{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/MIT-LCP/sccm-datathon/blob/master/04_timeseries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dImagXnW2Lfz"
   },
   "source": [
    "# eICU Collaborative Research Database\n",
    "\n",
    "# Notebook 4: Timeseries for a single patient\n",
    "\n",
    "This notebook explores timeseries data for a single patient.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mUTUkkJb2YTK"
   },
   "source": [
    "## Load libraries and connect to the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F9DjPZSV2Vyn"
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Make pandas dataframes prettier\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "# Access data using Google BigQuery.\n",
    "from google.colab import auth\n",
    "from google.cloud import bigquery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MmKc7haE2bbQ"
   },
   "outputs": [],
   "source": [
    "# authenticate\n",
    "auth.authenticate_user()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3I7O9JpE2c4q"
   },
   "outputs": [],
   "source": [
    "# Set up environment variables\n",
    "project_id='sccm-datathon'\n",
    "os.environ[\"GOOGLE_CLOUD_PROJECT\"]=project_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yiPWgbRb2hDV"
   },
   "source": [
    "## Selecting a single patient stay\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JS3FAZa7G_pg"
   },
   "source": [
    "### The patient table\n",
    "\n",
    "The patient table includes general information about the patient admissions (for example, demographics, admission and discharge details). See: http://eicu-crd.mit.edu/eicutables/patient/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u0WMh7hv2fLQ"
   },
   "outputs": [],
   "source": [
    "# Display the patient table\n",
    "%%bigquery\n",
    "\n",
    "SELECT *\n",
    "FROM `physionet-data.eicu_crd_demo.patient`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "colab_type": "code",
    "id": "vfS1ibvPHU6V",
    "outputId": "b22bfa53-e356-4b2b-d06a-54f9db768b3a"
   },
   "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>patientunitstayid</th>\n",
       "      <th>patienthealthsystemstayid</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>hospitalid</th>\n",
       "      <th>wardid</th>\n",
       "      <th>apacheadmissiondx</th>\n",
       "      <th>admissionheight</th>\n",
       "      <th>hospitaladmittime24</th>\n",
       "      <th>...</th>\n",
       "      <th>unitadmitsource</th>\n",
       "      <th>unitvisitnumber</th>\n",
       "      <th>unitstaytype</th>\n",
       "      <th>admissionweight</th>\n",
       "      <th>dischargeweight</th>\n",
       "      <th>unitdischargetime24</th>\n",
       "      <th>unitdischargeoffset</th>\n",
       "      <th>unitdischargelocation</th>\n",
       "      <th>unitdischargestatus</th>\n",
       "      <th>uniquepid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>210014</td>\n",
       "      <td>182373</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>73</td>\n",
       "      <td>89</td>\n",
       "      <td>Hypertension, uncontrolled (for cerebrovascula...</td>\n",
       "      <td>178.0</td>\n",
       "      <td>13:08:59</td>\n",
       "      <td>...</td>\n",
       "      <td>Direct Admit</td>\n",
       "      <td>1</td>\n",
       "      <td>admit</td>\n",
       "      <td>116.0</td>\n",
       "      <td>112.7</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>4424</td>\n",
       "      <td>Skilled Nursing Facility</td>\n",
       "      <td>Alive</td>\n",
       "      <td>002-10665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200026</td>\n",
       "      <td>174624</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>71</td>\n",
       "      <td>87</td>\n",
       "      <td>Ablation or mapping of cardiac conduction pathway</td>\n",
       "      <td>177.8</td>\n",
       "      <td>10:41:00</td>\n",
       "      <td>...</td>\n",
       "      <td>Operating Room</td>\n",
       "      <td>1</td>\n",
       "      <td>admit</td>\n",
       "      <td>106.1</td>\n",
       "      <td>106.1</td>\n",
       "      <td>17:40:00</td>\n",
       "      <td>1548</td>\n",
       "      <td>Home</td>\n",
       "      <td>Alive</td>\n",
       "      <td>002-10715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>221131</td>\n",
       "      <td>190993</td>\n",
       "      <td>Male</td>\n",
       "      <td>83</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>71</td>\n",
       "      <td>87</td>\n",
       "      <td>Endarterectomy, carotid</td>\n",
       "      <td>175.3</td>\n",
       "      <td>21:43:00</td>\n",
       "      <td>...</td>\n",
       "      <td>Operating Room</td>\n",
       "      <td>1</td>\n",
       "      <td>admit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.1</td>\n",
       "      <td>17:46:00</td>\n",
       "      <td>1203</td>\n",
       "      <td>Home</td>\n",
       "      <td>Alive</td>\n",
       "      <td>002-10249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>221215</td>\n",
       "      <td>191054</td>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>71</td>\n",
       "      <td>87</td>\n",
       "      <td>Infarction, acute myocardial (MI)</td>\n",
       "      <td>185.4</td>\n",
       "      <td>03:16:00</td>\n",
       "      <td>...</td>\n",
       "      <td>Emergency Department</td>\n",
       "      <td>1</td>\n",
       "      <td>admit</td>\n",
       "      <td>145.3</td>\n",
       "      <td>146.6</td>\n",
       "      <td>19:07:00</td>\n",
       "      <td>1562</td>\n",
       "      <td>Home</td>\n",
       "      <td>Alive</td>\n",
       "      <td>002-10627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>217835</td>\n",
       "      <td>188445</td>\n",
       "      <td>Male</td>\n",
       "      <td>57</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>73</td>\n",
       "      <td>92</td>\n",
       "      <td>CABG alone, coronary artery bypass grafting</td>\n",
       "      <td>172.7</td>\n",
       "      <td>01:09:00</td>\n",
       "      <td>...</td>\n",
       "      <td>Operating Room</td>\n",
       "      <td>1</td>\n",
       "      <td>admit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.4</td>\n",
       "      <td>08:25:00</td>\n",
       "      <td>4719</td>\n",
       "      <td>Floor</td>\n",
       "      <td>Alive</td>\n",
       "      <td>002-10324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   patientunitstayid  patienthealthsystemstayid gender age  ethnicity  \\\n",
       "0             210014                     182373   Male  45  Caucasian   \n",
       "1             200026                     174624   Male  50  Caucasian   \n",
       "2             221131                     190993   Male  83  Caucasian   \n",
       "3             221215                     191054   Male  49  Caucasian   \n",
       "4             217835                     188445   Male  57  Caucasian   \n",
       "\n",
       "   hospitalid  wardid                                  apacheadmissiondx  \\\n",
       "0          73      89  Hypertension, uncontrolled (for cerebrovascula...   \n",
       "1          71      87  Ablation or mapping of cardiac conduction pathway   \n",
       "2          71      87                            Endarterectomy, carotid   \n",
       "3          71      87                  Infarction, acute myocardial (MI)   \n",
       "4          73      92        CABG alone, coronary artery bypass grafting   \n",
       "\n",
       "   admissionheight hospitaladmittime24    ...           unitadmitsource  \\\n",
       "0            178.0            13:08:59    ...              Direct Admit   \n",
       "1            177.8            10:41:00    ...            Operating Room   \n",
       "2            175.3            21:43:00    ...            Operating Room   \n",
       "3            185.4            03:16:00    ...      Emergency Department   \n",
       "4            172.7            01:09:00    ...            Operating Room   \n",
       "\n",
       "  unitvisitnumber  unitstaytype admissionweight  dischargeweight  \\\n",
       "0               1         admit           116.0            112.7   \n",
       "1               1         admit           106.1            106.1   \n",
       "2               1         admit             NaN             72.1   \n",
       "3               1         admit           145.3            146.6   \n",
       "4               1         admit             NaN             80.4   \n",
       "\n",
       "  unitdischargetime24 unitdischargeoffset     unitdischargelocation  \\\n",
       "0            15:00:00                4424  Skilled Nursing Facility   \n",
       "1            17:40:00                1548                      Home   \n",
       "2            17:46:00                1203                      Home   \n",
       "3            19:07:00                1562                      Home   \n",
       "4            08:25:00                4719                     Floor   \n",
       "\n",
       "  unitdischargestatus  uniquepid  \n",
       "0               Alive  002-10665  \n",
       "1               Alive  002-10715  \n",
       "2               Alive  002-10249  \n",
       "3               Alive  002-10627  \n",
       "4               Alive  002-10324  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8WkUTZ66Hmp3"
   },
   "source": [
    "### The `vitalperiodic` table\n",
    "\n",
    "The `vitalperiodic` table comprises data that is consistently interfaced from bedside vital signs monitors into eCareManager. Data are generally interfaced as 1 minute averages, and archived into the `vitalperiodic` table as 5 minute median values. For more detail, see: http://eicu-crd.mit.edu/eicutables/vitalPeriodic/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "__dKFPdlHh_a"
   },
   "outputs": [],
   "source": [
    "# Get periodic vital signs for a single patient stay\n",
    "%%bigquery vitalperiodic\n",
    "\n",
    "SELECT *\n",
    "FROM `physionet-data.eicu_crd_demo.vitalperiodic`\n",
    "WHERE patientunitstayid =  210014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "colab_type": "code",
    "id": "q2cCCvmLH8_K",
    "outputId": "49b8056f-926c-4c3c-b32c-11a57f27763c"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>vitalperiodicid</th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>observationoffset</th>\n",
       "      <th>temperature</th>\n",
       "      <th>sao2</th>\n",
       "      <th>heartrate</th>\n",
       "      <th>respiration</th>\n",
       "      <th>cvp</th>\n",
       "      <th>etco2</th>\n",
       "      <th>systemicsystolic</th>\n",
       "      <th>systemicdiastolic</th>\n",
       "      <th>systemicmean</th>\n",
       "      <th>pasystolic</th>\n",
       "      <th>padiastolic</th>\n",
       "      <th>pamean</th>\n",
       "      <th>st1</th>\n",
       "      <th>st2</th>\n",
       "      <th>st3</th>\n",
       "      <th>icp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58557726</td>\n",
       "      <td>210014</td>\n",
       "      <td>251</td>\n",
       "      <td>None</td>\n",
       "      <td>94</td>\n",
       "      <td>83</td>\n",
       "      <td>25</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>155</td>\n",
