notebooks/missing_values.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49ad14a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn as sk\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146adb1f",
   "metadata": {},
   "source": [
    "### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70199882",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = pd.read_csv(\"../data/train.csv\")\n",
    "test = pd.read_csv(\"../data/test.csv\")\n",
    "display(train.head())\n",
    "display(test.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d66232e",
   "metadata": {},
   "source": [
    "### Display Null Count Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f44e4d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_null(df):\n",
    "    null_df = pd.DataFrame(df.isnull().sum(), columns=[\"Count\"])\n",
    "    display(null_df[null_df[\"Count\"] > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c74d28d",
   "metadata": {},
   "source": [
    "### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f8b4f9e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Count\n",
       "Age         177\n",
       "Cabin       687\n",
       "Embarked      2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Count\n",
       "Age       86\n",
       "Fare       1\n",
       "Cabin    327"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_null(train)\n",
    "count_null(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "801cd9be",
   "metadata": {},
   "source": [
    "### Handle Missing Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "90e96b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Cabin\"] = train[\"Cabin\"].apply(lambda x: \"X\" if pd.isnull(x) or x == \"T\" else x)\n",
    "test[\"Cabin\"] = test[\"Cabin\"].apply(lambda x: \"X\" if pd.isnull(x) or x == \"T\" else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1afe51c2",
   "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Count\n",
       "Age         177\n",
       "Embarked      2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Count\n",
       "Age      86\n",
       "Fare      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_null(train)\n",
    "count_null(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4836ceec",
   "metadata": {},
   "source": [
    "### Handle Missing Embarked Port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e236ef4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train.loc[train['Embarked'].isnull(), 'Embarked'] = train[\"Embarked\"].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bdd46bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Count\n",
       "Age    177"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Count\n",
       "Age      86\n",
       "Fare      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_null(train)\n",
    "count_null(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f2a747e",
   "metadata": {},
   "source": [
    "### Handle Missing Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "116b6d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Count\n",
       "Age    177"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Count\n",
       "Age     86"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.loc[test['Fare'].isnull(), 'Fare'] = train['Fare'].median()\n",
    "count_null(train)\n",
    "count_null(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8871ca05",
   "metadata": {},
   "source": [
    "### Handling Age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1240b12",
   "metadata": {},
   "source": [
    "#### New Features Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f47fc6ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_new_title(df):\n",
    "    # Grouping Title\n",
    "    new_title = {\n",
    "        'Mr' : 'Mr','Ms' : 'Ms','Mrs' : 'Mrs','Rev' : 'officer','Sir' : 'royalty','theCountess' : 'royalty','Dona' : 'royalty','Capt' : 'officer','Col' : 'officer','Don' : 'royalty','Dr' : 'officer','Jonkheer' : 'royalty','Lady' : 'royalty','Major' : 'officer','Master' : 'kid','Miss' : 'Ms','Mlle' : 'Ms','Mme' : 'Mrs'\n",
    "    }\n",
    "\n",
    "    #Add Title\n",
    "    df['title'] = df['Name'].apply(lambda x: x.split(\",\")[1])\n",
    "    df['title'] = df['title'].apply(lambda x: x.split(\".\")[0])\n",
    "    df.title = df.title.str.replace(' ', '')\n",
    "    # Group Title\n",
    "    df['title'] = df['title'].apply(lambda x: new_title[x])\n",
    "    \n",
    "def add_cabin_section(df):\n",
    "    df[\"Cabin\"] = df[\"Cabin\"].apply(lambda x: \"X\" if pd.isnull(x) or x == \"T\" else x)\n",
    "    df[\"Cabin_Section\"] = df['Cabin'].str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e77f18fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_new_features(df):\n",
    "    df[\"Family_Name\"] = df['Name'].str.split(',', 1, expand=True)[0]\n",
    "    df[\"Family_Size\"] = df[\"SibSp\"] + df[\"Parch\"] + 1\n",
    "    df[\"Ticket_Group_Size\"] = df.groupby('Ticket')['Ticket'].transform('count')\n",
    "    add_new_title(df)\n",
    "    add_cabin_section(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3368715",
   "metadata": {},
   "source": [
    "#### Add New Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c48586a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.concat([train, test], sort=False)\n",
    "add_new_features(all_data)\n",
    "train = all_data[0:891]\n",
    "test = all_data[891:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a655577b",
   "metadata": {},
   "source": [
    "#### Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ebf80f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABGQAAAKwCAYAAADeC6nGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAADNK0lEQVR4nOzdd3xT1RvH8e9JW0oLtLRQaNlTQJCNbGTIdIDgwgG4cA9EWS5UBCe4Ufy5By4cqCggCCgyZE/Zu1CgpbSlOzm/PxJLS1tFIUkpn/fr1RdJ7nOT5+Ry701OnnOusdYKAAAAAAAAvuPwdwIAAAAAAABnGzpkAAAAAAAAfIwOGQAAAAAAAB+jQwYAAAAAAMDH6JABAAAAAADwMTpkAAAAAAAAfIwOGQAAAAAAcNYyxrxjjDlojFlXyHJjjHnZGLPVGLPGGNP8dLwuHTIAAAAAAOBs9p6kXn+zvLekup6/oZImn44XpUMGAAAAAACctay1CyQl/E1IX0kfWLfFksoaY2JO9XXpkAEAAAAAAChcZUl7ct3f63nslASe6hPAu7IOb7f+zgHHtT1vsL9TgMe8ayP9nQI8nvqylL9TgEeoNf5OAR61stgWRUnj4KP+TgEe87LD/Z0CPMq4/J0B/jJk30fF+qRRFL7TloiqfavcQ43+MsVaO+VfPEVB2+iU20WHDAAAAAAAKLY8nS//pgPmRHslVc11v4qk2FNKSnTIAAAAAAAAb3E5/Z3B6TBd0l3GmE8ltZZ01Fq7/1SflA4ZAAAAAABw1jLGTJXUWVJ5Y8xeSY9JCpIka+0bkmZI6iNpq6RUSTecjtelQwYAAAAAAJy1rLUD/2G5lXTn6X5dOmQAAAAAAIB3WGaQLgyXvQYAAAAAAPAxKmQAAAAAAIB3uKiQKQwVMgAAAAAAAD5GhwwAAAAAAICPMWQJAAAAAAB4hWVS30JRIQMAAAAAAOBjVMgAAAAAAADvYFLfQlEhAwAAAAAA4GN0yAAAAAAAAPgYQ5YAAAAAAIB3MKlvoaiQAQAAAAAA8DEqZAAAAAAAgHe4nP7OoMiiQgYAAAAAAMDH6JABAAAAAADwMYYsAQAAAAAA72BS30JRIQMAAAAAAOBjdMgAAAAAAAD4GEOWAAAAAACAd7gYslQYKmQAAAAAAAB8jAoZAAAAAADgFZZJfQtFhQwAAAAAAICP0SEDAAAAAADgYwxZAgAAAAAA3sGkvoWiQgYAAAAAAMDHqJABAAAAAADewaS+hSrWHTLGmIckXSPJKckl6VZr7ZJTfM5LJZ1rrX36NOSXYq0tfarPU5Q9PH6iFixcqsiIsvrmozf8nc5Z4YEn71X7bm2UnpahsfeN16a1m/PFPPLCSDVoUl/GGO3evkdj7x2vtNS0nOXnNqmvd394Q2NuHas5P8zzYfbFR0C9Zgrue4vkcChryWxl/TIt7/LajVRyyBi5EuIkSdnrFitr9mfHA4xDIfe9IHs0XunvjPNl6sXGJY8NUr0uTZWZlqkvH3hDset35ouJqBKlga/erZDw0opdv0OfD3tdziynOg69WE37tZMkOQICVKFOZY1rfqtKlQvTwFfvzlk/smoF/TzpSy185ydfNeuM1HPsINXp0kRZaZma/sCbOrBuZ76YslWj1P+Vu1SybGkdWLdT3wx7Xa4sp87p3kKdh18u67JyOZ2a9fiH2rPMfVy7+7cXlXksXS6nSy6nU29f8oiPW3ZmiencWC2fvF7G4dDWqfO04dXv8sW0ePJ6Ve7aVNlpGVo0bIqOrN0pSap/Sy/VvqazZK0S/9yrRcOmyJWRpWaPDFTl7s3kysxWyq6DWjRsirKSUn3bsGKgdKfmqvSY+5xx5LPZOvTGl3mWl+17gcrfNkCS5DqWrthHXlf6xp2SJEeZUqryzN0KPqe6ZK32jXhJqSs3+boJZ7RqnRur09jrZQIc2jB1npa/nn/f6PT49aru2Td+vn+KDnmOY92ev0U1ujVVWnySPrlwdE58r9fvUtlaMZKk4LBQZSSl6tNeD/mkPWeyyp0b6/wn3MepLVPnae1r+bfF+U9cryqebfHbsClK8GyLEmGhavf8zYqoV0XWWi0c/pYOLd+qpvf3V91rOisjIVmStPzpz7Vv7mpfNgsoVLHtkDHGtJV0saTm1toMY0x5SSVOct1Aa212QcustdMlTT99mRZv/fp01zUDLtWYJ5/3dypnhfZd26hqrSq6rN1ANWp+rkY/PVxDLro1X9zEx17RsRT3B+ZhY+/SlTf21/uvfixJcjgcuvvh27R43lKf5l6sGIeCL7tVaVMekz0ar5B7n1f2hqWycXvyhDl3bCi0syWo48Vyxe2RKRnqi4yLnXqdm6pczWg93/l+VW1WR/2eulGv93s0X1yvUQP129s/as13i9TvqRvV8qouWvLRz/p1yvf6dcr3kqT63Zqrw029lXb0mNKOHtMrfcZIkozDaPSS17R+5jKftu1MU6dLE0XWjNZrFwxX5WZ11GfcDXqn32P54rqNulpL3v5R679brD5P3ahmV3XW8o/maMfCddo8e7kkqUL9qhrw2j2a3O3BnPU+uHqc0o6k+Kw9ZyrjMGo1frDmXv20UvcnqNeMJ7R35nIlbYnNianUtYnCakZrevvhKte8ts6fMEQzLx6rkOgI1buph77vPFLO9Cx1eONu1ejbRts//1X7F6zVqvGfyTpdavrQVWp49yVa9dRnf5MJ8nE4VOmJ27Tj+keUfSBetb+dqKSflyhj6/FzRuaeOG2/arRcScdU+oIWqjz+Lm277AFJUqXHblHy/BXafcfTMkGBMiWD/dWSM5JxGHUeN1jfXPO0UvYn6Krvn9D22ct1JNe+Ub1LE5WtGa0POw5XxWa11Xn8EH1x6VhJ0sYvFmjNe7PV/cW8n7d+uuPVnNsdHrlGGXRU/iPjMGr91GDNGug+Tl084wntnrVcR3Nti8qe49RXHYYrqnlttZ0wRD9cMlaSu6Nm3y9rNG/oy3IEBSgw5Pi+sOGtn7T+zRm+bhLwj4rzHDIxkg5bazMkyVp72Foba4zZ6emckTGmpTFmnuf2WGPMFGPMLEkfGGOWGGMa/vVkxph5xpgWxpghxphXjTHhnudyeJaHGmP2GGOCjDG1jTE/GWOWG2N+NcbU98TUNMYsMsb8YYx50sfvh1+0bHqewsPK+DuNs8YFvTpoxhfuX+rXrdigMmGlVa5CuXxxf3XGSFJwyWDJ2pz7V900QHN/mK+Ew4lez7e4clSrK1f8AdmEOMmZrexVvyqw4fknvb4JL6eABi2VvXS2F7Ms3hr0aKGVX/0qSdqzcqtKlglVmaiy+eJqt2uodTPchZMrpv2qc3u0zBfT5NK2Wj3993yP12nfSPG74pS47/DpTb6YOad7C62Z5t4W+1ZuVcmwUJWuUDZfXI12DbVhhrsjePW0Barn2RZZqRk5MUGhwZJsvnXxz8o1q63knXFK2X1Iriyndn27WFV7tsgTU6VnC23/8jdJUvyKbSoRXkolPdvKBAYooGQJmQCHAkNKKDXuiCTpwPx1sk53Kfrh5dsUGhPpu0YVE6FN6ipz135l7YmTzcrW0e8WKKx76zwxqSv+lCvpmPv2yj8VFF1ekuQoHaJS5zfSkc9mSZJsVrZcycd824AzXMWmtZW4M05Jnn1j8/TFqtUj775Rq0cLbZzm3jfiVm5TcFgphXr2jdglm5Se+PedwnUubq3N3y7ySv7FSfkTjlM7vl2saiccp6r1bKFtnuPUIc9xKqRCWQWVDlHF1vW0Zeo8SZIry6lMOsGKDpfT/39FVHHukJklqaoxZrMx5nVjzAUnsU4LSX2ttddI+lTSlZJkjImRVMlau/yvQGvtUUmrJf31vJdImmmtzZI0RdLd1toWkh6Q9Lon5iVJk621rSQdOOUWAieIio7SgdiDOffj9h9ShZjyBcY+Omm0Zq75VjXqVNOn70zzrF9enXt30rQPvvVJvsWVCS8nm3j8S7pNjJcJz98xFlC9nkLuf1Elb35UjopVcx4P7nuzMr9/P09HGf6d8IoRSoxNyLl/9ECCwqIj8sSERpRRetIxuTxfJo/uj1dYxbwxQSVL6JwLmmjdj/krxhpf0lZrpvMB+5+UiY5UUmx8zv2kAwkqc8L7HBJRWulJx3K+2CfvT1CZXNurXs+Wun3Ocxr47oOa/uCUnMetrK79aJRu/n6cmg3s4uWWnNlCoiOUmmufSN2foJCYE/aJ6