Loan_Defaulter_Prediction_with_Decision_Tree_Visualization_30052021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Loan Defaulter Prediction \n",
    "\n",
    "## Supervised Learning Hands-on session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Dataset using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for handling dataset\n",
    "import numpy as np\n",
    "\n",
    "#additional array-based functionality\n",
    "import pandas as pd\n",
    "\n",
    "#visualization packages\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#machine learning functions\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#for manipulating dates and times\n",
    "import datetime as dt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset loaded in memory...\n"
     ]
    }
   ],
   "source": [
    "#Load the dataset into memory from secondary memory to primary memory\n",
    "data = pd.read_csv(\"LoanStats.csv\", encoding=\"Latin-1\", low_memory=False)\n",
    "\n",
    "print(\"Dataset loaded in memory...\")\n",
    "\n",
    "#set the maximum column display limit to 50\n",
    "pd.set_option('display.max_columns', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.iloc[0:2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 41)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Row display operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Details of 0th profile from the dataset :\n",
      "\n",
      "Attributes\n",
      "\n",
      "Loan ID                                                                                87023\n",
      "Amount Requested                                                                        7500\n",
      "Amount Funded By Investors                                                              7500\n",
      "Interest Rate                                                                         13.75%\n",
      "Loan Length                                                                        36 months\n",
      "Application Date                                                                  2007-06-07\n",
      "Application Expiration Date                                                       2007-06-21\n",
      "Issued Date                                                                       2007-06-27\n",
      "CREDIT Grade                                                                              E2\n",
      "Loan Title                                                                Consolidation Loan\n",
      "Loan Purpose                                                              debt_consolidation\n",
      "Loan Description                           I plan to consolidate over $7,000 of debt: a c...\n",
      "Monthly PAYMENT                                                                       255.43\n",
      "Status                                                                            Fully Paid\n",
      "Total Amount Funded                                                                     7500\n",
      "Debt-To-Income Ratio                                                                  14.29%\n",
      "Remaining Principal Funded by Investors                                                    0\n",
      "Payments To Date (Funded by investors)                                               9194.98\n",
      "Remaining Principal                                                                        0\n",
      " Payments To Date                                                                    9194.98\n",
      "Screen Name                                                                        Kingtrite\n",
      "City                                                                               Attleboro\n",
      "State                                                                                     MA\n",
      "Home Ownership                                                                           OWN\n",
      "Monthly Income                                                                       1833.33\n",
      "FICO Range                                                                           660-678\n",
      "Earliest CREDIT Line                                                              2003-10-27\n",
      "Open CREDIT Lines                                                                          7\n",
      "Total CREDIT Lines                                                                         8\n",
      "Revolving CREDIT Balance                                                                4175\n",
      "Revolving Line Utilization                                                             51.5%\n",
      "Inquiries in the Last 6 Months                                                             0\n",
      "Accounts Now Delinquent                                                                    0\n",
      "Delinquent Amount                                                                          0\n",
      "Delinquencies (Last 2 yrs)                                                                 1\n",
      "Months Since Last Delinquency                                                             11\n",
      "Public Records On File                                                                     0\n",
      "Months Since Last Record                                                                   0\n",
      "Education                                                                                NaN\n",
      "Employment Length                                                                   < 1 year\n",
      "Code                                                                                     NaN\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#displaying profiles of any given index\n",
    "i=0\n",
    "print(\"Details of \"+str(i)+\"th profile from the dataset :\\n\\nAttributes\\n\")\n",
    "print(data.iloc[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display the starting 5 profiles from the dataset :\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>Loan ID</th>\n",
       "      <th>Amount Requested</th>\n",
       "      <th>Amount Funded By Investors</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Loan Length</th>\n",
       "      <th>Application Date</th>\n",
       "      <th>Application Expiration Date</th>\n",
       "      <th>Issued Date</th>\n",
       "      <th>CREDIT Grade</th>\n",
       "      <th>Loan Title</th>\n",
       "      <th>Loan Purpose</th>\n",
       "      <th>Loan Description</th>\n",
       "      <th>Monthly PAYMENT</th>\n",
       "      <th>Status</th>\n",
       "      <th>Total Amount Funded</th>\n",
       "      <th>Debt-To-Income Ratio</th>\n",
       "      <th>Remaining Principal Funded by Investors</th>\n",
       "      <th>Payments To Date (Funded by investors)</th>\n",
       "      <th>Remaining Principal</th>\n",
       "      <th>Payments To Date</th>\n",
       "      <th>Screen Name</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Monthly Income</th>\n",
       "      <th>FICO Range</th>\n",
       "      <th>Earliest CREDIT Line</th>\n",
       "      <th>Open CREDIT Lines</th>\n",
       "      <th>Total CREDIT Lines</th>\n",
       "      <th>Revolving CREDIT Balance</th>\n",
       "      <th>Revolving Line Utilization</th>\n",
       "      <th>Inquiries in the Last 6 Months</th>\n",
       "      <th>Accounts Now Delinquent</th>\n",
       "      <th>Delinquent Amount</th>\n",
       "      <th>Delinquencies (Last 2 yrs)</th>\n",
       "      <th>Months Since Last Delinquency</th>\n",
       "      <th>Public Records On File</th>\n",
       "      <th>Months Since Last Record</th>\n",
       "      <th>Education</th>\n",
       "      <th>Employment Length</th>\n",
       "      <th>Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87023</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>13.75%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>2007-06-07</td>\n",
       "      <td>2007-06-21</td>\n",
       "      <td>2007-06-27</td>\n",
       "      <td>E2</td>\n",
       "      <td>Consolidation Loan</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>I plan to consolidate over $7,000 of debt: a c...</td>\n",
       "      <td>255.43</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>14.29%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9194.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9194.98</td>\n",
       "      <td>Kingtrite</td>\n",
       "      <td>Attleboro</td>\n",
       "      <td>MA</td>\n",
       "      <td>OWN</td>\n",
       "      <td>1833.33</td>\n",
       "      <td>660-678</td>\n",
       "      <td>2003-10-27</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4175.0</td>\n",
       "      <td>51.5%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90376</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>7.43%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>2007-06-12</td>\n",
       "      <td>2007-06-26</td>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>A2</td>\n",
       "      <td>JAL Loan</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.38</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>3.72%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5174.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5174.16</td>\n",
       "      <td>Laguna</td>\n",
       "      <td>Rockville</td>\n",
       "      <td>MD</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>16666.67</td>\n",
       "      <td>780+</td>\n",
       "      <td>1988-11-19</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>85607.0</td>\n",
       "      <td>0.7%</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90395</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.07%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>2007-06-12</td>\n",
       "      <td>2007-06-26</td>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>A4</td>\n",
       "      <td>MBA Loan Consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.84</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2.3%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5272.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5272.08</td>\n",
       "      <td>MGDesousa</td>\n",
       "      <td>Framingham</td>\n",
       "      <td>MA</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>8333.33</td>\n",
       "      <td>714-749</td>\n",
       "      <td>1998-10-28</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9698.0</td>\n",
       "      <td>19.4%</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90665</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>10.28%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>2007-06-13</td>\n",
       "      <td>2007-06-27</td>\n",
       "      <td>2007-07-03</td>\n",
       "      <td>C1</td>\n",
       "      <td>Retiring credit card debt</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>The rate of interest and fees incurred by carr...</td>\n",
       "      <td>275.38</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>6.4%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9912.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9912.49</td>\n",
       "      <td>Radiothermal</td>\n",
       "      <td>Greensboro</td>\n",
       "      <td>NC</td>\n",
       "      <td>RENT</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>679-713</td>\n",
       "      <td>1986-12-30</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8847.0</td>\n",
       "      <td>26.9%</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 years</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92187</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>8.07%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>2007-06-15</td>\n",
       "      <td>2007-06-29</td>\n",
       "      <td>2007-07-05</td>\n",
       "      <td>A4</td>\n",
       "      <td>Home Improvement</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>Our current gutter system on our home is old a...</td>\n",
       "      <td>78.42</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>11.33%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2822.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2822.72</td>\n",
       "      <td>PMJ7671</td>\n",
       "      <td>Lakewood</td>\n",
       "      <td>CO</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>9166.67</td>\n",
       "      <td>750-779</td>\n",
       "      <td>1990-11-11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7274.0</td>\n",
       "      <td>13.1%</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4 years</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Loan ID  Amount Requested  Amount Funded By Investors Interest Rate  \\\n",
       "0   87023            7500.0                      7500.0        13.75%   \n",
       "1   90376            5000.0                      5000.0         7.43%   \n",
       "2   90395            5000.0                      5000.0         8.07%   \n",
       "3   90665            8500.0                      8500.0        10.28%   \n",
       "4   92187            2500.0                      2500.0         8.07%   \n",
       "\n",
       "  Loan Length Application Date Application Expiration Date Issued Date  \\\n",
       "0   36 months       2007-06-07                  2007-06-21  2007-06-27   \n",
       "1   36 months       2007-06-12                  2007-06-26  2007-07-02   \n",
       "2   36 months       2007-06-12                  2007-06-26  2007-07-02   \n",
       "3   36 months       2007-06-13                  2007-06-27  2007-07-03   \n",
       "4   36 months       2007-06-15                  2007-06-29  2007-07-05   \n",
       "\n",
       "  CREDIT Grade                 Loan Title        Loan Purpose  \\\n",
       "0           E2         Consolidation Loan  debt_consolidation   \n",
       "1           A2                   JAL Loan               other   \n",
       "2           A4     MBA Loan Consolidation  debt_consolidation   \n",
       "3           C1  Retiring credit card debt         credit_card   \n",
       "4           A4           Home Improvement    home_improvement   \n",
       "\n",
       "                                    Loan Description  Monthly PAYMENT  \\\n",
       "0  I plan to consolidate over $7,000 of debt: a c...           255.43   \n",
       "1                                                NaN           155.38   \n",
       "2                                                NaN           156.84   \n",
       "3  The rate of interest and fees incurred by carr...           275.38   \n",
       "4  Our current gutter system on our home is old a...            78.42   \n",
       "\n",
       "       Status  Total Amount Funded Debt-To-Income Ratio  \\\n",
       "0  Fully Paid               7500.0               14.29%   \n",
       "1  Fully Paid               5000.0                3.72%   \n",
       "2  Fully Paid               5000.0                 2.3%   \n",
       "3  Fully Paid               8500.0                 6.4%   \n",
       "4  Fully Paid               2500.0               11.33%   \n",
       "\n",
       "   Remaining Principal Funded by Investors  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "\n",
       "   Payments To Date (Funded by investors)  Remaining Principal   \\\n",
       "0                                 9194.98                   0.0   \n",
       "1                                 5174.16                   0.0   \n",
       "2                                 5272.08                   0.0   \n",
       "3                                 9912.49                   0.0   \n",
       "4                                 2822.72                   0.0   \n",
       "\n",
       "    Payments To Date   Screen Name        City State Home Ownership  \\\n",
       "0            9194.98     Kingtrite   Attleboro    MA            OWN   \n",
       "1            5174.16        Laguna   Rockville    MD       MORTGAGE   \n",
       "2            5272.08     MGDesousa  Framingham    MA       MORTGAGE   \n",
       "3            9912.49  Radiothermal  Greensboro    NC           RENT   \n",
       "4            2822.72       PMJ7671    Lakewood    CO       MORTGAGE   \n",
       "\n",
       "   Monthly Income FICO Range Earliest CREDIT Line  Open CREDIT Lines  \\\n",
       "0         1833.33    660-678           2003-10-27                7.0   \n",
       "1        16666.67       780+           1988-11-19               17.0   \n",
       "2         8333.33    714-749           1998-10-28               11.0   \n",
       "3         1500.00    679-713           1986-12-30                6.0   \n",
       "4         9166.67    750-779           1990-11-11               13.0   \n",
       "\n",
       "   Total CREDIT Lines  Revolving CREDIT Balance Revolving Line Utilization  \\\n",
       "0                 8.0                    4175.0                      51.5%   \n",
       "1                26.0                   85607.0                       0.7%   \n",
       "2                20.0                    9698.0                      19.4%   \n",
       "3                 9.0                    8847.0                      26.9%   \n",
       "4                40.0                    7274.0                      13.1%   \n",
       "\n",
       "   Inquiries in the Last 6 Months  Accounts Now Delinquent  Delinquent Amount  \\\n",
       "0                             0.0                      0.0                0.0   \n",
       "1                             0.0                      0.0                0.0   \n",
       "2                             0.0                      0.0                0.0   \n",
       "3                             1.0                      0.0                0.0   \n",
       "4                             0.0                      0.0                0.0   \n",
       "\n",
       "   Delinquencies (Last 2 yrs)  Months Since Last Delinquency  \\\n",
       "0                         1.0                           11.0   \n",
       "1                         0.0                            0.0   \n",
       "2                         0.0                            0.0   \n",
       "3                         1.0                            5.0   \n",
       "4                         0.0                            0.0   \n",
       "\n",
       "   Public Records On File  Months Since Last Record Education  \\\n",
       "0                     0.0                       0.0       NaN   \n",
       "1                     0.0                       0.0       NaN   \n",
       "2                     0.0                       0.0       NaN   \n",
       "3                     0.0                       0.0       NaN   \n",
       "4                     0.0                       0.0       NaN   \n",
       "\n",
       "  Employment Length  Code  \n",
       "0          < 1 year   NaN  \n",
       "1          < 1 year   NaN  \n",
       "2          < 1 year   NaN  \n",
       "3           3 years   NaN  \n",
       "4           4 years   NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display first 5 records \n",
    "\n",
    "print(\"Display the starting 5 profiles from the dataset :\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting particular rows from the dataset : \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>Loan ID</th>\n",
       "      <th>Amount Requested</th>\n",
       "      <th>Amount Funded By Investors</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Loan Length</th>\n",
       "      <th>Application Date</th>\n",
       "      <th>Application Expiration Date</th>\n",
       "      <th>Issued Date</th>\n",
       "      <th>CREDIT Grade</th>\n",
       "      <th>Loan Title</th>\n",
       "      <th>Loan Purpose</th>\n",
       "      <th>Loan Description</th>\n",
       "      <th>Monthly PAYMENT</th>\n",
       "      <th>Status</th>\n",
       "      <th>Total Amount Funded</th>\n",
       "      <th>Debt-To-Income Ratio</th>\n",
       "      <th>Remaining Principal Funded by Investors</th>\n",
       "      <th>Payments To Date (Funded by investors)</th>\n",
       "      <th>Remaining Principal</th>\n",
       "      <th>Payments To Date</th>\n",
       "      <th>Screen Name</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Monthly Income</th>\n",
       "      <th>FICO Range</th>\n",
       "      <th>Earliest CREDIT Line</th>\n",
       "      <th>Open CREDIT Lines</th>\n",
       "      <th>Total CREDIT Lines</th>\n",
       "      <th>Revolving CREDIT Balance</th>\n",
       "      <th>Revolving Line Utilization</th>\n",
       "      <th>Inquiries in the Last 6 Months</th>\n",
       "      <th>Accounts Now Delinquent</th>\n",
       "      <th>Delinquent Amount</th>\n",
       "      <th>Delinquencies (Last 2 yrs)</th>\n",
       "      <th>Months Since Last Delinquency</th>\n",
       "      <th>Public Records On File</th>\n",
       "      <th>Months Since Last Record</th>\n",
       "      <th>Education</th>\n",
       "      <th>Employment Length</th>\n",
       "      <th>Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87023</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>13.75%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>2007-06-07</td>\n",
       "      <td>2007-06-21</td>\n",
       "      <td>2007-06-27</td>\n",
       "      <td>E2</td>\n",
       "      <td>Consolidation Loan</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>I plan to consolidate over $7,000 of debt: a c...</td>\n",
       "      <td>255.43</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>14.29%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9194.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9194.98</td>\n",
       "      <td>Kingtrite</td>\n",
       "      <td>Attleboro</td>\n",
       "      <td>MA</td>\n",
       "      <td>OWN</td>\n",
       "      <td>1833.33</td>\n",
       "      <td>660-678</td>\n",
       "      <td>2003-10-27</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4175.0</td>\n",
       "      <td>51.5%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90665</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>10.28%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>2007-06-13</td>\n",
       "      <td>2007-06-27</td>\n",
       "      <td>2007-07-03</td>\n",
       "      <td>C1</td>\n",
       "      <td>Retiring credit card debt</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>The rate of interest and fees incurred by carr...</td>\n",
       "      <td>275.38</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>6.4%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9912.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9912.49</td>\n",
       "      <td>Radiothermal</td>\n",
       "      <td>Greensboro</td>\n",
       "      <td>NC</td>\n",
       "      <td>RENT</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>679-713</td>\n",
       "      <td>1986-12-30</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8847.0</td>\n",
       "      <td>26.9%</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 years</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>101579</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10.28%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>2007-06-27</td>\n",
       "      <td>2007-07-11</td>\n",
       "      <td>2007-07-17</td>\n",
       "      <td>C1</td>\n",
       "      <td>Web Design Business Expansion Loan</td>\n",
       "      <td>small_business</td>\n",
       "      <td>Seven months ago I decided to drop everything ...</td>\n",
       "      <td>323.98</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10.7%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11662.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11662.13</td>\n",
       "      <td>WebDev</td>\n",
       "      <td>Fort Collins</td>\n",
       "      <td>CO</td>\n",
       "      <td>RENT</td>\n",
       "      <td>2281.33</td>\n",
       "      <td>679-713</td>\n",
       "      <td>2003-09-17</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3534.0</td>\n",
       "      <td>54.4%</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loan ID  Amount Requested  Amount Funded By Investors Interest Rate  \\\n",
       "0    87023            7500.0                      7500.0        13.75%   \n",
       "3    90665            8500.0                      8500.0        10.28%   \n",
       "19  101579           10000.0                     10000.0        10.28%   \n",
       "\n",
       "   Loan Length Application Date Application Expiration Date Issued Date  \\\n",
       "0    36 months       2007-06-07                  2007-06-21  2007-06-27   \n",
       "3    36 months       2007-06-13                  2007-06-27  2007-07-03   \n",
       "19   36 months       2007-06-27                  2007-07-11  2007-07-17   \n",
       "\n",
       "   CREDIT Grade                          Loan Title        Loan Purpose  \\\n",
       "0            E2                  Consolidation Loan  debt_consolidation   \n",
       "3            C1           Retiring credit card debt         credit_card   \n",
       "19           C1  Web Design Business Expansion Loan      small_business   \n",
       "\n",
       "                                     Loan Description  Monthly PAYMENT  \\\n",
       "0   I plan to consolidate over $7,000 of debt: a c...           255.43   \n",
       "3   The rate of interest and fees incurred by carr...           275.38   \n",
       "19  Seven months ago I decided to drop everything ...           323.98   \n",
       "\n",
       "        Status  Total Amount Funded Debt-To-Income Ratio  \\\n",
       "0   Fully Paid               7500.0               14.29%   \n",
       "3   Fully Paid               8500.0                 6.4%   \n",
       "19  Fully Paid              10000.0                10.7%   \n",
       "\n",
       "    Remaining Principal Funded by Investors  \\\n",
       "0                                       0.0   \n",
       "3                                       0.0   \n",
       "19                                      0.0   \n",
       "\n",
       "    Payments To Date (Funded by investors)  Remaining Principal   \\\n",
       "0                                  9194.98                   0.0   \n",
       "3                                  9912.49                   0.0   \n",
       "19                                11662.13                   0.0   \n",
       "\n",
       "     Payments To Date   Screen Name          City State Home Ownership  \\\n",
       "0             9194.98     Kingtrite     Attleboro    MA            OWN   \n",
       "3             9912.49  Radiothermal    Greensboro    NC           RENT   \n",
       "19           11662.13        WebDev  Fort Collins    CO           RENT   \n",
       "\n",
       "    Monthly Income FICO Range Earliest CREDIT Line  Open CREDIT Lines  \\\n",
       "0          1833.33    660-678           2003-10-27                7.0   \n",
       "3          1500.00    679-713           1986-12-30                6.0   \n",
       "19         2281.33    679-713           2003-09-17                4.0   \n",
       "\n",
       "    Total CREDIT Lines  Revolving CREDIT Balance Revolving Line Utilization  \\\n",
       "0                  8.0                    4175.0                      51.5%   \n",
       "3                  9.0                    8847.0                      26.9%   \n",
       "19                 4.0                    3534.0                      54.4%   \n",
       "\n",
       "    Inquiries in the Last 6 Months  Accounts Now Delinquent  \\\n",
       "0                              0.0                      0.0   \n",
       "3                              1.0                      0.0   \n",
       "19                             0.0                      0.0   \n",
       "\n",
       "    Delinquent Amount  Delinquencies (Last 2 yrs)  \\\n",
       "0                 0.0                         1.0   \n",
       "3                 0.0                         1.0   \n",
       "19                0.0                         0.0   \n",
       "\n",
       "    Months Since Last Delinquency  Public Records On File  \\\n",
       "0                            11.0                     0.0   \n",
       "3                             5.0                     0.0   \n",
       "19                            0.0                     0.0   \n",
       "\n",
       "    Months Since Last Record Education Employment Length  Code  \n",
       "0                        0.0       NaN          < 1 year   NaN  \n",
       "3                        0.0       NaN           3 years   NaN  \n",
       "19                       0.0       NaN          < 1 year   NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display selected rows from the dataset\n",
    "print(\"Selecting particular rows from the dataset : \")\n",
    "data.loc[(data['Amount Requested'] > 7000)].head(3)\n",
    "# data.loc[(data['CREDIT Grade'] == 'A2') | (data['CREDIT Grade'] == 'B2')].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 41 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   Loan ID                                  2000 non-null   object \n",
      " 1   Amount Requested                         2000 non-null   float64\n",
      " 2   Amount Funded By Investors               2000 non-null   float64\n",
      " 3   Interest Rate                            2000 non-null   object \n",
      " 4   Loan Length                              2000 non-null   object \n",
      " 5   Application Date                         2000 non-null   object \n",
      " 6   Application Expiration Date              2000 non-null   object \n",
      " 7   Issued Date                              2000 non-null   object \n",
      " 8   CREDIT Grade                             2000 non-null   object \n",
      " 9   Loan Title                               1999 non-null   object \n",
      " 10  Loan Purpose                             2000 non-null   object \n",
      " 11  Loan Description                         1994 non-null   object \n",
      " 12  Monthly PAYMENT                          2000 non-null   float64\n",
      " 13  Status                                   2000 non-null   object \n",
      " 14  Total Amount Funded                      2000 non-null   float64\n",
      " 15  Debt-To-Income Ratio                     2000 non-null   object \n",
      " 16  Remaining Principal Funded by Investors  2000 non-null   float64\n",
      " 17  Payments To Date (Funded by investors)   2000 non-null   float64\n",
      " 18  Remaining Principal                      2000 non-null   float64\n",
      " 19   Payments To Date                        2000 non-null   float64\n",
      " 20  Screen Name                              1999 non-null   object \n",
      " 21  City                                     2000 non-null   object \n",
      " 22  State                                    2000 non-null   object \n",
      " 23  Home Ownership                           2000 non-null   object \n",
      " 24  Monthly Income                           2000 non-null   float64\n",
      " 25  FICO Range                               1998 non-null   object \n",
      " 26  Earliest CREDIT Line                     2000 non-null   object \n",
      " 27  Open CREDIT Lines                        2000 non-null   float64\n",
      " 28  Total CREDIT Lines                       2000 non-null   float64\n",
      " 29  Revolving CREDIT Balance                 2000 non-null   float64\n",
      " 30  Revolving Line Utilization               1990 non-null   object \n",
      " 31  Inquiries in the Last 6 Months           2000 non-null   float64\n",
      " 32  Accounts Now Delinquent                  2000 non-null   float64\n",
      " 33  Delinquent Amount                        2000 non-null   float64\n",
      " 34  Delinquencies (Last 2 yrs)               2000 non-null   float64\n",
      " 35  Months Since Last Delinquency            1216 non-null   float64\n",
      " 36  Public Records On File                   2000 non-null   float64\n",
      " 37  Months Since Last Record                 816 non-null    float64\n",
      " 38  Education                                1544 non-null   object \n",
      " 39  Employment Length                        2000 non-null   object \n",
      " 40  Code                                     831 non-null    float64\n",
      "dtypes: float64(20), object(21)\n",
      "memory usage: 640.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting the required rows\n",
    "\n",
    "## Transforming \"Status\" values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display unique values from the attribute \"Status\":\n",
      "\n",
      "1 Fully Paid\n",
      "2 Charged Off\n",
      "3 Late (31-120 days)\n",
      "4 Performing Payment Plan\n",
      "5 Default\n",
      "6 In Grace Period\n",
      "7 Current\n",
      "8 Late (16-30 days)\n"
     ]
    }
   ],
   "source": [
    "#Display the unique values in the attribute of Loan status\n",
    "\n",
    "print(\"Display unique values from the attribute \\\"Status\\\":\\n\")\n",
    "i=1\n",
    "for item in data.Status.unique():\n",
    "    print(i,item)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove all other records except the  'Fully paid' and 'Charged off' records\n",
    "data_with_loanstatus_sliced = data[(data['Status']==\"Fully Paid\") | (data['Status']==\"Charged Off\") | (data['Status']=='Does not meet the current credit policy  Status: Fully Paid') | (data['Status']=='Does not meet the current credit policy  Status: Charged Off') | (data['Status']==\"Current\")]\n",
    "\n",
    "#map Fully paid class value to 0, and Charged off class value to 1\n",
    "di = {\"Fully Paid\":0, \"Charged Off\":2, 'Does not meet the current credit policy  Status: Fully Paid':0, 'Does not meet the current credit policy  Status: Charged Off':2, 'Current':1} \n",
    "data= data_with_loanstatus_sliced.replace({\"Status\": di})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in \"Status\" attibute : \n",
      " 0    1368\n",
      "2     305\n",
      "1     295\n",
      "Name: Status, dtype: int64\n",
      "\n",
      "Current shape of dataset : (1968, 41)\n"
     ]
    }
   ],
   "source": [
    "#print the new unique values of the attribute\n",
    "\n",
    "print(\"Unique values in \\\"Status\\\" attibute : \\n\",data['Status'].value_counts())\n",
    "\n",
    "print(\"\\nCurrent shape of dataset :\",data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values of the status column :\n",
      "0       0\n",
      "1       0\n",
      "2       0\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "1995    1\n",
      "1996    1\n",
      "1997    1\n",
      "1998    0\n",
      "1999    0\n",
      "Name: Status, Length: 1968, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Values of the status column :\")\n",