       "      <td>86</td>\n",
       "      <td>107</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58595556</td>\n",
       "      <td>210014</td>\n",
       "      <td>661</td>\n",
       "      <td>None</td>\n",
       "      <td>94</td>\n",
       "      <td>101</td>\n",
       "      <td>24</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58594652</td>\n",
       "      <td>210014</td>\n",
       "      <td>651</td>\n",
       "      <td>None</td>\n",
       "      <td>94</td>\n",
       "      <td>91</td>\n",
       "      <td>26</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58599780</td>\n",
       "      <td>210014</td>\n",
       "      <td>711</td>\n",
       "      <td>None</td>\n",
       "      <td>94</td>\n",
       "      <td>97</td>\n",
       "      <td>39</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58597662</td>\n",
       "      <td>210014</td>\n",
       "      <td>686</td>\n",
       "      <td>None</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vitalperiodicid  patientunitstayid  observationoffset temperature sao2  \\\n",
       "0         58557726             210014                251        None   94   \n",
       "1         58595556             210014                661        None   94   \n",
       "2         58594652             210014                651        None   94   \n",
       "3         58599780             210014                711        None   94   \n",
       "4         58597662             210014                686        None   94   \n",
       "\n",
       "   heartrate respiration   cvp etco2 systemicsystolic systemicdiastolic  \\\n",
       "0         83          25  None  None              155                86   \n",
       "1        101          24  None  None             None              None   \n",
       "2         91          26  None  None             None              None   \n",
       "3         97          39  None  None             None              None   \n",
       "4         94          30  None  None             None              None   \n",
       "\n",
       "  systemicmean pasystolic padiastolic pamean  st1  st2  st3   icp  \n",
       "0          107       None        None   None  0.6  0.3 -0.3  None  \n",
       "1         None       None        None   None  0.7  0.4 -0.3  None  \n",
       "2         None       None        None   None  0.7  0.5 -0.1  None  \n",
       "3         None       None        None   None  0.7 -0.2 -0.8  None  \n",
       "4         None       None        None   None  0.6  0.3 -0.2  None  "
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vitalperiodic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "colab_type": "code",
    "id": "VcZegYL3IB94",
    "outputId": "6a27edb7-7818-4f58-9ff7-9199a3698c61"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vitalperiodicid</th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>observationoffset</th>\n",
       "      <th>temperature</th>\n",
       "      <th>sao2</th>\n",
       "      <th>heartrate</th>\n",
       "      <th>respiration</th>\n",
       "      <th>cvp</th>\n",
       "      <th>etco2</th>\n",
       "      <th>systemicsystolic</th>\n",
       "      <th>systemicdiastolic</th>\n",
       "      <th>systemicmean</th>\n",
       "      <th>pasystolic</th>\n",
       "      <th>padiastolic</th>\n",
       "      <th>pamean</th>\n",
       "      <th>st1</th>\n",
       "      <th>st2</th>\n",
       "      <th>st3</th>\n",
       "      <th>icp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>58538807</td>\n",
       "      <td>210014</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>94</td>\n",
       "      <td>79</td>\n",
       "      <td>19</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>58539159</td>\n",
       "      <td>210014</td>\n",
       "      <td>11</td>\n",
       "      <td>None</td>\n",
       "      <td>96</td>\n",
       "      <td>74</td>\n",
       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>58539497</td>\n",
       "      <td>210014</td>\n",
       "      <td>16</td>\n",
       "      <td>None</td>\n",
       "      <td>97</td>\n",
       "      <td>77</td>\n",
       "      <td>18</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>58539727</td>\n",
       "      <td>210014</td>\n",
       "      <td>21</td>\n",
       "      <td>None</td>\n",
       "      <td>97</td>\n",
       "      <td>79</td>\n",
       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>58540110</td>\n",
       "      <td>210014</td>\n",
       "      <td>26</td>\n",
       "      <td>None</td>\n",
       "      <td>95</td>\n",
       "      <td>81</td>\n",
       "      <td>15</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vitalperiodicid  patientunitstayid  observationoffset temperature sao2  \\\n",
       "224         58538807             210014                  6        None   94   \n",
       "258         58539159             210014                 11        None   96   \n",
       "275         58539497             210014                 16        None   97   \n",
       "46          58539727             210014                 21        None   97   \n",
       "235         58540110             210014                 26        None   95   \n",
       "\n",
       "     heartrate respiration   cvp etco2 systemicsystolic systemicdiastolic  \\\n",
       "224         79          19  None  None             None              None   \n",
       "258         74          14  None  None             None              None   \n",
       "275         77          18  None  None             None              None   \n",
       "46          79          14  None  None             None              None   \n",
       "235         81          15  None  None             None              None   \n",
       "\n",
       "    systemicmean pasystolic padiastolic pamean  st1  st2  st3   icp  \n",
       "224         None       None        None   None  0.5  0.8  0.2  None  \n",
       "258         None       None        None   None  0.5  0.8  0.2  None  \n",
       "275         None       None        None   None  0.5  0.8  0.1  None  \n",
       "46          None       None        None   None  0.6  0.8  0.1  None  \n",
       "235         None       None        None   None  0.5  0.7  0.1  None  "
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the values by the observationoffset (time in minutes from ICU admission)\n",
    "vitalperiodic = vitalperiodic.sort_values(by='observationoffset')\n",
    "vitalperiodic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "colab_type": "code",
    "id": "rIRB7WPzIIdU",
    "outputId": "dc2b8ad0-a6b1-4bb8-a2a0-4cd463769d97"
   },
   "outputs": [
    {
     "data": {
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       "      <th>temperature</th>\n",
       "      <th>sao2</th>\n",
       "      <th>heartrate</th>\n",
       "      <th>respiration</th>\n",
       "      <th>cvp</th>\n",
       "      <th>etco2</th>\n",
       "      <th>systemicsystolic</th>\n",
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       "      <th>systemicmean</th>\n",
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       "      <th>padiastolic</th>\n",
       "      <th>pamean</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>6</th>\n",
       "      <td>None</td>\n",
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       "      <td>79</td>\n",
       "      <td>19</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <th>11</th>\n",
       "      <td>None</td>\n",
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       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>18</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
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       "      <td>15</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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      ],
      "text/plain": [
       "                  temperature sao2  heartrate respiration   cvp etco2  \\\n",
       "observationoffset                                                       \n",
       "6                        None   94         79          19  None  None   \n",
       "11                       None   96         74          14  None  None   \n",
       "16                       None   97         77          18  None  None   \n",
       "21                       None   97         79          14  None  None   \n",
       "26                       None   95         81          15  None  None   \n",
       "\n",
       "                  systemicsystolic systemicdiastolic systemicmean pasystolic  \\\n",
       "observationoffset                                                              \n",
       "6                             None              None         None       None   \n",
       "11                            None              None         None       None   \n",
       "16                            None              None         None       None   \n",
       "21                            None              None         None       None   \n",
       "26                            None              None         None       None   \n",
       "\n",
       "                  padiastolic pamean   icp  \n",
       "observationoffset                           \n",
       "6                        None   None  None  \n",
       "11                       None   None  None  \n",
       "16                       None   None  None  \n",
       "21                       None   None  None  \n",
       "26                       None   None  None  "
      ]
     },
     "execution_count": 52,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subselect the variable columns\n",
    "columns = ['observationoffset','temperature','sao2','heartrate','respiration',\n",
    "          'cvp','etco2','systemicsystolic','systemicdiastolic','systemicmean',\n",
    "          'pasystolic','padiastolic','pamean','icp']\n",
    "\n",
    "vitalperiodic = vitalperiodic[columns].set_index('observationoffset')\n",
    "vitalperiodic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 553
    },
    "colab_type": "code",
    "id": "iVe95ZiZILtR",
    "outputId": "6f220a4d-aadd-43b5-9fa7-218f085877c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absolute value')"
      ]
     },
