notebooks/report_height.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import display, Markdown, Image\n",
    "\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.options.mode.chained_assignment = None "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### CONFIGURE ###\n",
    "definitions = ['height', 'height_backend']\n",
    "covariates = ['_age_band','_sex','_ethnicity','_region','_imd','_dementia','_diabetes','_hypertension','_learning_disability']\n",
    "output_path = 'phenotype_validation_height'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Height\n",
    "## Count of Patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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></th>\n",
       "      <th>height</th>\n",
       "      <th>height_backend</th>\n",
       "      <th>all_filled</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>subgroup</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <th>with records</th>\n",
       "      <td>1000.0 (100.0)</td>\n",
       "      <td>1000.0 (100.0)</td>\n",
       "      <td>1000.0 (100.0)</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">age_band</th>\n",
       "      <th>0-19</th>\n",
       "      <td>125.0 (100.0)</td>\n",
       "      <td>125.0 (100.0)</td>\n",
       "      <td>125.0 (100.0)</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>145.0 (100.0)</td>\n",
       "      <td>145.0 (100.0)</td>\n",
       "      <td>145.0 (100.0)</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>120.0 (100.0)</td>\n",
       "      <td>120.0 (100.0)</td>\n",
       "      <td>120.0 (100.0)</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>125.0 (96.2)</td>\n",
       "      <td>125.0 (96.2)</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sex</th>\n",
       "      <th>F</th>\n",
       "      <td>505.0 (100.0)</td>\n",
       "      <td>500.0 (99.0)</td>\n",
       "      <td>500.0 (99.0)</td>\n",
       "      <td>505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ethnicity</th>\n",
       "      <th>Asian</th>\n",
       "      <td>135.0 (100.0)</td>\n",
       "      <td>135.0 (100.0)</td>\n",
       "      <td>135.0 (100.0)</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>270.0 (100.0)</td>\n",
       "      <td>265.0 (98.1)</td>\n",
       "      <td>265.0 (98.1)</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>30.0 (100.0)</td>\n",
       "      <td>30.0 (100.0)</td>\n",
       "      <td>30.0 (100.0)</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">region</th>\n",
       "      <th>East Midlands</th>\n",
       "      <td>70.0 (100.0)</td>\n",
       "      <td>70.0 (100.0)</td>\n",
       "      <td>70.0 (100.0)</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>140.0 (100.0)</td>\n",
       "      <td>140.0 (100.0)</td>\n",
       "      <td>140.0 (100.0)</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>115.0 (100.0)</td>\n",
       "      <td>115.0 (100.0)</td>\n",
       "      <td>115.0 (100.0)</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">imd</th>\n",
       "      <th>1 Most deprived</th>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>185.0 (100.0)</td>\n",
       "      <td>185.0 (100.0)</td>\n",
       "      <td>185.0 (100.0)</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">dementia</th>\n",
       "      <th>False</th>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">diabetes</th>\n",
       "      <th>False</th>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">hypertension</th>\n",
       "      <th>False</th>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">learning_disability</th>\n",
       "      <th>False</th>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      height  height_backend  \\\n",
       "group               subgroup                                                   \n",
       "all                 with records              1000.0 (100.0)  1000.0 (100.0)   \n",
       "age_band            0-19                       125.0 (100.0)   125.0 (100.0)   \n",
       "                    20-29                      145.0 (100.0)   145.0 (100.0)   \n",
       "                    30-39                      110.0 (100.0)   110.0 (100.0)   \n",
       "                    40-49                      130.0 (100.0)   130.0 (100.0)   \n",
       "                    50-59                      120.0 (100.0)   120.0 (100.0)   \n",
       "                    60-69                      130.0 (100.0)   130.0 (100.0)   \n",
       "                    70-79                      130.0 (100.0)    125.0 (96.2)   \n",
       "                    80+                        110.0 (100.0)   110.0 (100.0)   \n",
       "sex                 F                          505.0 (100.0)    500.0 (99.0)   \n",
       "                    M                          495.0 (100.0)   495.0 (100.0)   \n",
       "ethnicity           Asian                      135.0 (100.0)   135.0 (100.0)   \n",
       "                    Black                       75.0 (100.0)    75.0 (100.0)   \n",
       "                    Mixed                      270.0 (100.0)    265.0 (98.1)   \n",
       "                    Other                       30.0 (100.0)    30.0 (100.0)   \n",
       "                    White                      495.0 (100.0)   495.0 (100.0)   \n",
       "region              East Midlands               70.0 (100.0)    70.0 (100.0)   \n",
       "                    East of England             65.0 (100.0)    65.0 (100.0)   \n",
       "                    London                     140.0 (100.0)   140.0 (100.0)   \n",
       "                    North East                  65.0 (100.0)    65.0 (100.0)   \n",
       "                    North West                  60.0 (100.0)    60.0 (100.0)   \n",
       "                    South East                 115.0 (100.0)   115.0 (100.0)   \n",
       "                    West Midlands               60.0 (100.0)    60.0 (100.0)   \n",
       "                    Yorkshire and the Humber    75.0 (100.0)    75.0 (100.0)   \n",
       "imd                 1 Most deprived            215.0 (100.0)   215.0 (100.0)   \n",
       "                    2                          190.0 (100.0)   190.0 (100.0)   \n",
       "                    3                          215.0 (100.0)   215.0 (100.0)   \n",
       "                    4                          190.0 (100.0)   190.0 (100.0)   \n",
       "                    5 Least deprived           185.0 (100.0)   185.0 (100.0)   \n",
       "                    Unknown                     10.0 (100.0)    10.0 (100.0)   \n",
       "dementia            False                      990.0 (100.0)   990.0 (100.0)   \n",
       "                    True                        10.0 (100.0)    10.0 (100.0)   \n",
       "diabetes            False                      990.0 (100.0)   990.0 (100.0)   \n",
       "                    True                        10.0 (100.0)    10.0 (100.0)   \n",
       "hypertension        False                      990.0 (100.0)   990.0 (100.0)   \n",
       "                    True                        10.0 (100.0)    10.0 (100.0)   \n",
       "learning_disability False                      990.0 (100.0)   990.0 (100.0)   \n",
       "                    True                        10.0 (100.0)    10.0 (100.0)   \n",
       "\n",
       "                                                  all_filled  population  \n",
       "group               subgroup                                              \n",
       "all                 with records              1000.0 (100.0)      1000.0  \n",
       "age_band            0-19                       125.0 (100.0)       125.0  \n",
       "                    20-29                      145.0 (100.0)       145.0  \n",
       "                    30-39                      110.0 (100.0)       110.0  \n",
       "                    40-49                      130.0 (100.0)       130.0  \n",
       "                    50-59                      120.0 (100.0)       120.0  \n",
       "                    60-69                      130.0 (100.0)       130.0  \n",
       "                    70-79                       125.0 (96.2)       130.0  \n",
       "                    80+                        110.0 (100.0)       110.0  \n",
       "sex                 F                           500.0 (99.0)       505.0  \n",
       "                    M                          495.0 (100.0)       495.0  \n",
       "ethnicity           Asian                      135.0 (100.0)       135.0  \n",
       "                    Black                       75.0 (100.0)        75.0  \n",
       "                    Mixed                       265.0 (98.1)       270.0  \n",
       "                    Other                       30.0 (100.0)        30.0  \n",
       "                    White                      495.0 (100.0)       495.0  \n",
       "region              East Midlands               70.0 (100.0)        70.0  \n",
       "                    East of England             65.0 (100.0)        65.0  \n",
       "                    London                     140.0 (100.0)       140.0  \n",
       "                    North East                  65.0 (100.0)        65.0  \n",
       "                    North West                  60.0 (100.0)        60.0  \n",
       "                    South East                 115.0 (100.0)       115.0  \n",
       "                    West Midlands               60.0 (100.0)        60.0  \n",
       "                    Yorkshire and the Humber    75.0 (100.0)        75.0  \n",
       "imd                 1 Most deprived            215.0 (100.0)       215.0  \n",
       "                    2                          190.0 (100.0)       190.0  \n",
       "                    3                          215.0 (100.0)       215.0  \n",
       "                    4                          190.0 (100.0)       190.0  \n",
       "                    5 Least deprived           185.0 (100.0)       185.0  \n",
       "                    Unknown                     10.0 (100.0)        10.0  \n",
       "dementia            False                      990.0 (100.0)       990.0  \n",
       "                    True                        10.0 (100.0)        10.0  \n",
       "diabetes            False                      990.0 (100.0)       990.0  \n",
       "                    True                        10.0 (100.0)        10.0  \n",
       "hypertension        False                      990.0 (100.0)       990.0  \n",
       "                    True                        10.0 (100.0)        10.0  \n",
       "learning_disability False                      990.0 (100.0)       990.0  \n",
       "                    True                        10.0 (100.0)        10.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_patient_counts = pd.read_csv(f'../output/{output_path}/tables/patient_counts_filled.csv').set_index(['group','subgroup'])\n",
    "display(df_patient_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count of Missings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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></th>\n",
       "      <th>height</th>\n",
       "      <th>height_backend</th>\n",
       "      <th>all_missing</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>subgroup</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <th>missing records</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">age_band</th>\n",
       "      <th>0-19</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sex</th>\n",
       "      <th>F</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ethnicity</th>\n",
       "      <th>Asian</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">region</th>\n",
       "      <th>East Midlands</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">imd</th>\n",
       "      <th>1 Most deprived</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">dementia</th>\n",
       "      <th>False</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">diabetes</th>\n",
       "      <th>False</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">hypertension</th>\n",
       "      <th>False</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">learning_disability</th>\n",
       "      <th>False</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             height height_backend  \\\n",
       "group               subgroup                                         \n",
       "all                 missing records           - (-)          - (-)   \n",
       "age_band            0-19                      - (-)          - (-)   \n",
       "                    20-29                     - (-)          - (-)   \n",
       "                    30-39                     - (-)          - (-)   \n",
       "                    40-49                     - (-)          - (-)   \n",
       "                    50-59                     - (-)          - (-)   \n",
       "                    60-69                     - (-)          - (-)   \n",
       "                    70-79                     - (-)          - (-)   \n",
       "                    80+                       - (-)          - (-)   \n",
       "sex                 F                         - (-)          - (-)   \n",
       "                    M                         - (-)          - (-)   \n",
       "ethnicity           Asian                     - (-)          - (-)   \n",
       "                    Black                     - (-)          - (-)   \n",
       "                    Mixed                     - (-)          - (-)   \n",
       "                    Other                     - (-)          - (-)   \n",
       "                    White                     - (-)          - (-)   \n",
       "region              East Midlands             - (-)          - (-)   \n",
       "                    East of England           - (-)          - (-)   \n",
       "                    London                    - (-)          - (-)   \n",
       "                    North East                - (-)          - (-)   \n",
       "                    North West                - (-)          - (-)   \n",
       "                    South East                - (-)          - (-)   \n",
       "                    West Midlands             - (-)          - (-)   \n",
       "                    Yorkshire and the Humber  - (-)          - (-)   \n",
       "imd                 1 Most deprived           - (-)          - (-)   \n",
