diff --git a/Health/Risk prediction of heart disease/notebook.ipynb b/Health/Risk prediction of heart disease/notebook.ipynb
deleted file mode 100644
index bd7e8fca834ed65032866a5b898b852575e396b7..0000000000000000000000000000000000000000
--- a/Health/Risk prediction of heart disease/notebook.ipynb
+++ /dev/null
@@ -1,2752 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "%matplotlib inline\n",
- "#Ziel: Vorhersage, ob der Patient ein Risiko hat an koronare Herzkrankheit zu erkranken. Zielvariable ist TenYearCHD."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Risk_prediction_of_heart_disease_data.csv')\n",
- "#Quelle: https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset"
- ]
- },
- {
- "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>male</th>\n",
- " <th>age</th>\n",
- " <th>education</th>\n",
- " <th>currentSmoker</th>\n",
- " <th>cigsPerDay</th>\n",
- " <th>BPMeds</th>\n",
- " <th>prevalentStroke</th>\n",
- " <th>prevalentHyp</th>\n",
- " <th>diabetes</th>\n",
- " <th>totChol</th>\n",
- " <th>sysBP</th>\n",
- " <th>diaBP</th>\n",
- " <th>BMI</th>\n",
- " <th>heartRate</th>\n",
- " <th>glucose</th>\n",
- " <th>TenYearCHD</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>39</td>\n",
- " <td>4.0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>195.0</td>\n",
- " <td>106.0</td>\n",
- " <td>70.0</td>\n",
- " <td>26.97</td>\n",
- " <td>80.0</td>\n",
- " <td>77.0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>0</td>\n",
- " <td>46</td>\n",
- " <td>2.0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>250.0</td>\n",
- " <td>121.0</td>\n",
- " <td>81.0</td>\n",
- " <td>28.73</td>\n",
- " <td>95.0</td>\n",
- " <td>76.0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1</td>\n",
- " <td>48</td>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>20.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>245.0</td>\n",
- " <td>127.5</td>\n",
- " <td>80.0</td>\n",
- " <td>25.34</td>\n",
- " <td>75.0</td>\n",
- " <td>70.0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>0</td>\n",
- " <td>61</td>\n",
- " <td>3.0</td>\n",
- " <td>1</td>\n",
- " <td>30.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>225.0</td>\n",
- " <td>150.0</td>\n",
- " <td>95.0</td>\n",
- " <td>28.58</td>\n",
- " <td>65.0</td>\n",
- " <td>103.0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>0</td>\n",
- " <td>46</td>\n",
- " <td>3.0</td>\n",
- " <td>1</td>\n",
- " <td>23.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>285.0</td>\n",
- " <td>130.0</td>\n",
- " <td>84.0</td>\n",
- " <td>23.10</td>\n",
- " <td>85.0</td>\n",
- " <td>85.0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " male age education currentSmoker cigsPerDay BPMeds prevalentStroke \\\n",
- "0 1 39 4.0 0 0.0 0.0 0 \n",
- "1 0 46 2.0 0 0.0 0.0 0 \n",
- "2 1 48 1.0 1 20.0 0.0 0 \n",
- "3 0 61 3.0 1 30.0 0.0 0 \n",
- "4 0 46 3.0 1 23.0 0.0 0 \n",
- "\n",
- " prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose \\\n",
- "0 0 0 195.0 106.0 70.0 26.97 80.0 77.0 \n",
- "1 0 0 250.0 121.0 81.0 28.73 95.0 76.0 \n",
- "2 0 0 245.0 127.5 80.0 25.34 75.0 70.0 \n",
- "3 1 0 225.0 150.0 95.0 28.58 65.0 103.0 \n",
- "4 0 0 285.0 130.0 84.0 23.10 85.0 85.0 \n",
- "\n",
- " TenYearCHD \n",
- "0 0 \n",
- "1 0 \n",
- "2 0 \n",
- "3 1 \n",
- "4 0 "
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "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>male</th>\n",
- " <th>age</th>\n",
- " <th>education</th>\n",
- " <th>currentSmoker</th>\n",
- " <th>cigsPerDay</th>\n",
- " <th>BPMeds</th>\n",
- " <th>prevalentStroke</th>\n",
- " <th>prevalentHyp</th>\n",
- " <th>diabetes</th>\n",
- " <th>totChol</th>\n",
- " <th>sysBP</th>\n",
- " <th>diaBP</th>\n",
- " <th>BMI</th>\n",
- " <th>heartRate</th>\n",
- " <th>glucose</th>\n",
- " <th>TenYearCHD</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>4240.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4135.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4211.000000</td>\n",
- " <td>4187.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4190.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4221.000000</td>\n",
- " <td>4239.000000</td>\n",
- " <td>3852.000000</td>\n",
- " <td>4240.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>0.429245</td>\n",
- " <td>49.580189</td>\n",
- " <td>1.979444</td>\n",
- " <td>0.494104</td>\n",
- " <td>9.005937</td>\n",
- " <td>0.029615</td>\n",
- " <td>0.005896</td>\n",
- " <td>0.310613</td>\n",
- " <td>0.025708</td>\n",
- " <td>236.699523</td>\n",
- " <td>132.354599</td>\n",
- " <td>82.897759</td>\n",
- " <td>25.800801</td>\n",
- " <td>75.878981</td>\n",
- " <td>81.963655</td>\n",
- " <td>0.151887</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>0.495027</td>\n",
- " <td>8.572942</td>\n",
- " <td>1.019791</td>\n",
- " <td>0.500024</td>\n",
- " <td>11.922462</td>\n",
- " <td>0.169544</td>\n",
- " <td>0.076569</td>\n",
- " <td>0.462799</td>\n",
- " <td>0.158280</td>\n",
- " <td>44.591284</td>\n",
- " <td>22.033300</td>\n",
- " <td>11.910394</td>\n",
- " <td>4.079840</td>\n",
- " <td>12.025348</td>\n",
- " <td>23.954335</td>\n",
- " <td>0.358953</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>0.000000</td>\n",
- " <td>32.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>107.000000</td>\n",
- " <td>83.500000</td>\n",
- " <td>48.000000</td>\n",
- " <td>15.540000</td>\n",
- " <td>44.000000</td>\n",
- " <td>40.000000</td>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>0.000000</td>\n",
- " <td>42.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>206.000000</td>\n",
- " <td>117.000000</td>\n",
- " <td>75.000000</td>\n",
- " <td>23.070000</td>\n",
- " <td>68.000000</td>\n",
- " <td>71.000000</td>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>0.000000</td>\n",
- " <td>49.000000</td>\n",
- " <td>2.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>234.000000</td>\n",
- " <td>128.000000</td>\n",
- " <td>82.000000</td>\n",
- " <td>25.400000</td>\n",
- " <td>75.000000</td>\n",
- " <td>78.000000</td>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>1.000000</td>\n",
- " <td>56.000000</td>\n",
- " <td>3.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>20.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>263.000000</td>\n",
- " <td>144.000000</td>\n",
- " <td>90.000000</td>\n",
- " <td>28.040000</td>\n",
- " <td>83.000000</td>\n",
- " <td>87.000000</td>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>1.000000</td>\n",
- " <td>70.000000</td>\n",
- " <td>4.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>70.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>696.000000</td>\n",
- " <td>295.000000</td>\n",
- " <td>142.500000</td>\n",
- " <td>56.800000</td>\n",
- " <td>143.000000</td>\n",
- " <td>394.000000</td>\n",
- " <td>1.000000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " male age education currentSmoker cigsPerDay \\\n",
- "count 4240.000000 4240.000000 4135.000000 4240.000000 4211.000000 \n",
- "mean 0.429245 49.580189 1.979444 0.494104 9.005937 \n",
- "std 0.495027 8.572942 1.019791 0.500024 11.922462 \n",
- "min 0.000000 32.000000 1.000000 0.000000 0.000000 \n",
- "25% 0.000000 42.000000 1.000000 0.000000 0.000000 \n",
- "50% 0.000000 49.000000 2.000000 0.000000 0.000000 \n",
- "75% 1.000000 56.000000 3.000000 1.000000 20.000000 \n",
- "max 1.000000 70.000000 4.000000 1.000000 70.000000 \n",
- "\n",
- " BPMeds prevalentStroke prevalentHyp diabetes totChol \\\n",
- "count 4187.000000 4240.000000 4240.000000 4240.000000 4190.000000 \n",
- "mean 0.029615 0.005896 0.310613 0.025708 236.699523 \n",
- "std 0.169544 0.076569 0.462799 0.158280 44.591284 \n",
- "min 0.000000 0.000000 0.000000 0.000000 107.000000 \n",
- "25% 0.000000 0.000000 0.000000 0.000000 206.000000 \n",
- "50% 0.000000 0.000000 0.000000 0.000000 234.000000 \n",
- "75% 0.000000 0.000000 1.000000 0.000000 263.000000 \n",
- "max 1.000000 1.000000 1.000000 1.000000 696.000000 \n",
- "\n",
- " sysBP diaBP BMI heartRate glucose \\\n",
- "count 4240.000000 4240.000000 4221.000000 4239.000000 3852.000000 \n",
- "mean 132.354599 82.897759 25.800801 75.878981 81.963655 \n",
- "std 22.033300 11.910394 4.079840 12.025348 23.954335 \n",
- "min 83.500000 48.000000 15.540000 44.000000 40.000000 \n",
- "25% 117.000000 75.000000 23.070000 68.000000 71.000000 \n",
- "50% 128.000000 82.000000 25.400000 75.000000 78.000000 \n",
- "75% 144.000000 90.000000 28.040000 83.000000 87.000000 \n",
- "max 295.000000 142.500000 56.800000 143.000000 394.000000 \n",
- "\n",
- " TenYearCHD \n",
- "count 4240.000000 \n",
- "mean 0.151887 \n",
- "std 0.358953 \n",
- "min 0.000000 \n",
- "25% 0.000000 \n",
- "50% 0.000000 \n",
- "75% 0.000000 \n",
- "max 1.000000 "
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train.describe(include='all')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'pandas.core.frame.DataFrame'>\n",
- "RangeIndex: 4240 entries, 0 to 4239\n",
- "Data columns (total 16 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 male 4240 non-null int64 \n",
- " 1 age 4240 non-null int64 \n",
- " 2 education 4135 non-null float64\n",
- " 3 currentSmoker 4240 non-null int64 \n",
- " 4 cigsPerDay 4211 non-null float64\n",
- " 5 BPMeds 4187 non-null float64\n",
- " 6 prevalentStroke 4240 non-null int64 \n",
- " 7 prevalentHyp 4240 non-null int64 \n",
- " 8 diabetes 4240 non-null int64 \n",
- " 9 totChol 4190 non-null float64\n",
- " 10 sysBP 4240 non-null float64\n",
- " 11 diaBP 4240 non-null float64\n",
- " 12 BMI 4221 non-null float64\n",
- " 13 heartRate 4239 non-null float64\n",
- " 14 glucose 3852 non-null float64\n",
- " 15 TenYearCHD 4240 non-null int64 \n",
- "dtypes: float64(9), int64(7)\n",
- "memory usage: 530.1 KB\n"
- ]
- }
- ],
- "source": [
- "train.info()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "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>male</th>\n",
- " <th>age</th>\n",
- " <th>education</th>\n",
- " <th>currentSmoker</th>\n",
- " <th>cigsPerDay</th>\n",
- " <th>BPMeds</th>\n",
- " <th>prevalentStroke</th>\n",
- " <th>prevalentHyp</th>\n",
- " <th>diabetes</th>\n",
- " <th>totChol</th>\n",
- " <th>sysBP</th>\n",
- " <th>diaBP</th>\n",
- " <th>BMI</th>\n",
- " <th>heartRate</th>\n",
- " <th>glucose</th>\n",
- " <th>TenYearCHD</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " male age education currentSmoker cigsPerDay BPMeds \\\n",
- "0 False False False False False False \n",
- "1 False False False False False False \n",
- "2 False False False False False False \n",
- "3 False False False False False False \n",
- "4 False False False False False False \n",
- "\n",
- " prevalentStroke prevalentHyp diabetes totChol sysBP diaBP BMI \\\n",
- "0 False False False False False False False \n",
- "1 False False False False False False False \n",
- "2 False False False False False False False \n",
- "3 False False False False False False False \n",
- "4 False False False False False False False \n",
- "\n",
- " heartRate glucose TenYearCHD \n",
- "0 False False False \n",
- "1 False False False \n",
- "2 False False False \n",
- "3 False False False \n",
- "4 False False False "
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_missingValues = train.isna()\n",
- "train_missingValues.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "male 0\n",
- "age 0\n",
- "education 105\n",
- "currentSmoker 0\n",
- "cigsPerDay 29\n",
- "BPMeds 53\n",
- "prevalentStroke 0\n",
- "prevalentHyp 0\n",
- "diabetes 0\n",
- "totChol 50\n",
- "sysBP 0\n",
- "diaBP 0\n",
- "BMI 19\n",
- "heartRate 1\n",
- "glucose 388\n",
- "TenYearCHD 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_missingValues.sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.heatmap(train.isnull(),yticklabels=False, cbar=False, cmap='viridis')\n",
- "# Die Daten zeigen, dass es nur wenige Zeilen gibt, die keinen Wert haben "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAE9CAYAAACofLHtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAjTElEQVR4nO3dd5ykVZX/8c93QLIjoKiIZMMuIkGMgIoBM/4UFFdFXUyrGHAN67rqKuqqa2AVDCgKGFlAUDAikg2A5CjqKqKo66qEERAEzu+Pc4uu7unpgbq37nRT3/frVa/pp4o+/VDh1H1uOFcRgZmZ9bFoRZ+AmdkkcdI1M+vISdfMrCMnXTOzjpx0zcw6WnmuB3de9GxPbTAzu52Ou+UILesxt3TNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzDpy0jUz68hJ18ysIyddM7OOnHTNzHqKiCY34OWtYvWKvdDiLsRz9nPh58LPxfRby5buyxvG6hV7ocUdZ+yFFnecsRda3HHGXmhxxxm7SVx3L5iZdeSka2bWUcuk++mGsXrFXmhxxxl7ocUdZ+yFFnecsRda3HHGbhJXpYPYzMw6cPeCmVlHTrpmZh056ZqZdVSddCWtLun+LU5mlthrNoy1kqQPtoo3S/wtxxXbzPqTtLakh5TbXVrFrUq6knYBzgW+U463kXRM7UlJ2l7SxcAl5XhrSZ+oiRkRNwPbSVLt+S3DAZLOkLSXpLVbBpb0bEl3Lj+/TdJRkh7UKPaOkvYsP68nadNGcXeVtK+kD0t6ZqOYm0tatfy8k6TXtn6uW5F0X0lHS7pQ0qGSNpjPcUvs1891axD/HpI+K+nb5XgLSS9pEPd+ko6XdGE53krS2yrirSLpEOAycsbCgcBlkg6StErt+da2dN8JPBS4CiAizgU2qYwJ8F/AE4E/lbjnAY9qEPcc4GhJLyhJYVdJuzaIS0TsCDwf2BA4U9KXJe3cIjbw9ohYImlH8nn5HPDJ2qCS3gG8GXhLuetOwBcbxP0E8ArgAuBC4J8kfbw2LnAkcLOk+wCfBTYFvlxxnkskXTN0WzL8b+W5HgR8A9gNOBvYvzLeuOMC3Hk5t1qHAMcC9yrHPwVe1yDugeR7+G8AEXE+8A8V8d5GfhY2jIhtI2IbYCNgZeDtdadKXe0F4PTy7zlD953fYI3zbHHPaxD34FluBzVen70S+YG4gmyp/wTYtTLmOeXf9wHPm/ncVMQ9F9AYXr+LKNMRy/Ei4KIGcc8u/74JeE2r52EcN+Dc2c59vsbt9Jz8eOZrNvP/Zz7EJRsKa8xy/1rAhbXnu/IIeXrYhZKeB6wk6b7Aa4EfVsYE+LWk7YEozfnXUroaakTEntVntgyStgL2BJ4KHAfsEhFnS7oX8CPgqIrwV0j6FPB44D/LJXaLQdAbIyIkBTTtQ7+UbBn8qhxvCJzfIO7fJD0XeBGwS7nvTg3iImlr4JHl8JTI1lKN1SRtS36pAaw+fBwRZ8+zuEjab67HI+K1o8YurpV0V2Dwfns4cHVlTIA/Stp8KO6zgN9VxLslIq6beWdE/GXwWalRtThC0hrAW4EnkC/6scC7I+KvVScl3Q34KJlkBHwX2Dsi/lQZ937kZfk9ImLLkiifHhHvqYlbYp9CXuZ8JSKun/HYCyLiCxWx1wCeBFwQET+TtD7wwIj4buU5vxG4L7Az2Yp+MXBoRMz54bsNcU8GHgKcUe56CPnFcx1ARDx9xLhbkN0WP4qIQ0v/83Mi4v2V57s38DKmvhifCXw6Ika+dJd04hwPR0Q8dj7FLbFvJFt5hwO/ZSqxD4J/btTYJf6DyO6QLcvfWQ94dmT3YU3czci+1+2BK4FfAntExGUjxjsP2IkZ///FiRGx9WhnWuLXJN2FpiSDNwGfiohty30XRsS8nHkgad25Ho+IPzf4Gzsz/UvzlIi4oTLmo+d6PCJOroi9OrBRRFw6aoxZYp4PPCIiri3Ha5KJfatWf2MhKK3QZwPPAW4CDgOOjIgrG8VfFbgZuD/5frsUWFT7fhuKv2aJt6QyzmXALcyedCMiNquJP1L3gqSvU5rysxm1JTMUf7aW1tXAmRFxdEXoNSLiDE2fwHBTRbxble6V9wFbAKsN7q98gc4in2eRl+tXlp/XBi4nB5JGJumgiHgx2R2CpLWAbwGPq4kLPBD4UqsP64BytsyHgFWATSVtA7yr9v1GPqc3Dx3fzOwfuNsXVNoYuDYi/lgupXcEfh4RX5uPccuV5AHkTJwNgOcCF0l6c82V2pAfRcSDyD5/ACSdDVTNxClXKgcDS4ADS4v6X0e9EoyITWrOZ3lG7dP9UNOzWNpqwN8BR5Tj3cgX6iWSHhMRrxsxbuu+n2EHA+8gZ148huzfrfrgRsSmAJIOAI6JiG+V4yeTXS+1rpD0yYh4paR1gG+SXSS17gn8uHygDgKOjTaXVO8kZ8ucBDlbRm2muB0MnC7pq+X4GeTsiJFJ+ney7zkk/Tf5ep0EPFXSTqO+h8cVd8bfeBCZcHcGvk1++dfEuyewATP6n4HFwBo1sYsXR8RHJT0RuDv52TuY7JYc5Xzn/BKo6TcfBJh3N+AEYOWh45XLfSsBF1fE3Qz4Htm3eAXwfWDjRud8Vvn3gqH7Tm0Ze8Z9ZzaK/Z9k6+bHwG4NX0OR09v+G/g58F5g88qYY5ktU+JsRw7Y7g1s2yDexWSLfG3gGspoeHkvjzwCPq64JcY+ZIL9IvC04c9gZdwXASeSLdETh27HUDmzZ/g9QI4DPXPme2SEeMPneM2M4xNqz7dq9sKYLqkhvxXXZGpkc03gXhFxs6Sa/p91IuLxw30/5ZL1V8v7xdvgr5IWAT+T9Goyqd+9QVzIFvrbyA9DAHtQ5jCPYsbc5DPIuYdnkK2nXSOiZqYFkB1fkn4P/J7swlkH+Iqk4yLiX0YMO67ZMpDT535HufqTtFFEXF4R768RcSNwo6T/iTIaHhE3lQGr+RYX8n3wC2Drcntv6YpT/onR+rgjB+A+J2m3iDiy8hxnc5ak75LdbW9RLiS6ZdRgEfGYwc+Szhk+bqF2yljzS+riA8C5kk4q8R5FvgHWJFuqozpQ0osi4gIASf8A/DPw9crzhZzkvQaZCN4NPJb8hm/hueTz/FUy6Z5S7hvVLjOOzyGnXu1S4lclXUmvJf/f/wh8BnhTRPxt8KUEjJp0X0POlrmBXBRxLPlcV5H0GvL5/V+m+nMDqBlIW7t8uQlYPPRFJ6BmSem44kLlGMHyRMSRkp4KPIDpjbR3VYZ+CbAN8IuIuK4MCLaaHtp8pkHtlLGzImI7SRdExAPLfadGxCOX97u3Ifa9gBeQiwvWBH4TEadUxtwM+Aq5cmxH4IXA0yKixVzBsZO0VkT8ZUWfx/JIehfw2YhY6gpC0t9HxEhzriU9OyKOWN59I8T9OfCwqJySOCPmwXM9HiPOGR9X3Dn+3t2AP0VNopiKdQDZMHkM+WX8LOCMiGixFPjpTK1aPTkiWjSkkHR25OBfO5V9KT8gJ+kfBbyanN94aYM+mpeSS0ivJPtRrqdBX0qJfT+yX+xYYPVGMV9ELsm8ttzOBF7YInaJv30558vL8dbAJxrEvTfZev4D2co7Erh3Rbx157o1ON+lVl/Ndt8IcU+kUf/lQr4BDycH5Y4CtiXn0v6+vD+e1CD++TP+XQv4boO47weOJ+eZD2bjvK8i3v7AfuX2h6Gf9wP2qz3f2u6F1zH9kvoxZOux1t7khPrTIuIxkv6O7OQfiaQLmH6ZsC45KHe6JKJiPqakF5LPw+vJxCtyCswHS+zPjxp7yH+Rg1LHQNaikNSiFsXB5GX6s8vxHuW+UWtGDE9xW5/pE+yDHMi83cpsjacAG8yYTriYiil/miri8gvgJEnfJLsuAIiIfUeNPfQ3pk1nIt8bI09nmhG79aX6x4B/I7spTgCeHBGnlc/foZTCVhUGi4auK1eyf6JNl8ZTgG0i4hYASZ8ju8zeMudvLduZQz9XzdyYTW3SDeALwMZMLcc8kLq+MMjBgr9KQtKqEfET1ZWPfFrl+cxlL3LE9LKh+06QtBs5ct8i6RIRv54xv/jmZf23t8N6ETF8uXqIpNeNGizKFDe4dQBi25qTG/Jb8oPwdKZ/CJaQffKjGhRxubzcVim3lppOZxpY1qV65bmuPPgykPSuiDgNoHz+KkMD8A1lVbgPkg2UIM+9hbWBwWKh2r7tw4A7R8T/Dd8p6e7kbIYqtUn3S+QKrwuoGC2cxW/Ki/M14DhJV5IfvJHEUN+ipq+xPzUqlyACi2OW5YYRcZmkxZWxB8ZSi4KcFbEH2YqBHJxr1a/ZbACivEbnSfoy+Z5ttSLtA+SH6w/Dd0q6B21qAsBUS/8pwMHlKqVFBts+IraSdH5E7CPpw1QOgDL9M3z9jMeqX8+IGAx6HinpG2QLvcXipPcB5yiXSA8G3kdt5UJ2I3yHpZ/PncmxoFdWxK7u0/1+bf/GbfgbjyZbOKs0iLU32U/1rnK7gFKtqiLmUnNob8tjt/Nv3I38gvtfso/pi8BdG8TdiOyy+L8S92u0m7fcvPoVObviUuCX5XgbctHIqPE+zSzzRMmB1k82OudBq/ZnZMv0zi3eF0zNWT6NLJW4KvCzypg3ky25JWQyvGbo+G+VsTcAHjz4HJOt/vcCv230PK9f8sT/A+5ZGWuZawFoUC2vdvbC48jW0fFM7wurnuc5DhrDGntJ15GT/5d6CNgsIprtftGSpJWAz0XEHg1jDvpIRV72T+sTjco+UklnkVPxToqp2hnnj/r6Sbo4IrZYxmMXRcQDRj/bW+MsYmo601VlOtMGUVnFTNLbyQGfxwEfp1yqR0R9vdfGSpfVW8nPyarkIoZ9ya63D0RE1apQZZH8E6LMQipXyTvFiMuiJV0SEX9/ex+7rWq7F/Ykl+veialLk+p5nmM0jjX2VS/AXDTGUnuRC03Wk7RK5GT7FoYLXR8447hFd8NNEXF1o/5FmPu1b7V/4OFka/dcuLW+QXUXTsxyqR7zd+rjy4H7R8SfJW1EJt9HRekzbuAdEfHVwUH5cnsHeeU2ij9IemhETOsjl/QQ8qqwSm3S3TrK/NwF4mAar7GPiF+VVuOxEdGiHsKwVzBHqb0GLgN+oNxi6drBnaO2SCNiHwBJO0TED4Yfk7RDxXkOtF6RNtYPV3EA2TjZT9IRwCER8ZNRg0l6bEScoFl2PCmzZeZjg+evUSriRcTlkn7aMOHC7F+QNbntTcDhyi17BgO3DyZnZtXsSAHUJ93TJG0RERfXnkgPEbFvWeW2I5nA9oyIcxrEvVnSdZLu0ri1sT5jLLVHJvLfkm/aQau0RYt0f5auHDXbfbfX8Iq0Q6lfkTbWDxdARHwP+J5yY8PnkgPDvyavBL4YEX+7nSEfRU7nGqweHBisopuPSffeM67a7j58XHPFVpwpaV+mulleQ8VUr8hKhA8FXgX8Y7n7InIBzR+W+Yu3UW2f7iXA5mTR4BuoXKPdg7Ka1oYMfeFEbdWgjHs4Obn8OKa3GmvfUIP4g1J7rwealNprvcJL0iPIhRyvI+cWDywmp9VtPeq5zvg7i8n3WVXd1BLr7uSHa1BT+SLgYy0+XEN/467kHOgXkF9yXyK/+B8YETvdzlhvYGou9OBfys/V/ebjIGnO5fBRXxx9TbJuxPCmB+8ZjN2MGLP5mMdAbUv3SU3OohNJ7ya/uf6HqVZCkIMztb5Zbs2pcam9IW9hqnzmXPfdVquQq4xWZnp/7jXkPNIq5bL/oEFsSVeT82BrWjV/kPTniNhtxt/aOyI+WnXCGecoctzjC+QWToNBo8Mknbns31ymtcq/9ycXEB1NJppdyJoc884gqS7rS75B/GuBf62NMyPmOMY8gMqW7kIj6VKyddH0SRyK33RXA0n7kAs7LiEXWnwnIqrnNQ6t8Nqd7LIYWAxsEREPrYy/ccxSd6FWmX3yqog4tRzvSC6Hrrqy0izr61st7pC0O/m6XaOsFPcgshVWdXWlrKq126C1r6ysdUREzNuG0DKe5+raBmV+7lKJLCq2LipxP0W+Xk3GPAZqW7oLzYXkypVml44DGs+uBmMptcf4VngNrCrp08AmTO/Gqb2iWDJIuCXe9yWN3MWg3OTyeeTrdczQQ3em3SKRt0XE4eUL4onke+STwMMq424EDDcebiSf73lHY1rGPeSNQz+vRm560CLubGMe1SYt6Q5WrlzI9HnFtdu9wHh2NRhLqb0YWuEVWXLxTmSf5hWNBumOIEftP0OD5cqaquR/Rml9HEq2bJ5Deb5H9EOyhu7dgA8P3b+ENrsXw9T//1PJBRdHS3png7hfIJ+PQbnPZwJVfaNjNNYv+Vm6l36g3A+xNu7I9V7mMmndCxcBn2LGsuWo2ChxKPbpEfGw4cvSmon7Q3GfAdyH3JHi2NrzLDEPAPaPiIvKqPqPyOSwLvDGiDh0zgDLj39WRGzX4FQH8U6c4+Fo0IIeLP19SDk8o9VAWplDewU5yLMdubz2jBaDiuXLaHjb+OqZOONSBqY+HxHPH0Ps4Q1cF5HP834RUVOvBUnrkbWfZxYVqnq/TVpL949Rub34HJrvaiDpE+QL/kPg3WVOaXXRbuCREfGK8vOewE8j4hnKvay+zVQthlF9XdJeZNnI4SuKkXYvjsaV+2cqgzkfIlvNAvaX9KaI+EqD8LuTA84fKpP21yenqlUr/cLVM296KANTdx3HwBTTq9vdRM6mqq7RS84yOYwcV3kFWcK1ev72pLV09yWTwDFMTwYtpoytQc4hfUK561hywOSvFTEvJBeg3Fzin9qiBTmjNf5NcgDmkJmPVcT/5Sx3R9Rv4zSOcoZIOg/YedC6LS2c77Wa4mZpXANT46KpTRpuvWKVdHJEPLom7qS1dAfJ5OFD91VNGZO0GvkteB+y2+IRLWYYFDdGxM0AkduQtFqRdpWkp5F9bTtQWgWSVgZWrw0eQyUeW9J4yhlC7pc33J3wJ9otA7YpTQemZluVNyzqV+cNFq78rnzZ/5Ys/F9lolq64yDpMPLFORV4MnBZNNgGu8QeLqYjciHKz6mcvSDpfmT5unsCHxlq5T4ReEJEvKHyvNcgF3FsFBEvL90t94+Ib1TGPT+myhluJWkt4KiIeMJyf3nuuB8gZ4cMulWeQ+5u8OaauDZemn3rolsXjkTEiyvjP438XG9IrqhcDOwTEcfM+YvLMREt3TKd6/zB3FFJ/05OK/kV8NqYpR7u7bBFTO0P91natLwGxlJMJyJ+CjxJ0o4R8f2h+4+V1GIPtoPJfrbty/FvyBkNVUmX8e08EOQA62B5+KeZfjVkDbQemIqyH9yMVXqUn6+WtE1EnDvq+Q41Eq4mr66amJRLqP+gdICXb689yL2UjiE/bDVuXTvfsFthEO9XM29kX9jljRYfzDaouH+DuJtHxAcoz01EXE+bYj0zdx64jFw0UmvniDgqIl4fEf8cWbHqyQ3i2nRfIjea3ZTcfusy4McN4m5HdvGtT9YWfjmwE7n796g7TyPpfpKOL2MrSNqqLHCpEw0KCM/3G3De0M8HkbULBsdVxbaZKvw8s/jzEuCaythj2SgQeATwBuDXZDfA4PbO4eeqIv4Pyb7hs8vx5uQ0qZav6arAXSpjvJLsh7+WnJc7uP2SLEazwt+7d6QbpXg7ZWPK8vPJDeIeC6w1dLwWufPD6sxRkPw2xD2ZnHt/ztB9F9ae70R0LwAq/X/XkUWfPzH02Gqz/8ptExEr1fz+coxro8Cx1kgA3kGe24aSvkQO1v3jqME0vnKGXyanyL2P6Wv3l8SI09tsTmMZmGLp1Xl/I3dAuV7SDcv4ndtijciKY8P3VV/NTkrS/QhZRPoa4JKIOBNA0rbkiqRqkjYHfhMRN0jaidyc8/MRcVVF2LFsFBi5GORkSYfEGGokRMRxks4mW+oC9o6IP1aEHEs5w8gynFeTxYRs/N5TFuO8gamBqRbLzr9Mlpk9uhzvAhyqrD5WU3b2j+VzHQCSnkWDfDExsxeUpRHvTl4+D7ZqXp9MbL9uEP9cshbrJuTlzjHkiP1TKmLeWgxEMwqDzDweMf79yHXrm9CgRsLQct1ZxYjzobUAyxlaX5K2Y2og9PuDhtWIsf6FXBa+MTmouj1wJdnttEfUDbxPTEuXiLhC0ucj4nFD9/1O0vFkl0OtWyLiJuV+TR+JiP0l1S7L3FrSNeQbafXyM+W4qlukaFojgen1C2aqmQ+94MoZ2tLKl/wngXtExJaStgKeHhHvqY0dWX+hVcnTjUusV0XE40uLeVE0qN8ME9LSLQsY1gBOJEc1By2lxcC3o3KjufI3Tie7Md5K1k39paQLI2LLuX9zxWldI2HctADLGdoUZRGaNwGfiqkVkfPyM1Ku2vYnZ1t8kum1WqpWsE5KS/efyN0M7kV+gw2S7jXkFh8t7ElOW/mPknA3JbdKn8+a1kgYJmlLYAumz8f8fGXYBVPO0GY1loGpcYiIsyW9FTiSnH3TbNODiWjpDkh6TUS0mId6hzCuGgnKnVh3IpPut8g5r9+PiKqZEeVDsDv5JTEoZ3hYRLyvJq71IenbwKvJq5MHlYGpl0TEvJoTrdzC6cPAZsBekaVQ28WfpKQLIGl7lh44qm2BIekClq5efzVZR/Q9kVtvT4TyXGxNzm/cWlk28TMRsUuD2AumnKFNJ2kzlh6Yev44ZtDUkPQL4P3AgTGGBDlRSVfSF8hLhXOZGjiKaLB5ZFm/fzM5fQVyN1mRiXfHFgmntTHWSDgjIh4q6Sxy+eQSclL5A+rP2hYqSauS88A3IWs3X0N+/qqqxLUmab2IqC7huCyT0qc78GCyVsI4vml2iIgdho4vkPSDiNhBUvMdRRsZV42EM8ty3QNL/L/QtiaFLUxHA1eRS7h/u2JPZdkGCVfSDuQqzY3JXDkopFPV/TZpSfdCsrJWkwURM6wl6WERcTqApIcyNdVpXg4WkDUSnqPcK4yygqe6RkJE7FV+PEDSd4DFEdFq+xtbuO69wGaafJZcvHEWbaZUApOXdO8GXCzpDNrvkfZS4KCy3FjkpdNLyxy/+TrQc6NyB+PBipvNGXpeRlVWBh0GHF07kdzuUH4o6YERccGKPpHb6OqI+HbroJPWpztrxfdosEfa0N+4C/m8XtUq5rhI2hl4GznL4LuUGgkRcVJl3EeTNWmfSnYrHAZ8Iyp20bCFa2iQeWXgvuQO1zdQWRd63CS9H1iJXGbebKeZiUq6AJI2Bu4bEd8rA0kr1aw0kbRHRHxxaKnqNPN9iaqkuzJVI+G0yhoJM2OvRM5pfBlZFW1xq9i2cJTP3DLNt9kLA5p9Q9QYdZn8wER1L0h6GVlrc11yFsMG5DLYmmXAa5Z/15rlsXn5jTZLjYRBH/dGkjaq/SYvf2N1cpnuc8h9sebr9uA2ZvM1qS5PjGlD1IlKusCryPqYpwNExM/KROiRRcSgCPpmZDWtqwAkrcPctQhWpHHVSABu3cLoYWR5x48DJw2KDJktFGV++XuBe0XEkyVtQe6B+NmauJOWdG+IiBsHA/TKjRhbtUa3Gu7HjYgrS+nIeWdc3+BDDgaeF2VTTbMF6hDyvfzWcvxTcnyiKulOynY9AydL+jeyYtfO5JzUrzeKvai0bgGQtC4L4EtN0paSdpf0wsGtQdhTgLdI+nT5G/dVbpNkNu+VxhjA3SLicEqxm8jtuKobEvM+KTT2ZnJq1wVkEZxvkWUNW/gwOSXmK2TreXdyb7Z5a1k1EoDaZdHjWnRh1sMZ5DjEtWWgeTCl8uHkCtMqEzN7QdIicm+msZWRK30+jyVnAhwfETVV68duXDUSJJ0ZEQ+WdM5QCb/zImLrBqdtNlaD9+1QecctyYVV6wHPql3oMzEt3Yi4RdJ5ZXT+8jH9jYup2x6kt+vL83KTpMXkhpdVSxyLsSy6MOtkPUmvLz9/lbwKFPkefjy5eenIJibpFusDF5UVadcO7my0Im0hGleNhKYbU5p1thI5BXTmkvg1WgSfmO4F6LMibaGStAkNaiSUbpxnAcczpkUXZuOkBvsPzhl/UpJujz7dhWZGjYRrl/ff3464p0TEo1rFM+tpeCxiHCZmyliZnH+epI1W9LnMI/uSO6heLOkISc9S7idX6zhJb5S0oaR1B7cGcc16aLFR7TJNTEsXQNIJ5G6y7tMd0rpGQtkGaLY6FC0G6cwWtEkbSNtnRZ/AfDOmGglbAHuRregATiVrXJhNvIlq6dp0M2okHE6jGgmSDifrCX+p3PVcYO2I2L02ttlCN1FJV9ISpi57VwHuBFw7qSUHJT0JOK51jYTZFkJ4cYRZmpiBNICIuHNELC631YDdgI+t6PNagcZVI+GcsmSSEvdhwA8axDVb8CaqpTsbSadFxMOX/1/e8ZTuhbOAF0bElqV/90cRsU1l3EuA+wODlX8bAZeQhUPm7U4BZj1M1ECapF2HDheRuwNP8rfOWDamBBbS5oNmXU1U0iVH6QduAi4DJnm62FhqJCzUnQLMepi0pLuI2Xd3ePGKPKkVyDUSzDqbtKS7YHZ3GLeyLHodYFemaiTs7RoJZuM1aUl3kaR1IuJKWDi7O4xDKen46lIZ/5sr+nzMJsWkJZwFt7vDmB0n6Y1k0ZvhZdF/XnGnZHbHNnFTxhba7g7j5BoJZv1NXNK1KWXmwlI1EiLi+hV6YmZ3YE66E8w1Esz6c9KdYK6RYNbfRNVesKW4RoJZZ27pTjDXSDDrz0l3gknaeK7HvZzXrD0nXTOzjtyna2bWkZOumVlHTrpmZh056ZqZdfT/AeSvk7Vubr7uAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Wir l\u00f6schen alle Zeilen mit den fehlenden Werten und die Spalte die f\u00fcr die Auswertung nicht relevant ist oder nicht ben\u00f6tigt wird\n",
- "train = train.drop('education', axis=1)\n",
- "train = train.dropna(axis=0)\n",
- "sns.heatmap(train.isnull(),yticklabels=False, cbar=False, cmap='viridis')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "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>male</th>\n",
- " <th>age</th>\n",
- " <th>currentSmoker</th>\n",
- " <th>cigsPerDay</th>\n",
- " <th>BPMeds</th>\n",
- " <th>prevalentStroke</th>\n",
- " <th>prevalentHyp</th>\n",
- " <th>diabetes</th>\n",
- " <th>totChol</th>\n",
- " <th>sysBP</th>\n",
- " <th>diaBP</th>\n",
- " <th>BMI</th>\n",
- " <th>heartRate</th>\n",
- " <th>glucose</th>\n",
- " <th>TenYearCHD</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- "Empty DataFrame\n",
- "Columns: [male, age, currentSmoker, cigsPerDay, BPMeds, prevalentStroke, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose, TenYearCHD]\n",
- "Index: []"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train[train.duplicated(keep=False)] #keine Duplikate vorhanden "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "### Explorative Datenanalyse"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['male', 'age', 'currentSmoker', 'cigsPerDay', 'BPMeds',\n",
- " 'prevalentStroke', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP',\n",
- " 'diaBP', 'BMI', 'heartRate', 'glucose', 'TenYearCHD'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 3179\n",
- "1 572\n",
- "Name: TenYearCHD, dtype: int64"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train.TenYearCHD.value_counts()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', data=train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "#import cufflinks as cf\n",
- "#import plotly.offline\n",
- "#cf.go_offline()\n",
- "#cf.set_config_file(offline=False, world_readable=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "#TenYearCHD_male = train[train['male']==1]['TenYearCHD'].value_counts()\n",
- "#TenYearCHD_female = train[train['male']==0]['TenYearCHD'].value_counts()\n",
- "#df1 = pd.DataFrame([TenYearCHD_male,TenYearCHD_female])\n",
- "#df1.index = ['Male','Female']\n",
- "#df1.iplot(kind='bar',barmode='stack')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "#df1 =train.melt(var_name='male', value_name='TenYearCHD')\n",
- "#pd.crosstab(index=df1['TenYearCHD'], columns=df1['male'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', hue='male', data=train)\n",
- "# kein 10-Jahres Risiko und weiblich = 1828\n",
- "# kein 10 Jahres Risko und m\u00e4nnlich = 1351\n",
- "# 10-Jahres Risiko und weiblich = 253\n",
