{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# National mRS distributions\n",
    "\n",
    "This notebook creates a plot of the mRS distributions averaged across England and Wales.\n",
    "\n",
    "## Plain English summary\n",
    "\n",
    "This notebook loads the data that we created in a previous notebook. It loads the mRS distributions averaged across all parts of England and Wales where redirection from the nearest stroke unit to an MT centre makes a difference to which stroke unit is attended first.\n",
    "\n",
    "## Aims\n",
    "\n",
    "Plot the national mRS distributions for these cohorts:\n",
    "+ nLVO with IVT\n",
    "+ LVO with mixed treatments\n",
    "+ treated ischaemic population\n",
    "\n",
    "\n",
    "## Methods\n",
    "\n",
    "Load in the existing data.\n",
    "\n",
    "Create new no-treatment mRS distributions for the weighted combination by combining the separate nLVO and LVO no-treatment distributions in the required proportions.\n",
    "\n",
    "Plot the mRS distributions and the cumulative probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_output = 'output'\n",
    "limit_to_england = False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "import stroke_outcome.outcome_utilities\n",
    "\n",
    "pd.set_option('display.max_rows', 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.1.6'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stroke_outcome.__version__"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "Results from outcome model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mrs_national_cumsum = pd.read_csv(os.path.join(dir_output, 'cohort_mrs_dists_weighted_national.csv'), index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mRS&lt;=0</th>\n",
       "      <th>mRS&lt;=1</th>\n",
       "      <th>mRS&lt;=2</th>\n",
       "      <th>mRS&lt;=3</th>\n",
       "      <th>mRS&lt;=4</th>\n",
       "      <th>mRS&lt;=5</th>\n",
       "      <th>mRS&lt;=6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>drip_ship_lvo_ivt</th>\n",
       "      <td>0.10356</td>\n",
       "      <td>0.19544</td>\n",
       "      <td>0.32788</td>\n",
       "      <td>0.49012</td>\n",
       "      <td>0.70857</td>\n",
       "      <td>0.82316</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_lvo_ivt_mt</th>\n",
       "      <td>0.11472</td>\n",
       "      <td>0.22605</td>\n",
       "      <td>0.37176</td>\n",
       "      <td>0.54453</td>\n",
       "      <td>0.75047</td>\n",
       "      <td>0.85084</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_lvo_mix</th>\n",
       "      <td>0.11382</td>\n",
       "      <td>0.22365</td>\n",
       "      <td>0.36835</td>\n",
       "      <td>0.54031</td>\n",
       "      <td>0.74723</td>\n",
       "      <td>0.84869</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_lvo_mt</th>\n",
       "      <td>0.11449</td>\n",
       "      <td>0.22589</td>\n",
       "      <td>0.37170</td>\n",
       "      <td>0.54455</td>\n",
       "      <td>0.75052</td>\n",
       "      <td>0.85086</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_nlvo_ivt</th>\n",
       "      <td>0.36134</td>\n",
       "      <td>0.58906</td>\n",
       "      <td>0.70508</td>\n",
       "      <td>0.82416</td>\n",
       "      <td>0.92123</td>\n",
       "      <td>0.95515</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_weighted</th>\n",
       "      <td>0.18250</td>\n",
       "      <td>0.39015</td>\n",
       "      <td>0.51547</td>\n",
       "      <td>0.65399</td>\n",
       "      <td>0.81988</td>\n",
       "      <td>0.89575</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_weighted_treated</th>\n",
       "      <td>0.23655</td>\n",
       "      <td>0.40482</td>\n",
       "      <td>0.53531</td>\n",
       "      <td>0.68105</td>\n",
       "      <td>0.83350</td>\n",
       "      <td>0.90148</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_ivt</th>\n",
       "      <td>0.09705</td>\n",
       "      <td>0.18809</td>\n",
       "      <td>0.32101</td>\n",
       "      <td>0.48329</td>\n",
       "      <td>0.70384</td>\n",
       "      <td>0.81982</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_ivt_mt</th>\n",
       "      <td>0.15523</td>\n",
       "      <td>0.27670</td>\n",
       "      <td>0.42080</td>\n",
       "      <td>0.59378</td>\n",
       "      <td>0.78327</td>\n",
       "      <td>0.87247</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_mix</th>\n",
       "      <td>0.15072</td>\n",
       "      <td>0.26984</td>\n",
       "      <td>0.41306</td>\n",
       "      <td>0.58522</td>\n",
       "      <td>0.77711</td>\n",
       "      <td>0.86839</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_mt</th>\n",
       "      <td>0.15523</td>\n",
       "      <td>0.27670</td>\n",
       "      <td>0.42080</td>\n",
       "      <td>0.59378</td>\n",
       "      <td>0.78327</td>\n",
       "      <td>0.87247</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_nlvo_ivt</th>\n",
       "      <td>0.34442</td>\n",
       "      <td>0.57736</td>\n",
       "      <td>0.69431</td>\n",
       "      <td>0.81494</td>\n",
       "      <td>0.91617</td>\n",
       "      <td>0.95206</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_weighted</th>\n",
       "      <td>0.18504</td>\n",
       "      <td>0.39450</td>\n",
       "      <td>0.51975</td>\n",
       "      <td>0.65849</td>\n",
       "      <td>0.82301</td>\n",
       "      <td>0.89784</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_weighted_treated</th>\n",
       "      <td>0.24676</td>\n",
       "      <td>0.42231</td>\n",
       "      <td>0.55251</td>\n",
       "      <td>0.69911</td>\n",
       "      <td>0.84606</td>\n",
       "      <td>0.90988</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mRS<=0   mRS<=1   mRS<=2   mRS<=3   mRS<=4  \\\n",
       "drip_ship_lvo_ivt            0.10356  0.19544  0.32788  0.49012  0.70857   \n",
       "drip_ship_lvo_ivt_mt         0.11472  0.22605  0.37176  0.54453  0.75047   \n",
       "drip_ship_lvo_mix            0.11382  0.22365  0.36835  0.54031  0.74723   \n",
       "drip_ship_lvo_mt             0.11449  0.22589  0.37170  0.54455  0.75052   \n",
       "drip_ship_nlvo_ivt           0.36134  0.58906  0.70508  0.82416  0.92123   \n",
       "drip_ship_weighted           0.18250  0.39015  0.51547  0.65399  0.81988   \n",
       "drip_ship_weighted_treated   0.23655  0.40482  0.53531  0.68105  0.83350   \n",
       "mothership_lvo_ivt           0.09705  0.18809  0.32101  0.48329  0.70384   \n",
       "mothership_lvo_ivt_mt        0.15523  0.27670  0.42080  0.59378  0.78327   \n",
       "mothership_lvo_mix           0.15072  0.26984  0.41306  0.58522  0.77711   \n",
       "mothership_lvo_mt            0.15523  0.27670  0.42080  0.59378  0.78327   \n",
       "mothership_nlvo_ivt          0.34442  0.57736  0.69431  0.81494  0.91617   \n",
       "mothership_weighted          0.18504  0.39450  0.51975  0.65849  0.82301   \n",
       "mothership_weighted_treated  0.24676  0.42231  0.55251  0.69911  0.84606   \n",
       "\n",
       "                              mRS<=5  mRS<=6  \n",
       "drip_ship_lvo_ivt            0.82316     1.0  \n",
       "drip_ship_lvo_ivt_mt         0.85084     1.0  \n",
       "drip_ship_lvo_mix            0.84869     1.0  \n",
       "drip_ship_lvo_mt             0.85086     1.0  \n",
       "drip_ship_nlvo_ivt           0.95515     1.0  \n",
       "drip_ship_weighted           0.89575     1.0  \n",
       "drip_ship_weighted_treated   0.90148     1.0  \n",
       "mothership_lvo_ivt           0.81982     1.0  \n",
       "mothership_lvo_ivt_mt        0.87247     1.0  \n",
       "mothership_lvo_mix           0.86839     1.0  \n",
       "mothership_lvo_mt            0.87247     1.0  \n",
       "mothership_nlvo_ivt          0.95206     1.0  \n",
       "mothership_weighted          0.89784     1.0  \n",
       "mothership_weighted_treated  0.90988     1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mrs_national_cumsum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mrs_national_noncum = pd.read_csv(os.path.join(dir_output, 'cohort_mrs_dists_weighted_national_noncum.csv'), index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>mRS=0</th>\n",
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       "    <tr>\n",
       "      <th>drip_ship_lvo_ivt</th>\n",
       "      <td>0.10356</td>\n",
       "      <td>0.09188</td>\n",
       "      <td>0.13244</td>\n",
       "      <td>0.16224</td>\n",
       "      <td>0.21845</td>\n",
       "      <td>0.11459</td>\n",
       "      <td>0.17684</td>\n",
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       "      <th>drip_ship_lvo_ivt_mt</th>\n",
       "      <td>0.11472</td>\n",
       "      <td>0.11133</td>\n",
       "      <td>0.14571</td>\n",
       "      <td>0.17277</td>\n",
       "      <td>0.20594</td>\n",
       "      <td>0.10037</td>\n",
       "      <td>0.14916</td>\n",
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       "      <th>drip_ship_lvo_mix</th>\n",
       "      <td>0.11382</td>\n",
       "      <td>0.10983</td>\n",
       "      <td>0.14470</td>\n",
       "      <td>0.17197</td>\n",
       "      <td>0.20692</td>\n",
       "      <td>0.10146</td>\n",
       "      <td>0.15131</td>\n",
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       "    <tr>\n",
       "      <th>drip_ship_lvo_mt</th>\n",
       "      <td>0.11449</td>\n",
       "      <td>0.11139</td>\n",
       "      <td>0.14582</td>\n",
       "      <td>0.17284</td>\n",
       "      <td>0.20597</td>\n",
       "      <td>0.10034</td>\n",
       "      <td>0.14914</td>\n",
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       "    <tr>\n",
       "      <th>drip_ship_nlvo_ivt</th>\n",
       "      <td>0.36134</td>\n",
       "      <td>0.22771</td>\n",
       "      <td>0.11603</td>\n",
       "      <td>0.11907</td>\n",
       "      <td>0.09707</td>\n",
       "      <td>0.03393</td>\n",
       "      <td>0.04485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_weighted</th>\n",
       "      <td>0.18250</td>\n",
       "      <td>0.20765</td>\n",
       "      <td>0.12532</td>\n",
       "      <td>0.13852</td>\n",
       "      <td>0.16589</td>\n",
       "      <td>0.07586</td>\n",
       "      <td>0.10425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_weighted_treated</th>\n",
       "      <td>0.23655</td>\n",
       "      <td>0.16828</td>\n",
       "      <td>0.13048</td>\n",
       "      <td>0.14574</td>\n",
       "      <td>0.15245</td>\n",
       "      <td>0.06798</td>\n",
       "      <td>0.09852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_ivt</th>\n",
       "      <td>0.09705</td>\n",
       "      <td>0.09104</td>\n",
       "      <td>0.13293</td>\n",
       "      <td>0.16228</td>\n",
       "      <td>0.22054</td>\n",
       "      <td>0.11599</td>\n",
       "      <td>0.18018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_ivt_mt</th>\n",
       "      <td>0.15523</td>\n",
       "      <td>0.12147</td>\n",
       "      <td>0.14409</td>\n",
       "      <td>0.17298</td>\n",
       "      <td>0.18949</td>\n",
       "      <td>0.08921</td>\n",
       "      <td>0.12753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_mix</th>\n",
       "      <td>0.15072</td>\n",
       "      <td>0.11911</td>\n",
       "      <td>0.14323</td>\n",
       "      <td>0.17215</td>\n",
       "      <td>0.19189</td>\n",
       "      <td>0.09128</td>\n",
       "      <td>0.13161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_mt</th>\n",
       "      <td>0.15523</td>\n",
       "      <td>0.12147</td>\n",
       "      <td>0.14409</td>\n",
       "      <td>0.17298</td>\n",
       "      <td>0.18949</td>\n",
       "      <td>0.08921</td>\n",
       "      <td>0.12753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_nlvo_ivt</th>\n",
       "      <td>0.34442</td>\n",
       "      <td>0.23294</td>\n",
       "      <td>0.11695</td>\n",
       "      <td>0.12062</td>\n",
       "      <td>0.10124</td>\n",
       "      <td>0.03589</td>\n",
       "      <td>0.04794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_weighted</th>\n",
       "      <td>0.18504</td>\n",
       "      <td>0.20946</td>\n",
       "      <td>0.12525</td>\n",
       "      <td>0.13873</td>\n",
       "      <td>0.16452</td>\n",
       "      <td>0.07483</td>\n",
       "      <td>0.10216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_weighted_treated</th>\n",
       "      <td>0.24676</td>\n",
       "      <td>0.17555</td>\n",
       "      <td>0.13020</td>\n",
       "      <td>0.14660</td>\n",
       "      <td>0.14695</td>\n",
       "      <td>0.06382</td>\n",
       "      <td>0.09012</td>\n",
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      ],
      "text/plain": [
       "                               mRS=0    mRS=1    mRS=2    mRS=3    mRS=4  \\\n",
       "drip_ship_lvo_ivt            0.10356  0.09188  0.13244  0.16224  0.21845   \n",
       "drip_ship_lvo_ivt_mt         0.11472  0.11133  0.14571  0.17277  0.20594   \n",
       "drip_ship_lvo_mix            0.11382  0.10983  0.14470  0.17197  0.20692   \n",
       "drip_ship_lvo_mt             0.11449  0.11139  0.14582  0.17284  0.20597   \n",
       "drip_ship_nlvo_ivt           0.36134  0.22771  0.11603  0.11907  0.09707   \n",
       "drip_ship_weighted           0.18250  0.20765  0.12532  0.13852  0.16589   \n",
       "drip_ship_weighted_treated   0.23655  0.16828  0.13048  0.14574  0.15245   \n",
       "mothership_lvo_ivt           0.09705  0.09104  0.13293  0.16228  0.22054   \n",
       "mothership_lvo_ivt_mt        0.15523  0.12147  0.14409  0.17298  0.18949   \n",
       "mothership_lvo_mix           0.15072  0.11911  0.14323  0.17215  0.19189   \n",
       "mothership_lvo_mt            0.15523  0.12147  0.14409  0.17298  0.18949   \n",
       "mothership_nlvo_ivt          0.34442  0.23294  0.11695  0.12062  0.10124   \n",
       "mothership_weighted          0.18504  0.20946  0.12525  0.13873  0.16452   \n",
       "mothership_weighted_treated  0.24676  0.17555  0.13020  0.14660  0.14695   \n",
       "\n",
       "                               mRS=5    mRS=6  \n",
       "drip_ship_lvo_ivt            0.11459  0.17684  \n",
