04_bureau_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.0  Analysis of Bureau Dataset\n",
    "\n",
    "> In this notebook I analyze the `bureau` dataset which is described below:\n",
    "> - Client's previous credits provided by other financial institutions that were reported to Credit Bureau (for clients who have a loan in our sample).\n",
    "> - For every loan there are as many rows as number of credits the client had in Credit Bureau before the application date.\n",
    "\n",
    "#### Analysis Summary:\n",
    "> After analyzing the `bureau` dataset I created a vector for each loan id with following columns:\n",
    ">> - __COUNT_GROUP__: The number of accounts by bin size of 5( i.e. 1 means 0 to 5 accounts).\n",
    ">> - __OVERDUE__: Flag to indicate if a client has an overdue account.\n",
    ">> - __DEBT_OVER_50%__: Account group with credit limit 50% or higher.\n",
    ">> - __DEBT_OVER_75%__: Account group with credit limit higher 75% or higher.\n",
    ">> - __DEBT_OVER_100%__: Account group with credit limit higher 100% or higher.\n",
    ">> - __ACTIVE__: The number of active accounts by id group by bin size of 5. \n",
    ">> - __CLOSED__: The number of closed accounts by id group by bin size of 5. \n",
    ">> - __ADVERSE__: The number of adverse (bad debt or sold) accounts by id group by bin size of 5. \n",
    ">> - __CREDIT_TYPE_COUNT__: The number of different types accounts by id group. \n",
    "\n",
    "> I also created an aggregate table of four functions ('minimum', 'maximum', 'mean','standard deviation') `SK_ID_CURR` for each of the availble `float64` fields.  Each field would have a new column for each of the functions.  This created a total of 48 features.  By taking this approach I figure there will be many fields available during model selection that can be removed if there feature importance is zero.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "#import helper ultility module\n",
    "%load_ext autoreload\n",
    "%aimport utils\n",
    "%autoreload 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "pd.set_option('display.max_rows', 100)\n",
    "\n",
    "import math\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "\n",
    "import missingno as msno\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Import Datasets And Prepare ABT\n",
    "> - Import the train and test datasets combined using a utility helper \n",
    "> - Create Activity Base Table (ABT) to group the fields by type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset =  utils.get_bureau()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>CREDIT_ACTIVE</th>\n",
       "      <th>CREDIT_CURRENCY</th>\n",
       "      <th>DAYS_CREDIT</th>\n",
       "      <th>CREDIT_DAY_OVERDUE</th>\n",
       "      <th>DAYS_CREDIT_ENDDATE</th>\n",
       "      <th>DAYS_ENDDATE_FACT</th>\n",
       "      <th>AMT_CREDIT_MAX_OVERDUE</th>\n",
       "      <th>CNT_CREDIT_PROLONG</th>\n",
       "      <th>AMT_CREDIT_SUM</th>\n",
       "      <th>AMT_CREDIT_SUM_DEBT</th>\n",
       "      <th>AMT_CREDIT_SUM_LIMIT</th>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE</th>\n",
       "      <th>CREDIT_TYPE</th>\n",
       "      <th>DAYS_CREDIT_UPDATE</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215354</td>\n",
       "      <td>5714462</td>\n",
       "      <td>Closed</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>-497.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-153.00</td>\n",
       "      <td>-153.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>91,323.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>-131.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215354</td>\n",
       "      <td>5714463</td>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>-208.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,075.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>225,000.00</td>\n",
       "      <td>171,342.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>-20.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215354</td>\n",
       "      <td>5714464</td>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>-203.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>528.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>464,323.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>-16.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>215354</td>\n",
       "      <td>5714465</td>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>-203.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90,000.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>-16.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>215354</td>\n",
       "      <td>5714466</td>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>-629.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,197.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>77,674.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,700,000.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>-21.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  SK_ID_BUREAU CREDIT_ACTIVE CREDIT_CURRENCY  DAYS_CREDIT  \\\n",
       "0      215354       5714462        Closed      currency 1      -497.00   \n",
       "1      215354       5714463        Active      currency 1      -208.00   \n",
       "2      215354       5714464        Active      currency 1      -203.00   \n",
       "3      215354       5714465        Active      currency 1      -203.00   \n",
       "4      215354       5714466        Active      currency 1      -629.00   \n",
       "\n",
       "   CREDIT_DAY_OVERDUE  DAYS_CREDIT_ENDDATE  DAYS_ENDDATE_FACT  \\\n",
       "0                0.00              -153.00            -153.00   \n",
       "1                0.00             1,075.00                nan   \n",
       "2                0.00               528.00                nan   \n",
       "3                0.00                  nan                nan   \n",
       "4                0.00             1,197.00                nan   \n",
       "\n",
       "   AMT_CREDIT_MAX_OVERDUE  CNT_CREDIT_PROLONG  AMT_CREDIT_SUM  \\\n",
       "0                     nan                0.00       91,323.00   \n",
       "1                     nan                0.00      225,000.00   \n",
       "2                     nan                0.00      464,323.50   \n",
       "3                     nan                0.00       90,000.00   \n",
       "4               77,674.50                0.00    2,700,000.00   \n",
       "\n",
       "   AMT_CREDIT_SUM_DEBT  AMT_CREDIT_SUM_LIMIT  AMT_CREDIT_SUM_OVERDUE  \\\n",
       "0                 0.00                   nan                    0.00   \n",
       "1           171,342.00                   nan                    0.00   \n",
       "2                  nan                   nan                    0.00   \n",
       "3                  nan                   nan                    0.00   \n",
       "4                  nan                   nan                    0.00   \n",
       "\n",
       "       CREDIT_TYPE  DAYS_CREDIT_UPDATE  AMT_ANNUITY  \n",
       "0  Consumer credit             -131.00          nan  \n",
       "1      Credit card              -20.00          nan  \n",
       "2  Consumer credit              -16.00          nan  \n",
       "3      Credit card              -16.00          nan  \n",
       "4  Consumer credit              -21.00          nan  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "abt = utils.ABT(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total float64 types: 12\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>count</th>\n",
       "      <th>na_count</th>\n",
       "      <th>na_%</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT</th>\n",
       "      <td>1</td>\n",
       "      <td>1,716,428.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1,142.11</td>\n",
       "      <td>795.16</td>\n",
       "      <td>-2,922.00</td>\n",
       "      <td>-1,666.00</td>\n",
       "      <td>-987.00</td>\n",
       "      <td>-474.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREDIT_DAY_OVERDUE</th>\n",
       "      <td>2</td>\n",
       "      <td>1,716,428.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.82</td>\n",
       "      <td>36.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,792.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT_ENDDATE</th>\n",
       "      <td>3</td>\n",
       "      <td>1,610,875.00</td>\n",
       "      <td>105,553.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>510.52</td>\n",
       "      <td>4,994.22</td>\n",
       "      <td>-42,060.00</td>\n",
       "      <td>-1,138.00</td>\n",
       "      <td>-330.00</td>\n",
       "      <td>474.00</td>\n",
       "      <td>31,199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_ENDDATE_FACT</th>\n",
       "      <td>4</td>\n",
       "      <td>1,082,775.00</td>\n",
       "      <td>633,653.00</td>\n",
       "      <td>0.37</td>\n",
       "      <td>-1,017.44</td>\n",
       "      <td>714.01</td>\n",
       "      <td>-42,023.00</td>\n",
       "      <td>-1,489.00</td>\n",
       "      <td>-897.00</td>\n",
       "      <td>-425.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_MAX_OVERDUE</th>\n",
       "      <td>5</td>\n",
       "      <td>591,940.00</td>\n",
       "      <td>1,124,488.00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>3,825.42</td>\n",
       "      <td>206,031.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>115,987,185.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_CREDIT_PROLONG</th>\n",
       "      <td>6</td>\n",
       "      <td>1,716,428.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM</th>\n",
       "      <td>7</td>\n",
       "      <td>1,716,415.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>354,994.59</td>\n",
       "      <td>1,149,811.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>51,300.00</td>\n",
       "      <td>125,518.50</td>\n",
       "      <td>315,000.00</td>\n",
       "      <td>585,000,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_DEBT</th>\n",
       "      <td>8</td>\n",
       "      <td>1,458,759.00</td>\n",
       "      <td>257,669.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>137,085.12</td>\n",
       "      <td>677,401.13</td>\n",
       "      <td>-4,705,600.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40,153.50</td>\n",
       "      <td>170,100,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_LIMIT</th>\n",
       "      <td>9</td>\n",
       "      <td>1,124,648.00</td>\n",
       "      <td>591,780.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>6,229.51</td>\n",
       "      <td>45,032.03</td>\n",
       "      <td>-586,406.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4,705,600.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE</th>\n",
       "      <td>10</td>\n",
       "      <td>1,716,428.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.91</td>\n",
       "      <td>5,937.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3,756,681.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT_UPDATE</th>\n",
       "      <td>11</td>\n",
       "      <td>1,716,428.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-593.75</td>\n",
       "      <td>720.75</td>\n",
       "      <td>-41,947.00</td>\n",
       "      <td>-908.00</td>\n",
       "      <td>-395.00</td>\n",
       "      <td>-33.00</td>\n",
       "      <td>372.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <td>12</td>\n",
       "      <td>489,637.00</td>\n",
       "      <td>1,226,791.00</td>\n",
       "      <td>0.71</td>\n",
       "      <td>15,712.76</td>\n",
       "      <td>325,826.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13,500.00</td>\n",
       "      <td>118,453,423.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        num        count     na_count  na_%       mean  \\\n",
       "DAYS_CREDIT               1 1,716,428.00         0.00  0.00  -1,142.11   \n",
       "CREDIT_DAY_OVERDUE        2 1,716,428.00         0.00  0.00       0.82   \n",
       "DAYS_CREDIT_ENDDATE       3 1,610,875.00   105,553.00  0.06     510.52   \n",
       "DAYS_ENDDATE_FACT         4 1,082,775.00   633,653.00  0.37  -1,017.44   \n",
       "AMT_CREDIT_MAX_OVERDUE    5   591,940.00 1,124,488.00  0.66   3,825.42   \n",
       "CNT_CREDIT_PROLONG        6 1,716,428.00         0.00  0.00       0.01   \n",
       "AMT_CREDIT_SUM            7 1,716,415.00        13.00  0.00 354,994.59   \n",
       "AMT_CREDIT_SUM_DEBT       8 1,458,759.00   257,669.00  0.15 137,085.12   \n",
       "AMT_CREDIT_SUM_LIMIT      9 1,124,648.00   591,780.00  0.34   6,229.51   \n",
       "AMT_CREDIT_SUM_OVERDUE   10 1,716,428.00         0.00  0.00      37.91   \n",
       "DAYS_CREDIT_UPDATE       11 1,716,428.00         0.00  0.00    -593.75   \n",
       "AMT_ANNUITY              12   489,637.00 1,226,791.00  0.71  15,712.76   \n",
       "\n",
       "                                std           min       25%        50%  \\\n",
       "DAYS_CREDIT                  795.16     -2,922.00 -1,666.00    -987.00   \n",
       "CREDIT_DAY_OVERDUE            36.54          0.00      0.00       0.00   \n",
       "DAYS_CREDIT_ENDDATE        4,994.22    -42,060.00 -1,138.00    -330.00   \n",
       "DAYS_ENDDATE_FACT            714.01    -42,023.00 -1,489.00    -897.00   \n",
       "AMT_CREDIT_MAX_OVERDUE   206,031.61          0.00      0.00       0.00   \n",
       "CNT_CREDIT_PROLONG             0.10          0.00      0.00       0.00   \n",
       "AMT_CREDIT_SUM         1,149,811.34          0.00 51,300.00 125,518.50   \n",
       "AMT_CREDIT_SUM_DEBT      677,401.13 -4,705,600.32      0.00       0.00   \n",
       "AMT_CREDIT_SUM_LIMIT      45,032.03   -586,406.11      0.00       0.00   \n",
       "AMT_CREDIT_SUM_OVERDUE     5,937.65          0.00      0.00       0.00   \n",
       "DAYS_CREDIT_UPDATE           720.75    -41,947.00   -908.00    -395.00   \n",
       "AMT_ANNUITY              325,826.95          0.00      0.00       0.00   \n",
       "\n",
       "                              75%            max  \n",
       "DAYS_CREDIT               -474.00           0.00  \n",
       "CREDIT_DAY_OVERDUE           0.00       2,792.00  \n",
       "DAYS_CREDIT_ENDDATE        474.00      31,199.00  \n",
       "DAYS_ENDDATE_FACT         -425.00           0.00  \n",
       "AMT_CREDIT_MAX_OVERDUE       0.00 115,987,185.00  \n",
       "CNT_CREDIT_PROLONG           0.00           9.00  \n",
       "AMT_CREDIT_SUM         315,000.00 585,000,000.00  \n",
       "AMT_CREDIT_SUM_DEBT     40,153.50 170,100,000.00  \n",
       "AMT_CREDIT_SUM_LIMIT         0.00   4,705,600.32  \n",
       "AMT_CREDIT_SUM_OVERDUE       0.00   3,756,681.00  \n",
       "DAYS_CREDIT_UPDATE         -33.00         372.00  \n",
       "AMT_ANNUITY             13,500.00 118,453,423.50  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_table = abt.float_table\n",
    "print(\"Total float64 types: {}\".format(len(float_table)))\n",
    "float_table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1) ['DAYS_CREDIT']: How many days before current application did client apply for Credit Bureau credit\n",
      "----------------------------------------\n",
      "2) ['CREDIT_DAY_OVERDUE']: Number of days past due on CB credit at the time of application for related loan in our sample\n",
      "----------------------------------------\n",
      "3) ['DAYS_CREDIT_ENDDATE']: Remaining duration of CB credit (in days) at the time of application in Home Credit\n",
      "----------------------------------------\n",
      "4) ['DAYS_ENDDATE_FACT']: Days since CB credit ended at the time of application in Home Credit (only for closed credit)\n",
      "----------------------------------------\n",
      "5) ['AMT_CREDIT_MAX_OVERDUE']: Maximal amount overdue on the Credit Bureau credit so far (at application date of loan in our sample)\n",
      "----------------------------------------\n",
      "6) ['CNT_CREDIT_PROLONG']: How many times was the Credit Bureau credit prolonged\n",
      "----------------------------------------\n",
      "7) ['AMT_CREDIT_SUM']: Current credit amount for the Credit Bureau credit\n",
      "----------------------------------------\n",
      "8) ['AMT_CREDIT_SUM_DEBT']: Current debt on Credit Bureau credit\n",
      "----------------------------------------\n",
      "9) ['AMT_CREDIT_SUM_LIMIT']: Current credit limit of credit card reported in Credit Bureau\n",
      "----------------------------------------\n",
      "10) ['AMT_CREDIT_SUM_OVERDUE']: Current amount overdue on Credit Bureau credit\n",
      "----------------------------------------\n",
      "11) ['DAYS_CREDIT_UPDATE']: How many days before loan application did last information about the Credit Bureau credit come\n",
      "----------------------------------------\n",
      "12) ['AMT_ANNUITY']: Annuity of the Credit Bureau credit\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# print description of each field in the integer ABT\n",
    "field_table = utils.FieldsNames()\n",
    "field_table.print('bureau.csv',float_table.index.values.tolist()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total string types: 3\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>num</th>\n",
       "      <th>count</th>\n",
       "      <th>na_count</th>\n",
       "      <th>na_%</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>top_count</th>\n",
       "      <th>top_%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CREDIT_ACTIVE</th>\n",
       "      <td>1</td>\n",
       "      <td>1716428</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>Closed</td>\n",
       "      <td>1079273</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREDIT_CURRENCY</th>\n",
       "      <td>2</td>\n",
       "      <td>1716428</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>1715020</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREDIT_TYPE</th>\n",
       "      <td>3</td>\n",
       "      <td>1716428</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>1251615</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 num    count  na_count  na_%  unique              top  \\\n",
       "column                                                                   \n",
       "CREDIT_ACTIVE      1  1716428         0  0.00       4           Closed   \n",
       "CREDIT_CURRENCY    2  1716428         0  0.00       4       currency 1   \n",
       "CREDIT_TYPE        3  1716428         0  0.00      15  Consumer credit   \n",
       "\n",
       "                 top_count  top_%  \n",
       "column                             \n",
       "CREDIT_ACTIVE      1079273   0.63  \n",
       "CREDIT_CURRENCY    1715020   1.00  \n",
       "CREDIT_TYPE        1251615   0.73  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#exclude the ['SK_ID_CURR','TARGET','DATASET'] fields from the ABT\n",
    "other_table = abt.other_table\n",
    "print(\"Total string types: {}\".format(len(other_table)))\n",
    "other_table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1) ['CREDIT_ACTIVE']: Status of the Credit Bureau (CB) reported credits\n",
      "----------------------------------------\n",
      "2) ['CREDIT_CURRENCY']: Recoded currency of the Credit Bureau credit\n",
      "----------------------------------------\n",
      "3) ['CREDIT_TYPE']: Type of Credit Bureau credit (Car, cash,...)\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# print description of each field in the string ABT\n",
    "field_table = utils.FieldsNames()\n",
