Adult_income_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Abstract: Predict whether income exceeds $50K/yr based on census data. Also known as \"Census Income\" dataset.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 50 000 USD per year is about 4 167 USD monthly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SOURCE:<br>\n",
    "http:https://archive.ics.uci.edu/ml/datasets/Adult <br>\n",
    "https://www.kaggle.com/overload10/adult-census-dataset/tasks <br>\n",
    "http:https://cseweb.ucsd.edu/classes/sp15/cse190-c/reports/sp15/048.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from os import path\n",
    "from wordcloud import WordCloud\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from statsmodels.graphics.mosaicplot import mosaic\n",
    "from sklearn.neighbors import (KNeighborsClassifier, NeighborhoodComponentsAnalysis)\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import auc, confusion_matrix, plot_confusion_matrix, classification_report, roc_curve, roc_auc_score\n",
    "from sklearn.preprocessing import OneHotEncoder as SklearnOneHotEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, KFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression as SklearnLinearRegression, LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32561, 15)\n"
     ]
    },
    {
     "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education-num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       marital-status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week native-country income  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heading =['age', 'workclass', 'fnlwgt', 'education', 'education-num','marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country','income']\n",
    "df = pd.read_csv(\"data/adult_data.txt\", header=None, names = heading, na_values=\"?\", skipinitialspace = True)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The number of missing data points per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "workclass         1836\n",
       "fnlwgt               0\n",
       "education            0\n",
       "education-num        0\n",
       "marital-status       0\n",
       "occupation        1843\n",
       "relationship         0\n",
       "race                 0\n",
       "sex                  0\n",
       "capital-gain         0\n",
       "capital-loss         0\n",
       "hours-per-week       0\n",
       "native-country     583\n",
       "income               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset has missing values in \"age\", \"occupation\", \"native-county\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis and Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many people in dataset have income >50K or <=50K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<=50K    24720\n",
      ">50K      7841\n",
      "Name: income, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8fda739940>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df['income'].value_counts())\n",
    "df['income'].value_counts().plot.pie(autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More people earn less or equal 5OK $."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_distplot(column, title = \"\"):\n",
    "    \n",
    "    fig = plt.figure(figsize=(20,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.set_facecolor((0,0,0.10))\n",
    "    sns.distplot(column)\n",
    "\n",
    "    mean_value = column.mean()\n",
    "    plt.axvline(mean_value, c='red', linewidth=1.5, label=\"mean\")\n",
    "\n",
    "    median_value = column.median()\n",
    "    plt.axvline(median_value, c='blue', linewidth=1.5, label=\"median\")\n",
    "    plt.legend(loc='upper right', prop={'size': 20})\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_distplot(column=df['age'], title=\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precise median age for >50K income :  44.0 yrs\n"
     ]
    }
   ],
   "source": [
    "print('precise median age for >50K income : ',df[df['income']=='>50K']['age'].median(), 'yrs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precise median age for <=50K income :  34.0 yrs\n"
     ]
    }
   ],
   "source": [
    "print('precise median age for <=50K income : ',df[df['income']=='<=50K']['age'].median(), 'yrs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MEDIAN value for people earning <=50K is around 35yrs, presicely.<br>\n",
    "MEDIAN value for people earing >50K is around 45yrs.<br>\n",
    "50% people who earn <=50K are between around 25 and 45. <br>\n",
    "50% people who earn >50K are between around 38 and 50. <br>\n",
    "IQR for >50K is visibly narrower than for <=50K.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WORKCLASS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_countplot(countplot_x, countplot_hue, countplot_data, figsize_a=20, figsize_b=10, xticks_rotation=45):\n",
    "    fig = plt.figure(figsize=(20,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.set_facecolor((0,0,0.10))\n",
    "    plt.xticks(rotation = xticks_rotation)\n",
    "    plt.rc('xtick',labelsize=8)\n",
    "    sns.countplot(x = countplot_x, hue = countplot_hue, data = countplot_data)\n",
    "    plt.legend(prop={'size': 30})\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_countplot(countplot_x='workclass', countplot_hue='income', countplot_data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two values **Without-pay** and **Never-worked** are insignificant and I can drop them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is **missing data**. How many missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             22696\n",
       "Self-emp-not-inc     2541\n",
       "Local-gov            2093\n",
       "State-gov            1298\n",
       "Self-emp-inc         1116\n",
       "Federal-gov           960\n",
       "Without-pay            14\n",
       "Never-worked            7\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             22696\n",
       "Self-emp-not-inc     2541\n",
       "Local-gov            2093\n",
       "State-gov            1298\n",
       "Self-emp-inc         1116\n",
       "Other                 981\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workclass'].replace(['Never-worked', 'Without-pay', 'Federal-gov'],'Other', inplace = True)\n",
    "df['workclass'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is plenty of missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I create **Other** cathegory for missing values and for cathegories with a few members."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    30725\n",
       "True      1836\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df[\"workclass\"]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             22696\n",
       "Other                2817\n",
       "Self-emp-not-inc     2541\n",
       "Local-gov            2093\n",
       "State-gov            1298\n",
       "Self-emp-inc         1116\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['workclass'].fillna('Other', inplace = True)\n",
    "df['workclass'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FNLWGT - Final weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In other words, this is the number of people the census believes the entry represents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_distplot(column=df['fnlwgt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This column represents nothing relevant to the futher prediction. <br> So I'm dropping this column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['fnlwgt'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDUCATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_countplot(countplot_x='education', countplot_hue='income', countplot_data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDUCATION-NUM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_countplot(countplot_x='education-num', countplot_hue='income', countplot_data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no missng values. <br> Values aren't linear. Values are categorical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Bachelors, the Education Number is [13]\n",
      "For HS-grad, the Education Number is [9]\n",
      "For 11th, the Education Number is [7]\n",
      "For Masters, the Education Number is [14]\n",
      "For 9th, the Education Number is [5]\n",
      "For Some-college, the Education Number is [10]\n",
      "For Assoc-acdm, the Education Number is [12]\n",
      "For Assoc-voc, the Education Number is [11]\n",
      "For 7th-8th, the Education Number is [4]\n",
      "For Doctorate, the Education Number is [16]\n",
      "For Prof-school, the Education Number is [15]\n",
      "For 5th-6th, the Education Number is [3]\n",
      "For 10th, the Education Number is [6]\n",
      "For 1st-4th, the Education Number is [2]\n",
      "For Preschool, the Education Number is [1]\n",
      "For 12th, the Education Number is [8]\n"
     ]
    }
   ],
   "source": [
    "education_var = df['education'].unique()\n",
    "for edu_var in education_var:\n",
    "    print(\"For {}, the Education Number is {}\"\n",
    "          .format(edu_var, df[df['education'] == edu_var]['education-num'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I see that **Education Number** and **Education** are just the same, so, **Education** of them column could be droped. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['education'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     10501\n",
       "10     7291\n",
       "13     5355\n",
       "14     1723\n",
       "11     1382\n",
       "7      1175\n",
       "12     1067\n",
       "6       933\n",
       "4       646\n",
       "15      576\n",
       "5       514\n",
       "8       433\n",
       "16      413\n",
       "3       333\n",
       "2       168\n",
       "1        51\n",
       "Name: education-num, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education-num'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MARITAL-STATUS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married-civ-spouse       14976\n",
       "Never-married            10683\n",
       "Divorced                  4443\n",
       "Separated                 1025\n",
       "Widowed                    993\n",
       "Married-spouse-absent      418\n",
       "Married-AF-spouse           23\n",
       "Name: marital-status, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marital-status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OCCUPATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_countplot(countplot_x='occupation', countplot_hue='income', countplot_data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prof-specialty       4140\n",
       "Craft-repair         4099\n",
       "Exec-managerial      4066\n",
       "Adm-clerical         3770\n",
       "Sales                3650\n",
       "Other-service        3295\n",
       "Machine-op-inspct    2002\n",
       "Transport-moving     1597\n",
       "Handlers-cleaners    1370\n",
       "Farming-fishing       994\n",
       "Tech-support          928\n",
       "Protective-serv       649\n",
       "Priv-house-serv       149\n",
       "Armed-Forces            9\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the data have missing values ?. <br>\n",
    "All variables are unique.<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "displaying data only with team = NaN\n"
     ]
    },
    {
     "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>Other</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>32</td>\n",
       "      <td>Other</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>25</td>\n",
       "      <td>Other</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>67</td>\n",
       "      <td>Other</td>\n",
       "      <td>6</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>17</td>\n",
       "      <td>Other</td>\n",
       "      <td>6</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>34095</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32530</th>\n",
       "      <td>35</td>\n",
       "      <td>Other</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>30</td>\n",
       "      <td>Other</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32539</th>\n",
       "      <td>71</td>\n",
       "      <td>Other</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32541</th>\n",
       "      <td>41</td>\n",
       "      <td>Other</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>72</td>\n",
       "      <td>Other</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1843 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age workclass  education-num         marital-status occupation  \\\n",
       "27      54     Other             10     Married-civ-spouse        NaN   \n",
       "61      32     Other              4  Married-spouse-absent        NaN   \n",
       "69      25     Other             10          Never-married        NaN   \n",
       "77      67     Other              6     Married-civ-spouse        NaN   \n",
       "106     17     Other              6          Never-married        NaN   \n",
       "...    ...       ...            ...                    ...        ...   \n",
       "32530   35     Other             13     Married-civ-spouse        NaN   \n",
       "32531   30     Other             13          Never-married        NaN   \n",
       "32539   71     Other             16     Married-civ-spouse        NaN   \n",
       "32541   41     Other              9              Separated        NaN   \n",
       "32542   72     Other              9     Married-civ-spouse        NaN   \n",
       "\n",
       "        relationship                race     sex  capital-gain  capital-loss  \\\n",
       "27           Husband  Asian-Pac-Islander    Male             0             0   \n",
       "61     Not-in-family               White    Male             0             0   \n",
       "69         Own-child               White    Male             0             0   \n",
       "77           Husband               White    Male             0             0   \n",
       "106        Own-child               White  Female         34095             0   \n",
       "...              ...                 ...     ...           ...           ...   \n",
       "32530           Wife               White  Female             0             0   \n",
       "32531  Not-in-family  Asian-Pac-Islander  Female             0             0   \n",
       "32539        Husband               White    Male             0             0   \n",
       "32541  Not-in-family               Black  Female             0             0   \n",
       "32542        Husband               White    Male             0             0   \n",
       "\n",
       "       hours-per-week native-country income  \n",
       "27                 60          South   >50K  \n",
       "61                 40            NaN  <=50K  \n",
       "69                 40  United-States  <=50K  \n",
       "77                  2  United-States  <=50K  \n",
       "106                32  United-States  <=50K  \n",
       "...               ...            ...    ...  \n",
       "32530              55  United-States   >50K  \n",
       "32531              99  United-States  <=50K  \n",
       "32539              10  United-States   >50K  \n",
       "32541              32  United-States  <=50K  \n",
       "32542              25  United-States  <=50K  \n",
       "\n",
       "[1843 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating bool series True for NaN values\n",
    "bool_series = pd.isnull(df[\"occupation\"])\n",
    "# filtering data\n",
    "print(\"displaying data only with team = NaN\")\n",
    "df[bool_series]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data have missing values<br>\n",
    "How many of them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    30718\n",
       "True      1843\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool_series.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Missing values about Occupation is 5.66% of all data'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'Missing values about Occupation is {(df[\"occupation\"].isnull().sum()/(df[\"occupation\"].count()+df[\"occupation\"].isnull().sum())):2.2%} of all data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a percentage it looks as if the data loss was not very large. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll create **Other** variable for missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prof-specialty       4140\n",
       "Craft-repair         4099\n",
       "Exec-managerial      4066\n",
       "Adm-clerical         3770\n",
       "Sales                3650\n",
       "Other-service        3295\n",
       "Machine-op-inspct    2002\n",
       "Other                1843\n",
       "Transport-moving     1597\n",
       "Handlers-cleaners    1370\n",
       "Farming-fishing       994\n",
       "Tech-support          928\n",
       "Protective-serv       649\n",
       "Priv-house-serv       149\n",
       "Armed-Forces            9\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'].fillna('Other', inplace = True)\n",
    "df['occupation'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RELATIONSHIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Husband           13193\n",
       "Not-in-family      8305\n",
       "Own-child          5068\n",
       "Unmarried          3446\n",
       "Wife               1568\n",
       "Other-relative      981\n",
       "Name: relationship, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['relationship'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    32561\n",
       "Name: relationship, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df[\"relationship\"]).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two columns **Relationship** and **Marital Status** carry similar information.\n",
    "But no the same so I leave both columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RACE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_countplot(countplot_x='race', countplot_hue='income', countplot_data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White    27816\n",
       "Black     3124\n",
       "Other     1621\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['race'].replace(['Asian-Pac-Islander', 'Amer-Indian-Eskimo', 'Other'],'Other', inplace = True)\n",
    "df['race'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vast majority of people included in this dataset are of the **White** race. <br> I can sum no White and no Black races to one variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SEX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex     income\n",
      "Female  <=50K      9592\n",
      "        >50K       1179\n",
      "Male    <=50K     15128\n",
      "        >50K       6662\n",
      "Name: income, dtype: int64\n",
      "------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_income = pd.DataFrame({'sex': df['sex'], 'income': df['income']})\n",
    "data = sex_income.income.groupby([sex_income.sex,  sex_income.income]).size()\n",
    "plt.title('Gender division in data')\n",
    "print(data)\n",
    "print('------------------------------------------------')\n",
    "df['sex'].value_counts().plot.pie(autopct='%1.1f%%', colors=['#33cccc','#ded033'])\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.show()\n",
    "print('------------------------------------------------')\n",
    "mosaic(data, gap=0.01, title = \"Sex and income\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are more Male participants than Female participants.<br>\n",
    "More percentage of Male have an Income of more than 50K$ than Females."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CAPITAL-GAIN and CAPITAL-LOSS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Capital gain - a profit that is made by selling property or an investment <br>\n",
    "Capital loss - a loss as a result of the sale of an asset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ola/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_distplot(column=df['capital-gain'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        29849\n",
      "15024      347\n",
      "7688       284\n",
      "7298       246\n",
      "99999      159\n",
      "         ...  \n",
      "4931         1\n",
      "1455         1\n",
      "6097         1\n",
      "22040        1\n",
      "1111         1\n",
      "Name: capital-gain, Length: 119, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['capital-gain'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ola/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_distplot(column=df['capital-loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       31042\n",
      "1902      202\n",
      "1977      168\n",
      "1887      159\n",
      "1848       51\n",
      "        ...  \n",
      "1411        1\n",
      "1539        1\n",
      "2472        1\n",
      "1944        1\n",
      "2201        1\n",
      "Name: capital-loss, Length: 92, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['capital-loss'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HOURS-PER-WEEK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_distplot(column=df['hours-per-week'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most people work about 40h/week, but data is in range of 0 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NATIVE COUNTRIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "United-States                 29170\n",
      "Mexico                          643\n",
      "Philippines                     198\n",
      "Germany                         137\n",
      "Canada                          121\n",
      "Puerto-Rico                     114\n",
      "El-Salvador                     106\n",
      "India                           100\n",
      "Cuba                             95\n",
      "England                          90\n",
      "Jamaica                          81\n",
      "South                            80\n",
      "China                            75\n",
      "Italy                            73\n",
      "Dominican-Republic               70\n",
      "Vietnam                          67\n",
      "Guatemala                        64\n",
      "Japan                            62\n",
      "Poland                           60\n",
      "Columbia                         59\n",
      "Taiwan                           51\n",
      "Haiti                            44\n",
      "Iran                             43\n",
      "Portugal                         37\n",
      "Nicaragua                        34\n",
      "Peru                             31\n",
      "France                           29\n",
      "Greece                           29\n",
      "Ecuador                          28\n",
      "Ireland                          24\n",
      "Hong                             20\n",
      "Cambodia                         19\n",
      "Trinadad&Tobago                  19\n",
      "Laos                             18\n",
      "Thailand                         18\n",
      "Yugoslavia                       16\n",
      "Outlying-US(Guam-USVI-etc)       14\n",
      "Hungary                          13\n",
      "Honduras                         13\n",
      "Scotland                         12\n",
      "Holand-Netherlands                1\n",
      "Name: native-country, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['native-country'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total adults from each Country')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_count = df['native-country'].value_counts()\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_facecolor((0,0,0.10))\n",
    "plt.bar(country_count.index, country_count.values)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.xlabel('Countries')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Total adults from each Country')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is **\"?\"** category, which appears to be error. <br>\n",
    "The majority of people are from **United States** so for the rest I'll create **Other** variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8fda5eaa90>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.drop(df[df['native-country'] == '?'].index)\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_facecolor((0,0,0.10))\n",
    "countries = np.array(df['native-country'].unique())\n",
    "countries = np.delete(countries, 0)\n",
    "df['native-country'].replace(countries, 'Other', inplace = True)\n",
    "sns.countplot(y = 'native-country', hue = 'income', data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding target column with 0/1 values <br>\n",
    "0 when INCOME <=50K <br>\n",
    "1 when INCOME >50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = df['income']\n",
    "df['target'] = np.where(df['income'].isin(['>50K']), 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "id there any missing values in income?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in INCOME column  0\n"
     ]
    }
   ],
   "source": [
    "missing_values_income = df['income'].isnull().sum()\n",
    "print(\"Missing values in INCOME column \", missing_values_income)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No missing values in INCOME column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation matrix to identify their relation with income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20, 10))\n",
    "sns.heatmap(df.corr(), vmax=.9, square=True, annot=True, fmt='.1f', center=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It’s clear that there is no high linear correlation between any of the continuous features and the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll drop **Income** column, beacuse there is 0/1 **Target** column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset after all changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  education-num      marital-status  \\\n",
       "0   39         State-gov             13       Never-married   \n",
       "1   50  Self-emp-not-inc             13  Married-civ-spouse   \n",
       "2   38           Private              9            Divorced   \n",
       "3   53           Private              7  Married-civ-spouse   \n",
       "4   28           Private             13  Married-civ-spouse   \n",
       "\n",
       "          occupation   relationship   race     sex  capital-gain  \\\n",
       "0       Adm-clerical  Not-in-family  White    Male          2174   \n",
       "1    Exec-managerial        Husband  White    Male             0   \n",
       "2  Handlers-cleaners  Not-in-family  White    Male             0   \n",
       "3  Handlers-cleaners        Husband  Black    Male             0   \n",
       "4     Prof-specialty           Wife  Black  Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week native-country  target  \n",
       "0             0              40  United-States       0  \n",
       "1             0              13  United-States       0  \n",
       "2             0              40  United-States       0  \n",
       "3             0              40  United-States       0  \n",
       "4             0              40          Other       0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "education-num     0\n",
       "marital-status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "sex               0\n",
       "capital-gain      0\n",
       "capital-loss      0\n",
       "hours-per-week    0\n",
       "native-country    0\n",
       "target            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'target'\n",
    "num_features = ['age', 'capital-gain', 'capital-loss', 'hours-per-week', 'education-num']\n",
    "cat_features = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OneHotEncoder(SklearnOneHotEncoder):\n",
    "    def __init__(self, **kwargs):\n",
    "        super(OneHotEncoder, self).__init__(**kwargs)\n",
    "        self.fit_flag = False\n",
    "\n",
    "    def fit(self, X, **kwargs):\n",
    "        out = super().fit(X)\n",
    "        self.fit_flag = True\n",
    "        return out\n",
    "\n",
    "    def transform(self, X, **kwargs):\n",
    "        sparse_matrix = super(OneHotEncoder, self).transform(X)\n",
    "        new_columns = self.get_new_columns(X = X)\n",
    "        d_out = pd.DataFrame(sparse_matrix.toarray(), columns = new_columns, index = X.index)\n",
    "        return d_out\n",
    "\n",
    "    def fit_transform(self, X, **kwargs):\n",
    "        self.fit(X)\n",
    "        return self.transform(X)\n",
    "\n",
    "    def get_new_columns(self, X):\n",
    "        new_columns = []\n",
    "        for i, column in enumerate(X.columns):\n",
    "            j = 0\n",
    "            while j < len(self.categories_[i]):\n",
    "                new_columns.append(f'{column}_{self.categories_[i][j]}')\n",
    "                j += 1\n",
    "        return new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = OneHotEncoder()\n",
    "encoder_cols = encoder.fit_transform(df[cat_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add one-hot encoded columns to numerical features and target column\n",
    "df = pd.concat([pd.concat([df[num_features], encoder_cols], axis=1), df[target]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>education-num</th>\n",
       "      <th>workclass_Local-gov</th>\n",
       "      <th>workclass_Other</th>\n",
       "      <th>workclass_Private</th>\n",
       "      <th>workclass_Self-emp-inc</th>\n",
       "      <th>workclass_Self-emp-not-inc</th>\n",
       "      <th>...</th>\n",
       "      <th>relationship_Unmarried</th>\n",
       "      <th>relationship_Wife</th>\n",
       "      <th>race_Black</th>\n",
       "      <th>race_Other</th>\n",
       "      <th>race_White</th>\n",
       "      <th>sex_Female</th>\n",
       "      <th>sex_Male</th>\n",
       "      <th>native-country_Other</th>\n",
       "      <th>native-country_United-States</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  capital-gain  capital-loss  hours-per-week  education-num  \\\n",
       "0   39          2174             0              40             13   \n",
       "1   50             0             0              13             13   \n",
       "2   38             0             0              40              9   \n",
       "3   53             0             0              40              7   \n",
       "4   28             0             0              40             13   \n",
       "\n",
       "   workclass_Local-gov  workclass_Other  workclass_Private  \\\n",
       "0                  0.0              0.0                0.0   \n",
       "1                  0.0              0.0                0.0   \n",
       "2                  0.0              0.0                1.0   \n",
       "3                  0.0              0.0                1.0   \n",
       "4                  0.0              0.0                1.0   \n",
       "\n",
       "   workclass_Self-emp-inc  workclass_Self-emp-not-inc  ...  \\\n",
       "0                     0.0                         0.0  ...   \n",
       "1                     0.0                         1.0  ...   \n",
       "2                     0.0                         0.0  ...   \n",
       "3                     0.0                         0.0  ...   \n",
       "4                     0.0                         0.0  ...   \n",
       "\n",
       "   relationship_Unmarried  relationship_Wife  race_Black  race_Other  \\\n",
       "0                     0.0                0.0         0.0         0.0   \n",
       "1                     0.0                0.0         0.0         0.0   \n",
       "2                     0.0                0.0         0.0         0.0   \n",
       "3                     0.0                0.0         1.0         0.0   \n",
       "4                     0.0                1.0         1.0         0.0   \n",
       "\n",
       "   race_White  sex_Female  sex_Male  native-country_Other  \\\n",
       "0         1.0         0.0       1.0                   0.0   \n",
       "1         1.0         0.0       1.0                   0.0   \n",
       "2         1.0         0.0       1.0                   0.0   \n",
       "3         0.0         0.0       1.0                   0.0   \n",
       "4         0.0         1.0       0.0                   1.0   \n",
       "\n",
       "   native-country_United-States  target  \n",
       "0                           1.0       0  \n",
       "1                           1.0       0  \n",
       "2                           1.0       0  \n",
       "3                           1.0       0  \n",
       "4                           0.0       0  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df.columns.tolist()\n",
    "features.remove(target)\n",
    "X = df[features]\n",
    "\n",
    "y = df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    " def ConfusionMatrix(classifier, X=X, y=y, confusionMatrix = True, plotConfusionMatrix = True):\n",
    "        '''ConfusionMatrix function split data, fit data to model and give \n",
    "        a prediction for a given model and data. After that draw Confusion Matrix or \n",
    "        Plot Confusion Matrix to show the score'''\n",
    "        \n",
    "        #split dataset into train and test data\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, \n",
    "                                                            stratify=y)\n",
    "\n",
    "        # Fit the classifier to the data\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "\n",
    "        #computing the confusion matrix with each row corresponding to the true class\n",
    "        if(confusionMatrix):\n",
    "            print(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "        #drawing Plot Confusion Matrix\n",
    "        if(plotConfusionMatrix):\n",
    "            plot_confusion_matrix(classifier, X_test, y_test)  \n",
    "            plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GridSearch(param, estimator, X=X, y=y):\n",
    "    # split into a training and testing set\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "    grid_rf = GridSearchCV(estimator, param, refit = True, verbose = 3, n_jobs=-1) \n",
    "\n",
    "    # fitting the model for grid search \n",
    "    grid_rf.fit(X_train, y_train) \n",
    "\n",
    "    # print best parameter after tuning \n",
