All_GRADYpatients_transfusion_ver3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3bcfcbec-cbf6-4eb1-bf9e-c8825d458ba6",
   "metadata": {},
   "source": [
    "# **GRADY DATA: Transfusion Response Analysis with median SpO2, Hb, S/F (SpO2/FiO2), P/F (PaO2/FiO2) and blood volume (ml)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e941c3-55b6-4c67-a96b-765718ddc21e",
   "metadata": {},
   "source": [
    "**BeforeT: 24 hrs before T to T time, AfterT: T+4 hrs to T+4+$\\tau$ hrs, where $\\tau$=3,6,9,12**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c48ef9a7-8e5e-4749-80b1-0069aeee0cbc",
   "metadata": {},
   "source": [
    "find del_Hb, del_SpO2, S/F, P/F"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1dc4426-1f24-4ccd-abcb-c657454b0c4e",
   "metadata": {},
   "source": [
    "Remove bed criteria to include more patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e2061ea2-a007-481e-aefe-9e4e3b3e5944",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import os\n",
    "import pickle\n",
    "from datetime import timedelta\n",
    "from datetime import datetime\n",
    "from datetime import date\n",
    "from datetime import time\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from multiprocessing import Pool\n",
    "import numpy as np\n",
    "\n",
    "import wfdb\n",
    "from tqdm import tqdm\n",
    "\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import decomposition\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import time\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') \n",
    "# warnings.filterwarnings(\"ignore\", category=RuntimeWarning) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d50d4908-1405-4e4e-bc72-f7a17cecb086",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"/labs/kamaleswaranlab/MODS/Data/Grady_Data/2. Fluids & Meds/Blood Products/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0933cd15-72dc-46be-b612-e57aad328022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['7. Radiology',\n",
       " '6. Vent',\n",
       " '4. Patient Assessments',\n",
       " '8. Lines',\n",
       " '2. Fluids & Meds',\n",
       " '5. ICD Codes',\n",
       " '1. Administrative Attributes',\n",
       " '3. Labs & Cultures',\n",
       " 'meta_data']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('/labs/kamaleswaranlab/MODS/Data/Grady_Data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00262667-7b07-41dd-a814-db16370f7c3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['encounter_2014_decomp_03112020.txt',\n",
       " 'encounter_2015_decomp_03112020.txt',\n",
       " 'encounter_2016_decomp_03112020.txt',\n",
       " 'encounter_2017_decomp_03112020.txt',\n",
       " 'encounter_2018_decomp_03112020.txt',\n",
       " 'encounter_2020_decomp_07222020.txt',\n",
       " 'encounters_2019_decomp_04272020.txt',\n",
       " 'archive',\n",
       " '.encounter_2016_decomp_03112020.txt.swp',\n",
       " '.encounter_2017_decomp_03112020.txt.swp',\n",
       " 'encounter_2021_decomp_05032022.txt',\n",
       " 'encounter_2022_decomp_11082022.txt',\n",
       " 'encounter_2014-2022_decomp_by_MRN_04112023.txt',\n",
       " 'encounter_2021_09062023.txt',\n",
       " 'encounter_2022_09062023.txt']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('/labs/kamaleswaranlab/MODS/Data/Grady_Data/1. Administrative Attributes/Encounters/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c9c4b75-5b23-4c83-9220-fd01ec0f85d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8bb4a61d-fa8a-4438-9b86-b42dad843862",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# years = ['2016','2017','2018','2019','2020']\n",
    "\n",
    "# All_icu_pat = pd.DataFrame()\n",
    "# for year in years:\n",
    "#     dir1 = '/labs/kamaleswaranlab/MODS/Data/Grady_Data/'  \n",
    "#     ### ICU adult patients: \n",
    "#     files1 = [filename for filename in os.listdir(dir1+'1. Administrative Attributes/Encounters/') if filename.startswith('encounter_'+str(year))]\n",
    "#     if year == '2019':\n",
    "#         files1 = [filename for filename in os.listdir(dir1+'1. Administrative Attributes/Encounters/') if filename.startswith('encounters_'+str(year))]\n",
    "#     bedFilePath = dir1+'1. Administrative Attributes/Encounters/'+files1[0]\n",
    "#     bedPat = pd.read_csv(bedFilePath, sep='|', low_memory=False)\n",
    "#     icuPat = bedPat[~bedPat.total_icu_days.isnull()]\n",
    "#     icuPat = icuPat[icuPat.age>18]\n",
    "#     icuPat.csn = icuPat.csn.astype(int)\n",
    "#     icuPat['pat_id'] = icuPat['pat_id'].astype(str) \n",
    "#     icuPat['year'] = str(year)\n",
    "#     All_icu_pat = pd.concat([All_icu_pat, icuPat], axis=0)\n",
    "# All_icu_pat.drop_duplicates(inplace=True)        \n",
    "# All_icu_pat.reset_index(drop=True, inplace=True)\n",
    "# All_icu_pat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524b105c-c96a-4f7e-8d87-3500625c9562",
   "metadata": {},
   "source": [
    "**Adding CSNs into Patient Directory along with PAT_ID**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6edb1880-dce4-4dd0-9962-7f6bf2455dfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All total patients in 2016-- #pat_ids: 3647, #CSNs: 3853\n",
      "All total patients in 2017-- #pat_ids: 3455, #CSNs: 3629\n",
      "All total patients in 2018-- #pat_ids: 3460, #CSNs: 3626\n",
      "All total patients in 2019-- #pat_ids: 4178, #CSNs: 4439\n",
      "All total patients in 2020-- #pat_ids: 3015, #CSNs: 3216\n",
      "Total patients throughout the years-- #pat_ids: 17755, #CSNs: 18763\n",
      "Total patients admitted (no having waveforms criterion)-- #pat_ids: 16945, #CSNs: 18762, #Years: ['2016' '2017' '2018' '2019' '2020']\n"
     ]
    }
   ],
   "source": [
    "years = ['2016','2017','2018','2019','2020']\n",
    "\n",
    "All_Vent_Pat_bed_matched_total = pd.DataFrame()\n",
    "pat_total = 0\n",
    "csn_total = 0\n",
    "\n",
    "count=0\n",
    "for year in years:\n",
    "    dir1 = '/labs/kamaleswaranlab/MODS/Data/Grady_Data/'\n",
    "    \n",
    "    ###(1) ICU adult patients: \n",
    "\n",
    "    files1 = [filename for filename in os.listdir(dir1+'1. Administrative Attributes/Encounters/') if filename.startswith('encounter_'+str(year))]\n",
    "    if year == '2019':\n",
    "        files1 = [filename for filename in os.listdir(dir1+'1. Administrative Attributes/Encounters/') if filename.startswith('encounters_'+str(year))]\n",
    "    bedFilePath = dir1+'1. Administrative Attributes/Encounters/'+files1[0]\n",
    "    bedPat = pd.read_csv(bedFilePath, sep='|', low_memory=False)\n",
    "    icuPat = bedPat[~bedPat.total_icu_days.isnull()]\n",
    "    icuPat = icuPat[icuPat.age>18]\n",
    "    icuPat.csn = icuPat.csn.astype(int)\n",
    "    \n",
    "    pat = len(icuPat['pat_id'].unique())\n",
    "    csn = len(icuPat['csn'].unique())\n",
    "    print(f'All total patients in {year}-- #pat_ids: {pat}, #CSNs: {csn}')\n",
    "    pat_total = pat_total + pat\n",
    "    csn_total = csn_total + csn\n",
    "     \n",
    "        \n",
    "    icuPat['PAT_ID'] = icuPat['pat_id']\n",
    "    icuPat['pat_id'] = icuPat['pat_id'].astype(str)\n",
    "\n",
    "\n",
    "    All_Vent_Pat_bed_matched = icuPat \n",
    "\n",
    "    All_Vent_Pat_bed_matched['year'] = str(year)\n",
    "\n",
    "    #Creating a dataframe with all data pertaining to waveform identifiers from the list given\n",
    "\n",
    "    All_Vent_Pat_bed_matched_total = pd.concat([All_Vent_Pat_bed_matched_total, All_Vent_Pat_bed_matched], axis=0)\n",
    "        \n",
    "print(f'Total patients throughout the years-- #pat_ids: {pat_total}, #CSNs: {csn_total}')\n",
    "#debedWav = len(All_Vent_Pat_bed_matched_total['deBedTime'].unique())\n",
    "patWav = len(All_Vent_Pat_bed_matched_total['PAT_ID'].unique())\n",
    "csnWav = len(All_Vent_Pat_bed_matched_total['csn'].unique())\n",
    "yrsWav = All_Vent_Pat_bed_matched_total['year'].unique()\n",
    "print(f'Total patients admitted (no having waveforms criterion)-- #pat_ids: {patWav}, #CSNs: {csnWav}, #Years: {yrsWav}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d2e2489-9fc8-4d10-b6c9-fc6dc3df9e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_Vent_Pat_bed_matched_total.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44a9276e-9ed1-4b30-9ecc-d9e1c5dc4ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All_Vent_Pat_bed_matched_total['csn'] = All_Vent_Pat_bed_matched_total['csn'].astype('int64')\n",
    "# All_Vent_Pat_bed_matched_total['csn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d6c46df-4d14-408b-b3f2-d48b1d6db882",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_Vent_Pat_bed_matched_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b8f5dba-b09b-4a27-8845-063c647731d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_Vent_Pat_bed_matched_total=All_Vent_Pat_bed_matched_total.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40d282c3-27c7-4b63-9475-feb99fc43cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                             int64\n",
       "pat_id                           object\n",
       "csn                               int64\n",
       "har                             float64\n",
       "mrn                               int64\n",
       "study_id                        float64\n",
       "age                             float64\n",
       "zip_code                         object\n",
       "ed_arrival_source                object\n",
       "pre_admit_location               object\n",
       "ed_presentation_time             object\n",
       "hospital_admission_date_time     object\n",
       "hospital_discharge_date_time     object\n",
       "admit_reason                     object\n",
       "insurance_status                 object\n",
       "cpr_in_ed                        object\n",
       "total_icu_days                  float64\n",
       "total_vent_days                 float64\n",
       "total_hosp_days                 float64\n",
       "discharge_status                 object\n",
       "discharge_to                     object\n",
       "encounter_type                   object\n",
       "PAT_ID                           object\n",
       "year                             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_Vent_Pat_bed_matched_total.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b24ba9c9-61d1-4389-bda1-f65142c242b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18762\n",
      "16945\n"
     ]
    }
   ],
   "source": [
    "print(len(All_Vent_Pat_bed_matched_total.csn.unique()))\n",
    "print(len(All_Vent_Pat_bed_matched_total.pat_id.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005e0248-8ae5-4c08-ad67-6b06b4f78582",
   "metadata": {},
   "source": [
    "**Finding CSNs undergoes to Transfusion from Patient Directory**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ba102501-1055-4616-8b86-75d62237cf93",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transfusion = pd.read_csv('/labs/kamaleswaranlab/MODS/Data/Grady_Data/2. Fluids & Meds/Blood Products/blood_transfusion_2014-2022_decomp_by_MRN_04102023.txt', sep=\"|\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59bbd7f0-d715-4241-8ede-bbf922cb8de3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transfusion.drop_duplicates(inplace=True)\n",
    "df_transfusion.reset_index(drop=True, inplace=True)\n",
    "df_transfusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07a45553-f497-4a6e-89a1-0d643e52b374",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many years of patients bed-data present: ['2016' '2017' '2018' '2019' '2020']\n",
      "How many years of transfusion data present: [2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023]\n"
     ]
    }
   ],
   "source": [
    "yearsT = All_Vent_Pat_bed_matched_total.year.unique()\n",
    "print(f'How many years of patients bed-data present: {yearsT}')\n",
    "\n",
    "# #How many years of waveform data\n",
    "# yearsW = sorted((pd.to_datetime(All_Pat_file['DateStart']).dt.year).unique())\n",
    "# print(f'How many years of waveform data present: {yearsW}')\n",
    "\n",
    "yearsTrans = sorted((pd.to_datetime(df_transfusion['transfusion_start']).dt.year).unique())\n",
    "print(f'How many years of transfusion data present: {yearsTrans}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b05d67b-f5f9-4360-b7a9-46d91fa7130a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transfusion['year'] = pd.to_datetime(df_transfusion['transfusion_start']).dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8d25c8cb-7df2-44ff-a240-90e365bdee57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2014, 2015, 2016, 2020, 2017, 2018, 2019, 2021, 2022, 2023])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transfusion['year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e012f96-0c6d-4bd1-8d22-059c9bbd7d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "w = Counter(df_transfusion.blood_product_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9465cf08-b2ac-4543-a89f-a0dfc307cdf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[13,4])\n",
    "plt.bar(w.keys(), w.values())\n",
    "y_pos = range(len(w.keys()))\n",
    "# Rotation of the bars names\n",
    "plt.xticks(y_pos, w.keys(), rotation=90)\n",
    "plt.title('Transfusion frequency from 2014-2022 at Grady Hospital')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11eabe36-2824-4030-9848-413e4087f5f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transfusion_years = pd.DataFrame()\n",
    "for yr in years:\n",
    "    df_transfusion_y = df_transfusion[df_transfusion['year'] == int(yr)]\n",
    "    df_transfusion_years = pd.concat([df_transfusion_years, df_transfusion_y], axis=0)\n",
    "df_transfusion_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a419361f-c07e-4387-b105-6d90c4720dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Counter({'PREPARE PLATELET PHERESIS': 7552,\n",
       "         'TRANSFUSE RED BLOOD CELLS': 43474,\n",
       "         'PREPARE FRESH FROZEN PLASMA': 38294,\n",
       "         'FROZEN AND THAWED PLASMA': 306,\n",
       "         'TRANSFUSE FRESH FROZEN PLASMA': 8870,\n",
       "         'PLATELET CONCENTRATE': 73,\n",
       "         'TRANSFUSE RED BLOOD CELLS (INFANT/NEONATE)': 1387,\n",
       "         'PREPARE CRYOPRECIPITATE': 3822,\n",
       "         'ANE TRANSFUSE RED BLOOD CELLS': 10910,\n",
       "         'ANE TRANSFUSE CRYOPRECIPITATE': 876,\n",
       "         'ANE TRANSFUSE FRESH FROZEN PLASMA': 6779,\n",
       "         'TRANSFUSE PLATELET PHERESIS': 4587,\n",
       "         'TRANSFUSE CRYOPRECIPITATE': 1215,\n",
       "         'TRANSFUSE PLATELET PHERESIS (INFANT/NEONATE)': 320,\n",
       "         'ANE TRANSFUSE PLATELET PHERESIS': 1292,\n",
       "         'UNMATCHED RED BLOOD CELLS': 12424,\n",
       "         'UNMATCHED FRESH FROZEN PLASMA': 6399,\n",
       "         'TRANSFUSE FRESH FROZEN PLASMA (INFANT/NEONATE)': 403,\n",
       "         'UNMATCHED CRYOPRECIPITATE': 323,\n",
       "         'UNMATCHED PLATELETS': 365,\n",
       "         'ANE TRANSFUSE CRYOPRECIPITATE FOR FIBRIN GLUE': 15,\n",
       "         'TRANSFUSE CRYOPRECIPITATE (INFANT/NEONATE)': 64,\n",
       "         'UNMATCHED CRYO (FIBRIN GLUE)': 20,\n",
       "         'MASSIVE TRANSFUSION PROTOCOL': 1100,\n",
       "         'TRANSFUSE CRYOPRECIPITATE FOR FIBRIN GLUE': 3,\n",
       "         'UNMATCHED WHOLE BLOOD': 230,\n",
       "         'TRANSFUSE CRYO-POOR PLASMA (CPP)': 2})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ww = Counter(df_transfusion_years.blood_product_name)\n",
    "plt.figure(figsize=[13,4])\n",
    "plt.bar(ww.keys(), ww.values())\n",
    "y_pos = range(len(ww.keys()))\n",
    "# Rotation of the bars names\n",
    "plt.xticks(y_pos, ww.keys(), rotation=90)\n",
    "plt.title('Transfusion frequency from 2016-2020 in ICU at Grady Hospital')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n",
    "ww"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3533649a-244b-4bb7-ac3a-300cf098bd43",
   "metadata": {},
   "source": [
    "### Find the matched patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8df700c8-255e-441d-9943-f5d65667ab93",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transfusion_years.reset_index(drop=True, inplace=True)\n",
    "df_transfusion_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "189af7cd-c986-42c3-abb4-dc6937b82e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_Vent_Pat_bed_matched_total.year = All_Vent_Pat_bed_matched_total.year.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "59b19801-2975-48fb-82ea-580ab399bc5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pat_id                        object\n",
       "csn                            int64\n",
       "har                          float64\n",
       "mrn                            int64\n",
       "study_id                     float64\n",
       "order_time                    object\n",
       "order_id                       int64\n",
       "blood_transfusion_type        object\n",
       "blood_product_name            object\n",
       "unit                           int64\n",
       "product_code                  object\n",
       "product_status                object\n",
       "transfusion_start             object\n",
       "transfusion_end               object\n",
       "transfusion_complete_time     object\n",
       "year                           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transfusion_years.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e4834019-8e3d-4c17-b817-070f1ca3a464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                             int64\n",
       "pat_id                           object\n",
       "csn                               int64\n",
       "har                             float64\n",
       "mrn                               int64\n",
       "study_id                        float64\n",
       "age                             float64\n",
       "zip_code                         object\n",
       "ed_arrival_source                object\n",
       "pre_admit_location               object\n",
       "ed_presentation_time             object\n",
       "hospital_admission_date_time     object\n",
       "hospital_discharge_date_time     object\n",
       "admit_reason                     object\n",
       "insurance_status                 object\n",
       "cpr_in_ed                        object\n",
       "total_icu_days                  float64\n",
       "total_vent_days                 float64\n",
       "total_hosp_days                 float64\n",
       "discharge_status                 object\n",
       "discharge_to                     object\n",
       "encounter_type                   object\n",
       "PAT_ID                           object\n",