       "                    2                         - (-)          - (-)   \n",
       "                    3                         - (-)          - (-)   \n",
       "                    4                         - (-)          - (-)   \n",
       "                    5 Least deprived          - (-)          - (-)   \n",
       "                    Unknown                   - (-)          - (-)   \n",
       "dementia            False                     - (-)          - (-)   \n",
       "                    True                      - (-)          - (-)   \n",
       "diabetes            False                     - (-)          - (-)   \n",
       "                    True                      - (-)          - (-)   \n",
       "hypertension        False                     - (-)          - (-)   \n",
       "                    True                      - (-)          - (-)   \n",
       "learning_disability False                     - (-)          - (-)   \n",
       "                    True                      - (-)          - (-)   \n",
       "\n",
       "                                             all_missing  population  \n",
       "group               subgroup                                          \n",
       "all                 missing records                - (-)        1000  \n",
       "age_band            0-19                           - (-)         125  \n",
       "                    20-29                          - (-)         145  \n",
       "                    30-39                          - (-)         110  \n",
       "                    40-49                          - (-)         130  \n",
       "                    50-59                          - (-)         120  \n",
       "                    60-69                          - (-)         130  \n",
       "                    70-79                          - (-)         130  \n",
       "                    80+                            - (-)         110  \n",
       "sex                 F                              - (-)         505  \n",
       "                    M                              - (-)         495  \n",
       "ethnicity           Asian                          - (-)         135  \n",
       "                    Black                          - (-)          75  \n",
       "                    Mixed                          - (-)         270  \n",
       "                    Other                          - (-)          30  \n",
       "                    White                          - (-)         495  \n",
       "region              East Midlands                  - (-)          70  \n",
       "                    East of England                - (-)          65  \n",
       "                    London                         - (-)         140  \n",
       "                    North East                     - (-)          65  \n",
       "                    North West                     - (-)          60  \n",
       "                    South East                     - (-)         115  \n",
       "                    West Midlands                  - (-)          60  \n",
       "                    Yorkshire and the Humber       - (-)          75  \n",
       "imd                 1 Most deprived                - (-)         215  \n",
       "                    2                              - (-)         190  \n",
       "                    3                              - (-)         215  \n",
       "                    4                              - (-)         190  \n",
       "                    5 Least deprived               - (-)         185  \n",
       "                    Unknown                        - (-)          10  \n",
       "dementia            False                          - (-)         990  \n",
       "                    True                           - (-)          10  \n",
       "diabetes            False                          - (-)         990  \n",
       "                    True                           - (-)          10  \n",
       "hypertension        False                          - (-)         990  \n",
       "                    True                           - (-)          10  \n",
       "learning_disability False                          - (-)         990  \n",
       "                    True                           - (-)          10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_patient_counts_missing = pd.read_csv(f'../output/{output_path}/tables/patient_counts_missing.csv').set_index(['group','subgroup'])\n",
    "display(df_patient_counts_missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of Values\n",
    "\n",
    "### Comparison of Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>1.650973</td>\n",
       "      <td>5460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height_backend</th>\n",
       "      <td>1.650212</td>\n",
       "      <td>5450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean  count\n",
       "height          1.650973   5460\n",
       "height_backend  1.650212   5450"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image 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>avg_height</th>\n",
       "      <th>avg_height_backend</th>\n",
       "      <th>ct_height</th>\n",
       "      <th>ct_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_band</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-19</th>\n",
       "      <td>1.656213</td>\n",
       "      <td>1.648641</td>\n",
       "      <td>675</td>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>1.648138</td>\n",
       "      <td>1.652992</td>\n",
       "      <td>800</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>1.647384</td>\n",
       "      <td>1.649693</td>\n",
       "      <td>605</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>1.650518</td>\n",
       "      <td>1.650505</td>\n",
       "      <td>705</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>1.652859</td>\n",
       "      <td>1.647168</td>\n",
       "      <td>635</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>1.650272</td>\n",
       "      <td>1.651977</td>\n",
       "      <td>715</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>1.651002</td>\n",
       "      <td>1.650929</td>\n",
       "      <td>705</td>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>1.651800</td>\n",
       "      <td>1.648689</td>\n",
       "      <td>615</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_height  avg_height_backend  ct_height  ct_height_backend\n",
       "age_band                                                              \n",
       "0-19        1.656213            1.648641        675                690\n",
       "20-29       1.648138            1.652992        800                820\n",
       "30-39       1.647384            1.649693        605                615\n",
       "40-49       1.650518            1.650505        705                740\n",
       "50-59       1.652859            1.647168        635                640\n",
       "60-69       1.650272            1.651977        715                705\n",
       "70-79       1.651002            1.650929        705                670\n",
       "80+         1.651800            1.648689        615                570"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image 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>avg_height</th>\n",
       "      <th>avg_height_backend</th>\n",
       "      <th>ct_height</th>\n",
       "      <th>ct_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1.649943</td>\n",
       "      <td>1.650344</td>\n",
       "      <td>2750</td>\n",
       "      <td>2745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1.652018</td>\n",
       "      <td>1.650077</td>\n",
       "      <td>2710</td>\n",
       "      <td>2705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     avg_height  avg_height_backend  ct_height  ct_height_backend\n",
       "sex                                                              \n",
       "F      1.649943            1.650344       2750               2745\n",
       "M      1.652018            1.650077       2710               2705"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image 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>avg_height</th>\n",
       "      <th>avg_height_backend</th>\n",
       "      <th>ct_height</th>\n",
       "      <th>ct_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>1.650980</td>\n",
       "      <td>1.648047</td>\n",
       "      <td>715</td>\n",
       "      <td>770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>1.651823</td>\n",
       "      <td>1.655146</td>\n",
       "      <td>415</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>1.651591</td>\n",
       "      <td>1.649688</td>\n",
       "      <td>1460</td>\n",
       "      <td>1445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>1.651099</td>\n",
       "      <td>1.640307</td>\n",
       "      <td>175</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>1.650498</td>\n",
       "      <td>1.651009</td>\n",
       "      <td>2695</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           avg_height  avg_height_backend  ct_height  ct_height_backend\n",
       "ethnicity                                                              \n",
       "Asian        1.650980            1.648047        715                770\n",
       "Black        1.651823            1.655146        415                375\n",
       "Mixed        1.651591            1.649688       1460               1445\n",
       "Other        1.651099            1.640307        175                160\n",
       "White        1.650498            1.651009       2695               2700"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image 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>avg_height</th>\n",
       "      <th>avg_height_backend</th>\n",
       "      <th>ct_height</th>\n",
       "      <th>ct_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Midlands</th>\n",
       "      <td>1.651823</td>\n",
       "      <td>1.650797</td>\n",
       "      <td>390</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>1.652068</td>\n",
       "      <td>1.648478</td>\n",
       "      <td>390</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>1.651095</td>\n",
       "      <td>1.650719</td>\n",
       "      <td>765</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>1.652682</td>\n",
       "      <td>1.657523</td>\n",
       "      <td>350</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>1.647574</td>\n",
       "      <td>1.646869</td>\n",
       "      <td>320</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>1.648314</td>\n",
       "      <td>1.647974</td>\n",
       "      <td>620</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>1.655489</td>\n",
       "      <td>1.646319</td>\n",
       "      <td>330</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>1.646985</td>\n",
       "      <td>1.648131</td>\n",
       "      <td>430</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          avg_height  avg_height_backend  ct_height  \\\n",
       "region                                                                \n",
       "East Midlands               1.651823            1.650797        390   \n",
       "East of England             1.652068            1.648478        390   \n",
       "London                      1.651095            1.650719        765   \n",
       "North East                  1.652682            1.657523        350   \n",
       "North West                  1.647574            1.646869        320   \n",
       "South East                  1.648314            1.647974        620   \n",
       "West Midlands               1.655489            1.646319        330   \n",
       "Yorkshire and the Humber    1.646985            1.648131        430   \n",
       "\n",
       "                          ct_height_backend  \n",
       "region                                       \n",
       "East Midlands                           370  \n",
       "East of England                         385  \n",
       "London                                  775  \n",
       "North East                              360  \n",
       "North West                              320  \n",
       "South East                              600  \n",
       "West Midlands                           315  \n",