     "execution_count": 75,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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BwM3NLd8ftl999ZWkS9OxSpQooTvvvFPt2rXTihUrbFOalixZoi+++KLIGurXr6+EhARb\nAFq9erUqVKigKlWqqGHDhrYRujNnzui7777Ld/zDDz+snTt3Ki4uTlarVWPHjlVUVJRt+5kzZ/KM\nfhWkdevW+uabb2z3Fy1ZskQPP/xwkcdclp6eblswY+3atapbt648PT2VmJioWrVqyc3NTVu2bNGR\nI0dsf0Tn7g+XWSwWPfzww1qyZImkSyM6I0eOvOrUOovFogsXLshqtaps2bJyc3OzreR3PavwWSwW\nnT9/XhcvXlS/fv1s98DVqVNHFotFZrM5X3/+17/+JW9vbx07dkybNm2yfU7p0tS9//znP7r33nvz\njIZKl/qcm5ubbdRq+fLlBfbba5m2t27dOv3++++aNm2aoeAkSXfffbd8fX1t/4jxxRdfqHLlyrrz\nzjsVFBSkrVu36s8//5R06T7By9NjC/PYY4/pp59+0pYtW7RlyxY988wzeuaZZwhOAHAFRp6A21C1\natVsU76qVatW4D4tWrTQvHnz1K1bN/3vf//T6tWr1b59ewUGBmrYsGF66aWXNGnSpHwjDpf16tVL\nY8aM0YQJE2S1WtW2bVs1b95cO3bssO0zduxYhYWF6eOPP1aXLl1s9wAVpbDz5maxWFSrVi3t2bPH\ndr+Km5ubLl68qEceeUTnzp3TxIkTZTabFRQUpEOHDtnuXalatareeeedImvw8vLSf/7zH02YMEGp\nqany9fXV9OnTZTKZ9OSTT2r79u0KCgpSpUqVFBQUlC90VKhQQePHj9eAAQPk5uamevXq2RZiSExM\nVHx8fJFLmkuXAtzzzz+vPn36KCcnR7Vq1bIt/HA1lStX1o4dOzR16lRdvHjRthjHSy+9pPDwcL3/\n/vtq166dXnnlFc2cOVO1atVSUFCQpk6dqqNHj+aZtvb2229r7Nix+vzzzyVd+iO8YsWKRY483Xff\nfXrvvffUqlUrbdq0SUOGDNGzzz6r8uXLq1+/foY+Q24hISHq1auXJk6cqO7du9vuwTKbzRo9erRK\nliypNm3a6I033tCxY8f06quvaujQoQoODta9996r0NBQDRkyRPPnz9fAgQPVsWNHhYeHF/g8LXd3\nd02YMEGjRo2Sh4eHnnjiiSKnwxmxdOlSHTt2LM8Kd40aNVJ4eLhGjhypn3/+WfHx8XJ3d9fKlSvV\nt29f9e3bV++9957CwsI0a9Ys+fn5aerUqZKkgIAAjR07VoMHD1Z2drZq165tC22fffaZIiMjdeHC\nBV24cEEhISGqX7++pkyZckOfAQBuFyZrQfNbAMBBLk/Xki7djzV//nzVrFnzhs87Z84c/fXXXwoP\nD1dcXJw6dOig/fv33/B57W3p0qXatGnTVVczvF6xsbEaPXq0vvnmG7uc/1aQmZmptm3b6quvvlKZ\nMmWcXQ4A4CbCtD0ATjN06FDNnTtX0qWlta1Wq6pXr14s5+7du7c2b96c5zlLN7uLFy8qMjKywBEP\nOM78+fP18MMPE5wAAPkQngA4zauvvqqYmBgFBwfrnXfe0ZQpU1SiRIliObePj4/Gjx+v0NDQAheQ\nuBlFRESoffv2ql+/vrNLuW2FhIRo06ZNGj58uLNLAQDchJi2BwAAAAAGMPIEAAAAAAYQngAAAADA\nAMITAAAAABhAeAIAAAAAAwhPAAAAAGAA4QkAAAAADCA8AQAAAIABhCcAAAAAMIDwBAAAAAAGEJ4A\nAAAAwADCEwAAAAAYQHgCAAAAAAMITwAAAABgAOEJAAAAAAwgPAEAAACAAYQnAAAAADDA4uwC7CU+\nPtkp1y1b1ktJSalOuTZcB/0ERtBPYAT9BEbQT+zL39/H2SXAQRh5KmYWi5uzS4ALoJ/ACPoJjKCf\nwAj6CVA8CE8AAAAAYADhCQAAAAAMIDwBAAAAgAGEJwAAAAAwgPAEAAAAAAYQngAAAADAAMITAAAA\nABhAeAIAAAAAAwhPAAAAAGAA4QkAAAAADCA8AQAAAIABhCcAAAAAMIDwBAAAAAAGEJ4AAAAAwADC\nEwAAAAAYYHF2Abh2uxKTtenEGZ1Oy1T5kh56uKKvGvj5OLssAAAA4JZGeHIxuxKTtfTPk7b3J9My\nbe8JUAAAAID9MG3PxWw6ceaa2gEAAAAUD8KTizmVllFg++lC2gEAAAAUD8KTiymrCwW2lymkHQAA\nAEDxIDy5mEamPQW2NyykHQAAAEDxIDy5mIDUBN1v2m1776ckBZm3qEJqghOrAgAAAG59hCcXczLh\nbgWa/1ltr4clWneb/9aphLudWBUAAABw6yM8uZhqNZvp778DbO/PJ3tr566aqlqzmROrAgAAAG59\nPOfJxdSoHaBj5+60vd9/6CE1al5VNWoHFHEUAAAAgBtFeHJB/gE+UuKl108OauLcYgAAAIDbBNP2\nXJDZZHJ2CQAAAMBth/DkgsxmfjYAAADA0fgr3AVZxcgTAAAA4GiEJxdEeAIAAAAcj/AEAAAAAAYQ\nngAAAADAAMKTC7JanV0BAAAAcPshPAEAAACAAYQnl8TQEwAAAOBohCcXlEN2AgAAAByO8OSSSE8A\nAACAoxGeAAAAAMAAwpMLyr3aXg5z+AAAAACHIDy5pH8CU052jhPrAAAAAG4fhCcXlHusKTubkScA\nAADAEQhPLi47O9vZJQAAAAC3BcKTC7LmuumJkScAAADAMQhPLu5iFvc8AQAAAI5AeHJBuRfYy8oh\nPAEAAACOQHhycVmstgcAAAA4BOHJxWVlcc8TAAAA4AgWZ148LS1NoaGhSkxMVEZGhl5++WXVrFlT\nb775prKzs+Xv76+pU6fKw8NDK1euVGRkpMxms5588kn16NHDmaU7ldWao8u5l5EnAAAAwDGcGp6+\n/fZb1a1bV88995yOHTumZ555Ro0bN9ZTTz2ljh07avr06YqKilKXLl00e/ZsRUVFyd3dXd27d1f7\n9u1VpkwZZ5bvNEkJqZLlDknSd9/8puzm1VWjdoCTqwIAAABubU6dttepUyc999xzkqQTJ04oICBA\nsbGxateunSSpTZs22rp1q3bt2qV69erJx8dHJUqUUOPGjbVz505nlu40h/af0l+HEmzvL1xIV8zK\nX3Vo/yknVgUAAADc+m6Ke5569eqlN954Q6NGjVJaWpo8PDwkSX5+foqPj1dCQoJ8fX1t+/v6+io+\nPt5Z5TrVzq1/520wmyRJP1/ZDgAAAKBYOXXa3mVLlizRr7/+qhEjRuR5AGzu17kV1p5b2bJesljc\niq3Ga+Hv72O3cyclpspSIVeD2Wprt+d1Ufz4vWAE/QRG0E9gBP0EuHFODU979+6Vn5+fKlasqFq1\naik7O1ve3t5KT09XiRIldOrUKZUvX17ly5dXQsI/U9VOnz6thg0bFnnupKRUe5dfIH9/H8XHJ9vt\n/GX9vJRsyrS9N5n+abfndVG87N1PcGugn8AI+gmMoJ/YF8H09uHUaXvbt2/XJ598IklKSEhQamqq\nHnzwQa1du1aStG7dOrVq1UoNGjTQnj17dP78eaWkpGjnzp26//77nVm60zRuXjVvw///BRtd2Q4A\nAACgWDl15KlXr15666239NRTTyk9PV1jxoxR3bp19e9//1tLly5VpUqV1KVLF7m7u2v48OEaNGiQ\nTCaTBg8eLB+f2zPh16gdoMNnffXn/39f0ttdrR66i9X2AAAAADszWY3cQOSCnDU07Yhh8Z8O/Kov\nki/l3s4l3dS87r/sej0UP6ZPwAj6CYygn8AI+ol9MW3v9nFTrLaH65dza2ZfAAAA4KZDeHJBeVYk\nVI4TKwEAAABuH4QnF3T0osn2+rsMaVciw/AAAACAvRGeXMyuxGTtyPhnnY8LVmnpnycJUAAAAICd\nEZ5czKYTZ66pHQAAAEDxIDy5mNNpmQW3pxfcDgAAAKB4EJ5cTPmSHgW3lyi4HQAAAEDxIDy5mIcr\n+l5TOwAAAIDiQXhyMQ38fNTIM8v2/g7lqOe/KqiBHw9nAwAAAOyJ8OSCKluyba+bWDIITgAAAIAD\nEJ4AAAAAwADCkwuyZifYXrvl7FdK0l4nVgMAAADcHghPLiYlaa+U+ZvtvUlpSjy8nAAFAAAA2Bnh\nycWcP7m5kPYtDq4EAAAAuL0QnlzMxfT4PO+thbQDAAAAKF6EJxfjXsJfVplytZhs7QAAAADsh/Dk\nYkpVaFlIewsHVwIAAADcXizOLgDXxrtsXeVY4qXMS+9zVEJ+1Z+Qd9m6zi0MAAAAuMUx8uSKzH62\nlxdVg+AEAAAAOADhyQVZbctEXHoHAAAAwP4ITy7OSnYCAAAAHILw5IIITAAAAIDjEZ5cHDkKAAAA\ncAzCk0vinicAAADA0QhPAAAAAGAA4cnFMe4EAAAAOAbhyQVZmbUHAAAAOBzhyQXlzU6kJwAAAMAR\nCE8ujugEAAAAOAbhCQAAAAAMIDy5IKbqAQAAAI5HeHJxBCkAAADAMQhPLsiae7k9q8l5hQAAAAC3\nEcKTi2PcCQAAAHAMwpOrsxKfAAAAAEcgPLkg4hIAAADgeIQnF2Qt5DUAAAAA+yE8AQAAAIABhCdX\nlOs+J5YqBwAAAByD8OSCiEsAAACA4xGeXJG1wJcAAAAA7Ijw5OpYqhwAAABwCMKTC8obl0xOqgIA\nAAC4vRCeXJK1gFcAAAAA7InwBAAAAAAGEJ5cHEuVAwAAAI5BeHJBxCUAAADA8QhPAAAAAGAA4ckF\nWVkwAgAAAHA4wpMrIjEBAAAADkd4cnUEKQAAAMAhLM4uYMqUKdqxY4eysrL0wgsvaMOGDdq3b5/K\nlCkjSRo0aJBat26tlStXKjIyUmazWU8++aR69Ojh5Mqdx1rIawAAAAD249TwtG3bNh06dEhLly5V\nUlKSunbtqmbNmun1119XmzZtbPulpqZq9uzZioqKkru7u7p376727dvbAtbtjPAEAAAAOIZTw1OT\nJk1Uv359SVKpUqWUlpam7OzsfPvt2rVL9erVk4+PjySpcePG2rlzp9q2bevQem8WVsaeAAAAAIdz\n6j1Pbm5u8vLykiRFRUXpoYcekpubmxYtWqT+/fvrtdde05kzZ5SQkCBfX1/bcb6+voqPj3dW2QAA\nAABuQ06/50mSYmJiFBUVpU8++UR79+5VmTJlVKtWLc2ZM0cRERFq1KhRnv2t1quPtpQt6yWLxc1e\nJRfJ39/Hrud3t1ikzEuvzWaz3a8H++B3gxH0ExhBP4ER9BPgxjk9PH3//ff68MMP9dFHH8nHx0fN\nmze3bWvbtq3efvttBQcHKyEhwdZ++vRpNWzYsMjzJiWl2q3movj7+yg+Ptmu17h4Mcv2Ojsnx+7X\nQ/FzRD+B66OfwAj6CYygn9gXwfT24dRpe8nJyZoyZYr+97//2RZ/GDJkiI4ePSpJio2NVY0aNdSg\nQQPt2bNH58+fV0pKinbu3Kn777/fmaU7lbXQNwAAAADsxakjT19//bWSkpI0bNgwW9sTTzyhYcOG\nqWTJkvLy8lJ4eLhKlCih4cOHa9CgQTKZTBo8eLBt8QgAAAAAcAST1cgNRC7IWUPTjhgWX7n9R22z\nlpUk1b0Yr6cefNCu10PxY/oEjKCfwAj6CYygn9gX0/ZuH06dtgcAAAAAroLw5OJuyWFDAAAA4CZE\neHJFt+ZMSwAAAOCmRnhyccQoAAAAwDEITy7IWsQ7AAAAAPZBeHJBeeOSyUlVAAAAALcXwpPLY+QJ\nAAAAcATCk4sjOgEAAACOQXgCAAAAAAMITy6J8SYAAADA0QhPLojoBAAAADge4ckF5Q5PPC8XAAAA\ncAzCkyuyFvgSAAAAgB0RngAAAADAAMITAAAAABhAeHJJ1gJeAQAAALAnwpMLIjABAAAAjkd4ckFW\nq8nZJQAAAAC3HcITAAAAABhAeHJJue55YhAKAAAAcAjCk6vjBigAAADAIQhPAAAAAGAA4ckFWQt5\nDQAAAMB+CE8uyFrEOwAAAAD2QXhyeawYAQAAADgC4cnFMe4EAAAAOAbhCQAAAAAMIDy5IoabAAAA\nAIcjPLkgK+kJAAAAcDjCk4sjRgEAAACOQXgCAAAAAAMITwAAAABgAOHJBVkLeQ0AAADAfghPAAAA\nAGAA4ckFMdoEAAAAOJ7lWg84cOCAYmNjdeLECfXq1UvVq1eXJB09elSBgYHFXR8AAAAA3BQMhyer\n1aoxY8YoKipKVqtVJpNJ7dq1U/Xq1ZWZmanHH39cHTp00LvvviuzmQEtR2EUCgAAAHAMwyln8eLF\n+vzzz9W+fXtNmzZNVus/f7YAgNdAAAAgAElEQVRnZWXpscce04oVKxQZGWmXQgEAAADAmQyPPC1f\nvlytW7fWzJkzlZycnGebl5eX3n77bSUmJuqLL77Q008/XeyF4h+stgcAAAA4nuGRp7/++ktt2rQp\ncp/WrVvryJEjN1wUroLEBAAAADic4fBktVplsRQ9UJWVlcX9To5gcnYBAAAAwO3HcNKpUaOGNm7c\nWOj2zMxMLV26VDVq1CiOugAAAADgpmI4PPXs2VPffPONJk6cqIMHD0qSEhIStGfPHkVGRuqxxx7T\nr7/+qp49e9qtWOTHDD4AAADAMQwvGNG9e3f9/vvvmj9/vhYvXixJev311yXJtvLegAED1K1bNzuU\nidzyBibm8AEAAACOcE0PyQ0NDVWXLl20Zs0a/fHHH0pJSZG3t7fuvvtuhYSEqGbNmvaqE7lYi3gH\nAAAAwD6uKTxJUs2aNQlJAAAAAG47LI3nkhhtAgAAABzN8MhTrVq1DO1nMpm0f//+6y4IBlgLfAkA\nAADAjgyHp2rVqslkyr84QUZGhk6ePKmcnBw1aNBAXl5exVogroYFIwAAAABHMByeoqOjC92WkZGh\nhQsXatmyZZo5c2axFIbCWQlMAAAAgMMVyz1Pnp6eevbZZ/XAAw9o0qRJxXFKGMS0PQAAAMAxrnm1\nvaI0atTomsPTlClTtGPHDmVlZemFF15QvXr19Oabbyo7O1v+/v6aOnWqPDw8tHLlSkVGRspsNuvJ\nJ59Ujx49irN0F0NkAgAAABytWMPTyZMnlZWVZXj/bdu26dChQ1q6dKmSkpLUtWtXNW/eXE899ZQ6\nduyo6dOnKyoqSl26dNHs2bMVFRUld3d3de/eXe3bt1eZMmWKs3yXQXQCAAAAHM9wePrpp58K3ZaZ\nmam9e/fqo48+Uo0aNQxfvEmTJqpfv74kqVSpUkpLS1NsbKzGjRsnSWrTpo0++eQT3XnnnapXr558\nfHwkSY0bN9bOnTvVtm1bw9cCAAAAgBthODz169evwNX2LrNarfL09NTw4cMNX9zNzc22Ol9UVJQe\neughbd68WR4eHpIkPz8/xcfHKyEhQb6+vrbjfH19FR8fb/g6tzJGoQAAAADHMByeBg8eXGh4slgs\n8vf3V8uWLRUQEHDNRcTExCgqKkqffPKJOnToYGu3WguOBoW151a2rJcsFrdrrqU4+Pv72PX8ZvM/\n63yYTPa/HuyD3w1G0E9gBP0ERtBPgBtnODwNGTLELgV8//33+vDDD/XRRx/Jx8dHXl5eSk9PV4kS\nJXTq1CmVL19e5cuXV0JCgu2Y06dPq2HDhkWeNykp1S71Xo2/v4/i45Pteo3snBzbOolWq+x+PRQ/\nR/QTuD76CYygn8AI+ol9EUxvH8WyVPn1Sk5O1pQpU/S///3PtvjDgw8+qLVr10qS1q1bp1atWqlB\ngwbas2ePzp8/r5SUFO3cuVP333+/M0sHAAAAbllTpkxRz5491a1bN61bt87Z5dw0Ch15ateu3XWd\n0GQyKSYmxtC+X3/9tZKSkjRs2DBb26RJkzR69GgtXbpUlSpVUpcuXeTu7q7hw4dr0KBBMplMGjx4\nsG3xiNsd9zwBAAC4vu9+jtPn6w/p71PJqhrgox7tauihRlWcUktBK2LnvrXmdlZoeDJyX9GNHtez\nZ0/17NkzX/u8efPytYWEhCgkJOS6arrV5PmGC1/DAwAAAC7gu5/jNHXRDtv7wyfO295fb4A6fvy4\nRowYIbPZrOzsbE2dOlXjx49Xamqq0tPTFRYWpvr16ys2NlYzZsyQxWJRQECAwsPDC1wROzs7W25u\nzllP4GZSaHjasGGDI+vA9bKSngAAAG5mn6zapy27jhW6PfF8eoHtMz7bqcjV+wvc1qJBZT3TuU6h\n51y7dq0efPBBDR48WPv27dPx48fVo0cPBQUFaevWrZo7d65mzZqlsWPHat68eapYsaLGjx+vVatW\nqVu3bvlWxCY4XVKsD8ndsWOH1qxZo9GjRxfnaVEEpu0BAAC4tuzsgv+iyyqk3YgWLVrolVdeUXJy\nsoKDg1WzZk2NHz9eH3/8sTIzM+Xl5aWzZ8/KZDKpYsWKkqSmTZvmebZr7hWxcck1h6esrCwlJiYq\nOzs7T3t6ero+//xzwhMAAACQyzOd6xQ5SjTkvW91+MT5fO3VK5bSrDfaXNc177nnHq1YsUJbtmzR\n9OnT1bRpUwUEBGjq1Knas2ePpkyZIpPJlOeWm4sXL9oeTXTliti4xHB4ysnJ0dSpU7VkyRKlpxc8\ntGi1WnXvvfcWW3EwgFl7AAAALq1Huxp57nnK3X69Vq9ercDAQAUFBalMmTJas2aN7e/0mJgYXbx4\nUaVLl5bJZNLx48dVqVIl/fjjj7rvvvtsK2LPnz/ftiI2LjEcnpYsWWKbDxkYGKgff/xR9erVk5ub\nmw4ePCgPDw/16tWrwAUgULyYqgcAAHDruLwoxOfrD+noqWQFFsNqe9WrV9fYsWPl5eUlNzc3DR48\nWGFhYYqOjlafPn301VdfadmyZZowYYKGDx8ui8WiwMBAPfLII1q2bFm+FbEnT56sSpUq3fBndXUm\nq8Hl8bp27Sp/f399+OGHunDhgh544AEtXLhQTZo00fnz5xUeHq6kpCTNmjVL7u7u9q77qpz1IDhH\nPIRu3tYtOmQpL0mqcDFBQx9sbtfrofjxsEIYQT+BEfQTGEE/sS8eknv7MPyQ3L///lvBwcEym822\nuZCXc1epUqX07rvvKi0tTREREfapFAAAAACcyHB4ysrKso0olShRQiaTSefP/3Njm8lkUufOnbV6\n9erirxJF4KYnAAAAwBEMh6eqVatqy5YtkiR3d3f5+fnphx9+yLNPRkaGEhMTi7dC5GMt4h0AAAAA\n+zC8YETnzp01ffp0mc1mhYeHq2nTplqyZIl8fX3VrFkzxcXF6cMPP1RgYKA968UVrIw8AQAAAA5h\nODwNHDhQBw4csE3VGzp0qLZs2aLZs2dr9uzZki5N3RszZox9KgUAAAAAJzIcnjw8PDR9+nRlZmZK\nkqpVq6avvvpKX3zxheLi4lSuXDkFBwfrnnvusVuxuISJegAAAIDjGQ5PsbGxatq0qTw8PGxt5cqV\n03PPPWeXwmAMQQoAAABXio2N1eLFizVz5sxiOd/atWsVHBxsaN8DBw7I09NTd955Z7Fc+2ZiODwN\nGDBAlSpV0iOPPKLOnTszwuRMJCYAAIBbypa/f9IX+9cq7vwJVSlVUV1rB6tF1SbOLkuSFBcXp9Wr\nVxsOT998843q1q17e4en7t27KyYmRnPnztVHH32ke+65R48//rgeeeQRBQQE2LNGXIk1IgAAAG4Z\nW/7+Sf/d+ont/d/njtne30iASklJ0RtvvKGDBw8qODhYISEhGj9+vEwmk7y9vTVp0iSVKlVK4eHh\n2r17tzIyMtS7d2/16NFDoaGhcnd319mzZ5WRkaHdu3crIiJCVqtVR48eVVxcnObPn6+RI0fq1KlT\nSk1N1ZAhQ1SpUiXbonJ+fn7KzMzU9OnTZbFYVLFiRU2YMCHPTDZXYzg8TZw4UePGjdO2bdu0Zs0a\nxcTEaMqUKZo2bZoeeOABPfbYY+rQoYO8vb3tWS8kRp4AAABcyMJflmnb0Z2Fbj+Tdq7A9ohtkfp0\n15cFbmsW2Fj9GnYr8rp//PGH1qxZo5ycHLVr104//fSTxo8fr+rVq2vx4sVavHixnnnmGVWuXFkj\nR45Uenq6goKC1KNHD0lS6dKlNWHCBNsUwFdeeUWzZs3SxYsX9emnnyoxMVEtW7ZU165ddfToUb36\n6qtavny5WrVqpeDgYNWvX19dunTR/PnzVaZMGU2ZMkXR0dF67LHHDH5zNx/D4UmS3Nzc1KJFC7Vo\n0SJfkNq6davGjRuntm3bavr06faqFyI7AQAA3EqyrdnX1G5U7dq1VbJkSUmS1WrV7t27FRYWJknK\nzMxUvXr15OnpqXPnzqlXr15yd3dXUlKS7fj69esXeN7L7aVKldKePXu0dOlSmc1mnT17Ns9+CQkJ\nOnLkiIYMGSJJSk1NVdmyZW/oMznbNYWn3K4MUsuWLdN///tfrVmzhvDkQAQpAACAm1u/ht2KHCV6\nI3qi/j53LF97tdKVNTVk9HVf12LJ+6d+yZIltWDBAplM/9wD8uOPP2rbtm1auHCh3N3d1ahRI9s2\nd3f3As97uf2rr77SuXPn9Omnn+rs2bPq3r17vv3Kly+vhQsXXvdnuNmYb+TgXbt2acqUKerUqZPG\njh2rM2fOqGHDhsVVGwzhBigAAABX1rV2wQsxdCmk/XrVrFlT3333nSRp9erV2rp1q5KSklShQgW5\nu7tr/fr1ys7Otj2a6DKz2aysrKx850tKSlKVKlVkNpv1zTff2I4zmUzKzs5W6dKlJUm///67JGnh\nwoU6cOBAsX4mR7vmkaft27crOjpaMTExOnXqlKxWq+6991699tprevTRR1WpUiV71IncyEsAAAC3\njMuLQnyZa7W9LnZYbe+tt95SWFiY5s6dK09PT02bNk1ubm6aO3eu+vbtq6CgILVu3Vpvv/12nuPu\nuusu7d+/X++++658fHxs7R06dNBLL72kX375Rd26dVOFChUUERGh+++/XxMnTpS3t7feeecdjRw5\n0jYK1bNnz2L9TI5mslqthmZ+jR07VuvXr1diYqKsVqsCAwP1yCOP6NFHH9Xdd99t7zqvWXx8slOu\n6+/vY/drz926RX9Zyl+63sUzeu3Bpna9HoqfI/oJXB/9BEbQT2AE/cS+/P19rr4TbgmGR56WLl2q\ncuXKqW/fvurcuXOhN5ABAAAAwK3IcHiaN2+emjZtKrP5hm6TQjGzMoUPAAAAcAjD4al58+b2rAPX\nIs9ES9ITAAAA4AgMIwEAAACAAYQnF5R7qh7PeQIAAAAcg/DkirjRCQAAAHA4wpNLYrwJAAAAjvHS\nSy8Z3nf9+vXKzMxUfHy8xowZY8eqnOOaH5J72YULF+Tp6Sl3d/firAeGMPIEAABwK4n/brPiopYp\n9WicvAKrqEr3bvJ/qKWzy5IkffDBB4b3nT9/vpo1ayZ/f3+NHz/ejlU5xzWFpz/++EPTp0/Xjz/+\nqJSUFEVGRqpJk0tPPh43bpx69uypmjVr2qVQ/MNayGsAAAC4nvjvNuu3aTNs71OP/G17f70Bavny\n5fruu+90+vRptWrVSps2bZLZbFZQUJCeeeYZ7d+/X+PGjZOHh4c8PDw0Y8YMRUZG6uTJkzpx4oTi\n4+M1YsQIPfTQQ2ratKliY2PVr18/1ahRQ5L0/PPPa8SIEZKkrKwsTZ48WTt37tQvv/yi5557Tu+8\n846GDx+u5cuXKzY2VjNmzJDFYlFAQIDCw8P11VdfaceOHTpz5oz++usvDRo0SD169LjBb9L+DIen\nP//8Uz179lRaWppq1KihgwcP2radOXNGX375pVatWqVPP/1U99xzj12KRUEYhQIAALiZ/TUvUok/\nbC10e2bimQLbD/13po4sXFTgNr8Hm+vOpwcUed0TJ07ovffe06hRo/TZZ59Jknr37q2QkBAtX75c\nvXv3VpcuXbR161bFx8dLkk6dOqVPPvlEBw8e1L///W899NBDec5Zo0YN9e7dW7t379bgwYPVrFkz\nRUVF6dNPP1VoaKhmzpypuXPnKikpyXbM2LFjNW/ePFWsWFHjx4/XqlWrZDKZ9Ntvv2nJkiU6fPiw\nXn/9dZcIT4bveZo9e7YsFouWL1+uBQsWyGr9Z8zD19dXK1euVIkSJfT+++/bpVAAAADgVmTNzi64\nPavgdqPq1aunPXv26MiRI+rfv7/69++vlJQUHTt2TO3atdMHH3yg//znP/Lz89Ndd90l6Z9nu957\n7706depUvnPWr19fkuTv76+FCxeqT58+ioyM1NmzZwus4ezZszKZTKpYsaIkqWnTpvr1118lSQ0b\nNpSbm5sqVKig5OTkG/qsjmJ45Ck2NlZ9+/bVvffeW+CHCwwMVJ8+fbRw4cJiLRAFYLAJAADAZdz5\n9IAiR4l+HvqaUo/8na/dq3o1Nfrv9Ou+rru7u9zd3dW6desC7z+KiorSt99+q9DQUL355puSpJyc\nnKueU5Jmzpypli1bqnfv3oqOjtbGjRsL3N9kMuUZdLl48aJMpkt/zFos1738gtMYHnk6e/asqlat\nWuQ+VapU0fnz52+4KBjHPU8AAACurUr3bgW3d3vihs9dp04dxcbGKi0tTVarVRMnTlR6eroWLVqk\ns2fP6rHHHtOAAQNso0E7duyQJB04cECVKlUq9LxJSUmqWrWqrFar1q9fr4sXL0q6FJayc42klS5d\nWiaTScePH5ck/fjjj6pbt+4Nfy5nMRz3ypYtq6NHjxa5z/79++Xr63vDRaFoBCYAAIBbx+VFIeKW\nLVfa0TiVDKyiKt2eKJbV9ipVqqT+/furT58+cnNzU1BQkEqUKKGqVavq1VdflY+Pjzw8PBQeHq7P\nPvtMd9xxh1588UUdO3ZMo0aNKvS8PXv21IQJE1S5cmX169dPYWFh2rx5sx544AE99dRTCg8Pt+07\nYcIEDR8+XBaLRYGBgXrkkUe0cuXKG/5szmCy5h5HK8LIkSO1efNmffbZZypdurSaNGmihQsXqkmT\nJsrOztYXX3yhcePG6dFHH83zZTlLfLxz5k36+/vY/dofbv1Bf1v8JUlls85pRPP77Xo9FD9H9BO4\nPvoJjKCfwAj6iX35+/s4u4RiMWvWLJUtW1Z9+/Z1dik3LcMjT6+88oq+/fZbdenSRffdd59MJpPm\nzJmjjz76SHv27FFSUpLKlCmjV155xZ71AgAAAIBTGA5PlStXVlRUlCZNmqSNGzfKarXq+++/v3QS\ni0VBQUF68803VblyZbsVi0uuHCpc+vFPaty8qmrUDsjTfmj/Ke3c+rfOxKfIzc2knByrypbzLnBf\nAAAA3N6GDBni7BJuete0xEWVKlUUERGh9PR0HT58WCkpKfL29tadd94pT09Pe9WIK1hz/olPVpN0\nJj5FMSsv3eR3ORQd2n/K1iZJ2dmXjiloXwAAAABXZ3i1vYiICP3xxx+SpBIlSqhmzZq67777VLNm\nTVtwWrt27U1xv9OtLien4NvUtn37p+31zq35l7vM7eerbAcAAACQ1zWFpz///LPIfY4cOaL/+7//\nu+GiULTClvi4kJyhQ/svPcwsKSGlyHMkJaYWd1kAAADALa3IaXsxMTFav3697f2iRYu0YcOGAvfN\nzMzU999/rxIlShRvhcjHlOshudYrnpj789a/VaN2gMqW89aZ+MIDlJe3h73KAwAAAG5JRYYni8Wi\nuLg47du3TyaTSbGxsUWerESJEho5cmSxFoj8TGZTodsujyg1bl41zz1PV7o8SsV9TwAAAMht7dq1\nCg4Ovu7j58yZoyZNmqhRo0bFWNUlV6utadOmio2N1TvvvKP+/fsrMDCwWK9fZHhq3bq1WrdurZyc\nHNWuXVtvv/22WrVqVeC+bm5uKleunCyWa1qDAtfBZCo8PEnKE4qKClCXR6kAAADgXHt/PqbN6w8p\n/tQF+QfcoZbtaqhuI8evYh0XF6fVq1ffUHh6/vnni7Gif1xLbW+99ZZdajCUdMxms8LDw9WsWTNV\nrFjRLoXgGhQRnnJyrLbAdCLuXJGn4b4nAAAA59v78zEtX7TT9v70iWTb++sNUMePH9eIESNkNpuV\nnZ2tkydPav78+apatapOnjypl19+WREREXn2mTp1qsaPH6/du3crIiJCAwcO1KhRo3Tu3DllZ2dr\n9OjRqlmzpoKCgvTkk08qOjpa1apVU506dWyvp02bptDQUAUHB6tly5YKDQ3VsWPH5OnpqSlTpig7\nOzvfNYcNG6Zp06ZdU20DBgxQaGiozp8/r6ysLI0ePVp16tSxff5+/fopLCxMFSpU0BtvvKELFy7I\nx8dH06dPl7e393X/ViartbDlB1ybs56i7YgneL+/daviLOUkSaWyz6v0xuu73h0+nuo3uHlxlgaD\neNI7jKCfwAj6CYygn9iXv79Pkdu/WbVf+3cdL3R78vl05WTn/5Pc7GaST6mC1xOo3aCS2neuXeg5\n582bp9TUVA0ePFj79u3Tpk2bZDab9eKLL2rx4sVKT0+X2WzOs8/FixeVkZGhxYsXa+bMmZo9e7bK\nly+vHj166Pfff9c777yjefPmqW3btho/frxatGih1q1bKzQ0VB07dlTr1q21cuVKvfvuuwoODlZC\nQoJ+//13jRw5UqtXr9a5c+eUkZGR75p79+7VhQsXrqm2iIgIeXh46Pnnn9eePXs0efJkLVq0yDZt\n73J4Wr16tfz8/NS/f3/Nnz9fVapUUVBQUJG/V1EMz7GrVauWof1MJpP2799/3QXh6vL+p1X0FL6i\ncN8TAACA8xUUnIpqN6JFixZ65ZVXlJycrODgYPXu3VuDBg3Siy++qI0bN2rixIk6d+5cnn0aNWqU\nZ42Dn3/+WWfOnNHKlSslSWlpabZt9evXl8lkkp+fn2rXvhTifH19lZz8T0jft2+fmje/9A/1jzzy\niCTpt99+y3fNatWqXXNte/fu1UsvvSRJqlevno4cOVLg97B//369+uqrkqSBAwde9/d5meHwVK1a\ntQLvtcnIyNDJkyeVk5OjBg0ayMvL64aLguNw3xMAAIB9te9cu8hRog/f26jTJ/KPDAZULKUX3nj4\nuq55zz33aMWKFdqyZYumT5+ubt26qUKFCtq9e7dycnIUEBCggICAfPvkvkXH3d1dYWFhBS784Obm\nVuDr3JPa3NzclJOTc9W6unTpcs21mUymPNe68jpF1XAjDIen6OjoQrdlZGRo4cKFWrZsmWbOnFks\nhcGIG59xyX1PAAAAztWyXY089zxd1qLd3dd9ztWrVyswMFBBQUEqU6aMoqOj9fjjj2v8+PHq2bNn\noftUrlxZWVlZkqQGDRooJiZGjRo10u+//67vv/9eTz/9tOEa6tWrp23btqljx4769ttvdfDgQQUG\nBua7ZpcuXa65tnr16ik2NlYNGzbUL7/8oho1ahRYQ926dbVt2zbVr19fS5Yskaenp7p27Xrd32ux\nLI3n6empZ599VkePHtWkSZM0Y8aM4jgtrsIk643M2pPE854AAACc7fKiEFvW/674U8nyD/BRi3Z3\n39Bqe9WrV9fYsWPl5eUlNzc3jR49WoGBgQoLC7OtVlfQPmXLltX+/fv17rvvaujQoRo5cqSeeuop\n5eTkXPMKdp06ddIPP/ygvn37ymKxaPLkyUpISMh3TUlq06bNNdc2atQo9e/fX1arVWPGjCmwhgED\nBujNN99Uv3795O3trffee++6v1OpmBeM+PLLLzVp0iRt27bN8DG//fabXn75ZQ0cOFB9+/ZVaGio\n9u3bpzJlykiSBg0aZLv5LDIyUmazWU8++aR69OhR5Hlv5QUjIrZu1XFLOZmVo9LmdI24r4E+mLSx\n0P3dLCaVLOmhC8kZ+bbdUcpT/V5m0QhH48ZdGEE/gRH0ExhBP7Gvqy0YcbPYtm2bvvjiC02ePNnZ\npeRzM9eWW7E+lOnkyZO2oTQjUlNTNWHCBNuNZJe9/vrratOmTZ79Zs+eraioKLm7u6t79+5q3769\nLWDdbv4ZbPon93qWsCgjveDvPjvLWmBwkqTUC5nFWxwAAABuOjNnztTmzZs1a9YsZ5eSz81c25UM\nh6effvqp0G2ZmZnau3evPvroo0LnGxbEw8NDc+fO1dy5c4vcb9euXapXr558fC6l+saNG2vnzp1q\n27at4WvdSi5HJlOu1z6lSygj/cI1n6usHwt8AAAA3OqGDh2qoUOHOruMAt3MtV3JcHjq169fgavt\nXWa1WuXp6anhw4cbv7jFIoslfwmLFi3SvHnz5Ofnp7CwMCUkJMjX19e23dfXV/Hx8Yavc6sy/f/o\ndGj/KSWcuvbgJEmNmlctzpIAAACAW5bh8DR48OBCw5PFYpG/v79atmypgIAbW/b68ccfV5kyZVSr\nVi3NmTNHERER+ZZHNHKbVtmyXrJY3K66nz04dt6rSbt/ijO2p0nyvsNTF5IzVMa3pNp2qnVDNyLi\nxrjK/Gg4F/0ERtBPYAT9BLhxhsPTkCFD7FmHTe77n9q2bau3337b9oTiy06fPq2GDRsWeZ6kJOcs\nwe3YGzKtssqq+JPGruft46lGzarq+3WH1KTVnQqoUoqbR52EG3dhBP0ERtBPYAT9xL4IprcPs7ML\nuNKQIUN09OhRSVJsbKxq1KihBg0aaM+ePTp//rxSUlK0c+dO3X///U6u1Hms/3/JCJMkWU3yusPT\n6IFyd780GncxM9s+xQEAAAC3qEJHntq1a3ddJzSZTIqJiTG07969ezV58mQdO3ZMFotFa9euVd++\nfTVs2DCVLFlSXl5eCg8PV4kSJTR8+HANGjRIJpNJgwcPti0ecTszySqrjD/qKTUlU+4ehCcAAAAU\nbu3atbbnLV2POXPmqEmTJvluvblSSkqKOnfurA0bNui1116z/d1vVHR0tEJCQgrd3q9fP4WFhWnv\n3r3y8fFR+/btDZ+7MIWGp+t9/NO1HFe3bl0tXLgwX3tBP1ZISEiRX87tLOVCwcuQX6msn9c/4eki\n4QkAAOBmcebELzr513qlpZxWSe/yqnBnO/lWLPo2FXuIi4vT6tWrbyg8Pf/889d8zIwZM675mDlz\n5hjKB0888cQ1n7swhYanDRs2FNtFYB+XV9srW85bZ+JTrrp/o+ZVc408GX8eFwAAAOznzIlf9Nee\nxbb3aRdO2t5fb4A6fvy4RowYIbPZrOzsbJ08eVLz589X1apVdfLkSb388suKiIjIs8/UqVM1fvx4\n7d69WxERERo4cKBGjRqlc+fOKTs7W6NHj1bNmjUVFBSkJ598UtHR0apWrZrq1Kljez1t2jSFhoYq\nODhYLVu2VGhoqI4dOyZPT09NmTJF3t7eGjJkiDIyMnTffffZ6m3btq1WrVqlo0ePaty4cbJYLDKb\nzfrvf/8rb29vjRgxQgtS9c8AACAASURBVPHx8crMzNSQIUP022+/6eDBg3rllVcUERGhKVOmaOfO\nncrOzlafPn3UpUsX27lnzZqlsmXLqm/fvpo4caJ2794tNzc3jRs3Tvfcc881fa/F+pBcONbl6XqN\nm1dVzMpf822/o5Sn/h977x0YRZ3//z9ntiSbXgibAqG3gEqXKkWULhwKp1hODvX4HYjfu/vqKacn\np+jJx/L7fCyfn+XuOEU9QU4RpXnSlJIAAlKCQIAEElJJSNlkk+zO/P7YzGbLzOzM7mx2k7wef0B2\ndsprp7zn/Xy/yru+rgmJyVEYNjYT/bLMuF7mKGlOYXsEQRAEQRBtQ+G5b1BVelLy++bGGtHl+ac/\nQ9GFbaLfJZpvRrcBcyT3uXPnTowbNw7Lly/HmTNnsG/fPmzbtg3Lli3Drl27MHv2bK91ysvLsXTp\nUnzyySdYsWIF3nnnHUycOBELFy5EXl4eXnrpJaxbtw4cxyErKwuPPvooJk+ejDvvvBObNm3C5MmT\nUVPT+ls2b96MLl264PXXX8fWrVuxa9cu8DyPfv36YdWqVdi2bRu2bt3qZvf169fx3HPPISsrC//z\nP/+Dr7/+GsOHD0dVVRU++eQT1NTUYN++fXjkkUfwwQcf4O2338aRI0dw4cIFfPbZZ6ivr8ddd92F\nadOmeZ2TgwcPoqSkBBs3bsSRI0ewbdu24Ionq9WKjz/+GHv37kV+fj7q6+sRHR2NPn36YPr06Vi4\ncKHovE2EtvAuf/EA+mU5ysMfP3QFVdfr3cSSJ5TzRBAEQRAEEV7wvHi/TGq5EsaPH48VK1agtrYW\n06dPx3333YelS5di2bJl2Lt3L9asWYPq6mq3dYYNG4acnBznPo4fP47Kykps2bIFANDQ0OD87uab\nbwbDMEhOTkZWVhYAx1ystbWtVR3PnDnjrKQ9e/ZsAMALL7yAUaNGAQBGjx7tZXdycjJee+01WK1W\nlJWVYe7cuejduzcsFguefPJJ3HHHHc59CZw+fdq5z6ioKPTt2xcFBQVe+z5z5gyGDx8OABg1apRz\nGzUoVjpVVVW4//77cenSJQBAQkIC4uLi0NDQgOzsbOTk5GDLli1Yt26dqkQvwn9cau6hX5ZZVCx5\ncjW/CgBw7nQpykvrMFxCZBEEQRAEQRDa0G3AHFkvUe7B19FQV+K13BSThqxxv/frmP3798dXX32F\nAwcO4I033sDdd9+N1NRUnDx5EhzHwWw2w2w2e62Tlpbm3IfBYMBzzz0nWvhBp9OJ/u1a/0Cn04Hj\nOLfteJ4HyzoKfnt+BwAvvfQSHn30Udx22234+9//jvr6ephMJmzcuBHHjh3Dl19+iT179uCvf/2r\ncxvPuWibm5udx/C0WeyYalBcqvydd97BlStX8OSTT+LQoUPIzs7G3r17kZOTg/3792P58uX46aef\n8P777wdkEKEGHmrKelzILcX3O847P1eWW/DdlrO4kFuqvWkEQRAEQRCEIlJ7iVe5Tu011e99bt26\nFRcuXMC0adPwxBNP4PTp05g3bx5eeOEFZ5EFsXVYloXN5siNv+WWW5xVtPPy8rBu3TpVNtx0003I\nzs4GAOzZswfvvvsuevXqhdOnTwOAm5dL4MaNG8jMzERTUxP27duH5uZmnDlzBl9//TVGjhyJ1atX\n4+LFiwBahdqQIUOc+7JYLLhy5Qp69Oghao+wXm5uLv7yl7+o+j2ACs/Tnj17cO+992Lp0qVe33Xp\n0gUrVqxAWVkZtm3bhpUrV6o2hFAPo6pQOXDs0BXR5ccPXSHvE0EQBEEQRIgQikKUXN6NBkspTNFm\npPaaGlC1vZ49e+L5559HVFQUdDodnn32WXTv3h3PPfecs5Ke2DqJiYnIzc3Fyy+/jJUrV+KZZ57B\n4sWLwXEc/vSnP6myYdasWTh48CAeeOAB6PV6rF27FiaTCcuXL8evfvUrt4IRAg888ACWL1+O7t27\n48EHH8QLL7yACRMmYMuWLdiwYQN0Op1TjwwaNAj33HMPNm3ahCFDhuD++++HzWbDH/7wB0RFRXnt\ne9SoUdi1axcWL14MAHj++efVnlYwvMLa4jfddBNefPFFt8oVnnzxxRdYvXo1Tp6UTohrK0I1i3Zb\nzOD9P4eyUapPRjTqAYbFn0bepGi7d9fuhdjVZlkGv3lqksZWEnLQTO+EEug+IZRA9wmhBLpPgktK\nSvuYfzQ7Oxtffvkl1q5dG2pT2i2KPU+RkZGorKyUXae2thZGozFgowilqJuLS6qkeWKytzInCIIg\nCIIgOg5vvvkm9u/fj7feeivUprRrFOc8ZWVl4YsvvkBdXZ3o97W1tU6XGRFsXEtFKGf42EzR5cMk\nlhMEQRAEQRAdg5UrV2Ljxo0wmylVIxAUe56WLl2K3/zmN5gxYwZmz56Nvn37IioqCvX19Th37hy2\nb9+OqqoqrFq1Kpj2Em6oy3kS8pp2f/MzOI5Hckq0ZElzgiAIgiAIgiDcUSyebrvtNrz88sv461//\nig8//NCtJCDP80hKSsJrr73mrOVOBB8GgNpii/2yzMjZ6yg3v2ip+tr2BEEQBEEQBNFZUTWj7S9+\n8QvMmjULhw8fxqVLl1BfX++ciGrUqFGU79RGCJlOasP2BBiWgd0WWI17giAIgiAIguhsqBJPABAR\nEYGJEydi4sSJwbCHUIW6eZ4EWB2Lpib/Z6wmCIIgCIIgiM6I4oIRAHD69Gl89dVXbsveffddLFiw\nAPfeey+++eYbTY0j5GEAtQX3ADhKk/OcP7KLIAiCIAiC6Ojs3LkzoO3ff/99HD9+XCNrwgvFnqdj\nx45hyZIlGDFiBObNmwcAeOedd/DWW29Br9dDp9PhySefRFxcHG677bagGdzZuZBbilbd46fniWXA\nkXgiCIIgCIIIGw5fq8S2iyUorrMiLSYSs/qkYnR6UpvbUVhYiK1btzon0vWHxx57TEOLwgvF4un9\n999Heno6Xn31VQBAU1MT1q1bh8zMTGzYsAEmkwkPPfQQPvzwQxJPQeJCbim+23IWuM0EwOF54nnH\ncjUV80g8EQRBEARBhA+Hr1XigxP5zs9FtVbnZ38F1LVr1/Dkk0+CZVnY7XaUlJTgn//8JzIzM1FS\nUoLf/va3ePvtt93WefXVV/HCCy/g5MmTePvtt/Hwww9j1apVqK6uht1ux7PPPouBAwdi2rRpWLRo\nEXbs2IEePXpg8ODBzr9ff/11PP3005g+fTomTJiAp59+GkVFRYiIiMB//dd/4cCBAzhy5Aiqqqpw\n4cIF/O53v8M333yDixcv4rXXXsMtt9yCTz75BF9//TVYlsW0adPw61//GiUlJXjyyScBADabDWvX\nrkVmZibuuOMOTJs2DceOHUNsbCzef/99sKyq4DpVKBZPp0+fxiOPPILk5GQAjhmK6+rqsHLlSiQm\nJgIA5s2bhzfffDM4lhI4duiKxxKHADp+6Ip68WSnghEEQRAEQRBtwednC/FjyQ3J729Ym0WX/+On\nAnxx7prodyNSE7BwUDfJfe7cuRPjxo3D8uXLcebMGezbtw/btm3DsmXLsGvXLsyePdtrnfLycixd\nuhSffPIJVqxYgXfeeQcTJ07EwoULkZeXh5deegnr1q0Dx3HIysrCo48+ismTJ+POO+/Epk2bMHny\nZNTU1Dht2Lx5M7p06YLXX38dW7duxa5duxAZGYn8/Hx8+umn+Pzzz/Hee+9h8+bN+OKLL/DNN98g\nKSkJO3bswL/+9S8AwH333YcZM2agoqICy5cvx5gxY7Bp0yZ8+umnePrpp3H16lXMmzcPf/zjH7Fo\n0SKcO3cOgwYNUnJZ/EKxeKqpqUHXrl2dnw8dOgSGYTBlyhTnsoSEBFgsFm0tJJxUVbifWwYADwZV\n1+tV7YdlGfC8o8S8a8l5giAIgiAIou2x8+IRQVLLlTB+/HisWLECtbW1mD59Ou677z4sXboUy5Yt\nw969e7FmzRpUV1e7rTNs2DDk5OQ493H8+HFUVlZiy5YtAICGhgbndzfffDMYhkFycjKysrIAAElJ\nSaitrXWuc+bMGec0RrNnzwYAfPHFFxgyZAgYhkFKSgoGDBgAnU6HLl264NixYzh16hQKCgrw0EMP\nAQAsFguKiorQrVs3rFmzBm+99RZqamowePBgAEBMTAwGDhwIAEhNTXU7fjBQLJ6Sk5NRXFzs/Lxn\nzx5kZmaie/fuzmXl5eWIj4/X1kLCSWKXaFSWW1zynBx/JSZHqdoPq3O4MjmOh05H4okgCIIgCCKY\nLBzUTdZLtPqHXBTVWr2Wd4s14fmJ/nlR+vfvj6+++goHDhzAG2+8gbvvvhupqak4efIkOI6D2WyG\n2Wz2WictLc25D4PBgOeeew7Dhg3z2r9OpxP9m3cRfDqdDhznHe2k1+tF/+Z5HgaDAZMnT8YLL7zg\nts0zzzyDCRMm4L777sOOHTuwd+9er2N7Hj8YKA4IvOWWW/Dxxx/j22+/xSuvvIKCggLMmTPH+X1T\nUxO++uqroLrJOjvDx2YCcJnfqeXeGNayXCkM69gD5T0RBEEQBEGEnll9UkWXz+yjPC3Dk61bt+LC\nhQuYNm0annjiCZw+fRrz5s3DCy+8gBkzZkiuw7IsbDYbAEf//7vvvgMA5OXlYd26dapsuOmmm5Cd\nnQ3A4Xh59913fW4zePBg5OTkoKGhATzPY82aNbBaraiqqkJmZiZ4nseuXbvQ3Cwe6hhsFHueli1b\nhvvuuw9PPPEEeJ5Hz5498atf/cr5/aJFi3D+/Hm89957QTGUgDOv6ZuaAscCxvGPmnwnwBG2BwCc\nnQcMGhpIEARBEARBqEYoCrH9YimK6xqQFmPCzD7mgKrt9ezZE88//zyioqKg0+nw7LPPonv37nju\nueeclfTE1klMTERubi5efvllrFy5Es888wwWL14MjuPwpz/9SZUNs2bNwsGDB/HAAw9Ar9dj7dq1\nOHDggOw26enpeOihh3D//fdDp9Nh2rRpiIyMxC9/+Uu8+OKLyMjIwIMPPojnnnsO+/fv9/v8+AvD\nq/BtFRYW4rvvvoNer8fs2bOdhSIA4M9//jPGjBmDWbNmBcVQtZSXBzfeUYqUlNigH/vt48dwzRaD\nZKYGtXwUVo/KUrX9ji9O4/L5Cix5YjwiTaSeQkFb3CdE+4fuE0IJdJ8QSqD7JLikpMSG2gRFZGdn\n48svv8TatWtDbUq7RbHnCQC6deuGhx9+WPQ7z7hEIrgwfs3w5KDV80QV9wiCIAiCIDoDb775Jvbv\n34+33nor1Ka0a1SJJwDYt28fcnJycO3aNVitVphMJnTv3h1jx451VtMggkyLbmLAg4f6gg+sjnKe\nCIIgCIIgOhMrV67EypUrQ21Gu0exeKqoqMBjjz2Gs2fPilax+OCDDzBs2DD87//+LxISEjQ1knAn\nUMkjTBxG4okgCIIgCIIglKNYPL3yyivIzc3FXXfdhZkzZ6J79+6IiIiA1WpFQUEBtm7diu3bt2Pt\n2rX461//GkybCcAPf1MrLFXbIwiCIAiCIAjVKBZPP/zwA+6++2689NJLXt/169cP06ZNg16vx549\nezQ1kJDGMUmuekg8EQRBEARBEIR6FM/z1NjYiBEjRsiuM2bMGFit3hN8EdrCu/zrD26lygmCIAiC\nIAiCUIRi8dS3b1+UlJTIrlNeXo7evXsHbBShDMblXzUI4inYMzATBEEQBEEQREdCsXhasWIFNm3a\nhIKCAtHvCwsLsXHjRjz++OOaGUf4xq+wvZZqe3YqVU4QBEEQBEEQipHMeXr77be9lvXu3Rtz5szB\n6NGj0a9fP0RHR6OhoQGXLl3CoUOHMGrUKBQWFgbVYALgwbT4m/zzHDGU80QQBEEQBEEQqlElngQO\