cohort_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install lifelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T19:23:24.361875Z",
     "start_time": "2021-05-30T19:23:16.369968Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import scipy.stats as sp\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from lifelines import KaplanMeierFitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:16:45.085288Z",
     "start_time": "2021-05-30T20:16:32.082362Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>f.eid</th>\n",
       "      <th>f.31.0.0</th>\n",
       "      <th>f.34.0.0</th>\n",
       "      <th>f.52.0.0</th>\n",
       "      <th>f.84.0.0</th>\n",
       "      <th>f.84.0.1</th>\n",
       "      <th>f.84.0.2</th>\n",
       "      <th>f.84.0.3</th>\n",
       "      <th>f.84.0.4</th>\n",
       "      <th>f.84.0.5</th>\n",
       "      <th>...</th>\n",
       "      <th>f.42012.0.0</th>\n",
       "      <th>f.130706.0.0</th>\n",
       "      <th>f.130708.0.0</th>\n",
       "      <th>f.130710.0.0</th>\n",
       "      <th>f.130712.0.0</th>\n",
       "      <th>f.130714.0.0</th>\n",
       "      <th>f.131286.0.0</th>\n",
       "      <th>f.131294.0.0</th>\n",
       "      <th>f.132084.0.0</th>\n",
       "      <th>f.132085.0.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000047</td>\n",
       "      <td>Male</td>\n",
       "      <td>1943</td>\n",
       "      <td>July</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>2010-06-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000050</td>\n",
       "      <td>Male</td>\n",
       "      <td>1958</td>\n",
       "      <td>October</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000068</td>\n",
       "      <td>Male</td>\n",
       "      <td>1942</td>\n",
       "      <td>October</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>2017-05-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>2017-05-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000094</td>\n",
       "      <td>Male</td>\n",
       "      <td>1952</td>\n",
       "      <td>September</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000145</td>\n",
       "      <td>Male</td>\n",
       "      <td>1945</td>\n",
       "      <td>September</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 320 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     f.eid f.31.0.0  f.34.0.0   f.52.0.0  f.84.0.0  f.84.0.1  f.84.0.2  \\\n",
       "0  1000047     Male      1943       July       NaN       NaN       NaN   \n",
       "1  1000050     Male      1958    October       NaN       NaN       NaN   \n",
       "2  1000068     Male      1942    October       NaN       NaN       NaN   \n",
       "3  1000094     Male      1952  September       NaN       NaN       NaN   \n",
       "4  1000145     Male      1945  September       NaN       NaN       NaN   \n",
       "\n",
       "   f.84.0.3  f.84.0.4  f.84.0.5  ...  f.42012.0.0  f.130706.0.0  f.130708.0.0  \\\n",
       "0       NaN       NaN       NaN  ...          NaN           NaN           NaN   \n",
       "1       NaN       NaN       NaN  ...          NaN           NaN           NaN   \n",
       "2       NaN       NaN       NaN  ...          NaN           NaN    2017-05-30   \n",
       "3       NaN       NaN       NaN  ...          NaN           NaN           NaN   \n",
       "4       NaN       NaN       NaN  ...          NaN           NaN           NaN   \n",
       "\n",
       "   f.130710.0.0  f.130712.0.0  f.130714.0.0  f.131286.0.0  f.131294.0.0  \\\n",
       "0           NaN           NaN           NaN    2010-06-28           NaN   \n",
       "1           NaN           NaN           NaN           NaN           NaN   \n",
       "2           NaN           NaN    2004-04-01    2017-05-30           NaN   \n",
       "3           NaN           NaN           NaN           NaN           NaN   \n",
       "4           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "   f.132084.0.0  f.132085.0.0  \n",
       "0           NaN           NaN  \n",
       "1           NaN           NaN  \n",
       "2           NaN           NaN  \n",
       "3           NaN           NaN  \n",
       "4           NaN           NaN  \n",
       "\n",
       "[5 rows x 320 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = pd.read_csv(\"clinical_data/subset_cohort.csv\",low_memory=False)\n",
    "df = pd.read_csv(\"subset_cohort.csv\",low_memory=False)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:16:45.511655Z",
     "start_time": "2021-05-30T20:16:45.441529Z"
    }
   },
   "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>eid</th>\n",
       "      <th>sex_f31_0_0</th>\n",
       "      <th>year_of_birth_f34_0_0</th>\n",
       "      <th>month_of_birth_f52_0_0</th>\n",
       "      <th>number_of_children_fathered_f2405_0_0</th>\n",
       "      <th>diabetes_diagnosed_by_doctor_f2443_0_0</th>\n",
       "      <th>cancer_diagnosed_by_doctor_f2453_0_0</th>\n",
       "      <th>age_high_blood_pressure_diagnosed_f2966_0_0</th>\n",
       "      <th>cancer_yearage_first_occurred_f84_0_0</th>\n",
       "      <th>age_heart_attack_diagnosed_f3894_0_0</th>\n",
       "      <th>...</th>\n",
       "      <th>age_at_cancer_diagnosis_f40008_0_0</th>\n",
       "      <th>description_of_cause_of_death_f40010_0_0</th>\n",
       "      <th>date_of_myocardial_infarction_f42000_0_0</th>\n",
       "      <th>date_of_stemi_f42002_0_0</th>\n",
       "      <th>date_of_nstemi_f42004_0_0</th>\n",
       "      <th>date_of_stroke_f42006_0_0</th>\n",
       "      <th>date_of_ischaemic_stroke_f42008_0_0</th>\n",
       "      <th>date_n46_first_reported_male_infertility_f132084_0_0</th>\n",
       "      <th>source_of_report_of_n46_male_infertility_f132085_0_0</th>\n",
       "      <th>infertile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000047</td>\n",
       "      <td>Male</td>\n",
       "      <td>1943</td>\n",
       "      <td>July</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000050</td>\n",
       "      <td>Male</td>\n",
       "      <td>1958</td>\n",
       "      <td>October</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000068</td>\n",
       "      <td>Male</td>\n",
       "      <td>1942</td>\n",
       "      <td>October</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>69.7</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>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000094</td>\n",
       "      <td>Male</td>\n",
       "      <td>1952</td>\n",
       "      <td>September</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000145</td>\n",
       "      <td>Male</td>\n",
       "      <td>1945</td>\n",
       "      <td>September</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       eid sex_f31_0_0  year_of_birth_f34_0_0 month_of_birth_f52_0_0  \\\n",
       "0  1000047        Male                   1943                   July   \n",
       "1  1000050        Male                   1958                October   \n",
       "2  1000068        Male                   1942                October   \n",
       "3  1000094        Male                   1952              September   \n",
       "4  1000145        Male                   1945              September   \n",
       "\n",
       "   number_of_children_fathered_f2405_0_0  \\\n",
       "0                                    0.0   \n",
       "1                                    2.0   \n",
       "2                                    2.0   \n",
       "3                                    2.0   \n",
       "4                                    2.0   \n",
       "\n",
       "  diabetes_diagnosed_by_doctor_f2443_0_0 cancer_diagnosed_by_doctor_f2453_0_0  \\\n",
       "0                                     No                                   No   \n",
       "1                                     No                                   No   \n",
       "2                                    Yes                                   No   \n",
       "3                                     No                                   No   \n",
       "4                                     No                                   No   \n",
       "\n",
       "   age_high_blood_pressure_diagnosed_f2966_0_0  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                         62.0   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "\n",
       "   cancer_yearage_first_occurred_f84_0_0  \\\n",
       "0                                    NaN   \n",
       "1                                    NaN   \n",
       "2                                    NaN   \n",
