data_cleaning_EDA_profiling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2112851",
   "metadata": {},
   "source": [
    "# AKI - early/delayed renal treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c6d137",
   "metadata": {},
   "source": [
    "**Predicting AKI patients in ICU who receive delayed dialysis (>8h).**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4956ec4",
   "metadata": {},
   "source": [
    "*import libraries*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f71dc11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import stats\n",
    "from pandas_profiling import ProfileReport\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ce3f594",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import data\n",
    "aki_data = pd.read_csv('aki_icu.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa18851a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2110 entries, 0 to 2109\n",
      "Columns: 198 entries, subject_id to gcs_unable\n",
      "dtypes: float64(170), int64(16), object(12)\n",
      "memory usage: 3.2+ MB\n"
     ]
    }
   ],
   "source": [
    "aki_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b648ce0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-11ac1132c921>:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  aki_data.columns = aki_data.columns.str.replace(' |/','_')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['subject_id', 'hadm_id', 'stay_id', 'weight', 'weight_min',\n",
       "       'weight_max', 'charttime_aki', 'creat_low_past_7day',\n",
       "       'creat_low_past_48hr', 'creat',\n",
       "       ...\n",
       "       'spo2_mean', 'glucose_min.2', 'glucose_max.2', 'glucose_mean',\n",
       "       'urineoutput', 'gcs_min', 'gcs_motor', 'gcs_verbal', 'gcs_eyes',\n",
       "       'gcs_unable'],\n",
       "      dtype='object', length=198)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns, if necessary\n",
    "# changing all column names to lower case, and remove special characters and spacing.\n",
    "aki_data.columns = aki_data.columns.str.lower()\n",
    "aki_data.columns = aki_data.columns.str.replace(' |/','_')\n",
    "aki_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf10f890",
   "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>subject_id</th>\n",
       "      <th>hadm_id</th>\n",
       "      <th>stay_id</th>\n",
       "      <th>weight</th>\n",
       "      <th>weight_min</th>\n",
       "      <th>weight_max</th>\n",
       "      <th>charttime_aki</th>\n",
       "      <th>creat_low_past_7day</th>\n",
       "      <th>creat_low_past_48hr</th>\n",
       "      <th>creat</th>\n",
       "      <th>aki_stage_creat</th>\n",
       "      <th>uo_rt_6hr</th>\n",
       "      <th>uo_rt_12hr</th>\n",
       "      <th>uo_rt_24hr</th>\n",
       "      <th>aki_stage_uo</th>\n",
       "      <th>aki_stage</th>\n",
       "      <th>kigoord</th>\n",
       "      <th>new_event_flag</th>\n",
       "      <th>kigoord_flag</th>\n",
       "      <th>charttime_rrt</th>\n",
       "      <th>dialysis_present</th>\n",
       "      <th>dialysis_active</th>\n",
       "      <th>dialysis_type</th>\n",
       "      <th>rrtorder</th>\n",
       "      <th>delay_rrt</th>\n",
       "      <th>gender</th>\n",
       "      <th>dod</th>\n",
       "      <th>admittime</th>\n",
       "      <th>dischtime</th>\n",
       "      <th>los_hospital</th>\n",
       "      <th>admission_age</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>hospital_expire_flag</th>\n",
       "      <th>hospstay_seq</th>\n",
       "      <th>first_hosp_stay</th>\n",
       "      <th>icu_intime</th>\n",
       "      <th>icu_outtime</th>\n",
       "      <th>los_icu</th>\n",
       "      <th>icustay_seq</th>\n",
       "      <th>first_icu_stay</th>\n",
       "      <th>lactate_min</th>\n",
       "      <th>lactate_max</th>\n",
       "      <th>ph_min</th>\n",
       "      <th>ph_max</th>\n",
       "      <th>so2_min</th>\n",
       "      <th>so2_max</th>\n",
       "      <th>po2_min</th>\n",
       "      <th>po2_max</th>\n",
       "      <th>pco2_min</th>\n",
       "      <th>pco2_max</th>\n",
       "      <th>...</th>\n",
       "      <th>alp_min</th>\n",
       "      <th>alp_max</th>\n",
       "      <th>ast_min</th>\n",
       "      <th>ast_max</th>\n",
       "      <th>amylase_min</th>\n",
       "      <th>amylase_max</th>\n",
       "      <th>bilirubin_total_min</th>\n",
       "      <th>bilirubin_total_max</th>\n",
       "      <th>bilirubin_direct_min</th>\n",
       "      <th>bilirubin_direct_max</th>\n",
       "      <th>bilirubin_indirect_min</th>\n",
       "      <th>bilirubin_indirect_max</th>\n",
       "      <th>ck_cpk_min</th>\n",
       "      <th>ck_cpk_max</th>\n",
       "      <th>ck_mb_min</th>\n",
       "      <th>ck_mb_max</th>\n",
       "      <th>ggt_min</th>\n",
       "      <th>ggt_max</th>\n",
       "      <th>ld_ldh_min</th>\n",
       "      <th>ld_ldh_max</th>\n",
       "      <th>heart_rate_min</th>\n",
       "      <th>heart_rate_max</th>\n",
       "      <th>heart_rate_mean</th>\n",
       "      <th>sbp_min</th>\n",
       "      <th>sbp_max</th>\n",
       "      <th>sbp_mean</th>\n",
       "      <th>dbp_min</th>\n",
       "      <th>dbp_max</th>\n",
       "      <th>dbp_mean</th>\n",
       "      <th>mbp_min</th>\n",
       "      <th>mbp_max</th>\n",
       "      <th>mbp_mean</th>\n",
       "      <th>resp_rate_min</th>\n",
       "      <th>resp_rate_max</th>\n",
       "      <th>resp_rate_mean</th>\n",
       "      <th>temperature_min.1</th>\n",
       "      <th>temperature_max.1</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>spo2_min</th>\n",
       "      <th>spo2_max</th>\n",
       "      <th>spo2_mean</th>\n",
       "      <th>glucose_min.2</th>\n",
       "      <th>glucose_max.2</th>\n",
       "      <th>glucose_mean</th>\n",
       "      <th>urineoutput</th>\n",
       "      <th>gcs_min</th>\n",
       "      <th>gcs_motor</th>\n",
       "      <th>gcs_verbal</th>\n",
       "      <th>gcs_eyes</th>\n",
       "      <th>gcs_unable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19518348</td>\n",
       "      <td>24328934</td>\n",
       "      <td>30041848</td>\n",
       "      <td>104.50</td>\n",
       "      <td>102.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>07-04-26 4:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4657</td>\n",
       "      <td>0.6891</td>\n",
       "      <td>0.6891</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16-04-26 14:25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>IHD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>24-04-26</td>\n",
       "      <td>06-04-26 17:21</td>\n",
       "      <td>24-04-26 9:00</td>\n",
       "      <td>17.652083</td>\n",
       "      <td>66.262081</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>06-04-26 17:22</td>\n",
       "      <td>24-04-26 13:11</td>\n",
       "      <td>17.83</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>7.41</td>\n",
       "      <td>7.42</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>484.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>485.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>89.625000</td>\n",
       "      <td>95.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>132.500000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>68.5000</td>\n",
       "      <td>59.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>83.090909</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.673077</td>\n",
       "      <td>36.83</td>\n",
       "      <td>38.11</td>\n",
       "      <td>37.405000</td>\n",
       "      <td>93.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>96.909091</td>\n",
       "      <td>172.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>257.500000</td>\n",
       "      <td>2895.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18070922</td>\n",
       "      <td>27400972</td>\n",
       "      <td>30078723</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30-06-47 8:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4334</td>\n",
       "      <td>0.6007</td>\n",
       "      <td>0.9219</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05-07-47 19:15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CRRT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28-06-47 21:56</td>\n",
       "      <td>30-07-47 14:40</td>\n",
       "      <td>31.697222</td>\n",
       "      <td>70.489938</td>\n",
       "      <td>BLACK/AFRICAN AMERICAN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>28-06-47 21:59</td>\n",
       "      <td>30-07-47 15:03</td>\n",
       "      <td>31.71</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>7.35</td>\n",
       "      <td>7.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>84.208333</td>\n",
       "      <td>95.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>49.0</td>\n",
       "      <td>83.5</td>\n",
       "      <td>56.4600</td>\n",
       "      <td>62.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>74.019231</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17.134615</td>\n",
       "      <td>36.72</td>\n",
       "      <td>37.56</td>\n",
       "      <td>36.978000</td>\n",
       "      <td>92.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>96.680000</td>\n",
       "      <td>106.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>123.666667</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17414351</td>\n",
       "      <td>23018977</td>\n",
       "      <td>30254621</td>\n",
       "      <td>98.95</td>\n",
       "      <td>85.0</td>\n",
       "      <td>103.6</td>\n",
       "      <td>14-09-79 12:12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14-09-79 12:23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CRRT</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14-09-79 0:06</td>\n",
       "      <td>03-01-80 18:15</td>\n",
       "      <td>111.756250</td>\n",
       "      <td>37.700917</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>14-09-79 0:07</td>\n",
       "      <td>17-10-79 19:59</td>\n",
       "      <td>33.83</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.34</td>\n",
       "      <td>7.42</td>\n",
       "      <td>93.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>...</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.3</td>\n",
       "      <td>36.3</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>331.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>80.956522</td>\n",
       "      <td>82.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>101.958333</td>\n",
       "      <td>36.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>52.4375</td>\n",
       "      <td>51.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>67.541667</td>\n",
       "      <td>12.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>35.50</td>\n",
       "      <td>36.70</td>\n",
       "      <td>36.132143</td>\n",
       "      <td>92.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.250000</td>\n",
       "      <td>114.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>152.625000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17555267</td>\n",
       "      <td>21215796</td>\n",
       "      <td>30326472</td>\n",
       "      <td>105.70</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.4</td>\n",
       "      <td>20-12-28 5:38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.7933</td>\n",
       "      <td>2.7933</td>\n",
       "      <td>0.2665</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24-12-28 18:20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CRRT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>26-12-28</td>\n",
       "      <td>19-12-28 5:54</td>\n",
       "      <td>26-12-28 0:00</td>\n",
       "      <td>6.754167</td>\n",
       "      <td>59.967156</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>19-12-28 5:58</td>\n",
       "      <td>27-12-28 0:08</td>\n",
       "      <td>7.76</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>133.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>374.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>87.521739</td>\n",
       "      <td>75.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>56.4000</td>\n",
       "      <td>54.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>70.700000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>24.086957</td>\n",
       "      <td>36.39</td>\n",
       "      <td>37.00</td>\n",
       "      <td>36.776667</td>\n",
       "      <td>88.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>96.478261</td>\n",
       "      <td>47.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>102.714286</td>\n",
       "      <td>425.0</td>\n",
       "      <td>13</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18571406</td>\n",
       "      <td>23198317</td>\n",
       "      <td>30341422</td>\n",
       "      <td>69.00</td>\n",
       "      <td>66.7</td>\n",
       "      <td>71.3</td>\n",
       "      <td>23-01-80 9:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4909</td>\n",
       "      <td>0.6812</td>\n",
       "      <td>0.6812</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23-01-80 14:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>IHD</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23-01-80 2:04</td>\n",
       "      <td>28-01-80 15:00</td>\n",
       "      <td>5.538889</td>\n",
       "      <td>79.060470</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>23-01-80 2:05</td>\n",
       "      <td>24-01-80 0:04</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.318182</td>\n",
       "      <td>113.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>140.800000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>71.8500</td>\n",
       "      <td>60.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>88.285714</td>\n",
       "      <td>17.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>36.33</td>\n",
       "      <td>37.33</td>\n",
       "      <td>36.620000</td>\n",
       "      <td>93.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>95.782609</td>\n",
       "      <td>121.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>660.0</td>\n",
       "      <td>15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 198 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id   hadm_id   stay_id  weight  weight_min  weight_max  \\\n",
       "0    19518348  24328934  30041848  104.50       102.0       107.0   \n",
       "1    18070922  27400972  30078723  100.00       100.0       100.0   \n",
       "2    17414351  23018977  30254621   98.95        85.0       103.6   \n",
       "3    17555267  21215796  30326472  105.70       104.0       107.4   \n",
       "4    18571406  23198317  30341422   69.00        66.7        71.3   \n",
       "\n",
       "    charttime_aki  creat_low_past_7day  creat_low_past_48hr  creat  \\\n",
       "0   07-04-26 4:00                  NaN                  NaN    NaN   \n",
       "1   30-06-47 8:00                  NaN                  NaN    NaN   \n",
       "2  14-09-79 12:12                  NaN                  NaN    NaN   \n",
       "3   20-12-28 5:38                  NaN                  NaN    NaN   \n",
       "4   23-01-80 9:00                  NaN                  NaN    NaN   \n",
       "\n",
       "   aki_stage_creat  uo_rt_6hr  uo_rt_12hr  uo_rt_24hr  aki_stage_uo  \\\n",
       "0              NaN     0.4657      0.6891      0.6891           1.0   \n",
       "1              NaN     0.4334      0.6007      0.9219           1.0   \n",
       "2              NaN     0.0000      0.0000      0.0000           3.0   \n",
       "3              NaN     2.7933      2.7933      0.2665           3.0   \n",
       "4              NaN     0.4909      0.6812      0.6812           1.0   \n",
       "\n",
       "   aki_stage  kigoord  new_event_flag  kigoord_flag   charttime_rrt  \\\n",
       "0          1        8               1             1  16-04-26 14:25   \n",
       "1          1       32               1             1  05-07-47 19:15   \n",
       "2          3        4               1             1  14-09-79 12:23   \n",
       "3          3        6               1             1  24-12-28 18:20   \n",
       "4          1        7               1             1  23-01-80 14:00   \n",
       "\n",
       "   dialysis_present  dialysis_active dialysis_type  rrtorder  delay_rrt  \\\n",
       "0                 1                1           IHD         1          1   \n",
       "1                 1                1          CRRT         1          1   \n",
       "2                 1                1          CRRT         1          0   \n",
       "3                 1                1          CRRT         1          1   \n",
       "4                 1                1           IHD         1          0   \n",
       "\n",
       "  gender       dod       admittime       dischtime  los_hospital  \\\n",
       "0      M  24-04-26  06-04-26 17:21   24-04-26 9:00     17.652083   \n",
       "1      M       NaN  28-06-47 21:56  30-07-47 14:40     31.697222   \n",
       "2      M       NaN   14-09-79 0:06  03-01-80 18:15    111.756250   \n",
       "3      F  26-12-28   19-12-28 5:54   26-12-28 0:00      6.754167   \n",
       "4      M       NaN   23-01-80 2:04  28-01-80 15:00      5.538889   \n",
       "\n",
       "   admission_age               ethnicity  hospital_expire_flag  hospstay_seq  \\\n",
       "0      66.262081                 UNKNOWN                     1             1   \n",
       "1      70.489938  BLACK/AFRICAN AMERICAN                     0             1   \n",
       "2      37.700917                 UNKNOWN                     0             1   \n",
       "3      59.967156                   WHITE                     1             1   \n",
       "4      79.060470                   WHITE                     0             1   \n",
       "\n",
       "  first_hosp_stay      icu_intime     icu_outtime  los_icu  icustay_seq  \\\n",
       "0               t  06-04-26 17:22  24-04-26 13:11    17.83            1   \n",
       "1               t  28-06-47 21:59  30-07-47 15:03    31.71            1   \n",
       "2               t   14-09-79 0:07  17-10-79 19:59    33.83            1   \n",
       "3               t   19-12-28 5:58   27-12-28 0:08     7.76            1   \n",
       "4               t   23-01-80 2:05   24-01-80 0:04     0.92            1   \n",
       "\n",
       "  first_icu_stay  lactate_min  lactate_max  ph_min  ph_max  so2_min  so2_max  \\\n",
       "0              t          1.3          1.3    7.41    7.42     98.0     98.0   \n",
       "1              t          1.2          1.2    7.35    7.37      NaN      NaN   \n",
       "2              t          1.2          2.5    7.34    7.42     93.0     93.0   \n",
       "3              t          NaN          NaN     NaN     NaN      NaN      NaN   \n",
       "4              t          NaN          NaN     NaN     NaN      NaN      NaN   \n",
       "\n",
       "   po2_min  po2_max  pco2_min  pco2_max  ...  alp_min  alp_max  ast_min  \\\n",
       "0     90.0    147.0      39.0      40.0  ...     61.0     61.0     44.0   \n",
       "1     99.0     99.0      38.0      40.0  ...     36.0     37.0     12.0   \n",
       "2     36.0     85.0      29.0      36.0  ...    351.0    351.0    104.0   \n",
       "3      NaN      NaN       NaN       NaN  ...    133.0    164.0    162.0   \n",
       "4      NaN      NaN       NaN       NaN  ...      NaN      NaN      NaN   \n",
       "\n",
       "   ast_max  amylase_min  amylase_max  bilirubin_total_min  \\\n",
       "0     44.0          NaN          NaN                  0.3   \n",
       "1     13.0          NaN          NaN                  0.6   \n",
       "2    104.0          NaN          NaN                 36.3   \n",
       "3    189.0          NaN          NaN                  3.0   \n",
       "4      NaN          NaN          NaN                  NaN   \n",
       "\n",
       "   bilirubin_total_max  bilirubin_direct_min  bilirubin_direct_max  \\\n",
       "0                  0.3                   NaN                   NaN   \n",
       "1                  0.6                   NaN                   NaN   \n",
       "2                 36.3                  26.7                  26.7   \n",
       "3                  3.3                   NaN                   NaN   \n",
       "4                  NaN                   NaN                   NaN   \n",
       "\n",
       "   bilirubin_indirect_min  bilirubin_indirect_max  ck_cpk_min  ck_cpk_max  \\\n",
       "0                     NaN                     NaN       484.0       644.0   \n",
       "1                     NaN                     NaN         NaN         NaN   \n",
       "2                     NaN                     NaN        36.0        36.0   \n",
       "3                     NaN                     NaN      1011.0      1131.0   \n",
       "4                     NaN                     NaN       277.0       277.0   \n",
       "\n",
       "   ck_mb_min  ck_mb_max  ggt_min  ggt_max  ld_ldh_min  ld_ldh_max  \\\n",
       "0       15.0       23.0      NaN      NaN       485.0       485.0   \n",
       "1        NaN        NaN      NaN      NaN         NaN         NaN   \n",
       "2        4.0        4.0      NaN      NaN       331.0       331.0   \n",
       "3       14.0       16.0      NaN      NaN       374.0       378.0   \n",
       "4        6.0        6.0      NaN      NaN         NaN         NaN   \n",
       "\n",
       "   heart_rate_min  heart_rate_max  heart_rate_mean  sbp_min  sbp_max  \\\n",
       "0            66.0           138.0        89.625000     95.0    152.0   \n",
       "1            77.0            96.0        84.208333     95.0    116.0   \n",
       "2            71.0            93.0        80.956522     82.0    118.0   \n",
       "3            78.0            97.0        87.521739     75.0    161.0   \n",
       "4            74.0           100.0        90.318182    113.0    161.0   \n",
       "\n",
       "     sbp_mean  dbp_min  dbp_max  dbp_mean  mbp_min  mbp_max   mbp_mean  \\\n",
       "0  132.500000     48.0     91.0   68.5000     59.0     98.0  83.090909   \n",
       "1  107.500000     49.0     83.5   56.4600     62.0    157.0  74.019231   \n",
       "2  101.958333     36.0     63.0   52.4375     51.0     78.0  67.541667   \n",
       "3  108.000000     34.0    131.0   56.4000     54.0    139.0  70.700000   \n",
       "4  140.800000     26.0    134.0   71.8500     60.0    141.0  88.285714   \n",
       "\n",
       "   resp_rate_min  resp_rate_max  resp_rate_mean  temperature_min.1  \\\n",
       "0           18.0           27.0       21.673077              36.83   \n",
       "1           12.0           23.0       17.134615              36.72   \n",
       "2           12.0           36.0       20.000000              35.50   \n",
       "3           17.0           32.0       24.086957              36.39   \n",
       "4           17.0           25.0       21.000000              36.33   \n",
       "\n",
       "   temperature_max.1  temperature_mean  spo2_min  spo2_max  spo2_mean  \\\n",
       "0              38.11         37.405000      93.0      99.0  96.909091   \n",
       "1              37.56         36.978000      92.0      99.0  96.680000   \n",
       "2              36.70         36.132143      92.0     100.0  95.250000   \n",
       "3              37.00         36.776667      88.0     100.0  96.478261   \n",
       "4              37.33         36.620000      93.0      98.0  95.782609   \n",
       "\n",
       "   glucose_min.2  glucose_max.2  glucose_mean  urineoutput  gcs_min  \\\n",
       "0          172.0          321.0    257.500000       2895.0       10   \n",
       "1          106.0          140.0    123.666667       2400.0        3   \n",
       "2          114.0          228.0    152.625000         10.0        6   \n",
       "3           47.0          134.0    102.714286        425.0       13   \n",
       "4          121.0          126.0    124.000000        660.0       15   \n",
       "\n",
       "   gcs_motor  gcs_verbal  gcs_eyes  gcs_unable  \n",
       "0        5.0         1.0       4.0           0  \n",
       "1        1.0         1.0       1.0           0  \n",
       "2        4.0         1.0       1.0           0  \n",
       "3        6.0         5.0       2.0           0  \n",
       "4        6.0         5.0       4.0           0  \n",
       "\n",
       "[5 rows x 198 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 100)\n",
    "aki_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d86ff3b",
   "metadata": {},
   "source": [
    "**missing data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fcb05ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_id                      0\n",
