dataset_curation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data curation using data from WardWatcher (ICNARC) and Philips (ICCA).\n",
    "\n",
    "## Contents:\n",
    "### [1. Data Linkage](#section1)\n",
    "\n",
    "We show how data extracted from the ICNARC database (via the Ward Wacther software) is linked to the Philips data to provide more information on patient outcomes, medical history and diagnoses. These ICNARC data, curated as demonstrated here, are included in our research database as a new table: *D_Icnarc* (following the table naming convention for the Philips data), which links to other database tables on *encounterId* and *ptcensusId*.  \n",
    "\n",
    "### [2. Demonstration analysis.](#section2)\n",
    "\n",
    "We demonstrate some basic analysis of common interventions extracted from the research database. This is for illustrative purposes to show potential users what kind of data is available.\n",
    "\n",
    "***\n",
    "This script uses the following data files:\n",
    "* *encounter_summary.rpt* - a tab separated file with output from an SQL query run on ICCA to extract basic information about patient encounters (ITU stays).\n",
    "\n",
    "* *ICNARC 2015-2018 encounterIds and Readmissions.TXT* - a file containing ICNARC patient IDs and the corresponding *CIS Patient ID*, which link to the *encounterId* numbers in ICCA.\n",
    "\n",
    "* *Philips encounterId Issue List (New).xlsx* - a file documenting known issues with either *encounterIds* in Philips or *CIS Patient IDs* in WW. We clean up the IDs using this file before joining the two datasets.\n",
    "\n",
    "* *ICNARC_Dataset_2015-2018_clean_.xml* - xml file containing standard format output of ICNARC CMP dataset.\n",
    "\n",
    "* *ICNARC CMP Dataset Properties.xlsx* - description of the variables in the ICNARC dataset.\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "VERBOSE = False ## For reasons of data protection we supress printing of results and data summaries.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### 1. Data Linkage.\n",
    "\n",
    "#### Functions for cleaning up the encounter IDs and parsing the ICNARC xml file are imported: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from clean_encounterids import *\n",
    "from parse_ICNARC_xml import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean up ICNARC CIS ID numbers.\n",
    "\n",
    "Each record in the ICNARC data links to an encounterId in Philips. In some few cases the links are wrong (e.g. it links to an empty record). The following function fixes those encounter IDs in the ICNARC data by replacing the erroneous encounterIds in the column *CIS Patient ID*. It also creates a new column *CIS Patient ID Original* which contains the original encounterIds prior to replacement. At the time of writing there are only 5 such errors for GICU. These errors are logged by the ICNARC database team at UHB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "icnarc_numbers = clean_icnarc_cis_ids('../ICNARC 2015-2018 encounterIds and Readmissions.TXT', \n",
    "                                      '../Philips encounterId Issue List (New).xlsx',\n",
    "                                    verbose=VERBOSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean up Philips encounterId numbers.\n",
    "\n",
    "The Philips database creates a new encounterId for each intensive care stay. Sometimes this process doesn't work as desired e.g. erroneous records are created, patients are accidentally discharged and readmitted creating a new record etc. \n",
    "\n",
    "The following function fixes the class of errors that results in duplicate encounterIds which correspond to the same physical intensive care stay. It does so by replacing the erroneous encounterIds with the 'main' encounterId for that stay (i.e. the encounterId that links to the corresponding ICNARC record). It also creates a new column, *encounterId_original*, that contains the original ids prior to replacement. In the research database all instances of the erroneous IDs are replaced such that duplicate records are remapped. To the original ICCA table *D_Encounter* we add the *encounterId_original* column, and the *error_type* column described below.\n",
    "\n",
    "When the flag *log_error_type* is set to True, the function creates another new column *error_type* which specifies the type of error associated with each encounterId. It gives a string containing one of 16 error types identified by Josh Inoue (UHB) and described in the file \"Philips encounterId Read Me.txt\", or contains \"NA\" if there is no known error. This flag is useful because there are encounterIds which we know have an issue but which we do not alter. For example, very occasionally a patient is discharged from ICU and then readmitted but is never removed from the Philips system. In such cases we know that a single encounterId in Philips (and in ICNARC) corresponds to two physical ICU stays. With the *error_type* flag, the user of the data can decide how they want to deal with different types of error during data processing.      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "philips_data = clean_philips_encounterids('../encounter_summary (1).rpt', \n",
    "                                  '../Philips encounterId Issue List (New).xlsx',\n",
    "                                  verbose=VERBOSE, log_error_type=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we have replaced some erroneous encounterIds with their correct value, there are now multiple rows with the same *encounterId*, with each containing a fragement of data relating to a whole intensive care stay. We combine the stay fragements (and the data they contain) so that each row corresponds to a single stay. This is consistent with the desired behaviour of the *D_Encounter* table. \n",
    "\n",
    "Note: this combination of fragmented data means that the *D_Encounter* table in our research database is slghtly shorter than the original table. (The original table could be reconstructed using the *encounterId_original* column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "philips_data = combine_non_unique_philips_encounters(philips_data, combine='simple', verbose=VERBOSE)\n",
    "if not philips_data['encounterId'].is_unique:\n",
    "    print \"Warning: non-unique ID.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Link ICNARC to Philips.\n",
    "\n",
    "Having cleaned the encounterId numbers we can now have a one-to-one mapping between *Philips:encounterId* and *ICNARC:CIS Patient ID* and we can safely link ICNARC records to the corresponding ICU stays in Philips. The following function renames the *CIS Patient ID* column in the ICNARC data as *encounterId* and then does an sql-style inner join on the column using *pandas.DataFrame.merge*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'encounterId', u'ptCensusId', u'outTime', u'lengthOfStay (mins)',\n",
       "       u'tNumber', u'gender', u'age', u'error_type', u'inTime',\n",
       "       u'encounterId_original', u'Unit ID', u'ICNARC number',\n",
       "       u'CIS Patient ID Original', u'CIS Episode ID',\n",
       "       u'Readmission during this hospital stay'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icustays = join_icnarc_to_philips(philips_data, icnarc_numbers, verbose=VERBOSE)\n",
    "icustays.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This *icustays* table represents a clean link between ICNARC and Philips. \n",
    "\n",
    "#### Next we demonstrate the construction of the new database table *D_Icnarc* using data extracted from the local ICNARC dataset in standard XML format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "icnarc_data = parse_icnarc_xml(\"../ICNARC_Dataset_2015-2018__clean_.xml\",\n",
    "                               \"../ICNARC CMP Dataset Properties.xlsx\",\n",
    "                              verbose=VERBOSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ICNARC data links to 'icustays' on 'ICNARC number' and 'Unit ID'.\n",
    "\n",
    "This join produces a wide table with 208 columns corresponding to each of the varibales in the ICNARC CMP. The *D_Icnarc* table in our research database is a subset of this table. (In the future ICNARC may start to collect time-stamped data to capture clinical trajectories, in which case we will need to add another table *PtIcnarcData*). \n",
    "\n",
    "Note: *D_Icnarc* links to other tables on *encounterId* and *episodeId* and it is possible that two ICNARC entries share the same encounterId for different episdoes when looking across different intensive care units in UHB. We are currently only discussing the general adult intensive care unit (GICU) so this is not an issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "icnarc_data = icustays.merge(icnarc_data, on=['ICNARC number', 'Unit ID'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validation.\n",
    "\n",
    "We now conduct some sanity checks and produce summary statistics on the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First we use variables from the initial WW/Philips join:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "columns = [('Date of admission to your unit', 'Time of admission to your unit'), \n",
    "           ('Date of discharge from your unit', 'Time of discharge from your unit'),\n",
    "           ('Date when fully ready to discharge', 'Time when fully ready to discharge'),\n",
    "           ('Date of death', 'Time of death'),\n",
    "           ('Date of declaration of brainstem death', 'Time of declaration of brainstem death'),\n",
    "           ('Date body removed from your unit','Time body removed from your unit')]\n",
    "\n",
    "icnarc_data = combine_date_time_columns(icnarc_data, columns, drop_originals=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "icnarc_data['Date of Birth'] = pd.to_datetime(icnarc_data['Date of Birth'], infer_datetime_format=True)\n",
    "icnarc_data['inTime'] = pd.to_datetime(icnarc_data['inTime'], infer_datetime_format=True)\n",
    "icnarc_data['outTime']= pd.to_datetime(icnarc_data['outTime'], infer_datetime_format=True)\n",
    "icnarc_data = calculate_icnarc_outtime(icnarc_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "icnarc_data['icnarc_age'] = [(row['Datetime of admission to your unit'] - row['Date of Birth']).total_seconds()/(60*60*24*365.25) for i,row in icnarc_data.iterrows()]\n",
    "icnarc_data['icnarc_los'] = [(row['icnarc_outTime'] - row['Datetime of admission to your unit']).total_seconds()/(60.0) for i,row in icnarc_data.iterrows()]\n",
    "\n",
    "icnarc_data['icnarc_gender'] = icnarc_data['Sex'].apply(lambda x: 'Female' if x=='F' else 'Male')\n",
    "icnarc_data['icnarc_bmi'] = icnarc_data['Weight in kg'].astype(float)/((icnarc_data['Height in cm'].astype(float)/100.0)**2)\n",
    "icnarc_data = icnarc_data.drop(labels=['Sex'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### and get the same summary data as printer above from philips ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age, median years (IQR): 64.0 (50.0, 73.0)\n",
      "LOS, median days (IQR): 3.0 (1.7, 5.6)\n",
      "\n",
      "Gender, % female: 39.7\n",
      "(0.1 % of patients have no gender recorded in Philips.)\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print_philips_summary(icnarc_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age, median years (IQR): 64.2 (50.8, 73.4)\n",
      "LOS, median days (IQR): 2.9 (1.7, 5.4)\n",
      "\n",
      "Gender, % female: 39.6\n",
      "(0.0 % of patients have no gender recorded in Philips.)\n",
      " \n",
      "Readmission to ICU, #(%) : 147 (3.0)\n",
      "(0.0 % of patients have no recording for this variable in WW.)\n"
     ]
    }
   ],
   "source": [
    "print_icnarc_summary(icnarc_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are a total of 4831 ITU stays.\n"
     ]
    }
   ],
   "source": [
    "print \"There are a total of %d ITU stays.\" %len(icnarc_data.encounterId.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To calculate the in-hospital mortality status of each patient in the ICNARC data we need to combine the CMP variables DIS, HDIS, UDIS and UHDIS (see data flow diagram)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "icnarc_data = calculate_icnarc_in_hospital_mortality(icnarc_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icnarc_in_hospital_mortality</th>\n",
       "      <th>count</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>3924</td>\n",
       "      <td>0.812254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D</td>\n",
       "      <td>905</td>\n",
       "      <td>0.187332</td>\n",
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       "      <th>2</th>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000414</td>\n",
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      "text/plain": [
       "  icnarc_in_hospital_mortality  count      rate\n",
       "0                            A   3924  0.812254\n",