       "3                                    NaN   \n",
       "4                                    NaN   \n",
       "\n",
       "   age_heart_attack_diagnosed_f3894_0_0  ...  \\\n",
       "0                                   NaN  ...   \n",
       "1                                   NaN  ...   \n",
       "2                                   NaN  ...   \n",
       "3                                   NaN  ...   \n",
       "4                                   NaN  ...   \n",
       "\n",
       "   age_at_cancer_diagnosis_f40008_0_0  \\\n",
       "0                                 NaN   \n",
       "1                                 NaN   \n",
       "2                                69.7   \n",
       "3                                 NaN   \n",
       "4                                 NaN   \n",
       "\n",
       "   description_of_cause_of_death_f40010_0_0  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       NaN   \n",
       "4                                       NaN   \n",
       "\n",
       "   date_of_myocardial_infarction_f42000_0_0 date_of_stemi_f42002_0_0  \\\n",
       "0                                       NaN                      NaN   \n",
       "1                                       NaN                      NaN   \n",
       "2                                       NaN                      NaN   \n",
       "3                                       NaN                      NaN   \n",
       "4                                       NaN                      NaN   \n",
       "\n",
       "   date_of_nstemi_f42004_0_0 date_of_stroke_f42006_0_0  \\\n",
       "0                        NaN                       NaN   \n",
       "1                        NaN                       NaN   \n",
       "2                        NaN                       NaN   \n",
       "3                        NaN                       NaN   \n",
       "4                        NaN                       NaN   \n",
       "\n",
       "  date_of_ischaemic_stroke_f42008_0_0  \\\n",
       "0                                 NaN   \n",
       "1                                 NaN   \n",
       "2                                 NaN   \n",
       "3                                 NaN   \n",
       "4                                 NaN   \n",
       "\n",
       "   date_n46_first_reported_male_infertility_f132084_0_0  \\\n",
       "0                                                NaN      \n",
       "1                                                NaN      \n",
       "2                                                NaN      \n",
       "3                                                NaN      \n",
       "4                                                NaN      \n",
       "\n",
       "   source_of_report_of_n46_male_infertility_f132085_0_0  infertile  \n",
       "0                                                NaN         False  \n",
       "1                                                NaN         False  \n",
       "2                                                NaN         False  \n",
       "3                                                NaN         False  \n",
       "4                                                NaN         False  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colnames=['eid',\n",
    "          'sex_f31_0_0','year_of_birth_f34_0_0',\n",
    "'month_of_birth_f52_0_0','number_of_children_fathered_f2405_0_0',\n",
    " 'diabetes_diagnosed_by_doctor_f2443_0_0',\n",
    " 'cancer_diagnosed_by_doctor_f2453_0_0',\n",
    " 'age_high_blood_pressure_diagnosed_f2966_0_0',\n",
    " 'cancer_yearage_first_occurred_f84_0_0',\n",
    " 'age_heart_attack_diagnosed_f3894_0_0',\n",
    " 'age_diabetes_diagnosed_f2976_0_0',\n",
    " 'cancer_code_selfreported_f20001_0_0',\n",
    " 'interpolated_age_of_participant_when_cancer_first_diagnosed_f20007_0_0',\n",
    " 'smoking_status_f20116_0_0',\n",
    " 'body_mass_index_bmi_f21001_0_0',\n",
    " 'date_of_death_f40000_0_0',\n",
    " 'type_of_cancer_icd10_f40006_0_0',\n",
    " 'age_at_death_f40007_0_0',\n",
    " 'age_at_death_f40007_1_0',\n",
    " 'age_at_cancer_diagnosis_f40008_0_0',\n",
    " 'description_of_cause_of_death_f40010_0_0',\n",
    " 'date_of_myocardial_infarction_f42000_0_0',\n",
    " 'date_of_stemi_f42002_0_0',\n",
    " 'date_of_nstemi_f42004_0_0',\n",
    " 'date_of_stroke_f42006_0_0',\n",
    " 'date_of_ischaemic_stroke_f42008_0_0',\n",
    " 'date_n46_first_reported_male_infertility_f132084_0_0',\n",
    " 'source_of_report_of_n46_male_infertility_f132085_0_0']\n",
    "\n",
    "cols = [\"f.eid\",\"f.31.0.0\",\"f.34.0.0\",\"f.52.0.0\",'f.2405.0.0',\n",
    " 'f.2443.0.0',\n",
    " 'f.2453.0.0',\n",
    " 'f.2966.0.0',\n",
    " 'f.84.0.0',\n",
    " 'f.3894.0.0',\n",
    " 'f.2976.0.0',\n",
    " 'f.20001.0.0',\n",
    " 'f.20007.0.0',\n",
    " 'f.20116.0.0',\n",
    " 'f.21001.0.0',\n",
    " 'f.40000.0.0',\n",
    " 'f.40006.0.0',\n",
    " 'f.40007.0.0',\n",
    " 'f.40007.1.0',\n",
    " 'f.40008.0.0',\n",
    " 'f.40010.0.0',\n",
    " 'f.42000.0.0',\n",
    " 'f.42002.0.0',\n",
    " 'f.42004.0.0',\n",
    " 'f.42006.0.0',\n",
    " 'f.42008.0.0',\n",
    " 'f.132084.0.0',\n",
    " 'f.132085.0.0']\n",
    "\n",
    "df = df[cols]\n",
    "df.columns=colnames\n",
    "df[\"infertile\"]=df['date_n46_first_reported_male_infertility_f132084_0_0'].notnull()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:16:45.869190Z",
     "start_time": "2021-05-30T20:16:45.813944Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((456, 29), (181522, 29))"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infertile_df = df.loc[df[\"infertile\"]]\n",
    "fertile_df = df.loc[~df[\"infertile\"]]\n",
    "infertile_df.shape,fertile_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:16:46.311132Z",
     "start_time": "2021-05-30T20:16:46.227084Z"
    }
   },
   "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>eid</th>\n",
       "      <th>year_of_birth_f34_0_0</th>\n",
       "      <th>number_of_children_fathered_f2405_0_0</th>\n",
       "      <th>age_high_blood_pressure_diagnosed_f2966_0_0</th>\n",
       "      <th>cancer_yearage_first_occurred_f84_0_0</th>\n",
       "      <th>age_heart_attack_diagnosed_f3894_0_0</th>\n",
       "      <th>age_diabetes_diagnosed_f2976_0_0</th>\n",
       "      <th>cancer_code_selfreported_f20001_0_0</th>\n",
       "      <th>interpolated_age_of_participant_when_cancer_first_diagnosed_f20007_0_0</th>\n",
       "      <th>body_mass_index_bmi_f21001_0_0</th>\n",
       "      <th>age_at_death_f40007_0_0</th>\n",
       "      <th>age_at_death_f40007_1_0</th>\n",
       "      <th>age_at_cancer_diagnosis_f40008_0_0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infertile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>3.513190e+06</td>\n",
       "      <td>1950.887931</td>\n",
       "      <td>1.752885</td>\n",
       "      <td>48.201365</td>\n",
       "      <td>1801.641253</td>\n",
       "      <td>51.538585</td>\n",
       "      <td>50.544395</td>\n",
       "      <td>1831.944239</td>\n",
       "      <td>54.178025</td>\n",
       "      <td>27.846765</td>\n",
       "      <td>69.853325</td>\n",
       "      <td>67.010811</td>\n",
       "      <td>61.420108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>3.661175e+06</td>\n",
       "      <td>1954.945175</td>\n",
       "      <td>1.127193</td>\n",
       "      <td>46.373832</td>\n",
       "      <td>1547.025641</td>\n",
       "      <td>46.444444</td>\n",
       "      <td>46.352941</td>\n",
       "      <td>1039.564103</td>\n",
       "      <td>46.687179</td>\n",
       "      <td>27.810567</td>\n",
       "      <td>66.940000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.958333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    eid  year_of_birth_f34_0_0  \\\n",
       "infertile                                        \n",
       "False      3.513190e+06            1950.887931   \n",
       "True       3.661175e+06            1954.945175   \n",
       "\n",
       "           number_of_children_fathered_f2405_0_0  \\\n",
       "infertile                                          \n",
       "False                                   1.752885   \n",
       "True                                    1.127193   \n",
       "\n",
       "           age_high_blood_pressure_diagnosed_f2966_0_0  \\\n",
       "infertile                                                \n",
       "False                                        48.201365   \n",
       "True                                         46.373832   \n",
       "\n",