- "# 10 Jahres Risko und m\u00e4nnlich = 319"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='Density'>"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['age'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='count'>"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEECAYAAADK0VhyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAArIklEQVR4nO3de1zUZb4H8M9wG3EA8Z5nEZUUxQtH00Dzkugimbm2pigIxmrHMK2D2ipyVxEwjUxdzdw8biAparsnM3uZl6T1Qh08YiHmpdRQ80KYMCvDAM/5o5gzv/mNMhhzYz7vv5h5nnme7+/rON/5Pb/LKIQQAkRE5NCcrB0AERFZH4sBERGxGBAREYsBERGBxYCIiAC4WDuApjp9+jSUSqW1wyAisisajQYDBw58YLvdFQOlUomAgABrh0FEZFdKS0sf2s5lIiIiYjEgIiIWAyIighmPGRQXF2PNmjXIyclBeXk5kpKScO/ePdTV1eGNN96Ar68v8vPzsWPHDri4uGDu3LkICQkxVzhE5AC0Wi3KyspQXV1t7VCsplWrVvDx8YGrq2uTXmeWYrBlyxZ89NFHcHd3BwCsXr0aEydOxLPPPouTJ0/iu+++g7u7O3JycrBnzx5oNBpERkZi+PDhcHNzM0dIROQAysrK4Onpie7du0OhUFg7HIsTQqC8vBxlZWXo0aNHk15rlmUiX19frF+/Xvf41KlTuHnzJmJiYrB3714EBQXhzJkzGDRoENzc3ODp6QlfX1+cO3fOHOEQkYOorq5G+/btHbIQAIBCoUD79u0fac/ILHsGYWFhKCsr0z2+du0avLy8sG3bNmzYsAFbtmxB9+7d4enpqeujUqlQVVXV6NgajabRU6SIyDFptVqHXiJqoNVqm/w5aZHrDLy9vTFmzBgAwJgxY/DWW2+hf//+UKvVuj5qtVpSHB6E1xkQ0YOUlpbqlqcdmaurq+xzsrHiYJFiMHjwYBw9ehTPP/88vvrqK/Ts2ROBgYFYu3YtNBoNampqcOnSJfj7+1siHCJyEFlZWSgpKcHt27dRXV2Nrl27om3btli3bp1Jr/3Xv/6F5cuXAwDq6uowffp0zJ8/H08//fQjxaPRaLB27VoUFxdDoVCgdevWWL58Obp06YLo6GikpaXh8ccf1/UdP348Dh8+jPj4eJSUlMDb2xu1tbVo27Ytli5diq5duz5SHMZYpBgsWbIESUlJ2LFjBzw8PPDmm2+iTZs2iI6ORmRkJIQQWLBgAW8z8RtotHVQujrL/iZyZPHx8QCADz/8EN999x1ef/11k1+7cOFCTJ48GcePH8dTTz2F9957D4GBgY9cCABg5cqV8PPzQ15eHgDgs88+Q1xcHHbu3Nnoa//85z9j1KhRAID/+Z//QVxcHPbs2fPIsRgyWzHw8fFBfn4+AOB3v/sd/uu//kvWJzw8HOHh4eYKwaEoXZ0x+M/vAwCKVs+0cjREtkmr1SI1NRVXrlxBfX094uLiEBwcjIkTJyIoKAjffvstFAoFNm7cCE9PT6xatQoLFy7E22+/jU8//RQffPABKisrkZiYiIqKCgBAUlISevfujdzcXBw4cAC1tbXw9PTE+vXr8fHHH2PPnj2or6/HK6+8gsOHD2PZsmW6eEJDQzFkyJAmb8eQIUPg6uqKK1euoFu3bs2SG7u7NxER0aPatWsX2rZti4yMDFRUVCAqKgr79u2DWq3GhAkTkJycjEWLFqGgoAATJkxAv379MHHiRMTExGDbtm1QKpVYt24dhg4disjISFy+fBlLly7F9u3bcffuXWzbtg1OTk6YPXs2vv76awCAl5cXNm3ahFu3bqFDhw6yM53atm2r+3vJkiW6Yx719fUP3Zb27dujoqKCxYCIqKnOnz+PoqIinDlzBgBQW1ur+4bft29fAECXLl2g0Wh0r3n++edRUFCAPn366MY4efIk9u/fDwC4d+8enJyc4OrqioULF6J169b48ccfUVtbCwC68/3btm2Le/fuQQghKQh79+7FM888AwBYtWqV7JjBg1y/fh2PPfbYb0/Kr1gMyOYYHvPgMRBqLn5+fnjssccQGxuL6upqbNq0CW3atAEAk69N8PPzwx/+8AdMnDgR5eXl2LVrF86dO4eDBw9i165duH//PiZPngwhBADAyemXy7lcXV0xYsQI5OTkYObMX5ZyP/30U/ztb3/DxIkTm7Qdx44dQ6tWrVgMqGXTP/4B8BgINZ/p06cjKSkJUVFRqKqqQmRkpO7D2lSxsbFITExEfn4+qqqqMH/+fHTr1g3u7u6YPHky3Nzc0LFjR9y6dUv22qVLlyIzMxPTp08HALRp00Zyge7DrF69Glu2bIGTkxNUKhXWrl3bpLgboxAN5ctOlJaW8jqDB2hJB5BZDOhR8PPhF8by0FhueNdSIiJiMSAiIhYD0qPR1hn9m4haPh5AJh1euEbkuLhnQERELAZERA2ae3nUnpZbuUxERPQrw2tcfitTllvr6+uRlpaGb7/9Fm5ubkhPT2+2W0w0BfcMiIis6ODBg6ipqcHOnTuxaNEiZGVlWSUOFgMiIisqKirCyJEjAQADBw7EN998Y5U4WAyIiKyoqqoKHh4eusfOzs66m9xZEosBEZEVeXh4SH4CuL6+Hi4ulj+cy2JARGRFTzzxBAoKCgAAp0+fttrP//JsIiKiX2m0dc16waUpt18PDQ3FsWPHMH36dAghkJGR0WzzNwWLARHRr5r7dzNMGc/JyQnLly9v1nkfhdmWiYqLixEdHS15bu/evZg2bZrucX5+PiZPnozw8HAcOXLEXKHYPd4ziIjMzSx7Blu2bMFHH32k+y1P4Jd7ae/evVv36z+3b99GTk4O9uzZA41Gg8jISAwfPhxubm7mCMmu8Z5BRGRuZikGvr6+WL9+PRYvXgwAqKiowJo1a5CQkIDk5GQAwJkzZzBo0CC4ubnBzc0Nvr6+OHfuHAIDAx86tkajQWlpqTnCtlnGfqTiUfo0xzyWYOwHOBzt35wejVarxf37960dhtVptdom/58xSzEICwtDWVkZAKCurg6JiYlISEiAUqnU9amqqoKnp6fusUqlQlVVVaNjK5VKh/8lI1O2vzlyZEt5tqVYyHaVlpZKViQclaura5O/3Jn9AHJJSQmuXLmCtLQ0aDQaXLx4EStXrsTQoUMl59aq1WpJcSAiIssxezEIDAzEvn37AABlZWVYuHAhEhMTcfv2baxduxYajQY1NTW4dOmS1c6vdQSGp7iZcsobkaMRtRooXJSNd7TSeOZktVNLO3bsiOjoaERGRkIIgQULFkiWkah5Gd6NsaUfiGbxo0ehcFHi6vIBzTaeb8rXJvUrLi7GmjVrkJOT02xzN5XZioGPjw/y8/Mf+lx4eDjCw8PNFQI5MEcrfmS/jJ19aQ28HQURkRU1nH1pbSwGRERWFBYWZpUb0xliMaBmxaulieyT9csRtSi8WprIPrEYEBH9StRqTD4DyNTx7OXUUi4TERH9qrk/uE0dz9jZl5bGYkAWx+MKRLaHy0RkcTyuQGR7uGdARC1Kw23yHdWjbj+LARG1GK1atUJ5ebnDFgQhBMrLy9GqVasmv5bLRGQy3u+HbJ2Pjw/Kyspw+/Zta4diNa1atYKPj0+TX8diQCbj/X7I1rm6uqJHjx7WDsMucZmIiIhYDIiIiMWAiIjAYkBERGAxICIisBgQERHMWAyKi4sRHR0NACgtLUVkZCSio6Mxe/Zs3LlzBwCQn5+PyZMnIzw8HEeOHDFXKDbN8N48vFcPEVmDWa4zMPxNz5UrVyI5ORkBAQHYsWMHtmzZgpdeegk5OTnYs2cPNBoNIiMjMXz4cLi5uZkjJJvFc/eJyBaYZc/A8Dc9s7OzERAQAACoq6uDUqnEmTNnMGjQILi5ucHT0xO+vr44d+6cOcIhIqJGmGXPICwsDGVlZbrHnTp1AgCcOnUKubm52L59O7744gt4enrq+qhUKlRVVTU6tkajQWlpafMHbSUNRVKf4fYZ9jG2/Y31aY55bCnWxjTHGESOxGK3o/jkk0+wadMmvPvuu2jXrh08PDygVqt17Wq1WlIcHkSpVBr9j96SNLZ9pmx/c/SxlTFM7WOJMYjsVWNfhixyNtF///d/Izc3Fzk5OejatSsAIDAwEEVFRdBoNKisrMSlS5fg7+9viXCIiMiA2fcM6urqsHLlSnTp0gWvvvoqAODJJ5/Ea6+9hujoaERGRkIIgQULFkCptI/fCiUiamnMVgz0f9Pzyy+/NNonPDwc4eHh5gqBiIhMxIvOiIiIxYCIiFgMiIgILAZERAQWAyIiAosBERGBxYCIiMBiQEREYDEgIiKwGBAREVgMiFoE/mIe/VYWu4U1EZkPfzGPfivuGRCZmf63dH5jJ1vFYkD0AM219NLwrX3wn9+H0tW5OUIjanZcJiJ6AC69kCPhngEREbEYEBERiwEREcGMxaC4uBjR0dEAgCtXriAiIgKRkZFITU1FfX09ACA/Px+TJ09GeHg4jhw5Yq5QiIioEWYpBlu2bEFSUhI0Gg0AIDMzE3FxccjLy4MQAocOHcLt27eRk5ODHTt24L333kN2djZqamrMEQ6R2VjqtFGenkrmZpZi4Ovri/Xr1+sel5SUICgoCAAwatQoHD9+HGfOnMGgQYPg5uYGT09P+Pr64ty5c+YIh8hsLHXaKE9PJXMzy6mlYWFhKCsr0z0WQkChUAAAVCoVKisrUVVVBU9PT10flUqFqqqqRsfWaDQoLS1t/qCtJCAgQPac4fYZ9jG2/Y31aY55bClW3+5+ULkrAQDq+xpcvfxdk8dojKW291FieZScET2MRa4zcHL6/x0QtVoNLy8veHh4QK1WS57XLw4PolQqjb7xW5LGts+U7W+OPrYyxoP6NFwDULR6ZrPN81vHsJU4mmseajka+3JgkbOJ+vbti8LCQgBAQUEBhgwZgsDAQBQVFUGj0aCyshKXLl2Cv7+/JcIhAsB1eCJ9FtkzWLJkCZKTk5GdnQ0/Pz+EhYXB2dkZ0dHRiIyMhBACCxYsgFKptEQ4RACkVxjz6mJydGYrBj4+PsjPzwcA9OjRA7m5ubI+4eHhCA8PN1cIRHZBo62THBQ2fExkCbw3EZGV8R5IZAt4BTIREbEYEBERiwEREYHFgIiIwGJARERgMSAiIphYDHbt2iV5/P777z+gJxER2aOHXmfw8ccf4/DhwygsLMTJkycBAHV1dbhw4QJmzuS50ERELcVDi8HIkSPRsWNH3L17F9OmTQPwy03nunbtapHgiIjIMh5aDNq0aYPg4GAEBwejvLxc92M1dXW8qRcRUUti0u0oli1bhqNHj6JTp0663ybYsWOHuWMjIiILMakYFBcX4+DBg5LfJSCilkf/Jnm8YZ5jMenTvVu3brolIiJqufjzmo7LpD2DGzduICQkBN26dQMALhMR2SF+66eHMakYvPnmm+aOg4jMjD/mQw9jUjH4+9//Lntu/vz5zR4MERFZh0nFoEOHDgAAIQTOnj2L+vp6swZFRESWZVIxmD59uuTxSy+91OSJtFot4uPjce3aNTg5OWHFihVwcXFBfHw8FAoFevXqhdTUVJ6xRERkBSYVg++//1739+3bt3Hjxo0mT3T06FHU1tZix44dOHbsGNauXQutVou4uDgEBwcjJSUFhw4dQmhoaJPHJiKi38akYpCSkqL7W6lUYvHixU2eqEePHqirq0N9fT2qqqrg4uKC06dPIygoCAAwatQoHDt2jMWAiMgKTCoGOTk5qKiowA8//AAfHx+0a9euyRO1bt0a165dw/jx41FRUYF33nkHX331FRQKBQBApVKhsrKy0XE0Gg1KS0ubPL+tCggIkD1nuH2GfYxtf2N9mmMeR4vV0bbX1D7UMplUDPbv34+1a9fi8ccfx4ULFzB//nxMmjSpSRNt27YNI0aMwKJFi3Djxg28+OKL0Gq1una1Wg0vL69Gx1EqlUbf+LbqUc7tbmz7TNn+5uhjK2NYah5bGcNS8zRXrGQfGivsJhWDbdu24cMPP4RKpUJVVRVefPHFJhcDLy8vuLq6AvjlBni1tbXo27cvCgsLERwcjIKCAgwdOrRJY9oDnttNRPbApGKgUCigUqkAAB4eHlAqlU2eKCYmBgkJCYiMjIRWq8WCBQvQv39/JCcnIzs7G35+fggLC2vyuERE9NuZVAx8fX2RlZWFIUOGoKioCL6+vk2eSKVS4e2335Y9n5ub2+SxiIioeZl0Un94eDjatGmD48eP48MPP8SMGTPMHRcREVmQScUgKysLoaGhSElJwe7du5GVlWXuuOyCRlv30MdE9Gj0/y/x/5VlmLRM5OLigp49ewIAunbtyquEf6V/cBjgAWKi5sITLyzPpGLwb//2b8jOzsbAgQNx5swZdOrUydxxEZGd4q2y7ZNJX/EzMzPRrl07HD16FO3atUNmZqa54yIiO9XYD+RwedU2mbRnoFQqERMTY+ZQiMgRcHnVNnHxn4iIWAyIiIjFgIiagOv9LZdJxwyIiAD7Wu/nWU1Nw2JARC0Sr1VoGi4TEZHd4XJV8+OeARHZHXtarrIX3DMgIofFeyD9P+4ZEJHD4nGF/8c9AyIiYjEgIiIWAyIigoWPGWzevBmHDx+GVqtFREQEgoKCEB8fD4VCgV69eiE1NZW/lUBEZAUW++QtLCzE//7v/+KDDz5ATk4OfvzxR2RmZiIuLg55eXkQQuDQoUOWCoeIiPRYrBj885//hL+/P+bNm4fY2FiMHj0aJSUlCAoKAgCMGjUKx48ft1Q4RESkx2LLRBUVFbh+/TreeecdlJWVYe7cuRBCQKFQAABUKhUqKysbHUej0aC0tNTc4ZokICBA9pxhbIZ9Gmt/lDEsNY+jxepo22tPsVpqex2JxYqBt7c3/Pz84ObmBj8/PyiVSvz444+6drVaDS8vr0bHUSqVRt8ItqKx2EyJ3RJjWGoeR4vV0bbXUvPYyhj2rLFCZ7FlosGDB+OLL76AEAI3b97E/fv3MWzYMBQWFgIACgoKMGTIEEuFQ0REeiy2ZxASEoKvvvoKU6ZMgRACKSkp8PHxQXJyMrKzs+Hn54ewsDBLhUNERHosemrp4sWLZc/l5uZaMgQiIjKCJ/UTERGLARERsRgQERFYDIiICCwGREQEFgMiIgKLARERgcWAiIjAYkBERGAxICIisBgQERFYDIiICCwGREQEFgMiIgKLARERgcWAiIjAYkBERGAxICIisBgQERGsUAzKy8vx9NNP49KlS7hy5QoiIiIQGRmJ1NRU1NfXWzocIiKChYuBVqtFSkoKWrVqBQDIzMxEXFwc8vLyIITAoUOHLBkOERH9ysWSk61atQrTp0/Hu+++CwAoKSlBUFAQAGDUqFE4duwYQkNDHzqGRqNBaWmp2WM1RUBAgOw5w9gM+zTW/ihjWGoeR4vV0bbXnmK11PY6EosVgw8//BDt2rXDyJEjdcVACAGFQgEAUKlUqKysbHQcpVJp9I1gKxqLzZTYLTGGpeZxtFgdbXstNY+tjGHPGit0FisGe/bsgUKhwIkTJ1BaWoolS5bgp59+0rWr1Wp4eXlZKhwiItJjsWKwfft23d/R0dFIS0vD6tWrUVhYiODgYBQUFGDo0KGWCoeIiPRY9dTSJUuWYP369Zg2bRq0Wi3CwsKsGQ4RkcOy6AHkBjk5Obq/c3NzrRECERHp4UVnRETEYkBERCwGREQEFgMiIgKLARERgcXgoTTaOqN/E5FjMPx/35I/B6xyaqm9ULo6Y/Cf3wcAFK2eaeVoiMjS9D8DgJb9OcA9AyJq8USt5qGPiXsGROQAFC5KXF0+QPfYN+VrK0Zjm7hnQERELAZEZD5cnrEfXCYiaiaiVgOFi/KBj1si/W00tr1cnrEfLAZEzcQRP/j0t9kRtrcl4zIREVmV/tIRl5Gsh8WAyIJs5YPPltbyG/Yuri4f0OKX1WwZl4mILMhWllVMWdJq7HiALbGnWG0ViwERGWUrhcsU9hSrreIyERGRGdnL/Y0stmeg1WqRkJCAa9euoaamBnPnzkXPnj0RHx8PhUKBXr16ITU1FU5OrE9E1HLYy/2NLPbJ+9FHH8Hb2xt5eXnYsmULVqxYgczMTMTFxSEvLw9CCBw6dMhS4RDZLFs5yGwrbOlgtzEt5e7GFtszeOaZZxAWFqZ77OzsjJKSEgQFBQEARo0ahWPHjiE0NNRSIRHZJK5/S9n69Rst5e7GFisGKpUKAFBVVYXXXnsNcXFxWLVqFRQKha69srKy0XE0Gg1KS0vNGmuDgIAAyWPDeQ3bTeljjjEcPVbDs0e01Wq4tlI1+xj6fbTValz8/qrRWA3nfFifxtof1KexnNlSrE1lqTEeJa+m9HmUMWyBRc8munHjBubNm4fIyEhMnDgRq1ev1rWp1Wp4eXk1OoZSqfzNb5RHZcq8jfWxlTEsNY8lYjX1m+NvHcPwG7s189PY62wp1qay1BjGCqjhF4RHGdtSeWqqxgqQxYrBnTt3MGvWLKSkpGDYsGEAgL59+6KwsBDBwcEoKCjA0KFDLRUOOZiWdB66I94DyRJsfTnK3CxWDN555x3cu3cPGzduxMaNGwEAiYmJSE9PR3Z2Nvz8/CTHFIiaU0tah3f0Dy0yD4sVg6SkJCQlJcmez83NtVQIRERWZ6t7qbwCmYjIgmx1L9Vhr/Cyl6sCich2tORrQBx2z8Bergq0Fh6kJJKz1W/1zcFhiwE9HA9SEjkWh10mIttgym53S941p5bF1m+d8TDcMyCzMWWpyZTd7pa8a04tiz3vUbMYtECWOnWNP4ZO1HKwGLRAlvomzW/sRC0Hjxk4KK7DE5E+FgMHxR8hJ7IdtvCbCFwmIiKyMlv4TQTuGRAREYuBreFaPpFjs9a1ClwmsjGNnaHD20QQtWzWOiW7xRYDjbYOSldn2d/2jufuEzkmc3+mtdhiYAsHZIiImou5P9N4zMCC7Pm+JUTUsrXYPQNr4O0ZiMheWb0Y1NfXIy0tDd9++y3c3NyQnp6Obt26WTssmea66RoRkS2yejE4ePAgampqsHPnTpw+fRpZWVnYtGnTQ19jjYPD/FZPRLbC2CqE4WdhUz8brV4MioqKMHLkSADAwIED8c033zT6Gh4cJiJHZmwV4rf+eqNCCCGaL8SmS0xMxLhx4/D0008DAEaPHo2DBw/CxcV4nTp9+jSUSp5XT0TUFBqNBgMHDnxgu9X3DDw8PKBWq3WP6+vrH1gIADx0Y4iI6NFY/dTSJ554AgUFBQB++dbv7+9v5YiIiByP1ZeJGs4mOn/+PIQQyMjIwOOPP27NkIiIHI7ViwEREVmf1ZeJiIjI+lgMiIiIxYCIiGzg1NKmqKurQ1JSEr7//ns4OzsjMzMTarUaK1asgLOzM9zc3JCRkYG1a9dK+tTU1CA5ORlCCPj7+6OmpgaXL1/Wtfv6+gIA9u7di9zcXOTl5cnmqaysRGxsLLp37w4AmDZtGo4dOybpo1KpkJSUhHv37qG2thadO3fGrVu3dO1vvfUW7ty5AwC4du0aAgMD4e7uLtue1NRUODs7o1u3blAoFLI4UlNT4ebmhoCAACQmJsLJyQnl5eWYPHkytm7dChcXF8THx0OhUKBXr15ITU1FRUWFrr3hAH1GRgZ69OiBiIgIAJCMUVNTI8nrqlWroFAodO1CCF1O+/Tpg+TkZDg7O0vGaJinIa87d+6UtFdXV0tyGhERgWeffVbSx9vbW5fTuro6vPHGG/D19ZX02bBhgySv//7v/46kpCTJtjTktHv37li5cqUsZ9XV1bK8Tp48GZ6engAAHx8fxMbGSvJaXFwsac/MzJTl9fnnn5f0mTlzpiSvN2/eRNu2bXXts2fPluX1hRdeMDqPfl4N54mKipLk9tq1a/jd736na3/99ddleX3ttdckY1RXV0vyWllZiT59+ki2xTCvhjmLioqS5LVjx444cuQItFotIiIiEBQUJHuvbtmyBYcPH9b1mTp1qiyvmzdvlvTp37+/JK/9+vVDYWGhrn3QoEGyvP71r381Ok9DXseMGSNp79u3r+z9+sMPP0j6jBkzRpbX/fv3S/ocP35ckleVSoXWrVtLtsUwr4Y56du3ry6vbm5uqK2thUKhgEajQWlpKfLy8pCRkSHJq5NTI9/9hR357LPPRHx8vBBCiJMnT4rY2FgxY8YMcfbsWSGEEB988IF4+eWXZX3mzp0rvvzySyGEENHR0WLmzJmSdiGEOHv2rJg5c6aYOnWq0Xny8/PFe++999BYlixZIvbt2yeEEGLDhg0iJiZGNo8QQty9e1f84Q9/ELt27ZKN8corr4jPP/9cCCFEZGSkePHFFyXtf/zjH0VRUZEQQojs7Gzxj3/8Q9TU1IhXXnlFjBs3Tly8eFG8/PLL4uTJk0IIIZKTk8X+/fsl7eXl5WL27Nli7NixIi8vTwghZGMY5jU9PV3Srp/TJUuWiAMHDsjGMMyrYbthTo3FoZ/TEydOiCNHjhidRz+v165dk7Tr53ThwoXi0KFDsjEM87pr1y4xadIkSWz6eU1ISBBjxoyRtBvmtbq6WjaGfl7ff/99MXToUEm7YV737dsnG8Mwr8bm0c+tsXbDvB44cMDoPA15fe6558SECRMkzxvm9dNPP5WNoZ/XRYsWiUmTJom6ujpRVVUl1q1bJ3uvbtiwQbz88suSPoZ5PXnypKyPfl4zMjLEmDFjJO2GeTU2j35ex48fL2s3fL8ai8Mwr++8847ReRryOnbsWBETEyNpN8zrpk2bZGMY+xwQQoi0tDSxY8cOWV4PHDhg9N9Wn10tE/3+97/HihUrAADXr19Hhw4dkJ2djYCAAAC/7Dn4+/vL+qxfvx5PPvkkampq4Orqivnz50vaKyoqsGbNGiQkJDxwnm+++Qaff/45ZsyYgYSEBAwdOlTW59SpU7h58yZiYmJw/fp1vP3225L2BuvXr0dUVBSmTJkiGyMgIAB3796FEAKenp548cUXJe03b97EE088AeCXazSKioqwatUqTJ8+HZ06dQIAlJSUICgoCAAwatQo/OUvf5G0q9VqvPrqq5g0aZIuJsMxDPN66tQpSbt+Tm/fvo327dvLxjDMq2G7YU6rqqpkffRzunfvXgQFBcn6GOZ169atknb9nKrVari4uMjGMMzrkSNHcP/+fcyaNQszZ87E6dOnJXnt3r07fv75Z0m7YV7PnTsnG0M/r9evX0ddXZ2k3TCvVVVVsjEM82psHv3c/ud//if+9a9/SdoN8+rl5SUbQz+vISEh0Gq1knbDvN64cUM2hn5etVotAGDevHmIjY3F6NGjZe/VgwcPwt/fX9LHMK///Oc/ZX3083rhwgV06NBB0m6Y18uXL8vG0M9rZWWlrN3w/Xr48GFZH8O8VlRUyPro57VHjx4YMGCApN0wr+fOnZONYexz4Ouvv8bFixcxbdo0WV6PHz+ORjVaLmzQ4sWLxaBBg8QXX3yhe66oqEg888wzory83GifsrIyERoaKv74xz+Kn376SddeUFAg5s6dKy5evCh++OEHMXXqVKPz7N69W3z99ddCCCE2btwosrKyZH369u0rdu/eLYQQYv369WLt2rWyOO7cuSOeffZZUVtba3SevXv3iieffFKEhYWJKVOmiOrqakn7tGnTRGFhoRBCiNTUVBEeHi7+8pe/CCGEiIqKEhcvXhTDhw/Xjb1q1SoxceJESXuDdevWiby8PLFnzx7ZGPp5HT58uFizZo2sXT+n77//vmSM8+fPS/I6duxY2RyGOZ09e7asj2FOY2NjjcbakNddu3bJ2g1zunPnTlkfw7zOmTNH7Ny5U9TX14vvvvtOjB07VpLXnTt3iilTpkjatVqtJK/nzp2TjdHQp6ioSIwZM0Zs3bpV1q6f1y+//FIyRkhIiJgzZ47k/Wpsnp07d+pyu3z5cvGnP/1J0u7v7y/Ja3JystFYG/J69uxZWfvf//53SV7PnDkj66Of1/Hjx4vhw4cLjUYjLl26JMaNGyfJ6fHjx8Xvf/978ac//UnSp76+XpLXxMTEB/YpKioSgwcPFlFRUbJ2/by+/vrrkjFCQ0NFbGysLq9Dhw6VzWH4fn3uuedkfQzfr5MmTTIaa0NeExISZO2G79f4+HhZn/DwcMn79fXXXxfz5s0TJ06cEEIIWV4XLVokGmOXxUAIIW7duiVGjx4t1Gq12Ldvn3juuefE1atXH9inQX5+vli8eLGufcCAASIkJERERUWJqVOnikGDBon09HTZGD/++KPuuQsXLuiWmvT7BAcHi59++kkIIURJSYl46aWXZHHk5uaKjRs3PnB7goODxfnz54UQQuTm5oq0tDRJe2lpqZg1a5b4j//4D7Fu3ToxYsQIMWPGDBEVFSUGDx4sXnjhBREQEKAbd/z48WLEiBGS9lu3bgkh/v8/V2RkpGyMW7du6fL6wgsvGG3Xz+mwYcMkfXr37i3Gjh2ry2ufPn3EyJEjHzjGhQsXxODBg2Xz9O/fX5LTIUOGGI2lIa/GtqVfv36SnA4fPlzWp7CwUJLX5cuXi/v37+vie+GFF0SfPn10jz/55BORnJwsab9+/bokrxqNRjbG9evXdXm9ePGi0Xb9vC5atEjSxzCvgwYNEsuWLXvoOGfPnhVRUVGybdHP66xZs4yO0ZBXY9vi7+8vyWtycrKsj35ep0+fLiIjI3XtEydOFIGBgbrHn332mXj++eclSzETJ04Ud+7ckeR19erVRvs05DU5OfmBYzTkdcKECZI+AwYMEM8884wur/369RMREREPHOPChQti9OjRsnl69+4tyevYsWONxtKQV2Pb0rt3b0lejeXk9OnTkvdramqqGD9+vK7PyJEjJXldtmyZaIxdLRP94x//wObNmwEA7u7uUCgU+Oyzz5Cbm4ucnBx07drVaJ/58+fj8uXLAICzZ8/iwoULuvYOHTpg//79yMnJQXZ2Nnr27Il+/foZHePMmTMAgBMnTsDNzU3WJygoCEePHgUAbN26VXfPpYZ2Z2dnnDhxAqNGjXrg9nh7e8PDwwMAcPnyZZw6dUrS/vnnnyMjIwPvvvsu7t69i/T0dN32BwQEYNWqVRg1ahQKCwsBAEOGDMHSpUsl7R07dpTkdfv27bIxjh8/rntu9+7dsvbk5GRdTlUqFZ5++mlJn3379uHgwYO6vA4YMAAFBQWSMV555RVJTsPDw2XzhISE6HL61VdfYcqUKbI+HTt21OXV2Lb4+PjoctqpUycEBwfL+pSUlEjyWl9fj6ysLAC/LCFVVVVh+PDhurzm5OSgrKxM0m6Y1927d8vG+PLLL3VzFxYWytpTUlIkef3hhx8kfbp3745PP/1U8n7t2bOnbJx58+bpcrt582bcu3dP0h4aGirJa11dnWwM/bwa2xZfX19JXktKSmR9iouLdXn18vKCWq2GEAI3b97E/fv3MWzYMF1OCwoK8NRTT+GLL76Q9PH29pbkdfDgwbI+BQUFuryGhITI2hMTEyV57dChg6RP586d8fHHH+vy6uPjA6VSKRljzpw5kvdrv379ZPOMHTtWktfevXsb3Z6GvBrbFsO8qlQqWZ+ioiLJ+7Vt27Z46qmndDnq27evJK9DhgxBY+zqbKJx48Zh6dKlmDFjBmpra5GQkICEhAR06dIFr776KoBfbmR39epVSZ927dohPj4erq6ucHNzQ6dOnSTthndBNTZPly5dsGLFCri6uqJDhw7IyMhAenq6pE9AQACSkpKwY8cOuLu7w9vbWzbP999/j65duz5wHm9vbyxYsAAuLi5wcnJC586dJe1OTk6YM2cO3N3dERwcrLvbq74lS5YgOTkZ2dnZ8PPzQ1hYWJPyXF9fj5UrV0ry+uSTT+K1117T9ZkzZ44up+7u7khPT2/SHACQlpYmyWnD8RPDbWnIqYeHB958802jY+nn1VB6eroup66urkbn6datmySv8+bNw9KlSxEREQGFQoGMjAy0bdtWl9fu3bvrzuxoaDe8weKUKVMkY6xcuRJz587V5bW+vh4///yzZAwAkrxmZ2cjOzu7SfNkZGRAqVTqctuuXTt069ZN0t65c2dJXtesWYPMzEzZPA157dWrl2yO+vp6SV7XrFmDDRs2SPrcvXtXktfHH38cU6ZMgRACKSkp8PHxkbxXFy5ciDfffFPSx9lZek/+kJAQ3ReDhj6LFi2SvF+1Wq2kXaVSSfL6xhtvYNu2bQ+cx8vLCwEBAZL2du3aSd6vWVlZ2Lhxo6SPn5+f7P26efNm2TwNee3Xr59sW9zd3SV5zcrKQl5enqSPVquV5FWlUqFNmzaS/zdN/Qzg7SiIiIgXnREREYsBERGBxYCIiMBiQEREYDEgIiKwGBAREVgMiIgIdnbRGZE1VVVVITExEZWVlaioqMDUqVPRv39/LFu2DCqVCu3bt4dSqURWVhZycnLw8ccfQ6FQ4Nlnn8XMmTOtHT7RQ7EYEJnoypUrmDBhAsaNG4ebN28iOjoaKpUKb7zxBnr16oW33noLN2/exMWLF/HJJ58gLy8PCoUCMTExGDFiBPz8/Ky9CUQPxGJAZKIOHTrgb3/7Gw4cOAAPDw/U1tbi1q1b6NWrF4Bf7pnzySef4Pz587h+/TpiYmIAAD///DOuXr3KYkA2jcWAyERbt27FwIEDERkZiZMnT+Lo0aN47LHHcPHiRfTs2RPFxcUAAD8/P/Ts2RN//etfoVAosG3bNvj7+1s5eqKHYzEgMlFISAjS0tKwd+9eeHt7w9nZGSkpKUhISEDr1q3h6uqKzp07o0+fPhg2bBgiIiJQU1ODwMBAdO7c2drhEz0Ub1RH9Bts374d48ePR7t27fDWW29JfkmPyJ5wz4DoN2jfvj1mzZqF1q1bw9PTU3c/fyJ7wz0DIiLiRWdERMRiQEREYDEgIiKwGBAREVgMiIgIwP8BT05QC0K3+0wAAAAASUVORK5CYII=",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- " sns.countplot(x=train['age'], hue=train['TenYearCHD'], data=train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='count', ylabel='cigsPerDay'>"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- " sns.countplot(y=train['cigsPerDay'], hue=train['TenYearCHD'], data=train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', hue='currentSmoker', data=train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', hue='BPMeds', data=train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
- "source": [
- "#sns.set_style('whitegrid')\n",
- "#sns.countplot(x='TenYearCHD', hue='prevalentStroke', data=train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', hue='prevalentHyp', data=train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', hue='diabetes', data=train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Outliers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='totChol', ylabel='Density'>"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['totChol'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TenYearCHD', ylabel='totChol'>"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.boxplot(y=train['totChol'], x=train['TenYearCHD'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "352.0"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_totChol = train['totChol'].quantile(0.99)\n",
- "q_totChol"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['totChol']<q_totChol]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='sysBP', ylabel='Density'>"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['sysBP'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TenYearCHD', ylabel='sysBP'>"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.boxplot(y=train['sysBP'], x=train['TenYearCHD'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "199.95499999999993"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_sysBP = train['sysBP'].quantile(0.99)\n",
- "q_sysBP"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['sysBP']<q_sysBP]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='diaBP', ylabel='Density'>"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['diaBP'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TenYearCHD', ylabel='diaBP'>"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.boxplot(y=train['diaBP'], x=train['TenYearCHD'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "113.28999999999996"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_diaBP = train['diaBP'].quantile(0.99)\n",
- "q_diaBP"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['diaBP']<q_diaBP]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='BMI', ylabel='Density'>"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['BMI'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:ylabel='BMI'>"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#sns.boxplot(y=train['BMI'], x=train['TenYearCHD'])\n",
- "sns.boxplot(y=train['BMI'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "38.262399999999985"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_BMI = train['BMI'].quantile(0.99)\n",
- "q_BMI"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['BMI']<q_BMI]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='heartRate', ylabel='Density'>"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEECAYAAAAoDUMLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAzD0lEQVR4nO3de1yUZf7/8dccmOEww1kRBFQwCjVDUDM3T5RZ32xbRQHty25lW23tV7e1Nju55rpi225b7ZZr/bZt0w6aq1tW2xraRtlJSFwPpIKKKYiAgMyMzIG5f3+IU8RR5XYY+DwfDx4y9zX3fX+uAt5zn65LoyiKghBCiD5P6+0ChBBC9AwSCEIIIQAJBCGEEM0kEIQQQgASCEIIIZrpvV3AhSgqKsJoNKqybbvdrtq2vUn65VukX77FV/plt9tJSUlptdynA8FoNJKcnKzKtouLi1XbtjdJv3yL9Mu3+Eq/iouL21wup4yEEEIAEghCCCGaSSAIIYQAJBCEEEI0k0AQQggBSCAIIYRoJoEghBACkEAQQgjRTAJBCCEE4ONPKoveod7moMHuarHMbNQTEmjwUkVC9E0SCMLrGuwu8vdXt1g2MSlSAkGIi0xOGQkhhAAkEIQQQjSTQBBCCAFIIAghhGimykVlt9vNkiVL2LdvHwaDgWXLljFo0CBP+9atW3nuuefQ6/VkZGSQmZnJhg0b2LhxI3Bm8obi4mK2bdtGcHCwGiUKIYT4HlUCIS8vD4fDwdq1aykqKmLFihWsXLkSAKfTSW5uLuvXrycgIIA5c+YwZcoUZs6cycyZMwF4/PHHycjIkDAQQoiLSJVTRoWFhUyYMAGAlJQUdu/e7WkrLS0lPj6ekJAQDAYDaWlpFBQUeNp37dpFSUkJWVlZapQmhBCiHaocIVgsFkwmk+e1TqfD5XKh1+uxWCyYzWZPW1BQEBaLxfN61apV3HvvvV3az9lTS2pobGxUbdve1BP75dCbqDhe0WJZTbiGhuNlXd5GT+xXd5B++RZf75cqgWAymbBarZ7XbrcbvV7fZpvVavUExKlTpzh48CDjxo3r0n5kTuVz1xP7dbTWRvQApcWyiMgIYsPiuryNntiv7iD98i2+0q+LOqdyamoq+fn5ABQVFZGUlORpS0xMpKysjLq6OhwOBwUFBYwaNQqA7du3M378eDVKEkII0QlVjhCmTp3Ktm3byM7ORlEUli9fzqZNm7DZbGRlZbFo0SLmzZuHoihkZGQQFRUFwKFDh4iNjVWjJCGEEJ1QJRC0Wi1Lly5tsSwxMdHzfXp6Ounp6a3Wu+OOO9QoRwghRBfIg2lCCCEACQQhhBDNJBCEEEIAEghCCCGaSSAIIYQAJBCEEEI0k0AQQggBSCAIIYRoJoEghBACkEAQQgjRTAJBCCEEIIEghBCimQSCEEIIQAJBCCFEMwkEIYQQgASCEEKIZhIIQgghAAkEIYQQzSQQhBBCABIIQgghmkkgCCGEAFQKBLfbzeLFi8nKyiInJ4eysrIW7Vu3biUjI4OsrCzWrVvnWb5q1SqysrKYOXMmb775phqlCSGEaIdejY3m5eXhcDhYu3YtRUVFrFixgpUrVwLgdDrJzc1l/fr1BAQEMGfOHKZMmcLBgwfZsWMHr7/+OqdPn+all15SozQhhBDtUCUQCgsLmTBhAgApKSns3r3b01ZaWkp8fDwhISEApKWlUVBQwN69e0lKSuLee+/FYrHwq1/9qtP92O12iouL1egCjY2Nqm3bm3pivxx6ExXHK1osqwnX0HC8rJ01WuuJ/eoO0i/f4uv9UiUQLBYLJpPJ81qn0+FyudDr9VgsFsxms6ctKCgIi8VCbW0t5eXl/OUvf+Ho0aP87Gc/4/3330ej0bS7H6PRSHJyshpdoLi4WLVte1NP7NfRWhvRA5QWyyIiI4gNi+vyNnpiv7qD9Mu3+Eq/2gstVQLBZDJhtVo9r91uN3q9vs02q9WK2WwmNDSUhIQEDAYDCQkJGI1GTp48SUREhBolCiGE+B5VLiqnpqaSn58PQFFREUlJSZ62xMREysrKqKurw+FwUFBQwKhRo0hLS+Pjjz9GURQqKys5ffo0oaGhapQnhBCiDaocIUydOpVt27aRnZ2NoigsX76cTZs2YbPZyMrKYtGiRcybNw9FUcjIyCAqKoqoqCi2b9/OrFmzUBSFxYsXo9Pp1ChPCCFEG1QJBK1Wy9KlS1ssS0xM9Hyfnp5Oenp6q/W6ciFZCCGEOuTBNCGEEIAEghBCiGYSCEIIIQAJBCGEEM0kEIQQQgASCEIIIZpJIAghhAAkEIQQQjSTQBBCCAFIIAghhGgmgSCEEAKQQBBCCNFMAkEIIQQggSCEEKKZBIIQQghAAkEIIUQzCQQhhBCABIIQQohmEghCCCEACQQhhBDN9Gps1O12s2TJEvbt24fBYGDZsmUMGjTI075161aee+459Ho9GRkZZGZmAvCjH/0Is9kMQGxsLLm5uWqUJ4QQog2qBEJeXh4Oh4O1a9dSVFTEihUrWLlyJQBOp5Pc3FzWr19PQEAAc+bMYcqUKQQHBwOwevVqNUoSQgjRCVUCobCwkAkTJgCQkpLC7t27PW2lpaXEx8cTEhICQFpaGgUFBcTExHD69Gluv/12XC4Xv/zlL0lJSelwP3a7neLiYjW6QGNjo2rb9qae2C+H3kTF8YoWy2rCNTQcL+vyNnpiv7qD9Mu3+Hq/VAkEi8WCyWTyvNbpdLhcLvR6PRaLxXNaCCAoKAiLxYK/vz/z5s1j9uzZHD58mJ/+9Ke8//776PXtl2g0GklOTlajCxQXF6u2bW/qif06WmsjeoDSYllEZASxYXFd3kZP7Fd3kH75Fl/pV3uhpUogmEwmrFar57Xb7fb8Yf9+m9VqxWw2M2TIEAYNGoRGo2HIkCGEhoZSVVVFdHS0GiUKIYT4HlXuMkpNTSU/Px+AoqIikpKSPG2JiYmUlZVRV1eHw+GgoKCAUaNGsX79elasWAFAZWUlFouFfv36qVGeEEKINqhyhDB16lS2bdtGdnY2iqKwfPlyNm3ahM1mIysri0WLFjFv3jwURSEjI4OoqChmzZrFQw89xJw5c9BoNCxfvrzD00VCCCG6lyp/cbVaLUuXLm2xLDEx0fN9eno66enpLdoNBgN/+MMf1ChHCCFEF8hHcOHz6m0OHHoTR2ttnmVmo56QQIMXqxLC90ggCJ/XYHeRt7e8xZ1KE5MiJRCEOEcydIUQQghAAkEIIUQzCQQhhBCABIIQQohmEghCCCEACQQhhBDNJBCEEEIAEghCCCGaSSAIIYQAJBCEEEI0k0AQQggBdDEQvjsFphBCiN6pS4Hw17/+lczMTNasWcOpU6fUrkkIIYQXdGm00z/+8Y/U19fzzjvvsGDBAsLDw8nMzOTKK69Uuz4hhBAXSZevIVRXV1NeXk5tbS1hYWG8//77PPTQQ2rWJoQQ4iLq0hHC7Nmz8ff3JzMzkwULFmAwnBlnft68eaoWJ4QQ4uLpUiA89thjjBw50vP6yy+/ZOzYsfz1r39VrTAhhBAXV4eBUFBQQElJCS+//DK33XYbAE1NTbz22mu88847F6VAIYQQF0eH1xCCg4Oprq7G4XBQVVVFVVUVtbW1PPDAAx1u1O12s3jxYrKyssjJyaGsrKxF+9atW8nIyCArK4t169a1aKupqWHSpEmUlpaeZ5dET1Bvc3C01tbiq97m8HZZQogOdHiEkJSURFJSEpmZmfTv37/LG83Ly8PhcLB27VqKiopYsWIFK1euBMDpdJKbm8v69esJCAhgzpw5TJkyhX79+uF0Olm8eDH+/v4X1ivhdQ12F/n7q1ssk3mOhejZOjxCmD9/PgAzZ87k6quvbvHVkcLCQiZMmABASkpKiwfbSktLiY+PJyQkBIPBQFpaGgUFBQA88cQTZGdnn1P4iN7B4XLT5Fa8XYYQfVqHRwjPPvssAJ988sk5bdRisWAymTyvdTodLpcLvV6PxWLBbDZ72oKCgrBYLGzYsIHw8HAmTJjACy+80KX92O12iouLz6m2rmpsbFRt2950sfrl0JuoOF7RYllNuIaG4y1PH55qbOIvhafIL60lwE/LjZcG09+kb/O9He3L5XS22N+5rN+Tyc+hb/H1fnXpLqPt27dz+vRpFEXhN7/5DQsWLOCmm25q9/0mkwmr1ep57Xa70ev1bbZZrVbMZjOrV69Go9Hw2WefUVxczIMPPsjKlSvp169fu/sxGo0kJyd3pQvnrLi4WLVte9PF6tfRWhvRA1p+4o+IjCA2LM7z2u1WuPXl7Xx2qI7UQWEcqLTwjz313D0psdV7O9uX3q+c6AHR7e7LV8nPoW/xlX61F1pdejDtySefZPDgwbzyyiu8/vrrvPHGGx2+PzU1lfz8fACKiopISkrytCUmJlJWVkZdXR0Oh4OCggJGjRrFq6++ypo1a1i9ejXJyck88cQTHYaB8H0rPyolf38VC665hBmjYrlnylD8dFo+2Fvp7dKE6JO6dIRgNBqJiIhAr9fTr18/HI6O7xaZOnUq27ZtIzs7G0VRWL58OZs2bcJms5GVlcWiRYuYN28eiqKQkZFBVFRUt3RG+I5jdad5ZssBbrw8mptTYvj4QA0mo54Jl0SyeW8le8rriQ0L9HaZQvQpXQoEk8nEbbfdxty5c3n11VeJjo7u8P1arZalS5e2WJaYmOj5Pj09nfT09HbXX716dVfKEj7s6Q/2gwIP35iMonx7aumqhAg+Kanm75+WMW14xz9nQoju1aVAeOaZZzhy5AhDhw5l//79zJ49W+26RC92oLKBf3x1lNt+MISBoQEcrbV52ox+OsYODufjA9VUW+xEmoxerFSIvqVL1xBqamr48MMP+fOf/8zmzZu7fBeQEG158t/7CDTouXfK0DbbR8aF0qQovLeros12IYQ6uhQICxYswGKxEBkZ6fkS4nx8daSWzXsruXNiAuFBbT+kNiDYn4TIIN4uKr/I1QnRt3XplFFQUBD33Xef2rWIXk5RFJ7419dEmozMu3pIh++9dlh/Xsg/xNFam1xcFuIi6dIRwiWXXMK7777LwYMHOXToEIcOHVK7LtELfXHoJF8cOsn8a4YSZOz4s8g1yWfuPHt/9/GLUZoQgi4eIRQXF7d4kEGj0fDKK6+oVpTofdyKwl8+OkR8eCDZY+I7ff/A0AAuG2Bm895K7piQcBEqFEJ0KRBWr15NQ0MDx44dIy4ujqCgILXrEr3MjiN1lJyw8Ex2CgZ91ybqu25YFH/+sISTVke71xuEEN2nS7+Z//73v8nJyeGBBx7g5Zdf5vnnn1e7LtGL1J928u6uci4fGMJNI2O6vN7UYQNwK7ClWJ5cFuJi6FIg/O1vf2PdunWEhoZyzz33kJeXp3ZdopdQFIWNO47S5FZ45MbL0Go1XV53xMBgokP8ZSgLIS6SLgWCRqPBYDCg0WjQaDQEBASoXZfoJQrKatlfaeH64QPO+W4hjUbD1GFR5B+o4rSjSaUKhRBndSkQxowZw8KFC6msrGTx4sVcfvnlatcleoFam4P3dlWQEBnElQkR57WN64YNoNHp5pOS6s7fLIS4IJ1eVP7666/RarXs2bOHH/7whwQHB5OTk3MxahM+zK0o/OOroyhARmosWo0GV5O7xTAVZ9md7X/6vzIhHLO/ng/2HmfqMBkEUQg1dRgI//rXv3jxxReZM2cODzzwAOXl5axbt47o6Giuvfbai1Wj8EEbdxzjYJWVGSkDCWu+Q+i0082O0pOt3jsqPrTd7fjptKRf1p8txSdocivozuEahBDi3HQYCK+88gpr1qwhMPDbc78zZszgZz/7mQSCaNfhaisr/1NKUpSJ0YPDLnh7U4dF8VZROYVltYwdEt4NFQoh2tLhNQS9Xt8iDODMUNg6nU7VooTvanIrPLB+J3qtlhmjYtFoLvwT/eRL++Pvp+Xtnce6oUIhRHs6DIT2fpndbrcqxQjf9/Knh9l+uJYF115CSIBft2zTZNQzbfgANu2soLGD6w1CiAvT4SmjkpISFi5c2GKZoiiUlpaqWpTwTcfqTvOHzfuYcmk/rh8exccHarpt27PSYnmrqJwtxSe4caRMnCOEGjoMhKeffrrN5dnZ2WrUInyYoij8+q3dKAosvXkE3XCmqIXxiZEMCPZnfeE3EghCqKTDQBg7duzFqkP4uH/vqSSv+AQP3XAZceGBbd5eeiF0Wg2zR8fy5w9LOFhlIaGfqVu3L4To4oNpou+ptzk4Wmtr8VVvc7T5XovdxZK393DZADO3dzLPwYX4yfjBGHRa/vKRnLIUQg1dGu30XLndbpYsWcK+ffswGAwsW7aMQYMGedq3bt3Kc889h16vJyMjg8zMTJqamnj00Uc5dOgQOp2O3Nxc4uM7HyZZqKPB7iJ/f8ungycmRRIS2HrU0T9s3kdlQyPP/28qfjr1PmNEmoxkj4njtS+P8Itrk4gJlSFUhOhOqvz25uXl4XA4WLt2LQsXLmTFihWeNqfTSW5uLi+99BKrV69m7dq1VFVV8eGHHwLwxhtvMH/+fHJzc9UoTXSzXUfr+funh7nlynhS4y/8mYPO/HTimbkR/vjBftX3JURfo8oRQmFhIRMmTAAgJSWF3bt3e9pKS0uJj48nJCQEgLS0NAoKCrjhhhuYPHkyAOXl5TJvswrqbQ4celOr8/tmo77NT/6dcTW5eXjjLiJMRh6Ydll3ldmh2LBAbr96CKs+OsicixRCQvQVqgSCxWLBZPr2op9Op8PlcqHX67FYLJjNZk9bUFAQFovlTDF6PQ8++CAffPABzz77bKf7sdvtLWZy606NjY2qbdtbHHoT7+8sQ+/XcvL6a4fFYHBZWr234nhFi2U14Roajpd5Xv9jTx27jtWzaGJ/yg+XUN7J+pdFGlota2/59/f1XdMGulkfoONXbxTw9I0DaTKYcTmdLbbR0fq+pDf+HIL0q6dSJRBMJhNWq9Xz2u12o9fr22yzWq0tAuKJJ57g/vvvJzMzk3fffbfVk9LfZTQaSU5OVqEHZ6YNVWvb3nK01ober5zoAS1v24yIjCA2LK7Ve6MHKO2+70iNjTWvHeaay/pz1w2jWz3E2Nb6AYGBrfbd3vK2avquXythzH99B0UNQUy+NKJVv9pbv97moMHuarHsfI+QLobe+HMI0i9vay+0VLmGkJqaSn5+PgBFRUUkJSV52hITEykrK6Ourg6Hw0FBQQGjRo3in//8J6tWrQIgICAAjUYjQ2T0UIqi8PDGXei1WpbNGNEtw1Ocq5tGRnNVQgS/37yP+tPOLq939mL5d7++HxBC9FWqHCFMnTqVbdu2kZ2djaIoLF++nE2bNmGz2cjKymLRokXMmzcPRVHIyMggKiqK6667joceeohbbrkFl8vFww8/jNFoVKM8cYHWFx7lk5JqfvOjEUSHeOdOH41Gw+M3D+eGZz5m1UcHGXRuc+8IIdqgSiBotVqWLl3aYlliYqLn+/T0dNLT01u0BwYG8swzz6hRjuhGVQ12lr1bzJjBYdwy1ru3BSdFmbl1/GBe+uQQsy8PQZ5fFuLCyINp4pwsfWcvpx1N5M4ceU7zI6vlF9deQniQgY8OWVEUpfMVhBDtkkAQXfZZaQ2bdpZz75ShDO3fM4aOMPv7cefEBCotLvZVNni7HCF8mgSC6BKHy80fNu9naH8Td09O8HY5LUwbHoXZoOWj/VXeLkUInyaBILokr7iS46cayZ15OUZ9z7r7S6/TMiomgLIaG4errZ2vIIRokwSC6FRF/Wm2lVTzwytiGDO4Z05hOay/P4EGHZ+UVHf+ZiFEmyQQRKf+vec4/n467p7Us04VfZefTkPaoDC+Pn6KU+fwXIIQ4lsSCKJDpVUW9ldamHxpP4K7aUpMtYwdHI5bgYKyWm+XIoRPkkAQ7XIrCu/vPk5IgB/jEiK8XU6nIkxGhvYzsf3wSZrccguqEOdKAkG0a/exeo7VnWZqcpSq8xx0pzFDwqk/7eSLQ903n7MQfYVv/JaLi87Z5Gbz3koGBPuTEh/q7XK6bFh0MGajnn/uKO/8zUKIFiQQRJve3lnOSauDacOj0Hph8LrzpdNqSBscxucHazhWd9rb5QjhUyQQRCsWu4u/bTvMkMggkqLMna/Qw4wZHI6iwNovj3i7FCF8igSCaOXF/IPU2ZzcMGKAV4a2vlBhgQbGJYTzxvZvcDa5vV2OED5DAkG0cKKhkRc/PsiUS/sRG+a7Y0rfnDKQEw12thSf8HYpQvgMCQTRwp+2lOBwublzYtcfQqu3OThaa2vxZXc2qVhl58YlhhMd4s9rctpIiC5TZT4E4ZsOVVt5/csjzBkbT1x4IIeqbV1a7+wsZN81yst3Jum1WrLGxPF03gEOVllI6NczRmcVoieTIwThseydvfj76Zh/zSXeLqVb3HLlIIx6LX/5qNTbpQjhEyQQBAAffn2CLV+fYP41Q+lnbnvqUleTu9WpoZ5weqg9/cxGssfEseGrY3ILqhBdIIEgOO1o4vFNe0iIDOLW8UPaf5/T3WqC+vz91Tiaeu4wEXdOOjN168r/lHi5EiF6PgkEwar8Ug7X2Fg2YwQGfe/6kRgYGsDcK+N57YsjFFec8nY5QvRoveu3X5yz0ioL6wuPcev4wYxPjPR2Oar45dQkQgL8+PVbe2TeZSE6oEoguN1uFi9eTFZWFjk5OZSVlbVo37p1KxkZGWRlZbFu3ToAnE4nDzzwAHPnzmXWrFls2bJFjdLEdzQ6m/jHV0eJDQvgwesv83Y5qgkNNPCr6y/jy8Mn2bDjmLfLEaLHUuW207y8PBwOB2vXrqWoqIgVK1awcuVK4Mwf/tzcXNavX09AQABz5sxhypQp5OfnExoaypNPPkltbS0zZszgmmuuUaM80ey9XRXU25ysmHk5AYaeNS1md8saHce/9xznua2l3DUpgeiQgPPaTr3NQYPd1WKZ2agnJNDQHWUK4VWqBEJhYSETJkwAICUlhd27d3vaSktLiY+PJyQkBIC0tDQKCgq4/vrrmTZtmud9Ol3nf6DsdjvFxcXdXP0ZjY2Nqm3bWxx6Ey6nk4rjFRyudVBQdorUmACija5WfXXoTVQcr2ix7LJIQ6tl7S3v6rL2lteEa2g4XtbqvZ31q6P177oikJ3f1PLKpwfJHBGKv5/2vPaVt7flSKrXDovB4LJ0af1z1Rt/DkH61VOpEggWiwWT6dsHgXQ6HS6XC71ej8ViwWz+dsC0oKAgLBYLQUFBnnXnz5/PL37xi073YzQaSU5O7vb6AYqLi1XbtrccrbWh9ysnJLwf/9lxgP5mIz8anUBEZASxYXGt3hs9oOX59oDAQKIHRLfablvLu7qsveVt1dRZv767jfbW/61fOP/3WhEfljn4yfjB6LSac97X9/+7nMv656o3/hyC9Mvb2gstVa4hmEwmrFar57Xb7Uav17fZZrVaPQFRUVHBj3/8Y26++WZuuukmNUoTwPu7j2O1u5g9Og69j0x8011GxoZyc0oMJVUW/rW79dGKEH2ZKn8NUlNTyc/PB6CoqIikpCRPW2JiImVlZdTV1eFwOCgoKGDUqFFUV1dz++2388ADDzBr1iw1yhJApcVJYVkt4xMjGRh65jx6Ww+c9dSHzbrD6MHhjE+M4NPSGgoOn/R2OUL0GKqcMpo6dSrbtm0jOzsbRVFYvnw5mzZtwmazkZWVxaJFi5g3bx6KopCRkUFUVBTLli3j1KlTPP/88zz//PMAvPjii/j7+6tRYp+kKAofHbJiMupJv6y/Z/lpp5sdpS3/MHp7LCK13TAimhMNdt4qKufaYf19emRXIbqLKoGg1WpZunRpi2WJiYme79PT00lPT2/R/uijj/Loo4+qUY5otq20hkqLi4zUgfj79e67ijqj02qYMyae5/9TwiMbd5MSF0ZM6PndeSREb9G3TiD3YYqi8PdPDxNs1JISF+btcnqEAIOOnHGDsLvc/Py1r2QyHdHnSSD0ER8fqKa4ooG0gYHotL43C5pa+gf78+D1l/HVkTp+9/7X3i5HCK+S+RD6iJe2HSLSZCC5X9sjmfZl1yT3p+SEhRc/PsSYweFcN3yAt0sSwivkCKEP+OakjY/2V3HTFTFydNCOR6cnc/nAEO5/cyffnOzaxEBC9DYSCH3A618eQQPcNLL1Q2HiDKNex3NzU1GAe1/7Crur9952K0R7JBB6OWeTm3UF33BNchT9g+UW3o7ERwTy5Kwr+O/Ren77ru8OPyDE+ZJrCL3cJweqqbY4yBytztAKPdXZh+2+r7MH7q4fMYA7rh7C//vkELFhAdw5MbHD9wvRm0gg9HKbdpYT7K9nYlIkVQ12b5dz0bT1sB107YG7h/4nmYpTjSx/72vCAg3M7mNhKvouCYRerNHZxOa9ldx4eTRGfd9+EO1c6LQansq8gnqbk0UbdhEWaODaYVHeLksI1ck1hF7sP/tOYLG7uOmKGG+X4nOMeh2rctIYERPMPa99xacl1d4uSQjVSSD0Ym/vLCfSZGBcQri3S/FJQUY9L982liERQdzxSgGFZTIQnujdJBB6KYvdxZbiE/zP5dG9Zojrepuj1aisao/MGhZkYPUdY+lvNnLr37az73iDavsSwtt6x18K0Ure3krsLnevOl3UYHeRv7+61ZejSel85QvQ3+zPqz8dR7C/HwvX7eSk1aHq/oTwFgmEXmrTznJiQvxJi5eB7LrDwNAA1txxJU2KwiufHaaxF88XIfouCYReqM7mIP9AFdOviEHro0NV9MRJe4ZEBvGbm4dTbbGzvvAoitL+kUl7p7fqbXJ0IXouue20F/rX7uM4mxRuGum7p4t66qQ9oweHc/2IaN7bVcEnJdVMuKRfm+87e3rr+yYmRRISaFC7TCHOixwh9EJvF5UzJDKIEQODvV1Kr/SDxAiGxwTz7z3HOVRt7XwFIXyEBEIvU3mqkc8P1XDTFTFoNL55uqin02g0ZKTGEhZo4I3tR6ix9J0nwEXvJoHQy7zz3woUBX7Yi+4u6on8/XTccuUgGp1NPL5pLy6ZbU30AhIIvczbO8sZHhPM0P4mb5fS6w0I8efmKwby1ZE6nvpgv7fLEeKCqRIIbrebxYsXk5WVRU5ODmVlZS3at27dSkZGBllZWaxbt65F286dO8nJyVGjrF7vcLWVnd/UydHBRZQ6KIybrojm+f+UsqW40tvlCHFBVAmEvLw8HA4Ha9euZeHChaxYscLT5nQ6yc3N5aWXXmL16tWsXbuWqqoqAF588UUeffRR7HY5J3s+Nu0sB2C6BMJF9YtrL2F4TDD3rS2S2daET1MlEAoLC5kwYQIAKSkp7N6929NWWlpKfHw8ISEhGAwG0tLSKCgoACA+Pp4//elPapTU6ymKwts7yxkzOIyBoQHeLqdPMep1rLwlDQW49W9fcuJUo7dLEuK8qPIcgsViwWT69hy2TqfD5XKh1+uxWCyYzWZPW1BQEBaLBYBp06Zx9OjRLu/HbrdTXKzOzFaNjY2qbVsNJTV2DpywcO+VEe3W7dCbcDmdVByvaLH8skjDeS/rKet/v1/nsq+acA0Nx1ue1tQag2hsan2Xlkbn1+b6BpeFxyb1Y/GW48z4cz6PTUug4nhNq/Xb2ldHfO3nsKukXz2TKoFgMpmwWr+9P9vtdqPX69tss1qtLQLiXBiNRpKTky+s2HYUFxertm01vLLhv/j7ablzWhohgX5tvudorQ29XznRA1rOrRwQGHjey3rK+no/vxZt57KviMgIYsNaToJztNZGYRsPlo2Kb3/95GRIGHKSeX8vYOFbJcwYNZBhMSGd7qst9TYHDXYXjuoazJERnuVmo75XPNjma79fXeUr/WovtFQ5ZZSamkp+fj4ARUVFJCUledoSExMpKyujrq4Oh8NBQUEBo0aNUqOMPuNUo5N/7ijnh1fEtBsG4uIYPTicd+dfTVx4IGu+OMK/dlXQ5D73wffOPumct7e8xUB+DXaXClULcYYqRwhTp05l27ZtZGdnoygKy5cvZ9OmTdhsNrKysli0aBHz5s1DURQyMjKIipLZqC7EhsKjnHY2kTNusLdLEUBsWCDP35LKQxt28XFJNUdO2sgeG09IgIS16NlUCQStVsvSpUtbLEtM/Hay8vT0dNLT09tcNzY2ttWtqKJ9riY3L207zBVxoVweG9L5CuKiMOi13JwykMERQWwsOsafth4gc3QcE5MivV2aEO2Swe183FtF5Rw5aePRG3v+ecue6uzIqt/VXSOrXhEXSkxoAK9/eYSXPz2Mn07Dw/+TLMOKiB5JAsGHNbkVnvuwhOToYKbKJPDnTe2RVfuZjdw9KZGNO47y4seHqLE6yJ15OUa9rtv2IUR3kKErfNi6gm84WG1lfvpQ+cTZwxn0WjJHx3HH1UPY8NUx/vf/fSEzr4keRwLBR9VaHfzu/a8ZOySc60cM8HY5ogs0Gg23/mAwz84Zxc6j9fzouW2UnJA5mkXPIYHgo554/2tONbpYevPwNo8O2pqxy9szjokzfnhFDG/cOQ6bw8WM5z/l4wNV3i5JCECuIfik93ZV8Mb2b7h7UiKXDWh7Epy2ZuzqCTOO9Ubnc1E6NT6Mf977A+a9XMCtf9vOfddewl2TEvHTyWc04T0SCD6k3uZgb8UpHnhzJ8Oig8kaE8vRWluveXrVV53vRenYsEDW/+wqFm3Yxe837+ftneU8dEMyky9te1pOIdQmH0d8yOEaK/e8+hUK8D+XR/NZ6Ul5etXHmf39eG5uKi/+eDR2l5vbXt5O9gufs6e83tuliT5IjhB8RFWDnV+u24nF7uKOqxMID5Ijgt5k6rAoJiX1Y+32Izyz5QB3rf6K4THBpEXpaD0ikxDqkCMEH3Ckxkbmqs84Vnea/x03iLjwQG+XJFRg0GvJuWowHz0whXlXD+bACQuv7axlW0k1inLu4yEJca7kCKGH+7Skmnte+wq3W+GPmSnU2pzeLklcgLYuQH//GlCQUc9tPxhCpMmftV+U8u6uCspO2piVGnuxyxV9jARCD2V3NfHUB/t5If8gQ/uZePHHo9HrNK3uHAJ1h14Q3autC9ATkyLbvCkgJMCP6ZcGU2rx4/3dx6mx2Bk+0ExsmBwhCnVIIPRAXx8/xfzXd7C/0sIPr4jh5+mJ6HWadv/Iqz30gvAejUbDhEv6ERXszxvbj/DTvxfy4k9GM2ZwuLdLE72QXEPoQSx2F7nvFTP92U+oarDz43GDGJcQQcHhOvL3V+NokvPIfVVSlJl7Jg3FHKDnlhe/4B+FXZ9ZsDNtPcRYb5NhNfoiOULoAeqsdjYUlfPchyXUWBxMHxnNbeMHsa/S2vnKos+INBtZ9b9p/OadYha+uZPSKgsLr7sUnfbCxrFq6yHG9k5jid5NjhC8bE95Pbe+vJ2lm/bir9fxs0mJjE+MJNAok6mI1oID/Hhl3liyx8Tx/H9Kmfvi5xyrO+3tskQvIUcIXvLNSRt/2LyPfxaVE+yv50cpAxk9OAytjFoqOuGn05I783JSB4Xx+Nt7uPYPH3HnxARu+8FgQuVTvbgAEggXWY3Fzp+2lvDqF2XotBp+NjmRH14RzY4j8mSq6DqNRkPm6DiuSohgxb++5pktB1iVX8q04QOYfGk/RsSEEBceiL+fDkVRaHS6sdhdnLQ6qLbYm78cnDrtxGp3UdlgJyzAj5jQAIJlqs8+SwLhIimrsfK3bYd5s+AbTjubyBoTx4JrkhgQ4t/qllHRt1zIbcNx4YEsnzGCWaNj2fjVMT7aV8VbReWedoNOi9Pt5lyea4sIMrDrWB0zRsWSNijsgq9RCN8hgaCihkYnH+6r4u2icrZ8XYleq2H6yBjunZLI0P5mb5cneohzuW24vfCoqGtkXEIEY4eEc7y+kdBAPxqdTTTYXRh1WgIMegINOsKDDESajPQzn/k3JMCPspNW/r27kpNWB9/UnuZAZQNvFhzl9S+/IdJkYOqwAdwwYgBXJUbIaKy9nCqB4Ha7WbJkCfv27cNgMLBs2TIGDRrkad+6dSvPPfccer2ejIwMMjMzO13HF9RY7Ow6Vs+uo/UUlNXyWWkNjiY3kSYj904eSs5Vg4gK9vd2mcKHdRYeWo2GmNAAJiZFdvkBNr1Wi9nfD7O/H4Migrh6aCRpg0I5cMLC+7uP81bRMV7/8ggBfjrSBoUxdkg4I2NDGBQRxMDQAAx6CYneQpVAyMvLw+FwsHbtWoqKilixYgUrV64EwOl0kpuby/r16wkICGDOnDlMmTKFHTt2tLvOxaAoCm7lzDzF7uZzrvU2JzanC6u9CZvjzL91NgfVVgc1Fjs1FgdVDXa+qbVx5KSNhsYzo45qNJAQGcRPxg9i2vABjIqXw25xcbV1JAGg14LL3XJZW6engox6po+MYfrIGBqdTeTvr+LT0ho+K63mjx/s5+wZKK0GIkxGQgP8CA7wI8BPh4KConDmC4Umt4KjScHpctPobMLucmN3unBzlCa3gkGnwd+gw6DTYtTrMOi1GPVa/P10+Ps1/6tv/t5w9vvvtPlpCfDTYfzu+/x0GPVadFoNWo0GjeZMWJ75Aq322+81Z5edbde2fK9Go0FRmvsEuJUzfyPO9tGtKJ7lDc1/I9xnlyvf/m1RaP5XUVrsS6fRnKlTq0HXxvKLOT2uKoFQWFjIhAkTAEhJSWH37t2ettLSUuLj4wkJCQEgLS2NgoICioqK2l2nu/3lo1KeztuP233mf1pT8/+41g53uJ3QQD8iTUaig/25dICZgaEBJEWZSIoyExKg9/ziVdSfuS2wrV9GkGEmRPdr60gCzhxN7DhS12rZ930/UIbFBDMsJpiccfH8Z181lacaOWl1UGN1EGjQnflj2OjC5nCh0WjQcOaDkQYNfjotgQYtfjotLrebOpsTe6Mbc1AQGg1Emgy43AoOlxtHkxuny43d1cSpRid2pxu7y02jqwm70+15T89W1q1bOxtmuu+EVVSwP/9aMAF/P1337ktRYRjFRx55hOuuu45JkyYBMHnyZPLy8tDr9RQUFLBmzRqefvppAJ555hliYmIoKipqd532FBUVYTQau7t8IYTo1ex2OykpKa2Wq3KEYDKZsFq/fcrW7XZ7/rB/v81qtWI2mztcpz1tdUgIIcT5UeVqUGpqKvn5+cCZT/FJSUmetsTERMrKyqirq8PhcFBQUMCoUaM6XEcIIYT6VDlldPaOof3796MoCsuXL2fv3r3YbDaysrI8dxkpikJGRga33HJLm+skJiZ2d2lCCCHaoUogCCGE8D1yA7EQQghAAkEIIUQzCQQhhBCABIJHTU0NkyZNorS0lLKyMubMmcPcuXP59a9/jdvd0x+Ead+qVavIyspi5syZvPnmm72ib06nk4ULF5Kdnc3cuXN9/v/Zzp07ycnJAWi3H+vWrWPmzJlkZmby4YcferPcLvtuv4qLi5k7dy45OTnMmzeP6uozE/L4er/O2rRpE1lZWZ7XvtgvABShOBwO5Z577lGuu+46paSkRLnrrruUzz//XFEURXnssceUzZs3e7nC8/P5558rd911l9LU1KRYLBbl2Wef7RV9++CDD5T58+criqIon3zyifLzn//cZ/v1wgsvKNOnT1dmz56tKIrSZj9OnDihTJ8+XbHb7cqpU6c83/dk3+/XLbfcouzdu1dRFEV5/fXXleXLl/eKfimKouzdu1f58Y9/7Fnmi/06S44QgCeeeILs7Gz69+8PwJ49exg7diwAEydO5NNPP/Vmeeftk08+ISkpiXvvvZe7776byZMn94q+DRkyhKamJtxuNxaLBb1e77P9io+P509/+pPndVv9+O9//8uoUaMwGAyYzWbi4+P5+uuvvVVyl3y/X0899RTJyckANDU1YTQae0W/amtr+f3vf8/DDz/sWeaL/TqrzwfChg0bCA8P94yjBGcGnzo7oFRQUBANDQ3eKu+C1NbWsnv3bp555hkef/xx7r///l7Rt8DAQI4dO8YNN9zAY489Rk5Ojs/2a9q0aS2eyG+rHxaLBbP52+HSg4KCsFgsF73Wc/H9fp39sPXVV1+xZs0abr31Vp/vV1NTE4888ggPP/wwQUFBnvf4Yr/O6vPzIfzjH/9Ao9Hw2WefUVxczIMPPsjJk98OCma1WgkODvZihecvNDSUhIQEDAYDCQkJGI1Gjh8/7mn31b69/PLLXH311SxcuJCKigp+8pOf4HQ6Pe2+2i8Arfbbz2hn+9HecC++5r333mPlypW88MILhIeH+3y/9uzZQ1lZGUuWLMFut1NSUsJvf/tbxo0b57P96vNHCK+++ipr1qxh9erVJCcn88QTTzBx4kS++OILAPLz8xk9erSXqzw/aWlpfPzxxyiKQmVlJadPn+aqq67y+b4FBwd7fsFCQkJwuVwMGzbM5/sFtNmPkSNHUlhYiN1up6GhgdLSUp8b2uWtt97y/J7FxcUB+Hy/Ro4cybvvvsvq1at56qmnGDp0KI888ohP96vPHyG05cEHH+Sxxx7jqaeeIiEhgWnTpnm7pPMyZcoUtm/fzqxZs1AUhcWLFxMbG+vzfbv11lt5+OGHmTt3Lk6nk/vuu48RI0b4fL+g7Z89nU5HTk4Oc+fORVEU7rvvPp8a5bepqYnf/va3REdH83//938AjBkzhvnz5/t0v9rTr18/n+2XDF0hhBACkFNGQgghmkkgCCGEACQQhBBCNJNAEEIIAUggCCGEaCaBIPq8DRs28Pvf/75btmW323nzzTc92508eTI5OTnk5ORw88038/jjj3e4/r59+9i+fXu31CLEuZJAEKIbVVVVeQIBYPr06axevZrVq1ezceNGiouL2bVrV7vrb968mZKSkotRqhCtyINpQnBmSOPbb7+dkydPMmfOHIYMGcIf//hHdDodcXFxLF26FLvdziOPPEJDQwO1tbXMnj3bM6RzWFgYp06dIjY2lpKSEv785z8TExPTYh9Wq5WGhgbMZjMWi6XVtq655ho2btyIn58fw4cPp7GxsVUNfn5+XvovJPoCCQQhAL1ez1//+leOHTvGT3/6U9xuN6+99hoRERE8/fTTbNy4keHDh3PjjTdy3XXXUVlZ6XkaFeCmm25i6tSpHD16lP379/Pzn/+cDRs28M4771BUVERVVRVBQUHcfffdDB48mD179rS5rRkzZhAZGcnll1/O9ddf36qGzMxML/+XEr2ZBIIQnBlDSKPR0K9fP8rLy9FqtfziF78AoLGxkR/84AdMmjSJv//972zevBmTyYTL5fKsP2TIkDa3O336dO6//36++eYb7rjjDgYPHgxAZGRku9sCOHnyJCdOnGhVgxBqkkAQAjxDTgOEhYUREBDA888/j9lsZsuWLQQGBvLSSy+RkpLC3Llz+fzzz/noo49ara/VatucrS0uLo5f//rXLFiwgHfffbfdbWk0GtxuN2FhYQwYMKBVDUKoSQJBiO/RarU88sgj3HnnnSiKQlBQEL/73e/QaDQsWbKETZs2ERoaik6nw+FwtFg3IiICp9PJk08+SWJiYou28ePHM378eJ599lmmTJnS5rZGjBjB7373OxITE9usQQg1yeB2QgghALntVAghRDMJBCGEEIAEghBCiGYSCEIIIQAJBCGEEM0kEIQQQgASCEIIIZr9f/0VMFB1jNBcAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['heartRate'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:ylabel='heartRate'>"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#sns.boxplot(y=train['heartRate'], x=train['TenYearCHD'])\n",