       "drip_ship_lvo_ivt_mt         0.10037  0.14916  \n",
       "drip_ship_lvo_mix            0.10146  0.15131  \n",
       "drip_ship_lvo_mt             0.10034  0.14914  \n",
       "drip_ship_nlvo_ivt           0.03393  0.04485  \n",
       "drip_ship_weighted           0.07586  0.10425  \n",
       "drip_ship_weighted_treated   0.06798  0.09852  \n",
       "mothership_lvo_ivt           0.11599  0.18018  \n",
       "mothership_lvo_ivt_mt        0.08921  0.12753  \n",
       "mothership_lvo_mix           0.09128  0.13161  \n",
       "mothership_lvo_mt            0.08921  0.12753  \n",
       "mothership_nlvo_ivt          0.03589  0.04794  \n",
       "mothership_weighted          0.07483  0.10216  \n",
       "mothership_weighted_treated  0.06382  0.09012  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mrs_national_noncum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mrs_national_std = pd.read_csv(os.path.join(dir_output, 'cohort_mrs_dists_weighted_national_std.csv'), index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>mRS=0</th>\n",
       "      <th>mRS=1</th>\n",
       "      <th>mRS=2</th>\n",
       "      <th>mRS=3</th>\n",
       "      <th>mRS=4</th>\n",
       "      <th>mRS=5</th>\n",
       "      <th>mRS=6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>drip_ship_lvo_ivt</th>\n",
       "      <td>0.00262</td>\n",
       "      <td>0.00029</td>\n",
       "      <td>0.00022</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.00083</td>\n",
       "      <td>0.00055</td>\n",
       "      <td>0.00129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_lvo_ivt_mt</th>\n",
       "      <td>0.00983</td>\n",
       "      <td>0.00399</td>\n",
       "      <td>0.00143</td>\n",
       "      <td>0.00129</td>\n",
       "      <td>0.00452</td>\n",
       "      <td>0.00355</td>\n",
       "      <td>0.00729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_lvo_mix</th>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.00317</td>\n",
       "      <td>0.00112</td>\n",
       "      <td>0.00102</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00283</td>\n",
       "      <td>0.00580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_lvo_mt</th>\n",
       "      <td>0.01037</td>\n",
       "      <td>0.00373</td>\n",
       "      <td>0.00054</td>\n",
       "      <td>0.00080</td>\n",
       "      <td>0.00460</td>\n",
       "      <td>0.00347</td>\n",
       "      <td>0.00723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_nlvo_ivt</th>\n",
       "      <td>0.00667</td>\n",
       "      <td>0.00215</td>\n",
       "      <td>0.00039</td>\n",
       "      <td>0.00064</td>\n",
       "      <td>0.00160</td>\n",
       "      <td>0.00074</td>\n",
       "      <td>0.00116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_weighted</th>\n",
       "      <td>0.00128</td>\n",
       "      <td>0.00048</td>\n",
       "      <td>0.00015</td>\n",
       "      <td>0.00015</td>\n",
       "      <td>0.00049</td>\n",
       "      <td>0.00037</td>\n",
       "      <td>0.00074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_weighted_treated</th>\n",
       "      <td>0.00515</td>\n",
       "      <td>0.00192</td>\n",
       "      <td>0.00060</td>\n",
       "      <td>0.00060</td>\n",
       "      <td>0.00198</td>\n",
       "      <td>0.00147</td>\n",
       "      <td>0.00298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_ivt</th>\n",
       "      <td>0.00568</td>\n",
       "      <td>0.00090</td>\n",
       "      <td>0.00035</td>\n",
       "      <td>0.00004</td>\n",
       "      <td>0.00188</td>\n",
       "      <td>0.00128</td>\n",
       "      <td>0.00309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_ivt_mt</th>\n",
       "      <td>0.01227</td>\n",
       "      <td>0.00250</td>\n",
       "      <td>0.00093</td>\n",
       "      <td>0.00032</td>\n",
       "      <td>0.00475</td>\n",
       "      <td>0.00307</td>\n",
       "      <td>0.00577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_mix</th>\n",
       "      <td>0.00978</td>\n",
       "      <td>0.00199</td>\n",
       "      <td>0.00074</td>\n",
       "      <td>0.00025</td>\n",
       "      <td>0.00378</td>\n",
       "      <td>0.00245</td>\n",
       "      <td>0.00460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_mt</th>\n",
       "      <td>0.01227</td>\n",
       "      <td>0.00250</td>\n",
       "      <td>0.00093</td>\n",
       "      <td>0.00032</td>\n",
       "      <td>0.00475</td>\n",
       "      <td>0.00307</td>\n",
       "      <td>0.00577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_nlvo_ivt</th>\n",
       "      <td>0.01520</td>\n",
       "      <td>0.00444</td>\n",
       "      <td>0.00075</td>\n",
       "      <td>0.00131</td>\n",
       "      <td>0.00387</td>\n",
       "      <td>0.00187</td>\n",
       "      <td>0.00297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_weighted</th>\n",
       "      <td>0.00224</td>\n",
       "      <td>0.00060</td>\n",
       "      <td>0.00013</td>\n",
       "      <td>0.00016</td>\n",
       "      <td>0.00067</td>\n",
       "      <td>0.00038</td>\n",
       "      <td>0.00068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_weighted_treated</th>\n",
       "      <td>0.00901</td>\n",
       "      <td>0.00242</td>\n",
       "      <td>0.00053</td>\n",
       "      <td>0.00066</td>\n",
       "      <td>0.00271</td>\n",
       "      <td>0.00154</td>\n",
       "      <td>0.00275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               mRS=0    mRS=1    mRS=2    mRS=3    mRS=4  \\\n",
       "drip_ship_lvo_ivt            0.00262  0.00029  0.00022  0.00003  0.00083   \n",
       "drip_ship_lvo_ivt_mt         0.00983  0.00399  0.00143  0.00129  0.00452   \n",
       "drip_ship_lvo_mix            0.00784  0.00317  0.00112  0.00102  0.00360   \n",
       "drip_ship_lvo_mt             0.01037  0.00373  0.00054  0.00080  0.00460   \n",
       "drip_ship_nlvo_ivt           0.00667  0.00215  0.00039  0.00064  0.00160   \n",
       "drip_ship_weighted           0.00128  0.00048  0.00015  0.00015  0.00049   \n",
       "drip_ship_weighted_treated   0.00515  0.00192  0.00060  0.00060  0.00198   \n",
       "mothership_lvo_ivt           0.00568  0.00090  0.00035  0.00004  0.00188   \n",
       "mothership_lvo_ivt_mt        0.01227  0.00250  0.00093  0.00032  0.00475   \n",
       "mothership_lvo_mix           0.00978  0.00199  0.00074  0.00025  0.00378   \n",
       "mothership_lvo_mt            0.01227  0.00250  0.00093  0.00032  0.00475   \n",
       "mothership_nlvo_ivt          0.01520  0.00444  0.00075  0.00131  0.00387   \n",
       "mothership_weighted          0.00224  0.00060  0.00013  0.00016  0.00067   \n",
       "mothership_weighted_treated  0.00901  0.00242  0.00053  0.00066  0.00271   \n",
       "\n",
       "                               mRS=5    mRS=6  \n",
       "drip_ship_lvo_ivt            0.00055  0.00129  \n",
       "drip_ship_lvo_ivt_mt         0.00355  0.00729  \n",
       "drip_ship_lvo_mix            0.00283  0.00580  \n",
       "drip_ship_lvo_mt             0.00347  0.00723  \n",
       "drip_ship_nlvo_ivt           0.00074  0.00116  \n",
       "drip_ship_weighted           0.00037  0.00074  \n",
       "drip_ship_weighted_treated   0.00147  0.00298  \n",
       "mothership_lvo_ivt           0.00128  0.00309  \n",
       "mothership_lvo_ivt_mt        0.00307  0.00577  \n",
       "mothership_lvo_mix           0.00245  0.00460  \n",
       "mothership_lvo_mt            0.00307  0.00577  \n",
       "mothership_nlvo_ivt          0.00187  0.00297  \n",
       "mothership_weighted          0.00038  0.00068  \n",
       "mothership_weighted_treated  0.00154  0.00275  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mrs_national_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['drip_ship_lvo_ivt', 'drip_ship_lvo_ivt_mt', 'drip_ship_lvo_mix',\n",
       "       'drip_ship_lvo_mt', 'drip_ship_nlvo_ivt', 'drip_ship_weighted',\n",
       "       'drip_ship_weighted_treated', 'mothership_lvo_ivt',\n",
       "       'mothership_lvo_ivt_mt', 'mothership_lvo_mix', 'mothership_lvo_mt',\n",
       "       'mothership_nlvo_ivt', 'mothership_weighted',\n",
       "       'mothership_weighted_treated'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_each_scen = df_mrs_national_noncum.index.values\n",
    "\n",
    "cols_each_scen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference no-treatment mRS distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mrs_dists, mrs_dists_notes = (\n",
    "    stroke_outcome.outcome_utilities.import_mrs_dists_from_file())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mRS&lt;=0</th>\n",
       "      <th>mRS&lt;=1</th>\n",
       "      <th>mRS&lt;=2</th>\n",
       "      <th>mRS&lt;=3</th>\n",
       "      <th>mRS&lt;=4</th>\n",
       "      <th>mRS&lt;=5</th>\n",
       "      <th>mRS&lt;=6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stroke type</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>pre_stroke_nlvo</th>\n",
       "      <td>0.583</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.993</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_stroke_lvo</th>\n",
       "      <td>0.408</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.956</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_treatment_lvo</th>\n",
       "      <td>0.050</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.811</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_treatment_nlvo</th>\n",
       "      <td>0.198</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.918</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_effect_nlvo_ivt_deaths</th>\n",
       "      <td>0.196</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.908</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_effect_lvo_ivt_deaths</th>\n",
       "      <td>0.048</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.783</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_effect_lvo_mt_deaths</th>\n",
       "      <td>0.048</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.779</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t0_treatment_nlvo_ivt</th>\n",
       "      <td>0.445</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.967</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t0_treatment_lvo_ivt</th>\n",
       "      <td>0.140</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.838</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t0_treatment_lvo_mt</th>\n",
       "      <td>0.306</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.915</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mRS<=0  mRS<=1  mRS<=2  mRS<=3  mRS<=4  mRS<=5  \\\n",
       "Stroke type                                                                 \n",
       "pre_stroke_nlvo             0.583   0.746   0.850   0.951   0.993   1.000   \n",
       "pre_stroke_lvo              0.408   0.552   0.672   0.838   0.956   1.000   \n",
       "no_treatment_lvo            0.050   0.129   0.265   0.429   0.676   0.811   \n",
       "no_treatment_nlvo           0.198   0.460   0.580   0.708   0.856   0.918   \n",
       "no_effect_nlvo_ivt_deaths   0.196   0.455   0.574   0.701   0.847   0.908   \n",
       "no_effect_lvo_ivt_deaths    0.048   0.124   0.255   0.414   0.653   0.783   \n",
       "no_effect_lvo_mt_deaths     0.048   0.124   0.255   0.412   0.649   0.779   \n",
       "t0_treatment_nlvo_ivt       0.445   0.642   0.752   0.862   0.941   0.967   \n",
       "t0_treatment_lvo_ivt        0.140   0.233   0.361   0.522   0.730   0.838   \n",
       "t0_treatment_lvo_mt         0.306   0.429   0.548   0.707   0.851   0.915   \n",
       "\n",
       "                           mRS<=6  \n",
       "Stroke type                        \n",
       "pre_stroke_nlvo                 1  \n",
       "pre_stroke_lvo                  1  \n",
       "no_treatment_lvo                1  \n",
       "no_treatment_nlvo               1  \n",
       "no_effect_nlvo_ivt_deaths       1  \n",
       "no_effect_lvo_ivt_deaths        1  \n",
       "no_effect_lvo_mt_deaths         1  \n",
       "t0_treatment_nlvo_ivt           1  \n",
       "t0_treatment_lvo_ivt            1  \n",
       "t0_treatment_lvo_mt             1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrs_dists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mrs_dist_nlvo_no_treatment = mrs_dists.loc['no_treatment_nlvo'].values\n",
    "mrs_dist_lvo_no_treatment = mrs_dists.loc['no_treatment_lvo'].values\n",
    "\n",
    "mrs_dist_nlvo_no_treatment_noncum = np.diff(mrs_dist_nlvo_no_treatment, prepend=0.0)\n",
    "mrs_dist_lvo_no_treatment_noncum = np.diff(mrs_dist_lvo_no_treatment, prepend=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stroke type proportions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "proportions = pd.read_csv(\n",
    "    os.path.join(dir_output, 'patient_proportions.csv'),\n",
    "    index_col=0, header=None).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "haemorrhagic         0.13600\n",
       "lvo_no_treatment     0.14648\n",
       "lvo_ivt_only         0.00840\n",
       "lvo_ivt_mt           0.08500\n",
       "lvo_mt_only          0.01500\n",
       "nlvo_no_treatment    0.50252\n",
       "nlvo_ivt             0.10660\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate weighted no treatment dist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_lvo_treated = 0.0\n",
    "prop_nlvo_treated = 0.0\n",
    "\n",
    "for key, value in proportions.items():\n",
    "    if ('nlvo' in key) & ('no_treatment' not in key):\n",
    "        prop_nlvo_treated += value\n",
    "    elif ('lvo' in key) & ('no_treatment' not in key):\n",
    "        prop_lvo_treated += value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.10840000000000001, 0.1066)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_lvo_treated, prop_nlvo_treated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5041860465116279, 0.49581395348837204)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_lvo_treated / (prop_nlvo_treated + prop_lvo_treated), prop_nlvo_treated / (prop_nlvo_treated + prop_lvo_treated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "mrs_dist_weighted_no_treatment_noncum = (\n",