    "field_table.print('bureau.csv',other_table.index.values.tolist()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n9f2EzLyBUvV87pJdmSRJkiRJy68ht4xGfVlT87E/A6vW3c2bTj8OTGjYn9TQ73zg/KaxTqCs3SxJ6qDMPBo4uunYLynJ3x7T6vGpTe3GNe2fA5zTYo5RDdvTqeswZ+bPaLqf1KroCcDDmXlLP7Ef19d5SZIkSZKWR0OxslmSpE44HLgSOKTTgUiSJEmSNBwNucpmSZI6ITNPBU7tdBySJEmSJA1XVjZLkiRJkiRJktpmslmSJEmSJEmS1DaTzZIkSZIkSZKktplsliRJkiRJkiS1zWSzJEmSJEmSJKltJpslSZIkSZIkSW0z2SxJkiRJkiRJatvoTgcgSZIkjXQR8XzgM8D2wCLgL8ARmXnbEow1EzguM2f20WY6MDMzpy9BuAONYzIwLjO/OFhzDCURMQ+YlJnzlqDvdGAmcAlwPvD2zHx66UUnSZI0NFjZLEmSJA2iiFgBuAJ4FNgkMzcBpgEzImLtjga3hCJiDPAx4MxOxzKcZObjwNXA+zodiyRJ0mCwslmSJEkaXDsArwA+lZnPAGTmtRGxP9AVEaMpSdsJwLrAHGAfYEXgIuBFdZzjM/Oyuv2eiDgFWAP4YGZe3tvkdZ4jgG5gFnBIZj4eEYcA+wKrAQuAfTIzawXvBcAu9dx/ZeaspmHfDVyXmQsjYhzwPWAusCnwIKVy99GIeAtwAqXI5bfA+zLzwQHOQUTsSknMrwj8DjgoMx+JiLfXa1oFGAMckJk31qrvR4ENgb2BHwPfB7YC/gR8HTgMeBkwNTN/FhHbAycCq9bP80OZ+YNajbw2sD5wZENM44Ef1c/ul8D/AJOALmB6Zp4aEaOALwBvAR6o52bWIS4Cbo6IMzOz+1+/Y5IkScOXlc2SJEnS4NoUmN2TaO6RmVdk5kPAtsCCzNyGkthcA9gN2AOYl5kTgfcA2zV0/1tmbkZJnB7b28QRsRFwNLB9Zm4E/AP4VF3W462UZSEmAD8EDmno+khmbgmcBXyixdCTgesa9l8HnFLH+ivw7ohYB/gK8NbM3Bi4AfjyQOeIiBcCnwV2ycxNgSuBz9VK8YOBt2Tm64CTgY83dJ2TmZGZsynJ+xm1/8rAHpm5HXAccHhtfyhwYP08D6Qkxxtj3KAhmf9ySmJ9/8y8CTgIoPbdEtg9IrYD9qJ83zcE3k75vlLb/gV4HNi4xecqSZI0rJlsliRJkgbXM8D83k5m5nXAGRHxAeA04DXAWOBG4K0R8X1gC+DTDd2+X7/eBbygj7m3By7PzEfq/leBHTPzMeBdwDsj4jPAf9Y5e/y4fp0LrNVi3NcAf2jYfygzb2/qsyVwS8Max18FdlyMObaiVIRfGxGzKcnw19Sk/R7ALhExDZjaFPvNTePMqF/vB65p2F6zbk8BJkTEJynV0n2N9R3gt5n587q/EzC5xnczpWJ6I0ql83czc2Fm/pmyjEqj+ymfoSRJ0ohislmSJEkaXLcCm9WlFf4pIk6KiB3qi/a+ATwBnEupGB6VmfcC/1bPbQfcUqt6obxkEMrSGM8Zt0nz7/ujgNER8XLgF5Qq6hnA9KZxepLjvY3fDSxs0b6xT8u5e5sjIl4SEbPrf1dQlp74eWb2rHO9BbBXRIwFbgHWo3xWX2yK8cnGSTNzQcPuIv7V9ZTE+CzKchq9jkWpJH9VRLy57ncBRzbEuDVlqY7mz6153kWUP0JIkiSNKCabJUmSpMF1PfAQZfmKLoCI2AXYH/gVpTr24sw8l7IExQ6UtZwPoazT/B3g/cA6wPMXc+6ZlMrbnsrhg4BrKYnb32TmqZR1h/egJE4H6jfAuH7a3AxsXdd0BnhvnbulzHygJ2mbmbvV/tvUNZIBPgl8HhhPSeaeVMfbczFj/6f6uYynLEUyA9i9n7FuAf4bOD0iVqNUSh8UESvWJPjPKQnnq4F3RMSYiFgT2LVpnHGUz1CSJGlEMdksSZIkDaL6ErjJwKuBuRExBzgK2C0zHwTOBvaJiDspyzTcQKnaPR+Ievx64KOZ+dfFnHsO8BngZxHxa0ol8zHAVcAKEfEr4Dbg13XOgbqckhTva+4HKQnm70XEXZSlJQ5ejNj/BBwAXFw/g80oy1zcAcyuMd8F/Bl45WLE3jjHo8DX6jh3A88DVq2J5N76XEdJcp9AWW/6XuB2SgX7uZk5MzN/QEn0zwUuo/xRAYCIWANYvX5vJEmSRpRR3d2+AHkgZs2aNQ743dlXPc5jT/qZLY5vTpvU6RCWyKxZs5g4ceIS93/qqaeYO3cuwHoTJ06ct7TikjT8eA9Rb3q7R3oP0VAXEStTqni3ycyF/bXXsyLig8CizDy9v7Y9948JEyYwZsyYQY9tWWr3d+2hqtPX5f1DktRpVjZLkiRJWiyZOZ+yvvH7Ox3LcFKX2tgJ+EqnY5EkSRoMo/tvIkmSJEnPlZnf63QMw01mPg78Z6fjkCRJGixWNkuSJEmSJEmS2mayWZIkSZIkSZLUNpPNkiRJkiRJkqS2mWyWJEmSJEmSJLXNZLMkSZIkSZIkqW0mmyVJkiRJkiRJbTPZLEmSJEmSJElqm8lmSZIkSZIkSVLbTDZLkiRJkiRJktpmslmSJEmSJEmS1DaTzZIkSZKkZWrixImdDmFQLMvrWrDw6WU2lyRJAzW60wFIkiRJknr3wVNu4rEnuzsdhoaYb06b1OkQJEn6F1Y2S5IkSZIkSZLaZrJZkiRJkiRJktQ2k82SJEmSJEmSpLaZbJYkSZIkSZIktc1ksyRJkiRJkiSpbSabJUmSJEmSJEltG93pAIab0z68NWPGjOl0GMPKgoVPs9KKXZ0OQ5I6znuImnmPlCRJkjSSWNmsfs2aNaut/v4jWpKGtnZ/zg+mkR6b90gNdxHx/Ig4PSLmRsTsiLg2IjbrdFxDRURMjYjpdfuKiHhJRKwXEV/rcGiSJEmDwmSzJEmSpMUWESsAVwCPAptk5ibANGBGRKzd0eCGoMzcLTMfAF4JvLrT8UiSJA0Gl9GQJEmStCR2AF4BfCoznwHIzGsjYn+gCyAiPgFMAZ4GrgKOBF4OfA+YC2wKPAi8Hfg78HVgQh3/jMw8u1YGz8zM6XXM7swcFRHH1fnHAy8ETgR2BLYC7gDemZndEfEx4B01piuBoygJ3x8DDwNPZubOPRcVESsDpwNvABYCn87Mb0fEPOBmYBNgO2BX4HBKAc8s4AOZOT8i9gWOAR4D7gcer+POAyYBXwReFRGnZ+YHluBzlyRJGrKsbJYkSZK0JDYFZvckmntk5hWZ+VBEvAmYDGxe264PHFybvQ44JTMnAH8F3g1sC6yVmZsCb6YkdPuzESWB+17gXOBzlGT1ZsDGEbErMBHYosbw0joXQABTGhPN1aHAWGADYCfg2IhYqZ6bkZlBSW4fBGxbK7ofAj4SES8BTgb+HdgGeF6LmA8DbjXRLEmSRiIrmyVJkiQtiWeA+X2c3xG4KDOfAIiIrwP7AT8CHsrM22u7ucBa9WtExJWU5Tk+OoAYfpKZiyLifuCPmfmrOtf/AWtSksVbUSqPAVYBfg/8vMYwr8WY2wNfrUn0PwEb1jGhVDZDqep+DXBTPb4ScBslYX5jZj5Y+1xYPwdJkqTlgslmSZIkSUviVuD9ETEqM7t7DkbEScBP+NenKEfx7L8/GpPU3cCozHwkIjYEdgZ2A26r+921LxGxYtOYCxq2F7WIsQv438w8pfZfo7Z7AfBkL9e1sM7Zcz3rUxLUNPTpAi7OzMNqm7H12nbsibWPmCRJkkYsl9GQJEmStCSupywf8amI6FmjeRdgf+BXwDXAPhGxSkSMrsev7W2wiJgMXECpfD6MstbxyynrKm9Ym711MWO8Btg3IsbWGL4PvK2fPtcBe0fEqIhYB/gZMKapzUxgj4hYJyJGAWdS1m/+ObBNRLy0vkBx7xbjL8KiH0mSNEKZbJYkSZK02Go182Tg1cDciJhDefnebpn5YGb+EPghpQL6Lkp18Jf6GHIGpXL4LuAW4MLMvBM4C5hUx3898MfFiPFy4FLK8hdzgdnAef10OwP4B+Ulg1cDh2bm35vGvQM4npLMvotS6fzZunzGobXfLZSXBDa7G1gjIi4Y6HVIkiQNF/5FXZIkSdISycyHgX37OH8CcELT4XnAuIY2xzWc26/FGPdRXjLY4/DmfnXt5cYxJy1ODE3zPQW8r8XxcU375wDntGh3CXBJP/0ntJpbkiRpuLOyWZIkSZIkSZLUNpPNkiRJkiRJkqS2mWyWJEmSJEmSJLXNZLMkSZIkSZIkqW0mmyVJkiRJkiRJbTPZLEmSJEmSJElq2+hOByBJkiRJ6t1pH96aMWPGdDoMDTELFj7NSit2dToMSZKew8pmSZIkSdIyNWvWrE6HMCiW5XWZaJYkDUUmmyVJkiRJkiRJbTPZLEmSJEmSJElqm8lmSZIkSZIkSVLbTDZLkiRJktq2YOHTnQ5BkiR12OhOByBJkiRJ6t0HT7mJx57s7nQY/frmtEmdDkGSJHWYlc2SJEmSJEmSpLaZbJYkSZIkSZIktc1ksyRJkiRJkiSpbSabJUmSJEmSJEltM9ksSZIkSZIkSWqbyWZJkiRJkiRJUttMNkuSJEmSJEmS2ja60wFIkiRJUn8i4vnAZ4DtgUXAX4AjMvO2NsacCRwHPA4cnJkHRsRBwOOZeVHbQfc+73RgZmZOH6w5JEmSOsHKZkmSJElDWkSsAFwBPApskpmbANOAGRGxdrvjZ+atmXlg3X09MKbdMSVJkpZHVjZLkiRJGup2AF4BfCoznwHIzGsjYn+gKyImAScDXcBc4APA6cCEeuxzmXlRRIwBzgE2B+YBLwCo/Y8DTgAmA2+MiD9m5pU9AUTEK4FzgXWAJ4ADM3NORJwI7AisBTwA7J2ZD0bEn4FbgRcDWwCfA95S23QBM5f2hyRJktRpVjZLkiRJGuo2BWb3JJp7ZOYVmflQ3R0PvDEz9wOOAWZl5kTg34GjI+JVwKG13wbAYcCrm8a7GrgMOLYx0VydAVyamRMoieljImJ94N+AbTNzPPB7YEpt/wJKknsTYPd6DRsCbwfWb+fDkCRJGqqsbJYkSZI01D0DzO+nTWbm3+r2TsCqEXFA3V+NkuidBHylNr43Im5cjBi2B/apfa+gLOtBRBwBHBgRAWwD3NfQ5+b6dRLw3cxcCPw5Iq5YjHklSZKGDZPNkiRJkoa6W4H3R8SozOzuORgRJwE/AbqBJxvadwFTel4eGBHrUtZ7fi8wqqHdosWIYWHDvKOADYBVgIuAU4BLgKcbx8/Mnpi625hXkiRp2HAZDUmSJElD3fXAQ8CnIqILICJ2AfYHftWi/TXAf9d2LwbmUNZ8vhp4d0SsUNdg3rZF30W0Lsq5Dnhn3d4J+Cql2nlmZp4F3ENZk7mrRd+rgXdExJiIWBPYtd8rliRJGoZMNkuSJEka0mo182TKGstzI2IOcBSwW2Y+2KLL8cAqETGXkng+MjPvo6y7/BhwN3A25WWCza4GPhERb2s6fgiwV0TMruO/F/g28LqIuJPywr9bgfVaxP+Den4uZU3oVglySZKkYc9lNCRJkiQNeZn5MLBvL+dmUtZF7tl/jGdf1NfYbiFwUC9TTKptvgV8q0Xf/wfs0qLfVr3ENKpp/xjKiwslSZJGLCubJUmSJEmSJEltM9ksSZIkSZIkSWqbyWZJkiRJkiRJUttMNkuSJEmSJEmS2mayWZIkSZIkSZLUNpPNkiRJkiRJkqS2je50AJIkSZKk3p324a0ZM2ZMp8Po14KFT7PSil2dDkOSJHWQlc2SJEmSpLaZaJYkSSabJUmSJEmSJEltM9ksSZIkSZIkSWqbyWZJkiRJkiRJUttMNkuSJEmSJEmS2mayWZIkSZIkSZLUNpPNkiRJkiRJkqS2mWyWJEmSJEmSJLXNZLMkSZIkSZIkqW0mmyVJkiRJkiRJbTPZLEmSJEmSJElqm8lmSZIkSZIkSVLbTDZLkiRJkiRJktpmslmSJEmSJEmS1DaTzZIkSZKWmojoHkCbaxdzzHERMW9JY2oaa3pETF0aYw1grn/GHRHTImJy3V6s65ckSRouRnc6AEmSJEnLnUmdDmBZy8xjG3YndSoOSZKkwWSyWZIkSdJSFxGTgE8ATwAbAHcC7wI+X8/fnJlbRcR8Txm5AAAgAElEQVSuwDRgReB3wEGZ+UitCL4Z2ATYF1g5Ii4GArgPeE9m/iUi3g4cAawCjAEOyMwbI2ImcAuwHfBC4NDMnNEQ36rAVcBFmXl6U+wfAg4GngYuz8yjImI6sDawPnAk8CfgVGBV4GHgfZn5u4jYFPhaHeqOhjGnAzOBzRqvfwk+WkmSpCFrUJbRiIhJEfF4RMyOiDsi4u6I+OBijjGgR+X6e0wvIqbWX+xanfPxNUnSgEXE6yPilnp/+2lEvLLTMUnSELctcAgl2fwKYJfMPAygJppfCHy2Ht8UuBL4XEP/GZkZwEPAOsCXMvN1lGTzsRGxAiUp/JZ6/GTg4w39V8rMbYAPASc0Hge+C1zSItG8BfB+YEtgY2BiREyspx/JzA1qnOcA78rMzYAvAGfXNucDR9Xjv23+QBqvv99PT5IkaZgZzMrmWzNzEkBEPA/4VUT8JDN/NYhzLq5JnQ5AkoaziBiVmf2uzTmCfAOYnJlzIuIA4IvA7h2OSZKGsrmZ+QeAiLgbWKvp/FaUJPS1EQHQBTzacP7mhu3MzOvr9gXAeZn5TETsAfxnlAEmUaqRe/y4J46muT8NPAPs2SLm7SnVzH+r+zvV+BvjGQ+8GrisHgd4fkS8AHhJZv6kHpsOvKfFHJIkSSPSslpGYxXKL31/A+jjUbeWj5w1iohxwIXAWOCmhuNjgdOBCZRfUj+XmRfV0+tHxHWUXzB/SKl2OK328/E1SVpy10TEOzPzwcXpFBGjKJVsewCLgK9k5mn1kefjMnNm/Xk/MzPHtXh0+Us8+2j1dsCuwOGUJ3ZmAR/IzPkR8UfgEuANdZ531Eecd6JUoa0A3E95rPtHwLTM/EmN7x5g+8x8oMY8BjgmM+fUy5gDHLp4H5ckLXfmN2x3A6OazncBP8/MnhfnrUz5Pb/Hkw3bixq2VwAW1n8D3EL598F1lJ/Nh7SYv3nui+o8xwMfbYppYW1PjekllKVAGuPpAn6bmZvUNl3Aui3maYxZkiRpxBuUZTSqzetjxnOAeZT1yR7o51G3Ph85q74MTK+/2N3QcPwYYFZmTgT+HTg6Il5Vz60H7EVZH+0NlKo0H1+TpPb9Erg9InZezH5vA14PbER5THn/iHhRP30eycwNMvPyut/zaPULgYOAbeu94SHgI7XNi4Cf1kezrwMOqUnjbwD7ZeZGlDVE9wO+TlkTFEoC+zc9iWaAzHwqMy8EqPey44DvL+Z1S5KKpyNiNOUPh9tExPh6/JPUNZ1b2KAWpwDsD1xNqTDuBk4CrqVUKncNYP7ZlD9eTomITZrOXQ/sFhFja4wXAZs3tfk1sFZEbFf3DwC+mZmPAPdHxJvr8Xf1Mn/P9UuSJI0og5lsvjUzN8nMjSn/2B8PfCwzn6FUsu0SEdOAqcDYXh45a2US8O26/Q1K5QGUx9sOjojZlITCasCG9dxlmfnnzFwAXIzLZ0jSUpGZRwJ7A2dGxIk1CTsQ2wMX1wTu4/V+8ad++tzcy/4OwGuAm+o9YHfg3xraNT9CvRHwf5k5u17DxzPzS5T7w84RsRol+Ty9VRARsRLl/jOaktyQJC2+H1CeZPwrJVF7cUTcSSkOOaKXPr+hrNN8J+UPjSfVMWZTkr93AX8GBrSefmY+CnwMOLtWJvccv41S4PKLOv51mXl1U9+ngLcDX6jFNfvx7HIZU4BPRcTtlKU2er3+WsktSZI0YiyTv6Zn5mMR8W3KP+J7e9RtoI+cdfNskrybZ9dk6wKm1F8OiYh1Keu9vZsWj9y1e02SpCIzr4+I1wHnUqqc7284N7mXbs2PKI+jJAga7wUrNvV5spf9Lkri+rA61lga7m+Z2fwIdfPcqwPPy8w/RMQVlKrrHYEPNAddx74MeATYPTO9n0hSk8wcVb/OpKHIIzOnNmzv1dDl8vpf8zjjGrbn8dw/JDbap2n/g7VP49zzgHEt4jgPOK/F3KdTluhrPDa1af8XlKdzmvve1cvxxnn3aj4vSZI0EgxmZfM/1UqBScBt9PKo22I8cnY1pVqA2renGuAa4L/rfC+mJLFfUc/tFhFr1MqBd9YxwMfXJGlp+S9KhfH3gUsb/uvNdcBeEbFiRKxKqT5+KfAwzz6V8tYBzj0T2CMi1qlrLZ9JWb+5NwmsExGvrftHUpZ3grKUxomUJTrmt+h7IaWybu9a1SZJkiRJkqrBTLRuXh9n7qZUp90BfI7yko6eR92eAa6krKMMJYl8bkScQHlsrZVDgAsi4r3ArcDf6/HjgTMiYi6lyu3IzLyvrqP2a+AKYA3KWmpX1T49j69N7CWpIEnqQ31p0nTg5cB/ZObtA+mXmd+LiM0pf4RcATgtM++JiJOB8yLiAAa4HnJm3hERx1P+6LgC5R7z2T7az4+IKcD5dUmM+6hrNWfmDRHRTanSbr7WTSlLdPwKuC0iAB7IzN0GEqckSZIkSSPdoCSb6yNzY/to0tujbi0fOWsa+/+ANzYcek89/hjPVjw3tp9OL+tu+viaJLVtLqWC+a2Z+cTidMzMo4Gjm479Enhtw6Fp9fjUpnbjmvbPAc5pMceohu3p1PtBZv6Mppc91aroCcDDmXlLi7Fu57nLPUmSJEmSpAYuISFJasdBmdnXchnDyeHARykvfJIkSZIkSYvJZLMkaYll5qURsSPwp/p0ChFxOHBnZv60s9Etnsw8FTi103FIkiRJkjRcLZMXBEqSRqaImAx8E1iz4fCTwIUR8abORCVJkiRJkjrBZLMkqR0fB3bOzJ/3HMjMrwBvAT7ZsagkSZIkSdIyZ7JZktSOlTNzTvPBzJwFrNaBeCRJkiRJUoeYbJYktaOv+4j3GEmSJEmSliP9JgIiYs+G7TWbzh0zGEFJkoaN2yLiXc0HI2If4J4OxCNJkiRJkjpk9ADaHAN8t27/FNis4dyewAlLOyhJ0rBxNHBjfRngDZQ/Ym4DbA9M6mBckiRJkiRpGRvII86jetlutS9JWo5k5gPA5sC9wG7AfwB3Aa/LzN92MjZJkiRJkrRsDaSyubuX7Vb7kqTlTGY+DEzrdBySJI1UCxc9zZgxnY5CkiSpfwNJNkuS1FJEnEvvf3jszsz3LMt4JEkaiVYc3dXpECRJkgZkIMnmNSJiD8qSGas3vjAQWH1wwpIkDRNzWxx7AXA4MG/ZhiJJkiRJkjppIMnm3wOHNWwf2nROkrScyswvNO5HxE7AecA3ePbeIalDImLPzPxu3V4zM//ScO6YzPRFz5IkSZKWmoEkm3fOzIWDHokkadiKiNHAZ4CpwMGZeWlnI5JUHQN8t27/FNis4dyegMlmSZIkSUvNCgNo84eI+HREvHTQo5EkDTsR8RrgJmALYFMTzdKQMqqX7Vb7kiRJktSWgSSb3wS8ELgzIi6NiB0GOSZJ0jAREfsDNwPfy8xJmfmHTsck6Tm6e9lutS9JkiRJbel3GY3MvA04OCKOAPYBPhcRqwGnA+dn5uODHKMkaej6GvAM8LGIOKrh+CigOzOf35mwJEmSJEnSsjaQNZsByMx/AOcA50TE64D3A38A1hik2CRJQ996nQ5AUp/WiIg9KH8AWj0i9mw4t3qHYho2ImIScFxmTlpG8+0GnA38LDPftQT95wGTMnNeGzG8BDgnM3db0jHaFRHdmbnUlnmJiJmU7+PMpTVmOyJiS2CvzDyq38aSJEnDzICTzT3qL90HAm8ELlzaAUmShpXuzPx9qxMRsStw/zKOR9Jz/R44rGH70KZzGlreBhyfmV/tVACZ+QDQsUTzcuK1wLqdDkKSJGkwDCjZXF8OOBU4APgHcBZwsEtoSNJy7/vAZgARcWlm7tVw7iTgxx2JShIAy6oid3kUEZ8ApgBPA1cBR2bm0xFxIrAjsBbwALB3Zj4YEX8ELgHeACwC3pGZv2sY70DgrcBOEfEMcB3w1TrOP4DDMvOXETEdWBtYv855eVNox9WnEOcD78vMObXPzMycXufqzsxREbEjcDJl/e6/UJbMG1vbjqv9/gZMBF4KTMvMcyNiLGVJvQlAF/C5zLwoIjauMY+u8+8PzAO+XtsCnJGZZzd9luMoRSxjKS+c7Tne2zy3AQdl5qyI6KL8YXMz4JXAqcCqwMP1+n/XNNe/fN+AlwOXAb8GNqzjTcnMRyPiT5R73VbAn+q1HAa8DJiamT+LiPWBM+v35Qng0My8vdXnB3yvfh0bEUdn5olIkiSNIP2+IDAiZgD3AhsBB2Tmxpl5holmSRLl0fwer+rjnKQOiIiVI+LkiLgsIg6PiIG8HFr9iIg3AZOBzYFNKYnfg2vS8d+AbTNzPKV6fErt9iLgp5m5KSWRfEjjmJl5DiXheWzdvhD4YmZuDHwIuCQixtTmj2TmBi0SzQD31jk+DZzXz6UcQykg2Rz4CfWPh01eDmxXr/fzDf1mZeZE4N+BoyPiVTXOL9Txzga2BrYF1qoxvbmO1ezLwPTM3AS4oSm+VvNcQEmMQ3na8g7gr5Ql/96VmZsBX6gx/FNv37d6eiNKInxD4G7guHp8XWBGjX9lYI/M3K6eP7y2OY+S+N8MeC/wrd4+v8z8K3AscJmJZkmSNBIN5B8cvwBelZnvzMyfDXZAkqRhpbuX7Vb7kpa9M4FxwAxK1ezxHY1m5NgRuCgzn8jMRZRq1x0z8zfAEcCBEfEFYBtKtW6Pnqc95lIqlluqFb3rZ+Z3ATLzJuBRIGqTm/uI7Zza5wrglRHR1/tVLgO+FxFfBm7PzKtatLkqM7ubYt6JklyfTUmcr0apCP4R8OWI+BqlovebtV9ExJXA24GPtphjEvDtuv0NYGE/81wE7BURoyhJ5wuB8cCrgctq+8/xr38Ebfl9q+fuaVjT+TxKErvHjPr1fuCahu016/dqC+DcOu83KVXLa/fx+UmSJI1YA1lG44fAS+rLQv5FZt62dEOSJA0jVi9LQ9vmmbkRQER8i5Io+2RnQxoRmgs2RgGjI2IiJRF6CmXJjKdp+DmZmfPrZjd9//xsVRAyimd/d3+yj76LmvosbJwvIlZsiOfUiLgceAtwckRcQkn2Nppf23ZH9OS66aIsM3FbHXNd4NHMXBgRv6jjfQh4c2YeFBEbAjtT1oK+LSI2rBW+Pbobrrmb8rn1N09SktQ7UarEXwP8tlZHU5fXaF4XueX3rcXntkLjfmYuaDjX2K4nxvk989a5X0b54wC0/vwkSZJGrIFUNl/ax3+XDF5okqRhYIWIWDMi1gK6erZ79jsdnKR/VoiSmX/BPxAtLdcA+0TEKhExmrI28bXA9pT1js8C7qEkXRf7Z2FmPgb8NiL2BIiIrSnLcMwdQPd31z57AHdn5j8o6xdvWM+/tadhRNwMPC8z/5ey1nGrZTRauQb47zrGi4E5wCsi4tvAFpn5FcofNTaLiMmUZS9+RFnr+HHK0hKNrubZ5Ub2pCxX0es89dwFlKUyrs3MJyjrLa8VET3LdBxAqTJujrvV961OET0J4/15tpq5T5n5N+DeiJhSB9mZUoXdl0UswYvaJUmShoN+f8nJzPWWRSCSpGFpI0oSoyeB9UjDOZfRkIaeZzodwDC0XUQ0vqvkwsw8uCYmb6X8Pn0V8CVKJe13I+LO2vZWYEl/l54CnBURxwNPAXtm5oIBVMeOr8s5/B3Yrx47C/h2RMyhJFz/WI9/ApgeEYsoSeADBxjb8cAZETGXkkw/MjPvi4iTgHMi4lhgASVRfDuwF3AXpcr3wsy8s2m8Q4ALIuK9lM/s733NU899r17XUQCZ+VREvB04LSJWBh5ruH5qmx/28n3rqUQ+vq67PWcxPgsoCf6zIuLIet1791PJfAvlRY6fzcyPLcY8kiRJQ96o7u6+cwERcUFm7ruM4hmyZs2aNQ743YQJExgzZkx/zUeUWbNmMXHixE6Hscy1e91PPfUUc+fOBVhv4sSJ85ZWXJKGn6F+DxnKP+eX19iW1j0kIn4LfKTh0P/QsGZuz5rA0vIsIsZRKtLHdTiUfzHU7x/tGMo/39vR6evy3yCSpE4byONbEwY9CknSsBURqwFP1Zct9RxbBTgmM4/uXGSSgN8Dh/ay3w2YbJYkSZK01Awk2exj0JKkliJiX+Bs4PGI2DEz74iItwJfBJ4ATDZLnbVbXc9WUi8ycx4wrsNhSJIkjQgDSTZvHBGPtTg+CujOzOcv5ZgkScPH0cDrgVcDR0bEHyhrb54IfL6TgUkC4NaIeHtm3tXpQCRJkiSNfANJNiew22AHIkkalhZk5ixgVkR8EbgfmJCZv+twXJKKLwMzI+Jjmfm1TgcjSZIkaWQbSLL5qcy8f9AjkSQNR4satudTHtl/pFPBSHquzDwjIn4CnB8RbwQOclkNSZIkSYNlhQG0eXLQo5AkjQSPmmiWhp7MvJey3M1C4L6ImPP/2bv3eEvnsvHjnzEzhp6Rci6qUXI5DIYhVDI5FIUfOoiUQyqPxyl6lOoRQqhEOjjF6KSkiJ4IMRTlMBJDrg4anoicOjiOGfP74/tdZlnW3nvN7Jm99uHzfr3mte99r/v+3td9rz1rr32t676+jX9dDk2SJEnSMNNnZXNmvgkgIsZk5uy6vFJmPrCog5MkDXovi4idKH38l46InZsfzMwfdycsSQ0RMRY4htIW7RPA3d2NSNL8enb2HMaN63YUkiRJfesz2RwRSwMXA6cB59XV34yIlwD/LzPbTR4oSRoZ7gUObFo+oOmxuYDJZqmLImI94DvAw8AGmfnXLockaQGMHTO62yFIkiR1pJOezScAtwMXNq3bGTgZOB7YbxHEJUkaAjJzSrdjkNSr64HjgOMyc263g5EkSZI0vHWSbH4zsF5mzmmsyMxnIuJg4LeLLDJJ0pAQEVtT+jVPb1q3HvC1zHxz9yKTBGydmdd3OwhJkiRJI0MnEwTOak40N2TmM8DTCz8kSdJQERFfBL4JXBYRO0XE2Ig4BbgZ+GN3o5MEfLXbAUiSJEkaOTpJNj8dEcu1royIFSgTQkmSRq6dgHWBzYD/BH4BvBWYkpl7dTMwSYDv1SRJkiQNoE7aaJwO/Cgi9srMuwEi4nWUSrazF2VwkqRB79+Z+Q/gHxGxAXABcEBmPtvluCQVS0fETvSQdM5MJ/GUJEmStND0mWzOzHMjYmXg1oh4klINPRY4MTNPXdQBSpIGteealh8F9s/M2d0KRtKLLA8c2MNjcwGTzZIkSZIWmk4qm8nM4yLiZGANSmLhzsycFRGj2/VzliSNSI+baJYGnT9l5lu7HYQkSZKkkaGjZDNAZj4J3AIQES+PiEOA/wJetYhikyQNfqtExFfaLAOQmT1VVEqSJEmSpGGm42QzQESsARwEfAB4APjsoghKkjRkfK2HZUmDw/f72iAiNs/MawYiGEmSJEnDW0fJ5oh4G/AxYAvgSuBxIGyhIUkjW2Ye1e0YJPUsM0/oYLMvAxss6lgkSZIkDX99JpsjYgYwC/gOsGdmPhgRd5toliRFxBEtq+YAjwBXZOafuxCSpPk3qtsBSJIkSRoeFutgm6eBscBywDKLNhxJ0hCzfMu/VwJbATdExDbdDExSx+Z2OwBJkiRJw0Oflc2ZuWFEvAH4T2B6RNwBLBUR4zPz8UUeoSRp0MrMA9qtj4i1gLOAywY2IkmSJEmS1C2dVDaTmTdm5l7AypSJZh4D/i8ijl+UwUmShqbMvBNYqttxSJp/ETElIqYN4PHeERH3RcT3BuqYfYmIPSNiapdjeGlE3BwRMyJi9Q6271qFekSsEhHnDODxjo6IHeZj+4VybSJiav3ZeGVE/GwBx9gwIk5cGPFIkiQNRh0lmxsy87HM/FJmrg7sAqwOZRbzRRGcJGlIe67bAUjqSLd7Nr8bOCozd+tyHIPNJGBWZk7MzD90O5g+nAx0MhnlQpGZR2TmxQN1vDbHvz8z37GA+94MvCoi1lnIYUmSJA0KfbbR6ElmXg5cXr91FnNJ0vMi4j3A37odhzTSRcSbMvO6Pja7ej7G+xSwO2Uy0MuBwzJzTkQcC2xJmd/jfmCXOqn034ALgDcDs4H3ZuZfmsbbB9gR2CoingOuBc6o4zwBHJiZN9Uq42WB1eoxL2kaYyZwPrB1XbV3Zv62VmcfmZnTImICMC0zJ7SOBTwFfIlShHEP0Eh6r1bHeDXwi8z8cESMAb4BTARWBG4DdqXMb3IesFLd96jMvDgiVqvbLws8CRyQmb9tuaYrAt+sx5kNfAq4BTgbWCkiLs7MHZq2X7deozGUuVX2ysw/1sdOAzatm74rM/9UX48PBZYExtXrc309t0eBtSlFJCsBR9dz+Qvw4cx8JCK+WK/tc8BFmXlUS/yvA16ZmXc1PR/n1X1mA5+rx389cGhmnh8RE4FTgfHACsDnM/O0iDgS2KRei1OBXwJT67n+Etg2M1erz+G0+u9CYAawPvAg8J7MfJQ2ImI88DXK8zcaOCEzz4uIPYFtKD93rwUuz8z9ImIU5WdjO8rP9Wig3c/TP4HJlLtAj87McyJiaeBblJ+zu4FVgJ0ycybwXeDjwB7t4pQkSRrK5quyuRfdroiRJHVBRNweEbe1/LsfOB74WLfjk8SpfW2QmYd0MlBEbAvsAGxISeytBuxbE6prAG+sd7/dS0lIQ0lg/iIz16ckkvdvOfZZwMXAEXX5O8BXMnNdymvIBRExrm7+SGau2ZxobvJEPcYRwLkdnM4jmbkmJWH+XWCPzFwHuJ15CcBXAzsDawLbRsTawBsp1cab1vN/GfAOYCdgZmZOBj4EbFbHOJeSHN8A+AilHV2rU4Gr6jm/m5JkHgXsA9zcnGiuPgZ8KTM3BM6kJGcbrszM9YArgI9GxGLAvsB2df2JwOFN29+WmQHcR3ndfnu9jj8HToiI11ASvOsBbwLWioglWuLZHvhVy7oHany/Bz4JvI3yM9E49j7AMZm5EfBW4AtN+y6RmWtl5jfq9TsiMydRErbtCmXWA07KzInAP4D3t9mm4TPA9Po8vQX4dES8tj72RuBdwLrA9rXy+F2Un/W1gfdQnvN2XkV5zncAvljXHQFkZq4NHAU0VzJfW4/h31CSJGnYWVjJZmcxl6SRaX/ggKZ//0WpblwtM3/fzcAkAQu3IGBL4LzMfDIzZ1OSoltm5p8olav7RMSXKJW145v2a0wUOoNSOdpWrTpdLTN/DJCZv6FU3kbd5IZeYjuj7nMJsEpELNfHuTTGWge4LzNvrfsfnpmNBP21mfloZj4D/BlYLjOvBb4eEf8FnEKp1h0PXA/sGBEXARsBn6vnsxFwTkTcCnwPGB8Ry7bEsgWlspnMvLvGtnEvsf8v8NWI+Calora51/VF9esdNd7nKInwt0fE0cCevPC5aVyHjSnJ9atrrPvXc7sPeCoirgMOAj6RmU+3xPN64K8t6y6tX+8Brqk/L/cAL6/rDwWWiIjDgWPaxRQRywATMrPRG/nsHq7H35uqxXv9GQO2onxAcisl4fsflEQywPWZ+e/MfJKS2F4GmAL8ODOfzcyHgJ76NF+emXNbjr818G14vnXG7Y2NM/NflP+brT8LkiRJQ94Ct9GQJCkzrwGIiDE1mUBErFT/6JbUfctHRI+Vy5l50nyM1VqkMAoYExGTKW0TTqK0zJhDU5K7KTk5l96T3+2KIEYx7/3qU73sO7tlnDktxxvbsn1jrGdpKpqorQ8ak5s2jzkXGFUnpTuakmg+B1gOGJWZf4yINSitGLanJFM3Bp6uVbmN8VehJNCbtb2uPZ1oZl4QEb+mtHb4GPBO4MP1sUbMjXjHAzdSKsavpbT9aK4ub1yH0cCvGlXUtXp5fGbOjoiNgc0pFdy/jojNW3pIz6Vcx2azmpZn82LnUyYcv4RS7b1rm5he8HPUi+bkd18/Y6OB3TPzFni+hcmjlGroduO0jtfuXJ6PITPnRjQ+G2EOvRf2zMa5DSRJ0jC0sCqbJUkjUEQsHRHXUG4vbvhmRFwdES/tVlySnrckpXq33b+J8znWVcCuEbFk7V28F6Xf8+aU/rWnAX+gJEFHz2+gtdrz7ojYGSAiNqG04ZjRwe7vq/vsBPw+Mx8DHmZe1eqOPR0WWCEi1qrfH0ZpO9GTrYDzM/McSsuGtwKjI2J/Sp/mHwL7UfoQjwL+GBG719i2piR8W11Fab1BbenwJuDXPQUQET8ANsrM04H/ofd5U1anJEyPozxXO9P+ubkB2DQiVq/f/w/wxYhYH7iGUuX9ceBO5lWaN/wJmNBLDO1sTWmP8RNg23peL4grM/8J/Lm2b4HSS7u/H2ReBfxnPd4rKMn3V/ey/ZXAeyNiXES8nPJhQqeupPb/ri05JlLjj4ilAHrqLS1JkjSULazKZvuNSdLIdALl1uALm9btDJxM6f+5XzeCkvS8ezJzrwXYb7OIeLzp++9k5r4RMQm4mfIe8nJKv+EVgR9HRKNNwM3AqgsY7+7AaRFxFPAMsHNmzmqqFu3JmyLiQ5RJBRs9l08Ezo2IvZnXXuIFMvPpmgz+VkQsTmmX8QFK7+R2zgS+FxG7Uqp3r6Oc64nAefUazAb+OzP/ERHvr+dzWN1+lzZ3fhwInBERe1GSkftk5t+i55M+DjgrIo6oY/5nL9fld8CtwF2UKtqfUyZrbL0OD9TrdH5N+v6VUgH8SK2inhERT9bzvbRl959S+l7PjyOBX0XE0zXGmbT/mfkgcHaUCShvo/fq9k4cRWmDMoOSdD8sM/8cEZu12zgzfxIRG1E+8HiAkmzv1OcoLVRuo/xcPcC8+DenXDdJkqRhZ9Tcub0XCEQHs5hHxEmdTi4zVE2fPn0C8JeJEycybty4vjYfVqZPn87kyZO7HcaA6+95P/PMM8yYMQNg1cmTJ89cWHFJg0n9g329zJzTsn4c8NvMXKv9niPLYP8dMphf50dqbAvrd0hE/LZO+DZsRcRMYEpmzuxuJCNXRPyYUqncSRX6/Ix7BHBmTb7vDLw/M9+1MI+xqNQPMf6SmddFxKspFeKvy8zn6vX6bGbe3hLwvT8AACAASURBVPsog//3R38M5tf3/uj2efk3iCSp2zqpbD6V3m/P63gWc0nSsDOrNdEMkJnP1Io1Sd01Pz2ZpQX1MUov6z362nA+3QtcERHPUno8f2ghj78o3UWpah9NqSr/aE00bwTM7CTRLEmSNBR1kmy2RYYkqSdPR8Rymflw88qIaPQrldRdd0REj0UDjYnShrLMnNDtGEa6zLyHhZ9oJjOnAlMX9rgDITNvBjZss/4m4KaBj0iSJGlgdJJsXpizmEuShpfTgR9FxF6ZeTdARLwO+CZwdlcjkwTwo14emwu8dqACkSRJkjT8dZJsbsxi3k5/Z4SWJA1hmXluRKwM3Fonj1oMGAuckJmndjc6SZm5oBP1SZIkSdJ86yTZvKCzmEuSRoDMPC4iTgbWoPSlvDMzZzUej4jNM/OargUoiYhYnVI88CQwIzP/r8shSZIkSRqG7NmsPg3HWaI70el5z3p2DouPHb2Io5EGt8x8Euip9+uX6WOiWUmLRkSMB74HvAX4A+WutDUj4n+BPTPzmW7GJ0mSJGl46STZbE/mJged9Bv+9ZTdQzTP946e0u0QpMHODy2l7vkM8Fdgpcx8GiAiXkL5EOhY4ONdjE2SJEnSMNNJsnnYz2IuSVqk/IRO6p53ApMyc05jRWY+GREHA7/uXliSJEmShqNOks3OYi5JkjQ0zW5ONDdk5lMR8aL1kiRJktQffSabncVckiRpyOrtzgJb3EiSJElaqDqpbAacxVyStMBMaEnd87KI2In2/w+XHuhgJEmSJA1vfSabncVcktSTiHhTZl7Xx2ZXD0gwktq5Fziwl8ckSZIkaaHppLLZWcwlST05FehxElmAzDxkgGKR1CIzp3Q7BkmSJEkjx2IdbPNO4IBGohnKLObAwcBWiyowSdKQYIsMaRCLiP2bltdueezrAx+RJEmSpOGsk8pmZzGXJPVk+YjosXI5M08ayGAkvcjewFfr8rd54Z0Imwx8OJIkSZKGs06Szc5iLknqyZKUyWPb6e33h6SBMaqHZUmSJEla6DpJNjuLuSSpJ/dk5l7dDkJSj+b2sCxJkiRJC10nyWZnMZck9cRKSUmSJEmSBHSQbHYWc0lSL+zJLA1uKzT1VV+hpcf68t0ISJIkSdLw1WeyOSL2z8yv1uW1M/OOpse+npn7LcoAJUmD2h0RsUFPD2bmLQMZjKQXuYJ5fdWblwGuHPhwJEmSJA1nnbTRcBZzSVJPftTLY3OB1w5UIJJerLee6hGx+EDGsqhFxBTgyIG6Ky8i3gGcCVyTmbstwP5HAVdm5i8jYhol9mkLN8oFFxFzM7OrrZLqNdoLODkzF/qdNBFxNHBzZl68sMfu47hvAN6VmZ8YyONKkiQNhE6Szc5iLklqKzNX7XYMknpWE8p7AI9k5o+b1m8PfBlYrVuxDQPvBo7KzDMWcP/NgasXYjzD0QeArTLzD4ti8Mw8YlGM24G1gBW7dGxJkqRFqpNks7OYS5J6FRGrU27PfxKYkZn/1+WQJBVnAGsDL42IZYALgHOBtwEndDOwgRQRnwJ2B+YAlwOHZeaciDgW2BJYBrgf2CUzH4yIv1Gu1ZuB2cB7M/MvTePtA+wIbBURzwHXUq71MsATwIGZeVNETAWWpST1D8vMS+r+HwQ2BM6KiJ3qsB+KiJOAlwEHZeYlEbEicDrwKuA54PDMfEH7k4g4EngNsCawHHB6Zn4hIvYEpmTmnnW7acCRdbcTgdHADOBjwDeBNYBngEMy86q6z2nApnWfd2XmnyLiPcChwJLAOGDvzLy+9gPfo8Z5Y2Z+NCJGA18AptTjTc3ML3fy/ABfA1YBLoqI3TLz1qbttwGOBsYCfwE+nJmPRMTbgS8BTwPTgA0zc0pz5XhETACmZeaE+vxMq/8uBu6i/H+5B9g9Mx+NiAeAi4CNgQeAsymTp68C7JmZ10TEasA3KM/1k8ABmfnbOv4/gcnAyjXmC+vX8RHx6cw8tvV6SJIkDWWLdTsASdLQFRHjI+Ji4EbgE5RExh0RcV5EjOtqcJIANgPeBGwB7AlcDywOrJ2ZR3YvrIETEdsCO1CSu+tTEr/71gThGsAbM3N14F5KwhNgJeAXmbk+JZG8f/OYmXkWJTl5RF3+DvCVzFyXkry9oOk18JHMXLORaK77fwu4GdgnM2+vq/+ZmRtQEpmNittTgLMzc3I9h9MjYqk2pzkZ2Kp+/WhvvfSr1YEtMnMP4HPAnzJzTUolcXPy88rMXI/S7/ujEbEYsC+wXV1/InB4TSofTrnGk4HFI2Jl4MP1fDcA3gD8v4jYrDmQnp6fzNyX8gHAO1oSzcsDxwNvr8/Pz4ETImIJ4FvA+zJzQ0rif36sA3w9M9cGfs+8xPyKwKX1WEsAO2XmZvXxg+s251I+TNgA+Ajw/aZxX0X5f7gD8MXM/Afl+b3YRLMkSRqOOqlsdhZzSVJPPgP8FVgpM58GiIiXUG7PPxb4eBdjkwT/zsxZwH31DoSvZOYx3Q5qgG0JnJeZTwJExNnAHpn5tYg4FNgnIoJSwfvnpv0uq19nAG/pafCIGA+s1mhTkpm/iYhHgaib3NBhnBfVr3dQKpShJJDXqL2FoVTyvg64tWXf8zLz8RrPxZQPFx7u5ViZmf+sy5sDu9WVtzOvkrk1prdk5nO1Env7es2mAHNqlfj1wE3AT4AvZeZ9EbEVMCkitqjjjKckdX/ZdIy2zw+lsrmdjYFXA1eXEBgNPFrHvS8zZ9TtzgKO6+UatPpDU8/sc4HvNT12af16D/CrpuWX1+d/I+CcGg+UquVl6/LlmTk3ImYw/wlwSZKkIaeTZLOzmEuSevJOYFJmzmmsyMwnI+Jg4NfdC0tS1dwC7ZERmGiGF9/JNwoYExGTgfOAkygtM+bQND9J4wM0yjXsbd6SdncKjmLe++ynOoxzdpvjjaZUID8KEBGvAP7ey76NeGbz4rjHNi03x/QsTT8nEbEG8AeAzHxBTDWxeiOlkvta4DbmVX3vSJk8fFvgsoh4f43/sEYiPiKWAx5vib3t89PmHBtGA7/KzB3qmEtQktgrtWz3bNNy87UYS3vtriEA9QObdts14nk6Myc1VkTEKpQEOJSWHtSEcw+HliRJGj76bKORmXv19A/46ADEKEkavGY3J5obMvMpSuJGUnc1J5s7TXoON1cBu0bEkhExBtiLMjHf5pTevadRkqvbURKH8yUz/wXcHRE7A0TEJpTE54xedyxJy74KP64C9qvjrlXHfEmb7XaKiHER8XJge0rf44eBNSNiVESsCqzbwzGuBXatx1iDUtHd0zwtq9fHjqNcw52B0bW1xZ3A7XXSvcvr8a4CPhwRY2ui+leUhHTrObZ7fnpyA7BprdQH+B/gi0BSepOvX9fv1rTPw5RezFCS4u1ERDQSxnsxr5q5V7VC/I8RsXsdZGvKNe1NJ8+9JEnSkNTnmxxnMZck9aK3iWN7qwSUNDBeW9sqtC4D0KgOHUY2i4jmytnvZOa+NYl4M+W97+XAqZRevD+OiEbP5JuBVRfwuLsDp0XEUZRJ9nbOzFl9VLJeVvf5YC/bHACcERG3UV5Td8/Mf7fZ7ilKa4qXAp/PzDsj4s/A3pQk7F3Ma//Q6rPAmRHxO0oS9AO9VOH+jtLC4y7KRIA/B96cmQ9FxBnATRHxZD3m2cAs4PXAbynX/pymVhUAZOZPe3h+2srMByJib+D82iv6r/W6PBsR76P0tR5NmTiw4UTg3LrfRS8eFSiVyEfVXt63Afv0FEMb76c8l4fVc96lj0rmG4EjI+L4zPzkfBxHkiRp0Bs1d25veQKosyivTXnz+gVaZjEfKZPLTJ8+fQLwlzMvf5x/PdX7NdPI8r2jp7Rd/8wzzzBjxgyAVSdPnjxz4CKSBk5E3A0cSvvE8hcy83UDHNKg1PgdMnHiRMaNG3zzJk6fPp3Jkyd3O4y2RmpsC+t3SETs0Wb1ktQq58w8d0HH1uAQEUcCjJT35J2KiCnAkZk5pYNtJ1Cq3Ccs2qjm32D//dEfg/n1vT+6fV7+DSJJ6rZObt/aDFiTMhngD4BDKBNirJ2Zdy/C2CRJg9+9wIG9PCapu84DzgQuyswLASLip5S2AvNTuSlJkiRJfeok2ews5pKktjqpGJPUVUdR7k67rmndR4CvA0cCn+lCTFqIrGhur7brmNLhtjOBCYsuGkmSpJGjzwkCcRZzSVIPImL/puW1Wx77+sBHJKnF9sBumfn3xorMvB/4ILBT16KSJEmSNCzNb7J5pM5iLklqb++m5W+3PLbJQAYiqa1Zmfmi92+Z+S/KRHaSJEmStNB00kZjpM1iLknq3KgeliUNDnMiYqnM/HfzyohYChjbpZgkSZIkDVOdJJsPalr+Uf36/CzmkqQRbW4Py5IGh/OAsyJi78x8AiAi/gM4i3nv6yRJkiRpoegk2ews5pIkSUPTycBpwAMRcQelhdqawHeBo7sZmCRJkqThp5Nks7OYS5J6skJEHNJmGWD5bgQkaZ7MfA74SEQcC0wGngNuyMy/dTcySZIkScNRJ8nm7YGNmieXycz7I+KDwK8x2SxJI9kVwDptlgGuHPhwJLWTmfcA93Q7DkmSJEnDWyfJ5h5nMY8IZzGXpBEsM/fq6bGIWHwgY5EkSZIkSd21WAfbzKkzlr+As5hLkiJi8Yj4cETs3LJ+e+DOLoUlSZIkSZK6oNMJAp3FXJLUzhnA2sBLI2IZ4ALgXOBtwAndDEySJEmSJA2sTpLNzmIuSerJZpTfCcsDPwAOofSFXTsz7+5mYJIkSZIkaWD1mWx2FnNJUi/+nZmzgPsiYnXgK5l5TLeDkiRJkiRJA6+TymbAWcwlSW3NbVp+xESzJEmSJEkjVycTBEqS1JPmZPNTXYtCkiRJkiR1XceVzZIktfHaiLi4zTIAmblDF2KSJEmSJEldMCiSzRHxUuDzwObAbOAx4FDgpcCRmTllER33SIDMPHJRjC9JI8BBTcs/ql+XZJhWOUfEZpSJcxcH/gLskZmPdTcqSZIkSZIGh6630YiIxYCfAY8CkzJzEnA0cCmwbDdjkyT16TxgC+BfmXluZp4LbAe8FfhuVyNbNM4BPpCZ6wB3Av/d5XgkSZIkSRo0BkNl81uBVwOfzcznADLz6ojYCxjf2CgiVgfOAJYBngAOzMybImI34DBgDqXKbPfMfDoiPgm8FxgN/Bz4RGbOjYj/Bj4CPEypoL5xgM5Tkoajoyh3oVzXtO4jwNeBI4HPtNspIkYBxwM7Ue5oOT0zT4mIaZQ7WqZFxARgWmZOiIiplA8gV6O85p8K3ABMAjYDtgEOpnyIOh34r/q74G/ABcCb63Hem5l/iYitgC/V7e8BdgP+Fzg6M6+o8f0B2Dwz728Kfc3MfDYixgIrA7ct0FWTJEmSJGkY6nplM7A+cGsj0dyQmT8D/t606jvAVzJzXeBjwAURMQ44BnhbZk6mJJvXiIhtgMnARnX8lYH3R8SGwN513VbAKov0zCRp+Nse2C0zn3+9rsnZD1ISyT15N/AmYB3gDcBeEbFSH8d6JDPXzMxL6veXZmYAywMfBt5Y7475O/Dxus1KwC8yc33gWmD/+rvju5QWGOsAtwN7AGcDH6j7bQb8qSXRTE00rwP8lfJh6ff7iFmSJEmSpBFjMCSbnwOe7m2DiBgPrJaZPwbIzN9Q2m4EcAlwXUScCPwoM2+lJJI3plS33QJsCKwNTAF+lpmPZ+YTwA8XyRlJ0sgxKzNf1J85M/8FPNPLfpsD52fmM/U1eVJmPtDHsW7o4fu3Aq8HfhMRtwL/D1ijabvL6tcZlLtj1gHuq78vyMzDM/NU4Hxg64j4D0ryeWq7IDLz9sxcEfgc8IM+YpY0HyJiSr3DYaCO946IuC8ivjdQx+whjrlt1u0bEft2I5526usrEfGGiDihj20XyfMYEdMiYsoC7ntrL49NiIiZCxjWgsTS5zWUJEkaqgZDsvlmYIN6y/LzIuI4oLGuXZyjgDGZeRDwLkpLjO9ExO6U1hkn1+TFJEri+VhgbtOYUG6pliQtuDkRsVTryrpubC/7PUt5TW5sP6EmeZtfp1v3b01qN74fTUlcN17z3wDs39goMxsfaDbGbj320hGxSv0Q8meUqustgZ+0nNMSEbFj06rvAOv2co6SBr93A0dl5m7dDqRVZp6Wmad1O46G+voKsBawYjdjWRBN8Q8GQ/IaSpIkdWIw9Gz+JeWW589GxOcyc05EvB3YC/gdlAq5iLg7InbOzB9HxCaUW6NnRMQfKT01P197aK4PXAUcHRFnUKqmL6JUqP0C+GFEHEWpuNuJkliQJC2Y84CzImLvmqylJo3PAn7Uy37XAgdFxGmUpPJlwA6UfvprA1cDO/a8+wtMAz4eEccADwHfAP5M6RndTgIrRMRamXknpQf0XEp/6bMp1cqXNCWpG54FvhYR/5eZ0ynzAvyqwxgl9VNEfArYnTJPx+XAYfV947GUD4iWAe4HdsnMB3vq2d403j6U15mtIuI5yutSu/lBptLUM77RyiciDgVWyMxPRMTb6rGWyczZEfF7yh11qwKnAEtQXt8+mpl/qlW/j1Je73ZpiumNwLnAtvVcycwje+k/P4XSw3428Gtgrcyc0nLdJgGnAy+px3w/8ADltXIiJel5G7BrXb4YuKvGdg9lPpRHa/X1yykTeY+PiE/XY3+T0prulcCVwD69PIet571SHW8spR3ehzPzkVplfD6wdd1178z8bctYL5qfBXgNcCHlTpb1gQeB9zTiz8xREbElcCLldf+xet4AS0bE9+s1eQzYscbyAOVviY3rdTsbOLCe856ZeU1ErFav57LAk8ABmfnb+rPzT0p7v5XruV7YfA0z89ierpckSdJQ1PXK5sycS0kwvI6SPL6N8mbxHZQ3iA27AwdGxO3AV4GdM3MWcARwRUTcDGwCnFD/CPgR5RbrGcCtwLn1lumTgZuAayhvoCVJC+5kyh/SD0TEbyLiRsof449R/phuKzMvpEwqeAvlNfmUzPwDJQGwX0TcAizZSQCZ+TvKRIVXAXdQEg/H97L905TfKd+qv3PWamyfmddREhDntNlvDiU5cka9Hfvd9JJUkbTwRMS2lPeLG1KSiKsB+9Yk3xqUnu2rA/dSk7S06dnePGZmnkVJrB5Rl3uaHwRe3DMeyqSiW9blLSh3W2wQEatSXhcfo/R13z8z1wNOo3xA13BbZkajpU9ErEdJ3G6XmX9quQTt+s+PBb4NvL+uf7aHy/dd4HO1R/33gYOAN1LaIG1ar+XLKO+9obQa+npmrg38nqYP7jLzH5T33hfXJOk7KXOvbEppZ7Q5sEEPcbzgvIH7KK+9b6/x/xxobi3xRF1/BCUB/7ye5mepD68HnJSZE4F/NK1v+Aywb2ZuCFzRFO/yTfs9CLyvrl+RMkfA+pQPDXbKzM3qdTm4bnMu5YOIDSiT5Db3838VZR6AHYAvtrmGkiRJw8pgqGwmMx9m3qRMrabUbe5qLLfsex4vfOPeWH8MZfLA1vVfA7624NFKkhrq5K4fqZWFkyl9+G/IzL91sO+ngU+3rLuJkvxtOLqu37Nluwkt359FqaZuPcaopuWp1D7MmXkNJWn1vNrOaSLwcGbe2EPMv6Kcp6SBtSVwXmY+CRARZ1Mm+fxarTDeJyIC2JRyZ0NDc8/2t/Q0eLv5QSKiMT8IvLhnPJl5V23D83JKMvFrlGTrE5RE9OrAY/V1jcz8YUScERFL9zDmz4EfZmb2EGbruawD/D0zb6vrz6ZUUTef13LAKzLzpzWGbzQ99khE/BclWf96YHx96A+ZOa0unwv02M86M8+r/YcPBtakVPaO72n7qnHeGwOvBq4uTx2jKVXPDWfUY1wSEefWc2lonp8FyoeT91LuNvl7UxV0o1d/s4uBCyPiIuAnmXlFREwA7m967b8DaD7epfXrPcy7o+Ue4OX1Z2cj4Jx6HlCqlpety5dn5tyIaBeLJEnSsDMoks2SpKEtM+9h6N8tcjDw38B7uh2IpBdpvRtvFDAmIiZTig5OorSZmEPT/BxterZ3Ov7zx6jLL5oItbqM0pZtLmXS6s/V5SN6GXN0D2PuBnw7Is6qd2y8QJtzmdPDMZq19qhfgtLuYiLlw7xTKHdyLMe869M8p8li9DLHSUQcQLnL4wxKC42J9H6d4YX99n+VmTs0xdacqG6NY07T9435WU6q+76sbr8cL5x4/EXPe2Z+OSIuAbYDToyICyjV37N72q/eTdkurkYsTzf3hI6IVZiXOH+6jjG3KRktSZI0bHW9jYYkSYNBZn45M19ZW2lIGlyuAnaNiCUjYgxlbo+rKZXE0+pEen+gJBBH9zxMe5n5L+DuiNgZoHl+kD52/V/gU5Rq11sp1b2r18raBJaNiI3qmO8F7snMR9sNlJlXAYcDZ0ZEJ+/Rf0+prF2nfr8bTYnlOuY/gb/WntJQ7iQ8mlIZfH5mnkNpNfFW5l23qH2eoVznS3mh2cxLwm8NnJ6Z36W0mJhE59f/BmDTiFi9fv8/wBebHn9fDWYn4PeZ+VjTY1cBH4iI8fXn4SJK0rtPEXEDsFRmngx8mb7bfvSqXuM/1knKiYitKa1OetN8DSVJkoYVk82SJEkaTDaLiMeb/p1W20D8FLiZ0uLgXsrkdD8A1qtzekyrj6+6gMftaX6Q3kwDXkFJeM+lJJynAWTmM5Q+71+tLRT2p2kywHYy81vA48ABfQVbY2v0n59O6Q3crgJ7d+CI2mt+F8odHGdSkve3Az+k9NBvXLdHgaMi4g5gBV7clu5GYJOIOJ7St/+zdZyTgevp8Ppn5gPA3sD5df8NgEObNnlTjfnjwB4t+7adn6WT41I+HJhar9mHgE92uF9v3k9p5XIb8HnKJJVze9m++RpKkiQNK6Pmzu3tfZAapk+fPgH4y5mXP86/nvKaaZ7vHT2l7fpnnnmGGTNmAKw6efLkmQMXkaTBpvE7ZOLEiYwbN66vzQfc9OnTmTx5cLaiHqmx+TtEfanVz8cDR2XmExFxCLByZh7ax669jTmBkjifsHCiXOA4ZgJTMnNmN+MYDAb774/+GMyv7/3R7fPy94ckqdusbJYkSZKGmDpB66PATbUC+C3Acd2NSpIkSSOdvcIkSZKkISgzj6dUNy+s8WYCExbWeAuq25XVkiRJWnBWNkuSJEmSJEmS+s1ksyRJkiRJkiSp30w2S5IkSZIkSZL6zWSzJEmSJEmSJKnfTDZLkiRJkiRJkvrNZLMkSZIkSZIkqd9MNkuSJEmSJEmS+s1ksyRJkiRJkiSp30w2S5IkSZIkSZL6zWSzJEmSJEmSJKnfTDZLkiRJ0iA369k53Q5BkiSpTyabJUmSJGkQO+ik37D42NHdDkOSJKlPJpslSZIkSZIkSf1mslmSJEmSJEmS1G8mmyVJkiRJkiRJ/WayWZIkSZIkSZLUbyabJUmSJEmSJEn9ZrJZkiRJkiRJktRvJpslSZIkDVkRMSEi5kbE6S3rJ9X1e0bErQvpODP7O44kSdJwZrJZkiRJ0lD3CLBNRIxuWrcL8BBAZk7qSlSSJEkjzJhuByBJkiRJ/fQ4cCvwFuDquu5twJUAETE3M0dFxDLAN4E1gGeAQzLzqoh4CLgZeAWwEfDfwO7AHOBy4LDmg0XEinWcVwOzgU9l5mURsXJd/zLglcDUzDwiIvYEtgGWAV4LXJ6Z+y2KCyFJktRNVjZLkiRJGg7OB94NEBEbAbcBs1q2+Rzwp8xcE/gAcGxdvxxwQq2A3grYAdgQWB9YDdi3ZZxTgasyc916zLNrAnpX4LzM3ARYBzg4Ipar+7wReBewLrB9RKyzUM5akiRpEDHZLEmSJGk4uBjYNiIWo7TQ+EGbbTYHvg2Qmbdn5qZNj91Qv25JSRg/mZmzgbPrumZbUCqYycy7674bZ+YXgXsj4uPAKcDiwH/Ufa7PzH9n5pPA3ZQqZ0mSpGHFZLMkSZKkIS8zHwd+B7yZkgy+ss1mzwJzG99ExBo1OU1mPlVXt/6NNIoXtx9su01EfAk4ELgHOAZ4uD4G8HTT9nOb1kuSJA0bJpslSZIkDRfnA8cDN9eq5FbXUlpdEBFrAJfRlHyurgJ2jYglI2IMsBfz+kA3b/OhOs5rgTcBvwa2Br6QmT8EAlgZGI0kSdIIYbJZkiRJ0nBxCTCJ9i00AD4LvD4ifgd8F/hAZr4g2ZyZPwV+Spkw8A7gXkqP5mYHAltExO3ARcA+mfk34PPAtyNiBrB/HWPVhXFikiRJQ0Hr7WCSJEmSNGRk5kxgQl1+HHhJ02N71sWp9ft/AO9pM8aolu+PobTBaNZ8nPuB7dqMcx5wXg+hTm3abkoP20iSJA1pVjZLkiRJkiRJkvrNZLMkSZIkSZIkqd9MNkuSJEmSJEmS+s1ksyRJkiRJkiSp30w2S5IkSZIkSZL6zWSzJEmSJEmSJKnfxnQ7gKHmlEM2Ydy4cd0OQ4PIrGfnsPjY0d0OQ5IkScPUKYds4ntOSZI0JFjZrD5Nnz692yF0Rafn7Zt+SZIkLWq+55QkSUOByWZJkiRJkiRJUr+ZbJYkSZIkSZIk9ZvJZkmSJEmSJElSv5lsliRJkiRJkiT1m8lmSZIkSZIkSVK/mWyWJEmSJEmSJPWbyWZJkiRJkiRJUr+ZbJYkSZIkSZIk9ZvJZkmSJEmSJElSv5lsliRJkiRJkiT1m8lmSZIkSZIkSVK/mWyWJEmSJEmSJPWbyWZJkiRJkiRJUr+ZbJYkSZJGgIiYEBFzI+L0lvWT6vo9+9h/ZkRMaLP+6IjYoY4/c2HGvDBFxNIRcWE/9p8SEdMWYkiSJEnDjslmSZIkaeR4BNgmIkY3rdsFeGhBB8zMIzLz4n5Htui9HFi/20FIkiQNZ2O6HYAkSZKkAfM4cCvwFuDquu5twJWNDSJiO+AYSmHK3cBHM/PB+vCREbEe8HRdf1tETAWm1X+NMVYETgdeBTwHHJ6Zy4mTkwAAIABJREFUzx+jbvMS4ExgvbrNFzPzW7XCejtgBeAVwCXAoZk5NyI+CbwXGA38HPgE8BrgMuBh4CngXcA3gVWAV9Zz2wf4CvDKiLgwM3eKiL2AQ4G5wHRg/8x8PCIeAm6ux94oM59tvYgRsTpwBrAM8ARwYGbeFBETgVOB8TX+z2fmaRFxJLAy8Poa71mZeWyb50eSJGlIs7JZkiRJGlnOB94NEBEbAbcBs+r3K1CSxDtm5rrAdcBXm/b9Y2auD3wOOLeXY5wCnJ2Zk4EdgNMjYqmWbY4EHsnMicAWlET2uvWxNwPvAdYGNgF2iohtgMnARpQK5ZWB99ftA9g9M7cG3gncmpmbUpK7mwMbAAcC99dE8zrAp4HNM3MdSsL4s3Ws5YATMnNSu0Rz9R3gK/UafQy4ICLGUZLax2TmRsBbgS807bMuJbG/MfDJiHhZL9dPkiRpSDLZLEmSJI0sFwPbRsRilBYaP2h67A3AjZk5s35/BrBl0+NnAWTmz4DX9JIw3Qo4OiJuBS4FxgKva9lmC0oFMpn5MPATYEp97CeZ+WBmzgK+X7fdipKonQ7cAmxISUYD/L0Rc2aeB1wREQdTqoyXpVQaN9scuCQzH+nhPG/o4byIiPHAapn543q83wCPUhLehwJLRMThlOrw5uNenZmzMvPvdfulezqGJEnSUGWyWZIkSRpBMvNx4HeU6uEtaGqhwYv/PhjFC1vvzW55rKfK39HAFrU6eBIlSXx7yza9HWt2y3az65gnt4zZaEXxVGPjiDiAUlH8ECXZfGcdu9Njk5lP0bN2f0M19j8f2Kke89Mt2zzdtDy3TUySJElDnslmSZIkaeQ5HzgeuDkzmxO7NwCbRMSE+v1HmNfbGWrbiojYCfh9Zj7Rw/hXAfvVbdcCZgAvabPNh+o2ywE7Mq/v8zYRsXRELAHsSqmOvgr4QESMj4gxwEXUdiAttgZOz8zvAksAkyiJ6tnMSyhPA3aIiGXq9x9uOc8eZea/gLsjYuca+ybASvUctwaOyMyfANvWx0f3NJYkSdJwY7J5Pj07e063Q5AkSZL66xJKEra5hQZ1IsCPABdGxB2Uthb7Nm2yem2NcQiwRy/jH0BJWt9Wj7F7Zv67ZZujgWUi4nbgWuDYzLylPvYQ8DNKBfZPM/PnmXkJ8CNKQnwGZaLDdn2jTwY+W8c9GbgeWBV4ELg3Iq7OzNuAzwPXRMRdwMuAz/RyPq12Bw6sx/gqsHNt+XEk8KuIuBPYDJhZjy1JkjQijOl7EzUbO8bCBEmSJA09tafxhLr8OE2Vxpm5Z9PyJZRkdOv+E3oYd8+mbxvj3w9s10c8/6Ikbdu5q2Xcxj7HUHohN5vZOG7d5ipK/+R23ti03VnUHtQtx2jb3iIzp1F7SmfmXczrL928zUnASW12P7JluwlttpEkSRryrGyWJEmSJEmSJPWblc2SJEmSBo3MnApM7XIYkiRJWgBWNkuSJEmSJEmS+s1ksyRJkiRJkiSp30w2S5IkSZIkSZL6zWSzJEmSJEmSJKnfTDZLkiRJkiRJkvrNZLMkSZIkSZIkqd9MNkuSJEmSJEmS+s1ksyRJkiRJkiSp30w2S5IkSZIkSZL6zWSzJEmSJEmSJKnfTDZLkiRJ0iA369k53Q5BkiSpTyabJUmSJGkQO+ik37D42NHdDkOSJKlPJpslSZIkSZIkSf1mslmSJEmSJEmS1G8mmyVJkiRJkiRJ/WayWZIkSZIkSZLUbyabJUmSJEmSJEn9ZrJZkiRJkiRJktRvJpslSZIkDWkRMSEi5kbE6S3rJ9X1e87HWFcv9AAlSZJGCJPNkiRJkoaDR4BtImJ007pdgIfmc5wpCy0iSZKkEWZMtwOQJEmSpIXgceBW4C1Aozr5bcCVABGxHXAMpeDmbuCjmflgRMwEbgAmAb+o296QmRtHxHuBo4EngN8CYzJzz4h4D3AosCQwDtg7M6+PiInAVMrfWb8Ets3M1SJiReB04FXAc8DhmXnlIrwWkiRJXWFlsyRJkqTh4nzg3QARsRFwGzALWIGS7N0xM9cFrgO+2rTfpZkZmbkfQE00Lw+cDGwJbAQsU8ddDNgX2C4z1wNOBA6v45wLHJGZkygJ7UZxzynA2Zk5GdgBOD0illoE5y9JktRVJpslSZIkDRcXA9vWhPAuwA/q+ieBGzNzZv3+DEoSueGGNmNtBvw6M+/LzOcoiWTq8k7A2yPiaGBPYHxELANMyMyf1f3PbhprK+DoiLgVuBQYC7yuPycqSZI0GJlsliRJkjQsZObjwO+ANwNbUFto8OK/e0bxwpaCT7UZbk6b/YiI8cCNwKrAtcBX6nhz6td2RgNbZOakWvW8MXB7B6ckSZI0pJhsliRJkjScnA8cD9ycmbPruiWBTSJiQv3+I8zr69xqTkSMAa4HNoqIV0TEKOB9wFxg9fr1uDrGzsDozPwn8OeI2LaOs1vdDuAqYD+AiFgLmAG8ZCGcqyRJ0qBislmSJEnScHIJZbK/HzSte5CSYL4wIu4AplD6LrfzE0p19L+BA4ErgJsorS+eqo/dCtwF3AE8BLym7vtB4IiIuIVSvdyomD6Akuy+rca1e2b+u78nKkmSNNiM6XsTSZIkSRq8ai/mCXX5cZqqhjNzz6ZNL2mz74SW798FEBHLAusB62bmcxHxFeCPmTkH2LVlmIPq1/cAO2fm3yJiZ2CpOub9wHYLdnaSJElDh8lmSZIkSXqxR4GXATMiYjZwC3BmH/vcC1wREc8CjwEfWrQhSpIkDS4mmyVJkiSpRWbOZV7Fcqf7TAWmLop4JEmShgJ7NkuSJEmSJEmS+s1ksyRJkiRJkiSp30w2S5IkSZIkSZL6zWSzJEmSJEmSJKnfTDZLkiRJ0iB2yiGbMOvZOd0OQ5IkqU8mmyVJkiRpkFt87OhuhyBJktQnk82SJEmSJEmSpH4z2SxJkiRJkiRJ6rcx3Q5gQUTEu4HDKfEvBnwrM7/Qy/bTgCMzc1rL+qnAtMycuqhilSQNPxGxPvCbzBzX7VgkSZIkSRoshlxlc0Ss/P/bu/M4uaoy4eO/pkNYZJMhDMuoAZFH9rBEdo3AMC55eYUgKERBBUFAxBEXBDHDgOvAGAQl7FEcYQZBRAggQgZlx7wBAvJkHASUMLLoK4gGCPT8cU9LUVR1V3d1p7o7v+/nk09Xnbr3nOdU+nNO3adPnQucCuyZmVsBOwLvi4i9OhuZJGlZEBErA98Exnc6FkmSJEmSRpLRuLJ5LWB5YGXgqcz8U0QcBCyOiB2AmcCKwJPAYZn5q94TI6KLKlE9FVgEdANzl274kqQyHn8F2BtYAszKzJm130SJiIlU3z6ZWL6J8jfARsBnqJK9twOTgF2BdwDHUP0R9RfAkZm5OCIeAy4Fdint7JeZv46IPajmg+WAh4EDgKuAkzLzJyW+hcDbMnNRXfinAt8Adh76d0aSJEmSpNFr1K1szsy7gSuAByPijoj4KlXS+BHgYuCosuL5LOD7dadPA7YGNgPeS5W0kCQtfftSJWu3AN4CfCgi1unnnKcyc5PMvLI8n5OZAUwADgV2ysxJwOPAseWYdYCfZubWwE3AURGxAvA94KDM3AK4FzgIOB/4QDlvV+BX9Ynm8i2alTPz0sF2XJIkSZKksWo0rmwmMz8WEScDewL/ANwGfBn4Q2beWY75j4g4OyJWrzl1CnBZZr4APBERVy/l0CVJlbcB/56ZzwHPUa1QJiL6Ouf2Js/fDrwJuK2cPx6YV3PcNeXnAuCtVAnuRzNzPkBmHlfafg3wpfLzIODC2sZKMvwEYI8W+yhJkiRJ0jJl1CWbI+LdwCqZeQlwAXBBRBxK9RXoel1Uq5579ZSyXkuGLVBJUl9eoBqTAShbZjzBK8fp5evO+UuT591UieujS12rUDO/Zebi8rC37vq2VwdWzczflj9C7gvsDhxZ195Uqq08bupNikfEfGDXzHymlU5LkiRJkjSWjbptNIA/A18uiYnefT8nUa1u/puImFzK9wMezszf15x7PbBfRKwQEa+l2uNTkrT03QRMi4jlyw33rgHWp9pvf7NyzHtarGsusHdErF3mhG9T7d/cTAJrR8Sm5flngMPL4/OBU6i26Fj8ipMyz83MN2bmpLJdB+WxiWZJkiRJkhiFyebMvBH4J+DHEZHAA8CLwAxgf+CMiFgAHFWe1557BVVSYgHwI+D+pRa4JOmvMvNy4Gaq7S7uBGZm5kLga8ARETEPWKnFuu6mmhduAO6jWun8lT6OXwxMB74TEfcAm/Yen5k3U616vmBwPZMkSZIkadk16rbRAMjM2cDsBi/dCmzf4PgpNY9PoNpzU5LUQZl5PHB8XdmdVMnfXieV8oPrjptY9/xc4NwGbXTVPL6Qsg9zZv4nsF3tsWVV9ObAk5l5Rwvxd/V3jCRJkiRJy5JRt7JZkqRhcgxwLdU3YyRJkiRJ0gCNypXNkiQNtcz8V+BfOx2HJEmSJEmjlclmSZIkSYNSbtq9kJfvhbIcsBowOzO/OMg6e9rdqigiLgBmZObDfRxzKPCnzPx+i3XOAMjMGe3EJkmSNJa5jYYkSZKkdizKzEnl35bATsCxEbFJB2N6O9BfwnpnYIWlEIskSdIyw5XNkiRJkobSulSJ3mcAIuJzwH5AN9Xe+J/NzJ6IOAXYHVgTWATsn5m/a1RhRKwCnEl1I9du4KuZ+f2I2BI4m+q6ZjHwIWAasB5wdUTsCuwGfApYiSq5/GFgZWAvYLeIeAyYD8wCXge8BByXmdc362BETAVOplq88yBwWGb+LiLeW99WZt4SEXOBO4BdgQnAxzNzTsvvqCRJ0ijhymZJkiRJ7VgvIuZHxAMR8SRVEnbvzPxtRLwD2BaYDGwNrA8cGBEbAW8GdsrMjYFHgOl9tHEC8IvM3BZ4K3B8RGwIfBI4NTO3A84BdsjMr1Alr98F/AE4HJiamVsBX+PlRPKPgBMz81pgJnB+qX8vYFZErNookIhYmyox/Z6ykvtm4IyIWK5RWzWnjs/MHUvMJ7f0zkqSJI0yJpslSZIktWNRZk4CNgW+S3WN8ZPy2h7A9sAvgHnAdsBmmfkrqhXAh0TEqcCOwCp9tLEHcHhEzAduAl4DbAZcRZXoPQ/4I/BvtSdl5kvA3sA/RMRJwMFN2tkDOKnUPwdYHnhjk1jeAtyRmQ+V52cDu7fQ1jXl5wKq1dySJEljjslmSZIkSW0rydZPU61ePrYUdwPf6N3TmSrxfEpEbAtcR3U9cilwOX3vsdwNTK+pZwfgmsy8FNiGaouKTwJn1Z5Utt+4A9iAKkl9epN2uoHd6uK8t0ks9ddQXcC4FtpaXH729NNXSZKkUctksyRJkqQhkZlLqBLNX4iIdYAbgA9ExCoRMQ74IbAv8DZgbmaeBSwEplIlfJu5AfgYQESsC9wDvD4iLgEmZ+Ys4AtUiWeAJVT7OG9Mldz9EnAjsE9NO73H9NZ/RKl/U6rVxys3ieV2YIeImFief7TU3VdbkiRJywSTzZIkSZKGTGZeA9wK/HNmXgn8gCpBu4DqRnyzgUuArSLiXmAucBfViuBm/glYKSIWUCWGP5OZ/02V2D0+IuZR7ZH8sXL8j4GrqbbWmA88ANwHPAG8oRxzPfD5iNgX+DhVAvmeEtv0zHymSf9+R5Vgvjwi7gOmUO3VfHcfbUmSJC0TxvV/iCRJkiS9Wtm3eGKD8j1rHp/Mq2+I9yjVVhWN6nzVFhOZ+TQNbiCYmXdT3XywvvwY4Jjy9P11L3+iHHMxcHFN+dRG8dTUOaPm8ZXAlQ0Oa9bWlJpzH6LBeyZJkjQWuLJZkiRJkiRJktQ2k82SJEmSJEmSpLaZbJYkSZIkSZIktc1ksyRJkiRJkiSpbSabJUmSJEmSJEltM9ksSZIkSZIkSWqbyWZJkiRJkiRJUttMNkuSJEmSJEmS2mayWZIkSZIkSZLUNpPNkiRJkiRJkqS2mWyWJEmSJEmSJLXNZLMkSZIkSZIkqW0mmyVJkiRJkiRJbTPZLEmSJEmSJElqm8lmSZIkSZIkSVLbTDZLkiRJkiRJktpmslmSJEmSJEmS1DaTzZIkSZIkSZKktplsliRJkiRJkiS1zWSzJEmSJEmSJKltJpslSZKkMSYiJkbEQw3Ke/o5b0pEzB2msAYkIjaIiPM60G53RFwbERkRU2rKR8x7I0mSNFKN63QAkiRJktTAG4A3dqDd9YEtMnO9DrQtSZI0qplsliRJkpYxEXEw8A5gTWBD4LrMPKLumE8AewPvAq4G7gB2BSYAH8/MORHxt8B5wOuBJcDngV8A8zNz/VLPo8A/ZuYlEXFcOe41VEndN1Ellc/NzFPqwjwd2DAizgRWA27KzHNKnXOBzwJfBeYDbwVWBI7JzOtKXLOA1wEvAcdl5vV1/VsZOAfYqhzzL5n5HeDHwFoRcVdmbtfk/dsYOLu8f88CR2fmnRGxOfBNYBVgbeDLmXlWRMxoob+SJEmjnttoSJIkScumnYBpwJbA/4mILXpfKMnoacDUzPxzKR6fmTsCnwROLmXfBG7IzC2BfYHzqa4xfhMRm0fEm6kWuLytHP8OqmQupd09ge2Bz0XEGnXxHQ3clZlHlno/UGJ7AzAhM28vx62WmdsABwCzI2I8MBM4PzO3BfYCZkXEqnX1zwCeyszNgd2AGRGxZTl+UbNEc3ERcHrp9yeBSyNiBeAQ4OTMnAy8Hfh6zTn99VeSJGnUM9ksSZIkjT0v1RdERBdQu2fzLZn5TEkmP0i1Shdgc6oVvzMz8081x19Tfi6oOXY3qpXNZOaDwO1UydSrgd3L6zOBXSNideBvM/OX5dwbM/P5zHwc+D2weh/9mQusFxETgQ8C36l57ZzS/nzgMaqk7h7ASRExH5gDLM+rt+Sojf1J4ApgSh8xABARqwAbZeZl5dzbSvwBfApYsazgPplqhXOvgfRXkiRpVDLZLEmSJI09f+DVycy1S3mvxTWPe4Cu8vgZYB/g6xHxmgbH1x5bfz3RRbWS+SqqhO/bgcuAF6lWHl/bQvuvkpk9wGzg/cD+wHdrXl5S83i58rwb2C0zJ2XmJKoE+L111TaLvT+NrqF6z/13qq1H7geOrzum5f5KkiSNViabJUmSpDEmM58B/isiptUUfxS4vskptR7OzCupVhOf1M+xNwAfAYiIDYGdgVuBecDGwMaZ+QBwI3ACL2+h0YolvDL5eyFwOPBIZi6qKX9faX874LVUSeUbgCNK+aZUq7FX7iP2tYD3UPW5T5n5NPBgROxTzt0BWKe08ffAiZl5BfDO8np3i/2VJEka9Uw2D9ALS17sdAiSJElSK6YDR0TE3RHxS6rtMY4cwPmfBg6MiG36OOZoYLeIuBf4IXBIZj5WViL/HOjdMuMGqpv8/ecA2v8lsEZEfBcgM38DPEKVdK61YUTMo7ph3/6Z+SLwcWCHiLgHuASYXhLwtU4C1iyx3wSckpnzWoxtOnB0OfcMYJ/MfJ5qH+ifR8T9VDdTfAjYoPUuS5IkjW6tfE1MNZYf58IESZIkjXyZuZBq3+RGr11ITdI2M6fUvDyllD1FtWL3r2Wl/CFgYnm8CJjapI2P1Dy+Eli15vmMumMnNjj/KaoEee9+0+uWeK6oO3RmZs6tO7dpXDXHPE2VNK4vf4jSv7ryubz83jxAg/2dM/M04LQGzc2oO+5V9UuSJI0FrmyWJEmSNNJNA+4GjsvM5zodjCRJkhpzZbMkSZKkES0zLwUubVA+ZelHI0mSpGZc2SxJkiRJkiRJapvJZkmSJEmSJElS20w2S5IkSZIkSZLaZrJZkiRJkiRJktQ2k82SJEmSJEmSpLaZbJYkSZIkSZIktW1cpwMYRboBnn/++U7H0RHPPfdcp0PoiHb6XfO70j0kwUgazUb8HDKSx/llMTbnEEnFiJ8/2jGSx/d2dLJfzh+SpE4z2dy6dQEWLlzY6Tg6YsGCBZ0OoSOGqN/rAv89FBVJGrVG/Bwyksf5ZTw25xBp2Tbi5492jOTxvR0jpF/OH5KkjjDZ3Lo7gV2Bx4AXOxyLRoduqg95d3Y6EEkd5xyigXIOkQTOHxo45w9JUkd19fT0dDoGSZIkSZIkSdIo5w0CJUmSJEmSJEltM9ksSZIkSZIkSWqbyWZJkiRJkiRJUttMNkuSJEmSJEmS2mayWZIkSZIkSZLUNpPNkiRJkiRJkqS2mWyWJEmSJEmSJLXNZLMkSZIkSZIkqW3jOh3AaBARBwAnAMsD38jMMzscUr8i4ovAfuXpVZn5mYjYAzgNWAm4JDNPKMdOAs4FVgNuAg7PzCV19a0BfA/YEHgC2C8z/ycixgPnAdsBfwEOyMwHIqIL+DowFXgJODQzbx7WTr8y3n8B1srMg5v1LyJeD1wErA0kcGBm/qmungH3LyI+BRxK9cecz2XmZcPfY0kj1XDNIRFxI9X49UIpOgx4Y6O2mo3/dfU1HBMHMP4fCswGpmbmQ3V1D2gcbtZmXZ0tj8PA9cAtwNnA4bXvT5kvPwz8oVR9Til37pDUcSPtOmSsX2N4DSFJUvtc2dyPiFgfOAXYBZgEfDQiNu1sVH0rH/j2BLaminnbiHg/cD7wf4FNgMkR8c5yykXAUZm5MdBF9SGn3snAzzJzE+AcYGYpPxp4tpQfA1xYyqeVdjYF3gNcGBFL5Y8bEbE7cFBNUbP+fQv4Vma+GbgL+EKD6gbUv4iYDEynet93Ab4eEWsOYfckjSLDNYeUi9WNga0yc1JmTgJ+26itiFiJ5uN/rWZjYivj/yyqC/GNm4Q80HG4WZu1Wh2HTwduLbF9uv79oUoEvK/3faxJ5Dh3SOqokXYdMtavMbyGkCRpaJhs7t8ewA2Z+fvMfBa4FNi3wzH15zHgU5n5fGa+APyS6iL7vzLz12VFwUXAeyPiDcBKmXlbOfdC4L0N6nw31aoDgO8D74yI5WvLM/MmYEL5a/+7gYsz86XMXAg8Auw0DH19hfKh7BTgS+V5w/6V2N9K9f/51/IGVQ60f+8CLsvMxZn5ODCXauWCpGXTcM0hUX5eFxF3R8RRfbT1FhqM/6+orO8xsd/xH5gM/H/gd68KdHDjcLM2a7U6Dj8LXA78EbilwfuzHfD5iLgnIs6IiBWdOySNECPtOmTMXmN4DSFJ0tAx2dy/9ag+WPV6DPi7DsXSksy8r/eDUUS8ieqrbi/RuB+t9u+vx5UPkk8DE/o4v1Pv2yzgeF7+OnSzONYCnq75Kl+//a47bqT1W9LINFxjwmuBnwJ7A7tTbQ3x+iZttRJDX2Niv+N/Zh4C/JrG23MNZhxu1mYr9daX/wfw5xLb43XHTwT+H9WK522ANahWqDl3SBoJRtTYMMavMbyGkCRpiJhs7t9yQE/N8y6qD1UjXkRsBvyE6iL6QRr3o9X+dTV43tf5S/19i4hDgN9k5k9riluNjybxDbR/o/b3RdKwGJYxITNvzcwPZuYfM/NJqn0hT2rSVisx9DUmDmT8b2Qw43CzNgdTb+259eXPZ+a7MvOBkjg4lWp1mXOHpJFgRI4NY+0aw2sISZKGlsnm/v0WWLfm+TrAog7F0rKI2Jlq1dvnMnM2zfvRav8eLa9R9kVbFXhqCOodSvsDe0bEfKqky17AIU3ieBxYPSK6S/m6TeIbaP9G5e+LpGEzLGNCROxS9pbs1QU81KStVmLoa0wcyPj/ihs/Fc3aH0ybrdTbrHwJ1c2casufjYgP15R1Ud1wcTAxtxObc4ekRkbc2DBGrzG8hpAkaQiZbO7f9cDuETEhIlamuqnDNR2OqU8R8Trgh1R3Pb64FN9evRQblQ9HBwBzMvNhYHH54AjwAWBOg2qvBj5YHu9PdSOPF2rLI2IXYHFmPlLKD4yI7ojYiGo/tzuHuq+1MvPvM3PzcqOsE4EfZeaHaNC/EvvPSl8ofeiz3y32bw4wLSJWjogJVF9v/2mDeiUtG4ZrDlmD6uZBK0bEqlQ3NJrepK2G439tZf2MiS2P/8CL9YE2m2cG2WatgY7Di4Gd696fa4GvRcQG5aaLRwKXDzLm+ticOyS1a0Rdh4zVawyvISRJGlpDcufesSwzH42I44EbgfHAuZl5R4fD6s+xwIrAaRG995DiLOBg4Afltat5+cYWBwLnRMRqwDzgdICIOAlYlJlnUe1heWFE3Ed1E6gDy7nfBGaV8ueoPohR6t4euKc8/0hm/mXIe9qahv0DjgBmR8QJVDfneD9ARBwOrJeZJzLw/t0RERdRfWgcB3whMx8d7g5KGpmGaw7JzB9HxPZU+w13A2dm5s3N2oqIg2kw/kfEuVQX1T+iyZjIwMb/y3pjjIirgRMz8y4GOA43azMi9gL2KntED2gcjogXgdPK+7MBcF5mXhcRhwFXlvfs51RbaTDQmJ07JA21EXgdsqxdYzgPSJI0CF09PfVbTkmViNgG2DEzz+x0LEtTRKwFfDozP9vpWCRpOEXEPsBzmXnVMNT9j8C1mXnfENY5HpiZmR9rs57hiM25Q5JaMFavMZwHJEmquI2G+rIu8G+dDqIDNgXG1IdfSWpiHNXXtIfD48D9Q1znJsC3h6Ce4YjNuUOSWjNWrzGcByRJwpXNkiRJkiRJkqQh4MpmSZIkSZIkSVLbTDZLkiRJkiRJktpmslmSJEmSJEmS1LZxnQ5AI0dEdAOfAA6g+t0YD1wJnJiZzw1xW5OBj2Tm4UNZrySpM5xDJEmD4fwhSdLY4spm1fo2sCOwe2ZOAiYDAZw7DG1tBvzdMNQrSeoM5xBJ0mA4f0iSNIZ09fT0dDoGjQARMRG4D1g3M5+uKV8H2Bm4HjgTmAT0AHOAz2fmkojoASZk5pPlnB5gArA5cArwYHm8PHAY8AhwM7A6cFlmfmhp9FGSNDycQyRJg+H8IUnS2OPKZvXaFriv9kMeQGb+T2b+ADgdeArYAtgO2Ao4toV6twdOzcytgQuAL2Xmb4ATgZ/5IU+SxgTnEEnSYDh/SJI0xphRLSryAAABUElEQVRsVq+X6Pv34Z3AGZnZU/ZOO6uU9efhzJxfHs8D1mwvTEnSCOQcIkkaDOcPSZLGGJPN6nU7sElErFpbGBHrR8RVQDfVV9d6LUf1lbReXeX48XX1/qXmcU/vcZKkMcU5RJI0GM4fkiSNMSabBUBmLgK+B5wfEasBlJ/fovrq2jXAURHRFRErAB8FflJOf4Lqa21Q3UW6FUt45QdFSdIo5RwiSRoM5w9JksYek82qdQRwP3BLRMynWmlwP3AIcDSwNnBv+ZdUN96gvHZmRMwDNgEea6Gt24ANI+KyIe2BJKlTnEMkSYPh/CFJ0hjS1dPT0/9RkiRJkiRJkiT1wZXNkiRJkiRJkqS2mWyWJEmSJEmSJLXNZLMkSZIkSZIkqW0mmyVJkiRJkiRJbTPZLEmSJEmSJElqm8lmSZIkSZIkSVLbTDZLkiRJkiRJktr2v12uE/xCKZoiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.plot_hbar(other_table.index,dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Add `CREDIT_ACTIVE` Columns \n",
    "> - Lets create a table that counts the number of `CREDIT_ACTIVE` by `SK_ID_CURR`.\n",
    "> - We can combined this table with others to create a final features table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACTIVE</th>\n",
       "      <th>BAD_DEBT</th>\n",
       "      <th>CLOSED</th>\n",
       "      <th>SOLD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ACTIVE  BAD_DEBT  CLOSED  SOLD\n",
       "SK_ID_CURR                                \n",
       "100001           3         0       4     0\n",
       "100002           2         0       6     0\n",
       "100003           1         0       3     0\n",
       "100004           0         0       2     0\n",
       "100005           2         0       1     0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_active_table = pd.pivot_table(dataset,index =['SK_ID_CURR'], \n",
    "                     columns=['CREDIT_ACTIVE'],\n",
    "                     values=['DAYS_CREDIT'],aggfunc='count',fill_value=0)\n",
    "\n",
    "#let replace columns with no spaces and make upper case\n",
    "cols = [c.replace(' ','_').upper() for c in credit_active_table.columns.levels[1]]\n",
    "credit_active_table.columns = cols\n",
    "\n",
    "#lets column to cobined bad debt and sold\n",
    "credit_active_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_cat(feature,df):\n",
    "    \"\"\" Lets reduce the number of counts\n",
    "        by grouping them into bins of 5 and setting a ceiling value.\n",
    "    \"\"\" \n",
    "    df[feature]  = df[feature].apply(lambda x:int(x/5)+1)\n",
    "    utils.applyMax([feature],df)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ACTIVE\n",
       "1    276932\n",
       "2     28879\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets 'ACTIVE' into a category in bins size 5\n",
    "make_cat('ACTIVE',credit_active_table)\n",
    "credit_active_table.groupby(['ACTIVE']) .size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CLOSED\n",
       "1    218588\n",
       "2     68564\n",
       "3     14621\n",
       "4      4038\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets 'CLOSED' into a category in bins size 5\n",
    "make_cat('CLOSED',credit_active_table)\n",
    "credit_active_table.groupby(['CLOSED']) .size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ADVERSE\n",
       "1    299772\n",
       "2      6039\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets combine 'BAD_DEBT' and 'SOLD' into one field called 'ADVERSE' since \n",
    "# by themselves they are too unbalance.\n",
    "def adverse(x):\n",
    "    bad = x['BAD_DEBT'] > 0\n",
    "    sold = x['SOLD'] > 0\n",
    "    if bad or sold:\n",
    "        return 2\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "credit_active_table['ADVERSE'] = credit_active_table.apply(adverse,axis=1).astype('int64')\n",
    "credit_active_table.groupby(['ADVERSE']) .size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets drop columns\n",
    "credit_active_table.drop(['BAD_DEBT', 'SOLD'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>ACTIVE</th>\n",
       "      <th>CLOSED</th>\n",
       "      <th>ADVERSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  ACTIVE  CLOSED  ADVERSE\n",
       "0      100001       1       1        1\n",
       "1      100002       1       2        1\n",
       "2      100003       1       1        1\n",
       "3      100004       1       1        1\n",
       "4      100005       1       1        1"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#resent index\n",
    "credit_active_table.reset_index(inplace=True)\n",
    "credit_active_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Add Count Field\n",
    "> Count the number of bureau records by loan id and then create a category count group of bin size 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COUNT_GROUP\n",
       "1     370880\n",
       "2     682749\n",
       "3     401642\n",
       "4     166453\n",
       "5      60091\n",
       "6      20791\n",
       "7       7337\n",
       "8       2976\n",
       "9       1241\n",
       "10       890\n",
       "11       519\n",
       "12       450\n",
       "13       121\n",
       "16        78\n",
       "19        94\n",
       "24       116\n",
       "dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['COUNT'] = dataset.groupby('SK_ID_CURR')['SK_ID_CURR'].transform('count')\n",
    "dataset['COUNT_GROUP'] = dataset['COUNT'].apply(lambda x:int(x/5)+1)\n",
    "dataset.drop('COUNT',axis=1,inplace=True)\n",
    "dataset.groupby('COUNT_GROUP').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Add Count Orverdue Counts\n",
    "> - Create new coulumn to count the number of accounts\n",
    "that are overdue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def overDue(df):\n",
    "    name_flag = 'OVERDUE_FLAG'\n",
    "    name = 'OVERDUE'\n",
    "    \n",
    "    def fun(x):\n",
    "        days = abs(x)\n",
    "        if days > 0:\n",
    "            return 1\n",
    "        else:\n",
    "            return 0\n",
    "\n",
    "    df[name_flag] = df['CREDIT_DAY_OVERDUE'].apply(fun)\n",
    "    \n",
    "    df[name] = df.groupby('SK_ID_CURR')[name_flag].transform('sum')\n",
    "    \n",
    "    #lets increase the groups by 1 to make the base group 1\n",
    "    df[name] = df[name].apply(lambda x:x+1)\n",
    "    \n",
    "    #drop column\n",
    "    df.drop([name_flag],axis=1, inplace=True)\n",
    "\n",
    "#apply function\n",
    "overDue(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OVERDUE\n",
       "1    1686759\n",
       "2      26998\n",
       "3       1893\n",
       "4        441\n",
       "5        153\n",
       "6        104\n",
       "7         62\n",
       "8         18\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby(['OVERDUE']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Add Count Credit Type Count\n",
    "> - Create new column to count the number of different account credit types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>CREDIT_TYPE_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  CREDIT_TYPE_COUNT\n",
       "0      100001                  1\n",
       "1      100002                  2\n",
       "2      100003                  2\n",
       "3      100004                  1\n",
       "4      100005                  2"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groupby id and credit types\n",
    "credit_size = dataset.groupby(['SK_ID_CURR','CREDIT_TYPE']).size()\n",
    "\n",
    "#create new dataframe\n",
    "df_credit_size = pd.DataFrame(data=credit_size.values,\n",
    "                              index=credit_size.index,columns=['COUNT'])\n",
    "\n",
    "df_credit_size.reset_index(inplace=True)\n",
    "\n",
    "credit_type_size = df_credit_size.groupby('SK_ID_CURR').size() \n",
    "\n",
    "credit_type_count = pd.DataFrame(data=credit_type_size.values,\n",
    "                              index=credit_type_size.index,\n",
    "                                 columns=['CREDIT_TYPE_COUNT'])\n",
    "credit_type_count.reset_index(inplace=True)\n",
    "credit_type_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CREDIT_TYPE_COUNT\n",
       "1    109315\n",
       "2    166653\n",
       "3     27652\n",
       "4      2113\n",
       "5        74\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_type_count.groupby('CREDIT_TYPE_COUNT').size().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 Add Count High Debt Accounts\n",
    ">  Create two new columns to count the number of high debt accounts:\n",
    ">> - Accounts with used debt limit of 50% or higher.\n",
    ">> - Accounts with used debt limit of 100% or higher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill nan values\n",
    "dataset[['AMT_CREDIT_SUM_DEBT']]= dataset[['AMT_CREDIT_SUM_DEBT']].fillna(0)\n",
    "dataset[['AMT_CREDIT_SUM']]= dataset[['AMT_CREDIT_SUM']].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def debtPercent(x):\n",
    "    '''Calculate debt percent.'''\n",
    "    debt = x['AMT_CREDIT_SUM_DEBT']\n",
    "    limit = x['AMT_CREDIT_SUM']\n",
    "    if debt <= 0 :\n",
    "        return 0\n",
    "    elif limit <=0:\n",
    "        return 0\n",
    "    else:\n",
    "        return debt/limit\n",
    "    \n",
    "#apply function\n",
    "dataset['DEBT_PERCENT'] = dataset.apply(debtPercent,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def highDebt(name,df,threshold):\n",
    "    name_flag = 'HIGH_DEBT_FLAG'\n",
    "\n",
    "    df[name_flag] = df['DEBT_PERCENT'].apply(lambda x: 1 if x >= threshold else 0)\n",
    "    \n",
    "    df[name] = df.groupby('SK_ID_CURR')[name_flag].transform('sum')\n",
    "    \n",
    "     #lets increase the groups by 1 to make the base group 1\n",
    "    df[name] = df[name].apply(lambda x:x+1)\n",
    "    \n",
    "    #drop column\n",
    "    df.drop([name_flag],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "highDebt(\"DEBT_OVER_50%\",dataset,0.5)\n",
    "highDebt(\"DEBT_OVER_75%\",dataset,0.75)\n",
    "highDebt(\"DEBT_OVER_100%\",dataset,1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.7 Add Aggregation Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let get minimum, maximum, average and standard deviation\n",
