notebooks/shared_control_crowd_density_cluster_analysis.ipynb

{
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  {
   "cell_type": "markdown",
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    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Libraries-&amp;-settings\" data-toc-modified-id=\"Libraries-&amp;-settings-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Libraries &amp; settings</a></span></li><li><span><a href=\"#Metrics\" data-toc-modified-id=\"Metrics-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Metrics</a></span><ul class=\"toc-item\"><li><span><a href=\"#Crowd-related\" data-toc-modified-id=\"Crowd-related-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Crowd-related</a></span></li><li><span><a href=\"#Path-efficiency-related\" data-toc-modified-id=\"Path-efficiency-related-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Path efficiency-related</a></span></li><li><span><a href=\"#Control-related\" data-toc-modified-id=\"Control-related-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Control-related</a></span></li></ul></li><li><span><a href=\"#Pipeline\" data-toc-modified-id=\"Pipeline-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Pipeline</a></span><ul class=\"toc-item\"><li><span><a href=\"#Result-loading\" data-toc-modified-id=\"Result-loading-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Result loading</a></span></li><li><span><a href=\"#Analysis-given-different-dates\" data-toc-modified-id=\"Analysis-given-different-dates-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Analysis given different dates</a></span><ul class=\"toc-item\"><li><span><a href=\"#Mean-Std-statistics\" data-toc-modified-id=\"Mean-Std-statistics-3.2.1\"><span class=\"toc-item-num\">3.2.1&nbsp;&nbsp;</span>Mean-Std statistics</a></span></li><li><span><a href=\"#ANOVA-test\" data-toc-modified-id=\"ANOVA-test-3.2.2\"><span class=\"toc-item-num\">3.2.2&nbsp;&nbsp;</span>ANOVA test</a></span></li><li><span><a href=\"#Crowd-related-metrics\" data-toc-modified-id=\"Crowd-related-metrics-3.2.3\"><span class=\"toc-item-num\">3.2.3&nbsp;&nbsp;</span>Crowd-related metrics</a></span></li><li><span><a href=\"#Path-efficiency-related-metrics\" data-toc-modified-id=\"Path-efficiency-related-metrics-3.2.4\"><span class=\"toc-item-num\">3.2.4&nbsp;&nbsp;</span>Path efficiency-related metrics</a></span></li><li><span><a href=\"#Shared-control-metrics\" data-toc-modified-id=\"Shared-control-metrics-3.2.5\"><span class=\"toc-item-num\">3.2.5&nbsp;&nbsp;</span>Shared control metrics</a></span></li></ul></li><li><span><a href=\"#Analysis-given-cluster\" data-toc-modified-id=\"Analysis-given-cluster-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Analysis given cluster</a></span><ul class=\"toc-item\"><li><span><a href=\"#Cluster-according-to-crowd-density-metrics\" data-toc-modified-id=\"Cluster-according-to-crowd-density-metrics-3.3.1\"><span class=\"toc-item-num\">3.3.1&nbsp;&nbsp;</span>Cluster according to crowd density metrics</a></span><ul class=\"toc-item\"><li><span><a href=\"#Clustering-results-visualization-by-cluster\" data-toc-modified-id=\"Clustering-results-visualization-by-cluster-3.3.1.1\"><span class=\"toc-item-num\">3.3.1.1&nbsp;&nbsp;</span>Clustering results visualization by cluster</a></span></li><li><span><a href=\"#Clustering-results-visualization-by-date\" data-toc-modified-id=\"Clustering-results-visualization-by-date-3.3.1.2\"><span class=\"toc-item-num\">3.3.1.2&nbsp;&nbsp;</span>Clustering results visualization by date</a></span></li></ul></li><li><span><a href=\"#Mean-Std-statistics\" data-toc-modified-id=\"Mean-Std-statistics-3.3.2\"><span class=\"toc-item-num\">3.3.2&nbsp;&nbsp;</span>Mean-Std statistics</a></span></li><li><span><a href=\"#ANOVA-test\" data-toc-modified-id=\"ANOVA-test-3.3.3\"><span class=\"toc-item-num\">3.3.3&nbsp;&nbsp;</span>ANOVA test</a></span></li><li><span><a href=\"#Crowd-related-metrics\" data-toc-modified-id=\"Crowd-related-metrics-3.3.4\"><span class=\"toc-item-num\">3.3.4&nbsp;&nbsp;</span>Crowd-related metrics</a></span><ul class=\"toc-item\"><li><span><a href=\"#4-in-1-plotting\" data-toc-modified-id=\"4-in-1-plotting-3.3.4.1\"><span class=\"toc-item-num\">3.3.4.1&nbsp;&nbsp;</span>4-in-1 plotting</a></span></li><li><span><a href=\"#Individual-figures\" data-toc-modified-id=\"Individual-figures-3.3.4.2\"><span class=\"toc-item-num\">3.3.4.2&nbsp;&nbsp;</span>Individual figures</a></span></li></ul></li><li><span><a href=\"#Path-efficiency-related-metrics\" data-toc-modified-id=\"Path-efficiency-related-metrics-3.3.5\"><span class=\"toc-item-num\">3.3.5&nbsp;&nbsp;</span>Path efficiency-related metrics</a></span><ul class=\"toc-item\"><li><span><a href=\"#2-in-1-plotting\" data-toc-modified-id=\"2-in-1-plotting-3.3.5.1\"><span class=\"toc-item-num\">3.3.5.1&nbsp;&nbsp;</span>2-in-1 plotting</a></span></li><li><span><a href=\"#Individual-figures\" data-toc-modified-id=\"Individual-figures-3.3.5.2\"><span class=\"toc-item-num\">3.3.5.2&nbsp;&nbsp;</span>Individual figures</a></span></li></ul></li><li><span><a href=\"#Shared-control-metrics\" data-toc-modified-id=\"Shared-control-metrics-3.3.6\"><span class=\"toc-item-num\">3.3.6&nbsp;&nbsp;</span>Shared control metrics</a></span><ul class=\"toc-item\"><li><span><a href=\"#4-in-1-plotting\" data-toc-modified-id=\"4-in-1-plotting-3.3.6.1\"><span class=\"toc-item-num\">3.3.6.1&nbsp;&nbsp;</span>4-in-1 plotting</a></span></li><li><span><a href=\"#Individual-figures\" data-toc-modified-id=\"Individual-figures-3.3.6.2\"><span class=\"toc-item-num\">3.3.6.2&nbsp;&nbsp;</span>Individual figures</a></span></li></ul></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crowd_density cluster analysis with shared-control method\n",
    "\n",
    "> Analysis of data from 2021-03-25, 2021-03-27, 2021-04-10, 2021-04-10, and 2021-12-03 with shared-control method and advanced analysis with different density clusters generated by KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries & settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import datetime\n",
    "import collections\n",
    "import sys, os, fnmatch\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "use_serif_font = True\n",
    "if use_serif_font:\n",
    "    plt.style.use('./styles/serif.mplstyle')\n",
    "else:\n",
    "    plt.style.use('./styles/sans_serif.mplstyle')\n",
    "\n",
    "plt.ioff()\n",
    "import seaborn as sns\n",
    "sns.set_context(\"paper\", font_scale=1.2, rc={\"lines.linewidth\": 1.3})\n",
    "\n",
    "from qolo.utils.notebook_util import (\n",
    "    walk,\n",
    "    values2colors,\n",
    "    values2color_list,\n",
    "    violinplot,\n",
    "    categorical_plot,\n",
    "    barplot_annotate_brackets,\n",
    "    import_eval_res,\n",
    ")\n",
    "from qolo.core.crowdbot_data import CrowdBotDatabase, CrowdBotData\n",
    "from qolo.metrics.metric_qolo_perf import compute_rel_jerk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crowd-related\n",
    "\n",
    "1. Crowd Density (within an area of 2.5, 5m, 10m around the robot):\n",
    "2. Minimum distance to pedestrians: \n",
    "3. Number of violations to the virtual boundary set to the robot controller "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "crowd_metrics = (\n",
    "    'avg_crowd_density2_5',\n",
    "    'std_crowd_density2_5',\n",
    "    'max_crowd_density2_5',\n",
    "    'avg_crowd_density5',\n",
    "    'std_crowd_density5',\n",
    "    'max_crowd_density5',\n",
    "    'avg_min_dist',\n",
    "    'virtual_collision',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Path efficiency-related\n",
    "\n",
    "1. Relative time to goal (normalized by the goal distance)\n",
    "2. Relative path length (normalized by the goal distance in straight line):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_metrics = (\n",
    "    'rel_duration2goal',\n",
    "    'rel_path_length2goal',\n",
    "    'path_length2goal',\n",
    "    'duration2goal',\n",
    "    'min_dist2goal',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Control-related\n",
    "\n",
    "1. Agreement\n",
    "2. Fluency\n",
    "3. Contribution\n",
    "4. Relative Jerk (smoothness of the path as added sum of linear and angular jerk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "control_metrics = (\n",
    "    'rel_jerk',\n",
    "    'avg_fluency',\n",
    "    'contribution',\n",
    "    'avg_agreement',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "qolo_dataset = CrowdBotData()\n",
    "\n",
    "bagbase = qolo_dataset.bagbase_dir\n",
    "outbase = qolo_dataset.outbase_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0325_shared_control/ is available!\n",
      "0327_shared_control/ is available!\n",
      "0410_shared_control/ is available!\n",
      "0424_shared_control/ is available!\n",
      "1203_shared_control/ is available!\n"
     ]
    }
   ],
   "source": [
    "chosen_dates = ['0325', '0327', '0410', '0424', '1203']\n",
    "chosen_type = ['shared_control']\n",
    "\n",
    "eval_dirs = []\n",
    "\n",
    "for root, dirs, files in walk(outbase, topdown=False, maxdepth=1):\n",
    "    for dir_ in dirs:\n",
    "        if any(s in dir_ for s in chosen_dates) and any(s in dir_ for s in chosen_type):\n",
    "            dir_ = dir_.replace(\"_processed\", \"\")\n",
    "            eval_dirs.append(dir_)\n",
    "            print(\"{}/ is available!\".format(dir_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading results from 0325_shared_control\n",
      "Reading results from 0327_shared_control\n",
      "Reading results from 0410_shared_control\n",
      "Reading results from 0424_shared_control\n",
      "Reading results from 1203_shared_control\n"
     ]
    }
   ],
   "source": [
    "eval_res_df = import_eval_res(eval_dirs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis given different dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shared_control sequence on different dates\n",
      "0327    14\n",
      "1203    13\n",
      "0424     8\n",
      "0410     8\n",
      "0325     5\n",
      "Name: date, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Shared_control sequence on different dates\")\n",
    "print(eval_res_df['date'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eval_res_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean-Std statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_crowd_density2_5</th>\n",
       "      <th>std_crowd_density2_5</th>\n",
       "      <th>max_crowd_density2_5</th>\n",
       "      <th>avg_crowd_density5</th>\n",
       "      <th>std_crowd_density5</th>\n",
       "      <th>max_crowd_density5</th>\n",
       "      <th>avg_min_dist</th>\n",
       "      <th>virtual_collision</th>\n",
       "      <th>rel_duration2goal</th>\n",
       "      <th>rel_path_length2goal</th>\n",
       "      <th>path_length2goal</th>\n",
       "      <th>duration2goal</th>\n",
       "      <th>min_dist2goal</th>\n",
       "      <th>rel_jerk</th>\n",
       "      <th>avg_fluency</th>\n",
       "      <th>contribution</th>\n",
       "      <th>avg_agreement</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0325_mean</th>\n",
       "      <td>0.054568</td>\n",
       "      <td>0.054657</td>\n",
       "      <td>0.285206</td>\n",
       "      <td>0.063878</td>\n",
       "      <td>0.035999</td>\n",
       "      <td>0.211358</td>\n",
       "      <td>1.717142</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.379606</td>\n",
       "      <td>1.198281</td>\n",
       "      <td>36.137141</td>\n",
       "      <td>68.606075</td>\n",
       "      <td>1.840448</td>\n",
       "      <td>0.120737</td>\n",
       "      <td>0.954195</td>\n",
       "      <td>0.654564</td>\n",
       "      <td>0.681743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0325_std</th>\n",
       "      <td>0.005142</td>\n",
       "      <td>0.001659</td>\n",
       "      <td>0.027895</td>\n",
       "      <td>0.008102</td>\n",
       "      <td>0.003034</td>\n",
       "      <td>0.017082</td>\n",
       "      <td>0.179279</td>\n",
       "      <td>1.673320</td>\n",
       "      <td>0.074838</td>\n",
       "      <td>0.217506</td>\n",
       "      <td>4.866596</td>\n",
       "      <td>12.143353</td>\n",
       "      <td>1.638693</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.009416</td>\n",
       "      <td>0.162608</td>\n",
       "      <td>0.028696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0327_mean</th>\n",
       "      <td>0.187289</td>\n",
       "      <td>0.122211</td>\n",
       "      <td>0.665722</td>\n",
       "      <td>0.179175</td>\n",
       "      <td>0.068934</td>\n",
       "      <td>0.413803</td>\n",
       "      <td>0.941544</td>\n",
       "      <td>17.642857</td>\n",
       "      <td>0.290162</td>\n",
       "      <td>2.444374</td>\n",
       "      <td>43.154011</td>\n",
       "      <td>76.149608</td>\n",
       "      <td>7.315774</td>\n",
       "      <td>0.124112</td>\n",
       "      <td>0.963112</td>\n",
       "      <td>0.549693</td>\n",
       "      <td>0.715361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0327_std</th>\n",
       "      <td>0.030064</td>\n",
       "      <td>0.014814</td>\n",
       "      <td>0.064620</td>\n",
       "      <td>0.017112</td>\n",
       "      <td>0.008631</td>\n",
       "      <td>0.041709</td>\n",
       "      <td>0.139164</td>\n",
       "      <td>21.826614</td>\n",
       "      <td>0.122677</td>\n",
       "      <td>2.060132</td>\n",
       "      <td>12.005690</td>\n",
       "      <td>23.929199</td>\n",
       "      <td>7.531669</td>\n",
       "      <td>0.021247</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>0.133325</td>\n",
       "      <td>0.024030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0410_mean</th>\n",
       "      <td>0.088744</td>\n",
       "      <td>0.074453</td>\n",
       "      <td>0.420169</td>\n",
       "      <td>0.105671</td>\n",
       "      <td>0.043323</td>\n",
       "      <td>0.253056</td>\n",
       "      <td>1.311429</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>0.323986</td>\n",
       "      <td>1.458339</td>\n",
       "      <td>28.225303</td>\n",
       "      <td>57.129754</td>\n",
       "      <td>2.835881</td>\n",
       "      <td>0.136816</td>\n",
       "      <td>0.949613</td>\n",
       "      <td>0.428046</td>\n",
       "      <td>0.895437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0410_std</th>\n",
       "      <td>0.010305</td>\n",
       "      <td>0.008254</td>\n",
       "      <td>0.045144</td>\n",
       "      <td>0.010102</td>\n",
       "      <td>0.003511</td>\n",
       "      <td>0.030005</td>\n",
       "      <td>0.099335</td>\n",
       "      <td>3.370036</td>\n",
       "      <td>0.076341</td>\n",
       "      <td>0.573675</td>\n",
       "      <td>14.102476</td>\n",
       "      <td>28.293821</td>\n",
       "      <td>1.907087</td>\n",
       "      <td>0.024800</td>\n",
       "      <td>0.020554</td>\n",
       "      <td>0.256288</td>\n",
       "      <td>0.011826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0424_mean</th>\n",
       "      <td>0.143501</td>\n",
       "      <td>0.103763</td>\n",
       "      <td>0.598423</td>\n",
       "      <td>0.146364</td>\n",
       "      <td>0.062064</td>\n",
       "      <td>0.346958</td>\n",
       "      <td>1.080395</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>0.263567</td>\n",
       "      <td>1.573290</td>\n",
       "      <td>27.096940</td>\n",
       "      <td>60.335837</td>\n",
       "      <td>6.355908</td>\n",
       "      <td>0.167283</td>\n",
       "      <td>0.960120</td>\n",
       "      <td>0.543206</td>\n",
       "      <td>0.875827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0424_std</th>\n",
       "      <td>0.022295</td>\n",
       "      <td>0.011776</td>\n",
       "      <td>0.145967</td>\n",
       "      <td>0.017971</td>\n",
       "      <td>0.005076</td>\n",
       "      <td>0.033858</td>\n",
       "      <td>0.109319</td>\n",
       "      <td>3.058945</td>\n",
       "      <td>0.063369</td>\n",
       "      <td>0.502683</td>\n",
       "      <td>13.014723</td>\n",
       "      <td>17.450916</td>\n",
       "      <td>4.792163</td>\n",
       "      <td>0.007820</td>\n",
       "      <td>0.005858</td>\n",
       "      <td>0.143257</td>\n",
       "      <td>0.016074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203_mean</th>\n",
       "      <td>0.261741</td>\n",
       "      <td>0.142428</td>\n",
       "      <td>0.748273</td>\n",
       "      <td>0.180780</td>\n",
       "      <td>0.078690</td>\n",
       "      <td>0.421148</td>\n",
       "      <td>0.831233</td>\n",
       "      <td>10.923077</td>\n",
       "      <td>0.230464</td>\n",
       "      <td>2.283629</td>\n",
       "      <td>33.448947</td>\n",
       "      <td>101.372966</td>\n",
       "      <td>4.224270</td>\n",
       "      <td>0.160034</td>\n",
       "      <td>0.938521</td>\n",
       "      <td>0.744049</td>\n",
       "      <td>0.695130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203_std</th>\n",
       "      <td>0.038449</td>\n",
       "      <td>0.013137</td>\n",
       "      <td>0.093874</td>\n",
       "      <td>0.022898</td>\n",
       "      <td>0.010976</td>\n",
       "      <td>0.041409</td>\n",
       "      <td>0.089171</td>\n",
       "      <td>9.031994</td>\n",
       "      <td>0.108602</td>\n",
       "      <td>3.870622</td>\n",
       "      <td>2.954294</td>\n",
       "      <td>30.923000</td>\n",
       "      <td>3.916663</td>\n",
       "      <td>0.031648</td>\n",
       "      <td>0.014129</td>\n",
       "      <td>0.085531</td>\n",
       "      <td>0.031169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           avg_crowd_density2_5  std_crowd_density2_5  max_crowd_density2_5  \\\n",
       "Metrics                                                                       \n",
       "0325_mean              0.054568              0.054657              0.285206   \n",
       "0325_std               0.005142              0.001659              0.027895   \n",
       "0327_mean              0.187289              0.122211              0.665722   \n",
       "0327_std               0.030064              0.014814              0.064620   \n",
       "0410_mean              0.088744              0.074453              0.420169   \n",
       "0410_std               0.010305              0.008254              0.045144   \n",
       "0424_mean              0.143501              0.103763              0.598423   \n",
       "0424_std               0.022295              0.011776              0.145967   \n",
       "1203_mean              0.261741              0.142428              0.748273   \n",
       "1203_std               0.038449              0.013137              0.093874   \n",
       "\n",
       "           avg_crowd_density5  std_crowd_density5  max_crowd_density5  \\\n",
       "Metrics                                                                 \n",
       "0325_mean            0.063878            0.035999            0.211358   \n",
       "0325_std             0.008102            0.003034            0.017082   \n",
       "0327_mean            0.179175            0.068934            0.413803   \n",
       "0327_std             0.017112            0.008631            0.041709   \n",
       "0410_mean            0.105671            0.043323            0.253056   \n",
       "0410_std             0.010102            0.003511            0.030005   \n",
       "0424_mean            0.146364            0.062064            0.346958   \n",
       "0424_std             0.017971            0.005076            0.033858   \n",
       "1203_mean            0.180780            0.078690            0.421148   \n",
       "1203_std             0.022898            0.010976            0.041409   \n",
       "\n",
       "           avg_min_dist  virtual_collision  rel_duration2goal  \\\n",
       "Metrics                                                         \n",
       "0325_mean      1.717142           1.400000           0.379606   \n",
       "0325_std       0.179279           1.673320           0.074838   \n",
       "0327_mean      0.941544          17.642857           0.290162   \n",
       "0327_std       0.139164          21.826614           0.122677   \n",
       "0410_mean      1.311429           4.250000           0.323986   \n",
       "0410_std       0.099335           3.370036           0.076341   \n",
       "0424_mean      1.080395           4.250000           0.263567   \n",
       "0424_std       0.109319           3.058945           0.063369   \n",
       "1203_mean      0.831233          10.923077           0.230464   \n",
       "1203_std       0.089171           9.031994           0.108602   \n",
       "\n",
       "           rel_path_length2goal  path_length2goal  duration2goal  \\\n",
       "Metrics                                                            \n",
       "0325_mean              1.198281         36.137141      68.606075   \n",
       "0325_std               0.217506          4.866596      12.143353   \n",
       "0327_mean              2.444374         43.154011      76.149608   \n",
       "0327_std               2.060132         12.005690      23.929199   \n",
       "0410_mean              1.458339         28.225303      57.129754   \n",
       "0410_std               0.573675         14.102476      28.293821   \n",
       "0424_mean              1.573290         27.096940      60.335837   \n",
       "0424_std               0.502683         13.014723      17.450916   \n",
       "1203_mean              2.283629         33.448947     101.372966   \n",
       "1203_std               3.870622          2.954294      30.923000   \n",
       "\n",
       "           min_dist2goal  rel_jerk  avg_fluency  contribution  avg_agreement  \n",
       "Metrics                                                                       \n",
       "0325_mean       1.840448  0.120737     0.954195      0.654564       0.681743  \n",
       "0325_std        1.638693  0.025826     0.009416      0.162608       0.028696  \n",
       "0327_mean       7.315774  0.124112     0.963112      0.549693       0.715361  \n",
       "0327_std        7.531669  0.021247     0.010032      0.133325       0.024030  \n",
       "0410_mean       2.835881  0.136816     0.949613      0.428046       0.895437  \n",
       "0410_std        1.907087  0.024800     0.020554      0.256288       0.011826  \n",
       "0424_mean       6.355908  0.167283     0.960120      0.543206       0.875827  \n",
       "0424_std        4.792163  0.007820     0.005858      0.143257       0.016074  \n",
       "1203_mean       4.224270  0.160034     0.938521      0.744049       0.695130  \n",
       "1203_std        3.916663  0.031648     0.014129      0.085531       0.031169  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames_stat=[]\n",
    "for date in chosen_dates:\n",
    "    eval_res_df_ = eval_res_df[eval_res_df.date == date]\n",
    "    stat_df = eval_res_df_.drop(['date'], axis=1).agg(['mean', 'std'])\n",
    "    stat_df.index = date+'_'+stat_df.index.values\n",
    "    frames_stat.append(stat_df)\n",
    "    \n",
    "stat_df_all_shared = pd.concat(frames_stat) # , ignore_index=True\n",
    "stat_df_all_shared.index.name = 'Metrics'\n",
    "stat_df_all_shared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ANOVA test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "anova_metrics = (\n",
    "    'avg_crowd_density2_5',\n",
    "    'max_crowd_density2_5',\n",
    "    'avg_crowd_density5',\n",
    "    'avg_min_dist',\n",
    "    'rel_duration2goal',\n",
    "    'rel_path_length2goal',\n",
    "    'rel_jerk',\n",
    "    'contribution',\n",
    "    'avg_fluency',\n",
    "    'avg_agreement',\n",
    "    'virtual_collision',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1_anova_ = eval_res_df[eval_res_df.date=='0325']\n",
    "date1 = data1_anova_[list(anova_metrics)].values\n",
    "\n",
    "data2_anova_ = eval_res_df[eval_res_df.date=='0410']\n",
    "date2 = data2_anova_[list(anova_metrics)].values\n",
    "\n",
    "data3_anova_ = eval_res_df[eval_res_df.date=='0424']\n",
    "date3 = data3_anova_[list(anova_metrics)].values\n",
    "\n",
    "data4_anova_ = eval_res_df[eval_res_df.date=='1203']\n",
    "date4 = data4_anova_[list(anova_metrics)].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fvalue12, pvalue12 = stats.f_oneway(date1, date2)\n",
    "fvalue23, pvalue23 = stats.f_oneway(date2, date3)\n",
    "fvalue34, pvalue34 = stats.f_oneway(date3, date4)\n",
    "fvalue13, pvalue13 = stats.f_oneway(date1, date3)\n",
    "fvalue24, pvalue24 = stats.f_oneway(date2, date4)\n",
    "fvalue14, pvalue14 = stats.f_oneway(date1, date4)\n",
    "# total\n",
    "fvalue, pvalue = stats.f_oneway(date1, date2, date3, date4)\n",
    "\n",
    "statF_df = pd.DataFrame(\n",
    "    data=np.vstack((fvalue12, fvalue13, fvalue14, fvalue23, fvalue24, fvalue34, fvalue)), \n",
    "    index=['12', '13', '14', '23', '24', '34', 'total'],\n",
    ")\n",
    "statF_df.columns = list(anova_metrics)\n",
    "statF_df.index.name = 'Metrics'\n",
    "\n",
    "statP_df = pd.DataFrame(\n",
    "    data=np.vstack((pvalue12, pvalue13, pvalue14, pvalue23, pvalue24, pvalue34, pvalue)), \n",
    "    index=['12', '13', '14', '23', '24', '34', 'total'],\n",
    ")\n",
    "statP_df.columns = list(anova_metrics)\n",
    "statP_df.index.name = 'Metrics'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>avg_crowd_density2_5</th>\n",
       "      <th>max_crowd_density2_5</th>\n",
       "      <th>avg_crowd_density5</th>\n",
       "      <th>avg_min_dist</th>\n",
       "      <th>rel_duration2goal</th>\n",
       "      <th>rel_path_length2goal</th>\n",
       "      <th>rel_jerk</th>\n",
       "      <th>contribution</th>\n",
       "      <th>avg_fluency</th>\n",
       "      <th>avg_agreement</th>\n",
       "      <th>virtual_collision</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>46.554594</td>\n",
       "      <td>35.475322</td>\n",
       "      <td>60.511284</td>\n",
       "      <td>28.189055</td>\n",
       "      <td>1.656790</td>\n",
       "      <td>0.918200</td>\n",
       "      <td>1.254849</td>\n",
       "      <td>3.070732</td>\n",
       "      <td>0.214626</td>\n",
       "      <td>361.713180</td>\n",
       "      <td>3.031041e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>74.664454</td>\n",
       "      <td>21.808283</td>\n",
       "      <td>91.268102</td>\n",
       "      <td>64.663777</td>\n",
       "      <td>9.022480</td>\n",
       "      <td>2.430895</td>\n",
       "      <td>23.684589</td>\n",
       "      <td>1.682762</td>\n",
       "      <td>1.997105</td>\n",
       "      <td>249.860456</td>\n",
       "      <td>3.584294e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>138.962796</td>\n",
       "      <td>113.809388</td>\n",
       "      <td>120.469558</td>\n",
       "      <td>202.455216</td>\n",
       "      <td>7.839604</td>\n",
       "      <td>0.378181</td>\n",
       "      <td>6.074854</td>\n",
       "      <td>2.390352</td>\n",
       "      <td>5.161379</td>\n",