nHDiAAwcOeC3fv38/Dhw4gAcffFAb6wgZeIBh/NJPQqlynsQTQRAyHC09gZ35u1FSX4bUqK6Y3nMq\nRpqHhtosgiAIgggZkuLpo48+aks7CD+gansEQQSLo6UnsO7Mp87P1ywlzs8koAiCIIjOiqR4Gj16\ntF87LC0t9dsYQhk84BK253/BCBJPBEFIsTN/t+jydWc+xc783eSFIgiCIDoligtGyMFxHHbv3o1l\ny5bh9ttv12KXhAL8nSiXSpUTBOGLkvoyye8EL9TR0hNtaBFBEARBhB7Fk+SKUVhYiE2bNuGLL75A\neXk5eJ5H//79tbKNkIEDgwouFhwYvHm6AJPSknBLcqyibVs9T1RtjyAIcVKjuuKaRX56im8L9pD3\niSAIguhUqBZPNpsN//nPf/D5558jOzsbPM+DZVnccccdePDBBzFq1Khg2Em4YOX04KCDIH1KGpqw\n4ZKjk+MqoH66Xot9xZUobWiCjmFg53mYTUb0aXFZUdgeQRBSTO851S3nSYxiSykVlSAIgiA6FYrF\n0+XLl7Fx40Z89dVXqKqqAs/z6NKlC65fv45XXnkFc+fODaadhAsW3iC6fF9xpVM8/XS91imoAMDW\nMiFuSUMTSgAkm00kngiCkGSkeShOlefiaJl0aF68MY6KShAEQRCdClnx1NTUhO3bt+Pzzz/Hjz/+\nCJ7nYTKZcNddd2HBggUwm82YMWMGIiIi2speAoBdIlWtzNrk/HtfcaXsPmp6xFKpcoIgRBG8ScU+\nwvZqmmpEl68/uxEACSiCIAii4yErniZMmIDa2loAwIgRIzBv3jzMnDkTMTExAIArV64E30LCCx04\n2KHzWt410uj8u6yhyet7V5qj9eR5IogOSKBhdJ4lyuWw8+J5kzbO5uWBOlp6ApvztqKqsdpt3cSI\nBMzvO4uEFkEQBNEukBVPNTU10Ol0uPfee/GrX/0KmZmZbWUXIUMU04xa3ls8TUpLcv7d1WREiZyA\nYhkcbrYiKxgGEgQRErSYm0mqRLk/CAUl5ARZVeMNWRspp4ogCIIIJ2TF05IlS7B582Z88skn+PTT\nTzF8+HAsWLAAM2bMQHR0dFvZSHgQwdhQz9sQwdhRz0cg0ajHnd26uBWLmJSW5JbzJMZ52PHuphO4\nPSsN/bLMwTY7IKgDRRC+kRI+riJG6jmS8gwFQrGlVNYuMRtd6YgT9UpdA2rjCIIg2gey4umPf/wj\nfv/732Pnzp3YsGEDjhw5gmPHjmHNmjWYPn06VdYLITpwSGZqUc9HoKrJhn9fLsWVugbM7dFV1X6K\nukbguy1nASBsBVRH7EARRDCQmpupqK4YT32/GhZbvXOZ8BytO/MpovVRbt9pRbwxTtYuV67VeQ/2\n+BKD7Q2ptiy7+CjOVp73Wg5QG0cQBBFu+Jwk12AwYM6cOVi/fj127NiBhx9+GJGRkdi8eTOeffZZ\nMAyD/fv3o6qqqi3sJQAADJqhw1Wui3OJjedxqKwaXxeUeVXak8Me6Qj/O34ofPPX5DpQBEG0khol\nPXgiJ46CIZwAR0je0dITsnYJ6FjvUGQp0SV4tNobUm2Zq3Byhdo4giCI8EO3evXq1UpXTkhIwIQJ\nE/DQQw+hT58+qKqqQlFREXJzc7F+/XoUFRUhIyMDXbp08b2zIFNfL18wIVhER0cE/dgHi0tQzxsB\nMF7fFVkacbqqTtX+4i/XotFqw8jxPbUxUGM+v/AVeHgXt7A012NWr2khsChw2uI+Ido/au+TKIMJ\nJ8pPBdEi9ZQ3VGB6z6k+7eJ4Djvyd+F42UlEGUxIj0nF8bKTqG32bs90rA7bLv/Hbd1QIXiTPr/w\nlU97pNoyKZS2cdSeEEqg+yS4REdT5enOgupJcgHAaDRizpw5mDNnDi5fvowNGzZg8+bN2LRpE774\n4gvk5uZqbSehELX183RWOwAgMTlKe2M0IjWqK66JlExmGRZHS0+0WVgL5SQQ4Y5wPyqtltcWCOF4\nMYZo1DVbZNfleM4ZsnapOh/9EnuLPvs2zubYt0t4G4CW8uql0DE62Hk70qLNQX1O1YYUS7VlUsQb\n4/BSzhth0+a0lrBvu3PcVlD7ThCEUhie5zWpV93U1ISdO3di48aNWL9+vRa7DIjy8tqQHDclJTao\nx/7pei02XboGOxiIeZ7Ukny6ElGlDZh216B2k/PkyZLBi4P+kpOywd9jB/s+IToG/twnPM9jxZ4/\nBsmi8CQxIgFVjTckv9eyjXDtZDMQL9eeEZOGVaN/J7ptoMJW7Le0RXviy/ZJ3cZhUf/5QbVBS1yv\nY7wxVrRQSlu8W9oSeu8El5SUWN8rER0CvzxPYhiNRsydOxdz587VapeEB625TD5T1UQZ2zUepyrr\nUGezAzwPXaMdMbERmHprz7AVToDv0fS2mJBTKldh3ZlPsTN/N41SEkFH6cj4kZJjIbAutMgJJ0C7\nNkKp+BFysjae34wDRYdh423O76L1JlhsDX7boKZYhpbelM1522S/31d4EADahYDyvI5SFSbba2ES\ngiCCi2biiQg++4or/d7WpGOQGWPCobKWlwTDwB6pR0EPPerN4RuyJzDSPFSy0yJMyPnPM//yCiHR\nqvMgVy2MKmMRwUYqPOyj3M/A8bzbff/tlb1BtUXP6sHxnLOSni/hEg4IbcSl6vyAOvdK58CKN8bh\n7RN/Ey0EEYhwAhyVE5WgZZXSo6UnFF3nfYUH0Tu+Z9i3g0qvY3stTEIQRHAh8dSOKJOb9NYHDXYe\nOworRL/bV1zpNkdUOKIkupQH75UDoVXnQUmuAo1SEsFCqrMnhIy53tulFt9lwZUgFQr34KBFbvf5\n43ueBicSuhaOBNq5V1JyHXAIymCKSiW5nlqWeVczcfLmvG1h3w4qvY5p0eEbkUEQROgg8dSO6Goy\noiQAAVXdZBNdXmYNz+o7nsnJavi2YA/qm8VHeP3pPEzvOdVnuA6NUhLBQmlnb+O5zWAYFuDtAR9z\nft9ZABzPS7GlFGnRZtzZY4rXs6O2CEKokfJSKyFcfquSNkxuzq+N5zfjQtUlxR55pfcf0D48kUqv\n4509prSBNQRBtDdCLp7Onz+P3/72t3j44YfxwAMPoLi4GE899RTsdjtSUlLw6quvwmg0YsuWLfjw\nww/BsiwWLVqEhQsXhtr0NmdSWpLo/E0GhkFzAHU/YvXqhElb4Bly4pozoIRrdSWSJYHFJuP0hZIq\nZhzP4aWcNyj/SQOo8pU78cY4RZ1SreZrGpTU33m+fZ13JQML4Yanl1rpvRUuv7XYUur2jHSLS8Pt\n3Sa5/Q45gSDkJwHKPPLhIhoDxXVAzheJEQmdus0hCEKakIqn+vp6vPjiixg7dqxz2ZtvvonFixdj\n5syZeOONN7Bp0ybMnz8f77zzDjZt2gSDwYB77rkHd9xxBxISEkJofdvh2uDr9b2QahqOWsQjFtUY\nzuQihx2LZrvvIhLxBh2qm71HpKub7fjpem3IQ/dcf6eauVDEYBkGdglByYP3q8S5XN6VAOU/BY6W\nuRodAaX5JloRrY/CiqGPKF5/pHkoLlXnu3XI2xMf5X6GD3M/Q2pUV/RL7C3rkRlpHoqLNy7j+6JD\nAByitrqpxq/jRutNMOoi/Lq2HM+5PSNXqouwrvpTbM7bhhuN1dAxOtUDTnJFNdSIxsSI8Hwv+1Pp\nsC2nwiAIov0QUvFkNBrxwQcf4IMPPnAuy8nJwV/+8hcAwJQpU/CPf/wDvXr1wk033YTYWEfnfvjw\n4Th27BimTp0aEru15mjpCWzO2+qs+KNjWGcSeL/E3m6dkmbbJUxGMVJ0LCrsHJJZFntt4xRVLZ/R\nPQU7CitEw/dCnfekRQlfV8TKB7viT+heTvGPitfdnLfNb89JTm4pth7Kx7WKeqR3icLssT1xq4pq\niDm5pfh8Tx4qaxsBAEmxEVg4pS8AiO430OMFgqetABAxZD9YkRomfz/yNc5FRGHAzfU+z63UOXD9\nXWLHdoUBkJESjdljewJoPXcJMUZYrM1obObc1hWkOsMAgm5Pio1AWpdonL9ShWY7Dx0DmCINsDQ0\ng2UAu4u+F7MRAL48963k+QsGiwa0FlTwdY4EIoacFL1mUvA8wDdFgo2wqraPARPw4Iorrnljrt4V\n1/mmXAVVj9huznUeuekBvP7j//p1XIutAUadtpNqCkJMrXAC5AvvjDQPxQ9F2ci7cUnRvsJRdPiq\nFuhJVeONNh20CaQdlnpOY0wG3H9Hf9Xt+Sf/OY+9xwrd2idPPPcdyvcIQbQ1ms3zFAhvvfUWEhMT\n8cADD2Ds2LE4dMgxqnflyhU89dRTuP/++3Hq1CmsWrUKAPDf//3fSEtLwy9/+UvJfbaXeZ78EQ0P\nxpqc4qmLjsXfbPeBYcTVE8/xsFmaYSmogbW0AeYp3cCw3uuyDLBmZD9VdmjJSzlvtHlYyJLBizEs\n5SboWPmwxY3nN+P7wkMBd9ia8m6BqSETPVJjcf7qDTTbOBh0DG4bmoH77+gPwPHS2vVjYUDH0QJX\nMQAAOpbB5GGtdirB82U6IDMRx8+Xo9qQD336RTAmC/iGaNiu9YG9Ms25XeSonWAY73PNc0DzpVtg\n7PuT13dNebeAq0xDVKQeFqv6jqOW6JKKZX+f2n0Z+vwEicdbFTzvcl15FmA48E2RiDTqYGMbvHKa\ncnJL8d6WM4r2LXXNpODqYwHwYKPqVP+OcOLXgxfjH0EM4xOumQZT+vmF6zxHa4+8iSu1ytumYM2R\nJLQrReUW6HUMbByPjC7Rsp31QAbndAyLh7LudfstSoWC1Hqey+NjInDmsnc1XWEAJ2uoFRdtP4oO\nGCl9TiMMOsRGG3H9RoPXwE2w+M1dgxULqM9+3IeD5T/AZqiFvjkW41Im4t4Rk4JsobbQPE+dh5Dn\nPMkhpeuU6L3ExCjoQ5TLo+YB2vXjPtX7F96jwlng+TowjPcxbQ02VBx0L2trszTDEGv0Wjc9xhTS\nB19NQrJWrDvzKTab4jG97yRM6z0BcZHev/8fxzZoFo6kT7+EutNpbi/JZjuPXT8WwmQyYFDPpLAQ\nTgC8ZKKda7XzN7+42ef23x8vdHuhF5ZbUFhugS6p2E38MFF1MPb9CU15cAoMviEajEinmmEBffdz\nosfTp19CY2Var354TgAAIABJREFUWAgnX7/P330FSvPFW0RtaAQwZ0Iv/Ga2+3X99/eHFO9b6ppJ\nYbvWG4Y+JxWvH640sJag7l+4Zsb+R6FLEK+WGkx2F+7DzCETwfGc4hLpAl9f3o6ZQyZqao9nu9Lc\nogAKyy14b8sZxMVF4rZh3by28+c9K2BvCZGMi4vE+MxRom2b2LGl1iuqrMc3+y+7LS8sF7+PeADF\n9jxcr25tBwSPqGDPziNHFf2OxmY7Gm80tPwmRZsEzM4jVzFnUl+f6/1t3078UL0VMDr6N3ZjDX6o\n3gpTrgGPTJoefEMJQiVhJ56ioqJgtVoRGRmJ0tJSdO3aFV27dkVFReuLo6ysDEOHyo9oVVVpkzit\nlrOF1fj7V6dlw4VcKaxR90IScIYIAbA2HkaU6XavdeouesfSWwpqkDCki9fy3OxCPP1TBarrGtvM\n7e6a8MyCBYe2K3fMgMGkbuOQXXwUn53agk1ntmGUeRimdJ+AjBhHBzMntxQ7rn3v75zE3seMlO5c\nfrP/stsLNVwJxE5dUjEMvcU7zPr0S86Ove1aH0nRwBjFw7zkzm1bok+/KL68+zlV4slxrk4FbA/P\nA3xDLGzXesse/5v9l9HQ0OzmAa24oXw+IqlrJoTnAQBjaARvjXHaok+/qEpwhSMf7z0MXXJw9s1z\nrYKbjfIvrypQ8m8UYeFnvwVvjQJrEq/gyPMQ9YxW1Fd5RWH48tj4ChfTiURNuPLqxz/i1Y+9w6sj\nR10L2Hv7Ts5HePPQOvAcg8hRHPiGGDevstSxPVHbfkoNGG06tR39TQNxpSQ0UTZKuFpaKxmJ43ov\nGAaLh2rvvLYFOz/7CnxDNLjiPkhPiUa58TS4iFpZr76vflewIM9T5yHswvaee+45jBw5EvPmzcOa\nNWswYMAAzJ07F3PnzsW///1v6HQ6LFiwAJs2bXLmQIkRirA9uZArz4dZiFG29NilOnTloVgTklvC\n9rrqWLx+wwKjoS96JU5HmbUJMQyLKyfLYC0V7/yYMqIRPzAJ4Hk017WG9MmhdWOkdY6TWjJi0rBq\n9O/QYLMiu/go9hYeQEXDdQBAv4TeyMAQfHs0H4Y+J1W/dPlmAxhDs9dyrj4WjafHa2F+u8OXF4Xn\nGFiPto4wRo7aIXrepTpqYudWy/A5pUjZDThCC5UcX43HSep8OL/nAOvRGYr2FSiO830JTGSdm0iS\nwpCZC33qlaDZw/MAeAYMG7xXHM859m8r6wZ9V229xq73tNx9FWqk7kGeB6xHHPeejmUwsEeiaGha\nW+DIowyOUFf6XPuD3DPSls+2EgJpb9WG/boid/7VhA1qAYmnzkNIPU+nT5/G2rVrUVRUBL1ej507\nd+K1117D008/jQ0bNiA9PR3z58+HwWDAH/7wByxduhQMw2D58uWywikU5OSWyoZcVdY24r0tZ7xi\nk3Uyo+xSiL1D9U2VqMgpQXG5xWdmjrWkHvEDkxBZYUXJSWWhIIL9728540yi92yUpJJWXRPoBaSK\nArQVwvwdJn0kpnSfgEndxuHM9Z/xn8vf48KNS7iASzD6jjYQhed0YOAtnmzXegdicrtGyiMjwFtj\n3D83xIiH7kl0ID3PrZLwuaCIq5ZcIjFcvWty+DpXrvjqUDOs43cGWzQCjvOq9Di6pGLRTqH9RjJ0\nCdcDsoNrjITt6gD369z9nOriFHyzAWA5MDrpObMEYcbVJIHvUqSpUHO7p20GQGRAJizgIfpSEjyO\ngCPsN1TCCZD3ZgeK0udaDY579mewEdKFWrR8tg2ZudB1LXS0XTwLe1k3NF/JUmVvIOHKasN+3Wxv\n8dCLHefzvXlUtIIICiEVT0OGDMH69eu9lq9bt85r2YwZMzBjRviMsniy9VC+X9vZK9NgK62E3nxV\n1XY83N9ZNaUxuC4RN+21Led4wfM6xqswgJLjCrHbH397zplj4uyIDrQgwqMjKubbZEzBzRUQwy2E\nKTUNMAO19U04d+UGcguqcLagBqWV/cFEpiMiKweM3r/OCmO0uo3GenbmOiO+rreXsCztB/Q67nO/\nUudWMnyupaOjZW6SGxLCCVAeWqj1sxGMzl2gSF0fxtiEprxbvDxYbEyVIi8V1xiJxp8muy0TRF2r\nZ6wWjIJQXDHvsRT69MuAzMi5lDdacn3OXeSr2batsZd3g97sPXDIVXUNgTXisDFV4DnIXneu0SH2\n1IpsrUOG1XieDX1+gj79YkADP17eLYaDPvUKdOYrXqGJgn2eg05Sz7Oh9ykY+pyUHZzSJRUDOv/v\nb4blJNvuyhr5KqEE4S9hl/PUXimq8L/D01zUD3rzVWcIiD/oU6+Aq0tU1IAm8QDsPHgdi8FgcA08\n/BkTdBVOajuigYw0+Qvf0BoG88/tP2NbdgGulrXaEGHU4eY+yRjUoy+21B7w+zie3gCuqmvYdV7V\nEqiXRu56c42RwI103D7CvZrf8t2+xRPsBu8Xe/efwRglyo+3dHSk8giMPc6DbegOS4PvwhM6lkFa\nchRKKxvQbOccAxESHjPA27vmtMnDM8s3RYDx0YHjOQZgeEWhXFp27oTwXbcy8HERWDhZKIVfgOLr\nFsRHOzK/q2oboWdZ2DkO6V1iMCAzAeeu3ECFhEBkIutEPVj2yjTozFd8e9oM0p0l1/1G3LLXrzLp\nkseNrJO89lxjpOT9KAVvbY2sUOOJbCtcB6IAHhART2reSVJo4R1WGh7KGBphPTpddYgk36xtuXm1\nnmep963SgVGdRLip676XDB6MkeahLeH2O1rXafleKvmDYTm39ZryHMud17TJKOpd8xWOLEY4DhIR\nHRcSTxqhZxln5R+1MKyt5X9123tGSyhpPJIA9AGLIo4Hp2NgAoM+YABwfgkox3HlR/nFCGYYhRSu\nncjGZjuKr9djYGYCBvVMwqAeieiZGgu9zjE0+f33ybhh06a6VURKBZr9TO0Y3CtJNtxFKqZbqnyt\n1PpLX9kt+aLVwkvD1SRJ5hwsHb4AI2d6F4AxsHo0c/IixvWaKhmx7RaXilVPT8Xy3TvEVzA04K0n\nbpPdhxxHS5Mkc/mWjpqLkXPkC904Oie+O/Ux+hjU1vGKBiCkRJs/CHmPUqEwSkNkVu74FnajdxEE\nOVv5pkjfolLhb7VdHaBp+yN4yMT2abs6QHVxDFdPLGMKv6IawkCU45mTrpoYSIdWK++wlDjwRLh3\n5AZAxGAjrJqGxvrrefY812zL9BI+K7jKeMsFhHkRpebK0rOsz7kVAXiFzzISYYn+5PeJDRIlxWor\nbAlCgMSTRtgCqf0pE1MvBgNHAbiuOhYMgCWxJmRbm5HrY4RZxzBIazGTsXPgda0tVBoYVPo5j5FU\nYy834m2vTEPTRTuMfU4DQEBeN6V4dqze/j8TYTSIl7P/xYA7lRe0aDIBRumCG5yuAbeP6IZ9J67B\nZucUjaa6zqvkqEpUgKKKOuhZFnxCESK754Mz1uC72hPQlXpPEit0YgVPQFpyNGaP7SHZuc1IiRYt\nlxsdqQeXIT4xppqOERsnLgATIxIk54KJ0ptQ3SRf+IVvjnCOsCoZsRVy3YKBUD3Sk3hjHBb0myM7\n542wrdK5zhp4CyalzXSU9/WBrbg3dAwDewC1gSKMOjw8Y6Bm+QPjUiaK2i6XF6hE8CjNK7RXpqEp\nz3EPM6bagIsxCMUxnPsUKZohKqxKMsHVJcoX2pDJo1MKw+vBM94DEXyzAdDZwFtjwDcZFeebOT24\nPp45xlSLyFE7pds5mbbQn0E5T3RJxYrPnXDv+DOwF6jXw/U8gGdkQ0Cl8HzfpiVHOz35e48ViT7/\nSXERsDIseB+VbostpQBaJ2H2RIlwAqSrpWqB2MCJMDk8QWgNiSeNkOp8KoHRqZubJoJhYHB523fV\n63BXjA5cE3ACraFLWTF2jI00oIuOhZ1noWeB2tooXK+MR7UxDk2sARPH/Yi8S91RVJIie0y5l5xU\nSJavUWCu1qXGr1y+gEZVs1w7VklxEZLCCWidUX7j2a9RZ3d04IVTznOOzoy+KR7juk7AvSMmYfnu\np2SPPeDmevTNGIS/7f9OcjTV1JCJ++/oD11yMXbm70Z2/U7k5zgmRHxh6WgAgmdiGwS5Lcz54Wqz\ngJyHQNiXUCo+rl8SdPYMrw7AA3cOwMel28CJnHo2qhZ9ppxwm7BRisf3fCu6j+om6RLMjILEFDbC\nimEzLqHEUoqqRvnBg0ndxjntTIyIR1Vjtdc6iREJPo8phlz1yBVDH0F6TKrkthvPb1Y9l1hatBn3\njpiEvqXx+LZgD4otpUiLNqNvQi/k3bjs/HxnjykYOdX72rgKcvDS4T0xJgPuv6O/5knX946YhOz1\nJWhMPK+4Op+7OKkF0NrJ5JuU5xXqdQxsdt4tFyoQLxRXneQ8rlTRDClhFW/r6RXymJYcjdl39UBe\nUbXDaxCgcAIgKpxsJZluRQEYAIzCcEbeGuPoeEfJFyhytJm8ZMEWsbbQVlIFtvgmsD4G5WJMBoyb\nwElOHusZYub1G1oM97x3hGulZnJqnakO/3h6qvO4QruaGtUVffQjcDRb71ZISQh1vTXLjM9+3Icf\nXOZx8kc4Ad7v29ljewAA7r+jv1NEpaTEulUidpwj3/dXWrQ2z38wK0YaTU2I61mFyitJPgcLCSJQ\nSDxpxOyxPRXN8i0Kq87zFC0x18W4ODt+bonXHmTQ466Y1mpHbEuDHBdrQVysBcdsQ1APE+JiLRh+\ny8+IjYvA8gXznOu7Vs7zFT4hNVIniBUp4cUmt85xJVtuuSW2Xm0Hh+dYALxox0zosMgx0jwUI81D\nW15uraPkQhz3Q8NnO+PAZe0Aj3VnPgXPA4be4oKg19AyrBp9v1cn3FMciXk2AOCj3M/wYe5niDfG\nosneDIvNMc+ZjmHB8TzSos0iHYvW49ywVcDYtwIxZZFeL5/vartKekQE+y5V52NR//mS5yA1Snwf\nci/lJnuT2+d4Y6yoJ+ps5XnJfbhysjzXaeP8vrMlxc7R0hNeYtCzQyScSyUeowabdGf07RN/U2y/\nK4IHTbhH1eJLWLcF942YjPe2iA/aJMVFoLquCWnJ0ai3Njs7nmoq+gHS4s9VPLLVGY7Ocu+Tfg3Q\npJiBu8Yl+XUdXPG08dYsM/pmxOOTK4dgN3oLfTESIxJwc0qWm4Cub24Q9RhEJFbj10Pdw3jlwk5d\nEUJQX8rJUewtBVwKtrAMYvrkoUlsndQrmDSyGw4U6WDjvUUfw/LoM+UE+iX2dht0UNpWCthLMyUr\nytkr06Dvfs5nmKhAeqxjcESs/b6GrWAGMejj0QYLXLT5nhtKCXF1WahkGVXCQSoMz5M7e0zB0dIT\niudjZMGA8zOSxV9sTCPquubgN1MWB/wsEoQvSDxphNBQ7TxyFVdLaxU3YDm5pfjip4OQGjPnGiMd\nSdAuyeFSFy1ZxzrXGRNpkD2uHjbYoAfHM2AZHn16t1b7O1p6At/V7kZjVhniORMaefEJh4UXob0y\nDU0FVhh7OJLwnZ6Z9IvQxVZBZ25N+BGE1wATi3P1F2RtFBCED9coXm5YKuQvtmIUFtw8AQCwtbQA\nxazv8DUxpF5uvuLAPWEYSI4iC2ERUvsSjlVSXyb6vRA24elNEZYr7VhYU39ERFc7qhkd1pfY8V2t\nGf0Se/vsIO0rPIiT5bmobqrxGgEGgOk9p4p2yuTC6Jo49wpMtU2B5X64diBHmofiWOlJ/FRx2msd\nT0+elKBdn7sBNt73wMe3BXtQea7KTXgBwMZzX8JiUz4RLeDoHM/vO6tDdA6EZ1Cs+ITr8ymVvyeH\nL4+ZmHh8fM8pcD5CGxMjEnCjsRq8S8fwurVS0vsbKLdmmaFLlhb6riwZLN5pfHzP06Lr85G1Xudg\npHkoLlXny3pCXT24Us+1FPpoC/6/Fg/N8t3bJdfz5Ym9ZimRbJPWn90IAJJtpcCvhy7E51XeU2s4\nqU4FuubL7kNAaMek2lUevGSUgC87lZAYkYA1996tapuN5zdLhuEJJETE4xd9ZwOAquvc1sLJFeFd\nSRDBhMSThtyaZcacSX1VTdB7a5YZfGJ3rD97COPTb0V+zRVcqysFrDFoLOzVWh1qyH5naJwd4heu\nziUuqotOOuQpj8tEBZIAAJ/bZ2AEewZ9WUdSqWdHsYmxSHqFHPHsLWERNmPrcpcKO5BIvD3XcFx8\nwioXEiMSMCRyPA7UM7DAJlmxSqdj8Kus+9zClzzDlQIZZZd6ufmKA1dDWrQZR0tPSO5LOJaUB0cp\nvkSYraVAgzDiK3RS+if0xvkb4rlPAoLtYp2EkeahOF1xFkdKj4MBAz2rA8fzbi85T++OzaNYhDm6\nq/M8aEGSSTpEz/UFvDlPPK9IiXACgNPXzzr/dj03apHqHLdnlHjAhO/lBJQQMhUISp6tKIMJJn2k\n6HrB6rQJ+5QLz7znppnobxoour1ar++i/vPRO74nNudtc2uPxIS7mG11TXWoaapzE5gC8cY4HC09\nIflMaYGNs2HdmU8RbYiCpVl84C8jJs3nvbc5z4r/XMn3eTwhb/No6QlFbfP6sxtxqTofF6ouoaS+\nTLE3R46qxhtYvvspRBuisKj/fJ/34dHSE4pChX8//P9BsikJL+W8EZB9bYmW7wiCkILEUxjQ2BKe\nNCCxLxYPFB89+uxHnTN0zMLxiNd5K494HYtBBsclZSUUTx6Xie+48c7PVUjAd9x46AynkQnpkTMx\n3A5hEAvA8B+WYbFm/CoAwL0jHMteyhF/OaVFm/0OX1KCPyFnarmzxxTZcy8cS+1Iryf+irCL1QWq\nj+XZmUyKTAQA/J/hy7Dl4g5cqs4Hz/NgGEbUu+PJ9B5T8c/cf6m2QyDO6D6xdqNNuny06wtYLDeq\nrcmISetwwkkNt2aZRSfgBrSrqKXk2ZLrmAWz0+arffPMZXHFH6+vmvbUc92Pcjcgp0TcW+/q2Q02\ncuGySgrHHC+TriLoyvy+s2RzHj2xcTY34RKocHLF0lyvyAuq9D0vnEMtvGNthZbvZYKQgsRTiDla\negI78ncBADZf3AYevGijd++IScCPwA9V29EEHhzPiwqkSSajZE4UABzjBosuz7H1wySEz6hNvDHO\na5k/nQAt8HVcqeIDvuB5QM/owIHDzvzdsufeNcfly7ytuOFnh55lWDy+52nEewgJX9gVellcKaor\ndoyG6qNg1Bmdo9iXqwtg1BnAg4eNs8GgMyh6mY9KHYbLNQWqiysIDE0Z4va5wS4tnjiew/LdT0HP\nhEcTGex7vD2wcEpfUe+TVhW1lISspUWbwfN80AdTtETMO3RnjylBE+M94zKRU/IjovQm1KsMS9US\nTqICHANG0W+/bq3yuc7klhBGLTwzvuZlWjJ4sc/7U2D92Y3OHFiAcYZTL7x5FvqbBioWQ9svf4dH\nb34o4IiHtoTaSqIt0K1evXp1qI0IBvX12npClBIdHaH42MJoleB5arBZcaL8FMxRKaLVuYak90Qm\n24g4azFYAIyIeIpkGUmvEwDs50ZALF6ugWOx7cJ/Kw5FCjZWu9XrPKTHpMIclYLyhgpYmuuRHpOK\ne/rdFfQReV/HjYuIw4nyU6r3yzBwhrbUNkvn8yRGJGBgUj+sO/MpPr/wFcBDNJFaCRzPgQcPq4xw\n0JpmrhlWe+so8M9VF1rKkNcgKTIR689uVPRiNkel4I4ek2GOSlF1vpmW+/1KbRGOl51ClMGE9JhU\nHLp2BOUN8qWZtRwV9ocuUYn4Zf8FndrrJNAtJQapSY5JiS3WZmR0icF90/ppWvhicPJAmKNSUFBz\n1e2eFbin313om9BL9P67p99dslUVg4mv9056TComZozFrF7TMDFjbFDt/LnyAs5V5fmcoy1UZMSk\nYWLGWJ/rHSk9Lhr2p2NY3NZtLApqruLW1BHoEdcdG89vDoapTjJi0vDLAb/A4OSB2H75O5/ru7bz\nVrsVPHjUNtchp/A4LM0WNNmbZN85AiX1ZbA0W5BsSkRBzVWf64eaUIc2R0fTvFKdhfAYVu2kSI22\nS8XOW6pOI77qx4DqfSaiBpXwzvXguCo0cqERnFKInYdghufJIXdcYfn6sxu98nS0oMne5Ob5ahDp\n1LU3iuoclRY/Pfdvxdv4m1MiCFTPpG2xznE4kRGThv939p9V5VB2dNqiSqDwrB8tPSHrrWkrT057\n4mjpCWy5JF0ePBxQ6pnoE9cTZfXeE6U/lHUvjpY4KqxuOL8Z/77wjSZ2GVmDV5EcAVeb06LNAXmB\n9hUexKCk/or34cvTJUzvIEQWCBUfj5accFZ9bQvSo1PpGSTaDBJPQeJo6Qm3alo6hkX/xL4t89HI\nh1xdqxNv1GpK9gds13D2jFvOk0AX+ynJin+hIlxCCJUgNNrBiOdvyxdQW+FPGKBwPyiN1/d8qbvy\nUe5niid2DBVS7QDRNvgaMKGOmjdqcmZDgdyk3K4cLT2BQyVHvZZP6jYOl6rzcep6rnOZv1EAniRE\nxKOsoQKpUV1R3nAddt6OjJg0L2EeaN4roHx6ByVUN9XgrSmveC2/UHVJ83eXgTUgxhAt2qZPzBij\n6bEIQg7fs1ASqhHC8VzLENt5DmcrzyvKjdGx4pO3NlvL/bapnuOwpc6KXkwBprEHEAnHqHs8ajCN\nPYApxkK/9x0swjWHQIqR5qFYMngxMmLSwDJs2OTLdBRYhsXR0hOK4vWXDF6MNeNXSU7Cq1Y4sSqa\nSn8n2vWEB48DV45osi+CaAvaurCA2mdNblJuV6REYN6NyzhQdFjVMX2hZ/VYMngxYo2OSW7jjLEw\nsHpkxKRh1ejfiUZfLBm8WPS3M75K2AYBqfd0MO4FI2vAmvGr3N6z0YYoAEDv+J6aH48gpKDeXRAI\ndPTNzomPyhsiU9Bs9a9BimIdnT8GQF/2Csr4ZJzkB2Ka7iBSmCrYmfDT0e0x8dN1RFpqfhXCP4QS\nxIkRCT7LwwshflolOqdG+96Pa7z9swde1qSE/ebcnXhqhHgJaoIIN9qysIAgLgDgpZw3FB1X6YCc\n3PQUUoUo/IXjOYw0D8X3LeFxNt4Oq70R0YZoyW08PZ8f5n6GwyXHcE+/ufj8whZN7fOF1Hs6KPdC\nizZ0/f1bL/8H2y7/R7IsPUEEg/DrMXcAAh1xkUrmjUudENB+x0QaUGFvmYPJmQfiaI2u28MrhElp\neEU4kxrVNdQmdFqEED9hQtpAyYztJvu968ShgKN8sRYU1hRrsh+CaAu0et5cGZTUX3S5a6dd6XGV\nDshJtd1p0WbNIwpYODzqgjKobxEB0XqT4n1EtazbJ6GX396njJg0TOo2TvH60YYo2QINgdwLiREJ\nSIxIAMuwyIhJc4peS3M9Xsp5o+V8OYhpEZknK87gpZw38Piep73WIQitIfEUBMTKbKtBqoGPThyC\n5J4LYIg0A2DRrI/Dz3YD7Lyy2byTdSyyre4JqYJ48lweapSGV4Qz/RJ7h9qEDkl1U43Pl7zwstVC\ngGfEpKFRpjohAwaL+s93WzbSPFSTEJpucWkB74Mg2oqR5qGqQ+l0MlEPk7qNw4qhj7iFaWXEpHl1\n2pU8b2oG5KQ6/nf2mILxGaMV7UMpNt7hUc+vuQKgdfD1ePkpPHvgZUUiwNQinhpsDTBHpfhlx509\npmBR//lIjIhXtL4vT89I81DJdnrJ4MVIjxYfJM6IScOa8auwZvwqvDXlFdzZY4pb/rNQ9Ec4LzEt\nYXt7Cw/gmqUEHM95rUMQWkNhexpz4MqRgMJ1fDXw0YlDEJ3YOl9NHwCVV7ejrsJ3bsR1O4ezzTag\nzoqkyJYS2TYgp9HqWB4EEiMSUN1UA5MuUlXyaHvLd/JE6QzuHR3POZ7UbBcfESc5n86i/vPRO74n\nNudtE9236wCEkjA/Oe7sMUVy0k9A2lMcaFUsAJifNT2g7QmirZnfd5aiggaTuo3Dov7zJSeYdRVI\nSgp0+Hre1HiD5ebGEr77oShb0xA+sSI6rpMKy/3+KINDPJ0qz/Vrbi1XATu/72zFBSl8VUAV2mmp\nypRK5m30VZW4SKawjr8VWgnCFySeNORo6Ql8fHZjQPvwJ9ynsa5A0XqCd+lssw2DoQcigZwqAwoN\ngQunjJg09E3ohbwblyXL96qZhb095ju5Ipf3lh6dilvSB2L7hb0BHydaH4WEyHjnOb9WV+Isza0G\nBo75wbSuQNdgt+K/blvt/Kz0HmiwW7Go53zZl6vSktJKO3NS7MzfLVsdUOpeVVsVa1K3cV7Pz/jM\nUVSqnGhXuAoPYUoCMfYVHkTv+J6aTeIr9bwlRiRgft9ZqvcnJ9h6x/ds08ExXyLgdMVZAMDuQv8q\n8tp5zkukKWm7lFTElTqPSq+7XP4ZABwrOxmQfQThDySeNEKNMJDC3zwfqSp8PM+Dg8PjlG1tdvMu\nOSP9NCjOwzKsM3FXjpHmodiZv1t0dFDP6sHxXIeZM8VX3lukPlKT4zTYrfiv0audn5UmTnuSHpMq\n+6LJiElDsaXUGZKq1JPj6UF0fWHKCb20aLPil6uvUemR5qG4VJ3vd2dHOJ86RoeHsn6puJOn5rih\nntyRILREeCYf3/O0rHdGEAValH7XSoQpwVdRKGFQS048qkGubd54fjPOVeVpchzX6yH1rnYl0AgR\nJdddqvCEcOzrMhOdB5pCQRBSkHjSCC3mt/A3yVyqCl+5ncO6WnEXPsM4Oq2sigl3pcKf1DSgUqOD\nDw5a1KE6j3KVhq5ZSvDlWemJJNWEmXmee3/nACm2lEra7FrVSkDpYIGYV8b1hSm1H0/vUqBcqLoU\n8D7svB0783djes+pim1SctyMmLQOde8ThICvimtaewbaav4tX4NjRp0Rq0b/TpNBVUD+Hatl6XTX\n66HkXdIWESJSdgjHNkd3JQ8T0eZQwQiNUFthb1K3cbIJsGqQqsIXkTLaeYzEiARE66Oc3zk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q1fxr5CcvwVToDD1kCr6ClBLjHbn7BDAIgZMUL2e7EKW/7mawn4ura83VFEREw8dbvnbk2OQYQP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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "plt.rcParams['figure.figsize'] = [12,8]\n",
    "\n",
    "title = 'Vital signs (periodic) for patientunitstayid = {} \\n'.format(patientunitstayid)\n",
    "ax = vitalperiodic.plot(title=title, marker='o')\n",
    "\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "ax.set_xlabel(\"Minutes after admission to the ICU\")\n",
    "ax.set_ylabel(\"Absolute value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AKwbjEpzITad"
   },
   "source": [
    "## Questions\n",
    "\n",
    "- Which variables are available for this patient?\n",
    "- What is the peak heart rate during the period?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EtnH7O-5IZzB"
   },
   "source": [
    "### The vitalaperiodic table\n",
    "\n",
    "The vitalAperiodic table provides invasive vital sign data that is recorded at irregular intervals. See: http://eicu-crd.mit.edu/eicutables/vitalAperiodic/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IVSNgRD4INwW"
   },
   "outputs": [],
   "source": [
    "# Get aperiodic vital signs\n",
    "%%bigquery vitalaperiodic\n",
    "\n",
    "SELECT *\n",
    "FROM `physionet-data.eicu_crd_demo.vitalaperiodic`\n",
    "WHERE patientunitstayid =  210014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "colab_type": "code",
    "id": "Hz56o6w2IkYc",
    "outputId": "0956a427-7cf4-4b8b-8391-964db0750748"
   },
   "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>vitalaperiodicid</th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>observationoffset</th>\n",
       "      <th>noninvasivesystolic</th>\n",
       "      <th>noninvasivediastolic</th>\n",
       "      <th>noninvasivemean</th>\n",
       "      <th>paop</th>\n",
       "      <th>cardiacoutput</th>\n",
       "      <th>cardiacinput</th>\n",
       "      <th>svr</th>\n",
       "      <th>svri</th>\n",
       "      <th>pvr</th>\n",
       "      <th>pvri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2389995</td>\n",
       "      <td>210014</td>\n",
       "      <td>2204</td>\n",
       "      <td>93</td>\n",
       "      <td>52</td>\n",
       "      <td>64</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2389979</td>\n",
       "      <td>210014</td>\n",
       "      <td>1539</td>\n",
       "      <td>102</td>\n",
       "      <td>61</td>\n",
       "      <td>76</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2389973</td>\n",
       "      <td>210014</td>\n",
       "      <td>1334</td>\n",
       "      <td>111</td>\n",
       "      <td>61</td>\n",
       "      <td>79</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2389958</td>\n",
       "      <td>210014</td>\n",
       "      <td>889</td>\n",
       "      <td>120</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2389998</td>\n",
       "      <td>210014</td>\n",
       "      <td>2384</td>\n",
       "      <td>127</td>\n",
       "      <td>56</td>\n",
       "      <td>81</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vitalaperiodicid  patientunitstayid  observationoffset  \\\n",
       "0           2389995             210014               2204   \n",
       "1           2389979             210014               1539   \n",
       "2           2389973             210014               1334   \n",
       "3           2389958             210014                889   \n",
       "4           2389998             210014               2384   \n",
       "\n",
       "   noninvasivesystolic  noninvasivediastolic  noninvasivemean  paop  \\\n",
       "0                   93                    52               64  None   \n",
       "1                  102                    61               76  None   \n",
       "2                  111                    61               79  None   \n",
       "3                  120                    60               80  None   \n",
       "4                  127                    56               81  None   \n",
       "\n",
       "  cardiacoutput cardiacinput   svr  svri   pvr  pvri  \n",
       "0          None         None  None  None  None  None  \n",
       "1          None         None  None  None  None  None  \n",
       "2          None         None  None  None  None  None  \n",
       "3          None         None  None  None  None  None  \n",
       "4          None         None  None  None  None  None  "
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the first few rows of the dataframe\n",
    "vitalaperiodic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "colab_type": "code",
    "id": "9lKJNUHwIm4u",
    "outputId": "3cb7ec79-a1a4-47bb-9a1d-70b0b2aa2d20"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vitalaperiodicid</th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>observationoffset</th>\n",
       "      <th>noninvasivesystolic</th>\n",
       "      <th>noninvasivediastolic</th>\n",
       "      <th>noninvasivemean</th>\n",
       "      <th>paop</th>\n",
       "      <th>cardiacoutput</th>\n",
       "      <th>cardiacinput</th>\n",
       "      <th>svr</th>\n",
       "      <th>svri</th>\n",
       "      <th>pvr</th>\n",
       "      <th>pvri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2389927</td>\n",
       "      <td>210014</td>\n",
       "      <td>6</td>\n",
       "      <td>184</td>\n",
       "      <td>111</td>\n",
       "      <td>141</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2389928</td>\n",
       "      <td>210014</td>\n",
       "      <td>14</td>\n",
       "      <td>176</td>\n",
       "      <td>106</td>\n",
       "      <td>134</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <th>96</th>\n",
       "      <td>2389929</td>\n",
       "      <td>210014</td>\n",
       "      <td>29</td>\n",
       "      <td>167</td>\n",
       "      <td>106</td>\n",
       "      <td>129</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2389930</td>\n",
       "      <td>210014</td>\n",
       "      <td>45</td>\n",
       "      <td>197</td>\n",
       "      <td>117</td>\n",
       "      <td>147</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2389931</td>\n",
       "      <td>210014</td>\n",
