03-plot-timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# eICU Collaborative Research Database\n",
    "\n",
    "# Notebook 3: Plot timeseries data for a single patient stay\n",
    "\n",
    "The aim of this notebook is to create a series of plots using timeseries data available for a single patient stay, using the following tables:\n",
    "\n",
    "- `patient`\n",
    "- `vitalperiodic`\n",
    "- `vitalaperiodic`\n",
    "- `lab`\n",
    "\n",
    "Before starting, you will need to copy the eicu demo database file ('eicu_demo.sqlite3') to the `data` directory.\n",
    "\n",
    "Documentation on the eICU Collaborative Research Database can be found at: http://eicu-crd.mit.edu/."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Getting set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sqlite3\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plot settings\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "fontsize = 20 # size for x and y ticks\n",
    "plt.rcParams['legend.fontsize'] = fontsize\n",
    "plt.rcParams.update({'font.size': fontsize})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Connect to the database\n",
    "fn = os.path.join('data','eicu_demo.sqlite3')\n",
    "con = sqlite3.connect(fn)\n",
    "cur = con.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Display a list of tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query = \\\n",
    "\"\"\"\n",
    "SELECT type, name\n",
    "FROM sqlite_master \n",
    "WHERE type='table'\n",
    "ORDER BY name;\n",
    "\"\"\"\n",
    "\n",
    "list_of_tables = pd.read_sql_query(query,con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>table</td>\n",
       "      <td>admissiondrug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>table</td>\n",
       "      <td>admissiondx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>table</td>\n",
       "      <td>apacheapsvar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>table</td>\n",
       "      <td>apachepatientresult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>table</td>\n",
       "      <td>apachepredvar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>table</td>\n",
       "      <td>careplancareprovider</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>table</td>\n",
       "      <td>careplaneol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>table</td>\n",
       "      <td>careplangeneral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>table</td>\n",
       "      <td>careplangoal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>table</td>\n",
       "      <td>careplaninfectiousdisease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>table</td>\n",
       "      <td>diagnosis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>table</td>\n",
       "      <td>infusiondrug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>table</td>\n",
       "      <td>lab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>table</td>\n",
       "      <td>medication</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>table</td>\n",
       "      <td>pasthistory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>table</td>\n",
       "      <td>patient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>table</td>\n",
       "      <td>treatment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>table</td>\n",
       "      <td>vitalaperiodic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>table</td>\n",
       "      <td>vitalperiodic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     type                       name\n",
       "0   table              admissiondrug\n",
       "1   table                admissiondx\n",
       "2   table               apacheapsvar\n",
       "3   table        apachepatientresult\n",
       "4   table              apachepredvar\n",
       "5   table       careplancareprovider\n",
       "6   table                careplaneol\n",
       "7   table            careplangeneral\n",
       "8   table               careplangoal\n",
       "9   table  careplaninfectiousdisease\n",
       "10  table                  diagnosis\n",
       "11  table               infusiondrug\n",
       "12  table                        lab\n",
       "13  table                 medication\n",
       "14  table                pasthistory\n",
       "15  table                    patient\n",
       "16  table                  treatment\n",
       "17  table             vitalaperiodic\n",
       "18  table              vitalperiodic"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Selecting a single patient stay "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. 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": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# select a single ICU stay\n",
    "patientunitstayid = 141296"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT *\n",
      "FROM patient\n",
      "WHERE patientunitstayid = 141296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# query to load data from the patient table\n",
    "query = \\\n",
    "\"\"\"\n",
    "SELECT *\n",
    "FROM patient\n",
    "WHERE patientunitstayid = {}\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "print(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# run the query and assign the output to a variable\n",
    "unitstay = pd.read_sql_query(query,con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>hospitaladmityear</th>\n",
       "      <th>...</th>\n",
       "      <th>unitstaytype</th>\n",
       "      <th>admissionweight</th>\n",
       "      <th>dischargeweight</th>\n",
       "      <th>unitdischargeyear</th>\n",
       "      <th>unitdischargetime24</th>\n",
       "      <th>unitdischargetime</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>141296</td>\n",
       "      <td>129026</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>73</td>\n",
       "      <td>85</td>\n",
       "      <td>Hypovolemia (including dehydration, Do not inc...</td>\n",
       "      <td>162.6</td>\n",
       "      <td>2014</td>\n",
       "      <td>...</td>\n",
       "      <td>admit</td>\n",
       "      <td>None</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2014</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>2828</td>\n",
       "      <td>Floor</td>\n",
       "      <td>Alive</td>\n",
       "      <td>002-30269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   patientunitstayid  patienthealthsystemstayid gender age  ethnicity  \\\n",
       "0             141296                     129026   Male  63  Caucasian   \n",
       "\n",
       "   hospitalid  wardid                                  apacheadmissiondx  \\\n",
       "0          73      85  Hypovolemia (including dehydration, Do not inc...   \n",
       "\n",
       "   admissionheight  hospitaladmityear    ...     unitstaytype admissionweight  \\\n",
       "0            162.6               2014    ...            admit            None   \n",
       "\n",
       "   dischargeweight unitdischargeyear  unitdischargetime24 unitdischargetime  \\\n",
       "0             76.8              2014             17:00:00           evening   \n",
       "\n",
       "  unitdischargeoffset  unitdischargelocation unitdischargestatus  uniquepid  \n",
       "0                2828                  Floor               Alive  002-30269  \n",
       "\n",
       "[1 rows x 36 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the first few rows of the dataframe\n",
    "unitstay.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. 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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT *\n",
      "FROM vitalperiodic\n",
      "WHERE patientunitstayid = 141296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# query to load data from the patient table\n",
    "query = \\\n",
    "\"\"\"\n",
    "SELECT *\n",
    "FROM vitalperiodic\n",
    "WHERE patientunitstayid = {}\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "print(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# run the query and assign the output to a variable\n",
    "vitalperiodic = pd.read_sql_query(query,con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>vitalperiodicid</th>\n",
       "      <th>observationyear</th>\n",
       "      <th>observationtime24</th>\n",
       "      <th>observationtime</th>\n",
       "      <th>observationoffset</th>\n",
       "      <th>temperature</th>\n",
       "      <th>sao2</th>\n",
       "      <th>heartrate</th>\n",
       "      <th>respiration</th>\n",
       "      <th>...</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>141296</td>\n",
       "      <td>1933588</td>\n",
       "      <td>2014</td>\n",
       "      <td>12:40:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>2568</td>\n",
       "      <td>None</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</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",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141296</td>\n",
       "      <td>1960296</td>\n",
       "      <td>2014</td>\n",
       "      <td>19:45:00</td>\n",
       "      <td>night</td>\n",
       "      <td>1553</td>\n",
       "      <td>None</td>\n",
       "      <td>97.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</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",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141296</td>\n",
       "      <td>1979222</td>\n",
       "      <td>2014</td>\n",
       "      <td>07:35:00</td>\n",
       "      <td>midday</td>\n",
       "      <td>823</td>\n",
       "      <td>None</td>\n",
       "      <td>95.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</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",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141296</td>\n",
       "      <td>1959122</td>\n",
       "      <td>2014</td>\n",
       "      <td>20:30:00</td>\n",
       "      <td>night</td>\n",
       "      <td>1598</td>\n",
       "      <td>None</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</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",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141296</td>\n",
       "      <td>1953374</td>\n",
       "      <td>2014</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>midnight</td>\n",
       "      <td>1818</td>\n",
       "      <td>None</td>\n",
       "      <td>97.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</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",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   patientunitstayid  vitalperiodicid  observationyear observationtime24  \\\n",
       "0             141296          1933588             2014          12:40:00   \n",
       "1             141296          1960296             2014          19:45:00   \n",
       "2             141296          1979222             2014          07:35:00   \n",
       "3             141296          1959122             2014          20:30:00   \n",
       "4             141296          1953374             2014          00:10:00   \n",
       "\n",
       "  observationtime  observationoffset temperature  sao2  heartrate  \\\n",
       "0            noon               2568        None  98.0       98.0   \n",
       "1           night               1553        None  97.0       98.0   \n",
       "2          midday                823        None  95.0      112.0   \n",
       "3           night               1598        None  98.0      100.0   \n",
       "4        midnight               1818        None  97.0      110.0   \n",
       "\n",
       "   respiration  ...  systemicsystolic systemicdiastolic systemicmean  \\\n",
       "0         21.0  ...              None              None         None   \n",
       "1         18.0  ...              None              None         None   \n",
       "2         32.0  ...              None              None         None   \n",
       "3         22.0  ...              None              None         None   \n",
       "4         20.0  ...              None              None         None   \n",
       "\n",
       "  pasystolic padiastolic pamean   st1   st2   st3   icp  \n",
       "0       None        None   None  None  None  None  None  \n",
       "1       None        None   None  None  None  None  None  \n",
       "2       None        None   None  None  None  None  None  \n",
       "3       None        None   None  None  None  None  None  \n",
       "4       None        None   None  None  None  None  None  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the first few rows of the dataframe\n",
    "vitalperiodic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# display a full list of columns\n",
    "vitalperiodic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>vitalperiodicid</th>\n",
       "      <th>observationyear</th>\n",
       "      <th>observationtime24</th>\n",
       "      <th>observationtime</th>\n",
       "      <th>observationoffset</th>\n",
       "      <th>temperature</th>\n",
       "      <th>sao2</th>\n",
       "      <th>heartrate</th>\n",
       "      <th>respiration</th>\n",
       "      <th>...</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>25</th>\n",
       "      <td>141296</td>\n",
       "      <td>2001591</td>\n",
       "      <td>2014</td>\n",
       "      <td>18:05:00</td>\n",
       "      <td>night</td>\n",
       "      <td>13</td>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>...</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",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>141296</td>\n",
       "      <td>2001440</td>\n",
       "      <td>2014</td>\n",
       "      <td>18:10:00</td>\n",
       "      <td>night</td>\n",
       "      <td>18</td>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</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",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>141296</td>\n",
       "      <td>2001288</td>\n",
       "      <td>2014</td>\n",
       "      <td>18:15:00</td>\n",
       "      <td>night</td>\n",
       "      <td>23</td>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>...</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",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>141296</td>\n",
       "      <td>2001137</td>\n",
       "      <td>2014</td>\n",
       "      <td>18:20:00</td>\n",
       "      <td>night</td>\n",
       "      <td>28</td>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</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",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>141296</td>\n",
       "      <td>2000987</td>\n",
       "      <td>2014</td>\n",
       "      <td>18:25:00</td>\n",
       "      <td>night</td>\n",
       "      <td>33</td>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</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",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     patientunitstayid  vitalperiodicid  observationyear observationtime24  \\\n",
       "25              141296          2001591             2014          18:05:00   \n",
       "37              141296          2001440             2014          18:10:00   \n",
       "61              141296          2001288             2014          18:15:00   \n",
       "245             141296          2001137             2014          18:20:00   \n",
       "211             141296          2000987             2014          18:25:00   \n",
       "\n",
       "    observationtime  observationoffset temperature   sao2  heartrate  \\\n",
       "25            night                 13        None  100.0      124.0   \n",
       "37            night                 18        None  100.0      122.0   \n",
       "61            night                 23        None  100.0      124.0   \n",
       "245           night                 28        None  100.0      120.0   \n",
       "211           night                 33        None  100.0      124.0   \n",
       "\n",
       "     respiration  ...  systemicsystolic systemicdiastolic systemicmean  \\\n",
       "25          36.0  ...              None              None         None   \n",
       "37          29.0  ...              None              None         None   \n",
       "61          28.0  ...              None              None         None   \n",
       "245         23.0  ...              None              None         None   \n",
       "211         23.0  ...              None              None         None   \n",
