw8bQR7a9sRRXjx867ImqQn0dMqX0mnxeUNYDFoqW930+zwcOPYbFZvqQxLX3bXs/g+9na4FKUUkaludfHi5+XMOi+7kxeSzPD0+OP2vRwBpyIqfgidiluAf3VWfpQB1xOMkLwiCMIsoAKY/gOMQ4WK44bQ2n0JJr0GEYE/vraVd+eM/UFD81IIc0CSNWKSeHpmUdjKp/uY69rdrHh7Z8SYlSacBy4rDNMRkMr2lq8v6VHyunsYW3I+atvcxJkCPLtxP9eeO5BfnJXFsg/2yTX+io6b8MOH6k8GRLPJNJspIrwt/YBJPBaTXsMVo9OP+xiVbEqSh7990lAyE8NX5LVtLsobneytbVckBXv9IjWt4av8UI7GshkjsZq0zJmQI4tNrd5SiUEnEGvSc+1zX4aNQ3JYTHoNppCKLJNeg1mvJRAQj+q5PlyKUoo2znvlW6pbXTy7McgHOZ4OBUSPoOQdATH0uOtUiKL4rSiKRaIoFoqiOFUUxWZRFBtFUZwgimJu5/9Nx3scKlQcTyjV7i8szueFz8qOe7iyJyhNEve8tZ2lVxQq1u3Pu2gwuSmx3Dg2m/oOd1joU+lcd76xjfnFQT2I1VsqueqMTDkvfvclQxUlr7862IRGQE7RFKbHsaB4GACHmp08uHY3l43uzyPv78UfEJl1XjY3j89hcEqsYu2/dK4f6x6fqFCySSUNlMOly44nlGxKUp5USjFIP8ZKaYE543OZ/9Z2jDpNxCp/zoQc/jnrLPL7xbL1UKssXf7Mp2Vcc04Wf5qcr6iAedclQ5k7IYe7Ls7D0KmkJV1rzsvfRJXHPxwOd8+laOO7c8by8uyzeHfO2F5HC75P+qKnCEpvoSpqqlBxDKDTaZg6Ip3c5BiqW12d6pnw0LSRP2plQk9phoeuGME3h5p5aNoIDjbY0Qhg1GqY+3KwSuOZT8vCQp/RJpzSug6WzRjJnpqglsVVZ2TSP9GCRa8ssfxpaQPPfFrGiqtH8a9ZZ1HWYOfWbivVlRv2U1yYjt3jl1fPaTYT84uHyZO/Sa/h/suGMzozPiJMriLcJnvSQDkWjP+jRTSbklJz3VMMoSv4Fz8vZ/a4bDITLNS0ueSoQqvLI0copFX+nPG5gEhNq1uOfEnXemxdCctmjFQchxQRufXCweSlxjJnQg7+QFfK5UhX8RJ6c8+PhjD5fdMXvSF5Hw6qU6FCxTGCTqdhRP8ERvT/sUfShWiTRKI1KLv7lw+7Igk3XZDDio/2RA19RjtXu8vHkjW7uHx0Btl9rOSmxDIgycrBRrtySqizh8HNL33DP2edFdHcTOJJaDXgDxCmNZASa+TlX50ttzA/nFDY6Y7e2uSxKgs9UkSzKUkeXkAI4+KEljBXtTgJiLDyk/1sq2qTlSf9AdAIMHdCLnaPP6yKZH+9sgR8jFHbo60++uE+Xrj+TJavK4049mi5Fcfjnn/f9EWo0xbqlBxJ1Ep9GlWoOIXRU5i1+3vROAtS6LP7/llJZpbNGIlRp+GKMRms2VZFbkos2X1j0GgERab+wuJ8Pt1XJ5+7ya7cGEyrgVH94/mirJ6ZZ2fJHUtv/vc37KttRxAElqzZyfu7a48q/KzixICSfd45KY8Yo5Z7pxawubyJ6//xpZxmkCo7rlz5Ob/91zfc9upWLh6eRmF6HNeck8XKDWX86oUt/GVdCVqNgNWgRauBe6cMJzPBIpfOhiLIxQhEyJeHVh65vAHsHh9ZSWZuuiCHm8cH/2UlmU8oLs/3TV98n7SLBDVSoULFKQylShCdFr462ITd4yM3OYbXfn0Olc1O+sYY5dp9CaGhz9BzNdrdVDQ5ZX2K6UUZzLtoCGLnxK/RCDJTP7Si4MkNpRQXpsvaHSlxymTC0ZkJnDswiUSLgSuf/jxs5bXg7Z3MHpfNFaMz+fvG/eSlxqpVHycpQm2qts1FrEnH3toOHli7JywdtnTtboakxAIotkp/7rozuP4fX0WUiXZXgx2WFsu8iwbLKRCJRxRvNnBxQSoTh6VS0RRUxA2tKjHpNfRPsHDL+Nww3Y97pxbIolknAo5F+uL7RlBUp0KFilMcoZUg6/fWUlLbEVaRMe+iwfgDIhWNdu67bDh3h/Qr6B761GgEBiRZaXd5KW+087sLcxEQePTDfWHHTMpPlZn6ocqYgFytMXdCLi1Od0S4dekVhZw3qA86nQaHQqdNlzeoTrpozU4emjZCLSU9ySHZFEB9u1u2PwhPh+2uaUNAOZpW0eiIaifS3/Ne+ZZnri3CpNPIAm0aAbL7WsPKvbMSLTTaPTQ7PEAXwVWvEyJ0P+55cwejMxN+MPvrSYUTjk364vtCdSpUqDhNcLDRzrbK1rBoRPcVXVaSmSd/OQajTqM4aXUngs2ZkBNxPimHG23VNCQlllnnZctqm2vnhqttStcMBET0Wo1c/ifpBoTmul0eH8mxcag4edG9f0yovUh8mszEYGdOfWdlR/dSZItRx9wJObzSrd9OaGsrlzfAlweaeHVzZZgEd05nuk6CUoTt8fUlPHTFiB61JY43ekPC/DFJtxJUp0KFitMEtW0uAqLySk9a0ZU3Ovn1P7dEbVjUnQim02gUz1fb5uKsgUkRq6Y543O5/93dYf1FatpcstKmhJ70C64syuTFz8uDIelEi1pKepKju01JTkOazcTMs7PCdCYWXprPnZPyeGDtHkUdFElmvtnhkf+WYNIHJd+795U5d1ASA/p02V60CJvD4/vBJbND0VsS5o9FupWgOhUqVJwmSLOZyEuJlVf+G/bWMXZwMloN5CbHkmYzySqM0VZf3YlgA/tYFSdai17LwUY7fWMNrJp9Ng6PH4tBy5yXvwlzKKJNytH0Cx6ZNoL73t1Ns8PDkikFjOn/w6iUqjh+CLWpUMEqJQXVRe/s5OYLcph1XjZjMuP57UtfR/Ar/nr1aHZVt2HRa8NSGEuvKGTZB3sB5P4e/RMstLt87K/rkNVjo0XYMhOPLrVwuJTF0dwnCT9kpKS3UJ0KFSpOAwQCIruq2+XmYVlJZn49LodFa3aGRQJe/Dy4you2+kqODZ9wq1ocip0lazvcMsFSmnzPyErk9klDezUpR5tAReCuS4aSmWAmv59aTnoqIPRHXBKsmjshl4wEs6INePwBnviolNsmDlZ83+nzs+KjUhIsBmaPy2ZwSixDU+PISrSg12pYunY3N5w7EIfXH9ZMT0olROMlDOxjZWAf6xGlFo6F7LXSfZJwIirJqk6FChWnAbqv/IsL02WHAroiAbPHZZOXGseAJKviCkurCdb/SyFnp8fPm99WRXSW/MPEPLml+eotwWZP784ZK2sMVLe6SLOZyU8L8iHK6jvCrhN9tWg+7lLFKn5YdP8Rb3Z4yEqyMiQ5RtEGJuQlM6p/PHaPX/H9PlYjq2afg9fvJ9FqDPvhn5SfSnq8iXV76hS5QENuGYsgQLxZz79mnUWry0OS1Uh+mk0+x+FSC6HPjcWg/d6y19Hu049BwuwNBFE8OWq8i4qKxM2bN//Yw1Bx+qDHX61o9uh0etnX0IHT66fZ7iXWpMNs0NLi8GAz67GZtfSNM7Gvxk5tm5uUOCODU61hr1NtWurbgryExBgDZp0Wm0VPRryFimbHYcOooZNacmywmdi+2nZmv/i1vM/N43NYsb5UJsJJpLUzsuLpF2+h2eGhqtnJnSGVIPdfNpxBfazMf2sHYwcnY9RpGJIaS7Pdw5/e6Yp4LJ5SwCtflXPDTwaSHBdcRTk8wSqOOLMOh8dHjFGP0+PDatTR6vCSYDXgC4g02T3YLHrcXj8Wg45Wpxd/QOT1ryu4+qyBuHx+rAYdcUYtBp0Gj9+H2ydQ1+4mOdaIw+PFYtBTkBqLxWyQP6/H42fbd61B0aw4E8P72TAYtIc1gmMVuj4GOOxFlWyy++celGKRbS093oRGgKoWF31jjTg9PiwGHR6/D71WR2OHh37xZob0jWFvfTuNdjd9Y0w4PH4aO9wkxxlxen1kJRqpafN32W+clpo2P26fnzijHofXL9thfYcbg1aDwxPcptNCTasbl9eHSa/D5Q2Owe7xYTXoqGlzUVrXIbe4//P0EdjMeuweHxa9lhanl0fe38v0Mf1JtZlIjzfj9PqIM+kJiH5AS22bmzSbEYNOQ0OHG4teR4vTy/76Dv75eUWng2viF2dlMrRfLBa9DqfXh81koNHuxqTXEmvSIYrQ2OHBZNBi0AqYdFpqO9zEGnUkWnW0Of3UtLnJSjLh80Ory4vVoKO2zUWCxUBFk53vWt1A8Dn7yaC+6HQafL4AO6tbZUd7SN8Y9jd10O7y4fWJOL1+BvaxMqjTCTnYaI+IlHQ/R35aHDqd5ojsV9q31enBH4CGDnfYuXprj2qkQoWKYwSn08uG/Q20OL0sfLvrR3buhFxijDoWr9nFfZfl801FOwve7qp1Xzy5gM0H63llS7X8+omPSyhvdMrkNKtBYHN5c1iNvFIYVSncOu+iwQxLjY1Y1WUlmbmyKDMsdTEgqYAFbwfbPD+7MXwld9cb23l0xkimjcngtS2VTCpIY+7L38hh5sxEC6lxJl7YVMY15w6kw+3D3eykptUVRqabMz6X9XtquGJ0JovWfK1IuJNImVefmcUHu6qZUZTFr/+5JeyeZve10ub0yYqcocfddH4uxQUpWMwGPB4/b277jgUh+y2eUsDUwn49OhbHMnT9Y6D75751/ED218ey4O0dPd7zX4/L4ckNO2X7Wzy5gA93f8eFeanUtnlYFOJAPjK9kC8OeMK+g8WT8/H5PLS4CLOtuy7Ow+kNhJUfL7w0n9VbKhiflyqTcKX/Q49dMqWA/okmtlW28ftXu1IW9/xsKL89PyfseZszPpdvDzUyYWha2PZ5Fw3GqNWEaWDceuFg/rPtO35W2I+XviznSiF4/ZlnD+CR97/pShX+NCfsc8+dkIvVoEUQBFZ+W8mMokwWhFzrnp8NRafR8Kd3vg47ZvWWoHOUZhvGuzurGdTXyq7v2uX7V5Rl49pzB9Js92D3+MO+nz9PH8nFBakRkRKfL8CbW6si9DMmD+/Hh3vremW/kq3/feP+zudyZ9i5po5I73WqUU1IqlBxjLC9pg2vX5QnMugij9V3uCkuTEcraGWHQnp/wds7mDo6M+x1cWG6/HrROzuJtxgjauSVGi4pERyXfbAPo0Ebphj4ztYqFhTnRxDh7nkzeO1obdHbXV4SrEb+8P/yWLW5Qs6DL19Xyj1v7qDR7uayUZmU1nVgMeg50GCPaCq2fH0J15ybLU9cl4/OUNynuDCdRz/cxzXnZkfcs8fWleD1iYoS38WF6Sx4ewc7atoB2PZdq/zDKt/jt3aw7bvWHr/PaGz77vf8REX3z312Top8H3u654vW7AyzvwVv7+AXZw/EYtTLP6zSe/tqOyK+gwVv7yQnJSHCthrsHtmhkLYtemcn15ybLV879P/Q/ea/tYOGdm9E3466dnfE87Z8fQm/OHtgxPZlH+yj0eEJ2/boh/u4cdwgHv1wX9j1H3l/b3iq8J3IZ7rB7qG+w91pnzsjxvUnhWMuH52ByxtgyZpdlNZ10BriFANcc242JXUdNNg9Ed/P719Vtr2d1a2K+hnbvmvttf1Kth76XIaea2d1z89KKNRIhQoVxwi1bW4cbp/ij3FADIo+1bcry1I3drjDXgtC+PtNdq/icd2Z30oExwSLAYfbjyDAw9NGcKDBjtsX4GCDXfGc0rWV8tWVLU5ZoTC0NbR0bEqciYZ2D+k2MxaDFotep3gNZ8h9iubASNudUe6pPcp26bjatuA9rYlC+qxt61m6+GRh20dD989d1+7q9T3vbn8tDi8uT6QQWbQS5dr2yHsXbV+nxxdxbaX9/IGAzN2BIFcn9JxhqTwxaPehlUbSc9j9vCD2eP1o45HOpWSf0T6rNHbp+MaO8PnA6faFCXZ1P17J9qSKre77RrN7pXNIth7tWatpdfW6p9FxdyoEQTgItAN+wCeKYpEgCInAKmAAcBCYIYpi8/EeiwoVxxMpcUbq2pV/jDVCsDlW31ij4vtJMcaw16FUp2ADMH2vmN/dCY5pNhO/+Wk2WyqaCYhQWtdOosXAkxvKWHjpMMVzmnQa/vVFRURVx9wJuby3vZqbLsgh1qQlI97C3T8bSnmjnX9+XoFBJ3CoyRkWCn/i6tHKJadGXdj2aM2clPaV3realLdLx6XEBe9pWhTSZ0pcz6z5k4VtHw3dP3f3z9PTvetuf/EWPe0aIeIYraB8npTYyHsXbd94i4E5E3LITe5qba+UqvP4RTklJzm1Ok1w3wSLIULTQtKpCBXD6p61Muk1xIf0A4l2/WjPNKBon9E+q3RfpeOTYsLnA4tRh7YHp17J9tJsylL30exe6RySbUR71lJtRyDz3es9vx8uEEVxpCiKRZ2v7wDWiaKYC6zrfK1CxUmN4alx6DUCiybnh01Ocyfk0jfGyJptVfhFP4snhzfZWjy5gDe/rgh7vWZblfx64aX5tDjcEc25lJjf3Rs0XX9uFnaPn5UbylixvpSnNpTh8Pq5/twsEqx6Fl4aPtaFl+YTb9ZR3epi1eYKnrh6NLdNHMzD00bw3vZqJhWksWZbFYEA3PbaVm5+6Rte2XyIhZcOY9HkgohQ+OI1O5lfPCzsGnPG5/L8Z2UsLA5ee/WWSsVmTmu2VXHrhYN5/rMyFivcU71WYMmUAsXjFk8uoCA12CtieD8bi7vtt3hKAYX9bD1+nz01YzsZ0P1zbyqplW2vp3u+sDg/zP4WTy7gX58fwOHyRthLbkpMxHeweHI+pbXNzBkffv4kq4FbLxwctu2By4dT2exg5YYy7n93N3Mn5PLO1qqIY++8eKhiqmtgnxhumziE6UWRmhaPrSthelGGfI55Fw0myWIIO+8dk/LwBQIsmpwvX/edrVXcNnFIWKqw++eeOyGXPlYDfWOMivbZN9bInxSOef3rSvnvgX2svPZVhfwcADz/WRk5yTEkWQ0R38+fp49QtL38tLiIueHeqQUM72frtf1Kth76XIaeKz+t52clFMe9+qMzUlEkimJDyLa9wPmiKFYLgpAGfCyK4pCezqNWf6j4gfG9qz9aHF5ijDrMei0tTg9xJj3xliOo/mh3kWiNrP44XI28xOKua3fhD4jMen5zxMrj6WuKEIC739wucyhEEdZsq+K+qcPx+gPY3X6aHG7iLUa0AuytbWflhjJmnZctrxhDVQ9vHJvNivWlEeO54+IhtLv8GHUaBvSxYjFoiDN1Vn8YdLS6vCRYgtUfzXYPcWY9bp8fi16H3ePFatBj0An4AiINHR6sRh2xRi1GnQaP34/bF0wr9e1F9YfEgi88wuqPH0vyOATfq/pD+tw50ao/YoKVHHL1h0ZHk8NDWpyJIcmx7K1vp8nupk+MCafHT4PdTd8YI26fj/6JRmql6o/YoP3WdlZ/xHar/mjocKMPqf6we7zMeKqrYZwku12YYcPlCVDWYMcXCJCbHMMt//424jM/9cvRCBoBv1/kN//6OuL9lTPH4PEFSIkzsr+uHbNBR7zVQH27m/p2N0UDEvD6/FiNelxeP3aPD7Neh8PrQxAFnF4/VqOOOFMwqB9a/WHUaanvcGM16kgKrf5I7Kz+cHux6nXUdQTvS6xJS0WjC5NBg9mgZel7u9lc3kpRlo3fT8yj1eklzWYi3qKnqtlBQIQWhw+720eTw8PAPlYmDlMmCUvVHzWtLlJtJvLTbGHVH72xX2nfNqcHn1z90XWu3trjD8GpEIH3BUEQgadEUVwJpIiiWA3Q6VgkKx0oCMJsYDZAZmbmDzBUFSqiozf2aDbrGdE/4bDnOnOgqcfXmYnKx/VGfjdUpnf9nlrFHKnPH8DrF6PIEfu5cGgKa3fWcP+7QaZ8VpKZ2yYOicg5d1c9VAqdtrv88jVMeo3cZ0RCVpKZ+y4bTk2ri4omB692lg/ef9lwRmfGk5n4/X/IDQYtRQOi3NQe8GNLHh8Oh7NJpc/d3dZG9mJqPZxNZyWFv85MUt4vVA4bYNP+hjB7kUi///7VWWT3MdEn1kByrIkWh0fRtmwWA9c99yU3js1WfN+oF2h2+DjQ4OOON3ZGjOehK4bT0OHhxc/LWTZjBC6vyLeHGhW79T5//Znsrmln2Qf7Is4zZ0IOU0em4wuITH5iU8T7L88+i7y0eAandJV4zi8uUNTS2LS/gf/tbw6rvpLGEE0+X6fTMKJ/QgTv4UjsV9r3++KHSH/8RBTF0cDFwE2CIIzr7YGiKK4URbFIFMWivn37Hr8RqlDRC5yM9piVaJVDmRJM+mAbZ4tBq/ie1RiU2J44NIX/3DKWx38+kl+dNxCrQReRew51MCSJ5dB9Fk0OD6X/6dJ8Xt1cGXbN8kYnu75r48/vByfru382lP/cMpapI9MZ0CfmpCjf/LFwMtpkKKRcfigkvkt23xi5J0xBWmQK696pBdjdQQLz6i2VEamVeRcNprzBQbxZS1aSRfE6sSa9rCLr9YtMHJrC8HSboiP+v/0N5HYKcnU/jz8QJMJG+zzJsSa5bPOS5Z/y86e/4MqVm6hr90RED1LiglGdaCTLEx3HPVIhiuJ3nf/XCYLwBnAmUCsIQlpI+qPueI9DhYrTEQP7KKvwaTUCZfUdzLtosFymJ03E+2ramfX8ZpbNGMmwtFj+8No2HrtqFJXNDv76i9G0OLw8e20RO6taSYu3yCtESWJ59rhsMuLNJFoNIMAfJubJYkZGvQaDLtxJMOk1DE6J5W+/GEWc2fBjphhU/MDorUqkwaBlamE/cvpaqWx2UlrfwZ/f38eMoowuR6JbS3OzXsszG8uYMjKdC/Ms/OnS/DCRtkWT8ylvsjOjKIM+MUb2VLfiCwQipOihy3F4cO1ulkwpUNRGmT4mHX8AHpk2gpK6dlmwS/o8vW0INiDJyhlZib0mWZ5oOK6cCkEQrIBGFMX2zr8/ABYDE4BGURQfFAThDiBRFMU/9nQulVOh4gfGUXEqTkR0z6tmJlhYu7OGQ80OrAYtDXaPPBH3sRqwe/wsXbsXk17Dv2adxZcHm0hPMFPV7AwT47n1wsFsKW9gYn4697y5PWySffHzcq4/Nwu/SEQXydyUGG5+6ZuISfm+y4ZzbnYf1aFQxlFxKk4GHC7vH6oKCXDH69vw+EQuH51BrElLktXIoU6yZ/cf4VnnZRNr0pISa+Iv64JaFFoNDE2No93l4dEPS2l2eFg8OZ8nPi6lvNFJVpKZmy/IDXMcbr1wMP/47CDVrS4WFg9lQB8r3xxqwR8I8pDmFw/D4xPDnKP7LxvO4JQYnF4/SVYjtW0ufv70FxGf/+XZZ3F2dp+wbR6Pn7e2fRc2hiMVoTrO+NE4FSnAG0KwOFcHvCSK4lpBEL4CXhEEYRZQAUw/zuNQoeK0Rfe8all9ByV17YjAC5vK5dp+fwD+9kkZV4wJMuYTLAYONDpwev2U1nVE9Ep49MN9LJsxkqVrdzPrvGy0GshLjePJj0uDcsHxFv7Q2bBJOuaxdSX8efoIef+irEQe+e8eyhudfNfs5GCj/YTlL6g4Pugp76+kanrnpDxcvi5lzrsuHkK6Tbn5mFYDGfEWuXFYd27P5aMzeOKjUha8vZNZ52XzxEeleHwiTo8vLOph0nWlVUZnJZCfZiMryUpdu4srRqcjivCzxz8Ns/W73tgu84dMeg1PzyzqdfShstXJio9KwnrqPL6+hNGZCSf883FcnQpRFMuAEQrbGwlGK1SoUPEDo7bNxSubK3ng8gLe+rZK3i4IYNAJWA1abrogh8xEM4ea7FgMWjrckcJHLm+APTVtYWRPaXVY3+HBatCGNRWTRHo6XD55/zkTchg7OJl9dR2YDbqTRlhKxQ+DAw2RKYNGhyfMwW1z+7F7ghEGqZIJgqWghRnxsrhWKFzeADqNhoDYdV7puMtHZ3D/e3sifvylZntSQ7tQR6g74VQ6Z3q8mTSbiepWF/e8tZ2lVxRy++ptPaZ6IPiMKpGoT4bnQ1XUVKHiOOEEakYVBkkQS6/VRIR5H7h8OI0dHp7d2JWyWHhpPgOSlMW3/OHzKC5vgFiTlmvOyeK3L3X1PQhtq17fqR4qHS91Pq1ucZDfL+6HvBUquuFEstlAQGR3dVvEj7WlU3I+I8GC1x8gOdaIN+Cnf6I1LA0XbGx3kMGp8Yq2m5scI0tWm/RdwlTRFDQL+tkYPyRZMTWjESKFwUx6DVUtTmaencWLn5dT3ugkPd7Eu71onX4yC6+dEMkZFSpONXRnel+y/FPW7qwh0F0n+EcYV2l9B3aPny8ONMkh1pvH53Dj2Gzq21xhfQ9c3mB/hopGe0Rlx8LiYXJlhwSTXsPw9HjFvhLTizJYeGk+7++swaTXML94GF+U1TM4ORarQUt2csxJIyx1KuJEs9mDjXZK6trDqinSbCaSY4NKqY+8v4f99Xa+PNiETtDy+PrwviAL3tpBbko8q7dUskBBgO3BtbvxB0S5KkmyZUkNMxQmvYYd37Xy9rbvONjQQSAght2v3636VlFM7NXNlSxfH+z5YdJr6NOpnHs4KuPJLLymRipUqDgO6C3T+8cY17bKVlZuKON3F+ZGdIOcXzxMsWdCm9vP6i2VMhfirIGJVLU4mD1uEEvW7AojtNVF6TmQbjPz109K+cPEPHbXtLNyw35uuiCX/glmCtLjjokehYqjx4lms1KaLlQufnpRBgcbHbz1bVWE7Sr1ojHqNFS3unB6/GH8BGm/gX2s3DZxMK9tqeCOSUPZWd1Gfj8bf/h/Q3j4v3sVI21SGmRISqx8v6pbXbywqZxHpo1gT2172DUgGI1bcfUodlW396prqEYjMCk/lbxeRDVONKhOhQoVxwEnajOq2jaX3OwoXYFIuWTNrghxKik0XN3qCuNC5KXG0tTh5slfjsHu9hFj0uH2Btj5Xati6Lai2Ul5oxO7x8frXwc5Fgve2sG7c8ZGiCKp+OHxQ9psb9IsKXEmmh1BYSrJIUiPN3Oo2anYyXT5+hKZbAlBmxvR38bjPx+FUSfwcEgETno/1qRDxMyMov4sXrMrSDDuTA9GcxACIsx75Vv++ovREcJde2rbeebTyCqUCXnJxBj1EWTOea98S/rss2WeRihOdOG1aFDTHypUHAf0JILzYyIlziSHdw9E6VI6qG9MWNhV6lkgQeJCdLh8JMWaWPb+Xn7/6lZ2VrVR1eyQV5fdQ8FS3wMpz5xmM500gj6nA34om+1tmkVKATQ7PDzxUSnvbK2in82MViCqOJRW0zXuuRNy+fN/9wYrnUQUe9A0OzyU1LZjMujISwn+eFe3uvjbJ2X0iTXwzKdlPPFRaVhTMrHTKbcadRH3652tVSy9ojAibTE8PT6sS2zomNftqTshUqPHCmqkIgSiKNLW1kZcXByCcOKHmVScuOhJ1Of7kOFCj02zmcKU/HpznswEC4NTYpk7IRcBZVntQ00OZo/LZlDfGKwGLW0uL80Oj/y+pCtx28Q89lS3ceO4bL5rcfLvLyu4d8pweXV58wU5pMaZqGh2yKHj0DCy1EPkx3a0VATRWyGq74NAQGR7VYtimmXILWMRBMKeCykF0Gh3c6jJyT1vbeeGcweSbFMmMp41MJFlM0ZwsNHOl2WNXHlmppyey0oys2zGSErrOnD7AqzaXEFxYTrPbixj7oRc/u+ng/hoX7BFVbPDQ2qcKeJ+SPZr0mtIiTVGvH/7pKFMHJpCQT8bFU12LAad3C03GvnSH+CESI0eKxz3hmLHCj+EsEtraytXLnuHVfMuxWbrfVc2Fackvrf4lZKoDxBRdx8tr6p0PunYBIuBa87JChOWOtx5pOMrm+yY9FrirYYIQav5xcNod3npcPt5Z2sV9182nKXv7eHX5+ewp6ZNFvu56oxMXtgUdA7uujiP5FgT7W4fyXFGfP4Ai9fsorzRSVGWjXkX5XGoyYHFqOPpDfvZVtUGSCmUuF59dhU/jPjV8WygJtnfnpq2sPSahBVXj+K2V7eG2fPEoSlUNDuob3dz7XNf4vIGm9gtvaKAunZvWLXH/ZcNJ96iQ6fR4AsE0Go0/N+LWxQFsZ7dWBYmaGXSa3hq5hiu/ftXYc8SBMta9zd04PEGG5z5AwGGZ9gYPyQFoFfP+NIrCrl4WCof7q1TdFKqW12KIlgnMH7UhmInFfTmE59dq+LkgFJOtKy+46jJcKFEustHZ0RUWISeRymiUd5oZ29NGwkWPQ12D/e/t4cEi4FZ52UzsI8Fi0HH0rW7KW90yhOe1x/AoBP4rsXBkJRYzHotNpOW5z4rl/PPdo+f34VMlPOLh7FkSgE1bU68frjh+a/CJtH6Dg/NDg8ThiQzPCMyl6zix8PxzONL9hut+de+2vYwe166djdef4DbV2/jxrHZJFgMslCbgMDLXx7k5gty6J9gJsakZ/GanbLtzp2QS3KcUZF0PDjFyiPTRuDy+bliTIasoeL0+Hl59lmkxgWfly8ONJISZyIr0cLe2nZZQEtyYP63v55+NgsD+1gP+4zfvnobCRYDE4emsGr22azbU4c/0MXVOBFSo8cKvXIqBEFYJ4rihMNtU6FCRc+IRoYr70U6JPTYaLX0de0uBiRZe4xo3Du1gNROBUKJfPn4z0dFTITL15ew/KpRzCjK5ImPS8MkjvNSYrh8dAZ5qbGKZM+gHHcsN3dqVYSeUxYRUh2KUwK9TedJ9is1ngut3Lj/suE8/N+9YfsXF6az7IO9zDovm9y+Vm4Zn8PizlTGM53Kmv3izei0Ar/5V7idPbYuaGd3XzKUPbXtrN4S5ARdc04WARHK6rt6c0jpvORYI25fQE6zSA7Kk78cE/Fs3PXG9s6Ix5awiEptmwunV1kobnN5ExkJZoanx1PV4jquaaYfEz06FYIgmAAL0EcQhAS6Qh5xQL/jPDYVKk45RMurfnOoRZbzjZbG6H5sNHGcUBVCpYjGPW/u4KmZY8KOj0ba9PoDPPFxaUT53qLJ+fz141IEIV3xuP6JFprtHsX3RvWP56eDk1WH4hSAkox2NPuVGnVJjeek8uTxQ5LRaQWmF2UQELvUV20mrWx3UspCsqcEiwGH188tL3/DjWOzFe0sICJXY9w5KQ+3PxDWPE9KPSxfX8Iz1xRRWtfOH1fvkKXA290+XL4AHW6fYsRDcuznvfItT/5yDL/+ZzDVMndCTtizlWYzMb0og3SbmfoOdxhX5GQrF+0NDlf98X/AFiCv83/p31vAE8d3aCpUnHpQErWZOyFXbgcuTVKS0l+0Y1dvqYwQ25FWO+VNXQ5CtIjG3pp2Fk/Ol4/3BwKKzH+Hx69Yvrfw7Z0UF6Zj1msUjzvU5KC8yaH4XqLVcMpMoKc7omlbKNmvpJwqORbPbizDrNfi9PqZ9uQmlq8r5ZlPy5h5dhZZSWaGZ8TLdtfdjrs7y0p2JvXscHmD0t6SQyGNUxKlcnkDfHmwCW8g6ABIDsuKj0pZsb6U217dyjXnBKuVQs8v0RFd3gBfVzTL535lc9ezmWYzcc05WazcUMbtr2/n2r9/ydqdNQBhrd1Ppeehx0iFKIqPAY8JgnCLKIqP/0BjUqHilEV3URsBgfv+s0vOFUNwpaakDdD92NQ4ExOHpVLfEb7asRp0h41opNrM/Pn9Pcw6LxujTsPI/jbFNuhpNhOHmh2KjokggEYQmDshN6ITqfQ5uoe5JZ7GkeBEko5WEY7aNlcY1wGi268kEBUqQvXCpvKwdIH0Y7/qV2djD+k3IzmvSs7y6i2VLLw0n0Uhbc3nTsjFatBi9/gBZG2WUEg2HJTsjqW0rp1rzsmiw+2PiO5J6RQpmihFOaDreKnHh/Q5V84cQ5vTJ3MxpHOdSpUeSugVp0IUxccFQTgXGBB6jCiKLxyncalQccoilAx3sKGDi4enRfwop8Ypk7aUiHSDksMnp5Q4o/xDL0U0Qs8/Z3wu37U4KG908vrXlfzirEyaHV4G9LHy6IyRtLu8JFgMPL6+BK0gMCJDuXeCKILd06W0GfpDccWYjLAwd2aimYomJ6s2V/CTnCQ27W/olYPg8wX4z47qiCZMasXIiQFpJd4b+5XErEKbZEkllaFweQNUtjqJMwb7zSRYDMQYdGF2LGmtSLygVqeHmy/IoW+MEYtRR2WzI6zjbuj+odfWCMHoyf3v7qbZ4WFB8TC8flHRAUm3mblt4mDy0uJYsmanTLCcMz6XR97fI/f4qG510ezwkB5vwRdQTiv+2CJ4xxO9JWq+CAwCvgX8nZtFQHUqVKj4HvAHUFwVjc5MICAS9Ue3p9V7ZqKV3JQYuXWzVoB/XH8Gn+1vlBnn156bxYLioaTEmYg16Wi2eyir68Cs1/LkhjJZjjgt3syqrw6yZEpBWOMxiVNx6Yh0xR8KKTQshbkfnjYCnQZumziEL8oaaXP70QrIpXnRPuNnZY2yQyHdn1N9pXcyIZr9ThwWLMfsXoXUXddh6RWFLPsgnKCZlWTGZtLTaHdz/2XDqW1z8cDarkolQQCrQcu9Uwu4582gTTo9fp7aEKlkKYrB/wf2sbJ4cj4L3g5GM7KSzCy8NJ8Wh5fqVicQ5GnUtLkY1V/ZiW5yeBjYJ4bcZCsPXzGCjfsbwio4QrkfS68oZGAfqxwJUeI+narobUlpETBMPEpRC0EQtMBmoEoUxWJBEBKBVQQjHweBGaIoNh/NuVWoOBHR25B9Xbty+PjT0gae+bRMcVUejRw3LC2W6tbg9c7PTSanbwy1bW7sHh9Wg44+VgPP/u8A15yThc1sCAsXL56cj8WgRaMRuOacLJau3cvAPlb+vnE/4/NS5cZjWg0UZsTzylcHWTylgN3VbcES0pD+H4unFPDERyVAF2dEWgkumpzPv76sCCv9y+kboyjTfbDRzubyptNupXe8cSzTSdHst74j2FcjtAppelEGQ1JiWTX7HLx+P0lWI+1uL/MuGkJZfQevbK7EoBP4zU9zuPGFzbi8Ae6dMozUTuXVUJl4gL9ePYpHpo3A6fXTP8FMktUgl0lPL8ogu08MfWIMDE6xYjXocAhebr4gB7NeS5xZz2//1dVF99YLB2PSaWTnpXt0b95FgxmeHofLG8AfAEFDhNaGyxsgK9HMyplFnJudhEYjHJGg2KmS5uutU7EDSAWqj/I6c4HdBKtGAO4A1omi+KAgCHd0vr79KM+tQsUJgdBJwecXw8rSooXs02wmrv/JgAgugz8gRl2VRyPHSTnfrCQz9102nGa7l9K6rtK5BcVD+e35OVS1OHms06GQjl/w9k5mj8smxqgju1Om26TTclZ2X5kTEdpTYdZ52Tg9fv7yYUnYClIjQGaCiYXF+bh9AXbXtPHCpq6+CQvf3in3ZwiNyig5FVKfktNtpXcs0f2HKjPBwvu7a49KfE0J0ew3Nc4k22mCxcDMs7PCuDUrrh7FwUaHHGmQnNHMBDPX/eMr+fu2WYyUdnYqlZwXo05Ddl8rBp0Gly9AnFnPdf/4irkTcrnr4jzMBl1YVE0qGZ09bhBxJh19Yk0Rz8+jH+5j9rhs2Xl5YVM5s8dlyxUbRq2G6/+xOSzCkpVkprzRKd8Lk17DkNRY8tNscnlpSpyJiUNTDtvu/EiqaE509Lb3Rx9glyAI/xUE4W3pX28OFAQhA/gZ8EzI5inA851/Pw9M7eU4VKg4IdG9n8GvXtzMlUWZcn+LaIx4n1+MYKUv+2Afeq0gv+7eGyOa1kVADE7yVxZlcuPzm7nl39/w1IYgmz7BYqDV6WPh2zujktYCIsFrawQWT86nscPF0NRYbhwbbI0usd9d3mCPhTizjvnFw+T0xzOflmHSabn99e04PH52VgeVE5VK8UJfOzw+xXuaEmfina1VEX1Ell5ReMrU9B9PKPXY+M+Oapau3d2rao3eIJr9+vyibKeXj86IqB7aVtkqOxTStic+KkEQBO6dWsDjPx/FHRcPoa6zU+mdk/K45pwsnt1YxrIP9nHbq1vZV9vBw//dQ6vDw12X5JHdJ4aMBIvsUEjnXb6+hOLCdJas2UVWHyt7atp6fH5uuiCHK8YEy1ubnR6c3gAPrN0Tds7bV29jyZThEdVX+Wk23t9dG3bP399dy4Aka4+VHkdSRXOio7eRij99j2v8BfgjEBuyLUUUxWoAURSrBUFIVjpQEITZwGyAzMzM7zEEFSq+P3qyR6VJIbRrYrSQfUWUyopYox4I5pfNeq1MbMxMsGAxaJkzISespl/KHytN4NI4+sYae6wIkcrvPP4AWX3MHGxwcttrW+Vw8ryLBlPX7uKjPbXB1ufNLvrGBEmhdo8/rJujy+ePep3QJKpJryEzUdlBGJBk5fZJQ1m6dreceinKSpRDyyqO3CZvX70trJOntP1o00nR7PdQs4OsJCsmvQajThOxT3fHVnKGJSluKTWWlxZLs8NDu9vHis7nSLqG/Hx9XMqvf5rD3FXKmhUJFgN5nc4xIhh1GkW7TLTo5YiKZPMpcSaGphp4/etInQqdRoiIQBxt+/gTtavx0aC31R+fHM3JBUEoBupEUdwiCML5R3q8KIorgZUQ1LU/mjGoUHGs0JM9RpsUpFV5tJB9jFGnOMHFmfVkJZm5ZXwuV678XJ5o751awOPrS8KktFdtrpB7cVwxJiPqOJJjjbLGxa0XDubRD/eFTeAWvZasJDMHGuzohBgWvr1TMXS9eHIBd72xPYwXITk30vj7xhr58/v7IkpKpfFL+y2bMZKBfZSdCrmENjX2lBQJOhY4GpvUdotPf590UjT7jTHqZD6BElmxezWGkjP82Lqgouuc8bm4fMoqlYIQVN5cFJLO6y48dc05WbLqq0mv4a6L8yLs/96pBQzqa+XnT3+haPNzJ+SGpfFMeg0GnSaiEutonYNoongnY5qvV+kPQRDaBUFo6/znEgTBLwhCWy8O/QkwWRCEg8DLwHhBEP4J1AqCkNZ57jSg7ijHr0LFCYFobaOtnVGFR6aNQBSJaG9sMWgjRKzmTsglJdbI8qtGRYSI73lzB8WF6fLr5etLWH7lKHJTYsI6iXYfh0YAty8gt3sOiCKzxwXTGrPOy+aFTeU8sHYP91wyjBc2ldPh8UcNXS94O3wMj60rYXpRhnytOeNzKa3tkEtKZ4/L5rnrinh3zlimjkjnuevO5OXZZ/HunLGHzRlLJbSnokjQ8UY0myzKSlQUTYuGQECkrL6DTfsbKKvvCLPhaPZr7iT9TspPxdJpE6H7JFoMLJlSIG+L1sq82eFh1eYKirISFD+LKIYfK2mjSPtOL4pUlL3/vT0IiMw6L5s5E3JYNmMkL39ZTrvLF9XmlWy83eXp9T0/nHOgJIp3skp39zZSEZq6QBCEqcCZvTjuTuDOzmPOB24TRfGXgiA8DFwLPNj5/1tHMmgVKk40KLG8/3LlSBweP3e90dVJsTv5qs3p473t1Tw0bQROjw+LIdjJc0RGPII/umBP6Gunz8/4IUEy2HctDtJsw8LaPS8szsfp9WMz61m/p4aHp43A4fZx++vbIz5Hu9uHQSeQZDVg0muiKnJ2H0O6zczN43MQRVi1uYIpI4NOR7PDQ15qXJgs9/FqWKUiHNEqD87NTjoscVDC4QiE0ex3ZP94IOgUmvQ6Vm2uCNMy+ftnB3h0xki57Dk3OVZeqafZTFw+OqOTt6PnT5OH4fYGWHp5IWUNHRE9OxYW54dpVkiObEE/G35RWXPCZjbQ5nbiD8CSNbuobnXh8Ph7tHnJxoemxvLQf/ew/KpRvb7nh3MOugvbncxRuaPqUiqK4pudVRtHiweBVwRBmAVUANO/x7lUqPjRoTQpiCL87PFPw0ru9ta0MTQ1FpFgqNRq0HLx8DT+GBKenTshFxGRlFizYkh0SEosN4/PYfWW4OSaHGuSy9dEERyeAI9MG4FOCx3uAL99qat0btHkfFodbuKtRsVzH2y0c+/UAvRaIdgG3emNyr8IfV3V6mTF+lK5PE9AZMXVoxiaGsfAPifn5Hiyo6cfqt46dofjCMSZdYr2G2sM/rQEAiJmvYZbxudyqMkhl43eMWkojR0exmQmsODtHWww6Vl4aT5PfhLeZyYrycxN5+ey4O2uao4lUwpIsOgpb3Tw+4l5fNfi4L7LhnN3p/MeFJ4ykxJnpKHDo2i/kr2GbusTY+QP/28IbVFsvqLZybMby3h0xkjuumQoVoMuQsTt+zgHx7ND7A+J3opfXR7yUkNQt+KIOA6iKH4MfNz5dyOgdjhVcUqh+6SwaX+DYn42I8HCio+CvIh/XH+GonjQ3Am5pBeYI1Y9oZoPwU6gMQxIskasKLOSzCy9opBd1Y3cODZb5jwsfHsnf716NIvW7IzgO/zp0nz+u6Oa/p0M+gSLgWUzChVluLUhXJF7pw4nN9nKOdlJWAxavP4AiVbjSbvSOpXwfX+oDscRaHf5FO13ZP94xSjHfZcV4PGJ3Bq2bTjp8SZuX72N30/Mkx0UCPIlJIdCOv/8t3awbPoIDDoNpXXtBESIcft54upR7PyuHY0AAiLr9tSRFmdk8eSCMKfkT5fmk2DVy46D5Kis3FDCzwrTyU22Roi9SY7ykikF1LQ5SYszU7xio2L05lRxDo4WvY1UXBryt4+gYNWUYz4aFSpOIaTEBbsTds/Pzn9rh8zAl/K4oXB5A9g9fmraXPKqp7zRzjeHWsLIYo+tK+E/t4xFoxEoq++QJ2+JSX/dc1/Jk57Uq6C61cW3lS2UNzp58fNybr4gh+RYI6k2Ewcb7BSP7Mfyzh+J6lYXFY1OxX4Nd10ylJvH56ARIMmqx+MLfoZYk151Jk4hHI5A2Or0Ktpvq9OrGOUob3SwMkT50uUNcPcb2/nbL0ZT3uhkX2172PmipSIMeg1Or18+l+Ts9onRkxRjoqHDzcoNZcw6L5s126qYdV42sSYt/eItVDTa6RNj4MVZZ+DwBLC7/TR1uNle1cGGku3MHpfNq5srmT0um0F9Y7DotbS5vdz9xo6wa0mdS3tb4XG6oLeciuuP90BUqDjVMCDJyuDk2B45CXqtoDhpawTktEZ23xhq21yKCn7NDjdl9bCvtl2OSPRUVvrsxjL8gS5WfJxZL0sXS8JFs34ykDaXj5Q4ExkJZmUZbuCZT8uCzHxvgN/860vFVZuKkxuH4wj0iTFEVFtML8pAI0B9u5sEiwFATv+l28yKz4M3IHL7pCHkpcYqPg/dXxu0mgh9jMfWlbBy5hg2lzfLzoYgIPe4mXl2Vlia5t6pw2nqcNHm9vPO1iq5d0dADMrLS83DnrvuDG55+ZuIa4WW5nav8DhV1DGPBr1Nf2QAjxOs5hCBjcBcURQrj+PYVKg4qaHRCAxNi+tRq+GvH5WG9SSQVkGZiRY0AnLONjk2csWYlWSmqsXFL5/9Uk55zC8ehtOjXH6n1cADlxXQ7PAy76LBmA1aHv7vnjDOh93tIy81gdkvbsHlDVCUZWPR5HwWhvZMKM6n3e3l4WkjeP6zMs7N6Rs1594dp/NkezLicBwBj9/P4ikFLOhMl3VvLjbvosEYtRpZPGruhBxFO25zenlsXVAfIrRb7jtbqyI6kN45KY8Oly+oO0Gw4gOCjkur08eQlFgGJ8cwdnAyWYmWzmtqI9I097y5XXa0JdLn9KIMucGZRBitbXOHpRCl40PJyqHRm1NJHfNo0Nv0x3PAS3QRKn/Zue2i4zEoFSpOFQzsE7nSC9Vq2FfXAYj89epROH0BxAAcanbw0H/3yNoTzQ4PK64eFXGeJVOGM/vFzWEpj3mvfMuNY7MVHZmCfjZaHB4e+u/esFyx1PNA2ja/eJgc2j0ruy9//bhUDh/HmvQRxM9/dbaAltB91SbhdJ9sT1ZE4wgEAiK1rV5e+aqch6aNIMaglW0DutQ1JflrgFc2R3bNveviYcxd9Y2ccvMHRLkqRBTh318Eqzky4s002j24fAHmvbo1zMlw+QJhuhMS6VPSUgm1aQmSYxAayctMtPDn9/eRZjNFcKFCU4hSNBEiyz+PVgDrVEFvnYq+oig+F/L6H4Ig/O44jEeFilMKSiu9zAQLozMTqGt30cdqZFdNG99WtoblmoGwEOvNL33D2rljeXfOWJrsbvRaDdWtLsWUh1Sr331C3FfbHqFKGNrzQNq2ZM0uHp42gr217SSY9RQXpgdD1/EWWURI2ndhZ8+QbVVdsjXR6vJP98n2VMPBRjt3vhHsILu5/BtuHp+jGCELlWaR+mpI9qURwBsIhKVPMhIs7K1tB+D1r4PRgW1VbXLJ8rMbwzkZjQ5PBE9j0TvBHjOvf13ZGW1wcffPhnLff3aHCVhJEUMpkpcUY6DZ4WHWedk9phAXXppPotXAQ1cMJy81loL0eDQaQY7E9URuPdXRW6eiQRCEXwL/7nz9c6Dx+AxJhYpTA91D/WcO6JKXllY1tW0uBvW1okHokXvh8gaoaXNx5oAk9tS0h63254zPRRMiACTV6ksTtySf/fMzMw876Uvb9ta2887WKm4Zn8vD7wcjG3MmKP9oZCZYwpj00eryTyUpYhXK32c0flAomh0eyhvtcknn89efgUmvkSulQtUvpehAs8ODRoAAkcTNaL1sjDpNVGVMSefixc4om0mvYURGPC/872BQx8WjTKAekhLDiqtHs2TNTjkKcv9lwylI76p22VvTdsqoYx4NeutU3ACsAB4lyKn4rHObChUqFNBTqB+IKP984PLh8sQq8RuCUsZaoGtSitZj5KmZY8ImsupWF6V17TzzaZl8zpH9bb2a9KUVXHFhepiiZ7SOofUdbl684Ux8AbFHnsSpJEWsIvL7XL0lMrVx/2XD6XB5w5zOuRNy5XOY9BoMumATsfp2dwTvYfn6kmC30Hgz8RY9Xr9IVpJZjp4BxBi0inY1oI81rDxVIlj+7Rej8Yvw5MclssjW6MwE2pxuPtrXwJ7aDhYUD1M8Z1KMgVnPbw47511vbJfFvqSurN0jhSerOubRoLfVHxXA5OM8FhUqThn0FOoHIso/73x9O3dOysPh9YdNyrdeOJisJDO3TxrKgCQrXxxoVFxBba9slQmVUjOknOQYHp5WSGWzk8fWlZASl8fC4nwWrekivS28NB8tYtikv6B4GI+vL43oI6KUVpl30WDSbCZG9U9Ap+tZ9f9o1QZVnJjo/n02OzxYDVoev2oUu2vacfsC6LUCf/ukLKIk+YoxGXIk4o7Xt/PQFcPRasKjdRJRMjc5lma7m0NNPtbu+I7fnp8jE4dNeg0PXDac2yYO4ZH3u7hCC4vzqWlRbna2paKFz/fXc9WZWWGt15dMKZDbmT+7cT/3Ti0Ie3/pFYXsrGpVPGdtmwun1y/zQl78vKsMe2xOH84YkHja8IZ6W/0xELgFGBB6jCiKqqOhQoUCpNCwNDFKq6omuxuvv0s6+JpzsnB6/Vw6Ip2+cUaWdmux/OiH+3hl9tkMS7NxsNGORlAuQQVwe/08fU0R7S4vFY0Ont5Qxh8n5VFS18GNY7NJjTNz37u7wib4Jz8pZfqY/vI2jQBJMcawvHNo9GPV5goemjaC0rp2irISiDPpKehnO6xDAaeWFPGphqOpytFoBIalxYaRKv/2SZnMSXj960qWzRghlyRLz8L0ogyKshKwGrqadNW3eznYYA+T6v71uGwaHR721raTlxrLw//dQ3FhuuxQQPAZufON7cy7MJeHpo3A5fFR2eLk31+W86txg6JWXp2V3Teir878t3bw3HVn8L/9jUHn57ODYY6BTiNEff68fpGth1rk96pbXTzxUbAk9fJR6aeVjfc2/fEm8CzwDsG0lgoVKrohdGK2GHQUZdkYn5catrLPTY6hoJ9NTnXEmfVhkYlQhjkEJ7vvWl1UNDkpqWvnoz11ESHmWy8cTIJZR4vLx69e2CynVH7z0xzufnM7xYXpmPXBFtQenximOQHg8QfkCXDx5HwEIbhKfGdrlVwuKF3ryqJMHng3SHabd9Fgzhhw+AhFKE53tcEfGr1xFr5PVY6k59AdsSYtM8/OYn9dOw9cNpyaNhd9YoxUNgelulduKOO+ywpYUDyMNqcXq1HLR3vq5NLOuy4ZJgtYSZG3my/IJdakU6ziSIwx8sC7uwG4/icD2FfXwdMb9jO/uKsPTujzFa2bb6vTK3M36js88nNx5oBErn1xMwkWA3ddnEeD3UNADKYoh6TEMv+t7Xh8Yo9pj9OlnLq3ToVLFMXlx3UkKlScxFCWJB7O8nX7wngSZfUdjOxvY9mMkeypaZMnPAhnmEs//Ca9hl3VbbIQz5zxuXxZ1sjT1xTx1cEm/AEIiCKNDm9YPrq4MJ3XtlQwe9ygsElVqYVzTnIscybkMCIjnn9uOsimA00sKB5GSpwRjQaevbaILw4ErxVaUucLBLAYtD/ODVdxWPTWWVBK1S1du5v0eBMOj/+IeTJZSWaGpsXxfy9u4XcX5uL2ixGO89od1dS1uXlsXbhKZUlNG/OL82nscFPX5mJwcgyTCtIO24Y8zqiXX/sDIvMuzMVq0rNyw35mnZeNVgOFGfEs/3CfYhROen2gwR72rK3aXMGvf5rD/Le2y/s6vYEwJc/7LxuOxyf2mPY4ncqpe7vEeEwQhIWCIJwjCMJo6d9xHZkKFScRlCbmu9/YzvQx/Zl5dhbPbixjxfpSntpQxpbyFiYOTWFk/3jF1ZK286mUJtBXN1fK7y1fX8JlY/pzz5vbMem0PLuxDLvHT6LFEHauWJOWG84bFOG0KLVwfuDd3SxfV8pNL33NZWP64/IGWLxmF7ur2/nNP79hW2WrfC3JoZg7IZckiwGvXw1cnqiIxus52GgP2697FYfE87ly5ef8/OkvuGT5p6zdWRPW8lxC95bdWUlmbrogly3lzbi8AXx+kYc7dVGkMSxfX8KN4wZFkDJf/qqCc3L6cvNLX3P76u08taGMX5+f06s25E6vTx6T0+un1eVnyZpdlDc6eeKjUpavK+Xml77m/LxkgGAUbnK+PG7JpqVGedI4fz8xD4teQ3mjEwgKbEl6GNJ+d72xXR6PlPZ45tMy+sYaZYeht9/FqYDeRiqGAzOB8XSlP8TO1ypUnPaIVi6ZHGuU1TKlbRJbPCvRqrhaGpoax8PTCukba+TO17dHCvYgctvEPJrtHp6aOQatINDSrbNieryFPTVtimNKt5l57MqR7KvrYO2O6jDOhyh2jdPlC+oH2D1+Vm+plFdgo/vHs7e2nb9/doDnrjvzmN9LFccGvS3h7R5tUJJ5j6YnotEIXDgkmX/OOouaVhd9Y438cfVWLh2RjkmvQacVZLsB2LC3jrGDkwkEIluSFxemy028pOv2ZMOSbsWqzRUsmVJAms1Es8PD0LQ4dlcrHzcsLY6/XDmSVJuR2haHXD5a3+7mhU3l3HlJHo//fBRPb9jPtqo2DjbYGdnfxpwJOQREMGg1iucdnNIlL65EQD6dyql761RcBmSLoug5kpMLgmACNgDGzmu9JoriQkEQEoFVBImfB4EZoig2H8m5Vag4kRCtXDLVZlKcTJrsblqcXgV1wTxiTFoONdlJjjMyvSiDgIgsEWzSa9BqNCxdu5srizJZ8NYObjh3IBlJZuZOyOXlryooLkzH7fNHLQGtaHYyNC2WNduqwtpMSwx4aXIWxS5iW3Wri9e/rmR6UQb17W5cXj/zi4eplRsnMHpbwtu9ikMbonkiIdoPoM8X4O3t38mkR6lSqbrFyX2XFeD2BvjLh132tbA4nyc3lMpOR+h1lK4bzYal1uVSpGL+Wzu49cJc+ieaqW9zMTIjXvG40FTi/OJhtLc7cHZG+gw6gb01HUFxq+J8DF9XkJscI0vWm/QaHu2MyigtBN7tgYB8OpVT9zb9sRWIP4rzu4HxoiiOAEYCkwRBOBu4A1gnimIusK7z9XGDKIq0trYiipHhOxUqjgW6h4FNeg0PXVGIAMydkEOarWvyMOk16LUabn7pG7kD6M3jc5g7IZd4q4F73tyBLwD/9+IWXt1cSZxRy32XFfDI9EKevXYMNa1OfvvTHNw+P1edkckDa/fw2Acl9E80M3vcIJ7dWMbBRgfvbK1izvjcsDHdf9lwfpKTQKJFz6LJBYodVKcXZTC/eBhrtlWxZEoBa7ZVyQ3IVm4o4/bXg6Fpj099nk5kKNnkshkjyUywUFbfwab9DZTVdwAwKT+Vd+eM5eXZZzEhL0U+RkK0H8Cd37XKDkWazcQN5w6kvNFOjFFHcqyJxd3Sb4vW7KS4MF0uTw4d2/BOAnMo3tlaxb1Th4ftt2RKAedkJ3LXxUOYdV42L35ejscnUtnipKLJiUGvo8PtkbUmpOO6pxKXrNlFu8vPUxvKcHQ6ya9/XSmP89aL8nh24/6w8T+4djdLphRE3NOBfaxk943h7Ow+ZPeNieBJRPsuTkWnvLeRihRgjyAIXxF0FIDDl5SKwV/xjs6X+s5/IsG26ed3bn8e+Bi4vbeDPlK0tbVx5bJ3WDXvUmw22/G6jIrTHAadIJfXaQRocXh44L09NDs8wVWRy8u/v6zg9klDcXj8MoEz0aInNyWGNpePOJOOBcXDuOmlb0iwGPj1uGwcXj+//dfXYQ2bJEZ8v3gzf5g4hGc2HqCm1cUj7weJoVaDltnjBvHG14dkUqg/AI+t28evf5rDk58EV4tKK9KhqXFoBJE7Jg3l2Y37+f3EPDTAbd2EhEJD4qcLs/1kQ3eb1GsFPi6p4+aXvokgDEpVOYGA2Gs9kapWp2zHg5OtOLwBuY9MNAXWWJNOJjVK5clmvZaaNldE5O6W8YNB9PPXq0fT5PBQ0eRg2Qf7ZEXMXVUtzP/ZULwBEa1GYOUn+9lX18GfLs3n5S8reGTaCPbVtZObHMv97+6O2vvjsXUlPDpjZFjVVUOHm9+cn8OBBjstziBnY/WWStJsRv5zy1jqO3pfFn06lVP31qlYeLQXEARBC2wBcoAnRFH8QhCEFFEUqwFEUawWBCH5aM/fW+jNp55HqOLEwcFGuzxRSzDpNXIlx5I1u5g9Lpt5Fw1h4tAUKlscXHNOFi9/VcGVRZlhIdbFUwrkiTq0r8HlozNkh6K7/PBtE4eQZjPJ70n73X3J0DCCGCD3RZDG2H3MMUYdLU4vHS43m8tbOXtQsA9DtJD4gCTracNsP5lwoEHZJrv3eunOl+jtD2AgIJIaZ+Lm8TksWbNL7oshnTta6iInOYY0m0lWfV2+rpSbLsiRbTZUMyUzycyBejtmg5Z7XtoRdq5Vmyu4ZfzgsOZiC4vz+feX5fzpnZ08fU0RNZ0lrzePz6HZEZ69l1J70n3wi6I8LpNeg14jsK+2I8zJmTshl4wECwP6xDAo+ci4EKdLOXWv0h+iKH6i9K+Xx/pFURwJZABnCoJQ0NvBCYIwWxCEzYIgbK6vr+/tYSpUHBf0ZI/RiFgSQS3BYiA3OZb99R3srG5DFIMNw4oL0yNSEAve2sHdlwwlLzUWi0ErvyetqpSIdI+8v5dYk47f/DQbl8/PjWOzuWJMsJGSlF65eXwwDSONSykEPXdCLn9cvY0/vLYVrUZLUZZNnnijhcQrmuzsqWnjxrHB6yRYDKcss/1EQ082Wd6k3NhKqddLXbsLJXTPGAcCopw6+V9pAzuqWuWyzcwEc9j1Vm+pZH63FMSc8bksWbOTX5yVyV0XD+Hs7ERMeo1s21L1xOotlfgDcKDezqFmJ3sUiJdBGfntYc/BojU7uXHcoCDB2O3D3CnhvWFvHQuLw6s9Fhbn8+m+Ovn1oSYHl4/OkN9r6IiUDX9sXQlHW/Dk8wXYeqiZtTuq2XqoBZ/v1Kyc6q2i5tnA48BQwABoAbsoinG9vZAoii2CIHwMTAJqBUFI64xSpAF1UY5ZCawEKCoqUhO4Kn5U9GSP0YhYoojMR5AaJa3cUMb9lw0nwWKQJ9NQuLwB9tQG+3aESgdL54x2TIfbh9mgY+V7e2TSXGhDsNDae4l8+eLnwbbSQ1Pj2F3TFlb/v+DtHaycWcTtq7cBRISmpfz829u+C6vblwSGTkVm+4mGnmzSatAp2qRSr5dQvkQ0TYWJQ1N4f3dt2PaHrihk5tkDeOT9vdw4NjvsetWtLuwub5iCq6RzkpVkobLZyZ2vb2fO+FzcPr98rFLb8Xunhj8HoEzsdHkDOD0+THoNNrMel8/PnPG5uHx+ntxQGq4mu6GU4sJ09tV1cNvEITy78QB3XZLHQ9NG8MyG/Ywbkqx4/voO1xFFKQIBkUPNdr440BwmJHfv1AKmjkg/IvG4kwG9/TQrCHYmLQHMwI2d23qEIAh9BUGI7/zbDFwI7AHeBq7t3O1a4K0jGrUKFScYlIhYcyfkyhUT3Vc8Um27Wa9RjACIYhdxckHnCmv1lkqWTCnobDQWeUyCxRBWkte9IZjLG6y9X1Ccz5ptVUCwY6RZr0WjEVi+rjQi51zb5qK61UWzw0NuSgz/uSVI5nt3zlgm5adS3uTgrje2R1xjelHGKclsP5mQHGsMai+E2OS8iwYzLC2uR8JgNE2FndWtEdurWpxyzw2lyET/RKus0fLER6VyakFA4LF1JZQ3Onnx83JMeq18rFIk7p43d3D7pKFh5x7VP17xObAadMwvHkZ9u4vvWlys31NDus0sa1ZIYylvdJKZaGb2uGz0GgGDTkBA4I+vbWVbVZt8vu7nPxK7lhy07ZVtskMR+pl2Vrf2+lwnC3rLqUAUxVJBELSiKPqB5wRB+KwXh6UBz3fyKjTAK6IorhEEYRPwiiAIs4AKYPrRDF6FihMF3fPQfWNMtDo99I0x4hcja/Jd3gC5yTFUtzgjIgChLZld3gC7q9t4/oYzcHsDtLt8DOtnY+Gl+Tz5SXClJXVZdPn8YdeJHtHwMmVkukzeizHqONDQobiqHdjHysuzzwrLq4eu0qKF2HOTY05JZvvJBEGgk7DbRdQ06TQMTYvtsfwxWiqvujVye6joWnWri3aXV77ekJRYnt24P0K6+t6pw6nsbPYl9QOxe/wIAjw9cwwtzq6246G9c0RRZO6EXOweP6IINa3OiAZ5iybn02QPljw32z2s+KiUFVePkt/vbt8VTU5ZivtvvxyDTiNy/2XDueuN7YpdV4+0YkNy0BZdmq94T2taXYzo3+vTnRTorVPhEATBAHwrCMJDQDVw2DsriuI2YJTC9kZgwpEMVIWKEx3diVhl9R08/tG33DYxL0qNuhGH289f1gWluTMTzVS1OMN6f5j0GnJTYjnU5AzrmLj40mH837hBcsmeVHffPUQcrTZeK2hwev3Em/Xc++4uPD4xYgJdMqWAkRnxPYZno4XYU+NMKknzR0Z1q4u/fVIm/yj7A8GGX4NTY+XSRyVES+X1izdHbLeawr//5z8r5/qfDGDZB/vkaqWXv6qQpbKHpsZR3+ECMai+2V0nZdHkfJrtHjny1j0NMmd8Lqu3VNLs8DB7XDYf76njkWkj8Isieo3Ak53VH4/OGMniNbtIsBioa3Pz1IZI56a7897m9JKZYOaxL0rCBLvmTsglJzmGQX1jjrhiQ3LQLMYoz4nt1Ivm9dapmEkw0nAzcCvQH7jieA1KhYpTAZkJFuZdNIRH3t/DrRcOluV9pdRITVswrRDaxfG6cwdg0AncdEGOPAlXNTt4ftPBsImue68PlzdYdy+VAiZYDMQZtSyeXMCCt7uckfsuG87+unZsFiO1bS5SbUbunVJAbbubjAQzL9xwpqyMOKYX7cxT4owRzsjcCbn0jTUe79ur4jBIiTPJtiWhN+F7pRb1S68oxOcPRHzXfax6Hp0xkt01bQRE2FfTQprNJEcrtAIsLM7H6Q1GF8qb7Hyxv5HJI9O5Y9JQbu2WTln49k7mTsiVeRDd0yDL15cwe1w2MUYd/oDI+XnJWIxa/vpRCVUtbqYXZXD9eQP5riXoWE8vypAdb6kvh1YDBf1s7Ktt54oxQXntd7ZWsb++g2GpcREt0e+dWsAFg5OPivsgOWhPb9gfEVW5d2oB+WmnnsRBr5wKURTLO/90AYu6vy8IwmpRFE8aJ0MURdra2oiLi0MQ1NWUimOPQEDk/d21LPtgL1edkcnw9DgWTc7HYtBR2ezghU3lNDs8/OP6M1g0OZ+Fb+8EIMGi4+YLcmVuhEmv4aFphYrKl0rhVIfHx2NXjaLN6WX+WztIsBiYPS6bzAQLNW0uGttd3P/eXnmlds+bO7hz0lCsBh33/2c344YkMyLDxlkDk3q1IstMtJKbEhMWYs9NiSEzUU19/NhQcg6WzRiJRoBN+xui6omEpvJq21x4/SLz39rOz8/MxB8QWXRpPhajjhSbge2VbXJvDym18cj7e+VomURS7h4B+8u6fcz6yUBFG5Zk4e+6ZKji+3mpsVQ0OWRSsUmvYflVo2hxesOIkHMn5JIeUpEiVZak2Uz0v8jCio9KI0pRR/WPZ+qIdHKTY6hpdZFqM5GfZjtqMmXod/DvL8t5ZNoIRCAzwUx+v6M/74mMXnMqDoPsY3SeHwSqGJaK4w0pl5pgMSCKcMPzm8PCrhCcIFscXrw+PytnjqHD7Scgijz83z1hqzMNQtiKLcFiIMakHE490OBAq0GuxpBaU0v6BE4v8nmljqhOnx+v10+rK9j2Oc6k73WIV6MRGD8khew+Mae8qM/JBiWez4HGDiY99mmYk6GkJyKl8gAuWf4pCRYDsSZ9WMfbv149mof/uzesC++hJjtXnZHJ0rV7AWRtlVB7nv/WDu66OI/+iRZFG7YatFw+OgNrZzlo9/cFBBweP/MuGkxdu4t/fl6BWa9lzsvfhF3nsXUlPHfdGRHnmF6UEdFjZNGancwel43FoEOn0zCif8Ix4TqcTqJXEo6VU3HSlXuqYlgqjiecXh9/v+4MvL4A//fPLREh3FnnZbNmWxUef4B2tz9M/EpS3+xwB1dsZQ1dZEgpRfLge7sjcsRSWdy152aFpUqkviGZCRb+/ME+eYwub7DPw4EGO4OTY1k8pYA2p4+UuCNLXZwuoj4nI0K/m7L6DlkMSyJANnW42FLeRKM9SCrWaMBmNsg/fBIn4PLRGREdb7+tbFHkPSyenM8dFw+hw+0n3WZUbCjWN9bE3pp27pyUJytwmvQaHp42grp2F8+uK+F1iyEi3fKnS4P6EaElzPOLh+Hxh5OUpTFur2qNSDv0T7Ao7juwj/WIbf9Iv4PTAcfKqVChQkUnXC4fO6vaWfD2Dm4cm604gWk1ML84n22VLfIEKb0nqROu2VbF/ZcPl0tIXd4AvzgrU+ZmhOaIByfHkhRj4MbzBhJn1vOXD7vCwJI2RYwx/HE36TXkpcaxZM0uphdlkJloITfZqqYuTlFIDoKkA7F+Tw1XjM5k5t+/DEsDrP66gpnnDKRfvAlLJxHXqIvszhkQg6v+7lG0yhYn6TYzGjzotFpZZTMrycyvx+WE/cAvnlzA0zPH4PAGKKvvQK8RIqIfj0wbgU4r4PGLNLa7WNqtlfqSNbt4ZNoIxahGbnIMh5ocMu+jKCsBd5RKkJQ4I5mJVlVy/nviWCV01DuuQkUntle3yuRIUK51H5wcy67v2tBplFspx5q0XFmUyW/+uYXbV29n7oRcspLMpMR1dT2VcsTL15Wyp7adTWWNNDm8EaFdSZvimY37ueacLNJsQfLYguJhPPlxKc0OD/4A3PPmDqyG3qc+VJxckEiDkg7ENedmyz/w0JUGuObcbG5fvY2P9zYw5+WveXhaIbkpMRF2/M7WKgb2sYZF0Wae3dV0rsPjj9BN6X69BW/v4KvyZsrqO7AatHR4fHL049mNZTz43l5ue20rbS4ff35/D40Or+LzUtZgj9DkmDshl7ve2MH97+2hyeHh1c2V1LS6aHN7uW3ikLB9H7qikDOzkgBYu7OGS5Z/ys+f/oJLln/K2p01BLrLkKqIil47FYIgmAVBGBLl7ePWDOxYQupWKnUsVTuXqjiWkCSMa9rc8sQXTQr7vnd34/EHyO5rjZiss5LMDE2Lk+W2AV7YVM4fJuZR2exQdFI0nSWD0bQpWhwebjgvG60AC4qH8eiVI3n5ywr21XUwZ3yu3J2xwe7mSBEq3VxW36FOwCcQQr8bjQDLZoyUlSidbp+irYgBkRvHZpOXGhvs/tnslNNtoXZ88wW5xHaWSgIRolUBMRi5uG3iYJZePpy81FjF6wU6Jesb7B7SE8wR0Q+XNyhdf+fFQ6MKv7l9AV7YVM7D00aw9PLhzB6XLRM5pWjG9KIMUmwm6trcvPj5QVbOLOLfvwoKuRUX9kOn00QV/lIl53uP3sp0Xwo8QlCie6AgCCOBxVKXUlEU3z9uIzyGaGtr46aXtuB12fH7Atzw1Ef8/f8uwGazERfXa8VxFSoiECptvHLmGDm8GiqFPSwtDp9f5L7Obomrt1Ry5yV5YdwIKUT8fyEcC6mefk9tu+ykhOaw5xcPw+cL8OSGMq4Yk6EY2i1rcPDsxjLmTshl8ZpdcufUNqc3jEV/pCqY0SSd1WZiPz6UvpsVV49iQl4KKzeURdVO0GoEVqwvlW3P6xdl5cuHp42gvNFOdt8Ylq7djccnMu+iwSz7YF+EQxtj1MqaFS5vgLkTchSvJ6nH5ibH4vH6ye4To+h8+AMi52QnkZFgCauOkp6PZoeHkrp2AiKsWF8acfzAPlaMOg1nDEjg/+WnKqY1ogl/9VZyXk2d9J5T8SfgTIItyhFF8VtBEAYcnyEdX+jNMYCAv6MNQRD49QtfodVp+Odvxv/YQ1NxEiN0hfPcxgNymajLGwhKXCfHEmfW0eLwMqMog1c2B8mTTR1udBp4auYYtle2UpgRz+wXN0ekLx6aNgKNEJTVlrgUUifHgX0s3Pn6dtlRiSbyIzHiQzunzrswl8tHZ6DVwBlZiWQmWI76c0vj7d71UsWPA6Xv5uaXvmHt3LEsmzGSv2+M1E5YeGk+Kzfsl/dfvr6E5T8fxV0XDyEt3oLL62dk/3hufCFoo4XpcQxMsrJocj5pNhNFWTbOyu6LIEBWojVMh+KVzZEKlZJtmvQaSjpboI/sb1N0PuJMeq77x1ckdBI402xmyho6ZIdiyZQCEmP07K8NV4dNs5k6JfG1GHVBUS1JXK77j3404a/eONuqgx1Eb50KnyiKraeipoPebEWr0/7Yw1BxkiN0hfPRvgYA/nHdGXj8Ady+AA0dHrmhmJQCeW97NX4R/vVlBbdNzMPlC4SdJ1SiWCPAyk/2M794GEvW7JKlheeMz+XBd/dwZVEmqzZXUFyYjk4Dz15bREO7m3119jCFTpc3vHNqYoyJZR9uP+pJ8Puu7FQcP/Qkt23QCZyb0xdvIFjO3Or0kmg1sPS9PXLfCwjaiNvrx6TXylEAq1FLgsUAwJVnZnLvu7soLkynqsXJb8/P5a8fB4Wo+l+YG3b96lYXL2wq59EZI3H7AmEOQWi0YekVwxUd49K6dmadl02sSUt6vIXqFidnZycyLC2OXdVtvLjpIOfnJTOobwxP/XIMC97egccnRuhkzJ2QK+vEdLd3JW2P+y8bjkYIOg09PReqgx1Eb52KHYIgXA1oBUHIBeYAven9cVJA4leoKRAVR4vuK5xGu4dDzU7mv7WDWedl8+zGsjBGu8vr565LhnL769u4sihTdjikEHE0iWK9NpgX31PTxpisBBa8tUMWGpo9blCYjsD9lw1nzbaqsCZhUrgZgsz97q2jj3QSTI5VXtn1jTn15IdPNkRbdVsMWq7vXPFfPjqDunYPWgF+Orgv++o6wpzZvJRYvmtx4Be7tE+ykswsKB5Gh9tHs93N1WdmhanFLpqcj04rUNHoiLh+s8PD7po2CjNsDE6O5YoxGWHdSwE0goZVmyvCOoqu2lzB7ZOG8o9NB8OeF8lJ+HhPHT8r7Bc2jnunFhBr0jNXQb9CitZ1t3dJV2LILWPZXdPGvtp2Hv7vXkUHpDtUBzuI3hI1bwHyATfwEtAK/O44jem4QFLRVILP5WDWyo+jvq9CxeEQ2qU0zWbiN+fnyHlfQSCM0b56SyUiUNvu5p5LhrFqcwUJFkOnNLeGJ34xigXFw2SiZprNJIeiU+LMzHvlW17dXInD7ee2iUNY8fNR/N9PB0XoCNz1xnbmd3Y4hfDOqSa9hpxk5dx1Xbur+8eLCq0GRda99tQTCjzpoNQ5d9mMkXj8gTB7XLG+lKc2lHGg0cFTvxzNNed0bd9X1056gkVe6afZTFxZlMmtr3zLH17bRofHL/+QQ5fUtlGrxaDT8NC04az4+SiWXj6cFVeP4qErChiVGc+e6nZEghUkUvdSaYzVLQ5mjxskj+HZjWXcfEEuf9+4n99PzIt4Lh5bV8KvfzooYhz3vLkDnz+gaONStE7J3jUaAUGA217dKnfu7Q1hU3LiQnE0PKWTHYeNVHR2GH1bFMULgbuP/5COD3xuB7f8YyNxaQMU39eb1Np8FUcPaYWTPvtsGjs87KpuC5vMJEa7UgTizknB1MejH3Y1YVLKO1e3umho9zB3Qi5xZj0Prt0ty3dH08NocXiYdV42A5IsNHS4GZwSwx8nDSHVZiLZajzq/LEEKaQduqp8YVM5ozLjGdDn9FmdnYiIpuZ4sNGuWGFx9xvbeeH6M8MUMC0GLSJdBEylCg8lu9tT2847W6u46fwcFrzdlV5bPLmAFzaVsLm8VeZwPPlJsA255JCa9FpWfVkRxhvK7mthfF4qfwyJUIQ+F16/cifggKjcVE+K1kWLqh1N1CGaLPrp1q33sE6FKIp+QRAcgiDYRFE8qZu/60xHRkJToeJIoNEE5YMPNNgZntFFNlu9pZJ5Fw3G5Q1ETMoub4BGh0cOLf/irMwIWWNJgfPZjWXEW/SUN9nlEG7ouZQmT4MuKD701MwxJMcZKavrYOWnB1gyJZ8mjcDSKwq5ffW2iEmwtyz2lDgTBl3XdkEAg0447VZnJyqU1BwHJFkZnBwrpz+MumCL+6oWB06vn8HJMYwdnIwgQGaihdo2l2xbSiXL0X60iwvTWfB2pC7FQ9NGsLk8mJJY9M5Olk0fQYfbR58YI+WNdkQR9tV1sK2qTXYeHB7l5mJyatGqVxyHTiMoNrx7YVO5/HdJXTtNDjdJVqNs50dD2DwdJbmV0FtOhQvYLgjCB4Ac/xFFcU5PBwmC0B94AUgFAsBKURQfEwQhEVgFDAAOAjNEUWw+4tEfQ/SUHlGhordIs5k40GBnb3WbTDarbnXR2OEO9i1QmJSl1V6azRQmbiXB5Q0qcN43dTiL1+zk0hHpERO8UtXHwuJ8XvisjNsn5XHH6u1cMSZDLhUsqetg6dq9ZCWZWTmzCL1WkJ0HoNcs9swEC7d0NiYLzWUfaRWJih8OGo1Afr84xYjYgrd38Ouf5oRFD+6clMfCS/NZ9E6w6V3oj+3qLdErOq4Yk6Foy06PL+z1ntp2MhMtLFqzk/JGJ2k2k6wUW5SVyD8/LyMtPvpzcdfFeZgNGkXnQaMR0Arw+FWj8AQC9E8w8/n+RpnLIRE2JedEsvOjjTqcbpLcSuitU/Gfzn9HCh/we1EUvxYEIRbY0umYXAesE0XxQUEQ7gDu4EcW0JLSI+b4vj/mMFSc5PAHYPGaXdw4Npt3tlbJIdz8fnHceuFgHB5fxApIEvS5fHSGLG7VfYX0k0F9aOksm/MFAmF5conRL+lhDEmJJdakQxBEpozI4K+f7KfZ4UHsDAX/4f8N4ZlPDwBQ3uhk9oubeTeErFZW39FrFntFs0N2KKR973lzB6MzE07rifVEgc8XYGd1K9WtLtJsZvLT4tDpNLLglNLKf9E7O2Uio8sb4IG1e3jsqlE8Mm0ElS0OuQLJ5Q2WSyfHGblt4mDS4y3srmkLI10q2XKsUcdNF+QgCBBn1DIqM4E9NW386dJ8/vTOTjw+Ea0G+idY2PVdK7POy+Gz/Q2K5zpvUB9aXF4O1Nux6LVh3XKTO/t49Iu3kBZvYliajS8ONHL/e3sj7pPkoIfauRp1ODr0ik4liuLzSv96cVy1KIpfd/7dDuwG0oEpgHT888DUoxr9MYaaHlHxfVHX7pJTHlcWZcpks1aXj5e+LMek1zK/eJjsFGQlmRmVmcDSKwoZmhbLR3vqIpQLH5pWyNcVzeyt7aDN5cPjFfn7dUUkWfQ8OmMkWUlmIMistxp0PPTfPVS1uChvcDD/7Z00OzzcO6WAMwfE88INZ/LMpwfCKkK6k9V6yid3x5Hsq+KHhc8X4M2tVVy58nN+/c+vuXLlJt7cWoWvW+myhNDoV6h6gMsboLyhA7NBy18+LGHF+lJmnZfNzeNzmHdhLglmA0mdqYusJAvNDg8QJGEumpwfZut/vXo0Xn8AnQa+a7KTnRzLp6UN1LZ7+NM7O/n1T3O47f8NZuWGMv7w2jb+sq6E71qdvLI5Upl2fvEwatpdHKzvQK/T8r/9dRRmxBNj1OEPwCP/3cf2qlZu/vc3dLh9YWmNUIRyLEJtV4o6nJ3dh+y+MapD0Uv0VlEzF3gAGAbISSVRFHvd8rxTLGsU8AWQIopidec5qgVBSD6CMatQccJCmrSkyIEUxk206rnqjCBfQhLvyUmOoc3p49f/3BIWsn1ve7Uc4YgzavH5Rfm46UUZ9E+wcLDRjlmv5cG1u5kzYTAxndoBTR1uigvTefKTUu6bOpzlV42kocNNvFXP2NxkDjba5UlfQvdc8ZHkk7+PWJCK44ud1a2KUaTc5BiMOk1ULkToj6y0vSDDhl4rcN9lw7n7je088VEpWUlmbr4gl9++9LVsvwuKh/LMtUVUNTuxGHR8uq+Gv197Bh0eHx0uX9i+i6cUsKQz5SGlTZ78pJQpI9PDxnywwa4o+tbm9LJkzS5mj8vG3+rihp8M4tZXtoZFSaTPI9mjUlpDStdIx3wf21UVNXtfUvoc8DeC6YwLCPIkXuztRQRBiAFWA78TRbHXxAVBEGYLgrBZEITN9fX1vT3se6F7fxAVKiQczh4DARGNAEumFMiOxbMbyzDptDz9SRmpcSZmj8vm2nOziDPr2V7Vyt3ddCIeW1dCcWEaEJw4h6bZuOuN7XLVSOgKzuH1c9UZmdz9xnb8fpFr/v4l5U1OXv+6kvJGJw6Pn7R4E+cPSWb8kBQ0GiFqmWForrg3+xzNviqOPXqySakUMhQub4CaVhetLk/Eyn/O+FzWbKti4aX5rNlWJW+/c1Ieu75rZ+azXxFj1MlRit9PzAtrGJZgMdDq9HHj85u5ffV2/vDaVs4ZlIyIiFmv5a43wm19wVs7mD6mv/x6+foSigvT6d465pXNlcwvHkazw8MTH5XyzKfBZ+qFTeVyhcdj60pw+wOkxxsjPk+oPUpkynfnjOXfvzqLlTOLWLW5Qpap/z62Kylqnu7NyHrLqTCLorhOEARBFMVy4E+CIHwKLDzcgYIg6Ak6FP8SRfH1zs21giCkdUYp0oA6pWNFUVwJrAQoKir6Qb4Zn9sRJt1ts9l+iMuqOAnQkz2GSvT+7sJcZo/LRqfRMKK/jQVv7aC4MJ1/fXGQX43LIc6k4/p/fMVvz8+JmPQTLAbiLQaWfRhkzc+ZkBO1auSxdSU8PG0ELm+A9s7mUI9+uE8mneUkRxLGesNQPxIWu8p4/3HRk032izcrRiPSbCZEBFZt3sWs87Ix6jQM6GOlpsXBI9NG4PL6ePDyQjaVNeIPQLvbx4qPSkmwGIgz6chMNGMx6PB204C4fHRGBE/jzjeCHXb7xBgVHZzUOBNpNpPsACnpmzQ7PNhdXp6aOYYt5c34A11iWVI0wuUN0Ob0cdMFuXS4fdjMetz+AH+/9kwG9om0b4lMGQiIPHfdmcfEdlVFzSB6Xf0hCIIGKBEE4WagCjhsykII6no/C+wWRXFZyFtvA9cCD3b+/9YRjfo4Q5XuVnGkONDQNaE8/1k51507QNad+M1Ps0mONTEm04ZOq6HN5WXWedmKPQ6mF2WEleFJdfbRuo863EHip9mgk7dpNfS44uoNQ/1IWOwq4/3ERIxBq1gREWPUkZlojajaue+y4bj9Ab4qb8Go09DHauDZ/x3gtol5/O7CXGwmPdf/4ytc3gBFWTZu+395YfYbzUYTLYaoBOSKZgeXj86QZedH9o+n2eGR95XGnBpv5vF1+xifl6rY18ak11Db5qTE5cOg1fDI+/sw6TW8O2dshJOglKI4FrarKmoG0aNTIQjCi6IoziT4o28hKM+9BBhP0Bk4HH4CzCRYjvpt57a7CDoTrwiCMAuoAKYf1ehVqDhBUN5klyeU6lYXZr2Gx68aBQK0u3w8sHY3cybkUtfmpl+8GZ0Gqpod3Hrh4DBp4QFJ1rCJSSoVdfv8ipOy3eNjYXE+z3Q2gZIY8UUDEtVowWmOg00ORWGyvNRYspNjmToindzkGGpaXaTZTHzX6uLG5zfLtviXK0cwd8Jg/vDaVmadl81fPgzyen43fhCCRsPtq7eFlTFLVUwR/Jo4I81ODwuKh7E4REY+tPRUIl42tLt49MNSZo/LJruPlXiLgX01bdhMOmaPy+FgQwfLpo/A7QtwoNEu9wu5beIQXvz8IMWF6fS1BVMgSj/ogYDI+r21bKtsJSAGK6+GZ9jk9OD3gcovCuJwkYoxgiBkAb8AngYcwO97e3JRFDcC0b6pCb09jwoVJzqshq420mk2EwP7xlDX7sao0/DYun1cdUYmdW3usFXj0zPHcKDBLpfBWQ1aEq2GsImputXFqs0VPHD5cFLiTGGT8ryLBlOYYePB93bLQkFzJ+SSHGdUHQoVWA06mYcgwaTXYOmMaul0Gkb0T2BE/2AZ8e9Wfi7b7y/OysTnh/JGOwkWgyw1f925A7CaDNzWqWwZSkY+L7cPCRYDD67dExZluOuNHcFxXD1KtnWp30ezw8OQlFhmj8tGFEUe/XA/1a0ulq8r7ax8GsH97+2Vnxe/CLtq2okzahmTmUCMUUer08eLnx+Um+rd87Nh8mft/oNe0WSnpLZDFpuTxpjTN+Z7K8CqippBHM6peBJYC2QDWwg6CGLI/72u/lCh4lRGSpyRuRNyefmrCq4syuRXL2yWqzVuviCXfjYTd725XZbhzkiw4AuI3P/eHtmBuOmCHBa+vSNCxOqW8YPZUdnKc5+Frzqf+99B0uOHcu252fz8TD+pNhOtDi+ZiafXJKZCGZJNdk9/pHTqN4RCCt0Xpsdx7U8GUN7oYE9tO1oBfj0uG0EDd18ylNte28qiS/PDnF7JaRmaGsfQfkEHId1mpqrVyQubujQrFq/ZFdH0bsmUAqpaHOQmx3L/u7sjSp0PNtjlv78qb2ZMVgJfHWwOOhfVbQxIstIvXiQldiC+gMgf/18eRp2GldeMwagTIn7Qa0Mce+m8j60rYXRmwvd2KlR+URA9OhWiKC4HlguC8DdRFH/zA43phEBo59LuLd9D1TeV3ldx+iEz0UpuSgx/mJjHba9tVezxceuFg0kw62hx+fjDa1sj+nUIQlCMKrR0ThTBpBPIS4tTXHUeaLCz4qNS7p06HJEAGYmq1oqKICSbDBWEyk2JiXA6AwERi0FHVpKZORfmKq7k81Jj+eZQCy5vAItRFxHmz0oyE2vWcbDBwfJ1pdw8PocV60vDrlPe6CTWpOOhaSOoaXWSlxqL0xvApNei1wqKpc4D+1i5eXwO72ytwqzX4veLGLTBNOGDa3fL5aiLJufz7MfhPUQyEsxy5YXEoRARw2TIIZhidISofH4fqPyi3otfnVYOBfTcubStrY1f/m09v/zbelXaWwUQnEzGD0nBYtRGrdZ49MN9xJoNLPsgyKEw6zUyG/+mC3IYkhIrl6I+8VGp3KHRatQx/60dESWA9182nH99UdGpP7CdTftbmPHUptOyjE1FJCSbnDoynfNykpg6Ml3mDgQCImX1HWza38Cmsga2VzYzvzgfT+fKvftK3u72y6Th17ccihC1mnfRYL480AQizJ2QI9t2KEx6DXtrOvjz+3uINemZ/eIW/u/FLfzhta3UtbnkUuw0m4k5E3K4d2oBFU123tlaxe8mDMag0/Dbl75m2Qf7uPWVb7myKFPuVLrw7Z0UF6aHjbmkroOd37WGlXne8I/N/PysLNZsq2LF+mB56jXnZJ12KYrjid5Wf5yW6Klzqd58+nqiKpQh6UD0VK3h9nWV4WkEgbsuzsPu8YeJYoWGqxdemk+7y8P0Mf3RaWDlzCL21LSR0zeGT/fVyuHiUDXE07GMTYUylFbOgYDIeztq+P2r34ZFIwLNThKtBkW79foDmHTBdIVZr+Xh9/cw67xsMhPMJFgN7K8Pj27cdXFeBAn5vsuG09ju4vcT8+Ruo9L5739vD/MuzOWxq0bR5vTK+hcSobO+3cWyD5VlxSU58e4qoAERqlqdzHsl/FrdZcgfW1fCxGGpx/mbOH2gOhUqVBxDDEiycv9lwylvtCsywRNDuinaPX78AZEVnZMbgD8gsmhyPv1sJjrcfvbXdxDbx4pWI8iEteevPxODTuS5TYfCzt1dalh1KlQooay+Q3YooGtl//z1Z/BNRUvUCo4DjXbKmxyYdBrKG5088VEwzRFj0kVEN+5/bw//uK6IF244k4YON2a9FptZj9/vRwNyh9TQFESb24/XFwgT1JKcB0mPJRShjoSSCqjVoKWPVVkfo7sDUt/hYlCy+rwcC6hOhQoVxxAajUCMSYdJr2XJlIKwFdeSKQUgiHI0AsDTKSCUZjPJHAyJzBkasZhfPEwWCfrf/gaykqxkJZnDJI6PldSwilMbBxrtYT+0aTYT15yTRU2bm+c3lcuNvSTbWzw5n7ve2C7b2qOdCqqhWipKIm6VLa4wHQyJ59DicEfY99wJuWg7f+iVnACnQiO+UBnuhcX5PLmhVN4+d0Iu6fFmUmzGqMeFvlafl2MH1alQoeIYI9ak4+WvgqVtK64ezbbKFnKSY/nz+3uwmfTcPCGH5647g6qWYJtnkz7YoXTV5go5pPxdq5MEi0FWGlyyZpeslOkPwN1vbGflzDE4PX4Mei1/envHMZEaVnHqw6TXhpU/zzw7C60AWo1AeryRRKuBmy/IITXOhKkz1VHe6ASCP/APrt3N/ZcV0OLwkhxrIinGIDu4EqYXZUT0HXlsXQmzx2VzRlYiv3pxc8R7f/vlaA42KEf4+sYZI5x0KZ0y67xs/v1lOcWF6Wg1kJMcywPv7qbZ4eGV2WdHlHneO7WAx9eXyOdWn5djC9WpUKHiGMOoE/jT5AK0Asx+Mdgs7ObxOZQ3OilM19PY4WXOv4OTXFaSmUWT82l1eLiyKFNRLTBUwvjWCwfzj88O4vIGaHF4eWHTAR6ZPvKYSQ2rOPWRYNHL0TLJmZ09bhD/+F8ZV505gJ3ftSIC97y1gxvHZoc5CwAen0hAhIf+uzeM+/PkJ13VF5mJFsWIQ0CE6lan4ntNHZ4gJ+iiwTKZWXICnlhfQlWLm9njshnUNwajVsOqryo4Y2ASz24Mcjn21XUwZ3wuD4SUpla3uiLKPDMTLIzOTFCfl+ME1anoAT2VlapQ0R2S/K9Rq8Xr89Lo8kaU3d32/4Yw/60dcskowKubK5g3MY8bOiWQIZKIZtJryEuJZcl/dssRCYtByw3nDSIz0SoT8lSoCIWSJHVeShwHGuwsv2oUBp1GjoxNKkjjnk4tlXkXDZZt0aTXkGAxMOu8AaTGmUm0Grjh+XBbXfTOTh6ZNgKtRsAfELEYtIoRB40AZkNkSapJr8Go05JqM5Fo0XPbxMHEmfSk2czsrm6lqsUdJoq1bMZINh1oYk9tB7PHZTM4JZZ9te2yEy6dM9VmUiSrnu5ln8cTqlPRA3xuBzc89RF//78L6N+/v+pYqIgKqaHY3zfuZ8YZWTR1uMlItMiT54a9dfzmpznsqW6LiEjcOSmPZrsnKqHMpA+2iV763z2yQ3Hf1OHkpsTQP0FdZalQRk+S1BcOTmZ9ST07vut6LzkuWJ5Z3eqirj1oZ6u3VHLnpDx8osjDnZEJqcldKFzeAHtq2+mfYOavH5eyZEpBBDcj2FjMQG2bk3unFnCoycErmytpdnhYMqWA71ocmPRantt4gIuHp8k9cJSidqV1HbLIVkWzk5Wf7GfmOQNkrQspwpGfpjaE/KGhOhWHgSAIzFr5Ma/9YYrasVRFVEgdCpdNH8HS/+7hhnMHEgj4ue+y4dz9xnbGDk7mb5+UsnhKAQtCIhUxRi0+UWRfbbvi6m1YaiyPTBtB3zg9V52RSb94M1mJZgr6xaPT9UpmRsVpip4kqVscXiqaHGHvPXH1aNkG//l5hVwS2u728eqWQ7LN5ibHKtqq1aAlzqTnyqJMZr+4RS6RzkyyYDPpEQSRnd+1h6U25hcPw+7yYtJr6BdvJt5i4A+T8vjVC12ciwSLAZfPz+8m5HKoxck7W6vwBQKYdFr+sq5EdrQHp8SwavbZ1LS6SLWZyE+zqc/IjwD1jvcCPelVqFABQZnjBIsBvwjFhen8/bMDdLj96LUwe1w2w/vFcvukobQ4PMweN4hnN5axYn0poggP/3cvr2yujBC3undqAbFmHSaDBjEg4g+ILPtgLxpBUCdLFYdFNEnq2jY3bS5v2HsJFgP7att58PJC5k7IoW+MAbNew+xx2fRPMHNlUaZss4+8v4eFl+aH2WqwssOCx+uVo3DVrS6Wrt3L71/ZiiDA7uouh0Iaz5I1u2h1+fnDa9vYXdPBjS9sDuv2KRFJV24o4/bXt/PMp2XcdEEuY7LiWbW5QnYo5ozPxeMPMKJ/Av+vII0R/RPUZ+RHghqpUKHiGEAqy0uONVBSB1edkUmCxcihJjujMuMBgZ1VrQzPsNHutPPwtBEcaLAzJCVWrvIIlece3T8es0FDs92L1ajD7YdH3t8HBMlnhf1/1I+r4iSAw+OLWp4ZWsr8i7MySYkzUdnsYOnaPRh0AvdfNpydVa3kJsciAG6fnwSLAQg6zY0dblbOHMOu6jZEMaivUlLXztkDExmcHMO2qrawa1Y1O0mJM0dN8YX+39DuliMhSsq0C97awdwJuRQXpstS9qs2VzCpQBWwOhGgOhW9gETYhGCvDxUqusPnF1m3u4YxWQkU9LPR5vLy4Nrd3HDuQL5rcYU1Ubp3agHVLQ48/gCL1uzkmnOy5MZLEinz4WkjaHJ4SLOZaLR78Pi6SHOpNrWmXsXhkWg1UpRl45pzs3G6fViMOp7/rAwEgYAYJA4r8Xu8AZG73tjOlUWZ/KFT+VKq8DDoBO5+o6us89YLB2PWa/j7ZwcoLkzn8wNNzL1wMP/cdJBh6fEIQpCvYXf7ohI0Ja0JSTtCBLk6JZoybUaCmT+8tk0eh1oWeuLguMaHBEH4uyAIdYIg7AjZligIwgeCIJR0/p9wPMdwLOBzO/j1C1+pvT5UREWD3cUvzh7AxtIGvP4A97y5g6vOyCQpxig7FBCcEO95cwetLj/PfFrGlUWZvPxVBdOLMgDkybvV4aam1UVdmxu9RkPfWKNcm6+Sz1T0Bl6/j+lFmfzxta3c/vp2Hnl/D789P5cD9R3otQKLpxRERAEcXj+PvL+X4sL0iPcWvbOT8kZH2LZHP9yH2+eX0yPL15Vy00tfUzwynS/K6lmxvpSnNpThDYg4PV7unTo8LG0yZ3wua7ZVMb94GK9/XQmA0+vnhU3BqJ3UDycUJr2G1DgT784Zy8uzz+LdOWOZlJ+qEpZPEBzvpNM/gEndtt0BrBNFMRdY1/n6hIfebFX7faiICoNWS02ri5Ubythd006CxYDNrOdgN/VCCA/1Ll9fQnFhOuk2M3Mm5PDQtBE8+Ukp/eItvLrlEPFWPRoNuDw+nr32DC4tSFNzxSp6hYAosLCzgiLNZuLKokx++9LXLFqzm5te+oYWuzfCNvvEGMPsMxSSzkT3bWnxlggH5O43tnPDeYPkhl/LPthHRqIVh8fL7HHZzLtoMA9NG4FGA1NGpmN3eWV+xKj+8XJH3vvf3R3BNZpfPIy+sUay+8ZwdnYfsvvGqA7FCYTjOjuJorgBaOq2eQrwfOffzwNTj+cYjiWkNIgoqh0gjyek+3wy3esOty+M+Da9KIPFa3aR1dlgLBTd+3RoNVDR7GT5ulL21bZT3ujE6fVz1RmZlNS0gyig1WpJsuowGLQ/9EdTcZKivt0t26MSN6GsoUO2TalTbnJnRAxQtNvuv90mvSYqd2NPTRuXj86QX9e3u3l24wHS44Nlp3P+/Q3//rKCYWlxJMYYWXH1KJ64ehQGncD84mFyx95Vmyt4dMZIHp0xgsevGkVGgimifbuKEwc/xpInRRTFaoDO/5Oj7SgIwmxBEDYLgrC5vr7+BxtgNPTUDl3FscOJ2lq+J3t0eP3yxLphbx2Dk2NxeQM02z3cPikvIuQrhXpNeg15qXG8/nVlWH7ZatTxwqZy2tx+3H4/AVEkp6/K51ERjp5ssk+Ig6AUeXhlcyULiodRlGVj8eR8BqdYMeg03H/ZcFJjDfypW4XHkikF9LEawrbdf9lwUjul5kNh0mvwBwhr+JUca+T2SUNZu72ah6eN4K6LhzB3wmB+t+pbbnt1G7e9upUmuxePXyTGoOWpmWO44+I8lkwpIDFGT2aihdyUGMbmBH8ypNbtZfUdBLqHUFT8aDihiZqiKK4EVgIUFRWdEFajM1pOqB+6UxUnYqqpJ3tMjulqXHTx8DQsBi0Th/VhaFosrS4vj0wbQW2bi6w+Vpas2SmHeucXD+PJj0tpdniYMz6XVZsrmDshF7cvQLPDg0aAfjYzIzNUXQoVkejJJq16LQsvzWfROzsBwkiSaTYT04sy6Btr5OqzBnDvu7u44dyBtDq89E+0YDFoiTPpeOH6M6huc6MVBB5+fw8enyhXKGkEiDHq+PN//z97Zx4eVXn2/8+ZfSb7QhYSEgiELQlhCYh9gSpRS/uLgIK4FTd8ebVVaK1La0UqoG9dqnVX6lK1ti51p5ZqQUXeuqGyb4FAAiH7NpmZzHrO74/JOZnJnAECCQQ4n+vKBZnlnGcm53nO/dzL994V0ZdDvpbLxmQpBkl9m4vf/6ucFpeXokGJjB2UyM9e/S7Me7Lkva0snJanKGcuLs0nJ9nG4NTw1u2rt9WG9fN4eN5YLa+in3AyjIo6QRAyJUmqEQQhE6g/CWM4ZvweFzf/eT3xmYPRGzRXtEYQp9fHspkF3P3+NganxLB6azXnjRrI/Be+xu0TKclNYFHpCFo7vCybVciOmnZGZsRRb+/gvNEZLJwWg0EvcO/sIpqcHhrsHTxy6VhEUaTd7dMMCo0eU+9wo0fiobnFBCSRFbMLuevdrWFdcBdMyWPV5mru/MlonB4fdXYPN3be6IM362JMeh0ev8itF4zkT+v28uQne5Rz3D+niM3Vdl75Yj+PzBvLjlo7ARGln4jT7eOpK8cDEt9VtWEyCCyfVUggEMDjEw+btyHralwwOrxUVBaaCzVGbnljIyMXTdWkt/sBJ8OoeB+4Gvh957/vnYQxHBcGi+1kD0Gjn5FgNfPov8v5y4KzaHR4mDVuEAte2qD0TEiNtfBtZbMiS7z0wgLq7R1UNruVRdpi1LFy/gRe+WI/GyrblEqQ1DjzSf50GqciqbEW7vlgu6LnkJNk4akrxuP2BbjlzWCpaLLNyHU/GIIO2N8UVNhMspm4eHw2cWYDZoOeZau2KY3ClpYVwNeVbK62K+XNmQkWNlfbWbZqO7+fU8S3lS2UjcniibV7FI+c3GF3SdloXv+mkrPyBpCVHHPEtuRun0iDw83QtC5jIVQcK/R19e1uzajoB/R1SenfgC+AEYIgHBQEYQFBY+J8QRDKgfM7f9fQOKWJMxsoHZWBhER6vIkmp5ckm4n5k3N56KPd/OL1jTy7roL5k3NJspm454NtDEy0kWwzKsdw+0Rc3gDVrR7l93s+2IZF84hpHANxZgOXTexSwkywmrln1TbcfpEkm4lbLxjO6IFx6PUCbW4fohRU1rzmB4N5fn0Fv1+9k5//9TsuLclRqjjuWbWN66cNDfajmVnIgSYnN0zLIzPBQovLSyAg8diaPTz5yR6lsVdoNcnyVds5K28Aeh0YdQL3zIxU5pTzjeTH0uLCdVnS49VzOLq/TuPk0NfVH5dLkpQpSZJRkqRsSZKelySpSZKkUkmS8jv/7V4doqFxylFrd/N1RRMg4QuAThC46uzciIz7x9YG2027fSKHWjsYlt61s7IYdRxq7WD+5FwyOwWuZENDQ6On1Nrd/HNLDQ/MLeb+i4vwiSKXTcwhwWbgqrNzeeKTPTQ6fNS3ezjQ7CLerGdp2Wge+fdu1WtW/l1A4qVrJ/HPLYdY8eFOXL4A1/4gl8Wl+Up30lDUqp0KMhNoc/t5c0NVcHxzinhk3ljiLIawpmBqolY5STZWzC6MkLTPSdI8yP2Bfp2oqaFxqpCZYOG8ggwEBOrsHt769gCzx2UftvOoxWSgsT3olbAYddx6wQj+9Pk+WlzesJbn6fHaDkyj52QmWPhxUSa3d6pivnzdRAYl2dhd61DKn92+AKIUrFi64qxc2tyR2hXyNQvB61QvCHy1r5lPdjcC8Oiacp66YjwtHV6e/WwvS8pGhynILikbzRNru0J8xdmJPP1pOTdNH86GyjY2VH4fNuYH5xazq64dnQCjM+Miki+rWlw8vrZcSRiVJHh8bTnjc5K08Ec/QDMqNDR6gYAIy1dt54+XjmVAnJmiQYmKDkD3mLFOgGUzC3n5PxXc+qNRPH7ZWHbWOXh+/T7FZSwbHvddVIROCGa8a5ntGj0hIBKmnbK/yUlKjBmnN6DkTWTEmznY4uKckWn87oNtPDi3+LBS2vfMLOCF/6vgrLwByvNun4g3IPLS/+1nc7WdnXVdbcmr2zrChK2WlI3m6U/LmTMhB29AjDhXi8vLztp2Jc/oB0NTwio/IJhTUdnUEZYwCmg5Ff0ELaX8OJAkidbWVlpbW08ZkSaNvqG+PZg8Fm81EBADjM6Mx2LUs3xWuJt2SdloYk163D4/80pyeGH9Hmrsbt7+7qBiUFiMOiYPSWbhtDwe/NcuZjz6Oau31Wq1+Bo9Qr4mZf6+4SDJMUbSYoPVH19VNOD2iQxOjWHcoESSbCaqW10sLo1UsByTncDzV5fw7f5GSkdlROQ92Ex6dtc7gKBhYDHoeeqzPeQk2xibk8jjl4/lsUvH0e72cVbeAB5bU87S97ZFzI/uGi5qeRJaTkX/RvNUHAd+t4trHv8nBouNv982i4SEyJ4MkiRht9uJj49HELSd5ulKWlxwoYu3GPH6RWrtXh5dU87wtNiwUruV6/Zy07n5DEqysruunY+2N7KuvDmsNv+emQUk2ow8tqZrJ6aVzGn0FPmalA2LHxdlYu/wMTwjjjve2swNPxzGza99r4QpFpfm8+HmGuaMz2bhtDxEKahF4feL/O79bbS4vDx8STFNTm9Y3sMt5w/HHxBZMCUPq1HHuJxE9jY4mTU2i9wUG0ZBoCgrgRmPfq5Ihl88Phu9DpJsRl5ZMIkWpw+TQcfS97cqXo1oTcIGp8Tw8LyxEToVWkOx/oFmVBwnBosNoyX6xWy327n04Q94/ZYLVY0OjdMDvQ6WzyqkwxfA6xd5dM1uJeZb0egg1qRnYJINq3EQTU4PAvD0ZxVA0H08KiOeRy8dS1KMkVanh4Z2d9jxtZI5jZ6i13V1+3T7REZmxFHd4iIgwSUTBnHPB9sUgyPJZqLDF+DKybk0OjzEmvQkx5iptbt5Zl2F4kXbWdfOmxsOsnBaHjlJNhocHsx6HS5vINiRVCfg8gZodHgYm52Iw+NDQKDR5WVxaT6vfVMV0Rn1lvOHc8HodHKSY3jxmknUt7tJi7MwOCVGNeSn0wnMKMhg5KKpR3ytxolHMypOAEarZkGf7tS0uRmcaqXDG0CUxIiFc9nMAmLNet789gCzxmZRG3CHhTsCokR5gwN9I6TEmMjslsmuuXc1ekpNm5uXv6jkjh+NYFRmHC5fgJGZcbS7RQbEmcPUNedPzg27Xu+7qAiXx4/bH55bMSwtjssn5ZCXGkNAFHF4Arz2TRW/umAksWY98RYjN//t+zCDYWJuEpc/9xVJNhNL/t8oRSMDUJqNjRuUyODUWPIGxB6V4azTCUf9Wo0Ty2mfUyE3pzpR5zmVmmBp9B7p8RZc3gAdvgBmgyGilPTu97fxXVUrN52bz+jMON789gDQ1er8wY92Km2ind4AoiiFxZo1965GT0mPt2AyCMTbjLi8gc7QHPzug61khOQlqDUbu/OdLdTYPTz3eVBbJTfFyi3nD+d/P9zBU5/u4UCLi9+v3sXz6yu44YfD+MNHO2l3B1jWWfUhH+fhj3fjCQSVM2va3LS71ZuPtXb4TuyXo9FnnPaeCrvdzrVPrkZn7LtdniRJHDx4kN+sKgfgLzdO10IdZxiDU2KobnWSHGOkyaFelidKsOS9rTx+2TjunV2E3e0jwWrkzne2UNnUobzu0TXlPHdVCR9q7l2N42BwSgy/v3gMexscNDm85KXG4PQEqGzqwGzQKaGRaG3O5ccfW1vOE5eNo7zBwS/Pyycnxcb+Bge/On84cRYD9/1zB5VNHVGPY+/wK7kdNrNBtbok3mJE4/TgtPdUABitfSuKIvcD0RmtGCwxtLa20tLSQmtr6wnxkmicfHQ6AavRgCQJJNmMUQWA3D6RXXXtAAgIHGp1KwaFjNsn4vD4yRsQy+S8VPIGxGoGhUaP0ekEdIJAfbuHhz/ejS8gYTIIWIw66uxu0uLNLJyWx6iMuCMKVm2sbuPRNeUMiLew8UArB1o9/OHj3Ww5ZA+7ftWOk2A18vC8sViMOtXqksWl+aTHa1L0pwunvafiRCH3A5ErQgCsiQMQ/R3oDNaTOTSNE4TD48Prl0iOMbFidhF3vbslrGvjK19WYjHqGJEeBwI4vX4y4s3kplgjFuaUGNNJ/CQapw8BxmYncv3UPJJijPx72yGeunI8zU4voigyaXAyzS4v911UxJ3vRF6v0KWtcu9FRSTbjPzx3+URnga3T+Stbw+yaHp+WG6GbDBMHJzMyEVTaXZ6aHR4w6pL8tNjyUnWQnunC5pR0QfIBobRGoPo0xPwH1lmWSs9PbVp7XDT6PCTGW9mf6OTGLOBxy8bR5vbR1Wzi1e+rKTF5eXei4qoa3ez4rUdysJ7z8wCnvp0j9K0afmsQgbEaf0+NI6P1g43lc0e7u5sSf7l3gbmleTws1e/UzqV3v3+BqWL7lNXjmf7ITt5A2K5f/UOpbTz7rLRONw+BiVb+KqiKUwx84NN1SybVcjd7wVLQV/fUMVTV44nEBDZcsiuGAyhiZWiKDE8PU4L7Z2maEZFP0ErPT212V3r5K53t/DX688iO9nGNS9+oyzc+Wlx/PK8fAanxrCzxs59/9wZlsy29P1tvHTtJGrtbhJtRl79ch+DU4YxOPUkfyiNU5rdtU7FoAC46gd5imT3xeOzw9Q2N1S2cc8H2/jVBSOpanZy2wUjEQTY1+jk2XV7WVw6nAf+uZMNlW1kJlhYMCUPvQ6mDEvl7xsqeXb+BFpdPuX6vW7KUGaPzVI1GLTKjdMbzajoR2ilp6cudXZPZ5tmDz6/pGS73796l/KaJ68YR0qsWTWZbX+Tkzve2qI8NrN40Akbu8bpiXxNynR4uiov1JIqK5s62F3XznOfV7B8ViG+QABvQGRpWQFOr5cNlcH8sJo2tyKRPTwtjje+reGNb2vCjjVr7CAm52lW8ZnIGZGoqaHR16THm5UsdoNeUE1Yi7UY0OvUn7OaDGG/a4lrGseLfE3KyJUXMmrX4cTBSfz52okMT48hxmxkcl4KvkCARKu6NHb3c3Q9rmmqnKmcNE+FIAgzgEcBPfCcJEm/P1lj6WvkHiGSJJGQkKDlTJyGDM+I4YE5YzjY4iTeamLF7ELuenerkjextKyAx9fsZt6EQSy9sEBRMwwKYxVid3m4afow9AIMHRBLYUY8FQ0O6uxu0uMPH3cWRYn9Tc6jeq3GmYN8Te5pcCBK0OL0sHxWIUve28pb3x4MU9u0GHX87sLgNXrZpFweX1uu5PgsLs1nQJyJ311YwO9CrtuH542lKDOBB+aOYU998Bx6AYamxaLXQUWD45ivxd68prX5cWI5KUaFIAh64EngfOAg8I0gCO9LkrT9ZIynr/F7ghUherOVF/7nXOLj4wGOycDQEjr7Jza9EQlY8l4wgS03xcrTP51Ah9dPaqwZb8DPrT8aCUgMiDXx8nWTqLN7SI834/L6ueEvXQbIHy4p5v/2N3HTX78PW8BnFGRELIaiKLF6W21EHwS112qcWcjX5Mp1FV3XxiXFvHjNRJpdXjLiLLxwdQltbj8pMSbcfj8/KRpIhzfAlZNySE+wkmg1YjQIJNuMuH0iry+cjMsbUG7OAHpBF3aO2340gv/9cGewV8gxXIu9eU1r8+PEc7LCH5OAPZIkVUiS5AVeA2adpLGcEAwWG4IgcMPL33DZH1cx98H3OHDgQI/VN+WETrvd3kcj1TgWNh9q4463Nitx6sqmDm78y7dsO9TOvGe/5PqXvqOx3YtO0DE0LYFJQ1K4sHggqbFmbvjLd2GJm796cxObD7aFPXbLGxvZ3+SMOO/+JqeyYB7ptRpnFt2vSbdP5JY3N9Hg8PKrNzYx55kvuOK5r7nxL99x1Qtf81VFK8tW7eDeD3fQ5g5wx1ub+Xp/C9f9eQMev8TYnGSKByVx9tAu7ZT9TU5+9Wb49ffgv3Zx8fjsY74We/Oa1ubHiedkGRVZwIGQ3w92PhaGIAgLBUHYIAjChoaGhqM+uCyZ3R/lso3WGIyWWARB4LpnP6GqqqrHIllaQufJ4XDXY63dHZH4JqsSyv93ev3U2T1hr6mL8r7uXc7lhmLdifZ+tddqnH4cyzXZ4VWXyg69VuVETvnf2jb16yna9Rd6rJ5ei715TWvz48RzsowKNb9ThAUgSdJKSZJKJEkqGTBgwFEf3G63M+8P73PgwIF+rWgpCALXPP5PrnhiDQtfWIffF6C1tVUxMkRRPGbjqD8bVieT4+nRcrjrMTNePZFNPoXFqCPGZIhIwEyP8r7untloDcWivV9rPnZmcCzXpM1kOOK1Kknh/2YkqF9P0a6/0GP19FrszWtamx8nnpNlVBwEQmvmsoFDvXkCQRcMNSx8YR0Bv3jkN5wkDBab4r2Qcy+ueGINVz61hu3btzPvD+8r3gz5pztqBsTRhknOtEZodrudnz69lp8+vbZXQ0hFAxNYNqswQn747e8OKgJXRr1AYUZc2PsGp8QoEsby+x6eN5Yx2QlH1VAs2vu15mMaatfkspmFvPSfChZNj5TKlq/VRdPzWbW5Wvl3xexCCjLVtXPUrr/QYx3Ltdib17Q2P048J6v64xsgXxCEIUA1cBlwRW+fRFG0dJw6+QeykSH63Nz85/UYLDauefyfiuS3x+HAkpCqVJMAtLW1sfD5dfzp+h8SHx+vJHMaLDbFUxMtsVO+ycKRG6GdLkmiRmvvi+6YTHpmjxlIXmoMjQ4PqTEmApLEkNQYEm1GrEY9+akx2Kzh8ts6ncCMggxGdmseBhxVQ7Fo79eS0DRCr0m58qEwI56SwUk0Oz289t+TaWj3YDPrsRr1DE6xkRxjxuP3M2nIWNrdXh67bBwFmQkYDOr7z+7X34BYC3odjMtJPOZrsTevaW1+nHhOilEhSZJfEISbgH8RLCl9QZKkbSdjLP0ZWe67y9DQE/CLikcj1NAwWGzc8PI3yu/y+254+Rt0eoEnr5gQZgzIBondbldusrLX4nAGiKb6GR2TSU/J4OQevy+awuDRqg5qCoUa0VC7JvMsvXutqF1/g1OP7/i9eU1r8+PEctJ0KiRJ+hD4sBeOc1rsnntKd0MDiPi96zF3RJMz2fAAiM8cjE6v4+DBg/zqje9ZuWCaYjTIxocgCIr3o7W1FVEUw77v0PLYM/VvoqGhoXGmc8rLdNvtdubc9zov/HyGciNsa2vD1+FCZ3Ai+jvwu13K630d4Y91//1oH+ut15yo9x0O+TU3PrsNg9nKTx96G0tCCqLPjdcVLL2SfxcDIj996O2Ix15a9JOw7/+6J1eH/U16QvDv51D+35uEHltDQ0NDo3cRTpXkPEEQGoDKKE+nAo0ncDi9wak4Zjg1x30sY26UJGlGtCdPk+tRG2fv0pfjPOz1CP3+mjyZ5z+TP3tfnT/q9XjKGBWHQxCEDZIklZzscfSEU3HMcGqO+0SP+VT5jrRx9i79eZwne2wn8/xn8mc/GefXGoppaGhoaGho9AqaUaGhoaGhoaHRK5wuRsXKkz2AY+BUHDOcmuM+0WM+Vb4jbZy9S38e58ke28k8/5n82U/4+U+LnAoNDQ0NDQ2Nk8/p4qnQ0NDQ0NDQOMloRoWGhoaGhoZGr6AZFRoaGhoaGhq9wiljVMyYMUMi2B5d+9F+TsTPYdGuR+3nBP8cEe2a1H5O4E9UThmjorHxVBDU0zhT0K5Hjf6Gdk1q9AdOGaNCQ0NDQ0NDo3+jGRUaGhoaGhoavcIp36VUQ6OniKLE/iYndXY36fEWBqfEoNNpLdo1NDR6lzNxrdGMCo0zClGUWL2tllve2IjbJ2Ix6nh43lhmFGSc9pNdQ0PjxHGmrjVa+EPjjGJ/k1OZ5ABun8gtb2xkf5OzT84nSRIejwdNuVZD48ziRK81/QXNqNA4o6izu5VJLuP2idS3u/vkfF6vl0ufWIPX6+2T42toaPRPTvRa01/QjAqNM4r0eAsWY/hlbzHqSIuz9Nk59QZTnx1bQ0Ojf3Iy1pr+gGZUaJxRDE6J4eF5Y5XJLsc5B6fEnOSRaWhonE70dK0RRYmKBgdf7G2kosGBKJ6aIVMtUVPjjMNkEFg4LQ9RAp0Q/F1DQ0OjN9HpBGYUZDBy0VTq292kxUWv/jidkjo1o0LjjGJ/k5Ob/vp9WKzTYtTx4aKp5A2IPYkj09DQON3Q6QTyBsQecW2JltQ58hRcl7Twh8YZxZmaPKWhodF/OZ3WJc2o0DijOFOTpzQ0NPovp9O6pBkVGmcUWqKmhoZGf+N0Wpe0nAqNM4qeJE9paGhonAhOp3VJMyo0zjiONnlKQ0ND40RxuqxLmlGhccZxJjb50dDQ0Dgajnd91IwKjTMKUZRYu6uOzQfbECXQC1CUncD0EemaYaGhoXFG4/eL/KeiiQ2VzYgSfLCpmjtmjOqRXoZmVGicUVQ1Oymvc7ByXYUiMrO4NJ9hA2IZnHpqux01NDQ0jhVRlPjH1hrueGuzsjYump7P/at3MDIj7qjDMn1a/SEIgkUQhK8FQdgkCMI2QRDu6Xw8WRCEjwVBKO/8N6kvx6GhIVNn9/DomvIwkZlH15RTZ/ec5JFpaGhonDz2NzkVgwKCa+Nja8spG5PVI72Mvi4p9QDTJUkqBsYCMwRBmAz8GlgjSVI+sKbzdw2NPsfp9auKzLi8/pM0Ig0NDY2TTzQBLr2OHull9KlRIQVxdP5q7PyRgFnAS52PvwTM7stxaGjI5CbHqIrM5CSfevXgGhoaGr1FNAGuktzkHull9Ln4lSAIekEQNgL1wMeSJH0FpEuSVAPQ+W9alPcuFARhgyAIGxoaGvp6qBpnAENS1UVmhqQeedJo16NGf0O7JjV6CzUBrvvnjOEHeSk9SmIXJOnEtFcVBCEReAe4GVgvSVJiyHMtkiQdNq+ipKRE2rBhQ5+OUePMQC6ZOoLIzGFn0dFejx6Phyue+Zy/3jAVs9l8HKPWOMM54qqurZEax8tRro1wmOvxhFV/SJLUKgjCp8AMoE4QhExJkmoEQcgk6MXQ0DghnC4iMxoaGhq9SW+sjX1d/TGg00OBIAhW4DxgJ/A+cHXny64G3uvLcWhoaGhoaGj0PX3tqcgEXhIEQU/QgHlDkqRVgiB8AbwhCMICoAq4pI/HoaGhoaGhodHH9KlRIUnSZmCcyuNNQGlfnltDQ0NDQ0PjxKIpamr0O7TeHBoaGv0BbS3qOZpRodGvEEWJ1dtqueWNjYpU7MPzxvZIe15DQ0PjeNHWomOjz3UqNDR6wv4mpzKJIajodssbG9nf5DzJI9PQ0DiT0NaiY0MzKjT6FdGkYnuiPX8kRFGiosHBF3sbqWhwIIonRqtFQ0Ojf3G4teBErEWnI1r4Q6NfIUvFhk5mi1HXI+35w6G5NDU0NODIa0Ffr0WnK5qnQqNfoSYV+/C8sT3Snj8cmktTQ0MDjrwW9PVadCycCl5WzVOh0a/Q6QRmFGQwctHUo5GK7THRXJp1dremsKmhcQZxuLVAfn5EehyrF0+l1t77a1FPOVW8rJpRodHv6EsZ7RizQdWlGWPS9/q5NDQ0+i/Rwhu+gMRPHvu83924o3lWRi6a2q82RFr4Q+OMwucXWVyaH+bSXFyajy/Q/9yIGhoafUe0rpxL3tvSL8Ojp0riqOap0DijaHP7ePmLShZMyUMQQJLg5S8qGZkRd7KHpqGhcQJRC7U2OT1UNnWEvU6+cR+NN6AvxbJOlcRRzajQOKPITbLR4vLy5Cd7lMcsRh2DkmwncVQaGhonA7VQ67HeuPs650H2rHQ//slMHFVDMyo0zij0eoE7fzySRqcXUQK9ACkxJgz6/pPopKGhcWT6witwPDfuvs556Osk9t5CMyo0zigaHB46fCIr11Uoi8YvzxtOo8PD4NT+k+ykoaERnb7yChzPjftwOQ+9lUjZl0nsvYWWqKlxRmHS63jk37vDdhOP/Hs3Rr02FTQ0ThX6Um9GvnFPzkslb0DsURspcs5DKP0x56Gv6dFKKgiCVRCEEX01GA2NvsblDajuJlzewEkakYaGRk/pj5UQ/VEs62Rw1OEPQRAuBB4CTMAQQRDGAsskSZrZR2PTOEPxegNsPtRGrd1NZryFooEJmHpJRyJaBnV6/Jm1m9DQOJU52ZUQbrefLTVt1No9ZMSbKcpMwGIxnBI5D31NTzwVvwMmAa0AkiRtBAb39oA0zmy83gDvbj7ET5//ipv++j1XPv8V724+hLeXPAk5STZWzC4M202smF1Ijlb9oaFxynAyvQJut5/3t9Qw/4Wvuflv3zP/ha95f0sNbrf/mEMnpxM9SdT0S5LUJghn3pekceLYfKiNu9/bGhYrvfu9reSlxlAyOPm4j1/V4uK1ryt5YG4xHV4/NpOBl/5TwficpH6d/KShodFFTxMq/X6RbTVt1LS5yUywUpAZj8FwbHlUW2rauPv9bmvU+1sZkmpj4pCUY/5Mpws9MSq2CoJwBaAXBCEfWAT8p2+GpXGmUnsEPf7jpcnpYfrIDG7/+yYla3zR9HyanB7NqNDQOIU42koIv1/k3U3V3PXuVmXOr5hdyOzirGMyLGrtnihrlKfHxzoSfSmm1Vf05Bu9GSgAPMBfgTbgF30wJo0zmMwoGdS9lfMgIPDY2vKwXcZja8sR6N8TVUND49jYVtOmGBQQnPN3vbuVbTVtx3S8jHhzlDXKfNxjDUUum/3JY59z+Z++4iePfc7qbbX9sjNpKEdtVEiS5JIk6beSJE3s/LlLkqT+JTquccpTNDCBZbPCcx6WzSpkzMCEXjl+Q7v6LqPR0fu7DA0NjZNPTZu697O27dhuX0WZCSyb2W2NmllIUWbvrFEyfVk225f0pPrjY+ASSZJaO39PAl6TJOlHfTQ2jTMQk0nP7DEDyUuNUVx+Y3qx+iMl1qSaNZ5sM/XK8TU0NPoXmQlW1TmfkXBs3k+LxcDMokyGpNqos3tID6n+6E1OhJiWGscbcunJt5AqGxQAkiS1CIKQ1oP3a2gcFaIoIUkSokTw315092UkmLlnZgFL39+mxFfvmVlARmLvui41NDT6BwWZ8ayYXRiRU1FwBM9CtLJRCBoWfZ2UeTLKZntDqbQnRoUoCEKOJElVAIIg5AL9O7ijccrhdvv5YGsNS97rWgCWzyrkwsLMXtkJ+Pzw1Kd7wrqUPvXpHkpyJ/XC6DU0NPobBoOO2cVZ5KfFUtvmJiPBQkFmgpKkqVYZ4veLvL+lRqnykEMcM4uOfh063h3/yWgg1hv9S3qySv8WWC8Iwmedv08DFvZkwBoaR2JrrV0xKCB4US95bytDeqmktL7dTWVTR1iXUvnxoWla9YeGxumIwaCjeFASxYO6HhNFiYoGB9tq7FQ0OHhjw0FaXF5WzC5kcErMcZWN9saO/2Q0EOuNkMtRGxWSJK0WBGE8MBkQgF9KktTYg/FqaByRRoeHJJuJi8dnI0uivPXtQZp6KZHSZjKQm2KlbEyWcvwPNlVj66WcDQ0Njd7laHb8PfUKqN30F03P55UvK7nr3a08Mm9s2M01M8HCxeOzqbV7+HpfE6MzYoi1Rg9D9FbH0hPdQKw3Qi499SebgebO940WBAFJktb18BgaGlFJjzNz1dm5PLqmXJnsi0vzSYvrnZwHURK5Ydow7lnVlVOxtKwASdIieRoa/Y2j2fEfi1dA7ab/2NpyFkzJ48lP9hBrMSg318wEC/Mn5yql6HIo5CeFA6IaFse74+9Nsa6e0Bshl6MepSAI9wP/RzAMclvnz609HbSGxuHwBSTFoIDgRHx0TTm+wNHf9GW35hd7G6locIQleuoEQTEo5OPfs2obmlKshkb/42jKKo+l9DLaTV8QgjvzJKtRKRu9eHx2hLbN3e9vZXtt9OMfT8dSWazr0pVfcsNfvuPSlV/w7qZq/H7xiO89XuSQy4eLpvLawrP4cNHUHreT74mnYjYwQpIkraBfo8+oj6IjUX+U4Q+/X+Q/FU1sqGxGlIKhjTtmjFImhtalVEPj1CHazb8yJNRxLF6BtDh1N79OgBWzCxmZEc+w1FiGpNqOqKCpFno5nh1/NLGu/LRYigclHflLO06ON+TSE6OiAjASVNTU0OgT0jrV6iJjel3hD79fZNuhNqrbOkiNNZMeb2ZQUnCy/mNrDXe8tTksTnr/6h2MzIgjb0Cs1qVUQ6OfI4oS+xqdVDY7iTUbVOfr9wdaeWzNHixGHX+aX6L6mgGxFioaHKp5FnodLC7NDwuzLikbTUFmPIUDg5UhBoOOiUNS+HpfU5Q1w3zY0MuxJlkeTqwrNNG0v9ITo8IFbBQEYQ0hhoUkSYt6fVQaZywJVj33zi7it+9uUSbpvbOLSLAGEynVdPyXXljAwEQn2YkxikEB4XFSeddyMsq0NDQ0jo7uN+ncFCtLLyzgng+6cqAWl+bz8heVQOcu/r0t3D9nTNhm4uF5Y9nX5OCmv34fcbP3egPU2T2Mz43n5WsnUdceFLBav7uOoQNiMBh0ODs8bKt1UGf3kJ1oidC5WDazkNEZMUdMyDyWHX9vi3WdaHpiVLzf+aNxhtOXTW6sJjDqBRZOy0OUQCcEf7d2Cl5uOxR0DYZWiNS2dZASY6Ky2alq4et1KLFMnU7gnGEpvHzdJEUNrzAjrt836dHQOBPofpOubOrgmc/28NDcYmLMemwmA/f+Y3tEdVhKjJF/3DyVBkfQKyCKImVP/F/EzT7/51P4/kAr/95xiNJRmWEieMtmFjAw0Yyzw8M/ttaHaVQ8MGcMT1w+jianlxiTAaNBwKQ3Umd3qK45u+vaAY5pbTxWsa7+Qk9KSl/qy4FonBr0Rv314ahtC3B7iLcBglb6y9dNIjtRoqqlgySbSSUbu4CcFJuqhV+Sm6x4Ijo6fKzaWhchanNhYQZWq/G4x6+hoXHsqOVHVDZ1sLOunVGZcSTbTPy4KDOiOmz7ITt2d4AZBRl4PH4+2lmvWpq+r8nJ3e9v5dn5E/ifV77tlny5jZevm0RtmzdCo+L2tzbz7PwJ/PHf5dS0ubEYdfxlwVlRw6lbqu384vWNx7Q2Hkmsq7/Tk+qPfEEQ/i4IwnZBECrknyO8Z5AgCJ8IgrBDEIRtgiAs7nw8WRCEjwVBKO/8t++zTzR6hX2Nfdvkpi5KUlSTw8unu+tJsBq49ge5KtnY2/AFJO4uGx3W6GfF7CIGJVmVY22ptauK2myptffK+NUI+L14PFoqkobGkYhWNaETQIfAfyqaFIMiM8HC4tJ8BiXZGJQcg06AAy1OttTaqbN3cNXZuTy/voIn1u7huc8ruOrsXOKtBtw+kRanL0rypTvqGlTX5mb+5FwyEyzKa+Vwauias2h6Pm9/d/C41kZZrOtHhZkUD0o6ZQwK6Fn440VgKfAIcC5wLRyxX7Qf+JUkSd8JghAHfNvZmOwaYI0kSb8XBOHXwK+BO3o6eI0TiyhK7Kix92mTm4woiZo7au1KYtbyWYUk2UzUhHQZdPtEvq9q5a3vDvD4ZeNw+wOU1zv4w0e7aHF5eXjeWC4YlU6HL8BtF4xg9MB4Wl0+Ysx6Xly/T8nk1tDQOHmo5TwtLs0nLd7M71fv4MLiLMWguGFaHi5fgFv/vilM0j/easQbkHjikz0RpekvXF3C01eOJynGGFXkSa9D9bnUWDPVrR2Kjs6AOHOY6uXuuna2VNt55cvKTn2JoGDW8YRCTgYnsqGYVZKkNYIgCJIkVQK/EwThc4KGhiqSJNUANZ3/bxcEYQeQBcwCzul82UvAp2hGRb9nf5OT8vr2Pm1yE2vWs2xmAXd3a/i1eksN0CXbvXBaHo+t6ZLathh1ePwilU0dbDnUxsp1FWFjvH/1DnwBMSyZa0nZaA40+5g9PoshqbZeGX8okiRpHgoNjR4g36Sz/2cylU0dxJj0+ESJOnsHlU0dAIp2RJPLGzbP5bXh5esm4Q2IqpufRqeX2/++mbOHJKs2Fnz960rOykuJqAy55fzh/PbdrbS4vCwpG839c4pwef3KmLMTrDQ5POQkW7nzJ6N4+9sDTBySEhai7c0wcV9xohuKuQVB0AHlgiDcBFQDR92lVBCEwcA44CsgvdPgQJKkmmjdTgVBWEhnf5GcnJweDFWjL6izu3ljw0EWTc8Pmyz3XVTUK9UToihhdwd4UqXh168uGMknu4Oq8G6fSE5SV/5EqMQugCih7GbkmOqI9Dhu69zRyMdYvmo7C6flEWh1k5N0ZKOip9ej1+vlyqc/QdD1bktkDQ2Z03WNPNjiVuarxajj7rLR5KZYeevb4Prj9geUeQ6EzXWPX2RibpLq5seo05FkMylrycr5E2jr8JNgNbBhXyNzJ+Zw3Z+/IclmYsGUPMwGHXmpMQREkTkTsnnr24MsX7WdF64uwWLUs253PZIk0ejwhVWsrZhdyOPdQrS3vLGRtAVn0ejw9Eglsy8T47tzohuK/QKwAYuA5QRDIFcdzRsFQYgF3gJ+IUmS/WjVCyVJWgmsBCgpKdF0lE8y6fEWWlxeXvmyUrnp6wQYn5PYKxd5VbOTJqeHBIuRERlxdHj82MwGPt9tJBAIXxxyU2y8vnAyzQ4vOzvdi3MmZAMQa9KTm2Ll0pIcxfhZVDpMdeciSvDomnIK5k844viO5XrUG4y92rpdQyOU022NFEWJLdWt7Ky1c/3UPN769iA1bW6WrdrO01eO58ZXv+OVLyv57f8bRXld0GvaPXH7uc6NzkvXTkCUdDQ4PAyINQMif/hoN1ednYvDE0AQ4Ov9LRQOjOfF9fuYUZTJlxVNuH0iNW1ualqcXDg2i+8PtCpCejdPH0qz00eT04fNJLJs1TbKxmTx/Ppwj8ld725VJL9l3D6RdeUNShh3xexCZhdnHdaw6OvE+O6c0IZiwGBJkr4BHATzKRAE4RKCnoeoCIJgJGhQvCpJ0tvy2AVByOz0UmQC9T0Yh8ZJIjTe+eQne5QLPCe5d7wU31W1kp8Ww+Vn5XJ7yC5l6YUF5A0InkM+54TcZHQ6Aa83AAJ8f6AVCE78Wy8YwfJZRSx8ZYMyQURJPU4qde523N6+l8DV0NCIzuGafNW0uTEb9PxlwVnU2t1kJ1oxG4L5Fh2+QETi9qNrdnPz9OHc1ek9yE2xsrSsgKt/MIQ4i4Gl72+jsqlD8bQumJrH9S9v4PqpeViMOs4ekswP8tO48dXvlLH8ZsZIHJ6Akqshj0+nQ/VGrO9mK1iMOuS90dGqZPZWY7Kj5UQ3FPsN8OZRPKYgBF0SzwM7JEl6OOSp94Grgd93/vteD8ahcZLoq1a88u7kzne28OdrJ/LMZ13hD4BnPtvD/XPG8NrCs8LO6feL/GdfU9hOYuG0obz0n338/Nz8sIkhu01DwzbygmUx6shI7J2GZRoaGsfG4Zp8Pb++Ar8ocd3zX5FkM3FJSTaFAxM4a0gy1a2Ru+uyMVmKQZGZYOHSkhx+9tcuA2HF7EJcXj/17V4eXbObFRcV8MDcYnx+kRevmYhOgKtf/CZsLGo5HI+tLefBucVhN+I7ZwxjbE4qTp+fN/5nMvV2DymxJvxigNv/vlUZo9t3ZJXMw3kOBqfE9HpYpDfEAY9oVAiC8GPgJ0CWIAiPhTwVT7C643D8FzAf2CIIwsbOx+4kaEy8IQjCAqAKuOSoR6xxUuntVrzy7mRnrZ0km4l2tz8sbCHf/NvdfkpHpod1JlST5F65bi93zBhFTDd535o2N69vqOLVBWdxsLWDvQ0OXvmykhaXN5gxbtE0KjQ0TibRbqB6Hdw/ZwxL3tvC8LRYbjhnGDtr7WyubiM/LYaaNjeLSodh0OkYkhpDdauLZJtJOZZaQzC5vfnz6/fxq/PyqWn1cvd7WxWDZVhabMRYQnM4Qse3r9GpbFhuKc0jMcbKA//awZzxOdyzqsuQWTazkPsuGsm1f94IdKlkRutIKooSNpO6TPmAWEufhEV0OoELRqXz+sLJSgVLQWZCr1d/HAI2ADOBb0Mebwd+ebg3SpK0nuhlp6VHM0CN04vuSUc6AW55YyPXT83jkpJsEqzGiAXgsbXlvHzdJPY3ORVjZn+TM6okd0WDg8z4FJaUjWb5qu3KpLtsYg4Oj4+9dW38V346+WnBXiDrd9eRGmticGrvuxM1NDSOjmiu99KRaXgDIl6/xKWTcsJupA/MHYNRr2PluvBNSKvLqxxLENSNgR21dsrGZJEYY2bhK9+G5WbIYZDQ9+kF9RCqxy+yta6Vl66dRKvLy/YaOwumDOWX3bwud7+/lZevnaS8b8XsQkYMiItoO7BidiEziwby71313L96R4SH9eF5Y9Hr6JOwiChKfLSjrm+rPyRJ2gRsEgThr5Ik+QA6xaoGSZLUcsyj1zjjEEWJtbvqqKh3kJlo40Czi4wEC8PTYnnr24P86oLhNDu9qgtAs9OLKEnKhDncriZvQCyXPxd0ky6clkdOko1au5uXv6hk5fzxDEqJ4+oXvw6rbc+I18IfGhonA3mj0eT0qPbwKMpKZH+Tk6vOzlU2CRCc73vqHaohiZvOHaaUhYK6MRAQQRCguVMIK9SjoRYuHZYWGyGfvXxWIdlJZnJTbGFryv9eXKS6PtW1e3j2p+MVlcxoHUkHp8QoN3Y5MV6vg9KRaRRlJfLVvqbjTqhU40RXf3wsCMLMzvdsBBoEQfhMkqRbejhujTOUqmYnh1o6CEhw2983Ka7G66bk0eTwkGwzRYQtACXDe0BIp9Jou5ox2YksX7VNyeCWM60XTMmjxeXF01nL3r22/S/XTTpxX4SGhgag3kBs5fwSjHohLE8gJ8lGbortqEMSbn/QMHhwbjHVrS7uu6iIO9/pKvlcND2f1zdUUTYmi+ROIaxQj0ZNm5vVW2t4YG4xApAWZ0aSAjz72b6wyrcEqxG9oOfubmvK/kZn1M6mk4akKI9F7UgasmmqaXMrVSQ/GJqCTif0SkKlGr1R/dET7c8ESZLswMXAi5IkTQDO68H7Nc5w6uweGp1eHl1TrrgaV66r4Bevb+TBj3ZxsMWF2+dn0fT8CNlbt88fliykJo+7pGw0ta0uRSRHRvZgPDxvLA0OdU9Ig8PbZ59bFsGSpFO+4k9Do1dRayC28JUNpMdbyBsQq7jcq1pcJNlMERLeckgiFLmqq8XlZWdtO3/8dzkpsUYemTeWRaXDWDAlj9c3VHHFpFxWba6m1eVh2cyCsGNlJliYUZjJ7X/fxM1/+56rX/yaQ21ejAZ48pOg7HdmghUJibr2SFnvNzYcZNnMwrD1adnMQoZnhCc8yh1JQynJTSAtzqz6uWSjQW39641uy9Fk0vuq+sPQWf45D/htD96n0Q85FkEVUZTY1+ikstlJjMlAeryZnGT194UmH2UlWhEAh8dHwcAEHpxbTIxJr2RjQ/DGft8/d/LX6yfx+oaqMPGr1zdU8fAlxRHjLhgYx+v/PZm6dg/JMSYkJEx6fdS4bFFWIv+3t5HcFCtlY7KU6pIPNlUTa+k7gSox4GfBC1/y5i8uwGzWwiwaGjJHszMWRYlGhwe728fyWYWKp9Fi1JGTYotojS57IUL//cO/dvPjokxGZcTjFyUmDS7E7Re5bGIOz32+n5tKh5GbYiM7ycaS97aqJnc+umY3911UxOxxg0iNNRNv0dPs8GOwCRFrTovLS06KlZevnUS9w0NanJnhGTEkWsNvzt07kpbkJjCvJJfb/r5JNZdCNhr6ohJPFCV0AhFenV6v/ghhGfAvYL0kSd8IgpAHlPdw3Br9gGMRVFF7z+LSfPLTY5k+Ij3sfX6/GJF8dMv5w8lOtFDZ3MGja4KJUGqLSYfPzw3ThnHPqq5FYmlZAW5/gH2NTnbVtXPLGxtJspkUDf7Qz3DBqHTVkqiirKBAV4xZxw0/HBa2CC29sIAYU9827NEZTH16fA2NU5EjufHVwiOPzBtLeb0Dj1+kod3Dh5treOLycbS7/Rj1Oursbm67YCQGvcDDl4zlljc3UtnUQYPDy8/OGUqDw4MoBb0cqbFmysZksuhvwTVl4dQhPHXFeJzeQNiY5LLUBS9tCFv/Xv6iEpNBYNmsQiUEIhs2v3x9k9LR9OXrJkUYFBDZkTQ5xsT8F76OyKWYlj+A8TlJYetsb1bihX7Pci7asAGxDE6xMboPqj8AkCTpTUI0KSRJqgDm9GjkGv2CY0nGUXvPo2vKWTgtj7zU8At7Z52dqmYX10/NA4IaEQ9/vJuH5hYrRgCoJ08ZdHqeWRcu0/3MuqBORVVz1xguHp8ddiz5M3y4aGqEBZ+TZFO8G0a9TjEo5Pfd88E2/rLgrF7+lsORO5VqngoNjXCP45/ml/Doml2clTcAvQ4m5iaTGWtmw/5mGto97OosN69pc1PZ1MEv39jIwmlBtcrFpcPYXe/AF5D4TefuWsZi1PHKdZNYVDqc376zhYVTh+Dw+JXETtkwmDQkiReuKaG6xa14QRaXDgtbn9Q9F+WKauaTn5Tz/NUlNDu97Kx1KIJd8msP17BQ7khaPAhWb61RzaUoyIzv054hoet7aC7awml5HGx19271hyAIt0uS9IAgCI8DEUFhSZIW9fwjaJxMjiUZR+09STYT+Wlx7K5rRydAQIT6djduX4D3NlYrinWyyJTT48ftC4rRWAzBzOmDLS4+2VnPOSPTGJIaQ1uHH6+/6zITBPD6Jdo7/MRbjcoY4iz6MIEsWc5X/gzyT/edzqOXFat+9hZn3+VUaGhodKHm9Vw+q5AnPimnsqmD3BQrPz8nn7vfD9/5yzdqt09kZEYcj18+Dq/fz7JZhdjdPoanxXL9tKGKvP8nO2rxiRIGncBL100CJK5+4ZsIw+CpK8ez8UBrWBXJGxsOhjUV00dRzZTXn8qmDr7a18zZeSk8v35zhHGTlWhh04GWCC2K7sg5Ft3fn5HQOw0boxHtniBK9En1x47Ofzf0bJga/ZVjyRxOj7eE5SJYjTrizAaliqN7KGLR9HxWb61h6vA03P4Av/1/ozAZdJTkJnDdlKHsrLVT2eziy70N/HRyrtKV9M4fj4g41uLSfOKtwRwOuRIkzmLkj/8uV1yiS8pGU9HgwGo0BGODnVZ1dw9LWpz6Z4+3auJXGhonAjWv55L3unpllI3JUgwK+XlZg0ZuD7DtUDtPfrKHzAQLy2YVYDPpuHJyuLz/spkF/ObtzcrmZvmsQsXjIeP2iWzsVOQNXRNq2oIl6M/8dALfVbWQnxYXVeZf/n9AhLYOL0vLCsLCtw/MGUNVcwe/frurVDZa34/uORbyawsyE/rqzwFEvyfIbQx6tfeHJEkfdP770rEOWKN/cSxSrDlJNm6enh92sS8uzSfJZlINRTy2tjziHA/MKeKySbkR5w1dYLwBSdHWl4/16JpyXrluEjnJwXHvrLUr9epyrFM+xhOf7AnLD+lugbe6fKpy3Xa3rw+/cQ0NDZlou2J51x9NrEoQCPNaZCZYuOrsXG7+2/c8MLdYaWMuv/7u97cphohsuCyclsdja7qafMnGgM2kU022NOkFAiJUt7oixPTknIrQ5NARGSN567sqnr96Ii1OL2nxZmJMOuY882XY2KL1/eieYyHrWRxNN9PjQe2eENrGoFerPwRB+ACVsIeMJEkzj/psGv2CnmQOy7HPhnZPmEhLks1Ehy/AL0rzAfVFQJbellsS63Q6Hv94Z9jk2llrD3uv2y+qHquuPRiTnFGQccRYZ6i7LtICl1SrSx6aW3yc32okHo9H61CqodGNw+2KQ3/v/vzkIclMyE3id+9vpabNzR0zRiibmY7O0GoooYaK/HteaoxybIsx2FL92XV7uWxiTli4QzYajAad0oE0M8GiJE5OGZaKXxS5xJdNQAyuIT8/N58Ei57bZ4xidEYMsZ2JmaF5EqFjidb3IzTH4kQh3xNG3DyVHbV2dte1K20M+qL646HOfy8GMoC/dP5+ObC/JwPX6D8cTeZwaOwztFojM8ES1mq4e1ITBBcBq1Ef9rrusVEIiteEhlVGpKu7GdPizKzdVcf0EekMTulaGKLtaio7DSGX18+fr5nI9wdacXoD1Nrd3HL+cO54q6tkaumFBbj9R2pjo6GhcbxEK1tcMbuQx9eWk5lgId6sjygdXTarkOLsOHbUOrn1gpGkx5tpCVHfTY4xHZWhkhJrZuG0PEQpuKF47esqLpuYQ4cvwJsbDoZtNl7+ojKsZLWmzc3z6ytYNquQMZ3hCKNOR53dwznDiynKTMCiUpoeLU8iM8FCRYNDtaxfFCUONDuotXupb/eQEW+mMCMeax+GaXU6gaFpsQxJjWF0Zjw/GJpyTKWqRxP++AxAEITlkiRNC3nqA0EQ1vV86BqnCt1jn/LE6O4d6J7UJMcvGx0e1T4esksS4Mu9Dfz83HylHCs3xcqymQVKjoXFqOOemQUExACbD7aRlxob5qoLHZeM/PuNr36tmu9x549H8sI1JTS0e0m0GXn1y3387JzhJ/Cb1dA481ArWxyeHseojHhyk22U5CbxXVWwW7H8/NABsWQnWRmeZuOfWxvCkjfvLhtNboqVyqYOdDoi1qClFxbwzGfBdUYuTd/X6AgLfwA0OLw8fGkxK9dVKOuS/B5JhL99VckDc4vp8PqJMRnITDArxsPEEHXMaKjlSdw7u4iDLR3c8uaXYaHgGQUZAHxZ0cCBFrcS0pHFsy4szOhTwwKOv1S1JzoVAwRByOssJUUQhCHAgGM6q8YpQWjsM1QLv7t3QE5q+tNVJWw60EpuSgx/WreX+WcPVvUi6DvDgxajjp+dm8/PXu0Swaps6uDJT/fwwjUTaWj3KDf9+WcHdxdywpAcvml2eshPiw3rF7CkbDS/X71DMYC653vc98+dSmxVNlqspr4r19LQ0IhetviPm6dS2eyi1t6heC9Cn/9w0VR21DojkjeXrdqubC4a2728/EVlmKfhb19VsmJWEbWdnoDH1uxi8tAB6rkTOiEiwfKemQWsXLeXzdV2Fv3te+X1T185vkefOyJPIt6C3ePjv1/+VjVsC+AXUckR2crgVFuYzHd/pCdGxS+BTwVBqOj8fTCwsNdHdAagpmYJKI9lJliU8sxoapehTXhMeh1OT4AYswFvIEBKjLlHLqto4xGARaXDEKVgtUesWc9Dc4tJsBr5oJsq5Zd7G3C4/WQlWUmyGTEZBOrb3apehIm5ySwqHYbVqFdtIFbZ1MGXFU3KorK4NB+dIDEoyUqs2cDeeofy3YzPSWZ8DhRlJVDf7kZA4PsDLYpUd7TwyKiMeP546ViSY4z85ct9DEy0MiLjqP+EGhoaKkRT6hVFiYZ2T5h2jVweuqPWzq1vbuIX5+Wrlonv6RS6UpvHkiTxh0uKSY010eLyRngazEYdSzpbml88PpskqzHCa7BsZiEef4C3vqvigbnFSJJEvNWAUResKPlvk4E/dRoXFqMurAdRd6K1MQ/Nk9iwv5lv9reofp76djeS1NXkrPvzh9O76C/0RPxqtSAI+cDIzod2SpKkfEJBEM6XJOnj3h7g6UY0NUuTQeCmv34fVSkyVHxEPsb9q3dwaUlORM7C6xuquGPGqKMSLIk2noKBcWyublNqt3NTrPx6xijK6x0k2wwRIYufnTNMafUrW/lrdtREVFqsmF3Ijpo2bCY9AgIHml2qhsfwtDhumj4MnQDZSVbufGcrXr8U9bvJGxAMi/zfnkY6vIGwY6odf0etPcxTYe47lW4NjTOCaGvJBaPSI9ppy7lVLS4vu+vaI8rEQ9eyrYfaECX1ebyrrp03Nxzk5+cOVQ3BbqxsVB6Xy1Efu2wcL183iTq7h/R4M6MzYrAYTSwpM1Lb5iYrycaWanuE6q7p2younZiLUS/gdvsj8ifUlITVSkdr7e6on0eusvAFRNXn00+BbspCbzU5EgThO0mSeuYX6gElJSXShg2nvlRGRYODnzz2ecTFIrvjf37uMCXbOPT5D0PER+RjLJiSp/pa+fEPj0KwJNp4Xr52Eld1tvLtnpgZmnUNRB3zn64qQUBCJwjsqGlnYKKV36/eQWVTB4tKh7FyXYXSWCzU8Ljl/OEERIkOX4Bxg5J48pPdbKhsO+J3U9Hg4Pa/b+TKswZTa3crjcuu/a/BPPzx7ohSMDlZVJbR7eZWPKw1djTXo91u56fPfk7A70On0/PqjT8kPj7+sO/R0IjCEd2OJ3uNjLaWvL5wMpeu/DLi8VvOyyc/PZ4t1W0MSY3htk6NidDXPDJvLMtWbWdArIlLJ+WolnRePD6b59dXhFeaCTBlWArznv1KKT3NSrSyp8HBmxsO0uLyRtWK+GZfkyKVHTqWP80v4fa3NtPi8rJsZiEzizLDDItNB1pUP+frCyeHlY5+u7+ZW97cGLEhvO+iImaPzQJObk7FURL1euzN/ZkWlD4KDqdcBtHd9aHiI/IxDlfP3f09PR1Po7Or815oYmZmgoXUWHPYe6KNo67NzeOflHPZxBwGp8Qongzoallc0+ZWNO4FIZjU1ObykBhjxmLU4xdFNlS2HfY8lZ3u1g5fgLPyBvDHNbu5bGIOD84txuXxkxpnUjK+R6THcd+HOyIEcE4Ft6KGRn8m2lqi1t47yWYiOdbMja8G8woWlQ5TfW95vQOAGYWZrFy3VynpHJ+TxG/e3kJNm1tZF0JlrQFGZgQN+Jo2Nw5PgNvfCle6vOvdrQxPiyXWYgwL19TZI7uOun0izU6vsm7I+Q2CgBJulj9nZoJFMW4Amp3ha0vRwAR+fm4+T35SrnyesYMS+cHgFMWzPDlvAFnNDl66dhL17UGPSlEfV3/0Fr1pVGgF+UdBtBrt0CjFkdQuQ9vTRiujOpJgiRz784uS6jEGJVmVfIoR6XGKEt3F47M52KIesuj+e6zZwGUTc3h0TTkPXxIpjy2/R14MLEYdj146Do9f4hevBw2Q7uWq0c575ztbmVeSjV4XzMm4f/Uu5TU3TR/GE2v3KP9vcXkjxnEquBU1NPoz0da2gYmRJZWXlGSH6d5ECwf4xfBqM9loWFwaPo/V3ptkM3LT9GEAZMSZwvI1DjU7mTU+m8pmF2lxFj7aeoi/bajm4XljSYlVL09NjOm6obt9Iuv3NJIeb+Gd73Zw3ZShZCdZyU2xRngghl5UFKbyazLpmT1mIHmpMYoxM2ZgAiaTXjm+TieQmxpHbuqx/z1OFn0r06URgVwOGWoUPDxvLGOyE7AYdbz1bbA8s/vzoeIj8jE+2FTNounhr100PZ9Vm6sPK1ji94v8c1sNa3bW831VK49cOpbcFCuZCRYWlQ7j4UuK2VPvZOW6Cp5Yu4fb/r6Jq87OJTPBgiAES0hvvWCEct4PNlWz9MKCsHEsLs2nvt1NVpKV4WmxWIx65XlA9XMump7Pff/cjsvX1SFQLleN9t0smp7P71fv4Dc/GYVep2Pi4GRyU6xhn1ffqcQnn7f7d7ZsZiFFGVpYQkPjeFBb2564YhySKLFidiGLS4cF+/4YdeQk28Ju2mrz8q6fjGLYgFjVvhtvbDjIkrLRUdeFB+aOYeOBViA4/+NtJlZtruaJtXv4YFM1k4em8j+vfMvNf9vI1S9+zaiBSVxeksUtb2wk1qyPWM+WXlhAZaNTOb/FGFTiXL5qO9dNGcr9q3cQZzbwuwsLIsrof/POFvY3OcPGbzLpKRmczP8bM5CSwclhBsXhEEWJigYHX+xtpKLB0S/F9Xozp+JtSZIu7pWDqXCy44W9iZwhHapmCSiPZcQHqz8aHNHVLuVjNDs9GPU6XN4ANpMeSZIQBAGXN6AqqLK/ycmBZifVrW6WhcQn5fbft7+1hZvOHRYmlQ1deR+iBM99XsHi0nw6fAFFRObz3cGmYFkJVqpaOnj7u2DccuG0PEZlxGN3efAjsHLdXsrGZKHXBd2ANW0dmA165T01bW4ev3wsu+ocSvb3mKx4Fp03nM0HW7Eag5NvQKyZWrubV7+qoqbNzaLSYUri5bJZhTzZ2ZxIXti8fimsffLvZhbi9PhJiTGh10uU5KR2/461nAqN/kS/z6mA8LUtI97C9pr2sATNJWWjsXf4ECWJx9eGrzG5KVZ+dcFIOrx+bCYD9324gz9eWoQo6bj2z99ErEe3nJdPmzuA2aBjdGYcBr0Oty9AepyZbypbIvKo8gbEsqW6DYtBp7q+vXTtJC5d+SXv/Oxs6ts9+PwSzk5tCqNe4L5/7ohokiivPQERzh2Ril+E9Xsaga7qFYDXFp7F5LzjcztES4TtSQfRXqR3cioEQfgBwVJS5X2SJL3c+W+fGRSnG9HERbo/NjQtej6E2jEOl319sNXFtkN2dtW1MyI9TjEooKv998JpeSTZTKTHW1RjitmJVppdXpaUjabO7ubNDQeV2OG0EWm8ueEgcyZkK41+FkzJIyvBSkCScPlE9HqB/5k2NMyY+eV5w3H5Aopb02LUISHw3OcVysQ9Z2QaN/31u4hFYMGUPGra3MquQR7n3e9t5fWFk+nwBcKMtpGLplLZ5OT7A63c2RmPlY91NEmtGhoahyd0XapocEQ0Dlu+ajsLpuSxanN1hKrmpSU5/OGjncwam0VADOZC7G9088c15RFVZPddVIQgwKtf7+aKSbnc9Lfvleee+ekExaCQz/vomnIev3wcI9PjkJB4cG6xUiYqv6a+3cOi0mHoBB3T8lLZWmunzg7p8WZGZcTw4Nxi1u9pJCCiGBTy2pNg0VPd6g7TywmtcOlJ74xoqDVi62kH0RPBURsVgiC8AgwFNgKBzocl4OXeH5bGsRDtonv6pxMor7UTkGDluoowyW0ZOVn0xh/mkR5vVvIpZGvbYtSRnWQlLd5CW4eXoXnJWI36CK186JLxfn1DleKVKM5ORBAIE7py+0Qe+fdu/jhvLNAVzjjU6sLtC6pvLpyWx6Akm+p4uzcYCn2uwxeI2BnkDYilzu6OUNQ72qRWDQ2Noyda4qZeB5dNzCE7yczTV47n+wOtSv+MyybmkJtso7LZxU3Th5GZaKEoK5ahabE8dcV4nN4ADe1uMhNMtHeI/PrHo9hZY1dyvtw+kfoo53V5A2E3/aVlBfB1paI/kRZn5ua/fc/KdRWqHoDxg0wcaOngtyGGkFz2+vuLx4R5U0LXr5EZ8T3qnQHqeh/Rvs/+tnb1xFNRAoyWeiteotHrRLvovu9s3RtasqWWiJQZb8ag17HwlW9Jspm4pCSbW84fTpPDw8AkK3XtHn7z9hbVslJ5N3D7j0Zw8fhsXt9QFZGwdP+cMarj80sSN00fpjT3KhuTpTw3KiOeQ60dquOdOiyV80emsej178OqOQ6XpHosbd81NDSOjtCboc1kIDfFitcvKR7NWJOe4kEJNLR7sXcEeOazPZwzIp2hGbGMyoij2enhUJs7bLOybGYh9324XQk9PDBnDNWtnjA9iNBwRIPDozrH9YLA9VPzlI3SPau28ez8Cdz93lZuOjcfgy64Iappc6t6AAwGHRcWZpISY+K7qhbFELrl/BFKi/BQ3D6RoqwEEm1G9jc5I0QODydsqOZxjt4XqX+tXT0xKrYSbChW00dj0ThOot0w89PigK6Lft2ueu4uGx0Whlhcmk9uSgzXv7xBVTvivouKeHRNl0vR6Q2oTqKkGBMpMSbKxmRFJCxVNDhUx4cET6zdE+F1sBh1xJj15KTYeGDOGKUkTJ5kEwcnA3DHjFFHbOMeqkB6/5wxYTuWnnbh09DQiETtZrhidiEuj5/7/rkzbK15+YtgWECe7wNiTVx+Vi6NTq8iuAdd8tShLcz3NDh4b2N1WDXH6xuquHh8MPT6xoYD3Du7iN++uyXsnPd+uCPsnDVtbura3Nw8PZ9/bDrEF/uaw57bU++g3e0Ly08zGHQMTrFhNepxef1cPC6LIakx7G9yqq5tW6rblFyv7rld0XIionmcVy+eGtGevC/WrmiqqEdLT4yKVGC7IAhfA0rhrdb6vP+Qk2SLuGEuLs3nvg93MK8ku7PMysSMwkyeDan5HpkRzzOf7qGjs+pCrZ34ne8EEzgf+mi3cj61SbSv0cnwtLjDZmx3F7AxGXQ8etlYEqxGlna2Nc5NsXLHjFFsPtjGsLRYxuYk8GGUVu1HauPefbHLTbHyyLyxlNc78IsiJoMmsaKhcbyo3Qzvencri0vD5bdf+6bLAAhtMDisopEZRQOjhjplbCa9qpLwoORgGfyozHgsRoHnry6h1eVjV117mNidfM7n11dgMRm4/e+beGBuMZ/sbmTtzloeuXQs9XY3iTYjn+6owSvp0etgYm4yIiL/88p3ynmfuGIcAM2uyM2KbDzJn2HzwbYIg0nNIxLN41xrdx9xrTteeiMZtCdGxe+OaZQaERyvJRjtmB/tqOPhj3exYEoeuclWDrZ2KJNJLs3s8AUiar7lpEe5VjyayFRGgkVxD771baSBIFv5t14wnNGZ8YoRI7s+9QJkJ1mUKpKgBLcNCXj3u4NcND6bX88YxYFmJzEWY9iFLavNqcUOj9RVr/tiV9nUwS/f2KgsZlqipobG8RPtZjgg1syja8JDFTpd1/Nmg44xWfFcOC4LouhVhAbdsxNt3BoSypXzF1bOnxDWL2jogFhECdUcKr0OlpYV8Ny6vcEcLK+fMVnxzBmfwzWdSsLB0EsBG/Y38sa3NUr1yvC0WKYOT8Ns0OELSFz30tdUNnWQm2Jl5fwSjHoBvSDw3YEW5kzIVsItYpQQSfeciMOFaI+3g+iR6I1k0KPWqZAk6TO1n2Mb+pmLbAn+5LHPufxPX/GTxz5n9bba4643li8Grz94HElCqc64afow5kzI5p9bahiVER91J/DBpmBGdqiug4zFqKOq2cUlJdlAsLNfRryZhdPyuGn6MBZMyVMynVPjzDz3+V7uvaiQq87O5fn1Qb2LZ9dVUF7nVMpCAyJUt7r4n1e+pWhQIre/tRl7hw9vQFKMFXl8d6rUeh8JuaZ7d10710/NIzMhGHsckxXPA3OLyU+L5fHLxzE8LZb6dvcRjqahoSGjppcQKsonYzHqqGpxRRgAAxNtyvMjMuK4dFION/7lOx75eLeiPyE/v2J2EV9VNCi/i0iqa1iTw6v8/9E15Xj9Iga9oDqmCblJ/C0kSdNqMnD9tKFKl1L5OHe/v43Z43OU31eu28ulk3JYtbkaj19kR42dX88YxZiseCqbOljy3hbq2z1c9eLX/P6fu3ju8wrmTw5q/ERbV7vnRETTMjoRIdrDJYMeLT2p/pgMPA6MAkyAHnBKkqQV3veAvioLqrO7w3IhfnFefkTPi1vOH05qFLU4nQA3/HAYGfEmYswGls8qZMl7kYlQd5WNYlHpMEx6HTWtHWQn2pSWxBajjrvLRhNvNvCzc4dh1Ov57Tvh7Yof+mhXmIdgwZQ8LEYdwwbE4vaJJMaY0OuFo7LoD+fxUXPjLZqezzf7mrigIJPbO3c6skaHbHBoaGgcnsOVrneP+a+YXcgfQkKmEJzL+xudypy0GHUsX7VdCc2u7BaafWH9Xq44azALpxmItRhwefyqa1ic1ciYrHimDk9DEIJhktVbI0tX75lZwN3vbVUSP5deGPRYXDk5N4qx4mFMVjzXTxuKAFS3uLjuB0P439VdeSJLykbTsHYPZWOylBCI/H65CqQoO+GociJ0OuG4whzH4wnvjUT2noQ/ngAuA94kWAlyFZDfg/drcHhL8FiNCr9fxKjXcceMkRj0AsPTYjHohLCeGgCvflXJuJzEiG5+S8pGkxpr5r4Pt7O0rID/eeVbfv3jEUqYQpJQvBA6BNJiTWQk2th8sBWbKcDtPxpBSqyZvQ0OHl+7R0mGEiX1HYVcCrpidhFDUm1cMiFYl24x6hBFKWrn0tAL+0ixPzXj7bG15Uq2d+j38sxnexidMY7csH5ivYskSXg8HkWcTEPjVGV/k5P7V+8Im0P3r97ByIw4ZhRkMPymKexrcmIz6Ykx6VVl8cdkJ7BgSh6vb6jituSRYblcSTYTEJTu3lVrp3RUBne+s4WV8yfg8PhZ+dmeCEPhjhkjWb5qW1jSdkluApeU5PDomt1hPTb+U16niGwlWo2YjToWThtKWrxZdd1Jj7dwxVm5YRuRxaX5YWWssv6GWi6ZXAVyTn4aOp1wVMbCsYY5jjcnQvaSHE8yaI/EryRJ2iMIgl6SpADwoiAI/+nJ+zV6v6TR6w3w3uZDYV6FZTMLGZhkjkhm+uV5w2l2enn5iy5jQ+qsvJgzIZvKpg6anMEF4KX/VEZUgCwtK+Cd76u4oGCgIkYlX3ShN3AIJkM9OLdY9bNOGpyETsjjDx/tosXlDdvl7G1w8MaGgyot04vISbJR0eDoLFfTq3p8Uq6dxIA4c1Tjze0TVZO8ml1921BMDPhZ8MKXvPmLCzCbtT4jGqcuTU6P+hxyeshOsHKgxcW+BgcxFiOBgBixiVlcms++BgfPr69gaVkBDQ63ksulVnm2pGw0STYTmw+2MSoznpun59PW4Q/LzUqOMZKXEkNFg0NZ2yYOTuJ/XvlWyR+TvZElQwZgM+n5dEct+RnxythKchNYNrMwzPO6bGYBB5qd/O6D8LDIo2u6Ekzlx+RkzmhVIL6AxIyCjH6dE6HTCVwwKp3XF06mps1NZoKFgsyEHuX89aT3h0sQBBOwURCEBwRB+CWg1eH1kOONl4XGMvc3Ovj2QItiUEDwInry03JMOj1uf0DJJXD7gkJTNpOBFpeXt787iCQFO3/OK8kmxhTszWEz6ZUmPK9vqOLlayfx6GVjeeGaEuItei4en4PFqOcX5+Urx61ocETcwJNsJgw6geWzwjX/758zhsfW7CYgwpwJ2Vw/NY8X1u9l48FWDDo4a0gyJkOXl+Wm6cNYOC2P8YMS+GhHnZKLsmZnvarRsL/Jye1/3wjAotJhQQGdzsXEYtSRbDNGVLY8traceIvp2P6gPUBn6PtzaGj0NSa9TnUOGXU6/rG1hhtf/Y42d4Dlq4J9fAKixD0XFvD45eOUigib2cCCKXm89V0Vg5JsrJhdiF4INhrrfuzlq7ZzSUk2owfGs/lgK2ajnj0NDgw6HSPS47CZ9FQ0OLnx3KHkDYjl+fUVrNtVjwDKec8dnsot5+czPC0Ou9sPwIVjByKA4hnZUNnGGxsq+fO1k3j88rG8cM1EdhxqxR+I3g1axmLUMWVoKjkp1oj1fUnZaD7dWc8tb2zscV7Y0eL3i2w60ML2Gvtx5UTICf+XrvySG/7yHZeu/JKPdtT1KOevJ56K+QSNkJuAXwKDgDk9eL8Gh4+XqcXCoEssJS3Owr4mR3CSTRhEbkoMum6VGpkJFi4tyeGqkAzmsLpsu5tHLxuLvcMf5t1YemEBD84dw/4mJx9squaqs3PJSrKy8UALqzbXUFY8MEJL/9YfDeel/9tPbkpMmHWemWDhqrNzlVbnsvdkyAAbZoOO6SMzFDfnz8/J42fn5LOuvAFRCiaL/ur8EVS3duD0BtALQblynU4Is8CjdTVsc3mZMz5HUbeTP//rG6q4Y8Yo2t1+1Ulnd/v6+k+vhEBMJpMWAtE4ZXFF0ahpcXn5zTtbSLKZyEmykmQzEWcxRlSImQwCDe0ekmwGrvlBHm6/iMvrJzPejNVsVD12TrKNP368m7IxmRxo7lBKM+VjvruxmuwkG4ea2xmeFsvlk3IVEb+rzs7l0kk5xJgMdPgCVDQ6qW5xkTcghn/vqGX+5FxlfdxQ2cZ/9jby2Jo9ijLwoTZ18T158y7nkq34x3Z21zt4dv54nv7pBL7vFMhauW4vl5bk0PBlZZ+oX/r9Iu9uquaud7dy/dS8iLX4kpJsXN4AFQ2OI+ZX9EbO31EbFZIkVQqCYAUyJUm652jfpxFJT/p2mAwCN/21S9d++axC/nBJMc1OHy1OL0MGhN/Q1TQm5FyCx9fsJt5iRKcTIrwb93ywTand/uV5w/l4ew3X/VceDm+A2340krve2xLh/ls4LY8bzhlGs8MdFq64pCQ7Qm3z7ve3snBaHkNSYnh9QxVJNhM3TMtD0On4WUgo5TczRlJrD1fUu7tsNE0OrxLDhK6uht1dsN6AFJHB/djacl5fOJmirES+2d+sukDYTD2KBB4TYsDH/Gc/541F52khEI1Tlu4hXPnG5faL3PWTUQREiUNtHVxSkh1RxfXY2nKevGI8To+PP3y8W5HxHzcoEaMBrAaj6vxsdXmZUZiJwxvg4X9vjTjmgil5LHlvK3++dhJD0hO5/e+bOHtIMv+veGCY8ubi0nz+9nUVLS4vt5w/nNt+NJL/29vEw/OKWfnZXr7Y18y4QYlKc7M99e2YDcF1KTQxc8XsQrISg92QAyI8G2I4fFvZGqZHASjJmgNiLUoIt7fkBLbVtCmfMXRdlA2q0LX0SPkVvZHz15PqjwuBhwhWfgwRBGEssOxw4leCILwAlAH1kiQVdj6WDLxOsDHZfmCeJEktRzuO05VoFuLCacE+HZkJFi4en82BFhdWkx6XJ6j01trhC7u5RksU+rayhXklOTS0u6lr90R157l9In/9upIbfziMW97cFHbDXr21RsmsBkiwGKlocDAuJ5FHPt7Fw/PGUtHgIDclRvX4ogS/eWcLC6bkAdDkilTPU3ts2artLJyWx1Vn5yq6GzVtbl7fUMVDc4txevxUtXTwypeVzJmQrXruZqeXr/Y1YTPq+fWMkfw+ZIG47UcjiDEfXevh40ULgWic6gxOiVGEntRuXEvLRlGQmYBXFFXnotcfoLbNzcJpQ8O8GCtmF5IaS4Ta76Lp+egFgcfW7o7at0heuxra3XR4AgxPi+WnZw/m538N7zUk50K8/d1BHB4/+5tciBL8+u3N/OycYVz9X4M5KyeZX5QOD0vM/OV5w1lcmo/TG2DykGQGJlj4yePrIwwHWe9HbYzD0+PY1+QI2ySG3uSPtWpDThaV/y+Hjs8aksTCzpwSeQxH8jqc6OqP3wGTgE8BJEnaKAjC4CO8588Eq0ZCm479GlgjSdLvBUH4defvd/RgHKc8PWkWI0pdDbrCkiYvLMAXEAmIEq9vqFJa+8ZbDRFWssUY7KR39/vbeHBuMTV2D7kpVsrGZCkGwgebqhWBmbIxWRGJSa9vqIpYBJbPKiQ72caWajsLpgxVwh03TR8WVcBGNnxECdXJF21CihI8uqacxaX5ODwB9DoYlRHP05/u4ZyRaUrClNWoUz33ztp27l+9K7gDOX94WJJXaqyJE9XSJuD34vF4NE+FximJvHbFWfQ8O38C7R3+MCGqJJuJ1g4/96z6PmqitoBAmzvAw/8O92Y+vracB+cUk2A18tK1k3B6fcSajNS2u0mNNTE8LTbq/I4x6VlUOgyjXsfgARZump7PpoOtqmtJnEUfsZ4ump7PU5/u4cE5xexucPCbd8I9s4/8e7fiyT1/VBq76yPzyGTjRtaj6D7GnCQr81Z+qXqTH5wSc8xVG5kJ1rDz1bS5eX59BWMHjeux1+FEV3/4JUlq60ksWJKkdSqGxyzgnM7/v0TQSDljjIqeNovRCeEhDdljUdPWQUluEv6AyC9Kh+P0+qlocPDaN1WqYYFXvqwkyRbsy5FiM3Lz9Pwwt+A9Mwt4tbPnhpq3o2xMVoQrc8l7W/njvLG0u/2Uh0yyaKGJV76sxGLUMTY7kU0HW5XPGHquaBNSNkgyEixKUzPZnTl2UKJiSBl1gmq2eejnuP2tLRHH/8uCs3rpL6yhcXrSfe2SOxmHzqWLx3eFPqtbXfzyvOE88u/wXCynz4/ZED7H1XLBls0s4J5Pt4fpSegFiXsuHE11p0JlUKXXSovLF5ZnsWJ2IbbO5PPuc31gok3xQkB4CKWu3Y1ep66Tk5ts5f45Y/hibxNEWad0AhF6FLkpVu6ZWcjB1o6whmbyceUkymPNZSjIjGfF7MKw9XzF7ELyuuW6yWM8nNfheDUyoIcNxQRBuALQC4KQDywCjqWkNF2SpBoASZJqBEFIi/ZCQRAWAgsBcnJyjuFU/Y+eNItZMbuIjHgT2w8FM3rPHZ7KlWcPZvPBVkQJlry3laUXFtDi8tLa0TWpZPeXXgfD0uL43w93AHDV2blc99I3LJiSx/3/2hU2hqXvB3Mqdtc7GJURH3mzjxJW8UsSb393UOkt4vaJigtucWk+GQkW9jU6FZ2LZTMLiLcaiDHpSYkzRxgAOSk2br1gBA99tEvVIKkNcfXJ7swPbpqi9AVxeQP89p2tYSWzL38RDIsAUSXIGx1HLik9Ha9HjVObE3lNdl+7RAnSYk2KcQGQFmdS5l5Woo0WpydM70ZuJNbdi6GWC3b3+9t4YG4xu+vagaCezKOXjqW83hlmQDx15XiWvBfuWZV7jnTf3CwpG83+RqfqGqDXQXqcRdHR6X4zPtjaociA//K84RF5FvddVMT4nERykoO7+pGLptLk9HCguYMb/vJtWCjlz//ZT02bW7nJH08ug8GgY3ZxFvlpsdS2uckIKQM9Fq/D8UqB98SouBn4LcFmYn8F/gUsP6azHiWSJK0EVgKUlJScFi3XD9cs5oJR6Tx3dQm1bW6qml2KjsN9FxVxwejUCH2IRdPz2X7IzoBYM80uX1ifDQjKdF9Skk1Nm5tFpcOUm3e0G2tuspUH5hbz9Kd7IibjqMxIQyN4k++gxeUlb0BMWK6CLHjz4vp9TB2expwJ2egEyEm28eTaPZxXkMFja8pZ8F9DWDl/Ana3n+xEKwLwVXsTf7qqhDp78HuQDZJQj0PouOvsHQxMtCFJkBJjpsXlVcIh8jhDoxtqn+NoFDV743rURLA0epMTuUZ2X7u2HWwld8xAVnZ2IM1NsfKzc4bx/PquG/7vLx7D3e9vjzhWi8vL0gsLuKczzBpt07Knvl3pYPybGSPxBiQOtLjCdvwbD6iHOQbEmnn8k/Iu4avsRBJjjDg9AdU1YFxOEk6fj3iLkVvOHx5R7RbaHOyRf+/mpnOD7QnGZMWTnx4XsaPPGxCLw+1TRLq6v/eJT/aE3eSPJ5fBYNBRPCiJ4kHhj/d1AzLVsfTgtaM7fwydP7OAmcCYHp6zThCEzE4vRSZQ38P3n9KoJcLkplixGfV8srueQEBS3Fgyd76zhZeuncTVna5B6HLZPTi3GEGAeLM+ImFqcWk+kwYnk51oxWoyRFyw3X+PsxhZtmo7NW1uGhxeFk7LIyvBSozZwNOfRBoay2YV4vb5eWBuMVVNToZ29tJo6/Bh7wgaObvrHYq+/vJZhYiIfLGvmZ11Dq79QS5Wk0FJJrIYddx7URGrNtdg9wR469tg75I5E7KVXY7ceyT0u6tv93L9y98qC5uaK/DxteVAMHckQuBmViGj0uP75O/dHU0ES+NUpfvaddGEQWFhhLIxWSx9P9xjsK/RobrWZCVaqW5x8dDcYnbXt5Ofph7+DXT+mmQz4fIFuOqFyFL5aOXltXa3kjeWnxbHb98NdkC+YHRqxBqwYnYRjo4ObntrO09dOZ6AKCkelhHpcdz34Q4lZCF/tgGxZqrbOshIsEa9WR9qU99EDk2L5cPOXAqdTuiVXAY1+roBmRo9MSpeBW4FtgLiEV57ON4HrgZ+3/nve8dxrFOO7hdPboqVRaXDmbfyS5JsJm6fMSLC4/DWtwep7ezt0f3CbnN5GJoWhwDsrmsPk4597Zsqxg5KpKqlgxHpceSmWKls6lDNebj3oiIcHh8mg8DPzx3WWeaVRHWLEwkDu+sdNITIfusEcLp9dPhE7vuwK0t6+axCOrx+PH6RRJuBV66bRJPTG6bMJocqrEY9l3ZLXPrtO1t4ZN5YKhodqh6H0NCMxajj1zNGKQmiEOxA+nhnCWmHL0BanIWcJBvjc5KUcz75yW4emFtMh9eP1WTg5f9UUJKbdMImnlYBonEq0n3t6vCGa76oeUDf2HBQtY/QslXbuHl6Pvd+uIP5k3N56KOdYWtSboqVJWUFtLq8PDt/AoIEW2vawjwUch6E3AgxVLY7VJvHYtSxcFqe8v/zRmWyu7aFF66ZSJPDQ1qchfLaFm59fxcADe2eYDfnzu6mN00fpio1Xt0WDIesXFcRNaky3mpQNXjiLYaw9aY3chn6Cz0xKhokSfqgJwcXBOFvBJMyUwVBOAgsJWhMvCEIwgKgCrikJ8c81el+8cSZDfxrex3XT81j3KAEEFD1ONS1dXDtD3JpcwcwG3QMSY2hzeXBZjZGCD290plweWlJuAjU0rICnlm3h8qmDl7fUKWUgOYNiOWF9XuZWZylUuZVRJvLo7Q5lxuB3XL+cNx+MSIOuuS9rTw0t5jBqUZ+kJeCwRAp2ipbzl/sbVS14nfU2ok16SMMn4fnjcVm1oVVbqj1F6ls6qDDF2ByXqrqOT/aHvwJpS9EaTQ0Tie6r11GnXolRujvLS4vLU6PkhthMQTluC+flENKjIkWl5dXvqzk4vHZ6HTw52smEpAkatrc3PTX71RLVkMNBr0OfnbOMAw6iQVT8jAbdAxLi+X+1TsUI2LF7EIC/gCLSodRkpvEkve24vVLmE1mBAH21DuwdHZOthh1tLt9TMxNVnJF1u2qj1iLuodDQpMqQ6v74i1G1cTxgQlW1e/3RHsV+oKeGBVLBUF4DlhDMK8CAEmS3o72BkmSLo/yVGkPznvaIV88OUk23t1UrSQdvXB1Cbtq2yOEox5dU87dZaMw6vWK8It8k1VrmiXrQHS/4d+zahsPzS0mIElYDXoqmoKaEstXBWOeuakxfFvZErYbuOvdLfz52ok0tHt4aG4xFY1O/GKn66+1Q9UoiDHrmTIs9YhWdrSa6IAIiTYTzS4vD80tRkIiN8VG4cBEAAYlxSjWvBTF9ZkWZ1Et3e3t3isaGmcS8to1OCWGyiZnmBfig03V3D+niL0NTqUyIzXWzFOf7uWSkmze+vYg8yfnKloRv/tgm3KzfvKTPZTkJpBsM3Ow1aWsiaHVJBC+xj2/voIpQ1Npc/vYXdfO298F16yS3AR+N7NQUbR8fG05l5bk8OaGg2QlWPH6JdX+IrkpVq6YlItRp+Ou97Yowlw3njOMd74PbsL21DvIS43hXpVwyN4GB7vr2kmwGvnDRzvZUNlGboqVWy8YEbYRykm2IQjBappT0RNxJHpiVFwLjASMdIU/JCCqUaERHVGU+LqySYn9j8mKx6jXMSDOHHbDk0tIE20mdtWGhzd21qrrvMvhCbXnnB4/T30WbNH7/PoKxQC56uxcpflO991Ai9NHeb0DUUIxNixGHS9dN0n1Bp0cYzrqjniyiE7oeV/fUMX9c4qxmPQkx5r4+zdVzJuUoxwz1JoXRUk1FpmTZDvq9sy9EbvU0DhTCC0tTbKZWDgtj6EDYunw+PD6pbDKjN9dWEBRVixnDUkhK9FKm8vLTecOY0CsmQuLs1i9tSaiAViowFW0pHK9DpZeWMBtb21SSk6XlI3mne8OsGDKUG78y7dh75MVLTMSLVH7izx3VQk6HWw92KaqyfPMp3vYXG2PGg7ZUt2mVIcsLSvA669kc7Wdhz7axeOXjaOqpYPdde2s+McOpZHi0XYPPZXoiVFRLElSUZ+N5AyjqtlJnd2jGBQ/nZzLf7+yIUy7XU30KvRmHy1BafygRJxe9QznqpYOKps60OtQXHhqstqhu4EdtXZlsoT1EWnrUNWj8AXEiM+rhk4n8P8KM0mymdhQ2UxADDYxu+GHw7gjZLFYXJpPRry6JyFaLDJa6e6Hi6aeNrFLDY2TQejcqmlzK2vDn6+dyDUvfhM25373wTZeuHoiX+1r4pOd9fy4KJMnPtmjup7dP6dIeW/o2qW2jp2dl8Kv395MZVOHcq7lq7bz9E8nIKCu5Dl0QCyPr9nNpRNzVZ9vcXm57e+bWTAlj4f/XR6W23awxcWPizLZXG3ng03VSjg4WjjknlXBcthFf/u+Mxwrcuubm8LO29OeGqcKPelS+qUgCKP7bCRnGLV2D+0dPu788Qh+85NRvLGhigfmFjM4xcZTV4ynJDeB3/xklGofj4vHBysgPthUzYrZhViMXR3xlpYVUNXsVBKfQp9bUjaat787qCQ8Dh0Qy5wJ2eQk26LuBhaX5vPmhoMR57cYdQiCjtc3VCndRBdMyeP1DVUkxxx9VYPBoGPKsFRmj81ian4Kj106jmc+2xO2WDy6ppzD2SmyS3ZyXip5A4LNxw5X9632eg0NjaOj+9zKTLCwYEoeDe0epSuyjNsn0tAejJbfPD1fdfPy8LxiXrluIkNSg2JNciK5/P/FpeHr2OLSfBocHrz+8Apat0/k+6oWjHq98noZi1FHSoyJyUMHYO/wqT5v1OkUT6/cgv359RU8sXYPz66rIN5qJDfFyqUlObz+dde6t3L+BKV9QOhYOrx+5dhOr3ojw6PtHnoq0RNPxRTgakEQ9hHMqRAASZKknpaUntH4/SLba9todnoYnBrDztp29ta3M2d8TpjW/LKZhVQ1qYu0yOIs/zNtKK99XRkm9PTMuj1cNjEneOF33vBDJa3l+vCnP93DtBFpPLF2T1RZ7bOGJHPrm5sjJoteB/fPGcNL/6ng0pKcME/F/XPG9DiUEJqg9MXeRsWgCD1ng8PN0LSjt+i13AkNjd7F7xcpb7AjIR3Wm7qkbDTtbh8OT4APNlWzu97B8+srWDG7UHU9+6KiCatRT0qMUcnReOXLSiWskpFg5vYfjaDZ5QsT0Fo4LU+p0ICufKzvq1ojysqXXljAA6t3srvewW9mjIwQ2Ftcmo9XFPn1j0eQlWjj2h/kRsiIL1+1nUfmjaWh3c1/TxuKy+OnxeUl1mxQDYdYTQYlvJqb3HN1y1OVnhgVM/psFMfIsTZgOVnj8ftF3tt8iN92lj4tKh3GynUVPDt/gpLPALKa3Nao2vkj0uNYOC2PtHgzGyrb2FDZFjYOpzc4mX91wUh217UzOiOefU1Opo1IY+rwNJ75LGh4ODwBILqs9pbqNtXJMjwtjjirnuumDOX+1TsUw6UkN5kf5KUc19+gt4yBvqr71tA4E/H7RdbsqsNi1OPxidx7URG/fWeLqhLm8lXbldDp0rIC/vZ1JW6fyIFmV9TEbLnrcawp2FOkrs3NoGQbAUlkf4OLZat2RIwpL7XrRh2aj1U2JotYk4GnrhhPg8NDZoKFh/61i83VdgD+d/VOHr1snKrS54Ipedz2900sm1moWsIfkCQMeh23hWwAH798XIQRs3xWITlJFkWLAjhj1qMetT7vy4H0lGg9NE5W4ku08VwwKp2DrS6aHF68AZHKJqdSXSFKQTebw63uGqtudUXc7H953nD+tG4vU4en0eEVWVw6jDc2dGnJW4xB/flLS3L43w93KBMlVO8BYFCyjXv/EZyocsfPJy4fx8aDbUgSvPJlJSaDwP9eVKQ015En7r2dx129eCovXjNJyU3ISbJR1eI6LiOvt4yB06nuW0PjZFNeb8fjF6locPLomnKeuGIcC6bkkZNkVV27hqfF8uDcYlqcHn55wXAc7gCiKEbcfOWcCrdPJCvRSovLy7eVLVgMehJsRlzeADFmg1LeGZoonhJr5umfTlCqPF7fUKV4aM8aksRX+1qUZHTZoJDH1+ryhnk5ZOTE0Lvf36rqCREllA6q8rFu/tv3/HPRVF5fOFmRyR6VHs/Btg7q7MF1eXBKDOeNSOMvC86i1u4mM95C0cCE03I96omnol8RLRHvZCW+RBvPy9dNot7u4UCLK6xWednMAoYMsJEaYwrWb6tY8O3uAG9/d1DxBAxPi8Pl8fH/xgyMaNIjW9qy+NTTn1XQ4vKypGw0K9ftDRurLL4ieyEsRh1XnpWLyxf0cMgJkrJRNDDRyud7GhVjQzZgau1uJS+ht4y83jQGTpe6bw2Nk4nfL1Jj97Cn3sHKdRUk2UzoBR3Pr68ISyyXsRh1Ssjjth+NQC8IlNe3I0rw5d4GnruqhK/3BxOzQwWqqls7SI+3MCQ1lvs+3M6Dc4upaGiJ0KiQOyY/+u9d3DZjJMWdDQrLxmQpid4SEqs2V7O0bDRvfXeAn587LKwjc26KTXXcspy/nNjZ3RNyqNWlakTVtQfXwqIsiapmJ6u21oSJcT1xxThaXb4Ipd/ZxVmqWj6nMqesUXE8DVj6cjxyCah8AfsCAZJjTGHtgYOW8DaeuHwcv1+9kySbKcIjIQtV1bS5WbW5miVlBWw/ZCcvNYa7Pwi3lB9dU86frirBrNdR1+4mPT6GpWWjkBB4fv3eiLyHxaX57G908si8sZTXO/D4RV78v/20uLxhSpTyzXxAnJnnPq8Iy4bWC4RVZPSmkddXxkB/C5dpaJwKbKtpY+OBVqUj6Y0/zMMXEFk2q5A3vqlk2cwC7u6U5+7ufXjwX7uUHb/83B//vYuLxw9Sdvyh72lxeXni8nHcd1ERLm9ANbHz2fkT+PVbW6hpc1Nv9xBjMpCfFkeMSU9xdgISEodaOlg+qxAJiXkTc7k7RNFzaVkBL6zfq6r0KQsHWow6mhweHp43lg6vn4rGYA+iOROyVY0RWRdn9bZadtbalbJaedybD7ZFPHbXu1vJT4uleFDSCf6L9i2nrFHR3xLx0uMtSmZw6A18cEoREFR9HJMVzw0/HIo3IJEcY8QvikpZ1ishEtjjByXyyhf7+dUFI/H6A6TFb4X+kwAA2LRJREFUmdl2yI43IOKKkkXc6PCEtQT/3YUF/GtrDaWjMhicEhMmXJViM1Lv8NLotJOfFq5rLytRht6A0+IsPDt/PNsPtYftGkZkxJOTHHPEaov+4Cnob+EyDY1TBbl8XS9ASW4CZqOeRa99T5LNxP9eXIjLG1BE9XbXOcK8mW6fqHQwlY2CBVPycLh9vHBNCU0OHy6Pn0ZnsEIkyWaiweHlmc/28LMfDlNdUxraPVw8PpsEix5vQOK2kCaLy2cVMiDOhF4nsPCVb5XcjtCb+T2rgh2Z460GHppbjNsfIDPBwiMf71K8JksvLMCgg/tX7+D3F4/ht50eBrX8Mzk8K2+sQnU2ZLq3iJfHUtvmjmgCdqpzyhoV/SkRTxQl9Dr43YWF3Pjqt92s0S08OLeYktwE5kzI4ZY3uxJ87p1dpPTjqGlzKxLYj102jolDUrj975siZGoXl6pXauwLaefr9gXrw5+6cjy7att5+tM93PqjEXgDIjEmPS0dftVa8RaXN8ziDv1u758zhte+qYrqiUiLUzfyBsT23MjrC49CfwuXyd1KTaZgHxCv14vJZNI6l2r0O7ISrfzvP3ewqDSfSUNGsOClDcpmyGTQsaMmuNm4fmr4DRwiOwTL1WPjcpOoaHAqDcjkdUgQgi3OLy3J4VBbh+qakmQzcdf6rZ16EuEdQJe8F8yFyEywMjwtNqp4ltWoo9npU7qkylUr543OoN0d4JnP9jBrbBZlY7KAriRLOf/s6Z9OAEkiJzmGIamRG6vu49ZHaaeecRTdkU81Tlmjor8k4omixNpddYgiBMRw0ZXMBAtXnpWDIMCvfzyKzQfbuOfCAmxmA9WtLh5buzusIZYcmogx6xVLuLtM7RsbDkZoyd9dNprH14YnHbl9IhsPtBIQYXe9g4Ao8dznFaqWu6w2NzIjHp0An+6uZ1etPUy98463NkckfIZ6ImRNi+4a9/oehgv7yqPQ3zwpXq+Xq/60ntdvCirWX/rEGl6/qVTrXKrRqxyvgS6KEi6vn8sm5iCJIk5PgAVT8oiz6MlKtNHq8uP2BUiymXjr24PcXTaKWrtH8WykxJh4+rMK5XgWo44xWQk0tnt56tM9incWgomWt10wkrIxWTy2tlw1LHx32WiWrQoaAtEMhqxEK4daO/h5ZwWb2s18XE6iYhzJ75OrVuQ1TpRArwt6oc8aknLEe43sPVfzZhRlJ6h2Ty7ITOjR3/NU4JQ1KqB/JOIdbHWi1wm4PH4GxJm588cjyEg0k5lgY3+jiyXvbSXJZuLa/xrMwx+HJ1cuKs3HYhTCdOHT482K0iZEytTWtLl5+YtK/nRVCa0uLwICjQ63aulnQES52QdEicWl+XT4AqoTcWx2IiISMx79PMKDIRsW3Q2E0HCTPK5QzYyXv6hkXE4ig1OP/u/TVx6F/hYuA9CHdCvVa51LNXqZ3jDQ9zU62d/k4p9barj1RyNocHj5qqKBi8YPCiurXFI2moAoohN0YTLdy2YVYjIEzyXfSNs9fhrs7ohQ8aLp+ZiNOqyd87R7WHhEehzVra4wHRu1OW0zBW9rHp9ftRnYkrLRNDu9quugbODIVXQlucmKAXGke02o91zW2RieHseojHiGpMYgihL5abFKhUhBZsJpl6QJp7hRcaLx+0W21bRR0+ZmYKKVeLOebw+08dt3tpBkM3FJSTYj0uOwGvVs2N+VtXzx+GzFoICu5MrFpfkkWk0UDExgS3UbARHq2z04POES290nTovLy9bqYOnn6xuqWPBfQ1h6YUGYK0/Okl4+q5DWDh9+USLFZiTOpi7CkhJrCmtDnmQz4fYH+EVpPgdaO/hgUzUluclh2dCh4ab0eItqq/Ke3rT7yqNwosNlcngj2B5HQ+PE0xsGemWzkwSrkXNGpmHv8PPYmt3cMWNUxHGXr9rOQ3OLIxPS39vKi9dMxOn1k2A10uEN0OjwUjQokV939vyRX/vY2nIev2wcozLjlXUmNCwsGxfyc2oegcWl+dzXWfK+uDSfHxdl8to3VTx1xXjaPT521zl557sD3HLBSNUyVblB4eLSfPJSY1R1d6J5f47kPdfpBIoHJZ12ORTd0YyKo8TvF3l3U3WY++rJK8YpBkV3Vbn7LurSsY/mphsQa2ZvfTuDkmN469uDXFKSTZLVBJJX0YeQZWpDwwq/PG84f/7PfkWD4r5/7uSpK8bxxBXj2XywVanZ/vm5+Ty2ZjcbKtuU9/3lq6qI4z08byzeQFfoRk0lb8XsQiYPTubDKBOmt27afeVRONHhMq/Xy9UrP0NviAxndBkcGhp9R28Y6Ck2E80dXkZmxOH2iZSNyaKiwaF6XFcUL+ih1g6MBh3ldY6oLczl17a5fTy+tjzCWFg+q5CHP94NwG9mjKTJ5UWUwKCDJ68Yhy8gsb3GHiaX/eiacp6+cjxlY7K4Z9U2lpQV8FVFA/NKcrjuz99EbMAWlQ4n0Wpg8lUlZCVYGJwaKeF/JO9Pf/Cen2w0o+IIyFZpk8OjGBQQ3MnbO0Wr1FTlTAbdYb0NFqOOqhYXw9Li0OmEsGRMi1HHPTMLePSycUr1x3NXlfDlvmYkCf78n/3KxJENFm9AoqrJwbC0OPY3Oikbk8WTn5RzyYRBbKhsw+0TeeTfu1kwJY+Xv6jkpWsnISGFNeCSx6j2ee56dyvjc5KiTpjeumn3pUfhRE94vcGo+rgY8HHVM59itsWfkHFonJkci4Heveqruq2DW97YxIvXTEQi+Fxuirq3Mz3erPq42aBnd317REmlXAkiezeDYzNT2dQRFvaQJIi3BnV1kmwmPAExLMRy6wUjyE2xRYhZuX0iTm9AaYm++WArt/5oFNe8+HXEOB6aW6yI+j1xxTgkBL7a10RanAW9LhjeTY+3IEn0q4Tv/sgpbVQcaxLS0b5PFCU+31NPIAB+UeLBucXsa3TiDYgMSjBjNerJTbEyOjOO66cGW4jLrrSDLV1qmMEEptGqddlXnpXD+JykiHrspe9vY+G0PAIi3Pb3zfzx0rE897l6ZrXFqCPGbOC+f+6K+Azp8eHNfQQhGD5JjTUjCCiKbzlJNuVmHs2zcqQdTm/dtBNtelbOn0CL00dSjBGD/rgO1y/RRTE4NDR6i54a6Gq78Dt/PJI7fjQCnQ5aXX4m5iazo6ZNVdb/UIsrIhlRzrXISlBX3pTztCzGoCCgzRRsBiaHPeTnnrhsHA/PG4soShEhloc+2sUj88aqGjQGQeDOn4zioY92doaX1b03O+vaqWlzk5lgobzOwU1//T4spCKLCz40t/iwa2NoiDwzwUpBZvxpmTdxOE5Zo+JYk5B68r4DLU5q2zw89ekefv7DocRZTeQk20iOMZJgMeD0+vn5Ofn86s3whKV2tw868x2enT+BXTV2BqfawrTm5RLO0Znx+EUxwihx+4KKbgdbXMF68H/uUPT2u7vtll5YgNsXUJXsTo01kZlgUWKGOgH+cMlY9jWFTxxZPfPDRVNpcHhUDZgTkdR4oMVJnd3LvkZnMPu6EQanxnCgxUluirYT0NA4WnrqPeyeg5FkCyYPu/0iu2racXoDvPZNFb+eMYrfd/b8kT0Jcv7W3noHL14zkdo2N3FWI8tXbaOyqSNqKXxhZgL3X1yEzWygptWF02NmaVkB96zqyg9bNquQ/9tTT1uHn9njB6mulZXNLn553vAwpeFbzh/OPau2B5solhXw1ndVTM1PVR2HXPbavdpOzn+TPSrl9erqx2lxFtUQ+emqmnk4Tlmj4liTkNTed//qHWQlWoI68yYDHX4/To+fWLORhnYP980uwuHxIwI6HRh0AuUNTqqaXREuveWrtvPHeWOJsRj4ZcIIAqJIYoyZ37y9JSLbeemFBTi9gbAs6lDNiN11Qalb+bEkm4Gbzg0mFw1OjaG21cWKWUXc9d4WRVo71KpeXJpPeV07F4/P5vn1Fdx7USExRgN+SWJPnTOsZPSWNzbyYed3F22Hk5Nko6LB0aeKlE3tXmrb3GHuzcWl+TS1e8lN6dVTaWic9vTEe9g9B+Pi8dk0Or28t7GapRcW8LNXgyJTT3+6h4XThrI8xPN6z8wClry3VVmHnrpyvPJ6CJbC33L+8IgKOKfXz1Of7VHet2xmAd/sa+TZ+RNodfpIizdT3eJk8rABONwBFr6yQXWtLMiM54XOPh+CACPT47g3RNTvnlXb+OOlY4kxBccaqo8hr5kQPf9Nrgp5Y8NBlpSNDvvssvdnS3VrWIhcDhufjqqZh+OUNSqONQmp+/syEyxcWpKjVD7ISUHxVgPr9zQiSrC3wcGw9DiaHB7S4szY3X6aHR5GpEeGPdw+EYfXzy86b8hyJ1K3T1TihHIfjwMtLqViQx6/rBlhMegVqVv5sY0H2rAY9Lz6VVcy0qLSYUqJlWxVPzi3mPL6dmxGPc+sq+CW84fzwjUlbDlo57f/DndNyi2K3/r2oPLdqe1wcpJsfLSjrs8VKR1ev+pOYeX8Cb12Dg0NjUhCczBWzBzB8Iwk6trdPDCnmDp7hzInN1fb4esqHphbjIDEgDgzD/1rZ9g6tP2QPWydrWlzExClqJ1Bn+wU43vy0z3cdG6+0rXZYtTx25+MItYCv313i+paGWMycNd7W7jhh8NodXnp8AUiaq7cPpHddQ6qWzrITrLy1BXj+e5AKxaDDptRr5TkdxepykywcElJNlkJVm6aPowPNlVj7/CxcFoe4wYlkpsSo2yu5PW/+3lPR9XMw3HKGhXHWiXQ/X1qSYlL3tvKI/PGKs1zrjo7l4c/2sl1/5VHnd3DwEQL7XGWsLBHqNXc0N6lMxEqzxoaJ7xp+jDlfKG4fcH44x/XlIdlReck2fjDx7vDJqGsRdH9/S6vn4AIz6wLNhUzG/W0OH2Ka1B+XWiL4sWl+aR3qmmG5ptMGhwsqapocJyQBCWPX1T9TjzdHtPQ0OhdBqfEcP+cMTg7OjAYTFzVmdBoMep49LJiXlt4Fo0OLwMTLOxrcnF7iIf1l+cNp7rVo6xZasmcHb5A1M6gMmVjspR+HNApy+3w0ODwqK4L2YlWmpxeKps6uOeDbRF9RkIblvlFkTa3SGlGGg3tXSHezASLstk7f3Q6IzLiueWNjRFqxhajTmnlvrnazmsLzwpb+zITrKr3pNNRNfNwnLJGxeCUGJ64YhybD7Yp6m1F2QkMTonB7fazpaaNOruHoWk2nO4ADU4vcWYDRr3AS9dOxKjX4fb5ESVBuahDvQ1NDjePXjYOs0GHy+vnZ+fk89Sn5ZyVNwC9ICi5DRBuNVuNkVmF0WJ4gqD+XLPLq0xO+bEYs4E5E7IBiDUHk5nuLhvNs50dSOVGZnodxJgNvP3dQVpcXhZNz6e21UWC1RTVrSd7A84akszWQ22U1zsw6HTkDYihvK