tables/freetextitems.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../img/logo_amds.png\" alt=\"Logo\" style=\"width: 128px;\"/>\n",
    "\n",
    "# AmsterdamUMCdb - Freely Accessible ICU Database\n",
    "\n",
    "version 1.0.2 March 2020  \n",
    "Copyright &copy; 2003-2020 Amsterdam UMC - Amsterdam Medical Data Science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "azdata_cell_guid": "4a40ac09-522e-41f3-991b-d42bf3f5ec7d"
   },
   "source": [
    "# <a id='freetextitems'></a>freetextitems table\n",
    "The *freetextitems* table contains all observations, including laboratory results, that store text (non-numeric) data. Personal data, including references to hospital staff have been removed. All items have an associated admissionid from the [admissions](admissions.ipynb#admissions) table.\n",
    "\n",
    "## Fields\n",
    "\n",
    "|Name|Type|Description|\n",
    "|:---|:---|:---|\n",
    "|admissionid|integer|links the items with the admissionid in the [admissions](admissions.ipynb#admissions) table|\n",
    "|itemid|integer|id to identify the type of result|\n",
    "|[item](#item)|string|type of result|\n",
    "|[value](#value)|string|value of this text result|\n",
    "|[comment](#comment)|string|comment associated with this result|\n",
    "|measuredat|integer|time associated with this result in milliseconds since the first admission. Negative results imply that the sample was drawn before the first ICU admission|\n",
    "|registeredat|integer|time the result was saved to the database in milliseconds since the first admission. Negative results imply that the sample was drawn before the first ICU admission.|\n",
    "|registeredby|string|user group that updated the result ('System')|\n",
    "|updatedat|integer|time the result was updated, in milliseconds since the first admission|\n",
    "|updatedby|string|user group that updated the result ('System')|\n",
    "|islabresult|bit|determines wheter the observation is a labresult. Currently, only freetext labresults have been released|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Example Python and SQL scripts</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import amsterdamumcdb\n",
    "import psycopg2\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib as mpl\n",
    "\n",
    "import io\n",
    "from IPython.display import display, HTML, Markdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#matplotlib settings for image size\n",
    "#needs to be in a different cell from %matplotlib inline\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.rcParams[\"figure.dpi\"] = 288\n",
    "plt.rcParams[\"figure.figsize\"] = [8, 6]\n",
    "plt.rcParams[\"font.size\"] = 12\n",
    "\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = None\n",
    "pd.options.display.max_colwidth = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connection settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Modify config.ini in the root folder of the repository to change the settings to connect to your postgreSQL database\n",
    "import configparser\n",
    "import os\n",
    "config = configparser.ConfigParser()\n",
    "\n",
    "if os.path.isfile('../config.ini'):\n",
    "    config.read('../config.ini')\n",
    "else:\n",
    "    config.read('../config.SAMPLE.ini')\n",
    "\n",
    "#Open a connection to the postgres database:\n",
    "con = psycopg2.connect(database=config['psycopg2']['database'], \n",
    "                       user=config['psycopg2']['username'], password=config['psycopg2']['password'], \n",
    "                       host=config['psycopg2']['host'], port=config['psycopg2']['port'])\n",
    "con.set_client_encoding('WIN1252') #Uses code page for Dutch accented characters.\n",
    "con.set_session(autocommit=True)\n",
    "\n",
    "cursor = con.cursor()\n",
    "cursor.execute('SET SCHEMA \\'amsterdamumcdb\\''); #set search_path to amsterdamumcdb schema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of all fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "azdata_cell_guid": "af0db8dd-df4e-4305-b2ac-f10e8fefe9b4"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "``` mysql\n",
       "\n",
       "SELECT * FROM freetextitems\n",
       "\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\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>admissionid</th>\n",
       "      <th>itemid</th>\n",
       "      <th>item</th>\n",
       "      <th>value</th>\n",
       "      <th>comment</th>\n",
       "      <th>measuredat</th>\n",
       "      <th>registeredat</th>\n",
       "      <th>registeredby</th>\n",
       "      <th>updatedat</th>\n",
       "      <th>updatedby</th>\n",
       "      <th>islabresult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1064</td>\n",
       "      <td>11646</td>\n",
       "      <td>Afname (bloed)</td>\n",
       "      <td>Art.</td>\n",
       "      <td>None</td>\n",
       "      <td>933720000</td>\n",
       "      <td>933720000</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1090119264</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1064</td>\n",
       "      <td>11646</td>\n",
       "      <td>Afname (bloed)</td>\n",
       "      <td>Art.</td>\n",
       "      <td>None</td>\n",
       "      <td>952500000</td>\n",
       "      <td>952500000</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1090124180</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1064</td>\n",
       "      <td>11646</td>\n",
       "      <td>Afname (bloed)</td>\n",
       "      <td>Art.</td>\n",
       "      <td>None</td>\n",
       "      <td>976920000</td>\n",
       "      <td>976920000</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1090129377</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1064</td>\n",
       "      <td>11646</td>\n",
       "      <td>Afname (bloed)</td>\n",
       "      <td>Art.</td>\n",
       "      <td>None</td>\n",
       "      <td>981600000</td>\n",
       "      <td>981600000</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1090133484</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1064</td>\n",
       "      <td>11646</td>\n",
       "      <td>Afname (bloed)</td>\n",
       "      <td>Art.</td>\n",
       "      <td>None</td>\n",
       "      <td>1007160000</td>\n",
       "      <td>1007160000</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1090137480</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1064</td>\n",
       "      <td>11646</td>\n",
       "      <td>Afname (bloed)</td>\n",
       "      <td>Art.</td>\n",
       "      <td>None</td>\n",
       "      <td>1025280000</td>\n",
       "      <td>1025280000</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1090139154</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1064</td>\n",
       "      <td>11646</td>\n",
       "      <td>Afname (bloed)</td>\n",
       "      <td>Art.</td>\n",
       "      <td>None</td>\n",
       "      <td>1037160000</td>\n",
       "      <td>1037160000</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1090142714</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1064</td>\n",
       "      <td>11646</td>\n",
       "      <td>Afname (bloed)</td>\n",
       "      <td>Art.</td>\n",
       "      <td>None</td>\n",
       "      <td>1069320000</td>\n",