    "fun_agg = ['min', 'max', 'mean','std']\n",
    "\n",
    "agg_fun_df = dataset.groupby('SK_ID_CURR')[float_table.index.values].agg(fun_agg)\n",
    "\n",
    "#rename columns\n",
    "agg_fun_df.columns = [\"{}_{}\".format(c[0],c[1].upper()) for c in agg_fun_df.columns.values.tolist()] \n",
    "\n",
    "#let fill nan values\n",
    "agg_fun_df.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAYS_CREDIT_MIN</th>\n",
       "      <th>DAYS_CREDIT_MAX</th>\n",
       "      <th>DAYS_CREDIT_MEAN</th>\n",
       "      <th>DAYS_CREDIT_STD</th>\n",
       "      <th>CREDIT_DAY_OVERDUE_MIN</th>\n",
       "      <th>CREDIT_DAY_OVERDUE_MAX</th>\n",
       "      <th>CREDIT_DAY_OVERDUE_MEAN</th>\n",
       "      <th>CREDIT_DAY_OVERDUE_STD</th>\n",
       "      <th>DAYS_CREDIT_ENDDATE_MIN</th>\n",
       "      <th>DAYS_CREDIT_ENDDATE_MAX</th>\n",
       "      <th>...</th>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE_MEAN</th>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE_STD</th>\n",
       "      <th>DAYS_CREDIT_UPDATE_MIN</th>\n",
       "      <th>DAYS_CREDIT_UPDATE_MAX</th>\n",
       "      <th>DAYS_CREDIT_UPDATE_MEAN</th>\n",
       "      <th>DAYS_CREDIT_UPDATE_STD</th>\n",
       "      <th>AMT_ANNUITY_MIN</th>\n",
       "      <th>AMT_ANNUITY_MAX</th>\n",
       "      <th>AMT_ANNUITY_MEAN</th>\n",
       "      <th>AMT_ANNUITY_STD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>-1,572.00</td>\n",
       "      <td>-49.00</td>\n",
       "      <td>-735.00</td>\n",
       "      <td>489.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1,329.00</td>\n",
       "      <td>1,778.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-155.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>-93.14</td>\n",
       "      <td>77.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10,822.50</td>\n",
       "      <td>3,545.36</td>\n",
       "      <td>4,800.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>-1,437.00</td>\n",
       "      <td>-103.00</td>\n",
       "      <td>-874.00</td>\n",
       "      <td>431.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1,072.00</td>\n",
       "      <td>780.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1,185.00</td>\n",
       "      <td>-7.00</td>\n",
       "      <td>-499.88</td>\n",
       "      <td>518.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>-2,586.00</td>\n",
       "      <td>-606.00</td>\n",
       "      <td>-1,400.75</td>\n",
       "      <td>909.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2,434.00</td>\n",
       "      <td>1,216.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2,131.00</td>\n",
       "      <td>-43.00</td>\n",
       "      <td>-816.00</td>\n",
       "      <td>908.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>-1,326.00</td>\n",
       "      <td>-408.00</td>\n",
       "      <td>-867.00</td>\n",
       "      <td>649.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-595.00</td>\n",
       "      <td>-382.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-682.00</td>\n",
       "      <td>-382.00</td>\n",
       "      <td>-532.00</td>\n",
       "      <td>212.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>-373.00</td>\n",
       "      <td>-62.00</td>\n",
       "      <td>-190.67</td>\n",
       "      <td>162.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-128.00</td>\n",
       "      <td>1,324.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-121.00</td>\n",
       "      <td>-11.00</td>\n",
       "      <td>-54.33</td>\n",
       "      <td>58.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4,261.50</td>\n",
       "      <td>1,420.50</td>\n",
       "      <td>2,460.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            DAYS_CREDIT_MIN  DAYS_CREDIT_MAX  DAYS_CREDIT_MEAN  \\\n",
       "SK_ID_CURR                                                       \n",
       "100001            -1,572.00           -49.00           -735.00   \n",
       "100002            -1,437.00          -103.00           -874.00   \n",
       "100003            -2,586.00          -606.00         -1,400.75   \n",
       "100004            -1,326.00          -408.00           -867.00   \n",
       "100005              -373.00           -62.00           -190.67   \n",
       "\n",
       "            DAYS_CREDIT_STD  CREDIT_DAY_OVERDUE_MIN  CREDIT_DAY_OVERDUE_MAX  \\\n",
       "SK_ID_CURR                                                                    \n",
       "100001               489.94                    0.00                    0.00   \n",
       "100002               431.45                    0.00                    0.00   \n",
       "100003               909.83                    0.00                    0.00   \n",
       "100004               649.12                    0.00                    0.00   \n",
       "100005               162.30                    0.00                    0.00   \n",
       "\n",
       "            CREDIT_DAY_OVERDUE_MEAN  CREDIT_DAY_OVERDUE_STD  \\\n",
       "SK_ID_CURR                                                    \n",
       "100001                         0.00                    0.00   \n",
       "100002                         0.00                    0.00   \n",
       "100003                         0.00                    0.00   \n",
       "100004                         0.00                    0.00   \n",
       "100005                         0.00                    0.00   \n",
       "\n",
       "            DAYS_CREDIT_ENDDATE_MIN  DAYS_CREDIT_ENDDATE_MAX  ...  \\\n",
       "SK_ID_CURR                                                    ...   \n",
       "100001                    -1,329.00                 1,778.00  ...   \n",
       "100002                    -1,072.00                   780.00  ...   \n",
       "100003                    -2,434.00                 1,216.00  ...   \n",
       "100004                      -595.00                  -382.00  ...   \n",
       "100005                      -128.00                 1,324.00  ...   \n",
       "\n",
       "            AMT_CREDIT_SUM_OVERDUE_MEAN  AMT_CREDIT_SUM_OVERDUE_STD  \\\n",
       "SK_ID_CURR                                                            \n",
       "100001                             0.00                        0.00   \n",
       "100002                             0.00                        0.00   \n",
       "100003                             0.00                        0.00   \n",
       "100004                             0.00                        0.00   \n",
       "100005                             0.00                        0.00   \n",
       "\n",
       "            DAYS_CREDIT_UPDATE_MIN  DAYS_CREDIT_UPDATE_MAX  \\\n",
       "SK_ID_CURR                                                   \n",
       "100001                     -155.00                   -6.00   \n",
       "100002                   -1,185.00                   -7.00   \n",
       "100003                   -2,131.00                  -43.00   \n",
       "100004                     -682.00                 -382.00   \n",
       "100005                     -121.00                  -11.00   \n",
       "\n",
       "            DAYS_CREDIT_UPDATE_MEAN  DAYS_CREDIT_UPDATE_STD  AMT_ANNUITY_MIN  \\\n",
       "SK_ID_CURR                                                                     \n",
       "100001                       -93.14                   77.20             0.00   \n",
       "100002                      -499.88                  518.52             0.00   \n",
       "100003                      -816.00                  908.05             0.00   \n",
       "100004                      -532.00                  212.13             0.00   \n",
       "100005                       -54.33                   58.59             0.00   \n",
       "\n",
       "            AMT_ANNUITY_MAX  AMT_ANNUITY_MEAN  AMT_ANNUITY_STD  \n",
       "SK_ID_CURR                                                      \n",
       "100001            10,822.50          3,545.36         4,800.61  \n",
       "100002                 0.00              0.00             0.00  \n",
       "100003                 0.00              0.00             0.00  \n",
       "100004                 0.00              0.00             0.00  \n",
       "100005             4,261.50          1,420.50         2,460.38  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_fun_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.8 Combine All Datasets\n",
    "> - Combine all the datasets get the final feature dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['COUNT_GROUP','OVERDUE','DEBT_OVER_50%','DEBT_OVER_75%','DEBT_OVER_100%']\n",
    "\n",
    "#these features columns are same value by id so we can take the first row\n",
    "reduced_dataset = dataset.groupby('SK_ID_CURR').first()[features]\n",
    "\n",
    "reduced_dataset.reset_index(inplace=True)\n",
    "\n",
    "#set max values\n",
    "utils.applyMax(features,reduced_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = reduced_dataset.merge(credit_active_table,\n",
    "                                      how='left', \n",
    "                                      on='SK_ID_CURR',\n",
    "                                      validate='one_to_one')\n",
    "\n",
    "final_dataset  = final_dataset.merge(credit_type_count,\n",
    "                                      how='left', \n",
    "                                      on='SK_ID_CURR',\n",
    "                                      validate='one_to_one')\n",
    "\n",
    "final_dataset  = final_dataset.merge(agg_fun_df.reset_index(),\n",
    "                                      how='left', \n",
    "                                      on='SK_ID_CURR',\n",
    "                                      validate='one_to_one')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>COUNT_GROUP</th>\n",
       "      <th>OVERDUE</th>\n",
       "      <th>DEBT_OVER_50%</th>\n",
       "      <th>DEBT_OVER_75%</th>\n",
       "      <th>DEBT_OVER_100%</th>\n",
       "      <th>ACTIVE</th>\n",
       "      <th>CLOSED</th>\n",
       "      <th>ADVERSE</th>\n",
       "      <th>CREDIT_TYPE_COUNT</th>\n",
       "      <th>...</th>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE_MEAN</th>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE_STD</th>\n",
       "      <th>DAYS_CREDIT_UPDATE_MIN</th>\n",
       "      <th>DAYS_CREDIT_UPDATE_MAX</th>\n",
       "      <th>DAYS_CREDIT_UPDATE_MEAN</th>\n",
       "      <th>DAYS_CREDIT_UPDATE_STD</th>\n",
       "      <th>AMT_ANNUITY_MIN</th>\n",
       "      <th>AMT_ANNUITY_MAX</th>\n",
       "      <th>AMT_ANNUITY_MEAN</th>\n",
       "      <th>AMT_ANNUITY_STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>-155.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>-93.14</td>\n",
       "      <td>77.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10,822.50</td>\n",
       "      <td>3,545.36</td>\n",
       "      <td>4,800.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>-1,185.00</td>\n",
       "      <td>-7.00</td>\n",
       "      <td>-499.88</td>\n",
       "      <td>518.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2,131.00</td>\n",
       "      <td>-43.00</td>\n",
       "      <td>-816.00</td>\n",
       "      <td>908.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-682.00</td>\n",
       "      <td>-382.00</td>\n",
       "      <td>-532.00</td>\n",
       "      <td>212.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-121.00</td>\n",
       "      <td>-11.00</td>\n",
       "      <td>-54.33</td>\n",
       "      <td>58.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4,261.50</td>\n",
       "      <td>1,420.50</td>\n",
       "      <td>2,460.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  COUNT_GROUP  OVERDUE  DEBT_OVER_50%  DEBT_OVER_75%  \\\n",
       "0      100001            2        1              3              2   \n",
       "1      100002            2        1              2              1   \n",
       "2      100003            1        1              1              1   \n",
       "3      100004            1        1              1              1   \n",
       "4      100005            1        1              3              3   \n",
       "\n",
       "   DEBT_OVER_100%  ACTIVE  CLOSED  ADVERSE  CREDIT_TYPE_COUNT  ...  \\\n",
       "0               1       1       1        1                  1  ...   \n",
       "1               1       1       2        1                  2  ...   \n",
       "2               1       1       1        1                  2  ...   \n",
       "3               1       1       1        1                  1  ...   \n",
       "4               1       1       1        1                  2  ...   \n",
       "\n",
       "   AMT_CREDIT_SUM_OVERDUE_MEAN  AMT_CREDIT_SUM_OVERDUE_STD  \\\n",
       "0                         0.00                        0.00   \n",
       "1                         0.00                        0.00   \n",
       "2                         0.00                        0.00   \n",
       "3                         0.00                        0.00   \n",
       "4                         0.00                        0.00   \n",
       "\n",
       "   DAYS_CREDIT_UPDATE_MIN  DAYS_CREDIT_UPDATE_MAX  DAYS_CREDIT_UPDATE_MEAN  \\\n",
       "0                 -155.00                   -6.00                   -93.14   \n",
       "1               -1,185.00                   -7.00                  -499.88   \n",
       "2               -2,131.00                  -43.00                  -816.00   \n",
       "3                 -682.00                 -382.00                  -532.00   \n",
       "4                 -121.00                  -11.00                   -54.33   \n",
       "\n",
       "   DAYS_CREDIT_UPDATE_STD  AMT_ANNUITY_MIN  AMT_ANNUITY_MAX  AMT_ANNUITY_MEAN  \\\n",
       "0                   77.20             0.00        10,822.50          3,545.36   \n",
       "1                  518.52             0.00             0.00              0.00   \n",
       "2                  908.05             0.00             0.00              0.00   \n",
       "3                  212.13             0.00             0.00              0.00   \n",
       "4                   58.59             0.00         4,261.50          1,420.50   \n",
       "\n",
       "   AMT_ANNUITY_STD  \n",
       "0         4,800.61  \n",
       "1             0.00  \n",
       "2             0.00  \n",
       "3             0.00  \n",
       "4         2,460.38  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "abt_final = utils.ABT(final_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-670.37</td>\n",
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       "      <th>DAYS_CREDIT_STD</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>211.83</td>\n",
       "      <td>537.06</td>\n",
       "      <td>809.23</td>\n",
       "      <td>2,042.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREDIT_DAY_OVERDUE_MIN</th>\n",
       "      <td>5</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>16.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,776.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREDIT_DAY_OVERDUE_MAX</th>\n",
       "      <td>6</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.44</td>\n",
       "      <td>85.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,792.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREDIT_DAY_OVERDUE_MEAN</th>\n",
       "      <td>7</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,776.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREDIT_DAY_OVERDUE_STD</th>\n",
       "      <td>8</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.77</td>\n",
       "      <td>36.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,900.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT_ENDDATE_MIN</th>\n",
       "      <td>9</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1,021.00</td>\n",
       "      <td>2,611.70</td>\n",
       "      <td>-42,060.00</td>\n",
       "      <td>-2,122.00</td>\n",
       "      <td>-1,257.00</td>\n",
       "      <td>-435.00</td>\n",
       "      <td>31,198.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT_ENDDATE_MAX</th>\n",
       "      <td>10</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4,470.53</td>\n",
       "      <td>9,391.88</td>\n",
       "      <td>-41,875.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>900.00</td>\n",
       "      <td>1,685.00</td>\n",
       "      <td>31,199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT_ENDDATE_MEAN</th>\n",
       "      <td>11</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>656.82</td>\n",
       "      <td>3,269.02</td>\n",
       "      <td>-41,875.00</td>\n",
       "      <td>-692.25</td>\n",
       "      <td>-122.18</td>\n",
       "      <td>602.50</td>\n",
       "      <td>31,198.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT_ENDDATE_STD</th>\n",
       "      <td>12</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,245.52</td>\n",
       "      <td>3,982.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>399.05</td>\n",
       "      <td>891.52</td>\n",
       "      <td>1,416.56</td>\n",
       "      <td>38,718.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_ENDDATE_FACT_MIN</th>\n",
       "      <td>13</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1,284.81</td>\n",
       "      <td>880.78</td>\n",
       "      <td>-42,023.00</td>\n",
       "      <td>-2,118.00</td>\n",
       "      <td>-1,246.00</td>\n",
       "      <td>-512.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_ENDDATE_FACT_MAX</th>\n",
       "      <td>14</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-459.46</td>\n",
       "      <td>525.03</td>\n",
       "      <td>-2,887.00</td>\n",
       "      <td>-637.00</td>\n",
       "      <td>-283.00</td>\n",
       "      <td>-87.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_ENDDATE_FACT_MEAN</th>\n",
       "      <td>15</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-847.94</td>\n",
       "      <td>590.22</td>\n",
       "      <td>-8,376.50</td>\n",
       "      <td>-1,229.00</td>\n",
       "      <td>-833.00</td>\n",
       "      <td>-392.69</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_ENDDATE_FACT_STD</th>\n",
       "      <td>16</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>372.07</td>\n",
       "      <td>366.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>319.23</td>\n",
       "      <td>656.72</td>\n",
       "      <td>16,511.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_MAX_OVERDUE_MIN</th>\n",
       "      <td>17</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,979.56</td>\n",
       "      <td>221,537.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>115,987,185.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_MAX_OVERDUE_MAX</th>\n",
       "      <td>18</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6,276.27</td>\n",
       "      <td>286,423.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>193.50</td>\n",
       "      <td>115,987,185.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_MAX_OVERDUE_MEAN</th>\n",
       "      <td>19</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3,485.50</td>\n",
       "      <td>238,416.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68.29</td>\n",
       "      <td>115,987,185.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_MAX_OVERDUE_STD</th>\n",
       "      <td>20</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,302.23</td>\n",
       "      <td>125,494.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>67,042,382.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_CREDIT_PROLONG_MIN</th>\n",
       "      <td>21</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_CREDIT_PROLONG_MAX</th>\n",
       "      <td>22</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_CREDIT_PROLONG_MEAN</th>\n",
       "      <td>23</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_CREDIT_PROLONG_STD</th>\n",
       "      <td>24</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_MIN</th>\n",
       "      <td>25</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118,921.38</td>\n",
       "      <td>571,800.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15,795.00</td>\n",
       "      <td>39,414.33</td>\n",
       "      <td>90,000.00</td>\n",
       "      <td>142,290,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_MAX</th>\n",
       "      <td>26</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>991,750.68</td>\n",
       "      <td>2,317,435.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180,000.00</td>\n",
       "      <td>450,000.00</td>\n",
       "      <td>1,079,100.00</td>\n",
       "      <td>585,000,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_MEAN</th>\n",
       "      <td>27</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>380,731.17</td>\n",
       "      <td>879,277.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>103,960.12</td>\n",
       "      <td>197,292.88</td>\n",
       "      <td>397,859.33</td>\n",
       "      <td>198,072,344.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_STD</th>\n",
       "      <td>28</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>359,038.55</td>\n",
       "      <td>1,005,333.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>43,773.52</td>\n",
       "      <td>143,535.42</td>\n",
       "      <td>373,629.29</td>\n",
       "      <td>279,911,975.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_DEBT_MIN</th>\n",
       "      <td>29</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29,134.67</td>\n",
       "      <td>353,899.23</td>\n",
       "      <td>-4,705,600.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>51,750,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_DEBT_MAX</th>\n",
       "      <td>30</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>515,529.03</td>\n",
       "      <td>1,341,708.53</td>\n",
       "      <td>-25,959.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>140,463.00</td>\n",
       "      <td>487,302.75</td>\n",
       "      <td>170,100,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_DEBT_MEAN</th>\n",
       "      <td>31</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>138,213.09</td>\n",
       "      <td>473,019.80</td>\n",
       "      <td>-1,083,614.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35,587.35</td>\n",
       "      <td>121,944.88</td>\n",
       "      <td>51,750,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_DEBT_STD</th>\n",
