4_Selecting_best_parametrs_for _inductive_miner.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# After July 26 - Friday meeting with Douglas\n",
    "\n",
    "# p-values to select best set of parameters\n",
    "\n",
    "1. Event log - L1 (WITHOUT ’admission’ and ’discharge’), L2 (WITH ’ad-\n",
    "mission’ and ’discharge’)\n",
    "2. Noise threshold - 0.2 / 0.3 / 0.4\n",
    "3. Fitness threshold - 0.5 / 0.6 / 0.7"
   ],
   "id": "6da6690708db050f"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "success code to run - plot LOS / Thrombotic events / Mortality - for all 18 attempts as sub figures on a one large figure",
   "id": "109d5a68c8b420e2"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T00:54:36.139727Z",
     "start_time": "2024-10-07T00:54:36.137499Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import chi2_contingency\n",
    "import os\n",
    "from scipy.stats import mannwhitneyu\n",
    "import numpy as np"
   ],
   "id": "52debee2c89213ea",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2024-10-07T00:54:36.858567Z"
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   "cell_type": "code",
   "source": [
    "# Define your attempts with their respective file paths\n",
    "attempts = [\n",
    "#1\n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.2_fitness0.5/cluster_distribution_LogV1_Noise0.2_Fitness0.5.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.2_fitness0.5/clusters_hadm_id_level_LogV1_Noise0.2_Fitness0.5.csv'},\n",
    "#2   \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.2_fitness0.5/cluster_distribution_LogV2_Noise0.2_Fitness0.5.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.2_fitness0.5/clusters_hadm_id_level_LogV2_Noise0.2_Fitness0.5.csv'},\n",
    "#3    \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.2_fitness0.6/cluster_distribution_LogV1_Noise0.2_Fitness0.6.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.2_fitness0.6/clusters_hadm_id_level_LogV1_Noise0.2_Fitness0.6.csv'},\n",
    "    \n",
    "# new\n",
    "    \n",
    "#17   \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.2_fitness0.6/cluster_distribution_LogV2_Noise0.2_Fitness0.6.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.2_fitness0.6/clusters_hadm_id_level_LogV2_Noise0.2_Fitness0.6.csv'},\n",
    "    \n",
    "    \n",
    "#4    \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise02_fitness07/cluster_distribution_EventLogV1_noise02_fitness07.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise02_fitness07/clusters_hadm_id_level_EventLogV1_noise02_fitness07.csv'},\n",
    "\n",
    "# new\n",
    "    \n",
    "#18    \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise02_fitness07/cluster_distribution_EventLogV2_noise02_fitness07.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise02_fitness07/clusters_hadm_id_level_EventLogV2_noise02_fitness07.csv'},\n",
    "    \n",
    "#5    \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.5/cluster_distribution_LogV1_Noise0.3_Fitness0.5.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.5/clusters_hadm_id_level_LogV1_Noise0.3_Fitness0.5.csv'},\n",
    "    \n",
    "#6   \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.5/cluster_distribution_LogV2_Noise0.3_Fitness0.5.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.5/clusters_hadm_id_level_LogV2_Noise0.3_Fitness0.5.csv'},\n",
    "    \n",
    "#7    \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.6/cluster_distribution_LogV1_Noise0.3_Fitness0.6.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.6/clusters_hadm_id_level_LogV1_Noise0.3_Fitness0.6.csv'},\n",
    "    \n",
    "#8    \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.6/cluster_distribution_LogV2_Noise0.3_Fitness0.6.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.6/clusters_hadm_id_level_LogV2_Noise0.3_Fitness0.6.csv'},\n",
    "    \n",
    "#9    \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.7/cluster_distribution_LogV1_Noise0.3_Fitness0.7.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.7/clusters_hadm_id_level_LogV1_Noise0.3_Fitness0.7.csv'},\n",
    "    \n",
    "#10   \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.7/cluster_distribution_LogV2_Noise0.3_Fitness0.7.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.3_fitness0.7/clusters_hadm_id_level_LogV2_Noise0.3_Fitness0.7.csv'},\n",
    "    \n",
    "#11   \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.5/cluster_distribution_LogV1_Noise0.4_Fitness0.5.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.5/clusters_hadm_id_level_LogV1_Noise0.4_Fitness0.5.csv'},\n",
    "    \n",
    "#12   \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.5/cluster_distribution_LogV2_Noise0.4_Fitness0.5.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.5/clusters_hadm_id_level_LogV2_Noise0.4_Fitness0.5.csv'},\n",
    "    \n",
    "#13    \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.6/cluster_distribution_LogV1_Noise0.4_Fitness0.6.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.6/clusters_hadm_id_level_LogV1_Noise0.4_Fitness0.6.csv'},\n",
    "    \n",
    "#14    \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.6/cluster_distribution_LogV2_Noise0.4_Fitness0.6.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.6/clusters_hadm_id_level_LogV2_Noise0.4_Fitness0.6.csv'},\n",
    "    \n",
    "#15    \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.7/cluster_distribution_LogV1_Noise0.4_Fitness0.7.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.7/clusters_hadm_id_level_LogV1_Noise0.4_Fitness0.7.csv'},\n",
    "    \n",
    "#16   \n",
    "    {'cluster_distribution': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.7/cluster_distribution_LogV2_Noise0.4_Fitness0.7.csv', 'clusters_in_hadmid_level': '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/EventLogV1_noise0.4_fitness0.7/clusters_hadm_id_level_LogV2_Noise0.4_Fitness0.7.csv'}\n",
    "\n",
    "]"
   ],
   "id": "fa17109d81996016",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T00:54:46.589155Z",
     "start_time": "2024-10-07T00:54:46.580517Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Function to perform chi-squared test and determine significant clusters\n",
    "def perform_chi_squared_test(cluster_distribution_df, overall_positive_ratio, overall_negative_ratio, TP_to_TN_ratio, alpha=0.05):\n",
    "    chi_squared_results = []\n",
    "    for _, row in cluster_distribution_df.iterrows():\n",
    "        cluster_index = row['cluster_index']\n",
    "        observed_TN = row['TN_count']\n",
    "        observed_TP = row['TP_count']\n",
    "        total_cluster_patients = row['patient_count']\n",
    "        \n",
    "         # Expected counts based on overall ratios\n",
    "        \n",
    "        expected_TN = overall_negative_ratio * total_cluster_patients\n",
    "        expected_TP = overall_positive_ratio * total_cluster_patients\n",
    "        \n",
    "        observed = [observed_TN, observed_TP]\n",
    "        expected = [expected_TN, expected_TP]\n",
    "        \n",
    "        # chi2_contingency expects frequencies in a 2x2 table format\n",
    "        \n",
    "        contingency_table = [observed, expected]\n",
    "         \n",
    "        chi2, p_value, _, _ = chi2_contingency(contingency_table)\n",
    "        \n",
    "        significant = p_value < alpha\n",
    "        \n",
    "        # first condition inside 'if' to overcome zero division error when observed_TN = 0\n",
    "        \n",
    "        majority_class = 'TP' if (observed_TN == 0 or observed_TP / observed_TN > TP_to_TN_ratio) else 'TN'\n",
    "        significant_majority_class = f'True_{majority_class}' if significant else 'False'\n",
    "        \n",
    "        chi_squared_results.append({\n",
    "            'cluster_index': int(cluster_index),\n",
    "            'patient_count': int(total_cluster_patients),\n",
    "            'TP_count': int(observed_TP),\n",
    "            'TN_count': int(observed_TN),\n",
    "            'TP_percentage': round((observed_TP / total_cluster_patients) * 100, 2),\n",
    "            'TN_percentage': round((observed_TN / total_cluster_patients) * 100, 2),\n",
    "            'chi2_statistic': round(chi2, 2),\n",
    "            'p_value': round(p_value, 2),\n",
    "            'significant': significant,\n",
    "            'significant_majority_class': significant_majority_class\n",
    "        })\n",
    "        \n",
    "    chi_squared_results_df = pd.DataFrame(chi_squared_results)\n",
    "    \n",
    "# remove clusters if it doesn't contain any TP or TN patient = means, the cluster only contains either FP, FN, or both FN and FP.\n",
    "# comment this line, as our data set only contain TP and TN patients no\n",
    "    \n",
    "    #chi_squared_results_df = chi_squared_results_df[(chi_squared_results_df['TP_count'] != 0) | (chi_squared_results_df['TN_count'] != 0)]\n",
    "    return chi_squared_results_df.reset_index(drop=True)\n",
    "\n",
    "# Function to label OD and Non-OD patients\n",
    "def label_OD_Non_OD_patients(clusters_in_hadmid_level_df, chi_squared_results_df, event_log_df):\n",
    "    TN_and_TP_hadm_id_merge_with_cluster_info = pd.merge(clusters_in_hadmid_level_df, chi_squared_results_df, how='left', on='cluster_index')\n",
    "    \n",
    "    OD_patients_hadm_list = TN_and_TP_hadm_id_merge_with_cluster_info[(TN_and_TP_hadm_id_merge_with_cluster_info ['class']=='TP') & (TN_and_TP_hadm_id_merge_with_cluster_info['significant_majority_class']=='True_TN')]\n",
    "    OD_patients_count = len(OD_patients_hadm_list)\n",
    "    OD_patients_evets_list = pd.merge(OD_patients_hadm_list, event_log_df, how='left', on='hadm_id')\n",
    "    OD_patients_evets_list['OD_or_Non_od'] = 'OD'\n",
    "    OD_patients_count = len(OD_patients_hadm_list)\n",
    "    \n",
    "    Non_OD_patients_hadm_list = TN_and_TP_hadm_id_merge_with_cluster_info[(TN_and_TP_hadm_id_merge_with_cluster_info['class']=='TP') & (TN_and_TP_hadm_id_merge_with_cluster_info['significant_majority_class'] != 'True_TN')]\n",
    "    Non_OD_patients_count = len(Non_OD_patients_hadm_list)\n",
    "    Non_OD_patients_evets_list = pd.merge(Non_OD_patients_hadm_list, event_log_df, how='left', on='hadm_id')\n",
    "    Non_OD_patients_evets_list['OD_or_Non_od'] = 'Non_OD'\n",
    "    Non_OD_patients_count = len(Non_OD_patients_hadm_list)\n",
    "    \n",
    "    Event_log_TP_only_with_OD_and_Non_od_label = pd.concat([OD_patients_evets_list, Non_OD_patients_evets_list], axis=0)\n",
    "    \n",
    "    return (Event_log_TP_only_with_OD_and_Non_od_label, OD_patients_count, Non_OD_patients_count)\n"
   ],
   "id": "b944f20c6f7309c4",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 1. LoS",
   "id": "994b6c44028cbe22"
  },
  {
   "metadata": {
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     "end_time": "2024-10-07T00:54:51.139408Z",
     "start_time": "2024-10-07T00:54:48.769922Z"
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   },
   "cell_type": "code",
   "source": [
    "# Function to calculate Length of Stay (LoS)\n",
    "def calculate_LoS(event_log_df):\n",
    "    \n",
    "    # Filter admission, discharge, and expire flag events\n",
    "\n",
    "    admissions = event_log_df[event_log_df['event'] == 'admission']\n",
    "    discharges = event_log_df[event_log_df['event'] == 'discharge']\n",
    "    expire_flags = event_log_df[event_log_df['event'] == 'hospital_expire_flag_1']\n",
    "    \n",
    "    # Merge admission and discharge/expire times\n",
    "    \n",
    "    los_df = pd.merge(admissions[['hadm_id', 'event_time']], discharges[['hadm_id', 'event_time']], on='hadm_id', how='left', suffixes=('_admission', '_discharge'))\n",
    "    los_df = pd.merge(los_df, expire_flags[['hadm_id', 'event_time']], on='hadm_id', how='left')\n",
    "    los_df.rename(columns={'event_time': 'event_time_expire'}, inplace=True)\n",
    "    los_df['event_time_actual_discharge'] = los_df['event_time_expire'].combine_first(los_df['event_time_discharge'])\n",
    "    los_df['LoS'] = (los_df['event_time_actual_discharge'] - los_df['event_time_admission']).dt.total_seconds() / 86400\n",
    "    return los_df[['hadm_id', 'LoS']]\n",
    "\n",
    "# Load the full event log with admission and discharge times\n",
    "event_log_full_with_adm_and_dischge = pd.read_csv('/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Input/Input_for_clustering/May15_v1.csv')\n",
    "event_log_full_with_adm_and_dischge['event_time'] = pd.to_datetime(event_log_full_with_adm_and_dischge['event_time'])\n",
    "\n",
    "# Plotting\n",
    "fig, axes = plt.subplots(4, 5, figsize=(20, 20))\n",
    "#fig, axes = plt.subplots(4, 4, figsize=(20, 20))\n",
    "\n",
    "axes = axes.flatten()\n",
    "\n",
    "# main programme\n",
    "\n",
    "for i, attempt in enumerate(attempts):\n",
    "    cluster_distribution_df = pd.read_csv(attempt['cluster_distribution'])\n",
    "    clusters_in_hadmid_level_df = pd.read_csv(attempt['clusters_in_hadmid_level'])\n",
    "    \n",
    "    total_TN_count = cluster_distribution_df['TN_count'].sum()\n",
    "    total_TP_count = cluster_distribution_df['TP_count'].sum()\n",
    "    total_patient_count = cluster_distribution_df['patient_count'].sum()\n",
    "    \n",
    "    overall_positive_ratio = total_TP_count / total_patient_count\n",
    "    overall_negative_ratio = total_TN_count / total_patient_count\n",
    "    TP_to_TN_ratio = total_TP_count / total_TN_count\n",
    "    \n",
    "    chi_squared_results_df = perform_chi_squared_test(cluster_distribution_df, overall_positive_ratio, overall_negative_ratio, TP_to_TN_ratio)\n",
    "    \n",
    "    # Return event log of TP cases only\n",
    "    \n",
    "    # Call the function\n",
    "    \n",
    "    event_log_TP_only_with_OD_and_Non_od_label, OD_patients_count, Non_OD_patients_count = label_OD_Non_OD_patients(clusters_in_hadmid_level_df, chi_squared_results_df, event_log_full_with_adm_and_dischge)\n",
    "\n",
    "    # Return LoS of each TP patient \n",
    "    LoS_TP_only = calculate_LoS(event_log_TP_only_with_OD_and_Non_od_label)\n",
    "    \n",
    "    # Merge (performs an inner join by default. This means it will only include rows that have matching hadm_id values in both event_log_TP_only_with_OD_and_Non_od_label[['hadm_id', 'OD_or_Non_od']].drop_duplicates() and LoS_TP_only.) hadm_id and the status (either OD or Non-OD) of each TP case with corresponding LoS\n",
    "    \n",
    "    merged_cluster_with_LoS_with_OD_and_Non_od_label = pd.merge(event_log_TP_only_with_OD_and_Non_od_label[['hadm_id', 'OD_or_Non_od']].drop_duplicates(), LoS_TP_only, on='hadm_id')\n",
    "    \n",
    "    sns.boxplot(x='OD_or_Non_od', y='LoS', data=merged_cluster_with_LoS_with_OD_and_Non_od_label, ax=axes[i])\n",
    "    axes[i].set_title(f'Attempt {i + 1}')\n",
    "    axes[i].set_xlabel('TP category')\n",
    "    axes[i].set_ylabel('Length of Stay (LoS) / Days')\n",
    "    \n",
    "    # check output stats\n",
    "    print(i+1)\n",
    "    print(merged_cluster_with_LoS_with_OD_and_Non_od_label.groupby('OD_or_Non_od')['LoS'].describe())\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "b3ffe52acf0827fe",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        689.0  22.243166  20.343539  5.60000  10.642361  15.809028   \n",
      "OD            364.0  17.951152  14.455834  5.59375   9.553993  13.778472   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        26.852083  219.908333  \n",
      "OD            21.801736  177.950694  \n",
