proposal_figures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from scipy.stats import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PUMFID</th>\n",
       "      <th>VR2_080</th>\n",
       "      <th>PGM_034</th>\n",
       "      <th>PGM_P036</th>\n",
       "      <th>PGM_100</th>\n",
       "      <th>PGM_280A</th>\n",
       "      <th>PGM_280B</th>\n",
       "      <th>PGM_280C</th>\n",
       "      <th>PGM_280D</th>\n",
       "      <th>PGM_280E</th>\n",
       "      <th>...</th>\n",
       "      <th>FATHEDP</th>\n",
       "      <th>FATEDGRD</th>\n",
       "      <th>FATEDINT</th>\n",
       "      <th>MOTHEDP</th>\n",
       "      <th>MOTEDGRD</th>\n",
       "      <th>MOTEDINT</th>\n",
       "      <th>DDIS_FL</th>\n",
       "      <th>DTYPERP</th>\n",
       "      <th>DCLASSP</th>\n",
       "      <th>WTPF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28111</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28113</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>32.2057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28114</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28116</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>16.1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28117</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>10.6000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PUMFID  VR2_080  PGM_034  PGM_P036  PGM_100  PGM_280A  PGM_280B  PGM_280C  \\\n",
       "0   28111        2        1         6        2         1         2         2   \n",
       "1   28113        2        1         6        2         2         2         2   \n",
       "2   28114        2        1         6        2         2         2         2   \n",
       "3   28116        1        1         6        2         2         2         2   \n",
       "4   28117        1        2         3        1         2         1         2   \n",
       "\n",
       "   PGM_280D  PGM_280E  ...  FATHEDP  FATEDGRD  FATEDINT  MOTHEDP  MOTEDGRD  \\\n",
       "0         2         2  ...        6         3         3        6         3   \n",
       "1         2         2  ...        2         1         1        2         1   \n",
       "2         2         2  ...        6         3         3        6         3   \n",
       "3         2         2  ...        4         1         1        5         1   \n",
       "4         2         2  ...        4         2         2        4         2   \n",
       "\n",
       "   MOTEDINT  DDIS_FL  DTYPERP  DCLASSP     WTPF  \n",
       "0         3        1        1        1  10.0873  \n",
       "1         1        2        6        0  32.2057  \n",
       "2         3        2        6        0  11.0231  \n",
       "3         1        2        6        0  16.1977  \n",
       "4         2        2        6        0  10.6000  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"NGS_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PUMFID</th>\n",
       "      <th>PGM_P405</th>\n",
       "      <th>FATHEDP</th>\n",
       "      <th>MOTHEDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28111</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28113</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28114</td>\n",
       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28116</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28117</td>\n",
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       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19559</th>\n",
       "      <td>63858</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19560</th>\n",
       "      <td>63864</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19561</th>\n",
       "      <td>63865</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19562</th>\n",
       "      <td>63866</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19563</th>\n",
       "      <td>63868</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19564 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PUMFID  PGM_P405  FATHEDP  MOTHEDP\n",
       "0       28111         1        6        6\n",
       "1       28113         2        2        2\n",
       "2       28114         3        6        6\n",
       "3       28116         2        4        5\n",
       "4       28117         1        4        4\n",
       "...       ...       ...      ...      ...\n",
       "19559   63858         1        6        6\n",
       "19560   63864         1        5        5\n",
       "19561   63865         2        3        4\n",
       "19562   63866         1        1        5\n",
       "19563   63868         1        2        5\n",
       "\n",
       "[19564 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_of_interest = ['PUMFID', 'PGM_P405', 'FATHEDP', 'MOTHEDP']\n",
    "new_df = df[columns_of_interest]\n",
    "new_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PUMFID</th>\n",
       "      <th>FATHEDP</th>\n",
       "      <th>MOTHEDP</th>\n",
       "      <th>grade_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28111</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28113</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28114</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>C - D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28116</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28117</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19559</th>\n",
       "      <td>63858</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19560</th>\n",
       "      <td>63864</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19561</th>\n",
       "      <td>63865</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19562</th>\n",
       "      <td>63866</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19563</th>\n",
       "      <td>63868</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19564 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PUMFID  FATHEDP  MOTHEDP grade_average\n",
       "0       28111        6        6             A\n",
       "1       28113        2        2             B\n",
       "2       28114        6        6         C - D\n",
       "3       28116        4        5             B\n",
       "4       28117        4        4             A\n",
       "...       ...      ...      ...           ...\n",
       "19559   63858        6        6             A\n",
       "19560   63864        5        5             A\n",
       "19561   63865        3        4             B\n",
       "19562   63866        1        5             A\n",
       "19563   63868        2        5             A\n",
       "\n",
       "[19564 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a copy of the new_df\n",
    "new_df_copy = new_df.copy()\n",
    "\n",
    "# Define the mapping for substitutions\n",
    "grade_mapping = {\n",
    "    1: 'A',\n",
    "    2: 'B',\n",
    "    3: 'C - D',\n",
    "    4: 'No grade assigned',\n",
    "    9: 'Not stated'\n",
    "}\n",
    "\n",
    "# Replace values in the 'PGM_P405' column using the mapping\n",