       "hadm_id                         0\n",
       "stay_id                         0\n",
       "weight                          9\n",
       "weight_min                      9\n",
       "weight_max                      9\n",
       "charttime_aki                   0\n",
       "creat_low_past_7day           752\n",
       "creat_low_past_48hr           778\n",
       "creat                         752\n",
       "aki_stage_creat               752\n",
       "uo_rt_6hr                    1344\n",
       "uo_rt_12hr                   1344\n",
       "uo_rt_24hr                   1344\n",
       "aki_stage_uo                 1344\n",
       "aki_stage                       0\n",
       "kigoord                         0\n",
       "new_event_flag                  0\n",
       "kigoord_flag                    0\n",
       "charttime_rrt                   0\n",
       "dialysis_present                0\n",
       "dialysis_active                 0\n",
       "dialysis_type                  90\n",
       "rrtorder                        0\n",
       "delay_rrt                       0\n",
       "gender                          0\n",
       "dod                          1225\n",
       "admittime                       0\n",
       "dischtime                       0\n",
       "los_hospital                    0\n",
       "admission_age                   0\n",
       "ethnicity                       0\n",
       "hospital_expire_flag            0\n",
       "hospstay_seq                    0\n",
       "first_hosp_stay                 0\n",
       "icu_intime                      0\n",
       "icu_outtime                     0\n",
       "los_icu                         0\n",
       "icustay_seq                     0\n",
       "first_icu_stay                  0\n",
       "lactate_min                   471\n",
       "lactate_max                   471\n",
       "ph_min                        348\n",
       "ph_max                        348\n",
       "so2_min                      1081\n",
       "so2_max                      1081\n",
       "po2_min                       348\n",
       "po2_max                       348\n",
       "pco2_min                      348\n",
       "pco2_max                      348\n",
       "aado2_min                    1764\n",
       "aado2_max                    1764\n",
       "aado2_calc_min                774\n",
       "aado2_calc_max                774\n",
       "pao2fio2ratio_min             774\n",
       "pao2fio2ratio_max             774\n",
       "baseexcess_min                348\n",
       "baseexcess_max                348\n",
       "bicarbonate_min              2056\n",
       "bicarbonate_max              2056\n",
       "totalco2_min                  348\n",
       "totalco2_max                  348\n",
       "hematocrit_min               1394\n",
       "hematocrit_max               1394\n",
       "hemoglobin_min               1394\n",
       "hemoglobin_max               1394\n",
       "carboxyhemoglobin_min        2028\n",
       "carboxyhemoglobin_max        2028\n",
       "methemoglobin_min            2033\n",
       "methemoglobin_max            2033\n",
       "temperature_min              1222\n",
       "temperature_max              1222\n",
       "chloride_min                 1433\n",
       "chloride_max                 1433\n",
       "calcium_min                   780\n",
       "calcium_max                   780\n",
       "glucose_min                  1190\n",
       "glucose_max                  1190\n",
       "potassium_min                1088\n",
       "potassium_max                1088\n",
       "sodium_min                   1356\n",
       "sodium_max                   1356\n",
       "hematocrit_min.1                4\n",
       "hematocrit_max.1                4\n",
       "hemoglobin_min.1                6\n",
       "hemoglobin_max.1                6\n",
       "platelets_min                   4\n",
       "platelets_max                   4\n",
       "wbc_min                         5\n",
       "wbc_max                         5\n",
       "albumin_min                   863\n",
       "albumin_max                   863\n",
       "globulin_min                 2074\n",
       "globulin_max                 2074\n",
       "total_protein_min            2031\n",
       "total_protein_max            2031\n",
       "aniongap_min                    3\n",
       "aniongap_max                    3\n",
       "bicarbonate_min.1               3\n",
       "bicarbonate_max.1               3\n",
       "bun_min                         3\n",
       "bun_max                         3\n",
       "calcium_min.1                  73\n",
       "calcium_max.1                  73\n",
       "chloride_min.1                  3\n",
       "chloride_max.1                  3\n",
       "creatinine_min                  3\n",
       "creatinine_max                  3\n",
       "glucose_min.1                   6\n",
       "glucose_max.1                   6\n",
       "sodium_min.1                    3\n",
       "sodium_max.1                    3\n",
       "potassium_min.1                 3\n",
       "potassium_max.1                 3\n",
       "basophils_abs_min             670\n",
       "basophils_abs_max             670\n",
       "eosinophils_abs_min           670\n",
       "eosinophils_abs_max           670\n",
       "lymphocytes_abs_min           670\n",
       "lymphocytes_abs_max           670\n",
       "monocytes_abs_min             670\n",
       "monocytes_abs_max             670\n",
       "neutrophils_abs_min           670\n",
       "neutrophils_abs_max           670\n",
       "atypical_lymphocytes_min     1575\n",
       "atypical_lymphocytes_max     1575\n",
       "bands_min                    1523\n",
       "bands_max                    1523\n",
       "immature_granulocytes_min    1681\n",
       "immature_granulocytes_max    1681\n",
       "metamyelocytes_min           1552\n",
       "metamyelocytes_max           1552\n",
       "nrbc_min                     1800\n",
       "nrbc_max                     1800\n",
       "d_dimer_min                  2101\n",
       "d_dimer_max                  2101\n",
       "fibrinogen_min               1178\n",
       "fibrinogen_max               1178\n",
       "thrombin_min                 2100\n",
       "thrombin_max                 2100\n",
       "inr_min                       107\n",
       "inr_max                       107\n",
       "pt_min                        107\n",
       "pt_max                        107\n",
       "ptt_min                       116\n",
       "ptt_max                       116\n",
       "alt_min                       551\n",
       "alt_max                       551\n",
       "alp_min                       547\n",
       "alp_max                       547\n",
       "ast_min                       539\n",
       "ast_max                       539\n",
       "amylase_min                  1772\n",
       "amylase_max                  1772\n",
       "bilirubin_total_min           543\n",
       "bilirubin_total_max           543\n",
       "bilirubin_direct_min         1856\n",
       "bilirubin_direct_max         1856\n",
       "bilirubin_indirect_min       1882\n",
       "bilirubin_indirect_max       1882\n",
       "ck_cpk_min                   1082\n",
       "ck_cpk_max                   1082\n",
       "ck_mb_min                    1167\n",
       "ck_mb_max                    1167\n",
       "ggt_min                      2074\n",
       "ggt_max                      2074\n",
       "ld_ldh_min                   1011\n",
       "ld_ldh_max                   1011\n",
       "heart_rate_min                 10\n",
       "heart_rate_max                 10\n",
       "heart_rate_mean                10\n",
       "sbp_min                        23\n",
       "sbp_max                        23\n",
       "sbp_mean                       23\n",
       "dbp_min                        24\n",
       "dbp_max                        24\n",
       "dbp_mean                       24\n",
       "mbp_min                        10\n",
       "mbp_max                        10\n",
       "mbp_mean                       10\n",
       "resp_rate_min                  10\n",
       "resp_rate_max                  10\n",
       "resp_rate_mean                 10\n",
       "temperature_min.1             124\n",
       "temperature_max.1             124\n",
       "temperature_mean              124\n",
       "spo2_min                       10\n",
       "spo2_max                       10\n",
       "spo2_mean                      10\n",
       "glucose_min.2                   6\n",
       "glucose_max.2                   6\n",
       "glucose_mean                    6\n",
       "urineoutput                   339\n",
       "gcs_min                         0\n",
       "gcs_motor                      43\n",
       "gcs_verbal                     46\n",
       "gcs_eyes                       26\n",
       "gcs_unable                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 200)\n",
    "pd.isna(aki_data).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5f36245",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# double checking this happens in same rows?\n",
    "print(np.array_equal(aki_data.index[aki_data['sodium_min'].isnull()] , aki_data.index[aki_data['sodium_max'].isnull()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "321ef830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_equal(aki_data.index[aki_data['weight_min'].isnull()], aki_data.index[aki_data['weight'].isnull()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84f19d5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1720\n",
       "0     390\n",
       "Name: delay_rrt, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uneven sample\n",
    "aki_data['delay_rrt'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d538661",
   "metadata": {},
   "source": [
    "**drop columns not of interest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "942c6d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clinician's input\n",
    "aki_data.drop(columns=['weight_min', 'weight_max',\n",
    "                       'charttime_aki',\n",
    "                       'creat_low_past_7day','creat_low_past_48hr','creat','aki_stage_creat',\n",
    "                       'uo_rt_6hr','uo_rt_12hr','uo_rt_24hr','aki_stage_uo',\n",
    "                       'kigoord','new_event_flag','kigoord_flag',\n",
    "                       'charttime_rrt',\n",
    "                       'dialysis_present','dialysis_active','dialysis_type',\n",
    "                       'rrtorder',\n",
    "                       'dod',\n",
    "                       'admittime','dischtime','los_hospital',\n",
    "                       'hospital_expire_flag','hospstay_seq','first_hosp_stay',\n",
    "                       'icu_intime','icu_outtime','los_icu',\n",
    "                       'icustay_seq', 'first_icu_stay',\n",
    "                       'lactate_min',\n",
    "                       'so2_max',\n",
    "                       'po2_max',\n",
    "                       'aado2_min','aado2_max',\n",
    "                       'aado2_calc_min',\n",
    "                       'pao2fio2ratio_min', 'pao2fio2ratio_max',\n",
    "                       'baseexcess_max',\n",
    "                       'bicarbonate_min','bicarbonate_max',\n",
    "                       'totalco2_min', 'totalco2_max',\n",
    "                       'hematocrit_min','hematocrit_max',\n",
    "                       'hemoglobin_min','hemoglobin_max',\n",
    "                       'carboxyhemoglobin_min','carboxyhemoglobin_max',\n",
    "                       'methemoglobin_min','methemoglobin_max',\n",
    "                       'temperature_min','temperature_max',\n",
    "                       'chloride_min','chloride_max',\n",
    "                       'calcium_min','calcium_max',\n",
    "                       'glucose_min','glucose_max',\n",
    "                       'potassium_min','potassium_max',\n",
    "                       'sodium_min','sodium_max',\n",
    "                       'globulin_min','globulin_max',\n",
    "                       'total_protein_min','total_protein_max',\n",
    "                       'atypical_lymphocytes_min','atypical_lymphocytes_max',\n",
    "                       'bands_min','bands_max',\n",
    "                       'immature_granulocytes_min','immature_granulocytes_max',\n",
    "                       'metamyelocytes_min','metamyelocytes_max',\n",
    "                       'nrbc_min','nrbc_max',\n",
    "                       'd_dimer_min','d_dimer_max',\n",
    "                       'thrombin_min','thrombin_max',\n",
    "                       'inr_min',\n",
    "                       'pt_min',\n",
    "                       'ptt_min',\n",
    "                       'alt_min',\n",
    "                       'alp_min',\n",
    "                       'ast_min',\n",
    "                       'amylase_min','amylase_max',\n",
    "                       'bilirubin_total_min',\n",
    "                       'bilirubin_direct_min','bilirubin_direct_max',\n",
    "                       'bilirubin_indirect_min','bilirubin_indirect_max',\n",
    "                       'ck_cpk_min',\n",
    "                       'ck_mb_min',\n",
    "                       'ggt_min','ggt_max',\n",
    "                       'ld_ldh_min',\n",
    "                       'heart_rate_min','heart_rate_max',\n",
    "                       'sbp_min','sbp_max',\n",
    "                       'dbp_min','dbp_max',\n",
    "                       'mbp_min','mbp_max',\n",
    "                       'resp_rate_min','resp_rate_max',\n",
    "                       'temperature_min.1','temperature_max.1',\n",
    "                       'spo2_min','spo2_max',\n",
    "                       'glucose_min.2','glucose_max.2',\n",
    "                       'gcs_motor','gcs_verbal','gcs_eyes','gcs_unable'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f7f48d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2110 entries, 0 to 2109\n",
      "Data columns (total 78 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   subject_id           2110 non-null   int64  \n",
      " 1   hadm_id              2110 non-null   int64  \n",
      " 2   stay_id              2110 non-null   int64  \n",
      " 3   weight               2101 non-null   float64\n",
      " 4   aki_stage            2110 non-null   int64  \n",
      " 5   delay_rrt            2110 non-null   int64  \n",
      " 6   gender               2110 non-null   object \n",
      " 7   admission_age        2110 non-null   float64\n",
      " 8   ethnicity            2110 non-null   object \n",
      " 9   lactate_max          1639 non-null   float64\n",
      " 10  ph_min               1762 non-null   float64\n",
      " 11  ph_max               1762 non-null   float64\n",
      " 12  so2_min              1029 non-null   float64\n",
      " 13  po2_min              1762 non-null   float64\n",
      " 14  pco2_min             1762 non-null   float64\n",
      " 15  pco2_max             1762 non-null   float64\n",
      " 16  aado2_calc_max       1336 non-null   float64\n",
      " 17  baseexcess_min       1762 non-null   float64\n",
      " 18  hematocrit_min.1     2106 non-null   float64\n",
      " 19  hematocrit_max.1     2106 non-null   float64\n",
      " 20  hemoglobin_min.1     2104 non-null   float64\n",
      " 21  hemoglobin_max.1     2104 non-null   float64\n",
      " 22  platelets_min        2106 non-null   float64\n",
      " 23  platelets_max        2106 non-null   float64\n",
      " 24  wbc_min              2105 non-null   float64\n",
      " 25  wbc_max              2105 non-null   float64\n",
      " 26  albumin_min          1247 non-null   float64\n",
      " 27  albumin_max          1247 non-null   float64\n",
      " 28  aniongap_min         2107 non-null   float64\n",
      " 29  aniongap_max         2107 non-null   float64\n",
      " 30  bicarbonate_min.1    2107 non-null   float64\n",
      " 31  bicarbonate_max.1    2107 non-null   float64\n",
      " 32  bun_min              2107 non-null   float64\n",
      " 33  bun_max              2107 non-null   float64\n",
      " 34  calcium_min.1        2037 non-null   float64\n",
      " 35  calcium_max.1        2037 non-null   float64\n",
      " 36  chloride_min.1       2107 non-null   float64\n",
      " 37  chloride_max.1       2107 non-null   float64\n",
      " 38  creatinine_min       2107 non-null   float64\n",
      " 39  creatinine_max       2107 non-null   float64\n",
      " 40  glucose_min.1        2104 non-null   float64\n",
      " 41  glucose_max.1        2104 non-null   float64\n",
      " 42  sodium_min.1         2107 non-null   float64\n",
      " 43  sodium_max.1         2107 non-null   float64\n",
      " 44  potassium_min.1      2107 non-null   float64\n",
      " 45  potassium_max.1      2107 non-null   float64\n",
      " 46  basophils_abs_min    1440 non-null   float64\n",
      " 47  basophils_abs_max    1440 non-null   float64\n",
      " 48  eosinophils_abs_min  1440 non-null   float64\n",
      " 49  eosinophils_abs_max  1440 non-null   float64\n",
      " 50  lymphocytes_abs_min  1440 non-null   float64\n",
      " 51  lymphocytes_abs_max  1440 non-null   float64\n",
      " 52  monocytes_abs_min    1440 non-null   float64\n",
      " 53  monocytes_abs_max    1440 non-null   float64\n",
      " 54  neutrophils_abs_min  1440 non-null   float64\n",
      " 55  neutrophils_abs_max  1440 non-null   float64\n",
      " 56  fibrinogen_min       932 non-null    float64\n",
      " 57  fibrinogen_max       932 non-null    float64\n",
      " 58  inr_max              2003 non-null   float64\n",
      " 59  pt_max               2003 non-null   float64\n",
      " 60  ptt_max              1994 non-null   float64\n",
      " 61  alt_max              1559 non-null   float64\n",
      " 62  alp_max              1563 non-null   float64\n",
      " 63  ast_max              1571 non-null   float64\n",
      " 64  bilirubin_total_max  1567 non-null   float64\n",
      " 65  ck_cpk_max           1028 non-null   float64\n",
      " 66  ck_mb_max            943 non-null    float64\n",
      " 67  ld_ldh_max           1099 non-null   float64\n",
      " 68  heart_rate_mean      2100 non-null   float64\n",
      " 69  sbp_mean             2087 non-null   float64\n",
      " 70  dbp_mean             2086 non-null   float64\n",
      " 71  mbp_mean             2100 non-null   float64\n",
      " 72  resp_rate_mean       2100 non-null   float64\n",
      " 73  temperature_mean     1986 non-null   float64\n",
      " 74  spo2_mean            2100 non-null   float64\n",
      " 75  glucose_mean         2104 non-null   float64\n",
      " 76  urineoutput          1771 non-null   float64\n",
      " 77  gcs_min              2110 non-null   int64  \n",
      "dtypes: float64(70), int64(6), object(2)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 85)\n",
    "aki_data.info()\n",
    "# 78 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5158067",
   "metadata": {},
   "source": [
    "**missing data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "041e69fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_id                0\n",
       "hadm_id                   0\n",
       "stay_id                   0\n",
       "weight                    9\n",
       "aki_stage                 0\n",
       "delay_rrt                 0\n",
       "gender                    0\n",
       "admission_age             0\n",
       "ethnicity                 0\n",
       "lactate_max             471\n",
       "ph_min                  348\n",
       "ph_max                  348\n",
       "so2_min                1081\n",
       "po2_min                 348\n",
       "pco2_min                348\n",
       "pco2_max                348\n",
       "aado2_calc_max          774\n",
       "baseexcess_min          348\n",
       "hematocrit_min.1          4\n",
       "hematocrit_max.1          4\n",
       "hemoglobin_min.1          6\n",
       "hemoglobin_max.1          6\n",
       "platelets_min             4\n",
       "platelets_max             4\n",
       "wbc_min                   5\n",
       "wbc_max                   5\n",
       "albumin_min             863\n",
       "albumin_max             863\n",
       "aniongap_min              3\n",
       "aniongap_max              3\n",
       "bicarbonate_min.1         3\n",
       "bicarbonate_max.1         3\n",
       "bun_min                   3\n",
       "bun_max                   3\n",
       "calcium_min.1            73\n",
       "calcium_max.1            73\n",
       "chloride_min.1            3\n",
       "chloride_max.1            3\n",
       "creatinine_min            3\n",
       "creatinine_max            3\n",
       "glucose_min.1             6\n",
       "glucose_max.1             6\n",
       "sodium_min.1              3\n",
       "sodium_max.1              3\n",
       "potassium_min.1           3\n",
       "potassium_max.1           3\n",
       "basophils_abs_min       670\n",
       "basophils_abs_max       670\n",
       "eosinophils_abs_min     670\n",
       "eosinophils_abs_max     670\n",
       "lymphocytes_abs_min     670\n",
       "lymphocytes_abs_max     670\n",
       "monocytes_abs_min       670\n",
       "monocytes_abs_max       670\n",
       "neutrophils_abs_min     670\n",
       "neutrophils_abs_max     670\n",
       "fibrinogen_min         1178\n",
       "fibrinogen_max         1178\n",
       "inr_max                 107\n",
       "pt_max                  107\n",
       "ptt_max                 116\n",
       "alt_max                 551\n",
       "alp_max                 547\n",
       "ast_max                 539\n",
       "bilirubin_total_max     543\n",
       "ck_cpk_max             1082\n",
       "ck_mb_max              1167\n",
       "ld_ldh_max             1011\n",
       "heart_rate_mean          10\n",
       "sbp_mean                 23\n",
       "dbp_mean                 24\n",
       "mbp_mean                 10\n",
       "resp_rate_mean           10\n",
       "temperature_mean        124\n",
       "spo2_mean                10\n",
       "glucose_mean              6\n",
       "urineoutput             339\n",
       "gcs_min                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(aki_data).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b9ae86",
   "metadata": {},
   "source": [
    "**drop columns due to too many missing data**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "374a687f",
   "metadata": {},
   "source": [
    "Since there are 2110 rows, any columns with >125 (approx 5%) missing rows will be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4f1edb4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "aki_data = aki_data[aki_data.columns[aki_data.isnull().sum()<125]] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d0051ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2110, 47)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aki_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bd754a",
   "metadata": {},
   "source": [
    "47 columns left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8f6bea5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# aki_data.drop(columns=['lactate_max',\n",
    "#                        'ph_min','ph_max',\n",
    "#                        'so2_min',\n",
    "#                        'po2_min','po2_max',\n",
    "#                        'aado2_calc_max',\n",
    "#                        'baseexcess_min',\n",
    "#                        'albumin_min','albumin_max',\n",
    "#                        'basophils_abs_min','basophils_abs_max',\n",
    "#                        'eosinophils_abs_min','eosinophils_abs_max',\n",
    "#                        'lymphocytes_abs_min','lymphocytes_abs_max',\n",
    "#                        'monocytes_abs_min','monocytes_abs_max',\n",
    "#                        'neutrophils_abs_min','neutrophils_abs_max',\n",
    "#                        'fibrinogen_min','fibrinogen_max',\n",
    "#                        'alt_max',\n",
    "#                        'alp_max',\n",
    "#                        'ast_max',\n",
    "#                        'bilirubin_total_max',\n",
    "#                        'ck_cpk_max','ck_mb_max',\n",
    "#                        'ld_ldh_max',\n",
    "#                        'urineoutput'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1416d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_id             0\n",
       "hadm_id                0\n",
       "stay_id                0\n",
       "weight                 9\n",
       "aki_stage              0\n",
       "delay_rrt              0\n",