       "      <td>0.692441</td>\n",
       "      <td>5.292078e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>39.761354</td>\n",
       "      <td>10.888889</td>\n",
       "      <td>31.170247</td>\n",
       "      <td>19.571476</td>\n",
       "      <td>2.966760</td>\n",
       "      <td>0.181697</td>\n",
       "      <td>10.981691</td>\n",
       "      <td>1.230688</td>\n",
       "      <td>1.933507</td>\n",
       "      <td>7.724956</td>\n",
       "      <td>2.744165e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>152.359698</td>\n",
       "      <td>84.403077</td>\n",
       "      <td>75.765999</td>\n",
       "      <td>131.906656</td>\n",
       "      <td>4.513835</td>\n",
       "      <td>0.351972</td>\n",
       "      <td>3.107198</td>\n",
       "      <td>17.159577</td>\n",
       "      <td>2.162449</td>\n",
       "      <td>298.762109</td>\n",
       "      <td>3.958773e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>61.996024</td>\n",
       "      <td>8.289460</td>\n",
       "      <td>13.031589</td>\n",
       "      <td>32.621791</td>\n",
       "      <td>0.607833</td>\n",
       "      <td>0.261519</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>16.399761</td>\n",
       "      <td>16.653754</td>\n",
       "      <td>228.149897</td>\n",
       "      <td>4.011839e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>105.438428</td>\n",
       "      <td>36.896096</td>\n",
       "      <td>63.154225</td>\n",
       "      <td>84.064397</td>\n",
       "      <td>4.251100</td>\n",
       "      <td>0.334092</td>\n",
       "      <td>4.820695</td>\n",
       "      <td>6.826383</td>\n",
       "      <td>4.281824</td>\n",
       "      <td>181.354152</td>\n",
       "      <td>4.095647e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_crowd_density2_5  max_crowd_density2_5  avg_crowd_density5  \\\n",
       "Metrics                                                                   \n",
       "12                  46.554594             35.475322           60.511284   \n",
       "13                  74.664454             21.808283           91.268102   \n",
       "14                 138.962796            113.809388          120.469558   \n",
       "23                  39.761354             10.888889           31.170247   \n",
       "24                 152.359698             84.403077           75.765999   \n",
       "34                  61.996024              8.289460           13.031589   \n",
       "total              105.438428             36.896096           63.154225   \n",
       "\n",
       "         avg_min_dist  rel_duration2goal  rel_path_length2goal   rel_jerk  \\\n",
       "Metrics                                                                     \n",
       "12          28.189055           1.656790              0.918200   1.254849   \n",
       "13          64.663777           9.022480              2.430895  23.684589   \n",
       "14         202.455216           7.839604              0.378181   6.074854   \n",
       "23          19.571476           2.966760              0.181697  10.981691   \n",
       "24         131.906656           4.513835              0.351972   3.107198   \n",
       "34          32.621791           0.607833              0.261519   0.397212   \n",
       "total       84.064397           4.251100              0.334092   4.820695   \n",
       "\n",
       "         contribution  avg_fluency  avg_agreement  virtual_collision  \n",
       "Metrics                                                               \n",
       "12           3.070732     0.214626     361.713180       3.031041e+00  \n",
       "13           1.682762     1.997105     249.860456       3.584294e+00  \n",
       "14           2.390352     5.161379       0.692441       5.292078e+00  \n",
       "23           1.230688     1.933507       7.724956       2.744165e-15  \n",
       "24          17.159577     2.162449     298.762109       3.958773e+00  \n",
       "34          16.399761    16.653754     228.149897       4.011839e+00  \n",
       "total        6.826383     4.281824     181.354152       4.095647e+00  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statF_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_crowd_density2_5</th>\n",
       "      <th>max_crowd_density2_5</th>\n",
       "      <th>avg_crowd_density5</th>\n",
       "      <th>avg_min_dist</th>\n",
       "      <th>rel_duration2goal</th>\n",
       "      <th>rel_path_length2goal</th>\n",
       "      <th>rel_jerk</th>\n",
       "      <th>contribution</th>\n",
       "      <th>avg_fluency</th>\n",
       "      <th>avg_agreement</th>\n",
       "      <th>virtual_collision</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.864539e-05</td>\n",
       "      <td>9.508037e-05</td>\n",
       "      <td>8.518290e-06</td>\n",
       "      <td>2.488220e-04</td>\n",
       "      <td>0.224462</td>\n",
       "      <td>0.358536</td>\n",
       "      <td>0.286490</td>\n",
       "      <td>0.107503</td>\n",
       "      <td>0.652197</td>\n",
       "      <td>9.161777e-10</td>\n",
       "      <td>0.109544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.116572e-06</td>\n",
       "      <td>6.826151e-04</td>\n",
       "      <td>1.164758e-06</td>\n",
       "      <td>6.219310e-06</td>\n",
       "      <td>0.011999</td>\n",
       "      <td>0.147254</td>\n",
       "      <td>0.000497</td>\n",
       "      <td>0.221105</td>\n",
       "      <td>0.185267</td>\n",
       "      <td>6.556864e-09</td>\n",
       "      <td>0.084918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.661727e-09</td>\n",
       "      <td>1.108762e-08</td>\n",
       "      <td>7.407941e-09</td>\n",
       "      <td>1.681850e-10</td>\n",
       "      <td>0.012843</td>\n",
       "      <td>0.547226</td>\n",
       "      <td>0.025412</td>\n",
       "      <td>0.141635</td>\n",
       "      <td>0.037246</td>\n",
       "      <td>4.175826e-01</td>\n",
       "      <td>0.035214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.936870e-05</td>\n",
       "      <td>5.264049e-03</td>\n",
       "      <td>6.746714e-05</td>\n",
       "      <td>5.775682e-04</td>\n",
       "      <td>0.106999</td>\n",
       "      <td>0.676400</td>\n",
       "      <td>0.005119</td>\n",
       "      <td>0.285966</td>\n",
       "      <td>0.186085</td>\n",
       "      <td>1.476736e-02</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.606394e-10</td>\n",
       "      <td>2.025266e-08</td>\n",
       "      <td>4.681088e-08</td>\n",
       "      <td>5.414613e-10</td>\n",
       "      <td>0.046966</td>\n",
       "      <td>0.559996</td>\n",
       "      <td>0.094026</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.157786</td>\n",
       "      <td>4.437338e-13</td>\n",
       "      <td>0.061217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2.120866e-07</td>\n",
       "      <td>9.608771e-03</td>\n",
       "      <td>1.865812e-03</td>\n",
       "      <td>1.664902e-05</td>\n",
       "      <td>0.445208</td>\n",
       "      <td>0.614970</td>\n",
       "      <td>0.536040</td>\n",
       "      <td>0.000684</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>4.870230e-12</td>\n",
       "      <td>0.059658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>4.982790e-16</td>\n",
       "      <td>3.432643e-10</td>\n",
       "      <td>4.547228e-13</td>\n",
       "      <td>1.064003e-14</td>\n",
       "      <td>0.012876</td>\n",
       "      <td>0.800754</td>\n",
       "      <td>0.007415</td>\n",
       "      <td>0.001214</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>2.586102e-19</td>\n",
       "      <td>0.015015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_crowd_density2_5  max_crowd_density2_5  avg_crowd_density5  \\\n",
       "Metrics                                                                   \n",
       "12               2.864539e-05          9.508037e-05        8.518290e-06   \n",
       "13               3.116572e-06          6.826151e-04        1.164758e-06   \n",
       "14               2.661727e-09          1.108762e-08        7.407941e-09   \n",
       "23               1.936870e-05          5.264049e-03        6.746714e-05   \n",
       "24               1.606394e-10          2.025266e-08        4.681088e-08   \n",
       "34               2.120866e-07          9.608771e-03        1.865812e-03   \n",
       "total            4.982790e-16          3.432643e-10        4.547228e-13   \n",
       "\n",
       "         avg_min_dist  rel_duration2goal  rel_path_length2goal  rel_jerk  \\\n",
       "Metrics                                                                    \n",
       "12       2.488220e-04           0.224462              0.358536  0.286490   \n",
       "13       6.219310e-06           0.011999              0.147254  0.000497   \n",
       "14       1.681850e-10           0.012843              0.547226  0.025412   \n",
       "23       5.775682e-04           0.106999              0.676400  0.005119   \n",
       "24       5.414613e-10           0.046966              0.559996  0.094026   \n",
       "34       1.664902e-05           0.445208              0.614970  0.536040   \n",
       "total    1.064003e-14           0.012876              0.800754  0.007415   \n",
       "\n",
       "         contribution  avg_fluency  avg_agreement  virtual_collision  \n",
       "Metrics                                                               \n",
       "12           0.107503     0.652197   9.161777e-10           0.109544  \n",
       "13           0.221105     0.185267   6.556864e-09           0.084918  \n",
       "14           0.141635     0.037246   4.175826e-01           0.035214  \n",
       "23           0.285966     0.186085   1.476736e-02           1.000000  \n",
       "24           0.000553     0.157786   4.437338e-13           0.061217  \n",
       "34           0.000684     0.000637   4.870230e-12           0.059658  \n",
       "total        0.001214     0.012493   2.586102e-19           0.015015  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statP_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(statF_df.T.to_latex())\n",
    "# print(statP_df.T.to_latex())\n",
    "# print(stat_df_all_shared.T.to_latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Crowd-related metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "crowd_metrics_df = eval_res_df[['seq', 'control_type'] + list(crowd_metrics) + ['date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# max value in each metrics\n",
      "avg_crowd_density2_5     0.327009\n",
      "std_crowd_density2_5     0.167974\n",
      "max_crowd_density2_5     0.916732\n",
      "avg_crowd_density5       0.222670\n",
      "std_crowd_density5       0.093452\n",
      "max_crowd_density5       0.496563\n",
      "avg_min_dist             1.921854\n",
      "virtual_collision       92.000000\n",
      "dtype: float64\n",
      "# min value in each metrics\n",
      "avg_crowd_density2_5    0.049439\n",
      "std_crowd_density2_5    0.052378\n",
      "max_crowd_density2_5    0.254648\n",
      "avg_crowd_density5      0.053875\n",
      "std_crowd_density5      0.033797\n",
      "max_crowd_density5      0.190986\n",
      "avg_min_dist            0.564863\n",
      "virtual_collision       0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"# max value in each metrics\")\n",
    "print(crowd_metrics_df.max(numeric_only=True))\n",
    "print(\"# min value in each metrics\")\n",
    "print(crowd_metrics_df.min(numeric_only=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- group by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ozUE0IiIiUjqrsltbtLyA2JjCHETTf+ZduqXziZl5Pn8+EZGR7sCjL3Lw0RfZsGEjAEuWLCZSEmPee5YOy3m6Zoa7D7/ARhDlZhGR4S2dTLP7wW08/Ks/A3DaOacx7TXzKJo0PB9CHy03q9AVEZGcSSdS7PrtVv52318BOOWMU5h28TxKpnfv6R0OVOgOnHKziMjIcOaS0ymOFvOnx9cOy4fPbVToiojIsHXeKedQGivmtw8/SCgyfGfVqNAdOOVmEZGRYf78+QBUVVXlOJJjO1pu1mJUIiKSc/sbD7K/kWFd5IqIiMjIod8oREREREREJK+o0BUREREREZG8okJXRERERERE8ooKXREREREREckrKnRFREREREQkr6jQFRERERERkbyiQldERERERETyigpdERERERERySsqdEVERERERCSvqNAVERERERGRvKJCV0RERERERPJKJNcBiIiIiIiIZEt877M0PHkvnk5RuvQSCqcvznVIw1bji3XUVx3CoiEqF06gYEwRAK11cS6au4ySWBENzx+mdHZljiPtu5wVumZ2DVAPTAFudvd4pr0CuAk4E7jf3T+ZaX8XUAqMA77t7jU5CVxERERERIalxmf+zL6ffgw8DUDdY//LxDf/O6VLXp3jyIafQ+v3se+hHe2va9bvZ+aKlxCKhdlx1xaWzz4XgBd+8yzjz5jKhLOm5yrUfjF3H/o3NTsdeL+7X2lmK4C57n5L5thLgK1ADNjk7nPNbBrwTXd/g5ktBa5w91W9eB/PxecTEZG+mT9/PgBVVVU5juTYzAx3t1zHMZIpN4tIb6xcuZK1a9f2+bqPzt/LtKJEp7bq1ghffHpKn++1bNkyVq9e3efrsqUtNw6GkIX41PkfpihS1Kl9W83zNLQ2snTSok7tKU/yxT/fSksyPmgxHcuxfj84Wm7O1RzdFcCmzNfrgNe3HXD3Z9w9BUwG/jPTfDFB8QuwAXjtEMUpIiI5kE6kOLz5APsf2UXjC7W5DkdEREaI8bFkt7ZxsSSGHrB1VBQp7FbkAowrGsP44rHd2sMWoaKgbChCy5pcDV2eBGzOfN0MTO140MymA18BTjCzn2XOrwZw97SZTeh6QzO7Abi+a/uaNWuyGbeIiAyiNWvWQNIp3OiEmo/8UpKYbCTmaP1EEZHRor89qXt/vIqmqr+yceNGABYvXkzhzFN49q7c9cwO1MMPXzIo9z1wb5RUXXGntsI5LYQKjaYtFZ3aQ4Wt/PLzZ2GhoX1gcO659/f72lwVuvuAtr/VcmB/x4Puvgt4i5ndBrwqc/5sADOLAXVdb+juNwA3dGwzM1++fHlWAxcRkcGzfPlyDv5jNwciu6Djg+NGOOGUk4lVFuYsNhERGf7GXvxRWvdvBYJCN1wylnGv/URugxqmKs56lpo1i/FEUBKGy5opO+U5LJymde8YkjWlAFg4RflZzw55kTtQuSp07wHeB9wOLAHuNbMxPSww9XTmv2rg25m2xcC9QxWoiIgMXLo1xb6/vkDds9VYJMSYxRMYf8Y0LGQ072vgbYsupSRawqH1+4gfbOrxHvHqJhW6OXCMxSNLgGuBR4GXAV90d40zF5Gcio2fxYwP/4prfjwHM/jlz+4lFC3IdVjDUmxiHRPe+Hda94zBIilik2uwzOCpca95gtZ9FaTjMQom1xAq6D4kfLjLSaHr7o+b2SvN7ApgBnAj8LiZnU0wf3cF8Bugyt03ApjZfWb2QYJhztflIm4REemfvWufp/aZ6uBFIsXBx3Zj4RAl08vZ8cstLJ0UbP2w76EdxMb0UMyaUTihZAgjFmhfPHJhh8UjrwJuyRy+CDjo7vea2UzgFcCvchSqiEg7i0TZXB/MP1WRe2yhaIrCmQe7tZtBweSR/ewyZ9sLufuNXZraNrj6n8x/Xc+/bdCDEhEZpQZzZcdoKML1yz+GdVn/sPrRh9hTv4/FE09qb9uwIRhqtq9hP5NKJ7a3//G5P/OH//rLoMXY0XBf+XmIdV088iMcKXT/DlxvZvcRbP/3wNCHJyIi0rOcFboiIjLaGWUFpT0e+cXm31BRWM7Yokq21+xgT8P+Hs+TQXfUxSPdfY+ZXQf8ALjD3Zu7XqyFIkUk10byz5tVq4LdVNetqzjOmflr1argQXx//j/mZB/doaK9+kREeqetR3ewVnY8/NcFtDw/sVNb6dLnAGhYN6dTe6i4hQlveBQb4t1q21Z27M9effnKzL4APO/ut5vZfOD77n5+5thS4DTgToLe3OvcfU0v7qncLCKDbqTsz34sg52bR4KB5Gbt1SAiIoOu/Mwqik7Yg0VSWCxByaKdlCx6gZKTdlEw48jcoFBRnMqXPT3kRa4c1T0ExSx0WDwy8/oCoNrdm4CbgaU5iE9ERKRHGrosIiKDLhRNUXF2FRVnd3kiG3bGvHwzyYYC0i0xomPr21d8lNw7zuKR/w183MwSwEuA7+QwVBERkU5U6IqISM5FSuNQGs91GNKDYywe2QB8MvP1b4cuIhERkePTc3MRERERERHJKyp0RUREREREJK+o0BURkSGTrC8kvnsM6dZwrkMRERGRPKY5uiIiMujcoe5vJ9K8fRJgWDgVrMQ8V/vjioiISPap0BURkUHX8txEmrdPbn/tqTC1fz+RgqmHwKB5+yTS8SgF06qJTajPYaQiIiKSD1ToiojIoIvvGdO9MR2ieccEGjfOIt0SA6Bx00xKT91O6cJdQxyhiIiI5BMVuiIiMujCJT1vHRTfPba9yG3TuH4WxfP2EIqlhiI0Ecmi1urNxPc+Rrh4EkUzL8DCseNfJCI5k6wvJP7CeCySonDWAUIFSQDSiTAtOyaQjkcpnFZNpLIJCKYite4eS6KmhOjYBmJTajDL5Sc4OhW6IiIy6IpP3E3z1imk49H2ttiUQ3hrtNu5ngqTaigkNLZxKEMUkQGq23A79Rtua3/dsPkOxl/4XUIF5TmMSkSOpmXneA7/5STwoFKtXz+bcReuw6Ipqn9/CummAgAanppN+ZlbKZq3h8NrFxF/cVz7PQqmH6Ty5ZuHZbGrVZdFRGTQhYtbGXfxExQv2EXB9GrKXrqNMcs2ER1f1+1ciyaJlDfnIEoR6a9U0wEaNn6/U1ui9jkaq36Ro4hE5Fjcoe4fJ7QXuQAej1K/bjYNG2e2F7kBo/7JOcR3ju9U5ALEd42ntafpScOAenRFRGRIhEvjlL90e6e2koUvEH9xHKn6okyLU/bSbVgkPfQBiggrV65k7dq1fb5u4eQWrjq/plv7ugc+wW0Pf6nP91u2bBmrV6/u83Ui+ejcc+/P+j1LosV86vylQEun9sNPNtGYaGJaWXG3ax777X7OmDatW/vvf7OdtTseyXqMA6VCV0REciZclGD8ax8nvmscqZYYBdOqiZT2PJ9XRIav3bVR0g6hLsMXdx3Wr5oiw1FTopm6eD3lBWWd2vc07KOhtZFpZVM6tcdTcbYeeo4zpp3a7V57GvYNaqz9pZ8+IiKSUxZ2CmcdzHUYIgID6kWt27Ca+g2r2bhxIwCnnncpF13/XW7VHF2RAXn44UsG5b4tO+s4/Jdp7cOXLZbgZRcZoUgR1b8vJN3cNnzZKT9zK6+edwKH1ya7zNGtZvkXFmC2YFBiHEhvtgpdERERERmw8iUrKZx6Dvf+7EIONYV51Rfu0KrLIsNY4cyDjL/0MVpeGE+oy6rL4y99PFh1uSVK4fQjqy5XLttEfPdYkodKg1WXpx4algtRgQpdEREREcmS2LhFPLClFEBFrsgIEClr6XHv+lA0RfG8vd3azaBw2iGYdmgowhsQrbosIiIiIiIieUWFroiIiIiIiOQVFboiIiIiIiKSV1ToiojIkEq3hvHUMF25QkRERPKCFqMSEZEhkawvpPbhl5A4WIFFUhQveJGypc/nOiwRERHJQ+rRFRGRIXF47SISBysA8GSYxo0zado2KcdRiYiISD5Sj66IiLQbyMbsxzKpZAIfPmspUNupfdvDLdzz9COcNf00SqLFPFO9lfX7tgxKDCIiIjJ6qNAVEZFBl0wne2yPhaP885lXEMvst3nK5MVML5/K/VV/GMrwREREJM+o0BURkXYPP3zJoN370B/StO4d06ltyThIVld0ajtp4Sv51HdLCRUmBi2WngxWb7aIiIgMPc3RFRGRIVF5/maK5+8mVNxCZGw9lS/bjFkPqy97iFRzbOgDFBERkbyRsx5dM7sGqAemADe7ezzTPgv4FrAI+B1wpbu7mc0AHiGI+ZPu/oPcRC4iIv0RiqUoP3Mr5R3aEodLSRws73xeYSuRiqahDU5ERETySk56dM3sdGChu/8Q2Axc1eHwacAKYDFwAbAs0/4e4AR3n6wiV0QkP5QsfIHohCMLVFkkRcU5z2Ahz2FUIiIiMtLlqkd3BbAp8/U64CPALQDufnemvcHMNgEvmlkRQdF7lZn9h7t/c6gDFhGR7AtFU4x71TpaD5aRbokSm1RLKJrKdVgiArinie95FE+1UDDlLEKRovZjiZpnSdTtIDZ+MZGSKe3tqZZDvHRGM9WN4c73SrUS3/soAAWTz8TCR6YntB7cQLJxLwWTXkq4cOwgfyoZDTyZ4KSyZszAk61YRNNhjiadCNO6pxKLpIlNqaFtRpE7JPZXkI5HiU2uIRQ7kpuTtUUkakqJjm0gUt6co8iPL1eF7iSCnlyAZmBq1xPMbAzwortXZZouMLMJwN1mtsnd13Q5/wbg+q73WbNmTdcmERHpYtWqVQCsW1dxnDMHWXXu3nrVqvmA8oYIQKppPwf/cDXJ+hcACMXKGbvsK8TGL6Hm4etp3vFgcKKFKD/5g5Qteg+N2+6l9rEbed/ZhwE4+McPM27ZTSQbdlH9p4+QatoPQLhoAuMu+BqR0ulUP/SvxPf8PXOrCBVn/BslJ1w69B9Y8kaieid77riKlXMOArDzlkuZfPnXKZh8Yo4jG35a95dTs3YR3hoFIFzWxNgL12PhNIf+uITkoTIgM9rqvC0UTj9E3WMn0PTstPZ7FC/YRflLt+ck/uPJVaG7DyjOfF0O7O/hnPcB13ZscPcDZvYx4DxgTZdjNwA3dGwzM1++fHk24hURyWsf+MAHgMFddXm4u/LKYNXlqqqq45wpkv/q1n2rvcgFSLfWUfvof1K2ZOWRIhfA09St/w4Fk8+k9vGb8A5bicX3Pkpj1S9p2f3X9iIXINV8gNp/3ELh1PPai9zgVklqH7+JounLCBV0nrsv0lvVv/0qydq97a9TDdVU3/dlpr5/dQ6jGp5q/35ie5ELkKovpv6pOYQKW9uLXABPhqn7+4lY5OlORS5A09PTKZx5kNiEuiGLu7dyVejeQ1DI3g4sAe41szHuXgNgZpcDd7h7g5lNdffdZmbu7sAYuhS5IiIiIgLz58/Pyn0+/9r9jC3uOo1gI4/8+EHOmdPS7fy1v1zGsvmdF5HbuHEj6x74ZxZPaSHcZVWY9IaNbNj9U5ZO636vK7+xiM17Cwf6EfTQapRq2fFk97adT+HpNBYamRvODMb2dyXRYj51/lKgtlP74SfjNCaamFZW2+2aR39TzZnTurf//jc7WLvjkazHOFA5+b/t7o8DO83sCoJC91bgz2ZWamZfB64DHjCz9cDbzOwC4Akz+wwQcvcnchG3iIj0X7olQtO2STQ/PwFPdk4/rQfKadk1lnQifJSrRWQoHWjo/m+xIR5iT120h7PhuUNRvIc15A40hDnQ0L1fZX99pMf3cKfH80V6KzJ2eve2MdNGbJE7WJqTLTQnu8+vrW6u4WDToW7tKU/yYv3ebu3BNd3PHw56/ZPEzJ4DjrYMpgHu7nN7ez93v7FL0+LMn9cc5ZJTe3tvEREZXuJ7Kjm8dhGeCn6xrS+KM/bC9YSKWqn502ISB4K5wRZJUfmyLRRMG55Jc6TIds6WkefOeHxA14efKqD4gjh0WAG9ZV0Rc54LUzLbCJV1WJhmT4x/rQpTOC5GdO6RHlpvCfHOTRHCe6Ho/ARHviWNKU8WcPLBCCUzwArT7dckthfyjeoU0P9F6d5eUNDva2XkG/OKD7HvZx8/0mDG2AuuzF1AAzDYoxIOrd/Hvod2tL+2cIjXrXgroViYHXdtYd0T6wBYsmQx40+fymun6BYAACAASURBVGVnrOT5X2yh5UBj+zWFE0t467fei3UdtjEM9OWR2U3HWu3YzK7OQjwiIpKH6h6b117kAqSbC6h/ag6R8qb2IheCeUC1fzuRCW/4OxbWFkMDkLWcfYx9798MfANoBAoIphx9ZkBRy7CR2h+l8bcVRE+IY2EnsbOA1L6gN7fxgQpiJ8QJVaRIHYiQeC4oLFv+VkJyT5TIlATpxhCJqkK8JUSyKUzj78JE58YBJ7G9kPSh4FfQxvsric5vIVSSJrknSnKHVseVgSlZ8HKm/b//5tvveBkGvOr936dwxpJchzUsjT15EgVji6irOkQoGqJy4QQKxgarq8+5bBG33v1tSqLFvObaN1I2ZwwAs964gMNPH6TlQCOFE0qoPGn8sCxyoW+F7pYBHhcRkWFuMOYBFUUK+czLu88Dql/XQk1zPTMrus/3+cZ3H2dPw76sxzKKZCVnd9j3/kozW0Gw7/0tmcOH3X1K5rwPAQ/3N1gZntK1EeJP9PCrYmuI1i1F3dsxkjsKSO7o3qOaPhQhfqj7vbwlROuG4m7tIgNRMHUBv9odFGZfVpF7TCXTyymZ3n3xt1hFIb/fthaAL8+5tb09FAsz9uRJQxbfQPS6/Hb3Pw7kuIiIjE4tyTh18fpu7fsaDnKo+XC39rSnqI0Pv9UbR5Is5uyu+96/vsM9/tDhvCXuvr5PQYqIiAyiY/bomtks4A3AE+7+ZzMrAj7m7v8+JNGJiMiQGOx5QLVPH2T3H56jbbWaUDTEpSvehkVC3eYBjT1lMpd97wODGk8+GqSc3Zt976cAu48S0w1oj/sh1bYn9t6eVoYaJVaZAfo+k4C+DwZupP4dmh/jB6GZ/QX4GlAGFBJsB/QDd3/n0IQ3MEd2JBIRkVxrOdBE3dZqQpEwFS8ZR7Q8GN7YeriFj79jFaWxEq796mconzc2x5EenZnh7pbrOHoyGDnbzL4APO/ut5vZfOD77n5+l3M+DDzg7s/08p7KzYOobXuhgS5GNZK1LUal7YVGt7Z/C/o+6L+R8nd4tNx8vDm6W9z9fzM3MIIhTOMGIT6RAYvvf5K6J79B4nAV0XELqTj1GmLjFuU6LBHJKJxQTOGE7nPx4jXNTC+fQkmsmJZ9jZTOqiAU1TZD/TAYOfuY+95nnOjut/Z4tYiISI4cb47u18zsFAj2IXD3XwE/H/ywRI5I1D5H/eb/pnHbr0knmno8J9m4l+o/fYTW6k14qpXW/U9R/ccPk2rpPv9PRIaPhp217LqvitmVM5lQPJ7qJ/fw4gPbch3WSJX1nH2sfe8BzGwGsHNgYYuIiGTfMXt03X1jD223t31tZiF3T3c9R6Q3Vq5cydq1a495znlzmrjspbVkpttwuDnEzX8cx6Gmzt+6F76kgTec3H2xm5+uXsRftx97Ncdly5axevXqvgUvIllRs777ysoNzx8mUR8nWqa9MPtisHL2Mfa9x91fAL7c13uKiIgMtr5sLwSAmX0baAIeAJ42s7nuvibbgYlEw84blta1F7kAlUVpXnVSI79aV8bFCxs4aVKcmuYw9S09D04ImeaBiQxn6dZUz+0JPUPNBuVsEREZrfpc6BJsGv8IwdyfrxAsePGSbAYlo8PxelETh7ex//53dWs/bdlJrIoUE9/3DzZu3Mi0yiSLFy0E0mBHvqUtUsSFn72LcOHwXdhGZLQrmzeWpt2dR2MUjC1q37BeBkw5W0RERqX+FLpfBxa5+9UAZtafe4gcV7h0GqFoKelEQ6f2UOF4Wl78c+eTLURswml4ooHE4a1Exy6g4rRVKnJFhrkxiyeSqI2TWr+OaChC4YQSpr5qbq7DyifK2SIiMir1J+HdDYTN7GPATUBvN50X6ZNQpJDyU6/m8GNfbt97M1w8kaLZr+pe6AKhggrGXfQd3B2zYbn7h4h0ZWBhI5g6aljY9O83u5SzRURkVOpPofvfwA+A84HXAjcCp2QzKJE2JfPeSGzCUlp2/YVQQTlFsy7CQjHqCseRaqnudG7RjOUA+iVZZAQ5vOkA1U/sIRKKkvY0zXsb2PXbKua+Y0muQ8sXytkikhNte7DmWq7jGO570Oaz/hS6zwAXA/e5+/1Zjkekm2jFXKIVnYcyjn35jdQ88jlgI4mUUXrSP1E0++LcBCgi/VZXdahbW7y6mfihZs3TzQ7lbBERGZX6U+h+AqgEbjKz24E73f2Z7IYlcmyx8YuZ+Lqfc8N1c2mIh9hw4z/nOiQR6YdQpOcV0+0o7dJnytkyIFaawiJO+nAYODJiygrSWEmadG0YUh1GUoWcUGUKbwrhXXZECFUkAUjXaqr4aPLg15ODdm9PO16fwArCWGG407F0QwKAUGl00N7/WC66ZmR8n7s78YPNhKIhYpWFnY5VFpZTHC3C046FRt6IyV79HzCzse7e9tj9ve7+gplNAy4hmPNz6WAFKHI0ZsbBxpHxQ0REejZmyUQadhzu1FYys4JYufbQ7S/lbMmKaJqi8xuITG4FIF0XpmltOV4fJnZyEwULmyHkeCJEy2MlJJ8vIDyllaJzGrDCYM59oqqAlsdKsEKnaFkd4XFBwZOqjtD8UDnerAda0n+p6jiJ9TXQGmxHF55WTGRRBbSmaX2yBq8NvnetLErs1LFYUfhYtxuV4jXN7Lp/K601zQCUzKhg2mvmYSFj94Pb+ddzrwKMbT9ez7RXn0DRpNLcBtxHvf0Jc6mZfdTM3gHUALj7i+5+m7srYYqISL+Uzq5k2sXz2FW3m5qWw4w5eRLTLp6X67BGOuVsGbCCpU3tRS5AqDxF0TkNhKe0UrC4CULBIpEWTQfFbUmKovPailwAJzq/hejcOIVnNLYXuQDhcUkKT28cyo8jecZTTmLdkSIXIPViE6ldTSSermsvcgG8PkFi8+GebjPq7X5we3uRC9D4Qi0H/raLQ0/upX7bIdpGcSTq4rz4+214ZnHYkaJX3WHufgeAmU0C3mFmZcBzwP3uHh/E+EREJM+VzxvLtx+/A4AP3vHRHEcz8ilnC8DbCwY2KuJzM2oZF+rSHzIxxcNzU5zbtR340+IEryiErn0oT81Ms2hKkmiXa5IzEnxkgDHK6OW1rZBId2tPH4iTPtT9x1z6YFy7cnSRbE7Qsr/7A6eG5w8TKe4+3DtRG6e1pmVErZ/Rp3Gf7r4PuA3AzGYB7zezQne/eTCCExERkf5RzpaBqG8JMa4k1aktnjRqmnoe/nmgvudfKetaQtTHQ4wtTnVp1zBSGYCCnr9/LBbCCsJ4U5d5wbGQitwuQtEwoWiIdJcHBpHiKOGiHuY1mxEuHFlTBvsdrbvvAL6VxVhERERkEChnjz53xgfWeR/ZGKPo/M73aN1SzIxnIpTMDRajapM+HOHqzWGKJkWITOkwZDRpTNocIXzIuw1VnrqxaMAxHs1Ae7Nl+AuVRAhNKCR9oKVDoxGeVYJVRkluqu10fmT2yJpbOhRCkRBjlkyi+ok9ndrHnTqZcGGEhuc7D/euWDCux57e4WzAZbmZVbq7Br6LiIgMc8rZ0lvJFwpo+pMRm98CEUjuiJHYVgAYjQ9UULCwmVB5itSBCPEtRYDR/FAZsQXNRKYkSDeGaN1SRLouQrougjeHiJ4QB4PEtgKSO1WMysBEl44htaOR9MEWKAwTmV1KqCxKqCyKRUOkdjUBEJ5aTHjKyBluO5QmnDOdWEUhdVXVhGJhKhdPpHRmBQCz3rSAn/zfzymJFnPh+ZcwZsmkHEfbd70udM3sXUBP4wROBz6ctYhERERkQJSzJRtSe2I074l1a/eGMC2P9tBDljJaNxXTuqn7oeQLBSRfUHEr2WNhIzK3FOZ2/14MTyoiPEnF7fGYGZWLJlC5aEK3Y8VTyvjZxl8BcO3Szw11aFnRlx7di4DdQNdxJtOyF46IiIhkgXK2iIiMan0pdD/n7s91bTSzKVmMR0RERAZOOVtEREa1Xu/U3VPCzLTv6aldREREckM5W0RERrteF7pmdvpAjotkW8vuh3nLKbW88sQGUi1aW0VEpI1ytoiIjHa9LnSBMwd4XCRrap/4L6rXfJTl85t449J6Dvzu3aSa9uc6LBGR4UI5W0RERrW+zNH9vJl9/CjHDChFe/TJEEg17qPhmZ93bmvaT8MzP6fi1H/OUVQiIsOKcraIDAsXXTPg3UxF+qXX33nuPn4wAxHprWT9TvB09/a654c+GBGRYUg5W0RERrucPWIxs2uAemAKcLO7xzPtswieMi8Cfgdc6e6e2ROwFBgHfNvda3ITubSZP39+Tt63OJrmPy7dTzTs7W0bN27k1z/dwe9X3pOTmKqqqnLyviIiIiLD2YNfT+Y6hJxQT3bu9WWObtZkFsFY6O4/BDYDV3U4fBqwAlgMXAAsM7NpwFvd/bvAfcANQxqwDCtNiRC/eKqc9JE6l+eqo6zdWpy7oEREREREZNjo86MGM3u5uz80wPddAWzKfL0O+AhwC4C7351pbzCzTcCLwMXA1kz7BuC1wKoBxiBZcmc8PvRvus0oGBMlOjNOuiHM5CdjnNmYGPIw3l5QMOTvKSLSW1nK2SIiIiNOf/rU32NmFwC/dfe/9/N9JxH05AI0A1O7nmBmY4AX3b3KzN4KVAO4e9rMJvRw/g3A9V3b16xZ088Q5XhWrQqeNex1P86Z2TclfSfmu2kbDBM9McTB0NuI25QhjWOVGaDvM5Fs0b+lrMtGzpYRSA9iRWS063Oh6+7vBzCz15jZncCdwN3ufap29gFt40zLgZ72hXkfcG2H82dn3jcG1PUQ1w10GdJsZr58+fI+hCV98YEPfAAY+h7dUGWSkku675s75blv0/JI2ZDGsirzi4Tm6Ipkh35mZ1eWcraIiMiI05+hy5OBecCngBqgBfismf3e3R/p5W3uIShkbweWAPea2Zi2BabM7HLgDndvMLOpwP3AtzPXLgbu7Wvckj8s2vPvZ6Y5/yIinWQpZ8sIMlQPXt3TeGs9oYKKbseWLjyBlqTxzLNbO7Wnk80AhCJFQxKjjG6eDHbosEhOliSSYaA/pcEW4HHg423DoMzsdwRDkRf05gbu/riZvdLMrgBmADcCj5vZ2cCXgFcDqzK9t9939/8ys/vM7IMEw5yv60fckidSByN4cwgr6rzFUGJnLEcRiYgMWwPO2SJdNW67l/p13yHVUk2kfBaVZ/wbBZNOI1HzLDV//yJffsM+mlqN+s0/omzh5aQTTRx+9Eu07PwjAIUzX0HlmZ8iFNUikpJ9nkyT2FxLem8zOIQmFRJdVIlFVfCONv0pdG9w96/10P7TvtzE3W/s0rQ48+c1Rzn/tr7cX/KYG01ryik6v57wmCTplhCtW4pI7tB8JBGRLrKSs0XatFZv4vCjX4TM6Pdk3Q6qH/o4ky69i+q1HyfVFMxGK445dU99k0j5LFpe/AvNOx5sv0fzjv/DIkWMOevTOfkMkt+Sz9aT3tPc/jq9r4VEqJbYyWNyGJXkQp8fbfSUMN097e6fy05IIscXnR0nVJLGU4A73qCndCIiXSlnS7Y17/i/9iK3jSeaaNjyP+1Fbufzf0/zzv/r+T4igyC1t7lbW3pvC1qaYPTpc3VgZu/o8vrD2QtH5PjCU1qJndQM5pA2LAKFZzViRalchyYiMqwoZ0u2Wbjn0VOhaMlRzi/s8Zqj3UdkwELWvS0MZj20S17r9dBlM5sLfBE42cw+kGkOE6yafOsgxCbSo8jUHvbLNScypRVPhAhPSJKuD5N4rgCS+qEmIqNPNnO2mV0D1ANTgJvdPd7l+DsJdkN42t239nALySPFc19Lw9M/xVNHvg3CJZMpPeldNO9aS+LQ00dOthAl81YQLp5E/cbbO92nZP6bhipkGWUiM4pJbq3v1Bae3vODGMlvvS503X27mX0KuABom2jhBFv/iAwZb+55IEJ0Xpzw+GT769iJLTQ+UA5JDWsWkdElWznbzE4HFrr7lWa2ArgKuKXD8S8Cv3b3v2UnchnuImUzGHfBrdRvWE2ybgexCSdTvvRKLBxj3PKbqXvqW9Q8uoWaphDLln2V2PglRMctwiIFNG2/D3CK57yO0oX/lOuPInkqPLcUQkbqxSZwCE8tCtpk1OlVoWtm09z9xUziTHY5/GbgJ9kPTQSsME3BkibCkxKkG8K0biwisb2A2IJmrPDIqsvphjDh8QngSA9uqCJJdF6cxNPaxkBERo8s5+wVwKbM1+uAj5ApdM3sTOAiYIeZvQv4bNs2gZLfCiYspeCCr3drDxeOZczZn+G6y+8A4MrvnQOAWYiyhe+mbOG7hzROGZ3MjMicUiJzVNyOdr3t0b3DzC7KbDD/K6ABaJsQOQMVuqPa2wsGZ56N4Vx74UGmVWZ+T6tMkpjSwI0PjqP1TxO47KV1zBvfygs1UZ56sYA3n1Lf7R4Pj4WfDFJ8IiLDVDZz9iSCrYgAmgm2+GvzJuCH7v5dM7sOuAl4f8eLzewG4PquN12zZk0fQpCRSv+fR7dVq1YB8Gzr6FwEatWqoPMlH/4djNTP0KtC190v7PDyMnd/FsDMKoGywQhMZN6E1iNFbkY07Jw3t5mUw8LJwfygEya0Mrk8QcictHeek7u9WnvrisjokuWcvQ9o2+y0HOi4rG4RwdxcgHuAbtsAuvsNwA0d28zMly9f3scwZCTS/+fR7QMfCJYHePDrXQeWjA5Xfy0os6qqqnIcycCN1H/L/dlH9/Nm9mXgXOBa4MeZP2WUujMeP/5J/RChlaJ0ulv7jFic6MwEpDs8IYxCujpMqPLIysvJvTH+pSoE6cGJDwavN1tkNDm0fh8fP/dKSmPFvPjAViadP4tIcTTXYeWLgebse4D3AbcDS4B7zWxMZojyWuDlwI+AKPBoNgMXEREZiP4Uun8EngZ+DZwDvCKrEYlkJPdG8UQIi3YudtOHwzCztdv56cYwzX8tIzwxSbouTGpfhI5zdkVk+Kl7tpp9D+1gTGFl8LrqEImGBLPffFKOI8sbA8rZ7v64mb3SzK4gGPZ8I/C4mZ3t7r80s3My83MnAddlOXYREZF+60+hO55gIYo73f0FM3sl8N/ZDUsESBrNa8soPLuBUGkKTxqtG4to3VpIweIWCHWe85E6GCFdG/wnIkNr5cqVrF27ts/Xve+Uy5g3dk776w0bNsIGuOxz76G6uW/rGi1btozVq1f3OYY8N+Cc7e43dmla3OHYvw48RBERkezrT0XwE4IhUL80s/OAJ7IbksgRqf1RGn9diRWn8XgIUkEPbcsTxRSe3kSwWwakDkVofaYwh5GKSH+YadTFIFPOFhGRUanPha67Pw88n3n5VzOrzmZAIt0Z3hTu1JJ4tojk7hiRyQnSTSFSu6NomLJI7vS3J7X2mYPsfnB7p7aiyaX87btXZCOsUU85W0RERqs+F7pm9m/Ae4ECgspiDFCZ3bBEjs8bwiS2ho9/oogMWxUvGU+qOUn1E3tINicpm1PJ5GWzch1W3lDOFhGR0ao/Q5cvAd5CsC+fESxuISIi0i9jT5nM2FMm5zqMfKWcLSIio1KoH9f80t03ufuOzJCov2Q5JpE+i85tofjCWoouqCUya/C2ExLpyb49u/mnd17GgQMHch2KSFfK2SIiMir1p0f3NWZ2OcEm8QZMA07MalQifRBb2EzBKY3tryOTE7TEnESVFqeSweXJVqp/+1WeufdbvLnlIGu/sJs3feW3hApKch2aSBvlbBERGZX6u+ryX4BU5rX20ZWcip3U3GObCl0ZbIf+8C0O/e1Oag8dJGxOZPdj7Pz5Z5l9+VdzHZpIG+VsEREZlfozdHkr8HpgJ3AGcHdWIxLpKuKEKpMQ9h4OOlbQvd0K04Mfl4x6Det/y/79+zu1vfDnO/F06ihXiAw55WwRERmV+tOj+yngL+7uZnYv8D8EC12IZF10QTMFJzdhEcdbQ7T8o4TkcwVgTnRenMiUVjwJVsCR/goguSuWs5hl9LBwlMOHD+MePGxxdw4eOoy2upJhRDlbRET6rGbjfj56zgcpjhax+w/bmXTeTMKF/Skdc6c/0f6MYK4PQAw4P3vhyEj09oKCQbnvzDGtfOL0ZoKiwaAQ0uc2csPhYlacXM9LZ7QAEDYYU5zmcHOYZNp4dn+M2zeMobGgPwMWRHqv7PQ3UbnlCWpqanB3zIyW6edgIX3vybChnC0iIn1S92w1e9c8z7iisQDUbjlIsqGVmSsW5DiyvulPoRsHZpjZK4DPAb/PbkgigaXTuq+eHDI4d05Te5ELkHLjYGOEx3cW8usNZRxqGllPm2TkqnzZe5lZ38D2730GPMW6+gr+7Rt35DoskY6Us0VERqmVK1eydu3aPl93xanv4IQxs9tfb9iwETbAmz/zLg631B39wh4sW7aM1atX9zmGbOhPRXAXsBJ4E/Bz4HtZjUhGjKqqqkG9f8PTP6X2ia91az/7rZfTsOVHAGzcuBGAxYsXc/qrz+fTy74yqDGJdGShEDNf+2G2PVrNT3/6U975zncycfLUXIcl0pFytoiI9EnYeh6ZFjpK+3DVp0LXzM4HPgHMBp4GVrt76yDEJULR7NdQv/lHpFsOtbdFSqdRuvCfaNp6N+lEQ6fzCyafMdQhigBw9dVXU1VVxdVXX53rUETaKWeLiIxu/e1JPbzpAHv+9FyntqLJpfz9u1dkI6wh0+uy3MxeCfyaYAXHbxOs4PigmZ05SLHJKBcurGTCRd+jeO7riI1bSMmJb2H8hd8hXFBB5TmfJRQ9sldp0YzllMx/Uw6jldFs4sSJ/OQnP2HChAm5DkUEUM4WEZH+q1w0gQlnTSdcEAEzSmdXMu3iebkOq8+sbbXQ455o9ivgU+6+uUPbCcDn3f1dgxTfgJiZ9/bzyciTTjSx4hULqGkK85cnnjv+BSIiA2RmuPuwX1Z7OOds5eb8N3/+fGDwpzjJ8Nb2ffDg15M5jiQ3LromGDg7kv8duDukHQsP7yHLR8vNfRm6vKFjwgRw921mtvloF4gMplC0mKf3Dc6KzyK91bjlT9T/4248laR06SWUnfK6XIckAsrZIiIyQGYWbG8yQvWl0N13lPb92QhERGSkqX/i1xy45/Ptr5u3P0qq7gCVL39fDqMSAZSzRURklOtLoXuemW3vof1c4LYsxSMiMuT6u/z+J07cw6TCzkOyGp+6ms++/z8I9n/uvVwuvy95STlbRERGtb4Uui8DJvfQPjdLsYiIjCilkXS3tuJImrBB6hhTECuiSZpTIVrTw3vOi4xoytkiIjKq9aXQfZu7P9K10cxOy2I8IiJDrr89qQfu/hz1T93baT/n4vnn8uRN7+bQ72+ldf82CqYtYtzF/0LB1JOI732WA3ddR+v+bVikgPKz3s7Yi64J5sCIZJdytoiIjGq9XnU5629sdg1QD0wBbnb3eIdjy4FvufvCDm0zgEcIivNPuvsPevEeWtkxz2llR4Ej3weDaXwswdLKZuIp48nDxTSmwhSHU7xnVjXzSoMfXy80xfjV7kqunHuASOjIz57mVIgvPj2Zf5m/j7GxVKf73vnCWB6tKSFb9G9hcI2UVZeHM+Xm/KfcLKBVl/Nh1eWRIhurLmczmNOBhe5+pZmtAK4Cbmk77u5rzKyyy2XvAU7oWBCLiAyFUyqaeNfMakKZH6GvnlzHN7dOZG88yv/sHMf54+uJmvNQdRmnVjZ1KnIBisJplo+v71bkApxc0ZTVQldEREREclToAiuA/8/efYfZXZaJ/3/fUzIpk84kIVJCiRQTmoAoaoIV3VUsWBYVG65KZMWy6i5fJSvqyuqi2AsoiqL+xILYEJVkVUClhgSEUEJoSSZl0pNp9++PcyZMpsAkU86ZM+/XdXHxmedTzn2eTPKc+zxtWfH4duBcOiW6Rc0dBxExBngecHZEfDIzv9z1gRGxEDi/a/miRYsGJmKVNf+cR7ZZs2axYsWKQXl2kLx8ZtOuJBdgbHU7L56xkUWN43nnQY3UVRcS22fus4WlG8f0+Jzevs/eMYDzdGfNmuXfBUmSJEqX6E4HOvby2w7MfKKLM3M78LyIaAB+FhHLMnNRl2sWAgs7l0VEzp8/f2AiVlnzz3lke8c73jFoz66vaWdibfee2P3GNPNPMzbuSnIBagJmjdtJaxaOO+xsC/68djwHjWvmqfWPD0ppT/jL2voBi3XFihX+XZAkSaJ0ie5qYGzxeAJ93NcvMxsj4gPAycCiwQlN0nA1GPOAMpOdi6tg5+7JbtX0GtrX7YTWrnMN26g5ZjJt924mt7QSE2qpPXwiV01OsnUSrfdupn3tTqKumuqDxvGNfarpvb+37zrmAkmSJKl0ie5VwFuBS4G5wNURMTkzN/R2Qzy+esVkTHIlDZGIoPbwCbQs2QAdOe2oKmoOHU9rS9K+fvdlA6K+hprpY6iZPobM3G1F5aipovbwiUMYvSRJ0shUkkQ3M2+KiOdHxNuA/YELgZsi4qTM3BIRxwBTIuKUzLwuIp4H/G9E/AS4JTNvKUXcGnqtmx9i87Lv0LrxAUbt8zTqj3wz1WOmAtCycQXzD91K0/Yqsr2VqLJHS4OjesYYYkIt7Wt2QHUUfq6touaw8TT/vQVai/vpVgU1nRJZtw2SJEkqjZJlBpl5YZeiOZ3O3UZhSHPHz38Ejh2i0FQm2nasp/Haf6V9R6Gjv3ndMnY8egPTXnoFW5dfycZbLub0YzcB0HjNW9nn+V+hatT4UoasEhvc4bs1wOhuZWOqR3P0xG1UR7Jk41g2/6J6EGOQJI0Ea9as4dxzz+Xiiy+moaGh1OFIw9LALfcpDbBtD/x6V5LboXXzQ2xb8Vs23f7V3cpbNixn6z0/HsrwJKCwR+6N6+v5y7rxbG41yZU0crW3bGXjbV/mwy9YyzuetYHmtXeUOqRhp629ncvvvZkX/+gifjNlJx+65AulDmlYant0Gzv/tpadf11L60NbSx2OSiQqedN2N6Uvb2eddRaLFy/u9fxpdSeTdAAAIABJREFUczfxwsO7/+P0f/eO5bmHbutWvvTROr72lyl7HMe8efO45JJL9vg+qcPs2bMBN4UfCXrblF59Z9tcudb+/mx2rrmFpUuXAjD36ONoeNEl1E5+aokjGz4uXPJHfnjvzdxz9z20Z1IVwfde8U5eecTxpQ5tj3W0jYOxUOQTaV25lda7Nu5WVnPoeGoOGdpRfx2jzPxsMPh6a5ud1KiyteTR0d0S3faEGx8Yw7MP2bbbvqYAj2ysHcLoJEmqPE/2JXRv9pvUwkdeuHa3sjtuv4U//+zZ/PDmPV+EbyR+Cb21ZSe/WLmMxjWNu9Y+TOCT1145LBPdUmlbsaVbWeuDW4c80VXpmeiqZPrSgG2+63tsvuNSsnU7VaMmMPHp7+cl/30qm27/GpuXXbbrupr6fXnBxy7lotF73qMrSZL6Z9yo9j0qV3fb21ppaW+jqamJjlEPmcn9qx4pcWTDS7b0MGKkpb3bTgiqfCa6Kmvjj3gj4w59JW1bH6Nm/AFE9SgAJhz9Lur2fQY7H7uR6rHTGDPrVKpqx5U4WkmqPBFxDrAZ2Be4KDN3djr3GuCLFDqe5mfm3aWJUgNlb3tRs62ZVVed1m1tjef863/xkVkvHojQKt4+o8dx5KTpPDrpUdZv2LArMXvWlANKHdqwUj1tNG2P7j7FrWraaJPcEcjFqFT2qmrHUTvp0F1Jboe6accy4eh3M272q01yJWkQRMTxwJGZeRlwJ3B2p3MBzMnMGZm5r0nuyBbVo5jy7E9RPW7Grp/rj3gDY01y98jHjzuVkw46jAAik/oH1/H1N51T6rCGlZrDJ1A1tW7XzzF5FLVHuof9SGSPriT1Q7Y2M2fCNqoD2nduo6pubKlDkgbSacCy4vHtwLnA54o/HwO8PCL+BXhHZu75xE5VlLppxzL95T+ldfNDVI+e4pZ/e+GA+slc+eK3876/ruAXV/6UN73qNTxl+oxShzWsRG0Vo46fSu5og0xijOnOSOWfvCTtpea1D7LqO+/mrbPWAbDy8y9j3zd+kbqnHFniyKQBM51CTy7AdmBmx4nMvBU4NiKOAX4UESdm5m5LnUbEQuD8rg9dtGjRYMWrsvFAqQMY1uYd9XTuvvFmjj322GH79+W9730vAPc0l2iV9Y5xq82lefn3vrcwVHq4/vlVArcXkqS9tOr772PbPX/atZXGnDlzqJt5JE9553dLHJkGy0jbXigiLgBWZOalETEb+FZmPqeH6z4MXJuZt/ThmbbN0ghQqu2FyoXbCw0dtxeSpF7s7XYan3jaI4ypfnxF0aVLl8LSpbzgf6+ndQ9zoZG4lYaGhauAtwKXAnOBqyNicmZuiIiqzOz4C9DC4z2/kiSVnItRSdJeatzZ/bvC9c3Ve5zkSuUqM28CVkbE2ygkul8A/hQR9cB/RsTvIuJ9wB8zc0cpY5UkqTOHLkvSXtp2742svuJcsq04LCuqmPaqj1N/1KmlDUyDZqQNXR4Mts3SyODQZYcuD5Xe2mYTXUnqh+Y197P5tl9Ceyv1c091IaoKZ6Lbf7bN0sjQkeiOdCa6g89EV5KkfjLR7T/bZmlkMNEtMNEdfCa6kiT1k4lu/9k2SxoKHYm2iWbl661tdjEqSZIkSVJFMdGVJEmSJFUUE11JkiRJUkUx0ZUkSZIkVRQTXUmSJKmMrFmzhjPOOIPGxsZShyINWya6kiRJUpm47rF7Of37n+Pa2o18/OtfLHU40rBloiuNcH5rLElSefjm3Tfy/ht+xs071rLlsH35djzKH+69o9RhScOSia40Qm1p2cl/3XoNJ/zks/zi0Fre/p3P496WkiSVxo7WFr5/3800rmmkozVurwrO+9UVJY1LGq5MdKUR6oLbruXnDyxhbdMG2kbX8oeWNXz9tsWlDkuSpBFpU8sOtrW20NTUtOuL58zk7jWPlDgyaXgy0ZVGoM0tO1i86r7dvjVO4Mt/+W0pw5IkacRqGF3PAfWTmTRpEhEBQERwwtT9ShyZNDyZ6EojWNdvjR95+OESRyRJ0sgUEZx/zIuY/ZQDiGLZ6PVb+Orrzy5pXNJwZaIrjUDja0czb8Yh3b41PmXKgSWOTJKkkWvulH255qXv4vU5nWm/vJV/G3cYh8y0R1faGya60gj10WNeyBvnPpOq1jaqtzczZclDfPUt7y11WJIkjWijqmv477P+jZNnHcaCBQtKHY40bJnoSiNUfW0dnz75lXygdX9m/uhGzjrsJKZNm1bqsCRJGvGmTZvGFVdcQUNDQ6lDkYatmlK9cEScA2wG9gUuysydnc7NB76SmUd2KnsDUA9MBb6amRuGNmKpMi1YsIDly5f7rbEkSZIqRkl6dCPieODIzLwMuBPYbZZ9Zi4CJnW6/inAazLz68CvgIVDFatU6fzWWJIkSZWmVEOXTwOWFY9vB17ewzXNnY5PBe4tHt8B/NPghSaNHC3tbfy9cSXLNqwqdSjD2upHH+LMM15LY2NjqUORJEkSpRu6PJ1CTy7AdmBmH65fB5CZ7RHRrespIhYC53ctX7RoUX/ilCrWiubNfG39nWxsK3yndOCoet4zZQ711bUljmwYaWtm7B0/YNNtv+JVOzfxmw/9gwPP+DRZO7bUkUmSJI1opUp0VwMdnwQnAGv6cP0sgIgYBWzqekFmLqTLkOaIyPnz5/crUKlSvf66y2kfW8d46gBYDyxtqOIjR80vaVzDybrfXsS6xltZvWUzVcDkzcs54NHfcdCbP1/q0CRJkka0UiW6VwFvBS4F5gJXR8TkJ1hg6tfAV4vHc4CrBz9EaXg466yzWLx48R7d0zamlsde/6xu5Xdd/3cuffW79jiGefPmcckll+zxfcPdliW/Zc2a3b+ne/gvVzLrjZ8lqku21p8kSdKIV5I5upl5E7AyIt5GIdH9AvCniKgHiIhjgCkRcUrx+seAX0XEOynM7/1oKeKWhrMEWiaPo210LVU7W6lqae12TfXWHbRMHsfmOfuz7ZBpZLU7kD2RqB1NU1MTmQlAZtK4rgnCepMkSSql6PiAVokiIiv5/Ul9deu6R/joLb9hzfYtVEdw2oFzaBhdz9f/cQN3LF0KwDFz5/LS/Y7g6ofu3HXf/uMm8c1nv5Ypdc457UnTn7/L0u98hA0bNpCZRASb938uZ3zumlKHpkESEWRmlDqO4cy2WdJQmD17NgDLly8vcSQabL21zSa6Ugl1/CM8mLIqWPXak2gbM2q38sl/uZuqna1sn9VAtLYx5v41rHvBHLKmerfrxt/xEBNvun/Q4xyODVFm8tDvvsbvv/wRaG9jyZaJ/OePbmba9BmlDk2DxES3/2ybVe6Gom0eLoZj29zBRHfk6K1tdnydVOGaGyZ0S3IBduw/lVGNmxi1uom6VU0A3ZJcgJap9YMe43AVERzw4nfz4LHn8ul7ZjJp/jtMciVJksqAq6VIFa5qR0uP5W11taw6/Rm75uFWbW+mMJN39y/EatdvGeQIh78FCxawfPlyFixYUOpQJEkD5NpTuq9lMVK88DpTBA1//hZLFa524zZGP7yOHftN3VUWbe20Thq722JT7WNGUbN5O63jx+wqq9m8nfqlDw1pvMPRtGnTuOKKK0odhiSpzGypqmHx5Omsqx3NcZvXMWdrYQRVO/DXCQ3cPXYCB+7YyskbVzOqOKT//tH13DBxGmPaWpnftIoprYX97ptqalk0aQZbqmt5xqZGZm/fDEBLBNdPnMYDo+uZvW0TJ21qpPv4rMq0fcUtbF9+PdUTplF/1EuoHjMegJYNj/KCaZuoiqRl3Upqpx5Q4khVCia6UhkY7G+Nd7Yt4efbDuCW8VPZp2UH8zc8xicOOoZCD+7jZtQlH3rwb9w8YSqTW3by7I1rGHNS26DG5rfGkqRKtK5mFB8+9Hgaa0cD8JOGA3ntmhW8YfX9XHjgXG6c0LDr2l9PfQqfvP8WFk+awZf3O5wsjq66ctqBfOr+W6jJ5COHPJ3N1bUA/HjaLN75yN28aP2jfOygY7lz3KRdzzp+81r+34olVPpiAuv/8BWa/u9bu37eeP3lzDzr27SsfZBV3zuHl8zYCMDDX34t0173GcYd9pxShaoScTEqqYQ6FkoY6uFRbcDbjng2TTW7z909cVMjL1v7MI/VjWHulg3MbN4+6LF0JLouFqHhwMWo+s+2WeVuoBaj2njCwWyes/9uZdHezuTr7mT98+d0u37yX+5m09MPpm107W7lY1Y0Em3tbDtk+m7lVc0tTLzxPjY89/Buz9rnmiWMfnRDv99DKdvms846i8WLF/d4bnxNGx874lGquvxrfN2a8Rxcv5MDxzbvVr56Rw3/c8++exzDvHnzuOSSS/b4Pg2t3tpmu1KkEWBnVPHjabP4+4R9mNTazCsaV/KWx+7lC/sfQXvxO98x7a001o7mowcfC0CQvHHV/Zze+GApQ5dUYhFxDrAZ2Be4KDN3djk/G7gyM48uRXxSuWqZNK5bWVZVsXPm5B6v39kwoVuSC9AyZRzR2t6tvH1ULc3TJ/b82pPHDUiiW66m1bV0S3IB9h3Twoy67muTTB/dSpC7eso1MtijK5XQUPXofurAufy10xCpKpL/uv9WJrS1cMOEaYxub6M94LszDt3tviqSb/zjehpadnZ95ICxR1fDyUjr0Y2I44G3Z+a7I+I04ODM/Fyn82OAtwIfysxZfXymbbNGhK//4wYuveevu5XVVddw0YkvZ8ENP+12/Sef/hI+v+xPNO7YfRHIl+x3OHXVNfz8waW7lU+uG8uH557CR276VbdnffPZr+XoKTMH4F2Up7btm1j52VPJ1t17bifP/1d2PHgr2x/4+27ldTOP5Cnv/O5QhqghZI+uVMYGc55qa/1oVs2aVuiP2SV4y9YDmHrdnWQ8BAkbnnMY28Z1/TcieMU9Uxi7onHQ4pNU1k4DlhWPbwfOBT7X6fzbgEuADw1xXFLZ+5eDj2Xxqvu4d9NaACLgnCOfzQkNB/DqWUfxkxVLdl377OkH8bx9Z1NbVc15N/+GlvbC+hgNo+t55+HPpDqq+GvjSh7btgmA6gg+OGc+8/c9hFP2PZTrHrt317NedsCRFZ3kAlSPmcCUF72Xdb/5LBS/OBs146lMOOlfGHv4PB77zrtp316oq6pRY5l66vtLGa5KxB5dqYSGYlP65injWHPa8d3K61ZvpGpbMzsO3Idoa6N603Zapo7vdt30n/2d2qZtgx6nPboaDkZgj+43gOsy8wcRMR34v8w8rHjuVcDNmflgRKzoqUc3IhYC53ctv+666wY3cKlMtGU7d+xYT1NbM0eMnsz0msd3NljRvJkHmjezb+1YDq97fDGppradLNmxnjFRzdGjpzKqqrCGcku2c/v2dWzNFo4aPZXJ1XW77rln50YeadnKgaPqOXjUhKF7gyVWtWU1tY130j56Mi3T50KxrqJlO7WP3QrZTsu+x5Cj6kscqQbTKaec0mPbbKIrVbj2bOf0P36Xh4tbGnSYNrqeNTu2cMfSwlCoI488kqqqIDrNX/mn/Y/g/GNfPKTxSuVsBCa6FwArMvPS4lzcb2Xmc4rnFvP40u0nAd/LzLP68EzbZknSgOmtbTbRlYa5J1qVsEPL5HGsn38ELZPGEW3tjL1vNVtnzyiMo+qkZuM26u96hNYJY6h7rInRK9f1adkGVyXUSDECE93jgbdm5oJiD+6hwDczc0OX63rs0e3lmbbNkqQB4xxdaQSr3bCV6T+7idb60VQ1t0Im22bPoOtHzWhrp/6uR0sSo6Tyk5k3RcTzI+JtwP7AhcBNEXFSZm55ktslSSoZe3SlEeq8m3/NtY/cs1vZB+fO57UHHVOiiKTyN9J6dAeDbbMkaSA5dFnSbra3tvClu/7MtY/ew5jqWl570DG84ZDjSh2WVNZMdPvPtlmSNJBMdCVJ6icT3f6zbZYkDaTe2uaqUgQjSZIkSdJgMdGVJEmSJFUUE90SevCRVfzLGWfQ2NhY6lAkSZIkqWKY6JbATQ818brv3sSzLl7E1VVH8YGLv1vqkCRJkiSpYpjoDrEN25p5/y+WcffqTWzYsIG20RP4yeox/H35Q6UOTZIkSZIqgonuEFt8/zq2t7TR2LhmV1kSfPyyq0oYlSRJkiRVDhPdITaqulDlTU1NdGyvkJncfustpQxLkiRJkiqGie4Qm3/IVCaPqWXSpElEFLZ7qmpv5TXHH1ziyCRJklRqG3Zu4/I7/sLzF7yFR1avKnU40rAVlbxpe7luSn//uq1c+Ls7ueKaP1O9pZGGR/7O9b+4goaGhlKHJkl6Ar1tSq++K9e2WSoHf1n9AB/++y9Z8fBDrF+/ngMmTmXRW89jxtgJpQ5NKlu9tc326JbAwVPH8fV/OYH3H9jEtCVX8oYXn0xDQwMPNW3nH2s2097++AeAdVub+cpfVvDvV9/J5Tc/xPaWthJGLkmSpMHQnu18eskf2dq8gw0bNpDAQ5vW87lb/1Dq0KRhqabUAYxkCxYsYPny5bz1X9/Fv/18KTesWA/AUyaO5jP/fCTTx9fxlh/eyqrNOwFYdN9arrt3HZe+9uhdw54lSZI0/DXu2Mrq7ZtpXNNIR5dHAlffdiMXnvzKUoYmDUsjdujy7NmzB/3126tq2XzgieyYchBVzdsY//BNjN6wstt1TQc/hy37H79bWe2WtYxdvYyNh8zrdv0+S37S43P21vLlywfsWZJUyRy63H8OXZZ61tzWykt/901uvP1W2trbd5VPeGwjq7/44xJGJpU3hy6XwPojX8rmA06kpb6BnVMOZO3cV7Fz4lO6XbdjykHdylrq96FlXM9zdtvqnKchSZJUSUZV1/CuI56124Kl1a3tvGb6ESWOTBqeSjZ0OSLOATYD+wIXZebOTufOA1YC0zPzs8Wy/YEbKMT8H5n57aGPuu9aR09kx9QuKylHsOUpxxCtzWw85Dk0j59B7dZ1PT+gvZXR6+5n24wjdy/PpK5p4HpzJUmSVB5On3U0015Yw+sXfoi27TuY8lAT5//ywlKHJQ1LJenRjYjjgSMz8zLgTuDsTudOB9oy83KgJSI6JiW8GTgkM2eUe5IL0F5T12N5W+1Y1h79anZOPpCsqaN54kxa6qeRsfsfRf2jSxi7djnjHrkNOoZ4tbcy6d5F1OzYNNjhS5IkqQSee8jTeMfBxzNxyUO87iUvc1cOaS+Vqkf3NGBZ8fh24Fzgc53O/ajTuTdHxG+B5wFnR8QnM/PL/Q2gv/NSzzrrLBYvXtzr+dotjVRv30jbmIm7lVc3b6V50n67lUW2MWbVnVBdQ3t1HWPWLmfcY3cAMPne6xj/8M20jplC7ZbVVLds3+3eefPmcckll/TrvUiSJKl8dCxYumDBglKHIg1bpUp0p1PoyQXYDszscm5d53OZuR14XkQ0AD+LiGWZuajzAyNiIXB+1xdatGhR16IBsX79+ic8HyRTl13N+iNeQuu4qdDeSv2jd1CztZHt0w7rdn3tjo1MePDGHp9Vs2NTr72469evH7T3KEmSpKE3bdo0rrjiilKHIQ1rpUp0VwNji8cTgDV9OZeZjRHxAeBkYFHnB2bmQmBh57KIyPnz5w9c1J3syXMf3biDCaNrqK+rYeP2Fl7+rb+xrdN+uNUR/PB/3sKsKWOf4CmSJEmSpL4o1arLVwHHFY/nAldHxOTezgHE4xvHTqZLklvuZk4cTX1d4TuFiWNq+fwr5nBYQz0AB0waw4X/fIRJriRJkiQNkJLtoxsRHwYagf2BC4GbgJOArcAFwH0UhjFfCJwC/C/wE+CWzPx1H1+jrPfqa21rp6baHZ4kabhwH93+K/e2WZI0vPTWNpcs0R0KNqaSpIFkott/ts2SpIHUW9tsd6IkSZIkqaKY6EqSJEmSKoqJriRJkiSpopjoSpIkSZIqiomuJEmSJKmimOhKkiRJkiqKia4kSRpRHli3jZseamJna9tu5Ru2NfO3lRto3LJzt/KWtnZuebiJ5Y1bhjJMSVI/1JQ6AEmSpKHQ3NrOR359F3+6fx0AE+pq+ORLj+CkAyfzvZsf5ivXr6ClrZ3qCM48fn/OPnkWdzy2iX+/+k7WbWsG4Lj9JnLRy57GuDo/QklSObNHV5IkjQg/vO2RXUkuwKadrZx/zT+4e81mLv7T/bS0tQPQlsm3/76SWx5u4vxr7t6V5ALc8vBGLv3byiGPXZK0ZyIzSx3DoImIrOT3J0kaWhFBZkap4xhKEXEOsBnYF7goM3cWyycCnwVOBH6dmf/Rx+f12jbPnj17QGLuTeNRr2bn5AO6lY97+Da27ndMt/Kxjy5h28yjupXXbmlk+s3fG5QYOyxfvnxQny9JlaK3ttkeXUmS1KOIOB44MjMvA+4Ezu50egbwLuAk4HVDH92eq97ZwxzbTGq2re3x+ppt66G9tW/PkSSVFSeYSJKk3pwGLCse3w6cC3wOIDPvBoiIGcCnB/JFX9TYOJCP26Vp57VcN2kWbdW1u8pmrbyV4+5YxB+mHs7GCdN3lY/btoEX3LGIu1rgnkNP3lUe2c5zl/yOhnWDE+PvGhoG5bmSNNI4dFmSpD4aaUOXI+IbwHWZ+YOImA78X2Ye1un8fsDngUOAeZm5qcv9C4Hzuz73uuuu6/H1li5dCsCE1u69qANlTWsNf9tWz+b2ambX7eC40VupCtjeHvxtez2PtoxiWk0Lzxi7hfqqdjJhyY6x3LVzDGOq2jlhzBZm1rYMWnybagp9EHPmzBm015CkSnLKKaf02Dab6EqS1EcjMNG9AFiRmZdGxGzgW5n5nB6u+yZwTWZe2YdnPukc3cHq0R0OOnp0naMrSX3jHF1JkrSnrgKOKx7PBa6OiMk9XPeP4n+SJJUFE11JktSjzLwJWBkRb6OQ6H4B+FNE1EfEGyLi/4uIM4Hlmbm0pMFKktSJi1FJkqReZeaFXYo6Jo9+v/ifJEllxx5dSZIkSVJFMdGVJEmSJFUUE11JkiRJUkUx0ZUkSZIkVRQTXUmSJElSRTHRlSRJkiRVFBNdSZIkSVJFMdGVJEmSJFUUE11JkiRJUkUx0ZUkSSNKS00dW8dM7FbeHlVsHTuJtqrqEkQlSRpINaUOQJIkaSgkwZI5L+a+g06kvaqaCZsbOfGmK5m0aRUPz3watx31UnbU1TOqeTtzl/2Og1beUuqQJUl7yR5dSZI0Ijww6+ksP+SZtBd7bDeNb+CGZ7yerWMm8dfjX8OOunoAmkeN4eZjT6NpwvRShitJ6oeS9ehGxDnAZmBf4KLM3Nnp3HnASmB6Zn62WPYGoB6YCnw1MzcMfdSSJGmw/a6hYVCe23jw09lZW7tb2aaJ0/jVUc9ny6hR3a7//VNPZMKDNw5KLJKkwVWSHt2IOB44MjMvA+4Ezu507nSgLTMvB1oi4pUR8RTgNZn5deBXwMKhj1qSJA1nVW07ey5v3tZjebQ2D2Y4kqRBFJk59C8acQGwOjO/FBGzgG9n5inFc5cDP8rMX0bEfODNwJ+BIzLzgxFRBdyTmYf24XWyFO9PklSZIoLMjFLHMZyVsm2+5eEm3v2TO2jv9PrPmjWFT//TEZz+nZtYs+XxRHji6Fp+fObTmTy2e0+vJKl89NY2l2ro8nQKPbkA24GZXc6t63JuV1lmtkdEtzFNEbEQOL+H8gELWpIk9V85tc03AV/o5dyUdw9lJJKkgVSqRHc1MLZ4PAFY8yTnVgOzACJiFLCp6wMzcyHDcEhz8Zvt8mnxhyHrsP+sw/6zDvvPOhwZhsOfsb+L/Wcd9p912H/WYf8N5zos1arLVwHHFY/nAldHxOTezgG/Lh4DzCmWSZIkSZLUTUnm6AJExIeBRmB/4EIKo4dOArYCFwD3URiyfGFmZkS8g0JiPhP4Qmau6/HBw8xw/pakXFiH/Wcd9p912H/WocqFv4v9Zx32n3XYf9Zh/w3nOixZoquC4fzLUy6sw/6zDvvPOuw/61Dlwt/F/rMO+8867D/rsP+Gcx2WauiyHvdfpQ6gAliH/Wcd9p912H/WocqFv4v9Zx32n3XYf9Zh/w3bOrRHV5IkSZJUUezRlSRJkiRVFBNdSZIkSVJFMdEdJBFxTkS8JSL+IyLqIuLsiLg2Im6MiIbiNW+KiBsi4r6I+OdO974/IlZFxIMRMal076L09rYeI+IDEfFQRNxbrMuzSvtOSqdrHRbLIiJ+FRHziz/XR8SHI+KLne6ri4gLIuINEfHuEoVfFvpYhz3+fS6eOzkiri1B6GUjIo6JiOsjYlbx589ExO0RcVNEzC6WHRARH4+Id0bEizqVfTci7oqIBaV7B6oEts0Dw7a5/2yb+8+2uf8qvW020R0EEXE8cGRmXgbcCZwLXJOZLwTuAJ4REbXA5sx8JvBu4EvFe8cBozJzRmYemJlNJXkTZaA/9Qjclpn7Z+ahwDeBnw35GygDPdTh2cVTZ3a+LjO3ALcC4zsVnwMsy8zvA7Mj4jhGoL7U4RP8HhIR04DnA7VDFXM5yszbOo4j4mjgm5l5NPA74LziqS8DX83MrwPvi4ixQH1mngm8AHjrEIetCmLbPDBsm/vPtrn/bJsHRqW3zSa6g+M0YFnx+Hbg1My8LyKqgIeAazOzJTN/Xrzmr8AjxePnAm+MiFsiYu6QRl1+9roeM/MPnZ4zuVL2Xd4LXevw5RHxHAqNQmOXa5t7uHdp8XgJ8LLBCrLMPWkd9vZ7GBHVwOuBy4c04vLVDJCZt2fmPcWyvwKPRMRo4PjMfKxYvho4ITPvLP58ELBwKINVxbFtHhi2zf1n29x/ts0Dp2LbZhPdwTEd6PjHezsws/iX6r0UvnF6XpfrTwMuAMjM32TmHOBDwA8iYljuWzVA9roeO0TEM4C/DXKc5axrHe4HzMjMv+/FvTMHPrxhYU/rsPPv4ZnAdwCXt+/dc4EvAlOATZ3Kd/3ORcQRFOr0vcV/A6S9Yds8MGyb+8+2uf9smwdXRbTNJrqDYzWDW0QFAAAgAElEQVQwtng8AViTmW2Z+Tng1cCusewRsR9Qk5m/7fyAzPw98Cdg8tCEXJb6XY8U/mH7OSNX1zqcBSyIiEXAqcDnI+LQPt67ZvDCLGt9rsPOv4fFoY7nAFcBPwSOiYhPDHHsZS0i5gG/yMxVwFqgrtPpXb9zmXlXZp5SLD92aKNUBbFtHhi2zf1n29x/ts2DpJLaZhPdwXEV0DFnYi5wdadz91AYVkFETAeek5nfioiqiJhRHPrT4aHMXD8kEZenva7HYnlQmEPQ+ZuokaZrHZ6XmfMzcz7wW+DczLy3D/fOYff6H0n6VIddfw+B8Zl5XPG611OYm/b/ShB/WSrOK2vPzMURsU+xeEmxHgH2Ba7vctttwIohClGVx7Z5YNg2959tc//ZNg+CSmuba0odQCXKzJsi4vkR8TZgf+AbxW+Yfkqhy/+CiJhJYaL3joj4AIWhAc8EvhqFFQsXAT8qRfzlop/1CHAy8Jehj7x89FCHF/Z0XRRWKzyZwsIWT8nMRygs2vDxiKgHlmfmiBxm1pc6fJLfQwFRWL1xf2B+RDwAfBdoLH7o3ZKZ8yLi/RSGQD0KfCYzt0fEh4DDgMXArzNzbaneg4Y32+aBYdvcf7bN/WfbPDAqvW2OTIenS5IkSZIqh0OXJUmSJEkVxURXkiRJklRRTHQlSZIkSRXFRFeSJEmSVFFMdCVJkiRJFcVEV5IkSZJUUUx0JUmSJEkVxURXkiRJklRRTHQlSZIkSRXFRFeSJEmSVFFMdCVJkiRJFcVEV5IkSZJUUUx0JUmSJEkVxURXkiRJklRRTHQlSZIkSRXFRFeSJEmSVFFMdCVJkiRJFcVEV5IkSZJUUUx0JUmSJEkVxURXkiRJklRRTHQlSZIkSRXFRFeSJEmSVFFMdCVJkiRJFcVEV5IkSZJUUUx0JUmSJEkVxURXkiRJklRRTHQlSZIkSRXFRFeSJEmSVFFMdKUBFBFXRMT0fj5jVkTctpf3vjEivhQR9RHxvYiYVyy/NiK6/X2PiKMj4qL+xPsEsez2mhExOyJuH8Dn73U9SZI0WCJin4j40V7ee2pE/Lh4/J6IeOXARieNHCa60h6IiFdHRFtEvLRT2XnF5LIGeFNmru7hvgP6+hqZuQLYvJch3gDUZ+YW4AEgiuWnZmZ7D691O/Dve/laT+bUzGyPiKqI2C8zlwPbnuiGiDgqIv43Is6JiAsj4jW9XdvPepIkaY9FxLyI2BYRz+5S/paIWBkRR2TmWuCMvXyJRUBD8fgrmfmzXuLo8+cKaaQy0ZX2QGb+BLgaGNWpuAn4UGa2ZmZb13siYipw/h6+VLfn7MV9u457iqsv5/qj03P/DTi0eLyzt+sjYhJwBXB+Zn4xMz8MvC0invUELzMosUuS1JPMXAxcB3ygy6kXAPdn5l3F6/aqfcrMHZ2Ou31BDRARBwPn7s3zpZHERFfac18GzgaIiABGZea24lDaJcXyj0TEJRHxN2AOcHJEnBYRXyt+6zstIv5SvOfFEfHJiPhsRJzU24tGxCuKQ5N/GREHRMTEiFgYEe+IiM/1NDS5eN9REfHn4vFpEXFGRFwXEaMj4sMR8Z/Fc6+PiLMi4osRcWJEHBERfy4OnbohIl7Y6ZnVEfGjYq/rnIhYX/z//hHx7Y7XjIh6YB5wakTsV7z3dRHx+4h4e5dQ3wL8vdgb3eHHwIeK9727WHdfj4hDO9/Y17qQJGkA/BQ4OiIOAYiI+RR6Yin+/MaI+HJE7BcRt0bEGyLijxHxlt4eGBHPiIh/i4j/V/x5fER8v9iDPDUi/r3Yzr0TeBZwYkTML7aLH4mIr0bEwRHx2ihMo7ogIm6O4nSqiHhXRLwtIn5ebMNPLH4e+HZEvGLQakoqIT8MSnvu98D+EfFU4HnAH2HXUNpNxWv+DmwHnln89ndVZl4F3Fi8dg2wvHjtdOCbxXt2JZOdFXuF52Xm94AvAGOBDwPXZeY3iz+/qqd7M3NJpx9fCjwELKDQG3ozMCoiJgJnZuYlwKeBS4vfSo8pxnYB8JpOz2yj0Et9aGYuBa4HpgAHAJ/oeM1i0no78NvMfLh4+2+A9wKndwn1MGBVl7KHgdkRcRhwQmZeBlwGfL7LdX2qC0mSBkAb8BUe71U9Gfhzp/M3AuOK7d5W4FfAe3ji4cyfBr4IXASQmZt5fArS04ADgU8Un/1n4N7MXARMBr4ErCjGcQswNjM/CvwBeH4Upls1Zea3gF8DtcAHgfUUPsM8be+qQSpvJrrSHsrMBL5KIVk8ujjPtUNr8f9twLo+Dl36A4WkbypQ3cs1hwJZfP3fZeY/gGN5fI7q7TxxQ9UR10XF/xYCNZ3KD+84zsxHgH2KvdWbM3Mnhbm1nYdrU4xhRnEI1c3AmRQS3/u6vCZd7ttEoeEf3eVUI4/PS+owBXgEOAbo6Ont6b3uSV1IktRf3wROj4inA/d2Ode5/WvNzCZ6aEc7RMQ+wOQs6LyWRcdzrgfagWVAXZfbrwHeTqFNrS7e01Q81/Gac4vHZOY3isOjj8zM32bm5cB/9+0tS8OLia60dy4DXk0hOeuLKA6nbabQ4wiFJK4K+ByF+T53P8H9DwD/HBETImJSRJxMocE7vnh+HIVvcZ/MeOBECg3eSzqV3wscGYWFo6qAu4sJ/ZP5GfC/wMcpJKPNPVyTPP7+O4suP/8AmBeFRb06vIDClwp38sTvdW/qQpKkvZKZGylMr/kahaHMfVacbtO5DdwIzCyO3oLun8/nAu+jMG3q/7F7u3oZ8D0KI6B6s5zCl9FExHERsS+QEdEx+umlvd4pDWM1T36JpK4yc1NEfI9OjVtEzAD2i4gTgJMozJ+ZWGwMk8Ic1N8CvyjO69lavG4JheHIPwOeUZxPs39EPDMzbyi+3pqI+BpwG4Wh02dTSIy/UGwsA/glheT7qRExk8Lc4J0RsRI4pPia76Mw7GkVhaFPZ1Lo/dwGfIpCb++DwAeL82APjYjZwNOLz5icmRs6VcUPgOrMbIuInxZj61goo+M1lwDnUEiC94+IZwD7AU+JiBmZuar4Hu+KiA8Cn42Iq4rx39ax4mRE/CYi/ovCkPB/71xPFIZ8da0LSZIGVEScSOGL52uAiylMB4LC1KODI2Iuhak4hxYTygO6tHv7UviC+1MU2kcysyUiPkzh88H3gQkRcSCF9nkrhUT1/RR6di8H1lAYifUyCp8Lvkdh+tMhFBZ9PCQKqzIfTmFo8/uB10TEMuAbmXlxRLwH+G5EvA948yBVl1RS0bdOG0kaGhExHvguUA+8qI89y5IkSdIuJrqSyk6xZ/aVFL4B/01xD15JkiSpT0x0JUmSJEkVpWzn6BYn5N+amQdEYf/OuRSWQ/9hZj5Y2ugkSap8xa3HLgWOBu4Aziiu2Npx/jxgJTA9Mz9bmiglSequLFddLq4i96bi8Rjgo5l5EYWl3LvunylJkgbHyRQWqjmSwhZor+84ERGnA23F7UlaIuKVpQlRkqTuyjLRpbAS7Hcp7Bn2LAqry5GZ64G5EdHbXqOSJGmAZOavM3NrZrZQ2LbrkU6nTwOWFo9vB14+1PFJktSbshu6HBHzgNszc31xi7HpwLpOl7QA04DHuty3EDh/iMKUJI1Qmdl1D+iKFxG1wCSKW4gVdW6ftwMze7hvIbbNkqRB1lPbXHaJLoX9QacXk9wZFOYGXdnp/FhgQ9ebMnMhsLBzWUS4M4kkacAU26aR6C3Af3ZpVFdTaJMBJlAcfdWZbbMkabD11jaX3dDlzHxdZs7PzPnAKgobXR8AuxaoWtp5IQxJkjR4IuI04PeZ+VhEzIyIycVTVwHHFY/nAleXJEBJknpQ1tsLRcSKzJwVES8HDgXqgcsz84E+3u+3xpKkARMRI2rockR8EHgPsBaoBv4AnAqcBGwFLgDuozCM+cK+NLq2zZKkgdRb21zWiW5/2ZhKkgbSSEt0B4NtsyRpIPXWNpfd0GVJkiRJkvrDRFeSJEmSVFFMdCVJkiRJFcVEV5IkSZJUUUx0JUmSJEkVxURXkiRJklRRTHQlSZKK1qxZwxlnnEFjY2OpQ5Ek9YOJriRJGvHa2pNr72nkzM//mMUrt/KFL32l1CFJkvohKnnTdjellyQNpN42pVfflWPbnJmce9UyFi9fwz333E1mMmbLapZ+/mxmzphe6vAkSU+gt7bZHl1JkjSi/XVlE9evWE9j45pdZTvGz+BDX/p+CaOSJPWHia4kSRrR7l+3FYCmpiY6epszk8W3/aOUYUmS+sFEV5IkjWhzZkwAYNKkSUQURr9FBC849qmlDEuS1A8mupIkaUQ7auYEXjFnBg0N03aVjVt/H58+58wSRiVJ6g8TXUmSNOKd94Kn8v03ncBLp+1k2m0/ZsHceqZPm/bkN0qSypKrLkuS1Eeuutx/5d42r1mzhnPPPZeLL76YhoaGUocjSXoSvbXNJrqSJPWRiW7/2TZLkgaS2wtJkiRJkkYEE11JkiRJUkUx0ZUkSZIkVRQTXUmSJElSRTHRlSRJkiRVFBNdSVJJZVs7K2+6n4+9/T9YteLRUocjSZIqgNsLSZJKpnVbCw/+7C5WLLuf9evXM3nKFE4489lMPHyfUofWI7cX6j/bZknSQHJ7IUlS2Vl3y2NsXbOFDRs2kAlNGzbw0B/upb2lrdShSZKkYcxEV5JUMttXbaGxcQ0d/XsJrH5kFc1NO0oZliRJGuZMdCVJJVM3dQxNTU1keyHVzfZk7fq11E6oK3FkkiRpODPRlSSVzNTj9mXspHqiqjC1JqqCzQ3NVNfVlDgySZI0nLkYlSSppFatfIyPveM/qKsaxf2bVnLZzy+noaGh1GH1yMWo+s+2WZI0kFyMSpJUlmYcsC/7nnQg19y3iBNeeFLZJrmSJGn4KMtENyJqI+LzEfG3iLg8CsZFxN0RsSoiPlbqGCVJA2fBggUcf/zxLFiwoNShSJKkClCWQ5cjYhawBtgBLAOeAbwZ+FFmrtmD5zg8SpI0YBy63H+2zZKkgTSshi5n5orM3AZMAr4NbAbmAbdHxCciwg8ZkiRJkqQele2ylhExBfhv4IXADzPz9IioB75DoXf3si7XLwTO7/qcRYsWDXaokiRVrIg4BvgKcEZmruhUPg74CPA34NnApzJzY0mClCSpi7IcutxZRJwH7MzMzxZ/ng6cl5n/1od7HR4lSRowI3XockRcT/dE9xXAgZl5cUQsAB7JzJ/34Vm2zZKkATOshi53cQ9wZ0R0xLoP8IcSxiNJ0kjT3EPZX4G3RMShQD1wzdCGJElS78py6HJEnAJ8ELgS2AncByyJiJ8DSzPzh6WMT5KkkS4zH4uIj1JYS+M7mbm96zVOK5IklUrZD13uD4dHSZIG0ggeurwIeEuXoctHA8cBP6LQm/vRzFzUh2eVtG1et7WZL/75Af66cgMzxtfx1hMO4LmHTC1ZPJKk/hnOQ5clSVKZiIjJxcPnAeuKuyRcBBxduqj67r0/X8qv7lrN2q3NLF21mX//5Z3c/qhraElSpbFHV5KkPhqJPboRMRv4LXBBZl4WEUuBk4A6CtOM/o9Ckvu1zGzqw/N6bZtnz549YHH3pHn8dNYcd0a38rGr7mTy8t+zdcYcmsdPp3brWsY9tpSqtp6mJg+N5cuXl+y1JWk46a1tLss5upIkqTxk5nLgkE4/zykebgH+o3j8m6GOa29kVc8fe7KqmsajTqd54sxdZdumH8m0W39AtLcNVXiSpAHk0GVJkjQijNr4KNU7NnUrr96xabckF6ClvoFtDYcNVWiSpAFmoitJkkaEINnnjp8zatNjAFS1bGfifYupbt7W4/WtYyYNZXiSpAHk0GVJklRWXtTYOIhPb4QH/0FLTR3Vbc1UZbJh4r784cBndbvyOSuWMn1QY+nudw0NQ/p6klSp7NGVJEkjTm3rTqqKi2JN3vgYh9/zp93OH7TiJqY33leK0CRJA8AeXUmSNOLNuev3zFp5CxsmzWTiptVM2Dy0PbmSpIFloitJkgTUb11P/db1pQ5DkjQAHLosSZIkSaooJrqSJEmSpIpioitJkiRJqigmupIkSZKkimKiK0mSJEmqKCa6kiRJkqSKYqIrSZIkSaooJrqSJElAEuyoG0d7RKlDkST1U02pA5AkSSq1R2ccxm1H/RPbxkxk9I4tzF12DQc+vKTUYUmS9pI9upIkaUTbNnoCN57wOraNmQjAjtH13HTcq9g0vqHEkUmS9pY9upIkqaz8rmFoE8wtM4+mqW509zgOO4kJD944pLFIkgaGPbqSJGlEi7aWPSqXJJW/yMxSxzBoIiIr+f1JkoZWRJCZrlTUD+XYNm9rbuVVl93Eum3Nu8rG19Xw4zOPZ+q4USWMTJL0ZHprm010JUnqIxPd/ivXtnnlhm185foHuWv1Zg7ZZxzveuaBPLWhvtRhSZKehImuJEn9ZKLbf7bNkqSB1Fvb7BxdSZIkSVJFMdGVJEmSJFUUE11JkiRJUkUx0ZUkSZIkVRQTXUmSJElSRakpdQA9iYha4DPAs4C7gTOBFwBzgVrgh5n5YOkilCRJkiSVq7LcXigiZgFrgB3AMuAk4OrMfG5ETAEuzcxX9uE5bmEgSRowbi/Uf7bNkqSBNKy2F8rMFZm5DZgEfBs4nkLiS2auB+ZGRHUJQ5QkSZIklamyHLoMUOy5/W/ghcBCYF2n0y3ANOCxTtcvBM7v+pxFixYNYpSSJEmSpHJTlkOXO4uI84BWYE5mvqlY9iBwWGbueJJ7HR4lSRowDl3uP9tmSdJAGlZDl7u4B7gDOAAgIqYCS58syZUkSf0XEcdExPXF9TN6On9GRPxzRBw6tJFJktS7shy6HBGnAB8ErgR2ZuavI6ImIt4P1APvKWmAkiSNEJl5W0TPndgR8SngF5l549BGJUnSEyvLRDczrwOu61L2ixKFI0nSSNfctSAiTqSwjsaDEfEG4GOZuWHII5MkqQdlmehKkqSy9yrgssz8ekR8FPgs8PbOF7hQpCSpVEx0JUnS3hgDbCoeXwV8s+sFmbmQws4Ju0REzp8/f5BDkySNdMNhMSpJklQmImJy8XAx8PTicS3wt9JEJElSdya6kiSpVxExG9gfmF8s+lNE1GfmT4GW4vzcecBHSxSiJEndlP0+uv3hXn2SpIHkPrr9Z9ssSRpIw3kfXUmSJEmS+sxEV5IkSZJUUUx0JUmSJEkVxURXkiRJklRRTHQlSZIkSRXFRFeSJEmSVFFMdCVJkiRJFcVEV5IkSZJUUUx0JUmSJEkVxURXkiRJklRRTHQlSZIkSRXFRFeSJEmSVFFMdCVJkiRJFcVEV5IkSZJUUUx0JUmSJEkVxURXkiRJklRRTHQlSZIkSRXFRFeSJEmSVFFMdCVJkiRJFcVEV5IkSZJUUUx0JUmSJEkVxURXkiRJklRRTHQlSZIkSRWlptQBSJIkSYL2bOenD97BHx+9l3E1o3jNQUdzYsMBpQ5LGpbKrkc3IiZGxJURsTwifhoRoyNiXETcHRGrIuJjpY5RkiRVns07Wlm0dAWnv+HNNDY2ljocjUAXL/sT/7PkOm5a+xCLV93HOTf+lD+tur/UYUnDUmRmqWPYTUS8FFgMNAO/B74NjAd+lJlr9vBZWW7vT5I0fEUEmRmljmM4K9e2+ce3P8oX/nQ/9698mA1rG3nhvnDVJ88pdVgaps466ywWL168R/e011Tz2BnPIqt374eqW9VEw29u3+MY5s2bxyWXXLLH90nDTW9tc9n16GbmrzNza2a2ALcAjwDzgNsj4hMR4QcMSZI0YFZu2MZnrruXzdub2bBhA1lVzbVralh854OlDk0jSI6q7pbkArSPri1BNNLwV7ZzdCOiFpgE/D4zr42IeuA7wJuBy3q4fiFwftfyRYsWDWqckiRpePvLig0k0Ni4+8Cxz3z/auZ98j2lCUrD2t72pL5h8fdZvrGRO5YuBWDunDm86dCnc87/POf/Z+++4+Mq73yPf54zVaPe3btsY2xMMc0UGwglgSyBtBtSLklMdrOEODebskmW4N1ks5vdJBtu7rJZQrIkuyHJphDKQkK1IZhqg7ENxnLvlmX1MvU89w8VayQZW7I0Z0b6vl8vXmieOWf0layZM7952kjGExkXsrbQBW4Gvtozvsla22aM+UvgawxS6FprVwGr+rYZY+zy5ctHOaaIiIjksor8IABNTU30DKu21rL5lRcAFbqSOd84+xq+9PLDbOy+fXH1TFbMvcDTTCK5KuuGLgMYY66nqyf3oDFmkjGmJ2cF8KSH0URERGSMWTarnJllEUpKSuiZIeWPt/GhC+Z6nEzGm1mF5fz6so9R/ftXmPDrF/ne+deT59fQZZHhyLoeXWPMF+j6+LTeGOMDdgE1xpjfA5ustb/0Mp+IiIiMLUG/w93vX8y/VQX5zk9+hdNymIojm/g/333I62gyDhljCDS2ex1DJOdlXaFrrf0O8B2vc4iIiMj4UZIX4CvXnEH0+d/yi188wwdvuonKykqvY8k4tLX5CC1nTcfEUzTEOigLRbyOJJKTsnLosoiIiGQHY8yZxpi1xpgZx7m/xhgz9L1PstStt97KkiVLuPXWW72OIuPQg3s289Fnfk7LmTNoPm82H3jqp2xvqfc6lkhOUqErIiIix2Wtfe149xlj8oArgeLMJRpdVVVV3HffferNlYxLuCn+3xt/ou820y2JGHe/9YJ3oURymApdEREROZH4cdo/Afw4k0FExqqGWAdN8c4B7dvUoysyLFk3R1dERESynzHmRuBha22sZ6XiQY5Zhfa4FzkprrX4OmI0pY59rtTa2kpBKk/PGZFhMLbv+Igxxhhjx/LPJyIimWWMwVo7eFWXxYwxO4HjXRANYK21s97m/NXAzdbaXX3a1vR5zAuA/7LWrjiJLLo2ixzH0we38bV1j/Dq668DcPmSC7j7ovczOX/MzA4QGXHHuzar0BURETlJOVzo3mqt/ddTuH813YWuMabUWtvY7/5d1toZJ5lF12aRt1HX2criD1yHk0iy7eHV2kdX5ASOd23WHF0REZGx783h3m+MqQGmAsu7m541xhSMUC4R6acqr5CCLQeIbK9TkStyCtSjKyIicpJytUe3L2NMNRDqvnmJtfbnGf7+ujZLVqupqfHse1ugY95EOmdUYhJJ8t88QPhgk2d5amtrPfveIifreNdmLUYlIiIyThhj7gcmAh10zc2dDGS00BWR42s+bzZtp0/pvR2dVkH5E5sI72vwMJVIblKhKyIiMn7UW2tv6LlhjJntZRiRbPb4ZcmMfr9Ox8dHF0wkYdJHPCy8ejJ/v6Muo1mufFolguQ+zdEVEREZP57qN792ynGPFJGManf8JMzAt+bN/qAHaURynwpdERGR8eNSYIcxZkf3lkMPeB1IRLpUJGPMjLYOaF/SUu9BGpHcp0JXRERk/Ki21lZZa2dZa2cCl3gdSESO+fyeN5gU6+i9vaS1ng/W7fIukEgO0wB8ERGR8eN3xpiAtTbRfTuzkxBF5G1Ni7Vz19YX2BUuIM9NMiEe9TqSSM5SoSsiIjJ+rAT+yRgTpWvV5RKg1NtIItKXAWZG27yOIZLzVOiKiIiMH38HPGKtTQEYYy7yOI+InCQXOBoIUZKME9Be1CInpEJXRERkjDPGXGatfdpa+1Dfdmvtc33v9yadiJzIC0UV3D1pHkcDIYqSCT52aBtXNh70OpZIVtNiVCIiImPfaad4v4iMgk7Hx4n6Zuv9Ib4zbSFHAyEAWvwB/nXKfHaEC05wpsj4ph5dERGRse+LxpgvHOc+A1jgrgzmERnXNkeK+eHkeewJF1CRiPLRQ9tZ3nQYgJ3hAl4oqiTiJlnWdIhXisoH7K9rMawtrmKW5vKKHJexY3iMvzHGjuWfT0REMssYg7XWeJ0jl+naLNmupqZmVB/fDfo59IHzcQPH+puMtVQ+uJ5EeQFNF83Fmq6XGSeWoGDjXlqWzBrwOMUv76Bw095RzVpbWzuqjy8yEo53bVaProiIiIhIhkSnlqUVuQDWGDpmV9E5p7q3yAVwQwHilUX42qOk8sO97U48QWT7oYxlFslFKnRFRERERPp5/LLR2Wb6ueIk/1Q4cFTDtXMt/1MegH6zdifV5LFq5zp+EZrJ1kgx06Jt/K+Gncy4oHNU8gFc+bRKBMl9+isWEREREcmQJS31lCdivYtLAQSsy7X1+3ihqCqtHaCms4XqRJTP7Xsz01FFcppWXRYRERknjDHXG2P+3nT5kDFmqteZRMabkHX55o71nN9yhJJknNPbm1i18zUmxztZcWArfuv2HlueiHHT4R0ephXJXVqMSkRE5CTl+mJUxpjfAquttT8wxjjAI9baazKcQddmyWqjvRjViaQiQTqnV+DEk4R31+Mk3ROfNEq0GJXkAi1GJSIiIv8N9EzsqwLO8TCLiAzC1xGn4M0DXscQyXkqdEVERMaPGHCOMaYc+BLwnx7nEck62dCL2dOrnA1ZclGy+TBtG/8Ibor8hVcRKJvsdSTxgIYui4iInKRcH7oMYIy5CjgD2GKtfdiD769rs8gJqNAdvs7dr3LoP2/DJqIAGF+A6g99l0jNUo+TyWjR0GUREZFxzhhzHtBprf2OMebdxph8a22717lERPpbsWIFa9asGfJ5t80+zIz8eFrb4y9ezT9vnTDkx1q2bBn33HPPkM+T7JB1qy4bY4qNMb8xxtQaY35njAkbY640xnzeGPNlY8x0rzOKiIjkqL8FUt1fPwH80MMsIiIjbmJeYkDbhHACg0aSjDdZN3TZGPMuYA0Qp+si/EvgQ9baS40xZcCPrbU3nORjaXiUiIiMmFwfumyM+YC19r+7v/YDW621szKcQddmkRPQ0OXhO/Aff0501zo2bdoEwMKFCwlOnMeUv/i5x8lktBzv2px1PbrW2keste3W2gSwHtgB1HXf1wAsMsb4vMwoIiKSoyZ27597PfAI8HXBsjIAACAASURBVILXgURERlL5VStxwoW9t00wj/Jr/srDROKVrJ2ja4wJACVABXC0z10JurZEONjv+FXAHf0fZ/Xq1aOWUUREJMf8X+ATwEXAA8Dd3sYRkf5eqd9L04U1mESSPW2NTCso9TpSTglNXsDUzz3A7cvn4Ri49qcP4Cso8zqWeCDrhi73MMbcAjwMLAButtZ+tLt9NzDPWhs9icfQ8CgRERkxuT50GcAYUw2Eum9eYq3N6Hg+XZtFju++7ev5/uZn2Ng97PbcxWfyb0vfx+mlQ19IabzT8O/xI6dWXe4eUvWEtfagMcYA07rby4FNJ1PkioiISDpjzAPAUqAVMEARoIlrIiNsOCsGW8dw8EMX4gYDvW0vb3iNix9+koonNg05w3hdMdhaS8vz9/HFuYdwjKXxmZ9QcvHNGCfrZmzKKMu6QtcY8wXgM0B991zcJ4HvGmM+DxR03yciIiJDF7PWVvbcMMac5mUYETkmlRdMK3J7JIvzPEiTu5qfvZeGJ/+VCeGu1Zcbn7wLG2un7MrbPE4mmZa1Q5dHgoZH5b5URx1tW35JsmUnwYpF5M/7IE4gH5tK0L7td8QOvogvUkX+vA8SKJ7pdVwRGeNyfeiyMeZGukZMtXTfXmqtXZvhDLo2iwzCtS43PHkvBzta0tqvn7aQr535Do9S5Z4937uWZPPhtFWXnXABM76y2ttgMmpyauiyCIAba+bIYytIddQBED3wPJ37nqHy6p/QuPbrdO59uvfYjt2PUXnVTwgUz/AorYhITvgeYIwxKbqGLpfTNXxZRDzmGIevLX4HX3r5ITqSXb2R0wtK+fP5F3qcLLe4iYEzHG0ihnVdDV8eZ1ToimdONH/l8rlt3Li4tV/rJn7xvcf40JKWAcc/+5vz+NX64iHnGK9zWERkXHqPtfa1nhvGmEu9DCMi6c6rnMbDV67g+brdFASCnF85DceoOBuKgoVX0fLSr9Pa8k9/h4rccUiFrmSt0jx30PaJxclB20vyUqMZR0RkLGgwxnwY8NHVo/tO4Jm3O8EYcyZwF3CTtXZXn/bp3e2nA38APq0xySKnriAQ4srJc72OkbPKrvwsbqwdd2PX0OX8BZdT/q4veZxKvKA5upK1ogdf5OjTK9MbjUPlO3/O0Sc+hRtvTZt/UbLkC+TPfZ8HSUVkvBgDc3R/AxwBkkAjELHWfuEkzlvLwEL3BuAhIAysBz5lrV19Eo+la7OIjLrT580GYPNb2z1OIqPteNdm9eFL1gpPPJ+C0z4C3UN2jD9MyblfJFgyk9ILV+EEj00ry5t+JZE5N3gVVUQkV/zaWvtp4EVr7dc5+ZFd8f4N1tr7rbVJa20bsBnYP4I5RUROSdx1iLsqdcYzDV2WrFZ81mcomPdBkq17CZTMwQl1FbfhyRcx4T0P8Ynv1tDU6eP5b33D46QiIjnhWmPMRKDJGPMIXcOOP3cqD2iMKQX2W2trB7lvFXBH//bVq1efyrcUETlper0ZvzR0WXJaTU0NALW1A95fiYiMuDEwdLkIKLTW7jfGXAnsttZuPYnzVgM39x263Oe+zwN3d/fsnkwGXZtFZNTpPeL4oaHLIiIi8h5r7X4Aa+3jwIeG+gDdPbg9X38U+Km1ts0YM2nkYoqIiJwaFboiIiJjnDFmVvdQ5e8aY3YYY3YaY3YBl5/EuTXAVGB5d9OzxpgCY8wPgNuBPxpjXgc+MDrpRUREhk5zdEVERMY4a+2O7t7Xs6y1Twzx3Fpgdp/bC7u/vG0EI4qIiIwo9eiKiIiMA9bao0CZMWaxMWaRMeZ+Y8w7vc4lIiIyGlToioiIjB9XABuB/wAeBs72No6IiMjoUKErIiIyfjwDXAW0WWt/DLR7nEdERGRUqNAVEREZPwLAZ4HbjDEXAzd4nEdERGRUaDEqERHxRCqe4sjavbRsb8QX9lG2eAKlC6u8jjWmWWvvBe7tuW2MebdnYUREREaRCl0REfHEgce207arCYBUZ4JDq3fh+B2K51d4nGxsMcZ8wVr7ne6vv0r6tX8R8H5PgomIiIwiFboiInLKVqxYwZo1a076+IJgPl+5+DbApLU/9Oyj/Gj9fw0rw7Jly7jnnnuGde4Y19Dn6wpgM5Dsvl2Z+TgiciJ1dXV87nOf484776SyUk9TkeHQHF0REck4n3HoX+QC+B1f5sOMcdban/S5+TVr7Y+ttT+11v4U+CuvconI4B7b/xY33vcvPBZqYdXdP/A6jkjOMtZarzOMGmOMHcs/n0BNTQ0AtbW1HicRkaHa/bs36TjQCsDGjZsAeMet11J2RrWXsd6WMQZr7cAKPQcYYy4BvgTMALYA37DWvu5BDl2bc1yy/SBtb/6cZPMughULKZh/E06oCADrJkk01eLLq8KXV+5x0tzzwy1r+fc3nmPrW1txrcUB7n//Z7mq5gyvo+UcvUccP453bdbQZRER8cTkq2dz8KldtO1pJpaK8fzedbx/0ce9jjUmGWOuAH5D10JUjwKzgSeMMddZa1/yMpvkllS0ifo/riAVPQpA7PArRPf/icprfkrs8Doan1+FG20A45A/5z0UL/kCxmgA4cmIJhP8YserHKk7Qs9HQdYY/ubR+3Ky0O0pNL3mdQ4V2t5RoSsiIp7w5weZ+u65uEmX60/7MBaLMTnZWZoLbgMusta+0dNgjLkL+Dvgw56lkqxzovn2V8xt44bFrf1aN3HPP0/npnOaiQT79NZvfJ3/+uqdvLArMuQc43HOfUsiSmcyQVNTEz2jHqy11B4+4HEykdykQldERDzl+B0sGso6yjb2LXIBrLXbjTFvHO8EkcEUht1B2+dWxtOL3G4LJ8aGVeiOR1V5hcwoLONASQkNjY1Y2/Xh37nlU7yOdkoe/0HyxAeNQVfepjLLa/oXEBERGfsOH6e9LqMpJOudqBc1dngd9U/emt5oHJbd+h2Orvk8AJs2dc25X7hwIeffcCNf/tmXRiXrWLTqrKv4bHsrzzc2YoG8+lZ++Fd3eB1LJCdp0oSIiMjYd5Ex5l39/wOWeh1Mckuo+hwKF34S4wQAMIEIJed9hfDkpYQnXpB2rPGHyZ/7Pi9i5qwFJRP4w7v+gg+71VQ/uI7bCucza1Ju9+h6wVpL6miM1NEY1tWIofFKPboiIiJj38XAhEHaZ2U6iOS+ojNuIX/u+0i17sVfMgcn0DU0ufSSf6R9yy/Y9vRf0dDh4/Iv3k2gWH9iQ+V3fHzzls/StnkHt95664lPkDRuR5LEugZsR/eQ6bCP4DllOAUBb4NJxqnQFRERGfs+YK19vn+jMeZsL8JI7vOFS/GFS9PaHH+YwoUf5/urvwXA7aVzvYg2JlRVVXHfffd5HSMnJbe0HCtyAaIpkm80EzyvwrtQ4gkNXRYRERnjBityu9vXZzqLiMhochtiA9sa42j/7vFHha6IiIiIiIwJJs83sDHs0/Z145AKXclK1rok2/bjJjoG3JfqrCcVbfAglYiMlrA/REm4aEC7TbnEm6O4ycG3NBEREenLP7vwpNpk7MvKObrGmDOBu4CbrLW7jDH5wHqgGLjLWvt3ngaUURWre5WmF75Bsu0Axp9HwWkfoWjRJ0lFG2lcewexQy+BMYQnXUw44BJN6PMakVxlXcvhZ3fz1UtW4jM+dvxiE5OvmkWoPELzW/Uc/tNeUp0JfGE/VRdNo+Q0zbESEZHj803Iw4R8pPZ3dZb4JuXhlIU8TiVeyMpC11r7Wr/hBZ8ALrHWar+/Mc5NRml45su48RYAbLKT1o0/IlAyh85dj3YVuQDWEt3/LO9d3MLPXynxMLGInIrGjYdp3FiHz3QNNYsd7WDfo9uYcm0NB57YCd1zqlLRJAef2kledT6hsjwvI4uISJZzSoM4pUGvY4jHsrLQ7RYHMF0V7zLgq8aYHwO3W80mzwo1NTUj/pinT4zy6YsbB7S//D/v5ZxpUZx+0ysWTzb8/JXRyTIUtbW1nn5/kVPl1XPolrM/zIySab23N27cBMAP7vs3lk49d8Dx373/B6zZPei6SiNGz2cRkdzldiRxD0fBgG9iV++ujE/ZXOgC0F3Uvs8YUwD8FPjfwL39jzPGrALu6N++evXq0Q04jq1cuXLEH7PQ7qcq9asB7WdPOJMKuwW/jaa17z7cyYwZM7j++utHPMtQ6O9Mct1oPJ9PxvSmKoriEZqamgAoKekaoXHhrAupbi8bcPzlc67gzLzzRjWTns8iIrkpVRcl8Vpj72ig5LZWgueW