       "      <td>1069320000</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1090056954</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1064</td>\n",
       "      <td>11646</td>\n",
       "      <td>Afname (bloed)</td>\n",
       "      <td>Art.</td>\n",
       "      <td>None</td>\n",
       "      <td>1082340000</td>\n",
       "      <td>1082340000</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1090059330</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1064</td>\n",
       "      <td>11646</td>\n",
       "      <td>Afname (bloed)</td>\n",
       "      <td>Art.</td>\n",
       "      <td>None</td>\n",
       "      <td>1097580000</td>\n",
       "      <td>1097580000</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1119017974</td>\n",
       "      <td>Systeem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   admissionid  itemid            item value comment  measuredat  \\\n",
       "0         1064   11646  Afname (bloed)  Art.    None   933720000   \n",
       "1         1064   11646  Afname (bloed)  Art.    None   952500000   \n",
       "2         1064   11646  Afname (bloed)  Art.    None   976920000   \n",
       "3         1064   11646  Afname (bloed)  Art.    None   981600000   \n",
       "4         1064   11646  Afname (bloed)  Art.    None  1007160000   \n",
       "5         1064   11646  Afname (bloed)  Art.    None  1025280000   \n",
       "6         1064   11646  Afname (bloed)  Art.    None  1037160000   \n",
       "7         1064   11646  Afname (bloed)  Art.    None  1069320000   \n",
       "8         1064   11646  Afname (bloed)  Art.    None  1082340000   \n",
       "9         1064   11646  Afname (bloed)  Art.    None  1097580000   \n",
       "\n",
       "   registeredat registeredby   updatedat updatedby islabresult  \n",
       "0     933720000      Systeem  1090119264   Systeem           1  \n",
       "1     952500000      Systeem  1090124180   Systeem           1  \n",
       "2     976920000      Systeem  1090129377   Systeem           1  \n",
       "3     981600000      Systeem  1090133484   Systeem           1  \n",
       "4    1007160000      Systeem  1090137480   Systeem           1  \n",
       "5    1025280000      Systeem  1090139154   Systeem           1  \n",
       "6    1037160000      Systeem  1090142714   Systeem           1  \n",
       "7    1069320000      Systeem  1090056954   Systeem           1  \n",
       "8    1082340000      Systeem  1090059330   Systeem           1  \n",
       "9    1097580000      Systeem  1119017974   Systeem           1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT * FROM freetextitems\n",
    "\"\"\"\n",
    "display(Markdown(\"``` mysql\\n\" + sql + \"\\n```\"))\n",
    "freetexitems = pd.read_sql(sql,con)\n",
    "freetexitems.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admissionid</th>\n",
       "      <th>itemid</th>\n",
       "      <th>measuredat</th>\n",
       "      <th>registeredat</th>\n",
       "      <th>updatedat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>651248.000000</td>\n",
       "      <td>651248.000000</td>\n",
       "      <td>6.512480e+05</td>\n",
       "      <td>6.512480e+05</td>\n",
       "      <td>6.512480e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11764.303183</td>\n",
       "      <td>11861.890980</td>\n",
       "      <td>5.372257e+09</td>\n",
       "      <td>5.392230e+09</td>\n",
       "      <td>5.971335e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6768.820682</td>\n",
       "      <td>1180.242541</td>\n",
       "      <td>2.727173e+10</td>\n",
       "      <td>2.731192e+10</td>\n",
       "      <td>2.765062e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10765.000000</td>\n",
       "      <td>-5.172246e+10</td>\n",
       "      <td>-5.172246e+10</td>\n",
       "      <td>-3.061021e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6038.000000</td>\n",
       "      <td>11646.000000</td>\n",
       "      <td>8.940000e+06</td>\n",
       "      <td>9.540000e+06</td>\n",
       "      <td>5.882277e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11717.000000</td>\n",
       "      <td>11646.000000</td>\n",
       "      <td>2.193600e+08</td>\n",
       "      <td>2.220600e+08</td>\n",
       "      <td>3.485025e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17670.250000</td>\n",
       "      <td>11646.000000</td>\n",
       "      <td>1.180695e+09</td>\n",
       "      <td>1.186020e+09</td>\n",
       "      <td>1.392079e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23551.000000</td>\n",
       "      <td>21207.000000</td>\n",
       "      <td>3.916534e+11</td>\n",
       "      <td>3.916534e+11</td>\n",
       "      <td>3.916539e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         admissionid         itemid    measuredat  registeredat     updatedat\n",
       "count  651248.000000  651248.000000  6.512480e+05  6.512480e+05  6.512480e+05\n",
       "mean    11764.303183   11861.890980  5.372257e+09  5.392230e+09  5.971335e+09\n",
       "std      6768.820682    1180.242541  2.727173e+10  2.731192e+10  2.765062e+10\n",
       "min         0.000000   10765.000000 -5.172246e+10 -5.172246e+10 -3.061021e+08\n",
       "25%      6038.000000   11646.000000  8.940000e+06  9.540000e+06  5.882277e+07\n",
       "50%     11717.000000   11646.000000  2.193600e+08  2.220600e+08  3.485025e+08\n",
       "75%     17670.250000   11646.000000  1.180695e+09  1.186020e+09  1.392079e+09\n",
       "max     23551.000000   21207.000000  3.916534e+11  3.916534e+11  3.916539e+11"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freetexitems.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='item'></a> item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "``` mysql\n",
       "\n",
       "SELECT item, value, comment\n",
       "FROM freetextitems\n",
       "WHERE item ILIKE '%coombs%'\n",
       "\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item</th>\n",
       "      <th>value</th>\n",
       "      <th>comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dir.Coombs conclusie (bloed)</td>\n",
       "      <td>Poly AHG = negatief. Anti-IgG, IgA, IgM, C3c en C3d = negatief</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dir.Coombs conclusie (bloed)</td>\n",
       "      <td>volgt</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dir.Coombs conclusie (bloed)</td>\n",
       "      <td>volgt</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dir.Coombs conclusie (bloed)</td>\n",
       "      <td>Poly AHG = sterk positief. Anti-IgG = sterk positief, anti-IgA, IgM en complement = negatief. Anti-IgG1 1:1 = positief, 1:100 = negatief, Anti-IgG3 = negatief, totaal anti-IgG = sterk positief.</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dir.Coombs conclusie (bloed)</td>\n",
       "      <td>Poly AHG = negatief</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           item  \\\n",
       "0  Dir.Coombs conclusie (bloed)   \n",
       "1  Dir.Coombs conclusie (bloed)   \n",
       "2  Dir.Coombs conclusie (bloed)   \n",
       "3  Dir.Coombs conclusie (bloed)   \n",
       "4  Dir.Coombs conclusie (bloed)   \n",
       "\n",
       "                                                                                                                                                                                               value  \\\n",