Ail5tpWqbEJCo2OUNqBI9o4eYb6/fGS+q96VZnJqTowf12+16g9sJNi567xXiOKqcDocsraf/yckXUgXkHR+c8Zf4m8qoeS57s/kpaoGq3shKOq8tx9qvP9i6r89N0yIVTI/BuloiOUkmvfSNmfoNInnC/cMfF/G1OYSq3rKfXwUR3dGXd6Ei7GQqMjdCzXtji2330Myh8Tny+mTPUopccnq8Okobpk5ji1e+7mPBUyDW7orktnj1f7F25RiXCqj1F0FNsOGWttitwdLEMlHZL0mTFmyD+sNt1a+9dEGp9LusJz+0pJXxQQ/5mkqzy3r/a8RmlJ7SR9YYxZJelNuat1JKm9pKme2x/+m/YAJ8MYk+8xW8iX+ieGTVDvppdpx5Zd6nFpN0nS8Cfu0SvjJsvFpelOvxO2g3PvNh176halTbxPWb/9oJJD3MNgAhq0lE1JlGvfNn9kWXwUuC/8Y0i+oPoXNteuZZuVdjTvL84BQQFqcGELrZ2x+FQzLfYKep9PPC4VdOzKXQizaeYyTe72oD6/ZZI6D78i5/H3+j+u/130sD4Z/KxaDequaufXP11pFzv/9B57ggqIsSoRHqoqPZvr29bD9FWzuxUYGqwa/dvnCWt4z6Wy2S7t/Grh6Uv6bPEvzt2l2pyniCu768DT77lXDQxQSMPaiv94hrZefJ9cqemqcPvl3sy22Cn4s9PJxJzcjybn9G2rLVTHnJxTOE6ZgACVO6+G/vxgjr7r+bCyUzN03l2XSJL+/OBnTWt3v6b3eEipBxPV6tFrT3/u+HvW5f+/IqrYziEjSdZap6R5kuYZY9ZKGiwpW8c7okqesMqxXOvuM8bEG2May93pkn8iDvdcMhOMMZFyd/7MlVRKUqK1tmlhaf1T3saYoXJ3JOn1F8bp5kED/2kVnMWuGHKZ+l3rPuFsWP2noitV0F/TlFWMidKhA/GFrutyuTR7+lxdf/tAfffZDDVoUk/j3xgrSSobGa723doo2+nU/J9+9XIrihd7NF6m7PHKJFO2nGxSQt6gjOOTKDv/XC71v1UKLaOAGg0UcO75Cq3fQgosIVMyVMEDhylj6iRfpX/GanN9d7XyVEnsXb1dZStFapdnWXh0pJI9Qyz+ciwhWSXDSskR4JDL6VJ4TDklHUzME9PkkoKHK53Tuali1+1QyuEkbzTljNdyUHc1u9q9LWLXbFdYpeO/9odFRyrlhPc51bMtTIBD1ulSmZj820uSdi/9UxHVKygkorTSjqTkPE9qfJL+nLlMlZrW0u6lf3qtXWey1P0JCq10fDhRaEyk0g4cKSDm+LYKrRSp1LhERXdspJQ9h3ImxNwzY5miWtbN6XypeUVHVb6wmeZcNcEHLSl+svcfVlCuatag6HLKjkvIF1eyfg1Vfvpu7bxhrJyJ7m2Rtf+wsg4cVtoq90TXR39cqKjb6JD5N1L2J6h0rn2jdEykjp1w/HHHlDshJvEfn9sEOFS7Vyt92ocJx09G6v4Elcq1LUrFROYMj8wbU+6EmETJWqXuT9Dhle4ftHb+sDSnQyY917l6y8e/qNv7w73YCuDfKbYVMsaYesaYurkeaippl6SdcneeSNKAf3iaTyWNkBRurV174kJPFc5SuYcifW+tdVprkyTtMMZc4cnDGGOaeFZZKHcljSQV2jVrrZ1irW1prW1JZwz+yRfvfa1ru9+oa7vfqHk//qo+V/SSJDVqfq5SklMUfzB/h0yVGpVzbnfs3k47t7q/tvZtfZUuPf9KXXr+lZrz/Xw9M2oinTH/gWvPFjnKx8hEVpACAhXYtKOc6/MOeTFlyubcdlStKxmHlJqszB8/VOq4m5Q6fqgyPn5ezq1r6Iw5SYs/nK1X+ozRK33GaMOsZWrWv6MkqWqzOkpPTlPyocR862xftEGN+rjnamg+oKM2zjo+QW9wmRDVbN1AG2Yvz7dek0vbafV3/OJZmGUfzNZbfcborT5jtGnWMjUe4N4WlT3b4sQOGUnauWiDzu3jnmupyYBO2uR53yOqV8yJiW5UQwFBgUo7kqKgkGCVKOX+XSUoJFi1Op2nQ5v2erllZ674VdtVpma0SlWNkiMoQNX7ttHeWSvyxOydtUK1Lu8gSSrXvLYyk1KVfjBRx/bFq3zzOgoIcV8bIbpDQx3duk+S+8pNDe+8WPOHTJQzLdO3jSomUtdsUXCNSgqqUlEmKFDhl3RS0s95zxlBlaJUbfJo7b1/ojJ3HJ/gNPtworL2H1aJWu7zeul2TfJMBox/Frd6u8rWiFaYZ98459I22jE7776xY/YKNRjg3jcqNqutzORUpRZwHDtR1Y6NdGRbrI4dyN/BhvwOr9qusJrRKu3ZFjX7ttGeE45Te2atUG3PcSrKc5xKO5iotENHdSw2QWG13QMTKnVoqKOb3cepkFzzllXr3VKJnCtQhBTnCpnSkl4xxpSVuypmq9xVJw0kvW2MGSPpny6B/aXcnS1/NwHvZ3IPZ+qc67FrJU02xjwsKUjujp3Vku6V9Ikx5l5J03QWePCxp/XHyjVKTExSt37X6Y6brteAS3r6O61ia+GcRWrfrY2+WfSp0tPS9fiw479WvvTRs3py+DOKP5igx196SKXKhMoYo80bturpkS/4MetiyOVSxtdTFHLLWMk4lPXHHLni9iiwrbuzLHvRTwps3E6BbXu7JxnLylT6R1yJ7HTa9Msq1evSVA/Mn6SstAx9+eCbOcuGvDtC00ZOUfLBRP349FQNfOVu9Rh+hWLX79Ifn8/LiWvYs5W2/LpWWWkZeZ47qGQJ1e3QSF+P+Z+vmnNG2zp3lep0aao7F0xUtuey13+5+r0H9f2It5RyMFFzJkxV/1fvVucHrtCB9bu06rN5kqQGvVup8YCOcmY5lZ2Rqa/ufEWSVKp8mK6cMkyS5AgM0Lpvf9e2+cxfUhjrdGnZQ++r6ycjZAIc2vbpfB3dvE91r+8qSdry4VzFzlmlyt2a6NLfX5AzLVOLhrnn64lfuU27f1iq3jPHyWY7dWTdLm396BdJUqunBssRHKiun41yxy7fqqWj3vVPI89UTpdiH3tDNT943H3Z6y9+VsaW3Yq8xn3OSPjkJ1W452oFRoSp0pO3S5JstlPb+t4vSYp97E1VnTRcpkSgMnfHae+DL/qrJWck63Rp/iPv69KPRsgR4NCGz+YrYfM+NbrOvW+s+2iuds5dpepdm2jQby8oKy1Tc4Yfn8uq56t3qnKbBioZWVo3LH1ZS16Ypg2fzZcknXNpGybz/Res06XFD7+v7p+MkHE4tPWz+UrcvE/1PMepTR/O1d45q1S5axP1X+g+Tv12//FtseSR99XpldvlCApUyu6DOctaPny1Is+tLmutUvYe1qKR7/ilfWc1pkMolDnZ8Y/wj6zD29lARUjb8wb7OwV4zLuWK3kUFU99WcrfKcAj1BY0MQ78oVYW26IoaRx81N8pwGNedri/U4BHGb4jFxlD9n1UrE8aGevn+P07bXDDbkXyPS7OFTIAAAAAAMCfivCkuv5WbOeQAQAAAAAAKKrokAEAAAAAAPAxhiwBAAAAAADvYFLfQlEhAwAAAAAA4GNUyAAAAAAAAK+w1unvFIosKmQAAAAAAAB8jA4ZAAAAAAAAH2PIEgAAAAAA8A7LpL6FoUIGAAAAAADAx+iQAQAAAAAA8DGGLAEAAAAAAO9wMWSpMFTIAAAAAAAA+BgVMgAAAAAAwDuY1LdQVMgAAAAAAAD4GB0yAAAAAAAAPsaQJQAAAAAA4B0up78zKLKokAEAAAAAAPAxKmQAAAAAAIB3MKlvoaiQAQAAAAAA8DE6ZAAAAAAAAHyMIUsAAAAAAMA7XAxZKgwVMgAAAAAAAD5GhQwAAAAAAPAOJvUtFBUyAAAAAAAAPkaHDAAAAAAAgI8xZAkAAAAAAHgHk/oWigoZAAAAAAAAH6NCBgAAAAAAeAcVMoWiQgYAAAAAAMDH6JABAAAAAADwMYYsAQAAAAAAr7DW6e8Uiiw6ZIq4tucN9ncKyGXR2vf9nQI87mk5yt8pwOPXtO3+TgEeowPq+DsFeCQE+DsD5NZs3wp/pwCP/0V18XcK8NgTaP2dAnDWo0MGAAAAAAB4B5P6Foo5ZAAAAAAAAHyMDhkAAAAAAAAfY8gSAAAAAADwDsuQpcJQIQMAAAAAAOBjdMgAAAAAAAD4GEOWAAAAAACAd3CVpUJRIQMAAAAAAM5axphexphNxpitxphRBSwPN8Z8Z4xZbYxZb4y54XS8LhUyAAAAAADAO4r4pL7GmABJr0nqLmmvpD+MMdOttRtyhd0paYO19hJjTJSkTcaYj621mafy2lTIAAAAAACAs9X5krZaa7d7Olg+ldT3hBgrqYwxxkgqLSlBUvapvjAdMgAAAAAA4GxVWdKeXPf3eh7L7VVJDSTFSlor6V5rT730hyFLAAAAAADAO4rApL7GmKGShuZ6aIq1dspfiwtYxZ5wv6ekVZK6SqotabYx5ldrbdKp5EWHDAAAAAAAKLY8nS9TClm8V1LVXPeryF0Jk9sNkp621lpJW40xOyTVl7T0VPKiQwYAAAAAAHhHEZ/UV9IfkuoaY2pK2ifpaknXnBCzW1I3Sb8aYypKqidp+6m+MB0yAAAAAADgrGStzTbG3CVppqQASe9Ya9cbY27zLH9D0pOS3jPGrJV7iNNIa+3hU31tOmQAAAAAAMBZy1o7Q9KMEx57I9ftWEk9Tvfr0iEDAAAAAAC8owhM6ltUcdlrAAAAAAAAH6NCBgAAAAAAeAcVMoWiQgYAAAAAAMDH6JABAAAAAADwMYYsAQAAAAAA77AMWSoMFTIAAAAAAAA+RoUMAAAAAADwDib1LRQVMgAAAAAAAD5GhwwAAAAAAICPMWQJAAAAAAB4B5P6FooKGQAAAAAAAB+jQgYAAAAAAHgHk/oWigoZAAAAAAAAH6NDBgAAAAAAwMcYspSLMcYpaa3c78tGSYOttamFxI6VlGKtfd53GRZNDzx5r9p3a6P0tAyNvW+8Nq3dnC/mkRdGqkGT+jLGaPf2PRp773ilpablLD+3SX29+8MbGnPrWM35YZ4Psz97PDx+ohYsXKrIiLL65qM3/J3OWeHKx25Qwy7NlJmWoQ8eeF171u/IF3PBoJ7qeuNFqlAjWg80u0nHjiRLkhp3b6lL7r9K1lq5sp364on3tG3ZJl83odgY/dT96titrdLTMvTQPU9q49r87+UTk8aoYZMGMsZo57bdeuieJ5WWmqYb7rhWFw3oKUkKCAxQrbo11PHc3kpKTPJ1M85IMZ0bq+WT18s4HNo6dZ42vPpdvpgWT16vyl2bKjstQ4uGTdGRtTslSfVv6aXa13SWrFXin3u1aNgUuTKy1PjBy1WlZ3NZa5VxOEmL7ntTaXGJPm3Xmaha58bqNPZ6mQCHNkydp+Wv598WnR6/XtU92+Ln+6fo0LqdkqRuz9+iGt2aKi0+SZ9cODonvvy51dRlwo0KCA6Sy+nU/IfeU9yq7b5qUrEyaeIT6t2rq1LT0nTTTcO0ctW6fDFv/2+SOnVso6NJ7nPFTTcP0+rV63VBp7b6ato72rFzjyTpm29maNxTL/oy/TNa5c6Ndf4T7uPUlqnztPa1/PvG+U9cryqefeO3YVOU4Nk3SoSFqt3zNyuiXhVZa7Vw+Fs6tHyrJKn+Dd3V4IYecmU7tXfOKi1/6lNfNqtY6Dl2kOp0aaKstExNf+BNHfC877mVrRql/q/cpZJlS+vAup36ZtjrcmU5c5bHNK6lG795XF/d9Yo2zljqw+yRg0l9C0WFTF5p1tqm1tpGkjIl3ebvhIq69l3bqGqtKrqs3UA99eCzGv308ALjJj72iq658AYN7DZEB/bF6cob++csczgcuvvh27R4HgdIb+rXp7vemDjO32mcNRp2bqYKNaP1WOd79MmYKRr41M0Fxm1bvkkvXfek4vcezPP4poVr9VTvBzW+zwh9OGKyrnuGw9F/1bFbW1WrWVV92lyhsQ9M0CPPjigw7plHXtSArterf5frtH9fnK656XJJ0ruvf6zLuw3S5d0G6cWnJmvZopV0xpwk4zBqNX6wfrn2WX3feYRq9G2jsLqV8sRU6tpEYTWjNb39cC0Z8bbOnzBEkhQSHaF6N/XQT70f0Q9dR8s4HKrRt40kacPkHzTjwjH6sftD2vfzSp037DJfN+2MYxxGnccN1vRBz+rjriN0Tt82ijhhW1Tv0kRla0brw47DNXfk2+o8fkjOso1fLND065/L97ztHxqopZO+0qe9HtKS56ep3ZiB3m5KsdS7V1fVrVNT9c/toNtvH6nXXp1QaOzI0ePUslUPtWzVQ6tXr895/LffluY8TmfMyTMOo9ZPDdbs657VN11GqGa/Ngo/Yd+o7DlOfdVhuBaNfFttPccpyd1Rs++XNfr6ghGa3n2Mjm6JlSRFt2ugaj1b6NsLR+vbrqO0/o0ZvmxWsVCnSxNF1ozWaxcM1w+j31afcTcUGNdt1NVa8vaPer3zcKUfPaZmV3XOWWYcRt1GX61tC9b4KGvg36FDpnC/SqojScaYQcaYNcaY1caYD08MNMbcYoz5w7N8mjEm1PP4FcaYdZ7HF3gea2iMWWqMWeV5zro+bdVpdkGvDprxxU+SpHUrNqhMWGmVq1AuX9yxlOOFRsElgyVrc+5fddMAzf1hvhIOJ3o937NZy6bnKTysjL/TOGs06dFSi79aIEnasXKLQsuUUlhU2Xxxe9fvVMLeQ/kez0jNyLldIjRYNtc+g3+nS69Omv6F+4PwmuXrVSastMr/w3GqZMlgFfSW97msu2Z8PdtruRY35ZrVVvLOOKXsPiRXllO7vl2sqj1b5Imp0rOFtn/5myQpfsU2lQgvpZIVykqSTGCAAkqWkAlwKDCkhFLjjkiSslOOV1gGhrB/nIyKTWsrcWeckjzbYvP0xarVI++2qNWjhTZOc2+LuJXbFBxWSqGebRG7ZJPSE1PyPa+1ViXKhEhyVwoc82wj/DuXXNJTH378pSRpydIVCi8brujoCn7O6uxQ/oTj1I5vF6vaCcepaj1baJvnOHXIc5wKqVBWQaVDVLF1PW2ZOk+S5MpyKjPJfS6pN+hCrX3tO7kysyVJ6fF05P9b53RvoTXTfpUk7Vu5VSXDQlXac0zKrUa7htrgqXxZPW2B6vVombOs1ZCe+vPHP5R6mPcfRRMdMgUwxgRK6i1prTGmoaSHJHW11jaRdG8Bq3xlrW3lWb5R0k2exx+V1NPz+KWex26T9JK1tqmklpL2eq8l3hcVHaUDscd/2Y/bf0gVYsoXGPvopNGaueZb1ahTTZ++M82zfnl17t1J0z741if5Ar5StmKkjsQezrl/5EC8ykZH/qvnaNKzlR6bM0l3vjNaH46YfLpTPGtUjInSgX25j1MHVTEmqsDYJ198WPPXzVDNutX1yduf51lWMiRYHbq00ezvf/FqvsVJSHSEUmMTcu6n7k9QSExEnpjQ6AilxsYfj4lNUGh0hNIOHNHGyTPU74+X1H/Vq8pMTtWB+ceHcDQZeYX6LXtJNfq305rnpnm/MWe4UtERSsm1LVL2J6h0dEQBMfF/G3OiX8d+pPYPDdSQJS+pw8MDtejpz05v4meJypWitXdPbM79fXv3q3Kl6AJjn3xipFYsn60XnhurEiVK5Dzepk0LLV82W99P/1DnnnuO13MuLkKjI3Qs175xbL/7GJQ/Jj5fTJnqUUqPT1aHSUN1ycxxavfczQoMCZYkhdeKVsXz6+mi78aq15cPqVyTWr5pUDFSJjpSSbne96QDCSpTMe+2CYkorfSkY7JO95CY5P0JKuPZfmUqRqh+z5Za/tHPvksaBXO5/P9XRNEhk1eIMWaVpGWSdkt6W1JXSV9aaw9LkrU2oYD1GhljfjXGrJV0raSGnscXSnrPGHOLpADPY4skjTHGjJRU3VqbduKTGWOGGmOWGWOWHUo9cBqbd/oZY/I9VtgvlU8Mm6DeTS/Tji271OPSbpKk4U/co1fGTZarCO8kwH9SwL5RYMnF31g98w893m2Y3hj6nC69/6rTlNjZx+jkj1OP3DdOXRpfrO2bd6pX3wvzLOvco6NW/rGW4Ur/QkHnCJ341heyr5QID1WVns31beth+qrZ3QoMDVaN/u1zQlY/84W+aXmvdn71u865sfvpTbwYKvh8fTIxf3/cOu/6bvr18Y/1Xut79evjH6vbc7ecUp5nq5N97x96eIIaNuqkNm0vUkRkWY148A5J0oqVa1Wrzvlq0bK7Xnv9XU374h2v51xsnMJxygQEqNx5NfTnB3P0Xc+HlZ2aofPuusS9SoBDJcJL6YdLxmrZuKnq/MZdpz/3Yq7gt92eEFP49uvx2PWa8/Snsi6qKFF00SGT119zyDS11t5trc2UZJT/sHyi9yTdZa09T9LjkkpKkrX2NkkPS6oqaZUxppy19hO5q2XSJM00xnQ98cmstVOstS2ttS2jQgv+dcSfrhhymT6e/Y4+nv2ODsUdVnSl4yW1FWOidOhAfKHrulwuzZ4+V10vukCS1KBJPY1/Y6ymL/1c3S6+QCOfvl8X9Oro9TYA3nDB9T01ZsazGjPjWR2NO6KISserxSKiyynxP5byb126UeWrR6tUBEPOTtbVNwzQl3M+0JdzPtDBuMOKrpz7OFVBBw8cLnRdl8uln779Wd0v7pLn8d79LtSMr2d5LefiKHV/gkIrHa8MC42JVNqBIwXEHB9CFlopUqlxiYru2Egpew4pIyFZNtupPTOWKapl/lG+O7/+XdX6tPJeI4qJlP0JKp1rW5SOicw3vMgdU+6EmMS/fd76l3fUth//kCRt/X6JKjatffqSLuZuv22wlv0xS8v+mKXY/QdUperxeUsqV4lR7P64fOscOOCu9svMzNT773+mVi2bSZKSk1N07Jh7qMyPP81VUFCgypX7++omuKXuT1CpXPtGqZjInOGReWPKnRCTqNT9CUrdn6DDK7dJknb+sFSR59XwrHNEu39cJkk6vGq7rMsqOJLz+D9pOai7bpkxXrfMGK/kuESF5Xrfw6IjlXIwMU98akKySoaVkglwf60tExOpZM/2i2lcU/1fuUt3//aiGvQ5X72fHKJ6JwzVhI/4uzqmCP/4T4fMP5sj6UpjTDlJMsYUNOagjKT9xpgguStk5Imtba1dYq19VNJhSVWNMbUkbbfWvixpuqTGXm/BafbFe1/r2u436truN2rej7+qzxW9JEmNmp+rlOQUxR/M3yFTpUblnNsdu7fTzq27JEl9W1+lS8+/Upeef6XmfD9fz4yaqPk//eqbhgCn2fwPZ2p8nxEa32eEVs9aqjb9O0mSajarq7TkVCUdSjzp54qqXjHndtWGNRUYFJhzBSb8s0/fnZYzEe/cH+fr0iv6SJIat2iolOQUHS7gOFW1RpWc2517dNCOLbty7pcuU0ot2zbTLz8t8H7yxUj8qu0qUzNapapGyREUoOp922jvrBV5YvbOWqFal3eQJJVrXluZSalKP5ioY/viVb55HQWEuIdkRHdoqKNb90mSytQ8vn9U7tlcSVv3+6hFZ6641dtVtka0wjzb4pxL22jH7LzbYsfsFWowwL0tKjarrczkVKWe8OXnRMfijqhymwaSpCrtGypxR9Gu7C1KJr/xfs4kvNOnz9T117onEm99fnMlHU3K6XzJLfe8Mpde2kvrN/wpSapY8fgwzFYtm8rhcCg+nvl8TsbhVdsVVjNapT37Rs2+bbTnhOPUnlkrVNtznIryHKfSDiYq7dBRHYtNUFjtGElSpQ4NdXSz+zi1e+YyRbc/V5IUVitaASUClZHAefyfLPtgtt7qM0Zv9RmjTbOWqfEA9w+1lZvVUXpyWr4OGUnauWiDzu1zviSpyYBO2jR7uSTp1Q7D9EqH+/RKh/u0ccZS/fjIe9o0a7nP2gKcDC57/Q+steuNMU9Jmu+5LPZKSUNOCHtE0hJJu+S+bPZf3d/PeSbtNXJ37KyWNErSdcaYLEkHJD3h9UZ40cI5i9S+Wxt9s+hTpael6/Fhx68K8NJHz+rJ4c8o/mCCHn/pIZUqEypjjDZv2KqnR77gx6zPTg8+9rT+WLlGiYlJ6tbvOt1x0/UacElPf6dVbK37ZaUadWmuJ+a/rMy0TH3w4Os5y+58d5Q+Gvmmjh48oi5Deqv7rZcqLKqsHv7pOa3/ZaU+GvWmmvVuo9b9O8mZ7VRWeqb+d9ckP7bmzLbg59/VsVs7/bjkS6WlpeuRe49fbez1jyfqsfvH6/DBeI1/5dGc49Sm9Vv15IhncuK69ems3+cvVVpquj+acMayTpeWPfS+un4yQibAoW2fztfRzftU93p3ceiWD+cqds4qVe7WRJf+/oKcaZlaNGyKJCl+5Tbt/mGpes8cJ5vt1JF1u7T1I/f8PU3HXKWw2jGyLqtj+w5r6ch3/dbGM4V1ujT/kfd16Ucj5AhwaMNn85WweZ8aXefeFus+mqudc1epetcmGvTbC8pKy9Sc4VNy1u/56p2q3KaBSkaW1g1LX9aSF6Zpw2fzNXfk2+o09no5Ah3KzsjS3FFv+6uJZ7QZP85Rr15dtWnjQqWmpenmm+/PWfbdtx9o6G0Pav/+OH34/qsqHxUpY4xWr16vO+4cJUka0P8i3XrrIGVnO5Welq5rr7vDX00541inS4sffl/dPxkh43Bo62fzlbh5n+p5jlObPpyrvXNWqXLXJuq/0H2c+u3+4/vGkkfeV6dXbpcjKFApuw/mLNvy6Xy1f2Go+s6ZIFeWU7/e96Zf2ncm2zp3lep0aao7F0xUtuey13+5+r0H9f2It5RyMFFzJkxV/1fvVucHrtCB9bu06rN5/ksa+JcMVyYo2lrGdGQDFSGL1r7v7xTgcU/LUf5OAR6/pu32dwrwGB1Qx98pwCMh4J9j4DvD4piMu6j4X1SXfw6CT+wJ5GtGUfHIro8LmAyn+Ej77HG//2cLueqxIvkeM2QJAAAAAADAxxiyBAAAAAAAvKMIT6rrb1TIAAAAAAAA+BgdMgAAAAAAAD7GkCUAAAAAAOAdDFkqFBUyAAAAAAAAPkaFDAAAAAAA8A5LhUxhqJABAAAAAADwMTpkAAAAAAAAfIwhSwAAAAAAwDuY1LdQVMgAAAAAAAD4GBUyAAAAAADAO6z1dwZFFhUyAAAAAAAAPkaHDAAAAAAAgI8xZAkAAAAAAHgHk/oWigoZAAAAAAAAH6NDBgAAAAAAwMcYsgQAAAAAALyDIUuFokIGAAAAAADAx6iQAQAAAAAA3mGpkCkMFTIAAAAAAAA+RocMAAAAAACAjzFkCQAAAAAAeIV1WX+nUGRRIQMAAAAAAOBjVMgAAAAAAADv4LLXhaJCBgAAAAAAwMfokAEAAAAAAPAxhiwBAAAAAADvsAxZKgwVMgAAAAAAAD5GhQwAAAAAAPAOLntdKCpkAAAAAAAAfIwOGQAAAAAAAB9jyFIRN+/aSH+ngFzuaTnK3ynA4+VlT/s7BXiMbvmQv1OAR0g6k+YVFR2DUvydAnL5OaKdv1OAR5Az3d8pwKNaNr/Nw0dcfD4pDHshAAAAAACAj1EhAwAAAAAAvIMKmUJRIQMAAAAAAOBjdMgAAAAAAAD4GEOWAAAAAACAd1jr7wyKLCpkAAAAAAAAfIwKGQAAAAAA4B1M6lsoKmQAAAAAAAB8jA4ZAAAAAAAAH2PIEgAAAAAA8A4Xk/oWhgoZAAAAAAAAH6NDBgAAAAAAwMcYsgQAAAAAALzDcpWlwlAhAwAAAAAA4GNUyAAAAAAAAO9gUt9CUSEDAAAAAADgY3TIAAAAAAAA+BhDlgAAAAAAgFdYF5P6FoYKGQAAAAAAAB+jQwYAAAAAAHiHy/r/7x8YY3oZYzYZY7YaY0YVEtPZGLPKGLPeGDP/dLw1DFkCAAAAAABnJWNMgKTXJHWXtFfSH8aY6dbaDbliykp6XVIva+1uY0yF0/HaVMgAAAAAAICz1fmStlprt1trMyV9KqnvCTHXSPrKWrtbkqy1B0/HC9MhAwAAAAAAvMO6/P5njBlqjFmW629orgwrS9qT6/5ez2O5nSMpwhgzzxiz3Bgz6HS8NQxZAgAAAAAAxZa1doqkKYUsNgWtcsL9QEktJHWTFCJpkTFmsbV286nkRYcMAAAAAADwjpOYVNfP9kqqmut+FUmxBcQcttYek3TMGLNAUhNJp9Qhw5AlAAAAAABwtvpDUl1jTE1jTAlJV0uafkLMt5I6GmMCjTGhklpL2niqL0yFDAAAAAAAOCtZa7ONMXdJmikpQNI71tr1xpjbPMvfsNZuNMb8JGmNJJek/1lr153qa9MhAwAAAAAAvMPl8ncG/8haO0PSjBMee+OE+89Jeu50vi5DlgAAAAAAAHyMChkAAAAAAOAdRX9SX7+hQ+Y0MMY8JOkaSU65x5Pdaq1d4t+sfCOgXjMF971FcjiUtWS2sn6Zlnd57UYqOWSMXAlxkqTsdYuVNfuz4wHGoZD7XpA9Gq/0d8b5MvVi68rHblDDLs2UmZahDx54XXvW78gXc8Ggnup640WqUCNaDzS7SceOJEuSGndvqUvuv0rWWrmynfriife0bdkmXzeh2Ht4/EQtWLhUkRFl9c1Hb/zzCvhP+j42WA26NFVmWqY+e2Cy9q3fmS8mskqUrnv1HoWEl9K+9Ts1ddhrcmY5FRJWSlc+d6vKVauo7IxMfT7iTR3YvFeBwUG647NHFRgcJEdAgNb8uESzJn3p+8adoSp2aazGTw6SCXBo58e/aPOr3+VZXrpOJbV48VaVPa+GNjz9ubZM/iFnWfNJQxXdvZkyDidpTueRvk692ClzQXNVfuxmmYAAxX86Swcn5z1/R/S7QBVuGyBJcqWmac9Dk5W+cack6dzf3pLzWJrkdMk6ndp8yXBfp1/sRHZpqjrjbpAJcGj/x3O0+5Vv8iwPrVNJ9V66U2XOq6kdE6Zqz+Tj+06VWy9SzDXdJFmlbNytTfe+LldGlm8bUEyV7dJUtZ68QQpwKO7jOdr36jd5lkf176jKd/WTJDmPpWvbyClK3bDL94kWU5Fdmqhurv1i1yvf5lkeWqeSGrx0h8qcV1PbJnyaZ7+oeutFirmmqySrYxv3aCP7BYoohiydImNMW0kXS2purW0s6UJJe/yblY8Yh4Ivu1Vp/3tcqc/dpcBmHWUqVs0X5tyxQWmThilt0rC8nTGSgjpeLFfc2fF2+ULDzs1UoWa0Hut8jz4ZM0UDn7q5wLhtyzfppeueVPzeg3ke37RwrZ7q/aDG9xmhD0dM1nXP3OaLtM86/fp01xsT6YD0pvqdmyqqZrSe7jxMX455SwOeuqnAuItGXaMFb8/QM13uV9rRYzr/qi6SpG539lXshl2a2Hukpg6frL6PDZYkZWdk6Y1rxmli71Ga2GeU6l/QRNWa1fFZu85oDqMmE27Qwmue1exOD6rKZe1U5pzKeUKyElO05uH383TE/GXXZwv0+8BnfJVt8eZwqMqTt2r74Mf154V3KuLSTgqum/f8nbEnTluvHK1Nve7RgZc/U9UJd+ZZvvXqh7Spz310xpwODofqPn2T1lzzlJZ2HKYKl7VX6DlV8oRkJaZo60Pv5PnCKUkloiNV+eY+Wt5zlP64YLiMw6EK/dr7Mvviy+FQrQk3a/01T2llp2GKuqyDQk7YLum7D2rtZY9qVdfh2jPpS9V5ns9Np43DqN7TN2n1NeO1JGe/yH/O2PzQu9qdb7+IUJWbe2tZz1FaesEDksOhCv3a+TJ74KTRIXPqYuS+HnmGJFlrD1trY40xLYwx840xy40xM40xMcaYcGPMJmNMPUkyxkw1xtzi1+xPgaNaXbniD8gmxEnObGWv+lWBDc8/6fVNeDkFNGip7KWzvZjl2aVJj5Za/NUCSdKOlVsUWqaUwqLK5ovbu36nEvYeyvd4RmpGzu0SocGylvJCb2jZ9DyFh5XxdxrFWsMeLbTsq18lSbtXblXJMqEqU8C+UKddQ62Z4S5oXDZtgRr1aClJqli3irYsdE+cf2hbrCKqRKl0+XBJUqZnPwkIDJAjMEBiPzkpkc3q6NiOOKXuPiib5dTebxYppmeLPDEZh5N0ZNV22WxnvvXjF/+pzMQUX6VbrIU2rauMnfuVuSdONitbR777VeHdW+eJSV3+p5xJx9y3V2xSUEx5f6R6VghrXkdpOw4ofddB2axsHfxmocr3apknJutwkpJXbZPNys63vglwyFGyhEyAQwGhwco4kOCr1Iu1Ms3qKH3HAWXsdm+XQ98sVGTPVnlikpdtkvOoez9JXr5ZJWIi/ZFqsRTWvI5Sc/YLpw5+87uieuV9/4/vF/nPGXn3ixLKPHDEV6mjINbl/78iig6ZUzdLUlVjzGZjzOvGmAuMMUGSXpF0ubW2haR3JD1lrT0q6S5J7xljrpYUYa19y3+pnxoTXk428XDOfZsYLxNeLl9cQPV6Crn/RZW8+VE5clXQBPe9WZnfv8+XmdOobMVIHYk9vk2OHIhX2eh/9+GgSc9WemzOJN35zmh9OGLy6U4R8InwipFKjI3PuX/0QILCT9gXQiPKKC3pmFxO90k6cX+8wiu6Y2I37tJ5ng9+VZvUVkTl8jnrG4fRsBkTNHb5m9ry21rtXrXNF00645WMiVBarm2Stj9BIXx58Yug6HLK2n/8XJG1/7CCovOfv/8SeXV3Jc9bnnPfSqr90RM65/uJKjewpzdTPSsER0cqI9e+kRGboOC/2R65ZR5I0J7J36ntislqu+YtZSel6sj8Nd5K9axSIiZSmbk+U2Xuj1fw3xyzKl7TTYlzV/oitbNC/v0iXsEn+Zk288AR7Z78ndqtmKz2a6YoOylVCewXKKLokDlF1toUSS0kDZV0SNJnkm6V1EjSbGPMKkkPS6riiZ8taa2k1yQVPJ7kTHZC54pz7zYde+oWpU28T1m//aCSQ8ZIkgIatJRNSZRrH19kTitj8j/2Lzu8Vs/8Q493G6Y3hj6nS++/6jQlBviWKWBfOLHiq+DdxR0zd/J0hYaX0rAZE9RhcE/Frt8pl9P9C5x1WU3qM1pPtr1TVZvUVvQJJewoWEHbhA55fzn5bVG67Xkqd1V3xU54P+exLf1HavNFw7R98OMqP6iPSp3f0FuJnh0K2hw6uX0jMLyUyvdqpcWt7tSiJkMVEBqsigM6nuYEz1IncR75S3j7hqo4sKt2jvvI21mdPQp6///FfhHVq5UWtbpTC5vcqoDQkuwX/uay/v8ropjU9zSw1jolzZM0zxizVtKdktZba9ueGGuMcUhqIClNUqSkvQXEDJW7g0cvdW+sGxvX8Frup8IejZcpe7yE2ZQtJ5t0QplsRlrOTeefy6X+t0qhZRRQo4ECzj1fofVbSIElZEqGKnjgMGVMneSr9IuNC67vqfYDu0mSdq3epohK5SW5J+KNiC6nxLj/VqK5delGla8erVIRZXIm/QWKsnbXd1frgV0lSXtWb1fZSsd/YQ6PjlTSCfvCsYRkhYSVkiPAIZfTpbIx5ZR00B2TkZKmzx58Myd2zG8vK2FP3mF+6Ump2rZ4o+pd0EQHNuc7lOMEabEJCsm1TUJiIpVGCblfZB04nGcIUlBMeWXF5R/mUrJ+DVV95i5tH/y4nInHzwPZB92x2fFHdXTmYoU2ratjS9d7P/FiKmN/goJz7RvBlSKVeZLDjiI6naf03QeVFZ8kSTr0wxKFtaqnuGm/eiXXs0lmbLxKVDq+n5SIKVfgsJfQBtVV+4XbteGap5R9hGGVp0vG/vgT9ouC3/+CRHQ6T2m7Dyor3n3cOvTDEoW3Oof9AkUSFTKnyBhTzxhTN9dDTSVtlBTlmfBXxpggY8xfPx8N8ywfKOkdz/CmPKy1U6y1La21LYtqZ4wkufZskaN8jExkBSkgUIFNO8q5fmmeGFOmbM5tR9W6knFIqcnK/PFDpY67Sanjhyrj4+fl3LqGzpj/aP6HMzW+zwiN7zNCq2ctVZv+nSRJNZvVVVpyqpIOJZ70c0VVr5hzu2rDmgoMCqQzBmeM3z+crUl9RmtSn9FaP2uZWvZ3/xpWrVkdpSenKrmAfWHrovVq3Mc9d0bLAZ20fpZ7WEbJsFAFBAVIklpf3VXbl2xURkqaSkWWUcmwUElSYHCQ6rZvpIPbYn3QujPfkVXbVLpWtEKrRckEBahKv7baP2v5P6+I0y519RYF16ykElUrygQFKuKSjkqanffikEGVyqvmm6O1a9gkZew4/n/cERIsR6mQnNtlOjVV+qbdPs2/uEleuVUhtWJUsloFmaBAVejXXodnLjupddP3HVZY87pyhJSQJEV0PE+pW+ggPh2SV7m3S7Bnu0T1a6+EWX/kiSlRubzqv/OAttz1itK37/dTpsVT8sptCq0Vo5Kec0aFfu1Oer/IKHC/2OfNdIH/jAqZU1da0ivGmLKSsiVtlbu6ZYqkl40x4XK/zy8aY7LkHqZ0vrU22RizQO7hTI/5JfNT5XIp4+spCrllrGQcyvpjjlxxexTYtpckKXvRTwps3E6BbXtLLqeUlan0j573b87F3LpfVqpRl+Z6Yv7LykzL1AcPvp6z7M53R+mjkW/q6MEj6jKkt7rfeqnCosrq4Z+e0/pfVuqjUW+qWe82at2/k5zZTmWlZ+p/d9FJ5g0PPva0/li5RomJSerW7zrdcdP1GnAJ8zCcTht/Wan6XZpq1PwXlZWWkafa5aZ3R+iLkW8p6eAR/fD0VF33yt3qNfxK7Vu/U0s+/0WSVLFOZV39wu2yLpfituzT5yOmSJLCKkTo6hdul3E45HAYrf5hsTYyZ8BJsU6XVo15T+2njpIJcGjX1HlK3rRPNQe5K/x2fDBHwVHh6jpznALLhMi6rOrc0kuzO41QdkqaWk2+S1HtGqhEZBn1XvGKNjw3TbumzvNvo85UTpf2Pvqman0wVibAoYTPf1b6lj0qd637/B3/8U+KvvdqBUSUUdUn3VeN+evy1oHly6rmFPfwYwUGKPHb+Uqev8JPDSkerNOlLaPfVuNPH3Jf3nfqL0rdtFeVBnWXJMV+MFslosqqxaynFVAmRHJZVRl6kZZ2HKbkFVt16PvFajn7WVmnU8lrdyr2w5/93KJiwunS9jH/U8OpD0sBDh2cOldpm/YqelAPSdKBD2ap2v2XKyiijGo9fXPOOqt7jvRj0sWHdbq0efQ7aurZL2Kn/qJj+faLcLWc9XTOOaPq0D5a0vF+JXn2i1azn5F1OpWydqf2sV/4lXUV3Ul1/c1wFZWiLeWBvmygIuTBL4P9nQI8Xl72tL9TgMfolg/5OwV4tEsvYDIK+EWtYIYuFCWJ6Zy/i4ogw0fboiLDMliiqOga93mxPoGnjB7g9x2/9IRpRfI9Zi8EAAAAAADwMYYsAQAAAAAA7yjCVznyNypkAAAAAAAAfIwKGQAAAAAA4B1UyBSKChkAAAAAAAAfo0MGAAAAAADAxxiyBAAAAAAAvMO6/J1BkUWFDAAAAAAAgI9RIQMAAAAAALyDSX0LRYUMAAAAAACAj9EhAwAAAAAA4GMMWQIAAAAAAF5hGbJUKCpkAAAAAAAAfIwKGQAAAAAA4B1UyBSKChkAAAAAAAAfo0MGAAAAAADAxxiyBAAAAAAAvMPl8ncGRRYVMgAAAAAAAD5GhQwAAAAAAPAOJvUtFBUyAAAAAAAAPkaHDAAAAAAAgI8xZAkAAAAAAHgHQ5YKRYUMAAAAAACAj9EhAwAAAAAA4GMMWQIAAAAAAF5hLUOWCkOFDAAAAAAAgI9RIQMAAAAAALyDSX0LRYUMAAAAAACAj9EhAwAAAAAA4GMMWQIAAAAAAN7BkKVCUSEDAAAAAADgY1TIAAAAAAAAr7BUyBSKDpki7qkvS/k7BeTya9p2f6cAj9EtH/J3CvCYsOwpf6cAj85NbvZ3CvCornB/p4Bc+jpK+DsFeLzqOODvFOARyn5RZHT1dwLwG4YsAQAAAAAA+BgVMgAAAAAAwDsYslQoKmQAAAAAAAB8jAoZAAAAAADgHS5/J1B0USEDAAAAAADgY3TIAAAAAAAA+BhDlgAAAAAAgFdYJvUtFBUyAAAAAAAAPkaFDAAAAAAA8A4qZApFhQwAAAAAAICP0SEDAAAAAADgYwxZAgAAAAAA3uHydwJFFxUyAAAAAAAAPkaFDAAAAAAA8Aoue104KmQAAAAAAAB8jA4ZAAAAAAAAH2PIEgAAAAAA8A4m9S0UFTIAAAAAAAA+RocMAAAAAACAjzFkCQAAAAAAeAVXWSocFTIAAAAAAAA+RoUMAAAAAADwDib1LRQVMgAAAAAAAD5GhwwAAAAAAICPMWQJAAAAAAB4hWXIUqGokAEAAAAAAPAxKmQAAAAAAIB3UCFTKCpkAAAAAAAAfIwOGQAAAAAAAB9jyBIAAAAAAPAKJvUtHBUyAAAAAAAAPkaFzCkyxlwm6StJDay1f/o7H1+55LFBqtelqTLTMvXlA28odv3OfDERVaI08NW7FRJeWrHrd+jzYa/LmeVUx6EXq2m/dpIkR0CAKtSprHHNb1WpcmEa+OrdOetHVq2gnyd9qYXv/OSrZhULo5+6Xx27tVV6WoYeuudJbVy7KV/ME5PGqGGTBjLGaOe23XronieVlpqmG+64VhcN6ClJCggMUK26NdTx3N5KSkzydTPOWH0fG6wGnn3jswcma18B+0ZklShd9+o9CgkvpX3rd2rqsNfkzHIqJKyUrnzuVpWrVlHZGZn6fMSbOrB5rwKDg3THZ48qMDhIjoAArflxiWZN+tL3jSuGHh4/UQsWLlVkRFl989Eb/k7nrHDfE3epbdfWSk9L11PDntXmdVvyxYx6/gHVb1JPRtKeHXv11H3PKC01XdVqV9VDk0bonEZ1NeWZdzT1zc9934BiZNDYm9S0SwtlpmXojQde0c512/PF9BjcW71uvETRNWJ0a9NBSj6SLEkqFVZKQ5+7SxWrRysrI0tvPviq9m7e7esmnLGiuzRWsyeulwlwaPsn8/Tnq9/li2n25CDFdGsiZ1qmlt73po6s3SlJqntzT9W+totkjLZ//Is2v3X8c1LdG3uozg3dZZ0uxf68SmvGTfVVk4qte5+4U226tlZGWobGF3LMGvn8A6rf5BwZGe3ZsVfjPccsnLrbH79N53dtpfS0DL1w/wvaum5bvpiRL49Q3cZ15czO1qZVm/XSqJflzHZKkhq3OU+3jb1VgYGBOnokSQ9eMcLXTYB0Rkzqa4zpJeklSQGS/metfbqQuFaSFku6ylp7yh/IqZA5dQMl/Sbpan8n4iv1OjdVuZrRer7z/fp6zP/U76kbC4zrNWqgfnv7R73Q5X6lHT2mlld1kST9OuV7vdJnjF7pM0Yzn/1MO5ZsVNrRYzq8fX/O469e/JCy0jO1fuYyXzbtjNexW1tVq1lVfdpcobEPTNAjzxZ80nnmkRc1oOv16t/lOu3fF6drbrpckvTu6x/r8m6DdHm3QXrxqclatmglnTH/Qv3OTRVVM1pPdx6mL8e8pQFP3VRg3EWjrtGCt2foGc++cb5n3+h2Z1/Fbtilib1Haurwyer72GBJUnZGlt64Zpwm9h6liX1Gqf4FTVStWR2ftas469enu96YOM7faZw12nZtrSo1K+uqDtfr2ZET9cCE+wqMe3ns6xrS/RYN7n6L4vYd1IAbLpMkJSUma9Ijr9IRcxo07dJc0TUr6f4L7tD/Rk/WjeNuLTBu07I/Nf7ax3Roz8E8j/e963Lt2rBDo3oN0+T7X9KgsQUf75CfcRi1GD9EC659Vj9dMELV+7VV2DmV88TEdG2iMrWiNaPdcC178G21ePoGSVJ4vSqqfW0Xze7zqGZ2G62YC5updM2KkqQK7c5VpZ4tNLPbaP3UeaQ2Tf7B520rbtp0PV9ValbRwA6D9OzIiRo+4d4C414Z+7pu6D5UQzzHrP439PNtosVUqy6tVLlmJd3Q8Sa9NPJl3T3+rgLj5n79i27ufItuvfB2lShZQr0H9pLk7ji+66m79NiNj2vohbdp3G1P+TJ9nEGMMQGSXpPUW9K5kgYaY84tJO4ZSTNP12vTIXMKjDGlJbWXdJM8HTLGGIcx5nVjzHpjzPfGmBnGmMs9y1oYY+YbY5YbY2YaY2L8mP5/1qBHC6386ldJ0p6VW1WyTKjKRJXNF1e7XUOtm7FEkrRi2q86t0fLfDFNLm2r1dN/z/d4nfaNFL8rTon7Dp/e5Iu5Lr06afoXMyRJa5avV5mw0ipfoVy+uGMpqTm3S5YMlrX5n6vPZd014+vZXsu1OGrYo4WWefaN3X+zb9Rp11BrPPvGsmkL1Mizb1SsW0VbFq6TJB3aFquIKlEqXT5ckpSZmiHJXbnkCAxQgRsN/1rLpucpPKyMv9M4a3To2U4/fek+rqxfsVFlwkurXIXIfHGpuY5RwSWDZT3/3xPjE/Xn6k3KznL6JuFirEX38/XrtF8kSVtXblZoWCmVrRCRL27X+h06vPdQvscr162i9QvXSpJit+1TVJUKCvMcr/D3IpvVVvLOOB3bfUiuLKd2f7tYlXu2yBNTuVcL7fzCfT6JX7FVQWGhKlmhrMrUraT45VvlTMuUdbp0aPFGVendSpJUe3A3/fnqdLkysyVJGfH8oHKqOvRsr5++nCVJ2rBio0qf1DGrBKfo06Rtjzb6edocSdKfK/9UqbDSiizgOPXHL3/k3N60apPKx5SXJHXp11kLf1qoQ7HuY9jR+KM+yBpnqPMlbbXWbrfWZkr6VFLfAuLuljRN0sEClv0ndMicmn6SfrLWbpaUYIxpLqm/pBqSzpN0s6S2kmSMCZL0iqTLrbUtJL0j6Yzspg2vGKHE2ISc+0cPJCgsOu/BMTSijNKTjsnldNenHd0fr7CKeWOCSpbQORc00bofl+Z7jcaXtNWa6Yu8kH3xVjEmSgf2HT8+xO0/qIoxUQXGPvniw5q/boZq1q2uT97O+2tzyZBgdejSRrO//8Wr+RY34RUjlRgbn3P/6IEEhUfn/eAWGlFGabn2jcT98Qqv6I6J3bhL5/Vyf7Cu2qS2IiqXz1nfOIyGzZigscvf1Jbf1mr3qvwlu0BRFxVdXgdjjx+jDu4/pKjo8gXGjpk4Qt+t+lLV61TVl+987asUzxoR0eWUkOt4lXAgXhEV83/RLMzuDTvVqncbSVLtJnVVvnKUykXn/wEA+YVERypt3/H3PnV/gkJO+BwVEh2p1FzbJ21/gkJiInR0015FtamvEhGlFRBSQjFdmyq0knu7lakVo/Kt6+vCHx5Xl68eVmSTWr5pUDHmPmYd75A8tP+QyhdyzBo98UF9u+pLVatTTdM4Zp0W5aPL6VDs8R9nD+8/rHKFvP+S+0erbv27adk8d4V9lZpVVDq8tJ79/Bm9+sPLunBAN6/njIJZl///jDFDjTHLcv0NzZViZUl7ct3f63kshzGmsqTLJJ3WMe50yJyagXL3nsnz70BJHSR9Ya11WWsPSPrrG209SY0kzTbGrJL0sKQqBT1p7v8sq5K3ejP//8aYfA+d+EtAASH5gupf2Fy7lm1W2tFjeR4PCApQgwtbaO2Mxaea6VnHqKBtU/DPNI/cN05dGl+s7Zt3qlffC/Ms69yjo1b+sZbhSv+SKXDfsCfE5F/vr5i5k6crNLyUhs2YoA6Deyp2/U65nO5KAOuymtRntJ5se6eqNqmt6HMKPHwARdrJ7CN/GX//s+rb/Ert3LJb3S7t4u3Uzjonc57+O9Mnf6VSYaU0fsZE9RjSRzvXb5fTeQZMElAUnMx7X0hM8pZYbXztO3X+bJQ6fTJSiRt253TwOwIdKhFeSj9f9JhWP/GJ2k65u4Anwb/xd+fsE024/zld1vxK7dqyS90u7ezdxM4W/+KcIUl3P3Wn1i1Zp3VL10uSAgIdqnteXT0y+FGNue5hXXPvQFWuWbnQ9VG8WWunWGtb5vqbkmtxgUfdE+6/KGmktfa0lukyqe9/ZIwpJ6mrpEbGGCv35D9WUmFd4kbSemtt2396bs9/jimSNLrGNUWi6LHN9d3VaqD7A/He1dtVtlKkdnmWhUdHKjnuSJ74YwnJKhlWSo4Ah1xOl8JjyinpYGKemCaXFDxc6ZzOTRW7bodSDtMZcDKuvmGALr/OXVG3btVGRVeukLOsYkwFHTxQ+LAvl8uln779WTfcea2++fT4WPPe/S7UjK9neS/pYqTd9d3VemBXSdKe1dtVttLxX4jDoyOVVMC+EZJr3ygbU05JB90xGSlp+uzBN3Nix/z2shL25B0qkJ6Uqm2LN6reBU10YPNebzULOG36D+6rS6+9SJK0cdUmVah0/BhVISZKh+PiC1tVLpdLc6b/omtuv0ozPmeC91PVfVBvdbm6uyRp+5qtisx1vIqMLqcjB48Utmo+aSlpevPBV3Puv/Tbmzq0J+70JVuMpe1PUEjl4+99aEyk0uIS88WE5to+ITGRSjvgjtkxdb52TJ0vSTpv9JVK81Qtp+5P0N4Z7qEbCau2Sy6r4HJllBGf7MXWFD+XDe6rS67tI0n6c9UmVah0vNI4KiZK8f9wzJo7fZ4G3n6VZnx+2qaYOKtcMvjinDlgNq/erKhKxytiyseUV0Ih7/+1912j8HLhemnU8XnhDu0/rKMJScpIy1BGWobWLlmnWufW1L4d+7zbCORzBlz2eq+kqrnuV5EUe0JMS0mfen5cKi+pjzEm21r7zam8MBUy/93lkj6w1la31taw1laVtEPSYUkDPHPJVJTU2RO/SVKUMSZnCJMxpqE/Ev8vFn84O2fC3Q2zlqlZ/46SpKrN6ig9OU3JhxLzrbN90QY16tNaktR8QEdtnHV8gt7gMiGq2bqBNsxenm+9Jpe20+rvGK50sj59d1rORLxzf5yvS69wf4ho3KKhUpJTdPhg/hNX1RrHqys69+igHVt25dwvXaaUWrZtpl9+WuD95IuB3z+crUl9RmtSn9FaP2uZWnr2jWrN6ig9ObXAfWProvVq7Nk3Wg7opPWz3PtBybBQBQQFSJJaX91V25dsVEZKmkpFllHJsFBJUmBwkOq2b6SD2048RwBF01fvf6shPYZqSI+hWjDzN/W63N0h0LB5A6UkHVP8wYR861SuUSnndvvu7bRr6558Mfj3Zn/wo8b0uV9j+tyvZbOWqOMA9w8tdZqdo7TkVCX+iw6Z0LBQBQS5f9frcnV3/bl0vdJS0rySd3GTsGq7ytSMVqmqUXIEBaha3zbaNzPv56F9M1eoxhXu80m55nWUlZymdM8PW8HlwiRJoZXLqUqfVtr1jfvHrX0/LVfFDu45KEvXipYjKJDOmP/g6/e/1Y09btWNPW7VrzMXqtflPSRJ557kMatd97batZUrjv1X373/ve7odZfu6HWXfp+5KGeYUf1m9ZWafEwJBRynel3dUy0vaKEJdz2Tp4Jm0azFanR+IzkCHAouGaz6zeppN+cTFOwPSXWNMTWNMSXknh92eu4Aa21Nz/f+GpK+lHTHqXbGSFTInIqBkk68FNY0SQ3k7mFbJ2mzpCWSjlprMz2T+75sjAmX+71/UdJ6n2V8mmz6ZZXqdWmqB+ZPUlZahr7M9Yv+kHdHaNrIKUo+mKgfn56qga/crR7Dr1Ds+l364/N5OXENe7bSll/XKistI89zB5UsobodGunrMf/zVXOKlQU//66O3drpxyVfKi0tXY/ce/xXgtc/nqjH7h+vwwfjNf6VR1WqTKiMMdq0fqueHPFMTly3Pp31+/ylXK7xP9j4y0rV79JUo+a/qKy0jDzVLje9O0JfjHxLSQeP6Ienp+q6V+5Wr+FXat/6nVryuXtkY8U6lXX1C7fLulyK27JPn49wV1KGVYjQ1S/cLuNwyOEwWv3DYm2cu9IvbSxuHnzsaf2xco0SE5PUrd91uuOm6zXgkp7+TqvYWjRnidp2ba3PF36k9LR0jb//2Zxlz38wQU8/+LziDybo4RdHqVRp9zFq64Ztem70i5KkyKgIvf3jGypVOlQul9WVtwzQtZ1vyDOhJk7OqrnL1bRLC01aMFkZaRl684FXcpaNeO9hTRnxmhIPHlHPIRfp4tv6qWxUhJ6e+aJW/bJcb418XZXrVNXtE++Ry+nS3q179Vauahn8Pet0acWY93TB1JHuy15/Ol9Jm/ep9iD3F89tH8zR/jmrFNOtqS5aNFHZaZlaOuz4+aT92/eqREQZ2axsLR/9nrKOuv//75g6T60mDVWvX56WKytbS+49rdMcnJUWzVmiNl1b69OFHyo9LV0T7n8uZ9mzH4zXMw++oISDCXroxZEKzXXMemH0S37MuvhYOvcPteraSu/+9o4y0tL1wvBJOcuefP8JTRrxohLiEnTPhLsVt++gXvxmoiRp4Y+/6+OXPtGerXu0bN4yvTFrsqx16aepM7Vr067CXg5nMWtttjHmLrmvnhQg6R1r7XpjzG2e5V47oJq/G4eH/8YYU9pam+IZ1rRUUnvPfDL/WlEZsgS379K2+zsFePQKqenvFOAxYdkZOT95sdS5yc3+TgEe1QO54lBR0jcz1N8pwONVx3/6SAwvCHWU8HcK8Ji558eC5jApNuK6XOD377QVf5lfJN9jKmS843tjTFlJJSQ9+V87YwAAAAAAQPFEh4wXWGs7+zsHAAAAAAD8zhbJ4pQigUl9AQAAAAAAfIwOGQAAAAAAAB9jyBIAAAAAAPAK6/J3BkUXFTIAAAAAAAA+RocMAAAAAACAjzFkCQAAAAAAeIV1cZWlwlAhAwAAAAAA4GNUyAAAAAAAAK9gUt/CUSEDAAAAAADgY3TIAAAAAAAA+BhDlgAAAAAAgFdYy6S+haFCBgAAAAAAwMeokAEAAAAAAF7BpL6Fo0IGAAAAAADAx+iQAQAAAAAA8DGGLAEAAAAAAK+wLib1LQwVMgAAAAAAAD5GhQwAAAAAAPAKa/2dQdFFhQwAAAAAAICP0SEDAAAAAADgYwxZAgAAAAAAXsGkvoWjQgYAAAAAAMDHqJABAAAAAABeQYVM4aiQAQAAAAAA8DE6ZAAAAAAAAHyMIUsAAAAAAMArrPV3BkUXFTIAAAAAAAA+RocMAAAAAACAjzFkCQAAAAAAeAVXWSocHTJFXKjlP29RMjqgjr9TgEdIusvfKcCjc5Ob/Z0CPOat/p+/U4BH0uAb/J0CclmxrIS/U4DH7J+G+zsFeNgNS/ydAnDWo0MGAAAAAAB4haXIoFDMIQMAAAAAAOBjdMgAAAAAAAD4GEOWAAAAAACAV1imfiwUFTIAAAAAAAA+RoUMAAAAAADwCheT+haKChkAAAAAAAAfo0MGAAAAAADAxxiyBAAAAAAAvMIyZKlQVMgAAAAAAAD4GBUyAAAAAADAK6yLCpnCUCEDAAAAAADgY3TIAAAAAAAA+BhDlgAAAAAAgFdY6+8Mii4qZAAAAAAAAHyMChkAAAAAAOAVTOpbOCpkAAAAAAAAfIwOGQAAAAAAAB9jyBIAAAAAAPAKl2XIUmGokAEAAAAAAPAxKmQAAAAAAIBXWCpkCkWFDAAAAAAAgI/RIQMAAAAAAOBjDFkCAAAAAABeYa2/Myi6qJABAAAAAADwMTpkAAAAAAAAfIwhSwAAAAAAwCtcXGWpUFTIAAAAAAAA+BgVMgAAAAAAwCssFTKFokIGAAAAAADAx+iQAQAAAAAA8DGGLP0NY8xDkq6R5JTkknSrpFskTbTWbjDGpFhrSxewXhtJL0kK9vx9Zq0d67PEfaDn2EGq06WJstIyNf2BN3Vg3c58MWWrRqn/K3epZNnSOrBup74Z9rpcWU6d072FOg+/XNZl5XI6NevxD7Vn2WZJ0t2/vajMY+lyOV1yOZ16+5JHfNyyM0tM58Zq+eT1Mg6Htk6dpw2vfpcvpsWT16ty16bKTsvQomFTdGTtTklS/Vt6qfY1nSVrlfjnXi0aNkWujCw1fvByVenZXNZaZRxO0qL73lRaXKJP21UcVOzSWI2fHCQT4NDOj3/R5hO2Tek6ldTixVtV9rwa2vD059oy+YecZc0nDVV092bKOJykOZ1H+jr1Yum+J+5S266tlZ6WrqeGPavN67bkixn1/AOq36SejKQ9O/bqqfueUVpquqrVrqqHJo3QOY3qasoz72jqm5/7vgFniYfHT9SChUsVGVFW33z0hr/TKdaCWpyvUkPvlhwOpc/6QelffFJgXEDd+gp/4XWlPPO4MhfOlySVunekSpzfVq7EIzp65w2+TLvYKtelieqNGyIT4NC+j+dq5yvf5lkeWqeSGr50u8LOq6mtEz7Vrsnf5ywLDAvVuRNvVen6VWWttGHYZB1dlv8Yh5OzcNVGPfPu13K5rC7r1lo39bswz/Lk1DSNefkjHYhPVLbTqcGXdFG/Lq0lSR/PmK9pcxbLWqsB3drquosu8EcTio2FW/br2Z9WuLdF81q6seO5eZa/t3CjZqzZJUlyuqx2HE7SLw/2U8mgQN347hxlOV3Kdrl04blVdUeX8/zRBHhY6+8Mii46ZAphjGkr6WJJza21GcaY8pJKWGtvPonV35d0pbV2tTEmQFI9b+bqa3W6NFFkzWi9dsFwVW5WR33G3aB3+j2WL67bqKu15O0ftf67xerz1I1qdlVnLf9ojnYsXKfNs5dLkirUr6oBr92jyd0ezFnvg6vHKe1Iis/ac6YyDqNW4wdr7tVPK3V/gnrNeEJ7Zy5X0pbYnJhKXZsorGa0prcfrnLNa+v8CUM08+KxComOUL2beuj7ziPlTM9ShzfuVo2+bbT981+1YfIPWvPcl5Kkejf10HnDLtPSUe/6q5lnJodRkwk36LcrJyhtf7y6/DRO+2etUPLmfTkhWYkpWvPw+4rp1TLf6rs+W6Dt78xSi1du92XWxVbbrq1VpWZlXdXhejVs3kAPTLhPQy+5M1/cy2NfV2pKqiTp7sdu14AbLtNHr01VUmKyJj3yqjr1au/r1M86/fp01zUDLtWYJ5/3dyrFm8OhUrffp6SHh8t1+JDCJ72prMUL5dyzK3/cDbcqa8UfeR7O+PlHpX//lUrfP8aHSRdjDqP6T9+oFVc+pfTYeLWeOUGHZi7TsRPOGZseek9RvfOfM+qNG6L4X1Zrzc2TZIICFBAS7MvsixWny6Xxb0/Tmw/fporlyuqa0ZPUuWUj1a4SnRPz2U+/qVaVaL0y6hYlJKWo770TdFHHFtoZe0jT5izWx+OHKSgwQHeMf1Mdm5+r6jFRfmzRmcvpcmnCjGV64/ouqhgWomvfmq0L6lVW7QrhOTFD2jfQkPYNJEnzN+3TR4s2KTw0WNZavTW4i0KDg5TldOmGd35Whzoxaly1vL+aAxSKIUuFi5F02FqbIUnW2sPW2lhjzDxjTM7Z0BjzgjFmhTFmjjHmryNuBUn7Pes5rbUbPLFjjTEfGmPmGmO2GGNu8XGbTotzurfQmmm/SpL2rdyqkmGhKl2hbL64Gu0aasOMpZKk1dMWqF4P99uWlZqRExMUGiyJLtP/olyz2kreGaeU3YfkynJq17eLVbVnizwxVXq20PYvf5Mkxa/YphLhpVTSs61MYIACSpaQCXAoMKSEUuOOSJKyU9Jy1g8McZ/U8O9ENqujYzvilLr7oGyWU3u/WaSYE7ZNxuEkHVm1XTbbmW/9+MV/KjORTsnTpUPPdvrpy9mSpPUrNqpMeGmVqxCZL+6vzhhJCi55/P9+Ynyi/ly9SdlZ+bcVTq+WTc9TeFgZf6dR7AWe00DO2H1yHdgvZWcrY8FcBbXpkC+u5CX9lbFwvlxHj+R5PHv9GtnkZF+lW+yFN6+j1B1xStvlPmcc+OZ3RfVqlScm63CSklZtkz3hOBRQOkQRbRto38dzJUk2y6nspFThv1m3dbeqRpdXlYrlFRQYqF7tmmneH+vyxBhjlJqeIWutUtMzFF46VAEOh3bsi1PjutUVElxCgQEBatGgjuYuXeOnlpz51u1LUNXIMqoSWVpBgQHq2aia5m3aV2j8j2t3qdd51SW5t1FocJAkKdvpUrbTyhgmlfUnlzV+/yuq6JAp3CxJVY0xm40xrxtjCqo5LCVphbW2uaT5kv4qE5kkaZMx5mtjzK3GmJK51mks6SJJbSU9aoyp5MU2eEWZ6Eglxcbn3E86kKAyFSPyxIRElFZ60jFZp0uSlLw/QWWij8fU69lSt895TgPffVDTH5yS87iV1bUfjdLN349Ts4FdvNySM1tIdIRSYxNy7qfuT1BITN7tEBododRc2yo1NkGh0RFKO3BEGyfPUL8/XlL/Va8qMzlVB+Yf/8DRZOQV6rfsJdXo305rnpvm/cYUMyVjIpSW631P25+gkJj8HQDwjajo8joYezDn/sH9hxQVXfCvZGMmjtB3q75U9TpV9eU7X/sqRcCnHOXKy3X4+D7hOnxIAeXK54sp0bajMn6c7uv0zjrB0ZHKyHXOyIiNV3B0xN+scVxI9QrKjE9Sw5duV+ufn9a5E2+VI5QKmf/qYEKiosuVzblfoVy44hKO5om5ulcHbd8XpwtvfUyXD39WI27oJ4fDoTpVY7R843YlJh9TWkamflu5QQfiE33bgGLkYFKaosNCc+5XDAvRwaS0AmPTMrP1+9YDurBBlZzHnC6Xrpz8k7o+943a1K6o86qU83rOwH9Bh0whrLUpklpIGirpkKTPjDFDTghzSfrMc/sjSR086z4hqaXcnTrXSPop1zrfWmvTrLWHJf0i6XxvtcFbCupgPrGKosBe6Fwhm2Yu0+RuD+rzWyap8/Arch5/r//j+t9FD+uTwc+q1aDuqnZ+/dOVdrHzT++xJ6iAGKsS4aGq0rO5vm09TF81u1uBocGq0f/4cIzVz3yhb1req51f/a5zbux+ehM/CxS8bag08peCtkdhlV/j739WfZtfqZ1bdqvbpXQKo5gqaJ844X7o0LuV+u6bksvlm5zOZqfwy70jMEBlzqupPe/P1pILR8mZmq6ad/c9jcmdXQo6NZy4eX5f/afqV6+kn998XJ8/94AmvP2VUlLTVatKRd3Qt6tuHTdZd4x/U+dUr6RAB1+1/itbQAV9YbvKgs2xalqtvMJzdUYGOBz6/PZemnn/pVq3L0FbmQ8RRRRHib/hGW40z1r7mKS7JA34p1VyrbvNWjtZUjdJTYwx5U6MKeS+jDFDjTHLjDHLlqVsPYUWnD4tB3XXLTPG65YZ45Ucl6iwSsd7mcOiI5VyMDFPfGpCskqGlZIJcP8XKxMTqeS4vCXPkrR76Z+KqF5BIRHuuZH/ep7U+CT9OXOZKjWt5Z0GFQOp+xMUWul41UVoTKTSDhwpIOb4tgqtFKnUuERFd2yklD2HlJGQLJvt1J4ZyxTVsm6+19j59e+q1qdVvsfx99JiExSS630PKWDbwLv6D+6r92ZN0XuzpujwgXhVqFQhZ1mFmCgdjosvdF2Xy6U5039R54s6+iJVwOdchw/JUf74PuEoHyVX/OE8MYF16qn0yEdV9p1PFdz+ApW6Y1iBw5pw6jL2xys41zkjuFI5ZZzkOSM9Nl4ZsfFKWuH+vBj33RKVOa+mV/I8G1QsVzZPVcvB+KOqEBGeJ+bbX5aqW+vGMsaoWnSUKleI1I7YOElS/65t9NkzD+jdx+9WeOlSqsb8Mf9ZxbBQHcg1/C4uKU1RZUIKjP1p3S71alStwGVhISXUskYFLdx6wCt54uRYa/z+V1TRIVMIY0w9Y0zub6hNJZ0w250cki733L5G0m+edS8yx3+SrSv3VZoSPff7GmNKejpoOkvKO1OeJGvtFGttS2tty5al65yG1py6ZR/M1lt9xuitPmO0adYyNR7g/qJSuVkdpSen5euQkaSdizbo3D7uAqAmAzppk2ci34jqFXNiohvVUEBQoNKOpCgoJFglSrlHdwWFBKtWp/N0aNNeL7fszBW/arvK1IxWqapRcgQFqHrfNto7a0WemL2zVqjW5e4P0OWa11ZmUqrSDybq2L54lW9eRwEhJSRJ0R0a6uhW97jcMjWPb5/KPZsraet+H7Wo+DiyaptK14pWaLUomaAAVenXVvtnLfd3WmeVr97/VkN6DNWQHkO1YOZv6nW5u9KrYfMGSkk6pviDCfnWqVzj+AjS9t3badfWPT7LF/Cl7M1/KqByFTkqRkuBgQru1FVZSxbmiUm86Wol3uj+y1g4X8den6Ssxb/5KePiLWnlNoXWilZJzzkjul87HZq57KTWzTx0VOmx8QqtHSNJiuzYSMc289npv2pYu6p27z+kvQfjlZWdrZ9+X6kLWjbMExNdPkJL1rqvYhWfmKydsYdUpYK7Qy3+qHtupf2Hj2jO0jXq3b65bxtQjDSsFKnd8cnadyRFWdlOzVy3WxfUq5wvLjk9U8t3HlKX+seHKyUcS1dSWqYkKT0rW0u2H1DN8sxPhqKJqywVrrSkV4wxZSVlS9oq9/ClL3PFHJPU0BizXNJRSVd5Hr9e0iRjTKpn3WuttU5PH81SST9IqibpSWttrM4wW+euUp0uTXXngonK9lz2+i9Xv/egvh/xllIOJmrOhKnq/+rd6vzAFTqwfpdWfTZPktSgdys1HtBRziynsjMy9dWdr0iSSpUP05VThklyl+Cu+/Z3bZvPZGiFsU6Xlj30vrp+MkImwKFtn87X0c37VPf6rpKkLR/OVeycVarcrYku/f0FOdMytWiYe76e+JXbtPuHpeo9c5xstlNH1u3S1o9+kSQ1HXOVwmrHyLqsju07rKUjucLSv2WdLq0a857aTx0lE+DQrqnzlLxpn2oO6iZJ2vHBHAVHhavrzHEKLBMi67Kqc0svze40QtkpaWo1+S5FtWugEpFl1HvFK9rw3DTtmjrPv406gy2as0Rtu7bW5ws/Unpausbf/2zOsuc/mKCnH3xe8QcT9PCLo1SqdKiMMdq6YZueG/2iJCkyKkJv//iGSpUOlctldeUtA3Rt5xvyTAKM0+PBx57WHyvXKDExSd36Xac7brpeAy7p6e+0ih+XU8cmv6iwJ5+XHA5lzJ4h5+6dCu59qST947wxpUc8qqDzmsqEhavs+18o7eN3lTFrhi8yL5as06VNo99R80/HyAQ4FDt1no5t2qsqg9yXW977wc8qERWu1rMm5Jwzqg3to987DpczJU1/jnlX571+t0yJQKXtOqj19072c4vOXIEBARp94wDd/tSbcrlc6teltepUjdHns9wdllf2aK+hA3rokdc/0YDhz8rK6r5rL1ZEmLvSe/gL7+pocqoCAwM05qYBCisd+ncvh78RGODQqD4tdPuH8+WyLvVtVkt1KoTriz/c1WBXtHL/aD134161rR2tkBLHv9YeTk7XI98slstl5bJSj4ZV1amAzhz4TlGeVNffDFdQ8R1jzFhJKdbak76e55PVr2UDFSG1sjiYFBUhzGtQZLwQQBlwUTFv9f/8nQI8kgbf4O8UkMuKZdH/HASf6DhrsL9TgIfdsMTfKcAjZODjxfpLxpJK/f3+nbZ17FdF8j1myBIAAAAAAICPMWTJh6y1Y/2dAwAAAAAAvuL38pgijAoZAAAAAAAAH6NDBgAAAAAAeIXLGr///RNjTC9jzCZjzFZjzKgCll9rjFnj+fvdGNPkdLw3dMgAAAAAAICzkjEmQNJrknpLOlfSQGPMuSeE7ZB0gbW2saQnJU05Ha9NhwwAAAAAADhbnS9pq7V2u7U2U9KnkvrmDrDW/m6tPeK5u1hSldPxwkzqCwAAAAAAvMKexJAhbzPGDJU0NNdDU6y1f1W5VJa0J9eyvZJa/83T3STpx9ORFx0yAAAAAACg2PJ0vhQ2zKigHqMCLw5ljOkid4dMh9ORFx0yAAAAAADAK1z+TuCf7ZVUNdf9KpJiTwwyxjSW9D9Jva218afjhZlDBgAAAAAAnK3+kFTXGFPTGFNC0tWSpucOMMZUk/SVpOuttZtP1wtTIQMAAAAAAM5K1tpsY8xdkmZKCpD0jrV2vTHmNs/yNyQ9KqmcpNeNMZKUba1teaqvTYcMAAAAAADwClvgFC1Fi7V2hqQZJzz2Rq7bN0u6+XS/LkOWAAAAAAAAfIwOGQAAAAAAAB9jyBIAAAAAAPAKV4EXkIZEhQwAAAAAAIDPUSEDAAAAAAC8wnUGTOrrL1TIAAAAAAAA+BgdMgAAAAAAAD7GkCUAAAAAAOAVliFLhaJCBgAAAAAAwMeokAEAAAAAAF7h8ncCRRgVMgAAAAAAAD5GhwwAAAAAAICPMWQJAAAAAAB4BZP6Fo4KGQAAAAAAAB+jQgYAAAAAAHgFk/oWjgoZAAAAAAAAH6NDBgAAAAAAwMcYsgQAAAAAALyCIUuFo0IGAAAAAADAx6iQAQAAAAAAXsFlrwtHh0wRVyuL/7xFSUKAvzPAXzoGpfg7BXhUV7i/U4BH0uAb/J0CPMLef9ffKSCXsMYP+DsFeNjYrf5OAR427oC/UwDOegxZAgAAAAAA8DEqZAAAAAAAgFe4GPRRKCpkAAAAAAAAfIwOGQAAAAAAAB9jyBIAAAAAAPAKF1dZKhQVMgAAAAAAAD5GhQwAAAAAAPAK6+8EijAqZAAAAAAAAHyMDhkAAAAAAAAfY8gSAAAAAADwCpe/EyjCqJABAAAAAADwMSpkAAAAAACAV7gMl70uDBUyAAAAAAAAPkaHDAAAAAAAgI8xZAkAAAAAAHiF9XcCRRgVMgAAAAAAAD5GhQwAAAAAAPAKLntdOCpkAAAAAAAAfIwOGQAAAAAAAB9jyBIAAAAAAPAKl/F3BkUXFTIAAAAAAAA+RoUMAAAAAADwCpcokSkMFTIAAAAAAAA+RocMAAAAAACAjzFkCQAAAAAAeIX1dwJFGBUyAAAAAAAAPkaFDAAAAAAA8Aoue104KmQAAAAAAAB8jA4ZAAAAAAAAH2PIEgAAAAAA8AqXvxMowqiQAQAAAAAA8DEqZP6GMcYpaa3c79NGSYOttamn8Hw1JH1vrW10ejL0j5jOjdXyyetlHA5tnTpPG179Ll9MiyevV+WuTZWdlqFFw6boyNqdkqT6t/RS7Ws6S9Yq8c+9WjRsilwZWWr2yEBV7t5Mrsxspew6qEXDpigr6T+/1WeNap0bq9PY62UCHNowdZ6Wv55/W3R6/HpV92yLn++fokPrdkqSuj1/i2p0a6q0+CR9cuHonPjy51ZTlwk3KiA4SC6nU/Mfek9xq7b7qknFRpkLmqvyYzfLBAQo/tNZOjh5Wp7lEf0uUIXbBkiSXKlp2vPQZKVv3ClJOve3t+Q8liY5XbJOpzZfMtzX6Rc7g8bepKZdWigzLUNvPPCKdq7L/3+6x+De6nXjJYquEaNbmw5S8pFkSVKpsFIa+txdqlg9WlkZWXrzwVe1d/NuXzehWAhqcb5KDb1bcjiUPusHpX/xSYFxAXXrK/yF15XyzOPKXDhfklTq3pEqcX5buRKP6OidN/gy7bPSw+MnasHCpYqMKKtvPnrD3+kUe+Gdm6n6kzfKOBw6OPVn7X/16zzLy13WSZXu7CdJcqama+eoKUrdsFMlKpVT7ZfuUVCFCFmXSwc/mq24t3/wQwuKj4Ubd+nZr36Ty7p0WZtzdeOFLfIsf2/uCs1YtlmS5HRZ7Yg7ol/G3ajwUiX14bxV+nrxBhkZ1Y0pp8ev6argIL5u/VeO6g1V4oIrJYdD2et+U/aymfljqpzjiQmQTUtRxpcvHF9ojEoOHCObkqiM6a/5MHPg5FEh8/fSrLVNPR0omZJuO5mVjDHF9shrHEatxg/WL9c+q+87j1CNvm0UVrdSnphKXZsorGa0prcfriUj3tb5E4ZIkkKiI1Tvph76qfcj+qHraBmHQzX6tpEk7V+wVj90GaUZF45R0vb9anj3Jb5u2hnHOIw6jxus6YOe1cddR+icvm0UccK2qN6licrWjNaHHYdr7si31Xn8kJxlG79YoOnXP5fveds/NFBLJ32lT3s9pCXPT1O7MQO93ZTix+FQlSdv1fbBj+vPC+9UxKWdFFy3ap6QjD1x2nrlaG3qdY8OvPyZqk64M8/yrVc/pE197qMz5jRo2qW5omtW0v0X3KH/jZ6sG8fdWmDcpmV/avy1j+nQnoN5Hu971+XatWGHRvUapsn3v6RBY2/yRdrFj8OhUrffp6THRijx9sEK7tRNAVWrFxx3w63KWvFHnoczfv5RSY8+6KNk0a9Pd70xcZy/0zg7OByqMf4Wbbp2nNZ0vlfl+nZUSN0qeUIy9sRpw4BHtPbC+7Vv0heq+az7I6nNdmnXE+9rzQX3aP3Fo1RxSO986+LkOV0uTfhygV679WJ9Neoa/bRii7YdSMgTM6Rrc30+4mp9PuJq3XNxG7WoU0nhpUoqLjFFUxes0Sf3X6lpowbKaV36acUWP7WkGDBGJboMVMY3ryj9g7EKrNdKJjImb0xwiDtm+mtK//BxZfwwJc/iwKbd5Eo44LucUShbBP6KKjpkTt6vkuoYYy4xxiwxxqw0xvxsjKkoScaYscaYKcaYWZI+MMZUNMZ8bYxZ7flr53meAGPMW8aY9caYWcaYEL+16D8o16y2knfGKWX3IbmynNr17WJV7Zn3l4MqPVto+5e/SZLiV2xTifBSKlmhrCTJBAYooGQJmQCHAkNKKDXuiCTpwPx1sk736MLDy7cpNCbSd406Q1VsWluJO+OU5NkWm6cvVq0eebdFrR4ttHGae1vErdym4LBSCvVsi9glm5SemJLvea21KlHG/d+yRFiojnm2EU5eaNO6yti5X5l74mSzsnXku18V3r11npjU5X/KmXTMfXvFJgXFlPdHqmeFFt3P16/TfpEkbV25WaFhpVS2QkS+uF3rd+jw3kP5Hq9ct4rWL1wrSYrdtk9RVSoorHy4d5MuhgLPaSBn7D65DuyXsrOVsWCugtp0yBdX8pL+ylg4X66jeY892evXyCYn+yrds17LpucpPKyMv9M4K5RuVkfpO/crY7f7nJHw7W+K6Hl+npiUZZvkPOo+Z6Ss2KwSMeUkSVkHjyh1rbviz3UsXelb9yrIswz/3rpdB1W1fLiqlA9XUGCAejarq3lrdxQa/+OKLerVvG7OfafLKiMrW9lOl9IzsxUVXsoXaRdLjuiaskcPyiYdllxOZW9epoDaTfLEBNY7X86tq2STPeeLtOPnCFO6rAJqnqfsdb/5Mm3gX6ND5iR4Kl56yz186TdJbay1zSR9KmlErtAWkvpaa6+R9LKk+dbaJpKaS1rviakr6TVrbUNJiZIG+KQRp0lIdIRSY4//UpC6P0EhMXm/2IRGRyg1Nv54TGyCQqMjlHbgiDZOnqF+f7yk/qteVWZyqg7MX5fvNWoP7KTYuWu814hiolR0hFJybYuU/QkqHR1RQEz838ac6NexH6n9QwM1ZMlL6vDwQC16+rPTm/hZICi6nLL2H865n7X/sIKiC/+AHHl1dyXPW55z30qq/dETOuf7iSo3sKc3Uz0rRESXU0Ku/SDhQLwiKp58p+/uDTvVqre7mq92k7oqXzlK5f5me6JgjnLl5Tp8vPrIdfiQAsqVzxdTom1HZfw43dfpAX5TIrqcMnMdozL3xyvob36Yihp4oRJ/WZn/eapEKbRRTR1bsdkreZ4NDh5NUXRE6Zz7FcuW1kFPR9iJ0jKz9Pufu3Vh49o5sYO6NFWvx99X90ffVemQEmpXv5pP8i6OTKmyxztaJNnkIzKlyuaNiagolQxV8OX3q+TAMQpo0CZnWdAFVyrzt2kq2rURZw+X8f9fUUWHzN8LMcaskrRM0m5Jb0uqImmmMWatpAclNcwVP91am+a53VXSZEmy1jqttUc9j++w1q7y3F4uqcaJL2qMGWqMWWaMWTY3tWiVOhpTwP/mE49zBcZYlQgPVZWezfVt62H6qtndCgwNVo3+7fOENbznUtlsl3Z+tfD0JV1MFbQtrD2ZmL8/MZ13fTf9+vjHeq/1vfr18Y/V7blbTinPs1PB+0BBSrc9T+Wu6q7YCe/nPLal/0htvmiYtg9+XOUH9VGp8xsWuC5OTkGHpMK2R0GmT/5KpcJKafyMieoxpI92rt8up5PrBfxrBR2PTrgfOvRupb77puTi/cVZpMBjVMGhYe0aqcLAbtrz1Ad5HneEltQ5/xuhXY++I2dKWsEr4x8V9LYXeA6RtGDdTjWtGaPwUiUlSUmp6Zq3bod+eHSQZj0xRGkZ2fph2SbvJVvcncwXaOOQo0I1ZXzzqtK/fklB5/eRKVtBjprnyaYmyx5kvjcUfcV2rpPTJM1a2zT3A8aYVyRNtNZON8Z0ljQ21+KCu9Dzysh12ykp35Ala+0USVMk6eNK1xWpbt3U/QkKrXT8V5vQmEilHThSQMzxX49DK0UqNS5R0R0bKWXPIWUkuMsJ98xYpqiWdXM6X2pe0VGVL2ymOVdN8EFLznwp+xNUOte2KB0TmW94kTum3AkxiX/7vPUv76gFj30oSdr6/RJ1e/bm05f0WSLrwOE8Q5CCYsorKy4hX1zJ+jVU9Zm7tH3w43ImHi+zzT7ojs2OP6qjMxcrtGldHVu6Pt/6KFz3Qb3V5erukqTta7YqMtd+EBldTkcOnvxQvLSUNL354Ks591/67U0d2hN3+pI9S7gOH5KjfIWc+47yUXLFH84TE1innkqPfNS9PCxcJVq2kXU6lbWYknMUX5n741Ui1zGqREw5ZR3If84IaVBdNZ+/Q5uue1LZR44POTaBAar7vwd1+KsFOvLjEp/kXFxVDC+tA7ne27jEFEWFFTzs6KeVeYcrLd68V5UjwxRZ2v3RvlvjWlq144AualnPu0kXUzYlUabM8apuUyZC9ljiCTFH5EpPkbIzpexMufZtkSOqihwVqimgVhMF1GwkExAklQhRiZ43KnPmOz5uBfDPqJD598Il7fPcHvw3cXMk3S5JxpgAY0yYtxPzhfhV21WmZrRKVY2SIyhA1fu20d5ZK/LE7J21QrUud88LUK55bWUmpSr9YKKO7YtX+eZ1FBBSQpIU3aGhjm51v5UxnRur4Z0Xa/6QiXKmZfq2UWeouNXbVbZGtMI82+KcS9tox+y822LH7BVqMMC9LSo2q63M5FSlHkz82+c9FndElds0kCRVad9QiTuYDO3fSl29RcE1K6lE1YoyQYGKuKSjkmbn/ZAcVKm8ar45WruGTVLGjticxx0hwXKUCsm5XaZTU6Vv4heef2v2Bz9qTJ/7NabP/Vo2a4k6DugiSarT7BylJacq8V90yISGhSrAc5WMLld3159L1yuNX6D/tezNfyqgchU5KkZLgYEK7tRVWUvyVkMm3nS1Em90/2UsnK9jr0+iMwbFXsqqrSpZM0bBVSvIBAUqsm8HHZmVd1LrEpXL65z/jdC2e15S+vb9eZbVfOFOpW3ZpwNT8l9pEf9Ow2oVtPvwUe2LT1JWtlMzV27RBY1q5ItLTsvQ8m2x6tKoZs5jMWVLa82uA0rLzJK1Vku27FWtin8/TByFcx3YKVO2gkxYOckRoMBzWsq5bXWeGOe21XJUqisZhxQYJEd0TbkSDihr4TdKf3uU0t95SBk//k+uPf9v777DoyjXPo5/702hBUILJDSpAiq9iFgBFaygqMfej93X3nsFPdajx4IVez92BQTBIypSpShN6QQCCQFCevZ5/5ghpC0EZHdD+H28crk788zsPTPMzu699/PMfCVjoixYBf6qKlXI7Lx7gQ/NbBXwC9AmRLtrgFFmdhFeJczlQGqItnsMVxRk2h2jGfjOzVhMgD/fm8TGhavocM5AABa9OYHV42fRfFA3TvzpcYpy8vn5Om/E8/SZf7L8q185ZsyDuMIiNsxdxuK3vIE2+zx0HoEasQx8/1av7fTF/Hrra9HZyD2EKwoy6a7RnPjWzQRiAvz+/iQyFq7igLO9YzH3rQksnTCLfQZ249wfH6cgJ5/xN2wbfX7ws1fSvF9najZM4IJf/82Uxz/m9/cnMeGWVzjs3nMIxAYozCtgwq2vRGsT91xFQVbe/SJt37gXiwmQ8cF35C5aQaOzhgCQ/va3JF9zOjEN6tLyAf9OGf7trWMb16fNqNu99cTGkPnZJDZPmhHihaQyZk2YTvcBvXjyh+fJy8njxRufKZ538+t3Murm/5CZtoHB5x/H8ZcNo35SA0aOeYpZ30/npVueo3n7llz+xP8RLAqycvFKXipRLSM7IVjEluefot4Dj0EgQN64rylavpQax5wIsMNxYxJuvpu4Lt2xeonUH/0hOW+/Rt7YryMR+V7ppntGMnXmbDIzNzFo2NlccdE5DD9BY1qFRVGQpXe8TMd37sZiAqx7bzw5C1fQ5JyjAUh7cyzNrzuN2AZ1aT3iEgBcYRHzjrmZhL6dSDr1CLJ/X8oB47zb/a4Y8TYbJ+i6sStiYwLcOvxQLn/hc4JBx9ADO9M+pREfTvbGPDz14AMAmDD7Lw7q2JJaNeKKl+3SOpkju7XjjMc+ICYQoFOLxgzvry7Hu8wFyf/+PWqcdA1YgMJ5k3EZqcR2OQyAwjk/4DasoWjZPGqefRc457VJX72DFYtULbaj8SQkuqpal6W9XUZMtCOQrQ4NbIp2COJ7FJ0YVcUzB5Tv5iDRUW+0flSoSmZ0vTHaIYiv6ytHRDsE8bkF6g5dVdS+9sUqPOzs3/dii+h/p7105VtVch+ry5KIiIiIiIiISIQpISMiIiIiIiIiEmEaQ0ZEREREREREwsJVyc5CVYMqZEREREREREREIkwVMiIiIiIiIiISFlX5ttPRpgoZEREREREREZEIU0JGRERERERERCTC1GVJRERERERERMJCXZZCU4WMiIiIiIiIiEiEqUJGRERERERERMLCRTuAKkwVMiIiIiIiIiIiEaaEjIiIiIiIiIhIhKnLkoiIiIiIiIiERdCiHUHVpQoZEREREREREZEIU0JGRERERERERMIiWAX+dsTMhpjZAjNbbGa3VjDfzOzf/vzZZtZzF3ZFOUrIiIiIiIiIiMheycxigP8AxwD7AWeY2X5lmh0DdPD/LgGe3x2vrYSMiIiIiIiIiOyt+gKLnXN/OefygfeAoWXaDAXecJ5fgPpmlvJ3X1iD+oqIiIiIiIhIWFSmy1CUNQdWlHi+EjiwEm2aA6l/54VVISMiIiIiIiIi1ZaZXWJm00r8XVJydgWLuLKrqESbnaYKGRERERERERGptpxzo4BRIWavBFqWeN4CWL0LbXaaKmREREREREREJCxcFfjbgalABzNrY2bxwOnA52XafA6c699tqR+w0Tn3t7orgSpkRERERERERGQv5ZwrNLOrgDFADPCqc26emV3mz38B+Bo4FlgMZAMX7I7XVkJGRERERERERMIiWNHoK1WMc+5rvKRLyWkvlHjsgCt39+uqy5KIiIiIiIiISIQpISMiIiIiIiIiEmHqsiQiIiIiIiIiYRGMdgBVmCpkREREREREREQiTBUyIiIiIiIiIhIWlbjt9F5LFTIiIiIiIiIiIhGmhIyIiIiIiIiISISpy5KIiIiIiIiIhEVQnZZCUkKmiutaY2O0Q5ASeqyaEe0QxPddg/7RDkF8QwPx0Q5BfDOm6VhUFfW63hjtEKSEnrMfi3YI4puhc6PK6HBgerRDEF/ta6MdgUSLEjIiIiIiIiIiEha67XVoGkNGRERERERERCTClJAREREREREREYkwdVkSERERERERkbDQkL6hqUJGRERERERERCTCVCEjIiIiIiIiImGhQX1DU4WMiIiIiIiIiEiEKSEjIiIiIiIiIhJh6rIkIiIiIiIiImERtGhHUHWpQkZEREREREREJMJUISMiIiIiIiIiYRHUja9DUoWMiIiIiIiIiEiEKSEjIiIiIiIiIhJh6rIkIiIiIiIiImGhDkuhqUJGRERERERERCTClJAREREREREREYkwdVkSERERERERkbAIRjuAKkwVMiIiIiIiIiIiEaYKGREREREREREJi6CG9Q1JFTIiIiIiIiIiIhGmhIyIiIiIiIiISISpy5KIiIiIiIiIhIU6LIWmChkRERERERERkQhThYyIiIiIiIiIhIVuex2aKmRERERERERERCJMCRkRERERERERkQhTlyURERERERERCYughvUNSRUyIiIiIiIiIiIRpgoZEREREREREQkL1ceEpgoZEREREREREZEIU0JGRERERERERCTC1GVpF5hZETCnxKRhzrmlUQonqhIO60mze/4JgQAb3h/Huhc+KjW//tDDaXzZcACCW3JZfddz5P6xFIBA3Tq0eORqauy7DzjHqpufJnvmgkhvQrXz5BP3c8yQgWTn5HDRRdcxc9bccm1eeflJDju0Hxs3bQbgoouv47ff5nH4YQfxycevsmTpCgA+/fRrHnzoqUiGX200HNCd9g9egMUESH17PMuf+bTU/Nrtm9Hx6Sup26UNS0a8y4rnvyie1+LS40g5cxDgyPpjOQuueY5gXkFkN2APlzygKz3uPweLCfDXOxOZ/+wX5dr0eOBcUgZ1oygnn1+vfZENc5YC0OHiwbQ7awCY8dfb37PwpW+Ll+lw4dG0v+AoXFGQ1d/NYvaD70Zqk6qFRgO60fHB87GYAKvensDSZz4rNb92+2bs//Tl1OvShsUj3mPZ818Wz4utV5v9nriUhE4tcQ5+v+55Nk5bFOlNqDYSj+jBPg9ciAUCpL37HanP/rfU/EYnHUazK4cBUJSdy9JbR5H9+1LimzWi3dP/R1yTBrhgkLS3xrH2la+isAV7jzsffoIfJv9Kwwb1+fStF6IdTrWnc6PqiOvRl9oXXQ2BAHnffUXuJ+9U2C6mfSfqjXyOrMfvo+DnSdtmBALU+9coghnryHrotghFLRUJRjuAKkwJmV2T45zrvjMLmJkB5pyrPv8eAwGa3X8ZS865i8I16bT77Ak2fTeFvMUripvkr1jLX/+4jeCmLSQc3ovmD1/FnyfdCECze/7J5kkzWH7FSCwuFqtZI1pbUm0cM2QgHdq3odN+h3Bg357859kR9D/khArb3nLbg3zySfkPCj/++CtDTzov3KFWb4EAHUZexG+nPUDe6gx6jRnB+jHTyF64srhJQWYWi+94lcbH9C21aHxyQ5pffCxTD72OYG4++426jibDDmbN+xMjvBF7LgsYvR4+n4n/GEFOagZHffMAq8fOYNPCVcVtUgZ2o27bZL7ufwONeran18gL+O64e0js2IJ2Zw1g3LF3E8wv5LB3bmH1dzPJWrKWJv33o9ngXowZdBvB/EJqNKoXxa3cAwWMTiMvZMZpD5G7Op0Dx4xg3ZhpbClxXAoys1hwx+skHdO73OIdHzyf9O9/Y/bFT2JxMcTU0jVjlwUCtH74n8w//T7yU9PZ/+tHyRwzlZxF296j8las5ffhd1G0cQuJA3rQ5tHLmHf8rbjCIMvuH032nL8I1KnJAd8+xqYffiu1rOxew449ijOHn8jtDzwW7VCqP50bVUcgQO1LrmXzvTcQTF9HvUdfJP/XyQRXLivf7txLKZg1tdwqah5/CkUrl2G1a0coaJGdpy5Lu4GZJZjZeDObYWZzzGyoP721mf1hZs8BM4CWZnaTmU01s9lmdl90I/97anfrQP6yVApWrMUVFLLxix+od9SBpdpkz5hPcNMW7/HM+cQlNwYgkFCLOn0PYMP7YwFwBYUEN2+J7AZUQyecMJg33/aqlKb8OoPE+okkJzeJclR7n3o925OzZA25y9JwBYWkfTqZxkNKf8EsWL+JzbP+xBUUllveYgIEasZjMQFiatcgb01GpEKvFhr2aMfmpWvZsnwdwYIiln/2C80H9yrVpvmQXiz98H8ApM9YTFy92tRsUp+6HZqRPn0xRTn5uKIg6375gxbH9AGg3XmDmP/s5wTzvWOWl74pshu2h0vs2Z7sJWvJWZaGKyhizac/kTSkT6k2Bes3sWnWn7iColLTYxJq0eCgzqx6ewIArqCIwk3ZEYu9ukno0Z7cpankLfeu3xmf/UiDwaWTw1nTFlC00bsuZ81YSHxKIwAK0jaQPecvwKt8zV28kjh/noRH7+5dSKxXN9ph7BV0blQdsR06E0xdRXBtKhQWkv/jBOL7HlKuXY1jTyb/50m4jRtKTbdGScT16kfed1+WW0Yiz1WB/6oqJWR2TS0zm+X//RfIBU5yzvUEBgCP+xUxAB2BN5xzPfzHHYC+QHegl5kdFvnwd4/Y5EYUpK4vfl6wJp245NAXnob/OJrNk6YDEN8ymcKMjbT417W0//Ipmo+8GtOvnX9b82bJrFyxuvj5qpWpNG+WXGHbB+6/hRnTx/H4v+4lPj6+eHq/fr2YPm0cX37+Jvvtt2/YY66OaiQ3JG91evHzvNUZ1NjOuVFS/poMVjz/BQfNeJ6DZr9E4aZsNkyaHa5Qq6VayQ3JWbVt/2enZlAruUG5NtkljlFOaga1UhqwccFKkvp1Ir5BAjG14kkZ2J3azRoCULdtCo0P7MSRX93HgE/upGG3tpHZoGqi/HmRTo0yxyWUWvs0IT99E/s/fTkHfjeS/Z64lEBtXTN2VXxyI/JLHIv81HTiUhqGbJ90xpFkfj+z/HpaJFH7gDZsmbEwLHGKRJrOjarDGjamaH1a8fNg+joCjRqXaxPf71Dyxnxebvk6F15F9ugXIFh1v4iLgBIyuyrHOdfd/zsJMOBhM5sNfAc0B5r6bZc5537xHx/t/83Eq5jphJeg2TMV55y2ca7iN706/brQ4LSjWDPydW/R2Bhq7d+O9Le/ZvHx1xLMzqXJ5aeEM9q9glXymNxx5wj2P+Aw+h10HA0a1ufmm64AYMbMObRt35devY/iP8+9xscfvhr2mKul8oeh0pn52MQ6NB7Sh1/6XMnP3S4hpnYNmg4/dDcHWM1VsP8pex6EaLN50Wr++M8XHPH+rRz2zi1k/r6cYJHX0zQQGyA+sQ7fHXcPv93/DgeNunq3h16tVfD+VFmB2BjqdmnDitHjmHLkrRRl59Lm6qG7Mbi9TIX//ituWq//ATQ5YxArHnqj1PRA7Zrs+/LNLLv7VYqycnZ/jCLRoHOj6qjomlHmWNS56Gqy33gRgqVHhIjrfRDBjZkU/aWEmFR9SsjsHmcBSUAvf2yZtUBNf17JfjgGjCiRzGnvnHul7MrM7BIzm2Zm0z7avKzs7CqjMHU9cSnbMtVxyY0oXFu+a0XNTq1pPvJqll3yIEWZ3iCyBanrKViznpxZ3hvlxm8mU3P/dpEJvJq5/LLzmDZ1LNOmjmV16hpatGxWPK95ixRWp64tt8yaNd4vDvn5+Ywe/T59evcAYPPmLLZs8boBfPPtBOLiYmnUqHK/YMs2eakZ1Gi2rSKmRrOG5Fey21GDw7qQuzyNgvRNuMIi1n01hXp9OoYr1GopJzWDWs237f/aKQ3JWZtZrk3tEseoVkpDctZ4bZa8O4mxR9/J9yc9QH5mFll/rQG8SpuVX3t91DNm/QVBR41G6kZQWXmp6WXOi0bkrdmwnSW2yV2dTt7qdDbNWAzA2i+mULdLm7DEuTfIT00nvsSxiE9pREEF71G1Ou9Dm8euYOEFIyjckFU83WJj6PDyTaz/5Ac2fDMlIjGLRILOjarDpa8jpvG2bveBRkkEM9aXahPTriMJN9xN4ovvEX/Q4dS59Dri+h5CbKcDiO/Tn8QX3yPhhruJ69KTOtfeEelNkBKCVeCvqlJCZvdIBNKccwVmNgDYJ0S7McCFZpYAYGbNzazcAB/OuVHOud7Oud6n1A21qujLnr2IGq2bEdeiKRYXS+IJh7Hpu19LtYlrlkSr529j5fVPkL9kW1eawvWZFKSuJ75tcwAS+ncrNRiwVN7zL4ymd5+j6d3naD7/fAznnOVVGh3YtyebNm4qTr6UVHJcmRNPHMK83+cD0LRpUvH0Pr27EwgESE+v3Bcm2WbzzMXUaptCzVZNsLhYmgw7mPVjplVq2dxV66nXswOBWl43sgaHdiFbAwLulIxZf1G3TTJ1WiYRiIuh1dB+rBozvVSbVWNm0PpUr/KoUc/2FGzOITctE6B4sN7azRvR4tg+LPv0J2+Zb6fT9JD9AEhom0wgLpa89M0R2qo936aZf1K7bTI1WyVhcTEkD+vPukqeF/nrNpK7Op3a7VIAaHjoAWxZqPNiV2XNWkzNNinUaOm9RzUceggbxpYeEDO+eWP2fflm/vy/p8n9K7XUvDaPX0nOolWsGVX+7mUiezKdG1VH4aL5BFJaEGiSDLGxxB8ykIKpk0u12XjZ6Wy81PvL/3kSW158koJffyTnrZfI/OepbLz0dLIev5+COTPY8tRDUdoSke3TXZZ2j7eBL8xsGjALmF9RI+fcWDPrDPzsdy3JAs4Gyn9j3hMUBVl9zwu0eeM+77bXH35H3qLlNDxzCAAZ73xLk/87ndgG9Wj2wOUAuMIi/hx6PQCr73mRlk/egMXHkr98LStveipaW1JtfP3NeIYMGciCPyaTnZPDxRdfXzzvi8/e4JLLbiI1dS1vjn6WxkkNMTN++20eV1x5KwDDTz6OSy89l8LCInJzcjnr7CuitSl7NFcUZNFtr9D1vTu8216/+z3ZC1bS7NyjAFj9xjjik+rTa+xIYurWgqCjxSXH8euh17F5xmLWffkLvcc9iisqYvOcpax+87sob9GexRUFmXH76xz+7i3eba/fm8Smhatod+4gAP58Yzyp42eRMqg7x/38BIU5+fx63YvFyx/8yjXEN6iLKyhk+m2vU7DRqxpb8u5E+jx5CUO+H0mwoJAp1+j2szvDFQVZcNur9HzvdiwmwOp3J7JlwUpanHskACvf+I74pEQOHDuC2Lq1cEFHq0uO5adDb6AoK4f5t79Gl+euxuJjyVmWxrxrno/yFu3BioIsveNlOr5zNxYTYN1748lZuIIm5xwNQNqbY2l+3WnENqhL6xGXAN71e94xN5PQtxNJpx5B9u9LOWDc4wCsGPE2GyfMiNrmVHc33TOSqTNnk5m5iUHDzuaKi85h+AmDox1W9aRzo+oIFpH90lPUvecx77bX47+maMVSagw+EaDCcWNE9kQWaswPqRrmtDlBB6gK6bFKF9Wq4rsG/aMdgvjWBuJ33EgiomGw/F27JDrqxRREOwQpoeds3TK6qpjR9cZohyC+Dgem77iRRETD/07a9YHW9gBXtD4t6t9pn1v6QZXcx+qyJCIiIiIiIiISYeqyJCIiIiIiIiJhEfXymCpMFTIiIiIiIiIiIhGmhIyIiIiIiIiISISpy5KIiIiIiIiIhEVQnZZCUoWMiIiIiIiIiEiEqUJGRERERERERMIiGO0AqjBVyIiIiIiIiIiIRJgSMiIiIiIiIiIiEaYuSyIiIiIiIiISFk6D+oakChkRERERERERkQhThYyIiIiIiIiIhIUG9Q1NFTIiIiIiIiIiIhGmhIyIiIiIiIiISISpy5KIiIiIiIiIhIUG9Q1NFTIiIiIiIiIiIhGmChkRERERERERCQsN6huaKmRERERERERERCJMCRkRERERERERkQhTlyURERERERERCYug06C+oahCRkREREREREQkwlQhIyIiIiIiIiJhofqY0FQhIyIiIiIiIiISYUrIiIiIiIiIiIhEmLosiYiIiIiIiEhYBNVpKSRVyIiIiIiIiIiIRJgSMiIiIiIiIiIiFTCzhmY2zswW+f9vUEGblmb2vZn9YWbzzOyayqxbCRkRERERERERCQtXBf77m24FxjvnOgDj/edlFQI3OOc6A/2AK81svx2tWAkZEREREREREZGKDQVG+49HA8PKNnDOpTrnZviPNwN/AM13tGIN6isiIiIiIiIiYRGMdgB/X1PnXCp4iRcza7K9xmbWGugBTNnRipWQEREREREREZFqy8wuAS4pMWmUc25UifnfAckVLHrHTr5OAvAxcK1zbtOO2ishIyIiIiIiIiLVlp98GbWd+UeGmmdma80sxa+OSQHSQrSLw0vGvO2c+6QycSkhU8VNLEyMdghSwstJA6IdgvjiinKjHYL4ng2siXYI4hv37Q3RDkF8bvXiaIcgJczoemO0QxBfz9mPRTsE8RXOmRDtEGQvEfz7g+pG2+fAecBI//+flW1gZga8AvzhnHuisivWoL4iIiIiIiIiIhUbCRxlZouAo/znmFkzM/vab3MwcA4w0Mxm+X/H7mjFqpARERERERERkbDYDbedjirnXDowqILpq4Fj/cc/Araz61aFjIiIiIiIiIhIhCkhIyIiIiIiIiISYeqyJCIiIiIiIiJhEYx2AFWYKmRERERERERERCJMFTIiIiIiIiIiEhbO7dmD+oaTKmRERERERERERCJMCRkRERERERERkQhTlyURERERERERCYsg6rIUiipkREREREREREQiTBUyIiIiIiIiIhIWuu11aKqQERERERERERGJMCVkREREREREREQiTF2WRERERERERCQsnAb1DUkVMiIiIiIiIiIiEaYKGREREREREREJC932OjRVyIiIiIiIiIiIRJgSMiIiIiIiIiIiEaYuSyIiIiIiIiISFs6py1IoqpAREREREREREYkwJWRERERERERERCJMXZZEREREREREJCyC0Q6gClOFjIiIiIiIiIhIhKlCRkRERERERETCwqFBfUNRhYyIiIiIiIiISIQpISMiIiIiIiIiEmHqsiQiIiIiIiIiYRFUl6WQVCEjIiIiIiIiIhJhqpARERERERERkbBwThUyoahCRkREREREREQkwvaIChkzKwLmlJg0zDm39G+s70RgP+fcSDO7F8hyzj22k+s4HngAL6kVBzztnHvRzC4Dsp1zb+xqfFVdqyO6cti952AxAX5/dyLTn/uiXJvD7juHfQZ2pzAnj++uH8W6uUsBGPTYP2k9qDs56Zt458jbitsPee4q6rdNAaBGvdrkbcrmvSF3RGR79mTNj+hK3/vPwQIBFr07kTn/KX8s+t5/Di38Y/HjdaPI8I9FfL3a9H/sYhp0bIFzjsk3vMS66YsB6HTBUXS+4GiChUWsHD+L6Q+9F8nNqnbqD+hO2wcugJgAa98ez6pnPy01P+nkQ2l+1TAAirbk8ucto8j+fVnkA91LXHP/lfQbeCB5OXk8fN2jLJy7qFybWx67kU7d9sUwVixZycPXPkJOdm4Uoq1eJs/6g0de+y/BoOOkQQdy0bAjS83fnJ3D7f9+izXpmRQWFXHeCQMYNuBAAN7+ehIfj/8F5xzDBx3E2ccdHo1NqDYm/7GMRz/5kaALclK//bjwyF6l5r8+YQZfT1sIQFHQsWTtBr5/8EIS69TkzYmz+O8vv2MYHVIacd+ZA6kRt0d8pKyyEo/owT4PXIgFAqS9+x2pz/631PxGJx1GsyuHAVCUncvSW0eR/ftS4ps1ot3T/0dckwa4YJC0t8ax9pWvorAFe4c7H36CHyb/SsMG9fn0rReiHU61N3neEh79YLx3zTi4KxcOObDU/M05edzx6lesydhEYTDIuUf1YVj/LqzJ2MSdr39N+qYtmBnDD+nGWYN6hXgVkejaU66eOc657rtrZc65z4HPd3V5M4sDRgF9nXMrzawG0Npfd7V+d7aAccSD5/HpmSPJSs3gH1/ez1/jprNh0eriNvsM6Eb9Nsm8eegNNO3RjiMePp8PT7wXgD8+/IHZr4/jqKcuLbXeb694tvjxIXedSd6m7Ihsz57MAsaBD53H2DNGkp2awfFf38/ysdPZWOJYNB/YjXptkvnkkBtI6tmOg0acz1cn3At4iZpV389m4iX/JhAXQ2ytGgAk9+9Mq8G9+OzI2wjmF1KzUb1obF71EQjQdsTFzDvtfvJTM+j27Ugyxk4jZ+HK4ia5y9OYc9LdFG3cQv2BPWj/2GXMPva27axUdlW/gX1p0aYFZxxyLvv17MwNI67h0hOuKtfumXufIzvLex+66p7LOfmCYbz9HyUm/46iYJCHX/mYF++8jKaN6nPmbU9yRO8DaNciubjN+9/+SNsWyTxz6z/J2JTF0GtGcNyhvVi6eh0fj/+Ftx++jrjYGK54+EUO7bkf+6QkRXGL9lxFwSAjPvqBFy4/kab1EzjriQ85/IA2tEtuWNzm/IE9OX9gTwAmzV3CW5N+I7FOTdZmZvHuD7P55NYzqRkfy02vf8u3MxYx9MDO0dqcPV8gQOuH/8n80+8jPzWd/b9+lMwxU8lZtO06kbdiLb8Pv4uijVtIHNCDNo9exrzjb8UVBll2/2iy5/xFoE5NDvj2MTb98FupZWX3GXbsUZw5/ERuf2CnfseVXVAUDDLi3XG8cM1pNG1Ql7NGvMnhXdvRrlnj4jbvT5xJ25RG/PvKk8nYnM2we17huL77ERMT4IZTBtC5VVO25OZzxsNv0K/zPqWWlcjSoL6h7ZFdlswswczGm9kMM5tjZkP96a3NbL6ZvWxmc83sbTM70swmm9kiM+vrtzvfzJ4ts852ZjajxPMOZjY9RAh18ZJZ6QDOuTzn3AJ/uXvN7EYza2Zms0r8FZnZPmaWZGYfm9lU/+/gMOyisGnavR2ZS9eyafk6ggVFLPz8F9oeXTrj3PboXvzx8Y8ArJ35JzXq1aF2k/oArJ6ygNzMrO2+RvvjD2ThZz+HJf7qpHGPdmxeupYs/1gs+ewXWg0ufSxaDe7Fnx95x2LdjD+JT6xDrSb1iUuoRdMDO7Lo3YkABAuKyPeTYB3PPZI5//mCYH4hALnpmyK3UdVQ3R7tyV2yhrzlabiCQtZ9OpmGg/uUarN52gKKNm7xHk9fSHxKw4pWJbvBIYMP5tuPxgLw+4w/SEhMoFGT8vt7azIGoEbNeNT1+e+bu3g5LZMb06JpY+JiYxnSvwcTp84t1cbMyM7NwzlHdm4eiQm1iQkEWLJqLV077EOtGvHExsTQq3N7Jvw6O0pbsuebuyyNlo0TadE4kbjYGAb36MDEOUtCtv9mxiKG9OxQ/Lwo6MgrKKSwKEhufiFJiXUiEXa1ldCjPblLU8lbvhZXUEjGZz/SYHDfUm2ySlwnsmYsJD6lEQAFaRvInvMXAMEtueQuXkmcP092v97du5BYr260w9grzF2aSssmDWiRVN97n+rTiYmzF5dqYwZbcvNxzpGTl09inZrEBAIkJSbQuVVTAOrUjKdtciPSdvD9QyRa9pSETK0SiY3/ArnASc65nsAA4HEzM79te+BpoCvQCTgTOAS4Ebg91As45/4ENppZd3/SBcDrIdpm4FXYLDOzd83sLDMLlGmz2jnX3a/seQn42Dm3zI/tSedcH2A48PLO7YroqpPcgKzVGcXPs1IzSEhuUEGb9O22CaXZgR3JXr+RjUvX7p6Aq7HayQ3YUuJYbEnNoHaZ/ey1SS/Xpu4+SeSmb+aQJy/hhDEP0v9fFxdXyCS2TaZp344c98W9DPnoDhp1axuZDaqm4lMakr96ffHz/NR0amwn4dL0zEFkTpgZidD2SknJjUlbva74+brUdTROrvgXs9ueuInPZn1Eq/at+PjV/1bYRiovLSOT5Eb1i583aZTI2oyNpdqcPuQQ/lq1liMvvYdTbniUmy8YRiAQoH3LFKb/8ReZm7eQk5fPjzN/Z016ZmQ3oBpJ25hFcoOE4udN6yeQ5n/ZLysnv4Cf5i/nyK7titueO6A7Q+4bzVF3v0ZCrXj6d2oVkbirq/jkRuSXuFbnp6YTt53rRNIZR5L5ffnrRHyLJGof0IYtMxaGJU6RSErbkEVyg23Jr6b165K2oXRS5fQjerJkTTpH3fI8pzzwOjedNpBAwEq1WbV+I/NXrKVLm5SIxC0Vc1Xgv6pqT0nI5GxNbjjnTgIMeNjMZgPfAc2Bpn7bJc65Oc65IDAPGO+8YZ3n4Hcr2o6XgQvMLAb4B/BOqIbOuYuBQcCveMmeVytq51fAXAxc6E86EnjWzGbhJXXqmVndMstcYmbTzGza5KzyYxtE07a81zZlfzmuuE3lToJ9hx7EIlXHVE4F+7nce02FbRwWE0OjLq2Z/8Z4vhh8J4XZeXS56gRvkZgA8Yl1+OqEe5n24Lsc8UL57hyyE3bifEg8eH+anjGQpQ++Fe6o9loVnxIVH48R1/+Lk3qexrJFyxh04hHhDWwvUNFuLns8fvptPp32acZ3L97HB/+6kRGvfEJWdi5tWzTlgqEDufTB57ni4RfZd59mxAb2lI8wVU9F/+IrOjcAfpi7lO5tUkisUxOATdm5TJy7hK/uPpex959PTl4hX01bEL5g9wYV7fsQH5vq9T+AJmcMYsVDpYcqDNSuyb4v38yyu1+lKCtn98coEmGVeZ/6ad4SOrZowrhHLuf9O85j5HvjycrJK56fnZvPjaM+46bTBpLg//AoUtXsqZ9mzgKSgF5+BcpaoKY/L69Eu2CJ50F2PGbOx8AxwPHAdOdc+vYa+4mfJ4Gj8KpdSjGzFOAV4B/Oua0p3QBwUIkEU3Pn3OYy6x3lnOvtnOt9cEKHsquNqqzUDBKabfvVJiGlIVvWbqigTaMybTJ3uG6LCdBuSB8Wfj5lt8VbnWWnZlCnxLGok9KQ7DLHwmvTqEybTLJTM8hOzWD9zD8BWPrVrzTs0tpfZgPLv5kGwPpZf+GCjhoNVZ67q/JXpxNfos9yfEoj8tdsKNeudud9aPf45fxx/iMUblBZ7e500nlDeXXsi7w69kXWr0mnSbNt444kpSSRvjb0W30wGGTC5xM5/LjDIhFqtda0Uf1SVS1p6Rtp0iCxVJvPvv+VQQd2xcxolZxE8yYNWbLaq5g8eWA/3n/kRl6772oSE+rQSuPH7LKmiQmsKfE+szYzi6R6FXc7+nZm6e5KvyxcSfOG9WiYUIu4mBgGdW3LrCVrwh5zdZafmk58iWt1fEojCtZklGtXq/M+tHnsChZeMKLUdcJiY+jw8k2s/+QHNnyjz1BSPTRtkMCaDdu+Iq3N3ExS/YRSbT77eS6DeuzrXTOaNKB540SW+OdOQVERN4z6jGP7dmZQj30jGrvIzthTEzKJQJpzrsDMBgD77I6VOudygTHA88Brodr5Y9gcUWJSd2BZmTZxwAfALc65krWjY4GrSrTr/jfDjqi1v/1F/dbJ1GuZRCAuhn1P7MeScTNKtVkybgadhx8CQNMe7cjfnE12WuYO193y0APY8OdqtlTwIUTKWz/rL+q1SSbBPxZthvZjxdjSx2LF2Bm0O8U7Fkk925G/KZuctExy1m1ky+oM6rXzyjebHbI/GxeuAmD5mGkkH7wfAPXaJhMTH0teRqmcoeyEzbMWU6ttCjVaNcHiYkkadjAZY6eWahPfvDGdXr2RRVc9Q+5fqVGKtPr67+jPuPDoS7nw6Ev535jJDDnlaAD269mZrE1bSE8r/57TvHWz4sf9jzqIZYuXRyze6mr/di1ZnrqOlWnpFBQW8u1PMzm89/6l2iQ3bsCUOV5laHrmZpauXkeLJt4X1fSN3vtQ6voNjP91Nscc3DOyG1CN7N+qCcvXb2RV+iYKCosYM3MRhx/Quly7zTl5TP9zNQMOaFM8LaV+ArOXrSEnvwDnHFMWraRt08p1S5aKZc1aTM02KdRo6V0nGg49hA0VXCf2fflm/vy/p8tdJ9o8fiU5i1axZlT5Oy2K7Kn23yeF5WkbWLU+03ufmjqfw7u2L9UmpWFdpsz3voKlb9rC0jUZtEhKxDnHfW98S5vkRpxzZJ+KVi8RFnQu6n9V1Z5yl6Wy3ga+MLNpwCxg/m5e98l4iZNQDLjZzF4EcoAtwPll2vQH+gD3mdl9/rRjgf8D/uN3t4oFfgAu223Rh5krCjLprtGc+NbNBGIC/P7+JDIWruKAswcCMPetCSydMIt9Bnbj3B8fpyAnn/E3jCpefvCzV9K8X2dqNkzggl//zZTHP+b39ycBsO+J/TSY705wRUF+uXM0R71zMxYIsPj9SWQuXEXHc7xjseDNCawcP4vmA7tx8uTHKcrJ58frtx2LKXeN5rBnLicQF0vW8rTieYvem8TBj1/C0PEjCBYU8b9rX4zK9lUbRUH+uv1l9n/3TogJkPbuBHIWrCT5XC8psOaNsbS6/hTiGtSl7ciLi5f5bfAtUQy6+vp5/BT6DTyQ9ya/SW5OLiOu/1fxvEffeJhHbnqcjLQM7njqFmon1MbMWPz7nzx+29NRjLp6iI2J4bYLh3P5Qy8SDAYZNuBA2rdM4YOxkwE47eiDuWT40dz13DsMv+FRHI5rzzqeBvW8X0RvePw1Nm7OJjY2htsvGk69hNrR3Jw9WmxMgFuHH8rlL3xOMOgYemBn2qc04sPJ3iDLpx58AAATZv/FQR1bUqtGXPGyXVonc2S3dpzx2AfEBAJ0atGY4f33r/B1pJKKgiy942U6vnM3FhNg3XvjyVm4gibneNeJtDfH0vy604htUJfWIy4BwBUWMe+Ym0no24mkU48g+/elHDDucQBWjHibjRNmhHw52XU33TOSqTNnk5m5iUHDzuaKi85h+AmDox1WtRQbE+DWfxzJ5f/+iGAwyND+XWjfrDEf/jALgFMP684/j+3P3aO/5pT7X8MB1558GA0SajNz8Uq+nPI7HZo35rQHXwfg6qGHcWgXjYsoVY9VdmyPvYWZ3QgkOufuinYsAM+0PFsHqAqpG4x2BLJVh6LcaIcgvltjynfBkugY9/UN0Q5BfG714h03koiZfdHEaIcgvp6zdcvoqqJwzoRohyC+WgMuDjGSV/VwaPNBUf9O+79V46vkPt5TK2TCwr+DUztgYLRjEREREREREZHqSwmZEvw7OJXiJ2nalJl8i3NuTGSiEhEREREREZHqRgmZHagoSSMiIiIiIiIiOxas8EbmAnvuXZZERERERERERPZYSsiIiIiIiIiIiESYuiyJiIiIiIiISFioy1JoqpAREREREREREYkwVciIiIiIiIiISFg4pwqZUFQhIyIiIiIiIiISYUrIiIiIiIiIiIhEmLosiYiIiIiIiEhYaFDf0FQhIyIiIiIiIiISYaqQEREREREREZGwcKqQCUkVMiIiIiIiIiIiEaaEjIiIiIiIiIhIhKnLkoiIiIiIiIiEhXPqshSKKmRERERERERERCJMFTIiIiIiIiIiEha67XVoqpAREREREREREYkwJWRERERERERERCJMXZZEREREREREJCw0qG9oqpAREREREREREYkwVciIiIiIiIiISFhoUN/QVCEjIiIiIiIiIhJhSsiIiIiIiIiIiESYuiyJiIiIiIiISFg4dVkKSRUyIiIiIiIiIiIRpgoZEREREREREQmLoG57HZIqZEREREREREREIkwJGRERERERERGRCFOXJREREREREREJCw3qG5oSMlVc3WC0I5CSVsTqzaSqaFWoAr+qonYgPtohiM/9PiXaIYjPrV0T7RCkhA4Hpkc7BPEVzpkQ7RDEF9tlYLRDENnr6RuNiIiIiIiIiEiEqUJGRERERERERMJCd1kKTRUyIiIiIiIiIiIRpgoZEREREREREQkLDeobmipkREREREREREQiTAkZEREREREREZEIU5clEREREREREQmLPX1QXzNrCLwPtAaWAqc55zaEaBsDTANWOeeO39G6VSEjIiIiIiIiIlKxW4HxzrkOwHj/eSjXAH9UdsVKyIiIiIiIiIhIWLgq8N/fNBQY7T8eDQyrqJGZtQCOA16u7IqVkBERERERERGRasvMLjGzaSX+LtmJxZs651IB/P83CdHuKeBmIFjZFWsMGRERERERERGptpxzo4BRoeab2XdAcgWz7qjM+s3seCDNOTfdzI6obFxKyIiIiIiIiIhIWOwJg/o6544MNc/M1ppZinMu1cxSgLQKmh0MnGhmxwI1gXpm9pZz7uztva66LImIiIiIiIiIVOxz4Dz/8XnAZ2UbOOduc861cM61Bk4HJuwoGQNKyIiIiIiIiIhImER7QN/dMKjvSOAoM1sEHOU/x8yamdnXf2fF6rIkIiIiIiIiIlIB51w6MKiC6auBYyuYPhGYWJl1q0JGRERERERERCTCVCEjIiIiIiIiImHhXKXvAr3XUYWMiIiIiIiIiEiEqUJGRERERERERMIi+PcH1a22VCEjIiIiIiIiIhJhSsiIiIiIiIiIiESYuiyJiIiIiIiISFg4py5LoahCRkREREREREQkwlQhIyIiIiIiIiJhoUF9Q1OFjIiIiIiIiIhIhCkhIyIiIiIiIiISYeqyJCIiIiIiIiJhoUF9Q1OFjIiIiIiIiIhIhCkhIyIiIiIiIiISYeqyJCIiIiIiIiJhEVSXpZBUISMiIiIiIiIiEmGqkJGd1vyIrvS9/xwsEGDRuxOZ858vyrXpe/85tBjYncKcPH68bhQZc5cCEF+vNv0fu5gGHVvgnGPyDS+xbvpiul9/Mh3OPIK8jM0ATB/5Aasm/BbJzao2Bt97Lu0HdKMgJ5/Pb3yRNf6+L6l+yyROfuYqatZPYM3cpXx63XMEC4qK56d0bcuFn97HJ1c9wx9f/xrB6KuPhgO60eHBC7CYAKlvj2fZM5+Vml+7fTM6P30Fdbu04c8R77Hi+W3nUctLjyPlzIGAY8sfK/jjmucI5hVEeAuql8vvu4y+A/uQm5PH49c/zuK5f5Zrc8u/b6ZD1w4UFRayYNZCnr713xQVeudF135duOzeS4mNjWXjhk3cdOrNkd6EamHyolQe/XYGwaDjpJ5tufDQ/UrNf33yH3w9exkARUHHkvWb+P6mYdSMi+XC18ZTUBSkMBjkyP1acsWALtHYhGojsM/+xB9+GgQCFM79kcJpY8q3abGv3yYGl5NF3kePb5tpRs0zbsdlZZL3+X8iGHn1FNejL7UvuhoCAfK++4rcT96psF1M+07UG/kcWY/fR8HPk7bNCASo969RBDPWkfXQbRGKunqaPG8Jj34w3nufOrgrFw45sNT8zTl53PHqV6zJ2ERhMMi5R/VhWP8urMnYxJ2vf036pi2YGcMP6cZZg3pFaSuqvzsffoIfJv9Kwwb1+fStF6IdjuyAQxUyoWw3IWNmjYDx/tNkoAhYB7QH3nDOXbGdZbOccwmVCcLMjgDynXM/7aDd2cDNQAxQCEwFbnTOZVbmdXYXM6sNvAR0BQzIBIY457LM7CfnXP9IxhNJFjAOfOg8xp4xkuzUDI7/+n6Wj53OxkWri9s0H9iNem2S+eSQG0jq2Y6DRpzPVyfcC3iJmlXfz2biJf8mEBdDbK0axcv9/tK3zHvx60hvUrXSfkA3GrZJ5j+H30DzHu059sELeHXYPeXaDbr1dKa88g3zvviFYx+6kB7/OILpb3mnugWMQbedzp8/zI50+NVHwOg48iJmnvYgeavT6T1mBOvGTCN74ariJgWZWSy84zWSjulTatH45Aa0uPgYphx6HcHcAvYfdR1NhvVnzfuTyr6KVFKfAX1o3qYZFxx6EZ16dOLqh6/imhOvK9duwn+/55H/exSAW5+9hWPOGMKXb35FnXp1uOqhq7jjnDtZt3odiY0SI70J1UJRMMiIr6fxwjkDaFqvFme9NI7DOzanXZNt+/P8gztz/sGdAZi0YBVv/byAxNo1cM7x0nkDqF0jjoKiIBe8+h2HtE+ha8vG0dqcPZsZ8QPOIO+Tp3BZG6h5xm0U/TUbl5G6rU2NWl6bT/+N27wBatUttYrY7oMIZqzB4mtGOPhqKBCg9iXXsvneGwimr6Peoy+S/+tkgiuXlW937qUUzJpabhU1jz+FopXLsNq1IxR09VQUDDLi3XG8cM1pNG1Ql7NGvMnhXdvRrtm295r3J86kbUoj/n3lyWRszmbYPa9wXN/9iIkJcMMpA+jcqilbcvM54+E36Nd5n1LLyu4z7NijOHP4idz+wGPRDkXkb9lulyXnXLpzrrtzrjvwAvCk/zxhe8mYXXAEsN0khpkNAa4DjnHO7Q/0BH4CmlbQNmY3xlaRa4C1zrkuzrkDgIuAAoDqnIwBaNyjHZuXriVr+TqCBUUs+ewXWg0unf1vNbgXf370IwDrZvxJfGIdajWpT1xCLZoe2JFF704EIFhQRP6m7EhvQrW271G9mP3x/wBYNXMxNevVJqFJ/XLtWvffn9/9ypffPv6Bjkf3Lp7X5/zBzP9mKtnrN0Uk5uqoXs/2ZC9ZQ+6yNFxBEWmf/kTSkNKJl4L1m9g8609cicqkrSwmQKBmPBYTIKZ2PPlrNkQq9GrpoKP78d3HXsJx/sz51KmXQMMmDcq1m/r9ti85C2YtoHGK9yF6wLAjmPztZNatXgfAxvSNEYi6+pm7KoOWDevSomECcbExDD6gFRMXrArZ/ps5yxjSZR8AzIzaNeIAKCwKUljkMLOIxF0dBZLb4Dam4Tath2ARhQunEdOuW6k2sR37UrR4lpeMAcjZXDzPEuoT06YLhXN/jGTY1VZsh84EU1cRXJsKhYXk/ziB+L6HlGtX49iTyf95Em5j6WuCNUoirlc/8r77MlIhV1tzl6bSskkDWiTV996n+nRi4uzFpdqYwZbcfJxz5OTlk1inJjGBAEmJCXRu5X0tqVMznrbJjUjLzIrGZuwVenfvQmK9ujtuKFLF7dIYMmZ2hJl96T9OMLPXzGyOmc02s+Fl2jY2s5/N7DgzSzKzj81sqv93sJm1Bi4DrjOzWWZ2aIiXvQOvGmYVgHOuyDn3qnNugf86S83sbjP7ETjVzM7wY5prZo+UiCerxONTzOx1//HrZvaCmf3PzBaa2fHb2QUpQPGnSOfcAudcXsn1m9n9/vbMMrNVZvaaP/1sM/vVn/5iBJJHu1Xt5AZsWZ1R/HxLaga1kxtU0Ca9XJu6+ySRm76ZQ568hBPGPEj/f11cqkKm8wVHceK4hzn48X8Sn6hfeHZF3eSGbCqx7zetyaBu09LHp1aDBHI3bcEVBQHYnJpBXf8Y1m3agE6DezP9re8iF3Q1VCO5IXkljkPe6nRqJDes1LL5azaw/Pkv6D/jeQ6ePYrCTdlkTFK10t/ROLkR61avL36+PnU9jZJD/2IZExvDoJMHMW3iNABatGlBQmICj37wCM9+9W+OHD4o7DFXR2mbckiut+29vWm9WqRtyqmwbU5+IT8tXsORnVsUTysKBjnt+W8Z+K9P6deuKV1aNAp7zNWV1am/LdECuM0bsDr1S7dp0BRq1qbGKddT84zbiencr3he3OGnkf/jx6AS9N3CGjamaH1a8fNg+joCjRqXaxPf71Dyxnxebvk6F15F9ugXIKjj8XelbcgiucG2L/lN69clbUPppMrpR/RkyZp0jrrleU554HVuOm0ggUDpBPGq9RuZv2ItXdqkRCRukarOORf1v6pqdwzqexew0a8W6QpM2DrDzJoCXwF3O+e+Ap7Gq7LpAwwHXnbOLaV09c3/QrzO/sCMHcSS65w7BPgBeAQYCHQH+pjZsEpsS2vgcOA44AUzC1WH+ypwi59oetDMOpRt4Jy7268sOhxIB541s87AP4CD/XlFwFmViKvqqOgXybL/vits47CYGBp1ac38N8bzxeA7KczOo8tVJwAw/43v+Lj/9Xx+9B1kp2XS5+49a7dUFRXvelemTehjePQ95zB+5Hs4faj7eyrYx5XtOxubWIekIX34uc+VTO52KTG1a9J0eKg8tVRKRcdjOxfmqx+6krlT5jL313kAxMQG6NClA3eddze3n30nZ15zBs3bNA9buNVVRedAqCKXHxaupnurxiTW3pa0jwkE+ODyIYy5/kTmrspg8drMMEW6F6hMcZEFCDRpRd6nz5L736eJ63ssVr8JgTZdcNmbcWnLwx7mXqMSn63qXHQ12W+8CMFgqelxvQ8iuDGTor8WhjHAvUdFV4ayh+eneUvo2KIJ4x65nPfvOI+R740nKyeveH52bj43jvqMm04bSEKJHx5FRCqyOwb1PRI4fesT59zWn1zi8MafudI5N6lE2/1KfCGsZ2Y7XWtmZl2AN4G6wO3Ouff9WVv/3weY6Jxb57d/GzgM+HQHq/7AORcEFpnZX0AnYFbZRs65WWbWFjja36apZnaQc+6PMnEa8DZesmm6mV0F9PLbA9QC0ijDzC4BLgE4L7EvR9Qpl++JmuzUDOo02/ZLf52UhmSv3VBBm0Zl2mSCc2SnZrB+pjeY5tKvfi1OyOSW6B6z6O3vGTT6hjBuRfXS+9yj6HH6AABWz/6LeiX2fb3khmSlZZZqn52xmZr16mAxAVxRkLopDdnsH8OUrm04+ZmrAKjdsC7tB3QjWFjEgrHTI7Mx1UReajo1ShyHGs0aVbrbUYPDupCzPI2CdK97wLqvppDYZ1/WfhwqVy0VOeG84znmjCEALPxtIUkl+vA3TmlMxtr0Cpc769ozSWyUyNO3Plg8bV3qejZmbCIvJ4+8nDzmTJlL2/3asGpJ6O42Ul7TerVZU6Kb6tpNOSTVrVVh22/nLmPIAa0qnFevVjy9Wzdh8uI1tG9aPxyhVnsuKxOru6160uo2wG3JLNNmA8HcLCjMh8J8gqsWEUhqQaBJK2LadiOmzQFYTBzE1yJ+8IXkj3k1wltRfbj0dcQ0blL8PNAoiWDG+lJtYtp1JOGGu735dROJ69WPLUVFxO7bmfg+/YnrdSAWF4/VrkOda+9gy1MPRXQbqoumDRJYs2Fb97y1mZtJql96SMzPfp7LhYMPxMxo1aQBzRsnsmRNBl3apFBQVMQNoz7j2L6dGdRj30iHL1JlBVVRGdLuqJAxKk4oFwLTgcFlXu+grePSOOeaO+c2V7BsRebhjRuDc26OX2HyDV5SY6stJWIKpWSsZStgym5HyH85zrks59wn/lg6bwHHVtDsXmClc+61EnGNLrH9HZ1z91aw7lHOud7Oud5VKRkDsH7WX9Rrk0xCyyQCcTG0GdqPFWNLFy6tGDuDdqd4fZ+TerYjf1M2OWmZ5KzbyJbVGdRr55VvNjtkfzb6g5zWKjHOSatjepO5YGVkNqgamPbGOF469nZeOvZ2FoydRle/mqJ5j/bkbs4pl5ABWPrz7+x3bF8Aug0/jAXjvITLs4dcxzOHXMszh1zLH1//yjd3va5kzC7YPPNPardNoWarJCwuhibD+rN+zLRKLZu3aj31enYgUCsegAaHdiF7kb7476wvRn/JFUOu4oohV/HTmJ+Luxl16tGJ7M1byEgrnyAbcvpgeh/eixFXPVKqgubnsb9wQN8DCMQEqFGzBp16dGT54hUR25bqYv9mDVmevplVG7IoKCxizNzlHN6xfKXR5tx8pi9dx4BO27orZWzJZVNOPgC5BYVM+WsNbRpr7IBdFVyzFKvfBKvXCAIxxO7bm6I/S9/ZsOjP3wg06wAWgNg4AsltCGasoWDyp+S+ciu5r95B3jcvE1wxX8mYv6lw0XwCKS0INEmG2FjiDxlIwdTJpdpsvOx0Nl7q/eX/PIktLz5Jwa8/kvPWS2T+81Q2Xno6WY/fT8GcGUrG/A3775PC8rQNrFqf6b1PTZ3P4V3bl2qT0rAuU+Z7Ay6nb9rC0jUZtEhKxDnHfW98S5vkRpxzZJ+KVi8iUs7uqJAZC1wFXAtgZg38KhkHXAh8aGa3OudGlmj7L79td+fcLGAzUG8HrzMCeMzMhjrntn5br/inNZgCPG1mjYENwBnAM/68tX7XoQXASf5rb3WqmY0G2gBt/TblmNnBwO/OuQ1mFg/sB0ws0+Z44Ci8AYu3Gg98ZmZPOufSzKwhUNc5V2YY/arLFQX55c7RHPXOzVggwOL3J5G5cBUdzxkIwII3J7By/CyaD+zGyZMfpygnnx+vH1W8/JS7RnPYM5cTiIsla3la8bzed55Ow/32wTlH1sr1/HyLPtztisUTZtF+QHeu/OEJCv3bXm91+us38eXNL5GVlsn4Ee9y8rNXc8SNp7Jm3jJmvT8xekFXQ64oyMLbXqX7e3dgMQFWv/s9WxaspNm5RwGw+o1xxCcl0nvsSGLr1sIFHS0vOZYph17PphmLWfflL/QZ9wiuqIisOUtZ9abG9Pk7fp0wlT4D+/Daj6+Sl5PL4zc8WTzvgdH38+TNT5GxNoP/G3E1a1el8dSnTwAw+ZufePvpd1ixeAXTJk7jhbHP41yQb98dw7IFe8zbdpURGxPg1mN7cfmbkwi6IEN7tKV9k0Q+nOoNmHlqH+9Lz4Q/VnJQu2RqxW/7iLJ+cy53ffoLwaAj6ODo/VtyWAXJHKkkFyT/+/eocdI1YAEK503GZaQS2+UwAArn/IDbsIaiZfOoefZd4JzXJn31DlYsuyRYRPZLT1H3nse8216P/5qiFUupMfhEgArHjZHwiI0JcOs/juTyf39EMBhkaP8utG/WmA9/mAXAqYd155/H9ufu0V9zyv2v4YBrTz6MBgm1mbl4JV9O+Z0OzRtz2oOvA3D10MM4tEvbqG1PdXbTPSOZOnM2mZmbGDTsbK646ByGnzB4xwuKVDFW2QFuzOxeIMs595h/m+obnXPHm1kC8B+8rjhFwH3OuU+23vbaT1h8AXwGfOC37YyXDPrBOXeZme0LfAQEgatDjSNjZucBN+Ld9joTmAvc45xLNbOlQG/n3Hq/7ZnAbXhVKV875272p5+CN77MCn/5BOfc+f7gvhuA3nh3brreOVfhcPVmdq4fh+FV/XwF3OKccyW2+3u8xE6mv9jnzrm7zewfflwBvDszXemc+yXUfn+9+dmq76pCVsTqcFQVB+cWRDsE8T0SX9lCRwm3Tx/tG+0QxOfWrol2CFJC7qT50Q5BfLX+75xohyC+2C4Dox2C+OIat63Wtw5sXG/fqH+JWr9pYZXcx5VOyFR3fkLmS+fcR9GOpSQlZKoWJWSqDiVkqg4lZKoOJWSqDiVkqhYlZKoOJWSqDiVkqg4lZMKvqiZkdkeXJRERERERERGRcoIqAgmpyiVkzOwO4NQykz90zoV1hDLn3PkVxDIYr3tTSUuccyeFMxYRERERERERqd6qXELGT7xUieHhnXNjgDHRjkNEREREREREqpcql5ARERERERERkepB49aGFoh2ACIiIiIiIiIiextVyIiIiIiIiIhIWARRhUwoqpAREREREREREYkwJWRERERERERERCJMXZZEREREREREJCw0qG9oqpAREREREREREYkwJWRERERERERERCJMXZZEREREREREJCyC6rIUkipkREREREREREQiTBUyIiIiIiIiIhIWDlXIhKIKGRERERERERGRCFNCRkREREREREQkwtRlSURERERERETCQoP6hqYKGRERERERERGRCFOFjIiIiIiIiIiEhVOFTEiqkBERERERERERiTAlZEREREREREREIkxdlkREREREREQkLBzqshSKKmRERERERERERCJMFTIiIiIiIiIiEhYa1Dc0VciIiIiIiIiIiESYEjIiIiIiIiIiIhGmLksiIiIiIiIiEhbqshSaKmRERERERERERCJMFTIiIiIiIiIiEhaqjwlNFTIiIiIiIiIiIhGmhIyIiIiIiIiISISZBtiRSDCzS5xzo6Idh+hYVCU6FlWLjkfVoWNRdehYVB06FlWHjkXVouMhezJVyEikXBLtAKSYjkXVoWNRteh4VB06FlWHjkXVoWNRdehYVC06HrLHUkJGRERERERERCTClJAREREREREREYkwJWQkUtSvs+rQsag6dCyqFh2PqkPHourQsag6dCyqDh2LqkXHQ/ZYGtRXRERERERERCTCVCEjIiIiIiIiIhJhSsiIiIiIiIiIiESYEjJSITO7w8zmmdlsM5tlZgfuhnWeaGa37qb4snbHevZkZlbkH5u5ZvahmdXeTtt7zezGSMYnnnCcS7JrzOwkM3Nm1inasexNKjoHzOxlM9vPn1/h+7mZ9TOzKf4yf5jZvRENvBrametGJdfX2szm7q749mYljs3Wv9bRjmlPs7v3YcnPrbv6OcrMjjezmWb2m5n9bmaX+tMvM7Nz/0584WBmjUrsvzVmtsp/nGVmz+1g2Up/NjezI8ysfyXane1fO+b5+/BlM6tf2dfZXcystpm9bWZz/PfPH80swZ/3U6TjkeolNtoBSNVjZgcBxwM9nXN5ZtYYiK/ksrHOucKK5jnnPgc+332R7vVynHPdAczsbeAy4ImoRiSl/J1zScLiDOBH4HTg3uiGsncIdQ445y6uxOKjgdOcc7+ZWQzQMZyx7iV26bqxvWu77DbFx6ayzMzwxoMMhiekPc5O78Pt+bufW80sDm+w2b7OuZVmVgNo7a/7hd0S5G7mnEsHuoOXhAKynHOPheGljgCygJDJDDMbAlwHHOOcW+VfB84DmgKZZdrGOOeKwhDnVtcAa51zXfzX6wgUADjndphYEtkeVchIRVKA9c65PADn3Hrn3GozW+p/mMbMepvZRP/xvWY2yszGAm/4v2juv3VlZjbRzHqZ2flm9qyZJfrrCvjza5vZCjOLM7N2ZvatmU03s/9t/SXbzNqY2c9mNtXMHojw/tgT/A9oD2Bm5/q/JvxmZm+WbWhm//T3429m9vHWX0jN7FQ/6/+bmf3gT9vfzH71fx2ZbWYdIrpVe75Q51IvM5vk/zsfY2Yp/nmxwL/IY2bvmtk/oxp9NeL/knUwcBFeQgYzC5jZc/4vb1+a2ddmdoo/r9wximL4e7JQ58BEM+u9tZGZPW5mM8xsvJkl+ZObAKn+ckXOud/9tvea2ZtmNsHMFuk82WX/A9qb2Qn+dXummX1nZk2hwmt7UzP7r3+N+M22/bodY2Yv+efRWDOrFbUtqkbMLME/H2aY96v8UH96a/Mqxp4DZgAtzewm/7o+28zui27kVccO9uF886ot5ppX+XCkmU3231P6+u3ON7Nny6yznZnNKPG8g5lNDxFCXbwfv9MBnHN5zrkF/nL3mtmNZtbMSlf1FJnZPmaW5H9Gm+r/HRyGXVRp5lW0fOk/TjCz1/x9OtvMhpdp29i8z+zHVbQd5lUuXQZc52/zoSFe9g7gRufcKii+DrxaYh8uNbO7zexH4FQzO8O2VbA8UiKerBKPTzGz1/3Hr5vZC+Z931hoZsdvZxekAKu2PnHOLdh6Xdu6fjO7v8RxXGVmr/nTz7Ztn6VfNC+xJFJMCRmpyFi8C/xC876sHF6JZXoBQ51zZwLvAacB+F9imjnnii9WzrmNwG/A1vWeAIxxzhXg/ZJwtXOuF3AjsLU88mngeedcH2DN397CasTMYoFjgDnmJcLuAAY657rhZfTL+sQ518ef/wfeF1SAu4HB/vQT/WmXAU/7vzj1BlaGb0uqpXLnknm/mD0DnOL/O38VeMg/L64CXjez04EGzrmXohd6tTMM+NY5txDIMLOewMl4v1Z2AS4GDoLiXzXLHaMoxFwdVOZ6UgeY4ZzrCUwC7vGnPwks8JMAl5pZzRLLdAWOwztmd5tZszBuQ7VT8rqBVzXWzznXA+/6fXOJpiWv7f8GJvnXiJ7APL9NB+A/zrn98X61LvXlTCqtVokvc/8FcoGT/PNiAPC4mZnftiPwhn/MOuIdg754lQ29zOywyIdfJezMPmyP99myK9AJOBM4BO+z5+2hXsA59yew0cy6+5MuAF4P0TYDr8JmmXk/spxl/o+RJdqsds519z9nvQR87Jxb5sf2pP+5dzjw8s7tirC6C9jonOvinOsKTNg6w7yE7lfA3c65r6hgO5xzS4EX/OndnXP/C/E6++MlHbcn1zl3CPAD8AgwEO886GNmwyqxLa3xvo8cB7xQ5jpT0qvALX6i6UGr4AdK59zd/nE8HC8J96yZdQb+ARzszysCzqpEXLIXUZclKcc5l2VmvYBD8S5g79uOx3753DmX4z/+ABiH96H6NODDCtq/j/cG9T3er9XPmfcLdn/gw23XS2r4/z+YbR/y3sR7093b1TKzWf7j/wGvAJcCHznn1kPxh4GyDjCzB4H6QAIwxp8+GS8Z8AHwiT/tZ+AOM2uBl8hZFI4Nqa4qOpeAB4EDgHH+v/MYtlUBjDOzU4H/AN2iEnT1dQbwlP/4Pf95HPChX+6/xsy+9+d3JMQxkp1TyetJEO/cAHgL//3HOXe/ed1qjsb7snQGXpk7wGf+NSfHP259gU/DuCnVRUXXjY54xyUFr0vlkhLtS17bBwLngvdLNd6X0gbAEufc1nVOx++SITutVHcbPzH8sJ9cCQLN8bpqACxzzv3iPz7a/5vpP0/AS9D8EImgq5id2YdLnHNz/HbzgPHOOWdmc9jxv+GXgQvM7Hq8z7J9QzV0zl1sZl2AI/GSPUcB55dt51fAXIz3Xonffr8Sn4frmVld59zmHcQWCUfiV5oCOOc2+A/jgPHAlc65SSXaltuOnX1Bfx++iVd1dLtzbus1Y+v/+wATnXPr/PZvA4ex4+vCB/5ngEVm9hdecm5W2UbOuVlm1hbvXDsSmGpmBznn/igTpwFv4yWbppvZVXiJ7an+PqgFpO3Mtkv1p4SMVMj/sDURmOhfnM4DCtlWVVU2g7ylxLKrzCzdzLriXagureAlPgdGmFlDvDeqCXi/kmZup/+v27WtqbbK9ZX2LwQ72k+vA8P8cRnOx/+C45y7zLwBZ48DZplZd+fcO2Y2xZ82xswuds5NCLFeqUAF59KVwDzn3EFl2/q/nHUGcoCGqCJptzCzRnhfJg8wM4eXYHHAf0MtQohjJDsvxPVku4uUWPZP4HkzewlY5x/LUm1CPJeKVXTdeAZ4wjn3uZkdQenxlbawY3klHhfhfeGQv+8sIAno5ZwrMLOlbPvsVfK4GDDCOfdihOPbE2xvH5b8dxss8TzIjr8ffYz3o+MEYLo/7kpIfuJnjnndyJdQJiHjJ0NfAU50zm3tXhMADiqREK1KQn3WLMRLyg7Gq3aEENtRIkGzPfPwqvG+9/dhd/O6kJV8j9l6LmxvhSVjLfv9pdLXEv/YfAJ8YmZB4Fi8SvOS7gVWOudeKxHXaOfcbduJT/Zy6rIk5ZhZxzKleN2BZcBSvOQJ7LgkeWvZc+LWXyBK8t/UfsUrZfzS7xe6CVjiVwhgnq1VApPZlo1XqV9o44HTtn5p8RNeZdUFUv1fjor3pZm1c85Ncc7dDazH62bQFvjLOfdvvCRa17BvQTUS4lz6A0gyb7BTzBs7aeuYS9f5888AXvWPkfx9p+CV9+/jnGvtnGuJ96F4PTDcvLFkmrKt+mIBoY+R7ITtXE9KCuAdI/AqYX70lz2uRPeCDnhf9jP950PNrKb/XncEMHW3B7/3SGTb2AjbS5aNBy4HbwBNM6sX7sD2colAmp9IGADsE6LdGOBC23bHl+Zm1iRSQVZxld2HO8U5l4u3358HXgvVzryxVo4oMak7Zd7//Ov8B8AtfpfarcbidWPe2q773wx7dyobWwP/oQMuBDqVqIQMtR2b8T6Pbs8I4DG/SnurUAnfKcDh5o1fE4P3OWprUmitmXX2f/Q6qcxyp/qfAdoBbfGu/+WYN/ZNA/9xPLAf5Y/l8XgVUP9XYvJ44JSt56SZNTSz3fLvUKoPJWSkIgnAaPNuzzcb703nXuA+4Gkz+x/eB+Pt+QgvgfLBdtq8D5zNtnJD8BIEF5nZb3iZ8aH+9GuAK81sKt4FVirgnJuHN9bFJH8fVnT3jLvwLlzjgPklpv/L/MHQ8Eqdf8OrcJrrl7h3At4IY/jVUUXn0t14Xz4f8Y/RLKC/me2LV658g9+f+gfgzuiEXe2cQflqmI+BZnhVSHOBF/HOi43OuXwqOEYRi7Z6CXU9KWkLsL95A2MOBO73p5+DN4bMLLxS9bPctrto/Io3TsEvwAPOudVh3Yrq7V68rsL/w0tShnINMMCvcpqON76DhM/bQG8zm4b32Wh+RY2cc2OBd4Cf/WPzETv+oru3qNQ+/BvrdngJh1AMuNm8Aftn4X2OPr9Mm/543W3us23j3zTD+1Lf27xBc3/HG9OvqngQaGD+jSDwuqMCxRWRp+O9V1xB6O34AjjJtjOor3Pua7yxq77xryE/4X3/GFNB21TgNryhEH7DG5fsM3/2rcCXeBVNZbsfL8BL3HwDXOYn2yrSDu+z9Ry87oHT8D5HlHQD3ueKrQP43u+8wejvBMb618BxeAMEixQz51TlKyIieyczS/DHOWmE9yX/YOecBg6vwiy8t2IVEdkhM7sRrwr8rmjHIrvGvLstfemc+yjascjeTWPIiIjI3uxLM6uPN5jpA0rGiIjI9ph3B6d2eBV9IiJ/iypkREREREREdpGfpGlTZvItzrly3WukNDO7Azi1zOQPnXMPRSGWwZS/k+sS51zZsWdEdhslZEREREREREREIkyD+oqIiIiIiIiIRJgSMiIiIiIiIiIiEaaEjIiIiIiIiIhIhCkhIyIiIiIiIiISYUrIiIiIiIiIiIhE2P8DfdcKLNhuiYQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set plot size\n",
    "fig, ax = plt.subplots(figsize=(20, 12))\n",
    "\n",
    "# Drop PassengerId\n",
    "df_train = train.drop([\"PassengerId\"], axis = 1)\n",
    "# Convert Sex to numnerical\n",
    "df_train[\"Sex\"] = df_train[\"Sex\"].apply(lambda x: 1 if x == \"female\" else 0)\n",
    "\n",
    "dataplot = sns.heatmap(df_train.corr(), annot=True)\n",
    "# displaying heatmap\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f03b835",
   "metadata": {},
   "source": [
    "#### Get Age by Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c0a9496",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_age(params):\n",
    "    pclass = params[0]\n",
    "    title = params[1]\n",
    "    sex = params[2]\n",
    "    age = params[3]\n",
    "    if pd.isnull(age):\n",
    "        age = np.float(age_df[(age_df['title'] == title) & (age_df[\"Sex\"] == sex) & (age_df['Pclass'] == pclass)][\"Age\"])\n",
    "    return age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dfee5b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_df = train.groupby(['Pclass','title','Sex']).Age.mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "082b25dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aman\\AppData\\Local\\Temp/ipykernel_2288/2338220323.py:7: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  age = np.float(age_df[(age_df['title'] == title) & (age_df[\"Sex\"] == sex) & (age_df['Pclass'] == pclass)][\"Age\"])\n",
      "C:\\Users\\Aman\\AppData\\Local\\Temp/ipykernel_2288/4053746285.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train['Age'] = train[['Pclass', 'title', 'Sex', 'Age']].apply(lambda x: update_age(x), axis = 1)\n",
      "C:\\Users\\Aman\\AppData\\Local\\Temp/ipykernel_2288/2338220323.py:7: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  age = np.float(age_df[(age_df['title'] == title) & (age_df[\"Sex\"] == sex) & (age_df['Pclass'] == pclass)][\"Age\"])\n",
      "C:\\Users\\Aman\\AppData\\Local\\Temp/ipykernel_2288/4053746285.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test['Age'] = test[['Pclass', 'title', 'Sex', 'Age']].apply(lambda x: update_age(x), axis = 1)\n"
     ]
    }
   ],
   "source": [
    "train['Age'] = train[['Pclass', 'title', 'Sex', 'Age']].apply(lambda x: update_age(x), axis = 1)\n",
    "test['Age'] = test[['Pclass', 'title', 'Sex', 'Age']].apply(lambda x: update_age(x), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9b3f0e9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop([\"Survived\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d04fb36d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}