    "    (mrs_dist_nlvo_no_treatment_noncum * prop_nlvo_treated) +\n",
    "    (mrs_dist_lvo_no_treatment_noncum * prop_lvo_treated)\n",
    ")\n",
    "\n",
    "# Remove the non-treated patients:\n",
    "mrs_dist_weighted_no_treatment_noncum = (\n",
    "    mrs_dist_weighted_no_treatment_noncum / (prop_nlvo_treated + prop_lvo_treated)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(mrs_dist_weighted_no_treatment_noncum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same structure as the other distribution dictionaries:\n",
    "dict_no_treat = {\n",
    "    'nlvo': {\n",
    "        'values': mrs_dist_nlvo_no_treatment_noncum,\n",
    "        'cumsum': np.cumsum(mrs_dist_nlvo_no_treatment_noncum)\n",
    "    },\n",
    "    'lvo': {\n",
    "        'values': mrs_dist_lvo_no_treatment_noncum,\n",
    "        'cumsum': np.cumsum(mrs_dist_lvo_no_treatment_noncum)\n",
    "    },\n",
    "    # 'weighted': {\n",
    "    #     'values': mrs_dist_weighted_no_treatment_noncum,\n",
    "    #     'cumsum': np.cumsum(mrs_dist_weighted_no_treatment_noncum)\n",
    "    # },\n",
    "    'weighted_treated': {\n",
    "        'values': mrs_dist_weighted_no_treatment_noncum,\n",
    "        'cumsum': np.cumsum(mrs_dist_weighted_no_treatment_noncum)\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gather data for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gather_dists(scenario, df_mrs_national_noncum, df_mrs_national_std, df_mrs_national_cumsum):\n",
    "    d = {}\n",
    "\n",
    "    for cohort in ['nlvo_ivt', 'lvo_mix', 'weighted_treated']:\n",
    "        ind = f'{scenario}_{cohort}'\n",
    "\n",
    "        d[cohort] = {}\n",
    "        d[cohort]['values'] = df_mrs_national_noncum.loc[ind]\n",
    "        d[cohort]['std'] = df_mrs_national_std.loc[ind]\n",
    "        d[cohort]['cumsum'] = df_mrs_national_cumsum.loc[ind]\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_drip_ship = gather_dists('drip_ship', df_mrs_national_noncum, df_mrs_national_std, df_mrs_national_cumsum)\n",
    "dict_mothership = gather_dists('mothership', df_mrs_national_noncum, df_mrs_national_std, df_mrs_national_cumsum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "colour_drip = '#00517f'   # seaborn colorblind blue darker\n",
    "colour_moth = '#0072b2'  # seaborn colorblind blue\n",
    "colour_no_treat = 'DarkGray'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Arm/MH0AFCgqR/G60rnfzrPhYgKvA3AEAsDcPPPCAoqOj9fvvv0uSvvvuOy1ZskRr1qyRJGVlZWnOnDlasGCBevbsKalgi9NVq1bpvffe07PPPmvOy9WpU0c1atSw6L9Vq1aKiYmRJDVp0kRvvvmmkpOT1b17d61bt04bN25Uenq6XF1dJRWsml2xYoU+/PBD8x61OTk5WrRokWrXri1J2rx5sy5cuKB+/fqpQYMGkqSWLVtaPK+Xl5fefPNNOTo6qmnTpurVq5eSk5P18MMPF/laDBgwQA899JAkadKkSVq1apVmzZqlt956q1SvbVkpdgL3wIEDZf7kx48fV15ennx8fCzqfXx8tGvXrmL18dxzz8nf39+csE1NTTX38c8+L577p/j4+ELvagwAwOUwfwAVyK1qwQFUcswdAAB7U7t2bfXq1UsLFiyQYRjq1auXvL29zef37dun3Nxc3XbbbeY6Z2dntW3btlirU1u1amVR9vPzM2+d+vPPP+vs2bOqVauWRZs///xT+/btM5cbNGhgTt5KUuvWrXX77berZcuWioiI0B133KF7771XXl5e5jYtWrSwuH+Xn5+ftm/fftlY27VrZ1XeunXrFcdY3oqdwL2YzbYnr776qvkTgYv7X5RGdHS0oqKizOXMzEwFBASURYjlatmyZSVqP3DgwHKKBACuT5V1/gAA2A5zB1D58V4c16IRI0Zo7NixkqTZs2eXad/Ozs4WZZPJpPz8fEnS2bNn5efnZ17t+3d/X8nr4eFhcc7R0VGrVq3S999/ry+//FKzZs3Siy++qA0bNigoKOiKz1vZFDuBW5idO3fq0KFDVnd/u+uuu4p1vbe3txwdHZWWlmZRn5aWJl9f38teO23aNL366qv66quvLDL5F69LS0uTn5+fRZ/BwcGF9uXq6mpepg0AQHExfwD2KyUlRSkpKVb1fn5+Fn8jAhWNuQMAYI969OihnJwcmUwmRUREWJxr1KiRXFxc9N1335kXeObm5urHH3/UuHHjJEkuLi6SpLy8vBI9b5s2bZSamionJycFBgaW6FqTyaTbbrtNt912myZMmKAGDRrov//9r8UHpSX1ww8/aOjQoRblv9/MzVZKlcDdv3+/7rnnHm3fvt1iX1yTySSp+D8sFxcXhYSEKDk5WX379pUk8w3JLmb9CzN16lS98sorWrlypUJDQy3OBQUFydfXV8nJyeaEbWZmpjZs2KDHHnushCMFAABAZZSYmFjo19RjYmKs7nwMAABwvXN0dDRvh/D3bQekgtWvjz32mHmv2/r162vq1Kk6d+6cRo4cKangm/smk0n/+9//dOedd8rd3V2enle+V0F4eLjatWunvn37aurUqbrhhht09OhRffbZZ7rnnnus8n4XbdiwQcnJybrjjjtUp04dbdiwQceOHVOzZs2u6nX44IMPFBoaqg4dOug///mPNm7cqPfee++q+iwLpUrgPvnkkwoKClJycrKCgoK0ceNGnThxQk8//bSmTZtWor6ioqI0bNgwhYaGqm3btkpISFBWVpb5bndDhw5V3bp1FR8fL0maMmWKJkyYoMWLFyswMNC8r62np6c8PT1lMpk0btw4TZ48WU2aNFFQUJBefvll+fv7m5PEAAAAuLaNGjVK3bt3V4cOHSRJ69atk7u7O6tvAQBAhTGm97F1CCVSrVq1Is+9+uqrys/P14MPPqgzZ84oNDRUK1euNO85W7duXcXFxen5559XZGSkhg4dqgULFlzxOU0mkz7//HO9+OKLioyM1LFjx+Tr66tOnTpZ3d/qn7F+8803SkhIUGZmpho0aKDp06ebb7JWWnFxcVqyZIlGjx4tPz8//d///Z+aN29+VX2WhVIlcNevX6+vv/5a3t7ecnBwkIODgzp06KD4+Hg98cQT2rJlS7H7GjRokI4dO6YJEyYoNTVVwcHBSkpKMv+QDh06JAcHB3P7OXPmKCcnR/fee69FP39fTTF+/HhlZWXpkUceUUZGhjp06KCkpKSr2icXAAAA9s309KeWFbnnzQ87LD0qObtJOmKuWxpWQYEBAADYoSslWFesWGF+7ObmppkzZ2rmzJlFtn/55Zf18ssvW9QVtrft3/uVpKpVq16279jYWKtvUDVr1kxJSUlFxlLY2BISEizKBw8etGrj7++vL7/8ssh+baVUCdy8vDxVrVpwF2Bvb28dPXpUN954oxo0aKDdu3eXuL+xY8cWuWXCP3/Qhb24/2QymTRx4kRNnDixxLEAAAAAAAAAgL0oVQL3pptu0s8//6ygoCCFhYVp6tSpcnFx0dtvv62GDRuWdYwAAABAyZw9KZ1OvVRO3y85uUoeXpJnTdvFBSvczR0AAODySpXAfemll5SVlSVJmjhxonr37q2OHTuqVq1aWrp0aZkGCAAAAJTYtiTphyWXykufL/jvrYOl9vfZJiYAAADYLcMwbB1CkUqVwI2IiDA/bty4sXbt2qWTJ0/Ky8tLJpOpzIIDAAAASqVVD6lRIZvcenhVfCwAAADAVShVAvfv/vjjD0lSQEDAVQcDAAAAlAnPmmyVAAAAgGuCQ2kuunDhgl5++WVVr15dgYGBCgwMVPXq1fXSSy8pNze3rGMEAAAAAAAAgOtSqVbgPv7441q+fLmmTp2qdu3aSZLWr1+v2NhYnThxQnPmzCnTIAEAAAAAAADgelSqBO7ixYu1ZMkS9ezZ01zXqlUrBQQEaMiQISRwAQAAAAAVJiUlRSkpKVb1fn5+8vPzs0FEAACUnVJtoeDq6qrAwECr+qCgILm4uFxtTAAAAAAAFFtiYqJCQkKsjsTERFuHBgDAVSvVCtyxY8dq0qRJmj9/vlxdXSVJ2dnZeuWVVzR27NgyDRAAAAAAgH8yPf3ppcLZIGnQq9LS5wvKg16VnFwVl+KluL/aGdP72CBKANezZcuWVejzDRw4sEKfDxWn2Ancfv36WZS/+uor1atXT61bt5Yk/fzzz8rJydHtt99ethECAADgqpT0q8Vbt27Vjh07rOpbtGih4ODg8ggRAAAAFWz48OFauHCh4uPj9fzzz5vrV6xYoXvuuUeGYZS679jYWK1YsUJbt24tg0ivbMGCBRo3bpwyMjIq5Pmu5ODBgwoKCtKWLVvK5O/nYidwq1evblHu37+/RTkgIOCqgwEAAEDZMPWLvVTYvlLa+4N1o8a3Si0jCh4HhVyqXxotHbFO4KpuC2lQvCRWsgEoXyXe03ZbkvTDkkvliytxbx0stb+vnKIEgMrPzc1NU6ZM0ahRo+Tl5VXhz5+bmytnZ+cKf97KptgJ3Pnz55dnHAAAALAXXR+WThyyrq9Vv+JjAXBdSkxMVFxcnFV9TEyMYmNjrS9o1UNqFGZd71HxyQgAqEzCw8O1d+9excfHa+rUqUW2++ijjzRhwgTt3btXfn5+evzxx/X0008X2nbBggXm3+Emk0lSQV5x+PDhMplMeuutt/TFF18oOTlZzz77rGJjY/Xxxx8rLi5OO3fulL+/v4YNG6YXX3xRTk4FqcsZM2Zo/vz52r9/v2rWrKk+ffpo6tSp8vT01Jo1axQZGWnxfBfni8DAQD300EP67bfftHz5ctWqVUuzZs1Su3bt9NBDDyk5OVkNGzbUvHnzFBoaah7DunXrFB0drZ9++kne3t665557FB8fLw8PD0lSYGCgHnnkEe3du1cffPCBvLy89NJLL+mRRx6RVHCfMEm6+eabJUmdO3fWmjVrSvUzkkq5B+5Fx44d0+7duyVJN954o2rXrn013QEAAKA8NGkvBbSyrnfzLLx9nYYFBwDYyKhRo9S9e3d16NBBUsEbaXd398JX30qSZ82C4zJKuhcle0kCuB44OjrqX//6l+677z498cQTqlevnlWbTZs2aeDAgYqNjdWgQYP0/fffa/To0apVq5aGDx9u1X7QoEH65ZdflJSUpK+++kqS5Tf7Y2Nj9eqrryohIUFOTk769ttvNXToUM2cOVMdO3bUvn37zInQmJgYSZKDg4NmzpypoKAg7d+/X6NHj9b48eP11ltvqX379kpISNCECRPMeUpPz0t/577++uv617/+pZdfflmvv/66HnzwQbVv314jRozQa6+9pueee05Dhw7Vjh07ZDKZtG/fPvXo0UOTJ0/WvHnzdOzYMY0dO1Zjx461WOA6ffp0TZo0SS+88II+/PBDPfbYY+rcubNuvPFGbdy4UW3bttVXX32lFi1ayMXF5ap+TqVK4GZlZenxxx/XokWLlJ+fL6ngBz506FDNmjVLVapUuaqgAAAAUIbcqhYcAGDHLLZ+OX9GOnvKXOwQ/Z7k5FzwwdPF32d/3/oFAFBq99xzj4KDgxUTE6P33nvP6vyMGTN0++236+WXX5Yk3XDDDdq5c6dee+21QhO47u7u8vT0lJOTk3x9fa3O33fffeYVs5I0YsQIPf/88xo2bJgkqWHDhpo0aZLGjx9vTuCOGzfO3D4wMFCTJ0/Wo48+qrfeeksuLi6qXr26TCZToc935513atSoUZKkCRMmaM6cObrllls0YMAASdJzzz2ndu3aKS0tTb6+voqPj9f9999vfs4mTZpo5syZ6ty5s+bMmSM3Nzdzv6NHjzb38frrr2v16tUWi1xr1apVaEwl5VCai6KiorR27Vp9+umnysjIUEZGhj7++GOtXbu2yOXTAAAAAAAUy4FN0rd/28bv2/nS6rcL6gEAZW7KlClauHChfv31V6tzv/76q2677TaLuttuu0179uxRXl5eiZ/r71sVSNLPP/+siRMnytPT03w8/PDDSklJ0blz5yRJX331lW6//XbVrVtXVatW1YMPPqgTJ06Yz19Oq1aXvonm4+MjSWrZsqVVXXp6ujmeBQsWWMQTERGh/Px8HThwoNB+LyaPL/ZR1kq1Avejjz7Shx9+qC5dupjr7rzzTrm7u2vgwIGaM2dOWcUHAAAAO1TSryJLfB0ZQAkEhUh+N1rXF7X1CwDgqnTq1EkRERGKjo4udFVtWbq4j+x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2loiICHx9fRs4QiGEEP8VG7OKeXThfjYePwOAn7MN//t5G+/++DsllTVo1CoeH9ye527pTE1VBcuWLUOr1dKjRw+sra0bOHohhBBXYm9OGS/+lsGCfbnUGUw1zLUqsKotp7owhz015ajVKga0jGJs31b0SAo/pwSCoiiUl5ebE7UuLi4kJyfj6ekpyVshroAkcIUQjV5lTS2z16YzY+lWdmblgYM7eEWBjb15H393Z94Z3dVcj+k7Hy9cbK0IdrVDrW68q1wXFBSQmZmJs7Mz8fHxgKmGrZWVFQaDgYqKClxdXQHw9vZuwEiFEEL8lxwtquSpRQf5YfdpAOytNdyT6s+uXbt48esNALSM8OOzMTfQPMzUsahS7FCr1ajVaurq6iSBK4QQTUR1nYGS6jr8nG0BMCgKc/fkAOBuZaQk/xT6siL0RgOezvbc068D9/dqSai323nPV1NTw/r16yktLaVfv37Y2prOK+V0hLhyksAVQjRa+0/mM2PpNr5cvZuyKh04ekBQEmhMly47KzV3tgzkoQ5hJPqd3Yvb3N/lfKdsUDqdjvz8fLy8vMwPMTU1NZw8eRIXFxdzAlelUtG9e3fs7OzOWrBMCCGEuBaOF1cR+79V1BqMqFQwIjUQf6Wct+f8Qk2tHjtrLS8P68b4/mlo/jbiytbWlk6dOmFnZycriAshRCNmNCrsySljWUYByzIKWHu0mIHxPvwwPJXKmlo27z2Mn1JKzulTFNdWA5AWGcDYvq25pV0cttYXv8bb2NhgNJoWGSkuLpZSOkLUA0ngCiEaldo6PT9tOcj0pVtZve+4eXszP3fatUjgywMVBLvZMa59KHenBeNu33hH9/x9hVWAdevWUVRURKtWrQgLCwNMo2rj4uLw8fE561h7e3uEEEKIa+Xvq4qHuNvTN8aLyloDd7fw4vUfljPzWC4APZLC+eiBAYT7ulNdXc3aTZvOuo/JtFghhGi8vtp2kqUHC1h2uICCitqzvtt6vIgHP/yZb9ftpbTKtLaIrbWWod2SGdu3NS0jLpyENRgMZGVlERYWhlqtRqVS0bp1a6ysrOS9Roh6IglcIUSjcKKghI+XbefT5TvIK60ylUnwjyXWTcvUIc3pkRSOUYEhB/LpF+vd4IuMXUx1dTUbN26koqKCgQMHml+Ivb290ev1ZyV1bWxsSEhIaKhQhRBC/McpisKCvbm8tCyDX+5Ow9/FNEPk01sS+d/8Ndz5xm8YjApujra8M6oPw7s0N9/XDh06REFBAdu3b6dPnz7n1D8UQgjRcCp0enafLqN92F8LHn+wPovNJ0oAsLdSE+asxVhVypGs42Qdq+TDP/YL93Hjwd6pjOreAg+niydgFUVh5cqVnDlzBpVKRXh4OIB5MWYhRP2QBK4QosEYjUZ+23WEGb9u45ftGRhVWnD2QhXcDEVtujzV2TvQs3kEKpUKNTAooXEt4FVRUcGpU6ews7MjODgYMCVlS0pK0Ov1lJaWmmvYJiQkkJiY2IDRCiGEEH/ZdrKERxfuY83RYgCmrMxk2o0JrEw/yn0zfuZIrmnhsts7JDB1dB98XB3POj4hIQGdTkdcXJwkb4UQooEZjAo7skv5LSOfZRmFbMgqxqhA0Uu9cbGzQlenp52/HUpVGadPnyL7WA77UMzHB3o40y8lkhvTYuiVHPGvr+sqlYqQkBBqamqk9rkQFiQJXCHENVdYVskXK3fx0W/bTC+HNg7gEYrK0Q0FFQoQ7GbH2HamMgmNqQ5sRUUFtra2aLWmy2dubi67d+/Gy8vLnMBVq9W0bdsWJycnHB3/etltTL9DCCHEf9eJM1U8vfgg3+w4BYCtVs3EzuHc1zqQuz9YwOcrdgKml/kZ9/dnQGo0YJoie/r0aYKCggDQarWkpaU1zI8QQggBwLJDBXy86TgrDhdyprrurO8CXWx4Y9FW9mRksSL9GFW6v77XatS0jwmmX0ok/VIiiQ/2/lfvKzqdjr179xIREWEeqNKsWTPCwsKk/rkQFiQJXCHENaEoCpsyspmxdCs/bNiHrs4AgIu9Df5RsRwoVVCAzhEejO8QxqB4n0ZXJmHVqlXk5+fTvn17AgICAPDx8cHX1xc/P7+z9v3nZyGEEKIxeOm3DF5bcZgavWlxmbtaBjK5TzRbDh4j7fEPySupRKWCMX1a8eod3XG2N5VUMBqNrFmzhoKCAvR6vbmWuxBCiGunpLqO3zMLSQt2M5e8ySyqZO6eHABcbLUkettja6gi6/gJMnfl8Mquv473dXWkX0tTwrZHUjguDraXHcPu3bvJysqirKyMLl26mGZKqtUyE0MIC5MErhDCoiqqdcxem870pVvZnZUHGitw8iHWTcOjfVtwe4cEDhVWMWPDcR7qEEaSf8MvflJTU8PBgwepqKigQ4cO5u2Ojo4UFBRQWVlp3ubk5ESnTp0aIkwhhBDislXo9NTojXSO8OCtgXH42qsZ+/ECFmw5BEBsoCefPDiI9rHBZx2nVqvx8vKipKQEGxubhghdCCH+c+oMRjYfP8OyjEJ+yyhgy4kzGBWYPiSRB9uFApDia88NzZwoLSpg24HDrDvw1+JkarWKtlGB9EuJpG9KJMlhvlc0K/DvizPHx8dTXl5OfHy8zDAU4hqSBK4QwiL2nchnxq9b+XLVbsqra8HGAbVPOIq9Kwoq2qUFcXePZABSAq355FbXBonTYDBQXFyMWq3Gw8MDAI1Gw+HDh1EUhcrKShwcHABTrb/mzZvL1CAhhBBNgqIoLD6QT4CLLckBpsVknu4RSYcwd/rHevHJ8h088dVyyqp0WGnVPHVTR54e0hEbq79eEf750h4WFma+LwohhLCMrOIqJvy0l98ziyjX6c/6LtrLgeP5Z3j660wW7zhsGiTzN17O9vRNiaRvSjN6NY/A/RKLkF1MeXk5u3btwsXFhaSkJAAcHBzo3r37FZ9TCHFlJIErhKg3tXV65m8+wIyl21iz/zigAkc3bEL80altMP6xX6dwdwbGNcxiZIpiKtT/9xW09+7dS0BAAO3btwfAysqKhIQEHBwczirEb2t7+VOMhBBCiIaw61Qpj/28nxWHC+kY7s7qMe1QqVS42lkR7aal6wuzWLv/BABpkQF8OmYQCSE+5uONRiO7du2iqqqK9u3bo1KpUKlUkrwVQoh6VlihY8XhQmy0agYnmsqwudtbsehAPgajgoe9FR1C3XDX1pGfm8P6vfv435Ya8/EqFbRuFkDfP2rZtozwq7dyBuXl5eTk5FBQUEBMTIwsUiZEA5IErhDiqh3PL+HjZdv5dPkO8ktN5QXUahVOEc0p1WvQATZaNXekBPBQhzDzKKBrbfv27WRnZ9O+fXs8PT0BUw3bw4cPn5OcjY2NbYgQhRBCiCumNxhZfCCfz7ac4Of9eSgKWGvUtAl2o86ggGLgjQUbeOmH1dTqDTjYWvHaHT0Y06cVmn/UnS8vL+fo0aMYjUYKCgrw9vZuoF8lhBDXF53ewPpjZ1iWUcCyjAJ2nCpFUaB1sKs5getgpeHpTkHk5Bew88BRFizadNY53B3t6N0ign4pkfROboaXS/10rhmNRqqrq82ddX5+fsTFxREcHCzJWyEamCRwhRBXxGg08uuuI8xYupVFOw5jNCpg44CvmyP39WzJvT1S+Gx7Lp9uPsGY9qHcmxaMp+O1qZlXV1fH6dOnqaioID4+3rxdp9Oh0+nIy8szJ3Dd3d0ZNGiQ1G8SQgjRpH2x5QRPLz5IbrnOvO22ZH9e6xdLmIc9Ww5nc8/0haQfzwegb0ozZtw3gBBv1/Oez8XFhVatWmFlZSXJWyGEqCd3fL2DH/fmUF1nPGt7op8TrQOd+Wb1bpbszGTpzkyKyqvP2icl3I9+KZH0axlJ62YB53S8Xa3y8nLWr1+P0Wikd+/eaDQaVCoVCQkJ9dqOEOLKSAJXCHFZCkor+WLlTj78bRvH8kr4s0yCk08Q5UYt7w9vyZDm/gD8X1cHnukRibaeHy7+qa6uDr1ej52dnfnz5s2bUalUREZGmnuLY2JiiIqKwt3d3XysJG6FEEI0RVW1egxGcLI1Pc7bajXkluvwcrRmeMtARrcOJs7XiYpqHY98vpR3F21CUcDT2Z53R/dhaMfEc+6Bx48fx9PT0zzyKiQk5Jr/LiGEuB7klNWwPKOALSdKmHZjgvl6W2c0Ul1nxNfJhh6RnkS5WVFeXMjavZl8sDGb95W/zuFib0OvZNMo2z4tmuHr5lTvcRoMBjQaDQD29vbodDqMRiOlpaVnvTMJIRqeJHCFEJekKAobD51k+tKtzNmwn1q9ATRW2HgFoXHxpsoA5UZTmYTjJX/1FNtbW/4Sc+TIEXbt2kVISAipqammdu3tCQgIwMHBAaPxr95teQgRQgjRlCmKwraTpXy25QSzd5zi6e7NeLJ7JAA3Jvoyb0QqA+J8sNaaOk5/3ZnJ/R/+zPGCUgDu6pzE26N64+l87lTbQ4cOsXv3btzc3OjWrZv5hV4IIcSlVdXqWXO0mGUZBfx2qIC9ueXm78a2DyXGx5R8ndA+hNaeatIzj7N0+W6+/qP83J+SQnzo1zKSvi2a0TY6CCutZa7FpaWl7Nq1C6PRSNeuXQHTQs7t27fH2dlZyiUI0QhZJLvSuXNn7r77bm655RbziDghRNNTXq3jmzV7mLF0G3uO/7G6qUqFW1gsZSp7dApggAAXW8Ze4zIJf3JwcMBgMFBRUXHW9j8XJBNCCCGauqLKWr7ens1nW06QnvNXUmBlZqE5gWtrpeGmJFPtxJzicp74ahlfrd4DQIiXCx89MJDeLZpdsI3AwEAOHjyIv79/vS1+I4QQ/wXvrzvGowv3U2v4a+CISgUpAS70iPIkK6+YH9ftYsnOw2w4eBKD8a9hto621vRsHk7fFFPSNtDTcmuFKIpiHglsZWVFfr6ppE5lZaV55sWfZeaEEI2PRRK4LVq04LHHHuOhhx7i1ltv5e6776ZNmzaWaEoIYQF7j+cx49dtfLV6N+XVtQDYWmsZ2iGBB3qlMnbxUbadLKVDmDvjO4YxOMEXKwuXSfhTbW0tlZWVuLm5AeDr60u3bt3w8PC4Ju0LIYQQ14qiKIz+fjezd5wyJwZstGqGJPpxd1owXSLOvvdtOZzNtEWb+WHDPur0RlQqmNC/DZOHdsXR7twO1rq6OqysrABTh2jfvn1l1JUQQlxEblkNzy45xMhWgXQIN12Dw9ztqTUYCXazo2ekF+1DXVDXVLBu31G+/nEr/ysuP+sccUFe9G3RjH4tI+kQE4y1lWVnLZaVlXHgwAG0Wi0tW7YETDMWW7dufVbZHCFE42aRK8XUqVN58803WbhwIbNmzaJTp040a9aM0aNHc9ddd+Hj42OJZoUQV0FXp2f+pgPM+HUra/efMG3UaHEPikDl6M62hzsS6uUMwFQre+ys1KQEul7TGM+cOcOaNWtQq9X06dPH/NIpPcVCCCGuF6dLa/B3sQVMddrrDEZqDUZaBDhzd+tghqUE4Gb/V5K1tk7P3I37mbZoM5sPnzJvbxcdxNujepMWFXjedrKysti1axedO3c2d4pK8lYIIc7PaFT4aNNxnlp0gNIaPZ4O1uYEbtdmHiwakcS+IydZsnMns+aeQP+30bj2NlZ0SwyjX0okfVOaEertdk1j1+l0HD9+HI1GQ2JiovlaL3XOhWhaLNbVo9Vquemmm7jpppvIz8/n448/5rnnnuPpp5+mX79+jB8/nm7dulmqeSHEv5SVf4aPf9vOp8t3UFBWBYDa1oGAsGbk1FpRbFSgxsiiQ0WM/SOB2z6sYWrJOjk5odVq0Wg0VFdXmxO4QgghRFOm0xv4KT2Xz7acYPnhQnZN7EySv+me+0yPSB7tHEGLwLOn1eaVVPDxb9uZ8etWcs6YyghZazXc3iGBh/q1JrVZwAXbUxSFkydPUltby9GjR80jsoQQQpxrz+ky7p+7h03HzwCQGuRCn2gPft56iMU7DrN4+2FOFJaedUyknzv9UiLp1zKSTnEh2Fpfm/eWyspKMjMzcXBwoFkzU9kcT09PYmNjCQgIkI46IZowi68wtGXLFr744gu+++47vL29GTlyJKdOnWLAgAGMGTOGN99809IhCCH+wWAw8uuuTKYv3criHYdRFAAVrj7+OHgFcKrSyMkaAIX2oW6M7xjOjYm+1zxOo9HI6dOnCQw0jR7SarV06tQJe3t7WVxFCCFEk5eeU8Znm0/w9fZsiqrqzNtXHykyJ3Bjfc5edXz7kdNMW7SZ79btNS0qCvi5OfJg71bc16slPq6Ol2xXpVKRlpZGVlYWkZGR9fiLhBDi+lGp0zPptwzeWXMUg1HByUbLox2D2bU7nd7PrDZfgwFsrDR0TfhrlG0zv4Yp75aXl8ehQ4dwcHAgPDwctVqNSqUiMTGxQeIRQtQfiyRw8/Pz+eqrr/jiiy84fPgwAwcO5Ntvv6V3797motkjR46kT58+ksAV4hrKL6ng85U7+ei37WTll5i390gK55ZOyYxZcoKSSiPWGjVDW/jzUIcwWga5NkisRqORFStWcObMGTp06IC/vz9gGoUrhBBCNGXHi6u45cvtbD1ZYt4W4GLLqFZBjGodRLjH2fUI6/QG5m86wLRFm9lw6KR5e5uoQMb3T2NIm9hL1lAsLi6mqKjInLC1trYmKiqq/n6UEEJcZ95ec5Q3Vx0BYEiiLwmOOl7/8ieqdKYOt1BvV/q3jKRvi0i6JoZib3NtR7fqdDqysrJwdXU1l6kMDg4mNzeX0NBQc+5FCHF9sEgCNzAwkIiICEaPHs3IkSPx8vI6Z5+kpCRatWplieaFEP+w93geU35cx5wN+809xY4ubrSIa8and6QR5W+qIbuvTI23ozX3poXg7XTuYifXklqtxtvbm8rKSoxG46UPEEIIIRopRVE4VVpDoKsdAH7Othw/U4VWrWJQvA93pwXTO9objfrsl+2C0ko+Xrad6Uu3cvqPRXCstGpua28qk9A68vz1bf+poqKC33//HYPBgJOTE76+135WjRBCNAWKopgTnxM7hbM8o4Bb4z35aska5v1RZ7xzfAjvju5LUqhPgyZJDx06xMGDB/Hx8TEncLVaLe3atWuwmIQQlmORBO6KFSvo2LHjRfdxdnbm999/t0TzQog/7D+Zz4vfr2bOxn3mMgnh4eHg4sPRklrW5hpRWdma9393cEKDxaooCtnZ2Xh6emJnZ3rBjY+PJyYmBhubhk0mCyGEEFcit6yGWduy+WzzCXQGI0ef7o5GrcJaq2bO8FRivB3P22G682gO7y3ezOy16ejqTB2vPq4OPNArlft7peLnfnmzURwdHQkNDaWqqgp394apYy+EEI2ZwagwfX0Wiw7ksfieNNRqFVZq6OZZxyPTv6dOb8TZ3obX7+rJvT1TUKvV1zY+g4Hs7Gzc3d3NMxIjIiLIy8sjKCjorMSzEOL6ZJEE7gsvvMD8+fNxdXU9a3tZWRmDBw9m5cqVlmhWCPGHg9kFvDRnNd+t22tK3Ko1xMXHkafYc7RKDyW1WGvU3N7C/5zRPg0lPT2dgwcPEhQURNu2bQFTD7JWa/FS3UIIIUS90RuMLD6Qz2dbTrDoQD4GowKAo42GQ/kVxPmaXrw7RXj84zgDP20+yLTFm1m7/4R5e2qEPxMGpHFLu3hsLlEm4e90Op154U+AFi1aoFKp5AVfCCH+YUd2CffP3cO2k6aFyObuySHE3sjdHyxg38kCAAamRjH9vv4Eerpc7FQWs337drKysoiIiDAvPOng4EDPnj0bJB4hxLVnkczI6tWrqa2tPWd7TU0Na9euvezzffDBB7zxxhvk5ubSvHlz3nvvPVq3bn3B/adOncqMGTM4ceIEnp6e3Hzzzbz22mvY2tpe8BghrgcZpwt56YfVfLtuL8Y/Xhg7tYhjR5Uj+ysNgB4/ZxvGtAvlvjYNXybh74KCgjh8+DBOTk7SgyyEEKJJmr8nh7Hz08kt15m3tQt14+7Wwdya7I+jzbmP3oVllXy6fAfTl27lZGEZAFqNmlvaxTG+XxppUYGXfU88c+YM69evx9vbm1atWqFSqa75aDEhhGjsymv0PP/rQaatPYZRARdbLS/2imLD9t3cvngzigJezva8d08/bm0ff83eTxRFIT8/Hzc3N6ytTXV1Q0JCyMvLw9Hx0gtVCiGuT/WawN2zZw9guuDs37+f3Nxc83cGg4GlS5cSEBBwWef8/vvvmThxIh9++CFpaWlMnTqV3r17c+jQIby9vc/Zf/bs2Tz55JN8/vnntGvXjoyMDEaOHIlKpeLtt9++uh8oRCOVmVPE5Dlr+HrNHnPi9obW0Uy6rQuR/l6Ev7qCMA97nuoWyZAkP6y1Df8SV1xcTFVVFYGBpvp9bm5uDBgwQMolCCGEaDIqdXoqaw3mDlE/Zxtyy3V4O1ozPDWI0a2DiPU5f7mDPVm5TFu0mW/WplNTqwdMiYIHeqfyQO9U/N2drzguRVGoqqqisLCQ2tpaubcKIcQ/LNiby7j56WSX1gBwe7I/N0Y68sTMn82LPQ/v0py3R/XGw8n+msa2ceNGsrOzSU5ONi826e3tTf/+/aUzToj/sHpN4CYnJ5unZnXr1u2c7+3s7Hjvvfcu65xvv/029957L6NGjQLgww8/ZNGiRXz++ec8+eST5+y/YcMG2rdvz7BhwwAIDQ1l6NChbN68+Qp+kRCN25HcYl6es4avVu82TdFUqYiNj8fZ3Yv5Ezqj/qM8wvqHOhDubm/+3NDy8/NZvXo1Wq0WDw8Pc81becEUQgjR2CmKwtaTJXy2+QTf7jzNsJQAPrw5CYA2IW4svqc1PaK8sNKc+5KtNxj4eWsG7y7axOp9x83bW4T5MmFAG25rH4+ttdVlx6TX6yktLcXDw1SWwd3dnVatWhEQEGAevSWEEMLEYFR4aVkG2aU1hHvY879+0Sxet43bXt8FQLCnCx89MIA+KZHXJJ6ysjKcnJzMI3y9vb3Jzc3FYDCY95ESOEKIek3gHjt2DEVRCA8PZ8uWLXh5eZm/s7a2xtvb21yH69+ora1l+/btPPXUU+ZtarWaHj16sHHjxvMe065dO77++mu2bNlC69atOXr0KIsXL+auu+66YDs6nQ6d7q+pbmVlZf86RiEawrG8M7w8dw2zft9lTtzGxcWTp3LkQJUeqsr5eX8eNySYVplu5unQwBGfzdPTE1dX17MeVIRoiuT+IcR/R2GFjq93nOKzzSfYm1tu3r7lxBlz6R+VSkXfWJ9zji0ur+KzFTv5YMkWjheYaixq1CqGtIljfP802sUEXfH9sLy8nFWrVmEwGOjbt6+5MzQsLOyKzicsT+4dQlx7eoMRowLWWjUatYqPb05i7p4cEpz1jJv2LXkllahUMK5va165oztOdtdmYMn69es5deoUHTt2xM/PDzANQgsJCcHK6vI79IQQ1696TeCGhIQAYDQa6+V8hYWFGAwGfHzOfhD28fHh4MGD5z1m2LBhFBYW0qFDBxRFQa/X88ADD/D0009fsJ3XXnuNF198sV5iFsKSjueX8Mq8NXyxchd6gxFQERcbS6HGhf2VdYCeUHc7nusRRf/Yc0uMNJSqqiqOHDlCQkKCuQ5f165dZYEy0eTJ/UOI/4aHf9rLjA3HqTWYnnFttWqGJPlxd1owncM9Lph83Xs8j/cWb+Gr1bup/qNMgoeTHff3SuXB3qn1shiOg4MD1tbW6PV6qqqqZDZLEyD3DiGura0nSrh/7m4GxPnwUp8YAPwc1BxM382ULaa8QkyAJ5+NHUS7mGCLxlJbW3vWzAgHBwdUKhUlJSXmBK68IwkhzqfergwLFy6kb9++WFlZsXDhwovuO2jQoPpq9hyrVq3i1VdfZfr06aSlpZGZmcmECROYPHkyzz333HmPeeqpp5g4caL5c1lZGUFBQRaLUYjLdaKghFfnreXzlTup05teHjsmRpCp8mZ/eS1QR6CLLc/2jGRUq+BGUeP2TwaDgeXLl1NTU4ODgwPh4eGAPJiI64PcP4S4Ph0vriLAxRbtH2UQHG201BqMpAS6cHfrYIalBOBqd/6RUQaDkV+2ZzBt0WZWph8zb28e6sOE/m24vUMCdjZXPqqqqqqKo0ePEh8fb+4Ubd++PXZ2dpc10000HLl3CHFtlFbX8eySg3ywIQtFgdNlOh7vEsF3a/fw2KzfKK3SodWoeeqmDjxzcydsrCz3fmI0GtmyZQvZ2dn07t0bJydTffTo6GiioqKwt7+2dXaFEE1PvV2hBg8eTG5uLt7e3gwePPiC+6lUqrNquVyMp6cnGo2GvLy8s7bn5eXh6+t73mOee+457rrrLu655x4AEhMTqays5L777uOZZ545b9FvGxsbGa0gGqXswlJem7+OT5ZvNyduuyWG8eLtXWgfE0zrd9eioOLp7pHc2yYYW6vG9+Km0WiIjo4mOzsbd3f3hg5HiHol9w8hrh86vYGf0nP5bMsJlh8uZMGoVgyMNz1vjmkXys1JfiQHXHjEbEllNZ8t38n7S7aYF8BRq1Xc2DqG8f3T6BgXctVlgwwGA8uWLUOn0+Hk5GSe/Sarkjctcu8QwrIURWHenhzG/7SXnDJTuZI7UgJ4qI0/N7w229y5lhrhz2djB5EUev7cwtUyGo3m/INaraaurg6j0cjp06eJjo4GMK8FIoQQl1JvCdy/l02orxIK1tbWtGzZkhUrVpiTwkajkRUrVjBu3LjzHlNVVXVOkvbP0QiKotRLXEJY2uniMl6bt46Pl22nVm/q8IiLiULj6sOP4zvhbGsaufPdnS3xc7bB3rrxjGbV6/Xs27ePsLAwnJ1NK2hHRkYSFRUl9W6FEEI0OntOl/HZlhN8vT2b4qo68/atJ0vMCVx/F1v8XWzPe/z+k/m8v3gLs1btpkpnOt7d0Y57e6Ywpk8rgr1c6y1WjUZDZGQkubm55nusEEKIv5w8U80D8/aw+EA+YFoL5P0b49l76Ahdn/mU6lo9dtZaJg/txoQBaWgtMHNBr9eTnp7OqVOn6N27t7mWbWJiIgkJCbi5udV7m0KI61/jyfpcwMSJExkxYgSpqam0bt2aqVOnUllZyahRowAYPnw4AQEBvPbaawAMHDiQt99+mxYtWphLKDz33HMMHDhQppWJRi+nuJwpP67jo9+2oav7I3EbHUmNgxf7z+ggr4rp67N4srtpRdSIRrY4GcDOnTs5duwYxcXFdOnSxTy9UwghhGhM0nPKGP39LradLDVvC3SxZVTrIEa1CibM48LTWY1GI4t3HGbaos0s233UvD0h2JsJ/dMY1ikRexvrCx7/bxUVFbF7925SU1PNCduYmBhiY2OlU1QIIc7DoCj8nlmIlUbFk92aMbCZM2M+WsC2I6cB6JoQyidjBhHha7mZgRqNhtzcXKqqqsjOzjYvKunq6mqxNoUQ1796S+BOmzbtX+87fvz4f73vbbfdRkFBAc8//zy5ubkkJyezdOlS88JmJ06cOCs59Oyzz6JSqXj22Wc5deoUXl5eDBw4kFdeeeXf/xghrrG8kgr+9+M6Zvy6jZo/FjmJjYxA7+zL/uIaqNXhbKtlYqdwHmwX2rDBXkJcXBxFRUXExMTIy6UQQohGK8rLAWuNGiuNihvifbk7LZieUV5o1Be+d5VW1vDFSlOZhCO5ZwBTmYRBqdGM759Gl4TQer33HThwgMLCQtLT02nfvv0f7UmnqBBC/N3RokrCPUwDW0Ld7Zl5ewuiveyZu3oH7Z76Ab3BiIu9DW+N7M3o7i3q9TpdV1dHZmYmBQUFdOzYEZVKhUqlonnz5qhUqguWfhRCiMulUuqprsCfvUqXbFCl4ujRo5fesQGVlZXh4uJCaWmpTE8TFpVfUsHrP61n+tKt5tWp06ICKXcNZX9BNQCONhomdAzn0c7huNlf/Wie+qQoCkeOHMFgMJjrOP25XZK3wtIa47W6McYkhPhLdZ0Ba43anKQtqDDVRvRyvHg90kOnCnlv8WZm/r6LyhpTmQRXB1vu6WEqkxDmUz/TYfV607PAnwt9lpeXc/DgQRISEqROYj1qbNfqxhaPEE1FSXUdTy06wMebjrPywbZ0jvAEYMPBE9wzfSEHsgsBuDEthvfv7Ye/e/3//aqrq+Pnn39Gr9fTuXNn80AzcX2S67VoSPU2AvfYsWOX3kkIAUBhWSVv/LSB95dsMdfLax0ZwIu3daF3i2bcNXsnx0pyGNc+jMe7RuB5iRfLhpKbm8uOHTtQq9UEBASYF1GR5K0QQojG5sSZKgZ/sZUekV68PjAOuHji1mg0snRnJu8t3sLSnZnm7XFBXozvl8adnZNwsK2/jtXTp0+zY8cOwsLCiI+PB8DJyYlWrVrVWxtCCHE9UBSF73ed5uEF+8grN3XE/Z5ZREt/J57+ZgXvL9mCooCPqwMf3NufIW3j6q3tqqoqjh49SkJCAgBWVlbEx8djbW2Nh4dHvbUjhBD/1Ohr4ApxPSkqr+LNBRt4b/Fm8wiemPAQ7H2C+WpEa6K8nQD434BY3hwYh6/z+RdMaSx8fX0JCgrC09MTe/sL1woUQgghGtLao0UMmbWNgopasktrLto5WlZVw6zfd/Pe4s0czikGQKWCgX+USeiWGGaRjkq9Xk9VVRUnT54kNjZWSiUIIcR5HCmsZMy8dH7LKAAg2suBD29OoqbsDPETpnOi0FTXfFS3ZN4c0Qt3p/p7R6murmbFihVUV1cTGhpqHrzy95mIQghhKfWWwJ04cSKTJ0/GwcGBiRMnXnTft99+u76aFaJJKC6v4u2fN/LuL5upqKkFIDosCEe/ULbnVEFuFS8vz+TLYS0ACHBpnNMkc3NzOXz4MO3atUOj0aBSqWjbtm1DhyWEEEJc0Ecbsxg3fy96o0KyvzM/jWp13uTt4dNFvL9kC1+s3El5tele7Wxvw93dWzCub2vC63nBm6qqKnQ6nXk18qCgIPR6PcHBwZK8FUKI85i+PotHF+6jRm/ERqvm6e6R3NPKnye//I2vVu8BINTblY8fGEjP5Ih6b9/W1hYfHx+Ki4tlxqEQ4pqrtwTuzp07qaurM//7hciFTvyXnKmo5p2fN/Luos2UVZmm90SHBeLiH86W05WQU4VGrWJkahDP9oxs4GgvTq/Xs3nzZnQ6HYcPHyYmJqahQxJCCCEuqFZv5OEFe5mx4TgAtyX78/ltzbG3/uvx12g0smz3UaYt2sziHYfN26MDPBjfL43hXZrjaFf/ZYxyc3NZv349Dg4O9OrVC7VajUqlIjw8vN7bEkKI64WLrZYavZFuzTyZPiSBXRnHSX5kOgVlVahUMKF/G14e1q1ey9soioKiKObrdMuWLTEajVhZWdVbG0II8W/UWwL3999/P++/C/FfVFpZw9RfNvHOzxsp/SNxmxTig09EHMuOlcHpStQquCMlkOd7RdHM06GBIz6/vy9GptVqadGiBUVFRURE1H+PthBCCFFfFEVh8BdbWXIwH5UKXukbw5Pdmp01kGDRtgwem/UbB08Vmrf1bxnJ+P5p9EgKt+goWHd3dzQaDVZWVuh0OlmgTAghzqO4qpbDBZWkhZhmKgxLCcDDwZp4D2vGfvIzP2/LACA+yItPxwyiTXRQvbZvMBjYunUr1tbWpKSkAKDRaNBoNPXajhBC/BsWr4F78uRJwDQtTIjrXVlVDe/+spm3f95ISWUNYHqgmHRbF25qE8urKzJZnlXGrc39mdQrihgfpwaO+MLKysrYvn07iYmJeHqaVnQNDg4mODi4gSMTQgghLk6lUjGyVRDrs4r55o4UBsSdvSr4e4s2M+HzJSgKONlZM7pbC8b2bU2kv2UWoCkuLiYvL4/Y2FgArK2t6dGjBw4ODjI7TQgh/kFRFL7ens2jP+9HrVJx8ImuuNpZoSgKx4+f4LaXl1FWpcNKq+aZIZ146qYOWFvVf2qjsLCQEydOoFKpiIiIwMXFpd7bEEKIf8siCVy9Xs+LL77ItGnTqKioAMDR0ZGHHnqIF154QaYbiOtOebWOaYs289bCDZypMCVumwX5EhAezeM9Y+kX5wvAhI7h3JDgS6Kfc0OG+69kZGRQUFDArl276N69u7xgCiGEaPRKqutwtTM9Z96a7E/3SE88HP6aSms0Gnn8y2W8tXAjAPf2TOHNEb1wtrfcoqGVlZWsWLECRVHw9vY2r1L+5+I3Qggh/pJRUMGDc9NZmWmaHRHn40huWQ0FZ8q4d8ZCVu8zlcVJiwzgs7E3EB/sbbFYfHx8SE5OxsXFRZK3QogGZ5EE7kMPPcT8+fN5/fXXzQscbdy4kUmTJlFUVMSMGTMs0awQ11xFtY73l2zhzQUbKCqvBiA8wJvQqFjWnKgk80QFlb9l0DfWB5VKhZOttlEnb/9eMiExMRGDwUBCQoIkb4UQQjRqRqPCs0sPMnPrSbY93Al/F1NC9u/J25raOoZP+5E5G/YD8Ood3Xnypg4Wucf9/X7q4OBASEgIiqJIqQQhhLgAnd7AlBWZvLoik1qDEVutmud7RTGhQyjvL97MC9+voqZWj72NFa8M68ZD/dLQaOq/1E1ubi7u7u5YW5vuH1FRUfXehhBCXAmLJHBnz57Nd999R9++fc3bkpKSCAoKYujQoZLAFU1eZU0tHyzZwhsLNlBYVgVAmJ8XzWLiWX2ykqNZppHnvaK8eKlPdKNPgOp0Onbv3o1Go6Fly5YA2NjYkJaW1sCRCSGEEBdXWl3HnbN38sv+PAAW7MvlwXahZ+1TVF7FDa99y/qDJ7HSqvli7GDu6JxU77EoikJ2djYHDx6kc+fO5gRAamqqRWvqCiFEU1ah05P6zhoOFVQC0Dvai+lDEikrK6PjM5+z42gOAD2Swvn4wYGE+bhZJI4jR46wfft2fH196dChg1y3hRCNikUSuDY2NoSGhp6zPSwszPwgK0RTVKWrZcbSbbz+03ryS00PGBG+bqS1TGF+RhnHssoB6BLhweQ+0XQIt0wtvfpWWlpKVlYWKpWKmJgYHBwa56JqQgghxN9lFFRww+dbOZhfga1Wzae3NueOloFn7XM0t5h+r3zDoVNFuNjb8OMTt9M1Mcwi8SiKwr59+ygrKyMjI4OEhAQASQIIIcRFONpoSQtxo7RGz9Qb4hkU58XkOWt4/af1GIwKbo62vD2yNyO6Jlt0YIybmxsajQZ7e3uLtSGEEFfKIgnccePGMXnyZL744gtsbGwA0wi/V155hXHjxlmiSSEsqlpXx0e/bWPKj+vIKzElbsN8XHn+ls7c2TmJH/fmMXv/dtqHujG5bwxdm3k2cMSXZjAYzCuoent7Ex8fj6+vryRvhRBCNAlLD+Zz+1fbKa3RE+hiy4+jWpEa5HrWPlsPn2LAq7PJL60kyNOZJc/eWe/1EmtqarCxsUGlUqFWq2nRogWFhYVER0fXaztCCHG9UBSFWVuz6drMgxB3U7L0nRviUatUpB87TfKjH5FxugiAW9rFMe3uvvi6WWbx57+XvHF3d6dXr144Ojo2+hmUQoj/nnpL4N50001nfV6+fDmBgYE0b94cgN27d1NbW0v37t3rq0khLK6mto6Pl21nyvx15JwxlUUI8naneVIS/ZqHMLK9aQTPkEQ/Vo1pS6dwj0Z/szcYDBw6dIhjx47Rs2dP86j4+Pj4Bo5MCCGE+HcW7s3lxplbMSrQLtSNeSNS8XU+eyGyX7Yd4ra35lKlqyM5zJdFzwzD371+69BnZGSwd+9eUlNTCQ4OBkyL3vj4+NRrO0IIcb04mFfOA/PSWX2kiIFxPiwY3QqVSoUWI09+uZwZv24DwM/Nken39WdwWqzFYikrK2Pr1q20adPGPIjFyckyiWIhhLha9ZbA/eeqjEOGDDnrc1BQUH01JYTF1dTW8enyHbw2fx2ni01lEQI9XWmZkszqk9X8cqScLXkZDE8NwsFGi1qtonNE4x91+6esrCwqKys5fvw4kZGRDR2OEEIIcVm6RXoS5+NEWrAbHwxJwEarOev7D3/dythPFmM0KvROjmDO/92Kk51NvcdRV1eHXq8nOzvbnMAVQghxrpo6A6+uOMyUlZnUGRTsrTV0DHdHUWDx9gwe+OgXsovKALinRwpvjOiJq4NlF37csWMHRUVF7Nixg44dO1q0LSGEuFr1lsD94osv6utUQjQYXZ2ez1fs5JW5azj1R+LW38OFtJYtWHOqhgWHTdvifZ14sXcUdlaai52u0dDpdFhbW6NSqdBoNKSmplJTUyMdK0IIIZqMwgodHg6me5mjjZZ149rjbKs9a+aL0Wjk6W9W8L8f1wMwunsLPrx/AFba+rlfnzlzBisrKxwdHQGIjo7GyclJ7qdCCHERyzMKeHBeOpmFplJ0/WO9+eCmROw1Cne+O49v1+4FINzHjU/GDKRbYvg1iat169bs2rWLlJSUa9KeEEJcDYvUwBWiqfkzcfva/LWcLDT1/Aa4OzGwU2vmH6nixz8St1FeDkzqFc2tyf5o1I27VMKfDh06xL59+2jVqpX5BdPbu37r/wkhhBCWtP5YMUNmbWNip3Ae79YMABc7q7P20dXpGfX+T+ZEwEu3d+XZWzrVW2mjw4cPs3PnTvz8/MwjtbRarYy8FUKIi/hh12lu+2o7AP7Otky7MZ4bE3z5du1eJny+hKLyatRqFRMHtuXF27tgb2O5Rc+NRiNnzpzBw8O00LS9vT3t2rWzWHtCCFGfLJbAnTt3Lj/88AMnTpygtrb2rO927NhhqWaFuCzVOlOphP/9uM484tbPzZGnh3Tknh4pZBRW8+Hu1YR72PNCryiGtQhAq2laK0n/fXqnjBASQgjR1Hy66Thj5qdTZ1D4ducpHu4UjrX27HvxmYpqbvzfd6zedxytRs2nYwYxomtyvcbh4+ODWq3GysrqrIVAhRBCXNjAeB8iPR3oE+PNy32jKa2oYuCr37J4x2EAEkO8+WzMDbSKDLBoHHq9nvXr11NQUECXLl3w9Gw65e+EEAIslMCdNm0azzzzDCNHjmTBggWMGjWKI0eOsHXrVsaOHWuJJoW4LFW6Wj76bTuv/7ie3BLT4mT+7k50at2S8ABvxvWLAyDJ34pl97ehc4QHVk0gcasoCqdOncLFxcVcgD86OhoXFxcCAwMbODohhBDi36szGJm4YB/vr88CYEiSHzNvTz4neXs8v4S+L3/NgexCnOysmfd/t9EzOeKq2v7zfqrT6YiIMJ3L2dmZfv36YW9vf1XnFkKI69nenDI+WJ/F+zclolGrsLPSsOvRTthq1Xz46zae+Go5FTW1WGs1PHdLJx4f3B5rK8tPDFar1Wg0GlQq1TkDzIQQoimwyJVy+vTpfPzxxwwdOpSZM2fy+OOPEx4ezvPPP09xcbElmhTiX6mo1jHj1228uWAD+aWmGkxBni50TEth3ek6vjtUjvZwBXe3CSHcw7QSaY8or4YM+bKkp6dz8OBBAgICaN++PQBWVlYy8lYIIUSTUlih45Yvt7PqSBEAL/WJ5pnukaj/Ub5ox5HT9H9lNrklFQS4O7H42TtICvW96vbz8vLYsGEDWq0Wf39/7OxMC+lI8lYIIc6vqlbPS78d5q3VR9AbFeJ9nRjXIQyAkwUl3DN9IesOnACgXXQQn44dRGzgtXvPUqvVpKWlUVlZiaur6zVrVwgh6otFErgnTpww15Kxs7OjvNw0Nf2uu+6iTZs2vP/++5ZoVogLKquq4YMlW3lr4QaKyqsBCPVxpX1qCmtP65i931T31sfJhie6RuDnbNuQ4V6xkJAQMjMzcXFxQVGUeqv7J4QQQlwrOr2Bdu+t53BhJY42Gr4elsINCecmZZfsOMwtb/5AZU0diSHeLH7mDgI9Xa643b/fN318fPD29sbT0xMrK6tLHCmEEP9tiw/kMXZ+OlnFpveswQm+3BDvS53ewBs/reelOavR1RlwsLViyp09GNOnFWq15Wc3Hjt2jOrqauLiTLMrraysJHkrhGiyLJLA9fX1pbi4mJCQEIKDg9m0aRPNmzfn2LFjKIpiiSaFOK+SymqmLdrM1F82caaiBoBmfu7c3q01s/aV883+UgB8nWx4olsz7msTjL1101jbr6qqin379uHo6EhsbCwALi4uDBw4UF42hRBCNFk2Wg2PdA7nrVVHWDC6NfG+Tufs8+my7Tzw0S8YjArdk8KY93+34eJwZZ2vBoOBw4cPc+rUKbp27YparUalUtG5c2fpCBVCiIs4XVrDhJ/2MndPDgBBrra8f2MigxJ82X7kNAMnL2B3Vh4AfVo048P7BxDi7XpNYisuLmbr1q0AeHl54eXVdGZVCiHE+VgkU9WtWzcWLlxIixYtGDVqFI888ghz585l27Zt3HTTTZZoUoizFJdX8e6izbz7yyZKq3QARAd48OzNnbi9QwKlNQambl+Ov7MtT3SL4N42IdhZNa3FSAoKCjh27BhWVlY0a9bMnLSV5K0QQoimxmhUyK/Q4fvHDJgH2oYwvGUgDjZnP6oqisLz3/7Oy3PXADC8S3M+eXDgVdVP1Ov1HDx4kNraWrKzswkODgaQ5K0QQlzCyO92siyjEI1axcMdw5jUOxrFaOCRz5cybfFmjEYFd0c73r27D3d0Srqm11V3d3eio6PRaDSyYJkQ4rpgkQTuxx9/jNFoBGDs2LF4eHiwYcMGBg0axP3332+JJoUAoLCskrcXbuT9JVsorzYVp48N8qZ1SnOqVTbmBwcPBw3L7m9Lsr8ztk0kcWswGKiursbR0RGA4OBgCgoKCA0NlaStEEKIJqu8Rs9ds3ewL6+CLRM64GZvjUqlOid5W1un557pC/lq9R4Anr+1M5Nu63LVCQEbGxuSk5MBpGa8EEJchjcGxjFmXjof3JRIc39n5m86wITPlnCq2FRC8fYOCbw7ug/ero7XJJ6qqipsbGzQaEzvd0lJ1zZpLIQQlqRSpKbBOcrKynBxcaG0tBRnZ+eGDkf8C3klFby1cAPTl26lsqYOgIQQX1omJ/HrsQpyy02jcFeNaUvniKbXA3vmzBnzYio9e/a8JjWjhGjsGuO1ujHGJERjdqSwkhu+2Mq+3HJstGoWjGpF7xjvc/Yrrazhpte/Z2X6MTRqFR89MJC7e6RcUZuKorBnzx5CQ0Nxcbnymrmi6Wps1+rGFo8Q51Neo+f5Xw/iYK3l5b4x5u2KopCVX8K4TxazeMdhACJ83fjg3v70btHsmsVXVFTEunXr8PHxIS0tTRK3wiLkei0aksWKfZ45c4bPPvuMAwcOABAXF8eoUaNwd3e3VJPiPyinuJw3Fqznw1+3UV2rB6B5mD8tmiey5Eg5s/aYVq8OdrPj6e7NaBPi1pDhXjF7e3tqa2sxGAxUVlbi5HRuPUAhhBCiKVmeUcCtX27nTHUdfs42/DiyFWnnuU+fLCyl38vfsPdEPo621sx57Bb6pERecbuHDh3i0KFDZGVl0a9fP5nFIoQQF6EoCj/tzWX8j3vJLq1Bq1ZxX5tggt3sqa3T89bCjUyes5rqWj1WWjVP3tiBp27qiJ3Ntb226vV6amtrKSsro66uDmtr62vavhB/MhgM1NXVNXQYoomwsrIyzxq4FIskcNesWcOgQYNwdnYmNTUVgGnTpvHSSy/x888/06lTJ0s0K/5DsgtLef2n9Xy8bDu6OgMArSMDGNWrDc+vPs3u3YUAhLrb8XT3SEakBmGtbTqjVs+cOUN+fj7R0dGAaXpnx44dcXV1RattGousCSGEEOejKArvrj3Gowv3YVQgLdiV+SNb4e9y7iJku4/l0u+VbzhdXI6vqyOLn72DFuF+V9V+WFgYp06dIiIiQpK3QghxEceLq3jox738vN+0EFm4hz0f3JRIsJs9a/cf54GPfmH/yQIAuiaEMv2+/sQENsxiYT4+PnTo0AFPT0+5tosGoSgKubm5lJSUNHQooolxdXXF19f3kjMHLJIJGjt2LLfeeiszZswwZ5INBgNjxoxh7NixpKenW6JZ8R9wPL+E//24js9W7KRWb0rcto0K4oXbOtMrOQKDUeGtLQU42Wp5pnskd6UGYqVpOolbgMrKSpYvX46iKPj4+ODq6gogxfeFEEJcF95efZTHft4PwIjUQD68Oem89eiX7TrCkDe+p7y6lrggLxY/c0e9rF5uY2ND165dpRyREEJcQJ3ByLtrjvHCb4eoqjVgpVHxf10ieLZnFJXVNYx+/ye+WLkLAC9ne94e1fuaL1Km1+tJT08nNjYWW1tTB6Cf39V18AlxNf5M3np7e2Nvby9lPMQlKYpCVVUV+fn5wKWvYRZJ4GZmZjJ37tyzhgFrNBomTpzIl19+aYkmxXXuaG4xr81fx8zfd6E3mBbIaxcbSkxcLDtya+iSGIZKpUKrUbH03jSC3eyaVOLWaDSaXyQdHBwIDAxEpVJJ77EQQojrzojUQGZsyGJchzAmdAw77wvOzJU7uXfGz+gNRrokhDL/8dtwc7S74jYPHz6Mg4MD/v7+AJK8FUKIi8iv0PHiMlPytmO4Ox8OSSLG24GZv+/i/2Yto7iiGoD7erbktTu74+5kf81j3Lp1KydPnqSkpIQuXa5+QUshrobBYDAnbz08PBo6HNGE2NmZnm/z8/Px9va+aDkFiyRwU1JSOHDggHn6958OHDhA8+bNLdGkuE4dPl3Eq/PW8tXq3RiMpvX2OieGEREVy08Hi9mww9RT8e2O04xsbVo5OsLTocHivVwGg4GMjAyOHTtGjx49zLWa0tLS5OVSCCHEdeN4cRUh7qYXfE9HG/b+X5fzjrpVFIWXfljNpO9XATCsYyKfj7sBG6srf2TNzc1l586dqFQqevXqJQuXCSHEeVTXGbD747oc4GLH24Pi0ahUjGwVxIHsAro8P4e1+08AkBjizYf3D6BdTHCDxRsfH8+ZM2dISEiQ5K1ocH/WvLW3v/adGaLp+/PPTV1d3bVJ4O7Zs8f87+PHj2fChAlkZmbSpk0bADZt2sQHH3zAlClT6qtJcR07mF3AK/PWMnttOsY/ErfdmkcQ1iyaeQeKWb3DVIcpysuB53pGcXuyf0OGe8VUKhVZWVlUVFRw7Ngxc6eHJG+FEEJcL2ZtPcl9c/YwfUgid6eZXvbPl7yt0xu4/8OfzdNyn7qpAy8P63bV90Rvb2+Cg4Oxs7OTFaOFEOIfFEXh+12neWTBPr4e1oLuUaYatve2CaFKV8szs1fw5oIN6A1G7G2seOn2rozvn4aV9t8tulOfdDodNjY2ADg7O9OnTx95bxKNinQmiCvxb//c1FsCNzk5GZVKhaIo5m2PP/74OfsNGzaM2267rb6aFdeZfSfyeXnuGr5fv5c//yj1bxnJ2AHtGDbnECv/SNzGeDvyXM9IbksOQKNuOhdJRVEoKCjAy8sLlUqFWq2mRYsW1NTUEBIS0tDhCSGEEPVGbzDyf7/sZ+qaYwAsyyhgdOug8z6kllXVcPMbP7Bs91HUahXT7+3P/b1T6yUOtVpNWloaIC9WQgjxd0cKKxkzL53fMkwLkU1de8ycwF20LYNxny4mK78EgMGtY3j37j4Ee7le8zgVReHgwYMcPHiQbt26mWdSSPJWCPFfUm8J3GPHjtXXqcR/0O5jubw8dw1zN+43bxvYKpoXbu1MywjT6Nq2W/I5fqaK53pGcUtz/yaVuAXTg8e6devIycmhXbt2BAYGAuDr69vAkQkhhBD1q7iqltu+3M7yw4UAvNAriud7Rp03gXqqqIz+r3zD7qw87G2s+OHRW+ifGnVV7f+5kEhMTAwgiVshhPi7Wr2RN1Zl8vKyw9Tojdho1TzdPZInukWQXVjKhM+XMn/TAQCCPJ15/55+DGod02DxKopCbm4udXV1nDp1SkrhCNGIrFq1iq5du3LmzBnzAuyNSWhoKA8//DAPP/zweb/PysoiLCyMnTt3kpycfE1ju1z1lsCV0YPiSuw4cprJc9bw05aD5m0D0+LwCQ5n4cEi/Dzdzdu/GtYCNzsr1E0scfsnlUqFm5sb+fn5VFVVNXQ4QgghhEXsyy1n0OdbOFpUhYO1hi+HtuCmpPOvqrv3eB59X/6G7KIyvF0cWPTMMFKbBVxV+5WVlaxfvx6DwYC9vT3BwQ1Xo1EIIRqb9ceKuXfObg7kVQDQPdKTGUMSCXO34/3FW3ju29+pqKlFo1YxcVBbnr+lM452Ng0as1qtpl27duTk5BAaGtqgsQhxvRk5ciSzZs0CQKvV4u7uTlJSEkOHDmXkyJGXHOn+59/N+uhYeeutt3jvvffIy8sjODiYRx99lPvuu++qz3sxQUFB5OTk4OnpadF26oNFFjEDOHLkCFOnTuXAAVPPXVxcHBMmTCAiIsJSTYomZMvhbCbPWcMv2zIAUKlgcJsEPAJD+S49n4ptOQDM3HqSp3tEAuDhYN1g8V6Jmpoa9u/fT2RkJE5OTgBER0cTHh4uxc2FEEJclwoqdLR7bx1lNXrC3O1ZMLoViX7nrzu7Mv0oN/3ve0qrdEQHeLDk2TsJ83G76hgcHByIi4ujsLCQgICrSwYLIcT15nRZDQfyKvB2tObtQfEMSwlga+Ypbp3yNbuO5QLQLjqIGff3Jym04WYKlpSUUFJSYk7Y2tjYSPJWCAvp06cPX3zxBQaDgby8PJYuXcqECROYO3cuCxcuRKs9f+qwrq4Oa2vreplVvGbNGh577DGmTZvGwIEDyc7OpqCg4KrPeykajabJzIq2SNGYX3/9lbi4OLZs2UJSUhJJSUls3ryZ+Ph4li1bZokmRROx4eAJ+rz0FWlPfMov2zJQq1Xc3CGJu28eyG/F9ny6LYcKnYFkf2fmj0zlyW7NGjrkK7Zjxw4yMzNJT083b7OyspLkrRBCiOuWl6MNj3WJoFszT7Y+3PGCyduvV++mz+SvKa3S0SE2mA2v3l0vyds/xcbG0r59+4uu5CuEEP8FiqJwtKjS/PnmJD+mDU7g4BNd6R/jwdiPF9HmyU/ZdSwXN0dbPnlwIGtfGdWgyduKigpWrlzJ1q1byc/Pb7A4hLgaiqJQWVPbIP/8fW2qf8PGxgZfX18CAgJISUnh6aefZsGCBSxZsoSZM2ea91OpVMyYMYNBgwbh4ODAK6+8wqpVq1CpVJSUlAAwc+ZMXF1d+emnn4iMjMTW1pbevXtz8uTJi8agVqvRaDTcfffdhIaG0qFDB2688cZ/9d950qRJBAcHY2Njg7+/P+PHjz9rn6qqKkaPHo2TkxPBwcF8/PHH5u+ysrJQqVTs2rULwPx7Fi1aRFJSEra2trRp04a9e/f+u/+YFmSREbhPPvkkjzzyCFOmTDln+xNPPEHPnj0t0axoxNbsy+KlOatZscdUK1mjVnFX5+Y8ekN7un66g8JTpwFoEeDMC72iGRTv0+Tq1RmNRhRFMb8sxsfHU11dTbNmTTcJLYQQQlxKhU5PWY0efxdbAJ7pHslT3Zqh1Zw7TkBRFF6dt5ZnZ68E4NZ28cwaPxhba6uriqGsrIzMzEySk5PNU/1kcRshxH/dwbxyHpyXzt7ccg4+0RUPB2tUKhXjOoTy7dp0Js78lbwSU3J3eJfmvDG8J96ujg0ctWkmRWBgIJWVlVLvVjRZVbo6HIe92iBtV8x+Ggfbq5vB3K1bN5o3b878+fO55557zNsnTZrElClTmDp1KlqtlqNHj55zbFVVFa+88gpffvkl1tbWjBkzhttvv53169dfsL3k5GQCAgIYM2YMn3/++b9+jps3bx7vvPMO3333HfHx8eTm5rJ79+6z9nnrrbeYPHkyTz/9NHPnzuXBBx+kc+fOREdHX/C8//d//8e7776Lr68vTz/9NAMHDiQjIwMrq6t7Zr0aFkngHjhwgB9++OGc7aNHj2bq1KmWaFI0Qoqi8PveY7z0w2pW7zsOgFajZminZCbd0oFwX1N927taBrL2WBHP94xiQFzTS9wC5OTksHPnTkJDQ4mLiwPAxcWF7t27N3BkQgghhOUcK6rihi+2oFWrWDeuPfbWWtRqFWrOvZfrDQbGfLyIT5btAOD/Brdjyp09rjrRajAYWLNmDVVVVWi1WpKSkq7qfEII0dTV1Bl4dcVh/rfyCLUGI3ZWaraeLKFPjDeHTxcx5uNFLN9jSrpEB3gw474BdE0Ma9CYjUYjYOp8U6lUtGzZEkBmUgjRgGJiYtizZ89Z24YNG8aoUaPMn8+XwK2rq+P9998nLS0NgFmzZhEbG8uWLVto3br1OfsbjUYGDx5M8+bNKSkpYdiwYebkL0BiYiIjRozgscceO+fYEydO4OvrS48ePbCysiI4OPicNvr168eYMWMAeOKJJ3jnnXf4/fffL5rAfeGFF8yDT2fNmkVgYCA//vgjt9566wWPsTSLJHC9vLzYtWsXkZGRZ23ftWsX3t7el32+Dz74gDfeeIPc3FyaN2/Oe++9d97/6X8qKSnhmWeeYf78+RQXFxMSEsLUqVPp16/fZbctLp+iKCzbfYSXfljN+oOmYfLWWg1DOyejdQ9g9u4cHqiG8D/2f7VfDDZadZNM3P6ptraWiooKsrKyiImJkVE/QgghrnsrDxdy65fbKKqqw8fJhqziauJ8nc67b0W1jlvfmsOSHZmoVPDePf0Y2/fCz3KXQ6PRkJKSwr59+y76IC6EEP8FyzMKeHBeOpmFppG1/WK9ef/GRPycrHjx+1W8Nn8tujoDttZanr25E4/d0A4bK4stjfOv6HQ6Nm7ciIuLCy1atAAkcSuaPnsbKypmP91gbdcHRVHOydOkpqZe8jitVkurVq3Mn2NiYnB1deXAgQPnzeUtXbqU9evXc+rUKRwcHBgwYAADBw5k/vz5aDQaMjMz6dix43nbuuWWW5g6dSrh4eH06dOHfv36MXDgwLPq9v69c1+lUuHr63vJ8ixt27Y1/7u7uzvR0dHmNb4aikWu1Pfeey/33XcfR48epV27dgCsX7+e//3vf0ycOPGyzvX9998zceJEPvzwQ9LS0pg6dSq9e/fm0KFD500G19bW0rNnT7y9vZk7dy4BAQEcP34cV1fX+vhp4iIURWHJjsO89MNqNh8+BYCNlYZhXVJQu/rxza4cao6ats/Zc5p2YaYRuLZWTe/mXFpaisFgwN3d9BuCg4Opra0lNDRUkrdCCCGua4qi8P66LB5ZuA+DUSE1yIUfR7Yi0NXuvPvnFJcz4NXZ7Diag521lm8n3swNrWPqNSZ/f3/8/PyadGewEEJcDaNRYcR3O/l6u+l9y9/ZlncHxzMkyY8Ve47S++NFHM4pBqB3cgQf3NefiD9mRDa0oqIi8vPzKS4uJioqCgcHh4YOSYirplKprrqMQUM7cOAAYWFnj863xN/PPXv2EBwcbM6v/PTTT/Tq1Yvu3bszePBgwsPDzaN5/ykoKIhDhw6xfPlyli1bxpgxY3jjjTdYvXq1udzBP8seqFQq86j/psQiCdznnnsOJycn3nrrLZ566inA9GA9adKkc4oJX8rbb7/Nvffeax6i/eGHH7Jo0SI+//xznnzyyXP2//zzzykuLmbDhg3m/0myWqVlKYrCwq2HmDxnNduP5ABgZ63ljm4tMTj5MHtXDjq96UGiXagbL/SKomeUV0OGfFVOnDjB5s2bcXFxoUePHuZpPv8ccS6EEEJcb3R6A2PmpfP5FtMMmztbBvDxLc2xu0Bn7IHsAvpO/prjBaV4Odvz89PDSIsKvPo4dDp2795N8+bNsbGxAZDkrRDiP02tVmFnpUGtgrHtw3i5bzTVNTrunDqf2WtNiyr7uTkydXQfbmkX36iumf7+/qSkpODp6SnJWyEaiZUrV5Kens4jjzxy2cfq9Xq2bdtmHm176NAhSkpKiI2NPe/+AQEBHDt2jOzsbAIDA3FwcGDx4sV07dqVp556ivnz51+0PTs7OwYOHMjAgQMZO3YsMTExpKenk5KSctmx/2nTpk0EBwcDcObMGTIyMi4Y/7VS7wlcvV7P7NmzGTZsGI888gjl5eUAODmdf0rdxdTW1rJ9+3ZzEhhMNXF69OjBxo0bz3vMwoULadu2LWPHjmXBggV4eXkxbNgwnnjiiQtOw9DpdOh0OvPnsrKyy471v8hoNPLj5oNMnrOa3Vl5ADjYWjGmTysmDmxLl4+2cuiQKXHbIcydF3pF0T3Ss1E9LFwJHx8ftFotjo6O6PV6c10WIcR/j9w/xH/N+B/38vmWk6hV8PqAOCZ2Dr/gfX3NvixumPIdJZU1RPq5s+S5O+tttNfmzZvJzc2lpqaGTp061cs5hbhW5N4h6kt6ThlONlpC3e0BmNI/lvvahJAS4MzHy7bz5FfLKa3SoVLBuL6tmTy0Gy4Otg0ctcnx48fx9/c3D7qShZ+FaDg6nY7c3FwMBgN5eXksXbqU1157jQEDBjB8+PDLPp+VlRUPPfQQ06ZNQ6vVMm7cONq0aXPBUqhDhgzhxRdfpH///rz11luEhoaybds2iouLcXBw4IsvvuCGG24472znmTNnYjAYSEtLw97enq+//ho7OztCQkIuO+6/e+mll/Dw8MDHx4dnnnkGT09PBg8efFXnvFr1nsDVarU88MAD5toQV5K4/VNhYSEGgwEfH5+ztvv4+HDw4MHzHnP06FFWrlzJHXfcweLFi8nMzGTMmDHU1dXxwgsvnPeY1157jRdffPGK4/yvMRiMzN24n8lzVrPvZAEATnbWDO/RimcGt8HPzfT/fGLnCL7Zkc0LvaLp2syjSSZuDQYDmZmZVFVVmesx2djY0LdvX2xtG8fDjxCi4cj9Q/zXPNMjktVHinh3cAK9Yy68rsF369IZMe0navUG2kYHsvCpoXg619+oqubNm1NdXU3z5s3r7ZxCXCty7xBXq1Kn56VlGby9+ig9ojxZfE8aKpUKd3trTuQV0e6pOeaSdi0j/Pjw/gGkNgto4Kj/sm/fPvbt24efnx/t27eXEnRCNLClS5fi5+eHVqvFzc2N5s2bM23aNEaMGHFFfz/t7e154oknGDZsGKdOnaJjx4589tlnF91/w4YNPP3004waNYqCggLi4+OZPHkyqamppKWl8fDDDzNt2rRzjnV1dWXKlClMnDgRg8FAYmIiP//8Mx4eHpcd999NmTKFCRMmcPjwYZKTk/n5558bfPCeSlEUpb5P2qVLFx5++OGrzk6fPn2agIAANmzYcFYB4ccff5zVq1ezefPmc46JioqipqaGY8eOmUfcvv3227zxxhvk5OSct53z9YIHBQVRWlqKs7PzVf2G683OozncMXUeB7ILAXCxt2FEz9aUaF35dncOH92cxKjWpmHmRqOCStW0pzQWFxezfPlyAHr37o2Li0sDRySE+FNZWRkuLi4Neq2W+4f4L9ibU0aC319/ng1GBY36/Pd2RVF446f1PPGV6d55U5tYvp5wE3b1tJjGP9tqys8YouE09P1D7h3iaiw+kMeYeekcP1MNwE2Jvnw1rAUGg4EXvvuddxdtxmhUcLKz5tU7uvNg71ZoNI0rQVpcXMzvv/9ObGwssbGxci0XTcaF7h9/5qDCwsL+8wO9Zs6cycMPP0xJSUlDh3JFVq1aRdeuXTlz5sw1W0vr3/75sUgN3DFjxvDoo4+SnZ1Ny5Ytz6lj8/cV4C7G09MTjUZDXl7eWdvz8vLw9fU97zF+fn5YWVmdVS4hNjaW3Nxcamtrz5sxt7GxMddPExe26dBJ+kz+mtIqHW6OtozokUaRxpnpu3LQG6sAWHes2JzAVV/g5a6xq6qqwt7eNA3J3d2dyMhIXFxcrmo0uRDi+iT3D3E90+kNPPHLAaatO8b8EakMTvQDuGDyVm8wMP7TJcz4dRsADw9ow5sjetVL4sBoNLJjxw6ioqLML0zywi+aKrl3iCtxurSGCT/tZe4e06CkYDc7Prgpkf6x3vy0+SDjP1tCdpGpHMet7eJ5Z3Rv/N0bT4fA3zvd3N3d6devH3Z251/8UgghxLksksC9/fbbAc5asEylUpkv2gaD4V+dx9rampYtW7JixQrzaF6j0ciKFSsYN27ceY9p3749s2fPxmg0mod6Z2Rk4Ofn1+DDnZuyNfuy6P/KbCpqamkZHUpMfALv7c7FYKwAoEekJy/0iqJD+NUNU29INTU1bN68mZKSEvr162eux/Rn6QQhhBDivyKjoIKhX+9gR3YpAHtzy80J3POprKll6Ntz+XlbBioVvD2yNw8PbHvB/S/X3r17OXr0KLm5ufTt2/eC6xoIIcT1aPPxM/T8aBPlOj0atYpHOoUzqVcUBaXlDHrtW37ZlgFAuI8bH9zbjz4pjWtx5YKCAnbs2EGHDh3Mg7skeSuEEJfHIgncY8eO1du5Jk6cyIgRI0hNTaV169ZMnTqVyspKRo0aBcDw4cMJCAjgtddeA+DBBx/k/fffZ8KECTz00EMcPnyYV1999axksrg8y3cfYdBr31Jdq6dLYhjHrQP4Zqep57dXlBcv9IqiXVj9LErSkKytramqqqKuro7CwkL8/C78oiqEEEJcr77cdpIx89KprDXgYW/FF7cnMzD+/DOfAPJKKhj46my2Zp7G1lrL1xNuYkjbuHqNKTo6moKCAuLi4iR5K4T4z2nu74yfsw3x9k58eHMicd6OvL1wIy/+sIrqWj1WWjVPDO7A00M6WqRkzdVQFIXdu3dTWlrK3r17SUtLa+iQhBAWNHLkSEaOHNnQYVyxLl26YIFKs/XCIgncq13t7e9uu+02CgoKeP7558nNzSU5OZmlS5eaFzY7ceLEWUWVg4KC+PXXX3nkkUdISkoiICCACRMm8MQTT9RbTP8li7ZlMOSN79HVGeiXEsm8x2/lVFktzyw5yMOdwmkT4tbQIV6xkpISsrKyaN68OSqVCrVaTevWrbGxscHR0bGhwxNCCCGuqfIaPWPm7+Hr7aaFb7pEePD1HS0IcLnwKKlDpwrp+/LXHMsrwcPJjoVPDaVdTHC9x2ZjY0O3bt2kbIIQ4j+hvEbP9A1ZPNo5HK1Gja2VhhUPtMXf2ZYNh07Q4n9fmxeT7pIQyvT7+hMb6NXAUZ+fSqWibdu2HDhwgOTk5IYORwghmiyLJHABDh06xHvvvceBAwcAUx3ahx56iOjo6Ms+17hx4y5YMmHVqlXnbGvbti2bNm267HbE2eZv2s/tb8+lTm+kV2osP/7fEKyttER4WvHdXS0bOryrUldXx8qVK9Hr9Xh5eREQYFqV9WpXKhRCCCGaqt8zC/l6+yk0ahWTekXxVPfIC9a7BVh/4ASDXvuW4opqwn3cWPLcHUT5e9ZLLIqisHfvXjw9Pc0zYiR5K4S43imKwo/puYz/aS+nSmuw1qh4pHMEAHYahftmLOSzFTsB8HS2560RvbirS/NGd32sqamhrKwMb29vABwcHEhNTW3gqIQQommzSAJ33rx53H777aSmptK2ran+2aZNm0hISOC7775jyJAhlmhW1KPZa/YwfNqPGIwKLZKbs6rEml8zCi86hbKxq6mpMa/oZ2VlRWRkJBUVFbLarxBCCAEMSvDluZ6R9I72pv0lSiPN3bCPO9+dj67OQOvIAH5+aijervU3e+XkyZMcOHAAtVpN3759z1kQVwghrjfHi6sY9+NeftlvWsA7wsOeRD9nFEVh1u+7eGzWbxSVVwNwb88UptzZA3cn+4YM+bwqKyv57bffMBqNdOnSRQbICCFEPbFIAvfxxx/nqaee4qWXXjpr+wsvvMDjjz8uCdxG7osVO7l7+gIUBVqltGDrGTVgZMuJkiaZwDUajWzdupWTJ0/Su3dvnJycAEhISGh0vdVCCCHEtZJfruPRn/fxxoA4fJ1NHZwv9Ym56DGKovDOzxt5bNZvKArc0Dqa2Y8Mwd6mfheKDQgIIDAwEA8PD0neCiGua3UGI1PXHGXSbxlU1Rqw0qh4omsznu4RybHcIro8N5M1+48DkBjizYf3D7BIqZr6Ym9vj6enJzU1NWeVOhRCCHF1LJLAzcnJYfjw4edsv/POO3njjTcs0aSoJ9OXbGHsJ4sBaNOqJZsKTduf7RHJS30uv/xFY6BWq6mtrcVoNHL69GlzGQ9J3gohhPivWp5RwF2zd5JbrqO0Ws/Cu1tf8hiDwcjEmb8ybdFmAMb2bcW7o/ui0dT/C7pGo6Ft27ZyrxZCXPfGzEvn080nAOgU7s6HNycR4mrD5O9/540FG9AbjNjbWDHpti48PKANVtrGtZBjXV0dmZmZREVFodFoUKlUtG7dGisrK0ngCiFEPbJIArdLly6sXbuWZs2anbV93bp1dOzY0RJNinrw9sINPDrzNwDapbViQ74RgBd6RfFCr6gm8xJVWVlJRkYGiYmJaLWmP+LNmzcnPj4ed/eLTwkVQgghrmd1BiPPLz3E/37PRFEgzseRV/tdfNQtQJWuljunzufHzQcBeGN4Tx69oV29PhscP34cnU5HVFQUIB2tQoj/hoc7hfPL/jxe6xfLiFaBLN2ZSf9JiziWVwLAoFbRTLu7LyHerg0a5/koisLq1aspLi7GaDQSHx8PmBaeFEIIUb8sksAdNGgQTzzxBNu3b6dNmzaAqQbunDlzePHFF1m4cOFZ+4qG98rcNTw7eyUAndq1YU1OHQAv9o7m+V5RDRnaZVEUhbVr11JWVoatrS2xsbEAUudWCCHEf96xoiqGfbODTcfPAHB/2xDeHhSHvfXFHwcLSisZ9Nq3bMrIxlqr4cvxN3Jbh4R6ja2kpIQtW7agKApOTk7mhcuEEOJ6cqaqlldXZGIwKrx9gynZGe/rRNaz3SksreTWN+cwd+N+AII8nXnvnn7c0PrSnWwNRaVSERUVRXp6ugyUEUJcUJcuXUhOTmbq1KnXtN2RI0dSUlLCTz/9dMF9QkNDefjhh3n44YevWVxXyiIJ3DFjxgAwffp0pk+fft7vwHTBNxgMlghB/EuKovD8t7/z8tw1ALx0exeOGV1Zk3OSV/rG8HSPyAaO8NJqamqwsbFBpVKhUqmIiYkhKysLLy+vhg5NCCGEaBQ2HT9D7483UVajx9XOik9uSeLm5v6XPO7w6SL6vfINmTnFuDnasuDJoXSMC6n3+FxcXIiLi6OyshJf36ZXb18IIS6mVm9k+oYsJi/LoLiqDo1axfiOYYS626M3GPhw6Vaenb2SippaNGoVjwxsywu3dsbRrnGNZDUajRw+fBh3d3fzu1ZQUBD+/v7mmY9CiKZl5MiRzJo1i/vvv58PP/zwrO/Gjh3L9OnTGTFiBDNnzrzkuVatWkXXrl05c+YMrq6ulgm4nm3durXJrLdgkaus0Wi0xGlFPVMUhf+b9RtvLdwImKZDPja4PQajwk1JfgyI82ngCC9t7969HDp0iHbt2plH64SEhBAaGtqwgQkhhBCNSIKvE35ONiT4OjH7jhRC3C+9cvm3a9O5/8OfKa+uJdTblcXP3kFsoGU6R1UqFfHx8SiKIqUThBDXDUVRmLM7h6cWH+BoURVguh6/PiCWEDc7th4+xQMf/cKOozkAtI0O5MP7B5AU2jg7sg4cOMC+fftwdnamZ8+e5pq3krwVomkLCgriu+++45133sHOzg4wDZSbPXs2wcGNc9HE2tparK2vfhHdpjTwT6qK/0cZjUbGfbLYnLwdNbArDw9sC4BGrWoSyVsAg8GAwWDg1KlT5m3y4ieEEELA4YIKjEYFAEcbLcvub8vqMe0umbytqNYx+v2fGPbOPMqra2kfE8TG1+6u9+RtYWEh27dvP6vjX+7hQojrxaH8CtpOW8dtX23naFEVfs42fHprc3Y92pkEL1tGTPuRtCc/YcfRHFwdbPnogQGse2V0o03eAjRr1gxnZ2eioqJkgTIh/qVKnf6C/9TUGf71vtX/ct8rkZKSQlBQEPPnzzdvmz9/PsHBwbRo0cK8TafTMX78eLy9vbG1taVDhw5s3boVgKysLLp27QqAm5sbKpWKkSNHmo81Go08/vjjuLu74+vry6RJk86KoaSkhHvuuQcvLy+cnZ3p1q0bu3fvNn8/adIkkpOT+fTTTwkLC8PW1haAuXPnkpiYiJ2dHR4eHvTo0YPKysqzzv3mm2/i5+eHh4cHY8eOpa6uzvxdaGjoWaUdVCoVM2bMoG/fvtjZ2REeHs7cuXOv6L9rfZOusv8gg8HIfR/+zOcrdoIK+nbtzBd7y9B9u4uv72jRaF+eamtrOXToEGFhYTg6OgIQHR2Nt7e3TLcUQggh/qAoCh+sz+Kxn/fzar8YJnaOACDIze6Sx+48msPQd+Zy6FQRarWKZ2/uxHO3dEKrqd9Vz+vq6li3bh21tbXY29uba9YLIcT1wsPeigP5FThYa3i8azMe7RyOXq/n2dkrmPrLJmpqTYmWuzon8eaIXni7OjZwxGdTFIUTJ05QWlpKUlISYFqcrHfv3o32fVGIxsjx6SUX/K5frDeL7kkzf/ae9BtVtecvM9o5woNVY9qZP4e+soLCytpz9lPeGnhFcY4ePZovvviCO+64A4DPP/+cUaNGsWrVKvM+jz/+OPPmzWPWrFmEhITw+uuv07t3bzIzMwkKCmLevHkMGTKEQ4cO4ezsbB7NCzBr1iwmTpzI5s2b2bhxIyNHjqR9+/b07NkTgFtuuQU7OzuWLFmCi4sLH330Ed27dycjI8NcYzszM5N58+Yxf/58NBoNOTk5DB06lNdff50bb7yR8vJy1q5di6Io5nZ///13/Pz8+P3338nMzOS2224jOTmZe++994L/LZ577jmmTJnCu+++y1dffcXtt99Oenp6gz+vSgL3P0ZvMDBi2k/MXpuOSq2ib7fOLD5SDkCbELdGfTPeunUrp06dorq6mtatWwNga2srC50IIYQQfyiqrOXu73exYF8eAOuOFfNIp/BL3t8VRWHaos08/uUyavUGAtyd+OaRIXSOD7VInFZWVqSmppKZmUlkZOOvty+EEJdSUKHj252neKhDGCqVCk9HG364qyXN/Z3xsLfi42XbmfT9KgrLTKUUOseH8OaIXqQ2C2jgyM+vpKSEzZs3AxAQEICHhwcgMyWEuF7deeedPPXUUxw/fhyA9evX891335kTuJWVlcyYMYOZM2fSt29fAD755BOWLVvGZ599xv/93/+ZE63e3t7n1MBNSkrihRdeACAyMpL333+fFStW0LNnT9atW8eWLVvIz8/HxsZU+/vNN9/kp59+Yu7cudx3332AaVDfl19+aS57sGPHDvR6PTfddBMhIaY1GhITE89q183Njffffx+NRkNMTAz9+/dnxYoVF03g3nLLLdxzzz0ATJ48mWXLlvHee++ds8bXtSYJ3P+Q2jo9w96Zx7xNB9Bo1PTp1oVFmaUAfHBTImPahzZsgP9QV1eHWq1G88eon5iYGCoqKvD3v/SiK0IIIcR/zZojRdzxzQ6yS2uw1qh5fUAs4zuGXfJlu6C0klHv/8Si7YcBGNQqms/H3YCH06Xr5F6NwMBAAgICJBkghGjSqusMTF1zlNdWZFKu0xPp6UDfWFM5ul7RXizYcpAnvlpOxukiAGICPHl9eE8GpEY16uufm5sbERER2NnZNZnFiIRojCpe7XvB7zTqs68B+ZN6XXBf9T/2zXqm+9UF9g9eXl7079+fmTNnoigK/fv3x9PT0/z9kSNHqKuro3379uZtVlZWtG7dmgMHDlzy/H+O5P+Tn58f+fn5AOzevZuKigpzR9GfqqurOXLkiPlzSEjIWTVrmzdvTvfu3UlMTKR379706tWLm2++GTc3N/M+8fHx5pzSn+2mp6dfNNa2bdue83nXrl2X/I2WJgnc/4ia2jpueXMOv2zLwEqroU/3LvycUQLAhzcncn/b0AaN75+OHj1Keno6MTExREdHA+Dh4UGvXr0a9YOOEEIIca3pDUZeXn6YycsyMCoQ6enA93e1pEWgyyWP/T39GHdMnUfOmQpsrDS8NbI3Y/q0ssi9trKykj179tCyZUvzohNyTxdCNFVGo8JX27N5dslBsktrAEgJdMHVzgqAzRnZPDbrN9YdOAGAt4sDL97ehXt6pNR7WZr6kJ+fz759+2jfvr35Gt2yZcsGjkqIps/B5t+n3Sy17781evRoxo0bB8AHH3xQr+e2srI667NKpTKvg1BRUYGfn99Z5Rr+9PcOJAcHh7O+02g0LFu2jA0bNvDbb7/x3nvv8cwzz7B582bCwsIu2W5TY7HK40eOHOHZZ59l6NCh5qz6kiVL2Ldvn6WaFBdQpatl0Gvf8su2DGyttQzs1c2cvP34lqRGl7z9k06n49SpU2fVL5EXPSGEEOJsB/MreHXFYYwKjEgNZMfETpdM3uoNBp6dvYLuk2aRc6aCmABPNk+5l7F9W1vkXqsoChs3buTkyZNs37693s8vhBDX0vKMAlq+s4aR3+0iu7SGYDc7vh7Wgq0TOuJrp+L2t+bQ5slPWXfgBHbWWp69uROZ08fzQO9WjTJ5azQa2b59OwUFBf9qJJ0Q4vrUp08famtrqauro3fv3md9FxERgbW1NevXrzdvq6urY+vWrcTFxQGYO38MhvPX8b2QlJQUcnNz0Wq1NGvW7Kx//j4K+HxUKhXt27fnxRdfZOfOnVhbW/Pjjz9eVvv/tGnTpnM+N3T9W7DQCNzVq1fTt29f2rdvz5o1a3jllVfw9vZm9+7dfPbZZ41mBbf/gvJqHQNemc2a/cdxsLXil6eHUWvlwJLMrbx/YyKj04IbOkQMBgNHjx7F1dXVPBw+NDQUrVZLYGCgJG2FEEKIi0jwc+adQfG42llxR8vAS+6flX+GYe/MY+OhbADu6ZHC1NF9cLC1tliMKpWK1NRUtm3bRvPmzS3WjhBCWJreYGTs/HQyCipxsdXyTI9IHuoQRrWulse//I33Fm+hVm9ApYIRXZKZPLQrgZ6XnhFxrf05SEalUqFWq2nZsiUnT55sFEkKIUTD0Gg05k4czT86mxwcHHjwwQfNtW6Dg4N5/fXXqaqq4u677wZMJQ5UKhW//PIL/fr1w87OzrwA/cX06NGDtm3bMnjwYF5//XWioqI4ffo0ixYt4sYbbyQ1NfW8x23evJkVK1bQq1cvvL292bx5MwUFBVd9HZszZw6pqal06NCBb775hi1btvDZZ59d1Tnrg0USuE8++SQvv/wyEydOxMnJyby9W7duvP/++5ZoUpxHSWU1fSd/w6aMbJztbVjy7B20izElbI883R0/Z9sGjtBk//79HDhwAA8PD7p162Z+iAgObvjkshBCCNHYVNcZeHLRAUa1CiI5wJQUGNsh7F8dO2fDPu6dvpDSKh3O9jZ8/MBAbuuQYMlwzVxdXenevbt0zAohmpzTpTV4OlhjrVWj1ah5Y0AcKzILea5HJE42GqYv3cLkOas5U2EqpdAjKZw3R/SieZhvA0d+fiUlJWzfvp2oqCiCgoIA06JD3t7eDRyZEKKhOTs7X/C7KVOmYDQaueuuuygvLyc1NZVff/3VXHM2ICCAF198kSeffJJRo0YxfPhwZs6ceck2VSoVixcv5plnnmHUqFEUFBTg6+tLp06d8PHxuWisa9asYerUqZSVlRESEsJbb71lXmTtSr344ot89913jBkzBj8/P7799lvzKOOGpFL+Pj+9njg6OpKenk5YWBhOTk7s3r2b8PBwsrKyiImJoaampr6brFdlZWW4uLhQWlp60T+8jVlReRW9XvyKHUdzcHW0ZWDPrjzbJ54or0v3flia0WhEr9ebh9dXV1fz+++/ExUVRXh4OGq1xSp7CCGuI43xWt0YYxLXl/255dz+9XbSc8qJ8XYk/bHOaDWXvm9W6Wp5+POlfLJsBwBtogKZ/cgQwnzcLnHklaurq2Pbtm0kJCSc1aEvRENrbNfqxhaP+Et5jZ43VmXy1uqjvNo3hgmdws3fKYrCnA37eOrrFRzNOwNAQrA3bwzvSe8WzRp1Z9XevXvZv38/Tk5O9OnTp1HHKkRjcqHrdU1NDceOHSMsLAxb28YxUE5cGZVKxY8//sjgwYOvWZv/9s+PRUbgurq6kpOTYy4a/KedO3cSEBBgiSbF3+SVVNDzxS9JP56Pp7M97dp34Ktd+aw+Xs7BJ7piZ9VwdZdyc3PZvn07Pj4+5mHwdnZ29O3bVx4chBBCiAtQFIXPNp9g/E97qa4z4u1ozTs3xP+r5G368Txue2sOB7ILUangyRs78OLtXbHSWvZ5YPfu3Zw8eZLS0lJ69eolHbRCiCZDbzDy2ZYTvPBrBnnlOgBWHC40J3DXHzjBY7N+Y1OGqRSNn5sjk4d2Y2TXZDT/4rrcEAwGg3lKdExMDLW1tcTExMg7mBBCNBEWSeDefvvtPPHEE8yZM8e8wtv69et57LHHGD58uCWaFH84VVRGj0lfcvBUIb5ujrRq056FBwvRqFW8MSCuQZO3YKqjUllZSU5ODnq9Hq3W9EdQHhyEEEKI8yupruP+OXv4YfdpAHpGefLl0Bb4XqIUkqIozFi6lYkzf0VXZ8DPzZGvJtxE96Twix5XXxISEigrK6N58+aSvBVCNAmKorDoQD6P/7KfA3kVADTzdGBK/xhuSvTj8Okinvx6OfM3mWpEOtha8fjg9jw6qJ1F64hfjaqqKnbu3ImiKHTo0AEArVZLSkpKA0cmhBDiclgkgfvqq68yduxYgoKCMBgMxMXFYTAYGDZsGM8++6wlmhTA8fwSur0wi6N5Zwj0dCa5VVt+PliIVq3i2ztTuLm5/zWNR1EUsrOzURTFXM/Wy8uLNm3a4O/vb07eCiGEEOL8Tp6pptP09WQVV6NVq3ilbwyPdYlArb54x2dxeRX3TF/Ij5sPAtAvJZKZDw3Gy8XhWoQNgK2tLV27dpVOWiFEk/H04oNMWZkJgIe9Fc/3iuKBtqGUVVUz4bMlzPh1G3qDEbVaxT3dU5h0Wxf83Bt3iZi6ujpOnzZ1AJaVlUmZDiGEuAgLVJmtNxbJoFlbW/PJJ5/w3HPPsXfvXioqKmjRogWRkZGWaE4AmTlFdH/hS04UlhLm40ZcSmt++SN5+8PwltyY6HfNYzpx4gSbN2/G1tb2rIStLE4mhBBC/DsBLrZEeDigwtQZmxZy6Zq1a/ZlccfU+WQXlWGlVfP6XT2ZMKCNxROpiqKwY8cOAgMDzQtOSPJWCNGUDG0RwLR1x3iofRhPdm+GrQbeWbiBV+evpazKVEqhX0okrw/vSXxw413wS6fTYWNjA4CLiwstW7bE3d1dkrdCCNGEWSSBu27dOjp06EBwcLAk666Bg9kFdHthFjlnKogO8KBP1068u/4EVhoVc4anckPCtVn9VFEUamtrzQ8LgYGBHDx4kMDAwGvSvhBCCHE9yCmrwcVWi721FrVaxew7UrDRqnGxs7rocXqDgZfnrGHy3DUYjQqRfu58N/FmUiKuzQycI0eOcOTIEY4fP06/fv1kEQ8hRKNWUl3Hq8sPY1AU3hoUD0CSvzPZz/XAxVbLt2v38vQ3KzhRWApAcpgvb47odc3K0FwJg8HA7t27OX78OL1798be3h6A8PDGG7MQQoh/xyIJ3G7duhEQEMDQoUO58847iYuLs0QzAtiTlUuPSV9SUFZFQrA3yycNx9rami3Z5TzVvRkD469N8rakpIStW7ei1Wrp0qULKpUKjUZDr169ZPSNEEII8S8tOZDHiO92cVOiHx/enASAt5PNJY87WVjKHVPnsXb/CQBGdG3Oe/f0w8nu0sfWl7CwMPLy8ggKCpLkrRCi0arVG5mxIYuXlmVQXFWHRq1ifIcwQtxNyc5dR07x2Kzf2HE0B4BAD2deGdaNOzsnNfp63mq1mjNnzlBXV8epU6dkBqwQQlxHLJLAPX36NN999x3ffvstU6ZMISkpiTvuuIOhQ4fKaMx6tP3IaXq9+BXFFdUkh/my7IW78HQ21bZbN679Jevj1Sdra2tKS0tRqVRUVlbi6OgIyNRJIYQQ4t/Q6Q08tegg76w5CsCm42eo1OlxsLn0o9pPmw8w+oMFnKmowdHWmg/vH8AdnZMsHfI5NBoN7dq1k3u/EKJRUhSFuXtyeGrRAY4UVQEQ5+PIGwPjCHaz40B2AY9/uYxftmUA4GRnzVM3deThAW2ws7n4DIiGVFlZiZ2dHWq1GpVKRWpqKjU1NeZSNkIIIa4PFkngenp6Mm7cOMaNG8exY8eYPXs2s2bN4qmnnqJTp06sXLnSEs3+p2w8dJI+k7+mrEpHq2YBeDRL4JtdeUzoZJoeY+nkbX5+PqWlpeZeXXt7e9q2bYuHh4eMuhFCCCEuw+GCCm7/egc7sk3TdB/qEMbrA2KxtdJc9LhqXR2PzfqN6Uu3ApAa4c+3E4fQzM/D4jH/6eDBg2g0GvPzgCRvhRCNUUZBBSO/3cXG42cA8HGyYXKfaEa1CqKovIoHP/qFT5fvwGBU0KhVPNA7ledv6Yy3q2MDR35xGRkZpKenk5SUZL4Ou7i44OLi0sCRCSGEqG8WSeD+XVhYGE8++STNmzfnueeeY/Xq1ZZu8rq3el8W/V/5hsqaOtrHBuMQHMvSQ4WsPlrMkCQ/Al3tLNp+cXExq1atQq1W4+/vj4ODadRvQECARdsVQgghrjdfbTvJmPnpVOgMeNhb8cXtyf+q/NH+k/nc/vZc0o/nA/B/g9vx8tBuWFtZ/NHOrKCggD179gDg7u6Oh8e1SxwLIcTlcLezYl9eOfbWGv6vSwSPdYlApRh5bd5aXv9pPRU1tQAMbh3DlLt6EB3g2cAR/ztqtRqDwUBBQYGUSxBCiOucRZ/y169fzzfffMPcuXOpqanhhhtu4LXXXrNkk9e933ZlMnjKd1TX6umaGI7WL5LfMgqxs1Lz8+jWFk/eguklzdfXF0dHRzSai48OEkIIIcT5FVfV8vCCfVToDHSO8OCbO1oQ4HLx+7iiKHyybDsPf76U6lo93i4OfDn+Rnq3aHaNov6Lp6cnsbGxAJK8FUI0KoUVOr7bdZqx7UNRqVR4Otrw/V0tae7vjLeDNV+u2s2z367kdHE5AK2a+fPmiF50ig9t2MAvoaamBr1eby5XFx4ejr29PX5+fg0cmRBCCEuzSBX2p556irCwMLp168aJEyd49913yc3N5auvvqJPnz6WaPI/4eethxj46rdU1+rpkxKJ2rcZyw4XYm+tYfE9aXSP8rJY2waDAUVRzJ87duxISkqKlEsQQgghrpC7vTVf3JbMS32iWfFA20smb0sqq7ntrTnc/+EvVNfq6dk8nN1vP9AgyVswlUtITEwkISGhQdoXQoh/qq4z8L+VmUS8tpKHftzLr4cKzN/1ifEm/Wg2KY99xOgPFnC6uJxQb1e+nTiETVPuafTJ27y8PJYuXcrmzZvN72V/zoiU8jVCiCs1cuRIVCoVU6ZMOWv7Tz/9dNXXlkmTJpGcnHxV57gcM2fOxNXV9Zq1dylZWVmoVCp27dpVL+ezyAjcNWvW8H//93/ceuuteHo2jeknjd3cDfsY+s489AYjg9JiKXMKYlVmEQ5/JG87RVhu5IvRaGTDhg3Y2dmRkpJiLpAvhBBCiH/PaFR4d+1RIjwcGJRgKpMwKMHX/O8Xs+HgCYa9M4/jBaVoNWpevaM7jw5qe81XRD99+jQ5OTm0aNHC3LY8EwghGprRqPDNjmyeWXKQkyU1ACT7O+P8x0KQe7JyefzLZfy66wgArg62PHtzJ8b1a43NNSw9czWcnJwwGo3o9Xpqamqws7P8zEshxH+Dra0t//vf/7j//vtxc3O75u3X1dVhZdV4F4tsLCzy1L9+/XrGjBkjydt68s3qPdz29lz0BiNDOyZwc/e2rDpShKONhqX3WjZ5C1BYWEhubi7Hjx+nvLzcom0JIYQQ16P8ch0DPtvCxIX7GfX9LgoqdP/qOIPByCtz19Dp2S84XlBKuI8b618dzf8Nbn/Nk7c1NTVs3LiRI0eOcOzYsWvathBCXMjKw4WkTl3D8G93cbKkhiBXW74cmsz2RzoR4qzl7g8WkPzoh/y66whWWjUPD2hD5vTxPHpDu0advNXr9eTm5po/29vb07VrV3r27CnJWyGaEL1ej16vP2tGs8FgQK/XYzAYLrnvnx03/3bfK9GjRw98fX0vWfJ03rx5xMfHY2NjQ2hoKG+99dYF9505cyYvvvgiu3fvRqVSoVKpmDlzJmDq/J8xYwaDBg3CwcGBV155BYAFCxaYZ3qHh4fz4osvotfrzed8++23SUxMxMHBgaCgIMaMGUNFRQUAq1atYtSoUZSWlprbmzRpEgChoaG8/PLLDB8+HEdHR0JCQli4cCEFBQXccMMNODo6kpSUxLZt2876DevWraNjx47Y2dkRFBTE+PHjqaysNH8fGhrKq6++yujRo3FyciI4OJiPP/7Y/H1YWBgALVq0QKVS0aVLl4v/j7iEertjLVy4kL59+2JlZcXChQsvuu+gQYPqq9nr3mfLd3DvjIUoCozqlswnDw5Co1GTV15Hu1A32oW5WzwGb29v2rY1jfKRFU2FEEKIy7Mio4A7Z+8kt1yHrVbNK31j8HSwvuRxp4rKuOvd+fy+NwuAoR0T+PD+ATjbN0z5IltbW1q1asWpU6fMD6RCCNGQ9AYjD8zdw+HCSpxttTzVrRkTOoWj1+uZ9P3vvLlgA9W1ppf/W9rF8dqdPYjwtfz709XS6XQsX76cqqoqevXqZX4Ha4iRcUKIqzN//nzAlAf7swTloUOH2Lt3L2FhYbRq1cq874IFCzAYDPTv39+8WHxmZia7du0iODiYNm3amPddtGgROp2O3r17m68Rx44dIyIi4rJj1Gg0vPrqqwwbNozx48cTGBh4zj7bt2/n1ltvZdKkSdx2221s2LCBMWPG4OHhwciRI8/Z/7bbbmPv3r0sXbqU5cuXA5yVT5o0aRJTpkxh6tSpaLVa1q5dy/Dhw5k2bRodO3bkyJEj3HfffQC88MILgKlszLRp0wgLC+Po0aOMGTOGxx9/nOnTp9OuXTumTp3K888/z6FDhwDM9cIB3nnnHV599VWee+453nnnHe666y7atWvH6NGjeeONN3jiiScYPnw4+/btQ6VSceTIEfr06cPLL7/M559/TkFBAePGjWPcuHF88cUX5vO+9dZbTJ48maeffpq5c+fy4IMP0rlzZ6Kjo9myZQutW7dm+fLlxMfHY2196ef/i6m3BO7gwYPJzc3F29ubwYMHX3A/lUp1Ts+BOL8Plmxh3CeLAbi7Z0veHNELjcY02uaxrpf/l/JyKYpinhZ5vr/AQgghhLiwOoORF349xJSVmSgKxPk48v1dLUnwc77ksb9sO8TI936iqLwaB1srPri3P8O7NG/wcgXBwcEEBQU1eBxCiP+unLIaPOytsdaq0WrUvDEwjuUZBTzfKwo3Oy2fLd/JC9//Tl6JaZRUu+gg3hzZi7bRQQ0c+b9nY2ODi4sLiqKg0/27GRtCCHE1brzxRpKTk3nhhRf47LPPzvn+7bffpnv37jz33HMAREVF/X979x0eVZn2cfw7M+m9kgYkEDqEUEIXEUGCooKCsjZYdF1FsaG7iKsgNsCCWFCsq64iuirqKiqI9CK9RKrUUJJQ0ntmzvtHXmZlAU0gkzMJv8915Vrm5Mxz7oPs3HPu85z7YevWrTz33HNnLOD6+voSEBCAh4cH0dGntwu78cYbGTVqlPP1rbfeysMPP8zIkSOBykUan3zySf7+9787C7j333+/c/+Ts2rvvPNOXnvtNby8vAgODsZisZzxeFdccQV33HEHABMmTOD111+nS5cuXHfddQCMGzeOHj16kJmZ6ZyNfNNNNzmP2bx5c15++WX69OnD66+/7izGX3HFFdx1113OMV588UUWLlxIy5YtiYysXKcqPDz8jDFVV40VcH87Vftcp23Lf73w1Qoeen8eAHdd0Y2NxYFc+c4avru9O4E+rn/UJz09nd27d9OzZ8/zvksgIiJyoSkpt9P39ZWs2p8NwF+7N+bFwW3x8/r9HF5aXsHfP5jPy9/+DEDHJtF8PHYYLePMaUuVk5PDL7/8QteuXZ29yVS8FREzFJRW8Pyi3Ty3aDeTr2jFvb2bAjC4XTRXt43i23U7+fsH89l28BgAzWLCmHJzf67t3trtP7ccDgf79u2jcePGeHhU5omUlBRsNpv6QorUcddeey1QOcv1pJYtW9KiRYvTPpsGDx582r7NmjWjadOmp+07aNCg0/Y93yekpk6dyqWXXspDDz102u+2bdvmjO+kXr16MX36dOx2+ylxVEVKSsoprzdt2sTy5cud7RSgstVESUkJRUVF+Pn58eOPPzJ58mS2b99OXl6esyf4yd//nvbt2zv/HBUVBUBSUtJp27KysoiOjmbTpk1s3ryZjz76yLmPYRg4HA727t1L69atTxv3ZPE4KyurWn8XVeWSSuAHH3zA8OHD8fb2PmV7WVkZs2fPZsSIEa44bL3x1L8X89jHCwEYO+QiVuR4s2p/NiG+nuzLLiKpCjN3zkd5eTnr16+ntLSU3bt3O/9hioiISNX4eNroEBvEtsx83ro+meuSY//wPTsOHeNP0z5j497Knof3DerG1BGXmdaj0TAMVq1aRV5eHps3b6Zz586mxCEiF7YKu4N3V6cz8YcdZORXzkadv/OYs4C7fvdhHnp/nrPdTFiALxOH9+HOASl4uXGP299asWIFhw8fpqioiHbt2gE4Z3eJSN128qbMb52t2Hmmfa1W6xnXPTjbvufj4osvJjU1lfHjx59xVm1NOtki4qSCggImTZrkLHj/lo+PD/v27ePKK69k9OjRPP3004SFhbFscMETlQAAR/tJREFU2TJuu+02ysrK/rCA+9ubYSeL4WfadnJCakFBAXfccQf33nvvaWM1btz4jOOeHMdVk1pdktFGjRrFwIEDadCgwSnb8/PzGTVqlAq4Z2EYBo/O+olnPl8KwCPXXcKPWTZWH8gh1NeTH+/s7vLiLVT+A7z44ovZs2cPLVu2dPnxRERE6oPC0gqKyu1EBlTewJ42uC0PX9qM+LDf/0JpGAbvL9zImLfnUlhSTkSQH/8cM5grU8zNwRaLhc6dO7Nt27ZTZiiIiNSWTYdzufmjDaRlVC6k3DTcjymDWjOsfQwHjubwj1k/8eHizQB4e9q4b1B3xg+9iBD/urXIV0JCAkePHv3DAoSIiKtNmTKFDh06nFYLat26NcuXLz9l2/Lly2nRosVZC9JeXl5VbqHaqVMnduzYQbNmzc74+3Xr1uFwOHjhhRechepPP/30nI9XlXi2bt161niq4uTT7DUVk0sKuL/tnfpbBw8e1CJYZ2EYBg++9wMv/mcVAE/c2J+vDzpYm55DuJ8nP97Zgw5xtfd3Fxoaqpk2IiIiVbT5cB7D/7WOuGAf5v21O1arBV9P2x8Wb/OKSrjzjW/4eGkaAH3bJfDh/dcSG+b6G7Znkp6ejqenp7NPV2RkpLN/l4hIbZq94RB/nr2R0goHYX6eTLisBaN7JlBcWsb4D39k+jerKC2vvCi+6eIknr6xH/ENQswNugoqKirYtWsXISEhxMTEABAXF0dkZORpT7CKiNS2pKQkbrrpJl5++eVTtj/44IN06dKFJ598kuHDh7Ny5UpeffVVXnvttbOOlZCQwN69e9m4cSMNGzYkMDDwrJ9zEyZM4Morr6Rx48YMGzYMq9XKpk2bSEtL46mnnqJZs2aUl5fzyiuvcNVVV7F8+XJmzpx52vEKCgpYsGABycnJ+Pn5nfONsXHjxtG9e3fGjBnDX/7yF/z9/dm6dSvz58/n1VdfrdIYDRo0wNfXl++//56GDRvi4+NzXjXR85tf/T86duxIp06dsFgs9OvXj06dOjl/kpOT6d27N/3796/JQ9YLDoeDu9/81lm8nfrngXyZ7mBtei4R/l78NLqny4u3JSUlLFmyhPz8fJceR0REpD6psDuYtng3XV9ayvasArZm5rMvu6hK71296yAdH3yDj5emYbNaePrGS5k/cYRpxdv9+/ezcuVKVq9erUVzRMR0LSL9MQwY1LoBO8b15a6e8bzxwxqa3f0yU+csp7TcTp+28ax59nY+vH9onSjeAuzcuZMtW7awadMm52O2FotFxVsRcRtPPPHEaW0AOnXqxKeffsrs2bNp164dEyZM4IknnvjdVgtDhw5l4MCB9O3bl8jISD7++OOz7puamso333zDvHnz6NKlC927d+fFF18kPj4egOTkZKZNm8bUqVNp164dH330EZMnTz5ljJ49e3LnnXcyfPhwIiMjefbZZ8/576B9+/YsXryYnTt30rt3bzp27MiECROIjf3jtmgneXh48PLLL/PGG28QGxt7Wg/h6rIYhmGc1wi/MWnSJOf/PvjggwQEBDh/5+XlRUJCAkOHDq32olgzZszgueeeIyMjg+TkZF555RW6du36h++bPXs2N9xwA4MHD+bLL7+s8vHy8vIIDg4mNzeXoCDXXkTZ7Q5uf/1r/vnTRiwWeGv01fRMas7FM1ZgscBPd/ao0mrV52vlypWkp6cTFhZGv3793L7Rv4hIbX5WV5U7xiSus2LvCUZ/voXNR/KAyiLDP//UwdlC4WwcDgfPf7WCf8z6iQq7g/jIYD4eO8z0FdIrKipYsGABsbGxtGnTptqLUYjUFe72We1u8ZjpRFEZYX7/vVbccDCX9jGBfL1mB+P+NZ9dR04A0DIunOdGDODKlNMXAXI3hmFgt9ud/SrLyspYvHgxLVq0oHHjxm4fv4j819k+r0tKSti7dy9NmjRR/2qptqr++6nRFgoTJ04EKqctDx8+vEb+4X7yySeMHTuWmTNn0q1bN6ZPn05qaio7duw4rcfub+3bt4+HHnqI3r17n3cMrlJeYWfkK3OcM2/ev+cabupTuYLdgjt74GG10CY6sFZi6dChA2VlZXTs2FFfIkRERH5HfkkF93+Vxrur0wEI9fVkyqDW3N79jy/EM7LzGfHyHOZv2gPAdT3b8Oboq0zp12i32zl48KBzZoOHhwf9+/dX4VZEap1hGLyybC//+G47C0f3JKVRCACBtgr6TnyPpVsPABAZ5MekP/XlL/074enh/p9VOTk5bNiwAR8fH3r06AFUTmzq37+/rrlERKRaXNIDd+TIkTU21rRp07j99tsZNWoUADNnzuTbb7/l3Xff5eGHHz7je+x2OzfddBOTJk1i6dKl5OTk/O4xSktLT3lUMC8vr8biP5uy8gr+NO0z5vy8HQ+blZl3DaFJ44bO37ePrd27776+vvTp06dWjykiUteZkT/EfL6eVtam5wJwa9dGTBnU+g9n3QL8sOFXRrw8h6zcQny9PHjptsv5S/9OplzEOxwOFixYQE5ODhaLxbmaroq3Iq6n3HGq7KIybv1kE1+mZQDw4bqDdG4YzPsLN3LP299RUFKGr5cHY6/uwd+H9CLIr+7MbjMMg6NHj2Kz2SguLsbXt/JmnYq3IiJSXS4p4Nrtdl588UU+/fRTDhw4QFlZ2Sm/P3HiRJXGKSsrY926dYwfP965zWq10r9/f1auXHnW9z3xxBM0aNCA2267jaVLl/7hcSZPnuxs/1AbSsrKGfbcp3y7bhdeHjbeGnMtU1ZlsX/eQX64vRsXNQ2vlTi2bt1KRETE785kFhGRs6vt/CHm2Xgol9ZRAXh72PCwWXlneDJlFQ56Ngn7w/eWlVfwj1k/8fxXKwBIim/A7LHDaNPIvPxrtVqJjY2luLgYT09P0+IQuRApd/zXqv3Z/Olf69ifXYyXzcoLV7fhxuQohr/wb/69YisAF7eJ54N7r6kTPW5LSkrIzc0lKioKqFwYOiUlhejoaGfxVkRE5FzU6CJmJ02aNIlp06YxfPhwcnNzGTt2LNdeey1Wq5XHH3+8yuMcO3YMu93uTIAnRUVFkZGRccb3LFu2jHfeeYe33nqryscZP348ubm5zp/09PQqv7e6CkvKuOqZj/l23S58vTx47/5hPLMik22ZBYT6ehIVWDvN69PT00lLS2PJkiUUFhbWyjFFROqb2swfYo6c4nLGfLGFTi8u4flFu53bUxqFVKl4++uR4/R65F1n8faugV34ecrtphRvCwoKKCkpcb5u06YNqampzlXQRaR2KHeAw2Hw3MJf6f3qcvZnF5MY7seKe3qRFGoheexM/r1iKx42K8/c1I+fJo2sE8XbnJwc5s6dy4oVK06ZwNS0adNzXgVdRETkJJfMwP3oo4946623GDRoEI8//jg33HADiYmJtG/fnlWrVnHvvfe64rDk5+dzyy238NZbbxEREVHl93l7e9fKqp95RSVc+cwslm49gL+PJ+/dfz2PLEhn17FCGoX4sHB0TxIj/F0eB0BsbCxxcXGEhobi7187xxQRqW9qK39I7TMMgw/XHeSh/2wlq6DyQnzv8eJqjfHh4k2MfuNbCkrKCA3w4d27BzOkW2tXhPuHDh06xM8//0xkZCQXXXQRFosFq9WqhTZETKDcAZ9tPsLfv9kGwPAOscy4pi0vfLmMKXOWYRjQLCaMWfcPpUvzOJMjrbqgoCD8/f2x2WyUlpZWe+FuEan7DMMwOwSpg6r678YlBdyMjAySkpIACAgIIDe3sk/clVdeyWOPPVblcSIiIrDZbGRmZp6yPTMzk+jo6NP23717N/v27eOqq65ybnM4HEDlwhw7duwgMTGx2udTE3IKixn4xIf8vOsQQX7evH//dfxt/gF+PVZIfKgvC0f3pEl47d2Ztdls9OzZs9aOJyIiUlekHcnj7i+2sGRPZcunVg0CmHFtEpc2r9rN4fziUsa8NZcPFm0CKh///fD+a2kUEeyymP9IQEAADoeD8vJyysvLVVgQEVMNax/DtUnRpLZswCWN/Rk46QPW7j4MwK39OvLSrQMJ8HXfIrdhGBw8eJCDBw/SvXt3502xiy++GB8fH/W4FbnAnGxHVVRUpHYpUm1FRUUAf9jWzCUF3IYNG3LkyBEaN25MYmIi8+bNo1OnTqxZs6Zad5u9vLzo3LkzCxYsYMiQIcB/F90YM2bMafu3atWKLVu2nLLt0UcfJT8/n5deeolGjRqd13mdq2N5hQyY9C827M0gLMCXWQ/9ibu++ZU9x4tICKss3iaEub54m52dTXZ2Nk2bNgXUPF9EROR/vbc6ndv/vYkKh4Gfl43H+jdnbJ9EvDyq1nVq3e7D3DDtM3YdOYHVamHCdX14dNjF2Gwu6Vr1u367YE5wcDB9+/YlNDQUq7X2YxGRC5vDYfDGqv2M6NwQf28PrFYL/x7RmfcWbqTT3z6ksKSc0AAf3rzzKob1bGt2uH+orKyMNWvWUFFRQVxcnHMhSBVuRC5MNpuNkJAQsrKyAPDz81O9Rf6QYRgUFRWRlZVFSEjIHy4m7JIC7jXXXMOCBQvo1q0b99xzDzfffDPvvPMOBw4c4IEHHqjWWGPHjmXkyJGkpKTQtWtXpk+fTmFhIaNGjQJgxIgRxMXFMXnyZHx8fGjXrt0p7w8JCQE4bXttycwpoP/jH5B2IIvIID9+fHwErRo2IGlt5azihaN70DjU9cXbkpISli5dSklJCVarlYSEBJcfU0REpK65qGkYNquFK9tEMX1wW+KreIPVMAymf7OKcf+aT3mFg4bhQcx6YCi928S7OOLT2e12NmzYQHp6OgMGDHC2SgoPr51FUkVEfiszv5RbZq1n/s5jrE3P4Z3hHTiRX8RfX/8Pn6+qbKNwSbsEPrj3GlOfVPgjJSUlzrYz3t7etG3blvLycvURFxEA51PiJ4u4IlUVEhJyxi4D/8slBdwpU6Y4/zx8+HAaN27MypUrad68+SntDapi+PDhHD16lAkTJpCRkUGHDh34/vvvnQubHThwwG1nkhw8lku/xz9g5+HjxIQGsGDSSFo3jATg0xEpHC8qIyaodnrPeXt706RJEw4fPkxsbGytHFNERMTd7TpawLwdR7n7oiYANIvw55e/XVKtnvRZOQWMevUr5q7fBcCQrq145+6rCQs0Z9Eai8VCbm4u5eXlZGRkmNY+SkTkp13HuOmj9WTkl+LraaVXQhgLt+zllpe+4NCJfDxsVp664VIeGtzTlCcVqsLhcLBu3Tr279/PZZddRnBwZZG5ZcuWJkcmIu7EYrEQExNDgwYNKC8vNzscqSM8PT3/cObtSRZDXZZPk5eXR3BwMLm5uQQFBZ3TGPuysrl04vvszcyhcUQw7z5wPYv35zMptaWpU+krKirw8HBJ3V5EpFbVxGd1TXPHmOTMisvtTF6wi6k/7abc4WDlPRfRLT60WmM4HA7eXbCBcf/6kRMFxXh72nhx1EDuTE2p9VzvcDiwWCzO4xYUFFBYWOi84S0i/+Vun9XuFk9NsDsMnpy/kyfm78QwoE1UAB/d2IHZC9fy7JfLMQxoHhPGrAeGktLM/RcqW758OYcOHaJ9+/a0atXK7HBExCT18fNa6o4aq+R9/fXXVd736quvrqnDuqVdh4/T7/H3ST+WR9OoUN69/3pu/iSNg7kl+HjYeKR/81qJw+FwsHfvXpo2beq8oFPxVkRELnTfbM3k3jlp7D1RuWBAastIIvyrt6jX5n0ZjH7jW1bsSAegfXwUH95/LUnxtV8wzcvL4+effyYhIYHmzSu/YwQEBBAQEFDrsYiIZOSVcMOH61m0+zgAt3VtzD3do7ntpU9Zt/sIAH/p34kXR6W65UJldrudPXv2kJCQ4FxQpn379rRs2ZKIiKotZikiIlLTaqyad3KRsT9isViw2+01dVi39PCHP5J+LI+WceG8dc91/OnjLRzOK6F1VAC3dq29hdTWrl3Lvn37OH78OF27dq2144qIiLij/SeKuO/LNL76pbIPfVywD9MHt2Vo+5gqz5jNLy7l8dmLeOnbVdgdBv4+njzxp77cc0U3PD2q9vhTTcvKyiI7O5uSkhKaNm1a5cewRERcwWFAWkY+/l42Zg5LouREFj0ffpui0sqFyt4afTVDe7QxO8yzWrZsGZmZmZSVldG2beWCaoGBgQQGBpocmYiIXMhqrIDrcDhqaqg67527r8bf25O/DurF9R9t4kheKW2jA1lwZw+iAmvvLnN0dDTp6ek0bNiw1o4pIiLijirsDi5+bQUHsovxsFp44OKmTBjQggDvqn0VMgyDL1Zt4753vuPQiXwAhnZvzfRbB9LQ5EV3EhMTVbwVEVM5HAZWa+WNsNhgHz4fmYKX1cFz//6JL/5/obJLk5rw/j1DTP/MPBPDMJw38po2bUpeXp5zAUgRERF3oOfpXSDE35eHh1/GpTNXkplfSlJMID/e0YMGtVi8BWjcuDENGjRwrpYqIiJyofKwWZlwWQs+WJvOa0Pb0za66jOp9mSc4J63v3MuUtYkKoRX/3IFV3Ru4apwf1d6ejr79u2jV69eWK1WLBYL7dq1MyUWEZH07GJu/Gg991/chKHtKxdLrijK5caX5nDoRD6eHlaevrEfD17dw+0Wn87Ozmbz5s00b97cudBzw4YNiYmJUes5ERFxKy7JSk888cTv/n7ChAmuOKzbKCqr4LI3VpGZX0pybBA/3tGdiIDaKd5mZmYSFhbm7Nek4q2IiFyIjuSV8ODXW7k2KZphyZUX5aO6NOLWro2q3C6htLyC579awVOfLaGkrAJPDyvjhlzEI0N74+vt6crwzx5TaSlr166lvLycvXv3kpiYaEocIiIA327NZMTHGzhRVM6BnGJSm0fwxL8X8fxXKzAMaBEbzqwHhtI5MdbsUM8oPT2dzMxMSktLiYmJcS4GqeKtiIi4G5dkpjlz5pzy+uRFhoeHB4mJifW+gOvn5cGLg9vy/KLdfHd7N8KruTDKucrMzGTp0qUEBwfTp08fvLxq57giIiLuosLuYMbyfTz2/Q7ySytYvu8Eg9tF42mzOh/vrYqftuzhrje/ZcehykV4+rZL4LW/DqJVw0hXhV4l3t7edOrUifz8fJo0aWJqLCJy4SqrcPDI3G28sHgPAJ0bBvPMZU3o89g/Wb+ncqGyv17WmWmjUvH3cZ9rktLSUhwOB76+vgC0atWK0tJSWrduXeWbeyIiImZwSQF3w4YNp23Ly8vjz3/+M9dcc40rDul2ru8Qy9D2MdiqcbF4vjw8PPD09MTf3193jUVE5IKzYu8J7vpiC5sO5wHQtXEIrw9NwtNW9Ud2M3MKePC9H/hoyRYAokL8eWFkKjdenGTKxX1FRQVpaWkkJCQQEhICQHx8fK3HISJy0r4TRfzpX+v4+UAOAPf2bkILn2KGPPU+xWUVhAX48vZdV3NN99bmBvo/0tPTWbt2LTExMXTv3h0ALy8vunTpYnJkIiIif6zWqnxBQUFMmjSJq666iltuuaW2Dmuq2izeAoSHh9OvXz98fX3drr+UiIiIqxwtKOXhb7fx7up0AEJ9PZkyqDV/6da4yrNu7XYHb8xbyyMfLSC3