       "year                              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_Vent_Pat_bed_matched_total.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d0b968d-e405-4c65-996a-5ef659ed2574",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_csn_transfusion = All_Vent_Pat_bed_matched_total.merge(df_transfusion_years,on='csn',how='left') #Intersection\n",
    "All_csn_transfusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "723a9e63-ba85-4fef-8e8a-926eefb78df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_csn_transfusion[['pat_id_x', 'pat_id_y','year_x','year_y','transfusion_start']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "168cdc6c-6231-47ce-b991-71d593c7c76e",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_csn_transfusion['year'] = All_csn_transfusion['year_x']\n",
    "All_csn_transfusion['pat_id'] = All_csn_transfusion['pat_id_x']\n",
    "All_csn_transfusion['age_yrs'] = All_csn_transfusion['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5ea145d0-a29a-45cc-a5e6-ab63f0efc6a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'pat_id_x', 'csn', 'har_x', 'mrn_x', 'study_id_x', 'age',\n",
       "       'zip_code', 'ed_arrival_source', 'pre_admit_location',\n",
       "       'ed_presentation_time', 'hospital_admission_date_time',\n",
       "       'hospital_discharge_date_time', 'admit_reason', 'insurance_status',\n",
       "       'cpr_in_ed', 'total_icu_days', 'total_vent_days', 'total_hosp_days',\n",
       "       'discharge_status', 'discharge_to', 'encounter_type', 'PAT_ID',\n",
       "       'year_x', 'pat_id_y', 'har_y', 'mrn_y', 'study_id_y', 'order_time',\n",
       "       'order_id', 'blood_transfusion_type', 'blood_product_name', 'unit',\n",
       "       'product_code', 'product_status', 'transfusion_start',\n",
       "       'transfusion_end', 'transfusion_complete_time', 'year_y', 'year',\n",
       "       'pat_id', 'age_yrs'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_csn_transfusion.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7f20a6c2-462b-47a8-b0c3-f3fa3366d88a",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_csn_transfusion_new_selColumns0 = All_csn_transfusion[['pat_id', 'csn','age_yrs',\n",
    "       'transfusion_start',  'transfusion_end', 'blood_product_name','hospital_admission_date_time','hospital_discharge_date_time',\n",
    "       'unit','year']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0b13cb68-e646-49df-88c5-e38bd563c89c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PREPARE FRESH FROZEN PLASMA                       24329\n",
       "TRANSFUSE RED BLOOD CELLS                         18188\n",
       "UNMATCHED RED BLOOD CELLS                          7933\n",
       "ANE TRANSFUSE RED BLOOD CELLS                      6298\n",
       "TRANSFUSE FRESH FROZEN PLASMA                      6065\n",
       "ANE TRANSFUSE FRESH FROZEN PLASMA                  4423\n",
       "UNMATCHED FRESH FROZEN PLASMA                      4249\n",
       "PREPARE PLATELET PHERESIS                          4249\n",
       "TRANSFUSE PLATELET PHERESIS                        2510\n",
       "PREPARE CRYOPRECIPITATE                            2460\n",
       "ANE TRANSFUSE PLATELET PHERESIS                     822\n",
       "MASSIVE TRANSFUSION PROTOCOL                        773\n",
       "TRANSFUSE CRYOPRECIPITATE                           734\n",
       "ANE TRANSFUSE CRYOPRECIPITATE                       548\n",
       "UNMATCHED PLATELETS                                 236\n",
       "UNMATCHED CRYOPRECIPITATE                           202\n",
       "FROZEN AND THAWED PLASMA                            173\n",
       "UNMATCHED WHOLE BLOOD                               112\n",
       "PLATELET CONCENTRATE                                 21\n",
       "UNMATCHED CRYO (FIBRIN GLUE)                         14\n",
       "ANE TRANSFUSE CRYOPRECIPITATE FOR FIBRIN GLUE        13\n",
       "TRANSFUSE FRESH FROZEN PLASMA (INFANT/NEONATE)        3\n",
       "TRANSFUSE CRYOPRECIPITATE FOR FIBRIN GLUE             3\n",
       "TRANSFUSE CRYO-POOR PLASMA (CPP)                      2\n",
       "TRANSFUSE RED BLOOD CELLS (INFANT/NEONATE)            1\n",
       "Name: blood_product_name, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_csn_transfusion_new_selColumns0.blood_product_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dbc0b021-a213-4d10-a733-c3cd280d2edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          NaN\n",
       "1          NaN\n",
       "2          NaN\n",
       "3          NaN\n",
       "4          NaN\n",
       "         ...  \n",
       "96987     True\n",
       "96988    False\n",
       "96989    False\n",
       "96990      NaN\n",
       "96991      NaN\n",
       "Name: blood_product_name, Length: 96992, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_csn_transfusion_new_selColumns0.blood_product_name.str.contains('red', case=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7f3e3ee-4a8d-4df6-89d8-6969960745d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5c442fbc-10cf-482d-9429-6b5f48c237d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12631\n",
      "39727\n",
      "12631\n"
     ]
    }
   ],
   "source": [
    "### Remove columns with generated Nans from merging/matching process\n",
    "print(All_csn_transfusion_new_selColumns0['transfusion_start'].isnull().sum())\n",
    "print(All_csn_transfusion_new_selColumns0['transfusion_end'].isnull().sum())\n",
    "print(All_csn_transfusion_new_selColumns0['blood_product_name'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25f91011-ed56-480d-a9ee-10796b08aa45",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_csn_transfusion_new = All_csn_transfusion_new_selColumns0[~(All_csn_transfusion_new_selColumns0['transfusion_start'].isnull())]\n",
    "All_csn_transfusion_new.reset_index(drop=True, inplace=True)\n",
    "All_csn_transfusion_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3c6a4e18-2db7-4365-9345-093b56f8d77f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pat_id</th>\n",
       "      <th>csn</th>\n",
       "      <th>age_yrs</th>\n",
       "      <th>transfusion_start</th>\n",
       "      <th>transfusion_end</th>\n",
       "      <th>blood_product_name</th>\n",
       "      <th>hospital_admission_date_time</th>\n",
       "      <th>hospital_discharge_date_time</th>\n",
       "      <th>unit</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35671</th>\n",
       "      <td>Z460384</td>\n",
       "      <td>1021000315</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2017-09-29 13:29:00.000</td>\n",
       "      <td>2017-09-29 13:48:00.000</td>\n",
       "      <td>TRANSFUSE RED BLOOD CELLS (INFANT/NEONATE)</td>\n",
       "      <td>2017-09-29 10:28:00.000</td>\n",
       "      <td>2017-10-01 21:45:00.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pat_id         csn  age_yrs        transfusion_start  \\\n",
       "35671  Z460384  1021000315     33.0  2017-09-29 13:29:00.000   \n",
       "\n",
       "               transfusion_end                          blood_product_name  \\\n",
       "35671  2017-09-29 13:48:00.000  TRANSFUSE RED BLOOD CELLS (INFANT/NEONATE)   \n",
       "\n",
       "      hospital_admission_date_time hospital_discharge_date_time  unit  year  \n",
       "35671      2017-09-29 10:28:00.000      2017-10-01 21:45:00.000   1.0  2017  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_csn_transfusion_new[All_csn_transfusion_new.blood_product_name == 'TRANSFUSE RED BLOOD CELLS (INFANT/NEONATE)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4e68812a-a4f8-470e-8137-8b009bebb3c0",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'All_csn_transfusion_new' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_16680/3770466054.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAll_csn_transfusion_new\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'transfusion_start'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'All_csn_transfusion_new' is not defined"
     ]
    }
   ],
   "source": [
    "print(All_csn_transfusion_new['transfusion_start'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c3d72703-4b70-4168-89ba-4acee031fb05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_csn_transfusion_new['unit'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4e9cd9e1-aa2c-4d1d-948c-6d64142ecee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total patients transfused-- #pat_ids: 5807, #CSNs: 6131,  #Years: [2016 2017 2018 2019 2020]\n"
     ]
    }
   ],
   "source": [
    "# debedWavT = len(All_csn_transfusion_new_selColumns0['deBedTime'].unique())\n",
    "\n",
    "patWavT = len(All_csn_transfusion_new['pat_id'].unique())\n",
    "csnWavT = len(All_csn_transfusion_new['csn'].unique())\n",
    "yrsWavT = All_csn_transfusion_new['year'].unique()\n",
    "print(f'Total patients transfused-- #pat_ids: {patWavT}, #CSNs: {csnWavT},  #Years: {yrsWavT}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "687f293c-c26f-414f-94c5-aec38564c8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRANSFUSE RED BLOOD CELLS                     18188\n",
       "UNMATCHED RED BLOOD CELLS                      7933\n",
       "ANE TRANSFUSE RED BLOOD CELLS                  6298\n",
       "TRANSFUSE RED BLOOD CELLS (INFANT/NEONATE)        1\n",
       "Name: blood_product_name, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_csn_transfusion_new[All_csn_transfusion_new.blood_product_name.str.contains('red', case=False)].blood_product_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e596bd04-1578-4c1a-9a3d-96dc06ae7175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TRANSFUSE RED BLOOD CELLS', 'ANE TRANSFUSE RED BLOOD CELLS',\n",
       "       'UNMATCHED RED BLOOD CELLS',\n",
       "       'TRANSFUSE RED BLOOD CELLS (INFANT/NEONATE)'], dtype=object)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_csn_transfusion_new[All_csn_transfusion_new.blood_product_name.str.contains('red', case=False)].blood_product_name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a8e387ec-9338-4d44-8736-c05eabdc7803",
   "metadata": {},
   "outputs": [],
   "source": [
    "RBC_list = ['TRANSFUSE RED BLOOD CELLS', 'ANE TRANSFUSE RED BLOOD CELLS',\n",
    "       'UNMATCHED RED BLOOD CELLS',\n",
    "       'TRANSFUSE RED BLOOD CELLS (INFANT/NEONATE)']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3173ab-179a-40aa-97a8-18741e8e5ca4",
   "metadata": {},
   "source": [
    "### Focus on only Red cell transfusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba6d2061-175e-4886-ae8c-c4b79e75b550",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_csn_transfusion_new_RBC = All_csn_transfusion_new[All_csn_transfusion_new.blood_product_name.isin(RBC_list)]\n",
    "All_csn_transfusion_new_RBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fb4676a2-b72a-4f1d-9cc8-002ad9563754",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_csn_transfusion_new_RBC.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e88258e5-db79-46a1-b441-8baca74cc6e2",
   "metadata": {},
   "source": [
    "## Dicarding the patients with MTP showing bleeding/traumatic conditions \n",
    "> Greater than 3 transfusions in 6 hours\n",
    "\n",
    "> **(a) Sort dataframe by csn sothat similar csn could be grouped, (b) check for each csn, whether it has repitions >3, (c) if it has, then check the timings if all are within 6 hrs window ---> discard this csn row entries completely**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a8abcec-a245-46d3-aff4-6eb76fc94f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_csn_transfusion_new_RBC2 = All_csn_transfusion_new_RBC.sort_values(by=['csn'])\n",
    "All_csn_transfusion_new_RBC2.reset_index(drop=True, inplace=True)\n",
    "All_csn_transfusion_new_RBC2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "28e9d9e5-d58b-49ae-b2a3-f01239881478",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5403/5403 [01:19<00:00, 67.65it/s]\n"
     ]
    }
   ],
   "source": [
    "MTP_csns = np.array([],dtype=int)\n",
    "unique_csns = All_csn_transfusion_new_RBC2['csn'].unique()\n",
    "for i in tqdm(unique_csns):\n",
    "    csn_count = list(All_csn_transfusion_new_RBC2['csn']).count(i)\n",
    "    if csn_count >= 3:\n",
    "        #print(csn_count)\n",
    "        idx = np.where(np.array(All_csn_transfusion_new_RBC2['csn'])==i)[0]\n",
    "        trans_times = pd.to_datetime(All_csn_transfusion_new_RBC2['transfusion_start'].iloc[idx]) \n",
    "        trans_times_sort = trans_times.sort_values().reset_index(drop=True)\n",
    "        #print(trans_times_sort)\n",
    "        diff_tt_sort = trans_times_sort.diff()\n",
    "        Tcount_under_6hrs = (diff_tt_sort<pd.Timedelta(hours=6)).sum()\n",
    "        if Tcount_under_6hrs >= 3:\n",
    "            #print('MTP patient: discard it')\n",
    "            MTP_csns = np.append(MTP_csns, i)\n",
    "       \n",
    "      \n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2b2ca946-80aa-48f3-872c-8e7a2956b0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1388"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(MTP_csns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963ab980-a768-4b0d-8f80-7a01a429d7b4",
   "metadata": {},
   "source": [
    "# MTP\n",
    "> **Discarding MTP csns by merging**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "352f7ed7-a9fa-4034-bb1e-a283bdedd5ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "MTP_csn_array=np.array([MTP_csns, MTP_csns+10000000000000]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4fc14da5-f825-47c9-ae80-d596c875c6c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>csn</th>\n",
       "      <th>arbitrary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1015788298</td>\n",
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       "      <th>1</th>\n",
       "      <td>1015792003</td>\n",
       "      <td>10001015792003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1015794504</td>\n",
       "      <td>10001015794504</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1015805995</td>\n",
       "      <td>10001015805995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1015826433</td>\n",
       "      <td>10001015826433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>1039522856</td>\n",
       "      <td>10001039522856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>1039704019</td>\n",
       "      <td>10001039704019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>1039704637</td>\n",
       "      <td>10001039704637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>1039723458</td>\n",
       "      <td>10001039723458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>1039729700</td>\n",
       "      <td>10001039729700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1388 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             csn       arbitrary\n",
       "0     1015788298  10001015788298\n",
       "1     1015792003  10001015792003\n",
       "2     1015794504  10001015794504\n",
       "3     1015805995  10001015805995\n",
       "4     1015826433  10001015826433\n",
       "...          ...             ...\n",
       "1383  1039522856  10001039522856\n",
       "1384  1039704019  10001039704019\n",
       "1385  1039704637  10001039704637\n",
       "1386  1039723458  10001039723458\n",
       "1387  1039729700  10001039729700\n",
       "\n",
       "[1388 rows x 2 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMTP_csns = pd.DataFrame(MTP_csn_array, index=np.arange(len(MTP_csn_array)), columns=['csn','arbitrary'])\n",
    "dfMTP_csns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6af014eb-ff27-48d9-9b6d-c6c399a057f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_csn_transfusion_new_RBC3 = All_csn_transfusion_new_RBC2.merge(dfMTP_csns,on='csn',how='left')#MTP_csns_series\n",
    "All_csn_transfusion_new_RBC3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b53642e8-f51e-4352-95c5-4acddc2ce7a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "32415    False\n",
       "32416    False\n",
       "32417    False\n",
       "32418    False\n",
       "32419     True\n",
       "Name: arbitrary, Length: 32420, dtype: bool"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_csn_transfusion_new_RBC3['arbitrary'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c0666e5-b65d-4bbd-8e6c-60543d3b4d92",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_csn_transfusion_new_RBC4 = All_csn_transfusion_new_RBC3[All_csn_transfusion_new_RBC3['arbitrary'].isnull()]\n",
    "All_csn_transfusion_new_RBC4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "4ca8a7b6-72c5-4359-b7e8-1eca0503abf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "IntersectionBeforeAfterT_RBC = All_csn_transfusion_new_RBC4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f214511-26f8-4c7f-a17e-e3bd7efb91b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "IntersectionBeforeAfterT_RBC.drop_duplicates(inplace=True)\n",
    "IntersectionBeforeAfterT_RBC.reset_index(drop=True, inplace=True)\n",
    "IntersectionBeforeAfterT_RBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8719a6cb-cb52-4d09-8309-20f06b9e6b02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total patients having waveforms collected during transfusion-- #pat_ids: 3802, #CSNs: 4015, #Years: [2016 2017 2018 2019 2020]\n"
     ]
    }
   ],
   "source": [
    "#debedWavT3 = len(All_csndata_during_transfusion_list['deBedTime'].unique())\n",
    "patWavT3 = len(IntersectionBeforeAfterT_RBC['pat_id'].unique())\n",
    "csnWavT3 = len(IntersectionBeforeAfterT_RBC['csn'].unique())\n",
    "yrsWavT3 = IntersectionBeforeAfterT_RBC['year'].unique()\n",
    "print(f'Total patients having waveforms collected during transfusion-- #pat_ids: {patWavT3}, #CSNs: {csnWavT3}, #Years: {yrsWavT3}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "877aabe0-612f-4481-9256-82668d1877cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(82002, 21) (18763, 24) (311734, 16) (84361, 10) (10767, 11)\n"
     ]
    }
   ],
   "source": [
    "print(bedPat.shape, All_Vent_Pat_bed_matched_total.shape, df_transfusion.shape, All_csn_transfusion_new.shape, IntersectionBeforeAfterT_RBC.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3fc7adff-d215-422b-a862-304e4ba4fe87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10767, 11)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntersectionBeforeAfterT_RBC.drop_duplicates(inplace=True)\n",
    "IntersectionBeforeAfterT_RBC.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4466593b-4083-48bb-85d2-af102a4561ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove redundency\n",
    "IntersectionBeforeAfterT_RBC.drop_duplicates(subset=['pat_id', 'csn','transfusion_start'], keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "98c0f0f3-2c72-444c-829f-9a7673f00769",