      "2\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        212.0  35.705618  27.383815  7.936806  19.515799  29.727431   \n",
      "OD            841.0  16.991879  13.249448  5.593750   9.141667  13.336111   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        43.239757  219.908333  \n",
      "OD            20.061111  177.950694  \n",
      "3\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        787.0  22.111079  20.259237  5.59375  10.795833  16.082639   \n",
      "OD            266.0  16.760683  11.765794  5.60000   8.967882  12.621181   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.767708  219.908333  \n",
      "OD            20.158333   78.552083  \n",
      "4\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        801.0  23.092420  20.312281  5.59375  11.348611  17.589583   \n",
      "OD            252.0  13.344177   8.154922  5.60000   7.868924  10.902083   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        27.749306  219.908333  \n",
      "OD            15.571007   58.696528  \n",
      "5\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        866.0  22.015715  19.770256  5.593750  10.779514  16.043750   \n",
      "OD            187.0  14.941990  10.156708  5.936111   8.626389  11.854861   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        26.560590  219.908333  \n",
      "OD            17.966319   72.453472  \n",
      "6\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        812.0  22.344442  20.105580  5.593750  10.845139  16.701736   \n",
      "OD            241.0  15.419395  10.846089  5.810417   8.534028  11.854861   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        26.689236  219.908333  \n",
      "OD            18.445139   75.806944  \n",
      "7\n",
      "               count       mean        std      min       25%       50%  \\\n",
      "OD_or_Non_od                                                              \n",
      "Non_OD        1053.0  20.759507  18.626287  5.59375  9.934722  15.26875   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        24.355556  219.908333  \n",
      "8\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        371.0  26.029745  19.876131  5.59375  13.958681  20.675000   \n",
      "OD            682.0  17.892560  17.260918  5.60000   8.821875  12.833333   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        31.169097  177.950694  \n",
      "OD            20.022917  219.908333  \n",
      "9\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        918.0  22.071388  19.488096  5.593750  10.936979  16.232639   \n",
      "OD            135.0  11.838719   5.725333  5.810417   7.864931   9.915972   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        26.308160  219.908333  \n",
      "OD            13.770486   33.867361  \n",
      "10\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        953.0  21.692744  19.253063  5.593750  10.666667  16.052083   \n",
      "OD            100.0  11.865757   5.818576  5.891667   7.822049   9.914583   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.616667  219.908333  \n",
      "OD            13.736632   33.867361  \n",
      "11\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        697.0  17.329212  12.665953  5.59375   9.610417  13.762500   \n",
      "OD            356.0  27.475562  25.401762  5.60000  11.939583  20.878125   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        20.129861  177.950694  \n",
      "OD            32.789583  219.908333  \n",
      "12\n",
      "               count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                 \n",
      "Non_OD        1006.0  21.224500  18.890833  5.593750  10.385243  15.704514   \n",
      "OD              47.0  10.806693   5.630326  5.891667   7.671181   8.987500   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.030208  219.908333  \n",
      "OD            11.954514   33.867361  \n",
      "13\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        455.0  21.050516  17.057702  5.593750  10.742361  15.989583   \n",
      "OD            598.0  20.538088  19.747711  5.891667   9.794097  14.264583   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.348958  177.950694  \n",
      "OD            23.030035  219.908333  \n",
      "14\n",
      "               count       mean        std      min       25%       50%  \\\n",
      "OD_or_Non_od                                                              \n",
      "Non_OD        1053.0  20.759507  18.626287  5.59375  9.934722  15.26875   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        24.355556  219.908333  \n",
      "15\n",
      "               count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                 \n",
      "Non_OD        1013.0  21.150427  18.849290  5.593750  10.302083  15.666667   \n",
      "OD              40.0  10.859462   5.872291  5.891667   7.675868   9.001736   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.003472  219.908333  \n",
      "OD            11.884896   33.867361  \n",
      "16\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        956.0  21.669960  19.199054  5.593750  10.747917  16.053819   \n",
      "OD             97.0  11.786383   6.739397  5.891667   7.734028   9.790972   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.600000  219.908333  \n",
      "OD            12.927778   47.594444  \n",
      "17\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        945.0  21.503441  19.136232  5.593750  10.500000  15.897917   \n",
      "OD            108.0  14.250090  11.516637  5.891667   8.015104  10.807639   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.584722  219.908333  \n",
      "OD            15.807292   98.525000  \n",
      "18\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        645.0  19.817327  16.501545  5.59375   9.877778  14.956250   \n",
      "OD            408.0  22.248984  21.500232  5.60000  10.069271  15.995833   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        22.864583  177.950694  \n",
      "OD            26.823958  219.908333  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x2000 with 20 Axes>"
      ],
      "image/png": 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Ni+rq6owmK29aUd7cL4Fi6QWQCs9f0qUVQCq0Il1aAaRCK9LXpzNffNZZZ8Wxxx4b//M//xODBw+OJ554InK5XPz3f/93d80HsFG5XC5mzpwZ06dP73B7LpfLaLLyphXlzf0SKJZeAKnw/CVdWgGkQivSpRVAKrQifZ1aFN9jjz3iV7/6Vfz2t7+NxsbGGDx4cBx00EHRr1+/7poP4A0NGTIkampqYv78+ZHP5yOXy0VNTU0MHjw469HKllbgfgkUQy+AlHj+kiatAFKiFWnSCiAlWpG2Tp0+PSJiwIABcdRRR8WJJ54Yhx12WEREzJkzp8sHAyjWtGnTYuDAgRERMWjQoKipqcl4IrQC90ugGHoBpMTzlzRpBZASrUiTVgAp0Yp0dXpR/PX+/e9/xw9+8IOumAXgTamqqoozzzwzdthhhzjjjDOiqqoq65F4Ha0oP+6XwJuhF0CWPH/pHbQCyJJW9A5aAWRJK9LVqdOnA6RqwoQJMWHChKzHAF7D/RIA6G08fwGgEK0AoBCtSNMmf1IcAAAAAAAAAFJlURwAAAAAAACAklXU6dPPPffcje5ramrqsmEA6L20AoBi6AUAhWgFAIVoBQCdtcmfFK+qqoojjjiiC0YBoFRpBQDF0AsACtEKAArRCgA6UtQnxWfPnt3dcwDQy2kFAMXQCwAK0QoACtEKADqrqE+Kn3/++fH8888X/LrnnnsuZs2atclDAdD7aAUAxdALAArRCgAK0QoAOquoT4rvv//+cdRRR8WBBx4YH/rQh2LkyJGx2WabRUTEK6+8EsuWLYvbbrstfv/738c555zTrQMDkCatAKAYegFAIVoBQCFaAUBnFbUofvDBB8e+++4bV111VXzmM5+Jl156KQYMGBD5fD6ee+65GDBgQHzkIx+Jm2++ObbddtvunhmgncWLF8fcuXNj5syZMWHChKzHKUtaAaROK9KgF0DKtCINWgGkTCvSoBVAyrQiTbl8Pp/vzB9obm6O+++/P5588smoqKiInXbaKfbaa6+oqCjqTOyZa25ujmXLlsWoUaOisrIy63GALtDU1BQf//jHY/Xq1TFo0KCYP39+VFVVZT1Wr9RVj5FaAaRGK7pOVz5G9uZeaAWUHq3oOlqxnlZA6dGKrqMV62kFlB6t6Dpd/RhZ1CfFX6uysjJGjhwZI0eO3OQbB+gKCxYsiMbGxoiIaGxsjNra2jjhhBMynqq8aQWQGq1Ik14AKdGKNGkFkBKtSJNWACnRinSlf6gUwBtoaGiI2tra2HDSi3w+H7W1tdHQ0JDxZACkQisAKEQrAChEKwAoRCvSZlEc6LXy+XzMnTt3o9s7eXUIAEqQVgBQiFYAUIhWAFCIVqTPojjQa9XX10ddXV00Nze32d7c3Bx1dXVRX1+f0WQApEIrAChEKwAoRCsAKEQr0tepRfF58+bFE0880V2zAHRKdXV1jB07NiorK9tsr6ysjHHjxkV1dXVGk5U3rQBSohXp0gsgFVqRLq0AUqEV6dIKIBVakb5OLYrfd9998cEPfjCmT58e//M//xMvv/xyd80FUFAul4uZM2dudHsul8tgKrQCSIlWpEsvgFRoRbq0AkiFVqRLK4BUaEX6OrUo/r3vfS9+//vfx3ve8564+uqrY//9949Zs2bFvffe213zAbyhIUOGxJQpU9psmzJlSgwePDijidAKIDVakSa9AFKiFWnSCiAlWpEmrQBSohVp6/Q1xQcOHBjHH3983HLLLXHVVVfFfffdF1OnTo3DDjssFixYEOvWreuOOQHoRbQCgGLoBQCFaAUAhWgFAMXo9KL4q6++Grfffnuceuqpceyxx0Y+n48vfvGL8bnPfS5+9rOfxWc/+9luGBOgYw0NDXHDDTe02XbDDTdEQ0NDRhMRoRVAWrQiXXoBpEIr0qUVQCq0Il1aAaRCK9LWpzNfPGvWrPjVr34VERGHH3543HDDDfGOd7yjdf9b3/rW+NjHPta1EwJsRD6fj7lz5250+2WXXeY6HRnQCiAlWpEuvQBSoRXp0gogFVqRLq0AUqEV6evUoviTTz4ZF1xwQbzvfe+LzTffvN3+t771rXHllVd22XAAb6S+vj7q6urabW9ubo66urqor6+PYcOGZTBZedMKICVakS69AFKhFenSCiAVWpEurQBSoRXp69Si+FVXXdXh9meffTa22267GDBgQBxwwAFdMhhAIdXV1TF27Ni4++67o7m5uXV7ZWVljBkzJqqrqzOcrnxpBZASrUiXXgCp0Ip0aQWQCq1Il1YAqdCK9HVqUfzee++Nyy67LFatWhUtLS0Rsf56Hc8++2zcd9993TIgwMbkcrmYOXNmTJ8+vcPtTkWSDa0AUqIV6dILIBVakS6tAFKhFenSCiAVWpG+is588UUXXRTbb799HHDAAbHLLrvExz/+8aisrIzPfe5z3TUfwBsaMmRITJkypc22KVOmxODBgzOaCK0AUqMVadILICVakSatAFKiFWnSCiAlWpG2Ti2KP/zwwzF79uyYNm1aNDc3x/HHHx/f/OY349Zbb+2u+QDoZbQCgGLoBQCFaAUAhWgFAMXq1KJ4//79o6qqKoYOHRoPP/xwRESMGjUqnnjiiW4ZDqCQhoaGuOGGG9psu+GGG6KhoSGjidAKIDVakSa9AFKiFWnSCiAlWpEmrQBSohVp69Si+Nve9ra49tprY4sttogtt9wyHnjggXj00UedBx/IRD6fj7lz5250ez6fz2AqtAJIiVakSy+AVGhFurQCSIVWpEsrgFRoRfr6dOaLZ86cGSeffHLsv//+8clPfjKmTJkSlZWV8bGPfay75gPYqPr6+qirq2u3vbm5Oerq6qK+vj6GDRuWwWTlTSuAlGhFuvQCSIVWpEsrgFRoRbq0AkiFVqSvU4vio0ePjv/93/+NzTbbLKZOnRpvf/vbY+3atbH//vt313wAG1VdXR1jx46Nu+++O5qbm1u3V1ZWxpgxY6K6ujrD6cqXVgAp0Yp06QWQCq1Il1YAqdCKdGkFkAqtSF+nTp9+xBFHxBZbbBEVFev/2Dvf+c7Yf//9Y+LEid0yHMAbyeVyMXPmzI1ud5qkbGgFkBKtSJdeAKnQinRpBZAKrUiXVgCp0Ir0FfykeH19fXznO9+JiIhHHnkkzj333Db7165dG01NTd0zHUABQ4YMiZqampg/f37k8/nI5XJRU1MTgwcPznq0sqIVQMq0Ih16AaRKK9KhFUCqtCIdWgGkSivSVvCT4tXV1bHttttudP92220X3/zmN7t0KIDOmDZtWgwcODAiIgYNGhQ1NTUZT1R+tAJInVakQS+AlGlFGrQCSJlWpEErgJRpRbqKuqb4F77whYiIGDp0aJxyyindOhBAZ1VVVcWZZ54Zc+fOjZkzZ0ZVVVXWI5UlrQBSphXp0AsgVVqRDq0AUqUV6dAKIFVaka6iFsUjIlpaWuJjH/tY6+/vvPPOeOCBB+I973lP7LLLLt0yHECxJkyYEBMmTMh6jLKnFUDKtCIdegGkSivSoRVAqrQiHVoBpEor0lTw9OkREatWrYrDDz88LrvssoiIuPXWW+P444+PW2+9NY4++uhYvnx5tw4JQPq0AoBi6AUAhWgFAIVoBQCdVdSi+De/+c3Yc88946yzzoqIiHnz5sWJJ54YN910U8yaNSvmzZvXrUMCkD6tAKAYegFAIVoBQCFaAUBnFbUo/uc//znOO++8GDhwYDz55JNRX18fH/7whyMi4n3ve18sW7bsTQ/w7LPPxqRJk2LJkiWt2/72t7/F0UcfHfvss09MnDgxFi5c2ObP3HzzzTFp0qQYNWpUHHnkkXHPPfe86dsHoGtoBQDF6K5eaAVA6dAKAArRCgA6q6hF8bVr18Z2220XEesD0L9//9h1110jImKLLbaIV1999U3d+F133RVTp06N+vr61m3PP/98nHjiiXHEEUdEXV1dXHLJJTF79uy49957IyJiyZIlcfHFF8ell14adXV18eEPfzhOPvnkeOmll97UDAB0Da0AoBjd0QutACgtWgFAIVoBQGcVtSi+zTbbxLPPPhsREX/9619j9OjRrfv+3//7f7Htttt2+oZvvvnmOOuss+KMM85os/3222+PAQMGxLRp06JPnz6x3377xeGHHx4LFiyIiIiFCxfGYYcdFmPGjInNNtssjjvuuNh2223jtttu6/QMQOlYvHhxTJ06NRYvXpz1KGVLK4DUaUUauroXWgF0Ja1Ig1YAKdOKNGgFkDKtSFNRi+Lvfe974+KLL47bbrstbr311jjssMMiImLNmjUxd+7cOPDAAzt9wwcccED8+te/jkMPPbTN9ocffjj22GOPNtt22223ePDBByMi4pFHHnnD/UD5aWpqijlz5sSqVatizpw50dTUlPVIZUkrgJRpRTq6uhdaAXQVrUiHVgCp0op0aAWQKq1IV59ivuiMM86Iz372s/HFL34xDjvssDj88MMjIuKggw6K7bffPi688MJO3/D222/f4fZ///vf0bdv3zbbqqqq4sUXXyxqf7Gam5s79fVAuubPnx+NjY0REdHY2Bjz58+P448/PuOpeqdNeWzUCiBlWtF1NvWxsat7oRVAV9GKrqMVbWkFlA6t6Dpa0ZZWQOnQiq7T1Y+NRS2K9+/fP374wx+22z5v3rwYO3ZsbLHFFl02UN++feOFF15os62pqSm22mqr1v2vP6qiqamp06dDWb58+aYNCiRh9erVUVtbG/l8PiIi8vl8XHvttTF06NAYNGhQxtOVF60AUqUVaempXmgF0BlakRatAFKkFWnRCiBFWpG2ohbFN+aAAw7oqjla7bHHHvHnP/+5zbZHHnkkdt9994iI2H333ePhhx9ut//d7353p25nxIgRUVlZuWnDApnK5/NxzjnnRC6Xa7fv17/+dVx66aUd7mPjmpubu/xJuFYAWdKKrtcdrYjo+l5oBVAsreh6WtGWVkDvpxVdTyva0gro/bSi63V1KzZpUbw7TJo0Kb72ta/FNddcE9OmTYu77rorbr311vj2t78dEREf/ehH49RTT41DDjkkxowZEwsWLIjGxsaYNGlSp26nsrJSZKCXW7FiRSxdurTd9ubm5li6dGk88cQTMWzYsAwmo7tpBVAsrShfWgEUSyvKl1YAxdKK8qUVQLG0In3JLYpvu+228cMf/jAuueSSuOKKK2K77baL8847L971rndFRMR+++0X559/flxwwQWxatWq2G233eIHP/hBDBgwINvBgR5XXV0dY8eOjbvvvrvNtSUqKytjzJgxUV1dneF0dCetAIqlFeVLK4BiaUX50gqgWFpRvrQCKJZWpC+X33Bi+zdw3XXXxTHHHBOvvvpqbLbZZj0xV7dpbm6OZcuWxahRoxx5BSWgoaEhpk+f3iYyffr0iR/96EcxePDgDCfrnTblMVIrgFRpRdfa1MfIUumFVkBp0YqupRXraQWUFq3oWlqxnlZAadGKrtXVj5EVxXzR1772tYiIGD9+/CbfIEBXGjJkSNTU1LReiyOXy0VNTY3AZEArgFRpRVr0AkiRVqRFK4AUaUVatAJIkVakrajTp/fv3z9OPfXUeOWVV+Lcc8/t8Gtmz57dpYMBFGvatGmxaNGiWL16dQwaNChqamqyHqksaQWQMq1Ih14AqdKKdGgFkCqtSIdWAKnSinQV/UnxbbfdtrtnAXhTqqqq4swzz4wddtghzjjjjKiqqsp6pLKkFUDKtCIdegGkSivSoRVAqrQiHVoBpEor0lXUJ8X33Xff2HfffeOFF15wdBWQpAkTJsSECROyHqOsaQWQOq1Ig14AKdOKNGgFkDKtSINWACnTijQVtSi+wdy5c+Of//xn3HrrrfHEE0/E9ttvH4cffnhUV1d313wA9DJaAUAx9AKAQrQCgEK0AoBiFXX69A2WL18ehx12WNx+++3x/PPPx+9+97v48Ic/HHfddVd3zQdAL6MVABRDLwAoRCsAKEQrAChWpz4p/rWvfS1mzpwZn/jEJ1q3/ehHP4qvf/3rce2113b5cAD0PloBQDH0AoBCtAKAQrQCgGJ16pPi//jHP6KmpqbNtpqamnjooYe6dCgAei+tAKAYegFAIVoBQCFaAUCxOrUo3rdv33jqqafabHvqqadim2226dKhAOi9tAKAYugFAIVoBQCFaAUAxerUovihhx4aM2bMiD/+8Y/x2GOPxR/+8Ic4/fTT49BDD+2u+QDoZbQCgGLoBQCFaAUAhWgFAMXq1DXFZ86cGc8++2yccsop8eqrr8YWW2wRRx11VMyYMaO75gMoyuLFi2Pu3Lkxc+bMmDBhQtbjlDWtAFKlFWnRCyBFWpEWrQBSpBVp0QogRVqRplw+n8939g+98sor8fzzz8egQYMil8t1x1zdprm5OZYtWxajRo2KysrKrMcBukBTU1N8/OMfj9WrV8egQYNi/vz5UVVVlfVYvVJXPkZqBZASreg6Xf0Y2Vt7oRVQerSi62jFeloBpUcruo5WrKcVUHq0out09WNkp06fvsHmm28e22+/fa+KC1C6FixYEI2NjRER0djYGLW1tRlPRIRWAGnRinTpBZAKrUiXVgCp0Ip0aQWQCq1I15taFAdIRUNDQ9TW1saGk17k8/mora2NhoaGjCcDIBVaAUAhWgFAIVoBQCFakTaL4kCvlc/nY+7cuRvd/iauDgFAidEKAArRCgAK0QoACtGK9FkU501ZvHhxTJ06NRYvXpz1KJSx+vr6qKuri+bm5jbbm5ubo66uLurr6zOaDIiIuPrqq2PixIlx9dVXZz0KZUwrAChEKwAoRCsAKEQr0tenM1/c0tISv/zlL6O+vj7WrVvXZt9pp53WpYORrqamppgzZ06sXr065syZE6NHj46qqqqsx6IMVVdXx9ixY+Ouu+6KlpaW1u2VlZUxZsyYqK6uznC68qUVREQ899xzsWDBgmhpaYkFCxbEUUcdFQMGDMh6LMrQhlbcfffdbV6UaEX29AJIxcZeV1RUVMS+++6rFRnSCiAVWpEurQBSoRXp69Si+Pnnnx8///nPY88994zNNtusdXsul+vywUjXggULorGxMSIiGhsbo7a2Nk444YSMp6Ic5XK5mDlzZhx77LFttufz+Zg5c6bHpoxoBRERX/7yl1uf/LW0tMSsWbPiiiuuyHgqytGGVkyfPr3D7R6bsqMXQCo2NOHjH/94m+0tLS1akTGtAFKhFenSCiAVWpG+Ti2KL1q0KG688cbYddddu2seEtfQ0BC1tbWt1z7I5/NRW1sbBx98cAwZMiTj6WC9fD7v+hwZ0gqWLl0ay5cvb7Pt3nvvjaVLl8a+++6b0VSUsyFDhkRNTU3Mnz8/8vl85HK5qKmpicGDB2c9WlnTCyAl//znPzvc/tRTT+lFhrQCSIlWpEkrgJRoRdo6dU3xrbfeOnbZZZfumoXE5fP5mDt37ka3W4Skp2342Xv9EVa5XM7PZIa0ory1tLTERRdd1OG+iy66qM2pg6AnTZs2LQYOHBgREYMGDYqampqMJ0IvgFR4/pIurQBSoRXp0gogFVqRvk4tin/oQx+KH/7wh901C4mrr6+Purq6NtfjjIhobm6Ourq6qK+vz2gyytWGn8nXx6SlpcXPZIa0orwtWbIk1qxZ0+G+NWvWxJIlS3p4IlivqqoqzjzzzNhhhx3ijDPOiKqqqqxHKnt6AaTC85d0aQWQCq1Il1YAqdCK9BV1+vSJEydGLpeLdevWxapVq+J73/te9O/fv83X/Pa3v+2WAUlHdXV1jB07Nu6+++42C+OVlZUxZsyYqK6uznA6ypGfybRoBRER48ePj/79+3f4BHCbbbaJ8ePHZzAVrDdhwoSYMGFC1mOUPb0AUuP5S3q0AkiNVqRHK4DUaEX6iloUnzFjRnfPQS+Qy+Vi5syZMX369A63v/4U1tDd/EymRSuIiKioqIhZs2bFWWed1W7f+eefHxUVnTpJDXSpxYsXx9y5c2PmzJkWxzOkF0BqPH9Jj1YAqdGK9GgFkBqtSF9Ri+KTJ0+OiIirr746PvnJT7bbf/nll3fpUKRryJAhUVNTE/Pnz498Ph+5XC5qampi8ODBWY9GmRoyZEhMmTIlrr322tZtU6ZM8TOZAa1gg3333TdGjBgRy5cvb902cuTIGD16dIZTUe6amppizpw5sXr16pgzZ06MHj3aKdQzohdAivbdd9/Ye++94/7772/dNnz4cM9fMqIVQIq0Ii1aAaRIK9JWcFH82WefjUcffTQiIubNmxfvfOc7I5/Pt+5/4YUX4kc/+lF89rOf7bYhScu0adNi0aJFsXr16hg0aFDU1NRkPRKQMa3g9S6++OI48sgjo6WlJSoqKuKiiy7KeiTK3IIFC6KxsTEiIhobG6O2tjZOOOGEjKcqP3oBpGyvvfZq8+bVXnvtleE05UsrgJRpRRq0AkiZVqSr4KL45ptvHqeffnr861//ioiIj3/84+32T506tXumI0lVVVVx5plntp5+1KesyFJDQ0PccMMNbbbdcMMNceihh8aQIUMymqr8aAWvN2DAgJg2bVosWLAgpk2bFgMGDMh6JMpYQ0ND1NbWtr5Jks/no7a2Ng4++GCt6GF6AaSqoaEhbrrppjbbbrrppvjwhz+sFT1MK4BUaUU6tAJIlVakreCieL9+/eIvf/lLRER88IMfjF/+8pfdPhTpmzBhgmtxkrl8Ph9z586NlpaWNtubm5tj7ty5cdlll7mueA/RCjryyU9+ssNTmEFP0oq06AWQIq1Ii1YAKdKKtGgFkCKtSF+nruouLkBK6uvro66urs3pkSLWx6euri7q6+szmqy8aQWQEq1Il14AqdCKdGkFkAqtSJdWAKnQivQV/KT4a7397W/v8CiGPn36xHbbbRfvfe9745xzznE6baBHDB06NPr37x9r1qxpt69///4xdOjQDKZCK9hg8eLFrZfacHYRsqIV6dILIrSCNGhFurQCSIVWpEsrgFRoRfo69Unxc845J97+9rfHd7/73fjFL34R3//+92PEiBExffr0uOCCC+LRRx+Nr3/96901K0AbK1eu7DAwERFr1qyJlStX9vBERGgF6zU1NcWcOXNi1apVMWfOnGhqasp6JMqUVqRLL9AKUqEV6dIKIBVakS6tAFKhFenr1KL4DTfcEN/+9rfjoIMOire97W1x4IEHxrx58+J3v/tdvPe9743LL7/c6UqAHlNdXR1jx47tcN+4ceOiurq6hyciQitYb8GCBdHY2BgREY2NjVFbW5vxRJSrDa14/ScHcrmcVmRML9AKUqEV6dIKIBXeg0qXVgCp0Ir0dWpRfNWqVbHddtu12bbNNtvEU089FRER2223naP7gR6Ty+Vi6tSpHe6bOnVqh6dOovtpBQ0NDVFbW9t6/Zx8Ph+1tbXR0NCQ8WSUo1wuFzNnzmzXhIqKig6303P0orxpBSnRinRpBZAK70GlSyuAVGhF+jq1KL7PPvvExRdfHC+//HJERLz88svx1a9+NUaNGhX5fD6uv/762HXXXbtlUIDX