       "gender                 0\n",
       "admission_age          0\n",
       "ethnicity              0\n",
       "hematocrit_min.1       4\n",
       "hematocrit_max.1       4\n",
       "hemoglobin_min.1       6\n",
       "hemoglobin_max.1       6\n",
       "platelets_min          4\n",
       "platelets_max          4\n",
       "wbc_min                5\n",
       "wbc_max                5\n",
       "aniongap_min           3\n",
       "aniongap_max           3\n",
       "bicarbonate_min.1      3\n",
       "bicarbonate_max.1      3\n",
       "bun_min                3\n",
       "bun_max                3\n",
       "calcium_min.1         73\n",
       "calcium_max.1         73\n",
       "chloride_min.1         3\n",
       "chloride_max.1         3\n",
       "creatinine_min         3\n",
       "creatinine_max         3\n",
       "glucose_min.1          6\n",
       "glucose_max.1          6\n",
       "sodium_min.1           3\n",
       "sodium_max.1           3\n",
       "potassium_min.1        3\n",
       "potassium_max.1        3\n",
       "inr_max              107\n",
       "pt_max               107\n",
       "ptt_max              116\n",
       "heart_rate_mean       10\n",
       "sbp_mean              23\n",
       "dbp_mean              24\n",
       "mbp_mean              10\n",
       "resp_rate_mean        10\n",
       "temperature_mean     124\n",
       "spo2_mean             10\n",
       "glucose_mean           6\n",
       "gcs_min                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(aki_data).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5f743e45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows\n",
    "aki_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f68b6a3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1786, 47)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aki_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d647d9",
   "metadata": {},
   "source": [
    "1786 rows left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "055e958e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_id           0\n",
       "hadm_id              0\n",
       "stay_id              0\n",
       "weight               0\n",
       "aki_stage            0\n",
       "delay_rrt            0\n",
       "gender               0\n",
       "admission_age        0\n",
       "ethnicity            0\n",
       "hematocrit_min.1     0\n",
       "hematocrit_max.1     0\n",
       "hemoglobin_min.1     0\n",
       "hemoglobin_max.1     0\n",
       "platelets_min        0\n",
       "platelets_max        0\n",
       "wbc_min              0\n",
       "wbc_max              0\n",
       "aniongap_min         0\n",
       "aniongap_max         0\n",
       "bicarbonate_min.1    0\n",
       "bicarbonate_max.1    0\n",
       "bun_min              0\n",
       "bun_max              0\n",
       "calcium_min.1        0\n",
       "calcium_max.1        0\n",
       "chloride_min.1       0\n",
       "chloride_max.1       0\n",
       "creatinine_min       0\n",
       "creatinine_max       0\n",
       "glucose_min.1        0\n",
       "glucose_max.1        0\n",
       "sodium_min.1         0\n",
       "sodium_max.1         0\n",
       "potassium_min.1      0\n",
       "potassium_max.1      0\n",
       "inr_max              0\n",
       "pt_max               0\n",
       "ptt_max              0\n",
       "heart_rate_mean      0\n",
       "sbp_mean             0\n",
       "dbp_mean             0\n",
       "mbp_mean             0\n",
       "resp_rate_mean       0\n",
       "temperature_mean     0\n",
       "spo2_mean            0\n",
       "glucose_mean         0\n",
       "gcs_min              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double checking\n",
    "pd.isnull(aki_data).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ba952bf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-3d819d2c3372>:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  aki_data.columns = aki_data.columns.str.replace('.1','')\n"
     ]
    }
   ],
   "source": [
    "# remove the .1\n",
    "aki_data.columns = aki_data.columns.str.replace('.1','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "40ae90ee",
   "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>subject_id</th>\n",
       "      <th>hadm_id</th>\n",
       "      <th>stay_id</th>\n",
       "      <th>weight</th>\n",
       "      <th>aki_stage</th>\n",
       "      <th>delay_rrt</th>\n",
       "      <th>gender</th>\n",
       "      <th>admission_age</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>hematocrit_min</th>\n",
       "      <th>hematocrit_max</th>\n",
       "      <th>hemoglobin_min</th>\n",
       "      <th>hemoglobin_max</th>\n",
       "      <th>platelets_min</th>\n",
       "      <th>platelets_max</th>\n",
       "      <th>wbc_min</th>\n",
       "      <th>wbc_max</th>\n",
       "      <th>aniongap_min</th>\n",
       "      <th>aniongap_max</th>\n",
       "      <th>bicarbonate_min</th>\n",
       "      <th>bicarbonate_max</th>\n",
       "      <th>bun_min</th>\n",
       "      <th>bun_max</th>\n",
       "      <th>calcium_min</th>\n",
       "      <th>calcium_max</th>\n",
       "      <th>chloride_min</th>\n",
       "      <th>chloride_max</th>\n",
       "      <th>creatinine_min</th>\n",
       "      <th>creatinine_max</th>\n",
       "      <th>glucose_min</th>\n",
       "      <th>glucose_max</th>\n",
       "      <th>sodium_min</th>\n",
       "      <th>sodium_max</th>\n",
       "      <th>potassium_min</th>\n",
       "      <th>potassium_max</th>\n",
       "      <th>inr_max</th>\n",
       "      <th>pt_max</th>\n",
       "      <th>ptt_max</th>\n",
       "      <th>heart_rate_mean</th>\n",
       "      <th>sbp_mean</th>\n",
       "      <th>dbp_mean</th>\n",
       "      <th>mbp_mean</th>\n",
       "      <th>resp_rate_mean</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>spo2_mean</th>\n",
       "      <th>glucose_mean</th>\n",
       "      <th>gcs_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19518348</td>\n",
       "      <td>24328934</td>\n",
       "      <td>30041848</td>\n",
       "      <td>104.50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>66.262081</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>31.4</td>\n",
       "      <td>31.9</td>\n",
       "      <td>11.3</td>\n",
       "      <td>11.3</td>\n",
       "      <td>246.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>11.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>172.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>14.6</td>\n",
       "      <td>47.5</td>\n",
       "      <td>89.625000</td>\n",
       "      <td>132.500000</td>\n",
       "      <td>68.5000</td>\n",
       "      <td>83.090909</td>\n",
       "      <td>21.673077</td>\n",
       "      <td>37.405000</td>\n",
       "      <td>96.909091</td>\n",
       "      <td>257.500000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18070922</td>\n",
       "      <td>27400972</td>\n",
       "      <td>30078723</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>70.489938</td>\n",
       "      <td>BLACK/AFRICAN AMERICAN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.2</td>\n",
       "      <td>11.3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>103.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>106.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>13.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>84.208333</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>56.4600</td>\n",
       "      <td>74.019231</td>\n",
       "      <td>17.134615</td>\n",
       "      <td>36.978000</td>\n",
       "      <td>96.680000</td>\n",
       "      <td>123.666667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17414351</td>\n",
       "      <td>23018977</td>\n",
       "      <td>30254621</td>\n",
       "      <td>98.95</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>37.700917</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>27.1</td>\n",
       "      <td>29.4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>31.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>10.2</td>\n",
       "      <td>73.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>10.5</td>\n",
       "      <td>121.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>27.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>80.956522</td>\n",
       "      <td>101.958333</td>\n",
       "      <td>52.4375</td>\n",
       "      <td>67.541667</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.132143</td>\n",
       "      <td>95.250000</td>\n",
       "      <td>152.625000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17555267</td>\n",
       "      <td>21215796</td>\n",
       "      <td>30326472</td>\n",
       "      <td>105.70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>59.967156</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>14.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>47.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>28.4</td>\n",
       "      <td>51.3</td>\n",
       "      <td>87.521739</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>56.4000</td>\n",
       "      <td>70.700000</td>\n",
       "      <td>24.086957</td>\n",
       "      <td>36.776667</td>\n",
       "      <td>96.478261</td>\n",
       "      <td>102.714286</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18571406</td>\n",
       "      <td>23198317</td>\n",
       "      <td>30341422</td>\n",
       "      <td>69.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>79.060470</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>13.2</td>\n",
       "      <td>262.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>106.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>126.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>13.8</td>\n",
       "      <td>35.6</td>\n",
       "      <td>90.318182</td>\n",
       "      <td>140.800000</td>\n",
       "      <td>71.8500</td>\n",
       "      <td>88.285714</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>36.620000</td>\n",
       "      <td>95.782609</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id   hadm_id   stay_id  weight  aki_stage  delay_rrt gender  \\\n",
       "0    19518348  24328934  30041848  104.50          1          1      M   \n",
       "1    18070922  27400972  30078723  100.00          1          1      M   \n",
       "2    17414351  23018977  30254621   98.95          3          0      M   \n",
       "3    17555267  21215796  30326472  105.70          3          1      F   \n",
       "4    18571406  23198317  30341422   69.00          1          0      M   \n",
       "\n",
       "   admission_age               ethnicity  hematocrit_min  hematocrit_max  \\\n",
       "0      66.262081                 UNKNOWN            31.4            31.9   \n",
       "1      70.489938  BLACK/AFRICAN AMERICAN            34.0            34.2   \n",
       "2      37.700917                 UNKNOWN            27.1            29.4   \n",
       "3      59.967156                   WHITE            25.0            30.9   \n",
       "4      79.060470                   WHITE            38.0            38.0   \n",
       "\n",
       "   hemoglobin_min  hemoglobin_max  platelets_min  platelets_max  wbc_min  \\\n",
       "0            11.3            11.3          246.0          247.0     11.2   \n",
       "1            11.3            11.4          185.0          186.0     12.1   \n",
       "2             9.9            10.4          185.0          252.0     22.4   \n",
       "3             8.1            10.1           70.0          107.0      7.6   \n",
       "4            13.2            13.2          262.0          262.0     10.8   \n",
       "\n",
       "   wbc_max  aniongap_min  aniongap_max  bicarbonate_min  bicarbonate_max  \\\n",
       "0     11.2          20.0          23.0             23.0             24.0   \n",
       "1     13.5          14.0          16.0             20.0             21.0   \n",
       "2     31.1          26.0          30.0             15.0             18.0   \n",
       "3     14.5          11.0          13.0             19.0             23.0   \n",
       "4     10.8          18.0          18.0             21.0             21.0   \n",
       "\n",
       "   bun_min  bun_max  calcium_min  calcium_max  chloride_min  chloride_max  \\\n",
       "0    109.0    114.0          8.7          9.5         106.0         114.0   \n",
       "1     36.0     41.0          7.5          7.7         103.0         104.0   \n",
       "2     40.0     53.0          7.4         10.2          73.0          76.0   \n",
       "3     15.0     16.0          7.5          7.8         106.0         110.0   \n",
       "4     19.0     19.0          9.4          9.4         106.0         106.0   \n",
       "\n",
       "   creatinine_min  creatinine_max  glucose_min  glucose_max  sodium_min  \\\n",
       "0             3.0             3.1        172.0        317.0       146.0   \n",
       "1             1.1             1.1        106.0        140.0       134.0   \n",
       "2             7.8            10.5        121.0        228.0       118.0   \n",
       "3             1.1             1.2         47.0        127.0       140.0   \n",
       "4             1.3             1.3        126.0        126.0       141.0   \n",
       "\n",
       "   sodium_max  potassium_min  potassium_max  inr_max  pt_max  ptt_max  \\\n",
       "0       154.0            3.9            4.1      1.3    14.6     47.5   \n",
       "1       135.0            4.4            4.8      1.3    13.9     25.0   \n",
       "2       119.0            3.3            3.8      2.6    27.5     36.0   \n",
       "3       141.0            3.2            4.0      2.6    28.4     51.3   \n",
       "4       141.0            3.7            3.7      1.2    13.8     35.6   \n",
       "\n",
       "   heart_rate_mean    sbp_mean  dbp_mean   mbp_mean  resp_rate_mean  \\\n",
       "0        89.625000  132.500000   68.5000  83.090909       21.673077   \n",
       "1        84.208333  107.500000   56.4600  74.019231       17.134615   \n",
       "2        80.956522  101.958333   52.4375  67.541667       20.000000   \n",
       "3        87.521739  108.000000   56.4000  70.700000       24.086957   \n",
       "4        90.318182  140.800000   71.8500  88.285714       21.000000   \n",
       "\n",
       "   temperature_mean  spo2_mean  glucose_mean  gcs_min  \n",
       "0         37.405000  96.909091    257.500000       10  \n",
       "1         36.978000  96.680000    123.666667        3  \n",
       "2         36.132143  95.250000    152.625000        6  \n",
       "3         36.776667  96.478261    102.714286       13  \n",
       "4         36.620000  95.782609    124.000000       15  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aki_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "50730e8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1786 entries, 0 to 2109\n",
      "Data columns (total 47 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   subject_id        1786 non-null   int64  \n",
      " 1   hadm_id           1786 non-null   int64  \n",
      " 2   stay_id           1786 non-null   int64  \n",
      " 3   weight            1786 non-null   float64\n",
      " 4   aki_stage         1786 non-null   int64  \n",
      " 5   delay_rrt         1786 non-null   int64  \n",
      " 6   gender            1786 non-null   object \n",
      " 7   admission_age     1786 non-null   float64\n",
      " 8   ethnicity         1786 non-null   object \n",
      " 9   hematocrit_min    1786 non-null   float64\n",
      " 10  hematocrit_max    1786 non-null   float64\n",
      " 11  hemoglobin_min    1786 non-null   float64\n",
      " 12  hemoglobin_max    1786 non-null   float64\n",
      " 13  platelets_min     1786 non-null   float64\n",
      " 14  platelets_max     1786 non-null   float64\n",
      " 15  wbc_min           1786 non-null   float64\n",
      " 16  wbc_max           1786 non-null   float64\n",
      " 17  aniongap_min      1786 non-null   float64\n",
      " 18  aniongap_max      1786 non-null   float64\n",
      " 19  bicarbonate_min   1786 non-null   float64\n",
      " 20  bicarbonate_max   1786 non-null   float64\n",
      " 21  bun_min           1786 non-null   float64\n",
      " 22  bun_max           1786 non-null   float64\n",
      " 23  calcium_min       1786 non-null   float64\n",
      " 24  calcium_max       1786 non-null   float64\n",
      " 25  chloride_min      1786 non-null   float64\n",
      " 26  chloride_max      1786 non-null   float64\n",
      " 27  creatinine_min    1786 non-null   float64\n",
      " 28  creatinine_max    1786 non-null   float64\n",
      " 29  glucose_min       1786 non-null   float64\n",
      " 30  glucose_max       1786 non-null   float64\n",
      " 31  sodium_min        1786 non-null   float64\n",
      " 32  sodium_max        1786 non-null   float64\n",
      " 33  potassium_min     1786 non-null   float64\n",
      " 34  potassium_max     1786 non-null   float64\n",
      " 35  inr_max           1786 non-null   float64\n",
      " 36  pt_max            1786 non-null   float64\n",
      " 37  ptt_max           1786 non-null   float64\n",
      " 38  heart_rate_mean   1786 non-null   float64\n",
      " 39  sbp_mean          1786 non-null   float64\n",
      " 40  dbp_mean          1786 non-null   float64\n",
      " 41  mbp_mean          1786 non-null   float64\n",
      " 42  resp_rate_mean    1786 non-null   float64\n",
      " 43  temperature_mean  1786 non-null   float64\n",
      " 44  spo2_mean         1786 non-null   float64\n",
      " 45  glucose_mean      1786 non-null   float64\n",
      " 46  gcs_min           1786 non-null   int64  \n",
      "dtypes: float64(39), int64(6), object(2)\n",
      "memory usage: 669.8+ KB\n"
     ]
    }
   ],
   "source": [
    "aki_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f0107d7",
   "metadata": {},
   "source": [
    "## categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "68149b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# should be categorical\n",
    "aki_data[['subject_id','hadm_id','stay_id']] = aki_data[['subject_id','hadm_id','stay_id']].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f102c5b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only categorical columns\n",
    "nom_aki = aki_data.select_dtypes(include='object').copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "068aaeac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1786 entries, 0 to 2109\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   subject_id  1786 non-null   object\n",
      " 1   hadm_id     1786 non-null   object\n",
      " 2   stay_id     1786 non-null   object\n",
      " 3   gender      1786 non-null   object\n",
      " 4   ethnicity   1786 non-null   object\n",
      "dtypes: object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "nom_aki.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4ee2a419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no duplicates\n",
    "nom_aki['subject_id'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "639d5645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom_aki['hadm_id'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d94813c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom_aki['stay_id'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d1f83a5",
   "metadata": {},
   "source": [
    "**gender**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ecceda71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    1077\n",
       "F     709\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom_aki['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6a70320d",
   "metadata": {},
   "outputs": [],
   "source": [
    "nom_aki['delay_rrt'] = aki_data['delay_rrt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d737cf14",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_count=nom_aki['gender'].value_counts()\n",
    "sns.barplot(gender_count.index,gender_count.values);\n",
    "plt.title('frequency distribution')\n",
    "plt.ylabel('frequency distribution', fontsize=12)\n",
    "plt.xlabel('gender', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "705b1666",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert gender to numerical\n",
    "nom_aki['gender']=nom_aki['gender'].apply(lambda x: 1 if x=='M' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f04e062f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1077\n",
       "0     709\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom_aki['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3660f09e",
   "metadata": {},
   "source": [
    "**ethnicity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a01d024b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['UNKNOWN', 'BLACK/AFRICAN AMERICAN', 'WHITE', 'UNABLE TO OBTAIN',\n",
       "       'HISPANIC/LATINO', 'OTHER', 'AMERICAN INDIAN/ALASKA NATIVE',\n",
       "       'ASIAN'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom_aki['ethnicity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6f2ea545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WHITE                            1010\n",
       "UNKNOWN                           285\n",
       "BLACK/AFRICAN AMERICAN            230\n",
       "OTHER                              88\n",
       "HISPANIC/LATINO                    83\n",
       "ASIAN                              43\n",
       "UNABLE TO OBTAIN                   41\n",
       "AMERICAN INDIAN/ALASKA NATIVE       6\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom_aki['ethnicity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5b5411a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-36-9b6547cef298>:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  nom_aki['ethnicity'] = nom_aki['ethnicity'].str.replace(' |/','_')\n"
     ]
    }
   ],
   "source": [
    "nom_aki['ethnicity'] = nom_aki['ethnicity'].str.lower()\n",
    "nom_aki['ethnicity'] = nom_aki['ethnicity'].str.replace(' |/','_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "63910638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5655095184770437"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# whites has the biggest percentage\n",
    "max(nom_aki['ethnicity'].value_counts())/len(nom_aki)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8cd710ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['unknown', 'black_african_american', 'white', 'unable_to_obtain',\n",
       "       'hispanic_latino', 'other', 'american_indian_alaska_native',\n",
       "       'asian'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom_aki['ethnicity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e4ac832a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnic_label = {'unknown': 'unknown', \n",
    "                'black_african_american': 'black', \n",
    "                'white': 'white',\n",
    "                'unable_to_obtain': 'unknown', \n",
    "               'hispanic_latino': 'latino', \n",
    "                'other': 'minorities', \n",
    "                'american_indian_alaska_native': 'minorities', \n",
    "                'asian': 'minorities' }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "15924ccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouped 'unknown','unable to obtain' as 'unknown'\n",
    "# grouped 'other', 'asian', 'native' as 'minorities'\n",
    "nom_aki['ethnicity'] = nom_aki['ethnicity'].map(ethnic_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b3be5c92",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ethnicity_count=nom_aki['ethnicity'].value_counts()\n",
    "sns.barplot(ethnicity_count.index,ethnicity_count.values);\n",
    "plt.title('frequency distribution ')\n",
    "plt.ylabel('frequency', fontsize=12)\n",
    "plt.xlabel('ethnicity', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1f80d02c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "white         1010\n",
       "unknown        326\n",
       "black          230\n",
       "minorities     137\n",
       "latino          83\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom_aki['ethnicity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bfb11b2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nom_aki.drop(columns=['subject_id','hadm_id','stay_id'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c35c93f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# current categorical\n",
    "aki_data.drop(columns=['subject_id','hadm_id','stay_id'],inplace=True)\n",
    "aki_data['gender']=aki_data['gender'].apply(lambda x: 1 if x=='M' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1337fc2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-45-ecc270d2bdb2>:3: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  aki_data['ethnicity'] = aki_data['ethnicity'].str.replace(' |/','_')\n"