4xSrd3c8MtnaOWqMWQ3cbK3d1aetGviatfazJ/kY6vwdRT09QL+KDVzGffgskWua8ZX12f/MNbQ/Wox/WpzQovTFqaLr80ls8W4Y4wdDXXM+1AMkua7n+bx27bsy+n1jB0tofGoRcGy4RmhqPcUXvkX9g+fiRo+9OXHCcSquewUnlBzkkU7d0qWPAG//fDbGYK3V0p2nQNfmscFai01FcfwDr8E9ryd9n0vJtgNE963BBArIm3Y5TiA/Y1nFGz1/B4//YHReswcTe7YufQ9dwCkLETy3PGMZelx5W1d/ot4jjr7jXZuzvkcXwBjjWGtdoAJ40us8MpoMHU8VEVrUiX9CArfNIbY5D7fZT3yjDxszBGbFwDUktodIbA97HVhETkFoYhOll22i/c0puNEAockNFCzcg/G7lF31Gm2vTyfZWIC/tI2CRXtGrcgVkZPXuXcNLa/eSbLtAP7iGZSc81eEJnRNNbCpONPL4jR3Hhsu2rnnSRqf+zrWpgBo3fBDKt7xb/iLpg36+CLDYZPugCIXwG1NeJBGskFWFrrGmBpgKrDcGPM88FtjzO+BTdbaX3qbTkZd3CG2Lp+B/cSGxNY8Elu1EI3IWBKa1Eho0sC5+f7CKCUXveVBIhE5nkTLbhr/9NXeojXZvIuja75A9fX3k2jYSuMLf8sXrziKa6HppX+k6Jy/onndv/QeD5CKHqVl448ou+gbXv0YMgYZv4OJ+Af26BYFsLEUNprCFPgxPu3WMV5kZaFrra0FZvdpWuhVFskVlsCcGP5pcWzckHgrTOpIwOtQIiIiGTXaC8tdc1or1y1sG9B+x08X8q4FrUSCXcPSHQMv/v6b/P7v7uQ9Z7QOOL7u+Tf5u5tHt+9CQ0azQ88Q3tFUGUowPRIn4ivk2gnN+J2uv8POlMMbG3ycub4On+m6/Zt9pbzWHBn1TOK9rCx0RYYqdE4HwXmdvbcD0+J0PFVI6pAWHxARERkpSXfwKeoV+cneIrevmWVxWqMOhWE3rX1fkz6MlpHxzgnNXFHV0rvSwxstYba1hUhYh86k4SPTG3qPzfO5fGjaUba9GaItqdWYxzoVupL7Ai7Bmmi/RkvwtCidKnRFRGQcGtmFIo8xtQ758y3Gf6yotXGH6u0+8me7A46f0mZJ7c4j78I2wB47/rUgV49Sxp6FIiU7jOZiVG5rgvja5rS2uXQSWBzGNyFMYnMTqX0DP4D5zXXt+CaNbq9uJnqy5e3pX0BynglacAa+iDl5Ay+4IiIiMny200fHU0WEF3fglKRINfiJvRrBbfKTPBDEPyl+7NikIbE1jNvip/2oD//UODZhSO4OYWOaJymnzm2KH7fdN+Ft9tDV3rrjggpdyXm23Yfb4sMpSqW1J/drWJSIiMhIc+sDdDxZPKC989lCgvM68U9M4Lb7iG/pKnIB3BY/8c162zkejWbP5uz8EH85e+Bw+l+vDfFak0NxoIBbZ3eQ7z/W+bG3I8j3H47Qd1s7GZv0iiNjQuefCslb1oqT370K5IEgsc1aaEBERCRjUob4GxHib3gdRMaL7e1htrSGmV94bApbXczPvIJO3ju5EWNgZ3uQhlY/pYEUO9pDrD5SiIrc8UGFrowJbpOf9gdKcMpS2LjBtmlIiojISDDGnAncBdxkrd3V776vAXuAamvtdzyIJyJZKlOrXttUkraNfyS293UCVbOIHXiTttceZtOmTQD82flzyV94FdXv/1ZG8kj20AQJyUlOWRL/9Bgmr+9wZYPb4FeRK5JD3LiP6J4KYgdLsAOn2ksWsNa+Nli7MeZ9QMpa+59AwhhzQ2aTiYiA8fkpPPNaKt79FYrP/yAdbz0z4JiON5/Culq7ZbxRj67kFmPJu7QV/+SexQcM0VciJLbmeRpLRIYudqCUpmcXYLu3ePCXtFF2xUaccMLjZDKIwVZ8uR74VffXG4D/DdyfsUQiIoNwghHczpa0NhPIwzjq3xtvVOhKTgnMivUpcgEs4XM6SO4JYaN6ARPJFdZC8wtze4tcgGRTAW0bp1F07nYPk8kQVANHu7/uBCb1P8AYswq4o3/76tWrRzPXuLZy5UoADo3jIRIrTdf8S/2djU/hokXk7Ts2bLq1tZXohIvZpb+HcUeFruQUX/UgPT3G4qtMkNyrffNEckWqNQ+3c+BzNl43cCVXyVqHgZ5V/4qAuv4HWGtXAav6thlj7PLly0c52vh1yy23AKO3j24uWNm9j26m5ohKllm+nOYXz2Lt334Sn4FLb/waxUs/oh7dcUj/4pJT3NbB598er11EspOTF8f4UwPafX1WzpTsZIwp7f7yAeDs7q8XAQ95k0hEJF3x+R/ge7UT+OetEyi5+GMqcscp/atLTknUhrEd6UVtYk8It0mDE0RyiRNIkX/6nrQ2409RsHDPcc4QrxhjaoCpwPLupmeNMQXAb4FiY8zHgSDwa28SioiIDKTqQHKKjTq0P1pMYE4Up9AleThAcmfQ61giMgwFC/cSKGsjurcCJ5Akr+YgfvXoZh1rbS0wu8/thX3u/pvMJ5Kc4VicghRuhw+S2rdURDJLha7kHBtziG+OnPhAOSl1dXV87nOf484776SystLrODLOhCY1EprU6HUMERkuvyUwPYaJuKQOBkjVB7qap8cIL2nHhFxs0hDbECHxlnZIEBkLku0JnKCDE8juqYMqdEXGqY5knDs3P8uPnvsjR2Y6dN77fe7/4t97HUtERHKECblErmrGKeyeb78IYpsiJLaFyFvaBqZr5Wfjt4TPaSd1JIDboLeeMvpaNzzKp2fV4RhoWfd7is55j9eRxoRYQycHnthBtK4d43coW1RN5dIpGJOdIzY0R1dknPrma0/w39vXc7i5kWR+mMeSdfxow8BN1kVERAYTmBs9VuR2C53eSWBmrLfITTt+6mDbMYuMrOYX/5sjv7udOQUxZuXHqH/wmzQ+8xOvY+U8ay37HqklWtfedTvpcvTVgzRtPuJxsuPTx2pyyj4Y0rY+uaYtEeOpg7UcqTtCz1sRC/zg2Ue4ZfGlXkYTEZEc4StLDmw0FgKD7+HrxrKz10ey04oVK1izZs2Qz/vq/IOUB4/9bW7atIn21z7D1z/5D0N+rGXLlnHPPfcM+bxscaLfoc84XD7zEhZPWEDSTfLS/ldZu/eV3vunFE0kEoiws3EPFZFSPnPeJwc8xu+feZifvPqL434PL3+HKnRFxiHXWiyWpqYmrO16Q2KtZd/+/R4nExGRXJE66sc/uV8vrTUktoYJTIvjFBzr7bUxh+ROfTAuoy/icwe05flcDBaLPmzp67q5V3Le5LN7b19bcyUA6w68zscWf4AZJVMBiCajPLz1iUEfI5bM3j27VejKKRvPm9Lnam92UTDMxdWz2L//AA2NjVhrMcawrGya19FERGQEZOL6FNkV4HMzk0wqPtZ7tro2Qt1MS+KtIqaVJphenuBgs58/vFnAEfyQm5dN8cBwewHr7l9F22sPp7VF5i9n6+++MxKxcsrb/Q7dpMvWH63HptI/GDjn0iUUzirl6LqDbNy4CYBzz1rChRcvJVSeR/ue5rTjr/2z97Jq2rdHPvwIUKErMk7dceZVuNE4dz/7B0wiQcm2I/zwe1qMSkRETk5HwuHbT1SweFKU0kiK0kiK5TUdvffvaQzwf1eXEU1qSRjJnPJrPk+q9Qid218EIDz9LCqu+2uPU2Uha7HuwGkGNmlp39syoD3ZHmfKu+YQKsujbVcT/kiAsrMmkD+tOBNph0WFrsg4VRQM8y+Xvo/CJ1/nl7/4JTfddBPVVVVexxIRkRGQ0dFW2x1MGAre0wJ93jjPKo5x4fRm4m9kdkvAXB1tJSPDl1fExI/9K8nmw1g3RaB0kteRspIT8FE4s4TWHelb/BXVlJFoifUuOtXDOIZgcZjqi6dRfXFujABUoSvZzWcJzIzhFKVI1ftJ7glC9/wK38Q4/okJ3A6HxI4QxLs+MTaFKQIzYxgDiV1B3Gb9mb+dz9z6GbbVbuPWW2/1OoqMU6nOAJ07q3GjAcKTGwhWN5/4JBHJKk5pEpyBvUO+ikEWrBLJAH9xtdcRst7Ey2cC0LqzCeMYiudXUHHeZOINnbTuako7tnRxNb5wbr2nzq20Mr74LJGrmvGVHrtIJvcF6XymiNBZ7QRP6+xtD82P0v7HYpySJJFlrb0X2+CCTjqfKSS5P5jx+LmiqqqK++67z+sYMk4lW8M0/PEs3FgAgI43p1KweCcFC/d6nExEhsJt9tH1QXR6sZtq0ltNkWzlC/uZ8q4a3EQKjMHxd3UahavymfmB0/n3h35CfiDCNV+4nqKaco/TDp0mTUjWCsyMpRW5AP4pcfxTowRPi6a1m0iK4PxOQos70j9RNpbQWelDL0Qke7Rvntpb5PZo2zQdN+7zKJGIDIft8BF/M5zW5rb5SGwNH+cMcIqThC9qJfLOJkJL2jDhgavlisjoizV00vDqQZreOIIb71otPVSWx8NbH+dXmx/IySIX1KMrWcwpSg3a7qtO0v8T457jfcUDz+l6HAtaUl4k6ySb8wc2phxSbWGcMn1IJZJLYq/mkzwYwD8hgdvuI7EzCEkHHIt/ShwTcUkdDOA2+zF5KSJXtWACXcWtrzSJf0KC9v8pAavrtUim1L9ygCMv7Eu7PeN9C/BHAm9zVm5QoStZK3V08D/PxO4gwTmxAXOBUvV+jN/iq070aw+gIlckOwUqW0jUF6W1mWACf1Hncc4QEc/5LeGz2/FPj0HSEN8aJr45DzCkDgVJHeozXSjokn9lC06fLYii6/Mxfttb5PZwilL4JyU03UikW01Nzag+fn4gwpcv/gw+kz6K6s5f/Ct/2PZ0xnKcjNra2iGfo6HLkrWSe4IDLnbxN/NwjwSJrsunb/GaavATfytMdF0+Nnbsz9omHKLrMrvao4icvIIFe/EX9+m5NZaiJdsxfg1hFMlW4fPbCMyJYgIWk+cSWtxBcH500GOD86NpRS5A+MwOTGTw57gJ6rkvkimV+eUDilyAiQXVBH0Bzpl4BpdMO5/KiIYui4wsa+hcU4SvKoFTnCJ1xI/bvahFojZM8kCga9XldofUwa5eW7fJoe2BUvxT4mAguS8ACX2eI5KtnHCC8netJ7a/DDcaIDS5AV8k7nUskZw3WlvshPwu/zwjgWPSr60H5yb41s5izp4aZX51jIYOH3/aHuGjVZYFTr/rsAO/7QjxXif9uZ5IGb5+tIDWkOboi/S1du27RuVx3Zifut8Vgpv+HD17ThXXH/gb3I6e1xFL0fm1ROYcGpUcb2fp0keGfa4KXcl6qboAqbqB8wRsu4/EtkEuhklDcpf20JPMSbbUYVNJ7dU3TMaxhKce9TqGiJyE400EMsBNS5q5cOaxaQeXzO5g44GB1+OUC6/sycPnwDsXtBHyW1qiDr9cV0xrTEWuSKY4oSSFZ+6kdf3s3jZfYQc26etT5AIYWtfPIm9GXU6NuMrKQtcYcyZwF3CTtXaXMeZKYBEQAH5prd3taUARESDV2cqR336Njtq1AISnLabq/f+Iv6jS42QiMl4NZx7bUDU8+9d07l2d1nbB/P9F21u/AmvZtGkTAEuXLOAdc24gfugFkm0He48tXLSC9f+4AgA30UGqow5/4RTe72Tl21KRMS3/tP0EJzQRO1iKLy9GeFo9Rx87c8BxNuEn2RYmUNLhQcrhycpXFGvta8Z0fWZojMkDbrfWXmqMKQN+DNzgZT4REYCGx+7sLXIBons2UP/Qt5jw4X/xMJWIyOgqOf9vMP4wnXuewvjC5NfcSKDiDNjyywHHutEGKt/5czp3/5FUxxHCEy8gWHlG7/1OIIJTPCOD6UWkv0BpO4HSY+tlBMraSDYUph1j/El8+YPPxc9WWVnoduuZuLEUqAOw1jYYYxYZY3zW2sH3nhERGaIVK1awZs2aIZ/3dwv2k99vCI+7cRNfXvUQ7hBX+l62bBn33HPPkDOIiGSaEyyg9MJVlFxwBz0dE26sBeMLYVOxtGNDVWfiBCLkz1EfhUiuKDh9T9faGZ3H5ugWnrUTJ5A7w5YhuwvdHtVA38lbCaAKONj3IGPMKuCO/ievXr16FKONbytXrgTgkB24p+14sbL7Aq+/s9zW0NAwrPM6Us6AQjfqOoPs8nxyGbz8O+p5Pm/YUOxZBq+tXNm1fYKezyInp6fIBXBCRRQv+QLNL3+7ty004Vzya97rRTQROQW+ghgV171C26ZpuB0hIvP3Eaxo8zrWkBmbpUWKMWY1cDMwG7jZWvvR7vbdwDxr7Qn7zo0xNlt/vrGgZ0+tX8ViJzhy7OpZ1TITc6Ik+7S88jvqH/pW73y0hQsXUnrZn1O6/BaPkw1dz/N5tFZ2zAU9Kzu+3fPZGIO1VhtznwJdm8e2VOdRPnztYho6fDz2gpZUETkVXl2b3YSPxtWnk6grAbr2ty+55E1CE5oymgNO7dqcC/uuPAdMAzDGlAObTqbIFREZbUVLbqTqfX/PtrYQO9tDVLz7q5QsW+F1LBERz/jyylm3N4+dR4NeRxGRYWrfPLW3yAWw8QDNa+eRa59RZuXQZWNMDTAVWG6tvdcY811jzOeBAuAz3qYTETmmYNHV/NuOKgBWLrnR4zQiIiIipyZ+qHRAm9sZItUcwa9Vl0+NtbaWriHLPbcf9DCOiIiMAmuh463JRPeW4wRTRObt92RYlIiIiBzjy4+ShIWImAAAIABJREFUOJq+6jKOi5MXH/yELJWVha6IiIx9LS/PobN2Uu/t2L4ySi/fSGiiil2RXOQmOmh742d88Yp6Gjp8xI++QbB8gdexRGSI8hfsJbq/HFLHZrlG5h7ACSU9TDV0KnRFRKRXz6IPoy3PH+arlyzEMc1p7bXPpbj3tcxkEJGR1fDMl4gdfoXpZQmmlyWof+IvqLz6JwRK5ngdTUSGIFDeRsU16+monYQbDRCaWk94+hGvYw2ZCl0REcm4sD+MY3wD2guC+R6kEZEew91XfHJxgq9cVZ/WtnHDev50/1J+uW7o25Zpb3ER7yRb8ki2RMhfsBdffu7urqJCV05ZzxY7IpL7MrmFQf3DfpLN6YXtkkVN3PR9b7Y4ylRvtshYVBByB28PDt4uItmp+aW+04osBWfspmDRHk8zDZcKXRER8UTxRVtoenYBqdY8wBKaepSC03PzYioyVgy3F9UmYxx64M9wY+nTES655Q6+PPOdIxFNREZZ7EBp2toZYGh7fQbhqfU5tdpyDxW6Mmxvt3FzpvRspJ0NWURkaAKl7VS8+2WSTfk4wWROD48SGe+MP0TZJf9I49pVpDoOY5wA+XPfS96Ma7yOJpLzMjXi6Jo5l3HJtGkD2h9+YCfP71uXkQwjSYWuZDU31kz0wFqcYCGhiRdinIFz+kQkdxnTVfCKSO4LVZ1F9fX3k2zZhS9cjhMa+txcEfHO0Y7Gwds7c3M3BBW6krWiB56n4U9fwSajAPiLplFxxV348io8TiYiMn4YY24DWoGJwPestbHu9nzgr4GXgIuBb1lrm4/7QDIuGOMQKJ7ldQyRMSVT62e4CR9H/xAg1RLpbQtUNPPjry/ACczDmIzESHMqvdnOiQ8RyTxrXZpe+ofeIhcg2bKH1o1agVFEJFOMMUuABdbae4E3gL/sc/eVQL219iFgD3BZ5hOKiMhIcQIpyq96jYIzdxKecZi82QdJdQY58uuLqH/wXKJ7y72OOCQqdCUrpdoPkuqoG9AeP7LBgzQiIuPW9cDm7q83AH/W574XgZuNMXOAAuCPGc4mIiIjzAklKTh9L4Vn7aRzZzVuex4AqbY8mp49jWRr2OOEJ09DlyUrOeFynEA+biJ97p6/aLpHiURExqVqunpyATqB3uU4rbUHjTG3A/8B/NRa29n/ZGPMKuCO/u2rV68ejawiImPKypUrAdiwIQPz3ZMGfBa6hycHDocJtxUMOKz2T+cSnzTg5X7UrFzZtfDscK4bKnQlKzn+MIWLVtC8/s7eNhOIULjwEx6mEhEZdw4DPZO1ioDeoTbGmMVAJXA18EdjzDZr7eq+J1trVwGr+rYZY+zy5ctHLbCIyFhxyy23AKM7RzfRUEDzC3NJNhZgQgkKFu4hf/5+Omon0nKkZsDx1TN2kH/a/lHL09+nP901R3c4O6yo0JWsVTD/QwTK5hPd+zQmUEhk9nX48yd6HUtEZDx5APg48GNgEfCQMabUWtsIXA5st9Z2GGO+BywGVnuWVEREhsSmDI1PL8SNBrtuxwK0rpuNv6iD8LQjtL42AxsP9B5v/EnCM454FXfIVOhKVgtVnUWo6iyvY4iIjEvW2leMMVcYYz4BTAW+DbxijLkA+BnwBWNMApgH/NDDqCIiMkTxwyW9RW5fnTurCE1qpOTSN2h7bQap1gj+0jYKz9yJLy/uQdLhUaErOadz9xO0bLybVOt+brv0KP/9ate8hUTzTprXfY/44fX4CidTdMafkzftCo/TisiJtG+ZRPsbU3FjAUKTj1J07jZ8eQmvY0k3a+23+zUt7P5/G/CV7q8fzVwiEREZCcafGrzd59L8Qg2dOyaANfhL2ik6Zzv+ko4MJzw1WnVZckq8fiMNa28n2bIHa1PMq45z66UNuIlOjj69ktihl7E2RbJlDw3P3U786OYTP6iIeKZzVyWt6+bgdobAdYjtraTpmQVexxIRERnzApUt+EvSF37FWHAsndsngu1amSrZlE/js7l3bVaPruSUjh2PgLVpbWWRFG1v/GzgdkTWpWPnowTLT89gQhEZis7tEwa0JeqLSbbk4S/K3KqOIiIi2Wrp0kdG7bELg2u4es5lzC6dQVO0mad2/onLZ17MtOL8Acd+/84XOdJxdNSyjDQVujK29SuKRURERESkS2u8nd+88XBa24VTlww4zmKJJqOZijUiVOhKTonMfCft23+fVsA2dvgoWPAxOnb+T3qvrnGIzHynBylF5GTlzT5E/FBpWlugokW9uSIiMu4NZ0udkdC2p5m9D77Fxo2bAFi0aCFFNWV88N8/6Ume4dIcXckpwcozKFv6DfxF0zHGx5bDQf7fM2U4gTzKL7uT0ITzMMaHv2g6ZRd9g2DFwhM/qIh4Jm/GEQqXbMOJxMBxCU09QsmlmlsvIiLilYJpxUx991y2NezkYNthKs6bzMQrZnkda8iMHcNDO40xdiz/fAI1NV0bWXv1iZcIjI2/w56fYTQ3pc92PXOg3u7f0RiDtd2rc8iw6NosIpIbcuX9zfGuzRq6LCIyDNZ16diyms4dL3FZZQsvNBT03hc7+BZtrz8KxqHwzOsIVuXep6AiIiIiuUyFrojIMNQ/+E1aX30QgOsmNrO0vI1UWwOdu1+l7tdfAesC0PLCfVR/6HtEapZ6GVdERETklNmUS+vOJpJtcfKnFRMqy/M60nGp0BWRnNcztCZTqkIJvjzvUFpbWTDFZ6+ZzzklHVSEkmn3PfL8NXx/W3VGsmX78CIRERHJTalokt2/e5NYw7EFI6sumkb5WQO3CswGKnRFRIaosl8h26MqlBhQ5AJUhROjHUlERERkSFKxJIef3UNLbQNOwKF0URUV507GOIaW7Q38+TkfJT8Yoe65vVScN4mGDYfSilyAIy/so3h+Of68gEc/xfGp0BWRMePxHwxegI40G3OIrbHQbz2dBVcHSB1IYpvjae1OeZDH/3x0s115m17ORURE5OQdfGoXrdsbAEilXOpfPoAT8BEsDbP/0W1MK54CwNFXDxJvjmJT7oDHsCmX2NFO/FNU6IqI5DwT8uGfW0TyrZbeNqcshG9yBKc4QHxdAyS6LwZBB//8Io+SioiIyHi1YsUK1qxZM+h9IV+Q25d9HkP6YsV1L6ymKdrM3PLZvW0bN26CjZaX9r/KeZPPTjvetSlu+rdP0RZvH/T7LFu2jHvuuecUf5LhUaErIjIM/hkF+KrDuA1xTJ4PpywEgCkOErq0CvdIDAw4lWGMT7vRiIiISG4I+0ODtBrWH9xITfksSsMlva2rd609bpHrNRW6krOsm2JycYKWqON1lDEhnkqys62BCXmFFAezdwW9bGLy/PgmD3wZNX4H30T9Dk+GtZBsyscJJvHlx7yOIyIiMmacqCd1/x+20bKtIa2t6qJpGAcOP7snrT1UEeGpf/8EbjxF89ajJNu7Vl3+wMRPjHjukZJTha4x5nzgge6bH7HWPuFlHvFO7PB6Gp9fxVeuqse10PTStyk+94sYo6J3OFYf3Ma3NjxJU7yTgOPjw7PP5i9Pu8jrWDLGJRrzaXp2AanWPMASmnqUkovexPjsCc8VERGRUzPx8pk4QV/aYlRlZ1aDhURLjMZNR7Apl7wJBUx6xywAnKCP0oVVHic/OTlV6ALXApOstQNnQsu4YVNxGv70VdxYEwCOgfZt9xMoP4382X/mcbrc0xzv5Pb1fyCW6losKeGmuLf2ZRaWTuDSCbNPcLbI8DU/N7+7yAUwxPZW0LZ5GoVn7PY0l4iIyHjgBH1MvHwmEy+fmX6HgepLplN5wRTchIs/kn0LTZ2MnCl0jTGTgKuBm40x/8da+1uvM8mpebsJ8m+npjLGyuXpwyw2bdrE6499nLufKxvy43k5SX4kDPf32KNjRiUNly0Y0H7jbx+ndO3Wk3qMXP8dSuYlW8Mkm/MHtMf2lavQFRERyQJOwIcT8HkdY9hyptC11h4AzjfGzAQeNMa8bq2t7bnfGLMKuKP/eatXr85YRhmahoaGEx80iNbY4MOTW6PDeyI2NDTk9N/JcH+PPXzRwfd4daLxQduPl8HL3+HKlSsB2Bofv0NeV67sWvBquP8OPb/DDRuKRyrS20saCjsjA7ZoSgb87N+wJDMZ+lm5sgbQdUNERGQsMNbm3htDY8wHgYS19ncnOM7m4s8nJ3Z09eeJHljbe9v4glRe9WMCpTUepspNc2pqOPLus5m3/MLetoJAkJ8v+wgTI7mxLU5NTde/e6b20c1GPfvo1tbWnuDIwfX8DteufdeIZTqR5hdr6Nw2sU+LpfSyTYQmNWYsQ19Llz4CvP3v0BiDtVbLaJ8CXZtFRGQkHe/anDM9uib9ypgHPOtlHvFW2cX/QNuW+4gefB5fpIqC0z6sIneYDFDxx9e5+ZbPsu7oPqbll/CxOUtypsiV3FV0Xi3+4naieytwgiki8/YTmtDkdSwREREZA3Km0AU+aoz5C+B+4IXuocwyThl/iMKFH6dw4ce9jjImOPGkVlmWjDMG8ucfIH++Xs5FRERkZOVMoWut/RnwM69zjIQjbTF+8tJeNh9qYVZ5Pp84byrTSiNexxIP9AwXHUnJwjCti6eTKM0nUN9K0Ybd+DriWJ9D66KpRKeW4+uIUbBpH6HDzaOaZSiGO+Q2E2zKJbmzHfdIFBPy4Z+Zj1PatZl66mAnqX3tWBd8k/LwTYlgjEa2nio34aN981RiB0vxRWLkn76XYEUrAJ07qujYPgGAvFmHicw+7GVUERERyUI5U+iOFfGky6d+vYF9zVEA3qxr47ldDfzqo+dQFgl6nE5yXSrk58i1Z5HK6/pbilcUEptcSvX9r9Cw/DQ6p1V0H1lIdEo5FY+8RuhIi3eBc0RiQxPuka7nrCVBvD5K8LwK3NYEyTeOfViQbIpDzMU/p9CrqGNG0zMLiB8qBSDZUEjsQBnl17xK/FAJreuPbXuVqCvBjQUoWLDPq6giIiKShVToDtNwt3TpqJxLw4JrB7Qv+OX3Kdy3fsiPp21dxobHLxuZRZQeLJ/Aj6sCpC1lWxjm5huruXdiOf2XuL3knRP5wt5TW7X5VF359Mi9DPUsyDSSKoIJvjI/Rtds5mPWvdbJtEicylB6e8fODr5+ZwmW3OzV7VmQyUsTC6r4zHmLgea09pcfb2NueRXFofT2tg0h/uFP3ucWERGR7DH4Pi0yalx/ePD2QF6Gk8hY1OYbfEPvo4HQ4Mf7c3MD8EzK97vHbY/4Bt4Xdlyc3Kxxs0ZeYPDXyUggj8gg90X0+ikiIiL9qEd3mIbbi3qoJcp7/uNlUtayadMmABYuXMi93/wop0/QcEc5Nee11vOr6plpbQ6Wa+v38XxxFfX9ioTzmo9kMt6oG43thazrEHvGgVgqrX3BgiC22ZDa35HW7lSG+cOn0o/NhFPtzfZ6jnTPHPHa2lrcpMu2n24g1Zm+x/PV7/gz2vY007L1aFp74Zwyau++JWNZRUREJPupRzfDJhSF+dtr5lGS19WTZpJxPr9stopcGRFzOlv51IG3iLhdBV9hKsFn977JxESUv969kYnxrqLMZy3XHN3PNQ37vYybE4xjCJ5ZisnzdTeAb2o+vikR/POKcCqO9ZabkiCBBcUeJR07HL/DlHfOIVDY9bs1jqFs8QSK5pVTfck08qcc2/oqMrmQCZdO9yqqiIiIZCkzljdtz+ZN6eNJl7nnXIgv1sr2LW94HUc8MlorHbt+h1RBGH9LJ8Y99hywQLI4DyeawBcb+d7PU3EqPYo9v8fR6NHtYa3FticxQR8mmP4Zoe1MYa3FiXg3SKanR9frntnh6tuj28O6lnhTFH8kgC+c/rtNtMbAQqBo8GH5o+V4m9LLycvma7OIiOSe412bNXTZI0G/Q6DD20WAZOxyki5OU8eAdgMEmjvT2iwQm1JGKhIidKARf1u09754WT6JyiL8je2E6sb36szGGEzB4HOaTZ4vR5eeyqxUNEnrzkYcn0PBrFIcf9cHBjblclpFDfnBCIm2OIGCrlXDjWMIlQ0+/7ant1dERERkMCp0RbLASK26PFRtjp+vzzqL7XldQ+cdLH+x/y2ubjjAjybW8HDF1N5jL2yu40t7No34fIeRXHVZslf73hb2PbIVN9G1gJc/P8j0G+bjhHzsuX8LHznjfQBs++kGJr1jJsXzKt7u4URERETeluboioxjD1VM7S1yAVwMP5lYw8ZIcVqRC/B8cRUvFlVmOqKMAdZaDq3Z1VvkAiTb49Q9v4+j6w4Sa+jsezCHn9mDmxx8tWsRERGRk6GuFJFx7K38gQsnRR0fa4urBj8+UsSFLWNrpWYZGW+3t3ieP8zfXPp/BrS3vNJKU7SZacVTets2buxajX7FXZ/lYNvhIWXQvuIiIiLSQ4WuSBbwavhuU3snbaenzy41rsvP1rbScNnAWad3vxzl59v0siFDE03GaI23UhhMX12+rr1+QKELkHSTNEabMhlR3oYx5jagFZgIfM9aG+t3/01AC7DFWrvNg4giIiID6B2ryDhWsHkfnTMrSUWOLexTsGkfebvqCe89SnRqeW97sL6VyI46L2JKDjhRT2rzlnoOPLGj97bxO7z7PR/AF/az67dvpu2ZW3nBFG760adGLaucPGPMEmCBtfbTxpjrgb8E/qXP/d8CHrTWvuBVRhERkcGo0BXxUDZsBTN74Wm0z5nAX//937K0agbnVU4DIOW6PHmwls2Nh5hVWM41U+YT+p5eMmR4iudXECrPo6W2AeN3KJ5fQbB7a6BZH1pI85Z6kh0JCmeWEJlcdIJHkwy6Htjc/fUG4HN0F7rGmPOAK4HdxpgPA1+31jZ6klJERKQfvWv1iOtqD0HJPNe6OCZ9DTonlqRw8z4+d/qlae0+x+GqyfO4avK8TEYcU6y1GKONh3qEK/MJV+YPaPdHApSfPdGDRHISqoGezd47gUl97rsRuNda++/GmNuB7wCf7HuyMWYVcEf/B129evVoZBUREemlQjfD/uOlPdz36n5aoknqF7yb0tonvY4k48Dj+7dy15bn2N/ezKKyiXxx4WXMLxl8wSk5dW5jjMSWFmxLApPvxz+vCF9l2OtYIsNxGIh0f10E9J2/kEfX3FyAB4Af9T/ZWrsKWNW3zRhjly9fPsIxRURE0ml7oQy6f+NB7lq7i6bOBK61RCvncHTBdV7HkjHujaZD3L7+Ufa3NwOwseEgn33hfqLJxAnOlOGw8RTxdQ3Ylq7fr21Pkni1Abfdm72SRU7RA8DZ3V8vAh4yxpR2314DnNP9dQB4KcPZREREjmvc9ujW1NRk/HvWLf4A8ZLJaW3xksnMXHQO/mjLcc4afdkwT1RGzx/3vYVr04fKN8U7WVu3i8snZf55MNalDkch1W9qggX3UCfO7MLBTxLJUtbaV4wxVxhjPgFMBb4NvGKMucBa+ztjzIXd83Orgds9DStZwY21cLh2Dd/67t18/Z9+QmWl9l8XEW+M20LXG8eZl2vdzMaQccU5zhzR47XLKdLvVcYYa+23+zUt7HPfFzMcR7JYx67HaHrx79m/dyfXTWzgxR/fwLVffBrjC3gdTUTGoXFf6F515EjGvtfuLc/x8jk3prVV123nkn07M5ahr8f0Keu4cO3UBfxyx6uk+vTqVoTzWVo1w7tQo+T/t3fvMVKVZxzHv8/sjYVlYYXlKrBEvAYEFEFKFCtqTVsk1rahak29xSgheGnsxRrwkjStRhutsVY0amO1pq1S490ato1WlCqgYhW5yCogAosCu7CXefrHnIXdheV2ZuacOfv7/DX7zjnDMw9n59lnznvec/bs6D/SylMV/PL4bfQs2vMFVnPa+O1LvdnSHH18IiK5kG7azta3fk3zrh3U12cW3y7dtpT17z7GkAmXH2BvEZHs0zW6eTTis6WMW/YcvRrqKWnZxYi6JUxa/FTUYUnCjarsz50Tz+PYPgMoLy5h8oAa7pv8PUqL1HTlQmM6xR9WVfPJ9jJ2pY1PG0p5cHV/NbkikmhNmz/AWxrZuLHj/db/++oj0QQkIt2e/vLKs1Gr32LUaq3XIfk1ZeBIpgwcGXUYORP1deZt1/x3FcfsfAYjIhKBol6DANi6dSsezCByd95+bw3TowxMRLotNboxsrVyIJ8OH086VcTwz5bRb0td1CFJwi3eVEf95KNJtbSyZtsWanofEXVIsbVj+Ws0rHiD4spqep98PsWVmdsz7drwMecNrqfIYOfaZfQYfmLEkYqI5F9J5QjKa86h77p11NfX4+40NBfR65gfRh2aiHRTanRjYsOAUbx+6kW4ZWaTrxw5kQnvPE1N3ZKII5OkenLVu9z1fi07jhsCwMW1j3PfNy5g7BFDIo4sfjY9fydfL3py989fvfUUQ698lObNa9nw5+uYWr0dgHUPX071+bfQe+y3owpVRCQyVafOZWiPY3jtnp+yaZuzqO4I/vbcz6MOS0S6KXPvYiXgBDAz7+r9RXF7of3ZOH4mTZWDO4wV7dzGoEXzyfUarlFP+5RwrrjiCmpraw9pH08Z62dOJl3WcSXMHp9vof/L7x1yDFOnTmX+/PmHvF9c7C+HlcWt3Hz8OlKdfhFf31zB8PImhvVs6jBe31TE7f879C8LCj2H3YWZ4e5aWjuE/dVmSYa5c+fyxBNPcOGFFzJv3ryowxGRhOuqNuuMbkw0l1ftNdbaozekiiDdGkFEkmTpspK9mlyAlsryCKKJt36lLXs1uQDVpS30L2vZa7yqtJVic1rUC4lINzVr1ixWrFjBrFmzog5FRLqxbn9GN5+3F9qfNyb+iHWDj+swVrV1HdNqH8jZv9l2eyGd0e1+3J3vv/YodTu2dhifPvwEbh53TkRRxVO6qZG1d55LeteODuNV065h12fv0/DRvzqMlx05mqFXPpK/ACWvdEY3PJ3RFRGRbOqqNuv2QjFx4vsv0rNhT9NR1tTASUufjTAiSTIz46axZ1FRUrp7bERFFVcdOznCqOIpVVpO/+k3YcV7ctVj+Dj6TJpJv29dR3HfPZccFPWqov93dD2aiIiISNR0RjcmZ3QB0pbiiwFHkU4VMXDjSopbm3P67+mMrjS0NPHmxk8pLy5hUvVwUqbvvrrSuqOexlVvU1RZTfmI8bvHvbWFxpVv4q0tlI+aTKqkLMIoJdd0Rjc8ndEVEZFs6qo2q9HNcaPbUN6HnWUV9P1qPSlP7x5vLi7j697VVGzfTFlzY05j6IoaXRGRQ6NGNzw1uiIikk2JWIzKzGYD24DBwF3uvivikLqUNmPx+PNZO2wsAD12bWfi4r8yYNNqVo04mWWjz6WluJRUupXjPq7lhI8ObdVcERERERER2beCmadoZhOAE9z9EWA5cE20Ee3fqppTdje5ADvLKlg04Qd83asf746dTktwvV86VcTy487ky34jogpVREREREQkUQrpjO4M4IPg8VLgWuDusC/aNn032zaNHMfOkk63bynpywtjzmRHaele2y886iT6pBpyEouIiIiIiEh3UkiN7kAyZ3IBGoEh7Z80s3nA3M47LVy4cJ8vNmfOnFDBLFiwgDVr1nT5fKppxz7HixvrD2n7A6mpqWHGjBmHtW+brnIkIiIiIiJSiApmMSozuw1Y4+4PmdnRwMPuftoB9imMNyciIgVDi1GFo9osIiLZVuiLUS0ALgUeAsYAB7zJbCH8MRKsPhn7OONMOQxPOQxPOQxPOeweCuH/WMdieMpheMpheMpheIWcw4JZjMrdFwNrzewyMo3uPRGHJCIiIiIiIjFUMFOXk6qQvyWJC+UwPOUwPOUwPOVQ4kLHYnjKYXjKYXjKYXiFnMOCOaMrIiIiIiIicjDU6EbvlqgDSADlMDzlMDzlMDzlUOJCx2J4ymF4ymF4ymF4BZtDTV0WERERERGRRNEZXREREREREUkUNboiIiIiIiKSKGp0c8TMZpvZT8zsF2ZWZmbXmNkrZvammVUH2/zYzP5jZivN7Lvt9r3ezDaY2adm1je6dxG9w82jmd1gZnVm9kmQyyuifSfR6ZzDYMzM7DkzOyP4ucLMfmZm97bbr8zMbjOzi8zs6ojCj4WDzOE+f5+D56aY2SsRhB4bZjbOzN4ws5rg5zvMbKmZLTazo4Ox4WZ2q5ldZWbntBt7zMw+NLNZ0b0DSQLV5uxQbQ5PtTk81ebwkl6b1ejmgJlNAE5w90eA5cC1wEvufjbwHjDJzEqAbe4+Gbga+H2wby+g1N0HufsId98ayZuIgTB5BJa4+zB3HwU8CDyd9zcQA/vI4TXBU5e0387dtwPvAr3bDc8GPnD3x4Gjzeyk3EccPweTw/0ch5jZAGAaUJKvmOPI3Ze0PTazscCD7j4WeBm4KXjqPuB+d38AuM7MegIV7n4JcBZwaZ7DlgRRbc4O1ebwVJvDU23OjqTXZjW6uTED+CB4vBQ4191XmlkKqANecfdmd38m2GYR8Hnw+HTgYjN7x8zG5DXq+DnsPLr7P9u9TpW7b85X0DHTOYfnmdlpZIrCl522bdrHvu8Hj5cB03MVZMwdMIddHYdmVgTMBP6U14jjqwnA3Ze6+8fB2CLgczPrAUxw9/XB+BfAKe6+PPh5JDAvn8FK4qg2Z4dqc3iqzeGpNmdPYmuzGt3cGAi0fXg3AkOCX6o5ZL5xOrPT9jOA2wDc/QV3Hw3cCDxhZgV5g+YsOew8tjGzScBbOY4zzjrn8EhgkLu/fRj7Dsl+eAXhUHPY/ji8BHgU0PL2XTsduBc4Avi63fjuY87MjieT0znBZ4DI4VBtzg7V5vBUm8NTbc6tRNRmNbq58QXQM3hcCWx091Z3vxu4ANg9l93MjgSK3f3F9i/g7q8C/waq8hNyLIXOI5kPtmfovjrnsAaYZWYLgXOB35nZqIPcd2Puwoy1g85h++MwmOo4G1gAPAmMM7Pb8xx7rJnZVOAf7r4B2ASUtXt69zHn7h+6+zeD8fH5jVISRLU5O1RVddaPAAABrElEQVSbw1NtDk+1OUeSVJvV6ObGAqDtmokxwLPtnvuYzLQKzGwgcJq7P2xmKTMbFEz9aVPn7lvyEnE8HXYeg3Ejcw1B+2+iupvOObzJ3c9w9zOAF4Fr3f2Tg9h3NB3z350cVA47H4dAb3c/KdhuJplr034VQfyxFFxXlnb3WjPrHwwvC/IIMBh4o9NuS4A1eQpRkke1OTtUm8NTbQ5PtTkHklabi6MOIIncfbGZTTOzy4BhwB+Db5j+TuaU/21mNoTMhd47zewGMlMDJgP3W2bFwoXAX6KIPy5C5hFgCvB6/iOPj33k8Df72s4yqxVOIbOwxVB3/5zMog23mlkFsMLdu+U0s4PJ4QGOQwEss3rjMOAMM1sNPAZ8GfzRu93dp5rZ9WSmQK0D7nD3RjO7ETgWqAWed/dNUb0HKWyqzdmh2hyeanN4qs3ZkfTabO6ani4iIiIiIiLJoanLIiIiIiIikihqdEVERERERCRR1OiKiIiIiIhIoqjRFRERERERkURRoysiIiIiIiKJokZXREREREREEkWNroiIiIiIiCSKGl0RERERERFJlP8Da8s5PnjcGYgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[0, 0],\n",
    "    df=crowd_metrics_df,\n",
    "    metric='avg_crowd_density2_5',\n",
    "    category='date',\n",
    "    # title='Mean crowd density within 2.5 m',\n",
    "    title='Mean crowd density',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.00, 0.4],\n",
    "    kind='box',\n",
    "    # lgd_labels=['Mar 25, 2021', 'Mar 27, 2021', 'Apr 10, 2021', 'Apr 24, 2021', 'Dec 03, 2021'],\n",
    "    lgd_labels=['Date 1', 'Date 2', 'Date 3', 'Date 4', 'Date 5'],\n",
    "    titlefontsz=10,\n",
    ")\n",
    "categorical_plot(\n",
    "    axes=axes[0, 1],\n",
    "    df=crowd_metrics_df,\n",
    "    metric='max_crowd_density2_5',\n",
    "    category='date',\n",
    "    # title='Max crowd density within 2.5 m',\n",
    "    title='Max crowd density',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.25, 1.0],\n",
    "    kind='box',\n",
    "    # lgd_labels=['Mar 25, 2021', 'Mar 27, 2021', 'Apr 10, 2021', 'Apr 24, 2021', 'Dec 03, 2021'],\n",
    "    lgd_labels=['Date 1', 'Date 2', 'Date 3', 'Date 4', 'Date 5'],\n",
    "    titlefontsz=10,\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[1, 0],\n",
    "    df=crowd_metrics_df,\n",
    "    metric='virtual_collision',\n",
    "    category='date',\n",
    "    title='Virtual collision with Qolo',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[-0.1, 40],\n",
    "    kind='box',\n",
    "    # lgd_labels=['Mar 25, 2021', 'Mar 27, 2021', 'Apr 10, 2021', 'Apr 24, 2021', 'Dec 03, 2021'],\n",
    "    lgd_labels=['Date 1', 'Date 2', 'Date 3', 'Date 4', 'Date 5'],\n",
    "    titlefontsz=10,\n",
    "    loc='upper left',\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[1, 1],\n",
    "    df=crowd_metrics_df,\n",
    "    metric='avg_min_dist',\n",
    "    category='date',\n",
    "    # title='Min. distance of Pedestrain from qolo',\n",
    "    title='Min. distance',\n",
    "    xlabel='',\n",
    "    ylabel='Distance [m]',\n",
    "    ylim=[0.5, 2.0],\n",
    "    kind='box',\n",
    "    # lgd_labels=['Mar 25, 2021', 'Mar 27, 2021', 'Apr 10, 2021', 'Apr 24, 2021', 'Dec 03, 2021'],\n",
    "    lgd_labels=['Date 1', 'Date 2', 'Date 3', 'Date 4', 'Date 5'],\n",
    "    titlefontsz=10,\n",
    "    loc='upper right',\n",
    ")\n",
    "\n",
    "plt.savefig(\"./pdf/all_shared_control_crowd_grouped.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- without grouping by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(16, 5))\n",
    "\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.8, hspace=None)\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[0],\n",
    "    df=crowd_metrics_df,\n",
    "    metric='avg_crowd_density2_5',\n",
    "    category='control_type',\n",
    "    # title='Mean crowd density within 2.5 m',\n",
    "    title='Mean crowd density',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.00, 0.4],\n",
    "    kind='box',\n",
    "    # group='goal_reached',\n",
    "    # lgd_labels=['Not reached', 'Reached'],\n",
    "    titlefontsz=10,\n",
    ")\n",
    "categorical_plot(\n",
    "    axes=axes[1],\n",
    "    df=crowd_metrics_df,\n",
    "    metric='max_crowd_density2_5',\n",
    "    category='control_type',\n",
    "    # title='Max crowd density within 2.5 m',\n",
    "    title='Max crowd density',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.25, 1.0],\n",
    "    kind='box',\n",
    "    # group='goal_reached',\n",
    "    # lgd_labels=['Not reached', 'Reached'],\n",
    "    titlefontsz=10,\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[2],\n",
    "    df=crowd_metrics_df,\n",
    "    metric='virtual_collision',\n",
    "    category='control_type',\n",
    "    title='Virtual collision with Qolo',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[-0.1, 40],\n",
    "    kind='box',\n",
    "    titlefontsz=10,\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[3],\n",
    "    df=crowd_metrics_df,\n",
    "    metric='avg_min_dist',\n",
    "    category='control_type',\n",
    "    # title='Min. distance of Pedestrain from qolo',\n",
    "    title='Min. distance',\n",
    "    xlabel='',\n",
    "    ylabel='Distance [m]',\n",
    "    ylim=[0.5, 2.0],\n",
    "    kind='box',\n",
    "    titlefontsz=10,\n",
    ")\n",
    "\n",
    "plt.savefig(\"./pdf/all_shared_control_crowd_nogrouped.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Path efficiency-related metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_metrics_df = eval_res_df[['seq', 'control_type'] + list(path_metrics) + ['date']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- group by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_fig, path_axes = plt.subplots(1, 2, figsize=(16, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=path_axes[0],\n",
    "    df=path_metrics_df,\n",
    "    metric='rel_duration2goal',\n",
    "    category='control_type',\n",
    "    title='Relative time to the goal',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.0, 1.0],\n",
    "    # lgd_labels=['Mar 25, 2021', 'Mar 27, 2021', 'Apr 10, 2021', 'Apr 24, 2021', 'Dec 03, 2021'],\n",
    "    lgd_labels=['Date 1', 'Date 2', 'Date 3', 'Date 4', 'Date 5'],\n",
    "    group='date',\n",
    "    loc='upper right',\n",
    "    kind='box',\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=path_axes[1],\n",
    "    df=path_metrics_df,\n",
    "    metric='rel_path_length2goal',\n",
    "    category='control_type',\n",
    "    title='Relative path length to the goal',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.0, 3.0],\n",
    "    # lgd_labels=['Mar 25, 2021', 'Mar 27, 2021', 'Apr 10, 2021', 'Apr 24, 2021', 'Dec 03, 2021'],\n",
    "    lgd_labels=['Date 1', 'Date 2', 'Date 3', 'Date 4', 'Date 5'],\n",
    "    group='date',\n",
    "    loc='upper right',\n",
    "    kind='box',\n",
    ")\n",
    "\n",
    "plt.savefig(\"./pdf/all_shared_control_path_grouped.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- without grouping by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_fig, path_axes = plt.subplots(1, 2, figsize=(8, 5))\n",
    "\n",
    "# plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.8, hspace=None)\n",
    "\n",
    "categorical_plot(\n",
    "    axes=path_axes[0],\n",
    "    df=path_metrics_df,\n",
    "    metric='rel_duration2goal',\n",
    "    category='control_type',\n",
    "    title='Relative time to the goal',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.0, 1.0],\n",
    "    # lgd_labels=['April 10, 2021', 'April 24, 2021'],\n",
    "    # group='date',\n",
    "    loc='upper right',\n",
    "    kind='box',\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=path_axes[1],\n",
    "    df=path_metrics_df,\n",
    "    metric='rel_path_length2goal',\n",
    "    category='control_type',\n",
    "    title='Relative path length to the goal',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.0, 3.0],\n",
    "    # lgd_labels=['April 10, 2021', 'April 24, 2021'],\n",
    "    # group='date',\n",
    "    kind='box',\n",
    ")\n",
    "\n",
    "plt.savefig(\"./pdf/all_shared_control_path_nogrouped.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shared control metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "control_metrics_df = eval_res_df[['seq', 'control_type'] + list(control_metrics) + ['date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# max value in each metrics\n",
      "rel_jerk         0.201612\n",
      "avg_fluency      0.974402\n",
      "contribution     0.981833\n",
      "avg_agreement    0.909517\n",
      "dtype: float64\n",
      "# min value in each metrics\n",
      "rel_jerk         0.076921\n",
      "avg_fluency      0.907108\n",
      "contribution     0.206484\n",
      "avg_agreement    0.647263\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"# max value in each metrics\")\n",
    "print(control_metrics_df.max(numeric_only=True))\n",
    "print(\"# min value in each metrics\")\n",
    "print(control_metrics_df.min(numeric_only=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- group by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "control_fig, control_axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=control_axes[0,0],\n",
    "    df=control_metrics_df,\n",
    "    metric='avg_fluency',\n",
    "    category='control_type',\n",
    "    title='Average control fluency',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.90, 1.0],\n",
    "    # lgd_labels=['Mar 25, 2021', 'Mar 27, 2021', 'Apr 10, 2021', 'Apr 24, 2021', 'Dec 03, 2021'],\n",
    "    lgd_labels=['Date 1', 'Date 2', 'Date 3', 'Date 4', 'Date 5'],\n",
    "    group='date',\n",
    "    loc='upper right',\n",
    "    kind='box',\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=control_axes[0,1],\n",
    "    df=control_metrics_df,\n",
    "    metric='rel_jerk',\n",
    "    category='control_type',\n",
    "    title='Relative jerk',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0, 0.4],\n",
    "    # lgd_labels=['Mar 25, 2021', 'Mar 27, 2021', 'Apr 10, 2021', 'Apr 24, 2021', 'Dec 03, 2021'],\n",
    "    lgd_labels=['Date 1', 'Date 2', 'Date 3', 'Date 4', 'Date 5'],\n",