       "      <td>32</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>202,981.90</td>\n",
       "      <td>569,367.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50,994.93</td>\n",
       "      <td>188,386.72</td>\n",
       "      <td>52,679,220.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_LIMIT_MIN</th>\n",
       "      <td>33</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,024.73</td>\n",
       "      <td>19,780.97</td>\n",
       "      <td>-586,406.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4,500,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_LIMIT_MAX</th>\n",
       "      <td>34</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20,076.72</td>\n",
       "      <td>81,195.71</td>\n",
       "      <td>-1,747.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4,705,600.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_LIMIT_MEAN</th>\n",
       "      <td>35</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5,410.39</td>\n",
       "      <td>28,703.00</td>\n",
       "      <td>-97,891.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4,500,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_LIMIT_STD</th>\n",
       "      <td>36</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8,449.98</td>\n",
       "      <td>36,423.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,598,076.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE_MIN</th>\n",
       "      <td>37</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.44</td>\n",
       "      <td>3,831.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,617,403.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE_MAX</th>\n",
       "      <td>38</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>176.41</td>\n",
       "      <td>13,686.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3,756,681.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE_MEAN</th>\n",
       "      <td>39</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.95</td>\n",
       "      <td>4,956.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,617,403.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE_STD</th>\n",
       "      <td>40</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68.98</td>\n",
       "      <td>6,076.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,878,340.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT_UPDATE_MIN</th>\n",
       "      <td>41</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1,174.49</td>\n",
       "      <td>1,076.57</td>\n",
       "      <td>-41,947.00</td>\n",
       "      <td>-1,855.00</td>\n",
       "      <td>-1,057.00</td>\n",
       "      <td>-478.00</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT_UPDATE_MAX</th>\n",
       "      <td>42</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-139.31</td>\n",
       "      <td>344.79</td>\n",
       "      <td>-41,890.00</td>\n",
       "      <td>-49.00</td>\n",
       "      <td>-18.00</td>\n",
       "      <td>-9.00</td>\n",
       "      <td>372.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT_UPDATE_MEAN</th>\n",
       "      <td>43</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-542.97</td>\n",
       "      <td>446.43</td>\n",
       "      <td>-41,890.00</td>\n",
       "      <td>-773.70</td>\n",
       "      <td>-479.30</td>\n",
       "      <td>-209.00</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_CREDIT_UPDATE_STD</th>\n",
       "      <td>44</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>432.00</td>\n",
       "      <td>411.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125.48</td>\n",
       "      <td>401.14</td>\n",
       "      <td>665.73</td>\n",
       "      <td>29,612.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_ANNUITY_MIN</th>\n",
       "      <td>45</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,412.73</td>\n",
       "      <td>55,252.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24,016,036.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_ANNUITY_MAX</th>\n",
       "      <td>46</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15,029.99</td>\n",
       "      <td>400,579.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7,249.50</td>\n",
       "      <td>118,453,423.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_ANNUITY_MEAN</th>\n",
       "      <td>47</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6,034.02</td>\n",
       "      <td>95,062.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3,316.64</td>\n",
       "      <td>27,282,428.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_ANNUITY_STD</th>\n",
       "      <td>48</td>\n",
       "      <td>305,811.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5,573.36</td>\n",
       "      <td>150,295.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38,580,069.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             num      count  na_count  na_%       mean  \\\n",
       "DAYS_CREDIT_MIN                1 305,811.00      0.00  0.00  -1,764.36   \n",
       "DAYS_CREDIT_MAX                2 305,811.00      0.00  0.00    -490.94   \n",
       "DAYS_CREDIT_MEAN               3 305,811.00      0.00  0.00  -1,083.80   \n",
       "DAYS_CREDIT_STD                4 305,811.00      0.00  0.00     529.14   \n",
       "CREDIT_DAY_OVERDUE_MIN         5 305,811.00      0.00  0.00       0.15   \n",
       "CREDIT_DAY_OVERDUE_MAX         6 305,811.00      0.00  0.00       4.44   \n",
       "CREDIT_DAY_OVERDUE_MEAN        7 305,811.00      0.00  0.00       0.97   \n",
       "CREDIT_DAY_OVERDUE_STD         8 305,811.00      0.00  0.00       1.77   \n",
       "DAYS_CREDIT_ENDDATE_MIN        9 305,811.00      0.00  0.00  -1,021.00   \n",
       "DAYS_CREDIT_ENDDATE_MAX       10 305,811.00      0.00  0.00   4,470.53   \n",
       "DAYS_CREDIT_ENDDATE_MEAN      11 305,811.00      0.00  0.00     656.82   \n",
       "DAYS_CREDIT_ENDDATE_STD       12 305,811.00      0.00  0.00   2,245.52   \n",
       "DAYS_ENDDATE_FACT_MIN         13 305,811.00      0.00  0.00  -1,284.81   \n",
       "DAYS_ENDDATE_FACT_MAX         14 305,811.00      0.00  0.00    -459.46   \n",
       "DAYS_ENDDATE_FACT_MEAN        15 305,811.00      0.00  0.00    -847.94   \n",
       "DAYS_ENDDATE_FACT_STD         16 305,811.00      0.00  0.00     372.07   \n",
       "AMT_CREDIT_MAX_OVERDUE_MIN    17 305,811.00      0.00  0.00   1,979.56   \n",
       "AMT_CREDIT_MAX_OVERDUE_MAX    18 305,811.00      0.00  0.00   6,276.27   \n",
       "AMT_CREDIT_MAX_OVERDUE_MEAN   19 305,811.00      0.00  0.00   3,485.50   \n",
       "AMT_CREDIT_MAX_OVERDUE_STD    20 305,811.00      0.00  0.00   2,302.23   \n",
       "CNT_CREDIT_PROLONG_MIN        21 305,811.00      0.00  0.00       0.00   \n",
       "CNT_CREDIT_PROLONG_MAX        22 305,811.00      0.00  0.00       0.03   \n",
       "CNT_CREDIT_PROLONG_MEAN       23 305,811.00      0.00  0.00       0.01   \n",
       "CNT_CREDIT_PROLONG_STD        24 305,811.00      0.00  0.00       0.01   \n",
       "AMT_CREDIT_SUM_MIN            25 305,811.00      0.00  0.00 118,921.38   \n",
       "AMT_CREDIT_SUM_MAX            26 305,811.00      0.00  0.00 991,750.68   \n",
       "AMT_CREDIT_SUM_MEAN           27 305,811.00      0.00  0.00 380,731.17   \n",
       "AMT_CREDIT_SUM_STD            28 305,811.00      0.00  0.00 359,038.55   \n",
       "AMT_CREDIT_SUM_DEBT_MIN       29 305,811.00      0.00  0.00  29,134.67   \n",
       "AMT_CREDIT_SUM_DEBT_MAX       30 305,811.00      0.00  0.00 515,529.03   \n",
       "AMT_CREDIT_SUM_DEBT_MEAN      31 305,811.00      0.00  0.00 138,213.09   \n",
       "AMT_CREDIT_SUM_DEBT_STD       32 305,811.00      0.00  0.00 202,981.90   \n",
       "AMT_CREDIT_SUM_LIMIT_MIN      33 305,811.00      0.00  0.00   1,024.73   \n",
       "AMT_CREDIT_SUM_LIMIT_MAX      34 305,811.00      0.00  0.00  20,076.72   \n",
       "AMT_CREDIT_SUM_LIMIT_MEAN     35 305,811.00      0.00  0.00   5,410.39   \n",
       "AMT_CREDIT_SUM_LIMIT_STD      36 305,811.00      0.00  0.00   8,449.98   \n",
       "AMT_CREDIT_SUM_OVERDUE_MIN    37 305,811.00      0.00  0.00      11.44   \n",
       "AMT_CREDIT_SUM_OVERDUE_MAX    38 305,811.00      0.00  0.00     176.41   \n",
       "AMT_CREDIT_SUM_OVERDUE_MEAN   39 305,811.00      0.00  0.00      45.95   \n",
       "AMT_CREDIT_SUM_OVERDUE_STD    40 305,811.00      0.00  0.00      68.98   \n",
       "DAYS_CREDIT_UPDATE_MIN        41 305,811.00      0.00  0.00  -1,174.49   \n",
       "DAYS_CREDIT_UPDATE_MAX        42 305,811.00      0.00  0.00    -139.31   \n",
       "DAYS_CREDIT_UPDATE_MEAN       43 305,811.00      0.00  0.00    -542.97   \n",
       "DAYS_CREDIT_UPDATE_STD        44 305,811.00      0.00  0.00     432.00   \n",
       "AMT_ANNUITY_MIN               45 305,811.00      0.00  0.00   2,412.73   \n",
       "AMT_ANNUITY_MAX               46 305,811.00      0.00  0.00  15,029.99   \n",
       "AMT_ANNUITY_MEAN              47 305,811.00      0.00  0.00   6,034.02   \n",
       "AMT_ANNUITY_STD               48 305,811.00      0.00  0.00   5,573.36   \n",
       "\n",
       "                                     std           min        25%        50%  \\\n",
       "DAYS_CREDIT_MIN                   859.26     -2,922.00  -2,583.00  -1,822.00   \n",
       "DAYS_CREDIT_MAX                   533.53     -2,922.00    -623.00    -305.00   \n",
       "DAYS_CREDIT_MEAN                  558.44     -2,922.00  -1,431.00  -1,052.50   \n",
       "DAYS_CREDIT_STD                   373.74          0.00     211.83     537.06   \n",
       "CREDIT_DAY_OVERDUE_MIN             16.49          0.00       0.00       0.00   \n",
       "CREDIT_DAY_OVERDUE_MAX             85.57          0.00       0.00       0.00   \n",
       "CREDIT_DAY_OVERDUE_MEAN            24.96          0.00       0.00       0.00   \n",
       "CREDIT_DAY_OVERDUE_STD             36.18          0.00       0.00       0.00   \n",
       "DAYS_CREDIT_ENDDATE_MIN         2,611.70    -42,060.00  -2,122.00  -1,257.00   \n",
       "DAYS_CREDIT_ENDDATE_MAX         9,391.88    -41,875.00     107.00     900.00   \n",
       "DAYS_CREDIT_ENDDATE_MEAN        3,269.02    -41,875.00    -692.25    -122.18   \n",
       "DAYS_CREDIT_ENDDATE_STD         3,982.67          0.00     399.05     891.52   \n",
       "DAYS_ENDDATE_FACT_MIN             880.78    -42,023.00  -2,118.00  -1,246.00   \n",
       "DAYS_ENDDATE_FACT_MAX             525.03     -2,887.00    -637.00    -283.00   \n",
       "DAYS_ENDDATE_FACT_MEAN            590.22     -8,376.50  -1,229.00    -833.00   \n",
       "DAYS_ENDDATE_FACT_STD             366.53          0.00       0.00     319.23   \n",
       "AMT_CREDIT_MAX_OVERDUE_MIN    221,537.65          0.00       0.00       0.00   \n",
       "AMT_CREDIT_MAX_OVERDUE_MAX    286,423.13          0.00       0.00       0.00   \n",
       "AMT_CREDIT_MAX_OVERDUE_MEAN   238,416.51          0.00       0.00       0.00   \n",
       "AMT_CREDIT_MAX_OVERDUE_STD    125,494.01          0.00       0.00       0.00   \n",
       "CNT_CREDIT_PROLONG_MIN              0.04          0.00       0.00       0.00   \n",
       "CNT_CREDIT_PROLONG_MAX              0.22          0.00       0.00       0.00   \n",
       "CNT_CREDIT_PROLONG_MEAN             0.06          0.00       0.00       0.00   \n",
       "CNT_CREDIT_PROLONG_STD              0.09          0.00       0.00       0.00   \n",
       "AMT_CREDIT_SUM_MIN            571,800.65          0.00  15,795.00  39,414.33   \n",
       "AMT_CREDIT_SUM_MAX          2,317,435.70          0.00 180,000.00 450,000.00   \n",
       "AMT_CREDIT_SUM_MEAN           879,277.69          0.00 103,960.12 197,292.88   \n",
       "AMT_CREDIT_SUM_STD          1,005,333.83          0.00  43,773.52 143,535.42   \n",
       "AMT_CREDIT_SUM_DEBT_MIN       353,899.23 -4,705,600.32       0.00       0.00   \n",
       "AMT_CREDIT_SUM_DEBT_MAX     1,341,708.53    -25,959.65       0.00 140,463.00   \n",
       "AMT_CREDIT_SUM_DEBT_MEAN      473,019.80 -1,083,614.67       0.00  35,587.35   \n",
       "AMT_CREDIT_SUM_DEBT_STD       569,367.28          0.00       0.00  50,994.93   \n",
       "AMT_CREDIT_SUM_LIMIT_MIN       19,780.97   -586,406.11       0.00       0.00   \n",
       "AMT_CREDIT_SUM_LIMIT_MAX       81,195.71     -1,747.04       0.00       0.00   \n",
       "AMT_CREDIT_SUM_LIMIT_MEAN      28,703.00    -97,891.66       0.00       0.00   \n",
       "AMT_CREDIT_SUM_LIMIT_STD       36,423.12          0.00       0.00       0.00   \n",
       "AMT_CREDIT_SUM_OVERDUE_MIN      3,831.42          0.00       0.00       0.00   \n",
       "AMT_CREDIT_SUM_OVERDUE_MAX     13,686.50          0.00       0.00       0.00   \n",
       "AMT_CREDIT_SUM_OVERDUE_MEAN     4,956.64          0.00       0.00       0.00   \n",
       "AMT_CREDIT_SUM_OVERDUE_STD      6,076.07          0.00       0.00       0.00   \n",
       "DAYS_CREDIT_UPDATE_MIN          1,076.57    -41,947.00  -1,855.00  -1,057.00   \n",
       "DAYS_CREDIT_UPDATE_MAX            344.79    -41,890.00     -49.00     -18.00   \n",
       "DAYS_CREDIT_UPDATE_MEAN           446.43    -41,890.00    -773.70    -479.30   \n",
       "DAYS_CREDIT_UPDATE_STD            411.83          0.00     125.48     401.14   \n",
       "AMT_ANNUITY_MIN                55,252.17          0.00       0.00       0.00   \n",
       "AMT_ANNUITY_MAX               400,579.99          0.00       0.00       0.00   \n",
       "AMT_ANNUITY_MEAN               95,062.33          0.00       0.00       0.00   \n",
       "AMT_ANNUITY_STD               150,295.95          0.00       0.00       0.00   \n",
       "\n",
       "                                     75%            max  \n",
       "DAYS_CREDIT_MIN                -1,045.00           0.00  \n",
       "DAYS_CREDIT_MAX                  -149.00           0.00  \n",
       "DAYS_CREDIT_MEAN                 -670.37           0.00  \n",
       "DAYS_CREDIT_STD                   809.23       2,042.83  \n",
       "CREDIT_DAY_OVERDUE_MIN              0.00       2,776.00  \n",
       "CREDIT_DAY_OVERDUE_MAX              0.00       2,792.00  \n",
       "CREDIT_DAY_OVERDUE_MEAN             0.00       2,776.00  \n",
       "CREDIT_DAY_OVERDUE_STD              0.00       1,900.70  \n",
       "DAYS_CREDIT_ENDDATE_MIN          -435.00      31,198.00  \n",
       "DAYS_CREDIT_ENDDATE_MAX         1,685.00      31,199.00  \n",
       "DAYS_CREDIT_ENDDATE_MEAN          602.50      31,198.00  \n",
       "DAYS_CREDIT_ENDDATE_STD         1,416.56      38,718.34  \n",
       "DAYS_ENDDATE_FACT_MIN            -512.00           0.00  \n",
       "DAYS_ENDDATE_FACT_MAX             -87.00           0.00  \n",
       "DAYS_ENDDATE_FACT_MEAN           -392.69           0.00  \n",
       "DAYS_ENDDATE_FACT_STD             656.72      16,511.73  \n",
       "AMT_CREDIT_MAX_OVERDUE_MIN          0.00 115,987,185.00  \n",
       "AMT_CREDIT_MAX_OVERDUE_MAX        193.50 115,987,185.00  \n",
       "AMT_CREDIT_MAX_OVERDUE_MEAN        68.29 115,987,185.00  \n",
       "AMT_CREDIT_MAX_OVERDUE_STD          0.00  67,042,382.09  \n",
       "CNT_CREDIT_PROLONG_MIN              0.00           6.00  \n",
       "CNT_CREDIT_PROLONG_MAX              0.00           9.00  \n",
       "CNT_CREDIT_PROLONG_MEAN             0.00           6.00  \n",
       "CNT_CREDIT_PROLONG_STD              0.00           3.58  \n",
       "AMT_CREDIT_SUM_MIN             90,000.00 142,290,000.00  \n",
       "AMT_CREDIT_SUM_MAX          1,079,100.00 585,000,000.00  \n",
       "AMT_CREDIT_SUM_MEAN           397,859.33 198,072,344.25  \n",
       "AMT_CREDIT_SUM_STD            373,629.29 279,911,975.13  \n",
       "AMT_CREDIT_SUM_DEBT_MIN             0.00  51,750,000.00  \n",
       "AMT_CREDIT_SUM_DEBT_MAX       487,302.75 170,100,000.00  \n",
       "AMT_CREDIT_SUM_DEBT_MEAN      121,944.88  51,750,000.00  \n",
       "AMT_CREDIT_SUM_DEBT_STD       188,386.72  52,679,220.51  \n",
       "AMT_CREDIT_SUM_LIMIT_MIN            0.00   4,500,000.00  \n",
       "AMT_CREDIT_SUM_LIMIT_MAX            0.00   4,705,600.32  \n",
       "AMT_CREDIT_SUM_LIMIT_MEAN           0.00   4,500,000.00  \n",
       "AMT_CREDIT_SUM_LIMIT_STD            0.00   2,598,076.21  \n",
       "AMT_CREDIT_SUM_OVERDUE_MIN          0.00   1,617,403.50  \n",
       "AMT_CREDIT_SUM_OVERDUE_MAX          0.00   3,756,681.00  \n",
       "AMT_CREDIT_SUM_OVERDUE_MEAN         0.00   1,617,403.50  \n",
       "AMT_CREDIT_SUM_OVERDUE_STD          0.00   1,878,340.50  \n",
       "DAYS_CREDIT_UPDATE_MIN           -478.00          14.00  \n",
       "DAYS_CREDIT_UPDATE_MAX             -9.00         372.00  \n",
       "DAYS_CREDIT_UPDATE_MEAN          -209.00          14.00  \n",
       "DAYS_CREDIT_UPDATE_STD            665.73      29,612.22  \n",
       "AMT_ANNUITY_MIN                     0.00  24,016,036.50  \n",
       "AMT_ANNUITY_MAX                 7,249.50 118,453,423.50  \n",
       "AMT_ANNUITY_MEAN                3,316.64  27,282,428.75  \n",
       "AMT_ANNUITY_STD                     0.00  38,580,069.48  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abt_final.float_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>count</th>\n",
       "      <th>na_count</th>\n",
       "      <th>na_%</th>\n",
       "      <th>unique</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COUNT_GROUP</th>\n",
       "      <td>1</td>\n",
       "      <td>305811</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>153819</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OVERDUE</th>\n",
       "      <td>2</td>\n",
       "      <td>305811</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>301947</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEBT_OVER_50%</th>\n",
       "      <td>3</td>\n",
       "      <td>305811</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>121325</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEBT_OVER_75%</th>\n",
       "      <td>4</td>\n",
       "      <td>305811</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>155911</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEBT_OVER_100%</th>\n",
       "      <td>5</td>\n",
       "      <td>305811</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>270478</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE</th>\n",
       "      <td>6</td>\n",
       "      <td>305811</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>276932</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED</th>\n",
       "      <td>7</td>\n",
       "      <td>305811</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>218588</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADVERSE</th>\n",
       "      <td>8</td>\n",
       "      <td>305811</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>299772</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREDIT_TYPE_COUNT</th>\n",
       "      <td>9</td>\n",
       "      <td>305811</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>166653</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   num   count  na_count  na_%  unique  top  top_count  top_%\n",
       "column                                                                       \n",
       "COUNT_GROUP          1  305811         0  0.00       5    1     153819   0.50\n",
       "OVERDUE              2  305811         0  0.00       2    1     301947   0.99\n",
       "DEBT_OVER_50%        3  305811         0  0.00       6    1     121325   0.40\n",
       "DEBT_OVER_75%        4  305811         0  0.00       5    1     155911   0.51\n",
       "DEBT_OVER_100%       5  305811         0  0.00       3    1     270478   0.88\n",
       "ACTIVE               6  305811         0  0.00       2    1     276932   0.91\n",
       "CLOSED               7  305811         0  0.00       4    1     218588   0.71\n",
       "ADVERSE              8  305811         0  0.00       2    1     299772   0.98\n",
       "CREDIT_TYPE_COUNT    9  305811         0  0.00       6    2     166653   0.54"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abt_final.int_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BUREAU_COUNT_GROUP',\n",
       " 'BUREAU_OVERDUE',\n",
       " 'BUREAU_DEBT_OVER_50%',\n",
       " 'BUREAU_DEBT_OVER_75%',\n",
       " 'BUREAU_DEBT_OVER_100%',\n",
       " 'BUREAU_ACTIVE',\n",
       " 'BUREAU_CLOSED',\n",
       " 'BUREAU_ADVERSE',\n",
       " 'BUREAU_CREDIT_TYPE_COUNT']"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " [\"BUREAU_\"+ f for f in abt_final.int_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "bureau_int_f = ['BUREAU_COUNT_GROUP',\n",
    " 'BUREAU_OVERDUE',\n",
    " 'BUREAU_DEBT_OVER_50%',\n",
    " 'BUREAU_DEBT_OVER_75%',\n",
    " 'BUREAU_DEBT_OVER_100%',\n",
    " 'BUREAU_ACTIVE',\n",
    " 'BUREAU_CLOSED',\n",
    " 'BUREAU_ADVERSE',\n",
    " 'BUREAU_CREDIT_TYPE_COUNT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['COUNT_GROUP',\n",
       " 'OVERDUE',\n",
       " 'DEBT_OVER_50%',\n",
       " 'DEBT_OVER_75%',\n",
       " 'DEBT_OVER_100%',\n",
       " 'ACTIVE',\n",
       " 'CLOSED',\n",
       " 'ADVERSE',\n",
       " 'CREDIT_TYPE_COUNT',\n",
       " 'DAYS_CREDIT_MIN',\n",
       " 'DAYS_CREDIT_MAX',\n",
       " 'DAYS_CREDIT_MEAN',\n",
       " 'DAYS_CREDIT_STD',\n",
       " 'CREDIT_DAY_OVERDUE_MIN',\n",
       " 'CREDIT_DAY_OVERDUE_MAX',\n",
       " 'CREDIT_DAY_OVERDUE_MEAN',\n",
       " 'CREDIT_DAY_OVERDUE_STD',\n",
       " 'DAYS_CREDIT_ENDDATE_MIN',\n",
       " 'DAYS_CREDIT_ENDDATE_MAX',\n",
       " 'DAYS_CREDIT_ENDDATE_MEAN',\n",
       " 'DAYS_CREDIT_ENDDATE_STD',\n",
       " 'DAYS_ENDDATE_FACT_MIN',\n",
       " 'DAYS_ENDDATE_FACT_MAX',\n",
       " 'DAYS_ENDDATE_FACT_MEAN',\n",
       " 'DAYS_ENDDATE_FACT_STD',\n",
       " 'AMT_CREDIT_MAX_OVERDUE_MIN',\n",
       " 'AMT_CREDIT_MAX_OVERDUE_MAX',\n",
       " 'AMT_CREDIT_MAX_OVERDUE_MEAN',\n",
       " 'AMT_CREDIT_MAX_OVERDUE_STD',\n",
       " 'CNT_CREDIT_PROLONG_MIN',\n",
       " 'CNT_CREDIT_PROLONG_MAX',\n",
       " 'CNT_CREDIT_PROLONG_MEAN',\n",
       " 'CNT_CREDIT_PROLONG_STD',\n",
       " 'AMT_CREDIT_SUM_MIN',\n",
       " 'AMT_CREDIT_SUM_MAX',\n",
       " 'AMT_CREDIT_SUM_MEAN',\n",
       " 'AMT_CREDIT_SUM_STD',\n",
       " 'AMT_CREDIT_SUM_DEBT_MIN',\n",
       " 'AMT_CREDIT_SUM_DEBT_MAX',\n",
       " 'AMT_CREDIT_SUM_DEBT_MEAN',\n",
       " 'AMT_CREDIT_SUM_DEBT_STD',\n",
       " 'AMT_CREDIT_SUM_LIMIT_MIN',\n",
       " 'AMT_CREDIT_SUM_LIMIT_MAX',\n",
       " 'AMT_CREDIT_SUM_LIMIT_MEAN',\n",
       " 'AMT_CREDIT_SUM_LIMIT_STD',\n",
       " 'AMT_CREDIT_SUM_OVERDUE_MIN',\n",
       " 'AMT_CREDIT_SUM_OVERDUE_MAX',\n",
       " 'AMT_CREDIT_SUM_OVERDUE_MEAN',\n",
       " 'AMT_CREDIT_SUM_OVERDUE_STD',\n",
       " 'DAYS_CREDIT_UPDATE_MIN',\n",
       " 'DAYS_CREDIT_UPDATE_MAX',\n",