       "           cancer_yearage_first_occurred_f84_0_0  \\\n",
       "infertile                                          \n",
       "False                                1801.641253   \n",
       "True                                 1547.025641   \n",
       "\n",
       "           age_heart_attack_diagnosed_f3894_0_0  \\\n",
       "infertile                                         \n",
       "False                                 51.538585   \n",
       "True                                  46.444444   \n",
       "\n",
       "           age_diabetes_diagnosed_f2976_0_0  \\\n",
       "infertile                                     \n",
       "False                             50.544395   \n",
       "True                              46.352941   \n",
       "\n",
       "           cancer_code_selfreported_f20001_0_0  \\\n",
       "infertile                                        \n",
       "False                              1831.944239   \n",
       "True                               1039.564103   \n",
       "\n",
       "           interpolated_age_of_participant_when_cancer_first_diagnosed_f20007_0_0  \\\n",
       "infertile                                                                           \n",
       "False                                              54.178025                        \n",
       "True                                               46.687179                        \n",
       "\n",
       "           body_mass_index_bmi_f21001_0_0  age_at_death_f40007_0_0  \\\n",
       "infertile                                                            \n",
       "False                           27.846765                69.853325   \n",
       "True                            27.810567                66.940000   \n",
       "\n",
       "           age_at_death_f40007_1_0  age_at_cancer_diagnosis_f40008_0_0  \n",
       "infertile                                                               \n",
       "False                    67.010811                           61.420108  \n",
       "True                           NaN                           53.958333  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"infertile\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:16:47.021876Z",
     "start_time": "2021-05-30T20:16:46.655499Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SYSBJmiYMkuyU5OJBFSNJGo4pw6A9tuDbSQ4YUD2SpCHoZwB5H+CSJN8Eto5NrKrDO6tKkjRQ/YTBWzqvQpI0VP0cZ7AhyXLgYVX1pSRLgSXdlyZJGpR+TlT3SuBU4APtpAcDp3VYkyRpwPrZtfTVwMHAzQBV9T1g7y6LkiQNVj9hcFtV/XzsQZKdaa50NqUk+ydZn+TSJJckOWp7CpUkdaefMNiQ5A3AbkmeCfwr8Lk+5rsd+Iuq+hXgScCrkzxy9qVKkrrSTxisBW4ALgJeBZwJvHG6marq2qo6r71/C3ApzXiDJGkHk6ppe3xIsivwCJruoe/2dhv1tZJkBPgKcFBV3TzuuTXAGoBly5Y9/pRTTpnJohedI684cmDreu/y9w5sXVq4Xn3OVrb+ovv17L4LvP83d+9+RfPYqlWrNlXVigmfrKopbzRXOrsKGAU2AFcCz55uvp757wNsAp4/XdvHP/7xpaktP/r0Gc+zfv36gaxHmoif2R0HsLEm+f7t56CzdwKrqur7AEkOBM4APj/djEl2AT4NrKuqz/SxLknSEPQzZnD9WBC0Lgeun26mJAE+DFxaVe+aZX2SpAGYdMsgyfPbu5ckORP4FM2YwR8A3+pj2QcDLwYuSnJBO+0NVXXm7MuVJHVhqm6i3+m5fx1wSHv/BuD+0y24qr5Kc5lMSdIObtIwqKqXDrIQdWDdOjjmGA658ko44AA47jhYvXrYVUnaAU07gJzkIcCRwEhv+/IU1ju2detgzRq49dZm8+yKK5rHYCBIuod+9iY6jWYg+HPAnZ1Wo7lzzDFw663bTrv11ma6YSBpnH7C4GdV9Z7OK9HcuvLKmU2XtKj1Ewb/J8mxwFnAbWMTqz3VhHZQBxzQdA1NNF2SxuknDH6VZhfRp3N3N1G1j7WjOu64u8YM7rJ0aTNdksbpJwx+F/gfNcPzEWnIxsYFjjmGuvJK4t5EkqbQzxHI3wb27LgOdWH1ati8mQ1f/jJs3mwQSJpUP1sGy4DLknyLbccM3LVUkhaIfsLg2M6rkCQN1bRhUFUbBlGIJGl4+jkC+RbuvubxrsAuwNaqum+XhUmSBqefLYM9eh8neR7wxK4KkiQNXj97E22jqk7DYwwkaUHpp5vo+T0PdwJWcHe3kSRpAehnb6Le6xrcDmwGnttJNZKkoehnzMDrGkjSAjfVZS/fNMV8VVVv66AeSdIQTLVlsHWCabsDLwceCBgGkrRATHXZy3eO3U+yB3AU8FLgFOCdk80nSZp/phwzSPIA4M+B1cDJwOOq6seDKEySNDhTjRmcCDwfOAn41araMrCqJEkDNdVBZ38B7Au8Ebgmyc3t7ZYkNw+mPEnSIEw1ZjDjo5MlSfOTX/iSdhzr1sHICIc8/ekwMtI81kD0cwSyJHVv3bq7rtsdgCuuaB6DV+kbALcMJO0YjjkGbr1122m33tpMV+cMA0k7hiuvnNl0zSnDQNKO4YADZjZdc8owkLRjOO44WLp022lLlzbT1bnOwiDJPyW5PsnFXa1D0gKyejWcdBIsX04lsHx589jB44Hocsvgo8CzOly+pIVm9WrYvJkNX/4ybN5sEAxQZ2FQVV8Bbuxq+ZKkueOYgSRp+AedJVkDrAFYtmwZo6Ojwy1oHhhZe8bMZ/rCzObZfRf8v9Cc2ONX1vKrJ6+d+Ywnz3Q9MDq6+8zXIwBS1d217ZOMAKdX1UH9tF+xYkVt3Lixs3oWq5G1Z7D5hMOGXYYWqdl8/kZHR1m5cmXn61lskmyqqhUTPWc3kSSp011LPwmcC/xykquTvLyrdUmStk9nYwZV9cKuli1Jmlt2E0mSDANJkmEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSXQcBkmeleS7Sb6fZG2X65IkzV5nYZBkCfB+4NnAI4EXJnlkV+uTJM1el1sGTwS+X1WXV9XPgVOA53a4PknSLHUZBg8Grup5fHU7TZK0g9m5w2Vngml1j0bJGmANwLJlyxgdHe2wpIVr1apVUz6ft088ff369R1UI21rZO0Z95h2xdufM6tlLT/69Amn774Lfn9shy7D4Gpg/57H+wHXjG9UVScBJwGsWLGiVq5c2WFJC1fVPXL2LqOjo/i+alg2r5zkiRP8zO5Iuuwm+hbwsCQPSbIr8ALgsx2uT5I0S51tGVTV7UleA3wRWAL8U1Vd0tX6JEmz12U3EVV1JnBml+uQJG0/j0CWJBkGkiTDQJKEYSBJwjCQJAGZ6mClQUtyA3DFsOtYgPYCfjTsIqQZ8DPbjeVV9aCJntihwkDdSLKxqlYMuw6pX35mB89uIkmSYSBJMgwWi5OGXYA0Q35mB8wxA0mSWwaSJMNg3khyR5ILem4jM5j3eb3Xn07y1iTPaO+PJnGvDW23JFv6aPMbSS5pP8O7zWDZbxj3+D/af0eSXDzzajWeYTB//LSqHtNz29zPTEl2Bp4H3BUGVfWmqvpSN2VKU1oNvKP9DP90usZp7ARsEwZV9ZSuClysDIN5LMnjk2xIsinJF5Ps004fTfJ3STYARwOHAye2v8YOTPLRJL8/wfIOTXJukvOS/GuS+wz4JWkBSLKy/QyemuSyJOvaL/VXAEcAb0qyrm37l0m+leTCJG9pp40kuTTJPwDnAR8Gdms/v2Pz3WMrJMmSJCf2LO9VA3vRC0Cn1zPQnNotyQXt/R/Q/FG9F3huVd2Q5H8CxwEva9vsWVWHACR5GHB6VZ3aPr7HwpPsBbwReEZVbU1yNPDnwFu7e0lawB4LPIrmUrdfAw6uqg8leSrtZzHJocDDgCfSXDP9s0meBlwJ/DLw0qr6U4Akf1BVj5lmnS8HflJVT0hyL+BrSc6qqh908QIXGsNg/vhp7x9DkoOAg4Cz2y/3JcC1Pe3/ZYbLfxJNV9LX2uXtCpy7HfVqcftmVV0N0P6IGQG+Oq7Noe3t/PbxfWjC4Urgiqr6+gzXeSjw6J6t3vu1yzMM+mAYzF8BLqmqJ0/y/NZZLO/sqnrh9pUlAXBbz/07mPi7JsDxVfWBbSY2O0fM9PM7trwjq+qLs5h30XPMYP76LvCgJE8GSLJLkkdN0vYWYI9plvd14OAkD22XtzTJw+esWumevgi8bGxsKsmDk+w9SdtfJNmlj+X9r7F2SR6eZPe5K3dhMwzmqar6OfD7wNuTfBu4AJhsD4tTgL9Mcn6SAydZ3g3AS4BPJrmQJhweMdd1S2Oq6izgE8C5SS4CTmXyHy0nAReODSBP4kPAd4Dz2t1NP4C9H33zCGRJklsGkiTDQJKEYSBJwjCQJGEYSJIwDDTPDOLsrUnOTLJne/vTGda3U5L3JLk4yUXteXIe0j73hunmn0k7aS65a6nmlSRbqmrGJ9Brz976IXrO0TTu+VHg9VW1sWfaSNv+oBms54XA7wFHVNWdSfYDtlbVj/utfbavUdoebhlo3uvg7K2b2xP3nQAc2LY/McnHkjy3p926JIePm30f4NqquhOgqq5ug+AE7nnmzdPami9Jsqadtk27jDtff5LXJ3lze/+1Sb7TnqHzlLl6P7U4eXSe5ptOz946zlrgoLETBCY5BPgz4N+T3I/miO8/HjfPp4CvJvkN4Bzg41V1flWtTfKacWfefFlV3ZjmIi/fSvLp8e2m6QZbCzykqm5Lsud0L0aaimGg+abrs7dOqqo2JHl/e/6c5wOfrqrbx7W5OskvA09vb+e0p18+Z4JFvjbJ77b396c5w+Z/zaCkC4F1SU4DTpvZq5G2ZRhovpvrs7dO52M0V+t6AXdvfWyjqm4DPg98Psl1NFea2yYMkqwEngE8uapubccs7j3B4m5n2+7c3jaHAU+j6f76mySPGh9OUr8cM9B8N9dnb52u/UeB1wFU1SXjZ0jyuCT7tvd3Ah4NXNE+3XvmzfsBP26D4BE015NggnbXAXsneWCaC7Y8p2fZ+1fVeuCvgD1prgcgzYphoHltrs/eOm7Z/0VzsZ+Lk5zYTrsOuBT4yCSz7Q18rh30vZDml/372ud6z7z5BWDn9gyxb6M5Syzj21XVL2iuNvcN4HTgsrbNEuDj7dk+zwfeXVU3TfeapMm4a6k0A0mWAhcBj6uqnwy7HmmuuGUg9ak9QO0y4L0GgRYatwwkSW4ZSJIMA0kShoEkCcNAkoRhIEnCMJAkAf8NZFuVPosuCoAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.boxplot(list([infertile_df[\"number_of_children_fathered_f2405_0_0\"].values,\n",
    "#              fertile_df[\"number_of_children_fathered_f2405_0_0\"].values]))\n",
    "# plt.show()\n",
    "\n",
    "child_df = df[[\"number_of_children_fathered_f2405_0_0\",\"infertile\"]].copy()\n",
    "child_df[\"number_of_children_fathered_f2405_0_0\"] = child_df[\"number_of_children_fathered_f2405_0_0\"].replace({-3.0:np.nan,-1.0:np.nan})\n",
    "child_df = child_df.dropna()\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "child_df.boxplot(column=\"number_of_children_fathered_f2405_0_0\",by=\"infertile\",showfliers=False,ax=ax,showmeans=True,\n",
    "                 meanprops={\"marker\":\"o\",\n",
    "                       \"markerfacecolor\":\"red\", \n",
    "                       \"markeredgecolor\":\"red\",\n",
    "                      \"markersize\":\"6\"})\n",
    "ax.set_title(\"Number of Children Fathered\")\n",
    "ax.set_ylabel(\"Number of children\")\n",
    "ax.set_xlabel(\"Fertility Status\")\n",
    "ax.set_xticklabels([\"Fertile\",\"Infertile\"])\n",
    "plt.suptitle(\"\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:48:48.448564Z",
     "start_time": "2021-05-30T20:48:48.242897Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sns.boxplot(x=\"infertile\",y=\"number_of_children_fathered_f2405_0_0\",data=child_df, order=[True,False],\n",
    "            showfliers=False,showmeans=True,\n",
    "            meanprops={\"marker\":\"o\",\n",
    "                       \"markerfacecolor\":\"red\", \n",
    "                       \"markeredgecolor\":\"red\",\n",
    "                      \"markersize\":\"10\"},ax=ax)\n",
    "\n",
    "plt.title(\"Number of Children Fathered\")\n",
    "plt.ylabel(\"Number of children\")\n",
    "plt.xlabel(\"Fertility Status\")\n",
    "ax.set_xticklabels([\"Infertile\",\"Fertile\"])\n",
    "plt.suptitle(\"\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:46:27.986302Z",
