Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import seaborn as sns\n",
    "from sklearn.cross_validation import KFold\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier, \n",
    "                              GradientBoostingClassifier, ExtraTreesClassifier)\n",
    "from sklearn.svm import SVC\n",
    "import plotly.graph_objs as go\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the datasets\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "gender_submission = pd.read_csv('gender_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "PassengerId = test['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data = [train, test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Has_cabin'] = train[\"Cabin\"].apply(lambda x: 0 if type(x)==float else 1)\n",
    "test['Has_cabin'] = test[\"Cabin\"].apply(lambda x: 0 if type(x)==float else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in full_data:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in full_data:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in full_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('Q')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in full_data:\n",
    "    dataset['Fare'] = dataset['Fare'].fillna(train['Fare'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for dataset in full_data:\n",
    "    age_avg = dataset['Age'].mean()\n",
    "    age_std = dataset['Age'].std()\n",
    "    age_null_count = dataset['Age'].isnull().sum()\n",
    "    age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)\n",
    "    dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list\n",
    "    dataset['Age'] = dataset['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_title(name):\n",
    "    title_search = re.search(' ([A-Za-z]+)\\.', name)\n",
    "    \n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return \"\"\n",
    "\n",
    "for dataset in full_data:\n",
    "    dataset['Title'] = dataset['Name'].apply(get_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in full_data:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for dataset in full_data:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "    \n",
    "    title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "    \n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "    \n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] \t\t\t\t\t\t        = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] \t\t\t\t\t\t\t        = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] \t\t\t\t\t       = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp']\n",
    "train = train.drop(drop_elements, axis = 1)\n",
    "# train = train.drop(['CategoricalAge', 'CategoricalFare'], axis = 1)\n",
    "test  = test.drop(drop_elements, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Has_cabin</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Parch  Fare  Embarked  Has_cabin  FamilySize  \\\n",
       "0         0       3    1    1      0     0         0          0           2   \n",
       "1         1       1    0    2      0     3         1          1           2   \n",
       "2         1       3    0    1      0     1         0          0           1   \n",
       "\n",
       "   IsAlone  Title  \n",
       "0        0      1  \n",
       "1        0      3  \n",
       "2        1      2  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f62038b1278>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WeNs595aZ9QGeAq440m2rB19EREREpPR1AdY659Y559KB94EBedq0B6b7n/9QwPLDUql68CeNvIb+PY5lW1IynS8eEexwjkj986+kervjyMpIZ9t7r5K+6a98bWJvvJ/QyDpYaChp635n+4dvgHPZy2v3Oof6Ay7nr4duIitlTxlGXzztH72f6D6n4UtNY+ndD5O84rd8bU6Z8h+qREfhS0sDYN7lN5O+Iyl7eWz/vpwwcRyzz7mM3ctW5Vu/LDnnePL1qfy0aCXVIsJ58vYrad/iqHztVv65geET3iYtPYMex3dg+LUXY2ZM+OBLPvzfHOpG1gLgrsHn0fOEjqRnZPLIxMms/HMDIWYMu/ZiunQ8uqx3D4DZv8zl6fEv4svK4oLzzuH6Ky/PtTw9PZ3hjz7JqtV/UCcykmeeGElco4ZkZGbyyJNjWLX6D3yZPs7rfybXX/VPtiYkMvzRUWzfkURISAgXDfwH/7z0oqDsW1EWzf2Z1yeMI8uXxRnnDOCCy6/Otfzbzz5i2qdTCQkJoWq16twydDhNmrUITrAlMPvnX3h6rLdfFwwcwHXXXJVr+YJFixgzdjxr1q7l6SefoN8Zp2cvu/m2O1i+fAWdjzuWCc+PL+vQi23JvF94c8KzZGVl0af/eQwcnHsfv//8Y7797EP/uavGjXcPo3GzFixbMJfJr71EZmYmYWFh/POmO+h4/IlB2ovcZs1bxOgJ/8aXlcWF/ftyw+ALcy1PT89g2OjnWPnHn9SJrMW4EUOJi41h1+5k7np0DCt+X8vAM/vw0J03ApCyL5Ur7hyWvX7Cth2ce0ZPht12fZnuV3E553hm2nxmr4mnangojw7sSrtG9XO1SU3P5P6pP7EpaQ8hIUaPoxtzR9/jgxRx0f5YPI8v35hAVpaPk04/h57nDy6w3fJfZvLeuEe4dfSrNG7Vhn17dvPu2EfY/OfvHN/rLM67/s4yjvz/NzO7Ebgxx6xJzrlJ/udxwMYcyzYBJ+d5iaXAhcDzwPlALTOr75zbcSRxVaoE/+0v5vDyB9N54/Hy+QepuKq1O47wqFg2Pnk3VZq2osFF1xL/XP43LAlvvYDbnwpAzNV3UeO4U0hZ/AsAoXXqUa3NMWQkbSvT2Isrqnd3ajRvyo+nnUudzp3o+ORD/Hze5QW2XXLHAwUm76E1qtPs2sHsXLQs0OEWy0+LVvL3lkS+mfAIy9b8xaOT3ueD0ffla/fYpPd49ObBHHt0c24a9RKzFq+ix/EdALjy3D5cO6BvrvYf/m8OAJ+Nf4gdu/dw0xMTmPL0/YSElO0HcD6fj1Fjn2PSC+OIjY7ismtuovdp3WjZvFl2m48//4rIyFp8/eFkpn0/nfEvTWTsqEf4bvoPpKdn8Mm7b5KalsbAy67i7L6nExERwdA7/kX7tkeTkrKPS6++gVO7nJjrNcsDn8/Ha8+PYeTYCdSPiuG+m6/ipG49ciXwp51xJmcO8JKseXNm8sZL4xnxzIvBCrlYfD4fT44ew6SXJxATE82gK66iV8/TaNni4H41jI3liUdH8OY7/823/tVX/pO0tP18+NHHZRl2iWT5fLz+/DM8+MyL1I+KZtgtV3Ni19NonOPcdTu9H33PuwCABXN+4u1Xnmf4089Tq3Yd7hs1jnoNotiw/k+evO9OXp36ZbB2JZvP52PU8xN57ZlHiYmqz6W33Evvrl1o1axJdpuPpn1PZK2afPPfV/l6xiyenfQ240bcS0REBLdfM5i16zewZv2G7PY1qlfj49eey56++Ka76XvaqWW6XyUxZ008G5L28NkdA1i+aTtPfTWXt2/on6/dFV3bc1LzWDIyfdz09v+Ys2Yz3VrHBSHiQ8vy+fj8389z7YhniKwXxcsP3EzbE7sS06RZrnb7U/fxy9cf06R1u+x5YeER9L3sWhI2rCdh4/oyjjy4ykOJjj+Zn1TIYitolTzTQ4EJZnY18BOwGcg80rgqVYnO7EV/sHN3SrDDOGI1Op7AnvmzANj/91pCqlUnNLJOvnYHkntCQiEsLFfvff2BV5D0xeQyifdwxPTrzeaPvgBg1+JlhEfWokp0gxK9Rpuht7HulTfI2r8/ECGW2Iz5yxjQ82TMjGOPbs6elH1s27k7V5ttO3ezd18ax7VpgZkxoOfJTJ+39JCv++emLZxyTBsA6teuRa0a1Vnx54ZDrhMIy1f9xlGN42gS14jw8HDO7tuHH36anavND7PmcF7/MwHo27sncxcswjmHmZGamkpmZib79+8nPDyMmjVqENWgPu3bep9G1KhRnebNmpKQWP7elK79fSUN45oQ26gx4eHhdO/Tl3lzZuZqU71Gzezn+9PSMCvo73r5smLlSo5q0pjGjeMIDw/nrH79+OHHn3K1iWvUiKNbtybE8v+7OKVLF2pUr15W4R6Wtb+vIiauMTGN4ggLD6drn77M/zn3PuY+d6nZ56556zbUaxAFQJNmLcjI2E9GenrZBV+I5b+voUlcQ5o0iiUiPJz+fbrzw89zc7WZMWceA/r1BqBfz678umgZzjmqV6vKCce0JyIivNDX/3tTPEm7dnNCp/YB3Y8j8ePqjZx7rPd3tFOTKPakZbBtz75cbapFhHFS81gAwsNCadewHgnJ+wp6uaDbtPZ36sc2ol5MI8LCw+nUrQ+/zZ+Tr933779OjwGXERYekT0vomo1mrU7hrCIiHztJeg2AU1yTDcG4nM2cM7FO+cucM51Bh70z8udPByGSpXgVxahteuSuetgGYpvVxKhtesW2Db2pgdo+viruLRUUpZ6f+Crdzge3+6dpMeXfRJYXFVjo0mN35o9nbYlgaqx0QW27TTucbp/M4VWdx78BCyyQ1uqNoolcfpPBa4TDIlJu4htcPA8xdSvS8KOXbnaJOzYRUz9OrnaJCYdbDN52kwGDnmCB196h917vX9EbZo2Zsb8ZWT6fGxK2M6qPzewdfvOAO9NfonbthMbffAcxURHkbBte/42MV6bsLAwataswa7du+nbpxfVqlWjz7kX0G/AJVx1+aXUrh2Za93N8Vv4/Y81dOpY/pKKHdu2UT8qJnu6flQMSdvyvxGZ9skUbhk8kLdffYHr7hhaliEeloTEbcTEHNyvmJhoEgvYr4osaXsi9aNznLsG0ewsYB+//XQqd1x+Ae9OmsDVt92db/ncn2bQrFUbwstBEpWwPYmGOTpEYhrUJ2FbUq42iduTiPW3CQsNpVaN6uxKLl6p5lczZnFWr+7l+k1qYvI+YiJrZE9HR1ZnW3Jqoe33pKbz0+pNdPEn/OXN7qTt1G5w8O9r7fpRJCfl/vsav24Nu7cn0vbE8vvJiuQzH2htZs3NLAK4DPg8ZwMza2CW3YMyDHi9NDZcZIJvZnvMLLmwxyHWu9HMFpjZgkmTCvvkQgpS4B/VvB/o+G2dOJoNI2/FwsKp1roDFh5Bnb4DSZo2NbBBHqGC9tG5/Du5+I5hzOp7Ib9ceDX1uhxP3IX/ADPaj7yX3x4fWxahFltB8efdT1fAiTzQ4rIze/DtS4/x8bjhRNWJZMxbHwFwwemnElu/Lhff9zRPvfEhx7VpQWho2b83L3D/itPGjBUrfyMkJITpX37MtI/f5+3JU9i4+WAnxr59+xgybAT333U7NWvUyPcawVfAL2AB1/DZ51/CK5M/5YqbbufDd0rlb3RgFXi+ghBHABWwiwXu5JkDL+aFdz9m8I238fF/38i1bOP6dUye9BI3DMk3wl1wFOO8Fef3tTDTfphF/9NPO4zAgqyQHcz0ZTHso1lcdnJbGterVbYxFVdBF2qOk5qVlcVXb75E/6tuLcOgyr+QkNCgPw7FOZcJ3AZ8C/wGTHHOrTSzx8zsPH+zXsBqM/sDiAFGlcaxKbIG3zlXC8DMHgO2Au/g/RpdDhT6m5KnJsnd9sqvRxxsZRbZrS+1TvU+Tt2/YR1hdepxoPAktE49fMmF99i6zAxSVi6kescTydyzm/B6UTS+dzQAYbXr0fieUWwe/zC+PUf8ic8RaXrVpTQZ5NUo7166kmqNYjmwV1UbxrA/IX+v2v6tiQD4UvYR/+nX1DmuIwnf/UCtNq04Zcp/AKgS1YATX3+BBdfeUeY32k6eNpOp/hr5Y1o1zdWznrBjJ9H1audqH5unVz9hx06i6nk9+g3qHOzRvrhvd2558mXA63174JqDN54OHv4MTRsW/GlHIMVER7E1MTF7OiFxG9FRDfK3SUgkNjqazMxM9u5NoXZkJF999z+6n9qF8LAw6tery3GdOrLyt99pEteIjMxMhgwbwTlnnsEZvXuU9W4VS/2oaHZsS8ie3rEtgXoNCi8p696nH5PGjy6L0I5ITEw0CQkH9yshIZEof0lKZVE/KpodiTnO3fZE6h7i3HXt3Zd/P/f0wfbbEhg38j5uHTaS2LhSGZ76iMVE1WdL4sHe3YTtO4huUC9fm62J24mNakCmz8eelH3Ujiw6uf39z/X4fFl0OLpVqcd9pD6Yt5pPFq4BoENcfRKSD5bkJibvI6pWtQLXe+KLXzmqXi0uP7VdgcvLg9r1o9i9/eDf1907thFZ9+BNw+mp+0jYuJ7XRt4FwN5dSbzz9INccf8oGrdqU+bxSvE5574Gvs4zb0SO5x8CH5b2dkvSDXimc+5l59we51yyc+4VvLt+pRQkz/mezWOHs3nscFJWLKDWSV7vSZWmrchKTcWXnLvUwyKqHKzLDwmhervjyEiMJ2PLRv4ecQsbH7+TjY/fSebuJDaNezDoyT3A3299wOyzLmH2WZeQ8O0MrzceqNO5E5l79rA/MffHkRYaSnhdbx8tLIzo03uyZ/VaMvfs5ftje/JD17P5oevZ7Fq8LCjJPcDgs3vyybjhfDJuOKd36cRnM+finGPpH+upVb0aUXVzJ/hRdWtTo1oVlv6xHuccn82cS5+TOgHkqtf/39wltD6qEQCp+9PZl+a93ft56W+EhoTSqknDMtrDgzq2a8vfGzexKX4LGRkZTPt+Br1O65arTa/TuvH5198C8P0PM+lyYmfMjIYxMdn1+PtSU1m2YhXNmzbFOcfIUU/TollTrhp8aZnvU3G1atOeLZs2kLBlMxkZGcye8T0ndc39ZiR+08GSuIW/zqZhXP4RlMqbDu3b8/fGjWza7O3XN999R6+eFbDn9hBatm3H1s0bSdwST2ZGBj/P+J4TT8197rbkOHeLf51DwzivZDZl7x5GD7ubQdffStuOx5Zp3IfSsW1rNmzewqYtCaRnZPD1jNn0PrVLrja9u3bhs+9+AOC7mT9zcudjilVy8/X0WfTvUz6vgUu7tOH9W87l/VvOpVfbJny5dB3OOZZt3EbNKuFE1cp/P8hL0xezd38GQ886KQgRF19cq7Zs37KZpIQtZGZksGzODNqd1DV7edUaNXnojc+475X3ue+V92nSur2Se7ybbIP9KK9KMoqOz8wuxxvD0wGDAF9AojpM7zx1Ez1OaEODOjVZ981YHnv1M978dFawwyqx1FVLqN7uOJo8OB6Xvp/E9ydmL4sb+iSbxw4nJKIKMdfdg4WFYyEhpK5ZSfLP/wti1CWTOGMWUX1Oo9fsr/ClprHsnoezl3X/Zgqzz7qEkIgITv7vq1h4GBYSwvbZc9kw+aMgRn1oPY7vyE+LVnLWv0ZStUoEo/518Hsqzr/nST4ZNxyAETcOYviEt9mfnsFpnTtkj6Az9u1P+P2vTRgQF12fR272hkhL2r2HGx5/kRAzouvVYfQdV+XbdlkICwtj+NC7uPnOofiysjj/3P60atGcCZP+Q4e2bendoxsX/KM/wx4dRf+LBlM7shZjHh8JwKCLBvLQE6M5f/DVOOcYeO7ZtGndkkVLlvHFtO9o3bIFF11xHQB33HIDPbqeEpR9LExoWBjX33kfj917B1lZPk4/+zyOat6S915/lZZt2tGlW0+mfTKFZQvnERoaRs1akdw+bGSwwy5SWFgYw++7l1tuuwOfL4uBA/5Bq5YteemVibRv347ePXuwYuUq7hp6H8nJycycNYtXJk7ik6kfAHDVdTfw119/sy81lTPOPpdHH36Qbl3LV31waGgY194+lCfvv4MsXxa9zv4HTZq3YMobE2lxdDtO7NaDbz+dyvKF8wkNC6NGrVrcer9JiR8fAAAgAElEQVR37r75ZCoJ8Zv46J3X+chfcvXgmBeoXbfeoTYZcGGhoTx4+w3ceP+jZPl8nH/2GbRqfhQvvjGZDke3ok+3LlzY/wweePI5zvrnzdSuVYuxD9+TvX7fQTewd18qGRmZzJgzl0ljHskegefbmXN45amHC9t0udG9dRyz12xmwAufUjU8jEcGHEyGL3vlS96/5VwSdqfwn1kraNYgksETvwK8Nwnnn9A6WGEXKjQ0lPOuv4M3nrgPl5XFCX3OJqZJc75//3Uat2xDu5O6HXL9Mbdcxv7UffgyM1g1bzbXPPxMvhF45P8XK6hOr8CGZs3wxujshpfgzwHucs79VYzVXUTnaw8vwgogffHrrBtS8Hi1lUWL8ZP5qkmnYIcRUOdsXIZvxfSiG1ZgoR1PJ33n1qIbVmARdWNZuaXQ24MqvA4NvVKu/XuD/6lcoFSpWZslm3cV3bACOy6uDpmb83/3R2USFteOlPeeCHYYAVVj0EN8tDy+6IYV2IXHNCq3d+Y0vHhC8ZLYANoy9bZyeXyK3YPvT+RL5du1RERERESORHkukQm2Ytfgm9nRZjbdzFb4pzuZ2UOBC01EREREREqqJDfZvoY3PmcGgHNuGd54niIiIiIiZSrYN9iW508QSpLgV3fOzcsz74i/SldEREREREpPSRL87WbWEv83vpjZRcCWgEQlIiIiIiKHpSTDZP4L74ur2prZZmA93pddiYiIiIiUqfJcIhNsJUnw/3bOnWFmNYAQ59yeQAUlIiIiIiKHpyQJ/noz+wb4AJgRoHhERERERIqkHvzClaQGvw3wP7xSnfVmNsHMugcmLBERERERORzFTvCdc6nOuSnOuQuAzkAkMDNgkYmIiIiISImVpEQHM+sJXAqcDcwHLglEUCIiIiIih2KhKtEpTLETfDNbDywBpgD3OudSAhaViIiIiIgclpL04B/rnEsOWCQiIiIiIsWkm2wLV2SCb2b3OefGAKPMzOVd7py7IyCRiYiIiIhIiRWnB/83/88FgQxERERERESOXJEJvnPuC//TZc65xQGOR0RERESkSCrRKVxJxsF/1sx+N7PHzaxDwCISEREREZHDVuybbJ1zvc0sFm9ozElmFgl84Jx7ImDRiYiIiIgUQD34hStJDz7Oua3OuReAm/GGzBwRkKhEREREROSwFDvBN7N2ZvaIma0AJgA/A40DFpmIiIiIiJRYScbBfwN4D+jnnIsPUDwiIiIiIkUKCbFgh1BuFSvBN7NQ4E/n3PMBjkdERERERI5AsUp0nHM+oL6ZRQQ4HhEREREROQLmXL4vpy24odlE4HjgcyDlwHzn3LPFWL14GxERERGR8qTc1sG0uvXjoOeXa1++oFwen5LU4Mf7HyFArZJuaN2QwSVdpcJoMX4yEZ2vDXYYAZW++HXSUlODHUZAVa1WjfVDrwh2GAHVfOw7ZG7+reiGFVhYXDuej2wT7DAC5s7k1QBkblkT5EgCJ6xha262ZsEOI6BedX+Rvn1TsMMIqIgGjf9f/N9I/XR8sMMIqGoDhwQ7BDkMJRkH/9FABiIiIiIiUlxm5bLzvFwodoJvZj9QQKmNc65PqUYkIiIiIiKHrSQlOkNzPK8KXAhklm44IiIiIiJyJEpSorMwz6w5ZjazlOMRERERESmSxsEvXElKdOrlmAwBTgRiSz0iERERERE5bCUp0VnIwRr8TOAv4LrSDkhEREREpCimHvxCFZngm9lJwEbnXHP/9FV49fd/AasCGp2IiIiIiJRIcb7JdiKQDmBmPYCngLeA3cCkwIUmIiIiIiIlVZwSnVDnXJL/+aXAJOfcR8BHZrYkcKGJiIiIiBRMJTqFK04PfqiZHXgjcDowI8eyktTwi4iIiIhIgBUnQX8PmGlm24FUYBaAmbXCK9MRERERESlTIfom20IVmeA750aZ2XSgIfCdc+7ASDohwO2BDE5EREREREqmWCU2zrlfC5j3R+mHIyIiIiIiR0I19CIiIiJS4egm28IV5yZbERERERGpINSDLyIiIiIVjnrwC6cefBERERGRSkQJvoiIiIhIJaISHRERERGpcEJUolMo9eCLiIiIiFQi6sEXERERkQrH1E1dKB0aEREREZFKRAm+iIiIiEglUiFLdOqffyXV2x1HVkY62957lfRNf+VrE3vj/YRG1sFCQ0lb9zvbP3wDnMteXrvXOdQfcDl/PXQTWSl7yjD6IzNp5DX073Es25KS6XzxiGCHc9jmzJnD02PGkJWVxfnnn891116ba/nChQsZ88wzrFmzhqdHj6Zv374AxMfHc/c995Dl85GRmcmgQYO45OKLg7ELxVJvwBVUb3csLn0/2z6YRPrmv/O1ibn+Xu9aDQkhbf1qdnz8FjhHnTMvpEaH43HOkbU3mW0fTMKXvCsIe3HQrHmLGD3h3/iysriwf19uGHxhruXp6RkMG/0cK//4kzqRtRg3YihxsTHs2p3MXY+OYcXvaxl4Zh8euvPG7HW+mv4Tr03+EDMjqn49nh4+hLq1I8t61wrVc8yDNOvXk8x9aXx3ywNsW7oqX5uQ8HB6jX2Yxqd1wWU5fnlsPGs//45jrr2MTjcMxvmyyEjZx/Q7HiZp9Z9B2IvcZs1dyOgJk/D5srjwnH7ccHnu36H09AyGPfUsK1evpU7tWowbcT9xDWNY9ttqHhk7AQCH419XD+aM07pmr+fz+bjkpiHENKjPy6NHluk+FeWS50fSsX9v0vel8tbVQ9m4eGWu5VVq1mDorKnZ03UbxzL3v58ydchjnHbT5fT61xVk+bLYvzeFd28cxpbf1pb1LuQy+9d5PP3cS/iysrjgH/25/opBuZanp6cz/PGnWbX6D+rUjuSZxx4mrmEsAKvX/sljY8aTkrIPCwnh/X+/TJUqEWRkZDDq2RdZsHgJZiHcceO19O3dIxi7l8/h/s84YO/evQw8/3z69OnD8GHDyjL0YnPOMebzOcxevYGq4WE8dklv2sVF5Wv34jdz+XLRHySn7ueXx6/Pnv/MF3OY/2c8AGkZmSTtTWX2o9fmW78yMdNNtoWpcAl+tXbHER4Vy8Yn76ZK01Y0uOha4p/Ln+gmvPUCbn8qADFX30WN404hZfEvAITWqUe1NseQkbStTGMvDW9/MYeXP5jOGzl+qSsan8/Hk089xcRXXyUmJobBl19Or549admyZXab2NhYHn/sMd56++1c60ZFRfH2W28RERHBvn37uPDCC+nVsyfR0dFlvRtFqtb2WMKjYtg0eihVjmpJ/QuvYcsLj+Rrl/jOi7j9aQBEX3kHNY49mZQlv7L7x6/Y9e1HAER270edvgPZ8dGbZbgHufl8PkY9P5HXnnmUmKj6XHrLvfTu2oVWzZpkt/lo2vdE1qrJN/99la9nzOLZSW8zbsS9REREcPs1g1m7fgNr1m/Ibp/p8zH6pf/w+RsvUrd2JGMnvsnkT77iX1cPKiiEMtesXw/qtGzGW8f1I/akY+kz/hE+6HNJvnZd7r2Z1O1JvH38WWBG1bp1AFg99QuWv/4+AM3P7sNpTw3jswuC+7vrncdXeG3sE955vHkIvbudTKtmR2W3+ejr74isWYNvJr/G19Nn8uykNxk38n5aN2/KlInPERYWyrYdSVxw3e30OvVkwsJCAXjno89p0bQJKSn7grV7Bep4di+iWzdnROteND+5M4NfGcXTpwzM1Wb/3hRGde6fPT1swRcs/vgbAOZP/oxZE98FoNM/zuCiZx/mxbOvKrsdyMPn8zFq3AtMem4MsdFRXHb9rfTufiotmzfLbvPxl9OIrFWTr6e8w7T/zWD8y68x9vGHycz0Meyxp3jq4WG0ad2SXbt3Z5+/SW+9S726dfjy/bfJyspid3L56Pw6kv8ZB7z00kuceMIJZRXyYZm9egMbtu/m83sHsXxDIqM+mcV/b7sgX7ue7ZpxWdeOnPfMe7nm3/uPbtnP35uznN/jtwc8Zim/KlyJTo2OJ7Bn/iwA9v+9lpBq1QmNrJOv3YHknpBQCAvL1Xtff+AVJH0xuUziLW2zF/3Bzt0pwQ7jiKxYsYImTZrQuHFjwsPDOevMM/nxxx9ztYmLi+Poo48mJM+78/DwcCIiIgCvhyorx3ktb6p3OJ69C2YDsH/Dn4RUrU5ordr52h1I7gkJxXJcq9nzAYuoAkHe1eW/r6FJXEOaNIolIjyc/n2688PPc3O1mTFnHgP69QagX8+u/LpoGc45qlerygnHtCciIjxXe+cczjlSU9NwzpGyL5WoBvXKbJ+K0qL/6fz23qcAbJ2/lCq1I6kek79Hrf0/L2T+uInehHOkJe0EIH3Pwd/V8BrVcv0dCpblv/+R5zz24Ic5v+ZqM2POrww463QA+vXszq8Ll+Kco1rVqtnJ4P