qBSLBe4a2IWnbryUEH9fV4b/u7Zs2cKuXbvIzMzksssuU34XEVNl5pfScdoScorLCfH15KWrWzHnp1W8vHo7AP3aN+GDe68hNizI5EhPFxAQQHl5OXl5eVRUVGjCi4iI1Cm1mrVyc3PJzc2tzUPWeyUlJdjtducqqQEBASZHJCIiUrs2Hc5zFm9v7dqIKYNaE1mN3vPrdh/mzpnfsHb3YQA6J8bw+l+vpEvzOJfEWx2tW7cmKyuLpKQkFW9FxHRRgd7c0rkha9JzGNM5nL+98RlHsgvw9LAy+ab+PHBVd7f4rDIMgyNHjlBeXu58aiE0NJS+ffsSERGhdgkiIlLnuKSA+/LLL5/y+mQC/de//sXll1/uikNekMrLy1m2bBnFxcX07t3b+WiliIhIfZddVEaoX2Vfxf4tInm0f3Mub9WAnk3CqjxGbmEJj876idd+WIPDYRDk583TN17K6NQu2KrRdqEmFRQUcPToUWd/Wx8fHwYMGKBig4iYZvexQnw8rcQFVz6N8NTA5jz+ySJufuFHAFrFRTDrgaF0bBpjZpinOHz4MMuXL8fLy4uYmBjn2iCRkeb2MRcRETlXLingvvjii6e8tlqtREZGMnLkSMaPH++KQ16QKioqqKiowG63u8WdbhEREVfLKS7n0e+28+G6g/zy90ucBYUnL29V5TEMw2D2sjTG/vMHMnIKALihdzteGJlKTFigS+KuisLCQubNm4fdbicwMJCIiAgAFW9FxDSfbjzMXz7dRMe4IBbc2YNfjxznxhc/Z8PeDADuGFC5UJmft/kLlTkcDuc1UUxMDKGhoTRo0ECfoSIiUi+4pIC7d+9eVwwr/8PX15dLL72UwsJCgoLcr8+UiIhITTEMgw/XHeSh/2wlq6AMgDlbMhhzUZNqjbPz8DHuevNbFmyu/K7SIjacGbdfQf/kxBqPubr8/f2Ji4ujsLDQuUK6iIgZisvtPPDVL7yxcj8ADgNe/m4tj344j+KyCsIDfXnn7sEM7lr1m2euUlpaSlpaGjk5OVx66aVYLBasViv9+vXTJBcREak31Lm9DiosLHT2vPXy8nI+EiQiIlIfpR3J4+4vtrBkzwkAWjUIYMa1SVzaPKLKYxSXljP5i6VMnbOcsgo73p42/jH0Yv5+TS+8Pc37OpSRkUFERIRzMZ3OnTtjtVpVdBAR0+zIKuD6D9ax+UgeFgvc1yue3dt/4cF3dwBwWXJT3rtniNssVGYYBvv376eiooKjR4/SoEEDAH2OiohIveKSK5aSkhJeeeUVFi5cSFZWFg6H45Tfr1+/3hWHvSDs3LmTLVu20KNHD2JjY80OR0RExKX+MXcbzy7cTYXDwNfTyoTLWjC2TyJeHlW/MP9+/S7GvD2X3RnZAAzs2IxXb7+CxOiq98t1hc2bN7N9+3aaNWtGp06dALQquoiY6sN1B7nzs80UltmJDPDigW7RvPzZPDJyCvDysDH55n7cf6W5C5U5HA6OHTvmLNT6+PjQqVMn/Pz8nNtERETqG5dcJdx2223MmzePYcOG0bVrV/UdqiGGYZCZmYndbic3N1cFXBERqffK7QYVDoMh7aKZPrgt8WF+VX7voeN53P/u93y2cisAsWGBvHTrQIb2aOMW300aNGjAjh07sNlsGIbhFjGJyIWrrMLB5AW7KCyz06dpGC0983nk7TkAtG5YuVBZhybmLlRWVlbGggULKCgoIDU11dlGLiEhwdS4REREXM0lBdxvvvmGuXPn0qtXL1cMf8GyWCz06tWLgwcP0qhRI7PDERERqXG7jhZgdxi0iqpcTGzCgBb0bRbO5a2jqjxGhd3OK3NXM+HjhRSUlGGzWrh3UDcm/akvgb7ergr9D9ntdoqKiggMrDy36OhoBg4c6HwtImImLw8rn45IYeayX1myYhVv7ssEYHRqCs//eYBbLFTm5eVFYGAgZWVlWgdEREQuKC4p4MbFxelipAaVlZU5+9xarVYaN25sckQiIiI1q7jczpQFvzLlp1/p3DCYZWN6YbVaCPD2qFbxduWOdEa/8Q2b/r/w0L1FQ2becSXJTaJdFXqVFBYWsnz5csrKyhgwYIAzr+v7koiYxTAM3luTTm5JBfdf3BTDMFi8YRtvfzaPkrIKIoL8ePfuwVzVpaVpMRYXF7NlyxY6duyIp6cnUNkr3MPDw/laRETkQuCSAu4LL7zAuHHjmDlzJvHx8a44xAWjoKCAn376iRYtWtCyZUs9XikiIvXOt1szuWdOGntPFAEQ5ONBXmkFIb5Vvzg/kV/Ewx/+yFvzK/vshwb4MPWWy7itX0e3WMjGy8uL8vJyKioqyM/PJzw83OyQROQCVlBawejPN/PhukPYrBY6RPsz7bMF/GftTgAGdEjkvTFDiAkz7yaTYRgsXryYvLw8/P39adu2LQC+vr6mxSQiImIWlxRwU1JSKCkpoWnTpvj5+Z12d/TEiROuOGy9dPDgQUpKSjhw4ADNmjXT4iYiIlJv7D9RxH1fpvHVL5WzZeOCfZg+uC1D28dU+YalYRh8sGgTD70/j2N5lQXgP/ftwLMjLiMy2N9lsVfFb5+g8fT0pFevXnh7e6v4ICKm2nQ4l+s/WMfOo4XYrBZGJkfwpyn/IvP/Fyqbekt/7h3UzfSbXxaLheTkZNLS0mjYsKGpsYiIiJjNJdXAG264gUOHDvHMM88QFRWlWaPnoVWrVnh6ehIbG6virYiI1BvrD+Zw0avLKS534GG18MDFTZkwoAUB3lXPdb8cyGL0m9+wdOsBANo2iuT1O66kdxvzn/45cOAA69ato1u3bs5FR0NCQswNSkQuaIZh8Oaq/dz35S+UVjiIC/ahZ2g5737xPQBtGkXy8QNDaZ9gXsuZnJwcHA4HYWFhAMTExBAdHa3rSRERueC5pCK4YsUKVq5cSXJysiuGr/ccDgcWi8X5RSUxMdHkiERERGpWcmwwrRoEEOjtwWtD29M2uuqP6RaWlPHEp4uZ9p+VVNgd+Hl7MvH6PjxwVQ88PWwujLrqTpw4QXl5OXv27HEWcEVEzGIYBn+evZEP1h4EoE+TEI7u3sq/Nx4BYMzlXXl2xGX4epvXVzYjI4Nly5bh6+vLZZdd5nyCQcVbERERFxVwW7VqRXFxsSuGrvcMw2DNmjVYLBZSUlJMf3RJRESkJhzOLeHZhb8yeVBrfD1t2KwWfvhrdyL8vap1cf716u3c8/Z3HDiWC8Dgri156dbLiW8Q4qLIq87hcDjzdlJSEv7+/roJKyJuwWKxkNIwhFnrD3FVoj9zFy2mtKyCyCA//jlmCINSWpgdIuHh4fj6+hIUFIRhGGaHIyIi4lZcUsCdMmUKDz74IE8//TRJSUmn9cANCgpyxWHrhezsbA4cqHwUNDExUYuciIhInZaRV8LUhb8yc8V+SiochPh68nhq5YrmkQHeVR5nf1YO977zHV+v2QFAfGQwr/zlClNXRz+poqKCzZs3U1paSo8ePQCw2Ww0b97c5MhE5EJmGAZZBWVEBVZ+1l7fLoLPF+YzZ95aAAZ2bMY/xwwmOtS8hcry8/MJDKw8vqenJ5deeik+Pj6adSsiIvI/XFLAHThwIAD9+vU7ZbthGFgsFux2uysOWy+EhYXRq1cvysrKVLwVEZE6Kyu/lGcX/sprK/ZRXO4AoGdCKFe1iarWOGXlFbz4n1VM+nQRxWUVeNisPHh1Dx67rg/+Pl6uCL3aioqK2L17N4Zh0LJlS2fvRhERs2QXlXHrJ5vYciSP9WMvZsXWffz51S/JzCnE29PGsyMuY8zlXU172s8wDDZv3syOHTvo3bs3MTExAFrkUURE5CxcUsBduHChK4at104WtwH1yhMRkTrLMAzGf7udV5bvpais8oZtt8YhTEptyYCWkdWaVbX4l33c9ea3bE0/CsDFbeJ57a+DaNu4gUtir46ysjJnf8agoCDatWtHSEiIirciYrpV+7P507/WsT+7GC+bhVvemMfXi9cAlYs9fjx2GEnx1buZVtMsFgsOR+XNvWPHjjkLuCIiInJmLing9unTxxXD1luZmZls27aNHj164O1d9cdJRURE3I3FYmFfdhFFZXa6NAphUmoLBrZqUK3CbVZOAX/7YD4fLNoEQESQHy+MHMAtlySb/lhtRUUFmzZt4sCBAwwcONA5W6x169amxiUi4nAYTFu8h/Fzt1HhMGgY5I139n6+XnwIgHuu6MrUW8xbqMwwDBwOBzZb5WKT7du3JyYmhujoaFPiERERqUtcUsBdsmTJ7/7+4osvdsVh6yS73c6aNWsoKipi+/btJCcnmx2SiIhIlZ0oKmPa4j2M6tKIxAh/AJ4Y2JKbOzdkUOvqFW4dDgdv/7iehz/8keyCEgD+ellnJt/cj7BAP5fEX102m42cnBzKy8s5ePCg+tyKiFs4VlDKn2dv5NttWQB0bODNLxvWUFZWToNgf/45ZjBXdDZvobKysjLWrl2L1Wqle/fuQOXnqYq3IiIiVeOSAu4ll1xy2rbfXsCpB+5/2Ww2evfuzfbt22nXrp3Z4YiIiFRJdlEZLy7Zw0tL95JXUsGh3BL++acOALSIDKBFZEC1xtu49wij3/iWVTsPApCcEMXMO66ke8tGNR16tWVlZREREYHVasVisdCpUyfKy8tp0MD8Vg4iIgAP/Wcr327Lwttmpbl3IRt+XgfAFZ2a8+6YwUSFVO8zuabl5+dz6FDlTODWrVsTHBxsajwiIiJ1jUsKuNnZ2ae8Li8vZ8OGDTz22GM8/fTTrjhknRYcHEy3bt3MDkNEROQP5RaXM33JHl5csofckgoA2scEMaTduc2iyi8uZcLHC3l57s84HAYBPl48eUNfxlzRFY//f8zWTD///DP79++nU6dONGvWDIDQ0FCToxIROdWzV7ZhQ/oJ0nf+QtqJE3h72nh+5ADuvryr6a1nAMLDw+nUqROhoaEq3oqIiJwDlxRwz5SUL7vsMry8vBg7dizr1q1zxWHrjPLyclavXk1SUhJBQUFmhyMiIlIlLy7ezRPzd5FTXA5Au+hAHk9twTXtYrBaq1cgMAyDz1Zu5f53v+fwiXwAruvZhhdHDSQu3H1yY1hYGAcOHKC0tNTsUEREzqi4tJynPlnA5lWrAUiKb8Cs+4fSzsSFygoKCti4cSMpKSn4+PgAkJiYaFo8IiIidZ1LCrhnExUVxY4dO2rzkG5p06ZNHDp0iLy8PFJTU7FarWaHJCIi8odyiivIKS6nTVQAj6e2ZGhS9Qu3AL8eOc6Yt+byw8bdACRGh/LqX65gYCdz+8k6HA727t1LWFiYc5ZtYmIi0dHRBAYGmhqbiMjZvPrdal6ZW1m8vW9QN6bc0h8fL3MWKjtp9erVHDt2jA0bNtCjRw9TYxEREakPXFLA3bx58ymvDcPgyJEjTJkyhQ4dOrjikHVKu3btKCgoICkpScVbERFxSwWlFby6bC/d4kPp2ywCgAf6NKV1VADXJcdiO4fCbWl5BVPnLOOZz5dSWm7Hy8PGw9dexMPXXGTaqui/lZaWxvbt24mIiKBv375YLBasVquKtyLi1u4d1I2FaXu594pupt8IO6lz585s3LiR9u3bmx2KiIhIveCSAm6HDh2wWCwYhnHK9u7du/Puu++64pB1io+PD3369HGLflQiIiK/VVhawYzl+3hu0W6OFZbRPT6UFff0wmKxEOLryZ86xlV7TMMw+GbtTh587wd2HTkBQP/2TZnx1ytoERtR06dwzpo1a8b+/ftp2LAhhmEoT4tIneDt6cHcR282NYbjx49TUlJCXFxljggODqZPnz6mxiQiIlKfuKSAu3fv3lNeW61WIiMjnf2PLkS7du0iMDCQ6OjKRV50USgiIu6kqKyC11fs59mFv5JVUAZAswh/RveMxzDgXNJWeYWd2cvSePbL5aQdyAIgOiSAF29NZXivdqbmwvLycrZv3w5AUlISAH5+fgwaNEhPx4iIVMPRo0dZtGgRNpuNAQMGEBAQYHZIIiIi9Y5LCrjx8fGuGLbOysjIYMOGDVgsFgYMGKCVV0VExK3MWn+QsV9vJTO/cqGupuF+PNa/BTd3jsPDVv1iZmFJGW//uJ5pX6/kwLFcAAJ9vRid2oVHhvYm2N/8G7rHjx9n27ZtWK1WmjZtir+/P4CKtyIi1RQeHk54eDi+vr54eXmZHY6IiEi9VKNXKT/99BNt2rQhLy/vtN/l5ubStm1bli5dWu1xZ8yYQUJCAj4+PnTr1o3Vq1efdd+33nqL3r17ExoaSmhoKP379//d/WtDZGQkjRo1olmzZgQFuc/K2iIiIgAWLGTml5IQ5ss71yezfVxf/ty1UbWLt8fyCnl89kIa//VF7n/3ew4cyyUqxJ9nburHgTcfYOqIy0wt3paXlzv/HB0dTWJiIj169MDPz8+0mERE6qKjR4862+VZrVZ69+5N9+7dVcAVERFxkRqdgTt9+nRuv/32MxYpg4ODueOOO5g2bRq9e/eu8piffPIJY8eOZebMmXTr1o3p06eTmprKjh07aNCgwWn7L1q0iBtuuIGePXvi4+PD1KlTGTBgAL/88ouzJ1Nts9lsdO/eHVDrBBERMVdphZ23Vx0g0MeDESmNALi+QywOw+C65Fi8PKp/b3d/Vg4vfL2CdxZsoKi0skiaGB3K3wb3YmTfZNNXQy8tLWXjxo0cO3aM1NRUPDwqv/507tzZ1LhEROqiDRs2sGvXLjp06ECLFi0A8PQ0fyFKERGR+qxGC7ibNm1i6tSpZ/39gAEDeP7556s15rRp07j99tsZNWoUADNnzuTbb7/l3Xff5eGHHz5t/48++uiU12+//Taff/45CxYsYMSIEdU69vkoKCggMzOTxMREQIVbERExV1mFg3dXH+DpH3dxMLeE6EBvrkuOxdfThs1q4abODas95pb9mTz75XI+XroFu6NyJlanpjGMu6YXQ7u3wXYO7RdcwWazkZWVRXFxMZmZmabd0BURqQ9O9rgtKyszORIREZELR40WcDMzM3/37quHhwdHjx6t8nhlZWWsW7eO8ePHO7dZrVb69+/PypUrqzRGUVER5eXlhIWFnXWf0tJSSktLna/P1AKiOsrKyliyZAkFBQUYhkGzZs3OazwREXFPNZ0/XKHc7uC9Nek89eMuDmQXAxAX7MMj/ZpjO4ebi4ZhsHTrfqbOWc7c9buc2/u3b8q4a3rRr31T029aGobBsWPHiIyMBCq/f3Tp0gUvL6/f/T4gIlIb6kLu+C3DMCgvL3e2R2jWrBnh4eH6PBUREalFNVrAjYuLIy0t7awFy82bNxMTE1Pl8Y4dO4bdbicqKuqU7VFRUc6Vo//IuHHjiI2NpX///mfdZ/LkyUyaNKnKcf0RT09PEhIS2Lt3r2b5iIjUYzWdP2rajzuPcvu/N7HvRGXhNibIm/GXNuf27o3x8bRVayyHw8HXa3Ywdc5yVu08CIDVamFo99aMu+YiOifG1nj858LhcLB06VIyMzPp06eP8ztEdHS0yZGJiFRy99zxWyUlJaxevZry8nL69u2L1WrFYrGoeCsiIlLLavTZxiuuuILHHnuMkpKS035XXFzMxIkTufLKK2vykL9rypQpzJ49mzlz5uDjc/ZFU8aPH09ubq7zJz09/byOa7FYaNOmDQMGDMDX1/e8xhIREfdV0/mjpoX5ebLvRDFRgd5MH9yW3Y/0457eTapVvC0rr+CfCzbQ9r7XuGbqJ6zaeRBvTxt3DOjMjlfG8OlD17tN8RYqn9QJDAzEZrNRUFBgdjgiIqdx99zxW3a7nePHj5OTk0N2drbZ4YiIiFywanQG7qOPPsoXX3xBixYtGDNmDC1btgRg+/btzJgxA7vdzj/+8Y8qjxcREYHNZiMzM/OU7ZmZmX84k+b5559nypQp/Pjjj7Rv3/539/X29sbb27vKcVWVmvmLiNRvrsof56LC7mDWhkMczCnhkf7NAejUMIQv/pxCastI/Lyql/Lzi0t5c946XvzPSg6dyAcg2M+buwZ24d5B3YgODazxczgXdrudX3/9lcaNGztvmrZr146WLVvi7+9vcnQiIqdzp9xxJoZhOFvh+Pv70717d/z8/AgODjY5MhERkQtXjRZwo6KiWLFiBaNHj2b8+PEYRuWCJhaLhdTUVGbMmHFaO4Tf4+XlRefOnVmwYAFDhgwBKh+NXLBgAWPGjDnr+5599lmefvppfvjhB1JSUs7rnERERNyZ3WHw8YZDPDFvJ7uOFeJls3JL54Y0Cq0sZl6TVPXWRQBZOQW8PPdnZny3hpzCyidqYkIDeOCqHtwxoDNBfmd/osUMa9as4cCBA+Tm5tK1a1eg8vvDyV6NIiJSdXl5eaxevZqUlBRCQkIAqtUCT0RERFyjRgu4APHx8cydO5fs7Gx+/fVXDMOgefPmhIaGntN4Y8eOZeTIkaSkpNC1a1emT59OYWEho0aNAmDEiBHExcUxefJkAKZOncqECROYNWsWCQkJZGRkAJWrpZ5cMVVERKSuszsMPt14mCfm72R7VmWrgHA/T/7etxlhftV/AmRPxgme/2oF/1y4kZKyCgBaxoXzt8G9uLlPe7w9a/wrQ41o3rw5WVlZzgXLRETk3KWlpXHixAk2bNhA3759zQ5HRERE/p/LrsZCQ0Pp0qXLeY8zfPhwjh49yoQJE8jIyKBDhw58//33zpm8Bw4cwGr9byvf119/nbKyMoYNG3bKOBMnTuTxxx8/73hERETMtuFgLjfNWs+2zMrCbZifJw9dksiYXk0I9Kleat+w5whT5yzj3yu34nBUPjnTtXkcD19zEYO7tjwlx5qtrKyMX375heDgYJo2bQpAeHg4gwYNwmar3qJsIiJyuk6dOmG1WklOTjY7FBEREfkN95xO8z/GjBlz1pYJixYtOuX1vn37XB+QiIiIiRqG+HAgu5gQX08e7NOUe3s3Icin6rNuDcNgYdpeps5ZzryNu53bB3ZsxrhretGnbYKz/6E72b9/P7t27cLb25vGjRvj4VH5NUbFWxGRc5OVlUVOTg4tWrQAwMfHh+7du5sclYiIiPyvOlHAFRERuVA5HAZfpmWwYNcxZgxNAiAywJsvR3WhS6MQgn2rXri12x18uXo7U75YxtrdhwGwWS0M79WOvw/pRXKT318g1AwVFRXOQm1iYiJZWVkkJiY6t4mIyLnJyclh0aJFWCwWwsLCiIiIMDskEREROQtd/YiIiLghwzD4+pdMHv9hBxsP5wEwvEMsFyeGA9C/RdV7vpaWV/DBok089+Vydh05AYCPlwe39evIg1f3pEnUufWpd6WioiI2bNhAWVkZl1xyCRaLBavVSq9evcwOTUSkXggJCSEhofKJi+DgYLPDERERkd+hAq6IiIgbMQyDb7Zm8vi8naw/mAtAoLcH9/VuQlJMYLXGyi0sYeYPa5n+zSoycir75YYG+DDm8q7cc0U3IoP9azz+mmIYBhkZGTgcDnJycs55MVQREfmvgwcPEh0d7XyKISUlxa16nYuIiMiZqYArIiLiJtKzi7n2/TWsTa8s3Pp72bi3dxMe7JNIuL9Xlcc5ciKfl75dxes/rCWvqBSAhuFBjL26B7f370SAr7dL4j8fhmGQnZ1NWFgYAP7+/qSkpBASEqKZYSIiNWDDhg3s2rWLZs2a0alTJwAVb0VEROoIFXBFRETcRHSQNyeKyvHzsnFPryY8dElTIgKqXmzddfg4z321nPcXbqKswg5Am0aR/H1IL264qB1enu6Z9svKyli8eDG5ubmkpqYSGFg50zg+Pt7kyERE6o+YmBh+/fVXfHx8MAzDLRerFBERkTNzzys5ERGRC5CnzcrsmzsTH+pLg8CqF27X7DrE1DnL+OLnbRhG5bZerRox7pqLGNS5udvPsPL09MTb2xur1UpeXp6zgCsiIufO4XBQXFyMv39lu5zo6GiuuOIK52sRERGpO1TAFRERcSNdGodUaT/DMJi/aTdT5yznpy17nduvTGnBuGt6cVFr9529WlFRwe7du2nWrBk2mw2LxULnzp2x2Wz4+PiYHZ6ISJ1XXFzMqlWrKCwsZMCAAXh5VbbhUfFWRESkblIBV0REpA6psNv5fOU2ps5Zxoa9GQB42Kzc2DuJvw3uSbv4KJMj/H2GYbB48WKOHz+Ow+GgdevWgIoKIiI1ycPDg6KiIsrKysjJyaFBgwZmhyQiIiLnQQVcERGROqC4tJz3Fm7k+a9WsCczGwA/b0/+ellnHriqO40jQ8wN8Hc4HA6gcrEci8VCYmIixcXFapUgIlKDHA6Hs2WOp6cnPXv2xNPTk4CAAJMjExERkfOlAq6IiIgbyy4o5rXv1/Dytz+TlVsIQHigL/cO6sbdl3clPNDP5Ah/37Zt29i1axfJycnORcni4+Np2LAhHh76GiIiUhOys7NZtWoVycnJxMbGAhAaGmpyVCIiIlJTdOUkIiLihg4ey+XF/6zizfnrKCgpAyA+MpiHBvfk1n4d8fP2MjnC0xmGwfHjxwkPD3eubl5RUUFJSQmHDx92FnAtFouKtyIiNWj//v3k5+eTlpZGTEyM8zNYRERE6gddPYmIiLiRbQeP8tyXy/lwyWbKKypbDyTFN2DckIu4vldbPD1sJkd4ZoZh8N1331FQUED//v0JCwsDoEmTJkRERKj/ooiICyUlJWGxWGjVqpWKtyIiIvWQCrgiIiJuILugmFGvfslXq3c4t/VpG8+4ay5iYMdmbnVBfnKmbU5ODs2aNQMqZ9WGhoZSWlpKQUGBs4AbEBCg/osiIi5ms9lITk42OwwRERFxERVwRURE3ECwnzc7Dh3HYoEhXVsx7pqL6NaiodlhnVFBQQE//fQTFouFRo0a4e3tDUCHDh3w8vLCZnPPWcIiIiIiIiJ1kQq4IiIibsBqtfLm6KuIDPKjVcNIs8NxysvLY8+ePXh5edGmTRsAAgMDiYiIwN/fn4qKCmcB19fX18xQRURERERE6iUVcEVERNxE7zbxZoeAYRgYhoHVagUqZ9vu3LkTX19fWrdu7Wzl0LdvX7dq6yAiIiIiIlJfqYArIiIiAOzatYsdO3bQqlUrZ2/bqKgoEhISiIuLwzAMZ9FWxVsREREREZHaYTU7ABEREal9hmFw4sQJHA6Hc5vdbqeoqIjDhw87t9lsNrp27UpcXJxzVq6IiIiIiIjUHs3AFRERucAYhsGPP/5IdnY2l1xyCQ0aNAAgPj6egIAAoqOjTY5QRERERERETtJUGhERkXrMMAyys7PZtWuXc5vFYiEkJASbzUZ+fr5zu6+vLw0bNsTDQ/d3RURERERE3IWu0EREROqxkpIS5s+fD0BcXBx+fn4AJCUl0bFjRxVrRURERERE3Jyu2kREROqJwsJC9uzZg8VioV27dkDlrNro6Gg8PDyoqKhw7uvj42NWmCIiIiIiIlINKuCKiIjUUYZhYBiGc3GxwsJCtm3bhpeXF61bt8ZmswHQu3dvLBaLmaGKiIiIiIjIOVIBV0REpA7at28f27ZtIyEhgdatWwMQERFBQkLCaYuQqXgrIiIiIiJSd2kRMxERETdnGAa5ubnY7XbnNrvdTn5+PocPH3Zus1qtdO3alcaNGztn34qIiIiIiEjdphm4IiIibm7JkiVkZmbSq1cv4uLiAGjYsCEeHh7ExsaaHJ2IiIiIiIi4kmbgioiIuJHc3Fx27tx5yrbg4GCsVisFBQXObd7e3sTHx+Pp6VnbIYqIiIiIiEgt0gxcERERN1FeXs78+fNxOBxERUURHBwMQKtWrWjTpg1eXl4mRygiIiIiIiK1TQVcERERN+Hp6UlMTAwOhwOHw+Hc7uPjY2JUIiIiIiIiYiYVcEVERNxIz549sVgsZochIiIiIiIibkI9cEVERNyIirciIiIiIiLyWyrgioiIiIiIiIiIiLgpFXBFRERERERERERE3JQKuCIiIiIiIiIiIiJuSgVcERERERERERERETelAq6IiIiIiIiIiIiIm6oTBdwZM2aQkJCAj48P3bp1Y/Xq1b+7/7///W9atWqFj48PSUlJzJ07t5YiFREREREREREREak5bl/A/eSTTxg7diwTJ05k/fr1JCcnk5qaSlZW1hn3X7FiBTfccAO33XYbGzZsYMiQIQwZMoS0tLRajlxERERERERERETk/Lh9AXfatGncfvvtjBo1ijZt2jBz5kz8/Px49913z7j/Sy+9xMCBA/nb3/5G69atefLJJ+nUqROvvvpqLUcuIiIiIiIiIiIicn48zA7g95SVlbFu3TrGjx/v3Ga1Wunfvz8rV64843tWrlzJ2LFjT9mWmprKl19+edbjlJaWUlpa6nydm5sLQF5e3nlELyIirnTyM9owDNNiUP4QEal7zM4fyh0iInWT2flDLmxuXcA9duwYdrudqKioU7ZHRUWxffv2M74nIyPjjPtnZGSc9TiTJ09m0qRJp21v1KjROUQtIiK1KT8/n+DgYFOOrfwhIlJ3mZU/lDtEROo2M68/5MLl1gXc2jJ+/PhTZu06HA5OnDhBeHg4FovlnMbMy8ujUaNGpKenExQUVFOhugWdW92kc6ubdG5nZxgG+fn5xMbGuiC6qqnp/KH/3nWTzq1u0rnVTTVxbmbnD117VI/OrW7SudVNOrffZ3b+kAubWxdwIyIisNlsZGZmnrI9MzOT6OjoM74nOjq6WvsDeHt74+3tfcq2kJCQcwv6fwQFBdW7D76TdG51k86tbtK5nZnZd75dlT/037tu0rnVTTq3uul8z83M/KFrj3Ojc6ubdG51k87t7My+/pALl1svYubl5UXnzp1ZsGCBc5vD4WDBggX06NHjjO/p0aPHKfsDzJ8//6z7i4iIiIiIiIiIiLgrt56BCzB27FhGjhxJSkoKXbt2Zfr06RQWFjJq1CgARowYQVxcHJMnTwbgvvvuo0+fPrzwwgsMGjSI2bNns3btWt58800zT0NERERERERERESk2ty+gDt8+HCOHj3KhAkTyMjIoEOHDnz//ffOhcoOHDiA1frficQ9e/Zk1qxZPProozzyyCM0b96cL7/8knbt2tVq3N7e3kycOPG0x6PqA51b3aRzq5t0bheW+vx3onOrm3RudZPO7cJTn/9edG51k86tbtK5ibgvi2EYhtlBiIiIiIiIiIiIiMjp3LoHroiIiIiIiIiIiMiFTAVcERERERERERERETelAq6IiIiIiIiIiIiIm1IBV0RERERERERERMRNqYDrAjNmzCAhIQEfHx+6devG6tWrzQ6pRixZsoSrrrqK2NhYLBYLX375pdkh1YjJkyfTpUsXAgMDadCgAUOGDGHHjh1mh1UjXn/9ddq3b09QUBBBQUH06NGD7777zuywXGLKlClYLBbuv/9+s0M5b48//jgWi+WUn1atWpkdVo05dOgQN998M+Hh4fj6+pKUlMTatWvNDsst1Mf8UV9zByh/1BfKH3WH8seZ1cfcAfU3f9Tn3AEXTv6oT7kDlD9E6gIVcGvYJ598wtixY5k4cSLr168nOTmZ1NRUsrKyzA7tvBUWFpKcnMyMGTPMDqVGLV68mLvvvptVq1Yxf/58ysvLGTBgAIWFhWaHdt4aNmzIlClTWLduHWvXruXSSy9l8ODB/PLLL2aHVqPWrFnDG2+8Qfv27c0Opca0bduWI0eOOH+WLVtmdkg1Ijs7m169euHp6cl3333H1q1beeGFFwgNDTU7NNPV1/xRX3MHKH/UB8ofdYfyx5nV19wB9Td/1OfcARdG/qiPuQOUP0TcniE1qmvXrsbdd9/tfG23243Y2Fhj8uTJJkZV8wBjzpw5ZofhEllZWQZgLF682OxQXCI0NNR4++23zQ6jxuTn5xvNmzc35s+fb/Tp08e47777zA7pvE2cONFITk42OwyXGDdunHHRRReZHYZbuhDyR33OHYah/FHXKH/ULcofZ3Yh5A7DqN/5o77nDsOoX/mjPuYOw1D+EKkLNAO3BpWVlbFu3Tr69+/v3Ga1Wunfvz8rV640MTKpjtzcXADCwsJMjqRm2e12Zs+eTWFhIT169DA7nBpz9913M2jQoFP+f1cf7Nq1i9jYWJo2bcpNN93EgQMHzA6pRnz99dekpKRw3XXX0aBBAzp27Mhbb71ldlimU/6oH5Q/6hblj7pF+eN0yh31Q33NHVA/80d9zR2g/CHi7lTArUHHjh3DbrcTFRV1yvaoqCgyMjJMikqqw+FwcP/999OrVy/atWtndjg1YsuWLQQEBODt7c2dd97JnDlzaNOmjdlh1YjZs2ezfv16Jk+ebHYoNapbt2689957fP/997z++uvs3buX3r17k5+fb3Zo523Pnj28/vrrNG/enB9++IHRo0dz77338v7775sdmqmUP+o+5Y+6Rfmj7lH+OJ1yR91XH3MH1N/8UV9zByh/iNQFHmYHIOJO7r77btLS0upNvx+Ali1bsnHjRnJzc/nss88YOXIkixcvrvNfotLT07nvvvuYP38+Pj4+ZodToy6//HLnn9u3b0+3bt2Ij4/n008/5bbbbjMxsvPncDhISUnhmWeeAaBjx46kpaUxc+ZMRo4caXJ0IudO+aPuUP6om5Q/pD6qj7kD6mf+qM+5A5Q/ROoCzcCtQREREdhsNjIzM0/ZnpmZSXR0tElRSVWNGTOGb775hoULF9KwYUOzw6kxXl5eNGvWjM6dOzN58mSSk5N56aWXzA7rvK1bt46srCw6deqEh4cHHh4eLF68mJdffhkPDw/sdrvZIdaYkJAQWrRowa+//mp2KOctJibmtC/vrVu3rjePaJ0r5Y+6TfmjblH+qJuUP06n3FG31dfcAfUzf1xIuQOUP0TckQq4NcjLy4vOnTuzYMEC5zaHw8GCBQvqTc+f+sgwDMaMGcOcOXP46aefaNKkidkhuZTD4aC0tNTsMM5bv3792LJlCxs3bnT+pKSkcNNNN7Fx40ZsNpvZIdaYgoICdu/eTUxMjNmhnLdevXqxY8eOU7bt3LmT+Ph4kyJyD8ofdZPyR92k/FE3KX+cTrmjbrrQcgfUj/xxIeUOUP4QcUdqoVDDxo4dy8iRI0lJSaFr165Mnz6dwsJCRo0aZXZo562goOCUO3B79+5l48aNhIWF0bhxYxMjOz933303s2bN4quvviIwMNDZMyw4OBhfX1+Tozs/48eP5/LLL6dx48bk5+cza9YsFi1axA8//GB2aOctMDDwtF5h/v7+hIeH1/keYg899BBXXXUV8fHxHD58mIkTJ2Kz2bjhhhvMDu28PfDAA/Ts2ZNnnnmG66+/ntWrV/Pmm2/y5ptvmh2a6epr/qivuQOUP+oq5Y+6SfnjzOpr7oD6mz/qc+6A+ps/6nPuAOUPkTrBkBr3yiuvGI0bNza8vLyMrl27GqtWrTI7pBqxcOFCAzjtZ+TIkWaHdl7OdE6A8c9//tPs0M7brbfeasTHxxteXl5GZGSk0a9fP2PevHlmh+Uyffr0Me677z6zwzhvw4cPN2JiYgwvLy8jLi7OGD58uPHrr7+aHVaN+c9//mO0a9fO8Pb2Nlq1amW8+eabZofkNupj/qivucMwlD/qE+WPukH548zqY+4wjPqbP+pz7jCMCyt/1JfcYRjKHyJ1gcUwDMPlVWIRERERERERERERqTb1wBURERERERERERFxUyrgioiIiIiIiIiIiLgpFXBFRERERERERERE3JQKuCIiIiIiIiIiIiJuSgVcERERERERERERETelAq6IiIiIiIiIiIiIm1IBV0RERERERERERMRNqYArIiIiIiIiIiIi4qZUwBURERERERERERFxUyrgioiIiMhp9u3bh8Vicf6EhYXRp08fli5desp+RUVFjB8/nsTERHx8fIiMjKRPnz589dVXJkUuIiIiIlK/qIArIiIiImf1448/cuTIEZYsWUJsbCxXXnklmZmZzt/feeedfPHFF7zyyits376d77//nmHDhnH8+HEToxYRERERqT8shmEYZgchIiIiIq51ySWXkJSUhM1m4/3338fLy4unnnqKG2+8kTFjxvDZZ58RFRXFK6+8wuWXX86+ffto0qQJGzZsoEOHDgBs2bKF9u3b89VXX3H11VcDEBISwksvvcTIkSNNPDsRERERkfpLM3BFRERELhDvv/8+ERERrF69mnvuuYfRo0dz3XXX0bNnT9avX8+AAQO45ZZbKCoqOu29xcXFfPDBBwB4eXk5t0dHRzN37lzy8/Nr7TxERERERC4kmoErIiIicgG45JJLsNvtzh62drud4OBgrr32WmdhNiMjg5iYGFauXEl0dDRNmjTB19cXq9VKUVERhmHQuXNnVq5ciaenJwBLlizhpptuIjMzk+TkZC666CKGDRtGr169TDtXEREREZH6RDNwRURERC4Q7du3d/7ZZrMRHh5OUlKSc1tUVBQAWVlZzm2ffPIJGzZs4PPPP6dZs2a89957zuItwMUXX8yePXtYsGABw4YN45dffqF37948+eSTtXBGIiIiIiL1n4fZAYiIiIhI7fht4RXAYrGcss1isQDgcDic2xo1akTz5s1p3rw5FRUVXHPNNaSlpeHt7X3KuL1796Z3796MGzeOp556iieeeIJx48ad0m5BRERERESqTzNwRURERKRKhg0bhoeHB6+99trv7temTRsqKiooKSmppchEREREROovFXBFREREpEosFgv33nsvU6ZMcS50dskll/DGG2+wbt069u3bx9y5c3nkkUfo27cvQUFBJkcsIiIiIlL3qYArIiIiIlU2cuRIysvLefXVVwFITU3l/fffZ8CAAbRu3Zp77rmH1NRUPv30U5MjFRERERGpHyyGYRhmByEiIiIiIiIiIiIip9MMXBERERERERERERE3pQKuiIiIiIiIiIiIiJtSAVdERERERERERETETamAKyIiIiIiIiIiIuKmVMAVERERERERERERcVMq4IqIiIiIiIiIiIi4KRVwRURERERERERERNyUCrgiIiIiIiIiIiIibkoFXBERERERERERERE3pQKuiIiIiIiIiIiIiJtSAVdERERERERERETETf0fQ7WlW8zddSYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1500x700 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 3, figsize=(15, 7), gridspec_kw={'wspace': 0.1})\n",
    "\n",
    "bar_width = 0.25\n",
    "offsets = [-bar_width, 0.0, bar_width]\n",
    "\n",
    "legend_drawn = False\n",
    "for c, cohort in enumerate(['nlvo_ivt', 'lvo_mix', 'weighted_treated']):\n",
    "    dist_drip = dict_drip_ship[cohort]['values']\n",
    "    std_drip = dict_drip_ship[cohort]['std']\n",
    "    dist_cumsum_drip = dict_drip_ship[cohort]['cumsum']\n",
    "    dist_moth = dict_mothership[cohort]['values']\n",
    "    std_moth = dict_mothership[cohort]['std']\n",
    "    dist_cumsum_moth = dict_mothership[cohort]['cumsum']\n",
    "    \n",
    "    if 'nlvo' in cohort:\n",
    "        ax = axs[0, 0]\n",
    "        ax_cumsum = axs[1, 0]\n",
    "        dist_no_treat = dict_no_treat['nlvo']['values']\n",
    "        dist_cumsum_no_treat = dict_no_treat['nlvo']['cumsum']\n",
    "    elif 'lvo' in cohort:\n",
    "        ax = axs[0, 1]\n",
    "        ax_cumsum = axs[1, 1]\n",
    "        dist_no_treat = dict_no_treat['lvo']['values']\n",
    "        dist_cumsum_no_treat = dict_no_treat['lvo']['cumsum']\n",
    "    elif 'treated' in cohort:\n",
    "        ax = axs[0, 2]\n",
    "        ax_cumsum = axs[1, 2]\n",
    "        dist_no_treat = dict_no_treat['weighted_treated']['values']\n",
    "        dist_cumsum_no_treat = dict_no_treat['weighted_treated']['cumsum']\n",
    "    # else:\n",
    "    #     ax = axs[0, -1]\n",
    "    #     ax_cumsum = axs[1, -1]\n",
    "    #     dist_no_treat = dict_no_treat['weighted']['values']\n",
    "    #     dist_cumsum_no_treat = dict_no_treat['weighted']['cumsum']\n",
    "\n",
    "    ax.bar(\n",
    "        np.arange(7) + offsets[0], dist_drip, yerr=std_drip, capsize=2.0,\n",
    "        facecolor=colour_drip, label='Drip & ship', width=bar_width\n",
    "        )\n",
    "    ax.bar(\n",
    "        np.arange(7) + offsets[1], dist_moth, yerr=std_moth, capsize=2.0,\n",
    "        facecolor=colour_moth, label='Mothership', width=bar_width\n",
    "        )\n",
    "    ax.bar(\n",
    "        np.arange(7) + offsets[2], dist_no_treat,\n",
    "        facecolor=colour_no_treat, label='No treatment', width=bar_width\n",
    "        )\n",
    "\n",
    "    ax_cumsum.plot(np.arange(7), dist_cumsum_drip, color=colour_drip, linestyle='-', label='Drip & ship')\n",
    "    ax_cumsum.plot(np.arange(7), dist_cumsum_moth, color=colour_moth, linestyle='--', label='Mothership')\n",
    "    ax_cumsum.plot(np.arange(7), dist_cumsum_no_treat, color=colour_no_treat, linestyle=':', label='No treatment')\n",
    "\n",
    "    if c == 2:\n",
    "        ax.legend(bbox_to_anchor=(1.0, 0.5), loc='center left')\n",
    "        ax_cumsum.legend(bbox_to_anchor=(1.0, 0.5), loc='center left')\n",
    "        legend_drawn = True\n",
    "\n",
    "for i, ax_list in enumerate(axs):\n",
    "    ylim = [0, 0.4] if i == 0 else [0.0, 1.05]\n",
    "    for j, ax in enumerate(ax_list):\n",
    "        ax.set_ylim(*ylim)\n",
    "        ax.set_xticks(range(7))\n",
    "        if (j > 0):  # & (j < len(ax_list) - 1):\n",
    "            ax.set_yticklabels([])\n",
    "\n",
    "axs[0, 0].set_title('nLVO')\n",
    "axs[0, 1].set_title('LVO')\n",
    "axs[0, 2].set_title('Treated ischaemic population')\n",
    "# axs[0, -1].set_title('Total\\nischaemic population')\n",
    "\n",
    "axs[0, 0].set_ylabel('Probability')\n",
    "# axs[0, -1].set_ylabel('Probability')\n",
    "# axs[0, -1].yaxis.set_tick_params(labelleft=True)  # turn the tick labels back on\n",
    "axs[0, 1].set_xlabel('mRS')\n",
    "# axs[0, -1].set_xlabel('mRS')\n",
    "\n",
    "axs[1, 0].set_ylabel('Cumulative probability')\n",
    "# axs[1, -1].set_ylabel('Cumulative probability')\n",
    "# axs[1, -1].yaxis.set_tick_params(labelleft=True)  # turn the tick labels back on\n",
    "axs[1, 1].set_xlabel('mRS')\n",
    "# axs[1, -1].set_xlabel('mRS')\n",
    "\n",
    "# axs[0, 3].axis('off')\n",
    "# axs[1, 3].axis('off')\n",
    "\n",
    "plt.savefig(os.path.join(dir_output, 'mrs_dists_redirection_areas.png'), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find mean values\n",
    "\n",
    "Import utility scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "utility_dists, utility_dists_notes = (\n",
    "    stroke_outcome.outcome_utilities.import_utility_dists_from_file())\n",
    "\n",
    "utility_weights = utility_dists.loc['Wang2020'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_dicts = {\n",
    "    'drip_ship': dict_drip_ship,\n",
    "    'mothership': dict_mothership,\n",
    "    'no_treat': dict_no_treat\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_means = pd.DataFrame()\n",
    "\n",
    "for scenario, dist_dict in dist_dicts.items():\n",
    "    for cohort, d_dict in dist_dict.items():\n",
    "            \n",
    "        if 'nlvo' in cohort:\n",
    "            dist_no_treat = dict_no_treat['nlvo']['values']\n",
    "        elif 'lvo' in cohort:\n",
    "            dist_no_treat = dict_no_treat['lvo']['values']\n",
    "        elif 'treated' in cohort:\n",
    "            dist_no_treat = dict_no_treat['weighted_treated']['values']\n",
    "        else:\n",
    "            print('help')\n",
    "            # dist_no_treat = dict_no_treat['weighted']['values']\n",
    "\n",
    "        mean_mrs_no_treat = np.mean(dist_no_treat * np.arange(7))\n",
    "        mean_util_no_treat = np.mean(dist_no_treat * utility_weights)\n",
    "        mrsleq2_no_treat = np.sum(dist_no_treat[:3])\n",
    "            \n",
    "        dist = d_dict['values']\n",
    "        ind = f'{scenario}_{cohort}'\n",
    "        df_means.loc[ind, 'mean_mrs'] = np.mean(dist * np.arange(7))\n",
    "        df_means.loc[ind, 'mean_util'] = np.mean(dist * utility_weights)\n",
    "        df_means.loc[ind, 'mrsleq2'] = np.sum(dist[:3])\n",
    "        df_means.loc[ind, 'mean_mrs_shift'] = (\n",
    "            df_means.loc[ind, 'mean_mrs'] - mean_mrs_no_treat)\n",
    "        df_means.loc[ind, 'mean_util_shift'] = (\n",
    "            df_means.loc[ind, 'mean_util'] - mean_util_no_treat)\n",
    "        df_means.loc[ind, 'mrsleq2_shift'] = (\n",
    "            df_means.loc[ind, 'mrsleq2'] - mrsleq2_no_treat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_mrs</th>\n",
       "      <th>mean_util</th>\n",
       "      <th>mrsleq2</th>\n",
       "      <th>mean_mrs_shift</th>\n",
       "      <th>mean_util_shift</th>\n",
       "      <th>mrsleq2_shift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>drip_ship_nlvo_ivt</th>\n",
       "      <td>0.234859</td>\n",
       "      <td>0.102172</td>\n",
       "      <td>0.705080</td>\n",
       "      <td>-0.090856</td>\n",
       "      <td>0.016509</td>\n",
       "      <td>0.125080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_lvo_mix</th>\n",
       "      <td>0.451140</td>\n",
       "      <td>0.061546</td>\n",
       "      <td>0.368350</td>\n",
       "      <td>-0.068860</td>\n",
       "      <td>0.014031</td>\n",
       "      <td>0.103350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drip_ship_weighted_treated</th>\n",
       "      <td>0.343897</td>\n",
       "      <td>0.081689</td>\n",
       "      <td>0.535310</td>\n",
       "      <td>-0.079773</td>\n",
       "      <td>0.015260</td>\n",
       "      <td>0.114129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_nlvo_ivt</th>\n",
       "      <td>0.242964</td>\n",
       "      <td>0.100770</td>\n",
       "      <td>0.694310</td>\n",
       "      <td>-0.082750</td>\n",
       "      <td>0.015107</td>\n",
       "      <td>0.114310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_lvo_mix</th>\n",
       "      <td>0.419377</td>\n",
       "      <td>0.067532</td>\n",
       "      <td>0.413060</td>\n",
       "      <td>-0.100623</td>\n",
       "      <td>0.020016</td>\n",
       "      <td>0.148060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothership_weighted_treated</th>\n",
       "      <td>0.331910</td>\n",
       "      <td>0.084012</td>\n",
       "      <td>0.552510</td>\n",
       "      <td>-0.091760</td>\n",
       "      <td>0.017582</td>\n",
       "      <td>0.131329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_treat_nlvo</th>\n",
       "      <td>0.325714</td>\n",
       "      <td>0.085663</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_treat_lvo</th>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.047516</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_treat_weighted_treated</th>\n",
       "      <td>0.423670</td>\n",
       "      <td>0.066430</td>\n",
       "      <td>0.421181</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mean_mrs  mean_util   mrsleq2  mean_mrs_shift  \\\n",
       "drip_ship_nlvo_ivt           0.234859   0.102172  0.705080       -0.090856   \n",
       "drip_ship_lvo_mix            0.451140   0.061546  0.368350       -0.068860   \n",
       "drip_ship_weighted_treated   0.343897   0.081689  0.535310       -0.079773   \n",
       "mothership_nlvo_ivt          0.242964   0.100770  0.694310       -0.082750   \n",
       "mothership_lvo_mix           0.419377   0.067532  0.413060       -0.100623   \n",
       "mothership_weighted_treated  0.331910   0.084012  0.552510       -0.091760   \n",
       "no_treat_nlvo                0.325714   0.085663  0.580000        0.000000   \n",
       "no_treat_lvo                 0.520000   0.047516  0.265000        0.000000   \n",
       "no_treat_weighted_treated    0.423670   0.066430  0.421181        0.000000   \n",
       "\n",
       "                             mean_util_shift  mrsleq2_shift  \n",
       "drip_ship_nlvo_ivt                  0.016509       0.125080  \n",
       "drip_ship_lvo_mix                   0.014031       0.103350  \n",
       "drip_ship_weighted_treated          0.015260       0.114129  \n",
       "mothership_nlvo_ivt                 0.015107       0.114310  \n",
       "mothership_lvo_mix                  0.020016       0.148060  \n",
       "mothership_weighted_treated         0.017582       0.131329  \n",
       "no_treat_nlvo                       0.000000       0.000000  \n",
       "no_treat_lvo                        0.000000       0.000000  \n",
       "no_treat_weighted_treated           0.000000       0.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save a copy of data behind the figure\n",
    "\n",
    "Keep a copy of the separate LVO cohorts even though the figure only shows the combined LVO cohort.\n",
    "\n",
    "Add the no-treatment distributions into the starting data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in dict_no_treat.items():\n",
    "    ind = f'no_treatment_{key}'\n",
    "    df_mrs_national_noncum.loc[ind] = value\n",
    "    df_mrs_national_std.loc[ind] = pd.NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to file:\n",
    "df_mrs_national_noncum.to_csv(os.path.join(dir_output, 'fig_data_mrs_dists.csv'))\n",
    "df_mrs_national_std.to_csv(os.path.join(dir_output, 'fig_data_mrs_dists_std.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_means.to_csv(os.path.join(dir_output, 'fig_data_mrs_dists_means.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "For nLVO, the mRS distributions are weighted slightly lower (less disability) for the drip-and-ship scenario. For LVO, they are weighted lower (less disability) for the mothership scenario. For the combined cohort, the mothership scenario just wins out. This is most easily seen by looking at which scenario is highest on the cumulative probability plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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