       " 'DAYS_CREDIT_UPDATE_MEAN',\n",
       " 'DAYS_CREDIT_UPDATE_STD',\n",
       " 'AMT_ANNUITY_MIN',\n",
       " 'AMT_ANNUITY_MAX',\n",
       " 'AMT_ANNUITY_MEAN',\n",
       " 'AMT_ANNUITY_STD']"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[f for f in final_dataset.columns if f !='SK_ID_CURR' and f not in bureau_int_f ]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z5fNs4F7g1yjyC0sy85d1bU0HVgEnADuBxZm5OSJ6gJspju7cCyzLzMfLOtdSHDN6CPCFzPx2695WksbeuK1gzsxLMnNeZh4PLAWezszzM3M38Chwfln0QmB9gyYe5K1zm08DBjJzaxlfEhFTImIOxaWCT5VtnBMR746ImcC/BB5u4StKkiRJkqQuEhEnAo9R5BJq48cBX6grfgdwR2YeCzwNrGjQ5NXAG5k5D7iG4p4pgHOAecBxFHdQ9UXE1Ij4OMXCu+OB04CbI+J9Y/BqkjRuxi3B/A6uAC6LiJ8AnwSuA4iIyyPixrLM7UBvRGwCVgIXlPG1wCbgWeC7wKWZuTMzf0gxs/gUxX8sVmTmS+P1QpIkSZIkqeMtA66kZid1RPQCdwLX18SmAadT5CCgSByf26C9hcA3ATJzAzCzXPm8EFiTmXsz8zlgK3AK8P8A387Mgcz8GfAIxSpnSeoaLT8iIzOPbhB7BDij5nlL7XONtcDnyzIDwEUN2toHLC9/6r/7CvCVKv2WJEmSJEkTW2YuBYiI2vCXgHuAF2pi7wd2ZOae8nk7cFSDJo8sv6Ou3MHiTzWIN63RheILFiwYSRMdob+/v91dkFTReJ7BXMVxwFfb3QlJkiRJkjTxRcRvA7Mz899ExBk1Xx0C7KsrvrdBE/XlespyI403bf78+fT29o6kSkfqxqS4NJEMDg42nLBqRkcnmMvtJJIkSZIkSePhM8CHI+LHwHuBIyLiWxTnJP9KREzJzDeBWdQcq1FjW/nd8+XzEWW5oThNxHPsXkeSWq9TzmCWJEmSJElqq8y8JDPnZebxwFLg6cw8PzN3A48C55dFLwTWN2jiwfI7IuI0YCAzt5bxJRExJSLmUFwq+FTZxjkR8e6ImAn8S+DhFr6iJI25jl7BLEmSJEmS1CGuAP4yIq6juKTvMwARcTlwZGZeD9wO3BkRm4BB4IKy7lrgRODZ8vnSzNwJ/DAi7qVINk8FVmTmS+P1QpI0FkwwS5IkSZKkSS0zj24QewQ4o+Z5S+1zjbXA58syA8BFDdraBywvf+q/+wrwlSr9lqRO4BEZkiRJkiRJ1R0HfLXdnZCkdnEFsyRJkiRJUkWZuaHdfZCkdnIFsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkiqZ2u4OSJLGR0T8KXBe+bguM/9tRPwFcBrwRhm/ITO/ExGfBm4BDgW+lZnXNWhvNnAv8GtAAksy85cRcTjwTeAY4FXgvMx8JSKmA6uAE4CdwOLM3Nyq95UkSZIkSa3nCmZJmgTKhPHvAB8DjgcWRMQfUCR7T8/M48uf70TEocA9wO8D84CPR8SZDZq9A7gjM48FngZWlPGbgEczcx5wF3BbGb8aeKOMXwP0teBVJUmSJEnSOHIFsyRNDtuBazNzF0BE/E9gdvlzT0R8APgOcAPwCeAfMvOFsuy9wLnA+qHGImIacDpwdhnqA74P/AmwsPwOYDXw1bL8QuB6gMzcEBEzI2J2Zm5t1UtLUqeKiBnAE8CizHxxuJ0jEXE8cDcwA9gAXJ6Ze+raGtHOkYjoAW4GFgF7gWWZ+XjLX1qSJEkTkglmSZoEMnPT0OeI+E2KozI+CZwBXAH8I/A3wKXALykS0kO2A0fVNfl+YEdNkqO2zJFD9TNzT0TsAGbWxuvqNJ1g3rhxY7NFAViwYMGIyquz9Pf3t7sLUktExIkUOzzmls9DO0c+BfwUWBcRZ2bmeoqjiJZm5pMRsQpYBnytrsmhnSMLI+ICip0j51OzcyQiTqeYDDwJOIdih8pxwJzy982rT1xLkiRJzTDBLEmTSER8GFgHfD4zE/iDmu9uBy4E1gL7aqr1UKxwq3VIXRlqyvTUxYfq19dp1O5BzZ8/n97e3pFUURdzgkCtMDg4OOLJqhZYBlwJfKN8brhzJCJ+AhyamU+W5foodprUJ5hHtHOkjK/JzL3AcxGxFTiFYoV0U+r/GXbqv69OVEmSJLWeCWZJmiQi4lTgfuCazFwTEb8FzM3M+8siPcBuYBswq6bqEcDLdc39DPiViJiSmW+W5YfKvFTW2RYRU4HDgNdq2n3+IO1K0oSXmUsBImIoNNwOj+Hi9Ua6c6TZdofVLRN+nZr4llTokEk/SdIoecmfJE0CEfFB4AGK8zfXlOEe4M8j4lfLlW6XUZzD/IOiSsyJiCnAYmrOXwbIzN3AoxRbsKFY+TxU5sHymfL7R8vy++MRcRow4PnLkgQMv8Oj2Z0fI905MuodJRPFrt1vtrsLb9OJfZIkSToYVzBL0uSwHHgXcEvNirmvA18CHgemAfdn5mqAiLiYYrXzuygSw2vL+N3A9zLzexRnN/9lRFxHcY7yZ8p2VwB9EbEJ+DmwpIzfDtxZxgeBC1r1spLUZYbbOdLMjhIY+c6RZtud8KZPm8Li6x9pdzcOcN+NZ7S7C5IkSSNiglmSJoHM/BzwuWG+vqNB+YeBjzYo+yDwZllmC8UlgfV1XwfOahAfAC5qutOSNHns3zkCvECxc+SezNwSEQMRcWpmPk4xMbe+Qf2hHSJfpGbnSEQMxR+r3TlSxi+JiNXAb1BcNvhUq19SkiRJE5NHZEiSRmIq8Hft7oQkTSTlBNzFFDtHfgJsptw5QrEL5NaI2Ay8F1gJEBE3RsTlZZkVwEnlDpErKC4QhGLnSG8ZX8lbO0fWApuAZ4HvApdm5s6WvaAkSZImNFcwS5Kalpl/1e4+SNJEkZlH13xuuHMkM58BPtGg+gPAyWWZEe0cycx9FEcnLa/YdUmSJGk/VzBLkiRJ3WcWcF+7OyFJkiS5glmSJEnqMpm5rt19kCRJksAVzJIkSZIkSZKkikwwS5IkSZIkSZIqMcEsSZIkSZIkSarEBLMkSZIkSZIkqRITzJIkSZIkSZKkSkwwS5IkSZIkSZIqMcEsSZIkSZIkSapkaqt/QUTMAJ4AFmXmixFxGXA1sA94GvhsZu6KiOOBu4EZwAbg8szcU9fW4cA3gWOAV4HzMvOViJgOrAJOAHYCizNzc0T0ADcDi4C9wLLMfLzV7yxJkiRJkiRJk0FLVzBHxInAY8Dc8nku8HngFOAj5e+/six+L3BVZs4FeoBlDZq8CXg0M+cBdwG3lfGrgTfK+DVAXxk/B5gHHAecDfRFRMuT6pIkSZIkSZI0GbT6iIxlFAnkl8vnQeCKzNyRmfuAvwdmR8SHgEMz88myXB9wboP2FlKsYAZYDZwZEdNq45m5AZgZEbPL+JrM3JuZzwFbKZLbkiRJkiRJkqRRaulq3sxcChARQ89bgC1lbCZwFXAxcCSwvabqduCoBk3uL5eZeyJiBzDzIPWbbXdYGzduPOB5wYIFI6neEfr7+9vdBUmSJEmSJEkTUFuOi4iIDwDrgVWZ+UhEnEpxJvOQHoozk+v1NHjeS7ESu1H94eJNmz9/Pr29vSOp0nG6MSkuTSSDg4Nvm6ySJEmSJEmaCMY9wRwRxwL/FViZmV8pw9uAWTXFjuCtYzVqvVR+t608S/kw4LWa+s/X1W+2XUmSJEmSNAlFxAzgCWBRZr4YEZdR3PW0D3ga+Gxm7oqI44G7gRnABuDyzNxT19bhFEd4HgO8CpyXma9ExHRgFXACsBNYnJmbI6IHuBlYRLEgbllmPt76t5aksdPqM5gPEBGHAQ8B19Ukl4eOzhgoVzIDXECxwrneg8CF5efzKS78210bj4jTgIHM3FrGl0TElIiYQ3HZ4FNj/2aSJEmSJKnbRMSJwGMU+QIiYi7weYr7mz5CkTe5six+L3BVZs6l2CG9rEGTN1HkKuYBdwG3lfGrgTfK+DUUd08BnAPMA44Dzgb6ygV1ktQ1xjXBDCwFfh24NiJ+XP7cWH63BLg1IjYD7wVWAkTEjRFxeVlmBXBSRGwCruCtQf52oLeMr6RIUAOsBTYBzwLfBS7NzJ0tfUNJkiRJktQtllHkFoZ2Ow8CV2TmjszcB/w9MDsiPgQcmplPluX6gHMbtLeQYgUzwGrgzIiYVhvPzA3AzIiYXcbXZObezHwO2EqR3JakrjEus2KZeXT58dbyp1GZZ4BPNPjqAeDksszrwFkN6g4AFzWI7wOWlz+SJEmSJEn7ZeZSgIgYet4CbCljM4GrgIuBI4HtNVW3A0c1aHJ/uczcExE7gJkHqd9su8NqdN9LN97F1N/f3+4uSKqoG7ZdzALua3cnJEmSJEnS5BARH6A4unNVZj5SHum5r6ZID8WZyfV6GjzvpdhB3qj+cPGmzZ8/n97e3pFU6UjdmBSXJpLBwcGGE1bN6PgEc2aua3cfJEmSJEnS5BARxwL/FVhZc3/UNooFcEOO4K1jNWq9VH63rTxL+TDgtZr6z9fVb7ZdSepY430GsyRJkiRJUkeKiMOAh4DrapLLQ0dnDJQrmaG4+2l9gyYeBC4sP59PceHf7tp4RJwGDGTm1jK+JCKmRMQcissGnxr7N5Ok1un4FcySJEmSJEnjZCnw68C1EXFtGfteZl4PLAHuiogZwI+AlQARcSPwcmZ+HVgB9EXEJuDnZR2A24E7y/ggRYIaYC1wIvBs+XxpZu5s5QtK0lgzwSxJkiRJkia1zDy6/Hhr+dOozDPAJxp89QBwclnmdeCsBnUHgIsaxPcBy8sfSepKHpEhSZIkSZJU3SzgvnZ3QpLaxRXMkiRJkiRJFWXmunb3QZLayRXMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwSxJkiRJkiRJqsQEsyRJkiRJkiSpEhPMkiRJkiRJkqRKTDBLkiRJkiRJkioxwawD7Nr9Zru7MCLd1l9JkiRJkiRpIpna7g6os0yfNoXF1z/S7m407b4bz2h3F6SuERF/CpxXPq7LzH8bEZ8GbgEOBb6VmdeVZY8H7gZmABuAyzNzT117hwPfBI4BXgXOy8xXImI6sAo4AdgJLM7MzRHRA9wMLAL2Assy8/GWvrQkSZIkSWopE8ySNAmUieTfAT4G7AP+/4j4DPBl4FPAT4F1EXFmZq4H7gWWZuaTEbEKWAZ8ra7Zm4BHM3NhRFwA3AacD1wNvJGZ8yLidKAPOAk4B5gHHAfMKX/fvPrEtSRNZhHxR8C/Kx/XZ+ZyJ/0kSZLUyTwiQ5Imh+3AtZm5KzN3A/8TmAv8Q2a+UCYq7gXOjYgPAYdm5pNl3T7g3AZtLqRIZgCsBs6MiGm18czcAMyMiNllfE1m7s3M54CtwCkteFdJ6koR8W5gJcXE30eBT5YThPcCV2XmXKCHYtKv3tCk3zzgLopJP6iZ9AOuoRjT4cBJv7OBvohw8YkkSZJGzD8iJWkSyMxNQ58j4jcpjsq4nSLxPGQ7cBRw5DDxevvLZeaeiNgBzDxI/WbbHdbGjRtHUpwFCxaMqLw6S39/f7u7II23KRQLQN4DvAFMA3bz9km/G3j7rpKFwOnl59XAV2sm/a6HYtIvIt426Qc8FxFDk34bWvRukiRJmqBMMEvSJBIRHwbWAZ9fY0ytAAAgAElEQVQH9lCsYh7SQ7FN+hCKYzTq4/V6GjwfrH6z7Q5r/vz59Pb2jqSKupgTBGqFwcHBEU9WjZfM/EVErAA2A/8EfB/YRQdO+tX/M+zUf1/faaKqW/stSZLUSVqeYI6IGcATwKLMfNELpSSpPSLiVOB+4JrMXBMRnwJm1RQ5AngZ2DZMvN5L5Xfbym3VhwGv1dR/vmK7kjQpRcRHgEuADwH/SHE0xu/QgZN+3TLh16kJ5HfSrf2WRqqTJ/0kSc1r6RnMEXEi8BjlCrmIOBS4B/h9ijPfPh4RZ5bFPVtOklokIj4IPEAxAbemDP+g+CrmRMQUYDHFhVJbgIEyIQ1wAbC+QbMPAheWn8+nGJ9318Yj4jRgIDO3lvElETElIuZQ/LfhqbF+V0nqYr8LPJyZP8vMQYq/ac9gZJN+DDPpV1/fST9JkiSNiVZf8rcMuJK3/lj9BF4oJUntsBx4F3BLRPw4In4MXFz+3A/8hGJL9tqy/BLg1ojYDLyX4tIpIuLGiLi8LLMCOCkiNgFXUIz3UJzt3FvGV1IkqCnb3gQ8C3wXuDQzd7bkbSWpOz0DfDoi3lPuxPs9imMynPSTJElSx2rpat7MXAoQEUOh0Z4B1/YLpbpxu9pIznCb6O8nTVaZ+Tngc8N8/dEG5Z+hmBSs9wBwclnmdeCsBnUHgIsaxPdRJLqXN91xSZpEMvOhiPgY0E9xud8Pgf8EfAe4qzx67kfUTPoBL2fm1ykm/frKyb2fU0wUQjHpd2cZH+TASb8TKSb9wEk/SZIkVTTex0WM9gw4L5SqoBuTxiMx0d9P3W+CnS03C7iv3Z2QpIkqM78MfLku7KSfJLWQd0dJ0ui0+oiMeqM9A86z5SSpjTJzXWb+n3b3Q5IEOOknSaPm3VGSNHrjnWD2QilJkiRpDDjpJ0ljwrujJGmUxnVWLDMHIuJiigul3kWRAK69UMqz5SRJkiRJ0riYiHdHQXceJen9SlL3GpcEc2YeXfP5YbxQSpIkSZIkdR7vjmqTbkyKSxPJaO6PGu8jMqrwbDlJkiRJkjQevDtKkkao4xPMni0nSZIkSZLGiXdHSdIIdXyCWZIkSZIkaTyUR3BeTHF31E+AzRx4d9StEbEZeC81d0dFxOVlmRXASeUdUVdQXCAIxd1RvWV8JQfeHbWJ4u6o7+LdUZK60Lhe8idJkiRJktRpvDtKkqpzBbMkSZIkSVJ13h0laVJzBbMkSZIkSVJFmbmu3X2QpHZyBbMkSZIkSZIkqRITzJIkSZIkSZKkSkwwS5IkSZIkSZIqMcEsSZIkSZIkSarEBLMkSZIkSZIkqRITzJIkSZIkSZKkSkwwS5IkSZIkSZIqMcEsSZIkSZIkSarEBLMkSZIkSZIkqRITzJIkSZIkSZKkSkwwS5IkSZIkSZIqMcEsSZIkSZKkrrBr95vt7sKIdFt/pSqmtrsDkiRJkiRJUjOmT5vC4usfaXc3mnbfjWe0uwtSy7mCWZIkSZIkSZJUiQlmSZIkSZIkSVIlJpglSZIkSZIkSZWYYJYkSZIkSZIkVWKCWZIkSZIkSZJUiQlmSZIkSZIkSVIlJpglSZIkSZIkSZWYYJYkSZIkSZIkVWKCWZIkSZIkSZJUiQlmSZIkSZIkSVIlJpglSZIkSZIkSZWYYJYkSZIkSZIkVWKCWZIkSZIkSZJUiQlmSZIkSZIkSVIlJpglSZIkSZIkSZU0lWCOiFUNYmvHvjuSpHfimCxJncMxWZI6g+OxJLXP1IN9GRFfAz4AfDIiZtZ8NQ04ppUdkyQdyDFZkjqHY7IkdQbHY0lqv4MmmIFVwHzgo8D9NfE9wJOt6pQkqSHHZEnqHI7JktQZHI8lqc0OmmDOzKeBpyPi7zJz2zj1SZLUgGOyJHUOx2RJ6gyOx5LUfu+0gnnIByPiG8D7gJ6hYGZ+pCW9kiQdjGOyJHUOx2RJ6gyOx5LUJs0mmO8E+oAfAfta1htJUjMckyWpczgmS1JncDyWpDZpNsG8JzNvaWlPJEnNckyWpM7hmCxJncHxWJLa5JAmy22MiN9qaU8kSc1yTJakzuGYLEmdwfFYktqk2RXMxwD9EbEF2DkU9CwjSWoLx2RJ6hyOyZLUGRyPJalNmk0w//uW9kKSNBKOyZLUORyTJakzOB5LUps0m2D++5b2QpI0Eo7JktQ5HJMlqTM4HktSmzSbYP7fFLew9vDWbazbgaNa0SlJ0kE5JktS53BMlqTO4HgsSW3SVII5M/dfBhgR04HFQLSqU5Kk4TkmS1LncEyWpM7geCxJ7dPsCub9MnMX0BcRTwP/rsovjYg/qqm7PjOXR8TxwN3ADGADcHlm7qmrdzjwTYrD+18FzsvMV8r/eKwCTqA4zH9xZm6OiB7gZmARsBdYlpmPV+mzJHWisRiTJUljwzFZkjqD47Ekja+mEswR8b6axx6KRO6vVvmFEfFuYCUwF/g58HhEfBr4c2BpZj4ZEauAZcDX6qrfBDyamQsj4gLgNuB84GrgjcycFxGnA33AScA5wDzgOGAOsC4i5tUnriWpm4zlmCxJGh3HZEnqDI7HktQ+Vc5gBvgZRVK3iinAIcB7gDeAacBu4NDMfLIs0wfcwNsTzAuB08vPq4GvRsS0Mn49QGZuiIiZETG7jK/JzL3AcxGxFTiFYoW0JHWrsRyTJUmj45gsSZ3B8ViS2mTEZzCPVmb+IiJWAJuBfwK+D+yiOHx/yHAH8R85VC4z90TEDmBmbbyu/nDxpm3cuPGA5wULFoykekfo7+9vuuxEfz9pIhjLMVmSNDqOyZLUGcZ6PPZoT0lqXrNHZBwCLAfOpFhx/BDwxSpHTUTER4BLgA8B/wjcC/wOb93yCsWM494G1XsaPO+lWBHdqP5w8abNnz+f3t7ekVTpON2YNB6Jif5+6n6Dg4Nvm6wajbEckyVJo+OYLEmdYYzzFh7tKUkj0OwM35eAf0ExMN5CcczEzRV/5+8CD2fmzzJzkGJQPQOYVVPmCODlBnVfKr8jIqYChwGvAduGqT9cXJK62ViOyZKk0XFMlqTOMJbjce3RntMY/mjPcxvUXUixghmKoz3PrDna85tQHO0JvO1oz8x8Dhg62lOSukazZzD/K+CEzNwNEBHrgGeAf13hdz4D/OeIeA/FERm/R3FMxh9GxKnlVpALgPUN6j4IXAh8kWIG8NHM3B0RQ/HHIuI0YCAzt5bxSyJiNfAbFLOPT1XosyR1krEckyVJo+OYLEmdYczG424/2hO6c6dvs8dfTuR3k7pVswnmQ4YGaYDMHIyI3QerMJzMfCgiPgb0U8wA/hD4T8B3gLsiYgbwI4rtKETEjcDLmfl1YAXQFxGbKLapLCmbvR24s4wPUiSoAdYCJwLPls+XZubOKv2WpA4yZmOyJGnUHJMlqTOM2Xjs0Z7t0Y2J42ZN5HfTxDGa4z2bTTD/OCJuBf4LxcD3x7yVtB2xzPwy8OW68DPAJxoUfwA4uaz3OnBWg/YGgIsaxPdRnMG0vGpfJakDjemYLEkaFcdkSeoMYzke7z/aEyAi+ijyCiM52nPbMEd7Pl9X36M9JXW9Zs9gvhL4VeAJ4AfA+ykG6/EwC7hvnH6XJHWDdo7JkqQDOSZLUmcYy/H4GeDTEfGeiOjhraM9ByLi1LLMOx3tCTVHe9bGa4/2LONLImJKRMzBoz0ldaGDrmCOiOnAXcADmXlxGVsHvAnsaHnvgMxcNx6/R5I63ViMyeUxRE8AizLzxYj4C+A04I2yyA2Z+Z3yluxbgEOBb2XmdQ3amk2xXfDXgASWZOYvI+JwigtMjgFeBc7LzFfK/q8CTgB2Aoszc3OFfxSS1Had8HeyJKk147FHe0rSyLzTERk3AjOAx2tiy4A7gD8D3pZwkCS1zKjG5Ig4keKP77k14ROA0zNze025Q4F7gE8BPwXWRcSZmVm/QuMO4I7MXFNegrIC+BPgJoqVGgsj4gKKm7zPB64G3sjMeRFxOsXN2yc1//qS1FFa8ndyRPwe8KfAe4CHMvNzTvpJ0kG1ZDz2aE9Jat47HZGxiOKPzZ8NBTLzZYptHX/Qyo5Jkt5mtGPyMoqtgy8DRMS7gdnAPRHxbETcEBGHUPzR/A+Z+UJm7qFIWJxb21BETANOp1hxAUWyeKjMQopkBsBq4Myy/P54Zm4AZpYJEUnqRmP+d3JEHAN8HTgb+AjwzyLiTIpJv98H5gEfL2P1hib9jgWeppj0g7cm/eZRTDLeVsb3T/oB11CM45LUjTohb+HRnpImtXdawbyr0daMzNwREYMt6pMkqbFRjcmZuRQgIoZCRwD/DbiC4nbsvwEuBX4JbK+puh04qq659wM7ygR0fZkjh+pn5p6I2AHMrI3X1dn6Tn0fMtIbbb2tubv19/e3uwvSwbTi7+Q/oFihvA0gIs4HfpNy0q+MDU367d9VUjPpd3YZ6qM4K/RPKCb3Ti/jq4Gv1kz6XV/2eUNEzIyI2eV5oJLUTdqet/BoT0mT3TslmN+MiMMy8xe1wYg4DJjWum5JkhoY0zE5M/8XNas6IuJ2ipUeaylu3h7SA+ytq35IXRlqyvTUxYfq19dp1O5BzZ8/n97e3pFUURdzgkCtMDg4OOLJqmG04u/kOcCuiPgexQ6TvwE20YGTfvX/DDv139d3mqjq1n5LOoB5C0lqs3dKMK8G7o6ISzLzDYCIeA9wN3B/qzsnSTrAmI7JEfFbwNzMHKrbQ3GJyTaKbX5DjqA8VqPGz4BfiYgpmflmWX6ozEtlnW0RMRU4DHitpt3nD9KuJHWLVvydPJVitfEZFLtJvkdxPnLHTfp1y4RfpyaQ30m39lsaqTGa9DNvIUlt9k5nMP85xbbpVyLiyYj4IfAK8H8oDtKXJI2fsR6Te4A/j4hfLbdLX0ZxM/YPgIiIORExBVhMzVZsgMzcDTxKcXkfFCufh8o8WD5Tfv9oWX5/PCJOAwbcii2pi7Xi7+RXgL/LzFfL7d7fAT7NCCb9yudGk34MM+l3sHYlqRuYt5CkNjvoCubM3AtcFhH/EVhAsarhB5m5/WD1JEljb6zH5Mx8NiK+RHHj9jTg/sxcDRARF1Os+HgXRWJ4bRm/G/heZn6P4uzmv4yI6yi2VH+mbHoF0BcRm4CfA0vK+O3AnWV8ELigSr8lqRO06O/kv6EYVw8HfgGcSTH+fiEi5gAvUEz63VPXl90RMTTpdx+NJ/2+SM2kX0QMxR9z0k9SNzNvIUnt905HZACQmVuALS3uiySpCaMdkzPz6JrPdwB3NCjzMPDRBtUfBN6s6ccZDeq+DpzVID4AXFSx25LUkcby7+TM/EFE/GfgMYqJv78FvgZsxkk/SToo8xaS1D5NJZglSSpNBbwlW5JaJDPvoW6FMuCknyRJkjqWCWZJUtMy86/a3QdJ0n5O+kmSJKntTDBLkiRJXchJP0mSJHWCQ9rdAUmSJEmSJElSdzLBLEmSJEmSJEmqxASzJEmSJEmSJKkSE8ySJEmSJEmSpEpMMEuSJEmSJEmSKjHBLEmSJEmSJEmqxASzJEmSJEmSJKkSE8ySJEmSJEmSpEpMMEuSJEmSJEmSKjHBLEmSJEmSJEmqxASzJEmSJEmSJKkSE8ySJEmSJEmSpEpMMEuSJEmSJEmSKjHBLEmSJEmSJEmqxASzJEmSJEmSJKkSE8ySJEmSJEmSpEpMMEuSJEmSJEmSKjHBLEmSJEmSJEmqxASzJEmSJEmSJKkSE8ySJEmSJEmSpEpMMEuSJEmSJEmSKjHBLEmSJEmSJEmqxASzJEmSJEmSJKkSE8ySJEmSJEmSpEpMMEuSJEmSJEmSKjHBLEmSJEmSJEmqxASzJEnS/2Xv3qPtqupDj38PeZkqiJZYUMSWIj+DuSXeACIvY6XewQ1FrUKGoTxawXIBUeujWgElBXt9gUBBuDwMvRCggqISUCsVCfiopoINj58MiyIQrii2CJJ37h9r7bCz2edkn5Vzsvba5/sZ4wzOnnvOtX8Lkt9e/Nacc0mSJEmSKrHALEmSJEkNsnrNurpDeJZ+jEmSJG0dk+sOQJIkSZLUu6lTJrHg9FvrDmMTixfOrTsESZJUE2cwS5IkSZIkSZIqscAsSZIkSZIkSarEArMkSZIkSZIkqRILzJIkSZIkSZKkSiwwS5IkSZIkSZIqmVzHh0bEnwIfAZ4LfD0z3xURBwNnA9OBazPz1C7jdgGuBF4EJHBkZj4ZEdsDVwG7Ao8BR2TmoxExFbgM2At4GliQmfeN/xlKkiRJkiRJ0uDb6jOYI2JX4CLgTcAfAf89Ig4BLgfeCMwE9i7bOl0IXJiZrwB+AJxWtp8JLM3MmcAlwLll+ynAU2X7u4FF43JSkiRJkiRJkjQB1TGD+c0UM5QfAoiI+cDLgfsz84Gy7UrgcODm1qCImAIcRFGYhqJY/C3gb4B55XsAVwMXlP3nAacDZOZtETEjInbJzAd7DXb58uWbvJ4zZ85ozrUvLFu2rOe+g35+kiRJkiRtjiuvJal3dRSYdwNWR8SXgV2AG4G7gRVtfVYAO3eM2wF4IjPXdunz4tb4zFwbEU8AM9rbO8b0XGCeNWsW06ZN67V7X2pi0Xg0Bv381HyrVq161s0qSZIkSf2pbeX1q4H/B/xLucr6YuC1wM+BJRFxSGbe3DG8tfL6mog4jWLl9d/wzMrreRFxFMXK6/m0rbyOiIMoJtPtO+4nKUljqI6H/E0GDgbeDryGImHvCmxo6zMErO8Yt01HH9r6DHW0t8Z3jul2XEmSJEmSpJaNK68zcw1FIfi3lCuvy4lvrZXXG7WtvL6ubFrU1mcexQxmKFZeH9K28voqKFZeAzPKWdCS1Bh1zGB+FPhGZj4GEBFfpEi469r67Ag80jHuF8DzI2JSZq4Ddmrr83A55qGImAxsC/wKeKjs95MRjitJkiRJktTSqJXX3VZLNnGlb6/bXw7yuUlNVUeB+UbginL/od8Ah1Dc3ftgROwGPAAsoHjo30aZuSYillLcOVwMHM0zezTfVL7+WPn+0rJ/q/32iDgAWDma/ZclSZIkSdKEM5liJvJc4EngyxT7I/flyutB2NoTmlk47tUgn5sGx5Zs77nVt8jIzO8BnwBuB+4BfgZ8FjgWuL5su49ySUlEXBoRh5XDTwTeERH3AAcCrQ31TwP2jYi7yz4nle3nA9PK9vOAo8b15CRJkiRJUtNtXHmdmU8DX6TY6nOntj4jrrwuX3dbec0wK69HOq4k9bU6ZjCTmZfTMUMZuAXYs0v3myi3z8jMn1HcQew83uPAYV3aVwLHbGG4kiRJkiRp4nDltSSNQh0P+RutycA36g5CkiRJkiQNPldeS9Lo1DKDeTQy85/qjkGSJEmSJE0crryWpN41YQazJEmSJElSv3LltaQJre9nMEuSJEmSJPUrV15LmuicwSxJkiRJkiRJqsQCsyRJkiRJkiSpEgvMkiRJkiRJkqRKLDBLkiRJkiRJkiqxwCxJkiRJkiRJqsQCsyRJkiRJkiSpEgvMkiRJkiRJkqRKLDBLkiRJkiRJkiqxwCxJkiRJkiRJqsQCsyRJkiRJkiSpEgvMkiRJkiRJkqRKLDBLkiRJkiRJkiqZXHcAkqStIyK2A74NHJqZP42Ig4GzgenAtZl5atlvNnApsB1wG3BCZq7tONb2wFXArsBjwBGZ+WhETAUuA/YCngYWZOZ9ETEEfBI4FFgPHJ+Zd4z7SUuSJEmSpHFlgVmSJoCIeDVwCbB7+Xo6cDnwWuDnwJKIOCQzbwauBI7LzO9GxGXA8cBnOw55JrA0M+dFxFHAucB84BTgqcycGREHAYuAfYG3ADOBPYDdys+b2Vm4liRBRHwK2CEzj/WmnyRJkvqdW2RI0sRwPHAS8Ej5eh/g/sx8oCxUXAkcHhEvA6Zn5nfLfouAw7scbx5FMQPgauCQiJjS3p6ZtwEzImKXsv2azFyfmT8GHgT2G+NzlKTGi4jXA8e0NV0JnJyZuwNDFPm8U+um30yKm4nnlu0bb/oB76bI6bDpTb83AYsiwoknkiRJqsQLSUmaADLzOICIaDW9GFjR1mUFsPMI7Z029svMtRHxBDBjDI47ouXLl4+q/5w5c0b7Eeojy5YtqzsEaauKiBcCZwEfA/Yc5qbfGTx7Vck84KDy96uBC9pu+p0OxU2/iHjWTT/gxxHRuul3W6+xdubjfs23m8sjxj22zNuSJE1MFpglaWLaBtjQ9nqIYpn0cO2dhrq8Hml8r8cd0axZs5g2bdpoh6mh+rWAomZbtWrVqG9WbUUXAx8GXlq+7tubfk3Jx03NI8atiaLPc7IkqUdukSFJE9NDwE5tr3ek2D5juPZOD5fvUS6r3hb41RgcV5ImpIg4Dvh5Zt7S1tzXN/0kSZIksMAsSRPV94CIiN0iYhKwALg5M38GrIyI/ct+RwE3dxl/E3B0+ft8ir0/17S3R8QBwMrMfLBsPzIiJkXEbhQPG/z+OJ2bJDXRfOANEXEnsBA4DDgOb/pJkiSpz1lglqQJKDNXAscC1wP3APcB15VvHwmcExH3Ac8DzgOIiIURcULZ5zRg34i4GziR4gGCAOcD08r28ygK1JTHvhv4EfAl4O2Z+fS4naAkNUxm/klmzsrM2RT7Jn85M/8Cb/pJkiSpz7kHsyRNIJn5+22/3wLs2aXPXcA+XYbfALym7PM4xey6zrErgWO6tG8A3lf+SJJ6dyRwSURsB/wbbTf9gEcy8yKKm36Lypt7/1mOgeKm38Vl+yo2ven3aoqbfuBNP0mSJG0BC8ySpF7tBCyuOwhJGnSZuQhYVP7uTT9JkiT1NQvMkqSeZOaSumOQJG3kTT9JkiT1BQvMkiRJUsN400+SJEn9wof8SZIkSZIkSZIqscAsSZIkSZIkSarEArMkSZIkSZIkqRILzJIkSZIkSZKkSiwwS5IkSZIkSZIqscAsSZIkSZIkSarEArMkSZIkSZIkqRILzJIkSZIkSZKkSiwwS5IkSZIkSZIqscAsSZIkSZIkSarEArMkSZIkSZIkqRILzJIkSZIkSZKkSiwwS5IkSZIkSZIqscAsSZIkSZIkSarEArMkSZIkSZIkqRILzJIkSZIkSZKkSiwwS5IkSZIkSZIqscAsSZIkSZIkSapkcp0fHhGfAnbIzGMjYjZwKbAdcBtwQmau7ei/PXAVsCvwGHBEZj4aEVOBy4C9gKeBBZl5X0QMAZ8EDgXWA8dn5h1b6fQkSZIkSZIkaaDVNoM5Il4PHNPWdCVwcmbuDgwBx3cZdiawNDNnApcA55btpwBPle3vBhaV7W8BZgJ7AG8CFkVErUV1SZIkSZIkSRoUtRRbI+KFwFnAx4A9I+JlwPTM/G7ZZRFwBvDZjqHzgIPK368GLoiIKWX76QCZeVtEzIiIXcr2azJzPfDjiHgQ2I9ihrQkSZIkSdKwXHktSZtX12zei4EPAy8tX78YWNH2/gpg5y7jNvbLzLUR8QQwY4TxvR53WMuXL9/k9Zw5c0YzvC8sW7as576Dfn6SJEmSJPWibeX1krLpSuC4zPxuRFxGsfK6c2Jca+X1vIg4imLl9XzaVl5HxEEUE+v2ZdOV17sBSyJiZmfhWpL62VYvMEfEccDPM/OWiDi2bN4G2NDWbYjizl2noS6v148wvtfjDmvWrFlMmzZtNEP6ThOLxqMx6Oen5lu1atWzblZJkiRJ6l+uvJak3tUxg3k+sFNE3Am8EHgeRRF4p7Y+OwKPdBn7cPneQ+VeytsCvwIeKsf/pGN8q31zx5UkSZIkSWpp7MpraOZErF5XJw/yuUlNtdULzJn5J63fyxnMczPzLyJieUTsX+41dBRwc5fhNwFHU9xBnE+x7GRNRLTab4+IA4CVmflg2f6XEXE18AfA7sD3x/P8JEmSJElSc7nyuh5NLBz3apDPTYNjS1Zf17UHczdHApdExHbAvwHnAUTEQuCRzLwIOA1YFBF3A/9ZjgE4H7i4bF9FUaAGuA54NfCj8vXbM/PprXEykiRJkiSpkVx5LUmjUGuBOTMXUexbRGbeBezTpdsNwGvKPo8Dh3U5zkqKjfc72zcA7yt/JEmSJEmSRuTKa0kanX6awTycnYDFdQchSZIkSZImNFdeS1IXfV9gzswldccgSZIkSZImHldeS9LmbVN3AJIkSZIkSQ3mymtJE1rfz2CWJEmSJEnqV6681lhZvWYdU6dMqjuMUWlizBp7FpglSZIkSZKkmk2dMokFp99adxijsnjh3LpDUB9wiwxJkiRJkiRJUiUWmCVJkiRJkiRJlVhgliRJkiRJkiRVYoFZE8bqNevqDmFUmhavJEmSJEmSJh4f8qcJo2mb5btRviRJkiRJkvqdM5glSZIkSZIkSZVYYJYkSZIkSZIkVWKBWZIkSZIkSZJUiQVmSZIkSZIkSVIlFpglSZIkSZIkSZVYYJYkSZIkSZIkVWKBWZIkSZIkSZJUiQVmSZIkSZIkSVIlFpglSZIkSZIkSZVYYJYkSZIkSZIkVWKBWZIkSZIkSZJUiQVmSZIkSZIkSVIlFpglSZIkSZIkSZVYYJYkSZIkSZIkVWKBWZIkSZIkSZJUiQVmSZIkSZIkSVIlFpglSZIkSZIkSZVYYJYkSZIkSZIkVWKBWZIkSZIkSZJUiQVmSZIkSZIkSVIlFpglSZIkSeNu9Zp1dYfwLP0YkyRJTTO57gAkSZIkSYNv6pRJLDj91rrD2MTihXPrDkGSpMazwCxJE1xEfBN4EbCmbPor4A+BU4EpwGcy84Iu42YDlwLbAbcBJ2Tm2ojYBbiyPGYCR2bmkxGxPXAVsCvwGHBEZj46ricnSQ0TER8BjihfLsnMD0TEwcDZwHTg2sw8tcu4UeXeiJgKXAbsBTwNLMjM+8b59CRJkjSA3CJDkiawiBgCdgf2zMzZmTkbeAg4CzgAmA28IyL26DL8SuDkzNwdGAKOL9svBC7MzFcAPwBOK9vPBJZm5kzgEuDccTotSWqkspD8BuBVFIWKhQ0AACAASURBVPl3TkS8DbgceCMwE9g7Ig7pMny0ufcU4Kmy/d3AonE5KUmSJA08C8ySNLFF+c+vR8RdEXEycDDwL5n5eGY+BVwHvHWTQREvA6Zn5nfLpkXA4RExBTioHLOxvfx9HsUsOoCrgUPK/pKkwgrgvZm5OjPXAPdS3AS8PzMfyMy1FDf3Dm8fVDH3bmzPzNuAGeUsaEmSJGlU3CJDkia2FwC3AO+k2A7jVuBaiiJHywpgn45xL+7SZ2dgB+CJsgjS3r7JmHIrjSeAGcAjvQa7fPnyXrsCMGfOnFH1V39ZtmxZ3SFIW1Vm3t36PSJeTrFVxvl0z7ftquTe4fL4g73E2pmP+zXfbi6PGPfYGtS4JUnSyCwwS9IElpnfAb7Teh0Rl1Hs83lmW7chYH3H0G2ADV36dLbTNnaoo73bcUc0a9Yspk2bNpoharB+LUSo2VatWjXqm1VbW0S8ElgCvB9YSzGLuaWXnAybz73D5fGeNCUfNzWPGPfW1dS4B0ETcrIkafPcIkOSJrCIOCAiXt/WNAT8FNiprW1Hnj3L+KFh+vwCeH5ETCrbd2ob+3DZj4iYDGwL/GrLz0KSBkdE7E+xsuSDmXkFw+fbdlVyby/HlSRJkjbLArMkTWzbA5+MiOdExLbAMcCfA6+PiBkR8TvAW4Cvtg/KzJ8BK8tCCMBRwM3lnqFLgfll+9HAzeXvN5WvKd9fWvaXJAER8VLgBmBBZl5TNn+veCt2KwvIC3gmrwJQMfdubI+IA4CVmdnT9hiSJElSOwvMkjSBZeaNFMuwfwgsAy7PzDuADwPfBO4EFmfmvwJExE0RsVc5/EjgnIi4D3gecF7ZfiLwjoi4BzgQOLVsPw3YNyLuLvucNN7nJ0kN8z7gOcDZEXFnRNwJHFv+XA/cA9xH+TC/iLg0Ig4rx442954PTCvbz6O4UShJkiSNmnswS9IEl5mnURQg2tsWA4u7dP8G8HTZ5y6e/fC/1uzmuV3aHwcO62yXJBUy813Au4Z5e88ubTcB68qxo8q9mbmSYtWKJEmStEWcwSxJGo1fUMygkyTVbzLFjT9JkiSpNs5gliT1LDOvrDsGSVIhM/+p7hgkSZIkZzBLkiRJkiRJkiqxwCxJkiRJkiRJqsQCsyRJkiRJkiSpEgvMkiRJkiRJkqRKannIX0R8BDiifLkkMz8QEQcDZwPTgWsz89Qu43YBrgReBCRwZGY+GRHbA1cBuwKPAUdk5qMRMRW4DNgLeBpYkJn3jfPpSZIkSZIkSdKEsNULzGUh+Q3Aq4ANwFcj4m3Ax4HXAj8HlkTEIZl5c8fwC4ELM/OaiDgNOA34G+BMYGlmzouIo4BzgfnAKcBTmTkzIg4CFgH7jvtJSpIkSZKkxnJinCT1ro4tMlYA783M1Zm5BrgX2B24PzMfyMy1FMn48PZBETEFOAi4rmxa1NZnHkWiBrgaOKTsv7E9M28DZpTJXpIkSZIk6Vk6JsbNBuaUE+MuB94IzAT2johDugxvTYx7BfADiolx8MzEuJnAJRQT46BtYhzwbopahyQ1ylafwZyZd7d+j4iXU9wRPJ+i8NyyAti5Y+gOwBNlAbqzz4tb4zNzbUQ8Acxob+8Y82Cv8S5fvnyT13PmzOl1aN9YtmxZz30H+fwG+dwkSZIkSWNm48Q4gIjYZGJc2daaGLdx5XXbxLg3lU2LgG9RrLyeV74HxcS4C9omxp0OxcS4iJgREbtkZs91C0mqWy17MANExCuBJcD7gbUUybplCFjfMWQbii012rX6DHW0t8Z3jul23BHNmjWLadOmjWZI32liYXU0Bvn8BvncJpJVq1Y962aVJEmSpP7U9Ilx0Mz/l3TyWDPPDZwcp/oe8rc/cD3w7nI/5dcCO7V12RF4pGPYL4DnR8SkzFxX9m/1ebgc81BETAa2BX4FPFT2+8kIx5UkSZIkSdqEE+O2rqYWV3sxyOcGg39+E8WWTI7b6nswR8RLgRsoNq6/pmz+XvFW7BYRk4AFtC0zASj3a15K8fA+gKPb+txUvqZ8f2nZf2N7RBwArHSZiSRJkiRJGkk5Me4W4IOZeQXPTGBrGXFiXPm628Q4hpkYN9JxJamv1fGQv/cBzwHOjog7I+JO4Njy53rgHuA+yof5RcSlEXFYOfZE4B0RcQ9wINB6YutpwL4RcXfZ56Sy/XxgWtl+HnDU+J6aJEmSJElqMifGSdLo1PGQv3cB7xrm7T27tN0ErCvH/gyY2+WYjwOHdWlfCRxTNVZJkiRJkjThtE+Ma7VdxDMT455DUavYODEO+HJmfpli0tsVEXEqxT7KbyvHnwYsKifA/SdwZNl+PnBx2b4KJ8ZJaqDaHvI3CpMp9jySJEmSJEkaV06Mk6TR6fsCc2b+U90xSJIkSZIkDcOJcZImtL4vMEuSJEmSJPUrJ8ZJmujqeMifJEmSJEmSJGkAWGCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSJElSJRaYJUmSJEmSJEmVWGCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSJElSJRaYJUmSJEmSJEmVWGCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSJEnjavWadXWHMCpNi7dOk+sOQJIkSZIkSdJgmzplEgtOv7XuMHq2eOHcukNoDGcwS5IkSZIkSZIqscAsSZIkSZIkSarEArMkSZIkSZIkqRILzJIkSZIkSZKkSiwwS5IkSZI0jNVr1tUdwrP0Y0ySpIlrct0BSNpyq9esY+qUSXWH0bOmxStJkqSJa+qUSSw4/da6w9jE4oVz6w5BkqSNLDBLA6AfL3pH4gWxJEmSJEnSYHCLDEmSJEmSJElSJRaYJUmSJEmSJEmVWGCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSJElSJRaYJUmSJEmSJEmVWGCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSBtDqNevqDmET/RaPJI2VpuW3sY538pgeTZIkSZIk9YWpUyax4PRb6w5jo8UL59YdgiSNi37Lt5sz1vnYGcySJEmSJEmSpEosMEuSJEmSJEmSKrHALEmSJEmSJEmqxAKzJEmSJEmSJKkSC8ySJEmSJEmSpEosMEvqa6vXrKs7hFFrYsySJEmSJElVTK47AEkaydQpk1hw+q11hzEqixfOrTsESZIkSZKkrcIZzJIkSZIkSZKkSiwwS5IkSZIkSZIqscAsSZIkSZIkSarEArMkSZIkSZIkqRILzJIkacJbvWZd3SGoAv+7SZIkSfWbXHcAkiRJdZs6ZRILTr+17jA0SosXzq07BEmSJGnCmxAF5ohYAJwKTAE+k5kX1BySJE1I5mNJ6h/mZEnqH+ZkSU028FtkRMRLgLOAA4DZwDsiYo96o5Kkicd8LEn9w5wsSf3DnCyp6SbCDOaDgX/JzMcBIuI64K3Aws2MmwSwevXqZ72x3fShMQ5x/KxatWrUYwb5/Dy3/jDI5wbPPr+2PDJpqwfTX6rmYxghJ29O0/78qFDl+2tL+Welear8OTEnbzTw18i9/vkw7rFh3FvXaPJfP8XudfKwxjwnQ3/9t9+cQf5/xEE+Nxjs85to57YlOXlow4YNWxhSf4uIDwHPzcxTy9fHAftk5jtGGrds2bIDgKVbIURJE8eBc+bMub3uIOpSNR+DOVnSuDAne40sqX+Yk83JkvrHqHPyRJjBvA3QXkUfAtb3MO77wIHACsBHlEvaEpOAnSjyykRWNR+DOVnS2DEnF7xGltQPzMkFc7KkflA5J0+EAvNDFAm3ZUfgkc0NmjNnzipgwt5BlTTmflJ3AH2gUj4Gc7KkMWdO9hpZUv8wJ5uTJfWPSjl5IhSYvwF8NCJmAE8BbwE2uxxbkjTmzMeS1D/MyZLUP8zJkhptm7oDGG+Z+TDwYeCbwJ3A4sz813qjkqSJx3wsSf3DnCxJ/cOcLKnpBv4hf5IkSZIkSZKk8THwM5glSZIkSZIkSePDArMkSZIkSZIkqRILzJIkSZIkSZKkSiwwS5IkSZIkSZIqscAsSZIkSZIkSapkct0BCCJiO+DbwKGZ+dOawxlTEfER4Ijy5ZLM/ECd8YyliFgIvBXYAFyWmWfXHNKYi4hPATtk5rF1xzKWIuKbwIuANWXTX2Xm92oMSRq1Qf7u0NgY5O9gDa4m5ram/l1r+rVs065Tm3r9GRF/CnwEeC7w9cx8V80hqUZNzNG9amou70XT830vmvad0KsmfXc4g7lmEfFq4HZg97pjGWsRcTDwBuBVwGxgTkS8ud6oxkZEvBb4Y+CPgL2Ad0ZE1BvV2IqI1wPH1B3HWIuIIYq/b3tm5uzypy8TtDScQf7u0NgY5O9gDa4m5ram/l1r+rVs065Tm3r9GRG7AhcBb6L4s/LfI+KQeqNSXZqYo3vV1Fzei6bn+1407TuhV0377rDAXL/jgZOAR+oOZBysAN6bmaszcw1wL7BLzTGNicz8FvC6zFxLcTdpMvBUvVGNnYh4IXAW8LG6YxkHrS/Tr0fEXRFxcq3RSNUM8neHxsbAfgdroDUxtzXy71qTr2Ubep3a1OvPNwPXZuZD5Z/v+UDfFjc07pqYo3vVyFzeiybn+1409DuhV4367nCLjJpl5nEAA3YDCYDMvLv1e0S8nGK5yf71RTS2MnNNRJwBvA/4PPBwzSGNpYuBDwMvrTuQcfAC4BbgncAU4NaIyMz853rDkno3yN8dGhuD/h2swdTE3Nbkv2sNvpZt4nVqU68/dwNWR8SXKYptNwKn1RuS6tLEHN2rJufyXjQ43/eiid8JvWrUd4czmDXuIuKVwD8D78/M++uOZyxl5keAGRTJ7PiawxkTEXEc8PPMvKXuWMZDZn4nM4/OzP/KzF8ClwH/s+64JGk8DPJ3sNRPmvp3rWnXsk29Tm3w9edk4GDg7cBrgFczgMvQpZam5vJeNC3f96Kp3wm9atp3hwVmjauI2J/ijssHM/OKuuMZKxHxioiYDZCZvwW+QLGn0SCYD7whIu4EFgKHRcQ5Ncc0ZiLigHKPppYhntkwX5IGxqB+B0v9pol/1xp8LdvI69QGX38+CnwjMx/LzKeBLwL71ByTNC6amMt70eB834tGfif0qmnfHW6RoXETES8FbgDmZ+a/1B3PGNsVOCMiDqB4EusbgcvrDWlsZOaftH6PiGOBuZn5nvoiGnPbAwsjYj+KZSbHACfUG5Ikja0B/w6W+kaD/6418lq2wdepTb3+vBG4IiK2B34DHELx510aKA3O5b1oZL7vRYO/E3rVqO8OZzBrPL0PeA5wdkTcWf707V+G0cjMm4AlwA+BZcC3M/OaeqNSLzLzRjb9b3d5Zn6n3qgkacwN7Hew1Gca+XfNa9mtq6nXn5n5PeATwO3APcDPgM/VGpQ0PhqZy3thvm+upn13DG3YsKHuGCRJkiRJkiRJDeQMZkmSJEmSJElSJRaYJUmSJEmSJEmVWGCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSJElSJZPrDkCSJA22iDgB+F/AFGAD8G/Ah4HXAR/KzFd09P894D+AXYGPA38CPNZx2Msz87yIWNTx/jbA84CLMvMT5fE2AMuBdcAQsBa4LDMvLN+fC/xDZs7qiOMfgF9m5kcj4qPAScDDHXF8NTM/OMp/JZIkSZI0MCwwq69ExCTgXcACij+fU4GvAKdn5qqImAF8jKIo8RSwHlgMnJ2Z68pjbABmZOYv2477VuDkzJwbEccCnwX2zszlbX1uBK4DHgI+VTbvCEzimYLC32fmtZs5h4OADwF/WMb3NPDpzLyyfP9Y4FzggXLIELAdsBR4R2auLPu9Cvg74BXAk+XPpzLzhvL9uWy+ILKIZwovGyiKOz8Bjs/MX4x0HpI0FiLiU8CewKGZ+fOI2Ab4c+A7wKuBT0fE/pl5R9uwvwS+lJn/LyIAzsnMT3Ueu80m70fELsC9EfHlzLyvbH5d63shInYAboyI6Zn