2/C405Hrrruu9U1WepZWlLd8Ph9z587d6Hb3S7IwZMiQmDZtWptt06ZNi8GDB2c0ERF6Uc60ghQNGTIkjjrqqDbbjjrqKK3ImFYAqfAeVLq0AkiFVqSvU4viF154YSxbtizGjBkTBx54YIwZMybuueeeuPDCC2PJkiXxzW9+M84+++zumpWELF68OKZOnRqLFy/OehTKWH19fdTV1XW4r66uLurr63t4IiK0otxtuF82Nze32d7c3Ox+SaamTZsWgwYNioiI7bffPmpqajKeCL0oX1pBqv7+97+/4e/peVoBpMJ7UOnSCiAVWpG+Pp354sGDB8f//M//xD333BOrVq2KnXbaKd75zndGLpeLHXbYIf7yl79ERUWn1tnphZqammLOnDmxevXqmDNnTowePTqqqqqyHosyVF1dHXvssUc89NBD7fbtueeertGREa0obxuunXPXXXdFS0tL6/aKiorYd9993S/LWD6fz/yUdaeeemp85zvfiZNPPjny+Xy89NJLmcxRVVXllFmhF+VsQyvuvvvuNgvjlZWVMWbMGK0gE0uXLo3777+/zbb77rsvli5dGvvuu29GU6EVQCq8B5UurQBSoRXp69SieETEunXrYvDgwfHWt741IqL12hw77bRT105GshYsWBCNjY0REdHY2Bi1tbVxwgknZDwV5Sifz8eTTz7Z4b4nnngi8vm8RYeMaEX52nBNzmOPPbbN9nw+75qcZSyfz8eMGTPivvvuy3qUiFj/SYIsDR8+PObNm+f+EHpRrja0Yvr06R1ud9+gp7W0tMRFF13U4b6LLroofvazn3kzPUNaAaTAe1Bp0wogBVqRvk4tii9atChmzZoVa9eubd224X/iAw880OXDkZ6Ghoaora1tvfZBPp+P2traOPjgg2PIkCEZT0e5WbJkSZvHo9dau3ZtLFmyJPbbb78engqtoCO5XM51c4A29KK8DRkyJGpqamL+/Pmt/99rampcv5lMLFmyJNasWdPhvjVr1nhdkSGtAFLhPah0aQWQCq1IX6cWxefNmxfTpk2LyZMnR58+nf6QOb1cPp+PuXPnbnT7ZZdd5igXetT48eOjf//+Hb6Btc0228T48eMzmAqtKG8bmlBRUdHmlLi5XE4rylgul4t58+Zlevr0pqammDx5ckRE3HzzzZle+sXp09fTC6ZNmxaLFi2K1atXx6BBg6KmpibrkShT48aNi8rKynbXuY+I6NOnT4wbNy6DqYjQCiAdWpEurQBSoRXp61QlnnrqqTjttNPEpUzV19dHXV1du+3Nzc1RV1cX9fX1MWzYsAwmo1xVVFTErFmz4qyzzmq37/zzz3eKw4xoRXnTCjYml8tF3759sx4jItYvSqcySznTC6qqquLMM8+MuXPnxsyZMzM9WIXy1tDQ0OEbVxHrT8fa0NDg+UtGtAJIhVakSyuAVGhF+jq1YrT33nvHI4880l2zkLjq6uoYO3ZsVFZWttleWVkZ48aNi+rq6owmo5ztu+++sf3227fZ9pa3vCVGjx6d0URoRXnTCqBYegGkYsPzl454/pItrQBSoRXp0goiIhYvXhxTp06NxYsXZz0KZUwr0tepw6dGjx4dxx13XHzwgx+MQYMGtdl32mmndelgpCeXy8XMmTNj+vTpHW53GlCy0NDQEM8++2ybbY2NjdHQ0OA69xnRivKmFUCx9IKmpqaYM2dOrF69OubMmROjR4/2aXEykcvlYurUqR2e7Wbq1Kmev2RIK4BUaEW6tAKvK0iFVqSvU4vi99xzT+y+++7x6KOPxqOPPtq63f/I8jFkyJDYa6+9Yvny5a3b9tprrxg8eHCGU1GuNly7OJ/Pd7jdtYuzoRVoBVAMvWDBggXR2NgYEesPaqytrY0TTjgh46koR/l8Pq6//voO91133XUxevRoj00Z0QogFVqRLq3A6wpSoRXp69Si+E9+8pPumoNeoqGhIe6///422+6//36fyiUTG7t2cUtLi2sXZ0graGhoiPvuu6/Ntvvuu08rgDb0orw1NDREbW1t68GN+Xw+amtr4+CDD9YKetzGXldEhNcVGdMKIBVakS6tKG9eV5ASrUhfp64pHhHx6KOPxn/913/FaaedFv/6179i/vz53TEXCdrw6duOjmTp6NO60N2qq6tjjz326HDfnnvu6RodGdKK8rWhFRvbrhXAa+lFedIKUuN1Rdq0AkjB0KFDY8stt+xw35ZbbhlDhw7t4Yl4La0oT15XkBqvK9LXqUXxP//5z3H00UfHv/71r1i8eHE0NTXFt771rfj+97/fXfORkA1HuTQ3N7fZ3tzc3HqUC/SkfD4fDQ0NHe5buXKlJz4Z0YrytqEVHV3WQCuA19KL8uV1BanxuiJdWgGkYsWKFfHiiy92uO/FF1+MFStW9PBEbKAV5cvrClLjdUX6OrUoPmfOnPjmN78Z3/jGN6KysjLe+ta3xve///2NniOf0lJdXR1jx46NysrKNtsrKytj3LhxjnKhx915551v+ILkzjvv7OGJiNCKcjd06NDo379/h/v69+/v6HmglV6UL68rSI3XFenSCiAVTz755Cbtp/toRfnyuoLUeF2Rvk4tiq9YsSLe/e53R0S0nkJ7xIgR8fzzz3f9ZCQnl8vFzJkzN7q9o9OqQ3d661vfukn76R5aUd5WrlwZa9as6XDfmjVrYuXKlT08EZAqvShfXleQGq8r0qUVQCp22mmnTdpP99GK8uV1BanxuiJ9nVoU32mnneLuu+9us2358uX+R5aRIUOGRE1NTWtQcrlc1NTUxODBgzOejHI0bNiwN7ye07Bhw3p4IiK0otz5pDhQLL0ob15XkBKvK9KlFUAqtCJdWlHevK4gJVqRvk4tip900klx8sknxze/+c149dVX4wc/+EGceuqp8clPfrK75iNB06ZNi379+kVExNZbbx01NTUZT0S5Wrly5RuejsQnUrOhFeXNJ8WBYukF06ZNi4EDB0ZExKBBg7yuIDNeV6RLK4BUaEW6tAKvK0iFVqSvT2e++LDDDot+/frFggULYqeddoo777wzvvSlL8UHPvCB7pqPRDn1CCkYOnRo9OvXL9auXdtuX79+/XwiNSNaUd7cL4Fi6QVVVVVx5plnxty5c2PmzJlRVVWV9UiUKc9f0qUVQCq0Il1agdcVpEIr0tepRfGIiIMOOigOOuig1t83NzfHY489FrvsskuXDka6FixYEC+88EJERLzwwgtRW1sbJ5xwQsZTUY7q6+s7DExExNq1a6O+vj523nnnnh2KiNCKcuZ+CXSGXjBhwoSYMGFC1mNQ5jx/SZtWACnQirRpBV5XkAKtSF+nTp/ekdWrV8ehhx7aFbPQCzQ0NERtbW3k8/mIiMjn81FbWxsNDQ0ZTwakTCsAKIZeAFCIVgBQiFYA0JFNXhSPiNYFUkpbPp+PuXPnbnS7nwN62rBhw2LEiBEd7hs5cmQMGzashyfijXiMKA/V1dXRr1+/Dvf169cvqqure3gioLfRC6Cnef7S+2gF0NO0ovfRCqCnaUX6umRR3PWly0N9fX3U1dVFc3Nzm+3Nzc1RV1cX9fX1GU1GucrlcnH22Wd3uO/ss8/22JQY/z/Kw8qVK9/wNEErV67s4YmA3kYvgJ7m+UvvoxVAT9OK3kcrgJ6mFenrkkVxykN1dXWMHTs2Kisr22yvrKyMcePGOcqFzLz+SW4ul3M0KGRkQys6ohUAQIqqq6vf8AxUnr8AoBUAFKIV6etTzBfV1dVtdN+zzz7bZcOQtlwuFzNnzozp06d3uN3Rd/S0Dafuf/0ieC6Xi7lz58Zll13m57IHaQUR/2nCJz7xiWhpaWndXllZqRVAROgF0Ls42DYbWgH0JlqRDa0AehOtSENRi+LHHnvsG+73Bnf5GDJkSNTU1MT8+fMjn89HLpeLmpqaGDx4cNajUYY2nNL/9VpaWlpP6e+64j1HK9hgyJAhMW3atPjJT37Sum3atGlaAUSEXgDpqa+vj+XLl3e4b/ny5V5XZEArgNRoRXq0AkiNVqSvqEXxBx98sLvnoBeZNm1aLFq0KFavXh2DBg2KmpqarEeiTG04HUlHoXE6kp6nFbzWa1ux/fbbawXQSi+A1HhdkR6tAFKjFenRCiA1WpE+1xSn06qqquLMM8+MHXbYIc4444yoqqrKeiTK2Msvv9yp7UDP0AoAoDfxugKAQrQCgEK0Im1FfVIcXm/ChAkxYcKErMegzK1YsSIeeuihDvf94x//iBUrVsTOO+/cs0MBrbQCAOgNvK4AoBCtAKAQrUifT4oDvVZLS8sm7QcAAPC6AoBCtAKAQrQifRbFgV7rySef3KT9AAAAXlcAUIhWAFCIVqTPojjQa+VyuU3aDwAA4HUFAIVoBQCFaEX6LIoDvda73vWuqKjo+GGsoqIi3vWud/XwRAAAQG/jdQUAhWgFAIVoRfosivOmLF68OKZOnRqLFy/OehTKWENDw0avw9HS0hINDQ09PBHwWloBQCFaQQq8roC0aQUp0AoACtGK9FkUp9Oamppi9uzZsWrVqpg9e3Y0NTVlPRJlqrq6OkaMGNHhvpEjR0Z1dXUPTwRsoBUAFNLU1BRz5syJVatWxZw5c7SCzHhdAenSClKhFQAUohXpsyhOp/3oRz+KF154ISIiXnjhhfjxj3+c8UTQXj6fz3oEKGtaAUAhCxYsiMbGxoiIaGxsjNra2owngva8roBsaQW9gVYAUIhWpMGiOJ3S0NAQ1113XZtt1157rdM+kIn6+vpYvnx5h/uWL18e9fX1PTwREKEVABTW0NAQtbW1rW8M5PP5qK2t1Qoy4XUFpEkrSIlWAFCIVqTPojhFy+fz8dWvfrXdES0b2w7drbq6OsaOHdvhvnHjxjkdCWRAKwAoJJ/Px9y5cze6XSvoaU5zCOnRClKjFQAUMnTo0OjXr1+H+/r16xdDhw7t4Yl4PYviFG3FihVveJTLihUrengiyl0ul4upU6d2uG/q1KmRy+V6eCJAKwAopL6+Purq6qK5ubnN9ubm5qirq3P0PJl4+eWXO9zu+sWQDa0gRVoBwBupr6+PtWvXdrhv7dq1nr8kwKI40Gvl8/m48sorO9w3b948R44DACRow9l+Kisr22yvrKx0th8ysWLFinjooYc63PfQQw85qA8yoBWkRisgbVdffXVMnDgxrr766qxHoYwVWo+wXpE9i+IUbdiwYW94mqBhw4b18ESUu8ceeywef/zxDvc9/vjj8dhjj/XsQEBUV1e/4WmCvHkFQC6Xi5kzZ0ZLS0ub7fl8PmbOnOlsP/S4138StbP7ga63oRUb264V9DStgHQ999xzsWDBgmhpaYkFCxbEc889l/VIlKnXv8bt7H66n0VxipbL5eLss89u98KjoqKiw+3Q3e69995N2g90vZUrV77haYJWrlzZwxMBkKIhQ4bEwIED22wbOHBgDB48OKOJKGcbu/RLsfuB7jFkyJCoqalpfb8pl8tFTU2NVpAJrYB0ffnLX25dbGxpaYlZs2ZlPBHlSivSZ1GcThkyZEgcc8wxbbYdc8wxXpCQiQ996EObtB/oekOHDn3DT4oPHTq0hycCIEVLly6N1atXt9n2zDPPxNKlSzOaiHLmdQWka9q0aa0HUQ0aNChqamoynohypRWQpqVLl7ZbaLz33nu9riATWpE+i+J02vTp06Nv374REbHlllvGJz7xiYwnolzV19dv0n6g69XX17/hJ8XdLwFoaWmJCy64oMN9F1xwgVPK0eMaGho2aT/QfaqqquKQQw6JioqK+OAHPxhVVVVZj0SZ8h4UpKelpSUuuuiiDvdddNFFXlfQ47yuSJ9Fcd6UPn36REREZWVlxpNQzv72t79t0n4AAHrenXfe+YYHUN155509PBEAqWpqaopFixZFS0tLLFq0KJqamrIeiTLlPShIz5IlS2LNmjUd7luzZk0sWbKkhyei3OXz+U3aT/ezKE6nLViwoPVNrLVr10ZtbW3GE1GuRo4cuUn7ga5XXV39hqdPr66u7uGJAEjNW9/61k3aD12turo6ttxyyw73bbnllp6/QIYWLFgQjY2NERHR2NjoPSgy4z0oSM/48eOjf//+He7bZpttYvz48T08EZA6i+J0SkNDQ9TW1rYe0ZLP56O2ttZpH8hERcUbP4QV2g90vZUrV77hp/9WrlzZwxMBkJqdd9459thjjw737bnnnrHzzjv37ECUvfr6+njxxRc73Pfiiy86JS5kxHtQpMR7UJCeioqK+NjHPtbhvmOOOcb9EminT9YD0Hvk8/mYO3duu2txNDc3x9y5c+Oyyy6LXC6X0XSUI6cjgfQMHjx4k/YDUPpyuVzMmjUrPv7xj7fbN2vWLK8p6HHNzc2btB/oehveg9rYdu9Bla98Pp/JafQL3WZTU1O89NJLPTTNelVVVe4HlLWWlpa49tprO9x37bXXxtSpUy2MA21YFKdo9fX1UVdX1257Pp+Purq6qK+vj2HDhmUwGeXqqaeeKrj/bW97Ww9NA0RE/PznPy+4/4gjjuiZYQBI1pAhQ+Loo4+OhQsXtm6bMmWKg6fIxL333ltw/6677tpD0wARG38Pqrm52XtQZSyfz8eMGTPivvvuy3qUdk4++eQev83hw4fHvHnzLIxTtoq5pvh+++3Xw1MBKUvyMJnbbrst9tprr9hnn31a//n85z8fERF/+9vf4uijj4599tknJk6c2OZNFLrX0KFDN3qNjv79+8fQoUN7eCLK3Y477rhJ++ndtCJNw4cP36T9AF1JK9I2bdq0Nr+vqanJaBLK3YgRIzZpP72bVqSpuro6xo4dG5WVlW22V1ZWxrhx46K6ujqjyYBypRfpcU1xUvP6syx3dj/dL8lPii9fvjw+8pGPxOzZs9tsf/755+PEE0+M008/PaZOnRp1dXVx6qmnxp577hkjR47MaNrysXLlyjc88mrlypWO0i1TWZ26asWKFQX377TTTj00zXpOXdVztCJNhY7Yv++++2K33XbroWmAcqcVafvpT3/a5vc33XRTnHDCCRlNQzlbvnx5wf2ev5QurUhTLpeLmTNnxvTp0zvc7nV3ecrlcjFv3rxM3oOKiLjnnnvii1/8Yrvts2fPjlGjRvX4PN6D6ll6kZ6KioqYNWtWnHXWWe32nX/++U6dTo9zBqr0Jbsofsghh7Tbfvvtt8eAAQNaP1Gw3377xeGHHx4LFiwQmB6w4SjdpUuXtrlWcy6Xi7FjxzpKt0ylfOqqiy66qMdv06mreo5WpOnwww+PK664osMjHysrK+Pwww/PYCqgXGlFuhoaGmLBggVtti1YsCAOPvjgGDJkSEZTUa4K3e89LpQ2rUjXkCFDoqamJubPnx/5fD5yuVzU1NS41EaZy+Vy0bdv30xue8KECfGOd7wjHnjggdZtw4cPd3rmMqEXadp3331jxIgRbQ5yHDlyZIwePTrDqchaVh/i23PPPQvuf+mll3pomvUcQNVWcoviLS0tcf/990ffvn3jqquuiubm5jjooIPirLPOiocffjj22GOPNl+/2267xY033tjp22lubu6qkcvKjBkz4rjjjmuzKF5RUREzZsxw6ocy9dqfBdZrbm7utaHpLY+NWpG2z3/+8/HVr361w+0Rvq9k47U/d83NzX4ON0Fv+d5pRbry+Xxcfvnl7Z5HtrS0xOWXXx6XXnppr30uRe9U6DVNPp93X++k3vL90or0HXPMMXHLLbfEmjVrYuutt46pU6f6fpKp8847r3XxM5fLxYUXXuhn8k3qTd+3nuhFb/p+pOaCCy6Io48+OlpaWqKioiLOP/98388yls/nY+bMmXH//fdnPUo7p5xySo/f5vDhw+Pyyy/vta+xu/q+nNyi+LPPPht77bVXfOADH4grrrgi/vWvf8XZZ58dn//852P77bdvdyRgVVVVvPjii52+nUKnR2PjDjrooLjjjjva/P7pp5+Op59+OsOpyNJxxx0Xr7zySia33djYGN/4xjfabf/c5z4XAwcO7PF5Nt988/jb3/7W47dbbrQibRs7PVUul4tly5b17DDwf15++eXWX997772xxRZbZDgNPUEr0rVq1apYunRpu+35fD6WLl0at99+e+ywww4ZTEa5+uc///mG+//xj3/E888/30PT0JO0In2vvPJKrFu3LiIi1q1bF/fee29svvnmGU9FOXvt64qDDjooHn/88eyGocf0RC+0YtNMnDgxfve738XEiRPdL8tcPp+Pf//731mPkYy1a9fGsmXLeu2ieFdLblF80KBBbU6j17dv3/j85z8fU6ZMiSOPPLLdKQ+amppiq6226vTtjBgxIiorKzd53nJUV1fX5vdvectbMrluDmzw6KOPxs9+9rPW33/0ox+NQw89NLuBerHm5uZe8SRcK9KVz+fjnHPO6XDfr3/9a5/+IzOvPT3VyJEjMzvlYinQira0ovNaWlriu9/9bqxZs6bdvv79+8ekSZNc/48e1dzcHHPmzOnwUwiVlZUxadIk9/NO0oq2tOLN++///u/W53EvvfRS3H///XH88cdnPBXl7LWvK8466yyvKzZBb2lFRM/0Qis2zahRo+Lcc8/NegwScdVVV2Vy+vR8Ph9nn312/P3vf2+3b++9987kfdHefvr0rm5FcoviDz74YPz85z+Pz33uc63/o1555ZWoqKiIkSNHxo9+9KM2X//II4/E7rvv3unbqaysFJk3oaGhIRYuXNhm24033hgf+tCHXPuPzEyfPr11Ubxv377xqU99yv27xGlFulasWNHhp/8iIpYuXRpPPPFEDBs2rIengmhzX3bfLg9aka6GhoYOF8QjItasWRNPPvmkVpSprK79t2TJko2elq+5uTkWL14c48eP79GZevubV72FVqStoaEhrr322tZLHOTz+bjuuuvigx/8oPegyIzXFeWpJ3rh5wm6Vr9+/TK53S9+8Yvx8Y9/vM22XC4XX/ziF2PrrbfOZCb+I7lF8QEDBsSCBQtim222ieOPPz6efvrp+NrXvhaTJ0+OD3zgA/GNb3wjrrnmmpg2bVrcddddceutt8a3v/3trMcuC/l8PubOnbvR7ZdddpkX7WSiqqqq9ddf+MIX2vye0qQV6RoyZEhUVlZu9JNW3rwCeopWpKu6ujrGjh0bS5cubXMt51wuF2PHjo3q6uoMpyMr+Xw+ZsyYEffdd1/Wo7RzwQUX9PhtDh8+PObNm+c1djfTinR5DwpIiV4AxRoyZEgcffTRbT5c+rGPfSwGDx6c4VRskNw56Xbcccf43ve+F7/97W9j3LhxcdRRR8WIESNi1qxZse2228YPf/jD+OUvfxnjx4+P8847L84777x417velfXYZaG+vj7q6uraLXQ0NzdHXV1d1NfXZzQZ/IfHg/KgFen661//+oaftPrrX//awxMB5Uor0pXL5WLmzJkd7ps5c6ZFDqDHaEW6vAcFpEQvgM6oqalp/XX//v3jE5/4RIbT8FrJfVI8ImLcuHFx3XXXdbhvxIgRG91H96quro4RI0Z0eP7+kSNH+kQH0KO0Ik3jx4+PLbfcMl588cV2+7bccsseP/UoUN60Im2v/ZT4G22jPORyuZg3b14mp0/f4Mwzz4wHHnig9fd77713fP3rX89kFqdP7zlakaYNZxW5++672yyMV1ZWxpgxY7wHBfQ4vQCK9doz2Z5xxhnObJuQJBfFSdcLL7zQ4faNXRMQgPKzbt26Tm0HoLw4JS4bk8vlom/fvpnd/vnnnx/HHHNMRERUVFTEJZdckuk8UM42nFVk+vTpHW7XCQCgN3DWiLQkd/p00vX444/H448/3ul9AJSPv/zlL/HKK690uO+VV16Jv/zlLz08EQCp2XBK3I44JS5Z2mabbVp/PWXKlBgwYEB2wwAxZMiQqKmpaV0Az+VyUVNT45qcAAC8KRbFKdqTTz65SfsBAACGDh0a/fv373Bf//79Y+jQoT08EbT3+k+nAtmYNm1aDBw4MCIiBg0a1OYanQAA0BkWxSnaTjvttEn7ASh9++23X2y55ZYd7ttyyy1jv/326+GJAEjNypUrN3r5pTVr1sTKlSt7eCIAUlVVVRWHHHJIVFRUxAc/+EHX5ASgQ4sXL46pU6fG4sWLsx4FSJhrilO0nXfeOXbZZZd47LHH2u1729veFjvvvHPPDwVAUioqKuKiiy6Ks846q92+//qv/4qKCsfj9bR8Ph9NTU1Zj5G5134PfD/Wq6qqcj1OMrHhk+IdLYz7pDgAr9XU1BSLFi2KlpaWWLRoUUybNs3COABtNDU1xZw5c2L16tUxZ86cGD16tFYAHbIoTqf069evw+1bbbVVD08CQKp23HHHDre/5S1v6eFJiFj/4vCQQw7JeoykTJ48OesRkrBo0aLo27dv1mNQhor5pPiwYcN6eCoAUrRgwYJobGyMiIjGxsaora2NE044IeOpAEiJVgDF8nEtilZfXx/Lly/vcN/y5cujvr6+hycCIDX5fD6+/OUvd7jvy1/+cuTz+R6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otk41xZctW5b//M//zMUXX5wkWbBgQT74wQ/mkEMOyX777ZdnnnmmR4sEoPzJCgA6Q14AAAAAUGydaopffvnluf/++1cvAXLhhRdm0qRJuffeezNlypTMmjWrR4sEoPzJCgA6Q14AAAAAUGydWo/w5z//ea644oqMGjUqL774Yu67775cd911WW+99XLUUUfl4IMP7uk6AShzsgKAzpAXAAAAABRbp+4Uf/bZZzNq1KgkyQMPPJABAwZk++23T5IMHTo0L730Us9VCECvICsA6Ax5AQAAAECxdaopPmDAgLz++utJknvvvTc77LBDBgwYkCRZtGhR1ltvvZ6rEIBeQVYA0BnyAgAAAIBi61RTfMKECfnBD36QJUuW5Kc//WmmTJmyeu7mm2/Ojjvu2FP1AdBLyAoAOkNeAAAAAFBsnWqKn3zyyfnud7+b3XffPTU1NZk2bVqS5LDDDsv3vve9fPazn+3RIgEof7ICgM6QFwAAAAAUW6ea4ltttVV+9atf5YYbbsgtt9ySddddN0kyefLkXH/99dlhhx16tEiAjsydOzdTp07N3LlzS11KnyUrgHInK8qDvOCNnJcAAABAMXSqKZ4kgwcPztixYzNw4MA0NTXlgQceyNSpU7Pddtv1ZH0AHWpubs7MmTOzePHizJw5M83NzaUuqc+SFUC5khXlRV6QOC8BAACA4ul0UzxJXn755Rx33HHZddddc9hhh2XKlCn51Kc+laVLl/ZUfQAdmjNnTpqampIkTU1NqaurK3FFfZusAMqRrCg/8gLnJQAAAFAsXWqK/9d//VdeffXV/OxnP8tf//rX/PjHP05ra2suvvjinqoP4E01Njamrq4uhUIhSVIoFFJXV5fGxsYSV9Z3yQqg3MiK8iQv+jbnJQAAAFBMXWqK33nnnfmv//qvjBo1Kuuss0622WabXHzxxfn1r3/dU/UBrFWhUMisWbPWOr7qIivFJSuAciIrype86LuclwAAAECxdakp/tprr2X99ddvMzZkyJC0trZ2a1EAndHQ0JD6+vq0tLS0GW9paUl9fX0aGhpKVFnfJiuAciIrype86LuclwAAAECxdakp/t73vrfNO/dXvZN/zJgxPVIcwJupra3NhAkTUl1d3Wa8uro6EydOTG1tbYkq69tkBVBOZEX5khd9l/MSAAAAKLb+Xdn4tNNOyyc/+cn85Cc/yfDhw/P000+nqqoq//3f/91T9QGsVVVVVWbMmJEjjjii3fGqqqoSVda3yQqgnMiK8iUv+i7nJQAAAFBsXWqKb7PNNvnFL36R3/zmN2lqasrw4cOzxx57ZPDgwT1VH8CbGjFiRKZNm5Zrr702hUIhVVVVmTZtWoYPH17q0vosWQGUG1lRnuRF3+a8BAAAAIqpS8unJ8mGG26YQw45JMccc0z222+/JMnMmTO7vTCAzpo+fXo22WSTJMnQoUMzbdq0EleErADKjawoT/Kib3NeAgAAAMXS5ab4v3rllVdy1VVXdUctAG9JTU1NTj311Gy66aY55ZRTUlNTU+qS+BeyAig1WdE7yIu+xXkJAAAAFEuXlk8HKFeTJ0/O5MmTS10GAGVMVkD5cV4CAAAAxfC27xQHAAAAAAAAgHKlKQ4AAAAAAABAxerU8ulnnHHGWueam5u7rRgAei9ZAUBnyAsAAAAAiu1t3yleU1OTgw46qBtKAaBSyQoAOkNeAAAAANATOnWn+AUXXNDTdQDQy8kKADpDXgAAAABQbJ26U/yss87Kiy++2OF2L7zwQs4888y3XRQAvY+sAKAz5AUAAAAAxdapO8V33XXXHHLIIdl9993z7//+7xk7dmwGDBiQJFm2bFnuv//+3H777fnd736XL37xiz1aMADlSVYA0BnyAgAAAIBi61RTfJ999sn48eNz9dVX5zOf+Uxee+21bLjhhikUCnnhhRey4YYb5sADD8ytt96ajTbaqKdrBljD3LlzM2vWrMyYMSOTJ08udTl9kqwAyp2sKA/ygjdyXgIAAADF0KmmeJJsvPHGOf300/O5z30uDz30UBYtWpR+/fpl8803z+jRo9OvX6dWYgfods3NzZk5c2aWLFmSmTNnZqeddkpNTU2py+qTZAVQrmRFeZEXJM5LAAAAoHg63RRfpbq6OmPHjs3YsWN7oh6ALpszZ06ampqSJE1NTamrq8unPvWpElfVt8kKoNzIivIkL/o25yUAAABQLG7BAHq1xsbG1NXVpVAoJEkKhULq6urS2NhY4soAKBeyAsqP8xIAAAAoJk1xoNcqFAqZNWvWWsdXXWQFoO+SFVB+nJcAAABAsWmKA71WQ0ND6uvr09LS0ma8paUl9fX1aWhoKFFlAJQLWQHlx3kJAAAAFFuXmuKXXnppnn766Z6qBaBLamtrM2HChFRXV7cZr66uzsSJE1NbW1uiyvo2WQGUE1lRvuRF3+W8BAAAAIqtS03xBx98MB/60IdyxBFH5Cc/+Ulef/31nqoLoENVVVWZMWPGWserqqpKUBWyAignsqJ8yYu+y3kJAAAAFFuXmuJXXnllfve73+UDH/hAvvvd72bXXXfNmWeemQceeKCn6gN4UyNGjMhhhx3WZuywww7L8OHDS1QRsgIoN7KiPMmLvs15CQAAABRTlz9TfJNNNsmRRx6ZH//4x7n66qvz4IMPZurUqdlvv/0yZ86crFixoifqBKAXkRUAdIa8AAAAAKAYutwUX758eX75y1/m+OOPzyc/+ckUCoV86Utfyuc+97n86Ec/ysknn9wDZQK0r7GxMTfeeGObsRtvvDGNjY0lqohEVgDlRVaUL3nRdzkvAQAAgGLq35WNzzzzzPziF79Ikuy///658cYb8573vGf1/Dvf+c58/OMf794KAdaiUChk1qxZax2/6KKLfCZlCcgKoJzIivIlL/ou5yUAAABQbF1qii9atChnn3129txzzwwcOHCN+Xe+85257LLLuq04gDfT0NCQ+vr6NcZbWlpSX1+fhoaGbLHFFiWorG+TFUA5kRXlS170Xc5LAAAAoNi6tHz61VdfnQ9/+MNrXLR67rnnkiQbbrhhdtttt+6rDuBN1NbWZsKECamurm4zXl1dnYkTJ6a2trZElfVtsgIoJ7KifMmLvst5STmbO3dupk6dmrlz55a6FAAAALpRl+4Uf+CBB3LRRRdl8eLFaW1tTbLycwCfe+65PPjggz1SIMDaVFVVZcaMGTn88MPXmJsxY4ZlN0tEVgDlRFaUL3nRd606L4844oh2x52XlEpzc3NmzpyZJUuWZObMmdlpp51SU1NT6rIAAADoBl26U/zcc8/NsGHDsttuu+Vd73pXPvGJT6S6ujqf+9zneqo+gDc1YsSIbL/99m3Gtt9++wwfPrxEFSErgHIjK8qTvOjbRowYkWnTpq1ugFdVVWXatGnOS0pqzpw5aWpqSpI0NTWlrq6uxBUBAADQXbrUFH/sscdywQUXZPr06WlpacmRRx6ZSy65JLfddltP1QfwphobG/Pwww+3GXv44YfT2NhYooqQFUC5kRXlSV4wffr0bLLJJkmSoUOHZtq0aSWuiL6ssbExdXV1KRQKSZJCoZC6ujpZAQAAUCG61BQfMmRIampqMnLkyDz22GNJkh133DFPP/10jxQH8GYKhUJmzZq11vFVF7QoLlkBlBNZUb7kBTU1NTn11FOz6aab5pRTTrFMNSUjKwAAACpfl5ri7373u3PddddlnXXWybrrrpu//e1veeKJJ3zmG1ASDQ0Nqa+vT0tLS5vxlpaW1NfXp6GhoUSV9W2yAignsqJ8yQuSZPLkybnhhhsyefLkUpdCHyYrAAAAKl//rmw8Y8aMfPazn82uu+6ao446Kocddliqq6vz8Y9/vKfqA1ir2traTJgwIffdd1+bC1jV1dXZeeedU1tbW8Lq+i5ZAZQTWVG+5AVQLmQFAABA5etSU3ynnXbK73//+wwYMCBTp07Ndtttl5dffjm77rprT9UHsFZVVVWZMWNGjjjiiHbH3WlWGrICKCeyonzJC6BcyAoAAIDK16Xl0w866KCss8466ddv5be9973vza677popU6b0SHEAHRkxYkSmTZu2+kJVVVVVpk2bluHDh5e4sr5LVgDlRlaUJ3kBlBNZAQAAUNk6vFO8oaEh3/rWt5Ikjz/+eM4444w28y+//HKam5t7pjqATpg+fXruuOOOLFmyJEOHDs20adNKXVKfIyuAcicryoO8AMqZrAAAAKhcHd4pXltbm4022mit8xtvvHEuueSSbi0KoCtqampy6qmnZtNNN80pp5ySmpqaUpfU58gKoNzJivIgL4ByJisAAAAqV6c+U/z0009PkowcOTLHHXdcjxYE8FZMnjw5kydPLnUZfZqsAMqdrCgP8gIoZ7ICAACgMnX6M8VbW1vz8Y9/fPXXf/7zn/Pf//3fefLJJ3ukMAB6H1kBQGfICwAAAACKqVNN8cWLF2f//ffPRRddlCS57bbbcuSRR+a2227LRz/60SxYsKBHiwSg/MkKADpDXgAAAABQbJ1qil9yySXZdtttc9pppyVJLr300hxzzDG55ZZbcuaZZ+bSSy/t0SIBKH+yAoDOkBcAAAAAFFunmuJ//OMf85WvfCWbbLJJFi1alIaGhhxwwAFJkj333DP333//Wy7gueeey95775158+atHvvrX/+aj370oxk3blymTJmSm266qc333Hrrrdl7772z44475iMf+Ujmz5//lvcPQPeQFQB0Rk/lhawAAAAAYG061RR/+eWXs/HGGydZeWFpyJAhGTVqVJJknXXWyfLly9/Szu+9995MnTo1DQ0Nq8defPHFHHPMMTnooINSX1+f888/PxdccEEeeOCBJMm8efNy3nnn5cILL0x9fX0OOOCAfPazn81rr732lmoAoHvICgA6oyfyQlYAAAAA8GY61RTfYIMN8txzzyVJ7rnnnuy0006r5/73f/83G220UZd3fOutt+a0007LKaec0mb8l7/8ZTbccMNMnz49/fv3zy677JL9998/c+bMSZLcdNNN2W+//bLzzjtnwIAB+Y//+I9stNFGuf3227tcA1A55s6dm6lTp2bu3LmlLqXPkhX8K+cl0J7uzgtZAXQnv78AAABUpk41xT/4wQ/mvPPOy+23357bbrst++23X5Jk6dKlmTVrVnbfffcu73i33XbLr371q+y7775txh977LFss802bca22mqrPPLII0mSxx9//E3ngb6nubk5M2fOzOLFizNz5sw0NzeXuqQ+SVbwRs5LYG26Oy9kBdBd/P4CAABQufp3ZqNTTjklJ598cr70pS9lv/32y/77758k2WOPPTJs2LCcc845Xd7xsGHD2h1/5ZVXMmjQoDZjNTU1efXVVzs131ktLS1d2h4oX9dee22ampqSJE1NTbn22mtz5JFHlriq3untvDbKCt7IeQmV6+2+NnZ3XsgKoLv4/aX7eG0EAADKTaea4kOGDMn3vve9NcYvvfTSTJgwIeuss063FTRo0KC89NJLbcaam5uz3nrrrZ7/13drNzc3d3mZxQULFry9QoGysGTJktTV1aVQKCRJCoVCrrvuuowcOTJDhw4tcXV9i6xgFecl8GaKlReyAugKv78AAABUtk41xddmt9126646Vttmm23yxz/+sc3Y448/nq233jpJsvXWW+exxx5bY/79739/l/YzZsyYVFdXv71igZIqFAr54he/mKqqqjXmfvWrX+XCCy9sd461a2lp6faL+7Kib3FeQuXriaxIuj8vZAXQWX5/6X49lRUAAABv1dtqiveEvffeOxdffHGuueaaTJ8+Pffee29uu+22XHHFFUmSQw89NMcff3w+/OEPZ+edd86cOXPS1NSUvffeu0v7qa6udvEKermnnnoqf/nLX9YYb2lpyV/+8pc8/fTT2WKLLUpQGT1NVpQv5yVQLmQF0Fl+fwEAAKh8ZdcU32ijjfK9730v559/fmbPnp2NN944X/nKV/K+970vSbLLLrvkrLPOytlnn53Fixdnq622ylVXXZUNN9ywtIUDRVdbW5sJEybkvvvua/OZddXV1dl5551TW1tbwuroSbKifDkvgXIhK4DO8vsLAABA5etUU/z666/Pxz72sSxfvjwDBgzo9iIeffTRNl+PGTMm119//Vq3P/DAA3PggQd2ex1A71JVVZUZM2bkiCOOaHfcEofFJStInJdAx3oyL2QF8Fb4/QUAAKDy9evMRhdffHGSZNKkST1aDEBXjRgxItOmTVt9oaqqqirTpk3L8OHDS1xZ3yMrWMV5CbwZeQGUI7+/AAAAVLZO3Sk+ZMiQHH/88Vm2bFnOOOOMdre54IILurUwgM6aPn167rjjjixZsiRDhw7NtGnTSl1SnyQreCPnJbA28gIoV35/AQAAqFydvlN8o4026ulaAN6SmpqanHrqqdl0001zyimnpKamptQl9UmygjdyXgJrIy+AcuX3FwAAgMrVqTvFx48fn/Hjx+ell15y1wZQliZPnpzJkyeXuow+TVbwr5yXQHvkBVDO/P4CAABQmTrVFF9l1qxZ+X//7//ltttuy9NPP51hw4Zl//33T21tbU/VB0AvIysA6Ax5AQAAAECxdGr59FUWLFiQ/fbbL7/85S/z4osv5re//W0OOOCA3HvvvT1VHwC9jKwAoDPkBQAAAADF0qU7xS+++OLMmDEjhx9++Oqx//mf/8k3vvGNXHfddd1eHAC9j6wAoDPkBQAAAADF0qU7xR999NFMmzatzdi0adPy97//vVuLAqD3khUAdIa8AAAAAKBYutQUHzRoUP7xj3+0GfvHP/6RDTbYoFuLAqD3khUAdIa8AAAAAKBYutQU33fffXPiiSfm7rvvzpNPPpm77rorJ510Uvbdd9+eqg+gU+bOnZupU6dm7ty5pS6lz5MVQLmSFeVFXpA4L4HSaGpqynHHHZfx48dn0qRJOf/887NixYo3/Z6///3vee9735t58+YVqUoASklWAFSeLn2m+IwZM/Lcc8/luOOOy/Lly7POOuvkkEMOyYknnthT9QF0qLm5OTNnzsySJUsyc+bM7LTTTqmpqSl1WX2WrADKkawoP/IC5yVQKieffHI23XTT3H333VmyZEk++9nP5pprrsnRRx/d7vavvfZaPve5z6W5ubnIlQJQKrICoPJ06U7xddZZJxdeeGHuvffe3H333bn//vtz5plnZp111ump+gA6NGfOnDQ1NSVZ+S7Ourq6ElfUt8kKoBzJivIjL3BeAqXw1FNP5Z577snnP//5DBo0KCNHjsxxxx2XOXPmrPV7zjnnnOy1115FrBKAUpIVAJWpS03xVQYOHJhhw4alqqqqu+sB6JLGxsbU1dWlUCgkSQqFQurq6tLY2FjiypAVQLmQFeVNXvRNzkugVB577LFsuOGG2XTTTVePjRo1KosWLcrSpUvX2P5HP/pRnnrqqZxwwgnFLBOAEpIVAJWpS8unA5STQqGQWbNmrXX8oosucoEdoI+TFVB+nJdAKb3yyisZNGhQm7FVX7/66qsZMmTI6vEnnngil1xySa677rpUV1e/5X22tLSkpaXlLX9/b7fqsfflY5A4Dqs4Do7BKuX8+GVF8TkvVnIcVnIcHINVuvvxa4oDvVZDQ0Pq6+vXGG9paUl9fX0aGhqyxRZblKAyAMqFrIDy47wESmndddfNa6+91mZs1dfrrbfe6rHXX389p5xySr70pS9l8803f1v7fPjhh9/W91eKBQsWlLqEsuA4rOQ4OAblTFaUjvNiJcdhJcfBMehumuJAr1VbW5sJEybkvvvua/OOoerq6uy8886pra0tYXUAlANZAeXHeUk5mzt3bmbNmpUZM2Zk8uTJpS6HHrD11lvnhRdeyJIlSzJ06NAkK+/y22yzzbL++uuv3m7BggX5v//7v3z5y1/Ol7/85dXjn/nMZ3LggQfm7LPP7vQ+R48enYEDB3bbY+htWlpasmDBgowZM+Zt3UXZ2zkOKzkOjsEqy5YtK9tGsKwoPufFSo7DSo6DY7BKd2dFl5rira2t+fnPf56GhoasWLGizZzPywCKraqqKjNmzMgRRxyxxtyMGTMsu1kisgIoJ7KifMmLvmtt5+WqceclpdLc3JyZM2dmyZIlmTlzZnbaaafU1NSUuiy62ZZbbpmdd945X/va13Luuefm+eefzxVXXJFDDz20zXbjx4/PAw880GZs2223zbe//e1MmjSpS/usrq7u0xczV3EcVnIcVnIcHINyfuyyonQch5Uch5UcB8egux97l5riZ511Vn76059m2223zYABA1aPu2gBlMqIESOy8cYb59lnn109tskmm2T48OElrKpvkxVAuZEV5Ule9G0jRozI6NGj2ywFN3r0aOclJTVnzpw0NTUlSZqamlJXV5dPfepTJa6KnjB79uyce+652XPPPdOvX78cdNBBOe6445Ik48aNyznnnJMDDjigxFUCUEqyAqDydKkpfscdd+Tmm2/OqFGjeqoegC75y1/+0qbJkSTPPPNM/vKXv2T8+PElqqpvkxVAuZEV5Ule9G2NjY156KGH2ow99NBDaWxszIgRI0pUFX1ZY2Nj6urqUigUkiSFQiF1dXXZZ599PCcr0NChQzN79ux25+bPn7/W73v00Ud7qiQAyoysAKg8/bqy8frrr593vetdPVULQJe0trbm3HPPbXfu3HPPTWtra5ErIpEVQHmRFeVLXvRdhUIhs2bNandVgFmzZq1uSkKxrHpOrm3ccxIAAKD361JT/N///d/zve99r6dqAeiSefPmZenSpe3OLV26NPPmzStyRSSyAigvsqJ8yYu+q6GhIfX19WlpaWkz3tLSkvr6+jQ0NJSoMvoqz0kAAIDK16nl06dMmZKqqqqsWLEiixcvzpVXXpkhQ4a02eY3v/lNjxQIsDaTJk3KkCFD2m12bLDBBpk0aVIJquq7ZAVQjmRF+ZEX1NbWZsKECbnvvvvaNCGrq6uz8847p7a2toTV0Rd5TgIAAFS+TjXFTzzxxJ6uA6DL+vXrlzPPPDOnnXbaGnNnnXVW+vXr0mIYvE2yAihHsqL8yAuqqqoyY8aMHHHEEe2Ot7esOvQkz0kAAIDK16mm+MEHH5wk+e53v5ujjjpqjflvfvOb3VoUQGeNHz8+22+/fR566KHVYzvssEN22mmnElbVN8kKoFzJivIiL0iSESNGZNq0abn22mtTKBRSVVWVadOmZfjw4aUujT5qxIgROeyww3LdddetHjvssMM8JwEAACpEh03x5557Lk888USS5NJLL8173/veFAqF1fMvvfRS/ud//icnn3xyjxUJ8GZGjx7dptExevToElbTN8kKoNzJivIgL3ij6dOn54477siSJUsydOjQTJs2rdQlAQAAABWqw6b4wIEDc9JJJ+X5559PknziE59YY37q1Kk9Ux1ABxobG3PLLbe0GbvllltywAEHZMSIESWqqu+RFUA5kxXlQ17wRjU1NTn11FMza9aszJgxIzU1NaUuiT6ssbExN954Y5uxG2+8Mfvuu6+sAAAAqAAdNsUHDx6cP/3pT0mSD33oQ/n5z3/e40UBdEahUMisWbPS2traZrylpSWzZs3KRRdd5PP/ikRWAOVKVpQXecG/mjx5ciZPnlzqMujjZAUAAEDl69eVjV20AspJQ0ND6uvr2yy7mqy8qFVfX5+GhoYSVda3yQqgnMiK8iUvgHIhKwAAACpfh3eKv9F2223X7ruj+/fvn4033jgf/OAH88UvftGyd0BRjBw5MkOGDMnSpUvXmBsyZEhGjhxZgqqQFUA5kRXlS16QJHPnzl29fLo7xikVWQEAAFD5unSn+Be/+MVst912+fa3v52f/exn+c53vpMxY8bkiCOOyNlnn50nnngi3/jGN3qqVoA2Fi5c2O6FqyRZunRpFi5cWOSKSGQFUF5kRfmSFzQ3N2fmzJlZvHhxZs6cmebm5lKXRB8lKwAAACpfl5riN954Y6644orsscceefe7353dd989l156aX7729/mgx/8YL75zW9aBhEomtra2kyYMKHduYkTJ6a2trbIFZHICqC8yIryJS+YM2dOmpqakiRNTU2pq6srcUX0Vauy4l9Xr6iqqpIVAAAAFaJLTfHFixdn4403bjO2wQYb5B//+EeSZOONN/bufqBoqqqqMnXq1Hbnpk6d2u6SrPQ8WQGUE1lRvuRF39bY2Ji6urrVn+FcKBRSV1eXxsbGEldGX1RVVZUZM2askQn9+vVrdxwAAIDep0tN8XHjxuW8887L66+/niR5/fXX8/Wvfz077rhjCoVCbrjhhowaNapHCgX4V6ted9pz/fXXr77ISnHJCqCcyIryJS/6rkKhkFmzZqW1tbXNeEtLS2bNmuW8pCRGjBiR6dOntxmbPn16hg8fXqKKAAAA6E79u7LxOeeck2OPPTY777xzNtpoozz//PPZaqutMnv27MybNy+XXHJJvvWtb/VUrQBtNDQ0pL6+vt25+vr6NDQ0ZIsttihyVcgKoJzIivIlL/qutZ2XhULBeUlJTZ8+PT/60Y/y0ksvZciQIZk2bVqpSwIAAKCbdKkpPnz48PzkJz/J/Pnzs3jx4my++eZ573vfm6qqqmy66ab505/+lH79unTzOcBbVltbm2222SZ///vf15jbdtttffZficgKoJzIivIlL/ou5yXlzFLpAAAAlalLTfEkWbFiRYYPH553vvOdSbL6M/8233zz7q0MoAOFQiGLFi1qd+7pp59OoVBwUatEZAVQLmRFeZMXfZPzknI1Z86cvPTSS0mSl156KXV1dfnUpz5V4qoAAADoDl1qit9xxx0588wz8/LLL68eW3XB4m9/+1u3FwfwZubNm9fm9eiNXn755cybNy+77LJLkatCVgDlRFaUL3nRdzkvKUeNjY2pq6tb/Zn2hUIhdXV12WeffTJixIgSVwcAAMDb1aWm+KWXXprp06fn4IMPTv/+Xb7JHKBbTZw4MdXV1WlpaVljrn///pk4cWIJqkJWAOVEVpQvedF3TZo0KUOGDMnSpUvXmNtggw0yadKkElRFX1YoFDJr1qy1jl900UVWLwAAAOjlunT16R//+EdOOOEEF62AstDY2NhukyNZuRxrY2NjtthiiyJXhawAyomsKF/you/q169fzjzzzJx22mlrzJ111lk+S56ia2hoSH19/RrjLS0tqa+vT0NDg6wAAADo5bp0tWH77bfP448/3lO1AHRJbW1txowZ0+7c2LFjU1tbW+SKSGQFUF5kRfmSF33bZptt1u74O97xjiJXAiuzYsKECamurm4zXl1dnYkTJ8oKAACACtCl2zJ22mmn/Md//Ec+9KEPZejQoW3mTjjhhG4tDODtWPVZgBSfrAB6C1lRWvKi71q1JHW/fv3S2tq6erxfv36WqqYkqqqqMmPGjBxxxBHtjns+AgAA9H5daorPnz8/W2+9dZ544ok88cQTq8f9gQiUQkNDQxYsWNDu3IIFCyxzWCKyAignsqJ8yYu+a21LVbe2tlqqmpIZMWJERo8e3SYzRo8eneHDh5ewKgAAALpLl5riP/jBD3qqDoAuW7UkbnvNDkvilo6sAMqJrChf8qLvWrVUdXuNcUtVUyqNjY158MEH24w9+OCDaWxszIgRI0pUFQAAAN2lS58pniRPPPFEvvrVr+aEE07I888/n2uvvbYn6gLolJdeeqnd8aVLlxa5Et5IVgDlRFaUL3nRN1VVVWXPPfdsd27PPfe0WgBFt2pJ/7WN+7gNAACA3q9LTfE//vGP+ehHP5rnn38+c+fOTXNzcy6//PJ85zvf6an6ANbq//7v//J///d/XZ6jZ8kKoJzIivIlL/qu1tbWXHHFFe3OXX755W0+ZxyKYdWS/v/a/C4UCquX9AcAAKB361JTfObMmbnkkkvyX//1X6murs473/nOfOc738kNN9zQU/UBrNWiRYve1jw9Q1YA5URWlC950XfNmzdvrSs1LF26NPPmzStyRfR1I0eOzJAhQ9qdGzJkSEaOHFnkigAAAOhuXWqKP/XUU3n/+9+fJKuXtBszZkxefPHF7q8MoAObb77525qnZ8gKoJzIivIlL/quSZMmrbUBucEGG2TSpElFroi+buHChW/6Ro2FCxcWuSIAAAC6W5ea4ptvvnnuu+++NmMLFizIO9/5zm4tCqAztthii6y77rrtzq277rrZYostilwRiawAyousKF/you/q169fzjzzzHbnzjrrrPTr16U/U+Ftc6c4AABA5evS1YZjjz02n/3sZ3PJJZdk+fLlueqqq3L88cfnqKOO6qn6ANZq4cKFefXVV9ude/XVV93RUSKyAignsqJ8yYu+bbPNNmt3/B3veEeRKwF3igMAAPQF/buy8X777ZfBgwdnzpw52XzzzfPnP/85X/7yl/Nv//ZvPVUfwFqNHDkygwcPzssvv7zG3ODBg93RUSKyAignsqJ8yYu+q1Ao5Otf/3q7c1//+tcze/bs1UvqQzHU1tZmwoQJqa+vbzNeVVWVCRMmpLa2tkSVAQAA0F261BRPkj322CN77LHH6q9bWlry5JNP5l3vele3FgbQkYaGhnabHEny8ssvp6GhIVtuuWVxiyKJrADKh6wob/Kib3rqqaeyYMGCducWLFiQp556ynlJUVVVVWXGjBn5xCc+scbcjBkzvEkDAACgArztD2tbsmRJ9t133+6oBYAKJSsA6Ax5AZRSe83vQqFQgkoAAADobm+7KZ74IxEojdra2gwePLjducGDB1vmsMzICqAUZEXvIy8q3xZbbJExY8a0Ozd27NhsscUWRa6Ivq5QKGTWrFlrNMWrqqoya9Ysr0sAAAAVoFua4pYSA0ph4cKFb7ok7sKFC4tcEW9GVgClICt6H3lR+aqqqvKFL3yh3bkvfOELngMUXUNDQ+rr69Pa2tpmvLW1NfX19WloaChRZQAAAHSXbmmKA5RCbW3tm95l5O4/AGQFlK/27sp1Ry6lUFtbmwkTJqS6urrNeHV1dSZOnCgrAAAAKkD/zmxUX1+/1rnnnnuu24oB6C4uqBafrAB6G1lRGvKCNy5V/cbzcNVS1RdddJG7xSmqqqqqzJgxI4cffvgaczNmzPB8BAAAqACdaop/8pOffNN5fyACpdDQ0JAFCxa0O7dgwYI0NDT4TMoikhVAOZIV5UdesGqp6n/1xqWqnZcU24gRI7L99tu3yYztt98+w4cPL2FVAAAAdJdONcUfeeSRnq4DoMtqa2uzzTbb5O9///sac9tuu61lDotMVgDlSFaUH3nBqo81aO8NKz7WgFJpbGzMww8/3GbsoYceSmNjY0aMGFGiqgAAAOguPlMc6LUKhUIWLVrU7tzTTz9tWVwAZAX0Ms5JSmHVkv6tra1txltbWzNr1izPSwAAgAqgKQ70WvPmzcvLL7/c7tzLL7+cefPmFbkiAMqNrIDy05mPNYBiWrWk/782vwuFwuol/QEAAOjdNMWBXmv8+PFvax6AyicroPyMGDEi1dXV7c5VV1dbqpqiGzlyZAYPHtzu3ODBgzNy5MgiVwQAAEB30xQHeq2f/exnb2segMonK6D83HPPPWlpaWl3rqWlJffcc0+RK6Kva2hoeNNVRdwpDgAA0PtpigO91tixY9/WPACVT1ZA+Zk0aVKGDBnS7twGG2yQSZMmFbkiAAAAoNJpigO91pZbbpmampp252pqarLlllsWtyAAyo6sgPLTr1+/nHnmme3OnXXWWenXz5+pFFdtbe2bLp9eW1tb5IoAAADobq42AL3WwoUL09zc3O5cc3NzFi5cWOSKACg3sgLK0/jx4zNs2LA2Y+94xzuy0047lagi+rKFCxe+6fLpsgIAAKD30xQHeq3a2tqMGTOm3bmxY8e6owMAWQFlqrGxMU1NTW3Gmpqa0tjYWKKK6Mtqa2szYcKEducmTpwoKwAAACqApjjQq73++utdGgeg75EVUF4KhUJmzZqVqqqqNeZmzZqVQqFQgqroy6qqqjJ16tR256ZOndrucxUAAIDeRVMc6LWeeuqp/P3vf2937tFHH81TTz1V5IoAKDeyAspPQ0ND6uvr09LS0ma8paUl9fX1aWhoKFFl9FWFQiE33HBDu3PXX3+9N2oAAABUAE1xAAAAisbHGlBuVr1Roz3eqAEAAFAZNMWBXmvkyJHp16/9l7F+/fpl5MiRRa4IgHIjK6B3cUcupTBy5MgMGTKk3bkhQ4bICgAAgAqgKQ70Wvfcc09aW1vbnWttbc0999xT5IqAN5o7d26mTp2auXPnlroU+jBZAeWnoaEhCxYsaHduwYIF7sql6BYuXJilS5e2O7d06dIsXLiwyBUBAADQ3TTFgV5r/Pjxb2se6DnNzc2ZOXNmFi9enJkzZ6a5ubnUJdFHyQooP+7KpdyMGDEi1dXV7c5VV1dnxIgRRa4IAACA7qYpDvRat99++9uaB3rOnDlz0tTUlCRpampKXV1diSuir5IVUH7clUu5ueeee9LS0tLuXEtLi1VFAAAAKoCmONBr7bfffm9rHugZjY2NqaurW/25sIVCIXV1dWlsbCxxZfRFsgLKz8iRIzN48OB25wYPHuxOcYpu0qRJqampaXdu0KBBmTRpUpErAgAAoLtpigO91lNPPfW25oHuVygUMmvWrLWOr2qUQ7F09NnEPrsYiq+hoSEvv/xyu3Mvv/yy85KiKxQKef3119uda25u9vsLAABABdAUB3qtu+66623NA92voaEh9fX1ayxB2tLSkvr6eo0Oiu6BBx54W/MAVL6f/vSna218FwqF/PSnPy1yRQAAAHQ3TXGg1/rEJz7xtuaB7ldbW5sJEyakurq6zXh1dXUmTpyY2traElVGX2X5dCg/tbW1b7p8uqyg2GQFAABA5dMUB3qte++9923NA92vqqoqM2bMWOt4VVVVCaqiL/vLX/7ytuaB7rdw4cI3XT594cKFRa6Ivu7pp59+W/MAAACUP01xoNcaP37825oHesaIESMybdq01Q3wqqqqTJs2LcOHDy9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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T00:54:56.261934Z",
     "start_time": "2024-10-07T00:54:51.654641Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# box plot with histogram\n",
    "\n",
    "# Function to calculate Length of Stay (LoS)\n",
    "def calculate_LoS(event_log_df):\n",
    "    admissions = event_log_df[event_log_df['event'] == 'admission']\n",
    "    discharges = event_log_df[event_log_df['event'] == 'discharge']\n",
    "    expire_flags = event_log_df[event_log_df['event'] == 'hospital_expire_flag_1']\n",
    "    \n",
    "    los_df = pd.merge(admissions[['hadm_id', 'event_time']], discharges[['hadm_id', 'event_time']], on='hadm_id', how='left', suffixes=('_admission', '_discharge'))\n",
    "    los_df = pd.merge(los_df, expire_flags[['hadm_id', 'event_time']], on='hadm_id', how='left')\n",
    "    los_df.rename(columns={'event_time': 'event_time_expire'}, inplace=True)\n",
    "    los_df['event_time_actual_discharge'] = los_df['event_time_expire'].combine_first(los_df['event_time_discharge'])\n",
    "    los_df['LoS'] = (los_df['event_time_actual_discharge'] - los_df['event_time_admission']).dt.total_seconds() / 86400\n",
    "    return los_df[['hadm_id', 'LoS']]\n",
    "\n",
    "# Load the full event log with admission and discharge times\n",
    "event_log_full_with_adm_and_dischge = pd.read_csv('/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Input/Input_for_clustering/May15_v1.csv')\n",
    "event_log_full_with_adm_and_dischge['event_time'] = pd.to_datetime(event_log_full_with_adm_and_dischge['event_time'])\n",
    "\n",
    "# Plotting\n",
    "#fig, axes = plt.subplots(9, 4, figsize=(20, 40))\n",
    "fig, axes = plt.subplots(6, 6, figsize=(20, 12))\n",
    "axes = axes.flatten()\n",
    "\n",
    "# To store p-values\n",
    "p_values_LoS = []\n",
    "\n",
    "# Main program\n",
    "for i, attempt in enumerate(attempts):\n",
    "    cluster_distribution_df = pd.read_csv(attempt['cluster_distribution'])\n",
    "    clusters_in_hadmid_level_df = pd.read_csv(attempt['clusters_in_hadmid_level'])\n",
    "    \n",
    "    total_TN_count = cluster_distribution_df['TN_count'].sum()\n",
    "    total_TP_count = cluster_distribution_df['TP_count'].sum()\n",
    "    total_patient_count = cluster_distribution_df['patient_count'].sum()\n",
    "    \n",
    "    overall_positive_ratio = total_TP_count / total_patient_count\n",
    "    overall_negative_ratio = total_TN_count / total_patient_count\n",
    "    TP_to_TN_ratio = total_TP_count / total_TN_count\n",
    "    \n",
    "    chi_squared_results_df = perform_chi_squared_test(cluster_distribution_df, overall_positive_ratio, overall_negative_ratio, TP_to_TN_ratio)\n",
    "    \n",
    "    event_log_TP_only_with_OD_and_Non_od_label, OD_patients_count, Non_OD_patients_count = label_OD_Non_OD_patients(clusters_in_hadmid_level_df, chi_squared_results_df, event_log_full_with_adm_and_dischge)\n",
    "    \n",
    "    LoS_TP_only = calculate_LoS(event_log_TP_only_with_OD_and_Non_od_label)\n",
    "    \n",
    "    merged_cluster_with_LoS_with_OD_and_Non_od_label = pd.merge(event_log_TP_only_with_OD_and_Non_od_label[['hadm_id', 'OD_or_Non_od']].drop_duplicates(), LoS_TP_only, on='hadm_id')\n",
    "    \n",
    "    # Plot the histogram\n",
    "    categories = merged_cluster_with_LoS_with_OD_and_Non_od_label['OD_or_Non_od'].unique()\n",
    "    ax_hist = axes[i * 2]\n",
    "    for category in categories:\n",
    "        subset = merged_cluster_with_LoS_with_OD_and_Non_od_label[merged_cluster_with_LoS_with_OD_and_Non_od_label['OD_or_Non_od'] == category]\n",
    "        color = '#CD5C5C' if category == 'Non_OD' else '#4682B4'\n",
    "        ax_hist.hist(subset['LoS'], bins=30, alpha=0.5, label=f'Histogram {category}', edgecolor='black', color=color)\n",
    "    ax_hist.set_title(f'Attempt {i + 1} - Histogram')\n",
    "    ax_hist.set_xlabel('Length of Stay (LoS) / Days')\n",
    "    ax_hist.set_ylabel('Frequency')\n",
    "    ax_hist.legend(title='Category')\n",
    "    \n",
    "    # Plot the box plot\n",
    "    ax_box = axes[i * 2 + 1]\n",
    "    #sns.boxplot(x='OD_or_Non_od', y='LoS', data=merged_cluster_with_LoS_with_OD_and_Non_od_label, ax=ax_box, palette={'Non_OD': 'blue', 'OD': 'red'}, width=0.4, showfliers=False)\n",
    "    sns.boxplot(x='OD_or_Non_od', y='LoS', data=merged_cluster_with_LoS_with_OD_and_Non_od_label, ax=ax_box, palette={'Non_OD': '#CD5C5C', 'OD': '#4682B4'}, width=0.4, showfliers=False)\n",
    "    ax_box.set_title(f'Attempt {i + 1} - Box Plot')\n",
    "    ax_box.set_xlabel('Patient Group')\n",
    "    ax_box.set_ylabel('Length of Stay (Days)')\n",
    "    \n",
    "    # compare samples\n",
    "    data_OD = merged_cluster_with_LoS_with_OD_and_Non_od_label[merged_cluster_with_LoS_with_OD_and_Non_od_label['OD_or_Non_od'] == 'OD']['LoS'].to_numpy()\n",
    "    data_NonOD = merged_cluster_with_LoS_with_OD_and_Non_od_label[merged_cluster_with_LoS_with_OD_and_Non_od_label['OD_or_Non_od'] == 'Non_OD']['LoS'].to_numpy()\n",
    "\n",
    "    if data_OD.size == 0 or data_NonOD.size == 0:\n",
    "        p_val_LoS = np.nan\n",
    "    else:\n",
    "        stat, p_val_LoS = mannwhitneyu(data_OD, data_NonOD)\n",
    "        \n",
    "    # Add p-value to the box plot title\n",
    "    # ax_box.set_title(f'Attempt {i + 1} - Box Plot (p-value: {p_val:.10f})' if not np.isnan(p_val) else f'Attempt {i + 1} - Box Plot (p-value: NaN)')\n",
    "\n",
    "    ax_box.set_title(f'Attempt {i + 1} - Box Plot (p-value: {p_val_LoS:.4e})' if not np.isnan(p_val_LoS) else f'Attempt {i + 1} - Box Plot (p-value: NaN)')\n",
    "  \n",
    "    # Collect p-value\n",
    "    p_values_LoS.append((i + 1, p_val_LoS))\n",
    "\n",
    "    # check output stats\n",
    "    print(i+1)\n",
    "    print(merged_cluster_with_LoS_with_OD_and_Non_od_label.groupby('OD_or_Non_od')['LoS'].describe())\n",