    "    print(grid_rf.best_params_) \n",
    "    grid_rf_predictions = grid_rf.predict(X_test) \n",
    "\n",
    "    # print classification report \n",
    "    print(classification_report(y_test, grid_rf_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CVmodel(nFolds = 5, randomState=2020, features=features, lin_reg = False, lin_reg_without_fi = False,\n",
    "            cutoff=0.5, debug= False, model = SVC(), df = df, pipeline = False):\n",
    "    \n",
    "    kf = KFold(n_splits=nFolds, shuffle=True, random_state=randomState)\n",
    "    \n",
    "    # predictions for individual folds\n",
    "    predictions = []\n",
    "\n",
    "    # the index in the original file\n",
    "    indices = []\n",
    "    true=[]\n",
    "    \n",
    "    if lin_reg:\n",
    "        for train, test in kf.split(df.index.values):\n",
    "            if debug:\n",
    "                print(model)\n",
    "            model.fit(df.iloc[train][features], df.iloc[train][target])\n",
    "\n",
    "            # predsTrain = model.predict_proba(df.iloc[train][features])[:,1]\n",
    "            # preds = model.predict_proba(df.iloc[test][features])[:,1]\n",
    "            preds, y_test = model.predict(df.iloc[test][features], round(cutoff,1))\n",
    "            \n",
    "            # information about predictions for this fold\n",
    "            predictions.append(preds.tolist().copy())\n",
    "            # indexed in the original data frame\n",
    "            indices.append(df.iloc[test].index.tolist().copy())\n",
    "            \n",
    "            true = (df.iloc[test][target]==1)\n",
    "            if(lin_reg_without_fi == False):\n",
    "                feature_importance = model.coef_\n",
    "        if(lin_reg_without_fi):\n",
    "            return predictions, indices, true\n",
    "            \n",
    "        return predictions, indices, feature_importance, true\n",
    "        \n",
    "        \n",
    "    if(pipeline):\n",
    "        for train, test in kf.split(df.index.values):\n",
    "            if debug:\n",
    "                print(model)\n",
    "            model.fit(df.iloc[train][features], df.iloc[train][target])\n",
    "\n",
    "            # predsTrain = model.predict_proba(df.iloc[train][features])[:,1]\n",
    "            preds = model.predict_proba(df.iloc[test][features])[:,1]\n",
    "\n",
    "            # information about predictions for this fold\n",
    "            predictions.append(preds.tolist().copy())\n",
    "\n",
    "            # indexed in the original data frame\n",
    "            indices.append(df.iloc[test].index.tolist().copy())\n",
    "        \n",
    "        return predictions, indices, true\n",
    "        \n",
    "    for train, test in kf.split(df.index.values):\n",
    "        if debug:\n",
    "            print(model)\n",
    "        model.fit(df.iloc[train][features], df.iloc[train][target])\n",
    "\n",
    "        # predsTrain = model.predict_proba(df.iloc[train][features])[:,1]\n",
    "        preds = model.predict_proba(df.iloc[test][features])[:,1]\n",
    "\n",
    "        # information about predictions for this fold\n",
    "        predictions.append(preds.tolist().copy())\n",
    "\n",
    "        # indexed in the original data frame\n",
    "        indices.append(df.iloc[test].index.tolist().copy())\n",
    "\n",
    "        true.append(df.iloc[test][target]==1)\n",
    "\n",
    "        feature_importance = model.feature_importances_\n",
    "        \n",
    "    return predictions, indices, feature_importance, true"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DECISION TREE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pOaB/RMwBkHQTMIlWgl+rzd6I+ExErI+IHwMfAqak5q+ZFVUbf94maQTZMpYPp6QLJD0h6QZJA1LaUGB5yWUrUtrQtL97elktBj9J79p9AwYC3dK+mVlrBkmaX7JN3T2DpL7Ar4HPRcQGsibswWQzSK0CLm/K2sz9o0x6WeWavZeXORfASa3dPK8/L+zDxAOPbuvbWhV1Gzmk1kWwHLSiR9vcp/Ja3ZqIGNfifaTuZIHvloi4DSAiXio5fx1wZzpcAQwvuXwYsDKlD2smvaxyLzmf2NrFZlZAQZt83pZGZH8KPBURV5SkD0n9gQCnA0+m/ZnAzyVdQTbgMQqYFxENkjZKGk/WbD4b+EFrz/ei5WaWX9u853cs8AlgoaQFKe0S4CxJY9NTniN9WhsRiyTNABaTjRSfn0Z6Ac4DbgR6kw10lB3sAAc/M3sT2mi09yGa76+7q8w104BpzaTPB8bkeb6Dn5nlV4QvPJT5O0lfTccHSPKohFmRFWQm56uBY4Cz0vFG4EdVK5GZ1TVF5Vs9q6TZ+56IeJek/waIiFfSEpZmVlSdfDLTJtsldSVVYiUNptVPls2sM6v3Wl0lKmn2XgX8BthH0jSy6ay+WdVSmVl96wR9fpWs23uLpEfJprUSMCkinqp6ycysPnWA/rxKVDKZ6QHAa8B/lKZFxLJqFszM6lgRgh/ZSm1NHw/3AkYCT5PNqWVmBaRO0OtfSbP3HaXHaUaXT7eQ3cysQ8j9hUeadfXd1SiMmXUQRWj2SvqnksMuwLuAl6tWIjOrb0UZ8AD6lezvIOsD/HV1imNmHUJnD37p5ea+EfGldiqPmXUEnTn4SeoWETs8Zb2ZlRKdf7R3Hln/3gJJM4FfAq82nWyactrMCqZAfX4DgbVka3Y0ve8XgIOfWVF18uC3TxrpfZI3rpDUCX66mb1pnSAClAt+XYG+vMll4cys8+rszd5VEfGNdiuJmXUcnSD4lZvSquPPVmhmbS+y0d5KtnIkDZf0gKSnJC2S9NmUPlDSfZKeSX8OKLnmYklLJD0taUJJ+lGSFqZzV6VlMcsqF/w+0OpfgpkVU9vM57cD+EJEvB0YD5wvaTRwETArIkYBs9Ix6dxksklVJgJXp3eRAa4BppKt5TsqnS+rxeAXEetaLbqZFVJbrOEREasi4rG0vxF4ChgKnAZMT9mmA5PS/mnArRGxNSKWAkuAoyUNAfpHxJyICOCmkmta5KUrzSy/yvv8BkmaX3J8bURcu3smSSOAI4GHgX0jYhVkAVLSPinbUGBuyWUrUtr2tL97elkOfmaWT74p6tdExLhyGST1JZsv4HMRsaFMd11Lb568qTdSKlnDw8xsJ9F2S1dK6k4W+G4p+WrspdSUJf25OqWvAIaXXD4MWJnShzWTXpaDn5nl1hbBL43I/hR4KiKuKDk1E5iS9qcAd5SkT5bUU9JIsoGNeamJvFHS+HTPs0uuaZGbvWaWX9u853cs8AlgoaQFKe0S4FvADEmfApYBZwJExCJJM4DFZCPF50dEQ7ruPOBGoDdwd9rKcvAzs/zaIPhFxEO0/D5xs6/aRcQ0YFoz6fOBMXme7+BnZvkUaFYXM7NdOfiZWRF19slMzcya5WavmRVPvpec65aDn5nl5+BnZkXT9IVHR+fgZ2a5qbHjRz8HPzPLx31+ZlZUbvaaWTE5+JlZEbnmZ2bF5OBnZoUT/rzNzArI7/mZWXFFx49+Dn5mlptrfgZAn/47+Ny3n2PEoZsJ4MovjeTdJ67nmA+tp7ER1q/tzuVfGMm61T04cdJazpi6aue1I9++mQs+fDjPLt6jdj+ggD5y5rNM+MjzSHDPzAO4Y8bB/N3/+RPj37eKCLH+lZ5cOe1I1q3pRb/+27hk2iOMOmw99989nB9f8c5aF7+2/JJzeZJuAP4aWB0RuaaX7mjOvXQZj/5+T6addwjdujfSs3cjz/95CDddni0oddrfv8THP7uSH/zzCB64fW8euH1vAEa87TUuvX6JA187O3DkBiZ85Hn+6R+OY/uOLlx2+Vwe+eO+/PqWg7n5usMAOPWMZznrk0/zo+8ewbZtXfjZdYdx4EEbOfCgDTUufX3oDAMe1Vy97UZgYhXvXxf26NvAO96zkd/dOgiAHdu78OqGbry2qevOPL32aGi2i+SEj6xj9syB7VVUS4aP2MTTiwawdWs3Ghu6sHDB3hxz/Co2v9Z9Z55evV//N9u6pRuLn9ib7du82GETNVa21bOq/WtGxB+AddW6f73Y74Ct/GVtd77wvaX88K5FfO7bS+nZO1tQasqXVvCzOQs4cdI6fnbFGxeQP/7Udcy+w8GvvT3/bD/GHLGWfv230bPnDsYds5rB+24B4OypT3Hjbfdywl+t4ObrD6txSetUkA14VLK1QtINklZLerIk7WuSXpC0IG2nlJy7WNISSU9LmlCSfpSkhencVSqz8nmTmv+vTNJUSfMlzd8eW2pdnNy6dg0OGfMqd968DxeccjhbXuvC334m69Ob/t1hfOKYsTxw+0BOnbJ6l+veNnYTWzd34fk/u8nb3pY/349f3XII//r9OXzjirksXdKfhobsv5Wbrn07f//Rv2L2vcM49W+W1rik9autFi2n5RbilRExNm13AUgaDUwGDk/XXC2pqYl1DTCVbC3fUS3ccxc1D34RcW1EjIuIcd3Vq9bFyW3Niz1Ys6oHTy/oC8CDdw3kkDGv7ZLngTv25n0nv7JL2vtPdZO3lu6980A+e877+cr572Pjhh6sXN5nl/Oz7x3Ke09Y1cLVtnPQo7WttdvkayGeBtwaEVsjYimwBDha0hCgf0TMiYgAbgImtXazmge/ju6Vl7vz8qoeDDtoMwBHHruBZc/0Zv8Rr9dix39oPcv/5/XALgXHfXgdv3fwq5k999oKwOB9X+O971/F7+8fyv7DNu08P/64F1nxfN9aFa+uNb3kXGHNb1BTyy5tUyt8zAWSnkjN4gEpbSiwvCTPipQ2NO3vnl6WX3VpA1dfeiBf/rdn6d49WLWsJ1d8cSSf+85zDDtoC9EIL73Qgx9cMmJn/ne8ZyNrVvXgxeUdr6bbWVzyzUfo338bO3Z04ZrL38GmjT248KLHGXrAJqIRVr+4Bz/67uuvtNzwq/vYo88OunVr5JjjXuRfPn8My5/rV8NfUEMReSYzXRMR43I+4RrgMrK642XA5cA5NL/AeZRJL6uar7r8AjiBLPKvAC6NiJ9W63m19OziPbjw1MN3SfvXcw9pMf8Tc/vz+dNHV7tYVsZXPvO+N6R985/f3WL+c874UDWL0/FU8T2/iHipaV/SdcCd6XAFMLwk6zBgZUof1kx6WdUc7T0rIoZERPeIGNZZA59ZEbXhgMcb75314TU5HWgaCZ4JTJbUU9JIsoGNeRGxCtgoaXwa5T0buKO157jZa2b5BNBGa3g010IETpA0Nj3pOeDTABGxSNIMYDGwAzg/IhrSrc4jGznuDdydtrIc/MwsvzZq9kbEWc0kt9hKjIhpwLRm0ucDub4kc/Azs9w8sYGZFZKXrjSz4vGsLmZWRNlLzh0/+jn4mVl+dT5jSyUc/MwsN9f8zKx43OdnZsWU69veuuXgZ2b5udlrZoXjRcvNrLBc8zOzQur4sc/Bz8zyU2PHb/c6+JlZPoFfcjaz4hHhl5zNrKAc/MyskBz8zKxw3OdnZkXVGUZ7vWi5meUUWbO3kq0VaVHy1ZKeLEkbKOk+Sc+kPweUnLtY0hJJT0uaUJJ+lKSF6dxVaRW3shz8zCyfoM2CH9mKaxN3S7sImBURo4BZ6RhJo4HJwOHpmqsldU3XXANMJVvOclQz93wDBz8zy6+xwq0VEfEHYN1uyacB09P+dGBSSfqtEbE1IpYCS4Cj0zq//SNiTkQEcFPJNS1yn5+Z5ZbjPb9BkuaXHF8bEde2cs2+aSFyImKVpH1S+lBgbkm+FSlte9rfPb0sBz8zy6/y4LcmIsa10VOb68eLMullOfiZWT4R0FDV0d6XJA1Jtb4hwOqUvgIYXpJvGLAypQ9rJr0s9/mZWX5tN+DRnJnAlLQ/BbijJH2ypJ6SRpINbMxLTeSNksanUd6zS65pkWt+ZpZfG33hIekXwAlkfYMrgEuBbwEzJH0KWAacmT0yFkmaASwGdgDnR0RDutV5ZCPHvYG701aWg5+Z5RNAG63hERFntXDqAy3knwZMayZ9PjAmz7Md/Mwsp4Do+F94OPiZWT5BtQc82oWDn5nl51ldzKyQHPzMrHje0mssdcPBz8zyCaATTGnl4Gdm+bnmZ2bFU/XP29qFg5+Z5RMQfs/PzAqpjb7wqCUHPzPLz31+ZlY4ER7tNbOCcs3PzIoniIaG1rPVOQc/M8unDae0qiUHPzPLz6+6mFnRBBCu+ZlZ4YQnMzWzguoMAx6KOhqylvQy8Hyty1EFg4A1tS6E5dJZ/80OjIjBb+UGkn5H9vdTiTURMfGtPK9a6ir4dVaS5rfhws3WDvxv1vl53V4zKyQHPzMrJAe/9nFtrQtgufnfrJNzn5+ZFZJrfmZWSA5+ZlZIDn5VJulKSQ9K+rdal8VaJ2l/SY9J2iLJHwF0Yg5+VSTpXUCfiDgO6CHp3bUuk7VqHfABYG6tC2LV5eBXXccA96f9+4HxNSyLVSAitkTEK7Uuh1Wfg1917QVsSPt/AQbUsCxmVsLBr7rWA/3Tfv90bGZ1wMGvuuaQ9R8BfBD3I5nVDQe/KoqIx4Atkh4EGiNiXq3LZOVJ6i7pfuAI4B5J76l1maw6/IWHmRWSa35mVkgOfmZWSA5+ZlZIDn5mVkgOfmZWSA5+HYikBkkLJD0p6ZeS9ngL97pR0hlp/3pJo8vkPUHSe9/EM56T9IaFblpK3y3PppzP+pqkL+YtoxWXg1/HsjkixkbEGGAbcG7pSUld38xNI+IfImJxmSwnALmDn1k9c/DruB4EDkm1sgck/RxYKKmrpO9KekTSE5I+DaDMDyUtlvRbYJ+mG0maLWlc2p+YpnR6XNIsSSPIguznU63zOEmDJf06PeMRScema/eWdK+k/5b0E0Ct/QhJt0t6VNIiSVN3O3d5KsssSYNT2sGSfpeueVDSYW3xl2nF4/nKOqA0z9zJwO9S0tHAmIhYmgLIXyLi3ZJ6Av8l6V7gSOBtwDuAfYHFwA273XcwcB1wfLrXwIhYJ+nHwKaI+F7K93Pgyoh4SNIBwD3A24FLgYci4huSPgzsEsxacE56Rm/gEUm/joi1QB/gsYj4gqSvpntfQLa2xrkR8Uz6+uJq4KQ38ddoBefg17H0lrQg7T8I/JSsOTovIpam9L8C3tnUnwfsCYwCjgd+ERENwEpJ/9nM/ccDf2i6V0Ssa6EcHwRGSzsrdv0l9UvP+Gi69reSKpka6kJJp6f94amsa4FG4P+n9JuB2yT1Tb/3lyXP7lnBM8zewMGvY9kcEWNLE1IQeLU0CfjHiLhnt3ynAK19y6gK8kDWXXJMRGxupiwVfy8p6QSyQHpMRLwmaTbQq4XskZ67fve/A7M3w31+nc89wHmSugNIOlRSH+APwOTUJzgEOLGZa+cA75c0Ml07MKVvBPqV5LuXrAlKytcUjP4AfDylnUzr8xfuCbySAt9h7DrZaxegqfb6MbLm9AZgqaQz0zMk6YhWnmHWLAe/zud6sv68xyQ9CfyErIb/G+AZYCFwDfD73S+MiJfJ+uluk/Q4rzc7/wM4vWnAA7gQGJcGVBbz+qjz14HjJT1G1vxe1kpZfwd0k/QEcBm7Tvn1KnC4pEfJ+vS+kdI/DnwqlW8RcFoFfydmb+BZXcyskFzzM7NCcvAzs0Jy8DOzQnLwM7NCcvAzs0Jy8DOzQnLwM7NC+l8J8igsRwqN1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Decision Tree classifer object\n",
    "tree = DecisionTreeClassifier()\n",
    "\n",
    "ConfusionMatrix(classifier=tree, confusionMatrix=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = DecisionTreeClassifier()\n",
    "predictions, indices, feature_importance, true = CVmodel(model=tree)\n",
    "\n",
    "tree = {\n",
    "    \"name\":\"Decision Tree\",\n",
    "    \"predictions\" : predictions,\n",
    "    \"indices\": indices,\n",
    "    \"feature importance\": feature_importance,\n",
    "    \"true\":true\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the file for binary writing using \"with\"\n",
    "with open(\"models_results/decision_tree.p\", \"wb\") as fp:\n",
    "    pickle.dump(tree, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RANDOM FOREST ALGORITHM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:   36.3s\n",
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:  4.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 20, 'max_features': 10, 'n_estimators': 100}\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.94      0.91      6159\n",
      "           1       0.77      0.62      0.68      1982\n",
      "\n",
      "    accuracy                           0.86      8141\n",
      "   macro avg       0.82      0.78      0.80      8141\n",
      "weighted avg       0.86      0.86      0.86      8141\n",
      "\n"
     ]
    }
   ],
   "source": [
    "param = {'max_depth': [2, 10, 20],\n",
    "         'n_estimators': [100, 500],\n",
    "         'max_features': [10 , 20]}\n",
    "\n",
    "GridSearch(param = param, estimator = RandomForestClassifier())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best score: {'max_depth': 20, 'max_features': 10, 'n_estimators': 100}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 20, 'max_features': 30, 'min_samples_split': 30}\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.94      0.91      6159\n",
      "           1       0.76      0.63      0.69      1982\n",
      "\n",
      "    accuracy                           0.86      8141\n",
      "   macro avg       0.82      0.78      0.80      8141\n",
      "weighted avg       0.86      0.86      0.86      8141\n",
      "\n"
     ]
    }
   ],
   "source": [
    "param = {'max_depth': [20, 50],\n",
    "         'max_features': [10, 30],\n",
    "         'min_samples_split': [10, 30]}\n",
    "\n",
    "GridSearch(param=param, estimator=RandomForestClassifier())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best parameters: {'max_depth': 50, 'max_features': 10, 'min_samples_split': 30}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Random Forest model \n",
    "rand_forest = RandomForestClassifier(random_state=2020, n_jobs=-1, n_estimators=100, \n",
    "                                 max_depth=50, max_features=10, min_samples_split=30)\n",
    "\n",
    "ConfusionMatrix(classifier=rand_forest, confusionMatrix=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_forest = RandomForestClassifier(random_state=2020, n_jobs=-1, n_estimators=100, \n",
    "                                 max_depth=50, max_features=10, min_samples_split=30)\n",
    "\n",
    "predictions, indices, feature_importance, true = CVmodel(model=rand_forest)\n",
    "\n",
    "random_forest = {\n",
    "    \"name\":\"Random Forest\",\n",
    "    \"predictions\": predictions,\n",
    "    \"indices\": indices,\n",
    "    \"feature importance\": feature_importance,\n",
    "    \"true\":true\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the file for binary writing using \"with\"\n",
    "with open(\"models_results/random_forest.p\", \"wb\") as fp:\n",
    "    pickle.dump(random_forest, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LINEAR REGRESSION ALGORITHM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we use Linear Regression for Classification problem?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegression(SklearnLinearRegression):\n",
    "\n",
    "    def predict(self, X_test, cutoff):\n",
    "        y_pred = super().predict(X_test) \n",
    "        \n",
    "        # initialise a matrix full with zeros\n",
    "        y_pred_classes = np.zeros_like(y_pred)\n",
    "        # add a 1 if the cutoff was breached\n",
    "        y_pred_classes[y_pred > cutoff] = 1       \n",
    "        \n",
    "        return y_pred_classes, y_test\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ConfusionMatrixOrHeatMap(y_true, y_pred, confusionMatrix = True, \n",
    "                                       HeatmapConfusionMatrix = True):\n",
    "    #confusion matrix\n",
    "    c = confusion_matrix(y_true, y_pred)\n",
    "    \n",
    "    if(confusionMatrix):\n",
    "        return c\n",
    "    if(HeatmapConfusionMatrix):\n",
    "        #drawing Plot Confusion Matrix\n",
    "        sns.heatmap(c, annot=True, fmt=\"d\")\n",
    "        plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#split dataset into train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "\n",
    "#create object of LinearRegression class\n",
    "lr = LinearRegression()\n",
    "# Fit the classifier to the data\n",
    "lr.fit(X=X_train, y=y_train)\n",
    "#classifing prediction by threshold 0.7\n",
    "y_pred_classes, y_test_lr = lr.predict(X_test, 0.5)\n",
    "\n",
    "ConfusionMatrixOrHeatMap(y_test_lr, y_pred_classes, confusionMatrix = False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changing cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create object of LinearRegression class\n",
    "lr = LinearRegression()\n",
    "# Fit the classifier to the data\n",
    "lr.fit(X=X_train, y=y_train)\n",
    "#classifing prediction by threshold 0.7\n",
    "y_pred_classes, y_test_lr = lr.predict(X_test, 0.7)\n",
    "\n",
    "ConfusionMatrixOrHeatMap(y_test_lr, y_pred_classes, confusionMatrix = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking best cutoff by ROC AUC:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotROCs(results, saving = False):\n",
    "    # Determining the size of the drawing\n",
    "    fig, ax = plt.subplots(figsize=(10,9))\n",
    "    ax.set_facecolor((0,0,0.1))\n",
    "    \n",
    "        \n",
    "    for true, pred, label in results:\n",
    "        \n",
    "        # Calculation of the points needed to draw the ROC curve\n",
    "        # the roc_curve function concludes three data series, fpr, \n",
    "        #tpr, and cutoff levels\n",
    "        fpr, tpr, thresholds = roc_curve(true, pred)\n",
    "        \n",
    "        # We calculate the area under the curve\n",
    "        rocScore = roc_auc_score(true, pred)\n",
    "        rocScore = round(rocScore, 3)\n",
    "        \n",
    "        # curve thickness\n",
    "        lw = 2\n",
    "\n",
    "        # We draw the ROC curve\n",
    "        ax.plot(fpr, tpr, lw=lw, label=f'{label}: {rocScore}')\n",
    "  \n",
    "    # We draw a 45 degree curve as a reference point\n",
    "    ax.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    \n",
    "    # We add light margins to the range so that the curve \n",
    "    #does not coincide with the axes\n",
    "    ax.set_xlim([-0.01, 1.0])\n",
    "    ax.set_ylim([0.0, 1.01])\n",
    "    ax.set_xlabel('False Positive Rate')\n",
    "    ax.set_ylabel('True Positive Rate')\n",
    "    ax.set_title(f'Receiver operating characteristic - {rocScore}')\n",
    "    ax.legend(loc=\"lower right\")\n",
    "    if saving:\n",
    "        plt.savefig('images/ROC.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RfIQIPw4tSNhoZjpOC+C0BAix6IRYAjg1nRCrjsO8DbEYt06rUd8UYjX/bBBiBWfnfePWaWVUhVJ/eAPg9oHbL8xbaO+8b9y2BgQNQqNBEzRpFposhohqtWq47RrtNg2PXeB1aPgdGgGnIBCigVPAKYqsDnSfJNAqCbTqBFqM+3qLJLpBJ6URUpsh1S1I80F6QCNNCGyi57mMxwEpOebTKfXqlNtiaEuYS2jyPFwJ2QgBurcYv3stIaKdhbHL2ZzVxJ/d6eh1EHlBIqFs5egb64d1HaPZSuMZ4BIgXghRDtwD2ACklI8C6zDaaBzCaKVxy2iNRaFQKBTnGhKHhW5CJsQqcVqHsgxiZ0wnPPZd7N4m8OzGOc3XQxxBiE12WzZWQsnjh3Z/d6Hk9ok+BVPnsqBt3L6+l7n9wjiuFLTbNbwODZ9DEHAKRJiGJUzDGibQwgTWMIElUsMSLrCEmX+hw0/JBtymwGqVQX/m45buj/0txv1IN2QKjUybRpZdI9Nu3M+wa4RqfdSrm8Or9OmUeHWOmbelXp0Sn0SLzmRWZh45mXmkxBmJPqm342v/BK/7E+yanxlRC8mMyeOXobt59iEb8Ve140hzUPevX9O49+Cwr3/CeWsaNWeqz5lCoVBMHAwB5LR2F0X9RYP6XmaKqCBR1CWOei8LsY3NlXr8XeKo3U8PwSN6iyjzfpc4GnhZp2DqFF+gy6EpQs0JlnCtSzyFCSxh5uNwgTWsx7pwDUuoQHMMT3FKXRJo6yGuWgZ+7G+V6K0SGej7mOEaZNoM4ZVl18iyW8gwxVjkANG2Wr9OqRkFK/WaIsync8yr4zZlkNViIztlFrlZeeRkzicqLKZzf7e7krr6d4m0n8BmgURnJnNjVtAcFuAe/Qjv/M6H7vYTNisST+Xf8NcF15tV7JBSnncqz51qta9QKBTnFN1FTnfB1Dsa1PeyLgHktPYWRV3RqPEjlAYXTIbw8Uenok05j3ZppWbnBzSdrOwhmLoLpWARdSpCadgIsIQKLJGGgAoN6xGtCtOwhgu0UDOaFSTGxHBThX4ZFLEKima1SAJtfUSzWqQhstyyn2l+A+MQkGHvin5lmRGwTLtGvLX/aFxzQFLaEQXz6pR4A52CrEnve58wRziLMuaSm7WAmek5OGzOznV1zTUcPbGVUHGIzDhBXIjAaQlnSujFzIrP4WPHPu6t9rHnrx6kXyd0SgitRX+EgPvUL7oHSpwpFArFGCEw+iX1jAadSoTIae0uiroLpr7Td2NBe0fqzSd6pN16LxswHefrSxz1XuYJnLpQcqRnk3jjdxEBK7Xrn6JlVy1mNc6II6x0Ra7C+4hk9RXNCj/NVGF7P6nCQaJZumcEL9zECqTauguwLFOAJdv6v8Z2XXaLgBkpSOO2NjA0JRgfmUBuZh45WQuYkpSNFjQjtqy6hD2lO2ht28OcZI3ZrjBAIxCQOPQZLE9dibALXo54l1/tTKR8bT0A4bOttOz9PcNSo32gxJlCoVCMMFZN8puVXmbE690Kt3um9Jxj9Anc3rOmqK86oyCh1Lt+aeCapp7pu3Y/yD67J40frDEJuD5zO8JipemTt2jZtWlI+2lOsIT2IbD6EVwd0azhpgqBHrVWPaNZupFK7GOd9A/7lMNCA5Ksolv0q0OMpdo0rL0K8Q18UlLu655+7Lh/0i9PWf4IBJkJk8nNWkBO5nySY9M61wUCfvaX7aWwNJ+i8l1MSxAsneYiNjwCgDaPn5N14Vw37TpmuDIp12pZn7CZ3788jbr3qgAIn9NOy56/D+cp6hclzhQKhWKE+d75Pr692DekbYOFUndx1MeyIaToBhNRE0EonWm0kFASb/wa1sh2PCe246t6kajz7X3WZXWmDjtShdZh1mOZqUJ/T3HVIbD6e9w2vFThaBJvEUE1YF0iLN2m4ehnhoQuJceDC/A7C/IDVPgkp6sjbRYb01Nnk2PWj0WGRneua/O0su/YbgpLd7K3bDcOi5+Lp7m449JkQuyGLKpp9rCztJXLMq/gJxdeio7kHX8B+7MP8WzVCnBUgyZwTi6nZc/rpzna3qgJAQqFQjGCZEbp7PtWG6E2+MbrDvZWaf2m7zxKKI0owkKXgOoZzepWCN+7KL6fIM6g6J6+Zw52q8vqI5Wot4/stY82URpk2i2GCAsqyM+0a4QNMEW12h88A7IrFXnMp+MdYfkR7oxgdsY8crPymJGWg8Pm6FxX11xDQUk+BaU7OHyiiIAeIDUmhGXZCcxNj8FiXsOR6hY2F1WT68rjJ8s+T0xIBI16G8/63qN2ruT1o0vBIxEOneN/exhf+Z4hjExNCFAoFIoxRPKHKz2E2uCfe6w8+skYFXhNcIQdrOE9aq36EFQ9o1kW5/CFrvQ78DV4CDT7zYjWANGrDsHVJpFDC5BOCEIFRvuJHinILLtGtKX/OrDGgKTEG+hMQZYE1YO1jXL8JyEqiZzMPHKy8picOK1b/dix6qMUlORTWJLP8bpjgNHBbGZKJMuyE5iSYKQuA7pk57F6NhdVEWlL4MHLv09eyjQA3qz6hNLkUnYlJLDhp3birmjDkSY49j/3oDfVj9p1KXGmUCgUI8R1MwJcMz1AYzt8b7198B3OZgRoIR01Vv2Iq77qtEIFmm2YqcKA7BWh6jea1aYTMvlCIhdeT6Bd4+QzD+E9cfb3QbcJOltPdMyA7BBgCQPMhGzTZbcasBJvlwhr1M9cBk4IQVbCVHIy55OblUdSTGrnOn/Az4GyPRSW5FNYmk9Da5d4slkE52XFsTTbhSvCmJHZ7gvw4ZEa3j9Yjd9v5QdLPstX8i7HomlUNtfyl8r1TF5sYX3TDHbc04je1kr9psO0l/4J/KOrypU4UygUihEg3C75/RXGtLY737FT2TIePRGHgcVs3RDevdZq0GhWqDC8JIeB7jMFVI/aK3+rjt5Rp9VLcJ1aqjBk2jzC538RGdCoff3PeE+UDGus4xELkGITZNkt3XqCZdo0UmwCrZ8crlfv6ohfElyQ79WpHuJMyNHAZrUzI3U2OVkLmJMxj8jQqM51bZ5W9hzbRWFJPvvLCmj3dX8TRDitXDTVxQVT4glzGJKnrtXDloPVbD9Si8evc92si7j70i+TEB6NXw/w112vU5pQx+xLJY8WLuDo4yeQPh17qkb74T8Box8uVeJMoVAoRoCfXeIlPUry8XFtXKYzhb2rdYO139YNfYitkNOYVdjWf4d3f4/HwetHO1VoT8wgftWtCKFRv/kl2op2jO4JRwEBJHbMhAyKgGXaNNLtWh+WRAYBKTnWWQMW6FaQX+mX9NMO7IwT7oxkTqZZP5Y6B3tQ/VhNUzWFpTsoKMnncGUxut67Y21SlJNl2QnMz4jBaqZkj9W2sqmoisLjDegSpsal8MBlt3Jh5mwAPi4v4reFa1m4fDazcn38+pWLqXqpHCTY0z14y/7OmZqNocSZQqFQnCZzEwN8Z7GPgA5ff80xeo1IzVShJbR79Mras/aqj/ThsFOFuuy/9qpV4m/R0dv6iGa1Seiny/tYYgmPxrX6W2h2By2F22j6YN1YD2lAYi29BViWXSPDphEyQGTyREfxfY8Zkcd9+hmI+wyPxOjkzvqxSYlT0URX9Lm06ohRP1a6g4q68n6PkZ0YwbLpCUxPigSMWaGF5Q1sKqqipLYVgBCbg29f8BluW7QKu8VKbVsTD773FMcjG1jxmflo06t48M8X0LjJOI81+STespdH8cp7o8SZQqFQnAaakPx5lQerBv/zoY2dlX14+PXaiV7iydpXJKuPaNZppwoH8CvsjGYFNSHV28df64bhImx2XKvvwBoRQ3tZMbXrnxzrIQEQoRFUhG/pvJ85iCVRjb9LgPW0JGqfAK+ZEIJJidPIycwjNyuPxOjkznW+gI/9xwvN+rGdNLb1X3xv1QTzM2NYlp1AUlQIAF5/gO1H69hSXEVtq7dz28unncfPln+FtCgXutR5ctdb/O6jtVx65SKuzplN1eQ2Xtx3PdaIGtAElug9+E+8P3pPQn/XdMbPqFAoFGcJwgY3XaATM8nCm+12flHvJHqJNng0a6QNobtFs3T8LdKIZgWJMekd/NhnN4L4VbfiSMrAV19F9YuPQuDMdWV1mpZEPU25s+wacYNYEnVvxNplSdQ8XnKQp4Dd6mBG2hxys/KYnTGPiJDIznWt7S1d9WPlhXh8AxcRhtotXDg1noumuohwGqUEjW1eth6q4cMjNbi9XaHbjKgE7l1xMyum5gFQWHmUOzc8Ro2lic/dfAXp6W1sT4zkg4IFiIAkNDeEundfIVA3NpNElDhTKBTnPL0NoYcWzdLsgk+A1eZxEqYO7XydhtB9dXjvEb0Kjmbpbf0bQisGJvqS6wnNnk+gvZWq5x9Gd7eM+Dls9LYkyhyCJZHbnAnZzZjbvF83hoX4I0VESCRzMueTm7WA6amzsVu7ZjJXN56ksNRod3G4shhdDq44XREOlmYncF5mLDZT2B6vb2NTcRW7yxoIBM0etVusfGPxp7nj/Otw2uw0edr41eZn+cfODSxaPIebVywjKeUoT7VewI4fthK7sgUtupmKh+87o+K9J0qcKRSKswONXrVYPWuvrP1EsoZrCK3pklhdR2vXOVpB/41Hgyx1TscQWjE8wnMvImrx5chAgJqX/oy/rnLYx+qwJOruCWmkIlNsYkBLorI+BFiJV6dqGJZE452k6BRysox0ZWbClG71YyUnD1NgFvRX1h8f8jGnuMJZNj2BWSldszX3VzSyqbiKQ1W9xfbSrFx+ftktTI410qVr92zhgfeepDngZs1nVzBjRgaJk/fx0P5rOPKH4wRafdSuP4Cv8s+nceUjgxJnCoViXCFsHSJroA7vo2gIPYRoVqBVssTl5a3PteP2wexHQilpOEtaZ5xlODKmE7vySwDUbXyK9tIDQ9rPFWRJFFyQn2HTsA9gSVTeoxN+aZAl0dkc9NSExqSkaaaheB4JUUmd63x+L/uO7zMiZKX5NLU1nsJxYW66UU+WFhtqHk/nk1Kjnqyqubcre1J4DP/vUzdxzcwLACiuKeenG/7Gh2X7SEmJ57YbriQ+3kFkdiH3b1zNib8fRfp0LLFefJVPn+YzMTIocaZQKEYFzUmPNg19WOr0Ec0ariF0Z6qwr2L3ATq8D8cQ2mGRPPoFDwK4b5NdCbNxijUmAdd1tyMsFhq3b6Bl99Zu66O0vj0hM+0aoQNMvKjyd++C3yHGykbBkmg8Y7c6mJmeQ05mHnMy5hEeEtG5rsXdbNSPleazv6wQr7+3iBqIEJuFxZPjuHiai+hQIw3a0u7j/UM1bDtcQ6un9z+tVbNwy4LL+Y+L1hDuCKHN285D76/lsU/W4dMDLFw4i8svP5/w8BZ8U4/w839cR+0rh0GCJaaeQN1aGCfNRJQ4UygUp0TYDCuOVEu/fbFGxRC6v2jWGBlC/+RiL9PiJHurNH77wfjraaYAzRlGwg3/TrjVQmzB28z85GWy4uzdBNhAlkT1/h5WREHRsNG2JBrPRIZGGe0uMvOYnjoLW1D9WFVjJYUl+RSU5nO08uCQ6sd6EhtmZ8k0F4smxeGwGTOfTza62VRcTX5pHf5+3AgWpk7ngZVfZWZCJgBvFG3nZ2//HxXNtTgcNm645hJmz55MdEwFR1Pt/N8fVtC4qQQALeIIgfq3Tnmso4kSZwqFYsiETLEy6SeRg2+IaQjdopsiq69IVt+P9VP7gX3GyY7T+c+LjU5Rt7/uwKcr4/Kxxm5aEnX1AbMwLTWV9I8eJdbTbGyUEtJrv9ZgS6Ju6cgAjeMjgDIuSIpJJTcrj9zMBWQlTulcrkudoycPdvpXVjZUDPscmXFhLMt2MSc1Gs2MWhZXNrG5uJqiyqZ+f3fFhkTw00u/yGdzLgGgtOEk/2/jE7xzZCcAiYmxrFmzgri4SFyJhWyJXcAHO+dhj60Di0BYt6M37xz2uEcLJc4UCsWQcV1jeNK17PXRdtjfvRarTZ61htBdSB65yoPDCo/ttLL12BB6milGBAuQaloSZfSYEZls7cOSqMUo+vdIOOYN9PKFLPXq1JwFMyFHA01oTEnKJifLiJC5ohI713n9XoqO76WgZAd7SnfR7B56/Vjv88Cc1CpfOFkAACAASURBVGiWZieQFR8GgD+gs+NoHZuLqznR6B5gX8Hncz/FT5Z9nuiQcDx+H4989DKPfPgy7abv5YIFM7jiiguw2QSJqVv4p+3zHN2VgpASS2o7yH8iPcMf/2iixJlCoRgSzgwLkfPs6B5J2Z9aCLSce19sX8jxs3xygJo2+PFGx+A7KE6JDkui4BYUHSIszda/JZFfSspMAVYRm0HNjPM57oxm18a1lFWWj5MqovGNw+ZkZloOOVl5zMmYS5izq36s2d3EntKdFJbu5ED5nlOuH+t1LqvGoklxLJnmIjbc+D9q8/jZdriGbYeqaWofuAg0J3ESD668lXkpRu+aTUd3c9fGxympNwS5zWZl1aol5OZOxWZzE5uxmd+V/TtH/nCcmOWR+OVhap9+hPE8ZVqJM4VCMSTirzaiZnXvec5JYRbtlPx2pdHJ9QcbHNS6VTpzuMT1YUnU0ZTVOYglUc/oV4clkR8Izc7DddXtAFS9+Cfclf3b/CggKjS60y4pO3UWNktX/eTJhhOmXVI+R08eRMrT/5+PCrGxZJqLxZPjCbEbUefq5na2FFfzSUkd3sDAMjrSEcoPl9zITXmXoQmNE821/Oztv7Ou6KPObRISYlizZgXx8dHY7VWEZ+/nFx/8BxV/OUigxUfN6wUEGp447WsZbZQ4UygUg2JP1IhaaEf3S2re7D/VcDbzi+UeEsMlm0o0/m+3+ugcjA5LIkOAWbo1ZY0YgiVRTxFW5hvYksielEncqq8CUP/eWtzF46+OaDyQEptGTuYCcrLmk5XQvX7scGWxaZeUz8mGkeuMnxoTwrLsBOamx2AxxfeR6hY2FVWx70QjQ9F9n5l1MXd/6ku4wqLx6wH++vFrPPT+87R6u1wE5s3L5qqrLsJms2K17UWfAw+8eBvVTx9AenW0sBYCDc+P2HWNJuoTRqFQDIrr6hCEJqjf0o6//tyLmp2fFuD28/x4A/CN150YCThFSF+WRObjgSyJmjotiQLdCvJLfTotw8hBWiJicK2+A81mp6VgK00frT+Nqzq70DQLU5Omk5M1n5zMPOIjEzrXeX0eDhzfQ0FJPnuP7aLZ3TRi5xXAzJRIlmUnMCXBSJEGdMnOY/VsLqqirL5tSMfJjk/j/su+ygUZswDYXnaAn254jAM1ZZ3b2GxWrrrqIubNywYkdvvbVM+Zz9//9zzqXisGCSLkBHrr64yXVhmDocSZQqEYEFusRvSFdqQuqXl9YK+7sxGrJnn0aqPG5r+32dhfc271NLMBaf14QiYN0ZIoOAVZ6tOpH8FCfGFzkLD6W1jDo2gvLaJ2/VMjduyJitPmZGZ6rlk/No9QR1jnumZ3E4WlOyks2cGB43vx+UfWdNVmEZyXFcfSbBeuCKMUwu0N8NHRGrYerKahbWgzhUJtDr5z4Wr+beFV2CxWalobeeC9p3h+z+Zu28XHR7NmzQoSEmIIBNqJjn2ND9NuYuNDsTS+d9Qc1D6ke2sfZxm/KHGmUCgGJO4KJ8IqaPjAg7dqYvzqHEm+vdjH3CSdI/WCBzbbB99hAqIByTbRWQNmpB+NVGSqTWAZwJLoWE9LIvPxGbEkEoL4a76GPTEDX91Jql/6E+hncx/+/okOi+mqH0uZhdXS9fVeWX+cwtKdFJTkU1J1aETqx3oS4bRy0VQXF0yJJ8xhnLuu1cOWg9VsP1KLxz/0z44rshdy7/KbSYmMQ5c6f8/fwK82P0ujp7Xbdjk5U1m16mLsdhstLRVMnbWFp60/pjTfhSO+0ZjiK7eBb88IXumZQYkzhULRL5YIQewyYzZV9TkYNUuP1Ln3EiOy8K11Dtz+iZ3OdFl6ekIa99MHsSQqM3uAHfMaVkQdPcFOjLElUcwlqwmdNpeAu5Wq5/+A3j60VNnZQmpsRqd/ZYZrUudyXdc5dKKos36sqnH4XqKDkRzlZGl2AvMzYrCaTX2P1bayqaiKwuMN9NMztk8yoxO5b8XNfGrKfAAKThzmzg2PsbvySLftrFYLV155IXl5MwCoqsrnvOVl/LbqflqKnQggYD0KgaeAViYiSpwpFIp+iVvpRHMImnZ68ZSfexGJ31/pIdwO/9pr5c1DE+PjMtoiuqUgg2dEDmRJdNLXlyekUYjvG4dlhuFzlxC5aCUyEKD6pUfx11eN9ZBGHU2zMDV5OrlmQX9chKtzncfn4UB5YWf9WEt786iOJTsxgmXTE5ieZDSl1qWkoLyBzUVVlNSemiByWGx84/xP863zr8VptdPY3sovN/2Tp3a/hd4jyhcbG8WaNctJSorD7/dTXb2B+deGc99H93DiLweJviSN9uqtNL390ohd61gwMT5tFArFGUcLFcQtN6Nmr517MzQ/Pd3PdTMCNHnge+vHVzozVNCHJ6SRhowaYCZkXZAl0bGgFOSxCWZJ5MycSezKLwBQu/5JPMeKxnhEo4fTHsLs9LnkZM5nVsbcbvVjTW0NZv1YPkXH9+ILjG7nZ6smmJ9pmJAnRRmOC15/gO1HDRPy2tZTr1+7ZNJcfn7ZLWTFGEbpzxVu4sH3nqamD3P02bMnc801S3A47NTV1WOzv0nWdXnc98Yaqh7fR6DFR/XLO5HuiS3MQIkzhULRD3GfcmAJ1WjZ58N9+NyKmoXZJH+40pgEcNc7diqaz/wkAIeA9G41YF3RMNcAMyFbArIfT8izw5LIGpeE67qvIzQLjR++SWvh+2M9pBEnJjyOnExjduW0lJnd6sdO1JWb9WM7KK06whmo7CPMYeWCKXFcNNVFhNPohdbY5mXroWo+PFyL23fqnw/JEXHcs/wmrp6+GICi6jJ+uuExPio/0Gtbi8XC5Zefz8KFxozN4uIi5s77iLIZn+GhZxZR8+xepFdH2OuR7ldO40rHD0qcKRSKXgi7kdKEczNqds8lXjKiJJ9UaPzx49EzNrcCqcGekEFiLKkvSyITjy47RVdJj4L8s9mSSAsJJ2H1v6M5Q2kryqdh04tjPaQRIy0uk1zTLindldW5XNd1DlYcoLBkBwWl+dQ0nbn0rSvCwdLsBM7LjMVm/iA4Xt/GpqIqdpc3EDiVgjITq2bh1vOu5HsX3UCY3Umrt52H3n+exz55A38fkzliYiK44YblpKS48PsD7NixiTWfK+fVsDvY/Nd46tYVgS4R1mNI70YY0yrIkUOJM4VC0YuYpQ6skRpth/207hvYSuVsIychwPfO96FLuP01B7o8vUkAAkgyLYkyegiw1EEsiXp6QnYIsBNnYibkeMNixfWZb2CLceGpLKXm9b8xnu13BsOiWZiWPNP0r5xPbER85zqPr539ZYUUlOxgb9luWttbzujYprjCWTY9gVkpUZ3L9lU0srm4ikNVwx/L4rQZPLDyVqa70gF4vegj7n3775xoru1z+xkzsrj22mU4nXbq65s4cGA9t35L53eN97LvES8N75qtMrRCpP+DYY9rPKLEmUKh6IawgOvKczNqJpA8usqDVYM/bLex48TQjc3jLN09ITsEWMYAlkS6lFQE9wELEmAdlkQKg7grvowzfRr+5nqq1z6M9I1sf64zQYg9lFnpueRmLWBWei4hjtDOdY2tDRSW5lNQkk9xxT78o1w/1hOLJpibFs3S6QmkxRjj8vl1Pik16smqmofvpxkfGsWdl3yBNTnLACipr+TujY/z3tHdfW6vaRqXXbaI88/PAWD//iMIsYVbvh/LvSX/j9YjVhyJTUarjMD7oO8d9tjGK0qcKRSKbkRfZMcWZ6G93E/zrjP7BTHWfC3Pz4XpOhXNgrve6T0JILLDkshu6S7CbBrhg1gS9RRgHTMhPRM3+HPGiLzgKsLnXIDu9VD1/MMEWnoXi49XYsLjyDX7j01LnoElqH6sorasU5Adqz56RurHehJis3C+WU8WHWq855vbfWw7VMO2wzW0eob/E0ETgi/NW8GPln6OKGcY7X4vf/zwZf704St4+hGfUVHh3HDDctLSEggEdN5550MuvLCIqSunc2fhd9GrjGfJfXILBDYAZ+cPSCXOFApFFwLirzJmYVW/3j6Rs0anjCtU55crPOh+wS/eCOECm43MsO4CLHaAQvzGgDRFV6BXQX7rWVCIP1aEzlhAzNLrkFKn5tX/xVdVNvhOY0x6fFZn/VhafGbn8oAeoPj4PmOG5RmuH+tJbJidJdkuFmXF4bAZEeLKRjebi6vIL63HP4x6smDmJk3mwZW3kpts+He+e2QXd298nNKGk/3uk52dwXXXXUJIiIOGhmY2bNjI129vpjF7Kfe981lqnthL1JIUWvY/j3vP9tMa33hHiTOFQtFJ5EI7jiQL3qoAjR9NvLTRULF1zIQMmgF5RQrUbIig0m3hmwCpvfdr02W3FhTB0bCGs7gQf6ywJ08i7qpbAGh4by3uQ32nwcYaq2ZlWkpH/VgeMeGxnevavW72lRVSWLqDfccKaPWc2fqxnmTGhbFsegJzUqLQzHR7cWUTm4qrKKo8/d5oUY4wfrT0Rr40fwWa0KhoquWet5/gzeKP+91H0wTLly/kwgvnAlBUVMrugk3cdTdsDr+Rl15dRM0/9hBo9lL9Yj74z25hBkqcKRSKIFyrzFqzde0TxR+4XzQgxSZ6mHJbyLJppPRlSdQGfoIsiXqkIDssiRRnBktkLAnXfxPNZqd59xaatm8c6yF1I8QeyuyMeeRm5TEzPZcQe0jnuobWOgpLdlJQuoODx/fj18e2elATMCc1mqXZCWTFG33S/AGdHUfr2FxcxYnGkXH/uGHOUn56yReJD4vCF/Dz6Mev8btta2nz9V+vFhkZxurVnyIjIwld13n77Y+xWPbwi185+F/3t9n+bALVz+9DegKg1YD/9REZ63hHiTOFQgFA+FwbIRlWfPU6DVuHX/x7pkmw9vSEHNySKBDkCVnmC/DFRe2kJfv5/R7B3dusE12XTniE3UnC6juwhEfhLtlP3Yanx3pIAMRFuIz+Y1kLmJo8HYvWNWGkvPaYYZdUks+xmqNjOMouHFaNRZPiWDLNRWy40VC6zeNn2+Ea3j9UTXP7yIjG6fFpPLDyVhanzwTgw2P7+OnGv1FcUz7gflOnpvGZz1xKaKiTpqYW1q59m0suqeOGr0TxQMWdHHrWT+26YtAliBLQ3+ZsaZUxGEqcKRQKABLMqFnNm+3IcTZNMNrSXYB1iLCMQSyJKju64fdIQZYHWRLdvdTLjDwv+6sF934UqoTZWCM04j/9b9gT0vDVVlLz0p/HzMxcIEh3ZZGbtYCczPmkxmV0rgvoAYqO7zX9K3dS21w9JmPsi6gQG0umuVg8OZ4QuyEgq5vb2VJczScldXgDI/MuD7M7+d5FN3DreVdi1SxUtzZw/7tP8cLeLQPuJ4Tg0ksXsGSJ4aF56FAZ69a9x79/O8CMJcn8+PCdVP6zhvp3OuoLC0B+OCJjnigocaZQKAibYSV0mg1/i079e2NjcB6m0d0T0q6RaRvckqjW370HWLAlkXuQLOTUWJ07lxi1dbe/7sQbmNjG5mcDMZ9aQ+iUHALuFsPM3HNmzcytmpXs1FnkZuUxJ3M+0WFd9WNur5t9ZbspLMln77HduL3jy2g9LSaEpdkJzE2PwWL+aDlS3cKmoir2nWhEjmBW/urpi7ln+U0kR8ShS50ndqznv7c8S9Mgr1d4eCirV19KVlYKuq7z7rs7KC7exS9+YScwZRY/3fNt9BMB7CmhoEnQtwFnX6uMwVDiTKFQ4Fpl1MvUbmhHH8WMZoclUbApd8f9+AFmQjYHWRIdC5oRWerVaRp2EEDyx6s8OK3wxC4rm0uH3tNMMTqEz19G5HnLkQE/1S/8CX/DmYlGhTnCmZUx16gfS8vBGVQ/Vt9S22mXdKjiwJjXj/VEALNSolia7WJKQgQAAV2SX1rHluJqyupHVkBmxSRx/2W3sGySUby/q+IQd254jMKTg6dyJ01K4frrLyU8PJTm5jbWrn2H8PBK/viIg4LwZTyx/Ua0pgASSdPOf4K+h7O1VcZgKHGmUJzjhEyyED7HRsAtqXv79JVZsCVRT0/IgSyJ2oMsiUp7FOTXjsJMyM/N8bNySoDaNvjhRseIH19xajgnzSJ2xecAqH3j73jKD47q+eIjE8jJzCM3K4/JSdnd68dqSikozaegZAflNaWjOo7hYrMIFmbFsSTbhSvCKElwewN8dKSGrYeqaWgb2R6FTquNb55/Ld9cfC0Oq40Gdwv/tekZnil4B32QkJwQgqVL57NsWR5CCI4cOc4LL7zLkiVevv1dO8/5vsj6txZR82QhEee7aProL/hOjM/n/UyhxJlCcY4Tf7URJah7p51A69BEkACSraKzB1iHAOuwJLL2I8B8UlLahyVRiVfn5Bm0JIpySB663Ehn/ugtBzVtKp05ltjik3Fda5qZb1tH696Rry8SCDISJpNrFvSnxKZ1rgsE/Bwo30NhST4FpfnUt/RtJzQeiHBauWiqiwumxBPmML7C61o9bDlYzfYjtXj8I181eenkefx8xS1kxiQC8GzBuzz43tPUuQdvvREWFsL111/K5MmpSCl5770dbN26k9tus3DtDSH8rv477NqYRNUzewg0eal9ZTfox0b8GiYaSpwpFOcwjhSNqPPs6D5J7YbetWbxFtGnKXf6IJZEx4ML8DsL8gNU+OS4sCR6YLmXpHDJ1mMaj+9UH4NjiRYaYZiZO0JoPbCDhi0vj9ixrRYb01NnkZu1gDkZ84kKi+5c5/a0sdesH9tXVjDu6sd6khzlZGl2AvMzYrBajBKA0tpWNhVVsed4A6fZM7ZPUiLi+Nnyr3Dl9EUAHKg+xp3rH+Pj40VD2j8zM4nVq5cTERFKa6ubF154l5Mnj3P//TZmLoripxV3UvaGpGbtPnRPAEQV6G9yTnW/7gf1qaRQnMO4VoUQ5paEbvAwRVrJjOuejhzIkqi6D0uiUlOIecfxZ+vClADfOM+HL2AYm0tU1GzMsFhxXf9NrNHxeCqOUvv645zuF3OYM5w5GfPIMevHHDZn57q65hoKS/MpLNnJwRP7CYzRLNBTYXpSBMuyE8hOigSMHz8F5Q1sKqqitLZ1VM5p0yx8beHVfPfC6wm1O2nxuPnt+8/x+I71+If4nF188TwuvXQBmqZRUnKCtWvfITrazcN/tGNNS+FHh++k7tVaataVQEACJSDPnVYZg6HEmUJxDhAqIKNHH7CsUAuTt1uI3BQArJDc++OgMSAp8QZ6dcQv9eq0jWMB1h8WIfnzKg+agF99YGNvtZoEMJbEX/UVnKlT8DfVUf3CI0j/8FwpXJGJ5GSZ9WOJ2Wha1+SSsuqSzvqx47UTI11m1QR5mTEszU4gKcooO/D4AmwvqWVrcTW1raPn3nF++iweWPlVsuONtO+r+z/gvnf+TmVL/ZD2Dwlx8JnPXMq0aekAbNmyk3ff3cGCBYK77rZTGjqH3+65g+bXj1O3seP1KAQ+REXMulDiTKE4S7AFz4TsYcqdaOtnJqQb3JqkpK27JVGHCGscjVzJGHLHIh/zk3VKGgQ/39zb2Fxx5oi6aBVhsxaje9oNM/PWoZuZCwRZiVPIMQ3Fk2O6vLb8AT9FZXvMGZb5NLTWjcbwR4Uwh5ULp8Rz4dR4Ipw2ABrbvGw9VM2Hh2tx+0YvquQKi+KuS7/E9bOXAHCk7gR3b3yczSUFQz5GenoiN9zwKSIjw2lra+fFF9/l0KFyVq+2cNvXrbyrX8ZTO1aj1fvMVhkB0LdjiDNFMEqcKRQTCAvBlkSWbgX5fVoSmXh1yTFfUBNWiyRwWyiVcfDxfzXhOXH2t15NjdD5+aVGxOFb6xy0+VQ6c6wInbmQ6Is/jdR1al79K77qgTvJA9gsNqanzTE69GfOJzK0q36szdPK3mO7KSw16sfavROr/YIrwsHS7ATOy4zFZraUOV7fxqaiKnaXNxAYxR9JmhB8ef5KfrT0RiIdobT7vPzhgxd5dPureANDrxC98MJcli9fiKZpHDtWydq17+B2t/KDH1i5/Eor/2j7Cu98sghLuw9d91K34fegVwJj01dxvKPEmUIxzhAYlkTdPSGNCFi6XcPWjwALdPOEDHQryK/0y26d7xNvDME1WaPxY+85IcwAfneFhwgHrN1nYd1B9dE3VthTJhN/1c0A1L/7HO7D/UdNwp0RzMmcR07mAmamzcFu62p5UttcTUFJPoWl+Rw6UYQ+AerHejLFFc6y6QnMSonqXLavopFNRVUcrh59g/R5yVN5YOVXyU2aDMBbh/K5560nONZYNeRjOJ0OrrtuGdOnZwKwbdtu3n77YyKjJP/9azvZcxz8ou4/KNqWRPU/CwnLjaRp28MEmidORHMsUJ9QCsUYEWMRvfqADcWS6ERH8X2PGZHHfTpD6WxkCRPEXmoanL82sSIMw+XqaX5WzwrQ7IHvvKl6mo0Vlsg4Eq7/JsJqo3nnezR/8navbRKikoz6scw8JiVNQxNdKflj1UcoKMmnoCSfirqyXvtOBCyaYG5aNEunJ5AWEwqAz6/zSalhQl7dPPq+ttHOMH687PN8Ye6n0IRGeWM197z1BBsO7Til46SmurjhhuVER0fgdrfz0kubKC4+xtSpgvvuc+BIjOInx39K9VaoenYvgUYv3hMVwNDq185llDhTKEaRcNOSqMsT0kKGKcYiB7EkCvaEDLYkaj/NDEfcZU4sTkFzoZf20okXbThVQm2Sh68yvvDuftfO8eb+nQgUo4ewh5Bwwx1YwiJxH91H3VvPGsuFYFLCVHKy8sjJzCMpJqVzH3/Az4GKPRSU5LOnNJ+G1on7pR5is3D+lDgumuoiOtSod2xu97HtUA3bDtfQ6hn9JjMCwZqcpdx5yReJC43EF/DzyPZX+P0HL+L2nZooXLx4DpddtgiLxUJ5eRXPP/82jY0tLFmq8aMf2ahxpnPXoZ/QurmR6rWH0NsDQDWgWmUMBSXOFIrTxCHoFFyZPWZEDmZJ1L0Ra5clUfMoZRo1J8SuMCJH1a+eG7Uedy/1khUt2XlC4+HttrEezrmJ0HBd+2/YXal4aypoeO1xctLnmoJsPhEhkZ2btra3sPfYLgpLd7K/rIB238R+n8aF2bk428WirDgcNmN2cGWjm83FVeSX1uM/Q5NuZrgyeHDlrSxMmw7AttK93LXxbxysPX5Kx3E47Fx77VJmzpwEwIcfFrJx43ak1LnpJis3fcXKLn0uv9vzDdzvVFLz+lGzVUYp8DaMi06H4x8lzhSKIdBhSRQswDqasib3NxMScJuF+MEtKDp6gtWNgiXRYMRc4sQartFa7KOt+Oz/kJztCvD9C3zoEr7+moOAVJMAxoKY5TcSlzWHSc0+Mk66yf78b7Fbu2bL1jRVddaPHa4snpD1Yz3Jigtj6fQE5qRGdVqWFVU2sbm4iqLKwTvrjxTh9hD+4+IbuGXBFVg1C1UtDfz8nX/w0v73T/lYycnxrFmznJiYSNrbvbzyyib27y/B6YQf/djG0qUW1nmv4F8F19H2znHq1ndYMO0FtqEiZkNHiTOFwkQDkkxLop61YINZEpUFRcCCBdiZtCQaDGGD+Cs6as0mdjRiKAgkj67yYLPAHz+28XGF6ml2pkmMTua8C9eQm5VHSpOGIARS5wBQUnWYQlOQVdQNPltzIqAJyEk16sky48IA8Ad0dhwz6slONJ7Z/7tVM87nnk/dRFJELAFd52+fvMGvt/yL5mHMZl24cBYrV56P1WqhoqKa559/m/r6ZhIS4L777EyeZuXRpq/yUWEellYvtmQHCB/IHcDQ23EoDJQ4U5xzdFgS9RRgGTYNxwCWROU9G7EGWRJNhN/50Rc7sEVruEv9tBSMrCnyeOSW+X4uztCpbBH89G3V0+xMIIRgcuK0zoL+hOhkY0UA/HqAovI9FJTsYE/pThrbGsZ2sCOIw6qxaHIcS6a5iA0zygZaPX4+OFzD+4eqaW4/s1HqSTHJ3L/yFpZm5QKQX3GQO9c/xt6qklM+lt1u45prljBnzhQAPv54L+vXf0QgEGD2bMHP7rUTEuPknqofUL4nCc3vx9/eSM0L/wWyBRj9CQ5nI0qcKc4ZQgX8KzOMyY7+IyhV/u4pyA4xVjbOLYkGRQPXVefODM34UMmvVhhfCt9bb6fRo9KZo4Xd6mBG2hxys/KYnTGvW/2YG50jNp3dh7ez463H8Uzw+rGeRIfauHiai8WT4gmxG58r1c3tbC6uZkdJHd7AmW1T47TaueOC67h90TU4rDbq3c38YtMz/HP3uwwnhp+YGMuaNSuIi4vC4/Hy6qtb2Lv3CABXXGHhO9+10mKL5QfHfkrzTo3q5/bhnKzR/MkjSM/Z/zkzmihxpjhnWBZuZbLDQpsuKfYEukXAJrIl0VCIPt+O3WXBcyJA0ydnf9Tsvy/zEBcKGw5b+Oce9TE30kSERBnNYLPymJ46u1v9WHXjSQqP76FiVg6VURG0HPiYmjf+OoajHXnSYkJYmp3A3PQYLGa0/XBVM5uLq9lX0TgmpQzLp+Rx34qbyYhOAOCZ3e/wi03PUO8eXn1bXt50rrzyQqxWK5WVtTz33NvU1TWiafD1r1tZfYOVozKL+w/+EO+OVqqfK8Lf4MF7ogkYPXupcwX1qaU4Z1gZYczU+59qD082nEMfHgLirzb8+apfd5/1NbnLMv3cPM9Pu99wAkAZm48ISdEp5GYtICcrj0mJU7utO3ryEIUl+RSU5nOyuYrEz38fR3QEnuOHqV33xNgMeIQRwKyUKJZmu5iSEAFAQJfkm/3JyuvHJlKUGhnPvcu/wuXZCwHYV1XKnRseY8fx4mEdz2azsmrVxeTmTgMgP/8Ab7yxDb8/QHg43HWXjfMWWvjIu4A/Fd+Gb3st1S8cQnf76WqVMREKPcY3SpwpzglCBCwNM97ub7Wc/ZGjYCLm23CmWvDWBmj84OwWpTZN8qerjXTmg1vsHKpTPc2GiyY0JiVNIzczj9ysBbiiEjvXE6WpxAAAIABJREFU+fxeio7vNfqPHdtJU1uHL6Yg/tNfw5EyGX9jDVUvPIL0T+z/N7tF47ysWJZku3BFGKUBbm+Aj47UsOVgNY3usbk+m2bhtkWr+M6F1xNic9DsaeM3W5/jiR3rCcjhpVNdrhjWrFmOyxWD1+vjtde2Ulh4CIC0NMHP77eRlq7xbNM1vHHwajzbKql57QjSL4FjwFuoVhkjgxJninOCJWFWQjTBLrefSv9ZHjrqgWuVETWrWdeOPMt/0P7wIh8zXZKiGsEv31c9zU4Vu9XBrPQccjKN+rHwkIjOdS3uZvYc20VByQ4OlO/B6+9d6B118TWEzVyI7nFT9fzD6G1nrmXESBPhtHLRVBcXTIknzGF8Vda1ethSXM32o7V4/GNne3ZhxmzuX/lVpsUZhu8v73ufn7/7JCdbht+kd+7caVx99cXYbFaqqup57rm3qKkxJm2cd57GXXfbCAm38Ouqr7PvUA7uDyqoXVdi7r0f2MpZH5Y/gyhxpjgn6Ehprm8+t37Vhc22EjrZir9Rp37z2T1ranKMzl1LjMjgN1534A2odOZQiAqNZk7mfHIy85ieOgtbUP1YVcMJCkrzKSzJ58jJg0jZ/5dv2KzFRF+0CqnrVL/yV3w1FWdi+CNOcpSTpdkJzM+IwWoxIq+lta1sKqpiz/EGzlDP2D5JCIvm7k99metmXQTAodrj3LXxcd4v3TPsY1qtFq666iLmzzea0+7aVcy6de/j8xmflatXW7jt61Y8llB+VP4j6g+50Lx+LHEShAfkbmDXaV+bojtKnCnOehwC/j97Zx4YRX33/9fM7JXN5k42QBISICQcSSAB5A43guIJ9Hy0tT5Ve2KfPtVWUVHR1vZpf621rba11lptK+CJiNyE+0oggUAuSMhBspt7c+w58/tjQhAVQcixWeb1F+x3dua7m9nv9z2fc7ZFvdU3Owa2i+WLYr2ly2q2yYkS0B9d4YXFLoL08NoxHdvLtaXt8xgcEU9GUhbpSVkkWUd0vy4rMqdrS8gvP0JBRR51zVcmsIxxyUQtvhuApq3/wXn66sVCf5E6KIRZKVZSBqnZprKikF/VzM4iGxUN7f06N0kQuTtrIf8780uEGs10elw8v/dt/nxoPW7f1T9wRkWFsXz5fGJjI/F4vGzYsIejR9VYNb0eVqzQsWixDjsxPHLmZ3hLJERkXPVnqX/zN6im+MB+6OsvtBVMI+CZEazDLArkd/qouY5cmuZkHcGj9Pg6ZBq3BfYCunyMl8UjfTR1wo83aTXNPokoiIwYlEJ60gTSEzMvih9ze91d9cfU+DFHZ+sXOrcuLJqYO7+DoNPTemQ7jtztPT39XkMnCmQlRpCdYmVQmPog4/L4OFjewO5iOw3t/R+jmTkkmWcX3ktarNouaXPJYR7f8ipVrfZrOm9a2ghuuWUmBoOe+vpm1qzZis3WCEB4BKxaZSAtTeSEM5lfVf4Y5UQH9W+dQhfVQfvxv0IAdHHwZzRxphHw3Njl0tx0nSUCRC9Rg5cbtriQOwNXlIYaFX67SN1EH95ixN6hJQEAGPUmRsend9cfCzZZusccna0cr8gjvzyXU9XH8XivToQIxiCsy3+AZA6h8/Rxmrb+p6em36sEG3VMGxHNtORoQkzq+tDS4WZ3qZ39ZQ10evpfeISbLPxs9lf52rh5AFS22Hliy9/ZXHrkms4rSRKLF09lwoTRABQUlLJ+/W7cbnV9TE4WeOopA9ZYgY3NU/lX9T0ox5uxrSnB2+TCVf3FxLvG1aGJM42AxiDA7ODrz6VpGioROt6A7FJo2BxYhT8/yeq5boaEKOytFPlr7vW9pIWZI7qbiafEjUEvXUiKqG2qoaAij4LyI5yxlX5u/NgVIUrE3H4/+qjBuO3V2N/9C1xllmBfERNiJDvFysTESPQ6VcRXN3Wws8jGsapmfP0ZUNaFgMCXM2bzs1lfJdIcitvn5aUD7/P8vrdxXqWIPk9ERCjLl89j8OBovF4vGzfu48iRU93jM7NFHnpIjylI4MWapeyvXYgvvx7bulLkDi9Qj1Yqo2+4vlcyjYBnmlmHRRIodPqo9PT/wttXRN+sWs0ad7jwOQL3c08Y7ON7kzx4ZXhgvRHlOqxpNiQyQY0fS8wi0Tq8+3VZkSk7V0RBRR755UewtdT26HUj53+FoKQx+Npbsa19AeUq+jX2FclWC9kpVsYMCet+rbCmhZ1FNsrsbf04s4sZY03k2YX3MiEuBYBd5QU8tvkVyhqvPblizJhh3HprNkajgcbGFtas2UptbQMAggB33aXj7m/o8KJjZfl3qTk3GteRWurfP18qoxK1VMb185Dbn2jiTCOgWRii3uKbriOrmSFWJGySAdmrUL/RfzfMa0UUFF5a4kIU4Nf79BTYro/G5qIokTwotctClkV0aEz3mNvj4mTVcQoqjnC84hhtzt5xQYVMnEdI5iwUrwfbW3/E19rQK9e5FiRRYFxCOLNSrMRFmAHweGUOdxWNtTv8Jw7TYgjixzOXc0/WIiRRpM7RyFPbX+O9k/uu+dySJLJw4RRuuGEsACdOnOb993NwudQ10WSChx7Wk50t0eIL5qHyn+KpiqAjt5aG9We6znIK2IVWKqPv0MSZRsCiB+Zarr8SGjE3ByGIAk27nHibAncx/d4kDxOGyJxtEXhyR2AnAZj0JkYnZJCRNIGxQ8dhNgZ3j7V2tKjxYxW5FFWfuOr4sSslaEQ6EXOXA1C/4e+4a0736vW+KEF6iSkjopmRHE2YWb0vHE4Pe0vr2VtWT7vLv9aC20ZP47G5dxFricAny/z10AZ+vXsNbT1giQwPD2HZsnnExcXg8/nYtOkABw+e6B63WuGppw0kJ4ucbh/EU7U/QywXEXwyQpAD6ASOA3nXPBeNL4YmzjQClinBOkIlgSKXjwqPf8fC9BT6SJHwaQYUWaF+Q+DGmg0JkVk9VxUh399gpN0TeO7M8ODILndlJiOHjEEnXViuzzVVU1CeS0FFLuV1ZVfV1Ppq0MfEE33rtxEEkeZd79Jx8lCfXPdKiAo2MDPFyg3DIjHoVCtqbUsnO4tt5FU04fWDeLKPMyJyCKsXfIsZSWkAHK4u5tFNL1Noq+iR86emJnL77bMwmYw0NTlYu3YLNTX13eNjxwqsetJARIRAjn0Uf23+EVJpJ4gy7WVHqX/nL4CI1iezf9DEmUbAstBy/bk0oxaZEHQCzftduOsCV5D+vxvdhBrhnVMS7xcHzjIWFzVU7V+ZmMnQmGHdr8uyTOm5UxSUq/Fj9ta6Pp+bFByGddn3EQ0m2k8coGXvB30+h88iKSqYWalWxsaFIQqqSC+qbWVnkY3iOv/rUGDSGVgx7U7uu2EJBklHY0crz+54gzcLdvaIyBZFkfnzb2Dq1HQATp0q5913d+J0XhBZixZJrHhQh14v8OrpmWxz3o1Y4sC2rhQMtbjOvHHN89C4NgJnVdPQ+Bg6YF53vJl/uTF6CylEIHKWEQD7+sC1mi1K9vKlsV7a3PDDD439PZ1rQhQlRg4e1R3QHxkS3T3m8rg4WZVPQXkuJ84eo83Zf0JD0BmIWfo9dKGROKtKqf/w1X6bC4AoQHpcONmpVhKjVBev1ydz5KwaT3auxT/v/wXJE3hy/jdJCFPjBF8/upVf7PwXzc6eSUoIDQ1m+fJ5xMfH4vPJbNlykP37C7rHRRHuv1/H0mU6ZARWFX6ZctdclJJm6tYU4208H4enRwv87180caYRkEwyS4RLImUuH2XuwLUgfZyohSZEo0BrnhtXVWCmugfpFP5wk7qBPLHDQGXrwKtpFmQwMyYhg/SkLMYkZFwUP9bS3szxs6p1rLi6EI/PHzZIgagl92AcnIS3uR7723+Ca6hKfy0YdSI3DI9i5sgYIoNVYd7u8rKvrJ49pXYcTv98EEsIi+HJ+d9kQfIEAI7XneGRTS+TV1PaY9cYOTKB22+fjdlsoqWljbVrt1JVZeset1hg5Uo9EydJtHt0/KToh3S2jcRb1IBtbUlXqYwG1FIZ/nDfXd9o4kwjILnxOuulKZoFouadt5oFbobmo9luhkcoHKsVef7AwGlsHmGJIj0xk4ykCYwcPArp4/FjjVXkV+SSX57LWdvpPosfu1LCs28jOHVCVzPz3/dLM/Nws54ZI2OYPCyaIIMaT2Z3OMkptnO4vAGPz7++s/MYJB3337CEH069E5PeQKurg1/l/IfX8jbj66GacKIoMHfuJKZPHwdAcfFZ3nlnB52dF7JR4+MFnl6tJyFB5GxzKI/V/RSpMQzncTv175Z1lcqoAjajCTP/QBNnGgGHBMw7H292nXQFiJprRDKLtBV66CwLTKvZ6GiZn0zzICtw/3ojXtm/kwDioxPJSFT7VyZEJ3W/LssyJTUnKSjPJb8il/pW26VP0s8Ep00lbOpNKLIP+zsv4Wk416fXj48IYlZqLBnx4Uii+vcusznYWWzjZE2rn8nYi5mRmMbqBd9iRNQQAN46sYtntr+Orb25x64REmJm6dJ5JCYOQpZltm07zJ49xy46ZuJEkZWP6bFYBPaeHcIfPY9gqFLoOG6n/v3TXdUxioActFIZ/oMmzjQCjglBElE6kXK3j2JX4Ls0BYPq0oRAtpopvLjEiUGCFw/rOFDtfzXNJFFi5JDR3QH9EZao7jGXx0lh5YX4sXaX/xQ+vRTG+JFELboLgMbN/8ZZXtgn1xUEGDM4jFmpVobHqC2nfLJCbld9sqom/77HYy0RPD73Lm4dPQ2AkoZqHt30MvvO9uz3N2JEPHfcMZvg4CBaW9tZt24bZ89eXGh46TKJ++7TIUkCbxSk8aF5BYbTnQgK+LzloLShCrNrawml0fNo4kwj4Fh4vpfmdeLSjMg2ogsV6Tjtpb0wMD/zN8Z5yU6UqWsT+NlW/0kCCDKYGTt0XFf82DiCDEHdYy3tTRR0uSuLa07i9Yv4sStDFx6jNjOXdLQe3kLb0Z29fk2DJDIxKZKZKTHEhKgPG51uHwdO17OrxE5Lp39/f5Igcs+ERfzPjGWEGM10elz8ds86/nLoAzw92CRcEARmz85i5sxMBEGgtLSKt9/eTkfHhSQIvR5WPKhj0SJ1i1+9dy7FkV9DX9wGokDzgY207HwbLfDff9HEmUZAIQILurI0P7oOSmgIEsQs7rKave/fFoWrJSpI4f8WqvEz/7PJQLOzf92ZkZZo0pOyyEjKInlQ6kXxYzUNlV3xY0eotJf7XfzYlSAazViX/QApyEJHaT5N29b06vVCTDqmJ8cwdUQ0wUb1u2xsd7Gr2M7BMw24vP5v/Z4Yl8IzC+9ljDURgI3FB1m19R9Ut9Zf5p1fDIsliDvvnMuwYUOQZZnt24+wa9fFBWLDI2DVKgNpaSIdToGf5N5Fu3kGYnErdW+VIjtP47G/03V04K+RAxVNnGkEFJlBEjE6kUq3zMnrwKUZNs2APkrCWeXFcTQwF9rnFriINsPW0xJvFPTPkjU0epjaLikpi/iood2v+2QfRdWFFFTkUlCeS4PD3i/z6zFEiejb70cfNQi3rYr69/8C19og/RIMDjORnWIlc2gEOknNuq1oaGdnkY3j1c34Wc3YzyQyKIRHZn+NL2fMAeBss43Ht/ydrWW5PX6tpKQhLF06B4vFTFtbB+vWbaO8/OIYwORkgaeeMmCNFai0GVhZ9SCiOAylpIW6N8+XyggGDGjFZf0bTZxpBBQLryOrGYLaqgnA/oEzIGN5Zwz1cW+mF5cXvrvBCH3U2Fwn6hgZN5qMxAmkJWYSYYnsHnO6O9X4sQo1fqzD1d4nc+oLIhd+jaCk0fjaWrqamfd8/8lRg0LJTokhZVAoALKikF/VzM4iGxUNA+O7FBD46ri5/GzWVwkPsuDyevjTgff4w/53cHp7du0RBIGZM8cze/YEBEHgzJka1q3bRnv7xZbymdkiDz2kJyhIYH9ROM8rj2BqC8Zd3oxtTTFyhw9oBD5EE2b+jybONAIGAVjQ1UvzesjSDJ1kwDhIwm3z0XIg8BZbvajw4s2qOPj5bj3FDb1b08xsDFbjxxInMCYhHdPH4sea2hq7rWMlNSfxyoEX2xd6wwJCxs1E9rixvfUHfI7GHju3ThTISowkOyWGQWHq9+ry+Dh4poHdJXYa2gfO/ZsWm8SzC/+bzCHJAOScyWfl5lc409Tzmaxms4k775zDiBHxKIrCzp257NyZi/Ixa6YgwF136bj7G+p2/sbeBD4Y9Aimci/Ookbs75R2lcqoRi2VMXC+6+sZTZxpBAwZJolBepEaj8xxZ+C7NGOWdMWabXBCAH7c/5nqYaxVpqRB4Be7e6exeVRIjBo/lpjFiMGpSOKFLNCqhrMUlB8hvzyXyvryXrm+vxA0cjzhs5cC0PDBK7jPlffIeYONOqaNiGZ6cjQWk/rg1NzhZneJnQOnG+j0DJyyL6FGM/8780vcnbkQSRSpdTSyauurfFB0oFeuN3ToIJYtm0tISDDt7Z289dZ2Tp+uvugYkwkeelhPdraEz6fw848yOTX6+xhLO+gsasL+TlmXRb0YtVRGAC4UAYomzjQChhtDrp9empZxeoKG6vA0yTTv6XnXU3+TFC7z+Cz1Cf87Hxhx+XrGnSkgMDRmWLcgGxKV0D2mxo+dIL9ctZA1tvVsMLe/YogdSvSSexEEkaacd+gouvayCtYQIzNTrExMjESvUy2e1U0d7CyycayqGd9ACCj7GLePmc5jc+7CagnHK/v488H1/GbPWtrdvdMmavr0ccydOxFRFKmoOMe6ddtwODouOsZqhaeeNpCcLOJoU3h4x2LaUpZjLFFdw666g6DogVLgcK/MU6P30MSZRsBwPXUFsHZZzeo3OlECTosqvLDYhVkPr+fr2Hrm2pYpnaQnZcgYMpIySU/MIiw4onus09VxUfxYp7vjc84UeEiWcGKWfg/RYKStYC+t+zZc0/mSrRZmpVgZPSSs+7XCmhZ2Ftkos/t/bbdPMjIqjtULvsW0xLEAHKw6xaMfvcyp+speuV5QkJHbb59NSoqadLJ791G2bTt8kRsTIC1N4IlVBiIiBCqqYGXRfyMlTEZX5kAWFJo++idt+XvQSmUMXDRxphEQpJtEButFaj0y+c6B4yq5GoJH6TCP1ONtk2na4Z8Nnq+FO0f7uDnFR7MTfrzp6tyZwUYLY4eOIyNpAqMT0jHqTd1jTW0NqnWsQo0f8/VgDaqBhKDvamYeEoGzspiGj/55VeeRRIFxCeHMSrESF2EGwOOVOdxVNNbuGHiW3SC9kRXT7uS+STejl3Q0dLTyzPbXWXs8p9fKo8THW1m2bB5hYRY6Opy8/fYOSks/LQIXLZZYsUKHXi+wJ8/I7+WfEGSJg4o26t4qwdtchM+xp+toTZgNVDRxphEQnC88u7nNE4hJixcRs0QNqG7Y5EQeePve52IxKDy/SP1QP9tqpK79ypMAokOtXe2SJjBiUAqieOG9lfXl3e2SquorenzeAw+B6CX3YhyUiKfJhv3tF79wM/MgvcSUEdHMSI4mzKyKaIfTw55SO/vKGmh3DUwL9qKRk1g1/xvEhUYjKzKv5W3mlzn/ptnZe5mkU6emM2/eDUiSSGVlHWvXbqW19eLriSLcf7+OpcvUbfsfH0ayYfijBNlMyHXt1P3nFN5GNxAJmIDAe3C7ntDEmUZAsNByfXQFCBomYUnT4+tUaNwaYMoMeHqOm7hQhf1VIi8d/vzlSUAg0Tq8O35scGR895jP5+VUVSH55UcoqMijqa2ht6c+oAifdQfmlEx8znZsa19A7rxyl2NUsIGZKVZuGBaJQacmUNS2dLKz2EZeRRPeARZPdp6hYVaeWvBN5o3IAiC/9jSPbnqZo+fKeu2aJpOB226bxahRSQDs25fPli2HkOWLA/ctFli5Us/ESRIej8Kzbw6naMZPMZe58VQ7qHvzFHKHDDShlsrQhNlARxNnGgOe0UaRBIOI3SuT1xnYLqrorrpmjdud+NoH5iZ4KTIH+fjBDR58Mjyw3ojyGTXN9JKe1LixpCdlkTY0k7Dg8O6xDlc7hWePUVCRx4nKYzjdgdkx4VqxZEwnbMoiFNlH/Tsv4W2svfybgKSoYGalWhkbF4YoqH+botpWdhbZKK5z9OaUexWjpOeBybfw/Sm3Y9IbaHG286uc//Da0c3IvVSAF2DIkGiWLZtPREQITqeLd97ZSVHRp6268fECT6/Wk5Ag0tSk8LN3b6B9zncwFbfhLGvB/nYJihegBtiEViojMNDEmcaA53wiwGaHN6ATxY1DRMImGpA9Cg0fBdaTsSgovLTEhSTCb/bpOVZ3oaRFsMlC2tBMMpKyGBWfjlF/obdmo6O+O36s9Nyp6zZ+7EoxDk0lcuF/AdC46Q2cFac+93hRgPS4cGalWhkaFQyA1ydz5GwjO4tt1LYM7PswOymD1QvuYVjkYADWHs/hme2vU9/R0qvXveGGsSxcOBlJkqiutrN27Vaamz8tcCdOFFn5mB6LRaCkVOaJI7ejn3E7hqI2Okqbsb9VAoqAmpG5A61URuCgiTONAc/1UkLjvNWsKceFtyWwrGYPTPQwKU6mskXgiR0GYkJjyUiaQHpSFsNjR14UP3bWfqa73EV149l+nPXAQhdhJeb2BxAkidaDm2g7tuuSxxp1IjcMj2LmyBgig1Ux3O7ysq+snj2ldhzOgR0+MCgkksfn3sUto6YCUFRfycpNf2N/5cleva7RqOfWW7MZM2Y4AAcOHGfz5gP4fJ8WVUuXSdx3nw5JEtixS+APzu8TPDoT6XQbiiLTcWoDKDFABXCwV+et0ff0qjgTBGER8DtAAv6qKMovPjEeBvwTGNo1l/9TFOWV3pyTRmCRYhRJNEg0eGWOBLBLUx8jEj7FgOJTqP9wYFsrPskgi8zqOV7OuVN5vmgqD94+kUERcd3jXp+XosrjXRayPJrbe65y/fWCaAruamYeTEfJUZp2rPvM48LNemaMjGHysGiCDKr10u5wklNs53B5Ax7fwH4o0IkS35qwiP+ZsZxgg4kOt5P/t2cdLx/egKeXra6DBkWxfPk8IiPDcLncvPdeDoWFZz51nF4PKx7UsWiRuj3/9V8mNo36KRbjIITqdnw+D/Xv/AnnmULUbXNgC2WNz6bXxJkgCBLwB2ABUAUcEgThPUVRCj922PeAQkVRbhEEIQYoEgThdUVRNKe5xhVxPhFga5uXwJVmEL3YhCAJNO124akPDNeFXmdgVNxYfjh7HG87JtAph5PU1VO8w9XOibPHyC8/wsnKfJyewBKkfYooEXPHA+gjY3HXnaX+/Zc/1cw8IcJMdqqVjPhwJFGNJyuzOdhZbONkTWtAZEDfED+KZxZ+i1Ex6k22oegAT279BzWO3k8WmTBhNIsWTUWnkzh3rp41a7bS1NT6qePCI2DVKgNpaSJOp8Lqv0RTetMTBNdI0NxJ3boiPPaTyM7z26gmzAKV3rSc3QCUKopyGkAQhH8DtwEfF2cKECIIggBYULuyanebxhVz43XQ6FwXJhAx04giK9R/MLCD3C2mUNISx5OemMXo+DQMeiMuABma22zkncmloDyP0toiZC1+rEeIuvG/MA1NxetoxrbuBRSPmuUrCDBmcBizUq0Mj7EA4JMVcrvqk1U1Dex77TxR5lAenf11lqfPAqCiqY6VW15hx+mjvX5tg0HPkiUzSE9X+3AePnySjRv34fN9+t5OThZ46ikD1lgBm03hkddG0vmlhzGXOPE1dlL370K8TV4gBjAD11fB5OuN3hRnccDHK+hVAZM/ccwLwHuoaSYhwJcVRfmUWUAQhPuA+9T/SZ8c1rhOGWEQGWGUaPbJHOoI3I08apEJUS/QctiN69zAs5pZwwZ1x48Ni01GFC7Ej4UKpYwOPsDbeUd5dtuVZQ1qXDmhk2/EkjEd2ePGvu4FfI5mDJLIxGGRzBwZQ0yIWpy30+3jwOl6dpXYaekMjAcdURD42rh5PDzrK4SbLDi9bv64/z3+dOBdnN7e/4xWayTLl88jOjoct9vD++/v4vjxzy7LMTNb5KGH9AQFCZw4IfPkthkYv/RtjKcceOo6qPt3IXInQDNqqQxNmAU6vSnOPqsZ3iet4zcCR4G5wAhgsyAIuxRFucjeqyjKn4E/AwiCIRAs7Bo9wMIuq9lWhzdgza1SsEDknK4G5+8PDEuGIAgMsyaTnjSBjKQsYsMHd495fB5OVhdQUJ7LjfH7+d40GwV1Ir/cEcRnLxkaV0tQSiYRXc3M69e/jKmlhnnpg5k6PBqzUf3tNLa5yCmxc+hMAy7vwBP+lyI9dhjPLryX8UNUi9WO00d5bPMrlDfX9cn1MzNTWbx4Gnq9jrq6Rtas2UJDw6czQAUB7rpLx93fUP8eH2708ZfGLxNy083oTjroLG/FtrYIfAJwDrVURuDVN9T4NL0pzqqAhI/9Px7VQvZx7gF+oaiNw0oFQTgDjEJLPdG4As53BdjUFqjSDCLnG5FMAo4CN84K/7UO6nUGRsenk56YSVpiJiFBod1j7c42Tpw9Sn55LierCnB5nKRGyTy0VH36f+ADI15ZE2Y9iWFQItFL7lX/veff3BbewPj0segk1WpZXt9OTrGN49XNDNCasZ9JmDGYn2R/ibsyFyAKIuccDTyx5VU+LO6bLUWv13HzzTMYN24kAHl5RWzYsAev99O/XZMJHnpYT3a2hM+n8Ke/imwf+SNC09ORyhx0nG7BvqYY9aGlDLVUhv+uARo9S2+Ks0PASEEQhgHVwFeAr33imLPAPGCXIAixQCpwuhfnpBEgJOlFUo0SLT6F/e2BKc5EE0QtOG8187+A+JCgUNISM8lIzCI1Pg2D7kIfzPpWW1e5iyOU1RYjXxStoPCnm10YJPhLro69lVqoQk8ihYQTc8d3GeY5R3p5DsOGtAFRyLJCfmUTO4vtVDT0Xiui/mLp2Jk8OufrxASH4/F5eenwen5tLYXPAAAgAElEQVS7Zx0dnr6xNEVHh7N8+Xys1gg8Hi8ffLCbY8dKPvNYqxWeetpAcrJIW5vCU78Npvy2RwntjEI814nsddN66J9AMqqd40CffAYN/6HXxJmiKF5BEL4PfIQaKPY3RVFOCILwQNf4i8DTwN8FQShAfTx4WFGU+t6ak0bgcN6lua3NE7CtfSNmm9BZRNqLPXQU+4cAjQ0fQkZSFumJWSTFjrgofqy8roz8iiMUlOdxrqnqkue4K8PLnGE+7O3w0y3GSx6n8cXRG03Mue1WJrZ+RKSnCXTg8vg4eKaBXSV2GtsDLxE+NTqe1Qu+xZShYwA4UHmSRze9TFH9pe/BniYjYyQ33zwdg0GP3d7EmjVbsdubPvPYtDSBJ1YZiIgQqKqSefSPg3Hf/xjBlQKCw4mnw4F9ze/w2KuAE2g5ctcnvVrnTFGUDcCGT7z24sf+XQMs7M05aAQm3S7NAO2lKeghelGX1Wx9/1nNBEFgeOzI7oB+a9ig7jGP101hdSEFFbkcr8ijpaP5sueLMCn8eqFqyfjxJiONnZo7sycINuqYlhzNjDEJBLsLAGju9LC72Mb+0w04PYHnDjPrjfxo+jLunbgYvaSjvr2F1dv/yboTly6u29PodBKLF08jK2sUAMeOlfDBB7vxeD57XVq0WGLFCh16vcDhwz5+/n4apu//hKCidpQ2D3VrC3HXFqF4zwvLwFzfNC6P1iFAY8CRoBcYY5Jo8yns7QjMxSt8hhF9uEhnhZe2/L61DRp0RkYnpJOemEXa0PFYgkK6x9qdDo5XHCW/IpeTlQW4vV/MZfSL+S5igmH7GYnX8rXl51qxhhjJTrEyISkSvSQCXmy6SLbllZJXdBZfIAWUfYzFKTewat43GBIahazIvJr7Eb/KeZMWV9+5a6Oiwli+fB6xsVF4PF4+/HAveXlFn3msKML9D+hYulS959et9fJq1XzC7rsH/YkWfK1u6t4owNusALFAMBB4rmeNK0dbHTUGHOetZtvbPXgCce8RIeam81azvsnQDDWHkZ6YRXpiJqlxY9F/LH7M3lJHfvkR8ityOVNb8on4sStnWoKP+yZ4cfvgOx8Y0bIzr55kq4VZKVZGDwnrfu1MUCK5IenkvvcmrrOfbqAdCCSFx/LUgnuYM3w8AMfOlfHIppfJr+3bUOW0tBEsWTIDo9FAQ0MLb765BZvtsztXWCyw8jE9EydKeDwKv31eZs/QbxB+y3z0J1pw2zupfb0AxSVyoVSGJsyudzRxpjHgON8VIFBdmuFTDBhiJFznfLQe7j2r2aCIuO74sWGxyd2vy4rMmbqS7v6Vtc2fTLL+4uhEhRdvVq1sz+3RU9QgXuYdGp9EEgXGJYQzK8VKXIQZAI9XJq9B5lTm12gxRlG/4VVcZz/bejOQMUp6vjvlNr475VZMOgPNzjae2/lv3ji2FVnpuyc0SZJYtGgKEyeq8W0FBaWsX78bt/uzf6fx8QKrn9ETHy/S1KSw6lmRqtsfIjwsBanMQWd5C7a1p8AnAbWoIdpaqQwNTZxpDDCG6ATSgyQ6ZIXdgZilKVxocG7/oPPTlQGvAVEQGT4opVuQxYTFdo+5vW6Kqk+QX36E4xVHcXR+uibTtfDgFA/psTKljQLP7jJc/g0a3QQZJKYMj2ZGcjRhZvW7czg97Cm1c7BBJOwrDyMazbTs30h7wZ5+nm3PM3v4eJ6e/02SItR4xzUFO3lmx+s0dHy6/VFvEhERyvLl8xg8OBqv18dHH+3j8OFLN0qfOFFk5WN6LBaB0lKZx38TgnfF44S2hiHWdNJxuhH7mlLUfLkzwDa0Uhka59HEmcaA4rxLc0ebF1cAujRDMvWY4iTcDT5a9l17Zp1Rb2J0fBrpSRNIGzqOYNOF+DFHZ2t3/bFTVce/cPzYlTI0TGbVLPWzfG+DEadXc2deCVHBBmamWLlhWCQGnVpupLalk53FNvIqmpCNwQy662eIJjMdRbk073y7n2fcswwOiWLVvLu5KVVtLFNkr+TRTS9zoOpUn89l9Ogkbr11FiaTgcbGVtas2UJt7aV7ci5dJnHffTokSWBXjo9frUkgeOXjBJ+WETvc+DocNO98CRgH1AH76dEnMY0BjybONAYU50tobArQXpoxS1SrWf2HTpSrfIgOM4er9ceSJpASNwa9pO8eszWfI78il/zyXM7UlaD0uktI4YXFLoIN8O/jOjaVaUvO5UiKDmZWipWxcWGIgipki2pb2Vlko7jOoR4k6Yi94zvoI2Jw1VZQ/8HfCJTNXSdK3DtxMT+avoxgg4l2t5Pf7F7D345sxNvH/VZFUWThwslMnpwGQGHhGd57bycu12evP3o9rHhQx6JF6n3+j1e9/Kc0i/CVP8JY2A4uHy5bDfa3nsfnaEIt9alZyzQ+jbZSagwYBukExgfp6JQVdgWgSzN4rA7zcB3eFpmmnV/MijUkMp70xAmkJ2WSZB3R/bqsyJTVFlNQnktBRS51zed6etqfy22pPm5J9dHihB99pLkzL4UoQHq8Gk82NCoYAK9P5vBZtQl5bcvF5VSiFt2FKWEkXkcT9nV/QPEERv2yyfGjeObGe0mNVpvLrD+1nye3/YNax2cH2/cmYWEWli+fR1ycFZ/Px6ZNBzh48MQljw+PgFWrDKSliTidCs895+GI9VYiv/s19AXNKE4fdf8+hrvuNMjna6Bpwkzjs9HEmcaAYX5XIkBOu5fOwDASXES31WyTE+UyhkFREBkxOLU7fiw61No95va4OFV9nPzyXE6cPYqjs29jc85jMSj8frEqMh/ZZqC2TUsC+CRGncjk4VHMHGklIlgVr+0uL3vL6tlbasfh/PRDSOjUm7CkTUV2u7Cv+wO+tsvXl/N3os1hPDrn6yxLywbgTOM5HtvyCjvP5PfLfFJShnL77bMJCjLS3OxgzZqt1NTYL3l8crLAU08bsFoFbHUKj63yYrvpAcLTstEXNONr83DuH7n4HBIwCAgBHH31cTQGIJo40xgw3BjALs2gZB2W0Xp8HTKN2z7bambSmxidkEF6UhZjE8YRbLJ0jzk6WymoyKOg/Ainqk/g8fa/JWXVbDcJYQqHqkVePKy//BuuI8LNemaOtDJ5eBQmvRpPZnc4ySm2c7i8AY/vs58+zKMmEJF9O4oiU//+X3HXne3Lafc4oiDwX+Pn81D2VwgzBeP0unlh3zu8eOB9XL6+/52LosC8eTcwbVoGAEVFFbzzzk6czktbsrOzRR56WI/JJHDihMwTz+ngB48Sah6OrqQVd30Hta8dQ3HrgRbUUhmaMNP4fDRxpjEgiJEEMoMkXLLCzgBsdB6zRK1r1rDFhfwxs2B4cARpiVlkJGUxcsjoi+LHapuqKajII788l3JbaR/Ej10542J9rJjswSfD/euNyIqWBACQEGEmO9VKRnw4kqh+J2U2BzuLbJw81/q5UWOGwcOIuukeAJp3rKOz9FgfzLj3GD94BM8s+BYZg1U3/LayPB7f8ncqmuv6ZT6hocEsWzaPhIRYZFlmy5aD7NtXcMnjBQHuulvH3Xer2+jGjV5+/69ILE88QVCTBam6g47yJuxrikDWowb+fwT4X59cDf9DE2caA4L5ITpEQWB3m4cO/9EgPYJpqEToeAOyS6Fhs5O4yKGkJ2WRkZTJ0Jjh3cfJikzpuSIKKtT6Y7aW2n6c9aURBYUXl7jQifDb/Xryaq/vxuaCAGMGhzEr1crwGNXa6ZMVcisa2Vlso7rp8oWGpdBIrHd+F1FvwHFsF60HN/f2tHuNcFMwD2V/ha+Pn4coiFS31rNqy6tsLDnUb3NKTo7njjvmYDabaG1tY+3abVRWXlokmkzw8MN6ZmZL+HwKf37Jy3uFyYQ/9yimMi9iu4uO0jrs6ypQt9lyYCtajJnGlaKJM40BQXcvzQC0mllvMhPXkkLs0dF8+ZZ0okJiusdcHhenqgq648fanP7vDvl2lpcp8TLVrQKPb79+kwAMksjEYZHMHBlDTIhqGe10e9l/uoHdJXZaOq/MbScYjFiXfh/JEkZn+UkaN73Rm9PuNQQElqVl8+icrxNlDsXj8/KnQ+/zu71v0enpn8KrgiAwd+5EZsxQOw6UlFTy9tvb6ey89HysVnjqaQPJySJtbQrPrPaQb55G5BM/wHjCgeCWcTfU0LDpBWAq0ADsI1CyaTX6Bk2cafg9UZLAxCAJj6KwvS0w4s1MhiDGJGQwPnUCY8VxGAvNYAAM0NrR3BU/lktR9Qk8/RB7c7VYg2V+MV/d2FZsNOJwX3/uzFCTjukjY5g6PBqzUV1iG9tc5JTYOXSmAZf3C7S/EgSib/k2Bms8noZa6t95Cfq4nERPMCo6gWcW3ssNCWqD8H1nC3l008uUNFT325wsFjNLl84lKWkwsiyzffthdu/+fFdxWprAE6sMREQIVFXJPPqoh6bs5UQu+RL6Y43gVegoK6R+/Z9R3J3ABjRrmcbVoIkzDb9nnkWHJAjsavPQdnVtHf2CCEsU6YmZpCeq8WM6qevn5wO7XEPesSPklx+hwnYaZYA+Zf9moZtwE3xQLLHu5PXlzhwcFsSs1BjGJ0Sgk9TM1PL6dnKKbRyvbuZqepBHzF2OOTkDX2cbtrW/R3Z19PCse5dgg4n/mb6Mb01cjE6UsLU18/T213insH87GQwfHsedd84hODgIh6Oddeu2UVHx+WECixZLrFihQ68XOHzYx+qfK4j3PUh46mT0xxqR3T5q/3kIj72GC3FlmjDTuDo0cabh93S7NAdgL834qEQ1fiwxi4SYpO7XZVmm1H4KW9ZJyiOPc/jJUtx1A1h5AvOGefl6hpdOD3z/w+ujsbkApA4KJTvVSkqs2n1BlhXyK5vYWWyjouHqxZRl/CxCJ85H8Xmxv/UnvM2XLuXgj9ycOplV877BoJBIfLLMK0c28n+73qS1HwWmIAjMmpVJdnYWgiBQVlbF22/voL390nF/ogj3P6Bj6VJ1u1y31suf/xVE6GOPYDYkoCtuxdfuoeaVg8jtRsAKhKJmZmpoXB2aONPwa8IlgRvMA8elKYkSIwePJj0pi/TETCJDorvHXB4nJysLKKjI5fjZo4TcKRMdb6J5v2vACzOjpPDHrsbmT+UYKG8O7JpmOlEgKzGSWSkxxIap9elcHh8HzzSwq8ROY/u1lTIxDRtD5IKvANCw8TVcVSXXPOe+YljEYFYvuIfsYWo5iryaUh7Z9FeO15X367yCg4O48845DB8eh6Io7NhxhJycvM/NcrZYYOVjeiZOlPB4FH73Wy+bC2IJ/80qguqNSFUduO0Oal/LR/EYgVbUUhmaMNO4NjRxpuHXzLXo0AkCu9q9tPipfgkymBmTkEFG0gTGJGQQZDR3j7W0N6vZlRW5FFUX4u2KH5NCBBJnhQNgXz/wU+t/OsNNSpTCCZvIr/cGbk0zi1HH1BHRTE+OxmJSP2dzh5vdJXb2n27A6bl2N5Y+ejAxt92PIEq07NtA+/F913zOvsCk0/O9Kbfzncm3YtTpae5s4+c7/8W/jm3rdzd9UtJg7rxzLiEhZtraOnjrre2cOVPzue9JSBB4erWe+HiRpiaFJ1e5KRLHEvXrn2EscSG2uegoq8O+rgwUI2BDLZVx+exbDY3LoYkzDb/mxq6uAB/5WeHZCEsUGYlZpCdlMXLwKCTpwk+pprGKgvIj5JfnctZ+5jM3pqiFJkSjQGueG1fVwI5LGRkp87MZ6t/nOx8Y8ciB5860hhjJTrEyISkSfVc8WVVTBzuLbByrbLqqeLLPQjSHYF36A0RjEO2njtCc827PnLiXmTs8k6cWfJPE8FgA/pO/nWd3vEFjZ/9nF8+cmcns2VmIokh5eQ3r1m2jre3zBdSkSSKPrtRjsQiUlso8/pib1vHziPz2AxjzWxDcMu0nK6h/rxY1k6cCtVTGwAu90PBPNHGm4beEijAlWMKrKGzzgxIaCdFJ3fFj8dGJ3a/LskxJzUnyu/pX1rfaPvc8YpBA1DwjAPb1A/0pW+FPN7sw6uBveTp2nQ2sJIBkq4VZKVZGDwnrfu1EdQs5xTbK7G09ezFJR8yd30UXHo2r5gwNH7yCv5dfiAuNZtW8u1mUcgMAJ20VPLLpZQ5XF/fzzMBsNnHHHXNITo4HICcnjx07jly2WPPSZRL33adDkgR25fh47pdepK/eRcSC29DnNiHICp0Vp6jf+BIwC9WFuRd//1tpDCw0cabht8y16NELAnvbvTRfop1Nb6ITdYwcciF+LMIS1T3mdHdS2BU/Vnj2GO2uK9+oo+YZkcwibYUeOssGttXsa+le5g33Ud8BD2029vd0egRJFBifEEF2SgxxEaqL2uOVOVSuxpPZHb1Tkyt68TcwxY3A29qI/a0/ovhBC65LoRcl/nvSzTw47U7MBhNtrk5+vXsNrxzZiE/p//iDhIRYli2bS2iohY4OJ2+9tZ2ysqrPfY9eDyse1LFokbot/uNVL6/9RyT0Jw8RPDwT3dFGkBUcR/fQuPX1rpImH6FlZGr0Bpo40/BbFvZDL80gg5mxQ8eTkZTF6IQMggxB3WPN7Y0UlOdRUJFLcc3J7vixL4JgUF2aMPCtZuEmhd8sVAXETzYbaegc2O7MIIPE1OHRTB8ZQ1iQ6k5v7fSwp9TOvrJ6Oty9twmHTbuZ4LGTkV1ObGtfwNfuvwHlU4eOYfWCb5ESrVqk3ju5l6e3vUZtW1M/z0xl+vRxzJ07EVEUOXu2lrVrt+FwtH/ue8IjYNUqA2lpIk6nwi+f87C7IJTw/3sMszAIXVErssfHuVf24G1q5IKVTBNmGr2DJs40/BKLCNODdfgUha297NKMColR648lTSB5cCqSeME1V91wVnVXludSWV9+zYHNEdlGdKEiHae9tBf2v6v2Wvj5PBexFoWcCpG/Hx24S0mUxUD2SCuThkVi0Kl/+3PNneQU28g724S3pwLKLoF59CTCZ96GIsvUv/8XPPbPt/D0FzHBYayc81/cOXYmAGUNNazc/Dd2Vxzv55mpBAUZuf322aSkDAVgz55jbNt2CPkyf7/kZIGnnjZgtQrY6hQef9zNGW8Ckc8/galWh1Tfjtfh5Nzf9iM7LYAEhAH+IUY1ApOBu6JqBDRzulyaBzq8NPSwS1NAICEmifSuhuJxUUO7x3yyj6LqExSU51JQkUeDo+dqSwkSxCwODKvZlHgfD0z04vHBA+tNDMSaZknRwcxKsTI2LgxRUOdfVNvKziIbxXV9E8huGDKc6Ju+CUDT9jV0ll260XZ/IQkid2Uu4CfZXybUaMbpcfP8vrd46eB63D7/eMCIi7OyfPk8wsIsdHY6efvtnZSUnL3s+7KzRR56WI/JJHD8uMyqVW46RmQS9fDDGE91ILa5cJ5rpO714+CzAA7UUhnNvf2RNK5zNHGm4ZcstPSsS1Mn6kiJG0N6YhbpSZmEB0d2j3W6OymsPEZBeR4nzh6l0907RTLDphnQR0k4q7w48vwr+/SLIAkKL3bVNPvVXj0n6wdOTTNRgPT4cGalWBkaFQyA1ydz+GwjOcU2alv6rqyJFBqF9c7vIuj0OPJ24Di8tc+ufaVkDknmmQX3kj5oGABbSnN5fMsrVLb4T0HcKVPSmD9/MpIkUlVVx9q122hp+fwYUEGAu+7Wcffd6jqz8UMvv/udF92ixUR8878xHmtCcHVlZL5fBYoZqEcVZgP7wUpjYKCJMw2/wyzAjGAdsqKw5Rq6ApiNwYwdOp70xCzGJKRj+lj8WFNbQ3f/ypKak3jlXrYACBBzs3p9+wfOAZ3YtWKKh3GDZE43CazOGRiNzY06kcnDo5g50kpEsDrndpeXvWX17C2143D2rQVIMARhXfZ9pOBQOs8U0rjlP316/csRbrLw01lf5evj5wFQ1WLniS1/Z1PpkX6e2QVMJgO33jqL0aOTANi/v4DNmw8iy5+fkGAywcMP65mZLeHzKfz5JS/r3law3P/fhMxahP5II4JPwXG0kMaPWgEjcBbYglYqQ6Ov0MSZht8xy6LDKAoc6fBi/4IuzaiQGDKSJpCRlMXwQSkXxY9V1VeQX3EhfqwvCZ1kwDhIwm3z0XLAf7PwLkdCqMyTs9X5f3+DkU6vf7szw816Zo60Mnl4FCa9ei/YWp3sKrFxuLwRTz9kASOIxNz2bQwxcbjra7C/6z/NzAUEvpQ+i0dmf41Icyhun5c/H1zP8/veptPTO1mqV8PgwdEsXz6fiIgQnE4X776bw6lT5Zd9n9UKT682MGKESFubwjOrPRwuMBD25E8Ijk9Dl9eIALQV7KVx0z+BeUAHsJsB/USlMeDQxJmG33G+l+ZHV2A1ExAYah1ORldA/5DI+O4xn8/LqarjFJTnkl+RS1NbQ6/N+XLELOmKNdvghP6vNHDV/G6RC4sB1pyQ+LDUf5ePhAgz2alWMuLDkURVQJbaHOQU2Th5rrVft9mIeV8maHgavg4H9rUvoLj8w002OmYozyy8l0nxqQDsLj/Oys1/o6zx8yvp9zWTJo1h4cIp6HQSNTV21qzZSnPz5WME09IEnlhlICJCoKpKZuWjHmqckUT89gmCfFHoTrWgKApNO96j9eAHXe/azID+wWoMWPx3ddW4LgkSIDtYvS23XKKXpk7Sk3o+fiwxk7DgiO6xTlcHhZX55JcfobAyv9fix74IlnF6gobq8DTJNO/xH+vDF+WWFC93jPbR6oIHP/K/mmaCAGMGhzEr1crwGAsAPlnhSIUaT1bd1P8iKCRrDqET5qB4Pdjf+iPelvr+nhIWQxA/nrGcb064EZ0oUdfWxNPbXuPdk3v7e2oXYTDoufXWbMaOHQ7AwYMn2LRpPz7f5cXTosUSK1bo0OsFDh/2sfppD85Bw4n81eOYqgUkezs+p5tzf8vB55BQMzJ9aMJMo7/QxJmGXzEzWEeQKHC000ut94J9I9hkIa0rfmx0QjpGval7rNFRr/avLM+j5NxJfH7iIjqPtctqVv+RE2WA5gEE6xVeuEkVliu3Gahx+E8SgEESmTgskpkjY4gJUb/rTreX/acb2F1ip6XTP7500/A0IuZ9GYCGD1/FVV3WzzOCW0dP5fE5dxEbEolPlvnr4Q38ZtcaHO7+F7IfJzY2kuXL5xMVFYbL5ea993ZRWHj6su8TRbj/AR1Ll6pb3bq1Xl56yYt+ymQi//fHGAvbER1u3I0t1L56CMUdDriBcKD/LO0aGpo40/Arzrs0Nzm8RIdaL8SPxaYgihcEQaW9vDt+rKqhor+me1nMqTrMI/V422Satg/cBuePz3IzNEzhSI3IHw75R2PzUJOO6SNjmDo8GrNRXcoa21zklNg5dKYBl9d/rB766CHE3PptBFGkec962gsP9ut8hkcOZvWCbzEzKR2AI9XFPLLpZQpt/vdbysoaxeLFU9HpdNTWNrBmzRYaG1sv+z6LBVY+pmfiRAmPR+F3v/WycaMP87LbCfn63RjzGhFcMp0V1djeLAE5HGhDzcjUaphp9C+aONPwG0yCwIQhyZTFjCNpXBqrIuK6x7w+L0WVJyioyCW/PJfm9sZ+nOmVY71FzdBs2OREHqAezXSrj/+Z6kFW4P71RmSlf5MABocFMSs1hvEJEei6mpCX17eRU2zneHVzjzUh7ylEcwjWZd9Xm5mfPETL7vf6bS4mnYEfTr2D+yffgkHS0dTp4Nkdb/Cf/B3XXGC5p9HrdSxZMpOMjGQAjhw5ycaN+/B6L28ZT0gQeHq1nvh4kaYmhSdXuTl+UiTkwe9imToP/aEGNSMz7ySNmxoAC2qpjI2oCQAaGv2LJs40+hW9pCc1Po30xEwmJGVxIkhtMB0NdLjaKTx7jPyKXAor83H6mavlcgQNk7Ck6fF1KjRuHZjKTEDhxSUudCL8/qCeI+f6p7G5AKQOCiU71UpKbAgAsqxwrLKJnGIbFQ3+uaEKOj3Wpd9DFxaNq7qMhg1/77e5zB+RxVML7iEhLAaAN45t5Rc7/01TZ98U3P0ixMREsHz5PGJiInC7Paxfv4uCgitzA0+aJPLoSj0Wi0Bpqczjj7mxt5sJf/anmGNT0eU2IADN+/bTkgNgAipRS2X4hwtcQ0MTZxp9jsUUQlqiGj82Kj4do/5CcLnJ2cDZyjz+XXSI0nNFyH4WP/ZFiO6qa9a43Ymv3b+sElfKvVlepiXI1DgEVm7r+5pmOlFgQlIk2SNjiA1Tv0+Xx8eBM2o8WWO7P5clEYi66ZsYhwzH29KA7a0/onj7fvOPD43hyfnfYOHIiQCcqCvnkU0vk1tT0udzuRLGj0/hppumo9frsNkaWbNmK/X1V1aRf+kyifvu0yFJAjk5Pn75nAd3mJXI51dhcoWhO6n2LG3e/T4te95HLZXhBXLQSmVo+BOaONPoE6xhg0hPyiI9MYvhsSMvih87az/N8fJcHpKLiOms4anTDs56BvZCaRwiEjbRgOxRaPhoYMaaxZhlnpuvWvwe3Gig1dV37kyLUce05GimjYjGYlJj3Jo73OwusbP/dANOj/+L9rAZtxA8ehKyqxPb2heQO/rWQmWQdNw3aQk/nHYHQXojDlcHv9r1Jv/I3YRP8Z94vPPodBI33TSdzEy1lEdeXhEffrgXj+fyJXX0eljxoI5Fi9Qt7R+venntNS+6UalErlqJqVJBsrcjez00fPA6HafOZ6JuQxNlGv6IJs40egVBEEiyJpPRJcgGRQzpHvP6vJyqPE5+eS7HK3Jpbm9iVrAOa7yZk07fgBdmcMFq1pTjwtsyMD/P/y10ExkEG0sl1hT2zVJhDTGSnWplQmIk+q54sqrGDnYW2zhW2eR38WSXInjMZMKnL0GRZezv/QVPfXWfXn96YhqrF9xDcpQat/lO4R6e3vYatnb/7AkZHR3O8uXzsFoj8Xi8bNiwh6NHi6/ovRERsOpJA2PHijidCr98zkNOjoxx9kzCfrQC43EHYqsHb5uDc3/bhdxpRd36vJEJOIIAACAASURBVGjCTMNf0cSZRo+h1xkYFTeW9KQJpA0dT6g5rHusw9XO8bNHKSjP5WRlPk7PxdakhSHqrfhRD/XS7E/00SLhUwwoPoX6Dwem1Wx2kpe7x3np9MD3Nhjp7cbmyVYLs1KtjB584Z45Ud3CzmIbp+2f3yfR3zDGJRO1+G4Amrb+B+fp43127VhLBI/N+S9uGzMdgNKGah7d9Df2nj3RZ3P4oqSnJ7NkyQwMBj319c28+eYW7PYry5ZMThZ46mkDVquArU7h8cfdlJYqBH/9y4R86asYchsQnD5ctbXUvn4UvFYulMro/xpzGhqXQhNnGteExRRKemIm6UmZjIpLw/Cx+LH6VjsFFUfIL8+lrLb4kvFjemCu5cq7Avg70TeZECSBpt0uPPX+5z66HAbpQmPz1bsMnG7qnZpmkigwPiGC7JQY4iLMAHi8MofKG9hVYsfuGHhJFLqwaGLu/A6CTk/rke04crf3yXUlQeQbWTfyvzOXE2I0/3/2zjw8qvLs/5/nnDmzJJNkJskkbGFPkEUWWQSBhB0EFBWoXdyqP6u21trNlU1A39a+79vaaiu2Wm1tX1sWFdlkT8JO2INACBBIQshk32c/vz9OAmhdEpKQmXA+15XrgmTmmWeSM+d8z718b+q8bl7btYo/71+Lxx+cnymDQWbatFEMHdoXgKNHs1mzJr1RaUyA5GSJZ55VMJsFmZkBFi3yUF5tIPLZHxN+yxiUvUUIv0rNyVMUr84DNRbNKmMDEBrd3jo3Lro402ky8baOmjt/91voEd8bSVy5eOc4z3As5yDHzh/kYmleo9YbGW4gShaccvs57w09MXM1hiiBfawJNaBSvDa0uksbeGa0lz6xKieKBP+9q+U9zSxGmVE9Yxmd6CDKoq1fWedlZ3YRu88UU+sJ/nqyL0OYLDjmPIkcFkHd2UzKtlyfYeZDOyfx8uSH6R/fHYCNpzNYuPk98iqLrsvrXwvR0VHMnTuRDh1i8Pl8rF+/m4MHTzbquULA/Q8YeOAB7fK1Yb2P117z4bNEYH/1RSzRPa/qyNxPRVodEIVmKrsBqGmld6Wj03Lo4kznGxFC0CM+8XL9WLyt4+Wfef1eTuQf42jOATLPH6aitunmjVOs2mG4sR2kNGOmmZEUQUWGB3dB6AnN3tEBXhyrdUA+sdaEx99y6cwYq5HkxDiG94jGaNAsOQrK60jLcnLwQhn+UCko+zIkGcesxzDGdsJTlE/Rx3+GVi66t1sieD7lO3xn0AQALpQ7Wbj5XTafOdiqr9tc+vXryZ13jsVkMlJSUsHy5ZspLGxcJMtshmefVRibLOP3q7y1zMfKlX7kLp2IfnkR5lorhs+0urrSzRuoOhAFhAF5aHMyQ/8co3NjoIsznS/FaDBxU5cBDOx+C/27DibCEnn5ZzWuKjIvHOFYzgFO5GXi9l57XZUBmBjRIM6CM/3SWORwQfT4+gHnn4Ri1EzljeluzAZ497CB1PMtc3roHhtOSlIc/TtHIQlN7J26VEnqKSdZhcHnsXUtRE+6F0uPfvhrKnGueB21FT35BIJ7B47jhXHfxW6JwOP38ebe1fxh90e4fMFrLSLLElOnjmT48P4AHD9+ltWr0/B4GieY4uJgyVIjvXpJVFerLF3iJSMjgDJwALYFL2A670N2VqMG/JRs+Ds1x3YBKWj1kmnoczJ1QgldnOlcJsISVV8/dgt9OvfHaLjia1VUUXjZnf/spSwCLRQVGB4mY5Mlzrj9nPGE9skzepIJ2SyoOubBdT70UnP39vcxpZef0jr45abmDTaXBNzcxUZKUhxdY8IB8PkD7D9fSnqWk0uVodko8WVEDJtIxJBxqD4vzlV/xF/ZejMZ+8d15+UpDzO0cxIAaTlHmb/pr5wtLWi112wJbLYI5s6dSKdODnw+Pxs37mH//s8a/fwBAwQLFxmx2wV5eQHmveglL0/FPGUikU/+ENOxSqRKL35XDUUr/4I7r6EBQvcv0wlNdHF2g9PB1omB3Ydyc/chdIvr9bn6sXOF2ZcF2aWy1rECmBrRPhoBJDPETG6ImoWe8IgyqfxumhZ1eWaTieLaa0tnmhWJET1iGJsYhz1cE/c1bh+7sovYdaaYKldo/52/iKXXzdgnzAWgeN27eC5+8zDuayHCaOHnY7/FQ7dMRZYkCqtKeWnr3/nk5O5Web2W5KabujNrVjJms4myskqWL99CQUHjOyVvny7z1FMGFEWQkeFn6RIv1TWC8IfvJ2LWbIwHtI5Mb0kRBX/bieq5CchCS2HqwkwnNNHF2Q2GJCR6dEhkYH1Bf1xUh8s/8/o8fJZ/XPMfu3CIytqKVt2LDExsqDerDu1aEPs4MwarRE2Wl9qs0BMgL0/00MGqsuOCxDuHmn5asIcpjEmM49aeMZgVrZ7MWeki/bSTjJxSvP72d5FUHF2IvfNRhJAo37Ga2hP7W+V17uo7mnkT7iPeascX8PPn/Wv53x0rqA7ycWaSJDF58ghGjtSGq584kcPHH6fidjcu9SpJ8NjjBmbP1o7HlSt8LFvmI2AwEjX/Z4QNGH65I7Pu7GmcK7Mg0AXNKsMOOFvnjenoXAd0cXYDYDSY6JtwMwO7afVjVkvE5Z9V11WReeEwR3MOcDIvE4/v+tkXDLXIxBgkcjx+styhm9IUCsROq4+arQm9qNnwTn6eGObF69eaANQmeJolRIeRnBTHwC42ZEl7XrazirRTTk4UVLbbuIUcHqUNMzeaqTm+l4qda1r8NXrHdGLp5IcZ3W0AAPvzTvHixrc5UXShxV+rpYmKsjJnzkS6dInD7w+wadNe9u5tvN+b1Qrz5isMGybj9aq89jsfGzb4kew27EvmY4lM0DoyVag8mEHZpjIgHq0TcwNaZ6aOTuiii7N2SmRYlGZ30e0W+nTuh3JV/Ziz4hLHcg5yNOcA5wqzW6x+rKlMqU9phnojgG2MCcUmUXfeR/XR0IoAykJl2Uw3koDf7FHIdH7zYHMhoH+nKJKT4ujpsALgD6gcOF9KWpaT/LLgjug0F2Ew4pj9IwyR0bjysile/16Lrm9RTDw16m5+MGImRtlAaW0lr2z/J/8+looaAnI3Kakrd92VgsVipry8ihUrtpKf3/goVkKCYMlShS5dJMrKVF5a5CEzU0Xu3g37ywswVVkwHNc6Mks2baL6oBEtUlYKrEe3ytBpD+jirJ3RJbYb9455iB7xvS9/L6AGOHvp9OX6scLyi224Qw0JmNwepgJI4JjeEDULPVHy5AgvQzoGyCkXLE79+sHmRllieI9oxibGERuhNQzUeXzsOasNIa+oC+G/Y6MRxMz4PqaO3fGVF1P04Z+gBU1ep/QeykuTHqJLlIOAGuD9w5v5deoHlLuCf0qCJAkmTBjO6NGDADh16jwffZSKy9X4aPzw4RIvzlOwWgXZ2QEWzPfgdIJx2C1EvfgspnMe5EKtI7N49b+pPdUNMAIXgY1oKU0dndBHF2ftjIkDp9Mjvjcen4dTeZkcPX+QzPOHqapr3fqxpjLEIuMwSOR6ApwI4ZRm1K1GjA4Zd4GfyozQEiedIwIsGa9dzJ5cZ6LW++XpzEizgdGJDkb1jCXMpJ0ySqrdpJ8uYv+5Ety+0P37NRXb2FmE3zS0fpj5H1psmHlClIOXJj3E5N5DATh26RwvbHybwwXZLbJ+axMREc6cORPo2rUDgUCALVv2s2vX0SatMXuOzA9+YECWBWlpfl79tReXCyx3TifisUcxHSlHqvAScNdR9NGbuHJOAGPQZoykoltl6LQndHHWzujq6AHA71Yv5ULRuTbezVfTMEszpBsBBDhm1kfN1taFXGPYa9PcRJhg1QmZtaf/81TQyWYhOSmOwQk2DPVDyHOKq0k95STzYgVqiL3f5hI+YBRRt01HDfgp+mgZ3pLm21cYZQOPj7iDH4+6G7NipNJdy6tp/+LvhzYSCJFfcK9eXbjnnvGEhZmprKxmxYqt5OYWNvr5igI/edrAtGnaMfjeu17ef9+PKiSsTzyM9faZKPtLkOr8eMuLcC5fhq+0oe5uJyH3wdPRaQS6OGtHmBUz8baOeP1e8kuCt2hYAJOtDfVmoSvOIoYomDsb8JT4qdgdWumUGYk+ZvfzU+WGp9Zf8TQTQJ8OkaT0iSMxXmscCQRUjuSWkZbl5HxJbRvtuG0xdUkkZtr9AJRu+gBXTuM9ur6Ksd1vZunkh+kZrU3cWJmZzsvb36eoJrii3F+FEIJx44aSnDwEgOzsXD78cDu1tY1virHbYdFLRvr3l3C5VH79ay/paQGE2Yxt3jNY+gzWOjJ9Kq7csxT++wD4RgKFgBtdmOm0V3Rx1o5IiO0OwMWSXPxfMWQ8GBholumgSBR4AxxzhW4qwjHTAkDxehdq8P66/wOLQeX16Vod0ILtRvKrJAyyYGi3aJITHcRHae/L5fWz75xWT1ZaE1risyUx2BzaMHPZQGXGZqoPpzZrvQ5WOwsmPMAdfUcBkFWcx7xN77D7QvMF3/XCarUwe/YEunfvRCAQYPv2A6SnH27SGr0TBYsXG4mLEzgLVRYs8JCdrSLFxmB7eSHmsA4YDhQjVKjOPEDJunxQe6D5l9mBS63x1nR0ggJdnLUjEupTmrnFwZvOBJjaDhoBwvsbCOtpwFcZoCzt+tmPtAQLUjx0t6kcKpB457CFKf0d3NYrFqtZi2aW13rYcbqIPWdLcHlDSHW2ApIpjLg5P0a2WKk9c4yyrcuveS1ZSHx/6DR+PmYuVpOFWo+L3+1axV/2r8UbxDdTX6RHj07cc894rNYwqqpqWblyK+fPNy3Fm5Ii8ctnFMxmQWZmgEWLPJSXgSGxF7al8zGVmzBkah2ZZambqNyjAp2AWjSrjMab2OrohCK6OGtHNNSbnQ/iWjNoHxYal6Nmn7pQQyio1N/h5+ejvBS5wvjLya48PyMGpb6eLK+0ltQsJ0dyywjlGeQthiQTe9djKDEd8DjzKF79FtdaaDescxIvT3mEfnHdAFh/ah+LtrzHxarQ8eMSQpCcPISUlFsQQnD2bD6rVm2jpqbxXcpCwAMPGrj/fu3Ss2G9j9de8+H1gum2kUQ9/wuM2XXIl6q02r7Vq6g7FQeEA2VoVhnB37mqo9NcdHHWjmgQZ8HcCDDALNFJkbjkDXDEFTrRgqux9DZg7avgrw1QujV0omYClV9NM/NJfk/O1UTTswMEVJXM/HLSsoo4W6Rf9K4mesp3sXTvi7+6on6YedP/1tGWCF4Y913uHTgegPPlhSzY9C5bzx5q6e22KuHhFu6+exy9enVBVVW2bz9AWtoh1CaIVbMZnn1WYWyyjN+v8tYyHytXaueAsDl3YX34Ia0js9yjdcOu/Cvu3MHoVhk6NyK6OGsnWIxhxEV1wOvzUNBKczBbgin1jQCbqr0hW8rb0KFZstlNoC7434UsCQYn2Ll7cCyn3OHgBq8vwP6cEtKyiiiuDh2Beb2IGD6ZiEFjCXg9OFe9gb+qtEnPFwi+O2gCz6V8B5vFitvn5Y97P+aPez7G5QutdH63bh2YPXsCERHh1NTUsWrVNs6ebdo5Ji4Oliw10quXRHW1ytIlXjIyAiDLRPz4ccInTkbZp3Vk+ipLca74A96ifMAMhAHb0a0ydG4kdHHWTmhoBsgvySUQxPUroZ7SNHeViRxsJOBWKdkU3KOaLEaZUT1jGZ3oIMqi/d7DZA+nC5ws21VKrSd4j5O2xNJ7EPbxswEoWftXPAU5TXr+zfE9eHnKIwzppBlBp547wrxNfyWnLPQK2MeMGcz48UORJInz5wtYsWIr1dVN69gdMECwcJERu12Qmxtg/jwveXkqIjyMqIXPY+7ZH+Oe+o7MghyKVv6FQE3DRIE9Lf6edHRCAV2ctRMupzSDuBmgr0miq1GiyBfgUF1oCoPYGVrUrDTVjb8qOKNmMVYjyYlxDO8RjdGgjWOSA7VM6pxHfnEJP9xugibMz7yRUOISiL3j/yGERFnaR9SeOtDo50aawvjl2Hu5f8hkZEmioKqERVv+xrpTe1txx62DxWLi7rvHk5iYAEB6+iG2bTvQpDQmwO3TZZ56yoCiCDL2+1m61Et1NUgd4rG/shCj0YGSoXVk1pw6RPHHJ0GdAHwMhN7EDR2dlkIXZ+2EBEd3ILjrzabWR802VflCMkFhjJeIGm4k4FMpXh98F44eseEkJ8XRv3MUktDE18mCSsoqLrH8rkLcfrj772HowuzLka22+mHmJqqP7aJy97pGP/fufmOYP+E+HOE2fAE/y/at4bc7V1DjCe7o6peRkBDP7NkTiIqyUlvr4sMPt5OdndukNSQJHn/CwD33aJeYlSt8LFvmIxAApd9N2BbPw1gqYzhVBkDF7s2Up1UDiYAPsKGLM50bGV2ctRO6xgZ/M0CDhUaoGs/GTjcjJEFZugtfWXBEzSQBA7vYSE6Ko2tMOAA+f4D950tJz3JSUl3HkcdrEQJeSTeSXSq18Y6DE6HUDzOPsOPKzaLk0/cb9bzEmM68POURRnXtB8C+3JO8uPFtThY3TcwEC6NG3cykSSOQJInc3EJWrNhCZWXTBolbrTBvvsKwYTJer8prv/OxYYMWKTeNG0vUMz/FmFWDXFCFGghQsn4FNZmRQBc0QbYBKGrpt6ajE1Lo4qwdEGYKxxEVj8fn4VKQNgMkmSS6GWVKfAEOhGBKU4mWsI02oQZUite1fTTErEjc2iOWMYkO7OHawPIat49d2UXsOlNMlUur6Xt+jJe+DpVTxYJf71TacstBjCB25iOYOnTDW1ZE0YdvfuMw8zDFxE9um82jw6ejyAaKayp4efs/WJGZdp323LKYzSbuuiuFPn00q49du46yZcs+Ak30VElIECxZqtCli0RZmcpLizxkZmprhN93L9b7vofxcClSmYeAx4Vz+T9w5/UCrEA5mlVGy8wr1dEJZXRx1g640gxwnoAanAnDhi7NLdU+Qk+aQcw0M5JBUL7Hjaew7X7H9jCFMYlx3NozBrOi1ZM5K12kZTk5cL4Ur//KxbSnPcD8ZM164Im1Jjx+PZ35ZdhS7iYsaQgBV602zLzu6y1FpiUNZ9HEB+kcGUtADfC3gxt5Ne1fVLibFmEKFjp3djBnzkRstgjq6tx89NF2srKaPv5t+AiJeS8qhFsF2dkBFsz34HQCioHIn/2YsDHjUPYWIdX68VWVUfivZfhKxgImNLf/T9FGMuno6OjirB0QCv5moTwVQI4QRKdo8yeL1rRN1CwhOoyUpDhu7mJDljSRle2sIvWUk5MFlV9iS6Ly+u1uLAr8/YiBbTn6R/3LsA4cTdTIaZeHmftKv7qjspstnsWTHmJCL22W5NGCM7yw8W2OXDp7vbbb4tx6a38mT74VWZbJz3eyfPkWKiqa7nc3e47MD35gQJYFaWl+Xv21F5cLRGQEtsXzMCUkYtzjRPhUPIUXcK58HX9VOdoYJjuwDULytk1Hp3XQz9jtgGAXZ72MEr1MMuX+APtrQ+8EHDPFjGQSVB724M67fvsXAvp3iiI5KY6eDisA/oDKgfOlpJ1ykl/+1QXTc/r5uT3RT1kd/GKT8XptOaQwde1D9JT7ACjd+E9c5098+eNkhSdG3smPRs7CbDBS4arh1bQPeP/wZgLXODGgrTGZjNx5ZzL9+mnnjr17M9m4cS+BQNOiwooCP3nawLRp2qXkvXe9vP++H1UFuUsnbK8swijbUfbXd2SePkLxJ++Dt2G4e+O7YXV0biR0cdYOSLjcDJDTthv5CqbUR822VPkINXczySKImVgfNfvk+nSPGQ0Sw7tHMzYxjtgI7bXrPD72nNWGkFfUfX30MdKk8to0LT307GYTzhq9CeCLGOxxOO56HCHLVO7bSPWR9C993Lgeg1gy+ft0t3cAYPmxVF7Z/k+Kayu+9PGhQIcOMcydO4no6EhcLg+rV6dy4kROk9ex22HRS0b695eoq1N59VUv6WmauFMG3Yxt0QsoxaBk13dk7t9K+daLwAw0q4zQTAPr6FwPdHEW4oSbrcRGOnB73RSWX2zr7Xwpl41nq0NNmkH0BBNymET1Z17qzrRu1CzSbGB0ooNRPWMJM2kfzZJqN+mni9h3rgSPr3FRjSXjPXSKUNmdK/GXg/pH/ItI5oZh5uHUnj5M2faV//GYDhHRLJzwADNvGgnAqeJc5m18hz25Xx5dCxWGDevL1KmjMBhkCgqKWb58M2VlTS/A750oWLzYSFycwFmoMn++hzNntCiieeokIp/+kdaRebEWVQ1QunE51YcV4CauWGXo4kxH56vQz9whToOFRl5xTlA2A3RXJPqYZCr9KntqQkucCSPETtVMZ4vWtF7UrJPNQnJSHIMTbBjqh5DnFFeTespJ5sWKJs3aHtrRz5MjvPgC8NgaE6ruafZ5JBnH3U+gRMfjKbxA8Sdvf26YuUGSeWTY7fx09BzCjWZqPC5+u3MFb2esxxfEkze+CaNR4Y47xjJgQC8A9u//jE8/3YPf3/T3lJIi8ctnFMxmQWZmgEWLPJSXAUIQ/v37sd47F+VQCXKZh4DHTdGH7+HK6QrEolllfAo4v/Y1dHRudHRxFuJcNp8tzmnTfXwVDSnNrdVeQq0VwJ5swhApUXvWR81nLSssBXBTx0iSk+JIjI8AIBBQOZJbRlqWk/MlTRuRAyAJlWUz3UgC/me3wjGn3KJ7bg/ETL0Pc9c++KrKca58HdV7pTtwRJebeGXKI/RxaK74a0/t5aUtf6OgqqStttsixMdHM3fuJGJionC7PXzySTrHjze9iUEIeOBBA/ffr32mN6z38dprPrxewGgk6rmfYRkxCmWPs74jsxznv/6Kt2QwEAFUoFllVLbgu9PRaZ/o4izECfZmgFCdpSlkcNze8lEzgywY2i2a5KQ44iO19V1eP/vOafVkpTWea177h8O9DO0U4EKF4KXtehPAF4m8dSrWgaMJeD0UXe4WhJiwSF4c9z3m3pwCQE7ZJeZv+ivbzx1py+22CLfc0odp025DUQwUFpawfPkWSkqaXi9nNsOzzymMHSvj96u8tczHypVa1E2y24haMh9Txx4Yd9d3ZDrzKFy+jED1VLTh5YVoEbO29wjU0QkFdHEW4gTzZIAERdDPLFPtV9lVG1riLOo2I0qMjCvPR9Wh5sf8rCYDt/WO5bZesVjNmmAtr/Ww43QRe86W4PI2L2XWKSLAyxM0Yffj9SZqvHo682osSUOwj9OGmReveRtP4QUkIfjeoEk8k3IvNrMVl8/DG3s+5k97VuP2h1qc9/MoioEZM8YwaFAiAAcPnmT9+l34fE0/zuLiYMlSI716SVRXqyxd4iUjQyuhkLt3w/7KQhQ14nJHZt3ZTIo+fgvV4wKOAHHAVnSrDB2dxqOLsxDGao4kOiIWt9eFs6KgrbfzH0yuj5ptr/HhCSXHAQGOGRYAita6+BITsUYTH2lmbJKDod2iUerryfJKa0nNcnIkt4wmGrB/Jb+d6iHSBB+dlFl9Sv9YX42xQzdiZz4CQNn2VdRlHWJQh568POURBnXUarC2nT3Mgk1/Jae8sC232iI4HHbmzp2Iw2HH4/Gydu0Ojh7Nvqa1BgwQLFxkxG4X5OYGmD/PS16edtAah91C1ILnUJwBlNNaR2blwe2UbfqYKxGy0I8+6ui0BfpZPITpWl9vllucgxqEfksNUwFCzXg2crgRUwcZj9NPxd5rSzMmxkWQ3MdB345RAARUlcz8ctKyijhb1HSTz69jWm8f3+rvo8YDT603tejaoY4cYcNxz4+QFCPVR3ciDqfz8uSHuW/IJCQhcbGyhIVb3mVD1v623mqLMGhQIjNmjEFRDDidZSxfvpni4vJrWuv26TJPPWVAUQQZ+/0sXeqluv7Qtdw5nYgfPYZyqgpDfn1H5tYVVGe4gLvRrDL0MUw6OteKLs5CmGCuN+tkEAy0yNQGVHaEWJemY2Z9rdk6FzShAVaWBEMS7CT3iaOTTYu8eXwBMnJKSMsqori65UfTWAwqb0zX1l243Uhupe5p1oBQTMTNfhJDhA3XhVOMy8th3qP/S2x4FF6/j2UZa/jdzpXUekN/ZJDBIDN9+miGDOkDwOHDWaxbtxOvt+mfPUmCx58wcM892uVhxQofby3zEQhoP7Q+/gjhs+5EOVyKXOrWavg+fgfXGQfQjytWGbo409G5VnRxFsIEs/ns5ZRmtQ938AX1vhLrIAVLVwPesgDlOxt30bYYZUb1imV0bwdRFu19V9Z52ZldxO4zxdR6Wq/W5sVkDz3tKkcuSby2Vx9sfhkhiL3jEYzxXUmoKONpOYpbpz8BwJ4Ln/HipnfIKs5r4022DDExUcydO4n4+Gi8Xh/r1u3k8OGsa1rLaoV58xWGDZPxelVe+52PDRu041eYzUTNfwbzoKGXOzL91RUU/vstvEUDAAdaOvNTtAYAHR2da0UXZyHMlchZ8M32a7DQ2BhiKc24+qhZ8acu1G/YeqzVxNgkB8O7R2M0aLYVF8vrSMtycuhCGf6WKij7CvrGBvjlbdomH19rwhfQmwAasI2bTUziYB7yStwb0QVDlExRTTlLt/2DVce/fBpAKDJgQC/uuGMsRqNCcXE5y5dvweksvaa1EhIES5YqdOkiUVamsmihh+PHtWNYio3B9vJCjI4EbUamV8VTlE/hv94lUDMKiESzyFiPZpmho6PTHHRxFqJEWKKwW6Op89RRVBFcd6kdDIIhFgN1AZX0EEpphvUxEJao4KsOULbtq1v+e8SGk9Injn6dopCEJohOFlSSmuXkdOH1SuWo/GmGC6MMyw4Y2JOne5o1YB00hlkjpvOUaiZOkQioAd498Cm/Sf8Xle6m+8cFI7IsM23aKIYN6wvAsWPZrFmzA4/n2m6Gho+QmPeiQrhVkJ0dYMF8D856n1hDYm9sL89H8YWj7CvSOjLPfYbzw3fAOwuwoJnKbkC3ytDRaRl0cRaiNDQDRwJ3WwAAIABJREFU5BXnoDannbAVmFTfCJBW46MuuLb2tThmanViJRtdBL6Q0ZQEDOxiI6VPHAnR4QD4/AH2ny8lPcvJpcrre1F6cJCPlO4BnDWC5zbrTQAN9Ol/G0unPsZIFBBw+GI2L2x8m2OFwVeXea3Y7ZHMnTuRjh1j8fl8bNiwmwMHTl7zenPmyjz6qAFZFqSl+Xn1115c9Yez6baRRL3wCwyFPpQsLSJXdSSd0o3/hIAfOAh0RrPKCJ0bMR2dYEcXZyFKV0dPIDibAaaGYErT0kMm4mYFf51K6ZYrysysSNzaI5YxiQ7s4Zqxa43bx67sInZmF1Ptvv4XpGiLyn9P0fb4s0+NlLv0dKbZoPDkuO/xxC1TMQpBpc/Df235G/88soVAEHYyXyt9+/Zg1qxkTCYjpaUVLF++hUuXrm2CgaLA0z9VmDpVi7q+966X99/3X55mFTb3bqyPfh/lVCWGPC3iWLp1JVX707niWXa8/ktHR6cl0cVZiNI1tjsQfOLMIQuGWGTcAZXUEBp0Hlvva1a6zYW/RsUeZmRMooNbe8ZgVrSLl7PSRVqWkwPnS/H62+6C/+pkN7FhsOWszD+O6R/hcT0Hs3TKw3SLigNgdcUlFvxtISW17af2SZYlpkwZyYgR/QH47LOzrF6dhtt9bTdAdjsseslI//4SdXUqr77qJT0t0PBiRPz4ccJun6Z1ZJZoHZnFa96hLssCzEGzymg/v18dnWBDP7OHKMFqozEpwoAkBDuqvdSGSMDC1EkiapiRgFfFskfivpHdGdjFhiRpEanswipSs5ycLKhs8wTymK5+Hhniw+2DH64zwQ082LxjRAyLJj7A9D63AnAGP/9VksPG915C9V77GKxgw2aLYM6ciXTu7MDv97Nx41727bv2aFXvRMHixUbi4gTOQpX58z2cOaMd2SI8nKiFz2HqPxDjXidSjR9/TSWFK/6E91IvoDta1MyGLs50dFoPXZyFIFFhNqLC7dS6ayiudLb1dj7H5VmaIRQ1c0y30PWShT6HrHQeGQaAP6By4Hwpaaec5Je33GzN5qBIKn+aoaUzf7VTIavkxvQ0UySZR4ZN56ejZxNmNFPj9/GO7OODqkLyP/hNuxJmffp0Y9asFCwWE+XlVSxfvoWLF4uueb2UFIlfPqNgNgsyMwMsWuShXDP3R+oQj/2VRSj2Dii7C5G84C0poPBfy/BXjUAbw+QCNgKXWuDd6ejofBW6OAtBEuqjZrlB1gwQIwuGWWS8qsq26uCvNzMaJEYOiGWcN47Ig5qorPX42HOmhJ3ZRVTUBdd7+NkoLwPiApwuEfxX+o052HxkQl+WTnmYPrEJAKx3nmNZXCxOj4vClW/gr742N/xgQ5IEkyaNYNSogQCcPJnDxx+n4nJdm/AUAh540MD992un/PXrffz+NR/e+kNc6XcTtiXzMXhMKHudCFVQl3OColX/RPWO54pVxgagffyOdXSCGV2chSBXUpo5bbuRLzDRakAWgvRqL9VNcNa/3kRaFEb3jmVUr1jCjAaogwrhYeuBQvbnlOLxBd/mu9sCLEjRLsw/XGfC7b+x0pmO8CheHHcfsweMBeBcaQFLTuwga/RUVNVP8Sd/wVN4oY132TJERoYzZ85EEhLi8fsDbNmyj927j13zemYzPPucwtixMn6/yrI3faxadcUY2TQ+mahnnsZQ4MVwqgSBoProTko2fADqXCAMKEITZsERRdbRae/o4iwE6RobnOazl1OaVcGZ0uxks5CcFMeQrnbk+nqywigXx3tUsvUPBbgKgk+Uaai8frubMAX+eczA5rM3zsdWEoL7h0zhl2O/RZQ5HJfXw+t7PuLt80ex3/s0ElC+fSV12e1jwHZiYgJ33TWOsDAzFRXVrFixhby8ay9diI8XLF6i0KuXRHW1ytIlXjIyrhzn4ffdi/Wh+zGcrMCQWwMIylI/pHLP+vpHZADdgC3oVhk6OtePG+cs3464euB5sGCTBSPCgi+lKYCbOkaSnBRHYnwEAIGAypHcMo4lVOAbLajI8ASxMIN7+vqZkeSn3KVZZ9woDO7Yi5enPMLADpptzJYzB1mw6V3yAz46PvA8kmKk6kg6lfs2tfFOm48QggkThjFmzGAATp++wIcfbqeu7trnfg64WbBokRGbTZCbG2D+PC95efVlEIqByJ8/hWXCeJSDJcglblSfl+K1f6X25ImrVjlZ/6Wjo3M90cVZiGELtxMZZgu6ZoAJVgMGIUiv8VERBDrHIAuGdosmOSmO+EhtJJPL62ffuRJ2nC6iAi9JD9qQgaI1wZuqsRpVfj9Nu0A/v8VEYU37bwKwmcN5JvnbfG/wRCQhkV9ZzMLN7/Lp6QyE0USH7z2LbI2iLueEZoYa4kREhDF79kS6detAIBBg69YMdu5sXiTw9ukyTz1lQFEEGfv9LF3qpbpa+5mIjMC2eB7GpL4Y99R3ZNZW4Vz5Bp6L8cBcNKuMsua+NR0dnWtEF2chRrCaz061NqQ02zZqZjUZuK13LLf1isVq1vZUXush/XQRe8+W4PJqtTaOWWZks6DqmBdXTusNJm8ui8d76BypsjdP4q0D7fvjKhDMvTmZF8Z9j5iwSLx+H3/ct5rf7/6QOq+7fpj