       "Yorkshire and the Humber                420  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAAMgCAYAAAAz4JsCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde1xU1cL/8e8wCIOiEKaASWCK5hOpmWlWXjI8iKmJpaY+omaZT5mZ4TGtk1inPJX3S3eUTLt6oU6KeUvrmJVlWD1aiT9MSpS8gaiDyezfH76cp4mLAw6wYT7v12teL/eetddaexhhz3fWWttiGIYhAAAAAAAAwKR8qrsDAAAAAAAAQFkIsAAAAAAAAGBqBFgAAAAAAAAwNQIsAAAAAAAAmBoBFgAAAAAAAEyNAAsAAAAAAACmRoAFAAAAAAAAUyPAAgAAAAAAgKkRYAEAAAAAAMDUCLAAAAAAAABgagRYAAAAAAAAMDUCLAAAAAAAAJgaARYAAAAAAABMjQALAAAAAAAApkaABQAAAAAAAFMjwAIAAAAAAICpEWABAAAAAADA1AiwAAAAAAAAYGoEWAAAAAAAADA1AiwAAAAAAACYGgEWAAAAAAAATI0ACwAAAAAAAKZGgAUAAAAAAABTI8ACAAAAAACAqRFgAQAAAAAAwNQIsAAAAAAAAGBqBFgAAAAAAAAwNQIsAAAAAAAAmBoBFgAAAAAAAEyNAAsAAAAAAACmRoAFAAAAAAAAUyPAAgAAAAAAgKkRYAEAAAAAAMDUCLAAAAAAAABgagRYAAAAAAAAMDUCLAAAAAAAAJgaARYAAAAAAABMjQALAAAAAAAApkaABQAAAAAAAFMjwAIAAAAAAICpEWABAAAAAADA1AiwAAAAAAAAYGoEWAAAAAAAADA1AiwAAAAAAACYGgEWAAAAAAAATI0ACwAAAAAAAKZGgAUAAAAAAABTI8ACAAAAAACAqRFgAQAAAAAAwNQIsAAAAAAAAGBqBFgAAAAAAAAwNQIsAAAAAAAAmBoBFgAAAAAAAEyNAAsAAAAAAACmRoAFAAAAAAAAUyPAAgAAAAAAgKkRYAEAAAAAAMDUCLAAAAAAAABgagRYAAAAAAAAMDUCLAAAAAAAAJgaARYAAAAAAABMjQALAAAAAAAApkaABQAAAAAAAFMjwAIAAAAAAICpEWABAAAAAADA1AiwAAAAAAAAYGoEWAAAAAAAADA1AiwAAAAAAACYGgEWAAAAAAAATI0ACwAAAAAAAKZGgAUAAAAAAABTI8ACAAAAAACAqRFgAQAAAAAAwNQIsAAAgMckJyfLYrFUSVvdu3dX9+7dndtbtmyRxWLRihUrqqT9kSNHKioqqkraKq8LP4cjR45Ud1c8Zv/+/bJYLEpNTa3urgAAgGpAgAUAAEqUmpoqi8XifNhsNjVp0kRxcXGaP3++Tp486ZF2Dh48qOTkZGVkZHikPk8yc98AAAC8iW91dwAAAJjbU089pWbNmumPP/7QoUOHtGXLFk2YMEGzZ8/Whx9+qDZt2jjLPvHEE3rsscfKVf/Bgwc1ffp0RUVFqV27dm4ft379+nK1UxFl9e21116Tw+Go9D7gvMjISJ05c0Z16tSp7q4AAIBqQIAFAADKFB8frw4dOji3p0yZos2bN6tPnz7q16+f9uzZo4CAAEmSr6+vfH0r9/Li9OnTqlu3rvz8/Cq1nYshSCnOMAzZ7Xbn+8GTLowCBAAA3okphAAAoNx69Oihf/zjH/rll1+0bNky5/6S1sDasGGDbrnlFgUHByswMFCtWrXS1KlTJZ1ft+qGG26QJI0aNco5XfHCOkfdu3dXTEyMvvnmG3Xt2lV169Z1HvvXNbAuKCoq0tSpUxUWFqZ69eqpX79+ys7OdikTFRWlkSNHFjv2z3VerG8lrYF16tQpPfroo4qIiJC/v79atWqlmTNnyjAMl3IWi0Xjxo1TWlqaYmJi5O/vr2uuuUbr1q1zKXfy5ElNmDBBUVFR8vf3V+PGjdWzZ0/t3LmzWN9LcuTIEQ0aNEgNGjRQw4YN9fDDD8tutzuf79atm9q2bVvisa1atVJcXFyZ9UdFRalPnz76+OOP1aFDBwUEBOiVV15xPr9s2TJdf/31CggIUEhIiO6+++5iPwtJWrRoka666ioFBASoY8eO+uyzz4r9fEtbA2vz5s3q0qWL6tWrp+DgYN1xxx3as2ePS5kL78vMzEyNHDlSwcHBCgoK0qhRo3T69OkyzxEAAJgDARYAAKiQ4cOHSyp7Kt///u//qk+fPiosLNRTTz2lWbNmqV+/ftq2bZskqXXr1nrqqackSWPGjNGbb76pN998U127dnXWcfToUcXHx6tdu3aaO3eubr311jL79cwzz2jNmjWaPHmyxo8frw0bNig2NlZnzpwp1/m507c/MwxD/fr105w5c9SrVy/Nnj1brVq10qRJkzRx4sRi5f/zn//ogQce0N13363nn39edrtdd955p44ePeosM3bsWL300ku688479eKLLyopKUkBAQHFAprSDBo0SHa7XTNmzFDv3r01f/58jRkzxvn88OHD9d133+mHH35wOW7Hjh36+eef9d///d8XbeOnn37SkCFD1LNnT82bN8851fKZZ55RYmKioqOjNXv2bE2YMEGbNm1S165ddeLECefxL730ksaNG6emTZvq+eefV5cuXdS/f3/9+uuvF21748aNiouLU25urpKTkzVx4kR9/vnnuvnmm7V///4SX4+TJ09qxowZGjRokFJTUzV9+vSLtgMAAEzAAAAAKMGSJUsMScaOHTtKLRMUFGRcd911zu1p06YZf768mDNnjiHJ+P3330utY8eOHYYkY8mSJcWe69atmyHJePnll0t8rlu3bs7tTz75xJBkXHHFFUZ+fr5z/3vvvWdIMubNm+fcFxkZaYwYMeKidZbVtxEjRhiRkZHO7bS0NEOS8c9//tOl3F133WVYLBYjMzPTuU+S4efn57Jv165dhiRjwYIFzn1BQUHGgw8+WKzti7nwc+jXr5/L/gceeMCQZOzatcswDMM4ceKEYbPZjMmTJ7uUGz9+vFGvXj2joKCgzHYiIyMNSca6detc9u/fv9+wWq3GM88847L/+++/N3x9fZ37CwsLjYYNGxo33HCD8ccffzjLpaamGpJcfhZZWVnFfhbt2rUzGjdubBw9etS5b9euXYaPj4+RmJhY7PW45557XPqTkJBgNGzYsMxzBAAA5sAILAAAUGGBgYFl3o0wODhYkvTBBx9UeMFzf39/jRo1yu3yiYmJql+/vnP7rrvuUnh4uNauXVuh9t21du1aWa1WjR8/3mX/o48+KsMwlJ6e7rI/NjZWzZs3d263adNGDRo00P/7f//PuS84OFhffvmlDh48WKE+Pfjggy7bDz30kLOvkhQUFKQ77rhDb7/9tnOaY1FRkd599131799f9erVu2gbzZo1KzbVcNWqVXI4HBo0aJCOHDnifISFhSk6OlqffPKJJOnrr7/W0aNHdd9997msnTZs2DBddtllZbabk5OjjIwMjRw5UiEhIc79bdq0Uc+ePUv8eY8dO9Zlu0uXLjp69Kjy8/Mvep4AAKB6EWABAIAKKygocAmL/mrw4MG6+eabde+99yo0NFR333233nvvvXKFWVdccUW5FmyPjo522bZYLGrRokWJU8o86ZdfflGTJk2KvR6tW7d2Pv9nV155ZbE6LrvsMh0/fty5/fzzz+uHH35QRESEOnbsqOTkZJeA62L++lo0b95cPj4+Lq9FYmKiDhw4oM8++0zS+Wl5hw8fdk4RvZhmzZoV27d3714ZhqHo6Gg1atTI5bFnzx7l5uZK+r/XpEWLFi7H+/r6Fltf7K8uHNuqVatiz7Vu3VpHjhzRqVOnXPb/9TW/EJL9+TUHAADmxF0IAQBAhfz666/Ky8srFj78WUBAgD799FN98sknWrNmjdatW6d3331XPXr00Pr162W1Wi/aTmXd0a4kRUVFbvXJE0prx/jTgu+DBg1Sly5dtHr1aq1fv14vvPCCnnvuOa1atUrx8fHlbrOk846Li1NoaKiWLVumrl27atmyZQoLC1NsbKxbdZb083E4HLJYLEpPTy/xPAMDA8vdd09w5zUHAADmxAgsAABQIW+++aYkXfROdT4+Prrttts0e/Zs7d69W88884w2b97snEZWWphUUXv37nXZNgxDmZmZLiN6LrvsMpeFxC/46yip8vQtMjJSBw8eLDal8scff3Q+XxHh4eF64IEHlJaWpqysLDVs2FDPPPOMW8f+9bXIzMyUw+FweS2sVquGDh2qFStW6Pjx40pLS9OQIUMuKchr3ry5DMNQs2bNFBsbW+xx4403Svq/1yQzM9Pl+HPnzl10xNyFY3/66adiz/3444+6/PLL3ZoCCQAAagYCLAAAUG6bN2/W008/rWbNmmnYsGGlljt27FixfRfuUldYWChJzpChpECpIpYuXeoSIq1YsUI5OTkuI5aaN2+uL774QmfPnnXu++ijj5Sdne1SV3n61rt3bxUVFWnhwoUu++fMmSOLxVLuEVNFRUXKy8tz2de4cWM1adLE+dpdzKJFi1y2FyxYIEnF+jJ8+HAdP35c999/vwoKCty6+2BZBgwYIKvVqunTpxcb3WQYhvNOix06dFDDhg312muv6dy5c84yy5cvv+i0vvDwcLVr105vvPGGy8/nhx9+0Pr169W7d+9LOgcAAGAuTCEEAABlSk9P148//qhz587p8OHD2rx5szZs2KDIyEh9+OGHstlspR771FNP6dNPP9Xtt9+uyMhI5ebm6sUXX1TTpk11yy23SDofJgUHB+vll19W/fr1Va9ePXXq1KnEtZXcERISoltuuUWjRo3S4cOHNXfuXLVo0UL33Xefs8y9996rFStWqFevXho0aJD27dunZcuWuSyqXt6+9e3bV7feeqsef/xx7d+/X23bttX69ev1wQcfaMKECcXqvpiTJ0+qadOmuuuuu9S2bVsFBgZq48aN2rFjh2bNmuVWHVlZWerXr5969eql7du3a9myZRo6dKjatm3rUu66665TTEyM3n//fbVu3Vrt27cvV1//qnnz5vrnP/+pKVOmaP/+/erfv7/q16+vrKwsrV69WmPGjFFSUpL8/PyUnJyshx56SD169NCgQYO0f/9+paamqnnz5hcdAffCCy8oPj5enTt31ujRo3XmzBktWLBAQUFBSk5OvqRzAAAA5sIILAAAUKYnn3xSw4cP1/3336+5c+fKMAzNnTtX3333nWJiYso8tl+/frryyiu1ePFiPfjgg1q0aJG6du2qzZs3KygoSJJUp04dvfHGG7JarRo7dqyGDBmirVu3Vri/U6dO1e23364ZM2Zo3rx5uu2227Rp0ybVrVvXWSYuLk6zZs3Szz//rAkTJmj79u366KOP1LRpU5e6ytM3Hx8fffjhh5owYYI++ugjTZgwQbt379YLL7yg2bNnl/s86tatqwceeEAZGRmaNm2aHnnkEf3000968cUXNXHiRLfqePfdd+Xv76/HHntMa9as0bhx45SSklJi2cTERElye/H2i3nssce0cuVK+fj4aPr06UpKStKHH36ov/3tb+rXr5+z3Lhx4zR//nwdOHBASUlJ+uyzz/Thhx8qODi4zHBUOn8nx3Xr1qlhw4Z68sknNXPmTN14443atm1bhQNQAABgThaDVSsBAAC83rx58/TII49o//79Jd4hsSo5HA41atRIAwYM0GuvvVatfQEAAObACCwAAAAvZxiGUlJS1K1btyoPr+x2e7F1spYuXapjx46pe/fuVdoXAABgXqyBBQAA4KVOnTqlDz/8UJ988om+//57ffDBB1Xehy+++EKPPPKIBg4cqIYNG2rnzp1KSUlRTEyMBg4cWOX9AQAA5kSABQAA4KV+//13DR06VMHBwZo6darL2lRVJSoqShEREZo/f76OHTumkJAQJSYm6l//+pf8/PyqvD8AAMCcWAMLAAAAAAAApsYaWAAAAAAAADA1AiwAAAAAAACYGmtgweMcDocOHjyo+vXry2KxVHd3AAAAAAC1nGEYOnnypJo0aSIfH8bq1EYEWPC4gwcPKiIiorq7AQAAAADwMtnZ2WratGl1dwOVgAALHle/fn1J539xNGjQoJp7AwAAAACo7fLz8xUREeH8PIrahwALHndh2mCDBg0IsAAAAAAAVYZlbGovJoYCAAAAAADA1AiwAAAAAAAAYGoEWAAAAAAAADA11sACAAAAAJRLUVGR/vjjj+ruBrxInTp1ZLVaq7sbqEYEWAAAAAAAtxiGoUOHDunEiRPV3RV4oeDgYIWFhbFQu5ciwAIAAAAAuOVCeNW4cWPVrVuXIAFVwjAMnT59Wrm5uZKk8PDwau4RqgMBFgAAAADgooqKipzhVcOGDau7O/AyAQEBkqTc3Fw1btyY6YReiEXcAQAAAAAXdWHNq7p161ZzT+CtLrz3WH/NOxFgAQAAAADcxrRBVBfee96NAAsAAAAAAACmRoAFAAAAAICH7d+/XxaLRRkZGW4fM3LkSPXv37/MMt27d9eECRMutXtAjcMi7gAAAAAAeFhERIRycnJ0+eWXV3dXgFqBAAsAAAAAAA86e/as/Pz8FBYWVt1dAWoNphACAAAAALzWq6++qiZNmsjhcLjsv+OOO3TPPfdo3759uuOOOxQaGqrAwEDdcMMN2rhxo0vZqKgoPf3000pMTFSDBg00ZsyYYlMIi4qKNHr0aDVr1kwBAQFq1aqV5s2bV2Kfpk+frkaNGqlBgwYaO3aszp49W2r/CwsLlZSUpCuuuEL16tVTp06dtGXLlkt7UQATIsACAAAAAHitgQMH6ujRo/rkk0+c+44dO6Z169Zp2LBhKigoUO/evbVp0yZ9++236tWrl/r27asDBw641DNz5ky1bdtW3377rf7xj38Ua8fhcKhp06Z6//33tXv3bj355JOaOnWq3nvvPZdymzZt0p49e7Rlyxa9/fbbWrVqlaZPn15q/8eNG6ft27frnXfe0XfffaeBAweqV69e2rt37yW+MoC5WAzDMKq7E6hd8vPzFRQUpLy8PDVo0KC6uwMAAADAA+x2u7KystSsWTPZbLbq7o5H9e/fXw0bNlRKSoqk86Oypk+fruzsbPn4FB/3ERMTo7Fjx2rcuHGSzo/Auu6667R69Wpnmf3796tZs2b69ttv1a5duxLbHTdunA4dOqQVK1ZIOr+I+7///W9lZ2erbt26kqSXX35ZkyZNUl5ennx8fNS9e3e1a9dOc+fO1YEDB3TVVVfpwIEDatKkibPe2NhYdezYUc8++6xnXiCTKOs9yOfQ2o8RWAAAAAAArzZs2DCtXLlShYWFkqTly5fr7rvvlo+PjwoKCpSUlKTWrVsrODhYgYGB2rNnT7ERWB06dLhoO4sWLdL111+vRo0aKTAwUK+++mqxetq2besMrySpc+fOKigoUHZ2drH6vv/+exUVFally5YKDAx0PrZu3ap9+/ZV5KUATItF3AEAAAAAXq1v374yDENr1qzRDTfcoM8++0xz5syRJCUlJWnDhg2aOXOmWrRooYCAAN11113F1qWqV69emW288847SkpK0qxZs9S5c2fVr19fL7zwgr788ssK97ugoEBWq1XffPONrFary3OBgYEVrhcwIwIsAAAAAIBXs9lsGjBggJYvX67MzEy1atVK7du3lyRt27ZNI0eOVEJCgqTzodH+/fvL3ca2bdt000036YEHHnDuK2mU1K5du3TmzBkFBARIkr744gsFBgYqIiKiWNnrrrtORUVFys3NVZcuXcrdJ6AmYQohAAAAAMDrDRs2TGvWrNHixYs1bNgw5/7o6GitWrVKGRkZ2rVrl4YOHVrsjoXuiI6O1tdff62PP/5YP//8s/7xj39ox44dxcqdPXtWo0eP1u7du7V27VpNmzZN48aNK3EtrpYtW2rYsGFKTEzUqlWrlJWVpa+++kozZszQmjVryt1HwMwIsAAAAAAAXq9Hjx4KCQnRTz/9pKFDhzr3z549W5dddpluuukm9e3bV3Fxcc7RWeVx//33a8CAARo8eLA6deqko0ePuozGuuC2225TdHS0unbtqsGDB6tfv35KTk4utd4lS5YoMTFRjz76qFq1aqX+/ftrx44duvLKK8vdR8DMuAshPI67PwAAAAC1T22+CyFqBu5C6N0YgQUAAAAAAABTYxF3AABQjGEYstvt5Sp/4dbj/v7+slgsbh1ns9ncLgsAAADvRYAFAACKsdvtio+Pr/R20tPTnXdZAgAAAErDFEIAAAAAAACYGiOwAABAMTabTenp6W6Xt9vtSkhIkCStXr3a7cV9WQQYAAAA7iDAAgAAxVgslgpP7bPZbEwLBAAAgEcxhRAAAAAAAACmRoAFAAAAAAAAUyPAAgAAAADUat27d9eECRMqfHxycrLatWtXpW0CcEWABQAAAAC4JEVFRTp37lyVPYqKiqr0/JKSkrRp0yaP12uxWJSWlubxeoHaiEXcAQAAAAAVVlRUpAF3DVTe8WNV1mbQZSFateJ9Wa3WKmkvMDBQgYGBVdIWgJIRYAEAAAAAKswwDOUdP6aT7RMlSxVM8jEc0s6lMgyjXIc5HA79/e9/1+uvvy4/Pz+NHTtWycnJkqQTJ04oKSlJH3zwgQoLC9WhQwfNmTNHbdu2lXR+CmFaWpoyMjIkSefOndPEiRO1dOlSWa1W3XvvvTp06JDy8vJcRlSV1WZUVJQkKSEhQZIUGRmp/fv3V/x1AWo5phACAAAAAC6dxUfyqYJHBUOyN954Q/Xq1dOXX36p559/Xk899ZQ2bNggSRo4cKByc3OVnp6ub775Ru3bt9dtt92mY8dKHlX23HPPafny5VqyZIm2bdum/Pz8EqcCltXmjh07JElLlixRTk6OcxtAyRiBBQAAAACo9dq0aaNp06ZJkqKjo7Vw4UJt2rRJAQEB+uqrr5Sbmyt/f39J0syZM5WWlqYVK1ZozJgxxepasGCBpkyZ4hw9tXDhQq1du9btNnv27KlGjRpJkoKDgxUWFlYp5wzUJgRYqDEMw5Ddbi9X+cLCQkmSv7+/LBaLW8fZbDa3ywIAAMB9XM+hOrVp08ZlOzw8XLm5udq1a5cKCgrUsGFDl+fPnDmjffv2FasnLy9Phw8fVseOHZ37rFarrr/+ejkcDhmG4ZzeeO2118rhcDjLhYWF6fDhwy77HA6Hzp07J+n8ou7uvnfLUxaoDQiwUGPY7XbFx8dXejvp6ekKCAio9HYAAAC8DddzqE516tRx2bZYLHI4HCooKFB4eLi2bNlS7Jjg4OByt2MYhvbu3avTp0/r1KlT2rt3r/O506dPy2q1uuzLyckpMSi7mOjoaAIseBUCLAAAAACoRuUZmVbRUWkSI9NK0759ex06dEi+vr7OhdXLEhQUpNDQUO3YsUNdu3aVdP5OjDt37lS7du3K1XadOnVUVFRUkW4DXocACzWGzWZTenq62+XtdrtzTvrq1atls9ncbgcAAACex/VcyRiZVr1iY2PVuXNn9e/fX88//7xatmypgwcPas2aNUpISFCHDh2KHfPQQw9pxowZatGiha6++motWLBAx48fd07ri46OVt26dRUcHKzo6GjncfXq1VODBg2c+6KiorR79261b99efn5+at++vXx83FuknjAS3oYACzWGxWKp8B9cm83GH2sAAIBqxvVcLWc4JMfFi3mkHQ+yWCxau3atHn/8cY0aNUq///67wsLC1LVrV4WGhpZ4zOTJk3Xo0CElJibKarVqzJgxiouLk9VqdVmbymKxuARSF567sG/WrFmaOHGiUlJS1LhxY2VnZ7sdYAHexmJcWF0O8JD8/HwFBQUpLy9PDRo0qLZ+nDlzxvlNFt82AUDl4ncugMrgLb9byjOFsKKj0qRLn0Jot9uVlZWlZs2aubRbVFSkAXcNVN7xYxWuu7yCLgvRqhXvy2q1VlmbZXE4HGrdurUGDRqkp59+utzHXlgTKzo6mgCrDKW9ByXzfA5F5WEEFgAAAABUo4qOTDPLqDSr1apVK95XVY6NsFgs1Rpe/fLLL1q/fr26deumwsJCLVy4UFlZWRo6dGi19Qmo7QiwAAAAAACXxCwjoaqKj4+PUlNTlZSUJMMwFBMTo40bN6p169bV3TWg1iLAAgAAAACgHCIiIrRt27bq7gbgVQiwAAAAgBqmPGsmGYahwsJCSZK/v3+51kC61DWTAADwFAIsAAAAoIax2+3Oxc0rU21eOB0AULMQYAFVgG9JAQAAAACoOAIsoArwLSkAAPAkm82m9PR0t8ra7XYlJCRIklavXl3s1vMXawcAADPwqe4OoOI+/fRT9e3bV02aNJHFYlFaWtpFj1m+fLnatm2runXrKjw8XPfcc4+OHj3qUub999/X1VdfLZvNpmuvvVZr166trFMAAABABVgsFgUEBLj1+HMIZbPZ3D4uICCAkd0AANNgBFYNdurUKbVt21b33HOPBgwYcNHy27ZtU2JioubMmaO+ffvqt99+09ixY3Xfffdp1apVkqTPP/9cQ4YM0YwZM9SnTx+99dZb6t+/v3bu3KmYmJjKPqVai29JAQDVqTxT2S+Ur8h0dqayAwCAykKAVYPFx8eXa1ra9u3bFRUVpfHjx0uSmjVrpvvvv1/PPfecs8y8efPUq1cvTZo0SZL09NNPa8OGDVq4cKFefvllz56AF7nwLWl5XfiWFKgJWOsNMC+msgMAgJqOAMuLdO7cWVOnTtXatWsVHx+v3NxcrVixQr1793aW2b59uyZOnOhyXFxcnFvTEwF4Nz4gAwDgvYqKimQYRpW1Z7FYZLVa3S7fvXt3tWvXTnPnzq1Qe8nJyUpLS1NGRkaVtempfpRXamqqJkyYoBMnTlRaG6WxWCxavXq1+vfvX+Vtw/wIsLzIzTffrOXLl2vw4MGy2+06d+6c+vbtq0WLFjnLHDp0SKGhoS7HhYaG6tChQ6XWW1hY6BxJIUn5+fme7zwAAKiw8kxllyo+nb0yprIzuhMwv6KiIg0eOEBHjuVVWZuXhwTp3fdXlSvEuhRJSUl66KGHPF6vxWLRypUrdc0113i8bqC2IUWdlEkAACAASURBVMDyIrt379bDDz+sJ598UnFxccrJydGkSZM0duxYpaSkVLjeGTNmaPr06R7sKYCaiLXeAPOq6FR2qfqnszO6EzA/wzB05FieXut2VNYqyIGLDOm+rarSEV+BgYEKDAyssvYAFMddCL3IjBkzdPPNN2vSpElq06aN4uLi9OKLL2rx4sXKycmRJIWFhenw4cMuxx0+fFhhYWGl1jtlyhTl5eU5H9nZ2ZV6HgDMiTtiAQDg3awWyden8h8VDckcDof+/ve/KyQkRGFhYUpOTnY+d+LECd17771q1KiRGjRooB49emjXrl3O55OTk9WuXTvn9rlz5zR+/HgFBwerYcOGmjx5skaMGFFs6ltZbUZFRUmS7rzzTl199dXq0aOH2+fyyiuvKCIiQnXr1tWgQYOUl/d/o9927Nihnj176vLLL1dQUJC6deumnTt3uhx/4sQJ3X///QoNDZXNZlNMTIw++uijEtv6/fff1aFDByUkJDhHuf7www+Kj49XYGCgQkNDNXz4cB05csR5TPfu3TV+/PhSz12S9u7dq65du8pms+m//uu/tGHDBrfPH96JEVhe5PTp0/L1df2RXxhye+Hbi86dO2vTpk2aMGGCs8yGDRvUuXPnUuv19/eXv79/JfQYAIDyY8pZ7cLoTgCe8sYbb2jixIn68ssvtX37do0cOVI333yzevbsqYEDByogIEDp6ekKCgrSK6+8ottuu00///yzQkJCitX13HPPafny5VqyZIlat26tefPmKS0tTbfeeqvbbe7YsUONGzdWSkqKWrZs6fZ0yMzMTL333nv697//rfz8fI0ePVoPPPCAli9fLkk6efKkRowYoQULFsgwDM2aNUu9e/fW3r17Vb9+fTkcDsXHx+vkyZNatmyZmjdvrt27d5fYfnZ2tnr27Kkbb7xRKSkpslqtOnHihHr06KF7771Xc+bM0ZkzZzR58mQNGjRImzdvduvcHQ6HBgwYoNDQUH355ZfKy8tz+QwKlIQAqwYrKChQZmamczsrK0sZGRkKCQnRlVdeqSlTpui3337T0qVLJUl9+/bVfffdp5deesk5hXDChAnq2LGjmjRpIkl6+OGH1a1bN82aNUu333673nnnHX399dd69dVXq+UcUbvxIRNmUJ734YXyFXkv8j6sOkw5q124ky8AT2nTpo2mTZsmSYqOjtbChQu1adMmBQQE6KuvvlJubq7zi/mZM2cqLS1NK1as0JgxY4rVtWDBAk2ZMsUZmi9cuFBr1651u82ePXuqUaNGkqTg4GDnv91ht9u1dOlSXXHFFc6+3H777Zo1a5bCwsKKjeR69dVXFRwcrK1bt6pPnz7auHGjvvrqK+3Zs0ctW7aUJF111VXF2vnpp5/Us2dPJSQkaO7cuc7rmIULF+q6667Ts88+6yy7ePFiRURE6Oeff3bWWda5b9y4UT/++KM+/vhj52fRZ599tkr+fqPmIsCqwb7++muXhP/C3QNHjBih1NRU5eTk6MCBA87nR44cqZMnT2rhwoV69NFHFRwcrB49eui5555zlrnpppv01ltv6YknntDUqVMVHR2ttLQ0xcTEVN2JwWvwIRNmwPsQAADv0KZNG5ft8PBw5ebmateuXSooKFDDhg1dnj9z5oz27dtXrJ68vDwdPnxYHTt2dO6zWq26/vrr5XA43GrzUlx55ZXO8Eo6P4vG4XDop59+ci4J88QTT2jLli3Kzc1VUVGRTp8+7fxsmJGRoaZNmzqDppKcOXNGXbp00dChQ4vdRXHXrl365JNPSlwTbN++fS4B1p/9+dz37NmjiIgIZ3h14TyAshBg1WDdu3cvc+HC1NTUYvseeuihi949Y+DAgRo4cOCldg8AgGrBlDMAQEnq1Knjsm2xWORwOFRQUKDw8HBt2bKl2DHBwcGV0mZlGjFihI4ePap58+YpMjJS/v7+6ty5s86ePStJbn2h5u/vr9jYWH300UeaNGmSS2BWUFCgvn37ugyEuCA8PNz57+o4d9RuBFgAqg0fMmEG5XkfShV/L/I+rDpMOQMAlEf79u116NAh+fr6OhdWL0tQUJBCQ0O1Y8cOde3aVZJUVFSknTt3uiz07o46deqoqKioXMccOHBABw8edI5e+uKLL+Tj46NWrVpJkrZt26YXX3xRvXv3lnR+Has/L7Depk0b/frrry7T/f7Kx8dHb775poYOHapbb71VW7ZscbbXvn17rVy5UlFRUcXWWHZX69atlZ2drZycHGfo9cUXX1SoLngPAiwA1aamfshkzaTapaLvQ6n634sAAJhJkSGpCgbYFJU+CaVCYmNj1blzZ/Xv31/PP/+8WrZsqYMHD2rNmjVKSEhQhw4dih3z0EMPacaMGWrRooWuvvpqLViwQMePHy/3tVtUVJQ2b96s8PBw+fn5uXWMzWbTiBEjNHPmTOXn52v8+PEaNGiQ887x0dHRevPNN9WhQwfl5+dr0qRJLtcr3bp1U9euXXXnnXdq9uzZatGihX788UdZLBb16tXLWc5qtWr58uUaMmSIevTooS1btigsLEwPPvigXnvtNQ0ZMsR5l8HMzEy98847ev31191ajD42NlYtW7bUiBEj9MILLyg/P1+PP/54uV47eB8CLAAoJ9ZMAgAA+D8Wi0WXhwTpvq1V1+blIUEe+6LPYrFo7dq1evzxxzVq1Cj9/vvvCgsLU9euXRUaGlriMZMnT9ahQ4eUmJgoq9WqMWPGKC4uzu07CV4wa9YsTZw4Ua+//roaN26s7Ozsix7TokULDRgwQL1799axY8fUp08fvfjii87nU1JSNGbMGLVv314RERF69tlnlZSU5FLHypUrlZSUpCFDhujUqVNq0aKF/vWvfxVry9fXV2+//bYGDx7sDLGaNGmibdu2afLkyfrb3/6mwsJCRUZGqlevXvLx8XHrvH18fLR69WqNHj1aHTt2VFRUlObPn+8SoAF/RYAFAAAAAKgwq9Wqd99fVeb6vJ5msVjKFRaVtL5VWlqa89/169fX/PnzNX/+/BKPT05OVnJysnPb19dXCxYs0IIFCyRJDodDrVu31qBBg9xuUzp/p/jbb79de/fudes8/tyP//mf/ymxzHXXXacdO3a47LvrrrtctkNCQrR48eISjx85cqRGjhzp3Pb19dXKlStdykRHR2vVqlWl9tOdc2/ZsqU+++wzl31V+R5CzUOABQDlxJpJAAAArso78qim++WXX7R+/Xp169ZNhYWFWrhwobKysjR06NDq7hpQaxFgAUA5sWYSAACAd/Px8VFqaqqSkpJkGIZiYmK0ceNGtW7d+pLqvfbaa/XLL7+U+Nwrr7yiYcOGXVL9QE1GgAUAAAAAQDlERERo27ZtHq/3o48+KvWuhKWtxwV4CwIsAAAAAABMIDIy0u2F0AFvw/8MAAAAAIDbWGgb1YX3nncjwAIAAAAAXFSdOnUkSadPn67mnsBbXXjvXXgvwrswhRAAAAAAcFFWq1XBwcHKzc2VJNWtW1cWi6Wae1XzORwOORwOSefvXs0UwuIMw9Dp06eVm5ur4OBgr7vrJc4jwAIAAAAAuCUsLEySnCEWLp1hGDp8+LDz34SCpQsODna+B+F9CLAAAAAAAG6xWCwKDw9X48aN9ccff1R3d2oFu92u5ORkSdKrr74qm81WvR0yqTp16jDyyssRYAEAAAAAysVqtRImeIhhGM4Rbf7+/gRYQCmYXAsAAAAAAABTI8ACAAAAAACAqRFgAQAAAAAAwNRYAwsAAAAAAMgwDNntdrfLFhYWSjq/dld57p5os9m42yLKjQALAAAAAADIbrcrPj6+0ttJT09XQEBApbeD2oUphAAAAAAAADA1RmABAAAAAADZbDalp6e7VdZutyshIUGStHr1atlstnK1A5QXARYAAAAAAJDFYqnQ1D6bzcaUQFQ6phACAAAAAADA1AiwAAAAAAAAYGoEWAAAAAAAADA1AiwAAAAAAACYGgEWAAAAAAAATI0ACwAAAAAAAKZGgAUAAAAAAABTI8ACAAAAAACAqRFgAQAAAAAAwNQIsAAAAAAAAGBqBFgAAAAAAAAwNQIsAAAAAAAAmBoBFgAAAAAAAEyNAAsAAAAAAACmRoAFAAAAAAAAUyPAAgAAAAAAgKkRYAEAAAAAAMDUCLAAAAAAAABgagRYAAAAAAAAMDUCLAAAAAAAAJiab3V3AAAAAABQ8xiGIbvdXq7yhYWFkiR/f39ZLBa3jrPZbG6XBVB7EWABAAAAAMrNbrcrPj6+0ttJT09XQEBApbcDwNyYQggAAAAAAABTYwQWAAAAAKDcbDab0tPT3S5vt9uVkJAgSVq9erVsNpvb7QAAARYAAAAAoNwsFkuFp/bZbDamBQIoF6YQAgAAAAAAwNQIsAAAAAAAAGBqBFgAAAAAAAAwNQIsAAAAAAAAmBoBFgAAAAAAAEyNAAsAAAAAAACmRoAFAAAAAAAAUyPAAgAAAAAAgKkRYAEAAAAAAMDUCLAAAAAAAABgagRYAAAAAAAAMDUCLAAAAAAAAJgaARYAAAAAAABMjQALAAAAAAAApkaABQAAAAAAAFMjwAIAAAAAAICpEWABAAAAAADA1AiwAAAAAAAAYGoEWAAAAAAAADA1AiwAAAAAAACYGgEWAAAAAAAATI0ACwAAAAAAAKZGgAUAAAAAAABTI8ACAAAAAACAqRFgAQAAAAAAwNQIsAAAAAAAAGBqBFgAAAAAAAAwNQKsGuzTTz9V37591aRJE1ksFqWlpZVZfuTIkbJYLMUe11xzjbNMcnJyseevvvrqyj4VAAAAAACAUhFg1WCnTp1S27ZttWjRIrfKz5s3Tzk5Oc5Hdna2QkJCNHDgQJdy11xzjUu5//znP5XRfQAAAAAAALf4VncHUHHx8fGKj493u3xQUJCCgoKc22lpaTp+/LhGjRrlUs7X11dhYWEe6ycAAAAAAMClYASWF0tJSVFsbKwiIyNd9u/du1dNmjTRVVddpWHDhunAgQNl1lNYWKj8/HyXBwAAAAAAgKcQYHmpgwcPKj09Xffee6/L/k6dOik1NVXr1q3TSy+9pKysLHXp0kUnT54sta4ZM2Y4R3cFBQUpIiKisrsPAAAAAAC8CAGWl3rjjTcUHBys/v37u+yPj4/XwIED1aZNG8XFxWnt2rU6ceKE3nvvvVLrmjJlivLy8pyP7Ozsyu4+AAAAAADwIqyB5YUMw9DixYs1fPhw+fn5lVk2ODhYLVu2VGZmZqll/P395e/v7+luAgAAAAAASGIEllfaunWrMjMzNXr06IuWLSgo0L59+xQeHl4FPQMAAAAAACiOAKsGKygoUEZGhjIyMiRJWVlZysjIcC66PmXKFCUmJhY7LiUlRZ06dVJMTEyx55KSkrR161bt379fn3/+uRISEmS1WjVkyJDKPRkAAAAAAIBSMIWwBvv666916623OrcnTpwoSRoxYoRSU1OVk5NT7A6CeXl5WrlypebNm1dinb/++quGDBmio0ePqlGjRrrlllv0xRdfqFGjRpV3IgAAAAAAAGUgwKrBunfvLsMwSn0+NTW12L6goCCdPn261GPeeecdT3QNAAAAAADAY5hCCAAAAAAAAFMjwAIAAAAAAICpEWABAAAAAADA1AiwAAAAAAAAYGoEWAAAAAAAADA1AiwAAAAAAACYGgEWAAAAAAAATI0ACwAAAAAAAKZGgAUAAAAAAABTI8ACAAAAAACAqRFgAQAAAAAAwNQIsAAAAAAAAGBqBFgAAAAAAAAwNQIsAAAAAAAAmBoBFgAAAAAAAEyNAAsAAAAAAACmRoAFAAAAAAAAUyPAAgAAAAAAgKkRYAEAAAAAAMDUCLAAAAAAAABgagRYAAAAAAAAMDUCLAAAAAAAAJgaARYAAAAAAABMjQALAAAAAAAApkaABQAAAAAAAFMjwAIAAAAAAICpEWABAAAAAADA1AiwAAAAAAAAYGoEWAAAAAAAADA1AiwAAAAAAACYGgEWAAAAAAAATI0ACwAAAAAAAKZGgAUAAAAAAABTI8ACAAAAAACAqRFgAQAAAAAAwNQIsAAAAAAAAGBqBFgAAAAAAAAwNQIsAAAAAAAAmBoBFgAAAAAAAEyNAAsAAAAAAACmRoAFAAAAAAAAUyPAAgAAAAAAgKkRYAEAAAAAAMDUCLAAAAAAAABgagRYAAAAAAAAMDUCLAAAAAAAAJgaARYAAAAAAABMjQALAAAAAAAApkaABQAAAAAAAFMjwAIAAAAAAICpEWABAAAAAADA1AiwAAAAAAAAYGoEWAAAAAAAADA1AiwAAAAAAACYGgEWAAAAAAAATI0ACwAAAAAAAKZGgAUAAAAAAABTI8ACAAAAAACAqRFgAQAAAAAAwNQIsAAAAAAAAGBqBFgAAAAAAAAwNQIsAAAAAAAAmBoBFgAAAAAAAEyNAAsAAAAAAACmRoAFAAAAAAAAUyPAAgAAAAAAgKkRYAEAAAAAAMDUCLAAAAAAAABgagRYAAAAAAAAMDUCLAAAAAAAAJgaARYAAAAAAABMjQALAAAAAAAApkaABQAAAAAAAFMjwKrBPv30U/Xt21dNmjSRxWJRWlpameVHjhwpi8VS7HHNNde4lFu0aJGioqJks9nUqVMnffXVV5V5GgAAAAAAAGUiwKrBTp06pbZt22rRokVulZ83b55ycnKcj+zsbIWEhGjgwIHOMu+++64mTpyoadOmaefOnWrbtq3i4uKUm5tbWacBAAAAAABQJt/q7gAqLj4+XvHx8W6XDwoKUlBQkHM7LS1Nx48f16hRo5z7Zs+erfvuu8+57+WXX9aaNWu0ePFiPfbYY57rPAAAAAAAgJsYgeXFUlJSFBsbq8jISEnS2bNn9c033yg2NtZZxsfHR7Gxsdq+fXt1dRMAAAAAAHg5RmB5qYMHDyo9PV1vvfWWc9+RI0dUVFSk0NBQl7KhoaH68ccfS62rsLBQhYWFzu38/HzPdxgAAAAAAHgtAiwv9cYbbyg4OFj9+/e/5LpmzJih6dOne6BXAAAUZxiG7HZ7pdT953orqw1JstlsslgslVY/AABAbUeA5YUMw9DixYs1fPhw+fn5OfdffvnlslqtOnz4sEv5w4cPKywsrNT6pkyZookTJzq38/PzFRER4fmOAwC8kt1uL9eajxWVkJBQaXWnp6crICCg0uoHAACo7QiwvNDWrVuVmZmp0aNHu+z38/PT9ddfr02bNjlHZjkcDm3atEnjxo0rtT5/f3/5+/tXap8BAACAmqSyRo8ychSAtyLAqsEKCgqUmZnp3M7KylJGRoZCQkJ05ZVXasqUKfrtt9+0dOlSl+NSUlLUqVMnxcTEFKtz4sSJGjFihDp06KCOHTtq7ty5OnXqlMudCgEANVNNnYr35/oW3nJM/lbDY3UbhnTWcf7ffj6SJz+rFRZZNO4/IZ6rEECNUhWjRxk5CsCbEGDVYF9//bVuvfVW5/aFaXwjRoxQamqqcnJydODAAZdj8vLytHLlSs2bN6/EOgcPHqzff/9dTz75pA4dOqR27dpp3bp1xRZ2BwDUPLVhKp6/1ZC/1bN12jxb3Z94LmgDAADwdgRYNVj37t1lGKVfHKemphbbFxQUpNOnT5dZ77hx48qcMggAAADAfQXthsjw8dBHL8OQHOfO/9vH16NDRy2OcwrMeNtj9QGAJxFgAQDghTz6YUqqtA9UfJgCUBsYPr6StY4Ha/S7eJEKYNwoADMjwAIAwAt5/sOUVBkfqPgwBQAAAEnyqe4OAAAAAAAAAGUhwAIAAAAAAICpEWABAAAAAADA1AiwAAAAAAAAYGos4o5qZRiG7HZ7pdT953orqw1Jstlssnjw9sUAAAAAAMAVARaqld1uV3x8fKW3k5CQUGl1p6enKyAgoNLqB6pTZYXMBMwAAAAAyoMAC7hEnv7wzQd7mElVhMwEzABQs9X0EfVcE8EdvM+B6keABdMoaDdEho8H35KGITnOnf+3j6/kwV/YFsc5BWa8LalyP3zXtA/2Nf0Pu8Qf99qG0WOozfidW7vU5J+n3W6v1GuWCyqrDb7sgDtq+syR1atXy2azebRO/lagqhFgwTQMH1/JWsfDtfp5uL7zjEqptear6X/YJS5iy+LRkLkWBMyVXT/vRVwMv3Nrl9rw8wRgXlwToTYgwAI8YOEtx+Rv9UysZRjSWcf5f/v5ePRzvQqLLBr3nxDPVVgL1dQRO1XxzZTnQ2YCZgCobTx5TSRV3nUR10QXV1OviaTKvy6qKTNH/vylHlAbEGABHuBvNeRv9Vx9nh3c+2dV99G+pvxhl2rHiB2+mSpdTfkwJfGBChVXU3/nso5kyWrqz1Py/DWRVFnXRXzdcTE19ZpIqvzropoyc+Sv73KuiVDTEWABqBQ15Q+7xCVsbVdzPkxJvBtRUTX1dy7rSJaspv48AZgb10So6QiwAOAvasq3U3wzBQAAKlNNuSaSuC4CvAEBFgD8Rc35dopvpgDUfKwjCZhXzbkmkrguAmo/AiwAAABUG9aRBAAA7vCp7g4AAAAAAAAAZSHAAgAAAAAAgKkRYAEAAAAAAMDUCLAAAAAAAABgagRYAAAAAAAAMDUCLAAAAAAAAJgaARYAAAAAAABMjQALAAAAAAAApkaABQAAAAAAAFMjwALw/9m79ygpqntf4L8GdIbhDUFAniqIiAoi1wd4BKIuxRwUNIKICoJ6E+MxEZVzOF4jhkSjxleMiVGCg94kxhfEqwciKohEDhECxiiCIoiP8W3kIQwIdf/gTIfmPTiP6uHzWWvWmu7atXtXV1d31bd27QIAAIBUE2ABAAAAkGoCLAAAAABSTYAFAAAAQKoJsAAAAABINQEWAAAAAKkmwAIAAAAg1QRYAAAAAKSaAAsAAACAVBNgAQAAAJBqAiwAAAAAUk2ABQAAAECqCbAAAAAASDUBFgAAAACpJsACAAAAINUEWAAAAACkmgALAAAAgFQTYAEAAACQagIsAAAAAFJNgAUAAABAqgmwAAAAAEg1ARYAAAAAqSbAAgAAACDVBFgAAAAApJoACwAAAIBUE2ABAAAAkGoCLAAAAABSTYAFAAAAQKoJsAAAAABINQEWAAAAAKkmwAIAAAAg1QRYAAAAAKSaAAsAAACAVKtT3Q3YW7355puxdOnSOOGEE6Ju3bqRJElkMpnqbhYApFrpxupuwe7bsq1JklRfQwAAagABVhX79NNPY8iQIfHcc89FJpOJN954Iw488MAYNWpUNGnSJG699dbqbiIApNZls5tVdxP2SGlpaRQVFVV3MwAA8pZLCKvYFVdcEXXq1IkVK1bk7MgOGTIkpk2bVo0tAwAAKkpOz8uNG/LnDyCl9MCqYk8//XT86U9/ijZt2uQ836lTp3j77berqVUAkB9+cfynUVC7uluxe0o3/rPHWEFBQTW3BqhqpaWl2f8bvPxQNbYEoGYQYFWxNWvWbPcSgs8++8zOLQDsQkHtyJsAa0vGuQQA+HoEWFXsX/7lX+KBBx6I8ePHR8TmHdpNmzbFzTffHP369avm1gEAABVhy5PTq7qdE1F7n2pszW7auEFvMSC1BFhV7Oabb44TTzwx5s2bF+vXr48xY8bEq6++Gp999ln8+c9/ru7mAQAAFSCn52XtffIjwAJIMYO4V7HDDjsslixZEscff3ycccYZsWbNmjjzzDNjwYIFcdBBB1V38wAAAABSRw+satCoUaO45pprqrsZAAAAAHlBgFXFZs2atdPpJ5xwQhW1BAAAACA/CLCqWN++fbd5bsvr4zdu3FiFrQEAAABIPwFWFfv8889zHm/YsCEWLFgQ1157bfzkJz+pplZBxUiS5J8PNm6ovoaUVz61FQAAYC8kwKpijRo12ua5k08+Ofbdd98YPXp0zJ8/vxpaBRWjtLQ0+79bMAMAAFBRBFgp0aJFi1i8eHF1N4M9VJonV35u2c6c3lIAAACQYgKsKva3v/0t53GSJFFSUhI//elPo3v37tXUKr6uy2Y3q+4mlFtpaWkUFRVVaJ0FBQXZ/1d1Oyei9j4VWn+l2bhBjzEAAIAUq1XdDdjbdO/ePY488sjo3r179v/TTjst1q9fHxMmTChXXbNmzYoBAwbE/vvvH5lMJqZMmbLLeUpLS+Oaa66J9u3bR0FBQXTo0CEmTpyYnV5cXByZTCbnr7CwsNzLyd5pyxsSRO198usPAACA1NIDq4otW7Ys53GtWrWiefPmexQSrVmzJrp16xYjR46MM888c7fmGTx4cHz44Yfxm9/8Jjp27BglJSWxadOmnDINGzbMuZwxJ5Rgu35x/KdRULu6W7FrpRv/2Vtsy95SsCN5OTD/Vu3Ml0t8I1zmCwAAOyLAqmLt27evsLr69+8f/fv33+3y06ZNi+effz7eeuutaNq0aUREdOjQYZtymUwmWrZsWVHN3CsU1I68CLC2JJhkd9SEgfnz8RLfiMq5zLcmBJJAuuXLSQMnDNgb5cv2GWEbZfsEWFXg5z//+W6XvfzyyyutHU888UT07Nkzbr755njwwQejXr16cfrpp8f48eOjbt262XKrV6+O9u3bx6ZNm6JHjx5xww03RNeuXXdYb2lpac5B7sqVK3e7TXl5MBWRX20F+B81IZDkn2rKb2i+HFA5mNo9+XjSoDJOGEAa5eP2GWEb5Z8EWFXg9ttv361ymUymUgOst956K2bPnh2FhYUxefLk+OSTT+LSSy+NTz/9NO6///6IiOjcuXNMnDgxjjjiiPjiiy/iZz/7WfTq1SteffXVaNOmzXbrvfHGG+P666/fozY5mIJ0y8uB+bcalD9fLvGNcJkv5VNTfkPz8YDKwRQAVD0BVhXYetyr6rJp06bIZDLx29/+Nho1ahQREbfddlt8+9vfjl/+8pdRt27dOO644+K4447LztOrV6/o0qVL/PrXv47x48dvt96xY8fG6NGjs49XrlwZbdu2rdyFAarEdgfmzzP55TnngQAAIABJREFUeIlvROVc5lsTAkmoqWpKj7p8OWnghAF7o3zZPiNso2yfAGsv0qpVq2jdunU2vIqI6NKlSyRJEu+++2506tRpm3n22WefOPLII+PNN9/cYb0FBQV7/KWSlwdTEQ6ogLxUEwJJ/qmm/IbmywFVZR9M1ZQedfl40sC4oOwt8nH7jLCN8k8CrGrw7rvvxhNPPBErVqyI9evX50y77bbbKu11e/fuHY888kisXr066tevHxERS5YsiVq1au3w8sCNGzfGK6+8EqeddlqltMnBFGlkPBYgH9SU39B8PKByMAUAVU+AVcWeffbZOP300+PAAw+M119/PQ477LBYvnx5JEkSPXr0KFddq1evzukZtWzZsli4cGE0bdo02rVrF2PHjo333nsvHnjggYiIOPfcc2P8+PFx4YUXxvXXXx+ffPJJXH311TFy5MjsIO4/+tGP4thjj42OHTvGP/7xj7jlllvi7bffjosuuqji3gRIOeOxAFDVakqPOgCoLAKsKjZ27Ni46qqr4vrrr48GDRrEY489Fvvtt18MGzYsTj311HLVNW/evOjXr1/2cdk4VMOHD4/i4uIoKSmJFStWZKfXr18/pk+fHv/2b/8WPXv2jGbNmsXgwYPjxz/+cbbM559/HhdffHF88MEH0aRJkzjqqKPixRdfjEMPPfRrLjkAADtSU3rUAUBlEWBVsUWLFsXvf//7iIioU6dOrF27NurXrx8/+tGP4owzzojvfve7u11X3759d3rZUHFx8TbPHXLIITF9+vQdznP77bfv9l0ToaYyHgsAQP4MqxBhaAXYGwiwqli9evWy4161atUqli5dGl27do2IiE8++aQ6mwb8D+OxAADk57AKEZUztEJe3ik0X9oJu0mAVcWOPfbYmD17dnTp0iVOO+20uPLKK+OVV16Jxx9/PI499tjqbh4AAABbqSl3CoV8JsCqYrfddlusXr06IiKuv/76WL16dfzhD3+ITp06VeodCAEAAMojX4ZViDC0AuwNBFhV7IYbbojzzjsvIjZfTnjPPfdUc4sAAAC2lY/DKkRUztAKeXmnUHcJpYYRYFWxjz/+OE499dRo3rx5nHPOOXHeeedFt27dqrtZAAAA7IA7hUL1q1XdDdjb/PGPf4ySkpK49tpr46WXXooePXpE165d44Ybbojly5dXd/MAAAAAUkeAVQ2aNGkSl1xyScycOTPefvvtGDFiRDz44IPRsWPH6m4aAAAAQOoIsKrRhg0bYt68eTF37txYvnx5tGjRorqbBAAAAJA6AqxqMGPGjLj44oujRYsWMWLEiGjYsGE8+eST8e6771Z30wAAAABSxyDuVax169bx2Wefxamnnhr33ntvDBgwwG1eAQAAAHZCgFXFxo0bF2effXY0bty4upsCAAAAkBcEWFXs4osvru4mAAAAAOQVY2ABAAAAkGoCLAAAAABSTYAFAAAAQKoJsAAAAABINQEWAAAAAKkmwAIAAAAg1QRYAAAAAKSaAAsAAACAVBNgAQAAAJBqAiwAAAAAUk2ABQAAAECqCbAAAAAASDUBFgAAAACpJsACAAAAINUEWAAAAACkmgALAAAAgFQTYAEAAACQagIsAAAAAFJNgAUAAABAqgmwAAAAAEg1ARYAAAAAqSbAAgAAACDVBFgAAAAApFqd6m4AAMDuKt2YiYikwupLkoj1mzb/v2+tiEymwqr+n7YCAFARBFgAQN64bHbT6m4CAADVwCWEAAAAAKSaHlgAQF6YPHlyFBYWVmid69ati0GDBlVa/WUqq14AgL2FAAsAyAuFhYVRt27dvK0fAIA95xJCAAAAAFJNgAUAAABAqgmwAAAAAEg1ARYAAAAAqSbAAgAAACDVBFgAAAAApJoACwAAAIBUE2ABAAAAkGoCLAAAAABSrU51NwAAAKAmy2z6KpKKqixJIjZ9tfn/WnUiMpmKqjkyZfUCpJAACwAAoBLVX/j76m4CQN5zCSEAAAAAqaYHFgA1WunGTETFXbgRSRKxftPm//etVaFXbvxPWwGoCQoLC2Pq1KkVXu+6deti0KBBERExefLkKCwsrLT6AdJEgAVAjXbZ7KbV3QQA9kKZTCbq1q1bqa9RWFhY6a8BkBYuIQQAAAAg1fTAAqDGquxLKyqj/jKVVS8AAOQjARYAuyUfbwFe2ZdW5POlGxW6PiMqbZ26pTsAABECLFIkXw6mIhxQsXdyC/CaxfoEACCfCLBIDQdTNYtAEgAgv7mTL5AmAiygUggka4Z8vQV4GeNI5aqs9RlRNevU+mRv4SQQaeFOvkCaCLCoVvl8MLVl/VBTuQV4zVIV6zPCOoWvy0kgANiWAItq5WCqZhFIAgDkt3zenytT2T1286WXpB6S1DQCLKDC1JRAMl/GezDWA0DNkc+hgZNANUtN2Z+rTHpJQvUQYAFsxXgPAFQ1oQEA7JwACwAAAHYiX3tJ6iFJTSLAAoj83Skp4+5sAACVRy9JqH4CLICwUwJQXSpy3MHKGnMwwriDAFDdBFhQAex8A1Cd8uWOWBHb3hXLuIMAVSNfblQU4biF7RNgQQWw8w1AdXJHLNIiXw6QHRyzN3LMQr4TYAEAUC0qc7DiyhpzMMK4gzvjABmAyiLAgj1UWYN+2/kGYHfk+80nIjYvQ6YirznZqm5jDgJ7u5ryWwERAizYY1Ux6LedbwB2xM0nSIt8P0B2cExN5reCmkSABQAA7DEHyABUhVrV3QD23KxZs2LAgAGx//77RyaTiSlTpuxyntLS0rjmmmuiffv2UVBQEB06dIiJEyfmlHnkkUfikEMOicLCwjj88MPjv/7rvyprEQAAAAB2SYCVx9asWRPdunWLu+++e7fnGTx4cDz77LPxm9/8JhYvXhy///3vo3PnztnpL774YgwdOjRGjRoVCxYsiIEDB8bAgQPj73//e2UsAgAAAMAuuYQwj/Xv3z/69++/2+WnTZsWzz//fLz11lvRtOnmO8R06NAhp8ydd94Zp556alx99dURETF+/PiYPn16/OIXv4h77rmnwtoOAAAAsLv0wNqLPPHEE9GzZ8+4+eabo3Xr1nHwwQfHVVddFWvXrs2WmTNnTpx00kk5851yyikxZ86cHdZbWloaK1euzPkDAAAAqCh6YO1F3nrrrZg9e3YUFhbG5MmT45NPPolLL700Pv3007j//vsjIuKDDz6IFi1a5MzXokWL+OCDD3ZY74033hjXX399pbYdAAAA2HvpgbUX2bRpU2Qymfjtb38bRx99dJx22mlx2223xaRJk3J6YZXX2LFj44svvsj+vfPOOxXYagAAAGBvpwfWXqRVq1bRunXraNSoUfa5Ll26RJIk8e6770anTp2iZcuW8eGHH+bM9+GHH0bLli13WG9BQUEUFBRUWrsBAACAvZseWHuR3r17x/vvvx+rV6/OPrdkyZKoVatWtGnTJiIijjvuuHj22Wdz5ps+fXocd9xxVdpWAAAAgDICrDy2evXqWLhwYSxcuDAiIpYtWxYLFy6MFStWRMTmS/suuOCCbPlzzz03mjVrFhdeeGG89tprMWvWrLj66qtj5MiRUbdu3YiI+P73vx/Tpk2LW2+9NV5//fUYN25czJs3Ly677LKqX0AAAACAEGDltXnz5sWRRx4ZRx55ZEREjB49Oo488sj44Q9/GBERJSUl2TArIqJ+/foxffr0+Mc//hE9e/aMYcOGxYABA+LnP/95tkyvXr3id7/7Xdx7773RrVu3ePTRR2PKlClx2GGHVe3CAQAAAPwPY2Dlsb59+0aSJDucXlxcvM1zhxxySEyfPn2n9Z599tlx9tlnf93mAQAAAFQIPbAAAAAASDUBFgAAAACpJsACAAAAINUEWAAAAACkmgALAAAAgFQTYAEAAACQagIsAAAAAFJNgAUAAABAqgmwAAAAAEg1ARYAAAAAqSbAAgAAACDVBFgAAAAApJoACwAAAIBUE2ABAAAAkGoCLAAAAABSTYAFAAAAQKoJsAAAAABINQEWAAAAAKkmwAIAAAAg1QRYAAAAAKSaAAsAAACAVBNgAQAAAJBqAiwAAAAAUk2ABQAAAECqCbAAAAAASDUBFgAAAACpJsACAAAAINUEWAAAAACkmgALAAAAgFQTYAEAAACQagIsAAAAAFJNgAUAAABAqgmwAAAAAEg1ARYAAAAAqSbAAgAAACDVBFgAAAAApJoACwAAAIBUE2ABAAAAkGoCLAAAAABSTYAFAAAAQKoJsAAAAABINQEWAAAAAKkmwAIAAAAg1QRYAAAAAKSaAAsAAACAVBNgAQAAAJBqAiwAAAAAUk2ABQAAAECqCbAAAAAASDUBFgAAAACpJsACAAAAINUEWAAAAACkmgALAAAAgFQTYAEAAACQagIsAAAAAFJNgAUAAABAqgmwAAAAAEg1ARYAAAAAqSbAAgAAACDVBFgAAAAApJoACwAAAIBUE2ABAAAAkGoCLAAAAABSTYAFAAAAQKoJsAAAAABINQEWAAAAAKkmwAIAAAAg1QRYAAAAAKSaAAsAAACAVBNgAQAAAJBqAiwAAAAAUk2ABQAAAECqCbAAAAAASDUBVh6bNWtWDBgwIPbff//IZDIxZcqUnZafOXNmZDKZbf4++OCDbJlx48ZtM/2QQw6p7EUBAAAA2KE61d0A9tyaNWuiW7duMXLkyDjzzDN3e77FixdHw4YNs4/322+/nOldu3aNZ555Jvu4Th0fEwAAAKD6SCbyWP/+/aN///7lnm+//faLxo0b73B6nTp1omXLll+naQAAAAAVxiWEe6Hu3btHq1at4uSTT44///nP20x/4403Yv/9948DDzwwhg0bFitWrKiGVgIAAABsJsDai7Rq1SruueeeeOyxx+Kxxx6Ltm3bRt++feOvf/1rtswxxxwTxcXFMW3atPjVr34Vy5Yti3/5l3+JVatW7bDe0tLSWLlyZc4fAAAAQEVxCeFepHPnztG5c+fs4169esXSpUvj9ttvjwcffDAiIueSxCOOOCKOOeaYaN++fTz88MMxatSo7dZ74403xvXXX1+5jQcAAAD2Wnpg7eWOPvroePPNN3c4vXHjxnHwwQfvtMzYsWPjiy++yP698847ldFUAAAAYC8lwNrLLVy4MFq1arXD6atXr46lS5futExBQUE0bNgw5w8AAACgoriEMI+tXr06p2fUsmXLYuHChdG0adNo165djB07Nt5777144IEHIiLijjvuiAMOOCC6du0a69atiwkTJsRzzz0XTz/9dLaOq666KgYMGBDt27eP999/P6677rqoXbt2DB06tMqXDwAAACBCgJXX5s2bF/369cs+Hj16dEREDB8+PIqLi6OkpCTnDoLr16+PK6+8Mt57770oKiqKI444Ip555pmcOt59990YOnRofPrpp9G8efM4/vjj47//+7+jefPmVbdgAAAAAFsQYOWxvn37RpIkO5xeXFyc83jMmDExZsyYndb50EMPVUTTAAAAACqMMbAAAAAASDUBFgAAAACpJsACAAAAINUEWAAAAACkmgALAAAAgFQTYAEAAACQagIsAAAAAFJNgAUAAABAqgmwAAAAAEg1ARYAAAAAqSbAAgAAACDVBFgAAAAApJoACwAAAIBUE2ABAAAAkGoCLAAAAABSTYAFAAAAQKoJsAAAAABINQEWAAAAAKkmwAIAAAAg1QRYAAAAAKSaAAsAAACAVBNgAQAAAJBqAiwAAAAAUk2ABQAAAECqCbAAAAAASDUBFgAAAACpJsACAAAAINUEWAAAAACkmgALAAAAgFQTYAEAAACQagIsAAAAAFJNgAUAAABAqgmwAAAAAEi1OtXdAACAipQkSaxbt263ym5ZbnfnKVNYWBiZTKZc8wAAsGcEWABAjbJu3bro379/uecbNGhQucpPnTo16tatW+7XAQCg/FxCCAAAAECq6YEFANQohYWFMXXq1N0qmyRJlJaWRkREQUFBuS4JLCws3KP2AQBQfgIsAKBGyWQy5bq0r6ioqBJbAwBARXAJIQAAAACpJsACAAAAINUEWAAAAACkmgALAAAAgFQTYAEAAACQagIsAAAAAFJNgAUAAABAqgmwAAAAAEg1ARYAAAAAqSbAAgAAACDVBFgAAAAApJoACwAAAIBUE2ABAAAAkGoCLAAAAABSTYAFAAAAQKoJsAAAAABItTrV3QBg75UkSaxbt263ym5ZbnfnKVNYWBiZTKZc8wAAAJAeAiyg2qxbty769+9f7vkGDRpUrvJTp06NunXrlvt1AAAASAeXEAIAAACQanpgAdWmsLAwpk6dultlkySJ0tLSiIgoKCgo1yWBhYWFe9Q+AAAA0kGABVSbTCZTrkv7ioqKKrE1AAAApJVLCAEAAABINQEWAAAAAKkmwAIAAAAg1QRYAAAAAKSaQdwBAAAotyRJYt26dbtdfsuy5ZmvsLCwXHegBmomARYAAADltm7duujfv/8ezTto0KDdLjt16tRy3bkaqJlcQggAAABAqumBRd7QRRkAANKjsLAwpk6dutvlkySJ0tLSiIgoKCjY7X3uwsLCPWofULMIsMgbuigDAEB6ZDKZcu83FxUVVVJrgJrOJYQAAAAApJoeWOQNXZQBAABg7yTAIm/oogwAAAB7JwEWAABANSrPzYr29EZFEW5WBOQ3ARYAAEA12tObFZXnRkURblYE5DcBFkA5lecsacSenyl1lhQAAGAzAVYemzVrVtxyyy0xf/78KCkpicmTJ8fAgQN3WH7mzJnRr1+/bZ4vKSmJli1bZh/ffffdccstt8QHH3wQ3bp1i7vuuiuOPvroSlkGyEd7epY0onxnSp0lrRoCSQCqW3luVrSnNyoqex2AfCXAymNr1qyJbt26xciRI+PMM8/c7fkWL14cDRs2zD7eb7/9sv//4Q9/iNGjR8c999wTxxxzTNxxxx1xyimnxOLFi3PKAWwtX8fvEEgCUN3Ke7MiNyoC9kYCrDzWv3//PTro2m+//aJx48bbnXbbbbfFxRdfHBdeeGFERNxzzz3x1FNPxcSJE+M//uM/vlZ7oaYoz1nSiD0/U5pvZ0mN3wEAAFQWAdZeqHv37lFaWhqHHXZYjBs3Lnr37h0REevXr4/58+fH2LFjs2Vr1aoVJ510UsyZM2eH9ZWWlmYPziMiVq5cWXmNhxQo71nSCGdK00wgCQAA6SfA2ou0atUq7rnnnujZs2eUlpbGhAkTom/fvjF37tzo0aNHfPLJJ7Fx48Zo0aJFznwtWrSI119/fYf13njjjXH99ddXdvOBlMvX8TsEkgAAkH4CrL1I586do3PnztnHvXr1iqVLl8btt98eDz744B7XO3bs2Bg9enT28cqVK6Nt27Zfq61A/jF+BwAAUFkEWHu5o48+OmbPnh0REd/4xjeidu3a8eGHH+aU+fDDD3PuUri1goKCKCgoqNR2AlC13J0RAIA0EWDt5RYuXBitWrWKiIh99903jjrqqHj22Wdj4MCBERGxadOmePbZZ+Oyyy6rzmYCUMXcnREAgDQRYOWx1atXx5tvvpl9vGzZsli4cGE0bdo02rVrF2PHjo333nsvHnjggYiIuOOOO+KAAw6Irl27xrp162LChAnx3HPPxdNPP52tY/To0TF8+PDo2bNnHH300XHHHXfEmjVrsnclBACg+pWnl+Se9pCM0EsSgPQQYOWxefPmRb9+/bKPy8ahGj58eBQXF0dJSUmsWLEiO339+vVx5ZVXxnvvvRdFRUVxxBFHxDPPPJNTx5AhQ+Ljjz+OH/7wh/HBBx9E9+7dY9q0adsM7A5AzebujJBue9pLsjw9JCP0kgQgPQRYeaxv376RJMkOpxcXF+c8HjNmTIwZM2aX9V522WUuGQTYy7k7IwAAaSLAAgCAPFOeXpJ72kOy7HUAIA0EWAAAkGfK20tSD0kA8p0AC6qAgVYBAABgzwmwoAoYaBWA6lSeEykRe34yxYkUAKCyCLAAAGq4PT2RElG+kylOpAAAlUWABVXAQKsAsGdchg8ARAiwoEoYaBWA6lSeEykRe34ypTJOpLgMHwCIEGABANR45T2REuFkCsDeSK9X0kyABQBAarkMH6Dq6PVKmgmwAABILZfhAwARAiwAAAAg9Hol3QRYAAAAgF6vpFqt6m4AAAAAAOyMAAsAAACAVHMJIQAAAFSQJEli3bp1u11+y7Llma+wsLBc405BvhNgAQAAQAVZt25d9O/ff4/mHTRo0G6XnTp1arnGq4J8J8ACAACqhJ4pAOypTJIkSXU3gppl5cqV0ahRo/jiiy+iYcOG1d0cAABSYu3atXvcM6U89EyhOpU3qE2SJEpLSyMioqCgYLfDV0FtLsehNZ8eWAAAAFBBMplMuQPUoqKiSmoN1BwCLAAAoEoUFhbG1KlTd7v81+mZAkDNIsACAACqhJ4pAOypWtXdAAAAAADYGQEWAAAAAKkmwAIAAAAg1QRYAAAAAKSaAAsAAACAVBNgAQAAAJBqAiwAAAAAUk2ABQAAAECqCbAAAAAASDUBFgAAAACpJsACAAAAINUEWAAAAACkmgALAAAAgFQTYAEAAACQagIsAAAAAFJNgAUAAABAqgmwAAAAAEg1ARYAAAAAqSbAAgAAACDVBFgAAAAApJoACwAAAIBUE2ABAAAAkGoCLAAAAABSTYAFAAAAQKrVqe4GUPMkSRIREStXrqzmlgAAALA3KDv+LDsepeYRYFHhVq1aFRERbdu2reaWAAAAsDdZtWpVNGrUqLqbQSXIJOJJKtimTZvi/fffjwYNGkQmk6nu5lSalStXRtu2beOdd96Jhg0bVndz+Jqsz5rHOq1ZrM+axfqsWazPmsc6rVn2lvWZJEmsWrUq9t9//6hVy2hJNZEeWFS4WrVqRZs2baq7GVWmYcOGNfqHYG9jfdY81mnNYn3WLNZnzWJ91jzWac2yN6xPPa9qNrEkAAAAAKkmwAIAAAAg1WqPGzduXHU3AvJV7dq1o2/fvlGnjqtxawLrs+axTmsW67NmsT5rFuuz5rFOaxbrk5rAIO4AAAAApJpLCAEAAABINQEWAAAAAKkmwAIAAAAg1QRYsIeKi4ujcePGOy0zbty46N69e/bxiBEjYuDAgZXdtN1q297kyy+/jLPOOisaNmwYmUwm/vGPf1RbW5YvXx6ZTCYWLlxY6a/VoUOHuOOOOyr9dfJJ37594wc/+EF1N4NdqKrvSiqX9VhzzZw5s9p/