    "    group='date',\n",
    "    loc='upper right',\n",
    "    kind='box',\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=control_axes[1,0],\n",
    "    df=control_metrics_df,\n",
    "    metric='contribution',\n",
    "    category='control_type',\n",
    "    title='Contribution',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.0, 1.25],\n",
    "    # lgd_labels=['Mar 25, 2021', 'Mar 27, 2021', 'Apr 10, 2021', 'Apr 24, 2021', 'Dec 03, 2021'],\n",
    "    lgd_labels=['Date 1', 'Date 2', 'Date 3', 'Date 4', 'Date 5'],\n",
    "    group='date',\n",
    "    loc='upper right',\n",
    "    kind='box',\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=control_axes[1,1],\n",
    "    df=control_metrics_df,\n",
    "    metric='avg_agreement',\n",
    "    category='control_type',\n",
    "    title='Average agreement',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.60, 1.0],\n",
    "    # lgd_labels=['Mar 25, 2021', 'Mar 27, 2021', 'Apr 10, 2021', 'Apr 24, 2021', 'Dec 03, 2021'],\n",
    "    lgd_labels=['Date 1', 'Date 2', 'Date 3', 'Date 4', 'Date 5'],\n",
    "    group='date',\n",
    "    loc='upper right',\n",
    "    kind='box',\n",
    ")\n",
    "\n",
    "plt.savefig(\"./pdf/all_shared_control_perf_grouped.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- without grouping by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(16, 5))\n",
    "\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.8, hspace=None)\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[0],\n",
    "    df=control_metrics_df,\n",
    "    metric='avg_fluency',\n",
    "    category='control_type',\n",
    "    title='Average control fluency',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.90, 1.0],\n",
    "    kind='box',\n",
    "    # group='goal_reached',\n",
    "    # lgd_labels=['Not reached', 'Reached'],\n",
    "    titlefontsz=10,\n",
    ")\n",
    "categorical_plot(\n",
    "    axes=axes[1],\n",
    "    df=control_metrics_df,\n",
    "    metric='rel_jerk',\n",
    "    category='control_type',\n",
    "    title='Relative jerk',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.0, 0.5],\n",
    "    kind='box',\n",
    "    # group='goal_reached',\n",
    "    # lgd_labels=['Not reached', 'Reached'],\n",
    "    titlefontsz=10,\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[2],\n",
    "    df=control_metrics_df,\n",
    "    metric='contribution',\n",
    "    category='control_type',\n",
    "    title='Contribution',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.0, 1.25],\n",
    "    kind='box',\n",
    "    # group='goal_reached',\n",
    "    # lgd_labels=['Not reached', 'Reached'],\n",
    "    titlefontsz=10,\n",
    ")\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[3],\n",
    "    df=control_metrics_df,\n",
    "    metric='avg_agreement',\n",
    "    category='control_type',\n",
    "    title='Average agreement',\n",
    "    xlabel='',\n",
    "    ylabel='Distance [m]',\n",
    "    ylim=[0.0, 1.0],\n",
    "    kind='box',\n",
    "    # group='goal_reached',\n",
    "    # lgd_labels=['Not reached', 'Reached'],\n",
    "    titlefontsz=10,\n",
    ")\n",
    "\n",
    "plt.savefig(\"./pdf/all_shared_control_perf_nogrouped.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis given cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> cluster the shared control tests by density of the crowd: Max_crowd_density: \n",
    "> \n",
    "> Perhaps (<0.35,  0.35 to 0.7,  >0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster according to crowd density metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://stackoverflow.com/a/64583481\n",
    "# https://datascience.stackexchange.com/a/53932\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# possible input feature: \n",
    "# max_crowd_density2_5, \n",
    "# avg_crowd_density2_5, \n",
    "# std_crowd_density2_5, \n",
    "# max_crowd_density5,\n",
    "# avg_crowd_density5,\n",
    "eval_res_sorted_df = eval_res_df.sort_values('avg_crowd_density2_5', ascending=True, ignore_index=True).reindex()\n",
    "shared_control_data_ = eval_res_sorted_df[['max_crowd_density2_5', \n",
    "                                         'avg_crowd_density2_5']]\n",
    "\n",
    "# normalize \n",
    "# sklearn.preprocessing.StandardScaler\n",
    " \n",
    "# Three cluster\n",
    "model = KMeans(n_clusters=3)\n",
    "model.fit(shared_control_data_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21,  0, 15])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustered_labels = model.labels_\n",
    "unique, unique_indices = np.unique(clustered_labels, return_index=True)\n",
    "unique_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2], dtype=int32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_dict = {\n",
    "    0: np.where(clustered_labels == 0),\n",
    "    1: np.where(clustered_labels == 1),\n",
    "    2: np.where(clustered_labels == 2),\n",
    "}\n",
    "\n",
    "cluster_order = np.argsort(unique_indices)\n",
    "\n",
    "for idx, ii in enumerate(cluster_order):\n",
    "    if idx != ii:\n",
    "        clustered_labels[index_dict[ii]] = idx\n",
    "clustered_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shared_control sequence on different cluster\n",
      "1    20\n",
      "0    15\n",
      "2    13\n",
      "Name: cluster, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# add date col\n",
    "eval_res_sorted_df['cluster'] = clustered_labels\n",
    "\n",
    "# eval_res_df_cluster = eval_res_df.sort_values('avg_crowd_density2_5', ascending=True, ignore_index=True).reindex()\n",
    "print(\"Shared_control sequence on different cluster\")\n",
    "print(eval_res_sorted_df['cluster'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_viz_data = shared_control_data_['max_crowd_density2_5'].values\n",
    "y_viz_data = shared_control_data_['avg_crowd_density2_5'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Clustering results visualization by cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9578411208447014, 0.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmap = mpl.cm.get_cmap('hot')\n",
    "cmap(0.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_unique = np.unique(clustered_labels)\n",
    "\n",
    "label_color_list = values2colors(clustered_labels, cmap_name='hot', range=(0.5, 0.8))\n",
    "label_color_list = np.array(label_color_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "fig_cluster, axes_cluster = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "for label in label_unique:\n",
    "    index = np.where(clustered_labels == label)\n",
    "    axes_cluster.scatter(\n",
    "        x=x_viz_data[index], \n",
    "        y=y_viz_data[index], \n",
    "        s=100,\n",
    "        c=label_color_list[index], \n",
    "        alpha=0.9,\n",
    "        cmap='hot',\n",
    "        edgecolors='black', # None black\n",
    "        linewidths=2,\n",
    "        )\n",
    "axes_cluster.set_xlabel(\"Max crowd density within 2.5 m\", fontsize=16)\n",
    "axes_cluster.set_ylabel(\"Mean crowd density within 2.5 m\", fontsize=16)\n",
    "\n",
    "axes_cluster.tick_params(axis='x', labelsize=14)\n",
    "axes_cluster.tick_params(axis='y', labelsize=14)\n",
    "axes_cluster.legend(['Low density', 'Medium density', 'High density'], fontsize=14)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_shared_control_clustering_by_cluster.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Clustering results visualization by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "fig_cluster, axes_cluster = plt.subplots(figsize=(8, 8))\n",
    "date_data = eval_res_sorted_df['date'].values\n",
    "color_list = values2colors(date_data, cmap_name='hot', range=(0.35, 0.75))\n",
    "color_list = np.array(color_list)\n",
    "date_unique = np.unique(date_data)\n",
    "\n",
    "for idx, date in enumerate(date_unique):\n",
    "    index = np.where(date_data == date)\n",
    "    axes_cluster.scatter(\n",
    "        x=x_viz_data[index], \n",
    "        y=y_viz_data[index], \n",
    "        s=100,\n",
    "        c=color_list[index], \n",
    "        alpha=0.9,\n",
    "        edgecolors='black', # None black\n",
    "        linewidths=2,\n",
    "        )\n",
    "    \n",
    "axes_cluster.set_xlabel(\"Max crowd density within 2.5 m\", fontsize=16)\n",
    "axes_cluster.set_ylabel(\"Mean crowd density within 2.5 m\", fontsize=16)\n",
    "\n",
    "axes_cluster.legend(date_unique.tolist(), fontsize=14)\n",
    "axes_cluster.tick_params(axis='x', labelsize=14)\n",
    "axes_cluster.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_shared_control_clustering_by_date.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Desicion boundary plot\n",
    "# ref: https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Utilities/ML-Python-utils.py\n",
    "# ref: https://towardsdatascience.com/easily-visualize-scikit-learn-models-decision-boundaries-dd0fb3747508\n",
    "# ValueError: Buffer dtype mismatch, expected 'double' but got 'float'\n",
    "\n",
    "# h = .02     # point in the mesh [x_min, m_max]x[y_min, y_max].    \n",
    "\n",
    "# # Plot the decision boundary. For that, we will assign a color to each\n",
    "# x_viz_data = shared_control_data_['max_crowd_density2_5'].values\n",
    "# y_viz_data = shared_control_data_['avg_crowd_density2_5'].values\n",
    "\n",
    "# x_min, x_max = x_viz_data.min() - 1, x_viz_data.max() + 1\n",
    "# y_min, y_max = y_viz_data.min() - 1, y_viz_data.max() + 1\n",
    "\n",
    "# # Meshgrid creation\n",
    "# xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "# data_xy = np.c_[xx.ravel(), yy.ravel()]\n",
    "# data_xy.astype('double')\n",
    "# Z = model.predict(data_xy)   \n",
    "\n",
    "# plt.contourf(xx, yy, Z, alpha=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean-Std statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>avg_crowd_density2_5</th>\n",
       "      <th>std_crowd_density2_5</th>\n",
       "      <th>max_crowd_density2_5</th>\n",
       "      <th>avg_crowd_density5</th>\n",
       "      <th>std_crowd_density5</th>\n",
       "      <th>max_crowd_density5</th>\n",
       "      <th>avg_min_dist</th>\n",
       "      <th>virtual_collision</th>\n",
       "      <th>rel_duration2goal</th>\n",
       "      <th>rel_path_length2goal</th>\n",
       "      <th>path_length2goal</th>\n",
       "      <th>duration2goal</th>\n",
       "      <th>min_dist2goal</th>\n",
       "      <th>rel_jerk</th>\n",
       "      <th>avg_fluency</th>\n",
       "      <th>contribution</th>\n",
       "      <th>avg_agreement</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cluster0_mean</th>\n",
       "      <td>0.080525</td>\n",
       "      <td>0.069948</td>\n",
       "      <td>0.373484</td>\n",
       "      <td>0.094139</td>\n",
       "      <td>0.043284</td>\n",
       "      <td>0.246160</td>\n",
       "      <td>1.437776</td>\n",
       "      <td>2.933333</td>\n",
       "      <td>0.330378</td>\n",
       "      <td>1.459300</td>\n",
       "      <td>32.398050</td>\n",
       "      <td>63.274633</td>\n",
       "      <td>3.561321</td>\n",
       "      <td>0.136636</td>\n",
       "      <td>0.952597</td>\n",
       "      <td>0.512010</td>\n",
       "      <td>0.823473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster0_std</th>\n",
       "      <td>0.022977</td>\n",
       "      <td>0.014412</td>\n",
       "      <td>0.073720</td>\n",
       "      <td>0.024678</td>\n",
       "      <td>0.008648</td>\n",
       "      <td>0.038997</td>\n",
       "      <td>0.237672</td>\n",
       "      <td>2.939064</td>\n",
       "      <td>0.085942</td>\n",
       "      <td>0.571469</td>\n",
       "      <td>13.212398</td>\n",
       "      <td>23.758684</td>\n",
       "      <td>4.216360</td>\n",
       "      <td>0.028438</td>\n",
       "      <td>0.015865</td>\n",
       "      <td>0.228307</td>\n",
       "      <td>0.105218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster1_mean</th>\n",
       "      <td>0.179447</td>\n",
       "      <td>0.117265</td>\n",
       "      <td>0.644259</td>\n",
       "      <td>0.167873</td>\n",
       "      <td>0.067362</td>\n",
       "      <td>0.396614</td>\n",
       "      <td>0.955215</td>\n",
       "      <td>13.150000</td>\n",
       "      <td>0.303885</td>\n",
       "      <td>1.737466</td>\n",
       "      <td>37.073475</td>\n",
       "      <td>73.490796</td>\n",
       "      <td>5.936858</td>\n",
       "      <td>0.139646</td>\n",
       "      <td>0.956848</td>\n",
       "      <td>0.587719</td>\n",
       "      <td>0.748062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster1_std</th>\n",
       "      <td>0.030537</td>\n",
       "      <td>0.012966</td>\n",
       "      <td>0.062430</td>\n",
       "      <td>0.017593</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.044159</td>\n",
       "      <td>0.125633</td>\n",
       "      <td>19.282594</td>\n",
       "      <td>0.098394</td>\n",
       "      <td>1.279478</td>\n",
       "      <td>13.000379</td>\n",
       "      <td>20.771676</td>\n",
       "      <td>6.536596</td>\n",
       "      <td>0.028505</td>\n",
       "      <td>0.017663</td>\n",
       "      <td>0.140928</td>\n",
       "      <td>0.079222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster2_mean</th>\n",
       "      <td>0.258359</td>\n",
       "      <td>0.143615</td>\n",
       "      <td>0.779614</td>\n",
       "      <td>0.186518</td>\n",
       "      <td>0.078049</td>\n",
       "      <td>0.423107</td>\n",
       "      <td>0.849000</td>\n",
       "      <td>12.076923</td>\n",
       "      <td>0.201799</td>\n",
       "      <td>2.885694</td>\n",
       "      <td>33.447374</td>\n",
       "      <td>95.981751</td>\n",
       "      <td>5.224301</td>\n",
       "      <td>0.154770</td>\n",
       "      <td>0.946712</td>\n",
       "      <td>0.690510</td>\n",
       "      <td>0.716711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster2_std</th>\n",
       "      <td>0.044725</td>\n",
       "      <td>0.012799</td>\n",
       "      <td>0.063695</td>\n",
       "      <td>0.021492</td>\n",
       "      <td>0.011357</td>\n",
       "      <td>0.040649</td>\n",
       "      <td>0.109426</td>\n",
       "      <td>8.499623</td>\n",
       "      <td>0.096931</td>\n",
       "      <td>4.066183</td>\n",
       "      <td>7.144266</td>\n",
       "      <td>37.475163</td>\n",
       "      <td>4.015689</td>\n",
       "      <td>0.031837</td>\n",
       "      <td>0.011738</td>\n",
       "      <td>0.152139</td>\n",
       "      <td>0.050548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               avg_crowd_density2_5  std_crowd_density2_5  \\\n",
       "Metrics                                                     \n",
       "cluster0_mean              0.080525              0.069948   \n",
       "cluster0_std               0.022977              0.014412   \n",
       "cluster1_mean              0.179447              0.117265   \n",
       "cluster1_std               0.030537              0.012966   \n",
       "cluster2_mean              0.258359              0.143615   \n",
       "cluster2_std               0.044725              0.012799   \n",
       "\n",
       "               max_crowd_density2_5  avg_crowd_density5  std_crowd_density5  \\\n",
       "Metrics                                                                       \n",
       "cluster0_mean              0.373484            0.094139            0.043284   \n",
       "cluster0_std               0.073720            0.024678            0.008648   \n",
       "cluster1_mean              0.644259            0.167873            0.067362   \n",
       "cluster1_std               0.062430            0.017593            0.008615   \n",
       "cluster2_mean              0.779614            0.186518            0.078049   \n",
       "cluster2_std               0.063695            0.021492            0.011357   \n",
       "\n",
       "               max_crowd_density5  avg_min_dist  virtual_collision  \\\n",
       "Metrics                                                              \n",
       "cluster0_mean            0.246160      1.437776           2.933333   \n",
       "cluster0_std             0.038997      0.237672           2.939064   \n",
       "cluster1_mean            0.396614      0.955215          13.150000   \n",
       "cluster1_std             0.044159      0.125633          19.282594   \n",
       "cluster2_mean            0.423107      0.849000          12.076923   \n",
       "cluster2_std             0.040649      0.109426           8.499623   \n",
       "\n",
       "               rel_duration2goal  rel_path_length2goal  path_length2goal  \\\n",
       "Metrics                                                                    \n",
       "cluster0_mean           0.330378              1.459300         32.398050   \n",
       "cluster0_std            0.085942              0.571469         13.212398   \n",
       "cluster1_mean           0.303885              1.737466         37.073475   \n",
       "cluster1_std            0.098394              1.279478         13.000379   \n",
       "cluster2_mean           0.201799              2.885694         33.447374   \n",
       "cluster2_std            0.096931              4.066183          7.144266   \n",
       "\n",
       "               duration2goal  min_dist2goal  rel_jerk  avg_fluency  \\\n",
       "Metrics                                                              \n",
       "cluster0_mean      63.274633       3.561321  0.136636     0.952597   \n",
       "cluster0_std       23.758684       4.216360  0.028438     0.015865   \n",
       "cluster1_mean      73.490796       5.936858  0.139646     0.956848   \n",
       "cluster1_std       20.771676       6.536596  0.028505     0.017663   \n",
       "cluster2_mean      95.981751       5.224301  0.154770     0.946712   \n",
       "cluster2_std       37.475163       4.015689  0.031837     0.011738   \n",
       "\n",
       "               contribution  avg_agreement  \n",
       "Metrics                                     \n",
       "cluster0_mean      0.512010       0.823473  \n",
       "cluster0_std       0.228307       0.105218  \n",
       "cluster1_mean      0.587719       0.748062  \n",
       "cluster1_std       0.140928       0.079222  \n",
       "cluster2_mean      0.690510       0.716711  \n",
       "cluster2_std       0.152139       0.050548  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames_stat=[]\n",
    "for idx in [0, 1, 2]:\n",
    "    shared_cluster_stat_df = eval_res_sorted_df[eval_res_sorted_df.cluster == idx]\n",
    "    stat_df = shared_cluster_stat_df.drop(['date', 'cluster'], axis=1).agg(['mean', 'std'])\n",
    "    stat_df.index = 'cluster{}_'.format(idx)+stat_df.index.values\n",
    "    frames_stat.append(stat_df)\n",
    "    \n",
    "all_shared_cluster_stat_df = pd.concat(frames_stat) # , ignore_index=True\n",
    "all_shared_cluster_stat_df.index.name = 'Metrics'\n",
    "all_shared_cluster_stat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>avg_crowd_density2_5</th>\n",
       "      <th>max_crowd_density2_5</th>\n",
       "      <th>avg_crowd_density5</th>\n",
       "      <th>avg_min_dist</th>\n",
       "      <th>rel_duration2goal</th>\n",
       "      <th>rel_path_length2goal</th>\n",
       "      <th>rel_jerk</th>\n",
       "      <th>contribution</th>\n",
       "      <th>avg_fluency</th>\n",
       "      <th>avg_agreement</th>\n",
       "      <th>virtual_collision</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>$0.08 \\pm 0.02$</td>\n",
       "      <td>$0.37 \\pm 0.07$</td>\n",
       "      <td>$0.09 \\pm 0.02$</td>\n",
       "      <td>$1.44 \\pm 0.24$</td>\n",
       "      <td>$0.33 \\pm 0.09$</td>\n",
       "      <td>$1.46 \\pm 0.57$</td>\n",
       "      <td>$0.14 \\pm 0.03$</td>\n",
       "      <td>$0.51 \\pm 0.23$</td>\n",
       "      <td>$0.95 \\pm 0.02$</td>\n",
       "      <td>$0.82 \\pm 0.11$</td>\n",
       "      <td>$2.93 \\pm 2.94$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>$0.18 \\pm 0.03$</td>\n",
       "      <td>$0.64 \\pm 0.06$</td>\n",
       "      <td>$0.17 \\pm 0.02$</td>\n",
       "      <td>$0.96 \\pm 0.13$</td>\n",
       "      <td>$0.30 \\pm 0.10$</td>\n",
       "      <td>$1.74 \\pm 1.28$</td>\n",
       "      <td>$0.14 \\pm 0.03$</td>\n",
       "      <td>$0.59 \\pm 0.14$</td>\n",
       "      <td>$0.96 \\pm 0.02$</td>\n",
       "      <td>$0.75 \\pm 0.08$</td>\n",
       "      <td>$13.15 \\pm 19.28$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>$0.26 \\pm 0.04$</td>\n",
       "      <td>$0.78 \\pm 0.06$</td>\n",
       "      <td>$0.19 \\pm 0.02$</td>\n",
       "      <td>$0.85 \\pm 0.11$</td>\n",
       "      <td>$0.20 \\pm 0.10$</td>\n",
       "      <td>$2.89 \\pm 4.07$</td>\n",
       "      <td>$0.15 \\pm 0.03$</td>\n",
       "      <td>$0.69 \\pm 0.15$</td>\n",
       "      <td>$0.95 \\pm 0.01$</td>\n",
       "      <td>$0.72 \\pm 0.05$</td>\n",
       "      <td>$12.08 \\pm 8.50$</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        avg_crowd_density2_5 max_crowd_density2_5 avg_crowd_density5  \\\n",
       "Metrics                                                                \n",
       "Low          $0.08 \\pm 0.02$      $0.37 \\pm 0.07$    $0.09 \\pm 0.02$   \n",
       "Medium       $0.18 \\pm 0.03$      $0.64 \\pm 0.06$    $0.17 \\pm 0.02$   \n",
       "High         $0.26 \\pm 0.04$      $0.78 \\pm 0.06$    $0.19 \\pm 0.02$   \n",
       "\n",
       "            avg_min_dist rel_duration2goal rel_path_length2goal  \\\n",
       "Metrics                                                           \n",
       "Low      $1.44 \\pm 0.24$   $0.33 \\pm 0.09$      $1.46 \\pm 0.57$   \n",
       "Medium   $0.96 \\pm 0.13$   $0.30 \\pm 0.10$      $1.74 \\pm 1.28$   \n",
       "High     $0.85 \\pm 0.11$   $0.20 \\pm 0.10$      $2.89 \\pm 4.07$   \n",
       "\n",
       "                rel_jerk     contribution      avg_fluency    avg_agreement  \\\n",
       "Metrics                                                                       \n",
       "Low      $0.14 \\pm 0.03$  $0.51 \\pm 0.23$  $0.95 \\pm 0.02$  $0.82 \\pm 0.11$   \n",
       "Medium   $0.14 \\pm 0.03$  $0.59 \\pm 0.14$  $0.96 \\pm 0.02$  $0.75 \\pm 0.08$   \n",
       "High     $0.15 \\pm 0.03$  $0.69 \\pm 0.15$  $0.95 \\pm 0.01$  $0.72 \\pm 0.05$   \n",
       "\n",
       "         virtual_collision  \n",
       "Metrics                     \n",
       "Low        $2.93 \\pm 2.94$  \n",
       "Medium   $13.15 \\pm 19.28$  \n",
       "High      $12.08 \\pm 8.50$  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_density_df = all_shared_cluster_stat_df[list(anova_metrics)]\n",
    "metrics_len = len(export_density_df.loc['cluster0_mean'])\n",
    "for idxx, cluster in enumerate(['Low', 'Medium', 'High']):\n",
    "    str_out = []\n",
    "    for idx in range(metrics_len):\n",
    "        avg = \"${:0.2f}\".format(round(export_density_df.iloc[2*idxx,idx],2))\n",
    "        std = \"{:0.2f}$\".format(round(export_density_df.iloc[2*idxx+1,idx],2))\n",
    "        str_out.append(avg+\" \\pm \"+std)\n",
    "    export_density_df.loc[cluster] = str_out\n",
    "export_density_str_df = export_density_df.iloc[6:9]\n",
    "export_density_str_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llll}\n",
      "\\toprule\n",
      "Metrics &              Low &             Medium &              High \\\\\n",
      "\\midrule\n",
      "avg\\_crowd\\_density2\\_5 &  \\$0.08 \\textbackslash pm 0.02\\$ &    \\$0.18 \\textbackslash pm 0.03\\$ &   \\$0.26 \\textbackslash pm 0.04\\$ \\\\\n",
      "max\\_crowd\\_density2\\_5 &  \\$0.37 \\textbackslash pm 0.07\\$ &    \\$0.64 \\textbackslash pm 0.06\\$ &   \\$0.78 \\textbackslash pm 0.06\\$ \\\\\n",
      "avg\\_crowd\\_density5   &  \\$0.09 \\textbackslash pm 0.02\\$ &    \\$0.17 \\textbackslash pm 0.02\\$ &   \\$0.19 \\textbackslash pm 0.02\\$ \\\\\n",
      "avg\\_min\\_dist         &  \\$1.44 \\textbackslash pm 0.24\\$ &    \\$0.96 \\textbackslash pm 0.13\\$ &   \\$0.85 \\textbackslash pm 0.11\\$ \\\\\n",
      "rel\\_duration2goal    &  \\$0.33 \\textbackslash pm 0.09\\$ &    \\$0.30 \\textbackslash pm 0.10\\$ &   \\$0.20 \\textbackslash pm 0.10\\$ \\\\\n",
      "rel\\_path\\_length2goal &  \\$1.46 \\textbackslash pm 0.57\\$ &    \\$1.74 \\textbackslash pm 1.28\\$ &   \\$2.89 \\textbackslash pm 4.07\\$ \\\\\n",
      "rel\\_jerk             &  \\$0.14 \\textbackslash pm 0.03\\$ &    \\$0.14 \\textbackslash pm 0.03\\$ &   \\$0.15 \\textbackslash pm 0.03\\$ \\\\\n",
      "contribution         &  \\$0.51 \\textbackslash pm 0.23\\$ &    \\$0.59 \\textbackslash pm 0.14\\$ &   \\$0.69 \\textbackslash pm 0.15\\$ \\\\\n",
      "avg\\_fluency          &  \\$0.95 \\textbackslash pm 0.02\\$ &    \\$0.96 \\textbackslash pm 0.02\\$ &   \\$0.95 \\textbackslash pm 0.01\\$ \\\\\n",
      "avg\\_agreement        &  \\$0.82 \\textbackslash pm 0.11\\$ &    \\$0.75 \\textbackslash pm 0.08\\$ &   \\$0.72 \\textbackslash pm 0.05\\$ \\\\\n",
      "virtual\\_collision    &  \\$2.93 \\textbackslash pm 2.94\\$ &  \\$13.15 \\textbackslash pm 19.28\\$ &  \\$12.08 \\textbackslash pm 8.50\\$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print(export_density_str_df.to_latex())\n",
    "print(export_density_str_df.T.to_latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ANOVA test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster0_anova_ = eval_res_sorted_df[eval_res_sorted_df.cluster==0]\n",
    "c0_metrics = cluster0_anova_[list(anova_metrics)].values\n",
    "\n",
    "cluster1_anova_ = eval_res_sorted_df[eval_res_sorted_df.cluster==1]\n",
    "c1_metrics = cluster1_anova_[list(anova_metrics)].values\n",
    "\n",
    "cluster2_anova_ = eval_res_sorted_df[eval_res_sorted_df.cluster==2]\n",
    "c2_metrics = cluster2_anova_[list(anova_metrics)].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "fvalue01, pvalue01 = stats.f_oneway(c0_metrics, c1_metrics)\n",
    "fvalue02, pvalue02 = stats.f_oneway(c0_metrics, c2_metrics)\n",
    "fvalue12, pvalue12 = stats.f_oneway(c1_metrics, c2_metrics)\n",
    "# total\n",
    "fvalue, pvalue = stats.f_oneway(c0_metrics, c1_metrics, c2_metrics)\n",
    "\n",
    "statP_df = pd.DataFrame(\n",
    "    data=np.vstack((pvalue01, pvalue02, pvalue12, pvalue)), \n",
    "    index=['c0-c1', 'c0-c2', 'c1-c2', 'total'],\n",
    ")\n",
    "statP_df.columns = list(anova_metrics)\n",
    "statP_df.index.name = 'Metrics'\n",
    "\n",
    "statF_df = pd.DataFrame(\n",
    "    data=np.vstack((fvalue01, fvalue02, fvalue12, fvalue)), \n",
    "    index=['c0-c1', 'c0-c2', 'c1-c2', 'total'],\n",
    ")\n",
    "statF_df.columns = list(anova_metrics)\n",
    "statF_df.index.name = 'Metrics'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_crowd_density2_5</th>\n",
       "      <th>max_crowd_density2_5</th>\n",
       "      <th>avg_crowd_density5</th>\n",
       "      <th>avg_min_dist</th>\n",
       "      <th>rel_duration2goal</th>\n",
       "      <th>rel_path_length2goal</th>\n",
       "      <th>rel_jerk</th>\n",
       "      <th>contribution</th>\n",