       "      <td>47</td>\n",
       "      <td>178</td>\n",
       "      <td>107</td>\n",
       "      <td>137</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vitalaperiodicid  patientunitstayid  observationoffset  \\\n",
       "102           2389927             210014                  6   \n",
       "99            2389928             210014                 14   \n",
       "96            2389929             210014                 29   \n",
       "107           2389930             210014                 45   \n",
       "100           2389931             210014                 47   \n",
       "\n",
       "     noninvasivesystolic  noninvasivediastolic  noninvasivemean  paop  \\\n",
       "102                  184                   111              141  None   \n",
       "99                   176                   106              134  None   \n",
       "96                   167                   106              129  None   \n",
       "107                  197                   117              147  None   \n",
       "100                  178                   107              137  None   \n",
       "\n",
       "    cardiacoutput cardiacinput   svr  svri   pvr  pvri  \n",
       "102          None         None  None  None  None  None  \n",
       "99           None         None  None  None  None  None  \n",
       "96           None         None  None  None  None  None  \n",
       "107          None         None  None  None  None  None  \n",
       "100          None         None  None  None  None  None  "
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the values by the observationoffset (time in minutes from ICU admission)\n",
    "vitalaperiodic = vitalaperiodic.sort_values(by='observationoffset')\n",
    "vitalaperiodic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 228
    },
    "colab_type": "code",
    "id": "bWRvZ09XIo7d",
    "outputId": "25c68566-5868-4afa-e4cd-32650d409643"
   },
   "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>noninvasivesystolic</th>\n",
       "      <th>noninvasivediastolic</th>\n",
       "      <th>noninvasivemean</th>\n",
       "      <th>paop</th>\n",
       "      <th>cardiacoutput</th>\n",
       "      <th>cardiacinput</th>\n",
       "      <th>svr</th>\n",
       "      <th>svri</th>\n",
       "      <th>pvr</th>\n",
       "      <th>pvri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>observationoffset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>184</td>\n",
       "      <td>111</td>\n",
       "      <td>141</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>176</td>\n",
       "      <td>106</td>\n",
       "      <td>134</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>167</td>\n",
       "      <td>106</td>\n",
       "      <td>129</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>197</td>\n",
       "      <td>117</td>\n",
       "      <td>147</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>178</td>\n",
       "      <td>107</td>\n",
       "      <td>137</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   noninvasivesystolic  noninvasivediastolic  noninvasivemean  \\\n",
       "observationoffset                                                               \n",
       "6                                  184                   111              141   \n",
       "14                                 176                   106              134   \n",
       "29                                 167                   106              129   \n",
       "45                                 197                   117              147   \n",
       "47                                 178                   107              137   \n",
       "\n",
       "                   paop cardiacoutput cardiacinput   svr  svri   pvr  pvri  \n",
       "observationoffset                                                           \n",
       "6                  None          None         None  None  None  None  None  \n",
       "14                 None          None         None  None  None  None  None  \n",
       "29                 None          None         None  None  None  None  None  \n",
       "45                 None          None         None  None  None  None  None  \n",
       "47                 None          None         None  None  None  None  None  "
      ]
     },
     "execution_count": 79,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subselect the variable columns\n",
    "columns = ['observationoffset','noninvasivesystolic','noninvasivediastolic',\n",
    "          'noninvasivemean','paop','cardiacoutput','cardiacinput','svr',\n",
    "          'svri','pvr','pvri']\n",
    "\n",
    "vitalaperiodic = vitalaperiodic[columns].set_index('observationoffset')\n",
    "vitalaperiodic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 553
    },
    "colab_type": "code",
    "id": "yh6dETxTIr_h",
    "outputId": "8eb4e507-5ba9-46aa-9514-7ca4f96c590f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absolute value')"
      ]
     },
     "execution_count": 92,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a2dJ0Qy1muFhSSEREREQUqcIecB09ehRTp07F+vXrAQDff/89brjhBixYsAB3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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "plt.rcParams['figure.figsize'] = [12,8]\n",
    "title = 'Vital signs (aperiodic) for patientunitstayid = {} \\n'.format(patientunitstayid)\n",
    "ax = vitalaperiodic.plot(title=title, marker='o')\n",
    "\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "ax.set_xlabel(\"Minutes after admission to the ICU\")\n",
    "ax.set_ylabel(\"Absolute value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4cj_8AdxIz0l"
   },
   "source": [
    "## Questions\n",
    "\n",
    "- What do the non-invasive variables measure?\n",
    "- How do you think the mean is calculated?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pN5pMDMDI69_"
   },
   "source": [
    "## 3.4. The lab table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k55Wyi7rIxND"
   },
   "outputs": [],
   "source": [
    "# Get labs\n",
    "%%bigquery lab\n",
    "\n",
    "SELECT *\n",
    "FROM `physionet-data.eicu_crd_demo.lab`\n",
    "WHERE patientunitstayid =  210014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "colab_type": "code",
    "id": "wfIxq_ZcI88o",
    "outputId": "3d093094-401a-400b-d116-7cb03ea4c548"
   },
   "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>labid</th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>labresultoffset</th>\n",
       "      <th>labtypeid</th>\n",
       "      <th>labname</th>\n",
       "      <th>labresult</th>\n",
       "      <th>labresulttext</th>\n",
       "      <th>labmeasurenamesystem</th>\n",
       "      <th>labmeasurenameinterface</th>\n",
       "      <th>labresultrevisedoffset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51663768</td>\n",
       "      <td>210014</td>\n",
       "      <td>1174</td>\n",
       "      <td>3</td>\n",
       "      <td>-eos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51227579</td>\n",
       "      <td>210014</td>\n",
       "      <td>-8756</td>\n",
       "      <td>3</td>\n",
       "      <td>-basos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
       "      <td>-8711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51057397</td>\n",
       "      <td>210014</td>\n",
       "      <td>4024</td>\n",
       "      <td>3</td>\n",
       "      <td>-basos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
       "      <td>4113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55479877</td>\n",
       "      <td>210014</td>\n",
       "      <td>-3470</td>\n",
       "      <td>3</td>\n",
       "      <td>-basos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
       "      <td>-3450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51663765</td>\n",
       "      <td>210014</td>\n",
       "      <td>1174</td>\n",
       "      <td>3</td>\n",
       "      <td>-basos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      labid  patientunitstayid  labresultoffset  labtypeid labname  labresult  \\\n",
       "0  51663768             210014             1174          3    -eos        0.0   \n",
       "1  51227579             210014            -8756          3  -basos        0.0   \n",
       "2  51057397             210014             4024          3  -basos        0.0   \n",
       "3  55479877             210014            -3470          3  -basos        0.0   \n",
       "4  51663765             210014             1174          3  -basos        0.0   \n",
       "\n",
       "  labresulttext labmeasurenamesystem labmeasurenameinterface  \\\n",
       "0             0                    %                       %   \n",
       "1             0                    %                       %   \n",
       "2             0                    %                       %   \n",
       "3             0                    %                       %   \n",
       "4             0                    %                       %   \n",
       "\n",
       "   labresultrevisedoffset  \n",
       "0                    1212  \n",
       "1                   -8711  \n",
       "2                    4113  \n",
       "3                   -3450  \n",
       "4                    1212  "
      ]
     },
     "execution_count": 82,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "colab_type": "code",
    "id": "hkLUJy8YJDIb",
    "outputId": "e846f83b-584e-4256-fa73-0626c8dfbd75"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>labid</th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>labresultoffset</th>\n",
       "      <th>labtypeid</th>\n",
       "      <th>labname</th>\n",
       "      <th>labresult</th>\n",
       "      <th>labresulttext</th>\n",
       "      <th>labmeasurenamesystem</th>\n",
       "      <th>labmeasurenameinterface</th>\n",
       "      <th>labresultrevisedoffset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>57756605</td>\n",
       "      <td>210014</td>\n",
       "      <td>-9226</td>\n",
       "      <td>4</td>\n",
       "      <td>urinary specific gravity</td>\n",
       "      <td>1.021</td>\n",
       "      <td>1.021</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>-9169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>51227584</td>\n",
       "      <td>210014</td>\n",
       "      <td>-8756</td>\n",
       "      <td>3</td>\n",
       "      <td>Hgb</td>\n",
       "      <td>13.800</td>\n",
       "      <td>13.8</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>-8711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>51909118</td>\n",
       "      <td>210014</td>\n",
       "      <td>-8756</td>\n",
       "      <td>3</td>\n",
       "      <td>PT</td>\n",
       "      <td>11.900</td>\n",
       "      <td>11.9</td>\n",
       "      <td>sec</td>\n",
       "      <td>sec</td>\n",
       "      <td>-8701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>51909119</td>\n",
       "      <td>210014</td>\n",
       "      <td>-8756</td>\n",
       "      <td>3</td>\n",
       "      <td>PT - INR</td>\n",
       "      <td>1.100</td>\n",
       "      <td>1.1</td>\n",
       "      <td>ratio</td>\n",
       "      <td></td>\n",
       "      <td>-8701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>51227577</td>\n",