- "sns.boxplot(y=train['heartRate'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "110.0"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_heartRate = train['heartRate'].quantile(0.99)\n",
- "q_heartRate"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['heartRate']<q_heartRate]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='glucose', ylabel='Density'>"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['glucose'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='glucose'>"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#sns.boxplot(y=train['glucose'], x=train['TenYearCHD'])\n",
- "sns.boxplot(x=train['glucose'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "117.0"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_glucose = train['glucose'].quantile(0.97)\n",
- "q_glucose"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['glucose']<q_glucose]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='glucose'>"
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEECAYAAACLCeeIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAR/UlEQVR4nO3daWwUhR+H8e+WlZVaEFuVqMgfakWKQhS80NosRq1UPIIaREJLkMh9ShQ5pEhBMYIcGm8iVBFQMUZTQQgkSjhEtFrINrpVREWBtBBYCqWF+b8gXdlaSku3vx3t83nFdGZnft2yT4fpdPE4juMIAGAiLtYDAEBTQnQBwBDRBQBDRBcADBFdADDkrW1lQUGBfD7fWe24vLz8rB9rwe3zSe6fkfkahvkaxs3zlZeX69prr61xXa3R9fl8Sk1NPauDBgKBs36sBbfPJ7l/RuZrGOZrGDfPFwgETruOywsAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGav0/0oC6WrhwoYLBYKPtv7S0VJKUmJhY58eUlZUpPj4+ajOkpKRo1KhRUdsfmiaii6gIBoMq2B7Q8fi6R7E+mpWVSJJ+OVBRz0ceitLxS6OyH4DoImqOxyfqSKfMRtl3i6J8SWq0/df1+EBDcU0XAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADDXJ6K5evVqbN2+O9RhAk7V69WqtXr061mPERJOMbn5+vjZu3BjrMYAmKz8/X/n5+bEeIyaaZHQBIFaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYapTolpSUaO7cuSopKanz9qNHjw5vHwwGdc899ygYDEqS1q1bJ7/fr/Xr19f4+OrbV98fAHcbPHiw/H6/hg4dKkkaPXq0/H6/xo8fL+mfr/EFCxZo2LBheuWVV2pcP3v2bPn9fs2ZM0eSNGnSJPn9fj3zzDOSpE8++UR+v1+ffvqpJGnYsGHy+/0aNWqUpDM3pyEaJbqLFy9WMBjUkiVL6rx9YWFhePvc3FwdPnxYubm5kqRZs2ZJkmbOnFnj46tvX31/ANytKpZFRUWSpB9++EGS9O2330r652t85cqVkqQPPvigxvWff/65JIWjunHjRknSl19+KUmaN2+eJGnu3LmSpEAgIEkqLCyUdObmNETUo1tSUqJVq1bJcRytWrXqjGeb1bf/5ptvtHPnTknSzp07tXTpUlVWVkqSKisr//GdJxgMRmy/bdu2eh0fQGwNHjw4YjkjIyNieejQoRGv8WeffTZi/cyZMyPWT5o0KWL9ww8/HLE8aNAgOY4jSXIcR3379o1Y379//1qb01Aep+roNQgEAkpNTa3XDufOnav8/HxVVlbK6/Xqnnvu0bhx4+q8/bnnnqtQKHTa7b1er9auXRteHjhwYPgJl6SEhAQdPXq01uNnZWVp37596tixY70+N2tlZWWKj4+P9Rindep8wWBQB497VdblwUY5VouifEnSkU6ZjbL/M4kv/EitmlUqJSUlavv8N319oy0YDCopKUlLliyR3+9vlGNES/Xm1EVt7Yz6me7atWsjvkusWbOmXtvXFtyqbU51anAlKRQK1ev4AFCb6s1pKG9U9ybpjjvuiDhzvfPOO+u1fV3OdE/Vvn37Ws90azr+BRdcIJ/Pp/nz59fvkzN2Nv/SsHTqfGPGjNG2n/fEeKLG45zTQinJbaL6d+bf9PWNtjFjxjTKfhtD9eY0VNTPdLOzsxUXd3K3zZo1U1ZWVr22z8nJiVj/+OOPRyxPnjw5YnnKlCkRy9OnT6/X8QHEVvVLNj6fL2K5U6dOEcu33357xHL1E6tbbrklYvmiiy6KWE5OTo5YbtOmTcTyZZddFrFcvTkNFfXoJiUl6e6775bH49Hdd9+tpKSkem1//fXXq3379pJOnsU++uij4e80Xq9XPXv2jHh8SkpKxPbdu3ev1/EBxNZbb70Vsbx69eqI5ddeey3iNV5121eVyZMnR6yvuvOgStUdDlUWLVokj8cjSfJ4PFq+fHnE+vfee6/W5jRUo9wylp2drZSUlDqfZWZnZ6tLly7h7adMmaLzzjsvfBZb9dPI033Hqb599f0BcLeqs92qs9quXbtKkrp16ybpn6/xPn36SPr7zoTq63v16iVJuvfeeyX9ffabnp4uSRo7dqwkhe8DrrqM0qVLF0lnbk5DRP3uhWg8trGNGTNGZWVlevPNN2M9Sq3c/BxKNV/Tbay7C2J990KLonx155pu1FRd023I8+nm58/07gUAwOkRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAw5I31ALGQmZmp3bt3x3oMoMnKzMyM9Qgx0ySjm5GRoUAgEOsxgCYrIyMj1iPEDJcXAMAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAw5I31APjvaFZWqhZF+Y207xJJarT9n/n4pZLaxOTY+G8huoiKlJSURt1/aek5kqTExMQ6P6asrEzx8fFRmqBNo3+OaBqILqJi1KhRsR7hHwKBgFJTU2M9BhCBa7oAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGPI4juOcbmVBQYF8Pp/lPADwr1deXq5rr722xnW1RhcAEF1cXgAAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADHmjubOSkhL16dNHixYtktfr1cSJE+XxeHTllVdq2rRpiouLXeMfeOABtWzZUpLUtm1bDR061FXzvf7661q3bp0qKirUr18/3Xjjja6Zb+XKlfr4448lnbz/MBAIaOnSpZo1a5Yr5quoqNDEiRP1xx9/KC4uTjNmzHDV379jx47p6aef1m+//aaEhAQ988wz8ng8rpjv+++/14svvqi8vDz9+uuvNc60YsUKLVu2TF6vV8OGDVPPnj1jNqMkrVmzRqtWrdKcOXMknfx9gpkzZ6pZs2ZKS0vTyJEjTeerNydKjh075gwfPty56667nGAw6AwZMsTZvHmz4ziOM3XqVOeLL76I1qHq7ejRo879998f8TE3zbd582ZnyJAhzvHjx51QKOQsWLDAVfOdKicnx1m2bJmr5luzZo0zevRox3EcZ8OGDc7IkSNdNV9eXp4zZcoUx3Ecp7i42Bk0aJAr5nvjjTec3r17Ow8//LDjODW/Jvbu3ev07t3bKS8vdw4ePBj+c6xmnDFjhpORkeGMHTs2vM19993n/Prrr86JEyecwYMHO9u3bzeb72xE7Vvr7Nmz9cgjj+jiiy+WJO3YsUM33nijJCk9PV0bN26M1qHqraioSEeOHNGgQYOUlZWlgoICV823YcMGdezYUSNGjNDQoUPl9/tdNV+VwsJCBYNB9e3b11XzdejQQcePH9eJEycUCoXk9XpdNV8wGFR6erokKTk5WcXFxa6Yr127dlq4cGF4uaaZfvjhB1133XVq3ry5WrZsqXbt2qmoqChmM3br1k05OTnh5VAopGPHjqldu3byeDxKS0vTpk2bzOY7G1GJ7sqVK5WYmKjbbrst/DHHceTxeCRJ5513ng4dOhSNQ52Vc889V4899pjefvttTZ8+XRMmTHDVfPv379f27ds1f/58V85X5fXXX9eIESMkuevrGx8frz/++EO9evXS1KlTNWDAAFfNl5qaqvXr18txHBUUFGjPnj2umC8jI0Ne799XGGuaKRQKhS/LVX08FArFbMbMzMzwjNLJ6CYkJETM54bXSm2ick33o48+ksfj0aZNmxQIBPTUU0+ptLQ0vP7w4cNq1apVNA51Vjp06KD//e9/8ng86tChg1q3bq0dO3a4Zr7WrVsrOTlZzZs3V3Jysnw+n/766y/XzCdJBw8e1M8//6ybb75ZkiKuP8Z6vnfeeUdpaWl64okn9Oeffyo7O1sVFRWume/BBx9UcXGxsrKy1K1bN1199dXau3eva+arUtPXNCEhQYcPH474+KkRjrWa5nPDc1mbqJzpvvfee3r33XeVl5en1NRUzZ49W+np6dqyZYsk6csvv9T1118fjUOdlQ8//FDPP/+8JGnPnj0KhUK69dZbXTNf9+7d9dVXX8lxHO3Zs0dHjhxRjx49XDOfJG3dulW33HJLeLlz586uma9Vq1bhEJx//vmqrKx01XyFhYXq3r278vLydMcdd+jyyy931XxVapqpa9eu2rZtm8rLy3Xo0CEVFxerY8eOMZ70bwkJCTrnnHO0a9cuOY6jDRs2uOK5rE3U3/BmwIABysnJUVxcnKZOnaqKigolJycrNzdXzZo1i+ah6qzqp8e7d++Wx+PRhAkTdMEFF7hmPkl64YUXtGXLFjmOo3Hjxqlt27aumu+tt96S1+vVwIEDJUm//PKLa+Y7fPiwJk2apH379qmiokJZWVm65pprXDNfaWmpxo8fryNHjqhly5aaOXOmysrKXDHf77//rvHjx2vFihWn/ZquWLFCy5cvl+M4GjJkiDIyMmI2oyRt2bJFy5Yt00svvSTp5N0Ls2bN0vHjx5WWlqZx48aZzldfvMsYABjilyMAwBDRBQBDRBcADBFdADBEdAHAENGFawwYMEDFxcWxHgNoVEQXAAxF9a0dgbo6evSonnzySe3du1eXXHKJtm7dqg4dOkiSFi5cqAsvvFD9+vVTcXGxcnJylJeXp/Xr1+vll1+WdPK3p6ZPn65NmzZp3rx58vl8at26tWbNmqXKykqNHTtWjuOooqJC06dP11VXXaW8vDx99tln8ng8yszMVFZWViyfAjRRRBcxsXz5crVt21YLFixQcXGxevfuHY5uTSorKzVjxgx98MEHSkpK0ssvv6w///xTU6dO1fvvv682bdpo8eLFevXVV3XTTTepZcuWmjNnjoLBoEKhkILBoPLz87V06VJ5PB4NHDhQaWlpSk5ONvysAS4vIEaKi4vVrVs3SdIVV1yhxMTEWrffv3+/WrVqpaSkJEnSyJEj1aJFCyUkJKhNmzaSpBtuuEE//fST0tPTdcMNN2j48OFasGCB4uLi9OOPP2r37t0aOHCgsrOzdeDAAe3atatxP0mgBkQXMdGxY0d99913kqRdu3Zp//794XU+n0/79u2TpPC7wSUlJengwYM6cOCAJCk3N1e//fabQqFQ+B27vv76a7Vv315btmzRxRdfrEWLFmnYsGGaO3eukpOTlZKSoiVLligvL099+vRx1Ru3oOng8gJi4qGHHtLEiRPVv39/XXrppfL5fOF1vXr10tixY7V161Zdc801kk6+7eC0adM0ZMgQxcXFqXPnzuratatyc3M1atQoeTwenX/++Xruuefk8Xg0btw4LV68WHFxcRoxYoQ6deqkHj16qF+/fjp27Ji6du0aPkMGLPGGN4iJb7/9VmVlZUpLS9POnTs1ePBgrV27NtZjAY2O6CIm9u3bp/Hjx6uiokKVlZUaPXp0+L+0Af7LiC4AGOIHaQBgiOgCgCGiCwCGiC4AGCK6AGDo/9aaRMj95i8UAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.boxplot(x=train['glucose'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [],
- "source": [
- "#sns.pairplot(train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Checking for Multicollinarity"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {},
- "outputs": [],
- "source": [
- "from statsmodels.stats.outliers_influence import variance_inflation_factor"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create a new data frame which includes all VIFs (Variance Inflation Factor)\n",
- "# Each variable has its own variance inflation factor. This measure is variable specific\n",
- "variables = train.drop(['TenYearCHD'], axis = 1)\n",
- "vif = pd.DataFrame()\n",
- "\n",
- "# Make use of the variance_inflation_factor module, output the respective VIFs \n",
- "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
- "\n",
- "# Include variable names so it is easier to explore the result\n",
- "vif[\"Features\"] = variables.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "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>VIF</th>\n",
- " <th>Features</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>2.217100</td>\n",
- " <td>male</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>42.056992</td>\n",
- " <td>age</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.959553</td>\n",
- " <td>currentSmoker</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.287163</td>\n",
- " <td>cigsPerDay</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1.118613</td>\n",
- " <td>BPMeds</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>1.019975</td>\n",
- " <td>prevalentStroke</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>2.240536</td>\n",
- " <td>prevalentHyp</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>1.023187</td>\n",
- " <td>diabetes</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>37.313994</td>\n",
- " <td>totChol</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>153.968224</td>\n",
- " <td>sysBP</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>147.331914</td>\n",
- " <td>diaBP</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>54.537909</td>\n",
- " <td>BMI</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>45.298946</td>\n",
- " <td>heartRate</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>41.248874</td>\n",
- " <td>glucose</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " VIF Features\n",
- "0 2.217100 male\n",
- "1 42.056992 age\n",
- "2 4.959553 currentSmoker\n",
- "3 4.287163 cigsPerDay\n",
- "4 1.118613 BPMeds\n",
- "5 1.019975 prevalentStroke\n",
- "6 2.240536 prevalentHyp\n",
- "7 1.023187 diabetes\n",
- "8 37.313994 totChol\n",
- "9 153.968224 sysBP\n",
- "10 147.331914 diaBP\n",
- "11 54.537909 BMI\n",
- "12 45.298946 heartRate\n",
- "13 41.248874 glucose"
- ]
- },
- "execution_count": 55,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "vif"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 56,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAISCAYAAADFm6feAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzddVhU2RvA8e8MM9QMZa4tgoGBoAvY/uxdXbsDuztQRAXBxs51dVGxsbBj7Vq7u7ALkxpyZvj9MezAmNSKu3s+z+PzOPece+/Le8+998y5Z2YkiYmJiQiCIAiCIAjCf4A0qwMQBEEQBEEQhG9FdH4FQRAEQRCE/wzR+RUEQRAEQRD+M0TnVxAEQRAEQfjPEJ1fQRAEQRAE4T9DdH4FQRAEQRCE/wzR+RUEQRAEQRC+a5cvX8bd3f2j5QcPHqR58+a0bt2a9evXp2pbsswOThAEQRAEQRAyy++//862bdswMzMzWJ6QkMDkyZPZuHEjZmZmtG3blho1apAzZ84vbk+M/AqCIAiCIAjfrYIFCzJv3ryPloeEhFCwYEGsrKwwNjamfPnynDt37qvbE51fQRAEQRAE4btVr149ZLKPJytERUVhYWGhf61QKIiKivrq9sS0h0yW8OZ+VodgoGbZHlkdgl5d2Q9ZHYIBx7jv65e9x0ueZHUIBqyMzL5e6RsxlhhldQgGYhITsjoEA+/UqqwOQc/ayDyrQzBQSGaV1SEYsJB8P7fdbRE3sjoEA7ZmubM6BAMl5NmyOgQDvz/ckNUhAH9PPyf4wFnWrVunf926dWtat2791fWUSiUqVfL1T6VSGXSGP+f7OQsFQRAEQRCE/5zUdnY/ZGdnx6NHjwgLC8Pc3Jxz587RrVu3r64nOr+CIAiCIAhC6mg1WR0B27dvJzo6mtatWzNy5Ei6detGYmIizZs3J3furz9BEJ1fQRAEQRAE4buWP39+/VeZNWzYUL+8Zs2a1KxZM03bEp1fQRAEQRAEIXUStVkdQYaJzq8gCIIgCIKQOtp/fudXfNWZIAiCIAiC8J8hRn4FQRAEQRCEVEn8F0x7ECO/giAIgiAIwn+GGPkVBEEQBEEQUudfMOdXdH6/U1eu32LmwqUEzp+aqdutVKcinQd3QKPRsitoN9vX7DIot7KxxGfBaExMTXgT+obJQ6YRFxv3yfV+blWPn1vWBcDY1Bj7kvY0cW5B3oJ5mRI4gacPngKwZeV2Dm47nPZgJRLqT+hC7pIFUcclsMMzgPePQvXFpRpVxK3rT2i1Wl7dfMKuMcsgMZN/tU0iwXFKF6xKFUIbn8Clob+jepgcQ54GLhQd0AgS4eHKAzxecxiJzAjnWT0xL5ATqYmcO7M283LvhQyFUbVOJboP7YxGrWFb0C62rN5uUG6VzYqJv/pgYmrC65dv8BsymbiYOGo2qE6n/u0hMZHgVdvZumYHv7T6mYatfwbA2MSYYqXsqVe2CVERX/9JSF1KJAyaNAC7kkWIj09gxvBZPH/4XF9esXYFOgxuj1ajYfe6P9i1Zre+rIRzCXqM6sawlsP1yyr/VJnqv1RlUv8pac6LRCKh38R+FClZhIT4BGaPmM2Lhy/05W613Wg3uB0atYa96/ayZ+0ejGRGDJk+hNwFciM3lrN27lpO7ztNkZJFGDB5ABq1hmcPnjF7+GwS09ieJBIJQyYNxK6kHQnxCUwbPoNnH+Sm02B3NBoNu9btYeeaXZ9dx76kHQPH90er1RIfl8DkwVN4/yYszTmqXqcKvYZ1QaPWsGXtDjat3mZQbp3Niim/+mFqasKr0Df4DJ5AbEwc7r3a0LRdQ96/1e1z/HB/nj56xoS5PuQtkAetRoOvxxQe3nv01Rgq1alIpxTXjx2fue4Ym5rw9oPrzqfWa9+/LZXrVkIul7Fl+TZ2Bu3GvpQdg8YPQKvRkBCfwMRB/rx/8/6zMUkkEjpN6EnBkoVRxyUQ4Pkrrx691Jc71/qRJoNaodFoOLruAIeD9mMkM6LnzAHkzJ8LrUbLkpELeRHyjH7zhmKV0xqAHPlzEXLxDgsGzEzN4flsbG0mdCefQyHU8Qms9vyN1ymufQByU2MGrhrDKs/fCA15jkQqof2U3uQukgetRsvK4Qt58zj0M3tIu9r1qjN4eB80GjXrVm9mzYpNn6zXrXcHcuXKweRxs/XLxk4cQcjdh6wKXJ8psaT3PgZgYmrCrKCpTBk2ncchGf9VTYlEQvsJ3cnvUBh1fALLPX/jdYp2BLr745BV3iz3XMjLEN31QJnNkpGbJuD70zDUcd/XL0Z+lZj28O8UHBzM9OnTs2z/S1dvYOyUOcTHxWfqdo1kRgwY24eh7TwZ0HwIDds3IFtOG4M6nYe4s3/LQfo3G8zda/do7P7LZ9fbvf4PBrYcxsCWw7h95S5zfOYTFaGiWJmirPt9o74sXR1foES98shM5Cxr6stB/3XUGdNeXyYzkfM/j5asaDORwGZ+mFiYUayWc0bS80l5fv4RI1M5x34Zy40JQZTyTY4BqYSSo9tyouUkjjbwoWjfXzDOZkGBFlWIfx/F8SbjONnOnzKTOmcoBiOZEUP9BtC/zVB6NhtA0w4NyZ7T8Gc3ewzpzJ7g/fRo0p/b1+7S3L0xUqmU/qN60bfVELr80oeOfdpilc2KHet306v5QHo1H8jNK7eZ7j0n1R1fgMo/VcLYxJgBjQcTMHkJvb17GsTax7cXnu29GNLCgwbt6mOT1MZa92nJsGlDMDYx1tfv59eH7iO7IpWk71JUsV5FjE2NGdpkKMsmL6OHd/LPeRvJjOg5tiej249mRMsR/Nz+Z2xy2lCzWU0i30cyvPlwvN296Tu+LwDth7Rnzew1eDT3QG4sx7WWa5rjqfJTZYxNjOnXeCCLJwfQx7u3QTz9ffvg0d6TQS2G0rCd7jz63Dr9x/Vlrvd8BrccxrHdx2jbt02a45HJjBg+bhC9Wg+mS9O+NHdv/FHb6TW0K7s276Vzkz7cunqHFu5NAXAoU5zRA8bRrVk/ujXrx8OQx1SpVQkjmREdG/bkt5lLGTiy11djMJIZ0X9sH4a182TgZ647nYa4s2/LQQYkXXcaJV13PrWeU8WylP6xFP0aD2Rg8yHkypsTgIF+/ZjjPY9BLYdxdPdx2vX7cr7K13PF2ETOuKZerPNfRbsxnQ1ibu/TBf8Ofkxs5U2NdnWxymlN2RrlMDIyYlyzUWyZs4GWw9sBsGDATCa18WF2T3+iI1SsGr/sq3n5krJ1XZCZyJnebAxb/NfQbExHg/KCZYowdL0fOQol/2S8Y+0fAZjRwocds9bT3NtwnYyQyWT4TvSkffOetPilM+06tiRnruwGdUxNTZj72xQ6d2urX5Ytuw0r1y+kzk81Mi2W9N7HAIo7FmN+8CzyFsqbafE41XVBbmLMlGajCfZfTasPjlWhMkUYvn4cOVMcq1LVyjJk5Rgsc3xfP739XyI6v9+hAnnzMHvSmEzfbuGihXj28BlR4VGoE9RcPXsNR7cyBnUcXctw+tAZAE4dOsOPVct9db3ijsWwLVaI7at3Jr0uSsVabszbNAvP6R6YKczSFW8Bl+KEHLkMwLOL98jjaKsvU8erCWzmizpW9wZBKjP6W949Z3MtzquDVwB4f+Ee1mWLJBdqEzlY1QN1ZAzG2SxAIkGtiuXZtlPc8k/+DfZETcbeJdsWLcyTh8+ITMr/5TNXcXJzNKjj5ObIyUOnAThx8BSuVX9Eq9XSspo7qkgVVjaWIJEQo4rRr+NQtjh2xW3ZvMpwFPlryriU5uzhcwDcvHCL4mWL6csKFS3Is4fP9W3l2tnrlHEtDcDzRy/w7eFnsK3r524wZ9TcNO0/pVKupTh/+DwAty7eoqhjUX1ZAfsCPE8Ry/Wz1ynlWopjO46xYvoKfT2NWvdrRSHXQlBaKwEwU5qhVqvTHE8Zl9KcOXwWgBsXbn4xN1fPXqOMa5nPrjOu70Tu3QgBdDf89LwZti1amCcPnhIZHok6Qc3F01coV8HJoE4517L8efAUAMcPnqRCNV0nqqRjCboP7Ejg1t/oNkB3Q390/zFGRkZIJBKUFgoSUpGjQqm87pz54LrzufVcq//I/VsPmLjEj8nLJ3Jivy52v74TuHc9KV9GX89XMRcHrhy5CEDIxTvYOtrpy/La5yf04UuiI1RoEtTcOXuT4i4OvHzwHKlM9/ebWpihSTD8pavmQ9uwb/kuwl99fsQ5NexcSnDjyCUAHl68S6EydgblMmM5i3tNJzTkmX7Z5b1nWeO1CIBs+XIS+To8QzGkVLRYER4+eEx4eAQJCWrOnr6Aa8XyBnVMTEzYuG4bc2cu1i9TKMyZ6f8rwevTdo35kvTexwCMjeWM7j6Wx/ceZ1o8RV0cuJbUju5/5lj92msaL1McK602kZntx6EKT/2gw3dFq8n8f9/Yv37aQ3BwMIcOHSI2NpbXr1/TsWNHDhw4wN27dxkxYgQvX75k7969qNVqLCwsmDdvnsH6K1euZMeOHUgkEurXr0/Hjpn3bvpz6tSowrMXmfe46i/mSnOiIlX619FRMSgtlJ+tEx0Vg8JC8dX13Ae0Y9mslfrXNy/eYvuaXdy5ehf3ge3oMrQjv45flOZ4TZRmxEYmd9YSNVokRlJdZzIxEdWbCABcOtfFWGHK/WNX07yPr5FbmJEQGf3pGJJe56nvguPkzoTuv4Q2QQ1xukflMoUpLgGDuDklY4/6FBbmBiOzqqholJaGx02hNCcqUlcnOioapaUCAI1GQ4361fCcNJTj+0+iTkjurHQZ6M7iGWkfoTK3MEeVoj1oNFqkRlK0Gi3mSnNUEcllMarkWI7tOk7u/IY/O3l4+xHKVjTsyKcplg/2p00Ri8JCYRBnTFJ7jo2OBcBMYcboRaP1HeFnD5/Rb0I/2g5siypSxZWTV9Icj8LC8FzRarQYGUnRaLQolAqiUsQanZSbz63z7tU7AEqVL0nTzo0Z2HxomuNRWiiIjEzRdlTRWFgoPohZod+/Kipaf27v2bqPoGWbiIpUMXvZFKrdqszt63fJVyAPW48HYZPNmv7uHl/PidKwveiuK1+/7nxuPatsVvyQPzeenUaTp+APTF42gQ7VOvM2KV+lfyxJsy6N6d9syBfjMlOaE53i3E7ZdsyUZgZlMaoYzCwVxKpiyZk/F/4H52GRzYKZXSfp61hmt6Jk5TKsGpexUV8AU6UZMZ+JDeD++dufXE+r0dJxRj/K1nUhoG/6p118SGmhIMLgGqTC0tLCoE54eARHD52gZdvG+mVPHj/jyeNn1KhdNdNiSe99DODqueuZFsdfvnasQj5xrG4eT/u1Rchc//rOL4BKpWLp0qXs3LmTwMBA1q9fz+nTpwkMDKR06dIEBgYilUrp1q0bV68md6Du3bvHrl27WLNmDRKJhM6dO1OlShWKFCnyhb19f7qP6IKjS2nsHIpw4+It/XJzpdlHj7ujo6IxV5gTHxufVK7SL/vUekpLBYXsC3LxxCV9+dE9x/U3+WO7/2TwhP7pijsuKgYThan+tUQqNRxFlUioPaot2W1/YEOv2enax9ckRMYgU6aMQfLRSO6LXWd5sfsc5eb2pmCrajwOOoJp3my4LRvKg8B9PNt8Il377uPZHSdXR+wd7Lh28YZ+uUJpTtQHIwaqpGMUFxuPudKcyBTlh3Yd5fDuY/jOGUWDlj+xfd0ulJZKCtsX4vyJi2mOKzoy2mA0XyqV6C/00VHRmCuT24qZwtygw5fZoqOiMVOmjCX5pqOKVBm0WzOlmb6jnCNPDrwDvNm5YieHtxwGoLdvbzyae/D4zmN+6fQLPbx78OuYX9MUjyrS8FyRSiVo/oonSoV5iljNFbo3NV9ap0bD/9FhYDtGdhpD+LvUj+T19+yJs1tZijnYc/Vi8g1foTAn8oNzXhWpQqE0Jy42DoUyuXzV4nX6DsSx/ScoUboYrpXL8+fh08ydtJDceXMRsHE+zWt0+OQoa/cRXSiTgeuO6jPXnYj3ETwOeYI6Qc2TkKfEx8Vjnd2asLdh1Gz0P9wHtGdEx9FfzVdMVDSmik+3nZioGMxSnPdmCjOiI1T81L0hV49cZP3U1WTLkx2vtX6MqjeEhLgEXOpX5OTWYyRmwoeBYqNiDGKTpDjHvmbFsAVY5rRi+OZJjK8zlPiYuHTHMXzUAFwrlMOhVDEunk/usCmUCiLCI9K93fTI6H3s7/LhsZKm4Vj9Y4k5v/8MDg4OAFhYWGBnZ4dEIsHKyoqEhATkcjlDhw5l1KhRvHz50uBR5507d3j+/DmdO3emU6dOhIWF8fhx5j0u+VYCpi5jYMthNHJqQT7bvFhYWyCTyyjr5si18zcM6l49e42KtdwAqFDDlcunr/Lw7iPyF8n3yfXKujly7th5g23MWO2Pg1NxAMpXceb2lbvpivvJuTvY13ACIJ+zPa9uG344ocHkbshM5KzrMUs//SGzvTt7m9y1dDHYlLMn4lZyDDKlGZU3eyM1lkFiIuroOBK1WkxyWFIpyIvr49fyeO2RdO97oX8AvZoPpK5jIwoUzodlUv6dK5TlyvlrBnUvn7lK5VoVAahUswKXTl9GoTRnUfA85MZyEhMTiYmORZt0Yy5XoSxnjp5LV1zXzl3HraZuPqxDuRI8uPVQX/bo7mPy2Sa3FUe3Mtz4oI1lphtnb+BS0wXQfZjuwa0H+rIn956Q1zYvSmslMrmM0q6luXnhJtY5rJm4eiLLJi1j77q9+vqRYZH60b53oe9QWhmOJqXGtXPXqZCUm5LlHLifIp5Hdx+T/4PcXD9/47Pr1GlWi6adGzO45TBePH7x8c6+YL7/Yro160eNMvUpUDg/ltaWyOQyyldw4vI5w7Zz8ewVqiS1nSo1K3Lh9CWUFgqCD6/GzFx3U3etUp4bV24RER6p72hEhEUgk8swMvr0bSRg6jIGtRxGY6cW5P/gunP9E9edCimuO1dOX+XRJ64718/f4MrZa7j+T3fMs+fOjqmZKRHvI6jTrDZNOzdhYMuhqcrXnXO3cKqhexxu51yMJ7eTP7j3/N5TchfOg8JKiZFcRnG3ktw7fxtVuErfRlRhURjJZEilur+/VBVHLh9O+5vJTwk5d5tSNXSfYSjsXJTnt79+33FtWpV6fZsAEB8TT2Jiov58T69pk+bRslEXnIpXp7BtQaytLZHLZbhVLM/5s5cztO20yuh97O9y79wtyiS1oyLORXmaimP1j6fVZv6/b+w/MfIrkUg+uTwhIYH9+/ezYcMGYmJiaNasmcGnu4sUKYK9vT0BAQFIJBICAwMpVqzYJ7f1T6BRa5jv9xszVk9BKpWyM2gPb16+wcLaAs9pwxjTw5flc1YzevYIGrarT9i7cMb1m/TZ9QAK2hXg+Qc3mhlecxg8cQDq+ATevX7P1BHpe/x2a885ilQpQ+fgsUgkErZ5LKJ040rIzU14cfUBzq2r8/jMbdzXjgLgzLI/uP1H+jp0n/Ni1zlyVitD1e2+IJFwcfAi8jWthExhyqNVB3m66U+qbPFBm6Ah4uZjnmw8Tmk/d+TWCooPbUrxoboPD51s5482Nn1zkjVqDbN85zNv7QykUinb1u7k9cs3WFpbMGaGJyO6jWHJ7OX4zh1N0/YNCXsXxui+44iNiWVP8F5+3zwftVrN3Rsh7N6k6+wVsivIs8fPv7LnTzu++0/KVy3H3C2zkEgkTB06g5pNamCmMGPn6l385reIKasmIZVK2bNuD29evk3XflLjxJ4TOFd1ZsbmGUgkEmYOm8n/mvwPM3Mzdq/Zze/jfmfiqolIJBL2rt/L25dv6eXbC6WVkraD2tJ2kO7DOd4dvZkzYg4jF4xEq9GSEJ/AHM85aY7n2O7j/Fi1HPO3zEEikeA/dBq1mtTETGHGjtU7WeD3G9NWTUEilbA7KTefWkcqlTJgXD9ePXvF+N99Abh06jKBM1Z8OYAPqNUapo+dy29Bs5BKpGwO2sGrl6+xtLbEd4YXQ7t5sXjWMibO9aF5+8aEvQtjZN+xxETHMnfybywJnk98XAJnjp/j+IGTnD95iXGzRxO4ZSFyYznzJv1GTNI0ks/56/oxPen6sesT150Vc1YzKum6E/7BdefD9d68fENZN0cW7VyAVCpl1mjdnPFB4/oR+vwVE/T5usKyGcs/G9f5PacpXaUsPsGTQCLhd4/5VGxcFVNzUw6t3cea8YGMWOmDRCrh6PoDvA99x56A7fSY1o8xGyYgk8vYMG01cUkjq3mK5OX145ef3V9aXP7jDA5VHfHYNB4kElYO/5UfG1XGRGHKn2sPfHKdS3vO4D6tL0PW+WIkl7FxXGCmfQ5CrVbjN2YqqzYuRiqVsG71Zl6+eIW1tSXT5oyjR6fBmbKf1EjvfezvcvGPM5Ss6ojnpgm6fsLwBbg2qoKJwpRja/f/bfsVMkaSmNbv8vmHCQ4O5v79+3h4eHD06FF27drFlClTuHnzJtOmTUOtVhMVFYWxsTHGxsa0aNECtVqtXycgIID9+/cTHx+Po6Mj3t7eGBkZfXZ/CW/uf8O/7utqlu3x9UrfSF3ZD1+v9A05xn1fTX+8JONfu5OZrIzS90HFv4Ox5PPnXFaISfy+vpronfrve6ybVtZG5l+v9A0Vkn1fn6i3kHw/Y07bIv6+pzLpYWuW++uVvqES8mxfr/QN/f5ww9crfQNxIacyfZsmdhUyfZtf8q/v/H5rovP7eaLz+2Wi8/t5ovP7ZaLz+3mi8/t5ovP7ZaLz+2n/hs7v93MWCoIgCIIgCN+3f8EvvP0nPvAmCIIgCIIgCCBGfgVBEARBEITU+hd81Zno/AqCIAiCIAipkwW/yJbZxLQHQRAEQRAE4T9DjPwKgiAIgiAIqfMvmPYgRn4FQRAEQRCE/wwx8isIgiAIgiCkzr/gq85E51cQBEEQBEFInX/BtAfR+c1k39MvqgEcvPx7VoegZ5a3alaHYEAm/b5+NazTD25ZHYKBl9qYrA5Br5XaOqtDMLBJHpHVIRjolPj9/HpiMU1sVodg4KDMJKtDMPCC7+fXARtZluRC/KusDkOv8Hf2a3zFtd9X2xEyj+j8CoIgCMJ/0PfU8RX+Qf4F0x7EB94EQRAEQRCE/wwx8isIgiAIgiCkSmLiP/9HLkTnVxAEQRAEQUidf8EH3sS0B0EQBEEQBOE/Q4z8CoIgCIIgCKkjPvAmCIIgCIIgCP8cYuRXEARBEARBSB0x51cQBEEQBEEQ/jnEyK8gCIIgCIKQOlrxVWfCV1SqU5HOgzug0WjZFbSb7Wt2GZRb2Vjis2A0JqYmvAl9w+Qh04iLjfvkej+3qsfPLesCYGxqjH1Je5o4tyBvwbxMCZzA0wdPAdiycjsHtx3OtL/hyvVbzFy4lMD5UzNtm6n1S4M6jB49GI1aw7LAIJYsXWNQXqBAXgIWz0QmM0IikdC7ryd37oRkagz169dm1KhBqNVqVqxYz9Klaz9Zr3//buTOnRNv7ykAtGnThEGDeqDRaFm+fB2//74qw7FIJBLaTuhOAYfCJMQnsNLzN14/emlQR25qzOBV3qzwXEhoyHOkMiM6Te1L9vw5kRnL2TV/E1f2n8tQDL0n9qWwgy0J8QnMHzGXl49e6MtdarvSelAbNGot+9fvY9/aP/RlVtmtmLlzNj7tvXkW8hSP+SOwzmkDQK78ubhz8TbT+6eznUkkuE7ujHXJgmjj1ZzyCCDqYai+uFCTipToXo9ErZawG0844xWIVG5ExVk9URbMRUJUDGdHBRL5IPQLO/laCBJ6TuhN4ZK2JMQl8KvnfIPc/FjLhVaD2qDRaDiwbj/7g/YCMH3XbKIjVAC8ehLK/OFzscpuRZ8p/VFYKTEykjJnyCxCH7/85H5TGRwVJ3cmW8mCaOLU/Dk8gMgU+bFtXJFSSfl5d/MJJ70CsW9ZBfuW1QAwMpWTrWRB1jn3Jz4iOv1xJMViN6UH5qUKkRiv5t7QhcQ+TP7bsjdwI9+AppCYSOjK/YSuOYBEZkTRuf0xKZCLRI2WEI+FxNx7nrE4PhNb/QldyF2yIOq4BHZ4BvD+UXKeSjWqiFvXn9Bqtby6+YRdY5ZBYmImhyChzYTu5HMohDo+gdWev/H6kWG7lJsaM3DVGFZ5/kZoyHMkUgntp/Qmd5E8aDVaVg5fyJvH6W/LAFXqVKTrkE5o1Bp2BO1i65qdBuVW2awYt2CM/v41fog/cTFx1GlSkzbdW6DRarl34z7TvGYhkUjwmu5BIbsCaDRaJgzx59mj1B0/iURCpwk9KViyMOq4BAI8f+VViuuec60faTKoFRqNhqPrDnA4aD9GMiN6zhxAzvy50Gq0LBm5kBchzyhUughdJvZCHZ/AoxsPWeW7hMSMHD+JhNoTO5PLoSCaeDV/jAggLMWxKtGoIuW71UOr0fL61hP2jw5EaiSl3vQeWOXPiZGxjFPzthKy70L6Y/jW/gXTHkTn929kJDNiwNg+9GjQl9joWH7dMoc/953k3ev3+jqdh7izf8tBdq//g/b92tDY/Rc2LdvyyfV2r/+D3et1HYkhEweyM2gPUREqipUpyrrfN7Ju0YZM/xuWrt7A9j0HMTP99r9xLpPJmD5tLBUqNUCliubokS3s2LmP0NDX+jp+viNYsHAZ27b9Qd061Zk4YSQtW/XI1BimTfOhcuWGqFTRHDoUzM6d+w1iMDU1YeFCf1xcnNm8ebd++eTJYyhXrhZRUdFcunSADRu2ExYWnqF4nOq6IDcxxr/ZaGydi9JiTEcW9kjuLBYqU4T2E3tinSe7flmFplVRhUWybOg8FNZKxuyclqHOr1u9CshN5Hg29aCYc3G6endjUvcJgK7Nd/PpzrCGQ4iLjmNK8FTO7j9N2OswjGRG9J3cn7jYeP22/uroKqwUTAiazJJxv6c7rgI/lUdqImdvIz+yl7Oj3Nh2HO0ySxeXqZyyI1qws5YXmph4Kv/aj3x1nFHky06CKpY/GvpiYZeHHyd24lC79L/Jc61XAbmJMV5NR1DMuTidx3RlSo+J+tx08enOiIZDiYuJY9Imf84dOIMqqdPr02a0wbbcvTpzdMthTuz8k9IVy5DfPn+GOr+FfiqPkYmcnY38yFnODlefdhzompyfciNasKWWF5rYeKov6EeB2s7cW3+Me+uPAVBhYifuBh3JeMcXyPazKxJTOVd/GY2yXFEK+3biVmd/XaFUSqHRHbhczxONKpZyR2fxds8ZLF1KIJEZcbXhaKyqOVJwZDtud5+e4Vg+VKJeeWQmcpY19SWfsz11xrRnfY+ZAMhM5PzPoyWL6o5EHRtP07n9KFbLmTv7M7fjUrauCzITOdObjaGwc1GajenIoh7T9OUFyxSh7cQeBue5Y+0fAZjRwoeiFUrS3NtwnbQykhkxyLc/Xev3IiY6lsVb53Ns30nevX6nr9NtSEf2bj7AzvV7cO/fjqYdGrJ55TZ6jehG+1pdiYuJY9yv3lSpUxGJVDfLsmfjAZSr6MQg376M6DImVbGUr+eKsYmccU29sHMuRrsxnZndY4o+zvY+XfBpOIK4mDh8Nk3i4oFz2DkVxcjIiHHNRlG6SllaDm/H3N7T6Dq5N6t8l3D3/G1aeLSlYpOqnNh8NN15KprUXtY09SOPsx3/827Hlu6680pmIqfK8BYE1vFCHRtPg3n9sKvtjJm1ktj3Uewe/Bum1ko67p7wz+r8/guIOb9/o8JFC/Hs4TOiwqNQJ6i5evYajm5lDOo4upbh9KEzAJw6dIYfq5b76nrFHYthW6wQ21fvTHpdlIq13Ji3aRae0z0wU5hl2t9QIG8eZk9K3QUqszk4FCUk5CFhYeEkJCRw4s+zVKniZlBn+Ag/du06AOg6qrGxcZkaQ4kS9oYxnDhL5cquBnVMTU1ZvXoT/v7zDJZfu3YTKytLTE1NkEgkGRtdSGLv4sD1IxcBeHDxLoXK2BmUy4zlLOw1jZchz/TLzu88xdYZQfrXGk3GHlmVdCnFxcO6C/Wdi7exdyyqL8tvX4AXD1+gClehTlBz8+wNSrqWAqDLmG7sWbWbd6HvPtpmu6Ht2Rm4nfev3n9Ullo5XYvz4vAVAN5eCCG7o62+TBOnZm8jPzQxuo63xEiKJi4eq6L5eH5Qt05kyAusiuZN9/4BHFwcuHgkOTd2jvb6svz2BXj58AWqiOTcOLiUorCDLSamxvis9MNv7QSKORcHoMSPDmTPk4Oxq8dRrUl1rp28mqHYcrkW59kh3d/6+hP52dnYD03SGxOJTJefv2R3tMW6WH7urD6UoRj+YulagrCDlwCIunAXZdkiyYVaLReqDkITGY08mxIkEjSqWGLuP0diZAQSCTILMxLVf8+j1wIuxQk5chmAZxfvkSdFntTxagKb+aJOypNUZoQ6LiHTY7BzKcGNI5cAePiZ83xxr+mEpjjPL+89yxqvRQBky5eTyNcZe6NtW7QQTx8+IzLpPnT5zFWcPrh/lXUtw8mk+9fJg6dxqVqe+LgEejbqT1yM7lpsZGREXFw8R/ccZ8rwGQD8kD+3wSDQ1xRzceBK0nUv5OIdbB2T85HXPj+hD18SHaFCk6DmztmbFHdx4OUD3VMviUSCqYUZmgRde8n2Q3bunr8NwJ1ztyj2o0M6M6STz6U4D5KuOy8uhpD7g/aypqlfivYiRR0bz+2dpzk+faO+nlbzDxtJ1Woz/9839p/s/EZFRTFo0CC6du1K06ZNWbNmDVeuXKF58+Z07NiRIUOGMHLkSABWrlxJ69atadOmDStWrEjTfsyV5kRFqvSvo6NiUFooP1snOioGhYXiq+u5D2jHslkr9a9vXrzFr+MXMaD5EJ4/fk6XoR3TFOeX1KlRBZksax4QWFooCY+I1L+OjIrCytLCoM7bt+9Rq9UUK2aHv7834yfMytwYLC0ID0+OISoqCisrwxjCwsLZv//YR+tev36bEyd2cvHifnbtOkB4eESG4zFVmhETmTzylqjRIjVKPo1Dzt/m/Yu3BuvERccSp4rFRGFKr4XD2Do9iIwwV5qhStE+tRqNPgZzC3OiU5TFJLXpmi1qEf42nItHPx7dsMpuhWPlshzccCBDccktzEhIMSqZqNUi+Ss3iYnEvtHlv1jXOsgVprw8co331x+Rr44TANnL2WH2QzYkUkm6YzBXGv792hTHx0xpZpgbVQwKS3PiYuLYungL49zH8tuoXxk8ZyhSIym58udCFR6FX3sfXj97TdM+zdMdF4Cx0oz4yK/nx6FLHeTmpjw/ek1ft+yARlyaFZyh/ackszBDnSIWNFpI0Y7RaMlW3w2nAzOIOHWDxAQNGlUsJgVyUu74HOym9+ZFwK6PN5wJTJRmxEbG6F8nagzzpErKk0vnuhgrTLl/LGNvSj7lw/Nc+8F5fv8T5/lf9TrO6Ecr3y5c3H0qQzEoLBSoIqP0r6NV0SgtP7x/JdeJjtKVJyYm8u6NrmPbsmtTzBVmnDmie9Kk0Wjwnj2SYRMGcnDnkVTHYqY0J/oz+dCdV8llMaoYzCwVxKpiyZk/F/4H59FtSh/2BuoGi14/CaWEW0kAnGu7YGKesaeaH51XH7SX6KT24ty5Dsbmpjw6do2E6DgSVLHIFaY0+m0gx6dl/lNb4cv+k9MeHj16RIMGDahbty6hoaG4u7ujUCiYOnUqRYsWZdasWYSGhnLv3j127drFmjVrkEgkdO7cmSpVqlCkSJEvbr/7iC44upTGzqEINy7e0i83V5oRFRFlUDc6KhpzhTnxsfFJ5Sr9sk+tp7RUUMi+IBdPXNKXH91znKikR6fHdv/J4An9M5qiLDXObwSVK7lQpowDZ85c1C+3UCoJ+0QH8n/VKzFv3iQ6dx6YafN9fX09qFTJhdKlHTh7NjkGpVJJWNjXO7GlS5fg559rUqJEZaKiVAQGzqFZswYEB+/86rpfEhsVg2mKkX2JVJKqUQObPNnps2g4h1f+wdltxzMUQ3RUDGbKlDFI9TFER0ZjmqLtminNUEWo+KVLQxITEylbxQnbkrYMnjWUid3GEfY6jEr1K3N0yxG0GXz3nxAZgyxlXBIpiSlzI5Hg7N0GyyJ5ONpjDgAhQUewLJqX2htH8/rsHd5deUCiNv0j9NFR0QZPXqQpjk9MVAymKeIzU+hy8/zBM14+1M0LfvHgOZHvI7HJlY3I95Gc2acbVTt34CzthndId1wA8VExyD84bh/mx2WMLj8Hk/IDYGxpjpV9Xl6euJmh/aekjozBSGmavEAq1XWAU3i36zTvdp+h6Nz+5GpVHfMSBQk7fIlHk9ZgnDc7pTf6crHGUBIzeeQ1LioGE0VybJ/KU+1Rbclu+wMbes3O1H3/Jb3nOcCKYQuwzGnF8M2TGF9nKPExaXsa1mtEN8q6lkm6fyUfc3OFOZHhH96/VJgrzImLjcdcmVwukUjo792bgkXyM7KHj8E64wdPYcHExSzZ+Sttq3cmNib2qzHFREUb5EOa4poTExWDWYq2ZKYwIzpCxU/dG3L1yEXWT11NtjzZ8Vrrx6h6Q1jsMR/3sd1o0Lsp9y/fy/DIfXxUDMZfOa+qj2qDTZE8bO2VfF5Z5MlG498Hc2nFfm5tPZmhGL65f8Gc3//kyG+OHDnYv38/Hh4eLFy4ELVazatXryhaVPf4tnz58gDcuXOH58+f07lzZzp16kRYWBiPHz/+6vYDpi5jYMthNHJqQT7bvFhYWyCTyyjr5si18zcM6l49e42KtXSP8ivUcOXy6as8vPuI/EXyfXK9sm6OnDt23mAbM1b74+Cke1Ravoozt6/czViCspjP2KnUqtOSvPmdsLOzxcbGGrlcTpWqbpw6Zfi3/696JWbO9KPBL+05f+FKpsXg6zudunVbU7BgOezsCmNjY6WLoYobp0+f/+r6ERGRxMTEEhMTq/tgzKu32NhYZTiue+duUbpGOQBsnYvy7PbX26NFDisGrRxD8JRVnNiQ8cfWN8/doHwN3fzCYs7FeXTrob7s6b0n5LXNi9JKiUwuo6RbaW6dv8WoliMZ3cqLMa29eHDjAbOHzCTsdRgAZas4cf7w13P6Na/P3iFvzbKAbhQ37NYTg3K3qV0xMpFzpMss/fSH7E5FeH3mDvtbTOTJ7nNEPX6VoRhunbtJuZS5uf1IX/b03hPyFE6Zm1LcPn+LWq3q0Nm7KwA2ubJhpjTn/at33Dp3g/I1dNeikq6leHLn68f6S16dvUP+pPzkLGfH+5uG+ankr8vPga6z9NMfAHK7leD5sWtkpsizt7CppWvHynJFib6V/LcZKc0ovdkPibEMEhPRRMeSqNWiDlehThrZV4dFIZEbJY+wZaIn5+5gX8MJgHzO9ry6bZinBpO7ITORs67HLP3j7MwWcu42pWo4A1DYuSjPU3GeuzatSr2+TQCIj4knMTExXW8oF01dQt8Wg6lftin5C+fDMuk+5FzBkWvnrxvUvXL2GpVqVQCgYk03Lp/RXYNHTh2GsYkxI7qM0U9/+Kl5HTr2bwdAbEwsidrUx3fn3C2ckq57ds7FeJLivHp+7ym5C+fRfTBULqO4W0nunb+NKlylHxFWhUVhJJMhlUpxqlme34fPZ0aXiShtLLh2/HKac5TSs3N3sK2hO6/yONvx5oPrTt0pXZGZyNnSPbm9mOewpMUqT45OXse19emfb5xl/gXTHv6TI79Lly7FycmJdu3acerUKY4cOcIPP/zAvXv3sLe35/Jl3clQpEgR7O3tCQgIQCKREBgYSLFixVK9H41aw3y/35ixegpSqZSdQXt48/INFtYWeE4bxpgeviyfs5rRs0fQsF19wt6FM67fpM+uB1DQrgDPH78w2M8MrzkMnjgAdXwC716/Z+qImZmXrCykVqt1c3p3rkYqlRIYGMTz5y+xsbFm8aJptGzVgxkz/DA2NmbZktkA3L5zn779PDM1hhEjxrN9+yqkUinLl6/j+fNQbGysWLhwKm3a9Prkeo8fPyMgYA0HD24iISGB+/cfsWJFxh9tXfrjDA5VHRmxaYKuTQ5fgEujKpgqTDm2dv8n1/m5XzPMrZTUH9iC+gNbADCv0yQS4tJ34z615yROVZ3xD54