       "1                            D    905  0.187332\n",
       "2                            E      2  0.000414"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mortality = icnarc_data.groupby('icnarc_in_hospital_mortality').agg({'encounterId':'count'}).reset_index()\n",
    "mortality = mortality.rename(columns={'encounterId':'count'})\n",
    "mortality['rate'] = mortality['count'] / float(len(icnarc_data))\n",
    "mortality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "primary_reasons = icnarc_data.groupby('Primary reason for admission to your unit').agg({'encounterId':'count'}).sort_values('encounterId', ascending=False).reset_index()\n",
    "secondary_reasons = icnarc_data.groupby('Secondary reason for admission to your unit').agg({'encounterId':'count'}).sort_values('encounterId', ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "icnarc_codes = {'1.1.4.39.1' : 'Primary lung tumour',\n",
    "               '2.4.2.7.3' : 'Anoxic or ischaemic coma or encephalopathy',\n",
    "               '1.3.9.39.1' : 'Pancreatic or pancreato-duodenal tumour',\n",
    "               '2.1.10.7.3' : 'Anoxic or ischaemic coma or encephalopathy',\n",
    "               '2.1.4.27.5' : 'Pneumonia, no organism isolated',\n",
    "               '1.3.7.39.2' : 'Secondary hepatic tumour',\n",
    "               '2.2.1.30.1' : 'Acute myocardial infarction',\n",
    "               '1.3.2.39.1' : 'Malignant neoplasm of oesophagus',\n",
    "               '1.3.6.39.2' : 'Malignant large bowel tumour',\n",
    "               '2.1.4.27.1' : 'Bacterial pneumonia',\n",
    "               '2.2.5.32.2' : 'Ventricular tachycardia or fibrilation',\n",
    "               '2.2.12.35.2' : 'Septic shock, aetiology uncertain',\n",
    "               '2.7.1.13.2' : 'Acute renal failure due to haemodynamic causes',\n",
    "               '2.7.1.13.1' : 'Acute renal failure due to infection',\n",
    "               '1.3.6.39.2' : 'Malignant large bowel tumour'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: replace None with 'Other' below to count reasons outside of top 10 most frequent. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "primary_reasons['Primary reason'] = [icnarc_codes[code] if code in icnarc_codes.keys() else None for code in primary_reasons['Primary reason for admission to your unit']]\n",
    "secondary_reasons['Secondary reason'] = [icnarc_codes[code] if code in icnarc_codes.keys() else None for code in secondary_reasons['Secondary reason for admission to your unit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "primary_reasons = primary_reasons.groupby('Primary reason').agg({'encounterId':'sum'}).sort_values('encounterId', ascending=False).reset_index()\n",
    "secondary_reasons = secondary_reasons.groupby('Secondary reason').agg({'encounterId':'sum'}).sort_values('encounterId', ascending=False).reset_index()\n",
    "\n",
    "primary_reasons['percentage'] = 100*primary_reasons['encounterId']/float(len(icnarc_data))\n",
    "secondary_reasons['percentage'] = 100*secondary_reasons['encounterId']/float(len(icnarc_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UkiRJkpYTJuiTrKrOA8Z68NpyoaqmTHYMg6aqZi3Fvk4Axrw9QZIkSdLyyYfE\nSZIkSZI0AEzQJUmSJEkaACbokiRJkiQNABN0SZIkSZIGgA+Jk7TYhmvGPwclSZIkTRJX0CVJkiRJ\nGgAm6JIkSZIkDQATdEmSJEmSBoAJuiRJkiRJA8AEXZIkSZKkAeBT3CUtduPdxSFzH5jsMCQNw7+w\nIEnSis8VdEmSJEmSBoAJuiRJkiRJA8AEXZIkSZKkAWCCLkmSJEnSADBBlyRJkiRpAJigS5IkSZI0\nAEzQJUmSJEkaACbokiRJkiQNABN0SZIkSZIGgAn6CijJq5JUkk0fRR/Tkrx8acY1CJLsn+Sodvzm\nJK8do/6sJO8ZpnzMtq3ecUnmJ3nXkke9uK8P9L3+yaPtU5IkSdLgWGWyA9AysQ/w4/b7w0vYxzRg\nBvD9pRXUYy1JgFTVQ8Odr6rPL2nf42mbZENgm6p6xnj7TbJKVT04wukPAJ/siWH78fYrSZIkafC5\ngr6CSbI2sAPwRuA1fefen2RBknlJDmllZyeZ0Y7XT3JdktWAjwJ7J7k8yd5J1krypSQXJ5mbZLdh\nxp6Z5Jwk30nyiySHJNm3tVmQZJMk6yS5Nsmqrc3jh163VfsL24rzyUme2Oo8I8npLe7LWj9rJzmj\nvV4wFE+SKUmuTvIVYCHw1CSvT3JNkouBF/TEu3h1PMmbklzSxvhWkjXHuM69bc9Ocmib5zVJdmzV\nfgQ8pV3DHUcaI8kxST6f5CLgsDa3L7d5zU+yZ3u/1mh9zW7t7my/k+TwJAtbm7173o+zk3wzyVVJ\nZrcvLSRJkiQNIBP0Fc9uwA+q6hrg90mmAyR5WTv3vKraEjhspA6q6n7gYOCEqppWVScAHwTOrKpt\ngZ2Bw5OsNUzzLYE3A88B9gOe1dp8EXhbVd0BnA28otV/DXBSVT0AfAV4f1VtASzg4dX/2cB/tbi3\nB24A7gV2r6qtWzz/3pN8PhP4XFVtBtwPfIQuMd8BmDrCtE+qqm3aGFfSfcExEau0eb6zJ+5dgZ+3\na3jeGGNsDGxfVe8G/g24vaqe267FmVV1EHBP62vfvrH3oNvxsCWwC917s1E7t1WLaSrw1/R8QTEk\nyQFJLk1y6V233jLBaUuSJElaWkzQVzz7AMe34+Pba+gSty9X1d0AVfWHCfb7EuCgJJfTJdirA08b\npt4lVXVDVd0H/JxuFRm6hHtKO/4i8Pp2/Hrgy0nWBZ5QVee08mOBnZKsAzylqk5ucd/b5hDgk0nm\nA6cDTwECyZ4eAAAgAElEQVSe3Nr+sqoubMfPA86uqpvbFw8njDC/zZOcl2QBsC+w2biuysNOar/n\n9MxzImOcWFWL2vEuwH8NnaiqW8cYewfguKpaVFU3AecA27RzF1fV9W2b/+XDxVZVR1fVjKqasdYT\n1x9jKEmSJEnLivegr0CSrAe8CHhukgJWBirJe0dp9iAPf1Gz+mjdA3tW1dVjhHFfz/FDPa8fon3e\nqur8thV9JrByVS1sCfpE7AtsAEyvqgeSXNcT/10T7AvgGOBVVTUvyf7AzAm2H5rnIkb+dzXaGEsS\n80TigtFjkyRJkjTJXEFfsewFfLWqnl5VU6rqqcC1wI7AacDre+57Xq+1uQ6Y3tN+yB3AOj2vfwi8\nbWgbeZKtHmWsXwG+DnwZoKpuB27tuX97P+CctiX++iSvauM+rs1hXeB3LTnfGXj6CONcBLwwyZPa\nfe+vHqHeOsANrU7/FvKlZbxjnAb8y9CLoXvxgQeG7t3vcx7d8wJWTrIBsBNw8VKKWZIkSdJjxAR9\nxbIPcHJf2beAfarqB8ApwKVtm/rQnw77NPCWJHOB3v3NZwFT20PJ9gY+BqwKzE9yRXv9aMwGnggc\n11P2Orr7p+fT3VP90Va+H/D2Vv4TYMPWfkbbLv5a4KrhBqmqG4BZwAXA+XT3fg/n3+iS+fNH6msp\nGO8YHwee2B76No/uHnuAo+mu/+y++icD84F5wJnA+6rqxqUauSRJkqRlLlU12THoz1CSvYDdqmq/\nyY5FD9t46vQ6cPaFY1eU9Jg7aKvhNtBIkqTlQZI5VTVjrHrej6rHXJIjgZcBL5/sWCRJkiRpUJig\n6zFXVW+b7BgkSZIkadB4D7okSZIkSQPABF2SJEmSpAFggi5JkiRJ0gAwQZckSZIkaQD4kDhJi224\nZvxTTpIkSdIkcQVdkiRJkqQBYIIuSZIkSdIAMEGXJEmSJGkAmKBLkiRJkjQATNAlSZIkSRoAJuiS\nJEmSJA0A/8yapMVuvLs4ZO4Dkx2GpHHyzyJKkrRicQVdkiRJkqQBYIIuSZIkSdIAMEGXJEmSJGkA\nmKBLkiRJkjQATNAlSZIkSRoAJuiSJEmSJA0AE3RJkiRJkgaACbokSZIkSQPABP3PQJIPJrkiyfwk\nlyd53iTEMDPJqctLv31jTEvy8p7XuyY5aALtP7BsIpMkSZK0IjFBX8El2Q54JbB1VW0B7AL8enKj\nGluSVSY7hh7TgMUJelWdUlWHTKD9wCToA3ZdJUmSJPUwQV/xbQTcUlX3AVTVLVX1W4Ak05Ock2RO\nkh8m2aiVPyPJ6UnmJbksySbpHJ5kYZIFSfZudWcmOTvJN5NclWR2krRzf9vKLgP2GAooybZJLkgy\nN8lPkjy7le+f5JQkZwJnJPlKklf1tJudZLdh5rj2COOPNL+zk3y27SZYmGTbkeJKshrwUWDvVn/v\nFudRrc2Tk5zcrtW8JNv3BpbkEGCN1nZ2kilJFvacf0+SWT1xfSbJpUmuTLJNkpOS/CzJx3vavLvF\nvTDJO1vZWP3+Z5JLgXeM72MjSZIk6bHmatqK70fAwUmuAU4HTqiqc5KsChwJ7FZVN7eE+xPAG4DZ\nwCFVdXKS1em+yNmDbiV5S2B94JIk57YxtgI2A34LnA+8oCWDXwBeBPwfcEJPTFcBO1bVg0l2AT4J\n7NnObQ1sUVV/SPJC4F3At5OsC2wPvG6YOQ43/kWjzA9gzaqalmQn4EvA5sPFVVV7JjkYmFFVB0L3\nRULP2EcA51TV7klWBtbuDayqDkpyYFVNa22nDBN/r/urakaSdwDfAaYDfwB+nuQzwBTg9cDzgAAX\nJTkHuHWMflerqhnDnUhyAHAAwBM2fNoY3UiSJElaVkzQV3BVdWeS6cCOwM7ACe3+6UvpktLT2oLz\nysANSdYBnlJVJ7f29wIk2QE4rqoWATe1pHAb4I/AxVV1fat3OV0SeSdwbVX9rJV/jZYEAusCxyZ5\nJlDAqj0hn1ZVf2hjn5Pkc0k2oEvgv1VVDw4zzeHGv224+fW0Oa6NcW6Sxyd5ArDOKHGN5EXAa1tf\ni4Dbx9FmNKe03wuAK6rqBoAkvwCeCuwAnFxVd7Xyk+je21OG6avXCSOdqKqjgaMBNp46vR5V9JIk\nSZKWmAn6n4GWOJ4NnJ1kAd0q9By6BHC73rotQZ+o+3qOFzH25+pjwFlt1XlKi23IXX11vwL8I/Aa\nupXj8Y4fhplfj/5EtMaIa2l5kEfeWrJ63/mhuTzEI+f1EKNf17H67b+ukiRJkgaM96Cv4Np91M/s\nKZoG/BK4Gtgg3UPkSLJqks2q6g7g+qF7v5M8LsmawHl092Gv3Fa0dwIuHmXoq4ApSTZpr/fpObcu\n8Jt2vP8YUzgGeCdAVf10jLq9hp1fz/mhe+h3AG6vqttHiesOutX14ZwBvKX1tXLbit/vgXZLAcBN\nwF8keVKSx9E9wG8izgNelWTNJGsBu7eyR9uvJEmSpElmgr7iW5tu2/ZPk8wHpgKzqup+YC/g0CTz\ngMvp7vEG2A94e6v/E2BD4GRgPjAPOBN4X1XdONKgbWv8AcD30j0k7nc9pw8DPpVkLmOstlfVTcCV\nwJcnMukx5gdwbxv/88Abx4jrLGDq0EPi+oZ6B7Bz25kwh+769jsamJ9kdlU9QPfQuYuB0+i+yJjI\nvC6j+9LiYuAi4ItVNffR9itJkiRp8qXKW041uNrq/QK6PxP3aO/vHurzbOA9VXXp0uhvRbLx1Ol1\n4OwLJzsMSeN00FbjeVSGJEmabEnmjPTQ5l6uoGtgtSepXwkcubSSc0mSJEkaVD4kTgOrqk4Hnr4M\n+p25tPuUJEmSpEfLFXRJkiRJkgaACbokSZIkSQPABF2SJEmSpAFggi5JkiRJ0gAwQZckSZIkaQD4\nFHdJi224Zvy7ypIkSdIkcQVdkiRJkqQBYIIuSZIkSdIAMEGXJEmSJGkAmKBLkiRJkjQATNAlSZIk\nSRoAPsVd0mI33l0cMveByQ5D0gDxLztIkvTYcQVdkiRJkqQBYIIuSZIkSdIAMEGXJEmSJGkAmKBL\nkiRJkjQATNAlSZIkSRoAJuiSJEmSJA0AE3RJkiRJkgaACbokSZIkSQPABH0ZSrIoyeVJFiY5Mcma\nkx3TeCT5yyTfnOw4HitJdk1y0FLu881JXrsE7fZPctQYdWYm2X4J+r4uyfoTbSdJkiTpsWGCvmzd\nU1XTqmpz4H7gzZMd0HhU1W+raq/JjmMkSVZZmv1V1SlVdchS7vPzVfWVpdlnj5nAhBN0SZIkSYPN\nBP2xcx7wjCRTklyZ5AtJrkjyoyRrACTZJMkPksxJcl6STVv5MUkWJ8xJ7my/ZyY5J8l3kvwiySFJ\n9k1ycZIFSTZp9aYkOTPJ/CRnJHlaT79HJPlJa79XT/2FPcfnJbms/YyZGCa5M8knksxLcmGSJ48W\nR1/b9ZJ8u9W5MMkWrXxWkq8mOR/4apI1k3wjyU+TnJzkoiQzWt3/TnJpu74f6en7uiQfafNY0HN9\nF69aJ3l12/EwL8m5Pee/neS01seBSd6dZG6Lcb1h5jEryXva8dtbnPOTHD/aPPv6+Ls2r7lJTk/y\n5CRT6L7oeVfbnbFjkg2SfCvJJe3nBa39k9rn64okXwQy1nsnSZIkafKYoD8G2orvy4AFreiZwH9V\n1WbAbcCerfxo4G1VNR14D/C5cXS/JV3C9hxgP+BZVbUt8EXgba3OkcCxVbUFMBs4oqf9RsAOwCuB\n4VaRfwe8uKq2BvbuazuStYALq2pL4FzgTeOIY8hHgLmtzgeA3lXoqcAuVbUP8Fbg1qqaCvwbML2n\n3geragawBfDCvuT3ljaX/6a7xv0OBl7aYt+1p3xzYA9gG+ATwN1VtRVwATDWVvaDgK3anIZ2UYw2\nzyE/Bp7fxjkeeF9VXQd8HvhM251xHvDZ9nobus/SF1v7DwM/bp+zk4E/+UIEIMkB7QuNS++69ZYx\npiJJkiRpWVmqW4X1J9ZIcnk7Pg/4X+AvgWuraqh8DjAlydp025ZPTBYvdD5uHGNcUlU3ACT5OfCj\nVr4A2Lkdb0eXXAJ8FTisp/23q+oh4KdDK919VgWOSjINWAQ8axwx3Q+c2o7nAC8eRxxDdqB9YVFV\nZ7ZV4Me3c6dU1T099T7b6i1MMr+nj79PcgDd53sjusR+6PxJPXHtwZ86HzgmyTd66gKcVVV3AHck\nuR34bitfQPdFwGjmA7OTfBv49jjmOWRj4IQkGwGrAdeO0P8uwNSez83j2+dpp6E5VtX3ktw6XOOq\nOpruyyE2njq9xpiLJEmSpGXEBH3ZuqeqpvUWtCTqvp6iRcAadLsZbuuv3zzYzpNkJbpkbUhvXw/1\nvH6I8b2/ve2H2wL9LuAmupX6lYB7x9HnA1U1lOgtGmcc43HXWBWS/BXdyvg2VXVrkmOA1XuqDM13\n2Liq6s1Jnge8ApiTZHpfO5j4dX4FXbL8d8AHkzx3rHk0RwL/UVWnJJkJzBqh3kp0K+2PeG96EnZJ\nkiRJywG3uA+IqvojcG2SVwOks2U7fR0Pb+HelW5VeyJ+ArymHe9Lt5o/XusCN7RV9v2AlYdOJLlq\nGcRxXjtHS0pvadem3/nA37d6U4GhpPfxdIn87W1HwMsmEmCSTarqoqo6GLgZeOpE2g/T30rAU6vq\nLOD9dNdzbcY3z3WB37Tj1/WU3wGs0/P6Rzx8OwNttwN0txf8Qyt7GfDERzMXSZIkScuWCfpg2Rd4\nY5J5wBXAbq38C3T3Us+j2yY+5kpyn7cBr2/bwPcD3jGBtp8DXtfG3nRo7HR/rmuiS7TjiWMWML3V\nOYRHJqb9cW2Q5KfAx+mu1+1VNQ+YC1wFfJ0ukZ+Iw9sD5BbSfaEwb4Lt+60MfC3JghbXEVV1G+Ob\n5yy6Wx7mAL03h38X2H3oIXHA24EZ7YFzP+WR97nvlOQKuq3uv3qUc5EkSZK0DOXhncjS+CV5JfDX\nVTWeh8Yti/FXBlatqnvTPa3+dODZVXX/ZMSzoth46vQ6cPaFkx2GpAFy0FYT3bQlSZL6JZnTHmQ9\nKu9B1xKpqlPHrrVMrQmclWRVupX8t5qcS5IkSVqemaBrudSeqD7mN1CSJEmStLzwHnRJkiRJkgaA\nCbokSZIkSQPABF2SJEmSpAFggi5JkiRJ0gAwQZckSZIkaQD4FHdJi224Zvybx5IkSdIkcQVdkiRJ\nkqQBYIIuSZIkSdIAMEGXJEmSJGkAmKBLkiRJkjQATNAlSZIkSRoAPsVd0mI33l0cMveByQ5Dkgae\nf/FCkrQsuIIuSZIkSdIAMEGXJEmSJGkAmKBLkiRJkjQATNAlSZIkSRoAJuiSJEmSJA0AE3RJkiRJ\nkgaACbokSZIkSQPABF2SJEmSpAGwXCboSRYluTzJwiQnJllzsmPqlWT/JH/Z8/qLSaZOZkyTLcnM\nJNs/yj72T3LUUornmCR7LY2+evqckmThCOX/sDTHkiRJkrTiWS4TdOCeqppWVZsD9wNvXhaDpLMk\n12h/YHGCXlX/VFU/XYpxrbK0+lrC8VdegmYzgUeVoC/HpgADk6Av4fsnSZIkaRlbXhP0XucBzwBI\n8u0kc5JckeSAoQpJ7kzyiSTzklyY5Mmt/MlJTm7l85Js31Y7r07yFWAh8NQkL0lyQZLL2or92q39\nwUkuaSv5R7eEfi9gBjC7rfKvkeTsJDNam79t/cxLckb/ZJKsnuTLSRYkmZtk51a+f5JTkpwJnNHX\nZkqSq5LMTnJlkm8O7SoYLsZWfnaSQ5NcnOSaJDu28pWTfLrVn5/kba38ulb/MuDVSTZJ8oN2vc9L\nsmmr93dJLmqxn96u8RS6L1He1a7Jji3mM9sYZyR52nBvbpLXt/guBl7QU/6IFfAkd7bfSXJ4i39B\nkr17yo9q7+3pwF/0tJ2e5Jw2lx8m2WiMazSlzfmy9jPWFw+HADu2ub8rfTsBkpyaZObQPFr8V7Tr\nt22L4xdJdh3HZ2S0fv89yTxguzHilSRJkjQJlusEPd1K8suABa3oDVU1nS5BfnuSJ7XytYALq2pL\n4FzgTa38COCcVr41cEUrfybwuaraDLgL+BCwS1VtDVwKvLvVO6qqtmkr+WsAr6yqb7Y6+7ZV/nt6\n4t0A+AKwZxvz1cNM61+AqqrnAvsAxyZZvZ3bGtirql44TLtnt5ifA/wReOtIMfa0WaWqtgXeCXy4\nlR1At+I7raq2AGb31P99VW1dVccDRwNva9f7PcDnWp0fA8+vqq2A44H3VdV1wOeBz7Rrch5wJHBs\nzxhH9E+oJcofoUvMdwDGc5vAHsA0YEtgF+Dw1s/u7RpNBV5LW81PsmqLZa82ly8BnxjjGv0OeHH7\nPOw9XOx9DgLOa3P/zBh11wLObJ+9O4CPAy9u8X+01RntMzJavxdV1ZZV9ePeE0kOSHJpkkvvuvWW\nMbqRJEmStKxM6lbpR2GNJJe34/OA/23Hb0+yezt+Kl2i/Xu6bfCntvI5dAkPwIvokjWqahFwe5In\nAr+sqgtbnefTJXXnt8Xn1YAL2rmdk7wPWBNYjy7B/+4ocT8fOLeqrm1j/mGYOjvQJYxU1VVJfgk8\nq507bYQ2AL+uqvPb8deAtwOfHiPGk3quyZR2vAvw+ap6cJgYTwBIt4Nge+DEdk0AHtd+bwyc0JLi\n1YBrR4h3O7pkGuCrwGHD1HkecHZV3dzGPYGHr8VIdgCOa+/nTUnOAbYBduop/226nQjQJe2bA6e1\nuawM3NDT33DXaFXgqCTTgEXjiGki7gd+0I4XAPdV1QNJFvSMP9pnZCSLgG8Nd6Kqjqb7woWNp06v\nRxW9JEmSpCW2vCbo91TVtN6CtpV3F2C7qro7ydnA0KriA1U1lHgsYux539XbNV1ivE/feKvTrRrP\nqKpfJ5nVM96yctco5/oTqxpHjPe13+O5Jr3jrwTc1v8eNEcC/1FVp7T3ZNY4+gUW3xs9p708Bbhs\nlOoPtjhI95yA1cY7Tv+wwBVVNdK27+Gu0buAm+hW6VcC7p3gmItjb3rfk97P6kND41fVQxn72QOj\n9Xtv+3JCkiRJ0oBarre491kXuLUl55vSrVaP5QzgLbD43ut1h6lzIfCCJEP3ua+V5Fk8nPzc0laU\ne58Ifgewzgh97ZTkr1pf6w1T5zxg33b+WcDTgKvHMZenJRlKMv+Bbqv5aDGO5DTgn4eSweFirKo/\nAtcmeXWrkyRbttPrAr9px6/radZ/TX4CvKYd70u3BXxR2wY+raoOBi4CXpjkSW0reu8tAdcB09vx\nrnSr2tBdv73b+7kB3cr5xXS3NgyVbwTs3OpfDWwwdO2SrJpkszGu0brADVX1ELAf3ar7aPrnfh0w\nLclKSZ4KbDtG+34jfUYebb+SJEmSJtGKlKD/AFglyZV0D+W6cIz6AO+g2wK+gG7l9k/ucW7bq/cH\njksyn257+6ZVdRvd/eQLgR8Cl/Q0Owb4fHso2Bp9fR0AnNQe1nXCMDF9DlipxXQCsH9V3TdMvX5X\nA//S5v9E4L/HiHEkXwR+BcxvMY709PF9gTe2OlcAu7XyWXRb3+cAvTc0fxfYvV2THYG3Aa9v13Q/\nuvfiEarqhtbfBcD5wJU9p79Al7wPPfRsaHX/ZGA+MA84k+4e+Bv/f/buPMySqr7/+PvDJpuCgAuK\nOooKDigDM6AoICgatwgqBnBFE40xGJeoIWoQdxB/SVSCBjcQURQURDQiO4gMyzDAsLoBCoiCArLJ\n+v39UaeHS9vL7dnubeb9ep5+um7VqXO+daoa5lvnVN22/hfAJcDXW51U1V10Ny72a3Wdz+Rvmz8Q\neGMrvzETz2ygxXNvuhcDvrsdyxUtls8x8UyB8dof6xpZ3HolSZIkDVDun02r6SrdW9KPbS+CkxbZ\nBjNn156H9XNvS5KWb3ttvvLkhSRJapLMq6o5k5V7MI2gS5IkSZI0bU3Xl8SpR/saM0fPJUmSJGka\ncwRdkiRJkqQhYIIuSZIkSdIQMEGXJEmSJGkImKBLkiRJkjQETNAlSZIkSRoCvsVd0kKPXj1+t68k\nSZI0II6gS5IkSZI0BEzQJUmSJEkaAibokiRJkiQNARN0SZIkSZKGgAm6JEmSJElDwLe4S1routuL\nfeffPegwJEmadvwWFElLgiPokiRJkiQNARN0SZIkSZKGgAm6JEmSJElDwARdkiRJkqQhYIIuSZIk\nSdIQMEGXJEmSJGkImKBLkiRJkjQETNAlSZIkSRoCJuhaKpLcm+T8JBclOSLJ6uOU+1GStZd1fK3t\nGUkuWsptrJ3k7UuzDUmSJEkPDiboWlruqKpZVbUpcBfwtt6N6axQVS+pqpuWZMNJVlqS9S2mtYGh\nSdCTrDjoGCRJkiSNzQRdy8LpwJPbiPXlSb4OXAQ8LsmVSdZr2y5LcnCSnyc5LMmOSc5I8oskWwEk\n2SrJmUnmJ/lZko3a+j2SHJPkJODEJF9PsvNIAK2+ncYLsO1/QM/nY5Ns35ZvTfKJJBckmZvkUW39\nhu3zgiQfT3LrGFXvC2zYZhPsn2T7JMf2tHNAkj3a8pVJPtXKnptkiyTHJflVkre1Mmn1XNTa3bWt\nn6ze/ZKcB7y6/9MmSZIkaVkyQddS1UazXwwsaKueAhxYVZtU1VWjij8Z+H/Axu3nNcA2wHuBD7Qy\nlwHbVtXmwN7AJ3v23wLYpaqeC3wF2KPFsBbwbOCHi3gYawBzq2oz4DTgLW39Z4HPVtXTgavH2Xcv\n4FdtNsH7+mjrN1U1i+6mxsHALsCzgI+07a8EZgGbATsC+ydZv496/1hVW1TV4aM3JHlruyFw7m03\n3tBHVZIkSZKWBhN0LS2rJTkfOBf4DV3CDHBVVc0dZ58rqmpBVd0HXAycWFVFl9zPaGXWAo5oz47/\nF7BJz/7HV9WfAKrqVOApSR4B7A58t6ruWcRjuQsYGZ2e1xPL1sARbfmbi1j3aMe03wuAs6rqlqq6\nHrizPau/DfCtqrq3qn4PnAps2Ue93x5vQ1UdVFVzqmrOGg9fb3HjlyRJkrSIhulZXT243NFGghdK\nAnDbBPvc2bN8X8/n+7j/Wv0YcHJVvSLJDOCUnn1G1/114HXAbsCbJon3Hh54w2rVnuW7240CgHtZ\nvL+bidqBBx7z6P6YqN3J6p2o3yVJkiQNAUfQNd2sBVzTlveYpOzBwLsAquqSScpeCcxKskKSxwFb\n9RHLXOBVbXm3ccrcAjy05/NVwMwkD2kj4s/vo51epwO7JlmxzQ7YDjh7CdQrSZIkacAcQdd082ng\nkCQfYpJnyqvq90kuBY7uo94zgCuAS4BLgfP62OddwDeSfBD4MXDzGDH8sb3o7iLg/6rqfUm+Q/eS\nvCuA+X200+souqn1FwAFvL+qrgNYzHolSZIkDVjun7krPbi0715fAGxRVX+VPC+h+u+oqkqyG7B7\nVY37pvjpYIOZs2vPw8Z7RYAkSRrPXpuvPOgQJA2xJPOqas5k5RxB14NSkh3pXkz3X0sjOW9mAwek\ne7j+JuDNS6kdSZIkScsBE3Q9KFXVCcATlnIbp9N93ZkkSZIkLTZfEidJkiRJ0hAwQZckSZIkaQiY\noEuSJEmSNARM0CVJkiRJGgIm6JIkSZIkDQHf4i5poUevHr/HVZIkSRoQR9AlSZIkSRoCJuiSJEmS\nJA0BE3RJkiRJkoaACbokSZIkSUPABF2SJEmSpCHgW9wlLXTd7cW+8+8edBiSJElazi2v3yzkCLok\nSZIkSUPABF2SJEmSpCFggi5JkiRJ0hAwQZckSZIkaQiYoEuSJEmSNARM0CVJkiRJGgIm6JIkSZIk\nDQETdEmSJEmShsAyT9CT7Jykkmy8lOr/UZK1l0bdrf7HJDlyEfY7OMkuSyOmcdqbk+Rzy6q95UWS\nK5OsN4Xy2yc5dgnHsHaSty/NNiRJkiQte4MYQd8d+Gn7vcRV1Uuq6qYlVV+SlUbVf21VLbNEe1FV\n1blV9S+DjmNJGH0OxNrA2yctJUmSJGlaWaYJepI1gW2Avwd261m/fZJTkhyZ5LIkhyVJ2/b8JPOT\nLEjy1SQPSbJWksuTbNTKfCvJW9rywhHOJG9IcmGSC5IcOkY86yQ5upWZm+QZbf0+SQ5NcgZw6Kh9\nZiS5qC1vkuTsJOe3Op4ySbvbJflZkl+PjKYnWTPJiUnOa8e4U09br+up/3+TrNjW35pk/yQXJzkh\nyVat/36d5OU9fXpsTxtfa/VfmORVY/TFli22C1qbD02yas9+85Ps0Mru0frt+NbfeyZ5TyszN8k6\nrdxbkpzT6vxuktWXxDlo29/X6r4wyUd6zs2lSb7U+uYnSVZr257c+uqC1tcbTlLPyHV4absue2N/\nR8/52rjts1WSM1sf/Gzk2pzisZ6Z5Bc91/J418a+wIbtuti/rVszo/5+kjwvydE97b8gyVGj45Ik\nSZI0HJb1CPpOwI+r6ufAH5PM7tm2OfAuYCbwJOA5SVYFDgZ2raqnAysB/1RVNwN7Agcn2Q14eFV9\nqbehJJsAHwKeV1WbAe8cI56PAPOr6hnAB4Cv92ybCexYVRON9L8N+GxVzQLmAFdP0u76dDcoXkaX\nZAH8BXhFVW0B7AD8v5ZcPQ3YFXhOq/9e4LVtnzWAk6pqE+AW4OPAC4BXAB8dI87/AG6uqqe3Yz1p\nVF+tAnwbeGeLeUfgDuCfgWp9vztwSDsnAJsCrwS2BD4B3F5VmwNnAm9oZb5XVVu2Oi+luzEz2pTP\nQZIXAk8BtgJmAbOTbNc2PwX4n9Y3NwEjNyMOa+s3A54N/G6SejYCDqyqpwF/5oEj1je08/UF4L1t\n3WXAtq0P9gY+OcVjfQbwPGBrYO8kj2GcawPYC/hVVc2qqve1/f/q7wc4Gdg4ySNamTcBXx0jLkmS\nJElDYFkn6LsDh7flw3ngNPezq+rqqroPOB+YQZckXdESeoBDgO0Aqup4YAHwP8A/jNHW84AjquqG\nVv5PY5TZhjY6W1UnAesmeVjbdkxV3THJ8ZwJfCDJvwFPaOUnavfoqrqvqi4BHtXWBfhkkguBE4DH\ntt36o3cAACAASURBVG3PB2YD5yQ5v31+UtvnLuDHbXkBcGpV3d2WZ4wR5450/USL6cZR2zcCfldV\n57Ttf66qe1r/fKOtuwy4Cnhq2+fkqrqlqq4HbgZ+0BPPSAybJjk9yQK6mwubjBHbopyDF7af+cB5\nwMZ0iTZ018v5bXkeMCPJQ4HHVtVRrZ2/VNXtk9Tz26o6oy1/o8U54nu99bfltYAj0s2u+K9FONbv\nV9Ud7bo5me6mwXjXxlj+6u+nqqq197p072XYGvi/0TsmeWuSc5Oce9uNN4xTvSRJkqSlbZk929um\nPT8PeHqSAlYEKsnICOCdPcXvnSy2JCsATwNuBx4OXL2EQ75tsgJV9c0kZwEvBX6U5B8n2aX3GNN+\nvxZ4BDC7qu5OciWwatt+SFX9+xj13N2SL4D7Ruqtqvuy7J7X7j2W+3o+38f95+5gYOequiDJHsD2\nU2xjvHMQ4FNV9b8PWJnM4K+vo9UmqH+iempU2d7PI230Xqcfo7tp8Yq2/ykTtDuWsdob79oYy3h/\nP1+ju3nyF7obR/f8VcNVBwEHAWwwc/boOCRJkiQtI8tyBH0X4NCqekJVzaiqxwFXANtOsM/ldCOg\nT26fXw+c2pbfTTdt+jXA15KsPGrfk4BXJ1kXFt4gGO102rTxJNvTTV3+c78HlORJwK+r6nPA9+mm\nKffTbq+1gD+0BGwH4Alt/YnALkkeOVJPkieMV8kkjqebrj4S98NHbb8cWD/Jlm37Q1ui39s/TwUe\n38r266F0U8lX5v7p+aMtyjk4DnhzuncakOSxI/00lqq6he7xg51b+Ye0Z8onqufxSbZuy6+he7Hh\nRNYCrmnLe4xTZqJj3SndM//r0t3IOIfxr41b6Pp2UlV1LXAt3WMXX+tnH0mSJEmDsSwT9N2B0S+o\n+i4TvM29qv5C99zsEW2a9H3AF9sLuP4B+NeqOh04jS4B6d33Yrpno09NcgHwn2M0sQ/dc8cX0j0T\n/sYpHtPfARe1KeibAl/vs91ehwFz2vG9ge5ZZto0+A8BP2nxHU/3DPui+Djw8CQXtZh26N1YVXfR\nPe/++bb9eLqR2gOBFVps3wb2qKo76d9/AGcBZ4wc1xj2YYrnoKp+AnwTOLPFdiSTJ6yvB/6ltfMz\n4NGT1HM58M9JLqWbofGFSer/NPCpJPMZf/bHPox/rBfSTW2fC3ysJdbjXRt/BM5o53N/JncY3ZT9\nS/soK0mSJGlAcv9MaUmwcIr7sVW16TJqbx/g1qr6zFKq/wC6l9N9ZbKyG8ycXXseNndphCFJkiT1\nba/NR0+Qnt6SzKuqOZOV8/ulpQexJPPonuX/10HHIkmSJGliJujSKFV1Jd0jC8uqvX2WYt2zJy8l\nSZIkaRgs669ZkyRJkiRJYzBBlyRJkiRpCJigS5IkSZI0BEzQJUmSJEkaAibokiRJkiQNAd/iLmmh\nR6+eB913TkqSJEnThSPokiRJkiQNARN0SZIkSZKGgAm6JEmSJElDwARdkiRJkqQhYIIuSZIkSdIQ\nMEGXJEmSJGkImKBLkiRJkjQETNAlSZIkSRoCJuiSJEmSJA0BE3RJkiRJkoaACbokSZIkSUPABF2S\nJEmSpCFggi5JkiRJ0hBIVQ06BklDIsktwOWDjmOaWw+4YdBBTHP24eKzDxeffbhk2I+Lzz5cfPbh\n4rMPF99GVfXQyQqttCwikTRtXF5VcwYdxHSW5Fz7cPHYh4vPPlx89uGSYT8uPvtw8dmHi88+XHxJ\nzu2nnFPcJUmSJEkaAibokiRJkiQNARN0Sb0OGnQADwL24eKzDxeffbj47MMlw35cfPbh4rMPF599\nuPj66kNfEidJkiRJ0hBwBF2SJEmSpCFggi5JkiRJ0hAwQZdEkhcluTzJL5PsNeh4pqMkX03yhyQX\nDTqW6SrJ45KcnOSSJBcneeegY5pukqya5OwkF7Q+/MigY5qukqyYZH6SYwcdy3SU5MokC5Kc3+9X\nC+mBkqyd5MgklyW5NMnWg45pOkmyUbv+Rn7+nORdg45rukny7vb/k4uSfCvJqoOOaTpK8s7WhxdP\ndh36DLq0nEuyIvBz4AXA1cA5wO5VdclAA5tmkmwH3Ap8vao2HXQ801GS9YH1q+q8JA8F5gE7ey32\nL0mANarq1iQrAz8F3llVcwcc2rST5D3AHOBhVfWyQccz3SS5EphTVTcMOpbpKskhwOlV9eUkqwCr\nV9VNg45rOmr/1rkGeGZVXTXoeKaLJI+l+//IzKq6I8l3gB9V1cGDjWx6SbIpcDiwFXAX8GPgbVX1\ny7HKO4IuaSvgl1X166q6i+4/IDsNOKZpp6pOA/406Dims6r6XVWd15ZvAS4FHjvYqKaX6tzaPq7c\nfrwTP0VJNgBeCnx50LFo+ZRkLWA74CsAVXWXyflieT7wK5PzRbISsFqSlYDVgWsHHM909DTgrKq6\nvaruAU4FXjleYRN0SY8Fftvz+WpMijRgSWYAmwNnDTaS6adNzT4f+ANwfFXZh1P338D7gfsGHcg0\nVsAJSeYleeugg5mGnghcD3ytPWrx5SRrDDqoaWw34FuDDmK6qaprgM8AvwF+B9xcVT8ZbFTT0kXA\ntknWTbI68BLgceMVNkGXJA2VJGsC3wXeVVV/HnQ8001V3VtVs4ANgK3a1Dr1KcnLgD9U1bxBxzLN\nbdOuwxcD/9weA1L/VgK2AL5QVZsDtwG+I2YRtMcDXg4cMehYppskD6ebVflE4DHAGkleN9iopp+q\nuhTYD/gJ3fT284F7xytvgi7pGh54F2+Dtk5a5tpz098FDquq7w06numsTYc9GXjRoGOZZp4DvLw9\nQ3048Lwk3xhsSNNPG3mjqv4AHEX3OJX6dzVwdc8MmCPpEnZN3YuB86rq94MOZBraEbiiqq6vqruB\n7wHPHnBM01JVfaWqZlfVdsCNdO9/GpMJuqRzgKckeWK7y7wbcMyAY9JyqL3g7CvApVX1n4OOZzpK\n8ogka7fl1ehe/njZYKOaXqrq36tqg6qaQfffw5OqyhGjKUiyRnvRI21a9gvppniqT1V1HfDbJBu1\nVc8HfGHmotkdp7cvqt8Az0qyevt/9PPp3g+jKUryyPb78XTPn39zvLIrLaugJA2nqronyZ7AccCK\nwFer6uIBhzXtJPkWsD2wXpKrgQ9X1VcGG9W08xzg9cCC9gw1wAeq6kcDjGm6WR84pL2xeAXgO1Xl\n14RpWXsUcFT373lWAr5ZVT8ebEjT0juAw9rN818DbxpwPNNOu0H0AuAfBx3LdFRVZyU5EjgPuAeY\nDxw02Kimre8mWRe4G/jniV766NesSZIkSZI0BJziLkmSJEnSEDBBlyRJkiRpCJigS5IkSZI0BEzQ\nJUmSJEkaAibokiRJkiQNARN0SZIkSZKGgAm6JEmSJElDwARdkiRJkqQhYIIuSZIkSdIQMEGXJEmS\nJGkImKBLkiRJkjQETNAlSdLQSzInSSWZMehYlgdJ9kny+9bnewxBPEvk/Cc5OMmxSyimK5O8d0nU\nJUkjVhp0AJIkadlI8gjgI8BLgPWBm4CLgH2r6vhBxqbhkWRT4MPAK4EzgZsHG9ES9U4gS6iuLYHb\nllBdkgSYoEuStDz5LrA68PfAL4FHAs8F1h1kUMtKklWq6q4lVNfKVXX3kqhrCD25/T66qmpRKxnG\nPqqqJXazoaquX1J1SdIIp7hLkrQcSLI2sC2wV1WdWFVXVdU5VfWZqjq8p9wqSfZLcnWS25Ock+Rv\nRtW1cZJjktyc5NYkZyZ5etu2QpL/SPLbJHcmWZBkp559Z7Spyq9Kcnxr45IkLxjVxouSXJbkL0lO\nB546avu6Sb7V4rwjycVJ3jSqzClJvpDkM0muB85I8tXRU5xbzL9J8p5x+m77FvNLkpyd5C7gb9q2\nv00yr8V5RZJPJFmlZ9/XtT68JckfkhyR5LE921dO8rkk17b++m2SfXu2PzzJIUlubMd5QpJNerbv\n0c7B85NclOS2JCcneWJPmccl+X6SP7X+vizJbuMc6z7AUe3jfUlqiud19yQnJbkD+Mdx2piwT1qZ\nyc7/yHG/uJW7vV2TayXZJckv2vV5aJLVevZ7wBT3JNslmdvqurmd303btrXa/n9ocfw6ybt69n3A\nFPckj09yVDuuW5J8L8kGvX3bztFuSX7VyhydZL2x+knS8skEXZKk5cOt7eflSVadoNzX6EbVXwNs\nChwC/CDJZgBJHgP8FCjgBcAs4HPAim3/dwLvA/4NeDpdsve9JLNGtfOJtt9mwDnA4UnWbG08Djga\nOL7V/3ng06P2XxU4D3gZsAnwWeB/kzx/VLnX0U1p3hZ4A/Al4EVJ1u8p8wLg0cChE/QLwH7Ah4CN\ngbPS3bg4DDigxfBmYBfgkz37rEI3XXyzFut6wLd6tv8L8ApgN+ApwK7A5T3bDwaeCewEbAXcDvy4\nN+kEHgL8e2t/a2Bt4Is92w+kmzmxQ4vzXXSPN4zlM8Bb2vL67Qf6P6+fau3NpDuHY5mwT/o8/yPH\n/a/Aa4HnA3PoZom8EXgVsHOr/+1jBZFkJeD7dNfzZnT9/N/Ava3Ix9uxvgzYiK5/rxmnrhVaXY+i\n6+cdgMcARyfpnVI/g+4cvwJ4IbA53d+CJHWqyh9//PHHH3/8WQ5+6JKWPwF/oXu2+DPAM3u2bwjc\nBzx+1H5HAwe25U8AVwGrjNPGNcDeo9adAnyjLc+gS+7/sWf7Y9u6bdrnTwI/B9JT5kOtzIwJju9w\n4Muj2r1wjHIX0c0kGPn8beDICerdvrX9qlHrTwP+Y9S6neluhGScujZudW3QPn8OOHGs8nQJewHb\n9axbi+6Z8H9on/doZTbqKfNa4M6ROoELgQ9P4TrZpfsn4iKd139dhOtydJ9Mev7HOe7P0CXX6/Ws\nOxg4dqzPwDqtjueOE9cxwFcniPtK4L1t+QWt7Rk9259E9/e0Y/u8D93f3lo9ZT4I/HKqfeaPP/48\neH8cQZckaTlRVd+lG9X7W+D/gGcDc5N8oBXZgm60+ZI25ffWJLcCL6VL3qEb8ftpjfEsd5KHtfrP\nGLXpp3Qjqr0u7Fm+tv1+ZPv9NGBuVfU+/3zmqLZWTPLBJBcm+WOL85XA40e1M290nHSj6G9q9axD\nNzr9lTHKjXbuqM+zgQ+O6qtvAmvQjciTZIs2vfyqJLf01DES58F0o8Q/T/I/SV7aRmNH+uG+3mOv\n7hnqBTywP++sqt5R92vpRqkf3j5/FvhQukcRPp5kdh/HutAUz+voPhqrvsn6ZNLz34w+7t8D11XV\nDaPWPZIxVNWf6Pr/uCQ/TPKeJL3XzxeAXZNckO4xiedOcFhPA66tqit76v813bno7aOr6oHPwV87\nXnySlk8m6JIkLUeq6i9VdXxVfbSqnk2XmO6T7rnpFehGFLekSxpHfp5GN713sZoe9Xnhy8N6ErGp\n/LvkvXTTm/enm948i26kf5VR5cZ6y/ahwBOSbEM32nw9cFwfbY6uawW6t+L39tUz6Ea+r0+yRqv3\nduD1dP36orbvKgBVdR7d6PO/t/oOAY7vSdLH09uf94yzbYXWxleAJ9I9vvBU4GftWfMlYfR5nfCt\n5v30yRSMddyjX0pXTHBdVdWb6Ka2nwa8HLi8PbpAVf0f8AS6kfn1gB8m+doUYxyJYcSU4pO0/PE/\nCJIkLd8uoftWl1WB+XQj6I+uql+O+hl59nY+sE16XoQ2oqr+TDci+JxRm7Zp7fTrUuCZo57dfdYY\ndf6gqg6tqvOBXzHqRWLjaSOn36O76fBm4JCqum8K8Y04D9h4jL76ZVXdQzd1ez3gA1V1WlVdxhij\npVV1S1UdWVX/RDdb4Xl0b1K/lO7faluPlG2j2U9nav1JVV1dVQdV1d8BewNvncK+S+q8Qn990s/5\nX2Kq6oKq2q+qtqebtv/Gnm03tGtsD7pvP3hjkoeMUc2lwGPS8z3tSZ5EN/Ngqn0kaTnm16xJkrQc\nSLIucATwVbrp5bfQvVTr/cCJLQn7c5LDgIOT/CtdAroO3TPYv66q79G9AOxtwHeSfAK4kW4U9NKW\nKO8PfDTJL+iml7+O7gVtW0wh3C/SjY7/d5ID6RLSt40q83O66cfbADcA76AbJZ7fZxtfAn4MrEz3\nbP6i+ChwbJKrgO/QjehuCmxVVe8HfkP3LPieSf6HbibCx3orSPfm+N8B59ONrr4G+DNwdVXdnuT7\ndC+/eyvdi90+0bZ/s98gk3yW7pGGnwMPoxuxnmrSuCTOK/TRJ/R3/hdbujfd/yPds+bX0D0z/gy6\nqe0k+Sjd38DFdP9mfiXd38GdY1R3At3f1WFJ3tnWfb7tf9KSjl3Sg5cj6JIkLR9uBebSvY37VLqk\n45N0id6uPeXeRDcV+tPAZcCxwHZ0L4ajjaRvRzcd+WS6hPgd3D/d+HN0ydyn6V7G9gq6l6td0G+g\nVfUbumToRcAFwLuBvUYV+zhwNl3ieRrd1OrD+m2DbqT0auCU9qzwlFXVcXQj3ju0WM5ucf6mbb+e\nbjR2Z7qE+MPA6K9yu4Xu7ehn0yVzs4AXV9Xtbfub2rZj2u/VgRdV1R1TCHUFumTxEro3o/+enlHi\nPi32eYX++qTP878k3E436+IIupsXh9BdQ/u17XfS3RC5gO75+4fSvb/hr7THNHaie1zi5PZzHbDz\nqGfpJWlC8b8ZkiRpedO+puwa4B1VNZXEXpKkpcYp7pIkabnRXr62Ht1MgjvopqZLkjQUTNAlSdLy\n5PHAFXTT299UVaPfqi1J0sA4xV2SJEmSpCHgS+IkSZIkSRoCTnGXtNB6661XM2bMGHQYkiRJ0oPK\nvHnzbqiqR0xWzgRd0kIzZszg3HPPHXQYkiRJ0oNKkqv6KecUd0mSJEmShoAJuiRJkiRJQ8AEXZIk\nSZKkIWCCLkmSJEnSEDBBlyRJkiRpCJigS5IkSZI0BEzQJUmSJEkaAibokiRJkiQNARN0SZIkSZKG\ngAm6JEmSJElDwARdkiRJkqQhYIIuSZIkSdIQWGnQAUgaHtfdXuw7/+5Bh7FM7LX5yoMOQZIkSXoA\nR9AlSZIkSRoCJuiSJEmSJA0BE3RJkiRJkoaACbokSZIkSUPABF2SJEmSpCFggi5JkiRJ0hAwQZck\nSZIkaQiYoEuSJEmSNARM0LVUJLk3yflJLkhyXpJnL2I9OyeZuQj7vTzJXpOU2SPJAYsS17BKMifJ\n5wYdhyRJkqSpW2nQAehB646qmgWQ5G+ATwHPXYR6dgaOBS7pd4ckK1XVMcAxi9DetFZV5wLnDjoO\nSZIkSVPnCLqWhYcBNwIkWTPJiW1UfUGSnUYKJXlDkgvbqPuhbdT95cD+bTR+w/bz4yTzkpyeZOO2\n78FJvpjkLODTvaPjSf42yVlJ5ic5IcmjJgo2yT6t/TOT/CLJW9r67ZOckuTIJJclOSxJ2rbZSU5t\ncR2XZP22/pQkc9ryekmubMt7JDk6yfFJrkyyZ5L3tBjnJlmnlZvVPl+Y5KgkD++pd78kZyf5eZJt\ne2I8ti1v1Y5hfpKfJdloiZxNSZIkSUuFI+haWlZLcj6wKrA+8Ly2/i/AK6rqz0nWA+YmOQaYCXwI\neHZV3ZBknar6U9t2bFUdCZDkROBtVfWLJM8EDuype4O2/71J9uiJ5afAs6qqkvwD8H7gXyeJ/xnA\ns4A1gPlJftjWbw5sAlwLnAE8p90U+DywU1Vdn2RX4BPAmydpY9NW36rAL4F/q6rNk/wX8Abgv4Gv\nA++oqlOTfBT4MPCutv9KVbVVkpe09TuOqv8yYNuquifJjsAngVdNEpMkSZKkATFB19LSO8V9a+Dr\nSTYFAnwyyXbAfcBjgUfRJdlHVNUNAFX1p9EVJlkTeDZwRBu4BnhIT5EjqureMWLZAPh2G9VeBbii\nj/i/X1V3AHckORnYCrgJOLuqrm7xnA/MaOs3BY5vca0I/K6PNk6uqluAW5LcDPygrV8APCPJWsDa\nVXVqW38IcETP/t9rv+e1OEZbCzgkyVOAAlYeK4gkbwXeCrD2ox/fR9iSJEmSlgYTdC11VXVmGy1/\nBPCS9nt2Vd3dpnyv2mdVKwA3jST+Y7htnPWfB/6zqo5Jsj2wTz9hj/P5zp5199L9DQW4uKq2HqOe\ne7j/UZLRx9lb1309n++jv7/NkfIjcYz2MbqbAK9IMgM4ZaxKquog4CCADWbOHn3ckiRJkpYRn0HX\nUteeE18R+CPdqO4fWnK+A/CEVuwk4NVJ1m37rNPW3wI8FKCq/gxckeTVrUySbNZHCGsB17TlN/YZ\n9k5JVm3xbA+cM0HZy4FHtJkCJFk5ySZt25XA7La8S59tA1BVNwM3jjxfDrweOHWCXUbrPe49ptK2\nJEmSpGXPBF1Ly2rtxW7nA98G3timnx8GzEmygO4568sAqupiuue2T01yAfCfrZ7Dgfe1F51tCLwW\n+PtW5mJgJya3D920+HnADX3GfyFwMjAX+FhVXTtewaq6iy753q/FdT7dVHyAzwD/lGQ+sF6fbfd6\nI91L8i4EZgEfncK+nwY+1dp2towkSZI05FLljFapV5J9gFur6jODjmVZ22Dm7NrzsLmDDmOZ2Gvz\nMR/JlyRJkpa4JPOqas5k5RxBlyRJkiRpCDjtVRqlqvYZdAySJEmSlj+OoEuSJEmSNARM0CVJkiRJ\nGgIm6JIkSZIkDQETdEmSJEmShoAJuiRJkiRJQ8C3uEta6NGrx+8HlyRJkgbEEXRJkiRJkoaACbok\nSZIkSUPABF2SJEmSpCFggi5JkiRJ0hAwQZckSZIkaQj4FndJC113e7Hv/LsHHcZA+RZ7SZIkDYoj\n6JIkSZIkDQETdEmSJEmShoAJuiRJkiRJQ8AEXZIkSZKkIWCCLkmSJEnSEDBBlyRJkiRpCJigS5Ik\nSZI0BEzQJUmSJEkaAibokiRJkiQNARP0IZakknyj5/NKSa5Pcuwk+20/UibJy5PstbRj7Wl7VpKX\nTBbXMozn4CS7LKO2bh1j3dpJ3r4s2pckSZI0vZmgD7fbgE2TrNY+vwC4ZioVVNUxVbXvEo9sfLOA\nMRP0qUqy0pKoZ8DWBoYmQU+y4qBjkCRJkjQ2E/Th9yPgpW15d+BbIxuSbJXkzCTzk/wsyUajd06y\nR5ID2vKGSeYmWZDk4yMjvm1k+5QkRya5LMlhSdK27Z3knCQXJTmoZ/0pSfZLcnaSnyfZNskqwEeB\nXZOcn2TX8Q5qvNhbvMckOQk4MckKSQ5scR2f5EcjI+JJZic5Ncm8JMclWX+c5nZMcm6L82Vt31WT\nfK31xfwkO7T1P0zyjLY8P8nebfmjSd7Slt/X+uTCJB+Z5PztC2zY+mP/0bMIkhyQZI+2fGWST7Wy\n5ybZoh3Xr5K8rZVJq+eiFvuuPedwonr3S3Ie8OpJ4pUkSZI0ICbow+9wYLckqwLPAM7q2XYZsG1V\nbQ7sDXxykro+C3y2qp4OXD1q2+bAu4CZwJOA57T1B1TVllW1KbAa8LKefVaqqq3afh+uqrtaHN+u\nqllV9e0JYpko9i2AXarqucArgRktrtcDWwMkWRn4fCs3G/gq8Ilx2poBbEV3o+OLrS//GajWF7sD\nh7T1pwPbJlkLuKenH7YFTkvyQuAprb5ZwOwk201wnHsBv2r98b4Jyo34TVXNanEcDOwCPAsYuRHw\nytbuZsCOwP4T3Jjo9ceq2qKqDh+9Iclb2w2Bc2+78YY+qpIkSZK0NDwYphA/qFXVhUlm0CWRPxq1\neS26xPIpQAErT1Ld1sDObfmbwGd6tp1dVVcDJDmfLqn9KbBDkvcDqwPrABcDP2j7fK/9ntfKT8VE\nsR9fVX9qy9sAR1TVfcB1SU5u6zcCNgWOb4P6KwK/G6et77T9f5Hk18DGrd7PA1TVZUmuAp5Klxj/\nC3AF8EPgBUlWB55YVZe3UfQXAvNb3WvSJeynTfH4x3NM+70AWLOqbgFuSXJnkrVb3N+qqnuB3yc5\nFdgS+PMk9Y57s6SqDgIOAthg5uxa3AOQJEmStGhM0KeHY+iS6e2BdXvWfww4uape0ZL4UxajjTt7\nlu8FVmojygcCc6rqt0n2AVYdY597mfq1NFHst/Wxf4CLq2rrPsqOTjonSkLPAeYAvwaOB9YD3kJ3\nE2Kk3U9V1f/20e5Y7uGBM1dWHbV9pE/v44Hn5D4m7uPJ6u2nTyVJkiQNkFPcp4evAh+pqgWj1q/F\n/S+N26OPeuYCr2rLu/VRfiTJuyHJmnTTrSdzC/DQPsr1G/sZwKvas+iPortJAXA58IgkC6e8J9lk\nnDpe3fbfkG76/uV0I+Wvbfs+FXg