5oxjjuuAtuUTxR8vqXepDl549O3PPPeMJD7dQVVXDihVbuXDh2jshJQkef8LAPfdox8qKFT7eWuYj0GBhltAZ28sLUSIdKLsu1XdkXqJw+Rv4KwYCPdCsMqLQxZmOTtvRvs/27ZCEIDSfjZRgZLiMT1XZ0kYWGvGRZpKTHNzSLRpF1qJLuaW1pJ5ycjSvjKtnkEtmiJmsRdOKPgneqNmQDn6eGuHFH4DH1pgIqO23CeAmR1denvIwI7rcBMCu88eZt+kdTpfkX36MfcJcwnoPxF9XjXPFHwiE8JxMIQTjxt3C2LFDEEJw5kweq1Zto7b22seAWa0wb77CsGEyXq/Ka7/zsWHDFfGqDLoZ20svYnAZUPZoHZmuC6dwrnoH1Z0MxANuNKuMgua+RR0dnWagi7MQIxjNZ8dbFRQh2F3jo/w6O+cnxkWQ0ieOmzpGAhBQVTLzy0k75eRscc2XPsc+zozBKlGT5aU2KziLnCWh8uZMN7IE/7tb4Uhh+xxsHm408/Mxc/n+0GkYJBlndTlLtv6dj07s/NzjrINTiBw2CdXvo2jVn/CVX7vXV1sTHm5h9uwJ9OjRCVVV2bYtg/T0w6jNGDOVkCBYslShSxeJsjKVRQs9HD9+ZT3ztElE/vRJDBfdGE7Wd2Qe20XJ+o9AnYoWKatC68jUI2Y6Om2NLs5CjGAUZ1Ou8yxNWRIMSbCT3CeOTjZt7JLHFyAjp4S0rCKKq7+6iFooEDutPmq25voOK28Kjw/zMqJzgNwKwcLt7bMJYOZNI1k44QE6RETjDwR4J2M9/53+b6o8n49mmnv0I3rytwEo2fB33Hmn22K7LUL37p2YPXs8VmsY1dW1rFy5jZyci81ac/gIiXkvKoRbBdmnAyxY4MHZUI4qBNaH7yf8u9/SOjIvaB2Z5ekfU7FrA3AvYAWK0YRZ6EYjdXTaE60qzoQQ04DXABn4i6qqv/qSx4wDfgcoQLGqqimtuadQxm6NIcISSY2ritLq4rbeDgBWCUaHGQioKptbOaVpMcqM6hXL6N4OoixaPVllnZed2UXsPlNMreeb649sY0woNom6Cz6qjwZPV+nVdLAGeGWC5mn21AYT1Z72lc7sYe/I0snfJ7mHZrB66GI2L2z8C5mFOf/xWCWmI45ZjyEkmYrd66jJ3H2dd9syCCEYO3YwKSm3IEkS585dZOXKrdTUNC+tPmeuzKOPGpBlQWqqn9+86sXVcM9hNBL1/M8xj75N68gs1joyS9a/R81n++oftA/ojWaVEZyfBx2dG5FWE2dCCBl4A5gM5AH7hRCrVVX97KrH2IA/AtNUVb0ghIhrrf20B4IxajYu3IBREuyr9VHSSinNWKuJsUkOhnePxmjQ0nsXy+tIy3Jy6EIZ/kAjX1cCx/SGWrPgjZr97xQPUWb45JTMRyfbTzrTbFB4ctTdPD7iDkwGhfK6al5J/ScfHNn2pZMuJIsVx5wnkUwWak4eoDzt4zbYdfMJCzNzzz3j6dWrC6qqkpp6kNTUg81KYyoKPP1ThalTtePjvXe9vP++n4YlJbsd29L5KD16X+nIrKumaNUfcedd3XCQXf+lo6MTTLRm5GwEkK2q6lkAIcQHwCzgs6se811glaqqFwBUVQ0eb4ggJBjFWYPx7KetkNLsGRtOcp84+nWKQhJa9OhkQSWpWU5OF1Y1eb2oW40YHTLuAj+VGcE5f3FyTx/fudlHrRd+vL79DDaf2OsWXpr0IN1s8QB8cHQb/7X9n5TWfcXfUTbguOeHKDYH7ovnKFn7VwiiUWWNpWvXDsyePYHIyHBqaupYtWobZ8/mf/MTvwa7HV5abKRfP4m6OpVXX/WSnnbFv0bu3g37fy3CYLFf6cgsLaRw+R/wl/cExgKfoKUydXR0gpHWFGedgdyr/p8H3PqFxyQBihBiOxABvKaq6t++uJAQ4gfAD7T/tZ9IQlPp2tCpGSTms2ECxobXpzRbaCqAJGBgFzspfRwkRIcD4PMH2H9eqycrrLzGiJcAx8z6qNnauqC8zpsNKn+sH2y+aLuR8xWh72nWOTKWlyY+yNSk4QB85jzPCxvf5kB+1tc+L/b2BzF36Y2vspSiVX9E9QWnmP46Ro8exIQJw5AkifPnL7Fy5RaqqppX09U7UbB4sZG4OIGzUGX+fA9nzlw5mI3DhxK14DkMtRLKnkKtIzP3NM5Vy1BdI4CeQABtVqYuznR0gpXWFGdfdsv/xUuiARgKTAQswG4hxB5VVT935lZV9S3gLQAhjEF4Wb0+BFvkLMVqwCQJDtT6KGpmStOsSNzaM5YxvR3Yw7UC+Bq3j13ZRezMLqba3TzxFzFEwdzZgKfET8Xu4LzQPz/GQ+9olUynxG/3KG29nWahSDKPDp/B06NnY1FMVLlr+Z8dy3n3wKf41a93KY66bQbh/W8l4HbhXPE6/pqK67TrlsFiMXHXXeNISuoKwI4dh9m6NaNZaUyAlBSJXz6jYDYLMjMDLFrkofyqxkrLnTOI+PFjGPLrMJwoRSCoOb6X4nUfQGAi0AHwoFllNK8JQUdHp3VpTXGWByRc9f8u/OcZIQ+tCaAGqBFCpAGDgK+/rb4BiYlwEG6OoKqukrLqkrbeDnB1SvPahZM9zMjYJAcjesRgVrSoqLPSRWqWkwPnS/G1UB2bY6bW1Vm83oUahL6lfWICPDdGSw0/tsaELxC66czbuvZn6ZSHSYzpDMDHn+1kybb3Kaz+ZouGsL7DsY2dhaoGKP7kz3iL8lp7uy1Kly5xzJkzkagoK7W1Lj76aDunT+d+8xO/BiHggQcN3H+/drpev97H71/z4W2oJJAkrE88Qvg9szCcqsBwvr4jc+cnVOzYDswAbEA1sB7dKkNHJ/hptDgTQoTXi6jGsh9IFEL0APKBb6PVmF3Nx8DrQggDYERLe/62Ca9xwxBs5rMWAcnh2uGz+RpmaXaNDiOlTxw3d7Yh1Q8hzy6sIjXLycmCyhbNOob3NxDW04CvMkBZ2rXPKmw9VP40w41Rhj8fNLArNzRT93HhNuaNv4+7+48B4EzJReZteocd5zMb9Xxjp57ETn8IgLKtK6g7c6y1ttoqjBx5M5MmjUCWJXJzC1mxYguVlU05Zf4nZjM8+5zC2LEyfr/Ksjd9rFp15e5CmM1EzX8G0/DhKAeLkYs9qH4fJev/Rs3xfWhWGRFACZpVRvP2o6Ojc334RnEmhLgN+AuaGU5XIcQg4DFVVX/4dc9TVdUnhHgS+BStUOwdVVWPCyEer//5m6qqnhBCbACOohVC/EVV1cadyW8wgi2lOTbcgEUSHK7zccnXOCklBPTvFEVKUhw9HFYA/AGVAzmlpGU5yS9vHbf+y1GzT12oQZjRvG+gj/E9/BTVwHObTW29nSYjC4kHbpnCL8Z+i0hTGC6vh9d2reKt/Wvw+BsXVZUjY4i754cIg0LVoVSqMja38q5bDrPZyKxZKdx0U3cAdu8+yubN+wkEmjdkNj5esHiJQq9eEtXVKkuXeMnIuLKmFBujjWLq2h1lTyFyTQC/q4aiVX/CnduQfNgN9AU2o1tl6OiEDo2JnP0WmAqsBlBV9YgQIrkxi6uqug5Y94XvvfmF//8G+E2jdnsDE2zirCGlubERKU2jQWJ492jGJsURa9XER63Hx54zJezMLqKirvUuGpbeBqx9Ffy1AUq3Bl/UzG5W+Z8pmmL8xSYTpXWhlc4c0qk3r0x5hAHx2vG56XQGC7e8R25F4x38hdFM3JwnkcMjqTv3GaWbP2it7bY4nTrFMmfOJOz2CFwuNx99lMqpU+ebve6AmwWLFhmx2QS5uQHmzfOSn3flJsiQ2BvbywswmCJRdlxC8oG3rAjnij/gK706OpZT/6WjoxNKNCqtqapqrhCfu2gEYdVO+6ZBnOUWt704MwkYZ/3mqQCRFoUxvR2M7BVDmFF7fHG1m/QsJ/tzSvH4mhdZaAwNHZolm90E6oKvl+RXk9zEhatsz5H425HQGdhhM1t5ftx3+O6giQDkVhSxcPO7bMo+0LSFhIRj1g8wOjrjKb5I0cehM8x8xIj+TJlyK7Isk59fxIoVWygvb7rFyxe5fbrMU08ZUBTB/n1+li71UnOV3jKNHknUC79ErgFl9yWEKuHKP4NzxRuorn7AAGAtUNjsvejo6LQNjbka5NanNlUhhBF4CjjRutvSuZrYyDjCTOFU1pZTXtP2xbxjwg2ESYJjdX4ufklKs5PNQkpSHIO72pHr68nOFVeTesrJ8YsVNLNprdGYu8pEDjYScKuUbAo+09lRXfz8YKgPjx+eWGsmFDzNBIJvDRzHCynfITosEo/fx7K9n/D73R/iuga7C/vEb2HpOQB/bRVFK15HdQfvIPoGTCaFO+5Ipn//ngDs3ZvJpk178fubd7MhSfDEEwbuvkc7La9Y4eOtZT6uzo6Gzb0b62MPax2Zx8sQQqLmxH6K17wHgTFobv8BtDozXZzp6IQqjRFnj6ONYOqM1l25EfjaejOdliX4Upr1UbOrGgEEcFPHSFKS4ugdHwFAIKByJLeM1FNOLpRe/5l9sTO0qFlpqht/VXBFzQySyrKZWpr11Z0KJ4uD39OsX1w3Xp7yCMM6JwGQnnOM+Zv+ypnSa7NliLhlPJFDJ6D6vBSt+iO+iuD33erQIYa5cycSHR2F2+1h9eo0Pvus+Z/LiAiYN19h6FAZr1fltd/52LDhqgiiLBPx1BOEzZyGIasSQ041CEHFrnWUp68HpgCd0KwyNqH1YOno6IQqjRFnfVRV/d7V3xBCjAZ2ts6WdL5IQmy9OAsC81lFwPhwrd5sU5UXgywY2i2a5KQ44iM1MeTy+tl3roT0rCLKatumAt8YLxE13EjAp1KyIfiiZk+P9HJzfIAzpYKX04N7sLnVaOHnY+fy/VumIUsShdVlLN76N1afuPY5l+aeA7BPvBeAkvXv4c4/01LbbTWGDu3LtGkjMRgMFBQUs2LFFkpLK5u9bkKCYMlShS5dJMrKVBYt9HD8+JWbCREeTtTC5zENGXRVR6afkk/fp+bYEeBOwI7WibkBrTNTR0cnlGmMOPsDcEsjvqfTSgRT5Oy2MANWWZDpg5uS4nm4VyxWsybWyms9pJ8uYu/ZYlze1q8n+zpip5sRkqAs3YW3tG338kW6RgVYlKKJ1h+uM+HyBW86886+o1gw/n7iI6LxBwL8JWMd/5u+nCrPtacfldhOOO58FCFJlO9cc9UQ7uDEaFSYOXMMN9/cG4CMjBNs2LAbv7/5tXHDR0jMe1Eh3CrIPh1gwQIPzquG2Ekd4rG/sghDx84ou7WOzICrlqKP3sR1/hTwLSAKKEXzMNOtMnR02gNfKc6EEKOA2wCHEOJnV/0okht5htJ1RiBIiO0GQG4QiLOp8WEc7eLggs3KFFlLxeWW1pJ6ysnRvDIaO4O8NVGiJWyjTagBleJ1wRY1U/nD7W7CjfBBpoGNZ4KzCaBXdCeWTn6YMd0HAJCRn8WLG9/mM2fzOhGlsAjiGoaZn9hPxY7VLbHdViMuzs7cuZOIjbXh8Xj55JN0MjNbJso3Z67Mo48akGVBaqqf37zqxXXV4ar074tt8TxkJRxlRwGST+ArL8a54g94SwrqH7ULrQFgC1pKU0dHpz3wdVcGI5q3mQGturSBSmBOa25K5woNzQAVNWVU1Ja32T4S4yMYlxSHsWMkeYBQVTLzy0k75eRscXDdrcdMMyMZBOV73HgKgytqNquPnzv7+Klwwc8+Db50ptlg5KlRd/PYrXdglA2U1lbySur/8e+j21GbaQ0sDApxs3+EISoW98WzlKx7t2U23UoMHpzE9OmjURQDhYWlLF++mZKS5o+SUhR4+qcKU6dq97jvvevl/ff9n2uUMY1PJurZnyJXBVB2aR2Z7otnca58g8Dnyjdz+fwIYx0dnfbAV4ozVVVTgVQhxLuqqjbfuEfnmmjLlKYsCYZ0tZOcFEcnm2bkKgUCRBRV8vyBixRXB59vmBwhiE7RvNSK1gRX1Cxc0aJmAC9uNVJQHVxNAJN7D+WlSQ+REOUA4B+Ht/Cr1P+j3FXdAqsLYqY/hKlTT3wVJThXvoHqC05TVEUxMH36aAYP1hofDh06xbp1O/H5mp/GtNvhpcVG+vWTqKtTefXXXtLTP38DEX7ft7E+fD9yXg2GzPqOzJMHKF7zDvgHAwPRUpj6fEwdnfZKY3IqtUKI3wD9AXPDN1VVndBqu9K5TIM4O38dxVmYUWZUr1hG93YQadHqySrrvJgvlTG9poa3Cmsprg7OFErMFDOSSVB52IM7L7j8sl4a7yEhSmV/vsSfMoJnsHlClIOXJj7I5MRhAGQWnuOFjW9z6GJ2i71G1JiZhPcdTsBdh3PF6wRqm+8H1hrExtqYO3cScXF2vF4fa9fu4MiR0y2ydu9EweLFRuLiBM5ClfnzPZw5c1W4TDEQ+YufYJk0HvlUOUpOjdaRuWc95amrgWQgEc0qw9oie9LR0QlOGiPO/gH8C5iJZqvxINB4+2+dZnE9zWdjrSbGJjkY3j0Go0GL6lwsryMty8mxC2Vs6xmOUZYaNRWgLZAsgpiJ9VGzT4IrajYo3s9PbvXiD8Dja00E1LZvAjDKBh4bMZOnRt2DWTFS6a7lN2n/4u+HNuFXWy4dHN5vBLbRd6AGAhSt/jPe4uC0eRg4sDczZozBaFQoKipj+fItFBW1jK/guHESv/ilgtksyMwMsGiRh/KrlhaRkdgWv4ixf38MB4sxFHtQA35KN/6D6iP7gGlobkZetFFMeipTR6c90xhxFqOq6ttCiJ9clepMbe2N6WjNAF0uDzzPabXX6RkbTnKfOPp1ikKqnwRxsqCS1FNOTju1CMeoMBmbLHHG7SfbE1x1XA1ETzAhh0lUf+al7kzwCEiBypsz3RgkeG2vwsGCtu+nGdNtAEsnP0yvmE4AfHh8B0u3vY+zpmXrGk2dexFz+4MAlG35F66zwTc612CQuf3227jllpsAOHLkNGvX7sDrbf4xJAQ8+KCB++7XTrXr1/v4/Ws+vFdldOWEztheWYQhNh5ldwFyjUrAXad1ZOZcQLPKiAZq0dKZulWGjk57pzHirOE0UiCEmIFW6NCl9bak04AjKh6L0UJZdQlVdc0vRL4aScDALnZS+jhIiA4HwOcPsP98CWlZRRRWfj7y1DBL89MgjZoJI8RO1bLuRWuCy2X+B0N9jOwSIL9SMH9r2zYBxFvtLJhwP3f2vQ2A0yX5zNv4DrsuHG/x1zJExeKoH2ZeeWAbVQe3tfhrNJfo6Cjmzp1Ihw4x+Hw+1q3bxaFDp1pkbbMZnn1OYexYGb9fZdmbPlat+nyqXRk8ENtLLyILE8qOi0g+SavJW/EHvMUFaL1XdqAMTZi1RP2fjo5OsNMYcbZUCBEF/BzN3ywSeLpVd6UDQFeHNh6mJaNmZkXi1p6xjOntwB6uCYVql5ddZ4rZlV1Mtfs/xZcETLL+51SAYMKebMIQKVF71kfNZ8EjIOPCA/xqktYE8JMNJqo8bZPOlIXEQ0On8vMxc4kwhVHndfO7nSv58/61eFthlqUwWXDMeRI5LIK6s5mUbflXi79Gc+nfvyd33DEWk8lISUkFy5dvprCwtEXWjo8XLF6i0KuXRHW1ytIlXjIyPh9xNk+bROTPfoxc4ceQcQkJCXdBDkUr38Bf03AztgMYjG6VoaNzY/GN4kxV1TX1/6wAxsPlCQE6rUxLdmraw4yMTXIwokcMZkVLqzkrXaRmOTlwvhSf/6ttEoZZZGIMEjkeP1nu4EtpChkctwdn1Ox/pniwmWHdaZmVJ9omnTm0cxKvTHmEfnGaX96GrH0s2vI38itbaVySJOOY9RjG2E54ivIp+vjP0II1bM1FlmWmTh3J8OH9AMjMPMMnn6Tj8bTMjceAmwWLFhmx2QS5uQHmzfOSn3fV50sIrI88QPh3v4WcX4PhWClCSNRmHaJ4zduoXstVqxXUf+no6NxIfJ0JrYxmP90Z2KCqaqYQYibwAmABhlyfLd64dHV0B5rXDNA1OoyUPnHc3NmGVD+EPLuwitQsJycLKhvlXNWQ0gzWRoCo24woMTKufB9Vh4Insjexh4/7Bvqo88KP1pm43oPN7ZYIXhj3Xb49cDwAF8qdLNj8LlvOHGzV142edC+WHv3w11RStPJ11GZME2hp7PYI5s6dRMeOsfh8fj79dDcZGSdabP3bp8s89ZQBRRHs3+dn6VIvNVfbAJpMRD33M8zJo5GzylHO1YCQqNi3kfJtK9EGrwwBPkUv+tfRuXH5usjZ20ACsA/4vRDiPDAKeE5V1Y+ux+ZuZIQQJFxjM4AQ0L9TFCl94uhZO3fwAAAgAElEQVQRq7Xc+wMqB3JKSc1ycrG88RdLCZhcP+j806rgET6XEeCYoUUaita4aKZPaothklX+OENLZy5OM5JTfv08zQSC7wyawHMp38ZuicDt8/Lmvk94ffdHuHytmxqLGDaRiCHjUH1enKv+iK8ieIrXb7qpO7NmpWA2GyktrWTFii0UFLRM9FCS4IknDNx9j/ZZWb7cx5/f8hG4KmAo2e3Yls5HSUrCcLAIQ7EXNRCgdNP/UX04HUjh/7N33mFSlWf//zynTNneacvSEVBAepFeFhAsIOibRKPGFkuMMRqNVCnmVfPLq1Gj0Wg0msRkKUrvbSnSpPfO0rb33ann+f1xdhWRsiwzs7t6PtfFxSycmXPPTvvOXb43tMa0yggLSFwWFhZ1kyuJs65ABymlIYRwADlASynl+dCE9uOmXnQD7LqDvOIcSlxVW65s0xS6NY2jb+skEiJMS4kyj4+vjuay7kg2ReXXLq46OVUSNYUMj8H+WljSjOpqw15fxZPlp3BT7enJebGPh9bxkr1ZCv9vQ+g8zW6q15QZqQ/RuWErANae2MWEpX/neH7wS2POFu2JHTQOgJyFH+M5eyzo56wKiqIwdGh3evZsD8D+/cf58su1uN2Beb5ERsKEiTpduqh4vZI3/s/HkiXf7ePTmjUh5pUpqLHx6BvOopaB4XaRPfd9XMcOYlplJGPOX60ATgUkNgsLi7rJlcSZR0qzUURK6RJCHLKEWehoXNlvVoWSZrRT55aWifRsEU+YzXxIc0rcpB/KYsuJPDy+6ouq1MjaPQiQeFtFr9lCl5lwqAW0ijP4fR/z9/X4AjteI/jlzEibk+f63s39nYehKgrni/N4eeU/mH/gq6CfG0BPTCbh9kcQQqFg3VzK9m8JyXmvRnR0BGPHDiY5OQm/38+yZZvYtClwk6kpKWbjf3KyQn6+ZMpkD3v3fjd9a+vWhehJL6Kio6WfQfWp+IryyJr5Nt7sfEyrjHhMq4zFmN+DLSwsfsxcSZy1EULsqrgsgBYVPwtASik7BD26HzFVGQZoGOOkf+skbk6JRa3oJzueXcKaQ1nsPVv4nV191UEAQyMq+81qnziL6KjjTNHw5hsUrK8tq6TMcqZdg4+2a6SfCv4QwJ3tbmHiwPtIiojBZ/h5f/N8/rR+JqWe0BjxquHR5jJzm4PSvZsoXD//6lcKAa1bp3DnnQNwOu0UFBSTlraCs2cD55/dvbvC+PE64RGCI4cNJk3ykJX13WOcd4wk8qnHUAp96FvOoaDiPn+K7Flv4S8pBMZgCrMCTKuM2rk5wcLCIrRcSZy1DVkUFt/jcuJMAG0aRNG/dRIt65n76A1DsuNUPmsPZXEqr+zim6o2HRwq9XWFc16D3a5akpa6gKRRZtYsZ4kLWUu040/b+xjS3E9OGfxumT2o52oZ35AZQx+id5MbAdh8+gDjl37EgezQlcSEZiNxzBNoUXG4Th8hZ9EnITv35VAUweDB3ejduyMABw+e5Isv1uByBU7Ajxun8sijGooiWLPGz+uveXFdqIUVhYjHHyL8rjtRzpah78pFCJWywzvJmfc3pLcylnVAF2AlUFu+YFhYWNQ0V1p8bi07ryEUoZAcb9oeZFQMA2iqoGuTOPq1TiIpyhQlLq+fzcdzST+UTX5Z4PuthtXiQYCwGzTCWun4SgzyV9WOVU0xDsmfUs3H4flldnLLg1POdOp2ft17DI92G4muauSWFTFj1T+ZuWctMqQTEYL4kQ9ib9gMX0EO2XPeBX/NTvRGRYVz112DSEmpj2EYLF++mY0bdwfs9nUdnvmNzrBhZkb0k4+9fPaZ/ztZauF0Ej3hd9h7dvvORGbR1uXkr0wDGcW3QiwLM2NmYWFh8S1VMaG1CDH1Yhpi1+3kFGUjcDHsxvr0apFAhMMsMeaXelh3OJtNx3NweYOX0arNFhqJo8wJzdylLoxaknB4ZbCbehGStScVPtkRnJfWsFZdmTL4fpKjEzGkwafbl/Ha2s8pcJVe/coBJqbvHYS36VKxzPytGl9m3rJlMqNHDyQszEFRUQkzZ64kIyMzYLcfGwsvT7XRrp1CebnktVe9pKd/9/WnJCYQM2MyevNmaF9noeX4kIZB/or/VGxI6FzxZzlwImCxWVhY/LCwxFktJCWxGYY/B2/ZUiaMuhFNNW0YMvJKWXMwi12nCzCCnCC5yaHQUFfI9BrsdAXeQf56cDZTiWyv4y+X5K2oHcqsRyM/j3Xx4fWbQwAywJ5mKdFJTB36AINbdAZg1/ljjF/6ITvOHQ3oeapK+E29iO59K9Lwk/3lX/Hm1pxRqhCCgQO70Levab14+HAGc+asorw8cM+Nlq0EU6faSEoSZGZKJk30cPTod1+EWquWxMyYhBoVi77+DGqZwPC4yJn7AeVH9wD9gDaYkyvOS5zFwsLCwqRK4kwI4QRSpJSBWTpncVla1YtkaBvwFP2DWDsYUrDnTAFrD2ZxLCd02ZHUikGAZSXe2mId9g0JFb5meatc+EtrPjpVSP46yo0i4H836OzLDtwQgE3VeLzH7TzV804cuo1CVymvr/0Pn+5YhnG9Ex/VxJ7civjh9wGQt/xzXMf31UgcABERYdx11yCaNm2AYRisWrWVdet2BvQcAwYoPPe8jsMh2LPbYMoUDwUX7Ye339KT6JeeRzE0tLWnUf0qvuJ8cyIz6zymVUZjwIdplWF1jVhYWFyeq4ozIcRtwB8BG9BMCHEzMFVKeXuwg/uxoCqCTimx9GudRMMYJ+ZyY42959zM236QnJLQZ4dq66Jze0OF6K42DK8kd0nt6DV7uoeXjvUNjucLpq8N3GLzfk07MG3ogzSPawDArD3pzFj9Gdnf7F0MPVpMIoljHkeoGkVbV1CyfU2NxdKsWUPuumsQ4eFOiovLmDVrBSdPBs7tRwi4/36Ne+8z3yYXLfLx5zd9eC9qwQy7ewwRjz6IUuhF33QGRWh4MjPImvUW/mIPcBuQAJRjWmUEbmLUwsLih0lVMmdTgO7AagAp5Q4hRNOgRfQjIsym0qtFAre0TCTKaYqhonIv0dF9sTk78fkXz1DuCb0wa2tXSLEp5PgMtpfXrpJmZdYsf60bX2HNZ80aRxlMHWgOATy50E657/rLmfUjYpk0+Ofc1qYXAAdzMpiw9O98lVFzGSoAxR5G0thfoTojKDu6m/yV/62ROIQQ9OvXif79OyOE4NixM8yevYrS0sCtiXI44IUXdfr2VfH7JX99z8fs2Re9FlSVyF8/Qdio4SjnytB35iKERtnR3eTMfR/pcQN3YgozyyrDwsKi6lRFnPmklIVChHYv4A+ZhAg7/Von0rVpPDbN7Cc7W1DO2oNZZJaE88LY3mQXZlLuCZwtxrVQmTVbVuyrLb6uAOgJCjE9bUi/JGdR7ciavTncTYQN0vaqLDpyfS2cmqLyYJdhPHvLOCLsTso8Lv5v/Sw+3LoQr1HDIllRSbjzMfT4+niyTpMz932u20ivGoSHOxkzZiDNmzdCSsnq1dtYu3Y7MoCx1KsnmDZdp3lzhZISyfRpXrZu/e4rQYSHEz3lJeydO6Ieykc/XmZOZG5bRf6K/1yw6D0d87utZZVhYWFRdaryabJHCPFTQBVCtAKeBjYEN6wfJs0Twul3QxLtGkajVIjdA+eKWHMwi8NZ5jfqnjfcBFRtM0CwGFZLtwIkjHAgVEH+OjfenJqXjbe19jG6rZ9iNzyz5Po8zbo1uoFXhj1Em8QUABYe3MTLK/7B2eLasZsybuhPcDZti7+kkKyZb1dkhUJLkyYNuOuuQURGhlFSUsbs2as4fvxsQM/Rvr1g8hQbMTGCjAyDCRO8nDn9XeGnNqhPzCtT0Bono36diZ7jR0qD/BX/pXjbSiAGM1MGkItllWFhYXGtVEWc/QoYj/m171/AEmB6MIP6IaEI6JAcS/8bkmgcZy4z9voNtpzMZe2hbDKLvpsBqspmgGDSyqbQ1KaS5zPYWlZ7SppatCC2nx1pSHIWBK58VV3CdMlbI0yBMmGVjbPF1VtsHh8WxUsDfsrd7QcAcDI/k4nL/86qYzsCFep1E9ltKJE398Pwesia/Q7+4ryQx9C3780MGNAFRVE4ceIss2atoqQksJnlEbeqPP20hq4Ltmz2M326l9KLZnD0G9sSM20iSngk2rrTaOUKhsdNzry/UX5kJ9AJcy3xKuBIQOOzsLD48VAVcXaDlHI8pkCzqCIOXaFH8wT6tEwkNtxsEi9xedlwNIcNR3IocV+60T4lwRRnleazoWZYRUlzeYmP2iPNIH64A0UXFG714D5X81mzyf09NImRbDur8M7ma19srgjBTzsO5oX+/0OMIwK3z8tfNn3JX776Epev9mQsnS07EjvwLgByF/wdz7kTIT1/WJiD0aMH0LJlYwDS07ezatW2gJYxFQUef1xj9Bjz7TAtzccH7/swLnqaOQb1J+p3z6D4FLTVGaiGhq+kkOyZb+HJzAD6Yi5WkUBwt0NYWFj8sKmKOPuTEKIBkAZ8LqUM3NbgHyBx4Tb6tEqke7N4HLppqZBV5GLNoSy2nczD57/8h4qiqDSKNz+EMnJOhCLc75FaC7cCqOGCuIEVC87n13zW7KYkP8/28mJI+OUCO355bf2Y7es145XUh7i5YUsAVh/bwcTlH3MiP3CThoFAT2pMwm0PI4RC/tovKDu4LaTnb9y4HmPHDiIqKoKyMhezZ6/i6NHTAT1HZCRMmKjTpYuKxyN58w0fS5Z8/2tJ+H0/IeLBexEFbvSvTqMIHU/2GbLS3sJfXAQMA1IwrTJWYhnMWlhYXA9XFWdSyoFCiPrA3cD7Qogo4D9SSqu0eQEpcWH0vyGJ9o1iUCqWkB/JLGbNoSwOnCuqkldYg9hG6JqNrIJzNTIM0MKm0NKuUuA32FKLSppxQ+yoDkHxbi+uEzUbl8D0NNMUeGuzztazVfc0i7KH8Xzfe/h556EoQuFccS5TVvyDhQc3BTHi6qFGxFQsM7dTsmcjRRsXhvT8vXt3YPDgbiiKwqlT55k1ayVFRYH1+UtJEUydppOcrJCfL5k8ycO+fRe9UnWNqOd+jXPoIJSzpeZEpqJTfnwv2V/8FekRmFYZiYAL0yoj63vnsrCwsLgWqjReJqU8D/xZCLEK+B0wCavv7BvGdU2hR/N4APyGZOuJPNYeyuJswbVleb7pN6vhrNmKYh+1xd1McUD80NqTNXuos4/ejQ3OFQsmrKy6p9mYG/syYeDPSAyPwWf4eX/zPN7YMItST+2YOr0QodtIvOtJtMhYXBmHyV38acjO7XTaufPOAbRubQ5GrF+/k5Urt2AEeCVG9+4K48frhEcIjhw2mDTJQ9ZFmkpERREzdTy29jeiHMzDdqIcFJXi7WvIW/45GH6+FWaFmI3/RQGN08LC4sdJVUxo2wL3AGMxR48+B34b5LjqFJ1SYgBYdSCT9MPZFJVXryTYpIaHAb7ZpVlSW6QZxA5woEUolB7yUnawZuNKDDN4dYg5BPDMEhtF7quXM1snJDNj6C/omdIOgE0Z+xm/9EMO5gS2PBc4BAmjHsJevwne/OyQLjNv1CiJsWMHERMTSXm5iy++WMOhQ6cCfp5x41QeeVRDUQRr1vh5/TUvros0stq4kTmR2aAB6rbz6LkGUhoUrJ5F0eZlFxy5DuiBaQNZ+4S2hYVF3aQqmbO/A/8GUqWUgZ1b/wEQYdewaSplbh8Ldl3fr6dxQs2Js6a6wg12lSK/5KvS2iHOhA4JwyuzZjX/wffHVA9xTlh8ROW/e6/80gnT7Txzy1083PVWdFUjp7SQ6as+Y9be9BBFWz1i+t9JWOtOGK4yc5l5eUlIztuz500MGdIDVVU4fTqLmTNXUFgY2HPrOvzmWZ3UVLMU/cnHXj77zP89uzb95g7EvDwexRGGlp6B5lIxvB5y5n9I+aHtQCyQX3F0PmYp08LCwiJwVKXnrGcoAqmrxFVMYuaVeq7rdjRFo1G8Wco5XQNlzcqS5soSL7VlFCCmjx09RqH8lI+SXTUb1YCmPn7e0YfLZ24C4AqLzUe07s6UwffTMCoeQxp88vUSXl/7XwrdoduNWh3C2/cmuucIc5n5F3/Flxf8AQW73cYdd/SjbVvzi8lXX+1m2bLNGBePSl4nsbHw8lQb7doplJdLXnvVS3r698/hGD6UqGefQvGCtuokqrThLy0ia9bbFZOqHTEzZWuBAwGN0cLCwqKSy4ozIcR/pZR3CyF2w3f62QUgpZQdgh5dHSAu3ByZzyu9PlPOBnHJaKrG+fyzuLyhzxJ9U9KsLbs0FUi8tSJrNq9ms2Y2VfLuSPPxnb7WxrH8S3uaNY2px9ShDzKw+c0A7Dx3lPFLP2Tn+WMhi7W62Bu3Jn5YxTLzpf/CdXJ/0M/ZoEEC48YNJjY2CpfLzZdfruXAgRMBP0/LVoKpU20kJQkyMyWTJno4evSidJkQRDx8P+E/GYco8KBvPIOi2PDknDUnMovygFuAGyuucO32KRYWFhZV5UqZs19X/D0qFIHUVQKVOft2GCD0Jc3GuqCdQ6XEL9lQVjvEWXQPG7ZEFfc5P0Vbr+93e7387hYvbRIk+7MFr2/4/oeyXdV5vOftPNnzDhyajQJXCa+t+Q//3LkcowZWHF0rWmwSiaMfR6gqRZuXUrIz+KXXbt3akZraE01TOXs2m7S0FRQUBH7v5IABCs89r+NwCPbsNpgyxUNBwUUH2e1Ev/gsjv59EOdKse3IQSg2yk/sMycy3V4gFWiCaZWxCqi5DR4WFhY/fC4rzqSU5youPiGlfOHC/xNCvAq88P1r/fgItDirCfPZoRVZs9WlPjy1QUsISBxVkTVbUE6VfEiCRItYg/F9zcf28QV2PP7vljMHNOvItKEP0jS2PgBpu9cwY/U/yS2rG1N7iqNymXk4ZYd3kL96VlDPZ7Pp3HZbX266qQUAW7bsZcmSTfj9gbVIEQLuv1/j3vvMt7hFi3z8+U0f3ouq40psLDEzJqLf0BrlYC768XKEolG8M528pf8Cw4b5/TQJs+F/CZAZ0FgtLCwsLqYqAwFD+b4QG3GJf/tREjBx9s0wQOhLYKkRpjirLcazkZ10HI00PLl+CjfWZNZM8peRbhwafLJDY83Jb18uDSLjmTz454y8oQcAB7MzGL/0QzadrkN9SIpK4ujH0ePq4ck8Rc68D4O6zLxevTjGjRtCfHw0breHefPS2bs38M93hwNeeFGnb18Vv1/y3ns+5sz+vvjTmjUh5pUpqImJqFvPoedJEIL81bMo2rSk4qiBmMKsCNMqozDg8VpYWFhczJV6zh4HngCaCyF2XfBfkcD6YAdWV6jsOcu9jp4zTdVpEJeMIQ0yck4GKrQq0VATdHCqlBmSdbVkSjNxlBOAnEUuZA16zt5zo4/UFn7yyuG5ZebjrCkqD3UdwW9uGUu4zUGpx8Wf1qXx0bbF+IzaY9xbFeKH3Ysj5QZ8xQVkzXob6Q3eMvPOnW9gxIjeaJrG+fO5pKWtIC8v8EKnXj3BtOk6zZsrlJRIpk31sm3b9xv/bd27ED3xRRS7A23tKTS3hvR5yZn/0UWbENYBvYE1QM377FlYWPw4uFLm7F+YXxX/ALx4wb8XSylDv/m4FiIExISZWaf8supneBpWDAOcyz+Dxxe8D8hLUVnSXFPiw10LSprhN2qENdfwFRnkrw3t7+JCou2S/xtmPqa/W2Ynp0zQI7kNM1If4oZEc8XW/ANf8fLKf3C+BhaBXy9RPYYR0eEWDK+H7Nnv4C++uBErMOi6xqhRfejQoRUA27btZ/Hijfh8gRey7dsLJk+xERMjyMgwmDDBy5nT339SO+8YSeRTjyE8En3FCVTs5kTm7L/gOXsMiAMqH9MiLKsMCwuLUHMlcSallCeEEE9e/B9CiDhLoEG0U0dTFYrKvVfcmXk1UhKbAzXjb1ZpobG0lpQ0v8maLXEha7CiOWOwhwaRknWnFOYejOdPt/6Mce37A3A87xwTl/+dNcd3XeVWaifO1p2IHWAuM8+Z/yGe88HJ1iYmxjJu3GASE2PxeLzMn7+O3buPBOVct96q8vSvNTRNsGWzn+nTvZRe7FyiKEQ+/jBhd92BKPSgrz+Notrx5p4jK+0tfIU5QHugF7AB2BOUWC0sLCyuxtUyZ6OAbZgt2Rd2QkugeRDjqhMEykYjJaEpABkhFmf1NEEnp0a5IUmvBSVNZ0uNiLY6/jKDvJU1lzXr1tDP4129eHwK84/exqpHfkq0IxyXz8PbG7/gvU3zcPtrh5i9Vmz1UkgY9RAA+atnV5iqBp6OHVsxcmQfdF0jKyuPtLQV5OQEPjunKPD44xqjx5hvZWlpPj5438fFNmnC6SR6wu+w9+qOOFuCbUcuQrXjOnmA7DnvYbjLMcuXN1XecsBjtbCwsKgqV5rWHFXxd7PQhVO3CLiNRojF2dCKQYD0Uh9ltaCkWTmhmbvcjVFeMwGpQvLeKDc+2Yr9hU/yVK+WAKw8up1Jyz/mZEHdndRTI2NIvOspFN1Gya71FG0KfLlO01RuvfUWOnW6AYDt2w+yaNEGvN7Ai//ISJgwUadLFxWPR/LmGz6WLPl+uVRJTCDmlcnozZuhHMhBP+FCqBolu9eTu/gzMMCce2oK+DGtMmq/N52FhcUPl6rs1rwF2CGlLBVC3At0Bt6QUgZ+6V0dIxDiTK8cBjAMTueG9ldam0qajsYqUTfbMNyS3GU1Zzr72942miXcT6Z7OPHhCmeKcpiy/BMWH95SYzEFAqHbSbrrKbTIGFynDpK75LOAnyM+Pppx44ZQr14cXq+PhQvXs2PHoYCfByAlRTB1mk5yskJ+vmTyJA/79n1f0GutWxIzYzJqbCzKljPY8oU5kblmDkVfLQIcwHDMiUw3plVG8DcjWFhYWFyJqlhpvAt0FEJ0BH4HfAh8CvQPZmB1gUCUNRvGp6AqKmfzTod0GCBBFXR2qrgNyZpaUNJMqMia5a1x4y+umazZo1378nSfn1Hqj8GQPt7bNI83N8ymPIhTjCFBCBJuewhbvRS8eZlkz3kPAjxZ2r59C0aN6ovNppOTU0Ba2nKysvKvfsVq0L27wvjxOuERgiOHDSZN8pCV9f3j7H16Ef3ScwjNhrrmBLrHZk5kLvyYsv2VYrs/pjArxpx/Cs5ghIWFhcW1UBVx5pNSSiHEHcCbUsoPhRD3BzuwukAgMmdNaqikOSRSQxGC9SVeSgO7xvCasdVTiO5mw/BJcheHPmvWJqExM1IfonvjNkigqHw3d/7zYw7nngl5LMEgZsBdhLW6GX95qbnM3BW4HZ+apjJ8eC+6dGkLwK5dR1iwYB0eT3CysePGqTzyqIaiCNas9vP6615cl3jKhN0zhohHHkR4JNry42jCgb+shOzZ7+A+c/SCIytdgdZiWWVYWFjUFqoizoqFEL8H7gP6CiFUrMVyQGDE2bf9ZqHtcfnGeLakFmTNbnUgFEF+ugtvXuiUYrjNwbO3jOUXXUegKSoK+dj5kF4fb+Z00Q+jITyiYx+iu6ci/X6yv3gPX/4lUkzVJC4uinHjhlC/fjw+n4/FizeybVtwTHh1HX7zrE5qqgrAxx97+ezTS2T/VJXIZ54gbORwRJEHLT0DVXPgzcskK+3P+AqygQQgp+IKJZilTAsLC4vaQ1XE2T3AT4FfSCnPCyFSgNeDG1btR1UEUU4dvyEpuA6Ps8aVk5o5JwITWBWIUwXdwlS8UrKqpGb7zfQ4hZhb7EhDkrMwdFmzkTf0YMrg+6kfGYffMDB882gY/hm/XerldJEtZHEEE0eTNsQN/RkAeUs/w33qYMBuu1275tx+e1/sdhu5uYXMnLmC8+dzA3b7FxIXB1NettGunUJ5ueS1V72kp39fxIvwcKKnvIS9y82IM8XoO3NRNAeuU4fInvNuRcbwJkyrjC3AjqDEa2FhYXG9XFWcVQiyfwLdhBCjgM1Syn8EP7TaTWyYDUUI8krdGNVskdI1Gw1ik/Eb/pAOAwyJ0FCFIL3ES0kNlzTjhztQNEHBV248mcEPpmlsfaYPfZD+zToCsP3sEc4WvMtjnQ+y47zCW5ucQY8hFGhx9Um885cIVaVw0xJKdgVmqYeqKqSm9qR79xsB2Lv3GPPmrcXtDo7Ib9VKMHWajcREQWamZNJED0ePfv8FpzaoT8wrU9CaNEbsz8J20o1QdUr2fEXu4n+A348pytoHJU4LCwuLQFKVac27MTNlqzG9zt4SQjwvpZwZ5NhqNYEoaSbHN0FRFE7nnsLrC53jamrFVoClxTVb0lQjBXH9zaGK7PnBzZo5NJ0ne97J4z1ux67pFJSX8Ic1/2bX2WV8/VgZhoTH5tvxS3H1G6vlKM4IksY+heIIo+zQdgpWzw7I7cbERDJu3GAaNkzE7/ezZMlXbNmyLyC3fSkGDFB47nkdh0OwZ7fBlCkeCi7Rr6/f2JaYaRNRoqNQNp3CVqCCUChYN5fC9fMBFRgCNMO0ylgDBMcM18LCwiIQVKWsOR7oJqXMAhBCJALLAUucAbnX029WA+azMaqgey0pacanOlDsgqIdHtyng7eXcmDzm5k29EGaxNQD4D+7VvHK6n+RX17Emgdc6Cr8ZYvG5jNq0GIIGapG4uhfoscm4T5/kpz5H2J6Rl8fbdo05Y47+uFw2MnPLyYtbTnnzuVc/YrVQAi4/36Ne+8z354WLfLx5zd9eC/xdHUM6k/U755BKBrqymPoPgfS7yN34SeU7tsE2IFhQH1Mq4ylwLmgxG1hYWERKKoizpRKYVZBLpZ9dkBsNGrCfHZQhIYmBOmlPgprsKSpOAXxgyuyZvOCkzVrGBnPy0PuZ3jr7gDszzrJS0s/ZOsZ03vrwZt99G1ikFkieGmFPSgxhJr4YffiaNwaX3E+2bPeQXqvLyOrKApDh3anZ0+zHHjgwAm+/HINLldwMr0OB7zwok7fvip+v+S993zMmT+lWYsAACAASURBVH1p4R7+858Q8cC94PKjrTiKpoThLy8he/a7uE8frjiqL6YwK8bckRkcew8LCwuLQFIVcbZYCLEE+HfFz/cAC4MXUt0gEGXNxjUgzoZFVJY0azZrFjfIjhqmULLfS/nRwJZXdUXl4W4jeab3GMJsDkrc5fy/dWn8fdti/NJUpAlhkteHmsL6N0tsFLrrfjkzqucIItr3xvC4yZ71Dv6S6/Psio6OYOzYQSQn18PvN1i+fBNffRW8fZP16gmmTddp3lyhuFgyfZqXbdsu8Q1C14h67hmcQwdCoRs9PQNVDzMnMme+ddFE6gbM75LrgLKgxW5hYWERSKoyEPC8EGIM0Aez5+x9KeWcoEdWy7lecWbT7NSPaYjf7+NMXkYgQ7ssUQr0DFfxScmKGrTQEDZIGGaazmbPC6y3VM/G7ZiR+gtaJyQDMG//Rqau/AfnS76bMXltiJv4MFh2VOXfe6ryHaV2E3ZDF2L7j0ZKg5x5f8OTeX0DJq1apTB6dH+cTgeFhSWkpa3gzJnA2XBcTPv2gslTbMTECE6dMpg40cuZ098vx4qoKGKmjsfW4SY4U4htRx6K7sR1+gjZs/+CUV4CJALZFdcowyxlWlhYWNQdLvupJIRoBfwRaAHsBp6TUv4wXDkDQKU4y69mWTM5wRwGyMg5hS9ES7QHRujoQrCx1EeBv+aWacb2s6NFKZQd81G6LzAiMTE8mgkD72XMjX0BOJZ3jgnLPiL9xO7vHduviZ8HO/lw+eCJhXbM7xx1F1uDpsSPfBCAgtWzKD+ys9q3pSiCQYO6ccst5jTroUOn+OKL1ZSXB29Lwq23qjz9aw1NE2ze7GfGdC+ll/DJVRsnE/OHKWgNGyD2ZWI76UFoNkr3bSJn4Sfg9wE3Yi4w3w5sDVrMFhYWFsHkSimDj4B/YFpn3wa8BYwJRVC1HZumEOHQ8foNil3VExeVwwChLGnWhl2aQoXEERVZs/nXnzVThOC+Tqn8rt89RNnDcHk9vLVxDu9tnofH//3HRlck7440e9xeSbdxJK9ut0+qkXEkjXkSRbdRvDOdos3Lqn1bkZHhjB07iJSU+hiGwYoVW9iwYVcAo/0uigJPPKFx52jzeZmW5uOD930Yl6pkdupAzJTxKBHhKBtPYCvSQVEoWD+fwnVzK47qAXSsuBy8ARMLCwuLYHMlcRYppfyg4vJBIcTXoQioLvBt1sxT7Tm4ymGAjOwTgQnqKkQocEuYhiEly2uwpBnd24Yer+I646N4+/WJxJsbtOSV1IdoX9/8XS4/8jWTlv+djMLsy17nud5e2iVKDuYIXl1ftxddCJudpLFPoUZE4zp5gLyl/6r2bbVokczo0QMID3dSVFTKzJkryMjIDGC03yUyEiZO1OncRcXjkbz5ho8lSy4tqBzDhxL17FMIFJQVR7D5w8yJzMWfUrpnI6ZVxgDMJL+BaZVx+JK3ZWFhYVEXuJI4cwghOvFtzcd54c9Syh+tWAvs2qbQZM4GhGvYFMHmMh+5NVXSFJA40jR5zZ7vqrbDQ4wjghf6/w8/7TgIRSicLsxm8vKPWXpk2xWv1yzGYGI/8zF7fIEdj78OlzOFIOG2R7AlJePNPV/tZeZCCAYM6Ezfvp0QQnDkyGnmzFlFWVnwfOdSUgRTp+kkJyvk50smT/Kwb98lngxCEPHw/YT/ZBy4/WjLD6NpEfhdpWTPea9i48GFVhkezP6ys0GL3cLCwiIUXEmcnQP+dMHP5y/4WQKDghVUbed6bTTsuoOkmAb4/D7OhmgY4Fvj2ZoraUZ1tWGvr+LJ8lO46dqFrUBwd/v+vDTgp8SFReHx+3h/81z+vHEO5d6rPRaSd25149Th050aq07U7SGA2IHjCGvZAX95ibnM3H3tk4gREU7GjBlEs2YNMQyDVau2kZ6+PQjRfkv37grjx+uERwiOHDaYNMlD1qXmDOx2ol98Fkf/PlDoQl+bgWqLwFuQTVbaW/jyzlcc2BtTmJUAi7CsMiwsLH4IXPYTSko5MJSB1CWuN3OWnNDEzPjkncRnBL/EGCagb7j5UC+rwa0AibdV9JotdJnVp2ugTWIKr6Q+RLfkGwBYf3IPE5Z9xJHcqmVJxrbzM6KVn/xyeG5Z3d6dGXFzP6K6DUH6fWTPfrdimfe10bRpQ+66ayAREWEUF5cxe/ZKTpwIrjnruHEqjzyqoSiCNav9vP66F9clEnRKXCwx0yeit7kBMvKx7cxHsYXhOnOU7FnvVExkVrIR821sA3CJKQILCwuLOkjdTh/UENcrzpokhLak2S9Cw64ItpX5yK6hkmZERx1nioY336BgfdUzjhE2J8/2GcuDXYajKSqZJflMX/kZX+yv+q7ISJvkzeHmOV9cYSertO4OATiatiNu6E8AyF386QVmq1VDCEHfvjczYEAXhBAcP36WWbNWUloaWEuTC9F1+M2zOqmp5gaGjz/28tmnly7Bas2bEjNjMmq9JNhzFnuGH6HbKd2/hdyFHyN9XkyrjBzMBL4LqP4QhIWFhUVtxBJn1aCyrJlbzbJmqM1nh1WWNGtwECBplJk1y1niQlaxsnpbm15MHnQf9SLj8BsGH25dxP9L/y/FnmsTEtMHeWgYKdmYofDBtrr7lNfjG5B452MIRaVw48KKZviqExbmYMyYgbRokYyUkjVrtrFmzXakDJ5gj4uDKS/baNdOobxc8tqrXtLTL502tfXoSvTEF1CcTsT6Y9hL7KAoFG5cSMHaLzHFWFvgFmAP8FXQ4rawsLCoSeruJ1UNcr2Zs2+GAXKCL86cF5Q0l9dQv1nYDRphrXR8JQb5q67eaN48rgHThj5Iv6YdANh25hDjl37E3qwT13zuLg38PNnNi8+AXy6wI+uop5nijCBx7FModielB7ZViJWqk5JSn7FjBxEZGU5paTmzZ6/i2LHg2ha2aiWYOs1GYqIgM1MycYKHY8cuLQSdd44i8slHzYnMZYewyQik30/uks8o3V2ZJe0O3FxxuWY3XFhYWFgEk6uKMyGEAH4GNJdSThVCpAD1pZSbgx5dLSTMpuLQVco9fso91z4d57A5qRfTAK/fy7m800GI8Lv0CdcIUwQ7y32c89VMSTNxlDmhmbvMhXGFZKNDs/GrXnfyyx63Y1M18suLeWX1v/jPrtXIaox2KkLy3ig3qgJ/3KCzK7OOLjZXNRLHPIEek4j77HFyF/ydaxl17dOnIwMHdkVRFE6ePMfMmSspKQnuKqMBAxSee17H4RDs3m3w8hQPBZfaJqUoRD7xCGFjbge3H3XpAXRbFIa7nOw57+E6uR9z/dIAoCVms+Ja4FBQ47ewsLCoSaqSOfsL5jviIGAq5gbhWUC3IMZVa/k2a1bNkmZ8EwDO5J7CXw3rg2ulckpzSQ0NAjiaqkS21/GXS/KWX/53NqRFZ14e8gApMUkA/GvnCv53zefklxdX+9xPdPPStaHBqULBlNV1dwggYcT9OJJb4ivKI3v2X5C+qmVsnU47o0cPoFWrFADS07ezatW2oJYxhYAHHtD42b3mW8uiRT7+/KYP7yUSXcLpJHrC77D36g755ejpp1DtUfgKc8ia+RbenHOADUgFGmJaZSwDrEUlFhYWP2yqIs56SCk7CyG2A0gp84UQdfeT7jr51kajuiXN5kBozGftAgZG1OxWgMqsWd4qF/7S74uC5KhEXh5yP6mtugKwN/MELy39kK/PXp+JaIMIgxmDzMfoV4vslHrrZjkzuvdIwm/sgeFxkTXzbfylhVW6XnJyEmPHDiY6OoKyMhdz5qzmyJHg2rY4HPDi73X69FHx+yXvvedjzuxLfwFREhOIeWUyeovmcCoX265CFHsE7rPHyZr1NkZZpSjviSnMSoHFQG5Q74OFhYVFbaAq4swrhFCpqKMIIRK5ZiOEHw4B6zcLwTBAZUlzd7mfszVQ0rQ3VIjuasPwSnKXfLfXTFdUHu0+il/3HoNTt1PsLuOP6Wl88vUS/PL6n15vDPcQZYcvD6jMPVg3WyvD2nYjpu8d5jLzuX/Dm121MnivXu0ZPLg7qqqQkZHJzJkrKCoKrs1EvXqCadN1mjdXKC6WTJ/mZdu2Sz+OWuuW5kRmfBzsOo39jGFOZB78mtz5H12UGdyEmT3biGWVYWFh8WOhKp9afwbmAElCiBnAWGBCUKOqxVx3WTOxKRAacfbNLs2SmsmaJVRsA8hf68ZX+K047J1yI9NTf0Gr+EYAfLFvPdNXfUZmSWAMRIe39HH3jT5KPWbWrC5ia9iMhFsfACB/5UzKj159x6XDYeOOO/rTpk1TADZs2MWKFZsxjOAK8/btBZOn2IiJEZw6ZTBxopczpy99TnufXkS/9BzCbkekH8ZeFgaKSuGmJRSsno35HTAJyK647AaWBzV+CwsLi9rGVcWZlPKfQohtwGDM1U13Sin3Bz2yWkplWTO/Gpkzpy2MpOj6eH0ezhUEt29GFzAw3Ow3W1YDJU09QSGmpw3pl+QsMrNmSeExTBx0H3e2uwWAI7lnmLDs76w/uSdg53Vo5iYAgMmrbWQU1T1PMzUqnqQxTyI0neLtayjeenVx0rBhIuPGDSYmJpLycjdffrmGgwdPBj3WW29VefrXGpom2LzZz4zpXkovk+AKu2cMEY88iJACZcl+bCIaafjJW/pvSnaurTiqDdAHOAikBz1+CwsLi9pIVaY1U4AyYN6F/yalPBXMwGor11PWrMyanc49hRHkYYDeYRoRqmC/y88pb+hLmgkjHAhVkL/ejZELv+gyguf6jiPSHka5182bG2bzwZYFePyBHVSY0M9D81jJrkyFNzfVvcXmwuYwl5mHR1F+fB95yz+/6nW6d7+R1NQeqKrKmTNZzJy5koKC6g9SVAVFgSee0LhztPkWkpbm44P3fRiXqmSqKpHPPEHYyOHg8aMu3o9ujzEnMr/8K67j+yoO7Ap0rrgcPFNcCwsLi9pOVcqaCzDrCwJwAM0wv9beGMS4aiWCC8RZ2bWLs5QQbgb4pqRZA1kzLVoQ28+ONCSNv0rmw/sf5MZ6Tc14Dm9l8vJPOF107SuHrkbbBIPne5v397H5dnxGHRsCEAqJdzyKLbERnpyzZH/51ysuM7fbdW6/vR/t2plDJps27WHZsk34/cFtCY2MhIkTdTp3UfF4JG+84WPpkkvHKSIiiJ7ye+ydb4b8UrS1GWiOGHxFeWSlvYU35wymVUZ/oBVmO+s64EBQ74OFhYVFbaYqZc32F/4shOgMPBa0iGoxkU4dTVUocXnx+K79A7ByGCAjyOazOjAoouYsNOKHO4gREfxsz+3cfmtfAE4VZDF5+ccsP/p1kM4qeXekC5sKf92m8dXpuudpFjv4bpzNb8JfVkz2zLeR7stnj+rXj2fcuCHExUXhcnmYO3ct+/cHX/SnpAimTtNJTlbIz5dMnuRh375LZ2bVhvWJmTEFrUlj5Ikc7LsLUByRuM+dIHvWOxWTpzZgKNAI01h2ORDcqVILCwuL2s41j7FJKb8WQvzIPc5q96RmjzCVaFVwyO3nhDe0g7VauMJPuw7m4WN3EqVH4PH7eG/TXN7a+AWuKvpzVYf7O/ro39Qgq1Tw++V1bwggsvNAoroMQvq8ZM/+C77CnMse27VrW4YN64WmqZw7l0Na2gry84uCHmP37grjJ+iEhwuOHDaYNMlDVtalj9VvbEvMtIkoMdHIHSdxnAVhc1J2aAc58/+G9FY+F7phCrMyYBGWVYaFhYVF1XrOnr3gRwWzKSTwNak6QKU4y62GOAuzh5MQlYTH6+Z8/tlAh/YdKo1nQ13SbJfUhNf/5xE65LQEIP3EbiYs+4hjeeeCet44p+SPqeYQwLNLbOS76lY509H8JmIH3wNA7qJ/4D5z9JLH2Ww6t93Wl5tuagHA1q37WLz4K/z+4JsZ3323ysOPaCiKYM1qP6+/7sV1mU1cjkH9ifrdbxC6BqsP4HRHggpFm5eSv3oWfMcEdzNmt8QmoCTo98PCwsKiLlCVzFnkBZd9mD1os4ITTu0m/hsD2mu30ajsNzudewojAD5el0MDhnzTbxaakmakzclv+97NA52HoSoKuVoB0zZ+yqyV669+5QDw6hA3CWGw8rjKP3fXLU8zPaEhibc/glAUCjbMp3Tfpksel5QUx7hxg0lIiMHt9jB//jr27Lm0iAtofDo8+6zO0FSzTPzxx14++/TyYjD85z8h4oF7wZCIhXuwa3FIwyBv+b8p2b6m8t4AOZj9ZV5gRZDvhYWFhUXd4oqfZBXmsxFSyudDFE+t5nrKmqEqaXYLU4lRFY66/RzxBL+keUfb3kwcdB/1ImLxST9z4lbw18Iv2LsyNMnVPil+Hu7sw+2DxxfYoQ4tNlfCIkmqXGa+fwuF6fMueVynTjcwYkRvdF0jMzOXtLQV5OZWbVPA9RAXB1NettGunUJ5ueS1V72kp1/mOaXrRD33a5xDByLLvaiL92MLi8dwu8ie+z6uY5V2KTcAfYEjwOqg3wcLCwuLushlxZkQQpNS+ioGACwIjI1GsIcBvi1pBjdr1jK+IdOH/oJbmtwEwNYzB3m/43/JSDzHyX8G18ahEl2RvDvSzGL+73qdQ7l1x9NMaDpJY55Ai07AffYYuQs/5uJl5rquMXJkHzp2bAXA118fYNGiDfh8wS9jtmolmDrNRmKiIDNTMnGCh2PHLt34L6KjiJk6AVv7G5F5JehrTqGFxeMrzjcnMr/ZbNCl4g9Ybv8WFhYWl+dKmbPNmP1lO4QQc4E0LnhHlVLODnJstY646ylrVmTOTgYxc6YAQyKCuxXAqdt5utdoHu0+CpuqkVdWxCur/8WyhK9oOCSM8lM+SnaFptftN7283JRkcCRP8If0urXuNX7E/dgbtcBXmEvWrHeQvu/+zhITYxk3bjCJibF4PF4WLFjPrl3Xt2+0qgwYoPDc8zoOh2D3boOXp3goKLj0sWrjZGL+MAWtYQPksUzse4pRwqJxnz9F9qy38ZcUYD4z+2JmzQxgPfCj9bG2sLCwuCpVadCJwxyhGsS3fmcS+FGJM0VAtFPHkJKCsmsTH+GOCOIjE3F73WQWBG8YoItTJV5TOOnxc9Ad+JJmassuvDzkAZKjEwH4bMdyXl3zOQWeElq/Gg1A9rzLdIkHmKYxBpP7mxnMxxfYcfvrTjkzus9thLfrjuEuJ2vmhUu+TTp0aMXIkbdgs+lkZ+fz3/8uJyfnMuoogAgBDzyg8bN7zbeFhQt9/PlNH77LJGFtnToSPeUllMgI5LZjODJVhN1J2ZGd5Mz9G9LrxjR2GQok821/2Y/Sv9rCwsKiylxJnCVVTGru4VtRVknoLedrmJgwG4oiyC/14L/GXYXfDAPknEDK4P3qglXSbBydyMtDHmBoS7Mktfv8ccYv+5DtZ48AEN3Lhi1RxX3eT9HW4NllfIvk7RFuwnT4126N5cfqzhBAeLvuxNxyG9IwyJ77QYUJq4mmqYwY0ZvOndsAsHPnYRYsWIfXG/zBDocDXvy9Tp8+Kn6/5N13fXwx5/LlU8eIVKJ+8yRCVZEr9+L0xpgTmVtXkL/yvxdMZHbBFGZlwGLMQQALCwsLiytxpU81FYjg0h3WPzpxdj0Lz78ZBghiv5ng260ASwJkoWFTNR7rPoqne43Bodsocpfx+tr/8On2ZfgrJ04FJI5yAJC9oDwkz4zRbfyMbO2nwGVaZ9QV7I1aED/ifgDyV/zngiZ5iI+PZty4wdSrF4/X62PRog1s334wJHHVr28ayzZvrlBcLJk+zcu2bZfJvApBxMMPEP6TseZE5rydOOyJSMMgf+V/Kd628qIrbAWcFX+HphfRwsLCoq5zJXF2Tko5NWSR1HK+7Ter/jBAMCc1OzlVEjWF0x6DfQEoafZt2p5pQx6kRXxDAGbvTWf6qs/ILv3ulGBkJx1HIw1Prp/CDcHPmkXYJH8eYQrk36+wk1laN4YAtOgEEsc8gdB0iratovjrVd/83003tWDUqD7Y7TZycgpIS1tBVlZeSOLq0EEwabKNmBjBqVMGEyd6OXP6Mgrbbif697/F0e8WpMuLunAvtvBEDI+LnLkfUH50d8WBSZidEH5M951Vl749CwsLC4tLciVxVneaeELAddlofLNT80QgQ/oOqRWDAEuucxCgfkQsEwfdx+1tewNwKOc0E5Z9xMZT+y55fOIoJwA5i1zI4A8RMnWgh+QoyabTCu9vqxvlTGF3kjj2KdSwSMqP7SF/xX8AUFWV4cN70rVrOwB27z7C/Pnr8HhCM1AxcqTKr57W0DTB5s1+Zkz3UnqZIUolLpaY6ZPQ27RGZheip2eghSfiKy4ge9bbeDIr+8haYe7JPIG5isnCwsLC4lq50qfb4JBFUQeoFGf51yjOIhxRxEUm4PKUk1UYHKd8s6R5fVsBVKHwYJfh/LbPOCLsTso8Lt7YMJu/bVmA9zLLt8Nv1AhrruErMshfe+3l3mvl5vp+nu7uxW/ALxfYMWQd+P4gFBLveAxbQsOKZeYfgDSIjY1i3LjBNGiQgM/nZ/HijWzbFpoJRkWBJ57QuHO0+fJPS/Pxwfs+jMskXLXmTYmZMRm1XhLG4XPY9xWjhsfiycoga+bb+IvzK47shLmOCawSpoWFhUX1uaw4k1KGpq5SR6iujUbKN/5mJ4M2DNDBoVJfVzjnNdjtuvaSZtdGrZmR+hDtkpoAsOjgZqas+ISzxVfec/hN1myJCxnkiqYiJH8d5UZV4E8bdXacrxuLzeOG/A/OZu3wlxaRPfMtpKectm2bcvvt/XE4bOTlFZGWtpzz50OzUzIyEiZO1OncRcXjkbzxho+lSy6f8rT16Er0xBdQwsIwthzGmaUjHOGUH91N9tz3kR435teDvkAbTKuMjcDekNwfCwsLix8idaMuVAuo7l7NymGAYJrPDqvmIECcM5KXBvyUezoMBOBkQSaTln3MymPbr3pdZ0uNiLY6/jKDvJXBz5o91sVH90YGp4sEk1fXjSGAyC6DiOw8AOnzkjX7L8iSfIYP70WPHqZx7759x5g7dy1ud2jKmCkpZuN/crJCXp5kymQP+/Zd/guD885RRD75KEJRkMt2EWbEgwbFX68mb/nnIA1Mq4whQGPM/rIVwMmQ3B8LCwuLHypBFWdCiOHAm5iTn3+TUv7vZY7rBnwF3COlnBnMmKqDrgqinDo+v0GR69o+SEOxtulaLTQEgp90HMTv+/+EGGcEbp+XdzfN5Z2vvsDlq9r9q5zQzF3hxigP7ohm/QiDPww2BeDTi+yUeGp/OdPZvD2xg+4GIGfhx4SVZTP2wdto1CgJv9/P0qWb2Lw5dNml7t0Vxk/QCQ8XHD5sMGmih+zLbdhSFCKfeISwMbeDIeGLbTjDGiClQf7KmRRvvbCXrCOmMCvHtMoIzdouCwsLix8yQRNnFXs538F0oDwNbBFCzJVS7rvEca8CS4IVy/VSWdLML/NwrZXJxgnBFWc3ORQa6gqZXoOdrqt35N9UrymvpD5Mp4YtAVhzfCcTlv2dE/nnq3xOR2OVqJttGG5J7tLgm87+KdVDtAPmHVSZc6D2lzP1xEYkVC4zXzeXxkYWdzw6GqfTTkFBMWlpKzh7NnQi5u67VR5+RENRBGtW+3n9dS+uyzxswukkeuIL2Ht2Q5a6URbuwR7VAMPrIWfe3yg/vOOia2wHwoGvsfrMLCwsLAJDMDNn3YEjUspjAEKIz4E7gIvH/n4FzOLbTuJaR3UnNaPCoomNiKPcU052YWYwQiM1wsyaLSvxXtFiLMoexnN97+bnnVJRFYVzxbm8vOIfLDi46ZrPmVCRNctb48ZfHNys2dDmPn7S3keZF361qPYvNlfDo0ka+ysUu4PyvZvoEZ5NryGpABw4cIIvv1yDyxUKo17QdXj2WZ2hqaag/fhjL599enkBryQlEjNjMnqLZsjMfLT0DPSo+vhKCs2JzPOV5cokIA+zjOkH1gT5nlhYWFj8uAimOGsEZFzw82mgx4UHCCEaAaMxV0NdVpwJIR4FHjV/Cn3mpLrirNJCIyP7ODJI7qyVJc0lVyhpjm7XhwkD7yUpIgaf4eevm+fzf+tnUuq59qyXrZ5CdDcbhk+Suzi4WTOHJvlLxWLzl9fYOFlYuz3NhGYjccwTaFFxqOcPMSoph8aNO+D3G6xYsZmNG3df/UYCRFwcvPyyjbbtFMrLJa+96iU9/fLDItoNrYiZPgk1Pg7j4GnsB0pRI+PxZJ8ha+Zb+Isq54NaYlplnAaW8iP0o7awsLAIOsEUZ1XZLPAG8IKU0i/E5TMiUsr3gfcBhLCF/NOguga0jYO8GaCtXSHFppDjM9he/v2MSKv4RsxIfYheKaaP1uaMA4xf+iEHcjK+d2xVSbjVgVAE+ekuvHmB3995Ib/v46FlnGRPlsKfNupBPdf1I4gf+SD2hs2oX3aKfvWyCHMmUVhYwsyZKzh9OitkkbRqJZg6zUZioiAzUzJxgodjxy7/srH37U3073+LcDgwNu7HkedEcURQfnwv2V+8j/SUVxx5M2ZCHKAQS5hZWFhYBIdgirPTmJ3ClSQDF2/97gp8XiHMEoBbhRA+KeUXQYzrmqnu6qZvJjWDZD5bmTVbVuzjQpnk1O38uvcYHu02El3VyC0rYvqqz5i5Z+11nU+PU4i5xY40JDkLg5s1uyHe4MU+5nDCL+fb8Rm1u5wZ0/cOItp05maO0zEuB7Bz+PAp5sxZTXl58KdZKxkwQOG553UcDsHu3QYvT/FQcIWd6WH33EXkY78AKTEWbSNMqW9OZO5YS96yf4Phx/ye1QdoiynINmKu3LWwsLCwCAbBFGdbgFZCiGbAGeB/gJ9eeICUslnlZSHEx8D82ibM4PrLmiezjwU8JvjWQmPpBVsBhrfqxpQh99MoKgFDGny6fRmvrf2cAtdlrN+vgfjhDhRNULDJjSczmFkzybsj3dhU+NvXGuszavcQQPiNPWnQewj9WzwEZQAAIABJREFU9APUV0swDINVq7aybt3OkMUgBDzwgMbP7jWfEwsX+vjzmz58l6t2qypRzzyJc+QwMCRy9mbCIpMByF+VRtHmZRUHaphWGSmYPWYrMd3/LSwsLCyCRdDEmZTSJ4R4CnMKUwU+klLuFUL8suL/3wvWuQNNbDXEWXRYDNHhMZS5S8kpCnxJq5VNoalNJc9nsLXMT0p0ElOHPsDgFp0B2HXuKC8t/ZCd5wMjDNVIQVx/s7ybPS+4WbN7O/gY2MxPdim8sNz+/9k77/CoyrwN3+fMOVPSZtIDCb13RCmCAqFKExBQV90F6y5iL6ygdKwsawVsi2DhU3oJNbRQpQnSpQmBAOk9mX6+Pw6EQHoyo+zm3NfFdWVm3vPOO5NJ8vBrj1efq6oYohrSauAQuhqOYRKcZGfnsnjxZuLjy9/9WlWMRnhjnMw99+hwuRTmzHGyfFnJhf+Cnx/myeMwtGuLkpuPGHMUoyUKt8NOasxc8k79Umh1K1RhZkX9UfZOY4uGhoaGxg28OudMUZQ1wJpb7itWlCmKMsqbZ6ksRlmHj17C5nCRayvfHDEoPHz2vFfO1fdaSnNLvsBznR/gubuHYJT0ZFpz+WDbj3x/aCNuDzoSBPcxIhoEsg7ZsV3ynolmoFFhZh9VBL8WayAt//ZNZ8qBofR9ZARtDWcRBDh79hJLl24hL8/740WuExGhDpatX18kO1th2jQHvxwoOaqpqxmB5Z3JSLVr4b6SirzjErKlJq7cLJKWzMJ+5db6yEOAH3AYtc5MQ0NDQ8PbaA4BZVDplKaXh8/28ZfQhTWnb/eHqWGJAGDRkTje2bqAlDzP/hEVTQLBPf+YqNl7vWyE+SpsPS/y7a+378fTPyiIh595iJqGFBQFtmw9wPZtB71m0VUcrVsLTJykx2IRiI93M2GCg4RLJT+/3KIZlmkTEC1mXMfOYzxtRRcQgj3lMkmLPsWVdd1CKgxIBxyoNWbbvf9iNDQ0NDQKuH3/+t0mVFaceXP4bKegEFre8zhy5J34AL+lXOStDXP5+aJ3jLODehjQ+YjknHCQf7b80cOKcneUi2fudGJ3wejVRm7XmWZ160Uy4pEB+Eg28lwiS35ax7nTf6xl0YABOp5/QUKSBPbudfH2dAe5pZQVGnt2J+D1lxD0Mq7tRzBl+iGa/Mk/f4Lk5Z+j2K53ZDYAugNXUCf+e7cjV0NDQ0OjKJo4K4Pg62M08irXqelJcSaJOp648z7+ee8IZNmIw2Hj/R2L+M/+tTjd3kk1CnoI6asOnU2OyS9jdeWRRIXPB6rv8Qc7ZU6m3J4zzbp2vYNu3e9EFBQu200s/GYxmVdvbUL2HjodjB4tMWSo+qO7aJGTr7504i5FQ/mOfAS/kY+qHZkxe/CRaiLIItm/bidtw4JrHZmgWjFdH0WYgTYqQ0NDQ+PPQRNnZVCZyJnFN4gAHzO51hxSsz1j09Mhqilv93mCpqG1AXAkHOCV2PksT/Tu/KzArgakAJG8c05yj3kvavZSJwetw92cTRN4e/vtZ2zu42Nk6NBoGjaMQlHgkC2Mdd8uwPYHCjN/f5gwUaZdOx12u8JHHznZsL4UUS7LBLz+IqZe0SgOJ8rSPfhY6gKQvnUpWXvWXVsoAJ2BFtdu7wb+uIG5GhoaGho3o4mzMigQZznlF2eejJoF+wTwZvdHGdGqGwCXMq4SdHwh6VeOsDrJu16Ggg5C+3k/albb7GZyN/X9HbPGgNV5e6Uza9eOYNiwHgQE+GJVJLY56vPr8h+xXfaemX3RMwhMmy4TGSmSlqYweZKd48dLjmwJ5gAsU99C36oF7qxcxJgjmILrojgdpKyeS97JA9dWSkBPoA7qqIwtwB/3ujQ0NDQ0iqKJszKoTOTME52aoiDwaJtejO32EBajH1annVk/r8B6ah0vBOnYkuPA4eWsk7mzHjlYhzXBSfZBR9kXVJJP7rPhq4efjkqsP3t7fSS7dGlDjx53IYoiV50+bHM24vK2NYXEjffp0EHkzbdkfH0FTp92M3GCneRSArK6WlFY3p2MVLMG7ktJyDsTkINr4crLVjsyLxcer9IcVZhpozI0NDQ0bhdur7+EtyGBBdZN5a85uxE5q9yMsdYR9Xmnz5O0qdEAgC3nDjEx9hvOZySyuI4vULqXpkcQIHSACYDkGKvXyo8GN3EyuKmLTCu8vP72SWeaTAaGDOlO48ZqGvnXPAuHxAZkH91D5u41ZVztOR58UMdTT0uIokDcVhczZjiwltIwq7+jDeYp4xH9/HAdPoPxjAOdJQxH6lWSFn+KM+NWVXcE8Eed+K+NytDQ0NC4HdDEWSn4GyX0kkiuzYnNWf6utVohdQGIr6Btk9ngy9iuD/HYHb0QBZHLWalM2jSPdaf2qfvKAs2NOnJcCrvyvCvOAu7SY4jQYU92kbmnYp2q5cVXVvi0nyp639ys50rO7dEEEBkZxogRPTGb/cjPt7E1M5RESyOsF0+Tuu67P+QMsgyvvCLTu4/qjvDNNw5++L70pg9T/z74vzQGQZJwbj2IT44Z0TcA64XfSF42B7ct79rKMFQhZkNV3Tu9+Eo0NDQ0NCqKJs5KoTIpzUC/YPxNAeTkZ5OWk1Lu64a1uJe3oh8jxNeMw+Xki/0xfLRzCXmOGxG73tcGz27NdWL3ckozdNC1WrM1Vq9NU5jc3U4ts8L+yyJz9t8exuadOrWkV6+O6HQiFy8msjm7Bkr9JjjSk0leNgdcXo5YAkFBMGWKnmbNRfLzFd5/z8GOHaV8EwQBv6dG4fuX4QC4lu3A11QHQRbJObJLFZQFHZn1gGggBVgNeG+gsIaGhoZG5dDEWSkEVSWlmVK+ouomIVG83edJOtZqBsDP8cd5M3Yup1IuFVnbx08VMOuzvVf/BeDXRsZUW8KR7iZjh3dMu1uHu3ipkwOXG/4eY8Ct/LlNAEajnvvv70azZnUB2L37CPvtNfDveCduax5Jiz/FnZ/j9XM0biwwZaqe0FCBxESFCW/ZOXeuFCVuMGAe9yrGrl1Q7A6UxbvwDW4IQMa25bekYFsBd1/7OhVthpmGhobG7YkmzkqhSs0AZXRq+sgGXu4ynCfv6oesk0jOzWD6lh9Yeqz4aew1JYHWJh15boUdud6N3oQNVKNmKeutKF7QgQIKXwy0IYnw8R6ZX678ucbmNWqEMGJELwID/bFabSxfHsclfSQh/fuhuF0kL/8CZ5r3vTK7R4u8/rqMwSBw5IibKZPtZGSUvF4MCsQyfSJy08a4M7MRVx3GFNpQ7chcM5+8E3uvrRSATqjiDGAP8MeZsmtoaGhoVAxNnJVCpcTZNWeAC6WIs36NOzC550hqBgTjVtzM/2U9M7YtJNNW8oj36ynNuBwnNi+mNH2aSPg0knHmuEnf4h2rpqfvdNIpyk1ClsCEzX9uE0D79s3p06cTkqTj8uVkFi3aRL5/DcIH/RWAtA0LsF7wjvPCdQQBRj0u8eij6o/jmjVOPvnYibMUDS7Vr4flnUnowkJxXbiCvPsy+tC6uPJySF46G1vCmWsrdUAP1HSmC9gKnPXmy9HQ0NDQqCKaOCuFyqU16wLFzzirawlnWu/H6V6/LQCHLp/hzdi5HL5adldnH3/1W7XByynN0IFqh2ZqrBW3FzKaYb5u3uupbvziOgPZ9j8nnWkwyAwa1JUWLeoDsHfvMTZs+BkhIISIoaMRdDqy9m4g51fv+kqaTPDGOJkuXXS4XApz5jhZvqz0OjB9x/aYJ4xF9PHB+ctJjL+7kAIjcKQlqh2Z6YUHEzdFFWY2YAOqLZOGhoaGxu2MJs5KIbCCkbNg/1B8jf5k52eRkZtWcL9RkhndcTDPdrofo6Qnw5rD+3E/suDXTbjLYZQdLgncYZLIdyts92JK01hXh38rGVe+QtpG79SazexjJ9AEa07rWHLiz0lnhocHMWJEL4KDzdhsdlau3Mbx478jGn0IH/48OpMvead/JX3rEq+eIyJCYOo0mfr1RbKzFaZNc/DLgdLrwExDB+H/7NMIOh2O2L34WkMQ/QxYL54ieekc3NZbo6/HgADgBKolk4aGhobG7Y4mzkpAECDQRxVn6eUUZ8U5A3Sv35bpvR6nTmA4AAuPbOWdrQtIzcsq91l6X2sE2J7rJM+LKc3rUbO0LVZcuZ5/oh71nDzW2km+A55bY+DPMDa/886m3Hff3UiSxJUrKSxevIm0tCwQdYQOGY0cFI498SIpq76GcgjnytK6tcDESXosFoH4eDcTJjhIuFTK84ki/mOewWfoIACci7bg59cQQS+Sc+xnUtd+W6iTNBTIAa67Ouz22uvQ0NDQ0PA8mjgrAYtJRicKZObZcbrL90f6xnyzc9TwD2Zyz7/Rv4lqJH0yOZ43N8xl76WTFT7LH5HSNNQUMd+lx+1QSF3v+Vozg05hzgA1Gjd1m57fM/7YmWayLDFw4L20bq12Mu7ff4J163bjcqkpxOC+j2Ks0wRndgZJSz5DcXgncggwYICO51+QkCSBvXtdvD3dQW7J5YYIPibME/6JoWN7FJsd90/b8QtvCkDGjlVk7lxVaHVd1BqzNCAG1ZJJQ0ND4waBgWYmT36Jhg3rIIq3x3zJ/3bcbjdnzlxg8uSPSE+v+kBvTZyVQEG9WV7FOjUFoH1wGDOfmomv3kiOLZ8Pdy5m7oF1