       "      <th>avg_fluency</th>\n",
       "      <th>avg_agreement</th>\n",
       "      <th>virtual_collision</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c0-c1</th>\n",
       "      <td>4.712817e-12</td>\n",
       "      <td>2.446430e-13</td>\n",
       "      <td>7.107319e-12</td>\n",
       "      <td>5.931100e-09</td>\n",
       "      <td>0.411814</td>\n",
       "      <td>0.439067</td>\n",
       "      <td>0.758870</td>\n",
       "      <td>0.234817</td>\n",
       "      <td>0.467274</td>\n",
       "      <td>0.021096</td>\n",
       "      <td>0.050798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c0-c2</th>\n",
       "      <td>2.925535e-13</td>\n",
       "      <td>1.246060e-14</td>\n",
       "      <td>7.913741e-11</td>\n",
       "      <td>1.121289e-08</td>\n",
       "      <td>0.000962</td>\n",
       "      <td>0.189533</td>\n",
       "      <td>0.123396</td>\n",
       "      <td>0.024224</td>\n",
       "      <td>0.281178</td>\n",
       "      <td>0.002578</td>\n",
       "      <td>0.000584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1-c2</th>\n",
       "      <td>1.102468e-06</td>\n",
       "      <td>1.101239e-06</td>\n",
       "      <td>1.044076e-02</td>\n",
       "      <td>1.824328e-02</td>\n",
       "      <td>0.006326</td>\n",
       "      <td>0.245215</td>\n",
       "      <td>0.164810</td>\n",
       "      <td>0.056081</td>\n",
       "      <td>0.078527</td>\n",
       "      <td>0.215136</td>\n",
       "      <td>0.851864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>1.807321e-17</td>\n",
       "      <td>5.561238e-20</td>\n",
       "      <td>1.682817e-15</td>\n",
       "      <td>1.349264e-12</td>\n",
       "      <td>0.001939</td>\n",
       "      <td>0.228140</td>\n",
       "      <td>0.229460</td>\n",
       "      <td>0.035001</td>\n",
       "      <td>0.205079</td>\n",
       "      <td>0.003490</td>\n",
       "      <td>0.072113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_crowd_density2_5  max_crowd_density2_5  avg_crowd_density5  \\\n",
       "Metrics                                                                   \n",
       "c0-c1            4.712817e-12          2.446430e-13        7.107319e-12   \n",
       "c0-c2            2.925535e-13          1.246060e-14        7.913741e-11   \n",
       "c1-c2            1.102468e-06          1.101239e-06        1.044076e-02   \n",
       "total            1.807321e-17          5.561238e-20        1.682817e-15   \n",
       "\n",
       "         avg_min_dist  rel_duration2goal  rel_path_length2goal  rel_jerk  \\\n",
       "Metrics                                                                    \n",
       "c0-c1    5.931100e-09           0.411814              0.439067  0.758870   \n",
       "c0-c2    1.121289e-08           0.000962              0.189533  0.123396   \n",
       "c1-c2    1.824328e-02           0.006326              0.245215  0.164810   \n",
       "total    1.349264e-12           0.001939              0.228140  0.229460   \n",
       "\n",
       "         contribution  avg_fluency  avg_agreement  virtual_collision  \n",
       "Metrics                                                               \n",
       "c0-c1        0.234817     0.467274       0.021096           0.050798  \n",
       "c0-c2        0.024224     0.281178       0.002578           0.000584  \n",
       "c1-c2        0.056081     0.078527       0.215136           0.851864  \n",
       "total        0.035001     0.205079       0.003490           0.072113  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statP_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>avg_crowd_density2_5</th>\n",
       "      <th>max_crowd_density2_5</th>\n",
       "      <th>avg_crowd_density5</th>\n",
       "      <th>avg_min_dist</th>\n",
       "      <th>rel_duration2goal</th>\n",
       "      <th>rel_path_length2goal</th>\n",
       "      <th>rel_jerk</th>\n",
       "      <th>contribution</th>\n",
       "      <th>avg_fluency</th>\n",
       "      <th>avg_agreement</th>\n",
       "      <th>virtual_collision</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c0-c1</th>\n",
       "      <td>110.235075</td>\n",
       "      <td>138.131419</td>\n",
       "      <td>106.742172</td>\n",
       "      <td>60.388731</td>\n",
       "      <td>0.690954</td>\n",
       "      <td>0.613476</td>\n",
       "      <td>0.095805</td>\n",
       "      <td>1.464473</td>\n",
       "      <td>0.540854</td>\n",
       "      <td>5.865649</td>\n",
       "      <td>4.108938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c0-c2</th>\n",
       "      <td>182.392925</td>\n",
       "      <td>239.371893</td>\n",
       "      <td>109.833354</td>\n",
       "      <td>67.167596</td>\n",
       "      <td>13.849516</td>\n",
       "      <td>1.815012</td>\n",
       "      <td>2.535534</td>\n",
       "      <td>5.726470</td>\n",
       "      <td>1.211209</td>\n",
       "      <td>11.116893</td>\n",
       "      <td>15.324615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1-c2</th>\n",
       "      <td>36.453260</td>\n",
       "      <td>36.457997</td>\n",
       "      <td>7.432946</td>\n",
       "      <td>6.211823</td>\n",
       "      <td>8.579146</td>\n",
       "      <td>1.403055</td>\n",
       "      <td>2.024064</td>\n",
       "      <td>3.939287</td>\n",
       "      <td>3.310086</td>\n",
       "      <td>1.601388</td>\n",
       "      <td>0.035459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>102.328652</td>\n",
       "      <td>138.918265</td>\n",
       "      <td>79.547672</td>\n",
       "      <td>53.311269</td>\n",
       "      <td>7.198312</td>\n",
       "      <td>1.527409</td>\n",
       "      <td>1.521247</td>\n",
       "      <td>3.614986</td>\n",
       "      <td>1.641472</td>\n",
       "      <td>6.432868</td>\n",
       "      <td>2.789335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_crowd_density2_5  max_crowd_density2_5  avg_crowd_density5  \\\n",
       "Metrics                                                                   \n",
       "c0-c1              110.235075            138.131419          106.742172   \n",
       "c0-c2              182.392925            239.371893          109.833354   \n",
       "c1-c2               36.453260             36.457997            7.432946   \n",
       "total              102.328652            138.918265           79.547672   \n",
       "\n",
       "         avg_min_dist  rel_duration2goal  rel_path_length2goal  rel_jerk  \\\n",
       "Metrics                                                                    \n",
       "c0-c1       60.388731           0.690954              0.613476  0.095805   \n",
       "c0-c2       67.167596          13.849516              1.815012  2.535534   \n",
       "c1-c2        6.211823           8.579146              1.403055  2.024064   \n",
       "total       53.311269           7.198312              1.527409  1.521247   \n",
       "\n",
       "         contribution  avg_fluency  avg_agreement  virtual_collision  \n",
       "Metrics                                                               \n",
       "c0-c1        1.464473     0.540854       5.865649           4.108938  \n",
       "c0-c2        5.726470     1.211209      11.116893          15.324615  \n",
       "c1-c2        3.939287     3.310086       1.601388           0.035459  \n",
       "total        3.614986     1.641472       6.432868           2.789335  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statF_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# print(statF_df.T.to_latex())\n",
    "# print(statP_df.T.to_latex())\n",
    "# print(stat_df_all_shared_cluster_stat.T.to_latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Crowd-related metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "crowd_metrics_df = eval_res_sorted_df[['seq', 'control_type'] + list(crowd_metrics) + ['cluster']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>seq</th>\n",
       "      <th>control_type</th>\n",
       "      <th>avg_crowd_density2_5</th>\n",
       "      <th>std_crowd_density2_5</th>\n",
       "      <th>max_crowd_density2_5</th>\n",
       "      <th>avg_crowd_density5</th>\n",
       "      <th>std_crowd_density5</th>\n",
       "      <th>max_crowd_density5</th>\n",
       "      <th>avg_min_dist</th>\n",
       "      <th>virtual_collision</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-25-15-01-50</td>\n",
       "      <td>shared_control</td>\n",
       "      <td>0.049439</td>\n",
       "      <td>0.052378</td>\n",
       "      <td>0.254648</td>\n",
       "      <td>0.067806</td>\n",
       "      <td>0.033821</td>\n",
       "      <td>0.190986</td>\n",
       "      <td>1.686864</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-25-15-04-27</td>\n",
       "      <td>shared_control</td>\n",
       "      <td>0.051985</td>\n",
       "      <td>0.054652</td>\n",
       "      <td>0.305577</td>\n",
       "      <td>0.053875</td>\n",
       "      <td>0.033797</td>\n",
       "      <td>0.203718</td>\n",
       "      <td>1.765421</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-25-14-52-33</td>\n",
       "      <td>shared_control</td>\n",
       "      <td>0.053860</td>\n",
       "      <td>0.054466</td>\n",
       "      <td>0.254648</td>\n",
       "      <td>0.061735</td>\n",
       "      <td>0.033850</td>\n",
       "      <td>0.203718</td>\n",
       "      <td>1.776971</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-25-14-54-51</td>\n",
       "      <td>shared_control</td>\n",
       "      <td>0.054485</td>\n",
       "      <td>0.057059</td>\n",
       "      <td>0.305577</td>\n",
       "      <td>0.060614</td>\n",
       "      <td>0.040070</td>\n",
       "      <td>0.229183</td>\n",
       "      <td>1.921854</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-25-14-57-34</td>\n",
       "      <td>shared_control</td>\n",
       "      <td>0.063069</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.305577</td>\n",
       "      <td>0.075359</td>\n",
       "      <td>0.038457</td>\n",
       "      <td>0.229183</td>\n",
       "      <td>1.434601</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   seq    control_type  avg_crowd_density2_5  \\\n",
       "0  2021-03-25-15-01-50  shared_control              0.049439   \n",
       "1  2021-03-25-15-04-27  shared_control              0.051985   \n",
       "2  2021-03-25-14-52-33  shared_control              0.053860   \n",
       "3  2021-03-25-14-54-51  shared_control              0.054485   \n",
       "4  2021-03-25-14-57-34  shared_control              0.063069   \n",
       "\n",
       "   std_crowd_density2_5  max_crowd_density2_5  avg_crowd_density5  \\\n",
       "0              0.052378              0.254648            0.067806   \n",
       "1              0.054652              0.305577            0.053875   \n",
       "2              0.054466              0.254648            0.061735   \n",
       "3              0.057059              0.305577            0.060614   \n",
       "4              0.054728              0.305577            0.075359   \n",
       "\n",
       "   std_crowd_density5  max_crowd_density5  avg_min_dist  virtual_collision  \\\n",
       "0            0.033821            0.190986      1.686864                  0   \n",
       "1            0.033797            0.203718      1.765421                  1   \n",
       "2            0.033850            0.203718      1.776971                  2   \n",
       "3            0.040070            0.229183      1.921854                  0   \n",
       "4            0.038457            0.229183      1.434601                  4   \n",
       "\n",
       "   cluster  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crowd_metrics_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4-in-1 plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({0: (1.0, 1.0, 0.02720490955785083, 1.0),\n",
       "  1: (1.0, 0.7504899348656205, 0.0, 1.0),\n",
       "  2: (1.0, 0.482843310967855, 0.0, 1.0)},\n",
       " {0: (0.3, 0.3, 0.3, 0.8), 1: (0.3, 0.3, 0.3, 0.8), 2: (0.3, 0.3, 0.3, 0.8)})"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_unique, color_unique = values2color_list(\n",
    "        eval_res_sorted_df['cluster'].values, cmap_name='hot', range=(0.55, 0.75)\n",
    "    )\n",
    "\n",
    "value_unique, point_color_unique = values2color_list(\n",
    "        eval_res_sorted_df['cluster'].values, cmap_name='hot', range=(0.35, 0.5)\n",
    "    )\n",
    "\n",
    "# creating a dictionary with one specific color per group:\n",
    "box_pal = {value_unique[i]: color_unique[i] for i in range(len(value_unique))}\n",
    "# original: (0.3, 0.6)\n",
    "# scatter_pal = {value_unique[i]: point_color_unique[i] for i in range(len(value_unique))}\n",
    "# black\n",
    "# scatter_pal = {value_unique[i]: (0.0, 0.0, 0.0, 1.0) for i in range(len(value_unique))}\n",
    "# gray\n",
    "scatter_pal = {value_unique[i]: (0.3, 0.3, 0.3, 0.8) for i in range(len(value_unique))}\n",
    "box_pal, scatter_pal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 12))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[0, 0],\n",
    "    df=eval_res_sorted_df,\n",
    "    metric='avg_crowd_density2_5',\n",
    "    category='cluster',\n",
    "    # title='Mean crowd density within 2.5 m',\n",
    "    title='Mean crowd density',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.00, 0.4],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "axes[0, 0].set_xticks([0,1,2])\n",
    "axes[0, 0].set_xticklabels(['Low density','Medium density','High density'])\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[0, 1],\n",
    "    df=eval_res_sorted_df,\n",
    "    metric='max_crowd_density2_5',\n",
    "    category='cluster',\n",
    "    # title='Max crowd density within 2.5 m',\n",
    "    title='Max crowd density',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.25, 1.0],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "axes[0, 1].set_xticks([0,1,2])\n",
    "axes[0, 1].set_xticklabels(['Low density','Medium density','High density'])\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[1, 0],\n",
    "    df=eval_res_sorted_df,\n",
    "    metric='virtual_collision',\n",
    "    category='cluster',\n",
    "    title='Virtual collision with Qolo',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[-0.1, 40],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "axes[1, 0].set_xticks([0,1,2])\n",
    "axes[1, 0].set_xticklabels(['Low density','Medium density','High density'])\n",
    "\n",
    "categorical_plot(\n",
    "    axes=axes[1, 1],\n",
    "    df=eval_res_sorted_df,\n",
    "    metric='avg_min_dist',\n",
    "    category='cluster',\n",
    "    # title='Min. distance of Pedestrain from qolo',\n",
    "    title='Min. distance',\n",
    "    xlabel='',\n",
    "    ylabel='Distance [m]',\n",
    "    ylim=[0.5, 2.0],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "axes[1, 1].set_xticks([0,1,2])\n",
    "axes[1, 1].set_xticklabels(['Low density','Medium density','High density'])\n",
    "\n",
    "plt.savefig(\"./pdf/all_shared_control_crowd_grouped_cluster.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Individual figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "dense_fig1, dense_axes1 = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=dense_axes1,\n",
    "    df=eval_res_sorted_df,\n",
    "    metric='avg_crowd_density2_5',\n",
    "    category='cluster',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.00, 0.50],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "dense_axes1.set_ylabel(\"Density [1/$m^2$]\", fontsize=16)\n",
    "dense_axes1.set_xticks([0,1,2])\n",
    "dense_axes1.set_xticklabels(['Low','Medium','High'], fontsize=16)\n",
    "#dense_axes1.set_xlabel(\"Crowd density\", fontsize=16)\n",
    "\n",
    "# axes_cluster.legend(date_unique.tolist(), fontsize=14)\n",
    "dense_axes1.tick_params(axis='x', labelsize=14)\n",
    "dense_axes1.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_boxplot_mean_density_2_5.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "dense_fig2, dense_axes2 = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=dense_axes2,\n",
    "    df=eval_res_sorted_df,\n",
    "    metric='max_crowd_density2_5',\n",
    "    category='cluster',\n",
    "    xlabel='',\n",
    "    ylabel='Density [1/$m^2$]',\n",
    "    ylim=[0.25, 1.2],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "dense_axes2.set_ylabel(\"Density [1/$m^2$]\", fontsize=16)\n",
    "dense_axes2.set_xticks([0,1,2])\n",
    "dense_axes2.set_xticklabels(['Low','Medium','High'], fontsize=16)\n",
    "# dense_axes2.set_xlabel(\"Crowd density\", fontsize=16)\n",
    "\n",
    "dense_axes2.tick_params(axis='x', labelsize=14)\n",
    "dense_axes2.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "# significance\n",
    "bars = [0, 1, 2]\n",
    "heights = [0.99, 1.0, 1.03]\n",
    "barplot_annotate_brackets(0, 1, 2.446430e-13, bars, heights, line_y=0.75)\n",
    "barplot_annotate_brackets(0, 2, 1.246060e-14, bars, heights, line_y=1.05)\n",
    "barplot_annotate_brackets(1, 2, 1.101239e-06, bars, heights, line_y=0.95)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_boxplot_max_density_2_5.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "dense_fig3, dense_axes3 = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=dense_axes3,\n",
    "    df=eval_res_sorted_df,\n",
    "    metric='virtual_collision',\n",
    "    category='cluster',\n",
    "    xlabel='',\n",
    "    ylabel='Virtual collision',\n",
    "    ylim=[-0.1, 30],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "dense_axes3.set_ylabel(\"Virtual collision\", fontsize=16)\n",
    "dense_axes3.set_xticks([0,1,2])\n",
    "dense_axes3.set_xticklabels(['Low','Medium','High'], fontsize=16)\n",
    "# dense_axes3.set_xlabel(\"Crowd density\", fontsize=16)\n",
    "\n",
    "# axes_cluster.legend(date_unique.tolist(), fontsize=14)\n",
    "dense_axes3.tick_params(axis='x', labelsize=14)\n",
    "dense_axes3.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "# significance\n",
    "bars = [0, 1, 2]\n",
    "heights = [0.99, 1.0, 1.03]\n",
    "barplot_annotate_brackets(0, 1, 0.050798, bars, heights, line_y=20)\n",
    "barplot_annotate_brackets(0, 2, 0.000584, bars, heights, line_y=25)\n",
    "# barplot_annotate_brackets(1, 2, 0.851864, bars, heights, line_y=25)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_boxplot_virtual_collision.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "dense_fig4, dense_axes4 = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=dense_axes4,\n",
    "    df=eval_res_sorted_df,\n",
    "    metric='avg_min_dist',\n",
    "    category='cluster',\n",
    "    xlabel='',\n",
    "    ylabel='Distance [m]',\n",
    "    ylim=[0.5, 2.5],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "dense_axes4.set_ylabel(\"Distance [m]\", fontsize=16)\n",
    "dense_axes4.set_xticks([0,1,2])\n",
    "dense_axes4.set_xticklabels(['Low','Medium','High'], fontsize=16)\n",
    "# dense_axes4.set_xlabel(\"Crowd density\", fontsize=16)\n",
    "\n",
    "# axes_cluster.legend(date_unique.tolist(), fontsize=14)\n",
    "dense_axes4.tick_params(axis='x', labelsize=14)\n",
    "dense_axes4.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "# significance\n",
    "bars = [0, 1, 2]\n",
    "heights = [0.99, 1.0, 1.03]\n",
    "barplot_annotate_brackets(0, 1, 5.931100e-09, bars, heights, line_y=2.0)\n",
    "barplot_annotate_brackets(0, 2, 1.121289e-08, bars, heights, line_y=2.2)\n",
    "barplot_annotate_brackets(1, 2, 1.824328e-02, bars, heights, line_y=1.2)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_boxplot_mean_min_dist.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Path efficiency-related metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_metrics_df = eval_res_sorted_df[['seq', 'control_type'] + list(path_metrics) + ['cluster']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2-in-1 plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MGxubJsdOZMpMsX2tef1+ubm5CA4ObrB90Hfb7O3tjaqqKnHoAFA9fODQoUM6He/l5SUORduxYwc2bNggJn/68OD1BgUFib8g1igqKqr3e6Cx75ePjw/i4+O1hsZduXIFycnJ9e4vk8mwdu1aHDlyBFu2bBFvcIKCglBSUqI1L31RURGcnZ3RrVs3/PTTT6isrMSQIUMaFaOpY2LcCtX85FLz049UKsXy5cuRl5eHDz/8ULzznDFjBmQyGWbNmoU1a9Zg9erV4thUoLphrGkcaxr8gwcPIjk5WfxZKC4uDnfu3IGVlRWysrJQWFgornB0//E1zMzMMGHCBFy8eBEjRowQy+VyOaZOnYp9+/bhtddew+bNm/HKK68gJCSkzmvU5XxqtVoriay57vvv/AVBEI9p6P14kLW1NVJTU3Hy5Em4uLhg/Pjx2LJlCyoqKgAAhw4dQkREBLp161bv8WlpaUhPT8fNmzdrxVujpmzOnDmIi4vD7NmzsXnzZrz99tv13sHv3r0baWlp8PX1haOjIyQSidh7DAAvv/wyjh8/Lk4sn5mZiZKSEkycOBEKhQIKhQJHjhxBamoqtmzZAgC4du0abty4Ib5fHFpBbVFbaF+B6p/sa66lpjf86aefbrB9sLKyQkFBAfLy8nDy5Ml624sHY3/wtZrXu3btimHDhuGHH37A4sWLxecghg4dCqB6db2H1R0ZGQkvLy94eXnBw8MDycnJyMnJEV9vbDv8oAc/mzlz5qCsrEz8DJVKJU6dOoV58+bVezxQPWTk+PHjYvz1XdPTTz8NFxcXLFiwAF9//TXWrVuHb7/9tt7PctGiRQgMDESHDh3g4eGBhIQE8VeLJ598El26dNG66Th8+DDmz58PqVSKsrIypKenIz4+HjExMbh8+TLy8vJw5swZMaaHfY6myiwyMjLS0EGQ7s6dO4f169cjPT0dRUVF6NChAzw9PWFvbw8nJyf88MMPuHDhAmxtbdG7d2/06tULsbGxiImJQbt27bBkyRJYWloiLi4O69atQ3Z2Nnx9fREcHIzs7Gzs3LkTOTk5eOmll7B//37I5XKMGjUKLi4u2LNnD44ePYrnnnsOp0+fxubNm5Gfnw8/Pz+tB0U8PT1RUVGBxx9/XCv2sLAw5OXl4ciRI0hOTsbzzz9f79jchs5XWFiINWvWIC0tDZ6ennBwcEBUVBQSEhIgCAL8/f1x6NAh8aGKwMBAdOvWrd73oy7l5eVYs2YNevbsiV69emHQoEFITU3F2rVrceXKFeTn5+PDDz8Uhyc8yNraGhs2bEBBQQH69OmDH3/8ETdu3ICTkxOcnJywfv16XLt2DVKpFH369EFoaCgUCgWOHz+O+Ph4jB07Vpw+6UFWVlbYtGkTYmJisH//fkRHR+Pnn39GYWEhhg4dih49esDGxgYrV67E1atXce7cOXz88cfiz6VqtRo7d+7EtWvX8Morr+DQoUOoqKjAlClT8MMPP+DcuXNQKpUICAho8MFEIlPRVtrXw4cPo6CgACkpKYiNjcUff/yBf/7zn3Bzc2uwfXBzc0NMTAx27NiBadOmYdOmTTh//jzUajUCAgJw4MABREdHo7S0FL1794aLi4vWuWNiYrBp0ybcu3cPPXr0gLu7OwYNGoSMjAwcOnQIt27dwjvvvAM/Pz/ExsZi48aNyM7Oho+PD6qqqrB+/XokJSXB1dUVgYGByMvLw/bt2/H7779j//792LdvHzZv3ozQ0FB4eHg0uh1+8Pvgwc/Gw8MDvXv3Fv8uHD9+HHPmzKl3oRMnJydcunQJGzduxMSJE3Ho0CEcPHgQlZWVCAgIwMWLF7F161bk5+cjKCgIXbp0QVhYGOLj43H48GFxGrz6xv6mpqbil19+wf79+/Hbb79h7969+PnnnzFy5Ei0b98ew4YNE9+XuLg4dOvWDbNmzQIAdO7cGTExMdi9ezf8/f3FXyGfeuopFBcXY82aNUhJSYGXl1erWKBKXyRCYwbWEJFR2Lt3LxwcHMSfwDQaDe7evYu3334b//73vw0cHREZs/feew8A8Omnnxo4kqYRBAEfffQRPvjgAzGhVSgUuHDhAg4fPowPP/zQwBE2r6qqKnzyySeIjIwUh2xUVlYiOjoaRUVFnJv+EXFWCqJWpqysDJ999hn++OMPsUwqlcLNza1N3dUTUdsWHR0NjUaj1ctrYWEBNze3NvEg2YYNG+Du7l5rlg1XV1c4ODgYMLLWTecxxqdOncKUKVOQmZlZ7z7Hjx/HsmXLEBkZWWsJRCLSj4qKChQVFeHDDz/EjRs3UF5ejlu3buGzzz4TV6Giti02NhYRERHo378//vd//7fe/dhmt01qtVpryrPWKj8/HwcPHsSOHTuQk5OD0tJSxMXF4fvvv9daYc5U5efnY+vWrThw4ADy8/NRXFyMP/74A7///nutoTakO516jPPy8lBeXq71cM+DUlNTsXLlSuzcuRNSqRQzZsyAj48P/Pz89BYsEVVPDbRx40asWrUKf//736FSqRAcHIwFCxYgODjY0OGRgZWUlODixYvYvn07Ll26hFdeeQXDhw/HwIEDtfZjm9027d+/H7GxsVCr1Th48GCrvpmeOXMmBEHAunXrsHTpUjg4OCAiIkJraIUpe/PNNyGXy/HFF1/g9u3bcHNzw9ixY7F48eJmmdmprdB5jLFGo0GPHj1w5MiROp/iX7JkCRwcHPDWW28BqJ6zMSMjA//85z/1GzEREdWrsrISlpaW4hfjxIkT8e6779aaToptNhFRbTqPMX5w9ZkHnTt3TmuBAW9vb/z888861a3RaFBWVgZzc3Pe5RBRqyMIApRKJeRyeYNtZXOrmR4KqJ5ZxcfHp87ZCdhmE1Fb9bA2W28P3+Xk5GgN9pbL5cjNzdXp2LKyMly/fl1foRARGYSfn5/RLKkaFxeHr776Ci4uLqioqKi1WAPbbCJq6+pqs/WWGEskEq0xPQqFot75XR9Us5+fn5/W+u1ERK2BQqHA9evXdW7zWkKnTp0wYcIErFixAsuXL8eDU9azzSaituphbbbeEmNXV1ettc7Lysrg6uqq07E1P8VZWFi0iQHzRGSajGlYgaurKyZPngypVKq18tX9r7PNJqK2rK42W2+D4WpWBauRlpZW76o7RETUMoKCguDm5larnG02EVFtOifGNZNX3D+JRVRUFFJSUgAA06ZNw8mTJ8V9zp49i2effVafsRIRUQMqKipw5coVcfv48ePiErBss4mIHk6noRSlpaX49ddfAQC7d+/GzJkz4eTkhOjoaHh6eopzX86YMUOc6mfOnDlchYuIqIUlJyfjxRdfhI+PD3r37o1evXph2LBhAMA2m4ioATrPY9ycqqqqcPnyZQQFBXG8GhG1Om2tDWtr10tEpuVhbZhhJ9wkIiIiIjISTIyJiIiIiMDEmIiIiIgIABNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDEmIiI2oC8vDytJbBrFBQU4L333sO9e/cMEBURGRudFvggIiJqjfLz87Fx40ZkZmYCAHr27IkZM2bAwsICALB161ZcuXIFW7duxcsvv2zIUInICLDHmIiITNbmzZvFpBgAEhMTcfDgQQDVvcVHjhyBIAg4fPgwe42JiIkxERGZprKyMqSmptYqT0xMBABs2bIFKpUKAKBSqbB169YWjY+IjA8TYyIiMknm5uaQyWqPGLSxsUFpaSkOHToEjUYDANBoNDhy5EhLh0hERoaJMRERmSQLCwsMGDCgVvnQoUNx8OBB2NjYaJW3a9dOTJSJqG3iw3dERGSyxo0bBycnJ/z555+wsLDA4MGD0bNnTxw+fBgSiURrX4VCgfz8fLi4uBgoWiIyNCbGRERksqRSKYYOHYqhQ4dqlTs7O6O0tFSrrLS0FHZ2di0ZHhEZGQ6lICKiNmfkyJFwcHAQe40lEgmCgoJgaWlp4MiIyJCYGBMRUZvTsWNHfPjhh5BKq78GzczMsHDhQgNHRUSGxsSYiIjapG7dumHUqFGQSCQYNWoUHB0dDR0SERkYxxgTEVGbNW3aNGRkZGDatGmGDoWIjAATYyIiarOcnJzw6aefGjoMIjISHEpBRERERAQmxkREREREAJgYExEREREBYGJMRERERASAiTEREREREQAmxkREREREAJgYExEREREBYGJMRERERASAiTEREREREQAmxkREREREAJgYExEREREBYGJMRERERASAiTEREREREQAmxkREREREAJgYExEREREBYGJMRGRyfv/9dzz++OMICwvDxx9/DJVKVed+xcXF6Nu3L/z9/eHv749Dhw61cKRERMZFZugAiIhIf7Kzs3H48GF8/fXXuHHjBiIjI9GhQwfMnTu31r7bt2/HZ599BrlcDgAICwtr6XCJiIwKE2MiIhOSnZ2NTz75BDKZDEFBQbh+/TrOnTtXKzFWKBQ4deoURowYgS5duhgmWCIiI8OhFEREJiQkJAQy2X/7PNzc3NChQ4da+8XExCA+Ph4RERF48cUXUVBQ0JJhEhEZJfYYExGZsMTERDz//PO1yiMiIjBq1CjExcUhMjIS8+fPx+bNmyGV6t5fcvnyZX2GSkRkcEyMiYhMVHp6OpycnNC9e/c6X5dIJOjfvz82btyIMWPGID4+HsHBwTrXHxQUBEtLS32FS0TUIqqqquq9sedQCiIiE6RWq7F161YsXLiwwX2dnZ3x5JNP4vbt2y0QGRGR8WJiTERkgqKiojB37lxYWFjotL+ZmVm9PctERG0FE2MiIhPz/fffIyAgAJWVlbh16xZ27NiB9PR0REVFISUlBQBw4MAB3Lp1CwBw8+ZN2Nvbw8fHx5BhExEZnE5jjCsrK7FixQrI5XIUFRXhnXfega2tba39vv/+e2g0GkilUlRUVOCNN96ARCLRe9BERFS31atX41//+pdWmY+PDyZPnozo6Gh4enrCx8cHsbGxWLx4McLDwxEaGooFCxYYKGIiIuMhEQRBaGin999/H0OGDMGYMWPwxx9/YM+ePfjyyy+19omJicGRI0fw8ccf1zqmITWDoPkgBxG1Rm2tDWtr10tEpuVhbViDQylycnKwd+9eDB8+HAAwcOBAHD16FJmZmVr73bx5E2VlZeK2lZUVSkpK9BE/EREREVGzazAxjo2NhaOjI6ytrQEAMpkMHTt2xPnz57X2Gz58OI4ePYoDBw6gqKgI9+7dw7hx45onaiIiIiIiPWtwjHFOTg4cHBy0yuRyOXJzc7XKunbtii+++AILFy5ESEgIVq1aBSsrq0YFw8niiYiIiMhQGkyMJRJJrQRXoVBoLTlaQ6VS4dtvv8XSpUvx5ptvYtWqVTA3N9c5GI5XI6LW6GGTxRMRUevR4FAKNzc3FBcXa5WVlZXB1dVVqyw2NhaJiYkYNGgQfvrpJ1y9ehXr16/Xb7RERERERM2kwcQ4LCwMubm5qKysBAAolUpkZ2cjNDRUa78DBw7Ay8sLQHUy/cYbb+DChQvNEDIRERERkf41mBi7uLhg2LBhOHPmDADg9OnTiIiIgJubm9Zk8T169MCVK1f+W7FUip49ezZT2ERERERE+qXTAh+RkZFYsWIFkpKSkJeXh2XLlgGA1mTxkyZNQnZ2NtasWQNHR0fcvn0b8+bNa9bgiYiIiIj0RafE2N7eXly44367du0S/yyVSvH666/rLzIiIiIiohbU4FAKIiIiIqK2gIkxERERERGYGBMRERERAWBiTEREREQEgIkxEREREREAJsZERERERACYGBMRERERAWBiTEREREQEgIkxEREREREAJsZERERERACYGBMRERERAWBiTEREREQEgIkxEREREREAJsZERERERAAAmaEDICIiIgKA/Px8REdHIzU1FS4uLoiIiEDXrl0NHRa1IUyMiYiIyOBUKhW+++47FBQUAACKioqQlpaGhQsXwtXV1cDRUVvBxJiIiIge2dKlSxEXF9ds9c+dO1cv9YSEhOCjjz7SS11kujjGmIiIiIgI7DEmIiKiJtBXL2xVVRWWLVuGiooKJCcnAwB8fX2xYMECeHt76+UcRA1hjzEREREZnKWlJebOnQs3Nzex7JlnnmFSTC2KiTEREREZBR8fHyxatEjcDg8PN2A01FOumNYAACAASURBVBYxMSYiIiIiAhNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDp2oiITM7vv/+OTz/9FGVlZRg7dizee+89yGS1m/vjx4/j+PHj0Gg0CA8Px+jRow0QLRGR8WBiTERkQrKzs3H48GF8/fXXuHHjBiIjI9GhQ4daq4elpqZi5cqV2LlzJ6RSKWbMmAEfHx/4+fkZKHIiIsPjUAoiIhOSnZ2NTz75BEFBQRg/fjymT5+Oc+fO1dpv/fr1GDJkCKTS6q+BwYMHY/369S0dLhGRUWFiTERkQkJCQrSGTbi5uaFDhw619jt37hw6duwobnt7e9eZQBMRtSUcSkFEZMISExPx/PPP1yrPycmBg4ODuC2Xy5Gbm9uoui9fvtzk+Ige5sKFC4YOgdoYJsZERCYqPT0dTk5O6N69e63XJBIJLC0txW2FQgFzc/NG1R8UFKRVB5G+9evXz9AhkAmqqqqq98aeQymIiEyQWq3G1q1bsXDhwjpfd3V1RXFxsbhdVlYGV1fXlgqPiMgoMTEmIjJBUVFRmDt3LiwsLOp8fdCgQUhNTRW309LSEBYW1lLhEREZJSbGREQm5vvvv0dAQAAqKytx69Yt7NixA+np6YiKikJKSgoAYNq0aTh58iQAQBAEnD17Fs8++6whw6Y2SKPR4O7du6iqqjJ0KEQAOMaYiMikrF69Gv/617+0ynx8fDB58mRER0fD09NTnK94xowZ+Oc//wkAmDNnDnx9fQ0RMrVR165dw7Zt21BYWAhzc3M8/vjjiIiIMHRYVIfr16/jzJkzUKlU6Nu3L4KDgw0dUrNhYkxEZELmz5+P+fPn1/narl27tLbHjRuHcePGtURYRFoqKyuxYcMGsadYqVTi4MGD8PLyQkBAgIGjo/vFx8dj48aN4vZff/2FgoICPPHEEwaMqvkwMSYiIqIWdf369TqHTyQkJDAx1pOlS5ciLi6uWepeuXIlVq5c2eR6QkJC8NFHH+khIv3hGGMiIiJqUTY2No0qJ2op7DEmIiKiFtW1a1d4enoiKytLLDM3N