709Fy9Z1sTt/PTr/O58Jx+ZbczxdRpQD9+fftjANbPXUy1OrWIjM1/Hg+IbtWMWtH1WTtrHgBpe/ZmL4uoUR0X5PO4/LffOapxHE3iGhEeHs7Zp/fmh1k/52rzw6yfOa+/dy769urJ3IWLcM7x87wFHN2yBW1ae8lxndq1CQ31zuknX32T/UlASEgIdevk/3sVDEfyPwNg1apV7EhK4tRTTy2jiA/Pjyv/4twTjsbM6NQ0hj2p+9mWnP//faemMURF1jjka01bspazjm0VqFClAih2D76ZtQQ2Oef2m1kvoBPwtnOuTGsGQmvXJXNXUva0b1cSobXrFli6EHvTA1Q5qiWpvy0hZamXiFTvcDy+3TtJj9+Qr72UjcTERGJjY7Ono2NiWL58ebHX37p1K7fdfjsbN25kyF13lcvee4CwvNfq7iRCa9fDt2d3vrYxN9xLlSYtSf19KSnL5mXPr3vWRdQ8sTtZaalseeXJMom7MAnbk2gY3SB7OqZBfZb9tiZXm8TtScT624SFhlKrRnV2Je8ptOQmPCyMh++6mYHX30m1qlVpGteQh+64scC2wVCzUQx7N23Nnt67eSs1G8WwL+Hgp38RtWsBcOpDd9K4exd2r9/Ij0MfY9+2HQB0umEwnW+7htDwcD7+R/B6fQ9I2LaDhlEHk9uYqAYsW7U6V5vEbTuI9bcJCwulVs3q7NqdTN06tVm2ajUPjXme+K2JjH7w7uyEf/SESdxz07Wk7CtfvfcAdeJi2LkxPnt616at1ImLJXlrwZ/injjoPBZ+8GWueT1vvYIz7r6e0IhwnuszOKDxFiVx23Zio3Ocw+golq38rYA23t/GsLBQataowa7dyfy9cRNmxk1D7mfnrl2cdUZvrr38MpL9b2ImvPYGCxYvpXFcI4bffTsN6gX/DfeR/M/Iyspi3LhxjBo1irlz5xa9QhAlJqcQW7tm9nRM7ZokJqcUmcznFb9zD/E799ClVVxph1juaBz8wpWkB/8jwGdmrYD/AM2BQrvBzexGM1tgZgsmTZp0hGHmet38MwvpTNk6cTQbRt6KhYVTrXUHLDyCOn0HkjRtasErSJkoqPerJHV0sbGxfDh1Kl98/jmff/EFO3bsKM3wSk+B12rBF2vCa8+w8bHbsbBwqrbqkD1/5zcfsvGJu9i76Gciu/UtcN0yU+B5y9ukgDaHeMmMzEw++HwaH058lh+nvs7RLZrx2uSPjjDQUlSMcxgSGkatxg3Z8usi3utxAVvmLab7qPuzly97bTJvHduXOSPHctK9twQ64sOS9/evoKv0QJtO7dvw+Zsv88HE8bz27lT270/nx5/nUa9uHTq0KZ89hgX/3yi8F/6ky/7B/Pc+zzVv5svv8HCrnnxy/2jOfuj20g6xRAoKPd85LOT31efzsXjZCkaPHM5brzzP9Jmz+XXBInw+HwmJ2+h8TEemvDGRYzu2Z9yEiYHahRI5kv8ZH0yZQvfu3XO9QSivCvy9O+Rf0IJ9u3QtZxzTgtCQClekIaWoJDX4Wc65TDM7H3jOOfeimS0urLFzbhJwILN364b8eNhBRnbrS61TvY/9929YR1ideuz3LwutUw9f8s5C13WZGaSsXEj1jieSuWc34fWiaHzvaADCatej8T2j2Dz+4QJ7VSUwYmJi2Lr1YK9oYkIC0VGFf1xemOjoaFq2bMmiRYvy3VAVLLW6nkGtk3sBkL4xz7Vau+hrdd+qRdToeDxpa1bkWpay+GdirhvKru8+DlDkRYuJqs+WxIM1nQnbdxCdp5wmJqo+WxO3ExvVgEyfjz0p+6gdWavQ1/x97XoAjoprCMBZvbrx7/eCm+B3umEwHa/y6uwTFi2nZuODiUHNuFj2bknM1T4taScZKftY+8X3AKz59Bs6XHlRvtdd/eFX9H72Eb4PYOzFERNVny3bDvZcJ2zbXvB53LaN2OgGZGb62LM3/3ls2bQJ1apWZc36v1m8YhU/zpnLrF8XsD89nZR9qdz/xFiefmhomexTQXreegXdb/DKTf6ev5S6TRplL6vTOJZd8QkFrhfXqR0hYaFsWLSiwOUL3v+Cwa88wVulH3KxxUQ3YGtijnOYuI3oBvXztIlia2IisdFRZGb62JuSQu3ISGKiG3DCcZ2yy29OO/Vkflu9hpNP6Ey1qlU5vWd3AM7s3ZNPvphWdjt1CEfyP2PZ0qUsWryYKVOmsC81lYyMDKpXr85dd94ZqHBL5P2fV/DxPO/Tlw6No9i6+2A5WMLuvURFVi/xa36zdC3DBpxWajGWZ6Ye/EKV5O1dhpkNAq4CDnx2GX6I9qUmec73bB47nM1jh5OyYgG1TvIu3CpNW5GVmpqvPMciqhysyw8JoXq748hIjCdjy0b+HnELGx+/k42P30nm7iQ2jXtQyX0Z69ChAxs2bGDT5s1kZGTwzbff0rNnz2Ktm5CQQFqaV5uenJzMkiVLaNasWQCjLZk9P/+P+PEPET/+IVJWLqTmid4/yypHtcSl7ct3rVlElYN1+SEhVGt7LOmJXilBWIOY7HbV2x9PRmI8wdSxbWs2bN7Cpi0JpGdk8PWM2fQ+tUuuNr27duGz734A4LuZP3Ny52MO2dMW06A+f/69iaRd3nH5eeESWjRtHLidKIZlr01mcveBTO4+kD+/+h/tBnk3Y8aedCz7k/fkKs85YN03P9D4tJMBaNLzVJJ+90bKqdOyaXab5mf2Ytef+UdRKmsd2xzNhk3xbNqy1X8ef6J315Nztend9WQ++2Y6AN/NnM3Jx3fCzNi0ZSuZmT4A4rcm8tfGzcTFRjPkxquZ8eFbfP/B64wdcR8nd+4U1OQevB73UZ37M6pzf5Z8+h2nXOndrNj85M6k7d5TaHnOSYPOY/57X+SaF92qWfbzjuf0IXHNX4EKu1g6tm3L35s2syl+CxkZGUyb/gO9unfN1aZX91P5/OvvAPj+x5l0OaEzZkbXLiex5s91pKalkZnpY8GSZbRs3hQzo2e3U5i/eCkAvy5YRIvmTfNtOxiO5H/GU089xbfffMO0adO4e8gQzj333HKT3ANc1rUjU+66mCl3XUzvDs35cuEfOOdY9ncCNatGlLg8569tu0hO3c+xTWOKbiyVWkl68K8BbgZGOefWm1lz4L+BCatwqauWUL3dcTR5cDwufT+J7x/8CDFu6JNsHjuckIgqxFx3DxYWjoWEkLpmJck//6+sQw2Id566iR4ntKFBnZqs+2Ysj736GW9+OivYYZVIWFgYwx54gFtuuYWsrCwGDhhAq1ateOnll+nQvj29evVixYoVDLn7bpKTk5n500+8/MorfPLxx6xbt45xzz6LmeGc46orr6R169bB3qUCpf62lOptj6PxA2NxGels++C17GWNhjxB/PiHsIgqxFx7NxYaBiEhpK5dxZ5fZgBQr/+lhEc3hKwsMnft8IZ6DaKw0FAevP0Gbrz/UbJ8Ps4/+wxaNT+KF9+YTIejW9GnWxcu7H8GDzz5HGf982Zq16rF2IfvyV6/76Ab2LsvlYyMTGbMmcukMY/QqlkTbr3yUq6660HCwkJpGB3Fk/ffEcS9zO2vb2fSrF9Prlr6PZn7Uvn+1uHZywbP/pTJ3b3kf86IsZw5aQxVRg8ndXsS39/qDcPX6cZ/clSvU8nKyCRtVzLf3Xx/gdspS2FhoTx4583ceO8Ib8jBs/vSqnlTXnz9v3Ro05o+3U7mwv79eODJcZw1+AZqR9Zk7Agv7kXLV/HvyR8SFhpKSEgID991S7m5EfNQVnz9Ax379+bxtTO9YTKvuTd72YOLv841es4Jl5zDhP7X5Fq/121X0faMbvgyMtm3czdvXnUPwRQWFsrwIbdz89334/Nlcf65Z9OqRTMmvPYGHdq2ofdpXbng3P4Me/wp+l9yBbUjazHm0YcAqB1Ziysuu4hB192KmXHaqV3o0fUUAIbceiPDHnuKp59/iXp16vD48HsPFUaZOZL/GRXJaW2PYvbqDfxjzHtUjQjj0Yt7ZS+75LmpTLnLG852/Ne/MG3xWtIyMuk36h3O79KWW/qeBMC0JWs469hWGj5SsMMZDcDM6gJNnHPLirmKWzckuDclBVKL8ZOJ6Fy5x5pNX/w6aampwQ4joKpWq8b6oVcEO4yAaj72HTI3/1Z0wwosLK4dz0e2CXYYAXNnsndDbOaWNUW0rLjCGrbmZmsW7DAC6lX3F+nbNwU7jICKaND4/8X/jdRPxwc7jICqNnBIuX23cNKj3wV9aLL5I/uVy+NT7BIdM/vRzCLNrB6wFHjDzJ4NXGgiIiIiIlJSJSnRqe2cSzaz64E3nHMjzay4PfgiIiIiIqWmoO89EE9JbrINM7OGwCUcvMlWRERERETKkZIk+I8B3wJrnXPzzawFUHmLQEVEREREKqBil+g456YCU3NMrwMuDERQIiIiIiKHonHwC1fsBN/MqgLXAR2AqgfmO+cq9/AxIiIiIiIVSElKdN4BYoEzgZlAY2BPIIISERERETkUC7GgP8qrkiT4rZxzDwMpzrm3gHOAYwITloiIiIiIHI6SJPgZ/p+7zKwjUBtoVuoRiYiIiIjIYSvJOPiT/N9g+zDwOVATGBGQqEREREREDiGkHJfIBFtJRtH5t//pTKBFYMIREREREZEjUWSCb2Z3H2q5c+7Z0gtHRERERKRopm+yLVRxevBrBTwKEREREREpFUUm+M65R8siEBEREREROXLFHkXHzN4yszo5puua2euBCUtEREREpHAWEvxHeVWS0Do553YdmHDO7QQ6l35IIiIiIiJyuEqS4If4h8kEwMzqUbJhNkVEREREJMBKkqCPA34xs6mAAy4BRgUkKhERERGRQ9A4+IUryTj4b5vZAqAPYMAFzrlVAYtMRERERERKrDjj4FcFbgZaAcuBV51zmYEOTERERESkMKYe/EIVpwb/LeBEvOT+bGBsQCMSEREREZHDVpwSnfbOuWMAzOw/wLzAhiQiIiIiIoerOAl+xoEnzrlMfS2wiIiIiASbctLCmXPu0A3MfEDKgUmgGrDP/9w55yKLsZ1Db0REREREyqNym0X3fu6noOeXP9zVo1wenyJ78J1zoaWxoa+adCqNlymXztm4jLTU1GCHEVBVq1UjovO1wQ4joNIXv056UnywwwioiHqN2PnKA8EOI6Dq3jKaHRPuDXYYAVP/tmcA2P/ju0GOJHCq9LqctO/+E+wwAqpqv+tIee+JYIcRUDUGPcTyLbuDHUZAHdOwNjM6dQl2GAHVZ1n5rczWMJmFK8dfsisiIiIiIiWlBF9EREREpBIpyTfZioiIiIiUCxoHv3DqwRcRERERqUTUgy8iIiIiFU6oevALpR58EREREZFKRAm+iIiIiEglohIdEREREalwVKJTOPXgi4iIiIhUIurBFxEREZEKRz34hVMPvoiIiIhIJaIEX0RERESkElGJjoiIiIhUOCrRKZx68EVEREREKhH14IuIiIhIhaMe/MKpB19EREREpBJRgi8iIiIiUomoREdEREREKpwwlegUSj34IiIiIiKViBJ8EREREZEAMLOzzGy1ma01swcKaXOJma0ys5VmNrk0tqsSHRERERGpcMr7KDpmFgq8BPQFNgHzzexz59yqHG1aA8OAbs65nWYWXRrbVg++iIiIiEjp6wKsdc6tc86lA+8DA/K0uQF4yTm3E8A5l1gaG66QPfjtH72f6D6n4UtNY+ndD5O84rd8bU6Z8h+qREfhS0sDYN7lN5O+Iyl7eWz/vpwwcRyzz7mM3ctW5Vs/mObMmcPTY8aQlZXF+eefz3XXXptr+cKFCxnzzDOsWbOGp0ePpm/fvgDEx8dz9z33kOXzkZGZyaBBg7jk4ouDsQtHZNLIa+jf41i2JSXT+eIRwQ6n2Gb/Mo+nn5uAz+fjgvPO4forB+danp6ezvDHnmLV739Qp3YkzzwxkriGsXz57fe8+e4H2e3+WLuOKW9Oou3RrbLn3X7vg2yKj+eTd98os/0pinOOZ2cu45f1CVQJD+XhfifQNrpOrjZpGZkM/2oem3enEGJG9xax/Kt7RwC+XPk3E2avIKpGNQAuOq4FAzo2K+vdKFL10wYQ0bQtLjODvdM/wLdtc+4GYeHUOusKQmvXx2VlkfHXKvb9Mg2AKm1PpHq3c8jamwxA2vI57F81r6x34ZCcczz9wbfMWrGGqhHhPH71ANof1TBfuxc+ncEXvy4jeV8qc18Ylj1/yswFvP/jAkJDjOpVIhjxz3Np2SiqLHehSM45nv5oOrNXrvP28Z9n065JbL52L37xE1/MW0nyvjR+HTck3/LvF69m6OufMfneK+hQwDEqL5xzPDNtPrPXxFM1PJRHB3alXaP6udqkpmdy/9Sf2JS0h5AQo8fRjbmj7/FBirjkFs/9hTcmjCPLl8Xp5wzg/MuvyrX8288+4ttPPyQkJISq1apz09BhNGnWIkjRHp7W999D/dO6kpWWxqqHH2Pvb6vzten8n1eIiGpAVtp+AJbcfDsZSTvLOtSgKQ89+GZ2I3BjjlmTnHOT/M/jgI05lm0CTs7zEkf7X2cOEAo84pz75kjj+gjW/dkAACAASURBVD/27jusqesN4Pj3kIBskC2I4t571b1nbZ2tVjttq921rR2OLnedHdpW+9Na29o6qlXrrgvRVq0bcW9UloCAgEC4vz9igwgoKEmAvp/n4Xlyc9+bvCcJNyfnvufeYtfB927fCqcK5dnWuifuDepSe+IYdj06ONfYg298kGvnXefkSNCQQcTtP2zudAvMYDAwcdIk5nz7Lb6+vgwaPJh2bdtSqVIlU4yfnx/jxo7lh4ULs23r7e3Nwh9+wM7OjuTkZPr160e7tm3x8SmUoz0Ws3D1Tr5evJnvx71g7VTyzWAwMGH6F8z9Yip+Pt4MHPIS7Vu3oFKFIFPM8tVrcXVxYe2yn1m3aQszZ89h2viP6dm1Mz27Gn+knTx9ljfeH5Otc//ntmAcHOwt3KJ7++t8JJfibrD02c4cjYhjyuaDzH+iXY64wY2q0CjQm3RDJq/9FsKucxG0qGDsXHWqWpYR7etZOPP8sy1fHZ27F/E/fYbetxxObfuSsOyrHHEpB7aTcfkM2Ohw7T0U23LVSL9o/DJOO3WIG8G/Wzr1fAsJPc2FqGv8Me41Dp+7zPif17BoZM7/vbZ1q/JE+yb0/HBWtvt7NK3D420bA7D10AmmLt3It2/mvk+2lpCws1yMimP1Ry9y5PxVxi/exM8jnsoR17Z2ZQa2acgjY7/Lse5G6k0Wbd9HnaCi27H/185TV7gYm8jKN3pxJDyGSWt2s/DFHjninmpRkyYV/EjPMDBs4Z/sPHWZllUCrJBxwRgMBv73xRQ+mjYLD28fPnjpGRq3bJ2tA9+6U1e69uoHwN6dwfww+3PGTP3SWikXmGerFjiWD+Tvnv1wrVubamPeZ9/gIbnGhn3wEYlhOQc6hWXc6szPzWN1br9AtDuW9UAVoB1QFtihlKqtaVr8g+RV7Ep0fLu05/JvqwGIP3AYW1cXSvl4Fegxqo14jbPffE/mzZvmSPGBhIaGEhgYSNmyZbG1taVb165s27YtW0xAQABVq1bFRmX/3Nja2mJnZwcYR4sztTs/Q8VDyP6TxF2/Ye00CuRI2HHKlfUnMMAfW1tbunfqwNbgndlitu7YyaM9ugLQuX1bdv+zH+2O92jdps306NzBtJycnMLCX5Yy7LmcnRFrCz5zlR41AlFKUbuMB0lp6cTcSM0WY2+rp1GgcTTXVmdDNR93opJSrJHufbGrUIubx/cBkBF5EZtS9ihHl+xBGenGzj1ApoGM6MvYOLtZONP7t/XQCR55qB5KKepVLEtiyk2iryfmiKtXsSzebi457nd2KGW6nXIzHWX9AbUcth45zSNNa6GUom4FfxJTUom+npQjrm4Ff7zdnHN9jNlrQni2U1NK6Yv+uNi2E5foWa+isb2B3iSmphOdmJwtxsFOT5NbP7Rt9TpqlPEgMiE5t4crck4fP4pfQFl8/QOwtbWlZYcu7N0ZnC3G0SnrfbyZmkKR/GDehVf7NkSsXgtAwuFQ9C4u2Hl53mMrUQSFA4G3LZcFruQSs1LTtHRN084BJzB2+B9Isevg2/v5kHIlwrScejUSe7/cR6jrTh9Hq/VLqPxm1pET11rVsff3I2pzcK7bWFtUVBR+flmHjn18fYmMyn85VkREBP0fe4yu3brx3LPPFrvR++IqKjoGv9tea18fbyKjY3LG+Bpj9Hodzs7OxF9PyBazfvM2unfuaFr+au58nnnicezti94IfvSNFHxcHEzLPs4ORN+l856YmkbI2as0KZf1Om09dZnBP21m5B+7iUwsep0LG2dXMpOyBlEyk67ftfOu7OyxC6pJevhp0312lergNvBtnLs9VSQ7/lHxifh5uJqWfd1diIrL2cG/m1+37qXH6K+YufxPPhjQrbBTfGBR8Yn4lr6jjbn8iMnLsUuRRMQl0rZ25XsHFwFRCcn4ujqZln1cHYlOuMv/ZkoawSfCaVohZ9lSURQbHY2Xt69p2dPbh9jo6Bxx61Ys5dVBffjx2694/o13LJniAyvl40NqRKRp+WZkFKXy+D6vMe5Dmiz5iaChuY/wl2Q6Gxur/93DXqCKUqqCUsoOGAisuiPmd6A9gFLKC2PJztkHfW3y3cFXSo1TSulvW3ZVSlm8IFjl8iv8zlFQgANvjGRH53781e9ZPJo2JKDfI6AUNT9+l2Pjplki1fuSW1tya3Ne/Pz8WLZ0KatXrWLV6tVcu3atMNMTecjP+5Z7TNbtw0fDsC9ViiqVKgBw/ORpLoVfpmO71oWbbCHJ7QBRXp/UjMxMPlz3D4/Xr0SAm7Hj0bqiHyuGdOXnJzvSpJwPYzfsM1+y9y2XFuV1ZEzZ4Nx1MKmHQ8hMMM73STsfRtwPE7n+6wzSL53CudNAM+Z6f+71ucyPge2bsHbC6wzv25G5a3cUUmaFKNfPav4amZmpMW35Ft7p076Qk7KwPJqbYchk5G87GNisOmU9ch6hKYq0XN7Q3D6z3fs8xuxFK3hy2Gss+3G+BTIrRLkWduRs99GRH7Gn3yD2PzsU94b18XskZymWsB5N0zKA14ANwDFgiaZpR5VSY5VSj94K2wBcU0qFAVuBdzVNe+DOW0GONeqB3Uqp5wA/4Ktbf7m6fdLBnDlzeJCqvvLPDCDwCWMt3fVDR3Hw9+PfKST2ZXy5GZnzl/vNCOOot+FGMld+X4t7/dpEbtyKS7XKPLRkHgClvL1oPP9L/hnyRpGZaOvr60tERNYRiqjISHy8Cz5hzcfHh0qVKrF//37TJFxhPr4+3kTcdqQlMioanzsOp/r6eBMRGYWfjzcZGQaSkpJwc80aVVy3aWu28pxDoUcJO3GSrn0GkmEwEBsXz3OvDOf7rz83f4PysOzQWVYeOQ9ADT93ohKzRgWjklLwcnbIdbvJfx4gsLQTAxtmjYC63Vba0at2ELNDQs2TdAGVqtMC+5rGOVAZUZewcc6aOGzj7EbmjYRct3Nq3w9DfAyph0JM92mpWUclbobtxrFF0fjy/XXrXn4L2Q9ArSB/ImKz2hQZn4i3+/119Lo3rs2En9cWSo4P6tfg/SzfZZxnVaucH5Fxd7Qxj1KcO924mcbpqzG88OUvAMQk3ODNOcv5YljfIjXRdvGeE6zYdwqAWgGeRCZklTlGJSTj7ZL7/+b41X9TzsOFwc1rWCTPwuDp7UNMdNbo9rXoKEp75f092bJDF76b+ZklUnsgAQP649+vNwCJR8Ow9/Pl+q11pXx9uJnLUYq0KON9huRkItZuwLV2TVNpz39BUZhkey+apq0F1t5x30e33daAt2/9FZp8d/A1TRuplNoM7AbigDaapp2+S/ztkw60NeNm5RV6Txd+WMyFH4xnGfHp0Jryzz7BlZXrcG9Ql4zERG5GZS+FUDodelcX0uPiUXo9Ph3bEhPyNxmJSWyq19YU99CSeRwbP73IdO4BatWqxcWLFwm/fBlfHx/Wb9jApIkT87VtZGQkbm5u2Nvbk5CQwMGDB3n6qaJXu10S1a5RnQuXLhN+5Sq+3l6s+3MLn306JltMu1YtWLV2A/Xr1GLT1u00bdTANMqfmZnJxi3bWPDNF6b4AX17MaCv8Wxal69G8NqIkVbt3AP0r1eR/vWME9l2notg6cGzdK5WlqMRcTjb2eLllLOU6NtdYSSlZTDqjjN0xNxINcXvOHuVoCIyenjzyC5uHtkFGCfZ2tdtSdqpg+h9y6GlpaIl5yztcGjWFVXKgRtblmW7Xzm6mOLtKtTCEFcoZz97YAPbN2Fg+yYABB85yS9b99K9SS0On7uMi0OpXGvt83Ih8hrlfT1Nj1XOx8MsORfUwDYNGdjG+JkLDj3Dr8H76daoBkfOX8XZvlS+O/guDqXYPvl10/LzX/zC233aFanOPcCAptUY0LQaADtOhrN4zwm61g7iSHgMzqVs8XZxzLHN7M0HSLqZzkePNrd0ug+kcrWaXA2/ROTVy3h4+bBzy0aGjxmXLeZq+EXKlC0HwP6/d+IXEJjbQxUplxcv4/Ji4z7Es3VLyj7xGJHrNuJatzaGxCTSYrIP6iqdDr2LM+nx11F6HV5tWxH7915rpC6KoHx38JVSbYAvgLFAHWCWUmqIpml3ThYwq6gtO/Du0Jp2IWswpKRy+J0PTetarV9CSLfHsbGzo9lP36Js9SgbG2JCdnNx0W+WTPO+6fV6Rn7wAS+//DKZmZn07tWLypUrM/vrr6lVsybt2rUjNDSUt95+m4SEBLYHB/P1N9+wYvlyzp49y/QZM1BKoWkazzz9NFWqPPA8DYv7cdIw2jSqhpe7M2fXT2PstytZ8HsRPOx/G71ex6h33uCl4e9hyMykT8/uVK5YgVlz51OrRjXat25J30ceZuSnE+nRfzBurq5MGZf12d138DB+Pt4EBvhbsRUF0yLIl13nIui/YBP2eh1jumR14J/6aQs/PtmBqMQUFuw5QfnSzjzz81Yg63SYSw6cYcfZq+hsFK72dnzYpZG1mpKn9AvHsStfA/enPkDLSCNp8xLTOrcBb3F98UxsnNxwbNKJjNhI3AYMB7JOh+lQrxW2QTVBy0RLTSbpz8V5PZXVtK5dhR1HTvPwmFnGU0g+86hp3WPj5rD0w2EAzPhtE2v3hJKalk6n92fSt1UDXnmkHb9s28vuY+fQ62xwdbRn/HN3nuLZ+lrXqkhI2Fl6jv0Oe1s9Y5/sblr3+OQFLPngWQBm/r6NtfvCSE1Pp/OHX9O3eV1e7tHKSlnfv1ZVAgg5dZleX/6Ova2eT3q1MK0b+M0f/PpyTyKv32DejlCCvFwZNGcNYPyR0KdR0f/O0On1vPDmu4x/9w0yMzPp0P0RAitU4tf5c6hUrQZNWrZh3YqlHN63B71Oj5OLK6+P/NjaaRfItR078WzdguZrlmNITeXYh1k/YJos+Ym9jz+JsrOl3rdfYqPXg42OuN17uPJb0T1jl7AslVv9Za6BSu0Bnv336ltKqb7ARE3Tqudjc21NYN37z7KIe/jSYVJTis+ZQe6HvYMDdg1K9gSetAPzSYu16O9Vi7Pz8Cfum1yvlF1ilH55MtdmvWvtNMzG87WpANzc9rOVMzGfUu0Gk7pxnrXTMCv7Ls9z45fx1k7DrJyeGMORq9fvHViM1Snjxpa6Ta2dhll1OLynyNbBDF1y0OqnC5z7eP0i+foUpAa/uaZphn8XNE1brpTaboachBBCCCGEEPepIB18L6XURCBA07RuSqmaQHOgZA+zCCGEEEKIIqc4TLK1loKcB38BxlP5/Duz6CQwvLATEkIIIYQQQty/gnTwvTRNWwJkguncnoa7byKEEEIIIYSwpIKU6NxQSnly65IhSqmHgJI9e0YIIYQQQhRJuoJele8/pCAd/LcxXl63klJqJ+AN9DdLVkIIIYQQQoj7cs8OvlKqCXBJ07T9Sqm2wDCgH7ARCDdzfkIIIYQQQuQgk2zzlp8a/DlA2q3bLYDRwGyMV7Odm9dGQgghhBBCCMvLT4mOTtO02Fu3BwBzNU37DfhNKXXQfKkJIYQQQgghCipfHXyllP7WWXM6AkMLuL0QQgghhBCFSkp08pafDvovwHalVAyQAuwAUEpVRs6iI4QQQgghRJFyzw6+pmkTlFKbMV7gaqOmadqtVTbA6+ZMTgghhBBCiNzoZQQ/T/kqsdE07e9c7jtZ+OkIIYQQQgghHkRBrmQrhBBCCCGEKOJkkqwQQgghhCh2ZJJt3mQEXwghhBBCiBJEOvhCCCGEEEKUIFKiI4QQQgghih0p0cmbjOALIYQQQghRgsgIvhBCCCGEKHZkBD9vMoIvhBBCCCFECSIdfCGEEEIIIUoQKdERQgghhBDFjpTo5E1G8IUQQgghhChBlKZplngeizyJEEIIIYQoVEV2mHzC5pNW71+O7li1SL4+FivRMYRuttRTWZyudkfOjXjK2mmYVYVpP5IWe8XaaZiVnYc/dg2GWDsNs0o7MJ/U9XOtnYZZ2XcbWqI/q3Ye/gAYLhyycibmoytfj4uxSdZOw6zKeTiT9PNYa6dhVs6DPyIlNdXaaZiVg719ie7fgLGPI4ofKdERQgghhBCiBJFJtkIIIYQQotiRSbZ5kxF8IYQQQgghShAZwRdCCCGEEMWOjODnTUbwhRBCCCGEKEGkgy+EEEIIIUQJIiU6QgghhBCi2JESnbzJCL4QQgghhBAliIzgCyGEEEKIYkdG8PMmI/hCCCGEEEKUINLBF0IIIYQQogSREh0hhBBCCFHsSIlO3mQEXwghhBBCiBJEOvhCCCGEEEKUIFKiI4QQQgghih2dkhKdvMgIvhBCCCGEECWIjOALIYQQQohix0ZG8PMkI/hCCCGEEEKUINLBF0IIIYQQogSREh0hhBBCCFHs6KRCJ08ygi+EEEIIIUQJIiP4QgghhBCi2LGRK9nmqVh08DVNY+L8pQTvP4qDnS0TX3+amhXL5Yg7euYio2YtJDUtnTYNazFqyGMopZi1+A+W/bmT0q4uAAwf9ChtG9UmLT2DT+Ys4uiZi9goxcghj9G0dlVLNy9XHr2ewrFGPbS0m0Qvnkva5Qs5YnxfeBedqzvKxobUcye4tvwH0DTcu/bDqVZDNE0jMymB6MVzMSTEW6EV2YX8tYfPPp+FwWCg76MP88LTg7KtT0tLY9TYSYQdP4m7mytTx39MQBk//tiwiQU/LzbFnTx9liUL5lK9amXTfa+/O5rwK1dY8fP3FmvPg5j78XP0aFOP6NgEGjz2kbXTuS+apvHZ8q2EhJ3D3lbPuMHdqBHomyPuqz9CWL33KAnJN/l76hum+1fuDmXmymB83J0BGNi6Pn2b17VY/nkxx+f0uVeGE3MtllKl7ACY8/lUPD1KW7RdO/YeZNI332PIzKR/t468OLD3He1K54Opszh66izuLi7MGD2cAD8fAOb+soLfNmxBZ2PDqFeeo1Xj+nd9zL8OHGHadz+RmZmJk4M9E0a8SvkAP4u293Z7/9rF159PI9NgoPujvRn49HPZ1i/75SfWrfodnU6Hm3tpRoz+GN8yZUzrb9xI4vmB/WnZtj2vj3jf0unni6ZpTN2wj52nLmNvq+eTXs2pUcYjW0xKegbvL91BeFwSOhtF6yoBvNGpAQD7L0QybcM+TkfGM7FfKzrVzPkda007d+5kymefkZmZSZ8+fRjy/PPZ1u/bt4+pU6Zw6tQpJn/2GZ07d862PikpiT69e9OhQwdGjhplydTz7UH7OgA/rd3KonXb0dnoaNuoFiOe7mvpZogioliU6ATvP8qFq1Gsn/UJn748mE/n/ppr3Ni5v/DpS4NYP+sTLlyNYseBMNO6p3t2YMX0UayYPoq2jWoDsOzPnQCsnDmG/338BlN++I3MzEzzN+geHKrXw9bbl/DJI4hZNh/Pfs/lGhf141dcmTGay9NGonNyxaleMwCub1vD5RmjuTJzDMnHDuLeuXeu21uSwWBgwvQv+HrGZFb+soB1mzZz5tz5bDHLV6/F1cWFtct+5qmBjzFz9hwAenbtzLKF/2PZwv8x8aNR+Jfxy9a5/3NbMA4O9pZszgNbuHonPV+dYe00HkhI2DkuRsexeswQPhrYmfFL/8w1rm3tivz89uBc13VpWI0l7z3NkveeLhKde3N+Tid/Mtq03tKde4Mhk/Gz5jFnwihWfzeTtdt2cvpCeLaY39ZvwdXZiQ0LvuKZvg8zfd7PAJy+EM667btYPXcGcyeMZtxX8zAYMu/6mGO//B9TPnidFd9O5eH2rZiz6DeLtvd2BoOBr6ZPZuKML/nfL8vYumkDF86dzRZTuWo1Zn//I3N/WkybDh35bvYX2dYvmPsNdRs0tGTaBbbz9BUuXUvg99ceZUzPZkxasyfXuKea12D5q4+waGh3Dl2KZuepywD4uTnxaa/mdKsTZMGs88dgMDBp4kRmf/01y1esYP369Zw5cyZbjJ+fH2PHjaN79+65Psbs2bNp1LixJdK9bw/a19l95ARb9hzm9xmjWf3FhzzXq3Ou24v/hmLRwd+y9zC92jZDKUW9qhVIvJFMdNz1bDHRcddJSk6lfrWKKKXo1bYZm/ccuuvjngm/ykN1qgHg6eaCi5MjoWcumq0d+eVYqyFJ/4QAcPPiGWzsHdG5uOWI026mGm/Y6FB6PWha9vsBZVcKNPPnfC9Hwo5Trqw/gQH+2Nra0r1TB7YG78wWs3XHTh7t0RWAzu3bsvuf/Wha9uTXbdpMj84dTMvJySks/GUpw557yvyNKEQh+08Sd/2GtdN4IFtDz/BIk5oopagb5E9iyk2iryfliKsb5I+3m7MVMiw4c31Ore3IidOU8/cjsIwvdrZ6urdtwZZde7PFbPnrH3p3bgdAlzYP8feBUDRNY8uuvXRv2wI7O1vKlvGhnL8fR06cvutjKgVJN1IASLyRjLenZX/Q3O5E2FH8ywZSJqAstra2tOvUhV3B27LF1G/UBHt7BwBq1KpDdFSUad3J48eIj42lUbOHLJl2gW0/Ec7D9Yzff3XKepF0M43oxJRsMQ62eppUMB5JsdXpqF7Gg8hbMf7uzlTxLW0aCS5KQkNDCQwMpGxZ43vYtVs3tm3bli0mICCAqlWromxydmvCwsKIvXaN5s2bWyjj+/OgfZ1fN+zghT5dsbO1BYz9mpJOp5TV/4qqYtHBj4qNx88r6wvC17M0kdeyl5xEXovH19M9W0xUbFbMonXb6f3WeEbP/pHrSckAVCtfli17D5NhMBAeGUPYmYtExMSZuTX3pncrTUZ8rGnZcD0WnZtHrrG+L75LuU9mk5mawo3DWSM2pbv1J3DM5zg3bEHcBuuNnv0rKjoGPx8f07KvjzeR0TE5Y3yNMXq9DmdnZ+KvJ2SLWb95G907dzQtfzV3Ps888Tj29sVrBL8kiIpPwtc96wvE182FqFw6+Hez+dAp+k/+gXfmryIiLuHeG5iZuT6nAGPGf0b/p1/g2/kLc/wgMLfImFj8vD1Ny37enkRdi80zRq/T4eLkSHxCIlHXsm/r6+VBZEzsXR9z7Fsv8dKYSbQf9BKrNgfz4gDrHUWMiY7C2yerdMzLx5eY6Og849etXknT5i0AyMzMZM6XM3nxtTfNnueDikpMxtfV0bTs4+JIdGJynvGJqWnsOHmZphVyltUVNVFRUfj5ZZV4+fr4EBUZma9tMzMzmT59Om+9/ba50is0D9rXOX81in3HTjPggyk8/eEMjpw+b5G8RdGUrw6+UspXKTVPKbXu1nJNpdTz99qusOT2ZXjnKIOWyzD1vxEDu7Zhw+yxLJ8+Cm93V6b8YOzw9u3YHD/P0jz23mdM+n4Z9atVRKcrAr95cvtFmEeHIPK7qVwa+zpKb4t95Vqm++PWL+PS+OEk7d+Fa0vrH6bL13uYa0zW7cNHw7AvVYoqlSoAcPzkaS6FX6Zju9aFm6zIp3u/p3fTtnYl1n38Ass+eIZmVcsz5uf1hZncfTHH5xSM5Tkrfp7PD998yf5DR1i9bmPhJZ0Pue0f79zP5LoPVSrP9t7tMRcuX8O340eyddG39OnSns/mLLy/xAtBft7Tf/25fi0nj4fx2OCnAVj121KatmiJj6/15g/kV25fEXn9N2ZkZjLqtxAGNq1G2dJFf5S3IO/hnZYsXkyrVq2y/UAoqh60r2MwGEi4kcyvk95lxNN9eXv6PIsPJliajVJW/yuq8jvJdgHwPTD61vJJYDEwL68NlFJDgaEAc+bM4fkWlQqU2KJ121l6q0a+TuXy2UbWI6/F4eORvWTF745fupHX4vD2MP7K9XJ3Nd3/WOdWvDzxa8A4SvXBc/1N6waNmkr5Mlmjd5bk0qITLs3aAZB26Sx6dw9u3lqnc/PAkJD3kQUtI53ksP041W5I6qnQbOtuHNiF7/MjiN+43EyZ54+vjzcRtx32joyKxsfLM2dMZBR+Pt5kZBhISkrCzTXrvVu3aWu2sodDoUcJO3GSrn0GkmEwEBsXz3OvDOf7rz83f4P+o37dcYDlfx0BoFY5PyLjE03rIq8n4u3qlO/HcndyMN3u16IOX6wOLrxE75M5Pqf/bgPg5ORIjy4dORJ23FTmYwl+Xp5ERF8zLUdEX8PnjnkA/8b4eXuSYTCQeCMZNxdnfO/YNjImFh9PD9Pj3PmYsfEJnDh7gXo1qgDQvV0Lho6aYM7m3ZW3jy/RUVmjvTFRkXh6eeWI279nN4sWzGP6199hZ2ecDH0s9DBHDh1g9W9LSUlJJiM9AwdHB1545Y0c21vDkr0nWLHfWIte09+DyISsEfuoxGS8XBxz3W7CH7sJ9HRl0EPVLZLng/L19SUiIsK0HBkVhbdP/r6rDx0+zIH9+1myZAkpycmkp6fj6OjIm8OHmyvdAinMvo6fZ2k6N6tvLJusEoSNUsQlJOHxHyjVETnld7jaS9O0JUAmgKZpGYDhbhtomjZX07TGmqY1Hjp0aIETG9S9rWlSbMemdVm5fTeapnHo5DlcHB3wLp39Q+9d2g0nh1IcOnkOTdNYuX03HZoYJ+3dXsP25+6DVCnnD0DKzTSSU43d6F2HjqGz0VE5sAzWkLjrT67MHMOVmWO4cXQfzo1bAVCqXCW01GQMidnr8JRdqay6fBsbHKrXIy3qCgB6r6xDro41G5J+635rql2jOhcuXSb8ylXS09NZ9+cW2rVukS2mXasWrFq7AYBNW7fTtFED0+hFZmYmG7dso9ttHacBfXuxZfUyNqz4lYVzviKoXFnp3JvZwNYNTJNi29epzOq9YWiaxuHzV3C2L1WgWvvb6/W3HTlDBV/Pu0Rbhjk+pxkZBuLijf+/6RkZBO/8iyoVK2BJtatV4sLlq4RfjSItPYN123fRvnn2CYftmzfi903bANgY/DfN6tdCKUX75o1Zt30XaWnphF+N4sLlq9SpVjnPx3R1cSLxRjLnw437nb/2HaZSuQCLtvd21WrU5PKlS1y9cpn09HS2/bmR5q3bZos5feI4n0+ZwNipMyntkVUOOfLTCSz6fS0/rfiDxLj8GgAAIABJREFUoa8Pp1P3h4tM5x7g8SbV+GVYD34Z1oN21QJZc+gsmqZxJDwG51J2eLs45Njm6y0HSUpNZ0TXRlbI+P7UqlWLixcvcjk8nPT0dDasX0/btm3vvSEwadIk1m/YwLp163jr7bfp2bNnkencQ+H2dTo0rcvuIycAOH8lkvSMDEq7Fo/5T6Lw5XcE/4ZSypNbx+SVUg8B1+++SeFp07A2wfuP0u3Vj7EvZceEV7MmVPZ5ZyIrphtPefXR0CcYNWshN9PSad2gFm0aGktWpi1cwfHz4SggwMeTT14ynvYu9noiL477Chul8PFwZ/Ibz1iqSXeVcuwQjtXrU/aDaWjpaUQv/s60zv+t8VyZOQZlVwrfIW+jdHqwsSHldBiJf20BwKPHAGx9ykBmJhnx14hZZv1TR+r1Oka98wYvDX8PQ2YmfXp2p3LFCsyaO59aNarRvnVL+j7yMCM/nUiP/oNxc3VlyrgPTdvvO3gYPx9vAgP8rdiKwvPjpGG0aVQNL3dnzq6fxthvV7Lg9x3WTqtAWtesQEjYWXqOm4e9nS1jB2WNSD8+ZSFL3jOWOcxcuZ21+46Tmp5O54/m0Ld5HV7u3oJFwQfYFnoGvY0Nro72jBtsuRHtvJjjc5qWnsaw4e+SkWEgM9PAQ00a0a/Xw5Ztl07H6NeG8OKoCcbTDHZtT5WgQL76YTG1qlaiQ/PG9OvWgfc/m0XXZ1/H3cWZaaOMnaAqQYF0bdOcR158G53OhjGvPW8qZcztMQHGDh/Gm2OnY2Njg6uzE+Pfedmi7b2dTq/ntXfeY+Tw18jMNNC1Zy+CKlZiwdxvqFqjJi1at2XurC9ISU5h3GjjKTB9fP0YN3Wm1XK+H62q+LPz9GV6zVqFva2OTx7NmlD6xJy1/DKsB5EJycwLOUqQlyuD564D4PEmVenTsDJHL19jxJLtJKSmseNkOHO2H2bpyz2t1Zxs9Ho9H4wcycsvv0xmZia9evemcuXKfD17NjVr1aJdu3aEhoby9ltvkZCQQPD27Xxz64w7xcmD9nX6dmjBmK9/5NHh47DV65n4+jNFctJ0YZIr2eZN5ac+SynVEPgKqA2EAt5Af03TDufzeTRD6Ob7TrKo09XuyLkRxessLgVVYdqPpMVa/0iAOdl5+GPXYIi10zCrtAPzSV0/19ppmJV9t6El+rNq52H88WC4cPezhBVnuvL1uBhbsAnbxU05D2eSfh5r7TTMynnwR6Skpt47sBhzsLenJPdvAHS1OxbZbvSiA+FWn2QwqEHZIvn65GsEX9O0/UqptkA1jPM5Tmialm7WzIQQQgghhMiDXMk2b/nq4Cul7rwUWlWl1HXgiKZpUbltI4QQQgghhLC8/NbgPw80B7beWm4H/I2xoz9W07QfzZCbEEIIIYQQooDy28HPBGpomhYJxvPiA98AzYBgQDr4QgghhBDCYoryeeitLb+nyQz6t3N/SxRQVdO0WEBq8YUQQgghhCgi8juCv0Mp9Qew9NZyPyBYKeUExOe9mRBCCCGEEIVPTpOZt/x28F8F+gKtbi3vAcpomnYDaG+OxIQQQgghhBAFl68SHc14svwzGMtx+gAdgWNmzEsIIYQQQghxH+46gq+UqgoMBJ4ArgGLMV4cS0bthRBCCCGE1cgk27zdq0TnOLADeETTtNMASqm3zJ6VEEIIIYQQ4r7cq0SnHxABbFVKfaeU6ojxSrZCCCGEEEKIIuiuI/iapq0AVtw6W05v4C3AVyn1DbBC07SNFshRCCGEEEKIbHQ2Muacl/xOsr2hadrPmqb1BMoCB4EPzJqZEEIIIYQQosDye5pMk1sXt5pz608IIYQQQgiLk0m2ecvvlWyFEEIIIYQQxYB08IUQQgghhChBClyiI4QQQgghhLXppEInTzKCL4QQQgghRAkiI/hCCCGEEKLYkUm2eZMRfCGEEEIIIUoQ6eALIYQQQghRgkiJjhBCCCGEKHbkSrZ5kxF8IYQQQgghShAZwRdCCCGEEMWODODnTWmaZonnsciTCCGEEEKIQlVku9FbTkdbvX/ZobJ3kXx9LDaCnxYXYamnsji70n5kXD5m7TTMSh9Qg7hvPrB2GmZV+uXJpK6fa+00zMq+21DsGgyxdhpmlXZgPn8ci7R2GmbTs4YvABn71lg5E/PRN3qY9Igz1k7DrGz9KlHr7dXWTsOsjs54hEXeNa2dhlkNig7j4OV4a6dhVvUD3K2dgrgPUqIjhBBCCCGKHZ2cBz9PMslWCCGEEEKIEkRG8IUQQgghRLEjV7LNm4zgCyGEEEIIUYJIB18IIYQQQogSREp0hBBCCCFEsaOTYeo8yUsjhBBCCCFECSIj+EIIIYQQotiRSbZ5kxF8IYQQQgghShDp4AshhBBCCFGCSImOEEIIIYQoduRKtnmTEXwhhBBCCCFKEOngCyGEEEIIUYJIiY4QQgghhCh25Cw6eZMRfCGEEEIIIUoQGcEXQgghhBDFjlzJNm/y0gghhBBCCFGCSAdfCCGEEEKIEkQ6+EIIIYQQotixUcrqf/eilOqmlDqhlDqtlPogl/UvKaWOKKUOKqVClFI1C+W1KYwHEUIIIYQQQmRRSumA2UB3oCbwRC4d+EWaptXRNK0+MAWYURjPLZNshRBCCCFEsVMMzpLZFDitadpZAKXUr0AvIOzfAE3TEm6LdwK0wnhi6eALIYQQQghR+AKAS7cthwPN7gxSSr0KvA3YAR0K44mLRQc/5K/dfDbzKwyZmfR99GFeeHpwtvVpaWmM+nQiYSdO4u7qytTxHxPgX4b0jAw+mTiFsBMnMWQYeLRHV1545kkiIqMY9ekEYq7FYmNjQ//ej/DkgP5Wap3Rjj37mTzrfxgyM+nXozMvDuqXbX1aWjojJ3/O0ZNncHd1YfpHIwjw8yX+egLDP51C6PHT9O7agTFvDjVts2ZzMN8tWoZSCm9PDz4b9Ral3Vwt3bRcaZrGjO2H+etcJKVsdXzYpRHVfdyzxaSmZzBqzR4uX7+BjVK0qujHq61qA/DH0QvMCgnF28kBgP71K9KrdpClm3FXmqbx2fKthISdw95Wz7jB3agR6Jsj7qs/Qli99ygJyTf5e+obpvtX7g5l5spgfNydARjYuj59m9e1WP4Pau7Hz9GjTT2iYxNo8NhH1k7nvhzfv5vf//clmZmZNOv8MB37PZlt/a71K9m5djk2NjrsHBx47JV38QsMIiM9nWXfTOPS6eMoGxt6P/8Gles0sFIrctI0jUkLVxB88BgOdnZMeOkJalYomyPu6NlLjJ7zC6lp6bSpX4ORT/dBKcWx85cZO38pN9Mz0NvYMOa5ftStXJ6zlyMZM+dXws6H8+bjPXiuZ3srtM4oZPc/TP5qjnGf+nBXXhj8eLb1aWnpjJw4jbCTp3F3dWHaxyMJKOPLkWMn+GTaV4DxdXrl2cF0atOCcxfDGfHpZNP24Veu8tqQp3jqsd4WbVdeWlX35oPetdHZKH77+yL/23I62/r3e9WiaWVPAOxtdXi4lKL56PUAvNOzBm1q+qIU/HUymkkrjlo8//xoNHEU/p3akJGcwt9vjCLu8LEcMR1/X4CDrzeG1JsAbHnsBW7GxOIYUIbmsyZi6+aKsrHh0PiZXPkz2NJNuKuDe/5iwawZZGZm0qHHo/Qe9Ey29ZtWLWfDymXY2Nhg7+DA0LdHUjaoIof/2c2i72aTkZGBXq/nyWFvULthYyu14r9HKTUUGHrbXXM1TZv77+pcNskxQq9p2mxgtlJqEDAGeCbHVgVU5Dv4BoOBCdM+Z+6X0/Hz8Wbgc8No37ollSoEmWKWr1qDq6sLa5ctYt2mzcycPYdpEz5h4+atpKWls+LnBaSkptJ74DN079wROzs7RrzxKjWrV+XGjWQGPPsizZs2zvaYFm/jF3P4buqn+Hp7MuDld2nfoimVgwJNMb+t24SrizPrf/qWtVt2MGPuQqZ/9C52dna8/twgTp+7yKlzF03xGQYDk2fPY9X3X1HazZVpcxawaMUaXn32CWs0MYe/zkdyKe4GS5/tzNGIOKZsPsj8J9rliBvcqAqNAr1JN2Ty2m8h7DoXQYsKfgB0qlqWEe3rWTjz/AsJO8fF6DhWjxnCkQtXGb/0T35+e3COuLa1KzKwdX0eGT8/x7ouDasxqn9HS6Rb6Bau3snXizfz/bgXrJ3Kfck0GFg+ZybDPp2Bm6c3n787lFpNW+EXGGSKadimEy269QIgdE8Iq+bPYujH0/h702oA3v3yBxLj4/jf2Hd5c9pcbGyKxrSnHQePcSEihnUzRnH49AXGzl/Gr+OG54gbO38Znzz/OPWqlOelKd8Rcug4revXYMYvq3mlb1da169B8IEwZvzyBws+fBU3Z0dGPtOHLf+EWqFVWQwGA+M//5rvpk/Az9uLAcOG077lQ1QKKmeKWb5mA64uzqxbNI+1m7czY858pn8yksoVyrN4zhfo9Tqir8XSb8irtGvRjArlyvLbvFmmx+/Q/2k6tm5urSZmY6NgdN86vPjt30ReT2HxW63ZejSCM5FJppjPVmZ12ge1CqJGgBsA9YNK06CCB32mbgPgx9db0qSSJ3vPXLNoG+7Fv1MbXCqWZ3XTbng2qkuTKR+zsdvAXGN3vfQesYey/0ip/fYwLqxcz+kFi3GtWol2v3zLqkadLZF6vmQaDMz/Yiqjp36Fp7cPI19+lsYtWlM2qKIppmXHLnR+tC8A/+wMZuE3XzDqsy9wcXPnvQnT8fDy5uK5M0x8702+XfqHtZpiUTa59p8t61Znfm4eq8OBwNuWywJX7vJwvwLfFEZeRePb5i6OhB2jXNkAAgP8sbW1pXvnDmwNDskWs3XHTh7t0RWAzu3bsvuf/WiahlKKlJQUMjIyuHnzJra2epydnPD28qRm9aoAODk5UiGoPJFR0RZv27+OHD9FYEAZAv39sLO1pUeHVmzdtTtbzJade+jVxTga1qVtC/7efxhN03B0sKdRnZrY2dlmi9c0DU3TSElJRdM0biSn4O3lYbE23Uvwmav0qBGIUoraZTxISksn5kZqthh7Wz2NAr0BsNXZUM3HnaikFGuke1+2hp7hkSY1UUpRN8ifxJSbRF9PyhFXN8gfbzdnK2RoXiH7TxJ3/Ya107hvF08dw7NMAJ5+/uhtbWnQqiNHd2ff99g7Oplup6Wmom4VhEZeOk+Vuo0AcHEvjb2TM+Gnj1su+XvYsi+UR1s3RilFvSpBJCanEB2XkC0mOi6BGyk3qV81CKUUj7ZuzOZ/jtxaq0hKMf6/Jqak4l3aeGTQ082FOpXKobfy1WeOHDtJuQB/Av3LGL83OrRhS8hf2WK27PybXl07AdClbSt27z+Epmk42Nuj1+sAuJmWlmuR79/7DxHo74e/X84jctZQp1xpLsXcIDw2mXSDxtoDV2hf2y/P+B4NAlh74DIAmgZ2ehts9TbY6XXodTZcS7xpqdTzLaBbB84tXgnAtX2HsXNzwd7XK9/baxrYuhj3s3auzqRERJklz/t1+ngYvgFl8fUPQG9rS4sOndm7K/sRBkenrO+Jm6kppv1NhSrV8PAyflcGBlUkPf0m6Wlplkte3M1eoIpSqoJSyg4YCKy6PUApVeW2xYeBU4XxxAUawVdKBQDlb99O0zSzHuOKio7Bz8fHtOzr483ho8dyxvgaY/R6Pc7OTsRfv07nDu3YGryTDj37kpp6k3eHv4rbHSUql69c5fjJU9StXShnJbovkTGxlPHJ2lH5enly+Fj29zcqJha/WzF6nQ4XJ0fiExLzLLmx1ev5cPhL9H7hTRzs7SkfUIYxbwzNNdYaom+k4OPiYFr2cXYgOikFLyf7XOMTU9MIOXuVAQ0qme7beuoyBy7HUM7dmeFt6+Dr4mj2vAsiKj4JX3cX07KvmwtR15MK1JnffOgU+0+HU96nNO/2aYdf6aJRYvVfcD02BnevrH2Pm6c3F0+F5YgLWbuc4JVLyMhI5+VxnwPgH1SZ0D0h1G/dgfiYKMLPnCQ+JopyVa23n7ldVFwCfh5ZJXG+Hu5Exl03ddQBIuOu4+vhZlr283An6taPgA+e7s3QyXOY9vNqMrVMfv4kq7SsKIiKuWbaXwL4entx5NiJXGKMnSK9XoezkyPx1xMo7e7G4bDjfPjZ51yJjGLSqBGmDv+/1m3eTo+O7czejvzydbPnanzW4EdkfCp1y7vnGlumtANlPR3ZfSoGgEMX4thz+hrbPumCAhaFnOdsVM6BCGtzLOND8pUI03LylUgc/XxJjYzJEfvQlxPQMjO5tHojoTO+BeDI1Fl0WPI/qr0wGL2jA5v7PW+x3PMjNiYKT5+sH4yeXj6cPpazVGrD70tZs/QXMjLS+XD67BzrdwdvIahyNWzt7Myab1FR1CfZapqWoZR6DdgA6ID5mqYdVUqNBf7RNG0V8JpSqhOQDsRRCOU5UIAOvlLqM2AAxpm/hn9zB8zawde0nJOJ73w/c41RitCjx7CxsWHzH8tJSEjk2Zde56EmjQkM8AcgOTmZt0Z+xPvDX8fZySnHY1hMrvnfGXLv1+F26RkZLF61jmVzZhDo78eEL7/ju0W/8dJTj99lK8vJpTl5ticjM5MP1/3D4/UrEeBmfJ9aV/SjS7Wy2Ol1LD98jrEb9jG7f2vzJXxfcv9c5lfb2pXo3qg6dno9S0IOMebn9fzvtaLx/v0n5PYhzeVT2qpHX1r16Mv+7Zv4c+lCnnhzNE079SAq/AKfvzOU0j6+BFWvhY1Ol8vjWcf97lf/tfjPnbz/VC+6NK3H+r8P8uHcxcwb/XIhZ3n/cm+funfMrf/PujWrs/KHbzlz/iKjJ82gdbPGlCpl7DClp6ezbdduhg99tvATv1+57Fbyevt6NPBn46GrZN5aX87LkYq+znT8dBMA3730EI1OeLDvbKyZkr1Puew7c3sPd730HikRUeidHGn9/RdUePxRzi1ZRVCfhzn76+8c/2YBXo3r0eLrz1jT+tG8XygLy313k7PNXXs/RtfejxGyeQPLf/qeVz/42LTu0rmzLJo7m1FTvjRjpqKgNE1bC6y9476Pbrv9pjmetyDHUXsD1TRN66Fp2iO3/h7NK1gpNVQp9Y9S6p+5c/MqTbo3Xx9vIqKyDqVFRkXj4+2VMybSGJORkUFS0g3cXF1Zs/FPWjVviq1ej6dHaerXrc3RY8bD5OkZGbw18iMe7tqJTu3b3Hd+hcHX25OrUVmjEJEx1/C5o5zG19uTiFsxGQYDiTeScXN1IS/HT58DoFxAGZRSdGvXkoNHrVsisOzQWZ76aQtP/bQFL2d7ohKzRpyiklLwcnbIdbvJfx4gsLQTAxtWNt3n5lAKu1ujar1qB3E8Kt68yefTrzsO8PiUhTw+ZSHebs5Exiea1kVeT8TbNf8/JN2dHLDTG3+D92tRh2OXIgs9X5E3N09v4mOy9j3Xr0Xj5pF3SUD91h0JvVXCo9Pp6fX867zz+XyGjJpEyo0kvPwD89zWEhZtDKHvyGn0HTkN79KuRMRm/c9ExsbjU9otW7yfhzuRsddNyxGx8fjcGuFfGfwPnZsYJ3x3bVaPI2cvUpT4enuZ9pcAkdExOUoUjTHG0syMDANJuexTKwWVw8HenlPnzpvu27H7H2pUqYSXR2nzNaCAIuNTKeOetf/0dbcnKiE119ju9bPKcwA61inD4QtxJKcZSE4zEHI8inrli0bbqgx5gu5bl9N963JSIqJw9M8qO3L09yUlMmeZzb+lNxk3kjm/fA2eDesAUHFwPy6uNE4qjvnnELpSdpTyLBrtBPD09uFaVNY+/lpMFKW98t7ftGjfmb07t2fFR0cy/eP3eGXkx/gF5JwwL/57CtLBPwvY3jPqFk3T5mqa1ljTtMZDh95/aUjtGtW5cCmc8CtXSU9PZ92mLbRr3TJbTLvWLVm1dgMAm7Zup2njBiilKOPra6rHT05J4XBoGBXKl0fTND6e8BkVg8rzzKAB951bYaldvQoXL18l/GokaenprN0SQvvmTbPFtG/RlJUbtwKwcfsumjWoc9fRYF8vT85cCCc23vgFvWvfQSqWt+4/ff96FfnxyQ78+GQH2lbyZ+2xS2iaRujVWJztbHMtz/l2VxhJaRm81Tb72WNur9ffcfYqQR55/9ixpIGtG7DkvadZ8t7TtK9TmdV7w9A0jcPnr+BsX6pA5Tm31+tvO3KGCr6e5khZ5CGwSnViroZzLfIKGenpHAjZTK2m2fc90Veyzn527J+/8Cpj/B9Lu5nKzVTjD9gTB/ei0+myTc61hkFdWrF80giWTxpBx8Z1WLXjHzRN49Cp8zg72GcrzwHwLu2Ko0MpDp06j6ZprNrxDx0aGc9i5VPalb3HzgCw++gpyvt6W7w9d1O7elUuhl8h/GqE8XtjSzDtWz6ULaZ9y2as3PAnABu3h9CsQV2UUoRfjSAjw3iQ+kpEJOcvhRNwW6392s3b6dGxreUakw+hl+Ip5+1EgIcDtjpFjwb+bA2NyBEX5O2Eq6MtB8/Hme67GpdC40qe6GwUehtF44qenI0sGiU6p+b/wrr2fVnXvi/h6zZTYYBxQrtno7qkJyTmKM9ROh2lbpWeKb2egC5tiT9mPJtQ8uWr+LYxfgZcq1TExr4UN2OKzlGKStVrEHH5ElFXjfubXVs20bh59sHHq+FZP6QP/L2TMgHGQYMbSYlMHvk2T7zwCtVrF90TT5iDjbL+X1F1zxIdpdRXGGsNkoGDSqnNgGkGjqZpZi2+1Ov1jBoxnJfeHIEhM5M+PXtQuWIFZs2dR63q1WnfpiV9H+nByE8n0KP/INxcXZgyznjI6on+vRkzfjJ9Bj2Lpmn07tmdalUqsf/gYVav20iVShXp/5SxDu+Nl1+kTYuH7paK+dqo0zH69RcZ+v6nZBoM9OneicoVyvHV94uoVbUyHVo2pV+PTnww8XO6PfkSbi4uTPvwHdP2nZ94kaTkFNLTM9iyczdzp3xC5aBAXnl6AM8MH41er6OMjzcT3y86dbItgnzZdS6C/gs2Ya/XMaZLQ9O6p37awo9PdiAqMYUFe05QvrQzz/xs/HHz7+kwlxw4w46zV9HZKFzt7fiwSyNrNSVPrWtWICTsLD3HzcPezpaxg7qa1j0+ZSFL3nsagJkrt7N233FS09Pp/NEc+javw8vdW7Ao+ADbQs+gt7HB1dGecYO75vVURdKPk4bRplE1vNydObt+GmO/XcmC33dYO6180+n09H1xOHM/HYFmyKRppx74lavA+kXzKFu5GrWbtmLn2uWcPLQPnU6Pg7MLT7w5CoCk+DjmfjoCZaNw8/DmieFjrNya7NrUr0HwwWN0f2si9qVsGT8s6+xafUdOY/mkEQB8NKQ/o7/9hZtp6bSqV53W9WsA8MkLjzN54e9kZBooZWvLJy88BkB0fAIDxswkKSUVG6X4cX0wq6a8j7Nj7nNrzEWv1zFq+MsMGzHG+L3RowuVK5Rn1rwfqVW9Cu1bPkTfHl0ZOWEa3Qc9j5uLC1M/fh+A/YePMm/RUvR6PTZKMeatVyjtbjy6kZKayl//HODjd163aHvuxZCpMWF5KHOHPoSNjWLFnkuciUzitW7VOHopnq1HjSPDPRoGsO620XuAjYeu0KyKFyvebQsahByPYltY0TtaeGVTMP6d2vDInvUYUlL5+43RpnXdty5nXfu+2JSyo/2S71B6PUqnIzL4L878uBSA/R9NodnMT6k+zLjf/fv1UVZpR150Oj1DXh/BxPffINOQSbvujxBYoSJLvp9Dxao1aNyyDRt+X8qRfXvR6fU4ubjwyvvGvs76FUuJvBLObz/O57cfjWdjGz3lS9xKF50TawjLU3erswRQSt212F/TtB/y8TxaWlzO0YSSwq60HxmXc56PtyTRB9Qg7psPrJ2GWZV+eTKp6++/nKw4sO82FLsGQ6ydhlmlHZjPH8eKXgelsPSsYRxNzti3xsqZmI++0cOkR5yxdhpmZetXiVpvr7Z2GmZ1dMYjLPIuGhPLzWVQdBgHLxeNElFzqR/gXmTHqU9EJVh9EkU1H9ci+frccwT/3w68UsoJSNU0zXBrWQeUMm96QgghhBBCiIIoSA3+ZuD2WZAOwJ+Fm44QQgghhBDiQRTkPPj2mqaZZt5ompaklCpaJx4XQgghhBD/CUXhSrZFVUFG8G8opUwzIZVSjYDic1lRIYQQQggh/gMKMoL/JrBUKXXl1nIZjBe+EkIIIYQQwqKK+pVsrSlfHXyllA1gB1QHqmG8bt5xTdPSzZibEEIIIYQQooDy1cHXNC1TKTVd07TmQKiZcxJCCCGEEELcp4KU6GxUSvUDlmv3Onm+EEIIIYQQZlSUryRrbQXp4L8NOAEZSqlUjGU6mqZprnffTAghhBBCCGEp+e7ga5rmYs5EhBBCCCGEEA+uICP4KKVKA1UA+3/v0zQtuLCTEkIIIYQQ4m6kQidv+e7gK6VewHiqzLLAQeAh4C+gg3lSE0IIIYQQQhRUQc+D3wT4W9O