       "\n",
       "    pasystolic padiastolic pamean   st1   st2   st3   icp  \n",
       "25        None        None   None  None  None  None  None  \n",
       "37        None        None   None  None  None  None  None  \n",
       "61        None        None   None  None  None  None  None  \n",
       "245       None        None   None  None  None  None  None  \n",
       "211       None        None   None  None  None  None  None  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    <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",
       "      <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>13</th>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>36.0</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",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>29.0</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",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>28.0</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",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>23.0</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",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>23.0</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",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  temperature   sao2  heartrate  respiration   cvp etco2  \\\n",
       "observationoffset                                                          \n",
       "13                       None  100.0      124.0         36.0  None  None   \n",
       "18                       None  100.0      122.0         29.0  None  None   \n",
       "23                       None  100.0      124.0         28.0  None  None   \n",
       "28                       None  100.0      120.0         23.0  None  None   \n",
       "33                       None  100.0      124.0         23.0  None  None   \n",
       "\n",
       "                  systemicsystolic systemicdiastolic systemicmean pasystolic  \\\n",
       "observationoffset                                                              \n",
       "13                            None              None         None       None   \n",
       "18                            None              None         None       None   \n",
       "23                            None              None         None       None   \n",
       "28                            None              None         None       None   \n",
       "33                            None              None         None       None   \n",
       "\n",
       "                  padiastolic pamean   st1   st2   st3   icp  \n",
       "observationoffset                                             \n",
       "13                       None   None  None  None  None  None  \n",
       "18                       None   None  None  None  None  None  \n",
       "23                       None   None  None  None  None  None  \n",
       "28                       None   None  None  None  None  None  \n",
       "33                       None   None  None  None  None  None  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "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','st1','st2','st3','icp']\n",
    "\n",
    "vitalperiodic = vitalperiodic[columns].set_index('observationoffset')\n",
    "vitalperiodic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10db3ee50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pjx494Ofnh7///huSJGH06NHavbFM1bVrV6xZswb9+/fHk08+CQsLCwQGBiIg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Th19epZK2Juf4KzPv/eUXBkF9T/WMC9X3XNgeyPz1Cf0geLUdjmNss+92ppcxmh9sxs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\nAvd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oIpuiy+jUsQPWrd1Sb/2mKApWK4XAYCmqq+y8j7Y6nQ6btm4ERTkQGFI35zSFWCLq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XvbvQ/D3r85OTlWx5YVnjWhlzMRERE9+DipCBGRExNmEy4rK4OXl1ele4k5ipAuaW3MPMOZ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\nIiIiJ8KAIBERERERERERkRP5f16PlXnjRUuJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daf0bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "figsize = (18,8)\n",
    "title = 'Vital signs (periodic) for patientunitstayid = {} \\n'.format(patientunitstayid)\n",
    "ax = vitalperiodic.plot(title=title, figsize=figsize, fontsize=fontsize,\n",
    "                       marker='o')\n",
    "\n",
    "ax.title.set_size(fontsize)\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": {},
   "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": {},
   "source": [
    "### 3.3. 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/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT *\n",
      "FROM vitalaperiodic\n",
      "WHERE patientunitstayid = 141296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# query to load data from the patient table\n",
    "query = \\\n",
    "\"\"\"\n",
    "SELECT *\n",
    "FROM vitalaperiodic\n",
    "WHERE patientunitstayid = {}\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "print(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# run the query and assign the output to a variable\n",
    "vitalaperiodic = pd.read_sql_query(query,con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>vitalaperiodicid</th>\n",
       "      <th>observationyear</th>\n",
       "      <th>observationtime24</th>\n",
       "      <th>observationtime</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>141296</td>\n",
       "      <td>1507295</td>\n",
       "      <td>2014</td>\n",
       "      <td>13:56:06</td>\n",
       "      <td>noon</td>\n",
       "      <td>1204</td>\n",
       "      <td>160.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>122.0</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>141296</td>\n",
       "      <td>1507277</td>\n",
       "      <td>2014</td>\n",
       "      <td>22:26:00</td>\n",
       "      <td>midnight</td>\n",
       "      <td>274</td>\n",
       "      <td>152.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>112.0</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>141296</td>\n",
       "      <td>1507298</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:56:08</td>\n",
       "      <td>evening</td>\n",
       "      <td>1384</td>\n",
       "      <td>108.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>88.0</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>141296</td>\n",
       "      <td>1507270</td>\n",
       "      <td>2014</td>\n",
       "      <td>18:55:56</td>\n",
       "      <td>night</td>\n",
       "      <td>63</td>\n",
       "      <td>163.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>97.0</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>141296</td>\n",
       "      <td>1507302</td>\n",
       "      <td>2014</td>\n",
       "      <td>20:56:09</td>\n",
       "      <td>night</td>\n",
       "      <td>1624</td>\n",
       "      <td>151.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>114.0</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": [
       "   patientunitstayid  vitalaperiodicid  observationyear observationtime24  \\\n",
       "0             141296           1507295             2014          13:56:06   \n",
       "1             141296           1507277             2014          22:26:00   \n",
       "2             141296           1507298             2014          16:56:08   \n",
       "3             141296           1507270             2014          18:55:56   \n",
       "4             141296           1507302             2014          20:56:09   \n",
       "\n",
       "  observationtime  observationoffset  noninvasivesystolic  \\\n",
       "0            noon               1204                160.0   \n",
       "1        midnight                274                152.0   \n",
       "2         evening               1384                108.0   \n",
       "3           night                 63                163.0   \n",
       "4           night               1624                151.0   \n",
       "\n",
       "   noninvasivediastolic  noninvasivemean  paop cardiacoutput cardiacinput  \\\n",
       "0                  91.0            122.0  None          None         None   \n",
       "1                  80.0            112.0  None          None         None   \n",
       "2                  71.0             88.0  None          None         None   \n",
       "3                  78.0             97.0  None          None         None   \n",
       "4                  78.0            114.0  None          None         None   \n",
       "\n",
       "    svr  svri   pvr  pvri  \n",
       "0  None  None  None  None  \n",
       "1  None  None  None  None  \n",
       "2  None  None  None  None  \n",
       "3  None  None  None  None  \n",
       "4  None  None  None  None  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the first few rows of the dataframe\n",
    "vitalaperiodic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'patientunitstayid', u'vitalaperiodicid', u'observationyear',\n",
       "       u'observationtime24', u'observationtime', u'observationoffset',\n",
       "       u'noninvasivesystolic', u'noninvasivediastolic', u'noninvasivemean',\n",
       "       u'paop', u'cardiacoutput', u'cardiacinput', u'svr', u'svri', u'pvr',\n",
       "       u'pvri'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vitalaperiodic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>vitalaperiodicid</th>\n",
       "      <th>observationyear</th>\n",
       "      <th>observationtime24</th>\n",
       "      <th>observationtime</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>27</th>\n",
       "      <td>141296</td>\n",
       "      <td>1507268</td>\n",
       "      <td>2014</td>\n",
       "      <td>18:07:24</td>\n",
       "      <td>night</td>\n",
       "      <td>15</td>\n",
       "      <td>142.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>115.0</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>24</th>\n",
       "      <td>141296</td>\n",
       "      <td>1507269</td>\n",
       "      <td>2014</td>\n",
       "      <td>18:22:23</td>\n",
       "      <td>night</td>\n",
       "      <td>30</td>\n",
       "      <td>157.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>125.0</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>141296</td>\n",
       "      <td>1507270</td>\n",
       "      <td>2014</td>\n",
       "      <td>18:55:56</td>\n",
       "      <td>night</td>\n",
       "      <td>63</td>\n",
       "      <td>163.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>97.0</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>21</th>\n",
       "      <td>141296</td>\n",
       "      <td>1507271</td>\n",
       "      <td>2014</td>\n",
       "      <td>19:25:57</td>\n",
       "      <td>night</td>\n",
       "      <td>93</td>\n",
       "      <td>160.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>107.0</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>40</th>\n",
       "      <td>141296</td>\n",
       "      <td>1507272</td>\n",
       "      <td>2014</td>\n",
       "      <td>19:55:57</td>\n",
       "      <td>night</td>\n",
       "      <td>123</td>\n",
       "      <td>150.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>112.0</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": [
       "    patientunitstayid  vitalaperiodicid  observationyear observationtime24  \\\n",
       "27             141296           1507268             2014          18:07:24   \n",
       "24             141296           1507269             2014          18:22:23   \n",
       "3              141296           1507270             2014          18:55:56   \n",
       "21             141296           1507271             2014          19:25:57   \n",
       "40             141296           1507272             2014          19:55:57   \n",
       "\n",
       "   observationtime  observationoffset  noninvasivesystolic  \\\n",
       "27           night                 15                142.0   \n",
       "24           night                 30                157.0   \n",
       "3            night                 63                163.0   \n",
       "21           night                 93                160.0   \n",
       "40           night                123                150.0   \n",
       "\n",
       "    noninvasivediastolic  noninvasivemean  paop cardiacoutput cardiacinput  \\\n",
       "27                 101.0            115.0  None          None         None   \n",
       "24                  75.0            125.0  None          None         None   \n",
       "3                   78.0             97.0  None          None         None   \n",
       "21                  87.0            107.0  None          None         None   \n",
       "40                  81.0            112.0  None          None         None   \n",
       "\n",
       "     svr  svri   pvr  pvri  \n",
       "27  None  None  None  None  \n",
       "24  None  None  None  None  \n",
       "3   None  None  None  None  \n",
       "21  None  None  None  None  \n",
       "40  None  None  None  None  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>15</th>\n",
       "      <td>142.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>115.0</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>30</th>\n",
       "      <td>157.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>125.0</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>63</th>\n",
       "      <td>163.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>97.0</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>93</th>\n",
       "      <td>160.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>107.0</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>123</th>\n",
       "      <td>150.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>112.0</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",
       "15                               142.0                 101.0            115.0   \n",
       "30                               157.0                  75.0            125.0   \n",
       "63                               163.0                  78.0             97.0   \n",
       "93                               160.0                  87.0            107.0   \n",
       "123                              150.0                  81.0            112.0   \n",
       "\n",
       "                   paop cardiacoutput cardiacinput   svr  svri   pvr  pvri  \n",
       "observationoffset                                                           \n",
       "15                 None          None         None  None  None  None  None  \n",
       "30                 None          None         None  None  None  None  None  \n",
       "63                 None          None         None  None  None  None  None  \n",
       "93                 None          None         None  None  None  None  None  \n",