8cHmbpv9bume1z2zl3sv9I+THAW9u/UGSxyZ55ATxj+6Pq4CZ\nSR7SRsSfP8G+Yzmd7hn/FZM8AtgOOHsJ1CtJkiRpwEzQp4GqurqqPjfGpk8Dn0oyn/5GsN8FvCfJ\nhcCTgZsnafcm4EvARXSJ6Tl9tHEyXaI44UviphD7d+mel78E+AZwHnBzS6R3AfZLcgFwPvDscer4\nDV0S+3/A26rqL3QzA1ZIsoBu+vceVTUyYn068IequqMtb9B+U1U/oXs84My275FMcEOiqv4InNFe\n6rZ/Vf0W+A5dn36H+6fK9+so4ELgAuAk4P1Vdd0SqFeSJEnSgKXKR06XF+1Z6juqqpLsBuxeVTsN\nOq7JJFmzqm5Nsi5dov2cqrpu0HE9GG0wc3btedjcQYcxUHttPtmrHCRJkqSpSTKvquZMVs5n0Jcv\ns4ED0r1V7SbgzQOOp1/HtmnbqwAfMzmXJEmS9GBkgr4cqarT6b6ea1qpqu0HHYMkSZIkLW0+gy5J\nkiRJ0hAwQZckSZIkaQiYoEuSJEmSNARM0CVJkiRJGgK+JE7SQo9ePX7NmCRJkjQgjqBLkiRJkjQE\nTNAlSZIkSRoCJuiSJEmSJA0BE3RJkiRJkoaACbokSZIkSUPAt7hLWui624t959896DCGgm+zlyRJ\n0rLmCLokSZIkSUPABF2SJEmSpCFggi5JkiRJ0hAwQZckSZIkaQiYoEuSJEmSNARM0CVJkiRJYtJT\nSgAAIABJREFUGgIm6JIkSZIkDQETdEmSJEmShoAJuiRJkiRJQ8AEfSlKcm+S85NclOSIJKsPOqZ+\nJHlMkiMHHceykuTlSfZawnW+LckbFmG/PZIcMEmZ7ZM8exHqvjLJelPdT5IkSdKyYYK+dN1RVbOq\nalPgLuBtgw6oH1V1bVXtMug4xpNkpSVZX1UdU1X7LuE6v1hVX1+SdfbYHphygi5JkiRpuJmgLzun\nA09OMiPJpUm+lOTiJD9JshpAkg2T/DjJvCSnJ9m4rT84ycKEOcmt7ff2SU5N8v0kv06yb5LXJjk7\nyYIkG7ZyM5KclOTCJCcmeXxPvZ9L8rO2/y495S/qWT49yXntZ9LEMMmtST6R5IIkc5M8aqI4Ru27\nTpKjW5m5SZ7R1u+T5NAkZwCHJlk9yXeSXJLkqCRnJZnTyn4hybmtfz/SU/eVST7SjmNBT/8uHLVO\n8uo24+GCJKf1bD86yfGtjj2TvCfJ/BbjOmMcxz5J3tuW/6XFeWGSwyc6zlF1/G07rvlJTkjyqCQz\n6G70vLvNztg2ySOSfDfJOe3nOW3/ddv1dXGSLwOZ7NxJkiRJGhwT9GWgjfi+GFjQVj0F+J+q2gS4\nCXhVW38Q8I6qmg28Fziwj+o3o0vYnga8HnhqVW0FfBl4RyvzeeCQqnoGcBjwuZ791we2AV4GjDWK\n/AfgBVW1BbDrqH3HswYwt6o2A04D3tJHHCM+AsxvZT4A9I5CzwR2rKrdgbcDN1bVTOA/gNk95T5Y\nVXOAZwDPHZX83tCO5Qt0fTza3sDftNhf3rN+U+CVwJbAJ4Dbq2pz4ExgsqnsewGbt2MamUUx0XGO\n+CnwrNbO4cD7q+pK4IvAf7XZGacDn22ft6S7lr7c9v8w8NN2nR0F/NUNEYAkb203NM697cYbJjkU\nSZIkSUvLEp0qrL+yWpLz2/LpwFeAxwBXVNXI+nnAjCRr0k1bPiJZOND5kD7aOKeqfgeQ5FfAT9r6\nBcAObXlruuQS4FDg0z37H11V9wGXjIx0j7IycECSWcC9wFP7iOku4Ni2PA94QR9xjNiGdsOiqk5q\no8APa9uOqao7esp9tpW7KMmFPXX8XZK30l3f69Ml9iPbv9cT1yv5a2cAByf5Tk9ZgJOr6hbgliQ3\nAz9o6xfQ3QiYyIXAYUmOBo7u4zhHbAB8O8n6wCrAFePUvyMws+e6eVi7nrYbOcaq+mGSG8fauaoO\nors5xAYzZ9ckxyJJkiRpKTFBX7ruqKpZvStaEnVnz6p7gdXoZjPcNLp8c0/bTpIV6JK1Eb113dfz\n+T76O7+9+481BfrdwO/pRupXAP7SR513V9VIondvn3H047bJCiR5It3I+JZVdWOSg4FVe4qMHO+Y\ncVXV25I8E3gpMC/J7FH7wdT7+aV0yfLfAh9M8vTJjqP5PPCfVXVMku2BfcYptwLdSPsDzk1Pwi5J\nkiRpGnCK+5Coqj8DVyR5NUA6m7XNV3L/FO6X041qT8XPgN3a8mvpRvP7tRbwuzbK/npgxZENSS5b\nCnGc3rbRktIbWt+Mdgbwd63cTGAk6X0YXSJ/c5sR8OKpBJhkw6o6q6r2Bq4HHjeV/ceobwXgcVV1\nMvBvdP25Jv0d51rANW35jT3rbwEe2vP5J9z/OANttgN0jxe8pq17MfDwxTkWSZIkSUuXCfpweS3w\n90kuAC4Gdmrrv0T3LPUFdNPEJx1JHuUdwJvaNPDXA++cwr4HAm9sbW880na6r+ua6hBtP3HsA8xu\nZfblgYnp6LgekeQS4ON0/XVzVV0AzAcuA75Jl8hPxf7tBXIX0d1QuGCK+4+2IvCNJAtaXJ+rqpvo\n7zj3oXvkYR7Q+3D4D4BXjLwkDvgXYE574dwlPPA59+2SXEw31f03i3kskiRJkpai3D8TWepfkpcB\nT6qqfl4atzTaXxFYuar+ku5t9ScAG1XVXYOI58Fig5mza8/D5g46jKGw1+ZTnagiSZIkjS3JvPYi\n6wn5DLoWSVUdO3mppWp14OQkK9ON5L/d5FySJEnSdGaCrmmpvVF90jtQkiRJkjRd+Ay6JEmSJElD\nwARdkiRJkqQhYIIuSZIkSdIQMEGXJEmSJGkI+JI4SQs9evX49WKSJEnSgDiCLkmSJEnSEDBBlyRJ\nkiRpCJigS5IkSZI0BEzQJUmSJEkaAibokiRJkiQNARN0SZIkSZKGgF+zJmmh624v9p1/96DDGDp+\n9ZwkSZKWBUfQJUmSJEkaAibokiRJkiQNARN0SZIkSZKGgAm6JEmSJElDwARdkiRJkqQhYIIuSZIk\nSdIQMEGXJEmSJGkImKBLkiRJkjQETNAXU5Kdk1SSjRejjllJXrIk41ocSfZIcsAY6x+S5IQk5yfZ\ndYL9P5pkx7Z8SpI5SzPe1s7BSXZZAvXMSPKaKe7zmCRH9lHu1UkuTXLyIsS1R5LH9Hz+cpKZU61H\nkiRJ0vAyQV98uwM/bb8X1SxgkRP0JCstRttTsTlAVc2qqm+PV6iq9q6qExalgWV4LOOZAUwpQa+q\na6uqn5sDfw+8pap2WIS49gAWJuhV9Q9Vdcki1CNJkiRpSJmgL4YkawLb0CVeu43a9m9JFiS5IMm+\nbd3C0eQk6yW5MskqwEeBXUdGppOskeSrSc5OMj/JTmO0vX2S05McA1zS1r2u7XN+kv9NsmJbf2uS\nT7RY5iZ5VFv/t0nOam2cMLJ+nGN9JPANYMtW/4ZJ9k5yTpKLkhyUJK3smKPZSW7tWd4lycE95b+Y\n5Czg030ef5IckOTyJCcAj+zZdmWS9drynCSntOVJ6wX2BbZtx/juJKsm+Vo7l/OT/FVy3UbdL2rL\neyT5XpIfJ/lFkk+39XvTXStfSbJ/khXb73OSXJjkH3vqe8C10/pyDnBYi2u1UdfS7q38RUn26+3v\nsc67JEmSpOFkgr54dgJ+XFU/B/6YZDZAkhe3bc+sqs2AT49XQVXdBewNfLtnZPqDwElVtRWwA7B/\nkjXG2H0L4J1V9dQkTwN2BZ5TVbOAe4HXtnJrAHNbLKcBb2nrfwo8q6o2Bw4H3j9BnH8A/gE4vcX5\nK+CAqtqyqjYFVgNeNnF3TWgD4NlV9Z4+j/8VwEbATOANwLP7aKOfevfi/mP8L+Cfgaqqp9PNkjgk\nyaqTtDOL7lw8ne7Gy+Oq6qPAucBrq+p9dDd1bq6qLYEtgbckeeJY105VHdmz76yqumOkoXTT3vcD\nntfa3TLJzm3zeOf9AZK8Ncm5Sc697cYbJjk0SZIkSUuLCfri2Z0usaX9HpnmviPwtaq6HaCq/jTF\nel8I7JXkfOAUYFXg8WOUO7uqrmjLzwdmA+e0/Z4PPKltuws4ti3Po5vGDV1SfFySBcD7gE2mGOcO\nbQR+AV2CONX9ex1RVfe25X6OfzvgW1V1b1VdC5zURxv99muvbehmDlBVlwFXAU+dZJ8Tq+rmqvoL\n3eyGJ4wTyxtaLGcB6wJPYerXzpbAKVV1fVXdAxxG1zcw/nl/gKo6qKrmVNWcNR6+3iTNSZIkSVpa\nBv2877SVZB26pPTpSQpYEagk75tgt3u4/6bIRKOwAV5VVZdPEsZto/Y5pKr+fYxyd1dVteV7uf+8\nfx74z6o6Jsn2wD6TtHd/Y90o8oHAnKr6bZJ9mPiYAKpneXTZ0cfSz/GPZ7x+Xtx6+3Vnz3Jvf/cK\n8I6qOu4BK5O/WYJxjHfeJUmSJA0hR9AX3S7AoVX1hKqaUVWPA64AtgWOB96UZHVYmMwDXEk3yj2y\n/4hbgIf2fD4OeEfPM92b9xHPicAu7VlxkqyTZKyR215rAde05Tf20UavkcT3hnTP4vfzkrTfJ3la\nkhXopqiPp5/jP41u+viKSdanm7I+4kru7+dXTbHe0efidNqjAkmeSjfiviQS/OOAf0qy8kjdbbr9\neNfO6LhGnA08N907DVakm8Vx6hKIT5IkSdIyZoK+6HYHjhq17rvA7lX1Y+AY4Nw2hfm9bftn6JKy\n+UDvXOKTgZm5/+vLPgasDFyY5OL2eULtjd4fAn6S5EK6RG/9SXbbBzgiyTxgSg8fV9VNwJeAi+iS\nzXP62G0vuinXPwN+N0G5fo7/KOAXdFPIvw6c2bPtI8Bnk5xLN3I8lXovBO5tL1Z7N90sgRXaNP5v\nA3tU1Z1j7DdVX26xn9deMPe/wEoTXDsHA18ceUncSCVV9Tu6fj0ZuACYV1XfXwLxSZIkSVrGcv8M\nWEnLuw1mzq49D5s76DCGzl6brzzoECRJkjSNJZlXVXMmK+cIuiRJkiRJQ8AEXZIkSZKkIWCCLkmS\nJEnSEDBBlyRJkiRpCJigS5IkSZI0BEzQJUmSJEkaAisNOgBJw+PRq8evFJMkSZIGxBF0SZIkSZKG\ngAm6JEmSJElDwARdkiRJkqQhYIIuSZL+f3v3HXZZVd59/PujKFWqSlNQVJAiZQYMCDgg+toiGDGA\nCIItJvZOYsMasCb2oNJ0RCLFFoP0KnUYZhhqgmBEUECRKjjA/f6x1zMcHp429Zxn/H6ua67ZZ+9V\n7r32GeXea+19JEnSADBBlyRJkiRpAJigS5IkSZI0APyZNUnz/O6+4tCZc/sdxqTiz9JJkiRpUXEG\nXZIkSZKkAWCCLkmSJEnSADBBlyRJkiRpAJigS5IkSZI0AEzQJUmSJEkaACbokiRJkiQNABN0SZIk\nSZIGgAm6JEmSJEkDYEIJepI9k1SSTRe0oyRbJ3npgtZf1JIcmOSrI+x/fJLTklyeZO8x6n8iye5t\n+6wkUxdnvK2fo5LstQja2SjJa+azzrQkP1vYvhe10a7jEur7LUkO6EffkiRJkpY+E51B3xc4r/29\noLYGFjhBT7LcQvQ9P7YBqKqtq+q40QpV1Uer6rQF6WAJnstoNgLmK0HXY1XVN6vqmH7HIUmSJGnp\nMG6CnmQVYCfgDcA+w459MMkVSWYlObTtmzebnGTtJDcmeRzwCWDvoZnpJCsnOSLJxUlmJtljhL6n\nJTk3yU+Aq9q+17Y6lyf5jyTLtv33JPl0i+XCJE9u+/82yUWtj9OG9o9yrk8Cvgds19rfOMlHk1yS\nZE6Sw5OklR1xNjvJPT3beyU5qqf8N5NcBHx2guefJF9Ncm2S04An9Ry7McnabXtqkrPa9rjtAocC\nO7dzfHeSFZIc2a7lzCS7jjJEqyQ5Psk1Sab3jMVoY7RxkpOTzGjXcdOesfhGu06/atf5iCRXD41X\nK7dvi2lOksN69h+U5LokFwPPa/tWTXJDkuXb5ycMfW7fycPamFyXZOdWZqMW12Xtz45t/7QkZyf5\ncYvv0CT7tfpXJNm4lTskyfva9jPa92tWa2vjEa7nAUlmtzLfbftG/H72tt0+z2nxrpzkv1obc9JW\neSSZ0mKekeQXSdZt+9+R5KrW7w9Gua6SJEmSBsBEZtD3AE6uquuAPySZApDkJe3Yc6tqK+CzozVQ\nVX8BPgoc1zMz/SHgjKraHtgV+FySlUeovi3wzqp6VpJnA3sDz6uqrYGHgP1auZWBC1ss5wBvavvP\nA/6mqrYBfgB8YIw4bwXeCJzb4rwe+GpVbVdVWwArAi8fe7jGtAGwY1W9Z4Ln/0pgE2Az4ABgxwn0\nMZF2D+aRc/wS8FagqmpLulUSRydZYYS2twHe1eJ5Oi05ZvQxOhx4e1VNAd4HfL2nrTWAHYB3Az8B\nvgRsDmyZ7nGI9YDDgN3oVl9sl+5Ri3WBj7e+d2qxUFV3A2cBL2vt7wOcWFVz2+fl2pi8C/hY23cr\n8MKq2pbue/Xlnvi2At4CPBvYH3hWq/9t4O0jjM104Gvt+7cjcEvvwSSbAx8Gdmtl3tkOTfj72bwY\nuLmqtmrjfXK7KfEVYK821kcAn27lDwa2qarntPN5jCRvTnJpkkvvveP2cbqXJEmStLhMZKn1vsC/\nt+0ftM8zgN2BI6vqPoCq+uN89v0i4BU9s4QrAE8Frh5W7uKquqFtvwCYAlzSJmlXpEuyAP4CDD0j\nPQN4YdveADiuJXaPA4bamqhdk3wAWAlYE7gS+Ol8tjHkh1X1UNueyPnvAhzb6tyc5IwJ9DHRce21\nE12CR1Vdk+TXwLOA2cPKXVxVNwEkuZxuqfx5jDBGSc6kS1R/2K4VwON72vppVVWSK4DfV9UVrd0r\nW7sbAmdV1W1t//Q2Hgzbf1yLFbrk+QPAj4CDeOQmDcCJ7e8ZrX2A5YGvJhm62fOsnvKXVNUtrY/r\ngVPa/ivobnzMk2RVYP2qOqmN4f081m501//2Vmbo38v8fj+vAL7QVhT8rKrOTbIFsAVwahvrZXnk\nBsFsYHqSH7VxeYyqOpzuZgobbDalxulfkiRJ0mIyZoKeZE26xGLLJEX3H/6V5P1jVHuQR2bmR5qF\nndc88KqqunacGO8dVufoqvrnEcrNraqh5OIhHjm3rwBfrKqfJJkGHDJOf4901s0ifx2YWlW/SXII\nY58TQG+CM7zs8HOZyPmPZrRxXth2x/JAz/ZDwHJjjNEywJ/aSoex2np4WLsP0127uY+pMY6qOr8t\nA58GLFtVc0bor/e78W7g93Sz5csA949QfniMQ/EtKqN9P3uvL7RrXFXXJdmW7n0On0pyOnAScGVV\n7TBC+y+ju7Hxt8CHkmxZVQ8uwvglSZIkLSLjLXHfC/huVW1YVRtV1VPoZvh2Bk4FDkqyEsxL5gFu\npJvlHqo/5G5g1Z7PvwDensx7XnmbCcR7OrBXumfFSbJmkg3HqbMa8Nu2/boJ9NFrKPG9Pd2z+BN5\ng/rvkzw7yTJ0S9RHM5HzP4fuuf1l2wxr78ztjTwyzq+az3aHX4tzaY8KJHkW3Yz7RBP8Eceoqu4C\nbkjy6tZukmw1wTYBLgaen+49BsvSrdw4G7io7V+rLe1+9bB6xwDfB46cQB+rAbdU1cN0y9iXnY/4\n5mnL629KsifM+yWAlYYVOwN4dZK1Wpmhfy+jfT9vpHu8g5aQP61trwfcV1XfAz7XylwLPDHJDq3M\n8kk2b9/Bp1TVmcAHW1+rLMg5SpIkSVr8xkvQ96Wbnet1ArBvVZ1M9+zwpW2589CS6s8D/5hkJrB2\nT70zgc3yyM+XfZJuifHstqz5k+MFW1VX0T3He0qS2XQ3CdYdp9ohdMusZwDz9YBtVf0J+BYwhy7x\nvWQC1Q6mW2r/S4Y9hzzMRM7/JOB/6F6QdwxwQc+xjwP/nuRSulnh+Wl3NvBQe9HYu+lmwJdpy82P\nAw6sqgdGqPcY44zRfsAbksyiezRgpBfWjdbuLXRjeSYwC5hRVT9u+w+hG4vzeezS/el0z7cfO4Fu\nvg68rsW3KY9e4TC/9gfe0b6XvwTW6T1YVVfSPRd+duvvi+3QIYz8/TwBWLNdw7cB17X9WwIXt39z\nHwM+1d7xsBdwWGv7crrHC5YFvteu60zgy+16SZIkSRpAeWRVuDT5pXuz/h5VtX+/Y5mMNthsSr1t\n+oX9DmNSOXib5fsdgiRJkgZckhlVNXW8cv3+PW5pkUnyFeAldM9nS5IkSdKkYoKupUZVjfTzZ5Ik\nSZI0KUzkd9AlSZIkSdJiZoIuSZIkSdIAMEGXJEmSJGkAmKBLkiRJkjQATNAlSZIkSRoAvsVd0jzr\nrBR/11uSJEnqE2fQJUmSJEkaACbokiRJkiQNABN0SZIkSZIGgAm6JEmSJEkDwARdkiRJkqQB4Fvc\nJc3zu/uKQ2fO7XcY6gPf3i9JktR/zqBLkiRJkjQATNAlSZIkSRoAJuiSJEmSJA0AE3RJkiRJkgaA\nCbokSZIkSQPABF2SJEmSpAFggi5JkiRJ0gAwQZckSZIkaQCYoC9mST6U5Moks5NcnuS5C9jOtCQ7\n9nx+S5IDFjK2G5OsvTBttHbuWdg2xmn/wCTr9Xz+dpLNxqmzSM5tUCTZKMlrJlBuvSTHL4mYJEmS\nJC1ay/U7gKVZkh2AlwPbVtUDLWF83AI2Nw24B/glQFV9c5EEOTkcCMwBbgaoqjf2NZolLMlywEbA\na4Dvj1W2qm4G9loCYUmSJElaxJxBX7zWBW6vqgcAqur2lkCRZEqSs5PMSPKLJOu2/Wcl+fc22z4n\nyfZJNgLeAry77d85ySFJ3tfqPCPJaUlmJbksyca9QSRZOcl/teNzkuzdc/jtrc4VSTZt5ddM8qM2\n639hkue0/askObKVnZ3kVcP6WTvJBUleNtagJPlGkkvbyoKP9+x/zJgk2QuYCkxv575iG6Oprc6+\nLZ45SQ4bpb/3tONzkryrZ/9Hklyb5LwkxyZ5X5KNk1zWU+aZvZ979vfGsHaSG9v2gUlOTHJykv9J\n8tmeOi9uYz0ryek91+aIJBcnmZlkj552fpLkDOB04FBg5zYG724z6ue29i4bWl3R9s8ZLxZJkiRJ\ng8cZ9MXrFOCjSa4DTgOOq6qzkywPfAXYo6puawnzp4HXt3orVdXWSXYBjqiqLZJ8E7inqj4PkOQF\nPf1MBw6tqpOSrMBjb7y8GLi5ql7W6q7Wc+z2qto2yT8B7wPeCHwcmFlVeybZDTgG2Br4CHBnVW3Z\n2lljqJEkTwZ+Any4qk4dZ1w+VFV/TLIscHq7AXD1SGNSVa9P8jbgfVV1aetrqM/1gMOAKcAdwClJ\n9qyqH/XENQU4CHguEOCiJGfTffdfBWwFLA9cBsyoquuT3Jlk66q6vNU9cpzzGW5rYBvgAeDaJF8B\n7ge+BexSVTckWXNoLIAz2nmuDlyc5LR2bFvgOW2sprUxeHk7r5WAF1bV/UmeCRxLdyNj3Fiq6je9\nBZK8GXgzwOrrPHU+T1WSJEnSomKCvhhV1T0tQdwZ2BU4LsnBwKXAFsCpLdlcFrilp+qxrf45SZ7Q\nErcRJVkVWL+qTmp17h+h2BXAF9oM88+q6tyeYye2v2cAf9e2d6JLXqmqM5KsleQJwO7APj3nd0fb\nXJ5ulvetVXX2WGPS/H1LCpejW2WwGfAwY4/JSLYDzqqq2wCSTAd2AX7UU2Yn4KSqureVOZHueiwD\n/LiN1/1JftpT59vAQUneA+wNbD+Bc+p1elXd2fq7CtgQWAM4p6puAKiqP7ayLwJekbYaAlgBGMqS\nT+0pN9zywFeTbA08BDxrPmJ5VIJeVYcDhwNssNmUmp8TlSRJkrTomKAvZlX1EHAWcFaSK4DX0SXD\nV1bVDqNVG+fz/MZwXZJtgZcCn0pyelV9oh1+oP39EAv+fXiQ7pz+HzBmgp7kaXQz9dtV1R1JjqJL\nSsPYY7IknQB8DDiDblb9DyOUeZBHViqsMOzYAz3b441rgFdV1bWP2tm9TPDeMeq9G/g93QqAZehm\n6EcyP7FIkiRJ6iOfQV+MkmzSlh8P2Rr4NXAt8MR0L5EjyfJJNu8pt3fbvxPdkvI7gbuBVYf3UVV3\nAzcl2bPVeXxb/twbx3rAfVX1PeBzdEunx3IusF+rO41uGfxdwKnAW3vaHVriXnTL8zdN8sGe49eM\n0PYT6BLPO9uy+Je0/WONyYjnDlwMPL89A74ssC+PvUFwLrBnkpWSrAy8su07H/jbJCskWYXuZX7d\nyXSz6r8AvsHoy9tvpFtaDxN7KduFwC7tBgU9S9x/QfcegLT924xSf/gYrAbcUlUPA/vTrTiQJEmS\nNImZoC9eqwBHJ7kqyWy6pdyHVNVf6JK6w5LMAi4Hduypd3+SmcA3gTe0fT8FXtleErbzsH72B97R\n+vglsM6w41vSPdt8Od3M8KfGifsQYEpr71C6WX9avTXSvWxtFt2yfWDeSoF9gd2S/FO6N9ZneMNV\nNQuYCVxD90by89v+scbkKOCb7dxX7GnrFuBg4ExgFt1s94+H9XdZq38xcBHw7aqaWVWX0D0zPxv4\nb7rHAO7sqTqdbtn9KaOM0eeBf2zXadyfc2vL8N8MnNjO77h26JN0y9VnJ7myfR7JbOChdC+Yezfw\ndeB1ra1NGXu2XZIkSdIkkCofOR0kSc6i54Vok1WSlwNPr6ov9zuW0SRZpb0nYCXgHODNLaGnPRO+\nWlV9pK9BLmEbbDal3jb9wn6HoT44eJvl+x2CJEnSUivJjKoa6aXOj+LzqFosqupn/Y5hAg5Pshnd\nM+RH9yTnJwEbA7v1MzhJkiRJf11M0AdMVU3rdwx/LarqNaPsf+WSjkWSJEmSfAZdkiRJkqQBYIIu\nSZIkSdIAMEGXJEmSJGkAmKBLkiRJkjQATNAlSZIkSRoAvsVd0jzrrBR/D1uSJEnqE2fQJUmSJEka\nACbokiRJkiQNABN0SZIkSZIGgAm6JEmSJEkDwARdkiRJkqQB4FvcJc3zu/uKQ2fO7XcY0l8tf0VB\nkqS/bs6gS5IkSZI0AEzQJUmSJEkaACbokiRJkiQNABN0SZIkSZIGgAm6JEmSJEkDwARdkiRJkqQB\nYIIuSZIkSdIAMEGXJEmSJGkAmKCPIcmZSf7fsH3vSvKNBWzvwCTrjXH8E0l2X8C2b0yy9oLUnUhs\n49SdluRnC9r3wkpyVJK92va3k2y2mPt7fJLTklyeZO/ePpPcM8E23pLkgLY9L/4xyj/q+iyJ85Qk\nSZK0ZC3X7wAG3LHAPsAvevbtA3xgAds7EJgD3Dz8QJJlq+qjC9jufGv9PTSR2AZJkuWq6sHRjlfV\nG5dAn9u0vrZun49bgPa+OZ9hHEjP9Vkc5ylJkiSpv5xBH9vxwMuSPA4gyUbAesC57fP7k1ySZHaS\njw+VSXJ1km8luTLJKUlWbDOkU4HpbeZ1xTbrfViSy4BXD5sJ3i7JL5PMSnJxklXbLOpXh4JL8rMk\n04YHneRHSWa0/t/cs/+eJF9IMgvYoWf/SLF9tJ3bnCSHJ0kr+4w2ezwryWVJNm7NrJLk+CTXJJme\nzm5JftTTzwuTnNS2X9zqz0pyetu3fZILksxs575J239gkp8kOQM4vbX91STXJjkNeFJPH2clmdq2\nv5Hk0jYOHx/pAifZOsmF7RqelGSNnnb+LcmlwDt7yj8J+B6wXRurjXv7bGW+1Po8PckTR2ovySFJ\n3jdCPI8Z91GuT+957pvkilbnsGHX+9NtjC9M8uSRxkCSJEnSYDBBH0NV/RG4GHhJ27U3EiGTAAAe\nJUlEQVQP8J9VVUleBDwT2B7YGpiSZJdW7pnA16pqc+BPwKuq6njgUmC/qtq6qv7cyv6hqratqh8M\n9ZvuhsBxwDuraitgd2Co/ES8vqqm0CV170iyVtu/MnBRVW1VVef1nOdIsX21qrarqi2AFYGXt+LT\n27ltBewI3NL2bwO8C9gMeDrwPOBMYNOhJBU4CDiiff5WG5etgFe349cAO1fVNsBHgc/0nNO2wF5V\n9XzglcAmra8DWhwj+VBVTQWeAzw/yXNGKHMM8MGqeg5wBfCxnmOPq6qpVfWFnrG6FXgjcG4bq+uH\ntbcycGm79meP194IHjPuY3x3SLfs/TBgN7rv4XZJ9uyJ5cI2xucAbxqpwyRvbjcyLr33jtvHCE2S\nJEnS4mSCPr6hZe60v49t2y9qf2YClwGb0iXmADdU1eVtewaw0Rjtj7Q8ehPglqq6BKCq7hprWfcI\n3tFmyS8EntIT10PACRNsY9ckFyW5gi752zzJqsD6VXVSi+v+qrqvlb+4qm6qqoeBy4GNqqqA7wKv\nTbI63az9fwN/A5xTVTe0dv7Y2lgN+GGSOcCXgM174jm1p9wuwLFV9VBV3QycMco5/H261QkzW1uP\nemY7yWrA6lV1dtt1dGt7yHwtXW8e7qn3PWCn+WzvMeM+TvntgLOq6rb2HZnOI+fwF2Do3QCjfg+r\n6vB242Dqymss8GsMJEmSJC0kn0Ef34+BLyXZFlipqma0/QH+tar+o7dwumXwD/TseohuJnQ0985H\nLA/y6JsqKwwv0Ja87w7sUFX3JTmrp9z9w547H1GSFYCvA1Or6jdJDhmpr2GGn/PQd+tI4KfA/cAP\nq+rBtlp+JJ8EzqyqV7ZxPKvn2PyME0meBrwP2K6q7khyFOOfw3Dz1ecoaqLtLeC4j2Vuu0kCj74m\nkiRJkgaQM+jjqKp76JZqH8Ejs+fQvTju9UlWAUiyfns+eSx3A6tOoNtrgXWTbNfaXjXJcsCNwNZJ\nlknyFLrl9cOtBtzRkvNN6WarJ6I3tqGk8PZ2fnsBVNXdwE1DS6jTvc18pbEabTPcNwMfpkvWoZvZ\n36Ul0SRZsyf237btA8do9hxg7yTLJlkX2HWEMk+gS4jvbM9ev2R4gaq6E7gjyc5t1/50y9IXxjK0\n8QJeA5w3RtnhRhz3ZrTvzsV0y/fXTrIssC8Lfw6SJEmS+sAZtYk5FjiJR5a6U1WnJHk2cEGbEb4H\neC3dTOVojgK+meTP9Lykbbiq+kuSvYGvJFmR7vnz3YHzgRuAq4Cr6ZbWD3cy8JYkV9Ml+hdO8ByH\nx/YtureG/w64pKfc/sB/JPkEMJdHnh8fy3TgiVV1dTu/29K9vO7EJMsAtwIvBD4LHJ3kw8B/jdHe\nSXTLv68C/g+4YHiBqpqVZCbdc+2/oRu7kbyunfdKwK/onpNfGPcC27dzuBXYe6IVq+pPSUYb96MY\n4btTVbckOZjuJlKA/6qqHy/kOUiSJEnqgzyyAlZaPNK9eX5mVX2n37FobBtsNqXeNn2i93QkLWoH\nb7N8v0OQJEmLQZIZ7QXWY3IGXYtVkhl0s8rv7XcskiRJkjTITNC1WLWfe5MkSZIkjcOXxEmSJEmS\nNABM0CVJkiRJGgAm6JIkSZIkDQATdEmSJEmSBoAJuiRJkiRJA8C3uEuaZ52V4u8wS5IkSX3iDLok\nSZIkSQPABF2SJEmSpAFggi5JkiRJ0gAwQZckSZIkaQCYoEuSJEmSNAB8i7ukeX53X3HozLn9DkPS\nEuKvNkiSNFicQZckSZIkaQCYoEuSJEmSNABM0CVJkiRJGgAm6JIkSZIkDQATdEmSJEmSBoAJuiRJ\nkiRJA8AEXZIkSZKkAWCCLkmSJEnSAFjiCXqSPZNUkk0XU/s/T7L64mi7tb9ekuMXoN5RSfZaHDGN\n0t/UJF9eUv39tUhyY5K156P8tCQ/W8QxrJ7knxZnH5IkSZKWvH7MoO8LnNf+XuSq6qVV9adF1V6S\n5Ya1f3NVLbFEe0FV1aVV9Y5+x7EoDL8GYnXgn8YtJUmSJGlSWaIJepJVgJ2ANwD79OyfluSsJMcn\nuSbJ9CRpx16QZGaSK5IckeTxSVZLcm2STVqZY5O8qW3Pm+FMckCS2UlmJfnuCPGsmeRHrcyFSZ7T\n9h+S5LtJzge+O6zORknmtO3Nk1yc5PLWxjPH6XeXJL9M8quh2fQkqyQ5Pcll7Rz36OnrtT3t/0eS\nZdv+e5J8LsmVSU5Lsn0bv18leUXPmP6sp48jW/uzk7xqhLHYrsU2q/W5apIVeurNTLJrK3tgG7dT\n23i/Lcl7WpkLk6zZyr0pySWtzROSrLQorkE7/v7W9uwkH++5Nlcn+VYbm1OSrNiOPaON1aw21huP\n087Q9/Dq9r3sjf3tPddr01Zn+yQXtDH45dB3cz7P9YIk/9PzXR7tu3EosHH7Xnyu7Vslw/79JNkt\nyY96+n9hkpOGxyVJkiRpMCzpGfQ9gJOr6jrgD0mm9BzbBngXsBnwdOB5SVYAjgL2rqotgeWAf6yq\nO4G3AUcl2QdYo6q+1dtRks2BDwO7VdVWwDtHiOfjwMyqeg7wL8AxPcc2A3avqrFm+t8C/HtVbQ1M\nBW4ap9916W5QvJwuyQK4H3hlVW0L7Ap8oSVXzwb2Bp7X2n8I2K/VWRk4o6o2B+4GPgW8EHgl8IkR\n4vwIcGdVbdnO9YxhY/U44DjgnS3m3YE/A28Fqo39vsDR7ZoAbAH8HbAd8GngvqraBrgAOKCVObGq\ntmttXk13Y2a4+b4GSV4EPBPYHtgamJJkl3b4mcDX2tj8CRi6GTG97d8K2BG4ZZx2NgG+XlXPBu7i\n0TPWt7fr9Q3gfW3fNcDObQw+CnxmPs/1OcBuwA7AR5OsxyjfDeBg4Pqq2rqq3t/qP+bfD3AmsGmS\nJ7YyBwFHjBCXJEmSpAGwpBP0fYEftO0f8Ohl7hdX1U1V9TBwObARXZJ0Q0voAY4GdgGoqlOBK4Cv\nAW8coa/dgB9W1e2t/B9HKLMTbXa2qs4A1kryhHbsJ1X153HO5wLgX5J8ENiwlR+r3x9V1cNVdRXw\n5LYvwGeSzAZOA9Zvx14ATAEuSXJ5+/z0VucvwMlt+wrg7Kqa27Y3GiHO3enGiRbTHcOObwLcUlWX\ntON3VdWDbXy+1/ZdA/waeFarc2ZV3V1VtwF3Aj/tiWcohi2SnJvkCrqbC5uPENuCXIMXtT8zgcuA\nTekSbei+L5e37RnARklWBdavqpNaP/dX1X3jtPObqjq/bX+vxTnkxN722/ZqwA/Tra740gKc64+r\n6s/te3Mm3U2D0b4bI3nMv5+qqtbfa9O9l2EH4L+HV0zy5iSXJrn03jtuH6V5SZIkSYvbEnu2ty17\n3g3YMkkBywKVZGgG8IGe4g+NF1uSZYBnA/cBawA3LeKQ7x2vQFV9P8lFwMuAnyf5h3Gq9J5j2t/7\nAU8EplTV3CQ3Aiu040dX1T+P0M7clnwBPDzUblU9nCX3vHbvuTzc8/lhHrl2RwF7VtWsJAcC0+az\nj9GuQYB/rar/eNTOZCMe+z1acYz2x2qnhpXt/TzUR+/39JN0Ny1e2eqfNUa/Ixmpv9G+GyMZ7d/P\nkXQ3T+6nu3H04GM6rjocOBxgg82mDI9DkiRJ0hKyJGfQ9wK+W1UbVtVGVfUU4AZg5zHqXEs3A/qM\n9nl/4Oy2/W66ZdOvAY5MsvywumcAr06yFsy7QTDcubRl40mm0S1dvmuiJ5Tk6cCvqurLwI/plilP\npN9eqwG3tgRsV2DDtv90YK8kTxpqJ8mGozUyjlPplqsPxb3GsOPXAusm2a4dX7Ul+r3j8yzgqa3s\nRK1Kt5R8eR5Znj/cglyDXwCvT/dOA5KsPzROI6mqu+keP9izlX98e6Z8rHaemmSHtv0auhcbjmU1\n4Ldt+8BRyox1rnuke+Z/LbobGZcw+nfjbrqxHVdV3QzcTPfYxZETqSNJkiSpP5Zkgr4vMPwFVScw\nxtvcq+p+uudmf9iWST8MfLO9gOuNwHur6lzgHLoEpLfulXTPRp+dZBbwxRG6OITuuePZdM+Ev24+\nz+nvgTltCfoWwDET7LfXdGBqO78D6J5lpi2D/zBwSovvVLpn2BfEp4A1ksxpMe3ae7Cq/kL3vPtX\n2vFT6WZqvw4s02I7Djiwqh5g4j4CXAScP3ReIziE+bwGVXUK8H3gghbb8YyfsO4PvKP180tgnXHa\nuRZ4a5Kr6VZofGOc9j8L/GuSmYy++uMQRj/X2XRL2y8EPtkS69G+G38Azm/X83OMbzrdkv2rJ1BW\nkiRJUp/kkZXSkmDeEvefVdUWS6i/Q4B7qurzi6n9r9K9nO4745XdYLMp9bbpFy6OMCQNoIO3Gb74\nTJIkLQ5JZlTV1PHK+fvS0lIsyQy6Z/nf2+9YJEmSJI3NBF0apqpupHtkYUn1d8hibHvK+KUkSZIk\nDYIl/TNrkiRJkiRpBCbokiRJkiQNABN0SZIkSZIGgAm6JEmSJEkDwARdkiRJkqQB4FvcJc2zzkrx\nd5ElSZKkPnEGXZIkSZKkAWCCLkmSJEnSADBBlyRJkiRpAJigS5IkSZI0AEzQJUmSJEkaAL7FXdI8\nv7uvOHTm3H6HIWmS8FcfJElatJxBlyRJkiRpAJigS5IkSZI0AEzQJUmSJEkaACbokiRJkiQNABN0\nSZIkSZIGgAm6JEmSJEkDwARdkiRJkqQBYIIuSZIkSdIAMEFfCiXZM0kl2XQh2tg6yUsXZVyDIMmB\nSb7att+S5IBxyh+S5H0j7B+3bit3bJLZSd694FHPa+tfhn3+5cK2KUmSJGlwLNfvALRY7Auc1/7+\n2AK2sTUwFfj5ogpqSUsSIFX18EjHq+qbC9r2ROomWQfYrqqeMdF2kyxXVQ+OcvhfgM/0xLDjRNuV\nJEmSNPicQV/KJFkF2Al4A7DPsGMfTHJFkllJDm37zkoytW2vneTGJI8DPgHsneTyJHsnWTnJEUku\nTjIzyR4j9D0tydlJfpzkV0kOTbJfq3NFko2TrJrkhiTLtzpPGPrcZu0vbDPOJyVZo5V5RpLTWtyX\ntXZWSXJ6+3zFUDxJNkpybZJjgDnAU5IclOS6JBcDz+uJd97seJI3Jbmk9XFCkpXGGefeumclOayd\n53VJdm7FTgHWb2O482h9JDkqyTeTXAR8tp3bke28Zid5VbteK7a2prd697S/k+RzSea0Onv3XI+z\nkhyf5Jok09tNC0mSJEkDyAR96bMHcHJVXQf8IckUgCQvaceeW1VbAZ8drYGq+gvwUeC4qtq6qo4D\nPgScUVXbA7sCn0uy8gjVtwLeAjwb2B94VqvzbeDtVXU3cBbwslZ+H+DEqpoLHAN8sKqeA1zBI7P/\n04Gvtbh3BG4B7gdeWVXbtni+0JN8PhP4elVtDvwF+DhdYr4TsNkop31iVW3X+ria7gbH/Fiunee7\neuJ+BXB9G8Nzx+ljA2DHqnoP8BHgzqraso3FGVV1MPDn1tZ+w/r+O7oVD1sBu9Ndm3XbsW1aTJsB\nT6fnBoUkSZKkwWKCvvTZF/hB2/5B+wxd4nZkVd0HUFV/nM92XwQcnORyugR7BeCpI5S7pKpuqaoH\ngOvpZpGhS7g3atvfBg5q2wcBRyZZDVi9qs5u+48GdkmyKrB+VZ3U4r6/nUOAzySZDZwGrA88udX9\ndVVd2LafC5xVVbe1Gw/HjXJ+WyQ5N8kVwH7A5hMalUec2P6e0XOe89PHD6vqoba9O/C1oQNVdcc4\nfe8EHFtVD1XV74Gzge3asYur6qa2zP/ykWJL8uYklya59N47bh+nK0mSJEmLi8+gL0WSrAnsBmyZ\npIBlgUry/jGqPcgjN2pWGKt54FVVde04YTzQs/1wz+eHad+3qjq/LUWfBixbVXNagj4/9gOeCEyp\nqrlJbuyJ/975bAvgKGDPqpqV5EBg2nzWHzrPhxj939VYfSxIzPMTF4wSW1UdDhwOsMFmU2oxxSFJ\nkiRpHM6gL132Ar5bVRtW1UZV9RTgBmBn4FTgoJ7nntdsdW4EpvTUH3I3sGrP518Abx9aRp5km4WM\n9Rjg+8CRAFV1J3BHz/Pb+wNntyXxNyXZs/X7+HYOqwG3tuR8V2DDUfq5CHh+krXac++vHqXcqsAt\nrczwJeSLykT7OBV469CHoWfxgblDz+4Pcy7d+wKWTfJEYBfg4kUUsyRJkqQlxAR96bIvcNKwfScA\n+1bVycBPgEvbMvWhnw77PPCPSWYCa/fUOxPYrL2UbG/gk8DywOwkV7bPC2M6sAZwbM++19E9Pz2b\n7pnqT7T9+wPvaPt/CazT6k9ty8UPAK4ZqZOqugU4BLgAOJ/u2e+RfIQumT9/tLYWgYn28SlgjfbS\nt1l0z9hDN8s9e+glcT1OAmYDs4AzgA9U1e8WaeSSJEmSFrtUuaJVS16SvYA9qmr/fseiR2yw2ZR6\n2/QLxy8oScDB24y0qEeSJA2XZEZVTR2vnM+ga4lL8hXgJcBL+x2LJEmSJA0KE3QtcVX19n7HIEmS\nJEmDxmfQJUmSJEkaACbokiRJkiQNABN0SZIkSZIGgAm6JEmSJEkDwARdkiRJkqQB4FvcJc2zzkrx\nd40lSZKkPnEGXZIkSZKkAWCCLkmSJEnSADBBlyRJkiRpAJigS5IkSZI0AEzQJUmSJEkaACbokiRJ\nkiQNABN0SZIkSZIGgAm6JEmSJEkDwARdkiRJkqQBYIIuSZIkSdIAMEGXJEmSJGkAmKBLkiRJkjQA\nTNAlSZIkSRoAqap+xyBpQCS5G7i233FMcmsDt/c7iEnOMVx4juHCcwwXDcdx4TmGC88xXHiO4cLb\npKpWHa/QcksiEkmTxrVVNbXfQUxmSS51DBeOY7jwHMOF5xguGo7jwnMMF55juPAcw4WX5NKJlHOJ\nuyRJkiRJA8AEXZIkSZKkAWCCLqnX4f0OYCngGC48x3DhOYYLzzFcNBzHhecYLjzHcOE5hgtvQmPo\nS+IkSZIkSRoAzqBLkiRJkjQATNAlSZIkSRoAJuiSSPLiJNcm+d8kB/c7nskoyRFJbk0yp9+xTFZJ\nnpLkzCRXJbkyyTv7HdNkk2SFJBcnmdXG8OP9jmmySrJskplJftbvWCajJDcmuSLJ5RP9aSE9WpLV\nkxyf5JokVyfZod8xTSZJNmnfv6E/dyV5V7/jmmySvLv9/8mcJMcmWaHfMU1GSd7ZxvDK8b6HPoMu\n/ZVLsixwHfBC4CbgEmDfqrqqr4FNMkl2Ae4BjqmqLfodz2SUZF1g3aq6LMmqwAxgT7+LE5ckwMpV\ndU+S5YHzgHdW1YV9Dm3SSfIeYCrwhKp6eb/jmWyS3AhMrarb+x3LZJXkaODcqvp2kscBK1XVn/od\n12TU/lvnt8Bzq+rX/Y5nskiyPt3/j2xWVX9O8p/Az6vqqP5GNrkk2QL4AbA98BfgZOAtVfW/I5V3\nBl3S9sD/VtWvquovdP8DskefY5p0quoc4I/9jmMyq6pbquqytn03cDWwfn+jmlyqc0/7uHz74534\n+ZRkA+BlwLf7HYv+OiVZDdgF+A5AVf3F5HyhvAC43uR8gSwHrJhkOWAl4OY+xzMZPRu4qKruq6oH\ngbOBvxutsAm6pPWB3/R8vgmTIvVZko2AbYCL+hvJ5NOWZl8O3AqcWlWO4fz7N+ADwMP9DmQSK+C0\nJDOSvLnfwUxCTwNuA45sj1p8O8nK/Q5qEtsHOLbfQUw2VfVb4PPA/wG3AHdW1Sn9jWpSmgPsnGSt\nJCsBLwWeMlphE3RJ0kBJsgpwAvCuqrqr3/FMNlX1UFVtDWwAbN+W1mmCkrwcuLWqZvQ7lklup/Y9\nfAnw1vYYkCZuOWBb4BtVtQ1wL+A7YhZAezzgFcAP+x3LZJNkDbpVlU8D1gNWTvLa/kY1+VTV1cBh\nwCl0y9svBx4arbwJuqTf8ui7eBu0fdIS156bPgGYXlUn9jueyawthz0TeHG/Y5lknge8oj1D/QNg\ntyTf629Ik0+beaOqbgVOonucShN3E3BTzwqY4+kSds2/lwCXVdXv+x3IJLQ7cENV3VZVc4ETgR37\nHNOkVFXfqaopVbULcAfd+59GZIIu6RLgmUme1u4y7wP8pM8x6a9Qe8HZd4Crq+qL/Y5nMkryxCSr\nt+0V6V7+eE1/o5pcquqfq2qDqtqI7n8Pz6gqZ4zmQ5KV24seacuyX0S3xFMTVFW/A36TZJO26wWA\nL8xcMPvi8vYF9X/A3yRZqf1/9Avo3g+j+ZTkSe3vp9I9f/790cout6SCkjSYqurBJG8DfgEsCxxR\nVVf2OaxJJ8mxwDRg7SQ3AR+rqu/0N6pJ53nA/sAV7RlqgH+pqp/3MabJZl3g6PbG4mWA/6wqfyZM\nS9qTgZO6/55nOeD7VXVyf0OalN4OTG83z38FHNTneCaddoPohcA/9DuWyaiqLkpyPHAZ8CAwEzi8\nv1FNWickWQuYC7x1rJc++jNrkiRJkiQNAJe4S5IkSZI0AEzQJUmSJEkaACbokiRJkiQNABN0SZIk\nSZIGgAm6JEmSJEkDwARdkiRpHEk+leT2JL/rdyzjSbJRkkqyQD+nm+SeJE9fiP6vTDJtQetL0l8z\nE3RJkrRUSPJvSe5IckGSDXr2vybJlxei3acC7wU2q6p1Rjg+LclNw/b9vyTnJLk7yW1Jzk7yinbs\nkCTfG6GdSvKMBY1zUamqVarqVwtRf/OqOmsRhiRJfzVM0CVJ0qSXZHtgCrAOcB5wcNu/GvB+4MML\n0fxTgT9U1a0TjGUv4IfAMcAGwJOBjwJ/uxAxSJL+CpigS5KkpcHTgPOq6gHgdGBoifangc9V1V1j\nVU6yWpJj2mz3r5N8OMkySXYHTgXWa0u/jxqnnQBfBD5ZVd+uqjur6uGqOruq3rSgJ5fk4CTXtxn5\nq5K8sufYskk+35bg/wp42bC6Z7Ul+r9s5/DTJGslmZ7kriSXJNmop/y8mfwkL2393Z3kt0ne1/av\nneRnSf6U5I9Jzk2yTDt2Yxs3kjy+rWy4uf35tySPb8emJbkpyXuT3JrkliQH9cQxYt+StDQzQZck\nSUuDK4Gdk6wIvAC4MslUYJOq+v4E6n8FWI0usX8+cABwUFWdBrwEuLkt/T5wnHY2AZ4CHL9gpzGq\n64GdW4wfB76XZN127E3Ay4FtgKnAXiPU3wfYH1gf2Bi4ADgSWBO4GvjYKP1+B/iHqloV2AI4o+1/\nL3AT8ES6FQL/AtQI9T8E/A2wNbAVsD2PXs2wTjun9YE3AF9LssY4fUvSUssEXZIkTXpVNQc4AbiQ\nbkn6Z4EvA+9I8o72PPj0JKsPr5tkWboE9p+r6u6quhH4Al1CO7/Wan/fsgB1R1VVP6yqm9ts/HHA\n/9AluwB/D/xbVf2mqv4I/OsITRxZVddX1Z3AfwPXV9VpVfUg3XL8bUbpei6wWZInVNUdVXVZz/51\ngQ2ram5VnVtVIyXo+wGfqKpbq+o2upsLveM6tx2fW1U/B+6hu8kxVt+StNQyQZckSUuFqvpSVW1V\nVXvTJa3n0P23zpvpZtWvpj2bPszawPLAr3v2/ZpuVnd+/aH9ve4YZR5s/c2TZOjz3JEqJDkgyeVt\nSfmf6GaU126H1wN+01P8149pAH7fs/3nET6vMkqsrwJeCvy6vehuh7b/c8D/Aqck+VWSkcZ1KLbh\n47pez+c/tJsEQ+7riWW0viVpqWWCLkmSlipJnkyXlH+CLpGdXVVzgUuA54xQ5Xa6xHjDnn1PBX67\nAN1fS5csv2qMMv8HbDRs39PoEvfH9JlkQ+BbwNuAtapqdWAOkFbkFrpl9b2xLxJVdUlV7QE8CfgR\n8J9t/91V9d6qejrwCuA9SV4wQhM389hxvXlh+pakpZkJuiRJWtp8ETikqu4DbgC2S7IKMA14zM+H\nVdVDdMnfp5Os2hLi9wCP+Sm08bRl3u8BPpLkoCRPaC+b2ynJ4a3YycCmSfZPsnySNYHPACcMm00e\nsjLd8923AbQXqW3Rc/w/6Zbyb9Ce3x5tNnu+JHlckv2SrNZucNwFPNyOvTzJM9pL8e4EHho6Nsyx\nwIeTPDHJ2nRvsx93XMfqW5KWZibokiRpqZFkN2D1qjoJoKouBv6LblZ7V+DQUaq+HbiXLoE/D/g+\ncMSCxFBVxwN7A6+nmy3+PfAp4Mft+K10L577B+BWutnwPwH/OEp7V9E9E39Ba2tL4PyeIt8CfgHM\nAi4DTlyQuEexP3BjkruAt9A9Uw7wTOA0umfGLwC+XlVnjlD/U8ClwGzgihbfpxayb0laamXk93lI\nkiRJkqQlyRl0SZIkSZIGgAm6JEmSJEkDwARdkiRJkqQBYIIuSZIkSdIAMEGXJEmSJGkAmKBLkiRJ\nkjQATNAlSZIkSRoAJuiSJEmSJA2A/w/LNoHWaw5bJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd657220d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14,10))\n",
    "\n",
    "reasons = primary_reasons.iloc[0:9]\n",
    "ax = fig.add_subplot(211)\n",
    "ax.barh(np.arange(len(reasons)),reasons['percentage'], tick_label=reasons['Primary reason'], color='lightskyblue');\n",
    "ax.set_title('Primary reasons for admission', fontsize=14);\n",
    "ax.set_xlim([0,9])\n",
    "\n",
    "reasons = secondary_reasons.iloc[0:9]\n",
    "ax = fig.add_subplot(212)\n",
    "ax.barh(np.arange(len(reasons)),reasons['percentage'], tick_label=reasons['Secondary reason'], color='lightskyblue');#plt.cm.tab20b.colors);\n",
    "ax.set_title('Secondary reasons for admission', fontsize=14);\n",
    "ax.set_xlabel('% of ICU admissions', fontsize=12)\n",
    "ax.set_xlim([0,9])\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.savefig('reasons_for_admission.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "admission_type_groups = icnarc_data.groupby('Admission Type').agg({'encounterId':'count'}).sort_values('encounterId', ascending=False).reset_index()\n",
    "discharge_reason_groups = icnarc_data.groupby('Reason for discharge from your unit').agg({'encounterId':'count'}).sort_values('encounterId', ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "admission_types = {'L': 'Unplanned local surgical or medical admission ',\n",
    "                   'U': 'Unplanned transfer in (e.g. due to lack of capacity)',\n",
    "                   'P': 'Planned transfer in (tertiary referral)',\n",
    "                   'S': 'Planned local surgical admission',\n",
    "                   'M' : 'Planned local medical admission',\n",
    "                   'R' : 'Repatriation'}\n",
    "\n",
    "discharge_reasons = {'N' :'Ending critical care',\n",
    "                     'C' : 'Comparable critical care',\n",
    "                     'M' : 'More-specialist critical care',\n",
    "                     'R' : 'Repatriation', \n",
    "                     'P' : 'Palliative Care', \n",
    "                     'S' : 'Self-discharge'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "admission_type_groups['percentage'] = 100*admission_type_groups['encounterId']/float(len(icnarc_data))\n",
    "discharge_reason_groups['percentage'] = 100*discharge_reason_groups['encounterId']/float(len(icnarc_data))\n",
    "\n",
    "admission_type_groups['type'] = [admission_types[k] for k in admission_type_groups['Admission Type']]\n",
    "discharge_reason_groups['type'] = [discharge_reasons[k] for k in discharge_reason_groups['Reason for discharge from your unit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XVncB8NRRxpEkr2oJ4YI7bv71aFUkSZLWeQ/YgjakJlF+Wvu5EJg1UH5WVf0G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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd659e4990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, figsize=(12, 8))#, subplot_kw=dict(aspect=\"equal\"))\n",
    "\n",
    "ax[0].barh(np.arange(len(admission_type_groups)), admission_type_groups['percentage'], color='lightskyblue', tick_label=admission_type_groups['type'])\n",
    "ax[0].set_title(\"Admission type\", fontsize=14)\n",
    "\n",
    "ax[1].barh(np.arange(len(discharge_reason_groups)), discharge_reason_groups['percentage'], color='lightskyblue', tick_label=discharge_reason_groups['type'])\n",
    "ax[1].set_title(\"Discharge reason\", fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('admisson_types_discharge_reasons.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfd = icnarc_data[~icnarc_data['Datetime when fully ready to discharge'].isna()]['Datetime when fully ready to discharge']\n",