09kqp6sCn5klLCCGEEEKIvNnIifDzVJAr2aZqmpYKoJQqpWnacYznxBdCCCGEEEIUEQUZwQ9XSrkDvwOblFJxwJV7bCOEEEIIIYSwoIKcRafPrZufKKW2Am7AerNkJYQQQgghxF1IhU7e7tnBV0rZAy8BlYEjwDxN07abOzEhhBBCCCFEweVnBP8HIB3YAXQHamKccCuEEEIIIYRVFGQi6X9Nfjr4NTVNqwOglJoH7DFvSkIIIYQQQoj7lZ8fP+n/3tA0LcOMuQghhBBCCCEeUH5G8OsppRJu3VaAw61lBWiaprmaLTshhBBCCCFyoWSWbZ7u2cHXNE1niUSEEEIIIYQQD64g58EXQgghhBCiSLCRAfw8KU3TLPE8FnkSIYQQQghRqIpsN/pq/A2r9y/LuDsVyddHzjAkhBBCCCFECWKxEp2jVxPuHVRM1Srjyheu1aydhlm9mXCCa7PetXYIYIyCAAAgAElEQVQaZuX52lTSYq9YOw2zsvPw549jkdZOw6x61vDFrsEQa6dhNmkH5gOQsW+NlTMxH32jh5m6/bS10zCrd9tWZs7uC9ZOw6yGNSvPMt9a1k7DrPpHHmXX+WvWTsOsWgR5WjuFPMkc27zJCL4QQgghhBAliEyyFUIIIYQQxY6MUudNXhshhPg/e/cd3lT1BnD8e5q2tKV700Ipe8pQZMneQ2U5URQHU0VQUBkCIkumKAoURYEfKDJkKFP2kL333nTRllLa0ja5vz9SUkJbaKFJCr6f58lDbu6b9D3c5ObNueeeK4QQQjxBpMAXQgghhBDiCSJDdIQQQgghxGNHrmSbPenBF0IIIYQQ4gkiBb4QQgghhBBPEBmiI4QQQgghHjt2MkInW9KDL4QQQgghxBNEevCFEEIIIcRjRzrwsyc9+EIIIYQQQjxBpMAXQgghhBDiCSJDdIQQQgghxGNHTrLNnvTgCyGEEEII8QSRHnwhhBBCCPHYkSvZZk968IUQQgghhHiCSIEvhBBCCCHEE0SG6AghhBBCiMeOnGSbPenBF0IIIYQQ4gkiPfhCCCGEEOKxIx342ZMefCGEEEIIIZ4gUuALIYQQQgjxBHmsh+js3bGNGZPHY9AbaNK6De3f6Gy2ftWShaxYPB87OzucnF3o0XcARUKL2ybZXKo/ZiChzeqTlpjM6h5fEHXgaKYYOwcHGoz7ksJ1q6MZNP4dNpHTS1fz1LuvUalLRzS9gdRbiazt9SUxJ87YoBX351K3DY5Fy6KlpZKwdh76qCvmAfYOuLXohM7DB81gIPX8URL/XQFAgbLVcHmuNYaEeACSD23l9tGd1m6CmS3/7uSbbyej1+tp/2Jr3n+ro9n6lJQUBgwbxdHjJ/H0cGfs8CEEFwrkr1Vr+HXOPFPcydNn+ePXMMqWLsk7PXsTfT2GAgUcAZj27Vh8vL2s2q7sHN+7g8U/fYfBYKBG09Y07vCm2fptK5ewdfki7Ox0ODo783LPfgQWCSUtNZUFU8Zx6fRxlJ0dbd/rRcmnqtqoFY8mbMg7tKpXmaiYeKq+PNjW6eSYpmmMmvUnm/Yfw9nRkRHdX6d8scKZ4o6cvcTAab+RnJJKvSrl6P9WO5RSHDt/hWEz5nM7NQ17OzsGvdOBSiWLcvZKBIOm/c7R85f5+JVWvPN8Qxu07v4uHd7N9nlhaAYDZeo0o3LLV8zWn9y2hp0LZuDi6QNA+YYvULZuc1ukmivnDu5iw/+mYDAYeKp+C6q/8FqWcSd3buKvycPpOHQygcVLc2zbWnYvn29aH3XpHG8O+xH/oiWslXqOVR7Rn0KN65GWlMTuXgOJO3QsU0z9Rb/gFOCHPvk2AJtf7cLt6BgqD/scv+eqA6BzdqKArzdLS9eyav4PcmjXduZO/RaDXk+9li/Q+tW3zNav/+tP1i5biJ2dDidnZ97++HOCixYzrb8eGc7ALm/Q5s33aPlyx3tf/olkJ/PgZ+uxLfD1ej3TJ41hyLjJ+PgF8Fn3t3n2uXpmBXzdJs1p3qYDADu3buSXHyYyeOz3tko5x0Kb1cOzRCgzqzQj8NnKNJo4lHmNXskUV71fd5KiY5j1dAtQCicvTwBOzF/GoRm/A1CsZSPqjurPkvbvW7UND+JQtCw6T1/i/vcN9gEhFKzfnvgFmbdN0r6NpF05A3Y63Nt2xSGkDKkXTwCQcuoAtzYttnbqWdLr9YwYP4mwSWMJ9PfjtXe707BubUoUCzXFLFq2HHc3N5YvmMOKNeuY+MM0xg0fwvPNm/J886aAsbjv9fkgypYuaXre6KEDqVCujJVbdH8GvZ5F0ybS7asJePj48W2/rlSoXofAIqGmmKfrNaF2izYAHN65haUzJtN1yDi2r1kGQL/vZnIzLpafhvXj43Fh2Nk9fgcUZy3byo/z1vLL1/nr8/Ugm/cf40J4NCsmDODg6QsMm7GA37/unSlu2IwFDH3vFSqXKkr3MdPZcuA4dauUY8Jvy+jZvjl1q5Rj076jTPjtL3798gM8XF3o/3Y71u0+bINWPZjBoGfb3Cm07DOcgl6+LBnZh5DKNfEKCjGLK16tHrU79rBRlrlnMOhZN2syHT4bjZu3L3OGfESJp2vhE1zULC4lKZF9axYTWKKs6bFytRtTrnZjwFjcL/12SL4s7gMb18WtWFFW1myJ9zOVeHrMYNa1fD3L2J09Pyf2wBGzxw4M/sZ0v8R7HfF8qpxF880tg17P7B/G0XfUJLx9/Rn20XtUqVnXrICv2bAZDZ9vB8C+fzfz+7Tv+HTkRNP636Z+x1PP1rR67iJ/evy+UdOdPn6EQsFFCAwqjIODA3UaNWXn1o1mMS4FXU33bycnPzZXPCveqjHHfjMWruG7DlDAwx2XAL9MceXf7MCu8dOMC5pGckwsACk3b5liHAo6g6ZZPulccixWgdvH9wCQFnERuwJOKBc386C0VGNxD2DQkxZ1BTtXDytnmjOHjh4npHAQRYKDcHBwoGWTRqzftNUsZv3mrbzYytgT2LRhfXbs3ot2z7ZZsWYtrZo2slreD+viqWP4FArGJzAIewcHqtZpzJEdW8xinFwKmu6n3PX5i7h0nlKVngHAzdMLp4KuXD593HrJ56Ete08Se+PWgwPzmXV7DvNi3WoopahcKpSbiUlExcabxUTFxnMr6TZVSoeilOLFutVYu/tQ+lpFQlIyADeTkvHzcgfAx8ONp0qEYK/Ln18tUedO4u4fhLtfIXT2DhR/th4XDmy3dVqPLPzMCTz9g/D0N7arbM36nNm7LVPc1oUzebbVK9g7OGb5Oie2r6dMzfx31AUgqEUjLsxfCkDMnoM4uLvh5O/7UK8V0q4VlxYtz8v0HtnZE0fxDyqMf6Fg7B0cqN6gCfv+3WwW41wwY596OznJrKbZu20jfoWCzH4Q/BcoZftbfpXjHnxlfCe9ARTXNG2YUioECNQ0zSbjIq5HReHjF2Ba9vEL4NTRzL1GK/78g6Xz55KWmspXE6dYM8WH5hoUQMLlcNNywpVwXIMCSIyIMj3m6GEshmsN+pjCdapz49wlNvQdRmLUdQAqdelI1Q/fQefgwKIX3rZuA3LAztUdQ0KcadmQcAM7Vw/0iTezjFeOTjiGlif+QEYR6VjiKeyDiqOPiyJxy1IMCTcsnnd2IqOiCfT3Ny0H+Ptx8MixzDEBxhh7ex2urq7E3YjHyzPjR8vKtRv47pvhZs8bNPwbdDo7mjSoR7d3OuWLH6o3YqLx9M1or4ePHxdPZR5GtmX5IjYt+YO0tFR6fP0tAEGhJTm8cwtV6jYiLjqSy2dOEhcdSUjp8lbL/78uMjaeQG9P03KAtycRsTdMhTpAROwNArwz3puB3p5Epv8I+OKttnQdPY1xc5Zh0AzMGdrLesk/gsS46xT0zigKC3r6EnXuRKa4c3u3cu3UYTwCgqn5ShdcvTN3sOQnCbHRuPlk5Ojq7ce1M+Y/miPPn+ZmTBTFq9Zk94oFWb7OiR0badN7qCVTfWjOhfxJvJLxvZh0LQLnQgEkR0Zniq02aTia3sCVv9ZwbOJUs3UuhQvhElKYyC07LJ5zbsRej8L7rprG29ePM8cz71PXLl3IqkW/kZaaxmdjjEe9bycnsfyP/9F31CRWLphrtZxF/pabbpYfgVrAnWNiN4EfsgtWSnVVSu1WSu0OCwt7hBSzk0WvdBaFT8t2rzBl7mI6dfuIBbNnWCAPC8iqgLunp9dOZ49b4UJc276X3+q159rOfdQZ8blp/cHpc5lZuSlbh4zj2X758VDzg9uYEWqHa/M3SD64BUN8DAAp548SO3MkN36fQOqlU7g2yXq8qbXc2xMPZCrEs47JuH/wyFGcChSgVImMHpjRQwfy55wZzJzyHXsPHGLZitV5l/SjyHJbZd6mdVq1Z8C033n+re78M38WANWbtMLTx49vP+3Kkp+/J7RsBex0OgsnLO6W5XsxBzF3zPtnK593asPayYP5vFNbvgybl21sfpJlm+5peEilGrw26hc6DPmB4HJV2PjLBOskl8fUXQ3TDAY2zJ1K/de7Zht/7cwx7B0L4Fs4v/YAZ96/ZLU9d/T8nDUN2rHhxU741nyakJdfNFtfpG0rrvy1GgwGi2X6ULIsaTK3ufGLHRjz6wJefq8ny+b+CsCfs36iWbvXcHJ2sXCS4nGSmzH4NTRNe1optQ9A07RYpVTWx/mM68OAO5W9duRafHahD8XHz5/rURGm5etREXj7Zn+4rk6jZoRNHJ2nOeSlSl06UvFt4zj7iL2HcC0caFrnGhxIwrVIs/jkmFhSbyVyetkaAE4tXkmFt17K9LonFvxNwwlDWWPB3HOqwFO1cSpfA4C0yEvYuWb0INq5emC4lfV7pGDDDujjokm+q/deS0403b99dAcutVtZKOucCfD3IzwyYxtFREbh7+uTOSYikkB/P9LS9CQkJODhntFjumLN+kzDcwL8jb1yBQu60KpZYw4dPW4a5mNLHj5+xEVntPfG9Sg8vLP//FWp25iF04yFkk5nT5v3PjKt++7zHvgGFbFcsgKAuau3sGC9cThKxeJFCI/JOIIWEROHv5f58LdAb08iYjKOioXHxOGf3sO/ZNNu+r9lHAvcvEZlBk9/PAr8gl6+3IrJ6PG9FRdtOpn2DifXjM9kmbrN2bnwF6vl97BcvXy5eT3jCG9CTBSuXt6m5ZTkJKIvn2f+qH4A3LoRw5JvB9Om9zACi5cG4MT2DZTNZ8NzSrzzOsXeNH6vxew/jEtwINfT1zkXCiA5PDLTc+48lnYrkYuLluNd9Skupg/tASjctiX7vxie6Xm25uXrR8xdNU1MdBSePtnvU2s0aMLs78cCcPb4UXZvWc8fP/9AYkICdkrh4OhIkzaZa4InjcqHQ5Dzi9z04KcqpXSk/85USvkBNvsJXLJMea5dvkjEtSukpqayZd0anq1dzyzm6uWLpvt7tm+hUHDIvS+TbxycPpe5ddoyt05bzvz9D+VebwtA4LOVuR1/02x4zh1nV66ncF1jwVykfi1ijhvHq3uWyDixqljzBsSduWCFFjzY7UPbuDFvIjfmTSTl7GEKlDWOw7YPCEFLSUbLYniOc43mqALOJG5eavb43eP1HYtVQB+beUdvTRXLleXCpStcvnqN1NRUVvyzjgZ1a5vFNKhTm6XLVwGwZv1Gqj9T1dRDYzAYWL1uAy3uKvDT0vTExhkLrNS0NDZt/ZdSxfNH71qRUmWJvnaZ6xFXSUtNZd+WtVSo/pxZTNTVS6b7x3b/i28h4ywtKbeTuZ2cBMCJ/bvQ6XRmJ+cKy+jYrA6LRvVl0ai+NK72FEs370bTNA6cOo+rs5PZ8BwAPy93XJwLcODUeTRNY+nm3TR6piIA/l7u7Dpm3N/sOHKKolmcI5Qf+YWWJj7yCjejw9GnpXJ21yaKVq5hFpMYF2O6f/HADjwL5f8fn4HFyxAXcYUbUdfQp6VyfPtGilfNmCGmgEtBev64gPcnzOb9CbMpVKKcWXGvGQyc3LmZMjUb2KgFWTvzy2/807gD/zTuwNUVayma3hvv/UwlUm8mZBqeo3Q6HNOHnil7ewo1rU/88VOm9a4lQnH0cOf67v3Wa0QOFStTjsgrl4kKN+5Td274h6o165jFhF/J2Kce3LmNgGDje3PAhCmMm7WIcbMW0azdK7R+7e3/RHEv7i83PfjfAX8C/kqpEcBLwCCLZJUDOnt73v/4M4b164XBoKdxyxcJKVaC32ZMpUSZclR/rj4r/vyDg3t2otPZ4+rmzkf9h9gq3Vw5v2ojoc3q8/aBNaQlJrGm5wDTuo5bFjO3jrH43zp4HM3DxlBg9ACSomNY07M/AJW6vklIg1oYUtNIjotndffPs/w7tpR64TiORcvh2ekLtLQUEtb+YVrn8WofbsybiF1BD1yebUJaTAQerxpn+LgzHaZz5To4hJYHzYCWnEjCP7btQbS31zHg01507/0ZeoOBds+3pGTxYkwOm0GFcmVoWPc52r/Qmv5fjaTVS2/g4e7OmK+/ND1/z/6DBPr7USQ4yPRYSmoK3Xr3Iy1Nj8Ggp+azz9ChTWtbNC8Tnc6e9l16E/ZVXzS9gepNWhEYUoyVc3+mcMkyVKxeh63LF3HywB50OnucXd14/WPj+zghLpawr/qi7BQe3n683ttmu5FHNntUN+o9UwZfT1fOrhzHsKlL+HXx5gc/0cbqVSnHpv3HaNlnJE4FHBjeLWM2kvb9x7FoVF8ABr/7EgOn/sbtlFTqVC5L3SrGmUeGvv8Ko2ctJs2gp4CDA0PffxmAqLh4Xh00kYSkZOyUYvbKTSwd8zmuLk7Wb2QW7HQ6ar/egxXffolmMFD6uaZ4BRVlz5LZ+BYtRdEqNTmybikXDuzATqejgIsr9Tv3sXXaD2Sn09HwrQ9ZOGYAmmagYr3m+BYOZevCmQQWK02Jp+8/HeTlE4dw9fbF07+QlTLOvfB/NhHYuB4tdqxAn5TM7o8z9htN1i7kn8YdsCvgSN3fw1AO9ig7HZGb/+Xs/zLONwhp14pLS1bYIv0H0unseeODTxg/oA8Gg566zZ4nOLQ4f86cTmjpslStVZe1SxdwdO9udPb2FHR14/2+j+++M89o+WyoVT6i7jfOMlOwUmWBxhgHw63VNC3zJLRZy/MhOvlJhULuTHLPX9MY5rWP409wfXI/W6dhUT4fjiUl5qqt07AoR+8g/joW8eDAx9jz5QJwrPqurdOwmJR9xnOJ0vb8beNMLMf+mdaM3Xja1mlYVL/6JZm2I38cXbWUbjWKsiCggq3TsKiXIo6w7fz1Bwc+xmqH+th+ZodsJCfesvkYHSeXgvny/ydHPfhKKTvgoKZpFYHHcz47IYQQQggh/gNyVOBrmmZQSh1QSoVomnbxwc8QQgghhBDCcpQM0clWbsbgFwKOKKV2AqYru2ia9mL2TxFCCCGEEEJYU24K/K8sloUQQgghhBAiT+S4wNc0baMlExFCCCGEECLHZIhOtnI8D75SqqZSapdSKkEplaKU0iulntypcYQQQgghhHgM5WaIzmTgNWA+UA14CyhliaSEEEIIIYS4L7mSbbZyU+CjadpppZRO0zQ98ItSapuF8hJCCCGEEEI8hNwU+IlKKUdgv1JqDHANKGiZtIQQQgghhBAPI8dj8IFO6fEfYpwmswjQwRJJCSGEEEIIcV+awfa3fOqBPfh3Lm6ladqda2onI1NmCiGEEEIIkS/lpAd/8Z07SqmFFsxFCCGEEEKIHFGawea3/ConBb66635xSyUihBBCCCGEeHQ5KfC1bO4LIYQQQggh8pmczKJTOf2CVgpwvuviVgrQNE1zt1h2QgghhBBCZCUfD5GxtQcW+Jqm6ayRiBBCCCGEEOLR5epCV0IIIYQQQuQL0oOfrdzMgy+EEEIIIYTI56TAF0IIIYQQ4gkiQ3SEEEIIIcTjR4boZEt68IUQQgghhHiCSA++EEIIIYR4/BikBz870oMvhBBCCCHEE0RpmlUuTitXwBVCCCGEePwoWyeQnZToyzavLx19C+fL/x+rDdG5nXDDWn/K6gq4epB27ZSt07Ao+0KluL1hjq3TsKgCDd5Af+GArdOwKF3RyqTt+dvWaViU/TOtn+g22j/TGgDHqu/aOBPLSdk3g7SrJ2ydhkXZB5V5ot+nkP5Z/A9sx9TI87ZOw6Ic/ENtnUK2lJxkmy0ZoiOEEEIIIcQTRE6yFUIIIYQQjx/pwc+W9OALIYQQQgjxBJECXwghhBBCiCeIDNERQgghhBCPH+vMBPlYkh58IYQQQgghniBS4AshhBBCCPEEkSE6QgghhBDi8SOz6GRLevCFEEIIIYR4gkgPvhBCCCGEeOzIlWyzJz34QgghhBBCPEGkwBdCCCGEEOIJIkN0hBBCCCHE40eG6GRLevCFEEIIIYR4gkgPvhBCCCGEePxID362pAdfCCGEEEKIJ4gU+EIIIYQQQjxBZIiOEEIIIYR4/MgQnWxJD74QQgghhBBPECnwhRBCCCHEY0dpBpvfHpijUi2UUieUUqeVUl9ksb6AUmpe+vodSqnQvPi/kQJfCCGEEEKIPKaU0gE/AC2B8sDrSqny94S9B8RqmlYSmAh8kxd/Wwp8IYQQQggh8l514LSmaWc1TUsBfgfa3BPTBpiZfn8B0FgppR71D8tJtkIIIYQQ4vFjsP1JtkqprkDXux4K0zQtLP1+MHDprnWXgRr3vIQpRtO0NKXUDcAHiH6UvB67An/Ltn/5Ztx4DHoD7du24b133jZbv3vvXsaMm8ip06f5ZuRwmjVpbFrX/cNeHDp0mKpVKjN50kRrp35fm3fsYfTkMPR6Ax1aN6PLGy+brU9JSaX/qAkcOXEaTw83xg/+nOBCARw8doKh4yYDoKHxQeeONKlb2/Q8vV7PK936EODrw4+jh1i1TfejaRrfzFvF5sOncHJ04OvObSgfUihT3HeL17Fs+0HiE5PY8V1/0+N/bNzN7xt2o7NTuBRwZPCbz1MiyM+aTQBg8679jJryC3qDgZdaNKbLa23N1qekpPLF2MkcOXUWTzc3JgzsTXCgPwBhv/3JwlXr0NnZMaDnO9SpVuW+r/nvvkOMm/4/DAYDBZ2dGNH3A4oGB1q1vZqmMWrWn2zafwxnR0dGdH+d8sUKZ4o7cvYSA6f9RnJKKvWqlKP/W+1QSnHs/BWGzZjP7dQ07O3sGPROByqVLMrZKxEMmvY7R89f5uNXWvHO8w2t2q67/RfamFNhQ96hVb3KRMXEU/XlwbZOJ1c279zD6Mk/odfrjfvUji+ZrTfuUydy5ORpPN3dGT+kH8GBAWzbvY+JYbNITUvDwd6eT7t3pubTlbmVmEinXhn7oIioaJ5v2oD+H3axdtNMLPVevePQmYt0HDyJcb3eonmNytZsGpD32xCg62dDiLoei16v55lKFRj0cTd0Op3V25aVLTt2MXrSVPQGPR2eb8n7b75qtj4lJYX+I8Zy9MQpPN3dGffVAIILBXLlWjgvvtmF0BDjtq9UoSxD+n5siyb8J6UX82HZrM6qJ157iJhce6yG6Oj1ekaOHsOU7yaxeME8VqxaxZmzZ81iCgUGMvyrwbRs0SzT8zu/9SYjvv7KWunmmF6vZ8SkKUz95iuWzvyR5es2cvr8RbOYhctX4+5akJVzp/PWS22YEPYrAKWKFeWPad+y6OfvCRszjK/G/0Bamt70vNkLl1K8aBFrNidHthw+zYXI6/z19YcMfvN5hs/5O8u4+pVKM7f/e5keb1X9KRYN6c78L7vRuXltxs5fbemUM9HrDQyf/DPTRgxg2fSJLN+wldMXLpvFLFy5DnfXgqz69Xvebt+a8T/PAeD0hcus2LiNZWETCBsxkK+//xm93nDf1xz23U+M+eIj/pw6ltYN6zBt7kKrt3nz/mNcCI9mxYQBDH3/ZYbNWJBl3LAZCxj63iusmDCAC+HRbDlwHIAJvy2jZ/vmLBrVlw9fasGE3/4CwMPVhf5vt+Od1rYvev8LbcypWcu28vwHE2ydRq4Z96nTmDp6CEt//YHlazdlsU9dg7ubKyvnhPHWyy8yYZrxCLmXhzs/jBzE4hnfM7J/b/qPMnYGFXRxYdFPk0y3oAB/mtatZfW23c1S71UAvcHAhN/+4rlKZazSlntZYhsCTBjyOX/+/B1LfplMTNwNVm3catV2ZUev1zN8wg9MGTecpbOns/yf9Zw5d8EsZtHfq3B3c2XF77/S6ZX2TJj6s2ldkeBCLPxlCgt/mfLfKu41zfa3+7sM3F2EFQauZhejlLIHPICYR/2veawK/MNHjhBSpDCFCwfj4OBAi2bNWL9hk1lMcFAQpUuVwk5lblrN6tUp6OJirXRz7NDxkxQJLkSRoEAcHRxo1age67duN4tZt3U7bVoYj0Y0q1+H7XsOoGkazk5O2Nsbex9up6Rw97Ct8MhoNm3fRYfWmX/s2Nr6Ayd4oWZllFJULl6Ym0m3ibpxM1Nc5eKF8fNwy/S4q3MB0/2k26k8+mi13Dt04jQhQYEUKRSAo4M9LevXZt22XWYx6/7dTdumDQBoVq8m2/cdRtM01m3bRcv6tXF0dKBwIX9CggI5dOL0fV9TKUi4lQTAzVuJ+Pl4WbW9AOv2HObFutWM261UKDcTk4iKjTeLiYqN51bSbaqUDkUpxYt1q7F296H0tYqEpGQAbiYl4+flDoCPhxtPlQjBXmf7XdJ/oY05tWXvSWJv3LJ1Grl26PgpigTdvU+ty/qtO8xi1m3dQZvmjQBoVv85tu817lPLlSqBv68PACVDQ7idkkpKSqrZcy9cvkpM3A2eqVTBOg3KhqXeqwBzVm2mafVKeGex/7UGS21D14LGGiBNryc1LQ2VZeep9R06doKQ4CCKBBXCwcGBlo0bsG7Lv2Yx6zb/S5sWTQFo1qAuO/bsR3twgSlsaxdQSilVTCnlCLwGLL0nZilwZzjKS8A6LQ827AOH6CillnGfQwWapr34qEnkVERkFAEBAablgAB/Dh0+Yq0/bzERUdcp5JcxvCTAz5eDR0+YxURGXScwPcbeXoebqwtxN+Lx8vTg4NETDBoziavhkYwe+Imp4B89OYxPu73LrcRE6zUmhyLjbhLonfFlEuDpRmTszSyL+ez8vn4Xs/7ZTqpez099OlkizfuKiI4h0M/HtBzo58PB46eyjbHX6XAr6EJc/E0ir8dQqWwpU1yArzcR0TGm18nqNYf16U73QaNwKuBIQRdnfp80wmJty05kbDyB3p6m5QBvTyJib5gVBhGxNwjw9jAtB3p7EpledHzxVlu6jp7GuDnLMGgG5gztZb3kc+i/0MYnXUT0dQr5+5qWA/x8OXjsnn1q9HUC02PsdTrcXAsSF38TL4+M7bx60zbKlSyOo6OD2XP/XruJFg3rkAfnwT0SS71XI2LiWLvrEDMG9eRw2DwrtcacJbdhl35DOHz8JHWqP0Oz+rBoRyEAACAASURBVLXJDyKjrhPob14HHDp23DwmOtoUY2+vw7VgQeJuGLfllWvhvPRuT1xdXPioy9s8U/kp6yUvspU+pv5DYBWgA2ZomnZEKTUM2K1p2lLgZ2C2Uuo0xp771/Lib+ekK2kcMB44ByQB09NvCcDh7J6klOqqlNqtlNodFpbd0KRcyuIHjY33rxZz7xdHVr+w7sRUKl+Gpb/+yLxpE5k+Zz63b6ewYdtOvL08qVCmpBWyzb2sfpzmdlu+1vBZlo/4iN7tGxO2fHMeZZZzWlZbJdN2y6qdKtv23+81Zy36m6nD+7N+7lTaNWvIN9NmPVzijyDLvHMQc8e8f7byeac2rJ08mM87teVLGxUP9/NfaOMTL8vP1z2fzQds59PnLjIxbCZDPumZKW7F+s20alTvkdN8VJZ6r46etYRPXn8enZ0NjzZZcBtOH/sVGxbOJCU1lR37DuZJuo8qy+8KctBepfDz8WbNgv+xYMaP9PuoG58NG03CrcfvyNtD0Qy2vz0oRU1brmlaaU3TSmiaNiL9scHpxT2apiVrmvaypmklNU2rrmna2fu/Ys48sAdf07SNAEqprzVNu3uPtkwptSmbp9170oF2O+HGIyUKxh77iIgI03JERCR+vtY/sTKvBfj5cC0qyrQcERWNv693ppjwqCgC/X1JS9NzMyERD3fz3u4SRYvg7OTEqXMX2Hf4KBu27mDz9t3cTknhVmISnw8fxzeD+lqlTVn5ff0uFm7ZC0CF0CDCYzIOJUfE3cTP8+EOBbesVpERc5bnSY65EejrQ3jUddNyeNR1/L29sowJ9PMhTa/n5q1EPNxcCbjnuRHRMfj7eJte597XjImL58TZC1QuZ+z1b9mgNl0HWKcHf+7qLSxYbxwyVrF4EcJj4jLyjonD38vDLD7Q25OImIzPe3hMHP7pPYpLNu2m/1vtAGheozKDp+eP4ve/0Mb/kgA/X65FZkxAEREVbfp83R0THhlNoJ+v8bOZcMu0Tw2PiqbX4JGM/KI3IcHmJ/8fP30OvV5vs84Ta7xXj5y7RN/vZwMQe/MWm/cfw97OjsbPWq9X2JLbEKCAoyMNa1dn3dYd1K5W1bKNyQFjW8zrAD9fn3ti/AiPjCLQ34+0ND0Jt4ztVUrh6OgIQIUypSgSFMT5S1eoWLa0Vdsg8pfc/Dz3U0oVv7OglCoGWLW6rlC+PBcuXeLylSukpqaycvVqGtSva80ULKJimdJcvHyVy9fCSUlNZfm6TTSsbT6LUsPaNViyci0AqzduocbTlVBKcflauOmk2qvhkZy/dIXgQH/6dO3MugUzWTNvBuMGf0aNqpVsWtyDscd9/pfdmP9lNxpVKcOy7cbxkgfOXsbNuUCuhudciMgogjcdOkmIv/d9oi2jYpkSXLhyjcvXIklJTWPFxm00rFXNLKZhrWdYvGYDAKs3badGlQoopWhYqxorNm4jJSWVy9ciuXDlGk+VKZnta7q7FeTmrUTOXzaem/PvnoOUCAm2Sjs7NqvDolF9WTSqL42rPcXSzbuN2+3UeVydncyGAwD4ebnj4lyAA6fOo2kaSzfvptEzFQHw93Jn17EzAOw4coqiAfnjB/p/oY3/JRXLluLilbv3qZuz2KdWZ8mqdQCs3riVGlWN+9T4hAR6fDGM3u+/xdNP3Xs9Gli+bpNNe++t8V5dPWkQa777kjXffUmzGpUZ9E4Hqxb3YJlteCspiajrxqGQaXo9m3fsoVhI5lmHbKFi2TJcvHyFy1fDSU1NZcXaDTSsU9MspmGdmixZuQaA1Rs2U+Np43lsMbFx6PXGOuDS1WtcvHyFIkHWnWFN5D+5mSazD7BBKXXn0EEo0C3PM7oPe3t7BnzWjx4f9kKvN9C2zQuULFGCH6ZMo3z5cjSsX4/DR47Su+9nxMfHs3HzZqZMC+PP+cZeibff68L58xdITEqiScvn+erLgTxX27azIIBxLN3Aj7vTtd9gDAYD7Vo2pWSxonw/439UKFOKRs/VoEOrZnwxcjwtOnbBw92VcYM/B2DvoaP8NHcB9joddnZ2fNm7B16eHg/4i7ZXt2IpNh86TetBk43TZL6dcSrHy19PY/6XxrfWhIVrWL7zMMkpqTT5fCLt61Sl5wsN+G3DLnYcO4e9zg53FyeGv3PvdSMsz16nY+CH79JlwAjjdmvekFKhRfh+5jwqlC5Bo1rV6NCiEZ9/M5nmnT/C082VcQN6A1AqtAjN69XihS6foNPZMejD99Cln3yZ1WsCDOvdjY+HjcfOzg5314IM/7SH1dtcr0o5Nu0/Rss+I3Eq4MDwbq+b1rXvP45Fo4w/Ige/+xIDp/7G7ZRU6lQuS90q5QAY+v4rjJ61mDSDngIODgx93zgdbFRcPK8OmkhCUjJ2SjF75SaWjvkcVxcnaaMNzR7VjXrPlMHX05WzK8cxbOoSfl1s/eFwuWWv0zGwVze6fjY0fZ/ahJLFQvh+xhwqlClp3Ke2bsoXIyfQ4o2ueLi7Me7LfgDM/fNvLl29xtTZ85g62/jdMX3sV/h4Gce6r9qwhSn5ZMphS71X8wNLbENN0/hg4HBSU1PR6w3UeLoSr77Y0pbNNLG31zGgzwd0+3QAeoOBdq2bUbJYKJN/mkmFsqVpWKcW7Vu3oP/wMbR8rTMe7m6MHToAgD0HDjH551nodDp0djoG9+2Fh7v7/f/gE0LlYIjMf5XKzYm6SqkCQNn0xeOapt3O4VPzZIhOflXA1YO0a6ceHPgYsy9Uitsb5tg6DYsq0OAN9BcO2DoNi9IVrUzanqynJH1S2D/T+oluo/0zrQFwrPqujTOxnJR9M0i7euLBgY8x+6AyT/T7FNI/i/+B7Zgaed7WaViUg39ovj3b0XB2t82nEbIrXi1f/v/kuAdfKeUCfAIU1TSti1KqlFKqjKZpfz3ouUIIIYQQQuQp6cHPVm7G4P8CpAB3xrRcBobneUZCCCGEEEKIh5abAr+EpmljgFQATdOSyPryukIIIYQQQggbyc1JtilKKWfSp2RXSpUAcjoGXwghhBBCiLwjQ3SylZsCfwiwEiiilJoDPAd0tkRSQgghhBBCiIeT4wJf07Q1Sqm9QE2MQ3M+1jQt+gFPE0IIIYQQIu8Z9LbOIN/K8Rh8pdQwTdOua5r2d/rMOTHpPflCCCGEEEKIfCI3J9mGKKX6g2k+/MXAkz35uxBCCCGEEI+Z3IzBfweYk17kNwRWaJo20TJpCSGEEEIIkT3NICfZZueBBb5S6um7FicB04CtwEal1NOapu21VHJCCCGEEEKI3MlJD/74e5ZjgfLpj2tAo7xOSgghhBBCiPuSk2yz9cACX9O0hkopO+BlTdPmWSEnIYQQQgghxEPK0Um2mqYZgA8snIsQQgghhBDiEeXmJNs1Sqm+wDzg1p0HNU2LyfOshBBCCCGEuB8ZopOt3BT476b/e3dPvgYUz7t0hBBCCCGEEI8iN1eyLWbJRIQQQgghhMgpTS89+NnJTQ8+SqmKGGfQcbrzmKZps/I6KSGEEEIIIcTDyXGBr5QaAjTAWOAvB1oCWwAp8IUQQgghhMgnctOD/xJQGdinado7SqkA4CfLpCWEEEIIIcR9yJVss5WjaTLTJaVPl5mmlHIHIpETbIUQQgghhMhXctODv1sp5QlMB/YACcBOi2QlhBBCCCHE/cg0mdnKzSw6PdPvTlVKrQTcNU07aJm0hBBCCCGEEA8jt7PotAfqYJz/fgsgBb4QQgghhBD5iNI0LWeBSv0IlAR+S3/oVeCMpmkfZP8sk5z9ESGEEEIIkZ8oWyeQndRdS21eXzo8+2K+/P/JTQ9+faCilv6LQCk1EziU0yfvvxKXy9QeH1WCPemuQm2dhkVN1c6TvPpnW6dhUU7N3uNiTIKt07CoEG9XUsPP2DoNi3IILMHYjadtnYbF9KtfEoC0qydsnInl2AeVwbHquw8OfIyl7JuB/vx+W6dhUbrQKiQvn2LrNCzKqVUPkpOSbJ2GRTk5O9s6BfEQcjOLzgkg5K7lIsgQHSGEEEIIIfKVB/bgK6WWYRxi4wEcU0rtTF+uAWyzbHpCCCGEEEJkQebBz1ZOhuiMs3gWQgghhBBCiDzxwAJf07SNdy+nX+QqV7PvCCGEEEIIkZc0mQc/Wzku1JVSXYGvgSTAgPGsag25mq0QQgghhBD5Rm564vsBFTRNi7ZUMkIIIYQQQohHk5sC/wyQaKlEhBBCCCGEyDEZopOt3BT4/YFtSqkdwO07D2qa1ivPsxJCCCGEEEI8lNwU+NOAdRgvbiXzEgkhhBBCCNuRaTKzlZsCP03TtE8slokQQgghhBDikeXmSrbrlVJdlVKFlFLed24Wy0wIIYQQQgiRa7npwe+Y/m//ux6TaTKFEEIIIYTVaXo5yTY7OS7wNU0rZslEhBBCCCGEEI/ugUN0lFKf3XX/5XvWjbREUkIIIYQQQtyXQW/7Wz6VkzH4r911v/8961rkYS5CCCGEEEKIR5STAl9lcz+rZSGEEEIIIYQN5WQMvpbN/ayWhRBCCCGEsLx8PETG1nJS4FdWSsVj7K13Tr9P+rKTxTITQgghhBBC5NoDC3xN03TWSEQIIYQQQoic0uRKttnKzYWuhBBCCCGEEPmcFPhCCCGEEEI8QXJzJVshhBBCCCHyBznJNlvSgy+EEEIIIcQT5LHrwd+/819+nTwBg8FAo1Yv0rbj22br1yxdxKolC7Czs8PJ2Zmun/SncGhxDu7ewdzpP5CWloa9vT1vdutFxaer2agVD/bKpCFUbNWQlMQkZnbuy6V9R8zWF3AtSN/N803LXoUD2fG/xczvM4y63d6gwQedMOgN3E64xZyu/bl27LS1m3BfmqbxzcK1bDlyFidHB75+syXligRmivt+2SaW7TxCfGIy28f3ybR+zb4T9J2xhLn9OlEhpJA1Us+xXf9u48dvx2HQ62n5Yltee+sds/ULfvsfK5YuRqfT4eHpRd+BQwgolNGGW7cSeO+1l3iufkM+6vu5tdPP0pYduxn9/TT0BgMdWjfn/TdeMVufkpJK/5HjOHryNJ7ubowb0p/gQgEcOnaCoeO+B4zbvmfnN2hSrzbnLl6m71ejTc+/fPUaH77biU4vt7Vqu3Li0uHdbJ8XhmYwUKZOMyq3NG/7yW1r2LlgBi6ePgCUb/gCZes2t0WqD7R55x5GT/4JvV5Ph9bN6NLxJbP1KSmp9B81kSMnT+Pp7s74If0IDgxg2+59TAybRWpaGg729nzavTM1n67MrcREOvXKuA5iRFQ0zzdtQP8Pu1i7abkWNuQdWtWrTFRMPFVfHmzrdO5L0zRGTvmVTTv34exUgJGf9qB8qeKZ4o6cOsuAcT+SfDuFetWrMqBHZ5RSxMUn8OnIb7kSEUVwgB8TBvbGw82VGzcTGDRhKpeuRVDAwYHhn3anVGgI1yKj6T/2B6Jj41DKjldaNaZTu1Y2aHn6d8afG9ly7BxODg58/XozyhXxzxT3/d9bWbb7GPGJt9n+zQdm61btO8nUVdsBKBPsx+hOLa2Se05t3bqVb8aMwWAw0K5dO957912z9Xv27GHM2LGcOnWKb0aPpmnTpgBcvXqVTz79FINeT2paGq+//jqvvPyyLZpgfdKDn63HqsA36PXMmDSWgWO/x8fPn/49OlOtdl0Kh2bs4J5r3IymL7YHYPfWTcyaMokB30zCzcOTz0aMx9vXj4vnzjDys4+ZOv8vWzXlviq2bIB/qWIMLtWAYjWq0nHKCL6paV7w3E64xYiqGTva/ruXsW/RSgB2zV3C5mlzAKj0QhNemvAl37c0/yFka1uOnuViZCzLBnfh0PlrDJ+3hjl9O2WKq1+xJK/Ve5oXhk3PtO5W8m3mbtzDU6H5q7AH0Ov1fD9+NN9M+hFf/wA+fLcTterWp2ixjPdqydJl+OGX2Tg5ObNs0Xym/zCJQcMzit1fw6ZQqerTtkg/S3q9nuHf/sj08SMI9PPl1W69afhcTUqEhphiFv29Cnc3V1bM/ZnlazcyYdoMxg/tT8liRZk3bRL29jqirsfQ4d0PaFC7BsVCCrPw58mm12/00ls0rlvLVk3MlsGgZ9vcKbTsM5yCXr4sGdmHkMo18QoKMYsrXq0etTv2sFGWOaPX6xkxaRrTxw4jwM+HV7t/SsPa1Sl513ZcuHwN7m6urJwTxvJ1m5gwbSbjh3yGl4c7P4wchL+vD6fOXaDrZ0NYP/9XCrq4sOinSabnv9y1D03z4XbMyqxlW/lx3lp++fp9W6fyQJt27efClXBW/jKJg8dP8dX3PzPvuxGZ4oZ99xNffdyVyuVK0W3QaDbv3k+9Z6vy0x+LqVm1Il1ebcv0eYv5ad4SPn3/DcJ+X0zZEkX5fkhfzl68wtc/zOCXb77EXqfjs66dKF+qOLcSk3jpw/7UeroSJYsWtnrbtxw7z8WoWJYN6MyhC+EMX7CWOX1ezxRXv0JxXqtThRdG/mr2+IWoWH5eu4uZvV7B3cWJ6zcTrZR5zuj1ekaOGsW0qVMJCAig4xtv0KB+fUqUKGGKCQwM5Othw5g5a5bZc/38/Jg1cyaOjo4kJibSoUMHGtSvj79/5h9A4r/jsRqic/r4UQKCCxMQFIy9gwO1GzVl17ZNZjEuBV1N928nJ6GU8WK7xUqVwdvXD4AiocVJTb1NakqK9ZLPhUptmrF91iIAzu3Yh7OnG+6BftnG+5cMxc3fh9ObdwKQfDPBtM6xoAualv+uR7b+0GleqF4BpRSVigVxMymZqBsJmeIqFQvCz8M1i1eAH/7eQucm1Slgn/9+p544eoSgwkUoFFwYBwcHGjRpxrZNG8xiqjzzLE5OzgCUq/AUUZGRpnUnjx8jLiaGZ2rUtGba93Xo2ElCgoMoElQIBwcHWjaqx7ot/5rFrNu6nTbNmwDQrH4dduw9gKZpODs5YW9vnHH3dkoKqMwXwd6+9wBFggIJCgywfGNyKercSdz9g3D3K4TO3oHiz9bjwoHttk7roRw6fooiQYUoEhSIo4MDrRrVZf3WHWYx67buoE3zRgA0q/8c29O3Y7lSJfD3NR6hKBkawu2UVFJSUs2ee+HyVWLibvBMpQrWadAj2rL3JLE3btk6jRxZ9+8u2jSph1KKyuVKc/PWLaKux5rFRF2PJSExiSrlS6OUok2Teqzdtiv9+btp26Q+AG2b1Gftv8bHz1y8TM0qTwFQPCSYqxFRRMfG4efjZTpCUNDFmeJFgomMjrFWc82sP3yGF54tZ/zOCC3EzaQUorLYbpVCC+HnUTDT44v+PcxrdSrj7mK8fI+Pm4vFc86Nw4cPU6RIEQoXNn5ntGjenA0bNpjFBAcHU7p0aezu2X86ODjg6OgIQEpKCoZ8+J0vrC9XBb5SSqeUClJKhdy5WSqxrMRER+Ljn/Hl7+PrT2xUVKa4VYvn0+uN9swJm0znDz/JtH7HpnWEliyDQ/oHIr/xDA4g9tJV03Lc5XA8gzMPX7mj2usvsmee+dGI+j078fXpjbQf8wV/9BpqqVQfWmTcTQK83E3LAZ5uRN64mePnH7sUQXjsTepXLGmJ9B5ZdFQkfne9V339A4jO4r16x4plS6heqzYABoOBad9NpMuHH1s8z9yIjL5OoL+vaTnAz5fI6OtZxBh/jNrb63At6ELcDeO18Q4ePU6bt7vT7p2eDP7kQ1PBf8eKtRtp1biBZRvxkBLjrlPQO6PtBT19SYy9ninu3N6tLPzqA/6ZOpKEmOy3ty1FRF+n0D3bMSLL7WiMsdfpcHMtSFy8+edz9aZtlCtZHEdHB7PH/167iRYN65g6V0TeiYyOJdDPx7Qc4OtDxHXzgjviegwBvt53xXgTGW38EXA99gZ+Pl4A+Pl4ERNn/GyWKVaUf7YaO4gOHj/N1YgoIu4p5K+ER3LszDkqlbXNPjfyxi0CPN1MywGerkRm0SmUnQtRsVyIjOXtSfN489vf2XrsvAWyfHiRkZEEBmZ8z/sHBBBxV6fPg4SHh/PSyy/TvEUL3unc+T/Te68ZDDa/5Vc5LvCVUh8BEcAa4O/0W7ZjXJRSXZVSu5VSu8PCwh45UYAsf5Rm8SXSvO3LfDdnER27fsii//1itu7SubPMDfuBLn2+yJOcLCHLL8b7/CJ/9rUX2PXbUrPHNv44my9L1ufPz0fTctBHeZ3io8uiOYqcFQQGg8a4Rev4tF3DPE4q72R11CS7gueflcs5efwoL7/xFgBLF86neu3n8A/I/kedLWTZpnu22f3aXal8WZbMnMrvU7/lpzl/cPt2xhG01NRUNmzbQbMGdfI467yR5VGwezZnSKUavDbqFzoM+YHgclXY+MsE6ySXWzl4b2a9rTOcPneRiWEzGfJJz0xxK9ZvplWjeo+cpshMy2LHmaNt94Bda5dX23DjZgLtenzGnKUrKVcyFJ1dRnlwKymZj7+eQP/ub+Na0EY93w/RrrulGTQuRMfx04cvMbpTS4bO+4f4pOQ8TPDR5OY7IyuBgYEsmD+fZUuXsnTZMq5fz9wBIf5bcjO24WOgjKZpOXrXaJoWBtyp7LX9V+Jym1smPn7+XI+MMC1fj47Ey9c32/jaDZvy07ffZMRHRTB+yGf07D+EwGDrjyG8n/o9O1Gni3E84YVdB/AqEmRa51k4kLirEVk+L7hSOezsdVzcezjL9bt/X0bHKcOZmfcp59rvm/ayaNtBACqEBBIRG29aFxF3M9uhOPe6dTuF09eief+73wCIjr/Fx9MWMalb+3xzoq2ffwBRd71XoyMj8Mnivbp35w7m/voz43+cbjrEeuzwQQ4d2MeyhfNJSkokLTUNZxdn3u/Zy2r5ZyXAz5fwyGjTckRUNH539RRmxEQR6O9LWpqehFuJeLi7mcWUCA3B2cmJU+fOU7FsaQA279hNuVIl8PX2snxDHkJBL19uxWS0/VZctOlk2jucXDOOSJWp25ydC807F/KLAD9frt2zHf19stqO0QT6+ZKm13Mz4ZZpO4ZHRdNr8EhGftGbkGDzz9vx0+fQ6/VUKJM/j6w9juYuXcX8FWsBeKp0CcKjMr6CI6Kv43/PZybQ18es9z0iOsbUa+/j5UHU9Vj8fLyIuh6Lt6fxPeta0IWRfY0/1jRNo+nbH1E40NgDnJqWRu+vx/N8ozo0rVPDcg3Nwu9bDrDo30NA+ndGXMZRpIi4BPzcc/adARDg4Uql0EAcdDoK+3gQ6u/Fxag4Kobkj46UgIAAwsPDTcuRERH4+2U/NDc7/v7+lChRgr1795pOwhX/TbkZonMJuGGpRHKiRNlyhF+5ROS1q6SlprJt3Rqq1TLvKbp2+aLp/r7tWykUXASAWwk3Gd3/E15/vydlK1a2at45sfHH2Yyo2ooRVVuxf/Fqar5lPFG4WI2qJN+4SXx41of7n339RXb9tszsMf+Soab7FVs3IvLUeUulnSuv1XuaP77ozB9fdKZhpVIs23kETdM4eO4qrk4FclzguzkXYOPoj1jxVXdWfNWdSqFB+aq4ByhTrjxXLl3i2tUrxt7pf1ZTq259s5jTJ47z7ZgRDBs7ES/vjAKr/1cjmLt4Of/78y+6ftSbJi1b27y4B6hYtjQXL1/l8rVwUlNTWbFuEw2fMz9HoOFzNViy6h8AVm/cQo2qlVBKcflaOGlpxtkOroZHcP7SZYLvGmu/fO1GWjU2///JT/xCSxMfeYWb0eHo01I5u2sTRSubFzuJcRlF1cUDO/AsVMTaaeZIxbKluHjFuB1TUlNZvm4zDWubt6Vh7eosWbUOgNUbt5q2Y3xCAj2+GEbv99/i6afKZ3rt5es2Se99Huv4YnP+nDKGP6eMoXHtZ1nyzyY0TePAsZO4ubiYivc7/Hy8KOjixIFjJ9E0jSX/bKJRrWcBaFizGov/2QjA4n820qiWcSa5+IRbpKSmAbBgxTqqVSyLa/r5W19OmErxIsF07vC8FVtt9FqdyvzR703+6PcmDSuWYNmuY8bvjPPXcHV2zHKsfXYaPVWCXacuAxCbkMSFqFgK+3hYKvVcq1ChAhcvXuTyFeN3xspVq6hfP2f7xIiICJKTjUcj4uPj2b9/P6GhoRbMNh8x6G1/y6dy04N/FtiglPobuH3nQU3TrHYcWqez592P+jLy814Y9AYatHyBIsWK88cv0yheuhzVnqvHqsXzObRnFzp7ewq6udHz8yEArPxzPhFXL7Nw9gwWzp4BwMAx3+Hh5X2/P2kTh5evp2Krhnx9eqNxmsx3+pnWDdy33Gz2nGdeac3kVubTLzb48G3KNnkOfWoaibE3+PXtT62We07VrVCcLUfP8vyw6Tg52DPszYzpyl4Z/St/fNEZgImLN7B8z1GSU1Np+uWPtK9ViR6t8ucwjrvp7O358NPP6N/7QwwGPc2fb0No8RL8GjaF0uXKU7tufcImTyIpMYmvBxqnwPQPCOTrsRNtnHn27O11DOjdg259B6E3GGjXqhklixVl8s+zqVC2FA2fq0n7Vs3pP2IcLTu+h4ebG2OHGNu29+ARfp47H3t7e+yUYlCfnnh5Gr9ck5KT+Xf3PoZ8mg+HkqWz0+mo/XoPVnz7JZrBQOnnmuIVVJQ9S2bjW7QURavU5Mi6pVw4sAM7nY4CLq7U75x5Wtf8wF6nY2CvbnT9bKhxOr6WTShZLITvZ8yhQpmSNHquBh1aN+WLkRNo8UZXPNzdGPelcR8098+/uXT1GlNnz2Pq7HkATB/7FT5engCs2rCFKaOH2KxtD2P2qG7Ue6YMvp6unF05jmFTl/Dr4s22TitL9apXZdOufbR452OcCjgy4tOMGZva9fiMP6eMAWDwR+8zYNyP3E5JpW61KtR7tgpgHIrTZ8S3LFy5nkL+vkwcaHyPnr14hS/G/oDOzo4SRYP5uk93APYeOcHStZspXSyEdj0+A6D3O69Tv3pVazYbgLrlQ9ly7BzPj/gVJ0d7hr3WzLTulbH/449+bwIwcelmlu89YfzOGPoT7WtW0vJWPAAAIABJREFUoEeLWtQuW5RtJy7QbvQs7OwUfV6oi2dBZ6u3Izv29vb0/+ILevTogcFgoG2bNpQsWZIffvyRCuXL06BBAw4fPkyfTz4hPj6ejZs28eOUKfy5aBFnz55l/IQJKKXQNI2333qLUqVK2bpJwsZUTmdYUUpludfWNO2rHDw9T4bo5FdVgj3prkJtnYZFTdXOk7z6Z1unYVFOzd7jYkzOT9p6HIV4u5IafsbWaViUQ2AJxm7MX9d9yEv96huHv6RdPWHjTCzHPqgMjlXffXDgYyxl3wz05/fbOg2L0oVWIXn5FFunYVFOrXqQnJRk6zQsysnZOd+eMZ+0eKLNpwxybtsnX/7/5LgHP4eFvBBCCCGEEMKGHljgK6W+1TStt1JqGVnMfaJp2osWyUwIIYQQQgiRaznpwZ+d/u84SyYihBBCCCFETmn6/HuSq609sMDXNG1P+r8blVKOQFmMPfknNE3Ln5eCFUIIIYQQ4j8qx2PwlVKtganAGYzXPCmmlOqmadoKSyUnhBBCCCFElvLxlWRtLTfTZI4HGmqadhpAKVUC49VspcAXQgghhBAin8jNha4i7xT36c4CkXmcjxBCiP+zd9/xURTvA8c/k0YSSALpEErovXekFylSRUTADoL4U1RUEFQQULooCghYAZWiFEEpX6X33pv0TjokkIQkd/P740Ig5I7cQS536PN+vXhl9/bZyzPsZW92dmZWCCGEeAjWzKLzZPriYaXUcmABpj743YCddsxNCCGEEEII85z4SbKOZk0XnQ53LUcAt5+dHAUUyBouhBBCCCGEcBRrZtF5KTcSEUIIIYQQwlpaWvAtsmUWHU+gN1AR8Lz9utb63/08cSGEEEIIIR4htgyynQOEAq2B9UBhIMEeSQkhhBBCCCEejC3TZJbSWndTSnXSWs9SSv0CrLJXYkIIIYQQQliiZR58i2xpwU9N/3lNKVUJ8APCczwjIYQQQgghxAOzpQV/plKqAPAhsBTIBwyzS1ZCCCGEEELchzZIC74lVlfwtdbfpi9uAErYJx0hhBBCCCHEw7C6i45SarRSKv9d6wWUUp/YJy0hhBBCCCHEg7ClD35brfW12yta6zigXc6nJIQQQgghxP1pg9Hh/5yVLRV8V6VUntsrSikvIM994oUQQgghhBC5zJZBtj8Bq5VSPwAaeBmYZZeshBBCCCGEEA/ElkG245VSB4CWgAJGaa1lHnwhhBBCCJHrZB58y2xpwUdrvRJYaW6bUmqr1rp+jmQlhBBCCCGEeCA2VfCz4ZmD7yWEEEIIIYRFzjzI1dGU1jpn3kipPVrrGhY258wvEUIIIYQQuUk5OgFLrs0c6vD6Zf6+o53y/ycnW/DvK+3S0dz6VbnOLaw8KdEXHZ2GXXkEFubm3H/3Yw/y9viQGz+PdHQadpWv1zAqDlzm6DTs6vCkDszYfs7RadhNv7rFAEjb/aeDM7Eft5pPYDi7z9Fp2JVreDU8qr/s6DTsKmXv9wz2+Hc/F3Ncymm2nI1xdBp21SA8wNEpiAeQkxV8p7yCEUIIIYQQ/z7SRccyW55km1cp5ZK+XEYp1VEp5X5XyHM5np0QQgghhBDCJra04G8AGimlCgCrgV1Ad6AXgNb6UM6nJ4QQQgghRFZGg8HRKTgtW55kq7TWicCTwFda6y5ABfukJYQQQgghhHgQNlXwlVL1MbXY3x7dlWuDdIUQQgghhBDZs6WC/iYwBFistT6slCoBrLVPWkIIIYQQQlgmT7K1zOoKvtZ6A6Z++LfXTwMD7JGUEEIIIYQQ4sFYXcFXSgUBg4CK3PXUWq11czvkJYQQQgghhEUyTaZltvTB/xk4BhQHRgBngZ12yEkIIYQQQgjxgGyp4Adorb8DUrXW67XWLwP17JSXEEIIIYQQ4gHYMsg2Nf3nFaXUE8BloHDOpySEEEIIIcT9SRcdy2yp4H+ilPID3gG+AnyBt+2SlRBCCCGEEOKB2DKLzh/pi9eBZvZJRwghhBBCiOzJNJmWZVvBV0p9BWhL27XWMlWmEEIIIYQQTsKaFvxddy2PAIbbKRchhBBCCCHEQ8q2gq+1nnV7WSn11t3rQgghhBBCOIJRBtlaZMs0mXCfrjpCCCGEEEIIx7NlFh0hhBBCCCGcgkyTaVm2LfhKqQSlVLxSKh6ocnv59uu5kKMQQgghhBD/Kkopf6XUX0qpE+k/C5iJKaaU2q2U2qeUOqyUetWa9862gq+19tFa+6b/c7tr2Udr7fsgBRJCCCGEEOI/7n1gtda6NLA6ff1eV4AGWutqQF3gfaVUoezeWLroCCGEEEKIR86/oItOJ6Bp+vIsYB0w+O4ArXXKXat5sHL8rK2DbIUQQgghhBAPL0RrfQUg/WewuSClVBGl1AHgAjBOa305uzd+JFrwN+7Yw9gp32IwGunarhWv9OyaaXtKSipDxn7B4X9Okd/Xh8+GvUtYaAjXrsfz1ojxHDp2ks6tm/Phm30BuJmYxHNvDsnYPyIqhvYtmzDk9T65Wq67bdq2g3FfTMVgNPJkh3b0ea5Hpu0pKSkMHTWOI8f/Ib+fLxNGfkRYwVAAjp88xcjxn3PzZiLKxYV5304jTx4PUlNT+XTSV+zauw+lXBjQ92VaNWvsiOLdl9aaCSt2sunEZTzdXRnRuQHlCwVkiklKSWPwrxu4GJuAi4uicZnCDGhVw0EZW0drzYRVu9l84hKe7m583Kk+5Qv6Z4pJSk1j8K8buRh3A1cXRaPSYQxoWR2APecimLhqNycjrjG6a0NaVijqiGJY1LBcEO93roSri2LhtvN8u+Zkpu2DO1WkTinTcfR0d8XfJw/1P1gJwDvty9O4QghKwdZ/ohiz+HCu52+NMwd2su6nrzEajVRu0oY6HZ4xG/fPjg38MeUTen48hdASZTi6ZTW7lv+asT3qwhmeHTmN4GIlcyv1+9JaM2b2YjbsO4qXhwefvtqDCsULZ4k7fPoCH8yYS3JKKo2rlWfI811QSnH07CVGfv8rt1LTcHNx4cOXulKlVLGM/Q6eOk/PYZOZOOB5WtetmqvlGv31j2zYsRcvzzyMfqc/FUqXyFquE6cZOnEaybdSaFynOkP7v4hSimvxN3hn9BdciogiLCSISR+8hZ9PPq4n3ODDSdO5cCWCPO7ufPLOq5QOL8qVyGiGTJhKdNw1lHLh6XYteK5Lu1wrr7VmDn+Jdo2rEhUbT/VuwxydzgPrOGkYZds0JTUpmQW93+PyvqznDVd3dzpN/pgSTeqhjUZWDfuMQ4tXkr9oIbrNHE/eIH8SY68x/8WBXL901QGlsOzgzm38Mv0LjAYDjdt24Inuz2favvaPxaxethAXF1c8vbx44c3BhBUrnrE9JvIqH7zSi07P9qZtt565nf5/llKqL9D3rpdmaq1n3rX9byDUzK4fWPs7tNYXMI2DLQQsUUr9prWOuN8+Tl/BNxgMfDp5Bt9MGEFIUADd+79HswZ1KBVeJCNm4Yq/8PXJx8qfprN8zUYmzZzNZ8Pew8PDgzde6snJM+c5ceZ8Rnxeby8WffNFxnq3fgNp1ah+rpbrbgaDgU8/+5KZX4wnNDiIZ/q8RrOG9SlZPDwjZtEfK/D1ycfyBXNY8fcaPp/2DRNHfURamoEhI8cw5qMhlC1dkmvXr+Pm5grAzFk/418gP3/Mm43RaOR6fIJjCpiNzScucz42gd8HdOLgxWjG/Lmd2a9k/ZJ8rkEFahcPJTXNQL/Zf7P5xCUeKx3mgIyts/nkZS7ExLPk9Y4cuhTDmD93MLtPmyxxz9UvbyqXwcCrs1dnlCvULy8jOtVnztajDsj+/lwUfPBkZV6Zvo2I60nMf7sRaw9f5VTEjYyYcb/f+fLt2TCc8mF+AFQLL0D14v50mbAOgDlvPEbtkgHsPBWTq2XIjtFoYM3sKXQdNBYf/0B+Hv4GJWvUJyCsWKa4lKRE9v61hNCS5TJeK9+gBeUbtABMlfulXwx3mso9wMZ9Rzl3NZoVk4Zy4OQ5Rn7/G/NGvZUlbuT3v/Fx76epWroYr47/hk37j9GoWnkmzV3Ga0+2plG18mzYe4RJc//gx4/+DwCD0cikuX/wWJWyuV0sNuzcx7lLV1n5w2QOHDvBiK++Y/6Xn2aJG/nlt4x4sy9Vy5em34dj2bhrH41rV+fbBUuoV70Sr3TvzDfzl/Dt/N95p08vZs5bQrmSxfhq+LucPn+JUVO/54dxH+Hm6sqgvs9RoXQJbiYm8dTrQ6hfowqlimW9WHKk2cs2M23+an4Y5bhGrIdVtk1TAkuFM6FCc4rWqUaXKaOY2vDJLHHNh/wfN6JimFixBUopvPzzA/DEuKHs/nkRe+YsomTT+rT55D3mv/RObhfDIqPBwJypE3l3zGT8A4MZ+UZvqtVrlKkCX6/Z4zRr3wWAvVs3Mm/Gl7wz+vOM7XOnf0nl2vVyPXdH0kbHd9FJr8zPvM/2lpa2KaUilFIFtdZXlFIFgchsftdlpdRhoBHw2/1inb6LzsFjJygSVpAihULxcHenXfOGrN2yPVPMms076PR4MwAeb9KAbXsOoLXG28uTmpUr4OHhbvH9z128TOy169SsUsGu5bifg0ePUbRwGEXCCuHu7k7bFs1Yu3FLppi1G7fQsd3jALRq2oTtu/egtWbLjl2UKVmCsqVNlYf8fn64upoq+Iv/XJlxJ8DFxYUC+f1ysVTWW3f8Au2rlkApRZUiQSQkpxKVkJgpxsvDjdrFTRfA7m6ulC/oT0R8orm3cxrrj1/kifRyVS4cyI1bKUQlJGWK8XK/q1yurpQr6E9Eekyh/PkoHVIApVSu556dykULcCH6JhdjE0k1aJbvvUyzSuYaKEzaVQ9j+d5LAGgNHm4uuLu54OHmipurCzEJt3IrdatdPXWc/MGFyB9cEFc3d8rVa8KpPVuyxG1eOIva7Z7Gzd3D7Psc37aWsvWa2Ttdm6zZfYiOjWqhlKJq6XASEpOIiss8KVpUXDw3k25RrUw4Sik6NqrF6l0H07cqbiQlA5CQlExQgTvzLfy8aiOt6lTB388nt4qTYc3WnXRq2dhUrvJlSLh5k6iYuEwxUTFx3EhMolqFMiil6NSyMau37EzffxedWzYBoHPLJqzeanr91PmL1KtWGYASRcO4HBFFdNw1ggIKZNwhyOvtRYkiYURGx+ZWca22ac8/xF2/6eg0HkrFDi3Z/fNiAM7v2IdXfl98QoOyxNV64SnWjvsaMN3RSUw//iHlS3Fqjenv99S6rVToYLHO5RCnjx8huFBhgguG4ebuTp2mLdm7dWOmGK+8eTOWbyUnZfpu2LNlPUEFC2W6IBCPhKXAC+nLLwC/3xuglCqslPJKXy4APAYcz+6Nnb6CHxEdS8HgwIz1kMAAIqIyn0Ajo2MJTY9xc3XFJ68316xsrf5zzUbaNG3o0EpUZFQ0ocF3TlQhwUFEREWbiTF1zXJzcyVf3rxcux7PuQsXUUrR7+3BPP1SP77/eR4A8QmmltQp3/zA0y/1Y+CHI4iOdb4vHoDI+ERCfO+cuIJ9vYmKT7IYn5CUwobjF6lT3HKF0hlEJiQS4uudsR7s453lwuVuCckpbPznEnWKh+RGeg8lxM+TK9fuHKOIa8mE+HmajS1