T6kaW+/Lbs/W23pV/dburG1vvvlmZDKZ+Pvf/75Hdbdp0yZ+8Ytf7HD6162/OjzzzDORyWRi9erV5Zrv//yf/xM9e/aspFZVnz19P6heAiyq3YgRIyKTyWzzd+qpp1ZI/bu7k1VWrkmTJrFu3bqcaS+99FK2XWWGDBkSS5YsqZA25qO0rLfdMWnSpHjhhRfixRdfjJKSkmjUqNE2ZYqLi7e7PIWFhV/79dk+B7fpV7ad//SnP815fsqUKTnfh3uqIgPdqtiGM5lMTJkypcLqqyr5sh5ff/31yGQy8d///d85zx977LFRWFiY89u8bt26KCwsjN/85jdf6zUjqvbEQkX6+OOP47vf/W60a9cuCgoKomXLlnHKKafEn//85wp9nYoMI7a3jWYymXjooYcqpP40/K7cc8890aBBg/jqq6+yz61evTr22Wef6Nu3b07Zsn2dpUuXfu3X3d2Tl2XflV26dNlm2iOPPBKZTCY6dOiQfe6qq66KZ5999mu3b3uSJImTTjopTjnllG2m/fKXv4zGjRvHu+++Wymv/XUdcMABUVJSEoccckh1N6XCfPXVV5HJZOLJJ5+sktc7/vjj46qrrtrm+QkTJsQ3vvGNKmkD+UeARSqceuqpUVJSkvP3+9//vlra0qBBg5g8eXLOc7/5zW+iXbt2Oc/VrVs39ttvv6psWuqkab3tzNKlS6NLly5x2GGHRcuWLXd4wNawYcNtluftt9+u4tZCuhQWFsZNN90Un3/+eYXWu379+gqtL8I2vDP5sB4POeSQaNmyZcycOTP73KpVq+Kvf/1rNG/ePCfYmjNnTpSWlsY3v/nNCnv9fHPWWWfFggULYtKkSbFkyZJ44oknom/fvvHpp59Wd9N26v77799mO63u0Kki9evXL1avXh3z5s3LPvfCCy9Ey5YtY+7cuTlB7IwZM6Jdu3Zx0EEHVWkb69WrFx999FHMmTMn5/nt7e/Wr18/mjVrVintyGQycf/998fcuXPj17/+dfb5ZcuWxZgxY+Kuu+6KNm3alLveyvh92Vrt2rWjZcuWUadOne1OT5IkJ8Rk71EVn7+9mQCLVCg7c7jlX5MmTbLTb7vttjj88MOjXr160bZt27j00ktzunu+/fbbMWDAgGjSpEnUq1cvunbtGv/1X/8Vy5cvj379+kVERJMmTSKTycSIESN22pbhw4fHxIkTs4/Xrl0bDz30UAwfPjyn3PbOdP30pz+NFi1aRIMGDWLUqFHb9OTa2rRp0+L444+Pxo0bR7NmzeJf//Vfc87ClZ0Vfvzxx6Nfv35RVFQU3bp122aHo7i4ONq1axdFRUUxaNCgbXZeX3755ejXr180aNAgGjZsGEcddVTOjtWeSst6e+yxx6Jr165RUFAQHTp0iFtvvTU7rW/fvnHrrbfGrFmzIpPJbHP2c0uZTGab5WnRokVOXZdffnmMGTMmmjZtGi1btoxx48bl1PH666/H8ccfH4WFhXHooYdmuyfvqNfGxo0bY9SoUXHAAQdE3bp1o3PnznHnnXfmlCk7o/yzn/0sWrVqFc2aNYvvfe97sWHDhmyZjz76KAYMGBB169aNAw44IH7729/ucDnzwYoVK+KMM86I+vXrR8OGDWPw4MHx4YcfZqeXXTbw4IMPRocOHaJRo0ZxzjnnxKpVq7Jl1qxZExdccEHUr18/WrVqlfO5KPP555/HBRdcEE2aNImioqLo379/vPHGG9npZdv5n/5WtUYRAAAgAElEQVT0p+jSpUvUr18/G9zuLU466aRo2bJl3HjjjTstt7PtMGLzJa3jx4+PCy64IBo2bBiXXHJJHHDAARERceSRR253+9zZZ357drUN7+o7d/369XHZZZdFq1atorCwMNq3b59d7rIeCYMGDdqmh0I+yJf12K9fv5wAa/bs2XHwwQfHgAEDcp6fOXNmtG/fPvvamzZtihtvvDH7XdqtW7d49NFHs+U///zzGDZsWDRv3jzq1q0bnTp1ivvvvz8iYpftT6N//OMf8cILL8RNN90U/fr1i/bt28fRRx8dY8eOjdNPPz1bblffpdvrsfSDH/wg+x6MGDEinn/++bjzzjuzvaWWL1+eLTt//vzo2bNnFBUVRa9evWLx4sW7bHvjxo232U7Lekp++umnMXTo0GjdunUUFRXF4Ycfvs2JsUcffTQOP/zwqFu3bjRr1ixOOumkWLNmTYwbNy4mTZoUf/zjH7Nt3fIzU1U6d+4crVq12ubzesYZZ8QBBxyQE8TOnDkzu78TsXm9XnTRRdG8efNo2LBhfPOb34yXX345O31H+3MzZ86MCy+8ML744ovssm+9f7KlOnXqxLnnnpuzv/vuu+/GzJkz49xzz80pu/Vlehs3bozRo0dnv0fHjBkTSZLs9D158MEHo2fPntGgQYNo2bJlnHvuufHRRx9FRETbtm3jO9/5TnznO9+J//t//28cddRR0bFjxygoKIijjz46Ijb3DDv00EOjTp06Ubt27SgoKMjZz27Tpk3ccMMNcfLJJ0ft2rWjqKgojjzyyIiIeP/99yNic++i4cOHx6GHHhrvvvtuJEkS1157bbYHY+vWreOKK67IafeaNWtixIgR0aBBg2jfvn1Oj8+tLyEs29+bNm1a9OjRI/bdd9/sun788cfjyCOPjMLCwjjooINi/PjxOw235s6dGyeddFI0a9YsGjVqFP369cvpIVrWU2rixIlx+umnR1FRURx88MHx1FNP5dTz5JNPRqdOnaJu3bpx4okn7vKETtnv2oABAyKTyUTHjh1zpk+aNCnat28fjRo1imHDhuXs02/atCl+8pOfZL+Du3fvHo8//vhOX293nXfeefHtb38757nLLrssTjrppOzj448/Pn7wgx/E5Zdfnv2OmThxYqxevTqGDx8eDRo0iE6dOsXTTz+9Tf0vvPBCHH744VFYWBi9evWK1157LWf6rFmzonfv3lG3bt1o165dXHHFFfHll19mp5d9/s4777xo2LBhXHrppRWy3GyfAIu8UKtWrfj5z38er776akyaNCmee+65GDNmTHb69773vSgtLY1Zs2bFK6+8EjfddFPUr18/2rZtG4899lhERCxevDhKSkq2CQe2dv7558cLL7wQK1asiIjNO/MdOnSIHj167HS+hx9+OMaNGxc33HBDzJs3L1q1ahW//OUvdzrPmjVrYvTo0TFv3rx49tlno1atWjFo0KDYtGlTTrlrrrkmrrrqqli4cGEcfPDBMXTo0OwP39y5c2PUqFFx2WWXxcKFC6Nfv37x4x//OGf+YcOGRZs2beKll16K+fPnx3/8x3/EPvvss9O2VYSqWG/z58+PwYMHxznnnBOvvPJKjBs3Lq699tooLi6OiM07DRdffHEcd9xxUVJS8rV/TCdNmhT16tWLuXPnxs033xw/+tGPYvr06RGxeadu4MCBUVRUFHPnzo177703rrnmmp3Wt2nTpmjTpk088sgj8dprr8UPf/jD+M///M94+OGHc8rNmDEjli5dGjNmzIhJkyZFcXFxdhkjNh9kvPPOOzFjxox49NFH45e//GV25zDfbNq0Kc4444z47LPP4vnnn4/p06fHW2+9FUOGDMkpt3Tp0pgyZUo8+eST8eSTT8bzzz+fc4nU1VdfHc8//3z88Y9/jKeffjpmzpwZf/3rX3PqGDFiRMybNy+eeOKJmDNnTiRJEqeddlrOAfaXX34ZP/vZz+LBBx+MWbNmxYoVK7bb5b2mql27dtxwww1x11137fBSjl1th2V+9rOfRbdu3WLBggVx7bXXxl/+8peI2Lzjv/X2uavP/J7Y1Xfuz3/+83jiiSfi4YcfjsWLF8dvf/vb7A79Sy+9FBH/7D1S9jhf5Mt67NevX8yePTv7Gzdjxozo27dv9OnTJ2bMmJFT75YH/jfeeGM88MADcc8998Srr74aV1xxRZx33nnx/PPPR0TEtddeG6+99lpMnTo1Fi1aFL/61a+yl6jsrP1pVb9+/ahfv35MmTIlSktLt1tmd79Ld+bOO++M4447Li6++OJsb6m2bdtmp19zzTVx6623xrx586JOnToxcuTIr7Vc69ati6OOOiqeeuqp+Pvf/x6XXHJJnH/++dl1VFJSEkOHDo2RI0fGokWLYubMmXHmmWdGkiRx1VVXxeDBg3N6h/fq1etrtWdP9evXb5vP69af47Vr18bcuXNzPsdnn312fPTRRzF16tSYP39+9OjRI0488cT47LPPImLH+3O9evWKO+64I6cX6q5+p0aOHBkPP/xw9kC8uLg4Tj311JzQf3tuvfXWKC4ujokTJ8bs2bPjs88+2+bKha1t2LAhxo8fHy+//HJMmTIlli9fnnNismz4iUsvvTR7kqFTp04xcuTI+Mtf/hLnnHNOHHHEEVGrVq244IILIpPJxCeffJKzn33zzTfH7NmzY+TIkTF9+vR45JFHImLziY1169bFWWedFa+++mrMmjUr2rRpE3/4wx/irrvuivvuuy/eeOONePzxx+Owww7Lafctt9wSxx57bCxYsCAuueSS+N//+3/Hm2++udNlHTt2bNxyyy3x+uuvR9euXWPmzJkxcuTIuOKKK+K1116LX/3qVzFhwoS46aabdljHqlWr4sILL4wXX3wx5syZEx06dIjTTjst1qxZk1Nu3LhxMWzYsPjb3/4WJ598cpx77rnZoTeWL18eZ511VgwaNCgWLlwYw4cPj//8z//cadvLftcefPDBKCkpyQlbFy9eHE899VQ89dRT8cQTT8QzzzwTt9xyS3b6+PHj43e/+13ce++98dprr8Xll18eQ4cOrfBLmndm4sSJ0apVq3jppZfiO9/5TlxyySUxePDgOOGEE+Kvf/1rfPOb34zzzjtvmw4GV199ddxxxx3xl7/8JRo3bhynn3569jdoyZIlcdppp8WQIUPilVdeid///vcxY8aM+P73v59Tx8033xxHHXVULFiwYJfvM19TAtVs+PDhSe3atZN69erl/P3kJz/Z4TyPPPJI0qxZs+zjww8/PBk3btx2y86YMSOJiOTzzz/faTu2LDdw4MDk+uuvT5IkSfr165fceeedyeTJk5MtN5n7778/adSoUfbxcccdl1x66aU5dR5zzDFJt27dcpb1jDPO2GEbPv744yQikldeeSVJkiRZtmxZEhHJhAkTsmVeffXVJCKSRYsWJUmSJEOHDk1OO+20nHqGDBmS07YGDRokxcXFO13+8krLejv33HOTk08+Oee5q6++Ojn00EOzj7///e8nffr02Wk9999/fxIR2yzPqaeemi3Tp0+f5Pjjj8+Z73/9r/+V/Pu//3uSJEkyderUpE6dOklJSUl2+vTp05OISCZPnpwkyT/X6YIFC3bYlu9973vJWWedlX08fPjwpH379slXX32Vfe7ss89OhgwZkiRJkixevDiJiOQvf/lLdvqiRYuSiEhuv/32nS53ddrR9vD0008ntWvXTlasWJF9ruxzX7aM1113XVJUVJSsXLkyW+bqq69OjjnmmCRJkmTVqlXJvvvumzz88MPZ6Z9++mlSt27d5Pvf/36SJEmyZMmSJCKSP//5z9kyn3zySVK3bt3sfGWfizfffDNb5u67705atGhREW9B6m25jo499thk5MiRSZIk23wf7s522L59+2TgwIE5ZXa0PezqM789u7MNb23r79x/+7d/S775zW8mmzZt2m75LbflfJJP6/GNN95IIiJ58cUXkyTZ/B378MMPJ++//35SUFCQrF27Nvnyyy+TgoKCZNKkSUmSJMm6deuSoqKi7DxlRo0alQwdOjRJkiQZMGBAcuGFF273NXfnezmNHn300aRJkyZJYWFh0qtXr2Ts2LHJyy+/nJ2+O9+l2/se3vo3s0+fPtnvzTJlv9HPPPNM9rmnnnoqiYhk7dq1O2xzRCSFhYXbbKdvv/32Duf51re+lVx55ZVJkiTJ/Pnzk4hIli9fvt2yu9rPqir33XdfUq9evWTDhg3JypUrkzp16iQfffRR8rvf/S454YQTkiRJkmeffTaJiOyyv/DCC0nDhg2TdevW5dR10EEHJb/+9a+TJNn5/tzW+6U7smW57t27J5MmTUo2bdqUHHTQQckf//jH5Pbbb0/at2+fLX/dddfl7Mu2atUqufnmm7OPN2zYkLRp0ybnfd/eZ2ZLL730UhIRyapVq5Ik+efnqVGjRkmtWrWSyZMnZz9PZ511VtK/f/+c/ewrrrgiOeKII7L72a1bt05OP/30JCKSmTNnJkmS+13St2/fpE+fPjn7DDfddFPSpUuXZMOGDdttY+vWrZMRI0ZkH2/atClp1qxZct999+XUX/b7Uba/9+STT+bU06dPn5z3q2wdtG3bdofvz9a++uqrpKioKJk6dWqSJJvf84jI2Yf+4osvkohIpk+fniTJ5u/uI444IqeeK6+8Mud931pZvf/v//2/nOevueaapH79+snq1auzz11xxRVJ7969kyRJki+//DKpW7duzn5okmzeHs8///wdLlfv3r2TffbZZ5vvg4KCgpzjhWHDhuXsFyfJ5n3lE088Maeuvn375ixLYWFhzvf+O++8k0RE8tJLLyVJ8s919uijj2bLfPzxx0lhYWHy2GOPZZdh6+O7GTNmJLVr105KS0uTJNn8Wfn2t7+9w+WkYumBRSqUdY3d8u873/lOdvozzzwTJ554YrRu3ToaNGgQ559/fnz66afZs0aXX355/PjHP47evXvHddddF3/729++VntGjhwZxcXF8dZbb8WcOXNi2LBhu5xn0aJFccwxx+Q8d9xxx+10njfeeCOGDh0aBx54YDRs2DB7pr+s91eZI444Ivt/q1atIiKyvWt253VHjx4dF110UZx00knx05/+tEIGC41Ix3pbtGhR9O7dO+e53r17xxtvvBEbN24sV10NGjTYZnkmTJiQU2bLdRGxeX2UrYvFixdH27Zto2XLltnpZd3fd+buu++Oo446Kpo3bx7169ePe++9d5vPQNeuXaN27drbfd1FixZFnTp14qijjspOP+SQQ/L2TpSLFi2Ktm3b5pzlP/TQQ6Nx48axaNGi7HMdOnSIBg0aZB9v+Z4sXbo01q9fn7NtNG3aNDp37pzzOnXq1Mkp06xZs+jcuXPO6xQVFeWMT7Ll6+xNbrrpppg0aVLOe1Nmd7fD8tzFaGef+R3Z1Ta8q+/cESNGxMKFC6Nz585x+eWXb/dSg3yX9vXYsWPHaNOmTcycOTNWrlwZCxYsiD59+kSrVq2iXbt2MWfOnOz4V2U9V95888348ssv4+STT872TKpfv3488MAD2d+77373u/HQQw9F9+7dY8yYMfHiiy/u9jKk1VlnnRXvv/9+PPHEE3HqqafGzJkzo0ePHtkebrv7Xfp17Gz/ZEduv/32bbbT/fffPyI292QeP358HH744dG0adOoX79+/OlPf8puo926dYsTTzwxDj/88Dj77LPjvvvuq/Bx3SpC3759Y82aNfHSSy/FCy+8EAcffHA0b948+vTpkx0Ha+bMmXHggQdmx5x6+eWXY/Xq1dGsWbOcz/GyZcuyn+OK3p8bOXJk3H///fH888/HmjVr4rTTTttp+S+++CJKSkpyfjfr1Kmzy++E+fPnx4ABA6Jdu3bRoEGD6NOnT0Rsu787cuTI6NKlSwwcODD7eXr11Vejd+/eOfu7vXv3jiVLlsSxxx6bnbdXr14xYsSIOOWUU2LAgAHZ7WDw4MGxYcOGmDZtWs4+w5AhQ2LlypVx4IEHxiWXXBJTpkzZZr9xy893JpOJFi1a7PLzvfV78be//S1++MMf5qzT7373u/Huu+/usPdkSUlJXHTRRdGpU6do1KhRNGrUKNauXbvT44OGDRtGUVFRuY4PyuPAAw+MevXqZR9v+V2+ZMmSWLt2bfTr1y9nOX/3u9/t8jN6wQUXbPN9cN111+1RG7d8P+rUqRNNmzaNww8/PPtcWe/Crdfhlu/LN77xjejYsWP2O/Lll1+OCRMm5CzXt771rdi4cWPOJZk18S6NaSXAIhXq1asXHTt2zPlr2rRpRGzuAvuv//qvccQRR8Rjjz0W8+fPj7vvvjsi/jlI3kUXXRRvvfVWnH/++fHKK69Ez54946677trj9vTv3z/Wrl0bo0aNigEDBlTa4JUDBgyIzz77LO67776YO3duzJ07NyK2Hfxvy8v9ygYg3/oyw50ZN25cvPrqq/Gtb30rnnvuuTj00EN32d17d6RtvX1dtWrV2mZ5WrdunVNm60svM5lMudbF1h566KG46qqrYtSoUfH000/HwoUL48ILL9zpZ6AiXrcmqKr3ZHuvk+xivI+a6IQTTohTTjklxo4du8d1bLnzuyt7sn53tQ3v6ju3R48esWzZshg/fnysXbs2Bg8evM24G/kuH9Zj3759Y8aMGfHCCy9Ep06dsjdMKbv8asaMGdGxY8dsMFM2DstTTz2VcxD02muvZcfB6t+/f7z99ttxxRVXxPvvvx8nnnhijbgUuLCwME4++eS49tpr48UXX4wRI0aU6+CvVq1a23yf7WqsuS3tyf5Jy5Ytt9lOywbCvuWWW+LOO++Mf//3f4//397dB0dV3X8c/wSSDUk2T5AEwkOI5Dm0IBAjSajyEBqVYlBL7RRjqGALNkKoBOOQUWtgGoOkjHEqBbUCxg6ttI4PQAqROLIIhsdWxCCKhY6l2AhIeDI05/dHJnfYbEJCgOTa3/s1szPJ7t17z73n3HPPfvfu92zZskV79+5VVlaWdY727NlTmzZt0oYNG5ScnKzy8nIlJCTo8OHDHS5zV2gOxDa31+aATf/+/TVo0CBt27ZNW7ZscZuEoL6+XpGRkR4f5mtra1VQUCDp2o/npk2bpu3bt+vJJ59UTk5OmwnJr8aZM2eUlZWloKAgVVRUqKamxipzy7GOw+GwytDcntq63rZ8PiAgQL///e/1/vvvKz093coHlZKSor1791r9fbPBgwfrk08+UXl5uXx9fTVr1iyNHTvWLTdVZ/qvlv1jfX29Fi1a5Fanf//733Xw4EE5HI5W19E8Ln722We1bds27d27V8HBwd06Nrzctpr74I0bN3r0wWvXrr3sekNCQjz6g/DwcLdlOtpPtVbGq/0MVV9fr1/84hdu+7Vv3z598sknGjx4sLXclVwXcXUIYMH2du3apcbGRi1dulSjR49WfHy8lZDxUs1JIP/85z/rkUce0cqVKyXJujhcyd043t7euv/++63frXdEUlKSx8Wx5VTgl6qrq1Ntba2Kioo0YcIEJSUldepbxI5uNz4+XvPmzdNf//pX3X333Vby2uulq+otKSnJ4/f1LpdL8fHxbt/6d4WEhAQdPXrULUFue3lyXC6X0tPT9dBDD2nEiBGKjY294m9UExMTdfHiRe3atct6rra21sqD8G2TlJSko0eP6ujRo9ZzH330kU6ePKnk5OQOrSMmJkY+Pj5u58aJEyd08OBBt+1cvHjRbZnm87Kj2/n/pqSkRG+++abHRBKdPQ870z93Vkf73KCgIN17771auXKl1q5dq3Xr1ln5Z3x8fLqkrNeb3etx3Lhx2rZtmzZt2uSWUP2WW25RdXW1R+Lr5ORk+fr66siRIx4fhC69+yg8PFy5ubl65ZVXtGzZMq1YseK6lL87JScnW3lyOtKXhoeHe0xKcWmyaKnp+HTVsXG5XMrOztZ9992n4cOHa8iQIW79ttT0ITQjI0O/+tWvtGfPHjkcDisg0pVlbU/zhATV1dUe7XjDhg364IMP3NrxyJEjdezYMXl7e3u04+Z8bVLb47nO7Hvv3r1155136t133+3QeDc4OFiRkZFu182W44+WPv74Y9XV1amkpETf+973lJiYeEV3McfGxsrlcrmNd10ulxITEz3Gv1LTZAyPPfaYlQPL399fixYt0g9+8ANt3brVbVk/Pz9lZ2ervLxcVVVV2rp1q0cC76s1YsQIHTx40KNOY2Nj25wV2+VyKT8/X7fffrt1F+uVjumSkpKs3HHNLve5RGoKEPfs2fOK29F3vvMdORwOHT161GMfOzOLZEsd6aeuxqXHpa6uTocOHVJSUpKkpvNy//79rdZfWwFIXF/XPswOdMKFCxd07Ngxt+e8vb2t2zgbGhpUXl6uyZMny+Vyafny5W7LNnfy8fHxOnHihLZs2WJ1PIMHD5aXl5feeust3XHHHfLz85PT6Wy3TMXFxSooKOjw3Vdz587V9OnTlZKSooyMDFVUVGj//v0aMmRIq8uHhoaqT58+WrFihSIjI3XkyBEVFhZ2aFuXmjNnjjIyMvTMM88oOztblZWV2rhxo/X6uXPnVFBQoB/+8Ie64YYb9M9//lM1NTW65557rnhbLdmh3h555BHddNNNKi4u1r333qv3339fzz33XLsJ9FtjjPHYH0mKiIhQjx7tx/snTpyomJgY5ebmqrS0VKdPn1ZRUZEktTlIiYuL0+rVq1VZWakbbrhBa9asUU1NjTUrVkckJCTotttu089//nM9//zz8vb2Vn5+vvz8/Dq8ju5y6tQpj0FIYmKivvvd72ratGlatmyZLl68qIceeki33nprh2/RdjqdmjFjhnUOR0REaOHChW71GBcXp+zsbD344IP63e9+p8DAQBUWFmrAgAHKzs6+pvv5v6K5Xp599lm35zt7HkZERMjPz08bN27UwIED1atXLwUHB3e6fJc7hzvS55aVlSkyMlIjRoxQjx499Kc//Un9+vWzfo4bHR2tqqoqZWRkyNfX123W1W8Tu9fjuHHjdObMGb300kvWlxpS0x1YM2fOlCS3WZ4CAwM1f/58zZs3T42NjRozZoxOnToll8uloKAg5ebm6vHHH9eoUaM0dOhQXbhwQW+99ZZ1vbnW5e8KdXV1mjp1qh544AENGzZMgYGB2rlzp0pLS63+KzMzs92+dPz48VqyZIlWr16ttLQ0vfLKK/rwww+tGdykpna/Y8cOff7553I6ndad1p118uRJj/M0MDBQAQEBiouL02uvvaZt27YpNDRUZWVl+ve//20F3Hbs2KGqqip9//vfV0REhHbs2KEvv/zSqsvo6GhVVlaqtrbWmsGtKyatac24ceOsWTeb78CSmtpxXl6evvnmG7cAVmZmptLS0jRlyhSVlpZaX/y9/fbbuuuuuzR06NDLjueio6NVX1+vqqoqDR8+XP7+/vL392+3nC+//LJ++9vfXtF4t6SkRHFxcUpMTFRZWdllgytRUVFyOBwqLy/XrFmz9OGHH6q4uLhD25Ka7ta/5557NHXqVL3wwgs6d+6cXn31Vd1+++2qqqqyxtl1dXXWLJz9+/fXe++9J6npp2/5+fn673//qzvuuEOVlZVKS0vTSy+9JC8vL6WmpsrPz08VFRXy9/e3ftJ5rTzxxBPKzs7WwIEDrbrat2+fDhw4oKeeeqrV9zSPDUeMGKETJ05owYIF1kydHTV79mwtW7ZMhYWFVjL8NWvWXPY9Xl5eioqK0ubNm3XzzTd3+DoXHBysefPmae7cubp48aLS09N16tQpbd26VaGhocrJybmisrc0fvx4/eY3v1FFRYVSU1O1atUqffzxx7rpppuuar3NnnzySYWGhiosLEyPPfaYIiMjNXnyZElNSflHjx6tOXPmaMaMGfL399f+/fv1zjvveFxD0UW6Mf8WYIxpSo4nyeORkJBgLVNWVmYiIyONn5+fycrKMqtXr3ZL8J2Xl2diYmKMr6+vCQ8PNzk5OeY///mP9f6nnnrK9OvXz3h5eZnc3NxWy9Fe0vD2krgbY8zixYtNWFiYcTqdJjc31yxYsOCySdw3bdpkkpKSjK+vrxk2bJiprq5uN+H3iRMnjCSzZcsW67kXX3zRDBw40Pj5+ZnJkyebZ555xirbhQsXzI9//GMzaNAg43A4TP/+/U1eXt5lk6x2hF3qzZimRLbJycnGx8fHREVFmSVLlri9fiVJ3Ft7NCdlby0paXZ2tlvZDhw4YDIyMozD4TCJiYnmzTffNJLMxo0bjTGedXr+/Hkzffp0ExwcbEJCQszs2bNNYWFhu8n/W+7Tv/71LzNp0iTj6+troqKizOrVq83gwYNtn8S9teM9Y8YM849//MPceeedJiAgwAQGBpqpU6eaY8eOWe9tmVTWGOORePb06dPmvvvuM/7+/qZv376mtLTUow6/+uork5OTY4KDg612evDgQev11s7zln3B/7LW2t7hw4eNw+HwOAbtnYdttceVK1eaQYMGmR49elhtuiNtvqWOnMPt9bkrVqwwN954owkICDBBQUFmwoQJZvfu3dY23njjDRMbG2u8vb3d2prdfZvq8dLtXFp3zaKjo40k88UXX7g939jYaJYtW2YSEhKMj4+PCQ8PN1lZWebdd981xhhTXFxskpKSjJ+fn+ndu7fJzs42n3322WXLb2fnz583hYWFZuTIkSY4ONj4+/ubhIQEU1RUZM6ePWst115faowxjz/+uOnbt68JDg428+bNM3l5eW7HoLa21owePdr4+fkZSebw4cOtjpn27Nljvd6Wts7RX//618aYpsk2srOzjdPpNBEREaaoqMjcf//9Vjv66KOPTFZWlgkPDze+vr4mPj7elJeXW+s/fvy4mThxonE6nR5jpa7WfL1PTEx0e/7zzz/3GC81+/rrr83DDz9s+vfvb3x8fMygQYPMtGnTzJEjRzo0nps1a5bp03TnYkMAAAfGSURBVKePkWSeeOKJVsvVXrL39pK4NzQ0mLlz55qgoCATEhJifvnLX7rVkTGe46VXX33VREdHG19fX5OWlmbeeOMNt7FQc3t69NFHrW1d2p7++Mc/mqSkJNOzZ0/To0cP43A43MbZAwYMMIsXLzZTpkwxkZGR1vGR5DaxwdNPP22CgoLM9u3bzWuvvWZSU1NNUFCQcTqdJi0tzbzzzjvWsgMGDHBrW8YYM3ToUFNcXGyMaTuJe2sJ0tevX2/S0tJMr169TFBQkElNTTUvvvhim3Wwc+dOM2rUKKuNr1u3zq08bSVbDwgIMGvWrLH+f/31161x9q233mpeeOGFyyZxN6ZpjNN8nYuJiTHGNCVxHzVqlNtyS5YssV43pqkPLisrc+uDb7vtNvPee++1ua2MjAxrgoZLrVy50i2Je3MZIiIiTEhIiJk/f76ZPXu2RxL3lutqWYctj9ulifeTk5ONw+EwN998s1WnzbZv324yMzNNQECAcTqdZvjw4aakpKTN7eD68jLm/2EiDwDoIi6XS2PGjNGhQ4fcEoEDAAAAADqOABYAXEN/+ctf5HQ6FRcXp0OHDmnu3LkKDQ31yLsAAAAAAOg4cmABwDV0+vRpPfroozpy5IjCwsKUmZmppUuXdnexAAAAAOBbjTuwAAAAAAAAYGvtT6sFAAAAAAAAdCMCWAAAAAAAALA1AlgAAAAAAACwNQJYAAAAAAAAsDUCWAAAADYzffp0TZkypbuLAQAAYBvMQggAAGAzp06dkjFGISEh3V0UAAAAWyCABQAAcI188803cjgc3V0MAACA/zn8hBAAAKCTxo4dq7y8POXn5yssLExZWVk6efKkZs6cqfDwcAUFBWn8+PHat2+f2/sWLVqkiIgIBQYGaubMmSosLNSNN95ovd7yJ4QXLlzQnDlzFBERoV69emnMmDGqqamxXq+urpaXl5eqqqqUkpIif39/paenq7a29vofBAAAgC5AAAsAAOAqrFq1Sg6HQy6XS8uXL9fUqVN1/PhxbdiwQbt27dLIkSM1YcIEffXVV5KkiooKLV68WE8//bR27dqlqKgoPf/885fdxoIFC7Ru3TqtWrVKu3fvVmxsrLKysqx1Nlu4cKGWLl2qnTt3ytvbWw888MB1228AAICuxE8IAQAAOmns2LH6+uuvtXv3bknS1q1bNWnSJB0/fly+vr7WcrGxsVqwYIF+9rOfafTo0UpJSdFzzz1nvT5mzBjV19dr7969kpruwDp58qRef/11nTlzRqGhoXr55Zf1k5/8RJLU0NCg6Oho5efnq6CgQNXV1Ro3bpw2b96sCRMmSJLWr1+vSZMm6dy5c+rVq1dXHRIAAIDrgjuwAAAArsKoUaOsv/ft26f6+nr16dNHTqfTehw+fFiffvqpJKm2tlapqalu62j5/6U+/fRTNTQ0KCMjw3rOx8dHqampOnDggNuyw4YNs/6OjIyUJB0/frzzOwcAAGAT3t1dAAAAgG+zgIAA6+/6+npFRkaqurraY7mumFHQx8fH+tvLy0uS1NjYeN23CwAAcL1xBxYAAMA1MnLkSB07dkze3t6KjY11e4SFhUmSEhIS3BKwS/L4/1IxMTFWjq1mDQ0NqqmpUXJy8vXZEQAAAJvhDiwAAIBrJDMzU2lpaZoyZYpKS0sVHx+vL774Qm+//bbuuusupaSk6OGHH9aDDz6olJQUpaena+3atfrb3/6mIUOGtLrOgIAAzZ49WwUFBerdu7eioqJUWlqqs2fPasaMGV28hwAAAN2DABYAAMA14uXlpfXr12vhwoX66U9/qi+//FL9+vXTLbfcor59+0qSpk2bps8++0zz58/X+fPn9aMf/UjTp0/XBx980OZ6S0pK1NjYqJycHJ0+fVopKSmqrKxUaGhoV+0aAABAt2IWQgAAgG42ceJE9evXT2vWrOnuogAAANgSd2ABAAB0obNnz2r58uXKyspSz5499Yc//EGbN2/Wpk2burtoAAAAtsUdWAAAAF3o3Llzmjx5svbs2aPz588rISFBRUVFuvvuu7u7aAAAALZFAAsAAAAAAAC21qO7CwAAAAAAAABcDgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2BoBLAAAAAAAANgaASwAAAAAAADYGgEsAAAAAAAA2Nr/Aa6nvZKsPzJ/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image 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>avg_height</th>\n",