    "    print(f'p-value for attempt {i + 1}: {p_val_LoS:.4f}' if not np.isnan(p_val_LoS) else f'p-value for attempt {i + 1}: NaN')\n",
    "\n",
    "\n",
    "# Create DataFrame with attempt numbers and p-values\n",
    "p_values_LoS_df = pd.DataFrame(p_values_LoS, columns=['i', 'p_val_LoS'])\n",
    "print(p_values_LoS_df)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "d4a71a776a752918",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        689.0  22.243166  20.343539  5.60000  10.642361  15.809028   \n",
      "OD            364.0  17.951152  14.455834  5.59375   9.553993  13.778472   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        26.852083  219.908333  \n",
      "OD            21.801736  177.950694  \n",
      "p-value for attempt 1: 0.0004\n",
      "2\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        212.0  35.705618  27.383815  7.936806  19.515799  29.727431   \n",
      "OD            841.0  16.991879  13.249448  5.593750   9.141667  13.336111   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        43.239757  219.908333  \n",
      "OD            20.061111  177.950694  \n",
      "p-value for attempt 2: 0.0000\n",
      "3\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        787.0  22.111079  20.259237  5.59375  10.795833  16.082639   \n",
      "OD            266.0  16.760683  11.765794  5.60000   8.967882  12.621181   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.767708  219.908333  \n",
      "OD            20.158333   78.552083  \n",
      "p-value for attempt 3: 0.0000\n",
      "4\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        801.0  23.092420  20.312281  5.59375  11.348611  17.589583   \n",
      "OD            252.0  13.344177   8.154922  5.60000   7.868924  10.902083   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        27.749306  219.908333  \n",
      "OD            15.571007   58.696528  \n",
      "p-value for attempt 4: 0.0000\n",
      "5\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        866.0  22.015715  19.770256  5.593750  10.779514  16.043750   \n",
      "OD            187.0  14.941990  10.156708  5.936111   8.626389  11.854861   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        26.560590  219.908333  \n",
      "OD            17.966319   72.453472  \n",
      "p-value for attempt 5: 0.0000\n",
      "6\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        812.0  22.344442  20.105580  5.593750  10.845139  16.701736   \n",
      "OD            241.0  15.419395  10.846089  5.810417   8.534028  11.854861   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        26.689236  219.908333  \n",
      "OD            18.445139   75.806944  \n",
      "p-value for attempt 6: 0.0000\n",
      "7\n",
      "               count       mean        std      min       25%       50%  \\\n",
      "OD_or_Non_od                                                              \n",
      "Non_OD        1053.0  20.759507  18.626287  5.59375  9.934722  15.26875   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        24.355556  219.908333  \n",
      "p-value for attempt 7: NaN\n",
      "8\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        371.0  26.029745  19.876131  5.59375  13.958681  20.675000   \n",
      "OD            682.0  17.892560  17.260918  5.60000   8.821875  12.833333   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        31.169097  177.950694  \n",
      "OD            20.022917  219.908333  \n",
      "p-value for attempt 8: 0.0000\n",
      "9\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        918.0  22.071388  19.488096  5.593750  10.936979  16.232639   \n",
      "OD            135.0  11.838719   5.725333  5.810417   7.864931   9.915972   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        26.308160  219.908333  \n",
      "OD            13.770486   33.867361  \n",
      "p-value for attempt 9: 0.0000\n",
      "10\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        953.0  21.692744  19.253063  5.593750  10.666667  16.052083   \n",
      "OD            100.0  11.865757   5.818576  5.891667   7.822049   9.914583   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.616667  219.908333  \n",
      "OD            13.736632   33.867361  \n",
      "p-value for attempt 10: 0.0000\n",
      "11\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        697.0  17.329212  12.665953  5.59375   9.610417  13.762500   \n",
      "OD            356.0  27.475562  25.401762  5.60000  11.939583  20.878125   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        20.129861  177.950694  \n",
      "OD            32.789583  219.908333  \n",
      "p-value for attempt 11: 0.0000\n",
      "12\n",
      "               count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                 \n",
      "Non_OD        1006.0  21.224500  18.890833  5.593750  10.385243  15.704514   \n",
      "OD              47.0  10.806693   5.630326  5.891667   7.671181   8.987500   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.030208  219.908333  \n",
      "OD            11.954514   33.867361  \n",
      "p-value for attempt 12: 0.0000\n",
      "13\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        455.0  21.050516  17.057702  5.593750  10.742361  15.989583   \n",
      "OD            598.0  20.538088  19.747711  5.891667   9.794097  14.264583   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.348958  177.950694  \n",
      "OD            23.030035  219.908333  \n",
      "p-value for attempt 13: 0.0633\n",
      "14\n",
      "               count       mean        std      min       25%       50%  \\\n",
      "OD_or_Non_od                                                              \n",
      "Non_OD        1053.0  20.759507  18.626287  5.59375  9.934722  15.26875   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        24.355556  219.908333  \n",
      "p-value for attempt 14: NaN\n",
      "15\n",
      "               count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                 \n",
      "Non_OD        1013.0  21.150427  18.849290  5.593750  10.302083  15.666667   \n",
      "OD              40.0  10.859462   5.872291  5.891667   7.675868   9.001736   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.003472  219.908333  \n",
      "OD            11.884896   33.867361  \n",
      "p-value for attempt 15: 0.0000\n",
      "16\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        956.0  21.669960  19.199054  5.593750  10.747917  16.053819   \n",
      "OD             97.0  11.786383   6.739397  5.891667   7.734028   9.790972   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.600000  219.908333  \n",
      "OD            12.927778   47.594444  \n",
      "p-value for attempt 16: 0.0000\n",
      "17\n",
      "              count       mean        std       min        25%        50%  \\\n",
      "OD_or_Non_od                                                                \n",
      "Non_OD        945.0  21.503441  19.136232  5.593750  10.500000  15.897917   \n",
      "OD            108.0  14.250090  11.516637  5.891667   8.015104  10.807639   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        25.584722  219.908333  \n",
      "OD            15.807292   98.525000  \n",
      "p-value for attempt 17: 0.0000\n",
      "18\n",
      "              count       mean        std      min        25%        50%  \\\n",
      "OD_or_Non_od                                                               \n",
      "Non_OD        645.0  19.817327  16.501545  5.59375   9.877778  14.956250   \n",
      "OD            408.0  22.248984  21.500232  5.60000  10.069271  15.995833   \n",
      "\n",
      "                    75%         max  \n",
      "OD_or_Non_od                         \n",
      "Non_OD        22.864583  177.950694  \n",
      "OD            26.823958  219.908333  \n",
      "p-value for attempt 18: 0.1360\n",
      "     i     p_val_LoS\n",
      "0    1  3.810935e-04\n",
      "1    2  8.025723e-47\n",
      "2    3  9.684837e-07\n",
      "3    4  1.525175e-24\n",
      "4    5  7.493087e-10\n",
      "5    6  3.742653e-11\n",
      "6    7           NaN\n",
      "7    8  1.700006e-28\n",
      "8    9  2.486890e-18\n",
      "9   10  1.779764e-13\n",
      "10  11  5.890954e-15\n",
      "11  12  6.634443e-10\n",
      "12  13  6.330128e-02\n",
      "13  14           NaN\n",
      "14  15  1.279783e-08\n",
      "15  16  8.459127e-15\n",
      "16  17  9.922032e-09\n",
      "17  18  1.359515e-01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1200 with 36 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Thrombotic events",
   "id": "bcaadab6c1d93fd4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T01:26:23.710342Z",
     "start_time": "2024-10-07T01:26:21.933210Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Function to calculate Length of Stay (LoS)\n",
    "def Thrombotic_events_percentages(event_log_df, OD_patients_count, Non_OD_patients_count):\n",
    "    \n",
    "    # How many OD patients had thrombotic events?\n",
    "    Thrombotic_events_in_OD_patients = event_log_df[(event_log_df['event']=='First_Thrombosis_or_Embolism') & (event_log_df['OD_or_Non_od']=='OD')]['hadm_id'].nunique() \n",
    "\n",
    "    # How many Non-OD patients had thrombotic events?\n",
    "    Thrombotic_events_in_Non_od_patients = event_log_df[(event_log_df['event']=='First_Thrombosis_or_Embolism') & (event_log_df['OD_or_Non_od']=='Non_OD')]['hadm_id'].nunique()  \n",
    "    \n",
    "    # percentages\n",
    "\n",
    "    # OD_patients_percentage_with_thrombotic_events is set to zero if OD_patients_count is zero, preventing a ZeroDivisionError. Similarly, it also handles the case where Non_OD_patients_count is zero.\n",
    "    \n",
    "    OD_patients_percentage_with_thrombotic_events = (Thrombotic_events_in_OD_patients / OD_patients_count * 100) if OD_patients_count != 0 else 0\n",
    "\n",
    "    Non_OD_patients_percentage_with_thrombotic_events = (Thrombotic_events_in_Non_od_patients / Non_OD_patients_count *100) if Non_OD_patients_count != 0 else 0\n",
    "    \n",
    "    No_thrombotic_events_in_OD_patients = OD_patients_count - Thrombotic_events_in_OD_patients\n",
    "    \n",
    "    No_thrombotic_events_in_Non_od_patients = Non_OD_patients_count - Thrombotic_events_in_Non_od_patients\n",
    "\n",
    "    x = ['OD' , 'Non_OD']\n",
    "    y = [OD_patients_percentage_with_thrombotic_events , Non_OD_patients_percentage_with_thrombotic_events]\n",
    "\n",
    "    return x, y, Thrombotic_events_in_OD_patients, Thrombotic_events_in_Non_od_patients, No_thrombotic_events_in_OD_patients, No_thrombotic_events_in_Non_od_patients\n",
    "    \n",
    "\n",
    "# Plotting - blank figure\n",
    "#fig, axes = plt.subplots(5, 4, figsize=(20, 20))\n",
    "fig, axes = plt.subplots(3, 6, figsize=(20, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "# main programme\n",
    "\n",
    "p_values_thrombotic = []\n",
    "\n",
    "for i, attempt in enumerate(attempts):\n",
    "    cluster_distribution_df = pd.read_csv(attempt['cluster_distribution'])\n",
    "    clusters_in_hadmid_level_df = pd.read_csv(attempt['clusters_in_hadmid_level'])\n",
    "    \n",
    "    total_TN_count = cluster_distribution_df['TN_count'].sum()\n",
    "    total_TP_count = cluster_distribution_df['TP_count'].sum()\n",
    "    total_patient_count = cluster_distribution_df['patient_count'].sum()\n",
    "    \n",
    "    overall_positive_ratio = total_TP_count / total_patient_count\n",
    "    overall_negative_ratio = total_TN_count / total_patient_count\n",
    "    TP_to_TN_ratio = total_TP_count / total_TN_count\n",
    "    \n",
    "    chi_squared_results_df = perform_chi_squared_test(cluster_distribution_df, overall_positive_ratio, overall_negative_ratio, TP_to_TN_ratio)\n",
    "    \n",
    "    # Return event log of TP cases only - Call the function\n",
    "    event_log_TP_only_with_OD_and_Non_od_label, OD_patients_count, Non_OD_patients_count = label_OD_Non_OD_patients(clusters_in_hadmid_level_df, chi_squared_results_df, event_log_full_with_adm_and_dischge)\n",
    "    \n",
    "    #event_log_TP_only_with_OD_and_Non_od_label = label_OD_Non_OD_patients(clusters_in_hadmid_level_df, chi_squared_results_df, event_log_full_with_adm_and_dischge)\n",
    "    \n",
    "    # Return LoS of each TP patient \n",
    "    X, Y, Thrombotic_events_in_OD_patients, Thrombotic_events_in_Non_od_patients, No_thrombotic_events_in_OD_patients, No_thrombotic_events_in_Non_od_patients = Thrombotic_events_percentages(event_log_TP_only_with_OD_and_Non_od_label, OD_patients_count, Non_OD_patients_count)\n",
    "    \n",
    "    print(i+1)\n",
    "    print(OD_patients_count, Non_OD_patients_count)\n",
    "    print(Thrombotic_events_in_OD_patients, Thrombotic_events_in_Non_od_patients)\n",
    "    print(X, Y)\n",
    "    \n",
    "    #############\n",
    "    # check p-values using chi-squared\n",
    "    \n",
    "    OD_Thrombotic_Yes = Thrombotic_events_in_OD_patients\n",
    "    OD_Thrombotic_No = No_thrombotic_events_in_OD_patients\n",
    "    NonOD_Thrombotic_Yes = Thrombotic_events_in_Non_od_patients\n",
    "    NonOD_Thrombotic_No = No_thrombotic_events_in_Non_od_patients\n",
    "    \n",
    "    table = np.array([[OD_Thrombotic_Yes, OD_Thrombotic_No], [NonOD_Thrombotic_Yes, NonOD_Thrombotic_No]])\n",
    "\n",
    "    if OD_Thrombotic_Yes == 0 or NonOD_Thrombotic_Yes == 0:\n",
    "        p_val_thrombotic_events = np.nan\n",
    "    else:\n",
    "        stat_thrombotic_events, p_val_thrombotic_events, dof_thrombotic_events, expected_thrombotic_events = chi2_contingency(table)\n",
    "    print(p_val_thrombotic_events)\n",
    "    #############\n",
    "\n",
    "    # Collect p-value\n",
    "    p_values_thrombotic.append((i + 1, p_val_thrombotic_events))\n",
    "    \n",
    "    axes[i].bar(X, Y, color=['#4682B4', '#CD5C5C'])#, width = 0.4)\n",
    "    axes[i].set_ylim(0, 100)\n",
    "    \n",
    "    axes[i].set_title(f'Attempt {i + 1} - (p-value: {p_val_thrombotic_events:.4e})' if not np.isnan(p_val_thrombotic_events) else f'Attempt {i + 1} - (p-value: NaN)')\n",
    "    axes[i].set_xlabel('Patient Group')\n",
    "    axes[i].set_ylabel('Thrombotic Events percentage')\n",
    "\n",
    "# Create DataFrame with attempt numbers and p-values\n",
    "p_values_thrombotic_events = pd.DataFrame(p_values_thrombotic, columns=['i', 'p_val_thrombotic_events'])\n",
    "print(p_values_thrombotic_events)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "583bbc1e042747bf",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "364 689\n",
      "279 547\n",
      "['OD', 'Non_OD'] [76.64835164835165, 79.39042089985486]\n",
      "0.34196192672350734\n",
      "2\n",
      "841 212\n",
      "632 194\n",
      "['OD', 'Non_OD'] [75.1486325802616, 91.50943396226415]\n",
      "3.7034704033753875e-07\n",
      "3\n",
      "266 787\n",
      "195 631\n",
      "['OD', 'Non_OD'] [73.30827067669173, 80.17789072426937]\n",
      "0.023255807090014086\n",
      "4\n",
      "252 801\n",
      "149 677\n",
      "['OD', 'Non_OD'] [59.12698412698413, 84.51935081148564]\n",
      "2.639879938254409e-17\n",
      "5\n",
      "187 866\n",
      "125 701\n",
      "['OD', 'Non_OD'] [66.84491978609626, 80.94688221709006]\n",
      "3.256993829664412e-05\n",
      "6\n",
      "241 812\n",
      "157 669\n",
      "['OD', 'Non_OD'] [65.14522821576763, 82.38916256157636]\n",
      "1.8298835170635642e-08\n",
      "7\n",
      "0 1053\n",
      "0 826\n",