     "start_time": "2021-05-30T20:46:27.949070Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:             number_of_children_fathered_f2405_0_0\n",
      "infertile                                       \n",
      "False                                   1.778829\n",
      "True                                    1.163717 \n",
      "\n",
      "STD:             number_of_children_fathered_f2405_0_0\n",
      "infertile                                       \n",
      "False                                   1.256018\n",
      "True                                    1.292898\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-10.398017372627374, pvalue=2.573518493298454e-25)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean: \",child_df.groupby(\"infertile\").mean(),\"\\n\\nSTD: \",child_df.groupby(\"infertile\").std())\n",
    "sp.ttest_ind(child_df.loc[child_df[\"infertile\"],\"number_of_children_fathered_f2405_0_0\"].values,\n",
    "             child_df.loc[~child_df[\"infertile\"],\"number_of_children_fathered_f2405_0_0\"].values,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:16:48.180972Z",
     "start_time": "2021-05-30T20:16:47.858609Z"
    }
   },
   "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": [
    "disease = \"cancer\"\n",
    "Disease=\"Cancer\"\n",
    "variable = \"age_at_cancer_diagnosis_f40008_0_0\"\n",
    "\n",
    "df[disease] = df[variable].notna()\n",
    "df[\"age_\"+disease] = 2016-df[\"year_of_birth_f34_0_0\"]\n",
    "df.loc[df[disease],\"age_\"+disease] = df.loc[df[disease].notna(),variable]\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "t=np.linspace(30,round(df[\"age_\"+disease].max()))\n",
    "\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(df.loc[df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[df[\"infertile\"],[disease]], timeline=t, label=\"Infertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "kmf.fit(df.loc[~df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[~df[\"infertile\"],[disease]], timeline=t, label=\"Fertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "plt.title(\"Kaplan-Meier Survival Curves for \"+Disease)\n",
    "plt.xlabel(\"Age at \"+Disease+\" Diagnosis\")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:16:48.856221Z",
     "start_time": "2021-05-30T20:16:48.543413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P88ADD3DXXXcxadIknn76aZ599lnGjx/P7373OyZNmkRlZSWnnXYaV199NUuWLGHkyJF86UtfYubMmQwePJht27YxePBgBg0axPjx4xNrL9u3b+eyyy5j2rRpVFZWcuGFF/Ld73437/J4oHDONTtX/9883lrxYUGvObDXZ7jyy4MafPxrr73GvHnz6NWrF8OHD2fGjBmMGTOGF154gZNPPpnTTz+dKVOmsGjRIl555RXMjFGjRvHPf/6Tvn37smDBAu655x5uu+02AB5++GFef/31jM9511130aVLF2bNmkVlZSXDhw/n+OOPz2oobBIPFM451wiGDh1Knz59ABg8eDBLlizhC1+ovZrrlClTmDJlCgcffDAAmzdvZtGiRfTt25e99tqLww8/PKvnnDJlCnPmzOGRRx4BYOPGjSxatMgDRSFd/X/zeH7hGspbN3zU8PB9unHM/j0K8vwfVW/Pqr/D+zScS5bNN//G0rZt208el5WVUV1dXecYM+Pyyy+v0zy0ZMkSOnbMvlnbzLj11ls54YTE+Vhz5oEiD/NXbmL+yk3MeHttQa6XbdDJtiPdA4tzTcsJJ5zAr371K775zW/SqVMnli9fnnZqjfLycqqqqjJOvXHCCSdw++23c/TRR1NeXs7ChQvp3bt3TkEnzgNFzJVfHsTjry9v8KinqfNXFSxILF23FVibVaDItiPdO8uda1qOP/545s+fzxFHHAGE4bUPPPAAZWV1J92+4IILOOiggzjkkEMYP3584vXGjBnDkiVLOOSQQzAzunfvzuTJk/POZ7NbM3vIkCGWz8JF2QSKQrrmiXkAXHFy41WZ12z6iFGDk+ZodK70zZ8/n/3337/Y2SgJSa+VpFfNbEjS8b5mtnPOuYy86Snu75cxbPHsxM7stf1GsXpfn0HEOdfyeKBIUf7xBsqrajfHdfhwMV1WvUzP+ffUOd5alVHddpc66bkElqXrtn7SBNUQhRxx5Zxz6XigiBt5HW9pAl1bV9ZK7vL+i3Re/WriKdqxnfJt62ql5RJY/lz1MY91Gsbf66w6myyXzm/nnMuFB4oUa/Y4ElI6s9f1/3La47ssn07rj2vfAZptYAHos20553feyqEn/6RB+bzmiXlZ10AO6t3FO7Odc1nzQJGnjb2PqpOWbWAB6DPnVrJYDJDh+3QDGj40d+m6rVRV72jw8c45V8MDxU6WFFgAeix6kE7r5vC5x46vs29Dz+EsHXplrbRj9u+RVbPTNU/Mo7JqR9ob9PxmPOfyV1ZWxoEHfroqwuTJk+nXr1+Dzp08eTL77rsvAwcOBOCKK67gyCOP5Nhjj2XEiBHceOONDBmSOHq10XmgSNG5fXlWN6Z9VL29IDPIru03itaVG9CO7bXSO3y4mA4fLmaXlTPqnJMUQDJpW94q7T0ifjOec/lr3759vRP3Jamurmby5MmcfPLJnwSKa665psC5y50HihTZfqsu1FoUq/cdnThKaveFE9hjwf11AkjbLcvpsmpmQZ4b0s8z5TUN5/Lz6quv8uMf/5jNmzfTrVs37r33Xnr27MmIESMYNmwYM2bM4Pjjj+fxxx9n+vTpjB07lkcffZRrr732k1lm46ZMmcKVV15JZWUl++yzD/fccw+dOnVq1DJ4oMhTtjWQTJJqJ+kCyMApo7NqqqpPulqR1zRcSfr7ZfD+m4W95h4HwsjrMh5Ss2YEQP/+/Zk0aRLf//73eeyxx+jevTsPPfQQv/jFL7j77rsB2LBhA9OnTwdg0aJFiYEhbu3atYwdO5Znn32Wjh07cv311/OHP/yBK664ojBlTMMDRZ4K+W07m9pJfU1VHTf8u1b69Zu2MK3NUUDxZ9V0rrlKbXqaO3cuc+fO5bjjjgPCwkI9e/b8ZP8ZZ5yR1fVfeukl3nrrLYYPHw7Axx9//Mk8UY3JA0WJytxUdV+dIbh7Vy9HO6rZTMOG3zpX0ur55r+zmBmDBg1i5szkZuJsZ3U1M4477jgmTJhQiOw1mAeKZmb1vqOp7NirzhDcyhm3FSlHzrVc++23H2vWrGHmzJkcccQRVFVVsXDhQgYNqluz79y5M5s2bcp4vcMPP5wLL7yQxYsX89nPfpatW7dSUVHBvvvu21hFAPIMFJJ+AHwHEHCnmf23pKuitDXRYT83sycTzj0RuBkoA8aZ2XVR+vXASOB1Mzs7SjsL2M3Mbs4nvy1F0hDcji/dWaeZqiG8k9u53LVp04ZHHnmEiy++mI0bN1JdXc0Pf/jDxEBx5pln8p3vfIdbbrnlkxXqUnXv3p17772X0aNHU1kZZpAYO3ZsoweKnKcZl3QAMBEYCnwMPAX8F/BNYLOZ3Zjh3DJgIXAcUAHMAkYDy4EnzOyLksYD1wGLgSeAE82sqr585TvNeDE15hTnHSeMQju2s/mbfyvI9Zat30K71nXnzPcA4orFpxlvuGynGc+nRrE/8JKZbY2eZDpwWgPPHQosNrN3onMnAqcAfwTaSBLQHqgCLgVuaUiQcJlt207aKT+ynWDQR0k513LkEyjmAr+W1BXYBpwEzAbWARdJOjva/omZrU85tzewLLZdARxmZpskPQq8BkwFNgKHmlnTufOkETXmzX5d2pWnbXryCQadc5nkHCjMbH7Un/AMsBl4A6gGbgeuJUxcdC3we+DbKacr6ZLRdW8AbgCQNA64QtIY4HhgjpmNTT1R0gXABQB9+/bNtUhFl22TzT8WrG5wYBnQtjWf0Y7EFfQyTTDoU5m7UmJmhAYJl04u3Q15dWab2V3AXQCSfgNUmNmqmv2S7iT0L6SqAPaMbfcBVsQPkHRw9HAhcLOZHSlpoqQBZrYoJR93AHdA6KPIp0ylJKvA8lpbNqbW6yLpJhj0moYrJe3atWPdunV07drVg0UaZsa6deto1y67vtB8Rz3tbmarJfUFvgIcIamnma2MDjmN0ESVahYwQFJ/Qgf2mcA3Uo65llBLKCeMjALYAXTIJ8+urnQTDGYzhblzxdanTx8qKipYs2ZN/Qe3YO3ataNPnz5ZnZPvfRSPRn0UVcCFZrZe0v9KGkxoSloCfBdAUi/CMNiTzKxa0kXA04QgcLeZffKpJOlUYJaZrYi2Z0p6k9D09EaeeXbONUPl5eX079+/2NlolvJtevpiQtpZaY5dQejwrtl+Eqhzf0W0bzIwObZ9CXBJPnl10HHLewyckrw8q68J7pxLx+/MbikOPJ2tH6xKXF2v7ZbltK7c4IHCOZfIA0VLMeQ85q1tV2c9cAir67Xb9F6dmWhv2FTG9PLh+ESCzrVsHihakKT1wAHKqjYnrnnR395jW2UrrvRhs861aB4oXNqZaHv99au031r3Po35Kzcxf+UmZrxdd0jtQb27MGpw70bJp3OuODxQuLR26diGPh8tYGKb2vc4buj2MY/tGMbfObFW+tJ1W6mq3rEzs+ic2wk8ULi0wuJI6+t0gPfZtpzzO2/l0JNrr21xzRPzPFA41wx5oHBppVvbos+cW4lmXHHOtQAeKFxGSWtb9Fj0YOJ63TdsKuMfZcOAETsnc865ncIDRQuSaXbabGaiTbded397D9vuc+w419x4oGhBMk0imLSKXTrpRkl1nDAKtlfnlDfnXNPVqtgZcM4517R5oHDOOZeRNz05IH3/RTZ9FxDGQqVrxvL1tJ0rTR4oHJC+/yKbvguAVhLdE6YJAVi2fkvi9TyAONe0eaBwO026mkk264Q753Y+76NwzjmXkQcK55xzGXmgcM45l5H3Ubii+6h6u3dyO9eEeaBwGRVq2Gwm3sntXNPmgcJllO2w2f47lrJ9SvLa22v7jfJ1uZ0rQR4oXE6SahovlR0J26v5bMr6FQBttyyndeUGDxTOlaC8AoWkHwDfAQTcaWb/LWk34CGgH7AE+LqZrU8490TgZqAMGGdm10Xp1wMjgdfN7Owo7SxgNzO7OZ/8usJJqmmc8fIopnxwODcfVOft9jUsnCthOQcKSQcQgsRQ4GPgKUl/i9Kmmtl1ki4DLgN+lnJuGfAn4DigApgl6XFgOTDMzA6SNF7SgcBi4FxIWXfTNUlVbbqwrv8X6qT3WPRgnQWQnHOlIZ8axf7AS2a2FUDSdOA04BQ+XbnmPmAaKYGCEFwWm9k70bkTo/P+CLSRJKA9UAVcCtxiZlV55NU1Ae02Lamz2BHAhp7DWTr0yiLkyDnXEPkEirnAryV1BbYBJwGzgR5mthLAzFZKSuoN7Q0si21XAIeZ2SZJjwKvAVOBjcChZnZNpoxIugC4AKBv3755FMk1lnSLHbXdspwuq2YmnuPDZp1rGnIOFGY2P+pPeAbYDLwBNHTVmqRl0Cy67g3ADQCSxgFXSBoDHA/MMbOxCXm5A7gDYMiQId4QXkTLN2zjmifmJew5iOGfvZdj9u9RK3XglNFpm6R82KxzTUNendlmdhdwF4Ck3xBqBqsk9YxqEz2B1QmnVgB7xrb7ACviB0g6OHq4ELjZzI6UNFHSADNblE++XeM4ZXBv1m2uTNy3dN1WYG2dQAHeJOVcU5fvqKfdzWy1pL7AV4AjgP7AOcB10e/HEk6dBQyQ1J/QgX0m8I2UY64lNCeVE0ZGAewAOuSTZ9d4vnFYXzq1LUucZvyaJ+axdN3WOrWNkZVDOK31RrqmVEa9Scq5piPf+ygejfooqoALzWy9pOuASZLOB94DvgYgqRdhGOxJZlYt6SLgaUIQuNvMPvkEkXQqMMvMVkTbMyW9SWh6eiPPPLsiGL5PN2BtnfQ/bz6Sv3c9kStOHlQr3ZuknGs68m16+mJC2jrgmIT0FYQO75rtJ4En01x3MjA5tn0JcEk+eXXFdcz+PRKbnZL7M5xzTYnPHuuccy4jDxTOOecy8rmeXNEldXJfv2kL++7w0VDONQVeo3BFNXyfbuzVte5Atkc/PoJF7FUnPdNoKOdc4/AahSuq9J3c8AqjshoN5ZxrHB4oXMlJd4Peyq6HweDbipAj55o3DxSupKztN4rybeuwsvJa6e02LaXbjplpF1Tym/Gcy50HCldSVu87msqOveo0P/WZcyutW7VKvCsc/GY85/LhgcKVnI29j6qT5utdONd4fNSTc865jLxG4ZqspPsrIAypTRop5ZxrHB4oXJOUbhLBTNOVZ+IzzjqXOw8Urkkq9CSCPuOsc7nzQOFKTropP3YtT3NCBl7TcK5+HihcSUnXJPVR1Q427Mj+el7TcK5+HihcSUnXJNVuQivYsb0IOXKu+fNA4Qqqc/vytN/GP6renvYbvHOu6fJA4QoqU7v+PxasTgwiHkCca9o8ULidJl0QSTc/UzF5J7dzn/JA4VwC7+R27lM+hYdzzrmMPFA455zLKK9AIelHkuZJmitpgqR2kq6StFzS69HPSWnOPVHSAkmLJV0WS79e0hxJ98fSzpL0g3zy6pxzLjc591FI6g1cDAw0s22SJgFnRrtvMrMbM5xbBvwJOA6oAGZJehxYDgwzs4MkjZd0ILAYOBc4Mde8uqYt3ZDabEdD9d+xlFYJK98BbOg5nKVDr8w5j861ZPl2ZrcG2kuqAjoAK4B+DThvKLDYzN4BkDQROAX4I9BGkoD2QBVwKXCLmVXlmVfXRBViNNS0NkfxUVU17Terzr7+O5ayvXJm+KtzzmUt50BhZssl3Qi8B2wDppjZFEnDgIsknQ3MBn5iZutTTu8NLIttVwCHmdkmSY8CrwFTgY3AoWZ2Taa8SLoAuACgb9++uRbJlbAPB36La986Au2orrPvl9uuo71/zXAuZ/k0Pe1KqAX0BzYAD0v6FnA7cC1g0e/fA99OPT3hkgZgZjcAN0TPMQ64QtIY4HhgjpmNrXOi2R3AHQBDhgyxXMvkSle6qT0A2k240af3cC4P+XRmHwu8a2ZromahvxD6F1aZ2XYz2wHcSXKFvwLYM7bdh9Bs9QlJB0cPFwJnm9nXgQMkDcgjz84557KUT6B4DzhcUoeoT+EYYL6knrFjTgPmJpw7Cxggqb+kNoRO8MdTjrkWuAIoB8qitB2EvhDnnHM7ST59FC9LegT4F1BN6Fe4AxgnaTChKWkJ8F0ASb2AcWZ2kplVS7oIeJoQBO42s08WGJB0KjDLzFZE2zMlvUloenoj1zy7lmvb9uRFj7JdVtWn9nAtUV6jnszsSiB1zOFZaY5dAZwU234SeDLNsZOBybHtS4BL8smra7m6tCtHCX0U81duYv7KTcx4u+76FukCiE/t4Voin+vJNXu7dGxDt/KPuOLkQbXSp85flRgkcl2X27nmygOFa7IKdSNeOoVel9u55soDhWuyCjktebtNS/hcwl3bfse2c/XzQOGavbX9RlG+bR1WVl4rvd2mpeyycgZLC/Ac6Tq5wTu6XenzQOFKTrZNUqv3HU1lx160/vjDWul95tyKtSrMBMqZmsK8o9uVOg8UruTk0iS1sfdRddJ6LHqwTvCosXTd1oIMp3WuOfBA4Vq0pL6LOypb8Vi7L/A3TquVnutoKL/3wpU6DxSuxUrXd9Fr+1K+3eGffP7kX9ZKz3U0lN974UqdBwrXYu2MvgvnmgMPFK5Fy7bvwrmWyAOFc0XifReuVHigcC5BUif3DZvKmF4+HBiUfFKWvO/ClQoPFM6lSNfJ3d+WQtUMNhcpX84ViwcK51Ks3nc028s7UdW+e630HVOvBdtRpFw5VzweKJxLUN3mM5RvW1MrrdJ2gJJW8XWuefNA4VyCpNFQHV+6M3FdC+eaOw8UrtlINwcUFG5q8p3BR0O5psYDhWs2Mn2I5jI1ebH4aCjX1HigcC4LhVp727lS4vMUONdAXdqV076sbvrSdVsTl1R1rrnwGoVzDZRu7W1fOtU1d16jcM45l1FegULSjyTNkzRX0gRJ7STtJukZSYui37umOfdESQskLZZ0WSz9eklzJN0fSztL0g/yyatr2WpGRKX+LFu/pdhZc67Jy7npSVJv4GJgoJltkzQJOBMYCEw1s+uiAHAZ8LOUc8uAPwHHARXALEmPA8uBYWZ2kKTxkg4EFgPnAifmmlfn0o2I+seC1Vktq5pOuhXxoHAd3ZnW5U7Hh9S6Qsi3j6I10F5SFdABWAFcDoyI9t8HTCMlUABDgcVm9g6ApInAKcAfgTaSBLQHqoBLgVvMrCrPvDpXRy7LqqYavk83ILkzO9dV8ZLkch+ID6l1hZBzoDCz5ZJuBN4DtgFTzGyKpB5mtjI6ZqWkpP/E3sCy2HYFcJiZbZL0KPAaMBXYCBxqZtdkyoukC4ALAPr27ZtrkZzLyTH790gbCIrd0e0377lCyKfpaVdCLaA/sAF4WNK3Gnp6QpoBmNkNwA3Rc4wDrpA0BjgemGNmY+ucaHYHcAfAkCFDLLuSONdwSdOPA2zoOZylQ69MPCdds9TOuPfCb95zhZBP09OxwLtmtgZA0l+AYcAqST2j2kRPYHXCuRXAnrHtPoRmq09IOjh6uBC42cyOlDRR0gAzW5RHvp3LSbrpx9ttWsouK2ewNOGcdM1ShWySyoXXNFw28gkU7wGHS+pAaHo6BpgNbAHOAa6Lfj+WcO4sYICk/oQO7DOBb6Qccy2hOakcqLnNaQehL8S5nS6XNbbTNUsVu0nKaxouG/n0Ubws6RHgX0A1oV/hDqATMEnS+YRg8jUASb2AcWZ2kplVS7oIeJoQBO42s0/+cySdCswysxXR9kxJbxKant7INc/ONVS6CQaXdRjCnr1rf8j6Gtuuuctr1JOZXQmkNsxWEmoXqceuAE6KbT8JPJnmupOBybHtS4BL8smrc9koxGgo55oLn8LDuSIpZie3c9nwQOFcETTVTm7nknigcK4A0g2bheShs021k9u5JB4onMtTumGzkHnobFPkw2ZdEg8UzuUp3bBZyDx0tinyYbMuiQcK5wpgY++jEtN96KxrDjxQONfENMXRULnMXOt2vsZqIvRA4Vwjy2Z+qKY6GiqXmWvdztdYTYQeKJxrRNnOD+WjoVxT5IHCuUaUy/xQzjU1Hiica2RJHd3eye1KiX+lcc45l5HXKJzLQrpZZbNdY9u5UuKBwrks+KyyriXyQOFciUh3f0UmPhOtKwQPFM6VgHT3V2RS7HsvXPPhgcK5EpDu/opM/N4LVygeKJwrgHSd3FDcju6mOB2IKz0eKJwrgEzz6xSro7upTgfiSo8HCueaqUzTgXhNw2XDA4VzLYzXNFy2PFA418iS+i+qqnewo3pHUfLjEw+6bOUcKCTtBzwUS9obuALYBfgOsCZK/7mZPZlw/onAzUAZMM7MrovSrwdGAq+b2dlR2lnAbmZ2c675da5YEvsvXmtLdcV7DZ5+3LliynmuJzNbYGaDzWww8HlgK/DXaPdNNfvSBIky4E+EgDAQGC1poKQuwDAzOwgok3SgpPbAucBtuebVuSbnwNPZ1r4nVlZe66ft1hXssnJGsXPnXC2Fano6BnjbzJZKasjxQ4HFZvYOgKSJwCnAH4E2ChdpD1QBlwK3mFlVgfLqXPENOY+5a9vRtXVlrWSfftw1RYUKFGcCE2LbF0k6G5gN/MTM1qcc3xtYFtuuAA4zs02SHgVeA6YCG4FDzeyaTE8u6QLgAoC+ffvmVRDndpY1exwJndvVSvPpx11TlPdXF0ltgFHAw1HS7cA+wGBgJfD7pNMS0gzAzG6Imqx+AlwLXCFpjKRJkn6ZlAczu8PMhpjZkO7du+dXIOecc7UUokYxEviXma0CqPkNIOlO4ImEcyqAPWPbfYAV8QMkHRw9XAjcbGZHSpooaYCZLSpAvp0rqqY2Ggr8Tm6XrBCBYjSxZidJPc1sZbR5GjA34ZxZwABJ/YHlhKarb6Qccy2hOamcMDIKYAfQoQB5dq7o0o2G2pjaULuT+P0VLp28AoWkDsBxwHdjyTdIGkxoSlpSs09SL8Iw2JPMrFrSRcDThCBwt5nNi133VGCWma2ItmdKehOYY2Zv5JNn51wyv7/CpZNXoDCzrUDXlLSz0hy7Ajgptv0kUGfobLRvMjA5tn0JcEk+eXXO5S6XtTDczrd757aMGty74Nf1O7OdcxnlshaGa148UDjnMsplLQxXHOmmus+XBwrnmphWrZT4D1/MdS1cy+aBwrkmpnPb1ontzMVa18I5nyvAOedcRh4onHPOZeRNT841NbYd3nykTnL396ugc91pyZ1rbB4onGtq1i+BF26qk9yz7X+wcoAHCrfzeaBwrik58HTYsqZu+gfv0rXtVlbW3eNco/NA4VxTMuQ86NIHPtpYO336DVhl8SYLdC2bBwrnmpoBx9VNm30P9nFCTcO5ncADhXMlotOW9zjgr8fWSV/d/Qjmfe4Xief4TXquEDxQOFcKDjydsi1r6FxWXjv9g3fpvGk2+6SZCM5v0nOF4IHCuVKQoe8CX2PbNTIPFM6VijR9F3y0Ie0pSavogTdJuex4oHCuGUtcRQ9vknLZ8Tqrc865jLxG4VypSzPlBwDtuiQ2WXmTlMuGBwrnSl2aKT8A6HVIYqDwJimXDQ8UzpWydFN+AHzwLmyv2rn5cc2SBwrnSlm6YbPgQ2ddweQcKCTtBzwUS9obuAK4P0rvBywBvm5m6xPOPxG4GSgDxpnZdVH69cBI4HUzOztKOwvYzcxuzjW/zjVbScNmod6hs841VM6BwswWAIMBJJUBy4G/ApcBU83sOkmXRds/i58bHf8n4DigApgl6fHoGsPM7CBJ4yUdCCwGzgVOzDWvzrVY6Tq603RyO5ekUE1PxwBvm9lSSacAI6L0+4BppAQKYCiw2MzeAZA0ETgF+CPQRpKA9kAVcClwi5l5Y6tz2UrX0Z2mk9u5JIVqwDwTmBA97mFmKwGi30nDK3oDy2LbFUBvM9sEPAq8BrwLbAQONbPHMj25pAskzZY0e80an2HTOSB0dHfpWzf9g3dh2cs7Pz+uZOVdo5DUBhgFXJ7NaQlpBmBmNwA3RNceB1whaQxwPDDHzMbWOdHsDuAOgCFDhlhWBXCuufL5oVyBFKLpaSTwLzNbFW2vktTTzFZK6gmsTjinAtgztt0HWBE/QNLB0cOFwM1mdqSkiZIGmNmiAuTbueYvy/mh/EY8l6QQgWI0nzY7ATwOnANcF/1OajaaBQyQ1J/QgX0m8I2UY64FLgDKCSOjAHYAHQqQZ+datg/egT8OrZP8pb1HwEk31En3G/Fatrzqn5I6EEYu/SWWfB1wnKRF0b6aYa+9JD0JYGbVwEXA08B8YJKZzYtd91RglpmtMLMNwExJb4ZT7Y188uxci3fg6dBlTygrr/2zsQLemVbs3LkmKK8ahZltBbqmpK0jjIJKPXYFcFJs+0ngyTTXnQxMjm1fAlyST16dc5Eh50HbztCpR+30py7zO7ldIr8z27mWqF0X2Lyqdtr2qrSd3On6LlzT0rl9ef0H5cADhXMtUZad3OkmEXQtg4+Rc845l5EHCueccxl5oHDOOZeRBwrnnHMZeWe2c+5TaW7EI82NeK5l8EDhnAtqVssrSxli+cG7fiNeC+eBwjkXZLoRb93byTUN8NpGC+CBwjn3qaQb8XodAtWVdWsaEGobbz/niyM1czJrXrNyDxkyxGbPnl3sbDjXvLz5SN2aBoTaxgfvwm796+6rrgQljJfZ8zDY+6jC59HlFZwlvWpmQ5L2eY3COVe/pJoGfFrbSNK6bd20VXNh7QJY8a/C5s8FnfdolFqcBwrnXP3SffgceDoseqbu4kgAVR/BrnvVTlv4lHeMlyAPFM65/KQLIoueSejvODj8uMbRrkujXNYDhXOucXhHdrPhd2Y755zLyAOFc865jDxQOOecy8gDhXPOuYw8UDjnnMvIA4VzzrmMPFA455zLyAOFc865jJrdpICS1gBL87hEN2BtgbLTVHiZSkdzLFdzLBM0v3LtZWbdk3Y0u0CRL0mz082gWKq8TKWjOZarOZYJmm+5knjTk3POuYw8UDjnnMvIA0VddxQ7A43Ay1Q6mmO5mmOZoPmWqw7vo3DOOZeR1yicc85l5IHCOedcRi02UEhqJ+kVSW9Imifp6ih9N0nPSFoU/d612HnNlqQySa9JeiLabg5lWiLpTUmvS5odpZV0uSTtIukRSf+WNF/SEc2gTPtF71HNz4eSftgMyvWj6HNirqQJ0edHSZcpGy02UACVwNFm9jlgMHCipMOBy4CpZjYAmBptl5ofAPNj282hTABfMrPBsbHrpV6um4GnzOw/gM8R3rOSLpOZLYjeo8HA54GtwF8p4XJJ6g1cDAwxswOAMuBMSrhMWTOzFv8DdAD+BRwGLAB6Ruk9gQXFzl+WZelD+KM9GngiSivpMkX5XgJ0S0kr2XIBnwHeJRpQ0hzKlFDG44EZpV4uoDewDNiNsHz0E1HZSrZM2f605BpFTRPN68Bq4BkzexnoYWYrAaLfuxcxi7n4b+CnwI5YWqmXCcCAKZJelXRBlFbK5dobWAPcEzUTjpPUkdIuU6ozgQnR45Itl5ktB24E3gNWAhvNbAolXKZstehAYWbbLVSR+wBDJR1Q5CzlRdLJwGoze7XYeWkEw83sEGAkcKGkI4udoTy1Bg4Bbjezg4EtNKOmC0ltgFHAw8XOS76ivodTgP5AL6CjpG8VN1c7V4sOFDXMbAMwDTgRWCWpJ0D0e3Xxcpa14cAoSUuAicDRkh6gtMsEgJmtiH6vJrR5D6W0y1UBVES1WIBHCIGjlMsUNxL4l5mtirZLuVzHAu+a2RozqwL+AgyjtMuUlRYbKCR1l7RL9Lg94Y/h38DjwDnRYecAjxUlgzkws8vNrI+Z9SNU+58zs29RwmUCkNRRUueax4T24bmUcLnM7H1gmaT9oqRjgLco4TKlGM2nzU5Q2uV6DzhcUgdJIrxX8yntMmWlxd6ZLekg4D7CCIZWwCQzu0ZSV2AS0JfwB/I1M/ugeDnNjaQRwCVmdnKpl0nS3oRaBIQmmwfN7NfNoFyDgXFAG+Ad4Dyiv0VKtEwAkjoQOn/3NrONUVqpv1dXA2cA1cBrwBigEyVcpmy02EDhnHOuYVps05NzzrmG8UDhnHMuIw8UzjnnMvJA4ZxzLiMPFM455zLyQOHyJuk0SSbpP3bCc42QNCzNvnMl/TElbZqkIUnHFzBPp0oa2AjXfbLmXp88r3OVpOXRbK6LJP0lnt9o+pCC5z/LPBY9Dy49DxSuEEYDLxBu8mtsIwh3xTYJkloDpwIF/5Azs5OiWQMK4SYLs7oOAB4CnpPUPXqeMWb2VoGeJydNIQ8uPQ8ULi+SOhGmDjmfWKCQ1ErSbdEc/k9E345Pj/Z9XtL0aIK/p2umQUi57pclvRxNmPespB6S+gHfA34UfTv+Yhb5PF/STbHt70j6g6R+CutB3CdpjsL6EB0y5TOqpfxG0nTgZ4Q5jX4X5Wmf6Oep6Lzna2paku6VdIukFyW9E3s9ekr6Z3T+3JpyKazB0S16/ONo31xJP4zS+imsY3Fn9DpPiWYZyMjMHgKmAN+IlWdI9Ph2SbMVW6MlSj8pep1eiMpQs9bJVZLujq7xjqSLY+ck5bmjpL8prAMzV9IZ8TwoTNR5b7TvTUk/auh77BpRsaev9Z/S/gG+BdwVPX4ROCR6fDrwJOHLyB7A+iitPDque3TcGcDdCdfdlU9vCB0D/D56fBXhjvOkvJxLmJH19djPZmAI0BF4GyiP5fVAoB9hZtrhUfrdwCWZ8kmYF+y22PPeC5we254KDIgeH0aYSqXmuIej12QgsDhK/wnwi+hxGdA5erwE6EZY1+HNqAydgHnAwVHeq4HB0fGTgG8lvC51XjPgh4QJCWvKMyR6vFssH9OAg4B2hDut+0f7JvDpFPZXRa9T2yiv66LXLl2evwrcGctHl3geovOeie3fpdh/4/5jtMa5/IwmTG0OYSLC0YS1Pb4APGxmO4D3Jf0jOmY/4ADgGUkQPpBWJly3D/BQ9C2+DWHthoZ4yMwuqtmQNA3AzLZIeg44WdJ8QsB4M6qlLDOzGdEpDxAWqXmqnnw+lPTkUQ1rGPBwdB6ED9Eak6PX5C1JPaK0WcDdksqj/a+nXPYLwF/NbEv0HH8BvkiYa+jd2PGvEoJHQyhN+tcVpnFvTVhjYSAhsL1jZjXvwQTggtg5fzOzSqBS0mqgR4Y8PwXcKOl6QrB5PuX53wH2lnQr8DdCzccVmQcKlzOF+XuOBg6QZIQPU5P0U9J/EAmYZ2ZH1HP5W4E/mNnjCvNWXVWALI8Dfk6Y/PGeWHrqPDbWgHxuSZPeCthgYfr6JJWxxwIws38qTJv+n8D/Svqdmd2felwDrrcdqLfpKXIwMDueIKk/oTZ1qJmtl3QvoTaR6fmT8tA63TlmtlDS54GTgN9KmmJm18T2r5f0OeAE4ELg68C3G1gm10i8j8Ll43TgfjPby8z6mdmehG/+XyB0bn9Voa+iB6ETGsKqYN0lHQEgqVzSoIRrdwGWR4/PiaVvAjrnklkLU3rvSWibj89s2rcmP3zaMd/QfNbKk5l9CLwr6WvReYo++NKStBdhHZE7gbsI043H/RM4VWH20o7AaUDqN/EGk/RVwgy8E1J2fYYQADdG79nIKP3fhG/5/aLtMxrwNIl5ltQL2GpmDxAWA6pV1qhPppWZPQr8KnW/Kw4PFC4fo/l0VtcajxI+iB8lrLkwF/gf4GXCymAfEwLM9ZLeIPQjJI1iuorQfPM8sDaW/n/AacqyMztmEmF5zvWxtPnAOZLmEJa7vD2LfEJocrtUoeN9H+CbwPnRefMIi95kMgJ4XdJrhDb8m+M7zexfhP6NVwiv4zgze61hxf3Ej6LXbBGhX+loM1uT8jxvEGZGnUfoq5kRpW8D/h/wlKQXgFXAxkxPliHPBwKvKKws+QtgbMqpvYFp0f57gcuzLKdrBD57rGs0kjqZ2eaoieoVQofx+0XO0xOEoaJTo+1+hLbykl7dsLHF3ksBfwIWmdlN9Z3nmgevUbjG9ET0zfB54NpiBglJu0haCGyrCRIuK9+J3st5hGbB/yludtzO5DUK55xzGXmNwjnnXEYeKJxzzmXkgcI551xGHiicc85l5IHCOedcRv8fLtQbWjyYdLkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disease = \"htn\"\n",
    "Disease=\"Hypertension\"\n",
    "variable = \"age_high_blood_pressure_diagnosed_f2966_0_0\"\n",
    "\n",
    "df[disease] = df[variable].notna()\n",
    "df[\"age_\"+disease] = 2021-df[\"year_of_birth_f34_0_0\"]\n",
    "df.loc[df[disease],\"age_\"+disease] = df.loc[df[disease].notna(),variable]\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "t=np.linspace(30,round(df[\"age_\"+disease].max()))\n",
    "\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(df.loc[df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[df[\"infertile\"],[disease]], timeline=t, label=\"Infertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "kmf.fit(df.loc[~df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[~df[\"infertile\"],[disease]], timeline=t, label=\"Fertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "plt.title(\"Kaplan-Meier Survival Curves for \"+Disease)\n",