       "123                None          None         None  None  None  None  None  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10dfaf850>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REBCg80XsiBEjIITAJ598ojVLPT8/H+PGjdObwCUiIiKqKs6MJaJqp0ePHvD1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O7Caa1YmzRpQlBQEHv37nXWFMJZ4hLh2JFaH1a3HEXFNav1cd2BNnQM+oZP0LPPuHxufduXqM49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JPJ\n2o3cnTJlCu+88w7/+Mc/+Oyzz5DL5YwcOZL8/HxKSkr44osv3La/6667iI2NZe/evezevRubzUZq\naip33303ixYtIi0tzeP6zZs3j23btuFwODqMLL7lllvIzc3lX//6F9u3b2fLli1otVpSUlKYMWNG\nu9e/Pby9nt6UL5PJePvtt3n++ef55ptvWL58OSkpKSxYsIAHH3yQKVOmeHXvW9aFU7teEL2Qlg8d\nOsTSpUsBmDBhAnPmzKFvX3dRoqKigg8//NAVbvzEE0+0qdz3VM6ePdvpNvV6G/u/MzD5+raz/XWX\nA7sNyGQwdFTnnQRRFNm2+SRvvrGCGdMeaJWRMRQF2eIiMw31dr9kMr0Um01k3cfnmX5rTFj9sL0h\nKiqKxsbGYFcDgKqzVoqLzFw9xT273cHvDVjMIqOu1gX1Pjg+/wCMBmRz7+l0W7HoMI4P3kT+uNTo\nO1/nzJZ+vs7OwFwNGVntZ0vvLt4+z0WHTdjtIpcPD0xila5w4qiJxvN2Ro6LCHZVACg/Y+FMsaXV\n7zQccPzrebhiDLJx1+L46P9AJiCb5VlkQjBo73n+bmszqRlK0sNMEPeG0tJSnl36epvZooPdNrFa\nRAq+amDkuAgSk3q2KN4RVWetHDto5JppUR59m8tPQ3FRIxOui+z2N2DrfxsZPFxDYlJwEhp6iyiK\nFHzZyIixOr8mZ6qutHL8iIkJecEfvAtVqiqsnCoyM26yd9+wbzc10S9HRWq6873rr/azwyHy388a\nuPraSKKiw2fAwd9055tw8b4PPnqlR+WZzWZqa2t9UXUJCYleTkJCAmp16wE2r4ZNv/rqKwB+/OMf\n8+CDD7YSYgFSU1N56KGHuP766wH48ssvu1LfHo0zKZR/wqvP19mpKLMycKhn05MEQeCbgo9cQiw4\npwHmTbwvqL5sHVFdYSW5b2A6PwqFgFwuYLH0nHD4UEZf07bP2ZArtDQ22CkNclIDb5J4iYf3IuSO\n9HONQpvGBjvfb29m5+ZmEpOV5N0YTb8ctd+E2K7gtCkI3eh3h0Pk1HEz2YNCJ0ImJU3J+To7hqYw\n9B1PSXdGxuJM3iWEYfIuURRDwrrF34Sy96RSJTBstI4D3xmw23pn+0AURYoOO71iPR0kHZQbiUot\nUHioe0k5HA6Rhno7sX62qvIlLVYF5Wf8246pr+2ZXtK+xOkZ691332YVqaux0SfZ/+K/TCaQ3l9F\nabj6s/uJFctXu4RY+KG/+sJf/49dW5o6/O+Fv/6f274SEhISoYBXX+uioiIEQWDOnDmdbjtv3jzW\nr19PYWFhlyvXU1GpBew2EZtN9Kn/nyiKHN5rYNBQDSovpnMbDfaQ82VrD5vN2QkdPT5wEWIarYDJ\n4AiLJBHhTl2tnZzBrUUnuUJg9NURbN/YRFyiIniRAhcl8eqs8yke2Ytszt2BqVeI0dxkp+iwieoK\nG9mD1FwxVhd0r9P2iIiU0RzCYuzZUisRUXK/+2N7g1wukN5PyZlTFgYPC92I4jZJSYO93zr9pk6f\ngDAUY5ubHMhkhLzPsS8IZe/JlDQlZactFB0xhXRkvb+oqbZhtYqkpnkuTgmCwIixOjavb6RPsoLE\nLgpbjeftaCNkKJSh+V1pj76ZKr4taCJ3ZOdtiK5SVxvcRI/hgEYreJ3Aq6baRmyCImDPXGaWim3f\nNDF4uCakBrCDSXOjrc3+qkIpdmrpoFC29iCXkJCQCDZe9e6amprQ6XRERnY+rSMyMhKdTofB4Nvs\nqT0BQRBcXqSRPhSVKsqsWC0i/bK9a4SFqi9bW9RU2YiNV6AMYAO8JZI5Ji5gRfZKHA6Rer2NuIS2\nG0tRMXIGDdWwZ4eBiT+KDEryIE+TeImNDVBVDjmDA1i74HCxf5dKJWPCuJtxWJLJukxF3vTQT3Cj\ni5BhaA49MbblulaUmYlLUJIx4PaQiAhsISNLza4tTQzK1QTd99cbhJQ0HFXlCOcqQaVGCMMXe10v\niIoNF4aN0lLwVSN9M5QhNWASCI4fMXPZ5d7//tUaGSPG6ti708A110d1aaA7nJJ3XUxUtByVSkB/\nzk6CH+wtRFGkXm9n+JW961n0FpVawGYTsds9T0RZXWElKTVw1zUyWk5klIyqs1aXLUJvpr7WRsN5\nsc3+akyckpROBoVi4pSt9pWQkJAINl61gCIjIzEYDDQ1NXW6bVNTEwaDgYiI0PC4CzU0OhlGHyaG\nsttEjuwzkjtK53XD+J6F8/lm6zLMZqdwbjYb2LD5Ve5ZGHrJ16orrCQHsDEEXc+6KuEdjeftaLWy\nDqO6++Wo0EXKOHqge1Mcu0XmAMSSjq0KxKP7/j97dx7e1HnnC/x7Fm1HkvfdllewjTGYJewEEgJk\na2fazCRhmqUJWZo0nXQ66ZbOZJqU6U3b9OY2zM1SpoV2KBMmk4bOTTKTZiEhQNh3bDDYYFvGuyTb\nstaz3T+EDQYbJPlI50h+P8/T52mMZb+2j855z++87/cHVNaCYhMjyy5awxlcNWX3YfGcJzBj6v14\n6z//DVNn9qOq1qT5QiwQuimTJRnBoHbe45f/Xu9Y+S3MqXkAL67bALvdrvbQRqSmMzAYafR2C2oP\nJTK5BUBPB+TzZ4DSKWqPJirOXjHpm3clCoORRk2dEccO+CBJkyeuwNkrwOeRUFgS3TUuO0+HwhI9\nju33RtUVOVSMTcz3QEGxPmZRBV5PaNW8iUv+VfMTQVEUDEYKAX94131ZltHdySMnP75zuuJyPdpI\nVAE624PYt9ODxx6/+n51+67Xw7pfvfJelyAIQgsiulpXVlZClmW8/fbb1/3ct956C7Iso7KyMurB\nJTMTRylajG1uDCAtk42qkcSVuWwHjm/Gl+64X1OrsIDLJkMF8Z0MmRQunBNjc/WJ121qQVEU6m4w\nobM9iO4OPk4ju2IMJRVAa/O1P2mS5MWOld9124pvYssf3lJ5ZOGjKEpzubHj5aJpLcc7EW8UKSMH\ncBbIh/cgEfNiAVzMi028VYHJqqhUD52ewrnGgNpDiZuzp/yYMm1i+d/VtUb4fTJazkZ+Dul3Cgm5\nMhYACop16GznY1K873ckbpE63kK5seH9DdwDEmiKgsUa3yJ3vk0PV584ae9BZFlGc6MfJw/7sGCZ\nGXPmlUedI375vS5BEIRWRHTFvu2223DgwAH8z//8DwYHB3HXXXehqKho1Oc0Nzdj27ZtOHDgAIBQ\nsy/iako28fJ5JZw7E8Cy1dF3Tr08l00UZHzy/iAG+0WkpGlnsusekECpMBkycTT6utUp/E0mToeA\nzDBWe+kNNOYsMOPQHg+WrbbCaIrv8UCVToH0wTvj/rssy6G82Dvujt+gVJJIedPXwlkYeIckpGWo\nPZIQ79DYuWha+70WFutw6rgPAb8EgzExVmLZ29rwH6fbIR2oB1M9A/fULoCtuFjtYYUtEJDg90uw\npmrn2jzZDT8kfGdrI/5ty3vgeQkmjsHDa9do7qG2EvqdAgb7RdywZGI732iGwtxFHHZ9MoSMbBap\n6eEd04IgY8gtaWp+GgmzhYGJo+HoEZCdp+ziApdj7CaoxNUi2fU2HFEQq5zf8bAshXybDu0tQUyt\nCa8xc7KQJBn1R3xw9ApYcot1JCN9IjniWs4gJwhicoqoGFtbW4uvfvWr2LZtG3bv3o3du3cjJSUF\nGRkZEAQBfX198PsvbR++6667MH36dMUHnQyMJhoDTmVurBuO+VA2Va9YMw+GpVBeZcDZU37MXaSd\nmInhiIJ4T4aMHAVfhEH/RORcDhFTp4U32czMYVFSoceRfV4sXG6O7zFRMuXaTbw62gBWB+Tkx29M\nKmEYOmHypq/FbNZOEy+/T4J7nFw0rf1edXoaeQWhJkYVVdq/UbS3tWHjt5/A32cZwOXlwxvoxsvf\nfgJrX3kjYQqyrr7QyjfS0EVbHK4O7Nj977j15m/CYOAQCHjx4rrXw161lUjONgRQUW1UJLfdbGUw\nfZYJh/d4cONqa1iNHgf7RVhTGFVy45VSWKxDRxuveDG23ymiemZyxyMpxWiiEAhzbt/dyWNKmPNT\npRWX63F4rxdTphnifv+jFoGXcWiPB5IELFlhTYjIK4IgiGhEXL1bs2YNnn76aeTm5gIABgcH0dLS\ngvb29pFCbF5eHr797W/j3nvvVXa0ScRkpuFTIIfU0SvA2SegolrZSUJphQF93QKGBrWzEkuNvCaA\nxBTEQ8AvIRiQYEkJ/5Q0tcYIUZTRfDq+W0Mpa+qlJl5jkOsPg5o+O+knzbIsY9b0L+PDz16LKr9L\nSziLNpp4eT0Sdm8fwr1r7ok6Fy3ebOUGtJ0LRpX7GG9vvboef59GgWNDRW2OZfD3aRTeenW9yiML\nn5M079KkTRu3jhRiAe1Gi0yUe0CEyyGgOMJGsddSVKpHagaD+iO+sD4/UZt3Xa6gWI/OCzwkUbnz\npiTKGOwXkTbJGslFy2Ci4Q8jM5YPShh0ichSKac7LYMBQ4fu9yYDnzc0DzKaaCxYZiaFWIIgklpU\nV5YlS5ZgyZIlaGlpwblz5zA4OAgASElJQXl5OUpLS5UcY1IymSZe4JMlGScP+1BTZwprNUEkWB2F\nsqkGNJ0KYNYC9TtP8kEJA67YdJ+9nuGtTOOuhCQmzOUQkZ4Z2apnmqYwZ6EZOz9yIyuHRVpmHI+N\ni028qJyCq/5Jrj8Cennyx7PYzweRl1uEf3z+cfxu0xb4vCJMHJOQK8HMFhoddnWjSDxDIvZ8OoTy\nKiPKK6egbOrj2LRR+7/XzGwGshTKKrxe5rPaJJdjpBA7jGMZSP0OlUYUOWevgKoZ2l+FPNkkS2TL\n9Zxt8KO80qD4nHPGXA6ff+hGhz2IAtu1C739TiGq/ghaYuJoWKyhBoi5CvVBGOwXwVlosDoyTw2H\n0UjB0Xv992dvl4CMbBaMwsd8uCiKgu1iPntWTnKveh5widi/awhlUwyoqJ48K4EJgpi8JjSbKS0t\nJYXXKA2vtoymwGe327Fp41a4HDwEnsL3pt0HQPktlqVT9dj+vhtejwjOrO4qhN7uUJ6o0jcA4WBZ\nCgxDIRiQYTCSiUEsRJtzxplpzJhrwqG9XixbbYUuTjchI0285i8b9XE5GACaG4Fv/CAu41ALz8s4\nfcKP+UvNSMu0JnwGV6iBl3pFE/egiL2fDaFyuhElFQYAiZNtRlEUWFMP1v3kbRhM0HRWJp2eCW9P\nz6iCrFcQQedkqjiq8IkXV76lkwY9mmPimISIFpmIIbeI3m4BM25Q/gG9Tkdh7kIO+3Z6kJbBXjN2\nq98pYorCu8HUUFisR0dbULFirMsZeqhNhCe00OL6K5O7O3nkqrAr73JFpXqcqR8EH5Sg0ydGPnuk\nujt4HN3vxYy5pus+kCEIgkgW5KqtEp2eAoVQUUMfwRYMu92OF9dtCHXarg7lkv3sn2OTS6bX0yip\n0KPpVAAzYzD5jkR3hzoRBcNMJgp+X+I0qUk0rj4h6jyuApsevV0CThzyYs7C+GQcj9vE60w9YCsD\nxWknazkWzjb4kZOni+9q5BgycTQCfhmSKIOOcw7hgEvEvs+HMG2mCbayxLsBsdvteHPr73HL0ic1\nn5V5z1NP4+VvP4G/TxPBsQy8goiX+2WsfeFptYcWln6nCEsKQ1a+adDDa9fgxXWvh+ZmF98H23eF\n3gfJoulUAKVT9DF76JmWyaKiyoDDez1YfLNlzFxkPijB74ss0kir8m06NJ70QxRlRfJvXWE2QSVC\njCYagevEFMiyjJ5OAVXT1S3+Gww0snN1uNDGo3SKQdWxxEJLUwBn6v2Yt9SsqRgehmGQmZkYD2sJ\ngtA2hhn74XxEZ7yhoSE0NzeD4zhMnTp11L85nU78/ve/R0NDAwRBQF1dHR588EFkZGikPbUGGTka\nfq8MfQT335s2bh2Z7AOX55JtickqqvJKAz79Hzcqp0tx71o/bHgyVKniZMjI0fB5ZaSmqzaEpCVJ\nMvpd4oQ6AE+fbcLOj9ywtwRhK41DQWu4iZckgaIvfXscqQAAIABJREFUvS/k+iOgps+O/fdX0ZBb\nRNu5IG66zar2UBRD0xSMJhperwSLNX4r2fqdAvbv9KB2TuKuBNm0cetIIRaI/TVpImzFxVj7yhvY\n8Op6SP0O0DmZWPvC0wnUvEtARlbyrLRMJjabDc8+F4oW8XoE9DtkfPeHj2rugUS0vB4JXRd4rLgj\ntuf9imoDersFnG3wo6rWdNW/97tEpKYxSdHAzmiikZLOoKeTR37RxM///Y7kWDEcLwYjdd2VsQMu\nEXoDBc6i/nm3uFyP0yf8SVWMlSUZDcf86O7kseQWC8wa+D1fjmVZsKx2isMEQSSfiM4wH3/8Md58\n803ceeedo4qxwWAQP/7xj9HT0zPysT179uD8+fP4+c9/DqORTA7GMhxVkJIW/sUn3rlkBiMNW6ke\nzacDmD776olxPAy4ROj1lKoXaRNHw0+aeMXEYL8IjqMntPWKZUP5sXt3DCEjk4E5xgW1S028uoDc\nS7mxcsMR0A8lxiq7aDUc9WFKtUG1hzOxEooqiF8x1tkr4MBuD+rmccgrTNwcuETLyrQVF+OZn/9S\n7WFExdknoKgkMYv2k8Hl0SJH93sBIXnOkc2n/Sgp10NviO3PRFEUZi8I5cdm5eiu6hMQat6VPMWR\nApsOHW0TL8YGAxICfgnWJFgxHC96AwVBkK+5Mrm7Q1A9omBYdi6LYwdD/TNS07VVtIyGIMg4stcL\nPihh6S2WmJ9bCIIgtCiiM9+xY8cAAEuXLh318c8++ww9PT2wWCx47LHH8NRTTyEjIwNdXV344IMP\nlBttkhkuxkb2Gmakw/awWOeSVVQbYG8JXnc7T6z0dAqqRhQAF1fG+kgxNhZcCjX+SU1nUFljxKE9\nXkU7FI+reArk1qaR/5SdfcCgCyipiP33VklPJ4+hQQlllcmzMmMYZw4VY+Ohr5vHgd0ezFmY2IVY\nQJ1r0mQkyzKcfdpvkkaEFJfr0XY+CFmOw7Uoxvw+CRfaeJRXxee8bzTRqJvH4fA+D4KB0efkUDE2\nec4t+TYderp4CMLEjpN+p4jUDBZUEqwYjheKomA0Ute8t+np5JGTr41zLkVT0Bl78JPnX8KzP/gZ\nfvLCS7Db7WoPKyp+n4Q9nw6BZYEFy0khliCIySuis9/wyteioqJRH9+7dy8A4Gtf+xpWrlyJZcuW\n4Zvf/CYA4MCBA0qMMylFU4x9eO0afPDpayM3v8O5ZA+vXROLIQIITYwLbDqcOxOI2fe4lu4OHjkF\n6k6GTKbI/1ZEeFx90TXvGkvpVD2MJgqnT/oV+XrXQpVOCTXxukhuOAJq2ixQdPLcKF5OkmScPOJD\nzSyTIvl2WmO20PDEoRjb3cnj0B4v5i42IzsvsQuxQOiatH3X66OuSe99+Cq++pW/VnlkyWXILYHV\nUTBx5KY1EaRnMqAAOPu0uUI8Es2NARSV6OKamZ9boEN+kR7HDvpGFbT7nUJSFWMNBhrpmSy6O/gJ\nfR2XQ0R6Ev1e4sVgHL+JV8AvYcgtaibD1G63Y+tb/4ZFcx/E4jlPoKbsPry4bkPCFWTdAyJ2fTKE\nnHwdZi3gknI+SRAEEa6IZlaDg4Mwm83QXxZyKooizpw5A5qmsWjRopGP19bWgqZpdHR0KDfaJGPi\nqIgLfDabDbevug9HTm3GF4ffQMP5LXFplDJlmgGtzUEEg/EtSA5PhjJVngyZOIrEFMSIUitjgdBK\nh7r5HC60BtHTNbGbm+t+r5KKUStjUX8ESOK82PNnA+DMNHJVfjASK5yFhtcT2/d4Z3sQR/d5MX+p\nGVk5yfF7HM7KbDi/ZeSa9O3vPIKO8xnodwpqDy9pOHtJXmwioSgqtDr2nDoPsZUSCEiwnw+iQoUs\n0mkzjfAOSWhtDobG4pcgCqFzdTIZjiqYiH6ngDSFHmpPJkYTDf84u956ugRk5eri3tRzPJs2bsXK\nG8fKZ9+q8sjC19vN44tPh1BVa0RVrREUpY3fLUEQhFoiuhuUZRmBwOiJ5blz58DzPMrKysBxl3Lj\nKIoCx3Hw+2O/Qi1RGaPIIQ34JXDGfKz76ffj2sCAMzPIK9Ch5Wwwro20eroEZOWoPxkabuAVa3a7\nHZs2boXPK8LEMXh47ZqkaQAyloBfAs/LsFiVu7kyGGjMWsDh6D4vlq22xm41z2VNvAAZ8qljoO9e\nG5vvpbKAX0LTqQAWr7Ak7eQ5tDJWuVVsV76Xb7/1r+DqzsSCZeakyjwERmdlDuvK47F/pwcLl1si\nykUnxubsEzSzQosIT1GpHtv/exA8L0OnS8zz5vkzAeQX6VRZkc0wFOYs4vDF9iH4gp3Y/G//gQGn\ngP3H9Uk1N8or0qH+iC/q40SWZbgcIurmkfNDpIwmCoFxVsb2dPDI1UhEAZB4+exXzoFWr7wLA71Z\nmLs4eR5GEwRBTFREs6vMzEwIgoDW1taRjw3HEFRXV4/6XEmS4PP5kJKSosAwk5MpigJfT6eA7Fyd\nKp1kp9QYcP5sAAIfvww0reQ1DT89j2X+m91ux4vrNqCm7L6E3oIUCWdfaMuh0gW+7Fwdikr1OLrf\nG7O/2agmXq3NQGo6qIysmHwvtZ0+4UdhiR7WlOQtqnFmBl6PMu/xsd7Lr/7f36JoiivpCrHjySvU\noXa2CXt3DME9qM2bxUTi6tPOdlkiPAYjjawcHTragmoPJSp8UEJLUxBTpqmXEW5NYZCa3YdfvPiv\nmFPzAG675amkmxvp9TQyc1h0XYhudax3SALDIOmaasaDwUTDP0ZmrCTJ6O1Wv1/F5RIpn32sOdCv\nX9+IkkoXKcQSBEFcJqIrd21tLQDgN7/5DZqamnDw4EH8+c9/BgDccMMNoz63vb0doigiIyNDoaEm\nH1MUBT41i5MWK4OsHBYtzfHZdidJMnq7tDEZYlkKDEshGIhdMXbTxq1YsTSxtyBFyuWIXYGhqtaI\nYEDG+VhmHV9s4iXXHwaVpBEF/U4B3R08qqYnX9Ouy+n0FGhamff4WO/lL61+Cm+//Z8T/tqJpKBY\nj2l1Juz9bAgeNynIRivglxAMyLCmkmJLoglFFSRmMfZ8UxC5+SzMFnWLPR9+/A7uXPVUUs+NCmz6\nqIv2LoeI9ExS4IqG0UiNGVPgcogwcbSmCtxj5bN/sP21mPYMidZYc6A7Vz+Ft/5zcs2BCIIgriei\nq8xf/uVfwmg04syZM/iHf/gHvPTSS/D7/aiqqhop1A47ePAgAKCqqkq50SYZhqXA6sK/+dfCk9qp\nNUacawxAnGDn13AMT4a00rAkmoZrkUi0LUhKULJ515VoOrTF8eypAAZcscmuHG7iJdcfATV9Tky+\nh5pkWUb9ER+qao3Q6bXxPowlpZp4Tcb38nhspXpUTjdiz2dD8Hom38+vBGefgPQs5XcQELGXncfC\n75Mw2J9Yx77Ahx5kTqmJf1bslSbD+TSvUAdnn4BgIPLrD8mLjV5o19vV9zM9nbzm8vGvzGc/2fQH\nrFj2NRh0eWoP7SqT4T1LEAShhIiuNDk5Ofjxj3+MzZs3o6mpCSaTCbNnz8YDDzww6vMkScInn3wC\nAJgxY4Zyo01CRhMNn0cKK9fS5RDBmdV9UpuSxiAgduJHP3gDNCvHNNdUKxEFw0wcNW7XVWW+fmgL\n0uUTGK1uQVKCJMoY6BeRFsMVHWYLg9rZJnzw7hmcbn4ffr+yWbztBgveevU1SD4vmL4g7vnbTNiK\nixUYuTZ02HkIAlBcpr/+JycBzkLDOyRhomkTk+29fD0lFQZIErDnUw8Wr7Bo5gGb1tntdvxh8x/R\n2e6BwcigoOy+pMnJnCxomkJRqR5t54OonW1Sezhha20OIDOH1UQ0zWQ4n7I6Ctm5OnRd4FFcHtku\nFJdDRE3R5LhGK81oohEYY2VsTwePGTdwY7xCXVfms/d18zi814vlt8awP0IUJsN7liAIQgkRn7nL\ny8tHCrIbNmzAk08+CYvFMupzKIrCSy+9hE2bNmHWrFmKDTYZmTgKvnE6eV6pp0P94qTdbsd7//0H\nzKt7IOa5pqHwfPUjCoYZTbFdGfvQQ/fi/Y9eHbUF6ZNdr2tyC5ISBvtDDxdi3dhEorvx4SdbML1C\n2Sxee1sbNv3Lenwj14xnpuTh8b4z2PjtJ2Bva1No5OoSBBkNx3yonWMCpUJGtRqUWhk71nbC7Un8\nXg5H2VQDSqfoseezoXG7VxOXDGfuTS26B6uWP4UFsx5MqpzMyaS4XI8LrUGIYvzy9idCFGU0NwYw\ndZr6q2KByXM+LSjW4UJbZLmxoijDPSAiNYMUuaJhMFHw+0e/L31eCT6fjPQE+J1m5epgK9PjyL7Y\n9UeIxm23/hXe+/DVpH/PEgRBTBTz/PPPP6/0F6UoCjqdDjqdbtJtq3O73RF9vrNPAEVTYeU91R/1\nobzKqOqqold+tQELZz0y8rSTZXUoLpyFjz59E8tvWqLY9/F5JTSdDmD6bJNmjqHBfhHBgIzsvNgU\niAddJlg4G+qb/ojzbQfQ2n4Mq1bcgxsWlMf0d2AwGBAMxj/TrqOdB01TyCuMbcH9lV9twOI5yh+z\nv/lf6/AtyQGODU3YdTSNG/Qy/q3hHBavWq3I2NV0tsEPhqFQUa2NG/JwTeR49npC24nzJ7jKKDU1\nFTNnTcW7729B/al98IkN+Nu/+/qkX9WYkcWCD8o4dcyH/GIdWFYb53YteuVXGzC/bm3Mr7VE7On1\nNHo6Beh0FKyp2i/wtDWHCscVVcqf+6M5Pw+fTz/69E2cbzuAfs/JpDyfmsw0Go76UFymD/vcOOAS\n4XIIKK9MrOu0VjAMcOakHxXVhpHGyB1tQdBUKPP8etSaP18uM5tFS1MQgiBrosmjyyGguYHFHX9Z\ngx1fbNXke9Zqtao9BIIgCAARxhSMpaenB4ODgwCAlJQU5OTkTHhQk0m4OaQ+r4SAX/0ntfHKAerp\n5JGTx45MjrTAyNHo7Yyu2+31iKKMxpM+LL9lKu5aE9qCJIkydn0yhJazQZRVJl/zJJdDQE5e7CeO\nsTpmJdelQuwwjmUg9Tsm9HW1wOuRcP5sEMtWT64JK2dh0HZemRsrm82Gbz75HfR08pizyKzI10wG\nldONEEUZez/zYNHNZugnQRZxNEjmXnIpLgs18iqwaXs7uSTKaDrtx9zF2jpnXbk9OxmxLIXcfB06\n23mUTglvzudyiEjLUL8Al6goioLBSCHgk8BdbFTX3cmjIIFiH2iawtxFHHZ+PITMbFbV48HnlXBw\ntwd18zjkFabin6Yn93uWIAhioqI6Y/f09GDbtm3Yt28fPB7PqH8zm81YuHAhvvKVr5DCbBhMHI0B\n1/ULfD2dPLLzWNW3C8crB6inU0B+kXYiCgDAZAo/UiJSbc1BWFMZZGZfekvSTGiCteuTIWRks0hN\n1/6Kmki4+gRUTY/9ao5YHbN0eia8PT2jCrJeQQSdkzmhr6sFDcd8KJtqAGeeXIUy88XMWKUIvAw2\nxjEciah6hhGS6Me+HR4svMkS86iSREQy95JLfpEOJ4/44PVImj6vtrcGYbYyYe3WIpRXUKxHc6M/\n7GJsv0NAVi75W03EcBMvzhJaGOHoETBrnvbyYq+Fu9gf4fBeL5atsqoy7xAEGQd2eVA61RDzHW8E\nQRDJIuIZ4Z49e/DMM89g+/btVxViAcDj8eCTTz7BM888gz179igyyGRmDHNlbHcHjxwN5KfGI7tL\nFGX09YSKz1pi4mj4vcpnMgm8jLOn/KiecXVh0mxlMH2WCYf3eCAI2smDmii/T4IgAGZr7G9KY3XM\n3vPU03i5X4ZXCK1U8woiXu6Xcc9TT094zGrq6xHQ7xBQUZ18q7Gvx2iiwAdlxd5rPC+TQuMYKIpC\nzSwj0jIY7Pt8CAKfPOc2pXz5SyRzL5kwLIXCYh3aW9Td0nwtkiSj6VQAU2vIlne1ZOexcA9IYedq\nu5wiKZxPkMFEw+8P/b6dvQKsKQz0Bu0+MBlPYYkeGZksTh7xxf17y7KMY/u9sKTQmDIJ544EQRDR\niugK3tTUhPXr10OSJBQUFOBLX/oSampqkJGRAVmW0d/fj/r6erz//vu4cOEC1q9fj9zcXJSXl8dq\n/AkvnJgCUZTh6BUwa776T2ptNhuefe5xbNq4BV6PgH6HjO/+8FFFc4CGJ0Na6gwKhArnfp8EWZYV\nzXA9dzaAzGwWqeljvx2LSvXo6eJRf8SHugR7Wj8eZ5+A9EwmLnnAlx+zfd1BGIwMnn3u8Qkfs7bi\nYqx95Q1seHU9pH4H6JxMrH3hadiKixUaefzJkoz6I17U1JkmZZ4nRVEwmWn4PJIi2Y6CQFbGjoei\nKNTOMeHYAR/27/JgwY1mMJPwmBuLe1BEjz0Tz3z3Ubz3329hcMAPE6fMeYtQj61Mj4NfeDG1xqCZ\nLPzLddp5GIwUMrPJ6mu1MAyF3AIWHXYe5deJpwoEJAQDEixxeKidzIxGCn5f6IFgT6egiYUv0aqd\nY8LnH7pxoS2IwjAyb5VytiEAr0fC4hUWTZ7bCIIgtCqiYuw777wDSZIwc+ZMfP/734dON/qClZeX\nh7y8PCxfvhw///nPcfz4cbz99tv4/ve/r+igk4nRRCEQkCFJ8rj5qFp7Unt5dtfxg15IQWXHpdXJ\nEMNQYHUUggEZBqMyk41gQMK5xgCWrrRc8/NmzOXw+YdudNi1nzkXDpdDRHocGw0MH7Md9iDs54Ow\n2a79+w776xYX45mf/1KRr6UFreeCYHUU8m3ae//Fi9lCwzOkTDGWD8owmrRx3tYiiqJQd4MJR/Z7\ncWC3B/OWmsEwk/tGLhiQsH+nBzV1RtjKyrD4xucibgxKaFNaBgudjkJftxCzRqDRkmUZZxv8qJml\nnaapk1VhsR5n6v3XLcb2O0SkpasfX5bojCYagYsrkbs7ecxdlLiLHlgdhTmLOOz73IP0DGYkBzeW\nOuxBtJ4L4MaV1kl//SYIgohURHeJjY2NAIDHHnvsqkLs5ViWxaOPPjrqNcTYaJqCwXDpqexYujsF\n5BRoa+I+rHSKAa3NAUjSxLeZ2u12/OSFl/Avr/5vbN6yHna7XYERKstoCi9WIlxNpwPIL9LBYr32\nhEmnozB3IYcTh0KZc4nO1ScgIzP+q28ysli4HCJkmWyLvlIwKKHxpB+1syf3zThnpuEZUqZJElkZ\ne30UTWHWfA4sS+HQHo8i15JEJUkyDn7hRX6hDrYystUzGRWX6xVrEqikrgs8aIbSXDzUZJSVy8Iz\nJF13rtfvFJCmwjwq2RhNFPw+CR63CIGXkZKW2L/TtAwWU6YZcHivN+bX0wGXgBOHfJi3xEwePBME\nQUQhojNnMBgEx3FhNebKzc0Fx3Hg+dh0n08moSzS8SddPR08cvO1OUFOSWPAmWl0d0zs72y32/Hi\nug2oKbsPd676Fuqq78eL6zZoriBr4ijFirF+n4S2c0FUhtnEKi2TRUWVAYf3JnbBQhJlDPar0wHY\naKKh01EYGkz8grbSzpz0I79IN25cxmShZBMvgWTGhoWmKcxZyAEy4nIDqVUnD/vAMMC0mSSzM1kV\nlujQ08kjGNDONSi0Kjag2fiEyYamKeQV6tBhv3bR3uUgebFKMBhp+P3yyK68ZHgPlFcawOooNJ70\nx+x7+H0S9u/yYMZckyrzeYIgiGQQ0dkzLy8PFy5cAM/z11wZC4QKt4FAAEVFRVENbO/evWhoaEBr\naytaWlrg9/tx44034lvf+ta4r2lsbMQ777yDs2fPgud55OXl4eabb8Ztt90Gmh677nzo0CG8++67\naGlpgSRJsNlsWL16NZYvXx7VuKNh5Gj4xgnrH3KLEARtP6ktnWJAS1MQ+UXRb5/ftHErVix9cqRz\ntMHAYcXSJ7Fp45aRSAQtULKJ15l6P2xlepi48J+JVFQb0Nst4GyDH1W1JkXGEW8D/SLMVlq1FYMZ\nWQycfYIi29CThXtAxIU2HjfdZlV7KKrjLAx6uwVFvhbPy2C1ualBc2iGwtzFZhzY5cGx/V7MWsAl\nxU1xuFrOBuDoFbB0pZVsO05iej2N3HwdLrTyKLvONvR46e0SIEky6YCuIYXFOjQc82NK9dgPZmRZ\nRr9TRFoGmcdMlNEU6gfR3cmjuDzxY8CAUATQ7AUcdvzZjexcFlm5yr63RVHGwd0elJQbkiI6jSAI\nQi0RFWOXL1+OzZs346OPPsIdd9xxzc/9+OOPIYoibrrppqgG9s4776C1tRVGoxGZmZm4cOHCNT//\nwIEDePnll6HX67Fo0SJYLBYcOnQIv//979HY2IjvfOc7V73mgw8+wKZNm2C1WrFs2TKwLIu9e/fi\ntddeg91ux/333x/xuL/7/M/w9CP3oTiCJhsmU6hhzFh6OgXkavxJbV6RDvVHfXAPirCmRDcx9HnF\nkULsMIOBg8+rzHZhpRhN4xfOI+EZEtFh57HijsiKX8MTrM8/dCMrR4fMnMR7Gu3qE1RdzZGRzcLZ\nJ6CkQhs3wley2+3YtHErfF4RJo7Bw2vXxLRpjyzLOHnEh6k1Rs01zVPDcGasEsjK2MgwDIUblpix\nf6cHxw/6kJ7bh99t+o+4vRfU0tvN40yDH0tusZDjZRKwlevRcNSH0ql61eZ2l64zAoYGgAcfuhcU\nlaLKWIirZWaz8PskDLnFMWOsPEMSWBZka7gC+hzt+I+3t0CWJRTYDFj7aHJcZwxGGrMWcDiyz4tl\nt1phUKjviCzLOHbACxNHY2qNNufRBEEQiSKiM/Odd96J+fPnY/PmzfjTn/6EYPDqLTTBYBB/+tOf\nsHnzZsyfPx+33357VAN76KGH8Morr+D3v//9SP7seHw+H37961+Dpmk8//zzeOKJJ3D//ffjpZde\nQmVlJfbu3Ysvvvhi1Gt6e3uxefNmWCwW/OxnP8PatWvx4IMP4pe//CVyc3Px7rvv4uzZsxGP+3TJ\nrXhi3Xq0RbC9/lpb33s6eeQUaLvgxjAUbGV6tDYFov4aJo5BIOAd9bFAwAsTp62n/teLlAhX44lQ\nc4ZomrIZTTTq5nE4vM+jqa2O4VJ7a11GFgtnr7aK/MMuj+tYPOcJ1JTdF/O4ju4OAX6fhNIpZHUD\nAJjMoYdjsgJb5QUeJDM2QixLYf5SM5qbWvHCP/06ru8FNQy5RRze48WcRRzMcWi2QqgvK4eFwAMD\nLnWuQ6OvM09i+eKvY9PGTUn33kpkFE0hv0iHjraxI8BcDhFpJKJgwux2O375899g5fKH8KVbv4Xp\nFcl1ncnJ06GwWI9j+72K9UpoPh3A0KCEuvmTa/cKQRBELIxbCXrttdeu+t/rr78Ok8kEo9GIN998\nE48++iheeOEFrF+/HuvXr8cLL7yARx99FG+++SaMRiNMJhPeeOONqAZWU1ODvLy8sD53z549cLvd\nWLJkCcrKykY+zrIs7r33XgDARx99NOo127dvhyAIuP3225GVlTXycY7j8NWvfnXM14SD0ZtAL/oa\n1v92S9ivGS+mQBBkOPsExbeXxEJJhQHtrTwEIbqL/V98+a/w/oevjhRkAwEvtu96HQ+vXaPkMCfM\nyFETXhk72C+ir0e4bqfca8kt0CG/SI9jB30J14zK6RCQnqVe0cGSQoPnZfgVWOGstPHjOrbG5PuJ\nooz6oz5Mn2UCTbZGAwgVA3V6Cn7/xN9XoZgC8nuNFKujcOL0e7h9xVNxey+ogQ9K2L/Tg+oZRmTl\naP86TyiDokIPsNvOqdPIK97XGSI6BcX6cXNj+x0C0klEwYRNhvdC9Qwj/D4ZLU0TP990XeBx/mwA\n85aawbJkbkMQBDFR4z5W3bFjx3VfHAgE0NDQMOa/eb3eka/x5JNPRjm88NTX1wMAZs2addW/1dTU\nQK/Xo7GxEYIggGXZUa+pq6u76jWzZ88GAJw8eTKq8TB6Exze8C963Dg5pH3dAtIy2ITYtsiZaWRk\nMbjQGox4+7coynB2ZeFvv/0I3vvvLSNbUp997nHNbRUycTR8E8yMPX3ChynTjBMu0kybacSuj4fQ\n2hxE6ZTE2Crk80qQxNBWcLVQFIX0zFBurNayrsaL6xgaVCbD9ErnzwRgTaGRk08KQZcLRRWIEeU5\nX0mW5VBMAblhiYrfnxjRNdGSJBmH9niRk8dqNjKFiB1bmR47/uzG9FkymDifIxIlFmqyy8hiwAdl\nuAfEqzLuXQ4RBcXamr8kosnwXqAZCnMWcdj9yRAys9moe5AM9os4dsCL+TeaJzQ3IgiCIC4Ztxj7\n13/91/Ecx4R0dHQAAPLz86/6N5qmkZOTg/b2dvT09KCgoOC6r0lLS4PBYIDD4UAwGIReH9mERwz6\nkMmF/xojR48ZU9DTySM3P3G2IZVONeDUMT+KyyPLQTt3JgCzhcaceeWYM087zbrGYjTRCPgkyLIc\n1fYcZ6+AwX4RcxebJzwW5uIE64vtoQlWIjSkcjkEpGcyqm9tyshi4ewTUaCtWv9IXMflNweBgBfu\nQRk7PhhEQbEehcU6cApsZ/b7JDSdDuDGlZYJf61kw1loeIckICf6ryGKAEWHboSIyI33XtBadE20\nGo75IctAzazEbMRITIyJo5GWwaCznUdRaXyLasn+3koWFEWhwKbHhbYgqmdcOk+Iogz3oIjUdPL3\nmqjJ8l6wWBnU1JlwaI8HN66yRryqNeCXsH+XB9Nnm1SNGSMIgkg2455R77777niOY0K83tDWdo7j\nxvz34Y97PJ6IXhMIBOD1eiMqxopBH6Q9/46nn3s67NcYjBQEXoYoXFohIcsyujt5LFyeOIWS7FwW\nJwUZLoeIjKzwLtY+r4Tm0wHcuCoxfk6GocDqKAT8MoymyCYzsizj1AkfKqcbwShUoLGmMJg20xia\nYK20xn2FTaRcfermxQ7LyGbRcNSn9jCu8vDaNXhx3esj2+aG4zqefe4xmI0cLrQFsfPjIXBmGoXF\nOhQU66Nu4HHquA/F5XqYx2gOMtmZLcyEm3gJvEy28U3A+O+Fx9Ue2oS1NgfQ08Fj6SoLiQeZxIrL\n9WhpCsa9GPv1h+7F8//4Ku5c9VTSvbeSTUHi+7VmAAAgAElEQVSxDkf2eVFVaxx5iD3gCjX1IteX\niUvm68yVikp16O3m0XDUh5k3jH3vOxZJlHHwCw8Ki3UoKiGrsQmCIJQU86qI1+sdt+AZb5GsxhvO\n4Yx0BV9165/x9HNPoziC7fUURcFoCuXGDndNdQ9IoCgKFmvibAWhKAolU/RoaQqEXYytP+JD2VR9\nQjUuGW7iFWkRrLdLQNAvK37jZSvTo7dLQMMxH2bM1cZ7bTwuh4DqmUa1h4G0dAbuARGCoK2Cmc1m\nw/d++Bh+/tPfIS2TAmdmR8V1ZOawqJ0jo69bQEcbjzMNbqSkMSiw6ZBv04XdLdflENDbJWDFHaR7\n9lg4M43uzrEbp4RL4OWEiJjRKpvNhmefexybNm6BoycIhtFmdE2kHD0CTp/wY8kKC/T6xLm+E8rL\nK9DhxCEfPENiXOdAvsFsfPUvHsDRBm3HQhFAWgYDWQptEU9ND82r+y/uMCIm7vLrTLK/FyiKwoy5\nHD7/sxud7UHkF13/XkSWZRw/5INOT6F6hvpzd4IgiGQTk2KsLMs4duwYPvvsMxw6dAibN2+OxbcZ\nMVzsHV7teqWxVsFyHAe32w2v1wuL5epVmT5faNWcyTT+FsL6+vqR7FkAuOeee/Dr//3TyH8AABar\nDxSMsFpDFzv7uUEUFXNISUmsYsm0Wg7/761O6FgORtO1J4ud7X4MDki4cWUOWDZxbkotVj8AA6zW\n8AufsixjV303Zs1PR2qq8gXTxTdZ8D/bujDgZFFUEtm2V71eD6vVqviYriSKMgYHBmArTgOrU//v\nnZbpQ9CnR3qBtiaYmRnlePjr38HKO8ffI5+aClRUAqIgo/OCHy3NHpw+7kZWrgEl5RyKSk3jFnpk\nWcYX23swe3460jMmHpehNUocz1k5AbSe65/Q1wn6AzAY/XF5byWrmpoavPTLn8DnFfHe252oKM+H\nwZi4RYght4DDe7ux5OYs5BeGd96J1/mZUEfZVBHd7UDdvPj8jbs7/LCfH8Rda+bgPm5eXL7n5cjx\nHLnSKRL6uoCi4tDvbWgwiPwiDlZr8l2/1TB8nYlGIh7PS28x4PMP+1BoS4XZcu0ywOmTbrgHZKz6\nci50Gpi3K+mtt94a+f/Tp0/H9OnTVRwNQRCTlaLFWLvdjh07dmDnzp3o7+9X8ktfU0FBAc6dO4fO\nzk6UlZWN+jdJktDT0wOGYZCTkzPqNY2Njejs7MTUqVNHvaa/vx+BQACZmZnXjCgY6+Ttdruj+hl0\nBgnOPg/M1tBqLHvLECqqjVF/PTXlFepw6oQTU6aNf7MpiTL273ajps4En88z7udpkU4vwenwIi0z\n/ID/jrYgJElCWqYQs7/prAUm7NvpgN5ojShc32q1xuU4c/YJMFto+PwewB/zb3ddaekU2tvc4KwT\nWwGpNPt5H9KzqLD/JqkZQF2GAdNn6dHdyeN88yAO7nEiK0eHwmIdcgp0YFkKdrsdmzZuRb+DRzBI\n4fs/ug9ud3GMf5r4U+J4pmgJ7gF+Ql9noJ8HTcsJeQ7XorxCHeqPOTG1RlsPT8LF8zJ2f+zGlGoD\nLKk83O7wzjvxOj8T6sgvorB3xxBKK+mYR1YEAxJ2f+rGzHkcBNELNQ4rcjxHLitXxoHdXpRX06Ao\nCr3dfpRVMeT3qAGJeDwbTEBZpR47P+nBopvGj8rp6eRRf9SLpSut8Ps98Gtg3q4Uq9WKe+65R+1h\nEARBTLwY63a7sWvXLuzYsQPnz58f+TjDMKitrcWCBQsm+i2uq7a2Frt27cLRo0exePHiUf/W0NCA\nYDCImpoasCw76jWNjY04evToVcXYw4cPj3xOvJgua+LFByUMuERk5qifrRmN0il6HPzCi4oqA6hx\nLvLDTbtyCxLvZzRyNPy+8PMkJUnG6ZN+zJhjimnjqowsFqVTDDiyz4tFy83j/u7V4nIIyMjSzqq2\n9CwGrc1BtYdxlb5uPqI8r2GsjkJhsR6FxXrwQQmd7Tzazgdx7KAXYLqx7f9txurl34ShOpSL9rN/\nfj1pt+NNlN5AQZJk8EEJuii3kguCDJbEFCimvNKAvTuGUF5lUCxzO15kScaRvR6kZ7EonUoy94hL\nrKkMjCYavV0Ccgt0Mfs+sizj2AEfCmx65ObH7vsQyktJY0DTQL9TBGemEQxKCRVhRmhPRbUBvd0C\nzjYEUFV79QNO96CII/u8mLfEDM5MjjWCIIhYieoMK4oi9u/fj1/84hf4xje+gd/97ncjhdi5c+fi\nqaeewm9+8xv86Ec/wi233KLogMeycOFCWK1W7N69G+fOnRv5OM/z2Lp1KwBg9erVo15z0003gWVZ\nfPDBB+jt7R35+NDQELZt2wYAWLVqVczHPuzyYmxvt4CMbFZTWZaRSMtgYTBQ6OkSxvx3nzfUxb02\nxsXJWDGZLv2twmE/H4TRRCMrN/aF56nTDIAs4+zpQMy/V6S00rxrWEYWC5dDgCzJag9lRMAvweuV\nkJoxsaK1Tk+juNyAhcstWHFHCj7b+adQIfZix2CDgcOKpU9i08atSgw76VAUBbOZnlATLz4ogyU1\nD8WkpDFISWPQ0aatlezhOH3CD4GXY/5AjkhMxeV6tJ2L7YPB1uYgvB5JE5ntRGQoikJhsQ4dbTz6\nnSLSMlhyHiEmhKIozF7AobU5AEfv6Hu1YEDCgZ0e1NQZkZGtnTk7QRBEMoroLNvc3IwdO3Zg9+7d\nGBoaGvn4tGnTcOrUKQDAt771LUUadh04cAAHDhwAgJHIg8bGRrz22msAQlsMHnjgAQChXNdvfOMb\nePnll/H8889j8eLFsFgsOHToEDo6OrBo0SIsWrRo1NfPycnBAw88gE2bNuGHP/whFi9eDJZlsXfv\nXjidTnz5y1++asVsLJk4Gl0XQjeZPR0CchJ85ULpxUZeY630qD/qQ+mUxGradbnhBl7hEAUZZ+r9\nuGGxOS6TZ4qmMHuhGZ9/6EZWDht2I7VYk2UZLoeAaXXauRE0GGkYjDQGBySkpmvjWOzrEZCZzSq6\nXdVgpEEz8kghduTjBg4+b/hRG5MNZ2Hg9UhIy4ju9YIA0sBLYeWVBpw65kNRqS5hihH2liA67DyW\nrrKATrAVvUR8FBTrceqYHwG/BINR+VVog/0iGk/6seQWS8KtKidCJLob//e1f4deL0OnZ1BUcR/Z\n1UJMiNFEo24ehw/fO4PGlvcQ8EswmRjU1XwZUypLYCszqD1EgiCIpHfdSo3T6cTOnTuxY8cOXLhw\nYeTjxcXFWLp0KZYsWYKsrCzce++9ig6spaUFO3bsGPWxnp4e9PT0ALhUTB02b948vPDCC3jnnXew\nf/9+8DyPvLw8fP3rX8ftt98+5ve47bbbkJOTg3fffReff/45ZFlGUVER/uZv/gbLli1T9Oe5HqMp\nVOCTZRk9XTymTk/si2CBTY+GY/6rugT3doWe7M+ar3wTq3gxclTYK2NbmgJIzWCQHseiqIkLTbAO\n7/Vi+WpL1NusleTzypAkaG67U0YWC1efoJ1ibJeA7FzlH8SYOAaBgHdUQTYQ8MLEaePn1iLOQsM7\ngZWxAk9iCpSWncei4WjooUUs3idKc/YJaDjqw+KbLTAYtHXuI7RDp6OQV6hDe0sQFdXKPrAUBRmH\n94RWuVms5HyfiOx2O/7lV7/FLTc+CYMhFDP04joSM0RMXFDswic7tuC2FU+NHFv/9d6reH7dNwAk\nX08BgiAIrblmheinP/0pTp48CUkK3ZBmZWVhyZIlWLp0KYqLY3uSvvvuu3H33XdH9JrKykr88Ic/\njOg1c+bMwZw5cyJ6TSyYOAo+XygrVqejEnbV6DCGpWAr1aO1OYiaOhOAUNOuE4d9qJ1tStgIBiBU\nOA/4ZciSfM1cVp6X0XQ6gEU3WeI4upC8Qh16u3gcP+jDnEWc6qvIXA4B6VmM6uO4UkYWg75uAaVT\n1X/4Icsyert5lFcpP5aH167Bi+tex4qll27mtu8K3cwRYzObaQz0R79ymOdlGIzaOt4THUVRKK8y\n4FxjQPPFWK9HwsHdHsyaz8GamtjXcyL2bOV6HD/gRXmVQdHrZP1RH6xpDIpKSVZxotq0cevItRu4\nPGZoC/7px99TeXREItu0cetIIRYIHVt3rHwKv/sdObYIgiDi4ZrF2OPHjwMAli5dilWrVqG6ujou\ng5qMdHoKsgxcaOMTPqJgWMkUPXZ9PISq6UYwLJXQTbsuxzAUWB2FQECG0TT+TdO5Rj9y8likpKlz\nI15TZ8LOj92wnw+iuFzdYqOrT0CGhvJih2VksThTr40WsV6PBEkCLCnKr6Cz2Wx49rnHsWnjFvi8\nIkwcQ1bVXAdnodF5Ifp8UoGXSZOVGCgs0eP0CT/cgyKsKdoscgqCjAO7PKioMsS0KRORPDKyGMgA\nXA5RsXihzvYgersELFtt1dyDUCJ8Pq9IYoaImCDHFkEQhLrCmvENZ7f6/X7MnDkTNE1uMJXW3t6O\n9/+8GYIgIStXhyeyvpbwhRKzhUGA78Q/PPsGZFnCQL+M7zzzNVBU/FeKKm244ZrRNPZ7IeCXcP5s\nEMtWqfezMiyFuYvM+K+3G3Fm43vgeQkmjsHDa9fE/dhyOUTU1GlvZY7ZSkMUQ03lTJy657W+bgFZ\nubFrzGGz2chKhwiQmAJtYhgKpVNCq2Pr5mkn7sZut2PTxq3weQV4h4Dbb70L5VWVag+LSBAURaG4\nLNTIS4lirM8r4fhBH+YvNUOnJ+ehsbTZ7Vj/2y1weIPI5PR4+pH7UKzBeTeJGSJihRxbBEEQ6rpm\n9eG73/0u5s2bB0EQsGvXLrz44ot4/PHHsXHjRjQ2NsZrjEnPbrfjxXUbcPPSh/ClW7+F2dMewIvr\nNsBut6s9tAmx2+1474M/4IYZD2DpvCexctlDeOX//Dbhfy4glBvr941fqDl7KoDCYh04leMm+gc7\nsGPXv2Nm5f1YPOcJ1JTdF/djSxRluAdEpGZob3JHURTSsxg4+4Trf3KM9XYnRg7mZMFxNPw+CZIk\nR/V6nhRjY6akQo/Odh4Bf/TFciUNX8Nryu7D4jlP4sYFX8d/vfcHtLe3qz00IoEUlerR1c5D4KM7\n5wyTJRmH93pQXmmIa159Immz2/HEuvU4XXIrnHX34HTJrXhi3Xq0aXB++vDaNdi+63UEAl4AGIkZ\nenjtGpVHRiQ6cmwRBEGoi3n++eefH+8fCwsLsXjxYtx6663IyMjAwMAAuru70dzcjE8//RQ7duzA\n4OAgUlNT8eGHHwIAvvKVr0Cnm7wFhcHBwYhXtr3yqw2YX7d25Mkky+pQXDgLH336JpbftCQWw4yL\nV361AQtnPZJ0PxcAOHsFUDSF9DG23ns9Ek4c8mHuIrPqxZhXfrUBi2Zf+29gMBgQDAZjNoZ+h4h+\nl4gyDeSyjsXvlTDYL6q6nViWZZw45ENNnQk6UsCbEKWOZ4qm0HYugLwiHfRRNMFraQogr0AHk8aa\n1iUDlqXgGZLgGZKQmaN+sSmW1/BYn58J7WB1FJwOAbIMpKZHf1yfbQjA55VRd4NJc/EEWjme1/2f\n1+Go/SoYfainAc3ogIJpaP50G1bftFTl0Y2WmpqKmbOm4qNP38T5tgPo95zE3/7d1xN+91wy0Mrx\nHK3JemxZrVa1h0AQBAEgzJgCi8WC22+/Hbfffjva29vx2WefYefOnejt7cW2bduwbdu2kc/t6+uL\neXMvLfMLMkwRFlOSNbMnWX8uIBRT4PeOvSrrTL0fJRX6cSMM4kkLfwOnQ0B6pvZWxQ5Lz2LR3upT\ndQyD/SL0Bkr1qARiNLOFgWdIiqqhIlkZG1vlVQbs+XQIFdUGMIy6v2ctnGeJ5FBcZkDTKX/UOe/O\nXgEtTQHcuMp6zQajk53DGxwpxA5j9CY4vNosrJGYISJWyLFFEAShnojv/IuKinD//ffj9ddfx7PP\nPotFixaNWgn7ve99Dz/4wQ/wxz/+cVJu0fPykd98DWf2XC4ZMnuS9ecCAOPFzNgruQdFdHfwqKjW\nxipQLfwNXH3KNSSJhdR0Bp4hEfwEt4ZORF+3gOxc7f6OJquJ5MaSzNjYsqYwSE1ncKFV/eKJFs6z\nRHLIyWfh9UhwD0Y+l+SDEg7v9WDmDRx5sHcdmZweYnD0Q1gx6EMmp71se4IgCIIgklPUszWapjFr\n1iz83d/9HTZs2IDHHnsMlZWhZhUtLS1466238Mwzz+A73/mOYoNNBF4+8hv3ZM3sSdafC7jUwOtK\njSf8KK8yRLWtORbG+ht89PlrcfsbyLIMl0NAepZ2ixIMQyE1nYHLoV5ubO/F5l2Etky0GEsiJ2Kr\nvMqA5sYAZFm9BykAcMetf4X3P3o1Ka91RHzRNAXbxUZekZBlGccO+pBboENe4eSNCgvX04/cB373\nlpGCrBj0IbhrC55+5D6VR0YQBEEQxGShyN0/x3FYuXIlVq5cia6urpEYg76+PnR0dCjxLRJGNMVY\nm82GZ597HJs2boHPK8LEMXj2uccTPrMnWX8uADCZrm7g1e8U4OwTMGuBdjp8X/k3kEQKt6++H0VF\nRXH5/sMFa62v0snIYuHqE5CTF/+bWFGU4ewTMGeRdo4bIsRsoXHBwUf8OlmWIYgAS+rrMZWVw4Km\ngd4uATn56hSgggEJzu5M/P13H8W2PyXftY6IP1uZHrs/GcK0GUbQYUZw2M8HMTQoYvYqkoUYjmKb\nDXeteRDv/9cfkWFi4BVkVK2+G7Y4zY0IgiAIgiAUv1XMy8vDmjVrsGbNGpw8eRKff/650t9C06Ip\nxgLJm9mTrD+X0UTD75chS/JILtvpE35U1hjBstpaDXf530CSZHz2gRu93fEpPLr6RKRnspprInKl\njCwW5xoDqnxvl0OENYXRzGpq4hLOTMPriXy7sMADLAOS2RhjFEWhvNKI5saAasXY+qM+FNh0qK0r\nw4y65LvWEfFnsTKwptDo7uSRX3T9bfPuQRGnjvux+GaL6vnJieRU0Ir/9Y8/QG0uB0GS8e33z+Pg\nBQ/mFVnUHhpBEARBEJNATNft1NbWora2NpbfQnOiyYwlEg/NUNDrKQQCMowmCn09PDxuCcXl2s4b\no2kK1bVGnD7uR3Zu7IukWo8oGJaeycDlFCBJMug4F9D6unkSUaBRZgsNz5AEWZYjeq8IAsmLjZfC\nYh1OHfdhsF9ESlp8zzU9XTwcPQJuui0lrt+XSH62cgPazgWvW4wVRRmH93hQPcMIa6r619o2ux3r\nf7sFDm8QmZweTz9yH4o1uEK8fSCAfr+IadmhJl4sTeGRuTnYcLAbs/LN0JGiNkEQBEEQMUaWYinM\nF+XKWCLxGE2h3FhZlnH6uB9VteFvKVRTvk0HWQY62yPffh0p58WVsVqnN9DgOBqD/fF/mNJH8mI1\nS6enQdMUgoHIMkn5ICnGxgvNUCibasC5M/Fd2S7wMo4f9GHmDRz5WxOKyy/SweUQx8ymv9ypYz6Y\nLYwmHgS32e14Yt16nC65Fc66e3C65FY8sW492ux2tYd2lV2tbiwptoK57OHrnAILilL0eLfRqeLI\nCIIgCIKYLEgxVmHRxhQQiWe4iVd3hwBBkFFYnBhNMyiKwrSZRpw+4Yckxa7xjSDIGBoUkZau/mqd\ncKRnsXD2xbcYy/MyBgdEZGSRYqxWcebIm3gJAmneFU8lFXp0XeCvyvGOpdMn/cjMYlSLRyCSG8tS\nKLDpYG8Zv5FXdwePrgs8Zs4zaSIKaP1vt4Be9DUw+tBqU0ZvAr3oa1j/2y0qj2w0WZaxs3UQN5Zc\nvaJ97ZxcvNPghMunXkNPgiAIgiAmB1KMVZg3SIqxk4HdbsfW//wX/OIXv8S6n7yElMy+hMqHzM5j\nYTRSaL/Gjd5EDThFWFMZMBrL0B1PRhYLZ198b8AcPQLSM1mS86dhZgsNjyey8zrPk5Wx8aQ30Cgs\n1qGlKT6rY10OAR1tQUyfbYrL9yMmp+JyPezngpDlqx+a+n0Sjh3wYvZCs2byxh3e4EghdhijN8Hh\njd08Ixqt/QEEBAlVWcar/q0gRY+V5anYfLRXhZERBEEQBDGZaGMGl0RIZmzys9vteHHdBiya+yBu\nWfpN3LzkIfzmXzfBrsGteOOhKArVM004U++HKMRmdazLISA9MzFWxQJARjYDZ68w5o1vrJC8WO3j\nLFGsjOXJyth4K6s0oLU5CCFG57Nhkijj2AEvps82QW8gUygidlLTGbBs6KHd5WRZxpF9XpRUGJCZ\nrZ3rRyanhxj0jfqYGPQhk1M/QuFyO1vdWFKSMu5q4ntmZOJwpwdnHb4x/50gCIIgCEIJ5E5CYSSm\nIPlt2rgVK5Y+CYOBAwAYDBxWLH0SmzZuVXlkkcnIYpGSxuDs6aGYfH2nQ0B6Am2/58yh06EvwlWQ\nE9HXLSA7J3F+R5NRVDEFZGVs3FmsDNIzmZiu9geAptMBcGYaBTYST0DEFkVRoUZe50cf082nA5BE\nGVNrDCqNbGxPP3IfPJ//YaQgKwZ9kPb8O55+5D6VR3aJLMvYNU5EwTBOx+D+uiz868GeuD6cJQiC\nIAhiciHFWIWRYmzy83nFkULsMIOBg8+beKuiq2eY0HBsEAKv7A2HLMtwJUjzrmEURV2MKojP39Hv\nk+D3y0hNkEzdySoUUxDZMUGKseqoqDLi3JlAzAoo7gER588GMGMup4mMTiL5FZXo0NMhgL8YgeVy\nCGhuDGD2QjNojUUjFdtsWHDHvUhveBdpR/8DfTv/Ez/73jdRbLOpPbQRTU4/AKAi49qF7BXlqRAl\nGTtaBuMxLIIgCIIgJqHEqZQkCFKMTX4mjkEg4B1VkA0EvDBxiVdUS0ljkFcQKmBUTr86Py1aXo8E\nmgZMnLZuFq8nI4uBs09AUWnst1X2dQvIzGYTKmt4MuIsTMQrY3kSU6CKjGwGLEuhp1NAboGyK1dl\nKRRPUFVrhIkjz7GJ+NAbaEh0F/7xR2+AoiUMumSsfXQNOHOq2kO7SkCQ0CqnYcM//wOsBgb/srcT\nZ4N6zFR7YJfZ1erGjdeIKBhGUxQeuyEXv9h5AQuKrDDpyHueIAiCIAhlkdmFwkgxNvk9vHYNtu96\nHYGAF0CoELt91+t4eO0alUcWnRlzU3HuTACBgHLH7vCq2ERbPRbPJl593QKySV6s5plMFIIBOaJs\nZbIyVh0URaG8yoDmRuUbebU0BUHRQEmFtvIvieRmt9vxX+9uxryZD2DJ3Cex4saHsPG32syoP9Ht\nRXm6AVZD6MH0yopUfNw8oJmt/pIsY3frIJaWWMP6/OpsE2pzOfyx3hHjkREEQRAEMRmRYqzCfKSB\nV9Kz2Wx49rnH0XB+C744/AYazm/Bs889DpuGtuJFwprCosCmQ9Mp5QoYida8a1hKOgOvR0IwGNuH\nKrIso7eHNO9KBBRNwWSm4Y0gS5jnZbDkT6uKApsOHreIAZdyD1W8HgmN9X7U3UDiCYj42rRxK1Yt\n/2ZCZNTva3djftGlQmd1lgk0BTT0aqMRVmOfDwaWRkla+Fm7X5+djQ+a+tHljm0WNUEQBEEQkw+5\nXVSYh6yMnRRsNhv+6cffU3sYiqmcbsRnH7hRXmlQZAuus09EYUnirSCjaQppGSxcfSJyC2L3rMoz\nJAEyYLaS52GJgLtYjLWmhveAQeABnZ4U7dRA0xTKKkOrY+csnPgUR5ZlHD/oRUWVAZaUxHvARCS2\nRMmol2QZB9qHcNfqzJGPURR1cXVsP6bncNd4dXzsanXjxtLrRxRcLpPT4S+q0/G7Iz344bKiGI6O\nIAiCIIjJhlQCFOYjxVgiARlNNErK9ThT75/w1xIEGR63mLCNqYZzY2Opr1tAVm7ixThMVmYLHSqg\nh0ngZbAs+duqpaRcj55OAT7vxK/H7a08An4JFdXa6lxPTA7DGfWX02JGfZPDD4uBQb519EPYm8tT\nsc8+BK/Ku8ZEKbKIgst9ZVoGzrkCON7licHICIIgCIKYrEgxVmF+QYKkkXwsgohExTQDui7wGHJP\n7Kap3ykgJY0BwyRmMSoeubG93QKyc5VtMETEDmeh4R0K/31BGnipS6enUVSiQ0vTxKJXAn4JDUd9\nqJvH/X/27jOwrfJuG/h1jvaWLW9bllems8h24hDIZpfREBIoI1BC3zas0tLSQCCUXeDxQ1kl5GEk\nDaGFAoWSZhHi7EX2TixL3pKHpjXOOe8HYRPHS3v5/n1pcTRuyzrrf/73dcfdyvXEwJAoGfV7jDZM\nzJV3+7lazMfILCm2VVljMKqfHG9yQC3hI08Z+E0VIY/G3WMz8N6+RjAsOb8nCIIgCCI8SDE2zIQ8\nGu1e0h1LJB6hkEbRYBFOHQmtO7Zj8a5ElZLGR1sLA5aJzEUXx3IwN3pJXmwCkcl5gXfGkmJsTBUO\nFqH6vBteT/Db8dGDTmgLhFCnkm2ViI1EyajfU2Prkhd7sdnFamw41xrlEXVVqbeiXKcM+vmT8+RQ\niXn49kxsfw+CIAiCIJIHucIIM6mAhsPDQiqIrylkBOGPwsEibP7agrYWL1Qpwe0eWsxe5BUkXl5s\nB4GAgkxOo62FQUpa+HeRba0MRGIKYgm5FxZt1QYDKlauhtnhhkYqxNLFi5DvR1FDGuACXl5vfBZj\ng/39E5FMzoMmnQ9DlRuFgwLvhmuo9aC1mcHoubHPuiQGtnjPqG+wudHa7sUgjbjHf78sW4a/7q6H\nvtWFEYrAYwJCxbAcdlZb8dJcXdCvQVEU7h2fiWUbqzGtQAmliJzjEwRBEAQRGlINCLOOYixBJCI+\nn8Kg4WKcOBxcdyzHcWgxJ3ZnLBDZqAJTgxfppCs26qoNBixZUYGTurloHj0fJ3VzsWRFBaoNhn6f\nK5X7irGcnxE0njjsjA3l909URUNEOH/aBS7AqcUeD4fD+x0YPV5Csn8Joh97jDZMyJWD10uUB4+m\nMKNIFbPu2MMNDmTIBchShHaTWKcWYapOgb8fbgrTyAiCIAiCGMhIMTbMpAKaLOJFJDRdkRA2Kwtz\nY+DFSIeNBU0DEmli71p8xdjILDjS1Dl1fxoAACAASURBVOBFGsmLjbqKlatBly0ETygBAPCEEtBl\nC1GxcnW/z+XzKQgEFNqd/Rf1WIYDxwK8OGucCuX3T1QpGh6EQgr1tZ6AnnfikBMZWQKynRKEH3rL\ni73YrGIVtl6wwM1E//y4Um/BtBAiCi62cFQ6tuutqGoJfbFTgiAIgiAGtsSumMQhqYCG3R3bVWMJ\nIhQ0j8KQUjFOHHb63QnYodkcman90ZbyY2dsoL9/fxiGQ4vZC0164n9G8Y7lONRZ3dhtsGLdURMO\n1lo7C5EdeEIJzA63X68nldFw+JEb6/kxooCi4quj0uxwh/T7JyKKolD8Y3esv8xNXjTUejB8dM9T\nrgmC+InNxeCMuR1jsmV9Pi5bIUS+WoQdVdHtjvUwHHYZrJiSH554BIWIh1tHpuG9/Y1hPz8gCIIg\nCGJgIRWBMJMIeKQzlkh4eToBzp1sR2OdF5k5/neHtZi8SNXEWUtgEKQyGjQPsNtYyBXh+31aTF4o\nVTwIhPFVqIs3gWSbchyHZqcX+lYXqttcqLWbcK7JBkObC0oRDzq1CPlqEXJUIrS4nV0KkozbCY3U\nv6mrUjkNu42BJqPvw6bXw0EQZxEFAKCRCtEUwu+fqLLyBDh+yIlWsxfqfuJTGIbDob0OjBgrgUBI\n7lUTRH8O1NkxIlMKEb//7WV2sQr/OdmEcRk5URiZzw91duSrREiXha/Lfd4gNdafacUugw1lYSry\nEgRBEAQx8JBibJjJhCQzlkh8FE1hyEgxTh52IiOb73eXX4vZC21hcix4k5rGR3OTN6zFWF9EAdnt\n9qUj27RjSn2T24klKyrw9rKlSMnIQXWrC/o2l+9/fyzA8mgKOpWv6FqaqcIMnQz5amGXhRSvTLsb\nS1ZUAD++LuN2gt25BkuXLfVrXDK5f4t4eT0c+HE4u33O9Tfiu1feQtbsO4P6/RMVTVMoHCzCudMu\njCvre9s7fawdShUP2XnJXaAmiHDZY7RiYl7fEQUdyrQKrNzfiCa7J6zF0b5s01tQHqaIgg48msK9\n4zPwv7vqMS5XBiGP3LghCIIgCCJwpCoQZhKygBeRJLJyBTh7woWaag/ydP0XJ7weDnYrC5U68Ttj\nAV8xtsXEIL8ofK9pavBiGJn+3Keesk1RthC3LX8b2jl3Il8tgk4thE4twpR8BfLVIqjFPx3KFAoF\nrFZrt9fN12rx9rKlXTtuly3tteP2UlI5D011/WePejyIu8W7jjc68PF5Gs8/9ius/+IzNNhcOGN2\n4eMnf+3375/I8otEOHPcAoedhVTWc+GkrYVB9Xk3ps8lnW4E4Q8Pw+FAnR2Lx2X69XgRn8YVxanY\ndL4NC0amRXh0gMvLYl+NDXePzQj7a4/KkqE4VYR/nWjG/BGR/10IgiAIgkg+pBgbZlIBDYeHZMYS\niY+iKAwbJcahfU7kaAWge1kpuUNrsxdKNQ80L74KUcFKTePhwhn/syb743GzsFoYpPQzVXqg6y3b\ntFAtxEfzB4WUxZqv1eKV5Y8H9VyZjEaVH5mx8RZTcNrkxAvf1+CRqTkYky3D7Mt8v/9Tm6rRQKtR\nEuPxRYNAQEFbIETVGReGj5F0+3eW9cUTDBslhlhCutwIwh/HGh3IVQiRIvH/mHb1sHQ8+e0ZzB+h\nAR3hXO0DtXYUp4oDGl8g7h6bgUe/1WNmkQoaaRxOhyAIgiAIIq6Rq44wk5LOWCKJpGUKIJXRqD7f\n/yI/ybJ4VweligdXOwtXe3i2Z1OjF6lpfPCSpFgdKRqpEIzb2eVnjNuJLIUopotiSQOKKYiPv/H5\n5nY8u9WI30zO7rbATrlOiW367h3EyapwsAjVF9zweLovunPhtAsCIQVtIYknIAh/7amx+R1R0GFQ\nmhQyIY3D9Y4IjeonkYgouFimXIh5JWp8cLApYu9BEARBEETyIsXYMJMKeKQYSySVYaPEOHO8HV5v\n3ysHt5i8SEmCxbs6UDSFFA0fLebwdLqbGrxI62fxJwJYungRXNtWdxZkO7NNFy+K6bhEYgqMl+ux\nmHcxj4cDnx/7Yqy+1YVnthjwwIQsTOihYDJZq8ChevuAmckhldFIz+TDcL5rt7vdxuDMCRdGjZfE\ntNhPEImE4zjsNVoxKS+wWA+KojC7WI2N51ojNDIfp4fFwTo7yrSBFYsDdXOpBkcbHDjRFPniMkEQ\nBEEQyYUUY8OMdMYSyUadykeKho+qPqbscxyHFjOD1CTqjAV+XMTL5A3La5nI4l1+yddqMefmRRD/\n8CVSD63DUP16vB1AtmukUBTl64619V289Ho4CISxLerVWNxYvtmAe8Zl9rrat0LEw/B0CfYYbVEe\nXewUDRHh/Bk3WNZXUOc4Dof3OjFomAgyefLcSCKISNO3ukBTFLSqwLvJpxcocaDWDqsrcjeC9tbY\nMDRNAqU4ssdciYDGLy5Lx3v7GsFyfd+oIwiCIAiCuBgpxoYZKcYSyWjISDHOnXLB4+75u223seDz\nkXR5i6lpPDQ3hV6MdTpYuFwcVCmk4NMfhuVwzKXA6888jlUvPYlXlj8e80JsB6ms/6gCb4w7Y+ut\nbjy5qRq3j07D5QV9T9Et1ylROYCiClI0fIglFOprfAuxGX6MLSgcLIrxyAgisewx2jAhTx5UN7lc\nxMO4HDm2VrVFYGQ+lXoLynXRWYxveoESPJrC5vOR+30IgiAIgkg+yVU5iQNSAQ/OATLtkxg4FEoe\nMnMEOHeq5+7YFpM3qfJiO6g1fFhaGTBMaB0vHREFZBp0/040OaEW85Cnir8CmUzOg72fRbw8MVzA\nq8nuwbJNBtxSqsHMYnW/j5+kleNYowO2CHaoxRuJognP//kv+P1jz+P55/6CtFxzv4sTEgTR1W6j\nDRNzg48AmFWiwsZzbeAi0E1qdzM40uDAJG10irEUReG+8Rn4+IemARP7QhAEQRBE6EgxNsxIZyyR\nrAaXilF11t3jglbNJgYpmuQrxvL5FORKHlqbQ7vAMjV4SESBnyr1FpTnR27RlVD4Ygr86IyNQTHW\n7PBg2aZqXDc0BVcNTvHrOVIBD6OypNhlHBjdsQaDASvfW4XpU+5E+fgHMPPyu/DG/6yEwWCI9dAI\nImGYHR402NwYniEN+jVGZkrh8LA419x7/FGwdhttGJEphVwYvZkogzQSjM2R45Mj5qi9J0EQBEEQ\niY0UY8NMIqDh6GUqN0EkMqmMRp5OgDPH27v9W4vZi9QkWrzrYqlpvJByYzmOg6nRi3RSjO0Xw3LY\nUW2N2vTSQEnldL+dsV5v9Iuxre1ePLnJgFnFalw/NDWg55bnK7FtgEQVrHp/LWaUPwCRyFdEEomk\nmFH+AFa9vzbGIyOIxLG3xoaxOXLwQ+gopykKM4tUEVnIy3dDL/rHkDvGpGPT+TbUWNxRf2+CIAiC\nIBIPqQ6EWTQ6Y6sNBlSsXA2zww2NVIilixfFTaYikdwGDRdjy3+sKBrCQCrzFV89Hg4OOwulOkmL\nsel8GKuCv7iyWVlQlK+QR/TtSIMDGXIBshSBLwoTDTJZ/52xnih3xlpcDJ7cZEC5ToFbSjUBP39C\nnhxv7qlHW7sXqggvdhNrTgfTWYjtIBJJ4XSQqcUE4a89RhtmFKlCfp0ZRSo8/M0F3D02AyJ+eI6P\nFheDE01OPFaeG5bXC0SKhI+bhqfi9W8Pgju+hZyjD2DkOo0gCILwB6kOhJlEQMPFsBFbVbXaYMCS\nFRU4qZuL5tHzcVI3F0tWVKCaTLMkokAkplFQIsSpoz91x7aavVCpeaB5yZm7mJrGR7OJCTrbztTg\nRVqmgOTF+mFbFBddCYZERqPdyYJle/8ueD0cBILojMfmZrB8swHjcmRYMDItqNcQ82mMzZFhpyH5\nu2MlUh5cLkeXn7lcDkikyXkjiSDCzelhcbzRicuyZSG/VrpMgEEaSVj3PbsMVozJlkEiiM3lzRip\nHVu/+Ds5Rx/AyHUaQRAE4S9SjA0zmqIg4tFwRqg7tmLlatBlC8ETSgAAPKEEdNlCVKxcHZH3I4hL\nFQ8Ro7HOC2ubr5usxcwk5eJdHcQSGgIBBZsluG26ieTF+sXDcNhtsGJqnObFAgCPR0EkpuB09P5d\n8HgQlc5Yh4fBM1sMGJYuwS/GpIdU7J+mGxhRBXffswCbK9/qLMi6XA5srnwLd9+zIMYjI4jE8EO9\nHUPSxJCFKY91VokKG861heW1AN8NvWkxvKH35v+tQcasO8k5+gBGrtMIgiAIf5FibAREMqrA7HB3\nHuA78IQSmB0ko4qIDoGQQslQEU4e8XXHtpi9SEnSvNgOKUHmxrIsh+ZGhuTF+uFQvR15KhHSZVFq\nKw2SVM7rM6rA1xkb2WKsy8vi2e+MKFCLce+4jJC7rsfmyHChpR3NzuCzkROBVqvFH5b9EscvrMaO\nA2/j+IXV+MOyX0JLpo8ShF/2GK2YmBe+YufEXAUMrS7UWUM/h211enHO3I5xOfIwjCw45BydIN8B\ngiAIwl+kQhABUmHkOmM1UiGa3M4uB3rG7YRGGp8Zi0RyKigRYfeOE/jim3+jqd6NrDwh7r3vtqQt\naviiCrzQFYsCel5bCwOxlIJITO579WdbVXxHFHSQyXyLeKX38G8cx8Eb4cxYN8Piz1uNyJQLsGRi\nZljiL4Q8GhNy5dhRbcG1QwJbACzRaLVaPPnUY7EeBkEkHIblsK/GjttG9rT3C46AR2F6oRIbz7Xh\njjGhve72aivG5crDlj8bjN7O0VOl8X2TkQgfcp1GEARB+ItUCCIgkp2xSxcvgnXrx2DcTgC+Azy7\ncw2WLl4UkfcjiJ7U1hnxXeUajBl6O66d+2uMLLkdz694F4YkzcTqyI0NVEdeLNE3N8Nib60triMK\nOkjlNBz2nvfvDANQNECHsMp4XzwMhxe/r4FCxMOvJ2WDDmMO8TSdEtuqkj+qgCCI4JwyOaGR8pEh\nD+8xbXaxGpvPt4HpI4vbH5UxjigAfOfo7M41Xc7RGzd+CEHpTLS2J/fMA8LnN/cshHnzh12v03aQ\n6zSCIAiiO1KMjQCJgAeHJzKrM+drtRg+6xZknPwagj1/h3XHP/Hmn35DVukkomrV+2sx54pfda5M\nLhJJMaP8Aax6f22MRxYZChUNj5tDuzOwmyxNDV4SUeCH/bV2FKWIkSKJ/89KKqd7jSmIZESBl+Xw\nyvYa8GgKD0/JAS/MBd/RWTLUWN1osnvC+roEQSSHPUYbJuaFPwIgXy1CmpSPg3X2oF/D5PCgus0V\nloXFQpGv1eLtZUsxVL8eqYfWYah+Pdb++SEMLynAQ19fwO4BsFDiQGfk1Bgy4xYMrvoWqYfWoX33\nZ7jrrrvJdRpBEATRTfxf+SagSHbGmhweWEUa/N/zT4BHAY9+q0cdlYKCiLwbQfTM6WA6C7EdRCIp\nnI7I3ISINYqikKLx5cbmaP2baub1cmht9kKTHtuLw0SQKBEFwE8xBT2JVEQBw3J4fUctPAyHP1ye\nC34EOm8FPAqT8uSo1Ftw43BN2F+fIIjEtqfGhkem5ETktWeXqLHhXCvG5wZX7N2u92XZCnix7zHJ\n12rxyvLHu/ysqACYkCvH6zvrsMtow33jMyAVJHfW/kBkdTH42/5G/P7ayzAsfSoAXx7+u3sbcDPL\nhf0mKkEQBJHYSDE2AiJZjN1lsGJCnrzzYnz+CA3WHTVhcp48LNmBBOEPiZQHl8vRpSDrcjkgkSbv\nxUVqGh8tJgY5fjY3tJi8UKp5Ec0PTQbtXhYH6+xYMiEz1kPxiy+mgAHHcd32uR4PBz4/PH/vaoMB\nFStXw+xwo8nBoKT8Krz084kRLTZM0ynx0Q9NpBhLJL2Lty+NVIilixeRzrU+GC0uOD0silMDy033\nV7lOgf872IhWpxfqIGZIVOotuG1UWgRGFj7DMqR4/epCrDrQiAe/voClZdkYmUlu1kZKLLbx9w80\nYopWjmHpP50bj8qUQiqgsctgxVRd/EcxEQRBENET+1vISchXjI1Mh+BOgw2TtT91kE3Mk8PLAAdq\ng5/eRRCBuvueBdhc+RZcLgcAXyF2c+VbuPueBTEeWeR0LOLlLxOJKPDLHqMNQ9IkUIoT47MSCH3F\nVo+7e76h18N1/nsoqg0GLFlRgZO6uWgePR8Y9zOc3vRP1NfWhPzafRmZKUWTwxOWlc0JIl5dun2d\n1M3FkhUVqE7SzPNw6IgoiNRNf6mAh0l5Cmy50BbwcxtsbtTbPBiVFf+FTYmAxq8mZeH+CVl4dXsd\n3t/fADcTmeaNgSwW2/gPdXYcqbfj9ksWoqMoCvNHpGHdUTM4LrRcZIIgCCK5kGJsBESqM7at3YsL\nze1dMrFoisItIzTkIE9ElVarxR+W/RLHL6zGjgNv4/iF1fjDsl9Cm8SdRepUHqxtDLxe/7azJrJ4\nl1/iYdGVQFAUBZmc12NUgSdMMQUVK1eDLlvYuRozTygBPWUhKlauDvm1+8KjKUzRKlCpt0T0fQgi\nlnrcvsoiv30lsr1GGyYGGSHgr9nFKmw41xbwuWyl3ooyrSIi8S2RMj5Xjv+5ugBNDi8e+U8VzjW3\nx3pISeW19z6O6jbe7mXx5p56PDAxq8f4ifG5MlAUsLfGFpH3JwiCIBITKcZGgFTAi0gxdrfRhjHZ\nMggvmaY6NV8Bi8uLo42OsL8nQfRGq9Xiyacew/MvPo4nn3osqQuxAMDjU1CqeWg1998d63axsFsZ\npKQmb2xDONjdDI40ODBJmzjFWACQynpexMvr4SAIQ0yB2eHuvIjswBNKYHZEvmN1mk6JSj1ZZIZI\nXrHcvhJRW7sX+lYXRmVJ+39wCIal+/4mJ5ucAT1vmz5xMscvphTz8bvyHNxSqsHTmw1Yd8QEhiVN\nFcFq97Ko1Fvwwvc12KG3RHUbX32oCUPTJBjXyw0LiqLw8xEafEoaZwiCIIiLJMa80AQjFdBwRqAY\nu7PaipnFqm4/59EUbi71dceS/KngkQw5oj8paXw0m5h+O15NjV6kpvNB8xKnUycWdhttKM2QQi5M\nrKK1TE7Dbu+5GMsPQzO0RipEk9vZ5WKScTuhkfq3eFwohmVIYHExMLS5oFVFJh+SIGJJIeLHbPtK\nRPtr7RiVJYv44lgURWFWka87dliGf4XfGosbrU4vSv18fLyhKApXFKpQmiFFxa467K2x4aEpOchV\nku+iP1xeFvtrbajUW/FDnR2D0yQo1ylQl6/EuSht46dNTnxfZcH/XlPY5+PKtAqsOWTCoXoHxmST\nazWCIAiCdMZGhFRAw+4ObzHW5mZwosmJsTk9H8CvKFSh3uoOuKOA8CEZcoQ/UtN4fuXGmhq8SMsg\n97r6k2gRBR2k8l46Y70IS0zBkjtvQ/2GD8C4fftzxu0Eu3MNli5eFPJr94emKEzVkagCInmpL5uD\nti0fxWT7SkR7jFZMzItsREGHK4tU2GWw+r3uQqXegik6ZcKvUp8uE+DpGVpcUajC7/+rxzenW0gH\nZS/cDItdBiv+UlmLuz87i2/PtGJMtgxvX1+E5TO0mFWsxqP33Q5255qIb+MehsUbu+pxz9iMfnPv\naYrCLaUafHrUFNYxEARBEImLFGMjQCKg4QzzAl77amwYmSXtMYsIAPg0hZuGk4N8sEiGHOGP1DQ+\nWsxecP1MJTSRvNh+WV2+G0wTonSRH06+Ymz3fbzHzUEQhmLsaY8Ss25ciKH69Ug9tA5D9evx9rKl\nUevU74gqIMUAItnsq7GhgZeCVU8/iKH69eB2rgGz9/Oobl+JxM2wOFTvwPheGgHCLUXCx4hMqd9R\nKdv0FkzLT7wbej2hKQrXDEnBC3Pysfl8G5ZvMcLs8MR6WHHBw7DYY7Tite21uOuzs/j3qRYMz5Dg\nzeuL8MzMfMwpUXcphuZrtXh72VIM1a8Hs2M1qP3/isg2vvaHeqTJ+Li8QOnX4y8vUKLJ4cVxEitH\nEARBgMQURIRMGP7M2J0G3wIFfZlZrMInR80439yOolRxWN8/2ZEMOcIfIjENkYiG1cJCqe75xojD\nzsLj4aBUB3eva6DEZew0WDE6S9brDaZ4JpPTPS7g5fVy4AtCu8fJsBy+ONGMh64YgWE/nxjSawVr\nsEYMN8OiqtWFwhRyLCGSg8PD4K099Vhalo2SLBleWf44bG4Gv/zXOagzcmI9vLh0uN6BArWo366/\ncJpVrMI/jpkxp0Td5+P0rS44PCyGpEv6fFyiyVOK8OIcHT49ZsbD31Th3vGZKOC1DYjzgot5GA6H\n6u2o1Fuwt8YGnVqEqflK3DU2AymS/r+P+VotXln+OMwOD5Z+fQEZ2blhHZ+hzYXPjjTgL/N0oCj/\nbsLy6I7uWDOempGY0RoEQRBE+JDO2AiQCOiwFmPbvSwO1zswoZ+VbIU8GjcOS8Wnx8xhe++BQsDj\ndU5n6kAy5IiepKbz0dzUe1SBqcGDtAy+3yfnFxtIcRmVegumFSRmR5NYQsPt4sAwXTtHPZ7QO2N3\nVFuRKuFjWHrsLtQoikI5WciLSDIfHmzCmGwZRmf91OUpF/IwPleOrRfaYjiy+LXHaItaREGHcTly\nNNq9qG5z9fk4X8yNEnQQx9p4x6MpLBiZhmVX5uH9LUew4I+vDYjzAi/L4UCtDRU763D3Z2fwj2Nm\nFKeKUXFNIZ6brcM1Q1L8KsReTCMVYFi6BNvDGL3Dchze2FWPu8bnIl0W2CyoKwuV0Le5cMZMYuUI\ngiAGOlKMjQBpmIuxB2ptGKwRQyHqv4Ns7iA1jjU6+j2JJX5yvrkdzYXT4Px+NcmQI/rVX26sqdGL\ntMzguogGSlxGq9OLs+Z2jM9JvIgCAKBpChIpDccli3j5FvAKvjDAcRw+P2HGjcNTQx1iyHzFWAuJ\nKiCSwvFGB3Ybbbj7soxu/zar2LdoFPmud8VyHPbW2DApL7o3zXg0hRmFSmw829rrYziOwza9BeUJ\nmDkeiEEaCVKqvkfqjDuS9ryAYTn8UGfHG7vqcNdnZ7H2iAk6tQivX1OIF+bocN3QVGikocU+zSpW\nY+O58N1w+c/pVtAUcF1pesDPFXQ0zhwljTMEQRADHSnGRkC4i7E7q20o8zMTS8yncd2QFPyTHOT9\nom914ZktBjw67zKseuanDLlI5UsRiS81jd9rMZbjOJgavEgPshg7UOIydhisGJcrh4ifuIegnhbx\nCrUYe7jBAZeXw/h+ZkFEQ1GKCBQFnG1uj/VQCCIkbobFG7vrcd/4DMh7uKk9IlMKl5cl3/VLnGtu\nh1RAI0cZ/RlCs4rV+O6CBR6m5wL5+RYXOA4oGQCRXK1OT9KdFzAsh8P1dry5ux53f3YWHx9qQq5S\niFfnFeCluQW4YVgq0kIswF5sfK4ctVY3jJbQG1Wa7B6sPWLC/5uUFXRX9pwSNU6ZnKhqIfscgiCI\ngYxkxkaAmE/DzbBgWC7kFV49DIv9dTbcM657N0dvrh6cgvu/PI86qxvZCjLNvjdGiwtPbTbgnnGZ\nPxa7FXhl+eM40mDHxz+YSCGW6JFMQYNhAKeDhUTatZhos7Dg8ShI5cHloGqkQjS5nV0uvJIxLqNS\nb8ENw2Lf/RkKqax7MTbUmILPjvm6YuNh2i1FUZ0LeQ3SJFcmIzGwrDtihlYlxJT8nhfZoSkKM4tV\n2HC2jXzXLxKLiIIOOUohtCoh9tZYe/y7bauyoFynDCoOKNH0dl6QGsZiZbj1lH2fm5uHE01OVOot\n2GmwQiPlY2q+Ei/P0yFTHtlzHD5N4cpCFTada8OdPXTH+4vjOLy1px7XDU1BnkoU9OuI+DRuGJqK\nfxwz47fl4c2yJQiCIBJH4rYlxTGaoiDm03B6Q++OPVTvgE4lCigjSSbk4apBanx2nHTH9qbO6saT\nmwy4Y3Rat1VQtUoRjBYXmbJI9IiiKKT0ElXQ1BB8RAEALF28COyONV3iMuo3fIA5198Y9GvGG7PD\nA32rC2Ozo7M6d6TI5DTsYYwpON/cDkObG9MLVOEYXlh0RBWwZF9IJKiqlnasP9uKX47P7PNxM4tU\n2F5tgSsM523JYo/Rhokx7NLvbWo5x3HYXp38EQUdli5eBHZn1/MC06YPQQ2fAYeHifHouusp+37h\nE6/htpWVeG9/A9KkArwwR4dXryrEzaWaiBdiO8wqVmHL+TZ42eCPZ1urLDA7vLhpuCbk8cwbrMah\negdqLInb4UwQBEGEhhRjI0QioOEMQ1TBjmqr3xEFF7tuSAp2VFvRZPeEPIZk02T34MlN1fh5qQYz\ni7uv1qsS+7oa29rj7ySXiA+paT0v4mVq8IRUjM3XarHkvnvg3PUZUg+tw1D9ejz/2K/w8Xkaxxsd\noQw5bmyvtmJingICXmIffnwxBV33EaEUYz8/3ozrhqZAwIufTi+dWgSpgMapJrLQCJF4GJbD/+6q\nxx1j0vvNnNRIBRiSJsH2arJoHQA02NxocXoxOC12ncJT8hU4ZXLC5Oh6Hnva3A4hj0aBOvjOxESS\nr9Xi7WVLMVS/vvO8YM2fH0ahLh9/3FDd7fOJtZ6y79VX3gFN1fd4/epC3DJCE5NZe3kqEbIUQuyv\nsQX1/LZ2L1YdaMSvJ2eBH+KsRwCQCni4ZnAK/kkWXSYIghiwki6m4MCBA/jmm29QU1MDq9WKlJQU\nFBYW4tprr8XgwYO7Pf7UqVP47LPPcObMGXg8HmRlZeHKK6/EvHnzQNPBFwtkYciNZVgOe2psuG1U\nWsDPVYr5mFWsxucnmvvtCBlIzA4Plm2qxnVDU3HV4JQeH0NRFLQqEQwWF9QBrtpKDAypaXwc0Xct\nULEsB3OTF6MnSEN67TMeFR599BFcfdH3Mz3Ljhe+r8GfrsiL6cVxOFTqLVgwMvB9WryRyniwXxRT\nwLEcvAzAD2KX0WBz42CdDQ9Mir99dblOiW3VVgzLCO17TRDR9tWpZkgENGYX+9dtPrtYja9ONWNG\nUfx0p8fK3hobxuXKQ47aCoWIT6Ncp8Tmc22Yf9ExwxdRoBgQEQUd8rVavLL88S4/+1U+h8+PN+N3\n6/X40/Q8FMVJfq6+1QneoO4ZdnfY2gAAIABJREFUt+1x0MXbsVjfJG3gTS7v7W/EFYWqsEaZXDMk\nBQ98eQ4Ntuh1CBMEQRDxI6kqTR9//DG++uorKBQKTJgwAQqFAvX19di/fz92796N3/zmNygvL+98\n/N69e/Hqq69CKBSirKwMcrkc+/fvxwcffIBTp07h4YcfDnosEgEv5OlDRxsdyJQJkC4LLhfqZ8NS\nsfiD7Tj6xUpYXd7O3KaBloXakV3VYHNB3+rB7bfNx/VDi/t8Tp5SCEObGyMzE3sqNeHTU35ZKNuB\nKoUHu5Xp0gnZ2sxAKqMhEgd/E8flZbG/xobFY7tmmo3JluE3k7Px7FYjll+pjZuLrkA12Nyos3ow\nKivxtyuZnIbDzoLjOFAUBa/XV4gNpkDwxYlmzC5RQyoILms4ksp1SjyxQY/FYzNiWpghwifc+8N4\nVGd14x/HmvHyXJ3f2+T4XDne2luPWos7JotWxZM9RluXG4KxMqtYhZcra3HLCA1oigLLcdhebcUz\nM5Pr+xoMiqJwU6kGmXIBntpswINl2TFd/NHlZfHRoSbUWr1IidPs+6n5Srx/oBFmh6ffbvmL7aux\n4bTJiYprCsM6HoWIhzklanx+vBlLJmaF9bUJgiCI+JfY80Qv0train//+99Qq9V47bXXcP/992Ph\nwoV45JFH8MQTTwAAPvnkk87HO51OvPPOO6BpGsuXL8eSJUtw++234+WXX8bgwYOxa9cu7NixI+jx\nSAU0HO7QOmN3BhlR0MFqqkPjji9wvuiqztymJSsqUG0whDSuRHJxdpXlsluhnHoz/vXJR/1+BlqV\nCMa20FddJWKvp/yyULcDHo+CKoWHFvNPUQWmBi/SMkNbUGNfrQ3FGnGPHdkT8uR4YEIWnt5igL41\nMb+b2/VWlGkVYZniF2t8AQU+n4Kr3Zc/5wkyosDS7sXWKguuGxqfC5rlKoVIkfBxLEliMga6SOwP\n4w3HcXhzdz1uGp4a0HRoAc+3yM/Gc60RHF38s7sZnDa1Y0wc5HqXpIoh4dM42uDb/5xodEIp4kEb\nwuJJyWaqToknpufhjV11+OZ0S0zGcNbcjkf+U4Vmhxd/++N93TJu2Z1rsHTxopiM7WISAY0pWgW2\nnLf4/RyHh8Fbe+rxq0lZEPHDf9l8/bBUbNNbYI6zuAmCIAgi8pKmGGsymcBxHEpKSqBQdC1gDh8+\nHGKxGBbLTwffnTt3wmq1YurUqSgs/OlOJ5/Px6233goA2LBhQ9DjkYYYU8ByHHYZbSgLYipNh4qV\nq6G+8o4uuU102UJUrFwd9Gsmmpfe+ahbdpU/n0GeUggDCdVPCj3ll4VjO0hN53dZxCvUvFgAqNRb\nMU3X84rfAFCWr8DicZl4arMBRkviFWS36ZNr0RWZnO6MKvB6OAj4gRdjvzndijKtAqlxHIniW8iL\nZGkmg1ff+zgi+8N4sul8G+weFjcEcYNjVrEKmy9YwISwyE+i219rR2mGBOIIFJ4CRVFU59RyIPmO\nIeEyNF2CF+bo8O9TLXh/f0PUvr8My+GTIyY8s8WAW0em4bHyHAwvLuiWcfv2sqVx030/u0SNjedb\n/V6k98ODTRiTLcPoCM3oUYv5uLJIhS9ONEfk9QmCIIj4Fb9XfwHKysoCn8/H2bNnYbVauxRkjx8/\njvb2dkycOLHzZ8eOHQMAjBkzpttrDR8+HEKhEKdOnYLX6wU/iBBAqYCGM4RVeU+ZnFAKecgNYaqc\n2eHuMk0I8F14mR2JX2Tsa5plq9OL7dVWVOot2Gu0ImtY4J+BrzM28T+ngc7mZnC80Q5BD/lloW4H\nqWl8nD/tK4h6vRxaWxho0oLfpTo8DH6os+NX/UxVu7xACQ/D4slNBvx5Vn5MFsIIRq3FtyBMaRJl\nj/oW8WKhSQ9u8a52L4tvTrfguTn5ERpheJTrFPjtt3r8ckJmUnQ1DzQuL4sDtXZs01uwU29B1uDu\n+8MGW+Ld3OlJs9OLDw82YfkMbVCxGlqVCBkyAfbX2jAxb2AW/fYa4+t3n16owvtbjkD/7f9hj9GK\nsbkKTLr/jrgp7sWLLIUQL87R4YXvjXhxWw0emZoT0YK60eLC6zvqIBPy8NrVBV2m/feUcRsvBmvE\n4NMUjjU6MSKz7/OR440O7Dba8L9hjie41I3DUrH06wu4uVQDlThpLs0JgiCIfiTNHl8ul2PRokX4\n8MMP8cgjj2DChAmQy+WdmbGjR4/Gfffd1/n42tpaAEB2dna316JpGhkZGTAajWhsbEROTk7A45EK\naNjdwWfG7qy2YnJ+aNlPGqkQTXGa2xSKjmmWHd09TW4n7n+6Ar+46y6cdClwxtyO8bly3DRcA5dO\nidNBfAZpMj4cHgZ2NwOZMP5yHIm+OT0svj7Vgi9ONoOmaTAR2A5SNDy0mr1gWQ7NTV6oUnhBTVPv\nsNdow7B0CRSi/r9vM4vVcDMcntxUjedm64LOlY6mSr0FU/IVSZU7KpXRcNh9+3mPN/Bi7KZzbRia\nLkGeMr6n3GbKhciSC3C43o6xObHLJCT852U5/FDnK8DurbGhOEWMaQVKGPOVON/D/vBsswsfHGzE\nTcM1fu2D4tW7exswu0QdUq727GIVNp5ri6uCZLR4WQ4H6my4a2x6rIfSqbWxFg07voBnxh3IHCpB\ntduJJSsq4qrbMl4oRDwsn5GPN3bX4YkN1fjTFXlICfOsC5bj8J/Trfj7ERNuG5mGqwerE2oxNYqi\nMLtYjQ3nWvssxroZFm/srsd94zMgj/A+USMVYGq+El+ebMEdY+Jn2yMIgiAiK/ZzkMLo6quvxqOP\nPgqGYbBp0yZ88cUX2L17N9LS0jB9+nQolT9N/3U4fPlTUmnPB+KOn9vt9qDGIhXwgo4p4DgOOw02\nTAkhogAAli5e1C23ybVtdVzkNoWip2nnvKkL8eGaTzCnRI1VN5Xgkak5mJAnx0P33h5UdhVNUchV\nCmEkUQUJxc2w+PJkM5Z8eQ4XWtvx/Jx8vPn7eyOSXyYU0RBLaVhaGZgavEgPMS+2stqK8j4iCi51\n1eAUXDc0Fcs2VSdE1phveqn/v18ikMl5XWMKAijGMiyHf51oxs2lmkgNL6xIVEH8Y1gOh+vteHN3\nPe767CzWHTVjkEaMN64twopZ+ZhTosZv7+v5mPjm7++F3c3iga/OY+1hU8gLkMbCToMV+lYXbh0Z\n2jY1VafA0UYHmp3e/h+cZI43OpCtEAa0uFGkVaxcjdQZAztyKxACHoWHyrIxIU+O362vQnUYM+ZN\nDg+e3mzAdxfa8OIcHa4ZkpJQhdgOVxQqsddo67NpZt0RM7QqIabkR+e85ebSVKw/0wJbCI08BEEQ\nRGJJms5YAPjiiy+wdu1aXH311Zg7dy7UajVqa2uxevVqVFRUoKqqCosWBVaA6esk49ixY51xBwAw\nf/78zniEFIUd9VZ3t/xaf5xusoPPozFCmxbSSU7p8OH46IU/4OW33ofZ5gJL0XBNvRH5xYOhECXu\nn77VxfQYv5CtEmHeiNwuP7/0M9DIRXjshT+gQKfr930KNTI0uSiMD+JvmEiEQmFQ39N44mFY/Oek\nCR8fqMXgdBlevm4oijU/3mjJTev8DphsLpxscqLiDw+hdPjQkN83M9sDh5WPZpML48tSoFAE1+Fo\nc3lxtMGBP80eBHkA2+aiCQpQfCGWb6nBazcMRYokfi6gL3ah2Qmnl8OEogzQEb5wi+b3OS3DBWNV\nKxQKBfg8GyRSyu/33nTGjEylCOMLMyI8yvCYO1yEez89CpFUBiEvqe7jxrX+vs8cx+FEox2bzzZj\n67lmpEgFmFGcijsn5iOrh47rvo6JlwOoaWvHh/tr8cCXFzB/dBZuGJEBiSD+O2VtLi/+tu8c/jSr\nGBp1aNu/AsDlRanYUdOO2y7rPnsqmR083IJpRZqI7UOD2T/3ds7X5mIS/twlku6bokRBmgnLNhnw\nxKwijMtTBf1aHMdh89lm/HV7NW4cmYmFl2Un9CwXhQIYp1VhT70b15d2PwafMzvw33Nt+NvPS6GQ\n9T6LKpznGwoFMKWgDRurHLhjXOAzMonArFu3rvP/l5aWorS0NIajIQhioErcitwljh8/jjVr1mDS\npEm44447On9eUFCAxx57DA8++CD+/e9/Y/bs2cjIyOjsfO3okL1Uf52zQM87b6vV1znEYz1os7d3\n/ncgNp1swuQ8GWw2W8DPvZQmNRUvPPHbzv9+d18DXtp0Fo+V5yTk3WzAFwHR07RzlYjX4+d96WcA\nwK+/S5aUxtkGC6y5wU93TAQKhSKo72k8YFgOW6ssWHvEhGyFEL8vz8HgNAkApsvvdPF3YM3hJuxr\nZjA2DL+z1WHAW+98AreLweZKEe5evADaIKZNbjrXipGZUnBuJ6wBNmNfVyKHzeHEI1+cwLOz8qGM\nwynG6483YYpWDnsY9mn9ieb3maJZWNo8sFqtsFrbAXB+vTfHcVhzoAaLRqUnzLYnBqBVCrHtdP2A\nnL4dC9UGA9788BM0tDm6ZKNzHIeqVhe+r7KgUm+FgEfhcp0Sz8zIQ17nKvNuWHvZmfR1TFTSwK8n\npKN6sBJ/P2zCp4frcUtpKuaWqCGI4yL8G7vqMCFXhiKFf8f3/kzPl6JiZz2uKZYl7LlSoDiOw/YL\nzfjj5bkR2y8Fs39Wi3ioD+Ccj/jJ5GwRFOU5eHbjOfxiTDpmFasDfg2Li8Hbe+qhb3Vh2RV5KNGI\n4bBH/lgeaVfqZFh9qAFX5nct9DMshxc36XH76DSIWBes1t47i8N9vnH9ECX++N9qzC2UQSKI3/2t\nP/pa2yPWFAoF5s+fH+thEARBJE9Mwf79+wH4Ft+6lFAoRHFxMViWRVVVFQB05sDW1dV1ezzLsmhs\nbASPx0NGRnBdSxIBHfQ0v50GKyaHGFHQm7suS0eNxd25Mm2isbkZtA+6Am1bPgr7tPNL5alEMLQl\nx6ImyYblOFTqLVj69QVsONuKBydn4+kZ2h8LsX2bWaTC93or3EzwC+wBgMFgwKpVqzC97E5cO/fX\nGF60CM+veBcGgyHg19qmDyyi4FILRqZhXI4MyzdXx90UN+7Hv1WyRRQAgEhMwevl4PVwAS3gdaje\nAS/LYVxuZFZnjhQSVRA9HdnoR3Nmonn0fJzUzcW9y/8Hf/3vIfz63xfw3NYaUAD+OD0Xf722EAtG\npV1UiA1dvkqE30/LxZNX5OFgrR1LvjyP/55thTdKq7QH4nC9HQfq7LjzsvBlLQ5Nk4CmgONNzrC9\nZrzTt7rAcYBOHV8Z1j1FbkXinC9ZlWZK8dzsfHx61IyPfmgCy/m/De+vseHBry8gTcrHq1cVoEST\nPM0Jo7NkaG33oqqlvcvPvzrVDImAxuzi4DuJg5WnFGFEphTfnmmJ+nuHU8fx66Rubufxa8mKClQH\ncX5MEASRzJKmGOvx+DITLRZLj//eceeSz/c1A48YMQIA8MMPP3R77PHjx+F2uzFkyJDOxwdKFmRm\nrKHNBaeXxaAInfAIeTQeLc/BRz80JVyh0eFh8MwWAyYMK8LHzz6Eofr1SD20DkP16yOykIOWZMbG\nHY7jsMdoxSP/qcLnx5uxeFwGnpudj9J+VsS9WKZciKIUEXYZQuvsWPX+Wsya9gBEIt97i0RSzCh/\nAKveXxvQ61javThlcmJCbvALI1EUhV+MScewdCme2WKIq7zHCy0uMBwitk+LJYqiflzEiw2oGPvZ\ncTNuGq6JeGRDuE3NV2BfjQ0ub2g3Moj+9ZSNLixfhA1ffY6lZdl494Yi/OKyDBSmiCPauVmUKsay\nK7V4rDwX2/QW/L+vzuO7C21g4qQo6/Ky+OvueiyZkAlpGOMUKIrCrGIVNp5rDdtrxrs9NTZMzJPH\nXSdwvlaLt5ctjfg5XzLLU4rw0lwdjjY48Jfttf3ejHZ6WLy5ux5v763HI1Ozcc+4TIj4SXPJCADg\n0RRmFKm6NKfUWd34x7Fm/L9JWTHbDuaP0OCLE80JfZzt6fhFcp4JgiC6S5oj67BhwwAAmzZtQnNz\nc5d/O3jwIE6ePAmhUIjBgwcDACZPngyFQoHt27fj/PnznY/1eDxYu9ZXTJkzZ07Q4/F1xgZ+IN1Z\n7euKjeRFer5KhDvGpPt1QhYvXF4Wz35nRIFajHvHZUCXn49Xlj+OVS89iVeWPx6Rk/IshRDNTm9C\nnxAlC47zrQz+u/V6fPyDCQtGpuGVeTqMzQnuwnHWjyvphsLpYDoLsR1EIimcjsAKoTsNNlyWHfqU\nNIqicO+4DBSoxVixxYj2OPnebtNbUJ6viLsL/HCRyWnYbQw8Hg4CPyJ7z5rbYWxzY1oCdgqrJXwU\na8TYX5v4U1Tjndnh7jEnM0VCY0iaJOrb09B0CVbMzMf/m5SFb0634sFvLmBHtSWgLrtI+PthE0o0\n4ohEZ1xZpMJugy2ubm5F0h6jrxgbj/K12oif8yU7lZiPFbO04Dhg2UYDLO09L1B3osmBh765AA/L\n4fWrCzEyM7FmcARiVrEK31dZ4GFYcByHN3fX46bhqchW9J4TG2kFKWIMSpNgY4LOYAR6P36ZHaTB\nhSAI4mJJkxk7efJkjBo1CocPH8bDDz+MiRMnQq1Ww2g04sCBAwCARYsWQS73nWhKJBLcf//9ePXV\nV7F8+XJMmTIFcrkc+/fvR21tLcrKylBWVhb0eKQBFmM7snV2GSwYmi7FvMw7I3qyObtYhQO1dnxw\nsAn3jc+M2PuEg5th8eetRmTKBVgyMTNqF6F8mkKmXIBaqxuFKcnX1RevLs2ZuuqGm7DZJEKz04vb\nRqWjXBf6zYrJWjne3deABpsbmfLgTrolUh5cLkeXgqzL5YBEGlh31ja9BdcMTglqDJeiKApLJmbi\nf3fV4bmtRvzpiryYLrbkiyiw4o/Tc/t/cIKSymg4bCy8HvjVGfv5CTOuH5YCAS8xi9PTdEps01uj\ntsL0QKWRCtHUQ06mRhq7IgEAjMqS4cVMKfbX2rH6UBM+PWrGotHpGJcjg8FojGpG4FlzOzZfaEPF\nNYUReX21mI+RWVJsq7Ji7qDAszYTSbPTizqrG6UZ/s8yIRKPkEfjt+U5WH3IhN/9V497SiisW7cO\nZocbKRIB8svm4Qe7HEsmZkUsLi2eZMqF0HjNWPz4WljaPWhzsbjniV8C0MR0XD8v1eDFbTWYU6JO\nyHOFeD1+EQRBxBve8uXLl8d6EOFAURTKy8shl8thsVhw8uRJnDhxAjabDSNGjMDixYsxderULs/J\nzc3FqFGjYDKZcPjwYZw+fRpKpRI/+9nPcPvttwdV9OuIQ/CyHL4+3YqfDUvt9zkd2TrmETdCVjIe\n1pRCfLX6PUwbMwwqVWQyiyiKwpgsGVbub0C2QohcZXweID0Mhxe/r4FMyMODZTlRX731cL0DKjE/\n7jLUwkkkEsHtjo+71RdvC67cMWhS6PDFR+/hhikj8djMwWGbksujKZgcXhjb3BiVFVzXR1FxPj76\n+B3k544Bny+Ay+XA5sq38JuH7vR7u21xerH6cBMemJgFfpi+2xRFYUKuHAfr7NhywYIp+cqYrXp8\n2tyOvTU2LBqVFrWbKNH+PtutLGxWFi4nh7QsPmTy3ovx9VY3PjpkwkNTsuN6MaS+pMsEeG9fA64Z\nnLgF5UTAyNPw1eqVkBWUguYJOnMyVzx0X8TOC/xFURRylELMLVFDJebjo0NN+Hrvaby/ahVaR/20\n747keYyX5fDsViNuHZmGYemRKyBK+DS+PNmMOSXJXYzdpreAR1ERz/aOp/ONgYqiKIzOkqGtsQ7L\nXn0L7WNvhit3DEyKAuz98kO8cGsZxhbGd5NGuFQbDHjnvVXwjr8FlG4s+DmD8PWalX7vtyL1fdZI\nBThQawPLAcWpidcMosnMwdpV73Y5fjVv/ggv/fY+qGN8/AJ8C3gRBEHEA4rjYjzHLMnU1tYC8OUt\n3fXZGXxy65B+n/Pb5S/gpG5utzuIQ/Xr8cryxyM2VgA43ujAS9tq8OrVhUiVxFejtJfl8HJlDTgO\n+N203LAVqwKx+lATAGDR6PAtDBJvorn6fH+iuS1caGnHiu+M+NsNxUEXKw0GA1a9vxZOBwOJlIe7\n71kAbQCdYF+fasFpkxMPT80J6v370rH9AMBj5bHZft7b3wCpgMbCUdHbfqL9fW6o9eDCGRfcLg6j\nxkugTu19P/r2nnrIhDzcMSax9yfPbDHgikIVLi8g3bGRYGhz4YkN1birmMN/v/kSDW2OuFuN+mIM\ny+Gu369Ay/DronYe84+jZhxpdGD5lXkRvdHDsBzu/dc5LJ+hTeqbsiu2GDA9Ctt0PJ1vDHSxvPaI\nF6F+BpH8Ph9rcKBiVx3evK4oZjfUg8FxHJ7abEAeWnF62zedXdftg67A5aMGYcGotFgPsXMRb4Ig\niFiLr+pbEhHzKbgZDgzL9XsQjWW2zvAMKeYNSsFrO2rx9Axt3Cwow7AcXt9RCw/D4Q+Xx6aQBABa\nlQg7DeTCIVqiuS0UpoihFvNxqN6OsTnB5eRptVo8+dRjQY9hm96Cm4dHZjocn6bw26k5eP77Gry+\noxYPT4luZznLcdiht2L5zPgrHoWTVO6LKQD6jiloa/fie70Ff722KFpDi5hynRLb9BZSjI0Ai4vB\ns98Zcedl6ZhRrMYNlz8d98UrHk2BT6HHffeFZidcXjasi/8YLS7862Qz/jJPF/GOex5NYWaRChvO\nteLeccnZLdjuZXGsMTI3BYn4RXI94/szKM2UIlXCxza9BVcUxr6b1F+bzrfB6mKweN5I8GaO6vx5\ni9OLx76tglYlxNQEzMwnCIKIBFKMjRCKoiDh03B6WMhFvU9b5TgObe0smBhm6/x8hAaVfz+LBY+s\ngkxAxawD5+Ks0CYHg5Lyq/DiLRNjOp03TymEoc0Vs/cfaFQSQVRzpmYX+1bSDbYYG4omuwfGNhfG\nZEducQwBj8bjl+fi2e+MePar/bAc3IBmhycq2/iJJifkIh7yVcnbTQb4MmOdDhY8PgVBH8XYr0+3\nYGq+AilxNgMhGJPy5PjbvgbY3AzkwvCtYD/QeVkOL2+rwWStAjOLE2tafG8ZgQ43i7s/P4sJuXJc\nrlNidLYspJurLMfhr7vqcesITdB534GaVazC79brceeY9ISNF+nLD3V2DEoTk215gCG5nvH/Gcwf\nmYaV+xtweYEybppl+tLi9OLDg01YPkPb7eZ/ioSPP07Pw1ObDchSCGMSv9Bxnbnm3YqovzdBEERP\nku+sMo70t4gXy3F4e28D0ifMBbN9DRi3EwA6s+GWLl4UlXHW1Bhxdss/4Bh1PZpHz8dJ3VwsWVGB\naoMhKu8P/JQVelI3F82j5wPjfobTm/6JhrqaqI2hJ7lKIeqtHjAsSfOIBl3ZPLRs/ihq28K0AiUO\n1dnR1suqwpG0vdqCSVpFxHM3hTwadxRx+ObT1Tilmxe1bXxblQXluuTP5eLxKAhFFDxuDnx+z3/L\ndi+L/5xuxc+GxXZRkHCRCXkYmSnFHqMt1kNJKu/ta4CAR+EXCRhjsXTxIrA7u5/HrHzil3jj2iKU\npIrxyVEz7v7sLN7cXY/D9fagjqvrz7SC4ThcHaZFD/2RpRAiXy1K2u/7HqMNE3Ojf0OSiK3ettlo\nXXvEg3j/DMZkSSHi0dhtSIx9z7v7GjCrWIWiXgqtRaliPDAxE89tNaLZGd3z7ouvMwmCIOIFKcZG\nkFTAg8PD9PhvXpbDazvqYGxz4bUFE/HOU0sxVL8eqYfWYah+Pd5etjRqnakVK1eDP3VR551hnlAC\numwhKlaujsr7A8BL73wEumxh1zFMie4YeiLi00iV8lFni/2UpWRXa3Fjv02Ot578TdS2BbmQhwm5\ncnx3wRKR1+9Lpd4a8cVSOrzzwd+RPvMXUdvGGZbDDoMV0wbIVDSpnAZFA3QvjWUbzraiNEMStwsl\nBqNcp8S2quhvN8nqm9MtONrowG/Lo79QZTjka7V4e1nP5zGpEj6uG5qKl+bq8Mo8HbLkArx/oBGL\n/3UO7+1rwCmTE/4sX2ByeLDmsAm/npQd9c9o1o+zKJINw3LYV2PDxDxSjB1o+tpmB4p4/wwoisL8\nERqsO2ryax8ZS7sMVlS1tOPWkX1nwk7JV2JOiRrPbzXCzfTesBRuFStXd7nOJAiCiAeJP18yjkkE\nvpiCS7kZFi9tqwXHcXjySi1EfBr5Wm3MAvMjmZl0cfTApVOjm+we7Ki2Ynu1BXuNVmQNi8/cJq1S\nCGObG3nK5J5uHWvvH2jEjcNSMWqQJqrbwqwSFd7d24Drh6ZEPH+wQ73VjUabB6MyI7cK+MWinYt2\npMGBNKkA2YrkKT72xmAw4B+fr4bD7sWB46LORdw69n0muxtnm114duldsR5qWE3IleOtPfWwtHuh\nFJNTiVAcqrfjkyMmvDBHB6kgcaeK+3MekykX4qZSDW4q1cDY5kKl3or/2VkHD8Nhmk6BaQVKFKhF\nnfvii88haq1eXH/jzciPwUJaZVoF3tvXgCa7B+kyQdTfP9w6PtfqtnY02rxwTbofkMdHAYqInlhe\ne8SLeP8MJuTJsfqwCftr7Rgfpx3sNjeDd/c24NGpOX7lg88foUF1mwtv7KrHw1Oyo3Lu3dN5MEEQ\nRKyRK6gIkgpo2C8pxjo8DJ7bWgO1mIeHpsRuYaqL9ZaZpBCF9vXomBLScSeyye3EL5/+H/x80Z04\n5Vag1uLGJK0CC0amwbtTidNxmtuUpxLB0ObCZG3yT7mOlQO1NhjaXPj9tOgvIDIiQwo3w+G0uR1D\n0qJzolapt6IsXxG17q7etnGzk0G7l4U4jIvrAEClfmBEFBgMBjy/4l3MKH8AIpEULpcDz694C79Y\nfB2eWflp575P7Xbipb++i6I46rgJlURA47JsGXYabJg7KLHyTeNJrcWNv2yvxW+n5gyImxcXy1OJ\nsGCUCLeO1OBCiwvb9BY8t9UIIY/GNJ0Shfw2PP36253bkcDtxNeffozrhqVGfTsS8WmU65TYdL4N\nC/rp/Ip3l56byd1OLFmzqufYAAAgAElEQVRREVcdgQRB+NAUhZ+XarDuqBnjcmRRaxoIxAcHGzE+\nV45SPxsMKIrC0snZ+OOGavzzeDNuKY18hJOAz+u2PgtBEESskZiCCJIKu2bGWl0MntpkQLZCgIen\n5MRFIRboOTPJ8t1HsJdcAaur55gFf1w6JYQnlIA/dRH+9c9/YMHINPzfzYPwm8nZGJsjx0OLb4/b\n3CatytcZS0SGl+Wwcn8j7hmXEZPFUSiKwqxiFTaea43ae1ZWW6I6hb+nbZzZsQZjZ16Hh765gJNN\nzrC9l4fhsMtoQ3l+8kcUrHp/bWchFgBEIilmlD+AF156s3vsSpSjX6Jhmk6JSj2JKgiWzc3gz1uN\nWDgqDaOyIreQX7yjKApFqWLceVkG3r2hGEvLsmF1M3jklffjKr5odokam861gY3z6cL96encLBn3\nTwSRLKbkK2B1MTjS4Ij1ULo50mDH/lo77rwssKxzEZ/GH6fn4ptTLdhtsEZodD5mhweWwsvh/H51\n53kwQRBEPCDF2AiSXhRT0OL04omN1RieIcWvJmbFVSZcT5lJH614COUjS7B8swF2d3AF2d6mRqdJ\neRibI+9SjI7n3CatSgSDhRRjI+Wb0y1IkwliuoDIjCIVtldb0e6NfH6Vsc2F1nYGw9Kjd3e+p+3r\nnSeXYvnPxuMXY9Lx3PdGfPRDEzxM6EWGQ/V25CiEyJAn/lTe/jgdTGchtoPvv0VRjYWIlbE5Mpxr\naUdLlBfiSAYMy+GVylqMzpZh3qDoLUYV7yiKwpA0Ce4bn4lBmvjajopTxZAJaRyuj7+CSCCiHVtD\nEERoeDSFW0pT8elRc6yH0oXLy+Kvu+tx/4RMyISBR+xopAI8fnku3thdj6qW9giM0DfG57bW4Jay\n4Vj1zIMYql8fkfchCIIIBokpiJBqgwEbV6/EfxgGX6ZIYCm6HNdNHIqfl2ricopJT5lJd2k5/G1f\nA57eYsTyGXkBZdlZXQwa7F7QAUQPxGtuU55SiBqLCyzHgY7Dv10ia2v34tOjZvx5dn5MtwuNVIBh\naRJs11swsziyU64r9VZMjWJEQYfetq8p+UoMS5fir7vr8Lv1VXh4Sk5ImYyVegumDYCIAgCQSHlw\nuRxdCrIulwOAq9t0uHiJXQknEZ/GhBw5dlRbcc0QUlAMxKqDjWA5DovHZsR6KHErTSaEKc62o9nF\namw814ox2YnbyayWCHqMrUm2/RNBJJPphSq8/90R3PeHD+Fl2G7rcMTC2iMmFKWIMSkv+HO+wT/e\nfPvz1hq8PE8HdRgz6DmOQ8WuOuQphbi5NBUUFd01KQiCIPpDOmMjoCOPy3PZDeBNWQR98VUwfP85\nJquccVmI7Q1FUbh3fCbyVUL8+TsjXH52DR6oteHBry/gymtuBLsjPqMHAiET8iAR8GCyk+6vcFt9\nyITpBUrkq2K/ONqsEjU2Rni1bI7jsE0f3YgCf6RI+Hhieh6uGpyCJzZW418nzGDYwLtk3QyLPTU2\nTMkfGMXYu+9ZgM2Vb/1YgPUVYjdXvoXHf/eruI1dCbdyElUQsP/+f/buO67Jc+0D+C+DAAECskWG\nOFDAgVqtuHFbtW9tK3rsOD12vLa2dhw7TlvX8bSe02XV2trpqUetq2prtahYByAqihsBcbA3YoAA\nCSTvH7zJMRJGICQQft/Px4/65Bn3k1y5k1zP/Vx3WinOZZfjrVHd2tVdMu2NofIqln4fje0uQ2JO\nRatKOFlSsUKFil5jceeP/7Sr55WIGpeTnYXsmL3IDZqOkoGRSA6YggUr1yIjM9Mi7blRUoUjN+/i\nhQe8Wr2vMd1lGNtdhn+dyIaq1nR3qG2/UozCChUWDvfuUL+/iajzEC1fvny5pRthTcrKyrBy9Vco\n7jdLN+pAKLKBjV8Ibhzdg8njRlm4hcYRCAQY4uOIywUKHL5xt9ERfZUqNb47l49fr5Xg1RFd8egg\nf4weFIwbR/cAWZfgr7iNla893y5KDxjrXHY5uskkVjnBiq2tLZRK89+eeLOkCpsvFeLt0d0gMfEE\nUi3R1UmCLRcLMdTXEbJWTl7XkPTSahy9eRd/GezZ7r4YCgQC9HS1wwg/J/x8tQRHb91FPy8pHI24\n9SwhuxwF5SrM6Ovahi1tnDnj2dnZGQPCeuPw0Z9wKyMBpRVX8Mprf0ZoSAhGh1lH39cUTwcbbLpQ\ngDGBMqPunuisruYr8NWZPKyY4Ad3h6ZLeViqf24PnJ2d2937SCIW4vadapSrahFkpgkfTeX2nSos\nPZKJyaHdsOihIbh5bK/Zn9fOHM9kfcwZzytXfwX5wEf1flvCJ9givy1r1Br841gW5vR3R7Bn8ybt\nako/LynO5pTjYp4Cw3wdW/0dOS5djt1Xi/H3Cf71vsc6OXWOAQNE1P6xTEEbsLZ6XCJh3ayXn8bl\n4KOYbLw92hc2Iv0PyeTCSnwen4O+7vb4fHqg7oOvvZYeMJafswSZd5UY7GPpllgHjaauBMa8Ae5w\ntG0fCRyxUIBxgc44cuMu/jyobW4djkkvw8gAWbtLxN7L20mCf0z0xy/JJVgclY4/D/LAhB7OzWpz\nbLoco9rZqN+25ufnh6XL3qy33Fr6vqbYiAR40M8Jcell+J9gyyXhO4L8ciU+js3G6yN94Cuz/N0A\nHUF7fB9N7OWMjYkFmB7UpV335fdKzCnH5ydz8dwDXhjTva6Pbm/PKxE1rD39ttx7rQTOdmJEBJru\n+55QIMBr4T742+F0/Jp8p1XfJ9KKq7AhIR8rxvuhiz1THUTUfrGHagNuUonV1eMSCQV4Y6QPPorJ\nxtI9Z6G8Eo0ShQpd7G0QMGIaEssdsGCoN8Kt9PZkX2db3Gqj4vKdUVxGGSpr1JjUxvVZjTWppzPe\nj87AEwM99CaYMwWNRoPYdDneHNXNpPttCyKhAI+GuGFwVwesPpmL01nlWDjMGy6NfKmtqlHjXE4F\nnjfBLWvUsfSykePj1f/BXhdJu6hj1x4pVLX44Fg2Hu/nhkEduN4oAf29pFCo1LhRUo1ebnaWbk6T\nDl4vxdZLhXhnTDeEmGgUGxGZV3v5bZktV2LvtRJ8OjXA5Bej7G2EeHeML946lA5fmQRDWjCxb0ll\nDT48kYWXhnmjh2v775+JqHOz/L3BVqg91jkzBbFQgLkBtTi2dytSAqaiZGAkUrtPxbbNP2LxALHV\nJmKBukm8Mu92zJHN7U11jRr/TizA80O82l29RF9nW3g7SXAuu9zk+04rqUvm93TtOCPiunexwydT\nA+DvbIvXDtzCqcyyBtc9m12OIDc7OJtw8gVq/zIyM7Fuw3eQhj/WLurYtUe1ag0+i8tFXw97TA/i\nRGcdnVAgwIQezoi+UWrppjRKrdHgx/MF2HutGKsmBTARS9SBGfptWRD9I3wenAK1xvga/y2h1miw\n/nQuIvu5wcuxbZLAno42eHu0D9bE5yLzbrVR21bXqPHh8SxM7eVi1b9Jich6MBnbBvz9/LBhySL0\nTT8I14s70Df9IDYsWWQVI4W+/PdPcJvwtO7KrEhiD7fxT+PHLdst3LK25edsi6y71dCY6QuPNdud\nVIwgd3uEerXPH4YTezrjcBtM5BWbXobR7bxEgSE2IiGeCvPA26O7YWNiAdbE56BCWX/ymth0OUZ3\n71wlCghY+/0WCEfM0/tMEIbPw9rvt1i4Ze3H5ouFqFTV4vkHvDrc+58MG9/DGbHp8mZPbGpu1TVq\nfBybg+TCSvxrSnf4yDrunVlEZPi35b///hoKRa74KCbHLH3RobRSqGo1bX5RMdhDij8P8sA/jmVB\n3szJEjUaDdadykVXRwlm93Nr0/YREZkKhzC1kfZY58wU2lPNInNysRNBA+BudS1cOPKvxQorVNif\ncgefTQu0dFMaNNJfhh8SC1BSWQNXE9WaUms0iEuX4/1xvibZnyUEe0rx+UOB2JhYgNcO3MIrw7ti\ngLcDMjIz8dl3mxGfLkeuvwxBzz9pFReeqHk662dCcx29eRcnM8rw8ZSAerXWqePycLBBbzd7xGeW\nYVygs6Wbo6e0qgYfHMuCt6MEKyb4QSLiuAsia2Dot+Xfu6ux9lQe3o/OwHvjfNvsN0qxQoUtF4vw\nj4n+ZrmrbUJPF2TcVeKjmGwsH+/XZOmwnVeLkVeuwgcT/XnRk4g6DH5DI6O4SSW6W2S0Ono93OYQ\nCATwldkii6UKWmVjYgGm9+kCT8emZxG3FHsbIUb4OeGPm6YbHZtSVAlbsRABLh2nRIEh9jZCvPSg\nN/53qDdWn8zFJwfOY8Hf1yKt+1R4T/ozbgRO5S3qnUxn/UxojpSiSmxMLMB7Y30h40U8qzOxV9vc\nRdEamXer8dbBdIR1dcAbI7syEUtk5WxEQrwxoivCujrgrYPpRt/a3xwajQYbEvIxLcjFrN9jnw7z\ngK1IgO/O5je6XnxGGQ5eL8W7Y31hK2afR0QdB3ssMoq11sNtDl9nSZt8yeksruYrkFJUiUdD2v/t\nQ5N6ueDIjVKTlaWITS/D6O4dr0RBQx7o5og10wNx5Lc9vEW9kzP0mZB/+EcMjJhh4ZZZVmGFCv88\nkY1F4V3h38EvwpBhw7o5IbO0Grll7eMi7aW8Crx3OAOR/dzwxEAPq/m8IaLGCQQCPDHQA3P6ueG9\n6Axcyqsw6f7jMsqQI1didqh5v7+LhAL8dZQPrhQocCD1jsF1bpZU4aszeXh3rK/J7mYjIjIXJmPJ\nKNZcD7cp/s62yJS3jx9dHU2tWoNvz+XjmUGeHeKqdZCbHURCAZIKKpteuQm1ag3iMsowKsC6JhOQ\n2Yrg7SjmLeqdXEOfCcdK7HCknU9w1FaqatT44HgWHu7bBQ+0YDZo6hhsRAKMDZQhuh2Mjv3j5l18\nEpuDxaN8MLGni6WbQ0QWMKGnCxaP9MEncTkmu7tLXl2L787m45XhXWFjgZH2UhsR3h/ri+2Xi3Ah\nVz/JfKeyBh8ez8L/DvNCT1c7s7eNiKi1eAmJjGat9XCb4iuT4FxOeZseIyMzE2u/34JihRJuUgkW\nPftEqxPdbbFPY0XfuAt7sbDDJCQFAgEm9XTB4RulrZ5oLKlQARc7EXxl1jc6zk0qQaGyUi8hy1vU\nOx9DnwkrulVjSXQmbERCjOkEE7vd28/mlddg+JSH8Uhwd0s3i9rYpJ4uWPZHJuYNcDdLHcX7aTQa\n/HS5CEdvyvGPSf7wd7a+zxkiar4B3g74YKI/Vh7LQm6ZEvMGuLdqlPzGxHyMCJChr4d90yu3EW8n\nCRaP8sHHsTl4JViE7Tt2oKiiGhl3VXhs9myM9Lf+7xhEZJ3a/xA1onbC11nSpjVjMzIzsWDlWiQH\nTEHJwEgkB0xpdf3Nttinscqra7H1UmGHm0l8XKAMZ7LKUaFs3kyuDYlNL8OoAOv8otiZy5ZQ43xl\ntlg+3g8/nMtHfEaZpZvTpu7vZ0VDZ+H0b9uRmZVl6aZRG/N3sYW7VIzzuaa9Lbg5VLVqrD6Zi8Sc\nCnw8JYCJWCICAPg52+KjKQG4kFuBz07mQlWrbtF+zudW4Eq+Ak8N9DBxC43X38sBkz2UWPhB3Wft\nnbA5kIY/hv07NnOeAiLqsETLly9fbulGWJOyMuv+0dmZSW2E2HqpCA8FdWmTSTFWrv4Kxf1m6UYZ\nCkU2gE8w4n/bAfegQcgorTb6zydffI2qwY/V2+eNo3swedwoAICtrS2UyrZLMm+6WIiujhJM7t2x\nbp20EwtxvbgKylo1erm1bERArVqDL07l4dkhnnC0FZm4hZbn7OyM0WF18YSsS/BX3MbK1563aNmS\nto5naj4XOzEGeDvgk7gc+DnbwkdmnSOm/776S5QY6Lvv7WdbivHcMcRmyDHajBfdyqprsfJYFsRC\nId4b69thPl8Yz2RN2nM824mFGNtdhriMMkRdL8UwXyejyoRVqtT4+9EsvDjMu93UPd+08QdUNvGb\npjmcnDrGXXpEZP1YpoComYQCQd3oWLkSfdxNf7tOsUJpsP5m9t0qxGe2LMmffbcKjhas6Zl5txrH\nb8nxxYxAsxzP1Cb1dMZPl4swtXeXFm1/KV8BT0cbeDtZZxIK6LxlS6h5erja4f2xvvjHsSy8MdIH\nYV0dLN2kFtNoNLhTVYuM0mqkl1Yj427d3zG35ejam7WTO6tRAU749/kClFbWwMUME8jklinx96NZ\nGObriD8P8oCwA91xQkTmYysW4q3RPth0vhBvH0zH0ghfdG3m99EtlwoR4mGPwT7tp+55Q7+T+FlL\nRB0Vk7FERvCV2SLrbnWbJGPFIhFqDdTfHOTjhLdGd2vRPguOOCHZwD7VaPsfbxqNBt+dK8Dsfm5w\ntuuYXU1YVwesP5OH23eq0L2L8ZMDxKabd7QUUXsU5G6Pd8Z0wz9PZOOt0d3Qr5V1mFujuTW0y6tr\ndcnWjLt1dxqk/3+ZmgAXWwQ4S9DbzQ4TezhDdVKGG6yd3GlJbUR40NcJR2/dxawQ0882fm/MioVC\n3O0xBn8Z1x/Tglp2kZCIOg+hQIBnBnvC28kG7xxKxzujuyHYs/HP4JSiSsTelmPt9PY1kILzFBCR\ntWGZAhNjmQLrlimvRklljclHd90oqcK+XDHK4nZB5BsMochGV39z5WvPw9nZuUX77dszAPu2fAf4\n/Hef1TFb4DB4GtIrxejvJYWT1K5NbrNKyC5HfEYZFoV37bAjd4QCAcqVtUgtqjJ6dICqVoMvTufi\n+Qe84CDpGLeQWoP2fNtgZ+bhYIOernb4ODYHwR5SuDvYmL0N2tquxf1mobpbGAqdArBv83fw7R6I\nG+UiHL8txy/XSrDpfCF2JZXg1p0q1Grq6u+NCJBhXn93/GmAOyb0dMGQbo7o5WYPDwcbhPbqXq+f\nbW3frcV47hhktiJsv1yMh4JcTFob/f6YlXcJRNnJXXh+0uBWx5YlMJ7JmnSkeO7lZo8AF1t8EpsD\nd6kNAhooPaCq1WDlsUw8GeaJoDYYeNIahn7TtOSzlmUKiKi9YDLWxJiMtW7y6lpcyK3AmO6m+xGU\nXlqN5X9kYtG43ngqIsyk9TcN1fT88I3n8adRobiar8D35wrQ080BbnamTZaqatX44Hg2nn/AC91k\n7aPWVEt5Otjg23MFmNmni1GzZSfmVCCnTImHg13bsHV0v47046iz8XKUIMDFFh/H5aC/lwNcpeYd\nMd9QXe5Du3+CS68weDraYGg3RzwW6oqnB3lgSu8ueNDXCX3c7eHtKIGdjdBgoq0taycznjsGd6kY\n+1NL0bOLHTxMdKFBoarF3z5aj4qwR/ViVuRrmnrElsB4JmvS0eK5q5MEg7o6YO2pXChr1QjxsK/3\nmbbzSjGUtWo8OdCj3U26a6rPWiZjiai96Jj3DhNZiJ9Mgsy7pvvilXW3Gsv+yMSzQ7wQ7ucEwMnk\n9Tcbqum5YJg3huWU459Hb2K4ryOeDvMwqrh/Y/Yl34Gfs6Rd1ZpqKW8nCbq72OJ0VjlGGVFyIDZd\nbtT6RJ3BYB9HLBzmjZXHMrFivF+Lyn+0hEajwa07lRAZqO0a6CLBGyN9WrV/1k7u3AQCASb2cMbh\nG3ebvAX4fspaNbLuKnVlMdL/fwJOeXUtcgsq4NqPNRKJyDS6d7HDR1MC8I9jWcgrV+HFYd4Q//9A\ng4zSauxPvYPVD3Vvd4lYLX7WEpE1YTKWyAjeThIUK2pQXaNudeIyt0yJpX9k4ukwD4zpbpmk3WAf\nR3w32wOfHk3D67/fxusjuqK3W+tuSyqprMHuayX4aHKAiVppeRN71v3Ibm5ytbpGjYTscjwz2LON\nW0bU8Tzo5wSVWoPlR7OwcoIf/JzbdvT8xbwKbLlYiGJFLRxZb47aSG+JHB998y3O7raFp4NtvXrE\ntWoNcsuVumRremldArawQgVvx7rbhv1dbDGllwv8XWzh5WiDt5JlBuu+M2aJqKXcpDb4cFIAPo3L\nxuIdp6FJOoq7lSpkyJX4y7y5cJeav4wQEVFnxGQskRHEQgG8HG2QU6ZEYCtGdBWUq7D0SAYi+7lh\nfA/L1n2T2YmxeFQ3nLgtx8pjWZjW2wWz+7nrrpQb6z8XCjGppzN8ZNbzY3G4nxO+PVeAgnIVPB2b\n/pKamFOBnq526GKGmbWJOqJRATLUqDVYdiQTH0zyb/YMz8a4VqDAlktFKFao8KcBHnhpyQt46R/r\ngPB5EEnsdfXmFi1ZZPJjU+eSkZmJtz/+Eu5j5uGuxB4lykr8ZekazHnyaVTYuSP9bjWy5Uq42ovr\nkq7Othjh74Q/ubjDx0kCG5Hhz9tFzz6BBSvXMmaJyKTsbYR4IlCDp37YAZeIpyCS2EOqrMRPm3/E\n2B6LTFJmh4iIGifQaDQaSzfCmuTk5Fi6CdTG/nkiGyP8nVo8mrVYocK7hzMwo08XzOxr+XqiTk5O\nulrHxQoV1p3KQ1l1LV4f0RW+Ro5YSy2qxIcnsvHlzEBIbaxr0qqvE/LgbCvG3AHuTa77UUw2Bno7\nYEpvFzO0jO51bzxT+3corRQ7Lhfhg0n+8HI0TUI2rbgKWy8VIvNuNeb0d0dEoLOu3vO9M9O7SSX1\nRi+2N4znjmHx8n8iOWBKvRGsNud/wcJXX0OAiy38nG1h14I7ajpazDaG8UzWpKPHc0P9Vt/0g1Zd\nCsDHp3VliYiITIXDtoiM5OcsQZa8ukXbllbWYMmRTEzu5dIuErH3c5PaYFmEL6Kul+KdwxmY088N\n0/t0gbAZtaPUGg2+PZuPpwa6W10iFgAm9XTBqhNZiOzv1ujzUalS43xuBRYM9TJj64g6psm9XKCq\n1WDp/4+Qbc3tkeml1fjpUiFSiqowu58b/jamG2xE+skv1pujtlCsUOolNIC62q4udkJM7Nm6i3KM\nWSJqCw31W6xJTURkHqaZrYeoE/GVSZDVgkm85FU1WHokE2MCZHgs1K0NWmYaAoEA04K64KPJAYhJ\nl2PZkUwUVqia3O74LTk0ACIsXHahrfRwtYOjRIRLeYpG10vILkdfd3vI7Hiti6g5pvfpgqm9XbAk\nOhN3KmuM3j5HrsSncTlYciQDfT3sseHhHngoqEu9RCxRW3GTSlCrrNRbxtquRNSesd8iIrIs/lIh\nMpKfsy0y7xo3MrZcWYtlf2RiSDcHzOnffhOx9/KRSbBqUgAGeEvxxu+3cfTmXTRU1aRSpcamC4V4\nbohXs0bRdlQTe7rg8I3SRteJTZdjVICTmVpEZB1mhbghIlCGpUcyIK9qXkK2sEKFdady8dahdPjJ\nJNjwcA88EuzW6skViYy16NknoI7fqkts6Gq7PvuEhVtGRGQY+y0iIsviLxYiI3WTSZBXrkKtunnl\nlhWqWqz4IxOhnlI8HeYBQQdKVoqEAszu544V4/2wO6kY/4rJxl0DiZJdV4sxwEuKvh72BvZiPcZ2\nl+F8TgXk1bUGH69Q1uJyvgIP+jEZS2SsyP7uGObrhKV/ZKK8gfcYAJRU1uCbhDy8fuAWXOzE2DCz\nByL7W2d5FOoY/P38sGHJIvRNPwjXizvQN/0gNizhJDhE1H6x3yIisixO4GVinMCrc3jhlxtYFuGH\nbrLGb+WpqlFjxR+Z8HO2xYvDvNplIra5ExAoa9XYcrEIx2/LsXCYN7w0d7D2+y3ILavCzTtKbHjn\nOQwM6mGGFlvWp3E5CHKzM1jz94+bdxGfWYb3xvpaoGUEdPwJNTo7jUaDjYkFSCqsxPNBwLebtukm\nLpr/5ByckTsg+kYpxvdwxqOhbnCx8nIgjGeyJoxnsiaM546JE3gRUXvBkbFELVBXN7bxUgXVNWp8\ncDwL3k4SLGiniVhjSERC/GWwJxaP9MHnBy9g3rurkRwwBeWD58Jt9Gz87ZOvkJGZaelmtrlJPZ0R\nfcNwyYbYdDlGB8gs0Coi6yAQCPCXwZ5wU5Xg6aVrkBwwBSUDI5EcMAVPL1mD3OwsrJkeiPlDvKw+\nEUtERERERNaJyViiFqirG9vwJF6qWjX+FZMNZ1sRXn7Q26rqqPbzksIjIwYu45/SzcIqkthDGD4P\na7/fYuHWtb1+XlJU1qiRVlKlt1xeXYtrhZUY2s3RQi0jsg4CgQBF5w7CY8LTen2M9+Q/o/jcIbhJ\nbSzcQiIiIiIiopZjMpaoBfycJciUGx4ZW6PW4OPYHNiIBHhthA9EQutJxGrdrVTpkiRaIok9ihUN\nJ6ithVAgwMQezjicdldv+anMMoR1dYC9DbtVotYqUXTePoaIiIiIiKwbswZELeArs0WWgZGxtWoN\nVp/MQa1ag8Uju0FshYlYAHCTSnSzr2rVKivhJm28hq61GN/TGXEZclTXqHXLYtLlGB3AibuITKGz\n9zFERERERGS9mIwlagFfZwmy5Eq9uqFqjQbrTuWirLoWb4/pBhuRdSZiAWDRs09AHb9VlyypVVZC\nHb8Vi559wsItMw93qQ36uNsjLqNu4obSyhrcKK7CEB+WKCAyhc7exxARERERkfViMpaoBRwlItjb\nCFGkqAFQNwP4hjP5KKhQ4d2xvpCIrPut5e/nhw1LFqFv+kG4XtyBvukHsWHJIvj7+Vm6aWYzsacz\nom+UAgDiMsowpJsjbMXW/boTmQv7GCIiIiIislYCjaEpwanFcnJyLN0EMoOMzEw8+8E3cLYVIqCL\nHbyGTkWRuAuWj/eD1EZk6eYZxcnJCWVlZZZuRoejqtXgT9/HwDM9FlfzyxHoao+/v/IMk0UWxngm\na8J4JmvCeCZrwnjumHx8fCzdBCIiABwZS2S0jMxMLFi5FrYPPgrVsD8hJWAqft66CX/pjQ6XiKWW\ny83JQn7sXmT0mgaXiKdRFDwDC1auRUZmpqWbRkRERERERETtFJOxREZa+/0WCMPn6Wb6Fkns4THx\naXy/aZuFW0bmtPb7LXCOeEovDoTh87D2+y0WbhkRERERERERtVdMxhIZqVih1CXgtEQSexQrlBZq\nEVkC44CIiIiIiIiIjMVkLJGR3KQS3QzfWrXKSrhJJRZqEVkC44CIiIiIiIiIjMVkLJGRFj37BNTx\nW3WJuFplJdTxWyYQyxYAACAASURBVLHo2Scs3DIyJ8YBERERERERERlLoNFoNJZuhDXJycmxdBPI\nDDIyM7H2+y0oVijhJpVg0bNPwN/Pz9LNahHOBtty1hQH1oLxTNaE8UzWhPFM1oTx3DH5+PhYuglE\nRACYjDU5JmOpo+GXSbImjGeyJoxnsiaMZ7ImjOeOiclYImovWKaAiIiIiIiIiIiIyAyYjCUiIiIi\nIiIiIiIyAyZjiYiIiIiIiIiIiMyAyVgiIiIiIiIiIiIiM2AyloiIiIiIiIiIiMgMmIwlIiIiIiIi\nIiIiMgMmY4mIiIiIiIiIiIjMgMlYIiIiIiIiIiIiIjNgMpaIiIiIiIiIiIjIDJiMJSIiIiIiIiIi\nIjIDsaUb0BYuX76MqKgoXL9+HRUVFXB0dIS/vz+mT5+OsLAwvXVTUlKwe/duXL9+HSqVCt7e3oiI\niMDUqVMhFDJXTURERERERERERKZhdcnYzZs3Y9++fXBzc8MDDzwAmUwGuVyOW7du4erVq3rJ2ISE\nBHz22WeQSCQIDw+Ho6Mjzp07hx9//BEpKSl4/fXXLXgmREREREREREREZE2sKhkbHR2Nffv2Ydy4\ncXjhhRcgEon0Hler1bp/V1ZW4uuvv4ZQKMTy5csRGBgIAJg7dy5WrFiBU6dO4eTJkxgxYoRZz4GI\niIiIiIiIiIisk9Xch19TU4Pt27fD3d3dYCIWgF7Zgfj4eJSVlWHkyJG6RCwAiMVizJkzBwBw+PDh\ntm84ERERERERERERdQpWMzL20qVLkMvlmD59OgQCARITE5GZmQkbGxv06tULQUFBeutfvXoVAOrV\nkAWAkJAQSCQSpKSkoKamBmKx1TxNREREREREREREZCFWk2VMS0sDUDey9a233kJmZqbe48HBwXjj\njTcgk8kAADk5OQCArl271tuXUCiEp6cnsrKyUFBQAB8fnzZuPREREREREREREVk7qylTIJfLAQD7\n9u2DQCDAypUrsWnTJnzyyScYOHAgrl27htWrV+vWVygUAACpVGpwf9rlFRUVbdxyIiIiIiIiIiIi\n6gysJhmrnZxLJBLh7bffRlBQEGxtbeHn54fFixfD1dUVSUlJuH79ulH7FQgEbdFcIiIiIiIiIiIi\n6mSspkyBg4MDACAwMBDu7u56j0kkEgwcOBBHjx5FWloaevfurRv5qh0he7+mRs4CdXVntbVnASAy\nMpIlDahDcnJysnQTiEyG8UzWhPFM1oTxTNaE8dwx7dixQ/fv0NBQhIaGWrA1RNRZWc3IWG0StKHk\nqaOjIwBAqVTqrZ+bm1tvXbVajYKCAohEInh6ejZ4zNDQUERGRur+EHVE934hIeroGM9kTRjPZE0Y\nz2RNGM8d172/35mIJSJLsZpkbP/+/QEAWVlZBh/XTuilTa7269cPAHDhwoV66yYlJUGpVKJPnz4Q\ni61m8DARERERERERERFZkNUkY93d3TFkyBAUFRVh//79eo9dvHgRFy5cgKOjI8LCwgAAw4cPh5OT\nE+Li4nDz5k3duiqVCtu2bQMATJ482XwnQERERERERERERFZNtHz58uWWboSpBAcH4/Tp04iPj0dy\ncjIyMzNx+PBh7NixA0KhEC+//DK6d+8OALCxsUHXrl1x8uRJxMTEID8/H8nJydi0aRPS09MRHh6O\n2bNnW/aEiMyksXIcRB0N45msCeOZrAnjmawJ45mIiFpKoNFoNJZuhCmVlZVh165dOHv2LEpLS2Fv\nb4/g4GA88sgj6NmzZ731U1NTsXv3bqSmpkKlUsHb2xsRERGYNm0aBAKBBc6AiIiIiIiIiIiIrJHV\nJWOJiIiIiIiIiIiI2iOrqRlLRERERERERERE1J4xGUtERERERERERERkBkzGEhEREREREREREZmB\n2NINIKKWW7hwIYqKigw+5uLigq+//rre8pSUFOzevRvXr1/Xm7Ru6tSpEAoNX585d+4c9u3bh9u3\nb0OtVsPPzw+TJ0/G2LFjTXo+1DmcOnUKSUlJSE9Px+3bt1FVVYXRo0fj5ZdfbnAbc8XtsWPHcOjQ\nIWRlZUEoFCIwMBAzZ87E4MGDW33eZH2MieXCwsJGY3zEiBF49dVXDT5mbFyq1Wr8/vvvOHbsGHJz\ncyGRSNC7d2889thjCAoKatnJktUrLy/H6dOncf78eWRkZKCkpARisRj+/v4YN24cIiIiDE5uy/6Z\n2iNj45l9NBERmZNo+fLlyy3dCCJqmQMHDgAAHnnkEYSGhtb7c/8XuoSEBKxatQp37tzB8OHDERIS\nguzsbMTGxiI7Oxvh4eH1jhEVFYX169ejuroao0aNQq9evZCWloYTJ06guroaAwYMMMu5kvVYu3Yt\nzp07h/Lycri5uaGsrAwBAQEYNmyYwfXNFbebNm3C1q1bIRQKMWbMGPj5+eHKlSs4evQoZDIZevXq\nZfLngjo2Y2JZoVDgwIED6N69OyZNmmSwz/b19a23XUvicvXq1Thw4AAcHBwwZswYeHh44MKFCzhy\n5Ai6d+8OHx+fNnk+qGM7fvw4vv32W1RWVqJv374ICwuDh4cHkpKScOrUKWRmZtbrb9k/U3tlbDyz\njyYiIrPSEFGH9dJLL2kWLlzYrHUVCoXm2Wef1cybN09z8+ZN3XKVSqV5//33NZGRkZq4uDi9bQoK\nCjTz5s3TzJ8/X1NYWKhbXlFRoXnllVc0kZGRmtTUVNOcDHUaV69e1eTm5ur+HRkZqVm3bp3Bdc0V\ntykpKZrIyEjNokWLNBUVFbrlhYWFmvnz52ueeOIJvX0RaTTGxXJBQYEmMjJSs379+mbvvyVxGRMT\no4mMjNQsXbpUo1KpdMtv3LihmTdvnua5557TVFZWGnOa1ElcuXJFc+7cuXrLS0tLNS+++KImMjJS\nc/r0ad1y9s/Unhkbz+yjiYjInFgzlqiTiI+PR1lZGUaOHInAwEDdcrFYjDlz5gAADh8+rLfNH3/8\ngZqaGkybNg3u7u665VKpFLNmzTK4DVFTQkJC4O3t3ax1zRW3hw4dAgA8+uijkEqluuXu7u6YMmUK\nVCoVjh49asRZUmdgTCy3REviUhvbc+bMgVj832pUPXr0QHh4OORyOU6dOtVmbaaOKzQ01OBt1c7O\nzpg0aRIA4OrVq7rl7J+pPTM2nluCfTQREbUUk7FEHZxKpUJMTAz27NmDAwcO4OrVq1Cr1fXW037h\nDAsLq/dYSEgIJBIJUlJSUFNTU2+bgQMH1ttm0KBBAIArV66Y5DyIDDFX3Da2jfbYjHUyhTt37iA6\nOhp79uxBdHQ0MjIyGlzX2LisqalBamoqbG1t0bdv33rbsN+mlhKJRHp/A+yfqeMyFM9a7KOJiMgc\nOIEXUQdXWlqKL774Qm+Zp6cnXnzxRYSEhOiW5eTkAAC6du1abx9CoRCenp7IyspCQUGBrlZVY9u4\nuLjA1tYWxcXFUCqVkEgkJjsnIi1zxG11dTVKSkpgZ2cHFxeXetto95Obm2uy86LO69KlS7h06ZLe\nspCQECxcuFBvpGBL4jIvLw9qtRpeXl4GJ05iLFNLqNVqHD9+HIB+4pX9M3VEDcWzFvtoIiIyByZj\niTqwiIgIBAcHw8/PD3Z2digoKEBUVBQOHz6MVatW4YMPPoC/vz+AuokJAOjdRnUv7fKKigrdsuZs\nU11dDYVCwWQstQlzxG1LjkFkLFtbWzz22GMYNmwYPD09AQAZGRnYuXMnrly5gpUrV+Ljjz/W9aVt\nFfv3rkfUHJs3b0ZWVhYGDx6sN7kW+2fqiBqKZ/bRRERkTixTQNSBPf744wgNDYVMJoNEIoGvry+e\ne+45zJgxA0qlEjt27DB6nwKBoNnrajQao7chagvmiFvGObWGTCZDZGQkunfvDqlUCqlUir59++K9\n995Dr169kJeXhyNHjhi935bEPlFzHThwAPv374evry9efvnlFu2D/TO1F43FM/toIiIyJyZjiayQ\ndmKCa9eu6ZY1dbXd0NX6praprKwEANjb27eyxUSGmSNuW3IMIlMRCoWYMGECAPP12Yxlao6oqCj8\n+OOP8PPzw9KlS+Hg4KD3OPtn6kiaiueGsI8mIqK2wGQskRVydnYGUFfPSktbr81QHSq1Wo2CggKI\nRCLdrVlNbVNaWorq6mq4ubmxRAG1GXPEra2tLVxdXVFVVYXS0tJ622j3Y6jGIZEpyGQyAPp9dkvi\n0tvbG0KhEPn5+QYncmQsU3Pt378fGzduhL+/P5YuXar7XnEv9s/UUTQnnhvDPpqIiEyNyVgiK5SS\nkgIA8PLy0i3r168fAODChQv11k9KSoJSqUSfPn0gFoubtU1iYqLeOkRtwVxxGxoa2uA258+fBwD0\n79+/JadA1KTU1FQA0EtaAcbHpVgsRlBQEKqrq5GcnNysbYjut3fvXmzatAmBgYFYtmyZLhF1P/bP\n1BE0N54bwz6aiIhMjclYog4qOztb7wq9VmFhIX744QcAwOjRo3XLhw8fDicnJ8TFxeHmzZu65SqV\nCtu2bQMATJ48WW9f48aNg1gsRlRUFAoLC3XLy8vLsWfPHgD/LYlA1BbMFbfafezZs0dvso2CggIc\nPHgQNjY2GDdunEnPjTqXW7duGawHeOXKFezfvx+Afp8NtCwutdts27YNKpVKtzwtLQ3x8fFwdnbG\ngw8+aJJzIuuza9cu/PTTT+jZsyeWLFkCR0fHBtdl/0ztnTHxzD6aiIjMSaBhpXCiDmnnzp347bff\nEBwcDHd3d9jb2yM/Px+JiYlQqVQYPHgwFi9eDJFIpNsmISEBn332GWxsbDBixAg4Ojri3LlzyMnJ\nQXh4OF577bV6x4mKisLGjRvh6OiIESNGQCwW49SpUygpKcHMmTPx5JNPmvO0yQokJCQgISEBQN1t\nqRcvXoSnpyeCg4MBAE5OTnjqqaf01jdH3G7atAn79++Hq6srhg8fjpqaGpw8eRLl5eV49tln6yUV\niIyJ5RUrViA3NxdBQUFwc3MDUDdT95UrVwAAc+fOxaxZs+odoyVx+dlnn+H06dPw8fHBkCFDUFZW\nhvj4eKhUKixevBhDhgxpk+eDOrZjx47hq6++glAoxNSpUw3WrfTw8NBLLrF/pvbK2HhmH01ERObE\nZCxRB5WUlITo6GjcunVLV2fNwcEB3bt3x5gxY+pdvddKTU3F7t27kZqaCpVKBW9vb0RERGDatGkN\nzviamJiIffv24ebNm9BoNPD19cXUqVMxZsyYtjxFslI7d+7Erl27Gnzc09MT69at01tmrrg9ceIE\noqKikJWVBYFAgB49euDhhx/GoEGDWnayZNWMieWjR4/izJkzyMzMRFlZGWpqauDi4oKgoCBMmTIF\nffv2bXA/xsalWq1GVFQUjh49iry8PNjY2CAoKAiPPfYYevfu3bqTJqvVVDwDQEhICJYtW6a3jP0z\ntUfGxjP7aCIiMicmY4mIiIiIiIiIiIjMgDVjiYiIiIiIiIiIiMyAyVgiIiIiIiIiIiIiM2AyloiI\niIiIiIiIiMgMmIwlIiIiIiIiIiIiMgMmY4mIiIiIiIiIiIjMgMlYIiIiIiIiIiIiIjNgMpaIiIiI\niIiIiIjIDJiMJSIiIiIiIiIiIjIDJmOJiKhF5syZgzlz5qCoqMjSTem08vLy8Pnnn+OFF17A3Llz\nMWfOHHz55ZeWbla7cezYMcyZMwcrVqww63HXr1+POXPmYNeuXWY9rhbfm6Zn6deUiIiIiKyH2NIN\nICKitvfll1/i+PHjAAChUIivv/4aMpmswfXPnDmDTz/9VPf/l156CWPHjm3zdraGQqHA/v37AQCz\nZ8+2cGvaXnl5OZYsWQK5XA4AcHJygkgkgoODAwAgISEBt2/fRmhoKEJCQizZ1E5HIBBYugkdyoED\nB1BRUYGIiAi4u7ub/fg7duyAQCDA9OnTIZVKDa7TXl/ThQsXoqioCLNnz8bjjz/e4HoVFRU4cuQI\nLl26hOzsbMjlcojFYri4uKBnz54IDw/HkCFDIBT+d5xGUlKS7kLG+vXrG31tjFmXiIiIqLNjMpaI\nqJNRq9WIjY3FQw891OA6J06caHI/Pj4+EAgEEIlEpmxei1VUVOhGrXWGZGxcXBzkcjl8fHywfPly\nODs76z2ekJCA48ePQyAQMBlrZi4uLvDx8YGTk5NFjt/e3ptN2b9/P4qKihAaGmqRJN7PP/8MAIiI\niGgwGduRHTlyBJs3b4ZCodAtk0qlqK2tRV5eHvLy8hAXF4euXbvijTfegL+/vwVbS0RERGT9mIwl\nIupE3N3dUVRUhBMnTjSYjC0vL8f58+dhZ2cHsViM8vJyg+utXr26LZtKTcjMzAQADBkypF4ilixr\n3rx5mDdvnsWOz/cmae3atQs7d+4EAPTu3RuPPPII+vXrBzs7OwBAWVkZLly4gP379+PWrVu4desW\nk7FEREREbYzJWCKiTiQoKAgikQi3bt1CVlYWfH19660TFxeHmpoajBw5EpcvX7ZAK1tGo9FYuglm\npVQqAUCXVCEiuteFCxd0idgJEybghRdeqLeOk5MTRo8ejdGjRyMqKgpiMX8aEBEREbU1fuMiIupk\nxowZg507d+LEiRMGR+9pSxSMHTu20WTsnDlzANSvD7hz507s2rULY8eOxUsvvYRjx47h0KFDyM7O\nBgD06NEDs2bNwoABA+rt8/5tDVm/fj1OnDihVyNx+fLluHbtWr22aRmqp1hYWIh9+/bh0qVLKC4u\nhlAoRNeuXREeHo6pU6fC1ta23rGrqqrw22+/4ezZs8jNzYVKpYKTkxNcXV0RGhqKsWPHws/Pr8Hn\n7H5lZWU4efIkLl68iNzcXJSUlECtVsPDwwNhYWGYOXMmunTporfN/ee6c+dOXcIFAJYtW6Y3YdX9\njwPA9u3b9f6v0WgQExOD48ePIz09HQqFAk5OTggODsaMGTPQq1evem2/97V68cUXcfDgQZw4cQK5\nublQKBT46KOPEBAQ0ORzUFJSgtjYWFy5cgX5+fkoKSmBUCiEt7c3hgwZghkzZjR66/idO3ewY8cO\nnD9/HmVlZXB1dcXQoUMbrZ8J6MevUqnErl27cPXqVSgUCvj4+GD69OkYM2aMbv3o6GhER0cjNzcX\nIpEIAwYMwJNPPmnwtnpDMaoll8vxyy+/4MKFCygoKIBarYZMJoO7uzv69++P8ePH19tnQkICoqOj\ncfPmTZSXl8Pe3h4ymQyBgYEYOnQoRowY0eC5GWpffn4+fvnlF1y+fBklJSWQSCTw9/fH2LFjMW7c\nOL26oVrauHvppZcQHh6OvXv34uTJkygqKoK9vT1CQ0Mxd+5ceHt7N/q830sbQ1r3T7QWEhKCZcuW\ntbrtDdG+TloLFy7Ue7yhfkitVuP333/HsWPHkJeXBxsbGwQFBSEyMhI9evRo8HhVVVU4cOAAEhIS\ndP2Hu7s7wsLC8PDDD8PNza3ZbW+OzZs3A6jrc5977rkm1586dapJj09EREREhjEZS0TUyWiTsTEx\nMfWSsbm5uUhLS4O7uztCQ0NbfawNGzbg6NGjEAqFsLOzg0KhQFJSEq5du4Y33ngDw4YNM3qfhibS\ncXJygkwm001m5eLiovf4/aNHT58+jXXr1kGlUgEAbG1tUVNTo7tNNzY2FkuWLNGb5EyhUOD999/X\nJZWFQiGkUinkcjlKS0tx8+ZNCIVCo25P37t3L3777TcAgEgkgr29PRQKBbKzs5GdnY2YmBgsWbJE\n77ZhJycnuLi4QKFQQKlUws7OTu/8tJPyNPT4/aqqqvDxxx/jypUrAOqeX3t7e5SWliI+Ph6nTp3C\nM88802ii5pNPPsHZs2chFAphb29v1GRH//73v3H69Gld27Vxcvv2bdy+fRuxsbFYvnw5XF1d622b\nlZWF5cuXo6ysDEDd61xaWor9+/fj3LlzmDRpUpPHv379OjZs2ICqqipIpVKoVCrcvn0b69evh1wu\nx4wZM7BmzRqcPHkSYrEYYrEYFRUViI+Px/Xr1/Gvf/0Ljo6Oevts6PyLiorw3nvvobS0FMB/Y+jO\nnTsoKSlBamoqXF1dMXHiRN02P/30E/bu3av7v729PVQqFXJzc5Gbm4ukpKR6ydjGnDt3DqtXr9bF\nvlQqhVKpRHJyMpKTk3Hy5Em89dZbkEgkBs9JoVBgyZIluH37NmxsbCAQCCCXyxEfH4/Lly9j1apV\n8PT0bFZb7Ozs4OLiArlcDrVaDUdHR72RmffX3G1p2xvi4OAAFxcX3eshk8n0krnayfDuVVtbi3/+\n85+4ePGiXjycP38eV69exdKlS9G7d+9622VlZWHVqlUoKioCUPd+t7GxQV5eHqKiohATE4N33nkH\nQUFBzWp7U1JTU3WlTP7nf/7HqCQ1EREREbUtJmOJiDoZT09P9OnTBykpKbhy5Qr69eune+z48eMA\ngNGjR7f6OAkJCaipqcHzzz+PMWPGQCKRoLCwEF9++SWSkpLwww8/4IEHHjBJkuCvf/0rCgsL8fLL\nLwMAvv766wbXTUtLw5o1ayAQCPDYY49h4sSJcHV1hVqtxo0bN7Bx40bcuHEDX3zxBd59913ddgcO\nHEB2djZkMhkWLlyIAQMGQCgUQq1Wo6CgAGfOnKmXlGuKh4cH/vSnP2HIkCHo1q0bhEIhNBoNbt++\njW3btuHChQtYu3YtPvnkE71zBYAvv/wSx48fx8yZM+uNvvz6668bffxe69atw5UrV9CjRw/MmzcP\nwcHBEIvFUCgUOHjwIHbu3Ikff/wRPXr0MJgoOn36NGpra/VeZ7lc3uyEmK+vL0JCQhAWFqYbValW\nq5GSkoLNmzcjLS0N33zzDd555x297Wpra/HZZ5+hrKwM3t7eePHFF9G3b18AQGJiIr766ivdxEyN\n+eabbxAaGor58+fD3d0dVVVV2Lx5Mw4fPowdO3agoqICiYmJeOWVVxAeHg6RSISUlBR89tlnKCoq\nwi+//IInnniiWee6Y8cOlJaWwtvbGy+99BL69OkDAKipqUFeXh5OnjypdyGhsLAQv/zyCwBg1qxZ\nmDFjhi7G5HI5kpOTkZiY2KxjA3WjStesWQOVSoXQ0FC88MIL8Pb2Rk1NDY4fP46NGzfi8uXL2Lhx\nI/73f/+3wXNwdHTEe++9h/79+0MgECA5ORlr1qxBSUkJtm7ditdee61Z7Zk5cyZmzpyJhQsXoqio\nCIsXL0ZwcHCbtf1+zzzzDJ555hndSOJVq1Y1OYFYVFQUhEIhXn/9dQwdOhQikQgZGRlYt24dMjIy\n8O9//xsffPCB3jYKhUKXiA0PD8ejjz4KPz8/CAQCFBQUYPv27YiNjcWnn36K1atXm2QSMe3FFaFQ\niEGDBrV6f0RERERkOrxMTkTUCWlvv9YmX7ViYmL0Hm8NhUKBBQsWYOLEibrEnIeHB1599VWIxWLc\nuXMHqamprT6OsX788UfU1tZi/vz5iIyM1I24FAqF6N27N9577z24uLjg4sWLuHnzpm6769evA6hL\nIIWFhemSyNpb6h9++GGMHz/eqLZMnToVjzzyCPz8/HT7EwgECAwMxJtvvglfX19kZmbqlSUwpcuX\nL+Ps2bPo1q0bli1bhv79++tGJkqlUsyaNQtz5syBWq3Gnj17DO6jqqoK8+fP13udZTJZs2vZRkZG\nYurUqXq3twuFQgQHB+Pdd9+FTCbD+fPnUVhYqLddXFwcsrOzIRaL8be//U2XiAWAwYMH469//ave\n7PENcXFxweLFi3VJODs7Ozz77LPw9vZGdXU1du/ejeeeew6jRo2CSCQCAPTp00eXgD116lSzzhOo\nuxAAAHPnztUlYoG6EcG+vr6IjIzEAw88oLe+RqNBt27dMHfuXL1kv0wmw7Bhw7BgwYJmH3/37t2o\nrq6Gt7c33nnnHd1zLhaLMWHCBDzzzDMAgKNHjyI/P9/gPmpqarBkyRIMGDBAN1q2b9++um3Pnj2L\n2traZrfpXo3VfTZF201BoVDg7bffxvDhw3Xx4O/vjxdffBFA3WumHf2q9euvv6KoqAijRo3Ca6+9\nBn9/f91z5+npiVdeeQVhYWEoLS3FH3/8YZJ2akfwe3t7Gyy5QkRERESWw2QsEVEnNGLECNjY2ODM\nmTO6iaCSkpJQVFSEnj17wsfHp9XHcHd3x8iRI+std3Fx0dUgzcjIaPVxjJGfn4/U1FQ4ODggIiLC\n4DoODg66kWSXLl3SLbe3twdQV6PUHMRiMfr37w8ASElJaZNjHDt2DAAwfvz4BpOno0aNAgBcvXrV\nYLLMyckJ48aNa5P2OTg46Ebj3p+41yZBH3zwQYN1Svv27dvgKMt7zZgxo97obIFAoCvT4ebmZnCk\nuPa1KSgo0L2HmmJsDGnX15acaK0zZ84AAKZPn25w5PKECRPg6uoKjUbTYJJ5+PDhBssQaJPIKpUK\neXl5rW7r/UzRdlMIDg42OEK8R48eugs7WVlZeo9pL3rNmDGjwf1q+8p7+5zWKC8vBwCjR+sTERER\nUdtjmQIiok5IKpViyJAhOHXqFE6dOoUxY8boEnNjx441yTF69uzZ4GPaSakqKipMcqzm0iY1q6qq\nGh1RWFVVBQAoLi7WLRs8eDDi4+Nx4MABlJWVYdSoUejbt2+zR4A2JCcnB7///juSk5NRUFCgO/a9\nSkpKWnWMhmgTnD///DN+/fXXRtetrq5GWVmZXh1doO51bm2pibS0NBw6dAjXr19HcXExqqur661z\nfwLz1q1bAOomeWpISEhIk6OK763Hey9nZ2cAdWUUGnscqIvj5pRlGDRoENLS0rBlyxbk5uZi+PDh\n6N27d4Pb9u7dG46Ojrhz5w7ee+89TJkyBQMGDGh2TdZ75efn60YKN1QPWiAQICQkBLGxsbrn934N\nva9FIhGcnZ1x9+5dk7+vTdV2U2isX3N1dUVJSYkuEQrU9SHa9++HH37YYD3hmpoa3fpEREREZN2Y\njCUi6qTGjh2LU6dOISYmBsOHD8eZM2cgFosNjmZticaSlNrkU0tvZ24p7UQ9tbW1uHv3bpPr3zsa\nccyYMUhJed0yHgAADIJJREFUSUF0dDRiYmIQExMDgUCAgIAADBkyBJMnT643cVhT4uLisH79et3z\nIBQK4eDgABsbGwB1SeGqqiqDyUlT0D4fzbmdXyAQGBydef8kS8b69ddfsWXLFt3/hUKh3kRO2lGh\n9yeptZO1aRP7hhia9Ot+DW2vTTA39Jrem4Bubhw/8sgjuHXrFs6ePYtDhw7h0KFDEAqF6NWrF4YO\nHYqJEyfq1Qt1cHDAyy+/rKtH+u233+raNGDAAERERDSajL6X9vkCGn9e3Nzc6q1/L+1oXUO0catN\nLJqKqdpuCo2dv6F+7d6LCM1plylGQAP/HRF7b2KYiIiIiNoHJmOJiDqpsLAwyGQyXLlyBVFRUais\nrMTQoUOt+rZWtVoNAOjevTv+9a9/Gb39888/j2nTpiE+Ph7Xrl3D9evXcfv2bdy+fRu//fYb3nzz\nTd3t602Ry+X45ptvUFtbi5EjR2LmzJkICAjQS/Jt374du3fvbrSWZmton4+33noLQ4YMadE+WjMq\nNisrC1u3bgUATJs2DZMmTYKPj4/e6MEvvvhCV8vYWG31vLWUWCzGm2++ibS0NJw5cwbXrl3DzZs3\nkZqaitTUVOzbtw9LlizRG607aNAgrF+/HvHx8bh06RJSUlJQUlKCEydO4MSJE5gwYQJeeOEFo9qh\nUqkaTSq2Zx2t7ffG4I8//tjqkfTN1a1bNwBAXl4elEplsyfUu5c2uQ40nSS+94JRS45FRERE1Jkw\nGUtE1EkJhUKMHDkSv//+O7Zt2wYABmtjmpN2QhyVStXgOs0ZxdkQ7SjH+yfYMYavry9mz54NoG4E\n3MWLF/HTTz8hIyMD69evx5dfftmsBOWFCxdQVVUFX19fLFq0yOA6zRm92xouLi4oKiqqNzmWuZw6\ndQoajQZhYWG6CZju19BzIJPJUFJS0mj9VXPV9zVWr169dHWTlUolzp49iy1btqCoqAgbNmzAhx9+\nqLe+vb09xo8fr5sgLjs7G/v378eRI0dw5MgRDB06VFfnuCH3lpcoKiqqV25CS3ubfEOPW0JHbvu9\n5SwKCgoaLIthav369cP27duhVquRmJiI4cOHG72Pe0e937lzp9Fa4tr3mnZ0PxERERE1jBN4ERF1\nYmPGjAFQl1R0dHRs8ehIU9Heot1YjdSbN28aXN5QLcZ7aSfeKS8v181s3xoikQiDBw/G66+/DqAu\nIZGbm9usbbWJo4CAgAbXuXLlSovbpn0+Ghsd2rt3bwDA+fPnW3yc1tC+zt27dzf4eHV1db2Ju7QC\nAwMB1E0815Cm6sW2BxKJBCNGjNCNbr1582aToxC7deuGF154Qff6NfYcaHl5eeneXw3FlUaj0e1L\n+/yaQ1MXL8zV9rYYSe3p6alLyF64cMHk+29IUFCQLvG7d+9e3Sh4Y3h5eelGITc1iaD2ferr66u7\nqEZEREREhjEZS0TUifXo0QOzZ8/GzJkz8ec//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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dbf02d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "figsize = (18,8)\n",
    "title = 'Vital signs (aperiodic) for patientunitstayid = {} \\n'.format(patientunitstayid)\n",
    "ax = vitalaperiodic.plot(title=title, figsize=figsize, fontsize=fontsize,\n",
    "                        marker='o')\n",
    "\n",
    "ax.title.set_size(fontsize)\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": {},
   "source": [
    "### Questions\n",
    "\n",
    "- What do the non-invasive variables measure?\n",
    "- How do you think the mean is calculated?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4. The `lab` table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT *\n",
      "FROM lab\n",
      "WHERE patientunitstayid = 141296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# query to load data from the patient table\n",
    "query = \\\n",
    "\"\"\"\n",
    "SELECT *\n",
    "FROM lab\n",
    "WHERE patientunitstayid = {}\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "print(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# run the query and assign the output to a variable\n",
    "lab = pd.read_sql_query(query,con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>labid</th>\n",
       "      <th>labresultyear</th>\n",
       "      <th>labresulttime24</th>\n",
       "      <th>labresulttime</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>labresultrevisedyear</th>\n",
       "      <th>labresultrevisedtime24</th>\n",
       "      <th>labresultrevisedtime</th>\n",
       "      <th>labresultrevisedoffset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141296</td>\n",
       "      <td>49726053</td>\n",
       "      <td>2014</td>\n",
       "      <td>12:10:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-342</td>\n",
       "      <td>2</td>\n",
       "      <td>Tacrolimus-FK506</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>ng/mL</td>\n",
       "      <td>ng/mL</td>\n",
       "      <td>2014</td>\n",
       "      <td>17:12:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141296</td>\n",
       "      <td>49726051</td>\n",
       "      <td>2014</td>\n",
       "      <td>10:40:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>1008</td>\n",
       "      <td>2</td>\n",
       "      <td>Tacrolimus-FK506</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>ng/mL</td>\n",
       "      <td>ng/mL</td>\n",
       "      <td>2014</td>\n",
       "      <td>17:31:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141296</td>\n",
       "      <td>46043498</td>\n",
       "      <td>2014</td>\n",
       "      <td>21:20:00</td>\n",
       "      <td>night</td>\n",
       "      <td>-1232</td>\n",
       "      <td>1</td>\n",
       "      <td>CPK</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>Units/L</td>\n",
       "      <td>Units/L</td>\n",
       "      <td>2014</td>\n",
       "      <td>22:13:00</td>\n",
       "      <td>midnight</td>\n",
       "      <td>-1179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141296</td>\n",
       "      <td>56145945</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1827</td>\n",
       "      <td>3</td>\n",
       "      <td>Hgb</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>2014</td>\n",
       "      <td>12:50:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141296</td>\n",
       "      <td>50351418</td>\n",
       "      <td>2014</td>\n",
       "      <td>12:10:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-342</td>\n",
       "      <td>3</td>\n",
       "      <td>Hgb</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>2014</td>\n",
       "      <td>14:11:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>-221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patientunitstayid     labid  labresultyear labresulttime24 labresulttime  \\\n",
       "0             141296  49726053           2014        12:10:00          noon   \n",
       "1             141296  49726051           2014        10:40:00          noon   \n",
       "2             141296  46043498           2014        21:20:00         night   \n",
       "3             141296  56145945           2014        11:25:00          noon   \n",
       "4             141296  50351418           2014        12:10:00          noon   \n",
       "\n",
       "   labresultoffset  labtypeid           labname  labresult labresulttext  \\\n",
       "0             -342          2  Tacrolimus-FK506        7.2           7.2   \n",
       "1             1008          2  Tacrolimus-FK506        8.2           8.2   \n",
       "2            -1232          1               CPK     2018.0          2018   \n",
       "3            -1827          3               Hgb        9.3           9.3   \n",
       "4             -342          3               Hgb        9.4           9.4   \n",
       "\n",
       "  labmeasurenamesystem labmeasurenameinterface  labresultrevisedyear  \\\n",
       "0                ng/mL                   ng/mL                  2014   \n",
       "1                ng/mL                   ng/mL                  2014   \n",
       "2              Units/L                 Units/L                  2014   \n",
       "3                 g/dL                    g/dL                  2014   \n",
       "4                 g/dL                    g/dL                  2014   \n",
       "\n",
       "  labresultrevisedtime24 labresultrevisedtime  labresultrevisedoffset  \n",
       "0               17:12:00              evening                     -40  \n",
       "1               17:31:00              evening                    1419  \n",
       "2               22:13:00             midnight                   -1179  \n",
       "3               12:50:00                 noon                   -1742  \n",
       "4               14:11:00              evening                    -221  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the first few rows of the dataframe\n",
    "lab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'patientunitstayid', u'labid', u'labresultyear', u'labresulttime24',\n",
       "       u'labresulttime', u'labresultoffset', u'labtypeid', u'labname',\n",
       "       u'labresult', u'labresulttext', u'labmeasurenamesystem',\n",
       "       u'labmeasurenameinterface', u'labresultrevisedyear',\n",
       "       u'labresultrevisedtime24', u'labresultrevisedtime',\n",
       "       u'labresultrevisedoffset'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list columns in the table\n",
    "lab.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>labid</th>\n",
       "      <th>labresultyear</th>\n",
       "      <th>labresulttime24</th>\n",
       "      <th>labresulttime</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>labresultrevisedyear</th>\n",
       "      <th>labresultrevisedtime24</th>\n",
       "      <th>labresultrevisedtime</th>\n",
       "      <th>labresultrevisedoffset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>141296</td>\n",
       "      <td>47645495</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1827</td>\n",
       "      <td>1</td>\n",
       "      <td>anion gap</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17</td>\n",
       "      <td></td>\n",
       "      <td>mmol/L</td>\n",
       "      <td>2014</td>\n",
       "      <td>12:19:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>141296</td>\n",
       "      <td>47645499</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1827</td>\n",
       "      <td>1</td>\n",
       "      <td>glucose</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86</td>\n",
       "      <td>mg/dL</td>\n",
       "      <td>mg/dL</td>\n",
       "      <td>2014</td>\n",
       "      <td>12:19:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>141296</td>\n",
       "      <td>46021317</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1827</td>\n",
       "      <td>1</td>\n",
       "      <td>alkaline phos.</td>\n",
       "      <td>512.0</td>\n",
       "      <td>512</td>\n",
       "      <td>Units/L</td>\n",
       "      <td>Units/L</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:45:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>-1507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>141296</td>\n",
       "      <td>56145942</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1827</td>\n",
       "      <td>3</td>\n",
       "      <td>Hct</td>\n",
       "      <td>29.5</td>\n",
       "      <td>29.5</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
       "      <td>2014</td>\n",
       "      <td>12:50:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>141296</td>\n",
       "      <td>46021316</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1827</td>\n",
       "      <td>1</td>\n",
       "      <td>total bilirubin</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>mg/dL</td>\n",