YwIvCAd5sP2H6TO8/F8eOkzGs+/hx1n3cis3HojgdecPsvo50Iy4an4A7f5f5/INIiMt8lyHy7EkSYqMoUd1yq9nx7espV7+pvdJ8IJFx8YSmt2wChPjnJyLueqaYiLjrhPjfaRQI9c9PZPpFwPvPd2biL8to8fpIJv68lLe7P2HaJ/Yaq3cepHvLBrlQiqwio+MIDbrTvS8kMICImMznvYiYWEIC/e+K8Scy2lQJjIm7TlCAaZa6oIACxF4zlbds8WL8vXkHAAeOneRyRBQR91TkL12N5OipM1QpVyrnCybwLRTK9QtXMtavX7yKb6HM3wGe6ReVrT8eyIDtS+k1dwr50usGlw8co1IX0x3Uip1b4+nrg/ddfwOOFhcThX/QnXO/f2AQcdFRWeJWL13IoBefYsG30+j52tuAqbK/fMFPdHr25VzL11log9Hh/x7SWKCVUuoE0Cp9HaVULaXUt+kx5YHtSqn9wHpgotb6oNl3u4vVFXylVBml1Gql1KH09SpKqQ9tLIjtdNaH595bF9fmYqx8+xVrN9KuRaMHSCznmEk/ywWH2TIqU/eevQcOMXb4UGZ9PZnV6zexbdceDAYDEZFRVK9ciQU/zKBqpQp8NmWGvYqQ8ywcwDSDkSELN/JM3XIU9s/9FkJbmD2uFmLTjEaGLtzEM3XKUriAc5cLMFsQc+UFaFe9EP/bfwVj+vaigd6UCMlHixF/0XzEX9QtHUDNEv7md3Yy6q6Ca6ORdb9Mp0mPvhbjr5w6iptHHgILO1ermjXnTHMxt83/ezODn+vE6inDGPxcZz6aOR+AsbN/Z2CP9ri6OKbtSJt52Lq159L7eaV7J64n3KBL/0H8vHQl5UuFZyrjzaRk3hw1iSGvvkC+vN73eSfxwMwdpHuOpYubG/mLFOLs1t18Wbcj57ft5YlxpvF2fw4eTYnGdRmwYxklGtXh+sUrGNMMuZG5dayoBwC06NiV8T/+Rrfer7Hslx8BWDz7Wx7v8gyeXvLZe9RorWO01i201qXTf8amv75La90nffkvrXUVrXXV9J8WuwPdzZY++N8A7wEz0n/hAaXUL8An5oLvHnQwY8YMXn7iwSrRIUEBXIm805odER1DcKB/lpirkdGEBgWSZjCQcDMRP9/sK0nHTp3BYDBSsYxjW1xCggO5GnnnSj0iMorgwIB7YoK4GhlJaHAQaWkGbty8iZ+vLyHBgdSsViWj+02j+nU5evwEdWtWx8vTkxZNGgLQulkTFi9bkXuFysb8HcdZvPsEABXDAoiIv3P7ODI+kSAfL7P7fbJsG0X9fehVv3yu5GmrBTuPs3jPKQAqFMrcjSgyIZFAH/Mn4E//2E6RAF961itndruzibiWTMH8d45RSH5PIuOTzca2rRbGJ4vuNDa0qFyQA+fiSEwxfbluOhZJ1WIF2H3aue4w5SsQSELMnb/LG7FR5Ctw59yTkpxE9MWz/DrmPQBuXo/l9y+G0emtkYSWKAPA8W3rKOck3XN++d8mflu7DYBKJYpwNfZaxraI2GsEF8jchS/UPz8RsXda9a/GXiM4vSvO7xt2MeR5U1/g1nWrMuwbUwX/8JkLvPvVHADiEm6ycd9R3FxcaFG7sv3KtXQVv65YDUDlMiW5GnXnLktEdAzB/pmfGxMaGJCp9T0iOjaj1T6ggB9RMXEEBRQgKiYO//ym8ubL683od18DTBcIrV54g8KhpjuqqWlpvDXqM9o3b0irhnXtVs7/ovqvPked3t0BuLjrAH5FCmZs8yscSvyVzGMME2PiSLmZyOElqwA4sHA5tV/qBkDClUjmPN0fAI+83lTu0oZkJxqXViAwiNioO+WJjY4if0Cgxfi6TVsy56sJAJw+doRdm9ay4LupJN64gYtSuHt40LLTU3bPWzgvW5pZvLXWO+55Lc1SsNZ6pta6lta6Vt++llu4slOpXGnOX7rCxSsRpKSmsnzNJprVr5MpplmDOvz+v7UA/G/9FupWr2xVl5vlqzfSrrljW+8BKpUrx7mLl7h4+QqpqamsWL2Wpg0z3+Ju2rA+S5f/D4C/1q2nTs3qKKVoUKc2J06dJik5mbQ0A7v2HaBk8WIopWjyWD127t0PwLZdeyhRvFiW3+0o3euUZV7/9szr356m5Yrwx/7TaK05cCGKfHncCTJTEZ66ei83bqXybpvaDsjYOk/XLsvcfu2Y268dTcsW4c/0ch28GE2+PB5mL1ymrdnHjeRU3m1d0wEZP5hDF65RNCgvYf5euLsq2lUvxNpDWWekCA/Ki6+3O/vO3ukHfSUuiVolA3B1Ubi5KGqVCOB0hPN10QktUZZrEZe4HnUFQ1oqx7atp0T1O4Px83jn5bVpv9Fn0hz6TJpDwZLlM1XutdHIPzs2UrZeUweVILOejzdk0Zh3WTTmXVrUqszSjbvQWrP/xFnyeXlm6kcPEFTAF2+vPOw/cRatNUs37qJ5zUoABBfwZedR04Xs9sMnKBZi6sr0v8kf8teXH/HXlx/xeN2qfPhSV7tW7gF6dmzN4q/Hs/jr8bRoUJvf/95gKtfRf/Dx9s6ovGeUK6AAeb092X/0H7TW/P73BprXN51TmtWrxZK/1wOw5O/1NK9fC4D4GzdJSTV93f22Yg21KpUjX15vtNZ8NGk6JYqE8WLX9nYt53/R1ulzmFy7PZNrt+fw0r+o2ct0UVm0TjWSryeQcDVrF5ajf66mRBNTl7lSzRoQcdQ0u5d3wJ3xTM0G92fnrF+z7OtIxcuWJ/LSRaKuXiYtNZUd6/6mer2GmWKuXrqQsXxgxxZCwkyTjQyd9DUTZy9i4uxFPN7laZ545oX/TOXe0d1znHkeflta8KOVUiVJv5GklHoK09O17MrN1ZUP3niFvoNHYDQY6NK2JaWKF+WrH36hYplSNH+sDl3bteT90V/Q5tlX8fPxYeJHd0bGt+rxCjcSk0hNTWPN5u3MHP9xxgw8q9Zv5usxH9m7CNlyc3Nl6Ntv8OrAwRgMRrq0b0upEuFM+eYHKpYrS7NGDXiyfTuGjBpDu6efw8/Xh/EjTL2j/Hx9eO6Zp+jR+zWUUjSqX4fGDUwnt7df68uQkWMYN3kq/vnzM2roe44spkUNS4ex6cQlOn25JH06yTsXN898/Qfz+rcn4vpNvtt4iPBAX3rO+BMwXSR0qVnaUWlnq2HpQmw+eYlOU5bi6e7Kxx3vVA57zFjO3H7tiIhP5LtNhwkP9KXXTNMdlqdrl6FLjVIcvhTDuwvWE5+cwsZ/LjJj/QF+7e8clQiDUfPpokPM7FsPFxfF4h0XOBVxg9fblOXwhWusPWxqiWpXI4wV6YNrb/vf/svULR3I4veagDa14K87ct/ZvhzCxdWVZs+/zsLxQ9HaSKXGrQksHM7mhbMILV6GkjXuP/PWxeMHyecfSP7ggveNc4TG1cqzYd9R2r49Gs887nzS7860vE8OmciiMe8CMOzlp/hg+lxupaTSsGo5GlUz3Tn7uM/TjJ29hDSjgTzu7nzcp5tDynGvxnWqs2HnXtq89CaeeTz49J3+Gdu69B/E4q/HAzDsjT4MnTiNWympNKpVjca1qwGmrjhvf/oFC1eupWBwIJ9/YOrjfPr8Jd6fMBVXFxdKFgtj1NumJ8XvOXycpas3UqZ4Ubr0HwTAWy/1oEmd6rlZ7GzNGdOPxjXLEpg/H6dXTmTk9N/5ccnG7Hd0IsdWrKVsm6YMOrqWlKRkfu0zKGPbmzv/YHJt07lx+dBxdP9hEh0++4ibUbH8+ooprmSTerQZ9R4azZmNO1gyYLhDymGJq6sbvf5vIJ8NfRuj0UCjx9sTFl6CxbO+IbxMOarXb8Tqpb9xZM8uXN3cyJvPhz7v2r+XtHh0qfv1s8wUqFQJTNMANQDigDPAs1rrs1bsrtMuOd9UfznFLaw8KdEXHZ2GXXkEFubmXLO9sf418vb4kBs/j3R0GnaVr9cwKg5c5ug07OrwpA7M2H7O0WnYTb+6pjtxabv/dHAm9uNW8wkMZ/c5Og27cg2vhkf1f/egyJS93zPYI+tzCP5NxqWcZstZ55riN6c1CA9wvqnc0p0d/IJ1lVg7Ch83yyn/f6xuwddanwZaKqXyAi5aa+fpvCaEEEIIIYQAbJtFx6CUGgsk3q7cK6X22C0zIYQQQgghhM1s6YN/GNMFwf+UUt3Tp/JxytsSQgghhBDi382ZB7k6mi2z6KRprQdhmi5zo1KqJmZnbhVCCCGEEEI4ii0t+ApAa71AKXUYmAsUtUtWQgghhBBC3Ic2ONHDypyMLRX8PrcXtNaHlVINgc45n5IQQgghhBDiQWVbwVdKNddarwGKKaXufVKS8z2dRgghhBBCiP8wa1rwmwBrgA5mtmlgUY5mJIQQQgghRDa0UQbZWpJtBV9rPTz950v2T0cIIYQQQgjxMKzpotMBOKC1Ppe+PgzoCpwD3tRan7FvikIIIYQQQmQm02RaZs00mZ8CUQBKqfbAs8DLwFJguv1SE0IIIYQQQtjKmgq+1lonpi8/CXyntd6ttf4WCLJfakIIIYQQQghbWTPIViml8gGJQAtg2l3bPO2SlRBCCCGEEPchXXQss6aC/wWwD4gHjmqtdwEopaoDV+yYmxBCCCGEEMJG1syi871SahUQDOy/a9NVQGbWEUIIIYQQuc4oLfgWWdMHH631JeADoI1SyiX9tSta6/P2TE4IIYQQQghhG6sq+OmmA72AE0qpsUqpcnbKSQghhBBCCPGArOmDD4DW+m/gb6WUH9AD+EspdQH4BvhJa51qpxyFEEIIIYTIRJ5ka5ktLfgopQKAF4E+wF5gMlAD+CvHMxNCCCGEEELYzOoWfKXUIqAcMAfooLW+PYPOfKXULnskJ4QQQgghhLCN1RV8YIrWeo25DVrrWjmUjxBCCCGEENmSefAty7aCr5R60tzybVrrRTmdlBBCCCGEEOLBWNOC3+E+2zQgFXwhhBBCCJGrtEE7OgWnZc2DruRhVkIIIYQQQjwilNb3v/pRSj2rtf5JKTXQ3Hat9SQrfo9cYgkhhBBCPHqUoxOw5MjzHRxev6wwe5lT/v9Y00Unb/pPn4f5RTfnfvIwuzu1vD0+JDkpydFp2JWnlxcHr1x3dBp2VbmgH0nJyY5Ow668PD35JaiCo9Owq55RR/gtpKKj07CbpyIOA5B2+biDM7Eft0JlSV7+taPTsCvPdv0Z7FHC0WnY1biU03hUf9nRadhVyt7v2f90W0enYVdVF6xwdAoWGWWQrUXWdNGZkf5zhP3TEUIIIYQQQjwMW+bBLw68AYTfvZ/WumPOpyWEEEIIIYRl2ujwHjpOy5Z58JcA3wHLALknIoQQQgjCcEPdAAAgAElEQVQhhBOypYKfrLX+0m6ZCCGEEEIIIR6aLRX8yUqp4cD/gFu3X9Ra78nxrIQQQgghhLgPo8yDb5EtFfzKwHNAc+500dHp60IIIYQQQggnYEsFvwtQQmudYq9khBBCCCGEsIaWaTItcrEhdj+Q316JCCGEEEIIIR6eLS34IcAxpdROMvfBl2kyhRBCCCGEcBK2VPCH2y0LIYQQQgghbKBlkK1FVlfwtdbr7ZmIEEIIIYQQ4uHZ8iTbesBXQHnAA3AFbmqtfe2UmxBCCCGEEGbJNJmW2TLIdgrQAzgBeAF90l8TQgghhBBCOAlb+uCjtT6plHLVWhuAH5RSW+yUlxBCCCGEEOIB2FLBT1RKeQD7lFLjgStAXvukJYQQQgghhGUyD75ltnTReS49/nXgJlAE6GqPpIQQQgghhBAPJtsWfKVUUa31ea31ufSXkoER9k1LCCGEEEII8SCs6aKzBKgBoJRaqLWWVnshhBBCCOFQRqPMomOJNV101F3LJeyViBBCCCGEEOLhWdOCry0sCyGEEEII4RDyJFvLrKngV1VKxWNqyfdKXyZ9XcuDroQQQgghhHAe2VbwtdauuZGIEEIIIYQQ4uHZ9KArZ6O1ZsKKnWw6cRlPd1dGdG5A+UIBmWKSUtIY/OsGLsYm4OKiaFymMANa1XBQxtbZvHkz48aPx2g00qVLF3q//HKm7bt372b8hAmcOHGCcWPH0qpVq0zbb9y4QecuXWjevDlDhwzJzdQfyN7tW/lhymcYDUZaPNGJLr1eyLR91e8LWbXkN1xcXPD08qbfu0MoEu78w0E2b97M+HHjMo7jy717Z9q+e/duJowfz4kTJxg7bpzZ49ilc2eaN2/OkKFDczN1q9UcPZRCLRuTlpjEtgFDiTtwNEtMiyU/4hUShCH5FgBruvXhVnQs3mEFqT9lNO5+vigXF/Z/8jmX/96Q20XIVtVPh1CwRWPSkpLYNeADrh3MWsYmi37A864ybuz+CreiY6k6cjBBj9UBwNXLkzyB/iwtUz9X8zdn447djJ3yLQaDga5PPM4rPZ/KtD0lJZUhYz7n8D8nye/ry2fD3yMsNIQtu/by+czZpKal4e7mxjuvvki9GlUB6DtoOFExcRgMBmpWqciHb/bD1dU52oe01oxbvJ5NR8/g6e7OqB6PU75IcJa4r/7czLJdR4lPvMW2cf+Xaduqvf8wfdU2AMqGBTH2uba5krstOk4aRtk2TUlNSmZB7/e4vO9wlhhXd3c6Tf6YEk3qoY1GVg37jEOLV5K/aCG6zRxP3iB/EmOvMf/FgVy/dNUBpXgwM4e/RLvGVYmKjad6t2GOTueBFXrpVXyr18Z46xYXpn1G0plTFmPDBw3HIziUf97tD4BfvYaEdnuWPGFFODH0LZJOn8ittB3GKPPgW/RIV/A3n7jM+dgEfh/QiYMXoxnz53Zmv9IuS9xzDSpQu3goqWkG+s3+m80nLvFY6TAHZJw9g8HA6DFjmDF9OiEhIfTs1YumTZpQsmTJjJjQ0FBGjRzJrNmzzb7H1KlTqVWzZm6l/FAMBgPfTh7PsIlT8A8K5v1XX6DWY40yVeAbtWxN606myZt2bt7ArKlf8OGELx2VslUMBgNjRo9m+owZhISE0KtnT5o0bZrlOI4cNYrZs2aZfY+pU6dSs1at3ErZZoVaNsanRDGW1WlDQM0q1B4/nP+1ecZs7JZXBxG7P3Nlo9LAfpz7fSUnf5yPb5mSNJ07naU1W5nd31FCWzTCp3gxVtZri3/NKtQYP4w1bXuYjd3x2mDi7inj/mHjMpZL9u5J/srl7ZqvNQwGA59OnsE3E0YSEhRA91ffoVmDOpQKL5oRs3D5X/j65GPlzzNZvmYDk2bM4rPhgyjg58vU0R8SHBjAiTPn6DtoOGt//RGAScMHky+vN1pr3ho+llXrN9OueWMHlTKzTUfPcj4qjmVDX+Tguat88ttqfn4763FsUrEEzzSsRofRP2Z6/VxUHN+t3smsAU/j6+1JTEJiLmVuvbJtmhJYKpwJFZpTtE41ukwZxdSGT2aJaz7k/7gRFcPEii1QSuHlnx+AJ8YNZffPi9gzZxElm9anzSfvMf+ld3K7GA9s9rLNTJu/mh9G9XF0Kg/Mp3pt8oQW4tiA3niXLkdYn9c5+cHbZmP96jTAmJyU6bXkC+c4O3EUhfsOyI10hZOz5UFXTmfd8Qu0r1oCpRRVigSRkJxK1D0nXi8PN2oXDwXA3c2V8gX9iYh3vpPzbYcOHaJIkSIULlwYd3d32rRuzbp16zLFhIWFUaZMGVyUyrL/kSNHiImNpX59x7cSWuPkscOEhhUmpFAY7u7uPNb8cXZuztyK6503X8byreQkMFNuZ3PvcWzdpo3F46hcsv4ZHjlyhNiYGKc+jmFtmnNm/u8AxOw+gIefD54hgVbvrzW4+5iOrYdvPpKuRtolz4dRqE1zzv26FIDY3Qdw9/XBM9j6Mt6taJd2XFi0PCfTeyAHj52gSKGCFCkUioe7O+2aN2Lt5u2ZYtZs3k6n1s0BeLzJY2zbsx+tNeVLlyQ40HSXtFR4UW6lpJKSkgpAvrzeAKQZDKSmpaFwnr/TtYdO0aF2edN3RXhBEpJSiLp+M0tclfCCBPllfUD7oq2HeKZhVXy9PQEI8PG2e862qtihJbt/XgzA+R378Mrvi09oUJa4Wi88xdpxXwOmOxuJMXEAhJQvxak1WwA4tW4rFTq0zKXMc8amPf8QZ+aYPkr8atUjbsNqABJPHMM1bz7c8hfIEueSx5Og9k8SsXBeptdvXbrArSuXciVXZ6EN2uH/nNUjXcGPjE8kxPfOyTjY15uo+CSL8QlJKWw4fpE66RV+ZxQZGUlo6J38gkNCiIi0ruJjNBr57LPPGPi2+St+ZxQbFUVgUEjGekBQMLFRUVniViz+lf/r2YU507+i9wDnb1W69ziGBAcTGRFh1b63j+PbAwfaK70c4V0wmMTLd27hJ16OwDs0xGxsvS8/pe3aRVQa+GrGawcnTKH4Ux3ovH8NTedOZ9eQT+2es628CgaTeFc3haQrEXgVNF/GWpM/oeXqhZR/+9Us27wLF8S7aGEiN203s2fuioiOoeBdFykhQYFERMdkiomMjiE0PcbN1RWffHm5Fp+QKeZ/G7ZQvlQJPDzcM1575b3hNO7yHHm9vHi8SQM7lsI2kddvEpLfJ2M9JH8+Iq/fsHr/c1FxnIuM44XJ83n2i3lsPnrWDlk+HN9CoVy/cCVj/frFq/gWyvxd5+ln+j9o/fFABmxfSq+5U8iXfpwvHzhGpS5tAKjYuTWevj54p7fui9zh7h9AanR0xnpqTDTu/lkbFEKfeZ7IZYswpiTnZnriEWN1BV8pFaKU+k4ptSJ9vYJSqnd2++U6C41GaQYjQxZu5Jm65Sjs72M+yAlonfVqUFnZYj1/wQIaNmyYqWLp7LSZmVfNFbdtl25M/WUxz/Z7nd/mfJ8LmT2chzmOC+bPfzSOo5nymCv3llcHsbxJZ/5q/yxB9WpS/OmOAIR3eYLT85awpGpz1vV4lQbTxjnh3Rnryrj9tcH81bQL6zo+R2C9GhTt1jHT9iKd23Hpj/+B0Qn6i1rx2TT7+b1r+eSZ83w+cxbDB76WKeabCSNYt3AWKampbN97IEfSzRFmy2z97mlGzbnoa3z7+lOMfa4tH8//m/gkJ6tcmSvQPeV2cXMjf5FCnN26my/rduT8tr08Mc40TuvPwaMp0bguA3Yso0SjOly/eAVjmiE3Mhe3WXEMPYuVwCO0EPE7t+RSUuJRZUsf/B+BH4AP0tf/AeYD35kLVkr1BfoCzJgxg145VKeev+M4i3ebBo5UDAsgIv7OLbnI+ESCfLzM7vfJsm0U9fehV33H94G9n5CQEK5evdNiGBkRQXBQ1tus5hzYv589e/eyYMECEpOSSE1Nxdvbm7fefNNe6T60gKBgoqPutGzHREVSINByeR9r/jjffD7O4nZnce9xjIiMJCg466A+c/YfOMDePXtYsGABSYmJGcfxzbfesle6Viv9cg9KPdcNgJi9B/G+q4XQu1AISRFZ7zbd7nqTdjORs4v+JKBGZc4sWEqJXl1Z170vANG79uOax4M8AQW4FR2bCyWxrORLPSj+rGnQaey+Q3iHhXK7fdurYAjJZroSJd9VxvOLluNfvTLn07v2ABTu3JZ9739i99ytERIUyJXIO62EEVHRBAf4Z4m5GhlNaFAgaQYDCTdu4udrOolfjYpmwLDRjH7/LYqGFczy/nk8PGjWoA5rNm+nQa3q9i3MfczbtJ9FWw8CULFoKBHX7tyBiLh2gyDffJZ2zSLELx9VwkNxd3WlcIAf4cEFOB91jUpFHXsRXv/V56jTuzsAF3cdwK/InePhVziU+CuZ7xomxsSRcjORw0tWAXBg4XJqv2T6e064Esmcp02DNT3yelO5SxuS77lrI3JeQOv2BLQw3TlJPPUP7oGBcNy0zT0gkNS4zHfX8pYpj3fxUpSf8iO4uuLm50fJ4eM4NWJwLmfuHJy5i4yj2VLBD9RaL1BKDQHQWqcppSxe3mutZwIzb6/enJszX27d65Sle52yAGz85yLzdxyndaVwDl6MJl8ed4LM9I2cunovN26lMqyj8/Znvq1ixYqcP3+ei5cuERIczMpVqxgzerRV+44ZMyZj+ffff+fwkSNOXbkHKFW2AlcuXiDiyiX8A4PZvOZ/vPXhqEwxVy6ep2Bh0wDAPds2ExpWxBGp2uT2cbx08SLBISGsWrmS0Xcdn/u59zgeOXzYKSr3ACe+n8uJ7+cCUKhVY8r07sW5xcsJqFmF1PgEkiOiM8UrV1c8/Hy4FXsN5eZG2ONNuLreNBNJ4qUrhDSux5l5S/AtXQIXzzwOr9wDnPphLqd+MJUxtGVjSr3ckwuLl+NfswqpCTdIjsxaRnc/H1LSy1iwVRMiN2zN2J6vZDgefr7E7NqXq+WwpFK50py/dJmLV64SHBjA8jUbmfDhu5limjWow++r1lCtYjn+t34zdatXQSlF/I0b9H9/JG/1eZ4alStkxN9MSiIxMYmgAH/SDAY2bt9NjSoV7v3VueqZhlV5pqFphp8Nh88wb9M+2lQvy8FzV8nn5WG2r70lzSuXZMWe43SqU5G4G0mci4qjcICfvVK32tbpc9g6fQ4A5do2o0H/59g/fxlF61Qj+XoCCVezdnc8+udqSjSpx6l1WynVrAERR08C4B1QgKTYa2itaTa4Pztn/ZqrZfmviln1BzGr/gBMg2wD23Tg2ub1eJcuhzHxJmnX4jLH//UnMX/9CYB7UDDFB4/4z1buxf3ZUsG/qZQKIP1ptkqpesB1u2RlpYalw9h04hKdvlyCp7sbH3e60+fzma//YF7/9kRcv8l3Gw8RHuhLzxmmP4rudcrSpWZpR6V9X25ubgx5/3369++P0Wikc6dOlCpViqnTplGxQgWaNm3KoUOHeHvgQOLj41m/YQPTvv6axYsWOTr1B+Lq5kafN9/jk/cGYDQaad62A0WKl2Te9zMoWbY8tR9rzIrFv3Jg9w7cXN3I6+PLG0OGOzrtbLm5ufH+kCEZx7FT586UKlWKaVOnUqFixYzjOPDtt4mPj2fD+vV8PW0aixYvdnTqVrv81wYKtWxMhx0rMSQls23ABxnb2q5dxIpmT+KSx4NmC75BubmhXF2J2LCVU3NMFYc9w8ZT9/MRlOv3PADb3nC+qUCv/r2B0BaNabN9BYakZHa9+WHGtparF/J3i6645PGg0byZKHc3lIsrkRu3cvqn3zLiinZpx4XfVzgifbPcXF35YEA/+g762DSFa9uWlCpelK++/5mKZUvR/LG6dH2iFe+PnkSbXn3x8/Vh4kfvAfDL4j+5cPkK0+fMZ/qc+YCpW47Wmv/74BNSU1MxGIzUrVGF7h2dZxrJRhXC2XT0DO0//RFPDzdGPvN4xranJ/zEgveeBeDzpRtZvuc4yamptPr4W56sV5H+berToFwxthw/R5exs3FxUbzdoRH585q/W+wox1aspWybpgw6upaUpGR+7TMoY9ubO/9gcu32ACwfOo7uP0yiw2cfcTMqll9fMcWVbFKPNqPeQ6M5s3EHSwY4/3n2bnPG9KNxzbIE5s/H6ZUTGTn9d35cstHRadkkYe9OfGvUptyX32NMSebCtM8ztpUZP4V/Br1+3/19azcg7OX+uPn6Ufz9ESSfPc3p0R/ed59HnUyTaZky19fSbKBSNYCvgErAISAIeEprbU1HyxxrwXdGeXt8SHKS5cG9/waeXl4cvOLQ6zm7q1zQj6RkJ+tXm8O8PD35JcixLav21jPqCL+FVHR0GnbzVIRpKs60y8cdnIn9uBUqS/Lyrx2dhl15tuvPYA/nf57HwxiXchqP6i9nH/gIS9n7Pfufdp6LWXuoumCFsw2OyrCx/mMO76PTaOtmp/z/sboFX2u9RynVBCiLabzVca11qt0yE0IIIYQQQtjM1gdd1QHC0/eroZRCa23+aUtCCCGEEELYiTY6vAHfaVldwVdKzQFKAvuA24NrNSAVfCGEEEIIIZyELS34tYAK2tpO+0IIIYQQQtiJUabJtMiWJ9keApz8yTtCCCGEEEL8t9k0Dz5wRCm1A7h1+0WtdUfLuwghhBBCCCFyky0V/I/tlYQQQgghhBC20DIPvkW2TJO5XikVAtROf2mH1jrrM9uFEEIIIYQQDmPLLDpPAxOAdZjmwf9KKfWe1vq3++4ohBBCCCFEDtMyyNYiW7rofADUvt1qr5QKAv4GpIIvhBBCCCGEk7BlFh2Xe7rkxNi4vxBCCCGEEMLObGnBX6mUWgXMTV/vDizP+ZSEEEIIIYS4P5kH3zJbBtm+p5TqCjyGqQ/+TK31YrtlJoQQQgghhLCZLS34aK0XAgvtlIsQQgghhBDiIWVbwVdKJQDm7oEoQGutfXM8KyGEEEIIIe5DG2UefEuyreBrrX1yIxEhhBBCCCHEw7Opi85tSqm8QGegp9b6iZxNSQghhBBCiPuTQbaWWT3NpVLKQynVWSm1ALgCtASm2y0zIYQQQgghhM2s6YPfCugBtAbWAnOAOlrrl+ycmxBCCCGEEMJG1nTRWQVsBBpqrc8AKKUm2zUrIYQQQggh7kNLFx2LrKng1wSeAf5WSp0G5gGuds1KCCGEEEII8UCsmUVnL7AXGKyUegxTdx0PpdQKYLHWeqadcxRCCCGEECITbZBpMi2xepAtgNZ6s9b6dSAM+Byob5eshBBCCCGEEA/Elll0HkufHhOgJ9AG+NgeSQkhhBBCCCEejNLaugEKSqkDQFWgCqaZdL4DntRaN7FidxkFIYQQQgjx6FGOTsCSP4tUcXj98okLB5zy/8eWB12laa21UqoTMFlr/Z1S6gVrd1548LLt2T0iulYuRNKSzx2dhl15dX6bNVXqODoNu2p+YAeGQ6sdnYZduVZqwb5L1xydhl1VC8vPlrMxjk7DbhqEBwCQGnnWsYnYkXtwOMlJSY5Ow648vbz+1Z9TMH1W9z/d1tFp2FXVBSvwqP6yo9Owq5S93zs6BfEAbKngJyilhgDPAo2VUq6Au33SEkIIIYQQwjKZJtMyWwbZdgduAb211lcxDbSdYJeshBBCCCGEEA/E6hb89Er9pLvWzwOz7ZGUEEIIIYQQ4sFkW8FXSiVgfpCsArTW2jfHsxJCCCGEEOI+jFZOFPNfZM2DrnxyIxEhhBBCCCHEw7NlkK0QQgghhBBOwSAt+BbZ9CRbIYQQQgghhHOTCr4QQgghhBD/ItJFRwghhBBCPHJkGnzLpAVfCCGEEEKIfxFpwRdCCCGEEI8cGWRrmbTgCyGEEEII8S8iFXwhhBBCCCH+RaSLjhBCCCGEeOTIIFvLpAVfCCGEEEKIfxGp4AshhBBCCPEvIl10hBBCCCHEI0dm0bFMWvCFEEIIIYT4F5EWfCGEEEII8ch51AfZKqX8gflAOHAWeFprHWcmrijwLVAE0EA7rfXZ+723tOALIYQQQgiR+94HVmutSwOr09fNmQ1M0FqXB+oAkdm9sVTwhRBCCCGEyH2dgFnpy7OAzvcGKKUqAG5a678AtNY3tNaJ2b2xdNERQgghhBCPHGcYZKuU6gv0veulmVrrmVbuHqK1vgKgtb6ilAo2E1MGuKaUWgQUB/4G3tdaG+73xo9cBf+fvTv444cpGI0Gard4giZdepqNO7h1PXM/+5jXxk6ncKmyJCZc5+eJH3Pp1DFqNG1Dxz5v5nLm1tNaM37pZjYdP4+nuxsjn25G+bCgLHFfrdzOH3v+IT7pFltH9cl4fcKyzew8dRmA5NQ0Ym8ksWnEy7mW/4MoPfgdAho1wJiczJGPRnLj6PEsMdW/+xqPoECMybcA2PfqG6TGZumq5jS01oz+/lc27DmMl4c7o994ngolimaJO3zqPEOnzCY5JZXGNSoy9OVuKKUA+Gn5Wn5ZsR5XF1ea1KzIu88/mdvFsGjfjq38OGUSRqOR5u060rnnC5m2/7V0Eat+/w0XFxc8vbzoO3AIhcNLcGDXdn75ZippaWm4ubnxbL8BVKpRy0GluL+DO7fxy/QvMBoMNG7bgSe6P59p+9o/FrN62UJcXFzx9PLihTcHE1aseMb2mMirfPBKLzo925u23cyfqxxt0/adjJ08HYPRQNf2benzbPdM