   "metadata": {},
   "outputs": [],
   "source": [
    "IntersectionBeforeAfterT_RBC.drop(['arbitrary'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "221915ef-b8f9-4ffc-a5de-10588522461c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10765, 10)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntersectionBeforeAfterT_RBC.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91ed0d3e-065e-4b46-a5f9-7a7fe09c40bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "IntersectionBeforeT_RBC_sorted = IntersectionBeforeAfterT_RBC.sort_values(by=['csn'])\n",
    "IntersectionBeforeT_RBC_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c16b03-7e2e-4682-a1b5-f8482371b52c",
   "metadata": {},
   "source": [
    "## Exclude RBC Transfusions followed last 24 hours for a CSN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06479990-a1d5-4a0d-884d-3ab9649f2bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = IntersectionBeforeT_RBC_sorted[['csn','transfusion_start']]\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e437dc29-b1bb-4692-91e2-a4dd2d6772f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4015it [00:15, 253.62it/s]\n"
     ]
    }
   ],
   "source": [
    "#exclude RBC Transfusions followed last 24 hours for a CSN\n",
    "\n",
    "valid_Transf = pd.DataFrame([],columns=['csn','transfusion_start'])\n",
    "csn_uni =x1.csn.unique()\n",
    "for i, csnn in tqdm(enumerate(csn_uni)):\n",
    "    idx = np.where(x1.csn == csnn)[0]\n",
    "    TT=pd.to_datetime(x1.iloc[idx]['transfusion_start']).sort_values()\n",
    "    dTT = TT.diff().dt.days\n",
    "    Tsep = dTT < 1 #Trasfusion separation less than 1 day\n",
    "    Tsep.iloc[0] = False\n",
    "    z = np.where(~Tsep)[0] #Indexes need to save for csns and transfusions\n",
    "    a = np.array([csnn]*len(z))\n",
    "    b = TT.iloc[z].values\n",
    "    z1 = pd.DataFrame([a,b],index=['csn','transfusion_start']).T\n",
    "    valid_Transf = pd.concat([valid_Transf, z1],ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6342f666-d93b-4f5b-8fba-54bdd11aacb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_Transf['Arb'] = 50\n",
    "valid_Transf['csn'] = valid_Transf['csn'].astype(int)\n",
    "valid_Transf['transfusion_start'] = pd.to_datetime(valid_Transf['transfusion_start'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "464fee5b-d08a-4982-9711-7baff754e105",
   "metadata": {},
   "outputs": [],
   "source": [
    "IntersectionBeforeT_RBC_sorted['csn'] = IntersectionBeforeT_RBC_sorted['csn'].astype(int)\n",
    "IntersectionBeforeT_RBC_sorted['transfusion_start'] = pd.to_datetime(IntersectionBeforeT_RBC_sorted['transfusion_start'])\n",
    "IntersectionBeforeT_RBC_sorted1 = pd.merge(IntersectionBeforeT_RBC_sorted, valid_Transf, on=['csn','transfusion_start'],how='left')\n",
    "IntersectionBeforeT_RBC_sorted2 = IntersectionBeforeT_RBC_sorted1[~IntersectionBeforeT_RBC_sorted1['Arb'].isnull()]\n",
    "IntersectionBeforeT_RBC_sorted2.drop_duplicates(inplace=True)\n",
    "IntersectionBeforeT_RBC_sorted2.reset_index(drop=True, inplace=True)\n",
    "IntersectionBeforeT_RBC_sorted2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ddb46cb0-86ee-49d9-b90c-b9ac8a5b138b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6894, 11)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntersectionBeforeT_RBC_sorted2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9bee7d-6911-4786-9a5c-0e38671778c3",
   "metadata": {},
   "source": [
    "## Add count of all product transfusions as a feature column in last 7 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c026b314-4332-4745-bcf2-14fede00c65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_transfusions = All_csn_transfusion_new[['csn','transfusion_start','blood_product_name']]\n",
    "all_transfusions['transfusion_start'] = pd.to_datetime(all_transfusions['transfusion_start'])\n",
    "all_transfusions['arbbb'] = 1000\n",
    "all_transfusions.drop_duplicates(inplace=True)\n",
    "all_transfusions.reset_index(drop=True, inplace=True)\n",
    "all_transfusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d248d392-2b90-441b-9740-729deb5ddb85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6894/6894 [02:46<00:00, 41.37it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntersectionBeforeT_RBC_sorted2['Tcount_7d'] = np.nan\n",
    "for i in tqdm(range(IntersectionBeforeT_RBC_sorted2.shape[0])):\n",
    "    est_TT =  IntersectionBeforeT_RBC_sorted2.iloc[i].transfusion_start\n",
    "    csnn = IntersectionBeforeT_RBC_sorted2.iloc[i].csn\n",
    "    \n",
    "    all_TT_csnn = all_transfusions[all_transfusions.csn==csnn].transfusion_start\n",
    "    Tdiff = est_TT - all_TT_csnn\n",
    "    # Tdiffd = Tdiff.dt.days\n",
    "    cond1 =  Tdiff>pd.Timedelta('0 days 00:00:00') \n",
    "    cond2 = Tdiff<pd.Timedelta('7 days 00:00:00')\n",
    "    Tcount1 = (cond1 & cond2).sum()\n",
    "    IntersectionBeforeT_RBC_sorted2['Tcount_7d'].iloc[i] = Tcount1\n",
    "    \n",
    "IntersectionBeforeT_RBC_sorted2['Tcount_7d'].isnull().sum()    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "fa3dbdea-05e0-47f5-8120-37e7f11d6622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1190    2016-01-01 01:51:00.000\n",
       "1191    2016-01-01 01:52:00.000\n",
       "1192    2016-01-01 02:34:00.000\n",
       "1193    2016-01-01 06:14:00.000\n",
       "1194    2016-01-04 11:25:00.000\n",
       "Name: transfusion_start, dtype: object"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# est_TT \n",
    "# all_TT_csnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "481348dc-798d-4921-b87d-1f9db39d7398",
   "metadata": {},
   "outputs": [],
   "source": [
    "IntersectionBeforeT_RBC_sorted2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "d5723105-1a42-4442-9cf0-bca77e463ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "IntersectionBeforeT_RBC_sorted2['year'] = IntersectionBeforeT_RBC_sorted2['year'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2912d2b-25f6-401d-b1d4-b6b896a2c8d1",
   "metadata": {},
   "source": [
    "# Checkpoint 1: Save CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f5e981ce-e453-42a7-bcf8-7511744d4a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# IntersectionBeforeT_RBC_sorted2.to_csv('DATA_RESULTS/df_GRADY_RBCT_beforeFeat_extraction_v3.csv')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# IntersectionBeforeT_RBC_sorted2 = pd.read_csv('DATA_RESULTS/df_GRADY_RBCT_beforeFeat_extraction_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "874dd53a-f071-4c8b-a943-70d4118757db",
   "metadata": {},
   "outputs": [],
   "source": [
    "''' **Designed for GRADY data** \n",
    "Assumptions are: (i) T: Transfusion time = 4 hrs\n",
    "Before T: 3-24 hrs\n",
    "After T: T+4 to T+4+tau hrs, where tau = 9 \n",
    "Include baseline Hb (as variable) just before the transfusion started'''\n",
    "\n",
    "\n",
    "def vitalsign_feature_extract(csn, year, transT, tau, row_index): #tau = 6 # (in hrs, duration for three afterT segments) 3, 6, 9, 12, 24 for 3 consecutive windows\n",
    "    \n",
    "    #AllFeatureTable = pd.DataFrame([])\n",
    "\n",
    "\n",
    "    ## Assessing super table\n",
    "    pkl_path = '/labs/kamaleswaranlab/MODS/Encounter_Pickles/gr/'+str(year)+'/'+str(csn)+'.pickle'\n",
    "    with open(pkl_path, 'rb') as pf:\n",
    "        patient_enc_dict = pickle.load(pf)\n",
    "    super_table = patient_enc_dict['super_table']\n",
    "    super_table['Time']=super_table.index\n",
    "    \n",
    "    super_table0 = super_table.pop('Time')\n",
    "    super_table.insert(0, 'Time', super_table0) #First column is \"Time\" now [moved from last to first column]\n",
    "    \n",
    "    \n",
    "    super_table.index=np.arange(len(super_table))\n",
    "    super_table.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "    #super_table.dropna(subset=super_table.iloc[:,1:].columns, how=\"all\", inplace=True)\n",
    "    \n",
    "    super_table.fio2.loc[super_table.fio2<0.21]=0.21\n",
    "\n",
    "    # SF_ratio = super_table['pf_sp']\n",
    "    hemogb = super_table['hemoglobin']\n",
    "    # PF_ratio = super_table['pf_pa']\n",
    "    spo2 = super_table['spo2']\n",
    "    fio2 = super_table['fio2']\n",
    "    pao2 = super_table['partial_pressure_of_oxygen_(pao2)']\n",
    "\n",
    "    if fio2.isnull().all():\n",
    "        fio2 = 0.21\n",
    "    else:\n",
    "        fio2.fillna(method='ffill', limit=24, inplace=True)\n",
    "        fio2.fillna(method='bfill', limit=24, inplace=True) \n",
    "        \n",
    "    SF_ratio = spo2/fio2\n",
    "    PF_ratio = pao2/fio2\n",
    "    \n",
    "    # super_table.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "    # super_table.dropna(subset=super_table.iloc[:,1:].columns, how=\"all\", inplace=True)\n",
    "    \n",
    "    \n",
    "    #Get these measures for beforeT and afterT segments\n",
    "    a = np.array((transT-super_table['Time']).dt.days)\n",
    "    \n",
    "    #Find the sign change to identify transfusion time\n",
    "    id1=np.where(a>=0)\n",
    "    beforeTid = id1[0][-1] #it will throw indexError if id1 is empty \n",
    "    id2=np.where(a<0)\n",
    "    afterTid = id2[0][0] + 4  #Considering 4 hrs of transfusion duration\n",
    "    \n",
    "    #Inter-sample interval is 1 hr in super_table\n",
    "    if beforeTid >= 23: \n",
    "        beforeT_Inds = np.arange(beforeTid-23, beforeTid+1)\n",
    "        Hb_beforeT0 = hemogb[beforeT_Inds]\n",
    "        SF_beforeT0 = SF_ratio[beforeT_Inds]\n",
    "        PF_beforeT0 = PF_ratio[beforeT_Inds]\n",
    "        spo2_beforeT0 = spo2[beforeT_Inds]\n",
    "\n",
    "#         #Removal of NAN entries\n",
    "        Hb_beforeT = Hb_beforeT0[~Hb_beforeT0.isnull()]      #Hb_beforeT0[np.logical_not(np.isnan(Hb_beforeT0))]\n",
    "#         SF_beforeT = SF_beforeT0[~SF_beforeT0.isnull()] \n",
    "#         PF_beforeT = PF_beforeT0[~PF_beforeT0.isnull()] \n",
    "#         spo2_beforeT = spo2_beforeT0[~spo2_beforeT0.isnull()] \n",
    "        \n",
    "        med_Hb_beforeT = Hb_beforeT0.median() #np.median(Hb_beforeT)\n",
    "        med_SF_beforeT = SF_beforeT0.median()\n",
    "        med_PF_beforeT = PF_beforeT0.median()\n",
    "        med_spo2_beforeT = spo2_beforeT0.median()\n",
    "        # print(('loop if '+str(row_index)+'->'+str(np.array(Hb_beforeT0))))\n",
    "        if Hb_beforeT.empty:\n",
    "            Hb = np.nan   ### Baseline Hb just before the transfusion started \n",
    "        else:\n",
    "            Hb = np.array(Hb_beforeT)[-1]   ### Baseline Hb just before the transfusion started \n",
    "        #print(SF_beforeT0) \n",
    "        #print(Hb_beforeT)\n",
    "        # print(('loop if '+str(row_index)))\n",
    "\n",
    "        my_table_BeforeT = pd.DataFrame({'Hb':Hb, 'med_spo2_beforeT':med_spo2_beforeT, 'med_Hb_beforeT':med_Hb_beforeT, \\\n",
    "                                         'med_SF_beforeT':med_SF_beforeT, 'med_PF_beforeT':med_PF_beforeT}, index=[0])\n",
    "        #my_table_BeforeT.reset_index(drop=True, inplace=True)\n",
    "    elif beforeTid > 2 and beforeTid < 23: \n",
    "        # print(('loop elif'+str(row_index)))\n",
    "        beforeT_Inds = np.arange(0, beforeTid+1)\n",
    "        Hb_beforeT0 = hemogb[beforeT_Inds]\n",
    "        SF_beforeT0 = SF_ratio[beforeT_Inds]\n",
    "        PF_beforeT0 = PF_ratio[beforeT_Inds]\n",
    "        spo2_beforeT0 = spo2[beforeT_Inds]\n",
    "        \n",
    "        #Removal of NAN entries\n",
    "        Hb_beforeT = Hb_beforeT0[~Hb_beforeT0.isnull()]      #Hb_beforeT0[np.logical_not(np.isnan(Hb_beforeT0))]\n",
    "#         SF_beforeT = SF_beforeT0[~SF_beforeT0.isnull()] \n",
    "#         PF_beforeT = PF_beforeT0[~PF_beforeT0.isnull()] \n",
    "#         spo2_beforeT = spo2_beforeT0[~spo2_beforeT0.isnull()] \n",
    "\n",
    "        med_Hb_beforeT = Hb_beforeT0.median() #np.median(Hb_beforeT)\n",
    "        med_SF_beforeT = SF_beforeT0.median()\n",
    "        med_PF_beforeT = PF_beforeT0.median()\n",
    "        med_spo2_beforeT = spo2_beforeT0.median()\n",
    "        if Hb_beforeT.empty:\n",
    "            Hb = np.nan   ### Baseline Hb just before the transfusion started \n",
    "        else:\n",
    "            Hb = np.array(Hb_beforeT)[-1]   ### Baseline Hb just before the transfusion started \n",
    "\n",
    "        \n",
    "        #print(SF_beforeT0)         \n",
    "        #print(Hb_beforeT)\n",
    "\n",
    "        my_table_BeforeT = pd.DataFrame({'Hb':Hb, 'med_spo2_beforeT':med_spo2_beforeT, 'med_Hb_beforeT':med_Hb_beforeT, \\\n",
    "                                         'med_SF_beforeT':med_SF_beforeT, 'med_PF_beforeT':med_PF_beforeT}, index=[0])                         \n",
    "        #my_table_BeforeT.reset_index(drop=True, inplace=True)  \n",
    "    else:\n",
    "        my_table_BeforeT = []\n",
    "        #print('Removed')\n",
    "        # print(('loop else'+str(row_index)))\n",
    " \n",
    "    #####-----------------------------------------\n",
    "     \n",
    "    \n",
    "\n",
    "    med_Hb_afterT = []\n",
    "    med_SF_afterT = []\n",
    "    med_PF_afterT = []\n",
    "    med_spo2_afterT = []\n",
    "    for tauId in range(3):\n",
    "        if afterTid+((tauId+1)*tau) <= len(hemogb): # for total tau hrs range post-transfusion\n",
    "            #print('flag1') \n",
    "            afterT_Inds = np.arange(afterTid+(tauId*tau), afterTid+((tauId+1)*tau))\n",
    "            Hb_afterT0 = hemogb[afterT_Inds]\n",
    "            SF_afterT0 = SF_ratio[afterT_Inds]\n",
    "            PF_afterT0 = PF_ratio[afterT_Inds]\n",
    "            spo2_afterT0 = spo2[afterT_Inds]\n",
    "            \n",
    "            \n",
    "\n",
    "            #Removal of NAN entries\n",
    "#             Hb_afterT = Hb_beforeT0[~Hb_beforeT0.isnull()]               #Hb_afterT0[np.logical_not(np.isnan(Hb_afterT0))]\n",
    "#             SF_afterT = SF_afterT0[np.logical_not(np.isnan(SF_afterT0))]\n",
    "#             PF_afterT = PF_afterT0[np.logical_not(np.isnan(PF_afterT0))]\n",
    "#             spo2_afterT = spo2_afterT0[np.logical_not(np.isnan(spo2_afterT0))]            \n",
    "\n",
    "            med_Hb_afterT.append(Hb_afterT0.median())    #med_Hb_afterT.append(np.median(Hb_afterT))\n",
    "            med_SF_afterT.append(SF_afterT0.median())\n",
    "            med_PF_afterT.append(PF_afterT0.median())\n",
    "            med_spo2_afterT.append(spo2_afterT0.median())\n",
    "            \n",
    "        elif afterTid+((tauId+1)*tau) > len(hemogb) and afterTid+((tauId+1)*tau) <= len(hemogb)-3: # for atleast having 3 hrs range post-transfusion\n",
    "            #print('flag2')\n",
    "            afterT_Inds = np.arange(afterTid+(tauId*tau), len(hemogb))\n",
    "            Hb_afterT0 = hemogb[afterT_Inds]\n",
    "            SF_afterT0 = SF_ratio[afterT_Inds]\n",
    "            PF_afterT0 = PF_ratio[afterT_Inds]\n",
    "            spo2_afterT0 = spo2[afterT_Inds]\n",
    "            \n",
    "             \n",
    "\n",
    "#             #Removal of NAN entries\n",
    "#             Hb_afterT = Hb_afterT0[np.logical_not(np.isnan(Hb_afterT0))]\n",
    "#             SF_afterT = SF_afterT0[np.logical_not(np.isnan(SF_afterT0))]\n",
    "#             PF_afterT = PF_afterT0[np.logical_not(np.isnan(PF_afterT0))]\n",
    "#             spo2_afterT = spo2_afterT0[np.logical_not(np.isnan(spo2_afterT0))]            \n",
    "\n",
    "            med_Hb_afterT.append(Hb_afterT0.median())    #med_Hb_afterT.append(np.median(Hb_afterT))\n",
    "            med_SF_afterT.append(SF_afterT0.median())\n",
    "            med_PF_afterT.append(PF_afterT0.median())\n",
    "            med_spo2_afterT.append(spo2_afterT0.median())\n",
    "            \n",
    "        else:\n",
    "            med_Hb_afterT.append(None)\n",
    "            med_SF_afterT.append(None)\n",
    "            med_PF_afterT.append(None)\n",
    "            med_spo2_afterT.append(None)\n",
    "            \n",
    "        \n",
    "        \n",
    "       \n",
    "    \n",
    "    ######-------------------------------------\n",
    "        \n",
    "#     # Tracking missing data at the output (Trying to find the reason of csns reduction after this fun executed)\n",
    "#     csn_yr_Ttime = str(csn)+'/'+str(year)+'/'+str(transT)\n",
    "#     if len(my_table_BeforeT) == 0: \n",
    "#         BeforeT_no3hrsdata = csn_yr_Ttime\n",
    "#     else: \n",
    "#         BeforeT_no3hrsdata = 0\n",
    "#         if pd.isnull(med_Hb_beforeT): med_Hb_beforeT_missing=csn_yr_Ttime\n",
    "#         else: med_Hb_beforeT_missing = 0\n",
    "\n",
    "#         if pd.isnull(med_spo2_beforeT): med_spo2_beforeT_missing=csn_yr_Ttime \n",
    "#         else: med_spo2_beforeT_missing = 0\n",
    "\n",
    "#         if pd.isnull(med_SF_beforeT): med_SF_beforeT_missing=csn_yr_Ttime \n",
    "#         else: med_SF_beforeT_missing = 0\n",
    "\n",
    "#         if pd.isnull(med_PF_beforeT): med_PF_beforeT_missing=csn_yr_Ttime \n",
    "#         else: med_PF_beforeT_missing = 0\n",
    "\n",
    "    \n",
    "#     if all(pd.isnull(med_Hb_afterT)): med_Hb_afterT_all3missing=csn_yr_Ttime \n",
    "#     else: med_Hb_afterT_all3missing = 0\n",
    "    \n",
    "#     if all(pd.isnull(med_spo2_afterT)): med_spo2_afterT_all3missing=csn_yr_Ttime \n",
    "#     else: med_spo2_afterT_all3missing = 0\n",
    "    \n",
    "#     if all(pd.isnull(med_SF_afterT)): med_SF_afterT_all3missing=csn_yr_Ttime \n",
    "#     else: med_SF_afterT_all3missing = 0\n",
    "    \n",
    "#     if all(pd.isnull(med_PF_afterT)): med_PF_afterT_all3missing=csn_yr_Ttime\n",
    "#     else: med_PF_afterT_all3missing = 0    \n",
    "    \n",
    "    \n",
    "    #Discarding patient with no data present beforeT 3hrs window\n",
    "    #and Discarding patient if all afterT features are empty (None/NaN) \n",
    "    Ls = len(my_table_BeforeT)\n",
    "    if Ls>0: #and (not pd.isnull(med_Hb_beforeT)) and (not pd.isnull(med_spo2_beforeT)) and (not pd.isnull(med_SF_beforeT)) and (not pd.isnull(med_PF_beforeT)) and (not all(pd.isnull(med_Hb_afterT))) and (not all(pd.isnull(med_spo2_afterT))) and (not all(pd.isnull(med_SF_afterT))) and (not all(pd.isnull(med_PF_afterT))): \n",
    "        z = pd.DataFrame(IntersectionBeforeT_RBC_sorted2.iloc[row_index]).T\n",
    "        z['discharge_to'] = patient_enc_dict['static_features']['discharge_to']\n",
    "        z['age'] = patient_enc_dict['static_features']['age']\n",
    "        z['gender'] = patient_enc_dict['static_features']['gender']\n",
    "        z['race'] = patient_enc_dict['static_features']['race']\n",
    "        z['ethnicity'] = patient_enc_dict['static_features']['ethnicity'] \n",