    "plt.xlabel(\"Age at \"+Disease+\" Diagnosis\")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:16:50.149774Z",
     "start_time": "2021-05-30T20:16:49.815646Z"
    }
   },
   "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": [
    "disease = \"dm\"\n",
    "Disease=\"Diabetes\"\n",
    "variable = \"age_diabetes_diagnosed_f2976_0_0\"\n",
    "\n",
    "df[disease] = df[variable].notna()\n",
    "df[\"age_\"+disease] = 2021-df[\"year_of_birth_f34_0_0\"]\n",
    "df.loc[df[disease],\"age_\"+disease] = df.loc[df[disease].notna(),variable]\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "t=np.linspace(30,round(df[\"age_\"+disease].max()))\n",
    "\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(df.loc[df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[df[\"infertile\"],[disease]], timeline=t, label=\"Infertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "kmf.fit(df.loc[~df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[~df[\"infertile\"],[disease]], timeline=t, label=\"Fertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "plt.title(\"Kaplan-Meier Survival Curves for \"+Disease)\n",
    "plt.xlabel(\"Age at \"+Disease+\" Diagnosis\")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:23:52.235754Z",
     "start_time": "2021-05-30T20:23:52.231940Z"
    }
   },
   "outputs": [],
   "source": [
    "# disease = \"mi\"\n",
    "# Disease=\"Heart Attack\"\n",
    "# variable = \"age_heart_attack_diagnosed_f3894_0_0\"\n",
    "\n",
    "# df[disease] = df[variable].notna()\n",
    "# df[\"age_\"+disease] = 2021-df[\"year_of_birth_f34_0_0\"]\n",
    "# df.loc[df[disease],\"age_\"+disease] = df.loc[df[disease].notna(),variable]\n",
    "\n",
    "# fig,ax = plt.subplots()\n",
    "# t=np.linspace(30,round(df[\"age_\"+disease].max()))\n",
    "\n",
    "# kmf = KaplanMeierFitter()\n",
    "# kmf.fit(df.loc[df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[df[\"infertile\"],[disease]], timeline=t, label=\"Infertile\")\n",
    "# ax = kmf.plot_survival_function()\n",
    "\n",
    "# kmf.fit(df.loc[~df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[~df[\"infertile\"],[disease]], timeline=t, label=\"Fertile\")\n",
    "# ax = kmf.plot_survival_function()\n",
    "\n",
    "# plt.title(\"Kaplan-Meier Survival Curves for \"+Disease)\n",
    "# plt.xlabel(\"Age at \"+Disease+\" Diagnosis\")\n",
    "\n",
    "# ax.yaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:16:50.757955Z",
     "start_time": "2021-05-30T20:16:50.430208Z"
    }
   },
   "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": [
    "disease = \"death\"\n",
    "Disease=\"Death\"\n",
    "variable = \"age_at_death_f40007_0_0\"\n",
    "\n",
    "df[disease] = df[variable].notna()\n",
    "df[\"age_\"+disease] = 2021-df[\"year_of_birth_f34_0_0\"]\n",
    "df.loc[df[disease],\"age_\"+disease] = df.loc[df[disease].notna(),variable]\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "t=np.linspace(30,round(df[\"age_\"+disease].max()))\n",
    "\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(df.loc[df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[df[\"infertile\"],[disease]], timeline=t, label=\"Infertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "kmf.fit(df.loc[~df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[~df[\"infertile\"],[disease]], timeline=t, label=\"Fertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "plt.title(\"Kaplan-Meier Survival Curves for \"+Disease)\n",
    "plt.xlabel(\"Age at \"+Disease+\" Diagnosis\")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:23:41.257859Z",
     "start_time": "2021-05-30T20:23:38.854761Z"
    }
   },
   "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": [
    "disease = \"mi\"\n",
    "Disease=\"Heart Attack\"\n",
    "variable = \"date_of_myocardial_infarction_f42000_0_0\"\n",
    "\n",
    "df[disease] = df[variable].notna()\n",
    "df[\"age_\"+disease] = 2021-df[\"year_of_birth_f34_0_0\"]\n",
    "df.loc[df[disease],\"age_\"+disease] = [int(x.split('-')[0])-df.iloc[i][\"year_of_birth_f34_0_0\"] for i,x in enumerate(df.loc[df[disease],variable])]\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "t=np.linspace(30,round(df[\"age_\"+disease].max()))\n",
    "\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(df.loc[df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[df[\"infertile\"],[disease]], timeline=t, label=\"Infertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "kmf.fit(df.loc[~df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[~df[\"infertile\"],[disease]], timeline=t, label=\"Fertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "plt.title(\"Kaplan-Meier Survival Curves for \"+Disease)\n",
    "plt.xlabel(\"Age at \"+Disease+\" Diagnosis\")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=None))\n",
    "# df[\"age_mi\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:27:15.229307Z",
     "start_time": "2021-05-30T20:27:12.242435Z"
    }
   },
   "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": [
    "disease = \"death\"\n",
    "Disease=\"Death\"\n",
    "variable = \"date_of_death_f40000_0_0\"\n",
    "\n",
    "df[disease] = df[variable].notna()\n",
    "df[\"age_\"+disease] = 2021-df[\"year_of_birth_f34_0_0\"]\n",
    "df.loc[df[disease],\"age_\"+disease] = [int(x.split('-')[0])-df.iloc[i][\"year_of_birth_f34_0_0\"] for i,x in enumerate(df.loc[df[disease],variable])]\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "t=np.linspace(30,round(df[\"age_\"+disease].max()))\n",
    "\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(df.loc[df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[df[\"infertile\"],[disease]], timeline=t, label=\"Infertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "kmf.fit(df.loc[~df[\"infertile\"],[\"age_\"+disease]], event_observed=df.loc[~df[\"infertile\"],[disease]], timeline=t, label=\"Fertile\")\n",
    "ax = kmf.plot_survival_function()\n",
    "\n",
    "plt.title(\"Kaplan-Meier Survival Curves for \"+Disease)\n",
    "plt.xlabel(\"Age at \"+Disease)\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=None))\n",
    "# df[\"age_mi\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:23:57.807318Z",
     "start_time": "2021-05-30T20:23:57.797941Z"
    }
   },
   "outputs": [],
   "source": [
    "# [int(x.split('-')[0]) for x in df.loc[df[\"date_of_myocardial_infarction_f42000_0_0\"].notna(),\"date_of_myocardial_infarction_f42000_0_0\"]]#.split(\"-\")#.sum()#,df[\"age_heart_attack_diagnosed_f3894_0_0\"].notna().sum()\n",
    "\n",
    "# # df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:00:36.379920Z",
     "start_time": "2021-05-30T20:00:36.323614Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cancer</th>\n",
       "      <th>htn</th>\n",
       "      <th>mi</th>\n",
       "      <th>dm</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infertile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>0.175477</td>\n",
       "      <td>0.304222</td>\n",
       "      <td>0.041119</td>\n",
       "      <td>0.064775</td>\n",
       "      <td>0.088474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.234649</td>\n",
       "      <td>0.019737</td>\n",
       "      <td>0.037281</td>\n",
       "      <td>0.065789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cancer       htn        mi        dm     death\n",
       "infertile                                                  \n",
       "False      0.175477  0.304222  0.041119  0.064775  0.088474\n",
       "True       0.157895  0.234649  0.019737  0.037281  0.065789"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"infertile\")[[\"cancer\",\"htn\",\"mi\",\"dm\",\"death\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-30T20:32:10.792204Z",