    "new_df_copy['grade_average'] = new_df_copy['PGM_P405'].map(grade_mapping)\n",
    "\n",
    "new_df_copy = new_df_copy.drop('PGM_P405', axis=1)\n",
    "\n",
    "# Display the modified DataFrame\n",
    "new_df_copy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gUCgwfvx47Ny5s9Y0IdnZ2fj6668xfPhweHh4NJnrynxlxqzKMHnyZBw/frzW1Brl5eX4+OOPER4ejp49eza5j6aY6nX9199//43y8vI6jx8/fhz5+fm1Lt+5ubnVXPK72pV7566eikSv1+Pjjz+utd3gwYPh7++PDz/8EBqNpubxjRs31nmfZ82ahcuXL+OTTz6pc7zKysp6Mzeloa9pQUFBnW379+8PAHWmgKlPU9+TV/j5+WHSpEn46quvsHnzZkycOLFmtHBjHnzwQQQGBuKJJ57ApUuX6ny+OWeGr+T573P+u4KKQqHAhAkTsGPHDqSmptY8fuHCBezZs6fWtjNnzoRCocDy5cvr7FeSpHqneyGyNTzDR9SEqVOn4oYbbsCLL76I5ORk9OvXD3v37sXOnTvx+OOP17oRHwB69+6NCRMm1JoCAwCWL19es82qVatq5lV76KGH4ODggI8++ghVVVV4/fXXjcp15Sb8F198EbNnz4ajoyOmTp1a78S1zz33HLZs2YJJkyZh8eLF8PHxwaZNm5CUlIQffvjBZJexTfG6/uvLL7+smS5k0KBBcHJywoULF/DZZ5/B2dm51lx0gwYNwrfffosnn3wSQ4YMgUqlwtSpU9GrVy9ce+21eP7551FQUAAfHx9888030Ol0tY7l6OiIVatW4f7778eYMWNw++23IykpCZ9//nmde/jmz5+PrVu34oEHHsCBAwcwbNgw6PV6XLx4EVu3bsWePXuavIXgvxr6mq5YsQJ//PEHpkyZgg4dOiAnJwfvv/8+2rdvX2sgTkOM+Z684s4778Stt94KAFi5cqVRuX18fLB9+3ZMnToV/fr1w+zZszFkyBA4OjoiLS0N3333HYD672P8Lw8PD4wcORKvv/46tFot2rVrh71799bMl3m15cuXY/fu3RgxYgQeeugh6HQ6vPvuu+jVqxdiYmJqtuvcuTNWrVqF559/HsnJyZg+fTrc3d2RlJSE7du347777sPTTz9t1GslslqihgcTtZUrU3KcOHGi0e0WLFggubm51fu50tJS6YknnpBCQkIkR0dHqWvXrtIbb7whGQyGWtsBkB5++GHpq6++krp27SoplUppwIABNdN+XC0yMlKaMGGCpFKpJFdXV+mGG26Q/vzzz2ZlX7lypdSuXTtJLpfXms7jv9OySJIkJSQkSLfeeqvk5eUlOTs7S0OHDpV+/vnnWttcmabku+++q/X4lWlUPv/883pzNPd1NWdalpiYGGnJkiXSwIEDJR8fH8nBwUEKDg6WbrvtNikyMrLWtmVlZdLcuXMlLy8vCUCtKVoSEhKkcePGSUqlUgoMDJReeOEFad++fbWmZbni/ffflzp27CgplUpp8ODB0h9//CGNGjWq1rQsklQ9jctrr70m9erVS1IqlZK3t7c0aNAgafny5VJxcXHNdle+L/6rvq9TfV/T/fv3S9OmTZNCQkIkJycnKSQkRJozZ4506dKlJt+/5nxPSpIkVVVVSd7e3pKnp6dUWVnZ5P6vlpmZKS1ZskTq2bOn5OLiIimVSqlTp07SnXfeWWs6FUn6d1qW+qaaSU9Pl2bMmCF5eXlJnp6e0m233SZlZGRIAOpMEXPo0CFp0KBBkpOTk9SpUyfpww8/rNn3f/3www/S8OHDJTc3N8nNzU3q0aOH9PDDD0uxsbHNep1E1kgmSYLvwiayITKZDA8//LBRN7oTWSKdToeQkBBMnTrVpCOgiUgs3sNHREQ1duzYgdzcXNx5552ioxCRCfEePiIiwt9//42YmBisXLkSAwYMwKhRo0RHIiIT4hk+IiLCBx98gAcffBABAQH44osvRMchIhPjPXxERERENo5n+IiIiIhsHAsfERERkY1j4SMiIiKycSx8RERERDaOhY+IiIjIxrHwEREREdk4Fj4iIiIiG8fCR0RERGTjWPiIiIiIbBwLHxEREZGNY+EjIiIisnEsfEREREQ2joWPiIiIyMax8BERERHZOBY+IiIiIhvHwkdERERk41j4iIiIiGwcCx8RERGRjWPhIyIiIrJxLHxERERENo6Fj4iIiMjGsfARERER2TgWPiIiIiIbx8JHREREZONY+IiIiIhsHAsfERERkY1j4SMiIiKycSx8RERERDaOhY+IiIjIxrHwEREREdk4Fj4iIiIiG8fCR0RERGTjWPiIiIiIbBwLHxEREZGNY+EjIiIisnEsfEREREQ2joWPiIiIyMax8BERERHZOBY+IiIiIhvHwkdERERk41j4iIiIiGwcCx8RERGRjWPhIyIiIrJxLHxERERENo6Fj4iIiMjGsfARERER2TgWPiIiIiIbx8JHREREZONY+IiIiIhsHAsfERERkY1j4SMiIiKycSx8RERERDaOhY+IiIjIxrHwEREREdk4Fj4iIiIiG8fCRzYpKysLjz76KDp16gSlUonQ0FBMnToV+/fvb9V+Dx48CJlMBplMBrlcDk9PTwwYMADPPPMMMjMzTZSeiIjItBxEByAyteTkZAwbNgxeXl5444030KdPH2i1WuzZswcPP/wwLl682OpjxMbGwsPDAyUlJYiMjMTrr7+ODRs24ODBg+jTp48JXgUREZHpyCRJkkSHIDKlyZMnIyYmBrGxsXBzc6v1uaKiInh5ebV43wcPHsQNN9yAwsLCWvuprKzEgAED4OfnhyNHjrR4/0RERObAS7pkUwoKCrB79248/PDDdcoegFaVvca4uLjggQcewNGjR5GTk2OWYxAREbUUCx/ZlPj4eEiShB49erT5sa8cMzk5uc2PTURE1BgWPrIpLb1D4fDhw1CpVDUfmzdvbvGxZTJZizIQERGZCwdtkE3p2rUrZDJZswdmDB48GFFRUTV/DgwMbPaxL1y4AAAIDw9v9nOJiIjMiYM2yOZMmjQJZ86cETJoIzAwEIcOHWrx/omIiMyBl3TJ5rz33nvQ6/UYOnQofvjhB8TFxeHChQtYt24drrvuOpMcIycnB1lZWYiLi8M333yDYcOGIS8vDx988IFJ9k9ERGRKvKRLNqdTp06IjIzEq6++iqeeegqZmZnw9/fHoEGDTFbIunfvDplMBpVKhU6dOmH8+PF48sknERQUZJL9k5EMBqCqpPpDXQxoKwG9BtBrAYOu+r96zb//b9BW/1cmA+SOgMIRkDv8898r/+/wz+ecAKU74OwBKD2q/5/3ZxKRleIlXSKyHFVlQGkWUJoJlGVX/7c0CyjPBSqL/il2/5S7qhKgqhRAG/0TJpMDTlcKoHt1CXT2AFx8AFUA4B5U/aG68t9AQKlqm2xERE1g4SOitlOeBxQk/vtRmAyUZPxb7DRlohOalpOquvi5BwNeoYB3R8CnI+DTqfr/3XxFJyQiO8HCR0SmVVUG5JwHcmPrlruqEtHpLIvSA/AOry6APh0Bn85AQE8gIAJwchWdjohsCAsfEbWMJAGFSUDWWSD7HJB9tvqjMAVtdpnVVsnk1UUwsBcQ2Lv6vwE9q4sh7yMkohZg4SOiphkMQF4skH4CyDhdXfJyztveJVhL5+hWffYvuB/Qfkj1h18X0amIyAqw8BFRHcVVxYjOjYZ79kUMOPsTkBHFy7GWysUHaDeouvyFDgHaDa4eTEJEdBUWPiJCVnkWjmcdR2R2JKJyopBYnAgJEsZ798Jbkb+KjkfNIZMDft2qC2DHkUDHUYB781eOISLbwsJHZIfKNGU4nnUcf2X+hWMZx5BcklzvdoEufvjtfGTbhiPT8+9RXfw6jQLChwPOnqITEVEbY+EjsgM6gw5n8s7gWMYxHMs4hrN5Z6GTdEY9d1+hHkFFl82ckNqMTAGE9P+3AIZdBzgoRaciIjNj4SOyUbkVuTiQdgCH0w/jRPYJlGvLW7SfN506YkIs1we2WU4qoPMNQPfJQNcJnBuQyEZxaTUiG5JSkoL9qfuxP3U/zuSegWSC6VGiXd0xwQTZyEJpyoALP1V/yORA+6FA90nVBdC/m+h0RGQiPMNHZOXO5Z/D/pT9OJB2APFF8Sbff1+PztgcfcDk+yUr4NsF6DYR6HETEHYt5wAksmIsfERWRpIkROZEYl/KPvye+jsyyzPNejxHuSP+Sk6Hk77KrMchC+fRHug9E+hza/U8gERkVVj4iKxEcnEyfkr8Cb8k/oLLZW07iOJLnQ/6p0W16THJgvl2rS5+vW/lxM9EVoKFj8iCFamL8Gvyr/g54WfE5MUIy/G0ey8siOF8fFSP4H5An9uAXjMBz3ai0xBRA1j4iCyMVq/FwfSD+CnhJxy+fBg6g3HTp5jTjd69sJYTMFNjZHKg8xhg4ILqQR8KR9GJiOgqLHxEFiK2IBbfXfoOvyb9ihKNZS1jFuDsh/0XOAEzGcktAOg/p7r8+XYWnYaIwMJHJJRap8bu5N34LvY7oZdsjbG30IDgonTRMcjadBgODFoARNwMODqLTkNkt1j4iARILUnFN7HfYGf8Tos7m9eQN5w6YWLsQdExyFo5ewH9ZgNDFgF+XUWnIbI7LHxEbUSSJBy+fBhbLm7B0ctHTTIpclu6w7svno38WXQMsnoyoMtY4JoHgC7jOLcfURvhShtEZlahrcD2+O34+sLXSC1NFR2nxaL1ZaIjkE2QgPjfqj98uwLX3A/0nwc4uYoORmTTeIaPyEyK1EX4+uLX2HJxC4qqikTHaTUHuQP+SsmAUqcWHYVsjYs3MPhuYOh9gHuQ6DRENomFj8jEMssysen8JmyL24ZKXaXoOCb1hc4XA9JOi45BtkrhVD2n3/AneJ8fkYmx8BGZSFxhHD4/+zl+TfoVOkn83Hnm8JR7LyzkBMxkbjI50HM6MHIJENhTdBoim8B7+IhaKTI7Ep+d/Qx/pP9hdQMxmiua/2JQW5AMwLltwLntQMRN1cWP6/cStQrP8BG1UFROFN49/S6OZx0XHaXN+Dv74PcLUaJjkD3qNhEY+QzQfpDoJERWiYWPqJliC2Kx7vQ6/JH+h+goQuwpAkIKrXe0MVm5zmOAUc8CYdeKTkJkVVj4iIyUVJyE96Lew97kvTZ/6bYxrys7YdLFg6JjkL3rNhEYtwwIiBCdhMgq8I4coiZklGXgg+gP8FPCT9BLetFxhIt2UWGS6BBEl3YDcXuBfnOAG14APNuLTkRk0eSiA1iagwcPQiaToaioSHSUei1cuBDTp08XHQOAZWUxh7zKPLz616u4aftN2BG/g2XvH9GGUtERiKpJBiBqM/DuIGDPi0BFgehERBbLJIVv4cKFkMlkWLNmTa3Hd+zYARmXzTGpd955Bxs3bhQdw6ZV6avwUfRHmLxtMr6J/QZag1Z0JItysSwNakdn0TGI/qVTA8fWA+v6A4fXAlrbmv+SyBRMdobP2dkZr732GgoLC021yxbTaDSiI5iNp6cnvLy8RMewWXuT92LajmlYH7Xe5iZNNhWdQYfzwZwbjSyQuhjYvxxYNxCI+hrgLepENUxW+MaNG4egoCD83//9X6Pb/fDDD+jVqxeUSiXCw8Px1ltvNbnvVatWISAgAO7u7li0aBGee+459O/fv+bzVy4tvvrqqwgJCUH37t0BAF9++SUGDx4Md3d3BAUFYe7cucjJyam17127dqFbt25wcXHBDTfcgOTk5DrHP3LkCEaMGAEXFxeEhoZi8eLFKC8vbzBvQkICpk2bhsDAQKhUKgwZMgS//fZbrW3ef/99dO3aFc7OzggMDMStt95a87nvv/8effr0gYuLC3x9fTFu3Lia4/33MmppaSnmzZsHNzc3BAcH4+2338bo0aPx+OOP12wTHh6O1atX4+6774a7uzvCwsLw8ccf18qTlpaGWbNmwcvLCz4+Ppg2bVqt90Kv1+PJJ5+El5cXfH198cwzz8CWxvvEFsTi7j1346lDT+Fy2WXRcSxetFeg6AhEDSvNAHY8CGwYD2RwZRgiwISFT6FQYPXq1Xj33XeRnp5e7zanTp3CrFmzMHv2bJw5cwbLli3Dyy+/3Oglys2bN+PVV1/Fa6+9hlOnTiEsLAwffPBBne3279+P2NhY7Nu3Dz///DMAQKvVYuXKlYiOjsaOHTuQnJyMhQsX1jwnLS0NM2fOxNSpUxEVFVVTJq+WkJCAiRMn4pZbbkFMTAy+/fZbHDlyBI888kiDmcvKyjB58mTs378fp0+fxsSJEzF16lSkplZPZXHy5EksXrwYK1asQGxsLHbv3o2RI0cCADIzMzFnzhzcfffduHDhAg4ePIiZM2c2WK6efPJJHD16FD/++CP27duHw4cPIzIyss52b731FgYPHozTp0/joYcewoMPPojY2Nia92nChAlwd3fH4cOHcfToUahUKkycOLHmbOlbb72FjRs34rPPPsORI0dQUFCA7du3N/geWItCdSGWH1uOWT/PwomsE6LjWI1oB9sp+2TD0o8Dn4wBfnqM9/eR3TPJtCwLFy5EUVERduzYgeuuuw49e/bEhg0bsGPHDsyYMaOmrMybNw+5ubnYu3dvzXOfeeYZ/PLLLzh37ly9+7722msxePBgrF+/vuax4cOHo6ysDFFRUTXH3717N1JTU+Hk5NRgzpMnT2LIkCEoLS2FSqXCCy+8gJ07d9Y69nPPPVdzadrLywuLFi2CQqHARx99VLPNkSNHMGrUKJSXl8PZ2bh7mXr37o0HHngAjzzyCLZt24a77roL6enpcHd3r7VdZGQkBg0ahOTkZHTo0KHOfq5+r0tLS+Hr64uvv/665gxhcXExQkJCcO+99+J///sfgOozfCNGjMCXX34JAJAkCUFBQVi+fDkeeOABfPXVV1i1ahUuXLhQc8+lRqOBl5cXduzYgfHjxyMkJARPPPEElixZAgDQ6XTo2LEjBg0ahB07dhj1HlgSrUGLLRe24MOYD1Gq4SCE5vJT+uDAxSjRMYiM5+IN3PAiMPhuQK4QnYaozZl8lO5rr72GTZs24cKFC3U+d+HCBQwbNqzWY8OGDUNcXBz0+vpHQMbGxmLo0KG1HvvvnwGgT58+dcreqVOnMHXqVISFhcHd3R2jRo0CgJozbRcuXMA111xT6znXXXddrT9HR0dj48aNUKlUNR8TJkyAwWBAUlJSvZnLysrw9NNPIyIiAl5eXlCpVLhw4ULNcW+88UZ06NABnTp1wvz587F582ZUVFQAAPr164exY8eiT58+uO222/DJJ580eF9kYmIitFptrffD09Oz5pL21fr27Vvz/zKZDEFBQTWXt6OjoxEfHw93d/ea1+jj4wO1Wo2EhAQUFxcjMzOz1nvl4OCAwYMH15vL0h3PPI5bfrwFb5x8g2WvhfKqCnDZJ0x0DCLjVRYCu54GPh4FpBwTnYaozZm88I0cORITJkzA888/b+pdN8rNza3Wn8vLyzFhwgR4eHhg8+bNOHHiRM0lyOYM6igrK8P999+PqKiomo/o6GjExcWhc+fO9T7n6aefxvbt27F69WocPnwYUVFR6NOnT81x3d3dERkZiS1btiA4OBhLly5Fv379UFRUBIVCgX379uHXX39Fz5498e6776J79+4NlktjOTo61vqzTCaDwWCoeY2DBg2q9RqjoqJw6dIlzJ07t1XHtSRF6iK8eORF3LP3HiQVt+79JCA6oP7vfyKLlnUG+HwisO1+oDxfdBqiNmOWefjWrFmDn376CceO1f4tKiIiAkePHq312NGjR9GtWzcoFPWfYu/evTtOnKh9b9V//1yfixcvIj8/H2vWrMGIESPQo0ePOgM2IiIicPx47XVQ//rrr1p/HjhwIM6fP48uXbrU+Wjo8vHRo0excOFCzJgxA3369EFQUFCdwSAODg4YN24cXn/9dcTExCA5ORm///47gOoyNmzYMCxfvhynT5+Gk5NTvffLderUCY6OjrXej+LiYly6dKnJ9+e/rzEuLg4BAQF1XqOnpyc8PT0RHByMv//+u+Y5Op0Op06datZxRPox4UfcvONm/Jjwo+goNiPa1a3pjYgsVcw3wHtDgJjvRCchahNmKXx9+vTBvHnzsG7dulqPP/XUU9i/fz9WrlyJS5cuYdOmTVi/fj2efvrpBvf16KOPYsOGDdi0aRPi4uKwatUqxMTENDm/X1hYGJycnPDuu+8iMTERP/74I1auXFlrmwceeABxcXFYsmQJYmNj8fXXX9cZQPLss8/izz//xCOPPIKoqCjExcVh586djQ7a6Nq1K7Zt21ZzNnDu3Lk1Z9MA4Oeff8a6desQFRWFlJQUfPHFFzAYDOjevTv+/vtvrF69GidPnkRqaiq2bduG3NxcRETUXT7I3d0dCxYswJIlS3DgwAGcO3cO99xzD+RyebPmP5w3bx78/Pwwbdo0HD58GElJSTh48CAWL15cMwDnsccew5o1a7Bjxw5cvHgRDz30kMVOTn01TXo6Ypc+g6VHXkJhlfgpg2xJtJ6Xw8nKVeQD2xYBm28DiusfbEhkK8y20saKFStqlRyg+kzS1q1b8c0336B3795YunQpVqxYUWvk7H/NmzcPzz//PJ5++mkMHDgQSUlJWLhwYZODJfz9/bFx40Z899136NmzJ9asWYM333yz1jZhYWH44YcfsGPHDvTr1w8ffvghVq9eXWubvn374tChQ7h06RJGjBiBAQMGYOnSpQgJCWnw2GvXroW3tzeuv/56TJ06FRMmTMDAgQNrPu/l5YVt27ZhzJgxiIiIwIcffogtW7agV69e8PDwwB9//IHJkyejW7dueOmll/DWW29h0qT6F7Nau3YtrrvuOtx0000YN24chg0bhoiICKMHkwCAq6sr/vjjD4SFhWHmzJmIiIjAPffcA7VaDQ8PDwDVZX3+/PlYsGABrrvuOri7u2PGjBlGH6OtSQYDCjZtQuLUm2HY+hNeTOkvOpLNiS1Lg9rRRXQMotaL2wu8dy1w/BPO3Uc2yySjdNvajTfeiKCgoJpRp/Sv8vJytGvXDm+99Rbuuece0XGEqEpIQOaLL6Hyn1HcACBzccGyh7xxzimn4SdSs23U+2NQqvVc2idqUtj1wM3rAL+uopMQmZTFr6VbUVGBtWvX4ty5c7h48SJeeeUV/Pbbb1iwYIHoaBbh9OnT2LJlCxISEhAZGYl58+YBAKZNmyY4WduTtFrkffABkmbMrFX2AECqrMTzf/iICWbDor0CREcgMq3UP4EPhgGH3wIMXD+bbIfFFz6ZTIZdu3Zh5MiRGDRoEH766Sf88MMPGDdunOhoFuPNN99Ev379albkOHz4MPz8/ETHalNViUlIvn02ct9ZB6mBUdhOJ87i4Zy+9X6OWoYTMJNN0lcB+1cAn00ECjiin2yDVV7SJbpa0fffI2v1/0H6Zy7Dxsi8vfD4vXJcVpS0QTLb56v0xsGL0aJjEJmPkwqYuAYYOF90EqJWYeEjq6UvKUHm0ldQunt3s55XPHYg7h0aY6ZU9ufXYqB9QaroGETmFTEVmLoOcOWtIWSdLP6SLlF9KiIjkTR9RrPLHgB47o/ErJIeZkhlnzgBM9mFCz8B718HxO8XnYSoRVj4yKpIej1y17+HlPl3QpuR0eL93LazAO4GpQmT2a9oF07ATHaiLAv46hZg1zOAVi06DVGzsPCR1dBmZiJlwQLkrV8PNLD2srGk9AysuNjLRMnsW7Se90OSPZGA4x8BH48GcmNFhyEyGgsfWYWSPXuROH0GKk+abs63dr9EYnRlB5Ptz15dKktHpZOr6BhEbSv3AvDxDcCZ70UnITIKCx9ZNENlJTJfXorLjz0GQ3GxaXeu0+HBXyU4SPxr0Bo6SYdzwXWX/iOyedpy4Id7gJ+fBHT1TwdFZCn4k44sljr2EpJuvQ1F35lvcXNZbCJeTO1vtv3bi2hPTsBMduzkBuCz8UBhiugkRA1i4SOLVPLrr0ieMweahASzH6v39hj00bCwtAYnYCa7l3Ea+GgkENv8mQOI2gILH1kUyWBAzptv4vITTxo1kbJJjlmpxnOHvNvkWLYquiJTdAQi8dRFwJbZwG/LuCwbWRwWPrIY+uJipN13P/I/3dDmx3Y8eQ6P5vRr8+PaioKqQqT5cgAMESABR94GvpwOVBSIDkNUg4WPLII69hKSbpuF8iNHhGUYuS0B7XWewo5v7aL9OQEzUY2kP4BPxwK5l0QnIQLAwkcWoGTfPiTPmQNtqtjluaTCIiw7xbNULRXtyqlZiGopSAQ+HQfE/SY6CRELH4mV9+FHuLz4sTa7X68pHr9HYnYxl11riWgdJ2AmqqOqGPh6FvDnetFJyM7JJEni8DpqcwaNBpkvvYSSH38SHaUOWUgQFt1ZhmIZl05qDgeZA/5Mz4KLxjLKO5HFGXAHMOVtwMFJdBKyQzzDZyNGjx6Nxx9/XHQMo+jy8pA6/06LLHsAIGVkYfn5nqJjWB2dpMPZYL5vRA06/RXwxc1AeZ7oJGSHrLLwLVy4EDKZDGvWrKn1+I4dOyCTyZq1r/DwcPzvf/9rVZ6W7sOaSpqpqC9eRNKsWaiMjhYdpVEhv5zCmIpw0TGsTrQX5zMkalTqMeCTG7gOL7U5qyx8AODs7IzXXnsNhYWFoqOQkcr/Po6UO+ZDl2EFc7bp9bh/t4HLrjVTtNwgOgKR5StKBT6bAKT+LToJ2RGr/Wk2btw4BAUF4f/+7/8a3e6HH35Ar169oFQqER4ejrfeeqvmc6NHj0ZKSgqeeOIJyGSyBs8OSpKEZcuWISwsDEqlEiEhIVi8eHGj+8jPz8ecOXPQrl07uLq6ok+fPtiyZUvNPhcuXIhDhw7hnXfeqXlecnIyAODs2bOYNGkSVCoVAgMDMX/+fOTl/XsJoLy8HHfeeSdUKhWCg4NrvSZLVfrbb0i7914YyspERzGaLDYRLyf3Fx3DqsRUZoiOQGQdKguBL6YBF38RnYTshNUWPoVCgdWrV+Pdd99Fenp6vducOnUKs2bNwuzZs3HmzBksW7YML7/8MjZu3AgA2LZtG9q3b48VK1YgMzMTmZn1n3n64Ycf8Pbbb+Ojjz5CXFwcduzYgT59+jS6D7VajUGDBuGXX37B2bNncd9992H+/Pk4fvw4AOCdd97Bddddh3vvvbfmeaGhoSgqKsKYMWMwYMAAnDx5Ert370Z2djZmzZpVk2fJkiU4dOgQdu7cib179+LgwYOIjIw01VtrckU/bEP6Y49D0ljf4uI9d8SgryZQdAyrUVBVhDTfcNExiKyDrhL4dj5w8nPRScgOOIgO0BozZsxA//798corr2DDhrqrM6xduxZjx47Fyy+/DADo1q0bzp8/jzfeeAMLFy6Ej48PFAoF3N3dERQU1OBxUlNTERQUhHHjxsHR0RFhYWEYOnQoADS4j3bt2uHpp5+u+fOjjz6KPXv2YOvWrRg6dCg8PT3h5OQEV1fXWs9bv349BgwYgNWrV9c89tlnnyE0NBSXLl1CSEgINmzYgK+++gpjx44FAGzatAnt27dv4btoXvkbNiDnjTdFx2gxSa3Gswc9MW98tugoViMqoBNC85NFxyCyDpIe+PlxoDQLuOF50WnIhlntGb4rXnvtNWzatAkXLlyo87kLFy5g2LBhtR4bNmwY4uLioNcbv87hbbfdhsrKSnTq1An33nsvtm/fDp1O1+hz9Ho9Vq5ciT59+sDHxwcqlQp79uxBahOTC0dHR+PAgQNQqVQ1Hz16VM8Ll5CQgISEBGg0GlxzzTU1z/Hx8UH37t2Nfj1tJfuNN6y67F3heOo8HsvmsmvGinZxER2ByPocWgP8uJhr8JLZWH3hGzlyJCZMmIDnnzffb0ahoaGIjY3F+++/DxcXFzz00EMYOXIktFptg89544038M477+DZZ5/FgQMHEBUVhQkTJkDTxGXNsrIyTJ06FVFRUbU+4uLiMHLkSFO/NLOQ9HpkvPgiCjZ8JjqKyQzfFo8wvZfoGFYhhhMwE7VM5Cbg2zsAbaXoJGSDrL7wAcCaNWvw008/4dixY7Uej4iIwNGjR2s9dvToUXTr1g0KhQIA4OTkZNTZPhcXF0ydOhXr1q3DwYMHcezYMZw5c6bBfRw9ehTTpk3DHXfcgX79+qFTp064dKn2mor1PW/gwIE4d+4cwsPD0aVLl1ofbm5u6Ny5MxwdHfH33/+O7iosLKyzb1EMGg3SH3sMxT9sEx3FpKSiYrxyIlR0DKtwqSwdFU5uomMQWafYXcDm2wBNuegkZGNsovD16dMH8+bNw7p162o9/tRTT2H//v1YuXIlLl26hE2bNmH9+vW17q0LDw/HH3/8gcuXL9caCXu1jRs3YsOGDTh79iwSExPx1VdfwcXFBR06dGhwH127dsW+ffvw559/4sKFC7j//vuRnV37PrDw8HD8/fffSE5ORl5eHgwGAx5++GEUFBRgzpw5OHHiBBISErBnzx7cdddd0Ov1UKlUuOeee7BkyRL8/vvvOHv2LBYuXAi5XPyXUl9WhrRF96Lst/2io5iF+4HTmFscITqGxdNLepwL4QTMRC2WfBj4ciag5tlyMh3xLcFEVqxYAYOh9hxgAwcOxNatW/HNN9+gd+/eWLp0KVasWIGFCxfWel5ycjI6d+4Mf3//evft5eWFTz75BMOGDUPfvn3x22+/4aeffoKvr2+D+3jppZcwcOBATJgwAaNHj0ZQUBCmT59ea79PP/00FAoFevbsCX9/f6SmpiIkJARHjx6FXq/H+PHj0adPHzz++OPw8vKqKXVvvPEGRowYgalTp2LcuHEYPnw4Bg0aZKJ3smV0BQVIvXMBKv4ZhWyrZuzIhafBWXQMixftWf/fJSIyUtpfwJfTgcoi0UnIRnAtXWo1bUYGUu++B5p/5hG0dZlTh+Cx3qdFx7Boo70j8G7kHtExiKxfUF/gzp2Aq4/oJGTlbOYMH4mhzcpCyoKFdlP2ACB4VyTGVXQUHcOixVRYwWoqRNYgKwbYeBNQlis6CVk5Fj5qMW12DlIWLIA2LU10lLal1+O+XTo4SQrRSSxWQVURUv1YiolMIuccsHFK9Vx9RC3EwkctosvNReqCBdCmND6voM2KS8LLyX1Fp7Bo0f4sfEQmkxcLfD4JKL4sOglZKRY+ajZdXh5SFt5lV5dx69Njxxn01zS8Qou94wTMRCZWkFi9/i4v71ILsPBRs2gKi5B6113QJCSIjiKcpFbjmQMeomNYrGhOwExkevlx/4zeLRSdhKwMCx8ZTV2uxfb3LyGzx02io1gMh8jzeCKLy67VJ64sHRVKlegYRLYn+yzw1S1AVanoJGRFWPjIKBq1Dj+vj0ZeWhmiSrsib/oS0ZEsxvXb4tBB5yU6hsXRS3qcDeZE1URmcfkU8PXtXIaNjMbCR03SafT45b0YZCf9e4kupigcube8KDCV5ZCKS/DKcS67Vh9OwExkRilHq9fe1TW+RjsRwMJHTTDoDdj9yVlkxBXV+dyZ/BBk3baszTNZItWh05hXxLNZ/xUtb3qdaiJqhfjfgB/uBgz8u0aNY+GjBkmShANfXkTKmfwGtzmf64+M2a+2YSrLNX1HDpdd+4+YigzREYhs34WfgB0PAlw4ixrBwkcN+nNbAi7+1fREnxezvJA+5zVIMlkbpLJcUmY2Vp3jWb6rFWqKkeLXSXQMItsX8y2wb6noFGTBWPioXlG/pSJqn/GTKl/KVCF1zpuQ5Pa9+kTgrlMYX86CczVOwEzURv5cBxz/RHQKslAsfFRH3MlsHP0hvtnPS8hwRvLstyApHMyQykoYDFi0S8Nl164S7cLL3ERt5tdngIu7RKcgC8TCR7Wkxxbit43ngRbeCpKU4YjE29fC4OBk2mDWJD4ZSxM5N98VnICZqA1JBuCHe4D0U6KTkIVh4aMa+Rll+PXDMzDoWnfjb0qGAgmz1kJyst8zO913RmOQJlh0DIsQzwmYidqWtgL4elb1UmxE/2DhIwBARYkGv7wXA02lziT7S8uQIfaWtTC42OcPeqmqCk/9bp+v/b/0kh5nQjiYhahNVeQBX90KlDc8ywLZFxY+gk6rx68fxqA0X23S/WZkSrg47Q0YVJ4m3a+1cDh9AU9l9hcdwyJEe3ACZqI2V5AAbJnN1TgIAAsfAfh9WzyyEs1zn1VWlgHnp7wGg4evWfZv6a7ddgnhXHaNEzATiZJ+HNj5sOgUZAFY+Ozcu/vj8MSZRLh2djfbMXKy9Tg7cTX0PoFmO4alkkpK8MrfXHaNEzATCXT2B+DwWtEpSDAWPju2+2wW1v52CeU6A1bk58AQ4WG2Y+Xl6HBmzAro/dub7RiWyu2P05hf1FN0DKGKNMVI9u8sOgaR/fp9JXBpr+gUJBALn526kFmCJ7dG1azEIwF4KzMbGRFuUDia59uiIE+H6JFLoQ+2v4l4b96WBW+Di+gYQkX7hYuOQGS/JAPwwyIgL050EhKEhc8O5ZdVYdGmk6jQ1L2vanNmHo6FKeCscjTLsYvytTh97fPQhXY3y/4tlZSdg5Vne4iOIRQnYCYSrKoY2DIHUBeLTkICsPDZGb1BwkObI3G5qOFRW4fyS/CNlxaqQPOckSop1CJy4FPQduxtlv1bqoBfT2Fiuf1e1ozW8ocMkXD5ccAP9wIGg+gk1MZY+OzMm3tj8XdSQZPbxZVVYq2+GG4dzTOYo6xYi8g+j0LbZYBZ9m+RDAbc/YsaSjtddi2+PB3lSvMNDiIiI8Xtqb6nj+wKC58d+f1iNj48lGD09sU6PVYU5kDWwzyDOcpLdDjV4wFoIq4xy/4tUkIKXrHTZdcMkoETMBNZiiNrgXPbRaegNsTCZycuF1Xiya3RNYM0jGUA8HpWNvIi3CBXyEyeq6JMh5Od7oa67wiT79tSdd0RhcFVIaJjCBHt4Sc6AhFdsfNRIN/4kwBk3Vj47IBWb8DDmyNRVKFt8T4+z8xDZCdHKF0dTJismrpch5Pt5kE9YKzJ922JJI0GT+13hax1SxZbJU7ATGRBNKXAdwsAXZXoJNQGWPjswOpdFxCVVtTq/ezLLcY2Px3c/E0/2lJTqceJgFtROXSyyfdtiRTRF/F0Vn/RMdpcTMVlSDD9mWIiaqGsM8CeF0SnoDbAwmfjfj2Tic+PJptsf+dLKvGurBSqDiqT7fMKbZUBJ7ymouL66SbftyUa+kMsOum8RcdoU8WaEiT7dxIdg4iuduJT3s9nB1j4bFhKfjme+T7G5PvN1+iwsiQPjt1NP5hDpzHghNsElI263eT7tjRSaSle/qud6BhtLtrf/ibeJrJ4Py4GChJFpyAzYuGzUWqtHg9+FYnSKp1Z9q+TJKzOzkZJhApyuWkv0em1Bpx0GIXSMXeYdL+WyO1wFBYU9hIdo01FOytFRyCi/6oqAb67C9BpRCchM2Hhs1HLfzqP85klZj/OR5m5ONdFCScX0w7mMOglnML1KBl/j0n3a4lu2p4BX4Or6BhthhMwE1mozChg74uiU5CZsPDZoL3nsrDleGqbHe/nnEL8HKCHq69pz9wYDBJOaQeiaPKDJt2vpZGyc7HijP0sNZdQno4yZ/PM7UhErXT8Y+DCz6JTkBmw8NmY/LIqvLD9TJsfN7q4Ah86lkMV6mbS/UoSEFnZGwVTHzfpfi2N/+5TmGQny64ZJAPOBNv3usJEFu2nx4CyXNEpyMRkktTcqXjJkj3w5SnsPpcl7PhKuQxL/AJQdcn0l5P7eiXDb8cbJt+vxejUAfNnZaFK1rq56rK3ZyN3Z+1/rJ2CnNBtTbd6ty88XIjLGy7XekzmIEOvT/+9tzDv1zzk7qrep/9kf/hN+ncC5YqECmR8kYHOSztDZuTk3A979MYD0buM2paIBOg+GZizRXQKMiGe4bMh2yLThZY9AKgySFiVk43Knu6QmXi6tZiicOTc+pJpd2pJElOwLME0y64p2ynR/X/daz46vdj4VChyF3mt7bu/9e8lZnWaGtnbsxH6YChCHwxF9rZsqNPUAABJLyFjUwZCFoQYXfYAIFpunsFERGQisbuA01+JTgEAWLhwIWQyWc2Hr68vJk6ciJgY089CYctY+GxEZnElXvnxnOgYNdZn5CC+mwsclAqT7vdsXjCyZi0z6T4tSZedURha1fqpWmRyGRy9HGs+HNybHlRTa3vPf7evyqyCc3tnqHqqoOqpgnOoM6oyq2fmz/s1D27d3eDaqXmDTjgBM5EV2P08UNR294M3ZuLEicjMzERmZib2798PBwcH3HTTTaJjWRUWPhsgSRKWfBeDUrVlnTXZll2A34IBFy8nk+73fI4/Mma/atJ9WgpJo8ETvzm3etm1quwqXHz8ImKXxCLtwzRo8hufasFQZUDsU7G4+ORFpLyTAvVldc3nlO2V0GRroMnXQJOnQVVWFZTtlajKqULh4UIEzAxodr4STSmSAuzjnkUiq1VVAux4CM1ehN0MlEolgoKCEBQUhP79++O5555DWloacnN5r6GxWPhswJd/peBIfJ7oGPU6UVSGz1zUULUz7WCOi1leSJ/zGiRTXze2AIqYWCzJ7N/i57t2dkX7Re0R/lQ4Qu4MgSZPg6TVSdBX1n9voDJYiXb3tEPY4jCE3hcKGIDEVYnQFlSvvewc4ozAWwKR/EYykt9MRtCtQXAOcUbGxgwEzQpC2dkyxL0Yh/il8SiPLTc6Z7RfeItfIxG1keTDwF/vi05RS1lZGb766it06dIFvr6+ouNYDRY+K5eUV47/23VRdIxGpVdWYY26AC5d3E2630uZKqTOeROS3LSXjS3BkG0X0Vnn06Lnuvd1h+dQTziHOsO9jzvCnwiHvkKP4uP1z3/n2sUV3sO84dLBBW493BD2aBgc3B1QcKCgZhufMT7otqYbuq3pBp8xPig8Ugi5sxyuXVxx+bPLCHs0DEFzgpD2QRoMWoNROTkBM5GV2L8CyI0VGuHnn3+GSqWCSqWCu7s7fvzxR3z77beQy1ljjMV3yorpDRKe3BqFSm3rRnW2hUq9ASvycqDt6Q5T3rqVkOGM5NlvQVKYduJn0aTSMrx8LNgk+1K4KaAMUkKTY9wM+jIHGZzDnBvcXleqQ87OHITcEYKKxAoog5RQBimhilBB0kvQZBl3nGhtkbEvgYhE0qmB7fcDBnE/a2644QZERUUhKioKx48fx4QJEzBp0iSkpKQIy2RtWPis2Ed/JOB0apHoGM3yv4wcpHV3hcLRdN96SRmOSLx9LQxOtnXGyPVINO4qaP2ya3q1HpocDRy9HI3aXjJIUKer4eBVf4nO/DoTfuP94OjjCBiqR+rWPFcvQTIYd79PYvllTsBMZC0yTgN/fSDs8G5ubujSpQu6dOmCIUOG4NNPP0V5eTk++eQTYZmsDQuflUrJL8c7v8WJjtEi32Tl40iYHM4ephvMkZKhQPytayEpXUy2T0sweftl+Bmad/9j5jeZKL9YDk2uBhVxFUh9NxWQA57XeAIA0j9OR9Z3/07fk7MzB6VnS6HJ0aAyuRLpH6VDm6+F90jvOvsuO1sGTbYGPmOrLze7dHRBVWYVSmNKUXCwADK5DMpg44q3QTIgJjiiWa+NiAQ6sNpiRu3KZDLI5XJUVlaKjmI1bOs6mB15eec5VOmMu1fKEh3JL8VllTMWurqhLKvCJPtMzwAMM99Ctx3PQF5ZZpJ9iibl5GFFzGA81D/K6OfoCnRI+zAN+jI9FO4KuHV1Q6eXO8HBo/qvuyZfU+uyur5cj4zPM6Ar1kHhqoBzuDM6vdQJzu2ca+3XoDEg46sMhD4YCpm8egeOPo4IviMYlz+9DJmjDO0XtYfcyfjfI6M9/XC90VsTkVDacuCXp4B537X5oauqqpCVVf2LamFhIdavX4+ysjJMnTq1zbNYK660YYV+jsnAI1+fFh3DJNwdFHjK0xflCaUm22dQkBw9fn4O8rL6BylYHZkMmx7pjl9U8aKTmNwwrx748PRe0TGIqDlu/RzoPbPNDrdw4UJs2rSp5s/u7u7o0aMHnn32Wdxyyy1tlsPasfBZmVK1FuPWHkJ2SZXoKCYjA/BUcABkF0xX+gICFej564uQl+SbbJ8iyTqGYf7tOVDLLGuuxdZyd1Th6KULkIH/DBFZDVUg8PBxwMVLdBJqBt7DZ2Xe2nvJpsoeAEgA3szMQVaEGxQOphnCm5Otx9mJq2HwCTLJ/kSTklKxLL6v6BgmV6otQ1JAF9ExiKg5yrKB314RnYKaiYXPipy9XIwv/7LdIehfZubh73BHKFXGjSZtSl6ODjFjlkPv394k+xOt087TuMYEy65Zmmi/DqIjEFFzndoEpBwTnYKagYXPShgMEl7cfgZ6I6e8sFYH8orxrZcWbgGmGW1bkKdD9Mil0Ad3NMn+hNJq8cQ+ZauXXbM0nICZyBpJwM+PAzrj5t0k8Vj4rMRXf6cgOt1GBiE0Ia6sEu9IJVB1VJlkf0X5Wpy+9nnoQrubZH8iyc9cwrMZA0THMClOwExkpXIvAn+Lm5uPmoeFzwrklKrxxh6xy9q0tUKtDssLc6HoYZqJeUsKtYgc+BS0HXubZH8iDdp2Hl21trN+ZEJZOkqdPUXHIKKWOPQGUJotOgUZgYXPCrz6ywWUqm1rdKYxDADWZGWjsKcKckXrB3OUFWsR2edRaLtY9xkyqawcLx2zjcEoACBBwpkQTsBMZJU0pcBvy0SnICOw8Fm4UymF2BmVITqGUJ9m5CK6kxOcXFs/T3h5iQ4nezwATcS1JkgmjsvRaNxTYP1nK6+I9rCdM5ZEdid6C5B+UnQKagILn4V79ZfzoiNYhN25RfjRTw83P+emN25CZZkOJzsuhLrvCBMkE2fitnQE6E1zn6No0TKt6AhE1GIS8OuzAKf1tWgsfBbs1zOZiEwtEh3DYpwpqcB6eSlUYa0vOeoKPU62mwf1wHEmSCaGlJuH5TG2MYddTHk6JJhmDkYiEuDySeDM96JTUCNY+CyUVm/Aa7svio5hcfI0OqwqzYNj99YP5tBU6nHC/xZUDp1sgmRi+O45hZvLuoqO0Wql2jIkBlj/6yCya78tA7SVolNQA1j4LNTmv1KQnF8hOoZF0koSVmdno7SnO2Ty1p0V0lYZcMJrKiqun96i539TWIjpSUkYEncJQ+IuYU5KMv4oK2v0OV8UFGByYiIGXIrFmIR4rMnJRpXBUPP5n0qKMSYhHtfGXcJrObVHv13WajApMQFlen31A5KE+T+VwdVgmsmqRYr2CxMdgYhaoyQd+HO96BTUABY+C1Si1mLd7/GiY1i8DzNycLGLEo7OilbtR6cx4ITbBJSNur3Zzw10dMAT/v74rkM4vusQjmtc3fDI5XTEVdW//N3PJcVYm5eLh/x88XPHjlgZFIxfS0rxv7xcAEChToelWVlY4h+AT9qH4qeSEhy8qkCuyM7Gk/4BUCn+fc1SchqWxfdpdnZLwwmYiWzAkbeBshzRKageLHwW6IODCSgo5+zlxvgxpxC7AyW4+rSuLOi1Bpx0GIXSsfOb9bwbVO4YpVIh3MkJ4U5OeNzfH65yOWIq67+sEVVZiQEuLrjJwxPtHJ0wzM0Nkz3ccUatBgCkabVQyeWY5OGBPi4uGOrqigRNdXn8paQEDjIZbnR3r7Pf8B9P4zq1dS8hF60tFB2BiFpLWw788aboFFQPFj4Lk1FUic+OJImOYVUii8vxsVMFVO3dWrUfg17CKek6lExY1KLn6yUJu0pKUClJ6OdS/9Jw/V1ccF6trimEaRoNDpeXY4RbdfYOTk5QSxLOq9Uo0utxVq1Gd6USxXo91uXl4qWAwPoPrtXisX1OVr3sWmLZZZS4cAJmIqt36nOgKE10CvoPmSRxHLUleXJrFLZFXhYdwyo5y+VY4usHdVxpq/YjkwEDXM7Ba9f7Rm1/qUqNOSkp0EgSXOVyvB4cglGqhkcSf1lYgDdyqi956ADc7umFV4L+nUj5t9JSvJuXB7VkwFQPDzzi54+XsjLRzUmJns7OWJ2TDZ0k4WE/P0xwrz145fT8Ifi/9qeb/6ItxIeyEAxL/Et0DCJqrf53ANPfE52CrsLCZ0HOZRRj6rtHYOBXpFUeDQmAy4XS1k0JJQP6u8fB58f/NbmpRpKQqdWizGDAntIS/FBcjE2hYeiirHuZ+XhFOZ7KyMBjfv7o6+KMVI0Wq3OycZunFx7086t3/ycqKvBGbg42hYZhYmIi3gwJgZ+DArenpODXjp3g6/DvhNQylRteeFCFOIf8Fr90kR707IOHon4RHYOIWkumAB7+G/Dj6HtLwUu6FuT13bEseybwbkYOErq7wMGpFd/eEhBV0hX5M55pclMnmQwdnJzQy9kZT/oHoLtSiS8L678fbV1eHm728MStXl7opnTGOHd3PO7nj08K8mGop6FqDAasyM7CssAgpGo00EPCEFdXdHRSItzJCTHq2vcKSmXlePlIA5d9rUC0jPeuEtkESQ/8vkp0CroKC5+FiEorwqFLuaJj2IwfsgpwoJ0cLp5OrdpPdGEH5Nz6UrOeIwHQSoZ6P6c2GPDfmWQUMlnN8/7rw4J8DHdzQ09nZ+gB6K4qhVpJgr6eJzkfi8G9+dY5avdM+WVOwExkK87vBDKjRaegf7DwWYh398eJjmBz/iosxUY3NVQhrq3az9m8YGTNWl7v59bm5uBkRQUuazW4VKXG2twcHK+owE0e1YMPnsvMwNrcf6coGK1S4ZuiIuwqKUG6RoM/y8uxLi8Xo1WqmuJ3RXxVFX4tKcGjfv4AgE5OTpDLZPihqAiHysqQpNGgj3P9S81N2JaKAEPrBrGIUKotQ0JgN9ExiMgkJGD/StEh6B+tX42eWu3s5WLsv8h5i8whtaIKrym0WNLFDxXxLR/McT7HD4bZqxHyzQu1Hi/Q6/FcZgZy9Xq4y+XoplTik/ahuP6fUbeZWm2t36oe8PWDDDK8k5eLHJ0O3goFblCp8Ng/pe4KSZKwLDsLzwYEwlVevQdnuRyrg4KxMjsLGknCSwGBCHSsf8JlQ14+VkQNxgMDo1r8mkWJ9gtFl+xY0TGIyBTi9wEpx4AO14lOYvc4aMMC3P/lSew5l930htQqTwQHwuFiSf3XTo3UNbgc7b95FjJr+Gsjk2HzIz2wU2VdZ49nePfBikgO3CCyGR1HAQt+FJ3C7vGSrmAXs0qw9zzLXlt4OzMb6d1doXBs+bd9XKYbUue8CUneutU92oQk4Y4fS61u2TVOwExkY5IOAZdPiU5h91j4BHv39/jWTR9CzbIlKx9HwxRwdm95CUrIcEby7LcgKSz/jggpJR3L46xrAEdS2WUUu3iJjkFEpnR4regEdo+FT6D4nDL8eiZTdAy7czi/BF97aKAKqn81DGMkZTgi8fa1MDhZ/vqvHX6MxDB1qOgYRpMg4UxIhOgYRGRKF38Bci6KTmHXWPgEeu9APOfdEyShXI239MVw61R3XVpjpWQoEH/rWkjKlhfHNqHT4dG9DlBY0XQn0e4+oiMQkUlJwJG3RYewaxy0IUhyXjnGrj0EPRufUFVpZ+EX/TNSky6gpCIf945fjn4dhze4fXF5Prb99SFSc2ORV5yBUb1nYPGtj6DbjmcgrywDAPxZXo6V2VnI0+sxRqXCyqBgOP0z5UqpXo9ZKcn4NDQM7RoYYWsu0XcMwauh1rHs2nVe3fHx6X2iYxCRKckdgEcjAe8OopPYJZ7hE+S9A/EsexbAoFEj1aMdJs5eYtT2OoMW7s6emDjwDrTz7QwAyMiUcHHamzCovGCQJCzJzMDtXt74OqwDzqnV+K6oqOb5a3NzcbuXd5uXPQDov/08umvrX77N0pwpT4dBxn+eiGyKQQf8uU50CrvFf1EFyClRY0fUZdExCIBL58HwHjkff/v3BgA4Ojc++tbXPQi3DnsE13QbD2enfyc2zsrS4/yUNch39UChXo85Xl7oqlTiBpUKiZoqAMDpygqcVasx39vbfC+oEVJ5OV464t/0hhagTFuOhACuwUlkc05/BZRx3lkRWPgE+OqvFGjrWxOLhPtTpYebf/2rVzQlJ1uPjJvegL+jE45WlKPSYMCpikp0UyqhlSSsyM7GsqCgOitqtCXlX2dwf551jNqN9gsTHYGITE2nBv56X3QKu8TC18aqdHp8fTxVdAxqwOVKDdahFKpwVYuen5+rx4Nz3sIHxaW4OTkJEc5KzPT0wqf5+Rjq6gonmQzzUlIwOTERmwvFzDd34/YUq1h2LVrZunWQichCnfwc0FSITmF3WPja2E/Rmcgr04iOQY0o0OqwsjgPih4eLXp+gEtPPL/4Z+weNg4vBwbhslaLnSXFWOznj+cyM3Cblxe+DAvDB/l5iFWrTZy+aYa8Aqw83aXNj9tc0ZoC0RGIyBzURUDMt6JT2B0Wvja28c8k0RHICDpJwpqsbBT2VEEub/4l2KJ8LU5f+zy0Yd2xLCsLzwQEQJIkXKiqwgR3d/g6OGCwiytOVIr5Ldd7XyRmlHYTcmxjJZdncAJmIlt1/GPRCewOC18bOpFcgLOXS0THoGb4NCMXZ7o4wcml+atqlBRq8T9Fd7h7eGOMyh36fx7X/TMTkg6SuHkYJQlzfyyGSrLcy6YSJMRwAmYi25RzHkj6Q3QKu8LC14Y2Hk0WHYH+w6CphCY7EZrsRACArjgbmuxE6EqqR5EVHtqILz57GT/56+HqWz2YIz0vHul58ajSVqJMXYz0vHhkFibX2XdpZSF+PPoFpsxcB03XgfBUKNDJyQlfFBYiqrISf5VXYICLuEmbpdTLWB7bW9jxjcEJmIls2N8fiU5gVyx/MVAbkVFUiT3nskTHoP/QZMUhe8sLNX8u/P1TAIBb77Hwm/IE9GWF0JXkIqakAtnOWjwU5ok1H91fs31a3iWcjN8PH1UgVsz7uta+vz/6Hsb0vQ1OBi+c6n4/Bjl8jtWVlXghKxNfFRbgbh8f9BFY+AAg9KdIDA8PxRHnNKE5GhIt4/2uRDYr9legKA3wsp6lH60ZV9poI6/tvogPDiaIjkGtpJTLsMQvAFWXmn9p3tlVgYHpm+Ecc9gMyVpOiuiCudNToIfl/VPg5uCKP+MvQS4ZREchInMY9hhw4wrRKewCL+m2AbVWj284FYtNqDJIWJWTjfKe7mjudHrqCj1OtpsH9cBx5gnXQrIL8XghbYDoGPUq11UgPsCyB5cQUStEfgFoK0WnsAssfG1gZ9RlFFZoRccgE3o/IweXujnDUdn4yhz/panU44T/LagcOtlMyVqm77az6Km1zFU4ov14uYfIZlUWAme+E53CLrDwtYGvj1vm/VHUOjuyC7E3GHD1VjbredoqA054TUXFsBlmStZ8UkUFnj9smevscgJmIht3apPoBHaBhc/M4nNKEZ1WJDoGmcnJojJ8qqyAql3zVq7QaQw44Toe5aNmmylZ8yn/PoMHc/uKjlEHJ2AmsnGXTwI5F0WnsHksfGb23al00RHIzC6rNVijLoBLV/dmPU+vNeCEw0iUjp1vpmTNN2Z7MoL0LVtWzlxSyjNQ7OotOgYRmVPUV6IT2DwWPjPSGyRsj7wsOga1gUq9AStyc6CJcAeaMZjDoJdwSroOJRMWmS9cM0j5BVhxurPoGLVIkBAdzAmYiWxa9LeAXic6hU1j4TOjPy7lIqe0SnQMakPvZOYgpbsrHJyM/6tlMEg4pRmAoskPmTGZ8bz2ncItpd1Fx6gl2p1n+IhsWnkOELdXdAqbxsJnRt/zcq5d2pqVj4Pt5HDxMH6wgSQBkZW9UHDzE2ZMZrzZOwstatm1aBl/cSKyeVGbRSewaSx8ZlJcocW+C9miY5AgxwpLsUmlhirY1fgnSUBUSRfkzXjGfMGMjZKWgRUXLWfZtbNl6TDI+M8VkU27tAcozxOdwmbxX1Az+TH6MjQ6rg5gz1IqqvC6thCunZs3mCOmsANybn3JTKmM1/7nSIyq7CA6BoDqCZjjAi3rMjMRmZhBC0R/IzqFzWLhMxNeziUAKNcZsCI/B4YIj2Y972xeMDJnLTdTKiPpdHhoD6BozigUM4r2bS86AhGZGy/rmg0LnxnEZZciOr1YdAyyEBKAtzKzkRnhBoWj8X/lLuT4IWP2avMFM4LsQgJeTOkvNMMVnICZyA7knAeyzopOYZNY+Mzg+0ie3aO6vsrMw7EwBZxVjkY/52KWJ9LmvA6puQv3mlCf7WfRSxMg7PhXxGjyRUcgorZwbrvoBDaJhc8Mfo7OFB2BLNSh/BJ846WFKtDF6OfEZbohdc6bkOTNW7fXVKTKSjx/2EfIsa+WXJ6BIlfxOYjIzM7vEJ3AJrHwmVhMehEuF1WKjkEWLK6sEmv1xXDraPxgjoQMZyTNXgtJ4WDGZA1zOn4WD1nAsmsxwT1ERyAic8uPBzJjRKewOSx8Jvbr2SzREcgKFOv0WFGYA3kzBnMkZzgg4fa3YXBSmjFZw27YloRgffNGHJtaFCdgtntrjlRBtrwEj+9W1zym1kl4+JdK+L5eCtXqEtyytQLZZY3PkrDsoBo91pfBbXUJvF8rwbgvyvF3+r8rPVTpJMzfXgmP/ytBt3fL8Fti7VUg3jhahUd38Zd7s+FlXZNj4TOxPSx8ZCQDgNcys5EX4Qa5wrh79FIz5Ii/dS0kpfGXhE1FKijEishObX7cq8WAEzDbsxOX9fjolAZ9A2v/6Hpitxo/XdLhu9tccGihGzJKJczc2ngZ6+arwPrJzjjzoApH7nJDuJcc47+qQG55dVH8+JQWpzL0OHaPG+4b5Ii5P1RCkiQAQFKhAZ9EavHqWGfzvFDiZV0zYOEzodisUiTmlYuOQVbm88w8RHZyhNLVuMu16RlA7My3YHBRmTlZXZ6/ncJtJeLmwztTng69TMy9jCRWmUbCvG2V+GSqC7yd//0FqVgtYcNpLdZOcMaYjg4YFKLA59Oc8WeaHn+lN7w269w+jhjXyQGdvOXoFaDA2gnOKKkCYrKrC9+FPD1u7u6AXgEKPDzECbkVEvIqqgvfg79U4rVxSngoLWPKIptUkAhkRIlOYVNY+ExoN8/uUQvtyy3GNj8d3PyNO2OQkSnh4rQ3YVB5mTdYPWbtLIS7Qcxl5QpdBeI5AbNdeniXGlO6OmBcp9q/GJ3K1ENrQK3He/gpEOYpw7E0vVH71uglfHxKA08l0C+o+sdiv0AFjqTqUamVsCdBh2CVDH6uMmyO0cLZQYYZEcaPtqcW4mVdk2LhM6Hd51j4qOXOl1TiXVkpVB2MO3OXlaXH+SlrYPD0M3Oy2qT0DKy42KtNj3m1aD9OwGxvvjmrRWSmHv83ru4vGlllEpwUgJdz7bNtgW4yZJVJje7350taqFaXwHlVKd7+S4N9893g51r9Y/HuAY7oFyhHz/fL8OrhKmy9zQWFamDpQTXeneSMl35Xo8u6Ukz4qhyXS7iqklnwsq5JsfCZSEp+OS5kloiOQVYuX6PDypI8OHY3bjBHTrYeZ8a/CoNPkJmT1dbul0iMFrTsWrQTz6zYk7RiAx7brcbmmS5wdjDtJdQbwh0Q9YAKf97jiomdHTDr+wrk/HMPn6NChvemuCDpMXecuFeF4WEOeGqvGouHOuF0lh47LuoQ/YAK17ZTYPFVA0jIhAqTOVrXhFj4TISXc8lUdJKE1dnZKIlQQS5v+gdcfq4OMWOWQx8Q1gbp/qHT4cFfJThIbf9PSDQnYLYrpzL1yCmXMPCjcjisKIHDihIcStFj3d8aOKwoQaCbDBo9UKSufTYvu1xCkKrxvz9uTjJ08ZHj2vYO2DDNBQ5yGTZEauvd9kCSDudy9HhkqBMOJusxuasD3JxkmNXLEQeTjbt0TC0Qt0d0ApvBwmcinI6FTO2jzFyc66KEo3PTgxQK8nSIHvESdCFtN4pWFpuIF1P7t9nxrkgpz0Chm2+bH5fEGNvRAWcedEPUA/9+DA6RY15fx3/+XwFHObD/qmlTYvP0SC2WcF1o8wb4GCQJVfq6l4HVOgkP71Ljo5tcoJDLoDcA2n86ntYA6A2NXzqmVojbJzqBzWDhM4GsYjWi04tExyAb9HNOIX4JNMDVp+lBEkX5WkRd8xy0YW03qKH39jPorQ1ss+NdwQmY7Ye7UobeAYpaH26OMvi6VD/u6SzDPQMc8eReNQ4k6XAqQ4+7dqpxXXsFrm1/1UCO9WXYfqH67F25RsIL+9X4K12HlCIDTmXocffOSlwukXBbz7q3DKw8VIXJXR0wILi6QA4LU2DbRS1isvVYf1yDYWFiJkS3C+kngIoC0SlsAgufCey/mA2Jv+CRmUQXV+Ajp3KoQt2a3LakUIvTA56EtmPvNkhWvezac4e82uRYV4sWMDqZLNfbE51xU1cH3LK1AiM3liNIJcO222vPVRmbb0BxVfU/1Ao5cDHPgFu2VqLb+jJM3VKB/EoJh+9yQ6+A2mcFz+bosfW8DstH//tL1609HTClqwNGfF6OmGw93pnI+fjMRjIA8b+JTmETZJLEqtJaD3x5iiN0yeyUchme8QuA+lLTg4PcPBww4MJHcIqLbINkwB/3DMT6gLa7uXqoZzdsiOIPASK70PtW4NYNolNYPZ7hayW9QcKfCXmiY5AdqDJIWJmTjcqe7pA1MZajvESHU93vhybi2jbJNmpbItrpjV8mrrXOlKdxAmYiexH/G2DgwJjWYuFrpZj0IpSoG57NncjU1mfkIL6bCxyUjReeyjIdTnZciKq+I82eSSoswvJT4WY/zhWVukrEBXECZiK7oC4C0o6LTmH1WPha6Ugcz+5R29uWXYDfggEXL6dGt1NX6HGi3VyoB95o9kwe+yNxe3HbDaaI9uUEzER2g9OztBoLXysdjmfhIzFOFJXhMxc1VO0aH8yhqdTjhP9MVA6dbPZMt+7Mh6fUNjewcwJmIjvC6VlajYWvFcqrdDidWig6Btmx9MoqrFEXwKWLe6PbaasMOOE1FRXDZpg1j3Q5E8vP9zTrMa6IruIvW0R2I/scp2dpJRa+Vvg7KR/aeibpJGpLlXoDVuTlQNvTHWhkMIdOY8AJ1/EoHzXbrHlCfjmFMRXhZj0GAKRWZKLArW3XESYiUSQg+YjoEFaNha8VDvP+PbIg/8vIQVp3VygcG/5rrdcacMJhJErH3mm+IHo97t9taJNl1zgBM5EdYeFrFRa+VjjK+/fIwnyTlY8jYXI4ezQ8mMOgl3BKuhYlExaZLYcsNhEvp/Q32/6viHb3NPsxiMhCsPC1CgtfC2WXqHEpu0x0DKI6juSX4itVFVRBrg1uYzBIOKUZgKIpD5stR88dMeijCTDb/gEgWlKbdf9EZEFyzvM+vlZg4Wshnt0jS5ZUocabuiK4dWp4MIckAZEVPVFw8xNmySBVqvHcQS+z7PuKs+XpnICZyG7wPr7WYOFroZMpHJ1Llq1Up8fyghwgopEVMCQgqqQL8mY8Y5YMjqfOY3F2P7PsG6iegPkSJ2Amsh8sfC3GwtdCp1OLREcgapIE4I3MbGRHuEHh0PAQ3pjCDsi59SWzZBixPQHtdea7144TMBPZERa+FmPha4EKjQ6XsktFxyAy2heZefg73BFKVcOTFZ/NC0bWrOUmP7ZUWIRlJzuYfL9XRDs5mG3fRGRhcs4D5fmiU1glFr4WiE4rht7A+ffIuhzIK8a3Xlq4Bbg0uM35HD9cnr3a5Mf2OBCJOcURJt8vwAmYieyLBKSfEB3CKrHwtcDpNN6/R9YprqwS70glUHVUNbhNbJYn0ua8DknWyCzOLTBzRy48DaZfdi2tIosTMBPZk8unRCewSix8LcD798iaFWp1WF6YC0WPhgdzxGW6IXXOm5DkphsBK2VkYcV5M53lC+EEzER2IyNSdAKrxMLXAlFpRaIjELWKAcCarGwU9lRBrqj/TF5ChjOSZq+FpDDdPXLBuyIxrqKjyfZ3RbSKEzAT2Y3LLHwtwcLXTGkFFcgtrRIdg8gkPs3IRXQnJzi51l/qkjMckHD72zA4KU1zQL0e9+3SwUky7dx5nICZyI5UFgAFSaJTWB0WvmY6zbN7ZGN25xbhRz893Pzqv78uNUOO+FvXQlI2PNijWeKS8FKyaefmO1eWBp2co3WJ7AYv6zYbC18znU7lgA2yPWdKKrBeXgpVWP2DOdIzgNiZb8Hg0vBgj+aI2BGD/pogk+wLACr1ak7ATGRPeFm32Vj4mokDNshW5Wl0WFWaB6fu9Q/myMiUcGHaGzCovFp9LEmtxpKDjawA0gLRPpyAmchusPA1GwtfM+gNEs5nloiOQWQ2WknCq9nZKOvpDpm87mCO7CwDzk1ZA4Nn66dBcTx1Ho9