       "0                                                                                                                                     Poly AHG = negatief. Anti-IgG, IgA, IgM, C3c en C3d = negatief   \n",
       "1                                                                                                                                                                                              volgt   \n",
       "2                                                                                                                                                                                              volgt   \n",
       "3  Poly AHG = sterk positief. Anti-IgG = sterk positief, anti-IgA, IgM en complement = negatief. Anti-IgG1 1:1 = positief, 1:100 = negatief, Anti-IgG3 = negatief, totaal anti-IgG = sterk positief.   \n",
       "4                                                                                                                                                                                Poly AHG = negatief   \n",
       "\n",
       "  comment  \n",
       "0    None  \n",
       "1    None  \n",
       "2    None  \n",
       "3    None  \n",
       "4    None  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT item, value, comment\n",
    "FROM freetextitems\n",
    "WHERE item ILIKE '%coombs%'\n",
    "\"\"\"\n",
    "display(Markdown(\"``` mysql\\n\" + sql + \"\\n```\"))\n",
    "df = pd.read_sql(sql,con)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='value'></a> value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "``` mysql\n",
       "\n",
       "SELECT DISTINCT value\n",
       "FROM freetextitems\n",
       "\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dyserythropoïese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Poly AHG = pos, IgG/IgA/IgM en C3c = neg, C3d = sterk Positief</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Allo DLI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>studie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Klasse 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            value\n",
       "0                                                           50-60\n",
       "1                                                dyserythropoïese\n",
       "2  Poly AHG = pos, IgG/IgA/IgM en C3c = neg, C3d = sterk Positief\n",
       "3                                                        Allo DLI\n",
       "4                                                              13\n",
       "5                                                          studie\n",
       "6                                                            8100\n",
       "7                                                             0.3\n",
       "8                                                             182\n",
       "9                                                        Klasse 0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT DISTINCT value\n",
    "FROM freetextitems\n",
    "\"\"\"\n",
    "display(Markdown(\"``` mysql\\n\" + sql + \"\\n```\"))\n",
    "df = pd.read_sql(sql,con)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "``` mysql\n",
       "\n",
       "SELECT item, COUNT(item) AS \"Number of results\"\n",
       "FROM freetextitems\n",
       "WHERE NOT itemid = 11646 -- not a real result: location of bloodsample: arterial, venous, etc.\n",
       "GROUP BY item\n",
       "ORDER BY \"Number of results\" DESC\n",
       "LIMIT 20\n",
       "\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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tba3w7bUrooSEBNy/fx/R0dFITExERkYGDA0NYWpqCmtrazRs2PCTDb2qU3JyMh4/foznz5/jw4cPSE1NhYaGBgwNDWFmZoYGDRqgQYMGxapeWtpSU1Px6NEjvHr1CnFxcUhNTYVEIoG+vj7MzMxQr1492NraQk9Pr8zndv36dYwfP17adnFxgY+PT4nHjY6ORmhoKGJiYpCTkwNLS0s0a9YM9evXL/HYxfHvv/8iLCwMsbGxSEhIgI6ODiwsLGBjY4PGjRurNIz75s0b/PPPP4iLi8OHDx+gra0NY2NjNGzYEE2bNlXZ+/zmzRvcuXMHb968QXZ2NiwtLdGoUSOVVT3OzMxE586dpZVtTU1NcfnyZZWep6wAS0QlxVtqEZGqcV0hIlXimkJEqsZ1hYhUiWsKEaka15XPQ9WqVcr0eAzAEhERERFRoVxcXKSVjQ0MDHD16lVUrly5iL3oU3f58mVMnjxZ2p4yZQpmzZql0mPwf3wQUUnxf6gSkapxXSEiVeKaQkSqxnWFiFSJawoRqRrXlc9DWQdgNcv0aEREREREVOGMGzdO+ueUlBQcPnxYjbOh8mLPnj3SP+vp6WHkyJFqnA0RERERERERERERERERfW4YgCUiIiIiokINGDAA9erVk7bzBx/p8xQVFYUrV65I28OHD4elpaUaZ0REREREREREREREREREnxsGYImIiIiIqFBaWlqYMWOGtP348WNcu3ZNjTMiddu5cyeys7MBAAYGBpg8ebKaZ0REREREREREREREREREnxsGYImIiIiIqEh9+/aFnZ2dtP3f//5XjbMhdYqNjYW/v7+0PXHiRJibm6txRkRERERERERERERERET0OWIAloiIiIiIiqShoYHFixdDUzP3rxC3b9/GjRs31DwrUoetW7ciLS0NAFCvXj1MmjRJzTMiIiIiIiIiIiIiIiIios8RA7BERERERKSQZs2aYeTIkdK2r68vcnJy1DgjKmtv376Fn5+ftL1w4ULo6uqqcUZERERERERERERERERE9LliAJaIiIiIiBTm6emJhg0bAgBCQ0Nx5MgRNc+IytLy5cuRmpoKABgxYgQ6deqk5hkRERERERERERERERER0eeKAVgiIiIiIlJYpUqVsHLlSmnVz5UrV0oDkfRpCw0NxfHjxwEAjRo1gpeXl5pnRERERERERERERERERESfM211T4CIiIiIiCoWGxsb3Lt3T93ToDLWsmVLREREqHsaREREREREREREREREREQAWAGWiIiIiIiIiIiIiIiIiIiIiIiIiIgqGAZgiYiIiIiIiIiIiIiIiIiIiIiIiIioQmEAloiIiIiIiIiIiIiIiIiIiIiIiIiIKhQGYImIiIiIiIiIiIiIiIiIiIiIiIiIqEJhAJaIiIiIiIiIiIiIiIiIiIiIiIiIiCoUBmCJiIiIiIiIiIiIiIiIiIiIiIiIiKhCYQCWiIiIiIiIiIiIiIiIiIiIiIiIiIgqFAZgiYiIiIiIiIiIiIiIiIiIiIiIiIioQmEAloiIiIiIiIiIiIiIiIiIiIiIiIiIKhQGYImIiIiIiIiIiIiIiIiIiIiIiIiIqEJhAJaIiIiIiIiIiIiIiIiIiIiIiIiIiCoUBmCJiIiIiIiIiIiIiIiIiIiIiIiIiKhCYQCWiIiIiIiIiIiIiIiIiIiIiIiIiIgqFAZgiYiIiIiIiIiIiIiIiIiIiIiIiIioQmEAloiIiIiIiIiIiIiIiIiIiIiIiIiIKhQGYImIiIiIiIiIiIiIiIiIiIiIiIiIqEJhAJaIiIiIiIiIiIiIiIiIiIiIiIiIiCoUBmCJiIiIiIiIiIiIiIiIiIiIiIiIiKhC0Vb3BKhwGRkZiIyMxJMnT/Dhwwd8/PgR+vr6MDY2Rq1atdCsWTMYGhqqe5pE9In4999/cfv2bURHRyM9PR0GBgYwNTVF3bp1YWtrCz09PdE+GRkZCAkJwdOnT5GYmAgdHR1UqVIFVlZWsLa2hqWlpRqeCRHR5+3+/fsYOnQoJBIJatWqhVOnTkFXV7fI/SIjI/HPP/8gNjYWOTk5MDQ0hKWlJerWrYsmTZqUwcypPAsKCoKHhwcAoFmzZvD394eOjo6aZ0VEREREREREREREREREnysGYMuhlJQUHDt2DKdPn0ZwcDAkEkmB22pqaqJ58+YYNGgQXFxcULlyZYWP8+rVK3Tv3l3Qd/78eVhZWRV77iRfcHAwxowZI+h7+PBhqRxr9OjRuHnzprQ9ffp0zJgxQ+623bp1Q3R0tLS9dOlSDBw4sFTmRcWnzGe1uOdaYmIiFi9ejJMnTyI7O1vuNseOHYO1tbWgz8/PD6tWrUJCQoLcfcaPHw8vL69yc64p8/p4e3vj4MGD0rabmxuWLVtWqvOj0sH3svRs2rQJK1euFPQ1btwYR48eVdOMiq9jx46IjY1Vah8dHR3o6enBwsICVlZWaNGiBbp27YovvviilGapmIyMDHh5eUl/Q86ZM6fI8Ov9+/exYMEChIeHy328evXquHz5ssrnShVLhw4d8NVXX+HixYsIDw/Hhg0b8O2336p7WkRERERERERERERERET0mWIAthxJT0/H1q1bsX379gLDZLKys7MRFhaGsLAw+Pr6Yu7cuRg0aBA0NDRKebafjqtXryIuLg4DBgxQ91SI1CI+Ph7jxo1DREREgdvo6uqifv36gj5fX19s3Lix0LFtbGxUMkdSrSNHjsDY2BiOjo7qngpVcPmDxXkiIyNx584d2Nvbq2FGZUsikUAikSA5ORnPnz/HtWvXsGHDBtja2mLevHlo3769Wua1atUqREZGAgDs7OzQp0+fQre/ffs2Jk2ahOTk5AK34XpOeb7//ntcuXIFWVlZ2LRpE7p27Qo7OzuVHyfrxhUAgInKRyai8i6+0af/G4KIiIiIiIiIiIiIiFRDU90ToFx3796Fq6srVq9erXD4VVZCQgJ++OEHTJkyBSkpKSqe4acnJiYG3377LSZOnIioqCh1T4dIbVauXFlo+BUArK2toa39/9dMhISEFBl+BRiYKm+ePHmCMWPGYO7cuYiLi1P3dKiCu337Np49eyb3MX9//zKeTfny4MEDjB07Flu2bFHLsf/8809p29PTs9ALoyQSCby8vAoNvwJcz+n/NWjQAIMGDQIAZGZmYsGCBcjKylLzrIiIiIiIiIiIiIiIiIjoc8QKsOXAyZMn8f333yMjI0PQr6mpiU6dOsHR0REtWrRA1apVUblyZXz8+BFv3rzB3bt3cfjwYVFw7dKlSxg1ahS2b9+OKlWqlOVTqTD279+PX3/9lUFh+uzFxMQgICBA0FetWjV4eXmhTZs2qFSpEj58+CD6rMiGXzU0NDBhwgQMHDgQVatWRWpqKl69eoWGDRuW+nMgxaxbtw4bN26U3hKdqKRkq7/q6ekhPT0dQO5vm/nz58PIyEgdU1MJGxsbfPnllwU+LpFIkJaWhg8fPiAqKgqPHj1CZmam9PHs7GwsX74cJiYmGDx4cFlMGQDw888/Izs7GwDQqVOnIqvQHj9+HC9fvhT0tWvXDjNmzIC1tTVycnIQGxvL35QkMH36dBw+fBjp6el4+PAh9u7di5EjR6p7WkRERERERERERERERET0mWEAVs1OnTqF2bNnS4MKebp374558+ahdu3aon2MjIxQo0YNtGrVCuPHj8fp06fx448/Cqr5hYeHw9PTExs2bICmJgv9yjpy5AjDr0QAwsLCRFXbfHx80LZtW2nb2NhYtN/t27cFbTc3N8ydO1faNjIyQrVq1VQ8WyqJgIAAhl9JZdLS0nDixAlpu27durC2tsa5c+ekjx86dAhjxoxR1xRLrGXLlvDy8lJ4+w8fPmDjxo3Yvn27YF1dsmQJOnXqhOrVq5fGNAWOHj2KkJAQafu7774rch/Z9dzMzAwbN26EoaGhoI8ov2rVqmHYsGHYvn07AGDNmjXo06cPTE1N1TwzIiIiIiIiIiIiIiIiIvqcMACrRhEREfD29haEX7W0tLBgwQKMGDFC4XGcnZ1ha2sLDw8PvH79Wtp/6dIlbN++HePGjVPpvEl57du3x8OHD9U9DZELFy6oewqkYsqea/nXDAAwNTUVhF/lSUpKEt0q29nZudB9ysu5Vl4/i1S6li1bhmXLlql7Gp+UM2fOCNaB1q1bo127dtIALAD4+/tX6ACssoyNjeHl5YXq1avj119/lfanpqZi8+bNWLRoUakeXyKRwNfXV9ru1KkT7OzsitxP9nugU6dOgvArUUEmTJiAPXv2QCKRICEhAZs3b1YqNE5EREREREREREREREREVFIsDaomWVlZ8Pb2RmpqqqB/yZIlSoVf89SpUwdr166Fjo6OoH/9+vV4//59ieZKRJ8u2UrIVlZWSu+j6H5E9OkICAgQtFu3bo1u3boJfoc8fvwYf//9d6nNwc/PD02aNJH+V9oBU0V5eHigWbNmgr6jR48iMzOzVI976NAhREdHS9ujR49WaL/ifA8QAblVYPNfALN3717Ex8ercUZERERERERERERERERE9LlhAFZN9u7diwcPHgj6hg0bhoEDBxZ7zObNm4uqvSYmJmLHjh3FHpOIPm35b9MNAPr6+kXuIy/EValSJZXNiYjKt9evXyM4OFja1tDQQOfOnWFsbIyOHTsKtt23b19ZT69ckP09l5iYiMjIyFI7XmZmJjZv3ixtV61aFZ07d1Zo3+J8DxDlyX+up6Sk4M8//1TjbIiIiIiIiIiIiIiIiIjoc8MArBpkZWVh69atgj4TExPMnj27xGNPmDABBgYGgr5Dhw4hJyenxGMT0aenOGsD1xOiz9vBgweRnZ0tbdvZ2aFatWoAgL59+wq2PXXqFBISEsp0fuVBw4YNRX1v3rwpteOdP38eL1++lLZdXV2hpaWl0L5c06kkHBwcUKNGDWnbz89PdIcLIiIiIiIiIiIiIiIiIqLSoq3uCXyOLl68KLhFLQAMHjwYxsbGJR7bxMQEvXr1kt6auF69emjXrh0+fPgAExOTEo8vT1JSEm7fvo2YmBgkJCRAV1cX5ubmqFu3Lpo3bw5NzZLnrF+9eoV79+4hNjYWycnJqFKlCkxMTFCrVi20aNEC2trqPZVv3ryJBw8eQCKRoE6dOmjbti1MTU1VeozY2FjcvXsXr169Qnp6OiwtLVG3bl3Y29tDQ0NDpccq7yIiIhAeHo64uDgAgLm5OZo2bQpbW9tC98vMzMTdu3fx8OFDJCYmwsDAAJaWlmjdujUsLS3LYuoCOTk5ePjwIe7fv4+4uDjo6OigevXqaNGiBWrVqlXm8ynvkpOTce/ePTx79gxJSUnQ1taGsbExGjduDFtbW8Gt18uTJ0+e4NGjR4iLi0NycjJMTU1hYWEBe3t7la8TZaUs1v3yKiMjA7dv38abN2/w7t076Ovrw8LCAl988YUgBFYSqamp0tc3NjYWurq6sLCwkL6+6l7zDx06JGj36NFD+mcnJycYGBggJSUFAJCeno5Dhw5h7NixZTlFtSut31wFka206+rqWqbHz/P8+XM8fvwYCQkJSEhIQEZGBvT19WFubo569erBxsYGurq6KjlWVFQUHjx4gNjYWCQlJcHY2BgWFhZo2bIlqlatqpJjALmf+QcPHiAyMhLv378HABgZGaFu3bqws7ODoaFhicbPyclBeHg4Xrx4gdjYWKSnp8Pc3BzVqlVDq1atRBe2FUdSUhLu3buHFy9eIDExETo6OjA1NYW5uTlatmxZor+DaGpqon///ti0aZP0WKdOnYKbm1uJ501EREREREREREREREREVBQGYNXgxIkToj7ZW+WWxPjx49GlSxe0bdsWFhYWKhs3v+zsbJw9exbbtm3D3bt3BZXg8jMxMUG3bt0wbdo01K5dW6ljfPz4ETt37sTBgwfx/PnzArczNDREhw4dMHz48EJv99utWzdR8DjPunXrsG7dOmnbzc0Ny5Ytk7aDg4MxZswYaXvkyJFYtGgRHj9+jNmzZ+Phw4eC8XR0dODk5IQFCxbAwsJCtD8A0T4FCQ4OxsaNGxEUFCT3dbaysoKHhwdGjRqldOhM9jVZunSp3HOxJPNXdv+AgADMmzdP2l64cCFGjRoFADhy5Aj++9//Fng+1KtXD7Nnz4azs7OgPz09HVu3bsWOHTuk4RVZXbp0gaenJ5o0aaLo0yq2jIwM7Nq1C7t378arV69Ej2toaODLL7/EzJkzYWdnp9TYRb3W8h7P7+bNm6LXYPr06Th48GCBnx8A6N69u6Bdq1YtXLhwQdpW9FyT58aNG9i+fTuuXbsGiUQidxtDQ0P0798fkyZNKjQ8XNJzWVHJycnYunUrjh49iqioKLnbaGlpoWXLlhg3bhx69uyp8jkAKPR8njdvnuCzNn36dMyYMaPA7Utj3T948CC8vb0FffXq1cPhw4dRqVKlQvcFcsPwQ4YMQUZGhrSvQYMGCAgIENzG3dvbGwcPHpS2Zdf4/PK/ZtbW1jh27BgAICYmBmvXrsXp06eRmJgod99mzZph8uTJ6NWrV5Fzlyc4OBj/+9//EBwcLHhO+VlYWKBXr16YOnVqqX3HF+bWrVuCSqMaGhqC52tgYABnZ2fB6+3v7//ZBWBjYmJEfaV1YUN0dDQCAwOl7Ro1aqBx48YFbr9ixQr873//K/BxHx8f+Pj4CPr8/f3xxRdfyN0+JCQE+/btQ2BgIP79999C52pgYAAnJydMnDixWN+3aWlp2LFjBw4dOoQnT57I3UZDQwPNmjXDqFGj4OrqWuxA/qNHj/DHH3/g7NmzSEpKkruNtrY2unXrhokTJ6Jly5ZKjR8TE4NNmzbh9OnTiI2NlbuNrq4u2rdvj6lTp6J169ZKP4dz585h9+7dBf6GBHIDrM2aNYOLiwvc3d0Fa6eiunTpIg3AArnnCwOwRERERERERERERERERFQWPt0SbeVUdna2IKQA5AYi5N0qt7isra3Ru3fvUgvGPH36FIMHD8a3336L27dvF/gP6gCQkJCAgIAA9O7dG8uXL0dWVpZCx7h//z569eoFX1/fQsOvQG5Q9vz585g4cSKmTJlSYDBJ1aKjozFmzBi54TmJRILLly8rFOAqSFpaGubNm4cxY8YgMDCwwNf51atX+OWXXzBmzJgCw50VXVJSEr777jvMnTu30PPh+fPn+Pbbb7Fy5UppX1RUFAYNGoTVq1cX+vpcuXIF7u7uuHTpkgpnLhYREYF+/frht99+kxt+BXKrwV2/fh3Dhw8XBEo+NzExMZg0aRLGjh2LixcvFhh+BXLXAT8/P7i4uOCvv/4qw1mKHT9+HE5OTli/fn2B4VcAyMrKwu3btzFjxgyMHDlSbmCuvCitdd/NzU0UFn3+/DnWrFlT5JzS09Ph6ekpCIrq6urC19e3WAGuwpw8eRL9+vXD/v37C/2OCQ8Px3fffYdJkyYhLS1N4fETEhIwdepUjBkzBlevXi0w/ArkVgPftWsXevTogd27dyv1PFQhr8J8ntatW4uCzrLVR58+fYpbt26V+tzKkzt37gjaFhYWsLa2LpVjHT9+XPCZ7NSpU6kcR1Z8fDymTp2KESNG4NChQ0WGXwEgJSUFR44cwYABAwQXHiniypUrcHZ2ho+PT4HhVyD3O/Sff/6Bt7c3Bg4ciGfPnil1nLS0NCxZsgT9+vVDQEBAgeFXILey/JkzZzBs2DAsXbq00LUxvy1btsDZ2Rm7d+8uMPwK5F4wc/XqVYwYMQIzZ87Ex48fFRo/NTUVU6ZMwTfffFPob0gg9+8m9+7dw9KlS9GnTx/RuauIL774ApUrV5a279y5U+BvHCIiIiIiIiIiIiIiIiIiVWIAtow9ffpUFMJr27atmmajvMDAQAwcOBDh4eFK7SeRSLBlyxZ8/fXXSE5OLnTbZ8+eYfTo0QoFKWRdvHgRkydPVjhoW1zZ2dmYM2cO4uLiCtymV69egjCAMtLS0jBp0iRR0Kgwt27dwtdff43U1NRiHbO8yszMxLRp03Dq1CmF99m0aRNOnTqFV69eYeTIkXj06JFC+6WmpmLOnDmlFtoICwvD8OHDiwx158nMzMTKlSvx559/lsp8yrMnT55g6NChuHLlilL7paSk4IcffsC2bdtKZ2JF2LBhA2bPnq10GP3vv//G0KFDS6UabUmV9rr/448/wtLSUtC3bds23Lt3r9Dxf//9d9Fn29vbGzY2NkrNsyjnzp3D7Nmz8eHDB4X3uXLlCmbOnKnQtlFRURg2bJigYrIiUlJS8NNPP+HXX39FTk6OUvsWV0pKimgtHjBggGi7Dh06iKqd7t27t1TnVp4kJibC399f0DdkyBBoaWmVyvFkz52yCMC+e/cO7u7uSp+3ebKzs7F27VqFzwt/f39MmTIFb9++Veo4Dx48wLBhwxASEqLQ9klJSRg3bhx27typ1OcqOzsb27Ztw9y5cwvdLzMzE/Pnz8fy5cuV/r128uRJjBo1qsjfx9nZ2ZgyZQouXryo1PgA8Pr1a4wfPx4RERFK7aetrY127doJ+kr7giIiIiIiIiIiIiIiIiIiIgDQVvcEPjfyKlbZ2tqqYSbKe/DgAaZPny76B/t69erB3d0dX375JSwtLZGWloZnz57h9OnTCAgIEFRtvHr1KubOnYv169dDQ0ND7nG8vb0FYSkdHR0MHDgQPXr0QP369WFkZASJRILo6GhcuXIF27dvF1Tku3PnDgICAjBkyBDBuEOHDpUGmE6cOCEIUdjb28Pe3l7abtGiRaGvxcmTJ5GQkCBtt2/fHk2bNkViYiJCQ0Px+PFjDB06tNAxCvPjjz/i5s2bgj4DAwOMHj0aTk5OsLKyQkpKCiIiIrB//35pyCA0NLTYxyyvNm/eLA0aa2tro1evXhg4cCDq168PXV1dPHr0CNu2bRMFLXx9fWFgYCCtqlmtWjWMGzdOep4mJCTgxo0b2Lx5M968eSPdL+/W9f/5z39U+jzi4+MxZcoUpKSkCPqbNGmC0aNHo23btjA2NkZcXByuXbuGHTt2IDo6GgCwa9culc2jRo0aGD9+vLR9584dQbW36tWro0+fPoJ97O3toaOjI/38JCcnY9++fYJthg4dKgh8GxsbF3uOiYmJmDx5sijoZGZmhhEjRqBLly6oUaMGtLW18eTJExw5cgQHDhwQBN9///132NnZoVWrVsWeh7L27NmDVatWCfo0NTXRu3dv9O7dG7a2tqhcuTLev3+P0NBQHDhwQPA5f/v2LSZPnoxDhw7B1NRUJXPK/17v27dPsLZ27txZUJEy/xqYpyzWfRMTEyxbtgwTJkyQBseysrIwf/58BAQEQEdHR7TP5cuXRZ8LJycnjBw5sqiXRCn//vsvPD09pdUTa9WqhVGjRsHBwQHVqlXDx48fERkZiT179uDatWuCfS9evIjLly/D0dGxwPGTk5MxYcIEvHjxQtBfq1YtjBgxAg4ODqhRowYkEglevXqFixcvws/PT/A+bt++HRYWFpg8ebIKn7l8p06dEqxhlSpVQu/evUXbaWhowNXVFevXr5f2nTlzBu/fv1fZuV1eJSQk4OuvvxaE4GvUqIEJEyaUyvHi4+NF3/3yPsv5tW3bVrBeHj16FO/evZO227RpAzs7O8E+siH1uXPn4uXLl4I+W1tbDBkyBC1btkT16tWhp6eH5ORkPH/+HIGBgdi/f7/o4gAfHx/0798fBgYGBc737NmzWLRokaj/q6++Qr9+/dCiRQsYGRnhw4cPCA8Px+HDhwW/CRISEjBlyhQcPHgQVlZWBR4nJycHc+bMwe3btwX9urq6GDx4MHr06IF69erBwMAAr1+/xrlz57B9+3bB5/HYsWOws7ODh4eH3GMsW7YMBw4cEI0/aNAgODk5oUGDBtDX10dsbCxuRxTy5QAAIABJREFU3ryJvXv3IjIyUrrt/fv38c0332DPnj1y10YA2LlzJ4KCggR9bdq0wZAhQ9C8eXOYmZlBS0sLcXFxuHv3Lnbu3In79+9Lt01JScGyZcuUvpCkVatWgkD0xYsXMWrUKKXGICIiIiIiIiIiIiIiIiJSFgOwZUxe5cc6deqU/USUJJFIMH36dNGtV8ePH49Zs2ZBV1dX0F+zZk107NgRHh4e+OabbwS3n71w4QK2bt2KiRMnio5z584d3L17V9rW1tbGli1b0KFDB9G25ubmsLOzw+DBgzFq1CjBrcY3b94sCsBOmTJF+ud//vlHEK7r2LEjZsyYUdTLIJUXfq1cuTLWr1+P9u3bCx6/efNmkQGUgty4cUNU+bVRo0bYvHmzoKKemZkZrKys4OTkhGPHjsHb27vQW8RXVHnhV2NjY2zYsAGtW7cWPG5hYYEOHTrgu+++w+nTp6X9+T9rX331FXx8fGBoaCjtMzU1Rf369dGjRw+MGDFCEOQ5evQoFi1aVGBIuziWLl0qqhg8ZswYeHl5QVv7/5diU1NTNGrUCEOHDsUPP/yAEydOqGwOQO564+XlJW2vXbtWEICVfTxP/oqCr169EgVgv/7660KDRcr4/fffRVV4e/TogaVLl6JKlSqCfjMzM7Rt2xZ9+/bF1KlTpeG8rKwsLFmyRKkqyiURERGBZcuWCfpq1KiBNWvWiIJkJiYmqF+/PgYMGICDBw9i4cKF0s/u27dv4e3tjU2bNqlkXvnfy9OnTwuCWn369MHAgQML3Les1n0gdw0ePXo0duzYIe2LjIzEpk2bMH36dMG2cXFxmD9/vqCvRo0a+OWXXwp8LsWVv+rr6NGj8f333wuet5mZGWrXro3u3bvjf//7H1asWCHYf+/evYUGYP/zn/+Iwq/yjgPkhvhbt24NDw8PzJo1C7du3ZI+tnr1arRr1w5ffPFFsZ6nomQ/T05OTgVWOndzcxMEYDMyMnDo0CGMGzeuVOdYlnJycpCeno4PHz4gKioKV69exa5duwSfsypVqmDt2rWitUtV7ty5I7i9vZGREapVq1boPo6OjoLzMiQkRBCAdXR0LDRQffnyZdy4cUPQN3z4cCxatAiamsKbS1SpUgU1atSAg4MDxo0bhxkzZuDvv/+WPp6YmIhLly6JLrzIEx0dLfq8m5qaYuXKlfjyyy8F/SYmJqhbty769OmDCxcuYM6cOdLvhMTERMyePRt+fn4FVuL966+/cPnyZUFf48aNsX79etSuXVt0rKZNm8LNzQ3jxo0T/AZdtWoV3NzcYGRkJNjn3Llz2Llzp6CvadOmWLNmjWh8U1NTWFtbY9iwYdiwYQPWrl0rfSwsLAwrV66U+12dk5MjWEcBYOTIkXIDxMbGxmjQoAFcXV2xYMECwef7xo0buHfvXpEXhOWX/4IK4P/PTdlzgoiIiIiIiIiIiIiIiIhIlRiALWP5q4bmKSqoUB7s379fFEibOnVqkbd4btiwIfbs2YMhQ4YI9t+wYQPc3d1FgZDAwEBBu3fv3nLDr/lVr14dS5cuFVSZevnyJaKiokSBAlXz8fERhV8BiG4Dq4x169YJ2ubm5tIKfwVxcXGBhoYGZs+eXezjlnfr1q0ThV/zaGhowNPTUxCAzWNjY4M1a9aIwmR5qlatilmzZmHWrFnSvqSkJDx9+hQNGzZUydxfvHiB48ePC/oGDBiAH374ocB9DAwMsGLFCqSkpHxWtxF+8eKFKGTn6OiI1atXF3r78A4dOmDRokXw9vaW9oWHh+Pvv/9GmzZtSm2+eXx8fJCeni5tm5qaYufOnUWuQW5ubjAwMMC3334r7bt06VKZzbswZbXu5/H09MSNGzfw6NEjad/GjRvRs2dPNG7cWNo3f/58xMbGSttaWlpYsWIFTExMlHp+ynBzc8OCBQsK3WbSpEkICgoSVIINDAyERCKRW6kxLCwMx44dE/SNGzdOcA7LU7VqVWzZsgVjxoyRVv7MzMzEqlWrlK7YqIyoqChBcBHIfV0KUqdOHbRu3Vpw63l/f3+FA7A7d+7E69evC91G9jbtoaGh+O233wrdR1tbG3PmzFFoDv7+/vD391doW3kaNWoEX19fwfmravfu3RO0VfW9VRg/Pz9Bu0mTJliwYEGRQUczMzP4+PjAyclJcMFOSEhIgQHYdevWCar8GxgY4I8//kDTpk0LPVa3bt2wefNmeHh4SKvdhoaG4uzZs+jVq5doe4lEIgiZArmVmHft2lVoRfPatWvjv//9LwYNGiR9TikpKdi/f7+g6m92djZ+//13wb6NGjXCjh07Cg1Ha2lpYfr06dDW1oavr6+0f+fOnRg7dqzo7xAvXrwQrLsGBgb4/vvvCxw/7xg//vgjgoODpZXnAeD69etKBWAbNWokaH/8+BFPnz4V9RMRKUJTU3UXIgIQXdiooaEB5vOJqCS4rhCRKnFNISJV47pCRKrENYWIVI3rCpUGBmDLmOztzwEUetvX8iAnJwebN28W9LVo0ULhiqlmZmb47bffBLemTk5Oxu7duwVVWYHc203nV7VqVYWO0bZtW9SpUwcfPnxA7dq1UadOHUEFttLQtGlTdO3aVaVjPnv2TBQwmjt3bqHh1zx9+/bFiRMncO7cOZXOqTz46quvigwV16lTB/Xr1xdUnQSAGTNmFBh+zdO5c2dR36tXr1QWJDpw4IDgdtPGxsaiinbyaGlp4aeffkKvXr3krh2fItnXysDAAEuWLCk0/JpnwIAB2LJlCx4/fiztu3DhQqkHSZ88eYKrV68K+ubNm6dwAN/Z2RnOzs6CAPeff/6p1gBsWa77efT09LBixQoMHjxYGiSTSCRYuHAh/Pz8oKmpid27d4sC4dOmTSvV10pHRweenp4Kbevu7i4IwKalpeHt27dyz4U//vhD0G7QoIHCx6lUqRKWLFmC/v37IycnB0BuxcYHDx7A1tZWoTGUFRAQID0WkHvxjmwFTllubm6CAOyzZ88QHBws98IRWUePHpUGfBUVEREhCsXK0tXVVTgAWxLVq1fHL7/8UqrhVwCCW9cD4iqcqpaeno7r168L+iZNmiSoZF6Y6tWro3nz5oLK4/Hx8XK3jY2NFYXEp0+fXmT4NU/btm0xfPhw7Nq1S9r3559/yg3AXrlyBTExMYK+n3/+udDwa54mTZqgX79+gos3Lly4IAjAnj9/XlDtWUNDQ25V84JMnjwZJ06cwMOHDwHkro27du0Sncuyv6OrVKmCSpUqFTm+rq4u+vXrhz/++ANWVlaoU6cOTE1NFZpbnlq1asHAwEDweyU8PJwBWCIqFnNz+RXmVcXMzLDojYiIlMB1hYhUiWsKEaka1xUiUiWuKUSkalxXSBWYoS5jmZmZoj5FQwPq8vDhQ7x580bQN2nSJIUCaXnatGkjCrzIq9Yp+4/tsrfsLsyxY8dw8+ZNHDhwAL6+vqUWAsojLzRZUmfPnhW0jYyM0LdvX4X3/5RuLZ2fq6urQtvVq1dP0DYwMMBXX31V5H5VqlQR3cY7KSlJ4fkV5cyZM4J2nz59FArVALkhs969e6tsLuWd7GvVq1cvWFpaKrSvhoYGXFxcYGJiglatWmHw4MFo1qxZaUxT4PDhw4JgYM2aNdG/f3+lxhgxYoSgffnyZaSlpalkfsVRlut+fjY2NqIKs3fv3oWfnx+eP3+O5cuXCx5r164dpk2bpvCciqNTp04KXYQAQO73zocPH0R9KSkpovV+/PjxSv0eaNy4sSj4e+rUKYX3V0ZOTg4OHz4s6Ovfv3+RFT979+4NfX19QV9JKqpWJG/fvoW7uzvGjBlTZCi3JKKiogTt+vXrl9qxgNx1duvWrfj1118xdepU9O/fH926dVNqjFq1agnaqampcrc7ceIEMjIypG1DQ0NBtX9FyK6td+/exdu3b0XbyX4eGzZsiI4dOyp8nP79+8PAwADNmjWDi4sLHB0dBY/Lfn4cHBxgZ2en8PiampoYNmyYoE/e5132d3RMTIzo7goF+eabbxAWFoaTJ09i06ZNcHd3V3h+QO65IXv+yZ6fRERERERERERERERERESqVr6Tl58g2SAIALWGnBQRFBQkaBsaGioddgByqzMGBwdL2xEREUhMTISRkZG0T7aqV3R0NEaNGoV58+YVWTFOT09P6TmVRMuWLVU+pmz11y5duhRZvTS/1q1bo1q1aqIqZhWdorfgla2kZmNjo3Bgz9DQUBC2zh+6KYn4+HhRVVonJyelxujbty8OHDigkvmUZ7GxsaLXytnZWakxpkyZgqlTp6pyWkWS/dx27dpVVLa/KK1atYKenh7S09MB5Fb3Cw0NVahSZmkoy3Vf1vjx43H58mXcvHlT2rd69WoEBAQIgnImJiZYsWJFkSHMklLmFuDygrLyvuNDQ0NFF8R0795d6bk5ODjg1q1b0vbt27eVHkMRQUFBglujA7nVXYtSuXJl9OjRA0eOHJH2nTlzBvHx8TAzM1P5PFXNxsamyCq3EokEHz9+xLt37/D48WNRcDw4OBhDhw7Fr7/+ChcXF5XPUfZ4il5cUVy6urpo165dkVXZCyNbkTR/1e/8ZNfWjh07Kv1br2HDhrC0tBRURg0JCRFdXJT/cwQAPXv2VOo4HTp0EFS1lSX72SzOeip7Lr58+RLv3r0T3C2hTp06qFy5suD3zPTp0zFnzhwMGjSo0GqwyvzeLIiJiYmg/fr16xKPSURERERERERERERERERUGAZgy5ihobh088ePH9UwE8Xdu3dP0G7atCl0dHSUHkc2MJqdnY3w8HA4ODhI+7p164bq1asLqnM9ePAAY8aMQbVq1eDo6IhOnTrBwcGh0ABVWWjQoIHKx5StEtekSROl9tfQ0EDTpk0/qQCslpYWatasqfC2+SlatREovUrM8ir/2djYKDVGWVQxLQ8ePXok6lP2uSsbPC0piUSCf/75R9BXnNs96+rqom7duoiMjJT2hYWFqS0AW5brvixNTU38/vvv6N+/PxITEwHkVlGVraS6dOlSVKtWTek5Katu3boKbysvXJadnS3qkw3DmZmZFSsQKnuuhYWFKT2GIvLf2h3IDQU3bNhQoX3d3NwEAViJRIKAgABMnDix0P327dtX5Nh+fn5YvHixtO3u7o6ffvpJoXkpomXLlvDy8lJqnydPnmDdunU4efKktDJ0eno6vv/+exgZGaFLly4qm19ycrKoeqpsNfPyIDs7Gy9evEBoaCiCgoJw/vx50ePyyAZKi7O25u2XPwAbFhYmCMCmpKTg1atXgn2aN2+u1DEK++55/vw54uLiRHNSVp06daCrqyu4QCcsLEwQntfT08PAgQOxY8cOad/Hjx/x008/Yfny5ejQoQM6d+6MTp06KbW2KUr2/IuNjVX5MYjo8xAXp9hdYBSloaEhuI1WfPxHwR0ciIiUxXWFiFSJawoRqRrXFSJSJa4pRKRqXFc+D+bmZfvv1gzAljF5QT55t2ItT96/fy9oFzeA0KBBA2hpaQkqfcXHxwu20dbWxtKlSzFp0iRRdbyYmBjs27cP+/btg5aWFuzs7NC5c2d07dpVLQFBVVdZy8jIEAQ0AKBevXpKj9OoUSNcvHhRRbNSP2Nj42JXeDQwMFDxbJQnG6oxNDRUKpgL5FZUs7Cw+OSDJLKvlYGBgaCyXXkUHx8vrdqa56efflJJCE+d73dZrvvy1KhRA4sWLYKnp6fcx8eMGVOsCorFoUygUF4ITt6Pddnv/fj4eKUveJAnLS0NycnJKg1BJicni24Pr6mpid9++02h/bOzs6GpqSkIOe7btw8TJkwo88B6WWjYsCF8fX3RoUMHLFq0SNqflZWFH374ASdPnlTZ+5OSkiLqU2cANj4+Hk+ePMGLFy8QFRWFqKgovHjxAk+ePBEFdYuSlZWFd+/eCfrWr1+P9evXl3iesmur7HcPoFzwvSiyVXoBYOzYsSoZW973xIwZM3Dt2jU8ffpU0J+amoqLFy9KfyPWrl0bnTp1gqOjIxwcHAqtDqso2Qv9lH3fiYjyZGer9n92yv51MicnR+XHIKLPC9cVIlIlrilEpGpcV4hIlbimEJGqcV2h0sAAbBmT9w/q8v7hvTxJSEgQtItbeVVDQwOVK1cWVPGTHRvIvcXr6tWrsXDhwgKDUllZWbhz5w7u3LmDNWvWoGbNmujVqxcGDx6scFW6klJ1BdqkpCRRUKo4QRZ1V8ZVtZLckrc8hKtkq1YWN5xkZGT0yQdg86p95imPlQxlyc65ooxdlLJe9+Xp168fLl26hGPHjgn669evj7lz5xZrPsWhr6+v8jFl1wVVj63Kz86JEydEIbbQ0FCEhoYWe8wXL14gKCio0ErAFZ27uzvCw8Ph7+8v7fv3339x4MABeHh4qOQYaWlpor6yXjcjIiLw119/4fz58yq93X1iYmKpXekp+/mTt9aq8nUszbVc3lpiZGSErVu3wtPTEyEhIQXuGxUVBT8/P/j5+UFfXx+Ojo5wc3ODo6NjsX8/ValSRdBmAJaIiIiIiIiIiIiIiIiISlvxyipSsdnY2Ij+Ufn+/fsqP44q/8E5OVl4+8GShIFkq0vJVk7M4+TkhOPHj2Py5MkKVYB8/fo1/vjjD7i4uGDx4sWiOZcGbW3V5sfz39I2j56entLjVITQoDK0tLTUPYUSkX1fi1th7VN7X+WRDXOVRvBQ1Uoz2JSUlFRqYxdFHeu+oqKjo0WVDSua0nxvVT12QECASsfLkz8Y+qmaMGGCqIL5yZMnVTa+vJBicSumKyspKQleXl4YMGAAdu7cqVD41cDAAM7OzrC3t1do/NIiu77JW5NUUQ01T2l+TxT0e7dmzZrYvXs3li1bBjs7uyLHSU1NxalTp/D111/D3d292H83kT3/ysOFSERERERERERERERERET0aWMF2DJmYmICa2trREZGSvvu3Lmj0mPk5OSgR48eMDMzg4ODAxwcHNCmTZtiB+hkbyNfknDtx48fBe3CAgZmZmaYM2cOZs2ahZCQEFy4cAFXr17Fo0ePCtwnOzsbfn5+iIyMxB9//KHSAENpkxd2lVfdrSiZmZmqmI5KlVYVt4pA9n0t7uenPL6vqiZ76+TinP9lTd4aM2TIEFEVvOKwtrYu8RjFpa51P7/jx4+Lqr8CuaHyuXPn4sCBAyWqEK1OsutCo0aN0KVLF5WMbWpqqpJxAODZs2cq/42S59y5c4iLi4O5uXmpjF8e1K1bF3Xr1sWzZ8+kfeHh4cjJyVFJMFD2cwqUzboZGxuLsWPHFvp7rEqVKqhfvz4aNmwIW1tbtGzZEs2bN4e2tjZ++OGHIs8reb+JXFxcYGlpWeL516xZU9CW9zqWNKifn+yap6mpibFjx6pk7NatWxf4mIaGBtzc3ODm5oaoqCicP38ely5dQkhIiNyLrvKEhoZi9OjR2L59O5o3b67UfGS/K+S9tkREREREREREREREREREqsQArBp07NhREIB9/fo1IiIiYGNjo5LxHz58iHfv3uHdu3d4+PAhtm3bhj59+sDX17dY48ne+rq4t27OzMwUBaEUCeVqamqibdu2aNu2Lby8vPDu3TvcuHEDgYGBuHbtGt69eyfaJyQkBMuXL8fChQuLNVd1qFy5MjQ0NARh0eJUDSuL6rcAlArwFBa0+NQZGxsL2sWtaldW76s6ya41FeE5y76/ADB48GB88cUXapiN6qh73X/79i0WL15c4OORkZFYuXIlvL29izUvdTMxMRG0q1evDi8vLzXNpmAHDx4UtI2NjXH9+nXo6OgoPdaZM2cwY8YMaVsikeDAgQOYPHlyiedZntWqVUsQgM3IyEBSUpLoM1Yc8sKFZXHL+Xnz5onCr+bm5nB1dUWHDh1ga2tbaFBVIpEUeQzZzwgA9O7dG05OTspPuAjy3gtVfv/Ifk9kZ2dj6tSpKjkHFFW7dm2MHTsWY8eORVpaGm7fvo3AwEAEBgYiPDxctH1ycjKmT5+OU6dOKXUxmWxwmAFYIiIiIiIiIiIiIiIiIiptZXOfVBJwcXER9R0+fFhl48sbq2fPnsUer2rVqoL2kydPijXO48ePRZVAraysijWf/v37Y9myZbh69Sr27t0r9zX966+/RMGr8kxXVxc1atQQ9BXntVbkNsTKkhd0VaYiaWne/re8q127tqCdmpqq9HuUmZmJf//9V5XTKpfMzMwE7Y8fPyIuLk7pcZ4+fVpmoWtTU1PR5+P58+dlcuzSpM51PycnB15eXoJ1w8TEBO7u7oLttm3bhhs3bhRrXuome66/ePFCTTMpWHZ2Ng4dOiTo69GjR7HCrwDQtWtXUahx//79n3yFcNlbwgOquy28vr4+9PX1BX3x8fEqGbsggYGBuHLliqCvS5cuOHPmDLy8vODo6FhklVZFLgTR09MTVQUvrbVV9vMIFO8zGRUVJTc4K6/KsTq/JypVqoQvv/wSnp6eCAgIwOXLlzFz5kxR5fI3b97gzJkzSo0t+50t77UlIiIiIiIiIiIiIiIiIlIlBmDVoHnz5qJbW+/bt08lIcGUlBQcOHBA0GdgYABHR8dij9miRQtBOzw8XKHqXbJCQ0NFffXq1ZO7bU5ODmJiYoocU0NDA/b29vDx8cG0adMEj6WlpeH+/ftKz1OdbG1tBe1//vlH6TFK4znLC/Aoc5vl6OhoVU6nQrGxsRG9fsq+r0+ePCmT21qrm7xbLSt7Pr958wa9e/eGnZ0dunTpgpEjR+L9+/eqmqKIoaEhGjZsKOgLDg4u1ljv378vN2FAdaz7ebZt24agoCBB38KFC7FgwQLBa52TkwNvb+8KGbCXfX2joqKKtU6mpqaWWsXP69evi76H+/btW+zxdHV10atXL0Hfy5cvERgYWOwxK4K3b98K2gYGBgpVQVZUnTp1Cj2eqslWBTYyMoKvr69Sz+nly5eCdkHrnuznRHZdUFRCQgKys7MLfNzU1BS1atUS9D148EDp44wYMQKtW7dG+/btMXjwYNy+fRsAYG1tLaqiWtznokzAOSEhASkpKUVuV716dUydOhU7d+4UBdzznoOiZM+/unXrKrU/EREREREREREREREREZGyGIBVE9lb/iYnJ2P58uUlHnft2rWiW1UPHz68RLcgbd26taCdkpKC8+fPKz2ObGVaa2trQWWo6OhozJ49GwMGDIC9vT26du2qVAXXMWPGiPpKuxKaqjk4OAja169fR0JCgsL7R0dHFyu0URTZCnMAlAoVhoWFqXI6FYqJiYko2Hz8+HGlxjh37pwqp1RuWVhYiCrmKrvW3Lp1C8D/h+ifPXsGU1NTlc1RHtk18sKFCwqFjvKLi4tD586dYWdnB2dnZ4wfPx4XLlxQ5TSVUlbrvqzIyEisXLlS0Ne9e3e4uLhAV1cXv/zyiyBQ/vbtWyxevFjpealbmzZtRH3Hjh1TehwfHx988cUXcHBwwODBgzF37txCg37KCAgIELTNzc3Rvn37Eo3p6uoq6vP39y/RmOXZ69ev8ejRI0Ff06ZNVVYBFhAHDEujCnx+ss/HwcFBqfBrTEyMqKJ0VlaW3G1lPyfBwcF49+6dwscCci/W6dmzJ+zs7ODk5AQPDw9RZWMAsLe3F7SVXe9evnwprdSekJCAe/fuSStp6+rqws7OTrC9sr8DAODOnTtwcHCAvb09+vbti8mTJ4suqFmxYgVGjRoFBwcHtG/fHidPnlR4fFtbW7Rt21bQp+zv6Ddv3gjaRV3wQERERERERERERERERERUUgzAqknfvn3RqFEjQd++fftw9OjRYo8ZGBiI7du3C/r09fUxceLEYo8JAM2aNROF0rZs2VJgYEGeW7duISQkRNDXvXt3QdvU1BSnT5/GgwcPkJqaiuzsbFy6dEnhY2hpaYn6CgplyKtoWh707t0b2tra0nZGRgZ27dql8P67du1SWfgpP3mBNUWrmMbExODatWuqnlKF4uLiImifP39e4dsfp6Wl4a+//iqFWZVPPXr0ELRPnDihVAhcNtj05ZdfqmRehZGtapmQkICdO3cqNcaWLVsgkUiQkZGB58+f4/r166hWrZoqp6nUuldW635+GRkZ8PT0REZGhrTPxMQEP/74o7Rtb2+P0aNHC/Y7fvx4ib471aFq1apo1aqVoG/79u0K3Ro+T2x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5enoqhRn/+usvrdfbuHEjfH190aFDBwwYMADh4eEV3kJeMaB64sQJ3Lt3T1QlWNU5DACdO3cWtU+dOoUjR46I+gIDA7V9ClVGsbIooDrkqKni4mJRULgycrkcu3fvFvWVr8CrLalUqvSZ//PPP7We5+jRo0rXkg4dOojaLi4uSlVhd+3apfVae/fuRYcOHeDr64t+/frhgw8+wN27d0XHdOzYUdTOysrSKmxbWlqKmJgYrfemSPHcsLKyUll1l4iIiIiIiIiIiIiIiIjIkBiAJSIiolrD29sbzZs3F/UdOHAAR48e1WveVatWidpt2rRBw4YNNRp7/Phx3Lx5U6Nji4uLERkZKeoLCgqCmZmZZhs1AKlUWm1rGdratWshk8lEfaqqohobGyv1R0VFKVXerUhhYSF+++03AM+q9l65cgUpKSmoU6eO2jFNmjQRVfs8ffo0Tpw4ITpGXQBWsV8xAFuvXj1RldiaZm1tDRcXF1Hf9evX9Zrz119/1fjY3bt3Iy0tTdTXv39/vdYPDg4WtRMTE4UqvpooLS3FypUrRX3W1tZ49dVXK10rJiYGiYmJWq31yy+/AADy8vKQlJSE+Ph4pcrVPj4+SpVWV69erfE6Bw4c0CvYXEbx3FC8jhMRERERERERERERERERVQUGYImIiKhW+eijj0RtuVyOyZMnK1XL1NThw4exefNmUd/777+v8XiZTIZZs2ahuLi40mOXLFmiFAQbNWqUxmsZgomJiVJfUVFRte5BF8ePH8fatWtFfZaWlhgyZIjK49977z1Ru6ioCFOmTMHTp081Wu/HH39UqqY5fPjwSscFBAQIPz9+/FhUodjCwkJtZWFvb29RuPbQoUO4f/++aF6JRKLR3quLr6+vqH316lW95jt16hQ2bNhQ6XHp6emYM2eOqK9t27bw8fHRa/0BAwbAzs5O1Dd79mw8ePBAo/ErVqzA+fPnRX2DBg1SqkYMAGPGjBF9FuVyOaZNm4ZHjx5ptNaqVatw7do1Ud/gwYNVBtxHjx4tah87dgxbt26tdI3s7Gx8++23Gu2nMornhp+fn0HmJSIiIiIiIiIiIiIiIiKqCAOwREREVKsEBQUhKChI1Jefn48JEybg888/F4UGK5Kfn48lt0uPWgAAIABJREFUS5bgww8/RElJidDfpUsXvPnmm1rtKS4uDpMmTUJeXp7Kx2UyGRYvXozly5eL+oODg/UO7WlLVQVTxUqatUl2djYiIiIQFhYmep8AIDw8XO1t1D08PNC7d29RX3x8PEaPHo309HS165WWlmLJkiVC9dcyDRo0UBu2La98ABYAkpOThZ99fX1hamqqcpxUKoW/v7/QVgxUBwYGVrp2malTp6Jly5ai/zQNcWpDMcR45coVvef87rvvsG3bNrWP//vvvxg9ejSys7OFPqlUiv/85z96r21ubo6JEyeK+tLT0zFy5Ej8888/aseVlJTgp59+wqJFi0T9dnZ2SvOVcXFxwVtvvSXqS0lJwYgRIyqtKL1hwwZERESI+urWrasU+i4zbNgwpSqwX3zxBbZs2aJ2jbt37+Ltt99GRkZGhXvRlGJ12w4dOhhkXiIiIiIiIiIiIiIiIiKiihjX9AaIiIiIFH333XdIS0sTharkcjm2bNmCP/74A127dkXXrl3RunVruLi4wNLSEnl5ecjOzkZycjKOHTuGo0eP4uHDh6J5GzVqhO+//16nPR0+fBg9e/bEu+++i65du8LJyQm5ubk4f/48IiMjlQJgjo6O+PLLL3VaSx9mZmaoW7eu6LkvXLgQdnZ2aNSoER4/fgxbW1tYW1sbfO2EhATMnz+/0uOKi4vx+PFjJCcnIzExEaWlpUrHvPbaaxgzZkyF83z99ddITExESkqK0Hfp0iUEBwdj8ODBCAwMRJMmTWBpaYnMzEzExcVh48aNSkFOExMTLFiwQG14tbz27dvD3t5eFNAs06lTpwrHdu7cGYcOHVLqt7S0rHRsTVDcU0JCAp48eQIrKyut5zIxMUFxcTGKi4sxe/Zs7Nu3D8OHD0fLli1hZWWF1NRU7N27Fxs3blSq4jthwgSDVRQdM2YMzpw5g7///lvou3XrFgYMGIDQ0FCEhISgWbNmsLCwQGZmJk6fPo1Nmzbhxo0bonmkUikWLFiAunXrql3rk08+QVxcHOLj44W+GzduoG/fvggNDUVQUBDc3d1Rp04d5OTk4MKFC9iyZYtSlVmJRILvvvtOZbgdePbaLly4ECNGjBCC1TKZDJ9//jn27NmDESNGwMvLCxYWFkhLS8OBAwewfv165OfnA3h2zdC0crIqaWlpopC9iYmJUvVgIiIiIiIiIiIiIiIiIqKqwAAsERER1TrW1tZYt24dpkyZgmPHjokeKykpQUxMDGJiYrSa08PDA8uXL4eDg4NW4/z9/XHu3DkAwIMHD/D9999XGqK1s7PD6tWrYW9vr9VahuLt7S163a5du4ZBgwYJ7W+//Vajaqfaun79Oq5fv673PG+88QYWLVoEY+OKv6paW1tj5cqVGD9+vCgEW1hYiMjISERGRla6lomJCebNm4f27dtrtDcjIyN0794dUVFRSo9pEoBVpWvXrhqFb6tbkyZN4OHhIdzevri4GOfOncPrr7+u9Vw9evRAYWEhDh8+DAA4duyY0mdblaFDhyI8PFzr9Soyf/58TJ48GcePHxf6SkpKsH37dmzfvr3S8aamppg/fz66du1a6XFLly5FWFiYKCBfXFyMbdu2VVgJt4xEIsGsWbOUKg8r8vb2xo8//oipU6eKKimfOnUKp06dUjvO0tISH3/8Mb755ptK96LOyZMnRe1u3bpVScCeiIiIiIiIiIiIiIiIiEgRA7BERERUK1lbW+OXX37Bli1bsGTJEp1v1W1mZoa3334bkyZNgpmZmdbjBw4ciJ49e2LevHlKt61XpU2bNvjhhx/QsGFDXbZrEB999BFOnjwJmUym8vGKbvdek1q0aIEPP/wQPXv21HhM48aNsWXLFsycORMHDx7Uaj1XV1fMmTNH6+qrAQEBSgHYunXrwsPDo8JxzZo1g5OTk9K5HBgYqNX61al3795CABYAjh8/rlMA1sjICD/88INS8FQdExMTfPDBB/jwww+1XqsyZcHphQsXYv369Rp9rsu0aNECc+fOhbe3t0bHOzk5YcOGDfjyyy+xc+dOyOVyjddycHDAl19+iaCgII2ODwoKwtq1azFt2jSNrpc2NjZYuHAhzM3NNd6TKorvZ58+ffSar4zb2TAAQFZWHkpLNX/diIgUGRlJ4ODwv2A+rytERERERERERERERC8Oo5reABEREZE6EokEw4YNw99//42FCxciICAANjY2Go11d3dHeHg4Dhw4gI8//lin8GuZUaNGYevWrejUqRMkEonKYzw9PfH9999j06ZNNRp+BQAfHx+sXLkSjo6OKh+vyQCsRCKBqakp7O3t0aJFCwQGBmLy5MnYuXMndu3apVX4tYytrS2WLl2KTZs2oUePHrC0tKzw+KZNm2L69OnYvXu31uFXAOjSpYvS+dShQwcYGVX+1VpxPWNjY50CpdWlT58+kEqlQnv//v1ahTjLs7a2xqpVqzBz5ky4uLioPMbMzAzBwcH4888/qyT8WkYqlWL69OnYs2cPBg0ahLp166o9ViKRoF27dliwYAF27Nihcfi1jIWFBebPn48dO3agd+/eqFOnToXHu7q6Ijw8HHv37tU4/FrG398f+/btw4cffqj2829iYoKgoCBERUWhe/fuWs2vqKCgAEePHhXaderUqbRaLRERERERERERERERERGRoUjkuv4fbCIiIqIaIJfLcfPmTaSmpuLevXt48uQJCgsLYWJiAmtrazRs2BAtW7aEk5OTTvMHBAQgLS1NaH/33XcYOHCg0E5LS8OlS5dw7949lJaWwtnZGW3atEGjRo30fm7qzJgxQ1RxdMCAAZg3b16l44qKihAbG4vr16/jyZMnMDMzg4ODA1q2bFlptdLnWVFRES5fvow7d+7g4cOHKCwshLW1NZycnODl5YX69evX9BafK+Hh4di/f7/QjoyMhL+/v15zlpaWIi4uDrdu3UJmZiZsbGzg4uICf39/WFlZ6btlnfaTkJCAW7duISsrCwUFBcL1xNvbGw4ODgZbSyaTIT4+Hrdv30ZOTg7y8/NhbW2NevXqoXXr1ga7lpSUlCAxMRHXrl3Dw4cPYWlpCRcXF7Rv3x729vYGWWPPnj2YOnWq0B47diymT59ukLnLsFIjEemLFWCJyNB4XSEiQ+I1hYgMjdcVIjIkXlOIyNB4XXk5ODpWXBDI0IyrdTUiIiIiPUkkEri5ucHNza1G1nd1dYWrq2uNrF1GXRVaRaampujcuTM6d+5cxTuqXUxNTeHn5wc/P7+a3soL4e233xYFYHft2qV3ANbIyKhWvUdGRkbw8fGBj49Pla8llUrRtm1btG3btkrXMTY2Rps2bdCmTZsqW+PPP/8UfpZKpRg1alSVrUVEREREREREREREREREpKjy+7QSERERUY0qLi4WtU1NTWtoJ/Qy8vPzEwVDd+3ahcePH9fgjqg2SEtLw9GjR4V2cHBwjf9xABERERERERERERERERG9XBiAJSIiIqrlCgoKRG1zc/Ma2gm9rCZPniz8nJ+fj6ioqBrcDdUGmzZtgkwmA/Csgu6kSZNqeEdERERERERERERERERE9LJhAJaIiIiolrt//76o7ejoWEM7oZdVly5d4O/vL7QjIyOF8CO9fPLz87F161ah3bdvXzRr1qwGd0RERERERERERERERERELyMGYImIiIhqsby8PPzzzz+ivkaNGtXQbuhl9umnn8LI6Nk/H27duoW//vqrhndENWXjxo3IyckBAFhYWIgqBBMRERERERERERERERERVRcGYImIiIhqkZycHMTFxSE7OxtJSUmYMmUKioqKhMclEgl8fX1rcIf0svLx8cGQIUOE9rJly1BaWlqDO6KaUFBQgDVr1gjtsLAwuLq61uCOiIiIiIiIiIiIiIiIiOhlZVzTGyAiIiKi/4mJicHMmTPVPv7aa6/BwcGhGndE9D8ff/wxDh48iMzMTNy8eRNRUVEYNGhQTW+LqtFvv/2GrKwsAICbmxvGjh1bwzsiIiIiIiIiIiIiIiIiopcVK8ASERER1SJxcXFqH7OxscGsWbOqcTdEYra2tpg7dy4kEgkAICIiAk+ePKnhXVF1ycjIwIoVKwAAJiYmWLBgAUxNTWt4V0RERERERERERERERET0smIAloiIiKgWUReA9fT0xLp169CsWbNq3hGRWPfu3TF69GgAwIMHD4RAJL34IiIikJ+fDwD46KOP4OXlVcM7IiIiIiIiIiIiIiIiIqKXmUQul8trehNERERE9MyNGzdw5coVZGZmoqioCPb29vDy8oKnp2dNb42IqNplZeWhtJT/ZCUi3RkZSeDgYC20eV0hIn3xukJEhsRrChEZGq8rRGRIvKYQkaHxuvJycHSsU63rGVfrakRERERUIXd3d7i7u9f0NoiIiIiIiIiIiIiIiIiIiIhqNaOa3gAREREREREREREREREREREREREREZE2GIAlIiIiIiIiIiIiIiIiIiIiIiIiIqLnCgOwRERERERERERERERERERERERERET0XGEAloiIiIiIiIiIiIiIiIiIiIiIiIiInisMwBIRERERERERERERERERERERERER0XOFAVgiIiIiIiIiIiIiIiIiIiIiIiIiInquMABLRERERERERERERERERERERERERETPFQZgiYiIiIiIiIiIiIiIiIiIiIiIiIjoucIALBERERERERERERERERERERERERERPVcYgCUiIiIiIiIiIiIiIiIiIiIiIiIioucKA7BERERERERERERERERERERERERERPRcYQCWiIiIiIiIiIiIiIiIiIiIiIiIiIieKwzAEhERERERERERERERERERERERERHRc4UBWCIiIiIiIiIiIiIiIiIiIiIiIiIieq4wAEtERERERERERERERERERERERERERM8VBmCJiIiIiIiIiIiIiIiIiIiIiIiIiOi5wgAsERERERERERERERERERERERERERE9V4xregNEREREL7qpU6diz549AID//ve/GDlyZA3v6MWTkZGBpKQk3LlzB48fP0ZJSQksLS1Rt25dNGzYEK1atYKVlVVNb1MnsbGxwjnTqlUrbN26FaampjW6p2vXrmHw4MEoLi6Gi4sL9u3bB3Nz8xrdE1W92nguEhEREREREREREREREdHLiwFY0llAQADS0tKqZa3vvvsOAwcOrJa1iIjKREREYPny5UK7U6dOWLt2rcpjR4wYgbi4OKE9efJkTJw4Uem4kpIStG7dWtS3YcMG+Pn5GWbT5Wizf6o6e/bsEcKvTZs2xbBhw1Qep3gOVYUjR47A2dlZqV/VeanuHDaEkydP4t133xX16fI5SE9Px9atW7Fnzx78+++/FR4rlUrh7e2NkJAQDBgwALa2tlrvuzKrVq3CggULRH3NmjUT3n9d+fn54c0338SBAwdw7do1/Pzzz5g6dapec+qjqKgIn376KYqLiwE8C3irCr9269YN9+/fF9rff/89QkNDDb4fxfNJKpXiypUrBl/nRXXr1i0EBQWJ+tRdK2rbuUhERERERERERERERERELzcGYImIiIiqSEZGBr766iuh/Z///AfGxvz6pa+ioiIsWrQI69evR1FRkUZjZDIZLl68iIsXL2Lx4sV4//33MW7cOIO+Hzt27FDqS05ORmxsrN4h908++QQxMTEoKSnBL7/8gjfeeANt27bVa05dLVmyBElJSQCA1q1bo1+/fjWyD6oZ1XUunneLMPicRPTySqnpDRDRCyelpjdQzRqdGVfTWyAiIiIiIiIiIlLJqKY3QERERPSimjt3Lh4+fAgAaN++PXr06FHDO3r+ZWdnY/jw4Vi9erXG4VdFeXl5iIiIwJgxY/Do0SOD7Ovy5cu4fv26ysc2bdqk9/xNmjTBkCFDADwL83722WcoKSnRe15tJSUlYdWqVUL7k08+gUQiqfZ9UM2pLeciERERERERERERERERERFLkJHOhg4ditzcXI2OvXDhAi5cuCDqGzt2rMZrNW/eXKu9ERER1bQzZ85g7969Qvvjjz/Wanz9+vXRs2dPg+7JysrKoPNVt6dPn+K9995Tur29k5MT+vTpA19fX7i5ucHGxgampqbIy8vDgwcPkJCQgH379uHs2bOicefPn8e4ceMQGRkJU1NTvfb2xx9/iNpmZmZ4+vQpACA6OhqzZ8+GnZ2dXmtMmjQJO3bsQEFBAa5fv46NGzdizJgxes2prW+//RYymQwA0KlTJ3Tu3Lla16faoTaci0REREREREREREREREREDMCSzsLCwjQ+dvHixUoB2OnTpxt6S0RERLWCTCbDt99+K7S7dOkCPz8/reZo3Lgxf1cqWLRokVL49f3338dHH32kMsBqY2OD+vXro02bNhg5ciTOnz+P6dOnIzU1VTjm0qVLWLRoET799FOd91VUVIQ9e/YIbVdXV3h5eSE6Olp4fMeOHXj33Xd1XgMA6tWrhxEjRmDNmjUAnn2/6tOnD+zt7fWaV1N79uwRhYgnT55cLetS7VPT5yIREREREREREREREREREQAY1fQGiIiIXibGxsZISkoS/adtMJJqv507d+Kff/4R2h988EEN7ubFkJGRgcjISFHfpEmT8Mknn2hcvdXX1xeRkZFwcXER9a9btw53797VeW9///23qCq+n58fAgMDRcds3rxZ5/nLGzt2rPB8Hz16hGXLlhlk3sqUlJQgIiJCaHfs2BHt2rWrlrWpdqqpc5GIiIiIiIiIiIiIiIiIqAwDsEREREQGJJPJsHz5cqHt4eHBkLMB/PXXXygqKhLaDRs21ClY7OzsjC+++ELUV1xcjD/++EPnvW3fvl3U9vX1RUBAgCiYe/PmTZw5c0bnNco4OjoiJCREaG/ZsgWZmZl6z1uZXbt24fbt20J79OjRVb4m1W41dS4SEREREREREREREREREZVhAJaIiIjIgHbt2oVbt24J7YEDB9bgbl4ciuHRgIAAGBsb6zTXG2+8gebNm4v6Tpw4odNc9+/fx8mTJ0V9Xbp0gbW1Nbp27Srq37Jli05rKCp/ThUWFmL16tUGmVcdxVC3g4MDunfvXqVr0vOhus9FIiIiIiIiIiIiIiIiIqLyGIAlIiIiMqC1a9cKP5uYmKBPnz41t5kXSPnqowBgb2+v13xvvvmmqJ2cnKzTPDt27EBpaanQbt26NVxdXQFA6b2Pjo5Gdna2TuuU9+qrr6JBgwZCe/PmzcjPz9d7XnViYmKQkpIitPv16wcTE5MqW4+eH9V9LhIRERERERERERERERERladb2SyiWuzp06c4cuQIbt26BVNTU7Ro0QK+vr6i2xBXJDc3FxcvXkRGRgZycnJgYWEBBwcHtGjRAu7u7gbfb05ODmJjY5GamoqnT5/CxsYGbm5u8PHxgZWVlUHWKCoqwuXLl3H79m3k5ORALpfDwcEBzs7OaNeuHczNzQ2yTnnp6elISEhARkYGHj9+DHt7e9SvXx9+fn4wMzMzyBp37tzBlStXkJmZiUePHsHW1haOjo7w8fGBk5OTQdYoLysrC+fPn8edO3fw9OlTWFtbw83NDb6+vpW+hikpKcJ5JZFIYGdnBy8vL7Rs2RISiUSvfRUUFCA+Ph4ZGRnIzc3Fo0ePIJVKYW1tDRcXF3h4eMDZ2VmvNQzh/v37OH/+PO7evYuSkhLY2tqiefPm8Pb2Ntg5kZ2djfj4eOTk5ODhw4fIz8+HiYkJ7Ozs0KBBA3h6esLGxsYga9WkJ0+e4PLly0hOTkZeXh7s7Ozg6uoKf39/g72WNamwsBDnz5/Hv//+i8ePH8PCwgINGjRA+/bt4eDgUOHY7OxsxMbG4s6dOygqKoKtrS3c3NzQrl07jX8P6OPy5cu4evWq0O7WrZveQU165unTp6L2nTt39JqvdevWqFu3Luzs7GBnZ4d69erpNM8ff/whagcFBQk/BwQEwNraGnl5eQCA4uJiREVF4b333tN94wAkEgn69euHpUuXAnh2Tdi9ezeGDBmi17zqKFauDQ0NNci8jx49wtmzZ3Hv3j0UFhbCyckJjRs3ho+PD4yMqv/v9LKysnDhwgU8ePAAjx49gqWlJRwcHNCsWTO0bNnSoGtVx3cYuVyOpKQkXLlyBVlZWTAxMYGLiwu8vb1Rv359g6xR3eciEREREREREREREREREVF5DMDScykiIkJ0K96ff/4ZgYGB2Lt3L77++mul6mo2NjYYMWIEpk2bpnbO6OhorFu3DhcuXIBMJlN5jKurK0JDQ/Hee+/B2tpao72OGDECcXFxQvvIkSNwdnZGRkYGvv/+e+zZs0fleubm5ggJCcGHH36Ihg0barSWoosXL2L16tU4evQoCgsLVR5jZmaGjh07Yvz48fDz89No3q1bt+Kzzz4T2jNnzsQ777wD4FmVuDVr1uDcuXOQy+VKYy0tLREQEIDJkyejUaNGWj+np0+fYv369YiKisKNGzdUHiORSODp6YmRI0diwIABGoVoSkpK0Lp1a6Hdpk0bIfCTnJyMhQsX4vDhw6Iqf+Wf04gRIxAeHg4LCwvRY0eOHMHixYsRHx+vcl1XV1dMmjRJ61uk5+XlYdu2bYiOjkZ8fDyKi4srPL5FixYYOnQohg0bVmUhwG7duuH+/fsAAKlUiitXrgAAbt68iXnz5uHIkSMqzwkbGxuEhobigw8+qDTcqEpqaip+//13HD16FDdu3FC5RhmpVAp/f3+MGTMGPXr00HotQ1A81wBgw4YNGn3+bt26hZ9//hnR0dEqP9O2trYYMGAAwsPDNb5Glfnkk0+wa9cuoT148GDMmTOnSsYrXhcvX74MMzMzPHr0CD/99BO2bduGgoICpXFSqRSBgYGYPn26UGGzTHJyMiIiInDo0CGV11Rra2uMGjUKEydOrNKQ8NatW0Vt3ibecOzs7JCWlia09+/fj48//hh2dnY6zRcYGIjAwEC99hQXFyeqjAoAPXv2FH42NzdHz549sW3bNqFvy5YtGDt2rN5//NC9e3chdFg2b1WEDtPT03Hs2DGh7eTkBA8PD73mvH//PubPn48DBw6gqKhI6XFHR0f07dsXYWFhsLW11WutyhQVFWHnzp2IjIzEtWvX1B73yiuvICQkBOPHj9fpdxVQdd9h1K2zceNG0WemjJGRETp37owpU6bA29tb6/kVVde5SERERERERERERERERESkqPpLKxFVkb///hvTpk1TeWvhR48e4eLFiyrHpaamYvjw4fjoo48QGxurNvwKAGlpaVi6dCmCgoLw999/67zXy5cvIzQ0FLt27VK7XmFhIaKiotCrVy9s3LhRq/kfPXqEadOmYdiwYdi/f7/a8Cvwv4q5I0eOxAcffICHDx9qtVb5/c6cORNhYWE4e/as2hBifn4+/vrrL/Tq1UsUCNLE8ePH0bNnTyxYsEBtcAR4VvEsMTERs2bNQv/+/XW+rTUAbNq0CQMHDsShQ4dUhl+BZ89p9erVGDFihPD6lZSU4IsvvsD48ePVhl+BZ+fUzJkzRYHiyuzZswdvvvkmvvvuO8TFxVUafgWAf/75B99++y369u2r1+uhrZiYGAwcOBCHDx9We048evQI69ev1/pzJZPJEBERgZCQEKxevRrXr1+vMPxaNub06dOYOHEiJkyYgCdPnmj1fGrSr7/+it69e2Pnzp1qP9O5ublYu3Yt+vXrh8TExGreoX7Onz+P0NBQrF+/XmX4FXj2/kVHR2PAgAFISEgQ+rdt24b+/fvjwIEDaq+peXl5WL58Od566y08evSoSp5DSUkJ9u3bJ+rr0qVLlaz1MlIMjufm5mLSpEk6/94yBMXqr+3atUOTJk1Eff379xe1U1JScPr0ab3X9vb2FlW0vnz5Mm7duqX3vIp2794t+v2n7zl9+PBh9O7dG7t371YZfgWABw8eYM2aNQgODkZ0dLRe61UkLi4OISEh+OyzzyoMvwLPQrtr165FYGAgfvvtN63Xqq7vMFeuXEGfPn2wYMECleFXACgtLcXx48cxfPhw/PLLL1rNr0p1nYtERERERERERERERERERIoYgKUXQnp6OmbMmKE2oAhAZSWqy5cvY9iwYbhw4YJW62VlZSE8PBzr1q3Teq/Jycl47733VAZ1VSkqKsJXX32FJUuWaHT8vXv30L9/f+zevVvrvR06dAjDhg1TqmZXGblcjo8//lgpCFSR4uJifPbZZxoHHrdt24YJEybg7t27Wu0tKSkJI0aMwLlz57QaBwC///47vvjiiwoDxOVdvXoVs2bNglwux6effopNmzZpvNbWrVuxfv36So9bu3Ytpk6dqvH5oyglJQXvv/8+8vPzdRqvjbNnzyI8PFzjtfLy8jBp0iRs37690mNlMhkmT56M5cuXaxQAVuXw4cOYOXOmTmOr27x58zBv3jyNn2taWhrGjRv33ASQEhIS8N5772n8+S4LPubl5WHDhg2YPXu22iCdqrVmz56tz3bVio2NFYVrmzZtqlSplnRXvrJqmdjYWAQHB2PRokVITU2t1v0UFhZi7969oj7FsCsA+Pv7K1U837x5s97rS6VSdOzYUdQXExOj97yKFOfUJwB7+vRphIeH4/Hjxxodn5OTg6lTp4qqSxvK9u3bMWrUKNy5c0ercfn5+Zg7dy5mz56NkpISjcZU13eYCxcuYOTIkbh9+7ZGx5eUlGDhwoU6fZ8tr7rORSIiIiIiIiIiIiIiIiIiRcY1vQEiQ1i8eLEQpjAzM8Prr7+OBg0aID09HadPn4ZMJkNQUJBozN27dzFu3Djk5uaK+lu0aIHhw4fD398fjo6OKCgowK1btxAdHY1t27YJAbTS0lLMmTMHr7zyCoKDgzXe64wZM4SAlEQiQd++fTFw4EC4u7ujpKQEV69exZYtW5SCA4sXL4a7u7vKAFCZ3NxcjBs3Tqnil42NDYYPHy68LlKpFGlpaTh8+DA2btwoqp6XkpKCceOiAEC+AAAgAElEQVTG4Y8//hBV86rIr7/+ivv37wvtN954A6GhofDy8oK1tTXu37+PkydP4rfffkN6erpwnFwux9y5c9GtWzeYmpqqnf/QoUMqA2uvv/46+vbtC29vb9ja2iI3NxdXrlzBzp07Ra9fbm4uwsLCsGPHDjRs2FCj53Tr1i18++23QtvDwwNjxoyBt7c3HB0dkZGRgX379mHVqlV4+vSpcNzBgwcxbdo07NmzBwBgYmKCgQMHolevXnBzc4NcLkdSUhJ+++03HD9+XLTmsmXLMGzYMLWvxfnz5zF//nxRX9n8PXr0gJubG2xsbCCTyZCdnY2EhATs2rVLaZ20tDSsWbMGkyZN0ui10IVcLse0adOEz4uJiQmGDh2KPn36oHHjxsjPz0d8fDwiIyNx/vx50bjPP/8c7u7uaNOmjdr5V61ahQMHDoj6HBwcMGLECHTq1AkNGzaEhYUFCgsLkZ6ejri4OGzdulWp6l50dDTOnj2LDh06GPDZG9a2bdvw66+/ivokEglCQ0MRGhqKZs2aQSKRICUlBbt378a2bdtQUlKC7OxspXBebRUeHi5UfXV0dMSYMWPQqVMnuLq64smTJzh16hQWL16MjIwMYcy9e/cwa9YsUYi+a9euGDJkCLy8vGBhYYHU1FRERUVh8+bNoj+Q2L9/P65cuQJPT0+DPo/Dhw+L2u3atTPo/C+7zp07o127dkp/tPLw4UMsW7YMy5YtQ+vWrdGtWze89tpraNOmTYW/W/QVHR2NvLw8oW1qaopevXqpPDY0NBSLFy8W2n///TeysrLg4OCg1x7at2+P/fv3C+3Dhw/jnXfe0WvO8nJzcxEXFyfq0+e8Lv+HMubm5njrrbfQs2