    "                \n",
    "        z.reset_index(drop=True, inplace=True)\n",
    "        my_table_BeforeT.reset_index(drop=True, inplace=True)\n",
    "        my_table_BeforeT1 = pd.concat([z, my_table_BeforeT], axis=1)\n",
    "        \n",
    "        MSPO2_afterT1, MSPO2_afterT2, MSPO2_afterT3 = med_spo2_afterT\n",
    "        MHB_afterT1, MHB_afterT2, MHB_afterT3 = med_Hb_afterT\n",
    "        MSF_afterT1, MSF_afterT2, MSF_afterT3 = med_SF_afterT\n",
    "        MPF_afterT1, MPF_afterT2, MPF_afterT3 = med_PF_afterT\n",
    "        my_table_AfterT = pd.DataFrame({'MSPO2_afterT1':MSPO2_afterT1, 'MSPO2_afterT2':MSPO2_afterT2, 'MSPO2_afterT3': MSPO2_afterT3,\n",
    "                                        'MHB_afterT1':MHB_afterT1, 'MHB_afterT2':MHB_afterT2, 'MHB_afterT3': MHB_afterT3,\n",
    "                                        'MSF_afterT1':MSF_afterT1, 'MSF_afterT2':MSF_afterT2, 'MSF_afterT3': MSF_afterT3,\n",
    "                                        'MPF_afterT1':MPF_afterT1, 'MPF_afterT2':MPF_afterT2, 'MPF_afterT3': MPF_afterT3\n",
    "                                       }, index=[0])                         \n",
    "        my_table_BeforeT1.reset_index(drop=True, inplace=True)\n",
    "        my_table_AfterT.reset_index(drop=True, inplace=True)\n",
    "        my_table_BeforeAfterT = pd.concat([my_table_BeforeT1, my_table_AfterT], axis=1)\n",
    "\n",
    "        #AllFeatureTable = AllFeatureTable.append(my_table_BeforeAfterT)\n",
    "        total_eleminated = 0\n",
    "    else:\n",
    "        my_table_BeforeAfterT = []\n",
    "        #total_eleminated = csn_yr_Ttime\n",
    "        \n",
    "        \n",
    "    \n",
    "    # missingD = pd.DataFrame({'total_eleminated':total_eleminated, 'BeforeT_no3hrsdata': BeforeT_no3hrsdata,\n",
    "    #                          'med_Hb_beforeT_missing': med_Hb_beforeT_missing})\n",
    "\n",
    "    return my_table_BeforeAfterT #, missingD\n",
    "        # Hb, med_spo2_beforeT,\\\n",
    "        # med_Hb_beforeT, med_SF_beforeT, med_PF_beforeT, med_spo2_afterT, med_Hb_afterT, med_SF_afterT, med_PF_afterT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc6204b7-1ea1-4233-baf4-389ea78ae8cd",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ''' **Designed for EMORY data** \n",
    "# Assumptions are: (i) T: Transfusion time = 4 hrs\n",
    "# Before T: 3-24 hrs\n",
    "# After T: T+4 to T+4+tau hrs, where tau = 9 \n",
    "# Include baseline Hb (as variable) just before the transfusion started'''\n",
    "\n",
    "\n",
    "# def vitalsign_feature_extract(csn, year, transT, tau, row_index): #tau = 6 # (in hrs, duration for three afterT segments) 3, 6, 9, 12, 24 for 3 consecutive windows\n",
    "    \n",
    "#     #AllFeatureTable = pd.DataFrame([])\n",
    "\n",
    "\n",
    "#     ## Assessing super table\n",
    "#     pkl_path = '/labs/kamaleswaranlab/MODS/Encounter_Pickles/em/'+str(year)+'/'+str(csn)+'.pickle'\n",
    "#     with open(pkl_path, 'rb') as pf:\n",
    "#         patient_enc_dict = pickle.load(pf)\n",
    "#     super_table = patient_enc_dict['super_table']\n",
    "#     super_table['Time']=super_table.index\n",
    "    \n",
    "#     super_table0 = super_table.pop('Time')\n",
    "#     super_table.insert(0, 'Time', super_table0) #First column is \"Time\" now [moved from last to first column]\n",
    "    \n",
    "#     super_table.index=np.arange(len(super_table))\n",
    "#     super_table.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "#     #super_table.dropna(subset=super_table.iloc[:,1:].columns, how=\"all\", inplace=True)\n",
    "\n",
    "#     SF_ratio = super_table['pf_sp']\n",
    "#     hemogb = super_table['hemoglobin']\n",
    "#     PF_ratio = super_table['pf_pa']\n",
    "#     spo2 = super_table['spo2']\n",
    "\n",
    "#     #Get these measures for beforeT and afterT segments\n",
    "#     a = np.array((transT-super_table['Time']).dt.days)\n",
    "    \n",
    "\n",
    "#     #Find the sign change to identify transfusion time\n",
    "#     id1=np.where(a>=0)\n",
    "#     beforeTid = id1[0][-1]\n",
    "#     id2=np.where(a<0)\n",
    "#     afterTid = id2[0][0] + 4  #Considering 4 hrs of transfusion duration\n",
    "    \n",
    "#     #Inter-sample interval is 1 hr in super_table\n",
    "#     if beforeTid >= 23: \n",
    "#         beforeT_Inds = np.arange(beforeTid-23, beforeTid+1)\n",
    "#         Hb_beforeT0 = hemogb[beforeT_Inds]\n",
    "#         SF_beforeT0 = SF_ratio[beforeT_Inds]\n",
    "#         PF_beforeT0 = PF_ratio[beforeT_Inds]\n",
    "#         spo2_beforeT0 = spo2[beforeT_Inds]\n",
    "\n",
    "# #         #Removal of NAN entries\n",
    "#         Hb_beforeT = Hb_beforeT0[~Hb_beforeT0.isnull()]      #Hb_beforeT0[np.logical_not(np.isnan(Hb_beforeT0))]\n",
    "# #         SF_beforeT = SF_beforeT0[~SF_beforeT0.isnull()] \n",
    "# #         PF_beforeT = PF_beforeT0[~PF_beforeT0.isnull()] \n",
    "# #         spo2_beforeT = spo2_beforeT0[~spo2_beforeT0.isnull()] \n",
    "\n",
    "#         med_Hb_beforeT = Hb_beforeT0.median() #np.median(Hb_beforeT)\n",
    "#         med_SF_beforeT = SF_beforeT0.median()\n",
    "#         med_PF_beforeT = PF_beforeT0.median()\n",
    "#         med_spo2_beforeT = spo2_beforeT0.median()\n",
    "#         Hb = np.array(Hb_beforeT)[-1]   ### Baseline Hb just before the transfusion started \n",
    "#         #print(SF_beforeT0) \n",
    "        \n",
    "#         #print(Hb_beforeT)\n",
    "\n",
    "#         my_table_BeforeT = pd.DataFrame({'Hb':Hb, 'med_spo2_beforeT':med_spo2_beforeT, 'med_Hb_beforeT':med_Hb_beforeT, \\\n",
    "#                                          'med_SF_beforeT':med_SF_beforeT, 'med_PF_beforeT':med_PF_beforeT}, index=[0])\n",
    "#         #my_table_BeforeT.reset_index(drop=True, inplace=True)\n",
    "#     elif beforeTid > 2 and beforeTid < 23: \n",
    "#         beforeT_Inds = np.arange(0, beforeTid+1)\n",
    "#         Hb_beforeT0 = hemogb[beforeT_Inds]\n",
    "#         SF_beforeT0 = SF_ratio[beforeT_Inds]\n",
    "#         PF_beforeT0 = PF_ratio[beforeT_Inds]\n",
    "#         spo2_beforeT0 = spo2[beforeT_Inds]\n",
    "        \n",
    "#         #Removal of NAN entries\n",
    "#         Hb_beforeT = Hb_beforeT0[~Hb_beforeT0.isnull()]      #Hb_beforeT0[np.logical_not(np.isnan(Hb_beforeT0))]\n",
    "# #         SF_beforeT = SF_beforeT0[~SF_beforeT0.isnull()] \n",
    "# #         PF_beforeT = PF_beforeT0[~PF_beforeT0.isnull()] \n",
    "# #         spo2_beforeT = spo2_beforeT0[~spo2_beforeT0.isnull()] \n",
    "\n",
    "#         med_Hb_beforeT = Hb_beforeT0.median() #np.median(Hb_beforeT)\n",
    "#         med_SF_beforeT = SF_beforeT0.median()\n",
    "#         med_PF_beforeT = PF_beforeT0.median()\n",
    "#         med_spo2_beforeT = spo2_beforeT0.median()\n",
    "#         Hb = np.array(Hb_beforeT)[-1]   ### Baseline Hb just before the transfusion started \n",
    "\n",
    "        \n",
    "#         #print(SF_beforeT0) \n",
    "        \n",
    "#         #print(Hb_beforeT)\n",
    "\n",
    "#         my_table_BeforeT = pd.DataFrame({'Hb':Hb, 'med_spo2_beforeT':med_spo2_beforeT, 'med_Hb_beforeT':med_Hb_beforeT, \\\n",
    "#                                          'med_SF_beforeT':med_SF_beforeT, 'med_PF_beforeT':med_PF_beforeT}, index=[0])                         \n",
    "#         #my_table_BeforeT.reset_index(drop=True, inplace=True)                                 \n",
    "#     else:\n",
    "#         my_table_BeforeT = []\n",
    "#         #print('Removed')\n",
    " \n",
    "#     #####-----------------------------------------\n",
    "     \n",
    "   \n",
    "\n",
    "#     med_Hb_afterT = []\n",
    "#     med_SF_afterT = []\n",
    "#     med_PF_afterT = []\n",
    "#     med_spo2_afterT = []\n",
    "#     for tauId in range(3):\n",
    "#         if afterTid+((tauId+1)*tau) <= len(hemogb): # for total tau hrs range post-transfusion\n",
    "#             #print('flag1') \n",
    "#             afterT_Inds = np.arange(afterTid+(tauId*tau), afterTid+((tauId+1)*tau))\n",
    "#             Hb_afterT0 = hemogb[afterT_Inds]\n",
    "#             SF_afterT0 = SF_ratio[afterT_Inds]\n",
    "#             PF_afterT0 = PF_ratio[afterT_Inds]\n",
    "#             spo2_afterT0 = spo2[afterT_Inds]\n",
    "            \n",
    "            \n",
    "\n",
    "#             #Removal of NAN entries\n",
    "# #             Hb_afterT = Hb_beforeT0[~Hb_beforeT0.isnull()]               #Hb_afterT0[np.logical_not(np.isnan(Hb_afterT0))]\n",
    "# #             SF_afterT = SF_afterT0[np.logical_not(np.isnan(SF_afterT0))]\n",
    "# #             PF_afterT = PF_afterT0[np.logical_not(np.isnan(PF_afterT0))]\n",
    "# #             spo2_afterT = spo2_afterT0[np.logical_not(np.isnan(spo2_afterT0))]            \n",
    "\n",
    "#             med_Hb_afterT.append(Hb_afterT0.median())    #med_Hb_afterT.append(np.median(Hb_afterT))\n",
    "#             med_SF_afterT.append(SF_afterT0.median())\n",
    "#             med_PF_afterT.append(PF_afterT0.median())\n",
    "#             med_spo2_afterT.append(spo2_afterT0.median())\n",
    "            \n",
    "#         elif afterTid+((tauId+1)*tau) > len(hemogb) and afterTid+((tauId+1)*tau) <= len(hemogb)-3: # for atleast having 3 hrs range post-transfusion\n",
    "#             #print('flag2')\n",
    "#             afterT_Inds = np.arange(afterTid+(tauId*tau), len(hemogb))\n",
    "#             Hb_afterT0 = hemogb[afterT_Inds]\n",
    "#             SF_afterT0 = SF_ratio[afterT_Inds]\n",
    "#             PF_afterT0 = PF_ratio[afterT_Inds]\n",
    "#             spo2_afterT0 = spo2[afterT_Inds]\n",
    "            \n",
    "             \n",
    "\n",
    "# #             #Removal of NAN entries\n",
    "# #             Hb_afterT = Hb_afterT0[np.logical_not(np.isnan(Hb_afterT0))]\n",
    "# #             SF_afterT = SF_afterT0[np.logical_not(np.isnan(SF_afterT0))]\n",
    "# #             PF_afterT = PF_afterT0[np.logical_not(np.isnan(PF_afterT0))]\n",
    "# #             spo2_afterT = spo2_afterT0[np.logical_not(np.isnan(spo2_afterT0))]            \n",
    "\n",
    "#             med_Hb_afterT.append(Hb_afterT0.median())    #med_Hb_afterT.append(np.median(Hb_afterT))\n",
    "#             med_SF_afterT.append(SF_afterT0.median())\n",
    "#             med_PF_afterT.append(PF_afterT0.median())\n",
    "#             med_spo2_afterT.append(spo2_afterT0.median())\n",
    "            \n",
    "#         else:\n",
    "#             med_Hb_afterT.append(None)\n",
    "#             med_SF_afterT.append(None)\n",
    "#             med_PF_afterT.append(None)\n",
    "#             med_spo2_afterT.append(None)\n",
    "            \n",
    "        \n",
    "        \n",
    "       \n",
    "    \n",
    "#     ######-------------------------------------\n",
    "        \n",
    "# #     # Tracking missing data at the output (Trying to find the reason of csns reduction after this fun executed)\n",
    "# #     csn_yr_Ttime = str(csn)+'/'+str(year)+'/'+str(transT)\n",
    "# #     if len(my_table_BeforeT) == 0: \n",
    "# #         BeforeT_no3hrsdata = csn_yr_Ttime\n",
    "# #     else: \n",
    "# #         BeforeT_no3hrsdata = 0\n",
    "# #         if pd.isnull(med_Hb_beforeT): med_Hb_beforeT_missing=csn_yr_Ttime\n",
    "# #         else: med_Hb_beforeT_missing = 0\n",
    "\n",
    "# #         if pd.isnull(med_spo2_beforeT): med_spo2_beforeT_missing=csn_yr_Ttime \n",
    "# #         else: med_spo2_beforeT_missing = 0\n",
    "\n",
    "# #         if pd.isnull(med_SF_beforeT): med_SF_beforeT_missing=csn_yr_Ttime \n",
    "# #         else: med_SF_beforeT_missing = 0\n",
    "\n",
    "# #         if pd.isnull(med_PF_beforeT): med_PF_beforeT_missing=csn_yr_Ttime \n",
    "# #         else: med_PF_beforeT_missing = 0\n",
    "\n",
    "    \n",
    "# #     if all(pd.isnull(med_Hb_afterT)): med_Hb_afterT_all3missing=csn_yr_Ttime \n",
    "# #     else: med_Hb_afterT_all3missing = 0\n",
    "    \n",
    "# #     if all(pd.isnull(med_spo2_afterT)): med_spo2_afterT_all3missing=csn_yr_Ttime \n",
    "# #     else: med_spo2_afterT_all3missing = 0\n",
    "    \n",
    "# #     if all(pd.isnull(med_SF_afterT)): med_SF_afterT_all3missing=csn_yr_Ttime \n",
    "# #     else: med_SF_afterT_all3missing = 0\n",
    "    \n",
    "# #     if all(pd.isnull(med_PF_afterT)): med_PF_afterT_all3missing=csn_yr_Ttime\n",
    "# #     else: med_PF_afterT_all3missing = 0    \n",
    "    \n",
    "    \n",
    "#     #Discarding patient with no data present beforeT 3hrs window\n",
    "#     #and Discarding patient if all afterT features are empty (None/NaN) \n",
    "#     Ls = len(my_table_BeforeT)\n",
    "#     if Ls>0 and (not pd.isnull(med_Hb_beforeT)) and (not pd.isnull(med_spo2_beforeT)) and (not pd.isnull(med_SF_beforeT)) and (not pd.isnull(med_PF_beforeT)) and (not all(pd.isnull(med_Hb_afterT))) and (not all(pd.isnull(med_spo2_afterT))) and (not all(pd.isnull(med_SF_afterT))) and (not all(pd.isnull(med_PF_afterT))): \n",
    "#         z = pd.DataFrame(IntersectionBeforeT_RBC_sorted2.iloc[row_index]).T\n",
    "#         z['discharge_to'] = patient_enc_dict['static_features']['discharge_to']\n",
    "#         z['age'] = patient_enc_dict['static_features']['age']\n",
    "#         z['gender'] = patient_enc_dict['static_features']['gender']\n",
    "#         z['race'] = patient_enc_dict['static_features']['race']\n",
    "#         z['ethnicity'] = patient_enc_dict['static_features']['ethnicity'] \n",
    "                \n",
    "#         z.reset_index(drop=True, inplace=True)\n",
    "#         my_table_BeforeT.reset_index(drop=True, inplace=True)\n",
    "#         my_table_BeforeT1 = pd.concat([z, my_table_BeforeT], axis=1)\n",
    "        \n",
    "#         MSPO2_afterT1, MSPO2_afterT2, MSPO2_afterT3 = med_spo2_afterT\n",
    "#         MHB_afterT1, MHB_afterT2, MHB_afterT3 = med_Hb_afterT\n",
    "#         MSF_afterT1, MSF_afterT2, MSF_afterT3 = med_SF_afterT\n",
    "#         MPF_afterT1, MPF_afterT2, MPF_afterT3 = med_PF_afterT\n",
    "#         my_table_AfterT = pd.DataFrame({'MSPO2_afterT1':MSPO2_afterT1, 'MSPO2_afterT2':MSPO2_afterT2, 'MSPO2_afterT3': MSPO2_afterT3,\n",
    "#                                         'MHB_afterT1':MHB_afterT1, 'MHB_afterT2':MHB_afterT2, 'MHB_afterT3': MHB_afterT3,\n",
    "#                                         'MSF_afterT1':MSF_afterT1, 'MSF_afterT2':MSF_afterT2, 'MSF_afterT3': MSF_afterT3,\n",
    "#                                         'MPF_afterT1':MPF_afterT1, 'MPF_afterT2':MPF_afterT2, 'MPF_afterT3': MPF_afterT3\n",
    "#                                        }, index=[0])                         \n",
    "#         my_table_BeforeT1.reset_index(drop=True, inplace=True)\n",
    "#         my_table_AfterT.reset_index(drop=True, inplace=True)\n",
    "#         my_table_BeforeAfterT = pd.concat([my_table_BeforeT1, my_table_AfterT], axis=1)\n",
    "\n",
    "#         #AllFeatureTable = AllFeatureTable.append(my_table_BeforeAfterT)\n",
    "#         total_eleminated = 0\n",
    "#     else:\n",
    "#         my_table_BeforeAfterT = []\n",
    "#         #total_eleminated = csn_yr_Ttime\n",
    "        \n",
    "        \n",
    "    \n",
    "#     # missingD = pd.DataFrame({'total_eleminated':total_eleminated, 'BeforeT_no3hrsdata': BeforeT_no3hrsdata,\n",
    "#     #                          'med_Hb_beforeT_missing': med_Hb_beforeT_missing})\n",
    "\n",
    "#     return my_table_BeforeAfterT #, missingD\n",
    "#         # Hb, med_spo2_beforeT,\\\n",
    "#         # med_Hb_beforeT, med_SF_beforeT, med_PF_beforeT, med_spo2_afterT, med_Hb_afterT, med_SF_afterT, med_PF_afterT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e5a54be-11d1-42e7-a76c-0252f28438d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6894\n"
     ]
    }
   ],
   "source": [
    "tau = 9 #int(input('Enter tau = ')) #(in hrs, duration for three afterT segments) 3, 6, 9, 12, 24 for three consecutive windows\n",
    "\n",
    "L = len(IntersectionBeforeT_RBC_sorted2)\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "790e72ad-fed7-4157-a55c-968cf425fdc3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed in: 90.8263647556305 sec\n"
     ]
    }
   ],
   "source": [
    "##Using Parallelization: Multiprocessing\n",
    "\n",
    "t1 = time.time()\n",
    "def f1(i):\n",
    "    #for i,csn in tqdm(enumerate(IntersectionBeforeDuringAfter_RBC['csn'] )):  #.iloc[:3]\n",
    "    AllFeatureTable = pd.DataFrame([])\n",
    "    missingdata = pd.DataFrame([])\n",
    "    try: \n",
    "        transT = pd.to_datetime(IntersectionBeforeT_RBC_sorted2.iloc[i]['transfusion_start'])\n",
    "        year = pd.to_datetime(IntersectionBeforeT_RBC_sorted2.iloc[i]['transfusion_start']).year #dt.year\n",
    "        csn = IntersectionBeforeT_RBC_sorted2.iloc[i]['csn']\n",
    "        \n",
    "\n",
    "        my_table_BeforeAfterT = vitalsign_feature_extract(csn, year, transT, tau, row_index=i) #, missingD\n",
    "        #print(AllFeatureTable)\n",
    "        \n",
    "        # print(('try '+str(i)))\n",
    "        \n",
    "        AllFeatureTable = AllFeatureTable.append(my_table_BeforeAfterT)\n",
    "        #missingdata.append(missingD)\n",
    "            \n",
    "    except IndexError:\n",
    "        # print(('IndexError '+str(i)))\n",
    "        pass\n",
    "        #IndexErrorCases.append(i) #Record number\n",
    "    except ValueError:\n",
    "        # print(('ValueError '+str(i)))\n",
    "        pass\n",
    "        #ValueErrorCases.append(i)\n",
    "    except FileNotFoundError:\n",
    "        # print(('FileNotFoundError '+str(i)))\n",
    "        pass\n",
    "    except EOFError:\n",
    "        print(('EOFerror '+str(i)))\n",
    "        pass\n",
    "        #FileNotFoundErrorCases.append(i)\n",
    "    except pickle.UnpicklingError:\n",
    "        print(('UnpicklingError '+str(i)))\n",
    "        pass\n",
    "    # except TypeError:\n",
    "    #     print('TypeError')\n",
    "    #     pass\n",
    "    \n",
    "    return AllFeatureTable #, missingdata\n",
    "\n",
    "\n",
    "p1 = Pool(12)\n",
    "\n",
    "# LLL=[0, 1, 2, 3, 4, 5, 19679, 18067, 19189, 20703]\n",
    "results1 = p1.map(f1, range(L))  #range(L)\n",
    "# results1, DF2 = zip(*p1.map(f1, range(10)))  #range(L)\n",
    "p1.close()\n",
    "p1.join()        \n",
    "medianAllFeatureTable = pd.concat(results1, ignore_index=True)\n",
    "# missing_Data = pd.concat(DF2, ignore_index=True)\n",
    "t2 = time.time()\n",
    "print('Completed in: %s sec'%(t2 - t1)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "623afdc9-1652-4df4-8bc0-da73f5435f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "medianAllFeatureTable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a978f380-13f7-4f92-8b88-20571d282c7f",