      "['OD', 'Non_OD'] [0, 78.44254510921178]\n",
      "nan\n",
      "8\n",
      "682 371\n",
      "494 332\n",
      "['OD', 'Non_OD'] [72.43401759530792, 89.4878706199461]\n",
      "2.1516201346300458e-10\n",
      "9\n",
      "135 918\n",
      "61 765\n",
      "['OD', 'Non_OD'] [45.18518518518518, 83.33333333333334]\n",
      "2.472277096416277e-23\n",
      "10\n",
      "100 953\n",
      "50 776\n",
      "['OD', 'Non_OD'] [50.0, 81.4270724029381]\n",
      "9.155131567917149e-13\n",
      "11\n",
      "356 697\n",
      "305 521\n",
      "['OD', 'Non_OD'] [85.67415730337079, 74.74892395982783]\n",
      "6.357539316609815e-05\n",
      "12\n",
      "47 1006\n",
      "20 806\n",
      "['OD', 'Non_OD'] [42.5531914893617, 80.11928429423459]\n",
      "2.850041048223511e-09\n",
      "13\n",
      "598 455\n",
      "465 361\n",
      "['OD', 'Non_OD'] [77.75919732441471, 79.34065934065934]\n",
      "0.5874356598571037\n",
      "14\n",
      "0 1053\n",
      "0 826\n",
      "['OD', 'Non_OD'] [0, 78.44254510921178]\n",
      "nan\n",
      "15\n",
      "40 1013\n",
      "19 807\n",
      "['OD', 'Non_OD'] [47.5, 79.66436327739387]\n",
      "3.2242316150751848e-06\n",
      "16\n",
      "97 956\n",
      "45 781\n",
      "['OD', 'Non_OD'] [46.391752577319586, 81.69456066945607]\n",
      "2.2499765378721863e-15\n",
      "17\n",
      "108 945\n",
      "54 772\n",
      "['OD', 'Non_OD'] [50.0, 81.6931216931217]\n",
      "8.387796970637349e-14\n",
      "18\n",
      "408 645\n",
      "305 521\n",
      "['OD', 'Non_OD'] [74.75490196078431, 80.7751937984496]\n",
      "0.02525442409982693\n",
      "     i  p_val_thrombotic_events\n",
      "0    1             3.419619e-01\n",
      "1    2             3.703470e-07\n",
      "2    3             2.325581e-02\n",
      "3    4             2.639880e-17\n",
      "4    5             3.256994e-05\n",
      "5    6             1.829884e-08\n",
      "6    7                      NaN\n",
      "7    8             2.151620e-10\n",
      "8    9             2.472277e-23\n",
      "9   10             9.155132e-13\n",
      "10  11             6.357539e-05\n",
      "11  12             2.850041e-09\n",
      "12  13             5.874357e-01\n",
      "13  14                      NaN\n",
      "14  15             3.224232e-06\n",
      "15  16             2.249977e-15\n",
      "16  17             8.387797e-14\n",
      "17  18             2.525442e-02\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x800 with 18 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Mortality rates",
   "id": "3f20071626bd9bda"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T01:26:25.241692Z",
     "start_time": "2024-10-07T01:26:23.711684Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Function to calculate Length of Stay (LoS)\n",
    "def Mortality_rates_percentages(event_log_df, OD_patients_count, Non_OD_patients_count):\n",
    "    \n",
    "    # How many OD patients had Mortality events?\n",
    "    Mortality_in_OD_patients = event_log_df[(event_log_df['event']=='hospital_expire_flag_1') & (event_log_df['OD_or_Non_od']=='OD')]['hadm_id'].nunique() \n",
    "\n",
    "    # How many Non-OD patients had Mortality events?\n",
    "    Mortality_in_Non_od_patients = event_log_df[(event_log_df['event']=='hospital_expire_flag_1') & (event_log_df['OD_or_Non_od']=='Non_OD')]['hadm_id'].nunique()  \n",
    "    \n",
    "    # percentages\n",
    "\n",
    "    # OD_patients_percentage_with_throMortalitymbotic_events is set to zero if OD_patients_count is zero, preventing a ZeroDivisionError. Similarly, it also handles the case where Non_OD_patients_count is zero.\n",
    "    \n",
    "    OD_patients_percentage_with_Mortality = (Mortality_in_OD_patients / OD_patients_count * 100) if OD_patients_count != 0 else 0\n",
    "\n",
    "    Non_OD_patients_percentage_with_Mortality = (Mortality_in_Non_od_patients / Non_OD_patients_count *100) if Non_OD_patients_count != 0 else 0\n",
    "    \n",
    "    No_Mortality_in_OD_patients = OD_patients_count - Mortality_in_OD_patients\n",
    "    \n",
    "    No_Mortality_in_Non_od_patients = Non_OD_patients_count - Mortality_in_Non_od_patients\n",
    "    \n",
    "    x = ['OD' , 'Non_OD']\n",
    "    y = [OD_patients_percentage_with_Mortality , Non_OD_patients_percentage_with_Mortality]\n",
    "\n",
    "    return x, y, Mortality_in_OD_patients, Mortality_in_Non_od_patients, No_Mortality_in_OD_patients, No_Mortality_in_Non_od_patients\n",
    "    \n",
    "\n",
    "# Plotting\n",
    "#fig, axes = plt.subplots(5, 4, figsize=(20, 20))\n",
    "fig, axes = plt.subplots(3, 6, figsize=(20, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "p_values_Mortality =[]\n",
    "\n",
    "# main programme\n",
    "\n",
    "for i, attempt in enumerate(attempts):\n",
    "    cluster_distribution_df = pd.read_csv(attempt['cluster_distribution'])\n",
    "    clusters_in_hadmid_level_df = pd.read_csv(attempt['clusters_in_hadmid_level'])\n",
    "    \n",
    "    total_TN_count = cluster_distribution_df['TN_count'].sum()\n",
    "    total_TP_count = cluster_distribution_df['TP_count'].sum()\n",
    "    total_patient_count = cluster_distribution_df['patient_count'].sum()\n",
    "    \n",
    "    overall_positive_ratio = total_TP_count / total_patient_count\n",
    "    overall_negative_ratio = total_TN_count / total_patient_count\n",
    "    TP_to_TN_ratio = total_TP_count / total_TN_count\n",
    "    \n",
    "    chi_squared_results_df = perform_chi_squared_test(cluster_distribution_df, overall_positive_ratio, overall_negative_ratio, TP_to_TN_ratio)\n",
    "    \n",
    "    # Return event log of TP cases only - Call the function\n",
    "    event_log_TP_only_with_OD_and_Non_od_label, OD_patients_count, Non_OD_patients_count = label_OD_Non_OD_patients(clusters_in_hadmid_level_df, chi_squared_results_df, event_log_full_with_adm_and_dischge)\n",
    "    \n",
    "    #event_log_TP_only_with_OD_and_Non_od_label = label_OD_Non_OD_patients(clusters_in_hadmid_level_df, chi_squared_results_df, event_log_full_with_adm_and_dischge)\n",
    "    \n",
    "    # Return LoS of each TP patient \n",
    "    X, Y, Mortality_in_OD_patients, Mortality_in_Non_od_patients, No_Mortality_in_OD_patients, No_Mortality_in_Non_od_patients = Mortality_rates_percentages(event_log_TP_only_with_OD_and_Non_od_label, OD_patients_count, Non_OD_patients_count)\n",
    "\n",
    "    print(i+1)\n",
    "    print(OD_patients_count, Non_OD_patients_count)\n",
    "    print(Mortality_in_OD_patients, Mortality_in_Non_od_patients)\n",
    "    print(X, Y)\n",
    "\n",
    "    # check p-values using chi-squared\n",
    "    \n",
    "    OD_Mortality_Yes = Mortality_in_OD_patients\n",
    "    OD_Mortality_No = No_Mortality_in_OD_patients\n",
    "    NonOD_Mortality_Yes = Mortality_in_Non_od_patients\n",
    "    NonOD_Mortality_No = No_Mortality_in_Non_od_patients\n",
    "    \n",
    "    Contingency_table_Mortality = np.array([[OD_Mortality_Yes, OD_Mortality_No], [NonOD_Mortality_Yes, NonOD_Mortality_No]])\n",
    "\n",
    "    if OD_Mortality_Yes == 0 or NonOD_Mortality_Yes == 0:\n",
    "        p_val_Mortality = np.nan\n",
    "    else:\n",
    "        stat_thrombotic_events, p_val_Mortality, dof_thrombotic_events, expected_thrombotic_events = chi2_contingency(Contingency_table_Mortality)\n",
    "    print(p_val_Mortality)\n",
    "\n",
    "    p_values_Mortality.append((i + 1, p_val_Mortality))\n",
    "    \n",
    "    \n",
    "    axes[i].bar(X, Y, color=['#4682B4', '#CD5C5C'])#, width = 0.4)\n",
    "    axes[i].set_ylim(0, 30)\n",
    "    axes[i].set_title(f'Attempt {i + 1} - (p-value: {p_val_Mortality:.4e})' if not np.isnan(p_val_Mortality) else f'Attempt {i + 1} - (p-value: NaN)')\n",
    "    axes[i].set_xlabel('Patient Group')\n",
    "    axes[i].set_ylabel('Mortality percentage')\n",
    "    \n",
    "# Create DataFrame with attempt numbers and p-values\n",
    "p_values_Mortality_df = pd.DataFrame(p_values_Mortality, columns=['i', 'p_val_Mortality'])\n",
    "print(p_values_Mortality_df)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "e51db1c81e045c8c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "364 689\n",
      "85 151\n",
      "['OD', 'Non_OD'] [23.35164835164835, 21.91582002902758]\n",
      "0.6500503534705844\n",
      "2\n",
      "841 212\n",
      "181 55\n",
      "['OD', 'Non_OD'] [21.521997621878715, 25.943396226415093]\n",
      "0.19791466989218967\n",
      "3\n",
      "266 787\n",
      "38 198\n",
      "['OD', 'Non_OD'] [14.285714285714285, 25.158831003811944]\n",
      "0.00032888453439756444\n",
      "4\n",
      "252 801\n",
      "16 220\n",
      "['OD', 'Non_OD'] [6.349206349206349, 27.465667915106117]\n",
      "4.375912957442523e-12\n",
      "5\n",
      "187 866\n",
      "41 195\n",
      "['OD', 'Non_OD'] [21.92513368983957, 22.51732101616628]\n",
      "0.936695123272558\n",
      "6\n",
      "241 812\n",
      "15 221\n",
      "['OD', 'Non_OD'] [6.224066390041494, 27.216748768472904]\n",
      "1.2453557127156869e-11\n",
      "7\n",
      "0 1053\n",
      "0 236\n",
      "['OD', 'Non_OD'] [0, 22.41215574548908]\n",
      "nan\n",
      "8\n",
      "682 371\n",
      "132 104\n",
      "['OD', 'Non_OD'] [19.35483870967742, 28.03234501347709]\n",
      "0.0016420700158075391\n",
      "9\n",
      "135 918\n",
      "8 228\n",
      "['OD', 'Non_OD'] [5.9259259259259265, 24.836601307189543]\n",
      "1.5152123581898718e-06\n",
      "10\n",
      "100 953\n",
      "6 230\n",
      "['OD', 'Non_OD'] [6.0, 24.134312696747116]\n",
      "6.044925363781066e-05\n",
      "11\n",
      "356 697\n",
      "88 148\n",
      "['OD', 'Non_OD'] [24.719101123595504, 21.233859397417504]\n",
      "0.2282515362129406\n",
      "12\n",
      "47 1006\n",
      "2 234\n",
      "['OD', 'Non_OD'] [4.25531914893617, 23.26043737574553]\n",
      "0.004039759777563\n",
      "13\n",
      "598 455\n",
      "129 107\n",
      "['OD', 'Non_OD'] [21.57190635451505, 23.516483516483515]\n",
      "0.49967139641324065\n",
      "14\n",
      "0 1053\n",
      "0 236\n",
      "['OD', 'Non_OD'] [0, 22.41215574548908]\n",
      "nan\n",
      "15\n",
      "40 1013\n",
      "0 236\n",
      "['OD', 'Non_OD'] [0.0, 23.297137216189537]\n",
      "nan\n",
      "16\n",
      "97 956\n",
      "10 226\n",
      "['OD', 'Non_OD'] [10.309278350515463, 23.640167364016737]\n",
      "0.004075884359928394\n",
      "17\n",
      "108 945\n",
      "9 227\n",
      "['OD', 'Non_OD'] [8.333333333333332, 24.021164021164022]\n",
      "0.00034107094358322605\n",
      "18\n",
      "408 645\n",
      "81 155\n",
      "['OD', 'Non_OD'] [19.852941176470587, 24.031007751937985]\n",
      "0.13153645797248711\n",
      "     i  p_val_Mortality\n",
      "0    1     6.500504e-01\n",
      "1    2     1.979147e-01\n",
      "2    3     3.288845e-04\n",
      "3    4     4.375913e-12\n",
      "4    5     9.366951e-01\n",
      "5    6     1.245356e-11\n",
      "6    7              NaN\n",
      "7    8     1.642070e-03\n",
      "8    9     1.515212e-06\n",
      "9   10     6.044925e-05\n",
      "10  11     2.282515e-01\n",
      "11  12     4.039760e-03\n",
      "12  13     4.996714e-01\n",
      "13  14              NaN\n",
      "14  15              NaN\n",
      "15  16     4.075884e-03\n",
      "16  17     3.410709e-04\n",
      "17  18     1.315365e-01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x800 with 18 Axes>"
      ],
      "image/png": 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AAQNkGIaaNm1qdTk+g7sGnUzc6dOn9d1336ljx44qWLCgE4+sfXQyIW9vb/n7+8vX11exsbE6e/asRo4cqVOnTjm0W/KPP/5YJUqUsLvsE088oTVr1mjQoEHKnDmzzWX+/vvvBBsseXp6qlChQjZ3L5yc31U6cp8lOulKtDKhV155RY8//rg++ugjXbhwQWFhYRo7dqzy5s2r559//iEf6XtopW3+/v5av369unXrJi8vL4du39H1K0da6cg6pRS3gcHAgQPVpUsXm8fyTg/zOq7fB2sqtHDhQmXPnl21a9eWJDVu3FgTJkzQggUL1KNHDxUpUkS5cuWSt7e3ypcvL+neRGT+/Pktp02aNEmZM2fWrFmzLFviPP/886pdu7a+/vprDRgwwHKbzZo1U4cOHSTFvfhbtGihcuXKWT6ElS1bVuvWrdPu3but/oDXrl3bstuX6tWr68KFC5o6dapat26t0qVLy9vbW7ly5bKMyRZvb28VL17cocfGMAxVrlxZd+/eVY4cOazuQ1Js27ZNkqzuW7xcuXJp2rRpypAhg2rUqCFPT0+NHj1ax44ds3n8A0lq1KiRFi5cqJ07dyowMFBS3O7nihYtqvLlyysqKko3b97U4MGDVb9+fUlxv6S+efOmxowZo4sXL9r9Y2PG0ee9dOnSpte1d+9ehYaGau7cuXZ3HfGg2rVra9y4cRowYIA++ugjFS9eXDt37tTMmTMlSbdu3dKTTz6pChUqaNKkScqfP7+qVKmisLAwDR48WN7e3rp165YiIiIUHR2tWbNmqXDhwhoxYoSioqI0ceJEtW3bVj///LPy5ctnddsLFy5URESEunTpYnX677//rs2bN2v8+PGWx7t69eq6ffu2Pv30U7366qt2dwF9/fp1qy3Z4mXNmlU3b960/LtIkSIOPT4eHh4qW7Ysx5lMJeixbYMHD1ZAQIAaN26s7du3O/BIOo4eJ65w4cJ67733NGLECC1YsECvv/56gmUuXLggHx8fDRgwQBUrVpQkVa5cWadPn7baQjlexowZNWbMGL3++usaMWKE6a6A/P39+bAMK7QyofhJ8Tt37ujzzz/X1atXNWPGDLVt21bz589P8kadtDJxtBLOomMJhYeH65dfftFjjz2m/v37y8fHRzNmzFDHjh31448/JnlPGHTMtof5DB6/q8kHP7fG75ryxo0bVqdHRUXpu+++U/369VW0aFGr8w4dOqTr168rZ86cmjx5ss6cOaPJkyerdevW+umnn5QvXz41bdpUy5Yt04ABA9S7d2/5+vrqf//7n2WPb7dv39aBAwfc/hk83rBhw7RgwQJJUkhIiEqWLGl1Pp/BXYNOJm727Nny9vZW27ZtHb6MGTqZuOnTp1sO2dC8eXPLfbJnzZo1WrdunZYuXSoPDw+by+TIkcP0diMiImzu+jdr1qwJeisl73eVjtxniU66Eq1MKE+ePBo6dKj69OmjlStXSpIee+wxzZ492+Yu/Z1FK217cN7EWYmtXznSSkfWKSXp008/VZYsWdSlSxf9999/Ca4nPczrpPtfikdHR+vnn39W7dq1defOHUVERChz5syqXLmyFixYoJiYGIeva9u2bapcubIyZ86s6OhoRUdHy9fXVxUrVtTvv/9utWxAQIDl//PkySMpbncJ8eK39Hlwv/7xW8fEe/nll3X58uUEW6W5SnR0tKZNm6YZM2aobNmyat26tSV8DzIMw3K/4/+zt6VOWFiYsmfPbnM3Mg0aNLB6Y9WtW1eStHPnTpu3IcXtBrxgwYJavny5pLgPnWvWrLHs3sfb21szZ85U/fr1deHCBe3YsUPz58/Xhg0bJCnBFtfOcOZ5T8ydO3c0cOBAtWvXzuYfFXty5cql0NBQxcTE6PXXX9dzzz2nUaNG6b333pMU98dZkr788ks999xz6tGjhypWrKh27dqpRYsWyp07t7JkyWL1GHz99deqWbOmXn75Zc2YMUM3b9602qVLvO+//15BQUEJttjcunWrPDw8VKNGDavnKigoSBcvXtSxY8cUGxtr87mMjY21u+KY2AplYgoWLGh3tzNIOeixbUuWLNGuXbssuwxyBD1OWo8f1KZNG1WqVEljxoyxucKYL18+zZ49WxUrVtTZs2e1detWzZkzR7t377Z7f0qXLq3OnTtr6dKlWrduXaK3X7BgQV2+fFm3b99+6PuAtINW2ta+fXvNmjVLY8aMUeXKlVW3bl1988038vHx0bRp02xehlbSSiQPOmbb3bt3df36dc2cOVP16tVTjRo1NH36dPn6+uqrr76yeRk6ljyfwe//JdP94h97T0/rr9p++eUXXbp0SZ06dUpwmb59++r777+3bDDUqFEjzZw5U9evX9fs2bMlSVWrVtW4ceP0+++/q3bt2qpSpYo2btxomfDx8fF5pJ/B33jjDX333XcKCQnRjBkzbO7tg8/gSUMnExcZGamFCxeqefPmeuyxxxJdlk66bn0vKChIc+bM0cCBA7Vy5Up169bN7rLh4eEaOnSo+vfvr0KFCj3s3bCw1SLDMGyenlzfVTp7n+lkg1JdTwAAUWpJREFU0tFK25YuXaoePXooKChIM2fO1OTJk1WiRAm9/fbblr3dPIhWuvaz8cNwZP0qMY6sU27fvl3z58/X6NGj7U5sp4d5nXT/S/GNGzfq0qVLWrx4sRYvXpzg/A0bNli2NDJz9epVrVixwub+9e8/7omUcItiKe6DjJkHt3rJnTu3pLit5twhY8aMqlatmqS4rWReffVVTZ8+XVWqVEmw7B9//JFgC83Zs2ercuXKCZa9ceOG3fub2H1csmSJBg0aZHX+unXrVKhQITVs2FA//vijBg8erF9//VURERFWf2w2b96sUaNG6Z9//lHWrFnl5+dn2dIwKbvZcOZ5T8yECRMUGxur7t27W0ISP67o6Gh5eXnZjYe/v7+WLl2q8+fP6/bt2ypatKjl16TxHxDy5MmjKVOmKCIiQhcuXFCRIkXk6empYcOG6bHHHrM8FpUrV7baArNAgQIqUaJEgmN0xB+rvHfv3jYfE8MwVKFCBZvjvXDhgtauXatJkyZZnX7kyBFly5YtwW5ppLjdHz3s1mw+Pj4JVkSQ8tDjhM6fP69Ro0ZpwIAByp07t6Kjoy1fwsXGxiomJsbmLnnocdJ6/KD4XQO/9tpr+vDDD20et+nnn3/W559/rnPnzilHjhwqVaqU3V0axevevbvWrVunoUOHWn41aUv8xk3Xr1936LWJtI1W2la8ePEEW8xnz55dFSpUsBxb60G0klYiedAx27JmzaoSJUoof/78ltN8fX0VEBBg93iJdCx5PoPHfwl88+ZNqwm5W7duSVKCz62rVq3S008/bXOvJbb2AFC4cGGVKFHC6u/Xa6+9pldffVWnTp2Sj4+P8uXLp4kTJ8rT01PZsmV7pJ/B4zcgCAwMlGEYmjp1qkJCQqxeu3wGTxo6mbgtW7bo5s2batiwoemydNJ163vxu9mtVKmSsmXLpg8++EC7du3Sc889l2DZYcOGqUSJEmrevLmlr9K9ibfEvuN8kK+vr81fhN+6dcuqO1Lyflfp7H2mk0lHK22bNGmSKlSooPHjx1tOq1q1qurXr68vvvhCEydOTHAZWunaz8YPw5H1q8SYrVPevHlTgwYNUufOnfXUU08l+I45OjpaGTJkSBfzOul+UnzhwoUqWLCgRo8eneC8Xr16ad68eQ7HM1u2bHrhhRcsu8+4n70tL5z14PH04l9k8YFxlXXr1il79uyqVKmS5TRvb2/5+fnp2LFjNi9TpkwZLVy40Oq0B7fKi5czZ067L+YH7+OlS5ckxYWoVq1aCW4jPraNGjXStGnTtH37di1btkwVKlRQ4cKFJcUdOzskJEQvvfSSpk+fbtlFw9y5cy27HLPnwa3K4j/oxnPV875q1SqdOXPGamuzePHHInnwWF2SdOXKFW3cuFE1atSw2k3HgQMH5OnpaQni8uXLVaJECZUqVcryIX7fvn2KiYlR6dKllS1bNuXOnVtRUVEJbiM6OjrBF5YbNmyQj4+PatasmWD5bNmyKUuWLJatkB5UtGhRlSpVyuZlixUrpi1btig2NtZqC/tTp06