       "      <th>avg_height_backend</th>\n",
       "      <th>ct_height</th>\n",
       "      <th>ct_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 Most deprived</th>\n",
       "      <td>1.649954</td>\n",
       "      <td>1.648681</td>\n",
       "      <td>1190</td>\n",
       "      <td>1160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.652561</td>\n",
       "      <td>1.651856</td>\n",
       "      <td>1035</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.649338</td>\n",
       "      <td>1.650186</td>\n",
       "      <td>1175</td>\n",
       "      <td>1180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.652588</td>\n",
       "      <td>1.649782</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>1.650963</td>\n",
       "      <td>1.650514</td>\n",
       "      <td>1000</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>1.649098</td>\n",
       "      <td>1.654428</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  avg_height  avg_height_backend  ct_height  ct_height_backend\n",
       "imd                                                                           \n",
       "1 Most deprived     1.649954            1.648681       1190               1160\n",
       "2                   1.652561            1.651856       1035               1045\n",
       "3                   1.649338            1.650186       1175               1180\n",
       "4                   1.652588            1.649782       1000               1000\n",
       "5 Least deprived    1.650963            1.650514       1000               1020\n",
       "Unknown             1.649098            1.654428         60                 50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image 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>avg_height</th>\n",
       "      <th>avg_height_backend</th>\n",
       "      <th>ct_height</th>\n",
       "      <th>ct_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dementia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>1.650884</td>\n",
       "      <td>1.650145</td>\n",
       "      <td>5400</td>\n",
       "      <td>5395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1.658943</td>\n",
       "      <td>1.656877</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_height  avg_height_backend  ct_height  ct_height_backend\n",
       "dementia                                                              \n",
       "False       1.650884            1.650145       5400               5395\n",
       "True        1.658943            1.656877         60                 55"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image 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>avg_height</th>\n",
       "      <th>avg_height_backend</th>\n",
       "      <th>ct_height</th>\n",
       "      <th>ct_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>1.650980</td>\n",
       "      <td>1.650265</td>\n",
       "      <td>5405</td>\n",
       "      <td>5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1.650244</td>\n",
       "      <td>1.644609</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_height  avg_height_backend  ct_height  ct_height_backend\n",
       "diabetes                                                              \n",
       "False       1.650980            1.650265       5405               5400\n",
       "True        1.650244            1.644609         55                 50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image 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>avg_height</th>\n",
       "      <th>avg_height_backend</th>\n",
       "      <th>ct_height</th>\n",
       "      <th>ct_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>1.650841</td>\n",
       "      <td>1.650123</td>\n",
       "      <td>5410</td>\n",
       "      <td>5405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1.665277</td>\n",
       "      <td>1.661144</td>\n",
       "      <td>50</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              avg_height  avg_height_backend  ct_height  ct_height_backend\n",
       "hypertension                                                              \n",
       "False           1.650841            1.650123       5410               5405\n",
       "True            1.665277            1.661144         50                 45"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image 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>avg_height</th>\n",
       "      <th>avg_height_backend</th>\n",
       "      <th>ct_height</th>\n",
       "      <th>ct_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning_disability</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>1.651002</td>\n",
       "      <td>1.650194</td>\n",
       "      <td>5400</td>\n",
       "      <td>5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1.648192</td>\n",
       "      <td>1.652230</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     avg_height  avg_height_backend  ct_height  \\\n",
       "learning_disability                                              \n",
       "False                  1.651002            1.650194       5400   \n",
       "True                   1.648192            1.652230         55   \n",
       "\n",
       "                     ct_height_backend  \n",
       "learning_disability                     \n",
       "False                             5400  \n",
       "True                                50  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for suffix in [''] + covariates:    \n",
    "    df_distribution = pd.read_csv(f'../output/{output_path}/tables/avg_value{suffix}.csv', index_col=0)\n",
    "    display(df_distribution)\n",
    "    \n",
    "    img_exist = os.path.exists(f'../output/{output_path}/figures/distribution{suffix}.png')\n",
    "    \n",
    "    if img_exist:\n",
    "        img_distribution = Image(f'../output/{output_path}/figures/distribution{suffix}.png')\n",
    "        display(img_distribution)\n",
    "    else: \n",
    "        print('Plot redacted due to low counts.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Out-of-Range Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>oor_height</th>\n",
       "      <th>oor_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct25</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct75</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      oor_height oor_height_backend\n",
       "index                              \n",
       "count          -                  -\n",
       "mean           -                  -\n",
       "pct25          -                  -\n",
       "pct75          -                  -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAAMgCAYAAAAz4JsCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdeZRU5Z34/093I80mjQo0gggIxogGUBIRkeDSpEXEOBMVTUaWI264IWoUVMCVo0YkMSojRjTmTIJysuiguADG0ZBocHDiCCoqyKiAaOgGVJDu+/vDH/W16GbRAP2kfb3OqT/qqbs8t6oMqfe593ZBlmVZAAAAAECiCut6AgAAAACwNQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWACRmwoQJUVBQsEv2ddRRR8VRRx2Ve/7MM89EQUFBzJgxY5fsf9iwYdGxY8ddsq+vau3atTFixIho06ZNFBQUxKhRo+p6SjvEiy++GEcccUQ0bdo0CgoKYsGCBXU9pX9qm/+3BADsWAIWAOxE999/fxQUFOQejRo1irZt20Z5eXn87Gc/izVr1uyQ/bz33nsxYcKEJCNEynPbHjfddFPcf//9cd5558WDDz4YZ5xxxi7b96uvvhoTJkyIJUuW7NDtfvbZZ3HKKafERx99FLfffns8+OCD0aFDhx26DwCAHalBXU8AAL4OrrvuuujUqVN89tlnsXz58njmmWdi1KhRMWnSpHjkkUeiW7duuWWvvvrquPLKK7/U9t9777249tpro2PHjtGjR4/tXu/JJ5/8Uvv5KrY2t6lTp0Z1dfVOn8M/Ys6cOXH44YfH+PHjd/m+X3311bj22mvjqKOO2qFnqr355puxdOnSmDp1aowYMWKHbRcAYGcRsABgFxgwYEB8+9vfzj0fM2ZMzJkzJ0444YQ48cQTY+HChdG4ceOIiGjQoEE0aLBz/4n++OOPo0mTJtGwYcOdup9t2W233ep0/9tj5cqV0bVr17qexg61cuXKiIho0aLFV1p/3bp10bRp0x05JQCArXIJIQDUkWOOOSauueaaWLp0afzqV7/Kjdd2D6ynnnoqjjzyyGjRokU0a9YsDjjggBg7dmxEfH7fqu985zsRETF8+PDc5Yr3339/RHx+b56DDz445s+fH9/97nejSZMmuXW3dN+eqqqqGDt2bLRp0yaaNm0aJ554YixbtixvmY4dO8awYcNqrPvFbW5rbrXdA2vdunVx6aWXRvv27aO4uDgOOOCA+MlPfhJZluUtV1BQEBdccEH8/ve/j4MPPjiKi4vjoIMOilmzZtX+hm9m5cqVceaZZ0ZpaWk0atQounfvHg888EDu9U33A3v77bdj5syZublv7XK+jRs3xvXXXx+dO3eO4uLi6NixY4wdOzbWr19fY+4TJkyosf4X39P7778/TjnllIiIOProo3P7f+aZZ7Z6XHPmzIm+fftG06ZNo0WLFvH9738/Fi5cmHt92LBh0a9fv4iIOOWUU6KgoGCr927adBnsH//4xxg5cmS0bt069tlnn4iIWLp0aYwcOTIOOOCAaNy4cey1115xyimn1HiPNm3j+eefj9GjR0erVq2iadOm8S//8i/xwQcf5C1bXV0dEyZMiLZt20aTJk3i6KOPjldffbXW79vq1atj1KhRue9Kly5d4uabb97mWX0nnHBC7LfffrW+1rt377zYPG3atDjmmGOidevWUVxcHF27do277757q9v/4jFv/l5s+l5t/jn+5S9/ieOOOy5KSkqiSZMm0a9fv3j++efzllmzZk2MGjUqOnbsGMXFxdG6devo379/vPTSS9ucDwD8s3MGFgDUoTPOOCPGjh0bTz75ZJx11lm1LvO///u/ccIJJ0S3bt3iuuuui+Li4li8eHHux+2BBx4Y1113XYwbNy7OPvvs6Nu3b0REHHHEEbltfPjhhzFgwIA47bTT4t/+7d+itLR0q/O68cYbo6CgIK644opYuXJlTJ48OcrKymLBggW5M8W2x/bM7YuyLIsTTzwx5s6dG2eeeWb06NEjnnjiibj88svj3Xffjdtvvz1v+eeeey5++9vfxsiRI2P33XePn/3sZ/GDH/wg3nnnndhrr722OK9PPvkkjjrqqFi8eHFccMEF0alTp3j44Ydj2LBhsXr16rj44ovjwAMPjAcffDAuueSS2GeffeLSSy+NiIhWrVptcbsjRoyIBx54IE4++eS49NJL4y9/+UtMnDgxFi5cGL/73e+2+32LiPjud78bF110UfzsZz+LsWPHxoEHHph7T7fk6aefjgEDBsR+++0XEyZMiE8++STuuOOO6NOnT7z00kvRsWPHOOecc6Jdu3Zx0003xUUXXRTf+c53tvl9iIgYOXJktGrVKsaNGxfr1q2LiM9vBP+nP/0pTjvttNhnn31iyZIlcffdd8dRRx0Vr776ajRp0iRvGxdeeGHsscceMX78+FiyZElMnjw5Lrjggpg+fXpumTFjxsQtt9wSgwYNivLy8nj55ZejvLw8Pv3007xtffzxx9GvX794991345xzzol99903/vSnP8WYMWPi/fffj8mTJ2/xWAYPHhxDhgyJF198MRdYIz4Pcn/+85/j1ltvzY3dfffdcdBBB8WJJ54YDRo0iEcffTRGjhwZ1dXVcf7552/zfdsec+bMiQEDBkTPnj1j/PjxUVhYmAtn//Vf/xWHHXZYRESce+65MWPGjLjggguia9eu8eGHH8Zzzz0XCxcujEMPPXSHzAUAkpUBADvNtGnTsojIXnzxxS0uU1JSkh1yyCG55+PHj8+++E/07bffnkVE9sEHH2xxGy+++GIWEdm0adNqvNavX78sIrIpU6bU+lq/fv1yz+fOnZtFRNauXbussrIyN/7QQw9lEZH99Kc/zY116NAhGzp06Da3ubW5DR06NOvQoUPu+e9///ssIrIbbrghb7mTTz45KygoyBYvXpwbi4isYcOGeWMvv/xyFhHZHXfcUWNfXzR58uQsIrJf/epXubENGzZkvXv3zpo1a5Z37B06dMgGDhy41e1lWZYtWLAgi4hsxIgReeOXXXZZFhHZnDlz8uY+fvz4GtvY/D19+OGHs4jI5s6du839Z1mW9ejRI2vdunX24Ycf5sZefvnlrLCwMBsyZEhubNPn/PDDD29zm5u+w0ceeWS2cePGvNc+/vjjGsvPmzcvi4jsl7/8ZY1tlJWVZdXV1bnxSy65JCsqKspWr16dZVmWLV++PGvQoEF20kkn5W1zwoQJWUTkvTfXX3991rRp0+z111/PW/bKK6/MioqKsnfeeWeLx1RRUZEVFxdnl156ad74LbfckhUUFGRLly7d6jGWl5dn++23X97Y5t/7Tcf89ttv5y236b3f9JlWV1dn+++/f1ZeXp733nz88cdZp06dsv79++fGSkpKsvPPP3+LxwUA9ZlLCAGgjjVr1myrf41w032K/vCHP3zlG54XFxfH8OHDt3v5IUOGxO677557fvLJJ8fee+8djz322Ffa//Z67LHHoqioKC666KK88UsvvTSyLIvHH388b7ysrCw6d+6ce96tW7do3rx5vPXWW9vcT5s2beL000/Pje22225x0UUXxdq1a+OPf/zjV5p7RMTo0aNrzD0iYubMmV96m1/G+++/HwsWLIhhw4bFnnvumRvv1q1b9O/f/x/+7M4666woKirKG/vi2XifffZZfPjhh9GlS5do0aJFrZe1nX322XmXx/bt2zeqqqpi6dKlERExe/bs2LhxY4wcOTJvvQsvvLDGth5++OHo27dv7LHHHrFq1arco6ysLKqqquLZZ5/d4rE0b948BgwYEA899FDepanTp0+Pww8/PPbdd99aj7GioiJWrVoV/fr1i7feeisqKiq2uI/ttWDBgnjjjTfihz/8YXz44Ye541i3bl0ce+yx8eyzz+b+u2/RokX85S9/iffee+8f3i8A/LMRsACgjq1duzYvFm1u8ODB0adPnxgxYkSUlpbGaaedFg899NCXilnt2rX7Ujds33///fOeFxQURJcuXbZ6/6cdYenSpdG2bdsa78emy+Y2hY5NvhgaNtljjz3i73//+zb3s//++0dhYf7/FdrSfrZ37oWFhdGlS5e88TZt2kSLFi2+0ja/7P4jIg444IAarx144IG5KPJVderUqcbYJ598EuPGjcvdg6ply5bRqlWrWL16da1xZ/PPa4899oiIyH1em45h8/dwzz33zC27yRtvvBGzZs2KVq1a5T3Kysoi4v/dqH5LBg8eHMuWLYt58+ZFxOd/mXH+/PkxePDgvOWef/75KCsry91TrFWrVrl7yO2IgPXGG29ERMTQoUNrHMu9994b69evz+3nlltuiVdeeSXat28fhx12WEyYMGGbsRYA6gv3wAKAOvR///d/UVFRUeMH+xc1btw4nn322Zg7d27MnDkzZs2aFdOnT49jjjkmnnzyyRpnxWxpGzva5jea36Sqqmq75rQjbGk/2WY3fN+VtvS+bI+qqqodOJMdq7bv0IUXXhjTpk2LUaNGRe/evaOkpCQKCgritNNOqzWw7sjPq7q6Ovr37x8//vGPa339G9/4xlbXHzRoUDRp0iQeeuihOOKII+Khhx6KwsLC3I3zIz6PWscee2x885vfjEmTJkX79u2jYcOG8dhjj8Xtt9++1Yi8tf8+Nj+OiIhbb701evToUes6zZo1i4iIU089Nfr27Ru/+93v4sknn4xbb701br755vjtb38bAwYM2OrxAsA/OwELAOrQgw8+GBER5eXlW12usLAwjj322Dj22GNj0qRJcdNNN8VVV10Vc+fOjbKysn8omtRm01khm2RZFosXL45u3brlxvbYY49YvXp1jXWXLl2a9xfevszcOnToEE8//XSsWbMm7yysRYsW5V7fETp06BD/8z//E9XV1XlnYf0j++nQoUNUV1fHG2+8kXej9RUrVsTq1avztlnbe7dhw4Z4//3388a+7HsXEfHaa6/VeG3RokXRsmXLaNq06XZvb3vMmDEjhg4dGrfddltu7NNPP631e7E9Nh3D4sWL8874+vDDD2ucVde5c+dYu3Zt7oyrL6tp06ZxwgknxMMPPxyTJk2K6dOnR9++faNt27a5ZR599NFYv359PPLII3lnj82dO3eb2990xtjm78XmZ+JtugS2efPm23Use++9d4wcOTJGjhwZK1eujEMPPTRuvPFGAQuAes8lhABQR+bMmRPXX399dOrUKX70ox9tcbmPPvqoxtimMzXWr18fEZELE181HGzul7/8Zd59uWbMmBHvv/9+3o/kzp07x5///OfYsGFDbuw///M/Y9myZXnb+jJzO/7446Oqqip+/vOf543ffvvtUVBQsMN+pB9//PGxfPnyvL9+t3HjxrjjjjuiWbNm0a9fv6+0zYio8dfvJk2aFBERAwcOzI117ty5xj2a7rnnnhpn53yZ927vvfeOHj16xAMPPJC3/CuvvBJPPvlkbn47UlFRUY2zp+64446vfCbZscceGw0aNIi77747b3zz70PE52cjzZs3L5544okar61evTo2bty4zf0NHjw43nvvvbj33nvj5ZdfrnH54KYzxr54jBUVFTFt2rRtbntTmPri51xVVRX33HNP3nI9e/aMzp07x09+8pNYu3Ztje188MEHuXU3v2SxdevW0bZt29z/DgBAfeYMLADYBR5//PFYtGhRbNy4MVasWBFz5syJp556Kjp06BCPPPJINGrUaIvrXnfddfHss8/GwIEDo0OHDrFy5cq46667Yp999okjjzwyIj7/sdyiRYuYMmVK7L777tG0adPo1atXrfct2h577rlnHHnkkTF8+PBYsWJFTJ48Obp06RJnnXVWbpkRI0bEjBkz4rjjjotTTz013nzzzfjVr36Vd1P1Lzu3QYMGxdFHHx1XXXVVLFmyJLp37x5PPvlk/OEPf4hRo0bV2PZXdfbZZ8e///u/x7Bhw2L+/PnRsWPHmDFjRjz//PMxefLkrd6TbEu6d+8eQ4cOjXvuuSdWr14d/fr1ixdeeCEeeOCBOOmkk+Loo4/OLTtixIg499xz4wc/+EH0798/Xn755XjiiSeiZcuWedvs0aNHFBUVxc033xwVFRVRXFwcxxxzTLRu3brWOdx6660xYMCA6N27d5x55pnxySefxB133BElJSUxYcKEL31M23LCCSfEgw8+GCUlJdG1a9eYN29ePP3007HXXnt9pe2VlpbGxRdfHLfddluceOKJcdxxx8XLL78cjz/+eLRs2TLvjLTLL788HnnkkTjhhBNi2LBh0bNnz1i3bl387W9/ixkzZsSSJUtqvJ+bO/7442P33XePyy67LIqKiuIHP/hB3uvf+973omHDhjFo0KA455xzYu3atTF16tRo3bp1jbPlNnfQQQfF4YcfHmPGjImPPvoo9txzz/jNb35TI6wVFhbGvffeGwMGDIiDDjoohg8fHu3atYt333035s6dG82bN49HH3001qxZE/vss0+cfPLJ0b1792jWrFk8/fTT8eKLL+adAQcA9VYd/gVEAKj3pk2blkVE7tGwYcOsTZs2Wf/+/bOf/vSnWWVlZY11xo8fn33xn+jZs2dn3//+97O2bdtmDRs2zNq2bZudfvrp2euvv5633h/+8Iesa9euWYMGDbKIyKZNm5ZlWZb169cvO+igg2qdX79+/bJ+/frlns+dOzeLiOzXv/51NmbMmKx169ZZ48aNs4EDB2ZLly6tsf5tt92WtWvXLisuLs769OmT/fWvf62xza3NbejQoVmHDh3yll2zZk12ySWXZG3bts122223bP/9989uvfXWrLq6Om+5iMjOP//8GnPq0KFDNnTo0FqP94tWrFiRDR8+PGvZsmXWsGHD7Fvf+lZuXptvb+DAgdvcXpZl2WeffZZde+21WadOnbLddtsta9++fTZmzJjs008/zVuuqqoqu+KKK7KWLVtmTZo0ycrLy7PFixfXOvepU6dm++23X1ZUVJRFRDZ37tytzuHpp5/O+vTpkzVu3Dhr3rx5NmjQoOzVV1/NW2bT5/zwww9v85g2fYdffPHFGq/9/e9/z72HzZo1y8rLy7NFixbVOI4tbWPTPL54TBs3bsyuueaarE2bNlnjxo2zY445Jlu4cGG21157Zeeee27e+mvWrMnGjBmTdenSJWvYsGHWsmXL7Igjjsh+8pOfZBs2bNjmsWVZlv3oRz/KIiIrKyur9fVHHnkk69atW9aoUaOsY8eO2c0335zdd999WURkb7/9dm652r73b775ZlZWVpYVFxdnpaWl2dixY7Onnnqq1s/xv//7v7N//dd/zfbaa6+suLg469ChQ3bqqadms2fPzrIsy9avX59dfvnlWffu3bPdd989a9q0ada9e/fsrrvu2q7jBIB/dgVZVod3OQUAgG1YvXp17LHHHnHDDTfEVVddVdfTAQDqgHtgAQCQjE8++aTG2Kb7ih111FG7eDYAQCrcAwsAgGRMnz497r///