21NSUhjy6QSOHD9Bfl9fJo4YSljBUC5duUrHZ18hvGhhAKpULMfwd53zvLp582bGjR+P0WikS5cu9H458/lw9+7djJ8wgRMnTjBu7FhatWoFwOXLlxn4zjsYDQZS09Lo0aMHT3fr5ogiZOu/8Fkt9NKr+FavjfHWLS5M+4ykM6csxoYPGo5HcCj/vNsfAL96DQnt9ix5wopwYuhbJJ0+kVtp55iZw1+iXeOqRMXGU73bMEenI9KlV+YtVuiVUn8DoWY2fWDlr3ADGgHVgfOY+uy/CHyX3U6PDKPBwNJvJ/PysAn4+gcx7f1XKVerASFFwjPF3UpKZOvyRRQpXT7jNTd3D1o98zIR588QceFMLmdum03Hz3M++jpL3+vBwfORfLp4Iz+9nrVi16R8OM80qETHCXMzvf5eh8cyluduPsixy9F2z/lhBDRsgHexImxr3xXfKpUo++Fgdvcyf0Fy5P1hJBw5mssZPpgNew5z7kokK6d8zIETZxkxcx7zxw7KEjdy5lxGvNqTqmWK0+/TqWzce4TGNSqy/eBx1uw4wJJJH+Dh7k7M9QQHlMI8o8HA95Mn8MGErwgICmZI/xep1aARhcNLZMQ81uJxWnU0fW53bd7A7K8nM3TcZHz88jPo08/wDwzi/JlTjB70JtN//cNRRbHIaDAwZ+pE3h0zGf/AYEa+0Ztq9RplqhTVa/Y4zdp3AWDv1o3Mm/El74z+PGP73OlfUrl2vVzP3VoGg4FPJk3lm8/HEBoUSPdX3qDZY/UoWbxYRsyiP1fh65OPFfN+ZPnf65g0/Ts+G2H6XioSVpCFP3ztqPStYjAYGD1mDDOmTyckJISevXrRtEkTSpYsmRETGhrKqJEjmTV7dqZ9g4KCmD1rFh4eHiQmJtK1a1eaNmlCcLC5RjbH+S98Vn2q1yZPaCGODeiNd+lyhPV5nZMfvG021q9OA4zJSZleS75wjrMTR1G474DcSNcuZi/bzLT5q/nhrga9/7pHYZCt1rqlpW1KqQilVMH01vuCmO9bfxHYq7U+nb7PEqAe2VTwre6Dr5TyUkqVtTbeHi6ePEZAaCH8Qwrh5u5Olceac3Tn5ixxf837nsadnsHN3SPjNQ9PL8LLV8bNwyNLvLNZd/gs7WuWQSlFlWIhJCTdIir+Zpa4KsVCCPLNe9/3WrHvJG2qlrJXqjkisFljri5bDkD8gUO4+fjgERjg4Kwe3pqdB+jUpC5KKaqWKU7CzUSi4q5niomKu86NxGSqlS2BUopOTeqyesd+AOat2kifLq3xcHcHIMDPJ9fLYMnJY0cICStMSKEw3NzdadC8FTu3bMgU4503X8byreSkjLsSxUuXxT/QdEeqSHgJUlNvkZqSknvJW+n08SMEFypMcEFTGes0bcnerRszxXjlvfP3d3cZAfZsWU9QwUKZKlnO5uDR4xQNK0SRQgVxd3enbYumrNm0NVPMmo1b6dTG1KL9eNNGbN+9D+0Et8WtdejQIYoUKULhwoVxd3enTevWrFu3LlNMWFgYZcqUweWu4wfg7u6OR/p3RkpKCkYnLfd/4bPqV6secRtWA5B44hiuefPhlr9AljiXPJ4EtX+SiIXzMr1+69IFbl25lCu52sumPf8Qdz1rXUA80pYCt29/vwD8biZmJ1BAKXW7K0dz4Eh2b2xVBV8p1QHYB6xMX6+mlFpqzb456XpsNH6Bd1pO/AKCiI/N3Dp9+fQJrkdHUq5W/dxOL8dExt8k1O9O5SjELx+RZir42bkcl8DluATqlArLyfRyXJ7gYJKvRmSs34qIJI+FFrLyoz6i9oKfCO/r3F2OACJjrxEaeOcLKCSgABEx1zLFRMRcIyQgf6aYyFhTzNkrkew+epLu74/n+Y8mcfDk2VzJ2xqx0ZEEBIdkrAcEBhMXFZUlbtWSXxnQ60l+njmFF18fmGX79g1rCC9VFncnvPCOi4nCP+hOGf0Dg4iLzlrG1UsXMujFp1jw7TR6vmZqUbyVnMTyBT/R6Vnn/pxGRsUQGnyn+19IUCCR0ZnPqZHR0Rkxbm6u5Mubl2vX4wG4dOUqT738Gi++/i679x/MvcRtEBkZSWjonbvjwSEhRERmOwFFhqtXr/JUt260btOGl1580ela7+G/8Vl19w8g9a7PZmpMNO7+gVniQp95nshlizCmJOdmekI8qLFAK6XUCaBV+jpKqVpKqW8B0vvavwusVkodBBTwTXZvbG0L/seYpuW5lv7L9mGas9MipVRfpdQupdSumTOtHWuQDXOtJ3e1QhiNRv78cSrtXqx20EsAABjiSURBVHgtZ36fg5hrI1IoM6/e36r9J2lZuQSuLk4+WZK5opk51oeHDGNH157sebEv+WtUI7RDO/vn9hDMtXKqe1oItZmjfTvCYDAQfzOReWPe493nn2TgZ985Tcup2TRU1gPZunM3vvx5ET37vs6in37ItO3CmdP8MnMqr7xtaVYwBzN7uslaxhYduzL+x9/o1vs1lv3yIwCLZ3/L412ewdPL285JPhzzn797PqMWPsdBAf789dtP/Pb9NN57ox+DRo7lxk3na1205u/wfkJDQ/nt119ZtnQpS5ctIyYmJifTyxn/gc+qufPLvSciz2Il8AgtRPzOLbmUlHA0g3b8v4ehtY7RWrfQWpdO/xmb/vourXWfu+L+0lpX0VpX1lq/qLXO9ra3tX3w07TW1205Kd4z6EAvPHjZ6n0t8QsI4nr0nZaX6zFR+Ba405UjJSmRiAtn+Gb4WwDcuBbLnHEf8NzgTylcyqG9i7I1b8shFu0w9S2vWDiIq9dvZGyLuH6DIF/bT74r959kSKdGOZZjTgrr/hSFuppmg0o4fATP0P9v787DpKrOPI5/f3SDIEsr0ODGCLILCrJoAFFcAxP3HU0Ik3FGiXGN44Zx1xiNGpVBEyNiGDdUMI4LSFRQMQoqqybiiCj7JgqoIHS/88c5RRdNdVcD3VXV5ft5nn666ta9t86pe+rc97733FstSQxe2aVlCzamyAZ/vyJMK/n2W5a9NJEmXfffMrQnVzz+8hSe/lsYNnZAu31ZtqrsIuDlq9fQomnRVvPvUS6rv3z1Goqb7rbltWMO6R6GarVvTR2JNWvX0zQHhuo0K27B6hVlZ11Wr1rB7s23zaYl9D3iGP78h9+Vzb9yOXddfwW/vPp69th7nxot647avXkxX64sq+OXq1ayW7OK63jIgKMZc/+dAMz/50e899brjH34v/l2/XrqSNStV4+jTzytxsu9PVoWN2fZirLv2vKVqyguNzyuZXExy1asZI8WxWzeXML6b76hqEljJG0ZvtKlY3ta7bUXCxYupmunDhmtQzotW7Zk2bJlW56vWL6cFsXb3rQgnRYtWtC2bVs++OCDLRfh5op8bavNfnwczY4aCMC3n86jbvPmEO+/ULdZczat2fpgq2GHzuzaph2dR4yGggIKi4poe/3v+PTGKzNccueyr6oB/lxJZwMFktoDFwEZP0Teu10nVi1dzJfLl9KkaXNmT32NMy+5dsvr9Rs24tpHyoYvPXTdJQwaMizng3uAs/p25ay+XQF44x+f89TbcxnYrR1zvlhBo/r10o61L2/Byq9Y+91Guu3bMv3MWbD4qWdY/NQzADTr3499Bp/O8pdfocmBXSlZt57vV23dcauggMLGjdj01deosIDmhx/Kl+9Mz0bRK3X2oMM5e9DhAEx5fw6PvTyFfz20F7M/WUDjXRtQvPvWAX7x7kU0bLALs+Z9xoHtW/PXKe9yzqABABx58IG8O+djDu7agQVLlrNp82Z2b9Ko/FtmRdtOnVm2eCErli6hafNi3n5tEhcNv3mreZYu+oI99wl3DZrxzlT23LsVAN+sX8ftV1/G4HN/Saeu3TJe9qpq07EzKxYvYuWyJezerJhpk//GeVfdsNU8yxYvZI9Yr9nT3qZlfHzN3WUXnj435s/sUn/XnAiYyuvaqSNfLFrMoiXLaFncjJdfncwd1299RuWIQ3/EXydMonvX/Xll8psc0qMbkvhyzVcUNWlMQUEBC5cs5YtFi2m1V6obRWRXly5d+OKLL1i0eDEtW7RgwsSJ/Pa226q07PLlyykqKqJ+/fqsXbuWmTNnMuRnP6vhEm+/fG2rqye+wOqJ4QL8xgf1pvnA4/lq6hR2bd+J0m+/YfNXW99FbfWkF1k96UUA6ha3oM2VN3pwn+dy4TaZuaqqAf6FhNv5bASeACYCN1e6RA0oKCjghHMv4pFbrsBKS+l55CBatmrDpCdHsU/bjnTu3a/S5e8YdhYbv/uWks2b+GjaW/zbb+7c5g48uaB/p3/hrY+/4Pg7nqB+vUJuPH3AltfO+MPTjL0k3Kbtnpf+zssz/o8NmzZz7K1jOPngTgw7pjcAL8/8hIHd2m3XqehsWf3mVJr170ufF8dRsmED//hNWdPqPfZ/mH7GT1G9unR78D7qFBZCnQLWvDuNJc8+l8VSp3dYj6688cGHDLzgeurvUo9bLygLDE7+9W2Mv+saAK77z8FcM+IvbPx+E/0P6sJhPboAcMqRfbl25BhOuORm6hYWctuFP8+Z7VlQUMgvLryc2668iNKSUgYMOp5WbfZj7CN/ZL8OnenV7zAmPvc0c96fTkFhIQ0bN+aXV14PwITxT7N8ySKeHTOKZ8eMAmD4HfdRtHvTbFZpGwUFhZxzwWXcdc2llJaW0P/Y49i79X6Mf/QhWnfoxEF9+vPq88/w0QfvhTo2asy5l1+bfsU5pLCwgGsuvYDzfn0NJaWlnPyTY2nXpjUj/vwoXTp14IhD+3DKTwZy9S13MOisoRQ1acydN4R2+/6sOYx4+C8UFBRQUKeA6y6/iKImTbJboRQKCwu5+qqrGDZsGKWlpZx04om0a9eO/x45ki7778+AAQOYO3cul152GWvXrmXKG28w8oEHGD9uHPPnz+euu+9GEmbGz4cMoX379tmu0jZ+CG113YzpNOnRm073jaL0+w0sHFl2B6AOd4xg3hW/qnT5Jr37svcvhlHYpIg2V93IhgXzmX9b7foMxvz2PA7r2ZHmuzVi/oTfc9ODf2X0c2+mX9D9IClDY3qrZYhOrjr1gL347rl70s9YizU46VJeO/DgbBejRh05exolc1/NdjFqVEHXo5i5+Kv0M9Zi3ffejbcX5OA46WrSt3UYQrNpxYLsFqQG1W3Rmg3ffZd+xlqsfoMGed1OIbTVWWcMynYxalS3sS9T76DcvkB5Z30/Y1RuZJZSGFHUMesp/F99/XFOfj6VZvAl/S+pr/kEwMxOqPYSOeecc845l0ZtuA9+tqQbovP7jJTCOeecc845Vy0qDfDNbAqApIvN7N7k1yRdDEypwbI555xzzjmXkl9kW7Gq3iD95ymmDa3GcjjnnHPOOeeqQbox+IOBs4E25X65tjGQ31cHOeecc845VwulG4P/NrAUaA7clTR9HTC7pgrlnHPOOedcZfwi24qlG4P/OfA50CczxXHOOeecc87tjHRDdN4ys0MlrWPr22UKMDPLvV81cc4555xzec8vsq1YuiE6DQHMrHEGyuKcc84555zbSenuouOHRs4555xzztUi6TL4LSRdVtGLZnZ3NZfHOeecc865tPwi24qlC/ALgEaEMffOOeecc865HJcuwF9qZjdlpCTOOeecc865nZYuwPfMvXPOOeecyzl+F52KpbvI9qiMlMI555xzzjlXLdL90NWXmSqIc84555xzVVWa7QLksHQZfOecc84551wt4gG+c84555xzeSTdRbbOOeecc87lHL/ItmKewXfOOeeccy6PeAbfOeecc87VOv5LthXzDL5zzjnnnHN5xAN855xzzjnn8ogsMxco+EkU55xzzrnaR9kuQEWurLdf1uPL330/Pyc/n0xl8JXpP0nnZeN9vX5eR6+j1zHf/vK9fl7H/PnzOtbIn6uF8nmIzn9muwA1LN/rB17HfOF1rP3yvX7gdcwXXscfkBLL/l+uyucA3znnnHPOuR8cD/Cdc84555zLI/l8H/w/ZbsANSzf6wdex3zhdaz98r1+4HXMF17HHxD/JduKZeouOs4555xzzlWbSwvbZD2IvWfzZzl5IXI+Z/Cdc84551yeyuWLXLMtJ8bgSxou6UNJsyXNlHRINazzBElXVVP51lfHeipYd0ms81xJT0vatZJ5b5B0eU2VJRtqYtvnEkknSzJJnbJdluqwPe11O9Y5VNKI6ihfdUuqb+KvdbbLlEqKcla575M0QNILO/n+kyX12sFld/r9a7uaaGeSzpc0JD4eLem0NPP/QtKc2BfPlXRinH6TpKN3tjxVLHPafa2kg2Kf+uPtXTaXSWqWtP2XSVqc9PztOE9rSWcnLfOD/+64imU9gy+pD3Ac0MPMNkpqDtSr4rKFZrY51Wtm9jzwfPWVtMZ8Z2bdASQ9BpwP3J3dImXGzmz7WmQw8BZwFnBDdotSLXa4vUoqMLOSmixcDdhS3+2RhbruUDmrg6SCDL3PejNrlPR8KNDLzH6VifcvV5bRwAtm9ky56b2AIWZ20Xaustq3n5k9WNV5Je0DDCf0xV9LagQUx/VcV53lqgaJPnUwMDHLZak2ZrYaSPStNwDrzez35WZrDZwNPJ7RwrlaKRcy+HsCq8xsI4CZrTKzJZIWxIAPSb0kTY6Pb5D0J0mvAH+R9K6kLomVxUxSz0RWUFJRXFed+PqukhZKqiupraQJkt6X9GYiyyqpjaS/S5ou6eYMfhZvAu1iGYbETMosSWPKzyjpP2L5Zkl6NpFJlXR6zL7MkvRGnNZF0rSYCZgtqX0G61SZirZ9T0lT4naZKGlPSYWxvgMAJP1W0q3ZLHw6cSfZD/h3QoCPpDqSRiqctXhB0kuJzFqqemex+FWR3F6fi+X+UNKWezRLWh8zgO8CfST1lvR2bJ/TJDWOs+4Vv4ufSLojC3WpsphFe1PSB/Gvb5w+QNLrkh4H5sRpP0367v0xU8FwUlkXSLot9mfvSeoR29anks5PmrWJpPGSPpL0YFJ/+UBc7kNJN5Zb73WS3gJOT5peR9Kjkm6Jz4+N7/2BwhmfRnH6QEn/jMufkpEPIwPM7L0dCO5TStPOpkgaK2mepNslnRPb2RxJbeN825zxlXSUpPFJz4+RNA5oAawD1sd6rDezz+I8oyWdprAfTmSU50iy+HrK/ehO1n1PSW+o7Gxh/zhdwGnAUOBYSfVTLCtJd8bl5kg6M+lzmyzpmdj2Hovry/m+V2VnJ24H+sfP5dJy8zSUNEphPzlD8QxMvisxy/pfrsqFAP8VoFXsqEZKOrwKy/QETjSzs4EngTMgdArAXmb2fmJGM/samAUk1ns8MNHMNhGuRL/QzHoClwMj4zz3Ag+YWW9g2U7XsAokFQKDgDkKByzDgSPNrBtwcYpFxplZ7/j6PwhBJMB1wI/j9BPitPOBe2OGqBewqAarsj222faS6gL3A6fF7TIKuDWeqRkKPCDpGGAgcGNFK84RJwETzGwe8KWkHoRgpjVwAHAu0Aegonpno9BVkdxe46RfxHL3Ai6S1CxObwjMNbNDgGnAU8DFsX0eDXwX5+sOnEn4XM6U1CozNUmrQVJQkwiMVgDHmFkPQpnvS5r/YGC4me0vqXN8vV/87pUA52SgnDMTQU200Mz6EA7IRhMCpB8BN5Ur968Jn39byoLu4WbWCzgQOFzSgUnLbDCzQ83syfi8EHgMmGdm1yokaK4Fjo6f1XvAZTEoe4jQF/cH9tjZyks6XiHZM0PS3yS1jNMPT/pMZqjsgDLVOq6IAeEsSbfHaSkTKdHRMaCdJ+m4OP+WIRMxwB4Vg8r5kioL/Le3nSX2CwcAPwM6mNnBwJ+BCyt5n9eAzpKK4/N/Ax4h7COXA59JekTS8eUXjAcv3WNbngAksssV7Ud3xtmE/XR3Ql1nxun9gM/M7FNgMvCvKZY9hdCfJPqYO5MC9oOAS4D9gf2AfrWs770KeDNuh3vKvTYceC3GLUcQ6t0w4yV0OSPrQ3TMbL2knoSO/gjgKaUfP/q8mSUCg7HAJOB6QqD/dIr5nyJ0kK8TMqkjFTJJfYGn40E8wC7xfz/g1Ph4DPC77a3XdmggKdF5vQk8DJwHPGNmqwDM7MsUy3VVyJLtBjSi7FTlVGC0pLHAuDjt78BwhdOw48zsk5qpyvZJte2BW4CuwKS4XQqApXH+DxXOZvwv0MfMvs9KwatuMPCH+PjJ+Lwu8LSZlQLLJL0eX+9IBfXOManaK4Sg/uT4uBXQHlhNCGqfjdM7AkvNbDqAma0FiPV9NR6MI+kjYF9gYc1WpUpSDZ2oC4yQlAjaOyS9Ni2R+QSOIiQjpsc6NiAEbZkqZ0JiqOIcoJGZrQPWSdogabekcs8HkPQEcCjwDHCGwhmZQsIZt/2B2XGZp8q9zx+BsWaWCI5+FOefGutfj9AXdSIEaZ/E9/sfqvbLnMltD6BpUt3eAn5kZibpXOAKwgHL5cAFZjY19vkbUq1Y0iDCAfkhZvatpKbxpXFm9lCc5xZCIuX++FprQuKoLfC6pHYpVt2J0Lc1Bj6W9EBMLpW3ve1supktjeX6lJAsgbCNj0hVR4D4+YwBfirpEUKCYYiZlUgaCPQmtNt7JPU0sxvKr0PSGUAPQga9sv3ozpgOjIrB93Nmltjugwl9KfH/zyjbzyUcCjwRh8gtlzQl1mstoZ0vivWYSdiGX1E7+t50jgVOUNlZm/rAvxASgO4HKOsBPkD8Ik4GJkuaA/wc2EzZGYbyp+G+SVp2saTVMbN0JiE4Lu954Lex0+5JyGI0BL6qZKeYqfMu23TsCr1MuvcfDZxkZrMUxqIOADCz8xUuVP0JMFNSdzN7XGGIxE+AiZLONbPXqrkeOyTFtr8A+DBmHFM5gNAht8xMCXdMzGAfSTgQM8JOw4DxFS1C5fXOFana6wBCpqxPDI4mU/ad3ZA0Fr2ydr0x6XEJOdI3VeBSQrazG6GPSg4av0l6LOBRM7s6g2VLJfHZlrL151xK2edcfruYpDaEALm3ma1RGHee3Bd/U26Zt4EjJN1lZhsI9Z9kZoOTZ4oB6470r1u1vdjvJS7s3YeQHNqTcCCROMiaCtytcL3IuERwl8LRwCNm9i1slVSpKJEC4WCmFPhE0nxCMF/ei3EI4kZJKwj9VlXPoFbWzspvx+RtnO678wghSbKBkGzYDCH4J5xlmyZpUpzvhuQFFc4u3wgcFg8K6lD5fnSHmNkbkg4j7LPGSLqTcHboVEIQO5zQvppJahwPWrcUs5JVp+pnakvfm46AU83s42wXJJP8LjoVy/oQHUkdtfWY8O7A58ACQjAOZdn0ijxJyNgUmdmc8i+a2XpCx3Uv4cKokpg9/EzS6bEcktQtLjKVOGaamjulXplXCZmzZrFsTVPM0xhYGjMcW8ooqa2ZvRsvjFpFGAKzHzDfzO4jHOwcmGJ9GVfBtv8HUKxwAS4K10p0iY9PAZoBhwH3JWUfc9FpwF/MbF8za21mrQhBxyrgVIWxyi2JB2bAx1RQ71qgCFgTg/tOhMxtKv8kjLXvDSCpscJQn9qmiHAmopSQQaxoXP2rwGmSWkD4HkvaN0Nl3F4HK1x7VIeQKHkLaEII4r+ObXVQmnU8DLxEyOYWAu8QhkAkrtPYVVIHQjtoozhWnJCV3Vn3AyPM7ABCkqc+gJndThgK1wB4RxWPD6/o4HM08Ku43hvZ+gBnm4OiFMvvzIFrVdvZdjGzJcASwvCp0QCS9lIYQpiQ2A9vIamIsK8dYmYr47oq24/usPg9WRHPnjxMOGNwNDDLzFrFPnVfwtnBk8ot/gZhmF+BwlCkwwj7/4rUpr53HWHfn8pE4MKYIETSQRkrlctJubBzbQTcH4O1zcD/EU7XdgYelnQN8G6adTxDCN4ruyD2KcLwnQFJ084hjOm+lnA69EnCWMSLgcclXUzZ8IKMiUNRbgWmSCoBZhDGnyf7DeFz+ZxwWjbxpb8zBs0iBBizCOP2fippE+GagpvIDRVt+z8RAvgiQhv9g6TlhAuMjjKzhQq3VbyXcLYnFw0mlDfZs4R2vQiYC8wjbMOvzex7hYttt6o38GHmirzDJgDnS5pN2Fm+k2qmWMczCdu8AWH8fUZuv1fNRgLPxqDmdbbNZANgZh/FvuWVGDhvIpyh+jzV/Dup/PCVCWa2PbcJ/juhvR5ACJDGm1mppBmENjifkPiolJndHdvvGEL/OhR4QlJi2Ma1ZjZPYdjPi5JWEQ4mum5HWVMpAhbHx1v6hJjwmEO4tqkPIcv+zxTLvwJcJ+nxxBCdmMUvn0hZnLTM6ZIeBdoQxnN/TMUHtzuiSu1sBz0GFJvZR/F5XeD3kvYiZPZXEq7dSnYSYejcQzGGJGbuK9qP7owBwH/FfdZ6YAhhGG75M6DPAsMI7S1hPGHo0SzCQdcVZrasooO7Wtb3zgY2S5pFODibkfTazYRyz45B/gLCXery2oO2ICd/ZCoX+C/ZOpdhkhrF6w+aETJL/cwsIxdzO1dbqZLbZCrcMeQeQgD+DmFY0QBJ9xPGpJcAHwFD45CZVOu/ihBIfg+8ZGbXSBpGODu8JZFiZkMVhiutIQwRaglcZmYvKAxXu9zMjlO5Wx1KmgscZ2YLqvNz2RExQTLDzB5OO7NzrlbyAN+5DFMYo74bYazwHWY2OqsFcs79YEh6n3A24JiKDnacc7WfB/jOOeecc87lkVwYg++cc87VOEkHsPV4bYCNFn6nwTnn8oZn8J1zzjnnnMsjWb9NpnPOOeecc676eIDvnHPOOedcHvEA3znnnHPOuTziAb5zzjnnnHN5xAN855xzzjnn8sj/AzTKabpkouiGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = plt.cm.RdBu\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.heatmap(train.astype(float).corr(),linewidths=0.1,vmax=1.0, \n",
    "            square=True, cmap=colormap, linecolor='white', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kde.py:488: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in true_divide\n",
      "\n",
      "/opt/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in double_scalars\n",
      "\n",
      "/opt/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py:26: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in reduce\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f61ffb97da0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 829.475x777.6 with 90 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(train[[u'Survived', u'Pclass', u'Sex', u'Age', u'Parch', u'Fare', u'Embarked',\n",
    "       u'FamilySize', u'Title']], hue='Survived', palette = 'seismic',size=1.2,diag_kind = 'kde',diag_kws=dict(shade=True),plot_kws=dict(s=10) )\n",
    "g.set(xticklabels=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntrain = train.shape[0]\n",
    "ntest = test.shape[0]\n",
    "SEED = 0 # for reproducibility\n",
    "NFOLDS = 5 # set folds for out-of-fold prediction\n",
    "kf = KFold(ntrain, n_folds= NFOLDS, random_state=SEED)\n",
    "\n",
    "# Class to extend the Sklearn classifier\n",
    "class SklearnHelper(object):\n",
    "    def __init__(self, clf, seed=0, params=None):\n",
    "        params['random_state'] = seed\n",
    "        self.clf = clf(**params)\n",
    "\n",
    "    def train(self, x_train, y_train):\n",
    "        self.clf.fit(x_train, y_train)\n",
    "\n",
    "    def predict(self, x):\n",
    "        return self.clf.predict(x)\n",
    "    \n",
    "    def fit(self,x,y):\n",
    "        return self.clf.fit(x,y)\n",
    "    \n",
    "    def feature_importances(self,x,y):\n",
    "        print(self.clf.fit(x,y).feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Need to be analysed\n",
    "\n",
    "def get_oof(clf, x_train, y_train, x_test):\n",
    "    oof_train = np.zeros((ntrain,))\n",
    "    oof_test = np.zeros((ntest,))\n",
    "    oof_test_skf = np.empty((NFOLDS, ntest))\n",
    "\n",
    "    for i, (train_index, test_index) in enumerate(kf):\n",
    "        x_tr = x_train[train_index]\n",
    "        y_tr = y_train[train_index]\n",
    "        x_te = x_train[test_index]\n",
    "\n",
    "        clf.train(x_tr, y_tr)\n",
    "\n",
    "        oof_train[test_index] = clf.predict(x_te)\n",
    "        oof_test_skf[i, :] = clf.predict(x_test)\n",
    "\n",
    "    oof_test[:] = oof_test_skf.mean(axis=0)\n",
    "    return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest parameters\n",
    "rf_params = {\n",
    "    'n_jobs': -1,\n",
    "    'n_estimators': 500,\n",
    "     'warm_start': True, \n",
    "     #'max_features': 0.2,\n",
    "    'max_depth': 6,\n",
    "    'min_samples_leaf': 2,\n",
    "    'max_features' : 'sqrt',\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "# Extra Trees Parameters\n",
    "et_params = {\n",
    "    'n_jobs': -1,\n",