pRIkSNq/PTEREhEPHpELyoscJ/fbbb4qIiNAHH3xg2cVSvPbt26tgwYJav359gsvRY9c/70WKFFHv3r01atSoBPd7586dGjBggNq0aaOOHTtaVljHjh2rXbt22b1Ob29vjR49Wi1atNDIkSPtLnft2jV5eHg4tLskpH200rbly5crR44cqlq1qtXpd+7csbsOQCtpJZIHHbOtaNGiDn8Wi0fHkuczePxjfPLkSatfmJ88eVLe3t6Wx0CK+/XSb7/9ZvNX4nfv3tXSpUtVvHjxBLtKjYyMtPz9+vvvv7Vv3z41btzY6jjmBw4cUKlSpeTl5eX2z+DHjh3T3r171axZM6vL+vv7yzAM/ffff1Zf2vIZPGnoZOI2btyoQoUKyd/f33RZOpm05z0sLEzbtm3Ta6+9pkyZMllOj3/sbe0dSIrrqxS3G+n7nTlzRj/99JPdyTZbihUrplOnTlmdFhsbq9OnT+vll1+2Oj05v6t09j7TyaSjlbadOXMmwf328fGRv78/8zqP8LOxI5xdv7LHkXXK/fv3W3apPnnyZKtl4r93jp/YTuvzOul6UvzSpUvavHmz3n77bZt/iOvXr6958+YpLCwswe6vpIS7xAoMDNTx48f1zDPPWN40hmGob9++Klq0aJKPASZJ69evt9pK45dfflHBggUtMbA1zocRGhqqK1eu6Oeff7bcl+vXr2vPnj12f6Hh6+vr0AqpFPfr41u3bunatWsJdnW0fv16de3a1fLvVatWycPDQ1WqVFHOnDntvgmKFy8uf39/LV++XBs3btTAgQMt5+3fv1937txRly5drI5ZEB9Oe1sU+fr6JljB2717t9W/XfW8T506NcGXIEOHDpUkDR8+3O6udwzD0KBBgzRs2DC1bNlSUtzWNz/++KMCAwMtE+BTp05VyZIl9fnnn1suO2vWLGXPnt3y+n/xxRe1Zs0ahYeHW7aG+ueff3TixIkEx4b866+/VLp0aZtf0AQGBio0NFSGYVh9SbB48WKtXr1ao0aNUr58+Wwea6NatWqaNm2aNm/erBo1akiK2wXRjh07rF4Xzjh37pwKFiz4UJfFo0GPbbO1wnjgwAENHTpUw4cPt/kFnkSP3fW8t23bVqtXr9aYMWOsTt+zZ49iY2PVq1cvy5aPMTExll0tPbgyeL+yZcuqU6dOmjZtmnx9fW0ep+i///5Tnjx5HD7OJdIuWmnf999/rwsXLmj58uWW98r58+e1e/duu8eapJW0Eo8eHbOvRo0amjJliv7++2/Ll0ZXrlzR7t271bhxY5uXoWPJ8xk8ICBAWbJk0apVqyyfdw3D0Jo1axQYGGjVoaNHj+r27ds2f0mZMWNGffnllypQoIDmzp1rOf3AgQM6deqUZSL92LFjGjBggPz9/S2vjePHj2vLli3q3r275TFx52fwv/76Sx988IGKFStm9X7YvHmzzeOb8hn84dFJc3/99ZfdX7A9iE4m7Xk/ffq0PvzwQ2XOnNnql/nxY7S1BwxJCb7HkKRu3bqpbNmyCgkJsTvZZkvVqlU1c+ZMq+8qN2/erJs3bybYIDY5v6t09j7TyaShlfYVL15cu3btsjrEwJ07d3TgwAG7xyOnle573hPj7PqVPY6sU9ra8OHixYvq1q2bevToYZnkTg/zOun6mOJLlixRdHS0GjRoYPP8Jk2aKDY2Vj/++KOyZ8+uS5cuadOmTbpw4YKkuF1m7dmzRzt27JBhGOrevbtOnTqlLl26aO3atdq8ebN69uyp5cuX211JcNasWbM0adIkbdmyRUOGDNGGDRssx46OH9PBgwf1xx9/KDIy8qFvJyQkRCdOnFCvXr20efNmrVy5Uu3atdPt27fVs2fPJN+P+JWWB0MkSXv37lXfvn21efNmff311xo/frzeeOMNq62t7WncuLF++uknRUdH65VXXrGcXqZMGWXIkEHjxo3Tb7/9pg0bNqhnz57auHGjpIRbCcWrVauWduzYoWnTpmnbtm0aM2aMtm7darWMo8/7wYMHdfz4cbtj9/Pzk7+/v9V/WbNmVdasWeXv72/5oxEeHq4///zTsuugXLlyqUGDBpowYYKWLFmiX3/9VcHBwbpw4YL69etnuf633npLK1as0JQpU7Rt2zYNGTJEy5Yt03vvvWfZ7UtISIg8PDzUsWNHrV27VitXrlTXrl2VP39+NW/e3Gq8R48e1VNPPWXzvtSoUUOVKlVS9+7d9f3332v79u366quvNGzYMHl6eia6+5FKlSopMDBQ/fr104IFC7RmzRq1b99e2bJls0z6O8MwDO3Zs8dyGACkTPTYtpw5cyboQvwHqWLFill2X5YU9Nhx8bsGfvB4RfEriR999JG2bdum1atXq0OHDpZdXtq7T/FCQkL09NNP29wlnCTt2rVL1atXd3q8SHtopX0hISE6ffq0evbsqV9//VVLly5V27ZtlT17dnXs2DHJ94NWOo5WIjF0zL62bdsqf/78Cg4O1rJly7R+/Xp17txZHh4eNn9l7Cw6ltDDfgb38fHR22+/rZkzZ2rChAnatGmT3nnnHR04cMAySR3v6NGjkmT31zEhISHauXOnBg4cqN9++00//vijunTpIj8/PzVp0kRS3OfrIkWKqG/fvtq4caOWL1+ujh07qlChQmrXrp1lGXd+Bq9fv75KlCihPn36aMmSJdqyZYuGDx+uH374QT169LA6riifwZOGTiYuJiZG//zzj93vo5KCTiYUGBioypUr6+OPP9bcuXO1detWjR8/XhMmTFCLFi0sbXuwkw+21d/fX97e3sqRI4f8/f1t7n7anlatWilTpkzq0KGD1qxZowULFqhfv3568cUXE/xQIDm/q3TmPtPJpKOV9r3zzjv6888/9c477+jXX3/V2rVr1alTJ50/fz7BesrDoJUP78FWOrN+ZcZsnTJ+w4f7/ytZsqSkuON2x28UkR7mddL9pLi94zpJcV/cFC9eXIsWLVLjxo1VsGBBhYSE6KeffpIkde3aVfv27VPnzp117tw5lSpVSnPnzpWHh4f69++vXr166eLFi5o8eXKC3bk8rPfff1+bNm1St27d9Ndff2nixIl69dVXLee//fbbunTpkjp27Kj9+/c/9O288MILCg0N1bVr1/TOO+9o8ODBeuKJJ/Tjjz8+9K4O7le4cGGVKVNGmzZtSnBeu3btFB0drR49emjOnDnq2rWrZWttM/Xr15enp6dq1qxptaVS0aJF9dlnn+n8+fPq1q2bhgwZIkn67rvv5OHhoZ07d9q8vi5duuj1119XaGiounXrpvPnzyfYbaOjz3uPHj00fPhwh+5HYjZu3KgWLVrowIEDltOGDx+uevXq6bPPPlPv3r2VOXNmffvtt1a77GnRooUGDRqkJUuWWF67n332md58803LMoULF9a8efOUL18+9evXT4MHD1apUqX0/fffJ1hhvXz5st0we3p6asaMGWrQoIGmT5+u/2vv/uO9nu/H/9/POZVOP96UooUZo6wpDgmLIVq2aSLemd+9l7DSmwnF2EIyNqy3+S30YfjIjy1jfBvb2PwW8qOwsVJb9EOKUp1zvn/4dNp51+nxetXrdc55vV7X6+XicnFer9d5Pu+er+frVi+P148f/OAHcc8998TJJ59c7/tMGnLttdfGwQcfHFdccUWMGTMmtt5667j99tvXefVZJl577bX4+OOP49BDD836d2k8etx09Dg722+//TrfT7b33nvHRRddFNOnT49TTjklJkyYEF27dq37fp0NfSxwxNqPBl7f98PPnz8/Zs6cqWFEhFZuyDe+8Y245ZZbYunSpXHWWWfFxRdfHD169Ii77747qyeSDdHK7GglDdGxhm2++eZx9913x+677x4XX3xxnH322bH55pvHr3/964y/S3BDdGzjre85+MiRI2PUqFHx0EMPxRlnnBEffPBBXHfddeu8I3zNx4Y29Fz2qKOOiquuuirefvvtGDFiRFx99dXRr1+/uP322+vesVRZWRm33HJLbLXVVjF69Oi47LLL4hvf+Ebceeeddc/T8/0cvE2bNnHHHXfEvvvuG1dffXWcfvrpMX369Lj88svj1FNPrbctz8E3jU5u2McffxyrV6/Oyd/v/jedXFdFRUVcd911MXjw4Lj11lvjlFNOicceeyxGjx4dP/3pT+tut75O5krHjh1j8uTJ0aFDhxg9enRcffXVceihh663bf5fZenQyoYdfPDBcdNNN8WHH34YI0eOjAsvvDAqKytjypQpDX7iZDa0cuP971Zm8/erlEz+TpmJUmhlWe2mfBM9jea5556LE088MavvXGnuHnvssTj//PPjqaeeijZt2kTEF6/UHjlyZE7ejQ4REWPHjo0lS5bEdddd19SjUCT0mMZ07bXXxrRp0+LBBx+s+9grKARaSWPSSvJBx2DjeA5eOnQSNo5OlhathI2Tr1aW9DvFaVrf+ta3Yuedd45f//rXTT0KRWrevHnx+OOPx3//93839SjQrOlx87Rs2bK4++6740c/+pFFHmgGtLJ50krInI6Rb56DU+h0knzTSYqBVpJv+WylRXGaTFlZWVxxxRUxefLkWLRoUVOPQxH6+c9/HsOHD8/J9y5DMdPj5unGG2+Mgw8+OL75zW829ShAaGVzpZWQOR0j3zwHp9DpJPmmkxQDrSTf8tlKH58OAAAAAAAAQNFqsneKP/PMM3H00UfHHnvsEX379o1LLrkkVqxYERERr776ahx99NFRVVUV/fr1i/vuu6+pxgRoMjoJkKaVAGlaCZCmlQBpWgkUsiZZFF+0aFGceuqp8f3vfz9efPHFePDBB+P555+Pm266KZYsWRLDhw+PQYMGxQsvvBDjx4+PCRMmxGuvvdYUowI0CZ0ESNNKgDStBEjTSoA0rQQKXYum2GnHjh3jr3/9a7Rr1y5qa2vj448/js8//zw6duwYjz/+eGyxxRZx3HHHRUTEvvvuGwMHDoy77rorevXq1RTjAjQ6nQRI00qANK0ESNNKgDStBApdkyyKR0S0a9cuIiIOOOCAmD9/fvTu3TuOPPLIuOaaa6Jbt271brvTTjvFlClTMtpuTU1NrF69OsrLy6OsrCzncwPFrba2NmpqaqJFixZRXt5k3zAREfnrZIRWAptGKwE2rDl1MkIrgeZJKwHSSqWVOglsikxb2WSL4ms8/vjjsWTJkhg9enSMGjUqtt5666isrKx3m9atW8dnn32W0fZWr14dM2bMyMeoQAnp2bNntGrVqqnHiIjcdzJCK4Hc0EqADWtOnYzQSqB50kqAtGJvpU4CuZBqZZMvirdu3Tpat24d55xzThx99NFxwgknxNKlS+vdZsWKFdG2bduMtrfmFQA9evSIioqKnM9baKqrq+PNN990PGiQc6S+NcejObzyco1cdzJCK/+dxwCZcJ7Up5Wlx2OATDhP1mqOnYzQynzzGCATzpO1tLI0eQyQCefJWqXSSp2sz2OATDhP1sq0lU2yKP7yyy/H+eefH7/97W/rVuxXrlwZLVu2jJ122in+8pe/1Lv9u+++GzvvvHNG217z0RqtWrUq+ZMg4osTIcLxoGHOkfrWHI+m/piefHYyQiv/nccAmXCe1KeVpcdjgEw4T9ZqLp2M0MrG5DFAJpwna2llafIYIBPOk7VKpZU6WZ/HAJlwnqyVaSub5OVF3bt3jxUrVsQvfvGLWLlyZcydOzd+9rOfxVFHHRUDBgyIBQsWxO233x6rVq2KZ599NqZOnRqDBw9uilEBmoROAqRpJUCaVgKkaSVAmlYCha5J3inetm3buOWWW+Kyyy6Lvn37Rvv27WPgwIExYsSIaNWqVUyaNCnGjx8fEydOjI4dO8aPf/zj2GeffZpiVIAmoZMAaVoJkKaVAGlaCZCmlUCha7LvFN9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Pr3d5WVlZvPXWWxltY8mSJXHSSSfFPvvsE+PHj4/y8vK66/r37x9XXnll3H777XHcccfFSy+9FFOnTo3rrrsumzEBmpRWAqRpJUCaVgKkaSVAWjG3cs6CZfHuvz7J+36AwpfVovjMmTM3eYcPPPBAzJs3Lx599NH4/e9/X++66dOnx6RJk2L8+PExceLE6NixY/z4xz+OffbZZ5P3C9BYtBIgTSsB0rQSIE0rAdK0EiDLRfGIiJUrV8af/vSnmDt3bgwZMiT+8Y9/xC677JLx7w8dOjSGDh3a4PU9e/aMe+65J9uxAJoVrQRI00qANK0ESNNKgDStBEpdVovis2fPjv/6r/+KVatWxSeffBIHHHBADB48OK699to46KCD8jUjQEHRSoA0rQRI00qANK0ESNNKgIjy9E3WGj9+fBx55JHxxz/+MVq0aBE77LBDXHrppTFx4sR8zQdQcLQSIE0rAdK0EiBNKwHStBIgy0XxV155JYYNGxZlZWVRVlYWERGHH354zJkzJy/DARQirQRI00qANK0ESNNKgDStBMhyUbx9+/axYMGCepd99NFHsfnmm+d0KIBCppUAaVoJkKaVAGlaCZCmlQBZLooPHDgwRo4cGX/5y1+ipqYmXnvttRg9enR897vfzdd8AAVHKwHStBIgTSsB0rQSIE0rASJaZHPjH/7wh7FixYoYOXJkLF++PE488cQYPHhwjBw5Ml/zARQcrQRI00qANK0ESNNKgDStBMhyUbxly5Zx7rnnxtlnnx2ffPJJrFq1Kjp16hQVFRX5mg+g4GglQJpWAqRpJUCaVgKkaSVAlh+fPnPmzOjXr1+8+eab0bFjx5g0aVJ861vfir///e/5mg+g4GglQJpWAqRpJUCaVgKkaSVAlovi48ePjyOOOCJ69OgRERHnnHNOHHHEEXHppZfmZTiAQqSVAGlaCZCmlQBpWgmQppUAWX58+ltvvRWTJ0+OsrKyL365RYs4/fTTY5999snLcACFSCsB0rQSIE0rAdK0EiBNKwGyfKd4u3bt4r333qt32Zw5c+I//uM/cjoUQCHTSoA0rQRI00qANK0ESNNKgCzfKX7EEUfE6aefHsOGDYuuXbvGvHnz4tZbb40jjzwyX/MBFBytBEjTSoA0rQRI00qANK0EyHJRfOTIkVFeXh433HBDfPTRR/GlL30pjjzyyBg2bFi+5gMoOFoJkKaVAGlaCZCmlQBpWgmQ5aL4bbfdFj/4wQ/ijDPOyNc8AAVPKwHStBIgTSsB0rQSIE0rAbL8TvGbbropNttss3zNAlAUtBIgTSsB0rQSIE0rAdK0EiDLRfH9998/br755vjwww/zNQ9AwdNKgDStBEjTSoA0rQRI00qALD8+/aWXXorf/e538ctf/nKd6956662cDQVQyLQSIE0rAdK0EiBNKwHStBIgy0XxK664Il9zABQNrQRI00qANK0ESNNKgDStBMhyUbxPnz4REbFkyZKYM2dO9OjRI1avXh2tWrXKy3AAhUgrAdK0EiBNKwHStBIgTSsBsvxO8U8//TTOPvvs2HvvveP444+P999/P/r37x9///vf8zUfQMHRSoA0rQRI00qANK0ESNNKgCwXxa+44or47LPP4tFHH42WLVvGdtttFwcddFCMHz8+X/MBFBytBEjTSoA0rQRI00qANK0EyPLj05988smYOnVqbL755lFWVhYtW7aMMWPGxDe/+c18zQdQcLQSIE0rAdK0EiBNKwHStBIgy3eK19TU1H3HRG1t7TqXbYxFixZF//7947nnnqu77Cc/+UnsuuuuUVVVVffPvffeu9H7AGhMuW6lTgLFSCsB0rQSIE0rAdK0EiDLd4rvs88+cfHFF8dFF10UZWVlERFxzTXXRJ8+fTZq5y+99FKMGTMmZs+eXe/yGTNmxCWXXBJHHHHERm0XoCnlspU6CRQrrQRI00qANK0ESNNKgCwXxceOHRunn3567LXXXlFdXR1VVVXxla98JW644Yasd/zggw/GxIkT45xzzomzzjqr7vKVK1fG22+/HbvuumvW2/x31dXVWd2+oqJik/ZH08n2vt5YzpHClc05kovzKVetzHcnIxrv8dOcrTkGjkVuaGXh0sqG6YNW5ppWFq5MHwO5eqxoZWHRytzSysKllQ3TB63MNa0sXFq5ftZ1Sod1HTKR61ZmtSi+5ZZbxr333hszZsyIuXPnRpcuXaJXr14bdVLtt99+MXDgwGjRokW9eM6cOTNWr14dEydOjJdeeinat28fgwcPjmHDhkV5eeaf9j5jxoyMb1tZWRk9evTIan6aj1mzZsXy5cvzug/nSGFrjHPk3+WqlfnuZER2rSx2jsWm08rCppUN04e1HItNp5WFTSsbpg9rORabTisLm1Y2TB/Wciw2nVYWNq1cP+s6pcO6DpnI9XmS1aJ4RMT7778ff/7zn+Ojjz6Krl27xtZbbx1du3bNesedO3de7+VLly6NPn36xAknnBBXXXVVvPXWWzFixIgoLy+PYcOGZbz9nj17egVIiejevXtTj0Azl805Ul1dnZMnZrloZb47GaGVEWvvc8eCUqeVDdMHrYQ1Mm1lrjoZoZWFRCvhC1rZMH3QSlhDK9dPG0qHdR0yketWZrUoPm3atDjzzDNj1113ja5du8Yf/vCHuOGGG+Lmm2+O3r17Z7OpBvXt2zf69u1b93OvXr3ipJNOikceeSSreFZUVIhniXA/k9LY50i+W5mrTkZo5b9zLCh1WtkwfVjLsaDUaWXD9GEtx4JSp5UN04e1HAtKnVaunzaUDvczmcj1eZLVovjVV18dl156aQwaNKjusilTpsSECRPi/vvvz8lA06ZNiwULFsQxxxxTd9nKlSujdevWOdk+QL7lu5U6CRQDrQRI00qANK0ESNNKgIisvvhm3rx58b3vfa/eZUcccUS8//77ORuotrY2JkyYEM8880zU1tbG9OnTY/LkyTFkyJCc7QMgn/LdSp0EioFWAqRpJUCaVgKkaSVAlu8U79WrVzz++ONx6KGH1l32/PPPx+67756zgfr37x9jx46Nn/70pzF//vzo1KlTnHHGGXH44YfnbB8A+ZTvVuokUAy0EiBNKwHStBIgTSsBslwU33bbbePss8+OqVOnxvbbbx/z58+PadOmRe/evWPs2LF1t5swYUJWQ8yaNavez8ccc0y9j9kAKCT5aKVOAsVGKwHStBIgTSsB0rQSIMtF8ZqamrqP2Fi8eHG0atUqvvOd7+RlMIBCpZUAaVoJkKaVAGlaCZCmlQBZLopn+w5wgFKklQBpWgmQppUAaVoJkKaVABHlTT0AAAAAAAAAAOSLRXEAAAAAAAAAipZFcQAAAAAAAACKVlaL4s8991y+5gAoGloJkKaVAGlaCZCmlQBpWgmQ5aL4qFGj4pBDDolf/epXMW/evHzNBFDQtBIgTSsB0rQSIE0rAdK0EiDLRfGnn346zjnnnHj99ddjwIAB8V//9V/x8MMPx8qVK/M1H0DB0UqANK0ESNNKgDStBEjTSoAsF8VbtmwZAwYMiOuvvz7+9Kc/xSGHHBKTJk2K/fbbL8aNGxczZ87M15wABUMrAdK0EiBNKwHStBIgTSsBslwUX2PhwoUxderUeOihh+Ldd9+NvffeOzbbbLM4+eST44Ybbsj1jAAFSSsB0rQSIE0rAdK0EiBNK4FS1iKbG//ud7+L3/zmN/HXv/41dtxxxzjyyCPjhhtuiI4dO0ZExAEHHBAjRoyI0047LS/DAhQCrQRI00qANK0ESNNKgDStBMhyUXzcuHHx3e9+N+65557Ydddd17l+hx12iJNPPjlXswEUJK0ESNNKgDStBEjTSoA0rQTIclH8/PPPj0GDBq1z+b333htDhgyJLl26xKhRo3I1G0BB0kqANK0ESNNKgDStBEjTSoAMFsWXL18eixcvjogvXk209957R21tbd31S5cujcsvvzyGDBmSvykBmjmtBEjTSoA0rQRI00qANK0EqC+5KL5s2bL47ne/GytWrIiIiH79+tVdV1tbG2VlZXHIIYfkb0KAAqCVAGlaCZCmlQBpWgmQppUA9SUXxTt37hzTpk2L5cuXx8CBA+Phhx+ud/1mm20WnTp1ytuAAIVAKwHStBIgTSsB0rQSIE0rAerL6DvFt9xyy4iIeOmll6K8vDyvAwEUKq0ESNNKgDStBEjTSoA0rQRYK6NF8eHDh8dNN90UJ510UpSVla33NpMnT87pYACFRisB0rQSIE0rAdK0EiBNKwHWymhRfM8994yIiD59+jQYToBSp5UAaVoJkKaVAGlaCZCmlQBrZbQofuqpp0ZExBlnnJHXYQAKmVYCpGklQJpWAqRpJUCaVgKsldGi+NixY5O3mTBhwiYPA1DItBIgTSsB0rQSIE0rAdK0EmCt8qYeYNGiRdG/f/947rnn6i579dVX4+ijj46qqqro169f3HfffU04IUDT0kmANK0ESNPK/KusrGzqEYBNpJUAaVoJFKKM3imer1cKvfTSSzFmzJiYPXt23WVLliyJ4cOHx6hRo2LIkCHxwgsvxIgRI6J79+7Rq1evvMwBkAv5aKVOAsVGKwHSir2V1TW1UVFefN9pWVFRET169GjqMfKmWO83ClextxIgF7QSYK2MFsXXWLlyZUydOjXmz58fNTU1ERGxatWqePvtt+P666/PascPPvhgTJw4Mc4555w466yz6i5//PHHY4sttojjjjsuIiL23XffGDhwYNx1111ZxbO6ujqreSoqKrK6Pc1Htvf1xnKOFK5szpFcnE+5amW+OxnReI+f5mzNMXAsckMrC5dWNkwftDLXtLJwZfoYyNVjpVhbWVFREZc/OD3mLFiW1T5oOtt1ahdjjqhq1D8HtLJwaWXD/F3K3ytzTSsLl1aun3Wd0mFdh0zkupVZLYqff/758dRTT0WHDh1i1apV0aZNm3jnnXdi0KBB2WwmIiL222+/GDhwYLRo0aJePN95553o1q1bvdvutNN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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "\n",
    "### concat p-values of all three clinical parameters - LoS / Thrombotic events / Mortality"
   ],
   "id": "a0113cd6b0d03869"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T06:35:27.662800Z",
     "start_time": "2024-10-07T06:35:27.572240Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# merge p-values of all 3 clinical parameters in a one table and safe it as a csv file.\n",
    "\n",
    "p_values_merge_all = pd.merge(p_values_LoS_df, (pd.merge(p_values_thrombotic_events, p_values_Mortality_df, on='i', how='outer')), on='i', how='outer')\n",
    "\n",
    "p_values_merge_all_csv_path = '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/p_values_merge_all_18_attempts.csv'\n",
    "\n",
    "#p_values_merge_all.to_csv(p_values_merge_all_csv_path, index=False)\n",
    "\n",
    "p_values_merge_all"
   ],
   "id": "a30813f71699d0c4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     i     p_val_LoS  p_val_thrombotic_events  p_val_Mortality\n",
       "0    1  3.810935e-04             3.419619e-01     6.500504e-01\n",
       "1    2  8.025723e-47             3.703470e-07     1.979147e-01\n",
       "2    3  9.684837e-07             2.325581e-02     3.288845e-04\n",
       "3    4  1.525175e-24             2.639880e-17     4.375913e-12\n",
       "4    5  7.493087e-10             3.256994e-05     9.366951e-01\n",
       "5    6  3.742653e-11             1.829884e-08     1.245356e-11\n",
       "6    7           NaN                      NaN              NaN\n",
       "7    8  1.700006e-28             2.151620e-10     1.642070e-03\n",
       "8    9  2.486890e-18             2.472277e-23     1.515212e-06\n",
       "9   10  1.779764e-13             9.155132e-13     6.044925e-05\n",
       "10  11  5.890954e-15             6.357539e-05     2.282515e-01\n",
       "11  12  6.634443e-10             2.850041e-09     4.039760e-03\n",
       "12  13  6.330128e-02             5.874357e-01     4.996714e-01\n",
       "13  14           NaN                      NaN              NaN\n",
       "14  15  1.279783e-08             3.224232e-06              NaN\n",
       "15  16  8.459127e-15             2.249977e-15     4.075884e-03\n",
       "16  17  9.922032e-09             8.387797e-14     3.410709e-04\n",
       "17  18  1.359515e-01             2.525442e-02     1.315365e-01"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i</th>\n",
       "      <th>p_val_LoS</th>\n",
       "      <th>p_val_thrombotic_events</th>\n",
       "      <th>p_val_Mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.810935e-04</td>\n",
       "      <td>3.419619e-01</td>\n",
       "      <td>6.500504e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8.025723e-47</td>\n",
       "      <td>3.703470e-07</td>\n",
       "      <td>1.979147e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9.684837e-07</td>\n",
       "      <td>2.325581e-02</td>\n",
       "      <td>3.288845e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.525175e-24</td>\n",
       "      <td>2.639880e-17</td>\n",
       "      <td>4.375913e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7.493087e-10</td>\n",
       "      <td>3.256994e-05</td>\n",
       "      <td>9.366951e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>3.742653e-11</td>\n",
       "      <td>1.829884e-08</td>\n",
       "      <td>1.245356e-11</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1.700006e-28</td>\n",
       "      <td>2.151620e-10</td>\n",
       "      <td>1.642070e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2.486890e-18</td>\n",
       "      <td>2.472277e-23</td>\n",
       "      <td>1.515212e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1.779764e-13</td>\n",
       "      <td>9.155132e-13</td>\n",
       "      <td>6.044925e-05</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5.890954e-15</td>\n",
       "      <td>6.357539e-05</td>\n",
       "      <td>2.282515e-01</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>6.634443e-10</td>\n",
       "      <td>2.850041e-09</td>\n",
       "      <td>4.039760e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>6.330128e-02</td>\n",
       "      <td>5.874357e-01</td>\n",
       "      <td>4.996714e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>1.279783e-08</td>\n",
       "      <td>3.224232e-06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>8.459127e-15</td>\n",
       "      <td>2.249977e-15</td>\n",
       "      <td>4.075884e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>9.922032e-09</td>\n",
       "      <td>8.387797e-14</td>\n",
       "      <td>3.410709e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1.359515e-01</td>\n",