    "print(data['Status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The float64 type attributes\n",
    "\n",
    "## Attributes with no changes required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample values in the attribute - Amount Funded By Investors\n",
      "\n",
      "0        7500.00\n",
      "1        5000.00\n",
      "2        5000.00\n",
      "3        8500.00\n",
      "4        2500.00\n",
      "          ...   \n",
      "1995     9649.47\n",
      "1996     8210.44\n",
      "1997     5515.83\n",
      "1998    10775.00\n",
      "1999     5000.00\n",
      "Name: Amount Funded By Investors, Length: 1968, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Sample values in the attribute - Amount Funded By Investors\\n\")\n",
    "print(data['Amount Funded By Investors'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monthly PAYMENT : float64\n",
      "Monthly Income : float64\n",
      "Open CREDIT Lines : float64\n",
      "Total CREDIT Lines : float64\n",
      "Revolving CREDIT Balance : float64\n",
      "Inquiries in the Last 6 Months : float64\n",
      "Delinquencies (Last 2 yrs) : float64\n",
      "Public Records On File : float64\n",
      "Months Since Last Record : float64\n",
      "Amount Requested : float64\n",
      "Total Amount Funded :float64\n",
      "Remaining Principal Funded by Investors :float64\n",
      "Payments To Date (Funded by investors) :float64\n",
      "Remaining Principal :float64\n",
      "Payments To Date :float64\n",
      "Accounts Now Delinquent :float64\n",
      "Delinquent Amount :float64\n",
      "Months Since Last Delinquency :float64\n"
     ]
    }
   ],
   "source": [
    "#all the float64 type of attributes\n",
    "\n",
    "print(\"Monthly PAYMENT : \", end='')\n",
    "print(data['Monthly PAYMENT'].dtype)\n",
    "\n",
    "print(\"Monthly Income : \", end='')\n",
    "print(data['Monthly Income'].dtype)\n",
    "\n",
    "print(\"Open CREDIT Lines : \", end='')\n",
    "print(data['Open CREDIT Lines'].dtype)\n",
    "\n",
    "print(\"Total CREDIT Lines : \", end='')\n",
    "print(data['Total CREDIT Lines'].dtype)\n",
    "\n",
    "print(\"Revolving CREDIT Balance : \", end='')\n",
    "print(data['Revolving CREDIT Balance'].dtype)\n",
    "\n",
    "print(\"Inquiries in the Last 6 Months : \", end='')\n",
    "print(data['Inquiries in the Last 6 Months'].dtype)\n",
    "\n",
    "print(\"Delinquencies (Last 2 yrs) : \", end='')\n",
    "print(data['Delinquencies (Last 2 yrs)'].dtype)\n",
    "\n",
    "print(\"Public Records On File : \", end='')\n",
    "print(data['Public Records On File'].dtype)\n",
    "\n",
    "print(\"Months Since Last Record : \", end='')\n",
    "print(data['Months Since Last Record'].dtype)\n",
    "\n",
    "print(\"Amount Requested : \", end='')\n",
    "print(data['Amount Requested'].dtype)\n",
    "\n",
    "print(\"Total Amount Funded :\", end='')\n",
    "print(data['Total Amount Funded'].dtype)\n",
    "\n",
    "print(\"Remaining Principal Funded by Investors :\", end='')\n",
    "print(data['Remaining Principal Funded by Investors'].dtype)\n",
    "\n",
    "print(\"Payments To Date (Funded by investors) :\", end='')\n",
    "print(data['Payments To Date (Funded by investors)'].dtype)\n",
    "\n",
    "print(\"Remaining Principal :\", end='')\n",
    "print(data['Remaining Principal '].dtype)\n",
    "\n",
    "print(\"Payments To Date :\", end='')\n",
    "print(data[' Payments To Date'].dtype)\n",
    "\n",
    "print(\"Accounts Now Delinquent :\", end='')\n",
    "print(data['Accounts Now Delinquent'].dtype)\n",
    "\n",
    "print(\"Delinquent Amount :\", end='')\n",
    "print(data['Delinquent Amount'].dtype)\n",
    "\n",
    "print(\"Months Since Last Delinquency :\", end='')\n",
    "print(data['Months Since Last Delinquency'].dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Date type attributes\n",
    "\n",
    "## converting date to numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample values in the attribute - Earliest CREDIT Line\n",
      "\n",
      "0       2003-10-27\n",
      "1       1988-11-19\n",
      "2       1998-10-28\n",
      "3       1986-12-30\n",
      "4       1990-11-11\n",
      "           ...    \n",
      "1995    1991-04-20\n",
      "1996    1994-05-04\n",
      "1997    1990-08-23\n",
      "1998    1995-04-29\n",
      "1999    1996-11-19\n",
      "Name: Earliest CREDIT Line, Length: 1968, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#display samples of the \"Earliest CREDIT Line\" attribute\n",
    "\n",
    "print(\"Sample values in the attribute - Earliest CREDIT Line\\n\")\n",
    "\n",
    "print(data['Earliest CREDIT Line'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting \"Earliest CREDIT Line\" attribute to  Python Date time object\n",
    "data['Earliest CREDIT Line'] = pd.to_datetime(data['Earliest CREDIT Line'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Earliest CREDIT Line'].isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Finding median of day, month and year\n",
    "dd=int(data['Earliest CREDIT Line'].dt.day.median())\n",
    "mm=int(data['Earliest CREDIT Line'].dt.month.median())\n",
    "yy=int(data['Earliest CREDIT Line'].dt.year.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median of day is :  15\n",
      "Median of month is :  7\n",
      "Median of year is :  1996\n"
     ]
    }
   ],
   "source": [
    "#Printing median of day, month and year\n",
    "print('Median of day is : ', dd)\n",
    "print('Median of month is : ', mm)\n",
    "print('Median of year is : ', yy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining the median of day, month and year to a median date\n",
    "d = dt.datetime(yy,mm,dd)\n",
    "date = d.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median date is :  1996-07-15\n"
     ]
    }
   ],
   "source": [
    "print('Median date is : ',date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing null values with median date\n",
    "data['Earliest CREDIT Line'] = data['Earliest CREDIT Line'].fillna(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Earliest CREDIT Line'].isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#conversion of other date-type of attributes\n",
    "#replacing null values with median\n",
    "\n",
    "data['Application Date'] = pd.to_datetime(data['Application Date'])\n",
    "dd=int(data['Application Date'].dt.day.median())\n",
    "mm=int(data['Application Date'].dt.month.median())\n",
    "yy=int(data['Application Date'].dt.year.median())\n",
    "d = dt.datetime(yy,mm,dd)\n",
    "date = d.date()\n",
    "data['Application Date'] = data['Application Date'].fillna(date)\n",
    "\n",
    "\n",
    "data['Application Expiration Date'] = pd.to_datetime(data['Application Expiration Date'])\n",
    "dd=int(data['Application Expiration Date'].dt.day.median())\n",
    "mm=int(data['Application Expiration Date'].dt.month.median())\n",
    "yy=int(data['Application Expiration Date'].dt.year.median())\n",
    "d = dt.datetime(yy,mm,dd)\n",
    "date = d.date()\n",
    "data['Application Expiration Date'] = data['Application Expiration Date'].fillna(date)\n",
    "\n",
    "\n",
    "data['Issued Date'] = pd.to_datetime(data['Issued Date'])\n",
    "dd=int(data['Issued Date'].dt.day.median())\n",
    "mm=int(data['Issued Date'].dt.month.median())\n",
    "yy=int(data['Issued Date'].dt.year.median())\n",
    "d = dt.datetime(yy,mm,dd)\n",
    "date = d.date()\n",
    "data['Issued Date'] = data['Issued Date'].fillna(date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Percentage attributes\n",
    "\n",
    "## converting string values to numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample values in the attribute - Interest Rate\n",
      "\n",
      "0       13.75%\n",
      "1        7.43%\n",
      "2        8.07%\n",
      "3       10.28%\n",
      "4        8.07%\n",
      "         ...  \n",
      "1995    11.58%\n",
      "1996    11.26%\n",
      "1997    14.11%\n",
      "1998    15.68%\n",
      "1999     7.68%\n",
      "Name: Interest Rate, Length: 1968, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#display sample values from the attribute - interest rate\n",
    "\n",
    "print(\"Sample values in the attribute - Interest Rate\\n\")\n",
    "print(data['Interest Rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample values in the attribute - Interest Rate, after processing\n",
      "\n",
      "0       13.75\n",
      "1        7.43\n",
      "2        8.07\n",
      "3       10.28\n",
      "4        8.07\n",
      "        ...  \n",
      "1995    11.58\n",
      "1996    11.26\n",
      "1997    14.11\n",
      "1998    15.68\n",
      "1999     7.68\n",
      "Name: Interest Rate, Length: 1968, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#remove the '%' character from the string values of the attribute\n",
    "data['Interest Rate'] = data['Interest Rate'].replace({'%':''}, regex = True)\n",
    "\n",
    "#convert the attribute values to float\n",
    "data['Interest Rate'] = data['Interest Rate'].apply(lambda x:float(x))\n",
    "\n",
    "#display the new values of attribute\n",
    "print(\"Sample values in the attribute - Interest Rate, after processing\\n\")\n",
    "print(data['Interest Rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#processing of similar attributes with % at the end of the values\n",
    "\n",
    "data['Debt-To-Income Ratio'] = data['Debt-To-Income Ratio'].replace({'%':''}, regex = True)\n",
    "data['Debt-To-Income Ratio'] = data['Debt-To-Income Ratio'].apply(lambda x:float(x))\n",
    "\n",
    "data['Revolving Line Utilization'] = data['Revolving Line Utilization'].replace({'%':''}, regex = True)\n",
    "data['Revolving Line Utilization'] = data['Revolving Line Utilization'].apply(lambda x:float(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attributes with redundant information\n",
    "\n",
    "## removing the ' months' part from the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample values in the attribute - Loan Length\n",
      "\n",
      "0       36 months\n",
      "1       36 months\n",
      "2       36 months\n",
      "3       36 months\n",
      "4       36 months\n",
      "          ...    \n",
      "1995    36 months\n",
      "1996    36 months\n",
      "1997    36 months\n",
      "1998    36 months\n",
      "1999    36 months\n",
      "Name: Loan Length, Length: 1968, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#display sample values from the attribute\n",
    "print(\"Sample values in the attribute - Loan Length\\n\")\n",
    "print(data['Loan Length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample values in the attribute - Loan Length, after processing\n",
      "\n",
      "0       36\n",
      "1       36\n",
      "2       36\n",
      "3       36\n",
      "4       36\n",
      "        ..\n",
      "1995    36\n",
      "1996    36\n",
      "1997    36\n",
      "1998    36\n",
      "1999    36\n",
      "Name: Loan Length, Length: 1968, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#remove the ' month' part from the string values\n",
    "data['Loan Length'] = data['Loan Length'].replace({' months':''}, regex = True)\n",
    "\n",
    "#convert the strings into integer type\n",
    "data['Loan Length'] = data['Loan Length'].apply(lambda x:int(x))\n",
    "\n",
    "#display the processed values\n",
    "print(\"Sample values in the attribute - Loan Length, after processing\\n\")\n",
    "print(data['Loan Length'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical Attributes\n",
    "\n",
    "## mapping to class numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['E2' 'A2' 'A4' 'C1' 'B1' 'C4' 'B5' 'B3' 'A5' 'A3' 'B2' 'C2' 'F4' 'C5'\n",
      " 'E1' 'D3' 'B4' 'D1' 'F1' 'D4' 'C3' 'D2' 'F2' 'A1' 'E4' 'F3' 'E5' 'D5'\n",
      " 'E3' 'G5' 'F5' 'G3']\n"
     ]
    }
   ],
   "source": [
    "#rename the attribute\n",
    "data.rename(columns = {'CREDIT Grade':'CREDIT_Grade'}, inplace = True)\n",
    "print(data.CREDIT_Grade.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample values in the attribute - CREDIT_Grade\n",
      "\n",
      "0       E2\n",
      "1       A2\n",
      "2       A4\n",
      "3       C1\n",
      "4       A4\n",
      "        ..\n",
      "1995    B3\n",
      "1996    B2\n",
      "1997    D1\n",
      "1998    E1\n",
      "1999    A2\n",
      "Name: CREDIT_Grade, Length: 1968, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#display sample values of the attribute\n",
    "print(\"Sample values in the attribute - CREDIT_Grade\\n\")\n",
    "print(data['CREDIT_Grade'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample values in the attribute - CREDIT_Grade after processing\n",
      "\n",
      "['E' 'A' 'C' 'B' 'F' 'D' 'G']\n"
     ]
    }
   ],
   "source": [
    "#map the attribute values to correspondng numbers\n",
    "\n",
    "data['CREDIT_Grade'] = data['CREDIT_Grade'].map({'A1':'A','A2':'A','A3':'A','A4':'A','A5':'A',\n",
    "                                                             'B1':'B','B2':'B','B3':'B','B4':'B','B5':'B',\n",
    "                                                             'C1':'C','C2':'C','C3':'C','C4':'C','C5':'C',\n",
    "                                                             'D1':'D','D2':'D','D3':'D','D4':'D','D5':'D',\n",
    "                                                             'E1':'E','E2':'E','E3':'E','E4':'E','E5':'E',\n",
    "                                                             'F1':'F','F2':'F','F3':'F','F4':'F','F5':'F',\n",
    "                                                             'G1':'G','G2':'G','G3':'G','G4':'G','G5':'G'})\n",
    "print(\"Sample values in the attribute - CREDIT_Grade after processing\\n\")\n",
    "print(data.CREDIT_Grade.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display unique values of the attribute : Home_Ownership\n",
      "['OWN' 'MORTGAGE' 'RENT' 'NONE' 'ANY']\n"
     ]
    }
   ],
   "source": [
    "#renaming the attribute\n",
    "data.rename(columns = {'Home Ownership':'Home_Ownership'}, inplace = True)\n",
    "print(\"Display unique values of the attribute : Home_Ownership\")\n",
    "print(data.Home_Ownership.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display unique values of the attribute : Home_Ownership\n",
      "[1 2 3 5 4]\n"
     ]
    }
   ],
   "source": [
    "# mapping the attribute values to numbers\n",
    "data[\"Home_Ownership\"] = data[\"Home_Ownership\"].map({\"MORTGAGE\":2,\"RENT\":3,\"OWN\":1,\"NONE\":5,\"ANY\":4})\n",
    "print(\"Display unique values of the attribute : Home_Ownership\")\n",
    "print(data.Home_Ownership.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display unique values of the attribute : FICO_Range\n",
      "['660-678' '780+' '714-749' '679-713' '750-779' nan]\n"
     ]
    }
   ],
   "source": [
    "#rename the attribute\n",
    "data.rename(columns = {'FICO Range':'FICO_Range'}, inplace = True)\n",
    "\n",
    "#print unique values of the attribute\n",
    "print(\"Display unique values of the attribute : FICO_Range\")\n",
    "print(data.FICO_Range.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display unique values of the attribute : FICO_Range after mapping :\n",
      "[ 5.  1.  3.  4.  2. nan]\n",
      "\n",
      "\n",
      "Display unique values of the attribute : FICO_Range after filling nan\n",
      "[5. 1. 3. 4. 2.]\n"
     ]
    }
   ],
   "source": [
    "#map the categories of the attributes to numbers\n",
    "data[\"FICO_Range\"] = data[\"FICO_Range\"].map({\"640-659\":6,\"660-678\":5,\"679-713\":4,\"714-749\":3,\"750-779\":2,\"780+\":1})\n",
    "\n",
    "#print the unique values after mapping\n",
    "print(\"Display unique values of the attribute : FICO_Range after mapping :\")\n",
    "print(data.FICO_Range.unique())\n",
    "\n",
    "#replace the nan values with 3\n",
    "data['FICO_Range'] = data['FICO_Range'].fillna(3)\n",
    "\n",
    "#display the processed values\n",
    "print(\"\\n\\nDisplay unique values of the attribute : FICO_Range after filling nan\")\n",
    "print(data.FICO_Range.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display unique values of the attribute : Education\n",
      "\n",
      "nan\n",
      "Youngstown State University\n",
      "University of Connecticut\n",
      "Rochester Institute of Technology (RIT)\n",
      "Penn State-University Park\n",
      "Missouri State University\n",
      "Columbia University in the City of New York,Harvard University\n",
      "Aquinas College\n",
      "Western Washington University\n",
      "Georgia Institute of Technology\n",
      "Newbury College-Brookline\n",
      "Prince Georges Community College\n",
      "Vocatonal Electrical School\n",
      "Ashworth University\n",
      "MIT (Massachusetts Institute of Technology)\n",
      "Rhode Island College,Community College of Rhode Island\n",
      "Colorado State University (CSU)\n",
      "University of Phoenix-Online Campus and Howard Community College\n",
      "Mount Saint Mary College\n",
      "Western Carolina University\n",
      "University of South Carolina\n",
      "University of Kansas Main Campus\n",
      "The University of Alabama (UA),DCH School of Radiologic Technology\n",
      "Academy of Health Sciences San Antonio, TX\n",
      "New york school of Locksmithing and Alarms\n",
      "Rutgers University at New Brunswick/Piscataway\n",
      "Rutgers College - New Brunswick, New Jersey,Stevens Institute of Technology - Hoboken, New Jersey\n",
      "North Carolina State University at Raleigh,Tennessee Technological University\n",
      "Bentley College\n",
      "Wesleyan University,University of Massachusetts at Boston\n",
      "Auburn University\n",
      "Pacific Lutheran University\n",
      "college kachkatte\n",
      "Dillard University\n",
      "University of Maryland-University College (UMUC)\n",
      "New York University (NYU)\n",
      "Utah Valley State College\n",
      "Troy University at Troy\n",
      "Clemson University,Charleston Southern University\n",
      "Florida State University\n",
      "California State University Fullerton\n",
      "Boston College\n",
      "Penn State University\n",
      "Forsyth Technical Community College\n",
      "The Ohio State University\n",
      "Michigan State University,University of Washington at Seattle (UW Seattle),University of Rhode Island (URI)\n",
      "University of South Carolina-Columbia (USC)\n",
      "Northeastern University\n",
      "Florida State University \n",
      "Avation Institute of Maintenance\n",
      "Carnegie Mellon University\n",
      "University of Southern Mississippi\n",
      "University of Delaware\n",
      "Southeastern Louisiana University\n",
      "Art Instititute of Philadelphia,University of St. Thomas at Houston,St. Clair County Community College\n",
      "James Madison University\n",
      "Pennsylvania State University-Main Campus\n",
      "University of North Florida,CORRECTION TO LOAN DESCRIPTION: I need to *begin* buying...\n",
      "University of Washington at Seattle,Indiana University\n",
      "Los Angeles Mission College\n",
      "Cornell University\n",
      "University of Richmond\n",
      "university of humaine and sociol scince tunis 3\n",
      "Martin Community College\n",
      "Florida Community College at Jacksonville\n",
      "SUNY Alfred,SUNY Oswego\n",
      "Heald College at Concord,Silicon Valley College\n",
      "Ball State University\n",
      "University of Nebraska-Lincoln,Creighton University\n",
      "Arizona State University\n",
      "Tri County Technical School\n",
      "University of South Carolina-Columbia (USC),University of North Texas\n",
      "Auburn University Main Campus\n",
      "West Chester University of Pennsylvania\n",
      "New York Institute of Technology-Old Westbury\n",
      "Alabama State University,DePaul University\n",
      "Miami Dade College,FIU\n",
      "Grand Rapids Community College\n",
      "University of Rochester\n",
      "University of North Carolina at Charlotte (UNC),Central Piedmont Community College\n",
      "University of Louisville\n",
      "slcc,weber\n",
      "Quinnipiac University\n",
      "The College of New Jersey\n",
      "Nassau Community College NY,SUNY OF NEW YORK\n",
      "Louisiana State University and Agricultural & Mechanical College (LSU)\n",
      "Mercer County Community College\n",
      "University of Florida\n",
      "sun prairie truck driving school\n",
      "Kent State University-Kent Campus\n",
      "University at Albany,University at Buffalo\n",
      "University of Washington at Seattle\n",
      "jefferson community college\n",
      "Miami Dade College\n",
      "Johnson & Wales University-Providence,Ramapo College of New Jersey\n",
      "Mayaguez Campus, (CAAM) - University of Puerto Rico\n",
      "Buffalo State\n",
      "unc wilmington\n",
      "University of Central Florida\n",
      "JCC\n",
      "Ivy Tech Community College\n",
      "Northwestern University, Boston University\n",
      "Monroe Community College\n",
      "The University of Texas at San Antonio\n",
      "Brevard Community College\n",
      "Indiana University\n",
      "Queens College\n",
      "none\n",
      "Indiana University-Bloomington,Indiana University-South Bend,Goshen College\n",
      "Maryland\n",
      "William Paterson University of New Jersey,School of Audio Engineering, NYC\n",
      "Stevens Institute of Technology\n",
      "University of California-Irvine (UCI)\n",
      "University of California-Berkeley (Cal UC Berkeley),Stanford University,University of California-Santa Barbara (UCSB),University of Phoenix\n",
      "The University of Chicago\n",
      "Certified Automobile Engineer\n",
      "Everrett Community College,Pamlico Community College\n",
      "Indian River Community College,University of West Alabama\n",
      "University of New Mexico-Main Campus\n",
      "North Carolina A&T State University,Touro University International\n",
      "Kenyon College,George School H.S.\n",
      "San Jacinto College District\n",
      "Cal Poly Pomona\n",
      "National Hotel College\n",
      "Western Kentucky University\n",
      "University of Colorado at Boulder\n",
      "Kilgore Jr. College\n",
      "DigiPen Institute of Technology\n",
      "California State University-Fullerton (CSUF),Golden West College\n",
      "DeAnza, San Diego State\n",
      "Howard University\n",
      "Oregon State University,University of Oregon,University of Hawaii at Manoa\n",
      "University of Colorado\n",
      "NONE\n",
      "Pennsylvania State University-Penn State Erie-Behrend College\n",
      "Pennsylvania State University-Penn State Erie-Behrend College,Mercyhurst College\n",
      "Oklahoma State University-Main Campus\n",
      "University of Mary Hardin Baylor\n",
      "Akron University,Florida Community College\n",
      "UW-Washington County,Fox Valley Technical College,Moraine Park Technical College\n",
      "Southern Oregon University,Allied Medical X-ray Institute,Boulder College of Massage Therapy,Ft Meade Defense Information School,New Hampshire Community Technical College-Berlin/Laconia\n",
      "Flagler College\n",
      "Community College of Denver\n",
      "University of Arkansas at Fayetteville\n",
      "California State University-Los Angeles (CSULA)\n",
      "University of Wisconsin-Milwaukee\n",
      "Canisius College\n",
      "wesleyan university philippines\n",
      "MATC, WCTC\n",
      "Cal State LA\n",
      "Western Conneccticut State University,University of Connecticut\n",
      "University of Mary Hardin-Baylor\n",
      "University of Massachusetts-Lowell,Suffolk University,Middlesex Community College-Bedford\n",
      "Ferris State University\n",
      "The University of Texas at Austin\n",
      "University of New Orleans, B.S. and M.S. in Civil/Environmental Engineering\n",
      "Arizona State\n",
      "National Systems Contractors Association University\n",
      "young memorial vocational college,ITT Technical Institute\n",
      "Los Angeles Community College\n",
      "NYCCT\n",
      "Bryant University\n",
      "Central Lakes College-Brainerd,Mesabi Range Community and Technical College\n",
      "Georgetown University,University of Manchester (UK)\n",
      "The Art Institute of Washington,Thames Valley University,Excelsior College\n",
      "Mary Washington University,Averette University\n",
      "The College of Saint Rose\n",
      "currently Foley-Belsaw Institute\n",
      "Carroll Community College\n",
      "Yale University\n",
      "Broome Community College\n",
      "North Carolina State University at Raleigh,Duke University\n",
      "Orange County Community College\n",
      "Diablo Valley College\n",
      "University of South Floirda\n",
      "UC Davis\n",
      "Ohio University-Main Campus\n",
      "University of Colorado at Boulder,Johnson & Wales University-Denver\n",
      "Gloucester County College\n",
      "GW University (Two Degrees)\n",
      "Tarrant County College District\n",
      "Stanford University\n",
      "The University of Texas at El Paso\n",
      "Vermont Technical College, Castleton State College\n",
      "crafton hills college\n",
      "Brown University,University of Illinois at Urbana-Champaign\n",
      "Columbia College Chicago\n",
      "University at Buffalo\n",
      "Solano Community College\n",
      "Owens Community College\n",
      "Bladwin-Wallace College\n",
      "University of Washington at Seattle,Western Washington University,Indiana University-Bloomington\n",
      "ITT Technical Institute\n",
      "Bowling Green State University-Main Campus,University of Toledo\n",
      "Eastern Connecticut State University\n",
      "Cayuga County Community College\n",
      "SUNY College at Cortland,SUNY Cobleskill\n",
      "University of Pittsburgh-Main Campus,University of Vermont (UVM),Millersville University of Pennsylvania\n",
      "Hofstra University\n",
      "Norwalk cc Norwalk Ct.\n",
      "Cornell College,Kirkwood Community College\n",
      "The University of Texas at Arlington\n",
      "University of SD and University of Minn\n",
      "Clinton Community College\n",
      "New York University\n",
      "U of R\n",
      "San Diego State University-Main Campus\n",
      "University of Massachusetts at Amherst\n",
      "UMaine\n",
      "Iona College,,City University of London\n",
      "Salisbury University,Shepherd College\n",
      "Texas State University\n",
      "Iona Colleg\n",
      "Clemson University,The University of Alabama at Birmingham,University of Florida\n",
      "Colorado State University (CSU),Western State College of Colorado\n",
      "university of Minnesota\n",
      "UCSD, Portland community college, Orange coast College\n",
      "Kaplan University\n",
      "Sam Houston State University,University of New Mexico-Main Campus\n",
      "CUNY Hunter College\n",
      "University of Minnesota-Twin Cities\n",
      "ventura college, professional school\n",
      "northwestern bible\n",
      "Excelsior College\n",
      "Baptist Bible College and Graduate School,Tyndale Theological Seminary\n",
      "Bates College\n",
      "Moorehead State University\n",
      "University of Zambia,Fort Valley State University\n",
      "University of California, Irvine,Brussels School of Int'l Studies, University of Kent\n",
      "American Academy of Dramatic Arts\n",
      "Florida Atlantic University\n",
      "Wolmers School for Girls, Kingston, Jamaica\n",
      "kingsbrough community\n",
      "Northern Arizona University,Central Arizona College\n",
      "massasoitt college\n",
      "University of Phoenix-Online Campus\n",
      "Boston University\n",
      "University Of Maryland Europe Campus\n",
      "Kentucky State University,Kellogg School of Management - Northwestern University\n",
      "Cleveland State University\n",
      "Michigan State University\n",
      "Colorado State University at Pueblo (CSU)\n",
      "Pepperdine University,University of California-Riverside (UCR)\n",
      "CLST\n",
      "Central Piedmont Community College\n",
      "University of Missouri Kansas City,Kansas University Regents Center\n",
      "Rochester Institute of Technology\n",
      "University of New Orleans\n",
      "Georgia Southern University,Valdosta State University\n",
      "CUNY Lehman College\n",
      "SYmboisis Institute Of Business Management (UK)\n",
      "columbia university\n",
      "Florida Institute of Technology at Melbourne,The University of Texas at El Paso\n",
      "University of Utah\n",
      "Pierce College at Fort Steilacoom,Jackson State University,Ashworth College University\n",
      "Houston Community College System\n",
      "V.C.U.\n",
      "Elmhurst College\n",
      "Morebouse College,Long Island University-Rockland Campus\n",
      "New York Polytechnic University\n",
      "College of Commerce- Business College\n",
      "Hampden-Sydney College\n",
      "University of San Francisco (USF),De Anza College\n",
      "Midwestern State University\n",
      "California State University- Northridge\n",
      "County College of Morris,The Chubb Institute-Parsippany\n",
      "Rice University\n",
      "University of California-Irvine (UCI),Saddleback College\n",
      "Dunwoody College of Technology,Hennepin Technical College\n",
      "Stephen F Austin State University\n",
      "New Hampshire Technical College\n",
      "Copper Center Community College\n",
      "Columbia College\n",
      "University of Florida - BSBA - CIS\n",
      "University of Florida BSBA 1984 - CIS,University of Florida - Graduate School CIS\n",
      "University of Maryland, Baltimore County\n",
      "University of Hartford\n",
      "Los Angeles Valley College\n",
      "Texas A&M University\n",
      "University of Illinois,Southern Illinois University at Carbondale\n",
      "Northern Arizona University,International Coaching Academy\n",
      "Iona College\n",
      "Bob Jones University,Cleveland State University,Lakeland Community College\n",
      "DeVry University-Illinois\n",
      "University of Hawaii at Manoa,Leeward Community College\n",
      "Stony Brook University\n",
      "Earlham College\n",
      "Spokane Community College\n",
      "Macomb Community College\n",
      "Northern Illinois University\n",
      "University of Notre Dame\n",
      "Blue Mountain Community Collegeinternal\n",
      "UNC-Wilminton/Fayetteville Technical Community College/Methodist University\n",
      "Arkansas Tech University\n",
      "Westfield State College\n",
      "University of Michigan\n",
      "University of Washington at Seattle,Eastern Washington University\n",
      "Harvard University\n",
      "University of Virginia (UVA)\n",
      "University of Hawaii\n",
      "San Diego State\n",
      "UALR, South Central Career College, National Real Estate School\n",
      "Northern VA C. College\n",
      "Cerritos College,College of the Redwoods,U.S.Navy\n",
      "na\n",
      "Scottsdale Culinary Institute\n",
      "mount san jacinto college\n",
      "University of Colorado Denver\n",
      "Prescott College (BA Ecology and Psychology),Institute of Transpersonal Psychology (Ph.D psychology)\n",
      "Elon University\n",
      "BMCC\n",
      "Northwestern University,University of Phoenix-Online Campus,University of Colorado at Boulder\n",
      "Wenatchee Valley College\n",
      "Saint Johns University\n",
      "Robeson Community College\n",
      "Montgomery College Rockville\n",
      "HUNTER COLLEGE IN BRONX\n",
      "Arizona State University at the West Campus\n",
      "College of DuPage- Pre Med/EMT\n",
      "Los Angeles Pierce College\n",
      "University of Northwestern Ohio\n",
      "Delta College\n",
      "University of California-Riverside (UCR)\n",
      "Johnson and Wales University\n",
      "University of North Carolina at Pembroke (UNC),Bowling Green State University-Main Campus\n",
      "Ursinus College\n",
      "San Joaquin Delta College\n",
      "University of California @ Berkeley\n",
      "UNCW\n",
      "Goldenwest College\n",
      "Seton Hill College\n",
      "Queens University of Charlotte\n",
      "Pennco Tech\n",
      "Frederick Community College,University of Maryland-University College\n",
      "NJIT\n",
      "Metro State College of Denver\n",
      "Skyline College\n",
      "Emory University\n",
      "Winthrop University\n",
      "University of Oklahoma Norman Campus\n",
      "Davidson College,Mercer University\n",
      "Unversity of outh Carolina\n",
      "The Ohio State University,Webster University,The American College\n",
      "Middlebury College\n",
      "Southern Methodist University\n",
      "Grand Valley State University\n",
      "Monroe County Community College\n",
      "University of California-Berkeley (Cal UC Berkeley)\n",
      "St. Olaf, University of Iowa\n",
      "Shepherd College\n",
      "University of Maryland-Baltimore County\n",
      "Baruch College\n",
      "The American University in Cairo\n",
      "California State University-Chico (CSU Chico)\n",
      "Rio Hondo College\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imperial Valley College\n",
      "Georgia State University\n",
      "University of North Carolina at Chapel Hill (UNC)\n",
      "Humboldt State University, UNIVERSITY OF PHOENIX\n",
      "Art Center College of Design\n",
      "University of North Carolina\n",
      "California State University-Fullerton (CSUF)\n",
      "LACC\n",
      "Universal Technical Institute of Illinois\n",
      "palomar college, then ironworker apprenticeship school\n",
      "California State University-Sacramento (CSUS)\n",
      "Becker College,Mount Wachusett Community College\n",
      "Brigham Young University\n",
      "Indian Hills Community College\n",
      "Clarkson University\n",
      "The Culinary Institute of AMerica\n",
      "University of Georgia,Michigan State University,University of Pittsburgh-Main Campus\n",
      "Rhode Island School of Design (RISD)\n",
      "Boise State University\n",
      "University of St. Thomas at Saint Paul\n",
      "Tel-Aviv University\n",
      "Tunxis Community College\n",
      "The University of Tennessee\n",
      "University of Arizona\n",
      "Satter Community College,Elite College\n",
      "Covenant College,Reformed Theological Seminary\n",
      "Kilgore College,The University of Texas at Tyler\n",
      "Computer Education Inst\n",
      "CVCC\n",
      "College of Saint Rose\n",
      "Belmont University\n",
      "Portland State University\n",
      "Hanyang University\n",
      "University of Houston,Dallas Baptist University\n",
      "New Jersey City University\n",
      "California State University-Long Beach (CSULB),Cerritos College,UCLA extension\n",
      "Northern Michigan University,Gogebic Community College\n",
      "AMI\n",
      "Anne Arundel Community College\n",
      "Umass\n",
      "Binghamton University\n",
      "penn state university, cal state polytech & long beach\n",
      "Delmar\n",