56VGczrWZefIo+kvSqEXE71Ncr/172bQNxXXgZzLzn7pcS7b8KDOP7rghNkRxPf1vwAkU17W3lv2fB7wEyPL1P2fm+zcT264U1+J7lzGtorihd3mZ3+8HPpyZ13SMO5/iuvicjnNrtw/w4i7vP4/i+vwvM/M/NhPfnwJXAA+2NR+Ymb+JiHnA3wPTgB8Bb8/MJyLiD4HrgedSXOtfXh7rKGD3zDxtpM+UNNjMydVyckTsQVGraZkEzALekplfiIjrKf4f4cny/W9m5nvMyc1lgVn95rPAC4DXZ+Z/RcRzgauASyPincAdwCXA/8rMtRHxAuBi4P9SFKV7NQRcHRF7twq6LZn5DWA2QPllsEOvBYWIOAT4P8DbMvP2su33ga9FxG8z8wtl16WZeWjbuOcAtwPHABdHxN7AF4G/yswlZZ9XANdFxO9l5sWjONfOwsungQuBt47iGJI0ahGxM8XF80sz89cAmbke+MeImENxM+5yioLyHeWYIeA44Kgt+OidKfL8b7q9mZm/jIi/Bq6PiLO34HMkabw8nZmzWy8i4mXALRGxrmza5Fqyi403xMoJHDcAp2TmWTxznTuXYrLC7GGP0qa8pv0WcCrFte6GiHgJcFVEvDwzPxQRFwFvB65pGzcdOBJ4Tbdz6/iMbuc+BJwHnAW8bTNh7kcxIeNjHcedAXwO2D8z74+IjwP/GziRovDzceALwD3A5RGxbdn+x5v5PEkTgzn5mbaecnJm3tM6t3Lcp4F/b6uJvAbYKzMf6RhqTm4o92BW3ygT5JEUswn+CyAzn6IoTtxAsbz6h5n5ycxcW77/a+Bo4PVlUbZXtwCP8sxM5bHyCeA9reIyQGb+lKJY8tsRxv0u8Hzg8fL1mcBZreJyeZz7KAoufx8R07YgxlsoZkVL0nh7NXBvq7jc4RvAARQ3Ft8aEc8r2w8GnsjMb7f1fU9E3Nnx89+6vP8fEfFL4APAvMzs3M6i3V0Uq1R2GMX5zO8Sx/8YxXhJqiQzfwacDow4m20Yz6GYCbZiC8P4ELA4M6/IzA1lXA8D84F3RcROFDcN9yuLLy1HAD/IzHzWEXvzHIpZdI9vriNFgfmPI+KuiFhariwEeAPw/cy8v3z9WeDIslCyimJG3nMpZvQBfJSiUD3S9bukCcqc3HNOBiAiDqSY4HZC+foPgG2BSyLi3yPicxHxwrK7ObmhnMGsfjIHuDszn2hvzMxHKWaZfQX4auegzFwZEbdTFCq+3+NnbaAoTN8VEV/NzBu3LHSIiO0plnx8rUuMSzuaDoyIOymS8+8CP6ZImJ8v398P+Osux/lhuQXIHhVjnE5x3t+sMl6SKpgyTPs0YENmPlDm8CMoLoLfAfxDR9+etsgoV71cS3Fheutm4tpQ/vNpnrl47bQNxb7NLW6RIalOdwGtm2uta8l252bm58rf50fEARR57GUUW0Z8gS2zH/CsPefL7YzuBfbNzC9GxOeBv6AoCECR1z/RNmR6l9jvyMyTOt6fBLyIoojxBYrtLTbnVxRLsq8H9ge+FBF7Ai8Fft7W7yGKLeq2pZiJ93+Bk4H3R8RM4JWZ+d4ePk/SxGVO7t0nKbbqaNV6XkQx2eQU4BHgMxT/H/AmzMmNZYFZ/WQ9m59VP2Khovx9Q5f3O4sEZOaKiHg7xZKLPxpNoMMY6vz8iLgWCIp9ln6RmXPLt5Zm5qHlvkinUmzv8Xk2Ndy5Ti0/o9eCyHsi4s/L3ydTLKP50GbPRpK23HeBl0fEjuXNwnavA1qzlC8E/ra8kXggxY2wUcvMp8r92e4F3gOMtP3F3sADmflkOev5d7v0+T2KhwNKUj/YwDMr4kazHHsKcAHFDbgtXXXRy7X4BcDnI2Ihxaq5lwDtkzmGXY7d+X65SuRK4CuZ+eQIYwDIzD9re3l7RHyb4nkk29D9/xHWZeYKitUzlJ95M/DXUezZfCLwBHBSZvY8W0/ShGBO7kFE7AfMoG0/5sz8HvDmtj4fBR6NiKnm5OZyiwz1k+8BM8v9dTaKiJdExBKKQsTczkHlsuq9eaZQ0a1Q8HsUMxo2kZlfoSjs/iPPFIgrKZeA39seY2bOL5PxiXRZhp2Z6zNzIfBTYFHbW3fQ/Vz3BtYA99H9POHZ53pOZs4uf2Zl5km9fhlI0pYol+mdR7Hn/Uta7RHxF8BbKPZXA7iZYruKDwNXZubTW/CZvwbeC5zR/pntIuLF5We3ZkXfB6yMiPltffagKIL/c9VYJGmM7U33BzGNKDPXUNzIO2hzfTdjuOvTlwB/APxr+Xnfp3jA9OspZspdlOXDuEcrM79GcbPw8xGx3Uh9I2L7iPjbctuLliGKa+cHKZZ0t7wE+HW5HV/7MQ6n2NrpnvJzDweWUNy0lKR25uTezAf+sXwOSyvGAyPisLY+QxQT6DaJy5zcLBaY1TfKzd2vophRvB1A+c8LKQqmF1AUoD9YboxPFA/5uwK4LTP/tTzUzcAp5ezgVp9jgJuG+ej3Ulxwvn4MTuOvgfPKu3S0ncOhdCTLDicBb4iIN5avPwT8TUT8z7bjzKQoQp+WxYMJLYhI6nuZ+SGKmQ5fiojlEXE/xayE15T717Ue/HcRxVK4z3Y5TLc9mC8a4TOvApax6T773yzHLQO+THGhe2Hb5x8K/GW5D9xyimV6R7Xt1wnd92D+crV/M5LUu4jYHTgN+HTFQ8yjLDZsgbOAIyLimLa4dgauBi7seFDTBRTX338GXLqFn/spioe2nrGZfr+huKb+szK2VwH7UGyx93Vg34h4edn3BOBL7YMj4nco9lP9aNk0haLgsR74nS08B0kDxJzcU05ueS3Fc6DaPQ84v23f5fcD17UXvs3JzeMWGeo3J1Ik6m9HxFqKpR03AB/JzNUR8Rrg74B7ImINxbKPxWxaRHgXRaJfXh5jiGKG8hXdPrDcw/ltbHmCJzO/Wh7rw1E8tHBq+fk3UxQvhhv3kyieZn1ORHyt3Gv5DcDfRcQ5FMXpX1P8e7iuHLM+Ig4Fzo6IU8vPeZJnF0QkqVaZeRlw2Wb6fJJif7bO9mM3M67r+21bEpGZm12hkpl3M8Iyxcz8KM9c4ErSeGvfE3M9sBL4UGYuiYhj6b7f59rM3Kv8vbXf5waKZ378BxW3H2rJzJ+X1+JnRsTfAmvLuP5P+dPuGoqcflNm/nKEc2t3LPCfXT53TUScDHwtIi7LzK5bF2XmunKyxvkRcUYZ3/zW55erZ66LiKnAT3j2v4+/BS5o2yP0U8DdZUxv7faZkiYMc/Izn9tTTm7zcooV2+3HuDkizgPuKCcG/jtwfMc4c3LDDG3Y0G0rKkmSJEmSJEmSRuYMZmkUIuL9wJHDvP3Jclm2JEmS1CgR8TrgnGHe/mZm1r7npdfikiYKc7KaxhnMkiRJkiRJkqRKfMifJEmSJEmSJKkSC8ySJEmSJEmSpEosMEuSJEmSJEmSKrHALEmSJEmSJEmqxAKzJEmSJEmSJKkSC8ySJEmSJEmSpEosMEuSJEmSJEmSKrHALEmSJEmSJEmqxAKzJEmSJEmSJKkSC8ySJEmSJEmSpEosMEuSJEmSJEmSKrHALEmSJEmSJEmqxAKzJEmSJEmSJKmSyXUH0K+WLVs2DdgbWAGsqzkcSc02CdgJ+P6cOXNW1R1ME5mTJY0hc/IWMB9LGmPm5C1gTpY0xirnZAvMw9sbWFp3EJIGyoHA7XUH0VDmZEljzZxcjflY0ngwJ1djTpY0Hkadky0wD28FwO67787UqVPrjkVSg61evZof//jHUOYVVWJOljQmzMlbzHwsacyYk7eYOVnSmNmSnGyBeXjrAKZOncq0adPqjkXSYHDZWnXmZEljzZxcjflY0ngwJ1djTpY0Hkadk33InyRJkiRJkiSpEgvMkiRJkiRJkqRKLDBLkiRJkiRJkiqxwCxJkiRJkiRJqsQCsyRJkiRJkiSpEgvMkiRJkiRJkqRKLDBLkiRJkiRJkiqxwCxJkiRJkiRJqsQCsyRJkiRJkiSpEgvMkiRJkiRJkqRKLDBLkiRJkiRJkiqxwCxJkiRJkiRJqsQCsyRJkiRJkiSpEgvMkiRJkiRJkqRKLDBLkiRJkiRJkiqZXHcAkiRJkiRJdYiIhcBbgQ3AZZl5dkQcDJwNTAeuzcxTy76zgUuB7YDbgBMyc23H8bYHrgJ2BR4DjsjMRyNiKnAZsBfwNLAgM++LiCHgk8ChwHrg+My8Y7zPW5LGkjOYJUmSJEnShBMRrwX+GPgjisLvOyNiT+By4I3ATGDviDikHHIlcHJm7g4MAcd3OeyZwNLMnAlcApxbtp8CPFW2vxtYVLa/pfycPYA3AYsiwsmAkhrFArMkSZIkSZpwMvNbwOvKWcgvoljlvT1wf2Y+ULZfCRweES8Dpmfmd8vhi4DDuxx2HsUMZoCrgUMiYkp7e2beBsyIiF3K9msyc31m/hh4ENhv7M9WksaPd8W20Oo165g6ZVLdYfQ9/z1JqoO5p7n8byc106D/3R3085MmosxcExFnAO8DPg+8GFjR1mUFsPMI7Z029svMtRHxBDBjDI47rOXLl4+mOwAz95jF70yfNupxqt9vn17FvfeM/r+5NJ4sMG+hqVMmseD0W+sOo+8tXji37hAkTUDm6Obye0NqpkHPu+YmaTBl5kci4uPAV4DdKfZjbhmi2Bt5m2HaOw11eT3S+F6PO6xZs2Yxbdroi8WDnK8H2eKFc5kzZ07dYWgArVq1qtINK3CLDEmSJEmSNAFFxCvKB/eRmb8FvgDMBXZq67Yj8Ajw0DDtnR4u36PcS3lb4FcjjO/1uJLUtywwS5IkSZKkiWhX4JKImBYRUyke7HcxEBGxW0RMAhYAN2fmz4CVEbF/OfYo4OYux7wJOLr8fT7FA//WtLdHxAHAysx8sGw/MiImRcRuFDOovz8eJytJ48UCsyRJkiRJmnAy8yZgCfBDYBnw7cy8BjgWuB64B7gPuK4cV8jWVQAAIABJREFUciRwTkTcBzwPOA8gIhZGxAlln9OAfSPibuBE4KSy/XxgWtl+HkWBmvLYdwM/Ar4EvD0znx6XE5akceIezJIkSZIkaULKzI8CH+1ouwXYs0vfu4B9uhzmBuA1ZZ/HgcO6jF0JHNOlfQPFAwbfN+rgJalPOINZkiRJkiSpup2AxXUHIUl1cQazJEmSJElSRZm5pO4YJKlOzmCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSJElSJRaYJUmSJEmSJEmVWGCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSJElSJRaYJUmSJEmSJEmVWGCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSJElSJRaYJUmSJEmSJEmVWGCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSJElSJRaYJUmSJEmSJEmVWGCWJEmSJEmSJFVigVmSJEmSJEmSVIkFZkmSJEmSJElSJRaYJUmSJEmSJEmVWGCWJEmSJEmSJFVigVmSJEmSJEmSVMnk8Tx4RHwEOKJ8uSQzPxARnwMOAJ4q28/IzC9GxMHA2cB04NrMPLXL8XYBrgReBCRwZGY+GRHbA1cBuwKPAUdk5qMRMRW4DNgLeBpYkJn3jdf5SpIkSZIkSdJEMm4zmMuC8RuAVwGzgTkR8WaKYu9BmTm7/PliREwHLgfeCMwE9o6IQ7oc9kLgwsx8BfAD4LSy/UxgaWbOBC4Bzi3bTwGeKtvfDSwah1OVJEmSJEmSpAlpPLfIWAG8NzNXZ+Ya4F5gl/Ln8oj4UUScERHbAPsA92fmA5m5lmKW8uHtB4uIKcBBwHVl06K2PvMoZjADXA0cUvbf2J6ZtwEzylnQkiRJkiRJkqQtNG5bZGTm3a3fI+LlFFtlHAjMBU4E/gu4EXg78CRFQbplBbBzxyF3AJ4oC9CdfV7cGp+ZayPiCWBGe3vHmAd7PY/ly5eP+P6cOXN6PdSEt2zZsrpDkCRJkiRJkjSGxnUPZoCIeCWwBHh/Zibw5rb3zgeOppiVvKFt2BCwvuNQ23T0oa3PUEd7a3znmG7HHdGsWbOYNm3aaIZoGBbjNVGtWrVqszerJEkTxzDPKen6PJKImA1cCmwH3Aac0DbhonW8UT2PJCKGgE8Ch1JcGx+fmXeM60lLkiRpYI3nFhlExP7ALcAHM/OKiPhvEfGWti5DwBrgIWCntvYdgUc6DvcL4PkRMal8vVNbn4fLMUTEZGBb4Fc9HleSJEnaKoZ5TsnbGP55JFcCJ2fm7hTXzsd3Oexon0fylvJz9gDeBCwqr6ElSZKkURvPh/y9FLiBYqbENWXzEPCZiHhBuUfyO4AvAt8rhsRuZQF5AXBz+/HKfZyXAvPLpqPb+txUvqZ8f2nZf2N7RBwArMzMnrfHkCRJksZYt+eU7E6X55FExMuA6Zn53XLsIjqeU1Ia7fNI5gHXZOb6zPwxxfZx+43DuUqSJGkCGM+ZCu8DngOcHRGttouAvwfuAKYA1+f/Z+/+Yz296gO/vwfbTLxdHFLFESbESSnrE2cnxavhRxIcGrV0VRcERBRQ7PJDJaYIKFAtu4lUO1sQu/kjEgm48RIZnEFrMEQg2Ci2q23I7tohDQ2TLnRMfIK6BAo2gkITC+qZ2OD+8X3Ge7m5M77z9VzfO57XS7ry9/k855zveeaPo+vPc+7nzHlz1Rjj1dVHlz63thzmN8Z4b/W7c87fbVW7+f1jjGta/SL8C8u417baeXFn9ZfVVUv8uuq3lvix6hU79bAAAPBwTnBOyXVtfW7Iic4T2exUzyPZ7rgndKLST2dDSTTnigAAfK+dPOTvzdWbT3D7+i3af6J6+hZtb62+s7T5YqtDAjf3/Wb1wi3iR6tXbXvSAADwKNh4Tkn1QKtdzMed6nkip3oeiXNKHoGzIYkOjxZnlQA8NpwJtdbObfXLNwCPwAkOlfrt6vLq20v8bXPOj53osKlN413c6s+4f6ia1VVzzm+d6mFTO/W8AHvVck7JR6u3zDk/NMb4T9v63JDtnidy/DySL5/gPJL/a81xAQDgYe3oIX+nw5zzd+acx3Z7HgBnshMcKvXzrZK9z51zXrb8fGyMcX4nPmxqo+ur6+ecP159ulW5ojr1w6YAzhonOKdky/NIlr/eO7okpGtV7u22vzHoqZ9Hcmt11RjjnDHG01rtnv6T0/2sAACcHc6EHcwAPHIPHSpVNcb4s+ri5efGMcYPtzp09W3Vs1oOm1ra3tTqUKmHkhrL4VHPrV68hA5V/6b6pVaHRz13id9c/eaGw6Z+pVaHTY0xLhxjXOzwVeAsc6JzSl7dFueRtDpb5IYxxgXVn1bvrhpjvL26e875nk79PJKPVM+uPrtcv2bOed9pf1IAAM4KEswAZ4ETHCr1s63q2r+++qvq96rXVN/q4Q9/+sHq3jnnA1u0OdXDpradYD7VGn3qZJ7ZHKTFY9HDnFPyN84jmXN+ptWLv80+Xv300uaUziOZcz7YKtH91m1PHAAATkCCGeAssvFQqTnnrH5+w73rWv0p9Ud6+MOfNh8Q1YY2p3rY1LadzYdKnY28IGAnPIYOlLqo+uBuTwIAAPZ8DWYATo+lhucnql+ec75/jPGTY4yXbGiyr7q/7R3+9LXq+5daoS3tj7c5fthUJzhs6mTjArANc85b5pz/727PAwAAJJgBzgInOFRqX/UbY4wfWGokv7ZVHeYtD5vaON5yeNQdrQ6TqtXO5+NtTvWwKQAAAOAMpUQGwNnhRIdK/Wr1yeq86qNzzpurxhivbovDpsYY761+d875u61qN79/jHFNqzrKv7CMe6qHTQEAAABnKAlmgLPAwxwqdf0W7T/RFodNtUo2f2dp88VWhwRu7ntKh00BAAAAZy4lMgA4FedWv7/bkwAAAAD2BjuYAdi2Oefv7PYcAAAAgL3DDmYAAAAAANYiwQwAAAAAwFokmAEAAAAAWIsEMwAAAAAAa5FgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANYiwQwAAAAAwFokmAEAAAAAWIsEMwAAAAAAa5FgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANYiwQwAAAAAwFokmAEAAAAAWMu5uz0BAAAAgN0wxvjH1cuWy1vmnP9ojPHb1eXVt5f42+acHxtjPK96Z3V+9eE55zVbjHdxdVP1Q9WsrppzfmuM8cTqA9VTq69XL5tzfnWM8fjqfdUzqvuqK+ecd+3U8wLsBDuYAQAAgLPOkjD++9Xfqy6rDo4xfr5Vsve5c87Llp+PjTHOr26sXlRdWj1zjHHFFsNeX10/5/zx6tPVtUv8HdUdc85Lqxuqdy3xN1XfXuJvqQ7twKMC7Cg7mAEAAICz0T3VP5hz/nXVGOPPqouXnxvHGD9cfax6W/Ws6vNzzi8sbW+qXlrddnywMcZ51XOrFy+hQ9W/qX6pev5yr+rm6jeX9s+vfqVqznn7GOPCMcbFc84vbfchjhw5csoPfvDgwVPuw95x+PDh3Z4CfA8JZgAAAOCsM+e88/jnMcbfaVUq42ern6teX/1V9XvVa6pvtUpIH3dP9ZRNQ/5gde+c84Et2jz5eP855wNjjHurCzfGN/XZdoL5wIED7d+/f7vNeQzwgoCdcOzYsbVeWJUEMwAAAHAWG2P83eqW6h/OOWf18xvuXVe9svpI9eCGbvuq724a6nGb2rShzb5N8eP9N/fZalyAPU0NZgAAAOCsNMZ4TvWJ6pfnnO8fY/zkGOMlG5rsq+6vvlxdtCH+pOruTcN9rfr+McY5y/VFG9p8ZenTGOPc6gnVN7Y5LsCeJsEMAAAAnHXGGD9Sfby6cs75oSW8r/qNMcYPLDWSX9uqDvOnVl3G05YE8pVtqL9cNee8v7qjevkSeuWGNrcu1y3371jaPxQfY1xeHT2V+ssAe4ESGQAAAMDZ6K3V91XvHGMcj72n+tXqk9V51UfnnDdXjTFeXX106XNrq7IZjTHeW/3unPN3W9Vufv8Y45pWdZR/YRn32urQGOPO6i+rq5b4ddVvLfFj1St26mEBdooEMwAAAHDWmXO+uXrzCW5fv0X7T1RP36LtrdV3ljZfbHVI4Oa+36xeuEX8aPWqbU8aYA9SIgMAAABgfedWv7/bkwDYLXYwAwAAAKxpzvk7uz0HgN1kBzMAAAAAAGuRYAYAAAAAYC0SzAAAAAAArEWCGQAAAACAtUgwAwAAAACwFglmAAAAAADWIsEMAAAAAMBaJJgBAAAAAFiLBDMAAAAAAGuRYAYAAAAAYC0SzAAAAAAArEWCGQAAAACAtUgwAwAAAACwFglmAAAAAADWIsEMAAAAAMBaJJgBAAAAAFiLBDMAAAAAAGuRYAYAAAAAYC0SzAAAAAAArEWCGQAAAACAtUgwAwAAAACwFglmAAAAAADWIsEMAAAAAMBaJJgBAAAAAFiLBDMAAAAAAGuRYAYAAAAAYC0SzAAAAAAArEWCGQAAAACAtUgwAwAAAACwlnN3cvAxxj+uXrZc3jLn/EdjjOdV76zOrz4857xmaXtZ9d7qgur26nVzzgc2jffE6gPVU6uvVy+bc351jPH46n3VM6r7qivnnHeNMfZVv1a9oPpudfWc85M7+cwAAAAAAGeLHdvBvCSS/37196rLqoNjjF+obqxeVF1aPXOMccXS5abqjXPOS6p91dVbDPuO6o4556XVDdW7lvibqm8v8bdUh5b4S5bv+YnqxdWhMcaOJtUBAAAAAM4WO1ki457qH8w5/3rOeX/1Z9Ul1efnnF9YdiffVL10jPGj1flzzj9e+h6qXrrFmM9vtYO56ubqijHGeRvjc87bqwvHGBcv8Q/NOb875/zz6kvVz+zAswIAAAAAnHV2bDfvnPPO45/HGH+nVamM61olno+7p3pK9eQTxDd7qN2c84Exxr3VhSfpv91xT+jIkSMnvX/w4MFTGe6sdvjw4d2eAgAAAABwGu14uYgxxt+tbqn+YfVAq13Mx+1rVRv5cdWDW8Q327fF9cn6b3fcEzpw4ED79+8/lS6cgGQ8Z6tjx4497MsqAAAAgDPRTpbIaIzxnOoT1S/POd9ffbm6aEOTJ1V3nyS+2VeWey21lJ9QfeM0jAsAAAAAwCnayUP+fqT6eHXlnPNDS/hTq1vjaWOMc6orq9vmnF+sji4J6apXVLdtMeyt1SuXzy9vdeDf/RvjY4zLq6Nzzi8t8avGGOeMMZ7Wavf0n5zuZwUAAAAAOBvtZImMt1bfV71zjHE89p7q1dVHl3u3Vh9Z7l1V3TDGuKD60+rdVWOMt1d3zznfU11bHRpj3Fn95dKnVrWdf2uJH2uVoG4Z+9nVZ5fr18w57zvtTwoAAAAAcBbayUP+3ly9+QS3n75F+89Uz9qi7cern17afLN64RZ9j1av2iL+YKtE91u3PXGAx6gxxj9udeBq1S1zzn80xnhe9c7q/OrDc85rlraXVe+tLqhur14353xg03hPrD5QPbX6evWyOedXxxiPr95XPaO6r9Vfstw1xthX/Vr1glb18K+ec35yRx8aAAAA2FE7WoP5NLmo+uBuTwLgTLYkkv9+9feqy6qDY4xfqG6sXlRdWj1zjHHF0uWm6o1zzktaHZB69RbDvqNVqaJLqxuqdy3xN1XfXuJvqQ4t8Zcs3/MT1Ytb/UXKjh82CwAAAOycPf8/9nPOW3Z7DgCPAfdU/2DO+ddVY4w/a1WX/vNzzi8ssZuql44xPledP+f846Xvoept1T/bNObzq+cun2+ufnOMcd4S/5WqOeftY4wLxxgXL/EPzTm/W/35GONL1c+02iG9LUeOHDmlhz548OAptWdvOXz48G5PAQAAgIex5xPMADxyc847j38eY/ydVqUyrmuVeD7unuop1ZNPEN/soXZzzgfGGPdWF56k/3bHPaEDBw60f//+U+nCGcwLAnbCsWPHTvllFQAAcGJnQokMAE6TMcbfrf7X6h9W/656cMPtfa1qIz/uBPHN9m1xfbL+2x0XAAAAOENIMAOcJcYYz6k+Uf3ynPP91Zdb1bk/7knV3SeJb/aV5V5LLeUnVN84DeMCAAAAZwgJZoCzwBjjR6qPV1fOOT+0hD+1ujWeNsY4p7qyum3O+cXq6JKQrnpFddsWw95avXL5/PJWB/7dvzE+xri8Ojrn/NISv2qMcc4Y42mtakD/yel+VgAAAODRowYzwNnhrdX3Ve8cYxyPvad6dfXR5d6t1UeWe1dVN4wxLqj+tHp31Rjj7dXdc873VNdWh8YYd1Z/ufSpVW3n31rix1olqFvGfnb12eX6NXPO+077kwIAAACPGglmgLPAnPPN1ZtPcPvpW7T/TPWsLdp+vPrppc03qxdu0fdo9aot4g+2SnS/ddsTBwAAAPY0JTIAOBUXVR/c7UkAAAAAe4MdzABs25zzlt2eAwAAALB32MEMAAAAAMBaJJgBAAAAAFiLBDMAAAAAAGuRYAYAAAAAYC0O+QMAgEfZGOOC6o+qF8w5/2KM8dvV5dW3lyZvm3N+bIzxvOqd1fnVh+ec12wx1sXVTdUPVbO6as75rTHGE6sPVE+tvl69bM751THG46v3Vc+o7quunHPetZPPCwDAY5cdzAAA8CgaYzy7+sPqkg3hZ1TPnXNetvx8bIxxfnVj9aLq0uqZY4wrthjy+ur6OeePV5+url3i76jumHNeWt1QvWuJv6n69hJ/S3XotD4gAABnFQlmAAB4dF1dvaG6u2qM8beqi6sbxxifHWO8bYzxuOpZ1efnnF+Ycz7QapfySzcONMY4r3pu9ZEldGhDm+e32sFcdXN1xdL+ofic8/bqwmUXNAAAnDIlMgAA4FE05/zFqjHG8dCTqj+oXl/9VfV71Wuqb1X3bOh6T/WUTcP9YHXvkoDe3ObJx/vPOR8YY9xbXbgxvqnPl7b7DEeOHNkyfvDgwe0OccY6fPjwbk8BAGBPkWAGAIBdNOf8d9XPH78eY1xXvbLVruQHNzTdV313U/fHbWrThjb7NsWP99/cZ6txT+rAgQPt37//VLo8ZpwNSXR4tBw7duyEL6wAOHMokQEAALtojPGTY4yXbAjtq+6vvlxdtCH+pJayGht8rfr+McY5y/VFG9p8ZenTGOPc6gnVN7Y5LgAAbIsEMwAA7K591W+MMX5gqZH82upj1aeqMcZ42pJAvrK6bWPHOef91R3Vy5fQKze0uXW5brl/x9L+ofgY4/Lq6Jxz2+UxAABgIwlmAADYRXPOz1a/Wn2y+lz1b+ecN885j1avrj66xO9qOcxvjPHeMcYLlyFeX712jPG56mera5b4tdVPjTHuXNq8YYlfV+1f4u+uXrGzTwgAwGOZGswAALAL5pw/tuHz9dX1W7T5RPX0LbrfWn1nafPF6ue26PvN6oVbxI9Wr1pz2gCPKWOMf1y9bLm8Zc75j8YYz6veWZ1ffXjOec3S9rLqvdUF1e3V6zYcsnp8vCdWH6ieWn29etmc86tjjMdX76ueUd1XXTnnvGuMsa/6teoFrerhXz3n/OSOPjTAaWYHMwAAnHnOrX5/tycBcCZbEsl/v/p71WXVwTHGL1Q3Vi+qLq2eOca4YulyU/XGOeclrcobXb3FsO9oVZLo0uqG6l1L/E3Vt5f4W6pDS/wly/f8RPXi6tBSNx/gjCHBDAAAZ5g55+/MOY/t9jwAznD3VP9gzvnXS436P6suqT4/5/zCsjv5puqlY4wfrc6fc/7x0vdQ9dItxnx+qx3MVTdXVyz19R+Kzzlvry4cY1y8xD805/zunPPPqy9VP7MDzwqwY7wVAwAAAM46c847j38eY/ydVqUyrmuVeD7unuop1ZNPEN/soXZzzgfGGPdWF56k/3bHPaEjR46cSvOqDh48eMp92DsOHz6821OA7yHBDAAAAJy1xhh/t7ql+ofVA612MR+3r1Vt5MdVD24R32zfFtcn67/dcU/owIED7d+//1S6cIbzgoCdcOzYsbVeWJUSGQAAAMBZaozxnOoT1S/POd9ffbm6aEOTJ1V3nyS+2VeWey21lJ9QfeM0jAuwZ0kwAwAAAGedMcaPVB+vrpxzfmgJf2p1azxtjHFOdWV125zzi9XRJSFd9Yrqti2GvbV65fL55a0O/Lt/Y3yMcXl1dM75pSV+1RjjnDHG01rtnv6T0/2sADtJiQwAAADgbPTW6vuqd44xjsfeU726+uhy79bqI8u9q6obxhgXVH9avbtqjPH26u4553uqa6tDY4w7q79c+tSqtvNvLfFjrRLULWM/u/rscv2aOed9p/1JAXaQBDMAAABw1plzvrl68wluP32L9p+pnrVF249XP720+Wb1wi36Hq1etUX8wVaJ7rdue+IAe4wSGQAAAADru6j64G5PAmC32MEMAAAAsKY55y27PQeA3WQHMwAAAAAAa5FgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANYiwQwAAAAAwFokmAEAAAAAWIsEMwAAAAAAa5FgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANYiwQwAAAAAwFokmAEAAAAAWIsEMwAAAAAAa5FgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANYiwQwAAAAAwFokmAEAAAAAWIsEMwAAAAAAa5FgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANYiwQwAAAAAwFokmAEAAAAAWIsEMwAAAAAAa5FgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANZy7k5/wRjjguqPqhfMOf9ijPHb1eXVt5cmb5tzfmyM8bzqndX51YfnnNdsMdbF1U3VD1WzumrO+a0xxhOrD1RPrb5evWzO+dUxxuOr91XPqO6rrpxz3rWTzwsAAAAAcLbY0R3MY4xnV39YXbIh/IzquXPOy5afj40xzq9urF5UXVo9c4xxxRZDXl9dP+f88erT1bVL/B3VHXPOS6sbqnct8TdV317ib6kOndYHBAAAAAA4i21rB/MY431zztdsin1kzvlfP0zXq6s3VP986fO3qourG8cYP1x9rHpb9azq83POLyztbqpeWt224fvOq55bvXgJHar+TfVL1fOXe1U3V7+5tH9+9StVc87bxxgXjjEunnN+aTvPXXXkyJGT3j948OB2hzrrHT58eLenAI8Jj2BNBuA0syYD7B3WZIDdcdIE8xjjn1U/XP3sGOPCDbfOa1WO4qTmnL+4jHM89KTqD6rXV39V/V71mupb1T0but5TPWXTcD9Y3TvnfGCLNk8+3n/O+cAY497qwo3xTX22nWA+cOBA+/fv325zTkIynrPVsWPHHvZl1XY80jUZgNPHmgywd1iTAXbXw+1gfl91oHp69dEN8QeqPz7VL5tz/rvq549fjzGuq15ZfaR6cEPTfdV3N3V/3KY2bWizb1P8eP/NfbYaF+BMcVrXZAAeEWsywN5hTQbYRSdNMM85P119eozx+3POLz/SLxtj/GR1yZzz+IK/r7q/+nJ10YamT6ru3tT9a9X3jzHOmXN+Z2l/vM1Xlj5fHmOcWz2h+saGcf+vk4wLcEY4HWuyg1cBTo/T/XsyAOuzJgPsrm3VYK5+ZIzxz6v/sA27heec/8kpft++6jfGGH/QqizGa6v3V5+qxhjjadUXqitbHfr3kDnn/WOMO6qXVx9stfP5eI3mW5frf7rcv2Npfzz+h2OMy6ujp1J/GWCPWmtNXg5evaGtD169Z0O74wev/qfV/13dMsa4Ys55W9/r+MGrHxpjXNvq4NVf6t8fvPr8McYrWh28+vI2HLw6xnhuq1r6P3XKTw+wt5yu35MBeOSsyQC7YLsJ5t9qlQj40/5mmYptm3N+dozxq9UnW9VC+uic8+aqMcarW/0py/e1Shh/ZIm/t/rdOefvtqrd/P4xxjWt6ij/wjL0tdWhMcad1V9WVy3x66rfWuLHqlesO3eAPWTdNfmMP3gVYA86Lb8nA3BaWJMBdsF2E8wPzDnfue6XzDl/bMPn61vtetvc5hOt6iVtdmv1naXNF6uf26LvN6sXbhE/Wr1qzWkD7FVrrcmPhYNXT/WwRIeLntkOHz6821OA7XhEvycDcFpZkwF2wXYTzEfGGD855/w/d3Q2Wzu3umUXvhdgrzota/KZePDqgQMH2r9//6l04QzmBQE74dixY6f8suph7ObvyQB8L2sywC7YboL5qdXhMcYXWx3MVD06dYzmnL+z098BcIY5LWuyg1cBTotd+z0ZgL/BmgywC7abYP4fd3QWAJyK07UmO3gV4JHzezLA3mFNBtgF200w+/MSgL3jtKzJDl4FOC38ngywd1iTAXbBdhPM/0+rupn7+vf1M7c69AmAnfeI1mQHrwKcVn5PBtg7rMkAu2BbCeY55+OOfx5jPL7Vn0uPnZoUACe2y2uyg1cBNvB7MsDeYU0G2B2Pe/gm32vO+ddzzkPVf3H6pwPAqXi01+Q55+/MOY89Gt8FcKbxezLA3mFNBnj0bGsH8xjjP9xwua96RvUDOzIjAE7Kmgywd1iTAfYOazLA7linBnPV16o37ciMAHg41mSAvcOaDLB3WJMBdsEp12AGYHdZkwH2DmsywN5hTQbYHdstkfG46q3VFdV51b+s/umc84EdnBsAW7AmA+wd1mSAvcOaDLA7tvt271er/6x6V/XO6meqX9upSQFwUtZkgL3Dmgywd1iTAXbBdmsw/5fVM+ac91eNMW6pPlP9Dzs1MQBOyJoMsHdYkwH2DmsywC7Y7g7mxx1foKvmnMeq+0/SHoCdY00G2DusyQB7hzUZYBdsdwfzvx1j/Hr1P7c6kfW/rz67Y7MC4GSsyQB7hzWp+X7OAAAgAElEQVQZYO9Ya00eY1xQ/VH1gjnnX4wxfru6vPr20uRtc86PjTGe16r0xvnVh+ec12wx1sXVTdUPVbO6as75rTHGE6sPVE+tvl69bM751THG46v3Vc+o7quunHPetf4/AcCjb7s7mN9Q/UCrBfdT1Q+2WqgBePRZkwH2DmsywN5xymvyGOPZ1R9Wl2wIP6N67pzzsuXnY2OM86sbqxdVl1bPHGNcscWQ11fXzzl/vPp0de0Sf0d1x5zz0uqGVnWiq95UfXuJv6U6dGqPDLD7TrqDeXmTdkP18Tnnq5fYLdV3qnt3fHYAPMSaDLB3WJMB9o5HuCZf3Sox/c+Xfn+ruri6cYzxw9XHqrdVz6o+P+f8wtLupuql1W0b5nFe9dzqxUvoUPVvql+qnr/cq7q5+s2l/fOrX6mac94+xrhwjHHxnPNL233+I0eObLfpQw4ePHjKfdg7Dh8+vNtTgO/xcCUy3l5dUH1yQ+zqVm/k/qfqb/w5CAA7xpoMsHdYkwH2jrXX5DnnL1aNMY6HnlT9QfX66q+q36teU32rumdD13uqp2wa7gere+ecD2zR5snH+885Hxhj3FtduDG+qc+2E8wHDhxo//79223OY4AXBOyEY8eOrfXCqh6+RMYLWtX/+drxwJzz7uqV1c+v9Y0ArMuaDLB3WJMB9o7TtibPOf/dnPPn55z3zDn/v+q66r9qlT95cEPTfdV3N3Xf3KYNbfZtih/vv51xAfa0h0sw//Wc877NwTnnvdWxnZkSACdgTQbYO6zJAHvHaVuTxxg/OcZ4yYbQvur+6svVRRviT6ru3tT9a9X3jzHOWa4v2tDmK0ufxhjnVk+ovrHNcQH2tIdLMH9njPGEzcEldt7OTAmAE7AmA+wd1mSAveN0rsn7qt8YY/zAUiP5ta3qMH9qNeR42pJAvrIN9Zer5pz3V3dUL19Cr9zQ5tbluuX+HUv7h+JjjMuro6dSfxlgL3i4BPPN1XvHGP/B8cDy+b3VR3dyYgD8DdZkgL3Dmgywd5y2NXnO+dnqV1vVc/5c9W/nnDfPOY9Wr17G+1x1V/WR5bveO8Z44TLE66vXjjE+V/1s/77+87XVT40x7lzavGGJX1ftX+Lvrl5xKvMF2Ase7pC/36jeU311WeweV11afaBVEX0AHj3WZIC9w5oMsHc84jV5zvljGz5f3+qAwM1tPlE9fYvut1bfWdp8sfq5Lfp+s3rhFvGj1au2M0eAveqkCeY553dbvXn7J9XBVoXmPzXnvOdk/QA4/azJAHuHNRlg79gDa/K51S2P0ncB7DkPt4O5eugN3Bd3eC4AbIM1GWDvsCYD7B27tSbPOX/n0f5OgL3k4WowAwAAAADAliSYAQAAAABYiwQzAAAAAABrkWAGAAAAAGAtEswAAAAAAKxFghkAAAAAgLVIMAMAAAAAsJZzd3sCAABwthljXFD9UfWCOedfjDGeV72zOr/68JzzmqXdZdV7qwuq26vXzTkf2DTWE6sPVE+tvl69bM751THG46v3Vc+o7quunHPeNcbYV/1a9YLqu9XVc85P7vhDAwDwmGQHMwAAPIrGGM+u/rC6ZLk+v7qxelF1afXMMcYVS/ObqjfOOS+p9lVXbzHkO6o75pyXVjdU71rib6q+vcTfUh1a4i9ZvucnqhdXh8YYNp4AALAWCWYAAHh0XV29obp7uX5W9fk55xeW3ck3VS8dY/xodf6c84+Xdoeql24x3vNb7WCuurm6Yoxx3sb4nPP26sIxxsVL/ENzzu/OOf+8+lL1M6f5GQEAOEvYqQAAAI+iOecvVo0xjoeeXN2zock91VNOEt/soXZzzgfGGPdWF56GcU/oyJEjW8YPHjx4KsOckQ4fPrzbUwAA2FMkmAEAYHc9rnpww/W+VrWRTxTfbN8W1yfrv91xT+jAgQPt37//VLo8ZpwNSXR4tBw7duyEL6wAOHMokQEAALvry9VFG66f1Kp8xonim31luddSS/kJ1TdOw7gAAPCwJJgBAGB3faoaY4ynjTHOqa6sbptzfrE6OsZ4ztLuFdVtW/S/tXrl8vnlrQ78u39jfIxxeXV0zvmlJX7VGOOcMcbTWh02+Cc79GwAADzGSTADAMAumnMerV5dfbT6XHVX9ZHl9lXVr48x7qr+dvXuqjHG28cYr1vaXFv91Bjjzur1rQ4QrLqu2r/E390qQd0y9p3VZ6t/Ub1mznnfjj0gAACPaWowAwDALphz/tiGz5+onr5Fm89Uz9qi+8ern17afLN64RZ9j1av2iL+YPXW5QcAAB4RO5gBAODMc1H1wd2eBAAA2MEMAABnmDnnLbs9BwAAKDuYAQAAAABYkwQzAAAAAABrkWAGAAAAAGAtEswAAAAAAKxFghkAAAAAgLVIMAMAAAAAsBYJZgAAAAAA1iLBDAAAAADAWiSYAQAAAABYiwQzAAAAAABrkWAGAAAAAGAtEswAAAAAAKxFghkAAAAAgLVIMAMAAAAAsBYJZgAAAAAA1iLBDAAAAADAWiSYAQAAAABYiwQzAAAAAABrkWAGAAAAAGAtEswAAAAAAKxFghkAAAAAgLVIMAMAAAAAsBYJZgAAAAAA1iLBDAAAAADAWiSYAQAAAABYiwQzAAAAAABrkWAGAAAAAGAt5+70F4wxLqj+qHrBnPMvxhjPq95ZnV99eM55zdLusuq91QXV7dXr5pwPbBrridUHqqdWX69eNuf86hjj8dX7qmdU91VXzjnvGmPsq36tekH13erqOecnd/qZAQAAAADOBju6g3mM8ezqD6tLluvzqxurF1WXVs8cY1yxNL+peuOc85JqX3X1FkO+o7pjznlpdUP1riX+purbS/wt1aEl/pLle36ienF1aIyx40l1AAAAAICzwU4nW6+u3lD98+X6WdXn55xfqBpj3FS9dIzxuer8OecfL+0OVW+r/tmm8Z5fPXf5fHP1m2OM85b4r1TNOW8fY1w4xrh4iX9ozvnd6s/HGF+qfqbVDultOXLkyEnvHzx4cLtDnfUOHz6821MAAAAAAE6jHU0wzzl/sWqMcTz05OqeDU3uqZ5ykvhmD7Wbcz4wxri3uvA0jHtCBw4caP/+/afShROQjOdsdezYsYd9WQUAAABwJnq0y0U8rnpww/W+VrWRTxTfbN8W1yfrv91xAR7z1MQHAAAATrcdrcG8hS9XF224flJ190nim31luddSS/kJ1TdOw7gAj2lq4gMAAAA74dH+n/tPVWOM8bTqC9WV1Y1zzi+OMY6OMZ6z7Gh7RXXbFv1vrV5Z/dPq5a2SG/ePMY7H/3CMcXl1dM75pSX+344xbq7+o1aJlT/Z6YcE2IPO+Jr49fB18TdTmufMpnY/AADA3veoJpjnnEfHGK+uPlp9X6uE8UeW21dVNyx/wv2n1burxhhvr+6ec76nurbVrrc7q79c+lRdV/3WEj/WKkHdMvazq88u16+Zc963c08IsDc9Fmril7r4ZxsvCNgJ6uIDAMDp9agkmOecP7bh8yeqp2/R5jOtdtRt9vHqp5c236xeuEXfo9Wrtog/WL11+QHg31MTHwAAAHjEHu0azOu4qPrgbk8C4DFGTXwAAADgEdvzCeY55y1zzv93t+cB8BjzUE38McY5rWri3zbn/GJ1dIzxnKXdw9XErw018TfGN9bEX+JXjTHOWerwq4kPAAAAjwF7PsEMwOm3lBZ6daua+J+r7up7a+L/+hjjrupvt6Em/hjjdUuba6ufWmrfv77VAYK1qom/f4m/u++tiX9nq5r4/yI18QEAAOAx4VE95A+A3aUmPgAAAHA62cEMwHapiQ8AAAB8DzuYAdiWOectuz0HAAAAYG+RYAYAAADOWmOMC6o/ql4w5/yLMcbzqndW51cfnnNes7S7rHpvdUF1e/W6OecDm8Z6YvWB6qnV16uXzTm/OsZ4fPW+6hnVfdWVc867xhj7ql+rXlB9t7p6zvnJHX9ogNNIiQwAAADgrDTGeHb1h9Uly/X51Y3Vi6pLq2eOMa5Ymt9UvXHOeUm1r7p6iyHfUd0x57y0uqF61xJ/U/XtJf6W6tASf8nyPT9Rvbg6NMawGRA4o0gwAwAAAGerq6s3VHcv18+qPj/n/MKyO/mm6qVjjB+tzp9z/vHS7lD10i3Ge36rHcxVN1dXjDHO2xifc95eXTjGuHiJf2jO+d05559XX6p+5jQ/I8CO8lYMAAAAOCvNOX+xaoxxPPTk6p4NTe6pnnKS+GYPtZtzPjDGuLe68DSMe0JHjhw5leZVHTx48JT7sHccPnx4t6cA30OCGQAAAGDlcdWDG673taqNfKL4Zvu2uD5Z/+2Oe0IHDhxo//79p9KFM5wXBOyEY8eOrfXCqpTIAAAAADjuy9VFG66f1Kp8xonim31luddSS/kJ1TdOw7gAe5YEMwAAAMDKp6oxxnjaGOOc6srqtjnnF6ujY4znLO1eUd22Rf9bq1cun1/e6sC/+zfGxxiXV0fnnF9a4leNMc4ZYzyt1WGDf7JDzwawIySYAQAAAKo559Hq1dVHq89Vd1UfWW5fVf36GOOu6m9X764aY7x9jPG6pc211U+NMe6sXt/qAMGq66r9S/zdrRLULWPfWX22+hfVa+ac9+3YAwLsADWYAQAAgLPanPPHNnz+RPX0Ldp8pnrWFt0/Xv300uab1Qu36Hu0etUW8Qerty4/AGckO5gBAAAA1ndR9cHdngTAbrGDGQAAAGBNc85bdnsOALvJDmYAAIAd9Nf3f2e3p7BjHsvPBgBsjx3MAAAAO+jx553Tlb/yr3d7Gjvig2//ud2eAgCwy+xgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANYiwQwAAAAAwFokmAEAAAAAWIsEMwAAAAAAa5FgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANYiwQwAAAAAwFokmAEAAAAAWIsEMwAAAAAAa5FgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANYiwQwAAAAAwFokmAEAAAAAWIsEMwAAAAAAa5FgBgAAAABgLRLMAAAAAACsRYIZAAAAAIC1SDADAAAAALAWCWYAAAAAANYiwQwAAAAAwFokmAEAAAAAWIsEMwAAAAAAazl3tycAAACsjDH+VfVD1f1L6L+r/uPqmuq86jfmnL+5Rb/LqvdWF1S3V6+bcz4wxri4umkZc1ZXzTm/NcZ4YvWB6qnV16uXzTm/uqMPBwDAY5IdzAAAsAeMMfZVl1RPn3NeNue8rPpy9U+qy6vLqteOMX5ii+43VW+cc15S7auuXuLXV9fPOX+8+nR17RJ/R3XHnPPS6obqXTv0WAAAPMZJMAMAwN4wlv/+yzHGZ8YYb6yeV/3BnPObc85vVx+p/uvv6TTGj1bnzzn/eAkdql46xjiveu7S56H48vn5rXYwV91cXbG0BwCAU6JEBgAA7A0/UH2i+u9blcP419WHq3s2tLmnetamfk/eos1Tqh+s7p1zPrAp/j19llIa91YXVndvZ6JHjhzZMn7w4MHtdD+jHT58+JT7PNb/Xdb5NwEAHjskmAEAYA+Yc/5v1f92/HqM8b7qna3KWRy3r/rupq6Pqx7cos3meBv67tsU32rcEzpw4ED79+/fbvPHlMd6sngd/k1Y17Fjx074wgqAM4cSGQAAsAeMMS4fY/znG0L7qr+oLtoQe1J/c5fxl0/Q5mvV948xzlniF23o+5WlXWOMc6snVN945E8BAMDZRoIZAAD2hidWvzbG+L4xxhOqV1X/TfWfjzEuHGP8reol1f+ysdOc84vV0THGc5bQK6rb5pz3V3dUL1/ir6xuWz7fuly33L9jaQ8AAKdEghkAAPaAOefvVbdU/0d1uLpxzvnJ6n+s/lX1b6sPzjn/96oxxq1jjGcs3a+qfn2McVf1t6t3L/HXV68dY3yu+tnqmiV+bfVTY4w7lzZv2OnnAwDgsUkNZgAA2CPmnNe2Sv5ujH2w+uAWzX+/um9p85n+5uF/x3c3/9wW8W9WL3zkMwYA4GxnBzMAAJyZvlZ9brcnAQDA2c0OZgAAOAPNOW/a7TkAAIAdzAAAAAAArEWCGQAAAACAtUgwAwAAAACwFglmAAAAAADWIsEMAAAAAMBaJJgBAAAAAFiLBDMAAAAAAGuRYAYAAAAAYC3n7saXjjH+VfVD1f1L6L+r/uPqmuq86jfmnL+5Rb/LqvdWF1S3V6+bcz4wxri4umkZc1ZXzTm/NcZ4YvWB6qnV16uXzTm/uqMPBwAAAABwlnjUdzCPMfZVl1RPn3NeNue8rPpy9U+qy6vLqteOMX5ii+43VW+cc15S7auuXuLXV9fPOX+8+nR17RJ/R3XHnPPS6obqXTv0WAAAAADA/8/evUdbdpV1wv5VbkUUIijxIwEBacxLMGq0wj1gFNFmkEZsbp8JAVpIRG6NHyAol4YI2i1KBAWhSejQhAByES8JiiCBQAiSUsCAvHEoEElCgzDsCCaVW31/rHVg5+RU1alVdc4+l+cZI6POnnuutd+1OWfuxW/PNRebzjxmMNf47/uq6nsyBL//luSvuvvrSVJV70zyqCSnf2ujqrskObS7Lx6bzk7y0qo6M8mDkjxipv1DSZ6X5GHjc0ny1iSvqaqDu3th5jTApueqEgAAAGCqeQTMt0vygSTPyBBcXJDk7UmumulzVZJ7L9ruyCX63CnJ7ZNc3d03LGq/2TZj6HF1ksOTXLncYi+99NLdPr9t27bl7mrT2759+7xLABaZuarkLgvjaFXdMcnbkmxLsiPJRVX1we7+7KLNz0ny5O6+uKrOynBVyR/k21eVvK2qXpThqpLn5dtXlTysqk7JcFXJY1f+KAEAAICVsuoBc3d/LMnHFh6PocQrMwQPC7YkuWnRpgck