nme7SbrQT19QlshuZ0YBBLzqFVWHha4bk/HJodAbRMYjM7oOMHFzsooSjc90SlZutx5nxr8Lg0/pLssO2xyNM79Xq/QCcgJnIrmjLgbxLolNYFRa+ZojjcmpkR37MKcTuQAmuPnVH6Obn6hAzZjn0AWGtOoZUVIxXjoe2ah9XpFdkIV/lb5J9EZEVyDkvOoFVYeFrhkvZZaIjELWpyOJyfOxUAVV7tzqfK8jTIXrES9CFdGrVMdwPnsa8ItNMyBwdzAmYiexGzkXRCawKC18zxPIMH9mhTLUG/1dRAOeu7nU+V5SvRdQ1z0Eb1roRstN35Jhk2bVolWkHghCRBcu9IDqBVWHhawZe0iV7pTYYsDI3B+qe7vjvErslhVqcHvAktJ36tnj/UmY2Vp1r/Vk+TsBMZEd4hq9ZWPiMpNUbkJRXLjoGkVDvZuQgobsLHJxq/9NRVqzDqV4PQ9N1YIv3HbjrFMaXt+7y8HlOwExkPwoSAR1XvjIWC5+RkvPKodVLomMQCfdDVgEOtJPDxdOp1uMVpTqc6n4/NBHXtmzHBgMW7dK0atm1Sr0asUG8j4/ILkh6IC9OdAqrwcJnJN6/R/SvvwpLsdFNDVWIa63HK8t0ONlxIar6jmzZjuOTsTSpdXPzRfu2a9XziciK5PKyrrFY+IzEEbpEtaVWVOG1qkK4dqk9mENdoceJdnOhHnhji/bbfUc0BmiCW5wr2pETMBPZDRY+o7HwGYkDNojqqtAbsDwvB7qI2qNjNZV6nPCficqhU5q9T6mqCkt+b/mavdFVuS1+LhFZmRyO1DUWC5+RLrHwETXo7cxspPdwhcLx339StFUGnPC6CRXDZjZ7fw6nL+DJrP4tynK5Iht5qoAWPZeIrEx+vOgEVoOFzwiSJCGtoFJ0DCKLtiUrH0fDFHB2d6x5TKcx4ITLOJSPmt3s/V237RI66LxalCU6uHXzAhKRlShKE53AarDwGSGntAoaPdfQJWrK4fwSbPHQQBXkUvOYXifhhMNIlI69s1n7kopLWrzsGidgJrIT2nKgjLdxGIOFzwiXi3h2j8hY8eVqvKUvhlunfwdzGPQSTknXomTCombtS3XoNOYX9Wx2hmiJf2eJ7EZRiugEVoGFzwiXC/nDg6g5SrR6LC/IAa4azGEwSDilGYCiKQ83a183b8uCt8Gl6Q2vcr4sDVq5Y9MbEpH1Y+EzCgufETJ4ho+o2SQAb2RmIyfCDXKH6vXYJAmIrOiJgmlPGL+f7BysPNu8yZTV+ipcCuYEzER2oZCFzxgsfEbgJV2iltuUmYdT4Y5Quv2z5JkERBV3Qd6MZ43eR8CvpzCxvHOzjhvlE9Ks7YnISvEMn1FY+IzAM3xErfNbXjG+99HBzd+55rGYwjDk3PqycTswGHD3L2oom7HsGidgJrITRamiE1gFFj4jpPMePqJWu1haiXUohSr830mVz+YFIWvWCuN2kJCCpYnGL7sWwwmYiewDL+kahYXPCLykS2QaBVodVhbnwaHHv4M5zuf44vLs1UY9v9vOaAwyctm1yxXZyHMPbFFOIrIixWnVNwhTo1j4mlCq1qJUrRMdg8hm6CQJ/5eVjaKeKsjl1YM5YrM8kTbndUgyWaPPlaqq8NR+FWRG/tseHdSttXGJyNLpNUBloegUFo+Frwk8u0dkHp9k5OJsFyWcXKoHc8RluiF1zpuQ5I3fe+cQdQFPGbnsGidgJrITZTmiE1g8Fr4mZBapRUcgslm/5BTipwA9XH2VAICEDGckzV4LSeHQ6POu2XYJ4UYsu8YJmInsRDnv2W0KC18T8sqqREcgsmkxxRX40LEcqjA3AEByhgMSbn8bBidlg8+RSkrwyt/tm9z3OU7ATGQfWPiaxMLXhKIKregIRDYvW63F6rJ8KLtVX4JNzZAj/ta1kJQNr7Dh9kcUFhT2anS/VfoqxHICZiLbx8LXJBa+JhRVakRHILILVQYJq3KyUd7THTIZkJ4BxM58CwYXVYPPuWl7BnwNro3uN9qnnamjEpGlYeFrEgtfE3iGj6htvZ+Rg0vdnOGoVCAjU8KFaW/AoPKqd1spOxcrznRvdH/RjvxnjsjmsfA1if8SNqGokoWPqK3tyC7E3mDA1VuJ7CwDzk1ZA4OnX73b+u8+hUmNLLsWreboPSKbV8bC1xQWviYU8wwfkRAni8rwqbICqnZuyM3W48z4V2HwCaq7ocGAu35ueNm1jMoc5HrU8zwish08w9ckFr4m8B4+InEuqzVYoy6AS1d35OfqED1mBfQBYXU3TEzBsoSGl12LDupqxpREJFxFnugEFo+Frwm8h49IrEq9AStyc6CJcEdhvhZRw1+CLqRTne267IzCkKqQevcR7eZp7phEJFJVmegEFo+Frwm8pEtkGd7JzEFKd1eUl+kRdc1z0IbVHqwhaTR4cr9LvcuuRUsVbZSSiITQlItOYPFY+Bqh0xtQWsV1dIksxdasfBxqL4dWL8PpAU9C26lvrc8romPxdGb/Os87zwmYiWybtgKQjFxk206x8DWimCN0iSzOnwWl+EKlBlydcKrXw9B0G1zr80O3xaKTzrvWY1X6KlwMiWjLmETUpiSe5WsCC18jWPiILFNyRRVe1xYCAS441fVeaHpeV/M5qbQUS4/VnWw52rv++/uIyEZoeB9fY1j4GlGlM4iOQEQNKNcZsCI/B+VhrjgZvgBVfUfWfM71SBTuKqi97Fq0o6ytIxJRW+IZvkax8DVCq2fhI7JkEoC3MrOR1MEZkWHzoB54Y83nJm+/DD+DW82fOQEzkY3jGb5GsfA1QqvnDaBE1uCrzDwcCpQhJvx2VA6dAgCQcvKwIubf+fcyK3OR4xksKiIRmRvP8DWKha8RPMNHZD0O5ZfgK9cqnOtyCyqGzQQA+O0+hSllXWq2iQ7kBMxENotz8TWKha8RLHxE1iWurBJvaopxrsc0lI2eA0gSFv5cAWfJAQAQ7eYhOCERmY1OLTqBRWPha4SOl3SJrE6xTo9l+bk4GTERpeMWQEpKxbKE6vn6og285ENksySepGkMC18jNDzDR2SVDABey8zF7q6jUTLhHnTeGYWhVe2qJ2BWOImOR0TmwMLXKBa+RvAMH5F1+ywzHxtCr0HRuAV4Yp8ztHoNLgRzAmYim8TC1ygWvkbwHj4i67c3rwQrfPujNGwEnskYgGhvjtQlsklcWq1RLHyN4CVdIttwvrQSj7r1Rrvy/sjXKkXHISJz4Bm+RrHwNYKXdIlsR55Gh/sMPeBeMEh0FCIyB0kvOoFFY+FrhN7A3xaIbIlWkrAytQt+D31YdBQiMjWe4WsUC18jFHK+PUS26O64Yfg+5FlIMoXoKERkKix8jWKjaYSTA98eIlv1dGI/fBiwFJKC9/QR2QQWvkax0TSChY/Itr2W0hWveq2A5KQSHYWIWkvuIDqBRWOjaYSTgm8Pka379HIonnJZBYOLr+goRNQaDs6iE1g0NppGKHmGj8gubMsOwF3yFdC5txMdhYhayoG3ZzSGjaYRjjzDR2Q3DuV749aqV6Dx6iI6ChG1hIOL6AQWjY2mEbyHj8i+RJWoMLHkBVT49RMdhYiai2f4GsVG0wgWPiL7k1jhjFE5T6Io6HrRUYioOXgPX6PYaBrBQRtE9ilX44hhaQ8hq9140VGIyFg8w9coNppG8Awfkf0q18sxLPFOxIfeIjoKERmDZ/gaxUbTCJ7hI7JvekmOcXG3IDJsoegoRNQUnuFrFBtNI5SOfHuICJh5aTx+C30UEmSioxBRQxw5SrcxbDSN8HB2FB2BiCzEorjr8G3Ic1x/l8hSKT1EJ7BoLHyNcHFS8LIuEdV4LrEP3gt4BRLvFSKyLI6ugCP/XjaGbaYJHi48y0dE/3ozpQtWei6HpHQXHYWIrnDxFp3A4rHwNcHLlYWPiGr77HIoHndeCYOLn+goRASw8BmBha8JnjzDR0T12JkdgIWyFdC5txcdhYhY+JrEwtcELxY+ImrAHwVemKF+BVXe3URHIbJvLHxNYuFrgrebk+gIRGTBzpS64cai51Du3190FCL7xcLXJBa+Jviy8BFRE1IrnTEy+wkUBg0THYXIPrHwNYmFrwm+KhY+ImpavsYRw9MeREa7iaKjENkfVx/RCSweC18TfNy4VAsRGadcL8eIxDsQF3qb6ChE9sXVV3QCi8fC1wRe0iWi5tBLctwYNwMnQ+8SHYXIfrgHiU5g8Vj4muCn4hk+Imq+W+NuxN72i7n+LlFbcA8RncDisfA1IdiLS7UQUcvcF38ttoQ8B0nuIDoKkW3zCBadwOKx8DXBT6WEiyMXSyeilnkhsQ/W+3P9XSKzcXDhKF0jsPAZoZ23i+gIRGTF3krpjGUeKyEpPURHIbI9PLtnFBY+I4Sy8BFRK23KaIdHnVbB4OovOgqRbfHk8obGYOEzQntvV9ERiMgG/Jzrh/nSCug8QkVHIbIdnvz7ZAwWPiO05xk+IjKRo4WemFa5FFXe3UVHIbINLHxGYeEzAs/wEZEpnSt1w9jC51DmP0B0FCLrx0u6RmHhMwLP8BGRqaWrlRiV/TgKgkeIjkJk3bw7iE5gFVj4jMDCR0TmkK9xxLDU+3C53STRUYisl29X0QmsAgufEXxVSrg6cS4+IjK9Sr0CIxPnITZ0lugoRNbHyZ3TshiJhc9I7bx4lo+IzEMvyTEhbjr+Dl0kOgqRdfHtLDqB1WDhM1KYDwduEJF53R43Br+2f5zr7xIZy4+Xc43FwmekroHuoiMQkR14MH4oNoe8wPV3iYzB+/eMxsJnpB5BLHxE1DZeSuyFd/yWQXLgrSREjfLrIjqB1WDhM1I3nuEjojb0v9ROWOqxguvvEjWGZ/iMxsJnpC4BKjjIeV8NEbWdLzPa4WGnVdC7BYiOQmSBZIAvz/AZi4XPSE4OcnT0cxMdg4jszK5cP9xhWA6dR5joKESWxSMEcOKASmOx8DVDN97HR0QCHCv0xNSKpVD79BAdhchyBPQUncCqsPA1Qw/ex0dEglwoc8W4gmdRFjBIdBQiyxDcT3QCq8LC1wzdeYaPiARKVysxIvMx5AePEh2FSLzgvqITWBUWvmboEcTRckQkVqHWAcNTFyGt/RTRUYjE4hm+ZmHha4ZQHxeuqUtEwlXqFRiZMBcXQmeLjkIkhrMn4B0uOoVVYeFrBplMxhU3iMgiSJIMk+JuxrHQe0VHIWp7Qbyc21wsfM3UM5iXdYnIcsyJuwG/tH+C6++SfeHl3GZj4WumgWFeoiMQEdXycPwQfBn8IiS5o+goRG2Dha/ZWPiaaVAHb9ERiIjqWJrUE2v9lkFy5ES0ZAd4SbfZWPiaqZO/Ct6u/C2aiCzPu6kd8ZJqBSSlp+goRObj6Ab4dROdwuqw8LXAwDCe5SMiy7Q5MwQPOq2C3i1QdBQi82g/CJCzvjQX37EWGMjLukRkwXbn+mKufjm0nuGioxCZXth1ohNYJRa+FuAZPiKydH8XeWBq+UtQ+0SIjkJkWmHXik5glVj4WqB/qBcc5JwCgYgs28UyV4wteAalAYNFRyEyDZkCaD9UdAqrxMLXAi5OCkRwPj4isgKX1UoMz1yMvJDRoqMQtV5QH0CpEp3CKrHwtRDn4yMia1GsdcCw5EVIbT9VdBSi1uH9ey3GwtdCHLhBRNakyiDHqITZOB86R3QUopbj/XstxsLXQpyAmYisjSTJMDluKv4MvU90FKKW4Rm+FmPha6H23q4I9XERHYOIqNnmxo3GT+2fgiTjjwCyIt4dAXfOL9lS/NveCiO7+ouOQETUIo/GD8LGIK6/S1ak4wjRCawaC18rjOzGwkdE1mt5UgTe9F3O9XfJOnQeKzqBVWPha4XrO/tyPj4ismrvpYXjedVKGJy9REchaphMAXQaLTqFVWPhawV3Z0cM4PQsRGTlvskMxgMOq6B3CxIdhah+7QYBLl6iU1g1Fr5W4n18RGQL9ub54Hbdcmg9O4qOQlRXF17ObS0WvlbifXxEZCtOFrtjStlLqPTtJToKUW28f6/VWPhaqU87T3i7cpQbEdmGS+UuGJO/BCUBQ0RHIarm7AW0Gyg6hdVj4WsluVyG4bysS0Q2JFPthBGZjyI3ZIzoKETVgzXkCtEprB4LnwmM6OonOgIRkUkVax0wPPlupLS/WXQUsne8f88kWPhMgAM3iMgWVRnkGJ1wO86GzhMdheyWjPfvmQgLnwkEeTojIthDdAwiIpOTJBluipuCw6EPiI5C9ihkAODZTnQKm8DCZyKTenP+KiKyXfPjRmJH+6e5/i61rYipohPYDP7NNZHJfYJFRyAiMqvH4wdiQ+DLkBROoqOQvYjgPaSmwsJnIl0CVOgaoBIdg4jIrFYld8frPssgObqJjkK2zr8H4NdFdAqbwcJnQjzLR0T24IO0cDzjthIGZ2/RUciW8XKuSbHwmRALHxHZi++ygnCvYiX0Kv67R2bCwmdSLHwm1D3IHZ39eZmDiOzD/nwf3KZdBq1nJ9FRyNZ4dQCC+4lOYVNY+EyMZ/mIyJ5EFrtjctmLqPTtLToK2RKe3TM5Fj4Tm9SbhY+I7EtcuQtuyHsaJYHXiI5CtoKFz+RY+EysZ4gHOvrxsi4R2ZesKicMu/wwckK4KgK1klcYEMpfHkyNhc8MOAkzEdmjUp0DhiXfjaT200VHIWvW93ZAJhOdwuaw8JkB7+MjInulNcgwJuE2xITOFx2FrFXf2aIT2CQWPjPo3c4TPYLcRccgIhJCkmS4OW4SDoY+JDoKWZt2gznZspmw8JnJbYNDRUcgIhJqYdxwbGu3hOvvkvH68eyeufBvoZnMGNAOTgq+vURk355MGIBPApdy/V1qmtwR6H2L6BQ2i43ETHzcnDA2IkB0DCIi4VYnd8Ma7xWQnDiDATWi63jA1Ud0CpvFwmdGs3hZl4gIAPBRehiedlkFgwt/oFMD+t0uOoFNY+Ezo5Hd/BHk4Sw6BhGRRfghOxCL5CuhV4WIjkKWxtkL6DZRdAqbxsJnRgq5DLcMaic6BhGRxfg93xu3aZdB49VZdBSyJP1mAw5K0SlsGgufmc0aHMr5I4mIrhJZrMLEkhdR4ddHdBSyFIPvFp3A5rHwmVkHXzcMCec9