      "Sacramento City College\n",
      "CU-Boulder,UW-Milwaukee\n",
      "Russell Sage College,The College of Saint Rose\n",
      "Columbia State Community College,The University of Tennessee\n",
      "Transylvania University\n",
      "Shawnee State University\n",
      "Rockland Community College\n",
      "University of Maryland-College Park\n",
      "CUNY Brooklyn College\n",
      "live oaks technical for cosmetology\n",
      "Baylor University\n",
      "MIT (Massachusetts Institute of Technology),Stanford University\n",
      "Suffolk University\n",
      "California State University-Long Beach (CSULB)\n",
      "University of Houston-Downtown\n",
      "Trade school\n",
      "Univ of San Francisco\n",
      "UMKC\n",
      "st. petersburg college\n",
      "UNC-Chapel Hill\n",
      "Appalachian State University\n",
      "City College of San Francisco\n",
      "Marist College,The New School\n",
      "University of North Texas\n",
      "Drury University, Johns Hopkins University\n",
      "St. Francis College (NY)\n",
      "navy technical schools\n",
      "University of Northern Colorado,Adams State College,University of Colorado at Colorado Springs\n",
      "Marian College\n",
      "James Madison University,Northern Virginia Community College\n",
      "Unversity of Oregon,University of Maryland-College Park\n",
      "Umass Dartmouth\n",
      "doctoral degree candidate\n",
      "Salt Lake Community College\n",
      "Coffeyville Community College and Fort Hays State University\n",
      "West Virginia University,Dowling College\n",
      "Rice University,Cornell University\n",
      "Neumann College\n",
      "University of Maryland-Baltimore County,Case Western Reserve University,MIT (Massachusetts Institute of Technology),Georgia Institute of Technology\n",
      "Oregon State University\n",
      "CUNY York College,Stevens Institue of Technology\n",
      "Florida State University,University of Massachusetts at Amherst\n",
      "University of Phoenix\n",
      "Northeastern University,Eastern Illinois University\n",
      "University of Tennessee\n",
      "University of California-Berkeley (Cal UC Berkeley),Irvine Valley College\n",
      "Louisiana State University and Agricultural & Mechanical College (LSU),Oral Roberts University\n",
      "University of Delaware,University of Phoenix\n",
      "Elim Bible Institute\n",
      "CUNY Brooklyn\n",
      "Indiana Bible College\n",
      "College of the Sequoias,,Porterville College\n",
      "goldey beacom college\n",
      "Adirondack Community College\n",
      "California State University-Long Beach (CSULB)-BS\n",
      "Long Beach City College\n",
      "Middlesex Community College-Bedford,Peterson school\n",
      "DeVry University-California\n",
      "Fordham University\n",
      "Mississippi State University\n",
      "Central Missouri State University\n",
      "Brooks Institute of Photography\n",
      "Wright State University-Main Campus,Sinclair Community College\n",
      "University of Memphis\n",
      "Central Connecticut State University\n",
      "Mandle School of Allied Health New york, NY\n",
      "Lindenwood University\n",
      "Louisiana State University\n",
      "University of Florida,Miami Dade College\n",
      "Brooklyn College\n",
      "School of Hard Knocks\n",
      "Texas A & M University at College Station\n",
      "Gannon University\n",
      "Santa Clara University\n",
      "Plymouth State University\n",
      "Bucknell University,University of Virginia (UVA),University of California-Davis (UC Davis)\n",
      "Swarthmore College,CUNY Hunter College\n",
      "Florida State University,University of North Florida\n",
      "The George Washington University\n",
      "Naugatuck Valley Community College\n",
      "Assumption College\n",
      "Tulane University of Louisiana\n",
      "UNLV\n",
      "Emerson College,Middlesex Community College-Bedford\n",
      "Columbia University in the City of New York\n",
      "Loyola College in Maryland\n",
      "University of Michigan,University of Michigan Law School\n",
      "SUNY Stony Brook,State University of New York at Stony Brook,Univeristy of St. Andrews,St. Andrews\n",
      "Central Methodist University-College of Liberal Arts & Sciences,Missouri State University,Central Missouri State University\n",
      "Bloomsburg University of Pennsylvania\n",
      "Point Loma Nazarene University\n",
      "New Jersey Institute of Technology\n",
      "California State University - Sacramento\n",
      "Princeton University\n",
      "University of Kansas Main Campus,University of Liverpool\n",
      "Laguardia Community College\n",
      "The Art Institute of California in Orange County,Crafton Hills College\n",
      "Southern Oregon University\n",
      "William Rainey Harper College,IBEW Local 9 Electrical Training School\n",
      "Pune University, India\n",
      "CUNY City College,Howard University\n",
      "UCLA\n",
      "Temple University\n",
      "Seton Hall University\n",
      "Evangel University\n",
      "University of South Carolina-Columbia (USC),DePaul University\n",
      "Northwestern University, Evanston, IL\n",
      "University of San Diego,Oxford Univerty\n",
      "High School Graduate, no money to attend college yet.\n",
      "Iowa State University\n",
      "Pennsylvania State University and Waynesburg University\n",
      "University of California-Los Angeles (UCLA)\n",
      "West Minister\n",
      "Indiana University-Bloomington,Loyola University Chicago Law\n",
      "University of Maryland-University College,Eastern University\n",
      "East Carolina University\n",
      "Santa Clara University,Gonzaga University\n",
      "Louisiana State University-System Office (LSU)\n",
      "US Marine Corps\n",
      "Ashford University\n",
      "Rollins College,University of Colorado at Colorado Springs,Southwestern University\n",
      "Erie Community College,UNIVERISTY OF BUFFALO\n",
      "Penn Foster Online certification Course for Medical Billing\n",
      "Southern Utah University\n",
      "South Dakota State University\n",
      "Texas A & M International University,Blinn College,Excelsior College\n",
      "Widener University-Main Campus\n",
      "Baker College Center for Graduate Studies,Kansas State University\n",
      "Cerritos College,Mt. San Antonio College\n",
      "IRONWORKERS APPRENTICESHIP\n",
      "College of the Canyons\n",
      "Princeton University,UMDNJ - Robert Wood Johnson Medical School\n",
      "William Rainey Harper College\n",
      "Community College of Aurora\n",
      "Princeton University,Pace University\n",
      "Western State College of Colorado\n",
      "Chabot College\n",
      "University of North Carolina at Charlotte (UNC)\n",
      "State University of New York at Buffalo School of Civil and Environmental Engineering\n",
      "Pensacola Christian College\n",
      "University of Arizona,Colorado State University (CSU),Glendale Community College\n",
      "Cal State University San Marcos\n",
      "University of Texas at Austin\n",
      "Purdue University (BA),Ball State University (MA)\n",
      "Case Western Reserve University\n",
      "Purdue University-Main Campus\n",
      "Rogers State University\n",
      "Portland Community College\n",
      "Indiana University-Bloomington,Babson College\n",
      "Long Island University\n",
      "Sam Houston State University\n",
      "The Ohio State University,Tiffin University\n",
      "Glendale Community College\n",
      "Fairfield University\n",
      "Chaffey College\n",
      "Los Angeles Southwest College\n",
      "Dekalb Technical College\n",
      "Del Mar College\n",
      "Georgia Tech\n",
      "Santa Rosa Junior College, Pacific Union College\n",
      "Metropolitan State University\n",
      "Tyler School of Art\n",
      "Middle Tennessee State University---Computer Science Major (Senior),Jones County Junior College---AA Degree (Computer Science),Yorktowne Business Institute---Computer Programming (Graduate)\n",
      "University of Illinois at Champaign\n",
      "Kansas University\n",
      "Elmhusrt College\n",
      "SUNY Old Westbury\n",
      "Santa Monica College\n",
      "University of Hawaii at Manoa\n",
      "University of Phoenix-Online Campus,Community College of the Air Force (CCAF)\n",
      "University of North Florida,Florida Community College at Jacksonville,Northern Virginia Community College\n",
      "Medicine School Valencia , Venezuela\n",
      "Indiana University-Purdue University-Indianapolis\n",
      "Smith College\n",
      "tallahassee community college\n",
      "Physical Education and Sports University of Tunisia\n",
      "Shorter College,Kennesaw State University,California State University-Long Beach (CSULB)\n",
      "CHI Institute-Southampton,Eastern Montgomery County Votech Tech School\n",
      "Regent University\n",
      "University of New Hampshire-Main Campus (UNH)\n",
      "Butler Community College,Institute of Computer Technology,APICS Certification\n",
      "The Chubb Institute-White Plains,Binghamton University,Rockland Community College,Stony Brook University,Pennsylvania State University-Ogontz Campus\n",
      "U of MN\n",
      "University of South Alabama\n",
      "North Carolina Central University,Colorado Technical University\n",
      "University of Kentucky\n",
      "Syracuse University\n",
      "American Military University,Montana State University-Bozeman\n",
      "Wheaton College\n",
      "University of Illinois at Chicago\n",
      "Kansas State University\n",
      "SUNY at Buffalo\n",
      "Union County College\n",
      "Lincoln Technical Institute\n",
      "Youngstown State University,University of Pittsburgh\n",
      "Clemson University,Emory University\n",
      "Rochester Community College\n",
      "The Ohio State University,The University of Phoenix\n",
      "Bakersfield College\n",
      "J.Sargent Reynolds, Indiana University\n",
      "University of Puget Sound, Bastyr University\n",
      "Washington State University\n",
      "Oregon Institute of Technology\n",
      "York Technical Institute\n",
      "Omega Institute Paralegal Studies\n",
      "Purdue University, San Francisco State University\n",
      "Grand Canyon University\n",
      "Pensacola Junior College\n",
      "Northern Kentucky University\n",
      "Western Maryland College,Johns Hopkins University,UMBC\n",
      "John Carroll University,University of Michigan\n",
      "The University of Alabama in Huntsville\n",
      "New Hampshire College,University of Massachusetts\n",
      "University of Stonybrook,American University\n",
      "City Colleges of Chicago-Richard J Daley College,College of DuPage\n",
      "St. John's University,St Vincent Catholic Medical Center New York-Brooklyn and Queens\n",
      "Northwestern University,Loyola University School of Law\n",
      "Valdosta Technical College,Georgia Military College-Valdosta Campus\n",
      "Brandeis University\n",
      "Commomwealth College\n",
      "Malden High School\n",
      "U of Minn\n",
      "Lehman College\n",
      "Lee University,Coastal Georgia Community College\n",
      "University of Nevada-Las Vegas\n",
      "Wright State University-Main Campus\n",
      "University of Madras,\n",
      "Alameda Beauty College\n",
      "University of Hartford,Tunxis Community College,Connecticut Culinary Institute\n",
      "LorasCollege\n",
      "university of texas at el paso\n",
      "Bolsa Grande\n",
      "West Virginia University\n",
      "University of Nebraska at Lincoln,University of Illinois at Urbana-Champaign,United States Air Force Academy (USAFA)\n",
      "Louisiana Tech University,Northern Virginia Comm. College,George Masson Univeristy\n",
      "Northern Essex Community College\n",
      "Hofstra University,Life University\n",
      "University of Oregon\n",
      "far eastern university\n",
      "Fairleigh Dickinson University-Metropolitan Campus\n",
      "TCC\n",
      "Indiana University South Bend\n",
      "Delhi University\n",
      "Vassar College\n",
      "Appalachian State University,Coastal Carolina Community College\n",
      "University of San Diego\n",
      "University of California-Santa Barbara (UCSB),College of the Desert,California State University-San Bernardino (CSUSB)\n",
      "Western Oregon University,Chemeketa Community College\n",
      "Mandl College of Allied Health\n",
      "Abilene Christian University\n",
      "Kennesaw State University,Georgia State University,Western Carolina University\n",
      "Penn Foster technical Career School\n",
      "Warner Southern College,Birmingham School of Law\n",
      "Atlantic County Vocational Technical School\n",
      "boston college\n",
      "Augustana College at Sioux Falls\n",
      "University of Michigan,Wayne State University,Stanford University\n",
      "Andres Bello university\n",
      "SUNY Fredonia,Herkimer County Community College\n",
      "Laney Jr. College\n",
      "University of St. Francis\n",
      "University of Virginia\n",
      "University of Denver\n",
      "JMU\n",
      "University of Virginia's College at Wise\n",
      "Los Angeles Trade Technical College,Pasadena City College\n",
      "Soutwest Missouri State University\n",
      "Jackson State University\n",
      "U of M one semester 1993\n",
      "Kansas City Kansas Community College\n",
      "University of Missouri-Columbia\n",
      "University of Wisconsin-Madison,College of William and Mary\n",
      "University of Tennessee, Knoxville, TN\n",
      "Iowa State University,Webster University\n",
      "Chemeketa Community College\n",
      "Pima Community College,University of Phoenix-Online Campus,University of Arizona\n",
      "Rutgers University\n",
      "Kentucky State University\n",
      "Hope College,Scripps Insstitution of Oceanography\n",
      "Blue Mountain Community College,University of Oregon,Southern Oregon State University,Heritage University at Toppenish\n",
      "SOUTHWEST LA COLLEGE\n",
      "El Centro Community College\n",
      "Duke University,DePaul University\n",
      "Meramec Community College\n",
      "Western International University\n",
      "university of cincinnati\n",
      "University of Dayton, Ohio\n",
      "SUNY Oswego\n",
      "Community College of Allegheny County\n",
      "UMASS\n",
      "US Air Force\n",
      "Towson University\n",
      "Old Dominion University\n",
      "mt sac\n",
      "University of Central Oklahoma\n",
      "Pennsylvania State University-Penn State Abington,Temple College\n",
      "Mount Mercy College in Cedar Rapid, Iowa\n",
      "Music Tech\n",
      "None\n",
      "Millersville University\n",
      "University of Toronto\n",
      "George Mason University,George Washington University (GW)\n",
      "Inver Hills Community College\n",
      "UNIVERSITY OF AKRON\n",
      "California State University-East Bay (CSUEB)\n",
      "Western State College, University of Northern Colorado\n",
      "Queens University of Charlotte,Principia Upper School\n",
      "Victor Valley Community College\n",
      "University of Texas at Austin,State University of New York Regents College\n",
      "Mount Union College\n",
      "GEORGIA TECH,CAL STATE LOS ANGELES\n",
      "Manchester College,Northwestern Michigan College\n",
      "University of Central Florida - College of Engineering and Computer Science Bachelor\n",
      "University of Iowa\n",
      "Troy University at Troy,University of Maryland-Baltimore,Central Texas College,Jefferson Community College-Watertown,Austin Peay State University\n",
      "CAL POLY UNIVERSITY\n",
      "Franklin Pierce College\n",
      "Valencia Community College\n",
      "Penn State Erie, The Behrend College\n",
      "Maryland Institute College of Art\n",
      "MTSu\n",
      "Radford University\n",
      "UW-Madison, UA-Fayetteville\n",
      "Unversity of Pittsburgh\n",
      "Chapman University-University College\n",
      "Notre Dame\n",
      "CSI\n",
      "University of East Anglia, UK\n",
      "DePaul University\n",
      "College of the Holy Cross\n",
      "Riverside Community College\n",
      "University of Wisconsin-Eau Claire\n",
      "STRAYER UNIVERSITY AT RALEIGH, NC,American Intercontinental University at Atlanta,VANCE GRANVILLE COMMUNITY COLLEGE AT HENDERSON, NC\n",
      "Ohio State University Agricultural Technical Institute\n",
      "UC Santa Barbara, UC Berkeley\n",
      "University of Mary Washington\n",
      "delware county community college\n",
      "University of Stuttgart\n",
      "D'Youville College\n",
      "GEORGIA TECH,CALIFORNIA STATE UNIVERSITY - LOS ANGELES\n",
      "South Seattle Community College\n",
      "University of Illinois, Urbana-Champaign,Harvard University Graduate School of Design\n",
      "KEAN UNIVERSITY\n",
      "Rhode Island College\n",
      "N.C. State University\n",
      "The College of Wooster\n",
      "Gainesville College\n",
      "Carroll Community College,Hanover Prcatical Nursing School\n",
      "Collage of the Canyons\n",
      "Amarillo College\n",
      "Broward Community College\n",
      "Pennsylvania State University-Main Campus,Montclair State University\n",
      "University of Houston Clear Lake,University of St. Thomas\n",
      "New York Institute of Technology-Old Westbury,Stony Brook University\n",
      "Davenport University,Grand Rapids Community College\n",
      "Mott Community College\n",
      "university of the city of manila\n",
      "college\n",
      "Ouachita Baptist University,John Brown University,Southern Methodist University\n",
      "Texas Tech University\n",
      "Pepperdine University\n",
      "University of Pittsburgh\n",
      "Purdue University\n",
      "Tulsa Community College,Arizona State University\n",
      "Hampton University,University of Michigan\n",
      "George Mason University\n",
      "Rutgers University at Camden\n",
      "University of Cincinnati-Main Campus,Xavier University,University of Phoenix-Online Campus\n",
      "Queens College, CUNY\n",
      "SUNY College at Oswego\n",
      "Oklahoma City Community College,University of Central Oklahoma,Uniformed Services University of the Health Sciences\n",
      "ARCC/Dunnwoody\n",
      "Villanova University\n",
      "New York University,Tisch School of the Arts\n",
      "Indiana University-Bloomington\n",
      "Hillsborough Community College\n",
      "Northern Colorado. Ohio State\n",
      "Wilkes University\n",
      "Academy of Art University\n",
      "Texas State University-San Marcos\n",
      "vol state\n",
      "University of Marylanc University Europe\n",
      "Adelphi University\n",
      "St. Pete College\n",
      "Golden Gate University at San Francisco\n",
      "Virginia Commonwealth University,Washington State University\n",
      "University of Cincinnati-Main Campus,DAAP\n",
      "Audio Institute of America\n",
      "C Sun\n",
      "University of Georgia\n",
      "Calvin College\n",
      "Bucks County Community College\n",
      "Schoolcraft College\n",
      "David Grant Medical Center\n",
      "University of Illinois at Urbana-Champaign,Los Angeles City College\n",
      "University of Rhode Island (URI)\n",
      "MIT (Massachusetts Institute of Technology),Western Governors University,UPR Mayaguez,U. Interamericana\n",
      "Hampton University\n",
      "Santa Monica College,California State University-Northridge (CSUN)\n",
      "University of Oregon,San Francisco State University\n",
      "Wayne State University\n",
      "bmcc\n",
      "Portland Community College,Portland State University\n",
      "University Of Foreign Languages named after Brusov\n",
      "Sullivan County Community College\n",
      "Texas A&M\n",
      "SUNY Albany\n",
      "Texas Wesleyan University\n",
      "Saint Michaels College\n",
      "Johns Hopkins University\n",
      "University of Arizona,Illinois Institute of Technology Chicago-Kent College of Law\n",
      "SOME COLLEGE\n",
      "Clayton State University\n",
      "Texas A & M University at Kingsville,University of Texas- Pan American\n",
      "Texas Woman's University\n",
      "Abilene Christian University,University of Texas\n",
      "Cape Fear Community College\n",
      "University of Maryland University College\n",
      "Tarleton State University\n",
      "University of Montana-Helena College of Technology,Pensacola Junior College\n",
      "Academy of Art\n",
      "Florida Christian College Inc.,Hillsborough Community College\n",
      "Ashland University\n",
      "Northern Virginia Community College\n",
      "Art Institute of Seattle\n",
      "CMC, Alpine Campus\n",
      "Ohlone College\n",
      "Oklahoma State University-Main Campus,Murray State College,Oklahoma State University-Okmulgee\n",
      "Florida International University\n",
      "California State University-Northridge (CSUN)\n",
      "Connecticut College\n",
      "Erie Community College,Saint Bonaventure University\n",
      "St Petersburg College\n",
      "Bryant University,Woodstock Academy\n",
      "Valley Colledge\n",
      "San Jose State University\n",
      "University of Pittsburgh-Main Campus,Naugatuck Valley Community College,Western Connecticut State University\n",
      "UCLA Anderson School of Management\n",
      "California State Polytechnic University-Pomona\n",
      "Univeristy of Phoenix\n",
      "Elgin Community College\n",
      "Columbia University in the City of New York,New York University (NYU)\n",
      "Ohio State University,Kent State University\n",
      "BCCC\n",
      "Pace University-New York\n",
      "Pacific Western University\n",
      "University of Buenos Aires\n",
      "University of California-Davis (UC Davis)\n",
      "De Anza College\n",
      "University of Illinois at Chicago,University of Phoenix\n",
      "Brown Mackie College-Kansas City\n",
      "Central State University\n",
      "Indiana University Kelley School of Business\n",
      "Drexel University Marymount University\n",
      "University of Plymouth\n",
      "South Plains College\n",
      "Hawkeye Community College\n",
      "William Boyd School of Law,University Nevada Las Vegas- Undergrad/MBA\n",
      "St. Leo University\n",
      "NYC Community College\n",
      "San Jose City College\n",
      "Macon State College\n",
      "Full Sail Real World Education\n",
      "Fresno City College\n",
      "University of Utah,University of North Texas\n",
      "U of MN- Duluth\n",
      "Humboldt State University,ITT Technical Institute\n",
      "Western Michigan University\n",
      "University of Virginia (UVA) - Masters of Science,University of Delaware - Bachelor of Science\n",
      "Virginia Western Community College\n",
      "Cleveland State Community College\n",
      "Harvard Law School,Yale University\n",
      "Anne Arundel Community College,Ashworth University,Vermont College of Norwich Unversity\n",
      "Gloucester County College,Camden County College,University of Phoenix\n",
      "local154 trade school\n",
      "Drexel University\n",
      "del tech community college\n",
      "Kent State University - Tuscarawas Campus\n",
      "Cal State Fullerton\n",
      "Tompkins-Cortland Community College\n",
      "Nassau Community College\n",
      "Quinsigamond Community College\n",
      "Central Connecticut State University,Trinity College\n",
      "Frederick Community College\n",
      "Montclair State University\n",
      "College of the Sequoias\n",
      "The Pennsylvania State University\n",
      "University of Maryland-European Division\n",
      "CSUN\n",
      "University of Akron Main Campus\n",
      "El Dorado Center - Placerville, CA,Sierra College - Rocklin, CA\n",
      "Baltimore School of Massage\n",
      "Indiana University-Northwest,Purdue University-Calumet Campus,University of Missouri-Columbia\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brown College\n",
      "Penn state University - University Park\n",
      "Northern Michigan University\n",
      "REC Kurukshetra, India\n",
      "Sauk Valley Community College,Eastern Illinois University\n",
      "Florida College,Monmouth College,University of Colorado at Colorado Springs\n",
      "Eastern Illinois University,University of Illinois at Urbana-Champaign,Northern Illinois University,Aurora University,Rockford College\n",
      "Lander University,University of Colorado at Boulder,Front Range Community College\n",
      "Midwestern State University,The University of Texas at Austin\n",
      "SUNY College of Technology at Alfred\n",
      "Ottawa University-Milwaukee,Techskills.com\n",
      "Santa Barbara City College\n",
      "Youngstown State University, Stanford University\n",
      "Ozarks Technical Community College\n",
      "University of Cincinnati-Main Campus\n",
      "University of Dayton\n",
      "Virginia Polytechnic Institute and State University (Virginia Tech)\n",
      "University of Houston-Victoria\n",
      "Monash University\n",
      "Broward Community College,Union Institute & University\n",
      "Morehead State University\n",
      "Lander University,University of South Carolina-Columbia (USC)\n",
      "Baker University College of Arts and Sciences,University of Kansas Main Campus\n",
      "El Camino College Compton Center\n",
      "St Francis College\n",
      "Art Institute of Philadelphia\n",
      "Montclair Atate University\n",
      "nathaniel hawthorne college,johnson state coolege\n",
      "Park University\n",
      "De Anza College,University of California-Santa Cruz (UCSC),Golden Gate University School of Law\n",
      "Caribbean University-Bayamon\n",
      "Horry Georgetown Technical College\n",
      "Heartland Community College,University of Illinois at Urbana-Champaign\n",
      "Wright College\n",
      "Anne Arundel Community College,University of Maryland-Baltimore County\n",
      "Howard Community College\n",
      "South University\n",
      "Hamburger University, (McDonald's Corp)\n",
      "University of Illinois-Urbana/Champaign\n",
      "Ocean County College\n",
      "Worcester Polytechnic Institute\n",
      "Richland Community College,Arizona State University,Texas A & M University at Commerce\n",
      "Clemson University\n",
      "California Polytechnic State University (CalPoly)\n",
      "Ohio University-Chillicothe Campus,Ohio State School of Dentistry\n",
      "San Francisco State University\n",
      "Wellesley College\n",
      "University of Southern California (USC) Marshall School of Business\n",
      "Boston College,California Polytechnic State University (CalPoly)\n",
      "Emporia State University\n",
      "Master Insitute of Technology\n",
      "University of Kansas,University of Missouri - Kansas City,Lesley University\n",
      "university of wisconsin - green bay\n",
      "TCI - College of Technology\n",
      "slcc\n",
      "Clackamas Community College,ITT Technical Institute\n",
      "Everett Community College\n",
      "University of Houston-Clear Lake\n",
      "Roger Williams University\n",
      "Occidental College\n",
      "Ohio State University-Main Campus,Franklin University,Columbus State Community College\n",
      "University of California, San Diego\n",
      "Houston Baptist University\n",
      "Randolph-Macon College\n",
      "SUNY College of Technology at Canton\n",
      "University of Illinois at Urbana-Champaign\n",
      "Regis University\n",
      "CUNY Bernard M Baruch College\n",
      "University of California-Santa Barbara (UCSB)\n",
      "Ohio University-Main Campus,Kent State University-Ashtabula Campus,Youngstown State University\n",
      "cal state long beach\n",
      "Sonoma State University\n",
      "Palmer College of Chiropractic\n",
      "University of Texas-Austin\n",
      "Cal State Fullerton College\n",
      "New Hampshire Technical Institute,Boston University\n",
      "Collins College\n",
      "Aquinas College in Grand Rapids,Oakland Community College\n",
      "Indiana University Kelley School of Business,Butler University,Weber State University\n",
      "suffolk community college\n",
      "Staten Island CUNY\n",
      "Henderson State University,Community College of the Air Force (CCAF),Mississippi Gulf Coast Community College\n",
      "West Valley College\n",
      "Petaluma College PTA\n",
      "Eastern Illinois University\n",
      "Butte College\n",
      "University of Costa Rica\n",
      "University of Arkansas at Fayetteville,Xavier Labor Relations Institute,Delhi University\n",
      "University of California-Davis (UC Davis), NIU\n",
      "University of Duesseldorf,University of Konstanz\n",
      "California State University-Fresno (CSU Fresno),AIU\n",
      "Western Michigan University, BA,University of Michigan, MA,University of California, Berkeley, MBA\n",
      "Patrick Henry Community College\n",
      "Fashion Institute of Design & Merchandising in Los Angeles (FIDM)\n",
      "Georgia State University,University of Northern Colorado\n",
      "Christopher Newport University,Virginia Western Community College,Central Virginia Community College\n",
      "Athens Technical College\n",
      "Ohio University\n",
      "Vassar College,Oregon State University\n",
      "Eastern Michigan University\n",
      "University of California San Francisco\n",
      "Platt College-Aurora\n",
      "Millersville University of Pennsylvania\n",
      "OHIO INSTITUTE OF TECHNOLOGY (1975/76)\n",
      "glendale community college\n",
      "VCU\n",
      "TUI University\n",
      "Arizona State University,Northern Arizona University\n",
      "UNIVERSITY OF ILOILO\n",
      "George Mason University,University of Maine\n",
      "Colorado Technical University\n",
      "Oklahoma Baptist University\n",
      "Merced College,California State University-Fresno (CSU Fresno)\n",
      "James A Rhodes State College,Wright State University-Lake Campus\n",
      "Strayer University at Washington, DC,MTI College of Business and Technology,Kaplan University,American River College\n",
      "Montana State University-College of Technology-Great Falls,Montana State University-Northern\n",
      "Community College of Denver North Campus\n",
      "Fasion Institute of Technology\n",
      "University of Texas at Austin School of Law\n",
      "US Air Force Academy\n",
      "17 hours short of graduation from University of North Texas\n",
      "Utah State University\n",
      "NORTHWOOD UNIVERSITY\n",
      "The University of Alabama\n",
      "insurance broker 46 states\n",
      "Southern Maine Technical College\n",
      "University of Northern Colorado\n",
      "Georgetown, Harvard, UC Santa Barbara\n",
      "Denison University\n",
      "Metropolitan State University,Itasca Community College\n",
      "Drury University\n",
      "Erie Community College,Excelsior College\n",
      "California State University-Los Angeles (CSULA),Pasadena City College\n",
      "University of North Carolina at Greensboro\n",
      "San Jose State University,Sierra College\n",
      "Lakeland Community College\n",
      "University of Texas/Pan American,Texas A & M University at Kingsville\n",
      "Pennsylvania State University\n",
      "Cisco Systems Networking School,29 Palms Marine Corps Communications School\n",
      "Los Angeles City College,El Camino College Torrance,College of Marin,CEI\n",
      "Salt Lake Committe College\n",
      "Bemidji State University\n",
      "University of California-San Diego (UCSD)\n",
      "Cameron University\n",
      "University of the Sciences in Philadelphia,University of New Orleans,Loyola University New Orleans,Tulane University of Louisiana\n",
      "San Francisco State University,Central Connecticut State University,University of Connecticut\n",
      "Trinity Bible College\n",
      "CUNY John Jay College Criminal Justice,Marist College\n",
      "University of California-Irvine (UCI),University of Missouri-Columbia\n",
      "Rhema Bible Training Center,UAB Continuing Ed classes\n",
      "Empire Beauty School\n",
      "Belleuve Community College, Highline Community College\n",
      "GateWay Community College of Phoenix\n",
      "LSU - MBA,Louisiana State University and Agricultural & Mechanical College (LSU)\n",
      "University of Southwest Louisiana,Penn Foster\n",
      "Universität Bremen\n",
      "American River College\n",
      "California State University Northridge\n",
      "Worcester Polytechnic Institute,Yale University\n",
      "Daytona State College\n",
      "McNeese State University\n",
      "Universidad central de Venezuela\n",
      "Kansas City Art Institute\n",
      "Academia Naval Dominicana\n",
      "Babson College\n",
      "Community College of Rhode Island\n",
      "Elon University,Chapman University\n",
      "Oklahoma City University\n",
      "University of Kansas Main Campus,Washburn University\n",
      "Shippensburg\n",
      "COGNITIO COLLEGE,San Francisco State University\n",
      "Licensed Insurance broker 46 states\n",
      "Far Eastern University\n",
      "Cerritos College\n",
      "Seattle University\n",
      "Central Carolina Community College\n",
      "ccny\n",
      "California Lutheran University\n",
      "Coosa Valley Technical College\n",
      "Yerevan State University, Armenia\n",
      "Metropolitan State University,Southwest Minnesota State University\n",
      "Oberlin\n",
      "Missouri Western State College,William Woods University,Peru State College\n",
      "Johnson County Community College\n",
      "Wichita State University,Cowley County Community College\n",
      "San Diego State University-Main Campus,National University\n",
      "New England College of Finance\n",
      "Instituto Industrial Pablo Tavelli\n",
      "The University of Central Oklahoma\n",
      "middlesex county college\n",
      "Electrical JATC\n",
      "Punjab University,Ferris State University,Kellogg School of Management - Northwestern University\n",
      "UTI (Universal Technical Institute)\n",
      "northwest community college\n",
      "Siena Heights University\n",
      "Florida State University,United Kingdom - Open University\n",
      "Dutchess Community College\n",
      "Strayer University at Washington, DC,Villanova University\n",
      "Anoka Ramsey Community College\n",
      "SUNY at Stonybrook\n",
      "City College of Berkeley, Ex'pressions Center for New Media, UC Berkeley, Diablo Valley College\n",
      "Illinois State University\n",
      "Wichita State University\n",
      "Howard University,Colorado State University (CSU)\n",
      "auburn\n",
      "William Paterson University of New Jersey\n",
      "University of Wisconsin-Madison\n",
      "Universidad de Tijauan\n",
      "Tel Aviv University\n",
      "Marquette University\n",
      "SUNY Buffalo\n",
      "University of Maryland-University College,Defense Acquisition University\n",
      "Polytechnic University\n",
      "sequoia auto training\n",
      "University of Maryland-Asian Division,Touro University International\n",
      "San Francisco State University,University of California-Berkeley (Cal UC Berkeley)\n",