       "      <td>mg/dL</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:45:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>-1507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    patientunitstayid     labid  labresultyear labresulttime24 labresulttime  \\\n",
       "95             141296  47645495           2014        11:25:00          noon   \n",
       "99             141296  47645499           2014        11:25:00          noon   \n",
       "21             141296  46021317           2014        11:25:00          noon   \n",
       "23             141296  56145942           2014        11:25:00          noon   \n",
       "25             141296  46021316           2014        11:25:00          noon   \n",
       "\n",
       "    labresultoffset  labtypeid          labname  labresult labresulttext  \\\n",
       "95            -1827          1        anion gap       17.0            17   \n",
       "99            -1827          1          glucose       86.0            86   \n",
       "21            -1827          1   alkaline phos.      512.0           512   \n",
       "23            -1827          3              Hct       29.5          29.5   \n",
       "25            -1827          1  total bilirubin        0.6           0.6   \n",
       "\n",
       "   labmeasurenamesystem labmeasurenameinterface  labresultrevisedyear  \\\n",
       "95                                       mmol/L                  2014   \n",
       "99                mg/dL                   mg/dL                  2014   \n",
       "21              Units/L                 Units/L                  2014   \n",
       "23                    %                       %                  2014   \n",
       "25                mg/dL                   mg/dL                  2014   \n",
       "\n",
       "   labresultrevisedtime24 labresultrevisedtime  labresultrevisedoffset  \n",
       "95               12:19:00                 noon                   -1773  \n",
       "99               12:19:00                 noon                   -1773  \n",
       "21               16:45:00              evening                   -1507  \n",
       "23               12:50:00                 noon                   -1742  \n",
       "25               16:45:00              evening                   -1507  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>labid</th>\n",
       "      <th>labresultyear</th>\n",
       "      <th>labresulttime24</th>\n",
       "      <th>labresulttime</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>labresultrevisedyear</th>\n",
       "      <th>labresultrevisedtime24</th>\n",
       "      <th>labresultrevisedtime</th>\n",
       "      <th>labresultrevisedoffset</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1827</th>\n",
       "      <td>141296</td>\n",
       "      <td>47645495</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>1</td>\n",
       "      <td>anion gap</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17</td>\n",
       "      <td></td>\n",
       "      <td>mmol/L</td>\n",
       "      <td>2014</td>\n",
       "      <td>12:19:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1827</th>\n",
       "      <td>141296</td>\n",
       "      <td>47645499</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>1</td>\n",
       "      <td>glucose</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86</td>\n",
       "      <td>mg/dL</td>\n",
       "      <td>mg/dL</td>\n",
       "      <td>2014</td>\n",
       "      <td>12:19:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1827</th>\n",
       "      <td>141296</td>\n",
       "      <td>46021317</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>1</td>\n",
       "      <td>alkaline phos.</td>\n",
       "      <td>512.0</td>\n",
       "      <td>512</td>\n",
       "      <td>Units/L</td>\n",
       "      <td>Units/L</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:45:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>-1507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1827</th>\n",
       "      <td>141296</td>\n",
       "      <td>56145942</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>3</td>\n",
       "      <td>Hct</td>\n",
       "      <td>29.5</td>\n",
       "      <td>29.5</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
       "      <td>2014</td>\n",
       "      <td>12:50:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>-1742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1827</th>\n",
       "      <td>141296</td>\n",
       "      <td>46021316</td>\n",
       "      <td>2014</td>\n",
       "      <td>11:25:00</td>\n",
       "      <td>noon</td>\n",
       "      <td>1</td>\n",
       "      <td>total bilirubin</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>mg/dL</td>\n",
       "      <td>mg/dL</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:45:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>-1507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 patientunitstayid     labid  labresultyear labresulttime24  \\\n",
       "labresultoffset                                                               \n",
       "-1827                       141296  47645495           2014        11:25:00   \n",
       "-1827                       141296  47645499           2014        11:25:00   \n",
       "-1827                       141296  46021317           2014        11:25:00   \n",
       "-1827                       141296  56145942           2014        11:25:00   \n",
       "-1827                       141296  46021316           2014        11:25:00   \n",
       "\n",
       "                labresulttime  labtypeid          labname  labresult  \\\n",
       "labresultoffset                                                        \n",
       "-1827                    noon          1        anion gap       17.0   \n",
       "-1827                    noon          1          glucose       86.0   \n",
       "-1827                    noon          1   alkaline phos.      512.0   \n",
       "-1827                    noon          3              Hct       29.5   \n",
       "-1827                    noon          1  total bilirubin        0.6   \n",
       "\n",
       "                labresulttext labmeasurenamesystem labmeasurenameinterface  \\\n",
       "labresultoffset                                                              \n",
       "-1827                      17                                       mmol/L   \n",
       "-1827                      86                mg/dL                   mg/dL   \n",
       "-1827                     512              Units/L                 Units/L   \n",
       "-1827                    29.5                    %                       %   \n",
       "-1827                     0.6                mg/dL                   mg/dL   \n",
       "\n",
       "                 labresultrevisedyear labresultrevisedtime24  \\\n",
       "labresultoffset                                                \n",
       "-1827                            2014               12:19:00   \n",
       "-1827                            2014               12:19:00   \n",
       "-1827                            2014               16:45:00   \n",
       "-1827                            2014               12:50:00   \n",
       "-1827                            2014               16:45:00   \n",
       "\n",
       "                labresultrevisedtime  labresultrevisedoffset  \n",
       "labresultoffset                                               \n",
       "-1827                           noon                   -1773  \n",
       "-1827                           noon                   -1773  \n",
       "-1827                        evening                   -1507  \n",
       "-1827                           noon                   -1742  \n",
       "-1827                        evening                   -1507  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the index to the offset time\n",
    "lab = lab.set_index('labresultoffset')\n",
    "lab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>-1827</th>\n",
       "      <td>anion gap</td>\n",
       "      <td>17.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1827</th>\n",
       "      <td>glucose</td>\n",
       "      <td>86.0</td>\n",
       "      <td>mg/dL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1827</th>\n",
       "      <td>alkaline phos.</td>\n",
       "      <td>512.0</td>\n",
       "      <td>Units/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1827</th>\n",
       "      <td>Hct</td>\n",
       "      <td>29.5</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1827</th>\n",
       "      <td>total bilirubin</td>\n",
       "      <td>0.6</td>\n",
       "      <td>mg/dL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         labname  labresult labmeasurenamesystem\n",
       "labresultoffset                                                 \n",
       "-1827                  anion gap       17.0                     \n",
       "-1827                    glucose       86.0                mg/dL\n",
       "-1827             alkaline phos.      512.0              Units/L\n",
       "-1827                        Hct       29.5                    %\n",
       "-1827            total bilirubin        0.6                mg/dL"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subselect the variable columns\n",
    "columns = ['labname','labresult','labmeasurenamesystem']\n",
    "lab = lab[columns]\n",
    "lab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'anion gap', u'glucose', u'alkaline phos.', u'Hct',\n",
       "       u'total bilirubin', u'-bands', u'albumin', u'MCV', u'AST (SGOT)',\n",
       "       u'potassium', u'RDW', u'total protein', u'WBC x 1000',\n",
       "       u'creatinine', u'-monos', u'platelets x 1000', u'-polys',\n",
       "       u'calcium', u'MCH', u'bicarbonate', u'Tacrolimus-FK506', u'Hgb',\n",
       "       u'PT', u'direct bilirubin', u'PT - INR', u'chloride', u'BUN',\n",
       "       u'-lymphs', u'MCHC', u'sodium', u'ALT (SGPT)', u'RBC', u'CPK',\n",
       "       u'bedside glucose', u'paCO2', u'HCO3', u'FiO2', u'Base Excess',\n",
       "       u'pH', u'paO2', u'CRP', u'ESR'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list the distinct labnames\n",
    "lab['labname'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>labname</th>\n",
       "      <th>-bands</th>\n",
       "      <th>-lymphs</th>\n",
       "      <th>-monos</th>\n",
       "      <th>-polys</th>\n",
       "      <th>ALT (SGPT)</th>\n",
       "      <th>AST (SGOT)</th>\n",
       "      <th>BUN</th>\n",
       "      <th>Base Excess</th>\n",
       "      <th>CPK</th>\n",
       "      <th>CRP</th>\n",
       "      <th>...</th>\n",
       "      <th>direct bilirubin</th>\n",
       "      <th>glucose</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>total bilirubin</th>\n",
       "      <th>total protein</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>-1827</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>378.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1232</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>2018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-342</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>417.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-75</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "labname          -bands  -lymphs  -monos  -polys  ALT (SGPT)  AST (SGOT)  \\\n",
       "labresultoffset                                                            \n",
       "-1827               6.0      1.0     1.0    91.0        36.0       128.0   \n",
       "-1232               NaN      NaN     NaN     NaN         NaN         NaN   \n",
       "-342                2.0      1.0     4.0    91.0        39.0       159.0   \n",
       "-75                 NaN      NaN     NaN     NaN         NaN         NaN   \n",
       " 5                  NaN      NaN     NaN     NaN         NaN         NaN   \n",
       "\n",
       "labname           BUN  Base Excess     CPK  CRP      ...        \\\n",
       "labresultoffset                                      ...         \n",
       "-1827            31.0          NaN     NaN  NaN      ...         \n",
       "-1232             NaN          NaN  2018.0  NaN      ...         \n",
       "-342             49.0          NaN     NaN  NaN      ...         \n",
       "-75               NaN          NaN     NaN  NaN      ...         \n",
       " 5                NaN          NaN     NaN  NaN      ...         \n",
       "\n",
       "labname          direct bilirubin  glucose  pH  paCO2  paO2  platelets x 1000  \\\n",
       "labresultoffset                                                                 \n",
       "-1827                         0.3     86.0 NaN    NaN   NaN             378.0   \n",
       "-1232                         NaN      NaN NaN    NaN   NaN               NaN   \n",
       "-342                          NaN     95.0 NaN    NaN   NaN             417.0   \n",
       "-75                           NaN      NaN NaN    NaN   NaN               NaN   \n",
       " 5                            NaN      NaN NaN    NaN   NaN               NaN   \n",
       "\n",
       "labname          potassium  sodium  total bilirubin  total protein  \n",
       "labresultoffset                                                     \n",
       "-1827                  4.8   134.0              0.6            6.4  \n",
       "-1232                  NaN     NaN              NaN            NaN  \n",
       "-342                   5.4   138.0              0.5            7.0  \n",
       "-75                    NaN     NaN              NaN            NaN  \n",
       " 5                     NaN     NaN              NaN            NaN  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Hgb</th>\n",
       "      <th>total bilirubin</th>\n",
       "      <th>potassium</th>\n",
       "      <th>Tacrolimus-FK506</th>\n",
       "      <th>WBC x 1000</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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1827</th>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1232</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-342</th>\n",
       "      <td>5.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-75</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "labname          creatinine  pH  Hgb  total bilirubin  potassium  \\\n",
       "labresultoffset                                                    \n",
       "-1827                  4.00 NaN  9.3              0.6        4.8   \n",
       "-1232                   NaN NaN  NaN              NaN        NaN   \n",
       "-342                   5.67 NaN  9.4              0.5        5.4   \n",
       "-75                     NaN NaN  NaN              NaN        NaN   \n",
       " 5                      NaN NaN  NaN              NaN        NaN   \n",
       "\n",
       "labname          Tacrolimus-FK506  WBC x 1000  \n",
       "labresultoffset                                \n",
       "-1827                         5.2        14.6  \n",
       "-1232                         NaN         NaN  \n",
       "-342                          7.2        13.5  \n",
       "-75                           NaN         NaN  \n",
       " 5                            NaN         NaN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot laboratory tests of interest\n",
    "labs_to_plot = ['creatinine','pH','Hgb', 'total bilirubin', \n",
    "                'potassium', 'Tacrolimus-FK506', 'WBC x 1000']\n",
    "lab[labs_to_plot].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10e458810>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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is23vL774IuRyOWJjY3U1BBEREREREREREVGt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\n8MMP580PI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OGFF/Dz80On02EwGNi7dy9bt27lu+++w8fH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daf0cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "figsize = (18,8)\n",
    "title = 'Laboratory test results for patientunitstayid = {} \\n'.format(patientunitstayid)\n",
    "ax = lab[labs_to_plot].plot(title=title, figsize=figsize, fontsize=fontsize, marker='o',ms=10, lw=0)\n",
    "\n",
    "ax.title.set_size(fontsize)\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": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}