K0REV0uscMbo3KdRHHit6CgkWofhgH930SlsHgtfG+DgDSKiunKqHHH95YeRFXKj6Cgk0hCe3WsLLHxtYEqfYLgrHUTHICKyOOU6BUYkL0Ri+xmio5AIbgFAxM2iU9gFFr424OKk4MobREQN0BpkGBN/G6JC7xQdhdrawPmAwlF0CrvAwtdG7hoWDjkHbxARNWh63ET8Hvqw6BjUVmRyYNBC0SnsBgtfGwn1ccWNPQNFxyAismh3xw3D9yHPQpIpREchc+s6HvAKE53CbrDwtaG7h3UUHYGIyOI9ndgPHwW8DEnBedls2uB7RCewKyx8beiaTr7o3c5DdAwiIou3JqUbXvVaAclJJToKmYNvV6ArR2e3JRa+NnbPcJ7lIyIyxqeXQ/GUyyoYXHxFRyFTu/4RcFWCtsXC18Zu6huCAHdepiAiMsa27ADcJV8BnTuXqbQZbgFAvzmiU9gdFr425qiQ487rOoiOQURkNQ7le+PWqleg8eoiOgqZwtD7uG6uACx8Asy9pgOcHfnWExEZK6pEhYklL6DCr5/oKNQajm7AEA7WEIGtQwAfNyfMGMDLE0REzZFY4YxROU+iKOh60VGopQbcAbhyfXkRWPgEuWd4R96vSkTUTLkaRwxLfxBZ7caLjkLNJVMA1z0kOoXd4gKvgnQJcMek3kHYdSZLdBQyUvFf36Ho0Ca4D7oZPuPuAwBoCzNReGADqtLPQ9Jr4dJxEHxuvB8KN+8G91N6ehdKT++CrjgbAODoFwav6+fApfPgmm0K9n+C8rP7IXN0hteoBVD1uqHmc+UXj6D87H4E3PqKmV4pkWUr1ykwLPFO7O3ijs5pP4iOQ8bqeTPgHS46hd3iGT6BFo/tyrN8VqIq8xJKo3bD0T+85jGDRo2crS8DMhkC56xG0B1vQDLokPPDCkiSocF9Kdx94T1qAYIX/A/BC/4H5w79kLNtFTS5KQCAivi/UX7hEAJmrYT36LtQsPtd6CuKq49ZVY6iP76Az/gHzfp6iSydXpJjbNwtiAxdIDoKGev6xaIT2DUWPoF6BHlgYq8g0TGoCQZNJfJ+ehO+Ex+F3PnfSWCrLp+HrjgHfpOfgJN/OJz8w+E35QloMuOhTolpcH+uXa6BS+chcPRpB0efdvAeeSfkTs6oyogFAGjz0+Ac2gfK4K5w6zkKMifXmrOBhQc+h/uAyXDwCDDviyayEjPjJuC30Echgb89W7Su44F2A0WnsGssfII9No5n+Sxdwb4P4NJ5CFzC+9d6XNJrAQAyhWPNYzKFEyCToSr9nFH7lgx6lJ8/BINWDWW7HgAAJ/+O0GTFQ68uQ1VWPCRdFRy8Q6BOPwdNdgLcB001zQsjshGL4q7DtyHPcf1dSzb6OdEJ7B7v4ROsR5AHJvQMwu5zvJfPEpWfPwRNVgKCF7xd53PKkB6QOTqj8ODn8Bp1JyABRYc2ApIB+rLCRveryU1G1pdPQ9JpIHNyQcCMF+HkV72IuEunQXDrNRpZm56AzMEJflOegNxRiYI978N3yhPV9wBG/gyFiwd8JjwCJ3/O60j0XGIf5HV4BQ/nr4ZMpxYdh67WbSLQbpDoFHZPJkmSJDqEvbuQWYLJ6w6DXwnLoivJReamJxB4+0o4BVQviZf19XNwCuhUM2ijMikSBXvfh64oG5DJ4NZzFLR5qXAK7gbfCQ83uG9Jr4WuJBeGqgpUxB5BWfReBM5dU1P6/qvoyNcwVJVD1Wccsre+jJC730Nl/HGURv6M4IXvmP7FE1mpu9ul4eWyVZBVlYqOQlfcdwgI6S86hd3jGT4LEBHMs3yWSJMVD0NFETI3Pvbvg5IBVWnnUBr5M8Ke3g6XjgPR7v5Poa8ohkyugNxZhbT1d8DVq/F7M2UKRzh6hwAAlEFdoMmMQ+nJH+E78ZE622rz01B+/gCCF65DWcw+OLfvDYWrJ1x7jED+r+/AUFUBudLVpK+dyFp9djkU+YEr8bZ8FeSVeaLjUPcpLHsWgoXPQiwe2xV7zmfxLJ8Fce7QD8F3r6/1WP6ud+Do2x4e19wCmfzf+4UUrp4AgMqUaBjKi+Ha5ZpmHUuSpJp7Av/7eP6e9+A9ZhHkTi6AZIBk0FV/8sp/GxkRTGSPdmYHoNBnBT5zXw2H0nTRceyYjPfuWRAO2rAQPUM8ML5noOgYdBW50rVm9O2VD5mjEnJndzj9Mz1LWcw+VF2+CG1hJsrOHUDejjVwHzINjr7ta/aT/c0LKDn1U82fCw9thDrtLHTF2dDkJqPw0EZUpZ6BW8/RdTKURe+BwsWjpkAq20VAnRKDqssXUXJiJxx9w2qNHCaian8UeGFm1VJUeXcTHcV+9ZgCBPcVnYL+wTN8FmTx2K7Yez6bZ/msiLbgMgr/2ARDZRkcPAPged0suA+ZXnubwiwoK0tq/qwvL0bez2uhLy+AXOkGJ/9wBMxaAZeOA2o9T19eiOJjWxF0xxs1jylDusNj6AzkfL8ccldP+E15wqyvj8iaxZSoMEH7HH7xXwe33CjRceyMDLjhBdEh6CoctGFhHv46Er/EZIqOQURkM/ydtNgb8jG8s46KjmI/es0AbtsoOgVdhZd0LcyzE3rAyYFfFiIiU8nVOGJ42oPIaDdRdBT7oHACxnLpR0vDZmFhwnxdsfD6cNExiIhsSrlejhGJdyAu9DbRUWzfkHsBn46iU9B/sPBZoEfGdIGPm5PoGERENkUvyXFj3AycDL1LdBTb5eINjFoiOgXVg4XPAnk4O+KxsV1FxyAiskm3xt2Ive0Xc/1dcxi5pLr0kcVh4bNQ864JQyd/N9ExiIhs0n3x12JLyHOQ5JyswmS8O1ZfziWLxMJnoRwUcjw/KUJ0DCIim/VCYh+s91sKycFZdBTbMO4VwIG3I1kqFj4LdmPPQFzXyVd0DCIim/VWahcs81gJSekuOop1az+0eioWslgsfBbuxSkRkPM2EyIis9mU0Q6POr0Kg6uf6CjWa8KrohNQE1j4LFzvdp6YObB90xsSEVGL/ZzrhzuxEjqPUNFRrE/f2UDoUNEpqAksfFZgyYTucHNSiI5BRGTTjhR4YlrlUlR5dxcdxXo4ewLjV4lOQUZg4bMCgR7OeOJGLgBORGRu50rdMLbwOZT5D2h6YwLGvAyo/EWnICOw8FmJu4Z1RO92HqJjEBHZvHS1EqOyH0dB8AjRUSxbcH9g8D2iU5CRWPishEIuw//N6AsFR3AQEZldvsYRw1Lvw+V2k0RHsUwyOXDTWkDOGmEt+JWyIn3ae2L+tR1ExyAisguVegVGJs5DbOgs0VEsz6CFQLtBolNQM7DwWZmnJ3RHkAcnCSUiagt6SY4JcdPxd+gi0VEsh6sfMHap6BTUTCx8VkaldMCym3uKjkFEZFdujxuD3e0f4/q7AHDjCq6Xa4VY+KzQxN7BGBcRKDoGEZFdeSD+GmwOecG+198NHwH0nys6BbUAC5+VWjGtF+fmIyJqYy8l9sI6v2WQHFxER2l7jm7AtPWAjGc5rRELn5UK8XLh3HxERAK8ndoJSz1WQFLa2VRZ45YB3uGiU1ALsfBZsbuGdUSfdp6iYxAR2Z0vM9rhYadV0LsFiI7SNjoMB4beKzoFtQILnxVTyGVYO6sflA78MhIRtbVduX64w7AcOo8w0VHMy9GVl3JtAJuClesa6I5nJvYQHYOIyC4dK/TE1IqlUPvY8L/DY18BfDqKTkGtxMJnA+4eFo7rO/uKjkFEZJculLliXMGzKAuwwYmIOwwDrrlfdAoyARY+GyCTyfDmbf3g4WzHUwUQEQmUrlZiROZjyA8eJTqK6Ti6Aje/y0u5NoKFz0aEeLlgxbTeomMQEdmtQq0DhqcuQnr7KaKjmMa45YBvZ9EpyERY+GzI9AHtMKVvsOgYRER2q1KvwIiEubgYervoKK3TbSJwzX2iU5AJsfDZmFen90agh1J0DCIiuyVJMkyMm4a/Qq10GhNVEDDtfdEpyMRY+GyMl6sTXr+1n+gYRER2b3bcDfil/RNWtv6uDJjxIeDGgYC2hoXPBo3q5o/513YQHYOIyO49HD8EXwa/CEnuKDqKcYYtBjrfIDoFmYFMkiRJdAgyPbVWj5vXH8Gl7DLRUYiI7N6jYUl4svBVyLQVoqM0LGQAcM8+QGEl5ZSahWf4bJSzowLvzxsENyeF6ChERHbv3dSOeEm1ApLSQpfDdFIBt2xg2bNhLHw2rEuACmtu6Ss6BhERAdicGYIHnVZB7xYoOkpdk9/gFCw2joXPxk3tF4KF14eLjkFERAB25/pirn45tJ7hoqP8q+9soP9c0SnIzHgPnx3Q6g2Y9dExnE4tEh2FiIgA9FBVYIfHW3AuuCA2SFCf6vv2HF3E5iCz4xk+O+CokOO9uQPh4+YkOgoREQG4WOaKsQXPoDRgsLgQzl7ArC9Z9uwEC5+dCPFywdu394fcmqaDIiKyYZfVSgzPXCxo/V0ZcMungE9HAccmEVj47Miobv54dExX0TGIiOgfxVoHXJ9yL1LbT23bA49+Duh6Y9sek4Ri4bMzj43tihFd/UTHICKif1QZ5BiVMBvnQ+e0zQG7TgBGPds2xyKLwcJnZ+RyGd6ZPQAhns6ioxAR0T8kSYbJcVPxZ+h95j2Qd0dg5seAjPf32BsWPjvk4+aETxcM4aTMREQWZm7caPzU/ilIMjP8eHZ0BW7/CnDxMv2+yeKx8NmpniEeWDdnAAdxEBFZmEfjB2FjkKnX35UBMz4EgnqbcJ9kTVj47NjYiEC8MDlCdAwiIvqP5UkReNN3OSRHV9PscOxSoOc00+yLrBILn51bNKIT5l4TJjoGERH9x3tp4XhetRIGZ6/W7WjAHcCIJ02SiawXV9og6PQG3LXxBA7H5YmOQkRE/zHerwAfSKugKM9q/pPDRwDztwMKU14eJmvEwkcAgBK1FjPf/xPxOWWioxAR0X8M9SrBZuVrcCxOMv5Jvl2ARb8BLt7mC0ZWg5d0CQDg4eyIzxYM4fJrREQW6HiRB6aUvYRK317GPcHFB5i7lWWParDwUY0wX1d8PH8QnBz4bUFEZGkulbtgTP4SlAQMaXxDhRMwezPg27ltgpFV4E92qmVwuA9ev6Wv6BhERFSPTLUTRmQ+ityQMQ1sIQOmvQd0uL5Nc5HlY+GjOqYPaIcXOV0LEZFFKtY6YHjy3Uhpf3PdT45fBfSd1fahyOKx8FG97h3ZCY/c0EV0DCIiqkeVQY7RCbfjbOi8fx8c9hhw/SPiQpFF4yhdatTSnWfxxbEU0TGIiKgBX3b9AyP8yqov5RI1gGf4qFHLb+6F6f1DRMcgIqIGfOl4G/RT14uOQRaOhY8aJZPJ8OZt/TAuIlB0FCIi+o9hXXzx7twBUHBhdGoCCx81yUEhx/q5A3BtJx/RUYiI6B8Dwrzw8fzBUDooREchK8DCR0ZxdlTg0wVD0K+9p+goRER2r0eQOzYuHAo3pYPoKGQlWPjIaCqlAzbeNRRdA1SioxAR2a1O/m744p6h8HTl+rhkPBY+ahZvNyd8tegadPB1FR2FiMjudAlQ4Zv7rkWAu7PoKGRlWPio2QI9nPHtfdehk7+b6ChERHaje6A7yx61GOfhoxbLKVXjjk//xqXsMtFRiIhsWo8gd3x977XwcXMSHYWsFAsftUpBuQZ3fPo3zmeWiI5CRGSTeoV44Kt7roE3yx61AgsftVpxhRbzP/sbMenFoqMQEdmUvu098eXd13CABrUaCx+ZRKlaiwWfHUdkapHoKERENqF/qBe+uGcoPJxZ9qj1WPjIZMqrdLhr4wkcTyoQHYWIyKoN6uCNjXcNgTvLHpkICx+ZVKVGj3u/OIkj8XmioxARWaWh4T74/K4hnFSZTIqFj0xOrdXjwa9O4UBsrugoRERW5caegXh3zgA4O3K5NDItFj4yC63egCXfRWNHVIboKEREVmHuNWFYOa03FHKZ6Chkg1j4yGwkScKaXy/ioz8SRUchIrJoT93YDY+O7So6BtkwFj4yu8+OJGHlL+fB7zQiotoc5DKsntkHswaHio5CNo6Fj9rEzzEZeHJrNDQ6g+goREQWwdVJgffmDcQN3QNERyE7wMJHbebvxHzc/9UpFFVoRUchIhLK180Jny0cgn6hXqKjkJ1g4aM2lZBbhrs+P4HUggrRUYiIhOjg64pNdw1FuJ+b6ChkR1j4qM3ll1Vh0RcncZqrchCRnenb3hOfLRwCP5VSdBSyMyx8JIRaq8cT30bh17NZoqMQEbWJaf1D8NotfTnHHgnBwkfCSJKEd/bH4Z39cRzBS0Q2SyGX4dmJ3XHfyM6io5AdY+Ej4X47n40ntkahVK0THYWIyKQ8XRzx7pwBGNnNX3QUsnMsfGQREnPLcN+XpxCfUyY6ChGRSXQLVOGTOwejgy8HZ5B4LHxkMcqqdHhqaxT2nMsWHYWIqFUm9ArE2ln94aZ0EB2FCAALH1kYSZLw/sEEvLU3FgZ+ZxKRlZHJgMfHdsPisV0gk3FNXLIcLHxkkQ7E5uCxLadRwvv6iMhKqJQOePv2/rixZ6DoKER1sPCRxUrJL8d9X5xCbHap6ChERI3qHuiO9+YNQJcAd9FRiOrFwkcWrUKjwwvbzmBHVIboKERE9Zp3TRhevqkn59cji8bCR1Zh++l0vLzjHMqqeImXiCyDh7MDXrulLyb1CRYdhahJctEByPIcO3YMCoUCU6ZMER2lxowB7bFr8Qj050LjRGQBBnXwxq7HRrDskdXgGT6qY9GiRVCpVNiwYQNiY2MREhIiOlINnd6At3+7hA8OJnAULxG1ObkMeGh0Fzw+riscFDxnQtaDhY9qKSsrQ3BwME6ePIlXXnkFffv2xQsvvCA6Vh3HEvLx5NYoZBarRUchIjsR6KHE27P64/oufqKjEDUbfz2hWrZu3YoePXqge/fuuOOOO/DZZ5/BEn8nuK6zL359bAQm9OL0B0RkfmN6BODXx0ay7JHVYuGjWjb8f3t3Hxt1fcBx/PPr9dprr/SgR1voA6XHgBUoSMS2YAxsIhIdgs6IZjjNskRNzJZFV83MkrEsmsxplixx2RaWJeqYM5mGjmVT1lmMRlo3aUMf5aH04Qp9gF6vT9e7a/dHGZOhQ2rb39333