      "Boston Architectural Center\n",
      "Brown Institute\n",
      "Randolph Community College\n",
      "parsons\n",
      "Bowling Green State University-Main Campus,Kent State University-Kent Campus\n",
      "University of Phoenix-Online Campus,Truckee Meadows Community College,University of Nevada-Reno\n",
      "University of Louisville,Spalding University\n",
      "Old Dominion University,Columbia College-Columbia\n",
      "Spokane Community College,Clover Park Technical College\n",
      "El Camino College TorranceCISS\n",
      "University of Pittsburgh-Main Campus\n",
      "University of Wisconsin-Oshkosh\n",
      "Consumes River College\n",
      "Strayer University at Washington, DC\n",
      "Clemson University,University of Notre Dame,Medical University of South Carolina\n",
      "Hawaii Pacific University\n",
      "San Diego State University\n",
      "Princeton University,University of Rochester\n",
      "Eastern Washington University\n",
      "Hammel Business College\n",
      "Tulane University of Louisiana / University of Tulsa\n",
      "Dowling College\n",
      "Wayne State University,Macomb Community College,Oakland Community College\n",
      "Shasta College\n",
      "CUNY Bronx Community College\n",
      "CUNY College of Staten Island\n",
      "De Vry Institute\n",
      "Sierra College\n",
      "University of Massachusetts at Amherst,Fitchburg State College\n",
      "University of Tulsa,University of Oklahoma Norman Campus\n",
      "Miracosta College\n",
      "Stephens College\n",
      "Millikin University\n",
      "University of California-Davis (UC Davis),Notre Dame de Namur University\n",
      "Kentucky Christian College\n",
      "College of Suthern Nevada,UNLV\n",
      "Brookhaven College\n",
      "outside U.S.\n",
      "Miami University-Oxford\n",
      "Miami Jacobs\n",
      "CUNY Borough of Manhattan Community College,CUNY Hunter College\n",
      "University of Florida,University of Georgia,University of Wisconsin-Madison,Stanford University,West Virginia University,San Francisco State University\n",
      "Cuesta College\n",
      "Embry Riddle Aeronautical University-Extended Campus\n",
      "Unviversity of Central Florida,Valencia Community College\n",
      "Lynn University\n",
      "Samford University\n",
      "Anderson College / Universty Of SC\n",
      "Gwynedd Mercy College\n",
      "Ohio State University\n",
      "University of South Carolina-Columbia (USC),Winthrop University\n",
      "Drexel University,Atlantic Cape Community College\n",
      "Oregon State University,Oregon Technical Institute\n",
      "Golden Gate University\n",
      "California State University-San Bernardino (CSUSB),University of California-Riverside (UCR)\n",
      "Penn Foster\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "National University\n",
      "Pacifica Graduate Institute\n",
      "Scripps College,University of Arizona\n",
      "Heald College at Roseville\n",
      "U.A.B.\n",
      "Fullerton Community College,Fullerton State University\n",
      "Miracosta College,Cal State San Marcos\n",
      "UGA\n",
      "UTI-houston\n",
      "North Eastern University / UTS\n",
      "Framingham State College\n",
      "University of Tulsa,University of California-Davis (UC Davis)\n",
      "Delta Community College\n",
      "Ai Miami International University of Art and Design\n",
      "College of Oceaneering\n",
      "Eastern Arizona College\n",
      "Carnegie Mellon Tepper School of Business,University of Illinois at Urbana-Champaign\n",
      "University of Maryland-College Park,State University of New York - College of Environmental Science and Forestry\n",
      "Bethel University\n",
      "University of Arkansas at Little Rock\n",
      "Seattle University,University of Alaska Anchorage\n",
      "Virginia Commonwealth University,Tulane University of Louisiana\n",
      "University of Massachusetts at Amherst,Boston University\n",
      "Southern Methodist University,Dallas County Community College District\n"
     ]
    }
   ],
   "source": [
    "#display unique values of the education attribute\n",
    "print(\"Display unique values of the attribute : Education\\n\")\n",
    "for item in data.Education.unique():\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display unique values of the attribute : Education after processing\n",
      "\n",
      "[0 1]\n",
      "\n",
      "\n",
      "Sample values of the attribute :  Education after processing\n",
      "\n",
      "0       0\n",
      "1       0\n",
      "2       0\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "1995    1\n",
      "1996    1\n",
      "1997    1\n",
      "1998    1\n",
      "1999    1\n",
      "Name: Education, Length: 1968, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#replace the nan values with 0\n",
    "data['Education'] = data['Education'].fillna(0)\n",
    "\n",
    "#replace the strings with 1\n",
    "def repl_str(x):\n",
    "    if x==0:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1    \n",
    "\n",
    "data['Education'] = data['Education'].apply(repl_str)\n",
    "\n",
    "#display unique values of the attribute\n",
    "print(\"Display unique values of the attribute : Education after processing\\n\")\n",
    "print(data.Education.unique())\n",
    "\n",
    "#display sample values of the attribute\n",
    "print(\"\\n\\nSample values of the attribute :  Education after processing\\n\")\n",
    "print(data['Education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display unique values of attribute : Employment Length\n",
      "\n",
      "['< 1 year' '3 years' '4 years' '5 years' '1 year' '2 years' '8 years'\n",
      " '7 years' '6 years' '9 years' '10+ years']\n"
     ]
    }
   ],
   "source": [
    "#rename the attribute\n",
    "data.rename(columns = {'Employment Length':'Employment_Length'}, inplace = True)\n",
    "\n",
    "#display unique values of the attribute\n",
    "print(\"Display unique values of attribute : Employment Length\\n\")\n",
    "print(data.Employment_Length.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display unique values of attribute : Employment Length after processing\n",
      "\n",
      "[ 1  4  5  6  2  3  9  8  7 10 11]\n"
     ]
    }
   ],
   "source": [
    "#replace nan values with 0\n",
    "data['Employment_Length'] = data['Employment_Length'].fillna(0)\n",
    "\n",
    "#map the categories to numbers\n",
    "data[\"Employment_Length\"] = data[\"Employment_Length\"].map({'< 1 year':1, '3 years':4, '4 years':5, '5 years':6, '1 year':2, '2 years':3, '8 years':9, '7 years':8, '6 years':7, '9 years':10, '10+ years':11})\n",
    "\n",
    "#display the unique values after processing\n",
    "print(\"Display unique values of attribute : Employment Length after processing\\n\")\n",
    "print(data.Employment_Length.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique Loan Purpose in the dataset : \n",
      "14\n",
      "debt_consolidation    772\n",
      "credit_card           339\n",
      "other                 261\n",
      "small_business        135\n",
      "home_improvement      126\n",
      "major_purchase         70\n",
      "educational            70\n",
      "wedding                56\n",
      "car                    53\n",
      "medical                29\n",
      "moving                 25\n",
      "house                  17\n",
      "vacation               12\n",
      "renewable_energy        3\n",
      "Name: Loan Purpose, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#display unique values of the attribute\n",
    "print(\"Number of unique Loan Purpose in the dataset : \")\n",
    "print(data['Loan Purpose'].unique().shape[0])\n",
    "print(data['Loan Purpose'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of unique City names present in the dataset : \n",
      "1226\n"
     ]
    }
   ],
   "source": [
    "#display the number of cities present in the dataset\n",
    "print(\"Total number of unique City names present in the dataset : \")\n",
    "print(data.City.unique().shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of unique State names present in the dataset : \n",
      "50\n"
     ]
    }
   ],
   "source": [
    "#display the number of states present in the dataset\n",
    "print(\"Total number of unique State names present in the dataset : \")\n",
    "print(data.State.unique().shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique Loan Titles in the dataset : \n",
      "1563\n"
     ]
    }
   ],
   "source": [
    "#display the number of loan titles in the dataset\n",
    "print(\"Number of unique Loan Titles in the dataset : \")\n",
    "print(data['Loan Title'].unique().shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filling nan with means of the corresponding column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace all the remaining nan values in the dataset with the mean of the column\n",
    "data = data.fillna(data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving copy of data for analysis\n",
    "data_copy = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attribute selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining columns after selection : \n",
      "\n",
      "Amount Requested\n",
      "Interest Rate\n",
      "Loan Length\n",
      "Monthly PAYMENT\n",
      "Status\n",
      "Debt-To-Income Ratio\n",
      "Remaining Principal \n",
      " Payments To Date\n",
      "Monthly Income\n",
      "Open CREDIT Lines\n",
      "Total CREDIT Lines\n",
      "Revolving CREDIT Balance\n",
      "Revolving Line Utilization\n"
     ]
    }
   ],
   "source": [
    "#list of attributes ignored for our analysis\n",
    "dcolumns = ['Loan ID', 'Amount Funded By Investors','Loan Title','Loan Purpose',\n",
    "            'Loan Description','Total Amount Funded','Remaining Principal Funded by Investors',\n",
    "            'Payments To Date (Funded by investors)',\n",
    "            'Screen Name','City','Code' ,'Application Date', 'Application Expiration Date', 'Issued Date','Earliest CREDIT Line',\n",
    "           'Inquiries in the Last 6 Months','Accounts Now Delinquent','Delinquent Amount','Delinquencies (Last 2 yrs)',\n",
    "            'Months Since Last Delinquency','Public Records On File','Months Since Last Record',\n",
    "           'CREDIT_Grade','State','Home_Ownership','FICO_Range','Education','Employment_Length']\n",
    "\n",
    "#attribute removal\n",
    "data = data.drop(dcolumns, axis=1)\n",
    "\n",
    "#display the remainig attributes\n",
    "print(\"Remaining columns after selection : \\n\")\n",
    "for item in data.columns.values:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amount Requested              0\n",
       "Interest Rate                 0\n",
       "Loan Length                   0\n",
       "Monthly PAYMENT               0\n",
       "Status                        0\n",
       "Debt-To-Income Ratio          0\n",
       "Remaining Principal           0\n",
       " Payments To Date             0\n",
       "Monthly Income                0\n",
       "Open CREDIT Lines             0\n",
       "Total CREDIT Lines            0\n",
       "Revolving CREDIT Balance      0\n",
       "Revolving Line Utilization    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 sample profiles copied from the dataset : \n",
      "Last 5 profiles removed from the dataset... \n"
     ]
    }
   ],
   "source": [
    "# separate some sample profiles/records to test the trained models\n",
    "\n",
    "#copy few records\n",
    "samples = data.head(5)\n",
    "print(\"5 sample profiles copied from the dataset : \")\n",
    "\n",
    "#delete the records from the dataset\n",
    "data = data.iloc[5:]\n",
    "print(\"Last 5 profiles removed from the dataset... \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the values into two datasets - input and output\n",
    "\n",
    "#save the class variable\n",
    "status = data['Status']\n",
    "\n",
    "#save the remaining (predictor) attributes\n",
    "other_att = data.drop(['Status'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1963, 12)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_att.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "1958    1\n",
       "1959    1\n",
       "1960    1\n",
       "1961    0\n",
       "1962    0\n",
       "Name: Status, Length: 1963, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status.reset_index(inplace=True,drop=True)\n",
    "status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalizing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount Requested</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Loan Length</th>\n",
       "      <th>Monthly PAYMENT</th>\n",
       "      <th>Debt-To-Income Ratio</th>\n",
       "      <th>Remaining Principal</th>\n",
       "      <th>Payments To Date</th>\n",
       "      <th>Monthly Income</th>\n",
       "      <th>Open CREDIT Lines</th>\n",
       "      <th>Total CREDIT Lines</th>\n",
       "      <th>Revolving CREDIT Balance</th>\n",
       "      <th>Revolving Line Utilization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.183673</td>\n",
       "      <td>0.212264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.161805</td>\n",
       "      <td>0.622498</td>\n",
       "      <td>8.589770e-07</td>\n",
       "      <td>0.180548</td>\n",
       "      <td>0.034402</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.364865</td>\n",
       "      <td>0.443684</td>\n",
       "      <td>0.231388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.183673</td>\n",
       "      <td>0.112421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.158492</td>\n",
       "      <td>0.012410</td>\n",
       "      <td>8.589770e-07</td>\n",
       "      <td>0.177224</td>\n",
       "      <td>0.039316</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.229730</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.006036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.183673</td>\n",
       "      <td>0.410377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.168533</td>\n",
       "      <td>0.048439</td>\n",
       "      <td>8.589770e-07</td>\n",
       "      <td>0.187311</td>\n",
       "      <td>0.036859</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.364865</td>\n",
       "      <td>0.182659</td>\n",
       "      <td>0.161972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.183673</td>\n",
       "      <td>0.311321</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.165152</td>\n",
       "      <td>0.321457</td>\n",
       "      <td>8.589770e-07</td>\n",
       "      <td>0.183912</td>\n",
       "      <td>0.021132</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.190346</td>\n",
       "      <td>0.488934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.183673</td>\n",
       "      <td>0.261792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163473</td>\n",
       "      <td>0.477582</td>\n",
       "      <td>8.589770e-07</td>\n",
       "      <td>0.182224</td>\n",
       "      <td>0.086004</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.486486</td>\n",
       "      <td>0.406964</td>\n",
       "      <td>0.394366</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>0.591837</td>\n",
       "      <td>0.438679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.544062</td>\n",
       "      <td>0.392714</td>\n",
       "      <td>8.356944e-02</td>\n",
       "      <td>0.501616</td>\n",
       "      <td>0.034565</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>0.028341</td>\n",
       "      <td>0.014085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>0.346939</td>\n",
       "      <td>0.413522</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.317778</td>\n",
       "      <td>0.298239</td>\n",
       "      <td>5.009769e-02</td>\n",
       "      <td>0.299469</td>\n",
       "      <td>0.039314</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>0.059310</td>\n",
       "      <td>0.345070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.637579</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.312309</td>\n",
       "      <td>0.406325</td>\n",
       "      <td>4.902998e-02</td>\n",
       "      <td>0.294532</td>\n",
       "      <td>0.008353</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.067568</td>\n",
       "      <td>0.056783</td>\n",
       "      <td>0.904427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>0.591837</td>\n",
       "      <td>0.761006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.577896</td>\n",
       "      <td>0.795436</td>\n",
       "      <td>8.589770e-07</td>\n",
       "      <td>0.561011</td>\n",
       "      <td>0.056517</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>0.587936</td>\n",
       "      <td>0.969819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>0.183673</td>\n",
       "      <td>0.132075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.159150</td>\n",
       "      <td>0.231385</td>\n",
       "      <td>8.589770e-07</td>\n",
       "      <td>0.171751</td>\n",
       "      <td>0.017352</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.229730</td>\n",
       "      <td>0.035692</td>\n",
       "      <td>0.072435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1963 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Amount Requested  Interest Rate  Loan Length  Monthly PAYMENT  \\\n",
       "0             0.183673       0.212264          0.0         0.161805   \n",
       "1             0.183673       0.112421          0.0         0.158492   \n",
       "2             0.183673       0.410377          0.0         0.168533   \n",
       "3             0.183673       0.311321          0.0         0.165152   \n",
       "4             0.183673       0.261792          0.0         0.163473   \n",
       "...                ...            ...          ...              ...   \n",
       "1958          0.591837       0.438679          0.0         0.544062   \n",
       "1959          0.346939       0.413522          0.0         0.317778   \n",
       "1960          0.326531       0.637579          0.0         0.312309   \n",
       "1961          0.591837       0.761006          0.0         0.577896   \n",
       "1962          0.183673       0.132075          0.0         0.159150   \n",
       "\n",
       "      Debt-To-Income Ratio  Remaining Principal    Payments To Date  \\\n",
       "0                 0.622498          8.589770e-07           0.180548   \n",
       "1                 0.012410          8.589770e-07           0.177224   \n",
       "2                 0.048439          8.589770e-07           0.187311   \n",
       "3                 0.321457          8.589770e-07           0.183912   \n",
       "4                 0.477582          8.589770e-07           0.182224   \n",
       "...                    ...                   ...                ...   \n",
       "1958              0.392714          8.356944e-02           0.501616   \n",
       "1959              0.298239          5.009769e-02           0.299469   \n",
       "1960              0.406325          4.902998e-02           0.294532   \n",
       "1961              0.795436          8.589770e-07           0.561011   \n",
       "1962              0.231385          8.589770e-07           0.171751   \n",
       "\n",
       "      Monthly Income  Open CREDIT Lines  Total CREDIT Lines  \\\n",
       "0           0.034402              0.200            0.364865   \n",
       "1           0.039316              0.125            0.229730   \n",
       "2           0.036859              0.325            0.364865   \n",
       "3           0.021132              0.100            0.054054   \n",
       "4           0.086004              0.350            0.486486   \n",
       "...              ...                ...                 ...   \n",
       "1958        0.034565              0.200            0.243243   \n",
       "1959        0.039314              0.175            0.202703   \n",
       "1960        0.008353              0.100            0.067568   \n",
       "1961        0.056517              0.300            0.405405   \n",
       "1962        0.017352              0.200            0.229730   \n",
       "\n",
       "      Revolving CREDIT Balance  Revolving Line Utilization  \n",
       "0                     0.443684                    0.231388  \n",
       "1                     0.001451                    0.006036  \n",
       "2                     0.182659                    0.161972  \n",
       "3                     0.190346                    0.488934  \n",
       "4                     0.406964                    0.394366  \n",
       "...                        ...                         ...  \n",
       "1958                  0.028341                    0.014085  \n",
       "1959                  0.059310                    0.345070  \n",
       "1960                  0.056783                    0.904427  \n",
       "1961                  0.587936                    0.969819  \n",
       "1962                  0.035692                    0.072435  \n",
       "\n",
       "[1963 rows x 12 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the scaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "#fit and transform the dataset values\n",
    "trans_others = scaler.fit_transform(other_att)\n",
    "\n",
    "#create the dataset consisting of the predictor variables\n",
    "X = pd.DataFrame(data=trans_others, columns=other_att.columns.values)\n",
    "\n",
    "#display the values of the model input\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Models' performance on variable test data size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Decision Tree Classifier at test data- 0.5 :  0.9826883910386965\n",
      "Accuracy of the Decision Tree Classifier at test data- 0.4 :  0.9796437659033079\n",
      "Accuracy of the Decision Tree Classifier at test data- 0.3 :  0.9796264855687606\n",
      "Accuracy of the Decision Tree Classifier at test data- 0.2 :  0.9745547073791349\n",
      "Accuracy of the Decision Tree Classifier at test data- 0.1 :  0.9796954314720813\n"
     ]
    },
    {
     "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": [
    "# variable size of the test dataset\n",
    "test_list=[0.5, 0.4, 0.3, 0.2, 0.1]\n",
    "acc=[]\n",
    "\n",
    "#train model on various train/test sets and record the accuracies\n",
    "for num in test_list:    \n",
    "\n",
    "    #split the dataset according to the value in the list\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, status, test_size=num, random_state=1)\n",
    "\n",
    "    #create a new model object\n",
    "    DT = DecisionTreeClassifier(criterion='entropy')\n",
    "\n",
    "    #train the model on the train data\n",
    "    DT.fit(X_train,y_train)\n",
    "    \n",
    "    #predict the values, test data input\n",
    "    DTPredict = DT.predict(X_test)\n",
    "\n",
    "    #evaluate the accuracy of the model\n",
    "    Accuracy = metrics.accuracy_score(y_test,DTPredict)\n",
    "\n",
    "    #save the accuracy of the model\n",
    "    acc.append(Accuracy)\n",
    "\n",
    "    print(\"Accuracy of the Decision Tree Classifier at test data-\",num,\": \",Accuracy)\n",
    "\n",
    "#plot the graph - accuracy against the test-data size\n",
    "plt.plot(test_list, acc) \n",
    "plt.xlabel('Testing data size') \n",
    "plt.ylabel('Accuracy') \n",
    "plt.title('Decision Tree accruacy with variable testing data size') \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the data in the hightest accuracy\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, status, test_size=0.1, random_state=1) # 10% training and 50% test\n",
    "\n",
    "#display a record value from the test set\n",
    "# print(X_test.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Decision Tree Classifier :  0.98\n"
     ]
    }
   ],
   "source": [
    "#train the model\n",
    "DT = DecisionTreeClassifier(criterion='entropy')\n",
    "DT.fit(X_train,y_train)\n",
    "\n",
    "#make predictions\n",
    "DTPredict = DT.predict(X_test)\n",
    "\n",
    "#note the accuracy and ROC score\n",
    "Accuracy = metrics.accuracy_score(y_test,DTPredict)\n",
    "print(\"Accuracy of the Decision Tree Classifier : \",round(Accuracy,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing sample profiles on the trained models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#get the sample output separated\n",
    "sample_y=samples['Status']\n",
    "\n",
    "#print the values from the sample output\n",
    "# print(\"Classes of the sample profiles :\")\n",
    "# print(sample_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining attributes of the sample profiles :\n",
      "Index(['Amount Requested', 'Interest Rate', 'Loan Length', 'Monthly PAYMENT',\n",
      "       'Debt-To-Income Ratio', 'Remaining Principal ', ' Payments To Date',\n",
      "       'Monthly Income', 'Open CREDIT Lines', 'Total CREDIT Lines',\n",
      "       'Revolving CREDIT Balance', 'Revolving Line Utilization'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#remove the status attribute from the sample profiles\n",
    "samples = samples.drop('Status', axis=1)\n",
    "\n",
    "#display the remaining attributes\n",
    "print(\"Remaining attributes of the sample profiles :\")\n",
    "print(samples.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = DT.predict(samples)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted class:  Fully Paid\n",
      "\n",
      "Predicted class:  Fully Paid\n",
      "\n",
      "Predicted class:  Fully Paid\n",
      "\n",
      "Predicted class:  Fully Paid\n",
      "\n",
      "Predicted class:  Fully Paid\n"
     ]
    }
   ],
   "source": [
    "#print the prediction output\n",
    "for i in range(samples.shape[0]):\n",
    "    prediction = DT.predict(samples)\n",
    "    if prediction[i]==0:\n",
    "        prediction=\"Fully Paid\"\n",
    "    elif prediction[i]==1:\n",
    "        prediction = \"Current\"\n",
    "    elif prediction[i]==2:\n",
    "        prediction = \"Charged Off\"\n",
    "  \n",
    "   \n",
    "    print(\"\\nPredicted class: \",prediction)\n",
    "\n",
    "\n",
    "#     #print the actual class value\n",
    "#     if sample_y.iloc[i]==0:\n",
    "#         print(\"Actual class label : Fully paid\")\n",
    "#     elif sample_y.iloc[i]==1:\n",
    "#         print(\"Actual class label : Current\")\n",
    "#     elif sample_y.iloc[i]==2:\n",
    "#         print(\"Actual class label : Charged Off\")\n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis\n",
    "\n",
    "## Yearwise number of loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum Issued date :  2007-06-27 00:00:00\n",
      "Maximum Issued date :  2009-01-29 00:00:00\n",
      "\n",
      "Yearwise record count : \n",
      "2008    1536\n",
      "2007     249\n",
      "2009     183\n",
      "Name: Issued_year, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#obtain data before normalization\n",
    "data=data_copy\n",
    "\n",
    "#display the date range\n",
    "print(\"Minimum Issued date : \",data['Issued Date'].min())\n",
    "print(\"Maximum Issued date : \",data['Issued Date'].max())\n",
    "\n",
    "#convert the date into year\n",
    "data['Issued_year']=data['Issued Date'].dt.year\n",
    "\n",
    "#print the example values\n",
    "print(\"\\nYearwise record count : \")\n",
    "print(data['Issued_year'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2007 : 249\n",
      "2008 : 1536\n",
      "2009 : 183\n"
     ]
    }
   ],
   "source": [
    "#make yearwise groups and print the record count\n",
    "\n",
    "year_groups = data.groupby(['Issued_year'])\n",
    "y=[]\n",
    "c=[]\n",
    "for year, yr_data in year_groups:\n",
    "    y.append(year)\n",
    "    c.append(yr_data.shape[0])\n",
    "    print(year,end=' : ')\n",
    "    print(yr_data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Issued_year counts')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#print bar chart\n",
    "plt.bar(y,c)\n",
    "plt.xticks(range(min(y),max(y)+1,1))\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Amount')\n",
    "plt.title('Issued_year counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## States/cities that have the most fully paid and charged off applicants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total applicants :  1968\n"
     ]
    }
   ],
   "source": [
    "#print the total number of applicants\n",
    "total_rec = data.shape[0]\n",
    "print(\"Total applicants : \", total_rec)\n",
    "\n",
    "#make state-wise groups from the dataset\n",
    "state_groups = data.groupby(['State'])\n",
    "\n",
    "st=[]\n",
    "fp=[]\n",
    "co=[]\n",
    "ta=[]\n",
    "cu=[]\n",
    "\n",
    "#for each state group\n",
    "for state, st_group in state_groups:\n",
    "    #calculate the group size\n",
    "    state_rec = st_group.shape[0]\n",
    "    \n",
    "    #calculate the percentage of the applicants from this state\n",
    "    state_per = state_rec/total_rec*100\n",
    "    ta.append(state_per)\n",
    "    fp.append(st_group.loc[(st_group['Status'] ==0)].shape[0]/total_rec*100)\n",
    "    cu.append(st_group.loc[(st_group['Status'] ==1)].shape[0]/total_rec*100)\n",
    "    \n",
    "    co.append(st_group.loc[(st_group['Status'] ==2)].shape[0]/total_rec*100)\n",
    " \n",
    "\n",
    "    st.append(state)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   State_name  Total_applicants%  Fully_paid%  Charged_off%  Current%\n",
      "4          CA          18.394309    11.077236      3.404472  3.912602\n",
      "33         NY           9.298780     7.266260      1.168699  0.863821\n",
      "9          FL           6.656504     3.556911      2.235772  0.863821\n",
      "42         TX           6.148374     4.827236      0.508130  0.813008\n",
      "30         NJ           4.420732     3.099593      0.813008  0.508130\n",
      "44         VA           3.963415     3.150407      0.355691  0.457317\n",
      "10         GA           3.810976     2.439024      0.711382  0.660569\n",
      "19         MA           3.760163     2.947154      0.152439  0.660569\n",
      "14         IL           3.252033     2.134146      0.508130  0.609756\n",
      "34         OH           2.896341     1.930894      0.203252  0.762195\n",
      "5          CO           2.794715     1.930894      0.508130  0.355691\n",
      "20         MD           2.693089     1.930894      0.406504  0.355691\n",
      "37         PA           2.591463     2.032520      0.304878  0.254065\n",
      "3          AZ           2.439024     1.524390      0.558943  0.355691\n",
      "46         WA           2.235772     1.575203      0.406504  0.254065\n",
      "27         NC           2.184959     1.829268      0.355691  0.000000\n",
      "22         MI           1.727642     1.219512      0.152439  0.355691\n",
      "23         MN           1.473577     0.965447      0.203252  0.304878\n",
      "24         MO           1.473577     1.117886      0.152439  0.203252\n",
      "6          CT           1.422764     0.914634      0.203252  0.304878\n",
      "36         OR           1.321138     0.914634      0.203252  0.203252\n",
      "47         WI           1.067073     1.016260      0.000000  0.050813\n",
      "39         SC           1.067073     0.762195      0.152439  0.152439\n",
      "18         LA           1.067073     0.863821      0.050813  0.152439\n",
      "43         UT           0.965447     0.609756      0.304878  0.050813\n",
      "1          AL           0.965447     0.762195      0.152439  0.050813\n",
      "32         NV           0.965447     0.508130      0.254065  0.203252\n",
      "16         KS           0.863821     0.711382      0.050813  0.101626\n",
      "41         TN           0.813008     0.711382      0.101626  0.000000\n",
      "35         OK           0.609756     0.355691      0.101626  0.152439\n",
      "38         RI           0.609756     0.355691      0.152439  0.101626\n",
      "31         NM           0.508130     0.406504      0.000000  0.101626\n",
      "7          DC           0.508130     0.355691      0.152439  0.000000\n",
      "25         MS           0.508130     0.304878      0.050813  0.152439\n",
      "11         HI           0.508130     0.406504      0.101626  0.000000\n",
      "29         NH           0.457317     0.355691      0.050813  0.050813\n",
      "15         IN           0.457317     0.457317      0.000000  0.000000\n",
      "2          AR           0.406504     0.304878      0.000000  0.101626\n",
      "17         KY           0.406504     0.254065      0.000000  0.152439\n",
      "8          DE           0.355691     0.203252      0.101626  0.050813\n",
      "26         MT           0.355691     0.203252      0.000000  0.152439\n",
      "13         ID           0.304878     0.254065      0.050813  0.000000\n",
      "28         NE           0.254065     0.101626      0.152439  0.000000\n",
      "12         IA           0.203252     0.203252      0.000000  0.000000\n",
      "45         VT           0.203252     0.101626      0.000000  0.101626\n",
      "0          AK           0.152439     0.101626      0.000000  0.050813\n",
      "40         SD           0.152439     0.152439      0.000000  0.000000\n",
      "21         ME           0.152439     0.152439      0.000000  0.000000\n",
      "48         WV           0.101626     0.101626      0.000000  0.000000\n",
      "49         WY           0.050813     0.050813      0.000000  0.000000\n"
     ]
    }
   ],
   "source": [
    "#create a new dataframe to show the applicant percentage\n",
    "\n",
    "app_count = pd.DataFrame()\n",
    "\n",
    "#create new attributes\n",
    "app_count['State_name']=st\n",
    "app_count['Total_applicants%']=ta\n",
    "app_count['Fully_paid%']=fp\n",
    "app_count['Charged_off%']=co\n",
    "app_count['Current%']=cu\n",
    "#display the sorted valued based on the total applicants\n",
    "print(app_count.sort_values(['Total_applicants%'], ascending=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Earliest Credit Line analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example values of the attribute : \n",
      "0      2003-10-27\n",
      "1      1988-11-19\n",
      "2      1998-10-28\n",
      "3      1986-12-30\n",
      "4      1990-11-11\n",
      "          ...    \n",
      "1995   1991-04-20\n",
      "1996   1994-05-04\n",
      "1997   1990-08-23\n",
      "1998   1995-04-29\n",
      "1999   1996-11-19\n",
      "Name: Earliest CREDIT Line, Length: 1968, dtype: datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "print(\"Example values of the attribute : \")\n",
    "print(data[\"Earliest CREDIT Line\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       2003\n",
      "1       1988\n",
      "2       1998\n",
      "3       1986\n",
      "4       1990\n",