    "    'n_estimators':500,\n",
    "    #'max_features': 0.5,\n",
    "    'max_depth': 8,\n",
    "    'min_samples_leaf': 2,\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "# AdaBoost parameters\n",
    "ada_params = {\n",
    "    'n_estimators': 500,\n",
    "    'learning_rate' : 0.75\n",
    "}\n",
    "\n",
    "# Gradient Boosting parameters\n",
    "gb_params = {\n",
    "    'n_estimators': 500,\n",
    "     #'max_features': 0.2,\n",
    "    'max_depth': 5,\n",
    "    'min_samples_leaf': 2,\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "# Support Vector Classifier parameters \n",
    "svc_params = {\n",
    "    'kernel' : 'linear',\n",
    "    'C' : 0.025\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)\n",
    "et = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)\n",
    "ada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)\n",
    "gb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)\n",
    "svc = SklearnHelper(clf=SVC, seed=SEED, params=svc_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['Survived'].ravel()\n",
    "train = train.drop(['Survived'], axis=1)\n",
    "x_train = train.values # Creates an array of the train data\n",
    "x_test = test.values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:305: UserWarning:\n",
      "\n",
      "Warm-start fitting without increasing n_estimators does not fit new trees.\n",
      "\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:305: UserWarning:\n",
      "\n",
      "Warm-start fitting without increasing n_estimators does not fit new trees.\n",
      "\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:305: UserWarning:\n",
      "\n",
      "Warm-start fitting without increasing n_estimators does not fit new trees.\n",
      "\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:305: UserWarning:\n",
      "\n",
      "Warm-start fitting without increasing n_estimators does not fit new trees.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training is complete\n"
     ]
    }
   ],
   "source": [
    "et_oof_train, et_oof_test = get_oof(et, x_train, y_train, x_test) # Extra Trees\n",
    "rf_oof_train, rf_oof_test = get_oof(rf,x_train, y_train, x_test) # Random Forest\n",
    "ada_oof_train, ada_oof_test = get_oof(ada, x_train, y_train, x_test) # AdaBoost \n",
    "gb_oof_train, gb_oof_test = get_oof(gb,x_train, y_train, x_test) # Gradient Boost\n",
    "svc_oof_train, svc_oof_test = get_oof(svc,x_train, y_train, x_test) # Support Vector Classifier\n",
    "\n",
    "print(\"Training is complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:305: UserWarning:\n",
      "\n",
      "Warm-start fitting without increasing n_estimators does not fit new trees.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.13270218 0.21664601 0.04092846 0.0233126  0.08279594 0.02645058\n",
      " 0.06615848 0.08163996 0.01387697 0.31548882]\n",
      "[0.13403898 0.37882753 0.03204044 0.01591197 0.05892698 0.02958375\n",
      " 0.08687922 0.05069845 0.02048394 0.19260873]\n",
      "[0.04  0.22  0.022 0.156 0.03  0.02  0.024 0.084 0.008 0.396]\n",
      "[0.11566594 0.05980307 0.13675106 0.06996023 0.16746877 0.11229179\n",
      " 0.06299664 0.12862592 0.02762615 0.11881044]\n"
     ]
    }
   ],
   "source": [
    "rf_feature = rf.feature_importances(x_train,y_train)\n",
    "et_feature = et.feature_importances(x_train, y_train)\n",
    "ada_feature = ada.feature_importances(x_train, y_train)\n",
    "gb_feature = gb.feature_importances(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_features = [0.13570676, 0.21529563, 0.04069253, 0.02189311, 0.08459389, 0.02679918,\n",
    " 0.06244747, 0.08447483, 0.0137213,  0.31437532]\n",
    "et_features = [0.13234027, 0.37804863, 0.03154898, 0.01664447, 0.06114939, 0.02931924,\n",
    " 0.08480663, 0.05274789, 0.02061124, 0.19278327]\n",
    "ada_features = [0.04,  0.204, 0.024, 0.188, 0.032, 0.018, 0.022, 0.096, 0.004, 0.372]\n",
    "gb_features = [0.13052771, 0.05286675, 0.10816774, 0.05644885, 0.16589314, 0.13315788,\n",
    " 0.05803928, 0.14022956, 0.04328276, 0.11138633]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = train.columns.values\n",
    "# Create a dataframe with features\n",
    "feature_dataframe = pd.DataFrame( {'features': cols,\n",
    "     'Random Forest feature importances': rf_features,\n",
    "     'Extra Trees  feature importances': et_features,\n",
    "      'AdaBoost feature importances': ada_features,\n",
    "    'Gradient Boost feature importances': gb_features,\n",
    "    })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
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       "<div id=\"36ab1226-014c-4475-b541-8c1050cdce7d\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"36ab1226-014c-4475-b541-8c1050cdce7d\", [{\"marker\": {\"color\": [0.04, 0.204, 0.024, 0.188, 0.032, 0.018, 0.022, 0.096, 0.004, 0.372], \"colorscale\": \"Portland\", \"showscale\": true, \"size\": 25, \"sizemode\": \"diameter\", \"sizeref\": 1}, \"mode\": \"markers\", \"text\": [\"Pclass\", \"Sex\", \"Age\", \"Parch\", \"Fare\", \"Embarked\", \"Has_cabin\", \"FamilySize\", \"IsAlone\", \"Title\"], \"x\": [\"Pclass\", \"Sex\", \"Age\", \"Parch\", \"Fare\", \"Embarked\", \"Has_cabin\", \"FamilySize\", \"IsAlone\", \"Title\"], \"y\": [0.04, 0.204, 0.024, 0.188, 0.032, 0.018, 0.022, 0.096, 0.004, 0.372], \"type\": \"scatter\", \"uid\": \"0463a4c8-c1b1-11e8-a56d-ace0102288e3\"}], {\"autosize\": true, \"hovermode\": \"closest\", \"showlegend\": false, \"title\": \"AdaBoost Feature Importance\", \"yaxis\": {\"gridwidth\": 2, \"ticklen\": 5, \"title\": \"Feature Importance\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
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     },
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   ],
   "source": [
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['Random Forest feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['Random Forest feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Random Forest Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')\n",
    "\n",
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['Extra Trees  feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['Extra Trees  feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Extra Trees Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')\n",
    "\n",
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['AdaBoost feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['AdaBoost feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'AdaBoost Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')\n",
    "\n",
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['Gradient Boost feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['Gradient Boost feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Gradient Boosting Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>features</th>\n",
       "      <th>Random Forest feature importances</th>\n",
       "      <th>Extra Trees  feature importances</th>\n",
       "      <th>AdaBoost feature importances</th>\n",
       "      <th>Gradient Boost feature importances</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>0.135707</td>\n",
       "      <td>0.132340</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.130528</td>\n",
       "      <td>0.109644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>0.215296</td>\n",
       "      <td>0.378049</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.052867</td>\n",
       "      <td>0.212553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.040693</td>\n",
       "      <td>0.031549</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.108168</td>\n",
       "      <td>0.051102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parch</td>\n",
       "      <td>0.021893</td>\n",
       "      <td>0.016644</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.056449</td>\n",
       "      <td>0.070747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fare</td>\n",
       "      <td>0.084594</td>\n",
       "      <td>0.061149</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.165893</td>\n",
       "      <td>0.085909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.026799</td>\n",
       "      <td>0.029319</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.133158</td>\n",
       "      <td>0.051819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   features  Random Forest feature importances  \\\n",
       "0    Pclass                           0.135707   \n",
       "1       Sex                           0.215296   \n",
       "2       Age                           0.040693   \n",
       "3     Parch                           0.021893   \n",
       "4      Fare                           0.084594   \n",
       "5  Embarked                           0.026799   \n",
       "\n",
       "   Extra Trees  feature importances  AdaBoost feature importances  \\\n",
       "0                          0.132340                         0.040   \n",
       "1                          0.378049                         0.204   \n",
       "2                          0.031549                         0.024   \n",
       "3                          0.016644                         0.188   \n",
       "4                          0.061149                         0.032   \n",
       "5                          0.029319                         0.018   \n",
       "\n",
       "   Gradient Boost feature importances      mean  \n",
       "0                            0.130528  0.109644  \n",
       "1                            0.052867  0.212553  \n",
       "2                            0.108168  0.051102  \n",
       "3                            0.056449  0.070747  \n",
       "4                            0.165893  0.085909  \n",
       "5                            0.133158  0.051819  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_dataframe['mean'] = feature_dataframe.mean(axis= 1) # axis = 1 computes the mean row-wise\n",
    "feature_dataframe.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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          "reversescale": false,
          "showscale": true
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         "opacity": 0.6,
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         "width": 0.5,
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       "layout": {
        "autosize": true,
        "hovermode": "closest",
        "showlegend": false,
        "title": "Barplots of Mean Feature Importance",
        "yaxis": {
         "gridwidth": 2,
         "ticklen": 5,
         "title": "Feature Importance"
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      "text/html": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = feature_dataframe['mean'].values\n",
    "x = feature_dataframe['features'].values\n",
    "data = [go.Bar(\n",
    "            x= x,\n",
    "             y= y,\n",
    "            width = 0.5,\n",
    "            marker=dict(\n",
    "               color = feature_dataframe['mean'].values,\n",
    "            colorscale='Portland',\n",
    "            showscale=True,\n",
    "            reversescale = False\n",
    "            ),\n",
    "            opacity=0.6\n",
    "        )]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Barplots of Mean Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='bar-direct-labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>RandomForest</th>\n",
       "      <th>ExtraTrees</th>\n",
       "      <th>AdaBoost</th>\n",
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       "   RandomForest  ExtraTrees  AdaBoost  GradientBoost\n",
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     },
     "execution_count": 31,
     "metadata": {},
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    }
   ],
   "source": [
    "base_predictions_train = pd.DataFrame( {'RandomForest': rf_oof_train.ravel(),\n",
    "     'ExtraTrees': et_oof_train.ravel(),\n",
    "     'AdaBoost': ada_oof_train.ravel(),\n",
    "      'GradientBoost': gb_oof_train.ravel()\n",
    "    })\n",
    "base_predictions_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
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     "metadata": {},
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   ],
   "source": [
    "\n",
    "\n",
    "data = [\n",
    "    go.Heatmap(\n",
    "        z= base_predictions_train.astype(float).corr().values ,\n",
    "        x=base_predictions_train.columns.values,\n",
    "        y= base_predictions_train.columns.values,\n",
    "          colorscale='Viridis',\n",
    "            showscale=True,\n",
    "            reversescale = True\n",
    "    )\n",
    "]\n",
    "py.iplot(data, filename='labelled-heatmap')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.concatenate(( et_oof_train, rf_oof_train, ada_oof_train, gb_oof_train, svc_oof_train), axis=1)\n",
    "x_test = np.concatenate(( et_oof_test, rf_oof_test, ada_oof_test, gb_oof_test, svc_oof_test), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning:\n",
      "\n",
      "The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gbm = xgb.XGBClassifier(\n",
    "    #learning_rate = 0.02,\n",
    " n_estimators= 2000,\n",
    " max_depth= 4,\n",
    " min_child_weight= 2,\n",
    " #gamma=1,\n",
    " gamma=0.9,                        \n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'binary:logistic',\n",
    " nthread= -1,\n",
    " scale_pos_weight=1).fit(x_train, y_train)\n",
    "predictions = gbm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "StackingSubmission = pd.DataFrame({ 'PassengerId': PassengerId,\n",
    "                            'Survived': predictions })\n",
    "StackingSubmission.to_csv(\"StackingSubmission.csv\", index=False)"
   ]
  }
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