GEglzPWZTrXF1TBWm7F3zB0vHB+C7ahwSqZQD6/bxLvTjx7Mp5bPLl7FvMUjyZPc58lQrTd1tPoCEU0MXU7hpRWTmpry98gC7ttV5dfo2tTeMAuBWwB+8PnMLxxEtcOhdn4SIaE4NS/8H7gBO7zlF2SpOTAr2RyKRMN9jDlUbV8PU3Ix9a/8gcPwSfFb6IZFKOLB+P+9C33Fg3T76zxjExI1TSExMZMGIuWg1WgInLKWv/wDquf9MdGQ0swZk7MNdj3afI2+10jTY6gMSCceHLKZIk4rIFKa8vfyAYm2rE3r6Nj+t1+XnxpI/eLznHFZ2eYjM4JuCD73ddQbramUps30iSODe4AXkaFoFI4Upoav283rTMcpsGU9ighrVzUe83ngMqakxRWf3pfSW8UiNZTyatAZtdObO8Qe4teccRaqUoXPwWCQSCds8FlG6cSXk5ia8uPoA59bVeXzmNu5rdXk6s+wPbv+R/g+QfsrlpPPcY9N4kEhYOfxXfmxUGROFKX+u/fT0oEt7zuA+rS9D1vliJJexcVxghkY1NWoNc/wWMHvNNKRSCduDdvP65RssrS0YNX04I7v7sGz2SrzneNG4fQPC3oXj03cCxcsUpWHb+lw6fYUFG3TT0NYFbOTwrmOMmeXJwuA5yGQyZo2dT3wqr0Hn95ymdJWy+ARPAomE3z3mU7FxVUzNTTm0dh9rxgcyYqUPEqmEo+sP8D70HXsCttNjWj/GbJiATC5jw7TVxMXEEfrgBR6BY4iPjePGyWtcPpSxD5rd3XOOQlVL0zbYB4lEwh6PxZRoXBFjhSkvrzygTOvqPD1zm9ZBuvZyfukfFKjogKmVgooDm1BxYBMANnWc+rfMHxc+TZKYGZ/C+Yc5deoUvr6+2NjYYG1tzd27d5kxYwYTJ07E3NwcuVxO7ty5mTBhAgEBAezfv5/4+HgcHR3x9vbGyMjos9uumq/WN/xLvu7g5YzdzDOTWd6qWR2CAZn088cxK3T6we3rlb6hl9qYr1f6RlqprbM6BAOb5Bmfv52ZGqotszoEvWKarz/G/pYOmnz7b6r5kheS76eDcyE+c9/UZFRRebasDsFA2UTzr1f6hjweZ/zrMjND7LGVX6+URqZV3TN9m1/ynxz5rVChAnv27DFYtnr1an777TeyZcvGrFmzkMvlAHTv3p3u3btnRZiCIAiCIAhCJvtPdn4/JXv27HTt2hVzc3MsLCyYMmVKVockCIIgCILwXUlMFL/w9q/x008/8dNPP2V1GIIgCIIgCN+vLPiAWmb7T37bgyAIgiAIgvDfJEZ+BUEQBEEQhNQR3/MrCIIgCIIgCP8cYuRXEARBEARBSB0x51cQBEEQBEEQ/jnEyK8gCIIgCIKQOv+COb+i8ysIgiAIgiCkzr9g2oPo/GayurIfsjoEA9/TTwrHPD+W1SEYUO/6fn76GaDCiCNZHYIBE6k8q0PQ84l/ntUhGMglscrqEAzMUH8/P1P7Nu77+ulno/jva3bf+9iorA7BQP2cZbM6BL0Lsc+yOgQDYSY5szoEAx5ZHcC/iOj8CoIgCMJ/0PfU8RX+Qf4F0x6+r7fEgiAIgiAIgvA3EiO/giAIgiAIQuqIOb+CIAiCIAjCf8a/oPMrpj0IgiAIgiAI/xli5FcQBEEQBEFIHfGBN0EQBEEQBEH45xAjv4IgCIIgCELq/Avm/IrOryAIgiAIgpA6YtqDIAiCIAiCIPxziJHfrCaRUH9CF3KXLIg6LoEdngG8fxSqLy7VqCJuXX9Cq9Xy6uYTdo1ZBomJf2tIvzSow+jRg9GoNSwLDGLJ0jUG5QUK5CVg8UxkMiMkEgm9+3py507I3xpTSleu32LmwqUEzp/6t+9Lm5jIpN2XuPMqHLmRlLENylEwmxKAN1GxeG4+o697OzScQTVKYSwzYtuVRwDEqzXcDg1n/+D6WJoapzuO6nUq03NYVzRqDVvW7iB49TaDcutsVkz51Q8TU2Neh77BZ/BEYmPi9OXe0zyJCItgzsSFyI3ljJs9mvyF8qKKjGaS13QeP3iapniq1KlE9yGd0Kg1bAvaxdY1OwzKrbJZMX6BN6amJrwOfcO4IVOIi4mjbpNatOneAq1Wy70bIfh7zUJqJMV3zijyFPgBrUbLxOHTeHTvcZriqVmvGgM8eqBWa9i4ZivrVm42KLfJZs2sRRMxNTXlVehrRgzwJTYmlkYtfqZb3w5oNVo2rNnKmmUbMTaW4z/PlwKF8hMVGYWv5xQe3n+ShtxUpMvgjmg0GnYE7Wbbmp2GubGxxG/BGExMTXgT+pYJQ/yJi9UdKxNTE+YGTWfSsKk8CnmCkcwIn9le5CmQG41Gy5Th03kUkvpYPlS9bhX6DO2KWqNh89odbFq11aDcOpsVUxeOwyTpuI0ZNJ7YmDhKOzkw3G8QEomEN6/eMrKfL/Fx8emO4y91fvofQ0f0Qa3WELQqmNUrNn6yXo8+7uTMlYNJfrMAaNK8Pj366HJ88/odRg4bR2ImXBdr16vO4OF90GjUrFu9mTUrNn2yXrfeHciVKweTx83WLxs7cQQhdx+yKnB9huMA+Ll+Lby8BqJWa1ixYj2By4I+Wa9fv67kzp0THx9/cufOSeDyefoyR8eS+Pj4syRgdZr3L5FI6D6hN4VLFiYhLoHfPOfz8tFLfXn5Wi60GNQarUbDwXX7ORC0D4AmfZvjUscVmVzGHyt3c3DdfgbP88A6pzUAOfPn4u7FO8weMD3NMf2vbhX6DO2W1H63s/GT7Xc8pqYmvAp9bdB+R/gN1rdfz35jiY+Lp/vATtSoVxW5XE5Q4EaC12xPdW76TOyLrYMtCfEJzBsxlxePXujLXWq70nZQGzRqLfvW72Pv2j/0ZVbZrZi1czY+7b15GpJ83e3u052n95+xZ9XuNOflm/sXTHv47kd+z549y61btwC4cuUKXbt2pUuXLnTq1ImlS5dmaNvBwcFMn572EzAzlahXHpmJnGVNfTnov446Y9rry2Qmcv7n0ZIVbSYS2MwPEwszitVy/lvjkclkTJ82lp/rt6NGreZ0796e3LkNf9/cz3cECxYuo1adlkzxn8fECSP/1phSWrp6A2OnzMmUG29qHLr9nDiNhhWd/8egmqWZuf+qviyH0pQl7tVY4l6NgTVK4fCDNc2cbWlctpB+uUMeG0bULZuhjq9MZoTHuEH0bj2Yrk370ty9MdlzZjOo02toV3Zt3kuXJn25dfUuLdyb6MtauDemqIOd/nXzDo2IVsXg3qAnk0fPxGvSsDTFYyQzYohvPwa0HUav5gNp2qHhR/F0H9KJPzbvp2fTAdy+dpdmHRphYmpM7xHd6NNyMN0b9UNhqaRKnYpUrlUBIyMjujfqR8Cs5fTx7J7G/MgYM34YnVr0pV2j7rTp2IwcubIb1Bng0YPtm/bQpmE3rl+5RdtOzQHw8htMx2Z9aFW/C937uGNpZUFr92ZEq2Jo8VMn/LymMnZK6tu3kcyIQWP7MbjdcPo2H0zj9r+QLaeNQZ2uQzqxd8sB+jQbxJ1rd2nq3hCAEo7FWBg8h3yF8urrVqpZASOZET0bD2DprBX0SmNuUpLJjPAcN4ierQfRuUkfWnb4uB31GdaNncF76dS4Nzev3qZlx6YA+M7wYsygCXRs1Is/D50ib/4f0h1Hcjwy/CaNpE3THjRr0IkOnVuSM1cOgzqmpibMX+RP5+7tDJZ5jhlIi4adaVSvPRaWSur89L9Micd3oiftm/ekxS+dadexJTk/aEempibM/W0Knbu11S/Llt2GlesXUuenGhmOIWUs/v7eNGroTr26renate1H12FTUxOWLJlFz17u+mWhoa/5+ac2/PxTG8b6TOXypWssW7o2XTG41HPD2ETO6KaerPZfQccxXfVlRjIjOvt0Y0KHsYxtNZra7ephndOakhVKU7x8CcY0G8nYVqPJnkd3PGcPmI5vmzFM6zmZ6AgVgeOXpCMnRniOG0yP1gPp3KQ3LTs0Iccn2u+u4D/o2LgXt67eoVVS+/WbMYoxg8bj3qgnxw+dJG/+H3CpVA5nlzJ0+KUHnZv05oe8uVMdS4V6FTA2kTO8qQfLpwTS1bubQW66+3THu4M3Xq1G8lNSbv4q6ze5P/Gxyfcvy2yW+C73xbWOW5pz8l+i1Wrx8fGhdevWuLu78+jRI4Pybdu20bRpU5o3b86aNWs+s5Vk333nd9OmTbx69QqAcePGMXr0aJYtW0ZAQAA7d+7kxo0bWRxhxhRwKU7IkcsAPLt4jzyOtvoydbyawGa+qJNOFKnMCHVcwt8aj4NDUUJCHhIWFk5CQgIn/jxLlSqGJ+XwEX7s2nUA0F2kY2PjPrWpv0WBvHmYPWnMN9vfxSdvqVxEd1F0zJeN6y/ef1QnMTER/z8uM/pnJ4ykEv3y68/fE/I6ghblbD9aJy1sixbmyYOnRIZHok5Qc/H0ZcpVKGtQx9nVkT8PngLg+MGTuFVz0cVcvjSO5UuzceUWfd0ixWz58+BJAB6FPMa2aKE0xlOIpw+fERkehTpBzeUzV3ByczSoU9a1DKcO6UbFTx48jUvV8sTHJdC9UT/ikkakZUZGxMfF8zjkKUZJTxEUSnPUanWa4rErZsujB0+ICI8kIUHNudOXcKlg+CaxfAUnjhw8AcCRA39SuborALeu38PCUomJiQlIdMfSvrgtR/b/CcCDe4+wL1Y41bEU/iA3V85e/URuSifn5tAZfqxaHgBjY2NGdvc2GPV+fP8JRkZSXW4sFGnOTUpFitny+MFTIpLa0YUzlylfwcmgjrNrWY4ntY3jB05SsaoLhe0KEvY+HPeerVm2+VcsrS15GJK2kflPKVq8CA/vPyI8PIKEhATOnLqAW8XyBnVMTEzYELSVOTMW6ZfFxcXTsG57YmJiAd01KC4TrkFFixXh4YPHSfGoOXv6Aq6fiGfjum3MnblYv0yhMGem/68Er0/dqGFqlChhz/37jwgL0+Xm5IlzVKrkYlDH1NSENWuCmTZ1wSe3MWOmL4MGjUGbzlE6B5eSXDxyEYC7F+9g52ivL8tnn5+XD1+gilChTlBz6+xNSriUxKmaM49vPWL4Yi88l47h/IFzBttsNbQdu5fvJOzVx9fRr0nZfhOS2m+5D9pvuRTt99iBk1So6pqi/bYhcPNCrJLab+UaFbhzM4S5gVNZsGoGR/YdT3UsJV1Kcf7wBQBuX7xNUcei+rIC9gV48fAFqnBdbm6cvUEp11IAdB3Tjd2rdvMu9J2+vpnCjDWz1nAo+FCac5JlErWZ/+8r9u/fT3x8POvWrWPYsGFMmTLFoHzq1KksW7aMtWvXsmzZMsLDw7+4vTRPe4iNjcXLy4vnz5+TkJBAvXr1iIyMxMPDg7i4OH7++WcOHjyIu7s7NjY2RERE0KBBA7Zs2YJWq2XgwIGEhYURGBiIVCqlfPnyeHh4MG/ePJ4+fcrbt295/vw5Xl5e2NjYcOzYMa5fv469vT158+Zl9erVNGvWDAcHB9auXYuxsTHBwcEcOnSI2NhYXr9+TceOHTlw4AB3795lxIgR1K5dm23btrF8+XKMjY0pXLgw48aN0/9N7969o2/fvgwaNIgff/yRsWPH8ujRI7RaLYMHD8bNzY1ffvmFwoULY2xszMyZM9Oats8yUZoRGxmjf52o0SIxkpKo0UJiIqo3EQC4dK6LscKU+8eufm5TmcLSQkl4RKT+dWRUFFaWFgZ13r7VXbiKFbPD39+b5i268a3UqVGFZy9Cv14xk6jiElCayPWvjaQS1FotMmny+8Yjd19QJKclhbMb5mnJidv0ruqQ4RiUFgqiIqP0r6NV0SgtlAZ1FCnqqKKisbBQkCNXdvp4dGNI15HUbVRLX/f2tbtUq1OZg7uPUqZcKXLlyYlUKk31TVK3L5X+tUoVg9JSYVhHaW4Qj9JSQWJiIu/e6NpOq67NMFOYcfrIOXLlzUmeAj+w4ehKrLJZMbRT2p4kKC0URKbIjypKhYWlYX4slEoiI1LmR1d+59Y9th5YTUx0DH/sOEhkRBQ3r92hRt2q7N11CKfyZcidJ1eq86P7u5NzEx0Vg8Liw9woUCXViY6KRplUfuXctY+2FxMdQ54CPxB0dDnWNlZ4dB6VmpR8JrYPjltU9Ed5Ulokx69SRaO0VGKTzRqnH8swadQMHt9/woJVM7hx5Ranjxl2bNLKwkJJRETycYuKUmH5QTzh4REcOXSCVu2a6JclJiby5vVbALr2bI9CYc6RQycyFAvo2lHKeFRRKiw/uPaFh0dw9NAJWrZtrF/25PEznjx+Ro3aVTMcw18sLJSEhxtehy2tDGMJC4vgwIFjdOjQ4qP16zeozc0bd7l79366YzBTmhOdor1oNVqkRlK0Gi3mSnOiI6P1ZbGqGMwtFVhksyRnvpxM6TqBXAVy4xkwmkE1+wJgmd2KMpUdWT4u7aO+AErlh+f5p9qvgkh9+9VdB1K230f3H/Prqplcv3ILm2xW5Mmfh34dhpKvYF7mr5jOL5VbpSoWc6XZB7nRJOfGwjBvMUnXgFotahH+NpyLRy/Qsl9LfXnok1BCn4RSvsaP6crLf8X58+epWlV3jjk5OXHtmuH1snjx4kRGRiKTyUhMTEQikXxqM3pp7vwGBQWRL18+Zs2axZ07dzhx4gSRkZGfrNuwYUPq1KlDcHAwlpaWLFy4kLCwMNq1a8emTZswMzNj+PDh/PmnbpTF2NiYgIAA/vzzT5YuXcqSJUuoWrUq9evXJ2/evEyaNInly5fj6+vLkydP+OWXX/D09AR0DX3p0qXs3LmTwMBA1q9fz+nTp1mxYgXly5dn3rx5bN68GaVSyaRJk1i3bh3m5ua8ffuWPn36MGrUKMqWLcuaNWuwsbFh0qRJvH//ng4dOrBz506io6Pp27cvJUuWTGvKviguKgYThan+tUSa1PHVL5BQe1Rbstv+wIZeszN13ymN8xtB5UoulCnjwJkzF/XLLZRKwsIjPqr/v+qVmDdvEp07D/ym832/NYWJHFV88mibNjHRoOMLsPPaE9q52Bssi4iN5+HbSFwKGz6qTIt+nj1xdnOkmIM9Vy9e1y83V5jrO3J/UUWqUCgVxMXGo1Dqyus2rIl1Nivmr55BjpzZMTUz4cG9R2xZuwPbooUICJ7PpTNXuXnldqo6dr1HdKOsaxnsHey4fvGmfrlCYUZk+AfxREVjrjDXxxOVVC6RSBjg3ZuCRQrg2cMbgHY9WnHq8Bl+nfw7ufLm5Nf1s2lXq8tXp7YM9epL+QpOlChZlMvnky+ECqWCiHDDa1JkVBRKpTlxsXEolOZERERRvGRRatSpQvVyDYlWRTPztwn83Kg2G1Zvxa6YLau3LubC6ctcu3zzq/npOaIrZV3KYO9QxCA35kozoj48VlEqfW7MleYflafUpkdLTh8+y8IpAeTKm5P562fSoVZX4tPwBGjAyF6Ucy1LsZJ2XLmQ/KRMoTT/KE9RkdEo/sqTwpzIiEjC3ofz+OFT7t95CMDxQ6co6Vgi3Z1fz9EDca1YDodSxbl47op+uVKpMOjwfYlEIsF7nAdF7AvRveOgdMXxl+GjBuBaoRwOpYpx8XxyPLp29PG17+/kM3YYFSu6ULp0Cc6du6RfbqFUEh6W+ljatGnCrwuWZSiWmKhozBRm+tcSqQRt0r0pOioaU2XyfctUYUZ0hIqo95E8C3mKOkHN8/vPiI+LxzK7FRFvw6lQvxLHtx5N80j0wJG9cHYtS/GS9ly5kHwdVCjNP7ruREWqUrRfBREp2m/InQcAHD90klKOJQh7H879u49ISFDzMOQx8XHxZMtho3+D/iXRUTGYKVPmRpqcm8hozBTm+jIzpRlRESoadmkIiYk4VXHCtqQtQ2YNZXy3cYS9DktTPr4Lf8Oc33Xr1rFu3Tr969atW9O6dWv966ioKJTK5Dc7RkZGqNVqZDJdN7Zo0aI0b94cMzMz6tSpg6Wl5Rf3l+ZpD/fv38fJyQmAYsWKGezgww8c2NrafvT/x48f8+7dO3r27Im7uzshISE8eaL7AIeDg26U7IcffiA+3vDGFxcXx/Xr1+nXrx8bN25kz549PH/+XJ+sv9a1sLDAzs4OiUSClZUVcXFxPHnyBHt7e33iXFxcuHv3LgDHjh0jPj5ef0LeuXOHo0eP4u7uzsCBA1Gr1bx///6jvyezPDl3B/saTgDkc7bn1W3DD7M0mNwNmYmcdT1m6ac//B18xk6lVp2W5M3vhJ2dLTY21sjlcqpUdePUqfMGdf9XvRIzZ/rR4Jf2nL9w5TNb/HdwKpCd4yG6D3lcefaOojmtPqpz80UYTvkN555dePwGt8K5MrTvBf6L6d6sPzXLNKBA4fxYWlsgk8soX8GJK+cMnwBcOnuFKrUqAlClZkUunL7MmiUbaFuvK92b9Wfp/JXs3ryPbet2UcrJgYtnrtC9WX8O7j7C00fPUxXPb1OX0KfFYH4q24T8hfPp43GqUJar568b1L1y9hqValUAoGJNNy6d0bUTr6keGJsYM7zLaP30h4jwSP1oY8T7SGRyI6TSr1+aZk7+lfaNe+LmUIdCRQpgZW2JXC7DtWI5Lp41bJcXTl/mf7WrAFC9VmXOnrxAZEQUsTFxxMXGotVqefv6HVbWljg6l+LcqYu0b9yTvbsO8uTRs6/GsnjqUvq1HEJ9p2bkt02RG7eyXDtvODXrytlrVKylm0pUsYYrl05//hyK/DA3MhlSqdFX40lp3pRFdGnWl+ql61PQNj+W1pZJ7ciZyx+MNl88e4WqtSoBUKVWRc6fusyTR88wV5hToHB+AMq7ORFyO/0jiv4T59L8l844Fq1K4SIFsba2Qi6XU6HSj5w/eylV25g22xcTE2O6tBugn/6QXtMmzaNloy44Fa9OYduCWCe1I7eK5Tl/9nKGtp1W4/xm8PNPbbAt/CNFihTCxkaXm8pVXDl95kKqt+PsXOaj63Za3Tp3k3I1dNM+ijoX4/Ht5DmWz+49JU/hvCitlMjkMkq6leTO+VvcPHsD5+rlALDJlQ1Tc1Oi3uve0DhWKcvFw2mPaW5S+61W+mcK2iaf5+UrOHPpg+vgxbNXqJbUfqvWqsiFU5d4mtR+Cya133JuTty7/YALpy9TpabuGpUzdw7MzE0Je/flR+V/uXnuBj8mjdQWdy7Oo1sP9WVP7j0hr21ybkq5lebW+Vt4tRyJVysvRrX24sGNB8waMvOf2fEFXec3k/+1bt2a4OBg/b+UHV8ApVKJSpVitF2r1Xd8b926xeHDhzlw4AAHDx7k3bt37N795Q8Opnnk187OjqtXr1K7dm2ePHnCqFGjaNxY9wjo+nXDG2DKYee/bmb58+cnT548LF26FLlcTnBwMA4ODuzfv/+Tw9QSiUQ/hD18+HACAgIoVqwYNjY25MuXD2Nj44/29aH8+fMTEhJCdHQ05ubmnDlzRt+RbdKkCU2aNGHQoEFs2LCBIkWK8MMPP9C7d29iY2NZuHAhVlZWBn9DZrq15xxFqpShc/BYJBIJ2zwWUbpxJeTmJry4+gDn1tV5fOY27mt1jzvPLPuD239k7HHjl6jVat2c3p2rkUqlBAYG8fz5S2xsrFm8aBotW/Vgxgw/jI2NWbZkNgC379ynbz/Pvy2mrFSzeF5O3X9Fx8DDAPj9Up5d154QHa+mRTlb3qniMDeWfdT+Hr6NIp+N4hNbTDu1WsOMsXNZGDQbqUTClqAdvHr5BktrC3xneDG02ygWzwpkwlxvmrVvRNi7cLz6jv3s9h4/eEI/zx506tOOyIhIfIdMTlM8GrWG2X4LmLtmOhKphO1Bu3idFM/o6SPw7O7N0tkrGDvHiybtfyHsXTjefcdTvExRGrWtz6XTV/h1g+5T++sCNrF28Qa8Z3qyePM8ZHIZC6cEEJuGDo1arWai90wCNyxAKpWyYfVWQl++xsraksmzfejb2YP5MwOYPn8crd2b8u5dGEN6jSImOpa1KzaxbudSEuITePzwKZvWbkNpoWSIVx+69+tIZHgkIwf7pSk3c/1+ZdbqqUilUnYE7dbnxmuaB149xrJszip8Zo+kcTtdbsb2m/DZ7QUt3sDomZ4sDJ6DXC7ntzTmxjBPGqaOncPioNlIpFI2r93Oq5evsbS2ZNzMUQzuOpLFs5Yxca43LTo05v27MDz7+KBOUOMzZCJTf/NDgoRL565ydH/Gpxmo1Wp8R/uzNngxUqmUtauCefniFdbWVsyYN45u7p8e0S1T1oG27s05ffI8G7frRjcDflvJ7h0HMhyP35iprNq4GKlUwrrVm5PisWTanHH06DQ4Q9tPaywjR05g67YVSKVSVqxYz4vnodjYWLHgV3/ate392XVz5MhGVJTqs+WpdWbPKRyrODEh2B+JBBZ4zKVK42qYmpuyf+1elo9fyuiVvkilEg6uP8C70He8C31HSbdSTN42HalUQoD3Iv3AUt4i+Qh9nP4pa7r2O5vFQXMM2q+VtSV+Se130axlTJrrk9R+wxnRx5uEBDU+QyYw9bdxSJBw8dxVjibN6f+xgjPr9ixDIpUyYeS0VI9Kn9xzEqeqzkwNnoZEImGOx2yqN66OqcKUP9b8QcD4AMatGodEKmXfun28C32b7r9b0ClXrhyHDh2ifv36XLp0iWLFiunLLCwsMDU1xcTEBCMjI7Jly0ZExJeflEgS0/j9MHFxcYwaNYrQ0FA0Gg2jRo3C398fjUZDqVKlOH36NNu3b8fd3R1fX1/s7OwIDg7m/v37eHh4ALB161bWrl2LRqMhX758TJ48mYCAAHLkyEHbtm0JCQnB19eXlStXEhQUxOrVq5k9ezbh4eFMnToVjUaDRCKhTJkyeHl5sW3bNv32jx49yq5du5gyZQo3b95k+vTpLFmyhO3bt7N8+XKkUikFCxZk4sSJ7Ny5U7/eokWLePr0Kd7e3owZM4bnz58TFRVFu3btaNWqFTVr1mT37t26D8Z8wfhC7b9Y/q35vTic1SHoxTw/ltUhGFDv+j2rQzBQYcSRrA7BgIlU/vVK38jb+NQ9Dv9Wcpl8/AQgK0WqY75e6Rt5G/dtpwp8jdHfMGiREe9jPz/N5Vurn7Ps1yt9QzdiX3690jdUxCT909b+Dtsf7/h6pW8gZl3qBwRSy6z15wdsQDfS6+vry507d0hMTGTSpEncuHGD6OhoWrduzdq1a9m0aRNyuZyCBQsyfvx4/eDop6S58yt8mej8fp7o/H6Z6Px+nuj8fpno/H6e6Px+nuj8fpno/H5aVnR+M5v4kQtBEARBEAQhdf4FP3IhOr+CIAiCIAhC6vwLOr/f1/MgQRAEQRAEQfgbiZFfQRAEQRAEIXVS8Yts3zsx8isIgiAIgiD8Z4iRX0EQBEEQBCF1/gVzfkXnVxAEQRAEQUidf8E35IppD4IgCIIgCMJ/hhj5FQRBEARBEFJHTHsQPuQY9309DpBJjbI6BL3v7RfVZPV7ZHUIBow8v69fwPueKGWmWR2Cge/thzHNjb78s+vfktpYkdUhGPiecgNg9h3Fcyn6KT+aF8jqMPQ039m3CJhLRBfp30ocWUEQBEH4D/qeOr7CP8i/YORXzPkVBEEQBEEQ/jPEyK8gCIIgCIKQOt/Z9JT0EJ1fQRAEQRAEIVUStd/XZx7SQ0x7EARBEARBEP4zxMivIAiCIAiCkDriA2+CIAiCIAiC8M8hRn4FQRAEQRCE1BEfeBMEQRAEQRD+M8QH3gRBEARBEAThn0OM/GYFiQTHKV2wKlUIbXwCl4b+juphqL44TwMXig5oBInwcOUBHq85jERmhPOsnpgXyInURM6dWZt5ufdCpoVUv35tRo0ahFqtZsWK9SxduvaT9fr370bu3Dnx9p4CQJs2TRg0qAcajZbly9fx+++rMhSHNjGRSbsvcedVOHIjKWMblKNgNiUAb6Ji8dx8Rl/3dmg4g2qUwlhmxLYrjwCIV2u4HRrO/sH1sTQ1zlAsqXXl+i1mLlxK4Pypf9s+qtWpTI+hndGoNWwN2snm1dsNyq2zWTHx17GYmprw+uUbfIdMIjYmTl8+ZtoIwt9HMG/Sb0ilUryne1LIrgBarRbfwZN4+uh5umOrUqcS3Yd0QqPWsC1oF1vX7DAot8pmxfgF3rrYQt8wbsgU4mLiqFG/Gp36tycxMZEtq7ezdc3OdMdQvU5leg7rikatYcvaHQSv3mZQbp3Niim/+mFiaszr0Df4DJ5IbEwc7r3a0LTdL7x7GwbA+OFTefboGX6zRpO3wA8YmxizeFYgR/YeT0M+KtI1KR87gnZ99HdZZbNi3IIxmJia8Cb0DeOH+BMXE0edJjVp070FGq2WezfuM81rFlIjKWNmepKnwA/IjeUEzlnJsb0n0p2n7+FY1ahblb7DuqPRqNm0ZjsbVm0xKLfOZsWM3yZgYmrCq5dvGDXID6WFkpmLJurrlChdjBkT5rNueTAA2XLYsGnfSrq27MeDe49SHcv3dl7VrFuVfh490Kg1bFyzjfWrNhuU22SzZuZvEzExM+HVy9eMHOhHbEwsjZr/TNe+HdBoNGxas401gRuRSqVMnDUGW/tCaDVaRg704/HDp6mORSKR0HVCLwqWLIw6Ts1iz/mEPnqpLy9Xy4Vmg1qh0Wg4su4AB4P2Ua1FTaq3qAmA3EROoZK29HHpQnSECoBKjatRr3N9xjYdmaa8/CWz207wgVVERUQB8PTxc0YNGpfq3HSb0ItCJQuTEKdm0Sdy0yIpN4eSclM9RW6Mk3LTK0VuOnp35fn9Z+xf/Ue6cvNN/Qs+8Jalnd+jR4/y4sULWrdunep1Tp8+zeDBg7G3twcgLi6Ohg0b4u7unqr13d3diYmJwczMjISEBPLnz8/o0aOxsbFJ19+QHnl+/hEjUznHfhmLTTl7Svm250znmbpCqYSSo9typN5o1KpYah2dxss95/mhbjni30dxYcBC5DZK/rdvUqZ1fmUyGdOm+VC5ckNUqmgOHQpm5879hIa+1tcxNTVh4UJ/XFyc2bx5t3755MljKFeuFlFR0Vy6dIANG7YTFhae7lgO3X5OnEbDis7/48qzd8zcf5XZrSoCkENpyhL3agBcfvqW+Ydv0MzZFiOphMZlCwEwac8lGpct/M06vktXb2D7noOYmZr8bfuQyYwY5jeADj/3ICY6hmXbFnJ075+8ff1OX6fHkM7sCd7H9vW76dy/A83dG7N68XoAmrs3xr5EEc6fvARAtbqVAejauC/lKzoz1HcAQ7t4pSs2I5kRQ3z70bl+L2KiYwnYuoDj+04YxNZ9SCf+2Lyfnev30LF/O5p1aMS6JZvoN6oXnX7uSYwqhnVHlnN4z3HC36W97chkRniMG0S7n7oREx3D8u2LOLL3uEEMvYZ2ZdfmvWxbt4uu/d1p4d6EVYvX4VCmOKMHjOfmldv6uo3bNCDsfTijB4zDysaSdftS3/k1khkxyLc/XZPysXjrfI7tO8m7FLF0G9KRvZsPsHP9Htz7t6Nph4ZsXrmNXiO60b5WV+Ji4hj3qzdV6lTE0saS8PcR+A2chKWNJSv2/p7uzu/3cqxGjh9Cy7qdiImOYc2OJRzae4w3r97q6/Qb1p0dm/5g87od9BjQidYdm7F80Vo6Nu0NgNOPZRjs1YcNK7fot+k33Yu42Ng0x/I9nVcymYxRE4bRrI47MdExBO1cysG9Rw1y09+jB9uD9xActJ2eAzvTpmMzAhetwdNvMPWrtCRaFc3uPzeyY/MfuFYqD0CbBt1wrVQer3FD6NNxWKrj+bGeG3ITY8Y2HYm9czE6jOnCjB6TAV1bcvfpypiGHsTGxOG3aTLnD5zl6MaDHN14EIAu43tyeP0BfeeuUElbarSujQRJqmMwzE/mth1jE9094q+ytHBJyo1305EUdS6G+5guTE+Rm04+XRmVlJvxSbk5svEgR5Jy03V8Tw4l5cYimyX9Zg0ij20+ni/a/KXdCpkoS6c9VKtWLU0d379UqFCBlStXsnLlSlatWsWyZcuIiIhI9fr+/v6sXLmSoKAgqlWrho+PT5pjyIhsrsV5dfAKAO8v3MO6bJHkQm0iB6t6oI6MwTibBUgkqFWxPNt2ilv+G/TVEjWZ986rRAl7QkIeEhYWTkJCAidOnKVyZVeDOqampqxevQl//3kGy69du4mVlSWmpiZIJBISEzM2F+jik7dULpIbAMd82bj+4v1HdRITE/H/4zKjf3bCSJp8Ib3+/D0hryNoUc42QzGkRYG8eZg9aczfug/booV58vAZkeGRqBPUXDpzBWe3sgZ1nNwcOXHoNAB/HjyFa9UfAXAsX4oy5UqyaeVWfd3De44xYbhulDpP/ty8e/NxjlMfWyGePnxGZHgU6gQ1l89cwcnN0aBOWdcynDqkG7E/efA0LlXLo9VqaV29I6pIFVY2lkiQEKOKSWcMhXny4Kk+PxdPX6ZcBcP8OLs68ufBUwAcP3gSt2ouADg4FqfbwI4Ebl1I1wG6N9B7tx1kgf/v+nU1Gk0aYvkwH1dxcitjUKesaxlOfpCP+LgEejbqT1zSqKKRkRFxcfEc3H6ExVOXJMeiTn0sX4/t2x+rIsVsefzgKRHhkSQkqDl/+hLl3ZwM6pRzc+LYoZMAHD14gorVDK9FYyZ54DfCH23S6NMI38GsWx7Mq5dv0hTL93Ze2RUrzKMHTwxy82MFZ4M65d2cOHpQ9+bn6IE/qVzdDYDbN+5iYanExMQECbrr8P7dhxkzVDfima9AHt6k6NSnRnEXBy4f0Q2w3Lt4hyKO9vqyfPb5CX34AlWECk2Cmttnb1LCpaS+vEgZO/IXLcjBtXsBUFpb0NbTnRV+S0ivzG47JUoVxczMlCXr5xG46VfKli+d6lhS5ubuxTvYfZCblylyc+vsTRw+kZsDSbkxVZiycVYQx4IPpyUdWUurzfx/39g3HfmNjY3Fy8uL58+fk5CQQL169YiMjMTDw4MFCxawf/9+smXLRkxMDIMGDUImk+Hv749MJsPS0pLp06d/tM2oqCikUilGRkbcvn2bCRMmAGBtbc2kSZO4ceMG06dPRy6X06pVq4/Wb9SoEbNnzyYuLo7Lly8zf/58faz+/v6cOXOGhw8f4unpiUajoUmTJmzatAlj4/SPLMotzEiIjNa/TtRokRhJ9R3aRI2WPPVdcJzcmdD9l9AmqCFO16mUKUxxCRjEzSnr073/D1laWhAeHql/HRUVhZWVhUGdsLBw9u8/hrt7C4Pl16/f5sSJnURHR7Nlyx7Cw1P/JuRTVHEJKE3k+tdGUglqrRaZNPl92pG7LyiS05LC2Q1jXHLiNr2rOmRo/2lVp0YVnr0I/XrFDFBYKPSP5gBUUdEoLRUGdZRKBVGRujrRUdEoLZXkyJWdXh5dGdZ1FHUa1jSor9Fo8Jszmho/V2NEj/R33hUWCqIiVcmxqWI+ik2hNNfHljJ2jUbD/36uyohJQ/jzwEnUCep0xaC0SP7bAaJV0SgtlJ+IMzkGCwtdDHu27mfdsk1ERaqYtWwK1W6FcHSfrnNhrjBnRsBE5k9ZnOpYFBYKVB/GYmkYi7kyuc5fxyoxMVHfWWrZtSnmCjPOHDmXvI7CjMmL/Vg0Nf2dh+/lWEWmbMuqaCw+yE/KOqoow/Ia9apx7/Z9HoTopjY0bf0L796+5/ihU/Qc2DlNsXxv55XSQmmYm6iv5+avtnXnZgib968iJjqGvTsP6etoNBr85/tRt/7/GNDVM03xmCnNiU5xn9JqtEiNpGg12o/KYlQxmFua61837t+CTXOCAJBIpfSc2p8V45cSHxufphhSyuy2ExsTy9JfV7Fh1RYKFynI4qA5/FyxRare7JpnIDdNU+QG4PWTV7x+8gqn/5VPbSqyXgYHub4H37TzGxQURL58+Zg1axZ37tzhxIkTREZGcuvWLY4dO8bGjRtJSEigYcOGAOzfv586derQrVs3Dh48qB/dPXXqFO7u7kgkEuRyOd7e3igUCry9vZk0aRL29vZs2LCBgIAAKlWqRFxcHBs26EZNN23a9FFclpaWREREcPfuXaZNm0bu3Ln57bff2LNnD+7u7jRr1gwPDw+OHTuGm5tbhjq+AAmRMciUpvrXEqnko5HcF7vO8mL3OcrN7U3BVtV4HHQE07zZcFs2lAeB+3i2Of3z/v7i6+tBpUoulC7twNmzF/XLlUolYWFf78SWLl2Cn3+uSYkSlYmKUhEYOIdmzRoQHJz++YAKEzmq+OQbqzYx0aDjC7Dz2hPaudgbLIuIjefh20hcCudM976/N309e+Dk6khRBzuuXbyhX65QmhMZHmVQNypKhbnCnLjYeMyV5kSFR1K7YQ2ss1kzd9V0sufKhqmZKQ/vPWL7et20lbGDJjJ3wkJW7FpM82odiI1J/WPj3iO6Uda1DPYOdly/eDM5NoXZR7GpoqL1sSmU5kSlKD+8+xhH9hxn7Gwv6resx451u0mtfp49cXZzpJiDPVcvXtcvN1eYG9wkAVSRKhRKhT6Gv8pXL16n7xAe23+CEqWLcXTfCXLnzcWsZZNZHxjM7s37vhpLr6R82DkU4UaKfJgrPj5W0R8cq7/KJRIJ/b17U7BIfkb2SH4alStvTvyXTGDT8i3s3Xwg1fn5y/dwrAaN7E15NyeKlbTnyoXkY6VQmBOZ4o03QFSkCoXSnLjYuKS2nlzeqMVPrPg9uePQrF1DSIRK1VwpUboY/vP96NtxmMGj8A99b+fVEK8+lHdzonjJoly+cO2DeL6em+Il7flfnSrUKN+IaFU0MxaO56dGtdmzbT8Anv3HMi1XdjbuWc7PVVoQE5268zwmKhozhZn+tUQqQZt0n4qJisZUmVxmpjBDlTS9wdxSQV67/Nw4qftbipSxI49tHrpN6I3cRE6+ogXo6NONFeNS90bu72o7D0Ie8+iBbg70w/uPCXsXTs7cOXj5/OuDGdFR0Zh+ITdmX8nN9ZPXELLWN532cP/+fZycnAAoVqwYlpaWAISEhFCmTBmMjIwwNTWldGnd44fevXvz7t07OnXqxJ49e5DJdH31v6Y9rFixgiVLllC9enX9dvz8/HB3d2fTpk28evUKAFvbzz8GT0xM5M2bN2TPnp3cuXMzceJERo4cyenTp1Gr1SiVSlxcXDh+/DjBwcG0aNHis9tKrXdnb5O7li4PNuXsibj1RF8mU5pRebM3UmMZJCaijo4jUavFJIcllYK8uD5+LY/XHslwDAC+vtOpW7c1BQuWw86uMDY2VsjlcqpUceP06fNfXT8iIpKYmFhiYmLRarW8evUWGxurDMXkVCA7x0N0Hxy48uwdRXN+vL2bL8Jwyp/NYNmFx29wK5wrQ/v+3vzq/zs9mw+gjmNDChTOh6W1BTK5jHIVnLhy3vDiefnMVarU0s2NrlyzAhdOXyFoyUba1+tGz+YDCJy3Sj93sUGLenQZ0AHQjX5otVr9I+TU+m3qEvq0GMxPZZuQP0VsThXKcvX8dYO6V85eo1KtCgBUrOnGpTNXUCjN+W3THOTGchITE4mJjiUxjTEs8F9M92b9qVmmAQUK59fHUL6CE1fOXTWoe+nsFX1+qtSsyIXTl1FaKNh0eBVm5roblWuV8ty4cptsOWz4LWg2s8f/ypa1qXsjt2jqEvq2GEz9sk0N8uFcwZFrX8nH5TO6KVAjpw7D2MSYEV3G6Kc/ZMthw9y101kwcRE7glLf2UzpezhWc6b8RsemvalSqh4FbfNjZW2JXC7DpaIzFz84VhfPXKZ6Ld382Wo1K3Hu9CV9WamyDlxMyheAe+NeuDfpRcemvbl17Q6e/cd+seML3995NWvyQjo06UXFknUpZFsgRW7KcfHsFYO6589c4n+1q+hyU6sy505dJDIiirjYWOJidft8++Y9VlYWNG5Zn16DuujiidaVadIwXe7OuVs41dCNRto7F+PJ7eQPEj6795QfCudBYaXESC6jhFsp7p7XzZt3cC3JteOX9XVDLt9leJ2BjG8zhrkDZvDs7pNUd3zh72s7zds1wtNvMAC5cudAaaHgdWjqps7cPncL56TcFHUuxuMv5MbBrRR3UuTmaorc/GOJaQ9pY2dnx9WrV6lduzZPnjxh5syZNGnSBHt7e1auXIlWq0WtVnPjhu7d+Pbt22natCmenp4sWrSI9evX4+Li8tnt29ra4u/vT968eTl//jyvX+s+sCWVfr6Pv3HjRipUqIBUKmXMmDHs378fpVKJp6enfv5qq1at+P3333n//j0lSpTIcB5e7DpHzmplqLrdFyQSLg5eRL6mlZApTHm06iBPN/1JlS0+aBM0RNx8zJONxynt547cWkHxoU0pPrQpACfb+aONTchwPGq1mhEjxrN9+yqkUinLl6/j+fNQbGysWLhwKm3a9Prkeo8fPyMgYA0HD24iISGB+/cfsWLFhk/WTa2axfNy6v4rOgYeBsDvl/LsuvaE6Hg1LcrZ8k4Vh7mxDInE8EMTD99Gkc9G8Ykt/vOp1Rpm+s5nwdqZSKVStq7dyeuXb7C0tsBnxkg8uo0mYPZyxs0dQ9P2DQl7F86ovn6f3d6BnUfwnT2KgM3zkclkTPeZS3xc+h5HatQaZvstYO6a6UikErYH7dLHNnr6CDy7e7N09grGzvGiSftfCHsXjnff8cTGxLIneD+LgueiVqu5d+M+uzd9fYT1c/mZMXYuC4NmI5VI2BK0g1dJMfjO8GJot1EsnhXIhLneNGvfiLB34Xj1HUtMdCzzJi8iIHg+CXHxnD5+juMHTjJi/GAsrS3oObQLPYfqOg/92g0lLhWPbDVqDXP8FjB7zTSkUgnbg3br8zFq+nBGdvdh2eyVeM/xonH7BoS9C8en7wSKlylKw7b1uXT6Cgs2zAJgXcBGylVywsLKgq6DO9J1cEcAhnQYkapYPhXb93Cs/H1mE7BuHlKphE1rt/Pq5WusrC0ZP2sMA7uMYOGspUyZ50tL9ya8fxuGRx/d9AGb7NaooqK/soe0xfI9nVdqtZrJ3jNZun4+UqmUjWu2EpqUm0mzvOnXZTi/zlzC1Pl+tErKzdDeo4mJjiVoeTBrdywhIUHN44dPCQ7ajkwuY8pcX9Zs+x2ZTMbEMTPSFM/ZPacoU6UsfsFTQAKLPOZRqXE1TM1NObh2L6vGL8Nr5VgkUimH1+/nfahuTnEeu3y8epz5U8Eyu+1sWr2VyXPHsnr77yQmJjJ68PhUz+8/u+cUjlXKMi54ChIJLPSYR+Wk3BxYu5cV45cxOik3h1LkJu/flBsh7SSJGf2EUhrExcUxatQoQkND0Wg01K5dm/fv3+Ph4cHChQs5cOAANjY2vHjxAj8/P2QyGX5+fpibmyOXyxk3bhzPnz8nKCiIWbNmfbT9a9eu4e/vr2/AEydO5NWrVwb1U37bA0Du3LkZO3YsFhYWTJ48mWPHjmFpaUmOHDmwtrbWzyFu2LAh7du3p02bNl/8G7f+0C4zU5ZhrcNS/xVNf7f3i9tndQgGZPV7ZHUIBtzKdMzqEAzIJEZZHYJenDbjb/Iyk5n023ybSGpp+X7m4EWoM6+DmhnMjf6+b2JJj0h1+j4s+Hf40bxAVodg4GLMs6wOwUBZs3xZHYKBdY+2ZHUIAERP757p2zT3CMj0bX7JNx35NTExYcaMGR8tf/v2LZaWlmzcuJH4+HgaNGhAnjx5yJs3L8HBwQZ1CxQogJub2ye3X7p0aVauXGmwzNbW1qD+h+UpeXl54eX18VfTaLVazM3N+eWXX7749wmCIAiCIPyriZ83zhw2NjZcu3aN5s2bI5FIaNmyJXnz5s3qsAB48uQJ/fv3p3Xr1iiVyq+vIAiCIAiCIHy3vovOr1QqZfLkyVkdxicVKFCArVu3fr2iIAiCIAjCv532+5lmlV5Z+iMXgiAIgiAIgvAtfRcjv4IgCIIgCML3L61fdfg9EiO/giAIgiAIwn+GGPkVBEEQBEEQUudfMOdXdH4FQRAEQRCE1PkXfNWZmPYgCIIgCIIg/GeIkV9BEARBEAQhdcS0B+FD4yVPsjoEA51++PSv4WWFCiOOZHUIBow8j2V1CAZOX12R1SEY6Pnj8KwOQc9NY5bVIRjYytusDsGApfT7+QnfSrLv6ydzX0m/r0e0j4xjszoEA/cS3mV1CHo55ZZZHYKB6ER1Vocg/E1E51cQBEEQ/oO+p46v8A/yL/iqM9H5FQRBEARBEFLnXzDtQXzgTRAEQRAEQfjPECO/giAIgiAIQuqIrzoTBEEQBEEQhH8OMfIrCIIgCIIgpM6/YM6v6PwKgiAIgiAIqZL4L/i2BzHtQRAEQRAEQfjPECO/giAIgiAIQur8C6Y9iJFfQRAEQRAE4T9DjPx+Q1XrVKL70M5o1Bq2Be1iy+rtBuVW2ayY+KsPJqYmvH75Br8hk4mLiaNmg+p06t8eEhMJXrWdrWt28Eurn2nY+mcAjE2MKVbKnnplmxAVEZXmuCQSCW0ndKeAQ2ES4hNY6fkbrx+9NKgjNzVm8CpvVnguJDTkOVKZEZ2m9iV7/pzIjOXsmr+JK/vPpT85QPU6lek5rCsatYYta3cQvHqbQbl1Nium/OqHiakxr0Pf4DN4IrExcfpy72meRIRFMGfiQuTGcsbNHk3+QnlRRUYzyWs6jx88TXUs1epUpkfSsdoatJPNHxwr62xWTPx1LKZJx8p3yCSDWMZMG0H4+wjmTfoNqVSK93RPCtkVQKvV4jt4Ek8fPU9nlr7syvVbzFy4lMD5U/+W7YOuvbhP6EEBh8Ko4xNY5rmQVynaS9laP9J4YEs0Gg3H1h/kaNB+ZMYyuk3rT86CuYmJjGaVTwChD1/Qe94QrHJaA5Ajfy5CLt7htwGz0hsYVSd2JnvJgmjj1RweEUDEw1B9sX3jipTpVo9EjZa3N59wbHQgJCbSYvcE4iNjAIh48prDwxanNzVIJBL6TexHkZJFSIhPYPaI2bx4+EJf7lbbjXaD26FRa9i7bi971u7BSGbEkOlDyF0gN3JjOWvnruX0vtMUKVmEAZMHoFFrePbgGbOHzyYxMW0jLhKJhG4TelGoZGES4tQs8pxPaIpjVa6WCy0GtUKj0XBo3QEOBu2jeouaVG9REwBjEzmFStrSy6UL0REqADp6d+X5/WfsX/1HuvOERELNiZ3J4VAQTbya/SMCCH+UfKyKNaqIc7d6aDVa3t56wsHRgUgkUMu/OzZ2eUjUaNnnsZjwR6/SH4NBOBIaTejCDw6FUMcnsNnzd96liAd018Auq7wI9vydNyHPcW5RjXItqgEgM5GTp2Qhprj0JTYiOt0xdJ7Qk4IlC6OOSyDA81eDY+Vc60eaJh2rI+sOcDhoP0YyI3rNHECO/LlI1GgJGLmQFyHPyFs0P90m90EikfD45kOW+wSka55mlToV6TqkExq1hh1Bu9i6ZqdBuVU2K8YtGIOJqQlvQt8wfog/cTFx1GlSkzbdW6DRarl34z7TvGbp265NdmsC/1jMwDYePLr3OE3xVKpTkc6DO6DRaNkVtJvta3YZxmNjic+C0fp4Jg+ZRlys7rpsYmrCrKCpTBk2ncchTwDddfzXbXPpXKs78XEJqY5DIpHQZ2JfbB1sSYhPYN6Iubx4lHyeu9R2pe2gNmjUWvat38fetcnnilV2K2btnI1Pe2+ehjylQNEC9J/SHyQSHt54wCKfRWi/9zm1YuT373f69GkqVqyIu7s7HTp0oE2bNoSEhDBy5EgaNmyIu7s77u7utGvXjrt37wJQvHhxxo4da7CdCRMmULNmzVTvt3Llypn6dxjJjBjqN4D+bYbSs9kAmnZoSPac2Qzq9BjSmT3B++nRpD+3r92luXtjpFIp/Uf1om+rIXT5pQ8d+7TFKpsVO9bvplfzgfRqPpCbV24z3XtOujq+AE51XZCbGOPfbDSb/VfTYkxHg/JCZYowfP04chb6Qb+sQtOqqMIimd7Kh3mdJ9LWr1u69v0XmcwIj3GD6N16MF2b9qW5e+OP8tNraFd2bd5LlyZ9uXX1Li3cm+jLWrg3pqiDnf518w6NiFbF4N6gJ5NHz8Rr0rA0xTLMbwB92wyle7P+NOvQ6DPHah/dmvTjVtKx0u/bvTH2JYroX1erq2tLXRv3ZeHUJQz1HZDqWNJi6eoNjJ0yh/i4+L9l+38pV9cVuYkxE5uNYoP/KtqM6aQvM5IZ0da7M9PdxzGltQ//a1sHy5zWVG9Th1hVLBOaerHadwkdxnUH4LcBs/BvM5Z5PacSHaFi7bhl6Y7Ltl55ZKZytjTx49TkICp6t0uOy1SOy/AWbG81iS1Nx2FsaU6h2s4YmcgB2NZqIttaTcxQxxegYr2KGJsaM7TJUJZNXkYP7x7JMciM6Dm2J6Pbj2ZEyxH83P5nbHLaULNZTSLfRzK8+XC83b3pO74vAO2HtGfN7DV4NPdAbizHtZZrmuNxqeeG3MQY76YjWeu/AvcxXQzi6eTTlYkdfPFtNYba7epildOaIxsPMq7NGMa1GcP9ayEE+gYQHaHCIpslI5d7U75O2uP4kF298hiZyFnf1I8/pwRRNeWxMpFTcXgLNrWexIZm4zC2MMe2tjO2tcsBsKHZOE7O2ERV7/YZjuMvDnV/RGYiZ1Gzsez1D6L+GMNt5ytjS4/1PmQrlFu/7OLGoyxpM4ElbSbw/OoDdvquSHfHF6B8PVfkJnL8mnoR5L+KdmM668uMZEZ08OnClA5+TGjlTc2kY1W2RjmMjIwY12wUm+dsoOVwXR5bDW/P+qmrGdd8FMamxpSv45LmeIxkRgzy7c+gth70aT6Ixh0aku2D62C3IR3Zu/kAvZsO5Pa1ezTt0BATU2N6jehG35ZD6NmoP0pLBVXqVNRv03PqMOJSDBSkJZ4BY/swtJ0nA5oPoWH7BmTLaWNQp/MQd/ZvOUj/ZoO5e+0ejd1/AaC4YzHmB88ib6G8+rqu1X9kxlp/suUw3EZqVKhXAWMTOcOberB8SiBdvZPvf0YyI7r7dMe7gzderUbyU7t6WCe9wTeSGdFvcn/iY5Ov0R1HdGSF/wo8m43AxMwE1zpuaY5HSLvvvvMLUKFCBVauXMmqVavo378/U6fqRrWGDx/OypUrWblyJb169WLOnDkAWFtbc/bsWdRqNQAajYZr165lWfwAtkUL8+ThMyLDo1AnqLl85ipObo4GdZzcHDl56DQAJw6ewrXqj2i1WlpWc0cVqcLKxhIkEmJUMfp1HMoWx664LZtXGY5MpoW9iwPXj1wE4MHFuxQqY2dQLjOWs7DXNF6GPNMvO7/zFFtnBOlfazSadO8fkvLz4CmR4ZGoE9RcPH2ZchXKGtRxdnXkz4OnADh+8CRu1XQXdMfypXEsX5qNK7fo6xYpZsufB08C8CjkMbZFC6UtlofP9LFcOnMFZzfDWJzcHDmRdKz+TDpWulhKUaZcSTat3Kqve3jPMSYM17XZPPlz8+7N+1THkhYF8uZh9qQxf8u2Uyrq4sDVpPZy/+JdCqdoL3ns8/Pq0UuiI1RoEtTcOXeTYi4O5C2an6tHLgDw8v5z8tjlM9hmkyGt2R+4m/DXYemO6wfX4jw+fAWAVxdDyOVoqy/TxKnZ0sQPddJNR2okRRMXT3aHgsjMTGiw2pOGQV7kcrb75LZTq5RrKc4fPg/ArYu3KOpYVF9WwL4Azx8+JyrpGnD97HVKuZbi2I5jrJi+IjlWte5cCrkWgtJaCYCZ0kx/PUuL4i4OXE7K+92Ld7BztNeX5bPPz8uHL1AlHatbZ2/i4FJSX16kjB35ixbkwNq9AJgqTNk4K4hjwYfTHMeH8roU51HSsXp5MYTcKY9VvJoNTVMcK5kUTWw89/ee58DIJQBY5s9B9JvwDMfxl0IuxblzRBfPk4v3yFemiEG5kbGc1b1m8jrk4yc2+crYkqtYfs6uPZihGIq7OHAl6bwKuXgHW8fktpjXPj+hD5PPq9tnb1LcxYGXD3RP4SQSCWYWZmgSdG1nTu9p3D5zAyO5DOucNoS/CUtzPLZFC/H0o3tWGYM6ZV3LcPLQGQBOHjyNS9XyxMcl0LNRf30H18jIiLikN+QDffqwecU23oS+TXM8hYsW4tnDZ/rz5+rZazh+EI+jaxlOJ8Vz6tAZfqyqe8NkbCxndPexPE4x0qzVJjKkzQgiwiLTHEtJl1KcP6w7r25fvP3Ref7i4QtU4SrUCWpunL1BKddSAHQd043dq3bzLvSdvv7kXpO5fuY6sqRjFfY33R8yVaI28/99Y/+Izm9KERER5MuX76Pl4eHhmJubAyCTyXB1deXPP/8E4Pjx41SsWFFf9/bt2/oR4wEDBhAZGYlGo2HUqFG0atWKYcOGER+vO1n37t1Ly5Ytadu2LR4eHul+HKGwMDcYmVVFRaO0VBrWUZoTFamrEx0VjdJSAeg6ljXqV2PtgUAunrqMOiH5JthloDuLZ6R/tAzAVGlGTGTyiEWiRovUKLlphJy/zfsXhheruOhY4lSxmChM6bVwGFunB5ERSguF/m8HiFZFo7T4ID8p6qiiorGwUJAjV3b6eHRjktd0g7q3r92lWh3diGuZcqXIlScnUmnqmrvCQvGJY6UwjFep+OBYKcmRKzu9PLoyZdTMj7ap0WjwmzOaEROHsH/HoVTFkVZ1alRBJvv7ZzKZfdBetCnay4dlsVGxmFuY8/jGQ8rW1L1BKOJcFJsfsiFJOh4W2S0pWdmR4xszlhdjpRnxEYZxSf5qx4mJxLyJAKB05zrIFaY8PXoNdWwclxftZGd7f456LaPWvL7J66SDudIcVdL0gL9i+Cs3CgsFqsjkspioGBQWCmKjY4lRxWCmMGP0otH6jvCzh8/oM64Piw8txjqHNVdOXklXPNGfPVaGZTGqGMwtzfWvm/ZvwaY5yef16yevuHfpbppj+BRjpRlxH1xzUh6r6KRjVbZzHeTmpjw+dk1fr87MXlT368i9nWcyJRbQXQPjPpMngMfn7xD+4t2nVqV6v8YcnBOc4Rg+PB4fnlcpy2JVMZhbKohVxZIzfy6mHpxHtyl92Buom5aQqNWSPV9O/PfNRpnNghcpBi5SS9deP7gmf3DPMlcm1/nrOpiYmKh/g9+ya1PMFWacOXKOBq1+IuxtOKePnE1zLLp9mROV4vyJjor56B6Rsk500vkFcPXcdV49f21Q99yx80S8j0hnLGZER6Y8zzX6Y2VuYW5Q9td5XqtFLcLfhnPx6AWDbWm1WnLmy8mC/b9imc2SZ+k4Vt+cNjHz/31j/4g5v6dOncLd3Z34+Hhu377NokWL2Lx5M9OmTeP3339HKpWSK1cuhg8frl/nl19+YcOGDVSvXp0dO3bQp08ftm7VjcZ5e3szadIk7O3t2bBhAwEBATg5OREXF8f69et5/vw5f/yhm6OzY8cOOnfuTIMGDdiyZQtRUVFYWlqmOvY+nt1xcnXE3sGOaxdv6JcrlOZEhRtOU1BFRWOuMCcuNh5zpTmRKcoP7TrK4d3H8J0zigYtf2L7ul0oLZUUti/E+RMX05XXv8RGxWCqMNO/lkglaDVf7+Tb5MlOn0XDObzyD85uO56ufffz7ImzmyPFHOy5evG6frm5wpzID6ZxqCJVKJQK4mLjUSh15XUb1sQ6mxXzV88gR87smJqZ8ODeI7as3YFt0UIEBM/n0pmr3Lxy+6tvXPp69sDJ1ZGinzhWkR8cq6golcGxigqPpHbDGlhns2buqulkz5UNUzNTHt57xPb1uwEYO2gicycsZMWuxTSv1oHYmNh05SyrxXzUXqT69vJhmanSlOgIFRf2niGPfT48g/y4e+42D6/e189B/PHnipzaeizD3x0ZHxWDsdIwrsSU7VgiocLoNlgXycPenrqnRGH3XxKeNC84/MFL4t5HYZ7LGtVnOjpfEx0VjVmKGKQpcqOK1LWZv5gpzfQd5Rx5cuAd4M3OFTs5vOUwAL19e+PR3IPHdx7zS6df6OHdg1/H/JrmeD53bsd8EKuZIjkec0sFee3yc/3k3/PE7MNjxSeOVZVRbbApkoedveYYrLtv6CL+zBlE661+rKzliTodj9A/FBsVg7HCNHn3qbwGmlqak9MuLw9O3vhq3a+JiYrGTPHpthMTFYOpMjk+06Rj9XP3hlw5cpH1U1eTLU92Rq31w6veEBLiEnj77DUe/+vP/9rUpr13FxYNm5eqOHqN6EZZ1zLYORThxsWb+uXmio+vg9EfXAf/KpdIJPT37k3BIvkZ2cMHgF/a/ExiYiIuVctTtJQ9PnO8GN55NO9ef/lc6z6iC44upZPiuZUcj9Lso6l+0Un30PjY+KRy1YebyxTRUTEG507Ka2B0ZDRmH5znUREqGnZpCImJOFVxwrakLUNmDWV8t3GEvQ7j9bPX9Krek7pt6tLNpzuzh6bzcw9Cqv0jRn7/mvawbt06goODGTBgALGxsfppD8uXL2fatGnkypVLv0758uW5ceMG79+/JywszGC0OCQkBD8/P9zd3dm0aROvXr3i7t27ODrqpiHkzZuXPHnyAODl5cXZs2fp0KEDFy5cSPXo4V8W+gfQq/lA6jo2okDhfFhaWyCTy3CuUJYr5w1vLJfPXKVyLd0IdaWaFbh0+jIKpTmLguchN5aTmJhITHSsvhNXrkJZzhzN2IfMAO6du0XpGrrHQ7bORXl2++sfQrDIYcWglWMInrKKExvSP2K3wH8x3Zv1p2aZBhQonF+fn/IVnLhy7qpB3Utnr1AlKT9ValbkwunLrFmygbb1utK9WX+Wzl/J7s372LZuF6WcHLh45grdm/Xn4O4jqfqA2a/+v9Oz+QDqODY0OFblKjh98lj9FUvlmhW4cPoKQUs20r5eN3o2H0DgvFXsCd7H9vW7adCiHl0GdAAgNkZ3/L77DzR8wd1zt3BMai9FnIvy9PYjfdmLe0/JXTgPCislRnIZxV1Lcu/CHWzL2nP37C3824zlwh+nef0k+cNEpao4cvXwhY/2k1Yvz96hYE3d9JRczna8u/XEoLz6lK7ITOTs6TZL/0i9ROvqVEqaO2qe2xq50ozoV2HpjuHG2Ru41NRNxynhXIIHtx7oy57ce0Je27worZXI5DJKu5bm5oWbWOewZuLqiSybtIy96/bq60eGRepH+96FvkNpZTjKlRq3z93CuUZ5AIo6F+NximP17N5TfkhxrBzcSnHn/G0AHFxLcvX45bQnIJVenLtD4Rq6Y/WDsx1vPzhWtZKO1fbuKY5Vs8r82K8hAOqYeBK12kz7sv3H525TvIYTAAWc7Qm9/eTLKyQp7FqCe8cz5w3CnXO3KJt0Xtk5F+NJimP1/INjVcKtJPfO30YVrtI/aVGFRWEkkyGVShka4EXuwrp7WExUDIlpGFlbNHUJfVsMpn7ZpuQ3uGc5cu38dYO6V85eo1KtCgBUrOnG5TO6pxMjpw7D2MSYEV3G6Kc/9Gk2iL7NB9O3xWDuXr/HuEGTv9rxBQiYuoyBLYfRyKkF+WzzYpEUT1k3R66dN3zTcfXsNSrW0s2ZrVDDlcunr35qkxl289wNfqyhe5JV3Lk4j2491Jfpz3Mr3Xleyq00t87fwqvlSLxaeTGqtRcPbjxg1pCZhL0OY8wSb/IU1s1FjlbF/CN+QCJRm5jp/761f8TIb0o5cuRIVT2JREL16tXx9fWldu3aBmW2trb4+/uTN29ezp8/z+vXr5HJZOzcuZNOnToRGhpKaKju5rxu3ToGDBhA9uzZ8fHxYd++fTRt2jTNcWvUGmb5zmfe2hlIpVK2rd3J65dvsLS2YMwMT0Z0G8OS2cvxnTuapu0bEvYujNF9xxEbE8ue4L38vnk+arWauzdC2L1Jd4MsZFeQZ48z/q0Bl/44g0NVR0ZsmoBEIiFw+AJcGlXBVGHKsbX7P7nOz/2aYW6lpP7AFtQf2AKAeZ0mkZDOD1up1RpmjJ3LwqDZSCUStgTt4FVSfnxneDG02ygWzwpkwlxvmrVvRNi7cLz6jv3s9h4/eEI/zx506tOOyIhIfIdMTlMsM33ns2DtTKRSKVtTHCufGSPx6DaagNnLGTd3TNKxCmdUX7/Pbu/AziP4zh5FwOb5yGQypvvM/ds/lPZ3uvDHaUpVdWT0pokgkbBk+AIqNKqCicKMI2v3sXZCIMNWeCORSji2/iBhoe9QxyfQdGhbfurZiOgIFctGJI9g/lAkH6+ehH5hj6nzYM858lctTZPNPiCRcHjYYuybVERubsrrKw8o0aY6L87cptG6UQBcWfoHt4IOU2NmLxpv8gbgsMfvhiOQaXRizwmcqzozY/MMJBIJM//P3n2HNXX1ARz/3iTsAO49AQc4UcFRR121te66Le69Nw5AQHHvqnVVrRutVF8Xbq21bq0bUdwLB8hKGEl4/wgGgqKAqLQ9n+fxeUzOHT/OuifnnpuMmsvXrb7GwtKCvRv3ssJ3BX7r/ZAkif1b9vPq2Sv6efdDaauk07BOdBrWCQDPrp4sGLuAcYvHodPqSIhPYIH7gg+c/W1nA09RsXYlfAOmI0nw8+if+KplXcwtzTm0aT9rJ69m4rpJSDIZR7YcJDxpLWIh+8I8f/DxZZKW24HnKFanPO0CvJAkiQOjl1OmZU1MrMwJvXyXch3q8fjMTX7YrC+rv1ft4/beczSe05e2Wz2QKeT84bMebQae0H+f6/vO4VCnAn23eSNJEtvGLKNii1qYWZm/dy1vHrtChD/Mmm+cOBd4mvK1K+EVMBVJklg+ehE1W9bB3NKcI5sOsGHyGtzXeSHJJI5tOUR4aBh7V+6k76xBeG6dgtxEwZZZG4hTx7Hz5wD6zRmCJiGBeHU8K90zdscA9NesBT6Lmb9xFjKZxM7New394ITZYxjX24vV89fhuWA8Lbt8z+uwCLwGTqFMhVI079SUv09fZvFW/Qym/8rfOBaYubuDKeNZ5LOUORumI5PJ2L05kJfPXmKdwxr3WaPw6OPNrws2MHH+WJp3bsrrsAh8B039qHOm5WTgSSrXcWZmwCwkSWLB6PnUa1kPcytz9m3cx8rJK/Fd74skk3HA/wBh71nj/NuSrQyfOxxNvIY4dRw/uS/8JDELxqTEjH53zmd2+vRphg8fjoODAzKZjJiYGDp37syZM2do2rQpdevWfWufr776ihMnTnDz5k1++OEHTpw4gbm5Od999