8eAAQMMGJVp0VdP7NatWxEbG4vk5GQAgK+vL7y9vbFgwQK91G9smBgTERFRi5JIJJg3bx4OHTqEGzduoH379hg5ciRcXFwMHRo94G9/+xtycnLExNjZ2RlTp041cFTNh4kxERERtTi5XI7x48cbOgxqgK2tLV5//XUcPnwYAPD+++9DIpEYOKrmwzHGRERERKQTU06KASbGREREREQAdBxKUVlZiRUrVkAul6OoqAjvvPMObG1t691369atsLe3h7e3N/r06aPXgImIiIiImoNOPcZLly5Fv3798NZbb+GJJ57AkiVL6tyvqKgIb7zxBp544glMmDCBSTERERERtRoNJsY5OTnYu3cvhg8fDgAYOHAgjh49iszMzFr7vvHGG5g9eza8vLz0HykRERERUTNqMDGOjY2Fo6MjrK2tAQAymQwdO3bE+fPntfY7evQo0tPTceXKFbzwwgtYvnw5FApF80RNRERERKRnDY4xzsnJgYODg1aZXC5Hbm6uVtm+ffvQp08fPPvss5gwYQImTJgAQRCwaNEinYO5fPmyzvsSEREREelTg4mxRCKBlZWVVplCoYBMpn1ocnIypk6dCnNzczg6OmLKlCnYsmVLoxLjoKAgWFpa6rw/EZExqKqq4o09EZEJaHAohZubG4qLi7XKysrK4OrqqlWmVquhUqnEbX9/fxQVFekpTCIiIiKi5tVgYhwWFobc3FxUVlYCAJRKJbKzsxEaGqq1n7+/P1JTU8VtmUyGrl276jlcIiIiIqLm0WBi7OLigmHDhuHMmTMAgNOnTyMiIgJubm6IiopCSkoKAGDWrFk4ceKE+MDdpUuX8OyzzzZj6ERERGRKioqK8M0334jb69evR1VVlQEjorZGpwU+IiMjsWLFCiQlJSEvLw/Lli0DAERHR8PT0xM+Pj4IDg7G66+/jo8//hje3t6wsrLCpEmTmjV4IiIiMh3btm0TO9wAIDExEfv27WM+QS1Gp8TY3t4eH3/8ca3yXbt2aW2PGzcO48aN009kRERE1GYolUokJSXVKk9ISGBiTC1Gp8SYiIiI6EFqtRpHjx5FfHw8LCwsMGTIEAQHBz9SXVKpFBYWFrXWQLCxsWlUPSUlJUhKSoJcLkf37t0hleq0yC8RACbGRERE9Ih27tyJs2fPittpaWnQaDTo169fo+syMzPD4MGDcfToUa3yxx57TOc64uPj8e9//xtqtRoA4O7ujvnz58PW1rbR8VDbxMSYiIioDRk7dmyz1h8ZGam3upKTk/HJJ5806fgTJ07oLZ4H7d27t9nqJsPg7wtERNQmaDQaaDQaQ4dBREaMPcZERGTSqqqqsHv3bly8eBESiQShoaF4+umnYW5ubujQDGrV3Kav1rjlpCcy87VXxw33u4chPQqaXHdjVSmlWH2wC1RqiVZ5oFcJxvTN1eu5FkQF6bU+Y1FYWIiTJ08iPz8f3bp1Q1hYWK2Vjk1d27paIiJqc3bv3o3Y2Fhx+9SpU7CwsGj2IQVtwVN9c7DvghuyCqwglQA9OpZggN89g8Riaa5BuN89nLzqJJZZWWgQ5muYeFqbwsJCfPnllygtLQVQPV47KSkJc+fONXBkLYuJMRERtRpLly5FXFxck+tJTk7GmjVr9BBRbSEhIfjoo4+apW5jY2ejwvQhWSipMIPMTIC1hfZQlSqlFBoBtcqbywC/e/BwrMSNO3LYWKoR5FWMdtbqFjl3UxjrTVpycjL27NmjVWassd6vKWO/mRgTERFRkzyYfCpUEhz80xXXsqtng/BxK8eTwTktkiB3dqlAZ5eKZj8PmSYmxkRE1Go8Sk/szp07cerUKa2yUaNGYfTo0WLvF2cX0K9jfzkjKeu/U6TdvGODwwkuGBuSY8CojN+qPk0f9/2oEova4UCOq1aZmUTAPJ802Ji1jodWF/zZ9LHfTIyJiMikjRs3DhYWFoiLi4NUKkVYWBhGjRpl6LBMWk1P8f2u37aFRsiBVFLHAWRwgXYlSCu3QVJJ9WdnJhEw0vVuq0mK9YWJMRERmTSZTIaxY8fqPDby0qVLOHfuHDQaDfr374/+/fs3c4Smx0KmQaVCe0ZYczMNmBMbL6kEGNshBwOcClCkNIeHdSWs21hSDDAxJiIiEp0+fRo7duwQt2/cuIHi4mI88cQTBoyq9QnuUoTjV9prl3kXQcLM2Og5WyrhbKk0dBgGwwU+iIiI/uPYsWM6ldHDhfoW4omeeXC1V8DZToGhAfkY1F3/cxuXV0lRXsVUhvSHPcZERET/UTOH6/3Ky8uh0WgglTIBa4y+PkXo61PULHVXKqXYf9ENKTk2AFp21gsybfxXTkRE9B+BgYG1ygICApgUG5mjic64eccGggAIQvWsF0cvOxs6LDIB/JdORET0H+PGjUO3bt3E7c6dO2Py5MkGjIjqcv127Vkv6poJg6ixOJSCiIjoP2xtbTF//nzk5+dDo9HAxcXF0CFRHSxlGihVZrXKiJqKPcZERCbo1KlTmDJlCjIzM+vdp7i4GH379oW/vz/8/f1x6NChFozQuLVv355JsRHr61NYR1nzjGcmIL3cGr9mu2FXVgdcLTHtnnn2GJPJi4+Px5o1a/DWW2+ha9euhg6HqNnl5eWhvLwcCQkJD91v+/bt+OyzzyCXywEAYWFheo9F17mDDa01xGkKq/MpVRIcv9IeSVntIDMT0LtLEcJ87zV60Y/QboWwkGmQmGEHAAjyKkGwNxPj5nCtRI69t90h/Gf7ZpkNipUyhDnVvjkxBUyMyWSp1Wps2LABMTExKC4uRmRkJGbNmoWRI0caOjSiZuXs7NzgvLsKhQKnTp3CiBEj0KVLl5YJjNq8Qwku+OtWO3H75FUnmEkEhPo2LsmSSIBg72IEexfrO0R6wLkCRzEprhF7zxH9HQtNchVDJsZksi5evIj4+HiUlJRAEAQUFxdj37596Nu3L9q3b99wBUStWEOzKMTExCA+Ph4REREYOnQoPv30Uzg5OTXqHJcvX9Z532++ud6ouum/Xn3VDwBw4cIFA0fSNEq1BFez2tUqT8iwa3RibCxa+2eii1J17VSxUi2FSpDAQvJgymwcmvK5MDEmk5WamoqCggIIQvU/XEEQkJ+fj9TUVCbG1OZFRERg1KhRiIuLQ2RkJObPn4/Nmzc3alqyoKAgWFpaNmOUzUujAVJTrVFZKYW3dwVsbIz74a1+/foZOoSmEf7z34PFxplb6aTVfyY68LYpx+Vi7RsaT+tKWEiN94Nr6HOpqqqq98aeiTGZLFdXV7G3GKhOjEtKSuDq6mrgyIiMg0QiQf/+/bFx40aMGTMG8fHxCA4ONnRYLaKsTIqtW92Rn28OAJDJBDz99F1061Zh4MhMl7lMgJ9HKZKytB/eCvQqeehxF1PsEXvDAeUKM3R1K8eInncht1I3Z6h0n6HOeShQmCO70goA4GihxGi3XANH1XyYGJPJCgsLg6urK3JyciAIAiQSCbp06YJOnToZOjQio+Ls7Iwnn3wSt2/fbjOJ8alTDmJSDAAqlQSHDjnBxycLXMuj+YzqnQupVMC1rHYwM9OgT+dihPveq3f/q5m2OJL434U7rmfLUVpphhlDsloiXAJgI9NgRqcs3K2ygEqQwN2yChITHFtcg4kxmSxra2s899xzWL58uVj2yiuvGDAiIuNlZmaG7t27GzqMFpOVVXsISEmJDIWFMjg5qQwQUdtgaS7gqb65GBOcq1NyVTPrxP2yC6xQUGoOJ1tlM0RI9XGxVBg6hBbB+2IyWbdv38aOHTu0yn766ScolWxMyfTdP4SoRlRUFFJSUgAABw4cwK1btwAAN2/ehL29PXx8fFo+UANxcqrdDlhYaGBry5/oG1JULsONOzYoqTBreOd6mHKPI7Vu7DEmk3X+/Hnk5eVplaWmpuLatWsICgoyUFREza+0tBS//vorAGD37t2YOXMmnJycEB0dDU9PT/j4+CA2NhaLFy9GeHg4QkNDsWDBAgNH3bIGDChCWpo1qqr+2z80cGARLCyM94EifVsQ1XrbwY+2+Rs6BDJRTIzJZKnV6jofvlOr2SNEps3W1hbTp0/H9OnTtcp37dol/nnJkiVYsmRJS4dmNFxdlZg9OxuJibaorJTCz68cnTpVGTosIjIwJsZksvr164edO3eiuLhYfPjOycmpTY2jJKL62durMXhw210tbdVc3eehBoBTSY44fa32XNdDA/MR2q1p8xD/dLwj7hRqj/v2cq7AtEHZTaq3ubTm3nZ6OI4xJpPVqVMnPP/885D8ZzCbRCLB22+/3arnXSUiMhQ7m7ofSrS3adpzG4KAWkkxANy+Z9WkeokeBRNjMmnDhg3D8OHDIZFIMHLkSI4tJiJ6RN09StG+nXYS7GqvQDf3sibVK5EA7dvVnvHAuY4yfVCpJbh5xwbpd62haTtDyklHHEpBJu2XX35BWloarKyskJ6ejgsXLrSJlYqIiPTNXCbg74Mz8WeaPe4WWcDNoQp9uhTBTA9dbMMC87E7tgM0/1l80MxMwGMB+U2v+AE5hZbYcbYDyquqZ9RwlCsxZWA27OvpDaeWU6oyQ2qZDazMNPCRl8HMQDOXMDEmk3X58mUcP34cUqkUHTt2hFqtxtatW+Hn54d27do1XAEREWmxttBggF/9C3I8Kh+3cvxjeAauZtpCKgV6eJbAQf7oyWpBqTmOXnbGrTxrOMiVGNy9AL4dyvB7vIuYFAPAvTJzHP+rPZ7un6OPy6BHdL1Ejn133KH+Tw++o4US0zpmwVbW8g/LMzEmo7N06VLExcU1W/0PPqn/qEJCQvDRRx/ppS4iorbOyVaJQd2bnnSrNcDPZzxQXF6d4uQVW+CX8+6YOjCrzrHMGXk2TT4nPTq1ABzKdRGTYgC4pzDHmQJHjHTNq//AZsIxxkREZFAVFVKUlDz6YhFE90u/ayMmxTUEAUjKsoWtVe0eSAc5F30ypCKlOcrVtf/9Z1dYGyAa9hiTEdJXL2xRURE+//xz/PnnnwAAX19fdO7cGa+99po4UwURGY5KBRw40B5Xr8ohCBJ4elZh7Ni7sLPjXOOtTVaBFf640h65RZZws6/C0MA8dHA0zLzQQr0P1Ekw0L8Av8e7iCVSCTDQv6BF4iJAqZEgsdgOtyus4GShQB+HItjKVLCQClBotL+XnS0MswQ1e4zJZNnb2+ONN94Qt0ePHo158+YxKSYyEqdPO+DKFVsIQvW/yawsS0RHOxs4Kmqs0koz7Djjgcx8KyhUEtzKt8L2Mx4orzJMitHZpaJWz7BEAgR6FaN3l2JMHZSFXp2LEexdhJmPZcLHrdwgcbY1GgHYmeWBI7nOuFJii5P5Tth0qyMEARjYXvvmxMpMgzAnw9ywsMeYTFr79u3FP48aNcqAkRDRg65dqz2289YtK5SXS2FjozFARPql0QBnztjjyhU5zMwE9OpVin79SmBq9+ZJWbZQqLQvSqGU4lq2LYK9i1s8HpmZgMkDsnEk0RmZ+dawt6l++M7DqboHu5NzJTo5V7Z4XLpY8GfbmlI0o9wa8YWBdb629Kphlv1mYtxMqqqqcPnyZUilUgQEBHBRCSKiB1ha1k5+ZTIBMplpTC577Jgj4uLsxO2jR52g0UgQGtryyWJz0mjqzvQ1guHuAFzsFJg2KBuCAJO7EaHmxcT4EQmCALVaDZms9luYkZGBH374ARUVFQAAW1tbzJs3Dx4eHi0dJhGR0erXrwTR0dqdBj17lsLCovUnxhoNkJBgW6v80qV2RpMYN/eyxhtivLAhxqtZz2FqVvVp3DLd9blUaIfDuS61yoe75CHE0TDLoOcrzLEurVOt8oket9HVVj/DWfTR487E+BGcPHkShw4dQklJCXx8fDBlyhS4ubmJr+/evVtMigGgtLQUv/zyC15++WVDhEtEZJQCA8sglQq4dKkdFAopuncvM5qksakEAVCpandV1lVGpG/19dbfX64RAAlarke9vYUSQ5wLcDLPCTW3vr3si+EjN64x3m0+MW7qnLnJyck4ePCgTvvt37//kc7B+XKJyFT16FGOHj1qfzEWF5vh0qV2KC01Q5culejRowzSVvS4uJkZ4OdXjqQkuVZ59+5NWz5ZH/bu3dtsdavVapiZNX3qvbFjxwJo3lhNmZ9tKY7ntYf6vkTYTCLAv10pipQy/J7jivRya1ibqRHqVIj+joUtEle40z0EtCvBnUpLtLdUoL2F8U2V1+YTYyIiMi6FhTL89JM7KiqqE6y//rLFrVtWGD1a/0sEN6eRIwug0UiQnGxdvZpbjzI89ljLJCCGoo+k+FFUVFTAwsLCYOc3Nu3M1ZjocRtH7zojX2GB9hZKDHPJg725ChvSvXC3ygIAUK42w7G77WErU6FHu9IWic3OXAU7c+NdgrvNJ8aN7YndvHlzrR5mQRBgbm4OlUqF5ORkAICVlRUsLS3h6uoKqVSKuXPnokePHnqLm4ioNXn1Vb8mHZ+RYYl9+9o3vKMRS021wv79rfsajM2dO3ewbds2pKenw9raGsOGDcPIkSMNHZZR6CKvwBz5Lag0Esik1YMXcqssxKT4fleK2+k1Ma5QS3Gp0B65VZZws6xCsEMRrMxax0wzOiXGlZWVWLFiBeRyOYqKivDOO+/A1rb2QwU19u/fjy1btuCnn37SW6DGIiwsDCdOnEBFRQXMzMwgl8vh4eGBnJwcrflx3d3doVKpMGLECPTt2xfu7u4GjJqIiKjlKZVKxMXFISsrC56enggJCYG5uble6tZoNFi3bh3y8qqXDa6oqMBvv/0GZ2dnBAcH6+UcpqAmKQYAKep+sFUq0d8Dr0qNBJtvdUSBovpzTi6V41qpLZ7tdAtmrWCIvU6J8dKlSzFkyBCMGTMGf/zxB5YsWYIvv/yyzn1zcnLw3XffwcHBQa+BGovLly9DEAQoFAqo1dUTiD/zzDP4+eefkZSUJO5XXl6OwYMHY8yYMYYKlYjIaHzzzXWd9z1/3g4xMY5aZRYWGsyfn2l0M1YUFsqwZo1nrXI/v3KMH39XL+doam+7oajVaqxevRrp6eliWWxsLF599VW9DHnIzMwUk+L7Xbx4sdUmxoaaxzij3BpHc5tvcZ2McmtcuGffbPXrU4OPMuTk5GDv3r0YPnw4AGDgwIE4evQoMjMz69z/xx9/xLRp0/QbpZEoLi7GiRMnYGNjA3d3d3h6esLe3h4XLlyAnZ0dysv/+wBJWVkZHB0dH1IbERHVJTi4GN7e/53ZRyYTEBGRb3RJMQAoFHV3gVVVtYKusWZ2+fJlraQYANLT03H5sn6mJKtrulQAeuuRprapwR7j2NhYODo6wtrauvoAmQwdO3bE+fPn0bFjR619f/75Z4wdOxY3b95slmBrnlI1NjXjiu9XVlaGqKgoREVFGSAi3fBpXyIyRjIZMGVKLm7ftkBJiRm8vKpgbW2c4xNdXJRwdFTi3j3tZMzf37imoDKE3NzcRpU3loeHBzp37lwr+R4wYIBe6m9JreH7uK6ZQlJTU3H8+HGUlJQgICAAQ4cOxa1bt7Bq1apax7/99tvw9Kz964qxaTAxzsnJqTUsQi6X1/qLnZGRgaKiIvTu3fuRE2N93UWSbi5cuGDoEFpUW7teotauQwcFOnSoXV5eLkVhoQzOzkqD9yJLJMD48XcRHe2M3FwLyGQC+vQpQa9eLfOEvzHz9vaus9zHx0dv53j++ecRHR2Nq1evws7ODiNGjICvr6/e6qf6paWl4dtvv4VGU33TmpqaiqysLMyaNQsjRozAkSNHIAgCpFIpRo0a1SqSYkCHxFgikcDKykqrTKFQaP2EodFosGnTJrzzzjtNCiYoKEinpZMbM1ZN365etcH+/c5Qq6t/JvPwqMKUKTn4/ff2uHpVe77KQYMKMWiQYVaYeZia8Wr9+vUzcCQtq61dL7WcmiXgqfn98YcDzp+3g1otgaWlBiNGFCAw0LBzA7u4KDF79m2UlJjB0lJj8GTdWHTr1g3h4eE4e/asWBYeHo6uXbvq7RxyuRzPPPOM3upry8rLy7Fnzx4kJCTA2toajz32mDiMti5//PGHmBTX+PPPP/G3v/0NY8aMwYABA3Dnzh14eHjA3r51jC8GdEiM3dzcUFysvRJRWVkZXF1dxe2LFy9i+/bt2LNnD4DqxFmhUCAsLAznzp3Tc8iG1aNHOTp1ykR6uhVsbdXw8qqCRAKMGpUPMzMBSUlymJkJ6N27BAMGGF9STETUWqWkWOHs2f9+wVZVSXHgQHt06VIBubxlhlpkZFgiI8MKDg4qdO9ehvuHubZrp26RGFqTZ555BoMGDRJnpWgtvYZt0ebNm3HlyhUA1Xnc3r17YWVlVe/QlNLSun8VKSsrg5OTExwdHVvls1YNJsZhYWH48MMPUVlZCSsrKyiVSmRnZyM0NFTcp1evXvjtt9/E7QMHDuC3337Dv/71r+aJ2sDkcg0CArTHj1laChgzJh9PPpkPtRq4fdsShYUyODkZ7yTWREStSUqKTa0ytVqC1FRrBAU1f6/x4cOOuHjRTtyOi7PD9Ol32EPcACbExq+kpERMiu8XGxtbb2IcGBiIGzduaJU5ODi0+s+6wVkpXFxcMGzYMJw5cwYAcPr0aURERMDNzQ1RUVFISUmBhYUF3N3dxf/s7OzEsrYmPd0K333XEVu2uGPtWk/s2eMCFXNjIqImk8vrbkzl8uqe2oICGeLjbZGebgVBz7lqQYFMKykGgNxcCyQk1D+nP1FrIdTzD+bBoRL3Gzx4MEJCQsQ1HBwdHfHcc89B2prWbq+DTvMYR0ZGYsWKFUhKSkJeXh6WLVsGAIiOjoanp6deB9K3Nmo1kJ1tCSsrDZyclIiOdhaXMQWA69dtcPGiHUJDix9SCxERNaRnzzLExdlptbHu7lXo3LkSZ8/a4cQJBwhC9Zd0584VmDQpF/XM6NVoubm1VwsDgJycusuJWhM7Ozv4+/vj2rVrWuX9+/ev9xgzMzNMnz4dTz31FMrKyuDu7t7qk2JAx8TY3t4eH3/8ca3yXbt21bn/xIkTMXHixKZF1gpkZ1tgzx5XlJZWN9LOzlUoLTWD5IHpK1NTrZgYG4GKigpx2kEyrPj4eCQmJsLa2hoDBw5Eh7qmHiCT0lyLVGRkWCI21q7O8hMnmn+hqYwMS/z2G5d5bgkqlQopKSkwNzdHly5dtFabpaabMWMGdu3ahcTERFhZWeGxxx7DoEGDUFVVhc2bN4v7rVq1Cs8++6w4Y5m9vX2reriuIXq6l257BAHYt89ZTIoBICfHEhUVZrC11X4Agw9kGM79N29Lly7FpEmTHnoHTM1v//79OHz4sLh97tw5zJ8/H126dDFcUERk1DIzM7F27VpxMgAPDw+89NJLaNeunYEjMx22traYNWtWrfJ9+/YhMTFR3E5NTcX27dvx4osvtmR4LYaJ8SMqLJShsLD26jqCUD3HppWVBlJp9YpNISHsLTaExMREnDx5UtxWKBTYtm0b/P39YWdXu4eJHm7p0qWIi4trlroXLFigl3pCQkLw0Ucf6aUu0o+WWLjg9u3bWLFiRa3yp556Ck888cRDj31w0QK1Wo21a9dq/aQcHh4uTgmWkZGBGzduwNnZGYGBgXpZ2rgtKikpwdmzZ1FQUAA/Pz/07t1b/Bm+srKy1v7btm3TmiErOzsb0dHRJrvSrj40V5udnJyM5ORkvfzbNsY2m4nxI7K2VkMmE6BSVf+UU1UlQWGhOczNNTAzE1BWZoY+fUrw2GOFcHFRGjjatunq1au1yjQaDZKTkzmnMZEJ6dChA0JCQrSSAGdnZ/Fp+vLycly9ehWWlpbo0aPHQ5PZhISEWuMsz549i/DwcHTq1En8jx5dcXExvvrqKxQVVU9peu7cOVy9ehXTp0/H+fPnsXPnTnHf3bt3IyIiAllZWbXqGyOWAgAAIABJREFUuX7dcGsakOliYvyIrKyqVzeKi6vueSwpqX4rbW3V4tQ9giBhUmxAD67YKAgCJBKJSY2Fakn6uKvXaDT45JNPUFBQIC6l7uvri6FDh2LcuHFNrp/armnTpqFnz55ib27//v1hZWWFpKQkrF+/HkpldVvs7OyM+fPn12oflEolzM3NkZGRUWf9t27dYkKsJydPnhST4hpxcXEIDw/Htm3btGZCOHHiBHx8fGBtbY2KigqtY5ycnFok3tZKXz2xZ8+exfbt27XKHn/8cfztb3/TS/3GplUmxs31EIc+3Lnz3yeUMzIscexY8z/8QXULDw/X+qnnzp07CAwM1OuqS9Q4UqkUf//737Fu3TqxrHPnzhg5cqQBoyJTIJVK0bNnT/Ts2VMs02g02LFjh5gUA0BeXh4OHDiAcePGYe3atWL5smXL8I9//AMeHh511s8HROunr5/sX3/99VplycnJWLx4cZ37Jycn4+DBgzrXb4w/27cG4eHhMDc3x5kzZ6BUKtG3b18MGTLE0GE1m1aZGBPp4u7du1pPLcvlchQXF6OyspKzUxhQ165dERkZKc5cU9eXIZE+FBUVoaCgoFZ5SkoKDh06hNTUVLGsrKwMmzdvxrvvvovTp08jPT1dfK13795telpSon79+rWZIYitMjH+5hvjG1d044Y1fvnFBWp1dSJmaanB1Kk5cHdXGDiy2oy5x12f/vzzT8jum8TUzs4OKpUKSUlJCA4ONmBkZG5e+8FVIl3oo3cyOTlZXLTq/jIAmDRpUp3779ixQ+f621rPZGOvValUYt26deJYbqlUiokTJ2LAgAFYtWoV0tLSxH1tbW2xcOFCPjBNLaZVJsbGqFu3Csydm41r12wgkwno3r0Mcnn9K8ZQ87O0tGxUOZEpOXXqFFauXImvvvoKHTt2rHOf48eP4/jx49BoNAgPD8fo0aNbOEpqi8zNzfHSSy8hNTUV9+7dQ9euXcVnP1566SWcOHECKSkpcHFxwdChQ5kUU4tiYqxHDg4qhIVxajZjERYWhhMnTmiVOTs7w9/f30AREbWMvLw8lJeXIyEhod59UlNTsXLlSuzcuRNSqRQzZsyAj48P/PyM+xelR+mJTUlJQXx8PKysrBAaGor27dsjNTUVq1evhlr933nmTfmBImPk7e0Nb29vrTJLS0uMGDHCQBERMTFusrIyKU6dckBmpiUcHFQYMKAIHToY3/CJtsjFxQXz58/Hq6++CgAIDQ3F6NGjOe+ogSmVSq0FPuLj49G7d28DRmR6nJ2d/3979x4XZZX/AfwzN4bLIHdQUBMvSN4QuamkSKYoarbmpmualrtmbVZa2VYb6tbv5yrqquVuuaWgZF5WXfTnLXfNSyriJUVzDRQUEAS535lh5vz+YH1yAgUN5kH4vF+vfS3PmTPPfJkHv33nzHnOaXD93vXr12PIkCHS2rFPPPEE1q9fj8WLF1siRIvq2rVrnTnC3t7eePPNN3H8+HFUVlaiX79+nGJFRCyMfwmTCdiyxQN5ebUrUeTlWeHGDWvMmJENJ6camaNr3SorK5GYmIicnBx06dIFAwYMMJtPfMdjjz0m/Xy/heDLysqQmZkJNzc3uLhwe9fmtGXLFpw7d046jo2NxYsvvmi2mgD9cncK3ns5deoUZs6cKR17e3tj27ZtzR1Wi+Ll5SVt3EFEBLAw/kVu3LCWiuI7DAYlkpJ0CAsrkikqy7uzc1RL9yjEaYldwuRUVlaG77//vk77sWPHWBhbWE5OjtlavnZ2dsjNzX2gc1y6dKmpwyIikhUL41+gutp8RKa8XInKShUSEhzg6FgDP78ymSIjapmqq6shhKjTXt8WsNS8FAqF2Y2oer3+gVcL6dOnD29mJaJHTnV19T0/2LMw/gW8vSthZWWCXq9EaakK5eW1c1f1egUOHHCByQT4+7ed4viTmZYZPfr3RVecS627e52ncxWyCqzN2hxsa/Dbp24gIdkJx6/U3SVpQNcinEutuwnLgK7FGN43r+mCbsCcL/tY7LXk5OLigo4dOyIzM9OsnXOMLc/d3R0lJT/dLFxeXg53d3cZIyIikt/9J6HRfWm1As88cxvt2tWgokIJpRKwt6+BVls7Ivb99/YyR9g6dXSprLe9vKru57zbJVaI+bYT/pXkhqJyDYwmhdnjSkWdpwAA7G04R7y5vPDCC+jUqZN0PHDgQAwbNky+gNqo0NBQsw0url+/jpCQEBkjIiKSHwvjX6hLlyq8+GIWXF31cHPTm61d/POpFtQ0enQoh6/XTyPxCgUwuGcB3NpVm/WrMSlQVK7B7RIrKBQCVQYlCsqscOeLfKUSCOpWBC9n86/x7W1q0Lczl91rLq6urpg7d650/Nxzz3GlkGZwZ8rK3VNXvvzyS6SmpgKovRn1u+++k/okJCRg2rRplg+UiKgF4VSKhyAEUFGhhLW1CSpV7cixj08lrl2zNevXs2eFTBG2bkoFMC4wB0Hdi5BfagVPpyo46QzIKtAi7bYt9AYljCYFqg1K2GiNUCoAa40JdlojKvQq6A1K2NvW4Km+edDZGDFxUBbOX3dAVoE1nHV6DOhaDBsrbs5Cj66ysjLs2rULALBz505MnToVzs7O2LNnD7y8vKT1ip9//nlpebaXXnoJPXr0kDNsIiLZsTB+QBkZWuzf74LCQg2srY0IDS1GQEAphg8vwI0b1sjK0kKhALp1q8QTT7SdlSmAljtPtrDM/IainGItcoq1uJptV2//bSc9LREWUbPR6XSYMmUKpkyZYta+Y8cOs+Px48dj/PjxlgyNiKhF43f9D0CvV2DHDncUFtYWWlVVKvz7385IT9fi5ElH1NQo4epqgIuLASUlapw7xznGRERERI+KR3LE+LXXWtaWpUuXPlZve1xce8TFtbdwNPKx1KoU97L1hCdu3LYxayutUkMIQF+jhLPOgIkDs9Ctfcub4tJSR9uJiIjaEo4YU6uhszZfScJoUqCyWgVbqxq46PRQKgSOXHaFqe4yumRBQggkJiZKx+np6TJGQ0RE9JNHasRY7l3BTCYTlixZgtu3b0ttarUab731Fr755ht8//33SElJAQD06NEDI0eOxKhRo+QKt80J7FaEH7N0qDHWrsFWqVdBqzFBfdeCB/mlGlzPtUVXj5Y3atxW7Nq1C0eOHJGOV69ejVdeeQXdunWTMSoiIqJHrDCWm1KpxCuvvIL9+/fj6tWrcHNzw8iRI+Hh4YFf//rXUKlUUmE8bNgwjBgxQuaI2xZ3Bz2eH5KJs6mOKK9WobxKhZyiurtyVdfwixK5VFRU4Pjx42ZtJpMJ3377LQtjIiKSHQvjB+To6IjJkyfXabe2tsaUKVPw9ddfAwCefvppS4dGqC2OR/vnAgDS82wQd7QjyqpUMJoU0KgEnHQGeLtztFgu5eXlqKmpu3lKUVHbWsGFiIhaJg6dUatlb10Do1EBQ40SNUYF9DVKaFQmWKm4RrFcXF1d4erqWqfd19dXhmiIiIjMsTCmVutiuj2srYxwa1cNV3s9XO2rUW1Q4vpt24afTM1CoVBg6tSpaNeundTm4+PDaUdERNQisDCmVkv/37nECgWgVgkoau/J4xxjmXXu3BkffvihdDx79mxotXXnghMREVkaKwRqtXp6lgEAjKJ2GoUQClipBbpyjrHsVCpVw52IiIgsjIUxtVqdXKvg7lCN/FIrFJRpUFCuQb/HiqHVcI4xERER1cXCmFqtlGw75BZr4WZfDbd21XC20+NcmiPKqzhaSURERHWxMKZWo3blCYV0nJZbe5OdQgGolLX/bzKhzrbRZFm3b9/G8uXLpePNmzfDaDTKGBEREVEtrmPchC5fviz9nJaWBm9vbxmjaTuMJuDfF91wKcMeJpMC3TzKEdE/FzrrGlTXKFFWpUaNUQErtQn2NjXQ2dRdR5csZ8OGDbh586Z0nJiYCBcXF65MQUREsmNh3ESOHDmC+Ph46fjTTz/FSy+9hN69e8sYVdtw4kdnXLj+0/JfV2/ZAefd4e9dhJIKDYz/nVJcbVBCrVShg2O1TJFazrhx4+QOodFSUlKQkpKC1atXyx1KveTeip6IiCyHhXETMJlM+Ne//mXWJoTAwYMH21RhPOfLPnKHIEnPs8GhS3U3ktDXKDEvtu1cEyIiImo8FsZNwGAwoLy8vE57YWGhDNEQ/eST/pdkff0SgxpXSnVQKgR87cugUxux95Y7fiixN+v3K89sdNe1rGX05pxvOR/0iIjIMlgYNwGtVotOnTohIyPDrN3Hx0emiCxL7q+a9+/fj9jYWNTU1M4dVigUePLJJxEZGYn/+Z//QVZWFgDA3t4enp6e+N///V9YWVnJGXKbkFZug51ZHWAUtTdEHs93wUSvLER45MJeXYMLxQ6wURox1DWvxRXFRETUNnFViiYyadIkODg4SMcdOnTA2LFjZYyo7cjPz4dOp4NGo4FKpYJOp0N+fj6MRiMUip9WqbjzsxBCrlDblG9vu0pFMQDoTQocy3NBZqUNzhU5oNKoRIFBg5MFzqg0MhUREZH8+F+jJuLp6Yk//vGP0vHbb79tVihT80lLS4O9vT3at28PT09PODo6Ij8/HwkJCbC1tZX6eXh4wGQy4T//+Y+M0bYNRgHk6+uOyt+q1mL/LXfoTT+lnpxqLU7mO1syPCIionqxMG5Cd29ze/dIJTUvV9e6N9nZ2dmZFcV3U6s5g6i5qRSAm1Zfp91RY0BJTd33P72Sa0sTEZH8WBjTIy8iIqJOsRsREYGBAwfWaXd2doavr68lw2uzwt3yoFL8NG3FSmnCk263oVHUncripDFYMjQiIqJ6ceiMHnne3t54++23cerUKej1evTv3x/dunUDAMyaNQtz584FAPj5+WHs2LEcMbaQx2wr8bsuN5BcpoNCAfTUlcFObUSwcyGO3zV1QqMQCHbmCi5ERCQ/VgjUKri7u9e7qUX37t2ln6dPn27JkAiAvcaIAKdis7bBLoXw0FYjuUwHa5URfg4lcLbiiDEREcmPhTERWVw3XQW6cYk2IiJqYRo1x7iqqgofffQRVqxYgQULFqCsrKxOn9LSUrz++usYMGAAnnnmGZw/f77JgyV6UJWVldLPZ8+ehclkkjEaIiIiaskaVRgvWrQIAQEBmDdvHoYPH46oqKg6ff7+979j5MiR2LBhA9zd3fHqq6+iooIjQiSfyspK/OUvf5GOv/rqK8TGxsoYEd2RW22F4/lOOFvogAquYUxERC1Eg1MpcnJysHv3bqkYHjx4MF5//XVkZmaiY8eOUr9BgwZh0KBBAIDly5dj4MCBuHr1Kvr169dMoVNrtWjRIpw5c6bJz5uSkoKUlBTs2LGjSc4XGBiIBQsWNMm52pILRe1wMNcNd9amSChwwm863eQ8YyIikl2DhXFiYiKcnJxgY1O7zqharUbHjh1x+vTpOoXxHfb29tDpdGjfvn0zhExEjTXnfB+5Q2iUBZd7yh0CERFR40aMHR0dzdrs7OyQm5t7z+ekpaUhODgY7u7uDxTMpUuXHqh/S3b27Fm5Q3hkjR07tkm2005OTsbhw4fN2hQKBSZNmoR27dr94vMDvM5tAa8xEVHb0WBhrFAoYG1tbdam1+vvuxbsV199hfnz5z9wMH369IFWq33g57VEAQEBcofQ5vXv3x8VFRX44YcfpLbRo0cjPDxcxqgs65P+LevDZpVRgS+uP4ZKo8qsva9DKUZ53PvDthzujLY35t9ydXV1q/pgT0TUVjVYGHt4eKCkpMSsrby8/J6jwSdOnEBISAg6derUNBESPSSVSoWZM2ciLS0NeXl58Pb2rnf7aGp+xQY1DuS4I73CBjVCgSqjCnbqGgCAvboGg5wLZI6QiIioEatShISEIDc3F1VVVQAAg8GArKwsBAcH1+l77do1ZGRkYMSIEU0fKdFD8vb2RlBQEItiGf0zqwNuVNhAAFApBKxVRvTQlSOyfS5e6pIOB02N3CESERE1XBi7ublh2LBhOHnyJIDaEeGIiAh4eHjgyy+/RGpqKgAgIyMDW7ZsQWhoKDIzM3HlyhVs2rSpeaMnohYvr1qD3GorszaVQqBQr0F7bRWslOIezyQiIrKsRu18t3DhQkRHR+PKlSvIy8vDn/70JwDAnj174OXlBZ1Oh2nTpiE7O9tsndi715AlorZJqTA/NpiUKDJoUKjXYN2NzuhiW4nxntkskJtQVVUVoqOjYWdnh+LiYrzzzjvQ6XR1+pWUlGDYsGEoLy8HAHz66af8xo+I2rRGFcYODg74+OOP67TfvR7sz+/+b2uqq6uxc+dO6Xj37t2IjIyESqW6z7OIWj9nKwM62VQio7J2ycfiGjWMArBXGwEA1ytskFDghKGunGfcVBYtWoQhQ4YgMjISR48eRVRUFFasWFGn39atW7FkyRLY2dkBqJ06R0TUlnHLqSaybds2JCYmSsfffvstDh48KGNERC3HeM9b6OtQCitl7ZbcDhoDrFU/bc+dVm4nV2itzp1Nme6svjJ48GAcOnQImZmZZv30ej2OHz+OHj16YPDgwRg8eDA/yBNRm8fCuAkYDAacP3++Tntz7N5G9CiyUZkwyiMXL3vfgIdWD5u7imIA0Kl5811Tud+mTHf79ttvceHCBURERGDWrFkoKOCIPRFRo6ZSUMMUCkWj2ojaMmuVCf0di3G20EFqUyqAYKdCGaNqXRq7KVNERARGjhyJM2fOYOHChXj11VexadMmKJWNHy/h2s1E1NqwMG4CGo0Gvr6++Pe//y215ebmYvjw4TJGRdQyhbvmwc2qGsllOlirjPB3KIanTbXcYbUaD7Ipk0KhQFBQEGJjYxEZGYkLFy7A39+/0a/VmjZlIqK2436bMrEwbiJlZWXQarUoLS0FAGi1WpSVlckcFVHLo1DU7nTX16FU7lBapQfdlAkAXF1dMXr0aGRnZz9QYUxE1Nq0+cJ40aJFzTIXODc3F3FxcYiLi/vF5woMDMSCBQuaICoiyyk1qPBdvgtuVlnDWWPAIJcCdLCuHRmuMCqRVm4Ha6UR3nYVdZZ0o4cXEhKCDz/8EFVVVbC2tr7vpkx3U6lU8PX1tVCUREQtE2++I6ImZxTAlpteuFRij0K9BtfKbbEl0wvFBjWuldnis9Qu2HvLHTuyOmD9jc4or+FqCE2lsZsy7d+/HxkZGQBqdy11cHBA165dZYubiKglaPMjxk01EvvNN99g//79Zm3Tpk3j15Ikqznn+8gdgpk//lB3RDK9wgbni9rJEE3r1dCmTF27dkViYiL++Mc/YuDAgQgODsacOXNkjpqISH5tvjBuKiNHjoSTkxPOnTsHtVqNwYMH4/HHH5c7LCJqgxqzKVNUVBSioqIsGRYRUYvHwrgJBQUFISgoSO4wiLB7925ZX7+iogJ/+tOfoNfrzdpnzZqFmJgY6PV6pKSkAAB69OgBPz8/TJ8+XY5QiYiIJJxjTERNztbWFjNmzICzszMAwMbGBhMmTICvry8iIiLM+mq1WowYMUKOMImIiMxwxJiImoWvry/ef/99FBcXQ6fTQaPRAADCw8PRqVMnzJ07FwAwf/58ODk5yRkqERERABbGRNSMlEplvUVv9+7dpZ9ZFBMRUUvBqRRERERERGBhTEREREQEgIUxEREREREAFsZERERERABYGBMRERERAWBhTEREREQEgIUxEREREREAFsZERERERABYGBMRERERAWBhTEREREQEgIUxEREREREAFsZERERERABYGBORDK5evSr9XFhYKGMkREREP2FhTEQW9e233+Kvf/2rdLx06VJkZWXJGBEREVEtFsbU6hUUFOAPf/gDRyZbgOrqahw4cKBO28GDB2WKiIiI6CcsjKlVKysrw7Jly3D58mWsWrUKNTU1cofUppWUlECv19dpz83NlSEaIiIicyyMqdWqqKjA0qVL8cMPP0AIge+//x5/+9vf5A6rTXN2dka7du3qtHft2lWGaIiIiMyxMKZWKyEhAampqRBCAACEEDh16hTS0tJkjqztUqlUmDhxItRqtdTm7u6OESNGyBgVERFRLRbG1Grl5eWhtLTUrDAuLS1Ffn6+zJG1bX369EFUVJR0/M4779Q7ikxERGRpLIyp1erevTvs7e2hUCgAAAqFAu3atePX9i2ATqeTflapVDJGQkRE9BMWxtRq9e/fH0OHDjUrjCdPngxnZ2eZIyMiIqKWiIUxtVpKpRKzZs3CE088AYVCgfDwcIwZM0busIiIiKiFUjfchejRNnPmTOTn52P69Olyh0JEREQtGAtjavWcnZ3x5z//We4wiIiIqIXjVAoiIiIiIrAwJiIiIiICwMKYiCwsLy8PK1eulI63bt0Ko9EoY0RERES1WBgTkUVt3LgR6enp0nFCQgIOHz4sX0BERET/xZvviKhRFi1ahDNnzjT5eVNSUrBy5UqzUeSHFRgYiAULFjRBVERE1BZxxJiIiIiICI0cMa6qqkJ0dDTs7OxQXFyMd955x2xL1zuOHDmCI0eOwGQyYeDAgRg1alSTB0xE8miqkdi4uDicO3fOrG3GjBno169fk5yfmLOJiB5WowrjRYsWYciQIYiMjMTRo0cRFRWFFStWmPVJS0vDypUrsX37diiVSjz//PPo2rUrfHx8miVwIno0TZo0Cc7Ozrh48SJsbW0xdOhQFsVNjDmbiOjhNDiVIicnB7t370Z4eDgAYPDgwTh06BAyMzPN+q1fvx5DhgyBUll7yieeeALr169vhpCJ6FGm0WgQGRmJd999F3PmzIGfn5/cIbUqzNlERA+vwRHjxMREODk5wcbGpvYJajU6duyI06dPo2PHjlK/U6dOYebMmdKxt7c3tm3b1qgghBAAAL1e/0DBExG1BHdy151cJifmbCKi+7tfzm6wMM7JyYGjo6NZm52dHXJzc+/br74+92IwGAAAycnJjepPRNQSGQwGWFtbyxoDczYRUePUl7MbLIwVCkWdJ+n1eqjV6jr9tFqtWR+NRtOowOzs7ODj4wONRgOFQtGo5xARtRRCCBgMBtjZ2ckdCnM2EVED7pezGyyMPTw8UFJSYtZWXl4Od3d3szZ3d3ezfvX1uRelUgl7e/tG9SUiaonkHim+gzmbiKhh98rZDd58FxISgtzcXFRVVQGoHXbOyspCcHCwWb/Q0FCkpaVJx9evX0dISMgviZmIiB4QczYR0cNrsDB2c3PDsGHDcPLkSQDAiRMnEBERAQ8PD3z55ZdITU0FAEyePBnfffcdgNoh6oSEBEybNq0ZQyciop9jziYiengK0YjbqIuLixEdHQ0vLy/k5eVh3rx5sLOzw4QJEzBr1ixpUfj4+HhcvnwZABAUFISnnnqqeaMnIqI6mLOJiB5OowpjIiIiIqLWrsGpFEREREREbQELYyIiIiIisDAmIiIiIgLAwrhJ7du3D2PGjEFmZmaTn3vhwoX4+9//3uTnbUlycnKwcOFCREVFSW3jx4/H2bNnLfL6RqMRW7duRXh4eLOc/+WXX8aePXua5dzNYc+ePZg9e3aD/U6cOIFJkybh1KlTD3T+tLQ0vPXWW1izZs3DhnhPZ8+exfjx45v8vNS6MGf/MszZLQtzdtNoNYVxUlISZsyYgZ49e2Lt2rXIyMiweAweHh64evVqs5x77NixCAsLa5ZzP6jjx48jIiICffv2RX5+fr19fvvb38Lf3x/x8fHS9rEN0Wg0KC0tlfYwB2oTU/fu3Zsk7oaYTCbY29sjKyurWc4/ZcoU+Pv7N8u5G+vChQt44YUX0KtXL3z11VcoKiqCXq/Hrl27EBgYiHHjxklLePn7++M3v/lNg+ds3749rly58sCx6HQ63Lp1CyaT6YGf25Bu3brh5ZdfbvLzUtNhzrYc5uyHw5xtrq3k7FZTGPfr1w9jxoyBi4sLZs2ahU6dOlk8hv79+zfbuQMDA+Hj4yMdb9y4sdleqyGhoaEYNWoUFAoFNm/eXOfxGzdu4MyZM+jRowfGjx/f6G1mnZ2d0blzZ7O2yMhIODg4NEncDdFoNOjTp0+znT8sLAyenp7SsRzX0M/PD+PGjYOLiwuef/55ODo6wsrKCk8//TR69uyJ8PBwPPHEEwAAT0/PRv2HvWvXrnBycnrgWNzc3ODl5fXAz2sMR0dHREZGSsf5+fnYu3dvs7wWPRzmbMthzn44zNnm2krObjWFMQCoVCqo1Q3uct1slErLvJ1XrlzBypUrLfJa96JWqzF27Fhs3ry5zujC5s2bMXr0aKhUqgc+r6Xew3tRKBQWeZ3jx48jLi7OIq/1cyqVqt7fU6lUPvTv/7DXzRLXW6/XY/78+dJOcNRyMGdbDnP2L8Oc/cue9yDkztmtqjBuiBACa9euxapVq/D6669j8eLFMBqNSE1NxahRozBhwgQUFxdjy5Yt8PX1xZIlS1BTU4OrV69ixIgR+OGHH+qcs7CwEB988AHWrFmDDz74wOyx9PR0rFq1Ch988AGmT5+OW7du4caNG3j//fcRFRWFmJgYDB48GC+++CJqamoAAKdOncLatWuxcOFCDBkyBABw6dIlvPbaa1izZg30ej127tyJsrIyLFu2DImJiZg4cSICAwOl+K5evYonn3xSWri/uUyaNAmFhYU4cOCA1FZZWYmioiKzT9kAUF5ejr/+9a9YunQpnn32WZw+fVp6LD4+Hh9//DGWLFmCQ4cOSe379+/Hs88+i1OnTiEjIwOvvPKKtDPXxYsXMWrUKHzyyScoKSnBX/7yF0yYMAGJiYkYP348nnzySaSmpmLt2rUYOnQoJk6ciNLS0np/j4yMDLz33ntYs2YNli5davbYxYsX8cknn+DNN9/Ea6+9htLSUvznP//BnDlz8Omnn2LZsmUIDg7Gu+++Kz3nwIEDWLduHd566y0899xzAICTJ09ixowZ2LlzJwoLCxEfH4+8vDwsW7YMV65cwYgRI/DUU09JW/QmJiZHZQEiAAAOLklEQVRi5MiRsny9DAC5ubn4+OOPzb7aunjxIhYvXoyYmBj4+fkhIiIC69evlx4vKSnBa6+9Bn9/f2zduvWe5/7yyy8RHR2NRYsWISkpSWo3GAz44osvsHLlSjz77LM4cOAA9Ho9YmJiEBYWhitXrmDChAkIDQ3FxYsXAdT+Xa1atQqff/45wsLCcPToUZSWlmLlypWYMGECAODQoUO4du0a9u/fj/Xr12P58uXo2bOnVKhUVlbi5ZdfbpZ5c/TLMGc3LeZs5mzm7EYQrcj27dvFkCFD7vn4pk2bxPLly4UQQhiNRjFu3Djx2WefCSGE2LVrlxg7dqzUd+rUqWLTpk1CCCGKiorE559/Xu85X331VXHs2DEhhBBJSUnCx8dHZGRkiJqaGvH73/9e1NTUCCGEmDdvnpgzZ44wGAzio48+Es8884xISkoSpaWlIiQkRHz33XdCCCFeeOEF6TkrVqwQQghRXV0tXnrpJbF69WohhBAZGRnCx8dHiiE5OVn07t1bFBQUCCGEuHnzpli1atWDvHUPbPXq1SIjI0O8/fbb4rnnnpPav/76a3HixAmxevVqMXnyZKk9KipKZGZmCiGE2Lx5swgNDRUGg0FcunRJ/OY3v5H6zZo1S7z77rtCCCFKS0vFgAEDREJCghBCiH/84x9i6tSpUt/58+eL1atXC71eL+Lj40VwcLA4deqUMJlMYubMmWLatGkiOTlZGAwGMX78eLF58+Y6v4fJZBITJkwQV69eFUIIsW/fPum9LS0tFW+++abUd9KkSeLPf/6zMBgMYvbs2WLq1Kni+vXrIjs7Wzz++OMiLS1NCCHE888/Lz3nzjWsrKwUo0aNEtu3bxdCCJGQkCDCw8OlfocPHxYDBw4Uer1eCCHE6dOnxbZt2xp3MR7Q9u3bhb+/v4iOjjb7X1hYmNnf3N/+9jfp/TaZTCIsLEwkJSUJIYRYuHChmDRpknTO8PBwsXjxYlFRUSF2794twsLC6n3tgwcPinnz5knnHDt2rPR3/dlnn4nTp08LIYQ4duyY8PPzE7dv3xYXL14UPj4+YufOnUKv14sPP/xQOkdcXJzYs2ePEKL2PTty5Ij093D3+zt16lTpvRdCiIkTJ4qYmBjp+A9/+IMwGo0P/6bSQ2HOZs5mzm4Yc7Zlc3abGjHetGmTNJFeqVRiwoQJ2LJlCwBg+PDhyMzMxLVr1wDUzl2Kj48HAHzzzTcYPXp0nfNdv34dx44dQ2hoKACgb9++0mNJSUnIysrCxo0bERMTA0dHR1hbW0OtVqNdu3bo2bMn+vbtC51OB29vb9y+fRtA7c0Eb775JjIzM/HCCy8AAKysrODq6nrP36tHjx4YNGiQNHds165d+NWvfvWL3qvGmjZtGs6fPy99ikxMTMSgQYPM+gghcPDgQRw8eBAxMTHIyclBz549UVhYiHXr1mHo0KFS37vni+l0OrRr1046/vlXRneONRoN3N3dYWdnh+DgYCgUCvTp0weenp7o0aMH1Go1fHx8pPf4bidOnEBZWRm6detW5/UPHz6MoqIixMTEICYmRupz5xoGBwfjscceQ/v27eHq6oq8vDwAQHZ2NqKiopCfny9dQ2tra7i4uNzzfQwLC4OzszP27dsHoHYE4862vc1Bp9Ph7bffNvvf3XM8rays4ObmJh0XFBQgOzsb1tbWAGpvlPj511zh4eGwsbFB3759632vAeCLL77AsGHDANRev969e0uP7dixA0lJSYiJicHly5cREBCAvLw8ab7iM888A41Gg759+0rvta2tLZYtW4ZDhw4hICAAvXv3lv4e7ufFF1/Epk2bIIRAdnY2vLy8ZP9KmOpizm56zNnM2XcwZ9dPvsldMrh+/br09RcAdO7cGbm5uQBqL9aTTz6Jffv2ITw8HGPGjMGiRYuQnp6OzMzMem8M+fHHH2FjY1PvHJ/s7Gw4OjpixowZdR77eX+1Wi3d5blkyRK89957GDVqFH73u9/hjTfeqPc5Pzdjxgy8++67mDlz5j3jbQ79+vVDv379sHHjRvz6179GUFBQnT75+fkoLS3F9OnT6/weycnJ970BprHzp37e7+dz5ZRKJUQ9u5//+OOPsLOzq/ecWVlZ6NixY6OvodFoBACsWbMG7733Hp566im88847mDJlSqN+l+nTp2P9+vUYMWIEhBDQ6XT37d/c7o7XxcUFffr0wcmTJ9GjRw9kZmaa3ShxN5VKZfbv7G73e7+zs7MxZswYeHh4AABmzZoFAHWW0lKpVNK/l/HjxyMjIwNvvPEG+vTpg1WrVtWJvT4RERGIjo7GoUOHkJKS0mKWCSJzzNlNjzm7FnP2T5izzbWJIZKMjAzk5+fD09PTbGkeIQS6dOkiHT/99NPYu3cv/vWvf+GZZ57BsGHD8Pnnn6NDhw71nlen06GoqEj6JHQ3Nzc3nDt3TkriQO2yKw0xmUyIjY3F0qVLsW7dOpw8ebJRv2NoaCicnJywcOFCBAQENOo5TWXq1KnYt28fNmzYUO8fq5OTE4QQZnPRrl+/jqKiItjZ2UkjPg1RKpVSImsqOp0OGRkZZssN3eHu7o7Dhw+bfcpuzDXU6XTYvn075s2bh48++gjp6emNimX8+PG4desWPvroI4wcObLxv4SFLFiwABcvXkR8fDx69uyJ3/3udw98Dp1Od8/r7ebmZjb3saSkRJq/dy9ZWVl4/fXXsXfvXuj1eixbtqxRcahUKkydOhXr1q3DzZs3ZVkRge6NObt5MWfXPSdzdv3aYs5uVYWx0Wis86lHr9cjLi4OLi4umDx5Mv75z39Kd+QmJSWZrfkXGhqKgoIC2NnZQaVS4emnn0Z8fDwiIiLqfb0BAwbA3d0dy5cvhxBCmnSfn58PPz8/aRmiQ4cOYe/evThz5gyA2kT680/Cd47XrVsHoHbJm7u/rhJCSH3uLKVTXFxs9gc7ffp07Nu3757xNiWj0SglvNGjR8PBwQFeXl6wtbWt87hKpUJERATef/99/OMf/8Dx48cRGxsLR0dHjBo1CvHx8UhOTgZQ+0mzoKBAuo53/94uLi5IS0tDUVERLly4gOTkZKnv3f3uaOgYqP06zGAw4PPPP5deH6hduH7o0KHSJP9jx45h69atSE1NBdDwNVQqlZg2bRoef/xxs8fvvoZlZWWoqamREolWq8XkyZNx6tSpekdxmsrd1+ZuBoPBbH3Kn7+n77//PiZOnIhevXrB39/f7D8+9b239bWNGjUKcXFxuHXrFoxGI7Kzs6VrOHbsWCxfvhwxMTFISEhAdHQ0vLy8pPPcfb47P+/duxe3bt1Cp06d8Pvf/96s7939NRoNSkpKzP69PPfcc7h8+TIGDBjQ8JtGzYI5mzn7bszZ9WPOrmWpnK1auHDhwmZ9BQtJSkrChg0bkJycjEuXLuHIkSPYs2cPVq9ejY4dOyIsLAx+fn64efMm4uLikJqaCmtra7z44ovSEL5SqcTt27cxbdo02NjYoFOnTsjKysLYsWPrfU2NRoPAwEBs3rwZX3/9NbRaLZKTk+Hi4gI/Pz+Ehobi6NGj2LJlC5RKJd544w3cunUL69evx40bNxAQEID09HRs2rQJer0eQUFBiI6ORkJCAnJycmBvb4+JEyciJSUF69atQ2FhIUJCQtChQwd8//33+OqrrxAZGSmtR9ixY0fk5OQ06zwnoPYu7I0bN6KoqAje3t5wcnJCRUUFJk6cCEdHR+nxtLQ0dOzYEd7e3hg8eDBSUlKwadMmpKamYv78+XBwcECfPn1QUFCApUuX4tKlS1Cr1dDr9ejSpQuuXLmCbdu2wdraGgEBAejatSsSEhKwcuVKeHl5Qa1Wo6KiAl26dME///lPnDlzBr169YJCoUBsbCzS0tLg5+eHW7duIS4uDnl5eQgJCTFbY1On08HX1xdr167Fnj17oNVqpfc+ODgY/v7+2L9/P3bs2IEOHTpg5syZuHr1KtavX4/CwkIMGDAAFy5cwPbt26HVahEQEIA5c+YgIyMDaWlp6NWrF8LCwnD+/HnExsaipqYGQUFB6NChAw4cOIA9e/ZgwoQJsLGxAVC7lqNWq0VISEizXLukpCRs3LgRycnJ0Ol06NKlC5RKJf7v//4P27dvR0FBATp37gxbW1vExsbihx9+QGBgINq3b4+jR48iJiYGGzduRGxsLGJiYtC3b18kJydjx44dsLa2Rvfu3bFnzx6cPHkSnp6e6NWrl9nrBwQE4Mcff8SKFStw7do1KRn6+Phg+PDhyM3NxaZNm3D+/HnMnTsXLi4uiIuLQ2JiItzd3eHi4oLNmzfjwoULCAoKQmpqKhYvXoyKigqkpKRg9uzZUCqV2LBhA86cOYPevXujS5cu0Ov1+PTTT/HYY49Jc+S0Wi1SU1Px29/+ttHrtlLTYc5mzmbObhhztuVztkLU9xGBHknx8fHw8vJCYGCg3KHQQ/rss88wbty4ZltE/WHl5+fj66+/xmuvvQagdvQlJycHmzdvxty5c2WO7uFUVFRg1apVeO+99+QOhdoo5uxHH3O25VgqZ7epm+9aq23btsHW1hbHjh1r9HwdajmEENiwYQNcXV2lu21bmri4OKSnp6OoqAiOjo4AgISEBOlu5UfJpUuXcPHiRSQnJ2PSpElyh0NtEHP2o40527IsnbNZGLcC27dvlz5J0aPnzpxKd3d3rF69Wu5w6jV58mRER0cjMjISGo0GXbp0wezZs6WltB4lFy5cwOrVqzFv3jz4+vrKHQ61QczZjzbmbMuydM7mVAoiIiIiIrSyVSmIiIiIiB4WC2MiIiIiIrAwJiIiIiICwMKYiIiIiAgAC2MiIiIiIgAsjImIiIiIAAD/D0+Ooe0KgSjtAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "path_fig, path_axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=path_axes[0],\n",
    "    df=path_metrics_df,\n",
    "    metric='rel_duration2goal',\n",
    "    category='cluster',\n",
    "    title='Relative time to the goal',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.0, 1.0],\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    kind='box',\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    "    titlefontsz=16,\n",
    ")\n",
    "\n",
    "path_axes[0].set_xticks([0,1,2])\n",
    "path_axes[0].set_xticklabels(['Low density','Medium density','High density'])\n",
    "path_axes[0].tick_params(axis='x', labelsize=14)\n",
    "path_axes[0].tick_params(axis='y', labelsize=14)\n",
    "\n",
    "categorical_plot(\n",
    "    axes=path_axes[1],\n",
    "    df=path_metrics_df,\n",
    "    metric='rel_path_length2goal',\n",
    "    category='cluster',\n",
    "    title='Relative path length to the goal',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.0, 3.0],\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    kind='box',\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    "    titlefontsz=16,\n",
    ")\n",
    "\n",
    "path_axes[1].set_xticks([0,1,2])\n",
    "path_axes[1].set_xticklabels(['Low density','Medium density','High density'])\n",
    "path_axes[1].tick_params(axis='x', labelsize=14)\n",
    "path_axes[1].tick_params(axis='y', labelsize=14)\n",
    "\n",