     ]
    }
   ],
   "source": [
    "# current categorical ethnicity\n",
    "aki_data['ethnicity'] = aki_data['ethnicity'].str.lower()\n",
    "aki_data['ethnicity'] = aki_data['ethnicity'].str.replace(' |/','_')\n",
    "aki_data['ethnicity'] = aki_data['ethnicity'].map(ethnic_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "926eeb19",
   "metadata": {},
   "source": [
    "**summary**\n",
    "<br> For categorical data, 'subject_id', 'hadm_id', and 'stay_id' will be dropped, since they are unique, with low/none predictive power. This will leave **gender**, and **ethnicity**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e16e69",
   "metadata": {},
   "source": [
    "## continuous data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d5f8c2a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_aki = aki_data.select_dtypes(include=['int64','float64']).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bba00b96",
   "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>weight</th>\n",
       "      <th>aki_stage</th>\n",
       "      <th>delay_rrt</th>\n",
       "      <th>gender</th>\n",
       "      <th>admission_age</th>\n",
       "      <th>hematocrit_min</th>\n",
       "      <th>hematocrit_max</th>\n",
       "      <th>hemoglobin_min</th>\n",
       "      <th>hemoglobin_max</th>\n",
       "      <th>platelets_min</th>\n",
       "      <th>platelets_max</th>\n",
       "      <th>wbc_min</th>\n",
       "      <th>wbc_max</th>\n",
       "      <th>aniongap_min</th>\n",
       "      <th>aniongap_max</th>\n",
       "      <th>bicarbonate_min</th>\n",
       "      <th>bicarbonate_max</th>\n",
       "      <th>bun_min</th>\n",
       "      <th>bun_max</th>\n",
       "      <th>calcium_min</th>\n",
       "      <th>calcium_max</th>\n",
       "      <th>chloride_min</th>\n",
       "      <th>chloride_max</th>\n",
       "      <th>creatinine_min</th>\n",
       "      <th>creatinine_max</th>\n",
       "      <th>glucose_min</th>\n",
       "      <th>glucose_max</th>\n",
       "      <th>sodium_min</th>\n",
       "      <th>sodium_max</th>\n",
       "      <th>potassium_min</th>\n",
       "      <th>potassium_max</th>\n",
       "      <th>inr_max</th>\n",
       "      <th>pt_max</th>\n",
       "      <th>ptt_max</th>\n",
       "      <th>heart_rate_mean</th>\n",
       "      <th>sbp_mean</th>\n",
       "      <th>dbp_mean</th>\n",
       "      <th>mbp_mean</th>\n",
       "      <th>resp_rate_mean</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>spo2_mean</th>\n",
       "      <th>glucose_mean</th>\n",
       "      <th>gcs_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104.50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>66.262081</td>\n",
       "      <td>31.4</td>\n",
       "      <td>31.9</td>\n",
       "      <td>11.3</td>\n",
       "      <td>11.3</td>\n",
       "      <td>246.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>11.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>172.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>14.6</td>\n",
       "      <td>47.5</td>\n",
       "      <td>89.625000</td>\n",
       "      <td>132.500000</td>\n",
       "      <td>68.5000</td>\n",
       "      <td>83.090909</td>\n",
       "      <td>21.673077</td>\n",
       "      <td>37.405000</td>\n",
       "      <td>96.909091</td>\n",
       "      <td>257.500000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70.489938</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.2</td>\n",
       "      <td>11.3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>103.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>106.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>13.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>84.208333</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>56.4600</td>\n",
       "      <td>74.019231</td>\n",
       "      <td>17.134615</td>\n",
       "      <td>36.978000</td>\n",
       "      <td>96.680000</td>\n",
       "      <td>123.666667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.95</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.700917</td>\n",
       "      <td>27.1</td>\n",
       "      <td>29.4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>31.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>10.2</td>\n",
       "      <td>73.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>10.5</td>\n",
       "      <td>121.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>27.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>80.956522</td>\n",
       "      <td>101.958333</td>\n",
       "      <td>52.4375</td>\n",
       "      <td>67.541667</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.132143</td>\n",
       "      <td>95.250000</td>\n",
       "      <td>152.625000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>105.70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>59.967156</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>14.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>47.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>28.4</td>\n",
       "      <td>51.3</td>\n",
       "      <td>87.521739</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>56.4000</td>\n",
       "      <td>70.700000</td>\n",
       "      <td>24.086957</td>\n",
       "      <td>36.776667</td>\n",
       "      <td>96.478261</td>\n",
       "      <td>102.714286</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>79.060470</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>13.2</td>\n",
       "      <td>262.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>106.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>126.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>13.8</td>\n",
       "      <td>35.6</td>\n",
       "      <td>90.318182</td>\n",
       "      <td>140.800000</td>\n",
       "      <td>71.8500</td>\n",
       "      <td>88.285714</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>36.620000</td>\n",
       "      <td>95.782609</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight  aki_stage  delay_rrt  gender  admission_age  hematocrit_min  \\\n",
       "0  104.50          1          1       1      66.262081            31.4   \n",
       "1  100.00          1          1       1      70.489938            34.0   \n",
       "2   98.95          3          0       1      37.700917            27.1   \n",
       "3  105.70          3          1       0      59.967156            25.0   \n",
       "4   69.00          1          0       1      79.060470            38.0   \n",
       "\n",
       "   hematocrit_max  hemoglobin_min  hemoglobin_max  platelets_min  \\\n",
       "0            31.9            11.3            11.3          246.0   \n",
       "1            34.2            11.3            11.4          185.0   \n",
       "2            29.4             9.9            10.4          185.0   \n",
       "3            30.9             8.1            10.1           70.0   \n",
       "4            38.0            13.2            13.2          262.0   \n",
       "\n",
       "   platelets_max  wbc_min  wbc_max  aniongap_min  aniongap_max  \\\n",
       "0          247.0     11.2     11.2          20.0          23.0   \n",
       "1          186.0     12.1     13.5          14.0          16.0   \n",
       "2          252.0     22.4     31.1          26.0          30.0   \n",
       "3          107.0      7.6     14.5          11.0          13.0   \n",
       "4          262.0     10.8     10.8          18.0          18.0   \n",
       "\n",
       "   bicarbonate_min  bicarbonate_max  bun_min  bun_max  calcium_min  \\\n",
       "0             23.0             24.0    109.0    114.0          8.7   \n",
       "1             20.0             21.0     36.0     41.0          7.5   \n",
       "2             15.0             18.0     40.0     53.0          7.4   \n",
       "3             19.0             23.0     15.0     16.0          7.5   \n",
       "4             21.0             21.0     19.0     19.0          9.4   \n",
       "\n",
       "   calcium_max  chloride_min  chloride_max  creatinine_min  creatinine_max  \\\n",
       "0          9.5         106.0         114.0             3.0             3.1   \n",
       "1          7.7         103.0         104.0             1.1             1.1   \n",
       "2         10.2          73.0          76.0             7.8            10.5   \n",
       "3          7.8         106.0         110.0             1.1             1.2   \n",
       "4          9.4         106.0         106.0             1.3             1.3   \n",
       "\n",
       "   glucose_min  glucose_max  sodium_min  sodium_max  potassium_min  \\\n",
       "0        172.0        317.0       146.0       154.0            3.9   \n",
       "1        106.0        140.0       134.0       135.0            4.4   \n",
       "2        121.0        228.0       118.0       119.0            3.3   \n",
       "3         47.0        127.0       140.0       141.0            3.2   \n",
       "4        126.0        126.0       141.0       141.0            3.7   \n",
       "\n",
       "   potassium_max  inr_max  pt_max  ptt_max  heart_rate_mean    sbp_mean  \\\n",
       "0            4.1      1.3    14.6     47.5        89.625000  132.500000   \n",
       "1            4.8      1.3    13.9     25.0        84.208333  107.500000   \n",
       "2            3.8      2.6    27.5     36.0        80.956522  101.958333   \n",
       "3            4.0      2.6    28.4     51.3        87.521739  108.000000   \n",
       "4            3.7      1.2    13.8     35.6        90.318182  140.800000   \n",
       "\n",
       "   dbp_mean   mbp_mean  resp_rate_mean  temperature_mean  spo2_mean  \\\n",
       "0   68.5000  83.090909       21.673077         37.405000  96.909091   \n",
       "1   56.4600  74.019231       17.134615         36.978000  96.680000   \n",
       "2   52.4375  67.541667       20.000000         36.132143  95.250000   \n",
       "3   56.4000  70.700000       24.086957         36.776667  96.478261   \n",
       "4   71.8500  88.285714       21.000000         36.620000  95.782609   \n",
       "\n",
       "   glucose_mean  gcs_min  \n",
       "0    257.500000       10  \n",
       "1    123.666667        3  \n",
       "2    152.625000        6  \n",
       "3    102.714286       13  \n",
       "4    124.000000       15  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_aki.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a3876e22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1786 entries, 0 to 2109\n",
      "Data columns (total 43 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   weight            1786 non-null   float64\n",
      " 1   aki_stage         1786 non-null   int64  \n",
      " 2   delay_rrt         1786 non-null   int64  \n",
      " 3   gender            1786 non-null   int64  \n",
      " 4   admission_age     1786 non-null   float64\n",
      " 5   hematocrit_min    1786 non-null   float64\n",
      " 6   hematocrit_max    1786 non-null   float64\n",
      " 7   hemoglobin_min    1786 non-null   float64\n",
      " 8   hemoglobin_max    1786 non-null   float64\n",
      " 9   platelets_min     1786 non-null   float64\n",
      " 10  platelets_max     1786 non-null   float64\n",
      " 11  wbc_min           1786 non-null   float64\n",
      " 12  wbc_max           1786 non-null   float64\n",
      " 13  aniongap_min      1786 non-null   float64\n",
      " 14  aniongap_max      1786 non-null   float64\n",
      " 15  bicarbonate_min   1786 non-null   float64\n",
      " 16  bicarbonate_max   1786 non-null   float64\n",
      " 17  bun_min           1786 non-null   float64\n",
      " 18  bun_max           1786 non-null   float64\n",
      " 19  calcium_min       1786 non-null   float64\n",
      " 20  calcium_max       1786 non-null   float64\n",
      " 21  chloride_min      1786 non-null   float64\n",
      " 22  chloride_max      1786 non-null   float64\n",
      " 23  creatinine_min    1786 non-null   float64\n",
      " 24  creatinine_max    1786 non-null   float64\n",
      " 25  glucose_min       1786 non-null   float64\n",
      " 26  glucose_max       1786 non-null   float64\n",
      " 27  sodium_min        1786 non-null   float64\n",
      " 28  sodium_max        1786 non-null   float64\n",
      " 29  potassium_min     1786 non-null   float64\n",
      " 30  potassium_max     1786 non-null   float64\n",
      " 31  inr_max           1786 non-null   float64\n",
      " 32  pt_max            1786 non-null   float64\n",
      " 33  ptt_max           1786 non-null   float64\n",
      " 34  heart_rate_mean   1786 non-null   float64\n",
      " 35  sbp_mean          1786 non-null   float64\n",
      " 36  dbp_mean          1786 non-null   float64\n",
      " 37  mbp_mean          1786 non-null   float64\n",
      " 38  resp_rate_mean    1786 non-null   float64\n",
      " 39  temperature_mean  1786 non-null   float64\n",
      " 40  spo2_mean         1786 non-null   float64\n",
      " 41  glucose_mean      1786 non-null   float64\n",
      " 42  gcs_min           1786 non-null   int64  \n",
      "dtypes: float64(39), int64(4)\n",
      "memory usage: 613.9 KB\n"
     ]
    }
   ],
   "source": [
    "num_aki.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8ba15056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x3600 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot overview distributions of numerical variables\n",
    "# determining number of columns\n",
    "ncols = 2\n",
    "cols = num_aki.columns\n",
    "\n",
    "# determining number of rows\n",
    "nrows = round(int(np.ceil(len(num_aki.columns)) / (1.0*ncols)))\n",
    "\n",
    "fig, axes = plt.subplots(nrows, ncols, figsize=(15,50))\n",
    "axes = axes.ravel()\n",
    "\n",
    "for col, ax in zip(cols, axes):\n",
    "    sns.histplot(data=num_aki[col], kde=True, stat='density', ax=ax)\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883f9dbd",
   "metadata": {},
   "source": [
    "Most of these variables are not normally distributed. \n",
    "<br>'delay_rrt', our outcome variable is discrete [0,1].\n",
    "<br> 'aki_stage' is ordinal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b4bd985b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(num_aki['admission_age'] ,num_aki['delay_rrt']);\n",
    "plt.title('delay_rrt by age')\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('delay_rrt');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57afb80b",
   "metadata": {},
   "source": [
    "**glucose_mean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "859c546b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(num_aki['glucose_mean'] ,num_aki['delay_rrt']);\n",
    "plt.title('delay_rrt by mean of glucose')\n",
    "plt.xlabel('mean of glucose')\n",
    "plt.ylabel('delay_rrt');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "db9551c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1786.000000\n",
       "mean       1051.200171\n",
       "std       16100.410753\n",
       "min          62.000000\n",
       "25%         110.062500\n",
       "50%         136.333333\n",
       "75%         178.333333\n",
       "max      500037.500000\n",
       "Name: glucose_mean, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# glucose_mean looks suspicious\n",
    "num_aki['glucose_mean'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f08707f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers for glucose_mean\n",
    "sns.boxplot(num_aki['glucose_mean']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5e1a8254",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove outliers in glucose_mean\n",
    "num_aki = num_aki[num_aki['glucose_mean']<3000]\n",
    "aki_data = aki_data[aki_data['glucose_mean']<3000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07f0b00c",
   "metadata": {},
   "source": [
    "**weight**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ea13a840",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1778.000000\n",
       "mean       87.692438\n",
       "std        26.077158\n",
       "min        26.400000\n",
       "25%        70.000000\n",
       "50%        84.100000\n",
       "75%        99.975000\n",
       "max       296.800000\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# weight looks suspicious\n",
    "num_aki['weight'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6e701153",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers for weight\n",
    "sns.boxplot(num_aki['weight']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac82136e",
   "metadata": {},
   "source": [
    "There are lots of outliers in 'weight', but how do we remove them? Is it possible for someone to be 300kg? For a mostly caucasion dataset? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56bfe3ac",
   "metadata": {},
   "source": [
    "**wbc_min**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b9272d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1778.000000\n",
       "mean       11.277390\n",
       "std        10.857185\n",
       "min         0.100000\n",
       "25%         6.400000\n",
       "50%         9.600000\n",
       "75%        13.500000\n",
       "max       225.300000\n",
       "Name: wbc_min, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_aki['wbc_min'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "67534a76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEHCAYAAACQkJyuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAN20lEQVR4nO3df2zcdR3H8dd7u4L8MmwdTtIRCxRDMASFBhMFUrVs7f5hJv5BFNYYAyHTdpAQg2GJYPqHmDADjTFBXdIKEU3UOJJ2YcMJGInQkrGNDdiBJWvBMToqIFu3dh//uO/dere73l3t9/u+9Z6PpLm77/d79/l8v/fNc7fr7WYhBAEAkrfEewIAUK8IMAA4IcAA4IQAA4ATAgwATlLVbLxixYrQ3Nwc01QAYHEaGRl5P4RwUeHyqgLc3Nys4eHhhZsVANQBM3u72HLeggAAJwQYAJwQYABwQoABwAkBBgAnBBgAnBBgAHBCgAHACQEGACcEGACcEGAAcEKAAcAJAQYAJwQYAJwQYABwQoABwAkBBgAnBBgAnBBgAHBS1f8JF5e+vj6l0+m8ZePj45KkpqamvOUtLS3q7u5ObG4AEJeaCHA6ndauvfs1c+7y3LKln/xHkvTvqdSsZUcSnxsAxKUmAixJM+cu19Er1+Zun/PaoCQVXQYAiwHvAQOAEwIMAE4IMAA4IcAA4IQAA4ATAgwATggwADghwADghAADgBMCDABOCDAAOCHAAOCEAAOAEwIMAE4IMAA4IcAA4IQAA4ATAgwATggwADghwADghAADgBMCDABOCDAAOCHAAOCEAAOAEwIMAE4IMAA4IcAA4IQAA4ATAgwATggwADghwADghAADgBMCDABOCDAAOCHAAOCEAAOAEwIMAE4IMAA4IcAA4IQAA4CTRALc19envr6+JIaqyfEBoJhUEoOk0+kkhqnZ8QGgGN6CAAAnBBgAnBBgAHBCgAHACQEGACcEGACcEGAAcEKAAcAJAQYAJwQYAJwQYABwQoABwAkBBgAnBBgAnBBgAHBCgAHACQEGACcEGACcEGAAcEKAAcAJAQYAJwQYAJwQYABwQoABwAkBBgAnBBgAnBBgAHBCgAHACQEGACcEGACcEGAAcEKAAcAJAQYAJwQYAJwQYABwQoABwAkBBgAnBBgAnNRFgE+cOKG9e/eqra1NbW1t2rJlix588MHc7ezPbbfdpjVr1mj16tW5ZevWrVNbW5va29vV0dGhdDqtiYkJ9fT0aGJiIu96pcrdp3B99nY6nc67LFxfzRzmY2JiQhs2bNAdd9yhDRs2VD1eUvPE3LyfB+/xqxXnfOsiwIcOHdLMzEzu9sDAgHbu3HnadmNjY5qamtLx48dzyyYnJyVJ09PTOnbsmHp7e9Xf3689e/ZoYGAg73qlyt2ncH32dm9vb95l4fpq5jAf/f392rdvnw4cOKB9+/ZVPV5S88TcvJ8H7/GrFed8F32As69SF8ro6KgGBwcVQtDQ0JCGhoYUQtC2bdsqGmdiYkLbtm0reZ/C9el0Ond7dHQ077JwfaVzmI/svGYbGhqqeLxy+41keD8P3uNXK+75phb00UoYHx/X0aNHtXHjxqLr0+m0lhwPZR9nybEPlU5/VPJxihkbG6t420pNT09Lyry1kTUzM6OBgQHdc889c963v79fJ0+eLHmfwvW9vb2524UK11c6h/no7+/P218ps/+Vjlduv5EM7+fBe/xqxT3fsq+AzexOMxs2s+HDhw8v2MBJ+eCDD2J77BCCQsj8wTE9Pa3t27eXvc+OHTtyAS92n8L1o6OjuduFCtdXOof52LFjR25fs0IIFY9Xbr+RDO/nwXv8asU937KvgEMIj0l6TJJaW1vLv0wtoqmpSZL0yCOPFF2/ceNGjbx1qOzjnPzUp9Vy2cqSj1PM5s2btXXr1oq3r4aZScqEKJVK6eabby57n/b2dg0ODmp6errofQrXr1q1SmNjY0UjXLi+0jnMR3t7u5566qm8CJtZxeOV228kw/t58B6/WnHPd9G/B9zV1bXgj5lKZf7camhoyF1funSp1q9fX9F8lixZUvI+hes3bdqUu12ocH2lc5iPrq4uNTQ05C1raGioeLxy+41keD8P3uNXK+75LvoANzY2qrGxccEer7m5WWvXrpWZqbOzU52dnTIzdXR0VDROY2OjOjo6St6ncH1LS0vudnNzc95l4fpK5zAf2XnN1tnZWfF45fYbyfB+HrzHr1bc803kl3DeVq5cqcnJydxH0davX6+DBw+e9lG0VatW6fDhwwoh5D6KduGFF2pyclKpVEqpVEqbNm3SsmXLNDo6mvvTcPb1SnR1dc15n8L12ds9PT169NFHc5eF6+N+NdHV1aV0Oq0TJ05U9ep39v2TmCfm5v08eI9frTjna4W/WJlLa2trGB4ernqQ7KcWyr0HfPTKtbll57w2KEmnLbuuyveAKxkfAOJkZiMhhNbC5Yv+LQgAqFUEGACcEGAAcEKAAcAJAQYAJwQYAJwQYABwQoABwAkBBgAnBBgAnBBgAHBCgAHACQEGACcEGACcEGAAcEKAAcAJAQYAJwQYAJwQYABwQoABwAkBBgAnBBgAnBBgAHBCgAHACQEGACcEGACcEGAAcEKAAcAJAQYAJwQYAJwQYABwQoABwAkBBgAnBBgAnBBgAHBCgAHACQEGACcEGACcpJIYpKWlJYlhanZ8ACgmkQB3d3cnMUzNjg8AxfAWBAA4IcAA4IQAA4ATAgwATggwADghwADghAADgBMCDABOCDAAOCHAAOCEAAOAEwIMAE4IMAA4IcAA4IQAA4ATAgwATggwADghwADghAADgBMCDABOCDAAOCHAAOCEAAOAEwIMAE4IMAA4IcAA4IQAA4ATAgwATggwADghwADghAADgBMCDABOCDAAOCHAAOCEAAOAEwIMAE4IMAA4IcAA4IQAA4ATAgwATlLeE8ha+skRnfPa4KzbE5JUsOyIpJVJTw0AYlETAW5paTlt2fj4tCSpqWl2cFcW3RYAzkQ1EeDu7m7vKQBA4ngPGACcEGAAcEKAAcAJAQYAJwQYAJwQYABwQoABwAkBBgAnBBgAnBBgAHBCgAHACQEGACcEGACcEGAAcEKAAcAJAQYAJwQYAJwQYABwQoABwAkBBgAnFkKofGOzw5LenudYKyS9P8/7LjYci3wcj3wcj1MWy7H4XAjhosKFVQX4/2FmwyGE1kQGq3Eci3wcj3wcj1MW+7HgLQgAcEKAAcBJkgF+LMGxah3HIh/HIx/H45RFfSwSew8YAJCPtyAAwAkBBgAnsQfYzDrM7HUzS5vZfXGPV4vMbNTM9pjZLjMbjpYtN7PtZnYgulzmPc84mNkWM3vPzPbOWlZy383sR9G58rqZrfGZdXxKHI8HzGw8Oj92mdnaWesW7fEws0vMbKeZ7TezV81sY7S8fs6PEEJsP5KWSnpT0mWSzpL0iqSr4hyzFn8kjUpaUbDsZ5Lui67fJ+kh73nGtO83SbpW0t5y+y7pqugcOVvSpdG5s9R7HxI4Hg9IurfItov6eEi6WNK10fULJL0R7XPdnB9xvwK+XlI6hPBWCOG4pCcl3RLzmGeKWyT1R9f7Ja3zm0p8QgjPSTpSsLjUvt8i6ckQwlQI4V+S0sqcQ4tGieNRyqI+HiGEd0MIL0fXP5K0X1KT6uj8iDvATZIOzro9Fi2rN0HS02Y2YmZ3RstWhhDelTInoqTPuM0ueaX2vZ7Plx+Y2e7oLYrsX7nr5niYWbOkL0n6p+ro/Ig7wFZkWT1+7u2rIYRrJXVK+r6Z3eQ9oRpVr+fLLyVdLumLkt6V9HC0vC6Oh5mdL+mPku4OIXw416ZFlp3RxyPuAI9JumTW7VWS3ol5zJoTQngnunxP0p+V+WvTITO7WJKiy/f8Zpi4Uvtel+dLCOFQCGEmhHBS0q906q/Vi/54mFmDMvF9IoTwp2hx3ZwfcQf4JUlXmNmlZnaWpFslbY15zJpiZueZ2QXZ65JWS9qrzHHoijbrkvQXnxm6KLXvWyXdamZnm9mlkq6Q9KLD/BKVjU3km8qcH9IiPx5mZpJ+I2l/CGHzrFX1c34k8JvOtcr8dvNNSfd7/9Yx6R9lPgHySvTzavYYSGqU9IykA9Hlcu+5xrT/v1Pmr9UnlHkF87259l3S/dG58rqkTu/5J3Q8fitpj6TdykTm4no4HpJuUOYthN2SdkU/a+vp/OCfIgOAE/4lHAA4IcAA4IQAA4ATAgwATggwADghwADghACj5pjZxwmNc5eZrU9iLKAYPgeMmmNmH4cQzveeBxA3XgEjcWb2QzPria7/3Mz+Gl3/hpk9Hl1/2MxeNrNnzOyiaFmLme0ws1eidZeXePw2M3vWzP5gZm+Y2U/N7Dtm9mL0xfiXR9s9YGb3Rtf/ZmYPRdu8YWY3JnEsUN8IMDw8JykbuFZJ50dfynKDpOclnSfp5ZD5BrlnJf042vYJSb8IIVwj6SvK/JPeUq6RtFHS1ZJul/T5EML1kn4tqbvEfVLRNnfPGhOIDQGGhxFJ10VfUjQl6QVlQnyjMgE+Ken30baPS7oh2rYphPBnSQohHAshfDLHGC+FzBd+Tynz3QFPR8v3SGoucZ/st3GNzLENsGBS3hNA/QkhnDCzUUnflfQPZb6M5WvKfCfu/mJ3UfHvgp3L1KzrJ2fdPqnS5312m5k5tgEWDK+A4eU5SfdGl89LukvSrpD5rfASSd+Ktvu2pL+HzBd1j5nZOkmKvpLw3MRnDSwgAgwvzyvznzK+EEI4JOlYtEyS/ivpC2Y2Iunrkn4SLb9dUo+Z7VbmlfNnk50ysLD4GBoAOOEVMAA44RcNOGOZ2dXK/G8Ss02FEL7sMR+gWrwFAQBOeAsCAJwQYABwQoABwAkBBgAn/wMhLEZ+LspVswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers for wbc_min\n",
    "sns.boxplot(num_aki['wbc_min']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c536e2",
   "metadata": {},
   "source": [
    "**wbc_max**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7b8e6260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1778.000000\n",
       "mean       16.804556\n",
       "std        16.657864\n",
       "min         0.100000\n",
       "25%         9.500000\n",
       "50%        13.900000\n",
       "75%        20.075000\n",
       "max       378.300000\n",
       "Name: wbc_max, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_aki['wbc_max'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "910c5394",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers for wbc_max\n",
    "sns.boxplot(num_aki['wbc_max']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b76e665",
   "metadata": {},
   "source": [
    "**calcium_max**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "155f2107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1778.000000\n",
       "mean        8.748088\n",
       "std         1.209249\n",
       "min         4.900000\n",
       "25%         8.100000\n",
       "50%         8.600000\n",
       "75%         9.200000\n",
       "max        27.400000\n",
       "Name: calcium_max, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_aki['calcium_max'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7d29a060",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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IMJaFuz8rab+kJ81sTNIBSf2SvpbcK630y1h3SdpqZieU/wWM75/1dX+o/Fsyfl/SVyQdr8k3UF6/Zs2/6P1q/yj5JQOflHTIzJZydY8mx9tRAkAQroABIAg/BYGGlfwUxpdn7X7T3blnixWBWxAAEIRbEAAQhAADQBACDABBCDAABPl/uCTd68wfPkYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers for calcium_max\n",
    "sns.boxplot(num_aki['calcium_max']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1613195",
   "metadata": {},
   "source": [
    "**creatinine_min**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8c2e7d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1778.000000\n",
       "mean        3.921822\n",
       "std         2.961649\n",
       "min         0.200000\n",
       "25%         1.800000\n",
       "50%         3.200000\n",
       "75%         5.200000\n",
       "max        31.800000\n",
       "Name: creatinine_min, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_aki['creatinine_min'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3b2dfec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers for creatinine_min\n",