ON0VnykVohNoZ9JhcyvEeTGlGXLNDSB9mw1npucjRv+8x0HjYIXjyQIzd/2xM83CwgIZMaIXISEW7HYHq1Zt5+jRG0XxAR364tf6HtwOO8lLZ+HKTvfKOXQ6GD1aYshQ9cdu4UInX3/lxF1KJlMMC8Xy9iTkBvVwp2UgrDqMb0RTFJeT1LXfknvs50KrW6AamAuoozK0GWYaGhpFmTz5JTp0aIckSfwZGYz/TRSCgoKZPPklXnxxSpV308RZCVSmU/POmg3p4C8R0LY7AKtO7mbq5u+4mp1W+oWl0MtfQhQEduY4yPXSWCo5RMTSSY/iUkhZ6/moWaMgN+PvUd/Hf8QYcbj/uF8Gbds2pn//LsiyRFJSGgsXbiQ19cb/akyN2mLpPhSAlJj/YL96wSvn8PeHCRNl2rXTYbcrfPSRkw3rSxdPUpNGWN6ehC4oENfZi8h7rqKPaIArP5fkZXOwXTxVaHUnoPW1r/cCh7zyOjQ0NP77adiwjibMPI6AJEk0bFjHI7tp4qwEKiLOQnzMTOvzJL2C/AH4Pe0Kb8V+w7bzh6t8joLBszlejJr1MyLoBNJ32nCkeFoBKsweYMMgwdyDEtvj/5gmAFmW6N+/C23bNgbg4MHfWLNmJ07nDUGkD69NyKCnEASR9K1LyT910CtnqV1HYNo0mchIkbQ0hcmT7Bw/Xnp00nBvZ8zjXkUwGnHuO4rxAkjBNXGkJ6kdmWnXDcp1qBP/66NGyuKAMyVtq6GhoXEtlakJM88jeCxNrImzEijPGA1REPhr29683vUhzEZfXIrCjqsJPPnDOGyuqteHBekE2vvocCgKW3O8U28mmQUCu6qvNWV1fhmrK85fWjrpVd9Fah6MjTV4fP/iCAmxMGJEL8LCAnE4nKxevYNffz190xqdv4XQYc8hynpyDu8ka886r5ylY0eR8W/K+PoKnD7tZuIEO8m3eo/fgs/Dw/F/5nEAHGt24esMQ/Q3Yb10huSls29xKmiIKsy0URkaGhr/PezatZOZMz/A7XYzePBQRo16osgaRVGYOfMDdu7cgdFoZNKkqTRt2qzUfTMzMxk/fixXrlymRo2avPvuDAICAoqsy87OYvr0qZw9ewZBEJgwYTKtW7fh44//zfbt25BlmaioKCZOnIK/fwBOp4Pp06dw8uRJXC4X/fsP5PHHn/TY+3ErmjgrgYLIWU7xkbO2NRrwdp8naR2hzsk6lnqVy3Iwi4/t9IgwA+jpJ6ETBHbkOMj2Ukoz+D4joiyQud+O7bJnn8RiVPiwr/r+vR5rIDXf+/9Ta9WqIQMH3oNeL5OcnM6iRZtITr65hkyQDYQNew7J34I1/jdS13/vlbM8+KCOp56WEEWBuK0uZsxwYC0tayxJBLz4LKYBfQFwLIjFL7AJgkFH7vG9pKyZV4y352+AGTgNeKdWTkNDQ8OTuFwuPvjgXT777HPCw8MZOfJRunbtRv36DW5at2vXDuLj41m6dCVHjx7hvffeZt680n9fz58/l/btOzJq1BPMmzeX+fPn8vzzLxVZN3PmB9x9d2fef/9fOBwOrFY1ONGxYyfGjHkBSZL49NOPmDdPvX7jxljsdgc//rgYqzWfBx98gL5976NmzUjPvTGF0No0SqCktKbF6Ms7fZ5kxV+n0TqiPglZKTy19F+svppInrv44bOVpc81V4AN2d5Jaep8BYKirxmcx3g+avZOTxvhfgrbLojMO+Td/wdIko5Bg+7lgQei0etlDh8+zVdfLS8izBAEQgY9iT68No60RJKXfV7IFNwzyDL8858yz/xdRhQFvvnGwbRppQszwc+PwPenYhrQFyU/H9fc9fgHN0cQdWTsiiFl1X9uGZXhW+jqvWjCTEND47+FY8eOUqtWLaKiopBlmd69+xIXt7XIuri4rQwYMBBBEGjVqjXZ2dmkpJSeeoiL28rAgerIoYEDB7F165Yia3Jycjh48BcGD1brjWVZxt9fja516tT5Wj0etGzZmsREtYREEATy8/NxOp1YrTZkWcbX16/S70FZaJGzYpBEAbOPHpdbITNfFWcCAsNbduXN6EcJ9gnA4XIyZ98qPt61FKvDTq8uTwPlNzwvC7Mo0PFaSnOzl1KaQb0M6IwC2UccWM97VqB0jHTx9zudOFwwerUBxYv1DUFBZkaM6ElERDBOp5O1a3fxyy+/FbvW0n0YPo3a4rLmkrTks2KGtlb1LDBlip5mzUXy8xXef8/Bjh2lRyR1NSOwvDMZqXYtXEmpiKsP4xvZEsXlInXdt+QeLTynrA7QE8gEVgLedYzQ0ND432b44kte2Xfx8KgSH0tOTiI8PKLgdnh4OEePHilzXVhYOElJSYSEhPLii2N4661JhIaG3XRNWloqISGhAISEhJKeXrQhLyHhEhZLIFOmTOT06VM0a9acV18di8lkumndypXL6d1bzWT07NmLuLit9OvXG6s1n5dffg2z2VyOd6JyaOKsGK47A2Tk2XEr0DSkFm/3eZIOtdTZUrsuHOOt2LmcTk0AIMwcgcngQ0ZuGll5VZ9vAmpKUxIEduQ6yfRCSlM0QnBv70TNdILC5wNtiAK8v1vmeLL3mgBatKjPoEH3YjDoSU3NZNGijSQmFt8d69fmHswd+qC4XCQv+7xQUb1naNxYYMpUPaGhAlevKkycYOfcudIL/+WWzbFMewvRbMb52+/I+5Iw1GyM25pH8rI5WOMLi8zmqKMyRCAZbYaZhobGfyNKMQO+BaHof+BLW/fxx7Mq/fwul4vffjvJ66+/QcuWrfjXv95n3ry5jB49pmDN3LlfIUk6+vXrD6jRPlEUWbt2A1lZ2Tz99ON06NCJqKiSRWhV0MRZMVxPaebkw4Tox3jirn5Ioo6knAymbfmO5cd33rT+xvBZz6U0+15Laa730uDZwO5GJD+R3NMO8n7z7B/5Fzo6aBvh5vd0gWlx3jE21+l09O3bifbtmwNw9OhZVq3ajt1e/PtlrNOUoN6PApC24Xts8cVH1ipL92iR11+XMRgEjhxxM2WynYwy3JKMPbsT8PpLCHoZx66DmBIkpNAonBkpJC7+BGfq1UKrOwJtrn29D/BOZ6mGhkb1orQIl7cICwsnMfHG77fExMSCaFdp65KSEgkNLbquMEFBwaSkJBMSEkpKSjKBgUHF7hsWFkbLlq0A6NmzN/Pnzy14PCZmJTt2bGf27C8KxOC6dWvp3LkLkiQTFBREmzZtOXHimNfEmVZzVgxBvgZq+Tbl761f5JkOAxEQmLt/LdFfv1JEmEFh26bzHnn+ABE6+epwKgqbvDBCQ5AhpO/1qJln55pFBbiZGq2mgsesMZDv9Hw6MzDQnyeeGET79s1xOl2sXr2DJUs2lyjMpKAIQob8HUGnI3PPenIOF/0eVhZBgMefkHjrLT0Gg8CaNU5ef61sYeY78hHMb76OoJexr9iKb4o/UkAQtoSzXPnu3ULCTIeaxmwDuIEtaMJMQ0Pjv5nmzVsQHx9PQkICDoeD2Nj1dO3arci6rl27sXp1DIqicOTIYfz8/IoVcbdeExOjuqbExKyiW7fuRdaEhIQQHh7B+fPnAdi3bw/16qnNfbt27eTbb+cxc+ZHGI030pwRETXYt28viqKQn5/P0aNHqFu3XiXfgbLRIme3UC+wBpO7/4PmIU0AOHj5DOM3fM3RxPMlXlM7VP2meipyFu0nIwsCu3OdZLg8P63fco8BOVAkP95Jzq+ejcx9cp8NPz0sPq5j7RnPf7yaNq3L4MHdMBr1pKdnsWjRJq5cSSlxvWjyI2z4c+iMvuSdOkjG1qUeO4vJBG+Mk+nSRYfLpTBnjpPly8qo3ZNlAsa+hKlndxSXC8f36/EPa4kg6sg9uZ/U1d+gOAt/T+oBDQA7EAskeOz8GhoaGn8GkiQxduwbvPDCaFwuN/ffP5gGDVRrvSVLFgEwbNgIunS5l507dzB06CCMRiMTJ96YvF9SzdnIkU8wbtxYVq5cRnh4Dd57bwag1q9Nnz6lIB362mv/ZOLE8TgcDiIjI5k4cSoAM2a8h91uZ8yYfwDQqlVrxo17ixEjHmLq1Ik89NAwAAYNup9GjRp77T0Sisvp3s4Igl5Ru9U8i1GSee7uofyjwyAMkozNlc9XB5bwwZbVKJT8HgkIzHj8C4x6E+O+HUN2fvkNzUtiVqSJaD+ZKVfz+SnTw2lNERq/b0YfqiN+Vg5Z+8rvgFAWgxo7WfkXK9k2aDrLh8vZngvMiqJI794d6NRJDUOfOPE7K1Zsw2Yr5fw6ifCHXsJYqzG2qxdIXDADxeGZ1xsRoQ6WrVdfJDtbYdo0B78cKL04UDAHYJk2AX3L5rhzc3H/sBW/Om0ByNy9loxty6HYz9qdwO+ofpkaGhoaVWPt2nmEhISVvVCjwqSkJNGv36hb7r18QFGUuyqyjxY5A3rUv4OpvUdRxxIOwJGUA5zN3cmPhw+VKswAwiwRGPUm0nNSPSLM/ETo4iPhVhQ2eiGlae6oRx+qw3bVRdZ+zwkzH1nh037qwN63tug9KszMZj+GD+9JVFQYLpeb2Ng97NlztMzrgvs+hrFWY5zZ6SQvmeUxYda6tcCkyXrMZoH4eDcTJjhIuFT650RXu5bakVkzAtfVJISYw/jVaYvidpG2/gdyDu8otDoEdahs9rXbBzxybg0NDQ2N/w6qtTiLDAhhcs+/cV/jDgAcT7rAmxv+w6C7DPgapHJZN92oN/NMSrO7r4ReFNib5yTV0ylNAUIHXqs1W51ffJCmkkzqZqeOReGXKyKz9nrO2Lxx49oMGdINk8lIRkY2ixdvIiGhjBH7QECnfvi16ozbbiN5ySxcOWUUgZWTAQN0PP+ChCQJ7N3r4u3pDnLLmMahv6MN5injEf38cB4/jbw/GUOtZrht+SQv/xzr+ROFVtdGrTHLBZajpjM1NDQ0NKoT1VKcyaKOp9sP4KUuwzDJBnJs+fxrx0LmHViPpIOHDW1wON1kW8uOXNUK8aw4uzF41vNdmv53yBgjJeypLjJ3ee6PfsswF6/c7cCtwN9jDLiUqjcBiKJAjx7t6dJF7VD87bcLLF8eh9Vasp3WdXya3Elgt6Eoils1M0+Mr/J5dDoYPVpiyFD1R2bhQidff+XEXcaYE9OAvvi/+CyCJGGP24sp0YAcXgdnZgpJiz/FkVLYbqkZ0AW1TycRbVSGhoaGRvWk2omzu2s35+0+T9IoWLVcWHF8J9O2fE9ijjphPcxXjSyl5ZVPvBREzjwwfNZHgHt91W9JrBdcAUIHqp0nKWutKB6aOSug8PkAG5IIn+2V2X+56jPN/P19GT68B7VrR+B2u9m0aR+7dpXPRF4fUYfgAaovZcbWpeSfPuSB88CEiTLt2umw2xU+/NBB7IYyVJkg4Pf0KHwfHg6AbdEG/PX1EM0+2C7/TtLSWbhzC6fB2wN3XPv6AFoqU0NDQ6P6Uq3E2YAmHfl8yMsAnE29zFuxc9lx4ebapfIYnl9HEISCGWcXPTBGo6ufhEEUOJDnJNnDKU3fFhI+9SWcWW7St5X92srLk+2cdKnt5kq2wJubqz7TrEGDKB54IBofHyNZWTksXryZixfLNyxW5x9UYGae/et2svZuqPJ5atdRC/8jI0XS0hQmTbJz4ngZ3xujAfO41zDe2xnF6cQxP4aAGu0QdDryfvuFlJi5KM7r4l8EugGNUEdlbEf1y9TQ0NDQqK5UK3F2R81GACw5up2x677AXsREumRPzeIIN9fAIBtIzU4mx5pd5vqyuD54doMXGgEKombrrSgeymiG+rh5v5cq9F5aryfLVvl0piAIREffyb33qtGjM2cusmzZVvLyyjeHTdAbCBs+BsnPjPXCSdI2LKj0Wa7TsaPI+DdlfH0FTp92M3GCneQyyt3E4CAs0yciN2mEOzsb1/ebCajfHoDMPeuvjfIoLO7qoAozO7AR8I6VioaGhobGfw/VSpxZjKpZ9O6Lx4sVZlAxcXY9pemJqJmpUEpzo4frzUwNJfyaybjy3KRt9lzUbEZvO0EmWH9Gx8Jjlf8o+fn5MGxYNHXr1sTtdrNlywF27KhAOlIQCBn0NPqwWjhSr3rEzPzBh3Q89ZSEKArEbXUxY0bpxuUAUv16WN6ZhC4sFOely4hrjuBf/y61IzP2/8g5tK2Yq35HnfgfD6RW6cwaGhoa/y3s2rWTmTM/wO12M3jwUEaNeqLImvPnf2fq1EmcPHmC0aOf469/HVnmvpmZmYwfP5YrVy5To0ZN3n13BgEBAbfse57x48cW3L58OYFnnhnNI488xsfSGoSXAAAgAElEQVQf/5vt27chyzJRUVFMnDgFf/8ALl9O4MEHH6B27TrAjfln3qKaiTPVQT4zP6fENQVpzZyyRUwtD3Zq3uMr4SMK/Jrv5IrTsynN6x2aqZtsuPM9s3f3uk5GtnVidcKzawxQSWPzevVq8sAD0fj5+ZCdnceSJZu5cOFK2RcWIjB6BD4NW+PKzyFp8ae4bXmVOguALMMrr8j07qPWzn3zjYMfvi9b6Ok7tsc8YSyijw+OX0+gP5SKoU5LtSNzxRdYfz9eaHUwarH/dR9WbeK/hoZG9cHlcvHBB+/y2WefEx4ezsiRj9K1azfq129w07qAADOvvjqWuLgt5d57/vy5tG/fkVGjnmDevLnMnz+X559/6aY1devWZcGChQVn6d+/D9HRPQDo2LETY8a8gCRJfPrpR8ybd+P6yMioguu8TbWyb7KYVHGWYS1NnFU8cuYJcXajS9OzKU1jLR0BbfW4bQqpGzxj1aTXKcwZoIrX6dv0nEuv+MdIEAS6dWvHX//aHz8/H86dS+CLL5ZWWJj5te1KQPteKC4nyUvn4Mwoe8xGSQQFwb//rad3Hx35+QqTJ9nLJcxMQwdhmT4B0ccHW+wOjCdtGCLq4cxK4+r3H9wizGoBg4B+gLHSZ9XQ0ND4b+XYsaPUqlWLqKgoZFmmd+++xMVtLbIuKCiIFi1aIknljyPFxW1l4MBBAAwcOIitW0sXdvv27SEqKooaNWoC0KlT54Lna9myNYmJ5at59jTVKnJmvpbWzLCWPJiqvOJMFERqBavhzYsp56t0LoMA3f3Ub8WGHM+mNEOuRc3S4my4sj0TNXu9s4OmIQonUwRm7Kr4TDNfXxMPPBBN/fqRKIrC1q0H2LbtIBV1qzDWbU5Q778AkLruO2yXTlf4LNdp3FhgylQ9oaECV68qTJxg59y5Ms4jivg/9ww+Q9RfBNYFMfj7NEZn8cV25bw6Xy03s9AFTYB7Uf9PdAFthpmGhsafzV0xXb2y7/6BxZVxqCQnJxEeHlFwOzw8nKNHj1Ro/5Lsm9LSUgv8N0NCQklPL91ZZcOG9fTt26/Yx1auXE7v3n0Lbl++nMCjjz6Er68fo0eP4Y472lXozBWhWomz62nNkiJnvgYJg6wj3+4k31F6xCTcUhO9bCAlK4lcW8mRuPLQxVfCVxQ4anWR4PBcSlMfLmJur8ftVEhd55moWYNAN291VUXFP2IM2F0VS2fWqRPBsGE98ff3IScnj6VLt/D775crfA45uAahQ/6OIOrI3L2G3KO7K7zHdbpHi7z+uozBIHD4sJupU8o2Lhd8TJgn/BNDx/Yodgf2/yzHXKcDgk4i79QhUmK+vsWR4C7g+g/yQdQ6Mw0NDY3qR3H/EReEiv0tue6RWRUcDgfbtsUxZswLRR6bO/crJElHv379AVXorVq1DovFwokTx3nttZf56acl+Pn5VfkcxVE9xVkJNWeVS2mer/K5+lyPmnm4ESCkvxFBFEjfbsWRVsZcrnKhMKu/DaME8w9JxF2o2MfnnnvaEh19J6Iocv78ZZYs2UJOTsXrw0STH6HDn0M0mMj97QAZ21ZUeA8AQYBRj0s8+qj6OtasdvLJJ06cZWSWxbBQLG9PQm5QD3d6Bq7vN2Fu3BmArH2xpG9ZDAW/fESgK9AYdVTGDuBkpc6roaGh4WlKi3B5i7CwcBITrxbcTkxMLIh2VZWgoGBSUpIJCQklJSWZwMCgEtfu2rWDpk2bEhwcfNP9MTEr2bFjO7Nnf1EgGvV6PXq9qhGaNWtOVFQU8fEXaN68hUfOfSvVpubMKMkYZT1Wpx2rs3jxVTlxdq5K55IFiPZTU4OeHDwrB4lYuhhQ3AopazwTNXuohZO+DV2k5cPrseWfaWYyGXjkkfvo2bM9oiiyfftBvv12TaWEGTqJ0AeeRbaEYrtyntSYb6iMD5XJBJOnyDz6qITLpfDZpw7+/e+yhZnUpBFBsz9EblAP5/mLKD/uwr9xJxS3m9QNC0jfvKiQMAOIRBVmDmA9mjDT0NCo7jRv3oL4+HgSEhJwOBzExq6na9duHtm7a9duxMSsAiAmZhXdunUvce369evo0+e+m+7btWsn3347j5kzP8JoNBXcn56ehsulZtQuXbrExYvxREZGeeTMxVFtImcV6tQslzirC1TdGeBuHwl/ncBJq4sLDk9Et1SC7zMiSgIZe2zYE6u+r9mg8GFf9X0ZG2sgOa98ur5WrXCGD+9BQIAfeXlWli3bwpkzlZ/lFXzf3zBGNcSZlaaamZcgtEsjIkIdLFuvvkh2tsK0aQ5+OVD2e2S4tzPmca8iGI3Y9/+K/kgmxvptcNusJK/8Euu54szYLwK7gStASoXPqqGhofG/hiRJjB37Bi+8MBqXy8399w+mQYOGACxZsgiAYcNGkJKSwsiRj5Cbm4sgCPz44w/89NNS/Pz8Sqw5GznyCcaNG8vKlcsID6/Be+/NANQ6t+nTpxSkQ63WfPbu/Znx428ehzFjxnvY7XbGjPkHcGNkxsGDv/D557ORJAlRFHnjjbcwm83ee4+8tvNtRkEzgK08zQClj9EQRR2R15sBqpjW7OuvfgvWezClqfMXCOqmCs3kVZ6Jmk3vYaeGv8LOeJG5B8v3sencuXVBtCw+/ipLlmwmK6sMl/BSMHcegF/LTrjtVpIWf3ZLsX35aN1aYNJkPWazQHy8mwkTHCRcKjvy5vPwcPyfUW2hbKu34JNlRq7ZAGd2uuqRmVRYcAahRvPSr92uWKGrhoaGxv86XbrcS5cu9xa5f9iwEQVfh4SEsHp18U4vJdWcWSwW5sz5ssj9oaFhN11jNJrYuDGuyLply1YVu2+PHr3o0aNXsY95g2ojzm5EzqreqVnDEole0pOcmUi+vQoztYAe11Ka6z2Y0gzuY0Q0CGQdsmO7VHUTzfY1XTzb3oHDBf9YbUApY6aZ0WhgyJBuNGmiCthdu35l06Z9uN2Vb3bwaXoXlnsHq2bmK7/GkVzx6NuAgTqef15CkgT27nXx9nQHuWVpRUki4KUxmPr3AcA6byn+lhbogvywJ8aTtOQzXNmFuwcigd6onZjLgcp/PjQ0NDQ0qifVR5x5cMZZrespzSrON+voo8OsEzhlc3HeQylN0SQQ3NNzUTOdoPD5QBuiADN+ljmaVLqxeWRkKMOH98Ri8Sc/38ry5XGcOhVfpTPoa9Yj5JqZefrmxeSfLZ8J+nV0Ohj9rMSQIerHfeFCJ19/5cRdlne5nx+WKePR39EGxWrF9tVSzPU7I0gyeWcOk7LqKxR74ShrY9TifxF14r9nopYaGhoaGtWL6iPOyhijIQCBPqo4Sy9DnBU0A1Sx3uzG4FnPpTSDehjQ+YjknHCQf7bq0bgxHRy0q+HmQobA1LjSmwA6dmxJ794d0Ol0XLqUxOLFm8jMrNqYEV1AMGEPjEGQZLIPxpG9f2OFrvf3hwkTZdq102G3K3z4oYPYDWULYV3NGljemYRUuxaulFRc38Viaa4WrGYd2Ez6poWgFN7nzmv/AA4Beyt0Tg0NDQ0NjetUH3FmKn2MRoBJRtKJZFsd2F2l//H2hDOABPTyvz5CwzMpTUEPIX3VobPJMflV3i/S3830aFWoPrfWQJ6j+HSmwaBn8OCuNGumvi8//3yE2Ni9uMsKTZWBoDcSNvw5dL4B5P9+nLSNP1bo+tp11ML/yEiRtDSFSZPsnDhedmpVbtUCy9Q3Ec1mHGd+R1x3lIDm96IobtI3LST7wObCp0SNljVBHZWxCzhe3LYaGhoaGhrlotqIM7NBbQjILMEdoLwpTZ2oIzKoFlA1Z4D2PjosOpGzNhdn7J5JaQZ2NSAFiOSdc5J7rOqC76P7bPgbYOkJHTGniv+o1KgRwogRPQkMDMBqtbNyZRwnTpyv8nMjiIQOfgZ9aCT2lMskr/iiQmbmHTuKjH9TxtdX4NQpN5Mm2kkuh7OTsVc0Aa+9iKCXse3ej+F4DsZG7XDbbaSs+pr8M7/eckVNVGHmADahpjM1NDQ0NDQqT7URZ2XVnJV3jEaNwChkSU9SxhWs9spHpzztpSnoILSf56Jm/Rs5Gd7cRY4dXlxnKHbNXXc1o2/fu5EkHZcvJ7N48SbS07Or/NwAgT1GYKrfEldeDsmLP0Oxlf81PfiQjqeekhBFga1bXMyY4cBWto89viMfxW/kIwBYl63DNz8UOaoRzuwMkpd8hj2xOOGVAOwEkoDK+3pqaGhoaGhcp/qIszJqzso7RsMTKU0R6OVhL01zZz1ysA5rgpPsg1Xb0yQpfNZPfR8mbtFzKevmmWZ6vcygQffSsmUDAPbtO8b69XsKBvRVFb87uhNwV08Up4PkpbNxZpZvPpgswyuvyPTuozYtfDPXwQ8/lONMskzA2Jcw9eyO4nKRP3cx5pA26IL9sSddImnxp7iy0wtdEAjouDG37FhFXp6GhoZGtWfXrp3MnPkBbrebwYOHMmrUE0XWrF27mm+/nQeAyWTijTfepHHjJqXum5mZyfjxY7ly5TI1atTk3XdnEBAQcNOa8+fPM3782ILbly8n8Mwzo3nkkccYN24sFy6cByAnJxs/P38WLFjI2rWr+e67+QXXnDlzmu+++z+aNGlayXegdKqNODObrs05K2GURnnTmrU90Kl5p0lHsCRywe7iN5sHUpoChA5QJxknx1grMzD/JiZ0s1MvUOHQVZFP9txsbB4eHsSIEb0IDjZjs9lZtWo7x45VzSWhMMZ6LQjq9TAAqWu/xZZwplzXBQXBlKl6mjUTyc9XeP89Bzt2lP3eCuYALNMmoG/ZHHdeHrbPFxHYpBuCJJN/7ijJK75EsRfuuqwJ9AGcwDKg8nPbNDQ0NKojLpeLDz54l88++5zw8HBGjnyUrl27Ub9+g5vW1awZyRdf/IeAgAB27tzBO+9MY96870vde/78ubRv35FRo55g3ry5zJ8/l+eff+mmNXXr1mXBgoUFZ+nfvw/R0T0AePfdDwrWffjhzALvzH79BtCv3wBAFWavvvqS14QZVCP7pnJHznLK26l5vtJn8XRKM+AuPYYIHfZkF5l7Kj4xvzDNQ128drcDtwJ/jzHgUm40AbRr14QnnxxMcLCZq1dT+fLL5R4VZnJITUIHP4MgimTsiiH3+J5yXde4scDsOQaaNRO5elXhxRfs5RJmutq1CJr1IfqWzXFdTcQxeymBLXpd6wzdStLiz24RZo2AfoAedeK/NipDQ0NDo6IcO3aUWrVqERUVhSzL9O7dl7i4rUXWtWnTtiDq1apVa5KSEsvcOy5uKwMHDgJg4MBBbN26pdT1+/btISoqiho1at50v6IobNy4gb597ytyzfr1a4u935NUn8iZsayGgOs1ZyWnNSVRomZQbdyKm0uVFGcC0MfDrgChg67Vmq2xqg2DlURA4fMBNmQdzNkvsTdBTQ/KssSAAffQpk0jAH755SRr1+7C6fRMGhNA9PEn7LqZ+Yl9ZG4vfkrzrURHi7z2uozBIHD4sJupU+xkZJR9nb5dW8yTxyH6+eE4cQoh9jjmVtFqR+aWxWTvu3Vkxx1A+2tf/wqUTzhqaGho3M48fuwxr+z7TYuSI1zJyUmEh0cU3A4PD+fo0dKdVFasWEbnzvcU3C7JviktLbXARD0kJJT09LRS992wYT19+/Yrcv/Bg78QHBxM7dp1ijwWG7uBf/3ro1L3rSrVRpwVRM6KGaWhEwXMPjJut0J6XsmRpxpBUUg6iavpCVgdlYua3GHSESqJXLK7Oe6BlKZfaxlTbQlHupuMHeWoei+FUW2d3FvHTWKOwLiNqlgNDQ1kxIiehIYGYrc7iInZwZEj5Us1lhdBkgl74Fkkcwi2y+dIXTOPsnKzggCjHpd49FH1I7xmtZNPPinbuBzANKAv/i+NQdDpsMXtRn8qH1OT9rgddrUj8/Shws8E3AM0u3amXWg1ZhoaGhqVR1GK/n4XhJKdZ/bv38fKlcv56qtvCu4ryb6pIjgcDrZti2PMmBeKPLZhQ1FTdICjR49gNBpp2LBhlZ+/NKqFONMJImajL27FTZatqJ2OxUdGFATS8+yU5jDkkZSmhxsBrkfNUtZbUaqwZbBJYUZvVdy9vF5Ppk2gTZtGDBhwD7IskZSUzqJFG0lJKUdYqqLP3W8khsgGODNTSVoyC8VZ+gsxmeCNcTJduuhwuRTmzHayfHk5oniCgN/To/B9eDgA+T+twtcZgb52E1w5mSQt+Qz71Qu3XBQONEWtMdsE3Pq4hoaGxn8vpUW4vEVYWDiJiVcLbicmJhZEu27l9OlTBYblFoulzL2DgoJJSUkmJCSUlJRkAgODSly7a9cOmjZtSnBw8E33O51OtmzZxLff/l+RazZsWOf1lCZUk5qzgEIpTaWYiEx5UppQ9U5NAejtwXoznyYSvo1knDlu0rdUrf5pRm8bwT4Qe1bHopMG7r+/K0OGdEeWJQ4dOsXXXy/3ijAz3zMI3+YdcNvySVr8Ge680kdxREQIfPKJni5ddGRnK4x7w1E+YWY0YJ4yHt+Hh6M4neTN+o4AuSH6kJrYkxO48t27xQgzgKvAdiAGTZhpaGhoVJ3mzVsQHx9PQkICDoeD2Nj1dO3arci6q1evMHbsq0yZMp06dYqmF4uja9duxMSoZTExMavo1q17iWvXry8+OrZ37x7q1KlHeHj4Tfe73W42bYqld29NnHmE0lKaUIFOzZCqibNWRh01ZJErDjeHrVWv1wodqHZopsZacVcho9m1jovH73BidcIbu8N56qmh3HFHExwOJytWxLFiRRwOh+eM2a/j27wDli6DUNxukld+hSMlodT1rVsLzJqtp159kQsX3Dw3xs4vv5SdGhaDgwj68H2M93TGnZODdeY8LHXuKXAeuPr9B7iyCtclBAKF6xhOos4x09DQ0NCoKpIkMXbsG7zwwmhGjBhKr169adBATRMuWbKIJUsWAfD111+SmZnB+++/wyOPPMjf/vZIwR4vvjiG5OSiv5dHjnyCPXt+5oEHBrFnz8+MHKmO6EhOTuLFF8cUrLNa89m792d69OhZZI+SomMHDx4gLCycqKioqr0B5UAoLvd7OyMIegWKD3+WRNsaDVn1t+kcunyGQd+9VeTxfq1q0LNZBOuPXiH2+NVidgBJJzPz8S8RRJHX5j6D3VlxNfR6qIHHgwzMT7PxfnLV6sOMdXU0nGzGZVU49WoGrtzKfR9lUeHQP/JoHqow6XgLHA2j0etlUlIyWLRoI0lJ6WVvUgkMkQ0If/gVBEkmbeOPt1giFWXAQB3PPy8hSQJ797p4e7qD3HJMsZDq18PyziR0YaE4Ey7j/G4Dljt6A5B9aBtpsf93i/NADdRRGQqwHMiq5CvU0NDQuD1Zu3YeISFhZS/UqDApKUn06zfqlnsvH1AU5a6K7FMtas7K6w6QWkpaMzKoFjqdxJW0S5USZlBohEZO1aNQ16NmaVuslRZmAK91dtAoVOR7azRC81bogcOHz7B69Q7sds8ZshdGMgcTOnS0OrLily2lCjOdDkY/KzFkiPpRXbjQyddfOSmPbae+Y3vME/+JaDJhP3wMYdOJAmGWvmUxWXs33HJFA6A76oDZ39FmmGloaGho/BlUD3F2reYsowxfzfRS0pq1qlhv1sIgEimLJDrcHMqvWkrTUFPEfJcet0MhdV3la83qWdw819XEfxjIVWMYTqeTdet2c+DAySqdrzQEvYnQ4c+rKcVzR0nb+FOJawMC4K0JMu3a6bDbFT780EHshvJ1uJqGDsL/2acRdDryY+MwnnNgan43boed1NVzyfvtl1uuaAN0vPb1EeBnqjzNV0NDQ0NDoxJUE3FW9ZqzqnZqXo+axeY4qvwnP+SaG0D6NhvOzMrupjD1wVrMk3tix0BqaiaLF2/i6tXUKp6uFASR0CHPqEX4KZdJXvEVKMWLrdp1BKZNk4mMFElLU5g0yc6J4+V4raKI/3PP4DNEHUKY990i/IQ66Os2xJWbRdLSWdgvFxbYAtAFaI4qxnYDR6v2OjU0NDQ0NKpA9RBnpaQ19ToRf6OM0+UmK7/kNF7tkLpA5SNnfa+Js/VV7NKUQ0QsnfQoLoWUtZWLmul0IqMf6sCZGq0AOPvbWRYt247N5p005nWCej2MqV4LXLlZJC/+FKUE4/iOHUXGvynj6ytw6pSbSRPtJJfDU1zwMWGe8AaGjneh2B3kzfoWS+270fmZcaReIWnRJzgzbxWfIdwYlbEFNZ2poaGhoaHx51E9xNm1yFlx7gCB11OaefYSI1qyTqZGUBRut5tLqRUfp9DMIFJbL5LidHOwiinNkH5GBJ1A+k4bjpSKD7G1WPx56MEeBNcIQ8RF+tGdfL/ktyqdqTz439kD/3bdUZwOkpbOLkYkqTz4kI6nnpIQRYGtW1zMmOHAVo4SPzE8FMvbk5Hr18WdkYn1swUEtr0PUdaTf/4EKcu/wF3MjDtIBuJQC//LtgbR0NDQ0NDwNtVCnJkLas6KRs7Kk9KMDK6NTtRxOfUiDmfFvSsLUprZzqq4KyGZBQK7qs0LKauLjzqVRpMmdRgypBtGowELmTRNXEv/JVmoqT3vYarfisAeDwKQsmYe9stF/ThlGV55RaZ3H9Uy6pu5Dn74oXxCVmraGMv0ieiCAnGev4jzh/UE3jUQQRDJObyT1PXf39KRaQZ8UP0xAU5X/sVpaGhoaGh4mGohzkqrOatYvVllU5qecQUI7mtElAUy99uxXS6/zBNFkV69OnD33Woas7FyhkHKBu5ZJqKgq9KZykIOjSTk/qdVM/MdK8k7sa/ImqAgmDJVT7NmIvn5Cu+962DnzvK9PkPXLpjHvYpgMGA7cAhxy29Y7lJ90tLjlpH189pbrohAHZUhoo7K8PxgXQ0NDQ2N0tm1ayczZ36A2+1m8OChjBr1RJE1cXFb+Pzz2QiCgCRJvPLK67Rte0ep+2ZmZjJ+/FiuXLlMjRo1effdGQXm6YVZsOA7li9fhiAINGzYiIkTp2AwGNi4cQNffvk558//zrx539O8eQsA9uzZzWeffYLD4UCWZV544WXat+/gmTejGKrHENpSas7K4w5QFWeARnqRunodaU43+/Mqn9LU+QoERV8zOI8pf9QsIMCXxx8fyN13t8LlctM8cxt/EVbxxc9uDid6V5jpfM2EDX8e0WAk9/geMnfGFFnTuLHA7DkGmjUTuXpV4YUX7OUWZj5/GYFl8ngEg4H81bHo9ybi3+oeFKeD5BVfFiPM6gMDACNwGSjdjUBDQ0NDw/O4XC4++OBdPv54FgsXLmXDhnWcO3e2yLr27TuyYMFCFixYyIQJk5k+fUqZe8+fP5f27TuydOkq2rfvyPz5c4usSUpK5Kef/o9vv13ATz8twe12sWHDOgAaNGjIBx/8mzvuaHfTNRZLIP/+98f8+ONiJk2axqRJb1by1ZeP6iHOypPWzCklclYFZ4DrjQAbc5xUpdosqJcBnUkg+4gD6/ny7dSoUS3+/vcHiIoKJzMzh5y9S3nQfICLmQKTt+qrcJqyESSZ0AeeRQoIwppwlpQ184usiY4W+fAjPSEhAocPu3lujI3fz5WjI1OSCHj9RfyfHgVA7tff45vmj6l+S1x52ST+OJO8k/tvuag10At1htkxIBaq9B3R0NDQ0KgMx44dpVatWkRFRSHLMr179yUubmuRdT4+PgWG6Pn5+aWao18nLm4rAweq3foDBw5i69Ytxa5zOl3YbDacTidWq5XQUHW4fb169albt26R9U2aNCU0VB3c26BBA+x2O3Z7xcucyku1SmsW1xBQVlpTlvREBEbicrtISI2v8HP3uZ7SzK58SlM0QnDv8kfNRFGgR4/2dOnSBoBTp+LZHbuZX59Sp/0/v9ZArsObdWYCwQMex1CzHs6MFJKXzgbXjS5VQYBRj0s8+qj63qxZ7eSTT5w4y9HIKvj7YZn8Jvo7WqNYreR+8g2W+vci+QfiSL1K0uJPcWYUbu0UgM5Ai2u3d6POMdPQ0NDQ8H3sYa/sm/v9jyU+lpycRHh4RMHt8PBwjh4t/vfyli2bmTXrE9LT0/jww08L7n/xxTG89dakAsF0nbS01AIT9ZCQUNLT07iVsLBwHnvsbwwadB8Gg5GOHTvRqVPncr+2zZs30rhxU/R67wU5qoU4MxcyPr+VstwBooLrIIoil1LjcbgqJrAa6EUaGnRkuhT2ViGlGdjdiOQnknvaQd5vpSsYf38fhg3rSZ06EbjdbjZv3s/Onb/y4zArAQZYcVLHyt+8+2233Hs/vk3vUs3Ml3x6k5m5yQTjxsl07qLD5VKYM9tZPuNyQFezBpZ3JyPVisKVmob10+8JunMAot6INf43kpfNwW29tSMzEHVUhgt1VEbRZgQNDQ0NjT+O4mwjS4qKRUf3IDq6B7/8coDPP5/N7NlfAPDxx7Mq/fxZWVls27aVFStW4+/vzxtvvM6aNavp339AmdeePXuGTz/9mM8+m1Pp5y8P//PizFdvRNZJ5Nqt2F03CxuTXodJr8PqcJFnL14gVKXerPe1qNmmHAeVnW4myBDS93rUrPS5ZvXrR/LAA9H4+prIysplyZLNxMdfpW8DJw+1dJJrhxfWGSp5kvLh26IT5s4DUNwukld8gSPlSsFjERHqYNl69UWysxWmTXWUy7gcQG7VAsvUtxDNATjOnsP5/QaCOg5FEEVyjuwidd13t3RkXicNVZTlAcX7pmpoaGhUV0qLcHmLsLBwEhNv/D5OTEwsiHaVRLt2d5KQcJGMjHQslsAS1wUFBZOSkkxISCgpKckEBgYVWbN378/UrBlZ8Fh0dE8OHz5UpjhLTExk7NhXmDJlGlFRtUpdW1X+52vOCjo1KzlGoyrDZ/v4XfPSrMLgWcs9BuRAkfx4Jzm/Fh+5EwSB6Og7eeyxfvj6mjhz5hJffLGU+PirGCWF2QPUqOCkrXriM733LTdENSS4398ASNv4I9bfjx+2Qg8AACAASURBVBc81rq1wKzZeurVF7lwwc1zY+zlFmbGXtEEzngb0RyAbfc+WPwzgZ0GqR2g21eQumZeMaMyIgvdPocmzDQ0NDRuD5o3b0F8fDwJCQk4HA5iY9fTtWu3IusuXowviLKdPHkCh8OB2Wwpde+uXbsRE7MKgJiYVXTr1r3ImoiIGhw5chirNR9FUdi3bw/16tUvdd/s7Cxefvl5xox5gTZtSu8Y9QT/85GzUsdo+FwXZ2V3al6soDirI4s0NerIcinszqukOBMhtP+1qNmq4qNmvr4mhg3rQb16NXG73WzZcoDt2w8WPP7mvXbqByocThT5eI9cuXOUA8kSSujQZxF0Eln7N5FzMK7gsQEDdTz/vIQkCezZ4+Kdtx3kltNT3HfUY/j97S8A5C2NwXhFh0/bbihOB6lr55N7fO8tV4QDfVEL/1egRs40NDQ0NG4XJEli7Ng3eOGF0bhcbu6/fzANGjQEYMmSRQAMGzaCzZs3sXr1KiRJwmg08s47HxSkP0uqORs58gnGjRvLypXLCA+vwXvvzQDUOrfp06fw8cezaNmyFT179uKxx/6CTqejSZOmDB06DFBr3P71r/dIT0/n5Zefp3HjJnz66RwWLvyJixfj+frrL/n66y8B/p+9846Oqtrb8HPOtEwy6ZMGCQREpAgCiiggoUiTAAJivQofei9KsYuC0hRpil6kqoii1wYCCkGaAkFAEUFRuqKEnt4mmX7O98dJIWQmCe1ejftZi2VmZu8955ywFq+/9jJ37kIiIipH5i4Hkq/c758ZSTKqUHX481w61L+WT+5+gR1p+7nrk5cqfJZ0TTR9r6vL1iMZrPrpVKW9Rr2JV4e9haooPPXuv/BcQM3ZPyOMPBEVwBf5LsaevTibpdCbjSQMt+A86+XXsfmVfLgTE+swaFAXLJZAbLZili/fxLFj5WnEJlaFvQ8XY9RB+3fMfHvyyozOkE2BxN7/HIbIWIqP/kLm8rmgquh08MgIPbffrv0/wNJPPSxa5EGpScDMYCBkzOOYu3VG9XopevN9goOaYIqth9duI3PFfJwnfztvUwOgC9r/c6QBX8NFJ5QFAoGgdrJ27XtYrdHVLxRcMFlZGfTuPfS8d0/vVlX1hgs5p9ZHzi7FHSDeWh9ZkjmRc/yChBmUuwJcdEpTgqjkkqjZGnsFYSZJErfc0oqkpDbIsswff5xm+fJNFBWd28mpsrCPA6MO3tytv2LCDFmH9fbhGCJjcWWcJGvVW6CqhITAC+MNtGmjw+VSef11Nxs31CyNKYWFEvbSeIzNm6IUF1P02tuEN+mmdWTmpGsdmbkZ5+1qAdyE1p15ANhOJTUrEAgEAsFfgFovzmpSc5brR5xdrDNAvEGieYCOIkVl+0WmNINbGwioq8ed7SV/R/n1BQYGMHBgF666Kh5VVUlN3UNq6p5K3S8PXOchKVEho0hi7FdXrgkgovs9mBOb4rXlk7F8LqrLSb36WuF/3boyOTkqEye6OHigZkJJVz+BsJcnoa8Tizc9A/uc/xB5Y39kUwCOE7+SuWI+SoWuWwlNlLUoeb0T2Ht5b1IgEAgEgv8ifwNxVhI5s/sfo+Gv5uxiOzVLGwE22zy4LjJ4E5VsBiBzrQO1pNa9Xr1YBg3qSkhIEEVFdlas2Mzvv1dOx0aYVV7trt3TUxuM5DquzEyz4La3EtyqE4rbRcaK+XgLcmh3k8y4cQaCgiSOHFGYOMFFZmb1ZwEY27QidNJYZIsF96EjeD7cQGSHwVpH5v7vyF77foV5aRqhQFO0URlbgMpTpgUCgUAg+CtR+8VZVdZNgVWnNS+2U7M8pXlxg2eDmusJbKjHU6CQu1UTWR06XEfXrjcgyzJpaWdYvnwThYXnz/TSmHGrk6gg2PSHjv/8fGV+xeZG1xHe5Q4Asr98F9eZP7jrbh0PPqhHliW2bPbyyitunP57LSqe16cnwY+PRNLpcKRuR97xO+EdBwCQt201+dtX+9mZh1Zb5qLcyFwgEAgEgr8utV+c+XEHCA7QY9DLFDk9OD2Va6ECDAFEh8Xh8Xo4k3Oyxt9XRy/R0qyjWFHZVnRxKc3SqFnWegcBOhO339OZxo3rAbBt209s2vSDzyF+AB0SvDzUxoPTA4+sMaGl/S4vhuh4rH0fRJJk8rZ+jvvobp59zkD37lpd27uL3Xz4YQ2H7soyln8OJegurVOm6OPlmLMDCGzTFdXrIXvt+xTt/+68TSFoEbMTJa/TLsdtCQQCgUDwp6DWizN/DQHVpTTjrYnIkszJnGN4lJqLrFtLomZbbR4cF5HSNDfSY2lqwFusEHgolH8M70poqIXiYgcrV27ht99O+N1rkFUWJmv3M2O7gSPZl3+mmc5SYmZuDMC271t0h7/ktdeNNG0qY7erTJ/mrrFxOQEmQsc9TUDH9qgeD7Y57xASfi2mqxPxOorIXLEA54kj522KRhuVYQBWAzXMmQoEAoFA8Beh1oszf3POquvUvNiUZs8SV4D1F5nSjEoOABXif2pIu3tao9PJnDiRzmeffU1BQdXDwZ642c210Qq/5UhM/ebye35JBiNRA0eiDw7HceJXIo9+wIsLTFitEmfPqowf76qZcTkgR0YQ9vJEDI0boRTasL26kIhre6IPicCdm6l1ZOacPzg2EeiK9tf2OFpKUyAQCAR/NXbs2M6sWTNRFIX+/QcwdOgwv2v379/HsGEPMHXqDLp1617lufn5+YwbN4YzZ04TF1eHadNeISQkpMKaY8eOMW7cmLLXp0+f4l//eoR77/0Hhw8fYvr0l3E6nej1ep59dizNm7fg9OlT3HnnQOrVqw9AixYtGTv2hUt4AlVT+8WZn5qzasVZWTPAsRp/V4xeorVZj11R+eYiUpoBCToiWwRx/dkbqBtXB4AdO37m66+/R1GqFj31QxUmJmn3MmKNCaf3cqczJSL7DMMUl4g7N5Pm2fN46jUdJpPEzz8rTJ7kIj+/Zifpr2pI2NSJ6KKseE6fwf7GB1jbD0Q2mXGcOkrm8nkolYYGN0czMJeAg8A2xKgMgUAg+Ovh9XqZOXMac+cuJCYmhiFD7qNTpyQaNrzK59q5c2dz00031+jsJUsW07ZtO4YOHcZ77y1myZLFjB79eIU1iYmJfPTR0rLzb7utB126dAVgzpx/89BDw+nQoSPbt3/DG2/8mzfffAeAunXjy/Zdaf4+9k2VImeXv1Oze0mX5jdFHoovQjc06V+Hbqe7UddRB7vdySefbGDjxp3VCjNQmXubk0ADfPyLno2/X37NHZZ0O0HXtEF12OhtmMm4MQomk8SXazyMeabmwsx4U1vC35iJLsqK65f9OOd8ijXpbmSTmaKDu0j/eJYPYXYT0AFNmO0CvkEIM4FAIPhrsn//PhISEoiPj8dgMNC9e09SU7f4XPvppx/TpUs3nx6ZvkhN3UJycl8AkpP7smXL5irX79q1k/j4eOJKAiKSJFFUYmFjs9mIiqr50PvLSe2PnJXUnOU7K6YEI0siZ9m2ypGzAKOZ6LA43B4XZ3Jr3gzQoySleTFdmu27XUu3yHbIHpnT6Zks++Rr8vIKa7R3QBMvyY295DngifWXP50Z1KI9oTf1xiQV8UDCFG5q7cTrVVkw38Pnn9ew8B8wD+xH8CMPIel02DdsRv4+jcjOgwHI3/Eled98QWXRFYI2KkMBUoFfL89NCQQCgYCdi7KvyLntHor0+1lmZgYxMbFlr2NiYti375dK6zIy0tmyZTMLFrzFgQP7K3zmz74pJye7zETdao0iN7dqC78NG9bTs2fvstdPPvkMo0ePYPbs11BVhXfeWVL22enTp7jvvrsICrLwyCMjad26TZVnXwq1WpwZdXoCjQG4vR6KXBUtlKpKayaU1JudyjmBV6mZ+LDqJNqYdTgVldQLSGmaTAb69etEs2aa6eq+wkN8/vZ2vN6aFdVbjCpv9Naif+O+NpFedHmDoaaExkT2vB+rOZsRjWZSP85OYaHKSy+6a2xcjiwTPGo4gbcnA2B77yPMBRaC2nZH9XrJXv8fin7Z7mdzAbARTZydvgx3JBAIBIL/Jb6mDZR6Zp7La6+9wujRj6HTVXa4mT173iVfh9vtZuvWVEaOfLTsveXLl/Hkk0/TteutbNy4npdemsz8+W9itUaxevU6wsLCOHjwAE8//QSffroci8Vyydfhi1otzvx1asoShAYaUVSV3OLK4uxiUpq3BuuRJYntNjdFNdQssbGRDB7cjYiIUNySmx8if2DLwgM1FmYAL3ZxER+i8v0pmTd3X95fpz48mqgBD9PE+jsPN19ISKCbtDSFCePdnDpVs7SiFGgmdPxzmNrdgOpyY3ttASExbTBd0wDFUUzm5wtxpB06b1cwEAkcK3ld8+ilQCAQCGpOVRGuK0V0dAzp6eUNX+np6WXRrnM5ePAAzz//LAB5eXns2LENnU5H585d/Z4dERFJVlYmVmsUWVmZVaZDd+zYRpMmTYiMLH8GKSmreeoprVng1lt78PLLLwJgNBoxGrWgTtOmzYiPj+f48TSaNWt+AXdec2q1OCu3bqqY0gwLNKKTJfKKXXh91HPVs164OCt1BVhvq1nU7Prrm9Kr183o9TqyHDn80Oh7Tu3JwZVec2HWKtbLoze68SowPMWEol6+JgA5IJDoO0bT9eq93HvNUvQ6lZ07vUx92U1R1U2j5WfERBH28iQMDRNR8vKxzZhHROs+6EOtePKyyPhsDu7s8wfHRqGNyjABKUD6ZbsngUAgEPzvadasOcePH+fUqVNER0eXRKimVlr3xRdflv08adJ4brmlU5XCDKBTpyRSUlYzdOgwUlJWk5TU2e/a9evX0aNHrwrvRUVFsWfPD1x/fVt27fqehARtxmhubg4hIaHodDpOnjzJiRPHqVs3/gLu+sL4e4iz8wrMw6tzBiiJnJ2oYadmhE6ibaAOt6qyxVZ1vZnRaCA5uSMtWjQCYPfegxzrdRAMKlkpjir3nossqbyZ7EQnw+vfGfjp7GU0Npd1xA74F0Pab6VbvW8AWPqph0WLPCg11I76JtcQNmU8uohwPGknsM95H+stdyIHBOI8/TsZy+ehFJ9fU1cf6Ib21/IkUHWtgEAgEAj+euj1esaMeY5HH30Er1ehX7/+XHWV9m/i8uXLABg0aHCVZ/irORsyZBhjx45h1aqVxMTEMX36K4BW5zZlyuSydKjDYef7779j3LiK4zCef34Cs2bNxOv1YjQaGTduPAA//riHhQvno9frkWWZ5557gdDQ0Et/GH6o3eLM7NsdIMLiX5yZjYFEhcbg8rg4k1fZt9IX3Sx6dJLENpubwirES3R0BIMHd8NqDcPlcrN69TdkXHOa6AAzBT+5cJyoeXH98Os93FhX4WSBxITNl7cJoH7/O3h6wCaaRh7B5YbXX3OxcUPNI3qmTh0IHfsUksmEc89PeD7dhLXr/Ug6HUWHdpO9ZjGq53wR2wxtVIYMHAa2IjoyBQKBoHbSocMtdOhwS6X3/YmySZNeqvDaX81ZWFgYCxa8Ven9qKjoCnsCAsx89VVqpXWtWrXmgw8+rvR+16630rXrrT6/80pwRcWZJEm9gNmADlikqur08z6/D3i25KUNeERV1b2X6/vL05q+x2jk+hijkRCVCMCp7DSUGjYDlHtp+k9ptm59Db17t8dg0JOensOyZV+RW1zANSM05Z25uuZRs5gghWndtGt/dK0Jm+vypTObJXfk+WHbiQnMIidPZuILdg4erLlICrxnMMH/HApA8Zp16HafIrLrXQDkf7eWvNTPqSy6bgRalfz8A7DnEu9CIBAIBIK/LldMnEmSpAPmAd3RclS7JElaparqgXOW/QEkqaqaK0lSb+AtoN3lugb/1k3+I2fl9WbHavYdskS7kpTmJh8pTYNBT58+HbnuuqsB+PHHw3z55XY8Hi/WPgHoAmVsB93Yj9a8w/P1ni5CAyDliI6Vhy5fOrPTwEY8M/wXzHonvx0PYPyYPDJr6o6k1xPyxEjMvXugKgq2RUsItEcQ1K4XquIlZ8OH2PZu87HRQvmojK3A+XZNAoFAIBD8vbiSkbMbgd9UVf0dQJKkT4D+QJk4U1V1xznrvwMua3VdmTvABVg3XWinZjeLHr0ksa3IQ/55mT+rNYzBg28lOjoct9vDmjXb2LtXm9MlGcHaMwCAzBR7je+pe0MP97TwUOyGUV9ePmPzex+KZOjdp5Alle0/RzH1uRM4fc/nrYQUbCFs0vMYW7dEdTgofGUuoXVvxNTkKhSnXevIPHbQz24bsAEtnVmzNLJAIBAIBLWZKynO6gLnunSfpOqo2IPAWl8fSJL0L+Bf2quaR4rCyiJn59WcVeEOcKHizN/g2ZYtG9GnT0eMRgOZmbksW/Y1mZm5ZZ+HdzKhD5Ep/t1D0f6aRc0C9Crz+2jXPDnVSFr+pc80MxjgqWcDubWL9ow+3XYtb0/8ocb7dXXrEDZ1IvqEeLzZOdimzyHihv4YwqLw5GdrHZlZ588ns6AZmP9e8vr8jk2BQCAQCP6+XElx5iuk47N4SZKkLmjirKOvz1VVfQst5YkkGWtcAFVac5Z/TlpTr5MINRvwKip59oqCKshkwRoSjcvtJD2v+oGnITLcHKTHq6p8XTJCQ6/X0bt3e9q0aQLA3r2/smbNNtzucgEm6cDa+8KjZmM7umgUobIvQ+a1bw013uePyEiY9KKJpk0UHB4jCzZ3Y83MVTXeb2jRnLAXX0AODcF99HfNI7PrPegCgnCeOUbG8rkoRQXn7bICvYAAwIEYLCsQCAQCQUWupDg7CSSc8zoeH/8SS5LUElgE9FZV9bL6SJSbnpdHzkrHaOQWuzh/SHGpM8CJ7DQUtfruxM4WAwZJ4rsiD7lelYiIUAYP7kZsbCRut4e1a3fw44+HK+0LbW/EGKnDccpD4Y81s3q6JlLhuY7a2odTTHiUS0tnNm4s8eJLRqxWiSx7BK9vGciuue9CDZsgArp3IeTpx5AMBpzf7cLz2Waiev4fkk5H8eE9ZKUsRvWcnzZOAG4FDGh/PWpa0CYQCAQCwd+HKynOdgFXS5LUAK2Y6G7g3nMXSJJUD1gB3K+q6mWvBA81laQ1z6k5iyxLaV56vVlpSnO9zU3z5g3p2/cWTCYj2dn5LF36FRkZPuZ0SRDVxwxAZoqjhtMitHSmUQeL9ujZfuLSmgC6dJF5+hkDJpPEkdyrmLPzbn5dPBfFUbPpskFD/4HlgXsAKF7+BfLP6UR2vw+A/O83kLdlOZWUL03QAqMyWtH/VrQmAIFAIBD83dixYzuzZs1EURT69x/A0KHDKq3ZvXsXTz31BHXqaKbkXbp045//HF7lufn5+YwbN4YzZ04TF1eHadNeISQkpMKaY8eOMW7cmLLXp0+f4l//eoR77/0HCxbMY+vWLUiSREREBBMnvkhUVDR5eXk899zTHDiwn+TkfowZM/YyPAX/XDFxpqqqR5KkUcB6tEKxxaqq7pck6eGSzxcCE9B8euaX+Gp5VFW94XJdQ3nkrFyclTcD+K83O1EDcRYkQ8dAPW5Jwt3hRu64oRkAv/zyGykp23C5fEfEQm4wYorV4cr0kr/T9xDc8/lHSw9dG3jJLIJnvzLVaI8vJAmG/p+e++7Tfu2pJ9vzn30DOP3pXDw5NZjEbzAQ+uwTBHRNQvV6KZy/iCBPFEHt+2gdmRs/xvbTVh8b2wKtS37eXfJHIBAIBH9HvF4vM2dOY+7chcTExDBkyH106pREw4ZXVVrbunVrXn99To3PXrJkMW3btmPo0GG8995ilixZzOjRj1dYk5iYyEcfLS27lttu60GXLprzwP33D+GRR0YC8MknH7Fo0VuMHfsCJpOJhx8eydGjv3H06G8Xe+s15orOOVNV9Uvgy/PeW3jOzw8BD12p7/flEFCl4XnJjLOaRM46B+lxhVjYeFN7Wlgj8Hi8rF//LT/84K8rUSOqb0mt2ZeOGgWOwgNUZvXQrvXpjSZy7BeXzjSbYexYA+076PB64ZPDA9h0sjPZ69/Hebxy6vV8pLBQwl4aj7F5U5TiYgqm/5uw+h0IiG+E4nSQ+cWbOP7Y72NnIOWjMr5BGzArEAgEgr8r+/fvIyEhgfh4bUBD9+49SU3d4lOcXSipqVt4881FACQn92X48IcqibNz2bVrJ/Hx8cTFadG5c43M7XZ7mSG72WymVavWnDhx/JKvsSbUWocAWZIICQgEoMBZnq4rE2e2iuLMEhBMZHAUTreD9Pzquwc7t2jIhqSbcRuN5OQUsGzZV5w9W3XJnKWlAXM9Pe48hbxtNZtTMe1WJ9FBKluOyby/9+J+XbGxEi+9ZKBBQ5lCm8SCn4dx2HYd+TvXY/t5e7X7dfUTCJ86CV1cLN70DGzT5hB500AM4dF4CnLI+Gwu7kx/5uTFwDo0r8wTftYIBAKB4H9B6ivPX5Fzk5552e9nmZkZxMTElr2OiYlh375ffK795ZefuffeO7Fao3jssSfKbJ782Tfl5GSXmahbrVHk5lZtA7hhw3p69uxd4b358+ewZk0KFouFhQvfrnL/leLSZzH8SQkxBSJLMvmOIpRz6p/8jdEojZqdyEpDrVQvVY4syyT3uhlb9yTcRiNHD/7BW2+tqFaYQXnULGudA7UGfQA3x3sZfr0HlxceWRPAxcw0u+46mXnzjTRoKHP8BLyY+giHbddRfORH8rasqHa/8fpWRMyZhS4uFvehwxS9OAdr5/sxhEfjPHucsx9M8yHMgoCrz3mdgRBmAoFAIAB8/htbGqE6l2uuacqqVWv56KOl3HXX3TzzzBNln82ePa+SMLtQ3G43W7em0q1b9wrvjxgxmjVr1tOr120sXfrJJX3HxVJrI2ehfq2bfKc1y50B/Kc0Q0MtDB7cjbp1o5G9XiJ2/cjk9T/W6HoCr9ETdLUBj00hd3P1Vk16WWVhsiYgZ243cCjrwnV0crKOUaP16PUSO79XePvQcNTYJjjPppGV8g7VdSOY+/Qi+PERSDodjtRteD7fSlSfh5B0eop/3UvW6kWo7vMjgJFoozIC0UZlCFEmEAgEf1aqinBdKaKjY0hPP1v2Oj09vSzadS7nphg7dLiFGTOmkpeXS1hYuN+zIyIiycrKxGqNIisrk/DwCL9rd+zYRpMmTYiMjPT5ea9evXn88dEMHz6iJrd1Wam1kbPSAbT559SbBRhkAk16XB4vNmfFwa9lzQBZvsVZ48b1GD58IHXrRiMV2uj69Sa+/95XjZVvopK1Ds3sjQ6UGmQ0H2vnpmWMwtEciZe/uTBjc50ORj+q5/EnDOj1Eks/9fD6trtRY1vgKcwlc/k8VHcVzQiyjOXhBwl5arRmVv7RUvhqH9ZeQ5B0egp++IrMlfN9CLN4oC9a5OwMUIMmA4FAIBD8rWjWrDnHjx/n1KlTuN1uNm5cT6dOSZXWZWVllUXZ9u//BUVRCQ0Nq/LsTp2SSElZDUBKymqSkjr7Xbt+/Tp69OhV4b3jx9PKft66NZXExAY1va3LSq2NnPmecXbhYzRkWaJbtxtp374lAL8dTmPkL7sJ87j5qrBmM8oCEnUEtzDgdajkfFW9MqsXqjC5s3aNI7804fDUPJ0ZEgLjJxho3VqHy6Xy+utudhb0IDypI4rLSebyeXhteVVcrInQcc8Q0PFmVI+HgtfnEyTXxdKxL6qikPv1pxTu2exj4zXALWh6/zdgC2JUhkAgEAjOR6/XM2bMczz66CN4vQr9+vUvqyVbvnwZAIMGDWbTpq/47LOl6PV6TCYTL788vSz96a/mbMiQYYwdO4ZVq1YSExPH9OmvAFqd25Qpk5k9ex4ADoed77//jnHjXqiwf+7cN0hLO4Ysy8TGxjF2bHlNXr9+vSkqKsLtdpOaupk5cxZcliYGn8/oipz6J6DcHaByM0DueeIs2BxCuCUSh8tORl55qDUkJIg77uhGQkIMiqLw1VffY/nlIGF1A9lr93DGUzOzgtKoWc5mB96i6ve80ctJkBE+3adn/dGa/4rq1ZeYMsVAnToyOTkqEye4SFNaE3X7QFRVISvlHVzp/jtNZGskYVMmYGjcCKXQRv6UVwlvlERAvcYoLgdZq97GftRX0eb1JX8AfkQbcScQCAQCgW86dLiFDh1uqfT+oEGDy36+8867ufPOu33uLxVZ5xMWFsaCBW9Vej8qKrrCnoAAM199lVpp3cyZs/xe86pVPh0mrwi1XpxVmHFm8V1vlmAtTWmmoZbUYTVqFM+AAV0IDAwgP9/GZ599zcmTGbwSpwmtDYU188M01ZEJvcGI4lbJXld9rVm/azz0b+KlwAlPrK95OrPdTTLjxhkICpI4ckRhwngXBfr6xNyrDfbL27IC+68/+d2vv6ohYVMnoouy4jl1msKpb2DtMBhDZCyewlytIzPDV/1YAOWjMrYDVY8SEQgEAoFAUDW1V5yZaz7jrDyl+TuSJNG16w107NgKgF9/Pc7KlVuw252YJOhsKTE6t9UspWktcQPI3erEk1911CzIoDKnt5b2fH6TkTO2mpUE3nW3jgcf1CPLEps3e3n1FTceYwSxd41CNhgp3PsNBd9v8LvfePONhL4wBtlsxvXLforn/Yfonv+HLtCCK/0EGcvn4C30lwp1oPnVBwH/nfkvAoFAIBDUZmqtOAstaQg4N3JWat2Ufd4YjXolYzTSbSd44IE+JCbGoSgKmzf/wLZte8vWdQjSEyRL7HN4OeWuPj1psMqE3WRE9apkra0+ajaps4t6oSo/nJaZv6t6Y3ODAZ58ykD37pqd07uL3Xz4oRfJaCL2jpHoLaE40g6Rs+Ejv2cEDuqP5ZGHkGQZ+8bNeFZvI6bvcCS9geKjv5C16i1U1/l1ckFoxf+lA2WzS/4IBAKBQCC4VGqtOPOZ1qwicuYy5tKxd0PMgSYKC4v47LNNHD9+tsK6HqVRsxo2Alh7ByDpJHK3O3FnVV0c8l7x5AAAIABJREFU3zLGy+M3ufEqMDzFhKJW3QQQGQmTJhtp2lTGbleZPs3N9u0KSBLWvv/EGJ2AO/ssmSsX+jYzl2WCRw0n8PZkAGzv/gfdb/lE9dHSoAW7N5P79adQyQA+Am1UhgVwAsdq8igEAoFAIBDUkFoszkpGafhoCDhXnIUGhWGIKSA/6DhmycTRoydZsWIzxcUVI10GCbpYtGjWxhrUm+lDJcI7aZG6rDX2KtdKqCzs40Qvwxs7Dew5U7Wx+TXXSEx+0YjVKnH2rMr48S7++F2L5IV3GUxgo5Z47TYyPpuD4iyu/H2BZkInjMV04/WoLjcFr8wmyFQPS6fbUVWF3K+XUrh7k49vrgt0B4xoozKqd1IQCAQCgUBwYdRacRZ6Xs2ZxaTHqNdR7PLgcGuRpKAgM/fe25Niy3FUFVK37Gbr1h99Ti++OVBPsE7ikMNLmrv6ERGRPQOQDRL5P7hwnq56/T+v93BzgsLpQokXNlXdBNC1q8xTTxswmSR+/llh8iQX+fnaZ5ZWnQhpeyuq10PmigV48jIr7Zdjogh7eRKGhokoefnkvziT8CZdCajfBMXlJGv1Iuy/7a20DxoDndBGZRxFG5XhIyInEAgEAoHgkqi14uz8tOb5UbPExDgGDuxKcHAgktfA0T35pKbu8Xtez2DtUa2vQUpTFyQR0aXE4Dyl6qhZdJDC9G5aTddj60wUunynMyUJ/m+Ynnvv1a5jTYqHOXM8eEqCeAGJTYnofg8A2es+wHny10pn6JtcQ9iU8egiwvGknaBw6mysSXdjiIzDY8sn87M5fkZttAFuKPl5L7CzynsSCAQCgaAqduzYzqxZM1EUhf79BzB06DCf63bv3sWsWa/g8XgICwvnrbfeqfLc/Px8xo0bw5kzp4mLq8O0aa8QEhJSaV1hYQFTprzI0aO/IUkS48dPomXL63jrrQV8/vmKMheCkSNH+xz5caWpteKsvCFAS2ueK85uuaU1nTu3QZZlXAU6Yh1tOHDYv7mpAehaktLcYKs+pRlxqwmdWaLwFzeOY1VHl2b1cBFuhrW/6vjsgO90ptkMY8caaN9Bh9ersmC+h88/Lz9XHxlLVP/hSLKO/G/XUrTv20pnmJI6Evrck0gmE87dP2Ff+CHRvR9EFxSCK+MkGZ/NxVvoyyDWBDRBG5WxAzhQ7f0LBAKBQOAPr9fLzJnTmDt3ITExMQwZch+dOiVVGuhaWFjAjBnTeOONecTGxpGTU7WJOcCSJYtp27YdQ4cO4733FrNkyWJGj3680rpZs2Zy883tmTHjVdxuNw5HeSDlnnv+wf33D7n0G70EarF9U+kQ2tLImQlFpyei5XV07XoDsiyzdeuPBGU2Q6eYqvTUvDFQR6hO4lenlz9cVaco5QCI7F6zqFnXBh7+0dKD3a05AfgyNo+NlXjjDSPtO+goKFAZ+5y7gjCTzRai7xiNHBBI0eHd5G39vNIZgfcMJmziWCSTieI163C9+zkxt49EFxSC/fd9nP1wph9hBlrR/1pgI0KYCQQCgeBS2b9/HwkJCcTHx2MwGOjevSepqVsqrVu3bi1dunQlNjYOgIgI/z6ZpaSmbiE5uS8Aycl92bKlsqONzWbjxx/30L//AAAMBgPBwZWja/9LamXkzGwwYdIbcLhdODxaGjI+PpriBs0INRgpLnawYsVmss4UMej+cIqdRWQVZPg9r0dwSdSsBo0A4Z0D0Ftkin51U3zY/3qTTmVBHy2d+dJWI3/kVdbJ110nM2GigdBQibQ0hQnj3Zw6dU49nE5P1MARGMKicJ45RnbKu1QwM9frCXlyFOZe3VEVBdtb76JPKyaq7z8BKPwxlZyNH/voyAwE6lM+UDa35I9AIBAIahOxnxVckXPP3uFf7GRmZhATE1v2OiYmhn37KrvPHD+ehsfjYfjwBykuLubuu++lTx9NePmzb8rJyS4zUbdao8jNrRx4OHXqJGFh4UyePIFffz1C06bNeOqpMZjN2lzSZcs+4csvU2jatBmPP/6Uz7TolaZWRs7Orzdr374liTffiGowknkmk4ULV3D06Mmy+WZVRc10wK2WmtWbSQaw9iyNmlU91+zZjm4aR6ocyJR4dUflmWbJyTpmzNSE2c6dXh4d7aoozIDIXg8QEN8IT0GOZmbuKe9ClYIthM98SRNmDgf5k6cRUBBEeGfNyiln0zJyNnzoQ5iFA7ej+WQ2qvIeBAKBQCC4UHw13ZV6Zp6L1+vl0KGD/Pvfc5kzZz7vvPMWaWmaMfns2fMqCbOa4vV6OXz4EHfccScffvgpAQEBvPfeYgAGDbqTlStT+PDDT7Farfz73/7tnK4ktTJyVjpGI9OVzz339KRx43oAGLPOsOLTrygs1ISTP7Pzc2kbqCNcL/O708tv1aQ0wzqYMITL2I97sO31L+QaRSiM66gJqYdTAnAr5X8pdToYMVJP//7ar2bppx4WLfKgnPfVoe37YLn2JhSXQ6sXK8ovP6NuHcKmTkKfUBdvVjb5k2cQfm0PzIlNUdwurSPTp5VTHbRRGSbgLODLrkkgEAgEtYWqIlxXiujoGNLTy+eIpqenl0W7zl8XFhaG2WzGbDbTuvX1/PrrYerXr+/37IiISLKyMrFao8jKyiQ8vHIqNDo6hujoaK69tgUA3bp1Z8kSTZxFRkaWrbv99oE88cSjF32fl0KtjJyFBljIkPLZE51G48b1sNsdGNOOYMo8RZatfNp9vaiGAJzIOub3rJ4lKc311aU0ZYi6rSRqtrqqqJmWzjTp4d0f9XxzvLwJICQEps8w0L+/HpdLZcYMF2+9VVmYBTa5gbBb+mtm5qsW4c48WfaZoWVzIubOQp9QF/dvv5P/7ItYbxqMObEp3qIC0j9+1Y8wawT0RhNmvwNr0OrNBAKBQCC4fDRr1pzjx49z6tQp3G43Gzeup1OnpErrkpI68+OPP+LxeHA47Ozb9wuJiQ2rPLtTpyRSUlYDkJKymqSkzpXWWK1WYmJiOXbsGAC7du2kQQPt3Kys8hFUW7Zs4qqr/jcZpFoZOWvSKpHVxt2oksrJk+msT9nK40kNKLC78XjLw6kJ1kTAf+RMpjylWZ2XZmg7I8ZoHc6zXgp+cPldd8+1Hm5t6CW7GJ7ZaCp7v359iZemGKhTRyYnR2XiBBcHD1YO/RrrNMDa5/8AyN30GfajP5d9FtC9KyFPP4pkMOD89nvsb39CTJ/h6CyhuLJOk7FsDt4CXzZLrYAbS37+GfiuynsVCAQCgeBi0ev1jBnzHI8++gher0K/fv3LRNDy5csAGDRoMA0aNKR9+/bce++dSJJE//4DaNRIW+ev5mzIkGGMHTuGVatWEhMTx/TprwBanduUKZOZPXseAE8//SwTJozD7XZTt25dJkx4EYA33vg3R44cRpIk4uLqMG7cC/+VZ3I+kq/c758ZSTKqUDn8CWAyGenfvxNNm2rpSsNZmPD2OyRGmBnRtTHHsoqYu+kIAGFBEUz5x2yKHIU8u2SEz/PamnUsqRdEmstL7z+KfK7RLgoaTQkhoK6ek+/YyPvGtzgLC1A5NLKYGIvKsC9MvPuTFpVrd5PMuHEGgoIkjhxRmDDeRVZW5f26kAjiHhiHLihEK+bf8GHpQyFo6H1Y7tfmnBUt/xzvV7ux9hmGbDBiP3aAzM/fRHX66h41AAOBEOBbYJ//+xQIBALBX561a9/Dar24ei1B1WRlZdC799Dz3j29W1XVG3yt90etipzdf/9t1K0bheJW6KFex/oj36MoChElhuc5ReemNEvrzY75Pa+mXZrBrQ0E1NXjzvaSv8N/1GxqNycxFpVv0mTe+0l79HfdrePBB/XIssTmzV5efcWN00c2UTIGEH3HaG38xbED5Hz1ifaB0UjomMcJ6JqE6vVSOPdNDGdcRPT/F5IkU7j3G8343Je/JgButFEZ4UBalfcpEAgEAoHgylNrxJnJZKBu3Sg8Hg/e3U4SW0WXWTdFWCp7apaJsyzfKU0J6FFDV4CoZK39NnOtA9WPBmpX18vw6z24vfDwGhN6g8STTxno3l2rOVv8jpuPPvKzWZKJ6vdPjFF1cWefIfPzN0HxIoWFEvbSeIzNm6IUFZM/ZQaW0GsI7qLl7nO3LKdg53ofB5qBqyiPkhWU/BEIBAKBQPC/ptaIM71euxWHw4WVQMC/dRNU36nZ2qwjSi9z0qVwwOm/SzOomZ7Ahno8BQq5W30X0OsklYXJTmQJZnxrIF3R8drrRpo2lbHbVaZNc7Nju//vCO86GPNVLfAW28j4bC6q044usR7hL09EFxeLNz2D/AlTCW/VG3PDa1E9brJSFlN8eLeP08LQCv+D0aJmh/1+r0AgEAgEgv8+tUicaREoj8frw1fTR1rTWrU461HDRoCovlrULGu9A9VPRvPRdm5axSr8kSvxyRkT8+absFolzp5VGT/exR+/+6/7s7TuTMgN3VA9bjJXzMeTl4nx+laEThyHbAnCfegwhVPnYO1+P8boeLxFBWSsmI/r9O8+TosDeqB1ZKYj0pgCgUAgEPz5qNXiLN+HryZAuCUSizmYQnsBubbK3YsS0L0G9WbmRnosTQ14ixVyNvmOmsWHKLzYRfveOZlBTHvVhMkk8fPPCpMnucjP97kNgIAGzYm49W4Aste+j/PUb5iTexH82AgknQ5H6jbsi5cR3X8Eekso7uwzZCybgyffRzcBVwGd0cbq/gFsAqr2/RQIBAKBQPDfp3aKM3OJ6bndhk6WCDEbUBSVvGJNJNWvJqXZIkBHnEHmjFvhZ4d/AROVrM01y/7aiWL3Hf2a3ctJkBFSLEH0eUhbvybFw5w5HjxV9BkYrHWI6v8vJFkmb0cKRYd2YXn4QYLuHAhA0UdL8abuJeaOx5CNJhxph8hcuRDFWezjtOuAdiU/70PryvxrdekKBAKBQPB3odaIM4NBuxWPx1MhrRkeaESWJHKKnSgleqS64bM9SxoBNlTRCBCQoCOklRHFqZK9wffQ2eTGHvpdq7CncTDmKBNer8qC+Z4KxuW+kAODib5jFLLJTNHBXeTv2kDopHEEdLwZ1eOh4LW5GLIUogY8jCTJ2H7ZTva6//jpyNQDjUt+/hao7F8mEAgEAsF/kx07tjNr1kwURaF//wEMHTqs0poPPniPtWu/BDTLpWPH/mDDhs2Ehob6PTc/P59x48Zw5sxp4uLqMG3aKz69Mfv1601gYBCyLKPX63n//Y/KPvv0049ZuvQTdDodHTvewqOPPoHb7Wbq1Jc4ePAAsizz1FPPcP31bS/Dk/BNrRFnFSJnwSXizG4jLrxyM0B1w2fLRmjY/Ie2rH20KFhOqhNvYeUoVKBB5fVBbn5oFUpRkJ6CApUpL7nZs6dqCyhJbyB64Aj0oVacp38nd+cqImbPwHB1I5RCG3mTpmKJbEZIty4A5KaupOC7tVWc6EEblWEFjlX53QKBQCAQXGm8Xi8zZ05j7tyFxMTEMGTIfXTqlETDhldVWHf//UO5//6hAGzdmsrHH/+nSmEGsGTJYtq2bcfQocN4773FLFmymNGjH/e5duHCtwkLC6/w3g8/7CI1dQsff7wMo9FITo5mnL5y5XIAPvnkM3JycnjssZEsWfIhsnxljJZqjX1TqTjzehQsJjMexUuhy15Np2blovnmJpm6BpkMj8JPdt8RLmOMTOiNRhSPSvY631GzV+73cjpJE2bH0xRGj3JVK8wAInsPwVT3Kjz52eT89CXhc2ZiuLoRnlOnyX38OcKu6kTI9V20BoFVb/sRZma0VGYpNoQwEwgEAsGfgf3795GQkEB8fDwGg4Hu3XuSmrqlyj0bNqylR49e1Z6dmrqF5OS+ACQn92XLls0XdG3Lly9lyJD/w2jUtENEhObN+ccfv9O2bbuy9yyWYA4e3H9BZ18ItShyVnIrJUGsgrJmgIqdmpHBUQQFWCgozievKLfSOaVRs42FHr9VWdbbApBkidxvHLhzKguuf90r0eSeINyyxKGfPDw7wUNRFQYDpYR26EtQsxtRnA5y/9hK6PQJyGYzrp/3UThjLlE9h2KMScBbbCNzxTycp476OgVtVEYIWtTsyv3lEQgEAsFfm5SUlVfk3OTkAX4/y8zMICYmtux1TEwM+/b5L7lxOOx8++0OnnlmbNl7/uybcnKyy0zUrdYocnNzfJ4pSRKjRj2CJEkMGDCIgQPvACAtLY2fftrDggVzMRpNPPbYEzRvfi1XX92YrVs306NHT9LT0zl06ADp6ek0b96i+odxEdQicaZFznSqBEDe+Z2aNi1yVt18s3Kjc9/1ZoYImbAOJlRFJevLilEznQ5GjtTTr78eFcj+3smjz6uVjMt9Edj0RsI69kVRFPLtB7A8MwJJlrFv3IR9yXJiBoxCHxyOO/ssGZ/NwZOX6eOUGKAnEABkoBmYCwQCgUDw58GXbaQkSX7Xb926lZYtW1VIaZZ6ZF4sixa9R1RUNDk5OYwa9TCJiQ1o0+Z6vF4vhYWFvPvuBxw4sI9x48bw+edr6Nfvdo4d+4MHHriXuLg6tGx5HTqd7pKuoSpqnTiTVS1T628Abak4O+HDGaCJSaaeUSbLo7DHT0ozslcAsl4ib6cTV3q56goJgfETDLRurUNSVOJ+tjFwkh5F8f8XrhRT3YZYbxuCKkFRaDbm3lpI1rb4A5Rv9xN715PIxgAcx4+QuXI+isNXR2YDoAvarzQN+BotciYQCAQCgW+qinBdKaKjY0hPP1v2Oj09vSza5YuNG9fRs2f1KU2AiIhIsrIysVqjyMrKJDw8wue60ohbREQEnTt3Yf/+fbRpcz3R0TF06dIVSZJo3rwFkiSTl5dLeHgETz75TNn+YcMeICGhXo2u6WKoNTVnpd2aBjSRVmbdVCbOtLRmVZGz0pTmVzYPvoJdumCJiCQtTZqVUh41q19fYu48I61b69A7Fa7fm8/kxVDgrF6Y6UMjiRowAgKM2BtJGNq3QnW5yJ8yE/lAOlEDRyIbA7Dt+5b0pf/2I8xaAN3RhNl+YANCmAkEAoHgz0izZs05fvw4p06dwu12s3Hjejp1SvK51mYrZM+e3SQldanR2Z06JZGSshqAlJTVJCV1rrTGbrdTVFJrZLfb+e67b7nqqkYAdO7chV27dgFaitPtdhMWFo7DYcdutwOwc+e36PX6Sg0Ml5NaFzkzSNot5TlsGPUylgADbq9CoUMTK1V1alY3QiOyewCySaLgJxeOE1pkrd1NMuPGGQgKknBleOjwewGpByQ+3V/9o5WMZqLuGI0UGY6jRRByRDBKXj5546dgiW1BSPdu2r188wX5O9b4OUUHXF3y83fAz9V+r0AgEAgE/yv0ej1jxjzHo48+gter0K9f/zJxtHz5MgAGDRoMwObNm2jX7mbMZnOFM/zVnA0ZMoyxY8ewatVKYmLimD79FUCrc5syZTKzZ88jOzubMWOeBLTxW7169aZ9+w4A9Ot3Oy++OJG77hqEwWBg0qSXkCSJnJwcRo8egSzLREVFM3nylCv3gADJV+73z4wkGVWoHP685ZZWdO3aloJfC3gyYQDv7l7H/O8/4eleTckocDBz3UGsIdFMumcW+UV5PP+f0RX2NzLKrGpgIdej0OmordLsfNkscc2sUHSBMkdfKsB+1MNdd+t48EE9siyx/3s3I135uF1w7fxAjuZWE5SUZKIHj8bUqhWu68LBbMBz7Dj5E6cR3rYvgVdfh+pxk732fYoO7KzmqQSi1Zv5rqMTCAQCgaCUtWvfw2qNrn6h4ILJysqgd++h5717ereqqjdcyDm1KHKm3YpJ1lKTeQ5bpXqz+iXDZ4/7qDfrdU5K01e1WURXE7pAGdtBN94THp4ba+DWW7Vo3ZJ33YwNLUBnhUnfGKsXZkDErXdjvOkGXC3CQCfj3P0TtlfmYu39IKbYenjtNjJXLMB58lcfuwOApsCPJa+LEcJMIBAIBILaQS0SZ5pQCtBpgizPXkSEpeIYjYSoRMBfvZn2KHx1aUpGsPbUhs56t9p57XUjTZrI2O0q06a56SI7aNpV5VCWxMzthmqvNfj6rpgHJeNurHWeFKesw/HhF8QMegx9SATunHStIzM3w8fuELRRGaFo3pgijSkQCAQCQW2i1okzs6wJMl+Rs3rWkk7N88TZVUaZRiYd+V6V74srx83CO5nQh8hEZbp44XEdVqvE2bMq48e7kHO9vDBCO/+RNSZc3qqbAMxXt8Ly2MN44oNQFRXbm++g/HCE2HueRjYF4Dj5G5kr5qOUNDRUJBrohRY5ywR8RdUEAoFAIBD8lak14qy0WzOwLHJmo15YuTiTkEgo7dQ8L63ZvSRq9rXNXanHUdKBtXcAbQI93NXCjckgsXevwouTXeTnq6y7z0mAHt7fq2fLsaofp7FBY0JffgHFakZ1ucl/aTqGIhPWO0YhyTqKDuwk68sl4PXVaZkIdEX7lR0HvkJ0ZAoEAoFAUPuoNeKsdBhckEHr6Mh3FlVwB4gKjcFsNJNry6GgOL/C3h6WEi/NwspiJ6yDkQENvXQP8QASKSke5s7x4PHAnc099GzkJccOT28wVnl9hkaNCZ81HTXYhGqzk/PUWILjryOkYw8A8rankL9tlZ/d1wI3AxJwENgGfv0LBAKBQCAQ/JWpNeKsNK0ZrNfEWZ7dRkSQFdAiZ83q+R4+W98g0yRAR4FX5dviiuLMHAjjH9XTMsSD16syf56HL77Q0p6hJpV/99TSmc9+ZSKz2H8TgLF1K8JfngQBBsi2kf34U0Tc2JfAxq1RvR6y131A0b5v/eyWgUZowux74KcaPxOBQCAQCAR/PWqfOCuJnDm9dgIMOhxuL8Uur9/5ZqWNAJttbtznBKPi4iReftVIvXCFIg9MHudmz+7y0bRTurqIC1bZflzmnT3+H2NA926EjHkcdDLS6QKynxtHVLcHMMUl4nUUkblyIc7jh6u4MwVYB8QhOjIFAoFAUBvYsWM7s2bNRFEU+vcfwNChwyqtsdkKGT/+edLTz+LxePjHPx6gX7/bqzw3Pz+fcePGcObMaeLi6jBt2iuEhIRUWtevX28CA4OQZRm9Xs/7738EwIIF89i6dQuSJBEREcHEiS9WmqX236DWOQSEGAIB0Om0rsty26aSMRqVxFnllOZ118nMnWekXqxEulviuTfVCsLshjpeRrR141Hg4TUmVHw0AUgSQcPuJ3Tsk0g6GfloHvmTZxCd/C9McYm48zI5+8EMP8LMBFx/zmsHQpgJBAKBoDbg9XqZOXMas2fPY+nSFWzYsI7ffz9aad2yZZ/SsGFDPvpoKW++uYjZs1/D7fY9JL6UJUsW07ZtO1asWE3btu1YsmSx37ULF77NRx8tLRNmAPffP4SPP17GRx8tpWPHTixa9NbF3+glUGvE2bkOAYXOYkIDNbGWU+TUmgGs9QE4nnmsbE+8QaJ5gI4iRWV7SUqzb18dM2YaCA2V2G+XmXnEyKHVzrI9OknlzWQnsgSvfWtgX4YP41OjkdDnn8Hyj7tBVdHvz6HonQ+w9vk/9KGROE4d5ez70/DknK28l2CgP5o4a30ZnoxAIBAIBH8e9u/fR0JCAvHx8RgMBrp370lq6hYfKyWKiopQVZXiYjshIaHVmo2npm4hOVnzp05O7suWLZsv6NosFkvZz3a7vUpD9itJrUtr6pHJdxRVGKMRHRZLgNFMTmEWNkdB2Z7SRoDNNg9eGUaP1NO/v/ZI1qXJrJdNnP7SjnqOUB95o5s2cQppeRKTUys3AcjhYYS++ALG5k3BrWD4OQd7aipht/TXOjIP7iL7y/dQPb7UfxTaqAwzkAVUle4UCAQCgeDSyC8YeEXODQ1Z4fezzMwMYmJiy17HxMSwb98vldbdeefdPPXUY/Tu3Z3i4iKmTp2BLGsxJX/2TTk52WUm6lZrFLm5OT6vQZIkRo16BEmSGDBgEAMH3lH22fz5c1izJgWLxcLChW/X/KYvI7VOnOmQK804qxeleU+eP0KjNKW5FTfTZxho3VqHy6Uy72OFtK6BeGwKuVvKDc7rBCtM6aKlSUetNVHsrqiodYn1CJ86CV1sDBS7Me7Jxv3bUSzNbwIg/9svydv6Bb47LesD3dB+JSfQRmVUHb4VCAQCgeCvhi/bSF8Rqu++20HjxtewYMHbnDx5glGjHqZVqzZYLBZmz553SdewaNF7REVFk5OTw6hRD5OY2IA2bbRyohEjRjNixGjeffcdli79hOHDR1zSd10MtUicabeiQ6e5A5SO0bA5adqgdPjssbL1dfQSLc06CmMU7npBJq6OTHa2yqSJLopvsxAMZG90oJRrM2b3chJsgpUHdaQcqfjojDe0JnTCWGRLEGTZMP1cgDc3D1NcIqrXS/b6/1D0y3Y/V98caI/WkXkI+AYxKkMgEAgEV5qqIlxXiujoGNLTy8t60tPTy6Jd57J69RcMGTIMSZJISKhHnTp1SUv7g+bNW/g9OyIikqysTKzWKLKyMgkPj/C5rjTiFhERQefOXdi/f1+ZOCulV6/ePP746P+JOKt1NWd69bzIWbGLhBJngHObAW4NNuC4ViFvjIe4OjKHDyuMHOHkD7tMcAsDXodKzlfltWa3Xe3hjmZebC54dJ2pwneb+/YmbNpkZEsQ6tF0THvyUYud6C2hKI5iMpbNrkKYSUCDkv/uArYihJlAIBAIaivNmjXn+PHjnDp1CrfbzcaN6+nUKanSutjYOHbt2glAdnY2aWnHqFs3vsqzO3VKIiVlNQApKatJSupcaY3dbqeoqKjs5++++5arrmoEwPHjaWXrtm5NJTGxwUXd46VSayJnBkN5WjPfYSPcoomz3CJ3+RiNc9KaA+7SkTPIg06GzZu9vPqKG6cTEu7WRnHkbHbgLdJEklmvMre3JtQmbDZysqBE08oyluHDCBo8AADvt/sJLAgDRUE2GPHkZZHx2Rzc2WequHIV2ADURXRkCgQCgaC2o9ez9gR6AAAcnElEQVTrGTPmOR599BG8XoV+/fqXiaPly5cBMGjQYB588J9MnjyBu+++A1VVGTXqccLCwgH/NWdDhgxj7NgxrFq1kpiYOKZPfwXQ6tymTJnM7NnzyM7OZsyYJwHweDz06tWb9u07ADB37hukpR1DlmViY+MYO/b5/8ozOR/JV+73z4wkGVWtcL4izz//f+j1eoY6OvPeD18SHHkAm8PNwm9yeeHO6WQXZjLxoycxGmHc0wY6dtPE3AeL3Sz5UBssa6ojc/XUMBS3ypGn8/Dka89majcnYzu6+emszA1vmfGqElJAACHPP0NAh5tQPR5cK78mNORaVFVFkiScp/8gY/lclOJCH3dhAloAuxFRMoFAIBD8N1m79j2s1v/+7K6/A1lZGfTuPfS8d0/vVlX1hgs5p9ZEzsprzmTcJYViOUUu6p0zfDYyEia/aKRJExnJAWkLYcnqcqNzax8tapb7jbNMmDWL8vL0zW4UFYanmPCqErI1krCXJ2C4uhFKQSH2tz8lonlPQCtqLDq8m+yUd1E9Lh9XGgz0BsIoT2UKBAKBQCAQaNQKcVZab4YCEhJeVUtBap2a2vBZV8BPzJtvwmqV8GRB3Jt6XttVXu1vsMqE3WRE9apkfam9L6GysI8Tgw4W/KDn+1M69I0aEjZ1IjqrFc+p09heXUhM9weRJC3Vmf/dOvJSV+I7ImZFG5URCGQDB67I8xAIBAKBQPDXpVaJM6lUD8nl4qxJwwaE1NvJvbf/gMEo8f/t3Xd4VVW6+PHve0oaCZCQgoAYpIg4cKlyLZT5iSgSQBBR8I5RGEcdQFTGctERR8cyoF6Z0REVEVBxHAUVokgRgkqJoCjFipQgJZ00Us9Zvz/2TsyE5CRA2onv53nynHP2Xnvtd59F4GWtvdf6ZqeXoa8F4M0VEvN/WRUgckQQ4hSyNhVRkm6tBnBT71IGneMlJU+Y9XEggRcPpNUD9yLBQRTv3E3uUy9w1oQ/IU4XxhgyV79O3tefVhNlR6ypMtzAz8BadKoMpZRSSlXWrJIzp7F6rxz20k3HC4rof9kOoi9YC0BCQimprwnD2jhYf6KEfHtFJlcrIXyw9QRm+gcFALQJNsy93Ery7lwdQHHcWFrdNgVxOChYu578F5fQLv7PONwBGK+H1GX/pHDfyZPoWc4HLsF6OPZ79IlMpZRSSlWnmSRnv9xvBuB2l+Jwe5j0x1KiL1iL8Tp47rki3n/fw8IO1tqbqyuspdnmiiAcbiF7ezFFR6yMbc7lRbQJgY/2B/DBsBmEjR4JQN7C1yj6MJF28Q/hCAjEeD2kvPkMRT//6CPCWKzE7Av7RymllFKqas0kObPX1cR6bRmZT9dRBwmOKMJTFELisiG8//67RDiFASFOSowhMc/qXXO2ECJ+GwT80ms2qKOHyX1KSTMtmNL7UUJ698MUF5Mz51nMj0c56+YHcbisHrNjbz5N8c97a4hwHdABnSpDKaWUUjVpFpPQVlz0/ET4t/xmwg8ERxSRlRbGgY8fYNt266nJy0JdOEXYnF9Krj2kGTEsEGewkLe7hIL9HtwOw/y4QpID2zK4xz/x9O6HN+s4WTNn4UwpIub6u6zEzBhSl/2zmsQsALiQX77eEjQxU0oppSybN2/immvGMHbsKBYtWlhlmZycHO655y4mTryW+Pgb2Lu3po4QyM7OZurUWxk3bhRTp95KTk5OleVGjx7B9dePZ9KkCdx446Ty7evWrWHChHFceGEfvvlmz0nHHTt2lMGDL+K11xbX8kpPT7NKzqLaf8uhfk/hCvaQsS+E7cunUJIXXb4yQNlammvsIU1HELS53Oo1S11p9ZrNvLiEgnPP54reL5AVHUvpgWQypt5NSHgXIuMmIw7rXJmrl1Zzj1koMAboDQyst2tWSiml/JHH42HOnCeYN+95/v3v5axZ8xH79v10UrlXX11At27n8eabb/OXv/yVp5+eU2PdixcvZMCAgSxfvpIBAwayeHHViR/A/Pkvs3Tpv1myZGn5ts6duzBnzjP06dO3ymOeeeap8glr61OzSM6Cgpx07pJETLeN4PCQ8lUEG9+MoG2YteD5ofQDtHIIA+0hzfX2kGb40CBcoQ7yfyzhxPeldGrtpe+1lzCu97NkBUVQtH0HmTPuI6J/HK0vGVW+WGvOtrXkfb2xikjaAFcD4UAmsLMhLl8ppZTyG3v27Obss8+mQ4cOuN1uLr/8CjZuTDyp3P79+xgwwOrkiI3txNGjR8jIyPBZ98aNicTFjQIgLm4UiYkbTim2Tp3OJTY2tsp9iYnrad++Peee2/mU6jwdzeKes//5XQHt2v2A8ToJ3D6Oozt2U1hciNsVQGr2MQqKTzCipRuXCJ/ll5LtBXFD5BVWr1laQiFguHLWeKb3nALAiZWryH95CdGjbyXo7K4Y40XEwYkfvyZrwztVRHE2MAxrqozDWFNlVDUJrVJKKdU0xOXWz8wBCWFS7b60tFRiYtqWf46JiWH37pNHorp27caGDR/Tu3cf9uzZxbFjR0lNTaFNmzbVLt+UmZlRvoh6ZGQUWVmZVcYgIkybdjsiwtix1zBu3Hif11NQUMCSJYt47rn5vP56/Q5pQjNJznr0sJKgtJ1jaLvvfGA3OFoCcCjtAADDw6xLXZNr9Zq1viQQd7iDguRS8r4x9Hz8Tpb3H44YL96Fr1Dw4WbaTrwHd0QMprQEcbkpTjlE+soFcNKSV92BS7E6In/AmirDW89XrZRSSvmfqpaNFDk5mYuPn8zTT89h0qQJdOnSlW7dzsPptG4tmjfv+TOKYcGCRURFRZOZmcm0abcRG9uJvn37VVv+xRdfYOLEGwgJCTmj89ZWs0jOHPbgrDc/qnzppqDAGMBatqmlAy5q4cJjDB/nlYIDoq6ye83Wu4mc+1dSe/Uk2FNAz9f+xpqNWbT93f04g0PxFOThDA6lNPc4qcuew5QUVRFBR6zE7Etge71fr1JKKVUXfPVw1Zfo6BhSUo6Vf05JSSnv7aooNDSU2bMfAayEbsyYq2jXrr3PuiMi2pCenkZkZBTp6WmEh0dUWa6sxy0iIoKhQ3/Lnj27fSZne/bsYv36tfzjH8+Sm5uLw+EgMDCQCROur/F6T0ezuOfMaV+F07jwYCVnrcI6ApCcto+hoW7cImw74SHLY2g1MICAaCcnsqJwT34CZ6+exBSl8+jqO1m/XYi57i6cwaEUpx/BGRyKt6SYtOXP48nNqiaCj7GGMTUxU0oppXzp0eMCkpOTOXz4MCUlJaxdu5rBg4ecVC43N4eSEmu06733ltOnTz9CQ0N91j148BASElYCkJCwkiFDhp5UpqCggPz8/PL3W7duoXPnLj7rffnlV1mxYhUrVqxi4sQbuOmmKfWWmEEzSc7sByhxGheINcQZ1dp+GCDjYPmQ5uq8EhCIiguiOLA7eV3/gqtDe3rk/siK7bfx6M5eRI76PeJyc2LvTtxtrDHxjISFFB87WOGMAVhPYtonxoNOlaGUUkrVzOVyce+993PHHbdz7bVjGTbs8vLkaNmyt1m27G0A9u/fz3XXjWP8+KvZvHkTM2feW17HjBlTSUtLPanu+PjJJCVtZdy4USQlbSU+fjJg3ec2Y8ZUADIyMrjllpuZNGkC8fE3cOmlg8qfwNywYT0jRw5n166d3HXXdKZPv71ev4vqSFVjv02ZSICB/+z+XJkQQnCwl+xP7ib4aDa7MjcREnEXqdkpPPP2vWzqHIZLYOhPeRT9l4uY/x1BbvgUcLgYcHgzS356nOsP38j3EZdgvF5ytq0lrM9QHAGBZG1cTs7WjyqcrQUwAojAWrj8s4a7eKWUUuoMrVq1iMjI6JoLqlOWnp7KiBE3Vdp65AtjTP9TqadZ3XPmMC6KPQUUlDhpIQ6S0/YztIWLAIew7UQp6V7oMD2e3DZjAejy6TIWeV7l5oIZfB/RA29xIZlr3qT1kKtxBASSt2tzpcSsDXAlVoKWBXzVsBeqlFJKqWavmSRnVu+f0+ui0FuIF+tpikNp+xljTzy7qkiImHM/JedcCsaDvPwCL56zkWtlNvsc7SjNzSLtvZeIuHwirrBwCpN/IOOj1yqcpQPWVBkBwBFgDTpVhlJKKaXqWjNJzqxXp3FS7C3E7W4DQEr6fgaFusgMa8X6O+7EfX53xFuA572nmOk8zO94hEzTkuKUQ6Que46IYRMJbNuRkswU0t59Abwe+wznAYOwbtHbCySiU2UopZRSqj40i+RMxOo5c9nDmi2CO+E1XmILD3P0vI7M/OM9SGQ0jtJ0QpPnEvFdIK/0u49iAjAHd3Js+cu0ujiOkG698RTmk7rsObyF+RXO0B4rMdsBbGuEK1RKKaXUr0WzSM7Ke85wUewtxBkYTurxo3Qa8Bumz5hJfnAIkvUjrfPmkZvUk2P94gDofORj1r/1NqE9L6bVwCswHg9p786nNDOl0hk2AvuAAw14VUoppZT6NfL75KwsMTNGcNnDmuJoxeaOpXx7yyy8TifOzz+jddvFFB2KwxnRH8HL5II3eOSNTwjqeD4Rw28AIHPN6xQlf4+1BFN/rF6yUqypMg40xuUppZRS6lfG75MzeyUHjBGcRigyxXzaryN7uocBMGzluyQWvEdh0WQ8J7oQTCHz3M/z8BvfQOt2RF59K+J0kp20mrydm7CexLwS68nMAKxeM6WUUkrVlUcemc1nn31CeHgEb721rMoyxhiefnoOmzZ9RlBQELNnP0L37uf7rDc7O5tZs+7l6NEjnHVWO554Yi4tW7Y8qdzo0SMICWmBw+HA5XKxZMlSANatW8NLL83nwIH9LFr0Oj16XHDmF3sa/H4S2orJWYHDy7tXTCKpexiO0lLuXTIfPvoQd9QMPCe6EOY9zlsBj3Lgq69JSm9J9PjpOINacOKHHRxPXI41d9kYrMTsONZyTEoppZSqS3Fxo/n73//ps8zmzZ+RnJzM8uUrmDXrzzz55GM11rt48UIGDBjI8uUrGTBgIIsXL6y27Pz5L7N06b/LEzOAzp27MGfOM/Tp07f2F1MPmk1ylmEieejCs/kptjtBRV4e+cdcYr/bz6aRMzHFMbjNEVYFP0TbggPcvyGEqLG34w6PpuhYMukJrwDtgNFAKHAUeB/IbazLUkoppZqtvn37VdmjVdHGjYmMHBmHiNCzZy9yc3NJT0+r8Zi4uFEAxMWNIjFxwynF1anTucTGxp7SMfXB74c1HQ7YTyeecs3ieMtgWufkMOrDo3jz4N5Lb8M4A3CG/MCrhU/TQXL53ZpAHENuJKhjN2tus2XPYUrOAYZg5ao/YU2V4fF1WqWUUsrvjX/n53qp953xHc64jrS0VGJi2pZ/jo6OITU1lcjIKGbMmMqDD84uX8C8TGZmRvki6pGRUWRlZVZZt4gwbdrtiAhjx17DuHHjzzjeuuT/yVnrlvyVRymUYM7LyGX46kS+LW7L2xfeCIC7dRI9S/7FpQG5rN/vZEVIHOE9L8ZbXETasufx5B0HemElZl8DSY14NUoppZQC656zykQEgHnznj+juhcsWERUVDSZmZlMm3YbsbGd6Nu33xnVWZf8PjlzdYqlUIJp7z3EzE2HeCn4XHa3CQegTXACRW0/44kjmRQVwd3f9CH8smswxkt6wisUpyTbtXwKJKNPZCqllPo1qYservoSHR1DSsqx8s+pqSlERUX5OAIiItqQnp5GZGQU6elphIdHVFmurMctIiKCoUN/y549u5tUcub395zZSTQtSnJ5wRnJ7pBw3J5SpuxYRHHsZ/Q9XsB5xSU89XUUmYOnAZD18bsU/BiCNWUGgEETM6WUUqrpGDx4CB98kIAxhl27dhIaGlo+ZOnrmISElQAkJKxkyJChJ5UpKCggPz+//P3WrVvo3LlLncd/Jvw+OXM6HVBQytFtoXzrakGI18uTm16iuN0eAO4uyGbvcRevnvMgDncA2UmfkvtFC+A3wOBGjV0ppZT6NXrggfuZPDmegwcPMnLkcN5//10Ali17m2XL3gbgkksG0b59e8aOHcVjjz3CfffNKj9+xoyppKWlnlRvfPxkkpK2Mm7cKJKSthIfPxmw7l+bMWMqABkZGdxyy81MmjSB+PgbuPTSQVx88SUAbNiwnpEjh7Nr107uums606ffXq/fQ3WkqjHdpkwkwMAvmXPb4RcSMPAPSLGXtqUFXJuSx1Xb/sadf3DSNriIxSmpTEydwpaWl5G3cw8Zq9Kwnsg8DqxCn8hUSin1a7Jq1SIiI6NrLqhOWXp6KiNG3FRp65EvjDH9T6Uev7/nzNkyBin24grx8MdDuwnNcZMcAUfaCLOP5rD8+PlWYrbrABmrsrESs2PAaqCocYNXSimllKrE75OzsnvOXEFe3F5DWG4yW3oIFxQVcUGehxlB08jdcZjMNalYM/7vAzagU2UopZRSqiny+3vOXK5fLqHEuAnLTWZrdwe3HM9hrvkfUj1hHN+8HetSdwEfo4mZUkoppZoqv+85czodlNrvS42bnIDDuFuWEn6oLUs9l5Hx0WK8eVuBjsDBRoxUKaWUanxerxdrlgJp7FCaGWN/t2fO73vOnM5f/nBJSQmfn1fKxNQTjP3wejI+WUP+7i1Yfwg1MVNKKaX27j1IaWkp1r+Nqm4YSktL2bu3bnKNZtBzZidnRgg8kc3uswN5+fnRJB8rAvIaNTallFKqqXn44Wd5+OE76dLlHBwOv++jaRK8Xi979x7k4YefrZP66jU5E5ErgXmAE1hgjHmy0n6x918FnABuMsZ8eSrnCHC7OWG/P5KRzboXh5GRFQzkAFvP9BKUUkqpZiUrK5sZM/7S2GEoH+otORMRJ/A8cDnwM7BNRFYYY76pUGwE0NX+GQi8YL/WWlCANcv/iX15PLQ1hoJiFzgywPsBUHjmF6KUUkop1YDqsz/zQmCvMWafMaYY+BcwplKZMcASY9kKtBaRs07lJGFBgeR/k8nPr/xEQbELCTgG3vfQxEwppZRS/qg+k7P2wKEKn3+2t51qGZ/ahodSnJKP8RhaRx3CFK9Ep8pQSimllL+qz3vOqnpGt/KjIbUpg4j8AfiD/TEPjnxftu/1WfeWvY08nkb6acSpGlckaLv5KW07/6Tt5r+07fzTead6QH0mZz8DZ1f43AE4chplMMa8BLzk62Qisv1U165SjU/bzX9p2/knbTf/pW3nn0Rk+6keU5/DmtuAriLSSUQCgOuBFZXKrABuFMt/A9nGmKP1GJNSSimlVJNWbz1nxphSEZmGtcK4E1hojNkjIrfZ++cDH2JNo7EXayqNm+srHqWUUkopf1Cv85wZYz7ESsAqbptf4b0BptbR6XwOe6omS9vNf2nb+SdtN/+lbeefTrndxMqPlFJKKaVUU6DrNiillFJKNSF+mZyJyP+JyKciMq/S9nYisl5ENovIsMaKT1XNR7vNFpEt9s9ljRWfql51bWfvExH5WkR+3xixqer5+J0LEpFX7L8v/9FY8anq+Wi7ISKSJCJby+7hVk2HnYd8KSKFIuKqYl+tchS/S85EpC/QwhgzCAgQkQEVdt8PPAgMt19VE1FDuy0xxlyEtZzX7EYJUFWrhrYDGA2kNnxkypca2u0OYKkx5v8ZY6Y3ToSqOjW03UzgWuBi9CG6pigTuIyqF/eudY7id8kZcBGwzn6/DvjvCvt6AVuMMXlAroiENXRwqlrVtpsxZr/9togqJiFWjc7X7xzAROCtBo1I1YavdhsKjBaRRBEZ3dCBqRr5ars9QCsgEMhv4LhUDYwxhcaYrGp21zpH8cfkrDWQY7/PBsIr7HOaX55wqLxPNS5f7VbmYeDFhgpI1Vq1bSciVwAbgdJGiEv55ut3rjPwATAS+HPl4RfV6Hy13XtAAvAd8EYDx6XOTK1zFH9Mzo4DLe33Le3PZSouqll5n2pcvtoNERkLtDHGLG3owFSNfLXd74FXGzwiVRu+2i0b2GiMyceaZzKmgWNTvvlqu7nApUBXrEncQxo4NnX6ap2j+GNytgVrPBdgGP85rrtTRC4SkRZAS2NMzklHq8ZSbbuJSC+s+e7qas47Vbd8/c51xfqf/EzgThHp3sCxqer5arfNQC8RcQKxQFrDhqZq4KvtPMBxY0wx4AXcDRybOn21zlH8LjkzxnwJFIrIp4DXGPN5haeN5gCPYY3RP95YMaqT1dBuc7H+575aRN5vtCBVlXy1nTGmtzHmSuBp4FljzHeNGav6RQ2/c3/D+rtyE7DA/odeNRG1aLt1IrIF2GCMyW60QNVJRMQtIuuA/8L6N23g6eQoOgmtUkoppVQT4nc9Z0oppZRSzZkmZ0oppZRSTYgmZ0oppZRSTYgmZ0oppZRSTYgmZ0oppZRSTYgmZ0qpOiciHhH5qsJPrI+yeXVwvkUist8+15cictFp1LFARHrY72dV2rf5TGO06yn7XnaLyEoRaV1D+d4iclVdnFsp5T90Kg2lVJ0TkTxjTGhdl/VRxyIgwRjzjogMB54yxvQ6g/rOOKaa6hWRxcAPxpjHfJS/CehvjJlW17EopZou7TlTStU7EQkVkY/tXq1dIjKmijJnicgnFXqWBtnbh4vIFvvYt0WkpqTpE6CLfezddl27ReROe1sLEflARL62t19nb08Ukf4i8iQQbMfxhr0vz359q2JPlt1jd42IOEVkrohsE5GdInJrLb6WLUB7u54LRWSziOywX88TkQDgEeA6O5br7NgX2ufZUdX3qJTyf7rYrVKqPgSLyFf2+/3AtcBYY0yOiEQCW0VkhfnPrvtJwGpjzGP2skIhdtkHgWHGmHwRuQ+4Gytpqc4oYJeI9ANuBgYCAiSJyEbgXOCIMWYkgIi0qniwMeZ+EZlmjOldRd3/Aq4DPrSTp8uA24EpQLYxZoCIBAKbRGSNMWZ/VQHa13cZ8Iq96TtgsDGmVESGAY8bY64RkYeo0HMmIo8D640xk+0h0c9FZJ29RqZSqpnQ5EwpVR8KKiY3IuIGHheRwVjrAbbHWrLrWIVjtgEL7bLvGWO+EpEhQA+sZAcgAKvHqSpzReRBrHUip2AlP++WJS4ishwYBHwEPCUif8MaCv30FK5rFfB3OwG7EvjEGFNgD6X2EpHxdrlWWOuOVk7OypLWWOALYG2F8otFpCtgqH69xOHAaBH5k/05COgIfHsK16CUauI0OVNKNYQbgCignzGmREQOYCUW5Ywxn9jJ20jgNRGZC2QBa40xE2txjnuMMe+UfbB7oE5ijPnB7lW7CnjC7uHy1RNX8dhCEUkErsDqQXuz7HTAdGPM6hqqKDDG9LZ76xKAqcDfgUex1kkcaz88kVjN8QJcY4z5vjbxKqX8k95zppRqCK2AVDsx+y1wTuUCInKOXeZlrOG+vsBW4BIRKbuHLEREutXynJ8AV9vHtADGAp+KSDvghDHmdeAp+zyVldg9eFX5F9Zw6SCgLBlbDdxedoyIdLPPWSV7seo7gD/Zx7QCDtu7b6pQNBcIq/B5NTBd7G5EEelT3TmUUv5LkzOlVEN4A+gvItuxetG+q6LMUOArEdkBXAPMM8akYSUrb4rITqxkrXttTmiM+RJYBHwOJAELjDE7gJ5Y92p9BTwA/LWKw18CdpY9EFDJGmAwsM4YU2xvWwB8A3wpIruBF6lhZMKO5WvgemAOVi/eJsBZodgGoEfZAwFYPWxuO7bd9melVDOjU2kopZRSSjUh2nOmlFJKKdWEaHKmlFJKKdWEaHKmlFJKKdWEaHKmlFJKKdWEaHKmlFJKKdWEaHKmlFJKKdWEaHKmlFJKKdWEaHKmlFJKKdWE/H8z7r1NlnlbzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "res2plot = []\n",
    "true = (df[target]==1)\n",
    "for cutoff in np.arange(0, 1.1, 0.1):\n",
    "    predictions, indices, tru = CVmodel(model=lr, lin_reg=True, cutoff=cutoff, lin_reg_without_fi=True)\n",
    "    pred = pd.Series(sum(predictions, []), \n",
    "                     index=sum(indices, [])).sort_index()\n",
    "    res2plot.append((true, pred, \"%.1f\" % round(cutoff,1)))\n",
    "plotROCs(res2plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best ROC-AUC score has model with cutoff = 0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split dataset into train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "lr = LinearRegression()\n",
    "predictions, indices, feature_importance, true = CVmodel(model=lr, lin_reg=True, cutoff=0.3)\n",
    "\n",
    "linear_regression = {\n",
    "    \"name\":\"Linear Regression\",\n",
    "    \"predictions\": predictions,\n",
    "    \"indices\": indices,\n",
    "    \"feature importance\": feature_importance,\n",
    "    \"true\":true\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the file for binary writing using \"with\"\n",
    "with open(\"models_results/linear_regression.p\", \"wb\") as fp:\n",
    "    pickle.dump(linear_regression, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LOGISTIC REGRESSION ALGORITHM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Standardization of data and KNN model \n",
    "logreg_scaler = Pipeline(steps=[\n",
    "                ('scaler', StandardScaler()),\n",
    "                ('logreg', LogisticRegression())\n",
    "                ])\n",
    "ConfusionMatrix(classifier=logreg_scaler, confusionMatrix=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ola/opt/anaconda3/lib/python3.8/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": [
    "predictions, indices, true = CVmodel(model=logreg_scaler, pipeline = True)\n",
    "# define the model\n",
    "model = LogisticRegression()\n",
    "# fit the model\n",
    "model.fit(X, y)\n",
    "# get importance\n",
    "feature_importance = model.coef_[0]\n",
    "# feature_importance = logreg_scaler.steps[1][1].coef_[0]\n",
    "logistic_regression = {\n",
    "    \"name\":\"Logistic Regression\",\n",
    "    \"predictions\": predictions,\n",
    "    \"indices\": indices,\n",
    "    \"feature importance\": feature_importance,\n",
    "    \"true\":true\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the file for binary writing using \"with\"\n",
    "with open(\"models_results/logistic_regression.p\", \"wb\") as fp:\n",
    "    pickle.dump(logistic_regression, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-NEAREST NEIGHBORS ALGORITHM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:  4.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'knn__n_neighbors': 100, 'knn__p': 1, 'knn__weights': 'uniform'}\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.93      0.89      6159\n",
      "           1       0.71      0.55      0.62      1982\n",
      "\n",
      "    accuracy                           0.84      8141\n",
      "   macro avg       0.79      0.74      0.76      8141\n",
      "weighted avg       0.83      0.84      0.83      8141\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Standardization of data and KNN model \n",
    "pipe = Pipeline(steps=[\n",
    "                ('scaler', StandardScaler()),\n",
    "                ('knn', KNeighborsClassifier())\n",
    "                ])\n",
    "\n",
    "param = {'knn__n_neighbors': [10, 100],\n",
    "         'knn__weights': ['uniform', 'distance'],\n",
    "         'knn__p': [1, 2]}\n",
    "\n",
    "GridSearch(param=param, estimator=pipe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best parameters: {'knn__n_neighbors': 10, 'knn__p': 1, 'knn__weights': 'uniform'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of  10 | elapsed:   52.6s remaining:   22.5s\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'knn__n_neighbors': 50, 'knn__p': 1, 'knn__weights': 'uniform'}\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.93      0.90      6159\n",
      "           1       0.72      0.55      0.63      1982\n",
      "\n",
      "    accuracy                           0.84      8141\n",
      "   macro avg       0.79      0.74      0.76      8141\n",
      "weighted avg       0.83      0.84      0.83      8141\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Standardization of data and KNN model \n",
    "pipe = Pipeline(steps=[\n",
    "                    ('scaler', StandardScaler()),\n",
    "                    ('knn', KNeighborsClassifier())\n",
    "                ])\n",
    "\n",
    "param = {'knn__n_neighbors': [10, 50],\n",
    "         'knn__weights': ['uniform'],\n",
    "         'knn__p': [1]}\n",
    "\n",
    "GridSearch(param=param, estimator=pipe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Best parameters: {'knn__n_neighbors': 50, 'knn__p': 1, 'knn__weights': 'uniform'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Standardization of data and KNN model \n",
    "knn_standarization = Pipeline(steps=[\n",
    "                ('scaler', StandardScaler()),\n",
    "                ('knn', KNeighborsClassifier(n_neighbors=50, p=1, weights='uniform'))\n",
    "                ])\n",
    "\n",
    "ConfusionMatrix(classifier=knn_standarization, confusionMatrix=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardization of data and KNN model \n",
    "knn_standarization = Pipeline(steps=[\n",
    "                ('scaler', StandardScaler()),\n",
    "                ('knn', KNeighborsClassifier(n_neighbors=50, p=1, weights='uniform'))\n",
    "                ])\n",
    "\n",
    "predictions, indices, true = CVmodel(model=knn_standarization, pipeline=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = {\n",
    "    \"name\":\"K-Nearest Neighbours\",\n",
    "    \"predictions\": predictions,\n",
    "    \"indices\": indices,\n",
    "    \"true\":true\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the file for binary writing using \"with\"\n",
    "with open(\"models_results/k-nearest_neighbours.p\", \"wb\") as fp:\n",
    "    pickle.dump(knn, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SUPPORT VECTOR MACHINE ALGORITHM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:  5.8min remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:  5.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'svc__C': 1, 'svc__kernel': 'linear'}\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.93      0.90      6159\n",
      "           1       0.73      0.60      0.66      1982\n",
      "\n",
      "    accuracy                           0.85      8141\n",
      "   macro avg       0.81      0.76      0.78      8141\n",
      "weighted avg       0.84      0.85      0.84      8141\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# split into a training and testing set\n",
    "pipe = Pipeline(steps=[\n",
    "                ('scaler', StandardScaler()), \n",
    "                ('svc', SVC())\n",
    "                ])\n",
    "\n",
    "param = {'svc__C': [1, 10],\n",
    "         'svc__kernel': ['linear', 'poly']}\n",
    "\n",
    "GridSearch(param=param, estimator=pipe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best parameters: {'C': 1, 'kernel': 'linear'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:  3.6min remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:  3.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'svc__gamma': 'scale', 'svc__kernel': 'linear'}\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.93      0.90      6159\n",
      "           1       0.73      0.60      0.66      1982\n",
      "\n",
      "    accuracy                           0.85      8141\n",
      "   macro avg       0.81      0.76      0.78      8141\n",
      "weighted avg       0.84      0.85      0.84      8141\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# split into a training and testing set\n",
    "pipe = Pipeline(steps=[\n",
    "                ('scaler', StandardScaler()), \n",
    "                ('svc', SVC())\n",
    "                ])\n",
    "\n",
    "param = {'svc__kernel': ['linear', 'rbf'],\n",
    "         'svc__gamma': ['scale', 'auto']\n",
    "        }\n",
    "\n",
    "GridSearch(param=param, estimator=pipe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best parameters: {'gamma': 'scale', 'kernel': 'linear'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving SVM model to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Standardization of data and KNN model \n",
    "svm_standarization = Pipeline(steps=[\n",
    "                ('scaler', StandardScaler()),\n",
    "                ('svm',  SVC(kernel='linear', C=1, gamma='scale', probability=True))\n",
    "                ])\n",
    "\n",
    "ConfusionMatrix(classifier=svm_standarization, confusionMatrix=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardization of data and KNN model \n",
    "svm_standarization = Pipeline(steps=[\n",
    "                ('scaler', StandardScaler()),\n",
    "                ('svm',  SVC(kernel='linear', C=1, gamma='scale', probability=True))\n",
    "                ])\n",
    "predictions, indices, true = CVmodel(model=svm_standarization, pipeline=True)\n",
    "feature_importance = svm_standarization.steps[1][1].coef_[0]\n",
    "svm = {\n",
    "    \"name\":\"Support Vector Machine\",\n",
    "    \"predictions\": predictions,\n",
    "    \"indices\": indices,\n",
    "    \"feature importance\": feature_importance,\n",
    "    \"true\" : true\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the file for binary writing using \"with\"\n",
    "with open(\"models_results/support_vector_machine.p\", \"wb\") as fp:\n",
    "    pickle.dump(svm, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's also load previously saved models\n",
    "with open(\"models_results/decision_tree.p\", \"rb\") as fp:\n",
    "    tree = pickle.load(fp)\n",
    "    \n",
    "with open(\"models_results/random_forest.p\", \"rb\") as fp:\n",
    "    rand_forest = pickle.load(fp)\n",
    "    \n",
    "with open(\"models_results/linear_regression.p\", \"rb\") as fp:\n",
    "    lin_reg = pickle.load(fp)\n",
    "    \n",
    "with open(\"models_results/logistic_regression.p\", \"rb\") as fp:\n",
    "    log_reg = pickle.load(fp)\n",
    "    \n",
    "with open(\"models_results//k-nearest_neighbours.p\", \"rb\") as fp:\n",
    "    knn = pickle.load(fp)\n",
    "    \n",
    "with open(\"models_results/support_vector_machine.p\", \"rb\") as fp:\n",
    "    svm = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WvVzC2m1Fg5zb8IynODsC1GS9nwMcG6e5CIIgTAkcS1MS1JSHYU6Ri6U8y5IFV81LYQPVfkWlo+iOKgp6HCI9PopTPlx/ilSq9xGtgAI/xDttcBW23wWtiHf46Pb7aAsFAIxIwjyWelI+CjR0BRw6bYu4Y/daawIO+1xNzMl99GyHgZ9GhVmvy4Y/95aTuF5Rv3MSe3mkk7hrwFLYrkvKTYs6Y0lTynjIoj0+0o5K1xMzSlkY15lCKQvludH6/rZUD5pSlLLRWGhlYykLpWxsZaOUhW35PK2RVpYuyioCK2hmoyL0FU5K+UCFAB9KDSySTuHq9CORjJNMJUm6SZKpJCk3STKVIOmmSKYSpLK2DbjdTZJMZb/OGiuVJOkmsvbdn7U9QSqV6rPdG5sUdomLM8PCX20RmGHjr7YJVFs4FRbKGvi66KQm0RZEp6rAmo9OVOHGK0h1l9C95xBtjQeBCPDsSV2r8RRnjwF3KaV+AVwMtEm8mSAIgsFSmrnFmoWlLmdVuiRchWNp5hRpQj44r1zT0G4RtuHqmRDrtEl22yiliLU7uElFtNGP5Wh0SpGI2sSO2bSGA7iWiV2KKEV70E/Un+JgwDyGlYbWSJBgPIlW0BYO4pSliKa3uxo9yAMrH5J9jTixuPnRxmJmBR3aO2IkUynQ0JXSxFxNSkNc48X6WBSFAzR1aVL4sG0H2/IRcmyiCQfLsrEsC0vZ5rX32855baGUH00ByvLjs3zYlg/bNr99Wa9ty8Fn2di2J1WUY4TNAAwmZoInfcXyxwgUT7zoJMl4tnjp9sRJlrjJETZpUZQg5Y0x+PZkP5GVSAug7P2y+rluavgTGCsU+EqUEV4zbEIzLCPAZgRwKi0s3yACzNXEG1LE6lLE613iLQ7KWYxTfiFOxXmZz4BOJoju30bXjj8S3bsFHe/xRigC2k9qymOZSuNB4BqgQil1BPgS3udWa/09YBUmjUYtJpXGR8ZqLoIgCKcXzcwCTciBspAm7BgrlutCgba4fIbmjFJNT7fNGeUuJ+r8WCiqwppY0ggkx1JEmxycAiNSXA2xdodos4+epEOiwabEsen2OzzdZCxTrWETX+QqaIsE6Sm1Sdo2lqtxRyioOoP+zOtklvsrLcySKRefbXGgsZOW7gRaa7QGV2tCfpv69h78tkVLd4rmbhelHHx2AMvyowlgqSCOEyTohAg4QYL+IEEnSMANEQwGM23FTsi0O0H8TgBLjW/u9EQy7omc7qEtOWnLjGexSaUG227acrcPIpBytvcfb7qXY7QLFYFqG/8My/ttG2tYtY0VGPzzn2h2PQGWIlbvEq9LEatPkTjhogJFhJeuJLLsQiIrlnjWTiPIuve/RvfOjXTXbkHH/cC1wC4gLc5OTpjBJKytaWLOJM+ZIAinCxOMXhbS+CzwW1AThqUlmhCKi2a6LI0oaoo10WbHBH93ONgBl2ijH18ohdbQ0+ygU73CImFZJHzGxdUVcGiJBEl5rqX2cIAenw9ba7r8jomfGsTtdDK0dcfZ19hFynXRGkJ+m2OtUUKOTXNXkhQWYSdAZ9zCZ/nx+RxSOohtBbCsEI4vRNDviSpPOAXSQsuf22ZboxujFUvEiCWixBIxehJRYokeeuI9xBLmJ9FPuAxm0RnanZYzzkSw/ggAWCGVEVz+GVluyBkWdnhw4Z5sd4nVp4jXuUaEedawWH0KHe9zjEgR4aUXEFl2IYGaXEEW3b8tS5ClRVglJnNYCNgPrOlz9GObtNYrR3KekmpfEIRpScA2we0VYY1jw7xilwtnucQSsDxkc1UVuI0BuuoCOOEUPW0OuOAmvQdA+obupZes996mFMR8PjqCfrqCfnA1CZ9N3eIIPtcFoDPgH9EquTSuq7GyLGD7TnRSGHRojyapa0+i8REOOHT0WNi2Q8DnEE0GSaQK8Dkhz0plLFElZcYylX6/fLERUz57dB8LiWScHk849SR6iMWjue/Tr+PRnLaeRJRYPP2+d5/JZlAQRo5yyAguE/9lZ+LBfMWD/9+kul0juLKEl7GCubjdQ39uhhJk3fu3DCDI0swDrsPIqcOcbIxZX0ScCYIwRdHML9GcU+0yv8RlRZXLTYtSzC3uvUkneywSXTbRZofoiRDRRj+JbnNbbN7fO1Kiy4stwaz0i/tsogUW7XaQhsIAccdH1Ocn4Yz8ltrQ3oPPVnTFUtS3R6lr6yGWdPHbFq09mpQbwrKKiYRKKSkopyRSSkmkjJJIKfPmlhEJFgBw1slfqBxSqWQf8RTNWKZyhFK8J2O5yhFVWe09iR6xNgkDY4O/Im35svDPsHtflw9ubXXjup/7MV5nhFiqfWTCfXhBtskTZNFBRjgLuAyzwGIn8AKDJJ0YMSLOBEGYNBT4NUvLXYI+mFvssrzCBMvXFGtaoorLalIkXSgNmYV02oVEp49ok0NXXZj2J8LsAJRtguQHQwNxn80rPkUsFOa4zyFQGiYS8g+6z0C0dsdp6Y5zoLELrSGeSrHvRBfdsSTRhENhuIySgrKM2CqJlLGi0vwuLSgj4AwfRu66bo4YimWJpGyLU7/3OZaptKCKkUwlRnSOgjAoCpzy/qsg/dU2/koLZQ++EjJ+YmA3ZKLFPSX9c+qCLM1FwHne61eAzSc/qQEQcSYIwrgS9JkViItKXfw2zCsxgqsybGK9bliUYm+zYkl57h3ZTUG8w0dPi5/oUT/lJwIcfdVGu4p6S6PdIcRXljCL2xav2Q5dc8qIhgL0xFMEw7kirG8Wh3QwfHs0waHmLo639XCspZsDTV10x1MUBEuM2CoooyQy24gt7/3l55RSEinF8Q0v9GKJGC2dTbR2NdPW1UJrVzMt3u/WzmZau1ro6ulAj9K3dUE4GXzFynNDZgfjW/irbCxniJWQjb1Wr7T7MV6XIt7k9uZ4HQVGT5Bl046Z5PPAntGbrIcsCBAE4bRQ4Nd86NwE1y1I0dKj+NNlSUpD+e/f3ejQuK2QeIePRGd+3ytdrbGUImEp9iU0uyyb7bZDZ9ChojDI0hnDJ4lsjyZo6Ohh69E2WroT1Lf7KIqUURoxFq/iSNrSZX4XhUvyCoLvjnXR2tlMS1ezEVtdLUZwdTfT2mkEWDTendd5CsJYY0fUgKsg/dU2dmiIlZCt/d2P8TqX+IkUegyNtMMH9Z+MIOtLIdCRRz9ZECAIwgQg5NNURjSfuyzBx85PEB4mk2UiZXJjNkcVf9xvU99m4eu2odXPgkSA+dbQt6qdPSm2x1Js60nRWBSmqLyABVWFOLZFeUEg088HnDPIGAcau2jsjPHC7gY6Ykk0YWoq5jGnYgFzyudz3YXzqCyuzuv827vbPMHlia4sS1e6LZ6M5TWWIJwurIAJxPfP8NyPM+yMFcxXMMRKyM7sQPwUMc8VGa9P4faNnx/L+UeKCC89n8iylaNoIUtTCLwZYylLpzvOR5idHGI5EwThpAjYmivmpphfovn0xQlmFbo0RxVLy4e+pxxpV9z/ho8t9RZuCmoP+7km6KfcVtxc6BAYIh+XBp6Murxs+TiYMtmECoMOCyoinFPTt1xLLj2JFLalcGyLIy3d1Lf3UNfWw/ZjbSR1oSfE5jOnfB41FfMojvQfz3Vd2rpb+oiutLvRuB7bulpIuiOtQigIpwflA39VrvBKW8Cc0iFWQvaYQPxs92PaCpbqGj8dMZQgGz0LWSVwExDGLM9ePcL9R245E3EmCEJelAY1d12U4PwZKf50eX4BIT1JONSm+MzqANsPOSxybBb6Lc4O2iwJ2Mz1D/ww0EDSstiFoq64gK6yAuxIAGsEub5e2N1Ac3ecQ03dHG3tJpnSWMpiRuks5pQbIVZTMY/Z5XMJByL99o/GoxxtOsjhxoMc8X7qWo+SktWHwkTHAn95egWk1RsPVm3hlA9ekshNmIz4OekoPGtYsnXiaIXTI8jSzMNYzBxM1ck1wEj9sSLOBEEYFTSzCzVfvibOlXNTVBdoSgZYOFjfaYpW/2qbj+4EfG+jw6E2RX2XolBZ3FrkcGdFgOIBVmVpoNvvUF8U5kRRhCa/D9fxobXGHiYHWE8iRdCxeeNIK4mUi89SdMVT7KnvYHd9Bz2JFI7Pz+yymowIm1M+j1llcwYMxG/vbjUirMmIsMONB2hqPyGB9sLERYFTavWmochyQw5bkuiEm+t+9KxhiaZTWwk5lpxeQZbmTEyqDAuT+f95Tu4CScyZIAgnga1MjNjNi1N89Zo4NcWD34Ae3WnzRK2PB7f6aI/1PgBs4N6aMDOrLIIKyny9AituW7SEg3QFHA6WFBCNDJwiwuyR+1BJplyau+LUNnTwYm0j9e39g1hC/jA1FfOZU7GC85bOo6Z8HtUls7Cs/iKvsb3BE2AHOdJ0gMONB2jvbhvy+gjCeDFQSaJAeiXkUCWJmjz3Y5YIi9WZkkR6khh/xzaGbDhWAhd4rzcCr47BMQZHxJkgTFOKA5o1H4xSEdbMLNQEB7kbrDts8a0NDs8esKnv6hU7CqhxFO8r8fOnxX6KbUVKKTqCfo6VFrA34OdEUTivudTWd9DQ0cOO4+3sb+wkljRlhQbCsR3mVi5gfvViFlQtpqZyPuWF/a3pKTfFsabDHG5KuyUPcKTpkKyAFCYcVlhl8n9lErHmU5Kozc1dBZkWYg39SxJNFjKC7IyVBOaebkGWTSsmVcYLwO4xPlZ/RJwJwjSiPKS555YYZ1W5nFPt5mxrj0FRAF4+avHcQZt/esZPLCsfWKEFby30cWbQ5iNlZgWkBo6XRHh1RjnRwDBLMoE99R2c6OhBKcWmA80cbOoa1klQXljJguolLKhexILqxcwum4vdp8RQPBHjaPNhI8KajFvyePMREpJQVZggKD/4q/qUJPJqQ/qKhi5J1Lv6MbdA93AliSYLE0eQZVML1AGdp/GYvYg4E4RpQIFf8/j7olwz3+237YE3fHzxGT/7W3sfEH9S5HB3tY/zQzYBS2FDZhWlBg6XFbJ7RhmxQcoVtXbH2dvQyZGWbpq74uyp7yCe6n/svvh9AeZVLmBB9RLmVy9iftViisLFOX1c1+VI40EONNSyv34vB0/so6H1OK4efnxBGEuUDU5lbjHudFZ8Z6iSRDHtWb4GKEnUMTUEWF+GFmRvZNWyPF2CrAC4HlgLNHpt4yPMQMSZIExZ3rwgyVeuiXPF3P6iZXWtzd+t8fNGg3lgfKDEzz/M8THDp1gS6P8QaY4E2VVayLHSAtwB4rgON3fx3K4GdhxvJ5bMXyRVFc9gftWijBibXTa3X5xYR7SdA/W17G+oZX99LYdO7CeWOI3JkwQhG68kUToAP1uE+SvyLUmUlY6izlsJOTU1WA4TT5ClqQBuxqTKuAhYdZqP3x8RZ4IwhagpcvnOW2LcesbAEb8/2ezjY48FcFD8bWWAry9wqMlKZ6GBHp9Nd8Bhb0GY+spiehIpwsGBXZZrttexZttx3DweLEEnyLwqYw1bUL2YBdWLiAQLc/qk3BSHTuxjf/1e9tfXcqChlsb2hrzPXxBGC1+JyokBy8SCDVeS6ERuAH48uyTRNDTuTlxBlqYGYzFzgKPA0+M0j1xEnAnCJGdWocvfXpLgc5f1j6863qH49KoARw77me/YnBu02X6GSSXRHAnSGg6wKRKiJRIk7utvMbOAcFZai2d31bP1aBsHGrvymlskWMD5Cy9m5eJLWDhjKZbKtYq1dbWyv2EPB+r3sr9+D4caD5BITtJIZmHS0bckUbY1zA4OsRKyxRNfafejFwcWP+GOaUmiycLEF2RplgFXYO50uzGpMiaGghZxJgiTkKqIy903xrlwZorllf3NVqt3+diwvpDL7ABfdSyYY6xiDUVh1lWV0jpIKgvoLeq9u74Dx1Ks29vI64db8rKOAQScIOfMv5CViy9l2ZwVmTqTyVSSg437vFgx89PS2XQypy8IeWMFvZJE1f0Tsg5ZkqhjkJJEDae3JNFkIUeQ1SxBWRNVkKW5AJMuA0yajI3jOJf+iDgThEnC2VUpvnxNnHcMkp3//i0+fr4hwDtUhCsLfMz19Fe338eOWRXUF/fPgg/w0r5Gmjpj7K7voK6th1S+KiwLn+1wZs05rFx8KSvmnY/fS/SaclNsO/Q6G2vXseXAq5lEE0kAACAASURBVBIrJowJygF/ZXYusF5rmFMyxErIqO61gGXFgMXrx7ck0WRhSEG2dyIKsmyaMaky1mISzE4spEKAIExgbliY5Nfv6qF4EEPXs/sstj5bxnVhI4ZcBUdKCzleUkC33xkwvUVHT4LXDrXw7K4G2qIn74OxlMXSWWeycsmlnDt/JaFAb06zPcd2sql2PZv3v0xXz/iteBKmEBb4K/pnw/fPsHDKhilJVD9ISaK2yfX8mwgMJcii+7fTvWsT3Xten6CCrC8RIL8QjVNDKgQIwpRgRVWK1z4RpW8Vo2izw46tIY4eDLLY8VENVEQUm+ZW0eP4aBvCXbn9WBsPbDhIT+LU0oMvqF7MysWXcv7Ci3PSXBw6sZ9NtS+xae9LtHY1n9IxhGlKv5JEWVawSgs1WEmilM7J/5WdjiLRPHFLEk0WhrWQTQpBFgFuANYD9V7b6RBmJ4eIM0GYQFxek2LtR3NvcMmY4heri7iw27glQ8BiB1JK8eKS2XSGAv3GOdzcxdHWKLvrOthZ1058BOktBmJW2RxWLr6MCxZdQkVRr+W6vvU4m2rXs7F2PQ1tdad0DGH6YBcNUJLIy4pv+QcPxI83ZWXDz7aCNbrGQyWMGla4kPAZF0xyQZamHJMqI4JJlfH4+E4nD0ScCcIE4JbFSf7+8ngmSazWsH9LhNYdhYSwuLBP/6cKIiQXzchpa+6M8dMX93G8bXTiusoLK1m5+FIuXHwps8rmZNpbOpvZtNcIsiONB0flWMLUI1OSKG0By0pHMWxJorTwys4JNolLEk0WrHAh4aUX9NaynNSCLM0cTKoMP3AMeGp8p5MnIs4EYRy566I41y9Icdsy87Vfu3BsQwntB038Viir70bLx9Flc3Cc3JQXz+9u4PHXjo6K58a2bM6edwGXL7+W5TVnZ9o7ox1s3vcyG/euZ9/x3WjxEwmYkkS5wqvXGpZXSaI+2fDj9S5uVD5bp5OpKcjSnAFciUmVUQs8y0RJlTEcIs4E4TSj0Lx9WYrfvqfXwtW6P0TDlkJS0dx/yfaU5mtRuOyShfhsi77h/V997A3ae5KnPKeKoiouX34tFy+9MhNHFk/GeW3/K2zcs56dR7fiuuI3mo4oG5yq/u7HwAwbp2xwAZZdkqhvOoqpWpJosjC1BVma8zAuTIDNwCvjOJeRI+JMEE4TjqW566IEX7wqTlkIYm0+6l4tprs+N2YspWBHQYQDC6qxlOKqPuP8euMhXj3YTCJ1ag84n+XjnAUXcvnyazlj9lmZ9mNNh1m74xle2bOOaLz7lI4hTBLSJYn6ZMMPzLBxKoZYCZnUxBu8/F91ucH406Uk0WRhegiybJqAJGYBwI5xnsvIEXEmCGOIrTTXLkjx2UsT3LzYWJ6iTQ47Hq0Ach94zZEgj4UjVM0qAYwhPk00nuLJrcd4sbaRU6WqeAaXLb+Gi5deSWGoCIB4IsamvS/x4o4/cqBh7ykfQ5iY+EpUP/ejv9rGX2UNXZKoIdXHCmZ+J6ZpSaLJwvQTZNkcBn4BTM4vmCLOBGEMuH5hki9d3Vt03E0qmndHqH+1OKdfR9DPk5VlRMrMSsyqrG2Hmrp4dlcDW460nvJ8fLbDeQtWcvnya1kya3mm/UjjQV7c8UdeqV1Hz5S8QU8/7ALVJxu+lcmQP1xJon7Z8Ou8kkSn7jkXThO9guxCAjVLp5EgiwA3Ahswgf8wWYUZiDgThFGlMuzS8He9N4REt0XdxhI6j+XmH9tcU8nxMmO16pu3f+2eE/xuy1GSp+i2BKgumcXly6/h4qVXZIqMxxI9bKxdz4s7/sihE/tP+RjC6SddkijXDZlHSaJ2tzcXWHZh7voUbuw0noAwqkxfQZamDJMqowB4E/Do+E5nFBBxJgijxMWzU7x0h7n5uUnFrt/OMCn7s3jdcTh65tx++/5hRx0b9jXR3HXquQIc2+G8hRdx+fJrWTzzjEz7oRP7eXHHH9lUu54eKaM04VEO+Kv6x4D5q/MsSdQnG76UJJpaDCnI9r1B986pLsjSzMYkl/UDx5ksqTKGQ8SZIJwiF85MsfHj5gbYdjBE3SvFuMnch+dDPZqWpbOYXRrOaf/H37x2yoH9aYL+EFefdQNvPufmjJWsJx5lY+06XtzxLIcbD4zKcYRRxPZKEmW7IfMtSVTXawHLdkVKSaKpiwiyviwFrsJE6O7FpMqYGqvKRZwJwklQGtR846YYHzovidbQtKOAhteL+vX7QVOMyMWLKY34yZZl33p6F4eaRyceIuQPc83ZN3Ht2TcRDhgn6cGGfazd8Qybal8inhR/1biSLkmUlQ0/YwWryLMkUV2uG1JKEk0fRJANxjnAJd7r14CXx3Euo4+IM0EYAR86N8HnLkuwosollVA0bivgxBv9Rdm7D3VRUVPOjVctwO/rtaI9t6uBx18/OipzCQciXHv2TVyz4qZM0fE9x3awatPD7Dk2+ZaOT3bsItXP/ThcSSLtauKN2UlYe9NRSEmi6YsIsnw4gUmV8RKwfZznMvoorSfX1y+l/Boqh+8oCKNE2NH84G0x3n+OWbLmpmDXr2cN2Pcv21zmLazknDklOe0v7W3koU2HR2U+kWABbz7nFq4+6waCflNDYOeRrTz56qPUHt85KscQBqZfSaIsa5gdGmIlZJubWwsybQ1rSKETp/EEhAnLUIIsemC7CLIBCQGT4Xoc26S1XjmSPcRyJgiDMFARcq3h0LPlOW2vRpP8MlTA1efO5tY+Yzy3q4HVW48TT516MqiCYBHXnfsWrjrrOgKOWf254/AWntj0CPvq95zy+IIhU5KorxtyuJJEXW4/92NaiLmy/kIYALGQjYQwcBOwEZPDDCaHMDs5RJwJQh8umZNi/cf6/9NvfqGI4NGCzPvXXcU3Uz7+7JJFXN2n71PbjvPsrgbiyVMXZYWhYq4/9y1ceeZ1+B1TTWDrwdd48tVHJGHsSaJ84K/08n+lSxJ51rC8ShLlZMM3v1Odk8sLIYwPeQmy2tfRsakrPEZOKXALvakyRscLMZERcSYIWXziwgTfe1tuAP1f/TbEX8RKCCpF0lK8smAmLQXGnfhnWf3W1Z7g2V0No5IOA6A4XMr1572Vy5dfi9/nB2DLgU08uelRDjVKfrJhUeBUWH2sYOb3sCWJ0nnAvKLcaWtYslUEmDByRJCdCrMwqTICQB2wenync5oQcSYIgM/SbLgjygUzey1dn/x1iNtjxXzKZ9Ed8HGopIBdM8pA5T7Ud9e189jrR6lrGx3fVUmkjBvPv5VLl12NY5tS56/tf4UnNz3KkaaDo3KMqUR2SaJMNvwZNv7KPEsSpYPx67JKEokGE04REWSjwWLgasAG9gF/ZLqskhFxJkxrbKVJ/nNXTlsqrrjvFxX8td/BdWDjvBk0FOfm8X9k8xE2HWwmGh+9G0XQH+LWN72Ly5dfi8/24WqXV/du4MlXH+VY89Q34w+FXaBy3Y9ZcWBWYIhA/OasAPysotwJKUkkjAFGkJ3v1bIUQXZqrAAu815vwazKnD6IOBOmLZfOSbEuK7asq8HPG2uLKY47XOSHEwUhXlmUuyrzt5sOs/Fg86jEkmWzYu55vOfKj1BaUIarXTbuWceTmx+jrmV00m5MBgYrSRSYYWFHhi5J1Nf9GK83bXp0PMyCMCiDCrJUku5aEWQnTwOQwOQv2zbOczn9iDgTph1nVqbY9qneG2Wyx2Lno9XYWlGM8WhtnVPJ4fLe/GW76zv44XO1o+7tigQKeOdl7+eipVcAsL9+D/c/9+MpK8rSJYl6i3IbIeafYeMUD7ESstvNWv3o5gTlu93igxROLyLIxgpFb0xBA/ALpvKKzKEQcSZMG+66KM49t+SaUo4e8tO+rgLbe/+08hE/Z15On/98YjsNHaOfZf+8BSt59xUfpihcTDwR4/FXHuLZrauZbLkH+5EuSTSjNwlrWoQNWZIorvu5H9PxYKn2SX