    "plt.savefig(\"./pdf/all_shared_control_path_grouped_cluster.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Individual figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yBG5ubpg+fTqcnZ3x/fffo3fv3jh37hwGDhyI0tJSbNy4sSFjJmqSDh8+jN27d0OpVKKyshIfffQR/vWvf9VrqDiRmOq1vPmAAQMwYMAAk7I7QwIDAgLwyCOPoKKiAp6enpaLkKgZKCgoQEJCAqqqqowjpa5cuYKkpCSzvymixsqitzaOjo5MFkQ1uHLlCgDg1q1bJsNqd+zYIWZYRPXCtjCRFbRq1QqA+TyMO6MCiZoCJgwiKwgMDERwcLDZPIyIiAhxAyOqByYMIiuJjo7Gk08+aezklsvleOKJJ0SOikg4wQmjtLQUr7zyCl566SUA1cMDN2zYgKNHjzZYcETNiY2NjXEBT4lEwnkY1OQIThhLly5FamoqdDodAMDNzQ2zZs1CQkICvvnmmwYLkKi5iYqKQteuXREVFSV2KET1Ijhh5OTkICEhAZ06dTIp7927Nz799FOLB0bUXHl4eODdd99l64KaHMHzMLp3717jUuH79u1DeXm5RYMiao7Ky8tx+PBhZGVlwdvbG4888ohx9BRRU1CvpUGOHj0KvV6PW7du4dy5c/jss8+QkpKCuXPnNmSMRE2eXq/HmjVrcP36dQBAZmYm0tLSsGTJEri4uIgcHZEwEsOdQeECbNmyBWvXrkVeXh6A6rHlTz31FGbPnm0cKtgY3NkEhBso0f2KjY3FyZMnxQ6j3vr06YM333xT7DCoibrXZ2e9lgaJiopCVFQUKioqUFVVBWdnZwBAWVnZfW3XSkRETUe9EgZQvbRBZWUlDAYDSktLodPpsGnTJvzjH/9oiPiIRGHpu/SSkhIsW7YMer0eGRkZAICgoCDMnTuXG4NRkyE4YXz77bf417/+hdLSUpNyg8EAiUTChEFUBxcXF0ybNs1kCf9hw4YxWVCTIjhhxMXFYeTIkRgxYgQcHByM5QaDweSPgIhq1rt3b3Tr1g2TJk0CAIwePVrkiIjqR3DCCAgIwOLFi2tcjbZt27YWDYqoubK1tRU7BKL7Jnji3ooVK/Djjz/W+Np///tfiwVERESNU60tjAkTJqCkpMSkrKysDOvWrTMZQmswGJCfn4+pU6c2XJRERCS6WhNG//79odPpEBQUVOcWkjqdDgkJCQ0SHBERNR61Jownn3wScrnc2Gdx8+ZNeHl5mdXLzMxEr169Gi5CqlVBQQFOnjwJvV6Pnj17wtfXV+yQiKgZq7Xp4OPjY9LB/cknn9RYTy6X44svvrB8ZFSnK1euGPuVDh48iPfeew+nT58WOywiasbqHCV15coV7N+/H3q9Hunp6fj444/N6ly9ehU//fQTli9f3mBBNlXWXl7i9ddf/8vn4NISRFSbOhNGYGAghg8fjsWLF6OoqAgnTpwweV0ikcDBwQFvv/12gwZJRETiu+c8jKCgIHz11VfYvn07Zs+ebY2Ymo37vVM/dOgQ9uzZY1a+aNEi2NjYYM6cOQCA9u3bw8bGxvi6r68vXnzxxfsLlojoHgRN3HN1dWWysKKbN2/WWJ6amoqffvrJpJ67uzscHBxga2uLCRMmWCtEImqB6r34IDW8wMBAJCcnm5RJJBKcPXsWer3eWNa6dWs4ODhgwoQJCA0NhaOjo7VDJaIWRPBMb7KeBx98EKGhocZjiUSCMWPG4Pbt2yb1pFIptFotwsLCmCyIqMGJ0sJQq9WIj4+HQqFAcXExlixZUud+Gj/88AO+/vprfPnll1aMUjwymQwzZsxAUlISysvLERYWBg8PD5w9exaZmZkmddu3by9OkETU4tQ7YaSkpODcuXOQy+V46KGHEBgYWO+LxsbGYtCgQRg9ejSOHTuGmJgYrFy5ssa6SqUSa9asgZubW72v01RduXIFGzZsgEqlAgBcvnwZ0dHRGD9+PNasWWOsZ29vj/Hjx4sVJhG1MIITRlFREZ599lmkpKRUv1Euh06nQ2RkJOLi4gRvhapUKpGQkICYmBgA1UuQ/P3vf0dubi78/f3N6q9btw5RUVHYt2+f0FCbNL1ej6+//tqYLIDq2fQHDx5Enz59TJZpkUqlkMvZDUVE1iH40+att96Cvb09NmzYgK5du8LFxQUVFRU4cOAA3n//fcEbKCUnJxtH9gDVicff3x8pKSlmCWPr1q0YO3Ysrly5Uo9vqX7Gjh3bYOe2pIyMDKxdu9ak7PTp05g7d65IEd0frjtG1HQJThilpaVYt26dSZmDgwPGjRtXryUplEql2eMlhUKB/Px8k7Ls7GwUFxejR48e950w0tPT7+t91HBSU1PFDqHR4M+CmhrBCaNjx441lqvVapw9e1bwBSUSCezt7U3KNBqNyaMVvV6PTZs2YcmSJYLPW5PQ0FDBj8o+/vjSX7qWJR054obkZFfjsVxuwNSpSuzf74GCAtMNeFq10uCpp25YO8R6W7gwGED1rnNUjT8LamwqKyvrvNEWnDDs7Oywfv16DB06FE5OTigoKEBKSgq+/vpr9O3bV3BAPj4+ZvtsqFQqeHt7G49//fVXfPvtt9ixYweA6oSi0WgQHh5utjxJcxQRUQQfHw0uXXKEvb0evXqVwttbi4EDi7BzpxcMhur9SCQSAwYOLBI5WiJqKQQnjIULF+Kdd97BmDFjoNPpAFS3FsaPH49XXnlF8AXDw8PxxhtvQK1Ww97eHlqtFnl5eQgLCzPW6d69O/bu3Ws83rdvH/bu3YsPP/xQ8HWaui5dytGlS7lJWXBwBZ588gbOnq0eghwSUgYfH60Y4RFRCyQ4YchkMrz++utYsGABfvvtNwBA165d4eXlVa+ROl5eXoiIiEBSUhKGDBmC48ePIzIyEj4+Pli/fj2GDBmCgIAAtG7d2vgeFxcX2NrampS1VD4+Wvj43L53RSIiC6vXnt4A4OHhgaFDh2Lo0KFo06YNTpw4ga1bt9brokuXLsWhQ4ewZs0aHDt2DMuWLQMA7NmzB5cuNZ6+BCIi+kOdTYO8vDxcv34dQPW+FydPnoTBYDCpk5ubi5UrV2LKlCmCL+rq6lrj/hnbt2+vsf7EiRMxceJEwecnIiLLqzNheHh44MiRI4iPj4darUZiYqJZHQcHBzz55JMNFiCZ0+uBX391NnaKP/hgKdq3V4sdFhE1c3UmDHt7e0yfPh09evTArl276tW5TQ3nwAEPnD7tbDy+csUBkyfno0MHJg0iajiCeqtDQkLua80osryKCinS0kwXajQYJDh50oUJg4galODhTX+ebEfWodVK8N//uuH8eQXkcgOCg1XQ6yVm9crLZSJER9SyaLVaaLXaFrudAFeua+QOHPBAevofLYqUFFdIpQazpNGxY/mf30qN0N2jAHU6HWQyJvqmQK/XY8+ePfjpp5+g1WrRoUMHTJs2Da1atRI7NKviBkqNmEYjwfnzCrNyhaIKHh7VE/YkEgM6d1YhPLzY2uFRPW3ZsgWffvqp8fjjjz+GVsuJl03B8ePHcfjwYePvKysrCxs2bBA3KBHUq4VRWlqKGzduIDg4GMXFxVCpVPD19W2o2AjVI6L+TC4HoqPzUFhoAzs7PZydddYPjOolNzfXbNvda9euITU1FQ899JBIUTVfsbGxOHnyZINeIyMjA0ePHrXoOfv06YM333zToue0JMEtjJMnT2Lo0KF49913AVTPpTh37hzeeOMNlJWVNViALZmtrQFBQRUmZWVlMuTn22L1an+cPauAoyOTRVNwZz6T0HKixkhwC+Of//wnpk2bBo1GYywbNmwYMjIy6twxj/6ayMhCSKUGXLrkCJVKBoOhevValUqGEydcYTBUL1ZIjVtNm4PVVU5/jaXv0tPT0/Hvf/8bGRkZAICgoCCEhIQgOjraotdp7AQnDD8/PyxatMhsEx+5XG7xZpkY7iy/3RTcvv3Hry072xvbtnnXUZsaAz8/P/Tr1w9JSUnGsoCAADz44IMiRkVChYaGYsaMGcadQh9++GGMGjVK5KisT3DC8PHxAVC9Qu0dmZmZ2LhxI9q0aWP5yIiamUmTJkGhUBjvUqOjo2FjYyNyVCRUr169jF8/9thjIkYiHsEJY8yYMXjttddQUlKCzz//HOfPn8f+/fshl8sRHx/fkDFaRWPaQKk2Bw964NdfnU3KHnywFMOG3RIpIuGaUguuoWzcuBFpaWnG41WrVuH5558XvMkXkdgEJ4yePXuiXbt22Lx5M9LS0lBVVYXZs2fj8ccf57LjVjJ48G3o9TDOywgNLUNERONPFlQ9DPPuZAFUb1eckpKCgQMHihQVUf3Ua1ith4cHFi5caFaenp6O0NBQiwVFNbOxMWDEiFsYPrw6SUjMJ3xTI/XnPevvUCqVVo6E6P4JThg6nQ4//vgjsrOzTSYb6fV6HD58GN9//32DBEjmmCianvbt29ernKgxEpwwnn32WSQlJaFTp05mHXU5OTkWD4yoOfHx8cGwYcNw8OBBY1lISAh69uwpYlRE9SM4YRw/fhzbtm1Dx44dzV7bsWOHRYMiao5Gjx6NBx98EHPmzAGAFjeGn5o+wTO9IyIiat27u3///hYLiGqn1UpqXCqEmg4OEKGmTHDCWL58Ofbu3Yu8vDyT/3Jzc/H55583ZIwtXkGBDTZtao3332+HNWv8zYbWElHDMhgM2Lt3r/F4zZo1uHWr5Y1QFPxIateuXfj000+xatUqk3KDwQCJRIJXX33V4sFR9eKD333njeLi6l+VSiXDwYMecHOrQkBAxT3e3fyNHTtW7BDuW1OLPSEhQewQRJOUlIQDBw4YjzMyMrBhwwYsXrxYxKisT3DCWLlyJWbOnInx48ebbKak1+uxcePGBgmOgNxcO2OyuNvZswomDCIr+fXXX83KcnNzcfPmTXh5eYkQkTgEJ4z27dtj1qxZ8PDwMHtt3rx5Fg2K/lDb/joymcG6gTRyH0Wni3LdMrUMnx1ob9a3NKaPEp19y3DtpgNuldnC37MC3q5/LNyZd8sOB854I7/YFi6OVRjUuRBd25Zh90kfnL/+x4ZZEgkw6aEb6OAt3gZZz67nHKva+m9b2gZY9erDSExMrPG1lJQUiwVEpnx9K+Hl9ccHTUWFFLdvy3Hjhi3S0803VyLryil0qHEgwtV8R2xN8sXWJF8cSmuFjUfa4ti56pstTZUE353wRX6xLQCgpFyOH37zwZXfHXAh78/7tQOpma4N/n1Q3Woa2NO5c+cab6CbM8EtjPfffx/Xrl3Drl27TMoNBgMuXryI0aNHWzw4qr7DnDw5H0eOuCMtzQnl5TIoFDoUFtrihx9aQa2Wok+fUrHDbLFcHKpqLC9Ty3DtpoNJWfJld4S2K4Wy2A5qjem9msEAXLjuDEMNDcdKLTfGFFv37t0xffp0xMbGAqhOII8++qjIUVlfvZY3t7GxQVBQkEkzTKfTobiY24M2JGdnHcaOLUBurh3kctNPlJQUFyYMEfl5qPGAV4VJclDY6+Bga76xlcEA3LhtD5m05rHRzg46eDprUFhqa1Ie1EZl2aDpvvTp08f49eTJk0WMRDyCE8asWbNgY2NT41LmI0aMsGhQVLOKCvPnpTWVkXVNDL+B01ddkFPoAHeFFg8GFOHSDSeczzUd/qzVSfHzBXcUqWxQVG4DB1s97G2qE4tMCoS0LUFI2xIknGyNmyW2kEqA0HYl6BPADbKocRCcMNq1a1fra+Xl4nXItSQdO5bjwgWFWRmJSy4zoHdgMXoH/tHS7tauBGnXXHCzpLq1YIAEmiopisttIJEAzvZVKFPL4WhXhdZulRjY+RY8navXaJs1JAfF5XLYyvVwsOVMTWo8ak0Y8fHxCA0NNe4qtXr1auhr6N3T6/U4dOgQdu7c2XBREgBg6NBbUKlkyMmpHtbctq0aQ4e2vMlDTYGt3IDHB+Xi/HVn3CqzgU4vQeqVPzqv5TID3BRa9A4oRkRIodn7XR1r7hshElOtCSM3N9fk8VN6ejrOnz8PX19fSKV/dMLp9Xrk5ubW66JqtRrx8fFQKBQoLi7GkiVL4ORkOjqkqqoKMTEx2LdvH/z8/BAXF4euXbvW6zrNjUKhx7RpShQXVz+GcnU1f05OjYeN3IBOvmUoUtmgoNQGEon5aCephMOjqemoNWF8+OGHJsfR0dFwc3NDYGCgWd3ahtvWJjY2FoMGDcLo0aNx7NgxxMTEYOXKlQ+rGnEAACAASURBVCZ1du3ahaioKLz44ov4xz/+gbfeegtff/11va7TVBUVyXHwoAeysuzh4qJD//5F6NatuuPz7FkF0tKqk2u3bmUICWGHKNB05wpkFzhga5Kv2GEQCSJ4vJ5er68xWaSkpKB3796CL6hUKpGQkIAhQ4YAqB6elpiYaNZKGTVqFLp37w4PDw9ERUW1mAkyej2wbZs3MjMdYDBIUFwsx969rXD1qj1OnnTGnj2tkJ1tj+xse+zZ0wonT3JdKSKyDsGd3nv27EHfvn3NyoODgzFv3jxs2bJF0HmSk5Ph7u4OB4fqYYhyuRz+/v5ISUmBv7+/sd6d1wEgMzMTixYtEhrqfWnse07/6181DzrYsKENNmwwH7nW0og107s2P1/wwPGL7mblj4QWmHSONwVNtfVGlldnwrhy5QrWrVuHvLw8XL16FZmZmWZ18vLy6nVBpVIJNzc3kzKFQlHjFpa3b9/Gd999h02bNiEoKKhe1wGq+12ocUlNTRU7BKuoaR6G3iBBZr4jMm44wcNZg74db8Nd0XQ6t1vK706IlvqzqDNhBAYGIjY2Fq+//jpUKhXCwsLM6jg4OCAyMlLwBSUSicnihQCg0WhqXKtFoVAgLCwMZ86cwTPPPIMDBw7UOA+kNqGhobCzs6uzTmNbgVOv12PFihVmCXT+/PlISkrC6dOnkZGRAQAICgpCjx49MHPmTDFCpTp08S/FiQx3lKn/eJSqUsuQpXSERALkFNrjUp4TZg3JhpN90xi8UJ9Hz81dc/1ZVFZW1nmjfc9HUnZ2doiLi8PRo0eN/Q5/hY+PD0pKSkzKVCoVvL29zera2tqie/fueP/99xEZGYlTp07VK2E0RVKpFHPmzMGOHTtw8eJFuLm5YcSIEQgODkabNm1QWlpqTBgBAQGYOHGiyBFTTRxs9Zg+KBfJGe64WWILWxs9Lt9wNNmPvUIjRVq2C/oF3xYvUBJEp9Nh27ZtxuO4uDjMmDEDvr4ta8CCoD4MqVRqkWQBAOHh4XjjjTegVqthb28PrVaLvLy8Glsvd8hkMnTt2hU+Pj4WiaGx8/T0rHH7TmdnZyxcuBD79+8HACxcuNDaoVE9uDpWYXiPmwCA9GxnZCkdzeqUV7aMwRxN3bFjx3DixAnjsVKpxH/+8x+8/PLLkNx9F9DMWX1VMy8vL0RERCApKQlA9V7hkZGR8PHxwfr16439JJcuXTK2REpKSqDT6dCrVy9rh0tkEe29yyGt4a8twIcz9ZuCtLQ0s7L8/HwolUoRohGP4FFSlrR06VLEx8fjwoULKCgowLJlywBUj8Ty8/NDQEAAPvvsMxw/fhxDhw6Fp6cn3nrrrRaVyal5cbLXYVQvJQ6e8UKFRga5zICwjrdF3eeChHN0NG8dSiQSk9GcLYEoCcPV1RXLly83K9++fbvx6/fee8+aIRE1OGf7KjjaVqGsQgZHOx3cFFqxQyKBHn74YZw/f96krEePHnB1bVl7lQh+JKXVavHxxx9jxYoVAICioiJ89dVXuHDhQoMFR9RcqLVSbD/hi9sqW9jIDVBrZNh/yhs3btc9io8ah+DgYMyZM8d4HBkZiWnTpokYkTgEJ4x33nkHGzZsQHZ2NgDAzc0N06dPxwcffIBDhw41WIBEzUGm0hGaKtNHqtWbJjnV8g5qbDp37mz8OjIyEjY2NiJGIw7BCePUqVM4cOAAunfv/sebpVIMHjzY2OogoprJa9mDvbZyosZIcMLo0aMH3N3NlzpISkpCQUGBRYMiam4CvMvh8qcly+UyA0LacrdEajoEJ4xWrVrh8uXLxpFKWVlZWLJkCX788Ufu5010D3KZAVP7X0cnvzI42evQzqsCU/rlwcOJHd/UdAgeJTVv3jysWrUK27Ztw+rVq1FRUQG5XI7HH38cL730UkPGSNQsuCmq0OOBEni5aOCh0KCNu1rskIjqRXDCkMvlWLx4MZ555hnk5OSgqqoK7dq1q3F8MhGZ23/KC2euuRiPfT3UmNo/j/0Y1GQIfiQVFxcHoHptqY4dO6Jz585MFkQCKYttTZIFAOTdsse5XI6SoqZDcMI4d+4cYmJisHbt2hY3HZ7or8ovrnm+hbKWcmp8KisrjV///vvvIkYiHsGPpOLi4tC6dWvk5eXh+++/x82bN9GnTx8MGzasRY5HJqoPH9fKGstbu9VcTo3L7du38dFHHxmPV6xYgcmTJ6N///4iRmV9glsYrVu3BgD4+vpi/vz5WLBgAXbt2oWBAwfWuMwHNYy773Lu/poaj4t5Cmw43BarfuiAnSmtUVIhh7erBl39S1FaIUdhmS2Ky23QykWDLn5lYodLAhw6dAhFRUUmZbt3725xf4OCWxinT59G9+7dcezYMXz33Xc4fPgw3N3dERUVhcmTJzdkjPQ/169fx6effmo8Xr58OebPnw8/Pz8Ro6K75RTaI+Fkaxj+1499KU+BwlIbzIzIQX6JHexsdJBIDJDLDCitkKNCI4WzQ9PYQKkhjB07VuwQ7sudPWma0mefJTaLE5ww5s+fDwAoLS3F4MGDsWrVKgwePBjSmtZspgbx/fffQ6VSGY9VKhW+//577ouBxr3vdHaBA37LMl+kLr/YDq9u7iJCRET3R3DC0Ol0iI6OxqRJk9CqVauGjIlqcfXqVUFlRE3NRz1r3xa0MSjRyrE5xx+lVdUbXkkADPW+iV5uJXW/sRF49pTlbqYEJ4x//vOfGDZsmMUuTPXn7e1tNjqjpexCWJvGtif7hQsXsHbtWpMyDw8PPPvss1i+fDl0Op3Jnuzz589HcHCwGKFSPbjYVGH2A9dwocwZ5VUydHRSwctOI3ZYVif4eVJdySI1NdUiwVDdHn30UZNHgFKplMuyNDKdO3fG6NGjUVlZicLCQjg7OyM6Ohqurq544oknTDbcGTlyJJNFI3S9wh7fXW+Df19ti4P5rVCuq/6bs5MZ0MO1BP08b7fIZAHU0cKIj49HaGgoRo0aBQBYvXo19Hq9WT29Xo9Dhw5h586dDRclAQBCQkLw4osvGtflf/HFF42j16hxKCoqwn//+1/Y2dnB1tYWpaWl+PHHHzFz5kxoNBpUVf2xAKFG0zI/dBqzm5W2+CbXFzpD9Zp5hRpb3FDb44m2uZBIAFWVDBU6KTxttWiJG4DWmjByc3PRpk0b43F6ejrOnz8PX19fk7tcvV6P3Nzcho2SjO5OEEwWjc/x48dRWlq9Au2dhTpPnz6NS5cu4ZtvvjG56UpMTERgYCC6dGHHd2NxutjFmCzu+F1th5wKe6SVuOB8iTMMANxttRjb+nf42LespF9rwvjwww9NjqOjo+Hm5obAwECzuomJiZaPjKgJqm2p/zNnztTYQj9//jwTRiNSqa/5Kf2ZYhecL3U2Ht/W2CDh99aIfiC7RbU0BPdh6PX6GpNFSkoKevfubdGgiJqqjh07mpXJ5fIa/3YAwMXFpcZyEkewk8qszF6mx22N+WoWtzU2KKyhvDkTnDD27NlTY3lwcDDmzZtnsYCImrLw8HB069bNeCyXyzFp0iT06NED/v7+JnWdnJwQHh5u7RCpDkFOKgzwvAVbafXMSzcbLR5rcwMKuXnrUILqZNKS1Dms9sqVK1i3bh3y8vJw9epVZGZmmtXJy8trsOCImhqZTIbZs2fj+vXruHXrFjp06AAnp+oVaefPn49jx44Zh9U+99xzcHZ2rut0JIL+nrfRx70IFToZXORVkEgAA24jU+WIuxei7+JSCid5y5qlX2fCCAwMRGxsLF5//XWoVCqEhYWZ1XFwcEBkZGSDBUjUFPn5+Zkt2eLo6IiRI0fik08+AQB4enqKERoJYCs1wFb6x4i2do5q/J//daQWuRnnYfRxL6rjDM3TPSfu2dnZIS4uDkePHsWQIUPMXlcqlS1+8hgRNX/tHNVo59gylzW/Q1AfhlQqrTFZANUL4q1cudKiQRERUeMjeGmQhIQEvPPOOygqKoLBYLqlpIuLCxYvXmzx4IiIqPEQnDC+/PJLLF68GG5ubjh+/DhGjhwJg8GAHTt2IDo6uiFjJCKiRkBwwnjooYeMa7//9ttv6NOnD6RSKTw9PfHJJ5/g/fffF3xRtVqN+Ph4KBQKFBcXY8mSJcaRJHeUlpbitddew08//YR27dph6dKl6Nmzp+BrEDVGNU3eI2oqBM/DuHr1KrZt24bs7GyMHDkSb7zxBi5evIgffvgB//3vf+t10djYWPTu3RuLFy/G0KFDERMTY1Zn3bp1GDFiBP7zn//A29sbCxYsQHl5eb2uQ9SYnD9/3mR3yvr+3RCJTXDC+Nvf/obVq1djy5Yt6NatG/z9/TF+/HisXr0aw4cPF3xBpVKJhIQEYyd6//79kZiYaLYeVb9+/TBmzBiEhobivffeQ3FxMS5fviz4OkSNiUqlwoYNG0y2+fz+++9rnNtEjVOpVoafC91xML8VrpU73PsNzZDgR1Ldu3c3WTPq6aefxvjx46FSqRAUFCT4gsnJyXB3dzcu8yyXy+Hv74+UlBSTmbD9+vUzfu3s7AwnJycutkdWExsbi5MnTzboNTIyMvDcc89Z9Jx9+vTBm2++adFzElCoscHmHH+o/7fU+W9FrhjgeQv9PW+LHJl1CU4YNfH19QVQvUJn//79Bb1HqVTCzc3NpEyhUCA/P7/W92RlZSEsLAze3t73HywRNVqW3BXOWrLL/fB1jt+9KzYjtSaMmJgYaLXae55Ar9cjOTkZhw8fFnRBiUQCe3t7kzKNRgO5vPbctWnTJrz00kuCzn+39PTGve3jX8WNqxrOmDFjMGbMGIudT6/XY+vWrSguLjaW2djYYPLkyRZfHoT/Lqgmlvh3UeundEVFBbKzs/HAAw9AJpPVegKDwVCvkR8+Pj4oKTHdB1elUtXaejh+/DjCw8PRtm1bwde4IzQ0FHZ2dvV+X1PBVYKblqCgIOzfvx9ZWVnw9vbGiBEjzBYkbKka+57eh2964uRt0ycjjjId5nW4BrnUUMu7Goc7rTchnxeVlZV13mjXmjCio6Ph5OQk6B90fTJXeHg43njjDajVatjb20Or1SIvL6/GdaquXLmCnJwcTJ06VfD5iRorNzc3/ltuosI9buNauSNuVtoCAGQSA4Z732z0ycLSah0l1blzZ7NkUVpaikuXLgEAiouLjSvV1udO18vLCxEREUhKSgJQ3YKIjIyEj48P1q9fbxw1kpOTg2+++QYDBgxAbm4uLly4gM2bN9fvuyMi+otKtHKoquSY0TYHk/xuYHTrfMzrcA3BzuZ7ZzR3gju9T548iQULFiA0NBT//ve/4erqipSUFKxZswYvv/yy2cS7uixduhTx8fG4cOECCgoKsGzZMgDVe274+fnByckJM2bMwI0bN7Bx40bj++ozOZCI6K/Q6iXY87sPMsoUAAAPWy3Gt7mBAEXLnQ8mOGH885//xLRp00w2rh82bBgyMjIQExNTrwUIXV1dTSYw3bF9+3bj10eOHBF8PiIiS0u65W5MFgBwS2ODPb+3xswHckSMSlyCJ+75+flh0aJFcHd3NymXy+U4evSoxQMjIhLTlbuSxR35lbYo0f6l2QhNmuCEcWfPC8ldO55nZmZi48aNaNOmjeUjIyISkWMNu+lJAJRUyaBvWX3dRoJT5ZgxY/Daa6+hpKQEn3/+Oc6fP4/9+/dDLpcjPj6+IWMkIrK6vu5FyCl3gAGAARKUaOWQwoCvc/zhalOFcW1+R2v7SrHDtCrBLYyePXvihRdeQKdOnZCWlga1Wo3Zs2dj3759Jst4UMO6ewFGLsZI1HACFOWY4p+HQEU57KU62Ej0cJJXb9tarJUj4YYPDC2spVGvh3EeHh5YuHChWXliYiIeeeQRiwVFNcvOzsZnn31mPH777bcxb948tGvXTsSoiJqvBxwr8IBjBTZl+6FSb3p/XaS1QaHGBq3s7r0iRnMhuIVRl6+++soSp6F72LFjByoqKozHFRUV2LFjh4gREbUMilr6MxxkLWt/k3smjOzsbGzYsAF79uyBTmf6Q6uoqMALL7xgnIRHDSs7O1tQGRFZVm+3IkglpmUhLqU1JpLmrM5HUr/++itmz56NyspKSCQSbN26FV988QUkEgl+/fVXvPLKK7h586Zx4h01rNatWxtn199dRkQNq62jGlP9ryP1thsqdDIEOqnQ263o3m9sZupMGB999BEefPBBTJ8+HUVFRfjkk0+wd+9epKenY8OGDejZsyc+//zz+1oYkOpvzJgxWL9+vfFYJpNZdEVVIqqdv4Ma/g6/ix2GqOpMGMXFxfjmm29gY2MDoHr11ylTpkAqleL555/HnDlzTOZlUMMKDAxEaGgoLly4AKD69xEYGChyVETUUtTZh9G+fXtjsgCALl26oFu3bvj2228xd+5cY7Jo6J3JqNquXbtw+vRp4/Hp06exa9cuESMiopakzhbG9evXce7cORjuGmzs7++PoqIiY0e3RqPBpk2b0KdPn4aNtAlq6G0+MzIykJGRgQ0bNljsnNzik4hqU2fCOH36NCZNmmRSZjAYsHv3bhgMBkgkEuP/iYiag9wKe5wrcYZEYkCoSynatLDZ3HWpM2FERETgiSeegK2tba11Kisr8eWXX1o8sObA0nfqu3btMlvFd8iQIRg7dqxFr0PUUp0tccbe371x55nK6SJXjPf9HUFOKvxW5IKU2+6o0MnQUaHCEO+bcGxh8zDqTBhPPfVUjTvh/Vlz3ga1MXn00UchlUqRnJwMAAgLC8OoUaNEjoror7uzjWhjtOpyB7Oyy2WO2Kf0EiEacdWZMIQki/rUo7/mzjBaDqUlIjG03IXdiUh0CQkJYodg4qOPPkJWVpZJWUhICCorK3H58mVkZGQAAIKCggAAr776Klq1amX1OMVikbWkiIiag0mTJsHFxcV47OHhgXHjxqFv375mddu3b9+ikgXAFgYRkZGvry9ee+01ZGRkQCqVIigoCDKZDF5eXigrKzNuRd2tWzezEaQtAVsYRER3sbGxQdeuXdG5c2fIZDJj+ZAhQ4xfz54926Ql0lIwYRARkSBMGEREJAgTBhERCcKEQUREgjBhEBGRIEwYREQkCBMGEREJwoRBRESCiDLTW61WIz4+HgqFAsXFxViyZAmcnJzM6hUVFWH9+vUoKyvjpj5ERCITpYURGxuL3r17Y/HixRg6dChiYmJqrJednY2MjAxUVFRYOUIiIvozqycMpVKJhIQE4zT7/v37IzExEbm5uWZ1u3fvji5dulg7RCIiqoHVE0ZycjLc3d3h4OAAAJDL5fD390dKSkqN9aVSdrMQETUGVu/DUCqVcHNzMylTKBTIz8+3+LXS09Mtfk4iotTUVLFDEIXVE4ZEIoG9vb1JmUajgVxu+VBCQ0O5fSwRWVzv3r3FDqFBVFZW1nmjbfXnPT4+PigpKTEpU6lU8Pb2tnYoRERUD1ZPGOHh4cjPz4darQYAaLVa5OXlcV9wIqJGzuoJw8vLCxEREUhKSgIAHD9+HJGRkfDx8cH69euRmZlpUt9gMMBgMFg7TCIi+hNRJu4tXboU8fHxuHDhAgoKCrBs2TIAwJ49e+Dn54eAgAAAQFpaGk6cOIFbt24hOTmZrRAiIhGJkjBcXV2xfPlys/Lt27ebHHfr1g2bNm2yVlhERFQHTnIgIiJBmDCIiEgQJgwiIgFu3Lhh/PrkyZMtcjAOEwYR0T3k5OTg/fffNx5v3rwZO3bsEDEicTBhEBHdQ2JiIqqqqkzKfv75Z5SVlYkUkTiYMIiI7qGoqMisTK/Xm61a0dwxYRBZ0ZUrV7Br1y48++yzKCwsFDscEig4ONiszNXVFa1btxYhGvEwYRBZybZt2/DJJ5/g22+/xbVr1xATEwOtVit2WCTAI488gqCgIOOxg4MDpk+f3uK2X2hZ3y2RSK5fv47jx4+jqqoKpaWlMBgMyM3NxbFjx8QOjQSws7PD008/bTxeunSpSQJpKZgwiKzgzo6St27dMg7HNBgM2Lt3r5hh0X2ysbEROwRRMGEQWYGfnx8AGFsXQHXCyMrKEjMsonphwiCyAn9/f4SFhcHZ2RkSiQRA9fbDd/a2J2oKmDCIrCQqKgrPP/88ZDIZAEAmk+Hxxx8XOSoi4ZgwiKyoT58+GD58OCQSCYYPHw53d3exQyISTJTlzYlasqioKGRnZyMqKkrsUIjqhQmDyMo8PDzw7rvvih0GUb3xkRQREQnChEFERIIwYRARkSBMGEREJAgTBhERCcKEQUQkwN0rCxcUFIgYiXiYMIiI7qG4uBhxcXHG43feeQfJyckiRiQOJgwions4ePAgbt26ZTw2GAzYuXMnNBqNiFFZHyfuEVGzExsbi5MnTzbY+TMyMgAAkyZNsuh5+/TpgzfffNOi57QktjCIiEgQtjCIqNmx9F16YWEhVq1ahdLSUmPZY489hocfftii12nsmDCIiO7B09MTL7/8Mn799VeUlZUhJCQEbdu2FTssqxMlYajVasTHx0OhUKC4uBhLliyBk5OTWb2jR4/i6NGj0Ov1eOihhzBy5EgRoiUiAhwdHTFw4ECxwxCVKAkjNjYWgwYNwujRo3Hs2DHExMRg5cqVJnWysrLwwQcf4LvvvoNUKsXjjz+OgIAABAcHixEyEVGLZ/VOb6VSiYSEBOPWlP3790diYiJyc3NN6n3xxRcYNGgQpNLqEAcOHIgvvvjC2uESEdH/WL2FkZycDHd3dzg4OFQHIJfD398fKSkp8Pf3N9Y7ceIEoqOjjccdOnTA1q1bBV3DYDAAQIsbI01E9Ffc+cy88xn6Z1ZPGEqlEm5ubiZlCoUC+fn5ddarqU5t7kzhv3Tp0l+Mloio5dFqtbC3tzcrt3rCkEgkZoFoNBrI5XKzenZ2diZ1bGxsBF1DoVAgODgYNjY2kEgkfz1oIqIWwGAwQKvVQqFQ1Pi61ROGj48PSkpKTMpUKhW8vb1Nyry9vU3q1VSnNlKpFM7Ozn89WCKiFqamlsUdVu/0Dg8PR35+PtRqNYDqpk9eXh7CwsJM6g0YMABZWVnG46tXryI8PNyqsRIR0R+snjC8vLwQERGBpKQkAMDx48cRGRkJHx8frF+/HpmZmQCAqKgo/PTTTwCqm0m//PILZsyYYe1wiYjofySG2rrDG1BxcTHi4+Ph5+eHgoICLF68GAqFAhMnTsTcuXONE/R27tyJc+fOAQD69u2LYcOGWTtUIiL6H1ESBhERNT1crZaIiARhwiAiIkGYMIiISBAmDCIrUSqVWLp0KWJiYoxl48ePR2pqqohR0b3s2bMH8+fPv2e948ePY+rUqThx4oQVohIHE0YjcObMGcyaNQudOnXC2rVrkZOTI3ZILd7PP/+MyMhIdOvWDYWFhTXW+dvf/oZevXph586dxuVo6mJjY4PS0lKTNc7mzZuHjh07WixuEu706dN48skn0bVrV2zatAlFRUXQaDTYtWsX+vTpg7Fjx+Knn35Cr169MG3atHuer3Xr1rhw4YIVIhcPE0Yj0L17dzz66KPw9PTE3LlzW+TGLI3NgAEDMHLkSEgkEmzZssXs9WvXruHkyZMICgrC+PHjBS1b4+HhgXbt2pmUjR49Gq6urhaLm4Tr0aMHxo4dC09PTzz++ONwc3ODra0txo0bh06dOmHIkCEYOHAgfH19MXjw4HueLyAgAO7u7laIXDxMGI2ETCYzW0+LxCWXyzFmzBhs2bLFrAWxZcsWjBo1CjKZrF7nvLNcPzUOMpmsxvXmpFLpfa1D19x/v837u2smDAYD1q5diw8//BB///vf8c4770Cn0yEzMxMjR47ExIkTUVxcjG+++QadO3dGXFwcqqqqcPnyZQwfPhxnz54V+1tosqZOnYrbt29j//79xrKKigoUFRXB19fXWKZSqbB69WqsWLECkyZNQkpKivG1nTt3Yvny5YiLi0NiYqKxfN++fZg0aRJOnDiBnJwcPP3008bVDNLS0jBy5Eh89NFHKCkpwfvvv4+JEyciOTkZ48ePxyOPPILMzEysXbsWDz/8MCZPnmyy3zRZRn5+PpYvX4558+YZy9LS0vDOO+9gw4YN6NGjByIjI0326ikpKcHChQvRq1cvfPvtt2KE3WCYMJqALVu2oKysDM899xw++OADJCUl4fPPP0dAQACeeeYZaLVauLq6YurUqejbty/atWsHuVwOLy8vTJkyBSEhIWJ/C02Wp6cnRo0ahS+//NJYtnPnTowbN86k3ooVKzB+/Hi89NJL+L//+z8sWrQIVVVVOHv2LL755hu8/vrrePnll00W0Bw4cCCuXr0KAGjbtq3JSgbdunVDjx49AAAODg4IDAzE9evXAQA7duxAQEAAli5diiFDhiAxMRFVVVX44YcfGurH0KyVlZXhX//6l8l/d/oR3dzc0KpVK5SXlwOovnl79tlnMWbMGMyaNQsTJ06Eu7s7Zs+ebTxfamoq4uPj8dZbb2H16tWifE8NhQmjCdi8eTN69eoFoLrJO3HiRHzzzTcAgKFDhyI3NxdXrlwBUN2xunPnTgDAjz/+iFGjRokTdDMyY8YMnDp1CmfOnAFQvQlYv379jK8bDAYcOHAABw4cwIYNG6BUKtGpUyfcvn0b//73v/Hwww8b64aGhhq/dnJygouLi/H4z49A7hzb2NjA29sbCoUCYWFhkEgkCA0Nha+vL4KCgiCXyxEcHIybN282yPff3Dk5OeHFF180+e9OP6KtrS28vLyMdW/duoUbN24YV3QNDAw0LqR6x5AhQ+Dg4IBu3bo1u98JH5o3AVevXkVVVZXxuF27dsbNpBwdHfHII49g7969GDJkCB599FHExsYiOzsbubm57EC3gO7du6N79+748ssvMWXKFPTt29fk9cLCQpSWlmLmzJlmH/qXLl1Cz549az230Ofkf673574TqVRa6y5p9Nfc/bP39PREaGgokpKSEBQUhNzcXIwePbrG98lkMpO/2+aATeVxqAAAAylJREFULYxGLicnB97e3rh8+bKxzGAwoH379sbjcePG4YcffsDBgwfx2GOPISIiAp999hnatGkjQsTN0xNPPIG9e/fiP//5D8aPH2/ymru7OwwGg0n/xNWrV1FUVASFQmFs/d2LVCqFTqezaNxkeW+++SbS0tKwc+dOdOrUCXPmzBE7JKthwmgkdDqd2d2IRqPBV199hSeeeAI7duwwjtQ5c+aMybjwAQMG4NatW1AoFJDJZBg3bhx27tyJyMhIq34PzY1OpzN+gI8aNQqurq7w8/ODo6OjyesymQyRkZF49dVXsW3bNvz888/YuHEj3NzcMHLkSOzcudO4XXBubi5u3bpl/F0bDAZjy8DT0xNZWVkoKirC6dOncenSJWPdu+vdca9jure7f8d302q10Ov1AGD2s3/11VcxefJkdO3aFb169TJ5JFXT76A5/V74SKoROHPmDPbs2YPCwkLMmzcPzs7O0Gq1OH/+PAYOHIiZM2fi999/x4IFCxASEgJ7e3tMnz7d+H65XI7x48dj4sSJAIDBgwdj7NixzX5MeEM6ceIEDh8+DLVajRkzZsDPzw9RUVHGzu47r2dnZ2P37t147bXXEBsbi3feeQedOnVCXFwcgOqWSW5uLmbOnInw8HDY2dlBKpXi3LlzuH37Nm7evIkff/wRoaGh6N+/P7p27YpRo0Zh3rx5CAkJQVlZGS5fvox9+/bh5s2bOHLkCDp06IBffvkFhYWFSEtLg16vx6lTp3Dt2jVMmDCBjyEFOnPmDH744QcUFhZiw4YNeOyxx+Do6Ii9e/fi4sWLUKlU6NKlC44cOYIrV67g1KlT6NmzJ/z9/fHcc8+hrKzMuPf1J598ArVajZs3b2L//v3w9/fHnj17AADbt2/HpEmTRP5uLYPLmxMRCVRYWIivv/4aCxcuBADo9XoolUps2bIFixYtEjm6hsdHUkREAn311VfGx4Z3/PLLL4iIiBAvKCtiwiAiEigqKgoSiQSjR4/G4MGDMXv2bLRu3do47L254yMpIiIShC0MIiIShAmDiIgEYcIgIiJBmDCIiEgQJgwiIhKECYOIiAT5fzkmeiHPYW1ZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "dense_fig5, dense_axes5 = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=dense_axes5,\n",
    "    df=path_metrics_df,\n",
    "    metric='rel_duration2goal',\n",
    "    category='cluster',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.00, 0.7],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "dense_axes5.set_ylabel(\"Relative time to the goal\", fontsize=16)\n",
    "dense_axes5.set_xticks([0,1,2])\n",
    "dense_axes5.set_xticklabels(['Low','Medium','High'], fontsize=16)\n",
    "# dense_axes5.set_xlabel(\"Crowd density\", fontsize=16)\n",
    "\n",
    "# axes_cluster.legend(date_unique.tolist(), fontsize=14)\n",
    "dense_axes5.tick_params(axis='x', labelsize=14)\n",
    "dense_axes5.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "# significance\n",
    "bars = [0, 1, 2]\n",
    "heights = [0.99, 1.0, 1.03]\n",
    "barplot_annotate_brackets(0, 1, 0.411814, bars, heights, line_y=0.54)\n",
    "barplot_annotate_brackets(0, 2, 0.000962, bars, heights, line_y=0.6)\n",
    "barplot_annotate_brackets(1, 2, 0.006326, bars, heights, line_y=0.54)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_boxplot_rel_duration2goal.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "dense_fig6, dense_axes6 = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=dense_axes6,\n",
    "    df=path_metrics_df,\n",
    "    metric='rel_path_length2goal',\n",
    "    category='cluster',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.0, 3.0],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "dense_axes6.set_ylabel(\"Relative path length to the goal\", fontsize=16)\n",
    "dense_axes6.set_xticks([0,1,2])\n",
    "dense_axes6.set_xticklabels(['Low','Medium','High'], fontsize=16)\n",
    "# dense_axes6.set_xlabel(\"Crowd density\", fontsize=16)\n",
    "\n",
    "# axes_cluster.legend(date_unique.tolist(), fontsize=14)\n",
    "dense_axes6.tick_params(axis='x', labelsize=14)\n",
    "dense_axes6.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_boxplot_rel_path_length2goal.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shared control metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "control_metrics_df = eval_res_sorted_df[['seq', 'control_type'] + list(control_metrics) + ['cluster']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4-in-1 plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "control_fig, control_axes = plt.subplots(2, 2, figsize=(12, 12))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=control_axes[0,0],\n",
    "    df=control_metrics_df,\n",
    "    metric='avg_fluency',\n",
    "    category='cluster',\n",
    "    title='Average control fluency',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.90, 1.0],\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    kind='box',\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "control_axes[0, 0].set_xticks([0,1,2])\n",
    "control_axes[0, 0].set_xticklabels(['Low density','Medium density','High density'])\n",
    "\n",
    "categorical_plot(\n",
    "    axes=control_axes[0,1],\n",
    "    df=control_metrics_df,\n",
    "    metric='rel_jerk',\n",
    "    category='cluster',\n",
    "    title='Relative jerk',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0, 0.25],\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    kind='box',\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "control_axes[0, 1].set_xticks([0,1,2])\n",
    "control_axes[0, 1].set_xticklabels(['Low density','Medium density','High density'])\n",
    "\n",
    "categorical_plot(\n",
    "    axes=control_axes[1,0],\n",
    "    df=control_metrics_df,\n",
    "    metric='contribution',\n",
    "    category='cluster',\n",
    "    title='Contribution',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.0, 1.0],\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    kind='box',\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "control_axes[1, 0].set_xticks([0,1,2])\n",
    "control_axes[1, 0].set_xticklabels(['Low density','Medium density','High density'])\n",
    "\n",
    "categorical_plot(\n",
    "    axes=control_axes[1,1],\n",
    "    df=control_metrics_df,\n",
    "    metric='avg_agreement',\n",
    "    category='cluster',\n",
    "    title='Average agreement',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.60, 1.0],\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    kind='box',\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "control_axes[1, 1].set_xticks([0,1,2])\n",
    "control_axes[1, 1].set_xticklabels(['Low density','Medium density','High density'])\n",
    "\n",
    "plt.savefig(\"./pdf/all_shared_control_perf_grouped_cluster.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Individual figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "dense_fig7, dense_axes7 = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=dense_axes7,\n",
    "    df=control_metrics_df,\n",
    "    metric='avg_fluency',\n",
    "    category='cluster',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.90, 1.0],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "dense_axes7.set_ylabel(\"Average control fluency\", fontsize=16)\n",
    "dense_axes7.set_xticks([0,1,2])\n",
    "dense_axes7.set_xticklabels(['Low','Medium','High'], fontsize=16)\n",
    "# dense_axes6.set_xlabel(\"Crowd density\", fontsize=16)\n",
    "\n",
    "# axes_cluster.legend(date_unique.tolist(), fontsize=14)\n",
    "dense_axes7.tick_params(axis='x', labelsize=14)\n",
    "dense_axes7.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_boxplot_mean_fluency.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "dense_fig8, dense_axes8 = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=dense_axes8,\n",
    "    df=control_metrics_df,\n",
    "    metric='rel_jerk',\n",
    "    category='cluster',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0, 0.25],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "dense_axes8.set_ylabel(\"Relative jerk\", fontsize=16)\n",
    "dense_axes8.set_xticks([0,1,2])\n",
    "dense_axes8.set_xticklabels(['Low','Medium','High'], fontsize=16)\n",
    "# dense_axes6.set_xlabel(\"Crowd density\", fontsize=16)\n",
    "\n",
    "# axes_cluster.legend(date_unique.tolist(), fontsize=14)\n",
    "dense_axes8.tick_params(axis='x', labelsize=14)\n",
    "dense_axes8.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_boxplot_rel_jerk.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "dense_fig9, dense_axes9 = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=dense_axes9,\n",
    "    df=control_metrics_df,\n",
    "    metric='contribution',\n",
    "    category='cluster',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.0, 1.2],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "dense_axes9.set_ylabel(\"Contribution\", fontsize=16)\n",
    "dense_axes9.set_xticks([0,1,2])\n",
    "dense_axes9.set_xticklabels(['Low','Medium','High'], fontsize=16)\n",
    "# dense_axes6.set_xlabel(\"Crowd density\", fontsize=16)\n",
    "\n",
    "# axes_cluster.legend(date_unique.tolist(), fontsize=14)\n",
    "dense_axes9.tick_params(axis='x', labelsize=14)\n",
    "dense_axes9.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "# significance\n",
    "bars = [0, 1, 2]\n",
    "heights = [0.99, 1.0, 1.03]\n",
    "barplot_annotate_brackets(0, 1, 0.234817, bars, heights, line_y=1.0)\n",
    "barplot_annotate_brackets(0, 2, 0.024224, bars, heights, line_y=1.05)\n",
    "barplot_annotate_brackets(1, 2, 0.056081, bars, heights, line_y=0.9)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_boxplot_contribution.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.family'] = ['serif']\n",
    "mpl.rcParams['font.serif'] = ['Times New Roman']\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "dense_fig10, dense_axes10 = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "categorical_plot(\n",
    "    axes=dense_axes10,\n",
    "    df=control_metrics_df,\n",
    "    metric='avg_agreement',\n",
    "    category='cluster',\n",
    "    xlabel='',\n",
    "    ylabel='',\n",
    "    ylim=[0.60, 1.0],\n",
    "    kind='box',\n",
    "    lgd_labels=['Cluster 1', 'Cluster 2', 'Cluster 3'],\n",
    "    box_palette=box_pal,\n",
    "    scatter_palette=scatter_pal,\n",
    ")\n",
    "\n",
    "dense_axes10.set_ylabel(\"Average agreement\", fontsize=16)\n",
    "dense_axes10.set_xticks([0,1,2])\n",
    "dense_axes10.set_xticklabels(['Low','Medium','High'], fontsize=16)\n",
    "# dense_axes6.set_xlabel(\"Crowd density\", fontsize=16)\n",
    "\n",
    "# axes_cluster.legend(date_unique.tolist(), fontsize=14)\n",
    "dense_axes10.tick_params(axis='x', labelsize=14)\n",
    "dense_axes10.tick_params(axis='y', labelsize=14)\n",
    "\n",
    "# significance\n",
    "bars = [0, 1, 2]\n",
    "heights = [0.99, 1.0, 1.03]\n",
    "barplot_annotate_brackets(0, 1, 0.021096, bars, heights, line_y=0.92)\n",
    "barplot_annotate_brackets(0, 2, 0.002578, bars, heights, line_y=0.95)\n",
    "barplot_annotate_brackets(1, 2, 0.215136, bars, heights, line_y=0.9)\n",
    "\n",
    "plt.savefig(\"./pdf/pub/density_boxplot_mean_agreement.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4c6b102e5d13e1fecd509d7afca2a015b7aa951d14e261cb21795c62f51c11cb"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "366.17px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}