2blEn8Xtmdl2y6L2pfa7W8ccc0y2bt26N5uwC8J4NqsdO3bs8cuqOXJVCQAAADDZqgfMVXV8kq3d/YGxaUuSLyQ5YqbbHXLLWcZf2kWfryT5rqo6sLtvHPssbHvF2O9LVXVQktsk+dr+OxqAdW9DXVWymC+21jdXvgAAAKx981gi47ZJTq+q+2cIM56Q5HFJzqmqw5N8M8kjk5w2u1F3f7Gqrq2qB3T3R5OckuS93X19VV2Y4TLrc5M8Psl7x83OHx//xvj8hWbKAXybq0pYy3xBwEpY41eVAADAunPAar9gd/9ZkvOS/G2S7UneOAbGL0jywSSfTHJud/91klTV+VV13Lj5yUnOqKrPJbl1kleP7U9NclpVfTbJAzPcmCoZ1v28b1V9ZuzztJU+PoD1pKqOr6oHzzTtt6tKxvalriqJq0oAAABgY5jHDOZ098JNn2bbzs0wA3mx9ye5ZuzzqdzyMu109xeTnLBE+9eTPHzfKwbYsFxVAgAAS6iqDyb53iQL56y/mOQ/ZJjUdnCS3+3u1yyx3bFJzkxyWJIPJ3nKuETcnTPcKPt7k3SSk7v7G1V12yRvSXK3JF9N8pju/vKKHhzAfrTqM5gn+EqSz867CICNyFUlAABwS1W1JclRSX6ku4/t7mMzXMX38iTHJzk2wznvPZfY/JwkT+/uozJcIXjq2P7aJK/t7nskuSTfnnj3sgyTL47OcNPtV63QYQGsiLnMYN4b3X3OvGsA2MhcVQIAALdQ47/vq6rvyRD8/luSvxrPa1NV70zyqCSnf2ujqrskObS7Lx6bzk7y0qo6M8mDkjxipv1DSZ6X5GHjc0ny1iSvqaqDl3u135R7C7jXxfrmZtisNWs+YAZgTXFVCQAAm8HtknwgyTMyLIdxQZK3J7lqps9VueWEiyOX6HOnJLdPcnV337Co/WbbjEtpXJ3k8NzyPihLciPszccXBKyEfbkZtoAZgGVzVQkAAJtBd38syccWHlfVWUlemWE5iwVbkty0aNMDkuxcos/i9sxsu2VR+1L7BViz1sMazAAAAACrpqqOr6oHzzRtSfKFJEfMtN0ht5xl/KVd9PlKku+qqgPH9iNmtr1i7JeqOijJbZJ8bd+PAmB1CJgBAAAAbu62SV5RVbeqqtskeUKSxyV5cFUdXlXfkeSRSf58dqPxfiTXVtUDxqZTkrx3XE/5wiSPHdsfn+S948/nj48zPn/hctdfBlgLBMwAAAAAM7r7z5Kcl+Rvk2xP8sbu/miSFyT5YJJPJjm3u/86Sarq/Ko6btz85CRnVNXnktw6yavH9qcmOa2qPpvkgUleOLa/KMl9q+ozY5+nrfTxAexP1mAGAAAAWKS7X5Qh/J1tOzfJuUt0f3+Sa8Y+n8otb/63MLv5hCXav57k4fteMcB8mMEMAAAAsG++kuSz8y4CYB7MYAYAAADYB919zrxrAJgXM5gBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImBmXbnu+hvnXcK64H0CAAAAYDUcNO8CYG8ccvCBOenFF8y7jDXv3NNPmHcJAAAAAGwCZjADAAAAADCJgBkAAAAAgEkEzADApmft+vXJ/24AADB/1mAGADY9a/yvT+45AAAA82cGMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAKyq666/cd4lrJiNfGwAsJSD5l0AAAAAm8shBx+Yk158wbzLWBHnnn7CvEsAgFVlBjMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAANzMddffOO8SmGi1/7c7aFVfDQAAALiF666/MYccfOC8y1gRG/nYYCM75OADc9KLL5h3GUxw7uknrOrrCZiBXXIiuDzeJwAA9tVGDnJWO+gAYHUJmIFd2sgnufuTE2YAAABgs7IGMwAAAAAAkwiYAQAAAACYZFMskVFVJyV5YZKDk/xud79mziUBbErGY4C1w5gMsHYYk4H1bMPPYK6qOyZ5eZLjkxyb5LSquud8qwLYfIzHAGuHMRlg7TAmA+vdZpjB/FNJ/qq7v54kVfXOJI9KcvoetjswSa677ro9vsBhh27ZxxI3vh07duy3fXm/98z7vbr29H7PjCMHrngxa9vU8TjZizF5Mb/D69P+HMeWy+/K+jPl98SY/C0rdo68kf+W9mVs2qjvi/fklrwnt7Sr98SY/C0rnlssZaP+vm10q32e7PdkfVrt8+TNEDAfmeSqmcdXJbn3MrY7Ikkuu+yyPXY89advPamwzeTSSy/db/vyfu+Z93t17cX7fUSSf1zBUta6qeNxshdj8mJ+h9en/TmOLZfflfVnH39PjMkrdI68kf+W9uV3bqO+L96TW/Ke3NIy3hNj8grnFkvZqL9vG91qnyf7PVmfVvs8eTMEzAck2TnzeEuSm5ax3SeSPDDDwH7jCtQFbB4HZhigPzHvQuZs6nicGJOB/ceYPHCODKwFxuSBMRlYCyaPyZshYP5ShgF3wR2SXLmnjbZt27YjyUdWqihg09nMMzIWTBqPE2MysN8Zk50jA2uHMdmYDKwdk8bkzRAwvz/JS6rq8CTfTPLIJKfNtySATcl4DLB2GJMB1g5jMrCuHTDvAlZad1+R5AVJPpjkk0nO7e6/nm9VAJuP8Rhg7TAmA6wdxmRgvduyc+fOPfcCAAAAAIBFNvwMZgAAAAAAVoaAGQAAAACASQTMAAAAAABMImAGAAAAAGCSg+ZdAPtXVR2W5KIkJ3b3F+ZczoZWVf8tyWPGh+d196/Ms57NoKpOT/KoJDuTnNXdr5xzSTA3xnv2xOcUG51x8Ob8zS/N+eOuVdVvJ7l9dz9x3rXA/uTzgT3xmbn/mcG8gVTVfZJ8JMlR865lo6uqn0ry00l+NMmxSbZV1c/Nt6qNrap+PMlPJvnhJMcleUZV1Xyrgvkw3rMnPqfY6IyDN+dvfmnOH3etqh6c5AnzrgP2N58P7InPzJUhYN5YTk3ytCRXzruQTeCqJM/u7uu6+/okf5/kznOuaUPr7g8l+YnuviHJ92a4AuOb860K5sZ4z574nGKjMw7enL/5JTh/XFpVfXeSlyf5jXnXAivA5wN74jNzBVgiYwPp7icniS/lV153f2bh56r6gQyXVjxgfhVtDt19fVW9NMlzkrwjyRVzLgnmwnjPnvicYqMzDt6cv/ldc/64pNcneUGS75t3IbC/+XxgT3xmrgwzmGEfVNUPJvnLJM/t7n+Ydz2bQXf/tySHZzghPnXO5QCsaT6nYHPxN78054/fVlVPTvLP3f2BedcCME8+M/cvATNMVFUPSPKBJM/v7jfNu56NrqruUVXHJkl3/3uSd2dYTw+AJficgs3F3/wtOX9c0mOT/HRVfTLJ6UkeXlVnzLkmgFXlM3P/s0QGTFBV35fkPUke291/Ne96Nom7JXlpVR2f4S7gP5vkjfMtCWBt8jkFm4u/+V1y/rhIdz9k4eeqemKSE7r7l+dXEcDq8pm5MgTMMM1zktwqyStn1nZ6XXe/bn4lbWzdfX5V3TvJ3ya5Mcm7uvttcy4LYK3yOQWbi7/5JTh/BGAJPjNXwJadO3fOuwYAAAAAANYhazADAAAAADCJgBkAAAAAgEkEzAAAAKW2VNAAACAASURBVAAATCJgBgAAAABgEgEzAAAAAACTHDTvAgCAza2qDk5yeZJPdvdDZ9oPTPJfk5yU4ZzlkCR/muTFSQ5NcsHY9dZJ7pikx8d/2d3PraqdSQ5P8ookO7r7KYte95FJXtzdP1JVX0iyI8k1i8p7andftH+OFGDt2cNY+6tJbt/dT9/Ftj+Z5EVJ7pTk35N8Jcnp3X3hTJ9fGfe9JcmBSf48ya9193VV9cQkr0ry+UW7/nR3P76qXpLkaUmuGNsPTvLpDGP3P+zbkQOsDUudC1fVXZP8Y5K/G7sdkOQbSX63u/+wqr4/w7nv93f3FYv293cZxvDvyu7H2Ccuen5LksOSXJjktO6+tqpuk+SVSe6b5Kbxv9d095nja52d5CFJvrroNd7Y3a+e9IawLgmYWTXLGCCfmOWfYG7JcPL7N0mekuFk9YKx/5JBwx5qu1uS30hyr7GmHUle191vrKoDkvxDkhd099sWbfd7GQbYMxYd26x7JzlyiedvneRLSX6hu/9pd/XNvN4vJPm57v5Pi9qek+GE+/1Jntnd11fVdyQ5M8mPZnivn9fd7xm3+fUkP5/kyiQP7+5/HT/UPjQ+/pfl1AOwn/znJJ9MclxVHd3dfz+2/0GS2yV5cHf/36r6ziRvSXJmd5+S5NgkqaoTkvx+dx+7i/2/JskHquqXu3s2QD5tfG7Byd19yX47KoD1YZdjbYbz1yVV1cMzhA6ndPfHxrb7Jnl7Vf1Sd59fVY9O8nNJ7tfd11TVrZK8M8lLkvzauKsLu/vE3dT39tmAu6pOSfJXVfWD3X31xGMGWEt2dS58zez5bVXdJcM57Y3d/a6q+sskT0zy8pk+98sQLP9JklOy5zH2Zs+P4/RHkjwhyeuT/PcMGckPd/fOqjoyycVVdXl3v2/c7Izu/u19eQNY/wTMrLZdDpBj07JPMMfZFu/JEKi+PMsPGm5mDL4/lOSFSX5+HDTvmOQtVfUD3f2rVfW6JE9K8raZ7Q5NcnKS+y11bIteY6lj35Lk1Rk+DH5+DzV+d4YA/OSx1oX2Y5K8NMmPJflahv8z8MtJfivDifs3uvvoqrpzko9V1SVJ/i3JI5P8QJLnJXlckt9P8qwk5wqXgTn4pQzj6z9mmEX3lHFsPjnJEQsBQnd/s6qekuQBe7Pz7r6kqjrJo5K8OfnW2H9chuADYFNaxlj7Q7vZ/BVJnrEQLo/bXlxVzxqfOz/JERkmghya4Vz42qp6epLvnVpzd795DJlPSvK6qfsBWENucS68VKfu/mJVvTjJc5O8K8NEiVdX1W90986x22kZJsvdOOYQe+t7MgTUXx8fH5Hk/2SY0HZdd19ZVf955nlIImBmzhYNkHt7gnirJN+Z5Kp9LONXMwSrb5qp64qqemySz1fVq5O8MclLquou3f3FsdtjklzS3T2enO+tW2WY2fzlZfR9TIbZxs9J8p9m2n82yZ9091eTpKpenyG0/q0MoclJ4/FcPn67+ZgM7/MB4+sfluTrVXXEuK8fn3AcAJNV1T0zfFH3yCTbk3yoql6QZFuSzyyendbdX85wQr23Xpvhi8I3j49PTfK/u/vfZ/q8papmZzjv6O77THgtgPVit2NtVS0ZMFfV9yQ5KsmHl3j6/UneXVW3S/KmJCcm+XJVbU9yUZI/7u7Z7R5YVZ9ctI9Xdff/2k3dn8ruw2+AdWE358K7Mjv+/XmGq7t/PMkFVfVdGf5//T1m+u9pjF14/lYZwuXLkvx2d79jfP4lGa48+ZequijJRzNM/Ju9CvuXq+pxi17jlO5e6gpvNigBM2vB7AC5p8HvsVV1fIaA9C4Z1il69z6+/v2TPH9xY3f/n6r6+yT37e4/qqp3JPkvGQbYZPhm8LdmNjl0ido/2t1PW/T8gRlmbXx9rP0391Rgd78uScZlRGZ9X5IvzDz+UoY18Bae++fFz3X3v49Le1yS5LNJXpbkf2ZYC+/GAKyuX0ryZ939tSRfq6rPZxhfP5f9ezPityV5RVX9hyRfzHDZ308s6mOJDGCzuSn7NtYevETbIeO/O7v7/yb56XE5up9IckKS86rqtd39vLHfnq5gXMrODGs+A6x3uzoXfusu+n9r/Ovum8arrX8hw5Khj0tyXnd/Zab/spbIGJcGfWGGSWoL4XK6+9M1TIX+sQxB9kOSvKCqHt3dfzp2s0QGAmbWhNkTxL1ZIuPgDJeEvD3Jz+xjDUudHCfJ1rG+jK/1jqo6PcM3gndM8mczfXe5RMbi56vqZ5Kck+RPu/sb+1D3ATP1JcO3lzfu6bnufk3GdUer6oHj85dU1RszzKp+c3e/ZR/qAtijcZ3PU5LsGG+ylwxXVjw9w5d/R1fVbbr732a2uWOGL8UetWg95d0aL8s+O8MJ+CeSXOoGUQD5eHY/1l661Ebd/bVx6aETMixZN+snk/z9eI+PX0nykfFmqf+U5KxxssifZ1iqbap7JdndDGeANW8P58Lv3MVm98rN7+30xiSXVdVhGa7QW3J5jT3p7puSnF5V909ydpKHV9VBGXKDX+3u7RlmWL+yql6Y5Bcz3BAWkuzfmUEw1eIBclm6+/oMlzw/aB9f/6MZTo5vZjyx/v4kfz2+3icy3Bn1wZlZ12jKC3b3X2S4Kco7xg+CqS7PEAgvODLDTOU9PZfkW+tY/2aSX8nwbefnkzwsyfPHNaYBVtLJGdaPP7K779rdd01ytww3QX1AhnXl37gwTo7/vjbJ1/YmXJ7xB0n+3ww3Q/n9fa4eYJ3r7iuzm7E2ye7G2v8vye+ON/bLuO39MpzjLoTH35Hkv4/3E1nwQxlu1D1JVT0pw2fFH07dB8Aasbtz4Ucv7lxVRyV5UZLfWWgbZz7/aYZ7M93Y3RfvY01Py3Dlyc929w1JKsmLxgl+GUPno7MP4zgbkxnMzNXMAPlfkxw+YRcPyxgA74OXJ/l4Vf3twjrMVXWnJOcmee144r3gNRkuq35QhktE9sVvZ/i28qUZbsw3xZ8k+eOqenmG8Pu0fHsWyR+Pj39pPJ7/mGE5jFlPzbCG85VVtTXJDRkulTwow6zuKQEOwHL9UpJXzn5ZN854e3WGcfF+GT4jLqqqGzJcVfKeJP9tyot19z9V1ecyhBvnL9Fl8RrMyXDT2DOnvB7AOvHU7Hqs/bUkpy1apu3T3X3/7j6/qh6f5NfHiRkHZJjM8Pju/uDY99cznFteVFU7MywV94kM9wVZsNQSeTd093HjzwtL5O0cX6OTnNDd1+6PgweYoz2dC88uw3lTkmszzCY+b9F+XpPhipQnLfEaexpjb6a7/7Gq/keSM6rqLzLcJPu3MsyS/vcM4/AfJTl9ZrOl1mC+uLsnzaZmfdqyc+fOPfeC/WC8Ed4/5tuzlRcGyDO6+x3jieurMsyinXVDdx9XVS/J8G3aFRlOMG+V4VK7p87ceC9VdUKGQOCYvajtLhnC13tnCFmvzXBZ4P+cuRtrqupWGdY1Pr+7n7CbY5v1xCT/muFy7Fsvet2fTPIXSX60u5e8BHFR/ydmuCz8xJm2/5Lk2RkC4Y8nOW28FPzWGWbr/ViGk/mXdfc5M9vdPsMSHw/q7utmHt8+yZu6+9f3VA8AAAAAm5uAGQAAAACASSyRwYZXVT+R5IxdPP3B7p66PMV+U1XPzbD+0lJe4YZ7AAAAAKxFZjADAAAAADDJAfMuAAAAAACA9UnADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJDpp3AWvV9u3btya5V5Krktw453KA9e3AJEck+cS2bdt2zLsYAAAAgP1FwLxr90py4byLADaUByb5yLyLAAAAANhfBMy7dlWSHHXUUTnkkEPmXQuwjl133XW57LLLknFcAQAAANgoBMy7dmOSHHLIIdm6deu8awE2BsvtAAAAABuKm/wBAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImDeBK67/sZ5l3ALa7EmAAAAAGDvHDTvAlh5hxx8YE568QXzLuNmzj39hHmXAAAAAADsIzOYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADDJQSv9AlV1WJKLkpzY3V+oqvslOSPJbZJ8OskTuvu6qjo2yZlJDkvy4SRP6e4bFu3rtknekuRuSb6a5DHd/eWqOiTJWUmOS3JNkpO6+3NVtSXJK5KcmOSmJKd290dX+pgBAAAAADaDFZ3BXFX3SfKRJEeNjw9L8u4kp3X3D47dnjT+e06Sp3f3UUm2JDl1iV2+LMmF3X10kjckedXY/swk3xzbn5Xk7LH9kUmOTnLPJI9IcnZVrXioDgAAAACwGaz0EhmnJnlakivHxw9J8rHu/vT4+BlJ/qiq7pLk0O6+eGw/O8mjl9jfwzLMYE6StyZ5aFUdPNve3R9OcnhV3Xlsf1t339TdlyW5PMn99+PxAQAAAABsWis6m7e7n5wkVbXQdPck36iqtyW5R5KPJnl2kh9NctXMplcludMSuzxyoV9331BVVyc5fLZ90fa7al+2Sy+9dG+6r0nbtm2bdwlL2r59+7xLAAAAAAD2wWovF3FQkp9Jct8Ms4nPSvL8JH+ZZOdMvy0Z1kxebMsSj2/KMBN7qe131b5sxxxzTLZu3bo3m7BMazX4hv1tx44dG+LLKgAAAIDFVnqJjMW+nOTi7v58d9+Y5A+T3DvJl5IcMdPvDvn2shqzrhify7iW8m2SfG032y93vwAAAAAA7KXVDpjfl2RbVX3f+PjEJNu7+4tJrq2qB4ztpyR57xLbn5/k8ePPj81ww7/rZ9ur6vgk13b35WP7yVV1YFXdPcPNBj+xAscFAAAAALDprOoSGd39z1X1i0n+tKpuleSTSZ4zPn1ykjdU1WFJ/ibJq5Okqk5PcmV3vy7Ji5KcXVWfSfKv4zZJ8ntJXj+278gQUCfJO5PcJ8nCTQWf1N3XrOQxAgAAAABsFqsSMHf3XWd+Pi/JeUv0+VSG5TIWe0+S+419vp7k4Utse22SJyzRvjNDgP2cxc8BAAAAALBvVnuJjCmOSHLuvIsAAAAAAODmVnWJjCnGGc8AAAAAAKwx62EGMwAAAAAAa5CAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwyUErufOqOizJRUlO7O4vzLQ/PcmjuvuE8fGdk5yT5HuTdJKTu/sbi/Z1SJKzkhyX5JokJ3X356pqS5JXJDkxyU1JTu3uj47bPDvJqRmC9Od397tX7mgBAAAAADaXFZvBXFX3SfKRJEctar9nkucv6v7aJK/t7nskuSTJi5bY5TOTfLO7j07yrCRnj+2PTHJ0knsmeUSSs6vqoKq6V5LHJTk2yfFJXlFV370fDg0AAAAAgKzsDOZTkzwtyZsXGqpqa5LXJ3lxksePbQcneVCGcDgZguMPJXneov09bNwu3f3hqjp8nPn8sCRv6+6bklxWVZcnuX+Sn0jy7u6+Nsm1VXVBhlnO/3tvDuLSSy/dm+5r0rZt2+ZdwpK2b98+7xIAAAAAgH2wYgFzdz85Sapqtvk3k7wxyedn2m6f5OruvmF8fFWSOy2xyyPH57Ko3+7aP7FE+1455phjsnXr1r3djGVYq8E37G87duzYEF9WAQAAACy2ajf5q6qHJLlzd/+vJWrYuajtpiV2sbjflrHf3rYDAAAAALAfrFrAnOTnk/xgVX0yyZlJjquqtyf5SpLvqqoDx35HJLlyie2/ND634A5jv71tBwAAAABgP1i1gLm7f6G7j+7uY5M8Ockl3f3Y7r4+yYVJHjt2fXyS9y6xi/Pz7XWbj09ybXdfPrafXFUHVtXdM9xU8BPjPh5ZVd9RVYcneXCSD6zgIQIAAAAAbCoreZO/vfHUJG+qqhcmuTzDbOdU1VOSHNndL07ye0leX1WfSbIjySnjtu9Mcp8knx4fP6m7r0ny11V1Toaw+aAkL+ruK1brgAAAAAAANroVD5i7+65LtF2Q5ISZx1+cfTzjnUmeO/a5NskTltjXziTPGf9b/NzvJPmdKXUDAAAAALB7q7kG8xT3TPKaeRcBAAAAAMAtrZUlMpbU3R+edw0AAAAAACxtrc9gBgAAAABgjRIwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAet9AtU1WFJLkpyYnd/oapOS/LMJDuTXJLkF7v7uqo6NsmZSQ5L8uEkT+nuGxbt67ZJ3pLkbkm+muQx3f3lqjokyVlJjktyTZKTuvtzVbUlySuSnJjkpiSndvdHV/qYAQAAAAA2gxWdwVxV90nykSRHjY+PSvLcJPdP8sPj6z9t7H5Okqd391FJtiQ5dYldvizJhd19dJI3JHnV2P7MJN8c25+V5Oyx/ZFJjk5yzySPSHJ2Va14qA4AAAAAsBms9BIZp2YIkK8cH+9I8tTuvrq7dyb5uyR3rqq7JDm0uy8e+52d5NFL7O9hGWYwJ8lbkzy0qg6ebe/uDyc5vKruPLa/rbtv6u7LklyeIdwGAAAAAGAfrehs3u5+cpJU1cLjLyb54th2eJKnJ3likiOTXDWz6VVJ7rTELr/Vr7tvqKqrkxy+m+2Xu99duvTSS/em+5q0bdu2eZewpO3bt8+7BAAAAABgH8xluYiqumOS9yY5q7svqKoHZFiTecGWDGsmL7Zlicc3ZZiJvdT2u2pftmOOOSZbt27dm01YprUafMP+tmPHjg3xZRUAAADAYiu9RMYtVNU9Mtz0703d/etj85eSHDHT7Q759rIas64Yn8u4lvJtknxtN9svd78AAAAAAOylVQ2Yq+o2Sd6X5IXd/TsL7ePSGdeOM5mT5JQMM5wXOz/J48efH5vhhn/Xz7ZX1fFJru3uy8f2k6vqwKq6e4abDX5i/x8ZAAAAAMDms9pLZDw5yf+T5NlV9eyx7U+6+8VJTk7yhqo6LMnfJHl1klTV6Umu7O7XJXlRkrOr6jNJ/nXcJkl+L8nrx/YdGQLqJHlnkvsk+fT4+Endfc1KHiAAAAAAwGaxKgFzd991/PGM8b+l+nwqyb2XeOo9Se439vl6kocvse21SZ6wRPvOJM8Z/wMAAAAAYD9a9TWYJzgiybnzLgIAAAAAgJtb7SUy9lp3nzfvGgAAAAAAuKX1MIMZAAAAAIA1SMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEw74Xrrr9x3iXcwlqsCQAAAADYHA6adwHrySEHH5iTXnzBvMu4mXNPP2HeJQAAAAAAm5QZzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMctNIvUFWHJbkoyYnd/YWq+qkkr0xyaJK3d/cLx37HJjkzyWFJPpzkKd19w6J93TbJW5LcLclXkzymu79cVYckOSvJcUmuSXJSd3+uqrYkeUWSE5PclOTU7v7oSh8zAAAAAMBmsKIzmKvqPkk+kuSo8fGhSd6Y5GeTHJ3kXlX10LH7OUme3t1HJdmS5NQldvmyJBd299FJ3pDkVWP7M5N8c2x/VpKzx/ZHjq9zzySPSHJ2Va14qA4AAAAAsBms9BIZpyZ5WpIrx8f3TvIP3f35cXbyOUkeXVV3SXJod1889js7yaOX2N/DMsxgTpK3JnloVR08297dH05yeFXdeWx/W3ff1N2XJbk8yf338zECAAAAAGxKKzqbt7ufnCRVtdB0ZJKrZrpcleROu2lf7Fv9uvuGqro6yeH7Yb+7dOmll37r523btu3Npqtm+/btu31+vdYNAAAAAKxtq71cxAFJds483pJhbeRdtS+2ZYnHu9t+ufvdpWOOOSZbt27dm01W3VoNkPdkvdYNe2vHjh03+7IKAAAAYKNY6SUyFvtSkiNmHt8hw/IZu2pf7IrxuYxrKd8mydf2w34BAAAAANhLqx0wfzxJVdXdq+rAJCcleW93fzHJtVX1gLHfKUneu8T25yd5/PjzYzPc8O/62faqOj7Jtd19+dh+clUdWFV3z3CzwU+s0LEBAAAAAGwqqxowd/e1SZ6Y5F1JPpvkc0neOT59cpIzqupzSW6d5NVJUlWnV9VTxj4vSnLfqvpMkqdmuIFgkvxekq1j+6szBNQZ9/2ZJJ9O8sdJntTd16zYAQIAAAAAbCKrsgZzd9915ucPJPmRJfp8Ksm9l9j8PUnuN/b5epKHL7HttUmesET7ziTPGf8DAAAAAGA/Wu0lMqY4Ism58y4CAAAAAICbW5UZzPuiu8+bdw0AAAAAANzSepjBDAAAAADAGiRgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkywrYK6qs5Zoe+f+LwcAAAAAgPXioN09WVV/kOSOSR5YVYfPPHVwkrutZGEAAAAAAKxtuw2Yk5yV5JgkP5LkXTPtNyS5eKWKAgAAAABg7dttwNzdlyS5pKre391fWqWaAAAAAABYB/Y0g3nB91XVm5N8d5ItC43d/cMrUhUAAAAAAGvecgPm1yc5O8nfJNm5YtUAAAAAALBuLDdgvqG7X7milQAAAAAAsK4csMx+l1bVD61oJQAAAAAArCvLncF8tyTbq+qLSa5ZaLQGMwAAAADA5rXcgPkFK1oFAAAAAADrznID5r9b0SoAAAAAAFh3lhsw/0uSnUm2jP8myVVJ7rQSRQEAAAAAsPYtK2Du7m/dDLCqDklyUpJaqaIAAAAAAFj7Dthzl5vr7uu6++wkD9n/5QAAAAAAsF4sawZzVX33zMMtSY5LcrsVqQgAAAAAgHVhyhrMSfKVJM9ckYoAAAAAAFgX9noNZgAAAAAASJa/RMYBSZ6T5KFJDk7yviS/0d03rGBtAAAAAACsYcudmfybSX4yyauSvDLJ/ZO8YqWKAgAAAABg7VvuGsz/Mclx3X19klTVeUk+leSXV6owAAAAAADWtuXOYD5gIVxOku7ekeT63fQHAAAAAGCDW+4M5k9W1RlJfj/JziTPSPLpFasKAAAAAIA1b7kzmJ+W5HZJLkry8SS3zxAyAwAAAACwSe12BnNVHZLkDUne091PHNvOS3JjkqtXvDoAAAAAANasPc1gPj3JYUk+OtN2apLbJnnJCtUEAAAAAMA6sKeA+cQkJ3X3VxYauvvKJI9P8nMrWRgAAAAAAGvbngLm67r7msWN3X11kh0rUxIAAAAAAOvBngLmG6vqNosbx7aDV6YkAAAAAADWgz0FzG9NcmZVfedCw/jzmUnetZKFAQAAAACwth20h+d/N8nrkny5qj6TIZA+OslbMtwAEAAAAACATWq3AXN335TktKp6eZJtSW5K8vHuvmpfXrSqHpfkV8eH7+3u51TVsRlmRh+W5MNJntLdNyza7rYZwu27Jflqksd095er6pAkZyU5Lsk1GW5M+Lmq/7+9e4+28y7rBP4NSQkVqagUKZc601V4KGagGi4ChalScXW4uhA6tAOtw2UYQApjCRDhgAAAGjtJREFUcVBa0A7gjQVCRwSBTlAoqKhdnbbMIBUkqKAGQVPlgXG42AtjB5ZUSpM2JPPHfk/m5HCSnPOmOe9Jzuez1lnZ+7d/z7ufd6+z8sf3/Naza12SX8nsywp3J3led//JofQOAAAAAMDMwUZkJEm6+4vd/fvdffkdEC5/W5I3J/nXSR6S5DFVdUaSdyd5cXc/IMm6JM9bpPw1SbZ29ylJ3p7kTcP6S5LcMqy/NMmWYf1pmZ24flCSpybZUlUHO7UNAAAAAMASLClgvoOtH973rpl9UeAxSW5Pcmx3f3zYsyXJ0xepfUJmJ5iT2XzoM6vqmPnr3f3RJMdX1YnD+vu6e3d3fzbJl5I86nDcFAAAAADAWrPip3m7+5+r6qIkn0nyjSR/nOS2JPNPRt+Y5L6LlN97bl9376qqm5McP399Qf3+1pds+/btex9v3rx5OaUrZtu2bQd8/UjtGwAAAABY3VY8YK6qByf590m+N8nXMhuN8fgke+ZtW5fZzOSF1i3yfHdmJ6IXq9/f+pJt2rQpGzduXE7JilutAfLBHKl9w3Lt3Llznz9WAQAAABwtphiR8aNJrunuf+zunZmNwzg9yQnz9twryQ2L1F4/vJZhlvLdknwlyXX7qd/fOgAAAAAAh2iKgPnTSc6oqrtW1bokT8psTMaOqnr0sOdZST6wSO3VSZ49PD4rsy/8u33+elWdlmRHd39pWD+nqtZX1clJHpDkLw7TfQEAAAAArClTzGD+YFV9f5JtmX25358n+cUkf5Dk7VV1XJJPJnlzklTVxUlu6O63JrkoyZaqujbJPyU5Z7jsJUneNqzvzCygTpL3J3lEkr8enj+nu289zLcIAAAAALAmrHjAnCTd/UtJfmnB8qeTPHyR7ZcneeRQ99UkT17kejuSnLvI+p4kFww/AAAAAADcgaYYkbFcJyS5bOomAAAAAADY1yQnmJeju6+augcAAAAAAL7VkXCCGQAAAACAVUjADAAAAADAKAJmAAAAAABGETADAAAAADCKgBkAAAAAgFEEzAAAAAAAjCJgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAoAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAUQTMAAAAAACMImAGAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKAJmAAAAAABGETADAAAAADCKgBkAAAAAgFEEzAAAAAAAjCJgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAo2yY4k2r6klJXp3krkk+2N3nV9UZSd6Q5Ngkv93dFy5Sd2KSdye5Z5JOck53f72q7p7kPUlOSnJTkmd095er6s5J3pnkoUluTXJ2d3/m8N8hAAAAAMDRb8VPMFfVSUnemuSpSR6c5Aeq6swklyZ5SpJTkjxsWFvoLUne0t0PTPKXSS4a1l+TZGt3n5Lk7UneNKy/JMktw/pLk2w5LDcFAAAAALAGTTEi48cyO6F8XXffnuSsJN9I8rnu/nx378rslPLT5xdV1TFJHpvk/cPSlnl7npDZCeYkeW+SM4f9e9e7+6NJjh9OQQMAAAAAcIimGJFxcpLbquqKJCcmuTLJtUlunLfnxiT3XVB3jyQ3DwH0wj33nqvv7l1VdXOS4+evL6j50lKb3b59+97HmzdvXmrZitq2bdsBXz9S+wYAAAAAVrcpAuYNmZ1EPj3J15Nckdl85D3z9qxLsntB3Z0W7Mm8PesWrM/VL6xZ7LoHtGnTpmzcuHE5JStutQbIB3Ok9g3LtXPnzn3+WAUAAABwtJhiRMaXk3you2/q7luT/EGSM5KcMG/PvZLcsKDuH5N8R1WtH56fMG/P9UNNqmpDkrsl+UqS65ZwXQAAAAAARpgiYL4yyY9W1d2HsPjMzOYqV1WdPKydneQD84uGec1bM5vZnCTPnrfn6uF5hte3Dvv3rlfVaUl2dPeSx2MAAAAAALB/Kx4wd/cnkvxyko8l+dskX0zy60nOS/J7w9pnMnyZX1W9o6qePJS/MMnzq+pvkzwmyYXD+kVJfrCqrh32vGhYvyTJxmH9zUmedVhvDgAAAABgDZliBnO6+9Ikly5YvibJQxbZfnWSbw51X8xsdvPC6301yZMXWd+R5NxDbBcAAAAAgEVMMSJjuTYk+dDUTQAAAAAAsK9JTjAvR3f/ztQ9AAAAAADwrY6EE8wAAAAAAKxCAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAUQTMAAAAAACMImAGAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKAJmAAAAAABGETADAAAAADCKgBkAAAAAgFEEzAAAAAAAjCJgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAoAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAUQTMAAAAAACMImAGAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKAJmAAAAAABGETADAAAAADCKgBkAAAAAgFEEzAAAAAAAjCJgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgbpnzzqnp9knt093lVdWqSdyQ5LslHk7ygu3ct2H/3JO9JclKSm5I8o7u/XFV3TvLOJA9NcmuSs7v7M1W1LsmvJHlikt1Jntfdf7JCtwcAAAAAcFSb7ARzVT0uybnzlt6d5MXd/YAk65I8b5Gy1yTZ2t2nJHl7kjcN6y9Jcsuw/tIkW4b1pyU5JcmDkjw1yZaqmjRUBwAAAAA4WkwStlbVdyV5bZLXJXlIVX1vkmO7++PDli1Jfj7Jry8ofUKSxw6P35vk16rqmGH9VUnS3R+tquOr6sRh/X3dvTvJZ6vqS0keldkJ6SXZvn373sebN29ezm2umG3bth3w9SO1bwAAAABgdZvqNO/bkrwyyf2G5/dOcuO8129Mct9F6vbu6+5dVXVzkuMPUL/U6+7Xpk2bsnHjxuWUrLjVGiAfzJHaNyzXzp079/ljFQAAAMDRYsVHZFTVc5P8Q3dfs6CPPfOer8tsZvJC6xZ5vvsA9Uu9LgAAAAAAyzTFCeazkpxQVZ9K8l1Jvj2zEPiEeXvuleSGRWqvH167bpilfLckX0ly3VD/9wvq59YPdl0AAAAAAJZpxU8wd/ePdPem7j41s7nJV3T3TyTZUVWPHrY9K8kHFim/Osmzh8dnZfaFf7fPX6+q05Ls6O4vDevnVNX6qjo5yQOS/MXhujcAAAAAgLVkqhnMizknydur6rgkn0zy5iSpqouT3NDdb01yUZItVXVtkn8aapLkkiRvG9Z3ZhZQJ8n7kzwiyV8Pz5/T3beuxM0AAAAAABztJg2Yu3tLki3D408nefgi2y5P8shhz1eTPHmR6+xIcu4i63uSXDD8AAAAAABwB1rxERkjnJDksqmbAAAAAABgX6tpRMaiuvuqqXsAAAAAAOBbHQknmAEAAAAAWIUEzAAAAAAAjCJgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAoAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAUQTMAAAAAACMImAGAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKAJmAAAAAABGETCzqt12+zenbmEfq60fAAAAAJjShqkbgAO58zHrc/arPjJ1G3tddvHpU7cAAAAAAKuGE8wAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAoAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAUQTMAAAAAACMImAGAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKAJmAAAAAABGETADAAAAADDKhinetKpeneQZw9Oruvunq+qMJG9IcmyS3+7uCxepOzHJu5PcM0knOae7v15Vd0/yniQnJbkpyTO6+8tVdeck70zy0CS3Jjm7uz9zmG8PAAAAAGBNWPETzEOQ/Pgk35/k1CSbq+qZSS5N8pQkpyR5WFWduUj5W5K8pbsfmOQvk1w0rL8mydbuPiXJ25O8aVh/SZJbhvWXJtlyWG4KAAAAAGANmmJExo1Jfqq7b+vu25P8XZIHJPlcd3++u3dldkr56fOLquqYJI9N8v5hacu8PU/I7ARzkrw3yZnD/r3r3f3RJMcPp6ABAAAAADhEKz4io7uvnXtcVffPbFTGJZkFz3NuTHLfBaX3SHLzEEAv3HPvufru3lVVNyc5fv76gpovLbXf7du37328efPmpZatqG3bth3w9SO172R19r6UvgEAAABgLZhkBnOSVNX3JbkqycuT7MrsFPOcdUl2Lyi5U5I9C9bm9qxbsD5Xv7Bmsese0KZNm7Jx48bllKy41RjCLoW+WSt27ty5zx+rAAAAAI4WU4zISFU9Osk1SV7R3e9Kcl2SE+ZtuVeSGxaU/WOS76iq9cPzE+btuX6oSVVtSHK3JF9Z4nUBAAAAABhhii/5u1+Sy5Oc3d3vG5Y/MXupTh4C5LOTfGB+3TCveWuSs4alZ8/bc/XwPMPrW4f9e9er6rQkO7p7yeMxAAAAAADYvylGZFyQ5C5J3lBVc2tvTXJekt8bXrs6w5f5VdU7klzR3VckeWGSd1XVhZnNUX7mUH9Rki1VdW2Sf0pyzrB+SZK3Des7kzzrsN4ZAAAAAMAaMsWX/J2f5Pz9vPyQRdauTvLNofaLSU5f5JpfTfLkRdZ3JDl3bK8AAAAAAOzfJDOYl2lDkg9N3QQAAAAAAPuaYkTGsnT370zdAwAAAAAA3+pIOMEMAAAAAMAqJGAGAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKAJmAAAAAABGETADAAAAADCKgBkAAAAAgFEEzAAAAAAAjCJgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAoAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAUQTMAAAAAACMImAGAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKAJmAAAAAABGETADAAAAADCKgBkAAAAAgFEEzAAAAAAAjCJgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEww2Fw2+3fnLqFfay2fgAAAAA4OmyYugE4Gt35mPU5+1UfmbqNvS67+PSpWwAAAADgKLQmAuaqOjvJhUmOSfKr3f1rE7cEAAAAAHDEO+pHZFTVfZK8NslpSU5N8vyqetC0XQEAAAAAHPnWwgnmM5L8UXd/NUmq6v1JfjzJxQepW58kt9122z6Lxx277jC0ON7OnTuXtO9I7TtZXb2vhb654837f2T9lH0AAAAA3NHW7dmzZ+oeDquq+pkkd+3uC4fnz03y8O5+/oHqtm3bdlqSrSvQIrB2PGbz5s0fm7oJAAAAgDvKWjjBfKck81P0dUl2L6HuL5I8JsmNSb55GPoC1o71SU7I7P8VAAAAgKPGWgiYr8ssKJ5zryQ3HKxo8+bNO5M4aQjcUf5+6gYAAAAA7mhrIWD+UJKfq6rjk9yS5GlJDjgeAwAAAACAg7vT1A0cbt19fZJXJvlwkk8luay7/3zargAAAAAAjnxH/Zf8AQAAAABweBz1J5gBAAAAADg8BMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYZcPUDaxFVXVckj9N8sTu/sLE7SxJVb06yTOGp1d1909P2c9SVdXFSX48yZ4k7+zuN0zc0rJU1euT3KO7z5u6l6Wqqg8nuWeS24el/9Ddn5iwpSWpqicleXWSuyb5YHefP3FLAAAAAKueE8wrrKoekeRjSR4wdS9LVVVnJHl8ku9PcmqSzVX1Y9N2dXBV9a+T/HCSByd5aJKfrKqatqulq6rHJTl36j6Wo6rWZfa7/ZDuPnX4ORLC5ZOSvDXJUzP7ffmBqjpz2q4AAAAAVj8B88p7XpIXJblh6kaW4cYkP9Xdt3X37Un+LsmJE/d0UN39x0l+qLt3ZXaidkOSW6btammq6ruSvDbJ66buZZnmAvwPVtWnq+rFk3azdD+W5Le7+7rhd/ysJKs+GAcAAACYmhEZK6y7n5skR9BB2nT3tXOPq+r+mY3KePR0HS1dd99eVT+f5IIkv5vk+olbWqq3JXllkvtN3cgyfWeSa5L8ZJJjknykqrq7/3Datg7q5CS3VdUVmf3x5MokF03bEgAAAMDq5wQzS1ZV35fkD5O8vLs/N3U/S9Xdr05yfGZh7fMmbuegquq5Sf6hu6+Zupfl6u4/6+5nd/fXuvv/Jnlnkn8zdV9LsCHJGUmek+SRSR6RI2w8CQAAAMAUBMwsSVU9OrOTqa/o7ndN3c9SVNUDq+rUJOnubyT5/czm6652ZyV5fFV9KsnFSZ5cVW+cuKclqarThtnRc9bl/3/Z32r25SQf6u6buvvWJH+Q5OET9wQAAACw6hmRwUFV1f2SXJ7krO7+o6n7WYaTkvx8VZ2WZE+SpyS5dNqWDq67f2TucVWdl+T07n7ZdB0ty92TXFxVj8psRMa5SV4wbUtLcmWSd1XV3ZP8c5IzM/udBwAAAOAAnGBmKS5Icpckb6iqTw0/qz407O6rk1yV5K+SbEvyp939vmm7Orp195XZ9zO/tLv/bNquDq67P5Hkl5N8LMnfJvlikv82aVMAAAAAR4B1e/bsmboHAAAAAACOQE4wAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYZcPUDXDkq6r1Sc5PcnZmv1N3TvLfk7wqyTOTvCnJ54ft65Icl2Rrkud3946q2pLkR5LctODSl3b3m6vqI0m+N8nXhvVjklyV5L909z8PPXwhyY8neVCS/zTsOzHJrfOu+5PdvXU/9/CKJP92eHryUDP3fi9K8odJHt3dfzWv5oVJXpLkoUmunNfjnuEz+Msk/7G7v7HIPcx5TXe/f7Ge5r3PXZK8MskTM/v81id5d5Jf7u49w56TkrwuycOSfD3JziRv7e5Lh9f/RZLt3f3tC659QZJN3X1eVf1ckhcmeXB3f3nenu1JXpzZ57nszxYAAACAo5eAmTvCryf5ziSP6+6vVdVdk7wnyTuSXJNka3c/cW7zEJh+LMm5Sd42LL+xu19/gPd4+VwQW1XHJHlzksuSPGn+pu7+zSS/OezbklmoeqDrztX9YpJfHOo+kuS/zg9+q+oXkvxWVW3u7p1Vdf8kFyf54e7+elUt7HFdkt8Z9lyw8B6WarjO5Uk+m+SRQyD/3ZkF7N+e5KIhPP7jJBcmeWZ376mq+yR5T1Xdv7t/ZhlveVyS36yqH50Lr+eM/WwBAAAAOHoZkcEhGcLNc5I8p7u/liTdfUuSF2QWjC7mu5N8R5KvjnnP7r49s5O0j62qB465xgivTfKVJK8ZTmz/VpKf7e6/3k+Pe5J8OMmh9vfYJKckeVl37xiu/ZUkz0ry0WHPzyS5rLvfNRcKd/f1Sc5Kcn5VnbCM93t3knsn+alD7BsAAACANcAJZg7V5iTXdvfN8xeHEQu/V1XnJXlMVX0qyV0yC5c/m+T13f2780peVlX/bsG1n9Xdf7PYm3b3rVX12ST/Ksln7phb2b/u3j3098kk90zy+e7+jf3tr6rvzCzgvWLe8q9U1YULtj5uCIz356FJPtHd31zQz+eSfG54+qgkr1ik5/9TVX+X5AeT/NXC1/djR2ZjTT5WVX/U3Z9cYh0AAAAAa5CAmUO1Owc/Cb+1u59YVXfKbIzD2Ul+d8Geg43IWMyeJN9YZs1o3f0PVfWfk/xCZnOaF5oLkNcNz6/MbP70nGWPyMjSPt9kNpd6MRsz+5x27+f1OyVZGF7/zXAfl1XV5qU2CgAAAMDaY0QGh+oTSU6pqrvNX6yq+1TVVUmOnVvr7t3dfXGSLyTZcihvWlXfltnoiGsP5Toj/O8kN819ueACL+/uU7v7IcPPK7t71yG+38eTPGwYy7FXVT2sqn5rePonSU5fWDjMYf6XSf48s3Ekxw7zr+f7nsxGf+yjuy9J8r+yb0AOAAAAAPsQMHNIuvuGzL7Q79KqOi5Jhn/fkllweesiZS9K8viqesqY96yqY5P8apIPdPcXxlzjSNHdf5bZCJA3zIXDVfU9SS5J8vlh22uTPKOqzp2rq6r7Jnlvkrd09w3d/fXMvljx/Hl77pPk6Umu3s/b/0SSJ2Tx09oAAAAAYEQGd4gXJrkoyZ9W1a7MxjJcnuTVmY3D2Ed3/31V/VKSN1bV/xyWF5vB/PHufsHweG78xO7Mfm8/lHlh6RFisRnMvz+c6j6QpyV5XZJtw+e7Psm7krw+2Tu645GZfQHhzybZldks5d8Yfuack+RXq+razD7HXUle1d0fWexNu/umIbT+H8u4RwAAAADWkHV79uyZugcAAAAAAI5ATjCzZlTVG5P80H5efll3f3gl+5lTVVuT3G0/Lz9mP/OeAQAAAGByTjADAAAAADCKL/kDAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKP8P6pQYtH0gtFAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1512 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.plot_bar(abt_final.int_features,final_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.9 Save Final Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename columns  append all fields with 'BUREAU_' except id field\n",
    "\n",
    "col_name = []\n",
    "for c in final_dataset.columns:\n",
    "    if c =='SK_ID_CURR':\n",
    "        col_name.append(c)\n",
    "    else:\n",
    "        col_name.append(\"BUREAU_\"+c)\n",
    "\n",
    "final_dataset.columns = col_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File was saved.\n"
     ]
    }
   ],
   "source": [
    "final_dataset.to_csv('data_pre_processed/1_4_bureau.csv',index=False)\n",
    "print(\"File was saved.\")"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}