    "ddt = icnarc_data[~icnarc_data['Datetime of discharge from your unit'].isna()]['Datetime of discharge from your unit']\n",
    "\n",
    "delay = icnarc_data[~icnarc_data['Datetime when fully ready to discharge'].isna()]\n",
    "delay = delay['Datetime of discharge from your unit'] - delay['Datetime when fully ready to discharge']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sSZpgbHu0q+ruJL8KfBBYBLy5qq6d47AkSZOwzZak/Y1tog1QVe8D3tfzbg5p\n6MkC4PEf3g734wfPwayxzR6Jw/34wXPg8c8jqaq5jkGSJElacMZ5jLYkSZI0by2oRHuy1/8mOSbJ\nZUlubJ9HD1t3JvVHZYpjfHWSHUmubj9Pn6TekUmuSPKZJNcmec3Ast9N8tlW90NJjpuk/ookH01y\nXav/koFlIzlHSd6cZFeSawbKfq7Fc0+SKe9Cnu7V0El+Lcnn23b+cJptLEpyVZJLB8rG5vsx2flp\n5Qvl+Cb9Dib5o3Z8n03y3iQPmaL+VOdnXnz/F6qF3m7bZttmTxOfbfbh0GZX1YL4obv55gvA9wP3\nBT5D9xrgPwTWtXXWAX8wbN227ID1x+AYXw38xgHqBnhAm14CXA48vs0/aGC9FwNvmKT+scDj2vQD\ngX8d9TkCfhx4HHDNQNkPACcBHwNWzeS8tWU/CXwYOKLNf+80+/914B3ApQNl4/T9mOz8LKTjm/Q7\nCDwVWNzK/2CqGCc7P618Xnz/F+LPNG3agmi3pzm+V2Ob/TFss22zD4M2eyH1aE/1+t/VwMa2zkbg\n2TOoy5D1R+WgX3FcnW+22SXtp9qyrw+setTe8gn1d1bVp9v0N4Dr+e5b30Zyjqrq48BXJ5RdX1UH\neuHFdOftRcCGqrqzbW/XZBtIshx4BvDGCYvG5vsx2flhYR3fpN/BqvpQVd3dVvsXuuc3T1Z/svMz\nb77/C9RCb7dts22zp2SbDRwGbfZCSrSnev3vsqra2cpuBZYBJDkuyfsOUJep6s+R6eL8tXYp5c17\nL4NMOMa9l5muBnYBl1XV5QPL1ifZBvwC8DuT1R9Y9wTgNLoeFhivcwTM6Pf7KODHklye5B+T/NAk\n9QFeD7wCuGfCrsbu2CdYkMc3yXdwr18C3t/WmfT7O8X2FtT3fx5Z6O22bfaQbLPvtSCP73BusxdS\non1A1V0n2Nsj8OWq2m9c3LD1x8xf0F1iOxXYCfwJ7H+MVbWnqk6l+9/j6UkeM7Dst6tqBfB24Fcn\nqw+Q5AHAu4GXTvhf5d7tjMU5msHvdzFwDPB44DeBTUkyWD/JM4FdVXXlAfY5Fsc+wYI7vqm+g0l+\nG7ib7js8o3/jC+37v5As0HbbNnv/OGyzOwvu+A73NnshJdpTvf73tiTHArTPyS7DTPfq4GHqj8qk\ncVbVba1Bvgf4K7rLblOqqjuAjwJnTbL47cB/maxekiV0X9i3V9V7BhaN0zmazHS/3+3Ae9pl2ivo\negceOqGy4Q4oAAAMT0lEQVT+E4BnJbmZ7hLmk5O8rS0b92NfUMc31XcwyfOBZwK/0BrOg7UQv//j\nbKG327bZB8c2e4Ecn232wkq0p3r97yXA2rbOWuDiGdRlyPqjMmmce78wzc8A10ysmGRp2p29Se4H\n/BTw+Ta/cmDV1XvLJ9QP8Cbg+qp67YTF43SOJjPd7/fv6W4+Icmj6G68+cpg5ao6v6qWV9UJre5H\nquq5bfG4H/uCOb6pvoNJzqK7hPqsqvr2QWx3oX//x9lCb7dtsw+ObTbz//hss5uao7tR+/gBnk53\nZ+kXgN9uZd8DbAZupLuT95hWfhzwvunqTld/zI7xb4DPAZ+l+wIdO/EYgR8ErmrrXAP8zsA2393K\nPgv8A93NChPrP5Hu8spngavbz9NHeY6Ad9JdZr2L7n/959D9kdoO3AncBnxwhr/f+wJva8f/aeDJ\nk9UfWP9J7HuH99h8P6Y4Pwvp+Cb9DgJb6cZz7i17wxTfgf3Oz3z6/i/Un8n+bU51Tmfw73psfidT\nHJ9ttm32VOdnIR2fbXaVb4aUJEmS+rCQho5IkiRJY8NEW5IkSeqBibYkSZLUAxNtSZIkqQcm2pIk\nSVIPTLS1nyQPS/LNJIvmOpZDleStSX7vIOu+KMlt7Vx8T5+xJfmxJDcc4vYqySNnJzpJ88m4tNvp\nvCXJvyW5Yi5jmU6SVw+85GWY9e9tX5O8Icn/ewj7fn6STxxsfc0vJtoiyc1JnrJ3vqq+VFUPqKo9\ncxnXXGpvlHot8NR2Lm7vc39V9U9VdVKf+5C0cIxxu/1EupfrLK+qad94ORPjlJxW1a9U1e/OdRya\nH0y0NTZaT8i4fCeXAUcC18604pgdx4wlWTzXMUiatx4O3FxV35rrQA4Httfjb94mA5odSf4GeBjw\nD+2y4yuSnNAuky1u63wsye8l+ee2zj8k+Z4kb0/y9SSfSnLCwDYfneSyJF9NckOSs6fZ/8eSrE/y\nf4BvA9+f5MFJ3pRkZ5Idbd+L2vqPSPKRJLcn+UqL4SED2zstyaeTfCPJ39Ily3uXXZPkpwfml7Rt\nnDYhpkcBe4dx3JHkI638R9uxfq19/uh0xzHJsU4X25OSbB+Yf2U79m+0c3hGK1+U5LeSfKEtuzLJ\nioHdPCXJjUnuSPK/kmTI83Zz2+dngW8lWZzkcUmuavv5uyR/m4FhOEmemeTqtq9/TvKDU/2eJc2e\nMWi3j0tySVt3a5L/3srPAd4I/Ejb52smqXugtmhFkvck2d3W+fMkPwC8YWC7dwwc4y8P1N2n1zvJ\nnybZ1o73yiQ/NoNz/Jvtb9CXk/zShGWDw/4emuTS1g5+Nck/pXW0THYsE7bzx+mG2NyU5GkD5S9I\ncn1re7+Y5IUDy56UZHtrr28F3tLKXzEQ7y9n36EuR7R9fSndcMg3JLnfsOdCh2iuXs3pz/j8ADcD\nTxmYP4Hu1aWL2/zH6F6Z+gjgwcB1dK/GfQqwGPhr4C1t3aPoXq36grbsNOArwMlT7PtjwJeAU9r6\nS4D3An/ZtvW9wBXAC9v6j6S7LHkEsBT4OPD6tuy+wC3Ay9p2/ivdq1t/ry1/BfC3A/teDXxuirgm\nnoNjgH8DntfifE6b/56pjmPC9g4U25OA7W36pHYOjxuI5RFt+jfpXt18EhDgsQMxFHAp8BC6P8K7\ngbMOdN4GvgNXAyuA+w3E+5IW788C3xmI9zRgF/DDwCJgbdvGEXP9ffbHn8Phh7lttz8OXEDXWXBq\na2v2vir8+cAnpol7ujZ8EfAZ4HUtpiOBJ0613XaMvzwwv886wHPpXre9GHg5cCtwZFv2auBtU8R4\nFt3r4R/T4nhHO7ePbMvfOtAW/j7dfwKWtJ8fo2ubD3QsdwH/va33IuDLcO/bup/Rfm8BfoKu8+Zx\nbdmTgLuBP2jn8H4t3lvp/v7cn+4V7oPxvg64hO7v2APpXlv++3P9HT5cfuzR1rDeUlVfqKqvAe8H\nvlBVH66qu4G/o2uYAZ5Jd9nwLVV1d1VdBbwb+Llptv3Wqrq2besY4OnAS6vqW1W1i66RWANQVVur\n6rKqurOqdtONo/6Jtp3H0zV0r6+qu6rqXcCnBvbzNuDpSR7U5p8H/M2Qx/8M4Maq+pt2XO8EPg/8\n9MA69x5HVd01of6BYhu0h64BPTnJkqq6uaq+0Jb9MvA/quqG6nym9h0/vqGq7qiqLwEfpfsjeKDz\nttefVdW2qvr3Fu/iVnZXVb2H7j88e50L/GVVXV5Ve6pqI3BnqydpPMx6u92uoD0BeGVV/UdVXU3X\ni/2LwwR0gLbodOA44Ddb+/8fVXXQ47Kr6m1VdXs7pj+ha1eHuRfmbLpzd011Q2BePc26dwHHAg9v\nbeU/VVUNcSy3VNVfVTemfmPbxrIW9/9uv7eqqn8EPkSXwO91D/Cqdg7/fSDea6vq24Pxtqua5wIv\nq6qvVtU3gP9J+5uq/ploa1i3DUz/+yTzD2jTDwd+uF1Gu6Nd4vsF4Pum2fa2gemH0yWkOwfq/yVd\nzzZJliW5KN2wiq/TJc8PbXWPA3a0Rm6vW/ZOVNWXgf8D/Jd2qfJpwNuHOPa9275lQtktwPFTHMdk\n9aeMbVBVbQVeStdY7mrHe1xbvAL4wmT1mlsHpr9N+70c4LxNFv9k8U78Pb18wu95RasnaTz00W4f\nB+xN2Paa2BZO6QBt0Qq6BPTuYbY1xL5+ow3B+Fo7pgezf7s3mePYt72btK1u/ojuysGH2jCPda38\nQMdyb1vdkmP4bnv9tCT/0oai3EHX+TQY9+6q+o9p4h2cXkrXy33lwO/2A61cI2CiLeguMc2WbcA/\nVtVDBn4eUFUvGnL/2+h6Rh86UP9BVXVKW/4/2/r/qaoeRHdpMG3ZTuD49j/4vR42YV8bW52fAz5Z\nVTuGPK4v0/0xGvQwYLD+dOdxmNi+u6Gqd1TVE9s+i+4yIXTn5xFDxjxouvN2724PEO/gWPBtwPoJ\nv+f7t55+Sf2bq3b7y8AxSR44UDaxLZzOdG3RNuBhmfwGv8mO91t0SeRe9/7HoI3HfgVdb+/RVfUQ\n4Gvs3+5NZif7tnfTtdXfqKqXV9X3A88Cfj3dPTXTHcuUkhxBdzXhj4FlLe73TYh74rnYCSwfmB+M\n/St0/6k6ZeB3++CqegAaCRNtQdfLsd/NewfpUuBRSZ6X7mbDJUl+qN3MckBVtZPuMtmfJHlQkvu0\nm2f2Xlp8IPBN4GtJjqcbs7zXJ+nGrr247fdn6S7fDfp74HF0Y4//egbH9b52XP8t3Y2CPw+c3I53\nGMPEBkCSk5I8uTW4/0HXSN7TFr8R+N0kK9P5wQz3jO/pzttU8e4BfrUd7+oJ8f4V8CtJfrjFcVSS\nZ0z44yupP3PSblfVNuCfgd9PcmS6m6DPoeuZHsZ0bdEVdEnjhtamHJnkCW3ZbcDyJPcdWP9q4GeT\n3L/d+HfOhP3cTTd+fHGS3wEexHA2Ac9PcnKS+wOvmmrFdDeFP7J1SnyNrt285wDHMp370g1x2Q3c\nne4myacOEe8LkvxAi/feZ3xX1T107fXrkuy9Mnx8kjOHiEWzwERb0N3M8T/aZaXfOJQNtcuJT6Ub\n//Vlustje2/aGNYv0jU219HdcPguuvFrAK+hS5S/Bvxv4D0D+/4O3U17zwe+Cvz84PK2zr/T9Rac\nOHHZAY7rdrpxjC8HbqfrKXlmVX1lyPoHjG3AEcAGup6IW+mGzZzflr2WrlH9EPB14E10N8McyJTn\n7QDxngPcQdfrdCnd1QaqagvdjTx/Tvc72tqOTdJozGW7/Ry6my+/THfz+quq6sND7m66NnwP3X0v\nj6S7uXw7XVsJ8BG6x63emmRvu/s6upu0b6O7Wjk4FPCDdEMk/pVu6Md/MP3wvntV1fuB17d9bm2f\nU1kJfJjuPw+fBC6oqo8e4Fim2/c3gBfTtfP/Bvw3uhsZDxTvn9Hdl7MV+Je26M72+cq95W24zocZ\nbqy6ZsHeO1ylw0br2XhUVT13rmOZT5JcDryhqt4y17FIkibXrkRcQ/cUqFkZ766DZ4+2DitJjqHr\npb1wrmMZd0l+Isn3taEja4EfpOshkiSNkSQ/k+552UfTXY34B5Ps8WCircNGupcqbAPeX1Ufn+t4\n5oGT6J4DewfdkJn/2sbQS5LGywvp3m3wBbpx4tM9gEAj5NARSZIkqQf2aEuSJEk9MNGWJEmSemCi\nLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkH/xeK+T/1JPqJWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd65b26b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(1,2,1)\n",
    "p1 = plt.hist(rfd.apply(lambda x: x.time()).values, bins=20, color='lightskyblue')\n",
    "plt.xlabel(\"time ready for discharge\", fontsize=12)\n",
    "plt.ylabel(\"frequency\", fontsize=12)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "p2 = plt.hist(ddt.apply(lambda x: x.time()).values, bins=20, color='lightskyblue')\n",
    "plt.xlabel(\"time of actual discharge\", fontsize=12);\n",
    "\n",
    "plt.savefig('discharge_time_histograms.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now plot length of stay and discharge delay..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qTh1XcJIk6c5LshZYu3r16kmHIklaQXaUBP4a8ELgBGAV8Mwh2xRgEihJ0gpUVZuATfPz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\nknrEJFCSJEmSesQkUJIkSZJ65P8DOxRgDvhmo6oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd5a7b4150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.hist(icnarc_data['icnarc_los']/(60*24), bins=20, color='lightskyblue');\n",
    "plt.yscale('log')\n",
    "plt.xlabel('length of stay (days)', fontsize=12)\n",
    "plt.ylabel('frequency', fontsize=12);\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.hist(delay.apply(lambda x : x.total_seconds()/(60.0*60)), bins=20, color='lightskyblue');\n",
    "plt.yscale('log')\n",
    "plt.xlabel('discharge delay (hours)', fontsize=12);\n",
    "\n",
    "plt.savefig('stay_length_histograms.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 2. Demonstration analysis.\n",
    "\n",
    "#### We now load and process physiological data from Philips. These data contain a selection of variables most commonly used in our research and audits. \n",
    "\n",
    "This data comes from two tables in ICCA:\n",
    "* PtAssessment - containing flowsheet data, often recorded at high frequnecy (~1record/hr)\n",
    "* PtLabResult - containing laboratory results data (and similar), usually recorded at lower frequency (~1record/day) \n",
    "\n",
    "For simplicity we combine these data into a single table, 'chartevents'. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we combine and call it chartevents, but in the research database it remains two (or more tables):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chartevents = clean_philips_encounterids('../ptassess_physiological_data.rpt', \n",
    "                                  '../Philips encounterId Issue List (New).xlsx',\n",
    "                                  verbose=VERBOSE, date_columns=['chartTime', 'storeTime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chartevents = chartevents.append(clean_philips_encounterids('../labresults_physiological_data.rpt', \n",
    "                                  '../Philips encounterId Issue List (New).xlsx',\n",
    "                                  verbose=VERBOSE, date_columns=['chartTime', 'storeTime']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get only encounters that appear in our icustays table: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chartevents = chartevents.merge(icnarc_data[['encounterId']],how='right', on='encounterId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Extract numbers from variables that use valueString rather than valueNumber:\n",
    "\n",
    "Note: processing this large table is slow. We save it to disk after each stage of processing so it doesn't need to be repeated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chartevents['value'] = [row['valueString'] if row['attributeId'] in [16240,6847,6849,6851,8590, 34870, 34873, 8584, 3566, 25545] else row ['valueNumber'] for i,row in chartevents.iterrows()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get key that defines each variable/intervention/attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "interventions = pd.read_excel('interventions_and_attributes.xlsx', sheet_name='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chartevents = pd.merge(chartevents, interventions, how='left', left_on=['interventionId', 'attributeId'], right_on=['interventionId', 'attributeId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#chartevents.to_csv('cohort_chartevents.csv')\n",
    "chartevents = pd.read_csv('cohort_chartevents.csv', parse_dates=[2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many icustays have a recording of each variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "record_completeness = chartevents.groupby('Variable').agg({'encounterId':pd.Series.nunique}).sort_values('encounterId', ascending=False)\n",
    "record_completeness = record_completeness.rename(columns={'encounterId':'completeness'})\n",
    "record_completeness['completeness'] /= float(len(chartevents.encounterId.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>completeness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Serum sodium</th>\n",
       "      <td>0.998965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum potassium</th>\n",
       "      <td>0.998965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart Rate</th>\n",
       "      <td>0.996895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Respiratory Rate</th>\n",
       "      <td>0.996481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpO2</th>\n",
       "      <td>0.995239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCS</th>\n",
       "      <td>0.992962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCS Eyes</th>\n",
       "      <td>0.992962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCS Motor</th>\n",
       "      <td>0.992962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCS Verbal</th>\n",
       "      <td>0.992962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platelets</th>\n",
       "      <td>0.992134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pCO2</th>\n",
       "      <td>0.991099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum ionised calcium</th>\n",
       "      <td>0.991099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pO2</th>\n",
       "      <td>0.990892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum bicarbonate</th>\n",
       "      <td>0.990892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum pH</th>\n",
       "      <td>0.990892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haemoglobin</th>\n",
       "      <td>0.990892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airway</th>\n",
       "      <td>0.990685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum urea</th>\n",
       "      <td>0.990685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilirubin</th>\n",
       "      <td>0.989857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum creatinine</th>\n",
       "      <td>0.989857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peripheral Temperature</th>\n",
       "      <td>0.983647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arterial BP Systolic</th>\n",