x2HDx/m6tWrzJgxw/CQlp+fn2FAfO7cOQoVKsTff//NsWPHOHz4MPPnzydHjhxYWVkxdepUcuZM++nQagXrfLK8yIyqZgU+vNFncio2e61lkkvZ68bH6StrP7zRZ9S32pgPb/SZVNdafHijz2gHGX9g51OykZl96RAMaiVmfIb6U3ouy14zafcTs89yp9sJmVve86mYSPIvHYKRHPLs1e/sfLDrS4cAQNTQZll+TOuFn/dvy/aD338aMfhNmxj8vp8Y/KZNDH7fTwx+0yYGv2kTg9/3E4Pfd4sa3DTLj2m9aM+HN8pC2evqLwiCIAiCIAif0D9uza8gCIIgCILwhYhfeBMEQRAEQRCEfw4x8ysIgiAIgiCkz79g5lcMfgVBEARBEIR0+Td8T4JY9iAIgiAIgiD8Z4iZX0EQBEEQBCF9vsCyB51Oh7e3Nzdv3sTU1JQpU6ZQvHhxQ/rly5eZPn06iYmJ5M2bl1mzZmFmlvZXQIqZX0EQBEEQBCHbOnjwIPHx8fj7+zNq1CimT59uSEtMTMTT05Np06axadMm6tSpw+PH7/9dATHzKwiCIAiCIKTPF5j5PX/+PHXq6H9ErHLlyly9etWQdvfuXXLkyMGvv/5KcHAw9erVw87O7r3HE4PfLGabzX4R5plO/aVDMDCTmXzpELK17PSLagDLz8360iEYdK464kuHYCRGE/+lQzASm6j50iEY/CnPXg/DZLeHc15ps0+fbCEzQa1L+NJhZFsm2exXQP/LoqOjUSqTfz1SLpej0WhQKBSEh4dz8eJFPD09KV68OP3796d8+fLUrFkzzeOJwa8gCIIg/AeJga+QGYmfYObX398ff39/w+sOHTrQoUMHw2ulUklMTIzhtU6nQ6HQD2Fz5MhB8eLFcXBwAKBOnTpcvXpVDH4FQRAEQRCELPAJBr+pB7upValShSNHjtC0aVP+/vtvSpcubUgrWrQoMTEx3L9/n+LFi3Pu3Dnatm373vOJwa8gCIIgCIKQbTVu3JgTJ07QsWNHEhMTmTp1Kjt37kSlUtGhQwf8/PwYNWoUiYmJODs78/XXX7/3eGLwKwiCIAiCIKSP7vOfUiaT4evra/Sevb294f81a9bkt99+S//xsiwyQRAEQRAEQcjmxMyvIAiCIAiCkC6f4oG3z00MfgVBEARBEIT0+RcMfsWyB0EQBEEQBOE/Q8z8CoIgCIIgCOnzBR54y2pi5lcQBEEQBEH4zxAzv5+JJEkMmzoEeyc74uMTmDNmHk/uPTGk12xUgx+Hd0Gn1bLXfx97Nu41pJV1LkufCb0Y1S7552+/+vYr6jWrw9TB0zMdT3+/gZRwLElCfAKLxi7k2f2nhnSXRq50GNYRrUbHwS0HOLBpnyHNNrctc3fPx6uLJ49DHjF60Vhy5M0JQL4i+Qi+eJPZg2dmOKbajWvRe0Q3tBot/9u8hx0bdxml2+ayZfJiT8zNzXgR+hLfEdOJU8fxTauGdOzdFp1Ox+3rIcwYPw+ZXIb3ggkULFoAnVaH35hZ3L/9IFN5ldm46jetS7fBXUhMTGT7hp3s2Lg7U+eXJAm3KX0o6lgCTXwCq91/5vn9Z4b0Sg2r0XJoO7RaLce3HOaPzQdRmCroNWsweYvlRx2lYr3XSkLvPaX/TyOwzZsDgDxF8hFyMZilQ+ZlKq70uHwtiLk/r2LNoozXh/SQJIneU/pTwqkECXEJLHVfxLMUeVO1oQtth3VAp9Vy2P8ghzYfAKDVwB9waeyKwkTBvnV7Oex/EJvctvSfPggrWyUyuYxFI+YT+uBZWqc2UqtxTboN/xGtVseezXvZtXGPUbptThu8Fk/E1NyMV6EvmTZiFnGxcWnu12VwJ776phYmJgq2//o/dm9O7g8atWpAm56tGdhiSJp5MnzqUOyd7EiIT2DWmLlv9TVdh/+IVqtlr38guzfuTXOf4qWKMWrGCCRJIuR6CAs9F6PT6QznmbZ2Cif2nWTn+l3vjOVTlVXJ8naM+8WDp3f1f9f+9YH8tevP9BSVUTx9pvSnuFNJNHEJ/Oy+yKgPrNrQhXbDOqLVajnif5CDm/cDMGvPfFSR+l+aCn0YypIxCylZ3o6+fgPRxCdw9/pdVnuvSNfPKmdlWdk72TN08iB0Oh0JcQlMGz6DXPlyM9hngOF4Ts6OePSexNmj59KVR7Ub16RnUt+3a/Oet/ow21y2+C72wMzcjJehL5k8YgZx6jgat2pAx95t0ep03L5+h1lJfbLHXHcKFi2AiakJaxas4/j+vz4YQ63GNemeoo3sTKNtvYkhZdtKvZ9cIWfifHcKJF0XZo6Zw4OQh3gv8SBX0jWsQNECXL9wA++BU95bbn2n9KeEU0kS4hJYkqruVGvoQvukunMoRd2ZnaLuPH8YyqIxCxn50+i3rp9zh8xOR+l8OeKBtywQEBDAnTt3GD16dIb2O3DgABUrViR//vwcO3aMVatWIZPJ0Gq1tG3blhYtWvD69WuOHz9O8+bN03XMcePG0bRpU+rWrZuZP+W9vvq2FqZmpgxpORzHKmXp79kXr17eAMgVcgZ492Pg90OIVcWy4Pd5nDxwivAX4XQY0I5GPzQiVhVrONYgnwFUq1eNkGshmY6nepMamJiZ4N56NKWdy9DTsxdTe08xxNPLqzejmo8gThXH9ICZnD14mtcvXiNXyBk4bTBxsfGGY70Z6FrZWjFl8zR+8V2R4XjkCjkjvAfRvWk/1KpYVu5YzJ8H/uLVizDDNr1HdGPf7wfZvSWQroM70+bHFgSs20H/sb3o1LAHceo4Ji/xonbjmkiShFwup3eLQbjWrcYA996M6+P12eLy/2Ubgyb0o9t3fVHHqPE/9itHA/8kIiwiwzFU+cYVEzNT/NpMwM65FB09urGwzwxDfJ08u+Pbwp04dRwTf/Pj70PncPmuJrExsUxpPZ4CdoX40bc3c7pONgx0LW2scN/swybf1RmOJ71WbdjKzsDDWJibfbJzuDSpjqmZCRNbu1PKuTRdPXoys89UQJ833b16Ma75KH3d2Dad84fOUsi+CGWqlsWjzTjMLMxo3rcVAG7ju3F8+zFO7j5BuZoVKOxQOF2DX7lCzuBJA+j7/UBiVbEs3r6Avw6cJOxFuGGbbiPcOLD9MIFb9tFlUEdauDUjYPX2d+5XzKEY5auVY1DLoZhbmNGxf3vDcRzK2fN9p++QpLTjqf3tV5iamTK45TAcqzgy0LMfHr0mGWId5N2f/t8PJlYVy0+/z+evA6coX63cO/fp7d6TlTNWcfn0FdznjqHWNzX5M/AEAL3G9sAmh/UXKauS5e3ZuXIHu1bsSPf5U3NtUgMTM1Mmth5LKecydPPoyYw+fini6c245iOJU8cxZdsMzh06Q0zSwGVSx4lGx+o/bRCrvFdw83wQHUd3oXarehz//egHY8jKshriO5CFnosJuR5C8y7f02lgB5b4LmNEO/21td73dXkV+irdA1+5Qs4w78H0TOr7lu9YxPEDJwlL0ff1GtGV/b8fYveWQNwGd6b1j835fd3/6De2F10a9iROHYfvEk9qN66JTU4bIsIj8Rk6FZucNqzdv+KDg1+5Qs6QSQPok9RGlmxfwIlUbav7CDcObj/M3qS21dKtGdtWb3/nfk7OjvprWMuhVKtTlT7uPfHs62MY6CptlSzcOoefvJe8N643dWd867GUdi5Dd4+eTE9Rd3p49WZsUt2ZmqrueKWqO28GulY2Vvhu9mP15F/SVT5flFj28OWsXbuW6OhoALy9vfnpp59YvXo1S5YsYcGCBbx69YqbN29y+PDhLxypXgWX8oZO58aFIMpUSv5pvuKlivH43hOiI6LRJGi4evYaFVzLA/Dk/lO8+/gYHevauessmLDwo+JxcinHxaMXAAi+eBOHiqUMaUUcivL03lNiImLQJGi4cfY6Tq7lAOjh0YvA9XsJCw1765idR3Zh95qdhD8PfyvtQ0qWKs6je4+JSsqDS2cuU7l6RaNtKrlW4NSRMwCcPHwalzpViY9LoHeLQcSp4wBQyOXEx8XzIOQRcoUcSZKwUlqi0WgyHNPHxKXT6ehQrysxUTHY5rRBQkIdo85UDKVcHLly7CIAdy7eokSF5C/2LuhQhOf3n6GKjEGboCH43A1KuzhSqFQRrhzTl++zO08oaF/Y6JitRnTg4Jq9RLx4namY0qNooYLMn+rxyY4P4OjixMWkvLl1MRj7ig6GtMIORXh27ykxkfp6HHT2BmVdnKhc15kHQfcZs3w87qs8OH9I3y7LVHMkd8HceG7wpU6relw7eTVdMRQvVZzH9x4b2u+Vs1epWL2C0TYVXStwJqmOnDpyhmp1qqS5n2u9atwJuovfLz5M+9WPvw6eAsAmpw39xvfmp0nvvzBXcCnHmaNnAbhx4Qal39PXXDl7lYquFdLcZ1JfXy6fvoLCREGufDkJTxp01P2+DjqdjtNHzqYrjyBry8q+vD1VG1TDZ8tUBswcjLmVRbrjeKOsiyN/J7WRWxdvYpciniIORVPFcx1Hl3KUcCyJqbkpnut8mLRpCqWcywCQq0Aebp4PAuDmuRs4VnNMVwxZWVa+A/0Iua6fEJEr5MTHJRiOZW5hTvdRXVnouTjd+fN233eFyqnqdSXXCpx8R5/ct8VgQ58sl8uJi4vn8M5jLJ+ZPLDTarQfjKFEOtvW6VRtK639Ht55hFyedF2wtnwrhl6jurFt1XZePX/7+paSo4sjF48lXz/t31N3bqSoO2bmpnit88Fn0xRKJ9WdNzqO7MyeX3dn6vopZNwHB78BAQEMGjSIbt260aJFC/bt20ezZs0YPHgwI0eOJCoqiqFDh+Lm5oabmxs3b97k0KFDjB8/3nCMVq1a8erVK9avX0/Xrl3p3Lkz/fr1Iz4+3uhc69ato0OHDnTs2JG1a9cC+tlYLy8vevXqRfPmzbl27RpHjx7lxo0buLu7Ex8fT+7cuVm7di23bt3CysqKvXv3kjt3bpYuXcqpU6fw9/dn3Lhx9O/fn44dOxIREcH06dNp164d7dq149dffzWK49KlS7Rr146nT5/y9OlTevfujZubG7179+bp06dkhqW1JTFRMYbXWq0OmVyf/ZZKS8OnQgB1jAqljRUAx/f8iSbBuIEe3XksXbfU3huP0sIoHp1WmxyPtSWqFGnqaDVW1lY0aNuQiFcRXPzjwlvHs81tS8WvKnF466FMxWNlbUV0inPGxKgNeWDYRmlJdJT+A09MtD6PEhMTCXup7yza92yDhZUFp4+dQ6VSUbBoAbb+sY4Js8bg/8u2zxoXgFar5evv6rDh4Counr6EJiFzA3ALpQXqKJXhtS5F3UmdFhsdi6W1JQ+u36NSg2oA2DmXImeBXEgy/T7WuW1w+qoif/52JFPxpFfj+rVRKD7tzSULpXFd1aVqV6qUeROjxtLGCutcNthXdGDuwJmsmPAzwxaMBCBvkXxER8QwuYsXLx+/oNWAH9IVg5XSuG2rotVYWSuNtrFUWhrqkSqpPaW1n20uW8pWLI1XP1/mjJuH56IJyGQy3GePZpH3z6iiVbyPpbVVqrb9vr5GjZWNVZr76HQ68hfOx+rDK7HNacvDkIeUKFOChq0asHq2cb/5IVlZVrcu3WKd3xomtZ9A6INQ2g3vkKFYPhSPhdLCuA+MUWNpY0mcOo6dy7cz2W0SyycsYdiCkcjkMp4/fIZTdf0EQdVGrphZmqcrhqwsq7CkAVu5qk606t6SrSuS+7ymnb7l2O4/iAyPTHf+WFlbEZPUrwGoYlQobVLX6+RtVNH69JR9cruerbG0suDMsXOoVWpUMWosrSyYttyHZTM/PMOZst3oz6FGmY62ldZ+apWagkULsOGPNYydOZLfVgUYtsmROwdVa1dh75Z9fIhlBuuOVVLd2bF8O75uk1g6YQnDk+oO6K+fFb6qxJFMXj8/t0RdYpb/+9zSdWVSqVSsXr2asLAw2rXTry0cOHAgTk5OzJo1ixo1atC5c2fu3bvH+PHjWb9+PbNmzUKlUnH79m2KFStGzpw5ef36NWvWrEEmk9GrVy+uXLliOMft27fZs2cPGzduRJIkunfvTu3atQEoVKgQvr6+bNmyBX9/f3x9fXF0dMTb2xtTU1N+/vln1qxZw8iRIwkLC6Njx44MHjyY/v37s3nzZjp06MDFixepUaMG3bt358iRIzx69IgtW7ag0Wjo3LkzNWrUAODixYucPHmSpUuXkjt3boYPH46bmxv16tXj5MmTzJ49mzlz5mQ4o1VRKixSzE7IZBI6rf7egSpahaXS0pBmYWVJdIpO71NQRauxUCbHI8lkyfFEqTC3ShGP0oKYyBia9WhOYmIilWpXpqRTSYbPG4lfL19ev3hNraZf8cf2Y4b1gOnVf2wvKrlWwMHRnmsXbxjet7KyICoi2mjbmGgVllaWxMXG6wecSemSJDHEsz/F7Iri3scTgM592nPq6BmWTFtBvkJ5WbJlPp0b9iA+zvgD16eMC+Do3uMcC/yTSfPH07RdE3b57yWj1NFqo5mtlGWVOs1caY4qMoYL+89Q0KEw7pt9uHXuJveu3CExqWyqfVeTUzuOG17/k6mjjduVlKpdmSuTByHmVhaoImOIDo/iccgjNAkantx5THxcPDa5bYkOj+LcAf0M0rlDZ+g05sf3nrv32B5UcCmPvaMd1y8GGd63VFoQHWlcR1RJdSQ+Nj4pPcZQb1LvFxkeyYOQh2gSNDwMeUR8XDxlKpaiSMnCjJw2DFNzU0qUKs4Qn4HvnAVWRcVg+Z6+xsKor9Gf8337hD5+jlud7jTt9B0DJ/Un/GU4eQvkZu6WWRQokp+EBA3PHj374O30rCyrM/tOGdZOntl3ip4+fd577rTiMU/jb1an6h8trPR94JO7j3l2Tz8B8vTuE6LCo8iZLxeLRy+kx6Q+tOzfhpBLt9GkmHV9n6wuq/rN69FlaGfGd5totMSqUesGTOo7OV0x9Uvq+/T1Ornvs7SyfKvvU0XHGPo+S2VyuiRJDPbsTzG7IkbLzfIVysuMX6aw7dft7P897YFe77E9qPgRbUuVRttq36ctZ46eZdn0X8hXKC/zt8yme8PexMcl8HWzuhzYfihd1zBVdNrXc3W0GvMM1J1XT19Ss2ktju/I+PVTyLx0LXtwcXFBJpORJ08ebGxsCAsLo2TJkgAEBwezbds23Nzc8PT0JDIyErlcTpMmTdi/fz8BAQG0a9cOmUyGiYkJI0eOZMKECTx79szoVnRwcDBPnjyhe/fudOvWjdevX/Pggf4BJUdH/S2kAgUKvDVbHBERwZMnTxgzZgw7d+4kICCA48ePc+TI27Nab2IOCQmhWrVqSJKEiYkJlSpVIiREf7voxIkTREVFGWasgoODWbZsGW5ubixevJiwsPffDknL1XPXqN7AVf/3VCnL3aB7hrT7tx5QuGRhrHNYozBRULF6Ba6fv56p86TXjXPXqVpfPzNY2rkM91PE8+j2QwqVLITSVonCRIFT9fIEnQ9iQrtxTGw/Ho8O47l7/S7zR8zlddJt80q1K3P+6PkMx7F05i8MaDucbyu1okiJwtgk5UHlGpW4cv6a0baXz16lVkP9h5SaDarz95nLAIyfORpTM1PG9JhouNUWGRFl+OQfGR6FwkSOTJb+VT4fG5eV0pKl2xZgYmpCYmIialVspgebt84FUbF+FUA/i/vo5n1D2tPbj8hfoiBWtkrkJgrKuDpx+0IwJSs5cOtsEDM6TuLCvtO8eBhq2Kdc7YpcOfr27P0/UdC5G1SpXxWAUs6leZAibx7ffkTBEinrsRPB54O4cfY6zvX0+ZkzXy7MLc2JDo8yOpaTazkeBT9877lXzlzNsHajaFm5LUVKFjK030rVK3ItVfu9cvYqNRpWB6BGfVcun77C/Vv3KWJX+K39Lp+9iuvXLgDkzp8bcwtzbl6+RbcGvRjWbhQ+A6Zw79b9NJc/6Psa/bkcqzhyJ+iuIe3+rQcUKZnynPq+Jq19pqzypXBJ/ZIZdbQKnS6RZX4rGdh8KCPajSZw6362Lt+WrnWkWVlWHmu9caikX6pV/quK3LmS8ecf9PFUS4qnjFE8j24/NIrHsXo5gs8H0aB9Y7p69jTEY6m0JPx5GFUaVGPJmIVM6zEZZU5rLv35d7piyMqyatSmIa26t2REu9E8TbFW3craEhNTU148fZGumJbN/IWBbYfTtFJro77PuUZFrn6g77uU1CePmzkKUzNTxvbwMPTJufLkZOGm2Sz2W8auze+fBFg5czVD242iReW2FE7Vtq6+o23VTNG2Lp2+wr13tK2r568Tlfq6oFAgk8kBqFa7CqcPp28ZT8q6U9q5DPffU3ecqpfj5vkgGrZvTPcUdcciqe4AVKxdmYuZuH5+MbpP8O8zS9fM77Vr+gr/8uVLoqOjyZ07t2EgYWdnR4sWLWjevDmvXr1i69atALRt25ZJkyYRHh6Ol5cXQUFBHDx4kK1bt6JWq2nTpo3RrXs7OzscHBxYuXIlkiSxZs0aSpcuTWBgINI7nu6QJInExETi4+MZPnw4GzdupGDBguTNm5c8efJgamqKTCYz+iT15jj29vYEBATQvXt3EhISuHjxIq1btwZg8ODBhIaG4u3tzbx587Czs6Nnz55UqVKFkJAQzp5N/xq3lP7ce4KqdaqwcPs8JEli5sg5NGhVHwsrC3Zv2MNSn2VMXz8VmUxGoH8gL5+9ytR50utU4Ekq13FmRsAskCQWjp5P3Zb1MLcyZ//GfayavBLv9b5IMhmH/A8QFvr+eArbp+/hoLRoNVrm+yxm4cbZSDKJnZv38OLZS2xyWDNx9ljce3uyav5aJi0YT6suzXgdFoHnwMmUqVCKFp2a8vfpyyzZqn+Yy3/lNjYt34rnXHeW//4TChMFP09fSaw69gNRZF1csepYAgMOsixgIRqNhtvX77B324FM5c2FfacpV6ciE7f5gSTxy5jF1GhRGzMrC45tOsCmKWsYtdYTSSZxfMthXoeGoYlPoPXITnzbtwWqyBhWj00eKBWwK8zzFIPhf7IzgaeoWLsyUwJmIEmwePRCaresi7mlOQc37efXyauYuM4bmUzi8JZDhIWGERYahlP1ckz732xkMomVnsvQ6XT8OmUVA2YM5hu371BFxbBgSPru8Gg1Whb5LGX2hunIZDL2bA7k5bOXWOewxn3WKDz6eLN2wQYmzB9L885NiQiLwHfQ1DT3e/nsJZWqV2TZ7sXIZDLmTVyYoRmh43tPULVOVX7aPh9JkpgxcjYNk/qaXRv2sMRnKTPXT0Mmk9jrv4+Xz169cx+ATYs3M27uGBISEohTxzFrzNxMlRNkbVmt8PiZXr790MQn8PrFa5aNT/9a1pTxVKpdGb+AGSBJLB69ICkeCw5u2seayb/gsc4HSSZxZMtBwkLDOOx/gEFzhjH5t+mQmMiSsQvRaXU8vfuECWu8iI+N4+rJK1w8kr6BTFaVlUwmY4jvQJ4/foHvCv0Dc5dOXWbNnLUUsSvCs4cZ75u1Gi0LfBYzf+MsZDKJnZv3Gvq+CbPHMK63F6vnr8NzwXhadvme12EReA2cQpkKpWie1CcvNvTJv1GlVmWsba3pObwrPYd3BWDEj2ONHp5+VwyLfJYyJ6mN7H5H2/p1wQYmJrWt16naVur9tiz/jXFzx7AoYD4mJgqWT//FcF0oZl+UJw+epBlLSqeT6s7UgBlIksSi0Quok1R3DiTVHa+kunMoqe4c8j/A4DnD8PttOomJiSxOqjsAhewK8+zBv6NP/qeQEj+weDQgIAB/f3/Mzc2Jiopi+PDheHt7s3fvXszMzAgPD2fixIlERUURHR3N4MGDadiwIYBh0Dh48GDUajX9+vUjOjoaU1NTTE1Nadu2LRqNxvBtDytXruTgwYPEx8dTsWJFPD09mThxouEbGP744w/27NnD9OnTmTdvHsePH2fVqlWcP3+eJUuWoFAo9Ossv/7aMIjt3r07HTp0ICgoyOibHGbMmMGFCxdISEjg22+/pW/fvkbf9tCrVy9atWpF5cqV8fb2Ji4ujtjYWCZOnIizs3Oa+dWwyDdZWDwfTykz/dIhGDzVRH3pELK1cqZ5vnQIRpafm/WlQzDoXHXElw7BSKgm+sMbfUZyKfs8u5xHbvnhjT6jRLLX1zK90mbuwddPQa1L3/KMz8VEkn/pEIzkVVh9eKPPKOD+/750CAC8al4vy4+Ze+exLD/m+6Rr8JuZryL7rxKD37SJwe/7icFv2sTg9/3E4DdtYvCbNjH4fT8x+H23V99/gsHv7s87+M0+PaYgCIIgCIIgfGIfXPPbpk2bzxGHIAiCIAiCkM0l/gu+lELM/AqCIAiCIAj/GV/8540FQRAEQRCEf4h/wcyvGPwKgiAIgiAI6SKWPQiCIAiCIAjCP4iY+RUEQRAEQRDSRcz8CoIgCIIgCMI/iJj5FQRBEARBENLl3zDzKwa/Wcw0m/1CTXtNji8dgoFXfPp+N/1zUSrMv3QIRqprLb50CEay06+qbTw/70uHYCQ75Q1AcSn7/KqaT+/s1Qdq74Z+6RCMbN2X/0uHkEwGJxWxXzoKg9eJ8V86BCOFs1G7ylYSpS8dwUcTyx4EQRAE4T8oOw18BeFzEjO/giAIgiAIQrr8G5Y9iJlfQRAEQRAE4T9DzPwKgiAIgiAI6ZKoE2t+BUEQBEEQBOEfQ8z8CoIgCIIgCOnyb1jzKwa/giAIgiAIQrokiq86EwRBEARBEIR/DjHzKwiCIAiCIKTLv2HZg5j5FQRBEARBEP4zxMzvZyJJEoP8BmHnZEdCfALzx87n6b2nhvTqjarTeXhntBot+/33E7gpELlCzojZI8hfND8mpiZsWriJ0wdOY+dkx5BpQ9BqtDy++5j5Y+aTmJj4McHhOq07OZyKoYvXcGr0SqLvJf8kaPFWNSnbuwmJOh2vrz/kzPg1yEzk1JzXF2WxfCREqzk7YQ1RH/kzog2a1GXI6D5oNFp+27gD/3W/G6XnzJWDecv8MDc353noC8YO8SZWHUuLtt/Ra+CP6LQ6tm7cwcbVv2FqasKMn7wpWrwI0VHReLtP596dh+mOpV7jr+g7qidajZbtm3YRsOF/Ruk5ctkyfYkPZuamvAh9iddwP2LVcbj160jrzs0Ie/UagMljZvL4/mN85k2kUNECmJqZsnzeGo7t/zNzmSRJ1PHrTu6ksjo6diWRKcrKoWVNKvRqQqJWx6sbDzk+cQ0kJtJ27xTio9QARD58wdFRyzN3fvR1ufeU/pRwKkFCXAJL3Rfx7P4zQ3rVhi60HdYBnVbLYf+DHNp8AIBWA3/ApbErChMF+9bt5bD/QWxy29J/+iCsbJXI5DIWjZhP6INnaZ36o12+FsTcn1exZtHMT3L8rMybkuXtGPeLB0/v6n8WfP/6QP7alcl6kxRbmyk9KehYDG28hi3uy3l137jNmpib0nf9BLa4L+dFiP68I3ZPIzZKBUDYw+f4j1mW6RhSRIPpd92R5S8GWg1xu1aSGJ4ci6ygHaaNu4AEidERxG3/GUjErHlfpBz5IF5N3N41RvtkGUnC/MehyIraQUIC6l/nkvg8+afZZSVKY96hP0gSiRFhqFdMB01ClsdQa6q+nWvjNRwfs5KoFO3crmVNyvXWt/OwGw/5a8IaSrWtTan2dQGQm5mQy6kYm6oMJj5SlQXhSHSZ0oeijsXRxGv41f1nnt83bqem5qaMXO/FGvclPEuqO8pcNozf5sekb0eiict8HkmSRJ8p/SnuVBJNXAI/uy/i2f3k62fVhi60G9YRrVbLEf+DHNy8H4DWA9tSzahdHTDs092zF0/uPGb/hsBMx/UmtnZTelIoKW82uy/j5Tva1cD1E9nkvoznIcl1SZnbhtE7p7HEzc/o/ezu3/BVZ/+owW9AQAB37txh9OjRGdrvwIEDVKxYkfz58/PVV19x4sQJQ9off/zBnj17mD59elaHa6Rmk5r6zqHVSMo6l6WPZx98e/kCIFfI6TupL8OaDSNWFcuc3+dw+uBpqtWvRlR4FLOHz8Y6hzWLAhdx+sBpuozowsb5Gzl75CxjF47FtaErpw+eznRsRb+tiszMhP0tfMhdxZ4qkzrzR495+tjMTag0ti27G45Hq47nqyWDKNzYGavCuUmIiWVfc2+s7QtSza8bRzpnfkChUCjwmDyKVo1/RK1Ss2XPag7t+4OXz18Zthkyug87twWybfNO+g3tTqduP7B66QbG+wzn26/aoYpRse/ENnYF7KNl26aoYtS0/bYbJR2KM2n6OHq0H5TOWOSM9h1G5297oVap+XXnMo7t/5NXL8IM2/Qb2ZM9v+/nf/576DnYjbZurVi/3B/HCmWYOGQyNy7fNGzbsuP3vA6PYOIQX2xz2uB/IPOD35JNqqIwN2F7Kx/yOdtT07Mz+3oll5XLmLZsbTQeTWw8DRcNongjZx79cQWA/7X3y9Q5U3NpUh1TMxMmtnanlHNpunr0ZGafqfoYFHK6e/ViXPNRxKnjmLxtOucPnaWQfRHKVC2LR5txmFmY0bxvKwDcxnfj+PZjnNx9gnI1K1DYofAnG/yu2rCVnYGHsTA3+yTHh6zNm5Ll7dm5cge7VuzIktjKfVMNhZkJi9pMopizA809fmRNnzmG9CIV7PjBrxe2BXMZ3lOYmQDwc8fJWRLDG/IyVUFhQuwaH2SF7TFt1Jm4rfMM6abf9yJu20ISw0NRVP4ayTYPcrvyJMbHErfGGylXQUy/7Ubcpqz/EKNw/gpMTFFNHYbczhHz9v1QL5pkSLfoNhLVz74kPn+CSZ3vkOXOjy70UZbGUPzbqsjNTNjZ0oe8Veyp7tmZgynaedUxbQloNB5tbDxfLxpEsUbO3Np6nFtbjwNQc0o3gv2PZcnAF8D5G1dMzEyY1mYids6laOfRjcV9ZiTHW8EeN7++5ExRd8rVrcQP7j9ik8f2o8/v2qQGJmamTGw9llLOZejm0ZMZffT9mb5d9WZc85HEqeOYsm0G5w6dobChXbljZmFGi76tAbDJZcOQeSMoWLIQ/1v2+/tOmy4VvqmGwsyU+W28KO7sQCsPN1b2mW1IL1rBjvZ+vchRMLfRfjKFnA5Te5MQG//RMXxuHzPXll38J5Y9rF27lujo6C8aQznXcpw/eh6AoItBlKpYypBW1KEoT+49IToiGk2Chmtnr1HOtRzHdx1n7ey1hu20Gi0AIVdDUOZQAmChtECj0XxUbHldy/D06GUAXl0IIXfFksnnjNOwv4UPWrW+gUpyGdq4eGxLFebJYf0+USFPsS1V6KNisC9dkvt3HxIZEUVCgoZzp//GpYaz0TZVa1Tm2OG/ADh26ARf1XMFIOjabaxtlJiZmelnihITcShTkmMH9R9y7t6+j0PpEumOpWSpEjy8+4ioiCg0CRounr5ElRqVjLZxdq3IicOnAPjz8Emq13UBwLFiGXoN7cqaHT/Tc4gbAPv/d5jFM1YY9tVqtRnIGWMFXMvwIKmsnl8MIV+qstreygdNUmcqSyqr3I7FUFiY8f0Gd5pvHk8+Z/tMnx/A0cWJi8cuAnDrYjD2FR0MaYUdivDs3lNiImPQJGgIOnuDsi5OVK7rzIOg+4xZPh73VR6cP3QOgDLVHMldMDeeG3yp06oe105e/ajY3qdooYLMn+rxyY4PWZs39uXtqdqgGj5bpjJg5mDMrSw+KraSLmW4eewSAA8u3qZoBTujdIWpgjX95hhmfAEKORbDxNyUPmvH03+jB8WcHcgK8qJl0Ibo67HucQiygsn1WMpVENTRmLg2wdxtIpKFFYlhT5HlKWzYR//64/qcNGMrVQ7N1bMAaO/cQF6itCFNlr8IiTGRmDVqg+XYOUhW1lk+8AUo4FKGx0nt/MWFEPJUMm7nO1v5oH3TzhX6dv5GnoolyVmmCDc3HMmyeBxcynL12N8A3Ll4ixKp6o6JqYLF/WYaZnwBEnWJzOniQ0zEx197y7o48vexCwDcungTuxTtqohD0VTt6jqOLuWoVLcKD4LuM3b5BMat8uD8IX2ZmltZsGXeJv4IOPrRcQHYuZTlRlLe3E+jXf3Sby6hqWZ2W038kRMbDhLxPDxL4hAyJksHvwEBAQwaNIhu3brRokUL9u3bR7NmzRg8eDAjR44kKiqKoUOH4ubmhpubGzdv3uTQoUOMHz/ecIxWrVrx6tUr1q9fT9euXencuTP9+vUjPt7409G6devo0KEDHTt2ZO1a/QBx3LhxeHl50atXL5o3b861a9c4evQoN27cwN3d/a1jpPTnn38ydOhQw+uOHTvy/PlzGjZsyMiRI2nbti3jx49Hp8vcSm9LpSUxkTGG1zqtDplcn/1W1lbERCWnqaPVWFlbEauKRR2jxsLKgonLJhoGwo/vPWaA7wCWH1lOjjw5uHzycqZiesPE2oKEFDMEiTodUlJsJCYS+zISgNI9G2NiZc6zY1cJv3afwo0rA5C7ij0WBXIhyTJ/K0RpbUVUVHInGRMdg7WN0mgba6WSqMjopHQV1tb69OCg2+w4tIHAE1s5sv84UZHR3LgaTP1v6gBQuWoF8hfMh0yWvuqutLYiOkUsqhgVSmvjWKxSbKOPxQqAwB0HmTJ2Jr3bDsG5eiXqNq6FWqVGFaPC0sqSOSv9WDQ980sOTJUWRrM5Oq1xWamTyqp8d31ZPfrjKprYOC4t283uLjP4Y/xqGv40MHmfTLBQWqKKenddtlRaoopKji82Ro2ljRXWuWywr+jA3IEzWTHhZ4YtGAlA3iL5iI6IYXIXL14+fkGrAT9kOq4PaVy/NgrFp73ZlZV5c+vSLdb5rWFS+wmEPgil3fAOHxWbudLCsHwhdWwA984HE/E0zGifeHU8x1bsZkXXafw2cSWd5w822ifTzCwgLsWsZKIOJP1xJUslsiKlSDh/kNgN05GVKIesRDl0ofeRl6oMgKywPZJ1LpCy/varZG4FquQyRKeDpL5DsrZFbu9E/JH/oZozFoWjM3JH5zSOlHkm1hbEpyirRO27+2SnHo1RWJnz+I/kD42VhrTg4ryALI3HQmmB+j115/b5m4Q/fWW0z/U/LxPzOmsmnd7XriyUFkZp6hg1ljaW2OSyxr6iA3MGzmD5hJ8ZmtSunj8M5dbfwVkSF7xpV2rD68RUeXP3fDCvU+WNa9t6RL+KJOiPj7t2fymJOinL/31uWX4lUKlUrF69mrCwMNq1a4dWq2XgwIE4OTkxa9YsatSoQefOnbl37x7jx49n/fr1zJo1C5VKxe3btylWrBg5c+bk9evXrFmzBplMRq9evbhy5YrhHLdv32bPnj1s3LgRSZLo3r07tWvXBqBQoUL4+vqyZcsW/P398fX1xdHREW9vb0xNTYmIiMDNzc1wrNevX1OuXDm++uorpkyZQkREBC9evCBnzpzky5eP0NBQhg0bRvHixRk2bBgHDx7km2++yXi+RKuwUCbP3MhkMnRa/UA6JioGSytLQ5qF0sIwUM5TMA+eKz3ZvXY3R7cfBaC/d39G/zCaB8EPaNatGX08+7DEY0mGY3ojIUqNIkVskiQjUZtikC9JOHt2xMauIH/0WQBAyOZj2JQqRKPfJvLibDBhl++SqMv4vZCR4wdStUZlyjqV4tL55A7cSmlFZESU0bZR0dEolZbExcZhpbQkMjKaMk6lqN+4NvWqNEcVo2Lu0il816IRWzfswL50STbsWM6F05e4eunGBz+4DHLvi3P1ipR2dODKxWuG9y2tLA2D7jdiomKwUloRFxuPlTI5fcNyf6KTOuLjB/+ibPnS/HHgL/IXyse81dPYsiaAvb8fILPio9WYpiwr2dtlVWNiR3LYFWR/X31Zvb7zjIik9YIRd58RFx6NZb4cxKQa6KSXOlqFhVXKGCRDXVZFqzBXmhvSzK0sUEXGEB0exeOQR2gSNDy585j4uHhsctsSHR7FuQNnADh36AydxvyYqZiyi6zMmzP7TqFK6gfO7DtFT58+HxVbbLQaszRiS8uLu095eU+/DOXl3WeowqOwzpfjrUFyhsWpwTTFTLYkMzxCnqiOJjE8lMSX+pkybchl5AVLkHBqL6Z5CmHuNhHtw2B0T+9+kvuvibExYJ4yNkk/AAYSoyPRPX+C7ukDADRXzyIvXgrtjYtZGkNClBoTq/e3c9eJ+j75UFKfDGBqY4mtfSGe/nUjS+NRR6sxt0quu1KK69fnoI5WGd35kKWou+potdG11cJKf/2MCo/icchjQ7tKiEvAJrctka8isjQ2fbtKmTcfblfV230NJFK6dgUKOxXnxzkDWdFnFlEvsjY2IW1ZvuzBxcUFmUxGnjx5sLGxISwsjJIl9bdsgoOD2bZtG25ubnh6ehIZGYlcLqdJkybs37+fgIAA2rVrh0wmw8TEhJEjRzJhwgSePXtmdGs/ODiYJ0+e0L17d7p168br16958EDfGTk6OgJQoECBd8702trasm7dOsO/MWPGAPpF6y1atGDXrl1s27aNtm3bAlCwYEGKFy8OgLOzM3fv3s1Uvlw/ex2XBvpb42Wdy3I3KPk4D28/pFDJQihzKFGYKCjvWp4bF26QI08O/Db4sXrqavb77zdsH/U6yjCDFBYahtLWeFYyo16cDaZQA/1t/dxV7HkdZPxgWPWZPZGbmXCsxzzD8ofcle14cSaYg239eLj3HNEPnmfq3HOnLaFLy75Ud2xMcbui2OawwcREgWvNKlw8a/yp+MLpS3zdSP8hp17Drzh78gJRkdHEquOIi41Fp9Px6kUYtjlsqOhcjnOnLtKlZV/27znMw/uPPxjL4hnL6d1mMA0qfE/REkWwyWGNwkRB1RqVuXzuitG2f5+9TO2GNQGo3aAmF05fQmltxbaj67Gw1HfErrWrcv3yTXLlycnSzfOZP3kJ2zftzlQ+vfHsbDDFksoqn7M9YanKqt70nijMTAjsNc+w/KFsh3rU8uwCgGX+HJgoLVA9f53pGILO3aBK/aoAlHIuzYOb9w1pj28/omCJQiht9XXZqboTweeDuHH2Os71qgCQM18uzC3NiQ6PMjqWk2s5HgWn/6HE7Cgr88ZjrTcOlfTLo8p/VZE7V0I+KrZ754IpW78yAMWcHXh288N57dr+a1p46CcLbPLlxNzagqiPqDtvaB8FI3fQ12NZYXt0z5NjSQx/DqZmSDnzAyAvVgbdi8fICtmhfRhM7Do/tDfPkfg6c33OB2O7fQ1Fxer6c9s5onuc3FfrXjxFMjNHyqdfciEvVQHdk3tZHkPouWCKJLXzvFXebue1Z/REbm7CwV7zDMsfAApUL8uTP7N+6dDtc0FUqK+vo3bOpXh880GWn+N99O2qGgClnMsYtatHtx8atSvH6uUIPh9E0NnrVE7RrsyS2lVWu3vuJk719bP/xZ0deJKOdvVTBx9+6uDLoo6+PL5+n/WjlvyjBr5i5vcdrl3Tz5i9fPmS6OhocufObbjdbGdnR4sWLWjevDmvXr1i69atALRt25ZJkyYRHh6Ol5cXQUFBHDx4kK1bt6JWq2nTpo3RtxnY2dnh4ODAypUrkSSJNWvWULp0aQIDA5HecRtMkqR0fRvCDz/8wOjRo1Gr1YwaNQqA0NBQXrx4Qd68eblw4QItW7bMVL78FfgXznWcmfP7HCRJYu6ouXzd6mssLC3Yu3EvK3xX4LfeD0mS2L9lP6+evaKfdz+Utko6DetEp2GdAPDs6smCsQsYt3gcOq2OhPgEFrgv+MDZ3+/h3nMUrFueb/7nBUicGrmcEq1rorA059Xlu9h3qsfz0zdptHUCAEEr9/HiTBAVx7bFsX9TEiJVnBq14v0n+QCNRoOf51zWbF2MTCZj64YdhD57gW0OG6bN92Jg99EsmruS2Yt86eDWmrCw14zoNwG1KpZNa7fhv3sVCfEJPLj3iG2b/ofSWsmI8QPoPagrURFRjBvuk4FYtMyZtJCfN89HJkls37yL589eYpPDGu854xnZawLL561hykJP2nRpweuwCMYPnIRaFctP05axMmARCXHxnP7zHH8eOsnYycOxyWFN35E96DuyBwCDOo8kLhMPOtwNPEeROuVp9bsXSBJHRy3HoVVNTCzNeXH5LmU71uPpmZu08NeX1eVV+wjafJT6c/vRcpsnAEdHrzCeRcqgM4GnqFi7MlMCZiBJsHj0Qmq3rIu5pTkHN+3n18mrmLjOG5lM4vCWQ4SFhhEWGoZT9XJM+99sZDKJlZ7L0Ol0/DplFQNmDOYbt+9QRcWwYMicDweQjWVl3qzw+Jlevv3QxCfw+sVrlo1f/FGxXd13ltJ1KjB4mw9I4D9mGc4tamFqZc7pTYff/ff4H6HD7AEM2jqJxET9Plkx46cNOoe8ZHnMu+nrcdzO5cjL1UQyNUdz8Qhxu1Zi1mogSBK6R7fQ3v4bLJSYf90WkxpNSYxVEb/r4/qctGgunEDhVBXL8fNBkohdNRtF9fpIZhYk/LEH9Zq5WPYZD5KEJuQ6mstnsjyGe3vPUahOeZpt90KSJP4YuRy7VjUxsTLn5aW7lO5Yj2dnbtJ0i76dX/tlH/cDz2FrX5Co+1n/oeDivjM41anEuG1+SBKsHrMY1xa1Mbcy549NB7P8fKmdCTxFpdqV8QuYAZLE4tELktqVBQc37WPN5F/wWOeDJJM4suWgoV05Vi/P9P/NQZJJrPRcmulli+9zed9ZytSpwPBtviDBxjFLqdriK0ytzDm56VCWny87+Dc88CYlftR3ZBkLCAjA398fc3NzoqKiGD58ON7e3uzduxczMzPCw8OZOHEiUVFRREdHM3jwYBo2bAhAz549qVKlCoMHD0atVtOvXz+io6MxNTXF1NSUtm3botFoDN/2sHLlSg4ePEh8fDwVK1bE09OTiRMn0rRpU+rWrWv0LQ7z5s3j+PHjrFq1iu+///693/bQv39/SpUqZRj8VqtWjVq1avH06VMqVarExIkT3znAfuO7ot9lVXZmiR+1uT+80WfiFX/9S4dgRKkw//BGn9EAE7sPb/QZHZJl/SxJZm08P+/DG31GnauO+NIhGCkuWX54o8/Ep7f8S4dgRPuRX8GY1bbuy/+lQzA4qYj90iEYeZ2Yvb75oHA2alcAC+5t/tIhAHC3UuMsP2bJS5lfDpgZWT74zcxXkWUn/fr1Y8KECYalDqm/Gu1DxOA3bWLw+35i8Js2Mfh9PzH4TZsY/KZNDH7fTwx+3+1OhYw/9/Qhdlf2f3ijLPSf+Kqz9IiNjaVNmzaULVvWMPAVBEEQBEEQ/l2ydM1vmzZtsvJwn5W5uTkBAW9/PUxGZn0FQRAEQRD+zRIT//m/8CZmfgVBEARBEIT/jH/UzxsLgiAIgiAIX07i5/uK509GDH4FQRAEQRCEdNGJZQ+CIAiCIAiC8M8hZn4FQRAEQRCEdBEPvAmCIAiCIAjCP4iY+RUEQRAEQRDSJVH3z5/5FYPfLKZOTPjSIRjZZhL5pUMwyCfZfukQjGThjxtmiR28+tIhGInRZJ9fW8puv6iW3X5xrmWVwV86BIOOy7PXDcX4xOx1mZNJ2aida0FL9ukHi8iVXzoEI1c1YV86hGwpm106MyV79VKCIAiCIHwW2WngKwifU/b6SCwIgiAIgiBkW/+GZQ9i5lcQBEEQBEH4zxAzv4IgCIIgCEK6/Bt+5EIMfgVBEARBEIR0Ed/zKwiCIAiCIAj/IGLmVxAEQRAEQUgX8VVngiAIgiAIgvAPImZ+BUEQBEEQhHT5NzzwJmZ+BUEQBEEQhP8MMfP7mUiSxIipQ7F3sichPoFZY+bw+N4TQ3rNRjXoNtwNrVbLHv9Adm/ck+Y+Dk72DJ08GJ1OR3xcAtOGTyf85esMx9N3Sn9KOJUkIS6BJe6LeHb/qSG9WkMX2g/riFar5ZD/QQ5u3g/A7D3zUUXGAPD8YSiLxizENrctA6YPxspWiVwuY8GIeYQ+eJbhPKrduCY9hndFq9Wya/Ne/rdxt1G6bU4bfBZ7YGZuxsvQV0wZMYO42DgAzMzNWLh5NlNHzeR+yEPkCjle88dTsGh+tFod08fM5n7IwwzF0nNEN7QaLbs272FH6lhy2eJriOUlk0fMIE4dR+NWDejYuy1anY7b1+8wa/w8ZHIZHnPdKVi0ACamJqxZsI7j+/9KdyySJDHIbxB2TnYkxCcwf+x8nt5LLqvqjarTeXhntBot+/33E7gpELlCzojZI8hfND8mpiZsWriJ0wdOY+dkx5BpQ9BqtDy++5j5Y+an+2eeazWuSbfhP6LV6tizeS+7Nu4xzpOcNngtnoipuRmvQl8ybcQs4mLj0tyvy+BOfPVNLUxMFGz/9X/s3rzXcKxGrRrQpmdrBrYY8sG86T2lPyWcSpAQl8BS90U8u59c96o2dKHtsA7otFoO+x/k0OYDALQa+AMujV1RmCjYt24vh/0PUrK8HeN+8eDpXX273L8+kL92/ZmuvMmsy9eCmPvzKtYsmvlJjv+m7pR0LElCfAILxi7gaYp27trIlc7DkurOlv3s27QPmUzG0BlDKWJfBJ1Wx9zRc3l2/xl2Tnb09+2PTqsjIT6BOSPm8DoD/Y4kSQzwG2iI5aexC41icWnkSqdhHdFqdBzYcoD9m/YZ0mxz2zJv93y8unjyKOSR4f3eXr15dOcxgev3klGSJDHEbxAlU7SrJ6naVZcU7WpvUrsaOXsEBZLa1caFmzh14LRhn/qtvqZF9xaMaDUyU/FkVTsft3gcOfPmBCB/kfwEXQxi+qDpmcifwYZ45o2dZ5Q/NRpVp8vwLmg1Wvb57zPkz6jZIw3x6PPnlGGf/pP68jDkEbvX73nXKTNMkiR+nNKHoo7F0cRrWOP+M8/vG197TM1NGbXei9XuS3gW8iSNI2X8vMOmDsHeyY74+ATmjJnHk1TX8x+Hd0Gn1bLXfx97NibXz7LOZekzoRej2o0xvPfVt19Rr1kdpg7OWBl9Kf+Gb3v4Rw1+4+Li+O6772jYsCE9evSgUKFC79yuQYMG7N27FzMzsw8e8+zZs1hbW1O2bNmsDtdI7W+/wtTMlEEth+JUxZEBnv3x6OUFgFwhZ7D3APp9P4hYVSyLfl/AyQMnKVet3Dv3Gew7kIWei7h9PYTmXb6n08COLPFdmqF4XJvUwMTMlPGtx1LauQzdPXoyvY+fIZ4eXr0Z23wkceo4pm6bwblDZ4hJGvR6dZxodCy38d35Y/tR/tp9gvI1K1DEoUiGB79yhZxhkwbR8/v+qFWxLNv+E38e+IuwF+GGbXqO6Mb+7YfYs2UfboM60dqtOZtX/EbZiqUZO30k+QrmNWxbq0EN5Ao5fVsOwaVOVfq592ZC30npj8V7MD2b9kOtimX5jkUcP3CSsBfJv/Pea0RX9v9+iN1bAnEb3JnWPzbn93X/o9/YXnRp2JM4dRy+Szyp3bgmNjltiAiPxGfoVGxy2rB2/4oMDX5rNqmJqbkpI1uN1Hecnn3w7eVriLXvpL4MazaMWFUsc36fw+mDp6lWvxpR4VHMHj4b6xzWLApcxOkDp+kyogsb52/k7JGzjF04FteGrpw+ePoDESTV0UkD6Pv9QGJVsSzevoC/Dpw0Kp9uI9w4sP0wgVv20WVQR1q4NSNg9fZ37lfMoRjlq5VjUMuhmFuY0bF/e8NxHMrZ832n75DS0b+6NKmOqZkJE1u7U8q5NF09ejKzz1RDzN29ejGu+Sji1HFM3jad84fOUsi+CGWqlsWjzTjMLMxo3rcVACXL27Nz5Q52rdiR7rL5GKs2bGVn4GEszD/cT2VWzSY1MTEzYVTrUZRxLkNvz95M7j0ZSKo7Xn0Z3nw4sapYZgfM5szBM5R11veFo9uMpkKNCvTx7MPk3pPp592PpV5LuXP9Dt91+Y52A9qxYvKKdMdSo0kNTM1MGNN6NGWcy9DTsxd+vacYYunt1ZuRzUcQp4pjZsBMzhw8zesXr5Er5AyaNpj42HjDsWxy2TBy3kgK2RXm0bKATOVNrSY1MTE3ZURSu+rr2QfvFO2q/6S+DElqV3N/n8Opg6dxSWpXs5La1ZLARYbBr52THU06NEFKT8V9h6xs528GukpbJdP9p7PMZ1km8qcWpuamDG81Iil/+uLdy8cQT79J/RjSbCixqljm/T7XkD+R4ZHMHD4L6xzW/By4mFMHTmGby5ax80dT2K4wD0N+y1T+vIvzN66YmJkwtc1E7JxL0cGjGz/1mWFIL1HBnq5+fclZMFeWnRPgq29rYWpmypCWw3GsUpb+nn3x6uUN6PNmgHc/Bn4/hFhVLAt+n8fJA6cIfxFOhwHtaPRDI2JVsYZjDfIZQLV61Qi5FpKlMX5KX+KBN51Oh7e3Nzdv3sTU1JQpU6ZQvHjxt7bz9PTE1taW0aNHv/d4/8hlDxMnTkxz4JtR27Zt4/nz51lyrPep4FKeM0fPAnD9wg3KVCptSCteqhiP7z0hOiIaTYKGK2evUsG1Qpr7+A704/Z1fUORK+TEx8WTUY4ujlw8dgGA4Is3sa/oYEgr4lCUZ/eeEhMZgyZBw42z13F0KUcJx5KYmZvitc4Hn01TKO1cBoCy1RzJXTAPkzb4UrdVPa6evJLheEqUKs6je4+JSsqDy2evULl6RaNtKrmW59SRMwCcPHKGanWqAmBqasq43p7cv/3AsO2DOw+Ry2VIkoSVtRUajSbdsZRMFculM1eoXL1CqlgqcPJNLIdP41KnKvFxCfRtMZg4tX42Wi6XExcXz+Gdx1g+8xfDvlqNNgM5A+Vcy3H+6HkAgi4GUapiKUNaUYeiPElRd66dvUY513Ic33WctbPXvnXOkKshKHMoAbBQWqQ7X4qXKs7je4+N6mjFVHlS0bUCZ5Ly5NSRM1SrUyXN/VzrVeNO0F38fvFh2q9+/HVQPztkk9OGfuN789OkJemKy9HFiYvHLgJw62KwUT0u7FDEqB4Hnb1BWRcnKtd15kHQfcYsH4/7Kg/OHzoHgH15e6o2qIbPlqkMmDkYcyuLdMWQWUULFWT+VI9Peo5yLsl15+bFm+mqOyf3n2ThuIUA5CuSzzC7O33wdO5cvwPo63ZG+x0nl3KcP3ohzVie3ntKTIS+rK6fvU4513IA9PToxd71ewkLTf7waWFlwcZ5GzkScCSDOZKsnGs5zqXRroq9I2/Ku5bjj13H+fUd7co6hzW9xvdgaSYGmSnjyap2/saPI3/kf6v/R/jzcDKqvGs5zh09Z4intFH+FEsVz1XKu5ZPM38srMxZN3c9h7YdznAc71PKpSxXj/0NwJ2LtyhRwc4oXWGq4Kd+M3maRTO+b1RwKc/ZpLy5cSHovdfzq2evUcG1PABP7j/Fu4+P0bGunbvOggkLszS+f6ODBw8SHx+Pv78/o0aNYvr0t2fJN2/eTHBwcLqOl+1nfmNiYhg9ejSRkZEUK1YMADc3N7y9vbGyssLb25u4uDhev37NoEGDaNSoEQBeXl48fvyY3LlzM2PGDORyOZMmTeL+/fvodDqGDx+OlZUVx48f59q1azg4OHDp0iXWrFmDTCajatWqjB49mvPnzzNjxgwUCgU2NjbMnj0bpVKZ4b/DytqS6KgYw2udVodcLkOr1WGltCI6MjlNFaNCaWOV5j5hz/UXgXJVnWjdvSVDf8j4LTZLpSWqVMeWyWXotDoslBZGaeoYNVY2ljwOiWPH8u0c3LyfgiUL4fnrJAbXH0C+IvmIiYjGp4sX7YZ2oPWAH9g8d2PG8kdp/LeqotVYWVul2saKmKRtVNEqlEnpl89dfet4apWagkULsPmPX8mR05bR3SekPxZrK2KiopNjiVGhtDEuc0tl8jaqaH16YmIiYS/1F5l2PVtjaWXBmWPnkvexsmDach+WpRgIp4el0tIw6w7GZaWPNUVZJeXbm5kFCysLJi6baLhAPr73mEFTBtFpaCdiomK4fPJyumKwUloanUdfPqnzJLkM35RfWvvZ5rKlQJH8uHebSMFiBZi2egpdv+6J++zRLPL+2bCc5UMs3lOP9XVcZUiLjVFjaWOFdS4b8hbOy/SeU8hXND/uKycyrMFAbl26xaHNB7hzNYQ2g9vRbngH1vmtSVccmdG4fm0ePw39ZMcH3soDo/yxNk5Tp2hzOq2OkXNHUqtJLfz66+8IvRlAOVZ1pFm3ZoxtNzaDsVikKittqljerscN2zYk4lUEF/+4QLtB7QzpoQ9DCX0YStX61TIUg3E8abcry3S0K89lE/l19lpkMhkjZw9nqc9y4tNZbzMaT0bbOeiXilT+qjLLfZZneTyW1u9q16nzx4M1s38F4NnDUJ49DMWlvkumYkmLhdICdRr1G+D2+ZtZer43Uv/92lT9Tsp8UyddzwGO7/mT/EXyGx3r6M5jVKppPNGT3X2JB97Onz9PnTp1AKhcuTJXrxpf9y9evMilS5fo0KEDd+7c+eDxsv3g9/fff6d06dKMGDGCS5cucfp08i3aO3fu0KNHD6pXr86FCxf46aefDIPfTp06UblyZWbOnMmWLVtQKBTkzJmTqVOnEh4ezo8//sju3bupU6cOTZs2xdLSkp9++olt27ZhYWHBmDFjOHHiBH/++SeNGzemV69eHD58mMjIyEwNfmOiVFhaWRpey2QS2qQGGhMdg6UyeZbJ0sqS6Mjo9+5Tv/nX/Di0M+O6eRARFpHheFTRKixSzGzJZJKhw1BHqzFPEY+FlQUxkTE8ufuYZ0lrvp7efUJUeBQ58+UiKjyKMwf0M37nDp2l85gf0x1H37E9qeRSAQdHO65dvJGcB0oLoiOjjbaNiY7B0sqSuNh4/UArVXpKHfu04/TRs/w8fSX5CuVl0Za5/NiwJ/FxCWnu029sLyq5VsDe0Y7rKWOxsiQqwvhcqlSxvEmXJInBnv0pZleEcX28DNvnK5SXGb9MYduv29n/+6H0ZY7hXCoslCnLKrlzj4mKMaojFkoLQ8ebp2AePFd6snvtbo5uPwpAf+/+jP5hNA+CH9CsWzP6ePZhiUfas6y9x/aggkv5pDwJSs6Td5SPKlpfX+Nj45PSY4iJNq7Db/aLDI/kQchDNAkaHoY8Ij4unjIVS1GkZGFGThuGqbkpJUoVZ4jPwPfOAqtT1WMpRT1WRaswV5ob0sytLFBFxhAdHsXjkEdoEjQ8ufOY+Lh4bHLbcmbfKcN69jP7TtHTp0+a5/2neF/dUUUZ551FqjKdO3Iuq/OuZt6OefRr2I84dRx1m9elw+AOePfwJjIsMoOxqI1ikd6KxbgeR0fG0LxHc0hMpHLtypR0KsmIeSOZ3MuX1y9eZ+jc745HZdTvGscT8854APIWzIPXSk92rt3Nke1HKVO5NIVLFGbI1MGYmplSrFQx+k/ql+FZ4Kxs5wC1v6/N0R1H0el0GYojrXiM2laUCssUdcdSaUFMUt3JWzAPk1Z6sXPtLo6kiOdTUEerMbdKbuMpy/BTSt12ZKn6HUtlirKysjSa3BLezd/fH39/f8PrDh060KFDB8Pr6Ohoo7GXXC5Ho9GgUCh4/vw5ixYtYtGiRezdm771/9l+2cOtW7eoUEF/e7VSpUooFMnj9bx58+Lv78+YMWPYvHmz4RauiYkJlStXBqBKlSrcvXuX4OBg/vjjD9zc3Bg6dCgajYbw8ORbQQ8ePCAsLIy+ffvi5uZGSEgIDx8+pH///oSFhdGtWzcCAwONzp8RV89do0YDVwCcqjhyJ+iuIe3+rQcUKVkY6xzWKEwUVKxegWvnr6e5T+M2DWndvSXD243i6YOnb58sHYLO3aBK0qxJaecy3L9535D26PZDCpYohNJWicJEgVP1ctw8H0TD9o3p7tkTgJz5cmGhtCT8eRhB565Ttb5+CYKTazkeBj94+4RpWD5zFYPajaBp5TYUKVkYm6Q8qFy9ElfPXzfa9vLZq9RsWB2AmvVd+ft02rOWURFRhlnIyPAoFAoFMpn8vbEsm/kLA9sOp2ml1hQpkRyLc42KXD1/7a1YajWsoY+lQXUundHHMm7mKEzNTBnbw8Ow/CFXnpws3DSbxX7L2LU54w/mXD97HZcG+hmTss5luZui7jy8/ZBCJQuhzKEvq/Ku5blx4QY58uTAb4Mfq6euZr///uR8eR1lmO0LCw1Dafv+D3IrZ65mWLtRtKzcliIlCxnqaKXqFbmWqnyunL1KjaTyqVHflcunr3D/1n2K2BV+a7/LZ6/i+rX+b8qdPzfmFubcvHyLbg16MazdKHwGTOHerfsfXP6gr8f6ulfKuTQPUtTjx7cfparHTgSfD+LG2es416sC6OuxuaU50eFReKz1xqGS/tZu+a8qcufKP2cNXlqun7tOtaR2Xsa5DPeC7hnSDHUnKX/KVy9P0PkgGrRpQPtB+jXYsepYdDodOp2O+q3r06xbM9zbu/MsEw+03kgVy/33xFIuKZbx7cYxvv14JnQYz93rd5k3Ym6WDHzh7XZ1L0W7enD7IYVLFsI6qV1VSNGupm7w45cU7erm38H0bdSfse3dmTZoGg9uPcjU8oesbOcAzrWdOXvkbIbjeOPa2Wu4Jl1/9Plzz5D24PYDCpcsnCJ/KnA9KZ5pG6aycuoq9qWK51O4fS6ICvX1bdnOuRSPb6b/2vMxrp67RvWkvHGsUpa7KfLm/q03eZN8Pb+eqq/8p0tMlLL8X4cOHQgICDD8SznwBVAqlcTEpLgTodMZxmOBgYGEh4fTt29fli9fzq5duwgIeP+zANl+5tfOzo6///6bRo0acf36daM1igsWLKBdu3bUq1ePbdu28fvvvwOQkJDAjRs3cHR05Ny5c5QqVQqNRkOBAgXo378/sbGx/Pzzz9ja2iJJEomJiRQpUoSCBQuyatUqTExMCAgIwNHRkZ07d9K6dWvc3d1ZtmwZW7ZsYfDgwRn+O47v/ZNqdaqwaPsCJElixshZNGzVAAsrC3Zt2M1in6XMWj8dSSax1z+Ql89evXMfmUzGEN9BPH/8nMkrvAH4+9Ql1sxZ+/4AUjkdeIpKtSszNWAGkiSxaPQC6rSsi7mlBQc27WPN5F/wWueDJJM4tOUgYaFhHPI/wOA5w/D7bTqJiYksHrsQnVbHmimrGDhjCE3cvkMVpWLekNkZzh+tRstCnyXM2zATmUzGrs17efHsJTY5rBk/azTj+0xi9YL1eM0fR8vOzXgdFsGkQVPSPN7m5VuZONednwMWYGJiwtLpK4lVx6a5fepYFvgsZv7GWchkEjtTxDJh9hjG9fZi9fx1eC4YT8su3/M6LAKvgVMoU6EUzTs15e/Tl1m8dR4A/it/o0qtyljbWtNzeFd6Du8KwIgfxxIXm741k38F/oVzHWfm/D4HSZKYO2ouX7f6GgtLC/Zu3MsK3xX4rfdDkiT2b9nPq2ev6OfdD6Wtkk7DOtFpWCcAPLt6smDsAsYtHmd4Yn+B+4J058kin6XM3jAdmUzGns2BvHz2Eusc1rjPGoVHH2/WLtjAhPljad65KRFhEfgOmprmfi+fvaRS9Yos270YmUzGvIkLMzVDdSbwFBVrV2ZKwAwkCRaPXkjtlnUxtzTn4Kb9/Dp5FRPXeSOTSRzecoiw0DDCQsNwql6Oaf+bjUwmsdJzGTqdjhUeP9PLtx+a+ARev3jNsvGLMxxPdvOm7swOmI0kScwbPY+vW36NuZU5gRsDWTF5BVPWT0GSSRzwP8Cr0Fec2HuCkXNGMnPrTOQKOct9lqNN0NLfpz/PHz/HY7l+nfKV01fYMHdDumM5GXiSynWcmRkwC0mSWDB6PvVa1sPcypx9G/excvJKfNf7IslkHPA/QFjoq0+VLQCcCPyLKnWcmff7HEhqV/VbfY15UrtaltSuZJLEvqR21T+pXXUe1onOSe3Ko6un0cN4mZWV7Tw+Np4idkUy9SHFOH+qMO/3uUiSxJxRc6ifFM+ejXtZ5rucqeunIpMkApPiGeDdH6Wtki7DOtNlWGcAJnb1yJL8eZcL+87gVKcSE7b5gQSrxiymeovamFuZc2zTwU9yToA/956gap0qLNw+D0mSmDlyDg1a1cfCyoLdG/aw1GcZ09dPRSaTEZh0Pf83+RLLHqpUqcKRI0do2rQpf//9N6VLJ6+z7tq1K1276q+tAQEB3LlzhzZt2rz3eFJier/n6AvRaDSMHz+eR48eYWdnx7lz58iXLx/e3t7cuHGDhQsXkidPHgoWLEhQUBC7d++mSZMmVK1alfv371OoUCGmTp1KYmIiHh4ePHnyhOjoaDp37kz79u3ZvHkzGzZsYP78+Vy9epVNmzah1WopXLgw06ZNIzg4GB8fHywtLTExMcHX15eiRYumGe/XRRp9xtz5sFxyyw9v9Jk81UR96RCMZLeqn1P+aR+wyqgY3ae5YGVGfkXGlxp9ShvPz/vSIRhpWSXjH8g/FXk2u6EYn5ixB0w/teyUO1qyVx9YRJ692vl9TcaW9nxqhx59+tn09Dhd6P0Dy8yo/uT9M7Vvvu0hODiYxMREpk6dyvXr11GpVEazxG8Gvx/6todsP/j9pxGD37SJwe/7icFv2sTg9/3E4DdtYvCbNjH4fT8x+H23U59g8FvjA4PfrJad2qEgCIIgCIIgfFLZfs2vIAiCIAiCkD18iTW/WU0MfgVBEARBEIR0+Tf8vLFY9iAIgiAIgiD8Z4iZX0EQBEEQBCFdPv3PiHx6YuZXEARBEARB+M8QM7+CIAiCIAhCuiTyz1/zKwa/giAIgiAIQrrostfXQ2eKWPYgCIIgCIIg/GeImd8sFqaJ+dIhGOmWWOBLh2AwR/P8S4dgxFJu9qVDMGIjy17xxCZqvnQIBsWl7PNLhZC9flENYMeFRV86BIMFVby+dAhG1LLsNU0VLmWvX5x7kZh9fsnxqTZ7XT/zZKNfSM1OdP+CZQ9i5lcQBEEQ/oOy08BXED4nMfMrCIIgCIIgpMu/4YE3MfMrCIIgCIIg/GeImV9BEARBEAQhXf4NP3IhBr+CIAiCIAhCuohlD4IgCIIgCILwDyJmfgVBEARBEIR0+TcsexAzv4IgCIIgCMJ/hpj5FQRBEARBENLl3zDzKwa/giAIgiAIQrr8Gx54E4Pfz6he49r0G9UDrUbL9k272Lbhf0bpOXLZMn2JD+bmZjwPfYnX8CnEquNw69eR1p2bE/7qNQCTx8zg0f3HTFnoRaGiBdFptXiPns692/czF5gkUXNad3I5FUMbp+HEmJVE3Qs1JJdsWZNyvZuQqNMRduMhJ8evwaFdbRza1QVAbm5CLqdi+DsPJj5SlbkYUqn3TW0GjOyJRqvl90272LZ+h1F6jly2zPzZFzNzM16EvsRj2GRi1XGUr+zIGJ9hSJLEy+evGDfIm/i4j/sVo9qNa9F7RDe0Gi3/27yHHRt3GaXb5rJl8mJPzJNi8R0xnTh1HPWb1qXb4C4kJiayfcNOdmzcnanzS5JEryn9KO5UgoQ4DcvcFxF6/5khvUpDF9oOa49Wq+WI/yEObz5AvbYNqNe2AQCmZiYUdypJP5ceqCL1Px/a1bMnT+485uCGfemOYfjUodg72ZEQn8CsMXN5cu+JIb1moxp0Hf4jWq2Wvf6B7N64N819ipcqxqgZI5AkiZDrISz0XIxOpzOcZ9raKZzYd5Kd63elFU6aMbaZ0pOCjsXQxmvY4r6cV/dDjbYxMTel7/oJbHFfzosQffwjdk8jNkpfb8MePsd/zLIMnTd1DIP8BlHSsSQJ8QksGLuAp/efGtJdG7nSeVhntBot+7fsZ9+mfchkMobOGEoR+yLotDrmjp7Ls/vPsHOyo79vf3RaHQnxCcwZMYfXL19nOrYPuXwtiLk/r2LNopmf7BwGkkQjv+7kSyqrfWNX8jpFWZVtUZOqvZqg0+p4EfSQgxPXIJPLaDK7D7ZF8iI3VXDqpx2EHLjwSWJrOqUH+Z2KoYlLYJf7SsJTxFauRU2q9/wWnU7H8xsP2eOxGhKz9ieUJUnihyk9KeRYHE28hi3uy3j5jrrcf/1E/N2X8TypLo/cPY3YKDWgr8ubxyzNkli6TelLMacSaOISWOm+hOcp+h/nhtVoldT//OF/iKObD6IwVdBn9mDyFS2AOlrFr54rCL339D1n+XAMg/wGYZfUl8wfO5+nKY5XvVF1Og9Palf++wncFIhcIWfE7BHkL5ofE1MTNi3cxOkDp7FzsmPItCFoNVoe333M/DHzScxg+WWHPln4OP/YNb9xcXFs3bo1zfSzZ88SFBRk2HbGjBl07tyZLl260KdPH54+1TecBg0aEBcXl65zurm5ERISkql4FQo5Y3yH0a/DcHq0HsgPbi3JnTeX0Tb9RvZkz+/76d5qAEFXgmnr1hoAxwplmDjEl15tBtGrzSDuhTygdsNayBVyujbvy9K5qxg6rl+m4gIo/m1V5GYm7G7hw/lpm3H16mxIk5ubUGVsW/a2m8rulr6YWltStJEzt7ccJ7CdH4Ht/Hh1+S6nvdZl2cBXoZDj7juMvh2G0b3VANr9+HZeDRjVi90B++nWsj83rtykXVd9XnnPGY/HsCl0bdGPE0dOUahIgY+KRa6QM8J7EEM6jaLfD0Np/WPzt2LpPaIb+34/SN/WQ7h59RZtfmyBTCZj0IR+DOowkl7NB/LjgI7Y5rLNVAwuTapjYmaKZ+txbJqxFjePHkbxdfPqid+P3ni396BR52+wzZuDY78dxrejB74dPbhzNYQ13itRRcZgncuGcb96UrWxa4ZiqP3tV5iamTK45TCWT/uFgZ7J9U2ukDPIuz9juoxjeNtRNOv8PTnz5kxzn97uPVk5YxVDWg/HzMKcWt/UNByr19ge2OSwzlQ+lfumGgozExa1mcTuGZto7vGjUXqRCnYM3DKJ3MXzG95TmJkA8HPHyfzccfJHDXwBajapiYmZCaNaj2L19NX09uxtSJMr5PT16ovHjx64t3fnu87fkTNvTqo3qg7A6DajWTdnHX08+wDQz7sfS72WMq7DOP4K/It2A9p9VGzvs2rDViZNX/DRHxTTq1STqijMTNjY2oc/pm/ma8/kPkdhZkLtMW3x7zCVTW18MbO2xL6RM06tvyI2PJrNbSezressGvp2/SSxlU2KbXVrbw7P8KexRxej2L4e3Y61Hf1Y08YHM2sLSjd0zvIYyn9TDYWZKQvbeLF7xkZaeLgZpRepYMfgNOryko6+LOnomyUDX4CqTVwxNTPBt/V4/Gesp7NHd0OaXCGni1cPZvzog197T+on9T9fd2pMXEwsPq3HsXbSSrr69k77BOlQs0lNTM1NGdlqJKunrTa0kTcx9J3Ul4ldJjK23Vi+66JvVw3aNCAqPIoxP4zB082TgZMHAtBlRBc2zt/I6B9GY2JqgmvDjPWFkD365C9JJ2X9v8/tHzv4ffHixXsHv9u2beP58+cA+Pn5kT9/fjZu3MiGDRto3749w4cP/0yR6pUsVYKHdx8RFRGFJkHDxdOXqVKjstE2VVwrceLwKQD+PHySGnWrAeBUsSy9h3ZlzY6l9Bqi7/Dv33mAXC5HkiSU1lYkaDSZji2faxkeH7kMwIsLIeSuWNKQpo3TsLulD9pY/UVRUsjQprhA5q5YkhylixC84Uimz5+aXemSPLj7iMikvLpw5hJVU+WVs2sl/jx8EoA/D52kZh0XStgX43V4BG59O7D69yXY5LDhXsiDj4qlZKniPLr3mKiIaDQJGi6duUzl6hWNtqnkWoFTR84AcPLwaVzqVEWn09GhXldiomKwzWmDhIQ6Rp2pGMq4OHLpmH6G69bFYOwrOhjSCjsU4dm9p8RExqBN0BB09gaOLk6GdLsK9hQpVYxDm/YDYG5lzm/zNnM84GiGYqjgUo4zR88CcOPCDUpXKm1IK16qGI/vPSE6KY+unL1KRdcKae4zqa8vl09fQWGiIFe+nIS/CAeg7vd10Ol0nD5yNoM5pFfSpQw3j10C4MHF2xStYGeUrjBVsKbfHMOML0Ahx2KYmJvSZ+14+m/0oJizAx+jnEs5zh89D8DNizcpVbGUIa2oQ1GepMina2evUc61HCf3n2ThuIUA5CuSzzC7O33wdO5cvwOAXC7/pAPTooUKMn+qxyc7fmqFXcpw96i+z3l6MYT8KfocTbyGja190CT1OTKFDE1sPDd3n+bP2b8ZttNpP83Kw6IuZQhJqkePL96mYKrY1rTxThGbHE1cQpbHUNKlLEHH/gbgfhp1eXW/uYYZX4BCjsUxNTej39oJDNjoQfGPrMtvlHZx5PKxiwCEXAymZEX75HM6FCH03jNUSf1P8NkblHFxpHCpolw6qt/n2Z0nFHIo8lExlHNNbldBF4PS1a6O7zrO2tlrDdtpNVr933A1BGUOJQAWSgs0mbh2Zoc+Wfg4/9hlD0uXLuX27dssWrSIK1euEB0djVarZdiwYVhbW3P8+HGuXbuGg4MDhw8fxsfHx7Bv48aNqVatmuG1t7c3jx49AmDRokVYWloyYcIEHj58iFarpUePHjRt2vSj4lVaWxEVFW14HROjwtraymgbK2sroqP0t0BiolUorfUNNHDHATav3kZ0VAzzV0+nbtBX3Lx2i8JFC7Ljz83kzJWDwW6jMx2bqdKC+KjkWdtEnQ5JLiNRq4PERGJfRgLg2KMxJpbmPPnjqmHbSkNa8Pe8gEyf+12slMn5APq8sLZRGm2jtLZMzqsYFUobJTlz5aBytQpMnTCHB3cesnj9HK5fDuL08XOZj8U6VSwxapQ2qcpNaUl0UtnGRKsM6Vqtlq+/q8PYqSM4cegkmoTMfUCxVFqiSlE+Oq0OmVyGTqvDIlWaOkaNpY2l4XXrwW3ZtmCz4fWLh8958fA5lb+umrEYrK2ISZEPKWOwVFoSE5mcpo5RY2Vj9d598hfOx+zNM4mJjOFhyENKlClBw1YN8O7rS9cRxjO26WWutDAsX0h9PoB754Pf2ideHc+xFbs5vfkweUoWoPeaccxsMDLTA6v3lZWldaqyilZjldQH6LQ6Rs4dSa0mtfDr7wdA+HP9hwLHqo4069aMse3GZiqm9GhcvzaPn4Z+eMMs8lafozXuc1RJfY5z98aYWppz/3hyn2NiZU6LpUP5c1bakx8fw0xpYVg68K7YYpJic+n+DaZW5tw5fiXLYzBPFUN66nKCOo6jK3ZxavNh8pYsSJ8145jeYMRHf0hI3ccY9z8Wb/U/FjZWPLh2F+eGVTm/7zT2zqXJVSAXkkxGoi7z7SplH5MyBqtU/cybdhWritXHb2XBxGUTDQPhx/ceM2jKIDoN7URMVAyXT17OVDxfuk/+knRize+X079/f4KDg4mJiaFWrVp069aN0NBQOnXqxMGDB6lTpw5NmzZFoVCQJ08eJMm4sHLmzGn4/w8//EC1atUYN24cJ06cICwsjJw5czJr1iyio6Np06YNNWrUyFScg9374ly9EqUdHbhy8ZrhfSsrS6Iio422jYmKwUppSVxsHFbK5PT1y/0NA7DjB/+ibPnSuH5VlRNHT7Nw6s/kL5SPlb8t4of6P2Zqdig+Wo2J0sLwWpIldfSGNyRcPDpiY1eQw30WGN42tbHE1qEQz/66keFzvsuQcf2o4lqJ0k72XL5w3fC+ldKSyIgoo22jo1TJeWVlSVRkFK/DI3hw7xF3gu8B8OeRUzhVLJupwW//sb2o5FoBB0d7rl1M/vusrCyIikhVbtEqLK0siYuN1w+EU6Qf3XucY4F/Mmn+eJq2a8Iu/70ZjkUVrcLcKmX5SIYLmjpahUWKsrOwsjBcJCxtrChkX4RrJ6/ysVRRMVimiEGWIgZVtAoLZXLnbmFlQXRk9Hv3CX38HLc63Wna6TsGTupP+Mtw8hbIzdwtsyhQJD8JCRqePXrG2aPpL7vYaDVmaeRTWl7cfcrLe/q1ei/vPkMVHoV1vhxEPA1L93lTUqUqD5ksecCiilJhkSI+C6U+n96YO3Iuq/OuZt6OefRr2I84dRx1m9elw+AOePfwJjIsMlMxZUfx0WpMP9Dn1JvQkZx2BdnRL7nPsS6Yi5YrhvP32oME7Tj5SWKLi1ZjZmX+3tgaTehE7pIF2Npv/ieJIfatGD5cl5+nqMsv7j5FFR6FTb6cvH766qNiUafqf1LWaXW0GgtlcpwWVhaoImM4v+80hRyKMGGzL8Hng7h75U6mB77w/nYVExWDpVWK/keZ3AfmKZgHz5We7F67m6PbjwLQ37s/o38YzYPgBzTr1ow+nn1Y4rEkw/F86T75S8raFe5fxj922cMbISEhuLi4AJA/f36USiVhYckXrpw5cxIZGfnWgvadO3eSkKC/XVW+fHkA8uTJQ2xsrNExlUol9vb2PHz4MFPxLZqxnF5tBlG/QlOKliiCTQ4bFCYKqtaozKVzxg3g4tnL1G6oX/9Yu0FNLpz+G6W1FQFHN2BhqW9MrrWrcv1yEJERUYYLZ+TrSBQmCuTyzBXn87PBFGlQCYC8VewJv2H8t9aa0RO5mQmHes4zLH8AyF+9LE+OZ10j/mn6Mnq0GUi98k0pVjJlXjm/M6/qNKwFQO2GNTl/6hIP7z/G0sqSoiX0t9iqVq9MyM07mYpl6cxfGNB2ON9WakWREoWxyWGNwkRB5RqVuHL+mtG2l89epVZD/Yejmg2q8/eZy1gpLVm6bQEmpiYkJiaiVsVmuvO/eS4I5/r6WYFSzqV5cDP5wcbHtx9RoERBrGyVyE0UOFYvR/D5mwA4ujpx5c9LmTpnalfPXaN6A/3aVMcqjtwJumtIu3/rAUVKFsY6KY8qVa/A9fPX09xnyipfCpcsDOgvFDpdIsv8VjKw+VBGtBtN4Nb9bF2+LUMDX4B754IpW78yAMWcHXh288Nt1rX914b1lDb5cmJubUHU89cZOm9K189dp1p9/V2lMs5luBd0z5D28PZDCpUshNJWicJEQfnq5Qk6H0SDNg1oP6g9ALHqWHQ6HTqdjvqt69OsWzPc27vz7MGzd53uH+vxuWBK1tf3OQWd7XkZZFxW30zvicLMhO295xmWGFjmsaHtenf+mObP1S1/fLLYHp4LxiGpHhV2duB5qnr0/bReKMxM8O+THFtWu3fuJo719WuJizs78DQddbl6+/pGddnM2oLIpLsHHyP4XBCV61cBwN65NA9T9D9Pbj8if4r+p0x1J26fv4ldJQdunrvB1I5enA88zYsHH3dX4frZ67g00F+TyzqX5W6K/sfQrnIktSvX8ty4cIMceXLgt8GP1VNXs99/v2H7qNdRhpnZsNAwlLbGdxXTIzv0ycLH+cfO/MpkMnQ6Hfb29pw7dw4nJydCQ0OJjIwkR44cSJJEYmIiJiYm1K5dm3Xr1tG1q369bGBgIL/++ivNmzcHeGtW+M0xGzduTHR0NMHBwRQp8nFrljQaLbMnLWTp5nnIJBm/b97F82cvsMlhg/ec8YzsNZ7l81bjt9CLH7q05HXYa8YNnIRaFcvCaUv5JWAR8XEJnPnzHH8eOsn5k3/jO38ia7b/jImpCT9NXYo66TZPRt3fe45Cdcvz/Q4vkCT+HLEcu1Y1UViZ8+rSXUp3qkfo6Zt8u2UCANd/2ceDwHPY2hck6sHzj8qXd9FotMyctIDlm+cjyWT8vmmnIa98505geM9xSXnlSdsfWxIe9hr3AV5oEjR4jfBj5lIfJCT+PneFPw7+9VGxaDVa5vssZuHG2UgyiZ2b9/Di2UtsclgzcfZY3Ht7smr+WiYtGE+rLs14HRaB58DJxKpjCQw4yLKAhWg0Gm5fv8PebQcyFcPZwFNUrF0J34DpSBL8PPonvmpZF3NLcw5t2s/ayauZuG4SkkzGkS0HCQ/Vf/grZF+Y5x950Xnj+N4TVK1TlZ+2z0eSJGaMnE3DVvWxsLJg14Y9LPFZysz105DJJPb67+Pls1fv3Adg0+LNjJs7hoSEBOLUccwaMzdLYry67yyl61Rg8DYfkMB/zDKcW9TC1Mqc05sOv3OfM/5H6DB7AIO2TiIxUb/Px9wm/ivwL5zrODM7YDaSJDFv9Dy+bvk15lbmBG4MZMXkFUxZPwVJJnHA/wCvQl9xYu8JRs4ZycytM5Er5Cz3WY42QUt/n/48f/wcj+X6tbhXTl9hw9wNmY4tO7kVeI7idcrTKcALSZIIHL2csi1rYmplzrPLd6nQoR6Pztykw2Z9n3N+1T6K1nTE3NaKmkNbUXNoKwC2dZ2Z5WtugwLPYVe7At0DJiFJEv8bvYzyLWthYmnG0yt3ce5QjwdnbuK2SR/bmdX7uLkv80ur3uVKUl0ess0XSYLNY5ZSpcVXmFqZc2rToXfuc9r/MJ1mD2TwVm/Igrr8xvnA05SvXQmvgKkgSawYvYiaLetgbmnOkU0H2Dh5DWPXeSHJJP7Ycojw0DAS4hP4YVQnmvZtiSoyhpVjFn9UDG/a1Zzf5yBJEnNHzeXrVl9jYWnB3o17WeG7Ar/1fkiSxP4t+3n17BX9vPuhtFXSaVgnOg3rBIBnV08WjF3AuMXjDN+issB9wQfO/rbs0Cd/Sf+G7/mVEjP6HR/ZRFxcHO3bt6dChQqEhYURERFBbGwsw4YNo27dumzevJkNGzYwf/58ChUqxLRp0wgO1q+TsrW1xdvbm4IFC9KgQQP27t2LmZkZs2fPxs7OjmbNmuHp6cmDBw+Ii4vDzc2N1q1b4+bmhre3N/b29mnGVbFAzTTTvoQR8rRj/dzmaG5/6RCMWMrNvnQIRkqa5PzwRp/Rc23Mhzf6TKoq8nzpEIxc10V86RCM7Liw6EuHYLCgiteXDsGIWspel7hwSfulQzB4kfh5vt0jvV7qMvdQ8KdiI8te1wj/+9u/dAgABBTo/OGNMqjNs41Zfsz3+ccOfrMrMfhNmxj8vp8Y/KZNDH7fTwx+0yYGv2kTg9/3E4Pfd/utYJcPb5RBbZ9+3rta//g1v4IgCIIgCIKQXv/YNb+CIAiCIAjC55W97qVkjhj8CoIgCIIgCOnyb3jgTSx7EARBEARBEP4zxMyvIAiCIAiCkC66f/4PvImZX0EQBEEQBOG/Q8z8CoIgCIIgCOmi458/9SsGv4IgCIIgCEK6/Bu+7UEsexAEQRAEQRD+M8TMbxbLIbf80iEYKa2N/dIhGLyKi/zSIRjRmFp96RCM1FIU/dIhGPlTnn0+3/v0ln/pEIx0XJ695g2y06+qDbvg+6VDMKLZufRLh2Bks8eTLx1CCgoum2i+dBDJslezophk/qVDyJbEA2+CIAiCIPwjZauBryB8RmLmVxAEQRAEQUiXf8OPXIjBryAIgiAIgpAu2WdBXOaJZQ+CIAiCIAjCf4aY+RUEQRAEQRDSRTzwJgiCIAiCIAj/IGLmVxAEQRAEQUiXf8MDb2LmVxAEQRAEQfjPEDO/giAIgiAIQrr8G2Z+xeBXEARBEARBSJfEf8EDb2Lw+4nValyTbsN/RKvVsWfzXnZt3GOUbpvTBq/FEzE1N+NV6EumjZhFXGxcmvt1GdyJr76phYmJgu2//o/dm/fiUM6eYZOHoNNqSYhPwG/YDMJfhqc/SEnCfnofLMsVJzFew+2RPxN775khOff31Sk8pDUkJhK67iChGw8hKeSUWjgYs6L5SNTqCBn9M+rbWfeznY2//ZqRYweg0WjZvD6ADWt/e+d2fQa4kTdfHqb6zAOg1Q9N6TOgK1qtlhvXghk3ypfExIx/K2H9b+owcFRvtFoN2zbuZOv67UbpOXLZMmfpFMzMzXj+7CUThvmgtFYyd5mfYZuy5UszZ8oi/H8NACBXnpxsO7COnu0Gcff2/QzHBIAk0cCvO3kci6GN13Bw7Eoi7ocakku3qIlzrybotDpeBT3k8MQ1SBI0nNGbnPYFSdTqODB6ORH3n2fu/IAkSfSe0p8STiVIiEtgqfsint1Pri9VG7rQdlgHdFoth/0PcmjzAQBaDfwBl8auKEwU7Fu3l8P+BylZ3o5xv3jw9K6+7uxfH8hfu/7MbGSYftcdWf5ioNUQt2slieHJeSMraIdp4y4gQWJ0BHHbfwYSMWveFylHPohXE7d3jdE+GY5AkhjgN5CSjiVJiE/gp7ELeXr/qSHdpZErnYZ1RKvRcWDLAfZv2mdIs81ty7zd8/Hq4smjkEeG93t79ebRnccErt+b6biSgqORX3fyJdWdfWNX8jpF3SnboiZVk+rOi6CHHJy4BplcRpPZfbAtkhe5qYJTP+0g5MCFj4sjnS5fC2Luz6tYs2jmJz+XLjGRqfsuE/w8AhO5jElNK1MspxKAl9GxuO84Z9j25vMIhn3tRDvnkvxyMphjt56RoNXRvkpJWlcqnjUBSRI1p3Unl1MxtHEaToxZSdS95LIq2bIm5Xo3IVGnI+zGQ06OX4NDu9o4tKsLgNzchFxOxfB3Hkx8pOojQ5FoO6UnhRyLo4nX4O++jJf3jduIibkpA9ZPZLP7Mp6H6NvyqN3TiI1SA/Dq4XM2j8man5mWJIluU/pSzKkEmrgEVrov4XmK/se5YTVaDWuPVqvlD/9DHN18EIWpgj6zB5OvaAHU0Sp+9VxB6L2n7zlLxuJpMaUHBRyLo4lP4Hf3FYS9I396rB9PgPsKXoY8QaaQ03ZOf3IUyUuiVsfv41fyMiQ7/ez1v99/fvBbvnx5nJ2dSUxMRKVSMWDAABo3bkxAQAALFy6kaNGiAMTHx9OtWzeaNm2a7mPLFXIGTxpA3+8HEquKZfH2Bfx14CRhL5IHpt1GuHFg+2ECt+yjy6COtHBrRsDq7e/cr5hDMcpXK8eglkMxtzCjY//2AAz1GcQCz5+4fS2EFj82o/Ogjiz2+Tndceb6zhXJ3IQrzSairFKKEt7dCOo+Q58ok1F84o9cauKONiaWKn/M41XgGWxcyiIp5FxpPhHbuhUpNq4zN3vPTvc530ehUOAzdRzf1W+PSqXmf/vWsz/wKC+evzRsY25uxuwFvjhXq8ju/+03vOfuMZQGtVqhVseyZOUsGn/7Nfv3Hsng+eWMmzyCdt90Q61Ss3HXLxzZf5yXz18Zthk0qje7tu3jd/9d9BnSjQ5d2/Drsk10bd0fgMrVKjB8/AC2rttuOKbP7PHExcZ+VN7YN6mK3MyELa19KOBsTx3PzuzqrR/4y81MqDmmLRsaj0cTG8+3Pw2iZCNnJEn/MX1rG18K13CkjmcXwz6Z4dKkOqZmJkxs7U4p59J09ejJzD5T9TEo5HT36sW45qOIU8cxedt0zh86SyH7IpSpWhaPNuMwszCjed9WAJQsb8/OlTvYtWLHR+ULgLxMVVCYELvGB1lhe0wbdSZua/Lfafp9L+K2LSQxPBRF5a+RbPMgtytPYnwscWu8kXIVxPTbbsRtyvxgq0aTGpiamTCm9WjKOJehp2cv/HpP0cenkNPbqzcjm48gThXHzICZnDl4mtcvXiNXyBk0bTDxsfGGY9nksmHkvJEUsivMo2UBmc+YJKWaVEVhZsLG1j4UdLbna8/ObE+qBwozE2qPacuapLrz/U+DsG/kjEUOJbHh0ewdvhTzHEq67p3yWQa/qzZsZWfgYSzMzT75uQCOBD8lTqNlbde6XH4cxtxD15jftjoAeZTm/NKlNgCXHoex6NgN2lQqwdn7L7n0KIw1bnWITdDy6+nbWRZP8W/17Xx3Cx/yVrHH1aszh3omtXNzE6qMbcv2huPRxsZTb/EgijZy5vaW49zechyAGn7duLX52EcPfAHKf1MNhZkpC9p4UdzZgRYebqzqk9zXF61gRzu/XtgWzG14T2FmAsDijr4fff7UqjZxxdTMBN/W47F3Lk1nj+7M7zMd0LexLl498Go+ljh1HF7bpnLx0DlcmtYkLiYWn9bjKGBXiK6+vZnVdXKWxOP4TTUUZiYsazOJos4ONPXowvo+cw3phSuUpKVfL2wK5jK8V6Z+ZWRyOct/8Ma+dnkaj27PpgHzsySez+HfsOzhP//Am62tLevWrWP9+vX8+uuvTJo0yTBT2KxZM9atW8e6detYtmwZ06dPz9AsYvFSxXl87zHREdFoEjRcOXuVitUrGG1T0bUCZ46cAeDUkTNUq1Mlzf1c61XjTtBd/H7xYdqvfvx18BQAPgOncPtaCAByuZz4uHgywsa1LK8P/w1A9IVbKCvZJSfqdFyoMwxtlAqTXEqQJLQxsajvPEGSy0GSUFhbkKjRZuic71OqjB337twnIiKShIQEzpy6QPWaVY22MTMzY+vmHSyYs8zwXlxcPM2/6YJarR9gKhQK4mLjMnx+u9IleXD3EZERUSQkaDh/+m+qVq9stE2V6pU5fuQkAH8c/ouadV2N0j2mjsZn7Ax0On03MdZ7OP6/BvD82Us+RiGXMtw/ehmAZxdDyF+xpCFNG69ha2sfNEkDKJlChjY2njv7z3No3C8A2BTJg+plxEfF4OjixMVjFwG4dTEY+4oOhrTCDkV4du8pMZExaBI0BJ29QVkXJyrXdeZB0H3GLB+P+yoPzh/Sz6TZl7enaoNq+GyZyoCZgzG3ssh0XPKiZdCG6PNG9zgEWcHkvJFyFQR1NCauTTB3m4hkYUVi2FNkeQob9tG/LpTp8wM4uZTj/FH94PDmxZuUqljKkFbUoShP7z0lJkKfN9fPXqecazkAenr0Yu/6vYSFhhm2t7CyYOO8jRwJyNiHt7QUdinD3aS68zRV3dHEa9iYqu5oYuO5ufs0f85Ovuui036ey17RQgWZP9Xjs5wL4OKjML6yywdAxcK5uPbs9VvbJCYmMuPAZSY2qYhcJnHy7nMc8tkwctsZhv52mroO+bMsnnyuZXh8RF9WLy6EkDtlO4/TsLulD9qkspIUMrQp+vzcFUuSo3QRgjdkTb2xcylL0LG/Abh/8TZFK9gZpStMFazqN9cw4wtQyLE4puZm9F87gYEbPSju7EBWKe3iyOWk/ifkYjAlK9onn9ehCKH3nqGKjEGboCH47A3KuDhSuFRRLh3V7/PszhMKORTJsniKu5Qh+Ji+rB5evE3hVPkjNzVhQ7+5vEiRPy/vPEWmkCFJEuZKC3RZeP0U0udfMfi9e/cuHTt25Mcff6Rbt26MHz+eDRs2ABAREUGbNm0ICwuja9euuLm50bFjR27evPnWcaKjo8mfP79hpiylqKgozM3N35mWFiulJTFRMYbXqmg1VtZKo20slZZEJ22jT7dKcz/bXLaUrVgar36+zBk3D89FEwB49Vx/wSxfzYk2PVqyZfm7lwikRWFtgSYqxQyBVgdymdHrXE2rU/nQHCJPXScxQYs2Jhazonmp8ucC7Gf35+nKPW8fOJOsrZVERkYbXkdHx2BjY5xvERGRHDvyl9F7iYmJvHyhn53t2bcLVlaWb22THkprK6JSnD8mRoV1qvOn3CYm2ji9fpO63L55h7sh+qUNrTs0I+xVOH8eOZXhWFIzVVoQl6KsErU6pDdllZiI6mUkAJW6N8bE0pwHx68atms8tx/1fLpye/eZj4rBQmmJKkX91Gl1yJJisFRaokoRX2yMGksbK6xz2WBf0YG5A2eyYsLPDFswEoBbl26xzm8Nk9pPIPRBKO2Gd8h8YGYWEJeiHifqQNLHJVkqkRUpRcL5g8RumI6sRDlkJcqhC72PvFRlAGSF7ZGsc0EG2nhqlkqLVHmjTc4ba+N8Uye194ZtGxLxKoKLfxjPqIY+DCX47+BMx5KaqdKC+HTUHefujTG1NOf+8askqOJIiInFxMqcFkuH8uesrVkWz/s0rl8bheLz3ZiMiUtAmTRbCSCXSWh0xgP9Y7efYZfHhhK5rQEIV8dz/elrZrV2waNJRSbsPJ+pJVbv8lZZ6YzLKjaprBx76Nv5kz+uGratNKQFf8/7+DsFb5grLVAnLV8Afb2Rpbg+3D0fzOunr4z2SVDHcWTFLpZ2ncrWib/w4/whRvt8DItUfUzK/sdCaWGUpo5RY2FjxYNrd3FuqJ9AsXcuTa4CuZBkWROPeao+WZcqfx6cDybiaZjRPvGqWHIWycvwQ7NpNb0PJ1cHZkksn4vuE/z73P4Vyx7++usvypUrx7hx4zh37hw5c+bE09OTLl26sGvXLpo3b87ly5extrZmzpw53L59m+ho/cAlIiICNzc3dDodwcHB9OrVy3DcXbt2cenSJSRJwsLCgpkz03c7tPfYHlRwKY+9ox3XLwYZ3rdUWhCdYlAFoIpWYWllSXxsfFJ6DDFJ76XeLzI8kgchD9EkaHgY8oj4uHhy5M7B61evadDia9yGdGFs14lEhGVsZk8TpUauNE9+QybTD4BTCNtzmrC9Zyi1cDD52tfDsmwxXh/9m/tTN2JaKDflf/PmYv2RJMYlZOjcKblPHIprzSo4livDxXOXDe8rlVZERESl6xiSJOHpOxo7h+L07josQ+cfNq4/VatXprSTA5cvXDO8b2VlSVSq80dHxWCltCQuNg4rpXF6i7bfsnbFZsPrNp2bQyLUqutK2fKlmbHIh4FdRxkto0iv+Gg1psoUs6MyGYkpy0qSqD2hIzntCrK73wKjfQ+MXMaJvJvpsMOHdQ3d0agzPisOoI5WYZFihlaSSYYZQVW0CvMUdcncygJVZAzR4VE8DnmEJkHDkzuPiY+Lxya3LWf2nUIVqR8Qntl3ip4+fTIVEwBxajBNkTeSTD8ABhLV0SSGh5L4Uj/7og25jLxgCRJO7cU0TyHM3SaifRiM7uld+IgBjCpajYUyZd7IkvMmSoVFinZtkdTem/doDomJVK5dmZJOJRkxbySTe/ny+sXrTMfxLqnrjvSOulMvqe7sSFF3rAvmouWK4fy99iBBO05maUzZhZWZTnwt+AAAqjxJREFUCTHxGsNrXWIiilSDo93XHtG5WvKsXg4LE0rmyoeJXEaJ3NaYyeWEq+LJZfXxSzXio9WYfKCsXDw6YmNXkMN9ksvK1MYSW4dCPPvrxkfH8EZstBpzq+Q2nbK9p+X53ae8THpu5MXdp8SER2GTL+dbg+TMUEerjO4QyVK0MXW0GosU/Y9FUv9zft9pCjkUYcJmX4LPB3H3yh0SdVkz5IqNVmOawfyp1aspt/64zP6Z/tgWzEXPjR789K07mo+4fn5OWfMR78v6V8z8tm3blpw5c9K7d282bNiAiYkJVlZW3L59m507d9KyZUvq1q2Li4sLAwcOZOHChciSOrY3yx42bNjAkSNH2LVrF+fO6W/Jvln2sHbtWpYtW0blypXTFc/KmasZ1m4ULSu3pUjJQljnsEZhoqBS9YpcO3/daNsrZ69So6F+bVmN+q5cPn2F+7fuU8Su8Fv7XT57FdevXQDInT835hbmRIZH0rhNI1p3b8XQdiN5+iDji/ijzgaRs2EVAJRVSqEKemBIkystKP+7D5KpAhIT0apiSdTp0ETEoElaT6Z5HY1kIk+emcikGX4L+aFZdyqWqkMJu2LkyGGLiYkJNWpV4/zZv9N1jFnzvTEzM6VH5yGG5Q/ptWD6Urq27k/tck0oVrIItjlsMDH5P3v3HRbF0Qdw/LvHAXd07A0L2FBRUQFNLLHFNGvsLXaNXcSOiCj2XmOJDSxoxOQ1UewtiQ0kGgvYu2BD6eXK+8fhwRELTSFmPnl8ntzN7s6P2Zm5udnZPTku9ZwJDf7bYNvQM+dp1PRTABo2+YTg02nxVa3hSOiZtMF7j9YD6dFmID3bDiLs4lXGDZ2SrYEvwKPgq5RtXAOAYs4OPAu7Z5DedFYf5KbG7O63UH8Ju3K7T6kzpCUAqoRktBpNjjr+sOAr1Gqsm0Wp4FyRu+FpN+89uH6f4mVLYGFtgdxYThW3KlwNCePK2cs4N9LVMdsiBVCYKYiNisFzkzfla+iWBlT7tDo3/76R7bjU969iVF5XNrKSDmgep5WNNuoxmJgi2eouTRuVroTmyQNkJexR37tKop8v6vBgtC+yfyMgwJXgy9RpXAeASs6VuBN2W5927/o9SpRLK5uqbtUICwljQofxTOg4gYmdJnDr8i0WjlqQ6wNfgAfBVymXWneKOzvwNEPd+Ty17vycru6YFbKivf84js8M4OL247keU35Rs2QBfr+hu0npwoPnVChs9Y9trkS8oGbJtHWbzqUK8setx2i1Wh7HJJCQosZaaZIr8Tw+e5VSTXTnqnAtB6KuGJ6rT2b3wcjUmEN9FuqXPwAUdavMwxMXyU23gsNxbOwMQBnn8jwKv/eOPcCtY2NaefYAwKqILQpLJdGPs3AT9ltcDQ6jZmNdX+LgXJF76fqfh9fvU7RsccytLTAyllPJrQrXQ8Kxr1Ge8OArzOjsRUjQaZ7czf5NrRndDQ6nUuOaANg5lycyE+WT+DKOxNTZ4vgXcRgZG+XaTLSQOR/FzO+hQ4eoXbs2Q4cO5ddff2Xt2rV07NiRlStXUrRoUQoUKMDJkycpUqQI69atIzQ0lAULFuDn52dwHHNzcywtLUlJyZ1vX2qVmmVTf2De5lnIZDL2bAviacRTLG0sGTd3NJ79vdm0eDMTF42lZdevePn8JT5DZrxxv6cRT6nhVp1Vvy1HJpOxcNISAEb4DCHy4WOmr/EG4K9TF1g/f2Om43y25ww2DWvgtNsXJLg+cjmF2tbHyFxBpP9Bnuw8gdPP09CmqIi7cocnP51ApjChwqLBVPt5GjITOXdmbEETn72ZxIxUKhXek2azNXA1MpmMrf6BRDx6jI2NNfOX+tC3x+tndJ1qONKlx7ecPhnCT7vXA7D2Bz/2/nooi/mrme21iLUBS5HJJHZu3c3jiCdY21gxbaEnw3uPZeXCdcxa6k2HHm2IevYCj+916xNtC9oQF5vzm0ze5HpQMKUbVKNDoBeSJHHAYzWVWtfD2FxB5IVbVO3UiAdnwvl2m25JzF/r9nF9bzDN5w+g/Q5PZHIjjk/1R52DGYYzQaeoXr8m0wNnI0mw3GMJ9Vs3RGGm4ODW/Wycto5Jft7IZBKHtx/ieeRznkc+p4pbVWb+bx4ymcTayavQaDSs8VxJX5+BqJJTePHkBasmLM92XOqwYIzKVUPxnRdIEkm7V2NUtR6SiQJV6BGSfl2LaZvBIElo7l9Dff0vUFqg+Kw9xnW/QpsYT/Kva7KdP8DJoJPUbODMnMC5SJLEYo9FNGrdCIW5gn1b9rF22lp8/H2QZDIOBBzgeWTOZ8Iy61pQMGUaVKNLat0J8lhN5db1MDFXEHHhFk6dGnH/TDidUutOyLp92NVzRGFtTr3hbag3vA0AO3vO+dfMUGVWk0rFOXX7CT39joMWpn7tzJ5L94lPUdG+ZlmexydhZiI3WPbWsHwxQu49o9vG42i1WiZ8rlsLnBvu7A2mRMNqfP2Lri7/Pmo19m3qITdX8Oz8LSp2aUTk6XC+2K47V5d/3MfdoGCsHYoTczdnX+Ay+nvfWSo1cGL4Th8kCbaO+YFarT7F1FzBya2v71tPBxymy7zBDNvhDVrYNmZVrq0XDwk6TbX6NfAKnAGSxBqPZdRr3QCFmYIjWw+wZdoGxvp5Ickkjm8/RFTkc1KSU/h2dBe+GtCa+Og41o7Jfj+T0eV9wZRv4MSAnd5IksTOMauo3uoTTM0VnN16+LX7/PHjHtrNGUj/7V4YmcjZPyeAlGxeicsLmo/gUWeSNrcWKeWhu3fvMmbMGIyMjJDJZEyYMIEKFSrQsGFD5s6dS4MGDYiKimLUqFEkJCQgk8kYMmQI9evX1z/tAXRPdHBycmLSpEns2rWLmzdv4uHhkaVYGpZs+j7+xGybqbbN6xD02idefvdGH5C1iXleh2Dge0WlvA7BwO9S5paafAgb+lvmdQgGOq9+kdchGGiETV6HoDfiXO7f4Z8Tqt2584it3LLNM/880uqCserdG31AT7VZu1n7fSstKd690Qfke3tLXocAwOLS3XP9mCPu+uf6Md/mo5j5LV26NAEBAQbvJSQkULJkST79VHdp2tbWlg0bNvxj34sXX3+JqF27drkepyAIgiAIwr+ZeNRZPnXu3Dk6duzI4MGD9Wt7BUEQBEEQhJwRT3vIp2rVqsXu3bvzOgxBEARBEAQhn/koB7+CIAiCIAhC7vvX3yjGR7rsQRAEQRAEQRBeR8z8CoIgCIIgCJnyMTzqTMz8CoIgCIIgCP8ZYuZXEARBEARByJS8eDqDRqPB29ub8PBwTExMmD59OmXKlNGn//rrr2zcuBEjIyMqVqyIt7f3W5/2JWZ+BUEQBEEQhEzRvod/73Lw4EGSk5MJCAhg9OjRzJo1S5+WmJjIokWL2LRpE9u2bSM2NpYjR4689Xhi8CsIgiAIgiDkWyEhITRo0ACAmjVrGvxAmYmJCdu2bUOpVAKgUqkwNTV96/HEsodcVkZundchGDgsf3sF+JCMkvPXdy0zo/xTNgCPZfnrd3Py0y+fq29F5nUIBpK1+avrTJDln3OV335OWN5yUF6HYKDo+Il5HYJecxUcVOZ1FGmeaRPzOgQDxlL++szKLzR58LCz2NhYLCws9K+NjIxQqVTI5XJkMhmFChUCwM/Pj/j4eP2v+75J/urBBUEQBEH4IPLTwFf4bwsICCAgIED/ulOnTnTq1En/2sLCgri4OP1rjUaDXC43eD137lxu3brF0qVLkaS3P5JCDH4FQRAEQRCETHkf1ygzDnYzqlWrFkeOHOGrr77ir7/+omLFigbpXl5emJiYsGLFirfe6PaKGPwKgiAIgiAImZIXi6yaN2/OH3/8QefOndFqtcyYMYPdu3cTHx9PtWrV+Omnn6hTpw7fffcdAD179qR58+ZvPJ4Y/AqCIAiCIAj5lkwmw8fHx+A9BwcH/f+HhYVl6Xhi8CsIgiAIgiBkSv66NTt7xK2MgiAIgiAIwn+GmPkVBEEQBEEQMkXz9gcp/CuIwa8gCIIgCIKQKXnxnN/cJpY9CIIgCIIgCP8ZYuZXEARBEARByJR//7yvGPx+MJIk8d30AZSuUhZVUgprx63g8Z0Ifbpz0zq0GdERtVrN8YBDHN12ECO5EQMWDKNwqSJo1Bp+HL+SRzceMGSpO9aFbQAoVKoIN0KvsnzYgtwIkq+m96ZoldKoklL4ddxaou6k/axs1Vb1cOvzBRqNhsdX7rHHcz28p5/AbdaiESPHfI9arSJg8y62bNr52u36DupOkSKFmOmzSP/eFN+x3Lh2G/8N27Odf8Pmn9LfvRdqlZpftv3Grs27DdJtCljju2IKCoUpTyKe4j1qBokJSfp0z7ljeRkVzdIZPyCTyZg8bxxlHOzQaDR4j5zB/TsPsxWXJEm0mt6bYo5lUCWnsGvcGp7fMfzpX2OFCb39JxA4bg1PbzzEuX1DarVvCIDc1JjiVcowy2UwidHx2Y6h//RBlKlSDlVSCivHLSPiziN9eu2mLnQY0Rm1Ws2RgIMc3LYfgLl7FhEfrfuFnsh7kawYs4Ry1ewZ4DsYVXIKty7fYr33mtz5WWVJQtF9ODI7e0hJIWHjArSP08pcVrYiik6DQJLQvnxOwppZoErJhWwlhvkOoVwVe1KSU1g0dhEPb6eVjVszN7qN7IpapWZ/wH72bg3CSG6E+7xRFLMrirGJMVuWbOXUgdP6fRq3+YxWvVoxqo17juNLDTLP27lGq2XGvgtcffwSYyMZU76qSWlb3U+XPo1NZNwvwfptwx+/ZMRnVejgXI4fT17l2LUIUtQaOtYqR9saZXI1rre5cCmMBSvXsWHZnPefmSRRbXYfLKuWQZOk4m/3VcTfTjtHxb52xX5Ya0DLXb9D3N98RJ9mUsiKT/fP5ExHX+KuZ6+fMQxF4tvpfSjhWAZVsort41bx9DV9ziD/SQSMW8XjG7o83X+bSWJMAgDP7z1m25js/+y1JEkM8R1COcdypCSnsHjsYh6l63Ncm7nSdURqu9q+n31b9yGTyRg+ezilHEqhUWtY4LGAiDsR2FWwY/is4UiSxM3LN/nB6wc0mtx5foEkSXSf3h+71LLaMG6lwec8gInChNH+Xqwft4KIGzk/P0L2fLSD36SkJL788kuaNm1K7969KVGixGu369GjBwkJCSiVShISEvj0008ZNWoU9+/fp1WrVlStWhWA5ORk3NzccHfP3gdQ7RaumJga49N2Ag7OFenq2YtF/WcBYCQ3optXb7xajiUpIQmvnTMIPRSMQ80KGBkZ4dNuItXq16DDmK4sGTRXP9A1szJn4jYf/Ketz1ZMGVVuURu5qTHr23pT0rk8zT27sb2/Li+5qTGfeXRg1efjUSUm03bJECo2debqwXO5knd6crkcb99xfN20M/Hx8eza68+BoKM8efxMv41CYcqcRVNxru3Ent0HAChQ0JbFK2dQzqEsN65lv0zkciNGTx1G9y/7kxCfwPr/reT4/j949uS5fpv+o3oRFHiA3dv30mtod77t0ZrNq3WD7W97tKZ8ZXtCTv4FQMPPdb8x3qf1YGrXc8bdexjuvSdkKzbHz+sgNzVmVbsp2DmX5yvPbvj3T/viU9KpHK19+2JVvID+vdCfjhP603EAWvr04tz2Y9ke+AK4tqiLsakJk9qOpYJzJb7z7MPs/r6Ari738urH+JbuJCUkMX3nbIIPnSEuddA7pfMkg2MNmjmEdd5rCA8Jo7NHN+q3acSJXUezHdsrcudPwdiE+BkjMLJ3RNFxIAnLpujTld+5E7/SB+3jhxg3+BJZwaJoIu/nON9PWtTDWGHCqDbuVHauzIDJ/fHuq3s2pZHciEFTBjDsmxEkxieyYNd8Th08jUvjOsRExTB35DwsbSxZEbRMP/i1r2JPi04t3vlTnVmRH9r5kauPSFKp2dSzIRcePGfBoUssau8GQCELBT92qw/A+QfPWXbsCu1qlOXsnaecv/+cDT0akJiiZuPp67ka09us27yD3UGHUSpMP0h+Rb+sg8zUhJNfe2FTuzyOU3sQ8t08XaJMopJnF/74fCKquEQanphP5N5gUp7HIMmNqDa3H5rE5FyLpdrndZCbmrCknRdlnMvTyrMH6/rP06eXcrKng29frIsX1L8nNzUGYEVnn38cLzvqtaiHsakxo9uOppJzJfpN7se0ftMAXbsa4DWAkS1HkhifyLzAeZw5eIbKzpUB8GjngVNdJ/pP7s+0ftPoNbYXG2dv5OKZi4yaPwq35m6c3HcyV+J0/twVY1NjZrSbhL1zBTp5fsfS/rP16WWdHOjpOwDbdP3zv5F41Nm/wKRJk9448H1l9uzZ+Pn5sWPHDs6cOcPff/8NQPny5fHz88PPz4+tW7dy+vTpLD9I+ZWKLo5cOBYKwI3Qq5SrnvZw5hLlSxF5O4L46DjUKSqunr1CJRdHIm49RCY3QpIkFJZK1Clqg2N+696ZAxv38PJxVLZiysjOpRI3jp0H4EHodYpXL6dPUyWr2NDOG1VqpyqTG6FKyvlM2etUqGjP7Vt3efkympQUFWdPn8O1Xm2DbUxNTfkp4H8sWbBa/565uRkLZq8gcPvujIfMknIVynLv9gNiXsagSlHx15kLOLvVMNimplt1/jyiG6D8cfgUrg3qAFC9dlWcalVhp98v+m2PBp1g+hjdbFHxUkV5/jT756uMSyWuHrsAwL3Q65R0sjdINzIxZvPABTx5zYxCSadyFKlYirNbD2c7f4DKLo78dUw3GLoWGo599fL6tFLl7Yi4/Yi46DhUKSrCzl7G0aUqZR3LYaIwYbLfVKZsnU4F50oAFChWiPAQXZsKD76CYx3HHMX2ilGFqqgungVAffMKRmXTfgpTVrQU2rhoTJu1w2zsfCRzy1wZ+AJUda1K8NEQAMJCw6hQvYI+rXR5Ox7efkjsy1hUKSounb1ENdeqHP/1BBvnbdJvp1bp2rmljSV9J/Tmh6mrciW2V/JDOw+9/5xP7YsAUL1kAS5FvPjHNlqtltkHLjCpRXWMZBInbz2mfBEr3HeeYfhPp2lYvmiux/UmdiWKs2iG5wfLr4BbZZ4c+QuAFyHXsa6Rrp1rtByvPxpVTAImBSyRJAl1XCIAlb27c3fjQRIjcuczAaCcS2XCjuliuRN6HbsMfY7cRM76gQv0M74AJRzLYKIwZeCmiXy/xZMyzuXJiaouVQlJbVfhoeEG7cruNe2qqmtVTu4/yZLxSwAoUqoIL56+AMB3oC8Xz1xEbizHtrCt/v3cUMGlMhdTy+pm6DXKvqaslg6cwyMx45vnPqrBb1xcHN9//z3dunXD29sb0M3s3rhxg4iICAYNGkTv3r1p27YtBw8e/Mf+ycnJqFQqihQp8o+0xMREkpOTUSqV2YpNaWFGfEzabJtGrUFmJEtNUxqkJcQloLQyJzEukcKlijD78FL6zvqe/Rt+029jVdCaKp86cXxH2uWunDK1UOovUwFo1Rqk1BjRaol7Gg2AS6/PMTFXcPPE37mWd3oWluZER8fqX8fFxmFlZWmwzcuX0Rw/8qfBe/fuPiA0JOcxmVuaE2uQfzwWVuaGMVqYExuj2yY+Nh4LKwsKFSnIQI8+zJr4zyUoarWaqYsnMdZ3FAd/zf45U1goSXpDPQK4G3KVl4+ev25XGg1pzeHFgdnO+xVdXY57bQy6upyWlhCXgJmVGUkJSexe/TPTekxh9cQVjFjsjsxIxuN7EVRx011dqd3MFVMzRY7jA5AU5hCfFgcaDaT+3rtkaY2RQxWSj/yP+PljkTs6Y+TonCv5mlmY6We5wbBszCzNiUtfNrEJmFuakxifqGvz5komr5rExnmbkMlkuM8byQ9TV5MQm/1Z+tfJD+08LikFi9TZQQAjmYQqw6XnY9cjsC9kRdmCurYflZDM5UcvmNvWBc8W1Zm4OyR3lshkQvPG9ZHLP9yFUrmlElX0G85R6uuiX7lQ//Bsnp+6giZFRclOjUh+Fs3ToxdyNRZFhvqSsc+5HXKVF4+eGeyTkpDE0TW/sqrnDH6a9CPdFg0z2CerzN7y+WlmaZj2ql292s59gTvfT/2e33/7XfeeRkORkkVYeXAl1gWsuX8jd774gq7/S3hL/3w9JJyoDGX1b6RBm+v/PrSPatnDrl27qFixIqNGjeL8+fOcPp22bu7mzZv07t0bNzc3zp07x9KlS2nWrBkA48aNQ6lUcu/ePSpXroytrS2PHz/m+vXr9OjRAwAjIyN69uxJmTLZW2OWEBuPwjxt4CyTydCoNalpCSgt0j70leZK4qPj+KJfS/4+Fsr2OZspULwgE7ZOZWKLUaQkpeDyVT1O/nICbS6tVQJIik3A1DwtDkkmQ6tOd3xJotnELhQsV4wdAxflWr6vjJk4DNe6tXCsWpHQkLQO3NzCnOiX0bmeX0aDx/Wnpmt1Kjg6cDH0crr8zYh5GWuwbWxsHGbmZiQlJmNmYUbsyxiatWyMTQEblvjPo2CRAiiUCm5fv8Pu7XsBmDLClyXTV7Jpz2q+bdidxITELMeYGJuAicE5kvT16G0UVmYUdijBrZOX37ntu/yzLksZ6nJamtJcSVx0HA9vPSAide3ro1sPiYmKwbZIAZZ7LKH3lP60HtSOG+ev59osozYxDhTpvqhKkm4ADGhjo9E8fojm0V0AVBfPYlSmAuoroTnONz42HrN0f7+Urp3Hx8ShNDfTpyktlMSmDpQLFy+E19rJ7N70G0d+PkqlmhUpWbYkw2YMxcTUhNIVSjNoysBcmQXO63YOYG5qTFyySv9ao9UilxkOjn67dJ+uddJmzmyUxpQrUARjIxllC1piamREVHwyBcw/zFKED0kVk4BRus8EZJLhOQIi95wlcm8w1Zd8T6mODSnV+TO0Wi2FGjhhVa0MNZYNJrjHXJKfvMxRLIn/qC/v7nMe33rE09u6ta5Pbj0iPioGqyK2/xgkZ1Z8bLxBvyIzaFfxKNP1R7p2ldZfL3BfwPrC61n4y0IGNh1IUkISjx88pn+j/rTo3IL+Xv1Z4J4L98yg6/8UGdpWZvrnf5uP4Ya3j2rm99q1azg5OQFQo0YNg2/qhQsXJiAggDFjxrBt2zZUqrSO99Wyh8OHD2NjY8PatWsBw2UPGzZsoG3bttmO7WpwGDUb1wLAwbki98Lv6NMeXr9P0bLFMbe2wMhYTiW3KlwPCSfuZZz+G23ci1iM5HJkqR8QVetX5/zRnH9Yp3cv+CrlG9cEoKRzeR6H3zNI/3pmX+SmxgT0X6i/LJqb5s5YSodWvalZqRFly5XGxsYKY2M5bvVqE3L2fK7nl9GK2WsY8O0wmldviV3ZkljZWCI3llOrbk0uhFw02Pb8mb+p37QeAJ82qcu50xfY9uNPdGvRlwHfDmPDUn/9muCv27eg97DuACQmJKLRaLJ9g8Xd4HAqpZ4jO+fyRGY4R29S1rUy13+/+O4NMyEs+Aq1GuuWeVRwrsTddHX5/vV7FC9bAgtrC+TGchzdqnI1JIwmHZvTc3IfAGyLFMDMwoyox8+p1aQOK8YsYWbvaVjYWnL+979yJUb19UvIq+vWkBrZO6J5cEufpnnyCMlUgVREtxzKqIITmoe3cyXfy2cv49LEBYDKzpW5HZaW793r9yhZrgSWNrqycXKtxpVzV7ApZMOMzb78OGM9+wN0NweG/3WVAc0GMbbjOGYOmcnda3dzbflDXrdzgJolC/D7Dd1NUxcePKdCYat/bHMl4gU1S6atjXQuVZA/bj1Gq9XyOCaBhBQ11kqT9xJfXos6E06RprqrETa1yxNzJe0cyS2UuO3yQmYiB60WdXwSWo2WU22mcrqtD6fb+RB98Q7nh67I8cAX4HZwOI6NdbGUcS7Po0z0OW4dG9PKUzdxZFXEFlNLJdE5WJ53OfgydVL7nErOlbgddlufdu/6PUqUS+tzqrlVIywkjCbtmtBxSEfAsN/1+tGLEmV1bT8hLgGtJveGcteDw3BK/Zy3d67Ag/C7uXZsIXd9VDO/9vb2/PXXXzRr1ozLly8bDHAXL15Mhw4daNSoETt37mTXrl3/2F8mk1G0aFFSUnJ/jVtI0Gmq1a+BV+AMkCTWeCyjXusGKMwUHNl6gC3TNjDWzwtJJnF8+yGiIp8TtHY3/ecOwXPHdOTGcnbM3UxS6hMFituX4MndiHfkmjVhQcHY13eiV+AUJEnifx6rqNb6E4zNTHn09y2cOzXi7plwemydCMCZ9fsI3xf8jqNmnUqlYqrnHPx/Wo1MJhGweRcRjx5jY2PF3MU+9P9uZK7naZi/mgXey1i+dQEymYxftv7Gk4inWNlY4jV/PB59J7F20UZ8lnjStltLXjx/ycTBU994vEO/HcN70UTW7lqGXC5nntcSkpOyN6i4vC+Y8g2cGLDTG0mS2DlmFdVbfYKpueKta3kL2Zcg6t7jbOWZ0ZmgU9SoXxPfwNkgSSz3WEz91g1RmCk5uHUfG6b9iKffVCSZxJHtB3ke+ZzDAQcYMn8E036aBVotK8YuQaPW8OjWQyZu8CI5MYmLJ/8m9EhIrsSoOvcH8iq1MZuwCCSJxHXzkLs1RjJVknJ8DwkbFmDWfwJIEqobl1FdOJMr+f4R9Ce1GjizcNd8kCQWjF5A4zafoTBTsnfLXlb5rMHX3xeZJLFv+36eRTxjkPdALKwt6DqiC11HdAHAs+dkkt/TwDM/tPMmlYpz6vYTevodBy1M/dqZPZfuE5+ion3NsjyPT8LMRG5wo1/D8sUIufeMbhuPo9VqmfC5bi3wxyhiz1kKNXKi3q8+IMGFET9Qot2nGJkruOd3iIeBf1D3F280KSpiLt/lwU8n3lssf+87S8UGTgzb6YMkwbYxP1Cr1aeYmCs4tfXQa/c5HXCYLvMGM3SHN2ghYMyqHM2A/hn0J84NnJkXOA9JkljosZDPWn+GwlxB0JYg1kxbw3T/6UgyiQMBB3gW+Yw/9v6B+3x35uyYg5HciNVTV5OSlMKOFTtwX+BOSnIKSQlJLB63ONtxZXRu3xmqNKjBxJ2+IMG6Mctxa1UfhbmCY1v/udTy3+pjmMuWtB9q0dQHoFKpmDBhAvfv38fe3p7g4GCKFCmCt7c3V65cYcmSJRQqVIjixYsTFhbGb7/9ZvC0BwCFQsHcuXOJjY3F3d2d7duz9risHmXavY8/Ldsqkr01yu/Dqpj3P3ubFUVMbfI6BANfmpbO6xAMhGtj373RB7KuWcK7N/qAOuzPX/MG9WX55+5xD++SeR2CAXnLQXkdgoEDVSfmdQh6B/PPxwMAYZr3v7wtK4rJzN690Qe07vZPeR0CAB5lu+T6Mefd3prrx3yb/NWD55BcLmfu3LmvTXNwcOCbb775x/t+fn6v3d7GxibLA19BEARBEISP2cfw88Yf1eBXEARBEARBeH/+/UPfj+yGN0EQBEEQBEF4GzHzKwiCIAiCIGTKx3DDm5j5FQRBEARBEP4zxMyvIAiCIAiCkCnaj2DVrxj8CoIgCIIgCJkilj0IgiAIgiAIwr+ImPkVBEEQBEEQMuVjeM6vmPkVBEEQBEEQ/jPEzG8us5TyV5E+IiWvQ9CLSsw/P5cLoDQyzesQDNwxSczrEAw8U+efnxTesa9oXodgQCY9y+sQDERJ6rwOQW+b58O8DsFA0fH55+eEAZpfmpHXIeg1B9rWGpbXYehZSyZ5HYKBKE1SXoeQL/37533FzK8gCIIg/Cflp4GvIHxI+WuaUhAEQRAEQci3PoY1v2LwKwiCIAiCIGSKeNSZIAiCIAiCIPyLiJlfQRAEQRAEIVM+hl94EzO/giAIgiAIwn+GmPkVBEEQBEEQMuVjWPMrBr+CIAiCIAhCpohlD4IgCIIgCILwLyJmfgVBEARBEIRMEcsehGyRJInO0/tR0rEMquQUNo/7gSd3Ig22MVaYMNzfE/9xPxB54yGSTKLbrEEUtS+ORq3Bb8xKnt6NfEMO/+54AL78qikTJgxHpVKzadN2Nqzf9trthgzpQ9GihfHymk3RooXZsHGpPq169Sp4ec3mx7Wbs5x/k88bMMSjP2qVmp+2/I/t/rsM0m0L2LDgB19MlaY8jnjC+OFTSUxIpNW3X9JncHfUajU7t/yPLRt+QiaT4bvQk3Lly6BRaxg/fCp3b9/PdCySJNFr+gBKVymLKimFteNWEHknQp/u3LQObUd0RK1WcyzgEEe3HcRIbsTABcMoVKoIWrWGteNX8ujGA0pUKEXfmd8jSRJ3r9xmo9datJp3d2WSJDFyxnAcqtiTkpzC3DELeHg77Wds6zWrS8+Rur97b0AQv23Z+8Z9HKo4MHzaEDQaDSlJKcwcOZsCRQoydOr3+uNVcXbEs98Uzh4NznQ5IUl8MqMXBauURp2s4sSYtcTcTquT9q3rUbVfC7RqDc+v3OPPiRuo0L4+FTo2BMDI1JgCVUqztdZQkqPjM59vhnIa4jsE+9S/edHYRTy6/Uif7tbMja4ju6JWqdkfsJ+grUEYyY0YNW8URe2KYmxizNYlWzl94DTjl4/HtrAtAEVLFSUsNIxZQ2ZlK65XsX07vQ8lHMugSlaxfdwqnr6mnQ/yn0TAuFU8vqE7v+6/zSQxRvdT18/vPWbbmB+yHUO6YKg3sxcFqpRGnaTijwznqtyrc6XRnauTEzZQvkN9yndIPVcK3bkKcM7+ucoYT7XZfbCsWgZNkoq/3VcRny6eYl+7Yj+sNaDlrt8h7m8+ok8zKWTFp/tncqajL3HXP8xPO1+4FMaClevYsGzOe8tDkiQG+w6mnGM5UpJTWDJ2CY/upNVl12audB7RBY1KzYHtB9i3dR8ymYxhs4dR0qEUGrWGRR4LibgTgUM1B4bMGEJKcgo3L91itfcqtNrMXzqXJIk+0wem9oEqVo9bZtAH1mrqQrt0feDhbQdo2L4Jjdo3AcDY1JgyVcrxvUtvbIva0m/mYH0fuN5rTab6wIzxDPT9nrKpZbN87FIi0pVNnWYudEwtm0PbD3Bg634A5u9ZRHyMrr5G3otkmcdi/T69vfrx8OZ99vkHZSkWIXv+k4Pf06dPM3LkSMqXL49Wq0WlUuHr68uaNWs4ePAgf/75JyYmut8Yv3TpEu3atWPTpk2ULFkSd3d3tm/fnqP8a3zugtzUmHntPCnrXIF2nj1Z1X+uPr20kz1dfPtjU7yg/r3qzeoAML+9FxXqVuHbyYb7fEzxyOVyZs+eTMMGrYiLS+DQ4Z/Yu+cQkZFP9NsoFKYsXz6LOi41+eVnXWcRGfmEL7/oDICray28vT1Yv25rtvKfOH007Zr3ICE+gW2/rePw/uM8ffxMv81Qj/7sDgwicNtuBgzvReee7diwagvjpo7kq/odiI+LZ+8fP/Hrrn24flIbgM5f98X1k9pM8BnF9z1HZzqe2i1cMTY1ZmrbCTg4V6SrZy8W9tcNgozkRnT36s3klmNJSkhiys4ZhB4KxqFmBYyMjPBpN5Fq9WvQYUxXlgyaS8cx3dg+ZzPhZy4zYN5Qajd3IXjf6XfGUP+LTzExNWFo6xE41nJk8OSBePadoo9hiPcgBn09lMT4RJbuWsSfB05RrU7V1+4zzGcwSyYv58blG7Ts9jVdBndihc8qRnXwAKDR1w15FvksawNfoMwXtTEyNWZ366kUruWA2+SuHOy7UBejwpjaY9oT2GwC6sRkPls2hNLNnLm24wTXdpwAoN7077gacCxHg6l6LephojDBvY07lZ0r039yf3z6+ujLacCUAYz4ZgSJ8YnM3zWf0wdPU6dxHWKiYpg3ch6WNpYsC1rG6QOn9QNdC2sLZgXMYtXUVdmOC6Da53WQm5qwpJ0XZZzL08qzB+v6z9Onl3Kyp4NvX6zTtXO5qTEAKzr75CjvjF6dq99a6c6Vq1dXDvVJO1e1xrbn56a6c9Vo+RDsmjlzffsJrm/Xnau6vt9xbVvOzlV6Rb+sg8zUhJNfe2FTuzyOU3sQ8l1q2cgkKnl24Y/PJ6KKS6ThiflE7g0m5XkMktyIanP7oUlMzpU4MmPd5h3sDjqMUmH6XvOp26IexqYmeLT1oJJzJfpO7sf0ftMAXV3u59WfUS1HkRSfyJzAuZw+eIbKzpUAGNtuDE51neg3uT/T+01j6KxhrJqyirCQK3T36EGjNp9xdNeRt2VvoE4LN4xNTZjSdjzlnSvS3bM38/vP1MfSw6sPni09SExIYurOmYQcOsvxnw5z/KfDAPSeNoCj2w8RHx3HoHnDCZjjT9iZywyaNzzTfWB6bi3qYmxqwvi2Y6joXInek/sws5+vPp4+Xv0Y09KdpPgkZgTO4ezBM8RFxwEwudNEg2NZFbBixEJ3StiX4OdVmZ8UyUuaLHxxya/+s2t+69ati5+fH/7+/gwdOpQ5c3TfoAsXLszx48f12+3evRs7O7tczdvBpTKXj/0FwO3Qa5RxcjBIl5sYs3rgPCJvPNC/d37/WbZM0H34FShZmJgnLz/aeCpXLs/Nm3d48SKalJQUTv4ZzCefuBhso1CYsmVLIHPnLH/tMeYv8GbECE80WfxGD+BQsSx3bt0j+mUMKSkqQk7/RZ26zgbb1HaryfHDfwJw/NAffNrIDYDwy9ewtLLA1NQUCQmtVsvBvUfxdNd1jCXtivP0yfMsxVPJxZELx0IBuBF6lXLV085PifKliLwdQXx0HOoUFeFnr1DJxZGIWw+RyY2QJAmlpRJ1ihqAxYPmEn7mMkbGcmwK2/Ly6YtMxeDkUpUzR88CcOXcFSrWqKhPK1OhNA9uPyT2ZSyqFBV/n71IdVenN+7jM9iXG5dvALoPiuSkFP2xFEoFvUb3ZMnk15/XtynmUokHRy8A8OTcDQrVKKdPUyep2N1mKurUQYpMLkOdlDZgKVS9HLaVShG+OfMfyK9T1bUqIUdDAAgLDaNC9Qr6NLvydjxMV06Xzl6iqmtVTvx6gk3zNqXFqlIbHLO7e3f+t/5/RD2OylFs5VwqE5bazu+EXsfOyd4gXW4iZ/3ABfoZX4ASjmUwUZgycNNEvt/iSRnn8jmK4ZUirpV4cCTtXBWsbniufmuddq6kDOeqYPVy2FQsxdUcnqv0CrhV5smRvwB4EXId6xrpykaj5Xj90ahiEjApYIkkSajjEgGo7N2duxsPkhiRs3OTFXYlirNohud7z6eqSxXOpdbl8NBwKlRPO/d25e14dPsRcal1+fLZy1R1rcqp/adYOl539a1wqSK8SO1fChUrRFjIFQCuBF+mqkuVLMVSycWR88fOAXA99Cr26WIpWb4UkbcfEZeuD6yc7vj2Tg6UqlCaw6mzrwsHzSZM3wfaZLoPTM/RpQqhqWVzNTQch3TtvJS+bOJQpai4cvYyVVyrUtaxHKZKU6b4++CzdToVU78oKMyVbFu4haOBuVef3zfte/j3of1nB7/pRUdHU7JkSQC+/vprfv31VwA0Gg2XLl3CyckpV/NTWChJiEmbsdCoNciM0k7FzZBwoh49+8d+GrWGnvOH0NG7N6F7T3208VhaWvDyZYz+dUxsLFbWlgbbvHgRzaFDJ167/1dfN+PK5Wtcu3YzW/lbWFoQEx2rfx0XG4+llUWGbcz128TFxmORmn71yg12HfRnz+/bOXLghH4btVrN7GVT8Zo5hn27D2UpHqWFmf5SGRieH6WF0iAtMS4BMytzEuMSKVyqCHMOL6XvrO/Zv+E3ALQaDQVLFmb2gUVYFLDkUbovNG9jZmlOXEzca2MwszDTz2oAJMQlYG5l/sZ9nj/WDf6r1q5Cm16t2bFmp36br7p8wbHfjhMdFZ3p8nnF2FJJcrqy0Ko1SK/qsVZL4lPdMav0bo7cXMGD4xf129YY1orQhYFZzjOjjGWRvpzMM5RHQmwC5pbmJMYnkhCXgNJcyaRVkwwGwtYFran5aU0O7jiY49gUFkr98oWMsQHcDrnKiwztPCUhiaNrfmVVzxn8NOlHui0aZrBPdplYZDhXmtefK8fezTE2U/Aww7n6KxfOVXpySyWq6LSyMag7qa+LfuVC/cOzeX7qCpoUFSU7NSL5WTRPU79wfSjNG9dHLn//F22VFmYG9VWdvs1bmr2mLpsBuno1asEoBk0dxB+//Q5AxN0IqrlVA8C1mRumZoosx/LmPtAwLSEuATMrM/3r1kPbs3Nx2rI5rUZDoZKFmXtgCZYFrHh0I+tLVd4Wj5mlYVpibAJmluYkJSTxy+pdTO3uxQ8TVzBqyWhkRjIe34vk2l9XsxyDkDP/2cHvqVOn6NGjB506dWLixIm0aNECgOrVq3Pr1i3i4+M5deoUbm5uuZ53YmwCCnOl/rUkk9CoMzdDuWn0cqY2GUHXmQMxUebOZa/8Eo/XlNHsDdrG9h1rsUo32LS0sODli8wPhjp3bsO6dVuynP+oCd/j//MqfvBbgIWluf59cwszYtINxgFiY+IwtzAzSK9UpTyfNa9P49qt+KxWSwoWsuWLVs30+4wbOoXmddsxfYEnyix0/gmx8SjTnR+ZTKY/PwmxCSgs0o6lMFcSFx3Hl/1acuFYKGMaD2XiF+4MnD8M49RL2M8ePMHjs6Ec3ryfbpN7ZyqG+Jg4zAxiSKsj8bHxKC3SPmyU5kpio2Pfuk/jlo0YNWsEE76bxMvnaVcNmrVtwm9b9ma6bNJLiUnA2KAey9Cmr8eShKtnF0o0cOJQ/7S1diZWZlg7lODRn1eylW96urJ4/bmKi4nDzDxdOVko9QPlQsULMWv7LA4HHuboz0f129T/uj5HfzmarSsYGSXGJmBqnlZXMtPOH996RMgu3ZfMJ7ceER8Vg1UR2xzHkhybgLHF28+Vy+QulGjoxOGM56p8CSJy4Vylp4pJwChdO0ImGcYDRO45y+Eag5GM5ZTq2BC7Lp9RqKETboFeWFUrQ41lgzEpbJ2rceWlhLfU5fiYeIO2nb4uAyx0X8iAzwYwbPZwTJWmLPJYSIchHZmy3puXz14Q/TxrX24z9oHp625CbDyKdHEqzdNiMbMyp4RDKS6fvGhwvKcPnuD+2WAObg6iRyb7wIzxKCxeH098jGGsCgslcdGxPLz1gGOBRwF4eOshMVEx2BYpkOW88wMN2lz/96H9Zwe/r5Y9BAQEEBgYyLBhw0hM1F3KatKkCYcOHWL37t20atUq1/O+ERxO1ca6y+hlnSvwMPzuO/dxbduAFoPbAJCckIxWq82VD8T8FI/P1Pl8+UVnypWtg719GWxtrTE2NubT+q6cPnMu08dxdnbi1KmQLOe/cOZKurcZSL0qn1OmnB3WNlYYG8txqVeL0LOGszshZ/7is2b1AWjY9FOCT4USEx1LUmIiSYmJaDQanj2NwtraktYdvmLgCF0HmxivS1Nn8ssFwNXgMGo0rgWAg3NF7oXf0ac9vH6fYmWLY25tgZGxnMpuVbgeEk7cyzj9bH7ci1iM5HJkMhnuaydQtGxxQDdw1moy1+lcDL6EWxPdF0HHWo7cDLulT7tz7S6lypXE0sYSubGcGm5OXA65/MZ9mrVrSpterRnVwYNHd9NuWjG3NMPYxIQnj56QHZHBVynVpAYAhWs58DzsnkF6/dl9MFIYc7DvQv0ldYBibpV5+Lvhh2N2XT57GZcmuiU6lZ0rcytdOd27fo8S5UpgYWOB3FhONddqXDl3BZtCNvhu9mX9jPXsD9hvcDzn+s6cPXI2V2K7HRyOY2o7L+Ncnkfh996xB7h1bEwrzx4AWBWxxdRSSXQOl18APD5reK6irhjG8snsPhiZGnOoj+G5KupWmYcncudcpRd1JpwiTXVlY1O7PDHp4pFbKHHb5YXMRA5aLer4JLQaLafaTOV0Wx9Ot/Mh+uIdzg9dQXIuLv/Ka5eDL1Onsa4uV3KuxO2w2/o0fV22Tq3LbtUICwmjcbvGdBjSAYCkBF1fp9FocGniwuIxi5ja2xtLGyv+OhGapViuBodRs7Hu3onyGfrAB//oA6tyLSQcAEfXKlz8/bzBsTzWTqRYuj5Qk8k+ML0rwVeo3Vh330tF50rcDUuL5/71exRPVzZV3aoSHhJG047N6TW5LwC2RQugtDAj6nHWlsAJuec/ecNbRoUKFTJ43bJlS3x9fZEkidKlS+d6fuf3ncGxQXU8dk4DScJvzArqtPoUU3MFf2x9/SXxv4LO0GPuYEYFeGNkLOcnnw2o0q2V/JjiUalUjB8/nV/+twmZTMamTdt59DASW1trlq+YTdcug964b6FCBYiNjXtjembznzl5Aeu2L0Mmk/HTll+IjHiCtY0VMxZOZkjvMaxY8CNzlk2lY482RD17gfugSSTEJ7JtYyBbf/2RlBQVd2/fJ3DbbuTGcmYt8WbL/9Ygl8vx9ZxPclLmb5AJDjpNtfo18AqcgSRJrPZYRr3WDVCYKTiy9QCbp21gnJ8Xkkzi2PZDREU+Z+/a3QyYO4TJO6ZjZCxn+9zNJCUksXtlIAPnD0OVkkJyQjJrx63IVAwn9v5B7Qa1WfrzIiRJYrb7PJq2aYzSXMmvm/ewYuoPzPGfiUwmsTdgH08jnr12H5lMxjCfwTx+8ASfNbob5s6fusCG+ZsoZV+KiHsR74jkzW7vDaZEg2p887MXkiRx3H019m3qYWyu4On5W1Ts3IiIM+F8tV13w8mlH/dxJygYa4fixNx5nO180/sz6E+cGzgzf9d8JEliwegFfNbmM5RmSvZu2csanzX4+uv6lv3b9/Ms4hkDvQdiYW1BlxFd6DKiCwCTe04mOTFZVyZ3s18m6f297ywVGzgxbKcPkgTbxvxArVafYmKu4NQb2vnpgMN0mTeYoTu8QQsBY1Zl+qrQ29zZG0yJhtX4+hcvkCR+H6U7V3JzBc/O36Jil0ZEng7ni9RzdfnHfdx9da7u5s65Si9iz1kKNXKi3q8+IMGFET9Qot2nGJkruOd3iIeBf1D3F280KSpiLt/lwU+vX3L1MTkZdBLnBs7MDZyHJMEij0U0at0IhbmSfVuCWDttLT7+05DJZBwI2M+zyGf8ufdPRs4fxawds5HLjVgzdQ0pSSk8vP0Q7w1TSUpI4sLJCwQfydrNrGeDTuFUvwZTA2eBBKs8lvJJ64YozBQc3rof/2nrmeA3BUkm4+j2g0RF6gaVxR1K8jjDU4h+WbmTQfOHo0pRkZyQxOpxWb+/4HTQSWo2qMnMwDlIksRSj8U0aN0IhbmCA1v2sX7aWrz8fZDJJA4FHOB55HMOBRxg2PyRzNg5G61Wy7Ixi3OlLeWFj+FHLiRtVp438pFI/7QHmUxGXFwcXbt25cyZM3z11Vc0bNiQdu3a8e2339KtWzdGjRpF586dM/W0h8FlO37Av+TfZePjM3kdgoES5gXfvdEH5GZWJq9DMPBAHfPujT6Q7hTN6xAM/CT9cw18XnI0yj+X251S8tecSlGVKq9DMND80oy8DkGvba1heR2CAUvJOK9DMJCoVb97ow9o193deR0CAF3KtMn1Y26983OuH/Nt8lcv9YG4ublx8uTJf7zfrl07/f8HBqbdULFw4UL9/+f0MWeCIAiCIAj/Vv/O+WpD/8nBryAIgiAIgpB1eXGDWm77z97wJgiCIAiCIPz3iJlfQRAEQRAEIVM+hhvexMyvIAiCIAiC8J8hZn4FQRAEQRCETBE3vAmCIAiCIAj/GR/DE3LFsgdBEARBEAThP0PM/AqCIAiCIAiZIh51JgiCIAiCIAj/ImLmN5f9L/pyXodgoJSiUF6HoPdV4Rp5HYIBOVJeh2DgesrzvA4h3zppkpjXIRhQq/PXzMcTbXJeh6B3IX/9Qi357BdzWZHPflJ417mleR2C3ne1R+d1CAZeaPJXv5NfiBveBEEQBEH4V8pPA1/h30M851cQBEEQBEEQ/kXEzK8gCIIgCIKQKeKGN0EQBEEQBEH4FxEzv4IgCIIgCEKmiB+5EARBEARBEIR/ETHzKwiCIAiCIGSKeNSZIAiCIAiC8J8hHnUmCIIgCIIgCP8iYuZXEARBEARByJSP4VFnYvCbR5q1aMTIMd+jVqsI2LyLLZt2vna7voO6U6RIIWb6LNK/N8V3LDeu3cZ/w/YcxVC/eT36jPoOtUrNr9v28MuW3wzSrQtY47PcE1OFKU8jnzJt1GySEpJo3qYJnfu1R63RcP3yTeZOWIgkSUyY50EZBzvUag3TR83mwZ2HmYpDkiT6TR9E2SplSUlK4Ydxy4i4E6FPr93UhfYjOqFRqzkccJBD2w4A0Gbwt7g0d0VuLGef314OBxxk5FIPbArbAFC4VBGuhV5l0bB5WSoXSZLoM30gpauURZWkYvW4ZUSmi6dWUxfajeiIWq3mWMAhDm87QMP2TWjUvgkAxqbGlKlSju9dehMfHQfAJ60b0qLXV0xpOz5LsbySm+fq1Z26tgVt2LBvNcM7e3Dn+t08i0dmJMNzwTiK2xXD2MSYDYv9OLH/z2yVkyRJdJveHzvHMqiSVWwct5LH6c4dgInCBHd/LzaMW0HEDV0dtShgxYSdvkz5wh1VUkq28n6V/zDfodhXsSclOYWFYxfy8PYjfXrdZm50G9kNtUrNvoB97N0ahJHciNHz3ClqVxRjE2O2LNnKqQOn9PsMmjKAezfu85v/nmzH9Sq276YPSK3XKawdt8KgbJyb1qFNar0+HnCIo9sOIjeR03/eUIrYFSMhNp6Nk9cQme7vyUks7af3oUTqeQoYt4qndyINtjFWmPC9/yS2jVvF49TzNPq3mSTGJADw7N5jto35IcexvIrn23TxbH9DPIP8JxGQLh73dPE8z2E8kiQx2Hcw5RzLkZKcwpKxS3h0J62sXZu50nlEFzQqNQe2H2Df1n3IZDKGzR5GSYdSaNQaFnksJOJOBA7VHBgyYwgpySncvHSL1d6r3usd+hcuhbFg5To2LJvz3vJ4RZIkek8fSJnUz4s145Zn6J/r0HZERzRqDUcDDnFk2wGM5EYMWjCcwqWKoFFrWDt+BQ9vPMhyviNnDMchtW3PHbOAh7fTPuPqNatLz5HdUavV7A0I4rcte9+4T4Vq5fHdMI0Ht3Qx/G/Tbo7sPqbPZ+am6fyx7yS7/X/NhRITXiffDH4DAwO5efMmHh4eOT5WUlIS//vf/+jQoQOBgYEsWbIEOzs7AKKjo6lVqxZTpkx54/7h4eFER0fj4uKS41heRy6X4+07jq+bdiY+Pp5de/05EHSUJ4+f6bdRKEyZs2gqzrWd2LNbN9grUNCWxStnUM6hLDeurc9RDEZyI0Z4D6XPVwNJiE9k9S/LOHHgJM+fPNdv03dUT/bvOsRv24PoMbQrbbu3ZJff/xg4ti/dmvYhKSEJnxWTqd+8HpJMt4JmQOth1KpXkxHegxnb2zNTsbi0cMPE1JhJbcdRwbkiPT37MKf/DH2cvbz6Mr7laJISkpi2cxYhh85SwqEUlWpXxrPdeEyVprQc0AZAP9A1tzLHe9t0Nkz7MctlU6eFG8amJkxpO57yzhXp7tmb+f1n6uPp4dUHz5YeJCYkMXXnTEIOneX4T4c5/tNhAHpPG8DR7Yf0A98yVcrRuFMzJKQsx/Iqz9w8Vyf2/4mR3Ihxc3RlmtfxWNla8TIqmqnDZ2Bla8Wm/WuyPfh1/twVY1NjZrabhL1zBTp4fsfy/rP16WWcHOjhOwDb4gX071VtWINvx3XHqpB1tvJM75MWn2CiMGFkm1FUdq7MgMkD8O47FdCV28ApAxn2zXAS4xNZuGsBpw6exqVxHaKjopkzci6WNpasDFrOqQOnsC5gzdhFHpS0L8m9Gz/lOLbaLVwxMTXGp+0EHJwr0tWzF4v6z9LH1s2rN14tx5KUkITXzhmEHgrG5at6JMUlMrXteIrZl6CnTz/m9pyW41iqfV4HuakJi9t5Uca5PK08e7Cuf9qXVDsnezr49sW6eEH9e3JTYwCWd/bJcf5vimfJG+Ip9ZZ4VuRSPHVb1MPY1ASPth5Ucq5E38n9mN5PV9ZGciP6efVnVMtRJMUnMidwLqcPnqGycyUAxrYbg1NdJ/pN7s/0ftMYOmsYq6asIizkCt09etCozWcc3XUkV+LMaN3mHewOOoxSYfpejp+Rrn821vfP3Tx7syBd/9zdqw+TW44hMSEJ750zOHfoLOVrVsTIyAjvdhOoVr8GHcd0Y9GgrA3U63/xKSamJgxtPQLHWo4MnjwQz75T9PkO8R7EoK+HkhifyNJdi/jzwCmq1an62n0qOFVgx+qd7Fj9z3bdd2xvrGwsc15Q71FePOpMo9Hg7e1NeHg4JiYmTJ8+nTJlyujTDx8+zPLly5HL5Xz77bd07Njxrcf7KNf8PnnyhB07duhff/PNN/j5+eHn58euXbu4cuUKf//99xv3379/P9evX39v8VWoaM/tW3d5+TKalBQVZ0+fw7VebYNtTE1N+SngfyxZsFr/nrm5GQtmryBw++4cx1CuQhnu335AzMtYVCkqzp/5m5puTgbb1HB14uSRMwCcPHwalwa1SU5KYUCrofpBk5GREUlJyRwP+p1ZY+YDUKxUUZ4/icp0LI4uVQg9FgrAtdCrOFQvr08rWb4UEbcfERcdhypFRdjZK1R2qULNhs7cDbvDmNUTGLfOk5BDwQbH7Ojelb0bf+PF48zH8UolF0fOHzsHwPXQq9hniCcyNR51iorw1HhesXdyoFSF0hzeuh8ACxtLuozrwaapWR+Ev5Lb5wpguNf37Nr0P55GPiOrcjuew7uPsXpOWvmoVeosx/RKeZfKXDz2FwA3Q69R1sneIN3YRM7ygXP0M74AWo2W+d2mEvcyNtv5vlLNtSrBR3V1MSw0jIrVK+jTSpcvzcPbD4lNLbdLZy9SzbUax389wcZ5m/Tbvfr7leYK/Bb4c2jn4RzHBVDRxZELqe3sRuhVylV30KeVKF+KyNsRxKfW66tnr1DJxZGSFew4f1S3T8TNh5QoXypXYrF3qUxY6nm6E3oduwznSW4iZ93ABfoZVoASjmUwUZgyaNNEBm/xpIxzeXJLuUzEs/4N8QzcNJHvcyGeqi5VOHc0BIDw0HAqpOt37Mrb8ej2I+JS687ls5ep6lqVU/tPsXT8UkB3pevF0xcAFCpWiLCQKwBcCb5M1XR9VG6zK1GcRTMyN9GRGyqlq8e6/jljPf5n//zo1kNkciMkSUJpqUSdospyvk4uVTlz9CwAV85doWKNivq0MhVK8yBd2/777EWquzq9cZ+KThWo29SVRT/NZ8w8d5TmSgAaft0AjUbD6SNns1c4H4gGba7/e5eDBw+SnJxMQEAAo0ePZtasWfq0lJQUZs6cybp16/Dz8yMgIIAnT5689Xj5ZuYX4Pz58/Tp04fnz5/TpUsXypUrx8KFCzEyMsLOzg4fHx+SkpKYNGkSMTExREVF0aFDB7p27UqPHj2wtbUlOjqaUqVKcf36dZYtW0aJEiUM8oiLiyMmJgZLS0tiY2P/caymTZuya9cujI2NqVq1KomJif+IwdjYOEd/p4WlOdHRaR+0cbFxWFkZftN7+TKa40f+pEOX1vr37t19wL27D2jcrEGO8gcwtzQnLiYthvi4eCysLAy2MbNI2yY+Vpeu1Wp5/lQ3oOzQpy1m5krOHNN92KvVaiYvGs9nXzZgwoA3z6xnpLQwIz4mTv9ao9YgM5KhUWswszAjPiZen5YYl4CZlTmWBawoXLIws/pMp4hdUcatncSIJoMBsCpojdOn1dnok70BpzJDnunjyZiWEJeAmZWZ/nXroe3ZuXgbAJJMxoA5Q9k0bR3JicnZigVy/1x93fELXjx7yeljZ/luWLc8j0e/j7mSmaunsmpO9r8oKC2UJLzh3AFcDwn/xz6Xf7+Q7fwyMrMwIy76DXXZ0oy4dPU8PjYBc0tzEuMTdbGbK5m8ypMN8zYCEHEvkoh7kbg0zp0rUG+v18p/1GullTl3L93CuWltQvadxsG5IgWKFUCSydBqcvawI4WFkoTU5QIA2gzn6VbI1X/sk5KQxJE1v3Jq22EKlyvOgA3jmdlklH6fnMaTmC6ejPXm9hviOZounv4bxjMrB/EoLQzrh/otdSchNgFzSzN9rKMWjKJei0+YOUh3xSzibgTV3Kpx8fRFXJu5YWqmyFZMmdG8cX0ePIp894a5JGNdffvnRSJKKzMS4xIoXKow8w4vw7KAJXP7+GY5XzNLc4NzkDHf9O0+IS4BcyvzN+4T9lc4e7bu5erf1+g2rCvfjepB0I79NG3TBO8BPvQc1T3L8X3sQkJCaNBAN/apWbMmFy9e1KfduHGD0qVLY22tu3pXu3ZtgoOD+fLLL994vHw1+JXL5fz44488ePCA/v37o9Fo2LJlCwULFmTRokXs2rWLqlWr8vXXX/P5558TGRlJjx496Nq1KwAtW7akefPm3L9/n6tXrzJ06FACAwP59ddf+euvv3jy5Anm5uYMGjSIsmXLcunSpdceq23bthQqVAgnJye++OKLf8Twrun0NxkzcRiudWvhWLUioSFpH7jmFuZEv4zOlTJ8l4Fj+1LD1QkHR3suh17Rv29mbkZMhpmv+Ng4zMzNSEpMxswiLV2SJIZOHkRp+1KM7+9lsM+0kbNY7ruaH39bQZdGvUhMSHxnTAmx8fpvvgCSTNJ/gMTHxqOwSOu4FeZK4qPjiI2K4cGN+6hSVDy8+YDkpGSsCloT/ewldb/6hN9/OY4mmx/Qb4snITYehUVamtJcqe/0zKzMKeFQissndY3S3smB4uWK03f6IIxNjSlZwY6eXn3ZlMlB+fs6V990/hKtVotLg9pUqFoer8UTGNNrksGyhQ8ZD0CREoWZ/eN0dm78mf27DmWqfF4nITYBhXlafZFkslwZHGVWfGw8Sos31OWYeMzS1SszCyVxqV+CCxcvxJS1Xuze9CtHfj76XmJLiI1HkS5/WbqySYhNQJmunSlT21nIvtOUKF+Kidt8uBoSxq2/b+Z44AuQ+I/zJL3zPD2+9Yint3VrO5/cekRcVAxWRWx58SjrVy9eF49pDuOJz2E8CRnqTvrzk7HuKC2UBoOthe4LWV94Awt+WcD3TQexyGMhA7wH8u2g9ly7cJWU5OyvY89v/tnGDT8v0pehwlxBfHQcX/VrxYVjfxEwx58CxQviudWHcS1GkpKF9f3xMXEG50D2j3zTJkGU5kpio2PfuM+JoN/15+/3oN8ZPm0oWq2GwsUKsmD7XIqVKkpKioqI+xGcPWp4VTM/eB+POgsICCAgIED/ulOnTnTq1En/OjY2FguLtEkWIyMjVCoVcrmc2NhYLC3TJhDNzc2JjX37lbx8teyhSpUqSJJE4cKFefjwIY8fP2bkyJH06NGDP/74g4cPH1KoUCEOHjyIh4cHK1euRKVKu3xRrly51x73m2++wd/fn7Vr1xIXF0fZsmUB3nosgOfPn782huyaO2MpHVr1pmalRpQtVxobGyuMjeW41atNyNnz2T5uVqya8yOD24/kqxptKVW2JFY2lsiN5TjXrc7FkEsG2144e5FPmtYFoF4TN86f0Q3Yx88ZjYmpCWN7e+ovYX/xbXN6DtV9CUlMSESr0WZ68BkWfIVajXXLPio4V+Ru+B192oPr9yletgQW1hbIjeVUcavC1ZAwrpy9jHOjWgDYFimAwkxBbFQMANXr1yA09fJhdlwNDqNmajzlnStyL0M8xcoWx9zaAiNjOZXdqnItdTbR0bUKF39PO483zl9jTPPhTOvsyZJh83lw7V6mB77w/s7V9+1GMPjbkQxuP5Jrl67jM2LmOwe+7zOeAoVsWbJ1Hst9V/Hrtr2ZLp/XuR4chlNjXb2wd67Ag/Cs3ciXU5fOXsK1iSsAlZ0rczvstj7t7vW7lCxXEksbXV12cnXi8rkr2BSyYebmGaydsY59AfvfW2y6eq0rG4cM9frh9fsUTVevK7lV4XpIOPY1yhMefIUZnb0ICTrNk7u5M8N3Kzgcx8bOAJRxLs+j8Hvv3MetY2NaefYAwKqILQpLJdHZWNb0OrdzIR7THMZzOfgydVJn+Ss5VzKoO/eu36NEubR+sJpbNcJCwmjcrjEdhnQAICkhEY1Gg0ajwaWJC4vHLGJqb28sbaz460RotuPKb8KDr2Ton9Pa+MMM/bNjav8c9zJWPyMc9yIWI7kcmSxrw5+LwZdwa+IGgGMtR26G3dKn3bl2l1LlSmKZ2ifWcHPicsjlN+4zd/NMKtfUrdeuVd+Zq39fZZXvWga3HM6oDh4E7djPjtU78+XA933p1KkTgYGB+n/pB74AFhYWxMWlm0XXaJDL5a9Ni4uLMxgMv06+mvmVpLQbgmxtbVEqlaxYsQJLS0sOHTqEmZkZ69ato2bNmnTt2pVTp05x7Nixf+wvk8leO/Cys7NjypQpjBgxgt9+++2Nx5IkCY1Gg62tLcWKFftHDDmlUqmY6jkH/59WI5NJBGzeRcSjx9jYWDF3sQ/9vxuZ4zzeRa1Ss3jqchZtmYtMJrF7216eRDzFysaSifPGML6fF+sX+TF58QRad/uaF89f4jV4OpWcKtCyy1f8dfoCy3csBCBg7U8c3XMCz4XjWBm4GLlczsIpy0hOytyl/jNBp6hevybTA2cjSbDcYwn1WzdEYabg4Nb9bJy2jkl+3shkEoe3H+J55HOeRz6niltVZv5vHjKZxNrJq/TnvIR9SSJz8CF9NugUTvVrMDVwFkiwymMpn6TGc3jrfvynrWeC3xQkmYyj2w8SFakbOBZ3KMnjXBocpJfb5+pY0O/5Kp5an9TE0tqSPiN70mdkTwBGdR9LUjaWioTuO0OVBjUYv9MXSYL1Y5bj2qo+CnMFx7cezNHfnRl/BP1JrQa1WLhrAZIkMX/0fBq3+QylmZI9W/ayymc1M/xnIJMkgrbv51nEM773HoSFtQXdRnSl2wjdF8hJPT1ztFTmdUKCTlOtfg28AmeAJLHGYxn1WjdAYabgyNYDbJm2gbF+XkgyiePbDxEV+ZyU5BS+Hd2Frwa0Jj46jrVjludKLH/vO0ulBk4M3+mDJMHWMT9Qq9WnmJorOLn19TP/pwMO02XeYIbt8AYtbBuzKtdm9f/ed5aKDZwYlhrPttR4TMwVnHpHPENT4wnIYTwng07i3MCZuYHzkCRY5LGIRq0boTBXsm9LEGunrcXHfxoymYwDAft5FvmMP/f+ycj5o5i1YzZyuRFrpq4hJSmFh7cf4r1hKkkJSVw4eYHgIx/PICo46DRO9WviHTgTSZJS++cGqf3zAfynrWe8nxcymYyjqfV4z9rdDJw7FK8dvsiN5QTM9c/yzb4n9v5B7Qa1WfrzIiRJYrb7PJq2aYzSXMmvm/ewYuoPzPGfiUwmsTdgH08jnr12H4CFE5YwfPpQVCkqnj9+zvxxi95DSb0/mjy44a1WrVocOXKEr776ir/++ouKFdPWXDs4OHDnzh1evHiBmZkZwcHB9O3b963Hk7R5cdvea6R/2kNSUhJffvklPj4+LF++HK1Wi7m5OXPmzOHatWt4e3tja2uLjY0N165dY8+ePfTt2xdvb28cHBxISkqiY8eO1K9fHwcHh388RcLT0xNLS0saNWr02mP9+eefzJkzBy8vL1Qq1T9iKFiw4Bv/jlIFqn2I4sq0UopCeR2Cnp1xzu+mz03ybD594X25lfIir0PIt6qZ5J96DHBXnfOb43JTUVnOv5TnlkKSSV6HYCB/tXK4qonJ6xD0dp1bmtchGPiu9ui8DsHAo3zWzo/cP5DXIQDQoGTTXD/miQdvX+726mkPV69eRavVMmPGDC5fvkx8fDydOnXSP+1Bq9Xy7bff0q3b2+9nyTeD34+FGPy+mRj8vp0Y/L6ZGPy+nRj8vln+auVi8Ps2YvD7dv/lwW9uy1fLHgRBEARBEIT862P4hbd8dcObIAiCIAiCILxPYuZXEARBEARByJSPYeZXDH4FQRAEQRCETPkYbhUTyx4EQRAEQRCE/wwx8ysIgiAIgiBkysew7EHM/AqCIAiCIAj/GWLmVxAEQRAEQcgUrZj5FQRBEARBEIR/DzHzm8vKKYvmdQgGysrzz6+qnUt8kNchGFBrNXkdgoHCxlZ5HUK+9UKbnNchGChlZJHXIRh4pI7L6xDS5LMplWfaxLwOwYB1PvoFvJ613ZHy0W/gbQyZn9chGPjS+fu8DiFf+hie9iAGv4IgCILwH5SfBr7Cv4e44U0QBEEQBEEQ/kXEzK8gCIIgCIKQKR/Dsgcx8ysIgiAIgiD8Z4iZX0EQBEEQBCFTPoY1v2LwKwiCIAiCIGSKeM6vIAiCIAiCIPyLiJlfQRAEQRAEIVM04oY3QRAEQRAEQfj3EDO/giAIgiAIQqZ8DGt+xeA3j3zSvB69RnZHrdawZ9tedm/ZY5BubWuF1/JJmCpMeRr5lJmj5pKUmASAqcKUhdvmMGv0PO7euJet/CVJ4rvpAyhdpSyqpBTWjlvB4zsR+nTnpnVoM6IjarWa4wGHOLrtIEZyIwYsGEbhUkXQqDX8OH4lj248oEw1e3r7DkSVnMKdy7fx9/4xW88B/Ozz+nzv3heVWs2urbv5yf8Xg3SbAtbMWTkNhcKUx5FP8BwxjcSEJKrVdGTs1JFIksTTx88YN2QKyUnJ9Bv+HY1bNMDY2JhtG34icMvuTMfS+PMGDB7dD7Vaxc4tu9nh//M/Ypn/w3RMFaY8jnjKxBFTsbC0YMEqX/02latVZP70ZQRsDCTwkD+x0bEA3L/7kIkjfLJcPrldZ2wKWLPif0vo1bQfyUkp7y3/1+1nJDdi0qJxFLMrhkatYc6Y+dy9cQ/vFZ4UKGwLQDG7Ylw+dwXvwdPfGJMkSfSfPogyVcqhSkph5bhlRNx5pE+v3dSFDiM6o1arORJwkIPb9gPQdnB76jR3RW4sZ5/fXg4HHNDv02tyXx7efMD+zUHvLJPMkiSJ7tP7Y+dYBlWyig3jVhq0NwAThQmj/b1YP24FETce5lq+Q3yHYF/FnpTkFBaNXcSj22nl49bMja4ju6JWqdkfsJ+grUEYyY0YNW8URe2KYmxizNYlWzl94DT2VewZNnMYapWaB7cesGjMomw/7zM7/Y/cRE7/eUMpYleMhNh4Nk5eQ2S6vyW7ZVPOsRwpySksHruYR+nqjmszV7qOSC2b7fvZt3UfMpmM4bOHU8qhFBq1hgUeC4i4E4FdBTuGzxqOJEncvHyTH7x+QKPJ2s+nS5JEn+kDU8tExepxy4hMVya1mrrQLrVMjgUc4vC2AzRs34RG7ZsAYGxqTJkq5fjepTe2RW3pN3MwkiRx98pt1nutQZvFeNLH1Xv6QMpUKUtKUgprxi3PEFcd2o7oiEat4WjAIY5sO4CR3IhBC4brPyvWjl/Bwxsf7uftL1wKY8HKdWxYNue9HF+SJIb7DsOhiq7uzB+7iIe309ps3WZu9BjZDbVKTVDAfvZs3atPq1yzEv0n9mV0x7EA2BS0xn3OKCysLZDJZMweNdegHuZHYtlDPtCjRw9u3LiR12FkiZHciGFTvse96ziGfTuKlt2+1n/gv9JrVA8O/nyYoe1Gcu3idVr3+AaAStUrsixwISXKlMhRDLVbuGJiaoxP2wkEzPanq2cvg/i6efVmdvep+HacTOOun2Nd2IYajWthZGSET7uJ/Lx4Bx3GdAWgz8xBbPZZx/QOniTExFGvTYMsxyOXGzHOZyT9Ow2nV5tBdOjehkKFCxhs8/3ovuwJ3EfP1gMJ+/sqHXu2BWDq/Il4jphGj1YD+P3ISUqUKobLJ7VwdnGi+zf96dVmEMVKFM1SLOOnjaJvx6H0aD2Qjj3aUqhIQYNthozux68799G91QCu/B1Op57tePr4GT3bDqJn20Es8F3O5Qth7PD7GRNTEwB9WnYGvrldZ1wb1WH+1tkUKGR4jNzO/0371WvihpHciMGth7NhoR/9x/UBwHvwdIZ3GM3EflOIjY5lqfeKt8bl2qIuxqYmTGo7Fv/Zm/jOs49BzL28+jGtuxdTOk6kWdcW2BS2oWrdalSqXRnPduOY0nEihYoXAsCqgBWTNk6hTnPXTJVJVjh/7oqxqTEz2k3ip9n+dPL8ziC9rJMD47dPo0iZzNfTzKjXoh4mChPc27izfuZ6+k/ur08zkhsxYMoAJnWbxNgOY/my25fYFralSbsmxETFMObbMUzuMZnB0wYD0G1UN7Ys2oLHtx4Ymxjj2jT75ZSd/uezLs1JiktkatvxbJqylp4+/bKdP+jKxtjUmNFtR7N+1nr6TU47npHciAFeA/Ds7sm4juP4squubNyauQHg0c4Dv/l++vLsNbYXG2dvxKOdB6ZKU9yau2U5njot3DA2NWFK2/Fsnb2J7p69DeLp4dWHmd298enoSZPUMjn+02GmdfZkWmdPbl28wUbvtcRHx9FpTA8C5vjj/e0ETBSm1G7uku1y0sVlzJS249k2249uGeLq7tWHWd2npsbVHOvCNtRsXBsjIyO8200gcPF2Oo7plu38s2rd5h1MmbWY5KTk95bHpy0+wURhzPA2o1g7cx2DJg/QpxnJjfh+yiDGdZuIe4cxfJ3argA6DurA6Lmj9J8JAP0n9ePQrsO4t/dg/dyNlHawe29xC2n+9YPff6OyFcrw4PYDYl/GokpR8ffZi1R3czLYprqrE6ePnAHg1JEz1GlQCwATE2Mm9ZvC3et3cxRDRRdHLhwLBeBG6FXKVXfQp5UoX4rI2xHER8ehTlFx9ewVKrk4EnHrITK5EZIkobBUok5RA1CgWEGuhYQDcDU4jIp1HLMcj33Fcty9dZ/olzGkpKg4d+Y8terWNNimlmsNfj98EoATh05St4ErZR1K8yLqJT0GdGbDrpVY21hx+8ZdPm1cl6tXbrBkwxyW+8/n2IHfsx1LyOm/qO2WIRa3mpw4oovl+OE/qdfQcCDgOcODqWNno9FoqFy1Akqlgh+3L2XDzhXUqF0ty+WT23VGo9EyqvNYol/EvNf837TfvZv3MTLS1SVzSzPUKrXBsfqO/o6d637m2ePnb42rsosjfx07B8C10HDsq5fXp5Uqb0fE7UfERcehSlERdvYyji5VqdGwFnfD7jB29UTGr/Mk5NBZABTmSrYv3MrxwKOZKpOsqOBSmYvH/gLgZug1yjrZG6TLTeQsHTiHR7k04/tKVdeqhBwNASAsNIwK1Svo0+zK2/Hw9kP9ubl09hJVXaty4tcTbJq3Sb/dq3Nz4+INLGwsAFBaKFGpVNmOKzv9T8kKdpw/qtsn4uZDSpQvle38Aaq6pJVNeGh4psrm5P6TLBm/BIAipYrw4ukLAHwH+nLxzEXkxnJsC9vq38+KSi6OnE+ty9dDrxrU5ZLlSxGZWpfVKSrCz16hsksVfbq9kwOlKpTm8FbdlY2Fg2YTduYyRsZybArb8DIb8aSP69W50sWV8Vz9M65H6T4rlJZK1CnZrytZZVeiOItmeL7XPKq5VuXs0WAAroSGUTFd3SldvrRB3bl49hJOrro+/9GdR3j3N5z8qFqnKoWKF2LOllk0bduY8yfPv9fYc4P2Pfz3of2rlj0kJiYyduxYHj9+TPHixTl79izlypUDYOnSpRQqVIguXbpw48YNvL298fPz48iRIyxbtgyAKlWqMHXqVE6ePMmiRYswNTXFxsaGGTNmoFKpGDlyJFqtlpSUFKZOnUqlSpXw8/Pj119/RZIkvvrqK3r27Jnjv8PMwozYmDj96/jYBCwsLd64TXxsAuaW5gD8HXwpx/kDKC3MiI+J17/WqDXIjGRo1BqUFkqDtIS4BJRW5iTGJVK4VBFmH16KZQFLFvSZAcCTe5FUdqtC2OnLODdzwdTMNMvxWFiYExMTq38dFxuPpZVhmVhYmhOTWiZxcXFYWllgW8CGmnWcmDFxPndu3mWF/wIuXQjDtoA1xUsVZ0h3d0qWLsGyTfP45tOOmYvF0pyY6HSxxL0hltRtMsbauEVDroff5NaNOwAkJiSyboU/O/x/pqx9aVZvW8yX9dqjVhsO+N4mt+tM8ImQTOedk/zftF9CfALF7Yqx+fgGrG2tGNdrkn4bm4I21K5fi6XeK98Zl64epx3/n/U4LS0hLgEzKzOsClhSuGQRZvaZRhG7ooxbO4kRTQbz+F4kj+9F4vxZ7SyVTWYoLZQkvKG9AVxP/fKY28wszIiLfn35mFuaE5e+fFLPWWJ8oi5mcyWTVk3SD4Qf3H7AkOlD6DK8C3ExcVw4eSHbcWWn/7l76RbOTWsTsu80Ds4VKVCsAJJMlu3L+WZvicHM0jAtIV170qg1uC9w55MWn+A7SLfMSaPRUKRkEXy3+BIfE8/9G/ezHM/by8TsH2ViZmWmf916aHt2Lt6mf63VaChUsjATN08lISY+R1+qMp4Pg3LKEFdiXCJKKzMS4xIoXKow8w4vw7KAJXP7+L7u0O9F88b1efAo8r3m8fZ2ZWbQrtL3xSf2/k7RUoZXd4qVKkrsy1jGdh1P9xHd6DS4Exvnb0J4v/5Vg9+AgABKlSrFkiVLuHHjBt98841+8Ps6KpWKadOmsWPHDgoWLMiyZct49OgRkydPZuvWrRQtWpSNGzeycuVK3NzcsLS0ZP78+Vy/fp3Y2FiuX7/Onj172LJlC5Ik0atXL+rXr4+9vf0b83ybfmN7U92lGg6O9lwODdO/b2ah1K8HfSU+Nh4zczOSE5NT0+MyHi5HEmLjUZgr9a9lsrQP4oTYBJQWCn2a0lxJfHQcX/Rryd/HQtk+ZzMFihdkwtapTGwxitUey+gxpS9fD2rLzfPXUWVi/egrw8cPxNm1BpWqlOfCubRBmrmFGTEvDcskNiYOcwszkhKTMDc3Jzo6hhdRL7l7+z43rt4C4PcjJ6lavTIvol5y89odUlJU3L5xl+SkZAoUsuX506g3xjJi/CBqu9WkYsZYzM2IeWk4Q2oQi4Vheqv2X7BpTdoH0a0bd7lzS/dhePvmXV48f0nhooWIePjuDjqv60xO83/1Xsb9OvZvz5mjZ1k160eKlCjMou3z9GuPP/umIQd+PpSpNZP/rMdShnqclqY0VxIXHUdMVAwPbjxAlaLi4c0HpCSlYFXQmuhnL7NdTu+OMwGFeVqbktK1t/cpPjbeoAzSt/O4mDiDc6O0UOo/0AsVL8TktZP5bdNvHP35KACDvAfh8a0Hd6/e5ZvvvqH/5P6s8Hz7spQ3yU7/E7LvNCXKl2LiNh+uhoRx6++b2R74wtvLJj4mHmW6+JQZ6vsC9wWsL7yehb8sZGDTgSQlJPH4wWP6N+pPi84t6O/VnwXuC7IUT0KsYZ6SQV2OR/GaugxgZmVOCYdSXD550eB4Tx88wf2zwTTu3Iwek3uzcvSSLMWTFlfGupsWV8YyVJgriI+O46t+rbhw7C8C5vhToHhBPLf6MK7FSFKy8NmQn8XHxmNmkdZ20pdJXIa687q+Mr3oqGhO7tddRTx18BS9x/Z6P0HnIrHm9wO7ceMGtWrpLuU6ODhQoECBt24fFRWFlZUVBQvq1msOHToUpVKJhYUFRYvqvn25uLhw7do1GjZsiIuLC4MHD2bJkiXIZDKuXr3Kw4cP6dWrF9999x0vXrzg7t3sLzdYO2c9wzuMplXN9pQsVwJLG0vkxnJquFXnYshlg23/PnuRek1168bqNnbl/Om/s53v61wNDqNm49SydK7IvfA7+rSH1+9TtGxxzK0tMDKWU8mtCtdDwol7Gaf/lh/3IhYjuRyZTEbNJrVZM2YZ83v7YmFrycXfM3/ZZsmsVfRuN5iG1b6kdDk7rG2sMDaWU7uuM38FG/7NoWcv0LDpJwA0aFqPc6f+4v6dB5iZm1G6rO4SaC23mlwPv8W50+ep36QuAIWLFkJppuDF87cPbhbP+oGebQdRv2oLSpcrpY/FpZ4zoRljOXOeRk0/BaBhk08IPv2XPq1qDUdCz6TNin3btRXjpo4EoEjRQlhYmvMk8mmmyiev60xO87997Q6l7Ev+Y7+YlzH6GeHoqBjkcjkymREAderX4vThs5mKLyz4CrUa1wGggnMl7qarx/ev36N42RJYWFsgN5bj6FaVqyFhhJ29TM1GurpvW6QApmYKYqMyt/wju64Hh+GU2t7snSvwIDxny5Yy6/LZy7g00a33rOxcmVtht/Rp967fo0S5EljY6Mqnmms1rpy7gk0hG3w3+7J+xnr2B+zXbx/zIkbf/p9HPsfC2nDmPyuy0//Y1yhPePAVZnT2IiToNE/u5mx273LwZeqk1p1KzpW4HXZbn6Yvm9S6U82tGmEhYTRp14SOQ3RXkBITEtFoNGg0Grx+9KJEWd2a+oS4BLSarA8OdGWiu+pQPkOZPLh+n2LpyqSyW1X9UjNH1yr/6HM91k6kWNniunhiE9BkI55XwoOvZIgrre4+zBCXY2pccS9jX/tZ8bG4dPYyrqntytG5MrfS1Z271+9Sslxan+fk6sTlc1feeKyLZy/h2kS3bM7JzYk7V++8cVsh9/yrZn4rVqxIaGgozZo14+7du0RFpc3imZqa8uTJEwAuXdLN2hUsWJDo6GhevHiBjY0N06dPp2XLlsTGxvL48WOKFCnCmTNnKFu2LKdPn6ZIkSKsW7eO0NBQFixYwKRJkyhfvjxr165FkiQ2bNhAxYoVc/x3qFVqlk39gfmbZyGTyfhtWxBPI55iaWPJuLmj8ezvzcbFm5m0aCwtu37Fi+cv8RkyI8f5phcSdJpq9WvgFTgDJIk1Hsuo17oBCjMFR7YeYMu0DYz180KSSRzffoioyOcErd1N/7lD8NwxHbmxnB1zN5OUkETkrUd4bPAkOTGJyycvcv7IuSzHo1KpmTNlEau3LUaSydi1dTePI55gbWPF1AUTGdlnPKsWrmfGEi/ad29N1POXjP1+MikpKrxGTWfODz5ISIQG/83xg38AUKeuMwFB65FkMqaPn5vpu69VKjWzvRaxNmApMpnEznSxTFvoyfDeY1m5cB2zlnrToUcbop69wON73Roz24I2xMXGGxxv5+ZfmLlkCpt3r0Gr1TJp5LQsLXmAvK8z2c3/TfttX/0T4xeMYVngIoyN5aye9SOJCbrL7aUd7Hh4N3OXac8EnaJG/Zr4Bs4GSWK5x2Lqt26IwkzJwa372DDtRzz9piLJJI5sP8jzyOc8j3yOo1s1Zv1vPpJMYu3krN+Zn1Xn9p2hSoMaTNzpCxKsG7Mct1b1UZgrOLb14HvL98+gP3Fu4Mz8XfORJIkFoxfwWZvPUJop2btlL2t81uDr74skSezfvp9nEc8Y6D0QC2sLuozoQpcRXQCY3HMyi8cuZvzy8WjUGt3TEcYtznZc2el/UpJT+HZ0F74a0Jr46DjWjlmeK2UzL3AekiSx0GMhn7X+DIW5gqAtQayZtobp/tORZBIHAg7wLPIZf+z9A/f57szZMQcjuRGrp64mJSmFHSt24L7AnZTkFJISkrJVNmeDTuFUvwZTA2eBBKs8lvJJ64YozBQc3rof/2nrmeA3BUkm4+j2g0RF6tbDF3coyeMMXwR+WbmTQfOHo0pRkZyQxOpx2S+r4KDTONWviXfgTCRJSo2rQWpcB/Cftp7xfl7IZDKOpp6rPWt3M3DuULx2+CI3lhMw15+khKRsx5Df/B70B7Ua1GLxroVIEswdvYAmbRqjNFPw25a9/OCziln+vkiSjKDt+3gW8eyNx/ph2mpGzxlFyx7fEBcTx4xhsz7gX5I9H8OjziRtdp9VkwcSExMZP348T548oUSJEhw8eJBq1arh7e2NiYkJI0eOxMzMjGrVqnHx4kX8/Pw4duwYK1asQCaTUaVKFTw9PTl58iSLFy9GkiSsra2ZOVPXqEeNGkVCQgIymYwhQ4ZQv3591q5dy8GDB0lOTqZ69epMnjwZIyOjN8bYoGTTD1gi71ZWbp3XIeidS/xwj7rJDLX2/V92zorCxlZ5HUK+VVRuntchGLCSTN690Qf0SJ27y6JyopBM+e6NPqBn2sS8DsGAdT6qOxJSXodgYGPI/LwOwcCXzt/ndQgGDt7bl9chAOBQqFauH/PG06xPmuXEv2rwe+7cOeLj46lfvz63b9+mX79+HDz4/mZNskMMft9MDH7fTgx+30wMft9ODH7fTAx+30wMft9ODH5f72MY/P6rlj3Y2dnh7u7OsmXLUKlUeHl55XVIgiAIgiAI/xkfw7KHf9Xgt3Dhwvj5+eV1GIIgCIIgCMK/1L9q8CsIgiAIgiDkHW0+WzKYHWLwKwiCIAiCIGSK5iNY9vDxPHhPEARBEARBEN5BzPwKgiAIgiAImfIvekjYG4mZX0EQBEEQBOE/Q8z8CoIgCIIgCJnyMaz5FYNfQRAEQRAEIVM+hmUPYvCbyyobF8jrEAxU0pjmdQh6L0wL53UIBsyk/FX947WqvA7BgLGUf1ZFlZTM8joEAxdVz/M6BAOFjPJP+ZSWFHkdgoH8VI8BojRJeR2CgRea/PMLePntF9X2hq7M6xCE9yR/ffoLgiAIgvBB5KeBr/DvofkIZn7z11diQRAEQRAEQXiPxMyvIAiCIAiCkCnaj+CGNzHzKwiCIAiCIPxniJlfQRAEQRAEIVPE0x4EQRAEQRCE/4yP4Tm/YtmDIAiCIAiC8J8hZn4FQRAEQRCETPkYlj2ImV9BEARBEAThP0PM/AqCIAiCIAiZ8jH8yIUY/OYBSZLoNr0fpRzLokpOYeO4H3hyJ8JgGxOFCaP8J7Nx3EoibjwEwKKAFeN3Tsf7i9GoklJyMyCa+faiiGNp1Mkq9o1dy4s7kfrkyq3qUbtvCzRqDU/C7nFw0gZkRjJazOuPdanCGJnIObX0F24cOJfN7CW+9x1MOcdypCSnsHTsEh7deaRPd2nmSpcRnVGrNBzYfoD9W/fp06wLWrPwt0V4dZvM/Rv39e/38+rH/ZsPCPLfm614+k4fSJkqZUlJUrFq3DIi052fWk1daD+iI2q1miMBhzi87QCN2jehUfsmAJiYGlOmSjkGuvQmPjoOgJ6T+/Dw5gMObt732jw/VPnYVbBj6KyhIEncvnyLVV6r0Gg0WY5nwPRBlK1SjpSkFFaMW0ZEunjqNHWh44jOqNVqDgUc5OC2/QDM27NIXx6P70WybMwS3Jd6YFPYFoAipYpwNTScBcPmZbmMXsXVYXofSjiWQZWsYtu4VTxNV48BjBUmDPafxNZxq3ic2q4ALApa4bF7Jit6+Bq8n50YRswYhkMVe5KTU5g/ZiEPb6cdr16zunQf2Q2NWs3egH3s2ZJWPys7V6b/xL6M7jBG/96nX3xKo28aMGPorGzHk5/qcvq4Wk3vTTHHMqiSU9g1bg3PX3OuevtPIHDcGp7eeIhMbkT7+YOwKVUYrVrDrglreZqDc/W22LpP749daj3aMG4lj1/TP4/292L9uBX6/jk38h3o+z1lU9v58rFLDdtVMxc6juiCRqXm0PYDHNiqa1fz9ywiPiYegMh7kSzzWKzfp7dXPx7evM8+/6BMxzByxnAcqtiTkpzC3DEL/lF/e47sjlqtZm9AEL9t2fvGfSpUK4/vhmk8uPUAgP9t2s2R3cf0+czcNJ0/9p1kt/+vmYpruO8wHKroymb+2EUGcdVt5kaPkd1Qq9QEBexnz9Z07apmJV276jgWAJuC1rjPGYWFtQUymYzZo+Ya9Kfvw4VLYSxYuY4Ny+a813w+lI9h2UOuD35nzZrFpUuXePLkCYmJidjZ2WFra8uSJUsytW98fDw+Pj4AqNVqOnfuzNChQ2nUqFG24klKSmLRokWcP38eSZIwMzPDx8eH4sWL06NHD7y9vXFwcNBv++WXX3L48GHGjx/PpUuXsLGxQaVSYWtry4QJE7Czs8tWHOnV/NwFY1MTZrWbhL1zBTp69mR5/7RGUcbJnu6+A7AtXlD/XtWGNWg3rhtWhaxznH9GFVrURm5qzJa2Uynu7MBnk7vyc7+FAMhNjak/pj0bmk9AlZjM10uH4NDMGaWNBYlRsewd+QMKGwt67p2e7cFv3RZ1MTE1ZkxbDyo5V6LP5L749psOgJHciH5e/XBvOYqk+CTmBM7hzMHTvHjyAiO5EUNmDiU5MVl/LKsCVrgvdKeEfUnurwrMVjwuLdwwNjVhctvxVHCuSA/P3szrP1Mfz3defZjY0oPEhCSm7ZxJyKGzHPvpMMd+OgxAn2kDOLL9EPHRcVgWsGLIwhEUL1eSh6t25Xn59Bzbk02zN3HpzCVGzh+Ja3M3Tu07maV4XFvUxdjUhAltx1LRuRK9PPswq7+vPp7eXv0Y29KdpIQkZuycTfChM8SlDpy8Ok8yONarga65lTk+23xZP+3HbJURgNPndZCbmrConRdlnMvTxrMHa/unDaTtnOzp6NsXm3TtCkAmN6LTjH6kpCun7Pr0i08wMTVhWOuRONaqzKDJA/Dq6w3oyuZ774EM/noYifGJLN61kJMHThH1JIpO33eg2bfNSIxP+7nZIVO/p06jOty4dCPb8eS3uvyK4+d1kJsas6rdFOycy/OVZzf8+y/Qp5d0Kkdr375YFS+gf69S45rIjIxY/a03DvWr0dyjI1u/X5SjOF7H+XNXjE2NmZHaP3fy/I6l/Wfr08s6OdDTdwC26WLLDW6p7Wp82zFUdK5E78l9mNkvrV318erHmJbuJMUnMSNwDmcPprWryZ0mGhzLqoAVIxa6U8K+BD+vuv+PvN6k/hefYmJqwtDWI3Cs5cjgyQPx7DtFH8MQ70EM+nooifGJLN21iD8PnKJanaqv3aeCUwV2rN7JjtU//SOfvmN7Y2Vjmem4Pm3xCSYKY4a3GYWj82va1ZRBDPnmVbtawMmDunbVcVAHmn/b1KBd9Z/Uj0O7DnPs1+PUqFeD0g5273Xwu27zDnYHHUapMH1veQhZl+trfsePH4+fnx8DBgzgm2++wc/PL1MDXwB3d3fOnTvHn3/+CcCPP/5I9erVsz3wBfD19aVo0aJs2bKFzZs307FjR0aOHJmpfceMGYOfnx9bt26lT58+md7vXSq4OHLxWCgAN0OvUcbJwSBdbmLMioFzibjxQP+eRqNlQTcf4l7G5koM6ZV0qcStoxcAeBR6g6LVy+nTVMkqtrSdiip1YCCTy1AlJhP+22l+n5fWqWnUWZs9TK+KS1VCjuoGzuGh4VSoXkGfZlfejke3HxH3Mg5ViorLZy9T1bUqAH08+7LXfy/PI5/rt1eaK9mycAtHAo9kO55KLo6cP6aL51roVRyql9enlSxfiojbj4iLjkOdoiLs7BUcXaro0+2dHChVoTSHUmdlFOYKflq4jROBR7MdT26Wz8yBM7l05hJyYzk2hW158TQqy/E4ujgSmlo+V0PDDcqnVHk7ffmoUlRcOXsZR5eqlHUsh6nCBC+/qUzdOp2KzpUMjtnZvSt7Nv5G1OOsx/OKvUtlrhz7C4A7odexc7I3SJebyPlx4AIiM8zUtZnUnT82H+RlDvJ+xcmlGmePBgNw5VwYlWpU1KeVqVCaB7cfEvsyFlWKiotnL+HkWg2Ah3ce4d1/qsGxLgVfZvHEzPWdb5Lf6vIrZVwqcfWYrs+5F3qdkhnOlZGJMZsHLuBJunP19OYjZHIZkiShsFCiUalzHMfrVHCpzMXUenQz9BplX1OPlg6cw6NcnnV2dKlC6NEQ4FW7SmvnpTK08ytnL1PFNbVdKU2Z4u+DT7p2pTBXsm3hFo5msR90cqnKmaNnAbhy7goV31J//z57kequTm/cp6JTBeo2dWXRT/MZM88dpbkSgIZfN0Cj0XD6yNlMx1XNtWpauwoNo2K6sildvjQP39CuHt15hHd/H4NjVa1TlULFCzFnyyyatm3M+ZPns1RGWWVXojiLZni+1zw+NA3aXP/3ob33G95SUlKYOHEi3bp1o0uXLpw+fRqAli1bMm3aNLp3706PHj2IiYnBxMSE2bNnM3XqVMLCwggKCmLs2LHExMQwfPhwevToQY8ePQgPDwfA39+fnj170rVrVwYOHEhycjKBgYH6vE6cOMHhw4f57rvv9PE0b96cH374Ict/R506dTA2NubOnTs5LhOFhZKE1MtUoBs4yozSTsWNkHCiHj0z2OfK7xeIe5H7A18AEwslyeni0ao1SK/i0WqJfxoNgHOv5piYKbhz4iIp8UmkxCVibK6g1Q/D+X3ujmznb2ahJD4mTv9ao1bry8PM0swgLSE2AXNLc5q2b8rLZy8JPW442xx5L5Krf13Ndiy6eMz0lxF18aSdH2WGtIS4BMyszPSv2w5tz87F2/Svn9x7zPW/ruUwntwrH41GQ+GShVl+cAVWBax4kO4LVubjMcsQT/ryMYw1IS4BcyszkhKS+GX1z/j0mMIPE1cwcrG7fh/rgtY4fVqDIzsOZTmW9BQWShJjEvSvtRna1a2Qq7zI0K5c2zci9lk0Yccv5CjvV8wszYhL9/er08VgZmGmn6kDSIiLx8LKHIATe35HlWI4mDu6+1iOLy/mt7r8isJCSdJb+sC7IVd5+ei5wT7J8YnYlirMyEPzaDOrPyfXZ+5SflYp39E/X39N/5w7+b75XOnaeVpaYmwCZpbmqe1qF1O7e/HDxBWMWjIamZGMx/ciuZaNftDM0tyg/mreWn8TMLcyf+M+YX+Fs2r6Gka2H83DOxF8N6oHZSuVpWmbJqyftzFrcWXIO31c5hnaXHxqHwhwYu/vqDJ8SSpWqiixL2MZ23U8jx88odPgTlmKJauaN66PXC5WmOY37/2M7NixA1tbW2bMmEFUVBTdu3fnt99+Iy4ujq+//prJkyczevRojh8/ztdff03VqlVp2bIlvXr1YsOGDZiamrJkyRLq1q1L165duX37NhMmTGDz5s28ePGCDRs2IJPJ6Nu3L3///TcAVlZWrFy5ksePH1OoUCEkSTKIydbWVv//48aNQ6nUfSN919rHggULEhUVRZkyZXJUJomxCShSvwUDyGRSjmZOcyo5NgETi7R4JJkMbfp4JIlGEztja1+cXwamrSezLF6A1mtG8temg4T9krVL5+nFxyagzJD/q/KIj4lHaZ72gay0UBIbHUfL3i1Bq6Vm/ZqUq1KOUQvdmdbXhxdPXmQ7jrR44g3Oj5Tu/CTExhvEqjRX6jtlMytzSjiU4tLJizmOwTCe3C2fJw+eMLDRAD7v/Dl9vfqxyH1hFuOJ18/igGH9TYhNQPGa8nl46wERt3WXFh/dekhMVAy2RQrw7NFT6n31CSd+OZbltccZJcYmYGqu0L+WMtGu3Dp8BmipWN+JklXK0H3+YNb0n0vMk5fZikF3Pl5fNvGx8ZhZpDtX5mbEpvtAfx/yW11+JTE2AZMsnqtP+n7FteMX2D8nAOviBeizxZOlX4zL3fsfSK3DBrHJPkj/nBAbb9B20pdJxnqlsFASFx1r0K4eZmhX2REfE4fZW+qv0qD+KomNjn3jPieCftfXp9+Dfmf4tKFotRoKFyvIgu1zKVaqKCkpKiLuR+hndd8YV4a2k75s4jKUjZmFLq43iY6K5uR+3efVqYOn6D2217uKRcjgY1jz+95nfq9evcrx48fp0aMHw4cPR6VSERWlu7xYpYruElvx4sVJSkrS79OmTRtKly5N5cqV9cfYuXMnPXr0YPLkyURHRyOTyTA2Nsbd3Z2JEycSERGBSqUCoFw53WV7W1tboqOj/3Gidu/eTUqKrsOcPXs2fn5++Pn5sW7durf+LQ8fPqRYsWI5LpPrwWE4Na4FgL1zBe6H383xMXPiQfBVyjWuAUBxZweeht0zSP98Vh/kpsb83G+hfvmDWSEr2vuP4/jMAC5uP56j/K8EX6ZO4zoAVHKuxJ2w2/q0e9fvUaJcCSysLZAby6nqVo2wkDAmdBjPhI4TmNhpArcu32LhqAW5MvAFCA8Ow7lxbQAqOFfkbnjabP+D6/cpVrY45tYWGBnLcXSrytUQ3ZUIR9cq/P177l9Cy83y8fxxMsXLlgAgPi4BbTYGnGHBV6iVGk9F50rcSVc+96/fo3jZtHiquFUlPCSMph2b02tyHwBsixRAaWFG1GPdzF71+jX1l3tz4lZwOFUaOwNQxrk8D8PvvWMPWNppKks7+bCssw8PLt/Bf/SKbA98AS4GX8KtiSsAjrUqcyvdubpz7S4ly5XE0sYSubGc6m5OXA65nO28MiO/1eVX7gaHU6lxTQDsnMsTmYlzlfgyjsTU2c/4F3EYGRshyXL/Iyxj//zgA/XPV4KvUDtdu7oblqFdGbTz9O2qLwC2RQ3bVXbo6q8bAI61HLkZdkufdufaXUqlq781Uuvvm/aZu3kmlWvqlmHUqu/M1b+vssp3LYNbDmdUBw+Cduxnx+qd7xz4Alw6exnXJi66PJwN29Xd64btysnVicvnrrz5bzx7CdfUNurk5sSdqzm/mvtfo9Fqc/3fh/beZ37t7e0pVqwYgwYNIjExkZUrV2JtrbtpK+OM7NuO0apVK1q2bMmzZ8/YsWMHYWFhHDx4kB07dpCQkEC7du30g1xZaodobGxM/fr18fPzo2fPngAEBQWxceNGWrZsmaW/448//kChUOTK4Dd03xmqNKjOuJ3TkSSJDWOW49qqPqbmCk5sPZjj42fVtaBgyjSoRpdALyRJIshjNZVb18PEXEHEhVs4dWrE/TPhdNqmu6kiZN0+7Oo5orA2p97wNtQb3gaAnT3nZGsW5mTQSWo2cGZO4FwkSWKxxyIatW6EwlzBvi37WDttLT7+PkgyGQcCDvA8MvcvOaZ3NugU1evXwCdwFpIEKz2W8mnrhijMFBzaup9N09YzyW8KkkzGke0HiUpdU1vCoSSP70a+4+hZl5vl89OKHYxcMBJVsoqkhCSWjsv6mtLTQaeoUb8mMwJnI0kSyzwW06B1QxRmSg5s3ceGaT/i5TcVSSZxaPtBnkc+51DAAYbOH4HvT7PQarUsH7tEP3NTwr4kEblQbhf2naVSAydG7vQBCbaM+YHarT7FxFzBya05W1KRWb/v/YPaDWqx5OeFSJLEHPf5NGnTGKW5kt827+GHqauY5T8DmUxGUEAQTyP+W3X5lcv7ginfwIkBO72RJImdY1ZRvdUnmJorOLv18Gv3+ePHPbSbM5D+270wMpGzf04AKQlJr902J87tO0OVBjWYuNMXJFg3ZjlureqjMFdw7D32z6eDTlKzQU1mBs5BkiSWeiymQWo7P7BlH+unrcXL3weZTOJQwAF9uxo2fyQzds5Gq9WybMziHM1Sn9j7B7Ub1Gbpz4uQJInZ7vNomlp/f928hxVTf2CO/0xkMom9Aft4GvHstfsALJywhOHTh6JKUfH88XPmj1uU7bh+D/qDWg1qsXjXQiQJ5o5eoGtXZgp+27KXH3xWMcvfF0mSEbR9H8/e0q5+mLaa0XNG0bLHN8TFxDFjWPaepCL8u0na9zR/HRgYyM2bNxk+fDienp48fPiQ2NhYunbtSseOHWnSpAl79+7F1NSUefPmYW9vT7t27QC4f/8+7u7ubN++HYCoqCgmTZpETEwMsbGxDB06lE8++YSBAwcSGxuLiYkJJiYmtG/fHpVKxc2bN/Hw8AAgISGBmTNncvWqbv2TtbU13t7eWXrag0wmw9zcnClTplC0aNG3/t39y3Z4H8WZbZU0+ecO02O8yOsQDJhJ+WsdVrxWldchGDCW8s9v4NhJZu/e6AO6qMr+7Nr7UMgo/5RP+Xx2rh6R86d45KYoTe4P2LPrhSbx3Rt9QEb5qM8B2Bu6Mq9DMGBcyP7dG30A5mZlc/2YcfG3c/2Yb/PeBr//VWLw+2Zi8Pt2YvD7ZmLw+3Zi8PtmYvD7ZmLw+3Zi8Pt6H8PgN399+guCIAiCIAj51sfwC2/562uWIAiCIAiCILxHYuZXEARBEARByJSPYbWsGPwKgiAIgiAImaLNg19ky21i2YMgCIIgCILwnyFmfgVBEARBEIRM+RiWPYiZX0EQBEEQBOE/Q8z8CoIgCIIgCJnyMcz8isGvIAiCIAiCkCn//qGvWPYgCIIgCIIg/IeInzcWBEEQBEEQ/jPEzK8gCIIgCILwnyEGv4IgCIIgCMJ/hhj8CoIgCIIgCP8ZYvArCIIgCIIg/GeIwa8gCIIgCILwnyEGv4IgCIIgCMJ/hhj8CoIgCIIgCP8Z4hfe8pnbt29z584dKlWqRNGiRZEkScSTavTo0cyfPz/P8k8vJSUFY2PjvA7DQGxsLJIkceDAARo3boy1tXVeh5SvHDx4kJs3b1KhQgUaN26c1+HkuVu3br0xrVy5ch8wkvzp6tWrKJVK7Ozs8joUIZPy0+fVvXv3iIqKomjRohQtWjTP4hBeTwx+8xF/f38OHDjAy5cvadOmDXfv3sXLy0vEkyo5OZmwsDDKlSun79RMTEzyJJZ27dpRt25dOnToQMWKFfMkhvTGjh3Lp59+SmhoKBqNhgMHDrB8+fI8iycyMpK5c+cSFRVFixYtqFSpEjVq1MizeCZNmkRcXBzOzs78/PPPnDx5kokTJ+ZJLPHx8URHRyOXywkICKBNmzaULFnyg8fxprYsSRKbNm36wNHA3r17mTVrFgqFgrlz51K9evUPHsMrCxcu5PTp0yQnJ9OzZ0/atGmTZ7EAdOrU6R8DOa1WiyRJbNu2LY+iArVaTWBgII8ePcLNzY0KFSpQoECBPIklv3xe3b9/n5EjR2JsbEzBggV5+PAhSqWShQsXUqRIkQ8ej/AGWiHf6Ny5s1aj0Wi7d++u1Wq12nbt2