5NhEmPCLKxJoRJlfEacMBrm77XUsSZMC3483MTOcLsYKuicXU1Ye3dYIFvFhSxdFFvguPXDrVw30sHRn0uBcEi3n3Fh7hg0UWAyep//3M/orG9YdSPNWYocMqsfu5Hf7UJxFf2IIH4SZOKYiA3ZKJFAvGFiYUIstNFCSZVRiFwIXCQ6X4zEHEmTGkWl7ls+WQ3Iae3rfL/RXigupA5fnOjbYoE2bB4Nku97V2xJKveOMaGfU2jPp+Viy/lzy77IAWhQmKJHh556Res3f4MeoLeiHzFKld4pV9XjaAkUVYusHiTlCQSJjYiyE43M4EbMaky6jGpMibm/fB0IuJMmJK8ZUmS/7g+zoqq3MD9c+4J8/PqAub4LXp8NlurSmmoLM5sf/1wCz9ff2DU51McLuG9V36Es+dfAMCOw2/w4PM/obmzcdSPNVKssOoXA5a2hg1Zkqi1v/sxXucSPyEliYTJRVqQhc+4kODcM0SQnTYWAddgUmXsB55Bvr0ZRJwJU44H39nDe1fk5km452WHl9cX8stqU/boWEkBr82rzunz/edq2VPfMerzueSMK3nHpe8nHIgQjXXz2/UPsH7Xc6N+nKGwAuRavqp701L4CodYCdnp9q8HWS8liYTJjwiy8eZM4Arv9RuYVBliMUsj4kyYUvzv23OF2dX3hnjhoMX9cyN8odpHe9DP2jNqcvb5xcsH2XigedTnUlpQzu1XfZTlNecAsPXgZh58/qe0dbeM+rEgqyRRnxiwwAwbp3SIlZA9JhA/tySRsYKluuRmKUwdhhZkW+neuVEE2WmjAYgDmzDiTMhGxJkwJQg7mu2f6mZeSa+YsL8a4bygj5cWhym0FceLI2yePyOzvSuW5O6ndtIeHV0fnEJx+fJrefsl7yXoD9HV08GvX7yPjbXrTn1wC/zlVm8x7rQQq7aGLkmUMBnxc9JReNYwKUkkTGVEkE0kslNlNGJSZYgJfiBEnAmTnncuT/LQu3P/wa2vRPiXGSHeUeyny+9j1fLe9Bh1bVF+9/oxdta1j/pcKouqed9VH2Xp7DMBeG3fK/xy7c/oiLblP4jyShL1Lco9w8aptLB8gwfix+r7uB89a5iUJBKmEyLIJiLpVBlbgVqvTYTZYIg4EyYtc4tdDv5Nd07bhx4J8L+vO3ygxM9tJQGeXDEf18p16f3XUztxR1moWJbN9ee+hVsueDuOz09HtJ1frf0Zm/e9POg+dqHqU5Q7z5JETZ77MUuExeq8kkQSSytMU0SQTWSKMakyioDzgb3It8WhEXEmTEo+fmGC778tNzHsud8Lse+EzbrFBXRWFLN6blXO9l9vPDQm6THmVS3i9qs+yuzyuQBs2PUCv33pAbp6OrFCKiO4/DP6lCQKD1OSqI/7MVbnEm+QkkSCkEYE2WSgGmMxC2LizCRVRj6IOBMmHR86N1eYffEZP//2gp9ZPsWGJYW8uHQO7aFAZvvP1+/n9cOtoz6PgBPk1je9i6tWXI+lLJpiDfy+/T6OLtxN1aUWgRkl+IYpSdS7+jG3QLeUJBKEgRlWkO3aRPee10SQTQgWYlJl+DCJZf8AJIfaQfAQcSZMKlo+30mJyYZBR8wklI2lFBW24oklRTxx9sKc/l969A26YqNwM/BKEqXzgJ056zxuCd5OkS7DJcXmWU+zac6TJO0EpfQKQzdmAvFjdQOUJOoQASYI+SCCbDKyDLjKe70NWIdYzPJHxJkwKXjTrBQPvbsnI8ziKaj6uhFm10R83FMT4fmlvSkyntx6jKe314/sIAqccqvfKsjskkTheBGXH3gHi5rOBw0NkUM8O/9BjncdJvZGn2z4UpJIEE4aEWSTnXogBmwGtozzXCYfIs6ECc+Xr4nxpat70138apuP9zxkVNpP5oRZURZi9bK5me1rth0fUpj1liTKTkdhRJjlDLISMgVLai/m8hNvJ0iYmNvDUwcf5tnNq4k1pcAdcDdBEEaAFS4kvOR8wstEkE1OslNltGBSZcQG7y4MiogzYcJy1bwUj78vSlGvl5C3PRDk93t8+BX8tCbMigI/a7KE2Q+eq2X3IFn+rQAs+nIxgZn2oMdMtHirH7PcjyWdVbxj2YdZMnMZANsOvc4vXvgpLZ2jv7hAEKYbIsimCkFM4P8uYKfXJsLsZBFxJkw4rpib4oWP9L8RV/xnhKao4syAxb01EeJFIdYsnp3Zfv9LBwYVZgCF5/kJzLRxY5qew6n+JYkacksS2ZbNDee9jZuuug3HdmjvbuOhdT/n1b0bRvV8BWG6IYJsqpGdKiMI7EFqZJ4aIs6ECcVTH4hyw6Lcf+rvb/Lxqd8HcLWi2FI8MDeC6/fxfJYwW7PtOJsPDV0WqWilH4D6h7ppWjP0N7oZJbP48HWfYk6FSV67budzPPLSg3THuk7mtARh2iOCbKqSnSrjBPAkIsxOHRFnwoTgq9fG+KercssoLbknTG1zbyqK24ocPlMZAMfmmbPmZ9r/6eEtRBND3wyUHwrPcQBo3zR0uaYrz7yOP730dvw+Pyfa6nng+R+z59iOEZ6RIAgiyKY684E3Y6TEIeBpJFXG6CDiTBhXArbm53/aw7vO6hVXP3zVxyceD6AxwfkhBd+YFeKqAiOuVmdZzO5evXNYYQZGmFkBRXdtkkTzwNH7BcEiPnDNHayYdz4A63c+x0Pr7iOWkBIjgpAvVqiA8NILRJBNeZYCV2MWAewA1iJL00cPEWfCuFEVcan/XG75pfn/HeZgW6+1rMxWPD4/QqnPtO2YWU4qaNyTq7ce53hbfjf4ojeZfdpeGTi9/pk15/CBaz5OUbiY7lgXDz7/kyFLLwmC0IsIsulIPaY25hvAa+M8l6mHiDNhXLhuQZKn/7zXIvXYLpvbfhHK6fOmkM3P5kYAk6niyXMXZba9erCZNdvr8jqWcqDwXCPO2jfmijPHdrjt4vdyzdk3ArD76Hb+94/fp7WrecTnJAjTCRFk05HsVBltwK+QFZljg4gz4bRiKc3qD/Rw/cJeV+RfP+nnWxv8Of1mOyojzBKWYk1W5v9NB5t5cMPBvI9ZcLaDHVR070uSaOp1ac4qm8OH3/wpZpXXkEolefyVh/jDllVoLaZ5QRiIIQXZ3m1079xIdM/ruLHuYUYSJh8BTOD/PmCr1ybCbKwQcSacNu5/Rw+3n90bLNoVh0/+PsB9W5ycfheEbO7LCDOLNWcvyGx7fncDj712dETHLfZcmu2eS1OhuHrFDdx28XtwfH7qW49z7x++y+HGAydzWoIwpRFBJkAhJlVGCRDGxJjJisyxRMSZcFp48aPdXFbTa7V67oDFNT8LAbkZ+cOKjDDr8eWuyrz/pQPDpsvoi3JMfjMwLs2icDEfuObjnFlzDgBrtz/Db9c/QDwp3wAFIY0IMqGXSuBmIAQ0IqkyTg8izoQxRaHZcWc3Z1QYV+G2BouVPwzRk+xfJqnCVjy/uBCALr+PpxbNzpQQ/93rR0cszAAKznKwQ4rogSRnhM/l9lvuoDBURGe0gwee/zFbDmw66XMThKmECDKhP/OA6zBS4TAmVcbQqYiE0UHEmTBmRBxN5xd6k7a+dMTi0h+HB+w737FYtbAAMCsy91eVEABiiRTf+eMejrWeXFBx0Uo/tuvj8j23cdHNbzbjH36D+579AW3drSc1piBMFUSQCYOzGLgW493YCbyApMo4fYg4E8aEecUumz/Re0N/8ZDFFT8dWJjNdlQ/YZbmnmd2U9d2cnnGlA01Z83ipq0fobKghmQqyWMbfskf31iNlpuMME0RQSbkRx0QBbYBm8d5LtMPEWfCqFIZdvnpbTHeurQ3JuEzq/184yX/gP1n+RRrFhpX5u7q0hxh9sWHt9CTR4LZwbjsqit45+4P4XeDnGir56dPf4dDjftPejxBmKyIIBPyw8IkLgLoxKTKGDg3pDC2iDgTRo23L0vy8HtyrVzX3BvkuYMDf8zmORZPeBazrbMrOFRRDEBTZ4x/X7X9pG1bjs/Pn132AS5fei24sDX6Cvf+9of0xCXfkjB9EEEmjIx0qoxD9CaVFWE2Xog4E0YJnSPM/mejj8+sDgwY+A+w2G/x2AIjzFrCgYww29/YyXef2XPSwmxGySw+esNfMatsDkmV4MUFv+Wxb64iFh+4ZJMgTCVEkAknR3aqjAKMK1MC/8cTEWfCqLD6A73C7LzvhXi93h6072Vhmx/VmHQZ3X4f65fMAaAnkeI7z+w56TlccsaVvPvyD+F3AjR0H+fZS/6XY62HiR0TYSZMXYwgO5/wspUE5y5FWeZ/TwSZkB8VmFQZYaAJkypDhNl4I+JMOGXuuCDBjYtMbNh3X/ENKcz+tiLAX5SbBBkt4UBGmAF846mdJ3X8gBPkPVd8iIuWXgHAhl0v8Py8X1MYUbQ/LWZ5YeoxtCB7QwSZkCdzMakyHOAIsAYRZhMDEWfCSVPg15z4uy6C3qeoqRvuXBUctH+NozLC7FBZIVtrqjLb7vnDLpq6Ri6kZpfP5aPX30V1yUxiiRi/WnsvG2rXsuy/SwA1aKFzQZhsiCATRpeFwJsxiwB2Ac8jqTImDiLOhJNEs+uu7owwW7XH5m0PDC7M5jqKx70Ys6ZIMEeYffeZ3RxsGvkD5Yozr+Odl96O4/NztOkQP3n6O9S3HiOyzIevyCJWlyJ2RDJZC5MXEWTC2FEHdGOEmSTjnmiIOBNGzMJSl72f7n0YvO83AX6x1Rm0f6mteNJLl/HColl0FIQy2/71d1tp7R6ZGd1SFu+96qNctuxqANZu/wO/WXc/iZQZp6hPLU1BmEyIIBPGjuxUGd3ArxE35sRExJkwIu6+McZnLu39Z/7VNt+Qwgzgu7ONGGsP+nOE2TfX7BqxMHNshw9fdyfnLriQeCLG/c/9iE17X+rtoKDoQiPO2jaKOBMmB72CLL3KUgSZMNr4gRsxFrONXpsIs4mKiDMhb57+YJTrFva6CT/2WICfbB5amH2gxM+5IR9Rx2btGTWZ9s/9auQZp4P+EJ+46TMsmbWMrp5Ovvfk3eyvr83pE17iwymxiDek6DkoLk1h4jK4IEuJIBNGmQJMqoxSoBjYguQwm9iIOBPy4t7bejLC7GCr4szvhulODJzDLE21T/GF6iAa+OOZ8zPt/71m5KsyC0NFfOotf0dNxXxau5r59u//k7qWo/36Fa30rGbi0hQmIEMJsujeN+gSQSaMOuUYYRYGmoEnEGE28RFxJgzLv1wb40PnJQHYdMxi5Q8HrpGZjQXcPStESsHqcxZl2p/dVc+RlpFl6i8vrOSut36eyuJqGlqP8+3f/yfNnY39Oyoo9sRZu7g0hQmCCDJh/KgBrsekyjiKSZUh98bJgIgzYVCKA5rDf9tFocl+wf1bfHzg4UBe+/6/mSHOD/l4IkuYvXaohd+9fmxEc5hVVsOdb/l7iiMlHDqxj++uupvOnvYB+4YW+nDKLOKNKaL7xaUpjB8iyITxZz5GmFnAbkyqDEnIPVkQcSYMiM/StP5DV+b9oTblCbOhXZkAHyn1c0uRw8b5MzJtL+9v4levHBrRHBbNWMonbv4M4UCEXUe38YPV/00s0TNo/+I3idVMGD9EkAkTizpM8fJaehcACJMFEWfCgNx7WyzzetUem7c+ECQfYXZneYA7KwLUVpXQUGxKNP1m0yHW720a0fFXzDufj15/F36fn837XuZnf/gfkm5yyH2K3mQWJ7S9IiuQhNODCDJhYpGdKqMH+A2yInNyIuJM6Mc3borx/nOMEHqy1uatD4SG2cNwa5HDnRUBjpQWsntmeaZ9pMLs4qVXcPvVd2BbNmu3P8Mv196L1kNnrg4tsPGX2ySaXaL7hhZxgnAqDCvIdm0iuuc13B4RZMLpxA/cgKmPmU4vJMJssiLiTMhh4190c+Gs3riEW+4fPOt/Nm8K2fzHzBAxn82Wub3Z/7/48JYRHf+iJZfzwWs/AcCTrz7K7155KK/90oln2zbGpQKJMOqIIBMmNhHMiswyTLqM1zCWM2GyIuJMyPDjP+nJCLPDbYqF3wqTjyuz2FL8bG6EhGXxh7PmZ9q//Ogb9CTyD8xfXnM277/6DgAeXv8gf9iyKu99ZZWmMNqIIBMmB+XAzRiB1oJJlSHCbLIj4kwA4OU7unnTbCPMapsVS+8Jo/MQZgpYv8SUZlq3ZHamfc32Ojpj+bsX51Yu4I4bPo1t+3hq8+MjEmbBeTb+KptEq0v3HnFpCiePCDJhcjEHsyLTDxwDnkJSZUwNRJwJfPrieEaY/eMf/Pz7Wn/e+35jlolHe72miq6g2e+FPQ2s3no87zEqiqr4y1s+R8AJsmH3Wh57+VcjmH2W1WyTuDSFkWME2XleLUsRZMJkYS6mHJOFWZH5LJIqY+og4mya41iab95svmk9tdcekTCb51jcWOiwdXYFR8sKM+2Pbu6fuX8wCkNF3PmWv6cwVMSOw1u4/7kf5T95Dyl0LowUEWTC5KceaAMOAi+P81yE0WZMxZlS6mbgm4AN/Ehr/e99thcD92G+AviAr2utfzqWcxJ6ObsqxaaP92br/5MH8wv+T/PEwgJiPptDFcUAROMpvvRo/gsAAk6Qv7zls1QWV3PoxD5+tOYeXHdkyWMDc2wCM2yS7S5du8WlKQyOCDJh8pMONdFADHgYkPveVGTMxJlSyga+g1nbewR4RSn1mNZ6e1a3O4HtWutblVKVwC6l1P1aazGBjDH/cEWc/3td72W+47EAsdTwMWYAQQUvLDaWss3zqjPtX1u1DTdPt6Jt2dxxw18xt3IhJ9rq+Z8n7h4ywexgZBLPboqLRV/ohwgyYergYB6n7cBar02E2VRlLC1nFwG1Wut9AEqpXwC3AdniTAOFSikFFGCqssqnbYw5uyqVI8yW3BOmttnKe/9XlxYBsGNmOc0FJubsR8/vJRrPz+qlULz/6jtYXnMOHdF2vrPqP+mIDlySaTgyhc5llabgIYJMmHpEMCsyy4Fu4FXvtzBVxOqzawAAIABJREFUGUtxNhs4nPX+CHBxnz7fBh7DLDMpBN6jte5n/1BKfRz4uHlnj8FUpxdb/rLXlam+UjCifd9fYrLwNxaE2F9VAsDaPSfYWZe/uPqTi9/NRUuvIJbo4X9WfZ3G9oYRzSFNYJZFcLZNstOla6do+ulMRpCdsZLgPBFkwlSiDCPMCoBWTKoM+RxPdcZSnA3kI+vr9LoJky3vzcAiYI1S6gWtdc6TXmv9A+AHAEr5ZT3eKbDjzt56me//bX5FzDP9Sxz+T3WIuqIwry6YmWl/ZPORvMe4ZsWN3HDe20ilkvxozbc41Lh/RHPIJm0163g1AVLnfNohgkyY+szCrMj0A8cxqTJiQ+4hTA3GUpwdAWqy3s/BWMiy+Qjw79rU5qlVSu0HliFLT8aEb90SY1mF0bYdMXjgDSfvfW8u9PF/qkOcKAjlCLO7V+/Me4wLFl3MOy57PwD3P/cjdhx+I+99ByIdb9YmqzSnDUMKsn1bvVqWIsiEqcBsTNZ/C9iLSZUh30KnC2Mpzl4BliilFgBHgfcCt/fpcwi4DnhBKVUNnAHsG8M5TVt+8+4o71je+49d9O/5uzPfUujj67PCdAQcXlk0K9P+X0/t5HhbdIg9e1k+52w+eO0nsJTFoxt+yct7Xsx/8gPgn2ERrPGR6nLp2i7146YyIsiE6UkDJuP/EWDDOM9FON2MmTjTWieVUncBqzGBYj/RWm9TSn3S2/494F+Ae5VSb2DcoJ/XWjeO1ZymKz/6k56MMOtJQsHXInnv+9nKAB8rC5BSiheWzc20f331Dura8ltdecbss/j4TX+DYzv88Y3VrHntdyM7gQHIJJ7dnEDLl8kphwgyYXqivB8XU7T8UWSN3PRkTPOcaa1XAav6tH0v6/UxjENdGCPesTzJx843/9xP1tpeIfP8UmbM8ik+Vmbi0p5asSDT/sjmI3kLsyWzlvOJmz+D4/Ozdvsf+M26+0Z2AoOQjjeTxLNTBxFkwvTGwTiSejAuTBBhNn2RCgFTGIXmN+/uFVG33B8a0f4/rTEWtqZIEG0ZQffwq4d5sTY/4+aimWfwyZs/i9/nZ92OZ/nlCz8b0fEHw19pEZrvIxXVdG4Tl+ZkRgSZIACEMSsyK4AoJnVG15B7CFMbEWdTFFtpkv/c+899yY9GJszeUujj/7N33+GRX+Xd/9/nO00jjUa9S9v7entxWa97x8aYDgkkQAqhPJAEnpBfEsLz/EhyJSQkQIAEAnYCBuxA3HvZXXu99np7771qtavep5znjzOakVaj0UhTNbpf1+XLX42+MzrS2rMfnXPu+zQ4Te+zp4uLqAJ8gWDcwWxG1Ww+d+9XcDlcvHPoDX75xk/RSTr4cuC4po4d/Wj5xXLCGT2QbaPnyA4JZGKSKMFs/B/cKkOC2WQn4SxHDQ5mLx21sflc/P3h3l/k4JvVJsz1WIqqclM88PDG+Go1plXO5I/u+youRx7vHt7Ioxv+I2nBDKTx7EQkgUyIaGowO3tcwEXMFm1plSEknOWkTy6JLPW9ccrinjEsZ9ogHMw08NL8aTiB8609HG7sGPX5Uyqm8/n7/jdup5utR9/m5+t/jOmUkhyOcov8GXYCvZrOPbKkmc0kkAkRSy1mxswGnABeR1pliAESznLQf74v8pvXzY/kj+m5/1ATCXJ/6y1imd0sbR5v6hz1ufXlU/nCe/4Mtyuf7cfe5b9e/zeCww98SEi48eyufrRks6wjgUyIeF0CrgCNwDsM79EuJjMJZzlm46cif+nN//7Ygtm9hXbu9ZrGtN/oUaxeUg7AztMto54CUFc6hS++52vkuwrYeWILj7z+g6QHMxjUQkOqNLOG5faQPzt0lqUEMiFiUJimsgFMJeYzyGyZiEbCWQ7565v7WDPFBKJHdto5eDn+w8y9FvxTrQlzXUHN6utmhj/383dOxnxudUkdX7j/zyjI87Dn5HYefvX7BIPJf8NxlFrkz7IT7NN07JZps0ySQCbEWNmBOzD9y14LPSbBTEQn4SxHXF8f4Bu3mMDy6nEbn3oqb0zPf31mYfj6H5wF4RPq/+65fTGfl+8q4LP3/CmFbi97T+3kJ698j0AKghmAd4WZ1evY7UPLxFnaSSATYrzcmFYZFZg+ZoXA6Ht4xeQl4SwH5Ds0mz4TOUbpnp+PLZh9rsxJfqiP2Y+v9HHNmqkA7DrTwpWukVOQUorfue2PKPdWcOrScf7jle/iD6aut0W48axUaaaNBDIhElWM2fhfCLRhWmVIMBOxSTib8DRtX4u0zXj/Y3kEdHwnAACsybfxhXIT5jZ1+dnk9fAhl/nP4tfbzsR87r0rHmLhlCV09nSYYBZI3VKjvViRP9tO0Kfp2CXhLJWsvALy5yyTQCZEwqoxrTLyMBv/X8LMnAkRm4SzCe4D8wOECir5/PNOnjgY/x/pH5Y6+VKFCWbPtvt41ObiEyvN+Zn7z7fR0z/y8uQ1U5Zy34qHCAaDPPza92npvDL+byIO3hVOlKXo2NlPUN7bki4SyFaQN3WeBDIhElYFvAfTKuMkZp+Z7DET8ZFwNqFpHvugSSpbz1v8YItzTM8eCGbN/iB/dqGHb314HgCdvT5+GqPhbLm3kk/e9lkAntny3xw6F3tfWjKEG89KlWbSSCATIpUuE2mX8TbSKkOMhYSzCey79/ZjC82a/dFzrjE999UZnvD1e050cfc1NeGPv/XSwRGf57A7+f27vkS+q4BdJ7byys5nxzbocbB5FQVz7QT9mo6dUqWZCAlkQqTS4FYZAeB5ZLZMjIeEswnKZdN8cbUJKr1+2Ho+/uOZ/roqj1pH6NzMtn66FNyxoDr8+a6+kTf1f+ymT1NXNoXG1gv8bN2/j3P0Y+NdHlrS3N1PsEd++xwrCWRCpIMduD10/TJmpkyCmRgfCWcT0IySIMf+V+Qv0hnfib/Z7EynxUeKI8ufX7vYy9fuWxD++JvP7h3xuTctvIPVs9fQ5+vlxy9/h15fejZ/FUmV5phJIBMindzA3UAlZsO/F1OZKcT4SDibYNx2PSSYfelFJxc642s26wCemR5Zzlx4qJ0qbx7lHrMk+uLe87R2R182nFE1mw9c/1sA/Hz9j7nYcm6c38HY2DyKgvl2tF/TsUOWNGORQCZEJhRhWmV4gXZMqwwJZiIxEs4mEIXm1JcjbTM+8YSLn+92xP38b9VGzs28/VgHGvjSHXMBONPcxav7G6M+r9BdxGfu/CI2m53Xdj3PjuPvju8bGAfvcgfKpujY00+gS5Y0ryaBTIhMqsLMmOVhNv+/BPTEfIYQ8ZBwNoHs+MMeKgrM9R3/lcdrJ+L/41PAXYUmyL3Z6eeCX/OZtTNwhvpwrD90acTnfuymT1FUUMKR8wd56t3Hxz3+8fDKWZrDSCATIhtUYFpl2IFTmFYZqWvCLSYXCWcTyJJqc27m4StqTMEMTBHAgP91vpv/+75F5DvNa7x1pIldZ1qjPm/xtBUsnraCnr5uHnktNWdmjsTKV3gWONABTfv2yb2kOVog6z60je7DOwn2do3ySkKI5LgCXMQsYW5CWmWIZJJwNkF8flVk5uj6n8RfAADgseDDoSKAjV1+Vs2qCAezHadbeGLH2ajPy3Pk8aE1nwTg6Xcfp607eoBLFe9yB8qu6NzrI9A5+d74JJAJkW0UpqmsHwhiljGlIlMkn4SzCWBlbYB/vc+Es85+aO6J/3gmgL+tjuw1+6vWAF+9oR6AwxfbefSdkyM+7/5VH6LEU8qJxqNsPPD62AeeoHDj2UlUpSmBTIhsZQNuw5RWvYgJZxLMRGpIOMtyCs2W349sMC38u4IxPb/IgjtCe83+s7mPP757XvhzP3v75IjPm1oxg5uuuYNAMMAv3/gpWqd35spyKzzXONBBTfv23A5nJpANHC4ugUyI7JOH2fhfBfRhKjRbMjoikdsknGW59b8bCWYP/DIPM60ev+/URZZAfxGw8aXQkQI/XHeEHl/03/osZfGxmz6NpSxe2fUs55tjH4CeCoVLHVh2RecBH4H23FvSlEAmxEThxbTKKAI6MK0y0rvFQ0w+Es6yWI0nyE1TTRHAIzvtPHt4bH9cJTbF6nzznB9e7uOjq6aHP3esqXPE592y6G7qy6dyub2JF7Y9OY6RJ65oVe41nh0xkAUlkAmRnSoxM2ZuzFmZLyCtMkQ6SDjLYq99MvIm8Kmn8mLcGd1bswrD1+96C/hQkdl79syukRvIlnrKec/KDwDw+MZH6Pf3jfnrJsrKA8+i0JLmtokdziSQCTFRlQH3Y/6aPA28irTKEOki4SxLzSoNMr/CLOf9/jNjO9Qc4I/LI8/5fHuQD62dGv54Q4yeZh++8XdwOVxsO/oO+8/sHvPXTYbCJU4sh6LrkA9/68Rb0pRAJkQuaAbOAd3ARqRVhkgnCWdZ6sgXIw1E/2P72P6YXAp+v8yEs36bxXvWzgx/LtbZmctmrOaaqUvp7uvi15t+PsYRJ493Ap6lKYFMiFzhAHyYMPYKpipTiPSScJaF5pRF3gxW/MjNWIsAHp8aqej8p8IiloSuH3nr+IhnZ+Y53Xzwhk8A8NTmx+joyczZcMoJhYtNdWn7tuxuPBsOZHNXkDd1PsomgUyIicsG3ArkA89h2mRIMBOZIeEsC/3l2siM0fYLtjE9d3Gejdku85zHfBZLppYC8NbRJvaeGzlwve/aj1JUUMzxi0fYdGD92AedJIWLHVguRfdRP77m7HtjjBnITuyj++BWCWRCTDguzMb/aqAfKMacACBEZkg4yzKragN8YonZdPrJJ8a218yl4FehWTMNFK6MVGc+s3PkIoCl01dy44Lb8AV8/OrNn6IzuLfCuyr7Gs9KIBMilxViWmUUA52YikzpYSYyS8JZFvG6NO+GGs7+cKudn+12jOn5O+Z4w9ff1E5WhK7/441j+IPRA1eJp4yP3/x7ADz5zq843xz9KKd0UA5TDACZ328mgUyIyaACuIdIq4wXMQUAQmSWhLMs8vJvR1pn/NXrY5s1+8agg80PBGDF8gYA3jl+mYMX26M+x7JsfOr2z5PvKmDPye1s2PvyOEadPJ5FDmx5iu7jfnyX07+kKYFMiMmkBHgA89fgGUyrjOze5yomDwlnWWJuWZBr600g+at1Tq6M4fzMSrsKH2z+eqeP/XPqmRH63Kajl0d83ntWPMSM6tm0dDbz8/U/HvfYk6UoA1WaEsiEmKxagFOY3mVvIK0yRDaRcJYl/mxNJJD8zRtjW87869CsWVBr/jXg4PcqPAA8ueMs51ujd7OeW7eQO5c9QDAY5JHXf0BX38gnBqSDskPhsvSEMwlkQkxmA60yAF5HQpnIRhLOsoLmwwtNEcBHfu1Cj6F1xlSHxa0eE+Yeb/PxezfPB8DnD7LxSFPU5xS6vXzy1s9iKYtnt/6GYxcOJTj+xHkWOrC5FT2n/PRfSv6S5uiBbBvdR3YS7MlsSBVCpIoNuAVTAPAsZsZMgpnIThLOssDXb/ZR4AR/EJ4+NLY/kr8ctNfs1MwaFoWuv/XSgaj3KxSfuPUPKSoo5sj5A7y046nxDjupBqo027ckb9ZMApkQwnABdwE1RFpljLzlQ4hMk3CWBf6/G00gOdqs6PXHP2tWbVesKTB/hN+zu1lUXwzAK/sv0twVPeTctuReFjQsprOng0de+yFaZ/43R2UD7zIz+9eWYDiTQCaEGOrqVhkvYo5mEiJ7STjLsE8u8eEK/Snc96h7TM/9l9p8APyWYubC2vDjL+29EPX+qZUzee+qDwHws/X/Tlt3dvTyKVjgwFZg0XvGT3/j2Jc0rbx83LOXUTBPApkQYrByTKuMfExT2RcB2Usqsp+Es4zSfPeePgDOtClOtFpxP3OG02Kx24SQf3F7WBB6/KuP74h6f57Tzadu/zw2m53Xd7/AvtO7Ehp5MhUNNJ4dw6yZBDIhRGxFmFYZDuAs5pxMaZUhJgYJZxn0x9f5KAptGXvgl3mxb77KL6eYkwAu+YMsmFUJwOkrXSNub71n+YOUeys43XScpzc/Pt4hJ58NCpeHztIcpUpTApkQIn5twHHM2cRvIOdkiolEwlmGuO2ab99twsjX1znZ1Rj/GZpfLndRaDN7056tqaA69Ph/bjoR9f6i/GJuWngnAL9446f4g/7xDzzJCubZsXsses8F6Ds//M1TApkQYmycmE3/IP3LxEQl4SxDPrM8Mr3+92/F39dsutPiD8rM6QHr8vKorjVFAIcbO2jriT5lf/fyB3Hanew4/i5nL59KYNTJF63xrAQyIcTYWcDNQCnwNGYJU4KZmJgknGXIX641Qeq14zb6A/FXaD433RO+7plbB0CvL8CPNhyNen+pp5wb5t1CUAd5bstvEhhxCljgXWHCWcdui4JFaySQCSHGwYlplVGLmTUrAS5ldERCJELCWQYoNCVu8xvdwzvj/yP4ozJn+PqHpSVMDV0/8tbxEZ9z38qHsNvsbD68kYut58c13lTxXOPB7rXwdxZS8dA3UTbzs5BAJoSInwdTkVmKqcR8EVOZKcTEJeEsA66tD+K0QSAIj+6J/4/gi+WmaOCtgnymNpQCsPVkM0cvRQ8vVcU1rJ59I4GAnxe2PZH4wJNg8JJl8XVHgM0EeleAUhLIhBBjVIYJZgWY3mUvIK0yRC6QcJYB/3SXaZ/x3XcdEOdRTV8uN/vM/JaibVZN+PHHtoy8h+y+le/Hsiw27l/P5fbMTfFH30MWxO59EoArL56i9a2vSiATQoxBIaZVhhM4D7xMpBBAiIlNwlmaeZyaGxpMVeLG0/FVaFoQLgLYOKc+/PjfPLuXkRr815VNYcXM6/D5+3lx+5MJjXk8Ym/q30+gYwOF8zvpvxSg+dVtaR+fEGKi6wCOYvqYbUBaZYhcIuEszTr+PDLl/uTB+MLZP9eakwNa8110u8y+s5+8eYyW7pEbKt6/8gMAvLn/dVq70nMSwGiBrPvg1vCSZfXH84E82kbpbSaEEEO5gL7Q9VtIRabIRRLO0mh+eeQ3u0884SKoR1/SLLEp7ix0oIFNsyOzZgcutI/4nGmVM1k0bTl9vl5e3vFMQmMezVgCWZga1EIjiQedCyFymQXcBFQCT2ECmgQzkZsknKXR25/pBsxRTT/fHV9vsw0zTeuM7dOqw4/91wjNZgfcv+qDAKzf8xKdvSOHuPEaVyAbxD3DjqPUov9KgJ4TgaSPTwiRaxyYVhl1mP5lJcDFjI5IiFSScJYmDkuHj2r6PxucsW8OWe62YVeKHoedxiJzXNP6g43sPts64nPm1V/DvPpr6O7r4tVdzyc87gGjBrJDW+k+HF+VZdGq0HFNW+ScOyHEaAqAezGtMroxrTIuZ3REQqSahLM0+d9rTBBp6oKf7Ihv1uyPQxWaB2tKw489v2fkXmV2m4MP3/g7ALy84xl6+rvHO1wguYFsMG9oSVP2mwkhYivFBLMCoAXTKkOqukXuk3CWBlUFQb55mwkih69YcT3HqWBFvp1TZV4ulBQC8N9bTxOMscXi7mUPUFlUzfnms7y+58VxjTVVgWyAe7oNZ7kNX3OQnmPZc8anECLbFADvRVpliMlIwlkaXPxKZAbrt5/Ii+s5DzfkE1Swr74i/Njm4yN3va4qruHOpQ8A8Ks3HyYYHONeLqUouvEBim+4P/xQsgLZYENmzWQvrxBiRF3AQSAfWI+0yhCTiYSzFKsrjLyhfO1VJydbR585U8Ayt51NM+vCj/39C/tjPucjN/4udpudTQfWc/zi4TGN0VZYSsVDn8VVMw2AnpMH6D64JWmBbLCiVcMPOhdCiIg8oDd0/U4mByJExkg4S7FPLIks3f39W/EVAtyQb0MDrQVmlu3Nw5do6ugb8f5Vs9cwp24BHT3tPLX5sTGNr2DRGsru+jjKbvbBtaz/De2bXxrTa8Qrb6oNZ6UNX2uQ7iOypCmEGExhWmXUYFpl9GR2OEJkkISzFPvytaYQ4F/fja8IAODHDQWcKS0Mf/zMrnMj3pvvKuD9138cgCff+SVdffHNdFl5BZTd8wny5y4HoP/yeZp+/T38bak7MDjc22ybLGkKIQZzAHcC9YAfKEbCmZjMJJyl0PX1Aao8JoU8ti++H/X1oVmzPQ2VAFzu6ItZBPDgtR+h0O3lyPkDbD68Ma6vYbk9VH3sT3FW1BHs66Vt0zO0b30dxrpPbYy8q6TxrBDiavmYw8vLMYHsRaApoyMSItMknKXQd+81S5E/22WP+xzNh4qcnB00a/bwW8dHvHdG1WzWzL8Vf8DPr958OK7XV0431Z/4cxwlFfhamrj0+L/gb039G6Gr3oar2oa/PUjXYVnSFEKAaSZ7L+ABWjGtMjoyOiIhsoGEsxSxKc3KWlMM8Pj++H7MTgX3ex08H5o1O9PcTWN7b9R7LcvGR9d+CoBXdj5LY+uFuL5GzSdDway5kcZf/COBrra4npeocCHAtn4puhJCAG5MqwwXptv/S0TOzBRicpNwliIPzDVLhN0+ePZwfLNmP67PZ8v0yDFND288NuK9a+ffRm1ZA01tjby84+m4Xt9VNxNHmXn9pqf+PW3BDKTxrBDiaj3AXszs2TpAjnITYoCEsxT5q5tMCDneYmGqkGIrtikWFuex3muOadp/vo323ujLf3mOPO5Z8T4Annjnl/gCcRyDpCxK7zSFA22bnsN36Wwc30VyuGot8ups+DuDdB2UJU0hJjc3kc3+2zI5ECGyVnzt6sWYzCkLsrzGrN19fV187TO+UuHieEVx+ONHYuw1u2Pp/RS6vRy7cIjdJ+N7cytcfgvOqgb8bZdpe/uFuJ6TLAOzZh3bffLLsRCTlgLWAg9huv8LIUYi4SwFDn0hciLAkwdHX9JUwPsHFQLEOqapKL+Y2xbdY147zp5mVoGX4rUPAtD86mNof3qXFgf2m7VJlaYQk5QDuBuYj2kyWxz7diEmOVnWTLJl1ZGpoZsfcaPjWNLcMNNDk8dN0LLwBYK8G+OYpvtWvh+nw8XO41s40XgkrjFVvPf3sVxuuo/upuforriekyzOaou8BjuB7iBd++NYfhVC5Bg3piJzoFXGS8CljI5IiGwn4SzJvnKDCSDbL1i8cWr0WTOXgnK7xebKEgAcNmvE/qzVxbVcP/dmAsEAT7/7eFzjcc9YRN6UuQC0vDa20wOSIdx4drsPLUuaQkwyxZhgVgi0YVpltGd0REJMBBLOkmxWqdlrduRKfCvGf1vtptPl4EqhG4B/fuXgiPc+eO1HsCyLN/a9yqW2i3G8uqL4lvcD0Lrx6bT0M7vawH4zOUtTiMnGhWmVkQc0YmbMorcGEkIMJeEsqTSr60w4+8Xe0X+0i/Ns3Ot1sKW2LPzYuZboR5bMrJnLomnL6fP18sK2J+IajXf1HTgr6gj0dNL+7stxPSeZnBUW7ml2Aj2azn2ypCnE5NIH7AIqgdeRaiAh4ifhLInunxN583nl2OhLmn9QamaVmkLtMzYeGXlm633XfhSAV3c9T0fP6MsCyuWm6Ib7Abjy4s/QvvTPXIWrNHf2oyWbCTFJDG6Vkd49rkLkCqnWTKJ/vtt0tw4Eocc/eiHANKfFuRJP+ONX9kdfqrxm6jKmV82ivbuV13Y9H9dYitc8gOVy03vmMD2Hd8T1nGTzSpWmEJOIAm4EPoDZYyaEGC8JZ0nS4A0yq9Rs5X/wV3mj3l9qU8xw2cK9zQJBTVdf9Aatdy97LwCv7HyOfv/ox5s4ymooXHErOhik+dVfxfstJJWjzCJ/hp1Ar6Zzj0ybCZHb7MBdwALAibTKECIxsqyZJH9/R2R26Lkjo/9YV+fbaHW76HC7AHjs3VNR75tTu4DpVbPo7OngrQPr4hpLyR0fRVk2OnasT+tJAIOFlzR3yZKmELnNDdwDVGA2/L+EKQAQQoyXhLMk+dgiM+v1dxsdcd3/11VudjdUhD/efrol6n13LXsAgHV7Xopr1sw9eynuafMJ9HTR+mZ8Z26mQtFK83NolyVNIXJYEaZVhhfTIuMFTMsMIUQiJJwlwcraSCHAIztHD2cKsLsd4VmzH6yL3kx2auVM5tVfQ09/D2/se2X017U7KL3twwC0vvkUwZ7OOEaffI5Si/zZDoJ9mo7dMm0mRG5yYlpluDFNZV9EWmUIkRwSzpLgffPMrNmFDsXhOPqb3eqx8/asOgAOXWzneFP0EHV3aNbszX2v0tPfHfWewbyr78JeXE7/pbN07nwj3uEnnXeFCagdu31omTgTIkf1A9uBOkyrjOh7ZoUQYycFAUlwx3Qzc/YPm+Jb0vzHGjf9DpOLt55sjnpPbWk9i6etoN/fz7o9L476mrbCUrzX3QtgigB0MK6xpII0nhUil+UPut4HvIwEMyGSS8JZglbUBLi2PkhzD/x42+jhbJ7LorcgUs25c6S9ZkvNrNmmA+vj6mtWcusHsBxOug5soe/M4ThHn3z2YkX+bDtBn6Zjl4QzIXKHAtYAH8TsNRNCpIqEswT99c0mgDy800GXb/TeZh8ocnK0ypyjefpKV9RzNMu9lSyfeR2BgJ/Xdo/e18zVMIeC+asI+vppWfebMY0/2bwrnChL0bnHR1C2nwiRI2zAncBCwIG0yhAitSScJUChuXe2WdL8nwOjb99rcCh+q8RJk9csC4zUdPb2xfdhWRabj7xFS+eVUQZhUXqHOT2g/Z0XCHREXyZNl4ElTWk8K0SuyAMeAKZhNvw/B0Rv/SOESA4pCEjAZ5b7sVtwqUux6czoOfeXUwpo8rjRysywHW7sGHZPniOP1XPWAPB6HKcBeJbehLOyHn/r5YycnzmYzasomGsn6Nd07JQqTSEmvqtbZbwItGZ0REJMBjJzloCbpphZs/4AmP0YI5vqsCixW2ydXgNAY3svgeDwRc1Vs9fgcuRx5PwBLraej/maVl4BxWsfBKD59cfR/swGIu/y0JLmXh/BnmgLtkKIicOOmTHzAk3AU0gwEyI9ZOZ2KP/yAAAgAElEQVQsAZ9YYiqU/vw156j3/kmFi3Xzp6AtE+Ie3ng86n1rF94BwJv7Xxv1NYvXPojNXUDPif30HNkZ77BTpkiqNIXIIX5gKzAVeA2pyBQifWTmbJz+YEVkluqZQ6Nn3OvL8+l1mmrON49c4nLn8G7/M6vnUFtaT3t3K7tObI35es7qqXiW3YQOBGh+LTPnZw5m8ygK5tvRfk3HDlnSFGLiKhh0fRBzHJMEMyHSScLZOP3eskgAaeuLvaR5U4GdN+c2ANDrC/DUjnNR71u74HYANh3cQCAYiHoPgHK6qPzgF1HKomP76/ivRC8sSCfvcgfKpug84CPQJUuaQkw8Crge+BBQkuGxCDG5STgbp1V1psnrn748+pLmA+WRIoB1B6MfCOzJ87J0xmqCweCoB5znNczFVuDF39mW0fMzBws3nt0is2ZCTDw24A5gUeha+pgJkUkSzsahsiDSff/hHbEbzzqAhqll4Y9fOxA9nN0w72bsNjv7Tu8ctX2GZ7Gp5uzcvg7tG/0w9FSz8hWeBQ50QNO+XfabCTGx5AH3A9OBPuB54GQmByTEpCcFAePwwByz5HixU9HSG3tJ83O1BZytNksEbxy+FPUepRRrFtxq7tn/aszXsxUW4561BB0I0LF741iHnhLeZQ6UXdG5z0egU5Y0hZg4vJhWGUVAB6ZVRvRTS4QQ6SPhbBz+MFQM8NMdo//4Zi5tYGAu6fURZs0WNCymrLCCprZGDp7ZG/P1PEvWoiyLrgNbCHaNfqxTOnhXhRrPSpWmEBOIDTNj5gEuY4JZd0ZHJIQwUrqsqZS6Ryl1SCl1VCn1tRHuuUUptVMptU8ptSGV40mWOq+ZHXrnrC3mfRV5dvo9bgDW7ThDZ1/0iqeBQoC3DqxDRz3QKURZeBavBaBjR3b8qCy3wrPQgQ5q2rdJOBNi4ggA7wKngWeQYCZE9kjZzJlSygZ8H3Mg21lgi1Lqaa31/kH3FAM/AO7RWp9WSlWmajzJsrAiQG2hps8Pzx6OHc4+fd00AOw+P88duRz1nuKCEhY0LMEf8PPOoTdivl7+7KXYC4vxNTdm9HDzwQqXOrAciq6DPgLtsqQpRPbzAJ2h66Ohf4QQ2SSVM2ergaNa6+Na637gV8CDV93zceB/tNanAbTW0TdlZZFv3GJmh5q6FXqUUwEqKgsBsJ0b+bzL6+behGVZ7D65jc7e4cc5DVaw8FoAug9vH8uQU6pIztIUYgK5DtMqozzTAxFCxJDKcFYHnBn08dnQY4PNAUqUUuuVUtuUUp+M9kJKqT9QSm1VSm2FYLRb0uaDC0wxwMHLsX90n55VGr7+z73R+5ApFNfPuxmATQfWj/q1nTXTAOja904cI009Kw88i2VJU4jsN9AqY3Ho2pvZ4QghYkplQUC0aaWr173swArgdsANvK2UekdrPWTNTmv9I+BHAEo5M7Z29hdrIwHk88+7Yt57W0UBA5Fsa3f0hrJz6xdSVljB5fYmDp3bF/P1bAVF2AtLCPb14Luc+aazAIVLnGZJ85APf6ssaQqRnVzA3UA10A+8DMQ+t1cIkVmpDGdngYZBH9cz/B3hLHBZa90FdCml3gCWANmxoeoqX7/ZhDNfAA5fGXnmTAG9pR4Adu84NeJ9N8y7BYC3D26IXQgAOKunANB/8TTDM25meOUsTSGyXCGmVUYxZp/ZC0irDCGyX9zLmkqpgtHvGmILMFspNV0p5QQ+Clzdzv4pYK1Syq6UygeuBQ6M8eukRY0niDO0/7/227F/FPeVu2ktyMMKBnnseGvUezx5hSyetoJgMMjmw7ELAQCc1dMA6Lt4cizDThnlhMLFpgFv+zY5FUCI7GNhWmUUA1cwb7cSzISYCEYNZ0qpG5RS+wmFJqXUEqXUD0Z7ntbaD3wBc2ruAeBxrfU+pdRnlVKfDd1zANNcZzempvs/tNaxG31lyEPzI20wLnfHLgS4tqEYgIr2bvoC0ffIrZ5zozkR4MwuWrtGf8OMzJyNPBOXToWLHVguRfdRP77mzO4DFEJEEwTexmz9fRroyuxwhBBxi2dZ858xGxaeBtBa71JK3RTPi2utn8ecBTL4sX+76uNvAd+Ka7QZ9P37zNLdEwdit8+wAeVTyugGdp4ZOXQNLGluGuUczQEDM2fZEs6k8awQ2aoQ0+0fzDFMJzM2EiHE+MS1rKm1PnPVQ9F3uOcopy2yx2tfU+wf2eemFdHtMst9PzoavYXGjOo5VJfU0tbVwr7Tu0b9+jZPEXZPEcHebvytTWMYeWoohykGANlvJkR2WY1plVGV6YEIIRIQTzg7o5S6AdBKKadS6itk6b6wVLm2LrJs9/V1zpj3Tl85DQAd1HT4oi/3XT/XTDy+c/hNgnr0JcHIfrPsmDXzLHJgy1N0H/fjuyxLmkJkngXcBiwNXRdmdjhCiITEs6z5WeA7mB5lZzF12J9L5aCyzao6M1F4rj1249n3TysmaJm8+z9vHIl6j8uRx/KZppnsOwdHLwSA7NtvViRVmkJkESdwF1CLaZXxCnAuoyMSQiQmnnA2V2v9W4MfUEqtAd5KzZCyz0PzTDHAo3ti/7jWrJhqmlxozduXom++XTZjNS5HHkcvHKSpPfpB6FcL7zdrzHw4U3YoXCbhTIjs4MG0yijBbPh/EVOZKYSYyOJZ1vxenI/lrBunmKW77RdG/nE5LIW2mc+3nRu5EGBgSfPtOGfNAFxVoZmzC5kPZ56FDmxuRc8pP/2XZElTiMxRwHswwawZeBIJZkLkhhGngpRS1wM3ABVKqT8Z9CkvpihxUihyRYoBNp0Z+dv++vVTwtd/t/l01Hsqi6qZWTOX3v4edhx/N66vbyssxuYpItDThb8t+uHp6TRQpSmzZkJkmgY2AdcAr2GWNIUQuSDWOp0TM2duZ+ju0nbgg6kcVDa5d3akv9mZ9pFnzkrLC+kBrPZu/IHoHfyvC82abT+2mX5/X1xf31k1FciSJU0beJeZSlQ56FyITBncKuMMQ48wFkLkghHDmdZ6A7BBKfWI1jrzySBD3jvHFAM8tnfkHFvhcdETap/x663R3ygtZXHtnLUAvH0o/iVNZ00onGVBMUDBAge2AoveM376L8qSphDptwpzePkLyPmYQuSueAoCupVS3wIWAnkDD2qtb0vZqLLIkmoTzg43j1yleX+DFwBvdx+bLndHvWd+wyKKCoq52HKeE43RKzmjcVVlTzjzrgzNmsmSphBpZgE3A7Mxnf89mR2OECKl4ikIeBQ4CEwH/g+m3fSWFI4pa7jtmgUVZonyN/tHzrGL5lUDEGjuHPGegSXNd8YwawbgrM6ScGYD74rQfrMtcpamEOnjxFRkzgZ8mBPxDmd0REKI1IonnJVprX8C+LTWG7TWnwauS/G4ssKnl0VCyO7G6D+qFVNLCTpMcNtwLPqGfbczn2umLiOog2w5sinur28rLMFW4CXQ04m/LbNVWAVz7dg9Fr3nA/Sdn1QHRAiRQQXAezFtJrsxp+jJHjMhcl08y5oDCeWCUuo9mI0O9akbUvb40AJTDHD4ysjNZ+9baGbNvN19PHkp+pLm0umrcNgcHDq3j7bu0Q85HxCZNYte/ZlORQNVmlIIIESaKOA+TKuMFsw+s5Fn54UQuSOecPZNpVQR8KeY/mZe4MspHVVW0CytNpve/+nt6Ec2uewWRR4XAHUnLtIbvUiTVbNvAGDLkbH17XWFw9nJMT0v6VRkSVOqNIVIFw1sxBzJJK0yhJhMRg1nWutnQ5dtwK0QPiEgp62uC1IUKn/4+e7oP6ZrZ5SFr//tTEfUe4oLSplVOw+fv5+dJ7aOaQzZMnNWMNeO3WvRdzFA31lZ0hQitYowb7cAF0L/CCEmkxH3nCmlbEqpjymlvqKUuib02P1KqU3Av6ZthBnyx9eZ1dzNZy26fdGXNG+ZWwVAVVsXb3RG3yS/ctb1WMpiz6md9Pb3jGkMA+GsL8MzZ15Z0hQiTVYAHwIaMj0QIUQGxZo5+wnmHeJd4LtKqVPA9cDXtNZPpmNwmbSy1swQXeyMHsy8bgeFeebH5zh3hbYR2n6tHOeSps1bii2/kEB3J4H25jE9N6kGL2lKCw0hUsQCbgLmYFpl5Gd2OEKIjIoVzlYCi7XWQaVUHnAZmKW1vpieoWXWrFKzgezb70Tfb/bAkjqUUlS0d3GgKfoh57Wl9dSXTaGrt5P9Z3aN6es7s2S/Wf5sO45ii/5LAXpPyZKmEMnnAO7E1Fn5MPvLMl8EJITInFitNPq11kEArXUvcHiyBLM5ZZFpsM1no/+I5teYxrNlnT38sjX6jNLKWWbWbMfxdwkExxZsXFmy38y7UmbNhEidgVYZ9ZhWGc8gwUwIEWvmbJ5SanfoWgEzQx8rQGutF6d8dBnyW4vM/jF/EPoCw5c1lYI8hzkEvai7jyN90dc0V8wy7eDGuqQJWbLfTEHRStlvJkTq3A2UAa2YVhnRC4uEEJNLrHA2P22jyDJfv9mEs7990xH18yunloavO1q6iNZBo6aknrLCCtq72zh+Mf7jmgZkQ6Wme4YdR6lF/5UAPSdkSVOI5NuIKQJ4HejL8FiEENki1sHnmT/MMQPmDlrS/OmO6OHs+pnlAFS3dnJ8hFmzhVOWALD/zG501Pg2Mpu3DJvbQ6CrnUBH5ooBilaZ71+OaxIimQa3yriEmTETQoiIeI5vmlTWTo3MEJ1qi/7jmVJWAMDUy238YoT9ZgPhbN/pnWMeg6smNGvWKPvNhMgtyzGtMqZleBxCiGwWzwkBk8q9s8yRTU8fskX9vNsZeby4u4/N3cOX+/KcbmZUzyEQDHDw7N4xj8FZlfn9Zu7pNpzlNnwtQXqO+TM2DiFygwLWAvMwrTLcmR2OECKrxTVzppRyK6Xmpnow2eD9803YGumg82pvXvjapjXRdmLNq7sGm2XjROMRevqjn7cZi7Mm8/vNBmbN2rf2M8ZVWSHEEA7gHkww8wOvAAcyOiIhRHYbNZwppR4AdgIvhj5eqpR6OtUDywSHFUkh/7Y1+n6zylA4q27tZP0IpwJEljTH1ttswMDMWSZ7nA0cdC5naQqRiHzgAUw/7x5Mq4xJuZ1XCDEG8cycfQNYjan1Rmu9kxzdMHFdfWRz/7mO6D+a2+eZI5tKO3t4oWP4cp9CsaBh/PvN7EXl2NwFoWKA1jE/PxnypthwVtrwtQXpPiJLmkKM311AOebt80mgKbPDEUJMCPGEM7/Wum302ya+980zQeSpg9H3mwGUelwAVLV380z78JmzuvIpFBUU09J5hfPNZ8c8hkh/s8z9dh0+S1OWNIVI0JvAGeAppIeZECJe8YSzvUqpjwM2pdRspdT3gE0pHldGzCgxM2cd/dHP06wodIWv/+dS9L1kCxuWAgksaYb7m2UunBUNDmdCiDEqHnR9BdMqQ3qYCSHiF084+yKwEPPu8gtMg54vp3JQmfK+eWZ7/3c3R99vdltoSdMWCPJOd/TlvoT3m2U4nLnqbbiqbfjbg3QdkiVNIcZmGaZVxqxMD0QIMYHF00pjrtb6L4C/SPVgMml+eWS/2Y6L0TNrXYGZUSrp7uVg3/A6TU9eIdOqZuIP+Dl0bt+4xuGsngJkLpyFj2va1m8q/oUQcVDAjZiDVTTgin27EELEEM/M2beVUgeVUv+/UmphykeUIe+da2aJWnvBH4y+rFlbWQjAzMYWzvmGb8ZaMn0llrI4dG4f/f6xL2PYiyuw5RXg72wj0JmZYgBv6FQAaTwrRLzsmDMy5xNplTG+X86EEALiCGda61uBWzBlRj9SSu1RSv1lqgeWbqtqzUzYj7dHX9IsyXeGr399rjPqPctnmoPOtx/bPK4xZHxJs9Yir86OvzNI10FZ0hRidG5Mq4wpQC/wLHAykwMSQuSAuJrQaq0vaq2/C3wW0/Ps6ykdVZoVOjUfWGDC2aO7o6/0rmkoAsDT28/3G4cXAxS6i5hdMw9fwMfuk9vGNY5Mh7OBxrMd231E7a4rhLjKHUAFZivuk5izMoUQIjHxNKGdr5T6hlJqL/CvmErN+pSPLI0+vsjMEnX0wa7G6G00bllQDYDq7CHanNKyGauwLIuDZ/aM61QAAFeGw5k0nhVirDYCpzGtMtozPBYhRK6IpyDgYeCXwF1a6/MpHk9GrAgtaY500LmlAIf5UZ092xL1noElzW3jXNIEhbMqVAzQmP5w5qy2yGuwE+gO0rU/+skHQgiAEmDgfaCF0OEpQgiRNKOGM631dekYSCY9FGo++9Md0X8cN1R5wtffPzZ8o35xQQmzaubS7+9nz6nt4xqDvaQCKy8ff0crgc709/wNV2lu96FlSVOIESwBrgXeAA5meCxCiFw1YjhTSj2utf6wUmoPQ/vEK0BrrRenfHRp4LJpyvPN9X/til4McM+cCnNvXz/NgeFVmstmXAvA/tO76PP1jmsc2bLfTBrPChGNAm7AtHwEc5i5EEKkRqyZsy+F/n1/OgaSKTdNjUwTXemJ3kIjr9p0/G5sGqlK04SzbcfeGfc4wuEsE0uaFRbuaXYCPZrOfbKkKcRQduB2YCqmVcY64ERGRySEyG0jFgRorS+ELj+ntT41+B/gc+kZXurNDTWfPXQ5ejBTgx5+cs+FYZ8v9ZQzvWoWfb6+cZ8KAOCqCoWzC+kPZ+EqzZ39aMlmQgzixvx+OhXTKuM5JJgJIVItnlYad0Z57N5kDyRT7pphZs42nIpepXnPoCOb9ncMX/JbNnM1AHtP7RhX41lDhU8G6MvAzJlXqjSFGMGtQCWmEvMpoDGzwxFCTAqx9pz9EWaGbIZSavegTxUCb6V6YOnywFwTzvY1Rc+pty+qNRf9/qinGc2ruwZg3L3NIFQM4HLj72gh2JXecnxHmUX+DDuBXk3nHpk2E2KojZi9ZhuAngyPRQgxWcTac/YL4AXg74CvDXq8Q2vdnNJRpUl5fmRz/2N7h/8oygoipwI07z877POWZWNG9RwADp8/MO5xOKunAZkpBhhY0uzcJUuaQhilwMBbXDvSKkMIkW6xljW11vok8HmgY9A/KKVKUz+01FtSZWbNjrcoGruG/yjm1xaFr3c1DW8sO7ViOi6Hi4st5+joGX/7C1cGDzsvWilnaQoRsQj4IHBNpgcihJjERps5ux/YhmmlMXjHvAZmpHBcafH++aa/2bqT0febPbi0DoCa1k5e7Rg+rTS7Zj4ARy8k1u9oYOasL83hzF6iyJ/tINin6dwt02ZiMlPA9URCWVwn2wkhREqMGM601veH/j09fcNJr2nFZlnzVGv0N2IVKtUMdvTSPby9GbNq5wFw5Hwi4WzQyQBpDmfeFaEqzT0+guOtZRBiwrNhWmVMwxwquw44nskBCSEmuXjO1lyjlCoIXf+2UurbSqkpqR9a6t032yxrPndk+MyZ2xF5rOnc8CObLMvGzNB+syMXxr/fzF5aheXKw9/eTLC7Y9yvMx4DZ2m2S5WmmLTygAcwwawP0ypDgpkQIrPimbv/IdCtlFoC/G/gFPCzlI4qDfIdkamwg5eH/xhWTy8LX69vHT6t1FA+DZcjj8bWC7R3T7z9ZvYiRf5sO0GfpmOXhDMxWd2MaZXRgWmVcTGzwxFCCOILZ36ttQYeBL6jtf4Opp3GhPae2f7wdbdveAPauxaY/mYlXT1s6PQP+/zsmoElzfHPmkHmKjW9K5woS9G5x0dwfCdOCZED3sL8vvkkMPzcXCGEyIRRDz4HOpRSfw58AlirlLKRAwfL3TTVdC17+Vj0YgCX0/xopje10RYcvuFsdm2yigFCzWfTHc5WSuNZMVmVA5dD153ASxkcixBCDBfPzNlHMJsxPq21vgjUAd9K6ajS4K6ZZjYs2n4zb14ksz5zMsp+M2VF9pslUgygMlMMYCtUFMyzE/RrOnZKlaaYTK4BHgKWZnogQggxolHDWSiQPQoUKaXuB3q11v+V8pGlkKU0c8rMbNgv9gyfBLyu1hu+fuRK9P1meU43l9ou0tY9PLzFy15aheXMw992hWBP9EPVUyG8pLnXR7AnShmqEDlnoFXGDQztCiSEENknnmrNDwPvAh8CPgxsVkp9MNUDS6VFlZGDmC53D3+jXjvX7DdzXWqlJTA8vAzMmh1NqIUGuDK036wotKTZLo1nxaRgA+7ANJgNAK8DOzM6IiGEiCWePWd/AazSWl8CUEpVAK8Cv07lwFJp7VTTQqNnhBU9d2EeAFd6hxcCAEyrmgXA8cYjCY0jE/vNbB5FwXw72q/p2CFLmiLXuYC7gWrM7oyXgQsZHZEQQowmnnBmDQSzkCtM8PbZCyvMzFm0FhoFrsiP5IV90cvqp1XOBOBk49GExhGu1GxMXzjzLnegbIqOPf0EumRJU+S6tZhg1oE5I3P82xCEECJd4glnLyqlXgJ+Gfr4I8DzqRtS+mw5Pzyc3TGtBABPTx/bO4fPLBXll1BaWE5PXzeNrQn8Bq4UzsoGAPovnh7/64zRQJVm+xaZNROTwSbM75IbgeHn4wohRDYaNZxprb+qlHo/cCNmJ+2PtNZPpHxkKTRQDPDUoeHf/pwiFwBBy8IXZWJpWlVo1uzSMTTjn3lylFVjOV342y6nrRjAyld4FjjQAU37dtlvJnJVBdAUuu7GLGUKIcTEMWI4U0rNBv4RmAnsAb6itT6XroGljmZRpdlztrtx+MxZ1bQKAPYeHmlJ0+w3O3kpwSXNqqlAevebeZc5UHZF5z4fgU5Z0hS5aCGmInMHsDXDYxFCiPGJtXfsp8CzwAeAbcD30jKiFKss0FQUQGsvnG0fWqlpsyIfbz4b/Uim6aGZsxONxxIah7PahLN0Vmp6Q2dptkmVpshJ1wJrMBP8gQyPRQghxi/Wsmah1vrHoetDSqnt6RhQqg200dh7yeLqfkfzQ0uaKqjZ3TF8T5Zl2ZhSPh2AU5cmVjiz3ArPQgc6qGnfJuFM5BIbcAtmkj8IbAASq6QWQohMihXO8pRSy4gkGPfgj7XWEzKsXTMknA21utIDQElnN9G2y9eVNuB0uLjUeoGuvgT2iSlr0MkA6SkGKFzqwHIoug76CLTLkqbIFYNbZfRj9pedz+iIhBAiUbHC2QXg24M+vjjoYw3clqpBpdKiKhPO9kQJZ/NDzWcDncNPBQCYHupvdiLBWTNHWTWWw4mvtYlgb1dCrxWvIjlLU+SkGzDBrBN4AWmVIYTIBSOGM631rekcSLp8ZplpLLv30vAzNVWeCTAtgeCwz0Ey+5sNLGmmZ9bMygPPYnNMlSxpitzyNuZtbBOQnl90hBAi1SZ0M9mxslRkOe/g5ZGLATYcvEQ008IzZ8kKZycTep14eRY7zZLmYR/+VlnSFBNdBZHdFr3AK0gwE0LkkkkVzpZVR2bELnUN/dZnlOaHr99oG76sWZDnobKomn5fH+ebzyY0DleaZ86KVslZmiJXzAcexFRmCiFEbornhICcMa/chLOWnuGfW1tfBICno4eOKKua00P9zU5fPkkwmECZvrJwhE8GSH2lpnJC4cCS5lY5FUBMZKuBpaFr+W9ZCJG7Rp05U8ZvK6W+Hvp4ilJqdeqHlnxrpphQ9egex7DPzSwvAMDXF/1Nf0b1HACOXzyc0Bgc5TWmGKCliWBf6o+TKVzswHIpuo/58TVH30snRHazMPVHSzGtMtZjWi8KIURuimdZ8wfA9cDHQh93AN9P2YhS6Lo6E06uRJk5c5WaNhqNzdEDU7LCWbr3m3mlSlNMaE7gPmAWplXGC0Bi/w8KIUS2i2dZ81qt9XKl1A4ArXWLUsqZ4nGlxEAbjc1nh1ZqFrkjM2m7zw4vxbdbdqZWmOazxxsTa26Zzv1mygGFS2W/mZjIrgNqMRv+XwSuZHY4QgiRBvGEM59SyobpbYZSqgKztjDBaOyhecJDV4ZOGN46tzJ8vT1KMUBDxXQcdicXms/S3ZdYVVg6Z848ixzY8hTdx/34Lk/APzIh2IyZPXsbqcgUQkwW8Sxrfhd4AqhUSv0NsBH425SOKgUWVETCyfGWoW00VkwtBWDOhSuc9Q1vNTGjejYAxxJc0sSy4QwVA/Q1pn7mbKDxrMyaiYmlkkirjD7gVSSYCSEmk1FnzrTWjyqltgG3Y94x36e1PpDykSXZdfUmnG2/MPxMTbfL/BiKO6NsRgNmVCWpGKCsBmV34GtuRPdF/1rJouxQuEzCmZho5gE3AoeANzM8FiGEyIxRw5lSagrQDTwz+DGtdXqadCVJvdfMiHVdlVPstkhQO3BlpGIAM3OW8H6zmtCSZhpmzTwLHdjcip5TfvovyZKmmAhWAstD16n95UUIIbJZPHvOnsPsN1NAHjAd82vtwhSOK+kenGuObXpk19A2GvUlkeazrb7h/csqi6opdHtp62rlcnv0kwPi5axK334zrzSeFROGBdwMzMZsZ90IHMzoiIQQIpPiWdZcNPhjpdRy4A9TNqIUWV5jZo92Nw7dZre4vhgAhz/Apq7h4Wxm9VwAjjcmXr4/UAzQl+JKTWUD7zITQqWFhshuTuBOoA7TWPZV4ExGRySEEJk25hMCtNbblVKrUjGY1Ils8j98VaVmSWi/WV1LBy93Dm9Am9xigHog9W00ChY4sBVY9J7x039RljRFNluFCWbdmB5m0ipDCCHi2XP2J4M+tDCbQppSNqIUmFIUCWftV3XKWNhgZs5sbd10RskxM5N2MkBtpBigP7X7abwrQ7NmsqQpst67mN0Sm4HODI9FCCGyQzwzZ4WDrv2YPWi/Sc1wUmN6sUldQdOpLfy4zVJYlplJO98zfNbMk+elsriGPl8fZ68kNtsVLgZI9X4zG3hXhPabbZHzB0U2qgQuY/aX+YDXMjscIYTIMjHDWaj5rEdr/dU0jScl5leYmbNf7R367c6v8Yavt1wc/lv7wJLmyUvHEjvsnEgxQKr3mxXMtWP3WPSeD9B3PrExC5F8c4G1wFHMGZlCCCGuNmI4U0rZtbmrI2gAACAASURBVNb+UAHAhLaq1oSULeeH7jebXWkmBYu7elnf6R/2vMiS5qGExxA5GeBUwq8VS5FUaYqstSL0D0hTWSGEGFmsmbN3MfvLdiqlngb+m0HvqFrr/0nx2JLmIwtN8DpyVTFAg8cEmdLOHk76hm84C/c3u5hYfzNs9kgxQCp7nKnBS5oSzkS2sDCzZXMxS5lvAROuj7UQQqRNPHvOSjElVLcR6XemgQkRztx2TUHomPaNp4ceeF5X7gGguHv4eZp2m4OG8ukEdZATl44mNAZneS3KZsd35SK6vzeh14qlYK4du9ei72KA3jOypCmygQPTKqOeyP6yCdW/Wggh0i5WOKsMVWruJRLKBgw/gDJL1RSaoV7phra+yLdgKbA5TFg7dmX4EsuU8mnYbXbOXTlNb4LVlela0vTKWZoi66zABLNu4EVMIYAQQohYYoUzG+Dh6oMojQkTzmaXmuXK/KEHAzCltCB8vbl1+GzW9NCS5okEj2yCwc1nUxjOVCScSeNZkT22Au7QvzsyPBYhhJgYYoWzC1rr/5u2kaTI9Q1meW/vpaH7zaZXmHCW39fPxq7hxQAzqpK034z0zJzlz7LjKLbobwrQe0qWNEUmVWJ2QgQw3XfWZXY4QggxwVgxPhdtxmzCuXOGCSqbzg7dbza9yA1AUVs3B/uGFwNMr0rOYefY7Dgr6tA6SP+l1O21GThLU2bNRGbNBt4L3JrpgQghxIQVa+bs9rSNIoXqQnvOLnUNzZozK00xgLN5+FJLubcSb34RHT3tiR92XlEXKga4gO4fXniQFAqKVkqVpsi0ZZjjmECWMIUQYvxGDGda6+Z0DiRVphabcLb+5NCZM5fbhBkd5WSA6VWzgCTvN7uQuiVN9ww7jlKL/isBek7IkqZIN4VplTEP0yrjbWBfRkckhBAT2ZgPPp9IHFakbuGds5EV3CJ3qDpAa451DJ/Nmp6K/WaNqQtnRavM9yPHNYn0cwB3AA2Y/WWvAamtShZCiFwXa89ZwpRS9yilDimljiqlvhbjvlVKqYBS6oPJ/PpVnkg4C+rIsmZDaT4A9mCQTd3DZ5oGigGSMXPmGghnKZw5C1dpSgsNkXZLMMGsB3gGCWZCCJG4lM2chc7l/D6mA+VZYItS6mmt9f4o9/098FKyx1Dkit7xY1qZqdT09vSzo2dopWaeI4/a0gYCAT+nm04kNgCbHUf5QDHAmcReawTu6Tac5TZ8LUF6jg2vOhUitXYABcB2ZJ+ZEEIkRypnzlYDR7XWx7XW/cCvgAej3PdF4DdAYjvvoyjPN+Fsy7mh3+bcKnOmZmlXD+d8QwPc1MqZWJbFmcun8AUSWyZ0VtajbDZzMoAvNcUAQxrPTpjuc2JiqyTye10A2IAEMyGESJ5UhrM6YPB00dnQY2FKqTrgIeDfYr2QUuoPlFJblVJbzYbj+Awc21TvHZpa3JZZ4vT0+oblmaS10ACcVanvb1YkLTREWs0CHsAUc+dEtx0hhMg6qSwIiOdkgX8B/kxrHVBq5Dd6rfWPgB8BKOWMe35ocZUJcm9edaZmcajHWVfr8GObZiTxZABXTWrDWd4UG85KG762IN1HZElTpNpSzIQ4QBsyVSuEEKmRynB2FrNTeEA9cP6qe1YCvwoFs3LgPqWUX2v9ZDIGsKRq+Cyb3RYJgSc6hs42KRTTKmeaz02AmbOBxrOypClSSwE3AvMx/6G9jTlyVwghRCqkMpxtAWYrpaYD54CPAh8ffIPWevrAtVLqEeDZZAUzgLpCE86ONEcCWaHLfMtWMMjWqyo1y72V5LsKaOlsprWrJaGvrewOHBW16GDqigGKVslB5yLV7JhWGVMwrTJeB05mckBCCJHzUhbOtNZ+pdQXMFWYNuCnWut9SqnPhj4fc59ZMtSF9podb4lsrasozAPAHtDDKjVry8xE3/nmxMOUo6IeZdnobzqH9iU/PLnqbbiqbfjbg3QdkiVNkSqLMMGsF/O/cmNmhyOEEJNASpvQaq2fB56/6rGooUxr/bvJ/votPQpKNEebI+FsQW0RAJ6+ftqDQ9cCa0sHwtnZhL92qg87Dx/XtL1/LDUSQozRTsAD7MbsMxNCCJFqKW1Cm2luhwlfTYPO1ZwVakALDGujUVNSDyRn5syV4nDmDZ8KIEuaItkqMZ3/wewxexMJZkIIkT45Hc7mlZvw1Tkov1SHGtBOudI+bMKptjR54Sx8pmYKwpmr1iKvzo6/M0jnQVnSFMk0E9Mq405y/O1BCCGyVs6erWlTmlA7My52mosCV+TbPXW+dcj9dpuDyqJqAsEAja0XEvrayu7EUV6DDgbxXUp8ifRqA41nO7b7TA9QIZJiCXBt6LoVKQEWQojMyNlwNqUo8heLL2jCWb4z0u+s1T/0L57q4losy+Jiyzn8CZ4M4KgMFQNcOov2J3/ZUc7SFMmlgBuAhaGP3wb2ZG44QggxyeVsOGsoGv5bf4HTfLve7l6e7bqqUjO8pJn4TFd4v1lj8pc0nVUW7il2At1BuvYnFiKFMG8BtwNTMa0y1gEJnikrhBAiITkbzkrdJpw9fyQyWzaj1JwMkN/v59WOocGmJgWVmn0Xkh/Owmdp7vChZbuZSNgCTDCTVhlCCJEtcjac3TvLJJeWnkil5tKGEgCcXb10XzWxlopigFTMnIUbz0qVpkiKPUAhpuO/VGQKIUQ2yNlyrGLTa5Z6b6Qms7bcA0BL7/App0iPs8TCmXI4cZTVoIOBpBcDOCss3NPsBHo0nftkSVOMVyXgCl1r4C0kmAkhRPbI2XBW4zGh7MVjwycHz10ZeuC525lPiaeUfl8fVzqaEvq6zsp6lGXhu3we7U9ugApXae7sR0s2E+MyHbgfuBtzcIcQQohsk7PLmmunmnB2oMnkT/egSs3jrb1D7h1Y0rzQcg6tE2sf4KyeBqSm+ezAQedtsqQpxmURcH3o+gpytIQQQmSnnA1nA/aHwtnShmIA3H0+dvcObQ6WrCVNAGfVFCD5zWcdZRb5M+wEejWde2TaTIyFAq7DhDOAzcCuzA1HCCFETDkZziwVmf061WYKAupLzLFNWiku+4fOGNQMzJwlo1KzZhqQ/JmzgSXNzl2ypCnGwgbchlnODADrgWOZHJAQQohR5GQ4qyuMhLP+gAlnLmX+XdXexeVAag48Vw4XjtJqdCBAf5KLAYpWmrMOpfGsGJt5mGDWB7wMJHb6hRBCiNTLyXBW7zXhq70v8ticCnOmZklXL/2DsplCUVdmliLPNZ9O6Os6KxtQlkV/42kIJK8Jmb1EkT/bQbBP07lbps3EWOwDvMABzJFMQgghsl1OhrNCl0lffYPyUb7H9NZo6R4681TmrcDtdNPa1UxHT3tCX9dZnZr9Zt4VoSrNPT6CfaPcLAQVQCfQE/r47QyORQghxFjlZCuNgXM1XzgayZ46YPaZbWvuHnJvQ/k0AM5eTjxQpapSUxrPivhNAx7AtMrIyd+9hBAi5+Xku/cN9aYa82x75MBzZbOwBYIUBoYWAwyEszOXTyb8dQdmzpIZzuxFivzZdoI+TccuCWciloWYA8wVplVGIPbtQgghslJOhrOBPWdus4ee2uKBMzV9bL6qjUZDuTlq6UyCM2fK6cJRVo0O+OlvOpfQaw3mXeFEWYqOnf0Ee0e/X0xW1wGLQ9fvAjszOBYhhBCJyMlwNqvUzI4dbQ5VaHrNfrOAZdHkH1qpWZ+kmTNnZQNKWfQ1nU1qMUD4oHOp0hRR2YBbgRmYmbINwNGMjkgIIURicnLP2ZXQYedHm823Z7NCy5t9PpoHtdEoLiih0O2lq7eTls4rCX3NyH6zkwm9zmC2QkXBPDtBv6Z9p1RpimhmYYJZH/D8/2PvvsOjqrYGDv/O1NRJgSQ06UVB6QiIAmJFRK/STCgiqKBybSCiUgKIgqCgKCIoAiIoiPfDhoINrEgTkSZITwIJhCSTMpl2vj/OZJKQQiohw3qfJ5cpp+xJ8Gax1t5rI4GZEEJUfz6ZOfP3fKo4z5yzjg1rABCekcUxe+6cs8qZb1a+dhx5WdprJc303XbcmeXbVkr4qgNACHAQOFfFYxFCCFERfDI4axWpBWDZnga0Toc2z8zgcufLnOWUNE+eLf8EfnMlZM5CZC9NUagIIBPI8Dz/owrHIoQQoqL5XFlTITf4Ou7ZuinUszLAlZaV79icxQDlbaOhmPwwhEdqiwHOxJfrWjn0gQqBVxlQnSrWnVLSFDkaoLXKuB0wVvFYhBBCVAafy5yFanP/sWbnbt1kCTIDUPe8ifr1auSs1DxarnuaoupriwESK25ngOD2RhS9gnW3A1eGlDQFQEu0Vhk6IAmouIUnQgghLh0+F5z5GbRAJt2uBWbBfrkf8a/03AxUoF8Q4cE1yXZkk5h6qlz39M43O11x882k8azIrzPQxvN4K7CzCscihBCiMvlccFbHs+l5liepEOHJmgHsysrtcXZFjYaANt9MVcuXmTLX0jJwFTXfTBegENTSiOpSSdshwdnlTQ/0BJoAbrRWGQerckBCCCEqmc8FZ008Pc6yHFrmzN+kfcQgm528ewPUq6D5ZgCmqJzgrGIyZ5Z2RhSDQvoeB650KWle3hqhBWZ2YCNQcQ2OhRBCXJp8Lji72rNS8484ba1DeJBWHjQ7XPyRmTtHp6LaaCgmf21nAKejwnYGsOSs0pTGs4JDaK0yjgDJVTwWIYQQF4PPBWc5Zc0Az0I2P4MeAKPLla/HWZ3wegDEnS1ftssUdQWAFpi5y7+Xoc5fIaiVEdWtkrZdgrPLU020prJWz/PtVTgWIYQQF5vPtdLI9iTH4tO1smaIp41GWIbNu3WTTtEREVILgNMpCeW6n7l2xc43C25rRGdUyPzHiStNSpqXn/porTJ6A6YqHosQQoiq4HOZs051tOzYlpNa3Bnh2S7Az+EkZ61mjeAIDHoDydYz2J3Z5bpfRc83C+kojWcvX1cB3dD+zXQaaZUhhBCXJ58LzsL8tWxTzv6aFs+CgLPZub/oosLqAOXPmgGYPCs1sysgc6bzg6DWWqZPSpqXm05AO8/j7UgpUwghLl8+F5w5PdPK4q2e3QEsWlfaw5m5Pc6iQmsD5Q/OFLM/xvAoVKcDx5nyB3pBrU3ojAoZ/zhwpkhJ8/KgA3oAzdBaZfyEtl+mEEKIy5XPBWc1PJmzFJsWnBk9mbNzeYOzkJzgrHxbLZmjPM1nE09WyGIAb+NZWaV5GWmAFpjZgW+Bk1U7HCGEEFXOx4IzlYhA7VFyloJOyX0nLqPiM2cmb/PZ8vdKU0wQnFPS3CZ7aV4+jqB1/D8OnK3isQghhLgU+FRwFphnH2ibU8HfpPc+T3blttGICs2Zc1a+zFnufLPyB2fBrY3ozAqZ/zpxJLsvfIKoxmqgTfZP9TyXrZiEEELk8qlWGuGekmZShvY80FPSNDucnHRo7wX6BRHkH4zNnkVqZkq57leRmTOLrNK8TFxBbqsMvyoeixBCiEuRT2XO6lm0ACyntBlk1j6eQ69jr02bE5abNStfSVNnDsAYFonbYcdxtpwLC4wQ3Ebmm/m+FsANaP8mOoY2z0wIIYTIz6eCs8ZhWjnwlKcBbZMwfwAiUzO8HaNy55uVt6SpLQZwJJV/MUDQ1Ub0/gpZR5w4zkhJ0zd1BNp7Hu9Em2cmhBBCFORTwZnBU6TNCc6igs0AGPPNN6vYxQDZCeUvaYbIXpo+TAd0B5qjtcr4GdhfpSMSQghxafOp4Kx9bS0I+/WEFqWF+mkrBFKduT3DKnoxgP10+YIzxaBt2QRS0vRNddECMwdaq4wTVTscIYQQlzyfCs7qh2jBmWeqGQF6LYOWZS/YRuNURbXRKGfmLLCVEX2AjqzjTuynpaTpe04AvwEJwJkqHosQQojqwKeCsys8CwIOn9MyZzVDtDlnaZ4GtAadgZrBkbjdbs6kni7zfXR+ARhDIypkMYC38ays0vQh4YAKnPM8312FYxFCCFHd+FQrjSzPrP+jKVrGTOf2ZNJUzyrOkFrodDrOWpNwusu+qbQ3a5Z4AtSyZ7sUPVjaeUqvEpz5iLrAXWitMgKqeCxCCCGqI5/KnOVs3XTgjBZz6oK1zNkRazYAdcLrARCfXL4tckxROSXNo+W6TmBLI/pAHbYTTuynpKRZ/TVHm/yvQ+v4b6va4QghhKiWfCo4C9QqhCRmKBjy7N0Ul5IF5A3Oyjcp21Q7ZzHA8XJdx9LRkzWThQA+oIPnC+BP4I8qHIsQQojqzKeCs/ohWubM7oIgv9yPtjdTK2HWqaH1JitvcGbOyZydOlr2i+jB0iFnvpnspVl9KWjZshZorTJ+BfZW6YiEEEJUbz4TnOmU3HYZ6XaF4IDcfTXPubT3KiJzpvMLxBBaE7c9G8fZU2W+TmALA4YgHbZ4F9nx5WtiK6pSHbTAzAF8h1bOFEIIIcrOZ4KzcP/cxxkOhVaeGmdwho2zLhU/kz81giOwO+0klWOlZv7FAOoFji6ad5WmlDSruTjgFyARSKrisQghhPAFPhOcBZu0QCkuTZtrdoVF2x0gW68tDqgTpmXNTp2Lw12OFZYVstm5krekKcFZ9RMG6MntW7anCscihBDC1/hMK41aQVpwViNA+7OBUftoRqdWMqyo+WYVEZwFtjBgsOjIPuXCdkJKmtVLHeBu4HYgsIrHIoQQwhf5TOYsJyj767QWlEV6GtC6085bqXm2nIsBKiA4s3SUkmb11Axt8r8erfO/tMoQQghR8XwnOPP0ONuXpAVnQW43NuCcTlsYUDf8CgDiyrMYwD8IQ0gN3HYbjuQyLgZQcoMzaTxbnbQDOnke7wK2VOFYhBBC+DKfCc5y9tO0eaqEZj8DNiApNROAOp7grDxlzdzNzsu+GCCgqQFjqA57kgvbMSlpXvoU4HrgKrQtmX5F5pgJIYSoTD4TnF0TqU3yz3ZqCwJyQieroiMsqAb+5gCsWWlYs9LKfI+KmG9m8azSlMaz1UUUcCXgRGuVUb6N7oUQQogL8ZngLCpQC86CTCoKYA/W9jXcezaTuuENAYg7W74eVOWeb6ZASAdtVwBZpVldnAJ+ApLR2mUIIYQQlctngrMGoVqu7GiKDn8FHAZtrpnVpVI3MqekWc49NT3BWXYZgzP/xnqMNfTYz7rIOiwlzUtXGGAkNxjbX4VjEUIIcbnxmVYajcO0zNnhcwomnYLL09/sXKadWuF1AUgoR3CmCwjGYAnHnW3DmVy2JrYh3lWasl3Tpas2cBdaqwxLFY9FCCHE5chngrPEDG2u2b/ndATn+VTnMu1EWmppx6QmlPn6uYsBjpM7o610cuabSUnzUtUEuAMwAwlARtUORwghxGXJZ8qaLSO0gOl0ukKnELP39Sy7i4iQSAASy7Ftk3e+2emylTT9Guox1dTjOOcm819nmcchKksboLPn8W7gd8oahAshhBDl4TPBWY50O0SE+XmfB5gDCfQLxmbPwpqVWubrmqI8880Syhac5dtLU37nX0IUoBvQEu0H8xvwd5WOSAghxOXNJ4Izgy432jmTqdAwRAvOHBnZRIRoJc2ktLJnzQBMtcuXOcsJzqTx7KWmJrmtMn4AjlTtcIQQQlz2fCI4M+tzH6so2BQFI6C4XESGRAGQVI6Spi7QgiE4DHd2Fs7k0rdT8KuvxxSpx5HqJvOglDQvLUnAJiANKF8AL4QQQlQEn1gQYPB8ilTPVoeRYVqPszPJmURYcoKzMm63BJijyrcYwCIlzUtMCNqqzBwHkcBMCCHEpcKngjOH1k0De6C2IMDucFVIWdNUqz5Q9v5m+eabiSpWC7gbuA0IreKxCCGEEAX5RHBm1GvpKKdba6fhj/bnn6etRHjKmuVZqWmq1RAo284A5np6zLX0ONPcZByQkmbVagz0AfyAeMBatcMRQgghCuETwVnOnDOHS5tEl2XSptIdTLXlKWuWP3NWluDM23h2hx3cZR6CKLfWwM2AHm3j8o2A7NIghBDi0uMTCwICtO0qSbdDAz+9d3cAq9NMoF9Qudpo6ANDchcDnEsq9fmWTrKXZtVSgOuAVp7nv6H1MRNCCCEuTT4RnIX4aWXNLKfClUEm7+s1K2ClZv6sWelm85vr6PCra8CZ7iZ9v5Q0q0YYWqsMF1qrjMNVOxwhhBDiAnwiOKsbrAVN4f4qFrP2kYw2O+HBNQE4ay19xitHznyzsiwGsHhKmtYdDqmgVZlktKAsEyj7il0hhBDiYvGJ4EynaMHZ0RSFhhbPSk29nmD/EADSyrMzQDnmm+UEZ6mySvMiCwGCgDjPc8mWCSGEqD58YkFA03AtODueqkP11wIixe7EEuAJzjJTynztsq7UNEXp8K9vwJXpJmOvo8z3F6UVhdYq41YgvIrHIoQQQpSeT2TO/I1acBYVqHLO4ocTsGZkezNn1qy0Ml1XHxSCISgEty0TZ0rpSqM5WbO0nQ5UmW52kTQCbkT7a30Mreu/EEIIUb34RHDWzJM523dGB57MWXq6DUvN8mXOyjPfzNt4VlZpXiTXAF3QVmfuBX5BtmMQQghRHflEWdNi1n4JW+1g1mkNaA8lZ2IJsACQllm2OWdlnW9mjNDh39CAK0slfY+UNCuXAnT1fCnAFuBnJDATQghRXflEcJaWrQVkcWk6sjyZs9NnMwj217bnKeuCAO98s9OlC85yGs9a/7SjSmxWyUKAq9CWw34H7Kra4QghhBDl5BNlzfohWuv9U+kKzXQ6UFUSMl1Y/LXMWVnnnJmjPJmzhNIFZ975ZrJK8yJIQQvK7EBCFY9FCCGEKD+fCM5CPU1ojQbPPk6KgsMYgF5vIDM7A6er9OkrfXAo+qAQXFkZOFPPlPg8Yw0dAU0MuLNVrLslbVY5LGgZsxOe52XbkF4IIYS4FPlEWbNmgPan1a5lrAwuF3pzOeebRTUASl/StHTQtmuy7rKjSuKsEkSitcq4BYio4rEIIYQQFc8nMmeqZ+63yaAFZ25Fwb+cbTRMtT3BWSkXA+Ss0kyVVZqVoCHQC+2v7XG0kqYQQgjhW3wiOMv2bI0UoNd2BzDmbUCbVbZf4Oao0gdnhjCFgGZG3Nkq6X9JSbNitULbwFwB9iErMoUQQvgqnwjOgkzaL2l/nTbnzOhwYsnJnGWWMXNWq/TBmaWDZ5Xmbgfu7DLdVhSqC9Da83grsLMKxyKEEEJULp8IzsL9PX8GaJkzvyw7wQGeNhplaECrDw5DH2jBlZWOM/Vsic+TxrOVwYLWKsMNbAIOVu1whBBCiErmE8GZzQl+BnA6tQyaXacQ4ilrppYhOMvNmh0v8TmGEIWAZgbcDhXrLgnOKk4asBEtOIuv4rEIIYQQlc8nVmvqtR601AjUMmfJaTYsOZmzMsw5M3uDs6MlPsfSwYSiU0jf7cBtK/UtRT7BaJP/c5xEAjMhhBCXC58Izjw7NuH2PAjUKbnBWUbpW2mUJXMmjWcrSgRaq4ybgKgqHosQQghx8flAWVNF7wkxgyza5LO4dDthFVDWzC5h5kwfrBB4pQG3UyXtT1mlWXYN0IIyA1q2LLlqhyOEEEJUgWofnHmSZrhVUMxaA1ijWyXQLxiX20WGzVqq6+kt4egDgnFlpuNKK1lwYGmfU9K0486U9g5l0xKtVYYOOABsRlplCCGEuBxVallTUZTbFUU5oCjKIUVRJhTy/mBFUf7yfP2qKEqb0t4jJ2vmzvN7/Kxdm3tmzUpFLeUveFMZ5ptJ49nyuha4Hu2v4za0VZkSmAkhhLg8VVpwpiiKHngL6I2WFolWFKXleYcdAXqoqtoamA4sKu19cuabudx4twqIdwUCZWujYS7lfDN9oELgVQZUp4p1p5Q0Sy+I3FYZPwI7qnQ0QgghRFWrzLLmtcAhVVUPAyiK8hHaTO+9OQeoqvprnuN/B+qV9iY5wVmWUw+Kgs7txmYMBsq2r2Zp55sFtzei6BWsux24MiTbU3rpwAa0fyfEVfFYhBAVJSwshNjYJ2natAE6nU+sPROiSG63m0OHjhEbO49z58q2p3delRmc1QVO5Hl+EuhczPEjgfWFvaEoysPAw9ozfb73ctpopDu00qLZ6cJurgFcnB5nIbJKswyC0DYwP+x5nlCFYxFCVIbY2Ce59tr2GAwGcmcHC+GrVMLDaxAb+yRPPDG13FerzOCssP8aC00tKYpyI1pwdn1h76uqughPyVNRTPmu4ef5BA7V7L2Bn3/ZdgfQW2qg9w/ClZGGy3rhxQC6AIXAVkZUt0raDgnOSqYmcDvgB9iQ/mVC+KamTRtIYCYuIwoGg4GmTRtUyNUqMzg7CVyR53k9CvlNrChKa+BdoLeqqiXfK8nDqNdiNZtDS5srbhWLvwUAa1bp9tU01/ZkzU6XLGtmaWtEZ1BI3+vAZZWS5oVdAdwMGNH+eiRV7XCEEJVGK2VKYCYuJ0qFlfArMzjbCjRTFKUR2mSi+4CYvAcoilIf+BQYqqrqP2W5icHzfYhL0xYBGG12AsxBAKSXso2GKap0880sspdmKVxJ7orMf9BaZbirdERCCN/WuXN7mjRpitPpxGAw0KdPX6KjB5fpF+jChQto1649nTt3KfT9tWvX4OfnR58+fcs83kOHDjJ58gsAnD59iqCgIAIDgwgNDWPBgnfKfN3ifPHFZyxZshiAESMe4s477ypwzGuvzWbbtq0AZGfbSE5O5ocffmbbtq289tps73HHjh1lxoyZ9OzZi+nTY9m3by+qqlK/fgOmTJlGQEBApXwGX1RpwZmqqk5FUcYA36BNFFuiquoeRVFGe95fCEwGagALFEUBcKqq2rE098kJzlRPfOTW6wk0acFZpi2jVGM21S75fDOdHwRdLSXNkukEtPM83u75EkKIymU2m1m5cjUAycnJTJz4HOnpVkaNerTU1xo9uvhz+vUbUKYx5tW0aTPveGNjJ3HDDd256aZb8h2TE2hWhNTUVBYvfofly1eiKApDh0bTvXtPLBZLIgcrRQAAIABJREFUvuOefvoZ7+OPP17FgQP7AejYsZN3vKmpqdx7b1+6dOkKwFNPjSMoSPtdPHfuHFav/ojhw0dUyLgvB5XahFZV1a+Ar857bWGexw8CD5bnHjnBmVX1A0CfYSMgQsuipWeXLXNWkh5nwW1N6IwKGfsdOFOlpFm0AHJbZfyE1mBWCCEurvDwcJ5/fhLDhw/m4Ycfwe128+abr7N9+zYcDgcDBgzi3nv7A7B8+ft89dWX6HQ6unbtxn//+0S+YGn+/Nf56adN6PV6OnfuypNPPs2iRW/j7x/A0KH3c+DAfmbOnIHNZqNevXpMmjQVi8XCqFEjufrqa9i2bSvp6VYmToylXbv2Fxz7qFEjad26Dbt27aJ79x506NCRuXNfJSsrk9DQUKZMmUbNmhGcPHmCWbNeJiXlHH5+frzwwmQaNmxU5HV///1XOnfuQkiItqNO585d+O23X7jttt5FnvPNN+sZNeqRAq9/991Gunbthp+ftlNPTmCmqirZ2dl4EjCihKr9DgFBnvUBBlUrkaXr9QSaS585M4TURO8f6FkMcOGFBNJ4tqQyga8BM/kX7wohLhf9PzlZKdf9pH/pui/Vq1cPt9tNcnIymzb9QFBQMMuXr8Rut/Pgg8Pp3LkrR48e4ccff2Dp0g/w8/MnNTV/W4TU1FR+/PF7Pvnk/1AUBau14Nzm2NhJjBv3LB06dGThwgUsXryQsWPHA+B0uli27EN++eUnFi9+p8TlSqvVyqJF7+F0Onj44ZG8+uo8wsLC2bDhGxYseJPJk6cyY8Z0nnvuBerXb8Dff+9m1qyXePvtxWza9CP79u0tkP1LTEwkKqqW93lkZBSJiYlFjiEhIZ74+Hg6dry2wHsbN35DTMzQfK9NnTqZX3/9mUaNGvPkk0+X6HMKTbUPznL46RVwARk2Av08wVl2eonPz+1vduyCx+r8IKi1tlVU2nYJzgoKBOoABz3Pi/6PXQghLibV06x8y5bfOXToH777biMAGRnpnDhxjD/+2ELfvnd7M0A5WaUcgYGBmM1mXnxxKt263cANN3TP9356uhWr1UqHDtoMnTvv7MuECbllwV69egFw5ZUtSUgo+Wr1W265DYCjR49x+PC/PPbYaEDrr1WzZk0yMzPZvXtXvns5HFpj9B49etKjR8/CvhsFXikuw7VhwzfcdNPN6PX5W1qdOZPEoUOH6Nq1a77Xp0yZhsvlYvbsmWzY8A133fWfknxUgQ8FZ3H2QNCDRdGTZfTD5XJic9hKfH5uf7MLB2dBrT0lzX8cOFOkpJlfDbRWGQForTIkWybE5a60Ga7KcvLkSfR6PeHh4aiqyrhxE+ja9bp8x/z226/FBigGg4GlS1ewdesWNmz4hjVrPuLttxeXeAxGo1Z10et1uFzOEp/n7+/veaTSuHETlixZnu/99PR0goKCvXPASiIyMort27d5nycmnvYGlYXZsOFrxo9/rsDrGzduoGfPGzEYjAXe0+v13HLLbaxYsUyCs1LwmbbNZlX7F8JZnRZvZmSXcjFAKYKznJKmNJ49Xz2gL1rmLAE4XbXDEUIIj3Pnkpk580UGDBiEoih06dKVtWtX43RqvzuOHTtGVlYWnTt35bPP/g+bLQugQFkzMzOT9PR0unW7gaeffoZ//sk/jzYoKBiLxcLOndpWdF999QXt23eosM/RoEFDzp07x19/7QLA6XTw77+HCAoKok6dOnz77QZAyxCeP7bzdelyHVu2/EZaWhppaWls2fIbXbpcV+ixR48exWpNo3Xrgltgb9jwdb55aqqqcuLEce/jn37aTIMGRc99EwVV+8xZzr9vXJ440+HUIvfSlDQBTLXqAxcOzhQTBOeUNLfJXpq5WgA3oMX7h9D2yZRWGUKIqpOdnU1MzEDvCsfevfsweLA2L+o//7mXhIR4hgyJRlVVwsLCmDNnLtdd141//jnAsGGDMRgMdOt2PY899rj3mpmZGYwd+yR2ux1VVXnqqXEF7jtlyjTvgoC6desyefK0CvtMRqORmTNn8+qrr5Ceno7T6SQ6ejBNmjRl+vSXmTlzBkuWvIvT6eSWW26jefMWRc45CwkJYeTIh7n//sEAjBz5sLeMu3DhAq66qqW3HLphw3puueX2AlnF+Pg4Tp8+lS8AVVWV2NhJZGRkoKoqzZo1Z8KEFyrse3A5UHLq79WFtkNAhPd5u1oudozKYsmudpwzBZF1xElY+/H8e+of5q6bXqJrGkIjqDtqBs70VOLeeqbYYy0djdQfE0zmv04OTy9dk1vf1cHzBbATrcWdEOJytn79UmrWjKzqYQhxUZ05k0jv3sPPezV+e6nbhFXckKrWOU9vM3+91uQu01b6xQAlKWlaOsoqzfz8yG2V8Quwr2qHI4QQQlRzPhOcBTizyTSYyVD88KN0c85KGpwpRghuI/PN8rOh7VcfCJRs2yshhBBCFK3aLwjIKX+rntlnaYq2oqU0c87MJQzOgq42ovdXyDrixHHmcp5PFYg2xyzHWSQwE0IIISqGz2TOVFULzhSDp6xZ4syZginKsxjgdPHBmbfx7GWdNQtHa5URBGQDR6t0NEIIIYSv8ZngzJ2TQTPmZM5KFpwZwiLQ+QXgtKbgSk8t8jjFAMFtc1ZpXq7BWV3gFsCE1iojoWqHI4QQQvggnwnO7J7md6pnQUCWPatE55V0vllgKyP6AB1Zx53YT1+OJc3mQHe0Svi/aK0yXFU5ICGEEMInVfvg7Pw+zjpDTnCWWaLzvcHZhUqanlWaaZflKs32QM4q4F3AliocixBClEznzu1p0qQpLpeLOnXqMm3aiwQHW8p93c8/X8e+fXsL7ZZfHqNGjeTMmTOYzWYARo58iJtuuqVC7wFab7K//trF7bffccFj9+3by9Spk8nOzqZbt+sZO3Z8gV5nDoeDl16azr59e9HpdIwd+wwdOnQCYMGC+Xz55RdYrWls3vyb95yEhHimTYslJeUcFouFadNeIioqqmI/aDVW7RcEALjU3L8oOmMwUPLgzBzlCc4Sig7OFD1Y2muZuctvvpkZuBKtVcbPSGAmhKguzGYzK1eu5uOP1xISYmHNmo+rekgXNH36S6xcuZqVK1eXODBzOku+DRRogdE336wv0bEzZ87g+ecn8emnn3H8+HF+/fWXAsf8739rAfjoo094882FzJv3Gm63VmG64YYeLFu2osA5r7/+Gn363MmqVWt48MFRvPXWG6X6DL6u2mfOANIcZu9jo8mTOcsuSXCmeHcGyC4mcxZ4lRF9oA7bSSf2hMutpJmN1irDAly4D5wQQlyKrrmmDQcP/gPAnj27efXV2WRnZ2M2m5k8eRoNGzbk88/XsXnzJmw2G3FxJ+jZsxePP/4UAJ999n8sW7aEGjUiqF+/PiaTVk3JmwEKDQ1jypSp1KpVm9jYSfj5+XH06BESEhKYPHkqX375Obt3/0WrVlcTG1uyJumpqalMnz6FuLg4/Pz8eP75STRr1pxFi94mKSmJhIR4QkNDGTt2PC+/PINTp7S5wGPHPkObNu3Yvn0br776CqBtar5o0RLefPMNjhw5QkzMQO68sy8xMUMLvfeZM0lkZGR4t2zq0+dONm36gW7drs933JEjh+nUqTMA4eHhBAUFs2/fHlq1uoZrrmld6LUPHz7MU09pTd87duzEM888VaLvx+XCJ4KzZJu/97GfqeRlTUNYBDqzP07rOdwZRXf7t3S63LZrCgAakNtQ9pznSwghSq/jF90r5brb7txcouNcLhdbt27hrrvuAaBBg0YsWrQEg8HAli2/s2DBfF555VUA/vnnAB9++BFGo4n+/f/DwIHRGAx6Fi1ayAcfrCQoKJjRox+kRYsrAXjllZn06XMnd955F5999n/MmTOLOXPmAZCWlsbbby9m8+YfGTv2Cd59dymNGzfh/vsHc+DAfu818po06XlvWXPBgkUsXryQFi2uZM6ceWzd+gdTpkz0bm6+f/8+Fi9+Hz8/PyZOnEBMzBDatm3HqVMJ/Pe/j7Jmzf9YsWI5zz77HG3atCMzMxOTycSYMY+zYsVy5s6dD0BSUiIvvjiV119/K99YEhMTiYzMLTVGRkaRlJRYYMzNmjVn8+YfuPXW2zh9+jT79+/l9OnTtGp1TZE/k+bNm/P9998SHT2YH374noyMDFJSUggNDS3Rz9TXVfvgTFHAYdcD4Gd3ogaWPDgz1WoIXGAxgA4s7S+nXQHCgN5orTIcaPtkCiFE9ZOzt2ZCQjxXXtmSzp27AJCens7UqZM4fvw4iqLkKwt26nQtQUHa9JhGjRpz6lQCKSnn6NChI2Fh4QDccsttHD+u/d7YvfsvZs/WArs77ujDG2/M817rhhu6oygKTZo0Izw8nKZNmwHQuHETEhLiCw3Opk9/iZYtW3mf//nnTmbNetU7ttTUVNLTrQB0794DPz8/AP74YwuHDx/2npeRkU5GRgZt2rRl7txXuf32O7jxxpsKndcVERFZIDADbY/Mgs6f6Q133fUfjh49wrBhMdSuXYfWrdug1+sLOTfXE088zSuvzOSLLz6jXbv2REZGYjAUf87lpNoHZwD2nOAsPQui/HGrbrLttgueZy7BZueBLQwYgnVkJ7jIjvP11Yl10FplmIFTwImqHY4QwieUNMNV0XLmnKWnW3nqqcdZs+Zj7rsvhoUL36JDh07Mnj2X+Pg4Ro9+0HtOTrkSQK/X4XJpgZtSMCYpVN7J8jnX0ukUjEZTvmNcrpL9PikuQPLzy60aud0qS5Ys9wZrOYYPH8H119/AL7/8zIgRQ3nrrXdK9kGAqKgoEhNPe58nJp4mIiKiwHEGg4Gnn87dl3rEiGFccUX9Yq8dERHJ7NmvAZCZmckPP3znDYqFjywISLFrf0H1Oi1Is9mzULnwhu45mbPsYoKzkGsvl6xZU7SMmRk4DHyJNt9MCCGqt6CgYMaNe5YVK5bjdDrIyEgnMlLblP2LLz674PlXX30N27dvJyUlBafTwXffbfS+17p1GzZs+AaA9eu/om3bthU69vbtO/D1118BsH37VkJDQwkKCipwXJcuXViz5iPv8wMH9gNw8uQJmjZtxv33P8BVV7Xk6NEjBAQEkpFx4epSzZoRBAQEsHv3X6iqypdffkGPHj0LHGezZZGVpbWv2rLlNwwGA40bNyn22ikp57yLBpYufY++ff9zwfFcTnwiODO6tB9whl5LBNpK1OMsz84ARQVnClg6XA4tNNoCvQA98BfwLdLDTAjhS1q0uJJmzZqzYcM3DB06nLfeeoORI+/H5brwIq+aNSN46KFRjBw5jEcfHZ2vHDlu3Hg+/3wd0dED+OqrLxk7dnyFjvuhh0azb99eoqMH8OabbxS5kGDcuGfZu1c7buDAe/n0008AWLXqQwYN6kdMzEDMZj+uu+56mjVrhl6vJyZmICtXfkBSUiJPPPFYodedMOEFXnxxKvfc05d69epx3XXaYoBNm35k4cIFACQnJzNkyH0MGHAPy5YtZerUF73nv/HGXPr0uRWbzUafPreyaNHbAGzfvo3+/e+mX7+7SE5OZsSIBwve/DKmFJ4yvXQpikmF3LRqpzou5vWK5Dd7fUxnHeiaPsvJs8eZ+ckLxV7HEF6Lug9Nw5mWTNzbEwo9JvBKA40mWMg+5eLghKJ3D6jejMC9aKsxfwP+rtrhCCF8wvr1S6lZM7KqhyHERXXmTCK9ew8/79X47aqqdizs+KL4xJwzR7YOFMjGgD8la6NRkvlmlpzGsz7d28yB1iojDGmVIYQQQlQ9nyhrotMmR5r1WjCVYbNe8JQLrtRUcoMz35tv5g9cned5GhKYCSGEEJcGn8icZTv1oId0xYgFSLcV3bMsh7f5bBHBWUBTA8ZQHfYkF7ZjvjT/KhRt4n8wWtbsQNUORwghhBD5VPvgTFEgw20CPeg9mTNr1gUyZ8qFFwNYOnmyZj5V0qwN3Iq2IvM0ki0TQgghLj3VPjgD0Ou01TaqXuvkn36BsqYhPAqdyQ9n6lncWekFD1AgpINnVwCfKWk2AXqircg8AnyPrMgUQgghLj0+EZxZ0ba6cClaA7v0rOLLmuYLzDfzb6zHWEOP/ayLrMO+EMC0ATp7Hv+Ntiqzeq3SFUIIIS4XPhGcGbPdEAA6g9YZ+UKZswvNNwvxrtL0hb00DUBzz+PfgN1VOBYhhLh4unfvyubNv+V7be3aNfj5+dGnT9+LNo5Ro0Zy5swZzGYzBoOBF16YXOjWTVVh06YfOXLkMMOHj6jQ68bFxfHCC8+SlpZKixZXMW3aDIxGY4Hj3nhjLj///BOqqtK5cxfGjh2PoiisXv0Rq1Z9yMmTJ9i48QdCQ8MAWL/+S5YvXwqAv78/Eya8QPPmLSp07JeCar9aUwFUT4hpMGobpqZfYM6Zd6Xm6eLnm/lGSdOJ1ipjAxKYCSEud/36DajUwExVVW/n+7ymT3+JlStX07//QN54Y26F3KukW0AVp0ePnhUemAG8+eY8YmKG8Omnn2OxWFi37n8Fjtm160927fqTVavW8NFHn7B37x527NgGQJs2bXnrrYXUrl073zl16tTlnXfeY9WqNYwc+TAvvVR4U97qzicyZ+f02vZNiiEQuMBqTUXBFHkFAPZTxwu87ddQj6mmHsc5N5n/Ogu8Xz34o2XLdnmep3u+hBDi8rZo0dv4+wcwdOj9jBo1kquvvoZt27aSnm5l4sRY2rVrj8vl4s03X2f79m04HA4GDBjEvff2JzMzk7Fjn8RqTcPpdPLII4/Ro8eNxMfH8cQTY+jQoSO7d//FnDlzqV27TqH3b926DStWLAcgKyuL2bNncujQQVwuFw8/PJoePW7EZsti6tTJHD16lIYNG5GQEM/48c/RsmUrunfvSkzMUH7//VeefHIs8fHxfPzxShwOB1dffQ3PPvs8ANOnx7Jv314UReGuu+4mJmYoH320kk8//QS9Xk+jRo156aVZfP75Ovbt28v48c+RkBDPtGmxpKScIzQ0jClTplKrVm1iYycRFBTE3r17OHv2LI8//iQ33XRLkd9jVVXZunUr06e/DECfPn1ZvHgh/fsPzHecoijY7XYcDgeg4nQ6CQ+vAVBkZrFNm9ztsa65pnW+vT99iU8EZ34uJxl6EwbPxrLFlTWNNWqhM5lxpp4pdDFASE7WbLu9mk7LCkFrlWFBy5rtqdrhCCEuew/sGVIp132/1YpyX8PpdLFs2Yf88stPLF78DgsWvMO6df8jKCiY5ctXYrfbefDB4XTu3JWoqChmz36NoKAgUlLO8cADw+jevScAx44dZfLkqUyYUPzuNL/++ot3f8olSxbTseO1TJ48Fas1jeHDh3DttV345JPVBAdbWLVqDYcOHWLIkEHe87OysmjSpAmjRz/KkSOHWbbsfd57bykGg5GZM2fw9ddf0bhxE5KSEvn447UAWK1awmLZsvdZt+5LTCaT97W8XnllJn363Mmdd97FZ5/9H3PmzGLOnHkAnDmTxLvvLuXo0SOMHZsbnGlbQK3Od53U1BSCg4MxGLQQIzIyisTExAL3a926DR06dKJ375tRVRg4cBCNGjW+0I/Ma926/3m3k/I1PhGcuV1aE1qdPpis7Exc7qJTvaaoBkDR882qd+PZKOA2wA9IRNvAXAghRFF69eoFwJVXtiQhIR6ALVt+59Chf7wbnGdkpHPixDGioiJZsGA+O3fuQFEUkpISOXv2LAC1a9fmmmtaF3mfSZOex2bLwuVys2LFKu99Nm/exIoVywDIzrZz6lQCu3bt5L77BgPQtGlTmjZt5r2OXq+nV6+bAdi69Q/279/HsGGDPednEx4ezg039CAuLo7Zs2fSrdsNdOnS1XOtZkya9Dw9etxIz543Fhjj7t1/MXv2qwDccUcf3nhjnve9Hj1uRKfT0bhxE5KTz3pfPz8wAy1zdj5FUQq8duLEcY4ePcyXX24AYMyY0ezYsZ327TsU+X3MsW3bVj777P9YvPj9Cx5bHVX74ExRIMXPz/NMX4LFAFpwVthKTb/6esxRepypbjL/qW4lzUbAjWg/0mPAd2iZMyGEqFoVkeGqLEZPxUWv1+Fyaf+fqaoq48ZNoGvX6/Id+/nn6zh37hwffLASg8HIXXf1xm7PBsDPz7/Y+0yf/hLNmzdn/vw3mDXrZWbPfg1VVZk161UaNmyY79jitrw2mUzo9XrvOPv06cuYMY8XOG7lytX8/vuvrFnzMd9+u4HJk6cyb54WWG7e/CPvvbfIm1krSt6AymQy5Rlf8WWl0NAwrFYrTqcTg8FAYuJpIiIiChz344/fc/XVrQkICACga9du/P33XxcMzg4e/IcXX5zK66+/RWhoaLHHVlfVfkEAaGVNjY6M7OLnVhUXnHmzZtWupHkNcAtaYLYHbfK/BGZCCFEWXbp0Ze3a1Tid2or9Y8eOkZWVRXp6OuHh4RgMRrZt20pCQkKprmswGHnkkcf4++/dHDlymC5durJ69SpvsHPgwH4A2rZty7ffatmkw4f/5dChQ4Ver1Ona/n++40kJycDkJqaSkJCPCkp53C73fTqdTOjRz/K/v37cLvdnD59io4dO/H440+Snm4lKyv/PtStW7dhw4ZvAFi//ivatm1b4J4loSgKHTt25PvvvwXgyy8/95Z/84qKqs2OHdtxOp04nQ527NhOw4bFlzVPnUpg/PixTJ36Ig0aNCjT+KqDap85A1ByIinFSIatmOBM0eXZGaDgYoCQarlKUw/kpLx/B/6qwrEIIcSlw2az0afPrd7nMTFDS3Tef/5zLwkJ8QwZEo2qqoSFhTFnzlx6976Dp59+gmHDYmjevAUNGzYq9Zj8/PwYPHgoK1Ys55lnJvDaa7OJjh6AqqrUqVOHuXPn07//IGJjJxEdPYAWLVrQrFkzgoKCClyrceMmjB49hjFjRqOqKgaDgfHjn8NsNjNt2hTvqtHHHnsct9vF5MkvkJ6ejqqqREcPITjYku9648aNZ/r0WD74YJl3QcCFFDbnDGDMmCd54YVnefvtt2jRogV3330PAHv37uHTTz9h4sQp3HTTzWzb9gfR0QNQFIWuXa+je/ceAHz00Uo++GApZ8+eJTp6IN26Xc/EiVN4991FpKamMGvWSwAYDAaWL19Zuh9CNaBcKD15qVEUkwq56dEu9VwMvbYNWQYjZsuDbD28m+XfLyz0XGPNOtQZGYsjJYn4d/JP2jTX1dNsRghOq5v9T6RAwZXQl7AAtPlmR6p6IEIIAcD69UupWTOyqodRLblcLpxOJ2azmZMnT/Doow+zdu1nhfYJE5eWM2cS6d17+Hmvxm9XVbVjaa5T7TNnCuBWPXVxRSk2c5Zb0iwma7bDXg0CMz/gKmCn53kmEpgJIYRvsNlsPPLIQzidTlRV5dlnX5DA7DJT7YMzQIvQPA9KFpwdLfCepZNnL80/LvWSpgWtVUYI2t6YUsYUQghfEhgY6JOlOlFyPhGcqd7gTEdmMQsCzEVkzsx1dPjVNeBMd5O+/1KeSB8J3I6WOUsCDlbtcIQQQghR4XwiOLPrcz5GMZkzRYfRuzNA/pWaOas0rTscWjLqktQQ6IX2IzsOfIusyBRCCCF8T7UPzvL1tVN0RQZnxpq10RlNOM4l4c7Ov3zY20Jj26Va0rwa6IpWv90H/Ew16/UhhBBCiBKq9sEZgM7txq3TAXrSswtvQlvUfDNTlA7/+gZcmW4y9joqeaRloQOaogVmfwB/Vu1whBBCCFGpfKIJrepdEaCQdV5WLEdR881ysmZpOx2ol2SV0A18DWxEAjMhhCi57t27lvsae/fuYc6cWUW+Hx8fx9dff1Xi4883atRI+vW7m5iYgQwbFuNtRHsp2LTpR5YuXVLh142Li2P48CHce29fnntuvGfj84Lmz5/HoEH9GDSon7c57oXO3759KzExAxk48F4efnhkhY/9Yqn2wZni/R9NtsNW6HFFZc4uzcazZiDv9hU2pFWGEEJcfC1btmLcuGeLfD8hIZ5vvllf4uMLM336S6xcuZr+/QfyxhtzyzzWvFyu8k+g7tGjJ8OHj6iA0eT35pvziIkZwqeffo7FYmHduv8VOObnnzezf/8+PvzwY5YuXcGKFctIT08v9nyrNY1Zs17mtddeZ/XqT5k5c3aFj/1iqfbBGeSdfaVgs2cVPECnx+RZDJB9OjdzZozQ4d/QgCtLJX3PpVLSDAbuRgvO2lXxWIQQwrccOLCfBx4YSnT0AJ555inS0tIA2LPnb6KjBzBixDBef/01Bg3qB2iZmKee+q/n8TZiYgYSEzOQwYMHkZGRwZtvvsHOnTs9nfI/yHd8ZmYmU6dO5r77+hMdPcC7nVFRWrduQ1JSEgBZWVlMmzaFYcNiGDx4EJs2/QCAzZbFc889Q3T0AJ57bjzDhw9h7949gJYpXLhwAcOHD2H37r/46qsvuf/+wcTEDOSll6bjcrlwuVzExk5i0KB+3Hdff1au/ADQOvIPHHgv0dEDeP55Lbj8/PN1vPLKy4AWhD7yyMNERw/gkUce5tQpbeuq2NhJzJkzixEjhnH33X28m8UXRVVVtm7d6t28vU+fvt7PlteRI4dp374jBoMBf39/mjVrzm+//VLs+V9/vZ4bb+xFrVq1AQgPDy92LJeyaj/nTAfeVQEOlxOnu2Bt0lijNorBiCP5NGp2bvAWkrNK80876iURm0WgtcrwB84AB6p2OEIIUQECh9xXKdfNWPFRqc+JjZ3EuHHP0qFDRxYuXMDixQsZO3Y806ZN4fnnJ9GmTVvmz3+90HNXrFjOs88+R5s27cjMzMRkMjFmzOOsWLGcuXPnA1owl+O99xYRFBTERx99AuANBIvy66+/0KNHTwCWLFlMx47XMnnyVKzWNIYPH8K113bhk09WExxsYdWqNRw6dIghQwZ5z8/KyqJJkyaMHv0oR44cZtmy93nvvaUYDEZmzpzB119/RePGTUhKSvRuem61amM2qluoAAAgAElEQVRatux91q37EpPJ5H0tr1demUmfPndy55138dln/8ecObOYM2ceAGfOJPHuu0s5evQIY8c+yU033QIUvrVTamoKwcHBGAxa+BEZGUViYmKB+zVr1pzFi99h8OAh2Gw2tm3bSqNGjYs9//jxYzidTkaNGklmZib33RdDnz59i/2eX6qqfXCmz/PY7sgu9BhzbU9J83QR880uiVWaDYCb0H4kJ9BaZVwSEaMQQviE9HQrVquVDh20nXTuvLMvEyY8g9WaRmZmJm3aaBt93357b37+eXOB89u0acvcua9y++13cOONNxEVFVXs/f74YwszZsz0PrdYLIUeN2nS89hsWbhcblasWAXAli2/s3nzJlasWAZAdradU6cS2LVrJ/fdNxiApk2b0rRpM+919Hq9N6O0desf7N+/j2HDBnvOzyY8PJwbbuhBXFwcs2fPpFu3G+jSpavnWs2YNOl5evS4kZ49bywwxt27/2L27FcBuOOOPrzxxjzvez163IhOp6Nx4yYkJ5/1vl7YnpuFbRmp5Gu7oOnS5Tr27t3DiBH3ExYWxjXXtEav1xd7vsvlYv/+fSxYsIjsbBsjRgzj6qtbV8sN0qt9cGbO80O1OQopaQKmqILzzYw1dAQ0MeDOVrHuruogqBVwHdrkuf3AT0irDCGEryhLhutiKukW08OHj+D662/gl19+ZsSIobz11jsXuK5aaOBxvunTX6J58+bMn/8Gs2a9zOzZr6GqKrNmvUrDhg1LPFaTyYRer/feu0+fvowZ83iB41auXM3vv//KmjUf8+23G5g8eSrz5s1n584dbN78I++9t8ibWStK3s9lMpnyfebihIaGYbVacTqdGAwGEhNPExERUeixI0Y8xIgRDwEwceIE6tevX+z5kZFRhIaG4u/vj7+/P+3adeDgwQPVMjir9nPOcjNnRcw3I3cxQHaelZqWDtp2TdZddtQqTZwpQCPPn1uBzUhgJoQQFS8oKBiLxcLOnTsA+OqrL2jfvgMWi4WAgAB279a2w9uw4etCzz958gRNmzbj/vsf4KqrWnL06BECAgLJyCi8S0Dnzl1ZvTo3MC2urGkwGHnkkcf4++/dHDlymC5durJ69SpvsJOzirNt27Z8++0GAA4f/pdDhw4Ver1Ona7l++83kpycDEBqaioJCfGkpJzD7XbTq9fNjB79KPv378PtdnP69Ck6duzE448/SXq6lays/J+pdes23hWT69d/Rdu2bYv8LMVRFIWOHTt65999+eXndO/es8BxLpeLlJQUAA4e/IeDBw/SuXPXYs/v0aMnO3fuxOl0YrNl8fffu2nYsHGZxlnVqn3mLDd6V7AVtlJTp8cUWQ/I30bD4lmlmbq1qrNmKrABqIusyBRCiIpjs9no0+dW7/OYmKFMmTKNmTNnYLPZqFu3LpMnTwNg0qRYZsyYhr+/P+3bdyQoKKjA9Vat+pBt27ai1+tp1Kgx1113PTqdgl6vJyZmIHfe2ZcWLa70Hj9y5EO88spLDBrUD71ez4MPjqJXr5uKHK+fnx+DBw9lxYrlPPPMBF57bTbR0QNQVZU6deowd+58+vcfRGzsJKKjB9CiRQuaNWtW6FgbN27C6NFjGDNmNKqqYjAYGD/+OcxmM9OmTcHtdgPw2GOP43a7mDz5BdLT01FVlejoIQQH5y/Bjhs3nunTY/ngg2WEhoYxZcrUC37/C5tzBjBmzJO88MKzvP32W7Ro0YK7774H0NqQfPrpJ0ycOAWn08nDD2srRQMDA5k2bYZ3nllR52s/k+uIiRmIoijcffc9NG3a9ILjvBQpF0pBXmoUxaRqE+c1fRuo9OjcHtDzr7Unb6+fk+94Y+QV1HlgEo7k08QvngSAIVThynlhuO0q+/97DnfhU9UqkRm4BtiOZMmEEL5o/fql1KwZWdXDKLHMzEwCAgIAWLp0CWfOJJW6JcbF4HK5cDqdmM1mTp48waOPPszatZ9hNBqremgCOHMmkd69h5/3avx2VVU7luY61T5zpstTzi+srGkupL+Zdy/NvxxVEJgFA72BUHJLmUIIIarSzz//xNKl7+FyuahduzZTpkyr6iEVymaz8cgjD+F0OlFVlWeffUECMx9U7YOz/GXNgsFZYfPNQqpslWZNtFYZAcBZYO9Fvr8QQojC3Hrrbdx6621VPYwLCgwMZPnylVU9DFHJqn1wpsuzYqSw3QFydwY4BoAhRCGguQG3Q8X658UMzuqjtcowAifRtmOq6vluQgghhLjU+EBwlrPg1FlwQYBOjymiLpDb48zSwYSiU7DusuMufKenSnAV0A1tcewBZEWmEEIIIYpS7YMzvbeq6U+2PX+0ZYqoq+0McPYUquc9b+PZi7qXZkO0wGy750sIIYQQonDVPjjLrWoWnHN2fklTH6wQeKUBt1Ml7c+LWVL8FqiHtMoQQgghxIVU+ya0Sp5H5885y10MoAVnlvZaSTNjjwN3ZmWWFU3AteR+ex1IYCaEEBdX9+5dvY9/+eUn7r23r3fD7hx33dWb8ePHep9/991GYmMnXbQx5vX+++8W+V5Zxrl37x7mzJlV7DHx8XHeTd7PN2rUSO+m6lXt/fff4557+tKv39389tuvhR6Ts6l9TMxAhg2LYc+e3QA4nQ5iYydy3339GTDgHt5//z3vOQ6HgxkzptGv31307/+fC25Of7FU/8wZeRcE5O+LcX7mLMTbeLYyS5pBaK0ywtD2L/itEu8lhBDiQv74YwuzZ8/izTffplat2gXe37dvL//+e4gmTSquYanL5fJupVRS77//Hg888GCR75d2nC1btqJly1alGkNlU1UVVVXR6UqeGzp8+F82bvyGjz9eS1JSEo89Noq1a9cV+P7Onz+PBx8cRbdu1/PLLz/xxhvzeOed9/j2243Y7Q4++ugTbLYsBg68l9tuu506deqyZMliwsPDWbv2M9xuN2lpqRX9kcuk2mfOcnf0UnC68pQq9QZMEXVRVTf2xOPoAxUCrzKgOlWsOyurpFkD+A9aYJYM/FVJ9xFCCFESO3fuYMaMacybN5969a4o9JghQ4bly6bkyMrKYtq0KQwbFsPgwYPYtOkHQMs2PfTQAwwZch9DhtzHrl1/ArB9+1ZGj36QiRMnEB09AJfLxeuvv8awYTFERw/g008/AeDMmSQefngEMTEDGTSoHzt37mD+/NfJzs4mJmYgEyc+VyHj3L59K0899V8Azp1L5rHHRjFkyH289NJ0+vbtTUrKOQDcbjcvvjiVgQPvZcyY0dhsuVWo9eu/ZMSIYQwa1M+biUpNTWXcuCeJjh7AAw8M5eDBfwBYtOhtPvhgmffcQYP6ER8fR3x8HAMG3MPMmTMYMuQ+Tp8+RWzsJAYN6sd99/Vn5coPivrxAbBp04/ccsttmEwm6tatyxVXXMGePX8XOE5RFDIyMgBIT0/37rmpKApZWVmebZ2yMRqNBAZquyp89tk6hg8fCYBOpyM0NKzYsVws1T5zlidxht2VmxEzRdRF0RtwnE1AtWcT3NmEolew7nbgyqiMkuYVwM1orTLi0FplVOmmnUIIcUnY8u7ZSrlu5wdrFPu+3e5g3LgnWbjwXRo2bFTkcTfffCuffLKaEyeO53t9yZLFdOx4LZMnT8VqTWP48CFce20XwsPDefPNhZjNZo4fP8bEic95e4/t2fM3H320lrp16/Lpp58QFBTM8uUrsdvtPPjgcDp37soPP3xHly5dGTHiIVwuFzabjXbt2rNmzUeFbndU1nHmtXjxO3TseC0PPDCSX3/9hf/9L3dj8xMnjvPiiy8zceIUnnvuGb7//jvuuKMPoAV+S5YsZ8eO7UybFsvHH69l0aK3adHiSubMmcfWrX8wZcrEYscNcOzYUSZPnsqECS+wb99ekpISvZurW63anqNr164BoF+/AfnOTUpK5OqrW3ufR0ZGkZSUWOAeTz/9DP/976O8/vprqKqb997TAsWbbrqZTZt+pHfvW7DZsnjqqXGEhIR477tw4Vts376NevXq8cwzz1GjRvF/ry6Gah+cecuaioLTmZsRM0V55psleEqaldp49krgerRE5D9orTLclXAfIYQQJWUwGGjdug3r1v2v2K2Y9HodQ4fez9KlS7juum7e17ds+Z3NmzexYoX2Sz47286pUwlERETwyisz+eefA+h0Oo4fzw2WWrW6mrp163rPP3ToH777biMAGRnpnDhxjJYtWzF9eixOp5MePW7Mtx9ncUo7zrz+/HMns2fPBeC667phseTunVmnTh3vGK68siUJCfHe9267rTcA7dt3ICMjA6s1jT//3MmsWa8C2gbrqamppKdbix177dq1ueYaLcCqW7cecXFxzJ49k27dbqBLF21u4PlBWY7CtplU8vQ4zbF27RqefnocvXrdzMaN3zB9+lQWLHiHPXv+RqfTsX79BtLSrDz00ANce20XgoICSUw8TZs2bXnqqXF8+OEHvP76a0ybNqPYz3IxVPvgLLcuq+DIU9Y01fbMNzt9DF2AQmArI6pbJW1HZQRn9T0j2QFsq4TrCyFE9XWhDFdl0ekUXn75FR59dBTvv/8uw4Y9wNCh0QB0796T0aMf9R57xx19WLr0PRo3buJ9TVVVZs16lYYNG+a77qJFbxMeXoOVK1fjdru5/vrO3vf8/f3znT9u3AS6dr2uwNgWLXqPn3/+iSlTJjJ06P306dO3RJ+pNONMTi5ZxtJozJ0gpNPpcLmc3ufnB0GKohQaLIGCXm9AVXMTE3Z77u9bP7/c74vFYmHlytX8/vuvrFnzMd9+u4HJk4veSD0yMorTp095nycmnqZmzYgCx33xxeeMHTse0LKMM2ZoW3B9/fV6rruuGwaDkfDwcNq0acu+fXu4+eZb8fPzo2fPXgDcdNMtrFv3vyLHcTFV+zlnOZ9AQcHhzFPW9GTO7AnHsLQ1ojMoZOx34rJWRknzO7QypgRmQghxKfHz82fu3PmsX/8VX3zxGStXrmblytX5AjMAg8FIdPQQVq360Ptaly5dWb16lTcYOXBgP6DNZ6pZsyY6nY6vvvoCl8tV6L27dOnK2rWrvVWdY8eOkZWVRUJCPGFh4dxzTz/uuus/7N+/zzMGQ74KUGFKM8682rRpy7ffbgDg999/JS0trdj75Ni48RtAy7wFBQURFBRM+/Yd+PrrrwBtXltoaChBQUHUrl2H/fu1e+/fv4/4+LhCr5mScg63202vXjczevSj3s9flO7de7Bx4zfY7Xbi4uI4fvw4rVpdXeC4iIgIduzQfg9v3foHV1xRH4BatWqzdesfqKpKVlYWf/+9m4YNG6EoCjfc0IPt23PO2ULjxo1L9H2pbNU+c5Z3taY3c5ZvMcAJovpXdONZE9AOLRhzeb6kVYYQQlyKQkJC/r+9+w6PssoeOP49M0kIaRSDIYigq6gPTSFGROoiVX/A0gJEFBQpLkVdRUBYQJoICAKuoiKLqIgua0FEE4ooYqNXgcWlSBMRgoYAIcn9/fG+M0xCZjIhddzzeR6emXnrmbkJc3Lf997D7NkvMWDAQ5QvX55mzf6c63YdO3Zi/vxL01n07dufGTOm0bNnN4wxVKlShZkz59C1awLDhz/JqlUriIuLz9Zb5ukvf+nMsWNH6dWrJ8YYKlSowPTpM9m4cQNvvvkGQUFBhIWFMW7cRAA6depCz54J3HzzLUyc+KzX9+NvnJ769RvIqFEjWLEiifr144iOrkRYWDhpaWk+P7vIyCgeeugBzp49y5gx49zHGj9+LD17diM0NJRx4yYA0KLF3Sxf/jGJiQnUrFmLatWq53rMEydOMH78WLKyrF62QYOGAt7vObvhhhtp2bIVCQmdcTqdPPXUSPdIzYkTn6Fz567UrFmLUaPG8PzzU8nMzCQkJISnn7amGunWrTvjx49xTxnSvn0HatS4CYAhQx5l7NjRzJgxjfLlKzB2rPcevOIkuXdPll4iIQYudWf2uyWEm+vWQhzlGfvhBlLP/05I5erE9h5F+smj/Pz2OG6ZUwFxwp7HU8g4U9D3G441VUZFrMLlXxXweEop9cfz6acLiI6+uqTDULb09HQcDgdBQUFs27aVKVMm5XkTv8q/kydP0K5dnxxLj240xtyen+MEfM9ZttGa9mXNkMrXWa+PHyTythAcwcLZ3RcLITG7CmiLlaCdBrYU8HhKKaVU0Tt+/BgjRz6FMYagoCBGjRpT0iEpHwI+OfOsEHDRnZxZ15nTjx+kfEN74tkCj9KsijVVRghwFEhGp8pQSikVCKpVq87bb79b0mEoPwV8chbkvDTKxGD1jJWxe84unjpAZN1goKBTaNwMNMEafbAPWINOlaGUUkqpohDwyZnYyZnJsmY6lqBggqOrYLKyKFP5OI6QUM7+5yIZKQW5pHkNVmK2GVhf0JCVUkoppbwK+OTM6bB6xhyOigAEV6qKOJ2k/3KEqHrWRc+Cj9L8AvgvcKCAx1FKKaWU8i3g5zlzOK3kTOyOMff9Zr/8SOStrqoA+a2lGQw05FLumokmZkoppZQqDgGfnIm4qtJnv98sKHQnjjJC2o8ZXDyVn/vDwoEOQB2gUR7bKqWUKq3mz3+NhITO9OzZjcTEBHbs2F5isSxa9Bbnz5+7bPmrr77Miy/OzrZsz57ddOvWKV/H//333/jXvwp+w3+HDu3o1+/BbMtcBdqvxIABfdm1a+dly3ft2sn06c9d0TF9SU9PZ+TIp+jUqT19+vTyOhFucnISPXt2IyGhM7Nnz/R7/9TUVO65pxVTp3qfh64wBHxy5nAnZxZXz1mZqlZtsDP5uqRZEeiINWVGClY5JqWUUoFm27atfPXVWt56azHvvPMvXnrpFWJiYkoklszMTBYvfpvz589ftq5163buWfhdkpOT3DUt/fX777+zZEn+5i3zVtng7NmzHD9ulUvav/+/+Tqmv2rWrOWz3umV+uijD4iKiuKDDz4mMbEXc+bMumyblJQUZs+eyUsvvcJ7773PqVO/8v333/m1/9y5/6B+/bhCjzungE/OcFhvQfAYDGAuEH6zlZT5P0rzGqweswjgGPAR4LuQq1JKqdLp5MmTlCtXnpAQ6/aW8uUrUKmSNSluhw7tSEmxBpHt2rWTAQP6AlYv1pgxo3jkkX507tyeDz74N2CVKOrf/yGGDXuchITOPPvsRPfs9klJn9KjR1e6d+/CnDkvuM/ftGlD5s59iT59ejF//jx++eUXBg7sx8CBD2eL87rrriMyMjJbr97Klcm0bt2Gw4d/YsiQv3L//T3p1+9BDhywKtH8+uuvDBv2OImJCSQmJrB16xZefHE2R44cJjExgVmzZmCMYdasGXTv3oUePbqSnJzkfi8DBz7M6NEj6Nkz90LjrVq1dieMSUmfZUsUjx49Qr9+D9KrVw969erB1q2X5vtcuPCf9OjRlcTEhGxJzapVK+jd+z66dOnA5s2b3HE8/vgQ9+c+fvxYBgzoS8eO97J48SL3vsuXf0Lv3veRmJjA5MkTvCaULl9+ucZdp7RFi5busk2ejhw5TLVq1alQwbpX/Y477mT16pV57v/DD7s4deoUDRo09BlDYQj4AQEOj8uawVdXRRxOTOY6nKHCuf0ZXDzpzyXNGkAzrFz1R6ypMnz/ACillPLPF9NGFclxmw2b5HXdnXc2ZN68V+jSpQPx8Q1o1aoNcXF5T9K+b99/mD9/IefPn+O++3rQuHETAHbu3MG7775PbGwsQ4f+lc8/X0XdurcyZ84s3nxzEZGRUQwZ8ghr1qymefMWnDt3jhtuuMFdw/Pjjz9k7tzXKF++wmXnbNOmLcnJn1G7dh22b99GuXLlqFatOo880p+RI0dRrVp1duzYznPPTebll19j+vTnqFcvjmnTZpKZmcm5c2kMHjyUH3/c5571f/Xqlezdu4dFi94jJSWF3r3vo379+u73snjxv7nmmmty/QxatGjFM8/8nfvv783atV8wYcJkli9fBkDFihV58cW5lClThkOHDjJ69EgWLlzEunVfsWbN5yxY8CahoWU5c+aM+3gZGZm88cbbrFu3ltdee4WXXnrlsnMeOLCfuXPnkZZ2lq5d/0LXrt346aefWLEiiddfX0BQUDBTpkzis8+Wc++97bOVbfJ04sQJYmIqA1at0oiICM6cScn2uV97bTUOHtzP0aNHuPrqGNas+ZyLFy/63D8qqhwvvPA8zzwzifXrv8vrx6jAAj45E7GnoTUe95tFbAXyM/FsLFZithUo+g9dKaVU0QoLC+PNN99hy5ZNbNiwnqefHs7gwUNp376jz/2aNm1OaGgooaGh3H57PDt37iAyMpJatWpTtWpVANq0aceWLZsJCgoiLu52dw9M27b3sHnzJpo3b4HT6aRFi5Z+xdqqVVv69u3NY489QXKy1VOVlpbG9u1bGTFimHs7VwKxYcN6nnnGqsfpdDqJiIi8rJD5li2badOmHU6nk6uuuor69ePYuXMnERHh1KpV22tiBlCuXBRRUVEkJ3/G9ddfT2hoqHtdRkYGU6dOYe/ePTgcDg4dOgTA999/R/v2HQkNLWsfo5x7nxYtWgBwyy01OXbsaK7nbNy4CSEhIYSEhFChQkV+/fUU69d/z+7dP/DAA/cBcOHCBSpWtD7r0aPH5nqc3EtSSrZXUVFRDB8+iqefHo7D4aBOnVs5evSwz/2XLHmPRo0aU7ly5VzPW9gCPznzuDIbUrkaSAahsceA/FzSXAscQkdkKqVU4fPVw1WUnE4ncXHxxMXFc+ONNVi27GPat++I0xlEVpb1JZyenv17QrJ/j1/qAMjxBS8iXr7ILSEhIe7i3HmpXLkyVapUYdOmjaxevYr58xeSlZVFRETkFde/9BWbt0Ltnlq1asPUqc8yZsz4bMsXLXqLihWvYtGi98jKyqJx4wauM3p8VtkFB1uXlp1OB5mZGT63AXA4rO2MMdx7b3sGDx6aZ7wuMTEx/PzzcWJiYsjIyCA1NTVboujStGkzmjZtBsD77y/B6XT43H/btq1s2bKZJUveIy3tHBkZFylbNowhQx71O7b8CPh7zjx/FkIqX4czfB+OMlmcO5RB+s/eLmkGA3fZj2CN9DxQlGEqpZQqRgcOHODQoYPu13v37iE2NhaA2Ngq/PDDLgD3vUYuX3yxhgsXLpCSksLGjRvcl8127drBkSNHyMrKYsWKJG67rR61a9dh06aNpKScJjMzk6Skz7zeLB4WFs7Zs2le423dui0zZkyjatWqxMTEEBERQZUqVVi5Mhmwkq29e/cAEB9/h/vm/8zMTFJTUwkLCyct7dLx69WLY8WKJDIzMzl9+hSbN2+iVq3afn9+zZu34P77+9Cw4V3ZlqemphIdHY3D4WD58mXue8AaNGjI0qUfukekel7WvFLx8XewevUKTp065T6mt543lyZNmvHJJx8DVtvGx8fnmjS6jvnbb7+xZMl7dOzY2ef+Eyc+y7Jln7F06ac8+ujj3HPP/xVZYgZ/gJ4z118zghB8VSxBEesAXxPPhmEVL48GygKriiFGpZRSxencuTSmTZtCamoqTqeTqlWvZdSovwPQr98AJk4cx4IFr1OrVp1s+9WqVZvHHhvCzz8fo2/fflSqdDWHDh2kTp26vPjiLH78cR/16tWnefMWOBwOBg0awsCB/TDG0KhRY5o1+3Ou8XTq1IVHHx1EdHQ0c+fOu2x9y5ateP75aQwbdmkE44QJzzJlyiTmz59HRkYGrVq14aabbuaJJ55i8uQJLF36IQ6HgxEjRlG37q3ceuutdO/ehbvuasTQoY+zfftWEhMTEBGGDHmM6OhoDh7c79fnFx4eTu/eD162vGvXBIYPf5JVq1YQFxfv7oW7665G7N27hwceuI+goCAaNWrMoEH+93jl5k9/uoGBAwczePBAd8H2p54aSWxsFa/3nHXs2ImxY0fRqVN7oqKimDTp0nQdiYkJ7p7I55+fyn/+sxeAhx/uT/Xq1fPcvziJr67P0kgkxEAl9+sRjWsTXSWYYCow4/quhN84AUfwBfaOTCH9WM6eswpAO6wRmSnAp+iITKWUKnyffrqA6OirSzqMfHn11ZcpWzaM++/vnW35xo3reeuthcycOaeEIlOB4uTJE7Rr1yfH0qMbjTF5j0bxEPA9Z57dlc7w/+IIvsD5wxm5JGaxQGugDHAcSAIuFFucSimllFL+CPjkzFNQpDVPzOXlmm7EmirDiVUj83N0qgyllFKe+vd/JNflrkEFShWXgE/OxONJUKRVIuLyqgAxWInZduBbXKWelFJKKaVKmz9OcobgCErjwrFMLhzJ2Sv2NXAYOIhSSqmiZ82gb8g5BYVSf1zGXTmioAJ+Kg0XV1+Y1WsWhFW0vIzHWk3MlFKquOzbd5CMjAz0SoX632DIyMhg377CyTUCvufMxZ2cfefEmiqjEhAOJJdYTEop9b9q3LgXGDfuMW68sToOxx+mH0CpXGVlZbFv30HGjXsh7439UKTJmYi0BWZh3fA1zxgzJcd6sdffA6QBfYwxm/J3EvfBSN1dkQtH4oFI4AzW/WVKKaWK2+nTZ3j00WdKOgylAlKRJWci4gT+AbTCuuFrvYgsNcbs8tisHVbV8RpAA+Bl+9H/89iPP54I5eD7jYEQ4GesqTLOF+g9KKWUUkoVt6Lsa74D2GeM+a8xJh1YDOSsONsRWGgs3wLlRSQ2PycRcbD5UEVeXhOLSQ/BKsO0DE3MlFJKKRWIijI5uwb4yeP1YXtZfrfxyZQL5cjpMDKzBNgBrEDnMFNKKaVUoCrKe85yGz+dc9iOP9sgIv2B/vbLVDi6x7Vu4tIPXE+jgZP5D1OVMG23wKVtF5i03QKXtl1gujm/OxRlcnYYuNbjdVUgZzl5f7bBGPMq8Kqvk4nIhvzWrlIlT9stcGnbBSZtt8ClbReYRGRDfvcpysua64EaInK9iIQAPYClObZZCjwgljuBM8aYY0UYk1JKKSv2vLUAAAdvSURBVKVUqVZkPWfGmAwRGYw1bNIJzDfG7BSRgfb6ucByrGk09mFNpfFgUcWjlFJKKRUIinSeM2PMcqwEzHPZXI/nBhhUSKfzedlTlVraboFL2y4wabsFLm27wJTvdhMrP1JKKaWUUqWB1tRQSimllCpFAjI5E5GZIrJWRGblWF5FRFaLyNci0rKk4lO589FuY0XkG/vf3SUVn/LOW9vZ60REtorIwyURm/LOx+9cqIi8bv9/Oaek4lPe+Wi7ZiLynYh867qHW5Uedh6ySUTOi0hQLuv8ylECLjkTkfpAuDGmCRAiIvEeq0cAo4HW9qMqJfJot4XGmIZY5bzGlkiAyqs82g6gA3Ci+CNTvuTRbkOBRcaYFsaYISUTofImj7Z7AugG3IUOoiuNTgF3k3txb79zlIBLzoCGwEr7+UrgTo91dYFvjDGpwO8iElncwSmvvLabMWa//fQCuUxCrEqcr985gJ7Au8UakfKHr3ZrDnQQkTUi0qG4A1N58tV2O4FyQBngbDHHpfJgjDlvjDntZbXfOUogJmflgd/s52eACh7rnObSCIec61TJ8tVuLuOAV4orIOU3r20nIm2AL4CMEohL+ebrd+4G4BPgXuDvOS+/qBLnq+0+xCogvRt4u5jjUgXjd44SiMlZChBlP4+yX7t4FtXMuU6VLF/thoh0Aq4yxiwq7sBUnny13cPAP4s9IuUPX+12BvjCGHMWa57JmGKOTfnmq+2mAY2BGliTuIcVc2zqyvmdowRicvYN1vVcgJZkv667TUQaikg4EGWM+e2yvVVJ8dpuIlIXa767wprzThUuX79zNbD+kn8CeExEbinm2JR3vtrta6CuiDiB64Bfijc0lQdfbZcJpBhj0oEsILiYY1NXzu8cJeCSM2PMJuC8iKwFsowx33uMNpoKTMK6Rj+5pGJUl8uj3aZh/eWeJCIflViQKle+2s4Yc5sxpi3wPPCCMWZ3ScaqLsnjd+45rP8r1wHz7C96VUr40XYrReQb4HNjzJkSC1RdRkSCRWQlcCvWd1qDK8lRdBJapZRSSqlSJOB6zpRSSiml/sg0OVNKKaWUKkU0OVNKKaWUKkU0OVNKKaWUKkU0OVNKKaWUKkU0OVNKFToRyRSRLR7/rvOxbWohnG+BiOy3z7VJRBpewTHmiUhN+/nTOdZ9XdAY7eO4PpcdIvKxiJTPY/vbROSewji3Uipw6FQaSqlCJyKpxpiIwt7WxzEWAMuMMUtEpDUw3RhTtwDHK3BMeR1XRN4A9hpjJvnYvg9wuzFmcGHHopQqvbTnTClV5EQkQkRW2b1a20WkYy7bxIrIlx49S03s5a1F5Bt733+JSF5J05fAjfa+f7OPtUNEHrOXhYvIJyKy1V7e3V6+RkRuF5EpQFk7jrftdan247uePVl2j10XEXGKyDQRWS8i20RkgB8fyzfANfZx7hCRr0Vks/14s4iEAOOB7nYs3e3Y59vn2Zzb56iUCnxa7FYpVRTKisgW+/l+oBvQyRjzm4hEA9+KyFKTves+EUgyxkyyywqF2duOBloaY86KyHDgb1hJizftge0iEgc8CDQABPhORL4A/gQcNcbcCyAi5Tx3NsaMEJHBxpjbcjn2YqA7sNxOnu4GHgH6AmeMMfEiUgZYJyLJxpj9uQVov7+7gdftRbuBpsaYDBFpCUw2xnQRkTF49JyJyGRgtTHmIfuS6PcistKukamU+oPQ5EwpVRTOeSY3IhIMTBaRplj1AK/BKtl13GOf9cB8e9sPjTFbRKQZUBMr2QEIwepxys00ERmNVSeyL1by84ErcRGR94EmwGfAdBF5DutS6Np8vK9Pgdl2AtYW+NIYc86+lFpXRLra25XDqjuaMzlzJa3XARuBFR7bvyEiNQCD93qJrYEOIvKk/ToUqAb8kI/3oJQq5TQ5U0oVh/uASkCcMeaiiBzASizcjDFf2snbvcCbIjINOA2sMMb09OMcw4wxS1wv7B6oyxhj9tq9avcAz9o9XL564jz3PS8ia4A2WD1o77hOBwwxxiTlcYhzxpjb7N66ZcAgYDYwAatOYid78MQaL/sL0MUYs8efeJVSgUnvOVNKFYdywAk7MfszUD3nBiJS3d7mNazLffWBb4FGIuK6hyxMRG7y85xfAn+x9wkHOgFrRaQKkGaMeQuYbp8np4t2D15uFmNdLm0CuJKxJOAR1z4icpN9zlzZxaqHAk/a+5QDjtir+3hs+jsQ6fE6CRgidjeiiNTzdg6lVODS5EwpVRzeBm4XkQ1YvWi7c9mmObBFRDYDXYBZxphfsJKVd0RkG1aydos/JzTGbAIWAN8D3wHzjDGbgTpY92ptAUYBE3PZ/VVgm2tAQA7JQFNgpTEm3V42D9gFbBKRHcAr5HFlwo5lK9ADmIrVi7cOcHps9jlQ0zUgAKuHLdiObYf9Win1B6NTaSillFJKlSLac6aUUkopVYpocqaUUkopVYpocqaUUkopVYpocqaUUkopVYpocqaUUkopVYpocqaUUkopVYpocqaUUkopVYpocqaUUkopVYr8PwHnjJkbT5ZSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "listOfResults = []\n",
    "true = (df[target]==1)\n",
    "for result in [tree, rand_forest, lin_reg, log_reg, knn, svm]:\n",
    "    pred = pd.Series(sum(result[\"predictions\"], []), \n",
    "                     index=sum(result[\"indices\"], [])).sort_index()\n",
    "    listOfResults.append((true, pred, result[\"name\"]))\n",
    "plotROCs(listOfResults, saving = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best score has Random Forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "END OF THE PREDICTING\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=2020, n_jobs=-1, n_estimators=100, \n",
    "                                 max_depth=50, max_features=10, min_samples_split=30)\n",
    "#split dataset into train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "predictions = model.predict(X_test)\n",
    "output = pd.DataFrame({'index': X_test.index, 'income >$50K/yr': predictions})\n",
    "output.to_csv('prediction/income_prediction.csv', index=False)\n",
    "print(\"END OF THE PREDICTING\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which feature has the bigest importance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_facecolor((0,0,0.10))\n",
    "plt.bar([x for x in range(len(tree[\"feature importance\"]))], tree[\"feature importance\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a lot of features so I choose 10 top most important features in model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_facecolor((0,0,0.10))\n",
    "feat_importances_dt = pd.Series(tree[\"feature importance\"], index=features)\n",
    "feat_importances_dt.nlargest(10).plot(kind='barh')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<u>Marital status: Married civ spouse</u> has the most importance in Decision Tree model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll see top 10 important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_facecolor((0,0,0.10))\n",
    "feat_importances_rf = pd.Series(rand_forest[\"feature importance\"], index=features)\n",
    "feat_importances_rf.nlargest(10).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<u>Capital gain</u> has the most importance in Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_facecolor((0,0,0.10))\n",
    "feat_importances_linr = pd.Series(lin_reg[\"feature importance\"], index=features)\n",
    "feat_importances_linr.nlargest(10).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<u>Relationship: Wife</u> in Linear Regression is taken for the most important feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_facecolor((0,0,0.10))\n",
    "feat_importances_logr = pd.Series(log_reg[\"feature importance\"], index=features)\n",
    "feat_importances_logr.nlargest(10).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<u> Capital gain</u> has the most importance in Logistic Regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K Nearest Neighbours Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_facecolor((0,0,0.10))\n",
    "feat_importances_svm = pd.Series(svm[\"feature importance\"], index=features)\n",
    "feat_importances_svm.nlargest(10).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<u>Capita gain</u> has the most importance in SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary: I see that the most importance have capital gain or Relationship, Marital status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_importance_average(list_f, features=features):\n",
    "    '''Function return dictionary of features and average feature importance.\n",
    "    Avegare feature importance is counting using list of models' feature importance.'''\n",
    "    j = 0\n",
    "    dict_features = {}\n",
    "    for feature in features:\n",
    "        dict_features[feature] = 0\n",
    "\n",
    "    for feature_importance_list in list_f:\n",
    "        for i in range(len(feature_importance_list)):\n",
    "            f = features[i]\n",
    "            if(j == 0 ):\n",
    "                average = feature_importance_list[i]\n",
    "            else:\n",
    "                average = (feature_importance_list[i] + dict_features[f]) / j\n",
    "            dict_features[f] = average\n",
    "            i += 1\n",
    "        j += 1\n",
    "    return dict_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_f = [feat_importances_dt, feat_importances_rf, feat_importances_linr, feat_importances_logr, feat_importances_svm ]\n",
    "dict_features = feature_importance_average(list_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_wordcloud(dict_features, save = False):\n",
    "    '''Function draw, and save if needed wordcloud for dictionary- words and weights'''\n",
    "    \n",
    "    fig = plt.figure(figsize=(12,4))\n",
    "    #create object\n",
    "    wordcloud = WordCloud(background_color='black',max_font_size = 50)\n",
    "    wordcloud.generate_from_frequencies(dict_features)\n",
    "    # Display the generated image:\n",
    "    # the matplotlib way:\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis(\"off\")\n",
    "    if(save):\n",
    "        plt.savefig('images/wordcloud.png', transparent = True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_wordcloud(dict_features, save= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In second file I will show Data Preparation using Column Transformer and Pipeline"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}