       "      <td>0.954461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arterial BP Diastolic</th>\n",
       "      <td>0.953840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arterial BP Mean</th>\n",
       "      <td>0.953426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FiO2</th>\n",
       "      <td>0.840613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Invasive BP Systolic</th>\n",
       "      <td>0.839164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Invasive BP Diastolic</th>\n",
       "      <td>0.838336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Invasive BP Mean</th>\n",
       "      <td>0.834610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pain Scale (VAS)</th>\n",
       "      <td>0.752225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEEP</th>\n",
       "      <td>0.509211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central Temperature</th>\n",
       "      <td>0.245498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pain Scale (VAS) (on movement)</th>\n",
       "      <td>0.153798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Access (Arterial) Pressure</th>\n",
       "      <td>0.101014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                completeness\n",
       "Variable                                    \n",
       "Serum sodium                        0.998965\n",
       "Serum potassium                     0.998965\n",
       "Heart Rate                          0.996895\n",
       "Respiratory Rate                    0.996481\n",
       "SpO2                                0.995239\n",
       "GCS                                 0.992962\n",
       "GCS Eyes                            0.992962\n",
       "GCS Motor                           0.992962\n",
       "GCS Verbal                          0.992962\n",
       "Platelets                           0.992134\n",
       "pCO2                                0.991099\n",
       "Serum ionised calcium               0.991099\n",
       "pO2                                 0.990892\n",
       "Serum bicarbonate                   0.990892\n",
       "Serum pH                            0.990892\n",
       "Haemoglobin                         0.990892\n",
       "Airway                              0.990685\n",
       "Serum urea                          0.990685\n",
       "Bilirubin                           0.989857\n",
       "Serum creatinine                    0.989857\n",
       "Peripheral Temperature              0.983647\n",
       "Arterial BP Systolic                0.954461\n",
       "Arterial BP Diastolic               0.953840\n",
       "Arterial BP Mean                    0.953426\n",
       "FiO2                                0.840613\n",
       "Non-Invasive BP Systolic            0.839164\n",
       "Non-Invasive BP Diastolic           0.838336\n",
       "Non-Invasive BP Mean                0.834610\n",
       "Pain Scale (VAS)                    0.752225\n",
       "PEEP                                0.509211\n",
       "Central Temperature                 0.245498\n",
       "Pain Scale (VAS) (on movement)      0.153798\n",
       "Access (Arterial) Pressure          0.101014"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record_completeness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts = chartevents.groupby(['encounterId', 'Variable']).agg({'encounterId':['count'], 'chartTime':['min', 'max']})\n",
    "counts.columns = list(map('_'.join, counts.columns.values))\n",
    "counts = counts.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts = counts.merge(icnarc_data[['encounterId','icnarc_los']], on='encounterId', how='inner')\n",
    "counts['freq'] = [row['encounterId_count']/(row['icnarc_los']/(60.0)) for i,row in counts.iterrows()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freq_mean</th>\n",
       "      <th>freq_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Respiratory Rate</th>\n",
       "      <td>1.310394</td>\n",
       "      <td>0.923121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FiO2</th>\n",
       "      <td>0.922432</td>\n",
       "      <td>0.788986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart Rate</th>\n",
       "      <td>0.835824</td>\n",
       "      <td>0.311151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpO2</th>\n",
       "      <td>0.809720</td>\n",
       "      <td>0.309111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arterial BP Mean</th>\n",
       "      <td>0.699568</td>\n",
       "      <td>0.356515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arterial BP Diastolic</th>\n",
       "      <td>0.698276</td>\n",
       "      <td>0.355979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arterial BP Systolic</th>\n",
       "      <td>0.698062</td>\n",
       "      <td>0.356200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airway</th>\n",
       "      <td>0.671725</td>\n",
       "      <td>0.297497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Access (Arterial) Pressure</th>\n",
       "      <td>0.661339</td>\n",
       "      <td>1.218776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum bicarbonate</th>\n",
       "      <td>0.622238</td>\n",
       "      <td>0.491741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central Temperature</th>\n",
       "      <td>0.547355</td>\n",
       "      <td>0.666272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEEP</th>\n",
       "      <td>0.534654</td>\n",
       "      <td>0.387502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum sodium</th>\n",
       "      <td>0.453651</td>\n",
       "      <td>0.452378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum potassium</th>\n",
       "      <td>0.451158</td>\n",
       "      <td>0.451235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pain Scale (VAS)</th>\n",
       "      <td>0.362429</td>\n",
       "      <td>0.182010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum ionised calcium</th>\n",
       "      <td>0.351328</td>\n",
       "      <td>0.314051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pCO2</th>\n",
       "      <td>0.349795</td>\n",
       "      <td>0.312404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum pH</th>\n",
       "      <td>0.349642</td>\n",
       "      <td>0.311717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pO2</th>\n",
       "      <td>0.347998</td>\n",
       "      <td>0.312622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peripheral Temperature</th>\n",
       "      <td>0.292728</td>\n",
       "      <td>0.123345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCS Eyes</th>\n",
       "      <td>0.284299</td>\n",
       "      <td>0.131968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCS Verbal</th>\n",
       "      <td>0.284118</td>\n",
       "      <td>0.131910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCS Motor</th>\n",
       "      <td>0.284066</td>\n",
       "      <td>0.133167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCS</th>\n",
       "      <td>0.283856</td>\n",
       "      <td>0.133132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Invasive BP Systolic</th>\n",
       "      <td>0.258695</td>\n",
       "      <td>0.363691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Invasive BP Diastolic</th>\n",
       "      <td>0.258543</td>\n",
       "      <td>0.363552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Invasive BP Mean</th>\n",
       "      <td>0.254018</td>\n",
       "      <td>0.367398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pain Scale (VAS) (on movement)</th>\n",
       "      <td>0.206811</td>\n",
       "      <td>0.102814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platelets</th>\n",
       "      <td>0.111460</td>\n",
       "      <td>0.337193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haemoglobin</th>\n",
       "      <td>0.109208</td>\n",
       "      <td>0.336858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum urea</th>\n",
       "      <td>0.106984</td>\n",
       "      <td>0.183895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serum creatinine</th>\n",
       "      <td>0.106737</td>\n",
       "      <td>0.183941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilirubin</th>\n",
       "      <td>0.097640</td>\n",
       "      <td>0.160724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                freq_mean  freq_std\n",
       "Variable                                           \n",
       "Respiratory Rate                 1.310394  0.923121\n",
       "FiO2                             0.922432  0.788986\n",
       "Heart Rate                       0.835824  0.311151\n",
       "SpO2                             0.809720  0.309111\n",
       "Arterial BP Mean                 0.699568  0.356515\n",
       "Arterial BP Diastolic            0.698276  0.355979\n",
       "Arterial BP Systolic             0.698062  0.356200\n",
       "Airway                           0.671725  0.297497\n",
       "Access (Arterial) Pressure       0.661339  1.218776\n",
       "Serum bicarbonate                0.622238  0.491741\n",
       "Central Temperature              0.547355  0.666272\n",
       "PEEP                             0.534654  0.387502\n",
       "Serum sodium                     0.453651  0.452378\n",
       "Serum potassium                  0.451158  0.451235\n",
       "Pain Scale (VAS)                 0.362429  0.182010\n",
       "Serum ionised calcium            0.351328  0.314051\n",
       "pCO2                             0.349795  0.312404\n",
       "Serum pH                         0.349642  0.311717\n",
       "pO2                              0.347998  0.312622\n",
       "Peripheral Temperature           0.292728  0.123345\n",
       "GCS Eyes                         0.284299  0.131968\n",
       "GCS Verbal                       0.284118  0.131910\n",
       "GCS Motor                        0.284066  0.133167\n",
       "GCS                              0.283856  0.133132\n",
       "Non-Invasive BP Systolic         0.258695  0.363691\n",
       "Non-Invasive BP Diastolic        0.258543  0.363552\n",
       "Non-Invasive BP Mean             0.254018  0.367398\n",
       "Pain Scale (VAS) (on movement)   0.206811  0.102814\n",
       "Platelets                        0.111460  0.337193\n",
       "Haemoglobin                      0.109208  0.336858\n",
       "Serum urea                       0.106984  0.183895\n",
       "Serum creatinine                 0.106737  0.183941\n",
       "Bilirubin                        0.097640  0.160724"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqs = counts.groupby('Variable').agg({'freq':['mean', 'std']})\n",
    "freqs.columns = list(map('_'.join, freqs.columns.values))\n",
    "freqs.sort_values('freq_mean', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What do the distributions of variable values look like?\n",
    "\n",
    "First we remove variables we don't want to calculate distributions for:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variables = [i for i in interventions['Variable'].unique() if i not in ['Airway', 'GCS Motor', 'GCS Verbal', 'GCS Eyes', 'Pain Scale (VAS) (on movement)', 'Pain Scale (VAS)', 'Access (Arterial) Pressure']]\n",
    "len(variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chartevents_nona = chartevents.dropna(subset=['value'])\n",
    "#sum(~chartevents[chartevents['Variable']=='Pain Scale (VAS)']['value'].isna()) ## all Pain scale values recorded as ValueString"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_variable_histogram_grid(var_names, var_list=[]):\n",
    "    \n",
    "    plt.figure(figsize=(15,15))\n",
    "    for i,j in enumerate(var_list):\n",
    "        print var_names[j]\n",
    "        \n",
    "        ax = plt.subplot(3,3,i+1)\n",
    "        vvals = chartevents[chartevents['Variable']==var_names[j]]['value']\n",
    "        \n",
    "        if len(vvals)>0:\n",
    "            vvals = pd.to_numeric(vvals, errors='coerce')\n",
    "            plt.hist(vvals.dropna().values, bins=20, color='lightskyblue');\n",
    "            ax.set_xlabel(var_names[j])\n",
    "            if i in [0,3,6]:\n",
    "                ax.set_ylabel('frequency')\n",
    "        else:\n",
    "            print var + \" not in chartevents.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCS\n",
      "FiO2\n",
      "Heart Rate\n",
      "Haemoglobin\n",
      "Respiratory Rate\n",
      "SpO2\n",
      "Non-Invasive BP Mean\n",
      "Non-Invasive BP Systolic\n",
      "Non-Invasive BP Diastolic\n",
      "Arterial BP Mean\n",
      "Arterial BP Systolic\n",
      "Arterial BP Diastolic\n",
      "Peripheral Temperature\n",
      "Central Temperature\n",
      "Serum pH\n",
      "Serum sodium\n",
      "Serum potassium\n",
      "Serum ionised calcium\n",
      "Serum bicarbonate\n",
      "Serum urea\n",
      "Serum creatinine\n",
      "PEEP\n",