ol40vnmm2+0jRs31v9r0qRJnsWiVqu1R48e1Q4bNkzbvXt37fbt27WxsbF5Fk/Xrl21Wq1Wf6569uyZZ7FotVpt//79tX/++ae2e/fu2hs3bmg7dOiQp/G0b9/e4HVexvP9999rDxw4oB0zZox21apV2j59+uRZLK88f/5ce/78ee2zZ8/yLIZOnTppX7x4ob1796524MCBeRbHq1i0Wq02Li5O27t37zyNRavVau/fv//Gf3lp4sSJ2kWLFmk7duyoPXTokLZfv355Fkt++bwaMmSI9uzZswbv/f7779ohQ4bkSTzC64k1v/mINvWXpvN6VvOV/BbP7t27OXz4MIGBgRw6dIhDhw7lWSwymYyGDRvy7bffYmNjg5+fH3379iUgIOD/7d17XM3p/vfx10qnQaWDRaQUOW5tRtgYbGKYrcxki4zTzIgd4zTORMkpE9kagxlmDDqIDBtlY5rmR8xvTBiGsJsSMqSoKFKrWvcf7fXdLYd979/vnntd32au5+Ph8Vit/lhv0Vqf7/W9rs9HSB6dTseRI0do3bo1hYWFFBcXC8lhUF5eTs+ePdFoNHh4eGBlZSU0j6urK7m5uQA8ePAAZ2dnYVkePXqEj48P9+7dY/LkyVRUVAjLAjUrroGBgXzyySeMGjWKgwcPCslhaWmJnZ0dLVq0oKysTEiG2lkA6tevT2VlpdAsAHfu3HnpH5Fu3brFzJkzsbKyYsCAAZSUlAjLopbPq8LCQry9vY2e6927N6WlpULySC8mtz2oiK+vL2PGjOHOnTtMmjSJgQMHCs0zdOhQVeVJT08nPDycqqoqhgwZQrNmzQgICBCSJTIykq+//pru3bszadIkvLy8qK6uZvjw4YwaNcrkeYKCgkhOTmbRokXExMQwa9Ysk2eozdLSkrS0NKqrq7lw4YLwC6cLFy7wxhtv0KxZM+7du4elpSWvvfYaAKdOnTJpFp1Ox/bt2+nQoQNZWVk8fvzYpK//rB07drB//34aNGhAaWkpEyZM4M033xSayVDISDXGjRuHq6srnTp1AowLvW7dugnLVVVVRWFhIVBz5sDMTNx6mlo+P83NX1xWVVdXmziJ9O9o9PJdRlWys7PJzMzE3d2ddu3aiY6j5PHw8KBt27ZCs4wZM4ZNmzYxffp0PvvsM0aPHs3+/fuFZNm7dy9Dhw6lQYMGRs/fvn0bFxcXIZnUJC8vjw8//JDMzExatWrF/Pnzhf5cCgoKaNy4sbDXr+38+fOkpKQQHBzM4cOH6dSpk9D9rYGBgUb7Rt9++23i4+NNnqN///74+fmh1+tJSkrCz89P+d7s2bNNmqVr1654enqi1+vJyspSHovaY5uRkUFSUhIZGRn84Q9/wM/PTxUH8dLT01myZAkFBQU4OzsTEhJCr169hOVRw+fVsGHDmD9/vtFzer2edevWCburIj1PrvyqQFRU1HOHGa5evcqRI0dM/qZf26JFi5THJ0+exMLCgqZNmzJmzBghnQTMzMxo1KgRGo0GKyur5wpPU+revTuxsbHodDoA8vPzWb58uckLPMPqpU6no6ysDGdnZ+7du4eDgwOpqakmzVKbRqPhr3/9q/J1cnKy0OJ3xowZODg4MGLECPr16yd0herVV1/l6dOnHD16lK5duwrvrODq6sqaNWvw9vbm7NmzuLq6CskxY8aMFz4W4dChQ0Jf/1kdO3akY8eO6PV6vvvuO7Zs2cL9+/cZMGAAgYGBwnJZW1tz7NgxCgsLsbe3Jz09XViWn376idLSUpydnVm9ejXBwcH07NnT5Dk6dOhAcnLyC5+X1EOu/KrAgQMHXvo9f39/EyYxNnv2bFq0aIG3tzcXL17k0qVLtG/fnmvXrvHJJ5+YPE9ISAgODg6cOHECX19fsrOz+fDDD02eA2pWy/r378+ZM2fQarU8efKEjz76SEgWgLlz5zJnzhyl+I2IiGDDhg3C8owaNYpPPvkEc3NzwsLCKCkpYdu2bcLyQM2q0L59+zh37hw9e/ZkxIgRQlbP1q9fT15eHtnZ2YwdO5a0tDTWr19v8hwGlZWV7Nmzh+zsbFq1asWoUaNeeuvWlM6fP49Op6NHjx5CXj8jIwOtVou9vT2fffYZOp2OCRMmYGtrKySPwdOnT0lJSeFvf/sbRUVFfPnllybPcPbsWbKystixYwfvvvsuUHNbPy4ujqSkJJPngZo7FiEhIWzcuJHg4GDWrl1LXFyckCyS+ol/h5OUAreyspJLly5RWVmJXq8nPz9faK7CwkLlQ7lPnz689957zJo1izFjxgjJEx4eTmJiIl27duWVV15h5cqVQnJAzYrHX/7yF27cuEFERARvv/22sCxQs93CcIirSZMm3L17V2iekJAQpk6dquwhHTFihNA8AFqtlhYtWpCRkUFmZiarVq2iffv2zJw506Q5zp07R1xcHOPGjcPf35/du3eb9PWfVVZWhqOjo3InJSkpSUhrr5SUFMLCwmjSpAlDhgzh8OHD2NjY0KlTJ6O7UKYQHR3NmTNnqKqqwsHBgUaNGqHVapk3bx6ffvqpSbNAzZ2dkydPkpSUxI0bNxgwYAAhISHC7hrY2tpy//59KioqKCgoAGru9sybN09IHqjZa+vp6YlOp6Nz585UVVUJyTFgwICX9hcWeUhbMiaLXxWZNm0aOp2O/Px8qqqq0Gq1+Pr6CstTWlqqrAZlZ2fz+PFjioqKePLkiZA8Z8+eZfTo0UDNB/aKFStYvny5kCx6vZ6CggKePHnCkydPePjwoZAcBq1atWLevHl4eXlx4cIFunbtKiRH7cNjPXv25PTp0zRt2pRTp04pWzREmDlzJj/99BPDhg1j7dq1StP54cOHm7z4raqqory8HI1GQ1VVldAtGADvv/8+zZs3x8nJCUDYYIAtW7Zw7NgxSkpKeOutt0hNTaV+/frK77wpnT59mr1791JeXs6QIUP45ptvgJqDZyL06tULrVbL0KFDGT58OAA///wzP//8s5DfqzZt2tCmTRsCAgKMBjgYtoGJoNFomDNnDn379uXIkSO88sorQnIcPXoUqPmMmDhxItu3bxeSQ/r3ZPGrIqWlpcTGxhISEsLSpUuV20mihIaGMm/ePPLz83F2dmbp0qUcOXKE4OBgIXmio6Np0KAB1dXVhISEMGzYMCE5oOZC5auvvmLYsGH4+PgIb4K/YsUKTp48SVZWFn/605/w8fERkuPZvW4eHh7KcyKL35EjR9K7d+/nnhex6jphwgSGDx9OYWEhAQEBwn/P9Xo9ERERQjNATVuxhg0b0rBhQzw9PZWVaBGdQgyt+aysrIz2qou6MPDx8UGj0ZCbm0tubi7l5eVKPpG/V9988w1ffPGFcrfSwsKCY8eOCcny17/+lUuXLtGvXz/OnDljdObAlGr/fzUzMxPe6UZ6MVn8qki9evWAmlVNa2tr4f0/MzIyePz4MZaWljx48IC5c+dy/PhxYXk2bdrE1KlTqaioIDo6mlatWgnL8vvf/x4PDw8cHR2FFZq1PXnyhB9++IGCggJcXV25efMmbm5uJs9Ru4jKzMwkOzubli1b0r59e5NnAeNDm8/uRYyIiBDSf7hLly7Ex8dz8+ZNXFxchPVkNry/tGjRgh9++IGOHTsq3xPxgV27sBS9Gl5eXs6NGzeorq42evz06VMheSZPnkxkZCQuLi68/vrrTJ8+HcDk20GetXfvXmJiYtiyZQtDhgxh586dwrJYWlry3XffERcXR8uWLYV3J5LUTRa/KvL666+zadMm2rVrx6hRo7CxsRGaJzExURVvbLW7Ybi7u5OWlqa0jDF1N4zi4mJCQ0PJyMjAzs6OgoICevXqxdKlS2nYsKFJs9S2ePFi+vbtS3p6Ok5OToSEhBAbGyssT0xMDElJSXh5efH555/zxhtvMHHiRJPn+NOf/qQ8Xrt2rdA9iZmZmdy7d49169YpOS5fvkxUVJSQFkhDhgxBo9EoHQQMNBqNkL2J58+fV1Yxi4uLlccithRZWVmxdOnSFz4WISQkhOnTp1NcXMxf/vIXDhw4gIODA0FBQULvOtnb26PVann8+DE9evQQeuh38eLFdOvWjWHDhvH999+zcOFCIQezc3JylMdPnz7lxo0bSl9m0Z1dpH+Rxa+KGPZG6nQ6rK2tlZVgUdTyxubh4aE8dnd3p3v37kJyAKxevZpBgwYZ/SwSExNZvnw5kZGRwnIVFxczYsQIDh06xKuvvip8SEBSUhJxcXGYm5uj0+kIDAwUUvz26dNHebx161ajr03t0aNHHDlyhAcPHihbQTQajbDDks+2wqusrBTa5eHy5cvCXvtZMTExoiMYMTc3V/rn7tq1i5YtWwI1W0VEsrGxISUlRel/bBh4IUJRUZGyJ7t9+/bCtl+EhoYqj2tfOGk0Gnbt2iUkk/Q8WfyqSGRkJMuXLxfSQ/dF1PLGVrsbxp49e8jKyqJly5ZCDsLk5uYaNd8HCAgI4PDhwybP8qzs7GygZsCE6NvGer1eKaQsLCywsLAQmgfE7dc08Pb2xtvbm4yMDDp27MjDhw+xtbUVlisvL49Zs2bx6aefYmdnx9GjR9m1axcbN240OsRkaoZ+rWZmZqxfv15Yv1aoOZ0fHx+PTqdDr9dTXFws5He99v+R2ltSRE8NW7lyJbdu3WLOnDls376d8PBwYVnKy8uVYTb3798X9rN59sJJ9EWl9GJiPyElI56envTo0YN27dopf0RauXIlzZo1Y86cOdy4cYNly5YJzRMaGkpubi69e/fm559/ZsmSJSbP8LIiTnRhFRISwuLFi7ly5QozZsxg4cKFQvN07dqVGTNmsHPnTmbMmEGXLl2E5lGTx48f4+vrS2BgIB999BGJiYlCcoSFhREUFKRcbPv6+vLee+8RFhYmJE/tXJaWlmzZsoUPPviAjz/+WFiWTZs2MW3aNJydnfH396dNmzZCcmRlZTFnzhxmz55t9NhwwStCRkYGhYWFdOjQAa1Wy6xZszhx4oSwPDNnziQwMJC33nqLwMBAk3dxMcjLyyMwMFDZrnP06FFGjhzJvXv3hOSRXkxejqiIj48Po0aNMrrNL/IUdsOGDZWpNKKLKYCbN28qTcsHDhwoZLLRs3u4DMrKykyepba2bduyZ88eoRlqmzp1KufOnSM7O5vhw4fzxz/+UUiO2ifha+8jBeO2bKYUHR1NbGws06dPJzg4mNGjRxMQEGDyHI8fP2bgwIFGz4k+tATq6dcKNVu/unTpQkJCAsOHDxc2Tr32wJra73uiprutWLGCK1euUFpaypQpU3Bzc2PGjBkv7KhiKr179yYmJgZra2tu374tbGT4iy4qDcN+ROxBll5MFr8qEhMTQ1BQkPCDbmpVXl5OWVkZr7zyCk+fPhXyoWjYw2U4KFT7eRHU2lB98uTJ7N69W1jRa2AocHU6ndGqvci+zGoZ0/2yfeGi94urpV8r1NzpSU9Pp7KykrS0NGWgg6mJPOfwIufPn+fAgQOUlJQwYcIEysrKWLlypbDtKVBzZ7Bp06ZMnTqVzZs3c+jQISF3B9V6USkZk8Wvijg5ORmdTpeMjR8/njfffBNPT0+ysrKUdj+mZNjPdfDgQd58802Tv/6zBgwYwOXLl+nVqxd+fn40b95cdCQA7Ozs2LlzJ+7u7sr+YxH9SAsKCigtLWXBggVERkai1+uprq5mwYIF7Nu3z+R5AFxdXYmKiqKoqIitW7fSrFkzITm8vLzYtWsX48ePV56LiYkR3iLK0K+1b9++Qvu1Qs1UyevXrzNlyhSio6OZMWOGsCxqYuhsY2Njw+PHj/n888+N+iGLcPXqVWXo0ZIlS4RNIlXrRaVkTBa/KmJtbc3EiRPp0KGDsppn6lZeajZs2DD69u1Lbm4uLi4u2NvbC8uSmJioiuJ3yZIlVFdXc+rUKbZs2cLDhw8ZOHAgb7zxhtDm6vb29ly7do1r164pz4kofi9evMjOnTvJyclRTl2bmZkJHQxgGNPt7e1N/fr1WbFihZAcH3zwAatWreK1115Dq9Xy6NEjXnvtNeG9Y4OCghg2bBi///3v+cMf/iAkQ15eHk2bNuXJkyc0bdoUqPl5id7brxa1fw5arVZ44Qs1xWVRURH29vY8evRI2HYZtV5USsY0enk5ohoHDhx47jlDpwOppjXT/v37lelGANu2bROSZeTIkVRUVBitbEZFRQnJUltxcTHLli0jNTWVH3/8UViOxMREo32sz34YmNqJEyfo16+fsNevraKigj179pCTk6OMiBXZ1vDu3buYm5tjb2+Pubk5169fNzp3YGqPHj3i8OHDHD58GGdnZwICApQ2X6YSERHBokWLGDdu3HMFr2xXBf3798fPzw+9Xk9SUpJRBxxRCzbffPMNK1aswM7OjpKSEkJDQ+nbt6/Jc1RUVLBq1SpSU1Np3LixclG5cOFCrK2tTZ5HejFZ/Ep1xuDBg59rBSeqI8b333//3HOi9uVVV1dz+vRpkpOTuXr1Kn379sXPz0/IyfSkpCRSU1M5c+aMsmpXXV1NZmbmc6OPTenq1avs2bPH6MJJ1GHSDz74gObNm9O5c2fOnTtHYWEhH374oclzZGZmkp+fbzT8o7q6WtjQjWdlZ2ezefNmvv32W1xcXHj//fdNtofcsJfe8PFoYWGBTqfDysqKv//97ybJoGYvWqiBmhVhkUM3qqqqKCoqwtHRUfgq/alTp2jbtq1yUSmpi/wXkeoMQys4NejQoQPbtm2joKCAP/7xj8JuaYWHh5Oenk737t0ZOXIkr776qpAcBn369KFx48YUFxczatQooGabQYsWLYTmWrhwIWPHjlVuYYt0//59ZR/rwIEDGTt2rJAcjx49Ijk5WTVDNwzi4uI4ePAgDRs2JCAggDVr1lBZWcnIkSNNVvwePXoUvV5PeHg4gYGBeHl5ceXKFXbv3m2S11c7wx3JzZs3M3XqVOV5kXe/1LZKv2XLFqU7kaQ+sviV6gw1tYJTyzjh3bt306hRI44fP87x48eNvieilZednR2enp5s374dgP/6r//C0tJS6NAEqDlMKqKdWG0VFRUAuLi48OOPP+Ll5cW1a9eUaV2m9uzQjcLCQho1aiR8QEpGRgYrVqzAwsKCzz77DHd3d9q1a6ccZjIFw3753NxcpWVWhw4duH79uskyqFliYiL79u0jOzubkydPAjV3DXQ6HXPmzBGSyTBgQ6/Xk5GRYXTeQASNRsP7779vtDVOnuFRD1n8SnWGmlrBqWWcsOg3+GcdPnyYjz76iCNHjvDpp5+SlpaGk5MTFy5cMFohMrXmzZuzdetW2rdvr6wOmfrQ25AhQ5Rb6WfOnDG6lS5SSUkJPj4+2NjY8OjRI1asWCG0X+vdu3cpLi4mLi6OwYMHs2rVKmJiYoQMSrGxsWHDhg14eXlx4cIF1XRTEe3NN9+kZ8+efPrppwQHBwM1d3gcHR2FZaq9KNKqVSu+/PJLYVkA/vznPwt9fenfk8WvVGeorRWcGsYJZ2dnEx0dTf369Zk7dy5OTk5Cchh8+eWXHDx4EAsLCxISEti/fz9OTk4EBgYKLX51Oh05OTnk5OQoz5m6+E1NTTXp6/2noqOjiY+Pp0mTJty7d49p06YJLX4rKyvx9vZmy5YtDB06lPj4eGFZ1q1bx4EDBzh58iQeHh7CpoapjaWlJS4uLvz888+quSCoPeSnoKCAx48fC0wDfn5+XLp0icrKSvR6Pfn5+ULzSMZk8SvVGWpqBWcYJ5ydnc2MGTOEjYQNCwtj8uTJPHz4kLVr1wo5OFVbvXr1qF+/PllZWTg4OKDVagGE30qPiIggJyeHW7du0bZtWyWXCF9//TXx8fHodDr0ej3FxcUcPnxYWJ569eop21KaNGkifCVap9MRERGBt7c33333ndAJb/Xr1xfWL7YusLGxISUlxejWvru7u5AstQeQWFpaGk3FE2HatGnodDry8/OpqqpCq9Xi6+srNJP0L7L4leqM/v37i44AQGlpKa6urqoYJ2xmZqa08xF9mw9qTluXlpZy9OhRJVdeXh6VlZVCc8XGxvLVV1/x8OFD/P39uXnzJqGhoUKybNq0iaVLl5KQkECPHj04ffq0kBwGDRs2JCYmhm7dupGenk6jRo2E5lmzZg2nT58mICCAlJQU1q5dKzSP9HKFhYVGk8s0Go3JD5kZ7uYMHTrU6HmdTmfSHM8qLS0lNjaWkJAQli5dyrvvvis0j2RMFr9SnXDt2jX8/f2pqKggMTERS0tLIXuqYmNj2b59O+bm5ixdupQ+ffqYPMPLVFdXi47Au+++y7Bhw3BycmLLli38+OOPzJo1SxkwIUpycjLx8fGMHz+eCRMmCN2PZ29vT5cuXUhISGD48OHs379fWBaATp06cffuXTZs2ICHhwcODg5C87Rs2VI5BKimbU7S8wwTLw0MhzpNyXAR+6LWZiK7PRjam5WVlWFtbS28GJeMyeJXUr0vvviCI0eOsHv3biIjI7lz5w7NmjVj9erVJp/dnpSUxNGjRyktLWX+/PnCi9/i4mJOnTql3D6v3eFBxBSzfv36kZqaik6nw8LCAgsLC/bu3St02hz8a7So4QNSZB4LCwvS09OprKwkLS3N6HatKdU+sd+qVSsAJZck/ScSEhL44osvlH2tFhYWHDt2zKQZDAV4nz59ePDgAQ4ODhQVFWFpacnrr79OWFiYkD3sgwYN4uOPP6Zdu3aMHDlSGQktqYMsfiXVO3nyJAkJCWg0GpKSkjh27Bh2dnYEBgaaPIulpSWWlpY4ODio4kq+Y8eOSo/Wjh07KquIVlZWQorfgoICSktLWbBgAZGRkej1eqqrq1mwYAH79u0zeR4DX19fxowZw507d5g0aRIDBw4UliU8PJzr168zZcoUoqOjmTFjhpAcajyxL9Ute/fuJSYmhi1btjBkyBCjLRCm1q1bN6ZNm4aHhwe3bt1i06ZNTJ06lXnz5gkpfmvvFe/Xrx9ubm4mzyC9nCx+JdUzMzOjXr16ZGRk0KJFC2XCm+jhhKJfH2DSpElERkbi4uLC4MGDmTZtGgCLFi0SkufixYvs3LmTnJwcZauDmZmZkEK8trFjx9KzZ08yMzOVvrGmlpeXR9OmTXny5IkybOODDz4QNonKcGJ/xYoVQl5fqvvs7e3RarU8fvyYHj168NFHHwnLkpeXp7Q7c3V15c6dO7i5uQkbHf7TTz8RFhZGSUkJfn5+eHp6qubciiSLX6mOyMnJYf/+/QwYMACoeWMR0UEgKyuLOXPmoNfrjR5rNBoh041CQkKYPn06xcXFTJ48mQMHDuDg4EBQUJCQMaMDBw5k4MCBnDhxgn79+pn89Z8VFRX1XHF59epVjhw5YvJOIV988QWLFi0iNDRUVZOoJOl/y9DtQaPRkJCQQGFhobAsjRs3Zt26dXTp0oUffvgBJycnTp8+jYWFhZA8K1euJCIigiVLljBixAiCgoJk8asisviVVG/mzJnMnz+f5s2bM3v2bL7//nvmzZtHdHS0ybPUbp8jYtvFs8zNzenVqxdQU0AZDgrVr19fYCrQarUsW7aM8vJy5TkR0/hqN74X7auvviIlJUW5Y6CWIReS9L+1cuVKcnNzmTNnDtu3b2fZsmXCskRGRrJnzx5OnjxJmzZtmD59OleuXGH9+vXCMrm5uaHRaHBwcKBBgwbCckjPk8WvpHpeXl4kJiaSl5dHgwYN6Ny5MykpKeTm5po8S/fu3QF48uQJjx49wtzcnD179ghZZQXjE861D3GJ7vywcOFCxo4dq9zeF8Xf3x+oGZwguuH80aNH0ev1hIeHExgYiJeXF1euXGH37t0mzyJJv4RXXnmFy5cvc/fuXfr374+np6ewLFZWVowfP97oORFTAQ3s7OxISEigrKyM5ORkbG1thWWRnieLX0n1MjMzyc/PZ+3atcybNw+oKe6ioqI4ePCgkExz585l+PDhHD9+nNatWxMaGsrnn39u8hwv24ZhmD4nipOTEwEBAUIz1KaGhvOGi5Pc3Fy8vLwA6NChA9evXzdpDkn6pYSGhqLVavn222/53e9+x4IFC9i2bZvoWEKlpaXRp08fVq9ezSeffIK9vT2XL19m1apVoqNJtcjiV1K9R48ekZyczIMHD5TOBhqNhrfffltoJh8fH3bt2kVkZCRpaWlCcrxsG4boLRnNmzdn69attG/fXlmdFnnoTU0N521sbNiwYQNeXl5cuHBBNeNhJel/6tatW6xatYpz584xYMAAtm7dKjqScBs3buTEiRPMnz+fuXPnio4jvYQsfiXV8/b2xtvbm4yMDDp27Cg6DlAzPWj79u107NiRrKwsYXPkDdsw1Ean05GTk6NMXwKxxa/hxLcaGs6vW7eOAwcOcPLkSTw8PJg5c6awLJL0/6Kqqko55FZaWip8jLkaJCQk8NlnnzF69GgiIiJo06aN6EjSC2j0aujXJEn/gdOnT7Njxw6jQ1SiTsmfP3+elJQUgoODOXz4MJ06dVJuZUs1cnJyuHXrFm3btkWr1Qr9YIyLi1Ma36ekpFC/fn127NghLI8k/Rqkp6ezdOlSCgoKcHZ2ZvHixcoB3N+6f/zjH7zzzju4ubkpHYESEhJEx5L+SRa/Up3h6+vL4sWLjQ5RiTrNn56ebvS1ubk5zs7Owg94qUVsbCxfffUVDx8+xN/fn5s3bypjSEUoLCxUxvb+4x//wM3NDWtra2F5JOnXYO/evezYsYM7d+5gb2+PmZkZX3/9tehYwv33f/83y5cvx9fX1+gwtNzipB5y24NUZzg7O6tmVWHDhg3cv3+fjh07cuXKFSwsLKioqCAgIICgoCDR8YRLTk4mPj6e8ePHM2HCBP785z8LzTNx4kRcXV0ZOXKkkGlPkvRrlJCQwLZt22jcuLHoKKoRFhbG+fPniYqKokOHDqLjSC8hi1+pznB0dCQ0NJQOHTooh6hGjRolJIu1tTWHDh3CysqKiooKpk+fzsaNGxk7dqwsfvnX9DvDv1PtNmwiHDhwgEuXLrF//36ioqIYNGgQU6ZMEZpJkuo6e3t7uZr5jPr16/Pll18Kf8+T/j1Z/Ep1houLCwD3798XnASKioqU4QSWlpbKflLR/XXVwtfXlzFjxnDnzh0mTZrEwIEDRUfC09OTzp07c+vWLc6ePSs6jiTVWYbBERUVFUycONFoQcLUkxPVZsGCBUDNAcCTJ09SUVGhfE9UP3jpebL4leqMadOm8e2333L79m28vLxwd3cXlsXHx4fRo0fj5eXFpUuXGDBgAPHx8UKbvKvJ2LFj6dmzJ5mZmbi7u9OuXTuheRYtWsTFixcZPHgw4eHhyoWUJEn/c4b3XpHvwWo3depUtFotzs7OAM+NNJfEkgfepDpj/fr15OXlkZ2dzdixY0lLSxM6uvLatWtcv36d1q1b06ZNGwoLC7G3t/9Nv8lFRUW99O8vckUoNTWV/v37/6b/bSRJMp1x48YRExMjOob0EnLlV6ozzp07R1xcHOPGjcPf31/IWNjExEQCAgKMirxr164B8nYfiOu+8X/j4uLCmDFjKCkpwc/PD09PT/r37y86liRJv1Jt27bl4sWLtG/fXnlO7gNWD1n8SnVGVVUV5eXlaDQaqqqqhPSNNbQys7GxwcrKCltbW9avX897771n8ixq5O/vD0BlZSWXLl2isrISvV5Pfn6+0FyrVq0iIiKCJUuWMGLECIKCgmTxK0nS/zfff/89qampytcajUa2gVMRWfxKdcY777zD8OHDKSwsJCAgQMiI2j59+gBw/Phx1qxZQ+vWrfH29mbhwoVCR+aqzbRp09DpdOTn51NVVYVWq8XX11doJjc3NzQaDQ4ODjRo0EBoFkmSft0OHToEQHFxMXZ2dnLLlcrI4leqMxo1akR8fDw3b97ExcVFGVoggrm5Oa1btwagRYsWcqznM0pLS4mNjSUkJISlS5cKvzCws7MjISGBsrIykpOTsbW1FZpHkqRft/T0dMLDw6mqqmLIkCE0a9aMgIAA0bGkf5Kf2FKdsXHjRuzs7PDy8hJa+AI0a9aM9evXk5qayoYNG9BqtULzqE29evUAKCsrw9raGp1OJzTP6tWruX37Nvb29ly+fJlVq1YJzSNJ0q/bhg0biI2NxcnJieDgYCFnVKSXkyu/Up2h0Wh4//33cXd3V1ZaRR0yi4iIYPfu3Zw4cYJWrVoxdepUITnU6vXXX+fjjz+mXbt2jBw5koYNGwrNExYWRlRUlNAMkiT9dpiZmdGoUSM0Gg1WVlZyq5XKyOJXqjPeeustZUVRNCsrK9555x3RMVTrjTfeUFbn+/Xrh5ubm9A8FRUVXLt2DXd3d9VMnZMk6ddn1qxZbNiwAVdXV6KioigqKmLr1q00a9ZMdDSpFtnnV6oz3nvvPbZv3y46hvQf8Pf3x9XVlZEjR9K7d2/RcfDz86OkpITCwkIcHR0xMzOTJ68lSfrFjR8/nl27dlFZWUliYiKZmZm0atWKUaNGYWFhITqe9E+y+JXqjJkzZzJs2DBatmypbHuQE4bU69KlS+zfv5+LFy8yaNAgpkyZIiyLoTuHra0tpaWlLFu2jNdee01YHkmSfp369++Pn5+f8rWhxNJoNLIXvIrIbQ9SnVFYWMiOHTuUrzUaDbt27RIXSPq3PD096dy5M7du3eLs2bNCs2zevJnExEQcHR25f/8+wcHBsviVJOkXZ21tLRdl6gBZ/Ep1hhwVWXcsWrSIixcvMnjwYMLDw3FxcRGap1GjRjg6OgLg5OQk/ACeJEm/Tk5OTsqwH0m9ZPEr1RkDBgwwahRuY2PD3/72N3GBpJcaNGgQq1evVk1j94YNGzJx4kS6detGRkYGT58+Zf369YAcSy1J0i/nd7/7negI0n9AFr9SnXH06FGgZg/V5cuXla8l9XFxcWHMmDGUlJTg5+eHp6en0HHCPj4+yuMmTZoIyyFJ0q/bggULREeQ/gPywJtUZ40ZM4a4uDjRMaQXmDBhAsuXL2fJkiVER0cTFBTE/v37RceSJEmSJLnyK9UdUVFRym30goICOVJY5dzc3NBoNDg4OMgG75IkSZJqyOJXqjNsbGywsrLC1taWbdu2sW/fPtGRpJews7MjISGBsrIykpOTsbW1FR1JkiRJkgCQS2dSnXH8+HF69+6Nv78/x48fZ/Xq1aIjSS+xevVqbt++jb29PZcvX2bVqlWiI0mSJEkSIFd+pTrE3Nyc1q1bA9CiRQu57UHFwsLCiIqKEh1DkiRJkp4ji1+pzmjWrBnr16+nc+fO/Pjjj2i1WtGRpJeoqKjg2rVruLu7K/u0LS0tBaeSJEmSJNntQapDysvL2b17Nzk5ObRq1YrAwEBZUKmUn58fJSUlFBYW4ujoiJmZGV9//bXoWJIkSZIki19Jkn55x48fZ82aNdja2lJaWsqyZcvkOGFJkiRJFeS2B0mSfnGbN28mMTERR0dH7t+/T3BwsCx+JUmSJFWQJ4YkSfrFNWrUCEdHR6Bm1n3Dhg0FJ5IkSZKkGnLbgyRJv7hp06ZRVlZGt27dyMjIoKCggO7duwMwe/ZswekkSZKk3zK57UGSpF+cj4+P8rhJkyYCk0iSJEmSMbnyK0mSJEmSJP1myD2/kiRJkiRJ0m+GLH4lSZIkSZKk3wxZ/EqSJEmSJEm/GbL4lSRJkiRJkn4zZPErSZIkSZIk/Wb8H7oc/2c2S1W3AAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 864x576 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize=(12,8))\n",
- "sns.heatmap(train.corr(), annot=True)\n",
- "#Korrelationen zwischen currentSmoker und cigsPerDay, sysBPund diaBP, prevalentHyp und sysBP und diaBP "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train.reset_index(drop=True).copy()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Modeling"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {},
- "outputs": [],
- "source": [
- "estimators = ['male', 'age', 'currentSmoker', 'BPMeds',\n",
- " 'prevalentStroke', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP', 'BMI', 'heartRate', 'glucose']\n",
- "X_all = train[estimators]\n",
- "y = train['TenYearCHD']\n",
- "#currentSmoker & sysBP werden gedropt (siehe oben)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {},
- "outputs": [],
- "source": [
- "import statsmodels.api as sm"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Optimization terminated successfully.\n",
- " Current function value: 0.356399\n",
- " Iterations 7\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table class=\"simpletable\">\n",
- "<caption>Logit Regression Results</caption>\n",
- "<tr>\n",
- " <th>Dep. Variable:</th> <td>TenYearCHD</td> <th> No. Observations: </th> <td> 3444</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>Model:</th> <td>Logit</td> <th> Df Residuals: </th> <td> 3431</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>Method:</th> <td>MLE</td> <th> Df Model: </th> <td> 12</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>Date:</th> <td>Sun, 24 Oct 2021</td> <th> Pseudo R-squ.: </th> <td>0.1008</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>Time:</th> <td>19:01:14</td> <th> Log-Likelihood: </th> <td> -1227.4</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>converged:</th> <td>True</td> <th> LL-Null: </th> <td> -1365.0</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>Covariance Type:</th> <td>nonrobust</td> <th> LLR p-value: </th> <td>7.410e-52</td>\n",
- "</tr>\n",
- "</table>\n",
- "<table class=\"simpletable\">\n",
- "<tr>\n",
- " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>const</th> <td> -8.3986</td> <td> 0.805</td> <td> -10.431</td> <td> 0.000</td> <td> -9.977</td> <td> -6.821</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>male</th> <td> 0.6638</td> <td> 0.112</td> <td> 5.943</td> <td> 0.000</td> <td> 0.445</td> <td> 0.883</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>age</th> <td> 0.0703</td> <td> 0.007</td> <td> 10.266</td> <td> 0.000</td> <td> 0.057</td> <td> 0.084</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>currentSmoker</th> <td> 0.4561</td> <td> 0.113</td> <td> 4.031</td> <td> 0.000</td> <td> 0.234</td> <td> 0.678</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>BPMeds</th> <td> -0.1249</td> <td> 0.293</td> <td> -0.427</td> <td> 0.670</td> <td> -0.699</td> <td> 0.449</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>prevalentStroke</th> <td> 1.0221</td> <td> 0.540</td> <td> 1.892</td> <td> 0.058</td> <td> -0.037</td> <td> 2.081</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>prevalentHyp</th> <td> 0.1340</td> <td> 0.150</td> <td> 0.893</td> <td> 0.372</td> <td> -0.160</td> <td> 0.428</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>diabetes</th> <td> -0.0543</td> <td> 0.515</td> <td> -0.106</td> <td> 0.916</td> <td> -1.063</td> <td> 0.954</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>totChol</th> <td> 0.0020</td> <td> 0.001</td> <td> 1.468</td> <td> 0.142</td> <td> -0.001</td> <td> 0.005</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>sysBP</th> <td> 0.0138</td> <td> 0.004</td> <td> 3.760</td> <td> 0.000</td> <td> 0.007</td> <td> 0.021</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>BMI</th> <td> 0.0029</td> <td> 0.015</td> <td> 0.187</td> <td> 0.852</td> <td> -0.027</td> <td> 0.033</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>heartRate</th> <td>-6.527e-05</td> <td> 0.005</td> <td> -0.013</td> <td> 0.989</td> <td> -0.010</td> <td> 0.009</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>glucose</th> <td> -0.0009</td> <td> 0.004</td> <td> -0.196</td> <td> 0.845</td> <td> -0.009</td> <td> 0.008</td>\n",
- "</tr>\n",
- "</table>"
- ],
- "text/plain": [
- "<class 'statsmodels.iolib.summary.Summary'>\n",
- "\"\"\"\n",
- " Logit Regression Results \n",
- "==============================================================================\n",
- "Dep. Variable: TenYearCHD No. Observations: 3444\n",
- "Model: Logit Df Residuals: 3431\n",
- "Method: MLE Df Model: 12\n",
- "Date: Sun, 24 Oct 2021 Pseudo R-squ.: 0.1008\n",
- "Time: 19:01:14 Log-Likelihood: -1227.4\n",
- "converged: True LL-Null: -1365.0\n",
- "Covariance Type: nonrobust LLR p-value: 7.410e-52\n",
- "===================================================================================\n",
- " coef std err z P>|z| [0.025 0.975]\n",
- "-----------------------------------------------------------------------------------\n",
- "const -8.3986 0.805 -10.431 0.000 -9.977 -6.821\n",
- "male 0.6638 0.112 5.943 0.000 0.445 0.883\n",
- "age 0.0703 0.007 10.266 0.000 0.057 0.084\n",
- "currentSmoker 0.4561 0.113 4.031 0.000 0.234 0.678\n",
- "BPMeds -0.1249 0.293 -0.427 0.670 -0.699 0.449\n",
- "prevalentStroke 1.0221 0.540 1.892 0.058 -0.037 2.081\n",
- "prevalentHyp 0.1340 0.150 0.893 0.372 -0.160 0.428\n",
- "diabetes -0.0543 0.515 -0.106 0.916 -1.063 0.954\n",
- "totChol 0.0020 0.001 1.468 0.142 -0.001 0.005\n",
- "sysBP 0.0138 0.004 3.760 0.000 0.007 0.021\n",
- "BMI 0.0029 0.015 0.187 0.852 -0.027 0.033\n",
- "heartRate -6.527e-05 0.005 -0.013 0.989 -0.010 0.009\n",
- "glucose -0.0009 0.004 -0.196 0.845 -0.009 0.008\n",
- "===================================================================================\n",
- "\"\"\""
- ]
- },
- "execution_count": 60,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = sm.add_constant(X_all)\n",
- "reg_logit = sm.Logit(y,x)\n",
- "results_logit = reg_logit.fit()\n",
- "results_logit.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 61,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Der P-Wert bei folgenden Attributen: BPMeds, prevalentStroke, diabetes, totChol,diaBP,BMI,heartRate & glucose\n",
- "#ist relativ hoch und somit wei\u00dft es eine geringe statistiche signifikante Beziehung zur Wahrscheinlichkeit einer Herzerkrankung auf\n",
- "#(The closer to 0.000 the p-value, the better, Slides_AI - Part 4-2.pdf, S.27)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 5% 95% Odds Ratio\n",
- "const 0.000046 0.001091 0.000225\n",
- "male 1.560329 2.417551 1.942209\n",
- "age 1.058495 1.087278 1.072790\n",
- "currentSmoker 1.264052 1.969618 1.577878\n",
- "BPMeds 0.497151 1.566804 0.882575\n",
- "prevalentStroke 0.964058 8.010118 2.778888\n",
- "prevalentHyp 0.851976 1.534411 1.143364\n",
- "diabetes 0.345451 2.596858 0.947147\n",
- "totChol 0.999340 1.004611 1.001972\n",
- "sysBP 1.006617 1.021175 1.013870\n",
- "BMI 0.972975 1.033720 1.002888\n",
- "heartRate 0.990448 1.009513 0.999935\n",
- "glucose 0.990579 1.007774 0.999140\n"
- ]
- }
- ],
- "source": [
- "#Odds ratio & confidence intervals\n",
- "params = results_logit.params\n",
- "conf = results_logit.conf_int()\n",
- "conf['Odds Ratio'] = params\n",
- "conf.columns = ['5%', '95%', 'Odds Ratio']\n",
- "print(np.exp(conf))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 63,
- "metadata": {},
- "outputs": [],
- "source": [
- "#x_new = x.drop(['BPMeds', 'prevalentStroke', 'diabetes', 'totChol','diaBP','BMI','heartRate', 'glucose'], axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "metadata": {},
- "outputs": [],
- "source": [
- "#train = train.drop(['BPMeds', 'prevalentStroke', 'diabetes', 'totChol','diaBP','BMI','heartRate', 'glucose'], axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "metadata": {},
- "outputs": [],
- "source": [
- "#x = sm.add_constant(x_new)\n",
- "#reg_logit = sm.Logit(y,x)\n",
- "#results_logit = reg_logit.fit()\n",
- "#results_logit.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Odds ratio & confidence intervals\n",
- "#params = results_logit.params\n",
- "#conf = results_logit.conf_int()\n",
- "#conf['Odds Ratio'] = params\n",
- "#conf.columns = ['5%', '95%', 'Odds Ratio']\n",
- "#print(np.exp(conf))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Model Training"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 67,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(3444, 15)"
- ]
- },
- "execution_count": 67,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 68,
- "metadata": {},
- "outputs": [],
- "source": [
- "X = x\n",
- "y = y"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Undersampling (nachtr\u00e4glich) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 75,
- "metadata": {},
- "outputs": [],
- "source": [
- "import imblearn\n",
- "from imblearn.under_sampling import InstanceHardnessThreshold\n",
- "from sklearn.linear_model import LogisticRegression"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 76,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " n_iter_i = _check_optimize_result(\n",
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " n_iter_i = _check_optimize_result(\n",
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " n_iter_i = _check_optimize_result(\n",
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " n_iter_i = _check_optimize_result(\n",
- "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " n_iter_i = _check_optimize_result(\n"
- ]
- }
- ],
- "source": [
- "iht = InstanceHardnessThreshold(random_state=0,estimator=LogisticRegression (solver='lbfgs', multi_class='auto'))\n",
- " \n",
- "X_resampled, y_resampled = iht.fit_resample(X, y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 77,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.model_selection import train_test_split"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Methode von train_test_split (sklearn)\n",
- "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
- "#der Datensatz wird \u00fcbergeben ohne die Zielspalte TenYearCHD f\u00fcr X, daf\u00fcr wird diese in y eingesetzt\n",
- "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=365)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Scaling"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.preprocessing import StandardScaler"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "metadata": {},
- "outputs": [],
- "source": [
- "scaler = StandardScaler()\n",
- "scaler.fit(X_train)\n",
- "\n",
- "X_train = scaler.transform(X_train)\n",
- "X_test = scaler.transform(X_test)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Logistische Regression"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.linear_model import LogisticRegression"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 82,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Ein model wird angelegt\n",
- "log_model = LogisticRegression(random_state=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "LogisticRegression(random_state=0)"
- ]
- },
- "execution_count": 83,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Trainiere und fitten einer logistisches Regressionsmodell auf das Trainigsset\n",
- "log_model.fit(X_train,y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 84,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.metrics import classification_report"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 85,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "train performance\n",
- " precision recall f1-score support\n",
- "\n",
- " 0 0.94 0.99 0.96 365\n",
- " 1 0.99 0.93 0.96 380\n",
- "\n",
- " accuracy 0.96 745\n",
- " macro avg 0.96 0.96 0.96 745\n",
- "weighted avg 0.97 0.96 0.96 745\n",
- "\n",
- "test performance\n",
- " precision recall f1-score support\n",
- "\n",
- " 0 0.95 1.00 0.98 101\n",
- " 1 1.00 0.94 0.97 86\n",
- "\n",
- " accuracy 0.97 187\n",
- " macro avg 0.98 0.97 0.97 187\n",
- "weighted avg 0.97 0.97 0.97 187\n",
- "\n"
- ]
- }
- ],
- "source": [
- "#Precision= True positive / true positive + false positive \n",
- "#Recall = True positive / true positive + false negative\n",
- "#f1-score = zusammenfassung von der precision und dem recall\n",
- "#accuracy(genauigkeit) liegt bei 0.86 - also 86%\n",
- "print('train performance')\n",
- "print(classification_report(y_train, log_model.predict(X_train)))\n",
- "print('test performance')\n",
- "print(classification_report(y_test, log_model.predict(X_test)))\n",
- "#Bei der logistischen Regression sind die Trainings- und Testleistung sehr \u00e4hnlich.\n",
- "# erstellte Modell kann auf neuen Daten gut verallgemeinert werden kann."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 86,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Die Confusion Matrix zeigt eine Zusammenfassung der Vorhersageergebnisse zu dem Klassifizierungsproblem \n",
- "from sklearn.metrics import confusion_matrix"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[101 0]\n",
- " [ 5 81]]\n"
- ]
- }
- ],
- "source": [
- "print(confusion_matrix(y_test, log_model.predict(X_test)))\n",
- "#"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Decision Tree"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 88,
- "metadata": {},
- "outputs": [],
- "source": [
- "#from sklearn.tree import DecisionTreeClassifier\n",
- "# overfitting"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 89,
- "metadata": {},
- "outputs": [],
- "source": [
- "#tree = DecisionTreeClassifier()\n",
- "#tree.fit(X_train, y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 90,
- "metadata": {},
- "outputs": [],
- "source": [
- "#confusion_matrix(y_test, tree.predict(X_test)) #true negatives, false positives, false negatives, true positives"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 91,
- "metadata": {},
- "outputs": [],
- "source": [
- "#from sklearn.metrics import classification_report\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 92,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(classification_report(y_train, tree.predict(X_train)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 93,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(classification_report(y_test, tree.predict(X_test)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Random forest "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 94,
- "metadata": {},
- "outputs": [],
- "source": [
- "#from sklearn.ensemble import RandomForestClassifier\n",
- "# overfitting"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 95,
- "metadata": {},
- "outputs": [],
- "source": [
- "#rf = RandomForestClassifier()\n",
- "#rf.fit(X_train, y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "metadata": {},
- "outputs": [],
- "source": [
- "#confusion_matrix(y_test, rf.predict(X_test))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 97,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(classification_report(y_train, rf.predict(X_train)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 98,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(classification_report(y_test, rf.predict(X_test)))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.9"
- },
- "title": "Risk prediction of heart disease",
- "category": "Health"
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file