dPNGzYEE+ePEFCQgI2btyIs2fPCsfJZDLMmDED7u7u8PDw0Hnt8o4cOYL//ve/ShWjvby8MHjwYPj7+8PBwQF5eXlISkrC7t27sXfvXlGV8W3btsHa2rrSP6Soru8wmZmZ+OCDD5T+6MTDwwOjRo2Cn58fbG1tkZmZiWPHjmH9+vVCIDcyMrLC56CJqj4XiYiIiIiIiIiIiIiIiIhUYQCWXghlAU4PDw8sXboU9evXFx4rKirC1atXYWZmJvSVlJRg2rRpovCrVCrF1KlTMW7cOEgkEqHfzs4O9evXR6dOnfD2229j4sSJ+Pfff4XHZ8+eDS8vL42r/JUFuOrUqYPFixejU6dOosddXFwQEBCAnTt3YtasWaLqYl999RW6dOkCa2trlXN/+eWXSgG/Ll26YMGCBbC3txf116tXD23atMHbb7+NGTNmiMIWqampmD59OpYtW6bRcyoLv9atWxcLFy5E165dRY/b2dmhVatWGDBgAMLCwnDx4kXhsbS0NBw/fhwBAQEq505PT1cKl6hbp27dumjcuDFCQkJw5MgRTJkyRQiC5OXlYcqUKdi8eTOMjSu/9JUPBYeFhWHq1KlKa7Vo0QKtWrVCeHi46LGy8KujoyOWL18OLy8v0eOvvPIKunbtii+//FJUJTYrK6vC12LBggWiEF/dunWxdu1alYEge3t7uLu7o3///tizZw/+85//iM6lP//8s0oDsKWlpXjw4AEAwNnZGStWrECrVq2Exx0cHNCwYUOEhIRg1apVWLhwofCYTCbDrFmzsGvXLhgZKRcqz8nJwfLly0V9Xl5eWLNmDWxtbUX9NjY2cHJygo+PD0aNGoUFCxZg7dq1omP+/PPPWhuAzc7Oxty5c0V91tbWWLZsmdKenZyc0KFDBwwbNgzjx48XXv/nQVZWFoBn16uffvoJVlZWwmP29vZo2LAhOnfujNDQUFHgsOzW6CYmJvjmm28wYMAA0bz29vZo06YN/P39MW3aNNFjf/75p8EDsLGxsaJ28+bNDTo/PauGPGTIEFGl3fISExORmJiIZcuWwdTUFD4+PvDz80PHjh3h6+tr0ECsYtXzHj16qP3DkQEDBmDJkiVCgLK4uBh//PEH3n//fb324O7uLmpfvHgRMpkMUqlUr3kV5ytjZWUFFxcXvedt2rQpVqxYgcaNGwt9ZZ/1kJAQ/Prrr5g/f77wepWUlGD27NnYunWr3s8tOzsbU6dOFf1ONDIywsyZMzF69Gil74ANGzZEYGAgRo0ahfDwcOF6BTyryu7r66v0R1ZlqvM7zNy5c5GTkyPqGzt2LD7++GPRGDs7OzRv3hzDhg3DzJkzheuovqr6XCQiIiIiIiIiIiIiIiIiUoUBWHphWFtbY/Xq1UrV1ExNTZUqSe7du1epgtxnn32Gt956q8I1mjZtisjISAwZMkSosvr48WOsWLECX3/9tcZ7NTExwapVq9C2bVu1x4SGhkIul2P69OlCX3Z2NjZs2IAJEyYoHX/t2jWlao+vvfYali9fDhMTE7Xr1K1bF0uWLMGkSZNEIdhDhw4hNjYWfn5+Gj0nqVSKxYsXVxgktLOzw4IFC9CrVy/RrdyPHDmiNvT5888/i8KoFhYWWL16tVKoVFH37t2xatUqjBkzRgi5JCQkIDo6Gr1799boOQHAkCFDlMKv5QUFBaFt27aiUC8AGBsbY8mSJWr3KZFI8Omnn2Lv3r2iIPalS5dUvhbXrl1Tec5qUg2vV69eiIuLw/r164W+W7duITMzE/Xq1at0vD5sbW2xfv16pdt/l5FIJHj//ffx9OlTUXXEGzduYM+ePejTp4/SmF27dokq3JmammLRokVK4VdFxsbGmDFjBk6dOoWkpCShv3wF2tpm7dq1ePLkidCWSCQqw6/leXp6Yt26dRg0aJBSJcDazN3dvcLrVYMGDfDWW29h5cqVSo9NnTpVKfxaXu/evbF9+3acOHFC6FP8zOqrqKhIdF4BQLNmzfSa89SpU2jZsqVec5Rp3bq1UmDzedSkSROsXr0aYWFhoiCiKkVFRYiNjUV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      "text/plain": [
       "<Figure size 2304x1728 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT item, COUNT(item) AS \"Number of results\"\n",
    "FROM freetextitems\n",
    "WHERE NOT itemid = 11646 -- not a real result: location of bloodsample: arterial, venous, etc.\n",
    "GROUP BY item\n",
    "ORDER BY \"Number of results\" DESC\n",
    "LIMIT 20\n",
    "\"\"\"\n",
    "display(Markdown(\"``` mysql\\n\" + sql + \"\\n```\"))\n",
    "df = pd.read_sql(sql,con)\n",
    "cm = plt.get_cmap('RdPu')\n",
    "color_step = int(-255/len(df.index))\n",
    "ax = df.plot.barh(x=df.columns[0],y=df.columns[1], legend=False,color=cm(range(255,1,color_step)))\n",
    "ax.set(xlabel=df.columns[1])\n",
    "ax.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "``` mysql\n",
       "\n",
       "SELECT DISTINCT value, COUNT(item) AS \"Number of results\"\n",
       "FROM freetextitems\n",
       "WHERE itemid = 11646 -- location of bloodsample: arterial, venous, etc.\n",
       "GROUP BY value\n",
       "ORDER BY \"Number of results\" DESC\n",
       "\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>value</th>\n",
       "      <th>Number of results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ART.</td>\n",
       "      <td>247754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Art.</td>\n",
       "      <td>242585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ven.</td>\n",
       "      <td>7319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GEM.VEN.</td>\n",
       "      <td>5020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gem. ven.</td>\n",
       "      <td>3556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>VEN.</td>\n",
       "      <td>3373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>volgt</td>\n",
       "      <td>2496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Vervalt</td>\n",
       "      <td>1456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ZIE-OPM</td>\n",
       "      <td>812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAP.</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value  Number of results\n",
       "0       ART.             247754\n",
       "1       Art.             242585\n",
       "2       Ven.               7319\n",
       "3   GEM.VEN.               5020\n",
       "4  Gem. ven.               3556\n",
       "5       VEN.               3373\n",
       "6      volgt               2496\n",
       "7    Vervalt               1456\n",
       "8    ZIE-OPM                812\n",
       "9       CAP.                164"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT DISTINCT value, COUNT(item) AS \"Number of results\"\n",
    "FROM freetextitems\n",
    "WHERE itemid = 11646 -- location of bloodsample: arterial, venous, etc.\n",
    "GROUP BY value\n",
    "ORDER BY \"Number of results\" DESC\n",
    "\"\"\"\n",
    "display(Markdown(\"``` mysql\\n\" + sql + \"\\n```\"))\n",
    "df = pd.read_sql(sql,con)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "``` mysql\n",
       "\n",
       "SELECT DISTINCT value, COUNT(item) AS \"Number of results\"\n",
       "FROM freetextitems\n",
       "WHERE itemid = 11646 -- location of bloodsample: arterial, venous, etc.\n",
       "GROUP BY value\n",
       "ORDER BY \"Number of results\" DESC\n",
       "LIMIT 5\n",
       "\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2304x1728 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT DISTINCT value, COUNT(item) AS \"Number of results\"\n",
    "FROM freetextitems\n",
    "WHERE itemid = 11646 -- location of bloodsample: arterial, venous, etc.\n",
    "GROUP BY value\n",
    "ORDER BY \"Number of results\" DESC\n",
    "LIMIT 5\n",
    "\"\"\"\n",
    "display(Markdown(\"``` mysql\\n\" + sql + \"\\n```\"))\n",
    "df = pd.read_sql(sql,con)\n",
    "cm = plt.get_cmap('RdPu')\n",
    "color_step = int(-255/len(df.index))\n",
    "ax = df.plot.bar(x=df.columns[0],y=df.columns[1], legend=False,color=cm(range(255,1,color_step)))\n",
    "ax.set(ylabel=df.columns[1])\n",
    "ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='comment'></a> comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "``` mysql\n",
       "\n",
       "SELECT DISTINCT comment\n",
       "FROM freetextitems\n",
       "\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VERWISSELING.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Geen aanwijzing VWD type I.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFNAMEFOUT, MONSTER VERVALT.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Meest passned bij HCL-variant of SLVL.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  comment\n",
       "0                                    None\n",
       "1                           VERWISSELING.\n",
       "2             Geen aanwijzing VWD type I.\n",
       "3            AFNAMEFOUT, MONSTER VERVALT.\n",
       "4  Meest passned bij HCL-variant of SLVL."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT DISTINCT comment\n",
    "FROM freetextitems\n",
    "\"\"\"\n",
    "display(Markdown(\"``` mysql\\n\" + sql + \"\\n```\"))\n",
    "df = pd.read_sql(sql,con)\n",
    "df.head()"
   ]
  }
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