jj++OOjWbNm8dxzz8Wvf/3r+N73vhd9+vSp6+kBAHVEwAIAIBndunWLBg0axC233BKVlZW5G7vfcMMNdT01AKAOuYSwnnv22Wdj0KBB0bZt2ygoKIjf//7321znmWeeiUMPPTSKi4ujS5cucf/99+/8iQIARMShhx4aTz/9dKxatSo2bNgQy5Yti8mTJ0ezZs3qemoAQB0SsOq5devWRffu3ePOO+/cruXffvvtGDhwYBx99NGxYMGCGDVqVIwYMSKeeOKJnTxTAAAAgNr5K4RfIwUFBfG73/0uTjrppC0uc8UVV8TMmTPjlVdeyY2ddtppsXr16pg1a9aumCYAAABAHvfAIs+8efOirKwsb6y8vDxGjRq1xXXWr18f69evzz2vrq6Ojz76KPbaa68oKCjYaXMFAACAiIgsy2LNmjXRtm3bKCx0sVl9JGCRZ/ny5VFaWpo3VlpaGpWVlfHJJ59E48aNa6wzceLEuPbaa3fVFAEAAKBWy5Yti3322aeup8FOIGDxDxszZkyMHj0697yioiL23XffWLZsWTRv3rwOZwYAAMDXQWVlZbRv3z523333up4KO4mARZ42bdrEihUr8sZWrFgRzZs3r/Xsq4iI4uLiKC4urjHevHlzAQsAAIBdxm1s6i8XhpKnd+/eMXv27Lyxp556Knr37l1HMwIAAAC+7gSsem7t2rWxYMGCWLBgQUREvP3227FgwYJ45513IuLzy/+GDBmSW/7cc8+Nt956K3784x/HokWL4q677oqHHnooLrnkkjqZPwAAAICAVc/99a9/jUMOOSQOOeSQiIgYPXp0HHLIITFu3LiIiHj//fdzMSsiolOnTjFz5sx46qmnonv37nHbbbfFvffeG+Xl5XUyfwAAAICCLMuyup4E9UtlZWWUlJRERUWFe2ABAACw0/kdWv85AwsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAHra+DOO++Mjh07RqNGjaJXr17xwgsvbHX5yZMnxwEHHBCNGzeO9u3bxyWXXBKffvrpLpotAAAAQD4Bq56bPn16jB49OsaPHx8vvfRSdO/ePcrLy2PlypW1Lv8f//EfceWVV8b48eNj4cKF8Ytf/CKmT58eY8eO3cUzBwAAAPicgFXPTZo0Kc4666wYPnx4dO3aNaZMmRJNmjSJ++67r9bl//SnP0WfPn3ihz/8YXTs2DG+973vxemnn77Ns7YAAAAAdhYBqx7bsGFDzJ8/P8rKynJjhYWFUVZWFvPmzat1nSOOOCLmz5+fC1ZvvfVWPPbYY3H88cdvcT/r16+PysrKvAcAAADAjtKgrifAzrNq1aqoqqqK0tLSvPHS0tJYtGhRrev88Ic/jFWrVsWRRx4ZWZbFxo0b49xzz93qJYQTJ06Ma6+9dofOHQAAAGATZ2CR55lnnombbrop7rrrrnjppZfit7/9bcycOTOuv/76La4zZsyYqKioyD2WLVu2C2cMAAAA1HfOwKrHWrZsGUVFRbFixYq88RUrVkSbNm1qXeeaa66JM844I0aMGBEREd/61rdi3bp1cfbZZ8dVV10VhYU1m2dxcXEUFxfv+AMAAAAACGdg1WsNGzaMnj17xuzZs3Nj1dXVMXv27Ojdu3et63z88cc1IlVRUVFERGRZtvMmCwAAALAFzsCq50aPHh1Dhw6Nb3/723HYYYfF5MmTY926dTF8+PCIiBgyZEi0a9cuJk6cGBERgwYNikmTJsUhhxwSvXr1isWLF8c111wTgwYNyoUsAAAAgF1JwKrnBg8eHB988EGMGzculi9fHj169IhZs2blbuz+zjvv5J1xdfXVV0dBQUFcffXV8e6770arVq1i0KBBccjNMI8AACAASURBVOONN9bVIQAAAABfcwWZ68LYwSorK6OkpCQqKiqiefPmdT0dAAAA6jm/Q+s/98ACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3A+hq48847o2PHjtGoUaPo1atXvPDCC1tdfvXq1XH++efH3nvvHcXFxfGNb3wjHnvssV00WwAAAIB8Dep6Auxc06dPj9GjR8eUKVOiV69eMXny5CgvL4/XXnstWrduXWP5DRs2RP/+/aN169YxY8aMaNeuXSxdujRatGhRB7MHAAAAiCjIsiyr60mw8/Tq1Su+853vxM9//vOIiKiuro727dvHhRdeGFdeeWWN5adMmRK33nprLFq0KHbbbbevtM/KysooKSmJioqKaN68+T80fwAAANgWv0PrP5cQ1mMbNmyI+fPnR1lZWW6ssLAwysrKYt68ebWu88gjj0Tv3r3j/PPPj9LS0jj44IPjpptuiqqqqi3uZ/369VFZWZn3AAAAANhRBKx6bNWqVVFVVRWlpaV546WlpbF8+fJa13nrrbdixowZUVVVFY899lhcc801cdttt8UNN9ywxf1MnDgxSkpKco/27dvv0OMAAAAAvt4ELPJUV1dH69at45577omePXvG4MGD46qrroopU6ZscZ0xY8ZERUVF7rFs2bJdOGMAAACgvnMT93qsZcuWUVRUFCtWrMgbX7FiRbRp06bWdfbee+/YbbfdoqioKDd24IEHxvLly2PDhg3RsGHDGusUFxdHcXHxjp08AAAAwP/PGVj1WMOGDaNnz54xe/bs3Fh1dXXMnj07evfuXes6ffr0icWLF0d1dXVu7PXXX4+999671ngFAAAAsLMJWPXc6NGjY+rUqfHAAw/EwoUL47zzzot169bF8OHDIyJiyJAhMWbMmNzy5513Xnz00Udx8cUXx+uvvx4zZ86Mm266Kc4///y6OgQAAADga84lhPXc4MGD44MPPohx48bF8uXLo0ePHjFr1qzcjd3feeedKCz8fx2zffv28cQTT8Qll1wS3bp1i3bt2sXFF18cV1xxRV0dAgAAAPA1V5BlWVbXk6B+qaysjJKSkqioqIjmzZvX9XQAAACo5/wOrf9cQggAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoD1NXDnnXdGx44do1GjRtGrV6944YUXtmu93/zmN1FQUBAnnXTSTp4hAAAAwJYJWPXc9OnTY/To0TF+/Ph46aWXonv37lFeXh4rV67c6npLliyJyy67LPr27buLZgoAAABQOwGrnps0aVKcddZZMXz48OjatWtMmTIlmjRpEvfdd98W16mqqoof/ehHce2118Z+++23C2cLAAAAUJOAVY9t2LAh5s+fH2VlZbmxwsLCKCsri3nz5m1xveuuuy5at24dZ5555q6YJgAAAMBWNajrCbDzrFq1KqqqqqK0tDRvvLS0NBYtWlTrOs8991z84he/iAULFmz3ftavXx/r16/PPa+srPxqEwYAAACohTOwyFmzZk2cccYZMXXq1GjZsuV2rzdx4sQoKSnJPdq3b78TZwkAAAB83TgDqx5r2bJlFBUVxYoVK/LGV6xYEW3atKmx/JtvvhlLliyJQYMG5caqq6sjIqJBgwbx2muvRefOnWusN2bMmBg9enTueWVlpYgFAAAA7DACVj3WsGHD6NmzZ8yePTtOOumkiPg8SM2ePTsuuOCCGst/85vfjL/97W95Y1dffXWsWbMmfvrTn24xShUXF0dxcfGOPwAAAACAELDqvdGjR8fQoUPj29/+dhx22GExefLkWLduXQwfPjwiIoYMGRLt2rWLiRMnRqNGjeLggw/OW79FixYRETXGAQAAAHYVAaueGzx4cHzwwQcxbty4WL58efTo0SNmzZqVu7H7O++8E4WFboUGAAAApKsgy7KsridB/VJZWRklJSVRUVERzZs3r+vpAAAAUM/5HVr/OfUGAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWAAAAAAkTcACAAAAIGkCFgAAAABJE7AAAAAASJqABQAAAEDSBCwAAAAAkiZgAQAAAJA0AQsAAACApAlYAAAAACRNwAIAAAAgaQIWAAAAAEkTsAAAAABImoAFAAAAQNIELAAAAACSJmABAAAAkDQBCwAAAICkCVgAAAAAJE3AAgAAACBpAhYAAAAASROwAAAAAEiagAUAAABA0gQsAAAAAJImYAEAAACQNAELAAAAgKQJWPx/7d19rJZ14cfxzwF5Uh5EQRA6iokPYIryKPkjNMmzNJda08iJmuksMRybAs5ARio6TZ06Ta35MAlnGDpmaIE0RySISqlAamMgCUogTxYPh/v3R/P0Oz9BAXn4eny9trPBdV/X/f1et5vf+35z3dcBAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwAIAAACgaAIWAAAAAEUTsAAAAAAomoAFAAAAQNEELAAAAACKJmABAAAAUDQBCwAAAICiCVgAAAAAFE3AAgAAAKBoAhYAAAAARROwAAAAACiagAUAAABA0QQsAAAAAIomYAEAAABQNAELAAAAgKIJWAAAAAAUTcACAAAAoGgCFgAAAABFE7AAAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwAIAAACgaAIWAAAAAEUTsAAAAAAomoAFAAAAQNEELAAAAACKJmABAAAAUDQBCwAAAICiCVgAAAAAFE3AAgAAAKBoAhYAAAAARROwAAAAACiagAUAAABA0QQsAAAAAIomYAEAAABQNAELAAAAgKIJWAAAAAAUTcACAAAAoGgCFgAAAABFE7AAAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwAIAAACgaAIWAAAAAEUTsAAAAAAomoAFAAAAQNEELAAAAACKJmABAAAAUDQBCwAAAICiCVgAAAAAFE3AAgAAAKBoAtYXwD333JMuXbqkefPm6devX2bPnr3NfR944IEMGDAgbdu2Tdu2bTNo0KBP3B8AAABgdxOwGrjHH388w4cPz5gxY/Lyyy+nR48eqampyXvvvbfV/WfMmJHBgwfn+eefz6xZs1JdXZ3TTjstS5cu3cMzBwAAAPiPqkqlUtnbk2D36devX/r06ZO77747SbJly5ZUV1fnyiuvzMiRIz/1+Nra2rRt2zZ33313hgwZsl1jrlmzJm3atMnq1avTunXrzzR/AAAA+DQ+hzZ8rsBqwDZu3Ji5c+dm0KBBddsaNWqUQYMGZdasWdv1HB9++GE2bdqUAw44YHdNEwAAAOAT7bO3J8Dus2LFitTW1qZDhw71tnfo0CELFizYrucYMWJEOnXqVC+C/X8bNmzIhg0b6v6+Zs2anZswAAAAwFa4AottGj9+fCZOnJjf/va3ad68+Tb3u+mmm9KmTZu6n+rq6j04SwAAAKChE7AasHbt2qVx48ZZvnx5ve3Lly9Px44dP/HYW2+9NePHj89zzz2X44477hP3HTVqVFavXl33s2TJks88dwAAAICPCFgNWNOmTdOrV69MmzatbtuWLVsybdq09O/ff5vH3XLLLRk3blymTp2a3r17f+o4zZo1S+vWrev9AAAAAOwq7oHVwA0fPjwXXnhhevfunb59++aOO+7I+vXrc/HFFydJhgwZks6dO+emm25Kktx8880ZPXp0JkyYkC5dumTZsmVJkpYtW6Zly5Z77TwAAACALy4Bq4E777zz8v7772f06NFZtmxZjj/++EydOrXuxu6LFy9Oo0b/vRDv3nvvzcaNG/Pd73633vOMGTMm119//Z6cOgAAAECSpKpSqVT29iRoWNasWZM2bdpk9erVvk4IAADAbudzaMPnHlgAAAAAFE3AAgAAAKBoAhYAAAAARROwAAAAACiagAUAAABA0QQsAAAAAIomYAEAAABQNAELAAAAgKIJWAAAAAAUTcACAAAAoGgCFgAAAABFE7AAAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwAIAAACgaAIWAAAAAEUTsAAAAAAomoAFAAAAQNEELAAAAACKJmABAAAAUDQBCwAAAICiCVgAAAAAFE3AAgAAAKBoAhYAAAAARROwAAAAACiagAUAAABA0QQsAAAAAIomYAEAAABQNAELAAAAgKIJWAAAAAAUTcACAAAAoGgCFgAAAABFE7AAAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwAIAAACgaAIWAAAAAEUTsAAAAAAomoAFAAAAQNEELAAAAACKJmABAAAAUDQBCwAAAICiCVgAAAAAFE3AAgAAAKBoAhYAAAAARROwAAAAACiagAUAAABA0QQsAAAAAIomYAEAAABQNAELAAAAgKIJWAAAAAAUTcACAAAAoGgCFgAAAABFE7AAAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwAIAAACgaAIWAAAAAEUTsAAAAAAomoAFAAAAQNEELAAAAACKJmABAAAAUDQBCwAAAICiCVgAAAAAFE3AAgAAAKBoAhYAAAAARROwAAAAACiagAUAAABA0QQsAAAAAIomYAEAAABQNAELAAAAgKIJWAAAAAAUTcACAAAAoGgCFgAAAABFE7AAAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwAIAAACgaAIWAAAAAEUTsAAAAAAomoAFAAAAQNEELAAAAACKJmABAAAAUDQBCwAAAICiCVgAAAAAFE3AAgAAAKBoAhYAAAAARROwAAAAACiagAUAAABA0QQsAAAAAIomYAEAAABQNAELAAAAgKIJWAAAAAAUTcACAAAAoGgCFgAAAABFE7AAAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwPoCuOeee9KlS5c0b948/fr1y+zZsz9x/yeeeCJHH310mjdvnmOPPTbPPPPMHpopAAAAwMcJWA3c448/nuHDh2fMmDF5+eWX06NHj9TU1OS9997b6v5/+tOfMnjw4FxyySV55ZVXctZZZ+Wss87Ka6+9todnDgAAAPAfVZVKpbK3J8Hu069fv/Tp0yd33313kmTLli2prq7OlVdemZEjR35s//POOy/r16/PlClT6radeOKJOf7443Pfffdt15hr1qxJmzZtsnr16rRu3XrXnAgAAABsg8+hDd8+e3sC7D4bN27M3LlzM2rUqLptjRo1yqBBgzJr1qytHjNr1qwMHz683raamppMnjx5m+Ns2LAhGzZsqPv76tWrk/znfyAAAACwu330+dM1Og2XgNWArVixIrW1tenQoUO97R06dMiCBQu2esyyZcu2uv+yZcu2Oc5NN92UsWPHfmx7dXX1TswaAAAAds7atWvTpk2bvT0NdgMBi89s1KhR9a7a2rJlS1auXJkDDzwwVVVVe3FmwNasWbMm1dXVWbJkicurAWAHWEOhXJVKJWvXrk2nTp329lTYTQSsBqxdu3Zp3Lhxli9fXm/78uXL07Fjx60e07Fjxx3aP0maNWuWZs2a1du2//777+SsgT2ldevW3nwDwE6whkKZXHnVsPkthA1Y06ZN06tXr0ybNq1u25YtWzJt2rT0799/q8f079+/3v5J8vvf/36b+wMAAADsbq7AauCGDx+eCy+8ML17907fvn1zxx13ZP369bn44ouTJEOGDEnnzp1z0003JUmGDRuWgQMH5rbbbssZZ5yRiRMn5qWXXsr999+/N08DAAAA+AITsBq48847L++//35Gjx6dZcuW5fjjj8/UqVPrbtS+ePHiNGr03wvxvvrVr2bChAm57rrrcu211+aII47I5MmT85WvfGVvnQKwizVr1ixjxoz52Fd/AYBPZg0F2HuqKn7HJAAAAAAFcw8sAAAAAIomYAEAAABQNAELAAAAgKIJWAANVFVVVSZPnvyZnuPkk0/OVVddtcfHBeCLqyGtXzszjx110UUX5ayzztqtY2zNjBkzUlVVlQ8++GCPjw18MfkthABs05NPPpkmTZrs0uecMWNGTjnllKxatSr777//Ln1uAEisXwANkYAF8Dm0cePGNG3adLePc8ABB+z2MQD44rB+AbCzfIUQYA/YsGFDfvKTn+Sggw5K8+bN8z//8z+ZM2dO3eN//OMf07dv3zRr1iwHH3xwRo4cmc2bN9c9fvLJJ2fo0KG56qqr0q5du9TU1GzXuCtWrMjZZ5+dfffdN0cccUSefvrpeo+/9tpr+eY3v5mWLVumQ4cOueCCC7JixYp64/7frz68++67OeOMM9KiRYscdthhmTBhQrp06ZI77rhju8ZdtGhRTjnllCRJ27ZtU1VVlYsuumj7XkQA9jjr12dfvzZv3pyhQ4emTZs2adeuXX7605+mUqnUPf7oo4+md+/eadWqVTp27Jjvf//7ee+99+o9x+uvv55vfetbad26dVq1apUBAwbk7bff3up4c+bMSfv27XPzzTfXbXvqqafSs2fPNG/ePF/+8pczduzYev+dqqqq8uCDD37ia/7MM8/kyCOPTIsWLXLKKadk0aJF23X+ALuKgAWwB1xzzTWZNGlSHn744bz88svp2rVrampqsnLlyixdujSnn356+vTpk3nz5uXee+/NL3/5y/zsZz+r9xwPP/xwmjZtmpkzZ+a+++7brnHHjh2bc889N3/5y19y+umn5/zzz8/KlSuTJB988EG+/vWv54QTTshLL72UqVOnZvny5Tn33HO3+XxDhgzJP/7xj8yYMSOTJk3K/fff/7E32Z80bnV1dSZNmpQkWbhwYd59993ceeed2/syArCHWb8++/r18MMPZ5999sns2bNz55135uc//3kefPDBusc3bdqUcePGZd68eZk8eXIWLVpUL44tXbo0X/va19KsWbNMnz49c+fOzQ9+8IN6Aeoj06dPzze+8Y3ccMMNGTFiRJLkhRdeyJAhQzJs2LC88cYb+cUvfpGHHnooN9xww3a/5kuWLMk555yTM888M6+++mp++MMfZuTIkdt1/gC7TAWA3WrdunWVJk2aVB577LG6bRs3bqx06tSpcsstt1SuvfbaylFHHVXZsmVL3eP33HNPpWXLlpXa2tpKpVKpDBw4sHLCCSfs0LhJKtddd129eSSp/O53v6tUKpXKuHHjKqeddlq9Y5YsWVJJUlm4cGHduMOGDatUKpXK/PnzK0kqc+bMqdv/zTffrCSp3H777ds97vPPP19JUlm1atUOnQ8Ae5b167OvXwMHDqx069at3ms0YsSISrdu3bZ5zJw5cypJKmvXrq1UKpXKqFGjKocddlhl48aNW93/wgsvrHz729+uPPnkk5WWLVtWJk6cWO/xU089tXLjjTfW2/boo49WDj744Lq/f9q5jxo1qtK9e/d6zzFixAjrObBHuQcWwG729ttvZ9OmTTnppJPqtjVp0iR9+/bN/Pnz88EHH6R///6pqqqqe/ykk07KunXr8s477+SQQw5JkvTq1WuHxz7uuOPq/rzffvuldevWdf/iPG/evDz//PNp2bLlVud85JFH1tu2cOHC7LPPPunZs2fdtq5du6Zt27Y7NC4Anw/Wr12zfp144on1XqP+/fvntttuS21tbRo3bpy5c+fm+uuvz7x587Jq1aps2bIlSbJ48eJ07949r776agYMGPCJN6V/8cUXM2XKlPzmN7/52G8knDdvXmbOnFnviqva2tr8+9//zocffph99933U899/vz56devX73n7d+//06+IgA7R8AC+JzYb7/9dviY//9mt6qqqu6N8bp163LmmWfWu0fGRw4++OCdm+R2jAvAF4v1a9vWr1+fmpqa1NTU5LHHHkv79u2zePHi1NTUZOPGjUmSFi1afOrzHH744TnwwAPzq1/9KmeccUa981i3bl3Gjh2bc84552PHNW/evO7P1m6gdO6BBbCbHX744XX3/vjIpk2bMmfOnHTv3j3dunXLrFmz6t3QdebMmWnVqlW+9KUv7bZ59ezZM6+//nq6dOmSrl271vvZ2oeNo446Kps3b84rr7xSt+2tt97KqlWrdmjcj377VG1t7Wc7AQB2K+tXfTu7fr344ov1/v7nP/85RxxxRBo3bpwFCxbkn//8Z8aPH58BAwbk6KOP/tgVX8cdd1xeeOGFbNq0aZtjtGvXLtOnT89bb72Vc889t96+PXv2zMKFCz/2WnXt2jWNGm3fx8Fu3bpl9uzZHzsPgD1JwALYzfbbb7/86Ec/ytVXX52pU6fmjTfeyKWXXpoPP/wwl1xySX784x9nyZIlufLKK7NgwYI89dRTGTNmTIYPH77dbyx3xhVXXJGVK1dm8ODBmTNnTt5+++08++yzufjii7f65vzoo4/OoEGDctlll2X27Nl55ZVXctlll6VFixb1vhrxaQ499NBUVVVlypQpef/997Nu3bpdeVoA7CLWr/p2dv1avHhxhg8fnoULF+bXv/517rrrrgwbNixJcsghh6Rp06a56667xWOkbQAABE5JREFU8ve//z1PP/10xo0bV+/4oUOHZs2aNfne976Xl156KW+++WYeffTRLFy4sN5+Bx10UKZPn54FCxZk8ODBdTd5Hz16dB555JGMHTs2r7/+eubPn5+JEyfmuuuu2+5zv/zyy/Pmm2/m6quvzsKFCzNhwoQ89NBD2308wK4gYAHsAePHj893vvOdXHDBBenZs2feeuutPPvss2nbtm06d+6cZ555JrNnz06PHj1y+eWX55JLLtmhN5Y7o1OnTpk5c2Zqa2tz2mmn5dhjj81VV12V/ffff5sfPB555JF06NAhX/va13L22Wfn0ksvTatWrep9BeHTdO7cOWPHjs3IkSPToUOHDB06dFedEgC7mPXrv3Z2/RoyZEj+9a9/pW/fvrniiisybNiwXHbZZUmS9u3b56GHHsoTTzyR7t27Z/z48bn11lvrHX/ggQdm+vTpWbduXQYOHJhevXrlgQce2Oo9sTp27Jjp06fnr3/9a84///zU1tampqYmU6ZMyXPPPZc+ffrkxBNPzO23355DDz10u8/9kEMOyaRJkzJ58uT06NEj9913X2688cbtPh5gV6iq/N9rfgFgB7zzzjuprq7OH/7wh5x66ql7ezoAsF2sXwCfPwIWANvto38BPvbYY/Puu+/mmmuuydKlS/O3v/3tE387EgDsTdYvgM8/XyEE+Bx67LHH0rJly63+HHPMMbtt3E2bNuXaa6/NMccck7PPPjvt27fPjBkzvPkHYLs0pPVr8eLF2zyXli1bZvHixbvwDABwBRbA59DatWuzfPnyrT7WpEmTHbqvBQDsKQ1p/dq8eXMWLVq0zce7dOmSffbZZ89NCKCBE7AAAAAAKJqvEAIAAABQNAELAAAAgKIJWAAAAAAUTcACAAAAoGgCFgAAAABFE7AAAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwAIAAACgaAIWAAAAAEUTsAAAAAAomoAFAAAAQNEELAAAAACKJmABAAAAUDQBCwAAAICiCVgAAAAAFE3AAgAAAKBoAhYAAAAARROwAAAAACiagAUAAABA0QQsAAAAAIomYAEAAABQNAELAAAAgKIJWAAAAAAUTcACAAAAoGgCFgAAAABFE7AAAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwAIAAACgaAIWAAAAAEUTsAAAAAAomoAFAAAAQNEELAAAAACKJmABAAAAUDQBCwAAAICiCVgAAAAAFE3AAgAAAKBoAhYAAAAARROwAAAAACiagAUAAABA0QQsAAAAAIomYAEAAABQNAELAAAAgKIJWAAAAAAUTcACAAAAoGgCFgAAAABFE7AAAAAAKJqABQAAAEDRBCwAAAAAiiZgAQAAAFA0AQsAAACAoglYAAAAABRNwAIAAACgaAIWAAAAAEUTsAAAAAAo2v8CtwJEuek53/8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image 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>count_height</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>pct25_height</th>\n",
       "      <th>pct75_height</th>\n",
       "      <th>count_height_backend</th>\n",
       "      <th>mean_height_backend</th>\n",
       "      <th>pct25_height_backend</th>\n",
       "      <th>pct75_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_band</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-19</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count_height mean_height pct25_height pct75_height  \\\n",
       "age_band                                                      \n",
       "0-19                -           -            -            -   \n",
       "20-29               -           -            -            -   \n",
       "30-39               -           -            -            -   \n",
       "40-49               -           -            -            -   \n",
       "50-59               -           -            -            -   \n",
       "60-69               -           -            -            -   \n",
       "70-79               -           -            -            -   \n",
       "80+                 -           -            -            -   \n",
       "\n",
       "         count_height_backend mean_height_backend pct25_height_backend  \\\n",
       "age_band                                                                 \n",
       "0-19                        -                   -                    -   \n",
       "20-29                       -                   -                    -   \n",
       "30-39                       -                   -                    -   \n",
       "40-49                       -                   -                    -   \n",
       "50-59                       -                   -                    -   \n",
       "60-69                       -                   -                    -   \n",
       "70-79                       -                   -                    -   \n",
       "80+                         -                   -                    -   \n",
       "\n",
       "         pct75_height_backend  \n",
       "age_band                       \n",
       "0-19                        -  \n",
       "20-29                       -  \n",
       "30-39                       -  \n",
       "40-49                       -  \n",
       "50-59                       -  \n",
       "60-69                       -  \n",
       "70-79                       -  \n",