     "start_time": "2021-05-30T20:32:10.142355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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85jcDV0cfLEGYk2uvhINVUPxF2rv72FnfwWUL8k5+jDvdNXbf38lPv4CKZmtBmMg2vItp69atbN26lYsucsbkBgYGyMs7+v/m2muvHdX516xZw7Zt21i5ciUAvb29rFix4vQDH4ElCHNy1eud7wVL2XigFVVYeqIB6kFxyVC0Ana/SHHWFey1LiYzlk7yl/5YUVXmz5/PW2+95XP/aJf0VlUuuugiHnnkkUCE57egjkGIyKUislNEykXkDh/754rIWyJyWES+MppjzRipWgeeOJg8n/X7W/FECaXuXdInNesiaNzO4pROKixBmAlmzpw5NDY2HkkQfX19lJWV+SybkpJywpVcBy1fvpw33niD8vJyALq6uti1a1dgg/YhaAlCRDzAL4DLgBLgehEpGVasBfgX4L9O4VgzFqo3QN4iiI5lXUUr8/JSSIrzo+E54wIAlrGV9u4+Wg/1BjlQY8aP2NhYHn/8cb72ta+xePFiSktLefPNN32Wve666/jRj37EGWecwZ49e3yWyc7O5re//S3XX389ixYtYvny5ezYsSOYVQCC28V0NlCuqnsBRORR4CrgyIRfVW0AGkTk/aM91oyBgX6o3QRLPknfgJdNlW1ce1ahf8dmz4PETGZ3bwRmsbfpEGcmxQYzWmNCxteMotLSUl599dXjtr/88svHvF+5cuUx90H89re/9Vn2ggsuYO3atacd62gEs4spH6gc8r7K3RbQY0XkZhFZJyLrGhsbTylQcwKNO6CvC/LPZHvtQbr7Bjhz6gjjD4OioqD4PWQ3vQ2odTMZE4aCmSB8zYP09+EAfh+rqveq6lJVXZqdne13cMYPgwPU+UtYv78VwP8EATDtPKI7a5keVWf3QhgThoKZIKqAof0RBUDNGBxrAqVmA8SnQ8Z03q1sIyc1jinpCf4fP+18AC5PLmefTXU1QWYPpzq5U/n5BDNBrAVmicg0EYkFrgNWj8GxJlDqtjo3CInwblU7i0e7XEbmDEjJ49zo7dbFZIIqPj6e5uZmSxInoKo0NzcTHx8/quOCNkitqv0icivwHOABHlDVMhG5xd1/j4jkAuuAVMArIl8ESlT1oK9jgxWr8cHrhYZtsOSTtHX1sq/pEFefWTC6c4hA0Qrm7XqDis5DqKrvFWCNOU0FBQVUVVVh45AnFh8fT0HB6P4PB/VGOVV9Bnhm2LZ7hryuw+k+8utYM4Za9zkD1Dnz2VzlPBXuDH/ufxiu8GzSyp4gubeR1q4+MmwmkwmCmJgYpk2bFuowIo4t1md8q9/qfM9dwLuVbYjAgoK00Z+n4CwAzogq50CLrclkTDixBGF8qy8DiYLsubxb1caM7GRS42NGf57chXijYjkjajeVliCMCSuWIIxv9WWQORONjmdT5SkMUA+KjkNzF1kLwpgwZAnC+Fa/FXLmU9PeQ1PnYUoLT6F7yeUpOptFUfuobrbnUxsTTixBmOMd7nAeMZozn3cr2wBYfCoD1IMKziKeXqTeJqIZE04sQZjjNbqrRGbP493KNmI9UczNTT3187kD1Zlt7wYgOGPMWLEEYY7XvNv5njWbTZVtlExJJTb6NP6ppBXQGZPF9J5t9A14AxOjMSboLEGY4zXtAvEwkD6VLdXt/j3/4WREaMtYTKmUU9vWE5AQjTHBZwnCHK9pN2RMY19rL129AyzIP/UB6kH9+UspjqqnpqZy5MLGmHHBEoQ5XnM5ZM5ia7Uz62hB/mmMP7gSpy8H4HDFO6d9LmPM2LAEYY7lHYDmPZA1i7KaduKio5iZnXzap82ctYx+jSKmbn0AgjTGjAVLEOZYbQdg4DBkOS2IubkpRHtO/5+JJy6JPZ5iMls2nX6MxpgxYQnCHKvZeSi6ZjotiPkBGH8YVJUwj/yeXWBLMhsTFixBmGM1OfdA1EQXcLCnn/lTTn/8YVD7pPkk6yFnpVhjzLhnCcIcq2k3JExiS6uzEvyCKYFrQXhzFwHQdWBDwM5pjAkeSxDmWK37IGM6W6sP4okS5uSmBOzUyQWL6FUPh/atC9g5jTHBYwnCHKt1P6RPZWtNO7MmJxMf4wnYqQuy09mphUitLblhTDiwBGGO8g5AexVMmkpZzUFKAjj+AFA4KZGt3mkkt5TZQLUxYcAShDmqow68fRyMn0Jjx+GAjj8ApCXGUB49g/j+dmc6rTFmXLMEYY5q2w/A3r5MgIDOYBrUklbivKjbHPBzG2MCyxKEOcr9q35Lp9NyCHQXE4BmzcGLQMP2gJ/bGBNYliDMUa1OC+Kd1iSKMxNJOZVnUI8gJzOTSp2M1m8L+LmNMYFlCcIc1XYAUvIoazh8eg8IOonCjER2egvor7Onyxkz3lmCMEe17cebVkhF8yFmB/D+h6GKMhLZqYV4WvdC/+GgXMMYExiWIMxRbftpj8vHqzA3iAlil7eAKO0/su6TMWZ8sgRhHAP90F5NXVQ2ALNzgpMgpqQnsItC540NVBszrlmCMI6D1aAD7O3LJDY6iuLMxKBcJjY6ip6UaQzggXobhzBmPLMEYRyDU1y70pk1OTkgz4A4kbzMVKo9BdaCMGacC2qCEJFLRWSniJSLyB0+9ouI/NTdv1lElgzZ968iUiYiW0XkERGJD2asE17b4BTXZOYEqXtpUFFGIju9+dBoCcKY8SxoCUJEPMAvgMuAEuB6ESkZVuwyYJb7dTPwS/fYfOBfgKWqugDwANcFK1YDtB1AJYotHSkBXcHVl6KMRMr6ctHW/dDXE9RrGWNOXTBbEGcD5aq6V1V7gUeBq4aVuQp4UB1rgHQRyXP3RQMJIhINJAI1QYzVtB2gNyGHPqKDniAKMxLZ652CoNCyN6jXMsacumAmiHygcsj7KnfbiGVUtRr4L+AAUAu0q+rzvi4iIjeLyDoRWdfY2Biw4Cec1v20xTm5eSwSxB6d4rxxn2BnjBl/gpkgxMe24Ws8+ywjIpNwWhfTgClAkojc4Osiqnqvqi5V1aXZ2dmnFfCE1naAaiaTGh9Nbmpwh3uKMhLZq7nOm6bdQb2WMebUBTNBVMHghHcACji+m+hEZd4H7FPVRlXtA54AzglirBNbfy8crKa8N4O5uamI+MrbgZOZFIvEJtEWm2stCGPGsWAmiLXALBGZJiKxOIPMq4eVWQ18wp3NtBynK6kWp2tpuYgkivNpdSFgU16C5WAVoGw+lM7s3OSgX05EKJyUSHVUPjRbC8KY8SpoCUJV+4FbgedwPtwfU9UyEblFRG5xiz0D7AXKgV8Dn3OPfRt4HNgAbHHjvDdYsU547j0Q5b0ZzAnSIn3DFWYkstub53Qx2dPljBmXooN5clV9BicJDN12z5DXCnz+BMd+C/hWMOMzLneZ70pvdtDvgRhUlJHI5j2T+WBUJ3TUQuqUMbmuMcZ/die1gbYDeMVDHRnMmhz8LiaAoowEtve7M5ptoNqYcckShIG2/bRFTyYtKYFJSbFjcslp2cns8bqthsadY3JNY8zoWIIw0HaAGpnMjOykMbvkrMnJNJDO4egUW3LDmHHKEoSBtgPs6ctg5hh1LwHkpcWTHBdDXdw0aNgxZtc1xvjPEsRE19cDHbWU92YyI3vsEoSIMHNyMru1wGlB2EwmY8YdSxATXXsVAFWazYwxbEGA0820sScXuluhs2FMr22MGZkliImurQJwEsTMMWxBgPPUuo097pIbNg5hzLhjCWKic2+Sq/fkkJ+eMKaXnpmTzG5vgfPGxiGMGXcsQUx0rfvpJ5qUrAKiooK7BtNwsyYn00gah2PSrAVhzDhkCWKiaztArWQzPSdtzC+dn55AUmw0dXHF1oIwZhyyBDHBeVv3s38gc0zvgRg0OJNplxZCwzbwesc8BmPMiVmCmOC8rfup9GaP6T0QQ83OSeHtrnw4fPDIgLkxZnywBDGR9XYR3d1EpWaP6T0QQy0sSOPtHveRILWbQxKDMcY3SxATmTuDqZpspmWNfRcTwKKCdHZpAV6JhjpLEMaMJ5YgJjI3QfQmFxIf4wlJCPPyUvB64mhKKIbad0MSgzHGN0sQE1mb8xyIuKzikIUQF+1hXl4q23WakyBsyQ1jxg2/EoSI/FlE3i8illAiiLd1P4c1huy8wpELB9GigjTe7MqHQ43QURfSWIwxR/n7gf9L4KPAbhH5oYjMDWJMZoz0NO6jSrOYMXlsHjN6IosK0tnQW+S8sXEIY8YNvxKEqr6oqh8DlgAVwAsi8qaI3CQiMcEM0ARPf3OFswZTiKa4DiotTGebTnXe2EwmY8YNv7uMRCQTuBH4NLARuBsnYbwQlMhM0MV2VoV0iuugGdnJEJtMU1wh1G4KaSzGmKP8HYN4AngNSAQ+oKpXquofVfU2ILSfLubUHO4gvq+NlpjcMXvM6Il4ooQlUyex1TvVupiMGUf8bUHcp6olqvofqloLICJxAKq6NGjRmeBxp7gOpIZ2gHrQuTOzWNNV4MTV1RLqcIwx+J8g/t3HtrcCGYgZY26CiM6cFuJAHCtnZlGmxc6bui0hjcUY44g+2U4RyQXygQQROQMYXA86Fae7yYSpnsa9xAMpuTNCHQoAJXmpVMfNBMXpZpp+fqhDMmbCO2mCAC7BGZguAH4yZHsHcGeQYjJjoLNuL16NIzcvP9ShABAVJcybNYP6XZlMrn2XsX0yhTHGl5MmCFX9HfA7Efmwqv55jGIyY6Cv2bkHYlp2SqhDOeLcmVls3j6V86s2Edphc2MMjNzFdIOq/h4oFpEvDd+vqj/xcZgJA56DlVRpNudkjp+ewnNnZvFnLeZ9rU9C7yGIDc0CgsYYx0iD1IP/Q5OBFB9fJyUil4rIThEpF5E7fOwXEfmpu3+ziCwZsi9dRB4XkR0isl1EVvhdK3NyqqR0V9MSkxeyRfp8KcxIpCO9BEGhvizU4Rgz4Y3UxfQr9/t3RntiEfEAvwAuAqqAtSKyWlW3DSl2GTDL/VqGs6THMnff3cCzqnq1iMRig+KB091KgvcQPakFoY7kOEXzl8Pb0L5vHWmFZ4c6HGMmNH9vlPtPEUkVkRgR+buINInIDSMcdjZQrqp7VbUXeBS4aliZq4AH1bEGSBeRPBFJBc4D7gdQ1V5VbRtNxcxJuKu4yqTi0Mbhw3vOLKVFk6nf8U6oQzFmwvP3PoiLVfUgcAVOa2A2cPsIx+QDlUPeV7nb/CkzHWgEfiMiG0XkPhHx2SEtIjeLyDoRWdfY2OhndSa2zro9ACTkjI8prkNNn5xCRcxMohvsXghjQs3fBDG4IN/lwCOq6s+trr5mKg5f7P9EZaJx1nn6paqeARwCjhvDAFDVe1V1qaouzc7O9iMs01ZbDsCkKbNCHIlv3pyFFPTto771YKhDMWZC8zdBPC0iO4ClwN9FJBvoGeGYKmDoOg4FQI2fZaqAKlV9293+OE7CMAHQ27iXNk2icEpuqEPxqXD+CmJlgDfeeiPUoRgzofm73PcdwApgqar24fxFP3w8Ybi1wCwRmeYOMl8HrB5WZjXwCXc203KgXVVrVbUOqBSROW65C4FtmICQtgNUajZFGeNz3D9ntjM4XVn2ZogjMWZiG+lO6qHm4dwPMfSYB09UWFX7ReRW4DnAAzygqmUicou7/x7gGZxuq3KgC7hpyCluAx52k8veYfvMaUg4VMX+mAJio8fpAwIzZtDnSSC1fSflDR3MnDx+buYzZiLxK0GIyEPADGATMOBuVk6SIABU9RmcJDB02z1DXivw+RMcuwmnS8sEktdLRl8dXSnLQx3JiUVFoTkLmF+1n6c21vCVS+aMfIwxJuD8bUEsBUrcD3QTxrSjllj60PSiUIdyUrH5i1lY+we+vLGSL188GxFbncmYseZvH8NWYHyOaJpRaatxZjDFZo2PZb5PKHchCdqFtB9g/f7WUEdjzITkbwsiC9gmIu8Ahwc3quqVQYnKBE1L9W4mAWlTZoY6lJPLXQhAafQBntpUzdLijBAHZMzE42+C+HYwgzBjp7thLwA5RbNDHMkIJpeAeLh8chN3bK7lm1fMH7+D6sZEKH+nub4CVAAx7uu1wIYgxmWCxNtSQZ1OIj9rUqhDObmYBMiazdK4Ktq6+nh1l90lb8xY83ctps/g3Kz2K3dTPvBUkGIyQRTfWUVjdC7RnjD4azx3IZmdO8lIiuXJTdWhjsaYCcffT4nPAyuBgwCquhuYHKygTPCkHa6hM358PEVuRHmLkIM1XDsvnhe31dPR0xfqiIyZUPxNEIfdFVkBcG+WsymvYcbbd5gsbxN9qYUjFx4PppwBwD/lNnK438tzZfUhDsiYicXfBPGKiNwJJIjIRcCfgKeDF5YJhsbqPXhEic4c51NcB+UtBoSZ/bsoykjkqY3WzWTMWPI3QdyBs/z2FuCzOHdH/1uwgjLB0VS5C4CU3PG3zLdPcSmQNRup2cgHS6fw5p4m6g+OtEakMSZQ/J3F5MUZlP6cql6tqr+2u6rDz+BzILLH+xTXoaacATUbuap0Cl6Fp98dviCwMSZYTpog3FVWvy0iTcAOYKeINIrIN8cmPBNI/c376FMPk6dMD3Uo/stfAp31zIjvYFFBGk/ZbCZjxsxILYgv4sxeOktVM1U1A+eZ0StF5F+DHZwJrJiOSho92URFj2YR3xBzB6qp3sBVpflsrT5IeUNnaGMyZoIYKUF8ArheVfcNblDVvcAN7j4TRlK6q2mLmxLqMEYnZwGIB2o2cPlCZzmwF7bZbCZjxsJICSJGVZuGb1TVRo4+htSEgQGvkjtQx+GU8b2K63FiEyF3AVS+Q15aAgvyU3lxuyUIY8bCSAmi9xT3mXGmtq6WSdIBGWEyg2moohVQtQ4G+njfvBw2HGilqfPwyMcZY07LSAlisYgc9PHVASwciwBNYDRUOE9sTcoNoxlMgwqXQX831G3mopIcVOGlHQ2hjsqYiHfSBKGqHlVN9fGVoqrWxRRGOmt3ApBZNDfEkZyCIvfpdwfepiQvlSlpztIbxpjgCoMV20wgDDTtwatCRmEYPr4zdQqkFUHlGkSE95Xk8NruJnr6BkY+1hhzyixBTBBxByto8mQhMQmhDuXUFC2DA2+DKu+bl0N33wBvlB83f8IYE0CWICaI9O4DtMSHySJ9vhQug846aNvPsukZJMdF22wmY4LMEsQE0DfgZcpADT0pU0MdyqkbMg4RF+3hvNlZ/H17A7biizHBYwliAqiuqSZdDiGZ4/w51CczuQTiUuHAWwCsmj2Zho7D7KjrCHFgxkQuSxATQNOB7QAk5YXhFNdBUR4oWAqVbwNw/pxsAF6xR5EaEzSWICaAzmpnimvW1JIQR3KaCpdDw3bobiMnNZ65uSm8stMShDHBYgliAvA278GLkJYXhndRD1W0DFCoWgs4rYh1+1voPNwf2riMiVCWICaAuIMVNEZlh+8U10H5S52F+w6sAeD82dn0DShv2nRXY4LCEsQEMKmnkrZwnuI6KC4Z8hbB/jcBWDo1g8RYj41DGBMkQU0QInKpiOwUkXIRucPHfhGRn7r7N4vIkmH7PSKyUUT+Gsw4I1lPbz/53hp6UotDHUpgTF0J1eugr5vY6CjOmZHFK7sabbqrMUEQtAQhIh7gF8BlQAlwvYgMHyW9DJjlft0M/HLY/i8A24MV40RQVVNNmnThyQrz8YdBxe+Bgd5jxiGqWrvZ23QoxIEZE3mC2YI4GyhX1b2q2gs8Clw1rMxVwIPqWAOki0gegIgUAO8H7gtijBHv6BTXMFyDyZei5SBRUPEGAKtmu9NdbTaTMQEXzASRD1QOeV/lbvO3zP8AXwW8J7uIiNwsIutEZF1jo31IDHeoNkKmuA5KSIfchVDxOgCFGYlMz06ycQhjgiCYCUJ8bBveUeyzjIhcATSo6vqRLqKq96rqUlVdmp2dfSpxRrbmPQwQRUpuhHQxgdPNVLUW+noAZzbTmr3NtrqrMQEWzARRBQydOlMA1PhZZiVwpYhU4HRNXSAivw9eqJEr/mAFTVHZEB0X6lACZ+pKGDjsDFbjJIjD/V7W7G0OcWDGRJZgJoi1wCwRmSYiscB1wOphZVYDn3BnMy0H2lW1VlW/rqoFqlrsHveSqt4QxFgj1qTDVbQnRMAU16GmrgDkSDfT8umZxEVHWTeTMQEWHawTq2q/iNwKPAd4gAdUtUxEbnH33wM8A1wOlANdwE3Bimci6uzpo8Bby/60JSMXDicJkyB3wZEEER/jYdn0TEsQxgRY0BIEgKo+g5MEhm67Z8hrBT4/wjleBl4OQngR70BlJSXSRXR2GK/ieiLF74F1D0D/YYiO4/zZ2Xzvr9uobOmiMCMx1NEZExHsTuoI1nRgGwApUyJkiutQxedCfw9UO/MYVrmru768syGUURkTUSxBRLDuut0AZE+dF+JIgqDo2HGI6VlJFGUk8rLdD2FMwFiCiGQtzhTXuKzpoY4k8BIzIOfoOISIsGpONm/sabLprsYEiCWICJbQsZ8mTw5Ex4Y6lOAoXgmV7zjjEMB750ymp8/LO/taQhyYMZHBEkSEUlUyD1fSkRhhU1yHKj4X+ruhegNwdLrrP2wcwpiAsAQRoRo7eiikjr70aaEOJXiKznG+73e6mRJiPSyfnmnjEMYEiCWICFVZeYBU6SYme1aoQwmepEyYPP/IOATAe+dks6/pEBW2uqsxp80SRIRqrdwBQFrB3BBHEmTF57rjEL0ArJozGbDprsYEgiWICNVTvwuAjMIJkCD6uo48H6I4K4lpWUm8bHdVG3PaLEFEKE9LOX1E48koDnUowTV9FUTFwK5nj2xaNSebt/Y0091r012NOR2WICJU6qG9NMQUgCcm1KEEV3yqM931mAQx2VZ3NSYALEFEoP4BL/l9B+hIicAb5HyZfRk07YLmPQAsm5ZBfIxNdzXmdFmCiEA1TS0UUs9A5uxQhzI25lzqfHdbEfExHs6dmc3ftzfgrAdpjDkVliAiUN2+MjyixE+ZH+pQxsakYphcAjuOLhx8cUkO1W3dlNUcDF1cxoQ5SxARqLOyDIDM4oUhjmQMlXwQ9r8BB52HFl44bzJRAs9vqw9tXMaEMUsQEcjbsIMBokgvLAl1KGNnwYcBhbInAchMjmPp1AxesARhzCmzBBGBEg+WU+/Ji6znUI8kaybkLYYtjx/ZdPH8HLbXHqSypSuEgRkTvixBRBhVJaengrbkGaEOZewtuBpqNhyZzXRRSQ5g3UzGnCpLEBGmvqWdIuronygzmIZaeDVIFGz6AwBTM5OYk5PC82V1IQ7MmPBkCSLC1OzaQIwMEFdwRqhDGXupU2Dm+5wE4XXuor54fg5rK1poOdQb4uCMCT+WICJM137nGc3Zs88OcSQhcsbHoaMGyv8OwMUluXgVXrRuJmNGzRJEhIlp2MJBkpg0ZWaoQwmN2ZdCYhZs+B0AC/JTKcpI5OnNNSEOzJjwYwkiwmQc3M7+2FlI1AT91UbHwpKPw85noGUfIsL7F+Xx5p5m62YyZpQm6KdIZNL+Xor699GWFuFLfI/k7M+CeGDNLwG4YlEeA17l2a02WG3MaFiCiCAt+7cQRx/enEWhDiW0UvNg4TWw8SHoaqEkL5XpWUn81bqZjBkVSxARpH7nOwBMmjlBB6iHOudW50FCb99zpJtpzd5mGjsOhzoyY8KGJYgI0l+5jg5NYNrsCbQG04nkzId5V8Jb/wtdLVyxaApehWe31oY6MmPChiWICDKpeSM7PLNJSYwPdSjjw3vvhN5OeONu5uSmMGtyMk9vtgRhjL+CmiBE5FIR2Ski5SJyh4/9IiI/dfdvFpEl7vZCEfmHiGwXkTIR+UIw44wIPQeZ0ruPhvTFoY5k/Jg8z7m7+p17obOBDyyewjv7Wqhu6w51ZMaEhaAlCBHxAL8ALgNKgOtFZPjyopcBs9yvm4Ffutv7gS+r6jxgOfB5H8eaIQ5VvIMHLwP5Nv5wjPPvgP4eeP1/+GBpPgBPbawOcVDGhIdgtiDOBspVda+q9gKPAlcNK3MV8KA61gDpIpKnqrWqugFAVTuA7UB+EGMNey3bX8OrQvrsc0IdyviSNRMWXw9r76Mopo2ziifxxIYqe9KcMX4IZoLIByqHvK/i+A/5EcuISDFwBvC2r4uIyM0isk5E1jU2Np5uzOGrai07tYC5xZZHj3P+V0EH4LUf809LCtjTeIgt1e2hjsqYcS+YCUJ8bBv+Z9tJy4hIMvBn4Iuq6vPZkap6r6ouVdWl2dnZpxxsWPN6yWp9l23R85icYgPUx5lU7KzRtP53vL+on9joKJ7YYN1MxowkmAmiCigc8r4AGH6n0gnLiEgMTnJ4WFWfCGKc4a9+CwneTpomTcAVXP113u0gUaS+/RMumpfD6ndr6BvwhjoqY8a1YCaItcAsEZkmIrHAdcDqYWVWA59wZzMtB9pVtVZEBLgf2K6qPwlijBGhe9c/APBMXxXaQMaztHxYehNs+gMfndlPy6FeXtk5gbskjfFD0BKEqvYDtwLP4QwyP6aqZSJyi4jc4hZ7BtgLlAO/Bj7nbl8JfBy4QEQ2uV+XByvWcHdo5z/Y482jZO4EfEjQaJz7JfDEsqLqPjKSYnnSZjMZc1LRwTy5qj6DkwSGbrtnyGsFPu/juNfxPT5hhhvoJ6X+HZ7Tc/inwkmhjmZ8S8mBsz9D1Js/45/nXcVPt9TT3t1HWkJMqCMzZlyyO6nDXe0m4ga6qElfSkKsJ9TRjH8rvwixSdzQ8wd6+722gJ8xJ2EJIsz1lb8MQMzM80IbSLhIyoRlt5C+769clt3EH9dWjnyMMROUJYgw17P9WbZ5p1Iyc0aoQwkf59wKcWl8Pf4JNle1s9XuiTDGJ0sQ4ayrhaT6DbzoPYOlxRmhjiZ8JEyClbdR1Pgy50bv4NG1B0IdkTHjkiWIcLbnJaIYYE/aSjKSYkMdTXhZ/nlIK+SupD/w9MYqunr7Qx2RMeOOJYgw1rf9/2jWVPLm2/pLoxabCBd/j/zD5VzZ/yx/s2XAjTmOJYhwNdCP7n6Bf3hLWTU3L9TRhKeSD6LT38udMY/y0pq1oY7GmHHHEkS4qniN2L523vSczZlT7f6HUyKCXPkzPB4PH6//EWVVLaGOyJhxxRJEmOrf/DidmkB8ySXEeOzXeMrSC+m/+Aec49lG3ZP/FupojBlX7JMlHPX3ottW85z3TC5eXBzqaMJe4rIbWZd5FRc2P0z72kdDHY4x44YliHC05yVi+g7yWux5nDszK9TRRISsj9zNWu9sEv/vX6B2c6jDMWZcsAQRhrrWP0KrJlO49HKirXspIIpzJvFI8fdp9iajj1wPB20JDmPs0yXcdLUQu/tvrPau5Nrldvd0IF19/hI+dfhL9He1wu+uhM6GUIdkTEhZgggzh9b+nmjto2HmtRRMSgx1OBFlxfRMyFvMl6LvRNur4MEPQpfNbDITlyWIcKLKobfuZ5N3Bh+67JJQRxNxRIR/fd9snm6bxitn3g3N5fDQh+BQc6hDMyYkLEGEkb3rn2dyTwX7pl7NzMnJoQ4nIl04bzKLC9P5xrvZ9F39W2jYDvdfBM17Qh2aMWPOEkSY6Ortp+HZH9FKKhdcfWuow4lYIsKXL5pNdVs3v2uaC59cDd2tcO8q2PwYqIY6RGPGjCWIMKCq/OQPf2V5/1raF95IWlpqqEOKaO+ZlcWqOdn89wu7qEsrhZtfhpz58MRn4A/XQuv+UIdozJiwBBEGfvnKHubuuZ/+qDiKL/1iqMOJeCLCd66cT79X+d7ftsGkqXDj3+CSH0DF6/CLZfD6f8NAX6hDNSaoLEGMcw+t2c/Tzz3PP3lew3P2p50nopmgm5qZxOffO5O/ba5l9bs1EOWBFZ+HW9+BmRfCi9+G+y6E+rJQh2pM0FiCGMf+8PYB/t9TW/hx2p+QhHTk/NtDHdKE8v+tmsFZxZO448+b2V3f4WxMK4DrHoaPPAjt1fCr8+HVH1lrwkQkSxDj1L2v7uHOJ7fw9fwtlPRsQM7/mvMkNDNmYjxR/PyjS0iM9fCZB9dR2959dGfJVfD5d2DeB+Clf3daE3VbQxesMUFgCWKcUVV+/PxOfvDMDm6cO8DNHT+HonPgrM+EOrQJKSc1nl99fClNnb185FdvUdnSdXRnUiZc85shrYn3wOrboM0eYWoigyWIcWTAq3x7dRk/e6mcfz4jlW8d+gHiiYEP/xo80aEOb8I6c+okfv/pZbR39XH53a/x+PoqdOh015Kr4Na1sOwW2PQI3F0Kj34MNj4MHfUhi9uY0yUaQfO6ly5dquvWrQt1GKek83A///LIRl7a0cBt50zmS3W3I/Xb4IbHYdp5oQ7PAPubD3H7nzbzTkUL8/JSuWllMZeU5JKWGHO0UHsVvH0PbHkcOtzHmOYsgClnwJRSyCuF7LkQZzc6mvFBRNar6lKf+yxBhF5lSxefeXAduxs6+c+LJ/Ph7V+Axp1w7UMw57JQh2eGGPAqf95Qxa9f3cvuhk48UcIZheksm57BsmmZnDl1Eklx0c4NdXVbYPfzztTY2k3ODXcAiJM0ZrwXFl4DuQtBJJTVMhOYJYhx7Ol3a7jzyS2g8NBFA5S+/WXoaXeSw8wLQx2eOQFVZWNlGy9tb+D18ia2VLcz4FWio4SFBWksm5bJsukZnFWcQfJgwmivdJ41Ub/VSRoH1oC3DybPh4UfhilLnFlScakQnwrR8ZY4TNBZghiH9jUd4nt/3cZLOxq4ML+fu/NeILnsYZhUDNf8FvIWhzpEMwqHDvezfn8ra/Y28/a+FjZXtdE3oMR6ojhnZiYXleRw0bwcJqfGHz2oqwW2/hnefRSqj/93OxCbSlviVGqjC6j2FFAbU0hH0jSis6czIy+LObkpFGUkIpZEzGkIWYIQkUuBuwEPcJ+q/nDYfnH3Xw50ATeq6gZ/jvVlvCeIAa/yzr4WHllTzo6yTZwTvZubsndQ1PImIlGw9Ca48JsQlxLqUM1p6u4dYP3+Vl7Z1cBzZfUccGc/LS5I46ziDObnp5KXlkBSbDQDqrQ31tBVtYXm+gO0NDfR09lGLs3MkBpmRNWSK0eXHfeqUK1Z1DOJg1FpeFKySZ6US1ZOAXlTCohNnexMiY5JhJh4pyUSHQ8xCeCJOVHIZoIKSYIQEQ+wC7gIqALWAter6rYhZS4HbsNJEMuAu1V1mT/H+hLUBKHqLtTmflev+9oLqqh66R8YoLe3l77uDro72+lorae76QCd9RX0tRwguqOaKd4aiqSBaPE6503Nh0UfgTNvdFoPJuKoKrvqO3lhWx3/2NnIlup2evu9PstOToljYX4aC/LTmJeXyuycZIoyEonu73KWH28up7d+J501O+htr0c7G4jrbSXN245HRv6/rOKBmARkMGFEx7tJJOEE34ckF1/fo+NAPE5XmEQd+xXlgaiYY88zvNvsmNaPnHibX9uDVfYEsUVIy+1kCSKYcyfPBspVda8bxKPAVcDQD/mrgAfVyVJrRCRdRPKAYj+ODZz/nAF9XT4/+I+8HoEAMe4XQDqQN2R/u6TQEZeDJ2sxOnU+5Mxx+pyzZkXMPzTjm4gwJzeFObkp3HrBLHr7vRxoOURd+2G6+waIEshKjmNKegLZKXG+T+JJdmZBTSkldiFkDNvd0tnDlt0V7Nq3j6qqShob64geOEyc9BLPkC/pJb6vjwTpJUH6uHxuOnH0QX839PU44199PUff9/dAX7czVmL8FILklZwNX3j3lKI9mWAmiHygcsj7KpxWwkhl8v08FgARuRm42X3bKSI7fRTLApr8jjwoDgLVwAbgT4E44TioU0BFWn0g8uoUafWBiKlTLXzxSNIYbZ2mnmhHMBOErz+Lh7eBT1TGn2Odjar3AveeNBCRdSdqQoWrSKtTpNUHIq9OkVYfsDqNJJgJogooHPK+AKjxs0ysH8caY4wJomAutbEWmCUi00QkFrgOWD2szGrgE+JYDrSraq2fxxpjjAmioLUgVLVfRG4FnsOZqvqAqpaJyC3u/nuAZ3BmMJXjTHO96WTHnkY4J+2CClORVqdIqw9EXp0irT5gdTqpiLpRzhhjTODYaq7GGGN8sgRhjDHGp4hLECLygIg0iMjWIdsyROQFEdntfg+bR7OJSKGI/ENEtotImYh8wd0eznWKF5F3RORdt07fcbeHbZ3AWT1ARDaKyF/d9+FenwoR2SIim0RknbstbOvk3oj7uIjscP8/rQjz+sxxfzeDXwdF5IuBrFPEJQjgt8Clw7bdAfxdVWcBf3ffh4t+4MuqOg9YDnxeREoI7zodBi5Q1cVAKXCpO4stnOsE8AVg+5D34V4fgPeqaumQefXhXKe7gWdVdS6wGOd3Fbb1UdWd7u+mFDgTZ6LPkwSyTqoacV84S3VsHfJ+J5Dnvs4DdoY6xtOo219w1qiKiDoBiTi3ly8L5zrh3Kvzd+AC4K/utrCtjxtzBZA1bFtY1glIBfbhTswJ9/r4qN/FwBuBrlMktiB8yVHn/grc75NDHM8pEZFi4AzgbcK8Tm53zCagAXhBVcO9Tv8DfBUYunBXONcHnNULnheR9e6SNhC+dZoONAK/cbsB7xORJMK3PsNdBzzivg5YnSZKggh7IpIM/Bn4oqoeDHU8p0tVB9RpGhcAZ4vIghCHdMpE5AqgQVXXhzqWAFupqkuAy3C6NsP52bfRwBLgl6p6BnCIMOpOOhn3ZuIrCdAib0NNlARR764Si/u9IcTxjIqIxOAkh4dV9Ql3c1jXaZCqtgEv44wbhWudVgJXikgF8ChwgYj8nvCtDwCqWuN+b8Dp2z6b8K1TFVDltlQBHsdJGOFan6EuAzaoar37PmB1migJYjXwSff1J3H68cOCiAhwP7BdVX8yZFc41ylbRNLd1wnA+4AdhGmdVPXrqlqgqsU4Tf2XVPUGwrQ+ACKSJCIpg69x+ri3EqZ1UtU6oFJE5ribLsR5fEBY1meY6znavQQBrFPE3UktIo8Aq3CWvK0HvgU8BTwGFAEHgGtUteUEpxhXRORc4DVgC0f7t+/EGYcI1zotAn6Hs4xKFPCYqn5XRDIJ0zoNEpFVwFdU9Ypwro+ITMdpNYDTPfMHVf1+mNepFLgPZzHQvThL+0QRpvUBEJFEnEcjTFfVdndbwH5HEZcgjDHGBMZE6WIyxhgzSpYgjDHG+GQJwhhjjE+WIIwxxvhkCcIYY4xPliCMCQARGXBX1HxXRDaIyDnu9mIRURH53pCyWSLSJyI/d99/W0S+EqrYjTkRSxDGBEa3OitrLga+DvzHkH17gSuGvL8GOJ1H6BozJixBGBN4qUDrkPfdwHYRGVwy+1qcG5mMGdeiQx2AMREiwV2dNh5nieULhu1/FLhOROqAAaAGmDKmERozSpYgjAmMbnd1WkRkBfDgsBVqnwW+h7P8yx/HPjxjRs+6mIwJMFV9C2ctsOwh23qB9cCXcVbmNWbcsxaEMQEmInNxFiJsxnli3qAfA6+oarOzSK8x45slCGMCY3AMAkCAT6rqwNBEoKpl2OwlE0ZsNVdjjDE+2RiEMcYYnyxBGGOM8ckShDHGGJ8sQRhjjPHJEoQxxhifLEEYY4zxyRKEMcYYn/5/G2etD3mYH+UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this is to plot the kde\n",
    "sns.kdeplot(df.loc[df[\"infertile\"],\"body_mass_index_bmi_f21001_0_0\"],label='Infertile');\n",
    "sns.kdeplot(df.loc[~df[\"infertile\"],\"body_mass_index_bmi_f21001_0_0\"],label='Fertile');\n",
    "\n",
    "# beautifying the labels\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('Density')\n",
    "plt.title(\"Density Distribution of BMI\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# diabetes_diagnosed_by_doctor_f2443_0_0\n",
    "# cancer_diagnosed_by_doctor_f2453_0_0\n",
    "\n",
    "# cancer_code_selfreported_f20001_0_0\n",
    "# smoking_status_f20116_0_0\n",
    "# body_mass_index_bmi_f21001_0_0\n",
    "# date_of_death_f40000_0_0\n",
    "# type_of_cancer_icd10_f40006_0_0\n",
    "# \n",
    "# description_of_cause_of_death_f40010_0_0\n",
    "# type_of_cancer_icd9_f40013_0_0\n",
    "# date_of_myocardial_infarction_f42000_0_0\n",
    "# date_of_stemi_f42002_0_0\n",
    "# date_of_nstemi_f42004_0_0\n",
    "# date_of_stroke_f42006_0_0\n",
    "# date_of_ischaemic_stroke_f42008_0_0\n",
    "# date_n46_first_reported_male_infertility_f132084_0_0\n",
    "# source_of_report_of_n46_male_infertility_f132085_0_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# number_of_children_fathered_f2405_0_0\n",
    "# diabetes_diagnosed_by_doctor_f2443_0_0\n",
    "# cancer_diagnosed_by_doctor_f2453_0_0\n",
    "# age_high_blood_pressure_diagnosed_f2966_0_0\n",
    "# cancer_yearage_first_occurred_f84_0_0\n",
    "# age_heart_attack_diagnosed_f3894_0_0\n",
    "# age_diabetes_diagnosed_f2976_0_0\n",
    "# cancer_code_selfreported_f20001_0_0\n",
    "# interpolated_age_of_participant_when_cancer_first_diagnosed_f20007_0_0\n",
    "# smoking_status_f20116_0_0\n",
    "# body_mass_index_bmi_f21001_0_0\n",
    "# date_of_death_f40000_0_0\n",
    "# type_of_cancer_icd10_f40006_0_0\n",
    "# age_at_death_f40007_0_0\n",
    "# age_at_death_f40007_1_0\n",
    "# age_at_cancer_diagnosis_f40008_0_0\n",
    "# description_of_cause_of_death_f40010_0_0\n",
    "# type_of_cancer_icd9_f40013_0_0\n",
    "# date_of_myocardial_infarction_f42000_0_0\n",
    "# date_of_stemi_f42002_0_0\n",
    "# date_of_nstemi_f42004_0_0\n",
    "# date_of_stroke_f42006_0_0\n",
    "# date_of_ischaemic_stroke_f42008_0_0\n",
    "# date_n46_first_reported_male_infertility_f132084_0_0\n",
    "# source_of_report_of_n46_male_infertility_f132085_0_0"
   ]
  }
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