6dgYlWWjs8SqKqXgc6QcDpDVBr76DJ6SElxtwr4ZCMzh4qZWOywroKpif0ntKklkylcabQk2tdpxPISbbi81sUgwJgeFoszU4/i1/bFOF9heD68ci/d7Ny/gRqWt00Oj14fP6Ihl2jMuJV53aojLdeF7nRknuHqK2Vlc0dyrHcPvU+I/KmKSPBQnKMkZzkGHKSY8Kkt59fX8HD88YyOjOB0ZkJjFw0lYZ2D1d3qxSR+4bsrndEbGALMuPPGNXMw3HKGhUAXr8UFnt/eN5YPB4/H2yt5e73t3L2kGR+MmZg2IW9uDSfGJOeRJuBDp8UFlv7zYyRtHv8mPQ6clNi2FHbrsQAc1Os3DBtGPes2qbaMCbUw3BJSbbyuJrOhBx2+HBzjap4y/D08Ja7S8sK+P3qHUrcccXsIh69bBwN9g5u+OEwRSs/9DjLZhWSEmNk2artrJhViMMTiCr2Io+/2enlYEtH2He6aHo+f/p8L3Mm5ISJay0uzeetbw/y3OcVSsLoHTNGHXc4JN4KPz8nn7vf7/qbLZtZSIK1d0IsWjKVxplCT651UZSobHJS0ejCpBfC1rfrpw1VvBJw+OZYf1xTjskgsGj6cB5fu1vxAIzMiOfjbTXKGhqai/HmhioAxfDobjzoBXh3Y7WqMubKdXuV/hyh65k8ppEZsTw0txijQcd9H27nhasnKevT4ZJY8wbERg2x63WoVtEYDDpmF2edEaqZh+OUNSqiJWq+fN0k5YZ0zZQhSmxOfs2ja4IehcGpMdwe8tzwtFjSEyy01zvITLTi9AR49atwidjbQiaWuu5EBy0uLyNDJkaLy0tGvIXf/ngUVrOeqmYXT6zdo3gR1u6s5eF5Y9nT2YL85S8quXxSDgun5TEoyUZ6vIUlnYmY8me4690tSt7F6q0H+dUFI8MmvdsXVLJ77qoSFpXmU2v3RLT4fX1DFZeW5IS1+jUadKqthh+YWxxx/NCMaHlH0BvhkDp7QPn7KZ/l/a28fN2kXknU3FbT1m+Sqfw+D3a7nbi4uBN6Xo0zg6O91uX+RW6vSGWTkynDuiokMhMsmA26sB4dVqNOdf072NrB/Mm5mAyCkv8QWhaqto4sfX8bz86fQEO7h6pmF09/uifCeMgbEMOVZ+Uq67FspLywfi+XTczh5S8qIxIu5XMOiLNw2covuWl6MPesweFmaFpwfTpSEmu0EHvpyDSKshJVN08Gg+6MUM08HKesURHNigztm9Hi9Km+RpSgOeS5zAQLV5yVyy9e70pCfGDumLDd/6LSrja8avKwiirb9Hye6WyUk5Nspaq5gz+u2c1tF4wMq68GFNGV5au2h5WBevzByXjT9GHU2d1hWvfyZwjNu9hd1676Oe1uHzWtbqUjqfz4Y2vLWTm/hCXvbVE8Foum59PuVm+r3uFRfzzU7SgInW2L2z3HVR0S+vcLPVedvXcUKGva3CTZTFw8Pjss7HUykqnEgJ8FL3zJX2784Yk9scYZwdEmDlY1Oymvc7BmRy1X/SCPZqeXFbOLeHztbq6fkkeTw6M0IZQ9ut0rOeT8BZNB4BfnDVdfR7zq68i3lS3Emg0MT49j5boKGjr7ZuSlxmIz6rn3n9vx+iUuHp+N2aAjLzUGvU6gdFQGkwYnMzg1hniLgSaHR0m4lCtJ/rWlOkzPpydl8TlJNu6fM4Y7OuXEZW94NINCI8gpa1REsyLT482U5CZw1Q/yMOoEVe2GeLOe9HizEg5ItBr4XbcqDB2C0inv4vHB7N/QY8kNY3KTbQyIM7Ozxk7ZmCwlMWh3vSNMLc4vSqoTKiBJYRNBluC2GHWMTI8jzmpQ/ZzVbR0ReRfdX2Mx6HFHSXzceKCVWWOzGJ4eR4LVyF3vbuGun4xWPY7NrD4GKSTWGWPSc9XZuUoM8lirQ9LjzcybkMns8Tk0tnsYEGfmne+qSI/vnZyK7CQrN/4wj0anV8nFufGHeWQlWnvl+D1FZzDh8XgQxe756hoax8dAlcTB934+EY9fzwebDpEeb2Z4RgzNTi9mg475Zw9hb307n+ys58dFmdx3URFf7WsOy59IsplocnkZkR7H81eV0OjwoNPpqG51AcFEy/2NTtX1IjNBfc0OiPDwx7t56orxikekJDeJh/61i2kj0pRNVWhfoQfmFJESY+LVL/dz7qh0Fry0gZ8UpPPiNRNpdHhIi7Oweks1f9tQrWz4eqLMK4oSH+2o4+GPdynekZLcZNVeIBrhnLJGRTQtheEZMcwryQ3LTA7VbrjzxyMxG/VcG6L9/qCKStrBlg6Gp8VywznD2FlrV5pqXXV2rnKsWLMBh9vPS//Zz4+LMsOsedlyh+DE0esE1QmVYDWGdSm1GHSYDAKLS/ORkPD4xIi66FvOH87AxK4F44NN1RENxRaX5lPZ5FTO0/28flHEYtDz4L928sd5Y/nVBSNocXlYNrOAu98Pb0z20n8qVI8vux0XTc8HiAidHEs4ZHhGDCWDB4Rp8y+bWcjwjN5p/CUI4PQGInQwhDMr7KlxBiAhheVzvffzieyqdYfN7+WzCokzG3jgX7u6crguLOCZz/aQbBsWlj+h1hNIXgdMBoFlMwvw+INeyyevGMeyVduVPLDlswqpanKqCvjJpfNbD9mVqraJuRPYXe9g2og01fVrcEoMr31dyYfb6rjsrNywnkQTc5OpanFxQeFAZo8fhC8gMqMwo0ee09DwemgI50NNjvuInLJGRTS1uA2VzRExeVnnICBKxJgNXPVCV0bv2UOSyUgw8+I1Jbh9Ika9AEJQ4ColdnCY0bJoej6vfVPFnT8ZRXl9OwFR4n8/3qn003jmpxPYWt1G3oBY7l+9QwktrJhdyEv/qYgImdw7u4il72+NaOX7/NUlCMCf1lWws87BDdPywgyPgYlWbEYdL183iUaHhwGxZhxePw/NLaai0YlfFImzGHjyk72YDEJEFvaSstHYO3xK8x1PQGR0RjxlT6xneFosj8wbiyhJJNqM+AMiU4alIEoSry+cjMsbUPTwxw5KxGbS4wuISh+UUNy+nncX3V3rVM2pGJw6iUlDjr80q9npU9XBKMw6ORnaWjdTjb7iQEsH/9xSwwNzi+nw+PH49YpBAV3l8wun5YWFBGvbOvhdp2T2wEQL468uocnpJcFq4N4Pd0TMnTt+NAKzUc/Nr4VLWy8qzaeh3YPLGyAQCKDX6/nbV/sVHZ2ASFjJ55DUGHJTrNx0bj45ySZevnYSbW4fK2YXcde7XYbIPTMLePrTPXyxr5llMwuJMevJTrCGrTPH2y6gr7olnwmcskYFqCfaRIvJt7v9JNqMioZEZoKFX543DIvJwPdVrWHxwXtmFpCRYGHJe1vDJpvHH+CyiTnsa3Ty2JpgzoOcT9Di8uL2BujwiVQ1O/nVBSPZ3+jE4xdJshn5n2nDqGp28ez8CWw60IZfFIm3GlTzJWpa3Tz+STmXluSws87BM+squOrsXEZmxOH0BDjY7OKlzt3BjT8cxi1vdHll7ruoiHGDEhEEGJYWqwhXTchNorLJyfcHWnli7Z6wcFBanEWZRJur7dz46nfKeF5beBZjoiQwDk7t+t4rGhzHpBvSnb7OqXBH6aTo9gV65fg9RQz4+O8Xv9Cai2n0OoMSrfy4KFPx2j5++VjVa9+g04V5IHJTrOQNiKHB7ibeamJ7jR1RCpZ/ysndoRo6g1NjuDFEPVM2NhZOCwoDxpoNJMdaECWJDZVtVLfuiPB4LJqez0Mf7WRx6XCKsmL5Zr+dIakx5KfF4gv4efm6SdTZPaTHm4k167GZ9MyZMIiV6/ayu97BslmFzB4zEJOpdxoF9VW35DOBU9qoUCMjSqe8lFgzG/Y3MyY7gZLcBG744TBizHpaXX7FoICujOTlswpVW5ovKRtNaqw5LPlHJxB8PM6khEBCzz1qbjE7att5vlNG9vn1FSwpG83eevUbcVWLi7IxWUpVxdvfHQRQJq48CQWBiFyQO9/ZwoeLppKbHIModTUMG9yp/NbhE8NyOELjjGo15Ec7iXqv9bn636+3ciryonRYHNILXVCPFZ3BqHgsTCYTgqDFbDWOH5vZEOaVS4tTv1HmpcZw6983kWQzceVZOeSnxxFv0VPd6g5LXpdzEy4enx0WEqhvV98IiFKwau6+D3dw94WjSYkxYTHqlJw0OVdhcl4Kv3pjEzVtbu58ZwsvXzuJ29/awmsLzwrbuMhs2N/MbX/fHHbOu9/bSl5qDCWDk3vlu+vLbsmnO6e0UaEm7JIcYwiL/+emWFl6YQGHWjsYmRGHJEn899ShGPQC1a0emh3usHIpWcMhNdbEJSWRaptybw+5RnpJ2WgGxJqpae2gotERkXtwy/nDOdDiQq+DFbMLsRn1PDC3mEOtQW9D93wJOcY4Z0K24gW5pCSyyc1ja8tVc0HcPrGz3jy8Ydj9c8YwMNHC6Mw4/nHzVBoc4bXZOUk2bp6eH1bTvnxWIQLBpKUjxSJ7q/X58IwYls0sjNCpOJacCrVutEMGxPKHS8byqze7Fos/XDL2pLs0xYCPnz6zjuevPYu4uDgsFm1HpHF8NDo8YZ7W+vaOiLl130VFmI06kmwmfnbOUCRJAknEF9BFlKPKm5zQzqLLZxVS3+5WNVZ0AlQ2OWlxebEYdDi9PqXvhiw4dc/MAv702d4wz0d9u+ewXoHaqJV/btXXHwu9tZ6diZyyRoXfL7J+bwM6ISjU0tbh7fwdYsw6XrxmIk0OD+2egNLYRs5viLMY6PCKPL52NwunDeXhf0eWhhr1AnmpsaoXr0+UiDPr+dUFI/nDRzu540cjeWZdBQNiTVw7ZUhY/sOAWDOvfrWfa36QR7PTg9Wo5w8f7eTC4ixaXF7a3T7V7qWyF+SswUk4veoue5fXrzqZnZ4Aer3A8LRYNlcHNfjveGuz4iWRDYxQqlpcEYuIHG8dmRHPBaPSqWpxHbZctHs4ShSlHjcga3W4GZhkUrK4U2PNBKQArQ43idajv9HKtffdFVenj0jnnPzkMHdqQUZs/1gsBIFrn/s//vrz6ZpRoXHcZMRbImSm77+4kJevm0SDw0Oi1YjVKKAT9PzxsmLK6xwMSrZR0+ahpka9TF2vgwm5Sdw0fRiSFPQMP/FJuaowlShKPLc+mORd1eTig83V3D5jBH++diIN7V7S48w8t34Pn+xuVM4R1JYwH9YrkBm18q9350xPmrFpdHHKGhUVjXbcvgD+AHh8AexuAYMOUmLM1La50QkQazFy6983h1nrB5pdTBqSTHVLB2VjshQvAXRZ44/MG8v2Q3aGpMaqXrwHW1zkpsRw/+od3PjDYdz/r53UtLm5pCSb37y9JeL1T185AW9AxOkNkK4XFCGXRdPz+dvXVREd/2TD5u6y0bR2+NgTJUzS7PSqVmXc++EOWlxelpSNpqEzf0L2enQ3MOSyz2iJSaIEt7yxkZXzS8I6BB6pXPRYW9PXOwK8/J/9XDl5CAERfAGRv3y5n+unDmPwgKO7NkRRYntNG+V1jogqj9GZsawvb4nwhJQVpmOzmo7uBH2IzmA82UPQOE1od0cmJd/x9lbe+dnZ2Iw2dDqJdrdIfXsHg5KspMaa6fCK1LZ1YDaoC1yNyohn+yE7T6wNVmmkxozgsok5vPZNleLFGDcoEYtRYMtBO0vLCoix6Pi+qo2z8gaw+LXNtLi8PDxvLCv+sZ2fTh7MuvLmsLmYlWxm4uDkqOtE0cAEls0q5O6Q5PNlswoZM/DMksPur5yyRkWHV6LF5Q+7oS69sIBEqwFB0HH1i99wz/9n78zDq6jOP/6Zuy/ZE7KQkEBIwpJAWAJiK1RBLVoEVMStoIil/VWE1i4uFRTUtqjFStFa3LcqWnerVEUtWldc2GRJCCQQspH93pu7z++Pmxnu5M6FBBIION/nyQO5d+bMmTknc97zvt/3+15QqMqLyEyw0uzyqurMu31B2n0BHv+kgnk/yImYvFI6p04QmTYyk39+XiHzEDLjrartldc7uOPf27EYdTw8t4TB/excNi6b/gkWfnPuUEBk/hm5mA06BqbYOdAc4lTEmPX8rsMo6rwTuGNGEf1iTKx8dxf3zCrG5fFT1dIuZ3QAcsEwKU0rXJJbMjCktM9oxCSxI6VsY0Wj4uV0pHTRoy1N7w0EmDIsQ1GlcNn0QryBrhEpJWMmGBRVszzG5iRGyS6xMX7Qia2tHvD70On0Gr9CQ4/ggIr41emDkiivd2E366hr9bH09RAZfeHkPMXG5uapQ/n12QVyEULJ+9Dm9sqp5L89dwhPfLqX+T8cxE1Th7G9ppUfDE7mhS8qefnbakDKZhvHX99TejEe+rCMXXUOnB4f984qBgFSY82MSI/Daj28YW0y6Zk5sj+5KXbZCzqyf3yPkTQ1HBtOmFEhCMJU4H5ADzwiiuKfu3O+0xeIKBu+7I1tPHXNeDl90mY2qPIilr6+jYVn5TEsI1Jn3mLUcaA5JLddmBnPIxvKWTApl0EpdhJtJnbVtGLU6/juQItMytxc1QrA4il56qIvCVY5VcrrD3BdB9dh4eQ8HvmonGsn5qoSPB/66VjcvqCC2CQIcNrAJH7/0mYuHpvFrjoHO2vb0AlEVACUjAc13YxwA6Ouzc34gckRxCTpHEmgpnPbh0uvOtqULJNeL9djkc657fVtPDVvfNRzwiEZM8suKIwSd+3d7JJjhSiKtLW1cdUj/2PtwimYzT1XSE3D9wtqVTPnT8zli72NnJ6bzNLXQ++hi8ZkRXhs/7RuB4un5Mveh9NzkymtbaNfjJXfnFtA/3gLm6tamDEqE1GEP6/bzqUl2eyqdcgGhdRWs8vLM9eMp6rFze56h6JMwROf7uW6M/O5oCj9iMZEOEwmfY+RMjX0LE6IUSEIgh54ADgH2A98KQjC66IoftfVNhqdXlW55fBFo6rZRXaSTXURcfuD/P2DsggNByn0cNeFI4i3GDltcAour59Wl4+/vreLS0uyqTjoILefnT9dOIKbw4RcspNtEeGIRZPzWbFuO3fNHIHLF6DB4ZX7I8l9S/LeioJg0wuxGA+5IKWy6RajjpKcRKpb3LyxqYoVF49k5bs7uWnqsIjsjc921zMpL4XCjDiFEFZnAyM11iITk4ZcP5HtNa3sqm2T+R1/unAE9W1uFk7OUxQjO1xmyNGmZNVFWfTrHF1b9Gtb3RSkxpCRYGHFxSOwmQw8vGE3m6tayUm2Rs8uie0bi3cw4OfnT36Bzmiivr6elJQUjV+h4aiQGmtg5SXF7KhtIyhCjEmP0+snKCKn1gOy1zIcbl8oXPvox+XcfkEhf3lnBxeOGcDegw7irUZMBj3+gEheaqzsWV27sZIZozIV7UgciZJByQxr95ARb2FoeiwpMWaa233cM6u4S94JDScPTpSnYjxQJopiOYAgCM8DM4AuGxX94yNJSIun5CsWjSc/qZAXZzW3/q46BwMSrTw8p4QGp4e0OAttHj9LfjIcfzDI15VNZCdZiTUb2VXbJv/h3DR1GAcdHj4qreXxq8fJ6pqltQ6ykmwsPCsPtz8oEy+rW9x8sbeRnGSbnI4a7oG4pCSLrCQrd88qZm+HeFWCzYTL64+ocLp4Sj4mQ0id88apwzh3WBojMuNxenwsPCtfNpBykq1cd1a+XMJYYnpnxFu46eXNsmEQTojS6QQGp8YwKMXO8Iw4Ts9NxhcQ5YJm4UbXjVOHHTa96mhTslKjLPqpMV1b9PsnWLj8tBwWhIVPbptWSPrOas4t7M/v/rVJ1YAzn3g6hQydwSgbF89df7ZmVGg4Kri8/gj12AeuGINegH6xyr8ztb+5cQMT+VH+eJpcHn59zhC8/gC6RCs2k56gGCA93qJQLr5zZhGmMC6GxJEoTA95Ju1WM+MH9Q3jXUPvQRDF46/kJwjCLGCqKIrXdvw+BzhNFMWF0c4pKSkRN27cKP/+1d5Grnz084g/hLU/m8CuOoe8uJbkxHPZ+BxFqqS0MP7iR3nEmAX6xVqpaw0ZFTXNTnbVt/Pm5ip+eWYesWYDN7x46A/njhlFHHR4eP7LSi4bl01mooWcRBtlB1384ZUtXDsxl0c+igxl3D2rmD+9tZ1+MSYuPy1H4c1YNr2QBz8skxduSfq2X4yJeT8cxJ4Gp5xNktcvhkEpNuxmoyKborzewfmrPpKve91ZeaohlX9fP7EjvezIaVKd25Sf8YIJXSqqI6V0dicl6+OyOqqa3IqS9MumF5KVaOGHeamHPRdgU2UTlz78WUSfn75mPHM6lFQz4i1cNCYLvQ7OGJzCp2W1/LAgvbM79bAd7TwfO8Pj8TDrr+sAHcFgADEYRG8wEgwG0On0XfpMEHQ8Pv90UlJSNG6FhiNOgM5z8ovyBnlTISEnORS+MOsFWtoDLH19G4k2U8QG7Y8XjiCvnx1PIMDBNi8pMWbaPD6Mej1mg8CnpXVMyEtBEPRyFlVBuh0jAttqHIrMKrtVMyROQUSdjyfKU6HWoQjrRhCEBcACgOzsbMV3tW3qMfsDLe1kJJg6Ukq9JNiMxFoMPHZVCQ5PgFiLAY8/wF9mFdPk9hFjMlDf5uGgw0NavJmUOCtmk5Gi/nHsbXDy7GfK8ucWow5BgN+eO5TqZhc6dARFGJoew5PzxtPS7uOPF47glk769n96KyTbXd3ipn59KQ/PLcEfCDIg0UZVi4sZozIx6HQU9Y/jD69ulY99/H97uHbSYHRAenx0QlJnDkM0l2a9w82E3JQupUlF40W0+wJdSsE8mpQss0HP+u3V/GPOWJpdPhJsRp79bA8/m5jXpfOro+Wwh7l7pVCShIz4GAxdqP1xuPnYGwgG/Fzz6Kc8es0ETCYTFosFs9msGRgaZBz+HRkZSqxoaEdAwBMIMiwjlqfmjae2zUNmgoVn559Gdaub1Fgzta0u/vNdHW9uruK6s/LJTtJT0+ohLc6ISQ8XjM6OuknQvBHfb5woo2I/EF5oOgs40PkgURTXAGsgZIWHf5cWRR0uJcbMinXbWXhWPnFWAy6vH5NBh9MbMihsRj0mvYg/CMFgaPE16AT6x1sxCDoqmtpocwe4552dctubq1qxGHUsmJTL6bnJ6AUBly+ANyByzzs7ePzq8YqFMxgUGTUggYoGJ05PgHve2aGoKNrk8pKZcEirPrdfDAMS7dS1ubEaDbLipXTt3/9r0xEL2UTjMHSX09CVNntTqnZEejxnD+uvyP5YPr2IERldSxdTI6dZjDrSo9zL2JxE/rZ+F3fPGnXEtg83HztDqjyqO9ZCZYLAVf/4LzqdHr3RxDO/mERMTAw+n++YskNEUcTr9WI0Go+5LQ0nDod9R0YJJVY0uGj3BSmrczEpP4XMBAtVzW7SYs3kp9pIi5f+vvX8qKCYgnQ7CVYLhZkJx+u2NJzEOFG1Gb8E8gVBGCQIggm4DHi9Ow1IucoWY+gWpFzl/HQ7l47L4RfPfs3cx75k0fPfUtHgoiDNSoPTy/qddax+v5yyegeLn/+Gy9Z8zq9f2MSeBifL3thGg9PHU5/t5capQxVtL56Sz6BkO395Zwc7a9u469/bWf1BmSq3QNqh/6ggFYtJx2XjshVtdeYWSMdPyE1hRGY8K2ePOuzxapA4DNJ5b2yq4s6ZRd1u53BtHk0b3YXFYmD6iAyevmY8qy8fzdPXjGf6iAwslq7Zv4UZcRH3fefMUA5753tZNDmfpa9t5ZozBvdp+V2dwYjOYCQQ8HPZqnc5cOAAs/66jra2tqNu0+v1cunq9TQ0NHDJ/e/S1tbGiQiFSpDSaE9kH041SOq0nd9jz35eyaMflzMo2c7I/gmMyUniguL+jM9NZkh6AglWC+MHJYc+G5TcLdE5DRpOCKcCQBCE84G/EkopfUwUxbsOd7xaDNvrDbD5QEtErnJzu5tdNU5FrC/BalHE+NPjLASCUO9w0y8mVHWzptVNWqyFNrePBqeHeKuJBocXs1FHjMmALxgg2W7BoBeoae0aTyAYFKlsDPXF5fWTnWRnUMqRz+kuF0HtvOxEG5VNrmOSmT3avpxISPLtNS1u0uMtFGbEYzDoFLLdUnXVJLs52j0dFadC8gC43W6uePCDLvMn1D6L9r0A6IwmHr16PLGxsar983q9mM1mLBYLbrdboXshiiIOh4OfP/stq2YN4+dPf4leZ+DFX517wlJYPR4Pl65er6XRRke3ORWA4l2YHmcmxqKj2RXossKtBg1REHXinDCjors4EjFOg4YexlEZFdLi+PDccVz9yP96xaiQPiMYlL8TdDoCfp/8PYLA49f+kNjYWNra2rjm0f8R8PsxGM38/adj+MXTX6IzmPC5XQg6PUazjacX/IDY2NijCoNIxtThwiiHyryj4IaIosjBgwdZ8Mw3PPd/k+TjNf6IAkdlVGjQ0Evoc0RNDRpOWej0xo7Fs3cNdp3BCEEdYjCITqdHZyD0/w6jY97DH8uGiNFiQ2cQQBD42eOfIuj0CB3HisEgwYCPy1evlw0RCeFGQDg6f+71epn78Mc89bMzMJnU83O9Xi8/fehDgoGg4jper5er1vwXozkmZAA99gmCoOfpn0+U2xIEAZPJFMH/COeGeL1euU+djZGuGD0aNGg4dmieCg0a1HHUnopZf12H1+VE0Om77HU4Gk9Fb5wjBoOIHf+GDA7l/wWdPupnBpMl5EEBgn6f/Hn4OUaLTfU6gi5kHAEIOh06nR6/1x0yfjrYrg/PO52fP/kZTyz4EXFxcXg8HjweD/Mf+4RHr/kBV6/5L4LOwD+vm6xqBP307x/wzP+d1WfDK0fol+ap0NCXcPKHPwRBqAcqonydAhyM8l1fxcnYZzg5+300fT4oiuLUaF+eIvNR62fPojf7edj5CH1+Tp7I63+f7723rh91Pp40RsXhIAjCRlEUS050P7qDk7HPcHL2+3j3+WR5Rlo/exZ9uZ8num8n8vrf53s/Edc/USmlGjRo0KBBg4ZTDJpRoUGDBg0aNGjoEZwqRsWaE92Bo8DJ2Gc4Oft9vPt8sjwjrZ89i77czxPdtxN5/e/zvR/3658SnAoNGjRo0KBBw4nHqeKp0KBBgwYNGjScYGhGhQYNGjRo0KChR3DSGBVTp04VCUkUaj/az/H4OSy0+aj9HOefI0Kbk9rPcfyJipPGqDh48GTQvtHwfYE2HzX0NWhzUkNfwEljVGjQoEGDBg0a+jY0o0KDBg0aNGjQ0CPQqpRq6HPw+4Nsq26husVNRryVwow4DIbD27/BoMjeBie1rW7S4iwMTLaj0wmq36fHmmls94Xaj7Mwon88JpO+t2+rTyPa8zvSc+2t9o/muj3V12NBX+iDBg0nEppRoaFPwe8P8uqmKm59dStuXxCLUcedM4uYWZwZ1bAIBkXWbavhhhe+lc9ZOXsUUwvT5QVK+j7RZmLu6Tncv75UPnb5jCJmjuz/vTUsoj2/c4el8c722qjPtbfaP5rrHmkOHA/0hT5o0HCioYU/NPQpbKtukQ0KALcvyK2vbmVbdUvUc/Y2OOUXuXTODS98y94GZ8T3F43J4vkvK5l/Ri4LJ+dx7cRcHviglM0Hord/qiPa89tW3XLY59pb7R/NdY80B44HKhud7Khp5dqJobmVaDMd9z5o0HCioXkqNPQpVLe45YVBgtsXpKbFTfEA9XNqW9XPqWtzk9svRvF9rEXPpSXZrHr/kKdi0eR8mlzeXrmfkwHRnl+0sZCea3fbz4i3cNGYLASh5697pDnQ2wgGRb6ubGbNhnLFvHr6s4rj1gcNGvoCNE+Fhj6FjHgrFqNyWlqMOtLjLFHPSYuzqJ6TGmuJ+D4zwSYbFBBaeFa9X0qC1diTt3FSIdrzizYW0nPtTvs5yVbmTMjh0Y/LWf1+GY98VI7ZoO+x6x5pDvQ29jY4ueWVLRHz6pKSrOPWBw0a+gI0o0JDn0KCzcBtFxTKC4TFqOO2CwpJsEdf9Acm21k5e5TinJWzRzEw2R7x/Z6DTtUdrS9wWD2XUxrRnl9hRtxhn2t32r9jxogIY27p61tZcfHIHrnukeZAbyOap6QgLfa49UGDhr4ALfyhoU/hQLOb5z6v4O5ZxbR7/VhNBh7ZsJtBKTZyktVdyDqdwNTCdIYumkhdm5vUWCXrPvz7eocHi1GnWAAsRh3p8d/f3eThnt/hnmt32jfqhYhFt6KhncwEC2/1wHV7qq9HC8lT0nleDUuP00iaGr5X0IwKDX0KaXEWdtU5WPTcN/JnXXFj63QCuf1iosaupe+lHW1nhv73fTcZ7fkd6bl2FdEW3SS7uceu21N9PRpEm1eDUr7f80rD9w+aUaGhT6G3F/0TvaP9vuJUN+a0eaVBQwiaUaGhT+F4vJxP5I72+4rvw6KrzSsNGjSjQkMfhPZyPjWhjasGDac+tOwPDRo0aNCgQUOPQDMqNGjQoEGDBg09Ai38oaHPQSvKdPygPWsNGjT0JHrdqBAE4dfAtYAIbAHmATZgLTAQ2AvMFkWxqbf7oqHvQyvKdPygPWsNGjT0NHo1/CEIQiawCCgRRbEI0AOXATcB60VRzAfWd/yuQUOfKAz1fYH2rDVo0NDTOB6cCgNgFQTBQMhDcQCYATzZ8f2TwMzj0A8NJwEOVxhKQ89Ce9YaNGjoafSqUSGKYhVwL1AJVAMtoii+A6SJoljdcUw1kKp2viAICwRB2CgIwsb6+vre7KqGPoLuFIYKBkXK6x18uvsg5fUOgsHerd9xqs3HaM/aatQft2eq4dhwqs1JDSc/ejv8kUjIKzEI6A/YBUH4aVfPF0VxjSiKJaIolvTr16+3uqmhD6GrhaEkPsD5qz7i8oc/5/xVH7FuW02vLoKn2nxUe9Z3zixi0fPfHLdnquHYcKrNSQ0nP3qbqHk2sEcUxXoAQRBeBn4A1AqCkCGKYrUgCBlAXS/3Q8NJAp1O4OwhqTwz/zRqWt2kx1kY2T8+gjgYjQ8wdNFETVypi5BULgsWnsGeBicxZgM3vbyZioZ2QHumGjRo6D56m1NRCUwQBMEmCIIATAG2A68DV3UccxXwWi/3Q8NJAr8/yOtbDvDTRz9n4T+/4aePfs7rWw7g9ytj/xofoGcQDIp8u7+Z65/7hv/tbpANCgnaM9WgQUN30Nucis+BfwFfE0on1QFrgD8D5wiCUAqc0/G7Bg1sq27h1le3KjwQt766lW3VLYrjusO90BAdnZ+39kw1aNBwLOj17A9RFG8TRXGoKIpFoijOEUXRI4pigyiKU0RRzO/4t7G3+6Hh5EB1i7oHoqZFuVvuKvdCw+ER/rxf+mo/iybna89UgwYNRw1NUVNDn0JGvBWLUacwLCxGHenxyt3ysVS91FQkDyH8eVe3uHn6swoWTMplRGY8g/vFHLdno42JBg2nBjSjQkOfQmFGHHfOLJJd8lJGQmFGfMSxR1P1UlORVKLz825yeclOsnFWQSoGw/EpDaSNiQYNpw40o0JDn4LBoGNmcSb5qTHUtLhJj7dQmBHfYwucljWiRG8/765AGxMNGk4daEaFhj4Hg0FH8YBEigcc+qyn3OO1rW4SbSYuGpOF0HH6S1/tp67N/b1dwNSe9+HQ06GKw2XyHM8x0UIwGjQcOzSjQkOfR0+6xzPiLcw9PYf715fKbS2ekk96nJbh0BX0RqhCyuTpzKM5nlknWghGg4aewfHzcWrQcJToycJXgSCyQSG1df/6UgLBI5yoAeidImR9IZNHK66mQUPPQPNUaOjzOFr3uJo7u65Nva16h5vBqd/P8Ed30BuhivBMnkanB6Neh8sbYG+D86hDEN0NZfSVEIwGDSc7NKNCw3FF+Ms+I95CIAh1bcoXf+cFITW2++5xvz/Iv7dWc+NLmxXu7GHpsapt9YvRwh9dQVdCFe3tPrbUtFLb6iEtzsyI9DisVmPUNqXxbnB6ONDsjhiz7oYgjiaU0RdCMBo0nArQwh8ajhvCi4Dd8MIm3t5aw0/+piwI5vcHeXurslBY+UFHt9zjwaDIJ+UN8uIEh9zZbR4fy6YXKtpaNr0Qo2ZedwlHClW0t/t4Y2sNcx/7guuf+4a5j33BG1traG/3qbYXPic+3HlQdczUQhB+f5BN+5pYt7WaTfuaFTLuRxPK6AshGA0aTgVor1INxw3hL/uLxmRFcBtueOFb1v5sAr95UbkgXP/cN/z7+jN4q4tCV3sbnGysaFRX5mz28OCHZcw/IxdBAFGEBz8sY1ByMdlJmps7GsK9R8MzYvn39ROpd0SOxZaaVpa+rpRZX/r6Vgam2Bg/KBkIGQTbqluobnGTEmOW54Qg0KUQhN8f5NVNVRFaJjOLMzEYdEcVyjgWMTUNGjQcgmZUaDhuCH/ZR1tADkSR6d5d7+TcwvQuxbdrW90ERVTd2RaTjoqGdh74oExxzkGn52hv65RHd8IJta0e1fGrbQ09384GwaIpeRFjdKQQRLT6MPmpMRQPSDzqUMbRiKlp0KBBCS38oeG4oXMRMLXiVTEWg+rnVqO+W9d5Y1NVRB2LFRePJMluIifZynVn5bFwcugnJ9lKZrz1GO7s1EZ3wgmpceYoRcnMQKRBIBl/0PXaI0eqD9OXQxnBoEh5vYNPdx+kvN5BMCie6C5p0NCj0DwVGrqEnhAGkl72N7zwLS99tZ/FU/IVehGLJudjMggRny+eEvq8O9e5ceowVqzbzvwzctHroCQniR/kJhMMilx3Vj5LXzvkOl8+o4hh6XHdfSQnNboznt0JJ6THmlg2vZDbXt8mP99l0wtJjzMBkQaBZEiser+U6hY3azdWsmZOCUa9ELVfR6oP01dDGZoWhobvAzSjQsMR0VMvw84v+xhzaPo5vQFEEZ7+rILC/rHYTXoWTMolKIJOALtJT4yl61NVvk56bMSiUl7vkA0K6Ij5v7aVkpzE743bu7vj2Z1wwoCkGKqa21kzZyxNLh+JNiMGXehziDQIJEPiqWvGU9fmIT3Owsj+8ZhM0T1TXakP0xdDGZocuYbvAzSjQsMR0ZMvw/CXfTAoUtnYrljcjAaBRLuJg06vfE6i3cTgbpIooy0qmh5B98cz3MMUboSohRN0OoEJuf3Y2+DEZIj0EqgZBNedmc/d67azsaIlgnSphr5Qr+RooM09Dd8HaEaFhiOit16Gam5qh9vHyk92MfcHubR7/VhNBp78pJzsJBvFAxKP9VY0PQK6P57dDScczkvQ2SBIsptY0WFQSP0IJ11GQ3frlfQFaHNPw/cBmlGh4YjozZdh5wVo3dZqNla0sLHiG8VxNS3uHil41Z1d96mKoxnProYTusLVCDcIpPEOh0S6PJkMhq5Am3savg/oslEhCIIZuBgYGH6eKIrLe75bGvoSuvsyPBZS55FIeEfCkfgCOp3AucPSWLtgAtUtbjLirRRmxH2viHK9tbgFgyJvb62RdUYsRh1/uWQU5xVF594c63ifTOirBFINGnoSgih2LaVJEIR1QAvwFRCQPhdF8S+90zUlSkpKxI0bNx6PS2lQgWQoHOlleCykzmBQ5KOyOg40e1j2xqHsgSPF2MNRXu/g/FUfRSxSb3XwBbrRv8N29mSfj10dz+6gtLaNC1Z/HPHs31h4BvlpsarnHEnISoOMIw7OyT4nNZxUiDofuxP+yBJFcWoPdEbDSYiuur8lEmCizcRFY7IQBNhZ08rwjFgGphw6V82bsbfByc+f/pqC1BjunlVMu9eP3WRgRFZclxeYI/EFNAZ+CDqdIHsmalsP6TtEMxS74nnac9Cp+uz3HHRGNSpOVtKlBg0a1NEdo+ITQRBGiKK4pdd6o6HbCJc8llz50gu5J7QluovaVjeJNhNzJuSw6v1DWhM5yXaykw4VDFPzFvSLNSkXJRFEoMnpJSe5a9c/El/gVGPgRxv/I419Vz023fE8WYw6dRVT4+ENhHCOxeHmswYNGvo+umNUnAFcLQjCHsBDyP0hiqI4sld6puGIOJzrWKcTelVoJ9qilRZn4ZKSLNmggNCifcsrWxg1IOGw3oK1CyZQkhPPlRMGUlbXRlAEvQAiIkX9g11aXI7EFziVGPjRxn/6iP68t7Mu4hmcOyyNyiYXta1ubCYDK9ZtP6LHpjuenXiLUVW4LMluYtO+piMaClooRIOGkx/dMSrO67VeaOg2gkGRb/c3R62BEGsx9pqb/3C714HJdgpSYw/rDYjmLXB5A9x43jA27m1izYZyue0l04az7UALI7ISjmgQHYkMdyox8KPVwBiYbI8Y+xXrtqMT4LvqVtlYu+YHg3hoQznVHfLWah6b7nh2hqXHUdHkUgiX5fazs6OmrUuGwpFqemjQoKHvo8tGhSiKFYIgFAMTOz76SBTFTUc6TxCEBOARoIiQN/saYCewllAmyV5gtiiKTd3p+PcZ0qLe5vZFrYHg8gZ6zc1/pN3rsIy4w3oDonkL0uIs7G9yRVQvvePN71gwKZeqFneXPC2H43/odAJnD0nlmfmnUdPqJiPOwoj+8SclAz9qDQwVQ+CycdnsOehUGGuLp+Qz9/QcVqzbCah7bLrj2TGZ9Ewdls7m+BbZg2XUCSx+/rMuGQqHq+lxpPTSExHq6ymczH3XoKEzuuxTFARhMfAskNrx84wgCNd34dT7gXWiKA4FioHtwE3AelEU84H1Hb9r6CKkRd1mUi++lR5viSjeJX3XE27+aLvXigYnn+4+CMDqK0ZHFHTKTrRRXu+gwelhxcUjVQs+tfsCJNpMioJfiTYTQZGoRay6imBQpKy2jbe/q+Gj0nru+vd2rnz0c17fcgC/P3jkBvoYpHTMcFiMOtJVxn5Aoi3CWLt/fSkDEm0A5CRbWTOnhNpWt6LQVXeLc5lMekoGJvGTkf0pGZhEdZS5IhX/Amhud7NpXxPxViOrrxjNCz+fwLzTB3DdWXksmpJHSoz5sIW3JCP7/FUfcfnDn3P+qo9Yt63mpCjWdTL3XYMGNXQn/DEfOE0URSeAIAgrgE+Bv0U7QRCEOGAScDWAKIpewCsIwgzgzI7DngQ+BG7sXte/v5AW9Yc37Oa2aYUse1OZflmYEdp5H8nNf7Q7pGi71x01bTg8AfQ6GJeTxH9+NZHqllAIIjvRxjvba+X+SItY58JROUlW5p6eExGXj7cajsnTohayWTQ5n6c/qzhpXezRamCM7B8fMfY6naC6uJsMOl78+QSqW918VdHICxv30+TyyuEsAJNBUIQ0ulPc7Ug6FM3tbj7f3USjy6dII14+o4gXvqxgY0ULazaUH5YPdDJn9JzMfdegQQ3dMSoEwvQpOv5/pLdLLlAPPN4ROvkKWAykiaJYDSCKYrUgCKmqFxSEBcACgOzs7G509dSGtKhvrmqFLyq4e1Yxbq+f3H4xjMpKkGPVh+MWdIf939nwUOMl3HBOAWa9TmEMrLh4JD8pyqCyycWGsnp21rSSaDNR3eKmoqGdBU9vZN3iiQRF+HxPA2lxFtrcAdUd9cNzS47J06L28l71finzz8jlgQ/KuuRi72vzUacTSLAZFQt+gs2IwaBjamE6Q66fSGWjE5vJgMkgqC7uVpOeOY99oTC01m2tZkdNKxajjmS7mTve/I6KhnbFedK4HckgVTN8/njhCGpaPXj2NCAAvoAoGxRwqMjb3bOK2VjxzREX2pM5o+dY+97X5qQGDd0xKh4HPhcE4ZWO32cCj3ah/THA9aIofi4Iwv10I9QhiuIaYA2EhF260dc+h656BbpyXPiivrmqld//axMrZ49iTHai4tho3IJgUGRLVfMRd0iHMzzCDRYBga8rm/jTuzsU7a18NxSrv/GlzfL5N08dSpvHj9sfxGrUse1AK795cZP8/b2XFKu+ZJs7ds9HS6iM9vIWhK4rOB7P+diV1Mo9B50s/Oc3qkJfA5Pt7KxtU3iG7phRxJLXlIv7ra9uUYzZ2o2VLJg0mDve/C7CowNw0ZgszAYdextc3Pb6Vioa2rEYdfzpwhHE2wwk2y2KvobrUDQ6PYiiwLI3t8nn3X3xSHwBUXVsCBPmc/uC7KptAyL1NI42o6cvcBmONRvpVHpHajg10B2i5kpBED4klFoqAPNEUfzm8GexH9gviuLnHb//i5BRUSsIQkaHlyIDqOt+108e9LQmwLHI/UrX2FHTesQd0pFcs9JPeb2Ddl8kMXTayEzZoABItJlw+QKs/qBMEdqQvBduX5DUWHNUEmdqrPmoX/rRXt46AZZPL6Igpe/saLuSWhkMimyvjj6GgGLsKhraWf1BKc/MP40Gh4f0eAveQFDhgYDQmEkGhdTeqvdLWTwlH1FEoT0iGRvVLW5ufmUL88/I5dGPv4noq6RD8cWeBuZ2eEWktnU6gawoY5MeZ1H8vqWqlV+t/Tbib+JoMnqORfm1J3EqZSNp0ABdIGp28CIQBCGJUKbGM8DTQEXHZ1EhimINsE8QhCEdH00BvgNeB67q+Owq4LWj6fzJgmiLc2fSYVePg0NeiAm5KeT2i+mSBHZ5vYMv9zZywwvfEhQ5IpHzcK7Z8DZrW92cnpsc0Z5eh+L8i8ZkqYY2LhqTJR/j9Pq5/YJCBTHw9gsKcXr91LS6OVqoEQ6XTBuO1ajngQ9L2daxC+4LiJZaua36UOGtvQ1OSuvaoo5hS7uXu2cVs+KiEfzt8tGMzIyjoqEdfzDIj4syKB6QSLLdfMQxk64/INEWoT2y6v1DYyd5fdT6KqG21aNoOyPeQk1LOy1uH3fOLFKMzW3TCnH6fCycnEdOspVFk/N5+ev9qn8TkpH91qKJPL/gNN5aNPGIxkF3/tZ6E0fTdw0a+jK64qn4JzCNEB8i3L0mdPyee4TzrweeFQTBBJQD8wgZMy8IgjAfqAQu6Wa/Typ0NW7aW7Hh8F3ZtRNzcfuCvPTVfhZNzlfsPDvvkA7nmvX7g/x7a7XsiVBzr4/LSVKcLy06ne9PCHt/xluMVDW1K3gC/mCQeIuRBJvpqJ+B9PKOmTOWL/Y2IYqw+v0yWaOh9hgMlp5GV1Ira1vdvLAxcgz/eOEIsuKtfFXRpBiL26YVYvq6UmE0qu2SRw9IVB1zc6fPpD5JY2cx6uRoRee+Ajja3STHmOS2M+ItzJmQw73v7JLnz+orxtDs8mIy6Hnqk3J+/+NhPPJROXfOLOLfmw50hIIsXDQmKyIU0lUZeQl9iYfR3b5r0NCXcUSjQhTFaR3/DjqaC4ii+C1QovLVlKNp72REV+OmvaX22HlXZjHqqG5x8/RnFcw/Ixe9DqYMTWVEplJcKpprNjvRxiflDYrQhuReX7tgAu2+AKmxFrLiray4eKR8nL6Dv6AWgpD+b9DBnf/eHnHMCwtOO2aXsE4nYDcZeOSjctXwSl9BVyp3psVZaHJ55TEUhJABNiY7ge21rbJBAaHFctmb23j86nGKZyhVbF0zp4SNFY0EgvDAB7tUiZXR5qbY4fEK512ocVS+q3Gy8p0dcrbSRWOUqqsVDe0s/OfXHSGUcpZPL+TbyoOy5+PuWcXsqHVEyL8fbcjiVFJW1aChL6E7OhXru/KZhkh0Nde/u5oAXUX4rkzyUEiGxaMflzM0PS7CoIDortn9zS5a231cOzGXhZPzyOhYQCoa2mn3BZiQm8LAZDvv7axj5bs7mX9GLoum5DEhN5m/XBJ5fxeNzpTbr2iMooHR6O4Rl/CI/vEsn6F0tS+fEUrD7CuQMibC+yilCkuQ5kqTy8sDH5TxyEehccxOskf1dLS0+yI4PNuqW9hY0UhQhJe/3k9Vs4dGh4dHryrh71eOYfGUfO75z06uf+7riD6tuHgkZw5J4e9XjmHtxkqqW9yqfQVwef38bFIeWYkW/nrpKAr7x6n2cWh6DE/NG0+zs50/riuTP3d7/RGGyLGELHrrb02Dhu87juipEATBAtiAFEEQEjmURhoH9O/Fvp0y6Cqx8lgImIdD+K6susXNuq3V3DurGJ0OMhOssq5FNDZ8uGs2GBT5urKZW17ZIrutl0wbTlmdg0AwKJPrwr0jD3wQWhzWbChn3eKJvNVxf/1iLOh1IXe/dL1ml1d1B9m/C9kZXYHJpGd6UQaDkm3UtHpIjzMzIiMek0nfI+33BLpSubPzXJGe5ed7GugXoyS7ZsSH6rHodQLl9Q554exMVLx56lDc/iAr39slf7Zk2nD6xZjYXNXK395XeqKyE21UNrkA+Mslo2h0ekmLM0f01e320+r2U9XULnNqFk/JUx3nfrEW5j7+RcTng1LsxFmNRx2ycLv9bKluUYx5b/ytadDwfUdXOBU/B35FyID4ikNGRSvwQO9069RDV+OmPRlfDTcSHp5Twq2vbcHrFzlvRAa//dcmhQv53GFpCnGqaK7lvQ1O2aDIiLdwaUm24pwhHbvlaDHrmla37MlYt62GFeu2M21kpiyYFWPWqxal6ql3fTAo8n5p/Qln/R8JUsbEiMzQGH5Z0RiR9ijNFelZhqePLp9RxNLXtpJoM0WIia2cPYrhGbERRMUGl1eW8ZY+u+PN71g5e5SsVdHuCzB+YDJ7DjpZt62G0ro2hWCWmsdrS3ULZXUORdsvbNwfMc7LZxTx2MdlETyRO2eOoDA9lhqHusF5pJCF2+3n9S3VLH39UEhn+fQipo/I0LgMGjT0MLrCqbgfuF8QhOtFUYyqnqmhb0EtZW7FxSNJjTUx74mNES7ktT+b0CVlv3BjIZo7euiiiUeMWe9tcLJi3XYuLclWLCD3zCrmi/IG/jFnLM1OHwl2I098vIesRBujcw7dm2QspcZGejuOhvXfFxUMu5r2uOegMyJ99IEPSnnqmvG4fQEWPP1VxP0+PLckwugLiupE2h01rVw0JotHPy4nPc4i9ynRZuKSkixuOKeAujY3K9ZtZ2h6bMRzrGn1RLRd3eLmqU8reGRuCU0uH2lxZvzBIGNykslOsnHnzCLMBj37m1y0tXv5rtbBmOzEo0q/3FLdIhsU0j0tfX0rg1JsjBuU3M1R0aBBw+HQHZ2KvwmCUAQMByxhnz/VGx3TcGxQWzxvfGkz985SF5eqbGpX5zJ0CoeEGwvh2RwSK18QoL7Nw9gjLAC1rW6mjcyMMEqyk6xMH53JVxVNoWqaB2H66EyyE62A+kK7eEo+T31aoZCXjmZY9CXW/5HQVQOootEZcU8VDe3sqXdS1aI+rgea2yOMvjiznkVT8pDKTrz0VcgDEQiGUk3vnFmEPyDKBkVn0uSiyfk0OD0RzzE9zkxZXRs5yVamjcyUM0be2FRF/wQrCTYjW6paeei/ZR2er02KsYXQuB1teLCmUyqr9AxqWz1HGAENGjR0F102KgRBuI1QvY7hwFuESqF/DGhGRR9EtMVTRD0Dw2zUqS4o3+xrZtX6so6U0REYdIIcSpHO7bzAPPJRqFZDYf9Ynpw3HpfXT3aSnUEphxaAtDiLqiaCKIq0tfsUn4V+D3VMbaG9f/0hue0bXviW4YeRkD4ZWP+SJ2ZXbdsRDaBgUCTGbFC9J5vZIOuRdOZX6ASB+2aP4s/rtlPR0E5OspVEu5mV721VLOg2o57HPtnDb84dyl/e2cFtFxTi9gUVXirJoHT7A4hiKNxgsRx6tYzIiMfh9dE/wcptr4fV95heRFKMgdoDbpa9sY35Z+RGGJn3ry/lr7NHkRZn5pPdB2l0eukfb2FMViImkx5Hu5vvapzUtnpIizMzPN1OjFU5lulx0QTVzD07cBo0aOiWTPcsQlVGvxFFcZ4gCGmESppr6INIjVVfPPc3uSKKkN11YRGVDa6IsthpcWae+N9emTux4OmNilBKkt1ARvxwalvdqmGQBZNyWbW+TPZSDEo55KbOTrQxekBCRB/bfQGc3kBEX1y+UNmZw8ltQ0i1M5xI2jlk0NcVDDtrihzOAJKOXbFuewQPYfGUfPY3udiws44l04Zzx5vfqfIrlkwbTv94M7EWEz999POIBX3xlHyuGJ/Dn97aTnWLG3uHASN5qSS9CenaazaUy3yFcMPCbjJy2+vfKNpf+vpWBqaMp8HhjfB8SXD7gviCIi3tfm5745As+PIZRfy4sB//2VofwZU4v6ifwrAYkRHP8ulFEceNyOg7GT8aNJwq6I5R0S6KYlAQBH+HymYdRxa+0nCc0Jln4A345cUk3D391KcVmAwCd88qpqyujaHpcRxodskiRKDcIc45fSB2s152SUvf3/jSZl5YMAGnu4381FjVxcCg03HdWXkIAuysaWV4RiwDO+Sw9ze72NfoYvn0QpaG7V4DQVRVNx+ZG5I6OZxeAsAlJVmyQSGdHx4y6K0Mm2OFNH57G5zsrGmlIDUGu0kfMYbhBlC41yZcc6QgNZYGh4eXvt7P1KIM1mzYzfwzchmWHisTdOEQEfOZ+adRE8VYy0m24fMHuXhsFm9sqiIt1szK2aPY2VFwTI1Xs/T1rQxJj8EXEHF5fdS0etnf5FJtv9HpZWCKLeTxshpVx7aiwcnqD8pYMm04bW4fT35SwdLXtjIwebwqV2JgynjGDwqT+LYYmD4ig0EpNtmjMSIjXmH0HGlcTmR9EA0aTiZ0x6jYKAhCAvAwoSwQB/BFb3Tq+4hjeXlF4xm8vaWaBZNyyU60UdPqlus0AJgNOoanx9Hm8ZObEiPX35Dg9gX5rqaVVevLWDQlT51v0ehi5XulUXfUeakxij7lJNvJTgotiF9XNvPHt3eQaDMp+tjujawh4vYFafeGPBVqngaJU2Ex6iiIYuCEhwxOtIJh57HuXBZeUsB8aEMZXr/Igkm55PWLobB/vCKEFO61qW5x8/LX+2XZ7HZfgPNGZMgG2gMflHHDOQWqz2ZDaX1EmARCvweDsHjtJll/IivBRnaSnWHpsWQl2tinYiwk2kzsqnWwpKPS6NLXtkadI/6ASHm9k0euGoPXL0YIb0khmESbiTve/I4Fk3KZMyGHpz+riJD9lu6nweGNCMFYLIZukzL7Sn0QDRpOJnTJqBAEQQD+JIpiM/CQIAjrgDhRFDf3Zue+LzjWl9fheAZS+GH+Gbmy0ZCTbMXtCyiqh0oLc/gxeamxLJycR35qLDnJVkXxqZxkK/FWI7+aUkBOio27LiziD68cWgyWTBvOinXbO/VpFwMSrTS7fFQ0OClIjWFiQSpBEfY1uRg/KAmbSa+6+CTHHJLoHpIWy4NXjiHeasSgE2h0ern/slF4/AHiLCZKcuI5LbefghBoNeoJBsUTvhiojfWaOSUR47fszW0yT0Qaw7cWTYxanbNzGCIU1hrBr87Ox+EJsGFnHQVpMar8isx4KwedHm6eOpQ/rdshn79seiFrNuyW+3Trq1sZPSCRwakxDOoXw4BEG1uqmkmNMTEg2U6T04fZINDg8MiKnu0eP26fuiz8osn53PPODi4bl83gfils7iBrSh6XoelxPPRhGbvqHCyeErqP3JQYDHqB687MJS0KV2J7TSttbn9ECKa7OJkyhTRo6Cvo0l+cKIqiIAivAmM7ft/bi3363kHt5bVi3XYyEyy4vIHDei78/iC76x2H5Rm4fUH0HVpEOclW/nzRSOY98WWEESJxIHKSrfxiUh6/D9OykHbOEqnvF5Py5FRFi1HHr88u4Iaz80mPtzI8I47yg06FESLxMuZ0VKmU2gjndvRPsFKQFsNtFxSy7I1Dn992QSGCELpXaUevxg9YNDmf93fUMHtcDks71b24481tXHPGYKYWpuP3B9l8oIWaVjcZcRZG9D9+4ldqY72xolF1/MyGQ+EjgMZOmRXhXhu1MMQf5MqhofoZL39dKS/sas/v12cXsHhKPkl2E1XN7Xj9QeodXkWfKhudDE4N9cHvD1Ln8KDX6/l52FxYPqOIgtQYNle1YuvgYEiy8P+YM5avKpoIBJE9Z/evL2VMdqI85pJYmmQM1zu8xFuNSk2L6UUUpNsjuBKSXHiTy9utlFE1T+HJlCmkQUNfQXfM+M8EQRgniuKXvdab7yk6v7ykBfjSNZ8d1nMhlcg+2OZR3bElWA3y/0tyElnyk2FYTHo+LW9QfVnmp8ay+orRJNtNEUbHsje38fQ14/mo7CB5qbGywSF9f997u5h/Ri4r39vMW4smMrifclfcedGbNjJTNiikNm57fRtr5oyVd6uCAKIID/23jDtmFLGtukVekNUqnq56v5S7ZxVH9E3a9UuZIV/sbVIYHctnFDFzZP/jYlioLVTRQg+dw0f5qTGM6fC2SItggtXI41ePo6pZPXU0vHLoytmjeOjD0LMdmh7L71TGcMGkXCob23nggzJmj83gvktHUdfqpl+smVe+rsRmOvTK2FLdgs8vysaA1M7S17Zy76xiFj73DQ9v2C0Tg6tb3NS2uFm1viyin/UO9VCGIIR4Mss7lWOXuBPnF/UjI6GEz/c0IoooQnxdTRmN5ikcnhHb5zOFNGjoa+hy7Q/gLOBTQRB2C4KwWRCELYIgaOGPHoDkxpbQ1RoHUolsEVg8JV9Rx2DxlHzyU2O4ceoQ7pxZxJaqFryBEDEvWtnz0ro2Fv7zm6hGx4EWN1ajXnZpd/5eWsDq2twMSlHWVuicPhqN6e/yBTrEm8pY/X4ZD3wQ8o64fUEanIcWns4aGdedlce1E3OJMYXi79H6VtPqlg0K6bulr21l84HIUt29gc5jDaHwzIqLRyrG764LR0SEj258aTN7G5whVdCdtbz6bRWflDfwWXkD8Raj6piGVw7dUdPKxIJUHvigjJ1R0lWzE20hbsaoDEoGpnD141+w6PlvmffEl5QMTCEt9pDhVdPqwRllLgREUe5Pv1gTD145mud+dhqZCVbVfhJlTuoEGJBoU71GbauHGKsFi0HHIx+V88AHh6rOdidlNFqYIxBEqw+iQUM30R1PxXm91ovvOTqTD9X0G9TcrlLhKKc3wEtf7Zd396kxJlJizLS4/QxIsrHmv7vZVedg+YwiEm0mNuysUyXEPfVpqMpktJ3z7noHAPtUhJMsRh0Wg06xkxueEcsT88bxye4G8lPVd30Ru0C7epw82W6K0NhQ08hQ44dIi6vFqONgR/pi52d7vEqfqxFNb5w6jHOHpTEiM17OSGlwehThI6mfdW1u9DrwB0T58/+V1TMiM54/XzSSPQcdsmx258qhkogVELVibE2rm+oWN7PGZXNNJ2/V0te38dS88QQJ1Q9JjzNT26bejlEncMt5QzEb9dzx7+9k5dREm0lVhn3Nht0RnIs7ZhThCwSoa3MfVmfiWFNGo4U56h3uPpkppEFDX0Z3jArxyIdoOBp0TnO0Gg2KOgmg7naVSmQDcrXKkZlxXD4+hxteVPIhnvsilIa3YFIugSA8/0UFK2ePYkdNK/mpsfyxQ4cAQsJXaumoT39WwcVjs1RJd4un5GM36Vl9xWiyE20KKee5p+dw7zs7+O25Q7j3nZ24fUHe2FQVwZ347blDaHZ7FcdJn7e4vViNBnlBfumrUO2Idl9AVTApXCNj0eR81m6sZOXsUaTHqxstnUt19xYOl9LaOSNFtZ9xFr6qUBZ0+8WkPBY+9438vJZOG05qnJk73vxOrhy6ZNpw1mzYze/OHcrCyXnYTXqWTS9UilHNKOKBD0oBONgWRYWyzc3cx79g5exRnJWXQqPrYMQ4Lp9exGP/K2fuD3L5/b82KUStJHnuBZNyyYy3khZv4eaXt1Dd4qbe4ZUN49MGJlHd6uKWV7aREW+JrBMyvYjC9NCzslgMTCtKY2BYymhRemyXSZqHE0Q70ZlCGjScbOiOUfFvQoaFQEimexCwEyjshX6d0jhSNdBgUOySQJNUIvv5Lyr444UjuOWVLVw7abAqp+DuWcUseu4bubLkxooWqpq/k1MQm1xehdR2v1gzCyblEhSRY9VNLi+iiEy6m39GLgVpMexrdMky2f++fiKVTS6579IicklJFkWZcYo2n/s8tLiMzIwnzmrkd//axJ8vHsnTn+1VcCqe/mwvf75oJGlxFk4blCwvyOlxFsrrIyWq3b4gowck8NzPTsNm0uMLBJlalM7AZDt7Djq45byhHHR6QzLgAiTbTSRYjcdn8OlaSmt2oi3Cm3TnzCJ8AVGhw6HGTVn+5ncsPCtPlsQWRRCDQW6aOgyn18+4nEQqGpz0TzDz1DXjOejwkJlgJcZs4NafDMdq1GPu8Dp1XmhTYsxyeODf109kSGoMLe1+Hr96HA1OL2mxZgrTY8ntZ2d/h/R751BXdQevYuHkPHJS7DS5vPLnD3wQMgTHD0xkf1PIyG1yeUmyGXn6mvHUdBgNhekx2K0hT4XfH+St72ojntXM4kxFtdRo6Ow9ktRjJe+V5p3QoKHr6E7tjxHhvwuCMIZQBVMN3UBX0kcPt5sNBkUqG0OyxE6vn+LMBAafb8fh8XHvrGKCosi1E3N56av9ALKREGvWU5ITT0aChRiLgZumDuHJTyt44IMyzipI4aGfjuWgw8O+RhcvbNzPG5uquO7MPIUwVXiIpLrFzaMfl7NgUi4ZcRb6xZg6dpvukFSzyiIyPCMugqS3uaqVx68uYcyARG6cOox2rz+iyNiiyfl4/AGyE20RC3IgqL6jT7KZcPkCxFqMikXhQEs77b6gQrHz12cXUN3SzuDU2N4b+G4gGBTZVt3C394vVRhXf3u/lFt/MpxEm0ke1yFpsbLGSLhROCjFzp3/DnmfcpKtLJpSwK/D5tztFxQSZ9bj8YeeYbsvyAMffMc73x3EYtSx6vLRcqVT2eN1QSHVLS4y4i1Ut7jZXtPKb19UVrsdm5OETidw0OFBrxMUfITOYySK0NIRpuk83u3eAFZjqBbJhNxkfvPCJm77yRDSYq0yATM93kttqxdEqGx0yfO+usXNra9uJT81huIBiUd83uF/b41OD1XNboV6rKZNoUFD1yGI4tFHNQRB+FoUxTE92J+oKCkpETdu3Hg8LtWrKK93cP6qjyJesG91Iffd7w/yRUUDta0ealvciIREjkYPSMDpDfDbFzfJ4YbsJBs2k57S2jYe/ySkonndWfmKReL2CwpJjzfT7PJHxOLLaluZVTKAVrcfm0lPrNlAq9vHV5XN8g4/yWbioQ3lNLm8rL5iDOV1bfxgcAqejlRDu0mPyaDjhS8qyUq2MyE3iZtf3qIoKvXZ7nqu/kFImHVYeiwHnR5+/9JmxS77zc1VXDYum5xke2Qp9oMO3t5ao3CNL5teSIvLS6sngF6AMTkJZCXYO/gIAnM70lrDn/9T88YzPleRfnjYFaS35qNkdO7oEB7rjLULTuPryuYITsLbW6qZWpQhL845yVaWTiukyeUlM8HKTS9vVnA0Hri8CJvFyjeVocJtb2yq4pdn5rFuSzUf7AoZFk9eU0J9qw+n14/VZOCRDSFuzrPzx+MPQl2bh9Q4M+s2V/H4p/sU83jTvmbueHMrl47LYfUHpRGG4h0zilj57i5WXDyCJa9tjRjvm6YO49cvfMuy6YWs21KN0QBnD+sfwZswG+DGlyNTSqtb3Pzjp2P4cVFGt57/sfx99jKOaNGcKu9IDScFos7H7hQUuyHsVx0wBqg/hk6dcggPa9hMBryBACa9Hm8gQLLdHJH7rqjs6fAc1s0aDIq8tbWa33cSrHpx437WbChn8ZR8ClJj+MnI/tz33i7FMb+YlEubxx+R9XD7G9siuAdPf1bBqvdLefDKMVzz5MawF/hw9Hp9xA6/X0xo19zg8DAg2c6eBie/+5eyj2cOTeUv7+7iwx11/PLMPEUcf9n0Qu55Z4esf7HqstERx/z67AKe+GQvTS4vQ66fKOskBIMiDQ4vBWkx/GPOWNra/STajew56GTle6WKHfbS17ZR0dDOLecNVQ2XOL3+XpwZXYeUiSApUIZ7JfQC6HWCqoz5vbOKZQluKSX5un9+LT+DcInreadn0e7X85tnvlIsxg9+WMadM0fwwa6DIaJim4+Fz33DyMw4rp00mCtPyyHeZuRAi1sxxsunFzHvdHj8030ymbgwI44bzinAYjQwY1QmOh3cPauYvQed+INBshJD3o5nPtvDdWfmK42FGUXEWnU8MreERz8q59M9jTwxbxxXP96ZOLpVUXXX7QulFUvaHEfDk9G0KTRoODZ0h1MR7hv2E+JYvNSz3Tl5oRbWkAiCl5Zks3ZjJTdOHcaQtFjVrIVHPipn9RWjGZQcQ11bpFT33ganbFBAZHXO+9erazRIpMXMeKvqyzIYlnIovZAf+KCMb/c1y8cn2kzEWkyy+1w6/r73dqlKZkvu+PDrXzJ2AIBsLEht3PZ6SEPi5a/3c2lJNg6Pj36xJh69qoT9Te3YTAYe3rBbJpFWNjoRBGhwemhweKlsdLHy3V2KxfPhj8oV15AqYD7wQRkZCVbVEtxxluPHqTgcpEXtpa/2c/PUobh8AYVXIjPRpiqpHhBF+Z7VUpLDJa5HZScy9/EvVBfjRmeIW9PkCoUVSnLiuXhMtkIIrfMYL319K0/MG89zG6tkMrHb52V/k4cHPtzKpSXZ/PW9UObHJSVZDO4Xgwg8dvVY9je2YzUJrJkzliaXj0SbkWanhz+8so3FUwqYPS6beWcMoi6KJHdnY9DtC8pl2guPomDYyVDFVoOGvozucCqW9WZHTnao5bpLL2rp3xte+JZ1iyfKWRfhL/5Em4nSWgcL/3mIxR8eyz1cdU7J4xEMhvgUG3bWMbEgVV40bSa9rGyoFtcO95gMSQtJcgfCLnXRmCy217SqXt/h9nHtxFD44qWv9suGjlSHItaiZ1hGHK3tPmLMBlmaW+rbhp11FPWPY2j6MAQBYsxGdtQ0cc9/DmV//PrsAuodXppcXvQ6gfNXfSRLOYdnyUiLp2RAdH5OAO1ef4SS523TCvH4+4anQlrUqlvctHn8rP6gTLk778jgCQ+NhIzUQ8W4ommABEVY9X4p94Tt7sO/z0myIghB5p6eQ5zVyJoNu/ntj4ey4OmNFKTGcO2kwbR7/NgtBv7vR7ksff07+dz6Ng93XzwSh8eP3x/kuxqn7H14+rMKFk/JJ85qVGQULZ6ST3q8hXvf2YXXLyo8MpeNy+aWMEXQJ+aNU52/dpPyFWYx6piU349RWQldIml2Rl+vYqtBQ1/HEY0KQRDe4DDppKIoTu/RHp2kONyiH/5vTWso973zC1JNIVKqMzAw2Y4/IEbVhgj3eKjJXy+ZNhxRDETk8kux+M46D8tnFPHClxXydQQhunbF/uZ2Vr+vDJ8MSrGxcHIeazbs5tKSbIWE858uGkFNixunN0CcWc9PJ+QoCIR//+kY2aCQnsN97+1i8ZR8AHZUt8rPMyiqL576TmuJZDwBZCXZIvQXlr25jaeuGX80w97jUCxq/qDq/Q1KsctjYTHquOGcAtp9AZnwCNGJkW5fMGrNjP3N7QxItDFuYDzlB9vZXNVKTaubgtQYLh+fo/BWLJteyMjMODZXhaqVxlsNzH3sSznzwmzQy+1Xt7hxeAKqYZsFk3K5bFw2oghrN1YybWQmogAFabEUpMbIfzePfbxbVYsiEAwonsXK2aMYk52ITifg9wfZVt3SQWK1UpgRd0RDo69WsT2eEEURp9OJ3W5HEL4/962hZ9AVT8W9Hf9eBKQDz3T8fjmwtxf61GX0pbLEaXEWVbe6JLok/Svlvg9Mtite7IKAIn4OoZ1/XVvIzX3ra1si9BuWTBtOSoyZxc9/I7ejlmJ4x5vf8fjV47jp5c1yNoHFoMNu0nPm0NTI0tWvbeXxq8fx7b5mnN4AQ9NjeeJ/5araFFI2iOSZWTApF5NBz62vblXoE0Do/mo6aj24fUEWTclj5XvKvjZEEaca3C8GXyDInf/eLn8eTcBpWHqcYqG5c2YRf+tYbBud6u03OLom6dzbCF/UDjS384jK/fWLMXP/ZaMx6gQ8/iAJNiMHHR7iLHoenltCk9PDHTOK5KJe4QZfyKuhP2zNjKfmjWdAog0Aq1HPwsn5bD3QovBI3fb6Njnctnx6Ia9+Hco2cvuC/K3DG9J5fkfzngxNj+X2N7ZFkDmXTBtOa7sPgHe+O8jlp+Xwjzljae4Ik6TYQxk+axdMiKiRI0nYH02a6fddm8LpdHLZ/W/z/OLziIn5fj4DDUePIxoVoij+F0AQhDtEUZwU9tUbgiBs6MpFBEHQAxuBKlEUpwmCkASsBQYSMkxmi6LY1J2O97WyxNmJNq6fnK94id02rZCXOoo4SeJLkhu1s5s1zqyPKPC0eEo+6XEW6h0eLhuXTaLdyKpLR9Pq9lHZ5GL1+2VcUpKleFlHe3nXtXpk+Ws5XCL6KclJ5EWVGH1Vc7tSbKjDeyGFHUYNSOAPr2yNOC87yYbZoOPaibkMSFDyODp7Y9Q8Damx6rvoOKuB9DiLrGnw0lf7+cWkXG44p0DBqVg8JR+bWcebC89gX5MLm8lAeryZx64aT73DjU4QVNtPjumapPPxgGR0GvRELP7Lphfy1/d2MnloumIB/vXZBViNOn721EbZY/W3y0bT4vZR2eiSDYabpw5lY0UrH+yo5uG5JXxT2czAFDsHml2ysFm9w4NBJ1CSE8/LX+3j/JH9FQRdyQAREFl4Vh4PfFjGtJGZwKG6NXev267Q2YhmAOoEcHoCTBuZqcoD+dvlo2WOh17QceNLW2hyeVk+vYjx2UlRBa4kCfvw9rqTZvp9h85oweFwaN4KDd1Gd4ia/QRByBVFsRxAEIRBQL8unrsY2A7Edfx+E7BeFMU/C4JwU8fvN3ajL32uLHFlkyviJbbszW08M/80QJTFl6JpUdhMemb/47MI9/C4gUkEgiL9Ys1UNrgYkh7LrWFZHNHCEm5fUDYe9DpIjw95Urx+sUuy1pWNrgjvxZPzxlPT6ibBZsTnD8oLfPh1k2NM/N8zoayDxVPyjrhb7dz35o6Fr8HlVaSuun0BspNChtiKdduZNjKTNo+f03KTeGb+eJpcPuIsRtLizGQl2ORqpp0Nzg2ltaq6CC6vr6emQrfR2eOWnWjjw9I6Nu9vYVCylafmjae2zU1KjJmbXt6sugBLxcCkzyoa2rn++W9YPCUfq1HPLecPA8CoE/hVx3O57qwCkmNMESTMjDgLH5bWM3tcDhnxFtlQka4leaQEBNp9QSoa2mXvWjhJ9LwiH7ecN5ScFDstLh8PXDGG5W+GsnDCORX7m1xRpendvgB3zizE6Qmwr9HF3bNG4PYGGZhsP2wBOIlE2rm9mhY3xQN6ZNhOaQS8buat+S8v3vATzVuhoVvojlHxa+BDQRDKO34fCCw40kmCIGQBPwHuAqS01BnAmR3/fxL4kG4aFX0t9Staf/zBIBNyU1TPkdysA5PtvL21WvX86pZ2jHodbe0+jHodOkFQHNdZMvuNTVVymfJwd/KaDeXcMaOIWLNBXlSka9y/vpTFU/JZsW6n7HZe/X5kJcn/7T4op5/+8cKiCBf7XReO4Paw7I4XNu5XyCt33q1KUtvhHpEku4maVo9iZ3zDOQUk2kzodALnDkvDFwgV1wo3GIZnxMpG0bYDLaoGZ/K88Rh0etZurFSISq3dWMldMxXabscNah63B68cLdf22H2wnbe3VrNgUh4ub4Dfnjs06gIc7MR8SrSZGJRsx+0PUlbXxgsb98uerYx4Cw0Or0yclNq4f30pj8wt4cUOzZKVs0cpriUZqnkdVWgLUu0snhKS/QZl4TijQcDYqSz6nTOLSIkxoRd0lNW1Udvq5slPK1g6bXhUImaj081v/7VVYQBLfYvmmZQk7Du3d7zk2E8FGEzas9LQfXQn+2OdIAj5wNCOj3aIoigHogVBOEcUxXdVTv0r8HuUKalpoihWd7RbLQhCanc73tdSv7rTn847UwGiyiLvqHHw6MflLJk2HE8giD+oJGxWt7hZu7GSe2cV4/T6qWpu57kvKvjNuUMj0kuXvLaVZdMLVRek/glWbjlvCC3uAG1un6oXIi81loWT8wC4f30pd8wokmW3Q+91Ea//0MomSXSvmTOW0loH3kCQ3/14iEzEbHJ5yU6y8fDcElyeAL5AEItBL4czpL6tfHcXT3cQKSsaXbJBIX1/wwvfsmBSLi9u3M/c03No9wVU7/GjsoOMy0ngsnHZEWEmo/7EuHj3NjhZsW67bOTEmPXUtnoVtTTumFHEdwda+ePbO0i0mVh6gfoCHL62ZsRbmPfDgbIBKXlkku0GFk3JIz81lm/3N6s+p/o2j2ygxZj1Cs9XZy/Xosn5vPptFYumFHDLeUPISLDJx6fYzXINGqntW1/dysNzSvAGgqz4z05+dXY+l5RkUe9wy1Lz4W0ve3Mbf7popHx+eBr14TyTkoR9Z07F0aSZatCgoevojqeCDiNiU5SvVwAKo0IQhGlAnSiKXwmCcGZ3OycIwgI6vCHZ2dmK7/pa6ldX+6O2M10zZyzl9Y6IXbsUu5biy/fMKuaJ/5VHvCx/MWkwLq+PBz8MZVvsqnOwK0pp65goqaW76x0UZyVw3T+/Vq0kedu0Qv7SIVIl9W1HdWtEamPndMcml5eNFU3kpcay6LlvyIi38JdLivH6gxj1OvY1uWivDTA8PQanN0B1q/ouvLbVQzAosr1aPbU1KB7ibEjCUZ3vURTBZjZgN+kVxpDdpMfYOWVEBYebj0eLBqdH4VFaNCWPv3Yiry55LSTydO3EXOwmPX9+e3tECOe2CwrRCyKLpuQRFKEoIy7CI7V2YyXXT85nzYZymXSp9pxSYs3y/2NMBtkjpaZ/IaVL/6Ej/fPxtw71rc2tXha90eVl5bs7uW/2KPY1uWSv1C3nDelU8yUUkgsn0bp9h9KDD+eZNBh0zCzOJD81hpoWN+nxFgoz4o8qzbQvozfmpAYNx4JuGRVHgNpW74fAdEEQzidUhCxOEIRngFpBEDI6vBQZQJ1ag6IorgHWQEiCNvy7vpL6Fe51GJ4Ry9uLJlLXFqrLkZMUaeCocUFqWtwEgUBQZNkFhfSLNbOztk3Bc3D7glQ3u5h/xmC8gQAPzylhe3ULLe4AD23YzRXjc/jzRSNodQdYM2csrW6/6oJh0OuiViC1mQzySx3gH3PG8lVFEz8YnMKNL22SZZ6lxeQfc8bKngtJG2NwvxgWT8mTJb/vurCI6mY3oiiycHIeb2yqIivRyoHmUO2IoBiS6h6aHofNBGlxZnKSrQpJaYtRR2qcmb0NTkrr2joyGJRKkxajHqc35KFQq6Iq3WNR/zg6K9OLItQ7lJ4ZNRxuPh4tTHqd3M+MeEtUkbL9zS7sJj3ZSTZ+/+Oh1Le5WTwlH6c3gChCks1AmycgL9ArLh4R0c60kZkK3s8bm6ointOdM4todHpkUmhZfRuIsHhKPlmJ6n0LT5uubnHj9np5dv5p+IOiakZUv1gzd19cjEEHv+6oZHvRmCwyEmysDPOuAR3pqkbF79L4HckzaTDoKB6QeEpzKHpjTmrQcCzoSaMiYkKLongzcDNAh6fit6Io/lQQhHuAq4A/d/z72tFc8ESnfgWDIu/vrGXz/haCIsSY9AzJiMHpDuL0+HF5A+xvdvLD3H5UNrmobXXT7gtEpI4a9ZBsNyv4CZIug4ScZCt2i1Gh6bBocj4vd6TyBUURlzdAnMVAS7uPg21ufn12gUKye9HkfPYcdABww9n5ZCTYcHn8HHR6MBkE2tx+WTTKYtRxy3lDOT03mYMOj2KRh9Ai8lVFE6vfLyMn2cqvphSwp8FJaZ0DvQC//XEBGfEWvjvQJgs4SYvW/qZ2ftPhFs9JtvKLH+Up7mv59EIe+LBM9oosm14IBGhwenhho7rS5K/PLpBd9eFVVHOSrOxvbpd3vXaznv/truPKCYNocvpIsht55rM9XDsxr3cnSxS4OgyheacP4NwR/WlyeGWjTDIoc5KtJFhN3P6GsrhbZqKVZpeXRz/ew/hBRSxeu0meW/1iIrNo9LpDacsmvY6bpg7j0Y93yxk9xVkJrP1yL1dOGMSCSbm4PH5W/Gcn984qpsHlQ4iSOWMx6Fg0JY+h6XH86xcTaHB6+WJvI+u310RopiyfXsi/vqzkrW21rLh4pEJZVs1DdseMIp75bI98LYlTIXkCsxNtlNc7+kRauQYNGnrWqOgO/gy8IAjCfKASuOQE9eOYUNnopLTWIe8Oc5KtEXUrbrugkG/2NzHn0ZAs8vLpwxWpoznJVm6bVsgvO+o0gDp58sapw1QVOxeelYdRr+OfX1RE5Pn/6aIR/O3y0Tg9AcoPOnj6swpml2Txv7J6Lh6Tze/CWP/hglch0mUR/oDIvCe+jBpOkFQ3LxuXTU2rW0GulNJhJV0Nqc+3vrpVkaUwbWSmzB+Qjln6+jYeu3oc9a0eEuxGnv1sDz+bmIdJr6PJ5VVVmrzvvV0sPCtPXpSkKqqrrxhNjMUoc0S+2XuQs4f1V5AHl08vIjU2eiZBbyItzsLssRkM65/IvI7aFtLze3tLNWcOTWX0gAT+79nI+bFgUi7D0kM1NsrrHBSkxvCLM/PYUdPKrrq2iHTb0wYlYTXqFYv2kmnDSY8z4/QG+fuHpVw8Jpu//Gcnm6taWTg5D7cvyK66NgYk2thV3cydM0dw66uHeA9SKusf394hf3bf7FEy96GzZsrSDn2Ll7+tprzewbwf5ODwBuRwzNtbqmVZ+aqWdmLMBuZMGMTQjARMeh16AW46byjD0mPJTYmJmuWjGRYaNJwY9KRRsfdwX4qi+CGhLA9EUWwApvTgtU8Ials98gs6I96iSo5c9sY2Hr96HPfMKsbl8ZOZaOUPHS9lgDkTcqIS5jLDyJNldQ7VY9LjLVQ2urh9eiFOd4BlFxRiM4fqZdz88hZ+e24BcRYDw9LjuKQkC4tRz7UTB/OrtUoDZelrW1l5STETBrehEyA19lAq4eHCCQBZiTbZQJHau399Kfd1yhyQvgvPUoimq/FZeYOi0JnD48PjFHn0qhIOOtTrQAxMsTMw2cY5w9I46PTIITFADpPpBYE5j33RaaHbytPXjGegepJOr2Jgsp1ZY3MianHcv76U1VeMYeE/v+baibkR95toM5GfGkur24fdbCA72cal47N57OPd/GxSHkadgDcQ5IHLRxMUwO8X2VnTFqFqeceb3/HM/NNocDiY+4NcHtmwW1bJlATbAkFY8tpWHrxiDMveDNVRsRp1jMpOwOsP8Mtnv1G0KUm6Rxvb9o56HR/sqGPO6QNZ+V6kCNfFY7NkpdaH55ZwRl6KXBtkRHocVquR8npHn0or16BBQzeNCkEQfkAolVQ+TxTFpzr+vahHe3YSwOn1ywbFnAk5lNWpkyMPOjzc+85Opo3MpKqlnZumDuPvH5ZR7/CSHGOmuV2d/1BW76A4K55N+1sU0szhxyTZTDz/RQWZCValh2RaIc99UUH/BCsWg45f/vPQi/+GcwrUF3tCtT/irQZaXD75mPBwwtD0GJJjzNz88mbZPe/yRBLyEm0mEmxGmTj40lchd37nLAXpPqJ5QSSPzJo5Y/nFMyEZ6KXThqvyLrZXt/HbFzfJhdlqW0P9G5hsl8Nkb246oKpcWtt2YhQ1dTqB2rZDRlJ4HRZ3R6hMur/wY+aenqPwNN0xo4j/7qzhytMGUl7v4PkvQ5LXeh2MHpDAPz4uY8LgflHnp81kIDnGxLwf5vDs55Vcc8Zgyusd3Dd7FH//MOQV+nZ/syygJvVp2QWR2USSdkrnfku/x5pDr48zh6bKIT+pL5IGRvj4f76nkUc+KufOmSMYlhGDueP8o00r70tKvBo0nGroTunzp4HBwLdAoONjEXiq57t1fKAmOiRxH6K9bKRzGpweYi2hTAqJFX/txFxVYppBp5Mrlc6ZkIM/KPKbc4dgNurYXt2qSphbMm04a7+oZECijTUbylXjzUumDWfZm9u48cdDI1L3lr25jXtnFbOjpo2sBKuisqVep/6y31XbJnsHOi/cUjhh1aWj2by/mf/7UZ4c429yeVUXvXlPHHLnS6qii6YU4HT75OMlXY3wuHu4F0S6n7qORd/tC7L8ze8iMm2kc6IVZjt3WBqVTS7S4s2qyqVpcb2vqOn1Bth8oIWaVjcZcRZG9I/HZNKT2lGLo3PlWmmM39tWo+DHXFISWSdmyWtbZWLta99WqUpeN3caJ2ncv+vI4glxXkaw4Ed5WI16vtrbyItf7ZO9YHFWA7dfMIx/bNgji0vZLZHZRG9sqmLJtOGs2bBbNUvloCNEBM1OsqkaBdmJNv7y7i65f1LNkltf3cLqK8awq9bB5CFpR5VW3teUeDVoONUgiJ2p8NEOFITtwHCxqyf0MEpKSsSNGzf2WHudXy45ydYIme3OLxvpnBXrtstGwhXjc3B6/axaX8bIzDguH58TQUwDkQc+3M2cCQMVtTsWT8knJ9lGm9vPixsrmfuDXNq9fvrHW3ns43JGDEiQDYqLxmRhNujI7WfHbtLj9AQwGnQ88H4p8yfmsvj5byPu8f7LRvHnt3dgMgj87tyh7G92kZNkxx8M4vAEFFoIv/vxEB75aI9CVfO+2aMUBMo7ZhTh8fm59bXvuHPGcDISbGza34zVqMdk0Mn6E4um5Cmqh0rtrZlTQrvPT02zm+QYM7vq2ggE4aNdocwRvQ4m5qXw239tivBCLDwrj3vf2SV/dsM5BXj8Qbmy6h/f2k51i5vfnlsg8y3CFUVPz03mppc3c+tPhnP9c99E9O3Z+acxdmBS+OM77ArT3fno9QZ4dfMBloaRcZfPKGLmyP4caHHx+d4mqprbFc8tI97CJSVZFKTGUtvqxm7SEWczIYpw3T+/UR3v0joHogiPfhz5/BdPycdi1LNi3Q7FuL/2TZVcOVbKpLl/fSkrLh5Bi8un4EssnpJPjNnAgx/upsnlZfUVo9l70KWY1zecU8CQ9BiCQXB4/MRbjdS1eTAZ9DyyYTfzJ+Zi0usw6gUWqoxFOJdIMhalebloSh4FqbHYzHpyU+x8V93WLQOhvN7B+as+irjmW30/ZHJEi6en3pEOh4NL//o2OoOJ5xZOISYmRisypqEzok6C7oQ/thIqKFZ9zN3pA+ic2tk53U4tPiudE14oKyiKFKTFYjHqmFiQqkpMu3dWMdNGZnLvOzsVrne3L8D+RhcF6bHMLsmOkEselGKPuntd/X4ZTS4v980eRUBUr2Bq7HixXlqSzT3v7ODSkmzZSMhJtnLf7NAi5A+GilKFw+0LUtHoYvUVY9i8v5lAEFZ/UMp1Z+Zx/2XFxJqNCnJpRryFBZNyKUiLxR1FfGpjRSNjcxJ5aEM5c0/PIS3OIqe27qpzcOfMItp9/gjjSzJ4wu9NWjN0ArJXKCPeQlqcRRGSClcUXTQ5n911TtW+dRb76mlsPtAiGxTSNR/4oJS8fnYcHj9pcSYSbUZ5fsRa9MRalKXCb5tWyHvbavhJcWaUHbqZ8noHYhQuQ79YEya9TqHRkWw3cUnJAO56a7tifiXaTOyud0aUlpcIopeUZJEWZ2HVe6WcNyJDblMU4fH/7aXJ5eXRq0r43bObI/oZazbwy39+TUFqDLddUBhh3Hr9Qe6+eIQia0c6NxAM/R3e+84uLEYdq68Yzb+vn0i9o2tp5X1NifdkgVZkTENX0R2jIgX4ThCELwA5AH2ylj7v/HKJWogr7GUjnSMdmxFvIclm4q5/hwR/3H71xVSvE7BGcW/fPWskFqOe/c3tXDsxV+Ye3L++lCfmjeOSkkjBoTve/E7euW+vaeXDHXURIYQ7Z47gxY375NBM54qhFQ3t/LrDQHrgg5DrW/o/hF7gWQlW7nhzmxzOuaA4kwc+LOO35w6NIEtWt7hZtb6M+2YXYzboVRe9QBAaHV6qW9ysWLeTjHgL98wqprrZRfGAROodHswGPXEWpThVkt2EySDI7dxwTgFmvY7VHyh30AD7m1yKkFTneH3n6plSm6m9HP6o6TTfMuItXPODQXy+p5E99a3MHJON3Sww9/Qcnv+ykt+cO5SyujbFnFj25jb+MWcsS1/bGhFWuOvCEbS0eynJSaS947POHo8Ys5Ht1a28GJauajHqFNk40vyaf0Zu1NLyQRGK+sfxp7e3U9HQzqQhqbKsezgnxBcQuXnqUP4U5hlZNDmf1nYPCyblYjPpMRkExVj3izWDKJKVaMPQke0j9VMKod04dRgZ8RaqW9ws/Oc3vLVoYlQp/M7oa0q8JxP0JuuJ7oKGkwDdMSpu761OnAhEe7kc7mUjnQMh7YBLS7I50NJOk8vL059VcMfMQpmYCCECYJPLi9mgY0RWPJcEsuR8fGk36nD7+f2/NiteutLubPP+FnJTYlRf7OlxFjLiLdg6ypdDkAevHMO3+0Jehb+9v4tLS7Ix6JQCRZ3bCVcnlEQlpYXbaBAiYvOLJufjcnsZkBKj+rwsRj1//7AsQvVTXhB+PFQ+vsnlZX+TC1GEqzqyHyQDofPC99hVJbS0+9nb4CQn2a4o9y7toB+eW8Lv/7X5sAZeVbNLtYR7UHlojyOj03y7aEwWDS4vNc1OSgamcM0TX3Lf7FGs317DgkmDFV6r8DnR7PRR0dAuE2cl9cmGNje/eXsnJTnxLD57CH+6aAR7Dzr5YEcd543IUFVqlXgRnWuGSPPicJVF9YLALybl8dCGMoZ0eOrUjOZfn13ADWfnkxRjpqaD8HvL+cNYtb6M687K46/vbY1of/4ZucRajEwdlkqibSzf7GsiEAzVaLm0JJsV67Zz0ZgsHugIc3XHy9DXlHg1aDjV0J3aH//tzY4cb3R+ubyxqSpiIZReNsGgSGWjk4MOD3fPGsmBpnaWzyjkq4pmDLoQ9+CVbyo52OaN0GqwGfWU1bXJ1wx/8c4/I5e/vqcue/zox+Wkx1s50LHz7vzirWxyMff0HHSCwJoN5R2aANsVx63dWMmfLhop13pQy5gIVycsSI3l3ktGYjHosZhCxZzWbqyM6N+jV5Vw88ubVZU5/7xuOzf+eCgxFj0rZ49iR02rYkHY3+SSr/frswvwBYIRmhPh9R2kzxyeAAk2Iyue3SnrJ4TD7QviDwRlA++W84epPrc2d4DS2mYev3ocdW0eEmxGnvh4DwOSbIzK7r2S2CP6x7N8RpEcAtHrQlkSl582kKomN8suKMRkELjmjMGqeiTSnNDrQgJU1S1uhVdp/hm5ZMRbuHDMADkVOJSOOZYv9zbJOhAvfbVfbk/yUKll4+gEGNzPHlGPQ1LWTLAbsJoFFk8p4N53dsiGnFr11KfmjWfx2m+pbnGHvHv2UFZQNPVQvQ6+2deMIAiU1obmjyCEQpSSMSQIhzwwLm+A8npHl7I4+ooSrwYNpyq6k/0xAfgbMAwwAXrAKYpiXC/1rVeh9nLJTrQxJjtR8bIB+Kisjv1NbsUCunxGEa99WyWrPv79yjGqAkULz8rD7Q99lpWkU4QzonkP9DpYNDmfA80unvy0IqIa6K/PLiAoigxJi5V5DZ3byoi3cGlJNteEZWBI1UvDS09L6oSLJuezZsNuLh2fHeFhCI9ru31BvqkMpRa2uX2qtRra3H72NDh5YeM+OXQybWQmazdW8rtzh7Jwch46ASwGHb5A8LAeFOiIw1sMtPsCLJ6Sh9WkHl6RSqPf8MK3/PGt7RHZMisuHklanJGcZJsiM2XZ9EIyE3rX/W0y6Zk5sj+5KXZqW90dpewdlNY6Wfp66Hmvvnw0u6KkJet1IalstYwKaT7ccv4whV5Ios1EZWO7wtCVxlPo8ELcMaOIYPBQuET6LDfFzj82lBJjMvLQT8dS1+rGajJQ3RzyLK14ewen5faTCaFPf1bBr6bkq/a9ts1Dk8sr81zmPxkyehZPyVMdx6Hpcdzx5ndAyPBSI51aDLqILJ6uZnGcaCVeDRpOZXQn/LEauAx4ESgB5gL5hz2jj0Pt5dL5970HHXh9YkSJ6KWvbZV3e25fkG/2qQtY5STbaXJ6aHB62d/oYkCiLeIF2fn3vNRY/vLODqaNzKTJ5WVAolWOO1sMOhKtBva3uKlv88gx985tqXEKlr0ZUqrcvL8FfyBUivxXU/KpbAq50y8pyYq4z/BdrXSN9o7vHZ6A6gu/sqmdl7/eH+EKXzwln7s6sjSkY5+YNz6qi136/20XFHLTy5tlY+i2Cwq568Ii/vCK0qs0KMXOoBS7bCimx1k4d3i6gsT3dWVjBGHytte38cz88d2bPEcBk0lPSUeGid8fVIR9AMoPOmWNh87PY8KgZH7z4iaqW9zUO7wdIQI9IzMTqGl1U9HgpKrZpTjvojHq47lgUi7jcpKYlJ+CIAR5eEM5K2ePoqyDtJtoM1LR6GJ0djKF/eNY0KE+Gt6fu2cVK4rWVbe42dfcrtr3tDgzd8woYl+TSzEnX9i4XzVN+qEPy6hucRMU1euT/OnCEWQn2bjy0c8V96YJX/UsRFHE4XBgt2uhIQ1dR7dK9omiWAboRVEMiKL4OHBmr/SqiwgGRcrrHXy6+yDl9Q6CnYPDPYAGR0gWWs1gyE6ykhEf2uGGC/5IsBh1CEC8zcRpg5IYmBKDw+OTj5OUKqXfpZ3kU5+ESp0XZsRy7yXFBIIiqbFmHvko5P5ucPlYs6GcG1/ewiMflbNwch6JViP3zR5FTnKITKXXRVeqDAZFXvxqHwk2Ey3tXgQBZpdkMTDZHnWXLPVvybThvLm5Kmr/75xZxJubq+SS7PfNHsUN5xRwz6xiRYE0qW2dAMtnFEW0UdQ/nhUXj2DNnLG89FWlopjZsje20eT0cu+sYv52+WjW/mwC5w5LQ6cTZENxQm4KA1NiGJwa+n9uvxh0OoH6Nq/qPR7sQkGxnoROJ1DXpiS7egNBeRENfx5Lpg1Hr0Pe7Ut8nDiLkWue/JIbXtjEPzaUE2sxyuMP0cnH2Yk2bn1tC99UNvPbF7cwZVgGL35ZyYMflmEx6Klpacek19E/IdSWJMAV3oakihk+51/6an9H2uqhvi+fXsSw9JDSaX5qLNdOzJX/Zqpb3Dz1aQUPzy3h7z8dzeIp+ax+v4zNVa1AyKBYPKWAtRsrmX9GLoum5PG3y0bTP9FCfRRV1bo2Nxp6BgGvm3lr/ovT6TzRXdFwEqE7ngqXIAgm4FtBEO4mlFp6wkzY4yVi4xeDUd3tVc3tzJmQw9OfVbCrplkmSko7rF+emccTn5SzsaIlFCL56Rj6J1hlLoK08N5/6Wj2NDjJSbKRZDeQlWSltNah2MH98cIR3DNrJMl2M9c8+aVihyax9e95Zyd3ziyird3H8Mx4Va2IQBBWvV/KvbOKsZl1LA1T4fz7lWPUd8m5yeSnxlJa18baLypl8qbUf2mX6/EHaXH5ZCXH0wYl8dsXQ8qbCyfnRaRtWow6UmLMjMpMkMMCGfEWDrS4WfT8IfGqRZPzqWr2KEIw6fFWdtW2ERTh3nd2cOPUYV0a+2il3+3m41sGZ2+Dk9RYZdGvl77az7wfDuTZzyvkIl+jshIw6kXa2v3cf+koWt1+lnR4yTrzce7oJAqWbDeq3mu7N0BFQzv3vbeL+Wfkctvr23j86hIuHDuAmuZ2MhNt3P7GNtkzdMt5Q2l1++Uw3hubqrCaDLz01X5FaK7J5cVm1LPwrDxyku2kxZkZlm7n7W31Cn2O8JBak8tLvMXA6YOSeWd7rWw4SfocA5JsXDk+m1ZPgBS7CX9Q5H9lDeSnxmpZHD0MyTPhcDjkzwwm7Xlq6B668yadQ8izsRD4NTAAuLg3OtUVqJUQ70n3p6Sc6fEF+fPb26PWvmhyeWV2+y87OBXS7vLFjZWcltuPjRUtuH1BSmvaCIjw/JeVisqQTo8XURRpdHnZXhPapXXWB7jllS0d56jXAJF2pbe+GlpwXB5fRPEnqc9uX5AdtW1kJ1oVKYsPfFCqIBNKO80n/rebLVUO5p6ew646B/WfVbBgUi7ZSTZqWtyygSQRBqVQydqfT5ANCbX6IRIRVqcTKBmYRDAo8t/Sen7bSR1ULQQTrv65aHI+K9ZtZ2h6rGLs1eSY46yGCJf74in5xFmOr1FR2+qmyelWjJHJINAv1syMUZlyRkZ9mxur2cDv/7VZJmsejo9TVueQC3Llp8Wo3mtqh6dAaifRZmJfY7vCwFwybThtbh9vba7G6Q0oKs0un17Em5v2Ud3iJivRotCoeGhDOU0uL0/NG8/4Qcls3BsZbgonnq6cPYqizAR0OoFzh6XxwoIJ7G1wsbvewV1vbafJ5WXR5Hw27Kzj0vFKnZXOadRaFsexwel0csk9rxIIBDGYDqVYS8JXGjR0Bd3J/qgQBMEKZIiiuKwX+9Ql9ISIzeFkuv0BkVtf28LFYwbIKXz3zCpmZ22bgpQIMCwjnp89vTFi1yjt2G44p4BBKXZiLQZ+8UwoPh2+QP5jzliMegO/fuFbmaUfzXCIFnOXsjjcviC5KTaaXH7+9v4u2XgZmh4nx6olzkJFYzuPflwuGxun5fbjgQ9KFeTLBz4s5bZphUwfFSDFbuT3Px6CwxOgIDUGb1jmRrjRIvUpNcYs75wlr8aaOSUY9YKqDPreBifbqlpU790a5lKXCKbSd6veL5Vj/IC8sEierESbSd75FqTFkNvPrtBGyEy0MqRfbJfmTE8hLc7C4//bzaySHPl5TxiUxLVPbYwYW0lHorMhoTYP/MEgdpMBQRDZ19TOU58qU0+f+rSC35xbwHVn5aHXQX5qLPN+kCMbFHBo/i6YlMsvzsyLMN6Xvr6VJ+aN4+xhGegEkbx+MZTVOwgCV52eQ2H/OFw+P+X1Dura1P9Oh6XH8taiifIcCAbFiIqj0nySxje8WF9FQzsPbSjj7lnFmPUC+WmxWhbHMUDyUuhNFoRAQPGd0+nk6gffxWg7KTn5Go4zupP9cQFwL6HMj0GCIIwClp8o8atjFbHpikz3osn5DOpnl1P4dta28chHkSGFAy3tqi/O9HiLoujTny8aoXpcfZuHysZDJLto+gCiCC9/ffiKoRajDqNBL7+AO6cd7qpzyKmuD3V4Q6Sdo16HomCUhK/3NfPIR+WywNTKjroMOclW1swZS5vHT6zZyJLXtshGy+Ip+ew+6ODcYWm81cX0vdpWNznJ6oXTxg9K4rmfnYY3EOSml7ZEcDPK6g55LlbOHsXwjFjZoOhMGL1p6lAm5CZR3+qVS6sX9Y8n19L7BD/JkG12eTl7eH8SrEYe/bicgtQY8lPVNUnCqULSs1Hz/EhZG25/aFH4Yk8jTS6vYjwtRh0JNhOPfrxVcV54bZjw6+7oqDjauU+f7A5Vkc2It3DdmYMjUqmf+jTkxXvop2NVxzM93qIkRKt4HsM9VO3eSF5TRUM7ZXVtzByVqZEzjxGS4SDojarf67UwiIYuojtEzduB8UAzgCiK3xKqWHpCIOlMhJPCuuP+7IpM96r3S9EJAkumDcdi1KkS0RZPycfh9quSNMMNBbcvyJ6DzqjHSR6IDTvryE62RVxn0eR8Xv56v7zjv3tWMTecU8DK2aNYu7FSXsylVFS1hSAnycqjV5UAITd1OEdBr4PhGXGq/ZMKOt2/vpSsRJv8XUVDOy3tPhKsRrKTrMwYlcnCyXnMPyOXpz6tYOE/v6GyySUTJyWyZDjCybY2k4GDbW5V8mq710+Ty8f2A62q3Iy81FgWTs7j2om5rFi3ndrWEJFPLQvmz+t20OjwUVrv4OuKJn44OJVGZ+9XKZUM2fNXfcT+ZjdLX9tKgk3P8ulF/N+ZeZTXO1Sfv/TIwomx4XyWG84pYMGkXPonWPhibyPzn9zI/sZ2XtioTqS9o5OU/JLXtnJJSVbEdUUxOgFZqiJ60Zgs7nxru6K9+9eXctGYLNy+ILe9vpU7Z46IIG8WpsUqSNbRPI9S6mtevxjVfpTkJGkhjx6CZjho6Al0J5DsF0Wxpa8UkzlWERvpJSax6bMTrYrfpdsUxSCZCRYemVtCq9tHos3Io1eVUNMS0hq48aUtzD09J7TAv7tLsfuTdvQSXti4P0JzQqrjAXDz1KEIAjS0ecjtF8ODV4zB4w9gNuhZ9uY22XCYe/pAqppceANBKhudXDYum6xEGztq2nj6swouHpulujuMsRjZUtWiqHApfVeQGkuryxtxH+FeELcviMvjV5y3o8bBox+Xd8iC71fsdoHDhqPUyLZ/v3IMt7+xTeGyX7uxkntnFfPzZ74g0WaKrHw5rZC/vLNDJhYumpyPLxAIZd9E4R7sqmtj9ftlsmFoMeq7NG+OBQdanSTbTdwzayTpcWZe/r/TafcEyUu1U9Xslo2A8Hu7/YJCzAadwpB48MoxtLh87Glwcseb39Hk8oaKvfkD6AVCGRYJFlkITHqWOgGyEpUCaNLzGNzvkEJq+LibDEJEfY4l04bzytf7uO6sPPnvpnN70t9PRUM7jQ53REgtPd7CgqfDRLrmlKjOWZ0AK2ePojgrIUIJc8XFI/lBbrIW8jgGqJEzNWg4FnSroJggCFcAekEQ8oFFwCe9062u4VhEbNLiLLLUdnjZ8s6y1HfMKKK13UeD06cgvN1wTgGZiVZMhhA/oLbVze9/PITUOAuIYDToInbUmQlmshKt3HfpKGxGPb5gkJrmkMx3yN0syju9cFfy8P6x/PHCEexvbCfeasQXFBVhldumFWI16mQSn5pr/K4LR/DqN5VsqXKohk8kUtzjV5ew8Kw8+sWaqVIp6BRvNbJwch5vbAqV15aIn7e+uoUFk3JZtV7paj9cOErN5X37G9tYeFa+wvBaNDkfV4f7u7pD6llapCblp/C7sKqmkodp7c8msHL2KHbWtKouVtJOW3reY3pRTROgvd3HJ6VNstCV5DUw6gVufGkL107MpcnlZd3Wau6eVUy714/dZCDOqmdvg4s1c8bS7g2QYDOxaV8zA5JtjMiMpyAtln2NLla+u4sml5cl04YTBFat38Wy6YXc9vo2WTlz+Ywi3L6A6vPIiLfw/M8mUNEYIklKJORFk/N57vMQMTc/NRYBeOmrfVx52kD2NDiJt6lnmIQrtba4AxEhtY0VjYpxv/W1Lay4eCQ3vnRIsv6PF45gTHYC2Ul2TQmzlxCNnClBI2lq6C66Y1RcD/yBUDGxfwL/Ae7ojU4dDwxMtrNsepFMnHzpq/3cMbOIhf9UqmIueW0r984qZunrShGhle/uYslPhnHLecOIsRgIiiKJNhPL3wyl4uUkW+WXutsX5NzhKfy4KFOh5Lh4Sj7ZSTb+dNEI/vreLnKS7bJCpuQxafcFEBA46PDw4H/LmDYyUyE45faFRK1WXz5alhmXdrQPXDGG7dWttPuCrFq/i+vOymdnbSlPS9kbiTZqWt0Kw6GmxcPqD8pItJmYe3qOoqDT4in5LOvYGS+ZNpy1X1QqQigFYTUgJGKkKIY8EmovfzWXd0VDO01OT4Sn4u6LDxUCkySqLUYdY7MTVHfeLl+AqYXpDM+IJSfZrpCaDve+yMd7/fQmttS0ygaFdM2/vV/K784dyi/PzCM/LYa/XlrMnoMuRd2P5TOKCAZFVq3fxSUl2SxeqyQySvLn/WJC9WRqW90UpMVywchMXtxYyeNXj2Nfo4u0eAt/W7+Ls4enqxqVv/vXJq6fnM+OA038aGiGnM0h16GpamXV5aNoc/s4fXAyNa1uXvu2imSbSTXDRFJqXXHxSFa+u1PhAZTKq3ce98wEy2H5N5oSZu9AjZwpIeB184vHPsZgMqM7vglSGk5SdGeaDO/4MXT8zACmAyN7oV+9Dp1OwBtWdKq6xU2zS10Yye0PyAqGmQk29hx04g0ESY2z0OzyKV70UiqewxPgxQ7uw4FmFwVpsXLKqdSuVEY6LdbM9ZPz5cqfnct2Sy/qy8Zl4/SqF8r6dn8LceZD1T0L0mJlA0fC0te2cv+lo2l2eclIsLDkta0RtUAsRj0rLykGAfY3ukJeixgzVS3tCvEqSRtDEiqyGHUMS49j3eKJfF3ZrFjEo+mHRCPbZiXZWdkpC8Dt86sWgspOUid2psVZ0OkEBqbEkJ1kZ9SABOra3FgNehat/UYRprEYQxLfvQmJ4yFBklH/bZgB8fefjlWEpty+kHJrKAsjP8LgDa8+2/nZLJ6Sz5Rh6XxaHiJULpycx8aKFiYM7scbm6q4e1YxZXVtBIKHDIdbX93K6ivG4PT4VQnJlQ0unv28krsuLOKXz37N/DNy+dO6HSTaTLIRaDfpye0XwyUlWUwZkkph/3jsZn2E7soN5xTIlUal9pPsZs1o6IPonF4qqWxKoXDJmxH+mYbvL7pjVDwL/BbYCgSPcGyfglQQrLbVQ6vbR7zVSGqsmbQ4q2JBilayO9Fm4m+bS7nmB4MorQsJLukFsJr0XP+cslqmtNhKqZoHOmoluDwBRVGn8AqRKTFmtlW3Miw9VPRr2shM2aAI91iU5CTyXbW6O18U4fFPKuR6CNdOzFXdwTe7vNz48hZGZsaxYNJgRT2TX59dwN4GJ05vgDEDEnj8kwpZuErifYS3ZTXq5NTEcTlJ5CSFUnIlg0I6Lpp+iFrFyD92hGnunlVMu8ePzWzgyU/KGZuTyMT8pAj3N3DEqpPhO9xgUOTGqcMijh+U0ntGRXu7j7Q4szyX/u9HueSlxcp1WaQxrmt1c+3EXDbsrGNiQarMS7CZ9Gzery4DL3FGwrM0Em0m2n0B8lNDabKSgqVENp4zIUfOlunc3ub9zZyem6TK/Wlz+wCo7zCQpGuHFzcD+O25BZTkJOHyBahscjEwyc7Cfyr/Tla+u0sOl3WXZK3hxEAURerq6lj8/Nc8v/g8YmJCf89Op5PL7n9b8ZmG7y+6Y1TUi6L4Rq/1pJcQDIq8v7M2Yqf0ux8PYdSAeMXL88lPyhUhC2mRW/7mNi4bl43LF1CkzqXGWVRT8aSX7ar3S3ns6hL2HnQpdqThwlk6AXwBUX653nZBIQ2H8Vj8+uwCbp46lD+t2xHRniR7/NBPx+Lw+NVT+RIsjMyMY2pRBms27JZTSUdmxlPd4uauDiZ/uBtbOrdzW0WZ8XL4yGLUsfqK0egQVBc/NcKmWpzcoAePP6gIAdx2QSHp8eao7u/uxNqPd2y+vd3Hu7vqaHZ6uXvWSNxePwFR4LPyhqhj3Lnw25Jpw4HoacbhHBG19hZNzmfd1mo57PH0ZxX84SfqVVwDQfhybzMvfb2PB68YQ5vHz+56B6vfL5M5Fu2+Q9lO0eZFOAnz3lnFqnNi9IAEnl9wmsaPOEkghUIssQkR3+lN1sgTNHwv0Z2U0tsEQXhEEITLBUG4SPrptZ71EPY2ONm8vyXCrXzPf3by310HWfnuLlZdNprHriphzoSBpMaaWTNnLA9eOZoHrxzDwTY3FQ3tZCXaItq4483voqbiScd4fEGWd3gDMuItzD8jF7c/wB9+MoxbzhtKvxgz+5td8vHL3tjGiKx4LEadajrkfe/twuUL8Pcrx3D/ZcUsmJSr4EQ0ubw43H7u+vd21dTMpa9t5dLx2azdWClrUqxaX8bmqhbZoJCudf/6Ui4pyVJNpb3rwhHcFsYRSLSZKK11sGl/s2rqXzTCZnitjtx+Mfj8yJkG4c/Erx7yVW2jq1Uqu3r8sWBHXRtVTe388e0dGHU6bCYjy97YJqdqRiv8Nm1kpvz7mg27KcyIZ8VFI1k8JY+MeIs8nm9uruLOmSPkeixq7a16v5SJBams3VjJ6ivGcPn4bA40ubjtgkLFmEp1XTz+IF6/iMcf5MaXNrNqfZnsWVv1fintviDLpxeq1inpPC/cviCldW2qcyIn2X5cxkBDz0GNzKlBQzi646mYBwwFjBwKf4jAyz3dqZ5EbWuo2qHaTkmWQnZ4eOXrffxsYh7fdNTuyIo3s6PVI9cYcEUpKjYo5VBMvzMJ0GLU0eD0Rt2RLpteKEt5h7dZXufgzplFCp2L8O8HJtvZ1+Ti9W+ruKQkW1EvYXBKDLWtIQMjPEtizIAE/vreLioa2rnjze9krofEEUmLNateKyvBysVjs7Cb9DwxbxwNDi9pcRZZKEvCRWOyuH99qWrKZ3dc29EUGOva3AxOPflcq20ev/xcvIEg7g5OjJSh4/arc2Sk0IfEvfhZ2M5/+YwispOsfLuvmRt/PJSMBAs/nzSY5W9+F72IWFIorOb0+Niwq5ZrzhhMdXM798wqZs9BZ8iQ8AW4ceowyuoc3HL+MBo6UkHNBh2DUuxUNbtCaqppMbi8AWaMykSng/tmj0IQINZiwGLQ4/UrC/u9sHE/f7xwRATPRgt3nJyQeBWAVsFUQwS6Y1QUi6I4ojuNC4IwAHgKSCdkiKwRRfF+QRCSgLWExLP2ArNFUWzqTttdRVqc5bAKlReNyeKVr/fxf2fms2l/yKD4bHc9l47LYc2GkNLhbRcU0uTyqrbhaPdz96xiPL4A/RMs3PfuToWqpK2jGJnaDvK217fxt8tGs7O2jYWT84BQsaaB/eyU1rRRkpOoes2tB1p59ONy7phRxKdl9dx/2Wha231yGCcn2codM4soq3Xg8Pp5Y1MVOgGmFmVQ7/BS3eKmptlFMIicmrp4Sp7qtSoaD5Uxv/rxQ5krf7pwJDnJhzQPwuPr4cbMxLwUxg1M6vJO1GZSL/hlM/W+jkRvwOUNkGgzcfUPBqLXCdgtofuTntMt56uHISRvl9q8WfraVlZeUsxf3ysNeR4e/pxEm4kFk3IZPSBBtb3KDkn2G87OZ3ZJjkJJ9sapwyivdzAkI5abX94iZy/98sw8OdNIMpjf2FRFboqdg20hY/2tzdVMLcqIIBWHk3qbXF7GZCd0WVlVQ99GwOvmZ098jk6v59n/O/NEd0dDH0N3wh+fCYIwvJvt+4HfiKI4DJgAXNfRxk3AelEU84H1Hb/3CgYm2ylIi1VVwnz56/3EWvRcOGYA1/3za1atL+ORj8q5cMwAVn8Q2l1ePCYLnz9AjFnPXRcqlQF/9+MhIMDv/7WJ3/1rM/Of3MgVpw1kxYWF/GPOWHwBEaM+xIOIVorc5QsVa1r9fujaC8/Kp77Vzcr3SrnxpS0R/b5tWiEf7arD7Qulu2Yk2imvd8gGhbSzXfjPr/nzuh088lE5v5iUx4c76lj1fkjpMMStsCkWqxc2RoY4pGupLWw3v7KZ28Pc55LhBsjEvUc+KqdfrLlbi4c3EFAN2/gCwSOc2TfRL8bMJSVZ3PfeLtb8dzdGvSCHHapb3Dz5SXnEvLptWqEczog2b5zeAH+/ciw7qltlAvCq9WXc/sY27uhUSl4KayyanI83IMqprdJcueGFb7n3nV1c88RGLi3JJiPewrSRmTK3SLrmqvdLmTYyk1te2UKLO8AjH5XLoTS1sJl0fSlL53iFnDT0PgxmKwazxqPQEInueCrOAK4SBGEPIa0KARBFUYyaUiqKYjWhEumIotgmCMJ2IJNQOuqZHYc9CXwI3NjdzncFOp1AYf84qppc3DOrGJfHT5vHh0kfEqfKSbLLlQ9BmcFR0M9KaryNujYPqbFm9jU6uWdWMQC1re0MTrFzW5j6I8D963dx+/Qifv70IQLjLecNZUCSeqnm3fUOxbWXvLaVxVPy5V3/U58eEh7aUdPGQxtCWhWbq0Js/3iLntS4Q6qG0WL0UvqnXgeLJuez96BT0RfpWuFF06RrRXOpbzvQytoFE2j3BUiPszAkPe6wWRhdQbLdzPs7amQBKJsplP0xtSi9W+30FQxNs7O/OVQbpt7hxWLQYTcJ/P2nYymtaSXGYqTd42PNnLHUtLixmgy8/NU+Lhk7gNRYMwOSbKol7PsnWJn/5CHPkRR2q2hop8HpYcGkXHJTYjjocBNj1jNtZKastnq4uSKlqUYbc+lz6V/pbyU8+0MjYWrQ8P1Fd4yKqcdyIUEQBgKjgc+BtA6DA1EUqwVBSD2Wto+E7CQ7WUl2xYK3+orRvL1oIls6qmJ2lucekx1Pg8OnEKu64ZwCGpw+2n0BxmYn4g8GIxQ4F03OpzQsvc/tC/LHt3dww9n5EVyDO2cW8Zd3lFLebl+QJJtJ/r26xc2q9WWsuGiE/OKW+mgx6hjeP54v9zbKBsvhFgOLUceE3GRufnkzFxRnRhg5TS4vO2raFAuEXhe9Mmq7L0i7L8CE3BT5OR9rVkV2oo3Lxucosj/unFlEdljNkZMJdquZzASLHAJ79vO9/GxiHh+VHaSwfzy1Le1YjHoWhBmhN08ditsfZOnr20hUEZdaPqOI+97doWoMPPpxOW0dCpZS5sW+phAR+OKxWQxJi+3SXIHDZ5uEk5H1nfydEglT05vQoOH7h26VPj/aiwiCEAO8BPxKFMXWrgqkCIKwAFgAkJ2dHfF959LlaouYpFGRZDex6rLRxFj0JNmN+APQ7PKSaDNyy3lDsFuMCs2GB68cI4cUQD23/tGrSlR3end3eDMkuH1B0uIsmAx67plVjF4X0qbwB4KqxbHsZkPEZ/UOj/x/6cW+eEo+dW0eRc0I6ZjOi4FOgNsvKKS8ro3LxmXz/JeV3Dx1KA0ur6y7kWw38ff/livOy0uNpabZxV0zR/CHV5WqlGs3VnLxmEz5+J5QPKxodEUUdrv11a2MHpB4womaR5qPneFs9/BdjYO6Ng+PXT2OBkc7g1JszHksVMPEerqe9rA0ZQjdb4PLK38W7q0q7B+P1ajD7QswYXA/qppDc0Iyhoemx3L3xSNpbvcyMjOOiQWpiIgUD0hg5Ts72FjRolB6heiGw5ubq+SaH5JC6oBEGw0ODzdPHcpDG8rl40cPOMT90UiYxxfdnZMaNPQ2el14VRAEIyGD4llRFKVMkVpBEDI6vBQZQJ3auaIorgHWAJSUlCgo5WrFqDorN0bTqFg2vZDUOBMxZiO1bR4c3gAr31MaB2W1bYqwhiRYFQzboX1TqS5IVNmg1MrPSbbi8gW58eVDi/IfLxxBMBiIKNZ02wWFxFn1ipf0DecU8Pj/9sq71EAgIFcCvapDSvvpzypYeFYeAxKtEcJFy2cU4XT7+OfnFZw3IoO0ODNXjs/GZNQrdDfunFmEyRC6YYsxJLHcP95MUf84suKt9Is1s7GikUAwJJ1949RhPb54VDQ41Z9po/OEGxWHm4+d4Wz38O+tdYpaHw/PHcuXe5v45Zl5FA+IZ+lrW7mgODPifjtnK1W3uHlx437SJlsixMosBp1Cs+SGcwpAFLl8fA4PbSjjgQ9CWhe3X1DIT0b4aXD58PgCPH71OLbsb2bJtOGKNu+YUUSD08NVpw8kyW7khrPzibEY5bRo+RrQQdgdgd0iyEquOgF5DmnofXRnTmrQcDzQq0aFEHJJPApsF0VxZdhXrwNXAX/u+Pe17ratVoyqs3KjpFHReSd42+vbePDKMXxUepDXvq3ilz/KU7zEM+It2C1Gnv2ikmkjM9HrYOm04Tz68W7Z7Qvg9gdVd3qdKz7eNHVYBG/jlle2sPKSYlb8Z4eizsVD/y1jzoQcFkzKJSvBSqzFyN4GJ5eUZJGXGstf3tnBtJGZcohCBBZPyef5LyvRCQI3vrxFzgQY3C+GOIuBRpeXFf/ZidsXZFedg5unDiUn2S7Li0t9uvXVrTJHQi18cUZeClmJVura3Fw8JrNXYuUWk071mZoM3eEUn3hsq3Eoan0k2kxUNrbz2rdVTBuZyVcVTdw0dRj1DnfE/aplK11SkiUv/nBIs2TBpNwIb9o9s4r53b82yVwHty9UqG3BpFy5MusN5xRgNugVAmijBiTw13d3savOwZJpw0mxG0m2J/HTRz+PuMaT88aHSLgCTL3/o4jxektFQVWDBg2nPnrbU/FDYA6wRRCEbzs+u4WQMfGCIAjzgUrgku42rFaMyu1TKjfWtrox6HSqx327r5l4i5FLS7I50NKueIlfNCaLNRt2R/Al7rpwBG9+WyW388amKrmIl+wVmF5IvcPNwrPycPuDiCKU1jlU++Dw+GUBKonTcUFxJunxVhLtAe79zy4uHpslS2QvnJxHRUM74dGjdl+AFzfu5+bzh8k8BImHYTHquHtWMfVtIeLesPQ4th5o5aEN5QrCXnifwjkSnXE8CjpZDHrVAlW241CavCfRudaHlLrcWRp96bThEQqpeakxEfNqUIpddbyCYUauNIcCQZFrJ+YSY9arHisZBgvPypOJuIEg+AJBrjwth8qmdpxuH/6gQFAMql5XRCS3Xwyf7j54xL9DDRo0fH/Qq0aFKIofE8oSUcOUY2lbKl0uvRQhtMinxlrwegNsOdBCo9PLxPxkHvwwcuc7NC2WBLuJa574kkSbSeEG1utQ1N+A0IvyD69s4YErxtDg9LKrzsGlJdk8/0UFj109jtpWN3sOOrnvvVJZzlgKmUTTgMhMDNUeSbSZIoSxfnvuEEwGQVFCelh6LKuvGE0gGJTLj48ekMiaDeXsqm2LGorR6wSsRj2ZCRa217Ry8dgsivrHqfYpKIp8uaeBBpeXBKsRnz9IvM1EIBhEJwi4vCEvhl4XcsvbTAa8gQDJdnOPeC5MBh3jB8bx1Lzx1La5O3RGAhh7wFMhcXBqWtyYDTpa3F6S7RYKM+Iw9LAnRKr1IT3fJJuRa84YHOFdW/7mdyw8K48Fk3IZlGKnyelFJwi8s+2AwoMQDIpRuTKgLs+9ZNpwuWhXOLlSurbbH5Q9Xhajjr9fOYZnP69gV52D+2aPIi3OHJWkKymkRisKd7iS9xo0aDh1cdIWs81OtHH95HzFbu7OmUVkxJh5dfMBlr62ldMHJXHh2KyIne8dM4p47H/lXHHaQHln/962Gh6/ehzt3gAGvcDGiibVRXrT/mYWn11AZYMTtz/IhMH98AeC3PzyFsXx4Wz8vNRYbpo6lD+H7UYXT8mnosHJXRcWUdHgijBg7n1nJ3+7bDRLX98mH3/nv7fLBssbm6q4fnIBb28JkTQ9/kDUUIw/GCTRbmBfUztrNpSTaDORGpMb8VxumjqUulYPN72sJGS+v6OGi8dmK7gfksCR1B+JY6FWjbQ7MBvguwNulobVX1k+vZDR2aYjn3wYqHFwQv3+jusn5zOzOLNHDYvC9BjunjWSsjoH8RYjRZnx/C/Krj411kxFYzt/fW8Xl5Zkc/d/dnDJ2AHc25EZZDHqeOCK0SpZIIX4A0E5s6TzHAovbhdex0Vq094hKCY9i9vf2MaMUZlcOj4bm/lQ5dbDFWxTKwqnETW/P5AqlGrQIOGkNSoqm9SzBHKSbCztIClefcYgfv70V4rSzDoBmpweTsvtR0WDE4tRR0FqDNNG9efbfc2ypPLSC4arLtKBIDQ4PAxJj6HR6cfp8eMPiKqFxXKSrDw+bxwuj5/+mfEymU0UkRfkxVPyKUiNVV1snN4AN00dwu6DToVCoWSw3PrqFuafEar9Mff0HFXS3d8/LGNXnYNn5p/GtS+GSmdfNCaLP769I+K5FKTHcM0TGxXPVMpmkUIr0uf3ry+VY/ZSf6JVI+0OmlwB2aCQrrX09W08dc34o24T1Dk4h57jVvJTYygekHhM1wiHDxGvP8hr31ZxaUk2X+5tjLrrr2vzyCEwtz/AL3+UR79Ys+xlSLSZ0AkCuf3sPHb1OOrbPJj0Ourb3Pz9v+UhTZW0GNU5NDQ9lkfmllBe75AzjSSjMLdfDAsn5yGKh8qfF2bEMyQ9VC5eMg4PV4DteBdo09BzCJfbPlo4nU6ufvBdjLa4HuqVhpMdJ61REY1TURMWy25y+mRPRLj2wk3nhSqU6gR4eG4JTo8fk15HQ5tHNg7+/kFZRBaFtCO/bFw2ICiY/Z2liS1GHSkxZm56aTMVDe0smpIXUWoaoH9CKASittiUH3RQkBqrWqJa0hiQwhAr1u0kI97Cgkm5ZMZbqWwKiSBtrmoFoCbseUUrWZ2bMkqdZxGl7okUdgrvz7HG0jtzEaT2a1s9R91mqF31+TI0PZaC1BhqWtwUDzimSyiwq8bJra+GhMz0AgzNiGfJa1sjtEqWTBuO3x+UK8eqVRc9b0QGt7+xLYLjI1UvffTjcu6ZVaw6h2LMelrafbLxIRGCn/q0gktKshQl7S1GHelxZqpb3LS0+xVhrcNxaY4H10ZDz0MyCAS98Zja0Zu0UJeGQzhpjYposdz0sFh2Sowp4picZCtpcRaqm9tx+0OEtXDD4BeTcnloQzmbq1p5+tO9/GPOWGo7lA6rm11c84NBuHwBBbNf2rmHa1jcOaOIBz8slWtjRNulxlkMtHn8Ea7tX59dwBOf7I1aolrSqhiafogb0eTyYjHo+ev6EK9j/hm5Yc9F+bzU2kyyG1U/t5nV63GE8z2k/hxrLL0zF0FqPy3u2KojRpsvpXVtXH5aDtnJPSs5XNsaMlBT48zUtXpY8tpWLi0JSVovPCuP1FgzKbFmKhucPP7JXn5z7tAIb9Cq90u5d1Yxv+3I5FALbyyYlIvFoOfJT8pl/Ynw+XzLK1uZXZJFk8urMCAtRh0jsw7VCZFSrRet/UYuud6TYS0NfRN6k4Vg4DAlgA8DtdCH9JndbqerekQaTi2cXHl6YZBiuRbjoRoHK2ePYkRGPHfPGsmiKXl4/IGIehZLpxWy56CTg06vbFDAIcPA4w9w8/nDWDg5j2kjM2h0eFn6+jauf+6bkJaFP7oS4bD0OO6+eAQLz8qj0elhY0WL/L1UlTK8L3fMKKK0to3f/2szT30aKsK1cHJeR5hEpMnlpaalPeIepJLXi6fk89CHZSyYlMv9l43ir7NHsW5rNU0uL0umDeflr/fLXJOR/ePl56VWynz5jCKe+WyPop6HdK0nPymPKJMt1U4J709PxNKT7XqWT1fWrlg+vYhk+7Flf6jNl0WT83lx436WvbENj69nU/zT4kI1PyoaXNy/vhSvX0QQ4Jbzh5Maa2bp69u45omN/HndTi4tyUYMqmdZ0EkWu/P3mfFW1m4MpT4/21HIbcVFI1gwKVf2nKnVdVk2vZAXvtzL/DNyWTQlj4fnlPDgh2WyESwZNdNGZnLDC9+yt0GLm2tQIuB184vHPiYYCMihFIfDwWX3v63xLL7HOGk9FdFiuQB6QceaDeVcOzGXl77aL5dvzkuNYXt1KwZd6OXa+SVdkBpDUoxZ3jEumpLHyle2KAyP+97bFdXVrBMEKhpDKZ85ycpaH9UtbtZurOSJeeOobnaj1+v4yzs7ZPGjzqGIRVPyWDQ5n0c/3ku/GBOrrxjDjupWspPt1DS7QiJZFgNpcRb2Njj589s7aHJ5WTptOA63j2FpsSyZNozMeCuF/eMxGHSK55UeZ+Hc4enUO0LPLiveypDUGJrdXp6+ZjxNLi9xHdkfZ+QlExRF1i6YoMj+GDUgAZtJjy8QZGpReo/E0hudAR74sFThqn/gw9COffAxtCvNl9i5JXy+p1HBIwDkcvE9gWBQJDVWz+B+MZTWOTq4K4O4952dMnGys0fi8avHHdYjJf3e+ft+sWYuGTsAAdhV52BzVWtEqE1S5Xz0qhJa2n0Eg/DSV/sYnpkgh7Bq29yKUvZS33oqrKXh1ITBFPIgBrxu5q35L48v+BF6k1Zo7PuMk9aoAPVYbnm9g9+8eIiQJ7l9rzsrjxte+Jbbpg0jNd6C1y/K6ZwXjcnCbNBRkpPANU8eIip2VjaE0O9ef4DbLyjk9rBsiNsvKKTF5ZEXjHA5ZOmYX0zK497/7OCqH+Sys7ZNfomrLRYlOUnc+58dVLe4uaQki4X//DrimLtnFXPLK8qsk+VvfseaOSWMyk6MWODVnle4SmVxdoioGC5/nploi2osDEzp+UWmvs0ja3coPnccG6cCQvcfZzHyyEeRBbrS43suLry3wYkvAOnxZmpb2rlzZiHX/fObw3ocmlzeCA7PnTNH8PCG3SyZNpw1G3ar8jF21bVx7zu7yEm2smbOWJpdPtLjLRFFyJpcXjLiregEgcsf/hyAD3YdlL9ffcXowxo1WoqohiPBoHErNHCSGxVqCCfkvfTVfjkjItaiZ/4ZuWQm2th2oJXnv6zkD+cPw+Hxs357Ddf8MJeDTi/3zCrm4Q27ZYKj2ou2otFFvxgzC8/KwxsI8sPBKTS3e6lubpc/y0uN5ZWv9nH3rGLcXj/p8Rbu/c9ONle1MrvEL7cV3sdwgt6S17YwbWQmu+ocZCfZ1AmUXnUCpVEvHLXHoCvy572JlFh1TkWK/dg4FRIKM+IihKXunFlEYUZ8j7QPEAj6afn/9s48PqrqXuDf3+xL9gAhBMIaRXYRUfuKtdDnsxZFq1atS2318XyvFqzVal/d0L62Wtun1qWPItWqT1Fxq20tPrAudUFEWWUTSGRLCGSdyWSSmfP+uPcOM8lMEiBkZuB8P598MvfOnXt+c8/vnvubc35LS4TK2gADC7zUBds4bkAO15w+EgHmzhjFcyt2JDj1btjTRLFZPKx/rpt8jxOHQww9XG74NezYH+DeCydStS/AiP45LHz3c04Z0R+P08Z/nDGK7bUBHvvHNv7z7DGdfHTil6aSXd9hxb5OoaGWT4UOEdVoND3lqDMqfK4DToW7G0I0hdp4+uqpbN/Xwv3/twYRw3mxcl8LDS1t/H1jNRdMLueG5w9Uxbxj5lhYXhnzPYgfnG+bOYaHlm2JOUI+/OYWRvbPYe6znyas0180ZTBvbqrl/W37uX3mGG5evCaWhGhIkY98n4ufnz+e/3xpDU2htoTpfmtavrzIyyOXTaY1RTrw+O8av78k79B/MfQk/fmRJM9rT1kPpTdwOGycN7GMCjPiY2C+h7Gl+b2ao6IuGKGmqRWvy8nmmiYG5Li49JTEyqsd83w8+UFlTKceWLqW+VecxH1/2wjA6p2N3PP6Z9w9a3ys9so9r3/G3BnHkeN28N/fmhQLHZ47o4LKWsMQfe26L1MbaE0I80yVV2JMaT5jSvMZPWca1Y2hXl/W0mg0xwZHnVERVdHYNHKhz4VS0BZVVO4PcM20ERxfkktDsJWHLj2R9qhizozjYmWnwXiIznttXSw3w5AiL7+/Ygq76lvwuR3UBVtjvzCtcuJOc8C11sdnnz6CUQNyYzkAmkNtMYPixjOP54bnVrG7IcTCq07i4W9PpqUtwo3Pr+pkHOysb8HncvDzv3yW1LhpCLbyi/PH85OX1nT6RdqTCq7J6En68yNJIBTFLor7LpxIINyO3+UgGG4j0Brt/sM9xOGwMXFIYa+GkMazPximf66bytoAPpedUQNyeW/rPq6ZZhi0iz/ewQNLNzP/ipP4cFtdgm+HtTzS3NpOQ6gNMHTh4inlPLh0Izf9ywnsqm/hpjNHk++1U9McpnJfkNOPH8AZowdQVuChyO9i6tBiXC47o8hNkK27vBI6NFRzuFhOmzoC5Ngkq42K9vYo63Y3sLshRGm+l7GledhEKM1387NZYxCbnWc+3E5FiTGwep02IpF23E4HN5q/Gu+5YHzSh6hSihu+VkGoLcqNz69IeJiX5nuoC4axCdxxzlh+99bnsc8W+lxMLi9kfyDM8SW57K4PMrSfkWTIJkZ9BcvAsNtsbNzdiNNu1ICIrwQ5d0YFfpedHXVB6oJhyot8CfVErPwCt37jhE4VIqNRxZLPqg9pCSPdaZcbQm089UEV15w+EpRRMO2pD6r4oVkZMxsYlOehpb2NYf19+Bvt7KgPJVSDtWYm6lvaeGdTTcIyiOXDEI3CD6ZXsL+5lYZQhEUrqvj+Vyv4rz+vZ1NNMz/82nHsD9h47B/bYkXvJg0poDTPTcWArtOO67wSmiOBFU5qOW0+f8M3yMnROnaskbVGRXt7lJdX7ey0Nj66JJe2aJRCv4e7XlvH7NNHxpwcLaNg/ttG5cbSfA/lRV7mzBgVK7a0+OMd1AXDuOw2Kgbm8e9PJc5iWLkBhhT6GFzo4ddLNsb8L0rzPVx52lCuNT9jGQc1jaFYHZDrpo+K7b9l8ZpYVs2XPjFCRIcU+RBgZ10Qv8dBsC3C7NNHUOx38aMOsxlzZozqtM/jtLFo9qmHvISR7rTLw4v9zJw4KGGp4IZ/Pi5r1vTrW0JsqG7m+BI/rZEoHqeDHz2/MqEvrNksG2Lo5zOfJPgw3DFzLPPf/pxNNc088d2pvLOlllmTyij2Ozlj9ABu+pfRrNvdQKgtws/OG09LOEJJnrvXl3E0moPBCjF1uNw43DoC5Fgla0egdbsb+K2ZZvm66aO4ZtoIfrtsM8G2CB6Hg1U76pk5oaxTuei7X1vPzAlllOZ7uPb0EazZ2cj8t7fy0LItLHhnK1eeNpRffHM8EZXaEbJiQC51gVZueG4100cPjIX8XTRlcGyJwjr2gaWb2dvcyjcnD8bjtDF5SEFCDoEDibNGEokqdtUFCbVFGDMon71NrTS3GlVIb35xNfdcMCEhvDBVem/rvB331zR1HzZpTY//Zc40np19Cn+ZM+2QnTSjUcXWvc28/3ktW/c2E412nwuiPao65Q/5zRubaO/BZ3uj/cNl054AD7+5mXBEEQobtWKS9cWIfjnMf/tzIlHFjWcex2PfOZkhRV5+dOZonlleyeqdjbE+W/DOVgble3n071sY0S8Hp0MY2T+H6aNL+KeR/Thz7EAmDinUBoUm7Vghpppjl6ydqdgXaO2UtnjO9ApqmlqJRBVuhw2nWfbcKgltLe8V+5xcdko5+4LhhNA76wF/45nHcd+STSlzBwgwpNhPXTDMk2bCIREoK/AmfYBEFdhtMO/csVTuCyRNu72huim2pOFx2hKyc1rT5WUFHv4StxauUmTpLM0/vCWM3pgeP9Qoksp9gaTXsHJfILaM1ZO2q/YHWFlVHwu57asolurGVi46aQgKw4CpGJCbtC98Tjubaprpn+tmgJlF9OonVnd2us31cN1XR/HoW1v45TcncPLQIm08aDSajCVrR6d8j4tFK6oSZioWrahiQK6b0gI3Qwo9TCovYGixlytOHcpj7x6YjcjzuRha7EuZh6K+xZihqAuEk2aSHJDv5rnlVcyZXhHLg7Hgna3keZyxYy2MpFgwakAuj/x9C6MG5DK02Mv3vzqK66Ybf0OLvZ3KUls/qq3p8p+efQJOu51hxX5OHdGPEf1zGN4veVbRsaX5nfbfc8EE9gVa++wXe6ooku4yM/o9jqTXx+/umf1rGTMvfrIzIYdHT9s/XAbmuRk1wM8X+0PMfvLjmJNtx0yqO+uDzDt3LK2RCOt3NXL9ok/52XnjO2QSHUtLWxv3LdkUy2miDQqNRpPJZO1MRSDclnSmwi5RnHYHLW2KT6vquPmsEzo93H760hruu3Aidkn+S9+aASj0uyjwORIcIYt8Tm56fhWzJpXFZincDhsj+vnxumydkmLNnVHBgDw3v/jLZ+xuCLGrLsD3v1oRq6RqhUw+82FlJxkAJpTlcc3pI2mPKr6oC1Lf0sq0UQOw2aRLT35rf3VjiLaI4rZX1sRqOvTNL/ZDiyJpj0a49vRRzHstLqR05lii0Z7VJ7CMmWumjUhLFIvPZUfh5vpFy2NLUX98v5LZp49gcIGXXI+Tfc0hxgzKp7K2mTtfNfx+wu2KIr+DP353KtVmX4ba2rjlxXXA4YcKazR9jY4COTbJWqPC7XB0KrD04LLNPPm9qbRGouysb6FiQC5b9zYnfbiE2iP0y3EnLeT1v8srmTO9gvW7GhlU6GVyeSF7m1qpbTYqaIbbFUPN5Y+H39wSy374+LKt7KxvZe6MCsqLfdhFcNiFB/9vcyzio7xfDre8uDq2ZALwu7e2cNOZo9lQ3YRdoNjv4tG3tjKhLI9LpybmN7jjnLF8URdgaLHxYEy1VGHtBzj7wXf6PO/EoUaReOwO5r2W6Ng477V1PNnD0ufxxkyq9pNFDfXWDEBDKExdINEXZ3dDiAeXbuHeC8aztymEUrB2ZwO/MtN2W86/XqeDlvYIHqedYDjCnX9aH9MbnYBKk00opaipqWHusyt5du7XdRTIMUTWGhX7AslLZLdF2tlZb/hKFPpc3H7OmKQPl6r9QYp9LjwOG7+6cCIt4XZKC7ys29XAzAllLFpRxaxJZQy3+/loe10sbbTl7zAwz81Dl55IIBxhb1OIZ5dv56ovjSCiFCV5bjZVNzOowMvdr607MENw0UQaQ+1JZ1h21Qd5aNmW2PS4yyFcc/rITpUr5/1pHX/87tSYUdEd6co7cahRJPXBtljqdMvoWvzxDuqDbT1q1zJmrAJu8df5N9+axOB8b9KoofMmlvWKYeGy2ynJsyfVubJCL6G2CHeZzsKhtgNpu0f0y8Fr+sMM65dDNKr4w1VTk+aS0GgyHSsSxJ2Tr2crjjGy1qjol+NmaLGXmRPKYg+fP63aidPu4LZXVsamnh99c0vSNNjxGQzv+dtG7pg5lltfPrBEcNvMMUQiUbwuOy+u3BFrN9QWpbzQx3++tIZvTx3K/y6vZNakMs6fPIRd9UEiCm5evJq5MyrYUx/kwUtOpKXNKMIVjSo27GlKOsNy74UTY9u3vbKW+y6ciCK5z0dtIHkdjGQJr3or78TBJtPqLslSKopyXFx52tCE2aO5Myoo8rt6JGe8MfP62t2x61he5GNsaR7rdjewfOteFl51MrVNrfTPdfPSyioqBuQwcUjhwVySpFhGUbJkZSJGbZaLp5Tz5AeVCUttxTlOagNhJgw2ZNC5JDTZjsPl1jkrjkGy1qiIRCNc99WKhAJMd88aR21zOOEBunpnI3uXbeF3l5/Eisq6TtUpRw/M4b4LJ7I/2MpNZ44miqLA66RqX4ACv4s//OPz2LFgPAD2NBoVHf/7/zYZ9UTyvTzy1pbYw8KKIlnwnSmMLyuIPUiXbahma23y6IbttYGE7Q3VTZwyvCipQdA/x000qhIe0KmiLc48oeSw804caiTHoTwYW8KRpGG5C66c0qPPW8bMmLnTWFlVH0tyZslcVuDk5OH9+d7jHyXoTXsPfTa6IhRqp5/fxQ7TjyI+9fpDy7Zw6zdO4Nazx/DbZZu5aMpghhT62Nfcyv0XT2JosY/BBXo2QnP0EV9ozEqQpWcujl6y1pXcLvaYQQEHfuHn+xydIjDqgmGC4XYWvLOVh9/ckpDB0Cp5XeR3U+h3Ulbgob6ljTyfm3yPgzPHDkrwyJ8zvYKnP6yKtWm3QUm+h5kTyhKMlVBbFLskFvbyuxxEotGkESKt7Z2dRbfVNnPHzMTokztmjmXjnkZeX7cnIYojVbRFVV3wsPNOHGokx6HQ2h5JanS1tvf8oW+zCVFF0uiPcLsk1Zto9PAHuM21TYSjimK/K+Zv89CyLTz8plErpsjvYkCumytOG8b8t7dy0wuruX/pZppC7eR5Hdqg0ByVWA6blkFxyQN/JRA4slFYmvSRtUZFQ0tb0oePx2HvFMJ3+8wxLHx3K3OmJ+6/beYYnvygki/qgnyxP8jVT6zg/c/3M+eZT/nxC6v4uKqeXy/ZxOzTR/DAJZOYO6MiwXDwOG1MGlKA12njsXe3dprR6OitX5Lnptjv6iTfz2aN47XVO2Pbc6ZX8OLKHexrDrN4ZRX3XjiRey4Yz70XTmTxyip2N4Y7PdS78p2wZgysUNSDfXh1de7eJjdFWG6ux3lQ50kl894OM1nW/trm8KEJbBKNKjbVBHh3Sy0+l71TKPId54zFYQebXSU1ajbt0YOs5ugkEg5x1f/8nT179tDU1ITdpbNtHs1k7fJHnteZdGlgd2Pnqednl1dx8dRy5r/9OVd/eQR2G0wYXMCu+iCzJpXhc9pZ+N62WOXI+CqSlue+x2njkcsmUxcMx9r6+fnjqSjx89bG2k5r6PdeMKHTEkN5kZ9Bhc1srWmOOYcOKvQytbyIE8sL+WxPI5uqm2L+HuMH53P8wLxO5aitJZZ4Z8sjWbOjL+uB1AXbOjlYGvlAeuaoaZFK5jxP8squuZ7DuxW27wvw05fWcM20EewPtlLocyQURXM6bPTzO1i3K5jUqKluTO4no9EcDQhw5UNLAGLOmz6fj2AwqJdCjjLSZlSIyFnAA4AdWKCU+uXBfL4kr3M46NwZFQlTzxaWH8KPzhxN1b4A4wfnk+d2EAq7GVbsZ3N1M5ecXM6YQXncctbxOO027v7zZwkzD6G2KFtqmnn08pNAKcqL/AzvZxgNA/ObCbYmGgqnDivuNCNgswmnj+xPgdfFnsYQI/vnMH5QPi6XnZEDjGRWY0rz+NLI4phjI0DZ7FNZuqGGSPSAP0jHh3p5oY97LpjAzYtXx67HQ98+EaXg/c9rD6pSaUf6sh5Ioc8ZS2pmGYWLVlTxqwsm9orMxTnOpHpTlHNwMyEdsWZGFn+8g/5+FwPzXeR5nUSUIs/rQClFVBHLntkpc2aem9fX7u71EFeNJlOwUnhbMxcPX34yc59dyTNzzkJEtHFxlJAWo0JE7MDDwD8DO4CPRORVpdT6np6jvMjPmNJc49dgazt+jwO3w0aO28Ed54xlXlwCqv86fzx5HgetzWFOGlqIoGhXCqdD8DjtlBV6aY8o1u9q5NmPqrjznLGxGQkLj9PGSeWFOGzC/mCY2uZW7DZDjunHlzCiX44Z5ZCX8PC2UkZXN7YSbo9Q0xzmp3Gpo+PDGa1limHFfrbvC/Dhtn2U5HkYW5rPzvpQwgMy3mAozfewfncTT76/jXtNw2ZEfz81jWG+8dt3DitNtRX1Uehzsmj2abRFIhT53UcsxHFQgZPvnzGK21890H93nTuWQYU9V1UrD4VN4OmrT8Fugzyvi/JCH5X7A5QVehMSmpUVehlReHie6cV+VywaqSHUzsACL8HWCG6HDa/Tjs0WZU9DG/1yHdx17jhuf3Vt3Pcbh9sR4dqnVnYZ4nqo5ew1mkxDIBZyqvNZHF2ka6ZiKrBFKbUVQESeBWYBPTYq2tuj1DSFO0V/DCv2UJRk6jkQbqc+EOa+JRv4wfQK8j0O7nx1PS6HcPessTjsdhD4xfnj2VUf5JazRvPL1zck/JrdWR/kj+9vZ0VlQ2xfRUmOYVQkiXKIRhXLNlazubqZB5Yaxc8eezex1sitL69NCGfsKorDqvsxMM8wIiyDYc6MUbzy6U4unlIey2sxZ8aoTnVNDjbpVSpZJpcXHbGHWT+vj5EDWmOZJUtyPdjtUfp5ezYrkqp67bnj82Pl4At9Li6aMphhRT72NIb49ZKNDCv2xcI5DwWnPZrEGBpHntfOFQuXJyzltLWFze/XSkmuG7czwqyHPwKS6wQcegSORpOpWCGn1y58F09uAaCjQ44G0jXHWgZ8Ebe9w9zXY1bvakjq8LY/EGHuolVc98wn3Lx4Ddc98wlzn/2EtnaFz23n1xdOZHg/L2t2NXLBSYOZOaGMmxev5XuPf4RdhO89sYLt+0OUFhi/Zq+bPoqrv2xUFf3x4jVc+aURsfYeWLqZ1TsaUkZBbN8XYPWOhthUu5XoKJ5QW5Q9ccssXUVxWM6WUUXCMVEFMyeUJeS/SFXX5GCcK/sy6sNi7Z5GLluwgm/N/4AfPPMp35r/AZctWMHaPY09+vy63Q0xg8KS+daX17J6V0Psu1h+Mre+spaWtiiV+1rYU394Pg17myIxg8Jq9/ZX17J+d1OnnCRhZefKPyxnw54m3t5SGzMoLDrqBKSnLzRHL1ZERiYQX9lUR4dkP+maqUhmgnaqciUis4HZAOXl5Qnv7UkZkZAi02ZUEQxH+NELq7jlrBM6VQoFaAwdiCipD4aTHtMSbk84b1SRMjtldWOo08M92Xr6wHxPwmdSGQNWG8mOsds6GxGH61yZjmycqfrVCv3tjlRl31OdV8z6Lx5X9/Z1V/pYnULvOtZuizcurR9i3ekEpC8zqiaz6UonuyIQCHDVI28g9sPzJepNrFkKm9NDU1MTSqmYrwVw2DMYehakb0jXTMUOYEjc9mBgV8eDlFLzlVJTlFJT+vfvn/BeqendH4/l8JZq/4J3t3LxlHIq9weSHuNzOWIpngfle5Me43U5ErZtQsoHdUmeJ1a0DIiljk4IJz1vHGNL8xM+k6zd+DY6HrP44x2cUJrXaV/H0NWDda7siSy9Tep+7VmbpSn6bWCK89oEw1HT133Gzq70MZXedVyZiM+iqZSRBfauc8d1qRPG+fu+LzSZT1c62cVnaG5uxu7KHN2xaoVc9cgbtLUEuPKhJXz74WV8+5E3qa6uprm5mUse+CvNzc00NzcTjUZpamqKGR89IX4WJD53hqZ3SZdR8RFQISLDRcQFXAK8ejAnGD8on7tmJQ7Gd80ax3ED/Z0G6bvOHUehz87ds8azaEUVT7xX2emBO3dGBb9/+3PmzjDKmS98dyvzzk3MNTDv3LH88b2tCZ+ZMDg/5YN6WLGf8YPzY23tbgixaEUV/3P5Scy/4iQWzT61k0OeFbXQlTHQ8Zi6YBifyyhvHr+voiSHP//g0JNe9USW3iZVv04YlN/NJw3Glubxs/M6P6QnDOpcDv62mWPIcdkZXOhl9MC8w5I7md7dPWscY+KMPcun4rXVO/n5+eOZNCSfX180iXPGDWTR7FP5n8snJ9UJSE9faI5OrFmKaOTws8j2FpZvhTVz4nC5cbi9iAjfnf9WbAajpqaGSx74KzU1NVz0q5e56Dd/prq6GqVUzFDoyuCwcmToZZYjh6TLUhORs4H7MUJKFyql/qur46dMmaJWrFiRsC8cjrB6V0PMG36CGZ5Z3xJi0x4j4qIkz81xA/0UeD0x73nL2TEShb3NIfrneLDbjKn3klwPTaE2djeEKC/20tqm2NXQQj+/G7tNYbfZzYe4g5I8N+VFXXvgx0d/BMPtsVDU7j5jyZmqZkayY4BuP3ew9ESW3iZVv/YUK/pjT0OIgflG9IzDYUuInnDabTSFwhT53bH3O9Dll0ymjwl6l+tmSJGdduUg3GYskflcdtoi0UOOnklHX2gyhm47OplOQuK0P0B1dTVXL3jnsIwKT14R0fYw4eCR88uw2oi0t/PAt8Yz55mVhFuCuHPyY9vRSAQFPP5vZ+D3+7n0wddZcPU0rnrkDbA7+cPsryQsd1z+u7d46tqvEAgE+LcnV/D0v5+hI04OjZT6mDaj4mBJdcNoNEeIgzYqNJojyCEbFdbSwbNzvw7Ahfe+dNi+FH1pVISDzbSHWxMcOjtuK+Dhy0/mh4vX898XjIkZHO1hwwHb7nInvP/9J97D4c1lwXdPJScnB5/PF5u1sIyMQCDQaX9PfDEsI+4oT+6V8gtlbUZNjUaj0fQMu8sbe9jZXZ6MWvroCfEGRLJtK++Fw+WO/e94XMf3I+EQ//r4h4jNFjM0rNkNgGsee4cHLz0pYb812xNrVyRmhMTXN/nXhe+y4OppXPPYO1mXf+NwHVq1UaHRaDRHOZFwCzU1Nfzb75ch9u6dkrujvbWFaHs4NhNwJDiUNqxjU32m4/sOt5dIOMQ1v3sDh8sLkVa+/ZtXjPdc3qT743G4PDz5g7MAuPz+PxExjTVPbmFshiNTQnd7SnNzM1c98gYv/Pj8QzKGsmb5Q0T2ApUp3u4H1PahOL1BNsoM2Sn3ochcq5Q6K9WbR4k+ajl7lyMpZ5f6CBmvk+ls/1j+7keq/ZT6mDVGRVeIyAql1JR0y3EwZKPMkJ1y97XM2XKNtJy9SybLmW7Z0tn+sfzd09G+rlqk0Wg0Go2mV9BGhUaj0Wg0ml7haDEq5qdbgEMgG2WG7JS7r2XOlmuk5exdMlnOdMuWzvaP5e/e5+0fFT4VGo1Go9Fo0s/RMlOh0Wg0Go0mzWS1USEiZ4nIRhHZIiK3pFueVIjIEBF5U0Q+E5F1IjLX3F8kIm+IyGbzf2G6Ze2IiNhF5BMRec3czmiZRaRARF4QkQ3m9T6tL2XOVJ0UkYUiUiMia+P2ZVRfZst9IiIeEVkuIqtMOedlopymTH2qj5nSh+kctzJgDPqhee3Xisgzpr72WftZa1SIiB14GPg6MAa4VETGpFeqlLQDP1JKnQCcCnzflPUWYKlSqgJYam5nGnOBz+K2M13mB4DXlVKjgYkYsveJzBmuk48DHePKM60vs+U+aQWmK6UmApOAs0TkVDJMzjTpY6b0YTrHrXSOQWXAHGCKUmocRm2tS/qqfYBYdbds+wNOA/4Wt/0T4CfplquHsr8C/DOwESg195UCG9MtWwc5B5sKOB14zdyXsTIDecA2TF+huP19InOm6yQwDFjb19flMOTN+PsE8AErgVMyTc5M0Md09GE6x60MGIPKgC+AIoyM2a8BZ/bl9c/amQoOXDyLHea+jEZEhgEnAh8CJUqp3QDm/wFpFC0Z9wM/BqJx+zJZ5hHAXuAP5tTnAhHx03cyZ5tOZmxfZvp9Yk6vfwrUAG8opTJRzrTqYxr78H7SN26ldQxSSu0E7gOqgN1Ag1JqSV+1D1m8/EHyKmkZHcoiIjnAYuB6pVRjuuXpChGZCdQopT5OtywHgQOYDDyqlDoRCNC3U9BZp5OZSDbcJ0qpiFJqEsav4qkiMi7NIiUjbfqYrj7MgHErrWOQ6SsxCxgODAL8InJ5X7UP2W1U7ACGxG0PBnalSZZuEREnxk32tFLqRXN3tYiUmu+XYvzqyRT+CThXRLYDzwLTReQpMlvmHcAO81cjwAsYN3hfyZxVOkkG9mW23SdKqXrg7xj+KpkmZ1r0Mc19mO5xK91j0NeAbUqpvUqpNuBF4Et92H5WGxUfARUiMlxEXBjOKK+mWaakiIgAjwGfKaV+E/fWq8B3zNffwVh/zAiUUj9RSg1WSg3DuLbLlFKXk9ky7wG+EJHjzV0zgPX0ncxZo5MmGdWX2XKfiEh/ESkwX3sxBvINZJicpEEf092H6R63MmAMqgJOFRGf2RczMBxF+043j5SzRl/8AWcDm4DPgZ+mW54u5PwyxrTjauBT8+9soBjDoWiz+b8o3bKmkP8MDjg8ZbTMGN74K8xr/TJQ2JcyZ6pOAs9grLG2YfyaujrT+jJb7hNgAvCJKeda4HZzf0bJacrUp/qYSX2YrnErA8ageRhG7lrgScDdl+3rjJoajUaj0Wh6hWxe/tBoNBqNRpNBaKNCo9FoNBpNr6CNCo1Go9FoNL2CNio0Go1Go9H0Ctqo0Gg0Go1G0ytoo0Kj0Wg0Gk2voI2KPkBE5pglcJ9OtyxHEhG5XkR86ZZDk5mIyBlxpajP7aoUt4hMEpGz+046zbFIvE720vkSxkAR+YuVKO1YQRsVfcN/AGcrpS7r7kARcfSBPEeK6zGqNmo0XaKUelUp9csuDpmEkTRJo8kYxKCr5+b1xI2BSqmzlZHK/ZhBGxVHGBH5HUbluldF5GYRec+sXveelcpVRK4SkedF5E/AEhHxi8hCEfnIPHZWF+cfJiLviMhK8+9L5v4zROQtEXlORDaJyC9F5DIRWS4ia0RkpHncUBFZKiKrzf/l5v7HReTCuHaa4877dxF5QUQ2iMjT5o02B6OAzZsi8uYRupyag8DUjQ1iVEpca/bV10TkHyKyWUSmptK1bvSqU/93IcNZ5nHvAt+M23+ViDxkvr7IlG+ViLxtppS+C7hYRD4VkYtNWVPdOy+KyOvmd7q3Q9srzfMuNff1+N7S9D4ZrpN3isiNcdtrzTaHiTHT/AhGmfshIvKoiKwQkXUiMs88vtMYKCLbRaSf+foG85xrReT6uO/0mYj83jzXEjFSv2cvRzpVqv5TANuBfkAe4DD3fQ1YbL6+CiN1cpG5/XPgcvN1AUaaXX+Kc/sAj/m6Alhhvj4DqAdKMdK07gTmme/NBe43X/8J+I75+nvAy+brx4EL49ppjjtvA0ZxIhvwPvDl+O+Z7uut/2J9NgxoB8abffUxsBCjeuUsjBTCSXWtG71K2v9J2vdglN6uMNt8jgNpk68CHjJfrwHKLBk6vm9ud3XvbAXyzfYqMYpo9TfbHm4ed9D3lv475nTyTuDGuGPXmvIOwyijfmrce5Y+2TEKyk0wt7cTNwZyYOw/ydRzP5ADrMMoC29dj0nm8c9Z3z1b/7J5qj0byQeeEJEKjPz4zrj33lBK7Tdfn4lRac+ymj1AOUZhmI44gYdEZBIQAY6Le+8jpdRuABH5HFhi7l8DfNV8fRoHrPUngXvpnuVKqR3meT/FuDHe7cHnNH3PNqXUGgARWQcsVUopEVmD0W+DSa5ru0itVz3t/9Fm+5vNY58CZic57h/A4yLyHEZVxWR0de8sVUo1mG2sB4Zi1Ft4Wym1DeAQ7y3NkSEbdLIjlUqpD+K2vyUiszFKnZcCYzBqfaTiy8BLSqmA2e6LwDSMQl/blFKfmsd9bMqetWijom+5G3hTKXW+iAzDsHAtAnGvBbhAKbWxB+f8IVANTMSw0kNx77XGvY7GbUdJ3fdWMZh283xW5UFXivNGujiXJv10pwMRkuiaiNxJz/Squ/7vtriQUupaETkF+AbwqfnQ6EhX904yeSRF2wdzb2mODJmqk7Exz8QT9zo2PovIcOBG4GSlVJ2IPN7h2GSkXI6hs+xZvfyhfSr6lnyMZQgwpm1T8TfgB9a6oIic2M05dyulosAVGNNxB8N7GCWCAS7jgHW/HWPKDoxpSSfd0wTkHmT7mvSSStcOV6/AqJQ4XEz/HeDSZAeJyEil1IdKqduBWozli4661NN7x+J94CvmAwARKTL3H8y9pUkP6dLJ7cBks83JwPAU58jDMDIaRKQE+Hrce6nGwLeB88QoSe4HzgfeOQT5Mx5tVPQt9wK/EJF/0PUNcTfGQ3y1iKw1t1PxCPAdEfkAYzow0MWxyZgDfFdEVmPcqHPN/b/HGJSXA6f08Lzzgb+KdtTMJlLp2uHqFUqpEMbU8p9Np7jKFIf+Sgzn4bUYg+8q4E1gjJiOmvT83rHa3mu2/aKIrAIWdfN9NZlDunRyMVBkLp/8O4YvR7JzrAI+wfCLWIixfGeRdAxUSq3E8FNbDnwILFBKfXKw8mcDuvS5RqPRaDSaXkHPVGg0Go1Go+kVtINdliAi/wLc02H3NqXU+emQR6OJR0ReovMa9M1Kqb+lQx6NRutketDLHxqNRqPRaHoFvfyh0Wg0Go2mV9BGhUaj0Wg0ml5BGxUajUaj0Wh6BW1UaDQajUaj6RW0UaHRaDQajaZX+H9Krelg+NPfZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pairplot to visualize pairwise relationships between variables in the data\n",
    "sns.pairplot(df1[['fare_amount', 'mean_distance', 'mean_duration']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These variables all show linear correlation with each other. Investigate this further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Identify correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code a correlation matrix to help determine most correlated variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VendorID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.266463</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>0.001876</td>\n",
       "      <td>-0.000752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>0.266463</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014942</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>-0.024283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare_amount</th>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.014942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910185</td>\n",
       "      <td>0.859105</td>\n",
       "      <td>-0.025901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_distance</th>\n",
       "      <td>0.004741</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.910185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.874864</td>\n",
       "      <td>-0.046794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_duration</th>\n",
       "      <td>0.001876</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>0.859105</td>\n",
       "      <td>0.874864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rush_hour</th>\n",
       "      <td>-0.000752</td>\n",
       "      <td>-0.024283</td>\n",
       "      <td>-0.025901</td>\n",
       "      <td>-0.046794</td>\n",
       "      <td>-0.027499</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 VendorID  passenger_count  fare_amount  mean_distance  \\\n",
       "VendorID         1.000000         0.266463     0.001045       0.004741   \n",
       "passenger_count  0.266463         1.000000     0.014942       0.013428   \n",
       "fare_amount      0.001045         0.014942     1.000000       0.910185   \n",
       "mean_distance    0.004741         0.013428     0.910185       1.000000   \n",
       "mean_duration    0.001876         0.015852     0.859105       0.874864   \n",
       "rush_hour       -0.000752        -0.024283    -0.025901      -0.046794   \n",
       "\n",
       "                 mean_duration  rush_hour  \n",
       "VendorID              0.001876  -0.000752  \n",
       "passenger_count       0.015852  -0.024283  \n",
       "fare_amount           0.859105  -0.025901  \n",
       "mean_distance         0.874864  -0.046794  \n",
       "mean_duration         1.000000  -0.027499  \n",
       "rush_hour            -0.027499   1.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation matrix to help determine most correlated variables\n",
    "df1.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize a correlation heatmap of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation heatmap')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create correlation heatmap\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(df1.corr(method='pearson'), annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation heatmap', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heatmap shows that both mean_distance and mean_duration are correlated with the dependent variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lgPul2DiY6T4"
   },
   "source": [
    "\n",
    "## PACE: **Construct**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P_QYzJfVUrIc"
   },
   "source": [
    "### 8a. Split data into outcome variable and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "AzcDgLRET4d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   VendorID         22699 non-null  int64  \n",
      " 1   passenger_count  22699 non-null  int64  \n",
      " 2   fare_amount      22699 non-null  float64\n",
      " 3   mean_distance    22699 non-null  float64\n",
      " 4   mean_duration    22699 non-null  float64\n",
      " 5   rush_hour        22699 non-null  int64  \n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "### YOUR CODE HERE ###\n",
    "df1.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set your X and y variables. X represents the features and y represents the outcome (target) variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.435000</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID  passenger_count  mean_distance  mean_duration  rush_hour\n",
       "0         2                6       3.521667      22.847222          0\n",
       "1         1                1       3.108889      24.470370          0\n",
       "2         1                1       0.881429       7.250000          1\n",
       "3         2                1       3.700000      30.250000          0\n",
       "4         2                1       4.435000      14.616667          0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the target column from the features\n",
    "X = df1.drop(columns=['fare_amount'])\n",
    "\n",
    "\n",
    "# Set y variable\n",
    "y=df1[['fare_amount']]\n",
    "\n",
    "# Display first few rows\n",
    "X.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o3ArC_5xa7Oi"
   },
   "source": [
    "### 8b. Pre-process data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PdfTaopCcbTj"
   },
   "source": [
    "Dummy encode categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "4Y3T2poF28fP"
   },
   "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>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "      <th>VendorID_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4.435000</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  mean_distance  mean_duration  rush_hour  VendorID_2\n",
       "0                6       3.521667      22.847222          0           1\n",
       "1                1       3.108889      24.470370          0           0\n",
       "2                1       0.881429       7.250000          1           0\n",
       "3                1       3.700000      30.250000          0           1\n",
       "4                1       4.435000      14.616667          0           1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert VendorID to string\n",
    "X['VendorID'] = X['VendorID'].astype(str)\n",
    "\n",
    "# Get dummies\n",
    "X = pd.get_dummies(X, drop_first = True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data into training and test sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create training and testing sets. The test set should contain 20% of the total samples. Set `random_state=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "A2BNUvacwaZY"
   },
   "outputs": [],
   "source": [
    "# Create training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=17)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iDYyjWssbnBG"
   },
   "source": [
    "### Standardize the data\n",
    "\n",
    "Use `StandardScaler()`, `fit()`, and `transform()` to standardize the `X_train` variables. Assign the results to a variable called `X_train_scaled`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_scaled [[-0.4967541   2.10672801  2.50298377 -0.77308453 -1.11724471]\n",
      " [ 0.28736948  0.07835828  0.11517447  1.29351961  0.89505906]\n",
      " [-0.4967541  -0.41468394 -0.23109733 -0.77308453  0.89505906]\n",
      " ...\n",
      " [-0.4967541   0.60646125  0.99230285 -0.77308453 -1.11724471]\n",
      " [-0.4967541  -0.5934841  -0.86461668 -0.77308453  0.89505906]\n",
      " [-0.4967541  -0.45106226 -0.73785361  1.29351961  0.89505906]]\n"
     ]
    }
   ],
   "source": [
    "# Standardize the X variables\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "print('X_train_scaled', X_train_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wk0rjKeO3JLv"
   },
   "source": [
    "### Fit the model\n",
    "\n",
    "Instantiate the model and fit it to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "SClNm5hWotj6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit your model to the training data\n",
    "lr=LinearRegression()\n",
    "lr.fit(X_train_scaled, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HMj6QkK1cLmS"
   },
   "source": [
    "### 8c. Evaluate model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AromLx7t5hjt"
   },
   "source": [
    "### Train data\n",
    "\n",
    "Evaluate the model performance by calculating the residual sum of squares and the explained variance score (R^2). Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "33rE1x9e3U6t"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination:  0.8467708102914876\n",
      "R^2:  0.8467708102914876\n",
      "MAE  2.1735161146277355\n",
      "MSE:  17.122432955603934\n",
      "RMSE:  4.137926166040657\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model performance on the training data\n",
    "r_sqr = lr.score(X_train_scaled, y_train)\n",
    "print('Coefficient of determination: ', r_sqr)\n",
    "\n",
    "y_pred_train = lr.predict(X_train_scaled)\n",
    "\n",
    "print('R^2: ', r2_score(y_train, y_pred_train))\n",
    "print('MAE ', mean_absolute_error(y_train, y_pred_train))\n",
    "print('MSE: ', mean_squared_error(y_train, y_pred_train))\n",
    "print('RMSE: ', np.sqrt(mean_squared_error(y_train, y_pred_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test data\n",
    "\n",
    "Calculate the same metrics on the test data. Scale the `X_test` data using the scaler that was fit to the training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test_scaled [[-0.4967541  -0.05272685  0.30659491 -0.77308453  0.89505906]\n",
      " [ 0.28736948 -0.22803836  0.04343511 -0.77308453 -1.11724471]\n",
      " [-0.4967541  -0.53177952 -0.73268634 -0.77308453  0.89505906]\n",
      " ...\n",
      " [-0.4967541   0.2728016   1.84572724  1.29351961  0.89505906]\n",
      " [-0.4967541  -0.33121511  0.07145446  1.29351961 -1.11724471]\n",
      " [-0.4967541   2.71636785  0.91974117 -0.77308453 -1.11724471]]\n"
     ]
    }
   ],
   "source": [
    "# Scale the X_test data\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "print('X_test_scaled', X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "P5nXSpRCVXq6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination:  0.8399132414542425\n",
      "R^2:  0.8399132414542425\n",
      "MAE  2.1609783140606504\n",
      "MSE:  17.381695289884075\n",
      "RMSE:  4.169136036385005\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model performance on the testing data\n",
    "r_sqr = lr.score(X_test_scaled, y_test)\n",
    "print('Coefficient of determination: ', r_sqr)\n",
    "\n",
    "y_pred_test = lr.predict(X_test_scaled)\n",
    "\n",
    "print('R^2: ', r2_score(y_test, y_pred_test))\n",
    "print('MAE ', mean_absolute_error(y_test, y_pred_test))\n",
    "print('MSE: ', mean_squared_error(y_test, y_pred_test))\n",
    "print('RMSE: ', np.sqrt(mean_squared_error(y_test, y_pred_test)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L3MCKUhPJLi5"
   },
   "source": [
    "## PACE: **Execute**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F_l3bkxQdJ3a"
   },
   "source": [
    "### 9a. Results\n",
    "\n",
    "Use the code cell below to get `actual`,`predicted`, and `residual` for the testing set, and store them as columns in a `results` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "cSl5gbXfBPBN"
   },
   "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>actual</th>\n",
       "      <th>predicted</th>\n",
       "      <th>residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4540.000000</td>\n",
       "      <td>4540.000000</td>\n",
       "      <td>4540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.868659</td>\n",
       "      <td>12.926238</td>\n",
       "      <td>-0.057580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.421163</td>\n",
       "      <td>9.609615</td>\n",
       "      <td>4.169198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.837018</td>\n",
       "      <td>-26.219356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>7.382611</td>\n",
       "      <td>-1.767284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.500000</td>\n",
       "      <td>9.528786</td>\n",
       "      <td>-0.541160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.500000</td>\n",
       "      <td>13.989352</td>\n",
       "      <td>0.886530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>62.500000</td>\n",
       "      <td>88.719356</td>\n",
       "      <td>59.600511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            actual    predicted     residual\n",
       "count  4540.000000  4540.000000  4540.000000\n",
       "mean     12.868659    12.926238    -0.057580\n",
       "std      10.421163     9.609615     4.169198\n",
       "min       0.000000     2.837018   -26.219356\n",
       "25%       6.500000     7.382611    -1.767284\n",
       "50%       9.500000     9.528786    -0.541160\n",
       "75%      14.500000    13.989352     0.886530\n",
       "max      62.500000    88.719356    59.600511"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a `results` dataframe\n",
    "results = pd.DataFrame({'actual': y_test['fare_amount'],\n",
    "                        'predicted': y_pred_test.ravel()})\n",
    "\n",
    "results['residual'] = results['actual'] - results['predicted']\n",
    "\n",
    "results.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mwRmSDS3eyeH"
   },
   "source": [
    "### 9b. Visualize model results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y3vQ-mB51dfd"
   },
   "source": [
    "Create a scatterplot to visualize `actual` vs. `predicted`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "IBFU_dicBjwQ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Actual vs. Predicted')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot to visualize `predicted` over `actual`\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.scatterplot(x='actual', y='predicted', data=results,\n",
    "                s=20,\n",
    "                alpha=0.5)\n",
    "plt.plot([0, 60], [0, 60], c='red', linewidth=2)\n",
    "plt.title('Actual vs. Predicted')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IbO71S_R9IcY"
   },
   "source": [
    "Visualize the distribution of the `residuals` using a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "3a0UYoEr9Nx6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of residuals')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of the `residuals`\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.histplot(results['residual'], bins = np.arange(-20, 20, 0.5))\n",
    "plt.title('Distribution of residuals')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.057579881192733114"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate residual mean\n",
    "results['residual'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OCnELck-9h5M"
   },
   "source": [
    "Create a scatterplot of `residuals` over `predicted`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "7Kmr2U8A95fY"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatterplot of rediduals over predicted values')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot of `residuals` over `predicted`\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.scatterplot(x='predicted', y='residual', data=results)\n",
    "plt.axhline(0, c='red')\n",
    "plt.title('Scatterplot of rediduals over predicted values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9c. Coefficients\n",
    "\n",
    "Use the `coef_` attribute to get the model's coefficients. The coefficients are output in the order of the features that were used to train the model. Which feature had the greatest effect on trip fare?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>mean_distance</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>rush_hour</th>\n",
       "      <th>VendorID_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.04667</td>\n",
       "      <td>7.108085</td>\n",
       "      <td>2.881468</td>\n",
       "      <td>0.132942</td>\n",
       "      <td>-0.04049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  mean_distance  mean_duration  rush_hour  VendorID_2\n",
       "0          0.04667       7.108085       2.881468   0.132942    -0.04049"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the model's coefficients\n",
    "coeffcients = pd.DataFrame(lr.coef_, columns=X.columns)\n",
    "coeffcients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficients reveal that mean_distance was the feature with the greatest effect on the model's prediction. However, the data used to train the model was standardized with StandardScaler(). Therefore, each unit of mean_distance represents a standard deviation of mean_distance in X_train. The std as showed above is 3.566607. Thus the modelbuil relinear regression model is interpreted as \"With holding the other variables constant, for every 1 miles change in mean distance, the fare amount increases by a mean of $2 (7.13/3.566607)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the prediction of all the trip for machine learning buiding use\n",
    "nyc_preds_means = X.loc[:, ['mean_duration', 'mean_distance']]\n",
    "nyc_preds_means['predicted_fare'] = lr.predict(scaler.transform(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export the data as csv file\n",
    "df.to_csv('nyc_preds_means.csv', index=False)"
   ]
  }
 ],
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