   "metadata": {},
   "source": [
    "# Checkpoint 2: Save CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e649a9ca-18a7-4475-91b5-053b581a010c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# medianAllFeatureTable.to_csv('DATA_RESULTS/df_GRADY_RBCT_extractedFeat_NoImputation_tau9_v3.csv')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# medianAllFeatureTable = pd.read_csv('DATA_RESULTS/df_GRADY_RBCT_extractedFeat_NoImputation_tau9_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bfbf77d9-df64-4b6a-ac48-fd12f9092d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hb                    21\n",
       "med_spo2_beforeT       6\n",
       "med_Hb_beforeT        21\n",
       "med_SF_beforeT       909\n",
       "med_PF_beforeT      3267\n",
       "MSPO2_afterT1         94\n",
       "MSPO2_afterT2        189\n",
       "MSPO2_afterT3        296\n",
       "MHB_afterT1          113\n",
       "MHB_afterT2          217\n",
       "MHB_afterT3          331\n",
       "MSF_afterT1         1005\n",
       "MSF_afterT2         1060\n",
       "MSF_afterT3         1169\n",
       "MPF_afterT1         3436\n",
       "MPF_afterT2         3511\n",
       "MPF_afterT3         3708\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medianAllFeatureTable.loc[:,'Hb':].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ff106444-6f94-4d1a-8d2d-41143df8d7ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                       object\n",
       "pat_id                           object\n",
       "csn                              object\n",
       "age_yrs                          object\n",
       "transfusion_start                object\n",
       "transfusion_end                  object\n",
       "blood_product_name               object\n",
       "hospital_admission_date_time     object\n",
       "hospital_discharge_date_time     object\n",
       "unit                             object\n",
       "year                             object\n",
       "Arb                              object\n",
       "Tcount_7d                        object\n",
       "discharge_to                     object\n",
       "age                             float64\n",
       "gender                           object\n",
       "race                             object\n",
       "ethnicity                        object\n",
       "Hb                              float64\n",
       "med_spo2_beforeT                float64\n",
       "med_Hb_beforeT                  float64\n",
       "med_SF_beforeT                  float64\n",
       "med_PF_beforeT                  float64\n",
       "MSPO2_afterT1                    object\n",
       "MSPO2_afterT2                    object\n",
       "MSPO2_afterT3                    object\n",
       "MHB_afterT1                      object\n",
       "MHB_afterT2                      object\n",
       "MHB_afterT3                      object\n",
       "MSF_afterT1                      object\n",
       "MSF_afterT2                      object\n",
       "MSF_afterT3                      object\n",
       "MPF_afterT1                      object\n",
       "MPF_afterT2                      object\n",
       "MPF_afterT3                      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medianAllFeatureTable.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d11883ab-e114-4fdc-8bfb-3232e2c94e27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# del medianAllFeatureTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55573d56-5069-437c-a27e-4e54e06edd0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0 0\n",
      "pat_id 0\n",
      "csn 0\n",
      "age_yrs 0\n",
      "transfusion_start 0\n",
      "transfusion_end 410\n",
      "blood_product_name 0\n",
      "hospital_admission_date_time 0\n",
      "hospital_discharge_date_time 0\n",
      "unit 0\n",
      "year 0\n",
      "Arb 0\n",
      "Tcount_7d 0\n",
      "discharge_to 5689\n",
      "age 0\n",
      "gender 0\n",
      "race 0\n",
      "ethnicity 0\n",
      "Hb 21\n",
      "med_spo2_beforeT 6\n",
      "med_Hb_beforeT 21\n",
      "med_SF_beforeT 909\n",
      "med_PF_beforeT 3267\n",
      "MSPO2_afterT1 94\n",
      "MSPO2_afterT2 189\n",
      "MSPO2_afterT3 296\n",
      "MHB_afterT1 113\n",
      "MHB_afterT2 217\n",
      "MHB_afterT3 331\n",
      "MSF_afterT1 1005\n",
      "MSF_afterT2 1060\n",
      "MSF_afterT3 1169\n",
      "MPF_afterT1 3436\n",
      "MPF_afterT2 3511\n",
      "MPF_afterT3 3708\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Missing values (%)\n",
    "'''\n",
    "for i in medianAllFeatureTable.columns:\n",
    "    print(i, medianAllFeatureTable[i].isnull().sum()) #/medianAllFeatureTable.shape[0]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "23b88481-4000-4b4b-ae20-cf34c4f16481",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 424.57it/s]\n"
     ]
    }
   ],
   "source": [
    "# fio2.fillna(method='ffill', limit=24, inplace=True)\n",
    "medI_medianAllFeatureTable = medianAllFeatureTable\n",
    "indx = list(medI_medianAllFeatureTable.columns).index('Hb')\n",
    "for k in tqdm(range(indx,len(medI_medianAllFeatureTable.columns))):\n",
    "    medI_medianAllFeatureTable.iloc[:,k].fillna(medI_medianAllFeatureTable.iloc[:,k].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "113263f1-001b-42af-b80d-09f355490b06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0 0\n",
      "pat_id 0\n",
      "csn 0\n",
      "age_yrs 0\n",
      "transfusion_start 0\n",
      "transfusion_end 410\n",
      "blood_product_name 0\n",
      "hospital_admission_date_time 0\n",
      "hospital_discharge_date_time 0\n",
      "unit 0\n",
      "year 0\n",
      "Arb 0\n",
      "Tcount_7d 0\n",
      "discharge_to 5689\n",
      "age 0\n",
      "gender 0\n",
      "race 0\n",
      "ethnicity 0\n",
      "Hb 0\n",
      "med_spo2_beforeT 6\n",
      "med_Hb_beforeT 21\n",
      "med_SF_beforeT 909\n",
      "med_PF_beforeT 3267\n",
      "MSPO2_afterT1 0\n",
      "MSPO2_afterT2 0\n",
      "MSPO2_afterT3 0\n",
      "MHB_afterT1 0\n",
      "MHB_afterT2 0\n",
      "MHB_afterT3 0\n",
      "MSF_afterT1 0\n",
      "MSF_afterT2 0\n",
      "MSF_afterT3 0\n",
      "MPF_afterT1 0\n",
      "MPF_afterT2 0\n",
      "MPF_afterT3 0\n"
     ]
    }
   ],
   "source": [
    "for i in medI_medianAllFeatureTable.columns:\n",
    "    print(i, medI_medianAllFeatureTable[i].isnull().sum())#/medI_medianAllFeatureTable.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "365820e4-4dab-4cd8-999d-62dd87506380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83      99.0\n",
       "632     99.0\n",
       "934     99.0\n",
       "1724    99.0\n",
       "2711    99.0\n",
       "2712    99.0\n",
       "Name: med_spo2_beforeT, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medI_medianAllFeatureTable[medI_medianAllFeatureTable.med_spo2_beforeT.isnull()].med_spo2_beforeT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15e7524f-02dd-4741-b0cb-13309765426d",
   "metadata": {},
   "outputs": [],
   "source": [
    "medI_medianAllFeatureTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e97fc8f-cb5d-4206-aaa9-869bec7bbb4a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42b4555f-0c5d-415d-ae0e-bd01b25f0bdc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c26960f7-e1b2-465d-aec9-6d82d11db0b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def delta_calc(df):\n",
    "    df['del_SPO2_1']=df['MSPO2_afterT1'] - df['med_spo2_beforeT']\n",
    "    df['del_SPO2_2']=df['MSPO2_afterT2'] - df['med_spo2_beforeT']\n",
    "    df['del_SPO2_3']=df['MSPO2_afterT3'] - df['med_spo2_beforeT']\n",
    "\n",
    "    df['del_Hb_1']=df['MHB_afterT1'] - df['med_Hb_beforeT']\n",
    "    df['del_Hb_2']=df['MHB_afterT2'] - df['med_Hb_beforeT']\n",
    "    df['del_Hb_3']=df['MHB_afterT3'] - df['med_Hb_beforeT']\n",
    "\n",
    "    df['del_SF_1']=df['MSF_afterT1'] - df['med_SF_beforeT']\n",
    "    df['del_SF_2']=df['MSF_afterT2'] - df['med_SF_beforeT']\n",
    "    df['del_SF_3']=df['MSF_afterT3'] - df['med_SF_beforeT']\n",
    "\n",
    "    df['del_PF_1']=df['MPF_afterT1'] - df['med_PF_beforeT']\n",
    "    df['del_PF_2']=df['MPF_afterT2'] - df['med_PF_beforeT']\n",
    "    df['del_PF_3']=df['MPF_afterT3'] - df['med_PF_beforeT']\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "af6b6336-c5f0-4f4e-926f-1417b1760e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "medianAllFeatureTable = delta_calc(medianAllFeatureTable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bb813401-1ab0-4546-a979-8b11851ec6d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "medI_medianAllFeatureTable = delta_calc(medI_medianAllFeatureTable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7611d258-ebdb-4168-8233-7b6ff57dcc54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6375, 47)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medI_medianAllFeatureTable.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "23a605d3-6f7f-496d-b810-2b8536b4b6c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3757"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(medI_medianAllFeatureTable['csn'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4f320006-7264-4e32-a387-8fd64060de84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3552"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(medI_medianAllFeatureTable['pat_id'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b0ca8dc-bab7-4465-8f4f-c1e45896ec1a",
   "metadata": {},
   "source": [
    "# Checkpoint 3: **writing in CSV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5ee2782f-9f90-4fa7-862d-6121ccd3c49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #writing in CSV\n",
    "# medI_medianAllFeatureTable.to_csv('DATA_RESULTS/df_GRADY_RBCT_extractedFeat_medImputated_tau9_v3.csv')\n",
    "# medianAllFeatureTable.to_csv('DATA_RESULTS/df_GRADY_RBCT_extractedFeat_NoImputated_tau9_v3.csv')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "93b17edb-43d4-4ae8-a133-cdd7259ebab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'AfterT3 $\\\\Delta$Hb (g/dL)')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize =(18, 4))\n",
    "\n",
    "axs[0].scatter(medI_medianAllFeatureTable['del_SPO2_1'], medI_medianAllFeatureTable['del_Hb_1'],color='black')\n",
    "axs[0].set_title('$\\Delta$SpO$_2$ vs $\\Delta$Hb: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[0].set_xlabel('AfterT1 $\\Delta$SpO$_2 (\\%)$')\n",
    "axs[0].set_ylabel('AfterT1 $\\Delta$Hb (g/dL)')\n",
    "\n",
    "axs[1].scatter(medI_medianAllFeatureTable['del_SPO2_2'], medI_medianAllFeatureTable['del_Hb_2'],color='black')\n",
    "axs[1].set_title('$\\Delta$SpO$_2$ vs $\\Delta$Hb: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[1].set_xlabel('AfterT2 $\\Delta$SpO$_2 (\\%)$')\n",
    "axs[1].set_ylabel('AfterT2 $\\Delta$Hb (g/dL)')\n",
    "\n",
    "axs[2].scatter(medI_medianAllFeatureTable['del_SPO2_3'], medI_medianAllFeatureTable['del_Hb_3'],color='black')\n",
    "axs[2].set_title('$\\Delta$SpO$_2$ vs $\\Delta$Hb: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[2].set_xlabel('AfterT3 $\\Delta$SpO$_2 (\\%)$')\n",
    "axs[2].set_ylabel('AfterT3 $\\Delta$Hb (g/dL)')\n",
    "\n",
    "# plt.savefig(\"DATA_RESULTS/delSpO2vsdelHb_RedCellsT.eps\", format=\"eps\", bbox_inches=\"tight\", dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4e13c88a-f270-4794-999b-8d7d7e54fe7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Hb (g/dL) just before transfusion')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize =(18, 4))\n",
    "\n",
    "axs[0].scatter(medianAllFeatureTable['del_SPO2_1'], medianAllFeatureTable['Hb'],color='black')\n",
    "axs[0].set_title('$\\Delta$SpO$_2$ vs Hb: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[0].set_xlabel('AfterT1 $\\Delta$SpO$_2 (\\%)$')\n",
    "axs[0].set_ylabel('Hb (g/dL) just before transfusion')\n",
    "\n",
    "axs[1].scatter(medianAllFeatureTable['del_SPO2_2'], medianAllFeatureTable['Hb'],color='black')\n",
    "axs[1].set_title('$\\Delta$SpO$_2$ vs Hb: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[1].set_xlabel('AfterT2 $\\Delta$SpO$_2 (\\%)$')\n",
    "axs[1].set_ylabel('Hb (g/dL) just before transfusion')\n",
    "\n",
    "axs[2].scatter(medianAllFeatureTable['del_SPO2_3'], medianAllFeatureTable['Hb'],color='black')\n",
    "axs[2].set_title('$\\Delta$SpO$_2$ vs Hb: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[2].set_xlabel('AfterT3 $\\Delta$SpO$_2 (\\%)$')\n",
    "axs[2].set_ylabel('Hb (g/dL) just before transfusion')\n",
    "\n",
    "# plt.savefig('DATA_RESULTS/delSpO2vsHb_RedCellsT.png', format=\"png\", bbox_inches=\"tight\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0d3759a4-5689-4c8c-8475-52184983cc0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b04eb6fd670>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize =(6, 4))\n",
    "\n",
    "axs.plot(medianAllFeatureTable['del_SPO2_1'], medianAllFeatureTable['Hb'],'o',color='black')\n",
    "axs.plot(medianAllFeatureTable['del_SPO2_2'], medianAllFeatureTable['Hb'],'o',color='red')\n",
    "axs.plot(medianAllFeatureTable['del_SPO2_3'], medianAllFeatureTable['Hb'],'o',color='blue')\n",
    "axs.set_title('$\\Delta$SpO$_2$ vs Hb: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs.set_xlabel('Three regions $\\Delta$SpO$_2 (\\%)$ afterT')\n",
    "axs.set_ylabel('Hb (g/dL) just before transfusion')\n",
    "axs.legend(['afterT1 $\\Delta$SpO2 vs Hb','afterT2 $\\Delta$SpO2 vs Hb','afterT3 $\\Delta$SpO2 vs Hb'])\n",
    "\n",
    "# plt.savefig('DATA_RESULTS/ThreeRegions_delSpO2vsHb_RedCellsT.eps', format=\"eps\", bbox_inches=\"tight\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4fe27a7f-7cb2-420e-b882-04d8b1059141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.0, 9.0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize =(18, 4))\n",
    "\n",
    "axs[0].plot(medianAllFeatureTable['del_SPO2_1'], medianAllFeatureTable['del_Hb_1'],'o',color='black')\n",
    "axs[0].plot(medianAllFeatureTable['del_SPO2_2'], medianAllFeatureTable['del_Hb_1'],'o',color='red')\n",
    "axs[0].plot(medianAllFeatureTable['del_SPO2_3'], medianAllFeatureTable['del_Hb_1'],'o',color='blue')\n",
    "axs[0].set_title('$\\Delta$SpO$_2$ vs $\\Delta$Hb: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[0].set_xlabel('Three regions $\\Delta$SpO$_2 (\\%)$ afterT')\n",
    "axs[0].set_ylabel('AfterT1 $\\Delta$Hb (g/dL)')\n",
    "axs[0].legend(['afterT1 $\\Delta$SpO2 vs $\\Delta$Hb1','afterT2 $\\Delta$SpO2 vs $\\Delta$Hb1','afterT3 $\\Delta$SpO2 vs $\\Delta$Hb1'])\n",
    "axs[0].set_ylim([-5,9])\n",
    "\n",
    "axs[1].plot(medianAllFeatureTable['del_SPO2_1'], medianAllFeatureTable['del_Hb_2'],'o',color='black')\n",
    "axs[1].plot(medianAllFeatureTable['del_SPO2_2'], medianAllFeatureTable['del_Hb_2'],'o',color='red')\n",
    "axs[1].plot(medianAllFeatureTable['del_SPO2_3'], medianAllFeatureTable['del_Hb_2'],'o',color='blue')\n",
    "axs[1].set_title('$\\Delta$SpO$_2$ vs $\\Delta$Hb: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[1].set_xlabel('Three regions $\\Delta$SpO$_2 (\\%)$ afterT')\n",
    "axs[1].set_ylabel('AfterT2 $\\Delta$Hb (g/dL)')\n",
    "axs[1].legend(['afterT1 $\\Delta$SpO2 vs $\\Delta$Hb2','afterT2 $\\Delta$SpO2 vs $\\Delta$Hb2','afterT3 $\\Delta$SpO2 vs $\\Delta$Hb2'])\n",
    "axs[1].set_ylim([-5,9])\n",
    "\n",
    "axs[2].plot(medianAllFeatureTable['del_SPO2_1'], medianAllFeatureTable['del_Hb_3'],'o',color='black')\n",
    "axs[2].plot(medianAllFeatureTable['del_SPO2_2'], medianAllFeatureTable['del_Hb_3'],'o',color='red')\n",
    "axs[2].plot(medianAllFeatureTable['del_SPO2_3'], medianAllFeatureTable['del_Hb_3'],'o',color='blue')\n",
    "axs[2].set_title('$\\Delta$SpO$_2$ vs $\\Delta$Hb: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[2].set_xlabel('Three regions $\\Delta$SpO$_2 (\\%)$ afterT')\n",
    "axs[2].set_ylabel('AfterT3 $\\Delta$Hb (g/dL)')\n",
    "axs[2].legend(['afterT1 $\\Delta$SpO2 vs $\\Delta$Hb3','afterT2 $\\Delta$SpO2 vs $\\Delta$Hb3','afterT3 $\\Delta$SpO2 vs $\\Delta$Hb3'])\n",
    "axs[2].set_ylim([-5,9])\n",
    "\n",
    "# plt.savefig('DATA_RESULTS/ThreeRegions_delSpO2vsdelHb_RedCellsT_7_24_22.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f072b30-a3d8-4d3d-9481-4653af8ee7e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d48e0aa3-d3fe-47ef-b63a-0b0b02ba184c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median HB beforeT')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize =(18, 4))\n",
    "\n",
    "axs[0].scatter(medianAllFeatureTable['MHB_afterT1'], medianAllFeatureTable['med_Hb_beforeT'],color='black')\n",
    "axs[0].set_title('BeforeT and afterT Hb: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[0].set_xlabel('Median HB afterT1')\n",
    "axs[0].set_ylabel('Median HB beforeT')\n",
    "\n",
    "axs[1].scatter(medianAllFeatureTable['MHB_afterT2'], medianAllFeatureTable['med_Hb_beforeT'],color='black')\n",
    "axs[1].set_title('BeforeT and afterT Hb: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[1].set_xlabel('Median HB afterT2')\n",
    "axs[1].set_ylabel('Median HB beforeT')\n",
    "\n",
    "axs[2].scatter(medianAllFeatureTable['MHB_afterT3'], medianAllFeatureTable['med_Hb_beforeT'],color='black')\n",
    "axs[2].set_title('BeforeT and afterT Hb: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[2].set_xlabel('Median HB afterT3')\n",
    "axs[2].set_ylabel('Median HB beforeT')\n",
    "\n",