      "        ... \n",
      "1995    1991\n",
      "1996    1994\n",
      "1997    1990\n",
      "1998    1995\n",
      "1999    1996\n",
      "Name: ECLD, Length: 1968, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#convert the date into decade\n",
    "data['Earliest CREDIT Line'] = pd.to_datetime(data['Earliest CREDIT Line'])\n",
    "data['ECLD'] = data[\"Earliest CREDIT Line\"].dt.year\n",
    "print(data[\"ECLD\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Earliest Credit Line counts')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#convert the attribute values in list\n",
    "dec_list = list(data[\"ECLD\"])\n",
    "\n",
    "#print histogram\n",
    "n, bins, patches = plt.hist(x=dec_list, bins=100, color='#0504aa')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Amount')\n",
    "plt.title('Earliest Credit Line counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1P//5z5k4cSKXX355gdud/vpBPWpEREREpOJRjxoREWDo0KH885//BGDSpEksW7aMt956i7i4OFq3bs2SJUvybZ+UlERqaiqQ18tm79693HrrrcTGxjJ+/PiLjqlevXpnlJ2cialy5cpUruxN3BceHg54PWRatGhR6L6nq1q1Kg0aNMhX35dffsnhw4dJSEigT58+fPfdd/mOcfjwYa699toC64uKiqJWrVpnxHdyv927d/OTn/zkrNuJiIiIiFR0StSIiAB9+/YNTmf9wQcfEB8fz80338yKFStIS0tjypQp56xj4sSJJCUlkZaWxrZt20hPTw+uO3r0aPARoJNLr169LijWnJwckpOTGTx4cPDnmJgYRowYwY033nje9f3www/MmDGDfv36kZGRwWeffUZKSgpDhw5lwoQJgDd1d/v27Zk6dWqhiZqT9uzZE3wPT1q+fDmdO3c+53YiIiIiIhWdbmOKSIXknKt26s9xcXFMnjyZAQMGULduXapUqcKaNWt48sknycnJOWNcFufynsY5+fjU9u3befDBBwHvMaDdu3dTv359AKpUqRLsgXOxHnjgAe666y4aNWoEQKVKlVi1ahUff/wxSUlJpKSkFLkuM+Oee+4hOTmZyMhIIiMjadOmDdWrVycuLi6YoLriiitYu3YtKSkpPP/88zz33HMF1nfkyBEGDhzIjBkzqFSpUrB84cKFjBo16pzbiYiIiIhUdErUiEiF4Zy7FOgG9AVuOXVdeHg49evX5/nnnychIQGAZ599lrlz51KjRg2io6Pz1RUZGcmePXs4ceIEn3zyCQCNGzdm8ODBNGvWjJycnHzJnKNHj9KlS5cz6li8ePF5vYZZs2bhnOOuu+7KV3eVKlWoWbMmERERhe67e/durrjiinxljz/+OB06dCAuLg6Aq6++moyMDHJycti4cSMNGzbk+PHjhIWFERYWlu8YBdU3dOhQ7rvvPpo2bRosy8nJYcuWLbRs2fKs2xXGOfcmkAK8aWb7zrmDiIiIiEgZpkSNiJRrzrnaQA+85EwA+BDvon8EkK+bTEJCAnfeeSdPPfUUAL169eLWW2+lXbt2REVFcfq2ffr0ISUlJTjeyiOPPMKQIUPIysoiPDyclJQUatSoAVxYj5rk5GRef/11wEuKjBkzhvvuu48bb7yRQCBAXFwcY8eO5ZZb8nJO06ZNK3TfgQMH8v777we33bNnD7/97W+Jjo5m4cKF3HHHHQwdOpQhQ4YQCASoXLkys2fP5rvvvuMXv/gFYWFhVK1aNTjY7+n1rV27lgULFrBz505+97vfcf/999OzZ09WrVpFx44dz7ndWbyB9/ub4pz7G97vb5GZ7T2vN1REREREpAzQrE8iUu445+oBvfAu7tsCHwALgKVmduCU7c571qeStGvXLrp27cqIESMYPnx4sdZ94sQJRo8eXaSxdopi3759TJ06laSkpGKp79ChQ3Tu3Jl27drx7LPPBstPnfXJOXcJ0BVIwOsZ9Qne73WBme0slkBERERERHymRI2IlAvOuf+HdwGfADQH3sa7iF9mZlmF7BNSiRo5U2HTczvnqgKd8H7fvYCv8H7fKWb2eelGKSIiIiJSfJSoEZEyyzl3NV6vmQSgEbAE72L9fTM7XIT9lagJcYUlak7bpjIQi/c56APsIzdpA2zRL1lEREREyhIlakSkzHDe6LzXkZecqQMsxLsoTzOzY+dZn67hQ1xREjWnbR8GtMf7fPQFjpGXtPm7fuEiIiIiEuqUqBGRkJabnLmBvORMVbyL7gXAWjPLuYi6dd0e4s43UXPavg5oRV7S5hLykjYfXsxnR0RERESkpChRIyIhxzlXCYgmLzlzCO/iOgX4R3FlV6pVq7b38OHDdYujLikZERER3x46dKjexdaTm7S5Bu8z1ReoDyzC+0z99Xx7Y4mIiIiIlBQlakQkJDjnwvGmz+4L9Aa+JS85s1VdX6Q4OeeuIq+nzdXAUrzP2ntmdsjP2ERERESkYlOiRkR845yLADrjXSz3AL4g97EmM/vCz9ik4nDO/RfeIMR9geuBd/E+h8vMLNPP2ERERESk4lGiRkRKlXOuBvAzvIvirsAmvIvihWb2jZ+xiTjnfoQ33XdfvMfv/or3+XzTzPb7GZuIiIiIVAxK1IhIiXPO1QK64138xgFr8AZ1XWxm3/oZm0hhnHNReD29EoB4YC3e53aRPrciIiIiUlKUqBGREpHbM6E33kVuNLAC7yJXPROkzDmlJ1hC7r+b8D7PC9QTTERERESKkxI1IlJscsf6SMhdrgfewbuYfdvMfvAzNpHicsrYSglAT7yxlRYAKRpbSUREREQulhI1InJRcmfPOTmN9tXAm3gXrcvN7LCfsYmUtNzZymLx/gb64M1WtgBvXJtPNVuZiIiIiJwvJWpE5Lw45xzQlLzkTD1gEd7F6V/N7JiP4Yn4xjlXCWhP3t/GEfKmmP9YSRsRERERKQolakTknHKTM63IuwCtTl6vgTVmluNjeCIhJ/dvpjXe30xfoCq5Y9qgvxkREREROQslakSkQM65MPL3DjhGXu+A9eodIFI0uUmba8n7W/oReb3QUtULTUREREROpUSNiASdMt5GAt54G/8mLznziZIzIhfPOXc1eYNuX0XeuE7vaVwnEREREVGiRqSCc85VJf8MNl/hJWYWmNnnfsYmUt455/6bvKRNC2AZXtJmmWZKExEREamYlKgRqYCcc5cAP8O7OPwZsIXc8TPM7Gs/YxOpqJxzdYFeeI9ItQNW4P1dvmlmB/yMTURERERKjxI1IhWEcy4K6I53ERgPfIR3EbjIzPb6GZuI5Oecq03e3+vNwBq8nm6LzOw7P2MTERERkZKlRI1IOeacq0PeHfoOQCpecmaJmX3vY2giUkTOuUvxer71BboCG/CSNgvNbJefsYmIiIhI8VOiRqSccc5dgTcQcF+8KbXfxUvOvGVmmX7GJiIXxzlXDW9Mqb5AD+Bzcgf8NrMv/YxNRERERIqHEjUi5YBzrhHeeDN9gSbAUrzkzLtmdsjP2ESkZDjnqgABvL/73kA6ebO0faZZ2kRERETKJiVqRMoo59w1eBdofYErgEV4F2h/NbOjfsYmIqXLOVcJ7/HGvnhJ2yxyZ28D/qGkjYiIiEjZoUSNSBnhnHNAS/J6zlyKdxGWAqw2sxwfwxOREJH7XdGGvERuJXJndQPWmtkJH8MTERERkXNQokYkhDnnwvCm6U3IXYy8Rxv+rgsuETmb3KRNM/J62lwGLMRL2qSZ2XEfwxMRERGRAihRIxJinHOVgZvwLqr6APvJ6zmzWY8wiMiFcs41Ji9pcyWwBO/75X0zO+JjaCIiIiKSS4kakRDgnKsKxONdPPUCdpKbnDGz7X7GJiLlk3Pux+TNEHcd8Dbe9847ZpblZ2wiIiIiFZkSNSI+cc5VB7riJWduBT7F6zWz0Mx2+BiaiFQwzrn6eEnivsCNwAd430dLzew/fsYmIiIiUtEoUSNSipxzNYHueBdDnYC/410MLTKzdD9jExEBcM5dBvTA+56KBVbjfU8tNrN/+xmbiIiISEWgRI1ICcu96Dl5pzoGWIl30fOmLnpEJJTlJpe74X1/dQE+Jq/n3x4/YxMREREpr5SoESkBzrkGQG+8i5sbgPfwLm7eMrODfsYmInIhch/X7IL3vXYrsA3ve22BmX3lZ2wiIiIi5YkSNSLFxDl3Jd54M32BpsBbeBcx75pZtn+RiYgUL+dcFSAO7/uuF7CLvKTNZ37GJiIiIlLWKVEjchGccz8lb6rb/wcsxrtY+cDMjvoZm4hIaXDOVQY6kvdd+B9ykzbARlNDQ0REROS8KFEjch6ccw5oQd4FSRSwEO+iZJWZHfcxPBERXznnwvBmjeqbuxhewmYB8DczO+FjeCIiIiJlghI1Iudw2oVHAhCGl5hJQRceIiIFKiCxHYmX2F6AEtsiIiIihVKiRqQAzrlKeDM09QX6AAdRV34RkQuW+6joyXG8/hvvUdEF6FFRERERkXyUqBHJpcExRURKhwZfFxERESmcEjVSoTnnqgG3oOlmRUR84ZxrAPTG+x6+AXgP73v4LTM76GdsIiIiIn5QokYqHOdcTaAb3kVBF+BjvIuChWa2x8/YREQqMufc5UBPvO/nGGAl3vfzEjPb52dsIiIiIqVFiRqpEJxzlwE98Br/scBqvMb/YjP7t5+xiYjImZxzkXg9HfsCnYC/431vLzKzdD9jExERESlJStRIueWcq0ded/q2wPt4jfylZvYfP2MTEZGic85VB7rijWtzK/ApeT0hd/gYmoiIiEixU6LmLKpVq7b38OHDdf2OozyJiIj49tChQ/VKqn7n3I/xGvIJwHXA23izirxjZlkldVwRESkdzrmqQDze93wvYCfe93yKmW0v7uOpLVB6SrqNICIiUlYoUXMWzjnNwlzMnHOYmSvmOhvj9ZpJABqSN+Xr+2Z2pDiPJSIiocM5Vxm4ibwE/ffkJm2AzcVxEldboPSURBtBRESkLFKi5izUOCt+xdEIc845oBl5yZnLgIV4jfM0Mzt+0YGKiEiZ4pwLA9qRN+13DnlJm7+b2YkLrFdtgVKiRI2IiIhHiZqzUOOs+F1oIyw3OdOGvORMOLnTaANrL7QBLiIi5U/uOaMleUmbGngJ/RRgtZnlnEddaguUEiVqREREPGF+B1AerVmzhkGDBgGQnp5OfHw8AFFRUYwbNw6AxMRE2rVrRyAQ4PXXXwdg3LhxREVFFWssx48f58477yQmJoZnnnnmjPUrVqwgOjqa+Ph49uzZU2jZyJEjqVOnDrNnzy7W+M7GOVfJOXeTc+53wNfAHOA40A9oaGYPmNmHStKIiMipzPMPMxtrZtcAXYAM4AVgj3NuhnPuFudcFQDnXA3n3NO5498Uu7LeLvjd735H27Ztad++PWvXrgVg7ty5tG/fnq5du7J3795ijVFERKTCMzMthSwE23rn77bbbrNNmzbZoEGDbPXq1WZmFhsbG1w/cOBA++qrr87Y79RtCpOVlVXkOFJSUmzixIlmZta9e3fLyMjItz4uLs4yMzPto48+slGjRhValp6ebq+99pq99tprRT52QXLf09Pf577ApNz/hwO3AK8A3wIbgMeApqfvp0WLFi1atJzvAjQC/gdYizemzRy8GQIXAG8BEadtb8WhLLcLWrdubSdOnLBdu3ZZ79697dixY9axY0c7fvy4rV+/3kaPHl3k459NQW0ELVq0aNGipSIu6lFTQpKTkxkyZAiZmZl06NDhjPXOOe644w569OjB119/XaQ6N2/ezPDhw+nXr1+R41i7di2dO3cGIBAIsH79+uC67OxsLrnkEmrUqEHbtm3ZunVrgWUA9eqVzCQMzrl+wEt4dzj/AOwFkoAvgGgza2lm481sa4kEICIiFYqZfWlmz5pZe7zxztYBv8abSaoZ8Dfn3KXFfdyy2i4AuOqqqzh8+DAHDhzgsssuY9++ffzXf/0XlSpVokWLFnz00UdFPr6IiIicW2W/AyivrrrqKvbt28fo0aMLXP/cc89Ru3ZtVq9ezUMPPcQbb7xRaF3z5s1j/vz5NGzYkGHDhnH99dcDsGTJEiZPnpxv2379+jF8+PDgzwcOHKBmzZoAREZGsn///uC6/fv3B9cB5OTkFFhWUpxzY4EngEN4U6wuAMaY2a4SO6iIiEguM9vtnJuFN3NUBnAl3gD1XYE/F+exymq7ACA2Npaf/vSnHDt2jGXLllGnTh2++uorsrOz+fDDD/n+++/P890QERGRs1GipoS89tpr3Hbbbbz88sv069ePsLD8nZdq164NQMeOHRk7duxZ63r55Ze5+uqruffee2nevHmwvGfPnvTs2fOs+0ZFRXHw4EEADh48SIMGDYLratWqFVwHEBYWVmBZCboC2A7UxJup4+9K0oiISCk7ArwK7Aa+MLMjJXGQstouOHjwIPPmzeOf//wnGRkZDB06lLfffpvHHnuMbt260bx5c5o0aVK0N0FERESKRI8+lYDs7Gx+//vfk5SURNeuXZk3b94Z25xsCG3fvp3IyMiz1rd69Wp+85vfMH36dLp06cLcuXMB785ZIBDIt0yfPj3fvu3bt+eDDz4AIJ95/c4AACAASURBVDU1lRtuuCG4rnr16mRnZ5OVlcW6deto2rRpgWUlxcyGm1lTM/sv4HJgfIkdTEREpABmdsLMlpvZpyWVpCnL7YKwsDCqV69OlSpViIyMJCsrC4Bbb72V1NRUbrvtNjp27Hjhb46IiIicQT1qSsBzzz3HiBEjqFq1KqNHjyY+Pp7bb7893zYDBgxg//79OOfOaEQVpHnz5kyfPp3MzExSUlKAot0569GjB3fffTcdO3akR48e/OhHP2Ljxo1s3LiRxMREHn74YTp37kxERARz5swBKLAsOTk5OAvF7t27GTNmzHm/L2djZlnFWqGIiEiIKMvtgho1ahAXF0d0dDTHjx/n8ccfB7zZID/99FN+/OMfM23atAt8Z0RERKQgzsz8jiFkOeesON+fn/70p/Tr148nnniiwPXjxo3jj3/8I9u2bSu2Y4Ya5xxm5vyOQ0REpCiKuy1wKrUL8lMbQURExKNEzVmUZOOsolIjTEREyhK1BUqP2ggiIiIejVEjIiIiIiIiIhIilKgpRampqezYscPXGObPn0+HDh3o2bMnmZmZ+dZlZmbSs2dPOnTowPz58wFYvnw511xzDYFAILjd5MmTg4MU1qhRQ9NyioiIFJNQaCskJCQQFRVFamrqGesKaiuIiIhI8VKiphQV1vg6ceJEqRz/2LFjzJw5k5UrVzJgwABeffXVfOtnzpzJgAEDWLlyJTNnzuTYsWO0adOGjRs35tvu/vvvJzU1lYULF9KmTZvglKIiIiJycfxuKwBMmzaN0aNHF7iuoLaCiIiIFC8laopBVlYWt912G3FxcQwfPhyApKQkhg0bRqdOnRg0aBA5OTnMnj2b0aNH89BDDzF79mx+8Ytf0K1bN7Zs2UL//v2JjY2lf//+HD9+PLj+lltuoUePHhw9epTevXsHp++899572b59+3nF+fnnn9O8eXMqVapEp06d+Oijj/KtX7t2LZ07d6ZSpUo0a9aML774glq1alG1atUC61u6dCndu3e/gHdMRESkYikrbQWAevXqFbquoLaCiIiIFC8laorBzJkzuf3221mxYgVRUVGsW7cOgOuuu47333+fb775hszMTBITE3nhhReYNGkSAHXq1OHtt9/m888/p1mzZqSlpXHttdeyaNEiAGrVqsW7775LdHQ0CxcuJCEhgUWLFnH8+HG+/PJLmjRpEozh6NGjwceRTi69evXKF+eBAweoWbMmAJGRkezfv/+81p9u4cKF9O7d+yLeORERkYqhrLQVzuV82woiIiJy/ir7HUB5sH37dt544w1eeuklfvjhB9q2bQt4jS+ABg0a8J///OeM/Vq2bAnAv/71L1q3bg3ADTfcwKZNm6hbt25wfatWrdiwYQMjRowgMTGRunXrEh8fn6+uKlWqFPgs+amioqKCd9kOHjxIVFRUgetr165d4PpTHTp0iL1793LVVVed9ZgiIiJSdtoK53I+bQURERG5MErUFIPGjRvTrVs3evToAcDx48fZuHEjzuXNMGlmhIeHk5OTEywLC/M6NDVq1IiPP/6YW265hfXr19OkSROysrKCY8Ns2LCBhg0bcumll1K1alWmT5/OCy+8kC+Go0eP0qVLl3xlkZGRLF68OF+cW7ZsIScnh/fff5927drl2759+/Z88MEHJCQksGXLFq6++upCX/Py5cvPOJ6IiIgUrKy0Fc7lfNoKIiIicmH06FMxGDp0KPPnzycuLo5OnTrxzTffFLhdIBDgySefJDk5OV95nz592LRpE7GxsWzZsoU+ffoAsG/fPrp06cKHH34YLPv5z39ORkYGV155Zb46Tt4lO3U5veEVHh7OPffcQ0xMDHPnzmXw4MEAwQEDBw8ezJw5c4iJieGee+4hPDyc9evX06lTJzZt2kSnTp2CgwbqsScREZGiKyttBYBRo0YxZ84cHnjgAWbNmgWcva0gIiIixcuZmd8xhCznnPn1/syePRuAxMTEfOWLFy/m66+/ZuTIkaUfVDFwzmFm7txbioiI+M/PtsC5lLe2gtoIIiIiHj36VIb86U9/YsqUKSxdutTvUERERCQEqa0gIiJS9qlHzVmE8l20skp3y0REpCxRW6D0qI0gIiLi0Rg1ISwxMZEdO3aUSN0jR46kTp06wW7TAL1796Zjx47Ex8eza9cuAPr3709sbCzR0dFs27at0DIREREpPSXZRjh1+u4VK1YAag+IiIiUJj36VEGNGTMmOM3nSc8//zwNGzbkvffeY8qUKUyaNIk5c+YQHh5OWloa06ZNY8qUKQWWiYiISPlx+jTeag+IiIiUHvWoKQZr1qyhbdu2BAIBZs2axZEjR+jcuTMxMTHcfffdgNfg6dq1K926daNbt2689NJLtG/fnjFjxgDenbFf/epXxMTEMHbs2Hz17927l1tvvZXY2FjGjx8PwKOPPkrHjh2JjY1lz5495x1zvXr1zihr2LAhAJUrV6ZyZS+Hd3I2h6ysLFq0aFFomYiIiJypLLYRwsLCCAQC9O/fn/379wNqD4iIiJQm9agpBsuWLePpp58mPj6eEydOALB06VKqVq3KwIED2b59OwCXXnopf/7znxk2bBjOOdauXUu7du2CU3DGxMTw4osv0q1bN9LT04P1T5w4kaSkJNq0acOAAQNIT09n1apVpKWlUalSJU5/dj4hIYHvv//+jBirVat2zteSk5NDcnIyM2bMCP4cCATYtWsXS5YsKbRMREREzlQW2wh/+ctfqF27Nq+//joTJkxg0qRJag+IiIiUIiVqisF9993H+PHjmT17Nr/+9a9p0qQJgwYN4ttvv2Xnzp3cc889ADRt2hSA+vXrc8011wAQERERrKdly5YAtGjRgp07dwbLt2/fzoMPPgjAgQMH2L17N48++iiJiYlcfvnlJCcnU7169eD2CxYsuODX8sADD3DXXXfRqFEjACpVqsSqVav4+OOPSUpKIiUlpcAyEREROVNZbCPUrl0bgD59+jBv3jxA7QEREZHSpERNMYiKimLatGmkp6czdOhQBg4cSIsWLRgzZgy//OUvg3eznMubyODU/5+0ceNGGjduzObNmxk1alSwvHHjxgwePJhmzZqRk5ODc44jR47ws5/9jAkTJrBs2TL69u0b3P5Ce9TMmjUL5xx33XVXsOzo0aNUqVKFmjVrBhuMBZWJiIjImcpiG+HgwYPUrFmTDz/8MHjjRu0BERGR0qNETTF45ZVXWLBgAVlZWTzyyCO0bduWp556ir/97W/nVU9aWhpTp04lNjaW+vXrB8sfeeQRhgwZQlZWFuHh4aSkpNC3b1+ys7MJCwtj0KBB+eopyt2y5ORkXn/9dQB2797NmDFjuO+++7jxxhsJBALExcUxduxYbrnlluA+06ZN48SJE2eUiYiISMHKYhshLi6OatWqERERwezZsws896s9ICIiUnLc6c8uSx7nnJXW+5OYmEhSUhJXXnllqRzPL845zOzMW4UiIiIhqDTbAoVRG0FERKRi0axPIiIiIiIiIiIhQj1qziIU7qKVN7pbJiIiZYnaAqVHbQQRERGPetSIiIiIiIiIiIQIJWqKWWJiIjt27CiRunfs2EG9evWYPn06ACNHjqROnTrMnj07uE3v3r3p2LEj8fHx7Nq1C4D+/fsTGxtLdHQ027ZtK3Tf02VlZREXF8dNN91E9+7dyczMLHTbZ555hpiYGO68806OHz/OoUOHaNeuHYmJiRf9ukVERMqa0mwPrFixgujoaOLj49mzZ0+B+xR2Tt+wYQNdunQhNjaWpUuXAt7MUFFRUaSmphYaw86dO4mJieGmm27ijjvuICcnh61btxIIBAgEAjRq1IgXXnihwO3URhARETk7JWrKmK5duzJ8+HAAxowZwzPPPJNv/fPPP8/q1at5+OGHmTJlCgBz5swhLS2NCRMmBGdlKGjf01WuXJl58+axcuVKevXqxdy5cwvc7rvvvmPlypWsWrWK6667jjfffJNq1arxxhtvXOzLFRERkQKc2h5ITk5m+fLlPP300/z2t78tcPuCzulmxtNPP83ixYtJS0uje/fugDeD0+jRo896/MjISJYsWcLKlStp2LAh7777Lk2bNiU1NZXU1FRatWpF9+7dC9xObQQREZGzU6KmiIYOHco///lPACZNmsSyZct46623iIuLo3Xr1ixZsiTf9klJScE7USfvqu3du5dbb72V2NhYxo8ff9Ex1atX74yyhg0bAl6DrHJlb/b18PBwwLub1qJFi0L3PV3VqlVp0KDBGfWdbt26ddx8880AdOrUiY8++ug8X4mIiEjZEGrtgezsbC655BJq1KhB27Zt2bp1a4HbFXRO//LLLzl8+DAJCQn06dOH7777DihaGyEqKopatWrlq++kw4cPs3v3bn7yk5+cdTsREREpmBI1RdS3b18WLFgAwAcffEB8fDw333wzK1asIC0tLdh75WwmTpxIUlISaWlpbNu2jfT09OC6o0ePBrsLn1x69ep1QbHm5OSQnJzM4MGDgz/HxMQwYsQIbrzxxvOu74cffmDGjBn069evwPUHDhygZs2agHeHbf/+/RcUt4iISKgLtfbA/v37g+dg8M75Z3PqOT0jI4PPPvuMlJQUhg4dyoQJE84Z++n27NkTfB9OWr58OZ07dz7ndiIiIlIw3dYoori4OCZPnsyAAQOoW7cuVapUYc2aNTz55JPk5OSQkZGRb3vn8iYtODlbxPbt23nwwQcBL7mxe/du6tevD0CVKlXO+iz4+XjggQe46667aNSoEQCVKlVi1apVfPzxxyQlJZGSklLkusyMe+65h+TkZCIjIwvcJioqim+//RaAgwcPEhUVdfEvQkREJASFWnugVq1aHDx4MPhzWFjh9+BOP6dHRkbSpk0bqlevTlxcXJGSTKc6cuQIAwcOZMaMGVSqVClYvnDhQkaNGnXO7URERKRgStQUUXh4OPXr1+f5558nISEBgGeffZa5c+dSo0YNoqOj820fGRnJnj17OHHiBJ988gkAjRs3ZvDgwTRr1oycnJx8jbejR4/SpUuXM+pYvHjxecU5a9YsnHPcdddd+equUqUKNWvWJCIiotB9d+/ezRVXXJGv7PHHH6dDhw7ExcUVul2bNm2YMWMG999/P++//z7t2rU7r5hFRETKilBrD1SvXp3s7GyysrL49NNPadq0KVC0c/rVV19NRkYGOTk5bNy4Mfj4dEEKqm/o0KHcd999wWOC16Nny5YttGzZ8qzbiYiIyFmYmZZCFu/tybN48WKrWbOmZWdnm5nZjBkzrEWLFjZs2DCLjo42M7OBAwfaV199ZTt37rRWrVpZQkKCxcfH21dffWXp6enWvXt3u/nmm61Lly6WmZlp5+Orr76ygQMHBn9+6qmnrGnTpta0aVN76qmnzMysSpUq1rFjR4uNjbUnnnjCcnJyLBAIBJetW7cWum98fHy+4+3evdvCw8MtNjbWYmNj7ZVXXilwOzOziRMnWocOHWzAgAF29OjRAuM17021wt5vLVq0aNGiJdSW09sCZqHXHnjvvfesffv2dvPNN9s333xjZkU/p//f//2fdezY0QKBgO3YscPMzEaOHGkNGza0Vq1a2auvvlpgfWvWrLEaNWoE61u8eLGZmf31r3+1X//61+fcTm0ELVq0aNGipfDFmZmfeaKQ5pyzUHp/du3aRdeuXRkxYkRwpoficuLECUaPHn3Obs/79u1j6tSpJCUlnXW7Q4cO0blzZ9q1a8ezzz4bLHfOYWbuLLuKiIiEjFBrC8C52wNFPacXVVHP/UWlNoKIiMjZKVFzFqHYOCvr1AgTEZGyRG2B0qM2goiIiEezPomIiIiIiIiIhAglakREREREREREQoRmfTqLiIiIb51zdf2OozyJiIj41u8YREREikptgdKjNoKIiIhHY9SEAOfc/wHfmNlDPhx7ClDDzO4p7WOLiIjImZxzTwDNzayPD8ceBfwcCJjZidI+voiIiChR4zvnXDdgKtDMzLJ9OP6lwCfA3Wa2orSPLyIiInmcc9cCqcD1Zrbbh+NXAj4EZpnZzNI+voiIiChR4yvnXA28JMkQM3vPxzh6AJPx7t4d8isOERGRisw5FwasAuaZ2XQf42gOfIDXLkj3Kw4REZGKSoMJ+2s8sNLPJA2Amb0JbAAe9zMOERGRCu7e3H9f8TMIM9sMzASm+BmHiIhIRaUeNT5xzrUB3gSuM7N/h0A89YDNQKfcBpqIiIiUEufcFcBGINbMtoZAPNXw2gUPmNkSv+MRERGpSNSjxgfOuXC8O1X/EwpJGgAz2ws8Crya+3y6iIiIlJ4XgWmhkKQByH0UeijwonOupt/xiIiIVCRK1PjjfiADmO93IKf5PZAN/MrvQERERCoK51wCcA0wwe9YTmVmfwXeB5L9jkVERKQi0aNPpcw5dxXwN+BGM/vS73hO55xrgjfbQysz+9rveERERMoz51wk8CnQ38xW+R3P6ZxztfHi62NmH/kdj4iISEWgRE0pcs454D3gXTN7xu94CuOcGwu0A3qYPiAiIiIlxjk3HQgzs2F+x1IY59wvgDFAazM76nc8IiIi5Z0efSpddwG1gef9DuQcJgFXArf7HIeIiEi55ZzrCPQE/tfvWM7hj8A3wIN+ByIiIlIRqEdNKXHO1QE+AbqZ2cd+x3Muzrn2QApwrZnt9zseERGR8sQ5VxXYADxuZn/xO55zcc79GPgYiDazz/2OR0REpDxToqaUOOfmAXvN7H/8jqWonHMvAhFmNtjvWERERMoT59w4oBXQu6w8ZuycGw30AuLKSswiIiJlkRI1pcA5dwvwMnCdmWX5HU9R5U7H+Slwp5ml+hyOiIhIueCcuwZYBbQ0s2/8jqeonHOVgI+Al81slt/xiIiIlFdK1JQw59wleI883Wtm7/odz/lyzvUCngGam9lhv+MREREpy5xzYUAa8Ecze9HveM6Xc64F3sQIzczsW7/jERERKY80mHDJewL4sCwmaQDMbDGwGRjrdywiIiLlwBCgMjDd70AuhJltAn4P/M7vWERERMor9agpQc651sDbeHedMvyO50I55xoAm/CeSd/idzwiIiJlUXk5nzrnqgNbgFFm9pbf8YiIiJQ36lFTQpxzlYGZwENlOUkDYGZ78HrUzMx9Pl1ERETO3xTglbKcpAEws2xgGDDNOVfD73hERETKGyVqSs5oYB8wx+9AislM4Bgw3O9AREREyprcMd+aA0/5HUtxMLP3gb9STl6PiIhIKNGjTyXAOdcIWAe0NbN/+R1PcSmrs1SIiIj4qbzOouicuwzvdfU0s3V+xyMiIlJeKFFTzJxzDngHWGFmv/U7nuLmnBsHtAZ6mT48IiIi5+ScexGIMLPBfsdS3JxzA4CHgBvM7Jjf8YiIiJQHevSp+A0A6gKT/Q6khEwEfgL09TsQERGRUOecaw8kAA/6HUsJeR1IBx7wOxAREZHyQj1qipFz7nLgE6CHmf3d73hKinOuI/BH4Doz2+93PCIiIqHIOVcF+Acw3sz+6Hc8JcU51xD4O9DOzL7wOx4REZGyTomaYuSc+wPwvZn9xu9YSppzbjoQZmbD/I5FREQkFDnnxgLt8G7glOsGl3PuAaAb0Km8v1YREZGSpkRNMXHOdcabGek6M/vB73hKmnMuEm8AwTvMbKXf8YiIiIQS51wT4EOglZl97Xc8Jc05Vxn4CHjRzGb7HI6IiEiZpkRNMXDOVQe2ACPN7G2/4yktzrkE4GngejM77Hc8IiIiocA5F4Y3dfUCM/ud3/GUFudcS7wJFZqZWYbf8YiIiJRVGky4eIwD1lWkJA2AmS0APgMe8TsWERGREHIPEAG86HcgpcnMNgBzgOf9jkVERKQsU4+ai3TK3aPmZvat3/GUNufcFcBGIGBmn/odj4iIiJ+cc/WAzXhjtWz2O57SltvL+BNghJkt8zseERGRskg9ai5C7vPYM4GHK2KSBsDMduP1KJqR29VbRESkIpsCzKqISRoAM8sG7gWmO+dq+B2PiIhIWaQL64szEjgIzPY5Dr+9nPuvZoASEZEKyznXA2gJPOl3LH4ys+XAKuAJv2MREREpi/To0wVyzl0JrAfam9k//Y3Gf865a4FU4Hq8MWtGanpOERGpCJxzk4DngL8DiWa2wueQfOecq4M30UJ3M1vvdzwiIiJliRI1F8A554C3gZVmNsHveEKFc+5JoBlwM9DQzPb7HJKIiEiJym0T/IA3iG6Emd3tc0ghwzl3J3A/cKOZHfM7HhERkbJCjz5dmF8AVwDP+h1IqHDOTQSigRZAJtDA34hERERKRc3cfxOAKOfcPD+DCTHzgO+A0X4HIiIiUpYoUXOenHOXAZOBIbo7lM9jwFtAbbwk1tX+hiMiIlIqfgxUy122AEP9DSd05D4CfS/wv865Rn7HIyIiUlYoUXP+ngX+ZGZ/8zuQUGJmx8zseaAp3tg9lXwOSUREpDRUBrYBrc3s8dxZjySXmX0J/BZ4JfcxMRERETkHjVFTBM65K/ASEDl4Mzxda2aZvgYlIiIiUgY45yrjDbQ8GfgcyDSzrf5GJSIiEroq+x1AGXEL3gC57YD7lKQRERERKRozO+6cG4z3iPR84CCaultERKRQStQUzRVAI7y7QHc753aZ2UafYyp21apV23v48OG6fscRiiIiIr49dOhQPb/jEBEpK3ROKX5l9VzknKsPjAfeAzoC5a4NJSIiUpyUqCma64Ab8e4AJQOf+BtOyTh8+HBdPQpXMOecLjZERM6DzinFrwyfi/YCfwEmAFGAPhgiIiJnoTFqisA5tw5vfJq+ZrbH73hKinPO9HkomHMOM9MgiCIiRaRzSvEr6+ci51wtvLH+mpmZZoESEREphBI1EqRGdeHKeuNYRKS06ZxS/HQuEhERqRg0PbeIiIiIiIiISIhQokbOy5o1axg0aBAA6enpxMfHAxAVFcW4ceMAmDhxIldccQVJSUnB/caNG0dUVFSxxnL8+HHuvPNOYmJieOaZZ85Y379/f2JjY4mOjmbbtm0A7Ny5k+7duxMIBHj11VeLNR4REbl4RTnPfP/999x+++0EAgGeeuopwJ/zzMiRI6lTpw6zZ88OlhV07iloOxEREZFCmVmxLBEREXvxBofTUkxLRETE3uL6/RRl8T4O53bbbbfZpk2bbNCgQbZ69WozM4uNjQ2u37t3r61YscLGjRuXb79TtylMVlZWkWIwM0tJSbGJEyeamVn37t0tIyMj3/qjR4+amVlqaqqNHDnSzMzuvPNO+/e//13kY5yU+96U2u9CixYtWsr6UtRzSkHOdZ4ZNWqUff7552fsV9rnmfT0dHvttdfstddeC5YVdO4paLsLUZLnIrXjyl67TYsWLVq0lN+l2HrUnJzdQUvxLaE6rWlycjJDhgwhMzOTDh06nLG+bt26OHd+j9Bv3ryZ4cOH069fvyLvs3btWjp37gxAIBBg/fr1+daHh4cDkJWVRYsWLTh27Bhff/01Q4YMoWvXrnzxxRfnFaOIiJSOc51nPvvsM8aPH09cXBzr1q0rUp0lcZ6pV+/MmbJPP/cUtl2oUTuu/LbbRESk7NH03HLerrrqKvbt28fo0aMvuq558+Yxf/58GjZsyLBhw7j++usBWLJkCZMnT863bb9+/Rg+fHjw5wMHDlCzZk0AIiMj2b9/f77tc3JyCAQC7Nq1iyVLlvDvf/+bDRs28MUXX5CRkcHDDz/MX/7yl4t+DSIiUrzOdZ5ZtWoV//jHP6hduzb9+vUjNTW10LpK8jxTkNPPPSIiIiLnq0yNUZOamsqOHTt8jWH+/Pl06NCBnj17kpmZmW9dZmbm/2fvzsOjKrLGj3+LNWGQRBRZ9B0HHAFRBg0ykIUkZBNjZCeAiORlR0Xh56gDKAQmEURFlBEUUBCCo8gii4CAkLAoIgxKgBdkBJUlyoCsCZDt/P7opJNOOmShk+5Ozud56nng3tv3Vm4lXdWn656iS5cu+Pv7s3jxYifVsPzNnz+f3r178+6775KdnX1D53r33Xdp1KgRI0aMsA6eAbp06UJiYqJNyT94Bku+gosXLwJw8eLFQrkJqlevzrZt21i6dCmxsbF4e3vTqlUrGjRowL333svZs2dvqO5KKaXKR3H9TPPmzbnnnntKNIOzPPsZewr2PUrHb0oppVRpVYpAzY0GC0oqIyODuXPnsnXrVvr3718oGe3cuXPp378/W7duZe7cuWRkZFRIvSpSWloaH3zwAbGxsXTu3JmEhIQbOt/27dsZM2YMs2fPJiIigkWLFgGWbzqDg4NtyuzZs21e6+vry5dffglYfjcefPBBm/3p6ekA1KtXDw8PDzw9Palbty5paWmcPHnS+i2pUkop11GSfqZ58+akpKSQmppKVlbWdc9Xnv2MPQX7HqXjN6WUUqrUHPVcLjeQNPDy5cvSq1cv6dSpk4wYMUJERCZOnCjDhg2T0NBQGTRokGRmZsqdd94pbdq0keeff17mz58vffr0kYcffli+++476du3rwQGBkrfvn0lIyPDuj8iIkKioqLk2rVr0rVrV7lw4YKIiAwfPlwOHTpUqnru37/fmhjwzJkzEh0dbbO/V69ecvbsWRERefrpp+XgwYNlviciUuEJbEvShpMnT5bFixeLiEhaWpr4+vrKlStXbBI4zps3T3x8fKRp06bW+yVSfJLHixcvlirRYnp6uvTv31/8/f2tyR737t0r8+fPl6ysLAkODraW3LZITEwUf39/8fPzkz179pT4WhXdFlq0aNHi7qWs44KS9DMHDhyQwMBAad++vaxfv966vSL7GRGRuLg4adWqlbRq1Uri4uKK7HsKHldW5dkXlaW9dPxmS8cKWrRo0aLFUcWIiEMCPsYYKeu5ZsyYwe23307v3r0ZO3Ys3bt3Z+3atdxyyy2MGjWKiIgIlixZwowZM6zfei1YsIA9e/Ywc+ZMPv30nH8BMAAAIABJREFUU44cOcK4ceOIi4ujZcuWXL58mW+++YbZs2czZcoUmjVrxrVr1wB47LHHiIyMZMOGDdY6pKenExERYVMvLy8vVq5caf3/jh07WLduHXFxcWRmZhY6R3h4OOvWraNGjRq89NJLREZG4ufnV6Z7AmCMQURKl5X3BtxIG7Zs2ZI+ffowadIku/snTpzIJ598Yl2q1N1UdFsopZS7u5E+xZ7K3s+URHn2RWVpLx2/FbqHOlZQSinlEC6RTPjw4cN8/PHHvPPOO1y+fJn27dsDcN999wHQpEkTLly4UOh1DzzwAAA//vgjbdu2BeDBBx/k+++/p2HDhtb9Pj4+7N27l6eeeoqYmBgaNmxIaGiozblq1ap13WSEUPyz6rn769evX+Jn2SuL4gbGkyZNKnJwrZRSShVH+xnXo+M3pZRSqny4RKCmefPmREZG8uijjwKQmZnJd999Z5MgUESoWbOmzbPo1apZUuw0a9aMPXv28NBDD7F7925atGhBamoq3333HQB79+6ladOm3HTTTdSuXZvZs2czY8YMmzqU5BuZ5s2bk5ycTFZWFps2baJDhw42x+c+y96jRw+Sk5O5++67HXB3lFJKKaVcj47flFJKqfLhEsmEhw0bxuLFiwkJCSEsLIzjx4/bPS44OJjJkycTHx9vs7179+58//33BAUFkZycTPfu3QE4e/YsERER7Nixw7qtV69enD59mj/96U8258j9RiZ/yd/JA9SsWZNBgwbRsWNHFi1axJAhQwCsy4cOGTKEhQsX0rFjRwYNGkTNmjVv+N64OldfyeGXX34hJCSEwMBAlixZAsD06dOtU7Dr1q3L77//zoYNG7jnnnsIDg52wk+glFKqOK7e32RnZ/O3v/2N0NBQevbsCWC3b7F3nLvS8ZtSSilVPlwiR015WLBgAQAxMTE221euXMkvv/zCqFGjKr5SpeQOOWpiY2OtQY/8srOzrd+YlaeMjAzCw8P58ssvWbp0KadOnWLMmDHW/bnTpX18fHj44YdZu3YtNWpYJpKdO3eOHj16sGXLFs6dO0edOnV46KGH7E6h1ufOlVKqdBw9LnD1/uaTTz7h6tWrDBw40LrNXt9i77iScrUcNeXBncdvOlZQSinlKC4xo6aiLFmyhNdee40BAwY4uyouLzU1ld69exMSEsLIkSMByyB5+PDhhIWFMXjwYLKysliwYAGjR4/mhRdeYMGCBfTt25fIyEiSk5Pp168fQUFB9OvXj8zMTOv+hx56iEcffZT09HS6detmfW58xIgRHD58uFT1/OGHH/jLX/5C9erVCQsLY+fOnTb7jx49at3fsGFD/vOf/1j3rVmzhqioKABuvvlmateufSO3TCmlVBlUlv5mzZo17N+/n+DgYN5//33Aft9i7zh1fTp+U0opVdVU2kBNTExMoW9joqOj2b59uyaJK4G5c+cSHR3N5s2b8fb2ZteuXYAlQeCmTZs4fvw4ly5dIiYmhhkzZjBt2jQAGjRowNq1a/nhhx9o3bo1SUlJ3HvvvXz22WeAZdD6xRdf4Ofnx4oVK+jRowefffYZmZmZHD16lBYtWljrkJ6ebv32NLd07drVpp7nz5+nXr16gOWZ9HPnztnsb9myJYmJiaSlpbFz506b/StWrKBbt26Ov3lKKaVKrLL0N6dPn+buu+9m06ZNJCQkcObMGbs/b0mPq6p0/KaUUkq5SDJh5Xoqy0oOY8eOZcSIEbz11lu0bNmShg0bAnDlyhV+/fVX7rrrrtLcFqWUUg5WWfobLy8vgoKCqFGjBh06dODHH3/k1ltvLXSekh6nlFJKqaqr0syoiYmJKbckg6NGjaJBgwbW56YBunXrRkBAAKGhoZw4caLIbW+99Rbt27fH19eXr7/+ulzqVx6aN2/O+PHjSUxMZPfu3dZHhEq7kgPA7t27adasGcB1V3Lo06ePTR1K8g1ncSs53HbbbSxfvpxly5bh6elJ06ZNAUuCx4KrRCillKp4laW/8fPzY9++fQAkJyfzxz/+0e7PW9LjqqLyHMuVdNyW/3dg8+bN5VIXpZRSqlgi4pBiOZXzDBw4UI4dO1Yu505JSZH58+fL/PnzrduOHj0qIiIbNmyQ559/vshtbdu2lezsbDlx4oR069atVNfNuacOa6PiSv42vHz5svTp00c6deokoaGhcvToUZk4caJs2bJFRPLu9/bt2yUwMFDi4uJs7lF6erpER0dLYGCgREdHS0ZGhsyfP1+io6MlPDxcoqKi5Nq1ayIismzZMvH19S3Vvclv4cKF4uvrK1FRUXLhwgUREXn22WdFRGTNmjUSHBwsYWFhsnv3butrBg4cKHv37rX+/9tvv5XQ0FDx9vaW0NBQSU9Pt7lGRbeFFi1atLh7Kem4oLL0NxcvXpRu3bqJv7+/xMfHi4j9vsXecSVVnn2Rs8dxIuU7livpuC0oKKjM19CxghYtWrRocVSp8FWfvvrqK8aMGYOnpycDBgzg8ccfJyoqiqtXr/LnP/+Z+fPnk5iYyNSpU63flj3yyCMkJCQQEhJCfHw8MTEx1K1b17qkY1xcHDExMcTGxuLh4cHgwYO5fPkyYWFhvPzyy4wbN46tW7dSvXp1/vWvf9GkSZNS/3xFrUKwZcsWNm7cyCuvvGJ3W58+fViwYAFHjx7lzTffZN68eSW+pjus+lQaupKDUkpVHc5cRcid+5vrcZVVn9x1LAfFj9tCQkLIzs6mcePGzJo1i5tvvrnE59axglJKKUep8Bw169at45VXXiE0NJTs7GzAsgJC7dq1GThwoHUVhptuuolPP/2U4cOHY4zh66+/pkOHDsTHxwPQsWNH/vnPfxIZGUlKSor1/FOnTiU2NpZ27drRv39/UlJS2LZtG0lJSVSvXp2Cg5AePXrw+++/F6qjp6dnsT9LVlYW8fHxzJkzp8htQUFBtGzZkoyMDNatW1eGO1a5LVmyhLfffps1a9Y4uypKKaUqMe1vHMddx3IlGbctXbqU+vXr89FHHzFlyhRr8mqllFKqIlV4oObJJ5/kH//4BwsWLODZZ5+lRYsWDB48mN9++42ff/6ZQYMGAdCqVSsAGjduzD333AOAh4eH9Ty5SQLbtGnDzz//bN1++PBhnn/+ecCyQsPJkycZN24cMTEx3HrrrcTHx1OnTh3r8cuXLy/zz/Lcc8/xxBNPWJ+HL7jt4sWLJCQkcOTIEU6fPs2wYcNYu3Ztma/n7gp+swmWlRyio6MrvjJKKaUqLe1vype7juWKG7cB1K9fH4Du3buTkJBQuhujlFJKOUiFJxP29vZm1qxZTJs2jUmTJvHFF1/Qpk0bkpKSCAgIsH5Lkj+JYP5/58pNErhv3z7uvPNO6/bmzZszc+ZMEhMT2bNnDz4+PgQHB7No0SJuu+22QrNaevToUSiB4JUrV4r9Od5//32MMTzxxBNFbqtWrRp16tShVq1aeHl5kZqaWoo7VTWVZyJBTRColFJVU3n2Lf369SMoKAg/Pz8OHToEwObNm/Hz8yM0NJRTp04B7ru4gD3uOJYrybgNsK7stWPHDpuAjlJKKVWRKnxGzXvvvcfy5ctJTU1l7NixtG/fnri4OL755ptSnScpKYmZM2cSFBRE48aNrdvHjh3L0KFDSU1NpWbNmixbtoyePXuSlpZGtWrVGDx4sM15SvItTHx8PB999BEAJ0+eZPz48Tz55JP89a9/JTg4mJCQECZMmGB3W0hICH5+fmRmZjJhwoRS/YzK8YpbflUppZQqjYULF1KzZk2SkpKYNWsWb7/9NvHx8WzYsIEDBw7w6quv8tZbb7Fo0SK+/fZbTp06xdNPP82KFSucXfUyc8exXGnGbZ6ennh4eNis9qmUUkpVpApPJuwIucnm/vSnP1XI9ZzFlZMJu2MiQU0QqJRSFacs4wJ37FtyrV27lpSUFPr160ffvn1ZtWoVAOHh4WzcuPGGFhfI5SrJhB2hMo7ldKyglFLKUSp8Ro2qHNwxkaAmCFRKKdfmjn1LVlYWwcHBnDhxglWrVnHu3Dnq1atnsx90cQGllFJKlZxbBmp0KqrzuWMiQU0QqJRSrs0d+5bq1auzbds29uzZQ2xsLIsWLbLmOQFLvjpdXKAwHcsppZRSRavwZML5lWdyv59++olGjRoxe/ZsAEaNGkWDBg1sBgbdunUjICCA0NBQTpw4UeS2kiYAXLx4Mf7+/nTp0oVLly4Vedxrr71Gx44dGTBgAJmZmVy5coUOHTrYXaXCVbljIkFNEKiUUq7NHfuW9PR0AOrVq4eHhwd16tQhLS2N1NRUdu3aRatWrSr14gIVOZazl6TZnpKMx37++Wc6duxIYGAgjz32GFlZWXa3geX3Mrf9f//9d7cctymllHIvbjmjpqQ6d+7MyJEjARg/fjxt27a12f/mm2/StGlTNm7cyNtvv820adPsbitJAsCMjAzmzp3L1q1bWbp0KfPmzWPMmDGFjvvvf//L1q1b2bZtG6+++iqrV6+me/fufPzxx8TGxpbLfSgP7phIUBMEKqWUa3O3viU7O5uHHnrI+v9Zs2YB8Pe//53w8HA8PDxYuHAhdevW1cUFyij/WM5ekuaCSjoe8/LyYtWqVdx8882MHz+eL774Aj8/v0LbIiMjuf/++wstRuBu4zallFJuRkQcUiynshg6dKj88MMPIiLy6quvytq1a2XNmjXSqVMn8fHxkZUrV4qIyMCBA+XYsWMyceJE2bJli822lJQUiYyMlMDAQJk8ebKU1rFjx2TgwIE22+bPny/z588vdOzmzZtl7NixRW6Ljo6WtLQ02b9/vwwePNju9fbv3y+jRo0SEZEzZ85IdHS03ePWrFkjb7zxhoiI7N69W1544YUi65tzTx3WRsWV/G1YEXLb2h1UdFto0aJFi7uXiu5TcrlT31Ja5dkXFWwvVxvLpaamyqOPPmrdFxYWZvc1JR2P5TdhwgT54osvitx2yy23SEBAgIwbN85u3XLpWEGLFi1atDiqlMujTz179rR+C/Xll18SGhpKp06d2Lx5M0lJSbz99tvFniM34V9SUhKHDh2ySQaYnp5eaCpy165dy1TXrKws4uPjGTJkSJHbchMAhoeHM2rUKLvnOX/+vDV5oJeXF+fOnbuh45RSSimlnMXVxnJFJWkuqLTjrFOnTll/vqK2/fDDD2zdupUzZ85U+dxCSimlKka5PPoUEhLC9OnT6d+/Pw0bNqRWrVp89dVXTJ48maysLE6fPm1zfP7ny0Usz5/bS/iXO325Vq1ahaagltVzzz3HE088YZOzJP+2kiYA9Pb2tuZAuXjxIt7e3nav5+3tzW+//VbscVWBPn6klFLK0bRvcQxXG8vdfPPNhZI021PS8RjAtWvXGDhwIHPmzKF69epFbstdjKBbt24kJycTGRlZ4norpZRSZVEuM2pq1qxJ48aNefPNN+nRowcAr7/+OosWLWLVqlWFOlcvLy9OnTpFdnY2+/fvB+wn/MvlqBk177//PsYYnnjiiSK3FZUA8OTJkzbnat68OcnJyWRlZbFp0yY6dOhg97h27dqRlJQEYHOcO6vIRIL9+vUjKCgIPz8/Dh06ZPc1qamphISEEBgYSFRUVKVP7KyUUpWFqyWmLao/2bt3LxEREQQFBbFmzRrAknjY29u7RMGHZcuW0bRpUwAOHjxoHcs0a9aMGTNmuERf5GpjOXtJmqHs4zGAYcOG8eSTT1rPZW9bamqqdfaOLkaglFKqwjjqGSoKPNu8cuVKqVevnqSlpYmIyJw5c6RNmzYyfPhw8fPzE5G8Z5h//vln8fHxkR49ekhoaKj1ueaoqCjp1KmTREREyKVLl6Q0Cj47HBcXJ61atZJWrVpJXFyciIjUqlVLAgICJCgoSCZNmlTktvj4ePH19ZV27drJ6tWrRUQkNDS00DUXLlwovr6+EhUVJRcuXCjyuKlTp4q/v7/0799f0tPT7dZXLDdVpAKfgyvYhiVVnvkACt6X3PuVmJhofQa9oKtXr8rJkydFxPJ7984779g97vTp0xIVFSUiljZZvny53WuKVHxbaNGiRYu7l7L0KRXZn4SEhMilS5dk586d8swzz9h9jb3+JDs7W3r16mUd3+RKSUmxydNyPX379pWOHTsW2t6zZ085cuSI3fqKSIXmqBFxvbHcxo0bxdfXVzp16iTHjx8XkbKPx7766iupW7euBAUFSVBQkKxcudLutr1798oDDzwgAQEB8sQTT0hmZqbduomUb/to0aJFi5aqVRx3IiclDSzK8ePH5d5775VZs2Y5/NxZWVlFBgnyO3PmjEycOLHY49LS0sTf31+ee+45m+3ODtS4WiLB/D7//HOZN29esa//4IMP5L333rO7z5UTO2vRokWLOxagJtAZeB8XXmSgpIlp88vtT/7zn/9IVFSUdO7cWbp16yanT5+2HlOSQM2aNWvk/fffl6CgIJvtV65ckQ4dOtitby5AgHCghji+7Yq9BxWtuLGco8djJeUq4zYtWrRo0VJ5S6VdnvuOO+6wTr11tGrVqpUoid4tt9xSoqUbPT092b59uwNq5li5iQRffPFFvvzyS0aPHk1mZiaPPPIIly9fplu3bnTp0uW658hNJNiuXTv69+9PSkqK9fn09PR0IiIibI738vJi5cqVRZ4vKyuL4OBgTpw4wapVq6577cuXLzNnzhzWr19vd78mdlZKqRtnjKkFhAG9gK7AD8Cn+Y9xtf6kpIlpc+XvTw4ePMj//d//sW/fPpKSkpgyZQrTp0+/7uvz+/DDD0lISGDhwoU22zds2EB4eHhJThEHNDPGrMRynzeLSEaJK+BGihvLOXo8VlKuOm5TSilVeVTaQI26ca6WSBCgevXqbNu2jT179hAbG8uyZcvsHiciDBo0iPj4eLy8vOweo4mdlVKqbHKCM+FAb+BR4BCWoMFEETmec8wbuce7Wn9S0sS0udfP3594eXnRrl076tSpQ0hISIkCBbly8+LUqlWr0L4VK1bwzDPPFHsOEWlvjLkTS2BsErC4QNAmvcQVUkoppZRL0kCNKtL1EgnWrVsXPz8/m+OLSiQ4ZMgQWrduTVZWls3guywzatLT06lVqxb16tXDw8MDsCQIvP32222OmzBhAv7+/oSEhFi3FTyuXbt2zJkzh//3//5fpUnsrJRS5cUYUxuIwBKciQIOYgkOvCQiJ673WlfrT/Inpj1w4IBNYtri+pO7776b06dPk5WVxXfffWdNCmxPwfPt37+fVatWsX79evbt28fEiROZNGkSWVlZJCcn88ADD1zvNlqJyM/AG8Abxpg/Aj2BCViCNquwtMsmDdoopZRSbspRz1Dhgs82uztcIJmwKyUSzMrKkuDgYGs5ePCgiBROEHjy5EmpWbOmNRlgbo4ad0rsrEWLFi2uUAAPoAuwCDgHbAVGAbeX4LWSnyv1JyIlS0xbVH/yr3/9SwICAiQ4OFh++uknEREZNWqUNG3aVHx8fKw51Oz1O7ny56jZsmWLPPvss9etr4gU2xcB/wOMBnYAvwMLgEeA2td7ndhpL1V6OlbQokWLFi2OKkZEHBLw8fT0/PXq1asNHXIyBYCHh8dvV65caVRR1zPGiKN+HxzlxIkTdO7cmaeeeoqRI0cW2p+dnc3o0aOLnXp+9uxZZs6cWewz6leuXCE8PJwOHTrw+uuvW7cbYxARc52XKqVUpWCM8cCSELg3EAl8DywFlouI/XWs7Z/HpfoUR/UnJVXSfsceR/RFxpjbscy06Q3cC6zBMtNmg4hcK3i8juNuXEWP25RSSlVeDgvUKPfnaoNqV6KBGqVUZWaM8cQ2OLMXy4f65SLyaxnPqX2Kg5W1LzLGNMEStOkF/AVL0GYp8IWIXHVsLZVSSil1ozRQo6x0UF00DdQopSobY0wd4GEswZnOwB4swZkVIvKbA86vfYqDOaIvMsY0Bnpgafc2wFos7f6FiFy58VoqpZRS6kZpoEZZ6aC6aBqoUUpVBsaYP2CZMdMLS3DmW/KCM6ev99oyXEv7FAdzdF9kjGmEJWjTC/DBErRZCqzToI1SSinlPBqoUVY6qC6aBmqUUu4qJzjzCJYZFBHAN1iCM5+JyH/L8brapzhYefZFxpiGQHcsvydtgfVYfk/WiUhaeVxTKaWUUvZpoEZZaSLBommCQKWUOzHG1MWyhHYvIBz4GstMic9E5ExF1EH7FMerqL7IGHMb0A1L0OavwBdYgjZrRSS1vK+vlFJKVXUaqFEOZ4wxwCbgcxGZ7oTrtwNWA/dV1AcSpZRyNmPMTViCM72BUOArLB+uV4rIWWfWTbkvY0wD8oI27YEN5AVtLjuzbkoppVRlpYEa5XDGmBjgaaCDiGQ6qQ5vAvVFZKAzrq+UUhXBGFMPeBTLh+gQYBuWmTMrReR3Z9ZNVT7GmFvIC9r4YvlS5lNgjQZtlFJKKcfRQI1yqJzp0slAZxHZ68R61AX2A0NFZKOz6qGUUo5mjPEiLzjTCdiK5cPyKhE558y6qarDGFOfvKCNH/AlliDhahG55My6KaWUUu5OAzXKoYwxHwEnROQFF6hLJDATaK2JEJVS7swY4w10wfKhOAhIxBKcWS0i551YNaUwxtwMdMXy+9kR2Eze7+dFZ9ZNKaWUckcaqFEOY4x5GHgHS24YlwiMGGP+BfwiIi86uy5KKVUa+T789gICgS3kffi94My6KVWUnN/bLuT93iZimWmzSn9vlVJKqZLRQI1yCFd91ChnudF9OPlRLKWUKomcx0lyZyYEYHmcJDcHiM5MUG4l5zG93JlgwUASeY/p6UwwpZRSqggaqFEOYYx5A2ggIk84uy4FGWP+F3gKaC8iWc6uj1JK5VdEgtalWIIzmutDVQr5civ1Ii/xde6qZJpbSSmllMpHAzXqhhljHgTWYMkF819n16egnOXCv8TyuMCbzq6PUkoZY24lLzjTAdiI5UPr57p6jqrsclYry11KPgTYQV7QRlcrU0opVeVpoEbdEGNMTWAXMF1EFjm7PkUxxtwNfA08KCI/Obk6SqkqyBjTAOiO5cPpX4EvsMyc+VxEUp1ZN6WcxRhzE/AIlr+LMCx99afAZyJy1pl1U0oppZxFAzXqhhhjXsAysHpIXPyXyRgzFktiw0hXr6tSqnIwxtwG9MDyuEc7YD2WD6HrNDijlK2cfHe5QZtwYCeWYOYKETnjzLoppZRSFUkDNarMjDF3Ad8AfxWRo86uT3FyZv/sAaaIyL+cXR+lVOWUk8S8B5YPmz7AOizBmfWusiKeUq7OGPMHIBLL39FDWGbvfoolaONyj1krpZRSjqSBGlUmOXlfNgAbROQ1Z9enpIwx7YHPsCwhrlOqlVIOYYxpBPTEMnPmAWAtecGZK86sm1LuLido8zCWv6/OwG4sM22Wi8hpZ9ZNKaWUKg8aqFFlYox5AhgDtBORTGfXpzSMMW8B9UTkf51dF6WU+zLGNMYSnOkN/AX4HEtw5gsRuerMuilVWRlj6mAJ1vTGErz5N5a/u+Ui8psz66aUUko5igZqVKnlJMTcjyXXyx5n16e0chIXHgBiRGSzs+ujlHIfxpjbyQvO3AesxvLN/gYNzihVsYwxnliCNr2w5Lb5DkvQZpmI/OrMuimllFI3QgM1qtSMMYuA0yLynLPrUlbGmCjgTeAv+liCUup6jDF3kBecaYUlOPMpsFFErjmzbkopC2OMB5ZcNr2xBG32kRe0SXFm3ZRSSqnS0kCNKhVjzEPAu1hyvLj1iiXGmE+AoyIy1tl1UUq5FmPM/2D5lr430AJYhWXmzCYNzijl2nKCNhFY/oYfxTILODdoc9KZdVNKKaVKQgM1qsRykvklA0+KyHpn1+dG5ST/3AeEicg+Z9dHKeVcxpg/khecaQ6sxPLh7ksRSXdm3ZRSZWOMqY1lqe/eWII2B8kL2pxwZt2UUkqpomigRpWYMeY1oImI9Hd2XRzFGDMEGAb4ikiWs+ujlKpYxpg/kRecuQvLqnCfAptFJMN5NVNKOVpO0CYUy997F+Awlr/3pSJy3Jl1U0oppfLTQI0qEWOMD7AOaF2ZlsLMWWZ8C7BCRN5ydn2UUuXPGNOUvOBMU/KCM1s0OKNU1WCMqUVe0KYr8AN5M21+dmbdlFJKKQ3UqGIZY2oA3wAzRWSBk6vjcMaYFsAOwEdEfnF2fZRSjmeMaYblA1lv4I/ACiwfypI0OKNU1WaMqQmEYHl/6Ab8SN5Mm5+cWDWllFJVlAZqVLGMMc8BDwPhUkl/YYwxLwEdgEcr68+oVFVjjPkzeTNn7sA2OJPpzLoppVxTTtCmE3lBm2PkBW2OObNuSimlqg4N1KjrynlE4Fugg4j8x9n1KS85U6D/DfxDRD5xdn2UUmVjjLmbvJkzjYHlWD5kbdPgjFKqNHJmFAdjeT/pDvyC5f3kUxE56sSqKaWUquQ0UKOKlJO/ZR2QKCJTnV2f8maM8QWWAfeKyDln10cpVTI5jy/mzpxpiG1wRpOEK6VuWE7QJgjLe00P4CR5QZtK+0WWUkop59BAjSqSMaY/8DzQrqrkcDDG/BPwEJEhzq6LUqpoxpiW5M2cuRVLkPVTYIcGZ5RS5ckYUx0IxPL+0wNIIS9oc8SZdVNKKVU5aKBG2WWMuRXYjyVny7fOrk9FMcbUAw4AA0Qk0cnVUUrlY4xpRd7MmfrYBmeynVk3pVTVlBO0CcDyvtQTOE1e0OawM+umlFLKfWmgRtlljFkAnBeR0c6uS0UzxnQFXgP+IiJXnV0fpaoyY8y95M2c8QKWYvkQ9LUGZ5RSriQnaONPXtDmDHmJiP/PmXVTSinlXjRQowoxxoQB84D7ROSys+vjDMaYpcAhEXnJ2XVRqirJyY11H5YPOr2Am8gLzuzU4IxSyh0YY6oBfuS9l50jb6bNQWfWTSmllOvTQI2yYYypAyQDo0RkrbPr4yzGmMbAPiBERJKdXR+lKrOc4Exr8mbOeJKGv3TPAAAgAElEQVQXnNmlwRmllDvLCdr4khe0uUDOTBvggOhgXCmlVAEaqFE2jDFTgT+JSF9n18XZjDHDgf8F/DU5qVKOlROcaUPeB5fa5H1w2aUfXJRSlVFO0KY9ee99qeTMtAH263ufUkop0ECNyscYcz+wAWgtIr85uz7OljOYSgSWiMg/nVwdpdxeTnDmfvJmztQg7wPKbv2AopSqSnLGGX8lL2hzhbzZhPv0PVEppaouDdQowJoAbycwW0Q+cHZ9XIUx5h5gG/CAiBx3dn2Ucjc5wRkf8j6IGPJmzuzRDyJKKWV9r2xHXiA7nbxA9vf6XqmUUlWLBmoUAMaY0UBXLDlZ9JciH2PMRKAt0FXvjVLFy/nA0Za84IyQ94Fjr/4dKaVU0XLeQx/E8v7ZG8gib6aNvocqpVQVoIEahTHmTmAP4CsiR5xdH1djjKkN7AUmiMhSZ9dHKVdU4NvgXkAG+m2wUkrdkAKzEnvnbM59b/23vrcqpVTlpIGaKi5nAPA5sF1EXnF2fVyVMcYfWIJlyfJzzq6PUq4g5/2jPZbATC/gGnkfIDS/glJKOZCdPF/VyZtpo3m+lFKqEtFATRWVM4tmMrAeGAe0FZF059bKtRljZmMZFH0HnBGRJU6uklIVTlcsUUop5yuwcl5voCaWoI2unKeUUpWABmqqKGNMZ+AF4B6gP5ZO/bJza+W6cj6c/hnYjGUG0nkRedG5tVKqYuT8/vuSN3PmEnnBmQP6gUAppZwnJ2jTmrygjSd5M22+0fdopZRyPxqoqaKMMYOBscAvwL3AYBFZ49xauS5jTGPgeywrY7UHNolIf+fWyn15enr+evXq1YbOroe78vDw+O3KlSuNyvMaOcEZPyyD/p7AeXKCMyJysDyvrZRSqmxygjb3kRe0+QN5M212ikh2eVxX+/XyURH9vVLKNWmgpooyxrwPDAK+BkaIyD4nV8nlGWMaAFOw3LefRKSZk6vktowx+gXfDTDGICKmHM5bHfAnLzhzhpyltEXk/xx9PaWUUuXLGHMveUGbesAyLO/rXzsyaKP9evkor/5eKeX6NFBTRRljngJuBSZrz1o6xpgooIuIDHN2XdyVDuhujCMHbjnBmQAsg/gewH/Jmzlz2BHXUEop5XzGmFbkLfldn7ygzY4bDdpov14+NFCjVNWlgRqlVIXTAd2NudGBW05wJpC84EwKeTNnfnBMLZVSSrkqY0xL8mba3Ipt0CarDOfTfr0caKBGqapLAzVKqQqnA7obU5aBmzGmBnnBme7AKfKCM0ccX0ullFLuwBjTgryZNg2B5Vj6h20lDdpov14+NFCjVNVVzdkVcFeenp6/GmNES+Hi6en5q7PbJ5e2k3u00/V89dVXDB48GICUlBRCQ0MB8Pb2ZuLEiQDExMTQoUMHgoOD+eijjwCYOHEi3t7eDq1LZmYmAwYMoGPHjrz22muF9m/evBk/Pz9CQ0M5deoUAKNGjaJBgwYsWLDAetxbb71F+/bt8fX15euvv3ZI3YzFzQW21TDGhBlj3sMSmJkG/AT4i4iPiEzRII1SSlVtInJYROJF5H4gCDgJvAmcNMbMMsZ0MsZUN8b81RjzjTGmvqOuXZI+furUqdx+++3ExsZaX+eMPj44ONhaNm/eDMCiRYvw9fWlc+fO/PqrZVhVHn28UqoKEhEtZSiWW6fsybk3Tm8j0Xa6Lme2U2nbpXfv3vL999/L4MGDZfv27SIiEhQUZN0/cOBAOXbsWKHX5T+mKKmpqSWux7Jly2Tq1KkiIhIVFSWnT5+22R8SEiKXLl2SnTt3yjPPPCMiIikpKTJ//nyZP3++9bi2bdtKdna2nDhxQrp161bi6+cq2HaAAd4BPgZqAOHAHOA08C3wAtBMKridtWjRokWL+xbgz8DfgT3Ab8C7Of3Mv4H6BY6Vsiquj//1119l8+bNMnHiRJvXVXQfX/B6GRkZEhAQIJmZmbJ7924ZPXq0iNx4H5+fK42ptWjRUrFFZ9QopVxefHw8Q4cO5dKlS/j7+xfab4zhscce49FHH+WXX34p0Tn37dvHyJEj6dOnT4nr8fXXXxMeHg5YvlnbvXu3dV9aWhp/+MMfqFu3Lu3bt+fgQcsK1o0aFV5V86677uLq1aucP3+eW265pcTXt8dYltGeDXQCrmHJNxMH/AD8VUTaicg0ETl6QxdSSilVpYjIf0Rkqoi0BfyAX4E2QGvgsDGmriOuU1wf37BhQ4wp3dM/ju7jAapVq0ZwcDD9+vXj3LlznD17ljvuuIPq1avTpk0bdu7cCTi2j1dKVV01nF0BpZQqzl133cXZs2cZPXq03f1vvPEG9evXZ/v27bzwwgt8/PHHRZ4rISGBxYsX07RpU4YPH879998PwKpVq5g+fbrNsX369GHkyJHW/58/f5569eoB4OXlxblz56z7zp07Z90HkJVV9GP9QUFBtGzZkoyMDNatW3edn7xElgHdsExVzwIiReTbGz2pUkoplU894BngGLAOS3/jkKQ0xfXxpVFefTzA0qVLqV+/Ph999BFTpkxh6tSpHDt2jLS0NHbs2MHvv/8OOLyPV0pVUTqjxskSExP56aefnFqHxYsX4+/vT5cuXbh06ZLNvkuXLtGlSxf8/f1ZvHixk2rofNpOzjV//nx69+7Nu+++S3Z24RVE69e3PC4fEBBgfUa8KO+++y6NGjVixIgR1gEcQJcuXUhMTLQp+QdwYHlm/uLFiwBcvHjR5vn4m2++2boPLN+82XPx4kUSEhI4cuQIu3btYuzYscX89MWaCbwMLMWSBLLkXx8qpZRSJSAie0Wkvoi0FZEuItJdRFIdce7i+vjSKK8+HvLGGt27d2f//v1Uq1aNl19+mcjISFavXk2LFi3Ko49XSlVRGqhxsqICADfaUZVURkYGc+fOZevWrfTv35958+bZ7J87dy79+/dn69atzJ07l4yMjAqpl6vRdnKetLQ0PvjgA2JjY+ncuTMJCQmFjskdWB0+fBgvL6/rnm/79u2MGTOG2bNnExERwaJFiwDLt235EwUGBwcze/Zsm9f6+vry5ZdfApbfiQcffNC6r06dOqSlpZGamsquXbto1aqV3etXq1aNOnXqUKtWLby8vEhNvbFxrohsFpE4ERktIo+IyN9u6IRKKaVUBSlJH18a5dXHQ95YY8eOHTRr1gyARx55hMTERHr37k1AQIDD+3ilVBXm7CQ57looJmna5cuXpVevXtKpUycZMWKEiIhMnDhRhg0bJqGhoTJo0CDJzMyUO++8U9q0aSPPP/+8zJ8/X/r06SMPP/ywfPfdd9K3b18JDAyUvn37SkZGhnV/RESEREVFybVr16Rr165y4cIFEREZPny4HDp06Lr1Kmj//v0yatQoERE5c+aMREdH2+zv1auXnD17VkREnn76aTl48GCx58SFEp9pOxXNme1UXLvkN3nyZFm8eLGIiKSlpYmvr69cuXLFJqlfVFSU+Pv7S0BAgCQnJ1u3F5do8OLFizZJfouTnp4u/fv3F39/f2vCwb1791rPsXHjRvH19ZVOnTrJ8ePHRUQkLi5OWrVqJa1atZK4uDgREYmPjxdfX19p166drF69usTXz+VKf2NatGjRokVLafr1/ErSx8+bN098fHykadOm1rGQSMX38W3btpWAgAAJCwuTEydOiIhlzNWpUyeJiYmRtLQ0EbnxPj4/7e+1aKm6xYg45PHSKscYI9e7dzNmzOD222+nd+/ejB07lu7du7N27VpuueUWRo0aRUREBEuWLGHGjBnWyP6CBQvYs2cPM2fO5NNPP+XIkSOMGzeOuLg4WrZsyeXLl/nmm2+YPXs2U6ZMoVmzZly7dg2Axx57jMjISDZs2GCtQ3p6OhERETb18vLyYuXKldb/79ixg3Xr1hEXF0dmZmahc4SHh7Nu3Tpq1KjBSy+9RGRkJH5+fsXdG0SkdFnfyom203XvjdPaqbh2KYmWLVvSp08fJk2aZHf/xIkT+eSTTzh06NANXccVudLfmFJKKeWIfj2/qtzH56f9vVJVlyYTLieHDx/m448/5p133uHy5cu0b98egPvuuw+AJk2acOHChUKve+CBBwD48ccfadu2LQAPPvgg33//PQ0bNrTu9/HxYe/evTz11FPExMTQsGFDQkNDbc5Vq1YtEhMTr1vP4p7Hzd1fv359u/vdnbaT+ypucDZp0qQiB3hKKaWUcl3axyulqjrNUVNOmjdvzvjx40lMTGT37t1ERUUB2CwvKCLUrFnTZnWY3ASkzZo1Y8+ePQDs3r3b+izsd999B8DevXtp2rQpN910E7Vr12b27NmFliBMT08v9Dxu165dC9UzOTmZrKwsNm3aRIcOHWz25z6vm5WVRXJyMnfffbcjbo/L0HZyb66e5PmXX34hJCSEwMBAlixZAsD06dOt7Vy3bl1+//13srOz+dvf/kZoaCg9e/Z0xo+hlFJKuRRX7+MTEhLo2LEj7dq145///CcAGzZs4J577iE4ONh6nL2xgFJKFcvZz165a6EEuU/69OkjnTp1ktDQUDl69KhMnDhRtmzZIiIiAwcOlGPHjsn27dslMDBQ4uLiZP78+dbnYNPT0yU6OloCAwMlOjramvskOjpawsPDrblPRESWLVsmvr6+163P9SxcuFB8fX0lKirKmkfl2WefFRGRCxcuSFRUlPj6+srChQtLdD5c6HlabaeiObOdimuXksrfVvllZWU55PzFSU9Pl6CgIMnMzJSPP/5Ypk+fbrP/ySeflF27dklmZqaEh4dLRkaGdd/vv/8uwcHBIiLy8ccfy4IFC0p8XVf6G9OiRYsWLVoc1a/n5+p9fHp6uoiIZGZmygMPPCAilr796tWrNvlzrjcWKI7291q0VN2iOWrKyNHP4pbEggULAIiJibHZvnLlSn755RdGjRpVofUpiis9T6vtVDRXzlGTmppKTEwMZ8+epUWLFsyePZvY2FhSUlL48ccfufPOO5kzZw533XUX3t7eRERE0KpVK9avX8/FixeZMmUKU6dO5dSpUzRp0oRFixaRkJDA+vXrOXfuHLVq1WLZsmVER0ezcOFC6tWrx4gRIxgzZgwtWrQo8c9x4MAB3nvvPd5++23Onj3Lk08+ySeffGLd//DDD/PZZ59Ru3ZtBgwYwPjx42nZsiUAixYt4vTp0zz33HMMGDCARo0a8e233zJgwAAGDx5c3P1zmb8xpZRSqjTjrcrSx+e6cuUKQ4cOtVmxKjg42PpY+/XGAsXR/l6pqksffXJzS5Ys4bXXXmPAgAHOroq6Dm2n0pk7dy7R0dFs3rwZb29vdu3aBVhyB23atInjx49z6dIlYmJimDFjBtOmTQOgQYMGrF27lh9++IHWrVuTlJTEvffey2effQbAzTffzBdffIGfnx8rVqygR48efPbZZ2RmZnL06FGbAVxJHkk7f/489erVAywJoM+dO2ezv2XLliQmJpKWlsbOnTtt9q9YsYJu3boBcPr0ae6++242bdpEQkICZ86ccfAdVUoppVxDZenjAeLj42nevDn3339/kT/v9cYCSilVFE0m7EYKztAAiI6OJjo6uuIro4qk7XTjKkuS57FjxzJixAjeeustWrZsScOGDQHLt2+//vord911F2AZAAYFBVGjRg06dOjAjz/+yK233lri+6WUUkq5i8rSxwOMHz+ev/3tbwQGBjJkyBC7xxQ1FlBKqevRGTVuLCYmptySrPXr14+goCD8/Pwq/dKH5a0826lbt24EBAQQGhrKiRMnyuUazlBZkjzfdtttLF++nGXLluHp6UnTpk0BS7LB/Euy+/n5sW/fPgCSk5P54x//WMY7p5RSSrm2ytLHp6enA5agT506dahdu7bdn7eosYBSSl2PBmqUXQsXLiQpKYkpU6Ywa9YsZ1dHFeHNN99k+/bt/P3vf+ftt992dnUcZtiwYSxevJiQkBDCwsI4fvy43eOCg4OZPHky8fHxNtu7d+/O999/T1BQEMnJyXTv3h2As2fPEhERwY4dO6zbevXqxenTp/nTn/5kc47cb9vyl5UrV9ocU7NmTQYNGkTHjh1ZtGgRQ4YMAWD06NEAfP7553Tq1IkuXbrw4osvWgeh+R97Ahg8eDAfffQRAQEB+Pn50bhx4zLeOaWUUsq1VZY+/vXXXyc4OBh/f3+io6Px9PRk9+7dhIWF8f333xMWFkZGRkaRYwGllLoeTSZcRqVJmvbVV18xZswYPD09GTBgAI8//jhRUVFcvXqVP//5z8yfP5/ExESmTp1q/bbgkUceISEhgZCQEOLj44mJiaFu3brWjikuLo6YmBhiY2Px8PBg8ODBXL58mbCwMF5++WXGjRvH1q1bqV69Ov/6179o0qRJmX7OtWvXkpKSUmxy0wL3xmUSn1WVdtqyZQsbN27klVdeKc29cdlkwuXBXZI8l4Qr/Y0ppZRSzujX86tMfXx+2t8rVXVpjpoKsG7dOl555RVCQ0PJzs4GYM2aNdSuXZuBAwdy+PBhAG666SY+/fRThg8fjjGGr7/+mg4dOli/SejYsSP//Oc/iYyMJCUlxXr+qVOnEhsbS7t27ejfvz8pKSls27aNpKQkqlevTsGOs0ePHvz++++F6ujp6Wn9f1ZWFsHBwZw4cYJVq1aVy31xNe7YTmBpq/j4eObMmePwe1LZLVmyhLfffps1a9Y4uypKKaWUciDt45VS7kwDNRXgySef5B//+AcLFizg2WefpUWLFgwePJjffvuNn3/+mUGDBgHQqlUrABo3bsw999wDgIeHh/U8uUnS2rRpw88//2zdfvjwYZ5//nnAkqH+5MmTjBs3jpiYGG699Vbi4+OpU6eO9fjly5cXW+fq1auzbds29uzZQ2xsLMuWLbvBu+D63LGdAJ577jmeeOIJ6zPayj5N8qyUUkpVTtrHK6UqGw3UVABvb29mzZpFSkoKw4YNY+DAgbRp04bx48fz+OOPW2dS5H9m1d7zq9999x3Nmzdn3759PPPMM9btzZs3Z8iQIbRu3ZqsrCyMMVy7do2HH36YKVOmsG7dOnr27Gk9viQzNdLT06lVqxb16tWzCUJUZu7YTu+//z7GGJ544gmH3QellFJKKaWUUs6jgZoK8N5777F8+XJSU1MZO3Ys7du3Jy4ujm+++aZU50lKSmLmzJkEBQXZJBsdO3YsQ4cOJTU1lZo1a7Js2TJ69uxJWloa1apVK5RfpriZGtnZ2Tz00EPW/1eVZMLu1k5gmQX017/+leDgYEJCQpgwYUKp6qqw5hAqmGjQEd566y0++ugjqlWrxvTp0/H19aVfv36cOnWKjIwMPvjgA1q2bOnw6yqllFJVVXn26926dePMmTPUrl2bDz/8kDvuuMPutuDgYOtrJkyYQEhIiMPropSq3DSZcBlVdNK08ux0HM2VEp9pOxWtqiUTLkp5ttmDDz7It99+y6lTp3j66adZsWIFGRkZ1KxZk6SkJJYtW1am1bpc6W9MKaWUqir9+rFjx2jatCkbN25k48aNTJs2ze624OBgEhMTb/h62t8rVXXp8txKKbfw1Vdf0b59e4KDg3n//fe5du0a4eHhdOzYkf/93/8FIDExkc6dOxMZGUlkZCTvvPMOvr6+jB8/HrAM3p5++mk6duzISy+9ZHP+X3/9lUceeYSgoCD+8Y9/ADBu3DgCAgIICgri1KlTpa7zXXfdxdWrVzl//jy33HILYFnuEyA1NZU2bdqU+X4opZRS7swd+/WmTZsCUKNGDWrUqFHktmrVqhEcHEy/fv04d+5cGe6OUqqq00ef3ETusoPKtWk7lR93XJUrKCiIli1bkpGRwbp164CquaKaUkopVZA79utgf7XNgtuWLl1K/fr1+eijj5gyZQrTpk1z0F1TSlUVGqhRSrkFd1uV6+LFiyQkJHDkyBFOnz7NsGHDWLt2bZVcUU0ppZQqyN369Vz2VtssuK1+/foAdO/enYSEhNLdGKWUQh99qlAxMTH89NNP5XLun376iUaNGjF79mwA+vXrR1BQEH5+fhw6dKjI1y1evBh/f3+6dOnCpUuXijzutddeo2PHjgwYMIDMzEyuXLlChw4d7C6HWBlUZFtt3rwZPz8/QkNDi5yGm5qaSkhICIGBgURFRVXJtspdlWvatGlMmjSJL774gjZt2pCUlERAQECpVuUC2LdvH3feead1e/PmzZk5cyaJiYns2bMHHx8fgoODWbRoEbfddpt1RkyuHj16EBwcbFOuXLli3V+tWjXq1KlDrVq18PLyIjU1FbCsqAZUqRXVlFJKqYLcrV8H+6tt2tt28eJFAHbs2GET0FFKqZLSGTWVSOfOnRk5ciQACxcutCYsnTVrlt2EpRkZGcydO5etW7eydOlS5s2bx5gxYwod99///petW7eybds2Xn31VVavXk337t35+OOPiY2NLe8fq1LK31bx8fFs2LCBAwcO8Oqrr/LWW28VOr5GjRokJCTQpEkT5s6dy6JFi3jyyScLHVeZ28rdVuWqW7cuISEh+Pn5kZmZyYQJE6rsimpKKaVUQe7Wr4P91TbtbQsJCcHT0xMPDw99LF4pVTYioqUMxXLrLIYOHSo//PCDiIi8+uqrsnbtWlmzZo106tRJfHx8ZOXKlSIiMnDgQDl27JhMnDhRtmzZYrMtJSVFIiMjJTAwUCZPniyldezYMRk4cGCh7Z9//rnMmzfP7mv2798vo0aNEhGRM2fOSHR0tN3j1qxZI2+88YaIiOzevVteeOGF614z5944vY2kQDuJuF5bpaamyqOPPmrdFxYWVuzrP/jgA3nvvffs7itNWzmznQq2S0XJbUN350p/Y1q0aNGiRYv26+VD+3stWqpu0Rk1DtCzZ0+WL1/Oiy++yJdffsno0aPJzMzkkUce4fLly3Tr1o0uXbpc9xz2Ep7lfiuQnp5ORESEzfFeXl6sXLmyyPOVJGHp+fPnqVevnvV8RWWlL+lx7sDV2urcuXPWewuWdruey5cvM2fOHNavX293f2VqK6WUUkoppZSqijRQ4wAhISFMnz6d/v3707BhQ2rVqsVXX33F5MmTycrK4vTp0zbH53++VsTy/K29hGe5H/5r1apFYmJiqepUkoSl3t7e1mdoL168iLe3t91zeXt789tvvxV7nDtwtba6+eabrW0AlrwmRRERBg0aRHx8PF5eXnaPqUxtVR50+rFSSilVeWi/rpSqrDSZsAPUrFmTxo0b8+abb9KjRw8AXn/9dRYtWsSqVasKffj28vLi1KlTZGdns3//fsB+wrNc6enphZKbde3a9bp1spew9OTJkzbHNG/enOTkZLKysti0aRMdOnSwe1y7du1ISkoCsDnOHblaW9WpU4e0tDRSU1PZtWuXdWWDgm0AMGHCBPz9/QkJCbFuq8xtVZCrJXjO9e9//9smoDd16lQ6duxIz549rQmECzp37hx+fn4EBQURFRVlk6xw2bJlNG3aFKDSJIJWSimlCnK1fr2ohRt69OiBt7e39Yu4ki7wkJ2dja+vL3Xr1rX+nPb6f+3rlVJ2OfvZK3ctFHgWd+XKlVKvXj1JS0sTEZE5c+ZImzZtZPjw4eLn5yciec/R/vzzz+Lj4yM9evSQ0NBQa96TqKgo6dSpk0RERMilS5ekNPLnIMnKypLg4GBrOXjwoIiIhIaGFnrdwoULxdfXV6KiouTChQtFHjd16lTx9/eX/v37S3p6eqFr5ocLPU9bsJ1EXKutREQ2btwovr6+0qlTJzl+/LiIFG6DkydPSs2aNSUoKEiCgoKsOWpupK2c2U722qU45fkcesH7ExISIpcuXZKdO3fKM888c93XDho0SHx8fERE5NSpU9KlSxcREVm6dKnMmDHD7muysrIkMzNTRERiY2Nl2bJl1n19+/aVjh07Flk3Edf6G9OiRYsWLVoqQ79+9epVOXnypIhYxobvvPOOiIikpKTY5Cws6jh7UlJSbH7Oovp/dxhTa9GipWKLPvrkIF26dOHChQvW/w8dOpShQ4faHJN/euaePXsKnWP16tVlvn6NGjXYvXs3s2fPZuTIkWzZssVmf3Z2tnW2Rn4DBgxgwIAB1v+fPXuWgICAQse9+OKLvPjii9b/X7lyhccff9wtZ2y4WluFhYURFhZm3W+vrZo0aWKdJZWrsrTVsGHDeP7557n77ruZNm0arVu3Jjs7mzfeeIMLFy4wceJEm7xBsbGx1tlKMTExxMbG4uHhweDBg7l8+TJhYWG8/PLLZa5PWloaf/jDH6hbty7t27fnpZdeKvLYAwcO8D//8z/8+OOPAPzyyy/Wtrv//vv59NNPefbZZwu9Lv/MrezsbO6++24APv/8c8LDw1m4cGGZ66+UUko5k7v267Vr16ZJkyaAZawmYnnkvVGjRiU6zp6Cry2q/1dKqYI0UFNJ3HHHHdZHc+ypVq2a3SW6C7rllltKtIyzp6cn27dvL00VVQ5tK1vunOD5zTffZOrUqdbp0M2aNWPXrl1kZWWxefPm6yZz3rNnD8OHD6d27do899xzAHz44YckJCRooEYppZTbcud+HYpfuKG0xxVkr/9XSqmCNFCjlHIqd03wfOTIEby8vLj11lut2xo0aMBjjz1GaGgo7dq1o2HDhkVep23btuzevZs33niDDz/8kHvvvRc/Pz9q1apV4roqpZRSrsZd+/Xc6xe3cENpjrOnYP8/atSoUr1eKVU1aKBGKeVU10vwXLduXfz8/GyOLyrB85AhQ2jdujVZWVk2g77SfvOWP8HzgQMHbBI833777dbjkpOT+fbbb+ncuTP79u1jxIgRvPvuuwwePJjBgwezaNEia9Cl4GvT09Ot+3K/5du/fz+rVq1i/fr17Nu3j4kTJzJp0qTS31CllFLKidy1Xwf7CzfYU9QCDwXPV5C9/l8ppexydpIcdy0eHh6/AqKlcPHw8PjV2e2j7eTa7QRIfu6Y4Dm/oKAg67979eolIYX36bIAACAASURBVCEhMmbMGMnKyrL72r1790pgYKAEBwdL9+7dJTU1tcjzaTJhLVq0aNHi6qUy9OtFLdwwatQoadq0qfj4+Mi8efNKtcBD7969pXHjxuLv7y+rV68usv/XZMJatGgpWIyIoJRSFckYI6703nPixAk6d+7MU089xciRIwvtz87OZvTo0SXKHVTQ2bNnmTlzZonyCRV05coVwsPD6dChA6+//rp1uzEGETHXealSSilVYapSv25PefT1oP29UlWZBmqUUhXO1QZ07kYHbkoppVyJ9uvlQ/t7paquorNpKaWUUkoppZRSSqkKpYEapZRSSimllFJKKRehgRqllFJKKaWUUkopF6HLcyulKpyHh8dvxpiGzq6Hu/Lw8PjN2XVQSimlcmm/Xj60v1eq6tJkwkqpSsUY8zrQSEQed9L1pwJNRaSPM66vlFJKVUbGGAN8BuwVkVgn1WEpcEhEXnLG9ZVSVYcGapRSlYYxxgdYB9wnIv91Uh08gWRgtIiscUYdlFJKqcrGGNMT+AfwgIhcc1IdGv9/9u4/zsY6///44z0Mw5YZSkrtFm0KW1ry8WOMGTNYixSFSjKbSNYUn7b9VipDZkO/bIr8KMpoazcVWUrSTFNRS3639EvyY6T8CDOjMeP1/eOY8zHMMMPMXOeced5vt/et5rquc53XXIfr9fY+7+v1BtYCHcxsvRcxiEjloIEaEQkJzrmqwGfA383sJY9jSQBexDdgdMDLWERERIKdcy4K2AD0NbOPPI7lTiARaGdm+V7GIiKhS8WERSRUDAd2Ay97HYiZvQ98AIz1OhYREZEQMB542+tBmqOmA4eBu7wORERCl2bUiEjQc841AP4DtDKzb7yOB8A5dw6wHrjezD71Oh4REZFg5JyLAf4BNDWzn72OB8A51xjIwPcY1lav4xGR0KMZNSIS1I4WF3weeDxQBmkAzGw3cC8w3TkX7nU8IiIiwcY5Vx2YBtwTKIM0AGb2X2AS8OzRfoiISJnSQI2IBLt+QD3gKa8DKcI/gB3AX7wOREREJAg9CGwC3vA6kCKMAy4DbvA6EBEJPXr0SUSClnPuXHyPF11rZv/xOp6iOOcuAVYAbczsK2+jERERCQ7OuSZAOnC1mW33Op6iOOeigX/iWzxgr9fxiEjo0ECNiAQt59xLwB4zG+F1LCfjnPtfoBvQ0XTTFREROSnnXBi+GjBzzGyy1/GcjHNuChBmZnd6HYuIhA49+iQiQck51wmIBR72OpYSeAaIxLecp4iIiJzcnUBBDbpAdz/QzTnX3utARCR0aEaNiAQd51xNYB2QZGYLvY6nJJxzvwfeAa40s11exyMiIhKInHMXAquBODPb4HU8JeGc6wk8BjQzs1+8jkdEgp8GakQk6DjnxgO/MbObvY6lNJxzE4CLzOwWr2MREREJRM65N4D1ZvaI17GUhnPuTWCtmY3yOhYRCX4aqBGRoHLMzJSrzOwHr+MpjaMzgdYDfzazRV7HIyIiEkiOmZlytZkd8jqe0gjGmUAiErg0UCMiQcM5VwX4FHjOzGZ6Hc/pOFpbZzq+FSIOeh2PiIhIIHDORQIbgFvM7EOv4zkdzrm7gFuBGDM74nU8IhK8VExYRILJ3cB+YJbHcZw2M3sP+BAY43UsIiIiAeQxYGGwDtIcNfXof7UClIicEc2oEZGg4Jy7BFgBtDGzr7yN5sw4587F9wjUtWb2H6/jERER8ZJzLhr4J9DUzPZ5Hc+ZcM41AdLxPb613et4RCQ4aUaNiAQ855wDpgBPBvsgDYCZ/QTcB0x3zoV7HY+IiIhXnHPV8T0SPDzYB2kAzOwLYDIwyetYRCR4aaBGRILBTcCFwBNeB1KGUoFdwAivAxEREfHQ/wO+Bl73OpAy9BjQ5GhxZBGRUtOjTyIS0Jxz5+B7TOh6M/vU63jKknOuIfAZ0MrMvvE6HhERkYrknGsMZAC/N7OtXsdTlpxzMcA/8D3O9bPX8YhIcNFAjYgENOfci8ABM7vH61jKg3PuPqAz0Nl0QxYRkUrCORcGpAH/MrOQfEzIOTcVyDezoV7HIiLBRY8+iUjAcs7FAwnAQ17HUo6eBs4B+nsdiIiISAW6A6iGr55LqPp/wHVHiyWLiJSYZtSISEByztUA1gIjzGyB1/GUJ+dcC2Ah8Dsz+9HreERERMqTc+4CfDk+3szWeR1PeXLO3QiMwfd41y9exyMiwUEDNSISkJxzfwMuNbO+XsdSEZxzTwLnmZlm1oiISEhzzv0L+NLMRnodS3k7unLlW8BKMxvjdTwiEhw0UCMiAcc5dxWwBLjKzHZ6HU9FcM79Cl/R5CFm9q7X8YiIiJQH51wPfKs4XmVmh7yOpyI4534NfA60N7P/eh2PiAQ+DdSISEBxzlUBlgHTzGyG1/FUJOdcF2AKvkegsryOR0REpCw552oBG4D+ZpbmcTgVyjk3DOgLxJrZEa/jEZHApmLCIhJo/gxkAy94HUhFM7N3gE+AZI9DERERKQ8pwOLKNkhz1BSgKr4iyiIiJ6UZNSISMJxzvwFWAu3MbJPX8XjBOXcesA74o5l97nU8IiIiZcE51waYi2/W6B6v4/GCc+5KYCm+x74yvY5HRAKXZtSISEA4WmxvMvD3yjpIA2Bmu4C/AjOcc1W9jkdERORMOeeqAdPxreRYKQdpAI6ucDUVmOR1LCIS2DRQIyKBojdwCTDB4zgCwcvAHuAe51xD59zVXgckIiJSWs65Fs65i4H7gC3APz0OKRCMBa5yzl3ndSAiErj06JOIeM45VxtfccEbzGyZ1/EEAufcb4Hl+FbGuMzMBnockoiISKk45+YAa/DNFG1uZt97HFJAcM7FAbOBpma23+NwRCQAaaBGRDznnJsBHDKzYV7HEgicc9WBVGAn0BbYZWZ/9DYqERGR0nHOfQDUBtKAC4B+ZpbnaVABQn0fETkZ1T8QEU8d/VbpD0BTj0MJGGb2i3PuLeBx4GzgXI9DEhEROR2NgRr48tj/apCmkPuADc65OZpNLCLHU40aEfGMcy4CX1G9YZr6W5iZzcHXwV0A1PE4HBERkdNRC3gLaGxmqk9zDDPbC4wAph8ttiwi4qdHn0SkwjnnLgG2A6OAK8zsRk8DCnDOOWe6WYuISJBR/jq5oytevo2vJt0E4HzV8RER0ECNiHjAObcceBp4FrjKzDI9DklERESkwjnnfgN8DiQBd5pZnLcRiUgg0ECNiFQ459w24Ad806F3m9lkj0MSERERqXDOuSSgLr56fXXM7DKPQxKRAKBiwiJSoZxzVfCt/GDAPcBD3kZUtmrUqLHz0KFD9byOI1hERET8kJOTc77XcYiIVBTlifIR5PlkKJALnKPHxUQENKNGRCqYc+58IBP4N74pvts9DqlMqX9VOs45zMx5HYeISEVRnigfwZxPjj7+NAPoBNQyswMehyQiHtNAjYhUqKOF81qY2QqvYykP6oCXTjB3rEVETofyRPkIhXzinLsmVPtHIlI6GqgRESlD6oCXTih0rEVESkN5onwon4hIKAnzOgARkcrsk08+YeDAgQBkZmaSkJAAQFRUFKNGjQJgz5499OnTh7i4OMaOHQvAqFGjiIqKKtNY8vLy6N+/PzExMTz++OMn7E9KSqJu3brMmjXLv+3666+nXbt2JCQksG3btjKNR0REilaS3DFu3DguvPBCkpOT/a/zInfExcX529KlSwGYPXs2bdq0oUuXLuzcubNM4xERCQlmpqamFgQtIiJiJ74CvGolaBERETu9+Jx8t9XS6d27t61Zs8YGDhxoH330kZmZxcbG+vfffffd9uWXX57wumOPKU5WVlaJ45g7d66NGzfOzMy6d+9uu3btKrQ/MzPTZs6caTNnzvRv+/bbb83MbPHixXbfffeV+L0KHL1env/9UlNTU6uodjp5oiinyh07d+60pUuX2qhRowq9rqJzx/Hvd/jwYWvXrp3l5eXZihUrbPjw4SV+r5OpiHyivlhw9MXU1EKhaUaNSJA4dOhQPa9vGMHUgmlFjZSUFAYNGsSBAweIjo4+Yf9///tfHn30UeLj4/nss89KdM61a9dy11130bdv3xLHsWzZMjp16gT4vgFdsaLwY/Lnn3/iYhoNGjQAoGrVqlStqoUERUQqyqlyR7169fCVhSu58sgdYWFhxMXFcfPNN7N37152797NRRddRJUqVWjWrBnLly8vVYxeUl8sdPtiIoFGvWoREY9deuml7N69m+HDhxe5PyMjg88//5w6derQt29f0tLSij1Xamoqc+bMoUGDBtx5551cffXVAMyfP5+nnnqq0LF9+/blrrvu8v+8b98+atWqBUBkZCR79+4tUfz5+fmkpKQwbdq0Eh0vIiJn7lS5ozTKM3e8/vrr1KlTh1deeYXHHnuMcePGsXnzZrKzs/n444/Zs2fPGccvIhJqNFAjIuKxmTNn0rt3b55//nn69u1LWFjhyY6NGjWicePGAKf8dvT555/nsssuY8iQIVx11VX+7T169KBHjx4nfW1UVBT79+8HYP/+/dSvX79E8d97773cdtttNGzYsETHi4jImTtV7iiN8swdderUAaBnz56kpqYSFhbGww8/TNeuXbnqqqu4/PLLTztuEZFQpUefRCqxtLQ0vvvuO09jmDNnDtHR0fTo0YMDBw4U2nfgwAF69OhBdHQ0c+bM8SjC8pWdnc2LL75IcnIyXbp0ITU19YRjGjVqRGZmJllZWeTn55/0fB999BEjRoxgypQpdO7cmdmzZwO+b0WPLegYFxfHlClTCr22TZs2vP/++4Dvz8Y111xzyvhfeOEFnHPcdtttJf2VRUTkDJUkd5RGeeaOgkGcjz/+2D+g361bN9LS0ujduzft2rU7o9iDnfpiIlIkr59dVFNTK1mjjIoPHmvUqFH2wQcfnLA9Pz+/zN+rKLm5uRYbG2t5eXn26quv2lNPPVVo/5NPPmmvvvqq5eXlWWxsrOXm5pb43HhUpLa0n9OYMWNszpw5ZmaWnZ1tbdq0sZycnELFFzds2GDt27e3Vq1a2TvvvOPffqqCkPv37y9U+PdUcnNzrV+/fhYdHe0vDLlq1Sr/OcaOHWtNmjSxJk2a2NixY83MrFq1atauXTuLjY210aNHl/i9Cnj1Oampqal51coin5ckd8yYMcOaN29uDRo0sKSkJP/2is4dLVq0sHbt2lnHjh1t27ZtZmY2bNgw69ChgyUmJlp2dnaJ3+tkKiKfqC8WHH0xNbVQaM7MvBkhEpFScc5ZSf++ZmVlkZiYyO7du7n88suZMmUKycnJZGZm8s0333DxxRczbdo0Lr30UqKioujcuTNNmjThnXfeYf/+/f5nyHfs2EH9+vWZPXs2qampvPPOO+zdu5dq1aoxd+5c+vTpw8svv0ytWrUYMmQII0aMKNUU5g0bNjB16lSeeeYZdu/ezdChQ3nttdf8+3v37s3UqVOpU6cOSUlJDB061P8IUAmuF2ZWuiqKZaA0n9PJXHHFFfTt25fRo0cXuX/UqFG89tprbNy48Yzfy0tefU4iIl4pqzxRlMqSO4pSEflEfbHg6IuJhAI9+iQSgqZPn06fPn1YunQpUVFR/pWCfve737FkyRK2bt3KgQMHSExMZOLEiUyYMAGAunXrsnDhQr788kuuvPJK0tPTadq0KW+99RYAtWvX5t1336Vt27a8+eab9OrVi7feeou8vDy+/fbbQh2D3NzcE6ZLX3fddYXiPFUBwtMtbhsKNm7cWGxHG2D06NEh2dEWEZHTp9wRONQXE5EzoWLCIiFo06ZNvPrqqzz33HMcPHiQVq1aAb7OAUD9+vX5+eefT3jd73//ewC++eYbWrRoAcA111zDmjVrqFevnn9/8+bNWbVqFX/+859JTEykXr16JCQkFDpXtWrVTro6EZxYgDAqKqrI/XXq1Clyv4iIiEggUl9MRM6EZtSIhKBGjRoxcuRI0tLSWLFiBd27dwcKrxhkZoSHhxcqTluwYkTDhg1ZuXIlACtWrPAX/1u9ejUAq1atokGDBpx99tlUr16dKVOm0Ldv30IxlORbnEaNGrFu3Try8/NZsmQJrVu3LrS/oEBhfn4+69at47LLLiuLyxMUAr24YGpqKjExMbRs2ZJnn30WgMWLF9O4cWPi4uI8iFZERIIxd3zxxRdER0cTHR3Nww8/7EXI5UJ9MRE5ExqoEQlBgwcPZs6cOcTHx9OxY0e2bt1a5HFxcXGMGTOGlJSUQtt79uzJmjVriI2NZd26dfTs2ROA3bt307lzZz7++GP/thtvvJFdu3ZxySWXFDpHwbc4x7Z58+YVOiY8PJzbb7+dmJgYZs+ezR133AHA8OHDAbjjjjt4+eWXiYmJ4fbbbyc8PPyMr02wKK6zfeTIkQp5/8OHDzN9+nQ+/PBD+vXrx4wZMwrt79u3LxkZGSxfvpwXX3wRgJYtW/o7kCIiUvGCMXc8//zzTJgwgY8//pjly5ezb9++Com1vKkvJiJnQsWERYJEeRYfLIlZs2YBkJiYWGj7vHnz+P7770lKSqr4oE4iUIsJh0pxwQI5OTkMGjSo0NKwcXFxp5xqfcz1UqFBEalUTiefh3LumDp1KhdddBFdunShS5cuzJ8/nxo1apTq+kDgFRMuD+qLiVQemlEjIqftn//8J48//jj9+/f3OpSgESrFBQFSUlJo1KgRV199dblcKxER8Qnl3BEfH+8fEGrVqtVpDdJUZuqLiYQmFRMWkRI5/tsbgD59+tCnT5+KDyaIhUpxQYCRI0fyl7/8hfbt23PHHXeowKCISDkJ5dzx0EMP8eqrr3L11Vdzww038N13353wCI/4qC8mUnloRo2IlFhiYmK5FincsWMHERER/vfo3LkzcXFxNG/enOuvv77c3rcihUpxwdzcXMDXca9ZsybVq1c/42sjIiJFC+XcYWacc845hIWFERkZeUIBYimsovtis2fPpk2bNnTp0oWdO3eW2/uKSGGaUSMiAWPixImFOnWLFy8GYNKkSSEzFXrw4MEMHDiQp59+mrCwMKZPn17kcXFxcTz44IMsX76cCy+80L+9Z8+e3HrrrcTGxnL++edz//33k5qa6i8uWL16df73f/8X8BUXfOKJJ4otLngyxxYXPOecc5gzZw7gKy44ceJEnnjiCRYvXkxubi79+/enRo0arFixgvvvv581a9bQsWNHFi1apKKDIiJlIJRzx//7f/+Pm2++mSpVqtC4cWOuvPLK079QcsaO7Yvl5eUxbdo0PvroI1avXs348eN5+umnPY5QpHJQMWGRIFGaAnaffPIJI0aMoEaNGvTv359bb72V7t27c+jQIX77298yc+ZM0tLSGDdunP/btm7dupGamkp8fDwpKSkkJiZy1lln+VccGDt2LImJiSQnJxMREcHAgQM5ePAgHTt25OGHH+bBBx/kww8/pEqVKvzjH/+gfv36pfr9fvzxRx555BFycnJITk4u1EHs3LkzqampnHfeeaW5XgFZTLg8BFtxwWOp0KCIVDZeF6QtEMy5oyiBVkw4FPpiNWrUYPjw4fzjH/8gLy+PmJgYli1bVprrpRwvcpr06JNICFq0aBF/+9vfSEtL409/+hPh4eEsWLCAjIwMjhw5wqZNmwA4++yzWbhwIb/+9a9xzrFs2TLef/99/3liYmLIyMjg888/JzMz07993LhxJCcnk56ezsaNG8nMzCQjI4P09HTS09O54IILCsXTq1evE6ZL5+TkFDpm4sSJDBs27ITfZc+ePeTl5ZVqkEZUXFBEREpPuaPshEJfrG7dumzevJns7Gw++OAD9uzZUx6XSkSKoEefRELQ0KFDefTRR5k1axb33HMPl19+OQMHDuSHH35gy5Yt3H777QA0adIEgAsuuIDGjRsDEBER4T9PQZHBZs2asWXLFv/2TZs2cd999wG+FR62b9/Ogw8+SGJiIueeey4pKSnUrFnTf/wbb7xx0nj37dvH1q1badq06Qn73n77ba699trTuQyVhooLiohIaSl3lK9Q6IuFhYXx8MMP07VrV6666qpSLdUuImdGM2pEQlBUVBSTJ09mwoQJjB49mnfffZdmzZqRnp5Ou3btKJi2e2wRwmP/v0BBkcG1a9dy8cUX+7c3atSISZMmkZaWxsqVK2nevDlxcXHMnj2b8847j0WLFhU6z6m+xdm0aRNfffUVXbp04b333mPIkCH+fW+99VbIFBKuKOVZaPDmm28mNjaWtm3bsnHjRgC2bNlC9+7diYuLY8aMGeXyviIiUvbKM18kJSVRt25d/yNWUPQiAaGaQ0KlL9atWzfS0tLo3bs37dq1K7sLJCInpRk1IiFo6tSpvPHGG2RlZfHAAw/QqlUrxo4dy6efflqq86SnpzNp0iRiY2MLTaF94IEHGDRoEFlZWYSHhzN37lxuuOEGsrOzCQsLY+DAgYXOc6pvcVq1auV/5rng2WuAnJwcdu3aRYMGDUoVt5Sfl19+mfDwcNLT05k8eTLPPPMMDz/8MC+99BLnnHOO1+GJiEiAGDlypH9J8AJFLRIQqjkkVPpiSUlJbNiwgYsvvpjJkyeXKnYROX0qJiwSJCq6+GBBkj5+1YdgEQzFhIOx0GCBhQsXkpmZyW233UanTp2oU6cO2dnZPPvss/z2t78tzfVSoUERqVROJ58Ha74ormBxwSIBtWvXPqMccqxAKyZcFtQXE6m8NKNGRMQjBYUGExISOHLkCAALFiygevXqDBgwoFChwX/961/ceeed/kKDrVu3JiUlBfAVGnz22Wfp2rVrkYUGW7ZsSb9+/QoVGqxSpQrHdzZ79ep1QqHARYsWFVoaPT8/n7i4OLZt28b8+fP56aefWLVqFV9//TW7du3i/vvv5/XXXy+X6yUiUlkFY74ozrGLBGRmZiqHiIgUQQM1IlKkY58pl/IRbIUGAapUqUJGRgYrV64kOTmZ1NRUmjRpQt26dalbty67d+8+w6siIiLHC8Z8UZxjFwmIiopSDjkJ9cVEKi8VExYJYeVZJPC7777j/PPPZ8qUKQAsXbqUtm3bkpCQwI4dO0762s8//9xfMG/Pnj3+onZNmjRh+PDhxb7u8ccfJyYmhv79+5OXl0dOTg6tW7cucuWKYBBshQYBcnNzAahVqxYRERHUqFGDs846i+zsbLZv306tWrXK5uKIiIhfMOaL4hy7SEBlyCHB0BcD36yqmJgYbrjhBrKysop9Xaj1xUQClWbUiMhp69KlC3fddRcAKSkpLF68mA0bNjB+/Hj+/ve/F/u65557jubNmwNQp04d0tLSALj33nv54x//WORrfvzxRz788EMyMjIYP348b7/9Nj179uTVV1/1F7wLNsFWaPDIkSP84Q9/8P9cUFTwoYceonPnzpgZkyZNKlXsIiJyasGWL8DXL3jllVcA2L59OyNHjixykQDlkDNTFn2xzMxMli1bRkZGBnPnzmXGjBncc889J7wmFPtiIgHLzNTU1IKg+f66+gwaNMi+/PJLMzMbP368LVy40BYsWGAdOnSw5s2b27x588zMbMCAAbZ582YbNWqUffDBB4W2ZWZmWteuXa19+/Y2ZswYK63NmzfbgAEDzMwsKyvLrr32Wv++jh07Fvu69evX26hRoyw2NvaEfddcc40dPny4yNctWLDAnnzySTMzW7Fihf31r389IY5jHb1enn5OFaHg8wxWXn1Oampqal61is4TBYI9X5xKReSTUO2LLV++3O6//34zM/v666+tb9++Rb4uWPpiamqh0PTok0gQuuGGG/zfZr3//vskJCTQoUMHli5dSnp6Os8888wpz1FQODA9PZ2NGzcWKiqYm5t7wpTm6667rthz7d27t9B05fz8/GKPffrppxk2bNgJ29euXUvjxo2pWrXoiX779u3zv0dkZCR79+495e8oIiIiUh5CqS/WsGFDPvvsM/Lz81m6dGmxfSz1xUQqjh59EglC8fHxPPXUU/Tr14969epRrVo1PvnkE8aMGUN+fj67du0qdPyxzyCb+Z5jL6pwYME06GrVqvkfRyqJ2rVrs3//fv/PBUuDHu+rr74iMjKSc88994R9b775Jj179iz2PaKiovjhhx8A2L9/P1FRUSWOL5Sp0KCIiJSE8kXZCqW+WN26dbnllltISEigZcuW1KtXr8jXqi8mUnE0o0YkCIWHh3PBBRfw9NNP06tXLwCeeOIJZs+ezfz5809IzpGRkezYsYMjR46wfv16oOjCgQVK+y1OzZo1yc7OJisri88++8y/6sT27dsLHbdu3Tr+85//0KVLF9auXcuQIUP8+xYvXlyo/snxr23ZsiXp6ekALFmyhNatW5f4eomIiIiUpVDriw0cOJC0tDSuuuoqunXrVuRr1RcTqUBeP3ulpqZWssZxz7TPmzfPatWqZdnZ2WZmNm3aNGvWrJndeeed1rZtWzP7v2egt2zZYs2bN7devXpZQkKC/7no7t27W4cOHaxz58524MABK43jn0d+7733rE2bNtahQwfbunWrmZklJCQU+/pja9Rs3rzZrr/++kL7i3rtuHHjLDo62vr162e5ublFxlGAIKtRU561AzZv3mz16tWzyZMnm5nZ+++/b23atLH4+Hjbvn17ka85ePCgdejQwWJiYqxbt262f//+Io/bs2ePtWnTxtq3b2/dunXz/3k0M3v99dftkksuMTOz7Oxsa9Wq1QmflVefk5qamppX7XTyRKDlCDOznj17WmRkpL/uipnZ559/bp06dbL27dvb22+/bWZmcXFx1r59e4uNjbUffvihyHPl5+db69at7Ve/+pX/9ywqvxSXS8yswmvUmIVWX+zGG2+0+Ph4GzFihOXn5xf72mDoi6mphULzPAA1NbWStdMdACgvW7dutaZNm/o7dsfLz8+3pKSk0zr3Tz/9ZKNGjTrlcdnZ2RYdHW333nvvCfs0UPN/ju9AxcfH24EDB2z58uV2/IWL6AAAIABJREFU9913F/maQ4cO+Tvo06ZNs+eee67I4/Lz8y0vL8/MzJKTk23u3Ln+fTfddJPFxMQUG4eZd5+TmpqamlctEAdqSpsjzMwyMzMLFcg9cuSI3XjjjYUG7M3M/4/5WbNm+QvRFne+Y3/P4vKLl4MC6oudKBD7YmpqodD06JOInJaLLrqI9evX+5eEPF5YWFiJCukV5ZxzzinRMo81atTgo48+4oknnjit96kIgwcP5quvvgJgwoQJLFq0iH//+9/Ex8fTokUL5s+fX+j45ORk/zPpiYmJfPfdd+zcuZNu3boRGxvLo48+ekbxZGdn86tf/YqzzjqLVq1a8cUXXxR5XPXq1alfvz4AVatWLbbIc1hYGFWqVAF8y3dfdtllAPz73/+mU6dOxT4jLyIiwZsjAM4///xCP3/77bccOnSIXr160bNnT3788UfA94gQwKFDh2jatGmJz1dcfpH/o76YSOhSD1pEpBwF86oQAAcPHmTatGn07du32GNWrlzJNddcw5IlS7j44osBeOmll7j11ltP+buJiFRmwZ4jjrVr1y7++9//MnfuXAYPHsxjjz0G+OqctGnThkmTJp10oKYoReUXEZHKQKs+iYiUo2BdFaLg/W+//XZSUlKIjIws9rgWLVqwYsUKnnzySV566SWaNm1K27ZtqVatWonjEhGpjII5RxwvMjKSli1bUrNmTeLj4/2DTBdeeCHLli1j7ty5PP300zz55JMlPufx+SUpKanErxURCWYaqBERKUcnWxXirLPOom3btoWOL25ViDvuuIMrr7yS/Pz8Qh313NxcOnfufMI55s2bV2Q8x64KsWHDhkKrQlx44YWFjn3kkUeIjo4mPj7ev+3443Jzc/0DMgXfwq5fv5758+fzzjvvsHbtWkaNGsXo0aNLftFERCqJYM4Rx7vsssvYtWsX+fn5rF69mgYNGpCXl0dYWBhhYWHUqlWLiIiIEp+vqPwiIlJZaKBGJEhERET84Jyr53UcwSIiIuIHr2Mo0KtXL/r378/YsWMBuO666+jWrRutW7cmKirqhGN79uzJ3LlzqV27NgAPPPAAgwYNIisri/DwcObOnctZZ50FlP7bUoD777+fTp06ERERwcsvvwzAgAEDWLJkif+YHTt2MH78eNq2bcubb77JLbfcwuDBg0847osvvuCee+4hLCyM2rVrk5qaSs2aNbn77rsBiIuL0yCNiMhJBGOOALj77rtZsGABb7/9NkOHDmXgwIEMGjSIuLg4qlatyqxZs/jxxx+56aabCAsLo3r16syaNavY8/Xp04ePPvqIr7/+mvvvv5+LLrrohPziNfXFSieQ+mIiwcYVTJsUEZEz55yzQLuvbtu2jS5duvDnP/+5yIKDR44cYfjw4aeshbB7924mTZpUouKCx8vJyaFTp060bt26UMFB5xxm5k7yUhGRkBJoeaKsckRJlUcuAeUTEQktGqgRESlDgdYBD3TqWItIZaM8UT6UT0QklGjVJxERERERERGRAKGBGhERERERERGRAKFiwiIiZUiFBktHhQZFpLJRnigfyiciEkpUo0ZEJMA55+oC64GuZraygt+7CvAx8KKZTavI9xYRkdPnnLsH6Al0qOiiOM65tsDrQFMz21uR7y0iEgo0UCMiEuCcc6nATjP7i0fvfyWwFLjKzDK9iEFERErOOfcb4HOgrZl96VEMzwHVzGyQF+8vIhLMNFAjIhLAnHN/AJ4HfmdmWR7GkQI0MrPeXsUgIiKn5pxzwNvAcjMb62Eckfhmg95qZulexSEiEoxUTFhEJEA5534FTAGGeDlIc9SjQDPnXA+P4xARkZPrA1wMTPAyCDP7GUgCpjnnIryMRUQk2GhGjYhIgHLOPQGcb2a3eh0LgHMuDngZ3+ye/R6HIyIix3HO1QY2ADeY2TKv4wFwzs0FvjCzh72ORUQkWGigRkQkADnnmgOLgCvNbJfX8RRwzr0AZJtZktexiIhIYc65GcAhMxvmdSwFnHP1gTX4ihqv9zoeEZFgoIEaEZEA45yrCnwG/N3MXvI6nmM55+rgqzkQMN/WiohIYM96dM4NAW4D2pnZEa/jEREJdKpRIyISeIYDu/F1uAOKme0BRuCrOVDN63hERASO1oCZBgwLtEGao6YB+cBdXgciIhIMNKNGRCSAOOcaAP8BWpnZN17HU5SjK4osAD4xsxSv4xERqeycc2OBK8zsRq9jKY5zrjHwIfB7M9vmdTwiIoFMAzUiIgHi6ADIO8BSMxvvdTwn45z7DfA50NbMvvQ6HhGRyso5dyXwPtDMzDK9judknHPJwNVAT9M/QkREiqVHn0REAkc/oB7wlNeBnIqZfY9vye5pzjnlEhERDzjnqgDTgYcCfZDmqMeARkAvrwMREQlk6lyLiAQA59y5wBPAIDM77HU8JfQsUBP4k9eBiIhUUncBucAMrwMpCTP7BRgMPOOci/I6HhGRQKVHn0REAoBz7iVgj5mN8DqW0nDONQPeA64ys51exyMiUlk4536N7xHUGDPb6HU8peGcex7AzIZ4HYuISCDSQI2IiMecc53wTV3/nZkd9Dqe0nLOPQY0NLO+XsciIlIZHK1pNg9YYWZjvI6ntJxzkcAG4GYzy/A6HhGRQKNHn0REPOScqwk8DwwNxkGao8YALZxz3b0ORESkkrgB+C0Q0IXni2NmPwN346tzVt3reEREAo1m1IiIeMg5Nx74jZnd7HUsZ8I5lwC8iG9W0AGv4xERCVXOudrAeqCPmX3sdTyn6+isoDeB1WaW7HE4IiIBRQM1IiIecc79HngXuNLMfvA6njPlnJsJ7Deze7yORUQkVDnnpgL5ZjbU61jOlHPuQmA1EGtmX3gdj4hIoNBAjYiIB44uqfopMNnMXvQ6nrLgnDsH37e815vZp17HIyISapxz7YFXgKZHHx8Kes65oUA/fEWRj3gdj4hIIFCNGhERb9wN7Admeh1IWTGz3cC9wHTnXLjX8YiIhBLnXAQwDUgKlUGao54HHL5lu0VEBM2oERGpcM65S4AVQBsz+8rbaMrW0ZoDC4EPzewxr+MREQkVzrnR+B6V7eV1LGXNOdcUSAOuNrPtHocjIuI5DdSIiFSgYwYyMszsb17HUx5CeSBKRMQLlWEgwzk3Bl9B+pAbiBIRKS09+iQiUrFuAi4EHvc6kPJiZt8BfwOmHh2YEhGR0+ScC8P3yNMjoTpIc9TfgCbOuZ5eByIi4jUN1IiIVJCjxXafAgaZ2WGv4ylnzwC1gESP4xARCXZ3Hv3vVE+jKGdmdghfnZpJzrlIr+MREfGSHn0SEakgzrkXgYNmdrfXsVSEUFt+XESkolXG5audc9OAvFBYflxE5HRpoEZEpAI45+LxrfD0OzM74HU8FcU5NwG4yMxu8ToWEZFg45x7E1hrZqO8jqWiOOeigA1AHzP72Ot4RES8oEefRETKmXOuBr4p68Mq0yDNUclAK+fcH70OREQkmByt1dIYX+2WSsPM9gHDgenOuepexyMi4gXNqBERKWfOub8BvzWzPl7H4gXnXCdgOr7ZRAe9jkdEJNAdrdGyAbjZzDK8jqeiHS1EPw9YYWZjvI5HRKSiaaBGRKQcOeeuApYAV5nZTq/j8Ypz7mVgt5mN8DoWEZFA55ybAoSZ2Z2nPDhEOed+DawCYszsv17HIyJSkTRQIyJSTpxzVYBlwDQzm+F1PF5yzp2L79vh7mb2H6/jEREJVM65dsBrQNOjjwFVWs65JKAPvmLKR7yOR0SkoqhGjYhI+RkG5AAveh2I18zsJ+Av+GoO/N4596jXMYmIBBLn3Hjn3O+AacA9lX2Q5qjJQDhwh9eBiIhUJM2oEREpB8653wCfA9FmtsnreALB0ZoL/wK+BFqZWUuPQxIRCRjOuXVABnAhcJuZ/exxSAHBOXclsBTfI8SZXscjIlIRNFAjIlLGjhZBfBtYbmZjvY4nUDjnPgN2Ae2AQ2Z2vschiYgEDOfcPsDwPTJ7tpnFeBxSwHDOpQCXm9mNXsciIlIR9OiTiEjZ6wNcAkzwOI5AE4dvllE1oJ5zrqq34YiIBAbnXA0gEt/9cRnQyduIAs6jwFXOueu8DkREpCJoRo2ISBlyztXGVzT3BjNb5nU8gcg5dzm+uj3tzSzf63hERLzmnIsA3sf3yNM3XscTiJxzccBsfEWW93scjohIudJAjYhIGTj6Ld83wHB8j/UM8zgkERGRkOKcmwEcAl4A6pvZvz0OSUSkXGjauYhI2RgArAb+ADT1OBYREZFQ9FdgPfAT0AjQQI2IhCTNqBERKQNHC+WeDywA/gf4HzM74m1UZa9GjRo7Dx06VM/rOAJRRETEDzk5OSqQLFKGdM8pe8F6rzpa1+w/wHKgC7DVzNp7G5WISPnQQI2ISBlwzv0MVME3q+bPZrbG45DKhXPOlDeK5pzDzJzXcYiEEt1zyl4w36uccy2A54ArgRwzO9fjkEREyoVWfRIRKRtZwINATKgO0oiIiHjJzFYCbYFkIMfbaEREyo9m1IiISInp2+3iBfO31CKBSvecsqd7lYhI4NOMGhERERERERGRAKGBGhHxTI0aNXY650ytZK1GjRo7vf7MjvfJJ58wcOBAADIzM0lISAAgKiqKUaNGATBu3DguvPBCkpOT/a8ratuoUaOIiooq0/jy8vLo378/MTExPP744yfsv/nmm4mNjaVt27Zs3LjRv33Hjh1ERETw3XffAbBlyxa6d+9OXFwcM2bMKNMYRaTsleTelJiYSOvWrYmLi+OVV14BvLkPLV26lLZt25KQkMCOHTsAuP7662nXrh0JCQls27YNgL///e+0atWKNm3asGzZsjKNsbwp3wdPXheRAGFmampqap403y1ISuro9Qq4z6x37962Zs0aGzhwoH300UdmZhYbG+vfv3PnTlu6dKmNGjXqpNuOf11xsrKyTnlMgblz59q4cePMzKx79+62a9euQvtzc3PNzCwtLc2SkpL82++77z6LjY21zZs3m5lZ//797aeffjrpewXC56OmFmrtTPLEqe5NAwYM8P8dP1ZF34fi4+PtwIEDtnz5crv77rvNzOzbb781M7PFixfbfffdZ2ZmLVq0sCNHjti2bdvs+uuvL/H7H8+Le5XyfdGUN9TU1IprmlEjIiJnJCUlhUGDBnHgwAGio6NP2F+vXj2cc6fcdipr167lrrvuom/fviV+zbJly+jUqRMAcXFxrFixotD+8PBwALKysmjWrBkAP/74IwcOHOCSSy4B4PDhw3z//fcMGjSILl268PXXX5cqbhHxxqnuTc45brnlFq699lq+//77Ep2zrO9D2dnZ/OpXv+Kss86iVatWfPHFFwA0aNAAgKpVq1K1alUALr30Ug4dOsS+ffs455xzSvz+IiISfKp6HYCIiAS3Sy+9lN27dzN8+PByOX9qaipz5syhQYMG3HnnnVx99dUAzJ8/n6eeeqrQsX379uWuu+7y/7xv3z5q1aoFQGRkJHv37i10fH5+PnFxcWzbto358+cDMHHiRIYNG+Z/ROGnn35i1apVfP311+zatYv777+f119/vVx+VxEpO6e6Nz355JPUqVOHjz76iL/+9a+8+uqrxZ6rvO5De/fu9e8D3z3p2P9PSUlh2rRpAMTGxnLFFVdw+PBhFi1aVNLLICIiQUgzakQkpKSlpfnrinhlzpw5REdH06NHDw4cOFBo34EDB+jRowfR0dHMmTPHowjL1syZM+nduzfPP/88R44cKfPzP//885x//vkMGTLE/48jgB49epCWllaoHfuPI/DVo9i/fz8A+/fvP6H2RJUqVcjIyOD1118nOTmZffv2sXXrVpo2bVroHE2aNKFu3bo0bdqU3bt3l/nvKCJl71T3pjp16gDQrl07du48eamQ8roP1a5d278PICzs/7rm9957L7fddhsNGzZk//79pKam8tVXX/HZZ5/xwAMPlOJKhA7leBGpLDRQIyIhpbhOXHkMIBTl8OHDTJ8+nQ8//JB+/fqdUHh2+vTp9OvXjw8//JDp06dz+PDhComrvGRnZ/Piiy+SnJxMly5dSE1NLfP3+OijjxgxYgRTpkyhc+fOzJ49G/B9kx0XF1eoTZkypdBr27Rpw/vvvw/4/mxcc801hfbn5uYCUKtWLSIiIti0aRNfffUVXbp04b333mPIkCHUqFGDs846i+zsbLZv317o228RCUwluTcVDJBs2rSJyMjIk56vvO5DNWvWJDs7m6ysLD777DOaNGkCwAsvvIBzjttuuw3wDeDUrFmTatWqERkZSVZW1mlemeCmHC8ilYbXRXLU1NQqb6MUxQUPHjxoN954o3Xo0MGGDBliZmajRo2ywYMHW0JCgt1+++2Wl5dnF198sTVr1szuu+8+mzlzpvXt29f++Mc/2urVq+2mm26y9u3b20033WSHDx/27+/cubN1797dfvnlF7vuuuvs559/NjOzO++80zZu3FjiGM3M1q9f7y9K+9NPP1mfPn0K7b/xxhtt9+7dZmY2bNgw++KLL0p8bgKg6ODxn9mYMWNszpw5ZmaWnZ1tbdq0sZycnELFOGfMmGHNmze3Bg0a+K9NUdvMTl3Ec//+/TZz5sySXC4z8xUL7tevn0VHR/uLea5atcpmzpxp+fn5FhcX52/HfxbHFhpNS0uz6Ohoa9u2ra1cubLI9wqEz0dNLdRaafLEsUpyb+revbtFR0dbu3btbN26df7tFXkfMjN77733rE2bNtahQwfbunWrmZlVq1bN2rVrZ7GxsTZ69GgzM0tJSbE2bdpYy5Yt7e233y7x+x/Pi3vVqT7HyprjlTfU1NSKa87MvBkhEpFKzzlnJb0HTZw4kQsvvJDevXvzwAMP0LNnTxYuXMg555xDUlISnTt35p///CcTJ070f6s5a9YsVq5cyaRJk/jXv/7FV199xYMPPsjYsWO54oorOHjwIJ9++ilTpkzhscceo2HDhvzyyy8A3HLLLXTt2pXFixf7Y8jNzaVz586F4oqMjGTevHn+nz/++GMWLVrE2LFjycvLO+EcnTp1YtGiRVStWpWHHnqIrl270rZt25JeL8ysdBV4y1hJP7MrrriCvn37Mnr06BKfe9SoUbz22muFlskOJoHw+YiEmtLkiZI41b0p2O9DJeHFvepUn2NlzfHKGyJSHBUTFpGgsGnTJl599VWee+45Dh48SKtWrQD43e9+B0D9+vX5+eefT3jd73//ewC++eYbWrRoAcA111zDmjVrqFevnn9/8+bNWbVqFX/+859JTEykXr16JCQkFDpXtWrVSEtLO2mcp6qJUrC/Tp06Re4PFafzj5zRo0eXamBHRKS0TnVv0n3IG8rxIiKFqUaNiASFRo0aMXLkSNLS0lixYgXdu3cHKLTEs5kRHh5eaNWMgsKMDRs2ZOXKlQCsWLGChg0bArB69WoAVq1aRYMGDTj77LOpXr06U6ZMOWH51dzc3BNqEVx33XUnxLlu3Try8/NZsmQJrVu3LrS/oFZBfn4+69at47LLLiuLyxN0AqEgZK9evYiKiiqyY66CkCKhLxDuQycrTJuamkpMTAwtW7bk2WefLbSvR48eJCcnV2Ck5Us5XkSkMA3UiEhQGDx4MHPmzCE+Pp6OHTuydevWIo+Li4tjzJgxpKSkFNres2dP1qxZQ2xsLOvWraNnz54A7N69m86dO/Pxxx/7t914443s2rWLSy65pNA5Cr5tO7YdOyUaIDw8nNtvv52YmBhmz57NHXfcAeBfHvaOO+7g5ZdfJiYmhttvv53w8PAzvjbByOuCkACTJ08udtleFYQUCX1e34dOVZi2b9++ZGRksHz5cl588UX/9jVr1nDo0KEKibGiKMeLiBSmGjUi4pmyrj1QWrNmzQIgMTGx0PZ58+bx/fffk5SUVPFBnUQgPMt+qs8sKyuLxMREdu/ezeWXX86UKVNITk4mMzOTb775hosvvphp06Zx6aWXEhUVRefOnWnSpAnvvPMO+/fv57HHHmPcuHHs2LGD+vXrM3v2bFJTU3nnnXfYu3cv1apVY+7cufTp04eXX36ZWrVqMWTIEEaMGMHll19e6t8nOTnZ/83psXr37s3UqVOpU6cOSUlJDB06lMaNG5/q2nj++YiEmtPJE8FyH9qwYQNTp07lmWeeYffu3QwdOpTXXnvthONycnIYNGiQf+WqgQMHcu2117J69erTmlUTiDVqykMw5HjlDREpjmbUiIgc45///CePP/44/fv39zqUoDR9+nT69OnD0qVLiYqK4rPPPgN8dQaWLFnC1q1bOXDgAImJiUycOJEJEyYAULduXRYuXMiXX37JlVdeSXp6Ok2bNuWtt94CoHbt2rz77ru0bduWN998k169evHWW2+Rl5fHt99+W+gfRyWZvn4q+/bt8y/DHRkZyd69e8vi8ohIBQiW+1BJ7jMpKSk0atSIq6++GvDV2DnvvPNU++Q0KceLSLBQMWERqbSO/5YNoE+fPvTp06figwkRwVIQ8lRUEFIkeAXLfehUhWkBRo4cyV/+8hfat2/PHXfcwVNPPcWYMWNCelWqsqIcLyLBTAM1IiJSZho1akTXrl259tprAcjLy2P16tWlLgj5hz/8gRUrVnD55ZeTlZV10oKQEydOLBRDSZZYPZWCgpC9evVSQUiRIBMs96FTFabNzc2lWrVqVKtWjZo1a1K9enW2bNlCYmIie/bsYc+ePXTs2JF27dqVwVUTEZFAokefRKRSS0xMLLdVP26++WZiY2Np27at/9vP66+/nnbt2pGQkMC2bdvK5X29FCwFIQHuvvtuXn75Ze69915eeOEFQAUhRUJBsNyHTlWY9oknniAuLo7o6Gj69OlDjRo1ePfdd3nnnXeYMGECt956qwZpSqg8c32o53UR8YaKCYuIZ7wuJgy+zltycvIJneyycPjwYcLDw0lPT2fu3Lk888wzbN68mQYNGvDee+/x3nvv+WsjlEQgFB1UQcjiBcLnIxJqAiFPQPDch0qishQTPlZ55vpgz+siEpg0o0ZEgsInn3xCq1atiIuL44UXXuCXX36hU6dOxMTE8Kc//QnwLbXapUsXunbtSteuXXnuuedo06YNI0eOBHwdtWHDhhETE8NDDz1U6Pw7d+6kW7duxMbG8uijjwLw4IMP0q5dO2JjY9mxY0epYy6YhZGVlUWzZs0AaNCgAQBVq1alalU9fXq6VBBSRLym+1DZC8Zcr7wuIuVBdxMRCQqLFi3ib3/7GwkJCRw5cgSABQsWUL16dQYMGMCmTZsAOPvss/nXv/7FnXfeiXOOZcuW0bp1a//U9piYGJ599lm6du1KZmam//zjxo0jOTmZli1b0q9fPzIzM8nIyCA9PZ0qVapw/DeBvXr1Ys+ePSfEWKNGDf/P+fn5xMXFsW3bNubPn19oe0pKCtOmTSvbixSiVBBSRLym+1DFCMZcD8rrIlL2NFAjIkFh6NChPProo8yaNYt77rmHyy+/nIEDB/LDDz+wZcsWbr/9dgCaNGkCwAUXXEDjxo0BiIiI8J+nYNWOZs2asWXLFv/2TZs2cd999wG+JVO3b9/Ogw8+SGJiIueeey4pKSnUrFnTf/wbb7xxypirVKlCRkYGK1euJDk5mblz5wJw7733ctttt9GwYcMzuSQiIiIhJRhzPSivi0jZ06NPIhIUoqKimDx5MhMmTGD06NG8++67NGvWjPT0dNq1a+f/FuzYVT2O/f8CBat2rF27losvvti/vVGjRkyaNIm0tDRWrlxJ8+bNiYuLY/bs2Zx33nksWrSo0Hl69epFXFxcoZaTk1PomNzcXABq1arl70C+8MILOOe47bbbyuCqVE7lWRQSYMeOHURERJTre4hI8CnPe09SUhJ169b118IB6Ny5M3FxcTRv3pzrr7/evz2U71HBmOuV10WkPGhGjYgEhalTp/LGG2+QlZXFAw88QKtWrRg7diyffvppqc6Tnp7OpEmTiI2N5YILLvBvf+CBBxg0aBBZWVmEh4czd+5cbrjhBrKzswkLC2PgwIGFznOqb9mOHDnCH/7wB//PkydPBnzfFv7P//wPcXFxxMfH88gjj5Qqfil/EydOPGGZXBGR8jRy5EhatGhRaNvixYsBmDRpUqFHbUL5HhVsuR6U10WkfGjVJxHxTEWvAlGeqz5UhEBYHaI0n9knn3zCiBEjqFGjBv379+fWW2+le/fuHDp0iN/+9rfMnDmTtLQ0xo0bR1iYb4Jnt27dSE1NJT4+npSUFBITEznrrLP8S+WOHTvW/zlGREQwcOBADh48SMeOHXn44Yd58MEH+fDDD6lSpQr/+Mc/qF+/fql+vx9//JFHHnmEnJycUv9ZCYTPRyTUnE6eCMZ7DxS/slTnzp1JTU3lvPPOO6N7VIFQX/UpmHK98oaIFEePPomISLkoKAqZlpbGn/70J8LDw1mwYAEZGRkcOXKkUFHIhQsX8utf/9pfFPL999/3nycmJoaMjAw+//zzIotCpqens3HjxkJFIdPT0wt9iwolm8I+ceJEhg0bVo5XRUTKWzDee4qzZ88e8vLyOO+88wDdo0REKgs9+iQilcaxz/5L+Qu2opD79u1j69atNG3atAx+exHxSrDde07m7bff5tprr/W/l+5Rp6ZcLyKhQDNqRCSolGcxx++++47zzz+fKVOmALB06VLatm1LQkICO3bsKPZ1c+bMITo6mh49enDgwIEij9m7dy9t27YlNjaW7t27k5OTw5YtW4iJiaF9+/bccsst5Ofnk5OTQ+vWrYtcCjbYBFtRyE2bNvHVV1/RpUsX3nvvPYYMGVKm10NEKkaw3XtO5q233vIXEq5s96hgzfehnttFpGJoRo2IyDG6dOnCXXfdBUBKSgqLFy9mw4YNjB8/nr///e8nHH/48GGmT5/Ohx9+yOuvv86MGTMYMWLECcdFRkaSkZFBlSpVGD16NIsWLSI+Pp7SCSuhAAAgAElEQVT58+dTu3ZtRo4cybvvvkvXrl159dVXSU5OLu9ftdwFW1HIVq1asWzZMuD/ahyISPAJtnsP+PLNK6+8AsD27dsZOXIkOTk57Nq1iwYNGgC6R5W18sz3oZzbRaSCmJmampqaJ813C/IZNGiQffnll2ZmNn78eFu4cKEtWLDAOnToYM2bN7d58+aZmdmAAQNs8+bNNmrUKPvggw8KbcvMzLSuXbta+/btbcyYMVZamzdvtgEDBpiZWVZWll177bX+fR07dizyNevXr7ekpCQzM/vpp5+sT58+p3yfRx55xNauXXvCtnffffeEOI519HoFzGdWEQo+22AQCJ+PmlqotYq+5xQIpntPaXlxr6rM+f5kuV15Q01NrbimR59EJCDccMMN/m8d33//fRISEujQoQNLly4lPT2dZ5555pTnKKrAY4Hc3NwTpp5fd911xZ5r79691KpVy/9zfn5+kcft27fPf1xkZCR79+4t9pwrV67kmmuuYcmSJYWm0e/YscP/O4uIiISyypDvQbldRM6MHn0SkYAQHx/PU089Rb9+/ahXrx7VqlXjk08+YcyYMeTn57Nr165Cxx9bT8DMV2+gqAKPBdPVq1WrRlpaWonjqV27Nvv37/f/XLCE6/GioqL8x+3fv5+oqKhiz9miRQtWrFjBk08+yUsvvURSUhK//PILAwYMYNq0aVSpUqXE8VUWKgopIl7Qvaf8VIZ8r9wuImdKM2pEJCCEh4dzwQUX8PTTT9OrVy8AnnjiCWbPns38+fNP6DhFRkayY8cOjhw5wvr164GiCzwWKO03bDVr1iQ7O5usrCw+++wz/+og27dvL3Rco0aNWLduHfn5+SxZsoTWrVsXeVxubq7//2vVquVfWWTw4MEMHTrUf/7/z96dh0dRpX0f/x42AzIkwCAKzqPgiAsiivJCSEJCAnkYjBGQRUUkwyagODCOCm6AwoAbMjgOm44Mi4MLKsKjuGFQNhmU3X1DEBBlDQkISe73j056CHRIAkmqu/P7XNe5NFXVVXfXoU51nz7nrnAUbAkhMzMzSUxMpG3btqSkpCghpEiYCbY2B3wJhaOiogp0IKxdu5bk5GTi4+NZtGgRAO3atSM+Pp6EhIQTOizy5ebmEh0dTc2aNf3vM9CyYG2vwv1+DxXj3i4iZczruVcqKioVt3Bc7oEFCxZYrVq1LCsry8zMpk+fbs2bN7dbb73V2rRpY2b/nZ++ZcsWa9GihXXt2tWSkpL8c9ZTUlKsXbt2lpycbBkZGVYSx88ff+eddyw6OtratWtnW7duNTOzpKSkE143a9Ysi46OtpSUFNu/f3/A7dauXWtt27a1hIQE69Kli2VmZtqKFSusZs2aFh8fb/Hx8f55+eGWo6Yscz0cf64SExMtIyPDVq1aZXfccUfA1xw+fNh+/PFHM/P9G3v66acDbrd3717bs2ePmZnde++99n//938Bj5kvGOpHRSXcSji0OWZmO3bsKJBrJTc317p16+a/3+U7cuSImZnNnDnTnnjiiZPu7/j3GWhZsORFqUj3+5Lc23XfUFFRKaxo6pOIBI3U1FT279/v/3vAgAEMGDCgwDbHDkf/+OOPT9jHwoULT/n4VapUYc2aNUyZMoXBgwfTvn172rdv71+fm5sb8Nex3r1707t3b//fu3fvJjY2tsA2V1xxBUuXLi2wLDo6+oTRHIcOHeLmm2/2/1IXzAYOHMhdd93FhRdeyKOPPkqzZs3Izc3liSeeYP/+/YwaNYrU1FT/9qNHj/b/upn/xJKIiAj69evHwYMHad++PQ888MApx5OVlcWZZ55JzZo1adWqFffff3/A7c444wwaNGgA+OrczAJud+yw9ipVqlClim6ZIl4K1TYH4Oyzzy7w97fffsvhw4fp2rUrERERTJ8+nXr16lG1alUADh8+TNOmTYu9v8KWBatwvt+H+r1dRIKDPnWKiOQ599xz/cOqA6lUqVKxkhzWrVv3lB/BWb16dZYtW3ZKry1v+Qkh77nnHt577z2GDRtGdnY211xzDQcPHqRz584FvjQFkp8QsmXLlvTq1YsdO3b48wwcOXKE5OTkAttHRkayYMGCgPsqbkLIfAcPHmT69OksXrz4pNvlJ4R88MEHT7qdiJStUG9zjrVr1y4+++wzNmzYwNKlSxk/fjwTJ07kxx9/pFu3bmRkZBTZNsmpK+/7fSjd20UkOKijRkRETkmoJoTMP37fvn0ZN24ckZGRhW6nhJAiwSOU25zjRUZG0rJlS2rUqEFiYqK/U6Bhw4asXLmS+fPn8+STT/LEE08Ue58iIhI+1FEjIiKn5GQJIWvWrEmbNm0KbF9YQsj+/fvTrFkzcnJyCnyxKumv28cmhNy8eXOBhJANGzYssO2DDz5ITEwMiYmJ/mWBtlNCSJHgEcptzvEuvPBCdu3aRU5ODuvWraNRo0ZkZ2dTqVIlKlWqVCDpfHH2JyIi4UUdNSIicsq6du1K7969GTt2LADXXXcd11xzDa1btz7h0aVdu3alS5cuzJ8/n9q1awMwcuRIBgwYQGZmJlWrVmX+/PnUrFkTKPmv2wAjRoygQ4cOREREMGvWLAD69OnDu+++699m+/btPPLII7Rp04ZXX32Vm266iYEDB56w3cqVK3nllVfYsmULf/vb3/jzn/9c5LQKESlbodjmANxxxx0sWrSIhQsXMmTIEPr168eAAQNISEigSpUqzJw5k59//pkbbriBSpUqccYZZ/hztATaX48ePVi2bBlff/01I0aMICUlJeAyEREJTa6wJIoiImWtevXqOw8fPlzf6zhCRURExE+HDh3yNFukc86C6b6xbds2OnbsyG233cbgwYNPWJ+bm8uwYcOKzDWwe/dunnrqqSJzDRw6dIgOHTrQunVrHn/88QLrnHOYmSvkpSJyCsK1zSmu4rZNgRTWXnnRVul+H1gw3NdFJDipo0ZERIot2L40BRN11IiUPrU5pU9tlYhI8Ct+1jMRERERERERESlT6qgREREREREREQkS6qgREREREREREQkSeuqTiIgUW0RExE/OOSWEDCAiIuInr2MQCTdqc0qf2ioRkeCnZMIiIlLunHONgdXA/zOzbz04/mDgZiDOzHLL+/giUnzOuUuBaUBVYKCZbfA4pJDhnPst8ASQANxmZou8jUhERIpDU59ERKRcOeccvi9dj3jRSZNnGmDAII+OLyJFcM5FOOceBpYC84AYddKUjJn9YmZ9gH7AJOfcS865Bl7HJSIiJ6eOGhERKW+9gd8CT3oVQN4omoHAGOfcuV7FISKBOefaARuAS4ErzOxpM8vxOKyQZWbvAs2AL4H1zrnBzjl9DxARCVKa+iQiIuXGOVcP2AR0MrOPgyCeMcDlZtbF61hEBJxzdYHHgSTgdjN73eOQwo5z7jJget6fA81sk5fxiIjIidSTLiIi5WkiMCcYOmny/BW42DnX1etARCoy53MzsBk4ADRVJ03ZyOuYiQVmAe875/7qnKvucVgiInIMjagREZFy4Zz7X2AqcJmZZXodTz7nXCy+/BeXmdk+r+MRqWiccxfgaxvqAQPM7D8eh1RhOOfOAf4GtABuNbP3PA5JRETQiBoRESkHzrkzgSnA4GDqpAEws2XAImC817GIVCTOuarOuZHAR8BbwNXqpClfZrbDzHoAw4B/Oudm5U1RFRERD6mjRkREysNoYKWZLfY6kEKMAFLzRteISBlzzkUDnwBx+DpoHjezbI/DqrDyHtvdFPgF2OScS8t7Qp+IiHhAU59ERKRMOedaAG8Czcxsl9fxFMY5dz0wFt8TZn71Oh6RcOSci8Q3eq0zMBx40fRhNKg4567Cl2x4PzDIzL70OCQRkQpHI2pERKTMOOeqADOAu4O5kybPK8AX+EbXiEgpyksWfD2+ZMFV8CULfkGdNMEnL9l7K2AhsMI5d79zrprHYYmIVCgaUSMiImXGOXcn8AegQyh8IXPOnQusBdqa2WdexyMSDpxz/wP8Hfg9voS1H3ockhSTc+484GmgEb66W+ZxSCIiFYJG1IiISJlwzjUCRuIbOh/0nTQAZrYNXz6dGc453SNFToNzrrJzbhi+XDT/Aa5UJ01oMbMtwLX42sUXnHPTnHNR3kYlIhL+9CFURERKXV4SyinAY2b2tdfxlNAUfPfHAV4HIhKqnHNX4nua03VAGzN7WLmfQpP5vIQv2XAu8KlzrqeSDYuIlB1NfRIRkVLnnOsF3AW0NLOjXsdTUs65y4D3geZmtt3reERChXOuJjAGuBlfvqeZoTKiTorHOReDL9nw98BtZva9pwGJiIQhjagREZFS5Zz7LfAEMCAUO2kAzGwTMBV4yutYREKFc64TsAk4C7jMzJ5TJ034MbPlwJXACmCNc+7OvMTxIiJSSjSiRkRESpVzbiaw18yGex3L6XDORQDrgXvM7DWv4xEJVs65s4G/AVcBg83sHY9DknLinLsQX6d2bWCgma3xOCQRkbCgETUiIlJqnHPtgQTgAY9DOW1mdhgYCDzlnKvldTwiwcY5V8k5dyuwEfgWuFydNBWLmX0FtAcmAf/nnJvknPuNx2GJiIQ8jagREZFS4Zyrge8L21Aze8PreEqLc24GcMTMbvM6FpFg4ZxrCkwDKuMbSbHR45DEY3nTXh8HEvHlrlnocUgiIiFLHTUiIlIqnHMTgPPN7AavYylNzrnawGagm5mt8DoeES/lTQm8DxgEjAKmmlmut1FJMHHOJeGbDrUeuEMJ2UVESk5Tn0RE5LQ5564A+gJ/8jqW0mZme4FhwHTnXDWv4xHxinMuEdgAXILviWj/UCeNHM/M3gMuBz4H1jvnhjjn9J1DRKQENKJGREROi3OuMrAKmGJm//Q6nrLgnHPA68BqM3vY63hEypOmtMipypsiNx3fj8OaIiciUkzq3RYRkdM1FDgIPOd1IGUl7xHDtwF/cs5d5HU8IuXB+fTG98jtfUBTddJISZjZZiAOmAkscc791TlX3duoRESCn0bUiIjIKXPOnQd8DETnPf0jrDnn7gCuB9ppyoeEM+fc7/HlGakLDNBjl+V0OefOwfcY9xbAIDN71+OQRESClkbUiIjIKcmbDjQFmFgROmnyPA1E4MvHIxJ2nHPVnHP34pvO+CbQUp00UhrMbIeZ9cCXy+xZ59xs51w9r+MSEQlG6qgREZFT1RP4Hb7cFRWCmeUAA4C/OufO9joekdLknGuDb4RcLHC1mT1hZtkehyVhxsz+D2gK/Axscs79Ma/jX0RE8mjqk4iIlJhzrg6+R1Z3NrOPvI6nvDnn/gr8Pu/XYZGQ5pyLAsYD1wHDgRdNHxClHDjnrsKXbPgAcKuZfelxSCIiQUEjakRE5FQ8BrxUETtp8jwMXOmcu9brQEROVV6y4G74Ol0r4UsW/II6aaS8mNnHQCt8T9Vb4Zx7wDlXzeOwREQ8pxE1IiJSIs65dsC/8H2py/A6Hq845xLxPcmkQp8HCU3Ouf/Bl3PpAnyPTV7mcUhSweUlp/87+jcpIqIRNSIiUnx5j1WdDtxW0TsnzGwJ8C4wzutYRIrLOVfZOTcM+ARYDVypL8QSDMxsC5AKPAi84Jyb5pyr7XFYIiKeUEeNiIiUxP3AWjNb6HUgQeIvQHfnXCuvAxEpinOuBfARvlw0bczsYTP71eOwRPzM52XgUiAH2Oyc66lkwyJS0Wjqk4iIFItz7nJ8I0iam9kOr+MJFs65G4GRwFVmdtTreETyOeduAvYAy4AxwM3APcC/lIdGQkHek8imAz8AQ4CjwO1mNtLTwEREyphG1IiISJGcc5WBGcB96qQ5wTxgG3CX14GI5HPONQAmA+cBm4B6wGVmNlOdNBIqzGwF0AJfZ+MafJ2N1zvnUjwNTESkjGlEjYiIFMk5NxToBrQzs1yv4wk2eUkwPwaizewrr+MRcc69AlwCVAXuMLM3PA5J5LQ45y7lv52P1YELzeyQt1GJiJQNddSIiMhJOed+B6wFYs3sc6/jCVbOueHAtUCSRiyIl5xz1wGvAQeBKnn/vdjMdnsamMhpcM69B8Th+/dcG5hjZr29jUpEpGyoo0ZERAJyzg0BzgASgdVm9rDHIQW1vOlhq4ApwE1Av7ynmIiUK+dcLaA9sBHYYWYHPQ5JpFQ456oBZwNNgB/N7DOPQxIRKRPqqBERkYCcc38HagCtgD8CH5tZjrdRBS/n3NlAc2A28CXwYN4jvEVEREREiq2K1wGIiEjQOg9oC6wD5gL/D9jraUTB7QpgDvAZcD7QwNNowkT16tV3Hj58uL7XcYSKiIiInw4dOnS213GIlDW1DadP7YVI8NKIGhERCcg5txOoi++xvo+b2WGPQwp6zrn/wZfs8jrgGTMb4HFIIc85p5Q/JeCcw8yc13GIlDW1DadP7YVI8NKIGhERKcx04AUz2+x1IKHCzH4AOjvn+gO7vI5HREREREKPRtSIiIhI0NKv5iWjX8ilolDbcPrUXogEr0peByAiIiIiIiIiIj7qqBERKSPVq1ff6ZwzlYKlevXqO72um2OpnkKnro63YsUK+vXrB8COHTtISkoCICoqilGjRgEwYcIEGjZsyOjRo/2vC7Rs1KhRREVFlWp82dnZ9O7dm7i4OB577LET1i9ZsoQ2bdqQlJTE9u3bAejcuTOxsbEkJSWxbds2AP72t7/RqlUroqOjWblyZanGKFJRFKe92LNnDz169CAhIYGxY8cC3rQNgdqBhIQEf1myxPdAweTkZBISEmjRogWdO3cu1RhFxGNmpqKioqJSBsXXxMrx8s6L5/WTX1RPhQuGuiqqfrp3727r16+3fv362bJly8zMLD4+3r9+586dtmTJEhs1atRJlx3/usJkZmYWuU2++fPn24QJE8zMLCUlxXbt2lVgfWJiomVkZNiqVavsjjvuMDOzb7/91szM3n77bbvrrrvMzOyqq66y3Nxc27Ztm3Xu3PmkxwyGOlNRKY9yKm13Ue3FHXfcYV9++eUJryvvtiFQO3CyGCZPnmwzZswo9vHzqb1QUQneohE1IiIiErLGjRvHgAEDyMjIICYm5oT19evXxzlX5LKibNiwgcGDB9OzZ89iv2blypV06NAB8P0avmbNGv+6rKwszjzzTGrWrEmrVq349NNPAWjUqBEAVapUoUoV3zMfLrjgAg4fPsy+ffuoW7duieIWkf8qqr347LPPePjhh0lMTGT16tXF2mdptw0QuB2oVKkSCQkJ3Hjjjezdu7fA9gsXLiQ1NbXYxxeR4KenPomIiEjIuuCCC9i9ezfDhg0rk/3PmTOHuXPn0qhRI2699VauuOIKAF5//XUmTpxYYNuePXsyePBg/9/79u2jVq1aAERGRhb4crV3717/OoCcnJwC/z9u3DimT58OQHx8PBdffDFHjx7lzTffLP03KVJBFNVefPjhh3zyySfUqVOHnj17kp6eXui+yqptyHd8O/Dyyy9Tp04dnn/+ecaPH8+jjz4K+KZrZWdnc9ZZZxX/RIhI0NOIGhGRIJeens7333/vaQxz584lJiaG1NRUMjIyCqzLyMggNTWVmJgY5s6d61GE3lM9eeO5556je/fuTJ06ldzc3FLf/9SpUzn77LMZNGiQ/4sYQGpqKunp6QXKsV/EwJf74sCBAwAcOHCgQJ6L2rVr+9eB79fyfHfeeSe33HILjRs35sCBA8yZM4evvvqK1atXM3LkyFJ/jyIVRVHtRZMmTbjkkkuKNequrNqGfMe2AwB16tQBoEuXLmzatMm/3cKFC7n22muLeQZEJFSoo0ZEJMgV1gFQFl9KAzl69CgzZszggw8+oFevXjzzzDMF1s+YMYNevXrxwQcfMGPGDI4ePVoucQUb1VP5y8rK4p///CejR4+mY8eOzJkzp9SPsWzZMoYPH86UKVNITk5m9uzZgO9X82OTeyYkJDBlypQCr42Ojua9994DfP8+rr76av+6GjVqkJWVRWZmJqtXr+bSSy8F4Nlnn8U5xy233AL4OnBq1KhBtWrViIyMJDMzs9Tfo0hFUJz2okmTJuzYsYPMzMwCo9wCKau2AU5sBwB/x87y5cv9nTcAr732mhIJi4Qjr5PkqKioqIRroYhEhwcPHrRu3bpZu3btbNCgQWZmNmrUKBs4cKAlJSVZ3759LTs728477zxr3ry53XXXXfbcc89Zz5497Q9/+IOtW7fObrjhBmvbtq3dcMMNdvToUf/65ORkS0lJsV9//dWuu+46279/v5mZ3Xrrrfb555+fNK7jbdq0yYYOHWpmZr/88ov16NGjwPpu3brZ7t27zczs9ttvt08//fSk+yPIkheqngoXDHV1svp56KGHbO7cuWZmlpWVZdHR0Xbo0KECSTefeeYZa9GihTVq1Mh/fgItMys6YeiBAwfsueeeO+k2xzpy5Ij16tXLYmJi/IlD165d69/HO++8Y9HR0dauXTvbunWrmZlVq1bNYmNjLT4+3saMGWNmZuPGjbPo6Ghr2bKlLVy48KTHDIY6U1Epj1JU23284rQXmzdvtrZt21qrVq1s8eLF/uXl3TYEageuuuoqi42Ntfbt29u2bdv876NNmzbFPu7x1F6oqARvcWbmTQ+RiEiYc87ZydrYSZMm0bBhQ7p3787IkSPp0qULb7zxBnXr1mXo0KEkJyfz4osvMmnSJP+vcjNnzuTjjz/mqaee4qWXXuKrr77i3nvvZezYsVx88cUcPHiQjz76iClTpjB+/HgaN27Mr7/+CsBNN91Ep06dePvtt/0xHDlyhOTk5AJxRUZGsmDBAv/fy5cv580332Ts2LFkZ2efsI8OHTrw5ptvUqVKFe6//346depEmzZtTnZeMLOSZXItQ6qnk54bz+uqqPoJ5OKLL6Znz56MGTOm2K8ZNWoUL7zwAp9//nlJQwwqwVBnIuXhVNqGQIpqL8KlbQhE7YVI8FIyYRERj3zxxRfMmzePp59+moMHD9KqVSsALrvsMgAaNGjA/v37T3jdlVdeCcA333zDVVddBcDVV1/N+vXrqV+/vn99ixYtWLt2LbfddhtpaWnUr1+fpKSkAvuqVq3aSZMlQtFz6fPX16lTp9C59qFM9RR6TuUL1ZgxY0rUsSMi4aGo9kJtg4h4QTlqREQ80qRJE+677z7S09NZs2YNKSkpAAUSGJoZVatWLTBXPj/paOPGjfn4448BWLNmjX/O+rp16wBYu3YtjRo14je/+Q1nnHEGU6ZMOeHxoUeOHDlhLv111113QpwbN24kJyeHd999l9atWxdYnz/XPicnh40bN3LhhReWxukJGqqn0BXsCZ7ffvttLrnkEhISEvzLAiV9/vTTT4mJiSEmJoYHHnigPMMXCXvB3k7kS01NZfTo0QDMnDmTiy++mISEBO6++24AJk6c6L8/1KxZkz179pRX+CJSBtRRIyLikYEDBzJ37lwSExNp3749W7duDbhdQkICDz30EOPGjSuwvEuXLqxfv574+Hg2btxIly5dANi9ezfJycksX77cv6xbt27s2rWL888/v8A+8kdqHFuOnU4DULVqVfr27UtcXByzZ8+mf//+AP7Hm/bv359Zs2YRFxdH3759qVq16mmfm2CiegpdwZ7guWXLlv4Ou3yBkj5PnTqVRx99lOXLl7Nq1Sr27dtXLvGLVATB3k4ArF+/nsOHDxdYNmLECNLT0/2P6f7zn/9Meno6r776Ki1btvQ/JUpEQpNy1IiIlJHSmj9fEjNnzgQgLS2twPIFCxbwww8/MHTo0HKNJ5BgmxOveipcMNRVoPrJzMwkLS2N3bt3c9FFFzFlyhRGjx7Njh07+OabbzjvvPOYPn06F1xwAVFRUSQnJ3PppZeyePFiDhw4wPjx45kwYQLbt2+nQYMGzJ49mzlz5rB48WL27t1LtWrVmD9/Pj169GDWrFnUqlWLQYMGMXz4cC666KJix75582amTZvG5MmT2b17N0OGDOGFF144YbuEhAT/1Lbu3bszbdo06tSpw9ChQxkyZAgffPAB5557Lh07dqRjx468/vrrVK9evbDz5XmdiZSHotrucGon+vXrx7XXXsu6desYPXo0M2fO5PHHH6du3bqMHj2adu3a+bedPXs2u3bt4s477yzOOVR7IRKkNKJGRCTMvfjiizz22GP07t3b61DkJFRPxTdjxgx69OjBkiVLiIqKYvXq1YAvb9C7777L1q1bycjIIC0tjUmTJvl/ca5Xrx5vvPEGX375Jc2aNWPp0qU0bdqU1157DYDatWvz1ltv0aZNG1599VW6du3Ka6+9RnZ2Nt9++22BL1/FmY62b98+atWqBfiSP+/du7fI9xboNYmJif4vf61atSq0k0ZE/itc2onPP/+cs846q0Besc6dO7NhwwZefvll/vKXvxQY/fPqq6/qcd0iYUDJhEVEwsjxIzQAevToQY8ePco/GCmU6un0hEuC55O95tikz/fffz/z5s3jiiuu4Prrr+f7778/YXqciBQULu3ExIkTeeihhwokPc7fpl69evz+97/nl19+4ayzzuLQoUPs3LmTCy64oFjnSESClzpqRETCWFpaGqNHjy6TL3WdO3fml19+4YwzzuBf//oX5557bqkfo6JQPZVMkyZN6NSpE9deey0A2dnZrFu3rsQJnv/3f/+XNWvWcNFFF5GZmXnSBM+TJk0qEENxHpleVILnQPKTPnft2tWf9NnMqFu3LpUqVSIyMrLQZKMi8l/h0k5s2bKFtLQ09uzZw549e2jfvj2XX345tWrV4tChQ3z99dfUrVsX8CUoP/54IhKaNPVJREROyZNPPsmyZcsYMWIEkydP9jocKUQ41lO4JHhes2YN7du3Z/369bRv356jR48GTPp8zz33cOONNxIXF0e1atVo1qzZaZ9DkXAXLu3EW2+9xeLFi3n00Ue5+eabiY2N5cknnyQ6OpqEhATuueceKleuDGjak0g4UTJhEZEyUpIktWcCeqgAACAASURBVCtWrGD48OFUr16d3r17c/PNN5OSksLhw4f5/e9/z3PPPUd6ejoTJkzw/9p3zTXXMGfOHBITExk3bhxpaWnUrFnT/8Fy7Nix/pEaERER9OvXj4MHD9K+fXseeOAB7r33Xj744AMqV67Mv//9bxo0aHBK7/P999/nnXfe4a9//Wtxz0tQJS9UPZ303HheV+WV7DlUEjwXJRjqTKQ8KBH86VN7IRK8NPVJRCQIvPnmm/z1r38lKSnJnxRw0aJFnHHGGfTp04cvvvgCgN/85je89NJL3HrrrTjnWLlyJa1bt/b/EhgXF8ff//53OnXqxI4dO/z7nzBhAqNHj6Zly5b06tWLHTt28OGHH7J06VIqV67M8R92u3btyp49e06I8fgkpjk5OYwbN47p06eX+jkJRqqniuXFF19k8uTJLFq0yOtQRCRIqZ0QkbKgjhoRkSAwZMgQHn74YWbOnMmf/vQnLrroIvr168dPP/3Eli1b6Nu3LwCXXnopAOeccw6XXHIJABEREf795Cc5bN68OVu2bPEv/+KLL7jrrrsA3xMmfvzxR+69917S0tL47W9/y7hx46hRo4Z/+1deeaVYcd95553ccsstNG7c+DTefehQPYUvJXgWkaKonRCR8qKOGhGRIBAVFcU//vEPduzYwcCBA+nTpw/Nmzfnvvvu4+abb/aPpDg2CeKx/59v3bp1NGnShA0bNnDHHXf4lzdp0oT+/fvTrFkzcnJycM7x66+/8oc//IHx48fz5ptvcv311/u3L85IjWeffRbnHLfcckupnYdgp3oSERERkbKmZMIiIkFg2rRptG3blpSUFPr06UOrVq146aWXSE1N9T+2sziWLl1KXFwcV155Jeecc45/+ciRI7n33ntJTEykU6dOZGVl0blzZ+Li4li8eDFxcXEF9vPKK6+ckADx+Ok0Q4YMYc2aNf5EjBWB6qliSktL4/vvvy+TfScnJ5OQkECLFi38SUCXLFlCmzZtSEpKYvv27WVyXBE5fWXZNgwdOpR69er58+JA4W3D9u3biYiIKLNYRKT8KZmwiEgZKe9Eh2X5iOfSFGzJC1VPhQuGuvIiYejxyqPOnnrqKapXr07//v1JSkpiwYIFbN68meeff56//e1vxd5PMNSZSHkI97Zh586dLF682H8coNC24e6772b16tXMnDmzRLGovRAJXhpRIyIiImFhxYoVtGrVioSEBJ599ll+/fVXOnToQFxcHH/84x8BSE9Pp2PHjnTq1IlOnTrx9NNPEx0dzX333Qf4vhDdfvvtxMXFcf/99xfY/86dO7nmmmuIj4/n4YcfBuDee+8lNjaW+Pj40xr9snDhQlJTU8nKyuLMM8+kZs2atGrVik8//fSU9ykiPqHYNpx99tkF/i6sbfj555/JyMgIic5/ESk+ddSIiISJkv6SJt5QPZWd/Kdypaen88c//pGqVauyaNEiPvzwQ3Jzcws8leuNN97gd7/7nf+pXO+9955/P3FxcXz44Yd88sknAZ/KtXTpUj7//PMCT+VaunRpgWls4MshlJCQUKAcOnTohLj37NlDdnY2Z511Fnv37qVWrVr+dTk5OaV9mkQqnFBtG45VWNswadIkbr/99tM+RyISXJRMWERERMJCqD6Va+HChVx77bUA1K5du0C+o0qV9JuayOkK1bbhWIHahn379rF161aaNm1a4v2JSHDT3V9EJIiUZWLC77//nrPPPpspU6YAxUtYmpmZSWJioj+BbkZGRqH7f+yxx4iLi6N3795kZ2dz6NAhWrduHfBxpqEu2OoJYO7cucTExJCamlpoPW3ZsoW4uDjatm3LTTfdRE5OTljVU/5TuR599FHGjBnDW2+9RfPmzVm6dCmxsbEleioXwIYNGzjvvPP8y5s0acJTTz1Feno6H3/8MS1atCAhIYHZs2dz1lln8eabbxbYT3F/NX/ttdf8iYRr1KhBVlYWmZmZrF692v/FUUROXai2DccK1DZ88cUXfPXVV3Ts2JF33nmHQYMGnfI5EpHgohE1IiIVSMeOHRk8eDAA48aN4+2332bz5s088sgjAROWVqlShTlz5tCgQQNmzJjB7NmzGTJkyAnb/fzzz3zwwQd8+OGHPPLIIyxcuJAuXbowb948Ro8eXdZvK+yUtJ6OHj3KjBkz+OCDD3j55Zd55plnGD58+AnbRUZG8vrrr1O7dm3uu+8+3nrrLTp16hQ29TRt2jReeeUVMjMzGTlyJK1atWLs2LF89NFHJdrP0qVLeeqpp4iPjz/hqVwDBgwgMzOTqlWrMn/+fK6//nqysrKoVKkS/fr1K7Cf4vxqfujQIXbt2kWjRo38y0aMGEGHDh2IiIhg1qxZJYpdRE4Uim3DuHHjeP755wH48ccfue+++05oG84991xWrlwJ/DexsYiECTNTUVFRUSmD4mtifQYMGGBffvmlmZk98sgj9sYbb9iiRYusXbt21qJFC1uwYIGZmfXp08e+++47GzVqlL3//vsFlu3YscM6depkbdu2tYceeshK6rvvvrM+ffqYmVlmZqZde+21/nXt27cv8vX//Oc/bdq0aQHXLVq0yJ544gkzM1uzZo3dfffdJxwzX9558bx+8ks41NOmTZts6NChZmb2yy+/WI8ePYo8zoMPPmhvvfXWCcc8VjDU1bH1Ux7y6zFUBUOdqaiUR1HbcPrUXqioBG/R1CcRkXJw/fXX+39Be++990hKSqJdu3YsWbKEpUuXMnny5CL3EShZYb4jR46cMIz6uuuuK3RfJU1YevDgQaZPn07Pnj0Drt+3b59/f5GRkezdu7fI9xOMQrWeSnr+t2/f7n9/IiIiIhJcNPVJRKQcJCYmMnHiRHr16kX9+vWpVq0aK1as4KGHHiInJ4ddu3YV2P7YufFmvrnzgZIV5g+9rlatGunp6cWOpyQJS82Mvn37Mm7cOCIjIwNuExUVxU8//QTAgQMHiIqKKnYswSRU6ykqKsq/XVHn/9dff6VPnz5Mnz6dypUrFzuWimLmzJlehyAiQUhtg4iUJ42oEREpB1WrVuWcc87hySefpGvXrgA8/vjjzJ49m9dff/2EL+CRkZFs376d3NxcNm3aBAROVpivpCM1CktY+uOPP56w7YMPPkhMTAyJiYn+Zcdv17JlS5YuXQrAu+++S+vWrUtyeoJGqNZTkyZN2LhxIzk5OQXOf6D6HDhwIEOGDAnrJLXBmOx57dq1JCcnEx8fz6JFiwDf6Ku4uDiuv/56MjMzA74uNzeX6Ohoatas6X9P4ZQAWqQ8BWPbUFg78MknnwRMaJwvUNsAFSuxv0hY83rulYqKikq4Fo6bP79gwQKrVauWZWVlmZnZ9OnTrXnz5nbrrbdamzZtzOy/c+C3bNliLVq0sK5du1pSUpI/90lKSoq1a9fOkpOTLSMjw0ri+Dwk77zzjkVHR1u7du1s69atZmaWlJRU4DU//vijVa1a1eLj4y0+Pt6fo+b47czMJkyYYDExMdarVy87cuRIwGOa78RYSc9lWZZwqCczs1mzZll0dLSlpKTY/v37A263YsUKq1mzpr8+83PuhFuOmrLMJXH8uUpMTLSMjAxbtWqV3XHHHQFfk5uba926dfP/mzIz2759u6WmppqZ2csvv2yTJk0q9Jg7duw44T0Fc52pqJRHCYe24WTtQN++fa1FixYnPebxbcOuXbssJSXFzHz35FdeeSVgbPnUXqioBG/xPAAVFRWVcC2n8iGyLG3dutWaNm1q//jHPwKuz8nJ8SekPZlffvnFRo0aVeR2WVlZFhMTY3feeWeB5cH2wVD1FLiezIKjrgiDZM9ff/21paSkWMeOHa1z5862a9cuW7VqlY0YMcK/vmfPnic9rjpqVFQKlnBoGwprBzZt2mSjRo2y+Pj4Io97bNtQksT+Zqb2QkUliIty1IiIVBDnnnuuf3pOIJUqVSpWsty6desW6xGg1atXZ9myZSUJUVA9nUx+sud77rmH9957j2HDhpGdnc0111zDwYMH6dy5M6mpqSfdR36y55YtW9KrVy927NjhzyF05MgRkpOTC2wfGRnJggULAu6ruMmed+3axWeffcaGDRtYunQp48ePZ+TIkaxevZqcnByWLFkSsgm4RYJBqLYNjRs3DtgOPPnkk0yYMKFEOc0gfBL7i4iSCYuIiEiICNVkz5GRkbRs2ZIaNWqQmJjI5MmTqVevHjfddBNJSUm0bNmS+vXrF/u4IlJQqLYNgdqBr776isjISH77298W+3j5wiWxv4ioo0ZERERCxMmSPdesWZM2bdoU2L6wZM/9+/enWbNm5OTkFPjCVtJfzY9N9rx58+YCyZ4bNmzo3+7CCy9k165d5OTksG7dOho1agRAv3796NevH7Nnz6ZatWoBXysiRQvVtgFObAc2btzIf/7zHzp27MiGDRsYNGgQU6dOLVbb0LJlS6ZPn86f//znkE7sLyLqqBERKTMRERE/Oef0M/lxIiIifvI6hmOpngoXbHUF0LVrV3r37s3YsWMBuO6667jmmmto3br1Cb8ed+3alS5dujB//nxq164NwMiRIxkwYACZmZlUrVqV+fPnU7NmTaDkv5oDjBgxgg4dOhAREcGsWbMA6NOnD++++65/m6pVqzJgwAASEhKoUqWK/zG/3bt3Z8+ePTRv3pzHH3884GsBevTowbJly/j6668ZMWIEKSkpJYpRpCIIxbYBTmwHKlWq5O9sSkhIYOrUqYW+NlDb0KZNG2JjYzn//PMZPnx4iWIWkeDh8of7iYiIiAQb55wF02eVbdu20bFjR2677TYGDx58wvrc3FyGDRtWrDxCx9u9ezdPPfVUkbmFDh06RIcOHWjdurW/gyefcw4zK/yZviJhQm1D8ai9EAlN6qgRERGRoBVsX8aCnb54SUWhtuH0qb0QCV6BM1uJiIiIiIiIiEi5U0eNiIiIiIiIiEiQUDJhERERCVpK9lwywZgAWqQsqG04fWovRIKXctSIiIhIheKc6wDMAC4zs4PlfOx+wCCgtZnllOexRSoS51xH4F/AOOCpipDQxvmeKX4n8Begl5m953FIInKK1FEjIiIiFYZzrgawEbjdzN704PgOWAIsMLNJ5X18kXDnnKsE3AsMBnqa2TKPQyp3zrlEYC4wEXi8InRSiYQbddSIiIhIheGcewT4nZnd5GEMFwIrgavMbItXcYiEG+dcFDALqAt0N7PtHofkGefc74D5wBagr5lleBySiJSAkgmLiIhIheCcuxJIA4Z7GYeZfYXvl+4peSNsROQ0OecuA/4D/AC0q8idNABmthVoC+wFPnLOXexxSCJSAuqoERERkbDnnKuCLy/NCDMLhgSajwO/A3p6HYhIqHPO9QTeBx4ys9vN7IjXMQUDMztsZgPxdQx/4Jzr4nVMIlI8mvokIiIiYc85Nxy4FkgKlnwNzrlWwGtAUzPb43U8IqEmrwP2EaALcL2ZrfU4pKDlnGsJvAw8D9yvZOYiwU0dNSIiIhLWnHPnA2uA6LxpR0HDOTcZONPM+nkdi0goyXs09wvAYeAmdXYWzTlXD5gH5OA7Z794HJKIFEJTn0RERCRs5eWAmQI8EWydNHnuAzo459p5HYhIqHDOtcbX+fohcI06aYrHzH4G/hdYC/zHOdfC45BEpBDqqBEREZFwdgPQEF9OmKCT9ySW24DpzrnqXscjEsyczyDgdeA2M3tAU3hKxsyyzewe4C7gLefcH72OSUROpKlPIiIiEpacc3WBTUBnM/vI63hOxjn3IvCVmd3ndSwiwSivI/Np4P8BXc3sS49DCnnOuUuAV/ElYh5mZr96HJKI5NGIGhEREQlXjwMvBnsnTZ4/AQOcc5d7HYhIsHHOnQcsA2oArdVJUzrM7DN8HV/1gXTnXEOPQxKRPOqoERERkbDjnEsEEoH7vY6lOMxsB758NTOcc5W9jkckWDjnOgAfAXOBG83soMchhRUzOwBcDyzAl7cm3uOQRARNfRIREZEwkzdFYgMw3MwWeR1PcTnnKuGbgjDfzCZ7HY+Il/ISgd+Db7TZjWaW7m1E4S+vU2w2vkeeTzJ9URTxjDpqREREJKw458YDjc2sp9exlJRz7mJ8UzxamNkPXscj4gXnXC1gJtAA6GZm27yNqOLIm2b2CvAV0M/MMj0OSaRC0tQnERERCRt5OV764fsVPuSY2efA34Cn80YUiFQoeQluVwO7gHh10pQvM9sCxAKHgFXOuQs9DkmkQlJHjYiIiISFvNwuzwD3mtlOr+M5DY8AjYDuXgciUp6cc9cDHwCPmNkgPYXIG2Z2COiL7ylby51z13ockkiFo6lPIiIiEhacc38CugDtQj23gnMuGpgPNDWzvV7HI1KWnHNVgHFAT+B6M/vY45Akj3OuNfAS8BwwxsxyPA5JpEJQR42IiIiEPOfc/wCfAG3C5dG9zrmngWpmNsDrWETKinOuHjAPyMWXNPgXj0OS4zjn6gMv4JsO1cvM9ngckkjY09QnERERCWl5uVz+ge8pJWHRSZNnJNBRj8uVcOWcawmswZeTpqM6aYKTmf0EdAA+xfcI7ys8Dkkk7KmjRkREREJdD+A84FGvAylNZnYAuB2Y7pyL8DoekdLknOsHvAEMN7ORmlIT3MzsqJndCdwLvOOcu9nrmETCmaY+iYiISMhyztUGNuPLa7HS63jKgnNuPvCpmT3gdSwip8s5dwbwFBAHdMl70pmEEOfcZcCrwGLgTjM74nFIImFHHTUiIiISspxzzwCHzex2r2MpK865BsB6fEmSN3kdj8ipcs79Dl+S7B+AP5pZhschySlyzkUBs4C6QHcz2+5xSCJhRVOfREREJCQ55xKAZHxD8cNW3heg+4EZeY8gFwk5zrlEfLloXsb3xV6dNCHMzPYBnYE38eWtifU4JJGwoo4aERERCTl5OVumA7fn5XIJdzOAbGCQ14GIlITz+QvwPHCzmT1qGtIfFsws18zGAv2B+c65oXnJ3UXkNGnqk4iIiIQc59xY4GIz6+Z1LOXFOXcJ8AFwpZlt8zoekaI4534DPAs0wpdH6gePQ5Iy4pxrDLwCbAIGmlmWxyGJhDSNqBEREZGQ4pxrBgwEhnodS3kys8+AvwNP61drCXbOuYuAj4ADQJw6acKbmX0LtAFygZXOuQs8DkkkpKmjRkREREJGXo6WGcD9ZrbD63g8MAG4ELje60BECuOc6wwsA540s/5mdtjrmKTs5Y2i6YOvjV7hnOvkcUgiIUtTn0RERCRkOOduB3oACWaW63U8XshL2vkC0DQvoadIUMjrSH0I6A10M7PVHockHnHOxeBrp6YDYytqey1yqtRRIyIiIiEh79G+n+CbRvG51/F4yTk3BahkZrd6HYsIgHOuLr6EwVWBG8xsl8chicecc+cALwL7gN7qWBYpPk19EhERkaDmnOvhnKsPPA08VdE7afKMAK5xzrX1OhAR51wLYA2wAUhWJ40A5E1PTQS+xfcI72YehyQSMtRRIyIiIsHubiAN+D3wiLehBAcz248vmfJ059wZXscjFYtzrqZz7pa8/+8DvAXcbWZ3mVm2t9FJMDGzo2b2J2A0sMQ5dyOAcy7NOVfD0+BEgpimPomIiEhQc87tABwwFzjfzJRIN49z7lVgg5mN8joWqTicc3cBVwF7gCSgq5lt9jYqCXbOueb4HuH9OnA+sMTMnvI0KJEgpY4aERERCVrOuSrAEXxfCDcBt5vZJm+jCh7OuYbAOnzJlfVFWcqccy4C+B74CfgO6Gdmuz0NSkKGc64eMBM4B6gHNDazo54GJRKENPVJREREgll1IBO4B2inTpqCzOxH4EF8U6D0uU7Kw4PAWfiuzVhgsrfhSIiZArQEfgM0xJdvS0SOoxE1IiIiIiEsr4PmQ2AOMB+oZWZfexuVhCvn3MNAI+Al4D9mtt3jkCSEOOcc0ABfZ80NwGYze9jbqESCjzpqREREREKcc+5SYCnwV6C5maV5G5GIiIicKnXUiIiIiISwvDw+E/ElXG4BZJpZsrdRiYiIyKmq4nUAIiIiUrqqV6++8/Dhw/W9jiMYRURE/HTo0KGzvY6jNJlZtnPuG+ABoBqw1+OQwoKuo5IJx2tLSpeuqZLRNVWxaUSNiIhImHHOme7vgTnnMDPndRxlwTnXAPgXEG9m1byOJ9TpOiqZcL62pHTomioZXVMVmzpqREREwow+DBeuInzwdc5Fmdk+r+MIdbqOSqYiXFtyenRNlYyuqYpNj3EUERERCSPqpBEREQlt6qgREREJcytWrKBfv34A7Nixg6SkJACioqIYNWoUABMmTKBhw4aMHj3a/7pAy0aNGkVUVFSpxpednU3v3r2Ji4vjscceO2H9kiVLaNOmDUlJSWzfvr3QZbNnzyY6OpqOHTuyc+fOUo1RpDDFub7S0tJo3bo1CQkJPP/880DwXEsA27dvJyIigu+//x6AhIQEf1myZEmpxigSyKnepzZt2kRsbCyxsbFs2rQJCJ5rK9Cyzp07ExsbS1JSEtu2bSvVGCXMmJmKioqKiopKGBXf7b2g7t272/r1661fv362bNkyMzOLj4/3r9+5c6ctWbLERo0addJlx7+uMJmZmUVuk2/+/Pk2YcIEMzNLSUmxXbt2FVifmJhoGRkZtmrVKrvjjjsCLjt69KjFxsZadna2rVmzxoYNGxbwWHnnxtP6iYiI2AmYSvFKRETETi/qKdB1VJiirq8+ffrYd999d8LrguFaMjO76667LD4+3h9jceI6XjBcWyrBXYq6pk7lPtWlSxfbtm2bbdu2zbp27epfHgzXVqBl3377rZmZvf3223bXXXed9Ji6pip20YgaERGRCmDcuHEMGDCAjIwMYmJiTlhfv359nHNFLivKhg0bGDx4MD179iz2a1auXEmHDh0A3y/5a9as8a/LysrizDPPpGbNmrRq1YpPP/004LLdu3dz7rnnUrlyZZo3b86qVatKFHd5Onz4cH2vPwCGUgmFp8QUdX0557jpppu49tpr+eGHH4q1z/K4lgB+/vlnMjIyOP/88/3bVqpUiYSEBG688Ub27tVDxKR8nMp9av/+/TRs2JCGDRuyf//+Yh3Hq/sUQKNGjQCoUqUKVaroAcxSOP3rEBERqQAuuOACdu/ezbBhw8pk/3PmzGHu3Lk0atSIW2+9lSuuuAKA119/nYkTJxbYtmfPngwePNj/9759+6hVqxYAkZGRBb4Y7t27178OICcnJ+CyevXq8d1335GVlcXy5cvZs2dPmbxPkUCKur6eeOIJ6tSpw7Jly7j77ruZN29eofsqz2sJYNKkSdx+++0FpnO8/PLL1KlTh+eff57x48fz6KOPFvdUiJyyU7lP5ebmBvz/QLy+Tx37/+PGjWP69OnFfp9S8aijRkREpAJ47rnn6N69O1OnTqVnz55UqlS6g2qnTp3KhRdeyKBBg7j88sv9y1NTU0lNTT3pa6Oiojhw4AAABw4coEGDBv51tWvX9q8D3y/9gZZVqlSJBx54gE6dOnH55Zdz0UUXldZbEylSUddXnTp1AIiNjeX+++8/6b7K81rat28fW7dupWnTpgHj7dKlC3PmzDnpMUVKy6ncp47dpqjtvb5P5bvzzju55ZZbaNy4cRHvTioyTX0SEREJc1lZWfzzn/9k9OjRdOzYsUy+eC1btozhw4czZcoUkpOTmT17NuD7pfLYxKQJCQlMmTKlwGujo6N57733AEhPT+fqq6/2r6tRowZZWVlkZmayevVqLr300oDLAK655hrS09Pp3r07sbGxpf4eg1l6ero/EaxX5s6dS0xMDKmpqWRkZBRYl5GRQWpqKjExMcydO9ejCMtGca6v/C9sX3zxBZGRkSfdX3leS1988QVfffUVHTt25J133mHQoEEF4l2+fLm+TEq5ONX7VGRkJNu3b2f79u1BdW0Vdp969tlncc5xyy23FPvcSAXl9bxjFRUVFRUVldItHJew8aGHHrK5c+eamVlWVpZFR0fboUOHCiRbfOaZZ6xFixbWqFEjGzp0aKHLzIpO0njgwAF77rnnTrrNsY4cOWK9evWymJgYf7LGtWvX+vfxzjvvWHR0tLVr1862bt1a6LLbb7/d2rVrZ2lpaZaVlRXwWARBcsbj66c0jBo1yt5///0Tlufk5JT6sQI5cuSIxcfHW3Z2ts2bN88mTpxYYP0TTzxh8+bNs+zsbIuPj7cjR44Ue99e1Vlx66k411dKSorFxMRYbGysbdy40b88GK6lfMcmPL7qqqssNjbW2rdvb9u2bSvWsYPh2lIJ7nKya+pU71Pr16+3mJgYi4mJsfXr1/u3DYZrK9CyatWqWWxsrMXHx9uYMWNOekxdUxW7ODPzpodIREREyoRzzopzf7/44ovp2bMnY8aMKfa+R40axQsvvMDnn39+OiF6xjmHmZUsQ3Lpx1Cs+gHIzMwkLS2N3bt3c9FFFzFlyhRGjx7Njh07+OabbzjvvPOYPn06F1xwAVFRUSQnJ3PppZeyePFiDhw4wPjx45kwYQLbt2+nQYMGzJ49mzlz5rB48WL27t1LtWrVmD9/Pj169GDWrFnUqlWLQYMGMXz48BJNH9u8eTPTpk1j8uTJ7N69myFDhvDCCy/413fv3p1p06ZRp04dhg4dypAhQ7jkkkuKe748qbOS1FMgRV1foX4tHS8Yri0JbqdyTVXE+1Q+XVMVm6Y+iYiIVFCff/55iT78AowZMybkP/yGkhkzZtCjRw+WLFlCVFQUq1evBuCyyy7j3XffZevWrWRkZJCWlsakSZP8SV/r1avHG2+8wZdffkmzZs1YunQpTZs25bXXXgN8ORXeeust2rRpw6uvvkrXrl157bXXyM7O5ttvvy3QSXPkyJETpgVcd911BeI8WaLN4qwPR0VdX7qWRIqm+5RUVEomLCIiIhKkvvjiC+bNm8fTTz/NwYMHadWqFeDrqAFo0KBBwEfSPqJdQgAAIABJREFUXnnllQB88803XHXVVQBcffXVrF+/nvr16/vXt2jRgrVr13LbbbeRlpZG/fr1SUpKKrCvatWqkZ6eftI4j0+0GRUVFXB9nTp1Aq4XERGR/9KIGhEREQn6ZLSffvopMTExxMTE8MADDwDw9ttvc8kll5CQkODfLtCyUNakSRPuu+8+0tPTWbNmDSkpKYBvSHw+M6Nq1aoFHv+a/4SRxo0b8/HHHwOwZs0af2LYdevWAbB27VoaNWrEb37zG8444wymTJlCz549C8RQnBE1TZo0YePGjeTk5PDuu+/SunXrAuvzE3Hm5OSwceNGLrzwwtI4PUEt2K+pH374gcTERNq2bcuLL74IBL5+Al17IuUt2K+nZ599llatWtG6dWv+/e9/F7osX2pqKqNHjy6v0CUEqaNGRERECv0QnJubWy7HP3r0KDNmzOCDDz6gV69ePPPMMwXWT506lUcffZTly5ezatUq9u3bR8uWLf0dDvkCLQtlAwcOZO7cuSQmJtK+fXu2bt0acLuEhAQeeughxo0bV2B5ly5dWL9+PfHx8WzcuJEuXboAsHv3bpKTk1m+fLl/Wbdu3di1axfnn39+gX3kj6g5tixYsKDANlWrVqVv377ExcUxe/Zs+vfvD8CwYcMA6N+/P7NmzSIuLo6+fftStWrV0z43wS7Yr6lHHnmERx55hPfff59nnnmG7OzsgNdPoGtPpLwF+/WUlJTEqlWr+PDDD5k0aVKhywDWr1/P4cOHyyVuCV2a+iQiIhLGipuMdubMmbz22mueJaP98ssvufzyy6lcuTLt27dnyJAhBdY3bdqUffv2+UeNnHHGGVSvXv2E/dSuXfv0TliQOfPMM5k3b16BZcf+Cjtz5kwAzj//fJYuXXrC66tWrVogqW++P/zhD6SlpRVYVrlyZW688cZTjrV379707t27wLL8Lye1atVi4cKFp7zvYBIu19S3337rX1+/fn2+/vprLr744hP2E+jaEykt4XI95XdwV6lShSpVqhS6DPj/7N19nM11/v/xx3s0DF8xkqJ2f6UtilqlLQxjhkFCYpRqJZYlKaVsLiuTaJVcFyJLLja7NRXpWjKxdEEKXUi7snKZq2iGxozX748zc9aYM1fMzOecM8/77fa+3fI5n/M5r/N5937POe/zfr/eTJkyhf79+4fVjwpS/DSjRkREJIyFSzLali1b+j9YN2rUKOAgjZy+f/7zn4wbNy7XQIvkFi5t6vLLL2fFihWkpaXx8ccf55ngWW1PSlK4tKdsM2bM8C9RDXTs22+/5bzzzlOeLimQZtSIiIiEsXBJRvvII4+waNEirr76arp06cIPP/yQa4mOFM6pM2kAunbtSteuXUs/mBAULm1q2LBh9OvXj8mTJ3P55Zdz/vnnB7yO2p6UpHBpTwCffPIJb731ln+wKNCxCRMmMGrUKO1KJQXSQI2IiEgYq1OnDu3ateOmm24CICMjgy+++KLIyWhvuOEG1q5dS926dUlNTc03Ge3Ja/HB92tlmzZtchyrWrVqjjwnBSWjNTOqV69OREQEVatWzZXIUYqmZ8+eJCUllcgX7m3btnHvvffyyy+/cOedd/LnP/854LFQFS5t6rzzzuPVV1/l6NGj9OjRg9q1awd8v2p7UpLCpT3t2LGDQYMGsWTJEsqVK5fnsW3bttGzZ08OHDjAgQMHaNWqFc2aNTujeyjhSQM1IiIiYaxv37707t2biRMnEhERwaxZswKeFx8fz/Dhw/n444+58MIL/cc7d+7MnXfeSVxcHDVr1mTo0KEsWLDAn4y2QoUKPPTQQ4AvGe0zzzyTZzLa/JycjLZ69eosXLgQ8CWjnTRpEkOGDOGOO+6gXLlyXHHFFVx11VWsXbuWoUOH8uWXX9KqVSvefvttvvzyy1zHykLi2mDy6KOP8uKLL1K9evV8j4WqcGlTb775Js888wxnnXUWY8eOxTkXsE0FansixSVc2tOoUaPYs2cPiYmJgG8HtUDH3n33XQB/YnYN0khenJl5HYOIiIgUI+ecleTf9+wEtqcuoVm8eDH//e9/GTBgQIm99plyzmFmruAzSzSGQtfP6tWrefDBB6lYsSLdu3fnzjvvpEOHDhw7doxLL72UOXPmsGLFCsaOHev/hbl9+/YsWLCAli1bMmbMGHr27EnlypX9uz+NHj3aP6MmKiqK3r1788svv9CqVSseffRRhg8fzkcffUS5cuV46aWXuOCCCwr93o4fP07r1q0555xzSEtL49lnn+Wiiy7KdezSSy8tyv3ypM5Kuh2dLJTbVLZgaFsS3EqrTYVDewK1qbJOM2pERETkjP3zn/9kypQpLF261OtQwsrbb7/Nk08+SUJCgn8b2qVLl1KhQgV69OjB5s2bATj77LN5+eWXufvuu3HOsWbNGho3buzfrjs2NpZnn32Wdu3asWvXLv/1x44dS1JSEtdddx3dunVj165drFy5kpSUFMqVK8epX6oSExM5cOBArhizE8zu27eP9evX8/3337N3716GDh3K1KlTcx175ZVXSuyehQu1KZHio/YkoUYDNSIiIlIkSkZbevr3788TTzzB3LlzeeCBB6hbty69e/dmz549bNu2jV69egFQr149AGrVqsUVV1wBQFRUlP862Yk1GzRowLZt2/zHN2/ezMMPPwz4djXZsWMHw4cPp2fPnpx77rmMGTOGSpUq+c9/9dVX8403OjqaevXqUaNGDWrUqMH+/fsDHpOc1KZEio/ak4QDbc8tIiIixa5nz5788MMPJXb9nTt3EhUVVaKvEQyio6OZNm0aTz/9NI8//jjvvvsuDRo0ICUlhWbNmvlnvJycePPk/86WnVhzw4YNXHTRRf7jderUYerUqaxYsYJ169bRsGFD4uPjmT9/Pueddx5vv/12juskJibm2sb26NGj/scrVqxI5cqVSUtLY8eOHVSpUiXgMSmckmxHnTp1olmzZiQkJPDjjz8CMGDAAGrUqOFfOiISbkqyTU2ePJlGjRrRpEkT1qxZA8Add9xBXFwcMTEx2ulJikQzakRERCTkTJo0KdeuG+Ho+eef59VXXyU1NZVhw4bRqFEjRo8ezSeffFKk66SkpDB16lTi4uKoVauW//iwYcPo06cPqampREZGkpycTJcuXUhLSyMiIoLevXvnuE5BM2rAt51zmzZtMDOmTp2a5zHx1sSJE6lduzbvv/8+U6ZM4emnn2bEiBH+rY5FpGjmz5/PZ599xs6dO7nvvvt47bXXmDdvHpGRkaSkpDBt2jSmTJnidZgSIjRQIyIiUoaVVLLabLt37y7WZLUAP/30E0eOHCmRraWDzcCBAxk4cGCOY9mzY04WHx8PQFJSkv/YybuYPPzwwznu18kzJt54440c18releR0xcXFsWrVqgKPhZNQbEfZ23GfddZZnHWW7ytBzZo1i+mOiJyZUGxTv/vd7zh27BiHDh3y73CXvetgamoqDRo0KKa7I2WBBmpERETKsFBLVgu+2TT33Xcf48aNK5F7IlJUodiOADIzMxkzZgwzZ84s9nsiciZCsU3FxcVx+eWXc/z4cf+y0czMTOLj4/nxxx9ZsmRJ8d8oCVsaqBERESnDQi1Z7aFDh9i+fTv169cvhndfNijfSMkLtXaUbdCgQdx1111ccsklZ/DuRYpfqLWpw4cPs2DBArZs2cLevXvp27cvb731FuXKlWPlypWsW7eOpKQkkpOTi+HuSFmgZMIiIiJlWKglq928eTNbtmyhbdu2vP/++/Tr169Y70ewKskEmD/88AM1a9Zk+vTpACxfvpyYmBgSEhLYuXNnns9bv349bdq0IS4uzr/lbaBjgYwbN47Y2Fi6d+9ORkYGR48epXHjxgF3awkFodaOAGbPno1zjrvuuqvY7oNIcQm1NhUREUGlSpUoX748VatWJTU1FYD09HQAqlSpkmMASaQgmlEjIiJShoVastpGjRr5d9Po2bNnjpwscvratm3LPffcA8CYMWN47733+Oqrr3jqqaeYPHlyrvPNjCeffJLFixf7p/4HOhbITz/9xEcffcTKlSt56qmneOONN+jcuTOLFi0K2foMtXYEvhkL119/PfHx8bRs2ZLHHnuMMWPG8Pe//x2AHTt2MGLEiCLFL1JcQq1NVa5cmZYtWxITE0NGRgaPPfYYJ06c4IYbbvCfM23atCLFLmWcmamoqKioqKiEUfH9eS89PXr0sK1bt5bqa56urHsTNPXTp08f++6778zM7KmnnrK33nrLli5dai1atLCGDRva4sWLzex/93jkyJH24Ycf5ji2a9cua9eunTVv3txGjRpV5HuydetW69Gjh5mZpaam2k033eR/rFWrVgGf8/3331uHDh2sbdu21qlTJ9u7d2/AY4EsXbrUxo8fb2Zma9eutcGDB+eK42Re1ZnaUdEEQ9tSCe6iNlU0alNlu2jpk4iIiIhHunTp4v+l9oMPPiAhIYEWLVqwfPlyUlJSCrWVa3ZSzJSUFL799tscCTPT09NzTde/+eab87zWwYMHqVKliv/fmZmZAc/bu3cv33zzDcnJyfTt25e//vWvAY8FcujQIf9rVK1alYMHDxb4HkVERMoSLX0SERGRM6JktaevZcuWTJgwgW7dunH++edTvnx5Vq9ezahRo8jMzGTv3r05zj85B4OZL0dDoKSY2VP8y5cvn2Ob7oJUq1aNw4cP+/+dve3tqapWrcp1111HpUqVaNmyJVOmTAl4LJDo6Gj27NkD+BJwRkdHFzq+cKZ2JFK81KYklGmgRkRERMQjkZGR1KpVi4kTJ5KYmAjAM888w/z586lcuTIxMTE5zq9atSo7d+7kxIkTbNq0CfAlxfzzn//MVVddRWZmZo7BnPT0dNq0aZPrGosXLw4YT6VKlUhLSyM1NZWvvvrKv6PKjh07uPDCC/3nXXbZZezdu5fMzEy++OILateuHfBYoOded911zJw5k4ceeohly5bRuHHj0719IiIiYUlLn0RERAQIzp2FWrRoQVxcHPHx8blml2Q7ceIETZo0oXLlyv74Q2kXocTERF544QV/0smbb76Z9u3bM2TIkFyzTRITExk/fjy33nor1apVA3xJMYcPH07Lli1p164daWlp/vOzZ9ScXPIapMk2dOhQWrduzdChQxk8eDAAPXr0yHFOZGQkffr0IT4+nqFDhzJkyJCAxwI997zzziMmJoZmzZqxYcMGbrrpptO4a8ErGNtRYmIi0dHRuWZXff755wF3ysn29ddf+5fMXXLJJUyaNCmk2paEvmBsT4MHD6Zp06bExsayZcsWABYuXEjTpk3p2LEjR44cCfi8gwcPEhMTQ1xcHB06dODo0aNqT5I3r5PkqKioqKioqBRv4TQTNpZk4sVTE8W2bNnSjhw5Yh9//LHdf//9eT4vPT3dzMzmzp3rT0AbyK5du3LFHyg5LUGQnPF066ekbN++3erXr2/Tpk0L+HhmZqYNGDDgtK69b98+GzlyZIHnpaWlWdOmTW3QoEG5HvOqzsKpHe3atStHIupsvXr1soYNGxbqtbt06WJbtmwJGIdZcLQtleAup9Omgq097d+/359kfdWqVTZw4EBLT0+3uLg4y8jIsEWLFtmECRMCPjczM9MyMjLMzCwpKcmSk5MDxpFNbapsF82oERERCXN9+/b1/+r39NNP8/bbb/Pmm2/SsmVLrr32WpYsWZLj/KSkJP8v79m/Zu7evZv27dsTFxfHE088cUbxpKWl8X//939UrlyZRo0a8fXXX+d5bmRkJADHjh2jfv36eZ5Xs2bNM4qpLPvNb37Dpk2b/NtznyoiIqJQSY0DqV69eqG23K5YsSKrVq3imWeeOa3XKQ2h3I4CtY+vvvqK3/72t5x99tkFvtaxY8fYsWMHl1566RnFLJItVNtT5cqVOeecc8jMzOTQoUNUr16d7777jt///veUK1eOVq1a8fHHHwd8bkREBOXKlQN8M0Evu+yyM4pZwpty1IiIiIS57J2FhgwZwgcffMDAgQPJyMigffv2/PLLL3Tq1ImOHTvme43snYWuu+46unXrxq5du/wJa4uaB6WwOwuBL7/JLbfcwpEjR3jnnXcK+5ZFil0ot6NAJk6cyNixYwuVbPq9996jdevWRbq+SH5CtT2VL1+eiy++mLp163L8+HHWrFnD1q1bC72T3bp167j77rupUKECgwYNyvf9SdmmgRoREZEwF6o7CwFceOGFrFmzhuTkZCZOnMj48eML/ToixSmU29GptmzZQtWqVTn33HMLdf5rr73G/fffX+jrixQkVNvTN998w+bNm/nuu+/4/PPPeeSRRxg0aJD/uQXtZHfttdeydu1axo8fz4svvsiAAQMKHaOULRqoERERCXOhurNQRkYGERERREREUKVKFaKiogKeJ1IaQrUdBbJx40Y+++wz2rZty4YNG+jXrx8zZswI+NzMzEw2btzINddcU4i7JFI4odqezIxq1aoRERHBueeey88//0ydOnXYuHEjmZmZOXayO/W56enplC9fHiDH7B2RQDRQIyIiUgYkJibSvXt3Ro8eDfxvZ6HGjRsH3Fmoc+fOJCcn59hZqE+fPqSmphIZGUlycjKVK1cGiv7LJfxvZ6GoqCjmzZsH+HYHWrZsmf+cn376idtvv52IiAgqVKjA3LlzA54H0LVrV1atWsX333/P0KFD6dChQ5HiKU1RUVF7nHPnex1HqIiKitrjdQzZQrEdAdx///0sXbqUN954g/79+9O7d2//l+P4+HhmzJiR53NXrlxJs2bNihSXSGGEYnuqV68eFStWJDY2loyMDCZPnkxkZCS9evUiNjaW6tWrs3DhwoDP/frrr3nggQeIiIigWrVqLFiwoGg3TMoWr7MZq6ioqKioqBRvIch2FTIrvp2FznQXIbSLhkohSzi3o0DUtlRKugRbmwrm9mRmalNlvDgz82yQSERERIqfc8709z0w5xxm5go+U8o6taOiUduSgqhNFY3aVNmm7blFRERERERERIKEBmpERERERERERIKEkgmLiIiEGSWrzVswJaaV4KZ2VDRqW1IQtamiUZsq25SjRkRERPLknFsI7DSzhz147XbAVOAqM0sr7dcXKW7OuYuAdUCMmX1Xyq/tgLeBFWY2tjRfW6SkOOeSgKuBzqWdAMc5dyXwIdDAzHaW5mtL+NNAjYiIiATknGsLTMM3UJLqUQwvAdvMbKgXry9SXLIGSpYCq81sjEcx1AY+Axqb2fdexCBSXJxzVwAfAdeY2Y8exfAEUM/Munjx+hK+NFAjIiIiuTjn/g/YBNxtZu95GMf5wEagjZl94VUcImfKOXc7MAK41szSPYxjEHAj0Fpb8Eiocs5F4BukecnMnvMwjijgS2CImb3uVRwSfpRMWERERAIZBazycpAGwMz2AEOBWc65cl7GInK6nHPnABOBPl4O0mSZDEQDPTyOQ+RM9MX3XXa6l0GY2bGsWJ51zlX1MhYJL5pRIyIiIjk4564F3gKuNLOfgiAeBywHFpvZJK/jESkq59xsIM3MBngdC4Bz7hrgHXzLGvd6HY9IUTjnLsA3iyXezL7yOh4A59wsIN3M7vU6FgkPGqgRERERP+fcWcCnwCQzm+d1PNmcc5cBa/AtG9nmdTwiheWcawHMA+qb2WGv48nmnBsHXGBm3byORaQonHPJwNdm9qjXsWRzzlUDvgJuMbPVXscjoU9Ln0RERORkDwL7gPleB3IyM9sCTACmZ82wEQl6zrmKwEzg3mAapMkyEmjinLvR60BECss51wmoD3iSkDsvZnYQGIhvmW4Fr+OR0KcZNSIiIgKAc+4SfLNprjez/3gdz6mcc5HA58CTZvaS1/GIFMQ5NwaoY2a3eh1LIM65NvgGkq40s1+8jkckP865KvhmrdxpZilex3OqrB8RlgCfmtkTXscjoU0DNSIiIpL9AfNdYJmZPe11PHlxzjUCXse3jOSA1/GI5MU593vgA+D3ZrbL63jy4pybD+w1s0FexyKSH+fcc0B5M+vjdSx5cc79FlgPNDOzb72OR0KXBmpEREQE51x34CHgOjPL8Dqe/DjnpgCVzayX17GIBJK1Q9lqYLaZzfQ6nvw452oAG4EOZrbW63hEAnHOxQCv4BukP+h1PPlxzt0P3IIv2fEJr+OR0KQcNSIiImWcc+5cYBy+rYODepAmywiglXOupdeBiOShP/Ar8ILXgRQka2e3h/Hl1jjL63hETuWcKw/MAh4I9kGaLM8B5YHeXgcioUszakRERMo459w8YJ+ZPeR1LIXlnLsJX3Lh35vZUa/jEckWiksfTlr6+L6ZjfM6HpGTOeceBa4HOlqIfHkNlaWPErw0UCMiIlKGZSUTfR64KtSSiTrn/gl8b2bDvY5FBHIkE/3MzEZ5HU9RBHsycSmbnHOXAyuBhma23et4isI59yRwWbAmE5fgpoEaERGRMso5VwnYhG/r4Le9jqeonHM1gQ1AKzPb4HU8Is65W4Ek4BozS/c4nCJzzj0MtAZuCJWZCxK+nHMRwArgZTOb6nE4Reacq4jvb9QgM1vidTwSWpSjRkREpOxKAj4OxUEaADPbjS9fzays5K0innHOVQMm48v1FHKDNFkmAjWAO70ORARfjpfywDSvAzkdWcty+wLPZm0tLlJomlEjIiJSBjnnrgHewbfkaa/X8ZyurF9cPwSSzWyK1/FI2eWcmwUcN7P+XsdyJpxz1wJv4usbfvI6HimbnHO1gC+BBDPb6HU8Z8I59zcg1cwGeB2LhA4N1IiIiJQxWTu7fAw8a2ZzPQ7njDnn6gL/wpfD4L9exyNlj3MuDlgAXGlmP3sdz5lyzo0HapjZXV7HImWTc+5l4DszG+F1LGfKOXcO8BXQ2cw+9joeCQ1a+iQiIlL23A/8DLzodSDFwcw241ty8lxWMleRUuOciwJmAgPCYZAmy0ggNivZuEipcs51BBoAT3gdS3EwswPAg/iW6Zb3Oh4JDZpRIyIiUoY452oDnwGNzex7r+MpLlkffj8HRpnZP72OR8oO59wTQH0zS/Q6luLknGuLLzfIVWaW6nU8UjZk5XLZBPQwsw+9jqe4ZP2I8CbwLzMb43U8Evw0UCMiIlJGZH1QfAtIMbOxXsdT3JxzTYBkfF+aD3odj4Q/59yV+HIkNTCznV7HU9yccwuBHWY22OtYpGxwzk0FKplZb69jKW7OuYuAdUCMmX3ndTwS3DRQIyIiUkY45/4IDAH+YGbHvY6nJDjnngPKm1kfr2OR8JaVyHoVMM/MZngdT0lwzp0HbATamtl6r+OR8Oacawy8hm+w/YDX8ZQE59xA4GagpemLuORDOWpERETKAOdcdWA8vq2Dw3KQJsswoG1WcleRktQPOIEvP01YytoRbgjwQlYScpESkbV8dRbwYLgO0mSZClQG/uR1IBLcNKNGRESkDHDOzQF+NrOBXsdS0pxzNwNP41uOcszreCT8OOd+A6wH4szsa6/jKUlZSyaXAW+a2QSv45Hw5JwbAcQAHcJ9polzrgHwPr78T3u8jkeCkwZqREREwpxzLgH4G76tg494HU9pcM4lA1+b2aNexyLhJWvg4jXgCzNL8jicUuGcuxT4GLjOzLZ6HY+EF+dcHWA1cK2ZbfM6ntLgnBsLXGxmt3sdiwQnDdSIiIiEMedcRXw5Jh4wsze9jqe0OOcuAL4EWpjZJq/jkfDhnEsExgBXm9mvXsdTWpxzQ4F44MZwn/EgpSdr4HM5sNjMJnkdT2kpq3+bpfCUo0ZERCS8PQasLWsfBLN24HkEmJWV9FXkjDnnooEp+HI9lZlBmizjgVrAHV4HImGlF/B/+HK3lBlmdhRfnqtpzrnKXscjwUczakRERMLUSevgf29mu72Op7RlDdCkAIvM7Dmv45HQ55ybAWBm/byOxQvOueuBxfiWUe73Oh4Jbc65msAGoLWZfel1PF5wzs0FDpWF/HFSNBqoERERCUPOuXLAGuB5M5vtdTxecc5dAXwEXGNmP3odj4Qu51wz4B/4tg4+5HU8XnHOTQKizayn17FIaHPOLQJ+MLOhXsfilawdGb8COprZp17HI8FDU4FFRETC031AKr4kwmWWmX0DPAs8l5ULQaTInHMV8G0dfH9ZHqTJ8gjQIitJuchpcc61B/4APO51LF7Kmpk2CN8y3Uiv45HgoRk1IiIiYcY5dxGwDogxs++8jsdrWV+y1wOPmlmy1/FI6MiaRXMrcBC4GuisRLr+L9mT8W0vfNTreCS0OOfOBjYBvczsA6/j8VrWjwhvAyuAi4C/mdlnngYlntOMGhERkTCS9YFvGjBRgzQ+WUlf+wJTspLBihTWlUBN4F5gNFDR23CCQ1Zy8rXASK9jkZD0BLBcgzR+lYAk4C/4+pv6nkYjQUEDNSIiIuGlK/D/gHFeBxJMzGwV8AYw1utYJKRcADTFNyPrbeAyb8MJKgOBXllJy0UKJSsh9W34BiXEpx6wFPgcuBZfvyNlnAZqREREwoRz7hxgIr6tg9O9jicIDQE6OOdivQ5EQkYL4EJgD77d08rkzjSBZO0kNwxfbo1yXscjwS8rB8sLwCDtGvY/Wcucrsa3xPK3+PodKeOUo0ZERCRMOOdmA2lmNsDrWIKVcy4RGANcnbUkSiRPzrkhwDYzW+R1LMEoa6nlcmCxmU3yOh4Jbs65oUAc0E65ngJzzt0FVDeziV7HIt7SQI2IiEgIc86dh2+Hpw+Befi2Dj7sbVTBK+uL5WvAF/i2RD2gPAkip885VwdYjW/JxmBggJmd8DYqCRZZs2jGA1OAj4E/mNkPngYlEgI0UCMiIhLCnHOt8G2XewHwFzNb4nFIQc85dyG+gZqXgCNmNsLjkERCmnNuBBADXA9caWZ7PA5JgkTWLoQrgS3Am2Y2weOQRELCWV4HICIiImfkQuAcYCfwV+dcRTP7h8cxBS3n3AX4Eja+D9wIrPLZd8jwAAAgAElEQVQ2ovBQsWLF3ceOHTvf6zhCRVRU1J6jR4/W9DqO4uCcewJoDtQCUvlfTh8R8P3/cBzfbkadnXOVzGy0xzEVG/V9RRNOfV9J00CNiIhIaLsG3xbCO4EBwOvehhPczGync64N8CxwMaCky8Xg2LFj52uWduE558Lpi93jQD98Wy5XBergGwwVAbgcqI0vUe4UfMmEw4b6vqIJs76vRGnXJxERkdB2FHgFqGNmrylBY8Gydu5pjm/J2C6PwxEJaWaWYWbP4vtCvsbreCToGL6lT3XN7Hkzy/Q6IJFQoBw1IiIiInJGnHMaIywC5xxm5ryOQ0TOjPq+olHfV3iaUSMiIiIiIiIiEiQ0UCMiImVaxYoVdzvnTCV3qVix4m6v6yeb6ik06ik/q1evpnfv3gDs2rWLhIQEAKKjoxk5cqT/vKNHj1KzZk1WrFgBwMiRI4mOji7WWDIyMujevTuxsbGMGzcu1+Pbtm2jQ4cOxMfH88ILL+R57I477iAuLo6YmBi+/fbbYo2xpKlNhUabUj2FRj3lpzB93/vvv0+TJk1o0aKFvy/xou/r1KkTzZo1IyEhgR9//DHPY6Hc94UMM1NRUVFRUSmzxfenUALJujee15GpnvIVDPVU2Pq59dZb7csvv7TevXvbqlWrzMwsLi4uxzmTJk2yhIQE+/DDD/3HTj0nkNTU1ELFYGaWnJxsY8eONTOzDh062N69e3M83r17d9u3b1+Bx9LT083MbMWKFTZgwIBCv34o1VlZFAz1k11UT3kLhnoqrr4vLi7OUlNTbefOnda1a9ccxwtSnH3ff/7zHzMze++99+zhhx/O81go932hUjSjRkRERERKxZgxY+jTpw9HjhyhadOmuR5PT0/nk08+oVmzZoW+5oYNG7jnnnu47bbbCv2cNWvW0Lp1awDi4+NZu3at/7Hjx4/z3//+lz59+tC2bVu+//77gMcAIiMjAUhNTaVBgwaFfn0RKVsK6vsiIiKoVKkStWrV4t///nehrlncfR9A7dq1ATjrrLM466yz8jymvq/kaXtuERERESkVv/vd79i/fz8DBw4M+PicOXPo3r07n3zySYHXWrBgAQsXLqR27drcfffdXH311QAsWbKECRMm5Dj3tttu45577vH/+9ChQ1SpUgWAqlWrcvDgQf9j+/btY/369Xz//ffs3buXoUOHMnXq1FzHXnnlFTIzM4mPj+fHH39kyZIlRb4fIlI2FNT3nThxgp9++on9+/fzzTff5Hutkur7smVmZjJmzBhmzpyZ5zH1fSVPM2pERESKwYoVK/jhhx88jWHhwoU0bdqUjh07cuTIkRyPHTlyhI4dO9K0aVMWLlzoUYTeUz15a86cOdx6663MmDGDEydO5HgsIyODd999lxtvvLFQ15oxYwY1a9akX79+/i8qAB07dmTFihU5yslfVMCXG+Lw4cMAHD58OEceiOjoaOrVq0eNGjWoX78++/fvD3gMoFy5cqxcuZJXXnmFpKSk07klIU9tKjSonryVX98H8PTTT3Pbbbfx5JNPBpxxc7KS6vuyDRo0iLvuuotLLrkkz2Pq+0qeBmpERESKQV4fggN9ICsJx48fZ9asWXz00Ud069bNn+w026xZs+jWrRsfffQRs2bN4vjx46USV7BRPXknLS2Nv/3tbyQlJdG2bVsWLFiQ4/E9e/awfft2/2ODBw/2f6EIZNWqVTz44INMnz6dNm3aMH/+fMD3q3J8fHyOMn369BzPbdKkCR988AHg+3/iD3/4g/+xihUrUrlyZdLS0tixYwdVqlQJeAx8S7UAqlSpQlRU1JnfpBCkNhUaVE/eKajvA7j++utZvnw5I0eOpF69evler6T6PoDZs2fjnOOuu+7K95j6vlLgdZIcFRUVFRUVLwsFJAL85Zdf7JZbbrEWLVpYv379zMxs5MiR1rdvX0tISLBevXpZRkaGXXTRRdagQQN7+OGHbc6cOXbbbbfZjTfeaF988YXdfvvt1rx5c7v99tvt+PHj/sfbtGljHTp0sF9//dVuvvlm+/nnn83M7O6777Zvv/0237hOtWnTJn9Cv3379uVIRmhmdsstt9j+/fvNzOy+++6zr7/+usBrEkRJ/1RPeQuGeiqofszMRo0aZQsXLjQzs7S0NGvSpIkdPXo0YLLMkSNHFimZ8OHDh23OnDkFxpAtPT3dunXrZk2bNvUn1ly/fr3/GitWrLCmTZtaTEyMrVu3LuCxzMxMi4+P95fC1FW2UKgztSnv+z1TPeUrGOqpuPq+0aNHW4sWLaxLly45kpaXdt9Xvnx5a9asmcXFxdnjjz8e8Fio932hUpSjRkREJB+zZs2ia9eu3HrrrQwbNoxPP/0UgCuvvJLnn3+eNm3acOTIEXr27On/BWvu3LnUqFGDRYsW8fLLL3PVVVfx0ksvMXr0aF5//XUAqlWrxqJFi/jrX//Ka6+9RmJiIq+//jp//OMf+c9//kPdunX9MaSnp9OmTZsccVWtWpXFixf7/13QuvPCrEsPZaqn4Pfoo4/6/7tixYqsXr0agN27dzNy5Egef/xx/+MnT6UfOXIku3fnvwvv2WefTc+ePQsdS2RkZK5fta+++mr/MoK4uDhWrVqV4/FAxz788MNCv2aoUZsKDaqn4FeYvm/EiBGMGDEix/O86Pt+/fXXXM8JdCyc+75goYEaERGRfGzevJlFixbx3HPP8csvv9CoUSPA9yEY4IILLuDnn3/O9bxrrrkGgH//+99ce+21APzhD3/gyy+/5Pzzz/c/3rBhQ9avX8+9995Lz549Of/880lISMhxrfLly7NixYp84yxo3Xn24+ecc06e69JDmeopdH377bf5Pv7444/nGMSR0qE2FRpUT6FLfZ/kRzlqRERE8lGnTh1GjBjBihUrWLt2LR06dADAOec/x8yIjIwkMzPTfywiwvcn9pJLLmHdunUArF271p+I74svvgBg/fr11K5dm7PPPpsKFSowffr0XFttpqen51p3fvPNN+eKc+PGjWRmZrJs2TIaN26c4/HsdemZmZls3LiRyy67rDhuT9BQPYW2YE90euLECf7yl7+QkJBAly5dAJgwYYK/nitXrsyBAweYPXs2jRo1onHjxrz00ktevI1iozYVGlRPoS3Y+7733nuPK664gvj4eP+xQP3h3Llzufzyy4mPj2fw4MGlGX748nrtlYqKioqKipeFQqz/v+2226xFixaWkJBg//nPf3Lkz+jRo4dt3brVVq1aZc2bN7fRo0fbnDlz/Ou909PTrWvXrta8eXPr2rWrf/1/165drXXr1v71/2ZmycnJ1qRJk3zjyc+8efOsSZMm1qFDB38ugQceeMDMzH7++Wfr0KGDNWnSxObNm1eo6xFEa8lVT3kLhnoqqH4KcmpOmmyZmZlndN3CSk9Pt7i4OMvIyLBFixbZhAkTcjy+aNEimzt3bsDnHjhwwOLj483MbOvWrXbixAlLT0+366+/Ps/XC4U6U5vyvt8z1VO+gqGewr3vO3DggB07dixHrpxA/eHJ/0/lJxjqLFSK890vERGRssk5Z6X9t3Du3LkAudaVL168mP/+978MGDCgVOPJi3MOM3MFn1nyVE95C4Z6yqt+UlNT6dmzJ/v376du3bpMnz6dpKQkdu3axb///W8uuugiZs6cye9+9zuio6Np06YN9erV45133uHw4cP89a9/ZezYsezcuZMLLriA+fPns2DBAt555x0OHjxI+fLlSU5OpmvXrsybN48qVarQr18/HnzwwRw5NAry1Vdf8fzzzzNlyhT2799P//79+cc//uF/vHv37tSsWZPPPvuM7t2707t3b/9j8+fPZ+/evQwaNMh/zMxo1qwZ//rXv/K6X0FbZyVJbaroVE95C4Z6Cve+L1t8fLx/eVug/nDu3Lk888wzVK9enaSkJFq0aJHX/fK8zkKFlj6JiIgEgX/+85+MGzeO7t27ex2K5EP1VDTZiU6XL19OdHR0jkSny5YtY/v27f5Ep5MmTeLpp58GoEaNGrz11lt89913XHXVVaSkpFC/fv0ciU7fffddYmJiciQ6zcjICJjotKBlGQUlMt27dy+XXXYZy5YtY8GCBezbt8//2GuvvUanTp1ynD9jxgz/EhTJn9pUaFA9FU249H2BBOoPO3XqxIYNG3jllVf4y1/+UmrbvoczJRMWEREpZYF2aOjatStdu3Yt/WAkT6qnMxcuiU6rVq1KXFwcZ511Fo0bN+bf//435557LkePHmX37t387ne/85/7ySef8NZbb/m/WMn/qE2FBtXTmQuXvi+QQP1h9vurUaMGl156Kfv27eO8884r8FqSNw3UiIiIiEiJqFOnDu3ateOmm24CICMjgy+++KLIiU5vuOEG1q5dS926dUlNTc030emkSZNyxFCYrYMLSnQaExPDhg0bqFu3Lhs3buT+++8HfIk2T772jh07GDRoEEuWLKFcuXKnfd9EJLSFS98XSKD+8PDhw1SpUoWjR4/y/fffU7169SLeMTmVlj6JiIh4rGfPniW268Mdd9xBXFwcMTExBW4FKvkryXoaMGAANWrU8OeGCBd9+/Zl4cKFtGzZklatWrF9+/aA58XHxzNq1CjGjBmT43jnzp358ssviYuLY+PGjXTu3BmA/fv306ZNG/71r3/5j91yyy3s3buXiy++OMc1sn9VPrmc/EUFIDIykl69ehEbG8v8+fP585//DMDAgQMB6N27N3//+99p1qwZMTEx1KpVC8i97GnUqFHs2bOHxMRE4uPjSU9PP807VzaUZJsC2LlzJ1FRUZ7vqhPq1PcVXbj0fWvXrqVVq1Z8+eWXtGrViuPHjwfsDydOnEiTJk2Ij49nyJAhGqguBkomLCIiZZoXiRpP1bNnT5KSknJ9yCoOx48fJzIykpSUFJKTk5kyZUqhnxtMSf/CvZ52797NO++843+dogiGeirN+gmVRKf5KWt1lpeSbFMAgwcP5tNPP2Xu3LlFeo1gqJ9s4V5P6vsKT31f2aIZNSIiIoW0evVqGjVqRHx8PLNnz+bXX3+ldevWxMbG8qc//QmAFStW0LZtW9q1a0e7du147rnnaNKkCSNGjAB8H7Duu+8+YmNjeeSRR3Jcf/fu3bRv3564uDieeOIJAIYPH06zZs2Ii4tj586dRY45MjIS8O1A0aBBgzN5+yEjFOupZs2aZ/iuyzYlOi1ZodimfvrpJ44cOVJig0DBKBTrSX3fmVHfF76Uo0ZERKSQ3n77bZ588kkSEhL8OxosXbqUChUq0KNHDzZv3gzA2Wefzcsvv8zdd9+Nc441a9bQuHFj/9Tm2NhYnn32Wdq1a8euXbv81x87dixJSUlcd911dOvWjV27drFy5UpSUlIoV64cp/5ql5iYyIEDB3LFWLFiRf+/MzMziY+P58cff2TJkiUlcl+CTSjWkxSeEp2WvlBsU5MmTeK+++5j3LhxJXJPglEo1pMUnvq+skUDNSIiIoXUv39/nnjiCebOncsDDzxA3bp16d27N3v27GHbtm306tULgHr16gFQq1YtrrjiCgCioqL818netaFBgwZs27bNf3zz5s08/PDDgG/LzB07djB8+HB69uzJueeey5gxY6hUqZL//FdffbXAmMuVK8fKlStZt24dSUlJJCcnn+FdCH6hWE8iwSzU2tShQ4fYvn079evXL4Z3HzpCrZ5EJG9a+iQiIlJI0dHRTJs2jaeffprHH3+cd999lwYNGpCSkkKzZs38vyaevKvDyf+dLXvXhg0bNnDRRRf5j9epU4epU6eyYsUK1q1bR8OGDYmPj2f+/Pmcd955vP322zmuk52w9ORy9OjRHOdkJzOtUqVKjg/i4SwU60nOXGknPG3Tpg3x8fE0bNgwR0LhcBRqbWrz5s1s2bKFtm3b8v7779OvX79ivR/BKtTqSYpHSfZ9kydPplGjRjRp0oQ1a9YA0KlTJ5o1a0ZCQgI//vhjibyuaEaNiIhIoT3//PO8+uqrpKamMmzYMBo1asTo0aP55JNPinSdlJQUpk6dSlxcnH/3GIBhw4bRp08fUlNTiYyMJDk5mS5dupCWlkZERAS9e/fOcZ2Cfq08ceIEN9xwg//f06ZNK1KcoSrU6glgzJgx/P3vfwd8Wzxn54uQ4DBixAiuvfbaHMfee+89AKZOnRr2SzlCrU01atTI/6UyOxFuWRBq9QTq+4Ld/Pnz+eyzz9i5cyf33Xcfr732GhMnTqR27dq8//77TJkyhaefftrrMMOTmamoqKioqJTZ4vtTWHp69OhhW7duLdXXPF1Z98bzOjLVU76CoZ6KUj//+te/7Prrr7e4uDh74YUX7NixY9aqVStr1qyZ9ezZ08zMPvzwQ7vhhhvsxhtvtBtvvNGeffZZa9y4sQ0fPtzMfPVz7733WrNmzWzEiBH+Y1u3brVdu3ZZu3btrHnz5jZq1CgzMxs2bJg1bdrUmjdvbjt27CjaDc4yZ84cmzNnTq7jrVu3tj179hTpWqFWZ8VBbUr1VNyCoZ7Cve/r2rWrpaWl2aZNm6x37945Hlu+fLkNGzasSNcLhjoLlaIZNSIiIiJSasIp4emBAwfIyMjgvPPOO6N7IiLhLxT7vri4OC6//HKOHz+eY2lbZmYmY8aMYebMmcV7k8RPAzUiIiKl6OQcFxK8VE8lJ5wSnr7xxhvcdNNNp/38skRtKjSonkpOqPV9hw8fZsGCBWzZsoW9e/fSt29f3nrrLQAGDRrEXXfdxSWXXHKmt0XyoGTCIiIiRVSSift++OEHatasyfTp0wFYvnw5MTExJCQksHPnzjyf16JFC+Li4oiPj2fv3r15njdu3DhiY2Pp3r07GRkZHD16lMaNGwfc9jMcBGNdLVy4kKZNm9KxY0eOHDkS8JwTJ07QpEkTKleu7I8/XOoqnBKevv7662GfSPhkodqesiUnJ1O7dm0gfNpTXkK1rtT3BU/fFxERQaVKlShfvjxVq1YlNTUVgNmzZ+Oc46677iq+myO5aKBGREQkyLRt25Z77rkH8CVafO+993jyySd56qmn8nzOe++9R0pKCn/6059YsGBBwHN++uknPvroI1auXMmVV17JG2+8QcWKFVm0aFGJvI+yoKh1dfz4cWbNmsVHH31Et27deOGFFwKeFxERwWuvvcYtt9ziPxYudfX888/TvHlzOnToQI8ePWjUqBEvv/wyHTt25PDhw4W+TkpKCrGxsVxzzTW5Ep4OHz6cli1b0q5dO9LS0ujUqROxsbG88847xMbG5rjOq6++yooVK3KUU5c9jRkzhnHjxjFu3Dj/8oOjR4+yd+9e/xd/OXMl1Z6yvfLKK/z2t78Fwqc9eUV9X9GFWt9XuXJlWrZs6R+Ey56t079/f9auXUt8fDyjRo06w7siefI6SY6KioqKioqXhZMSAfbp08e+++47MzN76qmn7K233rKlS5daixYtrGHDhrZ48WIz+1/ivpEjR9qHH36Y41igZH5FsXXrVuvRo4eZmaWmptpNN93kf6xVq1YFPn/GjBn2zjvvBHxs6dKlNn78eDMzW7t2rQ0ePDjXa56MIEr6xykJG0O1rjZt2mQDBgwwM7N9+/ZZ165d832NUxN7BqqrYKinU+unpIVSwtNAgq3OykJ7Wrp0qc2ePdvi4uICvubJgqF+sov6vtyvmS0Y6kl9X9EEQ52FStGMGhERkSxdunTxr9n+4IMPSEhIoEWLFixfvpyUlBSmTJlS4DWyk/mlpKTw7bff5kj0l56enmua8c0335zntQ4ePEiVKlX8/87MzMzz3B07dtCkSROmTp1K/fr1A55z6NAh//WqVq3KwYMHC3w/wSpU6yqc6kDCR1loTy+++CJ33nlnge8j2JWFuhIRJRMWERHxa9myJRMmTKBbt26cf/75lC9fntWrVzNq1CgyMzNz5X45ee2474eiwMn8sqcmly9fnhUrVhQ6nmrVquWYDh0RkffvKxdeeCFr1qwhOTmZiRMnMn78+FznREdHs2fPHsCXJDA6OrrQsQSbUK2r6Oho/3mhXgdeUsLT4hXu7Sk7h0r58uULHUOwCve6kvyp7ys7NKNGREQkS2RkJLVq1WLixIkkJiYC8MwzzzB//nyWLFmS6wNo1apV2blzJydOnGDTpk1A4GR+2Yr6S2WlSpVIS0sjNTWVTz/91L8TxI4dO3Kcl5GR4d/qs0qVKv7dIU4977rrriMlJQWAZcuW0bhx4yLfo2ARqnVVp04dNm7cSGZmZo46OPW8siAYk50mJiYSHR2d64vq559/HjCpZ7aDBw8SExNDXFwcHTp04OjRoyGVADXc29OmTZtYsmQJbdu2ZcOGDYwcOfI075T3wr2uyoJg7PsGDx5M06ZNiY2NZcuWLUDhkj9v27aN2NhYmjdvzh//+EcyMzNDqu8Lal6vvVJRUVFRUfGycMr68sWLF1uVKlUsLS3NzMxmzpxpDRo0sLvvvttiYmLM7H9rxLdt22YNGza0xMRES0hI8K/979Chg7Vo0cLatGljR44csaI4dR3++++/b02aNLEWLVrY9u3bzcwsISEhx3N27txpzZs3t/j4eLvhhhts165dAc8zMxs7dqw1bdrUunXrZunp6QFfMxtBtJb81HoyC826MjObN2+eNWnSxDp06GA///xznufdeuutVqtWLWvatKm98cYbAV/TLDjqKVD9FKQkcy2cep9atmxpR44csY8//tjuv//+PJ+3a9euHDk9svXq1csaNmyY5/MyMzMtIyPDzMySkpIsOTk5YBzZgrHOwr09ZQv1HDVm4V9X6vtO3+n0ffv37/fnFlq1apUNHDjQ0tPTLS4uzjIyMmzRokU2YcKEgM89ePCgHThwwMzMhg8fbm+++WbAOLIFQ52FSvE8ABUVFRUVFS/L6XzIKknbt2+3+vXr27Rp0wI+npmZ6U/ImJ99+/bZyJEjCzwvLS3NmjZtaoMGDcr1WDB9oAq2ejILnroKhnoiDBLTZjt1oGbTpk02cuTIHF/w8/PYY4/Zhg0bcsVxsmCrs2AQLO3JLDjqJ7sEWz2ZBU9dBUM9hUPf9+uvv1rXrl0tIyPDli5dak888USRkz+b+fq+d999N1ccJwuGOguVohw1IiIiQeQ3v/mNf3p6IBEREYVKFlm9enWSkpIKPK9ixYqsWrWqKCFKFtVVYNnJTocMGcIHH3zAwIEDycjIoH379vzyyy906tSJjh075nuN7GSn1113Hd26dWPXrl3+HBrp6em0adMmx/lVq1Zl8eLFAa9VlKTcgUycOJGxY8cWmLdj3bp13H333VSoUIFBgwYV6TVE7SmUqK4CC9W+r3z58lx88cXUrVuX48ePs2bNGrZu3Vqk5M87d+7kgw8+4LHHHsv3PCk8DdSIiIiISLEJ1WSngWzZsoWqVaty7rnnFnjutddey9q1axk/fjwvvvgiAwYMKPTriEjoC9W+75tvvmHz5s189913fP755zzyyCMMGjSo0Mmff/31V3r06MHMmTMpV65coeOT/GmgRkRERESKTX7JTitXrkxMTEyO8/NKdvrnP/+Zq666iszMzBxfaIr6q/LJyU6/+uqrHMlOL7zwwnzfy8aNG/nss8/8SWj79evHjBkzcj03PT3dv6PQyb9gi0jZEap9n5lRrVo1IiIiOPfcc/n555/zTf58ar/Zt29f+vfv77++FA8N1IiIiIhIsUpMTKR79+6MHj0agJtvvpn27dvTuHHjXL/MJiYm0rlzZ5KTk6lWrRoAw4YNo0+fPqSmphIZGUlycjKVK1cGiv6rMsDQoUNp3bo1UVFRzJs3D4AePXqwbNmyHOfdf//9LF26lDfeeIP+/fvTu3dv/xeu+Ph4ZsyYEfC5X3/9NQ888AARERFUq1aNBQsWFCk+EQkPodj31atXj4oVKxIbG0tGRgaTJ08mMjKSXr16ERsbS/Xq1Vm4cGHA565Zs4ZXX32Vbdu2MXnyZB566KECl3dJIXmdJEdFRUVFRcXLEhUVtRswldwlKipqt9f1o3oKjXoCLJgUV7LTQMIlWa3aVHC3KdVTaNQTqO87VbD3faFSnO9+iYiIiIicHuec6TNl4TnnMDNX8JkiEszU9xWN+r7CK3w2NRERERERERERKVEaqBERERERERERCRIaqBERERERERERCRLa9UlEREREzkhUVNQe59z5XscRKqKiovZ4HYOInDn1fUWjvq/wlExYRERERIKOc+4hoD3Qyotsnc65ucBBM3uwtF9bRMou59x0IMLM7vbgtSOAj4C/m9m00n59+R8N1IiIiIhIUHHO1QY+Axqb2fcexXAusAm4ycw+8yIGESlbnHPNgH8A9c3skEcx1ANSgGvM7EcvYhDlqBERERGRIOKcc8B04BmvBmkAzGwfMAh4wTkX6VUcIlI2OOcqALOA+70apAEws6+B54Bns/pj8YAGakREREQkmNwB1ALGex0I8HdgF/CQ14GISNgbCnwHvOp1IMBfgbpAZ68DKau09ElEREREgoJzrjq+5UY3m9mnXscDwbEMS0TCm3PuCny5YYJmuVHWMqxFwJVezvApqzRQIyIiIiJBISuB7yEzG+h1LCdzzg0CbgRae5HYWETC10kJfF8ys+e8judkzrkZAGbWz+tYyhotfRIRERERzznnEoAWwCNexxLAZKAacJfXgYhI2OmD73v5dK8DCWAocFPW7BopRZpRIyIiIiKecs5VBDYCD5jZm17HE4hzriHwNnCVme31Oh4RCX3OuQuAL4EWZrbJ63gCcc51AUYDV5vZr17HU1ZooEZEREREPOWcGwtcbGa3ex1Lfpxz44ALzKyb17GISOhzziUDX5vZo17HkpesnZ9eA74wsySPwykzNFAjIiIiIp5xzjUA3sc3U2WP1/Hkxzn3f/hm/vQ3s3e8jkdEQpdzrhPwFNDAzI55HU9+nHO/AdYDzc3sG6/jKQs0UCMiIiIinqdV65QAACAASURBVHDOlQPWAM+b2Wyv4ykM51wb4Hl8O6Gkeh2PiIQe51wV4CvgTjNL8TqewnDO3QvcgW+w5oTX8YQ7JRMWEREREa8MAFKBv3kdSGGZ2XvAKmCU17GISMj6K/BOqAzSZJmOb/ygr9eBlAWaUSMiIiIipc45dxGwDogxs++8jqconHM1gE1AezNb63U8IhI6nHMxwCtAfTM76HU8ReGcqw+swJdYeIfH4YQ1zagRERERkVKVlZxyOjAx1AZpAMzsJ+BhYJZz7iyv4xGR0OCcKw/MBAaG2iANgJl9BcwApngdS7jTQI2IiIiIlLbbgN8C47wO5AzMB34CHvQ6EBEJGUOArcDLXgdyBsYA9bOSIUsJ0dInERERESk1zrlz8CXR7GxmH3sdz5lwzl0CfApcb2b/8ToeEQlezrm6wL+Aa8xsu9fxnAnnXBywEN/yrZ+9jiccaaBGREREREqNc242kGZmA7yOpTg45wYDrYAbTB+sRSQA51wE8CGQbGZhsWzIOTcLSDeze72OJRxp6ZOIiIiIlArnXAugNTDc61iK0QSgBnCn14GISNDqDVQAnvM6kGI0GOiclRxZiplm1IiIiIhIiXPOVQQ2AA+Z2Rtex1OcnHPXAm8CV2UlGhYRAcA5Vwtf35dgZhu8jqc4OeduBZKAhmb2q8fhhBUN1IiIiIhIiXPOjQEuM7OuXsdSEpxz44EaZnaX17GISPBwzv0T+N7MwmkmIeDfwW8x8JmZPeF1POFEAzUiIiIiUqKcc78HlgENzGyX1/GUBOdcZWAjcLeZved1PCLiPefcTcB4fH3fUa/jKQnOud8CnwOxZvat1/GECw3UiIiIiEiJcc6VA1YDL5jZLK/jKUnOubbANHxLoFK9jkdEvOOcOxvfDnc9zOxDr+MpSc65AcCtQLyZnfA6nnCgZMIiIiIiUpL6A8eA2V4HUtLM7B1gDTDS61hExHNjgGXhPkiTZRpQHl/SZCkGmlEjIiIiIiUia0r8eqBZWZkS75w7D98SqLZmtt7reESk9DnnGgOvAfXN7IDX8ZQG59xVwAeE8RLX0qQZNSIiIiJS7LKSTE4DJpeVQRoAM9sLDAFecM6d5XU8IlK6nHORwCzgwbIySANgZhvxve8pXscSDjRQIyIiIiIl4RbgEuAprwPxwIvAIeB+rwMRkVL3MLAd+IfXgXjgCaCBc66j14GEOi19EhEREZFi5Zyrhi+J5i1mttrreLzgnLsU+Bi4zsy2eh2PiJQ851wdfMnTrzWzbV7H4wXnXAt8g9VXmtlhr+MJVRqoEREREZFi5ZybBaSb2b1ex+Il59xQIB640fShWySsZS33XA4sNrNJXsfjJefcbCDNzAZ4HUuo0kCNiIiIiBQb51wcsABfEs0y/WtqVq6KtcBTZvZ3r+MRkeLnnPt/wBGgM3AP0NjMMr2NylvOuXPwzarsbGYfex1PKNJAjYiIiIgUC+dcFPAlMMTMXvc6nmDgnLseWIxvGcB+r+MRkeLlnJsLrAMeBdqY2RfeRhQcnHO3AyPwLQNL9zqeUKOBGhEREREpFs65J4B6ZtbF61iCiXNuEhBtZj29jkVEipdz7n3gLOAb4ACQZGYZ3kblvaylYEuB1WY2xut4Qo0GakRERETkjDnnrgQ+BBqY2U6v4wkmzrnK+JYB9DKzD7yOR0SKj3Puv0A0kA5MAsYoJ5WPc+4ifLONYszsO6/jCSUaqBERERGRM+KciwBWAfPMbIbX8QQj51x7YDJwlZkd9ToeESkezrnj+HJRdTOz/3gdT7Bxzg0EbgZaagCr8CK8DkBERP4/e3cfVlWV/n/8vVAEjAQ107SZ0krLUstsVBBBUDJTVCwcM5N8fsi+Ok2TaSmVpJmVaSMqaaZQTaWl06iZGvhYjU6W2fQ4Y5OCWYSCoCGwfn8g5yeBCibsc+Dzuq51JXuvvc999ooN52bte4mIeCZjjN+p6e1jgQJgscMhuS1r7T8o+jA3HYqunbMRicgF0tJa21lJmjOaD1wEDDNFdO8rB82oEREREZHzYozZDDxF0SpPXa21/3Y4JLdmjGkM7AXuBBZba1s5HJKISKUzxrQD3gOGAhOstb0cDsntKVEjIiIiIufFGLMf+IqiGgTvW2s3OBuRezPGdAVuBu4C2gG+1tpCZ6MSEalcxpjbgB7AtUBza+11Dofk9mo7HYCIiIiIeJ5Tjzw1A+oAN1D0e6USNWfXAXgYyAROApcAhx2NSKSK+Pn5HTpx4kRjp+PwFL6+vj8cP368idNxXCC3AdEU/by4yOFYPIJm1IiIiIhIhZ16jOcQ8AUw1Fr7kcMheQRjzHXAEqAzEGatTXU4JJEqYYxRLdkKMMZgrTVOx3GhGGOCgZeBqwB/a22OwyG5NSVqRERERKTCjDG1gAnAfGttgdPxeJJTs5HGAEnW2myn4xGpCkrUVEx1S9QAGGNqA/cBz+t/hrNTokZEREREREQqlRI1FVMdEzVSflqeW0RERERERBy3Y8cOhg8fDkB6ejoREREABAYGMn36dADeeOMN/vCHP9CxY0dWr14NwPTp0wkMDLygseTn5zNkyBBCQkJ4+umnS+3v168fXbp0ISIiggMHDgDw/PPP07FjRzp37szOnTvPuE3knKy1ampqampqampqVdR8fX0PAVatfM3X1/eQxsw9mzuMjZrnNMCWx5133mk/+eQTO3z4cLtt2zZrrbWhoaGu/cHBwTY3N9fm5ubarl27uraf3udMcnJyyhWDtdauXLnSzpo1y1prbe/eve3hw4dL7P/Pf/5jrbV2w4YN9sEHH7TWWnvzzTfbwsJCe+DAAduvX78zbiuPU9erysdJ9zv3uA9q1ScRERGRKnTixInGRb+DS3mcKlrsKI1Z2dxhbKT6iY+P5+677+bKK68kODi41P5WrVpx7NgxAOrVq1euc3766ackJCRw4MAB/v73v5frmJ07dzJo0CAAwsLC2LVrF7fddptrf/PmzQGoXbs2tWsXfay+6qqrOHHiBEeOHKFhw4Zn3ObOdL+rmMq6DypRIyIiIiIiIm7hqquuIiMjg4kTJ5a5v2/fvtx0000UFhaydOnSs54rKSmJ5ORkmjdvzujRo7nxxhsBWLNmDc8++2yJvgMHDmTs2LGur48cOeJKBAUEBJCZmVnq/AUFBcTHx7N48WIAQkNDufbaazl58iTr1q074zaRc1GiRkRERERERNzCSy+9xJ133snChQsZOHAgXl4ly6rGxcXx73//G2stvXr1omfPnmc818KFC7nmmmsYM2YMbdu2dW2PiooiKirqrHEEBgaSlZUFQFZWFk2bNi3V54EHHuCee+6hRYsWZGVlkZSUxNdff83hw4cZNWoUr732Wqlta9eurcjlkBpKxYRFREREPFxKSgr79+93NIbk5GSCg4OJiooiO7vkitPZ2dlERUURHBxMcnKyQxE6T+Mkcna5ubksXbqUuLg4evbsSVJSUqk+vr6+1K1bl4suuoi8vLyznm/btm1MmjSJhIQEIiMjWbFiBVA0oyYsLKxES0hIKHFs586d2bRpE1D0vduhQ4cS+5csWYIxhnvuuQcALy8v6tatS506dQgICCAnJ6fMbTWR7n0Vp0SNiIiIiIc70y/BhYWFVfL6J0+eJDExkS1btjB48GBefPHFEvsTExMZPHgwW7ZsITExkZMnT1ZJXO5G4yRyds888wzjx4/Hx8eHiRMnsnDhQk6cOFGiz6hRowgODiYoKIhRo0ad85xt27YlISGBlStXUlBQABTNqElJSSnRTn/sCaBPnz588skndOnSheDgYC699FL27NnDsmXLABg3bhy7du0iLCyMxx9/HH9/f8LDwwkKCiIiIoIHH3ywzG01ke5958GJStJqampqampqajW1Uc6VT6y19tixY/aOO+6w3bp1s2PGjLHWWjt9+nQ7atQoGxERYYcNG2bz8/PtFVdcYdu1a2cffPBB+9JLL9mBAwfa2267ze7Zs8f+8Y9/tF27drV//OMf7cmTJ137IyMjbe/eve0vv/xi+/bta48ePWqttXb06NH2iy++KHeM1lr72Wef2QkTJlhrrf3pp59sTExMif133HGHzcjIsNZae99999nPP/+83OfGoZVPTm/nGrOaOk7uMDZqntMqcu/7tVatWtlp06adcf+0adNsq1atzvv87sip7y/9jHKPn1GaUSMiIiLiphITE4mJiWHz5s0EBgby0UcfAXDDDTewceNGvv/+e7Kzs4mNjWXu3LnMnj0bgEaNGrF27Vq++uor2rRpQ2pqKtdffz1vv/02APXr1+fdd98lKCiIt956i+joaN5++23y8/P5z3/+Q6tWrVwx5OXllXpEoG/fviXiPFfRzfIU5fRkGieRyvXFF1/w2GOPnXH/Y489xhdffFGFEQno3leZVExYRERExE19+eWXvPbaa/z1r3/l2LFjdOzYESj6JRigadOmHD16tNRxN910EwDffvstN998MwAdOnTgk08+oXHjxq797du35+OPP2b8+PHExsbSuHFjIiIiSpyrTp06pKSknDXOXxfdDAwMLHN/gwYNytzv6TROIlIT6d5XeTSjRkRERMRNtWzZkqlTp5KSksKuXbvo3bs3AMYYVx9rLd7e3q7aC4BrlZQWLVqwe/duAHbt2kWLFi0A2LNnDwAff/wxzZs35+KLL8bHx4eEhAQGDhxYIoby/LWyZcuW7N27l4KCAjZu3EinTp1K7C8uyllQUMDevXu55pprLsTlcRsaJ5HK5e7FaJOSkggJCeGWW27hhRdeAODzzz8nODiY4OBgHn30UQA2bNjAddddR1hYWFWHXyl076s8StSIiIiIuKlRo0aRnJxMeHg43bt35/vvvy+zX3Exy/j4+BLb+/fvzyeffEJoaCh79+6lf//+AGRkZBAZGcn27dtd2+644w4OHz7MlVdeWeIcxX+tPL2tXr26RB9vb2+GDRtGSEgIK1asYMSIEQBMnDgRgBEjRrB8+XJCQkIYNmwY3t7ev/nauBONk0jlcvditAMHDmTr1q188MEHLF26FChaGnz27Nls376dDz74gCNHjnDLLbe4khDVge59lccU1b8RERERkapgjLFO/v5VvGJJbGxsie2rV6/mf//7HxMmTKj6oM7CGIO11py7Z6XGUOVj5gnj5A5jI56jrO+jnJwcYmNjycjIoFWrViQkJBAXF0d6ejrffvstV1xxBYsXL+aqq64iMDCQyMhIWrduzfr168nKymLmzJnMmjWLtLQ0mjZtyooVK0hKSmL9+vVkZmZSp04dVq5cSUxMDMuXL6devXqMGTOGSZMmlahzci779u1j0aJFzJs3j4yMDMaNG8ff/va3Uv2OHz/OyJEjSUpKYtGiRVx++eX07NmTnj17smbNGvz8/ICixMW5Htdx6vtLP6MqprLGSTNqRERERGq4119/naeffpohQ4Y4HYqchcZJqpvqUowWID4+npYtW3LjjTcCEB4e7koIdezY0ZWkkYqrifc+FRMWERERqUF+/VdKgJiYGGJiYqo+GDkjjZPUBNWlGC3A1KlT+fOf/0zXrl0ZMWIEjzzyCK+99ho33ngjAwYMYP/+/aUe25HSdO8rohk1IiIiIjVMbGxspRXmnDBhAo0aNXJNXy+WlpaGr6+v4wVBPVVljtmgQYMIDQ0lKChISxxLlaouxWjz8vKAoqRP3bp18fHxwVpLw4YN8fLyIiAgoFQBYjmzyrzfnT7OmzdvPuM2p2lGjYiIiIhcMFOnTnX9hft0c+fOLfXhRtzD8uXL8fb2JjU1lQULFjBv3jynQ5IaYtSoUQwfPpznnnsOLy8vEhMTy+wXFhbGlClT+OCDD2jWrJlre//+/bn77rsJDQ2lSZMmTJ48maSkJFcxWh8fH/70pz8BRcVo58yZc8ZitGdzejHahg0bkpycDBQVo507dy5z5sxhw4YN5OXlMWTIEPz8/HjooYcYNGgQtWrV4rrrrqNNmzbs2rWLyZMn88knn9C9e3fWrVvnFoVra5qyxvtc/w9UNSVqRERERNzUjh07mDRpEn5+fgwZMoS7776b3r17c+LECa6++mpeeuklUlJSmDVrlusvzLfffjtJSUmEh4cTHx9PbGws/v7+rpU1ZsyY4Tr/oUOHGD58OMeOHaN79+48+uijTJkyhS1btlCrVi1effVVmjZtWqGYmzRpUmrbjz/+SHZ2do2Y9u+JY1b8QTEnJ4d27dpduIshcg4XXXQRr732WoltcXFxrn8Xz8y78sorSU1NLXW8t7d3mUV9b7vttlKP0NSqVYtBgwadd6xDhgwpVSNl7ty5AEyZMoUpU6aU2HfzzTezY8eOEts6dOjAxo0bzzsGd+OJ9zsvLy/CwsK47LLLWLBgAfXr1y9zm9P06JOIiIiIm1q3bh1PPvkkKSkp3HvvvXh7e/POO++wdetWCgsL+fLLLwG4+OKLWbt2Lb/73e8wxrBz5042bdrkOk9ISAhbt27lX//6F+np6a7ts2bNIi4ujtTUVL744gvS09PZunUrqamppKamctlll5WIJzo6utQjAsePHz/n+5g7dy733XffBboq7s0Tx6ygoICQkBDGjx/PH/7wh0q8OiLOqInFaKuCJ97v3nzzTVJSUujTpw8zZ8484zanaUaNiIiIiJsaN24cTzzxBMuWLeP//u//aNWqFcOHD+eHH37gu+++Y9iwYQC0bt0agMsuu4zrrrsOAF9fX9d5igtrtmvXju+++861/csvv+TBBx8EilY1OXjwIFOmTCE2NpZLLrmE+Ph46tat6+q/atWqCr+HI0eO8P3333P99ddX+FhP5IljVqtWLbZu3cru3buJi4tj5cqVv/EqiDhHxWirjife7xo0aAAUPTaXlJR0xm1OU6JGRERExE0FBgayYMEC0tPTGTVqFEOHDqVdu3ZMnTqVu+++G2stULLw5un/LrZnzx5atmzJp59+yv333+/a3rJlS0aMGEGbNm0oKCjAGMMvv/zCbbfdxsyZM1m3bh0DBgxw9Y+Ojubnn38uce5169adddnZL7/8kq+//pqePXuyd+9eDh06xPr168/7mrg7TxyzvLw86tSpQ7169Up8eBKpbmJjY4mLi6uUxzD79evHTz/9hI+PDy+//DKXX345kZGR5OXlkZWVxe9//3vX8uHVhSfe77KysqhXrx7bt293FZ8ua5vTlKgRERERcVOLFi1i1apV5OTk8PDDD9OxY0dmzJjBhx9+WKHzpKamMn/+fEJDQ0tMFX/44YcZOXIkOTk5eHt7s3LlSgYMGEBubi5eXl4MHz68xHnK89fK+Ph4XnnlFQAOHjzI1KlT2blzJ/D/PyRVZ542ZoWFhdx6662urxcsWFChOEWkyHPPPUfz5s157733mDdvHrNnz2bDhg0AzJ8//6wJbU/lafc7gPDwcPz8/PD19XXVQCprm9NMcZZLRERERCqfMcZW5e9flfkX5KpgjMFaW/pPsFUbg8asDO4wNuI5KvJ9VFlFaou/t3x9fS94kdpi77//Pu+99x5PPvmka1tkZCRJSUlceuml5T6PU99fut9VTGWNk4oJi4iIiIiIiNvwxCK1UFSYOz4+nhEjRri2/fzzz+Tn51coSSOiR59EREREqjF3mcYt5acxk5rOE4vUAjzwwAPcc889Jeqc/P3vf6dPnz7ncxlqBN3vyqZEjYiIiIiIiLgNTyxSu2TJEowx3HPPPSX6vf322zz77LO/7YJIjaNHn0RERETcXGxsLPv376+Uc+/fv58mTZqQkJAAwObNmwkKCiIiIoK0tLQzHhcdHU1gYCApKSkltv/rX/8q8wPT6Z5++mlCQkIYMmQI+fn5HD9+nE6dOpW5rK6ncscxS05OJjg4mKioKLKzs8vsk5mZSVBQEKGhofTu3Zvjx49Xy/ER97Zo0SK6du1K7969GTp0KB07duSNN94gKiqKrKyscp8nNTWVkJAQbrrpplJFaqdMmUJ4eDi9evUiNzeXfv36ERISwvr16wkJCSlxnlWrVpGSklKi/bo48Lhx49i1axdhYWE8/vjjABw/fpzDhw/TvHnz33A13J873u+uvfZa12Nqn3/++Rm3lcUtfkZZa9XU1NTU1NTU1KqoFf36VTFDhw61//3vfyt8XHn897//tUOHDnV9HR4ebrOzs+0HH3xg77///jMel56ebqdPn27ff//9EtuHDRtm27dvf8bjDh8+bHv37m2ttXbWrFl21apVZcZR7NT10pid5nzGLC8vz4aGhtr8/Hz72muv2WeffbbMfgUFBTY/P99aa21cXJxduXJlma9prXuMjZrntPP5PvotKvN7sCo49f1VHe531lobGhparm2/5i4/ozSjRkRERMQho0aN4uuvvwZg9uzZrFu3jn/84x+Eh4dz8803s2bNmhL94+LiXDNYiv+CeejQIW6//XZCQ0N54oknflM8ubm5XHTRRfj7+9OxY8ez/sWxSZMmpbbt27eP3/3ud1x88cVnPO6jjz6iW7duAHTv3p0PPvjgN8Vc1Tx1zL766ivatm1LrVq1znrdvby8qFWrFlC0dPc111zzm+ITEc/lqfc7gB9//JGuXbsyZswYfvnllzNu+zV3+RmlRI2IiIiIQwYMGOAqUrlp0yYiIiLo1q0bmzdvJjU1lXnz5p3zHGWtXlIsLy+v1Eolffv2PeO5MjMzqVevnuvrgoKCCr2f5557jvvuu++sfY4cOeJ6jYCAADIzMyv0Gk7z1DGryHXfvXs3HTp0YOPGjVxxxRXnfD8i7mjZsmUeu+Szu/DU+x3A1q1b2bJlC7///e9ZsmTJGbf9mrv8jFIxYRERERGHhIeH8+yzzzJ48GAaN25MnTp12LFjB48//jgFBQUcPny4RP/Ta78Uzbgue/WS4loMderUKVVD5mzq169fov6Dl1f5/6b39ddfExAQwCWXXHLWfoGBgfzwww8AZGVlERgYWO7XcAeeOmaBgYGufue67jfffDO7du3imWee4eWXX2bChAnljkdEqg9Pvd8BNGjQAID+/fszf/78M277NXf5GaUZNSIiIiIO8fb25rLLLuO5554jOjoagDlz5rBixQrWrFlT6pfQgIAA0tLSKCws5LPPPgOKVi+ZP38+KSkp7N69m/bt27v6V/SvlXXr1iU3N5ecnBw++ugj19K3Bw8ePOd72bt3L//85z/p2bMnn376KWPGjCnz2FtuuYXU1FQANm7cSKdOnc55bnfiqWPWsmVL9u7dS0FBQYnr/ut+eXl5rn/Xq1evxFLHIlXN3YrU5uTkEB4e7ip0XFyUuzxFar/77jtCQkLo2rUrd911FwUFBW5fqNtT73d5eXmuR5u2b99OixYtytxW1rFu8zOqMgrfqKmpqampqampld34VaHG1atX23r16tnc3FxrrbWLFy+27dq1s6NHj7ZBQUHW2v9fqPG7776z7du3t9HR0TYiIsL+97//tenp6bZ37962W7duNjIy0mZnZ9uK+HWBxPfee8927tzZduvWzX7//ffWWmsjIiJKHTdhwgTbvHlz2759e/viiy+W2Hd6wcayjp01a5YNDg62gwcPtnl5eWXGUQw3KFhbXcZs+fLltnPnzrZ379726NGjZfb7+OOPbdeuXW1YWJjt37+/zcnJKfM1rXWPsVHznPbr76PycLcitSdOnLAHDx601hZ93//1r3+11pavSG1mZqb9+eefrbXWTpkyxf7jH/8oM45iTn1/VYf73aFDh+xNN91kQ0JCbFRUlM3KyipzW1nHWuseP6Mc/4ZVU1NTU1NTU6tJ7Xw+rFSm77//3l5//fV2wYIFZe4vKCiwEyZMOK9z//TTT3b69Onn7Jebm2uDg4PtAw88UGqfOyQDquuY/dbxcYexUfOcdvr30ciRI+1XX31lrbX2qaeesmvXrrXvvPOO7datm23fvr1dvXq1tfb/JwBOX2GueFt6errt1auX7dq1q3388cdtRZ3+wTsnJ8f26dPHta979+7nPH7p0qV20aJF1lprW7dubUNCQuzo0aPtiRMnznnstGnT7LvvvlsqjtO5S6LGaTX1Z5Rq1IiIiIjUYJdffrlrinpZvLy8ylUwsiwNGzYkLi7unP38/PzYtm3beb1GTXShxkzjI04pLlL70EMPsWnTJiZOnEh+fj633347x44do1+/fkRFRZ31HMVFam+55RYGDx5Menq6q/ZJXl4ekZGRJfoHBASwevXqMs9V0ULqx44dY/Hixaxfvx4oKlLboEEDnnzySZYsWcK4cePOeGxaWhqbNm1i2rRpZ30NKVJTf0YpUSMiIiIiIiJVxpOL1FprGTZsGPHx8QQEBADlK1IL8MsvvzB06FAWL15MrVq1yh2f1DxK1IiIiIiIiEiVOVuRWn9/f4KCgkr0P1OR2hEjRtCmTRsKCgpKJHMqOqPm9CK1+/btK1GktlmzZiX6Tps2jeDgYMLDw12vZa3Fx8enVJHaXx87atQoxo0b5zq/yJkoUSMiIiJShXx9fX8wxjR2Og5P4evr+4M7xKAxK80dxkY8V3R0NEOGDGHGjBkA9O3bl9tvv51OnTqVWhI5Ojqa/v37s3LlSurXrw/Aww8/zMiRI8nJycHb25uVK1fi7+8PVHxGDcDkyZPp0aMHvr6+LF++HIChQ4eyceNGV5+0tDSeeuopgoKCeOutt7jrrrvo27cvt912G/7+/tSvX5+kpKQyj925cyerVq3iu+++4/nnn+dPf/rTOR/vcoLudxVTWfdBUzx1TERERERERKQyGGOsO332PHDgAD179mT8+PGMHTu21P7CwkImTpx4XvVPMjIymD9//jnrnxw/fpwePXrQqVMn5syZU2KfMQZrrTnDoVLNKVEjIiIiIiIilcrdEjXuTomamu3MVZJERERERERERKRKKVEjIiIiIiIiIuImVExYREREREREKpWK1FaMinXXbKpRIyIiIiIigFCF3QAAIABJREFUIh7HGBMDTAdustbmVfFr9wdmAjdaa09U5WtL9adEjYiIiIiIiHgUY0x9YB9wh7V2h0MxrAI+s9ZOc+L1pfpSokZEREREREQ8ijEmEciz1o53MIZmwB4gzFq7z6k4pPpRokZEREREREQ8hjEmFEgCrrfWZjkcy1hgCNDFWlvoZCxSfWjVJxEREREREfEIxhhfYDEwwekkzSmLgEJgjNOBSPWhGTUiIiIiIiLiEYwxTwCtrbUDnI6lmDGmNZBKUVHjA07HI55PiRoRERERERFxe8aYG4D3gXbW2jSn4zmdMeYxoK21tr/TsYjn06NPIiIiIiIi4taMMbWAROBRd0vSnPIkcK0xJtrpQMTzKVEjIiIiIiIi7m4MkE9RfRq3Y639BRgFzDfGBDgdj3g2PfokIiIiIiIibssYczlFy2CHWGv/7XQ8Z2OMWQQUWmvHOh2LeC4lakRERERERMQtGWMM8DbwsbU2zuFwzskYEwjsAwZaa7c5HY94Jj36JCIiIiIiIu4qGrgGmOl0IOVhrT0C3A8kGmN8nI5HPJNm1IiIiIiIiIjb8dTZKadmAb1F0Sygx5yORzyPEjUiIiIiIiLidk7Ve7HW2jFOx1JRp+rqfAx0dfe6OuJ+lKgRERERERERt2KMCQFeBa631h51Op7zYYwZDwyiKFlT6HQ84jlUo0ZERERERETcxqnaLouB+z01SXNKAkWfuUc6HYh4Fs2oEREREREREbdhjHkMaAf0tx7+gdUYcwPwPtDOWpvmdDziGZSoEREREREREbdgjGkNpAI3WmsPOh3PhWCMeQJoba0d4HQs4hn06JOIiIiIiIg4zhjjBSQC06tLkuaUeOAGY0w/pwMRz6BEjYiIiIiIiLiD0af+u9DRKC4wa+0JYBQw3xhTz+l4xP3p0ScRERERERFxlDGmGbAHCLPW7nM6nspgjEkE8qy1452ORdybEjUiIiIiIiLiKGPMKuAza+00p2OpLMaY+sA+4A5r7Q6n4xH3pUefRERERERExDHGmP5Aa+BJp2OpTNbaTGAisNgYU8fpeMR9KVEjIiIiIiIijjDGBADzgVGnarlUd28A/wUecjoQcV969ElEREREREQcYYxZANS21o5yOpaqYoz5HfAx0MVa+4XT8Yj7UaJGREREREREqpwxJhh4HbjeWnvE6XiqkjHmfuAOioonFzodj7gXPfokIiIiIiIiVcoY4wMkAhNrWpLmlL8CdYDhTgci7kczakRERERERKRKGWOmAR2AvraGfig1xrQBNgHtrLXpTscj7kOJGhEREREREakyxpjrgK3ATdba752Ox0nGmHjgGmttjNOxiPvQo08iIiIiIiJS6Ywx440xVwCLgMdqepLmlBnAjcaYPk4HIu5DM2pERERERESk0hljPgb+AXQHgq21BQ6H5BaMMd2Al4EbrLVZTscjzlOiRkRERERERCqdMeZHip7qWAVcba3t5nBIbsMYswTIsdbe73Qs4jw9+iQiIiIiIiKVyhjjDVwCeAN+wF3ORuR2HgTuNMZ0cjoQcZ4SNSIiIiIiIlLZ6gBpwABr7d1a5agka+3PwCQg0RhTx+l4xFl69ElERERERETEYcYYA7wDbAdeBRpaa3c5G5U4obbTAYiIiIiIiIjUdNZaa4wZB+wGCoDrgXucjUqcoBk1IiIiIiIibsTPz+/QiRMnGjsdhyfw9fX94fjx402cjuNCMMbUBv4GHABCgQxrbYSzUYkTlKgRERERERFxI8YYq89p5WOMwVprnI7jQjHGxADPAvWAY9bapg6HJA5QMWERERERERERN2CtfR24DlhJUbJGaiDNqBEREREREXEjmlFTftVtRs3pjP5HqLE0o0ZERERERETEzShJU3MpUSMiIiIiIuIBduzYwfDhwwFIT08nIqKozmxgYCDTp08HYNasWTRr1oy4uDjXcZ999hldunShS5cufPbZZwBMnz6dwMDACxpffn4+Q4YMISQkhKeffrrU/s2bNxMUFERERARpaWkADBo0iNDQUIKCgvjiiy8ACAsLc7XNmzdf0BhFPIEefRIREREREXEjZ3viJSYmhkceeYR58+Zx7733EhwcTFhYGCkpKQD88MMPfP7556SmprqSNdHR0cyfPx+A+++/n5UrVwKUOO5McnNzqVu3brniXrVqFV9//TUPPfQQffr0YenSpTRq1Mi1PyIigtWrV7Nv3z5eeeUVnn/+eU6ePIm3tzepqamsXLmSefPmlSuuYu7y6JNW6ipbdVqVqyppRo2IiIiIiIiHiI+PZ+TIkWRnZxMcHFxqf+PGjTGmZN7i6NGjNGvWjGbNmnH06NFyvc6nn37K2LFjGThwYLlj27lzJz169ACKkkC7du1y7cvNzeWiiy7C39+fjh078vnnnwPg7e0NQE5ODu3atQPAy8uLsLAwBg0aRGZmZrlf30knTpxobK1FrWRT8ur81HY6ABERERERESmfq666ioyMDCZOnFjuYwoLC8v8d1mSkpJITk6mefPmjB49mhtvvBGANWvW8Oyzz5boO3DgQMaOHev6+siRI9SrV7RQUUBAQIkkS2ZmpmsfQEFBgeu/YWFhHDhwgDVr1gDw5ptv0qBBA1555RVmzpzJ7Nmzy/1eRaoDzagRERERERHxEC+99BJ33nknCxcuPGfSpZiXl1eZ/y7LwoULadKkCWPGjHElaQCioqJISUkp0U5P0kBRrZysrCwAsrKyStTAqV+/vmvf6XHUqlWLrVu38uabb7oe1WrQoAEA/fv3d9XUqSlSUlLYv3+/ozEkJycTHBxMVFQU2dnZJfZlZ2cTFRVFcHAwycnJDkVY/SlRIyIiIiIi4gFyc3NZunQpcXFx9OzZk6SkpHIdFxAQQFpaGmlpaQQEBJy177Zt25g0aRIJCQlERkayYsUKoGhGzelFfsPCwkhISChxbOfOndm0aRNQlHDo0KGDa1/dunXJzc0lJyeHjz76iNatWwOQl5cHQL169fD19QVwJXS2b99OixYtyvUeq4szJWrKm5T7rU6ePEliYiJbtmxh8ODBvPjiiyX2JyYmMnjwYLZs2UJiYiInT56skrhqGj36JCIiIiIi4gGeeeYZxo8fj4+PDxMnTiQiIoKYmJgSfZYsWcKCBQvIzMzk559/Zt68ecTFxbn6LViw4Jyv07ZtWxISEsjOznYVHo6KiiIqKuqsx/Xp04d7772XLl260KdPHy699FL27NnDnj17iI2NZfLkyfTo0QNfX1+WL19OYWEht956q+v44tjCw8Px8/PD19eXZcuWVeQSua2cnBxiY2PJyMigVatWJCQkEBcXR3p6Ot9++y1XXHEFixcvZtmyZbz99ttERkbSunVr1q9fT1ZWFjNnzmTWrFmkpaXRtGlTVqxYQVJSEuvXryczM5M6deqwcuVKYmJiWL58OfXq1WPMmDFMmjSJVq1alTvOr776irZt21KrVi26d+/OuHHjSuzfuXMnixYtolatWrRp04ZvvvmG66677kJfrhpPqz6JiIiIiIi4kbOt+lSWa6+9loEDB/LYY4+V+5jp06fzt7/9zbUktqdyl1WfzjVmc+fOpVmzZtx55508/PDD9O/fn7Vr19KwYUMmTJhAZGQkr7/+OnPnznXNWFq2bBm7d+9m/vz5vPHGG3z99ddMmTKFGTNmcO2113Ls2DE+/PBDEhISmDlzJi1atOCXX34B4K677qJXr15s2LDBFUNeXh6RkZEl4goICGD16tWur7dv3866deuYMWMG+fn5pc7Ro0cP1q1bR+3atXnkkUfo1asXQUFBZ7subjE+nkYzakRERERERDzY+SRbHnvssQolduS3+fLLL3nttdf461//yrFjx+jYsSMAN9xwAwBNmzYtc0Wum266CYBvv/2Wm2++GYAOHTrwySef0LhxY9f+9u3b8/HHHzN+/HhiY2Np3LgxERERJc5Vp06dcy57frY6Q6fvb9CgQZn75cJQokZERERERESkErVs2ZJevXrRp08fAPLz89mzZ0+JpdSttXh7e7tWxIL/X3S5RYsW7N69m1tvvZVdu3bRqlUrcnJy2LNnDwAff/wxzZs35+KLL8bHx4eEhATmzp1bIobyzKhp2bIle/fupaCggI0bN9KpU6cS/YvrEEVHR7N3716uueaaC3B15NdUTFhERERERKSacPdVg5YsWULHjh3p1KkTr776KlC0JHhISAi33HILL7zwAgDLli3j2muvJSwsjL/85S9V/h4utFGjRpGcnEx4eDjdu3fn+++/L7NfWFgYjz/+OPHx8SW29+/fn08++YTQ0FD27t1L//79AcjIyCAyMpLt27e7tt1xxx0cPnyYK6+8ssQ5imfUnN5OT9IAeHt7M2zYMEJCQlixYgUjRowAcC0HP2LECJYvX05ISAjDhg3D29v7N18bKU01akRERERERNxIRWvUnC4uLs5V4+R0hYWF51ya+0I4efIkPXr0YNOmTbz55pukpaUxadIk1/79+/dzxRVXkJ+fT5cuXfjwww85efKkaybJLbfcwr/+9S9XEeHY2Nizvp671ED5LWN2vs50jVavXs3//vc/JkyYUKXxlMVdxsfTaEaNiIiIiIiIm8vJyeHOO+8kPDycsWPHAkVJmdGjR9O9e3eGDx9OQUEBy5YtY+LEifzlL39h2bJl/PGPf6RXr17s3buXQYMGERoayqBBg8jPz3ftv/XWW+nTpw95eXn069fPVaNkzJgxfPnllxWK89erBn3wwQcl9l955ZUYY6hduza1axdV4iielZGXl+dathtgzpw5hIaG8v7775/3datpXn/9dZ5++mmGDBnidCjyGyhRIyIiIiIi4uYSExOJiYlh8+bNBAYG8tFHHwFFxWg3btzI999/T3Z2NrGxscydO5fZs2cD0KhRI9auXctXX31FmzZtSE1N5frrr+ftt98GoH79+rz77rsEBQXx1ltvER0dzdtvv01+fj7/+c9/SiztnJeX55qtU9z69u1bIs4jR45Qr149oKj+SWZmZpnvZ+HChfTu3dv1dXx8PC1btuTGG28EoF+/fnz66ae8+eab/PnPf6awsPACXcnqIzY2ttRsmpiYGLZt26Yivx5OxYRFRERERETcXHVZNQjgww8/ZO3ata5kEcDUqVP585//TNeuXRkxYoTruEaNGnH11Vfz008/cemll57zOolUB5pRIyIiIiIi4uZatmzJ1KlTSUlJYdeuXa7ZKBVdNQhg165dtGjRAuCsqwYNHDiwRAzlmVFzrlWDDh48yAMPPMDLL79MrVq1XOeFokRQ3bp18fHxcSV7jh8/zjfffEPDhg1/w9Wr/mJjYyutiPSECRNo1KiRqyaOVD7NqBEREREREXFzo0aNYvjw4Tz33HN4eXmRmJhYZr+wsDCmTJnCBx98QLNmzVzb+/fvz913301oaChNmjRh8uTJJCUluVYN8vHx4U9/+hNQtGrQnDlzzrhq0NmcvmpQw4YNSU5OBopWDZo7dy6PP/44P/zwA9HR0QBs2LCBOXPmsGHDBvLy8hgyZAh+fn7Mnj2b9evXU1hYyEMPPeRK6kjVmzp1qms2llQNrfokIiIiIiLiRqpqBSFPWDXoXNxlVaHyjtmOHTuYNGkSfn5+DBkyhLvvvpvevXtz4sQJrr76al566SVSUlKYNWuWazbU7bffTlJSEuHh4cTHxxMbG4u/v79rue4ZM2YQGxtLXFwcvr6+DB8+nGPHjtG9e3ceffRRpkyZwpYtW6hVqxavvvoqTZs2rfD7K+8qXL/mLuPjaTSjRkRERERERICiVYPmzZvHO++843Qo1dK6det48skniYiIcBVIfuedd/Dx8WHo0KGuVbYuvvhi3njjDUaPHo0xhp07d9KpUyfi4+MBCAkJ4YUXXqBXr16kp6e7zj9r1izi4uK45ZZbGDx4MOnp6WzdupXU1FRq1arFr5NJ0dHR/Pzzz6Vi9PPzq8zLIOegRI2IiIiIiEgNVNbsiJiYGGJiYqo+mBpi3LhxPPHEEyxbtoz/+7//o1WrVgwfPpwffviB7777jmHDhgG4lim/7LLLuO666wDw9fV1nae4CHS7du347rvvXNu//PJLHnzwQaBoBa6DBw8yZcoUYmNjueSSS4iPj6du3bqu/qtWrarcNyznRcWERURERERExKUyC9P269ePLl26EBERwYEDBwD47rvv6N27N2FhYbz44ouV8rruIjAwkAULFjB79mwee+wx3n33Xdq1a0dqaipdunRxzXg5vUj06f8uVlwE+tNPP+WKK65wbW/ZsiXz588nJSWF3bt30759e8LCwlixYgWXXnop69atK3Ge6OjoUgWijx8/XhlvXSpAM2pERERERESkSjz33HM0b96c9957j3nz5jF79mweffRRXn755RqxstOiRYtYtWoVOTk5PPzww3Ts2JEZM2bw4YcfVug8qampzJ8/n9DQUC677DLX9ocffpiRI0eSk5ODt7c3K1euZMCAAeTm5uLl5cXw4cNLnKc8M2ri4+N55ZVXgKJVu6ZOnVqhWKXiVExYRERERETEjVT3wrQA77//Pu+99x6PPfYYPXr0oEGDBuTm5vLCCy9w9dVXV+RauUWx2qoqAA24xufXq3K5I3cZH0+jGTUiIiIiIiIeyFML0xYUFBAfH8/ixYv56aef+Pjjj/nmm284fPgwkydP5s0337zg10rEkyhRIyIiIiIi4oE8tTDtAw88wD333EOLFi04fvw4rVu3plGjRjRq1IiMjIzfcEVqhuKlsqX6UjFhERERERERD+SJhWmXLFmCMYZ77rkHAD8/P/z9/cnNzeXgwYPUq1fvwlwcD1CZRZv3799PkyZNSEhIAGDz5s0EBQURERFBWlraGY9LTk4mODiYqKgosrOzy+xTWFhI586d8ff3d8V//PhxOnXqVOZKYlJxmlEjIiIiIiLigTyxMO24ceP4wx/+QFhYGOHh4UybNo1HHnmEyMhIrLXMnz+/QrHLmfXs2ZOxY8cCRQWBN2zYwL59+3jqqad4/vnnS/U/efIkiYmJbNmyhTfffJMXX3yRSZMmlern5eXFW2+9xeTJk13b/Pz8eO2114iLi6u091OTKFEjIiIiIiLigSZOnMjEiRNLbCueHXO6sLAwgBIfolNSUlz/fvDBB0sUpj390Zq///3vJc717rvvnne8AL/88kupbaGhoWzbtu03ndedjBo1igcffJBrrrmG2bNn06ZNGwoLC3nmmWc4evQo06dPJyoqytU/Li7ONQPpbIWcz1dubi4XXXQR/v7+dOzYkUceeaTMfl999RVt27alVq1adO/enXHjxp3xnE2aNDnveOTc9OiTiIiIiIiIyAUyYMAA1+yiTZs2ERERQbdu3di8eTOpqanMmzfvnOcoLuScmprKF198UaLIc15eXqlHzPr27XvGc2VmZpZ4pKygoKDMfkeOHHH1CwgIIDMzs1zvVy48zagRERERERGpoVSY9sILDw/n2WefZfDgwTRu3Jg6deqwY8cOHn/8cQoKCjh8+HCJ/qfXDSquK1RWIefix9Lq1KlTYkbUudSvX5+srCzX18VLtf9aYGCgq19WVhaBgYHlfg25sDSjRkRERERExMO5W2HanJwcwsPD6dq1K71793YVpr322mtds0A+//zzMo/19GK13t7eXHbZZTz33HNER0cDMGfOHFasWMGaNWtKJUoCAgJIS0ujsLCQzz77DCi7kHOxis6oqVu3Lrm5ueTk5PDRRx+5VgE7ePBgiX4tW7Zk7969FBQUsHHjRjp16lRmP6l8mlEjIiIiIiIiZ1XRwrS1a9cmKSmJpk2bkpiYyIoVKxg3bhxNmjQ552yQ6lCsNjo6miFDhjBjxgwA+vbty+23306nTp1KzVSJjo6mf//+rFy5kvr16wNlF3L29/cHKj6jBmDy5Mn06NEDX19fli9fDsDQoUPZuHGjq4+3tzfDhg0jJCSEhg0bkpycXGY/gJiYGLZt28Y333zD5MmT6d27d4XikbNTokZERERERMRNeWphWh8fH5o2bQoUJW2KH+n58ccf6dq1K61bt+b555/Hx8enzOM9vVhtVFQUR48edX09cuRIRo4cWaLP6Y+d7d69u9Q5fl3IuSJq167Nrl27SEhIYOzYsXTv3p3u3bu79hcWFrpm1pxuyJAhDBkyxPV1RkYGXbp0KdXv9ddfL/H18ePHufvuu12zcOS3UaJGRERERETETRUXpn3ooYfYtGkTEydOJD8/n9tvv51jx47Rr1+/EomashQXpr3lllsYPHgw6enprnoneXl5REZGlugfEBDA6tWryzxXeQvTFjt27BiLFy9m/fr1AGzdupUGDRrw5JNPsmTJkrOuLCTn7/LLL3c9RlUWLy+vchU1btiwYblmMfn5+VWrlbucpkSNiIiIiIiIm/LUwrTFrz9s2DDi4+MJCAgAoEGDBgD079+f+fPnl/t1RWoSJWpERERERETc1NkK0/r7+xMUFFSi/5kK044YMYI2bdpQUFBQIplT0Rk1pxem3bdvX4nCtM2aNSvRd9q0aQQHBxMeHu56LWstPj4+bN++nRYtWpzxWJGaTIkaERERERERN+aJhWnT0tJ46qmnCAoK4q233uKuu+6ib9++3Hbbbfj7+1O/fn2SkpLKPBZUrFZqNlM8HU5EREREREScZ4yx7vQ57cCBA/Ts2ZPx48e7Vn46XWFhIRMnTixXzZNfy8jIYP78+eesg3L8+HF69OhBp06dmDNnjmu7MQZrrTnLoVXCz8/v0IkTJxo7HYe78fX1/eH48eOeXRnaAUrUiIiIiIiIuBF3S9S4M3dJ1IhcSGeu/CQiIiIiIiIiIlVKiRoRERERERERETehRI2IiIiIiIiIiJvQqk8iIiIiIiJuxNfX9wdjjArTloOvr+8PTscgcqGpmLCIiIiIiEg1ZowxwD+AbdbaJx14/WDgdeAGa21mVb++iKdRokZERERERKQaM8b8EZgKtLfWnnQohgVAbWvtKCdeX8STKFEjIiIiIiJSTRljGgD7gH7W2g8djCPgVBx3WWu3OBWHiCdQokZERERERKSaMsYsBY5Za+93g1j6AzOBG621J5yOR8RdKVEjIiIiIiJSDRljwoFlwPXW2myHwwHAGLMK+MxaO83pWETclRI1IiIiIiIi1Ywxxg/4FPiTtfbvTsdTzBjTDNgDhFlr9zkdj4g78nI6ABEREREREbngHgU+dqckDYC19iAwDVhsjNHnUZEyaEaNiIiIiIhINWKMaQtsBNpaaw85Hc+vnUrQbAWSrbULnI5HxN0oUSMiIiIiIlJNGGNqATuAF621iU7HcybGmNZAKkWFhQ86HY+IO9FUMxERERERkepjPHACWOJ0IGdjrf0cWAC84HQsIu5GM2pERERERESqAWPM74F/AcHW2i+djudcjDE+FBUWnmqtXeV0PCLuQokaERERERERD2eMMcAa4CNr7RNOx1NexpgQ4FWKlhA/6nQ8Iu5AiRoREREREREPZ4yJoWg1pfbW2jyn46kIY8wioNBaO9bpWETcgRI1IiIiIiIiHswYUx/YBwyw1u50Op6KMsYEUhT/QGvtNqfjEXGaEjUiIiIiIiIezBiTCORZa8c7Hcv5MsYMAJ4AbrLW/uJ0PCJOUqJGRERERETEQxljQoEkimq8ZDkdz/k6VWPnbeBf1trHnI5HxElK1IiIiIiIiHggY4wv8AnwF2vtaqfj+a2MMZcDHwNdrbX/djoeEad4OR2AiIiIiIiInJepwGfVIUkDYK09AMQBi40x+qwqNZZm1IiIiIiIiHgYY8wNwPtAO2ttmtPxXCjGmFrANmCZtXaR0/GIOEGJGhEREREREQ9yarbJdqppMqO6JqFEykvTyURERERERDzLWCAfSHQ6kMpgrf0MWAjMczoWESdoRo2IiIiIiIiHqCkFd6tboWSRilCiRkRERERExAPUtCWsq8vS4yIVpUSNiIiIiIiIBzDGDACeAG6y1v7idDxVwRiTCPxirb3P6VhEqooSNSIiIiIiIm7OGBMI7AMGWmu3OR1PVTHG1KfofQ+w1u50Oh6RqqBEjYiIiIiIiJszxiwErLV2rNOxVDVjTAwwDWhvrc1zOh6RyqZEjYiIiIiIiBszxoQAr1JUq+Wo0/FUtVO1edYAH1prZzgdj0hlU6JGRERERETETRljfIA9wFRr7Sqn43GKMeb3wG6gi7X2S6fjEalMXk4HICIiIiIiIiUZY4o/qz0MfFGTkzQA1tr/UVRIefFp10akWtL/4CIiIiIiIu5nrzGmMzAe0IpHRf4K+ALDnA5EpDLp0ScRERERERE3cmrGyAlgF7ASSLHW7nY2KvdgjGkLbATaWmsPOR2PSGXQjBoRERERERH3cimQBzQCHgR6OhuO+7DWfgq8CMxzOhaRyqJEjYiIiIiIiHu5DrgIOA70ttbGOxyPu3kCuMkY08fpQEQqgx59EhERERERcSPGmEuA0cBMa22h0/G4I2NMOLCMoiXLsx0OR+SCUqJGREREREREPI4xZilwDPgU+Mla+7bDIYlcELWdDkBERERERESkIowxBngc2Al4A0cBJWqkWlCiRkREREREPJ6fn9+hEydONHY6Dnfk6+v7w/Hjx5s4HccF1oSiVbFSgShgs7PhiFw4evRJREREREQ8njHG6rNN2YwxWGuN03FcaMaYy4E5wJ3Af621VzscksgFoVWfRERERERExONYaw9Ya/8IDAY+dDoekQtFM2pERERERMTjaUbNmVXXGTUi1ZVm1IiIiIiIiIiIuAklakREREREpFrbsWMHw4cPByA9PZ2IiAgAAgMDmT59OgDvvfcenTt3plu3bnzxxRcATJ8+ncDAwAsaS35+PkOGDCEkJISnn3661P7NmzcTFBREREQEaWlpADz//PN07NiRzp07s3PnTgAmTJhAo0aNWLZs2QWNr7L4+fmXGgxLAAAgAElEQVQdMsZYtfI1Pz+/Q06PmThHjz6JiIiIiIjHO9ejTzExMTzyyCPMmzePe++9l+DgYMLCwkhJSQEgLCyMtWvXcvToUSZOnMjf/vY31/biPmeSm5tL3bp1yxXnqlWr+Prrr3nooYfo06cPS5cupVGjRq79ERERrF69mn379vHKK6/w/PPP06FDB/75z3+SlpbGfffdx1tvvcWhQ4dYv349ALGxsWd9TXd49EmPplWMO4yZOEczakREREREpNqLj49n5MiRZGdnExwcXGq/l5cXdevW5bLLLuPbb78t1zk//fRTxo4dy8CBA8sdx86dO+nRowdQlATatWuXa19ubi4XXXQR/v7+dOzYkc8//xyAq666ihMnTnDkyBEaNmwIQJMm1W21bREpVtvpAERERERERCrbVVddRUZGBhMnTixzf2FhIT/++CMZGRn8+9//Puu5kpKSSE5Opnnz5owePZobb7wRgDVr1vDss8+W6Dtw4EDGjh3r+vrIkSPUq1cPgICAADIzM137MjMzXfsACgoKAAgNDeXaa6/l5MmTrFu3rgLvWkQ8kRI1IiIiIiJS7b300kvceeedLFy4kIEDB+LlVfLhgtmzZzNw4EAuv/zyMmfcnG7hwoVcc801jBkzhrZt27q2R0VFERUVddZjAwMDycrKAiArK4umTZu69tWvX9+1D4pm+WRlZZGUlMTXX3/N4cOHGTVqFGvXri33+66uUlJSuPLKK7nyyisdiyE5OZkFCxbQsGFDkpOTufjii137srOzGTx4MBkZGYwbN47Bgwc7Fqd4Hj36JCIiIiIi1Vpubi5Lly4lLi6Onj17kpSUVKrPH/7wBzZv3sz06dNp3br1Wc+3bds2Jk2aREJCApGRkaxYsQIomlETFhZWoiUkJJQ4tnPnzmzatAkoSjZ06NDBta9u3brk5uaSk5PDRx99ROvWrV2PZNWpU4eAgABycnJ+6+WoFlJSUti/f3+p7YWFhVXy+idPniQxMZEtW7YwePBgXnzxxRL7ExMTGTx4MFu2bCExMZGTJ09WSVxSPWhGjYiIiIiIVGvPPPMM48ePx8fHh4kTJxIREUFMTEyJPvHx8WzatIkGDRqwaNGic56zbdu2JCQkkJ2dzcqVK4Hyzajp06cP9957L126dKFPnz5ceuml7Nmzhz179hAbG8vkyZPp0aMHvr6+LF++HH9/f8LDwwkKCiI/P59p06a54n3llVcAOHjwIFOnTj2fS+N2cnJyiI2NJSMjg1atWpGQkEBcXBzp6el8++23XHHFFSxevJhly5bx9ttvExkZSevWrVm/fj1ZWVnMnDmTWbNmkZaWRtOmTVmxYgVJSUmsX7+ezMxM6tSpw8qVK4mJiWH58uXUq1ePMWPGMGnSJFq1alXuOL/66ivatm1LrVr/r717ja6qOhc+/l+BQLBoQq1y0Z4KPYK3SNVaYiAmhoAYECRYWkUkcmvFG7aDw1WNQlRQC4UqBbSlID2WDkDUSlXUBFSUFyqI9hRrO/ACiC0FgYCGJOv9QLPLNgkCQvZO8v+NkSF7rbnnfrLmyIf9+MxnNiInJ4cRI0ZE3V+1ahWzZs2iUaNGpKam8t5773H22Wcf68elespTnyRJkiTVeUdzqtBZZ53FD37wA+6+++5q799111387ne/ixzXXVfFwwlCh7s+06ZN47TTTuP73/8+Y8eOpW/fvjz77LOcfPLJ3HLLLXTv3p2FCxcybdq0SNXS3LlzWbt2LTNmzOD3v/89f/3rXxk3bhyTJk3irLPOYs+ePbzxxhvMnDmT++67j3bt2vH5558DcO2115Kbm8vzzz8fiaG0tJTu3btHxZWcnMzSpUsjr1999VWWLVvGpEmTKCsrqzJHt27dWLZsGY0bN2bChAnk5uaSnp5+JM8r5mum2LGiRpIkSVKD9GUJmLvvvrvGJI6Oj40bN/LEE0/w8MMPs2fPHjp16gTAeeedB0CbNm349NNPq7zvggsuAOBvf/sbF110EQDf/e53Wb9+PS1btozcv/DCC3nzzTe56aabyM/Pp2XLlnTt2jVqriZNmnzpkexf7DWUkpJS7f2vf/3r1d6XDsUeNZIkSZIapJr6nNSmBQsW0LlzZ3r37s3u3buj7n3wwQdkZ2dz6aWXsnDhQgCef/55zj77bLKysiLj/vznP9O5c2c6d+7MHXfcUZvhH3Pt27dn/PjxFBUVsWbNGnr16gUcqDCpFIYhiYmJkVOxgEhz6Hbt2rF27VoA1qxZQ7t27QBYt24dAG+++SZt27blxBNPpGnTpsycObPK8eqlpaVVeg316dOnSpwbNmygvLyc5cuXk5aWFnW/shdReXk5GzZs4MwzzzwWj0cNhIkaSZIkSQ1SvDeknTx5MpMnT+bll1/m0UcfpaysjIsvvjiSdKj0y1/+kilTpvDqq6/y+uuvs3PnzlqJ/3gYPnw4CxYsIDs7m5ycHD788MNqx2VlZXHPPfdQWFgYdb1v376sX7+ezMxMNmzYQN++fQHYvn073bt359VXX41cu/rqq/nkk0+qnBxVWVFz8M/B254AEhMTGTx4MBkZGcyfP5+hQ4cCRI5/Hzp0KPPmzSMjI4PBgweTmJj4lZ+NGg571EiSJEmq8w7ugXK4DWm//e1vk5KSErOGtO+88w6zZs1i+vTpkWOcf/e730XuX3HFFTz55JM0bdqUgQMHMn78eM466yzgQKKicnvOrFmzOP300+nRowc9evTgqaeeolmzZgc/m5j3OzmaHkLHyty5cwHIz8+Pur506VI++OADbrnlltoP6kvEw5opdqyokSRJklSvzJkzh/79+/PSSy+RkpLC6tWrgQN9TpYvX86HH37I7t27yc/PZ9q0aUyZMgWAU045hWeffZZ3332X1NRUiouLOffcc3nyyScBaNGiBc899xzp6eksWbKEvLw8nnzyScrKyvj73/8elaQ5nO0zO3fu5KSTTgIONKvdsWNH1P2zzjqLoqIi9u7dy+uvv17lfqXs7OxIkqhTp05RSRpVb+HChTzwwAMMHDgw1qFIVdhMWJIkSVK9Ul8a0o4dO5Yf//jH/PznP+ess86iZcuW1c4zYcIEnnjiCb7zne/Qr18/Nm3aVGU7T0P2xUoagP79+1c5ol2KFyZqJEmSJNUr7du3Jzc3lyuvvBKAsrIy1q1bd8QNaS+//HLWrFlDhw4dKCkpOWRD2mnTpkXFcDhHPH9ZQ9pTTz2VxYsXs2/fPgYNGkTbtm2r/X3DMOTkk08mISGB5OTkKk2JdUB+fj4FBQXHJYl11VVX8c9//pOmTZvym9/8htNPP51rrrmGLVu2sH//fn71q19Ftq1JX8atT5IkSZLqlfrSkPYPf/gDl112Gb1792b06NEEQcCaNWvIyclh/fr15OTksH//fkaPHs0111xDRkYGTZo0ITU19Ss/Qx2ZqVOn8sorrzBmzBimT58OwLx58yguLua+++7jkUceiXGEqktsJixJkiSpzjvezWrrYkPaSvHQmPZI1ue1117j9ttvp1mzZgwcOJDrrruOXr168dlnn/Hf//3f/PrXv6aoqIj7778/UgXVs2dPHn/8cbKzsyksLCQ/P5/mzZtHEm6TJk2KVNQkJSUxZMgQ9uzZQ05ODnfccQfjxo1jxYoVNGrUiP/93/+lTZs2R/V7vvzyy7zwwgvce++9kWvPPvssW7duZciQIYc9TzysmWLHrU+SJEmSdBQWLlzI9OnTeeaZZ2IdSr2ybNky7r33Xrp27Ro5Kv2ZZ56hadOmDBo0iI0bNwJw4okn8vvf/54f/ehHBEHAqlWrSEtLi1RIZWRk8Itf/ILc3Fy2bt0amf/++++noKCAiy++mAEDBrB161ZWrlxJcXExjRo14osJpby8PP71r39VifGLTZvLy8spLCxk9uzZkddZWVl89NFHPPXUU8f2IaleM1EjSZIkSV/ChrS1Z8SIEUycOJG5c+dy22230aFDB4YMGcK2bdt4//33GTx4MADnnHMOAK1bt+bss88GICkpKTJPZfPnjh078v7770eub9y4kVGjRgEHTt7avHkz48aNIz8/n2984xsUFhZywgknRMYvXrz4sOL+6U9/yvXXX0+7du0AaNSoEStXrmTt2rUUFBSwaNGio30kamBM1EiSJEmS4kZKSgqPPPIIW7duZfjw4QwaNIiOHTsyfvx4rrvuukjFy8HNoQ/+d6V169bRvn173nrrLW699dbI9fbt2zN06FBSU1MpLy8nCAI+//xzrrjiCu677z6WLVtGv379IuMPp6LmscceIwgCrr/++si10tJSmjRpwkknnRSVQJK+jIkaSZIkSToGjuepQg3pBKFZs2axePFiSkpKGDt2LJ06dWLSpEm88cYbRzRPcXExM2bMIDMzk9atW0eujx07lmHDhlFSUkJiYiKLFi2iX79+7N27l4SEhCq9ZA6nombEiBF873vfIysri+zsbCZMmMDll18euW8zYR0JmwlLkiRJqvOOdzPhw3E8EzX79+8nMTGR4uJiFi1aFDlZ6HDEQ2Pa2l6f47kWtSEe1kyx4/HckiRJkhqU1157jU6dOpGVlcVjjz3G559/Trdu3cjIyOCGG24AoKioiB49epCbm0tubi4PP/wwl1xyCePHjwcOJAJuvvlmMjIymDBhQtT8H3/8MT179iQzM5OJEycCMG7cOLp06UJmZiZbtmw54pgTExMBKCkpoWPHjl/l15cU59z6JEmSJKlBqYunCnmC0JGpPE5dqotM1EiSJElqUOriqUKeICQ1HG59kiRJktSgVJ4qNGXKFO6++26ee+45OnbsSHFxMV26dDmiU4UA3nrrLb71rW9Frrdv354ZM2ZQVFTE2rVrufDCC8nKymL+/PmceuqpLFu2LGqevLw8srKyon727dsXNaa0tBSgQZ0glJ+fz6ZNm47L3Js2baJVq1bMnDkTgJdeeon09HS6du1a49a0kpISsrOzufTSS+nVqxe7d+8G4LLLLiMzM5OsrCw++eSTGj/zgQceICMjg4EDB1JWVsa+fftIS0ur9uh3NWxW1EiSJElqUOraqUIVFRWeIHQc9OjRgxtvvBGAwsJCnn/+ed555x0mT57Mz3/+8yrjGzduzOOPP06bNm2YM2cO8+fPZ8SIETz//PMkJibym9/8hscff5yf/OQnVd77j3/8gxUrVrBy5UomT57M008/Td++fXniiScoKCg43r+q6hgTNZIkSZIalJEjRzJy5Mioa5XVMQfLysoCiPoiXVRUFPn3qFGjok4VOrgvytNPPx0113PPPXfU8SYkJPDyyy8f9fvjzfDhwxk1ahRnnnkmU6ZMITU1lYqKCh566CE+/fRT7rrrLnr37h0ZX1BQEKk0qjzNKSkpiSFDhrBnzx5ycnK44447jjqevXv38rWvfY3mzZvTqVOnKs2hKzVt2pQ2bdoAB5I2lZVXlY2eP/vsM84999xq37t69Wouu+wyAHJycli4cCF9+/Y96phVv5mokSRJkiTVmn79+rF48WJGjx7Niy++yMiRIykrK6Nnz57s2bOHq666KipRU53qGjZXVjWVlpbSvXv3qPHJycksXbq02rl27NjBSSedFHldXl5+yM/es2cPs2fP5o9//CMAmzdv5uqrr2b37t2Ra1+0c+fOyGckJyezY8eOQ36GGjYTNZIkSZJ0hDxV6OhlZ2fzs5/9jAEDBtCyZUuaNGnCa6+9xj333EN5eXmVPi8H9weqrGKprmFzZaKmSZMmUZVPX6ZFixbs2rUr8johoeZWrmEYMnjwYAoLC0lOTgbgtNNOY9WqVSxatIipU6fy0EMPVXlfSkoK27ZtA2DXrl2kpKQcdnxqeGwmLEmSJKlBqivNar+ooqKCSy65hObNm0fir0uNaRMTE2ndujVTp04lLy8PgAcffJD58+fz1FNPVUmUJCcns2XLFioqKnj77beB6hs2VyotLa3SnLlPnz41xnPCCSewd+9eSkpKWL16deS0r82bN1cZe+edd9K5c2eys7MBKCsrixzxfnCj5y++9+KLL6a4uBiA5cuXk5aWdvgPTA2OFTWSJEmSdBwcq2a1X5SQkMCSJUsYM2ZM5FqzZs3qVGPavLw8Bg4cyKRJkwDo06cPPXv2JC0trUq1SV5eHn379mXRokW0aNECqL5hc/PmzYEjr6gBGDNmDN26dSMpKYl58+YBMGjQIJYvXx4Zs2XLFiZPnkx6ejpLlizh2muv5corr+SHP/whCQkJNG3aNFJp9cX3nnrqqaSnp9OlSxfOOOMMbr/99iOKTw2LiRpJkiRJ9Up9a1ZbnVatWh11PPGgd+/efPrpp5HXw4YNY9iwYVFjDt5etnbt2ipzfLFh85Fo3Lgxa9asYebMmdx4443k5OSQk5MTuV9RURGprKnUpk2byDHpB6uslKm0fft2unTpUmXc6NGjGT16dOT1vn37uO6666yuURUmaiRJkiTVK/WtWa2OvdNPPz2yjao6CQkJTJ8+/ajmPvnkkw+rsqlZs2a88sorR/UZqt9M1EiSJEmqV+pbs1pJDYuJGkmSJEn1yqGa1TZv3pz09PSo8TU1qx06dCipqamUl5dHJXOOtKLm4Ga177zzTlSz2tNOOy1q7Beb1dY0TlL9ZaJGkiRJUr1TX5rVDh8+vMo4gP79+/PKK6/w3nvvMWbMGHr16nVE8dS2pKSkbUEQtIx1HHVFUlLStljHoNgJDtWgSpIkSZLqgiAIwnj6bvPRRx/Ro0cPbrrppsjJTwerqKhg5MiRX9oHZfv27cyYMeNLe57s27ePbt26kZaWxoMPPhh1LwgCwjAManirpDhjokaSJElSnRdviZp4YqJGqltq7mIlSZIkSZKkWmWiRpIkSZIkKU6YqJEkSZIkSYoTnvokSZIkqc7zVKGaeYKQVLfYTFiSJEmS/i0IgnbAaqBTGIZ/i8Hnnw2sAC4Iw/Cj2v58SbHn1idJkiRJAoIgCIBfAlNikaQBCMPw/4BfAA//Ox5JDYyJGkmSJEk6YABwCvCzGMdxP3AmkBfjOCTFgFufJEmSJDV4QRB8A3gb6BWG4Zo4iKcL8Dvg3DAMd8Y6Hkm1x0SNJEmSpAYvCIJ5wD/DMPxJrGOpFATBTCAhDMMfxToWSbXHRI0kSZKkBi0Igu7AbOC8MAz3xDqeSkEQJAPvANeEYbgy1vFIqh32qJEkSZLUYAVBcAIHGgjfGE9JGoAwDD8FbgVmB0HQNNbxSKodVtRIkiRJarCCIJgCnB6G4bWxjqUmQRAsAd4Kw/CuWMci6fgzUSNJkiSpQQqC4ALgj0BqGIafxDqemgRBcBqwDsgMw/DPsY5H0vHl1idJkiRJDU4QBI2BOcDoeE7SAIRhuBm4C5gTBIHf4aR6zj9ySZIkSQ3RrcCnwG9iHchh+uW//+sJUFI959YnSZIkSQ1KEARtgf8HpIVh+F6s4zlcQRCcCxQB3/l3lY2kesiKGkmSJEkNRhAEAfAI8FBdStIAhGH4DjATmBHrWCQdPyZqJEmSJDUk1wBtgAdjHchRuhc4JwiCvrEORNLx4dYnSZIkSQ1CEAQnA+8AvcMwXB3reI5WEASXAr8Fzg3D8NNYxyPp2DJRI0mSJKlBCILg18CuMAxvi3UsX1UQBLOBsjAMR8Q6FknHlokaSZIkSfVeEARdgV8B54VhuDvW8XxVQRCkcKA6qH8Yhq/GOh5Jx449aiRJkiTVa0EQNANmASPqQ5IGIAzDncBtwJwgCJrGOh5Jx46JGkmSJEn13Z3A2jAM/xDrQI6xRcB7wOhYByLp2HHrkyRJkqR6KwiCjsALwPlhGH4c63iOtSAIvgn8Cbg0DMP/i3U8kr46K2okSZIk1UtBEDQC5gBj62OSBiAMww+Bu4FZQRD4/U6qB/xDliRJklRf3Qzs5UAT4fpsJtAEGBrrQCR9dW59kiRJklTvBEHwLWAtkB6G4buxjud4C4IgFXiJA1u8tsY6HklHz0SNJEmSpHolCIIAeBpYFYZhYazjqS1BEEwCOoRh+P1YxyLp6Ln1SZIkSVJ90x/4FvBArAOpZZOAjkEQ9I51IJKOnhU1kiRJkuqNIAi+DrwN5IVh+Hqs46ltQRBkAfOBc8Mw3BXjcCQdBRM1kiRJkuqNIAgeBfaFYXhLrGOJFZ+BVLeZqJEkSZJUL/y7mmQecF5DriY5qKqoXxiGq2Idj6QjY48aSZIkSXVeEATNgNnAzQ05SQMQhuG/gNuBOUEQNIl1PJKOjIkaSZIkSfXBeGB9GIZPxTqQOLEQ2ASMinEcko6QW58kSZIk1WlBEKQCLwIdwzDcGut44kUQBP8F/AlID8Pw3VjHI+nwWFEjSZIkqc4KgqARMAeYYJImWhiGHwATgdlBEPjdT6oj/GOVJEmSVJfdCJQCj8Y6kDj1C+AE4IZYByLp8Lj1SZIkSVKdEgRBItACaMqBrT0ZYRj+JbZRxa8gCDoCLwDnAyGwPQzDsthGJakmJmokSZIk1SlBEPwAyOVAsmZNGIb3xDikuBcEwX1Au3+//G0YhktjGY+kmrn1SZIkSVJd818cSNJ8G/gwCIJvxjieuBYEwRnARuAi4GsceH6S4pSJGkmSJEl1TVsgB2gM5HOgR41qth8YBpQBlwFnxDQaSYdkokaSJElSXXMpB3qt3ANkhWG4LcbxxLUwDDcDGcD9HPgOeFlsI5J0KPaokSRJklSnBEHwXeD9MAz/EetY6pogCFoBp4VhuDbWsUiqnokaSZIkSZKkOOHWJ0mSJEmSpDjRONYBSJIkSapdzZo1+/izzz5rGes44lFSUtK2ffv2tYp1HOA6HUo8rZN0rLn1SZIkSWpggiAI/R5QvSAICMMwiHUc4DodSjytk3SsufVJkiRJkiQpTpiokSRJkiRJihMmaiRJkiQdlaKiIjZt2hTTGBYsWEDnzp3p3bs3u3fvjrq3e/duevfuTefOnVmwYEGMIow910mqW0zUSJIkSToqNSUAKioqauXz9+/fz5w5c1ixYgUDBgzg0Ucfjbo/Z84cBgwYwIoVK5gzZw779++vlbjijesk1S0maiRJkiRFKSkp4fvf/z7Z2dnceOONABQUFPCjH/2InJwchgwZQnl5OXPnzmXkyJH8z//8D3PnzuWHP/whubm5bNiwgWuuuYbMzEyuueYaysrKIvcvv/xyrrzySkpLS7nqqqvYtWsXAD/+8Y/ZuHHjEcX57rvvcv7559OoUSNycnJ4/fXXo+6vWrWKbt260ahRI1JTU3nvvfeOzQOKE66TVD+ZqJEkSZIUZc6cOfTv35+XXnqJlJQUVq9eDcB5553H8uXL+fDDD9m9ezf5+flMmzaNKVOmAHDKKafw7LPP8u6775KamkpxcTHnnnsuTz75JAAtWrTgueeeIz09nSVLlpCXl8eTTz5JWVkZf//73+nQoUMkhtLSUrKysqJ++vTpExXnzp07OemkkwBITk5mx44dR3S/rnOdpPqpcawDkCRJkhRfNm7cyBNPPMHDDz/Mnj176NSpE3AgAQDQpk0bPv300yrvu+CCCwD429/+xkUXXQTAd7/7XdavX0/Lli0j9y+88ELefPNNbrrpJvLz82nZsiVdu3aNmqtJkyYUFRUdMs6UlJRIpceuXbtISUmp9v7Xv/71au/Xda6TVD+ZqJEkSZIUpX379uTm5nLllVcCUFZWxrp16wiCIDImDEMSExMpLy+PXEtIOFCw365dO9auXcvll1/OmjVr6NChAyUlJaxbtw6AN998k7Zt23LiiSfStGlTZs6cybRp06JiKC0tpXv37lHXkpOTWbp0aVScGzZsoLy8nOXLl5OWlhY1/pJLLuHFF18kLy+PDRs2cOaZZx6DpxM/XCepfnLrkyRJkqQow4cPZ8GCBWRnZ5OTk8OHH35Y7bisrCzuueceCgsLo6737duX9evXk5mZyYYNG+jbty8A27dvp3v37rz66quRa1dffTWffPIJZ5xxRtQclZUaB/8c/OUfIDExkcGDB5ORkcH8+fMZOnQoACNHjgRg6NChzJs3j4yMDAYPHkxiYuJXfjbxxHWS6qcgDMNYxyBJkiSpFgVBENb294C5c+cCkJ+fH3V96dKlfPDBB9xyyy21Gk9NgiAgDMPgy0cef65TzeJpnaRjza1PkiRJkmJi4cKFTJ8+nWeeeSbWoegQXCepdllRI0mSJDUwsajUqCviqVLDdapZPK2TdKzZo0aSJElSrcvPz2fTpk3HZe6rrrqKLl260LVrVz766KPj8hkNxfFcp1tuuYVTTjklst1K0gFufZIkSZJUr0ydOpW2bdvywgsvMH36dKZMmRLrkFSN8ePHR44Hl/QfVtRIkiRJqtFrr71Gp06dyMrK4rHHHuPzzz+nW7duZGRkcMMNNwBQVFREjx49yM3NJTc3l4cffphLLrmE8ePHAweqMm6++WYyMjKYMGFC1Pwff/wxPXv2JDMzk4kTJwIwbtw4unTpQmZmJlu2bDnimNu2bQtA48aNady4Yfy/6bq4Tq1atfqKv7VUP5mokSRJklSjZcuWce+991JUVMQNN9xAYmIizzzzDCtXrqSiooKNGzcCcOKJJ/Lss8/yzW9+kyAIWLVqFS+++GJknoyMDFauXMmf/vQntm7dGrl+//33U1BQQHFxMX/5y1/YunUrK1eupLi4mOLiYlq3bh0VT15eHllZWVE/+/btqxJ3eXk5hYWFkaOg67u6uk6SqmoY6WVJkiRJR2XEiBFMnDiRuXPnctttt9GhQweGDBnCtm3beP/99xk8eDAA55xzDgCtW7fm7LPPBiApKSkyzwUXXABAx44def/99yPXN27cyKhRowDYuXMnmzdvZty4ceTn5/ONb3yDwsJCTjjhhMj4xYsXH1bcP/3pT7n++utp167dV/jt6466uk6SqjJRI0mSJKlGKSkpPPLII2zdupXhw4czaNAgOnbsyPjx47nuuuuoPJUoCAX7q+YAAAcySURBVP5zAM/B/660bt062rdvz1tvvcWtt94aud6+fXuGDh1Kamoq5eXlBEHA559/zhVXXMF9993HsmXL6NevX2R8Xl4e//rXv6LmXrZsGc2aNYu8fuyxxwiCgOuvv/6YPYd4VxfXSVL1TNRIkiRJqtGsWbNYvHgxJSUljB07lk6dOjFp0iTeeOONI5qnuLiYGTNmkJmZGbVNZuzYsQwbNoySkhISExNZtGgR/fr1Y+/evSQkJDBkyJCoeQ6nUmPEiBF873vfIysri+zsbO68884jirUuqovrVFhYyG9/+1sANm/eHOmVIzV0QWVmVZIkSVLDEARBWJvfA/Lz8ykoKOCMM86otc88WkEQEIZh1VKTGHCdahZP6yQdazYTliRJkiRJihNW1EiSJEkNTG1XatQl8VSp4TrVLJ7WSTrWrKiRJEmS9KXy8/PZtGnTcZl706ZNtGrVipkzZwLw0ksvkZ6eTteuXdmyZUu17ykpKSE7O5tLL72UXr16sXv37hrnf+CBB8jIyGDgwIGUlZWxb98+0tLSyM/PPx6/TszF21oBLFiwgM6dO9O7d+8a16qiooJLLrmE5s2bR+Kv72slVcdEjSRJkqSY69GjBzfeeCNwoMns888/z7333svkyZOrHd+4cWMef/xxVqxYQZ8+fZg/f3614/7xj3+wYsUKVq5cyXnnncfTTz9Ns2bNeOKJJ47b71LfHela7d+/nzlz5rBixQoGDBjAo48+Wu24hIQElixZwtVXXx255lqpITJRI0mSJDVgw4cP569//SsAU6ZMYdmyZfzhD38gOzubiy66iKeeeipqfEFBAUVFRcB/Kjc+/vhjevbsSWZmJhMnTvxK8ezdu5evfe1rNG/enE6dOvHnP/+52nFNmzalTZs2wIGkTePG1R9ou3r1ai677DIAcnJyeP31179SfLFUV9fq3Xff5fzzz6dRo0ZfugatWrX6SjFJ9YHHc0uSJEkNWL9+/Vi8eDGjR4/mxRdfZOTIkZSVldGzZ0/27NnDVVddRe/evQ85x/33309BQQEXX3wxAwYMYOvWrZGjnUtLS+nevXvU+OTkZJYuXVrtXDt27OCkk06KvC4vLz/kZ+/Zs4fZs2fzxz/+sdr7O3fujMyXnJzMjh07DjlfPKura1Wf1kCqDSZqJEmSpAYsOzubn/3sZwwYMICWLVvSpEkTXnvtNe655x7Ky8v55JNPosYHwX/6t1Y2ut24cSOjRo0CDnwp37x5c+TLf5MmTSJVHYejRYsW7Nq1K/I6IaHmTQBhGDJ48GAKCwtJTk6udkxKSgrbtm0DYNeuXaSkpBx2LPGmrq5VSkpKZFxdXwOpNpiokSRJkhqwxMREWrduzdSpU8nLywPgwQcfZP78+TRv3pz09PSo8cnJyWzZsoWKigrefvttANq3b8/QoUNJTU2lvLw8KkFwpFUaJ5xwAnv37qWkpIR33nmHc845B4DNmzdz2mmnRY2988476dy5M9nZ2ZFrXxx38cUXM3v2bH7yk5+wfPly0tLSjvQRxY26ulbt27dnw4YNlJeXR61BdWsqyUSNJEmS1ODl5eUxcOBAJk2aBECfPn3o2bMnaWlpVaof8vLy6Nu3L4sWLaJFixYAjB07lmHDhlFSUkJiYiKLFi2iefPmwJFXaQCMGTOGbt26kZSUxLx58wAYNGgQy5cvj4zZsmULkydPJj09nSVLlnDttdcyfPjwKuNOPfVU0tPT6dKlC2eccQa33377ET+feFIX1yoxMZHBgweTkZHBySefzIIFC6odB9C/f39eeeUV3nvvPcaMGUOvXr2OKB6pPggqS+AkSZIkNQxBEITx9D3go48+okePHtx0002R04QOVlFRwciRI5k+ffoh59m+fTszZsygoKDgkOP27dtHt27dSEtL48EHH4y6FwQBYRgGNby1VsXbOkH8rFU8rZN0rJmokSRJkhqYeEwAxIt4SgC4TjWLp3WSjjWP55YkSZIkSYoTJmokSZIkSZLihIkaSZIkSZKkOOGpT5IkSVIDk5SUtC0IgpaxjiMeJSUlbYt1DJVcp5rF0zpJx5rNhCVJkiRJkuKEW58kSZIkSZLihIkaSZIkSZKkOGGiRpIkSZIkKU6YqJEkSZIkSYoTJmokSZIkSZLihIkaSZIkSZKkOGGiRpIkSZIkKU6YqJEkSZIkSYoTJmokSZIkSZLihIkaSZIkSZKkOGGiRpIkSZIkKU6YqJEkSZIkSYoTJmokSZIkSZLihIkaSZIkSZKkOGGiRpIkSZIkKU6YqJEkSZIkSYoTJmokSZIkSZLihIkaSZIkSZKkOGGiRpIkSZIkKU6YqJEkSZIkSYoTJmokSZIkSZLihIkaSZIkSZKkOGGiRpIkSZIkKU6YqJEkSZIkSYoTJmokSZIkSZLihIkaSZIkSZKkOGGiRpIkSZIkKU6YqJEkSZIkSYoTJmokSZIkSZLixP8HVYADkTYSna0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing Decision Trees\n",
    "plt.figure(figsize=(20,20))\n",
    "tree.plot_tree(DT);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}