    "sns.boxplot(num_aki['creatinine_min']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f9d406",
   "metadata": {},
   "source": [
    "**creatinine_max**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9a35ad7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1778.000000\n",
       "mean        5.082565\n",
       "std         3.631448\n",
       "min         0.200000\n",
       "25%         2.600000\n",
       "50%         4.200000\n",
       "75%         6.600000\n",
       "max        43.000000\n",
       "Name: creatinine_max, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_aki['creatinine_max'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9509b667",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers for calcium_max\n",
    "sns.boxplot(num_aki['creatinine_max']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a7c494",
   "metadata": {},
   "source": [
    "**inr_max**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5667cdf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1778.000000\n",
       "mean        2.091676\n",
       "std         1.670371\n",
       "min         0.800000\n",
       "25%         1.200000\n",
       "50%         1.500000\n",
       "75%         2.300000\n",
       "max        21.500000\n",
       "Name: inr_max, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_aki['inr_max'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "894d4e7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers for inr_max\n",
    "sns.boxplot(num_aki['inr_max']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "848ce845",
   "metadata": {},
   "source": [
    "**pt_max**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3d733d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1778.000000\n",
       "mean       22.285996\n",
       "std        16.177237\n",
       "min         8.900000\n",
       "25%        13.600000\n",
       "50%        16.600000\n",
       "75%        24.100000\n",
       "max       150.000000\n",
       "Name: pt_max, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_aki['pt_max'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "73ad8d0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers for pt_max\n",
    "sns.boxplot(num_aki['pt_max']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42db3b6a",
   "metadata": {},
   "source": [
    "**spo2_mean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "aab36d3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1778.000000\n",
       "mean       96.470320\n",
       "std         3.324450\n",
       "min        54.095238\n",
       "25%        95.392332\n",
       "50%        97.000000\n",
       "75%        98.500000\n",
       "max       100.000000\n",
       "Name: spo2_mean, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_aki['spo2_mean'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fa4de41b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers for spo2_mean\n",
    "sns.boxplot(num_aki['spo2_mean']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfef6c68",
   "metadata": {},
   "source": [
    "<br> Outliers, in general, are rare by definition. However, if there are many outliers, this might imply that something interesting is going on, which may affect or create a relationship between the variable and the predicted target (delay_rrt). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "77343d70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.pairplot(num_aki,hue='delay_rrt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2c3371",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c52ccbb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W35mOel61IwdIr0Z9J938GcmHhTHl1oSUBEGns3Xx8vYERvV9PgHuT38epSx3Ck/XUH2vkvIl01H7VJEPSW1rMPpb8lrdJ10AX76uwcCFVVUSER+S3IoDkjVHJbc2uKPCQg1DQ3htNHoOjGogIiaw9RPgT9Ofi9j6HyjTqyQ3bxPwH0n7QHJ5v6ST2HovkrpUcgn1pyX9VFLbtA2dJP0YuJlkGqkqBcBtkr5W8mYjaXeSP/Qlnxivrm6j0tG3/yXp/xDgNUknZb75SdpT0tclvZ3mLXGEkq8l+Y6k/Uo+bSpxBHBbmm8BUN1ADZKp0FdJfl8PSfpeuoas5HfWNz3mdeXKXU0yUrA38KzSS3yVfLXIKSSL75uR3KzuLzVoz/b4VXqcZiRX7F0pqXQhtpKvSjhZ0t+AbS5br8SOvo6mpj9PkbSjU1bb436SGxwKeFjSGemIL+l6wSeonanAkiCnZFF1+auy3ia5+GIoye9oDcn6uJqq7/MJydKCkpG4hySdnXFO9yN5T6zwYoyIeIxkLQ/AtUq+Tmefkv1KvoLo0PT/24eV1VXOcyQjPV2Af5acn7S+z6X7K1uMnqnk0v1j0p/PR8QHFWVuIEpeGxdV9YHTKpHt5kZ+VPwAvsnWm2gF8LtK8l5WLu8akhGNIFn3kPk1BH3Llb04TZ9XjTbNI8uN8UjWP72ScYxikju7lnyVwxMkf0yD9Pbz5cqPYesN6samzzdR9i7BAfyqgnaVlL+mgv0XUfbrCTaTXJm3sVz9V2eUGVFu36a0zOaMtAKy3MivGuexW5bzVXLlVUnao1nKncfWu+GWHH9DxnaVXwlSRbtq8lrYkyR4zzxHq9h69+WSxzZfcVOHr6P+GeejiGSdxrz00buq46f7So49opK+V/h6I7nMelFGPRvZ+lUpG0kuoijZd9h2vjccV+4c758lz9MZ+5+spK57qOAGhjU4nzt0zqrR373S13a2c1qdrwRpQ/LhKvOcrS332ip57FaD83NtubKfsPX/8FySEaPq/L/Lo+xXnuzQna5z9P//8xnt3UTyAXEeGV9V5UfVD48Y1dw/Sd4ASmxz76ISEXE7yR1ex5D8h29GcnnrzSTTEjUZ0aixSObCTwR+QfL9WZtJPjW/DfwPyRtXdaabNpGsofkx8B7JFR8FJPcTOjUiflpJ2cra90+SkZZfk6xZWEsyBLyR5I/7LSTrATJHad4huevubSSftpeTXO5bUub3JEFITUZEStqznOTN6vMkf8CWkSyWXZUe61q2Th1mlrsfGEgyIvQ+yfnZkrbn5yRfWzKjpu3ZHpF8oh0GfJEkYFlE0ocWJDfze4TkFhOH16DOHXodRcRskpHF0STntCvJhQl7kKNbhkTETJKbGN5E8odCJK+Z/5BcHZp5H5lPtvMwr5MEBABLImJ6ljyZo0gvbc9BGsL5TNsxFxhM8vUbH7D1nD4IHBERlS5Sjoj1EXEBSV/+QRK05JHc+mEpybn6PslXY1T7dgoR8UOS1//bJAFkc5K7cP+W5E70ld22IbOeYrbenLVRLLqOiHtJFoq/SnLFYy+S10Vd319sp6I0yjQza7LSWxI8RxLYtI+GvcDWckTSZJL1Rb+LiG0+FNnOySNGZtakpVfV/SDdfMFBkQGki8oHkUwd1/q9i6zhcmBkZjs9SSMl/UnSsPSKqZIF+0NJrpI8nmRdRmXf72VNhKRdgD+lmw9GxLz6a43lmqfSzGynJ+lMtl4FBcm6sdYkN+2DJCi6KiL+mOOmWQMi6T6Su5L3ZOuVgwdFw78azWqRvyvNzJqCN0lusXE8ydVUJbcxmEtyxeUtEVHdG5TazqsnyULlApILPX5QWVCU3iLk4Yr2V+D1iPjs9jfR6ppHjMzMzLZDug6pplcXvhwRI2q9MVZrHBiZmZmZpbz42szMzCzlwMjMzMws5cDIzMzMLOXAyMzMzCzlwMjMzMws5cDIzMzMLOXAyMzMzCzlwMjMzMws5cDIzMzMLOXAyMzMzCzlwMjMzMws5cDIzMzMLOXAyMzMzCzlwMjMzMws5cDIzMzMLOXAyMzMzCzlwMjMzMws5cDIzMzMLOXAyMzMzCzlwMjMzMwaHEl3SVoqaWoF+yXpJklzJE2WNKQ2juvAyMzMzBqie4CTK9n/aaB/+rgUuK02DurAyMzMzBqciHgFWFlJljOAv0fiTaCTpF47elwHRmZmZtYY7QbMz9hekKbtkGY7WoHVDx19cNR3G8zMLBFj31Vd1LtTv9e/OvHrJFNgJUZFxKga1JDtnO/w+XJgZGZmZjmXBkE1CYTKWwDsnrHdG1i4Q43CU2lmZmbWOI0GvphenXYYUBARi3a0Uo8YmZmZWYMj6d/ACKCbpAXAz4HmABFxO/AUcAowB1gPfLk2juvAyMzMrKHKa7oTOxFxQRX7A/hGbR+36Z5xMzMzs3IcGJmZmZmlHBiZmZmZpRwYmVnO/fWHP2fJ6BeY8rcH6rspZmZlODAys5y75+nHOfmqWl8zabbzUd7O+2igGm7LckDSNZKu2t79ddCeMyXtn6vjmdWXsZMmsHJ1QX03w8xsG006MKpPkppl2T4TcGBkZmZWT5pcYCTpaknvSfovMCBN6yfpGUnjJY2VtG+Wcl+T9I6kSZIektRGUntJH0hqnubpIGleyXaWOsZI+q2kl4HLy23/ADgduF7SREn9spS/VNI4SeNYvLwWz4qZmZlBE7vBo6ShwPnAwSR9nwCMJ/mulssiYrakQ4FbgePKFX84Iu5I6/k18NWIuFnSGOBU4NG07ociYnMlzegUEcem9ZxWbrs/8EREPJitYOb3yuzUXyxoZmaJvDr5blqrRJMKjICjgUciYj2ApNFAK+AI4AGp9AXYMkvZA9KAqBPQDng2Tb8T+D5JYPRl4GtVtOH+KrbNzMysnjS5qTSg/EhLHvBJRAzOeOyXpdw9wDcjYhDwC5KAioh4Degr6VggPyKmVnH8dVVsm+30/vXz3/HG7X9jQJ89mP/QM3zl1DPru0lmZkDTGzF6BbhH0rUkfT8N+AvwgaRzI+IBJcNGB0bEpHJl2wOL0vVDFwEfZ+z7O/Bv4Fc72L416XHMdmoX/uJH9d0EM7OsmtSIUURMIJm6mgg8BIxNd10EfFXSJGAacEaW4j8F3gKeB2aW2/dPoDNJcLQj7gO+J+ndbIuvzcysicnL23kfDZSSL6e1HSHpHOCMiPhCzo7pxddmZg1GjH23TlZJ64TDdtr3+vjvmw1yZXlTm0qrdZJuBj4NnFLfbTEzM7Md48BoB0XEt8qnSfozcGS55Bsj4u7ctMrMzMy2hwOjOhAR/hIoMzPbcQ34O8V2Vj7jZmZmZikHRmZmZmYpT6U1Uh//6Uv13QQzM7OdjkeMzMzMzFIeMTIzM2uoGvCNEHdWPuNmZmZmKQdGZmZmZikHRmZmZmYprzEyMzNrqLzGKOd8xs3MzMxSDozMzMzMUp5KM7M68fakj7jl769SVBycOnI/Ljx9SJn9H328iuv+8hKz5y3jq587lPM+M7h03wNPTeLJl2YgwV67d+UHXx9JixZ+uzKzuucRIzOrdUXFxdx491iu/f5nuOf683nh9TnMW7CyTJ727VryrS8dxedOHVwmfdnKtTz87BT+8ptzuPv351NUHLz4xpwctt6sAZF23kcD1egCI0n3SDonS3pfSVNrWNeukh6sYN8YScO2t53be1yzncHMOUvZtUdHdu3RgebN8jnu8L15bfy8Mnk6d2zDvv12oVn+tm9DRUXFFG7aUvqza+e2OWq5mTV1TXZsWlKziFgIbBNk1bX6Oq5ZrixftY5dum4NZrp3acuMOUurVbZ7l3Z87tTBnPetf9CyRTOGDdqdQw7cva6aamZWRoMfMZL0RUmTJU2S9I80+RhJr0uaW8HoUStJd0uaIuldSSPT9IslPSDpceC5zFEmSa0l3Zce636gdUZ9J0p6Q9KEtHy7Sto7T9Jv0/zjJA2R9Kyk9yVdlubJPO7Fkh6W9Iyk2ZJ+X2snz6yeRGybVt2R8zVrC3l9/Af8+8bP8+Cfv8jGws08/+qs2m2gmVkFGnRgJGkgcDVwXEQcBFye7uoFHAV8Brg2S9FvAETEIOAC4G+SWqX7Dge+FBHHlSvzP8D6iDgQ+A0wNG1DN+AnwAkRMQQYB1xZRdPnR8ThwFjgHpLRocOAX1aQfzBwHjAIOE9S1o/Hki5Ng61x9z78ehVNMKs/3bu0ZemKdaXby1auq/Z02PipC+i5Swc6dWhNs2b5HH3IXkydtbiummrWsOXl7byPBqrhtixxHPBgRCwHiIiS1ZuPRkRxREwHemQpdxTwj7TMTOBDYJ903/MZ9WQ6Brg3LTMZmJymHwbsD7wmaSLwJWCPKto9Ov05BXgrItZExDJgo6ROWfK/EBEFEbERmF5R/RExKiKGRcSwz3/2iCqaYFZ/9u23Cx8v/oRFS1ezeUsRL74xhyOG9q1W2V26tWP67CVsLNxMRDBh2gL22K1z3TbYzCzV0NcYCcgyKE9huTzZylVkXSX7sh1LJMHUBZWUK6+kfcWUbWsx2c95Zp6iCvKYNRr5+Xl8++Kj+f61T1BcHHx6xL7s2bsLo/87DYDTTxjIyk/W8/WfPMj6DZuQxIPPTOae35/P/nv34NhD9+LSHz9Ifr7o37c7nzlu/3rukZk1FQ39D/ALwCOSboiIFZK6VLPcK8BFwIuS9gH6AO8BQ6pR5iVJBwAHpulvAn+WtHdEzJHUBugdEV70YFaJww7eg8MOLjv4efoJA0ufd+nUhgdu+WLWsl8+ZzhfPmd4nbbPzCybBh0YRcQ0Sb8BXpZUBLxbzaK3ArdLmgJsAS6OiEJVvvrzNuBuSZOBicDbaRuWSboY+LeklmnenwAOjMzMzHYyimyXj1iDt3D8n/yLMzNrIHYdekWd3LFQpx2/077Xx+MvNMi7PDb0xddmZmZmOdOgp9IaMkmPAHuWS/5BRDxbH+0xMzOzHefAaDtFxFn13QYzMzOrXQ6MzMzMGqoGfCPEnZXPuJmZmVnKgZGZmZlZylNpjVTB/On13QQzM0vtOrS+W2C1xYGRmZlZQ+U1RjnnM25mZmaWcmBkZmZmlnJgZGZmZpbyGiMzM7MGqoovP7c64BEjMzMzs5QDIzMzM7OUp9LMrE5MeG8ld46eQ3EEnzqkF2eP7FNm/4Kl67n5gZm8//FaPn/Snpx57O6l+25+4D3GzVhBx3bNuenKQ3LddDNrwjxiZGa1rqg4+Mujs/nZVwZx85WHMHbSUuYvWVcmT7s2zbjk9L0585jdtyl/3NAe/Oyrg3LVXDOzUrUeGEnqK2lqlvQ7Je1fC/WPkPTEjtZTjWMcUcfHqJXzYdYQzZ6/ml5dW9Oza2uaN8vjqIN24a3pK8rk6dSuBf1370B+/raLSwfu1Yl2rZvnqrlmDVde3s77aKByNpUWEZfsaB2SctXeEcBa4PW6OkBtnA+zhmplwSa6dWpZut21Y0tmf7S6HltkZlY9dRWyNZP0N0mTJT0oqY2kMZKGAUg6WdIESZMkvZCmDZf0uqR3058D0vSLJT0g6XHgubT+DpIekTRd0u2S8tK8F0iaImmqpOtKGiNpraTfpMd7U1KPNP00SW+lx/yvpB6S+gKXAd+RNFHS0ZK6S3pI0jvp48iKOi7pmrTvz0maJ+mzkn6ftusZSc3TfJnnI2v7zBqryJboq47NrBGoq8BoADAqIg4EVgP/W7JDUnfgDuDsiDgIODfdNRM4JiIOBn4G/DajvsOBL0XEcen2cOC7wCCgH/BZSbsC1wHHAYOBQySdmeZvC7yZHu8V4Gtp+qvAYekx7wO+HxHzgNuBGyJicESMBW5Mtw8BzgburKL//YBTgTOAe4GXImIQsCFNL6+i9pUh6VJJ4ySN+89zM6pogln96dqxBcs/KSzdXlFQSJcOLSspYWbWMNTV1NT8iHgtfX4v8O2MfYcBr0TEBwARsTJN7wj8TVJ/kg+cmQsMns/IB/B2RMwFkPRv4ChgMzAmIpal6f8EjgEeBTYBJeuSxgOfSp/3Bu6X1AtoAXxQQX9OAPbPuNFWB0ntI2JNBfmfjojNkqYA+cAzafoUoG+W/BW1r4yIGAWMApjx6KVZP5SbNQT9e3dg0YoNLFm5gS4dWvLqpKVcef5+9d0ss8anAa/F2VnVVWBU/o925ray7Af4FcnIylnpdNaYjH3ryuXNVn9lA/WbI6KkTBFb+30z8MeIGC1pBHBNBeXzgMMjYkMlx8hUCBARxZIyj11M9nNeUfvMGqX8fPG1M/bmF3+dQlFxcMIhPenTsy3PvLkQgJMP25VVazZx1U3jWV9YhASPv7qAm797CG1aNeP//jWdqXMLWL1uM1/9zRuc/6m+fGp4r3rulZk1BXX1B7iPpMMj4g3gApIpq9PSfW8Af5a0Z0R8IKlLOhrUEfg4zXNxFfUPl7Qn8CFwHskoylvAjZK6AavS495cRT2Zx/xSRvoaoEPG9nPAN4HrASQNjoiJVdRt1qQN27crw/btWibt5MN2LX3euX0L/nr14VnLfvdCX7BpZvWjrsboZgBfkjQZ6ALcVrIjneq6FHhY0iTg/nTX74HfSXqNZPqpMm8A1wJTSaa/HomIRcCPgJeAScCEiHisinquAR6QNBZYnpH+OHBWyeJrkqnAYeli8ukki7PNzMxsJ6OtMzjWmHiNkZlZw7HfmaPq5LrLvPNO32nf64vvH90gr1X1qi4zMzOzlBf5bidJXwYuL5f8WkR8oz7aY2ZmZjvOgdF2ioi7gbvrux1mZmZWexwYmZmZNVS+j1HO+YybmZmZpRwYmZmZmaUcGJmZmZmlHBiZmZmZpbz42szMrKHKa5D3QNypecTIzMzMLOXAyMzMzCzlwMjMzMws5TVGZmZmDZU8fpFrPuNmZmZmKQdGZmZmZilPpVWDpCuAURGxvr7bYtZYTHhvJXeOnkNxBJ86pBdnj+xTZv+Cpeu5+YGZvP/xWj5/0p6ceezupftufuA9xs1YQcd2zbnpykNy3XQza8IcGFXPFcC9gAMjs2ooKg7+8uhsfnHJgXTt2JLv3TKB4ft3ZfcebUvztGvTjEtO35u3pq3YpvxxQ3twyhG7cuP9M3PZbLOGx18im3M+4xkk9ZU0U9LfJE2W9KCkbwO7Ai9JeqmSsmslXSdpvKT/ShouaYykuZJOz6h/rKQJ6eOINP2stIwk9ZI0S1LP3PTarPbNnr+aXl1b07Nra5o3y+Oog3bhrellA6BO7VrQf/cO5OdvewO7gXt1ol3r5rlqrplZKQdG2xpAMm12ILAaaAEsBEZGxMhKyrUFxkTEUGAN8GvgU8BZwC/TPEuBT0XEEOA84CaAiHgEWAx8A7gD+HlELK7tjpnlysqCTXTr1LJ0u2vHlqwsKKzHFpmZVY8Do23Nj4jX0uf3AkdVs9wm4Jn0+RTg5YjYnD7vm6Y3B+6QNAV4ANg/o/y3gB8BhRHx72wHkHSppHGSxv3nuRnV7Y9ZzkW2RH+zgZk1Al5jtK3y7+lZ3+Oz2BwRJXmLgUKAiCiWVHKevwMsAQ4iCUo3ZpTfLS3XQ1JeRBRv07CIUcAogBmPXlrddpnlXNeOLVj+ydYRohUFhXTp0LKSEmaWldcY5ZzP+Lb6SDo8fX4B8CrJ1Fj7Wqi7I7AoDXq+AOQDpIHT3cCFwAzgylo4llm96d+7A4tWbGDJyg1s3lLMq5OWMny/rvXdLDOzKnnEaFszgC9J+gswG7iNZJrsaUmLqlhnVJVbgYcknQu8BKxL038MjI2IsZImAu9IejIiPF9mjVJ+vvjaGXvzi79Ooag4OOGQnvTp2ZZn3lwIwMmH7cqqNZu46qbxrC8sQoLHX13Azd89hDatmvF//5rO1LkFrF63ma/+5g3O/1RfPjW8Vz33ysyaAm2d/TFJfYEnIuKA+m5LVTyVZmbWcOx35qg6WUWXd/F5O+17ffE99zfIlYeeSjMzMzNLeSotQ0TMAyodLZL0FlB+FekXImJKXbXLzMyaKH+JbM45MKqhiDi0vttgZmZmdcOhqJmZmVnKgZGZmZlZylNpZmZmDVVeg7xwa6fmESMzMzOzlEeMGqnioqL6boKZmdlOxyNGZmZmZimPGJmZmTVU/hLZnPMZNzMzM0s5MDIzMzNLOTAyMzMzS3mNkZmZWUPl70rLOZ9xMzMzs5QDIzMzM7OUp9LMrE5MmLWKu56YS3FxcMIhPfjssbuX2b9g6XpueWg2cxeu5cIT9+DMo3uX7rvloVmMm7mKjm2bc+MVQ3LddDNrwiodMZLUSdL/5qox20vSFZLa1Hc7zCxRVBzcMfp9fnLxQG68YghjJy1j/pL1ZfK0a9OMr562F2ccvds25UcO6cFPLx6Yq+aamZWqaiqtE1DvgZESlbX1CqBGgZEkj5aZ1ZE5C9bQq2srenZpRfNmeRx1YHfenrGiTJ5O7VrQv3d78rN8SebAPTvSvo3/i5opL2+nfTRUVbXsWqCfpImSrpf0PUnvSJos6RcAkvpKminpTklTJf1T0gmSXpM0W9LwNN81kv4h6cU0/WslB6mk3hmSbgUmALtLuk3SOEnTMvJ9G9gVeEnSS2na2oy6z5F0T/r8Hkl/TPNdJ6mfpGckjZc0VtK+FZ2ItOxtkl6SNFfSsZLuStt4T0a+EyW9IWmCpAcktUvTf5b2caqkUZKUpo+RdJ2ktyXNknR0tX5zZg3YioJNdO3YsnS7a8eWrFy9qR5bZGZWPVUFRj8E3o+IwcDzQH9gODAYGCrpmDTf3sCNwIHAvsCFwFHAVcCPM+o7EDgVOBz4maRdJZ1YSb0DgL9HxMER8SFwdUQMS+s5VtKBEXETsBAYGREjq9HnfYATIuK7wCjgWxExNG3rrVWU7QwcB3wHeBy4ARgIDJI0WFI34Cdp/UOAccCVadlbIuKQiDgAaA18JqPeZhExnGTk6+cVHVzSpWlgOO6B52dWo6tmZmZWEzUZqz4xfbybbrcjCWg+Aj6IiCkAkqYBL0RESJoC9M2o47GI2ABsSEdthpMEUBXV+2FEvJlR/nOSLk3b3QvYH5hcgz4APBARRelIzhHAA+ngDUDLiosB8HhGv5aU63NfoHfaptfSOlsAb6RlR0r6PsmUXxdgGklwBfBw+nM8Zc9XGRExiiSYY9pDX43qdNasPnTt2IIVBYWl2ysKCunSoUU9tsjMrHpqEhgJ+F1E/KVMotQXKMxIKs7YLi53jPJ/zKOKetdlbO9JMqpzSESsSqevWlXQ1szjlM9TUmce8Ek6GlZdmf0q3+dmQBHwfERckFlIUiuS0ahhETFf0jXl2lVSVxG+UtB2Anvv1p5FyzewZOVGunRowauTl/Gd8wbUd7PMGp28BrwWZ2dV1RlfA7RPnz8LfCVjzcxuknap4fHOkNRKUldgBPBODertQBLUFEjqAXy6gnYCLJG0X7pg+6xsDYmI1cAHks5NjytJB9WwP+W9CRwpae+0zjaS9mFrELQ87ec5O3gcswYtP19ccno/fnn3VL79pwkcOag7fXq05dm3FvHsW4sAWLVmE5dc+zaPv7aQB1+azyXXvs36jVsA+ON9M/nh7ZNYuHwDl1z7Nv8dt7g+u2NmTUiloxMRsSJdRD0VeBr4F/BGOk20Fvg8yShHdb0NPAn0AX4VEQuBhZL2q6reiJgk6V2SKai5wGsZu0cBT0talK4z+iHwBDAfmEoyPZfNRcBtkn4CNAfuAybVoD9lRMQySRcD/5ZUMi33k4iYJekOYAowjyQgNNupDR3QhaEDupRJO+nQXqXPO7dvwZ0/HJ617JXnV3gdhJlZnVJEbpaqpNNHayPiDzk54E7Oa4zMzBqOgWf/ddv7TtSC5v/7lZ32vX7zrXfVyTnbUV7PYmZm1kB5jVHu5SwwiohrcnWsHSHpauDccskPRMRv6qM9ZmZmljseMSonDYAcBJmZmTVBHqMzMzMzS3nEyMzMrIHyGqPc8xk3MzMzSzkwMjMzM0s5MDIzMzNLOTAyMzMzS3nxtZmZWQPlxde55zNuZmZmlnJgZGZmZpZyYGRmZmaW8hojMzOzBsprjHLPZ9zMzMws5REjM6sTE2at4q4n5lJcHJxwSA8+e+zuZfYvWLqeWx6azdyFa7nwxD048+jepftueWgW42auomPb5tx4xZBcN93MmjCPGJlZrSsqDu4Y/T4/uXggN14xhLGTljF/yfoyedq1acZXT9uLM47ebZvyI4f04KcXD8xVc83MSjWawEjSPZLOyZLeV9LUGta1q6QHK9g3RtKw7W2nmcGcBWvo1bUVPbu0onmzPI46sDtvz1hRJk+ndi3o37s9+XnapvzAPTvSvo0HtM3ypJ320VA1msCotkhqFhELI2KbIMvMaseKgk107diydLtrx5asXL2pHltkZlY9DTYwkvRFSZMlTZL0jzT5GEmvS5pbwehRK0l3S5oi6V1JI9P0iyU9IOlx4LnMUSZJrSXdlx7rfqB1Rn0nSnpD0oS0fLtK2jtP0m/T/OMkDZH0rKT3JV2W5mkn6YW0vimSzkjTD0mP30pSW0nTJB1QayfTzMzMqqVBBkaSBgJXA8dFxEHA5emuXsBRwGeAa7MU/QZARAwCLgD+JqlVuu9w4EsRcVy5Mv8DrI+IA4HfAEPTNnQDfgKcEBFDgHHAlVU0fX5EHA6MBe4BzgEOA36Z7t8InJXWNxL4P0mKiHeA0cCvgd8D90bENtODki5Ng65xDzw/s4qmmNWfrh1bsKKgsHR7RUEhXTq0qMcWmZlVT0OdxD8OeDAilgNExEol85GPRkQxMF1SjyzljgJuTsvMlPQhsE+67/mIWJmlzDHATWmZyZImp+mHAfsDr6XHbgG8UUW7R6c/pwDtImINsEbSRkmdgHXAbyUdAxQDuwE9gMUkwdM7JMHTt7NVHhGjgFEA0x76alTRFrN6s/du7Vm0fANLVm6kS4cWvDp5Gd85b0B9N8us0fF9jHKvoQZGArL94S8slydbuYqsq2RftmOJJJi6oJJy5ZW0r5iybS0mOdcXAd2BoRGxWdI8oGREqwvQDmieplXWXrMGLT9fXHJ6P35591SKA44f2oM+Pdry7FuLADjp0F6sWrOJ7/15IhsKi5DgidcWctMVQ2jTqhl/vG8mUz8oYM26LVxy7ducf0IfThjWs557ZWZNQUMNjF4AHpF0Q0SskNSlmuVeIQk+XpS0D9AHeA+o7EYoJWVeStf1HJimvwn8WdLeETFHUhugd0TM2p4OpToCS9OgaCSwR8a+UcBPgT2B64Bv7sBxzOrd0AFdGDqg7H/dkw7tVfq8c/sW3PnD4VnLXnn+vnXaNjOzijTIwCgipkn6DfCypCLg3WoWvRW4XdIUYAtwcUQUqvLLAm8D7k6n0CYCb6dtWCbpYuDfkkour/kJsCOB0T+BxyWNS481E5KF5sCWiPiXpHzgdUnHRcSLO3AsMzMzqyFFeKlKY+Q1RmZmDcfAs/9aJzfm6fy9b+207/Wrrr+5Qd7MqEGOGJmZmZkXX9cHB0Y1JOkRknVAmX4QEc/WR3vMzMys9jgwqqGIOKu+22BmZmZ1w2N0ZmZmZimPGJmZmTVQXmOUez7jZmZmZikHRmZmZmYpB0ZmZmZmKa8xMjMza6C8xij3fMbNzMzMUg6MzMzMzFIOjMzMzMxSXmNkZmbWQHmNUe75jJuZmZmlHBiZmZmZpTyVZmZ1YsKsVdz1xFyKi4MTDunBZ4/dvcz+BUvXc8tDs5m7cC0XnrgHZx7du3TfLQ/NYtzMVXRs25wbrxiS66abWRPW5EeMJL1e320w29kUFQd3jH6fn1w8kBuvGMLYScuYv2R9mTzt2jTjq6ftxRlH77ZN+ZFDevDTiwfmqrlmZqWa/IhRRBxRk/yS8iOiqK7aY7YzmLNgDb26tqJnl1YAHHVgd96esYLde7QpzdOpXQs6tWvB+Jkrtyk/cM+OLF21MWftNWuovPg695r8GZe0Nv05QtIYSQ9Kminpn5KU7psn6WeSXgXOraCeMZJukPSKpBmSDpH0sKTZkn6dke9RSeMlTZN0aZq2R5qvm6Q8SWMlnZiD7pvViRUFm+jasWXpdteOLVm5elM9tsjMrHqafGBUzsHAFcD+wF7AkRn7NkbEURFxXyXlN0XEMcDtwGPAN4ADgIsldU3zfCUihgLDgG9L6hoRHwLXpeW+C0yPiOfKVy7pUknjJI174PmZO9RRMzMz25YDo7LejogFEVEMTAT6Zuy7vxrlR6c/pwDTImJRRBQCc4GSlaffljQJeDNN6w8QEXcC7YHLgKuyVR4RoyJiWEQMO/dT+9aoY2a51LVjC1YUFJZurygopEuHFvXYIjOz6mnya4zKKcx4XkTZ87OuBuWLy9VVDDSTNAI4ATg8ItZLGgO0ApDUBii5LKcdsKaGbTdrMPberT2Llm9gycqNdOnQglcnL+M75w2o72aZNTr5XmOUcw6McqsjsCoNivYFDsvYdx3wT+BD4A7gM/XQPrNakZ8vLjm9H7+8eyrFAccP7UGfHm159q1FAJx0aC9WrdnE9/48kQ2FRUjwxGsLuemKIbRp1Yw/3jeTqR8UsGbdFi659m3OP6EPJwzrWc+9MrOmwIFRbj0DXCZpMvAeyXQako4FDgGOjIgiSWdL+nJE3F2PbTXbIUMHdGHogC5l0k46tFfp887tW3DnD4dnLXvl+Z4qNrP60eQDo4hol/4cA4zJSP9mxvO+1ahnRMbz8nWNyMj66QqqKB09iojPVnU8MzMzq31NPjAyMzNrqHwfo9xzYFRDkv5M2cv4AW70tJeZmVnj58CohiLiG/XdBjMzM6sbHqMzMzMzS3nEyMzMrIHyGqPc8xk3MzOzBkfSyZLekzRH0g+z7O8o6XFJk9LvH/1ybRzXI0aNVFHhhvpugpmZWZ2QlA/8GfgUsAB4R9LoiJieke0bJN8tepqk7sB7kv4ZETv0jdUeMTIzM7OGZjgwJyLmpoHOfcAZ5fIE0F6SSL5KayWwZUcP7MDIzMzMck7SpZLGZTwuzdi9GzA/Y3tBmpbpFmA/YCHJl7dfnn4J/A7xVJqZmVkDtTMvvo6IUcCoCnYrW5Fy2ycBE4HjgH7A85LGRsTqHWnXznvGzczMrLFaAOyesd2bZGQo05eBhyMxB/gA2OEvWnRgZGZmZg3NO0B/SXtKagGcD4wul+cj4HgAST2AAcDcHT2wp9LMzMysQYmILZK+CTwL5AN3RcQ0SZel+28HfgXcI2kKydTbDyJi+Y4e24GRmZlZA7UzrzGqSkQ8BTxVLu32jOcLgRNr+7hN94ybmZmZleMRIzOrNe/OKeDuZ+ZTXAzHD+nGWUf1LLM/Irj7mflMmL2als3z+MaZfdmrVxsAnnxzCS9MWE4AJwzpxqmH9QDg788tYPysT2iWn0ePLi35xhl70LaV37rMrG54xMjMakVRcfDXpz7i6ov6c8M39ue1qSuZv6zsHdrfnbOaRSsLuflbA/n6aX2448kPAfho6QZemLCc331tP/5w2f6Mn1XAohUbATioXwf++L8D+b//2Z9du7TkkbGLc943M2s6GmxgJOliSbfUsMxTkjrVsMxlkr5Yo8aZ2TbmfLyOnl1a0aNzS5rn53HkwM6Mm/lJmTzvzPyEYw/siiT26d2OdRuLWLVmMx8v20j/3m1p2TyP/Dyx/x7teTste1C/DuTnJbc06d+7LSvWbM5xz8zqT15e3k77aKgabsu2Q0ScEhGf1LDM7RHx9zpqklmTsXLNZrp2aF663aVDi22CmJVrNtO1Y4vS7a4dWrByzSZ236UVMz5cy5r1WyjcXMyEOQUsL9j2645emriCg/fuUHedMLMmr94CI0mPShqffiPupWnalyXNkvQycGRG3nsk3SbpJUlzJR0r6S5JMyTdk5FvnqRuktpKejL9xt2pks5L918rabqkyZL+kKZdI+mq9PlgSW+m+x+R1DlNHyPpOklvp+07upJ+9ZU0VtKE9HFEmp4n6da0v0+ko1vnpPuGSno5PR/PSupVQd2lt09/8MU5O/YLMKtt5e9Jy7a3ro1smYDe3VtzxpE9+dU/ZvGbe2fTt0fr0lGiEg+9soi8PHH0oC611GAzs23V5wrGr0TESkmtSb4190ngF8BQoAB4CXg3I39nktt+nw48ThI4XZKWHRwREzPyngwsjIhTASR1lNQFOAvYNyKigim3vwPfioiXJf0S+DlwRbqvWUQMl3RKmn5CBf1aCnwqIjZK6g/8GxgGfBboCwwCdgFmAHdJag7cDJwREcvSIO43wFfKV5x5+/TJ/7ow+18Ys3rSpUNzVqzeOkK0cvUmurRvXiZP1/YtWJExErRi9Sa6tE9GkI4f0o3jh3QD4F8vfFxm9GnMxBWMn13Az7+4D8n3RZqZ1Y36nEr7tqRJwJskt/3+AjAmIpal36R7f7n8j0dEkHxR3JKImJJ+Wdw0koAj0xTghHSU5+iIKABWAxuBOyV9FlifWUBSR6BTRLycJv0NOCYjy8Ppz/FZjpepOXBHesOpB4D90/SjgAciojgiFpMEfpDcqfMAku94mQj8hOTW52aNyt67tWXRio0sWVXI5qJiXpu2imEDOpXJM2xAJ16evIKIYNaCtbRpmU/nNHgqWJcEVcsKNvHWjFUceUAyMvTunAIefW0xPzi/Hy2b71Sz/2ZVypN22kdDVS8jRpJGkIy4HB4R6yWNAWaSfEtuRQrTn8UZz0u2y/QjImZJGgqcAvxO0nMR8UtJw0luH34+8E2SEajqKjlmUfnjlfMdYAlwEEnguTFNr+hVIGBaRBxeg7aYNTj5eeKrp/ThN/fOpjiCkYO7sfsurXlu3DIAThzWnSH9O/Du7AK+dfNUWjTP4xtn9C0t/4f/zGXN+i00yxeXnNKHdq2T/2Z/fWo+W4qK+dU/ZgOwT++2XPqZPXLePzNrGuprKq0jsCoNivYFDgNaAyMkdSUZ3TkXmLQ9lUvaFVgZEfdKWgtcLKkd0CYinpL0JlBmkU5EFEhalY4wjSUZwXp529qr1bcFEVEs6UsktzIHeBX4kqS/Ad2BEcC/gPeA7pIOj4g30qm1fSJi2nYc26xeDenfkSH9O5ZJO3FY99Lnkrjk1D5Zy/7qywOypt/y7QNqr4FmZlWor8DoGeAySZNJAoM3gUXANcAb6fMJbA0qamoQcL2kYmAz8D9Ae+AxSa1IRmm+k6Xcl4DbJbUh+SK6L2/HsW8FHpJ0Lsl02bo0/SGS0aqpwCzgLaAgIjali7BvSqfzmgF/IpkiNDMzsxxSsmzHckFSu4hYm46KvQ0cma43qjEvvjYzazgOvPBfdbJoZv8//Hqnfa+fftVPGuRCI99XP7eeSK+GawH8anuDIjMzaxoa8o0Qd1YOjLaTpJOA68olfxARZ1VUJiJG1GmjzMzMbIc4MNpOEfEs8Gx9t8PMzMxqj8fozMzMzFIeMTIzM2ugvMYo9xwYNVJbNm6o7yaYmZntdByKmpmZmaUcGJmZmZmlPJVmZmbWQHmNUe75jJuZmZmlHBiZmZmZpRwYmZmZmaW8xsjMzKyB8hqj3PMZNzMzM0t5xMjMak2H3Q6m92FfBeWxYtZ/WTL54W3y9D70q3TYfSixpZB5Y29mw4q5tOywK3uOvKo0T8v2PVg44d8sm/4Erbv0pc8Rl6H8FkQUMf/1UaxfPjuX3TKzJsQjRllIukJSm4ztH9dne8waBeWx++GXMue5XzHj4W/Tea+jaNWpd5ksHXoPoWXHXZn+4P/y4Wu30eeIrwNQuHohMx+7MnmMvoriLYUUfPgWALsd8iUWTfwPMx+7kkUT/s1uh3wx510zs6bDgVF2VwBtMrYdGJlVoW23/hSuXsSmNUuI4i2smvsqHfsML5OnY5/hrJzzEgDrl80iv0VbmrXuXCZP+16DKFyzmE3rliUJEeQ3bw1Afos2bF6/su47Y2ZNVpOeSpPUF3gGeAs4GJgFvALsCrwkaXm6r7WkicC0iLioknpeBQ4DJgF3A78AdgEuioi3JQ0H/gS0BjYAX46I9yRdCRwQEV+RNAj4NzA8ItbXUdfNal3ztl3YtG556fbmdSto032fMnlatOnKpnUrSrc3rVtBizZd2LJhVWla572OZtXcsaXbC966i71P+hm7HXIxSMx64kd11wmzBsaLr3PPZxwGAKMi4kBgNdACWAiMjIiREfFDYENEDM4WFGXYG7gROBDYF7gQOAq4iq0jTjOBYyLiYOBnwG/T9D8Be0s6iySg+rqDImt8lCUtqswSGXmU14xOfQ5h1Qevl6Z12/ckFrx1F1P/8zUWvH0XfY7+Ri2118xsWw6MYH5EvJY+v5ckmNkeH0TElIgoBqYBL0REAFOAvmmejsADkqYCNwADAdIyFwP/AF7OaE8Zki6VNE7SuIdfnredzTSrG5vXraBF226l283bdt1m2mvTuhW0aNu1dLtF265sXr91tKhD7yGsXzGXLRsLStO69h/JJx++CcAnH7xO227966oLZmYOjNjmI+0229VVmPG8OGO7mK1Tlr8CXoqIA4DTgFYZZfoDa0mm8bI3NGJURAyLiGGfPbbvdjbTrG6sWz6blh170aLdLiivGZ33OoqCj94pk6fgo3fosvdIANp034eiTevLTaMdxcqMaTSAzetX0a7nQCBdf7R6UR33xMyasia9xijVR9LhEfEGcAHJOqG9gfZAyYKJzZKaR8TmHTxWR+Dj9PnFJYmSOpJMwx0D3CLpnIh4cAePZZZbUcz8N+5g75N+jpTHitkvsPGT+XQbcBIAy997ltULxtNx96EMPOc2ircU8uHYm0uLK78FHXYdzEev3V6m2g9fu5XdD/0q5OURRZv58LVbc9ots/rkNUa558AIZgBfkvQXYDZwG7AJeFrSoogYCYwCJkuaUMU6o6r8Hvhbutj6xYz0G4BbI2KWpK+SLPx+JSKW7sCxzHJu9YIJTF8woUza8veeLbM9/41RWctG0SYm/2vbS/HXLZnBzNFXZSlhZlb7lCyDaZrSq8meSKe2GpUJd53VdH9xZmYNzJCvPJLt6oMdNvz2P+207/VvX3ZFnZyzHeUxOjMzM7NUk55Ki4h5QLVHiyR1BV7Isuv4iFiRJd3MzGy75XuNUc416cCoptLgZ3B9t8PMzMzqhkNRMzMzs5QDIzMzM7OUp9LMzMwaKK8xyj2fcTMzM7OUR4waqc3r19R3E8zMzHY6HjEyMzMzSzkwMjMzM0t5Ks3MzKyB8uLr3PMZNzMzM0s5MDIzMzNLOTAyMzMzS3mNkZmZWQPlNUa558DIzGpNxz6HsMfR/4uUx9LpT7Nown3b5Nnj6G/QaY/hFG8p5P0Xfs/6ZXMAyG/Rlr2O+y6tu/aFCOa++AfWLp5Bl37HsNvwL9K6Sx+mPfBN1i2dleNemVlT4sDIzGqH8uh77LeY+dgP2LR2GQM/92c++eB1Nqz6qDRLxz2G06rTbky690u067Efex57OdMe/BYAexzzDT756B1mP/NLlNeMvGYtAVi/ch6zn76GPUd+p166ZWZNS5MYo5M0QtIT6fPTJf2wAbTpKUmd6rsdZrWlXY8BbCxYSOHqRUTxFlbOHkPnvY4sk6fznkewfObzAKxdMoP8lu1o3qYL+c3b0H7XQSyb/jQAUbyFok3rANi46iM2frIgt50xsyaryY0YRcRoYHQDaMcp9d0Gs9rUom03Nq1ZWrq9ae0y2vbYt2yedt0oXLusTJ4W7boRxUVs2VDAXsd/jzbd+rFu6Sw+HHsrxVs25qz9Zg1Rfn6TGL9oUBrVGZfUVtKTkiZJmirpPEnHS3pX0hRJd0lqmeY9WdJMSa8Cn82o42JJt6TP75F0Tsa+tenPEZJelvQfSbMkXSvpIklvp8fpV0kb75F0m6SXJM2VdGzarhmS7snIN09SN0l90313SJom6TlJrWv/7JnVNW1XnohAefm07d6fJVMfZ+r9l1G8ZSO7Dj2/9ptoZlaFRhUYAScDCyPioIg4AHgGuAc4LyIGkYyA/Y+kVsAdwGnA0UDP7TjWQcDlwCDgC8A+ETEcuBP4VhVlOwPHAd8BHgduAAYCgyQNzpK/P/DniBgIfAKcna1SSZdKGidp3KOvfVzjDpnVpU3rltGi/S6l2y3adWfzuhVl86xdRst23bfJs2ntMjatXca6JTMBWDnnFdp075+bhpuZZWhsgdEU4ARJ10k6GugLfBARJZep/A04Btg3TZ8dEQHcux3HeiciFkVEIfA+8FxGG/pWUfbx9LhTgCURMSUiioFpFZT9ICImps/HV1R/RIyKiGERMezMI3erSV/M6tzaJe/RquNutGzfE+U1o0v/Eaz64PUyeT754A267fspANr12I+iTevYvH4lm9evonDtMlp16g1Ah92HsGHlhznvg5lZo1pjFBGzJA0FTgF+x9ZgJWv2alS5hTQ4lCSgRca+woznxRnbxVR93jLzlq8nW9nMPEWAp9Ks8Yli5r1yMwPOuBYpj2XTn2HDyg/ZZeBnAFg67Qk++fAtOu0xnIO+8HeKtxQy94XrS4t/+Mot9DvxR+TlNWfj6kWl+zrvdSR9j/kmzVp3ZMBnfsO65e/z3uh6v37CLCd8H6Pca1SBkaRdgZURcW+6HugyoK+kvSNiDsmU18vATGBPSf0i4n3gggqqnAcMBf4DnAE0r+s+mO3MCj58m8kfvl0mbem0J8psz3vl5qxl1y9/n2n/+cY26avmvsaqua/VXiPNzCrRqAIjkvU+10sqBjYD/wN0BB6Q1Ax4B7g9IgolXQo8KWk58CpwQJb67gAek/Q28AKwLhedMDMzs4ZJyVIYa2zeuuUE/+LMzBqIQ7/53+pcllljJ//zzp32vf6Ziy6pk3O2ozx5aWZmZpZqbFNpDYakq4FzyyU/EBG/qY/2mJnZzseLr3PPgdF2SgMgB0FmZmY7EYeiZmZmZikHRmZmZmYpT6WZmZk1UF5jlHsOjBqpos2b6rsJZmZmOx2HomZmZmYpB0ZmZmZmKU+lmZmZNVB5XmOUcz7jZmZmZikHRmZmZmYpB0ZmZmZmKa8xMjMza6B8H6Pc8xk3MzMzS3nEyMxqTac9hrPniG9DXh5Lpz7Jx+/8c5s8e474Np32PIzizYXMee53rFs6C4AhX7mfos0boLiIiCIm/+tSALr2H8Huh3+Z1l32YPK/v866Je/ltE9m1rQ4MDKz2qE89jruO0x7+Eo2rVnGgReOYuX7r7Jh5YelWTr1PYxWnXrz7t0X0q7n/ux13JVMue+y0v3THricLRsLylS7fsUHzHz8J/Q7/qqcdcXMmq6dLjCSdDEwLCK+WUme1yPiiNy1KmsbLgPWR8Tf67MdZrWlXc/92PDJxxQWLAJg+Xsv0KXfUXycERh16XcUy2Y8C8DaxdNp1rIdzdt2ZfO6FRXWmxlYmTU1XmOUeztdYFQd9R0UpW24vb7bYFabWrbrxqY1S0u3N61dRrue+5fJ06JdNwoz8hSuXUaLdt1KA6P9P/t/QLBkymiWTHk8J+02M8vUaEJRSV+UNFnSJEn/kHSapLckvSvpv5J6ZCnTQ9IjaZlJko5I09emP0dIeiIj/y3piBOS5kn6raQ3JI2TNETSs5LeT0d7KmrnCEkvS/qPpFmSrpV0kaS3JU2R1C/Nd42kq9LnYyRdl+aZJenoCuq+NG3LuMfeWLQDZ9OsLmjbpIhyOSrOM+X+/2Xyvy5hxiPfo+dBZ9Fht4PqopFmZpVqFIGRpIHA1cBxEXEQcDnwKnBYRBwM3Ad8P0vRm4CX0zJDgGk1PPT8iDgcGAvcA5wDHAb8sopyJW0cBHwB2CcihgN3At+qoEyzNM8VwM+zZYiIURExLCKGnXF4rxp2xaxuFa5dRov2u5Rut2jXnU3rlm+Tp2VGnpbturMpHS0qGTXavOETVs4ZS7ue++Wg1WZmZTWKwAg4DngwIpYDRMRKoDfwrKQpwPeAgRWUuy0tUxQRBVnyVGZ0+nMK8FZErImIZcBGSZ0qKfdORCyKiELgfeC5jHr6VlDm4fTn+ErymDVYaxfPpHXn3rTs0AvlNaPbgONZOfe1MnlWzX2V7vudBEC7nvuzZdM6Nq9bQV6zVuQ1bw1AXrNWdNzjENYvn5vzPpiZNZY1RgKiXNrNwB8jYrSkEcA121HvFsoGh63K7S9MfxZnPC/Zruzclc+bWU9F5UryFFVRt1nDFEXMffFP7P/ZPyDlsWTaU2xYMY8eB54OwJLJo1n1wZt06ns4Q778b4q2JJfrAzRv25l9T/sNAMrLZ9nM//LJh28D0KXf0ew58nKat+7Efmdcx7plc5jxiK9Qs6bBi69zr7H8AX4BeETSDRGxQlIXoCPwcbr/S5WU+x/gT5LygbYRsTpj/4fA/pJakgRFx5NM0ZnZdvhk3pu8e8+bZdKWTB5dZvuDl27gg3LlCgsWMener2Stc+X7Y1n5/tjabKaZWYUaRSgaEdOA3wAvS5oE/JFkhOgBSWOB5RUUvRwYmU63jafcdFtEzAf+A0wG/gm8WycdMDMzs0ZBEeVnqKwxeP2GY/yLMzNrII74zitZLrnccRc8dt9O+17/7zPOr5NztqMay1SamZlZk5Of3yBjh52aA6PtJGkQ8I9yyYURcWh9tMfMzMx2nAOj7RQRU4DB9d0OMzMzqz2NYvG1mZmZWS54xMjMzKyB8n2Mcs+BUSNVvLmw6kxmZmZWIw5FzczMzFIOjMzMzMxSnkozMzNroLzGKPd8xs3MzMxSDozMzMzMUg6MzMzMzFIOjMzMzMxSXnxtZmbWQHnxde45MDKzWtNpz8PY6/grkfJYMnk0C976+zZ59jr+SjrvdQTFmzcy6+lfsW7Je1t3Ko/BX7yHTWuXMf2h7wLQdcBx9Dnya7Tp2pdJ//gyaxfPzFV3zKwJcihqZrVDefQ74XtMe+AKJvz1fLrvdyKtu+5ZJkvnvY6gVefdGX/HOcx59lr2/tT3y+zfdeh5rF8xr0za+mVzmfnoD1g9/9267oGZWeMOjCTdI+mc+m7H9pD0lKRO9d0Os9rSvtf+bPxkAYUFC4niLSyb8Txd9z6mTJ4uex/D0mlPA7Bm0VTyW7WneduuALRotwtd+h3JksmPlSmzYeU8Nqz8KDedMLMmz1Np9SQiTqnvNpjVphbtdqFwzZLS7cI1S2m/68AyeVq2786m1VvzbFqzlJbtu7N53Qr2Ov47fDDmFpq1aJOzNps1dF5jlHuN5oxL+qmkmZKel/RvSVeV2z9PUrf0+TBJY9Ln7STdLWmKpMmSzk7TL0jTpkq6Lk3LT0ehpqb7vpOm95P0jKTxksZK2reSdt4j6TZJL0maK+lYSXdJmiHpnvLtldQ33XeHpGmSnpPUuoK6L5U0TtK40W8t3bETalbblCUtospMEdC535FsXr+SdUu8fsjM6lejGDGSNAw4GziYpM0TgPHVLP5ToCAiBqV1dZa0K3AdMBRYBTwn6UxgPrBbRByQ5u2U1jEKuCwiZks6FLgVOK6SY3ZO958OPA4cCVwCvCNpcERMLJe/P3BBRHxN0n/Svt5bvtKIGJW2hVd/f2j5vzhm9SoZ/elRut2y/S5sWru8TJ7CNUtp0aEHfJxst2i/C5vWLqPbgOPosvcxdN7rCPLyW5Lfsi37nHoNs568Joc9MDNrJIERcBTwWERsAJD0eA3KngCcX7IREaskHQOMiYhlaX3/BI4BfgXsJelm4EmSgKkdcATwgFT6abdlFcd8PCJC0hRgSURMSY8zDegLTCyX/4OMYGl8msesUVmzaAatO+9Oy4692LRmGd33+xTvPf7TMnlWzhlLryHnsHzGc7TvdQBFhWvZvG4FH75yKx++cisAHXcfwm7DL3JQZGb1orEERtkG6cvbwtapwVblylY9nk9p0HQQcBLwDeBzwBXAJxExuAbtLUx/Fmc8L9nOds4z8xQBWafSzBq0KOL9//6BA869CZTHkimPs37FB/QcfBYAiyc+wqq5r9F5ryMY+rWHKN6ykdlP/6rKarv2P5a9TriK5q07sf/ZN7Bu6SymPXB5XffGrEHwGqPcayyB0avAXyT9jqTNpwJ3lMszj2Rq7GmSqagSzwHfJAlwkNQZeAu4MV2TtAq4ALg53d4UEQ9Jeh+4JyJWS/pA0rkR8YCSYaMDI2JSHfXVrNFaNfd1xs99vUza4omPlNme+9/rK62jYP4ECuZPKN1eMftlVsx+ufYaaWZWiUYRikbEO8BoYBLwMDAOKCiX7Rckwc5YklGXEr8GOqcLqicBIyNiEfAj4KW0zgkR8RiwGzBG0kTgnjQPwEXAV9Py04Azar2TZmZmVu8U21w10jBJahcRayW1AV4BLo2ICVWV21l58bWZWcNx1Pffqs6Sjxr7xouP77Tv9X8+7rQ6OWc7qrFMpQGMkrQ/yfqhvzXloMjMzJoGrzHKvUYTGEXEhfXdhkySrgbOLZf8QET8pj7aY2ZmZjuu0QRGDU0aADkIMjMz24l4jM7MzMws5cDIzMzMLOWpNDMzswbKi69zz4FRI1W0eVN9N8HMzGyn41DUzMzMLOXAyMzMzCzlqTQzM7MGymuMcs9n3MzMzCzlwMjMzMws5cDIzMzMLOU1RmZmZg1Ufr7HL3LNgZGZ1ZrOex3B3id+DymPRRMfZf4bd2+Tp9+J36drvyMp2ryR9574OWsXz0T5LRj8xb+Sl98C5eWzbOZ/+fCV2wHotu8J9D3mMtp025MJd3+BtYum57pbZtaEOBQ1s9qhPPqf/EOm3PdN3vnL2ewy8GTadNurTJYu/Y6iTZc+vH3bGcx66tf0P/nHAETRJibdeynj7zyP8XeeT5e9jqD9roMAWL/sfaY9+F0KPpqQ8y6ZWdPTpAMjSVdKmi5psqQXJO1R320ya6w67HoAG1bOZ+MnHxPFW1g6/Vm67jOiTJ6u+xzL4slPALBm4RSatWpPi3bdACjevAEA5TVD+c2AAGD9ig/YsPLDnPXDzJq2pj6V9i4wLCLWS/of4PfAefXcJrNGqUX7XShcs6R0u3D1EjrsdkCZPC3b70Lh6sVl8rRovwub1i4H5TH0q/+idefd+Xjc/axZODVnbTdrqHwfo9xrtGdcUltJT0qaJGmqpPMkzZN0naS308fead490hGhkpGhPgAR8VJErE+rfBPoXcnxRkh6WdJ/JM2SdK2ki9LjTJHUL83XXdJDkt5JH0em6cMlvS7p3fTngDT9YkkPS3pG0mxJv6/L82aWU1FuW8qSJ80UxYy/83zeuOkkOux6AG2696vz5pmZlddoAyPgZGBhRBwUEQcAz6TpqyNiOHAL8Kc07Rbg7xFxIPBP4KYs9X0VeLqKYx4EXA4MAr4A7JMe607gW2meG4EbIuIQ4Ox0H8BM4JiIOBj4GfDbjHoHk4xUDQLOk7R7toNLulTSOEnjHn9neRVNNcutTWuW0rJ9j9Ltlh16ULh2WZk8hauX0LJDzzJ5NpXLU1S4lk8+GkeXvY6o2wabmWXRmAOjKcAJ6QjR0RFRkKb/O+Pn4enzw4F/pc//ARyVWZGkzwPDgOurOOY7EbEoIgqB94HnMtrSN31+AnCLpInAaKCDpPZAR+ABSVOBG4CBGfW+EBEFEbERmA5kXesUEaMiYlhEDDvtkG5VNNUst1YvnEbrLn1o1XFXlNeMXfY/iRWzxpTJs2L2y/Q88DMAtN91EFsK17Jp7XKat+lMfst2AOQ1a0nnvoeyfsW8HPfAzKwRrzGKiFmShgKnAL+TVBKkZA7elx/I3yZd0gnA1cCxacBTmcz9xRnbxWw9l3nA4RGxIbOgpJuBlyLiLEl9gTEV1FtEI/69WBMWRcx59joGXXArystj8aTHWL98Lr2GnAPAogkPsnLOq3TpdxTD/3d0ern+NQC0aNeNAaf9EikPKY9lM55n5ZyxAHQdMJL+J/6A5m06M+hzN7F2yXtMue8b9dVLM9vJNdo/wJJ2BVZGxL2S1gIXp7vOA65Nf76Rpr0OnE8yWnQR8Gpax8HAX4CTI2JpLTXtOeCbpKNPkgZHxESSEaOP0zwXZy1p1sitfP9VVr7/apm0RRMeLLM959lrtym3bulsJvz1gqx1rnjvJVa891LtNdKsEfHi69xrtIERyXqc6yUVA5uB/wEeBFpKeotk5KbknfbbwF2SvgcsA76cpl8PtCOZ4gL4KCJO38F2fRv4s6TJJOf3FeAykive/ibpSuDFHTyGmZmZ1QFFVDTb1PhImkdy+f1OvzL55d8cvPP84szMGrljr343yyWXO+6n77y0077X/+qQkXVyznaUx+jMzMzMUo15Km0bEdF3R+uQNIhkLVKmwog4dEfrNjMzqwmvMcq9nSowqg0RMYXkvkJmZmbWxDgUNTMzM0s5MDIzMzNLeSrNzMysgcrzGqOcc2DUSBVt3lLfTTAzM9vpOBQ1MzMzSzkwMjMzM0t5Ks3MzKyByleDvDn0Ts0jRmZmZmYpB0ZmZmZmKQdGZmZmZikHRmZmZmYpL742MzNroPLl8Ytcc2BkZrWmy95H0v/kH6C8fBZNeJgPX/3rNnn6f/qHdO1/NMWbNzL90Z+wdtEMWnbowf5n/ZYW7boRUczC8Q+y4K1/ArDnyG/Sfd+RRBSzed1Kpj/6EzatWZbrrplZE+FQtJZJulPS/lXkuUfSOVnS+0q6sO5aZ1aHlMeAU65m0j//l7f+fAa7HPBp2nTfq0yWrv2Ppk2XPXjzplOZ+fgvGHDqTwCI4iJmP/cH3vrzGYy/8yJ6Dz+/tOxHr9/N27edzTu3n8vyWS+z57GX5bxrZtZ0ODCqZRFxSURM387ifQEHRtYoddhtEOtXfsTGVQuIoi0snfo03QeMLJOn24CRLJ40GoDVCybTrFV7WrTrxqa1y1m7aAYARZvWs27ZB7Rs3yPZLlxXWj6/eWsiIkc9MrOmyIFRBSR9X9K30+c3SHoxfX68pHslnSjpDUkTJD0gqV26f4ykYenzr0qalabdIemWjEMcI+l1SXMzRo+uBY6WNFHSd3LYXbMd1rLDLhSuXly6Xbh6CS079Ngmz8Zt8uxSJk+rTrvSvte+rP54cmnaXsd9iyO+8zw9DjyVD176cx31wKzhyZd22kdD5cCoYq8AR6fPhwHtJDUHjgKmAD8BToiIIcA44MrMwpJ2BX4KHAZ8Cti3XP290ro+QxIQAfwQGBsRgyPihvINknSppHGSxj0xfmUtdNGsNm37Rrft6E6WN8OMLPktWnPA525g9jPXlRkpmvvizbx+w6dYMvlJeg+/oJbaa2a2LQdGFRsPDJXUHigE3iAJkI4GNgD7A69Jmgh8CdijXPnhwMsRsTIiNgMPlNv/aEQUp9NuPaiGiBgVEcMiYthnhnbZ3n6Z1Ylk9Kdn6XbLDj3YtGbpNnlalctTmOZRXjMO+NwNLJnyJMtmvJD1GEumPEX3/U+og9abmSUcGFUgDWbmAV8GXgfGAiOBfsAHwPPpyM7giNg/Ir5aroqqxgkLa5DXrMFbs3AqbbruQatOu6H8ZuxywKdZ/t6YMnmWv/cSPQ86HYAOvQ+kqHAtm9YuB2DfM37B+uVzmf/G38uUad2lT+nzbgNGsn75B3XbETNr0ny5fuVeAa4CvkIyffZHkpGkN4E/S9o7IuZIagP0johZGWXfBm6Q1BlYA5yd1lGZNUD7Wu6DWU5EcRGznvotg79wO1I+C999hHXL3mfXYecCsHDcA6yYPZau/Y/h8G8/RdHmjcx4LLkqrWOfg+l10OmsXTKLQy5LBlfnvnATK2aPpd8JV9CmW1+IYOMnC5n5xK/qq4tmOdeQ1+LsrBwYVW4scDXwRkSsk7SRZA3QMkkXA/+W1DLN+xOgNDCKiI8l/RZ4C1gITAcKqjjeZGCLpEnAPdnWGZk1ZCtmj2XF7LFl0haOKzuLPOup32xTruCjd3nxmkFZ65z6nyuzppuZ1QUHRpWIiBeA5hnb+2Q8fxE4JEuZERmb/4qIUZKaAY8Az6V5Li5Xpl36czNwfO31wMzMzGrCa4zq1jXp4uypJOuSHq3X1piZmVmlPGJUhyLiqvpug5mZNV55/q60nPMZNzMzM0s5MDIzMzNLOTAyMzMzSzkwMjMzM0t58XUjVVxcXN9NMDOzOtaUb/Ao6WTgRiAfuDMirs2SZwTwJ5Jb6yyPiGN39LgOjMzMzKxBkZQP/JnkS9gXAO9IGp1+v2hJnk7ArcDJEfGRpF1q49ieSjMzM7OGZjgwJyLmRsQm4D7gjHJ5LgQejoiPACJiKbXAgZGZmZnlnKRLJY3LeFyasXs3YH7G9oI0LdM+QGdJYySNl/TF2miXp9LMzMwaqPy8nXf8IiJGAaMq2J1tcVWU224GDCX5Kq3WwBuS3iz3he415sDIzMzMGpoFwO4Z271JvpC9fJ7lEbEOWCfpFeAgMr7QfXvsvKGomZmZNVbvAP0l7SmpBXA+MLpcnseAoyU1k9QGOBSYsaMH9oiRmZmZNSgRsUXSN4FnSS7Xvysipkm6LN1/e0TMkPQMMBkoJrmkf+qOHtuBkZmZWQPVlO9jFBFPAU+VS7u93Pb1wPW1eVwHRmZWa7rufRT7nPJDpHw+nvAQH469c5s8+5zyI7r1P4aizRuY/sjVrFmUjHzvf+av6LbPsWxat5I3/3xmaf52Pfdlv9N+Rl6zlkTxFmY+8WtWfzwlV10ysybGa4zMrHYojwGfuZqJ/7iMN245nZ6DTqFt935lsnTtfzRtuu7B6zd+mhmjr2Hf035Wum/hu4/y7j++vk21/U+8krljbuWt287m/Rdvof+JV9Z1T8ysCWuSgZGkvpJ2eB5yB9twuqQf1mcbzGpTx96D2LByPhtWLSCKNrNkylN033dkmTzd9z2ORROT9ZOrF0ymWav2tGjXDYBPPhzP5g0FWetu1rJd8rNVewrXLKvDXphZU+eptHoSEaPZdoW9WaPVsn0PNhYsKt3euHoJHXsfWDZPh13YWLC4dLtw9RJadujBprXLK6z3vaeuZcgXR9H/pKtAeYy746Lab7xZA5XXhNcY1ZcmOWKUaibpb5ImS3pQUhtJ8yR1A5A0TNKY9Pk1ku5K7645V9K3K6o0HY2aKelOSVMl/VPSCZJekzRb0vA038WSbkmf3yPpJkmvp/WfU0HdpXcJfXLCqlo/IWY7JOvt2Mrej03Z3uSj/D3byuo9/DxmPXMdr/7fCcx6+jr2O/NXO9BIM7PKNeXAaAAwKiIOBFYD/1tF/n2Bk0i+v+XnkppXkndvkm8EPjAtdyFwFHAV8OMKyvRK83wG2OYbhCG5S2hEDIuIYacO6VxFc81yq3D1Elp17FW63apDDwrXlP3qoo0FS2jVsWfpdsssecrbdfAZLJ3+PABLpz1Lx90G1WKrzczKasqB0fyIeC19fi9JUFKZJyOiMCKWA0uBHpXk/SAipkREMTANeCEiApgC9K2gzKMRUZx+c3BldZs1SKs/nkrrLn1o1Wk3lN+cHoNOYdnMl8rkWfbeS/QafDoAHXofyJaNayudRgMoXLOUzn0PAaDzXoeyfuWHddMBMzOa9hqj8uP3AWxha7DYqtz+woznRVR+7jLzFmdsF1dSLrOMJ5Wt0YniIt578jcc/MVRKC+PhRMeYd2y99lt2OcA+Hjcf1gx6xW69T+GI654muLNG5n2yE9Kyx9wzvV03vMQmrfpxFHffYG5L/2ZhRMeZvpj1zDglB+ivGYUbylkxmPX1FMPzawpaMqBUR9Jh0fEG8AFwKtAe5IvpHsaOLs+G2fWGK2YPZY3Zo8tk/bxuP+U2X7vyV9nLTv1we9lTS/4aAJv3/652mmgWSOTr6Y8sVM/mvIZnwF8SdJkoAtwG/AL4EZJY0lGhczMzKwJUVRxRYg1TP/92UD/4szMGogTfjmtTpZA3DVr0k77Xv+VfQ5qkMtGmvKIkZmZmVkZTXmN0Q6R1BV4Icuu4yNiRa7bY2ZmO5+m/CWy9cWB0XZKg5/B9d0OMzMzqz2eSjMzMzNLOTAyMzMzS3kqrZEqLt5pL1QwM7OU72OUez7jZmZmZikHRmZmZmYpB0ZmZmZmKa8xMjMza6B8H6Pc84iRmZmZWcqBkZmZmVnKgZGZmZlZyoGRmZmZWcqLr82s1nTtfxT7nvIjlJfPgvEPMu+VO7fJM+DUH9N9n2Mo2ryBqQ/9mDWLZgAw8Kxf033AsWxat5LXbz6jNH+PgSfR77hv0Lb7Xrx1+3msXjgtZ/0xq295Xnydcx4xyhFJ90g6p77bYVZnlMd+p/2ECX//Oq/ddBq9Bp1C2+79ymTpts8xtO26B6/ecDLTH/05+5/+89J9C999hPF/u3Sbatcunc3Ef3+bVR+Oq/MumJk5MGqgJHk0zxqVjr0HsX7FR2xYtYAo2sziKU+zy37HlcnTfb/jWDjxMQAKFkymWav2tGjXDYBV88azeUPBNvWuWzaX9cvn1Xn7zczAgVFWkn4qaaak5yX9W9JVkvpJekbSeEljJe2b5r1H0k2SXpc0t2RUSIlbJE2X9CSwS0b9QyW9nNb1rKReafoYSb+V9DJweX303Wx7terQg40Fi0u3N65eTMsOu5TN036XcnmW0KpDj5y10cysKh6VKEfSMOBs4GCS8zMBGA+MAi6LiNmSDgVuBUo+DvcCjgL2BUYDDwJnAQOAQUAPYDpwl6TmwM3AGRGxTNJ5wG+Ar6R1dYqIY+u8o2a1LstaiPLfdZxlvURsk8nMSuTnefwi1xwYbeso4LGI2AAg6XGgFXAE8IC2vrG3zCjzaEQUA9MllXz8PQb4d0QUAQslvZimDwAOAJ5P68oHFmXUdX9FDZN0KXApwOWf7skpQzpvdyfNatvG1Ytp1bFn6XarDj0pXLO0XJ4l5fL0oHB12TxmZvXJoei2sl0CkAd8EhGDMx77ZewvrKB8to/CAqZl1DMoIk7M2L+uooZFxKiIGBYRwxwUWUOz+uOptOm6B60774bym9Nz0KdZOvOlMnmWzXiRXQcnV5x17H0gWwrXsGnt8vporplZVg6MtvUqcJqkVpLaAacC64EPJJ0LpeuHDqqinleA8yXlp2uIRqbp7wHdJR2e1tVc0sA66YlZDkVxETOf+A1DvnQHR17+OIunPsu6pXPofch59D7kPACWz3qF9Svnc9SVz7D/mb9kxuhflZYf9LnrOfTr/6ZNt74c870X2W3oZwHYZb/jOeZ7L9Jp98Ec/MXbGPKlUfXSPzNrGjyVVk5EvCNpNDAJ+BAYBxQAFwG3SfoJ0By4L81TkUdI1iBNAWYBL6f1b0oXaN8kqSPJ7+BPgG/OYo3e8lmvsHzWK2XSFrxTdnZ45hO/zlp2yn++lzV96YwXWDrjhdppoFkj4y+RzT0HRtn9ISKukdSGZOTn/yLiA+Dk8hkj4uJy2+3SnwF8M1vlETGRZA1S+fQRO9pwMzMz234OjLIbJWl/kkXXf4uICfXdIDMzM6t7DoyyiIgL67sNZmZmlnsOjMzMzBqofPkaqVzzGTczMzNLOTAyMzMzSzkwMjMzM0s5MDIzMzNLefF1I7WlqKi+m2BmZnXMN3jMPY8YmZmZmaUcGJmZmZmlHBiZmZmZpbzGyMzMrIHK8w0ec85n3MzMzCzlwMjMzMws5cDIzMzMLOU1RmZWa7rtczT7f+ZqlJfH/HceYO7Ld2yTZ//Trqb7gGMp2rSRyQ/+kNULp1er7J5Hf4X9TvkBz//qMDavX5WT/pjVN9/HKPc8YmRmtUN5DDz9Z7xz9yW8csOp7HrQZ2i3S78yWboPOIY2Xfvy8h9OZOojP+WAM6+pVtlWHXvSbe8j2LDq4xx2yMyaIgdG5Uham6PjXCbpi7k4llkudNr9QNav+JANqxYQRZtZNOlJeux3fJk8PfY7no/ffRSAT+ZPolmrDrRs373Ksvud+iNmPn09QeSyS2bWBHkqrZ5ExO313Qaz2tSqQw82Fiwu3d6wegmddj+wbJ6OPdj4ydY8GwsW06pDj0rL7rLfcWxcvZQ1i9+r4x6YmTXBwEjS94GNEXGTpBuAgyLiOEnHA19O8/wfMBJYBZwfEcsk7Q3cDnQHioBzI+L9LPWPAH4BLAEGAw8DU4DLgdbAmRHxvqRrgLUR8QdJY4C30mN2Ar4aEWPr5ASY1ZksayGi/AjPtnkiosKyec1bsffIy3j7r1+plRaaNTZeY5R7TXEq7RXg6PT5MKCdpObAUcBYoC0wISKGAC8DP0/z/hP4c0QcBBwBLKrkGAeRBEKDgC8A+0TEcOBO4FsVlGmW5rki45hlSLpU0jhJ456e+Ek1umqWOxtXL6ZVx56l26079KBw9dKyeQoW06rT1jytOvakcM3SCsu27dKH1p17c9TljzHi+y/QqkNPjvrWw7Ro163uO2RmTVJTDIzGA0MltQcKgTdIAqSjSQKjYuD+NO+9wFFp3t0i4hGAiNgYEesrOcY7EbEoIgqB94Hn0vQpQN8Kyjyc0b6seSJiVEQMi4hhnx7cqap+muVUwYIptO3Wl9ade6P85vQ66FSWzHixTJ4lM15kt4PPBKDT7gexZeMaCtcsq7DsmiWzeOE3RzDm98cz5vfHs3H1Yl69+bNsWru8HnpoZk1Bk5tKi4jNkuaRTJu9DkwmmcLqB8zIVoSs4/yVKsx4XpyxXUzF57wkT1ElecwarCguYtroXzL8K3eC8lkw7iHWLp1Dn+HnA/DR2/ex7L2X2WXAsRx71fMUb97A5Ad/XGlZM7Nca6p/gF8BrgK+QjKK80dgfESEpDzgHOA+4ELg1YhYLWmBpDMj4lFJLYH8KkaNzJqcZe+9wsvvvVIm7aO37yuzPW30L6tdtrwxvz++0v1mZjuqqQZGY4GrgTciYp2kjWkawDpgoKTxQAFwXpr+BeAvkn4JbAbOBebmttlmZtaU5Oc1xRUv9UuxzVUj1hg89aMB/sWZmTUQp/zuvTq5fGzM4o922vf6ET37NMhL7hyKmpmZmaWa6lTaDpM0CPhHueTCiDi0PtpjZmZmO86B0XaKiCkkN3A0MzOrE3m+wWPOeSrNzMzMLOXAyMzMzCzlwMjMzMws5TVGjVRx8U57BaeZmaXy5fGLXPMZNzMzM0s5MDIzMzNLOTAyMzMzS3mNkZmZWQOV7/sY5ZxHjMzMzMxSDozMzMzMUg6MzMzMzFIOjMzMzMxSXnxtZjts4Ok/YZcBx1K0eQMT//NDVi+cvk2e1p17M+TCG2jRpiMFH0/n3fu/RxRtrrq88jj6Ww+zcfUS3rnn6wB06LUfgz77C/KatSSKtzD1kV/wyYLJOemrWS7l4cXXueYRIzPbIbsMOJa23fry0vWfYvLDP2XQWb/Imm+/U67ig1fv4aXrT2TzhgL6HHJOtcrvedSXWLv0/XJ1fY9Z/72FsTeewaznbmK/U75XN50zsyanwQdGkq6Q1KYW6xsm6abaqm8H2nGnpP3rux1mO6rHwONZMP4RAD75aBLNW7enZfvu2+Tr1u9wFk15BoD54x+hx8ATqizfqmMPeuw7go/eeaBMXUHQrGU7AJq1asfG1UvrpnNm1uQ0hqm0K4B7gfW1UVlEjAPG1UZdO9iOS+q7DWa1oVWHHmwoWFy6vbFgCa069KBwzbLStOZtOrN5w2qiuCjNs5hWHXpUWX7gaVcz46nf06xl2zLHnP74bzn0q39l/1N/gJTHa7eeV5ddNLMmJOcjRpL6Spop6W+SJkt6UFIbScdLelfSFEl3SWop6dvArsBLkl5Ky98maZykaZJ+kVHvtZKmp3X+IU07V9JUSZMkvZKmjZD0RPr8GklXZdQxNW1fSRvvTNP+KekESa9Jmi1peCX9uybt23OS5kn6rKTfp/16RlLzNN8YScPS52sl/SZt55uSelRQ96Vp38c9M7FgR38VZrUk2xqIsl9ynPUedVGSJ3v5XfYdQeHaFRR8PG2bvXscdgHTHv8tL/zuWKY98VsOPOe3NW20WaOQp5330VDV11TaAGBURBwIrAauBO4BzouIQSQjWf8TETcBC4GRETEyLXt1RAwDDgSOlXSgpC7AWcDAtM5fp3l/BpwUEQcBp9ewjXsDN6bH2Re4EDgKuAr4cRVl+wGnAmeQjHa9lPZrQ5peXlvgzbSdrwBfy1ZpRIyKiGERMezkwR1r2B2z2rPH4Rdx9OWPcfTlj1G4ZimtO/Ys3deqY49tprY2rVtF89YdUF5+mqcnG9ckeTauXpy1fJe+Q+mx//Ec94MXOfjCG+jW7zAGn3c9AL2HnsXiqc8BsGjy03Ta/cA67a+ZNR31FRjNj4jX0uf3AscDH0TErDTtb8AxFZT9nKQJwLvAQGB/kuBqI3CnpM+yddrtNeAeSV8D8mvYxg8iYkpEFAPTgBciIoApQN8qyj4dEZvTvPnAM2l6RWU3AU+kz8dXo36zevXhG/9k7I1nMPbGM1g87b/0HnoWAJ36HMSWjWvLTKOVWP7+m/QadDIAuw89iyXTXgBgyfQXs5af+cz/8cJvj+HF647j3X99h+Xvv8nE+5NF1htXL6XrXsnAbdd+h7Nu+by67rKZNRH1tcYoqs6yLUl7kozYHBIRqyTdA7SKiC3p9NbxwPnAN4HjIuIySYeSjNJMlDS4XJVbKBsctsp4XpjxvDhju5iqz1shQEQUS9qcBlSVlc3MU1SN+s0ajKUzx7DLgGMZ+f3/UrRpA5Me+FHpvuFfvoNJD15N4ZqlzHz6Dwy58AYGnHgFBQunMz9dUF1Z+YpMfugnDDztavLymlG0pZApD/+0zvpnZk1Lff0B7iPp8Ih4A7gA+C/wdUl7R8Qc4AvAy2neNUB7YDnQAVgHFKTrcD4NjJHUDmgTEU9JehOYAyCpX0S8Bbwl6TRg93LtmAd8Js07BNizznpsthOb+lj2S/TfvnvrrPD6lfN59ZZzalS+xIq5b7Ni7tul26vmjefVmz+7HS01a1zk+xjlXH0FRjOAL0n6CzAbuBx4E3hAUjPgHeD2NO8o4GlJiyJipKR3Saa25pJMlUESOD0mqRXJSs7vpOnXS+qfpr0ATAKOzWjHQ8AXJU1MjzkLMzMza7K0dQYnRweU+gJPRMQBOT3wTuaJH+yT21+cmZlV6DPXzaqToZ3xyxfvtO/1Q7v1bJDDYQ3+Bo9mZmZmuZLzqbSImAc0+tEiSV8mmQLM9FpEfKM+2mNmZjufvKw3AbO65KuftlNE3A3cXd/tMDMzs9rjqTQzMzOzlAMjMzMzs5Sn0hqp4uLi+m6CmZnZTseBkZmZWQPlaZ3c8zk3MzMzSzkwMjMzM0s5MDIzMzNLeY2RmZlZA+UbPOaeR4zMzMzMUg6MzMzMzFIOjMzMzMxSXmNkZjvsgDN+So99R1C0eQPv3v8DCj6etk2eNp17M/Tzf6J5604UfDyNCfddRRRtrrR8s1btGXzu72jfsz9EMPGBH7Hqw3cZetGNtNtlTwCat+rA5o2refmG03PXYbMc8ehF7jkwMrMdssu+x9K2W19euO54OvcZzIGf/QVjbz5nm3z7nfp93n/lbhZOepIDP/tL9hh+LvPe+Fel5Qed8VOWvvcK4/7xTZTfnPzmrQAY/8/LS+sd+JkfsXnjmtx01sx2eg5GAUlr67sNZo1Vz4EnsGD8IwCs+mgizVt1oGX77tvk67b3YSya8gwA88c/Qs+Bn6q0fLOW7eiy1yF89PZ/AIiizWzJEgDtetApfDzx8Trpm5k1PR4xMrMd0qpDDzZ8sqh0e0PBYlp17EHhmmWlaS3adGbLhjVEcVGS55MkT2Xlo7iITWtXMvi86+jYaz8+WTCVqY/9iqLNG0rzdtnzEArXLGfd8g/ruptm1kQ0iREjSd+X9O30+Q2SXkyfHy/p3vT5/0maIOkFSd3TtL0l/VfSpHRfvwrqHyHpZUn/kTRL0rWSLpL0tqQpJeUknSbpLUnvpvX2SNNvkvSz9PlJkl6RtM3vRtKlksZJGvfspNV1carMakzZ7rMSUT5ThXkqKq+8fDruNpB5r/+Ll/90OkWb1rP3cV8vk633wZ/h44lPbG/TzRo87cT/GqomERgBrwBHp8+HAe0kNQeOAsYCbYEJETEEeBn4eZr3n8CfI+Ig4AhgERU7CLgcGAR8AdgnIoYDdwLfSvO8ChwWEQcD9wHfT9N/CJwnaSRwE/DliCguf4CIGBURwyJi2EkHdajpOTCrNX2P+DzHfmc0x35nNBtXL6V1p16l+1p37MnG1UvL5N+0biXNWrdHeflJnk5b82woWJy1/MaCxWwsWMwn8ycBsHDKM3TabWBpPuXl0+uAk/h40pN11k8za3qaSmA0HhgqqT1QCLxBEiAdTRIYFQP3p3nvBY5K8+4WEY8ARMTGiFhfyTHeiYhFEVEIvA88l6ZPAfqmz3sDz0qaAnwPGJjWvR74GvA8cEtEvL/jXTarO/Nev5eXbzidl284nUVTn6f30LMA6NxnMJs3rikzjVZixZy36DXoZAB2H3oWi6f9F4DF017IWr5wzXI2fLKItt2Tq8+6730Ea5bMKa2vW/8jWbN0LhsLFtdpX82saWkSa4wiYrOkecCXgdeBycBIoB8wI1sRqPE4X2HG8+KM7WK2nuebgT9GxGhJI4BrMsoMAlYAu9bwuGb1aunMMfTYbwTH//BFijZt4N3//KB036FfuZOJD/6YwtVLmf7U7xl60Z/Y7+QrKfh4Oh+9/UCV5ac89kuGXvBH8po1Z92K+UzM2Lfb4FO96NrMal2TCIxSrwBXAV8hGcX5IzA+IiJdz3MOyfTWhcCrEbFa0gJJZ0bEo5JaAvlVjBpVpSPwcfr8SyWJkvYAvgscDDwl6dGIeGsHjmOWU1MeuSZr+lt3XVL6fP3K+Yy9+ewalV+9cAav3HRW1n0T7/9B1nQzsx3RlAKjscDVwBsRsU7SxjQNYB0wUNJ4oAA4L03/AvAXSb8ENgPnAnN3oA3XAA9I+hh4E9hTycrTvwJXRcRCSV8F7pF0SERs3IFjmZlZI+cvkc09RfmrR6xRGP29vf2LMzNrIE6/fk6dRDAzVy3fad/r9+3crUFGfU1l8bWZmZlZlZrSVNoOkzQI+Ee55MKIOLQ+2mNmZma1y4FRDUTEFGBwfbfDzMyaBk/r5J7PuZmZmVnKgZGZmZlZyoGRmZmZWcprjBqp4uKd9gpOMzNL+T5GuecRIzMzM7OUAyMzMzOzlAMjMzMzs5TXGJmZmTVQeXiNUa55xMjMzMws5cDIzMzMLOXAyMzMzCzlNUZmVqsGnfkzeuw3gqJNG5hw3/cp+HjaNnnadOnNsM/fSIs2nfjk42mM/9d3iaLNtNtlL4acdx0dew9kxtN/ZM6YOwHIa9aCo79xH3nNWqC8fBZOfoaZz96Y666ZWRPgwMjMak2PfUfQrltf/vu74+jcZzAHnf1LXrnp7G3yDTz1+7z/yt18PPEJDjr7V+wx/FzmvfEvNq0vYPKjv6TXASeWyV+8ZROv3vZ5ijatR3nNOPqb97Nkxsus+mhijnpmVj98f8fcq9WpNEl9JU2tzTrTegdLOqWW6uor6cLaqMvMyup5wAl8NP4RAFZ9NJHmrTvQsn33bfJ16384Cyc/DcBH4x6m16BPAbBp7Qo+mT+FKNq8TZmiTesByMtvRl5+M8B3fzez2tfg1xhJagYMBqodGKVlKtIXcGBkVgdad+zBhk8Wlm5vLFhM6449y+Rp0bYzmzesIYqLtubpUDZPVspj5JWP8+lfvM3SWa+x6qNJtdp2MzOom8AoX9IdkqZJek5Sa0n9JD0jabyksZL2BZB0mqS3JL0r6b+SeqTp10gaJek54O/AL4HzJE2UdF62g5Yvk44MjZU0IX0ckWa9Fjg6res7kvIlXS/pHUmTJX29oo5JGiHpZUn/kTRL0rWSLpL0tqQpkvql+bpLeiit8x1JR6bpwyW9nvb3dUkD0vSLJT2cnqPZkn5fO78Ks1zbdtw/thnZqU6eLKKYl/54Gs/+8kg69zmI9j332c42mplVrC7WGPUHLoiIr0n6D3A28GXgsoiYLelQ4FbgOOBV4LCICEmXAN8HvpvWMxQ4KiI2SLoYGBYR36zi2Jll2gCfioiNkvoD/waGAT8EroqIzwBIuhQoiIhDJLUEXpP0XER8UMExDgL2A1YCc4E7I2K4pMuBbwFXADcCN0TEq5L6AM+mZWYCx0TEFkknAL9Nzw8ko2IHA4XAe5Jujoj5mQdO23opwP+c0I0TD+xQxekwq3t7Hvl5+h6afF5ZNX8KrTvtCowHoFXHnmwsWFIm/6Z1K2neuj3KyyeKi5I8q5eUr7ZCmzeuYfn7b9Jj32NYs3hWrfXDrCHyDR5zry4Cow8iYmL6fDzJ1NURwAPauoqsZfqzN3C/pF5ACyAzGBkdERtqeOzMMs2BWyQNBoqAij5enggcKOmcdLsjSXBXUWD0TkQsApD0PvBcmj4FGJk+PwHYP6O/HSS1T+v+WxqoRdrGEi9EREFa73RgD6BMYBQRo4BRAI9+t58XWFiD8MFr9/LBa/cC0GO/Eex15Bf5+N3H6dxnMFs2rqFwzbJtyiyf8ya7HvhpPp74BH2GfZbFU/9b6TFatO1CFG1m88Y15DVrSff+RzL7xb/USX/MrGmri8CoMON5EdAD+CQiBmfJezPwx4gYLWkEcE3GvnXbcezMMt8BlpCM8OQBGysoI+BbEfFsNY+R2b/ijO1itp7PPODw8oGdpJvh/9m77/gqy/OP458vIHsLslRQHKiIKAiKiOLCPatWraMO3NbV2qpVq3W3P6vWhVZRa1Fx4MCBA0QRkCEQEBVlyRDZMwRIrt8f5yGGJAwlOTkk3/frlVeecz/Xc4+DJlfu+36ew8CIOElSK2DQeurNxXcM2hZozsRBNNntYA7/y8esWb2SL1+8If/cfhf+hzEv/4WVS35iwtv3se/ZD7LbUdeyeOYEpg3vC0C1Oo04+Op+VKleGyJofeB5fHTfkVSv25h9zrgfqTJSJWaO7c+ciQPLaphmVo6l45fvEmCKpFMjoq9S0yjtImIsqRmUmUncuRuoYylQ5xe2Ww+YERF5ks4FKq+nrveBSyV9HBGrJe0CzIyIX5OYrTUAuAK4H1J31SWzaAXHe95m1G+Wsca9dlux5cOeuiD/eMWCH/jkwZOLxOQsncf7d3QtUr5k9jcM+r/jS6yPZmbrk6670s4CLpA0FpgAnJCU30Zqie1TYN4Grh9IamlqvZuvi/EocK6kYaSW0dYmOuOANZLGSroGeAr4ChidPGrgCTY/YbwK6Jhs5v4KuCQpvw+4W9IQfk7UzMzMilWpHH9lKkV4q8qWyHuMzMwyx4n//L5Udkn/sHhhuf1Zv129Bhm5szyTkzYzMzOztNriNvhK+j3wh0LFQyLi8hJsY0/g+ULFORHRuaTaMDMzs8yzxSVGEfEM8Ewpt5FF6rlCZmZmZaaSPywt7byUZmZmZpZwYmRmZmaWcGJkZmZmltji9hhZSl5eub2D08zMrMw4MTIzM8tQ/hDZ9PNSmpmZmVnCiZGZmZlZwomRmZmZWcJ7jMzMzDKUn++Yfp4xMjMzM0s4MTIzMzNLODEyMzMzS3iPkZmVqHYn3ULT3Q4md/VKRvX5I4tmTCgSU7PhtnQ65yGq1qzPohnjGfHCdUTuampvsyMdzriP+tvuwVf9/8mkQU/lX7NV9Trs89t7qNt0F4JgdJ8bWDDty3QOzSzt/Byj9CuxGSNJrSSNL6b8KUm7l0D9B0t6e3Pr2YQ2upRmG2blWZPdDqZ241YMuOsQRr98I+1/c0excW2Pu4HvPnmaAXcdwqrsJbTqfBoAq1csZtxrtzNp4FNFrml38i3MmfgJH9xzOB/dfwxL53xXqmMxs4qp1JfSIuLCiPhqc+qQlK6ZrYMBJ0Zmv1LztocxfcTrACycNoatatSlet3GReIa77Q/M8e+C8D0L16l+Z6HA5CzbD4LfxhH5K5ZJ75Ktdo02rETU4e/DEDkrmb1yqWlORQzq6BKOjGqIulZSeMkvSKppqRBkjoCSDpS0mhJYyV9lJR1kvS5pC+T77sm5edJ6ivpLWBAUn9dSa9L+krS45IqJbFnSMqSNF7SvWs7I2mZpDuT9oZJapKUHydpeNLmh5KaSGoFXAJcI2mMpAMlNZb0qqQRydcB6xu4pNuSsQ+QNFXSyZLuS/r1nqStkrhbkrrGS+qllCpJ2cFJzN2S7izRfxmzNKherynZi2bnv85e9CPV6zVdJ6ZqrQaszl5C5OWmYhb/SPV6TTZYb62ttyNn2QI6nHEfh1z3FvucfjeVq9Yo+QGYWYVX0onRrkCviGgHLAEuW3tCUmPgSeCUiNgLODU59TXQLSL2Bm4B7ipQ3/7AuRFxSPK6E3AdsCfQGjhZUnPgXuAQoD2wr6QTk/hawLCkvcHARUn5Z8B+SZsvAn+KiKnA48ADEdE+Ij4FHkxe7wucAhSd319Xa+AY4ATgv8DAiNgTyE7KAf4dEftGRFugBnBsRKwBzgMek3Q4cCTwt8KVS+opaaSkkQOylmykK2bpp+IeuhKFP/C4uJiN1Fu5CvW33YPJQ17g438ex5pVK9j10Et+dT/NthSVpHL7lalKeonqh4gYkhz/F7iqwLn9gMERMQUgIhYk5fWAZyXtTOrH41YFrvmgQBzAFxExGUBSH6ArsBoYFBFzk/IXgG5AP2AVsHZf0ijg8OR4W+AlSc2AqsCU9YznMGD3Aj/s60qqExHrm8N/NyJWS8oCKgPvJeVZQKvkuLukPwE1gYbABOCtiJgg6XngLWD/iFhVuPKI6AX0Anjtmh038qvELD12POBsWu1/OgALp4+jRv1m+edq1G/KyiVz1olftXwBW9WoiypVJvJyqVGvaExh2Ytmk734RxZOHwvAzLHvOTEys1JR0jNGhX9ZF3ytYs4D3EFqZqUtcBxQvcC55ZtQ/4bSztUR+X+u5vJzIvgwqZmbPYGLC7VZUCVSSUr75KvFBpIigByAiMgr1HYeqWXG6sCjwG+Stp8s1PaewCJgw+sKZhlk8pDn+fgfx/LxP45l9vgP2H7fkwBo0LI9q7OXsnLJ3CLXzP1uGC32OgqA7TudwuzxH26wjZyl88heNJvajXcAYJudu7Dkx0klPBIzs5JPjLaXtH9yfAapJau1hgIHSdoBQFLDpLweMDM5Pm8j9XeStEOyt+j0pP7hSb2NJFVO2v1kI/UUbPPcAuVLgToFXg8Arlj7QlL7jdS7MWuToHmSagO/KVD3ycDWpGa7HpJUfzPbMku7H78ayPL5P3DETQPZ57S7GfPqLfnnulz0NNXrbgPA+LfvZaeDLuCIGz+mas36TB2W2lRdrU4jjrp1CDsdfD67HnEFR906hCrVagMw9tXb2Pfsf3HoH9+hXovd+ObDR9M/QDMr90p6KW0icK6kJ4BJwGOkZoGIiLmSegKvJYnNT6SWtu4jtZR2LfDxRuofCtxDamZlMPB6RORJ+gswkNTs0TsR8cZG6rkN6CtpJjAM2CEpfwt4RdIJwJWklgIfkTSO1Hs1mNQG7V8lIhZJepLU0tpUYASApEbJuA6NiB8k/ZvU/qZz11eXWaYa++qtxZZ//uT5+ccr5v/AoH+dVCQmZ+k83v1b8fc4LJ41kYH/d0LJdNLMbD0URTZG2pbAe4zMzDLHyQ9MLpXdxAuWLS23P+sb1q6TkTuw/ZEgZmZmZgl/JMgvJOn3wB8KFQ+JiMvLoj9mZmZWcpwY/UIR8QzwTFn3w8zMzEqeEyMzM7MM5Q+RTT/vMTIzMzNLODEyMzMzS3gpbQuVl5dX1l0wMzMrdzxjZGZmlqGk8vu18bHrSEnfSPpO0p83ELevpFxJv1lfzC/hxMjMzMwySvIRX48ARwG7A2dI2n09cfcC75dU206MzMzMLNN0Ar6LiMkRsQp4ESjuM4GuBF4l9TFjJcKJkZmZmWWaFsAPBV7PSMrySWoBnAQ8XpINe/O1mZlZhirPzzFKPli+Z4GiXhHRa+3pYi4p/Llx/wJuiIhcbcqmpU3kxMjMzMzSLkmCeq3n9AxguwKvtwVmFYrpCLyYJEWNgKMlrYmIfpvTLydGZmZmlmlGADtL2gGYCfwWOLNgQETssPZYUm/g7c1NisCJkZmZmWWYiFgj6QpSd5tVBp6OiAmSLknOl+i+ooKcGJlZqdnr5Ftptnt31qzOZuQL17NoxoQiMTUbbst+5/6brWrVY9EPE/jiv9cQuavZrsMJ7HrYJQDk5qxg9Ms3s3jWxHQPwczKSES8A7xTqKzYhCgiziupdn1XmpmViqa7H0ydxjvw3t8PZvSLN7LPqXcWG7fn8X/m20H/4f2/d2dV9mJ22O90AFbM/4FPHjqdD+89ionvP0yH0+9OZ/fNMkIlqdx+ZapymxhJOljS28nx8Rt6aqaZlbzmbY9g2ojXAFgw7Uu2qlGH6nUbF4nbZucuzByb+qNw2hev0nzPIwCYP3U0q7OX5B/XqN80TT03s4qsQiylRcSbwJtl3Q+ziqRG/SasWPTzTSTZi3+kRr2mrFwyN7+saq0GrM5eQuTlpmIWzaZG/SZF6tphv9P5ceKgUu+zmVnGzxhJqiWpv6SxksZLOl3SoZK+lJQl6WlJ1ZLYIyV9Lekz4OQCdZwn6d/Jce+Cn6ciaVny/WBJn0h6WdK3ku6RdJakL5J2Wm+gj70lPSZpoKTJkg5K+jUx2Sm/Nu4xSSMlTZD0t6SsXvJZMLsmr/tIumg97fRMrh/5wfilm/O2mqVB0anyiMKPISlmOr1QTOOd9qfVfqeT9eY9Jdg3M7PibQkzRkcCsyLiGEglEsB44NCI+FbSc8Clkh4HngQOAb4DXvoVbe0F7AYsACYDT0VEJ0l/IPXY8as3cG2DpO3jgbeAA4ALgRGS2kfEGOCmiFiQfLbLR5LaRcS4ZOd9b0kPAg0i4sniGij4zIdX/tCq8G8YszLXuuvZ7LD/GQAsmD6WmvWbMz85l5otmrNO/KrlC9iqRl1UqTKRl0uN+s3IXvzzk/3rNW9DhzPu4bPHz2PVikVpGoVZ5lCRZxpaacv4GSMgCzhM0r2SDgRaAVMi4tvk/LNAN6BNUj4pUn+W/vdXtDUiImZHRA7wPTCgQB9abeTat5J2s4A5EZEVEXnAhALXniZpNPAlsAepD8YjIj5IrnuEVDJltkX6/rPn+fD+o/nw/qOZlTWAlvumJm4bttyb1SuXrrOMttbcSUNpsdfRALTsdAqzxqf+t6vRoDn7n/84I56/hmVzp6RvEGZWoWX8jFEyK9QBOBq4m5+TlWLDN6HKNSQJoVKPy6xa4FxOgeO8Aq/z2Ph7VTC2cD1VkodUXQ/sGxELkyW26kk/KpGaqcoGGpJ64qfZFu3HrwbSdPfuHPnXT8hdlc3I//0x/9wBFz/DqD43sHLJT2S9dQ+dz32Ytsdcx6IZE5g69GUAdu9xFVVrNWDvU/8OQF7eGj7+5/FlMhYzqzgyPjGS1BxYEBH/TfYDXQK0krRTRHwHnA18AnwN7CCpdUR8D5yxniqnAh2Al0l9Uu9WpT2GRF1gObBYUhPgKGBQcu4aYCJwI/C0pP0jYnWa+mVWasa8ckux5UOe+H3+8fL5P/Dx/51YJGbUi39m1Iu+mdTM0ivjEyNgT+B+SXnAauBSoB7QV1IVUo8NfzwicpIPpOsvaR7wGdC2mPqeBN6Q9AXwEalkpdRFxFhJX5JaWpsMDAGQtAup5bNOEbFU0mDgZuDWdPTLzMwyWOSVdQ8qHBW9S8S2BN58bWaWOX7z4NRSeWLhiuVLy+3P+pq16mTkUx63hM3XZmZmZmmxJSylZQxJNwGnFiruGxHFf9aBmZmZbVGcGP0CSQLkJMjMzNLEe4zSzUtpZmZmZgknRmZmZmYJL6VtoVatXlPWXTAzMyt3PGNkZmZmlvCMkZmZWabyAx7TzjNGZmZmZgknRmZmZmYJJ0ZmZmZmCe8xMjMzy1jeY5RunjEyMzMzSzgxMjMzM0t4Kc3MSlSHU2+n+R6HsGZ1NsOeu4aFP4wvElNr6+044PxHqVarPgt+yGJo7z+Ql7uaFu2OoN1xf4S8PPLy1jD6lduY+/0IALaqUZfOZ91Pvea7AsHw569j3pTRaR6dmZV3FS4xktQeaB4R75R1X8zKm+Z7HEKdbXbgrdu6snWrfdj3t3cz4P7jisS1P/FGvvn4SaaNepN9z7ibHbv8lu8+fZ4533zGu+MGAFC/xW4ccMFj9L/9YAA6nPo3Zn81iM+euphKlbeictUa6RyaWdnwc4zSLq1LaUop9TYlbSjhaw8cXdp9MKuIWrQ7ginDXwFg/tTRVK1Zl+p1tykS12TXA5j+ZX8Apgzry3Z79QBgTc6K/JgqVWsAkTquXpttdurM95/3ASAvdzWrs5eU5lDMrIIq9RkjSa2Ad4GBwP5AP0nHAtWA1yPiVkm1gJeBbYHKwB0R8ZKkqcBLQPekujMj4rv1tNMbWADsDYyW9BLwL6AGkA38HpgC3A7UkNQVuBt4G3gY2JPU+3FbRLyxnjbOA05M+tgW+CdQFTgbyAGOjogFkloDjwCNgRXARRHxtaTjgJuTa+YDZ0XEHEm3AdsDOybf/xURD2383TXLLDXrN2XFwln5r1csnE3N+k1ZueSn/LJqtRqwesUSIi83FbNoNjXqN80/v+1eR7LXCX+mep1GfPLoOQDUbrQ9OcsWsN/Z/0f9bXdnwfQsRvW9hdxV2WkamZlVFOmaMdoVeA64AWgBdCI1c9NBUjfgSGBWROwVEW2B9wpcuyQiOgH/JpXobMguwGERcR3wNdAtIvYGbgHuiohVyfFLEdE+Il4CbgI+joh9SSVg9yeJ2vq0Bc5MxnAnsCJpYyhwThLTC7gyIjoA1wOPJuWfAfsl8S8CfypQbxugR1LvrZK22shYzTKPVKQoIjYaQ4GQGWPfo//tBzP4iQtS+42ASpWq0GC7tkz69Hneu/tIcletYI8jLi/JnpuZAenbYzQtIoZJ+gdwBPBlUl4b2Bn4FPiHpHuBtyPi0wLX9inw/YGNtNM3InKT43rAs5J2JvVjd32JxhHA8ZKuT15XJzVrM3E98QMjYimwVNJi4K2kPAtoJ6k20AXoq59/AVRLvm8LvCSpGalZoykF6u0fETlAjqSfgCbAjIINS+oJ9AS44KD6HLL7hvI3s/TYudu57HTAmQDMnzaWmg2a55+r2aAZ2YvnrBOfs2wBW9WsiypVJvJyqVm/GdmLfyxS79zvhlO7UUuq1WrAikWzWbFoNvOnpn50TB/dn917ODGyisB7jNItXTNGy5PvAu5OZmvaR8ROEfGfiPgW6EAqubhb0i0Fro31HG+oHYA7SCUxbYHjSCU8xRFwSoE+bR8R60uKILVktlZegdd5pBLNSsCiAvW1j4jdkpiHgX9HxJ7AxYX6VLDeXIpJWiOiV0R0jIiOToosU0wa/Czv3t2Dd+/uwYxx77FD598AsHWrfVidvXSdZbS1fvr2c7bf+xgAdtjvVGYkG65rN26VH9Ngu7ZUqlKVnOULWblkLisWzqLONjsC0LRNVxbPnlTKIzOziijdd6W9D9wh6YWIWCapBbA66ceCiPivpGXAeQWuOR24J/k+9Be0VQ+YmRwXrG8pUKdQn66UdGVEhKS9I+JLfqWIWCJpiqRTI6KvUtNG7SJibKE+nftr2zDLVLPGf0zzPQ7huL99Ru6qlQx7/tr8cwdf9hzDX/gj2Yvn8OXrd9H1gkdpd9yfWDhjPN9//iIA27U/mh06n0LkriF39UqG/OfS/OtHvvxXuvz+YSpVqcqyedMY9tx1aR+fmZV/aU2MImKApN2Aocky0zLgd8BOpPb25JFKlC4tcFk1ScNJzcSc8Quau4/UUtq1wMcFygcCf5Y0htTm6ztI7V0alyQxU4Fjf/Hg1nUW8Jikm0kt4b0IjAVuI7XENhMYBuywme2YZZyRL91cbPmgR8/JP14+fzrv31f0f7OJHzzKxA8eLVIOsGjGV7x/7zEl00kzs/VQkY2RGSS5K61jRMwr675kmv9dtm3m/sOZmVUwZz46o5i7CjbfiqVzyu3P+pp1mpTKe7a5KtwDHs3MzLYYfsBj2mV0YhQRrQqXSboJOLVQcd+IuLOk2pXUA7i3UPGUiDippNowMzOzzJPRiVFxkgSoxJKg9bTxPqlN2WZmZlaBpPUjQczMzMwy2RY3Y2RmZlZxeI9RunnGyMzMzCzhxMjMzMws4aW0LVReXrl9tIWZmVmZcWJkZmaWqfwco7TzUpqZmZlZwomRmZmZWcKJkZmZmVnCe4zMzMwylvcYpZtnjMzMzMwSTozMzMzMEl5KM7MS1fG022nR9hDWrMpm6LPXsOCH8UViam29HQde+ChVazVgwfQsPn/mKvJyV7PtXkew13F/JCKIvDWMfPlW5n4/AoBdD7mAnQ84EyS+++x/fP3xU+kemplVAJ4xMrMS07ztIdTZZgfeuKUrw1+4gU5n3l1s3D4n38TEj57kzVu6smrFYlofcAYAP379Gf3/fjjv3HkEQ5+7jv3O/gcA9Zrvys4HnMm79xxD/78fTos9D6PONjukbVxmVnGkJTGS1EpS0T8bS6+9qyXVLKG6Okp6KDk+WFKXEqr3HUn1S6Ius0yxXbseTBn2CgDzpoymao161Ki7TZG4JrsewPTR/QGYPLQv2+3VA4A1OSvyY6pUrQmResJ7vaY7M2/KaHJXryTycvlp0jC2a39kaQ/HrOxFXvn9ylDldcboamCzEyNJVSJiZERclRQdDJRIYhQRR0fEopKoyyxT1KjflOULZ+W/Xr5oNjXqN10nplqtBqxesZjIywVgxaLZ1CwQs137Iznutk/ofsWzDH3uOgAWzfqabXbej6q1GlB5q+o0b3sINRs0T8OIzKyiSeceo8qSniSVWMwETgCaA48AjYEVwEUR8bWk3kA20AZoCfweOBfYHxgeEecBSHoM2BeoAbwSEbdKuiqpd6CkeRHRXdIZwI2AgP4RcUNy/ZHAXUBlYF5EHCrptuT6VsA8Sb2A64ErgEuAXEm/A66MiE8LD/IX9H0q0BGoDbwLfFbwvYmI7F/5PpuVGUnFlEbhoGIifo75Ycx7/DDmPbbZqTN7Hf9HPnrwtyz58TsmvP8Ih/2hD6tzlrNwxlf5iZWZWUlKZ2K0M3BGRFwk6WXgFFJJwyURMUlSZ+BR4JAkvkFyfDzwFnAAcCEwQlL7iBgD3BQRCyRVBj6S1C4iHpJ0LdA9IuZJag7cC3QAFgIDJJ0IDAGeBLpFxBRJDQv0tQPQNSKyJR0MEBFTJT0OLIuIf2xkrJvS9429N/8tXKmknkBPgN8fWI9Ddq+1kW6Ylb5dDjqXnbqeBcD8aWOo1aA5c5Nzteo3I3vRnHXic5YtYKua9VClykReLjWLiQH46bvh1Gnckmq1GpCzfCHff/4i33/+IgDtT/gzKxbNLtVxmVnFlM6ltCkFEoJRpGZkugB9JY0BngCaFYh/KyICyALmRERWROQBE5JrAU6TNBr4EtgD2L2YdvcFBkXE3IhYA7wAdAP2AwZHxBSAiFhQ4Jo3N3PGZlP6XlBx700REdErIjpGREcnRZYpvv3kWd658wjeufMIZox5nx32+w0AjXbYh1Url5C95Kci18z55nO23+cYAHbc/1RmjBsAQO3GrfJjGm7XlkpVtiJn+UIAqtXZGoCaDZqz3d5HMXVEv1IclVmmyCvHX5kpnTNGOQWOc4EmwKKIaL+R+LxC1+YBVSTtQGqJa9+IWJgsYVUvpp7i5vbXlsd6zi1fT/mm2mDfNxAPqfemxma2b1YmZo7/iOZtD+GEO4Ykt+tfm3+u+xXPMez5P5K9eA5fvn4nXS98lPbH/4kFP0zguyF9ANh+76PZcb/fkJe7htzVK/n0yUvzrz+o55NUrd2AyF3DiD43sWrF4rSPz8zKv7J8jtESYIqkUyOir1KbE9pFxNhNvL4uqQRmsaQmwFHAoOTcUqAOMA8YDjwoqRGppbQzgIeBocAjknZYu5RWaNaoOEuTds1sPUa8eFOx5QP/fU7+8bJ503nvnmOLxHw14FG+GvBosdcP+OfJJdNBM7MNKOu70s4CLpA0ltQy0wmbemGSQH2ZXPc0qT1Da/UC3pU0MCJmA38BBgJjgdER8UZEzCW1X+e1pP2XNqHZt4CTJI2RdOCm9tXMzMy2DIpY32qSZbL/XtLC/3BmZhnid4/PXN+2jc2SvfC7cvuzvkaDnUrlPdtcZT1jZGZmZpYx/Flpv5Kkm4BTCxX3jYg7y6I/ZmZmtvmcGP1KSQLkJMjMzKwccWJkZmaWsTL3eT/llfcYmZmZmSWcGJmZmZklnBiZmZmZJbzHyMzMLFOF9xilm2eMzMzMzBJOjMzMzMwSTozMzMzMEk6MzMzMzBLefG1mZpaxvPk63TxjZGZmZpZwYmRmZmaW8FKamZWojqfdTou2h7BmVTZDn72GBT+MLxJTa+vtOPDCR6laqwELpmfx+TNXkZe7Ov/81i33oscNb/HZU5cyfXR/APY7+59su+dhrFw6j7fvODRt4zGzisUzRmZWYpq3PYQ62+zAG7d0ZfgLN9DpzLuLjdvn5JuY+NGTvHlLV1atWEzrA87IPydVYu+TbmL2V4PWuWby0Jf5+OGzSrP7Zpkn8srvV4bK2MRIUm9JvynrfpjZptuuXQ+mDHsFgHlTRlO1Rj1q1N2mSFyTXQ/InwmaPLQv