       "      <td>2.525442e-02</td>\n",
       "      <td>1.315365e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T01:26:25.262945Z",
     "start_time": "2024-10-07T01:26:25.250984Z"
    }
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   "cell_type": "code",
   "source": [
    "# Set p_values (LoS) to np.nan if the value<0.05\n",
    "\n",
    "p_values_LoS_df['p_val_LoS'] = np.where(p_values_LoS_df['p_val_LoS'] < 0.05, p_values_LoS_df['p_val_LoS'], np.nan)\n",
    "\n",
    "# Set p_values (thrombotic_events) to np.nan if the value<0.05\n",
    "\n",
    "p_values_thrombotic_events['p_val_thrombotic_events'] = np.where(p_values_thrombotic_events['p_val_thrombotic_events'] < 0.05, p_values_thrombotic_events['p_val_thrombotic_events'], np.nan)\n",
    "\n",
    "# Set p_values (Mortality) to np.nan if the value<0.05\n",
    "\n",
    "p_values_Mortality_df['p_val_Mortality'] = np.where(p_values_Mortality_df['p_val_Mortality'] < 0.05, p_values_Mortality_df['p_val_Mortality'], np.nan)\n",
    "\n",
    "# Merge all three forms of p values - LoS / Thrombotic events / Mortality\n",
    "\n",
    "p_values_all_LoS_Thrombotic_Mortality = pd.merge(p_values_LoS_df, p_values_thrombotic_events, on='i', how='outer')\n",
    "p_values_all_LoS_Thrombotic_Mortality = pd.merge(p_values_all_LoS_Thrombotic_Mortality, p_values_Mortality_df, on='i', how='outer')\n",
    "\n",
    "# Filter rows where all three forms of p_values are statistically significant (i.e., non of the values in the row should be np.NaN)\n",
    "\n",
    "statistically_significant_rows_only = p_values_all_LoS_Thrombotic_Mortality.dropna(subset=['p_val_LoS', 'p_val_thrombotic_events', 'p_val_Mortality'])\n",
    "\n",
    "statistically_significant_rows_only"
   ],
   "id": "6bb47ef2f90786a3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     i     p_val_LoS  p_val_thrombotic_events  p_val_Mortality\n",
       "2    3  9.684837e-07             2.325581e-02     3.288845e-04\n",
       "3    4  1.525175e-24             2.639880e-17     4.375913e-12\n",
       "5    6  3.742653e-11             1.829884e-08     1.245356e-11\n",
       "7    8  1.700006e-28             2.151620e-10     1.642070e-03\n",
       "8    9  2.486890e-18             2.472277e-23     1.515212e-06\n",
       "9   10  1.779764e-13             9.155132e-13     6.044925e-05\n",
       "11  12  6.634443e-10             2.850041e-09     4.039760e-03\n",
       "15  16  8.459127e-15             2.249977e-15     4.075884e-03\n",
       "16  17  9.922032e-09             8.387797e-14     3.410709e-04"
      ],
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       "      <th>i</th>\n",
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       "      <td>1.515212e-06</td>\n",
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       "      <th>9</th>\n",
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       "      <td>6.044925e-05</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
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       "      <td>6.634443e-10</td>\n",
       "      <td>2.850041e-09</td>\n",
       "      <td>4.039760e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
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       "      <td>4.075884e-03</td>\n",
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       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>9.922032e-09</td>\n",
       "      <td>8.387797e-14</td>\n",
       "      <td>3.410709e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 23
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  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T01:26:25.270639Z",
     "start_time": "2024-10-07T01:26:25.264644Z"
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   },
   "cell_type": "code",
   "source": [
    "# Sort each parameter column independently while keeping the corresponding 'i' values\n",
    "\n",
    "# 1. Sort by P_values of LoS\n",
    "statistically_significant_sorted_LoS_Pval = statistically_significant_rows_only[['i', 'p_val_LoS']].sort_values(by='p_val_LoS').reset_index(drop=True)\n",
    "\n",
    "statistically_significant_sorted_LoS_Pval"
   ],
   "id": "2240ec71ec689efd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    i     p_val_LoS\n",
       "0   8  1.700006e-28\n",
       "1   4  1.525175e-24\n",
       "2   9  2.486890e-18\n",
       "3  16  8.459127e-15\n",
       "4  10  1.779764e-13\n",
       "5   6  3.742653e-11\n",
       "6  12  6.634443e-10\n",
       "7  17  9.922032e-09\n",
       "8   3  9.684837e-07"
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       "      <td>4</td>\n",
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       "      <td>2.486890e-18</td>\n",
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       "      <td>8.459127e-15</td>\n",
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       "      <td>6</td>\n",
       "      <td>3.742653e-11</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>6.634443e-10</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "      <td>9.922032e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>9.684837e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
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     "start_time": "2024-10-07T01:26:25.599346Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 2. Sort by P_values of thrombotic_events\n",
    "statistically_significant_sorted_Thrombotic_Pval = statistically_significant_rows_only[['i', 'p_val_thrombotic_events']].sort_values(by='p_val_thrombotic_events').reset_index(drop=True)\n",
    "\n",
    "statistically_significant_sorted_Thrombotic_Pval"
   ],
   "id": "6e0a4e42e598bc51",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    i  p_val_thrombotic_events\n",
       "0   9             2.472277e-23\n",
       "1   4             2.639880e-17\n",
       "2  16             2.249977e-15\n",
       "3  17             8.387797e-14\n",
       "4  10             9.155132e-13\n",
       "5   8             2.151620e-10\n",
       "6  12             2.850041e-09\n",
       "7   6             1.829884e-08\n",
       "8   3             2.325581e-02"
      ],
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       "      <th>2</th>\n",
       "      <td>16</td>\n",
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       "      <td>9.155132e-13</td>\n",
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       "      <td>8</td>\n",
       "      <td>2.151620e-10</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>2.850041e-09</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>2.325581e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2024-10-07T01:26:25.967332Z"
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   },
   "cell_type": "code",
   "source": [
    "# 3. Sort by P_values of Mortality\n",
    "statistically_significant_sorted_Mortality_Pval = statistically_significant_rows_only[['i', 'p_val_Mortality']].sort_values(by='p_val_Mortality').reset_index(drop=True)\n",
    "\n",
    "statistically_significant_sorted_Mortality_Pval"
   ],
   "id": "6619e449c01d787",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    i  p_val_Mortality\n",
       "0   4     4.375913e-12\n",
       "1   6     1.245356e-11\n",
       "2   9     1.515212e-06\n",
       "3  10     6.044925e-05\n",
       "4   3     3.288845e-04\n",
       "5  17     3.410709e-04\n",
       "6   8     1.642070e-03\n",
       "7  12     4.039760e-03\n",
       "8  16     4.075884e-03"
      ],
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       "      <th>p_val_Mortality</th>\n",
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       "      <td>4.375913e-12</td>\n",
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       "      <td>6</td>\n",
       "      <td>1.245356e-11</td>\n",
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       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1.515212e-06</td>\n",
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       "      <td>10</td>\n",
       "      <td>6.044925e-05</td>\n",
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       "      <td>3.288845e-04</td>\n",
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     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### check OD and Non-OD patient counts for each attempt of clustering",
   "id": "dea9442545cc8aca"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T01:26:27.481446Z",
     "start_time": "2024-10-07T01:26:26.939057Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Initialize an empty DataFrame with the specified columns\n",
    "results_df = pd.DataFrame(columns=[\n",
    "    'Attempt', 'TP_count', 'TN_count', 'cluster_count', 'OD_TP_count', \n",
    "    'Non_OD_TP_count', 'OD_percentage', 'Non_OD_percentage'\n",
    "])\n",
    "\n",
    "for i, attempt in enumerate(attempts):\n",
    "    cluster_distribution_df = pd.read_csv(attempt['cluster_distribution'])\n",
    "    clusters_in_hadmid_level_df = pd.read_csv(attempt['clusters_in_hadmid_level'])\n",
    "    \n",
    "    total_TN_count = cluster_distribution_df['TN_count'].sum()\n",
    "    total_TP_count = cluster_distribution_df['TP_count'].sum()\n",
    "    total_patient_count = cluster_distribution_df['patient_count'].sum()\n",
    "    \n",
    "    overall_positive_ratio = total_TP_count / total_patient_count\n",
    "    overall_negative_ratio = total_TN_count / total_patient_count\n",
    "    TP_to_TN_ratio = total_TP_count / total_TN_count\n",
    "    \n",
    "    chi_squared_results_df = perform_chi_squared_test(cluster_distribution_df, overall_positive_ratio, overall_negative_ratio, TP_to_TN_ratio)\n",
    "    \n",
    "    # number of clusters in each attempt\n",
    "    \n",
    "    cluster_count = (chi_squared_results_df['cluster_index'].max())+1\n",
    "    \n",
    "    # Return event log of TP cases only - Call the function\n",
    "    event_log_TP_only_with_OD_and_Non_od_label, OD_patients_count, Non_OD_patients_count = label_OD_Non_OD_patients(clusters_in_hadmid_level_df, chi_squared_results_df, event_log_full_with_adm_and_dischge)\n",
    "\n",
    "    OD_percentage = round((OD_patients_count / total_TP_count * 100), 2)\n",
    "    Non_OD_percentage = round((Non_OD_patients_count / total_TP_count * 100), 2)\n",
    "    \n",
    "    # Create a DataFrame for the current attempt\n",
    "    attempt_df = pd.DataFrame({\n",
    "        'Attempt': [i + 1],\n",
    "        'TP_count': [total_TP_count],\n",
    "        'TN_count': [total_TN_count],\n",
    "        'cluster_count': [cluster_count],\n",
    "        'OD_TP_count': [OD_patients_count],\n",
    "        'Non_OD_TP_count': [Non_OD_patients_count],\n",
    "        \n",
    "        'OD_percentage': [OD_percentage],\n",
    "        'Non_OD_percentage': [Non_OD_percentage]\n",
    "    })\n",
    "    \n",
    "    # Concatenate the current attempt DataFrame with the results DataFrame\n",
    "    results_df = pd.concat([results_df, attempt_df], ignore_index=True)\n",
    "\n",
    "# Display the results DataFrame\n",
    "results_df.reset_index(drop=True)\n",
    "\n",
    "# Save cluster data to a CSV file\n",
    "\n",
    "clusters_hadmid_level_csv_path = '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Results_from_HPC/full_results_only_with_TP_and_TN/June21_only_TP_and_TN/results_changing_parameters_16_attempts.csv'\n",
    "\n",
    "#results_df.to_csv(clusters_hadmid_level_csv_path, index=False)\n",
    "\n",
    "results_df"
   ],
   "id": "38e92392ccd26105",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Attempt TP_count TN_count cluster_count OD_TP_count Non_OD_TP_count  \\\n",
       "0        1     1053    11288             5         364             689   \n",
       "1        2     1053    11288             2         841             212   \n",
       "2        3     1053    11288            12         266             787   \n",
       "3        4     1053    11288             6         252             801   \n",
       "4        5     1053    11288            32         187             866   \n",
       "5        6     1053    11288            16         241             812   \n",
       "6        7     1053    11288             6           0            1053   \n",
       "7        8     1053    11288             3         682             371   \n",
       "8        9     1053    11288            13         135             918   \n",
       "9       10     1053    11288            10         100             953   \n",
       "10      11     1053    11288            59         356             697   \n",
       "11      12     1053    11288            26          47            1006   \n",
       "12      13     1053    11288             7         598             455   \n",
       "13      14     1053    11288             3           0            1053   \n",
       "14      15     1053    11288            21          40            1013   \n",
       "15      16     1053    11288            11          97             956   \n",
       "16      17     1053    11288           113         108             945   \n",
       "17      18     1053    11288            36         408             645   \n",
       "\n",
       "    OD_percentage  Non_OD_percentage  \n",
       "0           34.57              65.43  \n",
       "1           79.87              20.13  \n",
       "2           25.26              74.74  \n",
       "3           23.93              76.07  \n",
       "4           17.76              82.24  \n",
       "5           22.89              77.11  \n",
       "6            0.00             100.00  \n",
       "7           64.77              35.23  \n",
       "8           12.82              87.18  \n",
       "9            9.50              90.50  \n",
       "10          33.81              66.19  \n",
       "11           4.46              95.54  \n",
       "12          56.79              43.21  \n",
       "13           0.00             100.00  \n",
       "14           3.80              96.20  \n",
       "15           9.21              90.79  \n",
       "16          10.26              89.74  \n",
       "17          38.75              61.25  "
      ],
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       "      <th>Attempt</th>\n",
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       "      <td>918</td>\n",
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       "      <td>953</td>\n",
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       "      <th>11</th>\n",
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       "      <td>455</td>\n",
       "      <td>56.79</td>\n",
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       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>1013</td>\n",
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       "      <td>90.79</td>\n",
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       "      <th>16</th>\n",
       "      <td>17</td>\n",
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       "      <td>11288</td>\n",
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       "      <td>945</td>\n",
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       "      <td>645</td>\n",
       "      <td>38.75</td>\n",
       "      <td>61.25</td>\n",
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       "</table>\n",
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      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Test after sending the initial results",
   "id": "e2a55d7ff7803726"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "8584e7a04866a2c2"
  }
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