    "# plt.savefig('DATA_RESULTS/Hb_BeforeAndAfterT_RedCellsT_7_24_22.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "23716441-b9b1-4026-bd4f-151f92bf1406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median SF beforeT')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r2HnBMAzDMAzDMAzDMEzWsPOCYRiGYRiGYRiGYZisYecFwzAMwzAMwzAMwzBZw84LhmEYhmEYhmEYhmGyhp0XTG/Y3d3F5uYmRqMRNjc3sbu7G32fQogDpQtStDXlftvqsb6+vtfn4/EYV1111b7vXHXVVVhZWdn7zmg0aj1elIVh+grltd21TuRyLXbR7hw02KYOV1xxxb7jcejQIQgh9mkyazHD2FO3aVZWVg7YPCZ2d3dx4YUX9uZ66UrrVP3i08fU9drc3Nynm6Z+UJ0jqm1Np9Nh6rCUsrfl4Q9/uGSWg8ViIVdXVyWAvbK6uioXi0W0fdb31SwxSdHWlPttq8doNGrt9/l8LqWUcj6fa49PzGILgBtlBjrZRWEtzhvKa7trnUilw026aHcOGmxTh+3t7WT663r8WYeZPqCyaSqbx8RisZCTySQbvTTRldYtFgs5nU61/WLbx9T1arbf1A86u1cIkbUOS0mrxaLYXj+5/PLLJa9pvRxsbm7i9OmDSwRvbGzg1KlTUfap8yrGvG5StDXlfm3rAQDj8Rhf/vKXsbKygrvvvruzOtWxPfZCiJuklJdHrk4WsBbnDeW13bVOpNLhJl20OwcNtqlDLiO5NsefdZjpAyqbprJ5TOjspoqcnve60jqbfrHtY0pM9Wrrh5R2r44UNjFPGxkYOYScxuDMmTNO7/eZVG3NpY91+6uEO0cBZ5iK3HSY8trORSe6Jna7d3d3lcbs6dOnOzuHVO05ffp0Fucykz+56V8budVRZdPY2jp909+Yelo/tibHBZDGnjS1s+1ztntrUIVwpCgcIrefHEJOY7GxsdEarrSxsRFtn237g2OYlA+qto7H46jHcjabte53NptF22cbqvZXfSCllOPxeKlC5HIvrMX3kKMOU+pn11rctQ4vFgu5sbEhhRByY2Nj77jFbLcuhLjrc0inv1U9Ummvz/FnHe6WHPWvSY51VNk0lc1jwnTdxtJLX2LpqY2W5tA3puPV1g8pp4ZQ9B2lFicX25CSg1DnRIoH/K5om883mUyWJudF7BusLs8EpfNC9WBgWxfOeZFnYS2+hxx1mNJYn8/nB4yooeS80PVTzAcemwcPm3PIRl9N37Ux/g8fPpxMf12PP+twt+Sof01iOyJtr8E6vjkvFouFcuAptl6GYNNen7601dLUfeNj56+vryfX3JC+o9Ti5GIbUnIQ6pxQeeWEEKmrFkxb0p3pdDpI54WURXtVnnhqI8DGWI21H5VILxYLuba2tve90Wh04CaeKmmcLZRCnXthLb6HXHXY16hubqN5DQshoiU8MzkpqTE91FD0YRsuI2oqXPVV992qnbp65DAKaAPrcLfkqn91YtUx1ME5n8/37L7xeGzluDAlo4yplyHYaK1PX/roUirHWl1n6/WezWYHtDgHvQ09ryi1OLnYhpQchDon+uDx9mVZpo3YCBW1EWAyUm3DFn3343MMfUMD+yrUuRfW4nvogw77PoR33TbT9DBKFotFZ5rbxGW00HUbbcfG9ruq76ki43LUYtbhbjA5vHLSv1g61rU+ukYZdIHtvcXkQPLtS9c+ST1dSEq1o2Y+n2c1TS/0vKLU4uRiG1LYYN5PjvP4qEjhze/6JmD7QE59I+zqpkd5DH1DA/sq1LkX1uJ7CF3yLjYh94mudbgLXZLSrL2xH7xcpsGpcDk2tt9NOT2PSotZh+Njun5ys0Nj2cpd66PraHxs2vp1MpnI2WzmnEPIti+bzhLXB/4czktVX6TM7RbjvKLU4uRiG1LYYD5IrLDW1CxD5IXNA3kMI8AkkFR9THkMl21N69wLa/E95B55EVK/PkReUM+T7uLBy3bOui4KrsvIi1yKDazD8TFFXORoh8awlfsYeUHZDy42rMmBZNOXuogFm7pQRRWHkvOUECodlpJWi5OLbUhhg3l56DqqRBdC7HKxumASsFhGgM2NhgLTjcYlydyyCXXuhbX4HnKf8x1Sv9g63DSkTXltqOqn094uImZstUdXF8qcFxW5G9WWfcs6HJncNa8ruo66c42MakKt57Z6UTkgdI4Tm7r5Rm/USw6OtdQ2bRc6LCWtFicX25DCBvNy0VVUSVcJLJvEHP3T9Z0uZI26j21C/FwM7mUR6twLa/E9DDnyQsp4OqwyVl2uRd+2pY68MGmOTfI+KWlWG6mTu1Ft2besw5HJXfO6ost+8LGHYtfXVi9snVomjfLNm1EvsRP/25CLbRtTh6Wk1eLkYhtS2GBmVIQY2DaCF6vOMeZdm+YhzmazA8vQdjVH1eXmmYshbQulUOdeWIvvIffcQ7r6hTomYuuu6Vr0HQFOnfNCN20k9pLgKhYL+yUYc9Zi1uH45K55Ibhomm6kn3rALaVequg6b5uqD2az2d5nNtEXs9mMpD4h6KJoxuOx0lbPodhCqcXRxRTAGMDNAK4r/38lgA8DuKUsDyvfFwBeDOAOALcCuMy0bTaYmTZCb6Q2Yhez7qp9+t5QbL3PbUmVYkORZG4ZhDq0xNRhyVp8gNxzD7XVL1Q3u9Bd07UYMpKYcrUR05KHXRvXfRkJtCEnHZYDtolz1zwfXDXNZGtROnRS66WK+nkwm80O6FrsqcjT6fTAA35Km963Har7Tw5Tp337jlKLuxDqZwH4rYZQP6nle1cCeFMp2I8E8A7TttlgzptUN7NQQY4ReeHSF9Rh3bYClCLE07ati8Uim8zLtlAKdWiJqcOStXgQ6LTCRr+70F3TtWh62DDpsIse+dzbdL8zzV+nxmcqYW7Fhpx0WLJN3CtMeuCz0kXsqANKvaSg6cxQDZKpHOomnZ3P53u24Xg8lmtra8p+71JfXTDVre2cycUeduk7Si2OLdKXAngrgG+1EOqXA3hK7f/bAVys2z4Ldb6kDCMMDYWjznnh2hchfdf2WxcPfdfYtDW3UUBbKIU6pMTWYclaPAhMOmHSoBi666PDKoPXV2t8vuPSPlXUS0xtNrUhlyg3Ci3ORYcl28S9Q6dpqmvItNIFVRRXbL2kxlX/2iIoQmxDk6alRFc31b0lp2WsbaHU4thC/TsAHg7gsQ2hvh1FGNwLARwq378OwKNrv30rgMtbtnkcwI0Abjx69Kh1pzHdEiMcrct960KIXYXOpz6+NxTVvmyN0RT4joYuk1CHlBg6LFmLB4fNdabTLCrdbWpB1zpMFZ3hsn9T31NPGzG1wVVzU+XGsCEXHZZsE/cOn2vWdA1R2sAx9ZKaEP1T9Z/r71Q6lTrnhaodpqT59aiTlMUWSi2OKdJPAPDS8nVdqC9GEQZ3CMCrADxXOhrNVWEvsx0ppm9QjML51pkq6oPqJtDlMmKmhFE5e59V5DYKaAulUPuWLnRYshaTkXLeuO0olsvvXXU3pjEeksyzXidVXUzb8dWxGNnwTX3hGu0mpf5+mVKLc9BhyTZxVGLppk7TfK4hquhjXXup9JK6jjrddNHGev/a/qYe4dHMwZHraiMu50oK7fU5ryi1OKZQ/wKAjwA4BeDvAJwFsGh8py7gHCIXgVTTN0K8zrGMYFeonA5deOBd9jUajVq/MxqNyOtDQWg0yRCE2rd0ocOStZiElFPt6nUwGZq6+sRwOquu8/F47NQ2Hx12mYbnG3mh22aM5ap1dam3oXksVfeN6jjkqsU56LBkmzgasXVTpWk2o/gxnCqm9oZeLxS46qZrBIXJuVqtNmKbWyMHQuql0uacdFhKWi3uSrTrgnxx+VcAeBGAXyz/fzz2Jyd6p2m7yyjUrnT54Fwn5IaSqs5N1tfXW+uxvr7utJ0uH0ps9qVKaLS2tkZeHwp855YOSagpSiwdlqzFJOSie1JK7chirPqo2q/S4fl87twmVx22dZz65rwI3aYvPn2hmmNdHYeuddhWi3PTYck2MSmpdDPVFARTe7e3t1s/397ejlovmzqqNM4154XO7hvKEr0upNBeVx0u69lr58X1AG4D8F4ACwDr8h7hfgmAD5afc6gyAV1OWWji60lMWedY9ejS22vaVy7964KuTV1HYNhCKdQUJZYOS9ZiEnK7LlXnf6z66K7jlZWVA//7aKirDttMw3PRc1Ooc5cjgj73pGZm/7oDqUsNdtHi3HRYsk1MSirdzHW/i8XiwEj8aDTq9IHeRzfb9EilUbrtL5vjQso02ls/txzq2S/nRayyjELtSk6jebbkUudc6kFNH9rlYlh3GX2RSqhzL6zF4eR2XXZdH9X+VAnJuuiXGH2Q23HW4aLDXSfttH1IZB0eNqmup1z3m4O+xK5DDm2MhY9T2VV7NzTTAH2KLZRanFxsQ8oyCrUrOcyjdiWXOudSD2pyb5dr/VyTy/VRqHMvrMXh5HZddl0f1f5CH15j1CmkD3I7zip8dLiZDC8HLWYdHjaprqdc95tDBF8XeUj6oKGu+LZrsVgcmGKjKtX2qJZadXEYUWpxcrENKcso1D7kmqBGRy51zqUe1NTnDVYjm7m0T+dVVx2PxWLRychfKqHOvbAW05Cb3nRdn7b9pR5li9EHuukXueDT76b56Cm0mHV4+KTSzRz3m1ovbeoYY/tVDrRc7p0+hBw7G+1t9kvoUquuDiN2Xiy5UDMMBbl6r3XzGU31NQn42tra3g1uNps5jRROJpNkQp17YS1mYpGrTvnSl/aEjODqNHhjY0OZVLCZ24TCaGYdZpaJvugLJUNpM0XUjEsi2bq9bJs7rvqej4OInRcs1AwTTC4eett6ucx9bwujG4/HB96bTCZyNpsd8N4D2DcncDabJRXq3AtrMROT3CJSQshVd5uEjgLqHiZUBnbTuVxpc/216/FnHWaWjSHppQ190VQTFO1om743nU4PnANtGm1yYISeS+y8YKFmmGBymBvZBtXc9+YNXGUwx7rBsdHMMEyTXHW3Sehopu4BSmcgU8M6zDDDpi+aaoIqgsTGeeU6vY+iLym1eASGYZaSo0ePOr3fFceOHcPJkyexsbEBIQQ2Njb2/m9DVd9jx47h1KlTOHfuHE6dOoVPfepTrd87c+YMWd0ZhmF05Kq7TVQ6fOzYMevf1/XX9ncMwzAu9EVTTYRqbn07Ju11tXtz60t2XjDMkrKzs4PV1dV9762urmJnZydRje6hTXxD6zuUGxzDMP0lZ91tEssBMZvNnN5nGIZR0SdNNdGV01dl985ms170JTsvGGZJofLydkVofYd0g2MYpp/0TXdjcPXVV2M6ne57bzqd4uqrr05UI4Zh+gprqjsqe/jqq6/uRV+y84Jhlozd3V1sbm5iNBrhxIkT2NnZ2fPy/vmf/zlWVlYghMDKygquuuqqaPW46qqr9vZVldFohPPOO2/fe0IIXHHFFQDCvNJ8g2MYpi/UdXpzcxO7u7vB26p0vf53fX0d4/F4n95W+7viiitadTiUY8eO4dprr92nxddeey1rMcMQQKkdfaFuG+7s7ODEiRP72p9Dn7TZvEIIHDp0aE+DR6PR3vsxbXCdPdyLKX9UyTNSFE5ORMOyZSZeZnQJgebzeWuinvl8Tl4P1b50ZXt7m7weMQEnimMGCN8vwrBJyka59F/btmyKKgke63C+hXV42Nho71CWDfWlrf3T6fTASnNd94mPzRvTBk8BpRYnF9uQwkIdzrILXZ+geGjQLcWkWop0PB6Tt0W1L1PpE2w0M0OD7xfhOmyzHB7l0n+uWeVZh/tbWIeHi632DmXZUF9c9G48Hndy71osFkF6G8MGN9U3xgAFOy9YqMlYdqHrC1QPDbolpbo0VtloHlZhLV4Olv1+QaHDNsv6UWqgSdtZh4dTWIeHi632DmXZUF9c9S6289038i2V5sYcoKDUYs55seSolsvh5SMLcpgnBwAnTpzA2bNn97139uxZnDhxwmk7uhU3xuNx62eq921o9t9VV12Fzc1N7+0xDJOOVPeL+lzh2Ll4dFDosM2qR5RazKspMUz/sdXeZV9VzbWdPna0DZXt+9SnPvXAPcMVXxvc5/mF6lkjOlRekBSFvczhLPtImo7FYnFgntxkMkkSIk3lTe8y5wWVx7le+gR4xI8ZGCnuF13m4jFBocM2I1uUGkitw1tbW851SAnrMDMEbLV3e3u79Xt9y1Xji23OixA72qcOIcXnXucbQREzcodSi5OLbUhhoQ6H5zCrmc1mrRfxbDbrvC6UDw26+Wzz+XwvH8V4PA56QKCea903hxobzczQSHG/GI1GrXowGo2i7VMFlQ6b5hRTO4kWi4V3nqEc7n8hsA4zQ8BWe3PSy1S06atOA6ltS0rb19fp5HsPiTlAwc4LFmpSOHt8OzpB6Zo+Opmo51r3LeMyG83MEOn6frGMOhxjP5SjgX2CdZgZCjbaO5Tr1gdT/3Sl35R2r2/9fCMo+pLzIrqYAhgDuBnAdeX/XwngHQDuAPBaANPy/UPl/3eUn2+ats1CzcQkt5tA35xM1JEXMUf8YvRtTkZzTB2WrMVMRJZVh2Pspx5Zl7vzgqr9OemwZJuYiUzq6zYVtg/dXeg3VZRbVShXmbLZFmUEdh1KLe5CqJ8F4LdqQv06AE8uX18DYF6+vgrANeXrJwN4rWnbLNRMTNbW1lov/rW1tdRV6wV9yXkRy9Ock9EcU4clazETkel02qoF0+k0ddV6RZ8iLyg1OScdlmwTM5FZVr3MKX8fpc0L+OWb8NXQvkReRF1tRAhxKYDHA/j18n8B4FsB/E75lVcB+J7y9XeX/6P8fLv8PsMkQXX68Wlpx7Fjx/D0pz892f5tMy33JruyJ6zDTJ+ZTqdO7/eJLlezatM5Hevr663vz2Yzkvro2j5UTWYtZmIzZL1so9KR06dPt35++vTpzlcJ3NjYIN2ez0oxx44dw8mTJ7GxsQEhBDY2NnDy5EkcO3ZM+7veaC+VF6StoBDchwN4LIDrAFwI4I7a5/cD8N7y9XsBXFr77IMALmzZ5nEANwK48ejRo8GeIIZRgYGG33UZ9uwz0qeaq+cybcTFexwruzIyGfGLocOStZjpiCHq8GKxaE0IHTOPkWsOosViIVdWVva9t7KyQlI/kz5TanIuOizZJmY6YIh6qcLWxux6lcD5fH5Aw1ZXV+V8PveaTt1lvre+rDYSLfJCCPEEAJ+QUt5EuV0p5Ukp5eVSyssvuugiyk0zzODZ3d3F8ePHcfr0aUgpcfr0aRw/fjyKZ9p1pK/iggsuOLCu9Xg8xtVXXx20b5X3eMjrosfSYYC1mGF8qDT4zjvvPPBZzBEuFz0bjQrT8Ny5c/veb/7vi0mfh6jJbBMzDC22NuaXvvQlPPOZz+ygRoW+v+pVr6ociwCKaO2nP/3peOlLX4pTp045R2a88Y1vpK6mkr5ob8xpI/8MwBOFEKcAvAZFaNzVAM4XQqyU37kUwEfL1x9F4XVG+fm9ARy8uzNMR6jCY6nCZlPQZUjYmTNnvH535513tjovKPbd9v7Ozg5WV1f3vbe6uoqdnR2nfWYK6zDDZITJ4PbVTRNtOqfi3LlzeNrTntbqvKB4CDDp80A1mbWYYQhx0co2Z3EM2vRdSonXve51e9PkPvnJTzptM9Y9oY2+aG8054WU8jlSykullJsokg1dL6U8BuAGAE8qv/Z0AH9Qvn5D+T/Kz6+XddcVw3TM937v9zq93wdcHupD8fXUjsdjfPGLX9z33he/+EUnB4uL99h3bmAfYB1m+s7a2prT+7lj0tpYI1x1nTMhhIDqsqd4CDDp8xA1mbWY6YKh6aWO3KIBALW+33nnnXsRz5///OedttllO/uivVETdir4aQDPEkLcAWAG4BXl+68AMCvffxaAZyeoG8PsoQrV6jKEi5qYIWHNBGxXXnml9UhfxWQywd133936mYuDxdV7fOzYMZw6dQrnzp3DqVOnshPqCLAOM73g8OHDTu/njk5rY49wVTpnyvsY+xnZRp+XSJNZixkyhqaXOlyiyaqI6dhJkkNt6RyiHnqhvVTJM1IUXhaKiUnMxDWpiLUMkmq78/ncOinRbDbbSyba9rnrklehiUlDf4+MEsXFLqzFTCyGpsOqJHOV/nWBT9K4ej0pcNHXEC1mHWaWiaHppQmdzViV6XQqF4tF1GVA6/XxXZK60rYuEur7kJNNnFxsQwoLNUNJ88Jsywbv8xCdGzHEUedw0N1Y2m4cXdxgTFDUgY1mhjmIq/5QOTNzIrWB6mtgVw8BqevqosWsw0wsUl/HbQxRL22wcQp31Te2zxIpbVxXcrOJk4ttSGGhZqhouzAnk4mcTqe9EphU6Lz9PiONqY0CipscG80Msx8fAygHZ+YQqWusjeOiy8iQOqFazDrMxCBXXcq1Xl1gshtTRaWoni9ms1lWji8dudnEycU2pLBQM1SoLszZbJadZz1HTMKW2hnhCsVNjo1mhtmPrwHUN/3oG7rouNT9HarFrMNMDHKOcGC9bCflMev7McnNJk4utiGFhZqhoiuPbN8FTMXQvP25eZlzL6zFjA25zMceqg77krN+c+QF63CO5KJlKeirfuasc7mTm02cXGxDCgs1Q0UXHtmhC2dfb2ht5Da/L/fCWszYkMNo5dB12Jdc9ZtzXrAO50gOWpaCvutnrjqXO7nZxMnFNqSwUDNUdCHIy3qz6ys5ZVbOvbAWMzbkYPiyDvcPXm2EdTg3ctCyFLB+Li852cSi2F4/ufzyy+WNN96YuhrMQNjd3cWJEydw5swZHD16FDs7O6TrG49GI7Rdb0IInDt3jmw/TB4IIW6SUl6euh5dwFrM2BJbZ02wDi8XrMNMLFJrWQpYPxlfKLWYnRcM0xGbm5s4ffr0gfc3NjZw6tSp7ivERIWNZobJD9bh5YJ1mGHoYP1kfKHU4hHFRhiGMbOzs4PV1dV9762urmJnZydRjRiGYZYL1mGGYRg/WD+ZHGDnBcN0xLFjx3Dy5ElsbGxACIGNjQ2cPHly8GGGDMMwucA6zDAM4wfrJ5MDymkjQohXSimf0W113OAQOYZhcoUqRI61mGEYxg/WYYZhmPR0NW3koRQ7YJhlYXd3F5ubmxiNRtjc3MTu7m7qKjHDgLWYYTSw9jIdwDrMLB2srUyO6JwXq0KIrxdCXNZWOqshY2R3dxcXXnghhBB7ZTwe46qrrkpdNStsxbErEa3vp96nVb+qfnP8+HGcPn0aUkqcPn0aT3va0yCE2FfXVDeC5n4f/OAHH2ibqnRNV310xRVX7GvnFVdc4fR5h7AW9wTW4jh10ulSm/YeP35cW/f6Mbrwwguj30uEEFhZWbHW3Fy0+Kqrrtqr98rKivI89jkf6r8577zzDtxvL7zwQlx44YUYjUZYX1/HeDw21iMyrMMt1M/xejnvvPOiXFdd21A2+6t/p37eUtfPtu1U2mFr13ZJs68PHTq018ZKK3LX1WY7Uj0jNG3ctlLXW9v7QSeo1lAF8DkA1wO4oaVcT7VWa0jhNa2LdXen02nrussA5Hw+T11FLbZrZXe1pnbbfpplNBod+J1q7et6XefzeVAbXNdYXiwWcjabaetlW7qiq+O8vb3d2s6trS3tsdze3rbeB4jWtGYt7ge5arGtbuSmxdW+TLqkul43NjZatzeZTA58dzqdkuuwzb0kZy2ez+dW57HP+UDRN7bXE+twPEzHcWVlhVQXutQe2/2Z+oCqfrZtp9QOG7s2pG0+Nm0MTe1SV1XtoHhGcEFlA6vK1tZWkA5LSafFUkrohPpmqp3EKmwwm8VlPB6nrqIWW8PTxUCNUR+T0AkhjL8Zj8febXB5sLBtQ47C3tVx7qIvqISatbgf5KjFLsZ+Ci02Ga+mPpVSrb1CiAP7UmlwDB3W7asPWqyqf/M89jkfKO5RttcT63AcbM9xynt3V/aBy/5szmWK+tnUxcbZ64KNXQv4OeZtHUP1+wPVYFxKXZVSfSxDnhHq2DiFqPrNxa6h0mIpJVioe46NuOSMreHZVft8hS7UGDNhe+Pqu1fa9nwIpYu+oBJq1uJ+kKMWuxj7XWtxm15NJhM5m832jC6b/qTURor+VEV39E2Lbevgo9m2D0UUfcE6TI+LrUF57+7KPjDtr7rmbc9jivrZtN30cO+Ki13r6sAwaWlMezalrkrprn8u5w9FhE6svqPSYikldEL97bXXRwB8tdOGgcMA3gngPQDeB+B55fuvBPBhALeU5WHl+wLAiwHcAeBWAJeZ9sEGs5245EyoN5l6NNN2tKxJiNCOx2Otp1TX/rqoxYi46Poc4sgL1uK+YjMq1DUuxr7ttWc7Im+CQq+ktDPWbPbVVv+mLtvocMzRwS7PIY68YB1W4XL8hhh54frw2VXkBbV2uNi1puuxrqU6jay0NKY9S9E3IcSMvLC9Rqj6LbvIi70vAN8F4HYAHy7/fxiAN1j8TgBYL19PALwDwCNRCPWTWr5/JYA3lb97JIB3mPbBBrN6Xmqqi9IVCsOTkhChq0/ZaN7cTDcAVR/M53Oj99+17j6lK7qa0+rbDylyXlSFtThfbEfbu8bF2O96TjXF6Hu97rowWdt91bfn4oToSoe7PIc45wXrsAqXa3doOS9cdavLnBcxtENn17bpYFWfprNClw+qTUtj6yhF3/gSM+eF7YCFa84LVUllE9sI9U0A7o1ayByA25x2AqwCeDeAR0At1C8H8JTa/7cDuFi33WU3mKU0P9jH8khTEmJ4UrfP1tvr0yZXb2sVPq2qQ1PUYs2xTiHsuvOBAh+vvotISxnFaGYtzhSb8ymFFrsa+zbXHtXoJ1XkBdW+QsKVXR4g+qTF8/l8774yHo+VDgMfza7/Zn193br9unq0wTpMj+2125bcPJQu7APd/nTtrR7U61PfqJ03IU7a0P2YcmpU95e2h3CbUr83xbRnU+uqqY9Dzm+X+zOFA8Plvk+pxTYi+/by782192612jgwRhEGdxeA55fvvbIU4VsBvBDAofL96wA8uvbbtwK4vGWbxwHcCODGo0ePWnfaUHF5uO0rutA96vZRzY223XbI6NN8Pre+qfZN2GNjc5yr6Ty+UAq1ZC3OGpPRmFKLqY19qtFPitF3qn3V6++jpXVi6nDuWhxyrtlEkc5mM+c6sQ7Ts1joV1bqy/nqA5XzNgYUx8Kk7zbXqa/joUsn8FDPU9/7s2+yf5d8HJRabCO2rwDw/aWwPhDArwC4xmknwPkolpN6CICLUYTBHQLwKgDPlQ5CXS/LPtonpVpIR6PRIBwXUqoNT9eRcN326wZX5RSIIXQuERm6MpvNBpvMKAZt/a7re5elE1VQCrVkLc4a3TXcxehg11A5ROrbWVtbazWMXHXJZkRLNzrqM52lvu+ctTjmqLXP6gHNY6Nrt68msw7HwWZaVe6r3fnQ9dQVU13qx4Ei8kJ1HdYHc+oRWVSl7vxZLOKt1hRDV02YdJdCl23vbyZcn0dyjrxYBbAD4F1l+TkAh513BDwXwE823nssgOvK19mGyOWM6iKfTCa9Helr+31b7geKG4Zu7lmo0Nn2g6oOOuPAJcw2d2GPjcnYaMtXQHH9UAq1ZC3OGpVepNRhCly0PET3VXPLL7nkEiddahsRdn3odTXe6tEAsSPgQrQ49kOXaUTadZ53M7LQt56sw3GwcfL5LKFJSSxnXeg0KaoIONvolzbtUE0J0x3XppbqHB0u9arfJyki8rrUVZvjZLI/Q3WZUtttImt890GpxSZxHQO4wWvDwEUAzi9fHwHwZwCeUIkvCk/ziwD8Yvn/47E/OdE7TftYdoNZSv1IT4owttCLyPVBPrSNvuJL3Q+qyACqJeVyFfYuMBnVps99IRVq1uJsMRlbKXSYAhcNC9V934f+Jqr7hMt0A9XxXF9fP3BfaBrzOWtxLJ2rMCWKc73XUtWLdTgOpms21vKltuQWIUFdFx/NrNAl4zVtt66lLg7Jahnstm3WdbQLBzClrvoeJ0r7k1LbbfvfxwFHqsXGLxShavd23jDwUAA3owitey/uCYW7HsBt5XsL3JN9WQB4CYAPlp9rw+PkkhvMFaYTLZf62F5ErsIVeoP0dQ7E7oeKFCKew3lEicmojrV2PKVQS9biLLENb+1j9IWLhoXqHZUOU2mYzdSTNuOti1BnX2LpXIXpHHA9xlT1Yh2Og80IeUpiO+tS1yVkeptuGWTX4+qqldQ6kVpXfY8Tpf1Jqe02/e973lJqsY3g/gGAMyjm+b24KlQVCCnLajDX0Z1o1AktbcLeTPUxeeu6Xj/bN/LC1BemFVJsQwdTeaG7EvYuGELkhWQtzg6X8NauRvwoQ5NdDKJQ4yk08sImZ4JvPzXnla+tre3NJ67mFvdBi2M/zJlGl4cQeSFZh/ehW60gxkojLlDZYDHrEuKg89FMW520yd0zm828phGa+qJrPQ3VVd/jRGl/Umq76fwIsWUotdhGqJ/eVqgqEFKW0WBuYjrRujJMbOtjOvldBJkiqWJbu6bTaWvyuGYbdG1StcM1b0eq+X9dCXsXmHJaxAovpRRqyVqcHa7GY+wRP+rz2MUgCjWeVHXf2toy6pKtRlYGtC7UuWls+8wrz1WLuwij1zmFVDqsynlBVS/W4XjoBnlCV+sKhcoGi1mXkHuCjzbZrLBWbdt2ILHKlVG/9mez2YFrvepnXV+k1NtYpM554Tqgoco/VT9GvlBqsa1YT1FkRX4IgAnVzkPLMhrMTUyGW1chobb1MYm27sJpFp9l01T71Imubam3yaUdphuYjRe8r8LeBW03xKbjK0ZiL0qhrgprcT6kCoNXYdJoCiMmVs4LVf1Mo3C6drcVXf6ktoeaVKOAsbQ4hs657FulwzHrxTocD9O5Gtthq4PSBnPdb5sTNIaDzkYjXcp8PnceMBNCtDogVf2s6wufaJIcdNXmOOn0jUL/KM+7WHpMqcU2Iv1YAKcB/AmAPwXwYQCPoapASFlGg7kN3bxrKlF2CXurn/g6wdO1p37h+GzDhfr+QuYsN+tD2Y5Ugg7QOYlSEWPUwwZKoZasxdmRW+SFTqO7MGJ8DB7Tb2wMzlySGndR+rz0JOvw8HTYZC/lkLSza1uSagTcpj2uemmjpz4OBFu7uepnVTtSausQSaW5Kii12EaobwLw1bX/HwTgJqoKhJRlNJhVxA4J9b0IKC6emBcg5bSMmH3R9ZrXVaGYmpOa2InqVFAKtWQtzg7d9IMUWe51+kKloZQjMjb3LBuDM+VoXYrSV1iHh6fDpmUVU0ZetBH7Ya5rW7WplyF2YlXHmM5gVT+kjrro+wCdilSaq4JSi22E+lab91KUZTSYdcQMvQwZuctpDeMmvoLZdV+4rL1MeaPpu+NCykGN+LEWZ4ZKc1OE5+v0hcKI6Sqnxng83us3U+4hXb10y6a6TE3MKedFbg+DLrAOD1OH5/O5HI1GrTZSbvZD7EG+mA+LOlu1Sp5pshNVA3U2iXV1xcZp4jLl0Lf4OF7q+c+GxrJHXlwL4NdRhMo9FsCvAbiWqgIhZVkN5lT4GuRdhMv54utlTtEXXTowhkRsg0UFpVBL1mLGApW+5BgBZ6O9JqNY126XEG5dtExzXnlztZGuNDnHh0EXWIeHrcMp86m4ELOeMR8WTXo5nU6NNmI9wqHS1mYf6CIK25zJqmjD6XS6p5O6fk4ZcTEajbI9TylIpbkqKLXYRqgPAXgWgNeX5ScAHKKqQEhhg5kJxUc4uw65sskhQln6PK9aRQrDKoLRzFrMeEFhxFCPKlIYrTbttr3ubb47n887mcJXOVT68DDoAusw6/CQyTFK2EUv6+1QXacU0YaxbNoNzRTJZhnClGgbcnIqUmqxTqDfWv59PtXOqAsLNROKb8ha7vXTFZPxXS19xYRBJdSsxQwFoUYM9agihbZ1SReRb0MOYU4F6zDTJbEeFvumlypi2LTA/kg5myVhWWe7h0qLpZRa58VfAvgmAO8H8PUALqsXqgqEFBZqhoL6zcZ2VK0rqEPqtra2lOI+Go3YcUEIodHMWswkJ8aooo/2pjLGY0dcVPPWGVpYh5mhsFioVxbMTS9VxJom0hYdAhzMgzGZTKymszD0UGmxlHrnxZMAvAnA5wDc0CjXU1UgpLBQM9QsFgs5mUycbgIxw7Kow+ool+1i9BAazazFTBZ0nRTapMUxULWRUodHoxEb0B3BOswMCVsbVaeXKe2/WNOfVdTb2paAue+5hPpEJ86LvS8A/4lqZ9SFhfrghZnSIBrKA3EzQZvpJtCWuT40gmF7e9tbxNfW1uT6+rryc4rkUbb07ZygrC+lUEvW4l6R43mfY52atNWxK+eFje5TlqYO53h8UteJYv+sw0wbqc/tEHy1qvqtbQRdSGL6w4cP79vH9va2lDJO5IWtTUs99TEGOZyXsZ4ru3ZejAA8FcBzy/+PAvhGqgqElGUXatPcri49ir4hxTlcqCpsjGaVGAohrNrSlv05pGxtbWnrVdWtuf+cltdNBXV9IxjNrMWZkvvoTt+uxTo2OuxKU/dUyz3GLjarnaQi9TlDtX/WYaYJ9bmdyo51zR8hpf0DvKqP5vP5PsdJc7rbYrFQaunW1pZx+Wuf0lYH1+i5HEituao6UNWHUotthPplAF4C4P3l//cB8C6qCoSUZRdqGw9mVx5FH49mDheqDhuh04XAmfqeOnFR5biwrVfs/u+Dl7sOdX0phVqyFmeL7XWc8rzv27VYh9rgjJUwLqSo9HqZzxmq/bMOM00oz+3UdmzzQd2kl7arRrlESNQTDVNGVrgM6OkSdlafqbaXywp7qTVXVweK+lBqsY1Qv7v8e3PtvfdQVSCkLLtQ28wd62pZT59l9Fwv1NhRAs1tm24CujbY9D2lyLvceCovdWyhpF5aMSa64+1bX0qhlqzF2WJ7Hac87ymvxS5GGev7sNFhFyh1N3YZyjlji81xd90/63B8co6gbYPy3M7hgbOOSS9t6+uam2JtbY00qXH9WNhq9mw2865DDuRgM8d8rqTUYhuhfgeAcU2wL6qLdsqyzAazlP2PvLC9UFXz+6i82ypPrY3QLRYL71EzysRFNiF/9aJL+EQllLnd1FWY+ioHL7NkLc4W2+vYd1SP4qGA6lpcLBYHpsRMp9Po081MOuxCrIRxpuKz3/F4nOyBsGv9jhXBxDocl9SRBz64nNsmDabWpxBsBt1Mx0s3sNVlqR+L2NFyudikOdjMNrlUcrCJbYT6GIA3APgogB0AtwP411QVCCnLbDBLqX9wrkrO81NtLtRYD5Y29bC9Kc3n8wPHwebmTZUczpRsKZWQ98Wo0fVRLvP7JGtxtthcYz7nEeX1o9uWi4NEpVmz2cy5Tq77MOmwLbEM89XVVWPdNzY2lPs33cvrodld0LV+x7qOWIfjksMDlys257btoFkuUxF0uSaaeqnSfJO93ZXjt+06r9eZMsKD2vkeQg42s+kelotNbCvWXwPgh8vytZa/OQzgnQDeA+B9AJ5Xvv+VKDzXdwB4LYBp+f6h8v87ys83TftYZoO5QneS2SaNpMJ1lNDmQrUxaELxEeSQtlN7tk397NI+aqHsQziprn9C6ksp1FVhLc4Pm1Ehn9WHqB8K2q5FV2PJVYd97gkhOmzbDzGMcFV/Nosqoet8Pt/rK9VDCKWTyLavutJv01QR3/2zDsdFd9xyRnduuwya+eoT5bVlG5lgQmeXbmwUiYWb2hWjmOxnylWhunYKm0htM5vy5eViE9sK9WUAfgzAjwK4zPI3AsB6+XpSiu8jAbwOwJPL968BMC9fXwXgmvL1kwG81rSPZTaYK0wPwam936YLMSQkD9B7t21FwHfJKd/+oBZ/Ey6OkpxEvCtijRxRCnVVWIvzRJcMrCrNjOgmqB/m2vTQ5dy3zQNU/77rKJKrFtu2swml/jbrYnMuTCYT7fJzIXrfV1iH89Zh1XWliyTqMvKX8oHPZDNVU2t117pKQ6tt+0TrqtpuG4lg2o6pvdX3mquNxNLStjq2OWmq5Tx995f6OSknYkZSUWqxjeA+F8BtAH4WwPNQeI3/o9NOgFUA7wbwCACfBLBSvv8oAG8uX78ZwKPK1yvl94Ruu8tuMEtpN1KVsm4hIVC2I2Qh+14sFtr8D9R9Si30hw4dsurH3FdDSEmsUD1KoZasxdljc525nFc2Tkfb7anOcdV22/IO6b7fFhHgagS5Rl206bDttUwZdgxAbm9vO/WXSW9d2jwUWIfz1WHTtLOUq+XEOG9MdmflIFFd4ypbM0QTbNvuox0ukSZtUGqpLrpMd09x7Yt6yTGJfCpiTl2h1GIbkb0dwOHa/0cA3G618SKp0S0A7gLwfAAXArij9vn9ALy3fP1eAJfWPvsggAtbtnkcwI0Abjx69GhwZ/Ydk8GXcgmgUA+ejfGu2pbtvl2iEmxuAiYoRd5lZKM+MqEKWx5y1EVoBJAPlEItWYt7gc0DuK3+UTodVTpnO2qo00lV2K1r5nQfLbbdRrM9lDq8srKiPH66NukM5i5yi+REva+qc5J1OB8dNl1XPud4V3Wj3CZqtpJOU9vOWxt9c+0vV80MaW9Fm61EpaWm/BOme4opCsUlSmbZaD4j6CIDfaHUYhuxvQHA+bX/zwdwvdNOit/cAODRoUJdL8s+2kc1zy0Wocv+mLzfugdu2337zn32hUrkgYNTPHzybuSUiyJWnWJ6knVQCrVkLe4Npuiqyuloc67Xv6fbngnd722uDZ+8MK4PFRS5h2x0P0bOCx2q80FnMLdNL+wysVyX94fY+sw6HI7puooZah5aNx900xOq89J1vzaa49pfrjrmsx1TLpDV1VW5trYWrKE2OmNznpmihFInxGwjtT3uEqkeUk9KLdaJ668AeDGA30eRVfmVAP4HgI8AeL3zjopQu59CD0Lk+oKN13WokRemC6eLyAufCzlU4FU3Ih9RTi2YzbrEuqmkMqyohJq1uF+YnBez2czrXA85j3W/tdEBn327XtM+Wmy7jfqSo9RT90x5l9qmJVaOCF3fp9Lnrg382PrMOhyO6RjN5/PWz30SFVPXzRfT9ee6X5O++eQIcdVMn+3U2+5rL/vWq4mtNs3n870oi/F4vO88zMnureqT2qES6hSyhUqLpZTQCevTdcW44WLt6/PL10cA/BmAJwD4bexPTnRV+fqHsT850etM+1h2g9nW65oKipwXvr938ST6zJPzrVsswbcx2m086KmEPKYBG2NkxgYqoWYt7hemKAefkXgpu9FD6t+7RoO5ajHFNqi1uI5K16oR3Jw02FTnWM7e2PrMOhyO6VxNNUBgU7dc9mujTRR18Nm+aTsxl0i1HWC1nVqWq66qSHntVNhoMEU9qbRYSgmSjbRuGHgogJsB3Ioi/O255fv3R7Fc1B2laB8q3z9c/n9H+fn9TftYdoPZxgva5QXQhs4DakPd+F1bW9tbQs5mWz6h2bPZTK6srFj1a9v7o9Fob64YRSid7Y3I5uYihNjrM1X919fXk3ilYxqwfY+8CC2sxd2icyTqpizYnOsho0ahI06LRfsSdT66blNHmwzyqm0cOnQoqvbWiyoPhWkuuOkeUvVt9d0haHEbtqN+vucu6zANumOQaoBAV7cuRthd97FYuK1OYtpvpRfVNkw2Z52mbb69vR3dZlUVUz+qnCt1m7bCJTrEdQWwGKS+dqS002CKelJqcXJBDynLbjCbvKWpvY2UHtDYYYlNw9zkDIjpibYtdWwFuwpNtN1HV+dQTAdDKk88pVDnXpZdi+u0TRWoJ7XMYaTFB9P9JpYW22pgxfb2dqc63Oa8mM/nxjwlrveQIWhxGyZ9DtVv1uH45KZpOY++x4iCq0rdkaHTS5U9naLYTKM02beVlvqsIrWyspL0vMjh2rE5JymSSFNqcXKxDSlsMNtliE01x4vyolSJEkVOj8XiYHI0U3Hx7sYozT50CR/c2NhwEvkuRDS2sZHiGqAU6twLa/E9tOlJPdli27leGV9d6rMrJs3z0eLmdTmfz520uI2utbhtWVkbx4RP7o0haLFqnyp9DrUjWIfjkzLnRRtdPBB2HQVXj7bwLRXUS0X7FttplLEHC1Ou4pSLo810TvbGeYEygVDOhQ1mMykvDMpwKBtB9sXnhmBroMYS/LbjVxcf3c2JIqt/DFI52WJBJdSsxf3CxnCuG6LN6zGXEcImNrrhgs6JE6JLVDprW5fmA5Ht/aRtxJG1+GAddMfHBtbh+OQwelwndih+13Y1VS6fCiqNNBWdHVrpSki+BcqSkhz01kRvpo0AeHft9a9Q7ZCysMFsJuVNhWrfumkOo9EouJ6+D/Nd3QAqgagLvk2fqdq1sbHhdDOg6OO+QHkToRJq1uJ+4XKTz83o10EdeUFhkDaniIROGWlGgZgeGFyXlW2eD7o58MukxW0ROKa+7zrygnVYTQ7z9uvE1lXd9kPzvLnsz7VUdBF5UZ0TJue8rbM/diJmRk+fIi9urr1+N9UOKQsbzGZS3lQovNMm0VpbWwuup8+Nwfd3PqVK9OdK27zrqv9d5zwOBZ1zgno0hUqoWYv7hYvhnJvRr2OxWGiTGbsmUO5CO310XVfn+XxudG7atq36vev0kT6jSqzoGoGTIucF67Ca3JywsSMjXAe8Qh0YVFG+FVtbW1H0UxVNqBt8sz1Wi8VCrq+vd6b9y8aQpo28u+11ToUNZjOpbyqhI9kmQ5DCyPdJXtSV8V1P9OeDrbG4DMJuuklSXytUQs1a3C9cDOfU+uyKzojWQTFFpItCEXVlo6+rq6tW0QVD0mLVdeHqvHE9NqzD8cll3n6zTrFC8V3tv9DcbFT2ZtUfMfVT95kKl2NVj2yhLMuMzfXbp2kjZ1Es6XRb7XX1/61UFQgpbDAXdDma3DUmoaUw8nMdBQT2J/qjwrW9FNEtqdGFZ1fnEPUoOJVQsxb3D9vQ4b7ps6/xp9Kctsiw+XzulcwyRvFdSk8XsWGzBKyuPn3EdXqMqvjc71mH49OMIPK5blR2bI75ANp025RomHp/fSuxogkpnBl91VUVrteMzSAKxUALlRZLKaET6g1doapASGGDWS1q9ZtHjuJvi87Iq+YMh5LDCKCuDtSjsK7t7buwm2781U0148gL1uKeoJoGoHNI9EWfdbmHAL1xbtK3tgeWXIz15rKdIccqpF2hUXip8G0zVSJb1uG42NigPttQRSfl4txtaoHOVqVaFS/ngTZTqfQy1r3OV2f6qqsqfAZEbAbuKAZaqLRYSgkX4Z4B+BcAHk6189DCBrN+FD0XkQ/BNEJFge0NwbSGdqyi8lj73ghcb4A5zr93wdTeyjmRa86LZmEtzhOT8ZTrVBBbTNeRz29VfZKbkV7pa9toa9vy5L59qNLfnJ1aJkxtblt1pXpwpXjQYR2OC4UNqtqGKVoyJ3QOWsolY3PTRpuickS56qcJ17xKvvnkcsZnEM72N6HOJ0ot1gnzdQAeUr6+GMDHAPwhgL8E8ONUFQgpbDCHTavow4ifrn1UkRe2HttUa2O3HcOQB23Xucc5Ggou6M6hZp9RXhNUQs1a3A9MxlIKJyDl+RwSHeaqVzlEwzWPnY0x7DvC5duvfcBGf2PaIqzDcaE4h12vixwHVFT6QD3tNqeoNFsNs40aoR5w7XJfueAz/bmr6atUWiylhE6o31d7/TMAfqN8fR6WfH5fTvgazDFO1hgGiO2oeSgpQ/J0GZRjJfqzTeQ5BIHXjerEbBuVULMW94Mu8vPYUNcyqtB7KfV5K2ydprb3h7W1tc512BQ1Y/uA5TPCRX2sciKV/lawDsdjsVAvyV4/r01QR16kGJjr0n5STU90LaFLS5uOu03CR9dj64Kqj6gGPnPE99mgi2uGSoullNAJ9S21128F8OS2z1IWNpj9Q5VDH35t6kEh3Lb5CqgwzeuOUdrCZqv3XUcmQ/ujD9E4rqRyylAJNWtxP9A9mHb1IGozKkep8UII0pDoaj9da3Cldbp8JbbObd8RriFqr5Tpk9KyDsfD5pqw0RvKnBcpz7eur+H5fO4dpTabzciiiduWRt3e3t7XF7bOFkqbfrFYyMlksm/7Q8tx0SS13uqg0mIpJXRC/YcAfhTFnL5PAzi/fP8Iah7olIUN5gKfJHHUD7/UzpA6i4V5pQhKKMTcVfhdb3ox+3uIpHgwoBJq1uJ+QJG4LhSbh4kQ47CL68g1Am46nR4wUF2K7dQx23DtHEa4ciNlm1mH42F6cHZ5aNJdd2wbteOqlc3jQmG/tuWnUeW3sNFp6uPEeptPm6m0WEoJnVD/EwDXAPgDAN9ee/9bAPwkVQVCChvM+3E5YakFPlYkQEWXHlQKQa+XaqlESgdMzt5VpoDQaGYt7gmpjQabUThbvUnVFpeRxKpe8/ncW5td2lXvk9lsduCexBqcH6zD8dA9PKd6aIpti0qZXucrfKIuKju0qn+obesyrXk2m7F+LjFUWiylVDsv+lDYYPaH+uE3trd7sVgcWEd7Op1GETpTeJvLXMP63DrqPs/lBsq0QynUuRfW4jwwjcSFJvXtQmNcRhNVdbUpFM5v1uD8YR2OR46DKF3Yorm02UUrhRBBiT5dcvLYOpBYP5cLdl4kEuqhQSkcsQW9i1BAm3nN4/HYyXnRnA/OYr08sNHMdI0qL0WllbZ6kzL02tYZsb6+rq2rrnQ5lYdJC+twXHKzaYZgi9ri4rilSIKcKrLbpT9yOheZ/bDzIqFQM2qo5iy20cW0FJXRX4XZtYW5mQxkZnlho3l5SWlE9UFvTdTboDK6K8fwMi4/ytjDOtwNOT04xqxLam1s0mxr2woiKysrJI4Llzp1HZ2SU0QM004vnBcA7gfgBhRrYL8PwDPL938WwEcB3FKWK2u/eQ6AOwDcDuA7TPtYBoNZJ8KqRJ1Nb6uvgOsiEdbW1g44J5rfGY/HWm9v/SbQ9tomE3JTUJv9tbW1FSzaPkUIIbe2tqwM63ofjUYjidLxUX9f1Y+q40axjFZVH1eax6CezGk2m8nZbLb3ur5M7Nra2r7Pqte2526z3W2jq/VzVXVsxuNxqwFw5MgRp35ABkYzOtBhuSRarMNmybm6Fs/n833nanXu13VPp1/1a6W6Pmwix2x0y+d31bXRdg1ub28f0PLKyA6Zc11pBIXOAeZlA8fjcevxaGpkzOUHY5+n1XFaW1vbuxfVj5lJh+va3zzm1f+2Kxzozo3JZGLdZmSgw7LnNnFdW1xWqGhO9a2uW1v9q3Staf+sra0duBan0+kBPTx8+PC+71xyySXWdd/a2trX9sou8blu2wb0TNuq7hNUy0ZTrSwSs0wmkyTLZFNRz21X5bprXj+mY948r7e3t72vVyEEieOKqu/QpfMCwEUo1rQ+CeDaqlj87mIAl5WvzwPw1wC2UAj1geRG5WfvAXAIwFcC+CCAsW4fQzeYdZ7EtgSWpjKdTq0fCG3C0apl8kLm0VFePG15MZahNB0F1H3g4sDwnX9uKjbLo7W1uz6vnaJuLg4MEBvN8NBidKDDcgm0WEfMh1XbMplMkmtfDnVIVSqN7OJcSHme6nQ4lvbrzjcbkIEOyx7bxF0f13qpHBI+50ZoMkqgcHZQalq1MkfI6kih+0+x3z6UUFRJo7e3t4P73cWBkeJ6tQUdOy/+D4DnA/heAP+qKs47KjI0fxvUQv0cAM+p/f9mAI/SbXPoBrNu3ljoCBugnwttu/3m6EyKUkE5CtfXQnHD1vWx7zlLUXRhi7r9Vr+jqpstoDeag7UYEXRYLoEW60h9zXPJo3R1LqQ+T1U6HFP7Q/oCGeqw7JFNnOK4DrmkjIBYLGiWRx1iCSX2cTVBEXkZu+/QsfPiluCdAJsAzgC4FwqhPgXgVhQe6/uU3/lVAE+t/eYVAJ7Usq3jAG4EcOPRo0etO62P6ObWxXhIrYfZx3oIjnnhpK7HkEvoOUtRdHNKdfutfkdVN1tAbzQHaTEIdVgumRbrSH1tcsmjdHUupD5PVTqcwmawbHdWOix7ZhP3yRbkYr5eUtch1xJKyvqljI5y6TsQavEIZq4TQlxp8b1WhBDrAH4XwI9LKT8L4GUAvgrAwwB8DMB/c9melPKklPJyKeXlF110kW+1esHRo0eV76s+C+HOO+/E8ePHsbu7G2X7zPCJed7otm3z2QDOaW8tptZhYLm0mGGYAp1dsiQslU28RMe1E8bj8VLvf6ik7NcTJ07g7NmzyfafAhvnxTNRiPUXhBCfFUJ8TgjxWZuNCyEmKER6V0r5egCQUn5cSnm3lPIcgF8D8I3l1z+KIqFRxaXle0vLzs4OVldX9723urqKnZ0d7OzsRLlYzp49i6c+9am46667MJlMyLcfk9lslroKS0/bOUtBdd7r9judTg+8P5lM9n73gAc8ILgeR44cCd5GAF5azDocxu7uLjY3NzEajbC5uYnd3d19n29vbyeq2T2Mx2MIIVJXY2kZjQpTKodzQQVF3XQ6HEv7VSS0T5bGJt7d3cVdd93V5S4HzerqKo4fP57Utj5+/HiyfQ8ZVb9ub28H66JJu8+cORO0/V5CFcLRLAAEgN8A8KLG+xfXXv8EgNeUrx+M/cmJPoQlT9gppX61kdhZefuSfK1ClTAnVt/kkGOji2PkurRgjquNuMz1HNhqI9F1WA5Yi22XX0udtDNFhvYcSg4aDGDf+RDzXBiPx0Hncw6rjVAUlyTSyECHZQ9t4tSh6L4l12ku1eoTVV6CFPniKrq0lftQZrMZyTWTarUR3baXdrWRYn+4Dwpv8GOqYvGbR5eNuxW1JaAA/CaA28r334D9wn0CRUbl2wE8zrSPoRrMNnQlPn1YXknK7lcaqYy4lMazzfKILjfytqzIQ1kn2+bGQd1WRDCa4ajF6ECH5YC1WJc0uaJtKT8u3ZTqgbrrRGXN+2JTO2I9QFXGcJ+h6BsXrUYGOix7aBPHuqZiP0zlXFI7g5rLvlYDRPVBpBSl6cipru9QrbD9vS6fWh+wHWSp49KPbatK+tjL6Dhh579BIayfRrFG9RcAXE9VgZAyVIPZhMtFTeHdTbWsk22RUn+jjSHO1XFI1eZmNATFyjT1BwGXiIc+YLpeYrQV9IniWIs7Rpc0Wcr+jk4OqXTd/yqHfl2TdZo7Ho/39HV7e9tqgKA+itd3XB6KF4uFVX/rAOuwF1QOuKZNYRrwqZYTdflNH0oOA4E53qt017etVoSeq67RxTniaru79g3FswE6dl7cBuAwygzLAL4GwOupKhBShmowm3C5+a+trbUKlq332+bGkVqUpTTfaKlHwkzHgXp5znpp83jqPK+2D1h99z7rMB2HGIDeaGYt7hhT5EUOywjmMoUtZdEZwK4PT7qi09G6fqoiASeTidLoMznKhoJtlGQ1RSa0X8A67AWVtjUfdHS2WH2qZx0bG2Z1dTWKDro8RHNxK6ZjajqedQ3wPUahEbd9HfCzvVZyjUa2Eep3lX9vAXCofP0+qgqElKEazCZcH8Tn8/mB30wmE2M0gm3oVqhjYDQa7UWIjMfjA3O+TEXK7h8iTMehnmvB1tutEpPZbGYljjoRbeaDaCtD8D6r0EXJUM13bEIp1JK1OAmmcMwU86vr0XSVsR8SAZLjaBxFO1RGaVtfTadTY4TheDzWTlFp6qcuB08bttsdAjb3oyrSJLRfwDrsBWVkaf1a1NlqugdJXS6tyt6hjoRzHQDSlT46QCaTyQFHYxUZU79+VRHeumeMumM5pE8rXH5Xj34LdVz0dar1YrEw3vPW19dJ94mOnRe/B+B8FGtR/ymAPwDwRqoKhJShGswmXC728Xisvfmr5sDVL2rT/kJFeX19/cDF7jKHXMruc17o+qX5MKxLYlb/TRdCqDIa+yK4IbTliZlOp9HaTSnUkrU4GTqnYNcGqRDCaNz7bK9qX4w6dxEZ0hZhobuu276r67+6PsbS6T4bwqGoEt1JGd4vrMP+UF639fBznSMg1FnX5uTwyUnUNgDkG2VcJSNv05WmI6CywWPqpaq0DZTZ6KruGtVd2zbng6o0o9hsjw2lpvbd4ay758WwjdF1ws69LwPfDOCJAKZUFQgpQzaYdbhe7KFhl7r9VUIQepNrGocuv63Xs83TG2NkUdUvJmE0/aarELS+hrqF0mW7KYW6WViL8yBWzou2SAAhhFXeAxfDtzmyQp0Iem1t7cADAHV/xXjAN+lEqI6ofr+sumwipF9Yh/3xjVJqK81pVTbfo8Q2otcU/UE1TVoXiRUrIb9Oe12iXlQODN9r1Mbxbopi0/3WFLHjy5Cm+nVx76HUYp0o36v8e0FboapASFlWg1lKt6V3KLyD9f1V4t00ukIjH6rtud4Y2+raFDrqkb9mv7gu6ckGanpiHwcqoWYtzptm9JpKM0yfN3XQ9/x0ib5oRl5sbBxcqq1eDh065KST6+vrrSN5VDrcR/1c5ggLX3JwXiyrDrtGKemu1TopRq1tIjBMg09UCexN7YzhwLCJtG5rs41eUdhTIeeEzW+ptTfFOdwFsWxjKi2WUkIn1NeVfz+MYn3pD9fKh6gqEFKW2WCu0I2y6ebrxRqtCnESCCGcw/sOHTp0wKuqqgNlYlEXTGFzTPd0cU1QCTVrcb+w0Y2Y559LdMNsNiOJ4nMpo9HIOa8RhQ7n4DTWjdz23eCNRS7TRliHD2KrB65Jxn3wnd5QL1WSWNU2KQfB2kbn67Yipeb66GXVfhu9ojqWNtvRRa2ZfkvtbBiiIzpmm6i0WEqJ5GIbUthgVl+MVXKxii7DVOvbdHEYdJEx33XkMPQmoPKeswMjDaYRI8oHCEqhzr2wFt+DrYGk017qaQmqddr7vkqJqQ907evayDQ9OPUx1FgHlW0R+sDBOhwP1bGhSDKuw1bfVA4MG02JNSVQdf7Gmipiq5fNfp3NZtpoapuVPqpj6nKMTffFkGnXMaZ55OAUD8HWQUdhG1NqsfoD4DJdoapASGGDOcxL1oXX0MVr3JUhbVplheImUKHKwjwajYy/7bsohhDLqWYyRigfIKiEmrW4X4Tqqu73IddF229TJYajKrq22oR32xpkMUOiXevSBRTOMyrbIpelUlmHD9J2nemWA6baZ/PcUp0jqmvKxiFmul5DSlsyRJWt2IVeqvrVVq9MEeAUOmB73GL+npIc7HuXY05hG1NpsZQSOqG+oSx/AeBLAG4EcFP5+i+oKhBS2GAu8L0IYnrYKlxuAH0ypG37n/JG0vdwNFtitd3mXKQ896mEmrW4f4QYJrrRTOoHhZgGetc67NMuG4OMSo9spnjmAEV7KR8SQrfFOhyPxeJgrrOYK3hJ6W5TNutb/b55PTbPcVt7tG05Uduytra2d4/oQi919yWXOthOx6CeIhdj8YEU2ptLPVyOeW8iL/a+ALwewNfV/n8IgN+hqkBIYYPZH13onI+HrRluViUCMoWeVaUKM+xCwClKvd06EbLZRhs5eYi7JlbbTcZIrjkvqsJavBy4OnGbSzPXUelyZbj6RF+srKwk11+Thtq2yUZTqPRIZ+Dn4riQkqa9lOHZueS8qArr8D2ksFNc9KpeD90oc1suMp0TuekAME1HpSgUg3u668h2+216pbpGdW3xgeJ8yyHiIRf7PiRnjQ+UWmwj1O+zeS9FYYPZH53Qul5AptCjyWSyz5nR9MZWnvo+hTGb+rHqQ1UC0rW1NW2fDmkJJlditd10zlPfxCiFWrIWLw0+RnAbJl2uDJL5fG6tu2tra3J7ezu5/urabduHtgYZlR7lMtpmgqK91MZ5yAMH63A8UtgpttOLbaMDVN/3uV5j6dzq6ureEp/U266uyVDNbLtGY+hAHzTURC72vYuDjgJKLbYR6lcD+HUAjy3LrwF4NVUFQgobzPtxucHrjFXXE9VG9JoecFU9Y4k/dTH1YyVCi8XBbM02I225eGZTEKvtXd/4KIVashYvBYtFe5JJ07zUNlx0WRdOHatMp9PgPEe6fjS12/a6p9SjHEb9TFCNbubykME6HI8UdopKM9qWZa5jo2vNetter7EjL3yms9iWuq3avGbrA48bGxt7DhSX5JvUOtAHDTWRi33fZ5vYRqgPA/gJAL9Xlp8AcJiqAiGFDeZ7cD0JdR43V2zE1NajGPMGQFXqy2nZiJCP2OZk/HVNzLZ3eeOjFGrJWjx4VJESs9lM6dTQabaPLpv0l9Jwrvbt+3tT0mOqnE7LpsVU7c3lIYN1OB4prg3fUWsb25JqWhN1qUOd2N7WVvU91rnoQE7kdE/pq01sK9ZHAHw11U6pChvM9+DqyaO8eFwcDqaLoy9TR2L0Y1tfLKvoD6HtlEJdFdbi4WLScNfkeK4RcVLqHR7Uulzt22U5bZUOt0GpzUPQIxeG1F7W4bh0fa74jlqborFsttHcnovt66uf9cEyKcOcFyF6mEu0wFAYksbaQqnFNiL9RAC3A/hw+f/DALyBqgIhxVWoh3yy+HijqfrD1fNsykZtu971+vq6U1LQtuzSPjeC5ojfkM8rxh9qo3koWszXSzs2Gu7SdyYdbVupRGWg2jgYXJwQtkmNTcUEn2sM6/CwCHFK6uy90OWsdaXSHp9kx81EoiFO5BA9zCVPA2t6f6HUYhuhvgnAvQHcXHvvNqoKhBRXgzmXMJ0YpPaK6sKa24ppeopuW22ZodtyS1SlSo7ZJnq+IX8MYyKC0dx7LR66DodAreGmUcE2J7Lu+Ji2V2mq7fds68k6zITAOjw8fB9gTVPzbHGNuKi2Xa93tfqT7rdNO9d135R6mfoZQ0q2H/oOpRbbCPXby78319671eJ390OxJvZfAngfgGeW718A4C0APlD+vU/5vgDwYgB3ALgVwGWmfbgIdQ4XHhWqh/C26IM28Yux//pntk4M3fZVq3Q0S/NmMJlMtMJGlVjJt7/alitk8iTUwx/BaO69Fg9Jh3XM5/M9Z2rd2arS7qpf2qLDfHMN2OhY3eHQTMrWVh9daUaI6JzCW1tbcrFYyEOHDkXX4Xp/VMekuZx3jprMI4w0sA7HJ/RcVemlbn91O3M2m+3TrWpbbXVR6eP29vaBtqi0wTX6wdQWnVZW+7WNSKbSy2abF4vFAfu63v82xzz0PPGxH9r0X3d+MPGg1GIboX4FgO8vxfOBAH4FwDUWv7u4ElsA5wH4awBbAF4A4Nnl+88G8Pzy9ZUA3lQK9iMBvMO0DxehziXkKRSV53E+n8vRaHSgfaYpGi77tTGwXULjVPuxmQKiKs0+qG5IUkqnJQFNhSq8sOo7NlTzgsLDTynUciBaPBQd1qEyMre3tw+cU9Pp9IBBWPVR03C00QefSDLV/cRHK10j8EKLCd/IOteRWF90Dwo8wkgD63Bc2s5VIYT1wJlKL1W/D3mIr65r1XLPW1tbRnvNRxsnk8mBPqtf910tP+17LKu+09n2qmcNSue8a9t0DhfW1e6h1GIboV4FsAPgXQBuLF87Z1YG8AcAvg3FXMGL5T1ifnv5+uUAnlL7/t73VGUZIy98ogZUa/ZSGsRVP9rWT/WwEsPwDR3dc+lTn+M1m83YUM0MCr2gFGo5EC0eig63QRXV1eyPNv2tlhlt6g/V/n1LmwM9ZjER0h+xNdhnis4QrpOuYR2Oi+pcrU+X0KGa7ttMVCllfxK6q67dkKnKuetlmz7ZtHU8HlvrrKltzWca2whu1tVuQNerjQTvBNgEcAbAvQB8pva+qP4HcB2AR9c+eyuAy1u2dRzFDePGo0ePWnfaUEYyKIS78h7r5v7VRcA2EZvrjaUtXLlrMacqqnMp5HixoKaDIkKAUqipSmotzlmHQ6KfYhikVR1sDMqqD/tq2PsWE6H9EVODdQ6KZYhQ6gqwDkcl9Ppxub5TO2f7XkyE6qXr0ttVsbUBdNsIuQezrnYDunBeAHiDrljvAFhHkeDoX5b/f6bx+aelg1DXyzKuNkIl3jqHRFsos01x+U1btEHfDe+2G3XI8WJBTUdOkRcYmBbnqMMhTpXFQp0sOLS4ZrRfNuPedC6F9kdMDdY5KDjygg6wDkdjsdAvP9p2/TSvWZ3d17yeU+tN34sJ6sgLF5veRtt0UTohdWdd7QZ05Lz4ewDvBvBTAB4D4JvrxWrjwATAmwE8q/Zer0PkUpMy5IyLvqhu1KY5lKqpMiyo6cgp5wVYi6Pj+7DoosdtOS+o9WfZoi9M12jo/TJV5EXOEUp9A6zDUXCZTqz7jS6PQnP63DJpW4xCcUxVZWVl5cBURheHgo2jWJcfxffcYF3tDnTkvBgD+E4ArwJwM4CfA/Bg6w0X4W+/AeBFjfd/CfuTE72gfP147E9O9E7TPvpuMPviMtJnO+eLS3hRGbqm7NVsqOZJaIQAlVCzFsfHN0zfxjgbj8dye3s7eiLLavnpPky/29ra6kyHTUlEZ7OZPHz4cOtnMVbrqtdLp/s5Rij1EbAOR8GkfW1LhLpey3X9tfk9hb1rs4RpX4sNPsdqfX29NfJa9X5bsXUUq1amcalzlZuJdbVb0HXOCwCHADwDhef5Ryx/8+jyRLkVwC1luRLADEX42wcA/DGAC+Q9wv4SAB8EcBsM4XFyAAZzCLZexrbpGVzCC7WzgQ3V4UEp1PIeXWUtjoBv5IVOh+tZ2rtIZFk5L0yh3ClLvT9j7UPlcNJpbKppGqz78WEdjoPJBq0vPeprg1Y52GwfTEejUfDUB8rEy7kVF2xW6qi0VtdfzeTSbfn2KAbrdCsxssbmAbpyXqAQ6H8J4LdRZFb+TwC+gmrnoaXvBnMItp7hyvtdXbyx5mbnUtpukmtra/uEkyL0kI1OxgSpULMWR8U3+iknI7fS+tTO6uoeY1oWL9bopo/DgRNkDhfW4TiYtM+0ck69qJy7a2trnepZ5QBOOT2l/qBPvYQq5fGta62pv9qi4WLYz2yX5w2pFis/KMLb3o0iNO4hVDukLH03mEOwNfxcEui4OjdGo9Ge0KYS+maxES8bjzLlTYBZTqiEmrW4G3wMnzZnQYgehkRpbGSQsNNl2sNisdDOd7fdn2r/Lsea8w4NF9bhONjmvDDpYS72Y6UdsRIw22hdc5oatZ67YHvcdPpZ/y7DUGmxlBI6oT4H4HNl+WytfA7AZ6kqEFKGYDD7Yiv4ttnXbT2obb/LJUy5zdBUzY8LjcBgGBNUQs1anDf10cUQo7cKcfX9fQ75jZrLbOucQBROZCqHU9sKW5x3aBiwDsejbl+1Fd3KOdXnqTWrqV2xIj2auXem0+meo7pum9ahroMLLsfNtEIhO4EZKem0WEqJ5GIbUpbZYLb1yDaNL122XindQ3lNN6euSpuhqWtraJ0ZxgSlUOdellmLpfSb1x2anT3H4jL9hqKtPqj2O5vNOOR4gLAOx8E28kKlCSZbc3V1tZNcQVU9pYw7DbDqMxeNoY4ACT2+uuOmSnTKTmCmglKLk4ttSFlmg9nWWG56PHWRF4vFQk6nUycxzGHaiOomoKrXaDQKrjPDmKAU6tzLMmuxlGajdzwe74uMqBLRNTHpTmqttdFiVfub7aVoSxObhwPOb7FcsA7HwTbnhZTt16Xu+u8yoree1DHmflQ2u+peIKX5fhCql6pjo/vMpJ+cd4JRQanFycU2pCy7wVwXCZVYNQ0ynfD4iHeV96KLm0xbUYWjmW58HHnBxIZSqHMvy67FpgfxlZUVq1wQfU6oLITQ9kNzBI468sI26sM0dZIZFqzDcbBxPuoeXm2uw64i0bpwCuvaEjM6TaWXUvolqmb9ZHyh1OLkYhtSlt1grtA9qFfZkyt0wuMr4CmjL1xvjPXfhcxtZBgTlEKde1l2LbYxMqsRNp2xmEJDKYupH+oGLsX8cptj0DSqfVeWYfoJ63AcbB+sVdeWzXXoEw2cY7Gxkdse/qlzcNgeQ50jwnTcOPKCUUGpxcnFNqQsu8FcYWMwVgISIw+EEIJ8SScfMbaJRAGKxHb171dtCLkJMEwTSqHOvSy7FpvCoOtarDNwU2goVanuM7rEbc1IwNA8H3VcpoOwgZ0HXRwH1uE42GpepQ2qbVTXfxV11jwP+q6LwMFknbY6VfURVT2a+E6hU1237BhmdFBqcXKxDSlDN5htlpqzFfZKpGIkQEo5D7vCJUt/1QdVn/p497s8zkw/oRTq3MvQtdhELisupSy2WjoajeR8PidJUFpnKOHMy3I/6OpBh3XYjO0S8/XvzOdzub6+bnWd6h7MVTZslUTeZF+qohNyyQ905MgRK12s+rRy4oxGI7m2tiaFEGTTCZvHUlcXH1Jr8LJoZypC+5dSi5OLbUgZssFsE5oVO5yuEtOu5h36lpAHh+l06nWT6+o4M/2FUqhzL0PWYhNDCW0OLeeff77TvYLCIG8eh75r6RDaYEtXDzqsw3psp2+ETF/wnRJheshuRtFWD1VbW1vJ9bBebGzMLvId2fR7iN6kTIa8TNqZAor+pdTi5GIbUoZsMJtu7LEdCqPR6ECdml7yXDzbKZwrXR1npr9QCnXuZchabCJ3526Xpet7QhOqkbdUI3jLdD8wJRqn6nfWYT0251yIxk0mkwNLQttus/q+KmJYFSGSWgdzLaZ+D73mdOdSbE1dJu2MTduxouhfSi1OLrYhZcgGs8mD2YWRWNEWLpjTPMQUTpSujjPTLZQ3WEqhzr0MWYtN5OLEzaF07ciJQcoRPN25NLQRRJtzpeqPEC1mHdZjY4OEJHNvRqVV15LNNoUQcrFYyJWVlQOftS2/zFFwZr2MaXOqtHM+nx94XwixNzWIAtPSu9U5E6onQ0d1DHXXqC1g58XwDWaTl6sL54FLTo2UhSMvGAqoH1oohTr3MmQtllLv1OqDRnZVXJL45aTDdVSa3Paw1NW+Q7UoR1ynIvi2n3VYT8zIC922bSMvdN8bj8f7BtViTb2ock90pWsx9TK2zekyal85pyjQ7YNST4aO7v4Xet6AnRfDN5hND1KxDebpdEq6RFPMksLb3tVx7pplTnhEfVOnFOrcC2txeh3MoUgpe6nDdXQPKbG12fRAPzSntu0KYSHtZx3WEzPnhSmSyPT7XLRVymFE2Nkeb2pMUREUmFa6irnvIWG6/4WcN2DnxfANZin1yxHFFLjxeJzNTcNWkF1WG6HaZ+zj3DW5OVK6hjqcklKocy9D1mKTUyuWUTsej+X29nYnidwoSpU8r8tIuC6Pd/O4x8J0fx8qttNIXGEdNuO62ojNtbm+vq7UrmrEPbVm2ZTqeu9C14QQe/k9ulhtpCubU9d3VFOkfQYxeXr2QXT2Dq82klio+0xo1ue2Mp1ODyRU6pOXWcpuDeb19fXEZ0Ecln0KC0desBa3YXJqxXD01s+5GJofo1Tzl7usbwxM9e/C4FU9rIzH4+j7ToXNecORF3nosMneWllZMY6Cp5ju61rq0xpiOFvqiUh1+V2o7HFXKBMgq9pAZV/6nE/LYtu6EHMQk1KLo4kogGsBfALAe2vv/SyAjwK4pSxX1j57DoA7ANwO4Dts9jFkg1kFteCPRqM9x8VsNtt73ZfRvsOHD0spzWFplM6Y7e3txGdBHJY9eehQc16wFodhcmrFilKbzWb7DOfcjf2mw6WL6L1YLBYLkjm+vqRocw7Uz/Pm/ajvOS+GpMPz+fzA8ak/fNtc+5XNmVq3XDSGsr6VY8TG7qDSfheo7aG2c4YystfVxl+mqGJXYkXmUGpxTKF+DIDLWoT6J1u+uwXgPQAOAfhKAB8EMDbtY8gGswrKh/DJZNLrzMx1r7jNqDnVDWCoo1/LHnkh5TBXG2EtDsNkxOm0IjSSbTKZHDgHu54i56LHbX1Xv56oH1ZSHveYsBbTaTHrMC1t10Vz5QgbvauOqYsN2vWgWvN6o45KltIuTD+FXsbQoJjTVVT1nc1me5/xaiNpodTi2GK9aSnUzwHwnNr/bwbwKNP2h2wwq6ASshiGZNfFlFCqaWhSGv1DZNlzXlBDKdShhbU4DJ3RpTOoqwf6tlEnF61u1oFSR6lGQG2MWuopJbFJlY+ItZgO1mFaKAaK6uey7XSMasnNrgbcVNcb1far/nJJkNilXvYtEpc1M38otTiFUJ8CcCuKELr7lO//KoCn1r73CgBPMm1/6AZzG67GX90orYchS5k2e/La2lrQ72ezWWvf6AxNjrwwk0vy0CFAKdShhbU4HjqtaE6lqK4tlxHEKsIsRh6Juo6G6KPLMqKUU0qGzNC1uKv2sQ7TYvNQq4rOqDSxeaxN+lf9xrSiROiSpvWlV1Xno0knbfZvEzUcI8rEhT5Gfw1dM7skRl9SanHXQn1fAGMAIwA7AK6VjkIN4DiAGwHcePTo0eDO7CMuoWS6Ey5l5IUqi/5oNLL2rNfDFG2gctZsbW2FHsI9WGzzYojTRiRrcVRUWqxbw94lEiNmlJzpgcNlO7rrpu26Cq37+eefr90+BbH1eVn132aUdGjTRuRAdFild/Xkk7PZTM7nc2tNqP+2XqoBt+p7NssYh0y1sIkqMDlQbErddlVdCzH0vtnvuusqJJLBZR+U+resekqNyhZoDoC7QqnFnQq16jNkHCKXMzbGZluEQkXqaSNtTor5fO40vcPFgUEVeVHdBFyPVVNUOcwtL4aasFOyFkfFZg646fsAWg34yWQSNcdF8/5ANce6GRbedl1R3H9027dxoLgeV0p9Xmb9N43qUvYN6zAttk7Otnw9LtuYTqdyPp9bP8jXE6n7OmJtogoWi4VcWVnx1qzmfaEZiVZ32ITqY7O4Xlfz+XxvkHE8HlvZ2y56TKl/y6yn1OjOvVxs4k6FGsDFtdc/AeA15esHY39yog8hk+REuWMTgqsy2nJcDnU2mym98G1lNBo59RWVR9v1GDU99ZPJRHnccg7LGzLUYZKUQh1aWIvjojJA21CdZ9VqT/X/68ZjLL1VtSd029V1o2pv6NTBSodNRn7Vj66Gbeyw6T6GZVNhmnpA2Tesw/TYToFTHS+ba9bnAb56uPZxjNo+lIU6Xet9onvgjjFV0Pa6Uj1X2PSRbh82503bOWPjeF5mPaXG9GyYg00cU6RfDeBjAL4E4CMAfhDAbwK4DcX8vjc0hPsEiozKtwN4nM0+lsVgtrlwTaJc/786MfuyHKqphPRlF/t0vdnlmhBp6FAnqKIU6pDCWkxPU0dcHo5tr/tYeS5M+6Qa8au2HdNJHrp9nREWO2Fd3xLiUWJ60KDsG9ZhWuoaYWNDtmmg6ZqtknP6XNO+v7MdTabQrfqDvO46oI7A8M1X0lY3FaEJSG3ug2331mXWU2pM51wONnFyQQ8py2Aw28w9ohgl63MJwdeB44LrttlTnIYhR17ELkPW4qajYnt7+4ChpDKcmqNsOkdmMwoiRtiwbp/UzhJT5AWV9oduXwVHXsTD9EAy1MiL2CWGDtf1bzabOed7aOaisE1Y7OuU9P2dLVTapdNaqlxEbdeP6bqy0VOfCAhbW7t5jdtqwTLrKTWhDiwVYOfFcA3mpqGsM3arm0JoGFs9w/LW1haZOOvKdDpNMoWjSRf71G2H5+jlw5BzXsQuQ9RiKWkMx9lsZjVFoum86GJa39ra2t7+qEf4Uua88KmnzbHnnBd06CJKh5rzInah1mHKB+ccpylXRZcbrgnFdLeq2EydoNRlm+vK5Ti55LLw3Z5tRMWy6yk1IVOHVICdF8M0mH1uFBTCVolAzMRwbfusGy8h2wrBt/9sjqVp281M2ipPNtMdlMeDUqhzL0PT4oouoh9UxlhX+66gfrioY5uw2LW4hrC3Fd0oUmx9Zv1XQ9U3rMP+xNagXKYu2yZ+XywW1qvh2RZKZ4KNJpvyM7ke8zb9bLt2dREZVLksWE/pydUmTi62IWVoBrPPjcJG2GxXJOnSM24b8mUryL74GtAU22RhHTaUQp17GZoWV7hoIoV+1o2DrpzJFZQPKiaDtIIiJL3+/3Q6dX4g4jnRw4Z12J/YNmE1iOVig1UP95T6aBsGT71CX/3BXqWXlLospTlCwfV42Oqnb2QER1QMB0otTi62IWVoBrPPjcIkpiavJxB/Ob62YhNq5iLIISwWC2eDV4ftzYYZNpRCnXsZmhZX2F7LQgi5vb1NMpJYJauzWUqwWQfXfY1GI2+j1aU9toYmxTTItlVbdN/nOdHDhnXYn9iRF9W155JIva4lVHarzQN4SG65tbU17wdwSl3WHdO6DrpERbvop+8oPkdUDANKLU4utiFlaAZzjBtF3TDtMgTaRkSb+NZRh63ouRr+OqgFn+knlEKdexmaFlfYGI5CCLm1tUU6Smlygqyvr7eueOJTh7oRHbo0q2nZxGbiPpUmh+ReansQUd1XdDkvmGHAOuxPjGkS9aJaUlpnB9aneFDZtOPx2KgDIfuy1b1m39ej0ionbMh9Rkr3VTl07eYICMYFSi1OLrYhZWgGM/XIVyXysUbUQsoll1yi7APX0GGX/lSJrcto35EjR7TH0XSTY8FfDiiFOvcyNC2uo4vMGo/H3k6DkFKPmKjjOxJZtS+kHWtra8bfTyaTAw9DVaQJVUi2ai5025LhtnPd27bHI4H9gHU4DMoElarSdGKYohyq71LWwWSXhWijTmds8wFNp1M5Go2C2iil+6ocqucHleMpNqy9/YVSi5OLbUgZosFMlcSyLvA5RVw0hbQJZeSFi0i7Gs6mY9hmKFf7ZrFdDiiFOvcyRC2uoxutstGsGA8A1WhhWwRGCj0fjUad32tcQrGpjF6eg90vWIfD6PJarkcKm7Qmhs7pVh0J0bbxeNy6TZWWUOfWqIpun7a5iVLasKy9/YZSi5OLbUgZusEcagjmvDRVJaRNfOqswiU8jnLaiJT5iD2TDkqhzr0sqxbbOppXVlaiaKgqkiGVpncd5ZdCZ11HLpm0sA6H4Xtt+thy1TWUcsBN5/x0jQqulza6bme9LX20T1l7+w2lFicX25AydIM5x+kesYXUZ761Chehc72JhBzTPt40GHcohTr3soxaXE13yGW5v9RlNBrt9VVXRnnI8fTVYdc540xaWIfD8IkCqKbTqSJQVaW6hkIH3kL1R6UJvhERqsiLrgcYfUlpt9pEo7P29gNKLU4utiFl6AazlG5ZmPtUzj///L32hAj4ysqKtu9sQ8xcHUW+x5JD3pYHSqHOvSyjFtusCLJMZW1t7UB/NfunLVIkNAGdz3EM0WEe/esXrMNhuCbtnEwmcjab7Us0aZtUmCrygmLgr00TfLVKlfNC1c7ZbBbl3uJ7/FPZrbbHkbW3H1BqcXKxDSnLYDA3GYID48iRI2TC7JKx2eQxdhkxpDx2deHlyIzhQCnUuRfW4rzK+vq68TsrKyukyUbbRr9UyeiaTiDfFQ2oj5uN5rITul+wDofTvGZdcvg0rw0bey5kikaVt4IiAqz5UOyzve3tbW2/qrSEUidj6OVsNotuq9r0N2tvf6DU4uRiG1KW0WDu+1QSysR1k8kkimjZJILywRRuzEbxsKAU6tzLMmpx1yG/LqNxpvDm0WhktTqISwkZ/VosFvvqvLa2ZmWoxzhuNprLTub+wDpMj6sdWtcGW+eh7xSNZpSDKfmnrjQdsrZLZ7voiYuWhE5T9MH2HhHDVjVNFWHt7ReUWpxcbEPKMhrMUnY7pzhE8JvrVIckO9Ld7CixcVyo5i+aMEVecDjysKAU6tzLMmpx1xpcLe1p+l4Vpk2hrS4PEFRGpMuoa6zjxpo7HFiH4+CSq6zuBDA5AKqHYF8Na7t2bSLRbLfls8oJhZ5QDFz64HKfo9ZNtomHBaUWj8AwxBw9ehQAcOzYMZw6dQrnzp3D+vo6vvSlL5Hv68yZM9rPd3d3sbm5idFohM3NTezu7mq/+7KXvcy4z7vvvtu5ngCws7OD1dXVfe+trq5iZ2cHgLotpjYyDNMtu7u7uOuuuzrd52g0MurT6uoqrr766j0N9qXSpauvvvqAZqk4duxY0D4rTpw4EeVeUXHllVcav8OayzB66vadySYajUZ7NhgAnDx5EhsbG63fPXv2LE6cOOGtYW3X7qFDh5y3I4TYs83qvPGNbySpkysnTpzA2bNng7fjSpvdqoJaN002M7PEUHlBUpRlHO3LfdpIPVlTPaQrVoi1zgPrOp/QJSQ75PipQgTZyzwswCN+gyRnDa6099ChQ16/b9Ml20g/KlzuFT5w5MVywTocB5tVINpKfXqBbiqtr85W+lW3s3x0UJVkU9deVaQahZ5Q2NC+LBYLq+kqMXSzOaVwNpvxVJGeQqnFycU2pCyTwVzhOw8wZqlPDWnOU65uVKZ5jrp26URbdYOR0i2Ts8uNoZlVnwrOeTEsKIU697JMWpzzlL1QHTehC7+m0imX/vWBIucF0x9Yh9X4JDRX2Xo+WmMasPGZIu2zRGtz37p+UNmqa2trUW04ivtOCKl0k+3i4UCpxdFEFMC1AD4B4L219y4A8BYAHyj/3qd8XwB4MYA7ANwK4DKbfSyTwSxlWOKhmMXXI2zjgQcgt7a2tDcaFTET6sU8xipjop6sqVpHXfe+z744ER0dlEIdUliLaclVT0P3OZ/Plde/zaokTS1ujpg1Nb352Ww2k5dcckl0HTY50ufzuVYDffXWdRusxTSwDrdj+1Cou45DysbGRqtt11aHLrXQdM259sVkMlH2f7WvtbU1ORqN9v2uSqpcaUSl0yFtC8Gkm/V7Rag+2uy36eCqL81b7zNVJGGuupqqfjbHLfTYUmpxTKF+DIDLGkL9AgDPLl8/G8Dzy9dXAnhTKdiPBPAOm30sk8Es5TBH/OohftSCvLKyEq3e1KgEy9Q3KseOSVTaDJfpdHogUV6XHu7cbyquUAp1SGEtpiW1ZsYqk8kkKJN9pYuxHnaodLhN++rOm6YGTiaTPYeGqh4qvW3TNFXCv/o22sK0x+Nx55o4BE0G63ArpodCKdNNkasPpqSONm5e2z4OhCNHjnSSwN5UQrBxdqm0rVrZSuVc0DmMKaYWra6uyvl83nouTafTbHQtVZSJzT3J5jsm0AfnRVFPbGK/UN8O4OLy9cUAbi9fvxzAU9q+pyvLZDBLOVyjWQght7e3vcW8jdhRKpQGnUqwXPJwNItpRRQXZ5FNOHkoQwwNRCZGs1wSLe7iQWt7ezu5XuZcun7Y8T3ebZEkq6ur3kt5t+mtStOaI6xt21DVI9aUxTaGoslgHW7FtHS7lMMcMPMp9TxuviuX5JAnqcL1XlkfRGuLaKBeAbGuMyrn1Ww2I9tnSB47SmwciiGojrtq0KJ+T1Ldt0ajkfX+0WPnxWdqr0X1P4DrADy69tlbAVyu2OZxADcCuPHo0aPWndZ3lsFo9h31a6PLm26oQaeqK8UoqAqX0YPmWucxiC3aKUDeRvOgtLiLB61cp+3lVFI+7Lgc7xj1pNiHzXnWFSk0OYYDEqzDrdgc3xRT2LjEK1K63ytN34/lsK7OQ53zgvL8zAEbh6IvuuOo6xcbx5QtGILzovz/09JRqOslh9G+LvBZV3qZShtd33RDDLoYda17TNsMwtwiL2KKdirQE6NZDkCLu3jQ4lFIc0n9sGN7vGPUM3Qf1bQQ04NAV3StybEckGAdbsWmv1nzhlV0x1SlnabvxzpHKp3R6RDlvnMgph2j27ZqoHQ0Glk5pmwBoRaP0C0fF0JcDADl30+U738UwP1q37u0fG8Q7O7uYnNzc2+d693dXevfXXjhhXjZy14WuYbDw3eNcF9C1rdW1XU8Hht/u7W11fr+8ePHARTn0PHjx3H69GlIKXH69GkcP34cV155JYQQVvW78sorrb4XgqoPuj6OS8SgtFh1/VGsO1/p9+nTp4O3NXRSX6+2x1tVz9GIziS64IILWt+fTqet7x8/fhwnTpzA2bNnldu01WwKutbktrafPXsWJ06ciLK/TEimw8eOHcPJkyexsbEBIQQ2NjZw8uRJHDt2bO87Ozs7WF1dpdwtkxjXe6XpfYp7bBuVzuh0iMo2XVtbI9lOKG3X2+rqKnZ2doK3rTuO1fNCkyNHjmjvR0DCvqPygrQVHPQy/xL2Jyd6Qfn68difnOidNttPPdpng+9oQqpESX0sqv4LWcrLtYR4Rn1yXtiuNqLztnbRttA+6Nv86jrIe8RvUFoca8SCdditpO4v2+Ot01yq/erCnVWabYrWGHLOi1iRHmAdDoI6n8GylrYE6V0nI5WyH5EXpmkp1edDy3khZbzcXabj2HZPMt2PRqORU/3Qh2kjAF4N4GMAvgTgIwB+EMAMRfjbBwD8MYALyu8KAC8B8EEAt8EiPE5mYDDb4GtUL9vNom2lC9uiopmpem1tjXyeHEBj0JlWG/EVMoqQu66mbgwhs30dZGI0Ywm0ONaDVkodnk6nUVdMilGqY1Fdx7PZzDsJpmtxPd4uy8JOJhPlg4Zqvz4P46bzretpdF1qciwHJFiHSejL9OXUq5Ooiupa6vIeI2U3OS8q7duorSSiO16m5alVfaez5at92zjT+zw92RYfG0l33HzuB+iD86KLktpgtsF3NCH13GHfospI2yxra2sHxMjG09cs29vbXscl9AbXl4dsnUFoO0raReTFEKEU6txLDloc40ErhQ432xRrP5dccslef62trVlrt6rolkyrj5JR9mksHdadS7bnmc/DuEmTh6zFy5DzInaJqcMxHrIptaB+rvjU9fDhw3vLs9avbyqnjYouo9Xq+3TRTtP3bbdHfY+20Vib5XaHrKt1fI47pSZTanFysQ0pORjMJnxHE2J5Y4UQVtMpdN9RPfhWJ3Vz5K2ZDGYymbSe/K5t9nVcSFlclCHrbPcFG695/Vg1j3vfp26khFKocy990GIfdPodQ6Pb7gs2+6mmPFTf1a1UJIRwGmWzKS7TGSgdGTkTMmW0zdheBi0e+mojsUtMHfa9VieTSesIeF2zquOtesish7NXOqkbrV8sFlZRa7YPraFab5qW0OYwaf5P4egZGi4au8y6GgKlJlNqcXKxDSl9MJipc16ECth8Pjd6IqfT6Z4TwvbBl8LbmiK7ebNeNsZ7l/OOKXARn6FN3UgJG839x+SkDXGA2hpRbXWo5kvrrlNVOK8uQqL6ncvIY4jxF+LIqK+olCshespaTAPrMA0+D/D189bmfKYc6V0sFvLQoUMkumVjF6r0q7KnQ7FxYOi0eqhTI6ijSJh4UGpxcrENKX0xmH0vlrph1/Q4r6+vW908qlDgZiJH2/oNYc6rK/V+X6abAEMLG83DoOnsnc1m+wxyijnW1TKZujqE3ENC9Vulh6Z6u6AafWQdZkJgHfanGZlpq2cUDk0qm1NlR7tu1xQd4Tqo59sWVVRdZSfrjgvDpISdF5GEOkdUnmibOVx98yjGmvPqSy7OFKafsNE8DGx0SfeQPZvN9qIk+jo1K6U2sw4zIbAO+9F2zbc9OLdN/8hdz/qMSYtVzo0+RKoxw4adF0tgMFf4hOr1mZxCunJzpjD9go3mYWDz8Owy5S0njXMhVb1Zh5kQWIf9UOnebDbrpX4NCZ0WD/XZgOk/lFosiu31k8svv1zeeOONqasRldFoBJdjtLGxgVOnTsWr0JKxu7uLEydO4MyZMzh69Ch2dnZw7Nix1NVieoAQ4iYp5eWp69EFQ9ZilQYLIXDu3DkAwObmJk6fPn3gO6zHNLAOM76wDvtho3tMfvC9iMkVSi0eUWyEicfRo0db35/NZlhdXd333urqKnZ2drqo1tJw7NgxnDp1CufOncOpU6fYYGaYJUOlwfX3d3Z2WI8jwjrMMN1io3tMfvC9iFkG2HmROSohuvrqq3Hy5ElsbGxACIGNjQ2cPHmSjTqGYRhCbIzBY8eOsR4zDDMY+CG4n/C9iFkGeNpID+CQWYbpHxyuPBxYgxmmn7AO+8O6xzAMFZRazM4LhmGYCLDRzDAMkxbWYYZhmPRwzguGYRiGYRiGYRiGYZYGdl4wDMMwDMMwDMMwDJM17LxgGIZhGIZhGIZhGCZr2HnBMAzDMAzDMAzDMEzWsPOCYRiGYRiGYRiGYZis6fVqI0KIvwdw2uOnFwL4JHF1coLb13+G3sahtw8AvlpKeV7qSnSBpxYvwzkw9DZy+/rP0NvIOtwtOZxPXAeuQ5Mc6rHsddiQUl5EsaEVio2kwrcThBA3DnnpLG5f/xl6G4fePqBoY+o6dIWPFi/LOTDkNnL7+s/Q28g63C05nE9cB65DjvXgOtDB00YYhmEYhmEYhmEYhskadl4wDMMwDMMwDMMwDJM1y+q8OJm6ApHh9vWfobdx6O0DlqONISxD/wy9jdy+/jP0Ng69fbmRQ39zHQq4DveQQz24DkT0OmEnwzAMwzAMwzAMwzDDZ1kjLxiGYRiGYRiGYRiG6QnsvGAYhmEYhmEYhmEYJmuWynkhhPhOIcTtQog7hBDPTl0fX4QQ1wohPiGEeG/tvQuEEG8RQnyg/Huf8n0hhHhx2eZbhRCXpau5HUKI+wkhbhBC/KUQ4n1CiGeW7w+ijUKIw0KIdwoh3lO273nl+18phHhH2Y7XCiGm5fuHyv/vKD/fTNoAS4QQYyHEzUKI68r/h9a+U0KI24QQt1TL8Q3lHI3NELSYdXgQbWQt7nn7WIe7JQfdU9ThZ4UQHy3Pg1uEEFfWPntOWYfbhRDfQbD/LLRRU48u+yK5hmrq8EohxIdr/fCw8v1oOpCD1rbUofN+iI6UcikKgDGADwK4P4ApgPcA2EpdL8+2PAbAZQDeW3vvBQCeXb5+NoDnl6+vBPAmAALAIwG8I3X9Ldp3MYDLytfnAfhrAFtDaWNZz/Xy9QTAO8p6vw7Ak8v3rwEwL19fBeCa8vWTAbw2dRss2/ksAL8F4Lry/6G17xSACxvvDeIcjdxvg9Bi1uFBtJG1uOftYx3uvL+T656iDj8L4CdbvrtV3mMOAfjK8t4zDtx/FtqoqUeXfZFcQzV1eCWAJ7V8P5oOIAOtbalD5/0QuyxT5MU3ArhDSvkhKeUXAbwGwHcnrpMXUso/BfCpxtvfDeBV5etXAfie2vu/IQveDuB8IcTFnVTUEynlx6SU7y5ffw7A+wF8BQbSxrKed5X/TsoiAXwrgN8p32+2r2r37wDYFkKIbmrrhxDiUgCPB/Dr5f8CA2qfhkGco5EZhBazDgPofxtZi3vePgWDOUdzIwfdU9RBxXcDeI2U8h+llB8GcAeKe1DI/rPQRk09VMToi+QaqqmDiijHIwetbdbBQG/1cJmcF18B4G9q/38E+ou8b9xXSvmx8vXfAbhv+brX7S5Dqb4ehSd1MG0sw7puAfAJAG9B4QH/jJTyy+VX6m3Ya1/5+T8AmHVaYXdeBOA/ADhX/j/DsNoHFDfHPxJC3CSEOF6+N5hzNCJD7otBHv+h6jDAWoz+t491OD259PePlOHv14pyykbsOuSijY16AB32RQ4a2qyDlLLqh52yH14ohDjUrENL/UJ4EdJrbbMOFV32Q3SWyXmxNEgpJfRex14ghFgH8LsAflxK+dn6Z31vo5TybinlwwBcisLz/TVpa0SHEOIJAD4hpbwpdV0i82gp5WUAHgfgh4UQj6l/2PdzlAljKMd/yDoMsBYPANbhjEjY3y8D8FUAHgbgYwD+W+wd5qKNLfXotC9y0NBmHYQQDwHwnLIu3wDgAgA/HWv/OWitpg6d9UNXLJPz4qMA7lf7/9LyvaHw8Srcp/z7ifL9XrZbCDFBIca7UsrXl28Pqo0AIKX8DIAbADwKRcjWSvlRvQ177Ss/vzeAO7utqRP/DMAThRCnUEwJ+FYAV2M47QMASCk/Wv79BIDfQ3HTHtw5GoEh98Wgjv+y6DDAWly+7lv7WIfzIHl/Syk/Xj7AngPwa7hnOkSUOuSijW316LovKnLQ0FodvrOcViOllP8I4H8gbj/koLUH6iCEWHTcD52wTM6LdwF4oCgyv05RJEh5Q+I6UfIGAE8vXz8dwB/U3v9/y6yyjwTwD7Wwtiwp5329AsD7pZT/vfbRINoohLhICHF++foIgG9DMVfxBgBPKr/WbF/V7icBuL706meJlPI5UspLpZSbKK6z66WUxzCQ9gGAEGJNCHFe9RrAtwN4LwZyjkZmyFo8mOM/dB0GWIvLr/W2fazD2ZC8v8X+ufr/AsV5UNXhyaJY3eErATwQwDsD95WFNqrq0XFfJNdQRR3+quZIEihyTdT7gfR45KC1ijo8tct+6AyZQdbQrgqKzKp/jWI+1onU9Qlox6tRhIJ9CcUcpR9EMVfqrQA+AOCPAVxQflcAeEnZ5tsAXJ66/hbtezSKcLtbAdxSliuH0kYADwVwc9m+9wJ4bvn+/VHcSO4A8NsADpXvHy7/v6P8/P6p2+DQ1sfinozHg2lf2Zb3lOV9lZ4M5RztoP96r8Wsw4NoI2txj9vHOpykz5PrnqIOv1nu41YUD2UX175/oqzD7QAeR7D/LLRRU48u+yK5hmrqcH3ZD+8FsMA9K5JE1QFkoLWNOiTph5hFlA1gGIZhGIZhGIZhGIbJkmWaNsIwDMMwDMMwDMMwTA9h5wXDMAzDMAzDMAzDMFnDzguGYRiGYRiGYRiGYbKGnRcMwzAMwzAMwzAMw2QNOy8YhmEYhmEYhmEYhskadl4wURBCSCHEovb/ihDi74UQ1zlu521CiMvL12+s1nIOrNt9hRDXCSHeI4T4SyHEG8v3N4UQXxBC3FIrU4ft/pIQ4n3l32cIIS5xrNeJ2n7vrr3+MSHEY4QQ7xZCfFkI8STz1hiGYViLI2jxs8q63iqEeKsQYsO13QzDLB+sxeRa/ENCiNvK//+3EGLLtd1MP1lJXQFmsHwewEOEEEeklF8A8G0APhqyQSnllSQ1A/4LgLdIKa8GACHEQ2uffVBK+TDP7R5Hsa733UKIt6FYU/lvHX7/fCnlTlmnu+r1EEJsAngGgJ/0rBvDMMsJazGtFn8LgMullGeFEHMALwDwfZ71ZBhmeWAtptXie0kprylfPxHAfwfwnZ71ZHoER14wMXkjgMeXr58C4NXVB0KINSHEtUKIdwohbhZCfHf5/hEhxGuEEO8XQvwegCO135wSQlxYvv59IcRNpUf3eO07dwkhdkrv8duFEPdtqdfFAD5S/SOlvNW2QaUX+s/KKIh3CyG+qXz/DQDWAdwkhPg+AJcD2C09wkeEEA8XQvxJWec3CyEuLn/3NiHEi4QQNwJ4pmq/UspTZT3P2daVYRimhLWYTotvkFKeLf99O4BLbevMMMzSw1pMp8Wfrf27BkDa1pnpOVJKLlzIC4C7ADwUwO8AOAzgFgCPBXBd+fnPA3hq+fp8AH+NQnyeBeDa8v2HAvgyilEuADgF4MLy9QXl3yMoPLmz8n8J4LvK1y8A8B9b6vYdAD4D4AYAJwBcUr6/CeALZV1vAfCSlt+uAjhcvn4ggBvrba69flut3hMA/wfAReX/31dr49sAvLSt/xT9+koAT0p9fLlw4dKPwlocR4vLz361rV1cuHDh0iysxfRaDOCHAXwQwN8AeGDqY8ylm8LTRphoSClvFcV0h6eg8DbX+XYATxRCVNMgDgM4CuAxAF5c+73K+/tjQoh/Ub6+HwrBvBPAFwFU8wdvQhGW16zXm4UQ90cRXvY4ADcLIR5SfmwKj5sA+FUhxMMA3A3gQZrvVnw1gIcAeIsQAgDGAD5W+/y1FttgGIbxgrV4DzItFkI8FcVI4jfb/oZhmOWGtXgPEi2WUr4EwEuEEN8P4D8CeLrN75h+w84LJjZvAPDLKLzLs9r7AsC/klLeXv9yKWJahBCPBXAFgEfJYt7x21CIPAB8SUpZhY7dDcU5LqX8FIDfAvBbokiW9BgUom7iJwB8HMD/g2La1f+1+I0A8D4p5aMUn3/eYhsMwzAhsBYTabEQ4goUo5PfLKX8R5vfMAzDlLAW09vFrwHwMsffMD2Fc14wsbkWwPOklLc13n8zgB8VpSoLIb6+fP9PAXx/+d5DUITINbk3gE+XAv01AB7pUiEhxLcKIVbL1+cB+CoAZyx/fm8AH5NSngPwNBTe4jY+B+C88vXtAC4SQjyq3OdECPFglzozDMMEwlpMoMVl/7wcwBOllJ9w+S3DMAxYiwEaLX5g7d/HA/iAy++Z/sLOCyYqUsqPSClf3PLRf0URanarEOJ95f9A4TldF0K8H0X24zav7/8CsFJ+5xdRJE1z4eEAbixD7/4CwK9LKd9l+duXAni6EOI9AL4Gau/wKwFcI4S4BYWQPwnA88vf3QLgm1wqLIT4BiHERwD8awAvL/uMYRjGCtZiGi0G8EsoktD9dpl47g2Ov2cYZolhLSbT4h8pk5PegiIvCE8ZWRLEPZFEDMMwDMMwDMMwDMMw+cGRFwzDMAzDMAzDMAzDZA07LxiGYRiGYRiGYRiGyRp2XjAMwzAMwzAMwzAMkzXsvGAYhmEYhmEYhmEYJmvYecEwDMMwDMMwDMMwTNaw84JhGIZhGIZhGIZhmKxh5wXDMAzDMAzDMAzDMFnz/wPkPyd7EYnhHAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize =(18, 4))\n",
    "\n",
    "axs[0].scatter(medianAllFeatureTable['MSF_afterT1'], medianAllFeatureTable['med_SF_beforeT'],color='black')\n",
    "axs[0].set_title('BeforeT and afterT SF: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[0].set_xlabel('Median SF afterT1')\n",
    "axs[0].set_ylabel('Median SF beforeT')\n",
    "\n",
    "axs[1].scatter(medianAllFeatureTable['MSF_afterT2'], medianAllFeatureTable['med_SF_beforeT'],color='black')\n",
    "axs[1].set_title('BeforeT and afterT SF: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[1].set_xlabel('Median SF afterT2')\n",
    "axs[1].set_ylabel('Median SF beforeT')\n",
    "\n",
    "axs[2].scatter(medianAllFeatureTable['MSF_afterT3'], medianAllFeatureTable['med_SF_beforeT'],color='black')\n",
    "axs[2].set_title('BeforeT and afterT SF: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[2].set_xlabel('Median SF afterT3')\n",
    "axs[2].set_ylabel('Median SF beforeT')\n",
    "\n",
    "# plt.savefig('Data_Transfusion_VitalSigns_12_18_22/SF_BeforeAndAfterT_RedCellsT_6_23_22.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c2b3dd5e-8d03-4d40-9fd9-d821f8a4c532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median PF beforeT')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize =(18, 4))\n",
    "\n",
    "axs[0].scatter(medianAllFeatureTable['MPF_afterT1'], medianAllFeatureTable['med_PF_beforeT'],color='black')\n",
    "axs[0].set_title('BeforeT and afterT PF: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[0].set_xlabel('Median PF afterT1')\n",
    "axs[0].set_ylabel('Median PF beforeT')\n",
    "\n",
    "axs[1].scatter(medianAllFeatureTable['MPF_afterT2'], medianAllFeatureTable['med_PF_beforeT'],color='black')\n",
    "axs[1].set_title('BeforeT and afterT PF: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[1].set_xlabel('Median PF afterT2')\n",
    "axs[1].set_ylabel('Median PF beforeT')\n",
    "\n",
    "axs[2].scatter(medianAllFeatureTable['MPF_afterT3'], medianAllFeatureTable['med_PF_beforeT'],color='black')\n",
    "axs[2].set_title('BeforeT and afterT PF: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[2].set_xlabel('Median PF afterT3')\n",
    "axs[2].set_ylabel('Median PF beforeT')\n",
    "\n",
    "# plt.savefig('Data_Transfusion_VitalSigns_12_18_22/PF_BeforeAndAfterT_RedCellsT_6_23_22.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9d6b508d-5649-4971-bfad-319d148d9d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median SpO2 beforeT')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize =(18, 4))\n",
    "\n",
    "axs[0].scatter(medianAllFeatureTable['MSPO2_afterT1'], medianAllFeatureTable['med_spo2_beforeT'],color='black')\n",
    "axs[0].set_title('BeforeT and afterT SpO2: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[0].set_xlabel('Median SpO2 afterT1')\n",
    "axs[0].set_ylabel('Median SpO2 beforeT')\n",
    "\n",
    "axs[1].scatter(medianAllFeatureTable['MSPO2_afterT2'], medianAllFeatureTable['med_spo2_beforeT'],color='black')\n",
    "axs[1].set_title('BeforeT and afterT SpO2: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[1].set_xlabel('Median SpO2 afterT2')\n",
    "axs[1].set_ylabel('Median SpO2 beforeT')\n",
    "\n",
    "axs[2].scatter(medianAllFeatureTable['MSPO2_afterT3'], medianAllFeatureTable['med_spo2_beforeT'],color='black')\n",
    "axs[2].set_title('BeforeT and afterT SpO2: Red cells transfusion (\\u03C4=9hrs)')\n",
    "axs[2].set_xlabel('Median SpO2 afterT3')\n",
    "axs[2].set_ylabel('Median SpO2 beforeT')\n",
    "\n",
    "# plt.savefig('Data_Transfusion_VitalSigns_12_18_22/SpO2_BeforeAndAfterT_RedCellsT_6_23_22.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7160236c-697e-42a5-8c99-7159439cfad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Transfused blood volume (ml)')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize =(18, 4))\n",
    "\n",
    "axs[0].scatter(medianAllFeatureTable['del_SPO2_1'], medianAllFeatureTable['unit'],color='black')\n",
    "axs[0].set_title('$\\Delta$SpO$_2$ vs Blood volume: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[0].set_xlabel('AfterT1 $\\Delta$SpO$_2 (\\%)$')\n",
    "axs[0].set_ylabel('Hb (g/dL) just before transfusion')\n",
    "\n",
    "axs[1].scatter(medianAllFeatureTable['del_SPO2_2'], medianAllFeatureTable['unit'],color='black')\n",
    "axs[1].set_title('$\\Delta$SpO$_2$ vs Blood volume: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[1].set_xlabel('AfterT2 $\\Delta$SpO$_2 (\\%)$')\n",
    "axs[1].set_ylabel('Transfused blood volume (ml)')\n",
    "\n",
    "axs[2].scatter(medianAllFeatureTable['del_SPO2_3'], medianAllFeatureTable['unit'],color='black')\n",
    "axs[2].set_title('$\\Delta$SpO$_2$ vs Blood volume: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs[2].set_xlabel('AfterT3 $\\Delta$SpO$_2 (\\%)$')\n",
    "axs[2].set_ylabel('Transfused blood volume (ml)')\n",
    "\n",
    "# plt.savefig('Data_Transfusion_VitalSigns_12_18_22/delSpO2vsBloodV_RedCellsT_6_23_22.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0a69a7c5-8112-4b3f-980b-fdef12f34ce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Blood volume (ml)')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots()\n",
    "\n",
    "axs.scatter(medianAllFeatureTable['Hb'], medianAllFeatureTable['unit'],color='black')\n",
    "axs.set_title('Hb vs Blood volume: Red cells transfusion patients (\\u03C4=9hrs)')\n",
    "axs.set_xlabel('Hb (g/dL) just before transfusion')\n",
    "axs.set_ylabel('Blood volume (ml)')\n",
    "\n",
    "# plt.savefig('Data_Transfusion_VitalSigns_12_18_22/HbvsBloodV_RedCellsT_6_23_22.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99966568-df80-43f4-ae05-51e875e059b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "# x = iris.iloc[:, :-1].values #last column values excluded\n",
    "# y = iris.iloc[:,   -1].values #last column value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32563e2e-3005-4ee0-8cc4-b4623d65073c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08f7f1e2-1491-4eb1-827c-9028bb831fb7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "284289d8-64f7-423b-91b8-bbbd378f50db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d05040b-2c96-4c2b-9618-3d11793fab04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa0273dc-0a19-4c58-a506-d44828b5bedb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "417f4966-4b8e-42b4-8749-199cd13b42b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e807773b-dc1d-4008-91fb-e3c2ca40cf48",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b12f083-675b-47d0-8e05-d260b184050b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dae8fc2-b806-4f94-b2b0-4087bd35df9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "704d8f3c-a164-4475-b216-6d0cd3740dfa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e7d9b2d-1e9e-4ba8-811e-a3de04b1e086",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84255d01-47a2-4c54-829c-14033a803734",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eaa250b-4541-4178-b11f-356481bd84fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(ResponderIndex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76a8791e-cf69-4b89-8f57-ce23819ba96e",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(NonResponderIndex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51ab0466-3864-4565-97f0-a558dac3879d",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(FileNotFoundErrorCases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6b6e985-4aa0-475a-b7b4-b23f9273b8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(np.unique(ResponderList))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad4c873f-1b9b-485d-82ce-1315e936894e",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(np.unique(NonResponderList))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ea8c198-a8e9-4817-9798-17f2d7f11418",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(IndexErrorCases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed5318f2-8813-4de2-9847-81689666bb92",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(ValueErrorCases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89cceabd-8824-4673-8894-f56a8a588144",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(LenLessThan24HrIndexAfterT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a787e724-f18a-418c-83c0-5d091681d75d",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(LenLessThan24HrIndexBeforeT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcd0e7a8-66ba-487a-990b-603c429723e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(ErrorLessThan5HrAfterTtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5c98fe4-2eef-433a-b907-2f178decb395",
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(GoodSpO2CasesBeforeT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceee5a18-2529-4249-87df-65faaa01729d",
   "metadata": {},
   "outputs": [],
   "source": [
    "TransfusionVar = {'ResponderIndex': ResponderIndex, 'NonResponderIndex': NonResponderIndex, \n",
    "                 'ResponderList': ResponderList, 'NonResponderList': NonResponderList,\n",
    "                 'FileNotFoundErrorCases': FileNotFoundErrorCases, 'IndexErrorCases': IndexErrorCases,\n",
    "                 'ValueErrorCases': ValueErrorCases, 'LenLessThan24HrIndexAfterT': LenLessThan24HrIndexAfterT,\n",
    "                 'LenLessThan24HrIndexBeforeT': LenLessThan24HrIndexBeforeT, 'ErrorLessThan5HrAfterTtime': ErrorLessThan5HrAfterTtime}\n",
    "                 #'GoodSpO2CasesBeforeT': GoodSpO2CasesBeforeT}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1318b9-6586-4881-b28d-cbdf9f1ed1f1",
   "metadata": {},
   "source": [
    "*Saving TransfusionVar in pickle*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36894385-13cc-4678-a7d2-28acfae76956",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ###Saving TransfusionVar in pickle\n",
    "# with open('TransfusionVar_6_22_22_improvFactor_2.pickle', 'wb') as handle:\n",
    "#     pickle.dump(TransfusionVar, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "\n",
    "###with open('TransfusionVar_5_27_22.pickle', 'rb') as handle:\n",
    "###     varAll = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086e4267-a51e-44a8-bf64-17d7d0ef1e7d",
   "metadata": {},
   "source": [
    "**Divide Responders vs Non-Responders**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3126a8ab-c650-45ba-b707-d722b15078e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Only Responders and NonResponders are included for this study\n",
    "#Starting 458 indexes (0 to ...) are---> Responders \n",
    "#After 459 index (... to ...) are---> NonResponders\n",
    "lenResponders = len(ResponderIndex)\n",
    "ResponderNonresponders = IntersectionBeforeDuringAfter.iloc[np.append(ResponderIndex, NonResponderIndex)]\n",
    "ResponderNonresponders = ResponderNonresponders.reset_index()\n",
    "ResponderNonresponders = ResponderNonresponders[['deBedTime','PAT_ID', 'csn',\n",
    "       'dispense_dt_tm', 'DateStart', 'DateEnd', 'estimated_transfusion_dt_tm', 'FileStart', 'FileStop', 'product_cd_disp',\n",
    "       'product_cat_cd_disp','Hb_collection_time','Hb','MedSPO2_beforeT','MedSPO2_afterT','del_SPO2']]\n",
    "ResponderNonresponders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098091ed-4434-4fcc-a158-b70642cd4470",
   "metadata": {},
   "source": [
    "*Writing in CSV*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "682cf77c-c966-4344-ba6a-800336e9c6f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ResponderNonresponders.to_csv(\"ResponderNonresponders_6_22_22_improv_factor_2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2a6329e-3254-4f12-859f-ec7410abfea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.scatter(ResponderNonresponders['del_SPO2'], ResponderNonresponders['Hb'],color='black')\n",
    "plt.title('$\\Delta$SpO$_2$ vs Hb: All Patients')\n",
    "plt.xlabel('$\\Delta$SpO$_2 (\\%)$')\n",
    "plt.ylabel('Hb (g/dL)')\n",
    "\n",
    "#plt.savefig('delSpO2vsHb_RedCellsT_6_22_22.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a9c87d7-9e4c-4ae4-a3de-4b764dd7bccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.scatter(ResponderNonresponders.iloc[:lenResponders]['del_SPO2'], ResponderNonresponders.iloc[:lenResponders]['Hb'], color='blue')\n",
    "plt.title('$\\Delta$SpO$_2$ vs Hb: Responders')\n",
    "plt.xlabel('$\\Delta$SpO$_2 (\\%)$')\n",
    "plt.ylabel('Hb (g/dL)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc9acca5-d98c-4156-90e5-e0ce28a0d875",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.scatter(ResponderNonresponders.iloc[lenResponders:]['del_SPO2'], ResponderNonresponders.iloc[lenResponders:]['Hb'],color='red')\n",
    "plt.title('$\\Delta$SpO$_2$ vs Hb: Nonresponders')\n",
    "plt.xlabel('$\\Delta$SpO$_2 (\\%)$')\n",
    "plt.ylabel('Hb (g/dL)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dc4f6ad-f101-4be8-858e-0740fcea82bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.scatter(ResponderNonresponders.iloc[:lenResponders]['del_SPO2'], ResponderNonresponders.iloc[:lenResponders]['Hb'], color='blue')\n",
    "plt.scatter(ResponderNonresponders.iloc[lenResponders:]['del_SPO2'], ResponderNonresponders.iloc[lenResponders:]['Hb'],color='red')\n",
    "plt.title('$\\Delta$SpO$_2$ vs Hb: Responders and Nonresponders') #: Responders and Nonresponders #Red Cells Transfusion: \n",
    "plt.xlabel('$\\Delta$SpO$_2 (\\%)$')\n",
    "plt.ylabel('Hb (g/dL)')\n",
    "plt.legend(['Responders ($\\Delta$SpO$_2$>2)','Nonresponders ($\\Delta$SpO$_2$<2)'])\n",
    "plt.ylim([2,18])\n",
    "\n",
    "# plt.savefig('delSpO2vsHb_improvFactor_2_RedCellsT_6_22_22.png')"
   ]
  }
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