       "      <td>210014</td>\n",
       "      <td>-8756</td>\n",
       "      <td>3</td>\n",
       "      <td>-lymphs</td>\n",
       "      <td>22.000</td>\n",
       "      <td>22</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
       "      <td>-8711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        labid  patientunitstayid  labresultoffset  labtypeid  \\\n",
       "129  57756605             210014            -9226          4   \n",
       "96   51227584             210014            -8756          3   \n",
       "90   51909118             210014            -8756          3   \n",
       "47   51909119             210014            -8756          3   \n",
       "30   51227577             210014            -8756          3   \n",
       "\n",
       "                      labname  labresult labresulttext labmeasurenamesystem  \\\n",
       "129  urinary specific gravity      1.021         1.021                        \n",
       "96                        Hgb     13.800          13.8                 g/dL   \n",
       "90                         PT     11.900          11.9                  sec   \n",
       "47                   PT - INR      1.100           1.1                ratio   \n",
       "30                    -lymphs     22.000            22                    %   \n",
       "\n",
       "    labmeasurenameinterface  labresultrevisedoffset  \n",
       "129                                           -9169  \n",
       "96                     g/dL                   -8711  \n",
       "90                      sec                   -8701  \n",
       "47                                            -8701  \n",
       "30                        %                   -8711  "
      ]
     },
     "execution_count": 83,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the values by the offset time (time in minutes from ICU admission)\n",
    "lab = lab.sort_values(by='labresultoffset')\n",
    "lab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 228
    },
    "colab_type": "code",
    "id": "Pnk3XWaYJE4V",
    "outputId": "659d3125-b927-4d74-d9f6-369170f71a6c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>labname</th>\n",
       "      <th>labresult</th>\n",
       "      <th>labmeasurenamesystem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labresultoffset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-9226</th>\n",
       "      <td>urinary specific gravity</td>\n",
       "      <td>1.021</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-8756</th>\n",
       "      <td>Hgb</td>\n",
       "      <td>13.800</td>\n",
       "      <td>g/dL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-8756</th>\n",
       "      <td>PT</td>\n",
       "      <td>11.900</td>\n",
       "      <td>sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-8756</th>\n",
       "      <td>PT - INR</td>\n",
       "      <td>1.100</td>\n",
       "      <td>ratio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-8756</th>\n",
       "      <td>-lymphs</td>\n",
       "      <td>22.000</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  labname  labresult labmeasurenamesystem\n",
       "labresultoffset                                                          \n",
       "-9226            urinary specific gravity      1.021                     \n",
       "-8756                                 Hgb     13.800                 g/dL\n",
       "-8756                                  PT     11.900                  sec\n",
       "-8756                            PT - INR      1.100                ratio\n",
       "-8756                             -lymphs     22.000                    %"
      ]
     },
     "execution_count": 84,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lab = lab.set_index('labresultoffset')\n",
    "columns = ['labname','labresult','labmeasurenamesystem']\n",
    "lab = lab[columns]\n",
    "lab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 138
    },
    "colab_type": "code",
    "id": "_96UMG-SJOK-",
    "outputId": "ebe9e3d5-8159-442d-cd22-d5c7d3be6987"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['urinary specific gravity', 'Hgb', 'PT', 'PT - INR', '-lymphs',\n",
       "       'bicarbonate', 'anion gap', 'creatinine', 'chloride', 'MCH', 'BUN',\n",
       "       'calcium', 'MCHC', 'RDW', 'Hct', 'RBC', 'potassium', '-basos',\n",
       "       'platelets x 1000', 'MCV', '-eos', 'sodium', '-polys', 'glucose',\n",
       "       '-monos', 'WBC x 1000', 'CPK', 'bedside glucose', 'paCO2', 'paO2',\n",
       "       'FiO2', 'HCO3', 'Base Excess', 'pH', 'LPM O2', 'lactate', 'PTT'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 85,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list the distinct labnames\n",
    "lab['labname'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "id": "bjb944cJJQ_2",
    "outputId": "76e07d9d-4e89-4387-a145-ee7f4d137dd3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>labname</th>\n",
       "      <th>-basos</th>\n",
       "      <th>-eos</th>\n",
       "      <th>-lymphs</th>\n",
       "      <th>-monos</th>\n",
       "      <th>-polys</th>\n",
       "      <th>BUN</th>\n",
       "      <th>Base Excess</th>\n",
       "      <th>CPK</th>\n",
       "      <th>FiO2</th>\n",
       "      <th>HCO3</th>\n",
       "      <th>...</th>\n",
       "      <th>creatinine</th>\n",
       "      <th>glucose</th>\n",
       "      <th>lactate</th>\n",
       "      <th>pH</th>\n",
       "      <th>paCO2</th>\n",
       "      <th>paO2</th>\n",
       "      <th>platelets x 1000</th>\n",
       "      <th>potassium</th>\n",
       "      <th>sodium</th>\n",
       "      <th>urinary specific gravity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labresultoffset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-9226</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-8756</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-3470</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2526</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-36</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "labname          -basos  -eos  -lymphs  -monos  -polys   BUN  Base Excess  \\\n",
       "labresultoffset                                                             \n",
       "-9226               NaN   NaN      NaN     NaN     NaN   NaN          NaN   \n",
       "-8756               0.0   3.0     22.0     9.0    66.0  16.0          NaN   \n",
       "-3470               0.0   1.0     12.0    11.0    76.0   NaN          NaN   \n",
       "-2526               NaN   NaN      NaN     NaN     NaN   NaN          NaN   \n",
       "-36                 NaN   NaN      NaN     NaN     NaN   NaN          NaN   \n",
       "\n",
       "labname           CPK  FiO2  HCO3            ...             creatinine  \\\n",
       "labresultoffset                              ...                          \n",
       "-9226             NaN   NaN   NaN            ...                    NaN   \n",
       "-8756             NaN   NaN   NaN            ...                   0.97   \n",
       "-3470             NaN   NaN   NaN            ...                    NaN   \n",
       "-2526             NaN   NaN   NaN            ...                    NaN   \n",
       "-36              63.0   NaN   NaN            ...                    NaN   \n",
       "\n",
       "labname          glucose  lactate  pH  paCO2  paO2  platelets x 1000  \\\n",
       "labresultoffset                                                        \n",
       "-9226                NaN      NaN NaN    NaN   NaN               NaN   \n",
       "-8756              131.0      NaN NaN    NaN   NaN             286.0   \n",
       "-3470                NaN      NaN NaN    NaN   NaN             370.0   \n",
       "-2526                NaN      NaN NaN    NaN   NaN               NaN   \n",
       "-36                  NaN      NaN NaN    NaN   NaN               NaN   \n",
       "\n",
       "labname          potassium  sodium  urinary specific gravity  \n",
       "labresultoffset                                               \n",
       "-9226                  NaN     NaN                     1.021  \n",
       "-8756                  3.6   140.0                       NaN  \n",
       "-3470                  NaN     NaN                       NaN  \n",
       "-2526                  NaN     NaN                     1.024  \n",
       "-36                    NaN     NaN                       NaN  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot the lab table to put variables into columns\n",
    "lab = lab.pivot(columns='labname', values='labresult')\n",
    "lab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 228
    },
    "colab_type": "code",
    "id": "MTyVuu4kJTde",
    "outputId": "b132b59d-543d-4348-9c00-cf05df06b211"
   },
   "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>labname</th>\n",
       "      <th>creatinine</th>\n",
       "      <th>pH</th>\n",
       "      <th>BUN</th>\n",
       "      <th>glucose</th>\n",
       "      <th>potassium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labresultoffset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-9226</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-8756</th>\n",
       "      <td>0.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-3470</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2526</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-36</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "labname          creatinine  pH   BUN  glucose  potassium\n",
       "labresultoffset                                          \n",
       "-9226                   NaN NaN   NaN      NaN        NaN\n",
       "-8756                  0.97 NaN  16.0    131.0        3.6\n",
       "-3470                   NaN NaN   NaN      NaN        NaN\n",
       "-2526                   NaN NaN   NaN      NaN        NaN\n",
       "-36                     NaN NaN   NaN      NaN        NaN"
      ]
     },
     "execution_count": 89,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot laboratory tests of interest\n",
    "labs_to_plot = ['creatinine','pH','BUN', 'glucose', 'potassium']\n",
    "lab[labs_to_plot].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 553
    },
    "colab_type": "code",
    "id": "BPd3TrJGJVfa",
    "outputId": "eecd0da2-0b06-4d92-f078-9f6a5cf4cf9c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absolute value')"
      ]
     },
     "execution_count": 90,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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JKlq0qG7cuGFWeQAAAADyIFNDjMVikYeHR6ptnp6ecnFxUVJSkr766iv5+/tL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\nmAohBgAAAICp/D++Rk9URIHXZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "plt.rcParams['figure.figsize'] = [12,8]\n",
    "title = 'Laboratory test results for patientunitstayid = {} \\n'.format(patientunitstayid)\n",
    "ax = lab[labs_to_plot].plot(title=title, marker='o',ms=10, lw=0)\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "ax.set_xlabel(\"Minutes after admission to the ICU\")\n",
    "ax.set_ylabel(\"Absolute value\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "04-timeseries",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}