       "80+                         -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "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>count_height</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>pct25_height</th>\n",
       "      <th>pct75_height</th>\n",
       "      <th>count_height_backend</th>\n",
       "      <th>mean_height_backend</th>\n",
       "      <th>pct25_height_backend</th>\n",
       "      <th>pct75_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count_height mean_height pct25_height pct75_height count_height_backend  \\\n",
       "sex                                                                           \n",
       "F              -           -            -            -                    -   \n",
       "M              -           -            -            -                    -   \n",
       "\n",
       "    mean_height_backend pct25_height_backend pct75_height_backend  \n",
       "sex                                                                \n",
       "F                     -                    -                    -  \n",
       "M                     -                    -                    -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "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>count_height</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>pct25_height</th>\n",
       "      <th>pct75_height</th>\n",
       "      <th>count_height_backend</th>\n",
       "      <th>mean_height_backend</th>\n",
       "      <th>pct25_height_backend</th>\n",
       "      <th>pct75_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count_height mean_height pct25_height pct75_height  \\\n",
       "ethnicity                                                      \n",
       "Asian                -           -            -            -   \n",
       "Black                -           -            -            -   \n",
       "Mixed                -           -            -            -   \n",
       "Other                -           -            -            -   \n",
       "White                -           -            -            -   \n",
       "\n",
       "          count_height_backend mean_height_backend pct25_height_backend  \\\n",
       "ethnicity                                                                 \n",
       "Asian                        -                   -                    -   \n",
       "Black                        -                   -                    -   \n",
       "Mixed                        -                   -                    -   \n",
       "Other                        -                   -                    -   \n",
       "White                        -                   -                    -   \n",
       "\n",
       "          pct75_height_backend  \n",
       "ethnicity                       \n",
       "Asian                        -  \n",
       "Black                        -  \n",
       "Mixed                        -  \n",
       "Other                        -  \n",
       "White                        -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "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>count_height</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>pct25_height</th>\n",
       "      <th>pct75_height</th>\n",
       "      <th>count_height_backend</th>\n",
       "      <th>mean_height_backend</th>\n",
       "      <th>pct25_height_backend</th>\n",
       "      <th>pct75_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Midlands</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count_height mean_height pct25_height pct75_height  \\\n",
       "region                                                                        \n",
       "East Midlands                       -           -            -            -   \n",
       "East of England                     -           -            -            -   \n",
       "London                              -           -            -            -   \n",
       "North East                          -           -            -            -   \n",
       "North West                          -           -            -            -   \n",
       "South East                          -           -            -            -   \n",
       "West Midlands                       -           -            -            -   \n",
       "Yorkshire and the Humber            -           -            -            -   \n",
       "\n",
       "                         count_height_backend mean_height_backend  \\\n",
       "region                                                              \n",
       "East Midlands                               -                   -   \n",
       "East of England                             -                   -   \n",
       "London                                      -                   -   \n",
       "North East                                  -                   -   \n",
       "North West                                  -                   -   \n",
       "South East                                  -                   -   \n",
       "West Midlands                               -                   -   \n",
       "Yorkshire and the Humber                    -                   -   \n",
       "\n",
       "                         pct25_height_backend pct75_height_backend  \n",
       "region                                                              \n",
       "East Midlands                               -                    -  \n",
       "East of England                             -                    -  \n",
       "London                                      -                    -  \n",
       "North East                                  -                    -  \n",
       "North West                                  -                    -  \n",
       "South East                                  -                    -  \n",
       "West Midlands                               -                    -  \n",
       "Yorkshire and the Humber                    -                    -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "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>count_height</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>pct25_height</th>\n",
       "      <th>pct75_height</th>\n",
       "      <th>count_height_backend</th>\n",
       "      <th>mean_height_backend</th>\n",
       "      <th>pct25_height_backend</th>\n",
       "      <th>pct75_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 Most deprived</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count_height mean_height pct25_height pct75_height  \\\n",
       "imd                                                                   \n",
       "1 Most deprived             -           -            -            -   \n",
       "2                           -           -            -            -   \n",
       "3                           -           -            -            -   \n",
       "4                           -           -            -            -   \n",
       "5 Least deprived            -           -            -            -   \n",
       "Unknown                     -           -            -            -   \n",
       "\n",
       "                 count_height_backend mean_height_backend  \\\n",
       "imd                                                         \n",
       "1 Most deprived                     -                   -   \n",
       "2                                   -                   -   \n",
       "3                                   -                   -   \n",
       "4                                   -                   -   \n",
       "5 Least deprived                    -                   -   \n",
       "Unknown                             -                   -   \n",
       "\n",
       "                 pct25_height_backend pct75_height_backend  \n",
       "imd                                                         \n",
       "1 Most deprived                     -                    -  \n",
       "2                                   -                    -  \n",
       "3                                   -                    -  \n",
       "4                                   -                    -  \n",
       "5 Least deprived                    -                    -  \n",
       "Unknown                             -                    -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "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>count_height</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>pct25_height</th>\n",
       "      <th>pct75_height</th>\n",
       "      <th>count_height_backend</th>\n",
       "      <th>mean_height_backend</th>\n",
       "      <th>pct25_height_backend</th>\n",
       "      <th>pct75_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dementia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count_height mean_height pct25_height pct75_height  \\\n",
       "dementia                                                      \n",
       "False               -           -            -            -   \n",
       "True                -           -            -            -   \n",
       "\n",
       "         count_height_backend mean_height_backend pct25_height_backend  \\\n",
       "dementia                                                                 \n",
       "False                       -                   -                    -   \n",
       "True                        -                   -                    -   \n",
       "\n",
       "         pct75_height_backend  \n",
       "dementia                       \n",
       "False                       -  \n",
       "True                        -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "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>count_height</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>pct25_height</th>\n",
       "      <th>pct75_height</th>\n",
       "      <th>count_height_backend</th>\n",
       "      <th>mean_height_backend</th>\n",
       "      <th>pct25_height_backend</th>\n",
       "      <th>pct75_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count_height mean_height pct25_height pct75_height  \\\n",
       "diabetes                                                      \n",
       "False               -           -            -            -   \n",
       "True                -           -            -            -   \n",
       "\n",
       "         count_height_backend mean_height_backend pct25_height_backend  \\\n",
       "diabetes                                                                 \n",
       "False                       -                   -                    -   \n",
       "True                        -                   -                    -   \n",
       "\n",
       "         pct75_height_backend  \n",
       "diabetes                       \n",
       "False                       -  \n",
       "True                        -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "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>count_height</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>pct25_height</th>\n",
       "      <th>pct75_height</th>\n",
       "      <th>count_height_backend</th>\n",
       "      <th>mean_height_backend</th>\n",
       "      <th>pct25_height_backend</th>\n",
       "      <th>pct75_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count_height mean_height pct25_height pct75_height  \\\n",
       "hypertension                                                      \n",
       "False                   -           -            -            -   \n",
       "True                    -           -            -            -   \n",
       "\n",
       "             count_height_backend mean_height_backend pct25_height_backend  \\\n",
       "hypertension                                                                 \n",
       "False                           -                   -                    -   \n",
       "True                            -                   -                    -   \n",
       "\n",
       "             pct75_height_backend  \n",
       "hypertension                       \n",
       "False                           -  \n",
       "True                            -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "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>count_height</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>pct25_height</th>\n",
       "      <th>pct75_height</th>\n",
       "      <th>count_height_backend</th>\n",
       "      <th>mean_height_backend</th>\n",
       "      <th>pct25_height_backend</th>\n",
       "      <th>pct75_height_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning_disability</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count_height mean_height pct25_height pct75_height  \\\n",
       "learning_disability                                                      \n",
       "False                          -           -            -            -   \n",
       "True                           -           -            -            -   \n",
       "\n",
       "                    count_height_backend mean_height_backend  \\\n",
       "learning_disability                                            \n",
       "False                                  -                   -   \n",
       "True                                   -                   -   \n",
       "\n",
       "                    pct25_height_backend pct75_height_backend  \n",
       "learning_disability                                            \n",
       "False                                  -                    -  \n",
       "True                                   -                    -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    }
   ],
   "source": [
    "for suffix in [''] + covariates: \n",
    "    df_oor = pd.read_csv(f'../output/{output_path}/tables/out_of_range{suffix}.csv', index_col=0)\n",
    "    display(df_oor)\n",
    "    \n",
    "    img_exist = os.path.exists(f'../output/{output_path}/figures/out_of_range{suffix}.png')\n",
    "    \n",
    "    if img_exist:\n",
    "        img_oor = Image(f'../output/{output_path}/figures/out_of_range{suffix}.png')\n",
    "        display(img_oor)\n",
    "    else: \n",
    "        print('Plot redacted due to low counts.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}