2+3VI//crt3PZ/qX/Vm5dP461/z03XByViwqvc6bmZHBiZGZbXlq1G/K8oWz8l8vXzSbGvWbrhNTrVYDVq9YTOTlArBi0WxqJjE16jdlu/ZHMmnw8+nrtJlZARmRGEn6q6SvJX0gqY+k6wudnyqpUXLcUdKg5Li2pGckZUkaJ+mUpPyMpGy8pHuTssrJLNT45Nw1SXlrSe9JGiXpU0ltNtDP3pIekzRQ0mRJB0l6WtJESb0LxB0haaik0ZL6SqqdlN8iaUTSh16SlJQPknSvpC8kfSvpwPW031PSSEkjP/5q+a9/w81KSfKfdCFROKiYiFRMx1P/xpev30Vk8DS7mZVvZb75WlJH4BRgb1L9GQ2M2sTL/wosjog9k7oaSGoO3At0ABYCAySdCPwAtIiItkls/aSOXsAlETFJUmfgUeCQDbTZIDl/PPAWcABwITBCUntgBnAzcFhELJd0A3AtcDvw74i4PWn/eeDYpA6AKhHRSdLRwK3AYYUbjoheSX/57yUtovB5s7Kwy0HnslPX1N6f+dPGUKtBc+Ym52rVb0b2ojnrxOcsW8BWNeuhSpWJvFxqFojZumU7ul74KADVajWkxR6HkJe7hhlj30/beMwyi/9ISLcyT4yArsAbEZENIOmtjcQXdBjw27UvImKhpG7AoIiYm9T3AtANuAPYUdLDQH9SCVNtoAvQt8BfutU20uZbERGSsoA5EZGVtDMBaAVsC+wODEnqrAoMTa7tLulPQE2gITCBnxOj15Lvo5J6zLYI337yLN9+8iwALdoeyi4Hn8fUkW/QaId9WLVyCdlLfipyzZxvPmf7fY5h2sg32XH/U5kxbgAA/W7ePz9m/3MfYGbWh06KzCytMmEprbi598LW8HNfqxe6tvDMSbH1RcRCYC9gEHA58FRS56KIaF/ga7eN9CUn+Z5X4Hjt6ypJ+x8UqG/3iLhAUnVSs1G/SWa4niw0lrV15ZIZCavZLzZz/EcsmzedE+4YQuff3ccX/7sx/1z3K56jRr0mAHz5+p3sdlhPTrj9M6rVasB3Q/pstO6uFzzCkX96k7pNW3PS3SNp3eW3G73GzOyXyoRfwJ8BT0i6m1R/jiGVNBQ0ldTS2Luklt3WGgBcAVwNqaU0YDjwYLInaSFwBvBw8npVRLwq6Xugd0QskTRF0qkR0TfZ89MuIsZuxniGAY9I2ikivpNUk9Qs0to/m+clM1W/AV7ZjHbMMtKIF28qtnzgv8/JP142bzrv3XPsBusZ+uw167z+7D+Xb37nzMw2osxnjCJiBPAmMJbUctJIYHGhsL+RSnY+JTWjstbfgQbJZuaxQPeImA38BRiY1Dk6It4AWgCDJI0BeicxAGcBFyTXTwBO2MzxzAXOA/pIGkcqUWoTEYtIJXxZQD9gxOa0Y2ZmFUBZP2uoAj7HSBFlv4dXUu2IWJbMrgwGekbE6LLuVybz5mszs8zxu8dnbsq2kF8se96Ycvuzvkaj9qXynm2uTFhKA+glaXdSe26edVJkZmZmZSEjEqOIOLOs+1CQpJuAUwsV942IO8uiP2ZmZpYeGZEYZZokAXISZGZmVsE4MTIzM8tYmbtJubwq87vSzMzMzDKFZ4y2UHl5/ivCzMyspHnGyMzMzCzhGSMzM7NMlcEPQiyvPGNkZmZmlnBiZGZmZpZwYmRmZmaW8B4jMzOzjOU9RunmGSMzMzOzhBMjMzMzs4SX0sysRO17+h202PNQcldlM6T31SyYnlUkpvbW23Fgz8epVrM+C6Zn8dnTV5KXuzr//NYt9+Kov/RncK+LmT66PwAn3/UFq3OWEXm55OXm8s5dR6ZtTGZWcTgxMrMS06LtIdRtsiP9bu5Cox32ofNZ9/Du3ccUidvnlJuZ+GEvpo54g85n3ctOXc/g20+eA0CqxD6n3MysCYOKXDfgn78hZ9mC0h6GWebwc4zSrlSW0iS1kjS+NOr+hf24TdL1G4kZJKljMeXHS/pzKfbtEknnlFb9ZmVhu/ZH8v3QvgDMmzKaqjXqUqPeNkXimrbpyrRRbwPw/dCX2b79Ufnn2hxyAdNH92fl0nnp6bSZWQHeY7QeEfFmRNxTivU/HhHPlVb9ZmWhZv2mrFg4K//1ioWzqVm/2Tox1Wo3ZNWKxURebn5MjfpNAahRvynb7X1U/uxRQUFw2NUvcsxN77Pzgb8rxVGYWUVWmolRZUlPSpogaYCkGpJaS3pP0ihJn0pqAyCpt6THJA2UNFnSQZKeljRRUu+1FUo6Q1KWpPGS7i1QfoGkb5PZnycl/btwZyS1lzRM0jhJr0tqUOD07yR9ntTbKYk/b209Sf8eSmImS/rN+gYt6WBJn0h6OenTPZLOkvRF0vfWSVz+bFbS73uTmG8lHbieuntKGilp5MCJK37Jv4VZekhFiiKicFAxF6Zi9j39dka/+neimOWD9+49nv5/P4KPHjqTXQ8+j2123q8EOmxmtq7S3GO0M3BGRFwk6WXgFOD3wCURMUlSZ+BR4JAkvkFyfDzwFnAAcCEwQlJ74CfgXqADsBAYIOlE4Avgr8A+wFLgY2BsMf15DrgyIj6RdDtwK3B1cq5WRHSR1A14GmhbzPXNgK5AG+BN4JUNjH0vYDdgATAZeCoiOkn6A3BlgXYLqpLEHJ307bDCARHRC+gF8FzPZoV/25iViV0PPo+dDzwLgPlTx1KzQfP8czUbNCN78Y/rxOcsm0/VmvVQpcpEXm4qZtEcILXputtFjwOpmaUWbQ8l8nL5Ycx7ZC9OxaxcOp8fxrxLo1bt+WnSsHQM0cwqkNJMjKZExJjkeBTQCugC9NXPf1VWKxD/VkSEpCxgTkRkAUiakFzbEhgUEXOT8heAbsm1n0TEgqS8L7BLwY5IqgfUj4hPkqJngb4FQvoARMRgSXUl1S9mPP0i9WfsV5KabGTsIyJidtL298CApDwL6L6ea15Lvq99r8y2CN8M6s03g3oD0GLPQ2nT/XymjuhHox32YXX2UrIX/1Tkmh+/GULLDscydcQbtN7/NH4Y8x4Ar9/YOT+my3n/Ysa4D/hhzHtUqVoDVIk1OcupUrUGzXY/iHFvP5CW8ZmVLW++TrfSTIxyChznAk2ARRHRfiPxeYWuzSPVzzXrua64eflfqvDsS3GzMQX7tLE2C/e/4NjW956vjcndQIxZRpuZ9REt2h7KSXcOZc2qbD7vfU3+uUOu/C9Dn7uO7MVzGP3q3+l20eO0P+EGFvwwnklD+myw3up1G3PwpU8DUKlyFaZ88TqzJgws1bGYWcWUzl/AS4Apkk6NiL5KTRu1i4jilr2KMxx4UFIjUktpZwAPAyOBB5I9Q0tJLdmt8+CUiFgsaaGkAyPiU+Bs4JMCIacDAyV1BRYn8ZsxVLOK64s+NxZb/vHDP2+YXjZvOu/cffQG6/m899XrxL99R5HVZTOzEpfumYmzgMck3QxsBbxI8fuBioiI2ZL+AgwkNWPzTkS8ASDpLlKJ0yzgK2BxMVWcCzwuqSapfT+/L3BuoaTPgbrA+b9mYGZmZrblU9E7RrY8kmpHxDJJVYDXgacj4vWy7ldp8uZrM7PMcU6v2aWyzJA957Ny+7O+RpOuGbk0U16eY3SbpDHAeGAK0K9Me2NmZmZbpHKxyTciNvh069IgaU/g+ULFORHRubh4MzMzy3zlIjEqC8njBNqXdT/MzMys5DgxMjMzy1ARuWXdhQqnvOwxMjMzM9tsTozMzMzMEk6MzMzMzBLeY2RmZpahIs+flZZunjEyMzMzSzgxMjMzM0s4MTIzMzNLODEyMzMzS3jztZmZWYbyAx7TzzNGZmZmZgnPGJlZidr39Dtoseeh5K7KZkjvq1kwPatITO2tt+PAno9TrWZ9FkzP4rOnryQvd3X++a1b7sVRf+nP4F4XM310f+o2aU23no//fH2jlox9834mfvRkWsZkZhWHZ4zMrMS0aHsIdZvsSL+buzD0+T/S+ax7io3b55SbmfhhL/r99QByVixmp65n5J+TKrHPKTcza8Kg/LIlc77n7TsO5+07Dqf/33uQuyqb6V++W9rDMbMKqMQSI0mtJI0vqfo2ox+3Sbp+IzGDJHUspvx4SX8uvd6ZlW/btT+S74f2BWDelNFUrVGXGvW2KRLXtE1Xpo16G4Dvh77M9u2Pyj/X5pALmD66PyuXziu2jaa7HcjSuVNZvmBGKYzALLNEXm65/cpUnjEqICLejIji/8Q1s42qWb8pKxbOyn+9YuFsatZvtk5MtdoNWbVicf4PxhULZ1OjflMAatRvynZ7H8W3nzy33jZ22PcEpozoV/KdNzOj5BOjypKelDRB0gBJNSS1lvSepFGSPpXUBkBSb0mPSRooabKkgyQ9LWmipN5rK5R0hqQsSeMl3Vug/AJJ3yazP09K+nfhzkhqL2mYpHGSXpfUoMDp30n6PKm3UxJ/3tp6kv49lMRMlvSb9Q1a0sGSPpH0ctKneySdJemLpO+tk7jjJA2X9KWkDyU1ScofknRLctxD0mBJTlptyyMVKYqIwkHFXJiK2ff02xn96t+JKP5jECpV3opt9+rBtJFvbWZHzcyKV9Kbr3cGzoiIiyS9DJwC/B64JCImSeoMPAocksQ3SI6PB94CDgAuBEZIag/8BNwLdAAWAgMknQh8AfwV2AdYCnwMjC2mP88BV0bEJ5JuB24Frk7O1YqILpK6AU8DbYu5vhnQFWgDvAm8soGx7wXsBiwAJgNPRUQnSX8Arkza/QzYLyJC0oXAn4DrgD8nY/4UeAg4Oor5zSCpJ9AT4Lyudem+W80NdMcsPXY9+Dx2PvAsAOZPHUvNBs3zz9Vs0IzsxT+uE5+zbD5Va9ZDlSoTebmpmEVzgNSm624XpTZZV6vdkBZtDyXycvlhzHtAag/TgulZ611mMzPbXCWdGE2JiDHJ8SigFdAF6Kuf/5KsViD+rSRJyALmREQWgKQJybUtgUERMTcpfwHollz7SUQsSMr7ArsU7IikekD9iPgkKXoW6FsgpA9ARAyWVFdS/WLG0y9JUL5aO7uzASMiYnbS9vfAgKQ8C+ieHG8LvCSpGVAVmJL0YYWki4DBwDUR8X1xDUREL6AXwHM9mxX+M9ysTHwzqDffDOoNQIs9D6VN9/OZOqIfjXbYh9XZS8le/FORa378ZggtOxzL1BFv0Hr/0/ITn9dv7Jwf0+W8fzFj3Af55wBadTqRKV+8XroDMssgfo5R+pX0ck1OgeNcoCGwKCLaF/jarZj4vELX5pFK2oqbc2cD5b9E4cSiuESjYJ821mbh/hcc29oE9GHg3xGxJ3AxUL3ANXsC84HmmG2hZmZ9xNK50zjpzqHsf84/GP6/v+SfO+TK/1KjXurvi9Gv/p3dD7uYE//+OdVqN2DSkD4brbty1Ro0360b0798p9T6b2ZW2s8xWgJMkXRqRPRVatqoXUQUt+xVnOHAg5IakVpKO4NUcjESeCDZM7SU1JLdOg9LiYjFkhZKOjAiPgXOBj4pEHI6MFBSV2BxEr8ZQ90k9YCZyfG5awsltSS1pLY38I6kfhExvLQ7Y1YavuhzY7HlHz/8u/zjZfOm887dR2+wns97X73O69xV2bx07R6b3T8zsw1JxwMezwIek3QzsBXwIsXvByoiImZL+gswkNSMzTsR8QaApLtIJU6zgK+AxcVUcS7wuKSapPb9/L7AuYWSPgfqAuf/moH9CreRWlacCQwDdkiSxf8A10fELEkXAL0l7RsRK9PULzMzMwNU9I6RLYOk2hGxTFIV4HXg6YioMJsPvMfIzCxznNNrdqksOSyZ+na5/Vlft9Wxpb5M82tsybeE3yZpDDCe1CbmfmXaGzMzM9vibbGflRYRG3y6dWmQtCfwfKHinIjoXFy8mZmZbVm22MSoLCSPE2hf1v0wMzOz0rElL6WZmZmZlSjPGJmZmWUoP+Ax/TxjZGZmZpbwjNEWKi+v3N7BaWZmVmY8Y2RmZmaW8IyRmZlZhoo87zFKN88YmZmZmSWcGJmZmZklnBiZmZmZJbzHyMzMLEP5OUbp5xkjMzMzs4QTIzMzM7OEl9LMrER1OuPvbLvnoaxZlc1nT/+BBdOzisTUbrQ9B/V8nGq16jN/ehafPnUFebmrabprFw65vDfL5k0HYNrodxj79v9Rs0FzDrzgYWrUa0zkBd8Ofp6JHz2V7qGZWQVQYRIjSVOBjhExr6z7YlZetdjzUOpusyOv3bg/jXfch/1/dy/97zq6SFyHU27mqw+eYMqIN9j/d/ey84Fn8s2gZwGYM2k4Hz189jrxkbeGES/fxoLpWVSpVovj/jqAWV8NZvHsb9MyLrOyEnl5Zd2FCsdLaWZWYrZv34Pvh74MwNzJo6lasy416m1TJK5ZmwOYOuptAL77/GW2b3/kBuvNXvxT/szTmpzlLJ49iZoNmpZw783MymliJKmWpP6SxkoaL+n05NQfJX2RfO2UxPaW9LikTyV9K+nYDdR7nqR+kt6SNEXSFZKulfSlpGGSGiZxrSW9J2lUUm+bpPw4ScOT+A8lNUnKb5P0tKRBkiZLuqqU3yKzUlGzfjOWL5iV/3r5wtnUrN9snZhqtRuyKntJ/hN9ly+cTc0GP8c0bt2B42/9iMP+8D/qN9+1SBu1t96Ohtu3Zd7k0aU0CjOryMplYgQcCcyKiL0ioi3wXlK+JCI6Af8G/lUgvhVwEHAM8Lik6huouy1wJtAJuBNYERF7A0OBc5KYXsCVEdEBuB54NCn/DNgviX8R+FOBetsAPZJ6b5W0VeGGJfWUNFLSyEFfr9j4u2CWblIxhYU/8LiYmEjFzJ82jldu6MibfzuUiR//h0Muf2adsCrVanLwZU/xxUu3sHrlspLps5lZAeV1j1EW8A9J9wJvR8SnSv3A7pOc7wM8UCD+5YjIAyZJmkwqSRmznroHRsRSYKmkxcBbBdpsJ6k20AXoq59/SVRLvm8LvCSpGVAVmFKg3v4RkQPkSPoJaALMKNhwRPQilXTR+8KmhX/bmJWJNt1/zy4HngXAvKljqNWwef65Wg2asWLRj+vE5yybT9UadVGlykRe7joxBZOdmVkfUemse6hWuyE5yxagylXoful/mDzsNaaPficNIzOziqhcJkYR8a2kDsDRwN2SBqw9VTBsPcfFvS4op8BxXoHXeaTez0rAoohoX8y1DwP/FxFvSjoYuG099eZSTv9trPz5euAzfD0wNbOz7Z6H0eaQ85nyRT8a77gPq7KXkr34pyLX/PjN57TqcCxTRrzBTl1OY/qY9wGoUbcx2UvmAtBoh71BImfZAgAOOPcBFs+exFcfPJGmkZmVPT/gMf3K5S9fSc2BBRHxX0nLgPOSU6cD9yTfhxa45FRJzwI7ADsC3/zatiNiSbL/6NSI6KvUtFG7iBgL1ANmJqHn/to2zDLVjKwPabHnoZx81zByV2Xz2TNX55877A8vMKT3tWQvnsPIV+7goIufYO+T/syC6eOZ9Nn/AGjZ4Th2PfhcIm8NuatW8kmvSwDYZqdO7NTlVBbM+Irjb/kQgFGv383MrI/SPkYzK9/KZWIE7AncLykPWA1cCrwCVJM0nNSszhkF4r8BPiG1fHVJRKzczPbPAh6TdDOwFan9RGNJzRD1lTQTGEYqETMrV4b/7y/Fln/44Fn5x8vmTaf/nUcVifl64NN8PfDpIuU/ffcFvS/0XWhmVvoUUbG3qkjqTWof0itl3ZdfwnuMzMwyx3lP/VjcnQebbf43/yu3P+u33vXMUnnPNld5nTEyMzPb4q19rIWlT4VPjCLivMJlknoA9xYqnhIRJ6WlU2ZmZlYmKnxiVJyIeB94v6z7YWZmZulVXh/waGZmZvaLecbIzMwsQ/k5RunnGSMzMzOzhGeMtlBr1vivCDMzs5LmGSMzMzOzhGeMzMzMMlTk5ZV1FyoczxiZmZlZxpF0pKRvJH0n6c/FnD9L0rjk63NJe5VEu06MzMzMLKNIqgw8AhwF7A6cIWn3QmFTgIMioh1wB9CrJNp2YmRmZmaZphPwXURMjohVpD6M/YSCARHxeUQsTF4OA7YtiYadGJmZmVnaSeopaWSBr54FTrcAfijwekZStj4XAO+WRL+8+drMzCxDlecHPEZEL9a//KXiLik2UOpOKjHqWhL9cmJkZmZmmWYGsF2B19sCswoHSWoHPAUcFRHzS6JhJ0ZmVqL2P+sutm13GLmrVvDJU1cxf9q4IjG1G23PIZf2olqtBsyfNo5BvS4jL3c1zdp04fCrnmfpvOkATB35Nl+++U8A2h5xMbse9DsigoUzJjL4P1eRuzonrWMzs7QZAewsaQdgJvBb4MyCAZK2B14Dzo6Ib0uqYe8xMrMSs227w6jbZEf63tCJT3tfxwHn3FdsXKfTbmH8gMfp++fO5KxYxK7dzso/9+O3w3j9lu68fkv3/KSoZv2m7HH4RfS77XBeu7kbqlSZHTuflJYxmVn6RcQa4ArgfWAi8HJETJB0iaRLkrBbgK2BRyWNkTSyJNou1cRIUitJ40uzjULtXS2pZgnV1VHSQ8nxwZK6lES9ZuVZy72PZNKQlwCY+/0oqtasR416TYrENd+tK1NGvAXApM9eouU+R2+0blWqQpWq1VGlylSpWoMVC38s2c6bZaK83PL7tRER8U5E7BIRrSPizqTs8Yh4PDm+MCIaRET75KtjSbzl5W3G6GpgsxMjSVUiYmREXJUUHQw4MTLbiFoNmrF8wc/bAJYvnEWtBk3XialWuyE5K5YQyQ/G5QtnUbNAzDY7deSk2wfS49oXqd98VwBWLPqRrPce5bf/HMOZ/xrPquwlzJwwqPQHZGYVTjoSo8qSnpQ0QdIASTUktZb0nqRRkj6V1AZAUm9Jj0kaKGmypIMkPS1poqTeaytMYkYmdf4tKbsKaA4MlDQwKTtDUpak8ZLuLXD9kZJGSxor6aOk7DZJvSQNAJ5LZoneltQKuAS4JpmqO7C4QW5m3+slT/fcNXndR9JFxbSRf2vj4G9Xbs6/iVnpUNEbSSKiUEgxN5skMfOmjuPF6/bh9Vu6M+HDpzj8qucAqFqzHi33PpKX/tiB/12zJ1Wq1WSn/X9T8v03swovHYnRzsAjEbEHsAg4hdTteVdGRAfgeuDRAvENgEOAa4C3gAeAPYA9JbVPYm5KpszaAQdJahcRD5Hasd49IrpLag7cm9TVHthX0omSGgNPAqdExF7AqQXa7gCcEBH5G7wiYirwOPBAMlX36QbG+mv7vpjUWmpvSb8FGkTEk4Urj4heEdExIjp226X6Brphlj67HXo+J90+kJNuH8iKRT9Sq2Hz/HO1GjRnxaI568SvXDqfajXrokqVi8SsXrmMNTnLAZgx7kMqValCtdoNabHHQSydN52VS+cTuWuYOrI/2+y0b5pGaGYVSTruSpsSEWOS41FAK1LLUn0L/OVYrUD8WxERkrKAORGRBSBpQnLtGOC05EFQVYBmpB4XXvjWl32BQRExN7n+BaAbkAsMjogpABGxoMA1b0ZE9maM9Vf3PSI+kHQqqUegl8jnvZilw8SPnmbiR08DsN1eh7P7oRcwefjrNG7dgVXZS8hePKfINbO+HsIO+x7H5OH92Lnr6Uz7MvVcthr1tiF78U8ANN5hb6RK5CxbwLL5M9imdQcqV61B7qpsmu/ejXlTx6RtjGZlpTw/xyhTpSMxKng/bS7QBFgUEe03Ep9X6No8oEpy6971wL4RsTBZpipu+qS4h0OtLS/2IVHA8vWUb6pf3XdJlYDdgGygIalnOJhtUX4Y+wHbtTuM0+77gjU52Qz+z1X553pc04dPn7maFYvmMOLl2+l+aS86nHwj86dn8c3gFwDYoeNx7HbIeeTlriF39Uo+fiz1INy5k0czZcRbnPS3j8jLXcP86Vl8Pei5MhmjmZVvZfEcoyXAFEmnRkRfpaaN2kXE2E28vi6pBGaxpCakPmBuUHJuKVAHmAcMBx6U1AhYCJwBPAwMBR6RtENETJHUsNCsUXGWJu1urg31/RpStyTeCDwtaf+IWF0CbZql1efP31Bs+fsPnJF/vHTuNN68vUeRmK8++g9fffSfYq8f3e8+Rvcr/vZ/M7OSUlZ3pZ0FXCBpLDCBQh8MtyFJAvVlct3TwJACp3sB70oaGBGzgb8AA4GxwOiIeCNZWusJvJa0/9ImNPsWcNKGNl9vTt8l7QJcCFyX7GEaDNz8a9sxMzOzX0eF7xixLcNT5zX2P5yZWYa4sPfc9W3f2CyzRz9cbn/WN9vnylJ5zzZXeXuOkZmZmdmv5s9K+4Uk3cS6t/gD9F37VE4zMzPbcjkx+oWSBMhJkJmZWTnkpTQzMzOzhGeMzMzMMpQf8Jh+njEyMzMzSzgxMjMzM0s4MTIzMzNLeI+RmZlZhoo87zFKN88YmZmZmSWcGJmZmZklnBiZmZmZJbzHyMzMLEP5OUbp5xkjMzMzs4RnjMys1Ox/1l1s2+4wclet4JOnrmL+tHFFYmo32p5DLu1FtVoNmD9tHIN6XUZe7moAmrXpwn5n3kmlylVYuXQB/e85Id1DMLMKxjNGZlYqtm13GHWb7EjfGzrxae/rOOCc+4qN63TaLYwf8Dh9/9yZnBWL2LXbWQBUrVmXLmffx4B//Y5XbzqQjx65IJ3dN7MKqsImRpKWFVPWXNIrv7Ce+pIuK7memZUPLfc+kklDXgJg7vejqFqzHjXqNSkS13y3rkwZ8RYAkz57iZb7HA1A6/1OYeqo/ixfMBOAlUvnpannZpkj8vLK7VemqrCJUXEiYlZE/OYXXlYfcGJkVkitBs1YvmBW/uvlC2dRq0HTdWKq1W5Izool+Q+xW75wFjWTmHpNW1OtVn2O+XM/TrztQ3bqclr6Om9mFVaFSIwk9ZM0StIEST0LnWskaaikYyS1kjR+A/XsIekLSWMkjZO0M3AP0Dopu19SbUkfSRotKUvSCQWu/6ukryV9IKmPpOuT8taS3kv6+KmkNqX1XpiljVSkKCIKhRSNIYlRpSo0atWO9//vTN79x2nsffx11G2yY6l01cxsrYqy+fr8iFggqQYwQtKrAJKaAG8CN0fEB5JabaSeS4AHI+IFSVWBysCfgbYR0T6pswpwUkQskdQIGCbpTaADcAqwN6n3fTQwKqm3F3BJREyS1Bl4FDikcONJUtcT4Oz9a9Ntl+q/8u0wKx27HXo+bQ46G4C5U76kVsPm+edqNWjOikVz1olfuXQ+1WrWRZUqE3m568QsXziLnGXzWbNqBWtWreDHb4ey9fZtWTJncvoGZGYVToWYMQKukjQWGAZsB+wMbAV8BPwpIj7YxHqGAjdKugFoGRHZxcQIuEvSOOBDoAXQBOgKvBER2RGxFHgLQFJtoAvQV9IY4AmgWXGNR0SviOgYER2dFFkmmvjR07x+S3dev6U700a/y84HnA5A49YdWJW9hOzFc4pcM+vrIeyw73EA7Nz1dKZ9+S4A00e/S5Nd9kOVKlO5ag0a77gPi2Z9m77BmFmFVO5njCQdDBwG7B8RKyQNAqoDa0jN2PQAPtmUuiLif5KGA8cA70u6ECj85+tZQGOgQ0SsljQ1aa+YNQMglZwuWjvjZFZe/DD2A7Zrdxin3fcFa3KyGfyfq/LP9bimD58+czUrFs1hxMu30/3SXnQ4+UbmT8/im8EvALBo9iRmZH3MyXd8QkQe3wx+gYUzvy6r4ZiVCT/gMf3KfWIE1AMWJklRG2C/pDyA80nN1Pw5Iu7ZWEWSdgQmR8RDyXE7YCxQp1B7PyVJUXegZVL+GfCEpLtJve/HAE8mS25TJJ0aEX2V2nTRLiLGbv7QzcrW58/fUGz5+w+ckX+8dO403ry9R7FxWe8+Qta7j5RK38zMilMRltLeA6okS1t3kFpOAyBSqfhvge6beMv96cD4ZMmrDfBcRMwHhkgaL+l+4AWgo6SRpGaPvk7aGkFqP9NY4DVgJLA4qfcs4IJkuW8C4KfYmZmZlQEVvkvESo+k2hGxTFJNYDDQMyJG/5q6njqvsf/hzMwyxIW9565vu8RmmT70znL7s377/W8qlfdsc1WEpbRM0kvS7qT2HD37a5MiMzOrGNY+48vSx4lRMST1AO4tVDwlIk7anHoj4szNud7MzMxKlxOjYkTE+8D7Zd0PMzMzS6+KsPnazMzMbJN4xsjMzCxD+TlG6ecZIzMzM7OEEyMzMzOzhBMjMzMzs4T3GJmZmWUoP8co/TxjZGZmZpZwYmRmZmaWcGJkZmZmlnBiZGZmZpbw5mszM7MMFbnefJ1uTozMrETtf9ZdbNvuMHJXreCTp65i/rRxRWJqN9qeQy7tRbVaDZg/bRyDel1GXu5qAJq16cJ+Z95JpcpVWLl0Af3vOYFaDZtz0EWPULPeNkTk8fWg55nwQa90D83MKgAnRmZWYrZtdxh1m+xI3xs60bh1Bw445z7evOPIInGdTruF8QMeZ/Lwfhxw7v3s2u0sJg7sTdWadely9n2898/TWb5gJtXrNAIgLzeX4S/eyvxp49iqei1OvO0jZk4YxKJZ36Z7iGZWzm1Re4wkDZLUcSMxV0uqmc66fi1Jn5dW3WZloeXeRzJpyEsAzP1+FFVr1qNGvSZF4prv1pUpI94CYNJnL9Fyn6MBaL3fKUwd1Z/lC2YCsHLpPACyF8/Jn3lavXI5i2Z9S60GzUp9PGZW8ZTHGaOrgf8CKzKsriIioktp1GtWVmo1aMbyBbPyXy9fOItaDZqSvXhOflm12g3JWbEk/8F1yxfOomaDpgDUa9qaSpW34pg/92Or6rUZP6AX333+8jpt1G60HVu33JOfvh+VhhGZlS0/4DH9MnLGSFIrSV9LelbSOEmvFJ65kfSYpJGSJkj6W1J2FdAcGChpYFJ2hKShkkZL6iupdjHtFYkpXJekypJ6SxovKUvSNRvo/yBJD0gaLGmipH0lvSZpkqS/F4hblnw/OLnmlWTcL0hSSbyXZmlVzH+2EVEopJj/tJMYVapCo1bteP//zuTdf5zG3sdfR90mO+aHValWi8OueIZh/7uZ1SuXlWzfzczI0MQosSvQKyLaAUuAywqdvykiOgLtgIMktYuIh4BZQPeI6C6pEXAzcFhE7AOMBK4tWMn6YgrXBbQHWkRE24jYE3hmI/1fFRHdgMeBN4DLgbbAeZK2LiZ+b1IzVLsDOwIHFA6Q1DNJBkcO/nblRpo3S4/dDj2fk24fyEm3D2TFoh+p1bB5/rlaDZqzYtGcdeJXLp1PtZp1UaXKRWKWL5zFjKyPWbNqBTnLFvDjt0PZevu2AKhyFQ674hm+G/oKU0f1T9PozKyiyeTE6IeIGJIc/xfoWuj8aZJGA18Ce5BKKArbLykfImkMcC7Q8lfEAEwGdpT0sKQjSSVrG/Jm8j0LmBARsyMiJ6lnu2Liv4iIGRGRB4wBWhUOiIheEdExIjp226X6Rpo3S4+JHz3N67d05/VbujNt9LvsfMDpADRu3YFV2UvWWUZba9bXQ9hh3+MA2Lnr6Uz78l0Apo9+lya77IcqVaZy1Ro03nGf/A3W3c7/F4tmf8v49x9P08jMrCLK5D1Gsb7XknYArgf2jYiFknoDxWUKAj6IiDM20M6mxJC0sxfQg9Tsz2nA+Ru4JCf5nlfgeO3r4t73gjG564kxy2g/jP2A7dodxmn3fcGanGwG/+eq/HM9runDp89czYpFcxjx8u10v7QXHU6+kfnTs/hm8AsALJo9iRlZH3PyHZ8Qkcc3g19g4cyvabJzZ3Y+4HQW/DCBk24fCMCIV+5kxrgPy2ScZuni5xilXyb/8t1e0v4RMRQ4A/gMOC45VxdYDiyW1AQ4ChiUnFsK1AHmAcOARyTtFBHfJfuUto2Igvf4bigmv65kyW1VRLwq6Xugd+kN3WzL9fnzNxRb/v4DP//tsXTuNN68vUexcVnvPkLWu4+sUzZn0nCeOq9xyXXSzGw9MnkpbSJwrqRxQEPgsbUnImIsqSW0CcDTwJAC1/UC3pU0MCLmAucBfZJ6hgFtCjaykZj8uoAWwKBkua038JcSHKuZmZllABW+YyQTSGoFvB0Rbcu6L5nqqfMaZ94/nJlZBXVh77mlcifxdwOuK7c/63c64p8Zefd1Ji+lmZmZVWh5fo5R2mVkYhQRU0nd2p7RJD1C0dvqH4yIjd3Kb2ZmZhkoIxOjLUVEXF7WfTAzM7OSk8mbr83MzMzSyomRmZmZWcJLaWZmZhnKD3hMPydGW6hVq9aUdRfMzMzKHS+lmZmZmSWcGJmZmZklvJRmZmaWobzHKP08Y2RmZmaWcGJkZmZmlnBiZGZmZpbwHiMzM7MMFXl+NEu6ecbIzMzMLOEZIzMrUV3PuYeW7Q9nzapsPnr8MuZNHVckpk7j7Tniyv9QrXYD5k0Zy4ePXkJe7mraH3slu3Q5FQBVrkKDFrvwzMU7kbN8UapMlfjNnQNZvmA27/zjt+kclplVEJ4xMrMSs337w6nXtDUvXNuBQU9dzUHn/7PYuP3PuI2x7z7G/67tSM7yxezW/WwAxrz9MC/f2I2Xb+zGsJduZ9bEIflJEUC7oy5h4cxv0zEUM6ugynViJOk2SddLGiSpY1n3x6y826HD0Xzz6YsAzPluJFVr1qNm/SZF4lrs0Y3vh78BwNef9mGHjkcXidl5/1OY9Pmr+a9rNWxOy/ZHMHHgc6XUe7PMk5ebW26/MlW5TozMLL1qNWjGsgUz818vXzCLWg2arRNTvU5DVi1fTOSlfjAunz+LWg2arxNTpWoNtt/rUCZ/8WZ+Wdez72Jon1uJyCvFEZhZRVfuEiNJN0n6RtKHwK4FTv1O0ueSxkvqlMTeJul5SR9LmiTpog3Ue7CkTyS9LOlbSfdIOkvSF5KyJLVO4hpLelXSiOTrgKS8U9L+l8n3XZPy8yS9Jum9pA/3baAPPSWNlDTys+9ySuLtMitRkoopjcJRxYSsG9NqnyP58dvh+ctoLffuQfaSecydMrZE+mlmtj7lavO1pA7Ab4G9SY1tNDAqOV0rIrpI6gY8DbRNytsB+wG1gC8l9Y+IWetpYi9gN2ABMBl4KiI6SfoDcCVwNfAg8EBEfCZpe+D95JqvgW4RsUbSYcBdwClJve2TPucA30h6OCJ+KNx4RPQCegE8emaDwr9tzMpE28MvZPfu5wDw0+TR1G7YIv9crYbNWb7wx3XiVy6dT9Va9VClykReLrW2bs7yRbPXidlp/5PXWUZrtktnWu1zJNu3P5wqW1Vjqxp1OOyyJ/jw0YtLcWRmVhGVq8QIOBB4PSJWAEh6s8C5PgARMVhSXUn1k/I3IiIbyJY0EOgE9FtP/SMiYnZS9/fAgKQ8C+ieHB8G7F7gL+e6kuoA9YBnJe1M6k/orQrU+1FELE7q/QpoCRRJjMwy0fgPnmL8B08B0LL9EbQ94iK+G/oqTXbqyKrsJaxYNKfINTO/+pTWnU/gu6Gv0ebAM5gy8t38c1Vr1KX5bgesk/QMe+l2hr10OwDNdzuA9sdc6aTIzEpFeUuMoOi8/frKYyPlxSm4fpVX4HUeP7+XlYD9k2Qrn6SHgYERcZKkVsCg9dSbS/n8d7EKYNqYAWzf/nDOemA0a3Ky+fiJy/PPHfOnlxnY6ypWLPqRYX1u4/Ar/0PnU29i7rRxTBz0fH7cDvseww9ZA1mTs6IshmCWUdbuxbP0KW+/gAcDvSXdQ2psxwFPJOdOBwZK6gosjojFyazOCZLuJrWUdjDw583swwDgCuB+AEntI2IMqRmjtbtSz9vMNswy1qe9/1hsef/7Tss/XvLTNF7962HFxn0zuA/fDO6z3vpnTRzCrIlDNq+TZmbrUa42X0fEaOAlYAzwKvBpgdMLJX0OPA5cUKD8C6A/MAy4YwP7izbVVUBHSeOSZbFLkvL7gLslDQEqb2YbZmZmVgoUUXH38Eq6DVgWEf8o6778Ut58bWaWOS7738LibsncbOP7nlduf9a3PbV3qbxnm6u8LaWZmZmVG5HBD0Isryp0YhQRtxUuk7Qn8Hyh4pyI6JyWTpmZmVmZqdCJUXEiIovUc4XMzMysgilXm6/NzMzMNodnjMzMzDKUn2OUfk6MtlB5FfhuQjMzs9LipTQzMzOzhBMjMzMzs4SX0szMzDJUnp9jlHaeMTIzMzNLODEyMzMzSzgxMjMzM0s4MTIzMzNLePO1mZlZhvIDHtPPiZGZlagDz72Xlu0PZ82qbD567DLmTh1bJKZO45b0uOo/VK/VgLlTx/LBIxeTl7uavY+9kl0OOA2ASpUr06DFrvynZ2tyli+i3ZGXsMch54DEVx8/x9h3H0v30MysAvBSmpmVmJbtD6d+0x357zX7MPDJP3DQBf8sNq7Lmbcx9p1H+e+1HchZvojdu58NwJdvP8xLfzmQl/5yIENfvJ1ZE4eQs3wRDbfdjT0OOYe+Nx/Kizd0pdXePajXdMd0Ds3MKogKkxhJaiVpfFn3w6w826HD0Xz96YsAzPluJNVq1qNm/SZF4rbdoxvfDX8DgK8H92HHjscUidmlyyl8+/krADRosQs/ThrJmlXZRF4uMycOYcd9jy3FkZhZReWlNDMrMbUbNmPZ/Jn5r5ctmEXths1YsWhOfln1Og3JWb44f+/EsvmzqNWw2Tr1VKlag+33OoxPnvkjAAt+mMj+p/+V6rUbsGbVSlq1P5yfpowp/QGZlbHwAx7TrsLMGCWqSHpW0jhJr0iqKWmqpEYAkjpKGpQc3ybpaUmDJE2WdNX6Kk1mo76W9JSk8ZJekHSYpCGSJknqlMR1kvS5pC+T77sm5ddKejo53jOpo2Yx7fSUNFLSyCHfrSqFt8dsM0lFiqLIBx4XjaFQTKt9jmT2N8PJWb4IgIWzvmXUmw9y/I39OO7PrzJv+njycteUUKfNzH5W0WaMdgUuiIghSSJy2Ubi2wDdgTrAN5Iei4jV64ndCTgV6AmMAM4EugLHAzcCJwJfA90iYo2kw4C7gFOAfwGDJJ0E3ARcHBErCjcQEb2AXgD/PqN+4d82ZmViz8MvZPdDzgXgp8mjqb11i/xztRs2Z/nCH9eJX7l0PtVq1UOVKhN5udTeumjMzl1OYVKyjLbWxEHPM3HQ8wDsd/pfWbZgVmkMx8wquIo2Y/RDRAxJjv9LKnHZkP4RkRMR84CfgKKbJX42JSKyIiIPmAB8FKk/lbOAVklMPaBvstfpAWAPgOSa84DngU8K9NEs42V98FT+hunJI/vT5sDfAtBkp46sWrFknWW0tWZO+JSdOp8AQJtuZzB51Dv556rWqEuL3Q5YpwygRt1GANTeelta73tckcTJzKwkVLQZo8KzLAGs4ecEsXqh8zkFjnPZ8PtVMDavwOu8AtfdAQyMiJMktQIGFbhmZ2AZ0HwDbZhltGlfDqBl+8M5+19fsiZnBR89cXn+uWP/9DIDn7yK5Qt/5PM+t9LjyqfpfNrNzJs6jq8GPp8ft+O+xzJ93MesyVl30vSoa56jeu2G5OWu4ZNnridn+eK0jcusrISXjNOuoiVG20vaPyKGAmcAn5FaJusAvEtqWas01QPW7kw9b22hpHrAg0A34N+SfhMR/nPYtkiDkw3Thb1932n5x0t+mkbfvx5abNzXg//H14P/V6T8tb8dXTIdNDPbgIq2lDYROFfSOKAh8BjwN+BBSZ+SmhUqTfcBd0saAlQuUP4A8GhEfAtcANwjaZtS7ouZmZkVoqJ3jNiWwJuvzcwyxxV9FhVzu+XmG/XkceX2Z32Hi94qlfdsc1W0pTQzM7MtRp4/Ky3tnBj9ApK2Bj4q5tShETE/3f0xMzOzkuXE6BdIkp/2Zd0PMzMzKx0VbfO1mZmZ2Xo5MTIzMzNLeCnNzMwsQ/lDZNPPM0ZmZmZmCSdGZmZmZgknRmZmZmYJ7zEyMzPLUOEHPKadZ4zMzMzMEk6MzMzMzBJOjMzMzMwS3mNkZmaWofwco/RzYmRmpebAc++lZfvDWbMqm48eu4y5U8cWianTuCU9rvoP1Ws1YO7UsXzwyMXk5a6mao26HH55L+o02hZVrsyYt//NxE9eKINRmFlF4qU0MysVLdsfTv2mO/Lfa/Zh4JN/4KAL/llsXJczb2PsO4/y32s7kLN8Ebt3PxuAPY+4kAUzv+bFP3fl9duP5YDf/Z1KlbdK5xDMrAKqEImRpNskXV/W/TCrSHbocDRff/oiAHO+G0m1mvWoWb9Jkbht9+jGd8PfAODrwX3YseMxyZmgao3aAGxVvTYrly0kL29NWvpuZhWXl9LMrFTUbtiMZfNn5r9etmAWtRs2Y8WiOfll1es0JGf54vxntSybP4taDZsBMO79Jznm+j78/tGv2apGbd5/6HyISO8gzMpYnp9jlHZb/IyRpFaSvpb0lKTxkl6QdJikIZImSeqUhO4l6eOk7KLk2oMlDZb0uqSvJD0uab3viaRlku6VNErSh5I6SRokabKk45OYypLulzRC0jhJFyfltSV9JGm0pCxJJxTo/0RJT0qaIGmApBql/LaZlT6pSFEUSWyKxqxNfrZvdwjzpmXxzGVteOnPB3LQefezVY06pdBRM7OfbfGJUWIn4EGgHdAGOBPoClwP3JjEtAOOAfYHbpHUPCnvBFwH7Am0Bk7eQDu1gEER0QFYCvwdOBw4Cbg9ibkAWBwR+wL7AhdJ2gFYCZwUEfsA3YF/Svm/OXYGHomIPYBFwCnFNS6pp6SRkkYO+W7VprwvZmm15+EXcvrdn3L63Z+yfOGP1N66Rf652g2bs3zhj+vEr1w6n2q16qFKlVMxW/8cs9vBZ/H9F28BsHjOFJbMnUaD5junaSRmVlGVl8RoSkRkRUQeMAH4KFJ/mmYBrZKYNyIiOyLmAQNJJUQAX0TE5IjIBfqQSqjWZxXwXnKcBXwSEasLtXMEcI6kMcBwYGtSiY+AuySNAz4EWgBrN1xMiYgxyfGoAnWtIyJ6RUTHiOh4wE5VN/yOmJWBrA+e4qW/HMhLfzmQySP70+bA3wLQZKeOrFqxZJ1ltLVmTviUnTqfAECbbmcwedQ7ACydN4Pt2h4EQI16janfbCeW/DQ1PQMxswqrvOwxyilwnFfgdR4/j7HwHH5spLw4q+PntYD8diIiT9LadgRcGRHvF7xQ0nlAY6BDRKyWNBWoXkz/cwEvpdkWb9qXA2jZ/nDO/teXrMlZwUdPXJ5/7tg/vczAJ69i+cIf+bzPrfS48mk6n3Yz86aO46uBzwMw8vX7OfSSRznj3iEg8Xmf21i5dEFZDcfMKojykhhtihMk3U1qOexg4M/ALkCnZKlrGnA60Gsz23kfuFTSx0kCtAswE6gH/JSUdQdabmY7Zhlv8DN/LLb87ftOyz9e8tM0+v710CIxyxf+yJt3b2hl26z88wMe068iJUZfAP2B7YE7ImJWkrQMBe4htcdoMPD6ZrbzFKmlsNHJHqK5wInAC8BbkkYCY4CvN7MdMzMzK2FbfGIUEVOBtgVen7e+c+uxIiJO38S2ahc4vq24c8k+pxv5edN3Qfuvp+qC/f/HpvTFzMzMSl552XxtZmZmttm2+BmjzRERg4BBhcslDQeqFSo+OyKy0tAtMzMzAMJPe0+7Cp0YrU9EdC7rPpiZmVn6eSnNzMzMLOHEyMzMzCzhpTQzM7MM5ecYpZ8Toy1UXl5eWXfBzMys3PFSmpmZmVnCiZGZmZlZwktpZmZmGSovz3uM0s0zRmZmZmYJJ0ZmZmZmCSdGZmZmZgknRmZmZmYJb742sxLV7bz7aLX3EazJWcEHj13K3Clji8TUbdySI//wDNVrN+CnKWMY8O+e5OWupmqNuvS48klqN9qWSpWqMPrth5g46IX866RK/PbuT1i2YDZv3XdaOodlVib8gMf084yRmZWYlu2PoH7T1jz3h/Z8/OQf6H7BA8XGHXDW3/jynUd47uq9yVm+iD0OOQeAdj0uYv6Mr+nzpwN47W9Hc+DZd1Gp8lb517U/+lIWzPw2LWMxs4op4xMjSYMkddxIzNWSaqazLjMrasd9j+brwX0A+HHSCKrVqkfN+k2KxG27x0F8N6wfABM/6cOO+x6bnAmq1qgDwFbVa7Ny2ULy8tYAULthc1rt3YMJHz9b6uMws4or4xOjTXQ1UFLJTEnWZVah1G7QnKXzZ+S/XjZ/JrUbNl8npnqdhuSsWEwkz2dZtmAmtRs2A2Dse71o2GIXLnj8W878x1AG974BIgDodu49fPbCLUT443DMrPRkTGIkqZWkryU9K2mcpFcKz9xIekzSSEkTJP0tKbsKaA4MlDQwKTtC0lBJoyX1lVS7mPaKxBSuS1JlSb0ljZeUJemaDfR/kKQHJA2WNFHSvpJekzRJ0t8LxPWTNCoZQ8+krGUS10hSJUmfSjqiJN5Xs7SSihRFktjkh7D+mJZ7HcrcqVn855Jd6POnrhx0/v1UrVGHVvscyYol85g7ZUypdNssU+XlRbn9ylSZtvl6V+CCiBgi6WngskLnb4qIBZIqAx9JahcRD0m6FugeEfMkNQJuBg6LiOWSbgCuBW5fW8n6YiLi9kJ1dQBaRETb5Lr6G+n/qojoJukPwBtAB2AB8L2kByJiPnB+MoYawAhJr0bENEn3Ao8Dw4GvImJA4cqTRKonwOkdqtGlddVNfV/NSk27Iy5ij0PPBWDO96Ops/W2zE7O1d66BcsXzl4nPnvpfKrVrIcqVSbycqndsAXLF/4IwG4H/45Rb/wfAIvnTGbJT9No0HwXmu/amR07HEWr9odTuWp1qtaowxFXPMmAf1+UtnGaWcWQaYnRDxExJDn+L3BVofOnJclBFaAZsDswrlDMfkn5EKX+eq0KDP0VMQCTgR0lPQz0B4okK4W8mXzPAiZExGwASZOB7YD5wFWSTkritgN2BuZHxFOSTgUuAdoXV3lE9AJ6ATx0et3MTbetQhk34EnGDXgSgFZ796Bdj558+/krNN15X3JWLGHFojlFrpnx1WB22u9EJn3+KrsddAaTR/YHYOm8H9iu7cHM+nooNeo1pkHznVn80xQ+7/M3Pu/zNwBa7N6VfY69ykmRmZWKTEuMCv+yz38taQfgemDfiFgoqTdQvZg6BHwQEWdsoJ1NiSFpZy+gB3A5cBpw/gYuyUm+5xU4Xvu6iqSDgcOA/SNihaRBa8eQLBtum8TXBpZuqG9mmWjql+/Tau8jOPfBsaxetYIPH/t50vf4P7/CR09cwfKFPzLkhVs58g/PsP/pf2Xu1LF89fFzAIx47T4Ov/Rxzrx/KJIY8sKtrFy6oKyGY2YVUKYlRttL2j8ihgJnAJ8BxyXn6gLLgcWSmgBHAYOSc0uBOsA8YBjwiKSdIuK7tQlHRBS8x3dDMfl1JUtuqyLiVUnfA703c3z1gIVJUtSG1MzVWvcCLwDTgCeBY4u53izjDXr6umLL37znN/nHS36ayss3dS8Ss3zhj/S768QN1j/zq8+Y+dVnm9VHsy1FXp5vNki3jNl8nZgInCtpHNAQeGztiYgYC3wJTACeBoYUuK4X8K6kgRExFzgP6JPUMwxoU7CRjcTk1wW0AAZJGkMqKfrLZo7vPVIzR+OAO5J2kXQQsC9wb0S8AKyS9PvNbMvMzMx+IRW+Y6SsSGoFvL12o7NtmPcYmZlljqteWlL0dssS8OEte5Tbn/WH3T6hVN6zzZVpM0ZmZmZmZSZj9hhFxFQg42eLJD0CHFCo+MGIeKYs+mNmZuVXJj/vp7zKmMRoSxERl5d1H8zMzKx0eCnNzMzMLOHEyMzMzCzhxMjMzMws4T1GW6iVObll3QUzMytl3nydfp4xMjMzM0s4MTIzMzNLODEyMzMzS3iPkZmZWYbKi4r7IbKSjgQeBCoDT0XEPYXOKzl/NLACOC8iRm9uu54xMjMzs4wiqTLwCHAUsDtwhqTdC4UdBeycfPWkwAfPbw4nRmZmZpZpOgHfRcTkiFgFvAicUCjmBOC5SBkG1JfUbHMbdmJkZmZmmaYF8EOB1zOSsl8a84t5j5GZmVmGKs/PMZLUk9QS2Fq9IqLX2tPFXFL4zdiUmF/MiZGZpcWhF97Pjh16sDonm3cfupg5k8cUidn76IvpeNzlNGjWmofP3p7spfPT31EzS4skCeq1ntMzgO0KvN4WmPUrYn4xL6WZWanbsUMPGjTbiScvbcf7j17B4Zf8q9i4mROH8dKtx7L4p2np7aCZZZoRwM6SdpBUFfgt8GahmDeBc5SyH7A4ImZvbsPlIjGSdJ6kf28k5vN09cfM1rVTp2OYMOh/AMz+dgTVa9WjVoOmReJ+mjKWJT9NT3f3zCzDRMQa4ArgfWAi8HJETJB0iaRLkrB3gMnAd8CTwGUl0XaFWUqLiC5l3QeziqpOw+YsmTcj//XS+bOo07AZyxf+WIa9Mst8eXkV9zlGEfEOqeSnYNnjBY4DuLyk283oGSNJ50gaJ2mspOclHSdpuKQvJX0oqUkx1zSR9HpyzVhJXZLyZcn3gyW9XSD+35LOS46nSrpL0lBJIyXtI+l9Sd8XyFCL6+fBkj6R9LKkbyXdI+ksSV9IypLUOokrtv+SHpJ0S3LcQ9JgSUX+bST1TPo1cvjUNZv13pqllYrukYzN3yNpZlbiMnbGSNIewE3AARExT1JDUrvN94uIkHQh8CfgukKXPgR8EhEnJQ+Iqv0Lm/4hIvaX9ADQGzgAqA5MAB7fwHV7AbsBC0hN7T0VEZ0k/QG4Erga+Gw9/f8zMELSp0n/j44o+rjTghvV7juxln+rWEbb+6ietDvi9wD8OGkUdRtty8zkXJ2tm7NsgWeLzCzzZGxiBBwCvBIR8wAiYoGkPYGXkgc4VQWmrOe6c5JrcoHFv7DdtZu7soDaEbEUWCpppaT6EbFoPdeNWLvpS9L3wIAC9XRPjrctrv8RsULSRcBg4JqI+P4X9tks43z5bi++fDd1w8mOHXqwz9GXMPHTvjTbZV9yli/xMpqZZaRMXkoTRZ9H8DDw74jYE7iY1EzOL7WGdcdduI6c5HtegeO1rzeUSBaOLVjP2us21P89gflA843032yLM3nU+yyaM4WLHs/iyMsf4YMnrs4/d8pfX6N2shF7n2Mu5dKnvqXO1i34/YPDOfLyR8qox2ZWUWXyjNFHwOuSHoiI+clSWj3In40/dwPXXQr8K1lKqxURSwqcnwbsLqkaqcTkUFJLXOlQbP8ltSS1pLY38I6kfhExPE19MkuLD3tdW2z5q3ecnH88uv9jjO5fIh93ZFYulOcHPGaqjJ0xiogJwJ3AJ5LGAv8H3Ab0TfbizFvPpX8AukvKAkYBexSq9wfgZWAc8ALwZakMoHi3Uaj/yacD/we4PiJmARcAT0n6NbNhZmZmthmUutvNtjTefG1mljn+1G95cR9Psdn6Xde63P6sP/Gf35fKe7a5MnbGyMzMzCzdMnmPUcZJ7op7vlBxTkR0Lov+mJlZ+eY9RunnxOgXiIgsoH1Z98PMzMxKh5fSzMzMzBJOjMzMzMwSXkozMzPLUBX5Q2TLimeMzMzMzBJOjMzMzMwSTozMzMzMEt5jZGZmlqH8HKP084yRmZmZWcKJkZmZmVnCiZGZmZlZwomRmZmZWcKbr80sLQ698H527NCD1TnZvPvQxcyZPKZIzN5HX0zH4y6nQbPWPHz29mQvnZ/+jpplEG++Tj/PGJlZqduxQw8aNNuJJy9tx/uPXsHhl/yr2LiZE4fx0q3HsvinaentoJlZYotLjCT1lvSbsu6HmW26nTodw4RB/wNg9rcjqF6rHrUaNC0S99OUsSz5aXq6u2dmlm+LS4zMbMtTp2Fzlsybkf966fxZ1GnYrAx7ZGZWvIxOjCT9VdLXkj6Q1EfS9YXOT5XUKDnuKGlQclxb0jOSsiSNk3RKUn5GUjZe0r1JWeVkFmp8cu6apLy1pPckjZL0qaQ2G+hnb0mPSRooabKkgyQ9LWmipN4F4h6TNFLSBEl/S8rqSfpG0q7J6z6SLirJ99GszElFigLvnTDbmLzIK7dfmSpjN19L6gicAuxNqp+jgVGbePlfgcURsWdSVwNJzYF7gQ7AQmCApBOBH4AWEdE2ia2f1NELuCQiJknqDDwKHLKBNhsk548H3gIOAC4ERkhqHxFjgJsiYoGkysBHktpFxDhJVwC9JT0INIiIJ9fznvQEegKcvFdVOrfK2H8+M/Y+qiftjvg9AD9OGkXdRtsyMzlXZ+vmLFvwY9l1zsxsPTJ5xqgr8EZEZEfEUlLJxqY6DHhk7YuIWAjsCwyKiLkRsQZ4AegGTAZ2lPSwpCOBJZJqA12AvpLGAE8AG5v3fysiAsgC5kREVkTkAROAVknMaZJGA18CewC7J/37ILnuEVLJVLEioldEdIyIjk6KLNN9+W4vnr1mf569Zn8mDX+LPQ4+E4Bmu+xLzvIlLF/oxMjMMk8mJ0ZF596LWsPPY6he6NrC8/TF1pckTXsBg4DLgaeSOhdFRPsCX7ttpC85yfe8AsdrX1eRtANwPXBoRLQD+q/ts6RKwG5ANtBwI+2YbXEmj3qfRXOmcNHjWRx5+SN88MTV+edO+etr1E42Yu9zzKVc+tS31Nm6Bb9/cDhHXv7Iemo0MysdmTzt8BnwhKS7SfXzGKDwEtNUUktj75JadltrAHAFcDWkltKA4cCDyZ6khcAZwMPJ61UR8aqk74HeEbFE0hRJp0ZEX0kC2kXE2M0YT11gObBYUhPgKFLJGMA1wETgRuBpSftHxOrNaMss43zY69piy1+94+T849H9H2N0/8fS1SWzjOfnGKVfxs4YRcQI4E1gLPAaMBJYXCjsb6SSnU+B3ALlfwcaJBuqxwLdI2I28BdgYFLn6Ih4A2gBDEqWzHonMQBnARck108ATtjM8YwltYQ2AXgaGAIgaRdSy2fXRcSnwGDg5s1py8zMzH4dpbbFZCZJtSNimaSapBKGnhExuqz7lQnuO7FW5v7DmZlVMH/qt3xTtn/8Yv+9pEW5/Vn/u8dnlsp7trkyeSkNoJek3UntxXnWSZGZmZmVpoxOjCLizLLuQ0GSbgJOLVTcNyLuLIv+mJlZ+ZaXl7nP+ymvMjoxyjRJAuQkyMzMrJzK2M3XZmZmZunmxMjMzMws4cTIzMzMLOE9Rluoecu8Ic/MrLzzAx7TzzNGZmZmZgknRmZmZmYJJ0ZmZmZmCe8xMjMzy1DeY5R+njEyMzMzSzgxMjMzM0s4MTIzMzNLODEys1Jz/OX/5E/PjueaXl/QYqf2xcZ0OeES/vTseO77MJuadbcucn7bXTtwz/vL2PPAk0q5t2aZJy8vr9x+ZSonRmZWKtp06kGjFq2579y2vPrAFZz0h4eKjZs6YShP/uloFvw4rcg5VarE0Rf+nW9HflDa3TUzA8pRYiTpYEldCry+RNI5G7mmo6Tif1qXorJq1yyddu9yLKM/+B8A0yd+QY3a9ajTsGmRuFnfjWXhnOnF1nHAiZeR9Wk/li2aW6p9NTNbKyMTI0m/5jECBwP5iVFEPB4Rz23ogogYGRFX/Yq2NktZtWuWTvUaNWfR3Bn5rxfNnUm9Rs03+fq6Wzen7QHHM+ztJ0uje2ZmxSqz5xglsznXAwGMA3KBBcDewGhJjwKPAI2BFcBFEfG1pOOAm4GqwHzgLKAGcAmQK+l3wJXAocCyiPiHpEHAcKA7UB+4ICI+lXQwcH1EHCvpNmB7YMfk+78i4qGkr78DrkraHA5cFhG56xnXsqTfhwELgRuB+5I6r46INze1XbMtmlSkKGLTn8ly/GX3885TNxMZvBfBrLT5OUbpVyaJkaQ9gJuAAyJinqSGwP8BuwCHRUSupI+ASyJikqTOwKPAIcBnwH4REZIuBP4UEddJepwkEUraOLRQs1UiopOko4FbSSUuhbUhlTzVAb6R9BiwE3B60tfVScJ2FrC+2ahawKCIuEHS68DfgcOB3YFngTc3pd2IWF3M+9YT6AlwRJsq7NXCz+e0zLL/8RfT+ejfA/DDt6Oo33jb/HP1G7dgyfzZm1zXtrvsw5k3pf43q1Vva9p06kFe7homfP5WyXbazKyAsvrNegjwSkTMA4iIBUr9ddk3SYpqk1oW66uf/+qslnzfFnhJUjNSMzhTNrHN15Lvo4BW64npHxE5QI6kn4AmpGaeOgAjkr7UAH7aQDurgPeS4ywgJ0mosn5huzMKB0VEL6AXwJ8Oq+E/IyzjDH3zCYa++QQAbTofSZcTLmHMwJfZfrdOZC9fwtIFP25yXfecvVv+8Wl/7MXEYe86KTKzUldWe4xEagmtsOXJ90rAoohoX+Br7U/Jh4F/R8SewMVA9U1sMyf5nsv6E8KcAsdr4wQ8W6Afu0bEbRtoZ3X8vF6Qt7bOiMj7he2abdG+Hv4eC2ZP4YbnJnDKtY/Q76E/5J87/87Xqbt1MyC1wfrGPt9Rr3ELru01gt9c+2hZddnMrMx+AX8EvC7pgYiYnyyl5YuIJZKmSDo1IvoqNVXTLiLGAvWAmUnouQUuWwrULaW+vpH09aekr3Uioui9xWa2jn4PX1Ns+dM3/fxMoiH9HmVIvw0nQy/f37NE+2W2pfAeo/QrkxmjiJgA3Al8Imksqf1FhZ0FXJCcnwCckJTfRmqJ7VNgXoH4t4CTJI2RdGAJ9vUrUpu9B0gaB3wANCup+s3MzCxz6JfcJWKZw3uMzMwyx30fZhe9DbMEPP67rcvtz/pL/ju/VN6zzZWRzzEyMzMzKwve5PsrSRrOz3fKrXV2RGSVRX/MzMxs8zkx+pUionNZ98HMzMq3TP6w1fLKS2lmZmZmCSdGZmZmZgknRmZmZmYJ7zEyMzPLUHl+pE7aecbIzMzMLOHEyMzMzCzhxMjMzMws4T1GZmZmGcrPMUo/zxiZmZmZJZwYmZmZmSWcGJmZmZklvMfIzMwsQ+Xl+TlG6ebEyMxKzfGX/5M2nXqwOmcFL9/Xk5nfjSkS0+WES+h68hU0atGa207elhVL5gOw414Hcu7tfVk4eyoA4z97gw//e3cae29mFZETIzMrFW069aBRi9bcd25btt+tEyf94SH+fWW3InFTJwxl4rB3uPifA4qeyxrCMzefko7umpkB3mNUJiR1lPRQWffDrDTt3uVYRn/wPwCmT/yCGrXrUadh0yJxs74by8I509PdPTOzYjkxKgMRMTIirirrfpiVpnqNmrNo7oz814vmzqReo+a/qI7td+/M1U8M5/y7+tGk5W4l3UUzsyIq9FKapL8CZwE/APOAUUA/4HGgMZALnAqsAF4C6pJ6zy6NiE/XU+cy4BHgMGAhcCNwH7A9cHVEvCnpYOD6iDhW0m3JuR2T7/+KiGJnkyT1BHoCHNGmCnu1qND/fJbppCJF8Qs+EHPmpDHcfeaurFq5nDadenDu317mvvP2LMkemmU8b75Ovwo7YySpI3AKsDdwMtAxOfUC8EhE7AV0AWYDZwLvR0R7YC9gzAaqrgUMiogOwFLg78DhwEnA7eu5pg3QA+gE3Cppq+KCIqJXRHSMiI5OiiwT7X/8xVz9+DCufnwYS+bPpn7jbfPP1W/cgiXzZ29yXTkrlrJq5XIAvv7ifSpV2Yqadbcu8T6bmRVUkX+7dgXeiIhsAElvATWAFhHxOkBErEzOjQCeThKWfhExZgP1rgLeS46zgJyIWC0pC2i1nmv6R0QOkCPpJ6AJMGM9sWYZa+ibTzD0zScAaNP5SLqccAljBr7M9rt1Inv5EpYu+HGT66rdoAnLFs4BYLtdO6JKlfLvWDMzKy0VOTEqOs9ffBkRMVhSN+AY4HlJ90fEc+upd3X8vF6QB+QkdeRJWt/7nVPgOJeK/e9i5cTXw9+jTace3PDcBFblrKDv/Rfnnzv/ztd55f8uY8n82Rxw4mUcdPq11GnYhGt7jeDrL97jlf+7jHbdTmK/4y4iL3cNq1et5H9/P6cMR2NmFUVF/gX8GfCEpLtJvQ/HAE8CMySdGBH9JFUDKpPabzQzIp6UVAvYB1hfYmRmiX4PX1Ns+dM3nZR/PKTfowzp92iRmM/feJzP33i81PpmtiXwHqP0q7CJUUSMkPQmMBaYBowEFgNnk0qYbgdWk9p8fSDwR0mrgWWA/3Q1MzMrh/RL7hIpbyTVjohlkmoCg4GeETG6rPu1Kf50WI2K+w9nZpZh7vswu9itGJvrHyfVKrc/669/fXmpvGebq8LOGCV6SdodqA48u6UkRWZmZlY6KnRiFBFn/tprJQ0HqhUqPjsisjavV2ZmZineYpR+FTox2hwR0bms+2BmZmYlq8I+4NHMzMysMCdGZmZmZgkvpW2h1uSVdQ/MzKy0eY9R+nnGyMzMzCzhxMjMzMws4cTIzMzMLOHEyMzMzCzhzddmZmYZKte7r9POM0ZmZmZmCSdGZmZmZgknRmZmZmYJ7zEys1Jz0hX/ZLfOPVi1cgV97uvJzEljisR0PfESup1yBY1atOavJ27L8iXzAWi914Gcf0dfFvw4FYCsT99gwPN3p7H3ZmXPW4zSz4mRmZWK3Tr3oFGL1tx1dlta7taJ31z9EA9e3q1I3JTxQ5kw9B0uf2BAkXOTs4bwn5tOSUd3zcyALXQpTdIlks4p635sDknvSKpf1v0wKy1tuxzLyA/+B8C0iV9Qo3Y96jRsWiRu5ndjWThnerq7Z2ZWrC1yxigiHi/rPmyuiDi6rPtgVprqNmrOop9m5L9eNHcm9Ro1Z+mCHze5jla7d+b6J4ezeN5s3nziL8yZOrE0umpmli9jZowk9ZM0StIEST2TsmWS7pQ0VtIwSU2S8tskXZ8ct0/OjZP0uqQGSfkgSfdK+kLSt5IOTMprSno5iX9J0nBJHZNzj0kamfThbwX6NrVAXV9I2mkD4+id1DNQ0mRJB0l6WtJESb0L1dlIUqvk3JNJuwMk1VhP3T2T/o0cN2vNZr/nZqVJUtHC2PQNEzMmjeGOM3blHxd15rN+j3H+7S+XYO/Mtgx5UX6/MlXGJEbA+RHRAegIXCVpa6AWMCwi9gIGAxcVc91zwA0R0Q7IAm4tcK5KRHQCri5QfhmwMIm/A+hQIP6miOgItAMOktSuwLklSV3/Bv61kbE0AA4BrgHeAh4A9gD2lNS+mPidgUciYg9gEVDspoqI6BURHSOiY7vmW+Rkn5VzB5xwMdf1GsZ1vYaxZP5s6m+zbf65+o1bsHj+7E2uK2fFUlatXA7AxOHvU7nKVtSqu3WJ99nMrKBMSoyukjQWGAZsRypZWAW8nZwfBbQqeIGkekD9iPgkKXoWKLi787Viru0KvAgQEeOBcQXiT5M0GviSVCKze4FzfQp8338jY3krIoJUojYnIrIiIg+YUHgMiSkRMWZ94zTbUgx54wn+2XM//tlzP7I+e4uOh58JQMvdOrFy+ZJftIxWp0GT/OPt23REqpR/x5qZWWnJiGkHSQcDhwH7R8QKSYOA6sDqJMEAyOWX9zenmGuLmd8HSTsA1wP7RsTCZNmreoGQWM/xhtrNK3C89nVxYygYkwsUu5RmtiWZOPw9duvcgxv/O4HVK1fQ576L889ddPfrvPSPy1gyfzYHnnQZ3X97LXUaNuH6p0Yw8f/bu/PwKqtz7+Pfn2BFFARRcD5YpLUojogTFgR9Ra0CVhwOx4EqGG0di7YMVeoLalv1qCgithWovqjgWKvY1oICFUQJk0XFV/BojQMiKPOQ+/yxF3GbBBIM2dkhv8915cqz17OG+wmX23uvtZ5nT5/A43dcwaEde3DcmX0o3rCedWtW86chtfp+CzOrJfIiMQJ2IbO8tVLSgcAxlWkUEcskfSHphIiYDFwAvFxBsynAOcBESW2Atqm8MbACWJb2Mp0KTMpqdy5wW/r9auUuy6xue/Kea8stf7B/j5LjyU8NZ/JTw8vUmfL0CKY8XevvszCrknzei7OtypfEaAJQIGkO8DaZ5bTKuggYIakh8B7Qu4L6w4HRaaxCMktpyyJigaRCMstd7wFTS7XbQdJ0MsuP529BfGZmZlZLKLbgLpFtgaR6wPYRsVpSK+Al4HsRsXYzbRYB7SJicY7CrNB1nXesW/9wZmZ57M5/rCp3m0ZV3XRaw232vf7Xz6+slr9ZVeXLjFEuNSSzjLY9mf1Gl28uKTIzM7O6o84lRhHxFZlHAmxJm5alyyQNBHqWKh4XEUO/fXRmZmZWk+pcYrS1pATISZCZmVWb4uKajqDuyafnGJmZmZnVKCdGZmZmZokTIzMzM7PEe4xqqRVrttk7OM3MLNlQxx6pkw88Y2RmZmaWODEyMzMzS5wYmZmZmSXeY2RmZpan/CWyuecZIzMzM7PEiZGZmZlZ4sTIzMzMLPEeIzOrNudecwcHH9uVtatXMmpoHz54Z1aZOp1+XECXc66k+T6tuO60vVmx7HMAGuzUmEtufIimLfalXv36/O3/3cU/nx+T4yswq1n+rrTc84yRmVWLg489heb7HMCvzj2Ih3/7U3r1u6fcev9/zqvcdfVpLC56/xvlJ/64gKJF8xlycXvu+Nn/4ewrb6Ne/e1zEbqZ1WF5mxhJukZSw63YXztJ5b8zm9lWd2iHM5g24REAFr75Gjs2akLjZnuUqffBgtl8/vH7Zcojgh0aNgJghx13ZsWXX1C8YX31Bm1mdV7eJkbANcBWS4wi4vWIuGpr9Wdmm9dk971Y8umHJa+Xfvpvmu6+V6XbT3zifvZseSC/fWYhN455ncfu+jnhr0cws2qWs8RIUktJb0kaLWmOpPGSGkrqIqlQ0lxJf5S0g6SrgL2AiZImpvb3S3pd0puSfp3V722S/pX6vD2V9ZQ0T9JsSa+ksk6SnkvHgyX1y+pjXopvY4y/T2WPSDpJ0lRJCyS138z1DU7X9ldJiySdJem36bomSNo+1btR0ozU/0hl1E9lnVKdWyUN3dr/Bma5JKlM2ZYkNge1P5kPFszmhm77M+Ti9px/3V00SDNIZmbVJdczRt8HRkbEIcCXwHXAKODciGhLZjP45RFxD/ARcGJEnJjaDoyIdsAhQEdJh0jaFegBHJT6HJLq3gicEhGHAmduYYwHAHencQ4E/hPoAPQDBlTQthVwOtANeBiYmK5rVSoHuDcijoqIg4EdgR9FxHrgYuB+SScDXYFfl+5cUt+UHL4+/+MNW3hZZtWv01mXMWjUdAaNms7SxUXs2nyfknNNmu/N0sVFle7ruNMvpPDlZwD47N/vsbhoEXv8x/e3esxm+aw4tt2ffJXrxOiDiJiajh8GugALI+KdVDYa+OEm2p4jaSZQCBwEtCGTXK0Gfi/pLGBlqjsVGCWpD1BvC2NcGBFzI6IYeBN4KTIfc+cCLSto+0JErEt16wETUnl22xMlTZc0F+icroWIeBP4E/Bn4CcRsbZ05xExMiLaRUS7H+yxpZdlVv0mPfkAQy4+miEXH82sV57lmK69ANj/oPasWr6MLz//uNJ9LfnkAw48MvO5qFHT5rTYrzWffbSwWuI2M9so14nRt8oRJe1PZsamS5oZ+gvQIM20tAeeALqTEpGIKAAGAfsCsyQ1K9Xler557Q2yjtdkHRdnvS6m4scbrEnjFwPr4ut1g2KgvqQGwHDg7DST9GCpsdsCS4EWFYxjlvfmvTqBxR8tZMjj/+KCXwxn7B1Xl5z72e1Ps8tuewJw4tlXcNtT79J09725ccwMLvjl/QD8ZdSttGp7DDeOeZ1r73mBp4YPKrmV38ysuuT6OUb7STo2Il4Fzgf+Dlwm6YCIeBe4AHg51f0KaAQsBhoDK4BlkloApwKTJO0MNIyI5yVNA94FkNQqIqYD0yWdQSZByrYI+FGqewSwf7Vd8TdtTIIWp9jPBsanOM4CmpGZMXtOUvuIWJqjuMyqxdg7rym3/N5+3UuOJ44fzsTxw8vUWba4iLuv/VE1RWZmVr5cJ0bzgYskPQAsAK4GpgHjJNUHZgAjUt2RwAuSiiLiREmFZJa23iOzVAaZxOmZNBMj4NpU/jtJrVPZS8BsoGNWHE8AF0qalcZ8hxyIiKWSHiSztLYojY2k3YDbyMyIfSDpXjL7nC7KRVxmZpaf8nkvzrZKubr9VVJL4Lm06diq6LLjG/g/FzOzPPHA1NVlb8PcCq7rvOM2+15/5z9WVcvfrKry+TlGZmZmZjmVs6W0iFgE1PrZIkm9ySwBZpsaET+tiXjMzMxs6/GXyG6hiHgIeKim4zAzs23fBj/tPee8lGZmZmaWODEyMzMzS5wYmZmZmSXeY1RLLVlZXNMhmJlZNSv2W33OecbIzMzMLHFiZGZmZpY4MTIzMzNLnBiZmZmZJd58bWZmlqf8JbK55xkjMzMzs8SJkZmZmVnipTQzqza9b7iTI47vyprVq7jvpktY+NasMnWuGjqaVm2OZP36dbw7bwYjh17BhvXr6XDq+XS/uB8Aq1ct58FbruT9d+bk+ArMrK7xjJGZVYvDO3Rlz/0O4MpubXhgyOX0GXBvufUmvzCWq3sczM97Hs53GuxIlx4/AeDTjxZy06Vd6HfukYx/8BYuGzQ8l+Gb5YXi2HZ/8lWtT4wkdZJ0XNbrAkkXVtCmnaR7qj86s7rrqI5n8PJzjwCwYO5r7NSoCU1226NMvcIpE0qO3503g2bN9wHgndnTWPHV0kz7OdNp1mLv6g/azOq8vEqMJH2bpb1OQEliFBEjImLM5hpExOsRcdW3GMvMKmnX5nvx+ccflLz+/JMP2bX5XpusX69+fX54ei8K//limXOdu/emcGrZcjOzrS3niZGkCyXNkTRb0p8kjZJ0p6SJwG8ktZI0QdIbkiZLOjC1O0PSdEmFkv4uqYWklkABcK2kWZJOkDRYUr/UZpKk30h6TdI7kk5I5Z0kPZeOB0v6Y6r7nqSrsmL9r9R2lqQHJNXbzHUtT2O9keJrn9XnmalOy3RNM9PPcam8R2ojSXumWMt+tDarRSSVLYxNz59f2n8Y82dO5q3Cqd8oP6hdRzp3783Ddw/Y2iGamZWR083Xkg4CBgLHR8RiSbsCdwLfA06KiA2SXgIKImKBpKOB4UBnYApwTESEpEuBGyLi55JGAMsj4vY0RpdSw9aPiPaSTgNuAk4qJ7QDgROBRsDbku4HDgDOTbGukzQc6AVsajZqJ2BSRPxC0lPAEOBkoA0wGngW+BQ4OSJWS2oNjAXaRcRTkn4M/BToCtwUER+X8/frC/QFOGKfenx3t7ya8DPjlHMKOOmsSwB4983XabbHviXnmrXYhyWfFZXb7uy+g2jcdHduH3LFN8r3a92WghtHcMvPzmT5siXVF7hZnvKXyOZeru9K6wyMj4jFABGxJH2qHJeSop3JLIuNy/q0uUP6vQ/wmKQ9ge8ACys55pPp9xtAy03U+UtErAHWSPoUaAF0AY4EZqRYdiST2GzKWmDjZom5wJqUUM3NGnd74F5JhwEbyCSEG10JzAOmRcTY8gaIiJHASICeh38nj7euWV314uMjePHxEQAc0eFUup53OVMnPEbrtu1ZuXwZSxeXyffp3KM3hx13MjdfdgqRNaO02x77cv3tjzHsV70p+p8FObsGM6vbcp0YCSjvf+gr0u/tgKURcVg5dYYBd0bEs5I6AYMrOeaa9HsDm77eNVnHG+sJGB0R/Ss5zrr4+l29eGOfEVGctXfqWuAT4FAy17o6q/3eqV0LSdtFhD8nWK02c8oLHN6hK8Oenc/a1au4b/ClJef6D3uGETcX8MVnRfQdcB+fFb3P0NGTAZj+j6cZP3IoZ/cdyM5NmtGn/zAANmxYzy97HVsj12JmdUeuE6OXgKck/XdEfJ6W0kpExJeSFkrqGRHjlJmqOSQiZgO7AP9OVS/KavYV0LiaYn0mxfppirVRRLxfhT53AT5MydJFQD0o2XT+EPCfwIXAdcDtVQvfrOb94baryy2/9cpuJcfnHdWw3Dojbi5gxM0F1RKXmdmm5DQxiog3JQ0FXpa0ASgsp1ov4H5Jg8gsPT0KzCYzQzRO0r+BacD+qf6fgfGSupFZjtpasf4rxfBXSdsB68jsAapKYjQceEJST2AiX8+UDQAmR8RkSbPILN/9JSLmV2EsMzOr5TZs5oYFqx4K/9FrJe8xMjPLH+MK15ZzG2bVXXrcDtvse/3v/7mmWv5mVeXbmszMzMwSf1faFpI0na/vlNvogoiYWxPxmJmZ2dbjxGgLRcTRNR2DmZmZVQ8nRmZmZnkqn79sdVvlPUZmZmZmiRMjMzMzs8SJkZmZmVniPUZmZmZ5yl8im3ueMTIzMzNLnBiZmZmZJU6MzMzMzBLvMTIzM8tTfo5R+STtCjwGtAQWAedExBel6uwLjAH2AIqBkRFxd0V9e8bIzMzMaptfAi9FRGvgpfS6tPXAzyPiB8AxwE8ltamoYydGZmZmVtt0A0an49FA99IVIqIoImam46+A+cDeFXXsxMjMzMxqmxYRUQSZBAhovrnKkloChwPTK+rYe4zMrNr0vuFOjji+K2tWr+K+my5h4VuzytS5auhoWrU5kvXr1/HuvBmMHHoFG9avp8Op59P94n4ArF61nAdvuZL335mT4yswq1nb8h4jSX2BvllFIyNiZNb5v5PZH1TawC0cZ2fgCeCaiPiyovpOjMysWhzeoSt77ncAV3ZrQ+u27ekz4F4GXNihTL3JL4zlnoEXAXD1rX+iS4+f8NdxI/n0o4XcdGkXVny1lMOOP4XLBg0vt72Z1U4pCRq5mfMnbeqcpE8k7RkRRZL2BD7dRL3tySRFj0TEk5WJq9YspUkqkHRhTcdhZpVzVMczePm5RwBYMPc1dmrUhCa7lf3wVzhlQsnxu/Nm0Kz5PgC8M3saK75ammk/ZzrNWlS4NcDM6o5ngYvS8UXAM6UrSBLwB2B+RNxZ2Y5rTWIUESMiYkxNx2FmlbNr8734/OMPSl5//smH7Np8r03Wr1e/Pj88vReF/3yxzLnO3XtTOLVsuZnVWbcBJ0taAJycXiNpL0nPpzrHAxcAnSXNSj+nVdRxjSZGkp6W9IakN9NaI5KWSxoqabakaZJapPLBkvql48PSuTmSnpLUNJVPkvQbSa9JekfSCam8oaTHU/3HJE2X1C6du1/S6ymGX2fFtiirr9ckHbCZ6xiV+pko6T1JHSX9UdJ8SaOy6pUZS9Iukt6W9P30eqykPpsYp29q//p7i/0FOpbfMh/WSolNb5i4tP8w5s+czFuFU79RflC7jnTu3puH7x6wtUM0s1oqIj6PiC4R0Tr9XpLKP4qI09LxlIhQRBwSEYeln+c333PNzxj9JCKOBNoBV0lqBuwETIuIQ4FXgPKShDHALyLiEGAucFPWufoR0R64Jqv8CuCLVP//Akdm1R8YEe2AQ4COkg7JOvdl6ute4K4KrqUp0Bm4Fvgz8N/AQUBbSYdtaqyIWAb8DBgl6TygaUQ8WN4AETEyItpFRLvv7lbT/3RmZZ1yTgG/e3QGv3t0Bks+K6LZHvuWnGvWYh+WfFZUbruz+w6icdPdGX3H9d8o3691WwpuHMFvr/0xy5ctqdbYzfLRhoht9idf1fT/Xa+SNBuYBuwLtAbWAs+l82+QeaplCUm7AE0i4uVUNBr4YVaVjZurstt2AB4FiIh5QPatLedImgkUkklksh/+NDbr97EVXMufIyLIJGqfRMTciCgG3syKo9yxIuJvqd19wKUVjGOWt158fATXn3cU1593FDMmPkvHH/UCoHXb9qxcvoyliz8u06Zzj94cdtzJ3N3/v4isN8vd9tiX629/jGG/6k3R/yzI2TWYWd1WY3elSeoEnAQcGxErJU0CGgDr4ut3xw1seYxrymlbzpw+SNof6AccFRFfpGWvBllVYhPHmxu3OOt44+v6mxtL0nbAD4BVwK7AhxWMZZb3Zk55gcM7dGXYs/NZu3oV9w3+OufvP+wZRtxcwBefFdF3wH18VvQ+Q0dPBmD6P55m/MihnN13IDs3aUaf/sMA2LBhPb/sVdHnEzOzqqnJ2/V3IbO8tVLSgWQe112hiFgm6QtJJ0TEZDIbq16uoNkU4BxgYnoceNtU3hhYASxLe5lOBSZltTuXzIauc4FXK3dZm7S5sa4l80TOAcAfJR0bEeuqOJ5ZjfvDbVeXW37rld1Kjs87qmG5dUbcXMCImwuqJS4zs02pycRoAlAgaQ7wNpnltMq6CBghqSHwHtC7gvrDgdFprEIyS2nLImKBpEIyy13vAVNLtdtB0nQyS47nb0F8ZUTE7PLGkvQ9Mstn7SPiK0mvAIP45r4pMzOrg4p9n03OKfJ4A9TWIqkesH1ErJbUiswXzn0vZryBFgAAAudJREFUItZups0ioF1ELM5RmFuk5+Hf2fb/4czMaolxhWvL3bJRVecese2+1z82s3r+ZlVVV5583ZDMMtr2ZPYbXb65pMjMzMzqpjqRGKVv1W23hW1ali6TNBDoWap4XEQM/fbRmZmZWb6oE4nR1pISICdBZmaWE9vyl8jmq5p+jpGZmZlZ3nBiZGZmZpY4MTIzMzNL6sTt+maWvyT1jYiRNR2HmRl4xsjMal7fmg7AzGwjJ0ZmZmZmiRMjMzMzs8SJkZnVNO8vMrO84c3XZmZmZolnjMzMzMwSJ0ZmZmZmiRMjM/tWJA2W1O/bnq+GeLpLapOr8cxs2+TEyMxqHUn1y3ndHXBiZGZV4sTIzCpN0kBJb0v6O/D9VNZK0gRJb0iaLOnActr1kTRD0mxJT0hqKKmRpIWStk91GktatPF1OX1MknSLpJeBq0u9/gVwJvA7SbMktaq2P4KZbdPqV1zFzAwkHQmcBxxO5r1jJvAGmdvtCyJigaSjgeFA51LNn4yIB1M/Q4BLImKYpEnA6cDTqe8nImLdZsJoEhEdUz9nlHrdGnguIsZvjes1s7rJiZGZVdYJwFMRsRJA0rNAA+A4YJykjfV2KKftwSkhagLsDLyYyn8P3EAmMeoN9KkghscqeG1mViVOjMxsS5R+8Nl2wNKIOKyCdqOA7hExW9LFQCeAiJgqqaWkjkC9iJhXQT8rKnhtZlYl3mNkZpX1CtBD0o6SGgFnACuBhZJ6Aijj0HLaNgKK0v6hXqXOjQHGAg9VMb6v0jhmZt+aEyMzq5SImElm6WoW8AQwOZ3qBVwiaTbwJtCtnOa/AqYDfwPeKnXuEaApmeSoKh4FrpdU6M3XZvZt+StBzKxGSTob6BYRF9R0LGZm3mNkZjVG0jDgVOC0mo7FzAw8Y2RmeUbSfcDxpYrvjoiq7kEyM6uQEyMzMzOzxJuvzczMzBInRmZmZmaJEyMzMzOzxImRmZmZWeLEyMzMzCz5X2PA1rPdLe6sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find correlation\n",
    "# only plots continuous, discrete and converted ordinal variables\n",
    "plt.figure(figsize=(8, 20))\n",
    "ax = sns.heatmap(num_aki.corr(method='spearman')[['delay_rrt']].sort_values(by='delay_rrt', ascending=False), annot=True, cmap='BrBG')\n",
    "ax.set_title('Variables correlating with delay_rrt', fontdict={'fontsize':25}, pad=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "264a3a95",
   "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>delay_rrt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>delay_rrt</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aniongap_max</th>\n",
       "      <td>0.219092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine_max</th>\n",
       "      <td>0.215134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potassium_max</th>\n",
       "      <td>0.178538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chloride_min</th>\n",
       "      <td>0.176414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aniongap_min</th>\n",
       "      <td>0.153351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gcs_min</th>\n",
       "      <td>0.145449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine_min</th>\n",
       "      <td>0.143292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bicarbonate_min</th>\n",
       "      <td>0.111833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pt_max</th>\n",
       "      <td>0.109496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperature_mean</th>\n",
       "      <td>0.109039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chloride_max</th>\n",
       "      <td>0.106527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inr_max</th>\n",
       "      <td>0.105324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose_max</th>\n",
       "      <td>0.102591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calcium_max</th>\n",
       "      <td>0.101555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_age</th>\n",
       "      <td>0.092150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platelets_max</th>\n",
       "      <td>0.083694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mbp_mean</th>\n",
       "      <td>0.080355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bun_max</th>\n",
       "      <td>0.079240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ptt_max</th>\n",
       "      <td>0.077768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dbp_mean</th>\n",
       "      <td>0.073974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platelets_min</th>\n",
       "      <td>0.061872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sodium_min</th>\n",
       "      <td>0.060589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aki_stage</th>\n",
       "      <td>0.060407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hematocrit_max</th>\n",
       "      <td>0.058377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sbp_mean</th>\n",
       "      <td>0.051832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calcium_min</th>\n",
       "      <td>0.050788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hemoglobin_max</th>\n",
       "      <td>0.046835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hemoglobin_min</th>\n",
       "      <td>0.045429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose_min</th>\n",
       "      <td>0.041269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose_mean</th>\n",
       "      <td>0.041170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  delay_rrt\n",
       "delay_rrt          1.000000\n",
       "aniongap_max       0.219092\n",
       "creatinine_max     0.215134\n",
       "potassium_max      0.178538\n",
       "chloride_min       0.176414\n",
       "aniongap_min       0.153351\n",
       "gcs_min            0.145449\n",
       "creatinine_min     0.143292\n",
       "bicarbonate_min    0.111833\n",
       "pt_max             0.109496\n",
       "temperature_mean   0.109039\n",
       "chloride_max       0.106527\n",
       "inr_max            0.105324\n",
       "glucose_max        0.102591\n",
       "calcium_max        0.101555\n",
       "admission_age      0.092150\n",
       "platelets_max      0.083694\n",
       "mbp_mean           0.080355\n",
       "bun_max            0.079240\n",
       "ptt_max            0.077768\n",
       "dbp_mean           0.073974\n",
       "platelets_min      0.061872\n",
       "sodium_min         0.060589\n",
       "aki_stage          0.060407\n",
       "hematocrit_max     0.058377\n",
       "sbp_mean           0.051832\n",
       "calcium_min        0.050788\n",
       "hemoglobin_max     0.046835\n",
       "hemoglobin_min     0.045429\n",
       "glucose_min        0.041269\n",
       "glucose_mean       0.041170"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clearer dataframe\n",
    "# for corr >= 0.05\n",
    "aki_corr = num_aki.corr(method='spearman').abs()\n",
    "aki_corr.loc[aki_corr['delay_rrt']>=0.04,['delay_rrt']].sort_values(by=['delay_rrt'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c351be29",
   "metadata": {},
   "source": [
    "There are 30 columns above. \n",
    "Clinician suggested dropping 'sbp_mean' and 'dbp_mean', since 'mbp_mean' is derived from them.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2444366f",
   "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>delay_rrt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>0.022518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.011053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hematocrit_min</th>\n",
       "      <td>0.038520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wbc_min</th>\n",
       "      <td>0.003966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wbc_max</th>\n",
       "      <td>0.006672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bicarbonate_max</th>\n",
       "      <td>0.016021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bun_min</th>\n",
       "      <td>0.018054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sodium_max</th>\n",
       "      <td>0.019904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potassium_min</th>\n",
       "      <td>0.004764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_rate_mean</th>\n",
       "      <td>0.012692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resp_rate_mean</th>\n",
       "      <td>0.035872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spo2_mean</th>\n",
       "      <td>0.031398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 delay_rrt\n",
       "weight            0.022518\n",
       "gender            0.011053\n",
       "hematocrit_min    0.038520\n",
       "wbc_min           0.003966\n",
       "wbc_max           0.006672\n",
       "bicarbonate_max   0.016021\n",
       "bun_min           0.018054\n",
       "sodium_max        0.019904\n",
       "potassium_min     0.004764\n",
       "heart_rate_mean   0.012692\n",
       "resp_rate_mean    0.035872\n",
       "spo2_mean         0.031398"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop these? \n",
    "aki_corr.loc[aki_corr['delay_rrt']<0.04,['delay_rrt']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ca863a49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.79437153551967 0.4345492783233209\n"
     ]
    }
   ],
   "source": [
    "# drop categotical columns ?\n",
    "# performing chisq for nominal ethnicity feature\n",
    "\n",
    "# relationship between ethnicity and delay_rrt\n",
    "obs = pd.crosstab(aki_data['ethnicity'],aki_data['delay_rrt'])\n",
    "chi2, p, dof, expected = stats.chi2_contingency(obs)\n",
    "print(chi2,p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90546f62",
   "metadata": {},
   "source": [
    "p=0.435. So do not reject null hypothesis. Both variables are independent (ie no correlation). Therefore, I am inclined to drop 'ethnicity'. Also, as a categorical variable (with >2 labels), I do not have to perform one-hot-encoding later. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0822a96b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1629194468840113 0.686482753454661\n"
     ]
    }
   ],
   "source": [
    "# performing chisq for nominal gender feature\n",
    "\n",
    "# relationship between ethnicity and delay_rrt\n",
    "obs = pd.crosstab(aki_data['gender'],aki_data['delay_rrt'])\n",
    "chi2, p, dof, expected = stats.chi2_contingency(obs)\n",
    "print(chi2,p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e1b8359",
   "metadata": {},
   "source": [
    "p=0.686. So do not reject null hypothesis. Both variables are independent (ie no correlation). Therefore, I am inclined to drop 'gender' too?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "50d039e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1778 entries, 0 to 2109\n",
      "Data columns (total 44 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   weight            1778 non-null   float64\n",
      " 1   aki_stage         1778 non-null   int64  \n",
      " 2   delay_rrt         1778 non-null   int64  \n",
      " 3   gender            1778 non-null   int64  \n",
      " 4   admission_age     1778 non-null   float64\n",
      " 5   ethnicity         1778 non-null   object \n",
      " 6   hematocrit_min    1778 non-null   float64\n",
      " 7   hematocrit_max    1778 non-null   float64\n",
      " 8   hemoglobin_min    1778 non-null   float64\n",
      " 9   hemoglobin_max    1778 non-null   float64\n",
      " 10  platelets_min     1778 non-null   float64\n",
      " 11  platelets_max     1778 non-null   float64\n",
      " 12  wbc_min           1778 non-null   float64\n",
      " 13  wbc_max           1778 non-null   float64\n",
      " 14  aniongap_min      1778 non-null   float64\n",
      " 15  aniongap_max      1778 non-null   float64\n",
      " 16  bicarbonate_min   1778 non-null   float64\n",
      " 17  bicarbonate_max   1778 non-null   float64\n",
      " 18  bun_min           1778 non-null   float64\n",
      " 19  bun_max           1778 non-null   float64\n",
      " 20  calcium_min       1778 non-null   float64\n",
      " 21  calcium_max       1778 non-null   float64\n",
      " 22  chloride_min      1778 non-null   float64\n",
      " 23  chloride_max      1778 non-null   float64\n",
      " 24  creatinine_min    1778 non-null   float64\n",
      " 25  creatinine_max    1778 non-null   float64\n",
      " 26  glucose_min       1778 non-null   float64\n",
      " 27  glucose_max       1778 non-null   float64\n",
      " 28  sodium_min        1778 non-null   float64\n",
      " 29  sodium_max        1778 non-null   float64\n",
      " 30  potassium_min     1778 non-null   float64\n",
      " 31  potassium_max     1778 non-null   float64\n",
      " 32  inr_max           1778 non-null   float64\n",
      " 33  pt_max            1778 non-null   float64\n",
      " 34  ptt_max           1778 non-null   float64\n",
      " 35  heart_rate_mean   1778 non-null   float64\n",
      " 36  sbp_mean          1778 non-null   float64\n",
      " 37  dbp_mean          1778 non-null   float64\n",
      " 38  mbp_mean          1778 non-null   float64\n",
      " 39  resp_rate_mean    1778 non-null   float64\n",
      " 40  temperature_mean  1778 non-null   float64\n",
      " 41  spo2_mean         1778 non-null   float64\n",
      " 42  glucose_mean      1778 non-null   float64\n",
      " 43  gcs_min           1778 non-null   int64  \n",
      "dtypes: float64(39), int64(4), object(1)\n",
      "memory usage: 625.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# current dataset\n",
    "aki_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1f0f36",
   "metadata": {},
   "source": [
    "## Pandas profiling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a68661",
   "metadata": {},
   "source": [
    "Experimenting with auto-EDA tools. Handy during a datathon with time constraints. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9305be88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf8a8d00fd89480099e5a7026a3a4b04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce363d362f71409fb667df450d341e82",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d0e1bc14b314378b95e5c3a0d1956c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0d9f6014d924b48ba34fd19f33f2243",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from pandas_profiling import ProfileReport\n",
    "# plt.switch_backend('agg')\n",
    "prof = ProfileReport(aki_data)\n",
    "prof.to_file(output_file='output.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a9fc120e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df5af3bb",
   "metadata": {},
   "source": [
    "export dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "efa9f309",
   "metadata": {},
   "outputs": [],
   "source": [
    "aki_data.to_csv('cleaned_data.csv',index=False)"
   ]
  }
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