q+EHM2F5PPPJW9+vd/ve+CA9u3bJ0nauXOnAoGAampqbF716Ramp+hXD27Ss3eXKo274wDMgTSnQz/atUYHHtqkLHeK3XOAGeNXuristbVV69atU3d3t3JyciRJjz/+uAKBgF5++WWb1/1/J3uDeuL1BtV3Dto9BYAhKnxZ+unXN2iZN93uKcAXRvDhssrKSj3//PNyOP57tWxqakqpqanq6emRx+Oxcd21TU5O6bfvndELb7VpLBy1ew6AOOVOcejpO0q0r3wZx6PBGAQfJEmRSEQFBQWqrKzUjh07rnhvz549evLJJ/Xoo4/atO76dAyM6uk/Nej9UwN2TwEQZ25ZuVjP3VOqgkVc1YNZCD5Ikt58803t3btXvb29V13Je+qpp1RdXa26ujqb1s3MwdoOPfuXZgU5jxfANSxITdYzd5bo/rJldk8B5gTBB0nSrl27NDk5qcOHD1/1Xm1trcrLy1VfX6/169fbsG7mzg+N65k3TuhI83m7pwCIUdtWZ+u5e0q11JNm9xRgzhB8SAhV9X7tr2pU//CE3VMAxAhPmlM//Noa3Xtjgd1TgDlH8CFhXByZ0I//3KQ3Puq2ewoAG1mWdN+NharcuVrejFS75wDzguBDwvng9ID2VzWpuWfI7ikA5tmGAo/2717HudxIOAQfElJ0ckoHazv0wlutujgatnsOgDnmdaeocudq3bepkEetICERfEhogdGwXny7Va8c61B0ko8CYBpHkqUHK4r0vdtWyZPmtHsOYBuCD5DUei6o/VWNPLsPMEhZcZb237VWJUsz7Z4C2I7gAz7hryd69JPDzeq6OGb3FAAzlJuZqh/cUaLdN+TbPQWIGQQf8D/Gw1H95uhp/bLmlEYnOKINiBeZrmQ9snWFvnVzsdJSHNf+B0ACIfiAz9AXDOmld07q1WMdmohM2j0HwGdwOZP08JZiPbZ1hTzpfE8P+DQEH3AN/sEx/aL6Y73+YZci3NgBxAynw9Lemwr1na+uVE6my+45QEwj+IDPqb1/RD8/0qZD9X7RfYB9LEvatT5PT+xYpSKv2+45QFwg+IDr1HouqBffbtXfGjmfF5hvX1mdre/f/mWtyePOW+B6EHzADDV0Depnb7XpaFuf3VMA4938Ja++e+sqlRVn2T0FiEsEH/AF1Z65oJfeOal3Wgk/YDYlWdLta5fosW0rtL5god1zgLhG8AGzpOXckH5dc1pVDX6Fo3ysgJlKcSTp7o35emSrT77sDLvnAEYg+IBZ1hMY04F3z+gPdZ0aDkXsngPEDXeKQw+ULdO3b/FpiYe7boHZRPABcyQwFtarx87qd++1qzcYsnsOELOy3Cl6eMtyPbR5Oc/RA+YIwQfMsYnIpN74qEu/Pnpap/pG7J4DxAzfYre+ublIe29axskYwBwj+IB5MjU1peqWXr3ywVnVtPXxLD8kpOQkS7etydW+iiJtWeGVZVl2TwISAsEH2KDr4qheq+vUa3Wd/LoXCWFJpkv3lxXqgbJlyuVUDGDeEXyAjSLRSR1p7tXvazv07sd94tMIk1iWtGWFVw9WFGl7Sa6SHUl2TwISFsEHxIjOC6M6WNuhP37Ypf5hrvohfnnSnLr3xgJ9o3wZj1UBYgTBB8SYcHRSbzed18HaDr1/akBRvuyHOOB0WNq6Kkd3b8zXrSU5cjm5CQOIJQQfEMP6giEdbvDrUL1f/+oYtHsOcAXLkjYVLdKejfm6s3SpFqan2D0JwGcg+IA40XlhVFUNfh067lfLuaDdc5DAVuZkaM/GfO2+IU8Fi9LtngPgcyD4gDjUdj6oQ8f9qmrw6+zAqN1zkAByM1N114Y87b4hX+vyPXbPAXCdCD4gzh3vHFRVvV9Hms8Tf5hVK3MytH1NrraX5Ghj4SIlJfHMPCBeEXyAQU72DusfLb36e8t5fdh+URFu+MB1SE6ydNPyrMuRV+R12z0JwCwh+ABDDY2HdbStT9Utvapp7dPAyITdkxCDFriStW11jraX5Gjb6hx50jjLFjARwQckgMnJKR3vGlR1c6+qW3rV1DNk9yTYJMmS1uZ5tHmFV1tXZausOEtOHogMGI/gAxJQ/3BIdWcu6NilP63nhjjb11BJllSyNFObfV5V+Lwq82Up08VVPCDREHwAFBgLq+7MBdW2TwdgY3eA7//FKcuSSpZkqsLnVYUvS+XFXnnSCTwg0RF8AK4yEoron2cvqvZSBDb5hzQcitg9C59iUbpT6/I9Ks33aEPhQpUXZ/EAZABXIfgAXNPU1JTaB0Z1ojugRv+QGv0BNfmHuBFkni1Md6o033M58ErzPSrM4sHHAK6N4AMwYz2BMTV2D+mEfzoEm/xD6h4cs3tW3LMsKc+TJl+2W2vzpsNufQFxB2DmCD4As2poPKz2/hG1D4xOv/aPqH1g+ucLXBG8gifNqeLFbvmy3fItdsuXnSFftlvLvW65nA675wEwCMEHYN4ExsI6OzCiM/0jau8fVfvAiLoHx9QfDKkvGFLQsO8JLkx3KneBSzmZqcrNdCk3M1VFWdOBV7zYLW9Gqt0TASQIgg9AzBgPR9UXDKn3UgD2BcenX4f/83NIQ+MRjYQiGpuIajQcVXQe7yZ2OZO0wOXUAleyMi+9Ls5InQ66Ba7LUZebOR15qclcpQMQGwg+AHFtPBzV6ERUI6HI9OvEdAyOhCIaC0cViU7Jsqa/F2fJkvWJ42Aty5KlK99LTU5SWopD6SnJcqc4Lv89IzVZKck8oBhAfCL4AAAADMd/VwEAAAxH8AEAABiO4AMAADAcwQcAAGA4gg8AAMBwBB8AAIDhCD4AAADDEXwAAACGI/gAAAAMR/ABAAAYjuADAAAwHMEHAABgOIIPAADAcAQfAACA4Qg+AAAAwxF8AAAAhiP4AAAADEfwAQAAGI7gAwAAMBzBBwAAYDiCDwAAwHAEHwAAgOEIPgAAAMMRfAAAAIYj+AAAAAxH8AEAABiO4AMAADAcwQcAAGA4gg8AAMBwBB8AAIDhCD4AAADDEXwAAACGI/gAAAAMR/ABAAAYjuADAAAwHMEHAABgOIIPAADAcAQfAACA4Qg+AAAAwxF8AAAAhiP4AAAADEfwAQAAGI7gAwAAMBzBBwAAYDiCDwAAwHAEHwAAgOEIPgAAAMMRfAAAAIYj+AAAAAxH8AEAABiO4AMAADAcwQcAAGA4gg8AAMBwBB8AAIDhCD4AAADDEXwAAACGI/gAAAAMR/ABAAAYjuADAAAwHMEHAABgOIIPAADAcAQfAACA4Qg+AAAAwxF8AAAAhiP4AAAADEfwAQAAGI7gAwAAMBzBBwAAYDiCDwAAwHAEHwAAgOH+DWnk0eCGmHw2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the occurrences of each grade\n",
    "grade_counts = new_df_copy['grade_average'].value_counts()\n",
    "\n",
    "# Plotting the pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(grade_counts, labels=grade_counts.index, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Proportion of Students by Grade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Assuming new_df_copy is the DataFrame with the modified 'FATHEDP' and 'MOTHEDP' columns\n",
    "# Replace 'new_df_copy' with the actual name of your DataFrame if it's different\n",
    "\n",
    "# Define the mapping for substitutions\n",
    "education_mapping = {\n",
    "    1: 'Less than high school',\n",
    "    2: 'High school diploma',\n",
    "    3: 'Trade certificate/diploma',\n",
    "    4: 'College/other non-university certificate',\n",
    "    5: 'University below Bachelor or Bachelor',\n",
    "    6: 'University above Bachelor/Master/Doctorate',\n",
    "    99: 'Not stated'\n",
    "}\n",
    "\n",
    "# Create a copy of the DataFrame\n",
    "education_df = new_df_copy[['FATHEDP', 'MOTHEDP']].copy()\n",
    "\n",
    "# Replace values in the 'FATHEDP' and 'MOTHEDP' columns using the mapping\n",
    "education_df['FATHEDP'] = education_df['FATHEDP'].map(education_mapping)\n",
    "education_df['MOTHEDP'] = education_df['MOTHEDP'].map(education_mapping)\n",
    "\n",
    "# Count the occurrences of each education level\n",
    "fath_edu_counts = education_df['FATHEDP'].value_counts()\n",
    "moth_edu_counts = education_df['MOTHEDP'].value_counts()\n",
    "\n",
    "# Plotting the bar chart\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "bar_width = 0.35\n",
    "bar_positions = range(len(fath_edu_counts))\n",
    "\n",
    "ax.bar(bar_positions, fath_edu_counts, width=bar_width, label='Father')\n",
    "ax.bar([pos + bar_width for pos in bar_positions], moth_edu_counts, width=bar_width, label='Mother')\n",
    "\n",
    "ax.set_xticks([pos + bar_width/2 for pos in bar_positions])\n",
    "ax.set_xticklabels(fath_edu_counts.index, rotation=45, ha='right')\n",
    "\n",
    "ax.set_ylabel('Number of Students')\n",
    "ax.set_title('Education Level of Father and Mother')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_skip_df = new_df[new_df[\"FATHEDP\"] != 99]\n",
    "no_skip_df = no_skip_df[no_skip_df[\"MOTHEDP\"] != 99]\n",
    "no_skip_df = no_skip_df[no_skip_df[\"PGM_P405\"] != 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        0\n",
       "2        1\n",
       "3        0\n",
       "4        0\n",
       "        ..\n",
       "19559    0\n",
       "19560    0\n",
       "19561    0\n",
       "19562    0\n",
       "19563    0\n",
       "Name: PGM_P405, Length: 17587, dtype: int32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = no_skip_df[\"FATHEDP\"]\n",
    "y = (no_skip_df[\"PGM_P405\"] > 2).astype(int)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train.values.reshape(-1, 1))\n",
    "X_test = scaler.transform(X_test.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(random_state=42)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Generate predicted probabilities for the training set\n",
    "y_pred_prob = model.predict_proba(X_train)[:, 1]\n",
    "\n",
    "# Scatter plot\n",
    "plt.scatter(X_train, y_train, color='blue', label='Actual', alpha=0.5)\n",
    "plt.scatter(X_train, y_pred_prob, color='red', label='Predicted', alpha=0.5)\n",
    "plt.xlabel('Father\\'s education level')\n",
    "plt.ylabel('Probability of getting a overall grade average of A- or higher')\n",
    "plt.title('Logistic Regression - Actual vs. Predicted Probabilities')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}