      "pO2\n",
      "pCO2 \n",
      "Bilirubin\n",
      "Platelets\n"
     ]
    },
    {
     "data": {
      "image/png": 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WfnWShf06H0mSJEma6/p5B3IL8M6qOgg4AjgxyUF05jO6oqoWAVe017R1y4GD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8NH+E5o++TqJH+d5CNJ0iN2aVKYg2Y22xx0IPDtqnqyTfftqvqbJD+X5M+HYnpVkk+3mC5LcneS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\n2O+0g999xQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd5a585190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oqvuq6tCqOhQ4HPgWcFVbfWZvXVVdC7ZPkmaGiaQkNUkOAH4WOK+v+FjgorZ8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7AAAg\nAElEQVRDk9zQPjs9SVr5TkkubuXXJNlva+KVJEmSJP1IP4nk64GD6B79cSFwP/DGrdzv3wAfqaqf\nBJ4J3AysAK6sqgOAK9t7khwILGsxHAW8J8kObTtnAK8BDmivo1r5CcC9VfVU4F3AaVsZr6TthB1d\nkiRJM5sxkayq/6qqt1bVz1TV4rb8wJbuMMkuwAuBs9v2v19V3wKWAue11c4Djm7LS4GLqurBqroN\nWAMclmQv4PFVdXVVFXD+pDoT27oUWDLxJU6SZmBHlyRJ0gz6mbX1E0mumvzain3uD6wH3pvkuiRn\nJXkMsGdV3dHWuRPYsy0vAm7vqb+2lS1qy5PLN6lTVRuB+4Ddpzi2E5OsSrJq/fr1W3FIkrYFdnRJ\nkiT1p5+hrb8P/EF7/S+6R4Gs2op9LgCeDZxRVYcA36X17k9oX7xqK/bRl6o6s11lXbxw4cJh707S\n+Bubji5J6pXkTUlWJ7kxyYVJftxh95JGqZ+hrZ/ref17Vb0ZOGIr9rkWWFtV17T3l9Illne1Xnza\nz7vb5+uAfXrq793K1rXlyeWb1EmyANgFuGcrYpa0fRibji5HTEiakGQR8LvA4qo6GNiBbli9w+4l\njUw/Q1t363nt0Xq1dtnSHVbVncDtSZ7eipYANwErgeWtbDlwWVteCSxrPWX70zV617arA/cnObz1\nph0/qc7Eto4Brmpf/iRpc8amo8sRE5ImWQDs3NqNRwPfwGH3kkZoQR/rfI6u9z3ARuA2ul6rrfF6\n4H1JHgV8BfgtuqT2kiQnAF8DjgOoqtVJLqFLNjcCJ1XVw207rwPOBXYGLm8v6O5vuiDJGmADXa+c\nJG1WVd2Z5PYkT6+qW/hRR9dNdJ1Tp/LIjq73J3kn8CR+1NH1cJL7kxwOXEPX0fXunjrLgc9gR5ek\nPlTVuiR/CXwd+B7wsar6WJLNDbu/umcTE8PrH6LPYfdJJobdf3MIhyRpGzBjIllV+8/2TqvqemDx\nFB8tmWb9U4BTpihfBRw8RfkDwLFbGaak7ZMdXZLGSrv3cSndfdzfAv4pySt716mqSjInw+6BEwH2\n3XffYe9O0hibMZFM8mub+7yqPjB74UjSaNnRJWkMvQi4rarWAyT5APA82rD7qrpjFofdr51p2D1w\nJsDixYsdTSFtx/qZtfUEuh70V7TXWcBvA78CvHR4oUmSJIluSOvhSR7d7ltcQveMW+eXkDQy/dwj\nuSNw4MQY/NbjdW5V/dZQI5MkSRJVdU2SS4HP0w2jv47uquBjcdi9pBHpJ5Hcp+dGboC7AAfFS5Ik\nzZGqehvwtknFD+Kwe0kj0k8ieWWSjwIXtvcvBz4+vJAkSZIkSeOsn1lbfyfJrwIvbEVnVtW/DDcs\nSZIkSdK46ueKJHRj8r9dVR9vN3o/rqq+PczAJEmSJEnjacZZW5O8BrgU+N+taBHwwWEGJUmSJEka\nX/08/uMk4PnA/QBVdSvwxGEGJUmSJEkaX/0kkg9W1fcn3rSH1PpcIUmSJEnaTvWTSH4qyR8COyd5\nMfBPwP8ZbliSJEmSpHHVTyK5AlgP3AD8D+DDwB8NMyhJkiRJ0vja7KytSXYAzq+qVwD/MDchSZIk\nSZLG2WavSFbVw8CTkzxqjuKRJEmSJI25fp4j+RXg35OsBL47UVhV7xxaVJIkSZKksTXtFckkF7TF\nlwEfaus+ruclSZIkSdoObe6K5KFJngR8HXj3HMUjSZIkSRpzm0sk/x64EtgfWNVTHrrnSD5liHFJ\nkiRJksbUtENbq+r0qvop4L1V9ZSe1/5VZRIpSZIkSdupGZ8jWVX/cy4CkSRJkiTNDzMmkpIkSZIk\n9TKRlCRJkiQNxERSkiRJkjQQE0lJkiRJ0kBMJCVJksZckickuTTJl5LcnOS5SXZLckWSW9vPXXvW\nPznJmiS3JDmyp/zQJDe0z05Pkla+U5KLW/k1Sfab+6OUNJ+YSEqSJI2/vwE+UlU/CTwTuBlYAVxZ\nVQfQPft7BUCSA4FlwEHAUcB7kuzQtnMG8BrggPY6qpWfANxbVU8F3gWcNhcHJWn+MpGUJEkaY0l2\nAV4InA1QVd+vqm8BS4Hz2mrnAUe35aXARVX1YFXdBqwBDkuyF/D4qrq6qgo4f1KdiW1dCiyZuFop\nSVMxkZQkSRpv+wPrgfcmuS7JWUkeA+xZVXe0de4E9mzLi4Dbe+qvbWWL2vLk8k3qVNVG4D5g9yEc\ni6RthImkJEnSeFsAPBs4o6oOAb5LG8Y6oV1hrGEHkuTEJKuSrFq/fv2wdydpjJlISpIkjbe1wNqq\nuqa9v5QusbyrDVel/by7fb4O2Ken/t6tbF1bnly+SZ0kC4BdgHsmB1JVZ1bV4qpavHDhwlk4NEnz\n1cgSySQ7tOEZH2rvnXlMkiRpkqq6E7g9ydNb0RLgJmAlsLyVLQcua8srgWXt+9D+dJPqXNuGwd6f\n5PD2nen4SXUmtnUMcFW7yilJUxrlFck30M04NsGZxySNnJ1cksbU64H3Jfki8Czgz4BTgRcnuRV4\nUXtPVa0GLqFLNj8CnFRVD7ftvA44i24Cni8Dl7fys4Hdk6wB3sykobOSNNlIEskkewO/TNeQTXDm\nMUnjwE4uSWOnqq5vQ0qfUVVHV9W9VXVPVS2pqgOq6kVVtaFn/VOq6ieq6ulVdXlP+aqqOrh99jsT\nVx2r6oGqOraqnlpVh1XVV0ZxnJLmj1Fdkfxr4C3AD3rKnHlM0kjZySVJktSfOU8kk7wUuLuqPjfd\nOs48JmlE7OSSJEnqwyiuSD4feFmSrwIXAb+Q5B9x5jFJIzROnVwtHju6JEnS2JrzRLKqTq6qvatq\nP7r7i66qqlfizGOSRmtsOrnAji5JkjTexuk5ks48Jmlk7OSSJEnq34JR7ryqPgl8si3fQ/dcpKnW\nOwU4ZYryVcDBU5Q/ABw7i6FK2n6dClyS5ATga8Bx0HVyJZno5NrIIzu5zgV2puvg6u3kuqB1cm2g\nS1glSZLmnZEmkpI0juzkkiRJ2rxxGtoqSZIkSZoHTCQlSZIkSQMxkZQkSZIkDcREUpIkSZI0EBNJ\nSZIkSdJATCQlSZIkSQMxkZQkSZIkDcREUpIkSZI0EBNJSZIkSdJATCQlSZIkSQMxkZQkSZIkDcRE\nUpIkSZI0EBNJSZKkeSDJDkmuS/Kh9n63JFckubX93LVn3ZOTrElyS5Ije8oPTXJD++z0JGnlOyW5\nuJVfk2S/uT4+SfOLiaQkSdL88Abg5p73K4Arq+oA4Mr2niQHAsuAg4CjgPck2aHVOQN4DXBAex3V\nyk8A7q2qpwLvAk4b7qFImu9MJCVJksZckr2BXwbO6ileCpzXls8Dju4pv6iqHqyq24A1wGFJ9gIe\nX1VXV1UB50+qM7GtS4ElE1crJWkqJpKSJEnj76+BtwA/6Cnbs6ruaMt3Anu25UXA7T3rrW1li9ry\n5PJN6lTVRuA+YPdZjF/SNsZEUpIkaYwleSlwd1V9brp12hXGmoNYTkyyKsmq9evXD3t3ksaYiaQk\nSdJ4ez7wsiRfBS4CfiHJPwJ3teGqtJ93t/XXAfv01N+7la1ry5PLN6mTZAGwC3DP5ECq6syqWlxV\nixcuXDg7RydpXjKRlCRJGmNVdXJV7V1V+9FNonNVVb0SWAksb6stBy5ryyuBZW0m1v3pJtW5tg2D\nvT/J4e3+x+Mn1ZnY1jFtH0O/wilp/low6gAkSZK0RU4FLklyAvA14DiAqlqd5BLgJmAjcFJVPdzq\nvA44F9gZuLy9AM4GLkiyBthAl7BK0rRMJCVJkuaJqvok8Mm2fA+wZJr1TgFOmaJ8FXDwFOUPAMfO\nYqiStnEObZUkSZIkDcREUpIkSZI0EBNJSZIkSdJATCQlSZIkSQMxkZQkSZIkDcREUpIkSZI0EBNJ\nSZIkSdJATCQlSZIkSQMxkZQkSZIkDWTOE8kk+yT5RJKbkqxO8oZWvluSK5Lc2n7u2lPn5CRrktyS\n5Mie8kOT3NA+Oz1JWvlOSS5u5dck2W+uj1PS/GP7JEmS1J9RXJHcCPxeVR0IHA6clORAYAVwZVUd\nAFzZ3tM+WwYcBBwFvCfJDm1bZwCvAQ5or6Na+QnAvVX1VOBdwGlzcWCS5j3bJ0mSpD7MeSJZVXdU\n1efb8reBm4FFwFLgvLbaecDRbXkpcFFVPVhVtwFrgMOS7AU8vqqurqoCzp9UZ2JblwJLJq4GSNJ0\nbJ8kSZL6M9J7JNuQrkOAa4A9q+qO9tGdwJ5teRFwe0+1ta1sUVueXL5JnaraCNwH7D7F/k9MsirJ\nqvXr18/CEUnaVoy6fZIkSRpnI0skkzwW+GfgjVV1f+9nrQe/hh1DVZ1ZVYuravHChQuHvTtJ88Q4\ntE92dEmSpHG2YBQ7TbIj3Ze091XVB1rxXUn2qqo72rCwu1v5OmCfnup7t7J1bXlyeW+dtUkWALsA\n9wzlYCRtU8alfaqqM4EzARYvXjz0xFWS5qtTr3uo73VXHLLjECORti+jmLU1wNnAzVX1zp6PVgLL\n2/Jy4LKe8mVtpsP96SatuLYNM7s/yeFtm8dPqjOxrWOAq9pVBEmalu2TJElSf0ZxRfL5wKuAG5Jc\n38r+EDgVuCTJCcDXgOMAqmp1kkuAm+hmVDypqh5u9V4HnAvsDFzeXtB9EbwgyRpgA92sipI0E9sn\nSZKkPsx5IllV/wZMN0PhkmnqnAKcMkX5KuDgKcofAI7dijAlbYdsnyRJkvoz0llbJUmSJEnzj4mk\nJEmSJGkgJpKSJEljLMk+ST6R5KYkq5O8oZXvluSKJLe2n7v21Dk5yZoktyQ5sqf80CQ3tM9ObxOC\n0SYNu7iVX9OepStJ0xrJ4z8kSZowyNT90nZqI/B7VfX5JI8DPpfkCuDVwJVVdWqSFcAK4P9LciDd\nRF4HAU8CPp7kaW0ysDOA1wDXAB8GjqKbDOwE4N6qemqSZcBpwMvn9CglzStekZQkSRpjVXVHVX2+\nLX8buBlYBCwFzmurnQcc3ZaXAhdV1YNVdRuwBjisPQf38VV1dXvs0PmT6kxs61JgycTVSkmaiomk\nJEnSPNGGnB5Cd0Vxz/bcWoA7gT3b8iLg9p5qa1vZorY8uXyTOlW1EbgP2H2K/Z+YZFWSVevXr5+F\nI5I0X5lISpIkzQNJHgv8M/DGqrq/97N2hbGGHUNVnVlVi6tq8cKFC4e9O0ljzHskJUnbvUHv01xx\nyI5DikSaWpId6ZLI91XVB1rxXUn2qqo72rDVu1v5OmCfnup7t7J1bXlyeW+dtUkWALsA9wzlYCRt\nE7wiKUmSNMbavYpnAzdX1Tt7PloJLG/Ly4HLesqXtZlY9wcOAK5tw2DvT3J42+bxk+pMbOsY4Kp2\nlVOSpuQVSUmSpPH2fOBVwA1Jrm9lfwicClyS5ATga8BxAFW1OsklwE10M76e1GZsBXgdcC6wM91s\nrZe38rOBC5KsATbQzfoqSdMykZQkSRpjVfVvwHQzqC6Zps4pwClTlK8CDp6i/AHg2K0IU9J2xqGt\nkiRJkqSBeEVSUt8GmZDEyUgkSZK2XV6RlCRJkiQNxERSkiRJkjQQE0lJkiRJ0kBMJCVJkiRJA3Gy\nHWkeGGSSG0mSJGnYvCIpSZIkSRqIiaQkSZIkaSAmkpIkSZKkgZhISpIkSZIGYiIpSZIkSRqIiaQk\nSZIkaSAmkpIkSZKkgZhISpIkSZIGYiIpSZIkSRqIiaQkSZIkaSALRh2AJEnzzanXPdT3uisO2XGI\nkUiSNBpekZQkSZIkDWSbviKZ5Cjgb4AdgLOq6tQRhyRJtk2Sxtaw2qdBruJLmh+22UQyyQ7A3wEv\nBtYCn02ysqpuGm1kkrZn20vb5JdGaf7ZXtonSbNjm00kgcOANVX1FYAkFwFLARtDjYVt/Yu295BN\ny7ZJ0riyfZLUt205kVwE3N7zfi3wnBHFou3Etp4calbMy7bJv+0tN6xzt511wGhuzMv2SdJobMuJ\n5IySnAic2N5+J8ktA1TfA/jm7Ec1b/Y/DjGMev/jEMOo9z8rMZw83P0/ees2Pxpb0T6Nw9/EVIxr\nMDPGtZX/brbUvD1fI7K5uGybRmDSv5uxiKnHuMUD4xeT8cxsa2Pqu23alhPJdcA+Pe/3bmU/VFVn\nAmduycaTrKqqxVse3tYZ9f7HIYZR738cYhj1/schhlHvfwvM2DbBlrdP43o+jGswxjUY45o1Q/vu\nNI7nYtxiGrd4YPxiMp6ZzWVM2/LjPz4LHJBk/ySPApYBK0cckyTZNkkaV7ZPkvq2zV6RrKqNSX4H\n+CjdFNbnVNXqEYclaTtn2yRpXNk+SRrENptIAlTVh4EPD2nzWzQkdhvaP4w+hlHvH0Yfw6j3D6OP\nYdT7H9g23jZNx7gGY1yDMa5ZMsT2aRzPxbjFNG7xwPjFZDwzm7OYUlVztS9JkiRJ0jZgW75HUpIk\nSZI0BCaSA0pyVJJbkqxJsmKO9rlPkk8kuSnJ6iRvaOVvT7IuyfXt9UtDjOGrSW5o+1nVynZLckWS\nW9vPXYe4/6f3HOf1Se5P8sZhnoMk5yS5O8mNPWXTHnOSk9vfxS1JjhxiDH+R5EtJvpjkX5I8oZXv\nl+R7Pefi74e0/2nP+Ryeg4t79v/VJNe38lk/B+NqprYondPb519M8uw5iGnKtmrSOkckua/nd/TH\nw46r7fcRbdikz0dxvqZs1yatMyfna9D2blLdof2/OEgbOEXdzf7OhxBXX/8fDfN8jatxOuZ+2qlR\nSLJDkuuSfGjUsQAkeUKSS9u/tZuTPHfE8byp/b5uTHJhkh8fQQxb3E7OcUx9tZGzoqp89fmiu/H8\ny8BTgEcBXwAOnIP97gU8uy0/DvhP4EDg7cDvz9GxfxXYY1LZnwMr2vIK4LQ5/D3cSfecm6GdA+CF\nwLOBG2c65vb7+AKwE7B/+zvZYUgxvARY0JZP64lhv971hngOpjznc3kOJn3+V8AfD+scjOOrn7YI\n+CXgciDA4cA1cxDXlG3VpHWOAD40gnP2iDZs1Odrit/pncCTR3G+BmnvBv1bHEJcU7aBg/7OhxDX\nlG3jXJ6vcXyN2zH3006NKK43A+8fRfs4TTznAf+9LT8KeMIIY1kE3Abs3N5fArx6BHFsUTs5gpj6\naiNn4+UVycEcBqypqq9U1feBi4Clw95pVd1RVZ9vy98Gbqb7RzVqS+kaGtrPo+dov0uAL1fV14a5\nk6r6NLBhUvF0x7wUuKiqHqyq24A1dH8vsx5DVX2sqja2t1fTPedrKKY5B9OZs3MwIUmA44ALt3Y/\n80w/bdFS4PzqXA08IclewwxqjNuqfsz5+ZpkTtq16QzY3vUa6v+Lo24DB4mrTyP5HjFiY3XM49hO\nJdkb+GXgrFHGMSHJLnQJytkAVfX9qvrWaKNiAbBzkgXAo4FvzHUAW9FOzmlMc9lGmkgOZhFwe8/7\ntcxx45NkP+AQ4JpW9Pp26fqcIV9OL+DjST6X5MRWtmdV3dGW7wT2HOL+ey1j08Rhrs4BTH/Mo/rb\n+G26qygT9m9Dqj6V5AVD3O9U53wU5+AFwF1VdWtP2Vydg1Hq51yPtL2aoq3q9bz293N5koPmKKSp\n2rBeo27fJ7drvUZxvqC/Nn7U521yG9hrpt/5MMz0/9Goz9cojO0xz9BOzaW/Bt4C/GDEcUzYH1gP\nvLcNtz0ryWNGFUxVrQP+Evg6cAdwX1V9bFTxTDKq78L92lwbudVMJOeRJI8F/hl4Y1XdD5xBN1Tk\nWXT/sP5qiLv/2ap6FvCLwElJXtj7YXXXz4c+BXC6ByS/DPinVjSX52ATc3XM00nyVmAj8L5WdAew\nb/s9vRl4f5LHD2HXIzvnU/gNNv3yPVfnQJsxRVvV6/N0v6NnAO8GPjhHYW22DRulKdq1XqM6X5sY\ndXs3lSnawMnm+nc+Tm2jZjBDOzWXcbwUuLuqPjeqGKawgG645BlVdQjwXbphmyPROmWW0iW4TwIe\nk+SVo4pnOuPWTvbRRm41E8nBrAP26Xm/dysbuiQ70jV476uqDwBU1V1V9XBV/QD4B2ZhGOF0Wm8Q\nVXU38C9tX3dNDP1qP+8e1v57/CLw+aq6q8UzZ+egme6Y5/RvI8mrgZcCr2gNF21I6T1t+XN096Q8\nbbb3vZlzPtfnYAHwa8DFPbHNyTkYA/2c65G0V1O1Vb2q6v6q+k5b/jCwY5I9hh3XNG1Yr5G170xq\n13qN6nw1/bTxo/o7ezWT2sDJ+vidz6o+/z8a5d/ZqIzdMc/UTs2x5wMvS/JVumG/v5DkH0cbEmuB\ntVU1caX2UrrEclReBNxWVeur6iHgA8DzRhhPr1F8F55RP23kbDCRHMxngQOS7N96kJcBK4e903Yf\n2NnAzVX1zp7y3vt3fhW4cXLdWdr/Y5I8bmKZ7ibeG+mOfXlbbTlw2TD2P8kmV6Dm6hz0mO6YVwLL\nkuyUZH/gAODaYQSQ5Ci6ITAvq6r/6ilfmGSHtvyUFsNXhrD/6c75nJ2D5kXAl6pqbU9sc3IOxkA/\nbdFK4Ph0DqcbCnTH5A3Npunaqknr/Le2HkkOo/t/6J4hxzVdG9Zrzs9Xj8lX1n9oFOerRz9t/Jz/\nvzhdGzhpnX5+57MdVz//H43ke8SIjdUx99NOzaWqOrmq9q6q/ejOzVVVNdKrbVV1J3B7kqe3oiXA\nTSMM6evA4Uke3X5/S+jubR0Ho/guvFn9tJGzpkY0A9N8fdHN7PefdFc63jpH+/xZukvlXwSub69f\nAi4AbmjlK4G9hrT/p9DNsvYFYPXEcQO7A1cCtwIfB3Yb8nl4DN0XqF16yoZ2Dui+2N0BPETXO3fC\n5o4ZeGv7u7gF+MUhxrCG7n6Tib+Fv2/r/nr7/VxPNxzuV4a0/2nP+Vydg1Z+LvDaSevO+jkY19dU\nbRHw2olzQjf76N+1z28AFs9BTNO1Vb1x/U77HX2BbhKA581BXNO1YSM9X22/U7Vrc36+Bmnv6IaW\nfXhzf4tDjmu6NvCHcU33Ox9yXFO2jXN5vsb1NU7HPF07Nepz1GI7gvGZtfVZwKp2nj4I7DrieP4E\n+BJdB80FwE4jiGGg74UjjGnKNnIYr7QgJEmSJEnqi0NbJUmSJEkDMZGUJEmSJA3ERFKSJEmSNBAT\nSUmSJEnSQEwkJUmSJEkDMZHUnEjy1iSrk3wxyfVJnjPqmLZEknOTHNOWz0py4KhjkrR587n9SbJf\nkt+c5W3+aZIXzeY2JY1XW5PkZUlWbGHdJyW5dBZieHWSv52F7XwyyeLNfL7Fx6qts2DUAWjbl+S5\nwEuBZ1fVg0n2AB41QP0FVbVxaAFuoar676OOQdLmbQPtz37AbwLvn60NVtUfz9a2JHXGra2pqpV0\nzzPdkrrfAI6ZrViGbWuOVVvHK5KaC3sB36yqBwGq6putkSLJoUk+leRzST6aZK9W/skkf51kFfCG\n3iuB7fPvtJ9HtPqXJflKklOTvCLJtUluSPITk4NJ8nOtp/D6JNcleVw6f5Hkxlbv5W3dJPnbJLck\n+TjwxJ7t/LCHbCKetnxMknPb8rlJzkhydYvviCTnJLl5Yh1JQzVu7c/bk1yQ5DNJbk3ymlY+ZRsE\nnAq8oLVXb2pXKP81yefb63mt/l5JPt3WuzHJC5Ls0GKf2Oab2rq9Iyu+2r7wkmRxkk/2xHle29fX\nkvxakj9v2/lIkh1n/TclzW/j1tb88GpgazeuSnel9Mok+7byc5OcnuQ/2naP6Vn/xrZ8UNvP9a3+\nAa38lT3l/zvJDq38t5L8Z5JrgedPdaKSPDbJe1vsX0zy6638jCSr0l3V/ZNp6h7V2r4vJLlyimOd\ntXOomZlIa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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd5a39d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hyeJBt0/S/NPPfkuSJGlUDfNO5TuBu7u+nwXcUFVLgRvad5IcDawEjgFOAS5K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\nSVJnJpWSJEmSpM7+P2z8IWC9AH/gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd2b036d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_variable_histogram_grid(variables, np.arange(9))\n",
    "plt.savefig('variable_hists1.png')\n",
    "\n",
    "plot_variable_histogram_grid(variables, np.arange(9)+9)\n",
    "plt.savefig('variable_hists2.png')\n",
    "\n",
    "plot_variable_histogram_grid(variables, np.arange(8)+18)\n",
    "plt.savefig('variable_hists3.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What are the average values of each variable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCS : 10.472 (4.753)\n",
      "FiO2 : 36.510 (14.576)\n",
      "Heart Rate : 85.878 (19.061)\n",
      "Haemoglobin : 101.869 (22.866)\n",
      "Respiratory Rate : 18.430 (11.187)\n",
      "SpO2 : 95.705 (3.567)\n",
      "Non-Invasive BP Mean : 83.908 (19.369)\n",
      "Non-Invasive BP Systolic : 124.320 (26.622)\n",
      "Non-Invasive BP Diastolic : 65.870 (18.173)\n",
      "Arterial BP Mean : 80.043 (18.327)\n",
      "Arterial BP Systolic : 119.991 (24.731)\n",
      "Arterial BP Diastolic : 59.305 (14.074)\n",
      "Peripheral Temperature : 37.060 (0.961)\n",
      "Central Temperature : 36.100 (1.801)\n",
      "Serum pH : 7.396 (0.093)\n",
      "Serum sodium : 137.267 (5.571)\n",
      "Serum potassium : 4.379 (0.599)\n",
      "Serum ionised calcium : 1.127 (0.154)\n",
      "Serum bicarbonate : 25.654 (4.837)\n",
      "Serum urea : 9.133 (6.912)\n",
      "Serum creatinine : 105.553 (89.350)\n",
      "PEEP : 8.018 (2.747)\n",
      "pO2 : 10.873 (5.714)\n",
      "pCO2  : 5.622 (1.414)\n",
      "Bilirubin : 23.915 (48.743)\n",
      "Platelets : 246.143 (151.254)\n"
     ]
    }
   ],
   "source": [
    "for i,var in enumerate(variables):\n",
    "    vvals = chartevents[chartevents['Variable']==var]['value'] \n",
    "    \n",
    "    mean = np.mean(pd.to_numeric(vvals, errors='coerce'))\n",
    "    std = np.std(pd.to_numeric(vvals, errors='coerce'))\n",
    "    \n",
    "    print var + ' : %.3f (%.3f)' %(mean,std) "
   ]
  }
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