experiments/analysis/analysis_test_limitations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the tests to compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "availeble tests:\n",
      " [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]\n",
      "tests_types:\n",
      " ['IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA', 'IMPALA']\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import sys\n",
    "import json\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "__file__ = globals()['_dh'][0]\n",
    "\n",
    "# get an absolute path to the directory that contains parent files\n",
    "project_dir = __file__ = globals()['_dh'][0]\n",
    "sys.path.append(os.path.normpath(os.path.join(project_dir, '..', '..')))\n",
    "\n",
    "from experiments.utils.constants import TESTS_RESULTS_PATH\n",
    "series = 73\n",
    "start_test = 0\n",
    "end_test = 27\n",
    "tests_ids = [i for i in range(start_test, end_test)]\n",
    "tests_names = [i for i in range(start_test, end_test)]\n",
    "\n",
    "tests = {}\n",
    "\n",
    "\n",
    "for test_id, test_name in zip(tests_ids, tests_names):\n",
    "    test_path = os.path.join(\n",
    "        TESTS_RESULTS_PATH,\n",
    "        'series',\n",
    "        str(series),\n",
    "        'tests',\n",
    "        str(test_id))\n",
    "    with open(os.path.join(test_path, 'info.json')) as cf:\n",
    "        info = json.loads(cf.read())\n",
    "    with open(os.path.join(test_path, 'episodes.pickle'), 'rb') as input_file:\n",
    "        episodes = pickle.load(input_file)\n",
    "    tests.update({test_name: {\n",
    "        'info': info,\n",
    "        'episodes': episodes\n",
    "    }})\n",
    "test_types = [test['info']['algorithm'] for _, test in tests.items()]\n",
    "print(\"availeble tests:\\n {}\\ntests_types:\\n {}\".format(\n",
    "    list(tests.keys()),\n",
    "    test_types))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Necessary stats of a test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_stats = []\n",
    "for test_id in range(start_test, end_test):\n",
    "    users_distances_episodes_average = [episode['users_distances'].mean() for episode in tests[test_id]['episodes']]\n",
    "    users_distances_test_average = sum(users_distances_episodes_average) / len(users_distances_episodes_average)\n",
    "    num_consolidated_episodes_average = [episode['num_consolidated'].mean() for episode in tests[test_id]['episodes']]\n",
    "    num_consolidated_test_average = sum(num_consolidated_episodes_average) / len(num_consolidated_episodes_average)\n",
    "    # print(users_distances_episodes_average)\n",
    "    # print(users_distances_test_average)\n",
    "    # print(num_consolidated_episodes_average)\n",
    "    # print(num_consolidated_test_average)\n",
    "    final_stats.append({\n",
    "        'test_id': test_id,\n",
    "        # 'checkpoint': tests[test_id]['info']['checkpoint'],\n",
    "        'network_id': tests[test_id]['info']['network_id'],\n",
    "        'network_id_test': tests[test_id]['info']['network_id_test'],\n",
    "        'trace_id_test': tests[test_id]['info']['trace_id_test'],\n",
    "        'sample_episode_users_distances': tests[test_id]['episodes'][0]['users_distances'],\n",
    "        'sample_episode_num_consolidated': tests[test_id]['episodes'][0]['num_consolidated'],\n",
    "        # 'experiments': tests[test_id]['info']['experiments'],\n",
    "        'penalty_latency': tests[test_id]['info']['penalty_latency'],\n",
    "        'penalalty_consolidated': tests[test_id]['info']['penalty_consolidated'],\n",
    "        'users_distances_test_average': users_distances_test_average,\n",
    "        'num_consolidated_test_average': num_consolidated_test_average\n",
    "    })\n",
    "\n",
    "final_stats_df = pd.DataFrame(final_stats)\n",
    "final_stats_df['test_types'] = test_types\n",
    "# final_stats_df.loc[df['penalalty_consolidated']]\n",
    "\n",
    "# TODO add the following information to the table (from the info.json file)\n",
    "# - penatly_latency\n",
    "# - penalty_conslidation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# compare tests - tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_id</th>\n",
       "      <th>network_id</th>\n",
       "      <th>network_id_test</th>\n",
       "      <th>trace_id_test</th>\n",
       "      <th>sample_episode_users_distances</th>\n",
       "      <th>sample_episode_num_consolidated</th>\n",
       "      <th>penalty_latency</th>\n",
       "      <th>penalalty_consolidated</th>\n",
       "      <th>users_distances_test_average</th>\n",
       "      <th>num_consolidated_test_average</th>\n",
       "      <th>test_types</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>0.648368</td>\n",
       "      <td>5.999421</td>\n",
       "      <td>IMPALA</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.782594</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>IMPALA</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0       0.869885\n",
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       "      <td>0       6\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.817866</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>IMPALA</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0       0.947351\n",
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       "2       0.96...</td>\n",
       "      <td>0       6\n",
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       "2       6\n",
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       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.832932</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0       1.425752\n",
       "1       1.502246\n",
       "2       1.42...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.375796</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0       1.473729\n",
       "1       1.549312\n",
       "2       1.47...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.480520</td>\n",
       "      <td>5.999710</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0       1.473061\n",
       "1       1.548511\n",
       "2       1.47...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.477645</td>\n",
       "      <td>5.999710</td>\n",
       "      <td>IMPALA</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0       1.418520\n",
       "1       1.493970\n",
       "2       1.41...</td>\n",
       "      <td>0       6\n",
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       "2       6\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.492846</td>\n",
       "      <td>5.999421</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0       1.454633\n",
       "1       1.585977\n",
       "2       1.50...</td>\n",
       "      <td>0       6\n",
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       "2       6\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.583836</td>\n",
       "      <td>5.999421</td>\n",
       "      <td>IMPALA</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0       1.298836\n",
       "1       1.320536\n",
       "2       1.27...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.318361</td>\n",
       "      <td>5.953953</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0       1.530937\n",
       "1       1.575612\n",
       "2       1.55...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.515720</td>\n",
       "      <td>5.959745</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0       1.544589\n",
       "1       1.589719\n",
       "2       1.56...</td>\n",
       "      <td>0       6\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.615163</td>\n",
       "      <td>5.969881</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0       1.710898\n",
       "1       1.667195\n",
       "2       1.78...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.757488</td>\n",
       "      <td>5.965248</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0       3.098763\n",
       "1       3.412156\n",
       "2       3.19...</td>\n",
       "      <td>0       6\n",
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       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3.094299</td>\n",
       "      <td>5.965827</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0       3.388860\n",
       "1       3.605170\n",
       "2       3.37...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3.435251</td>\n",
       "      <td>5.973646</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0       3.387391\n",
       "1       3.603434\n",
       "2       3.36...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3.429371</td>\n",
       "      <td>5.973646</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0       3.381995\n",
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       "2       3.36...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3.490816</td>\n",
       "      <td>5.961483</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0       3.617074\n",
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       "      <td>0       6\n",
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       "2       6\n",
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       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3.730679</td>\n",
       "      <td>5.962352</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0       2.072998\n",
       "1       2.126564\n",
       "2       2.15...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.969883</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0       2.579201\n",
       "1       2.560194\n",
       "2       2.68...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2.335489</td>\n",
       "      <td>5.998552</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0       2.639040\n",
       "1       2.616228\n",
       "2       2.79...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2.507059</td>\n",
       "      <td>5.999131</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0       2.635536\n",
       "1       2.480806\n",
       "2       2.78...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2.571952</td>\n",
       "      <td>5.994787</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0       4.112346\n",
       "1       4.411834\n",
       "2       4.34...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.235401</td>\n",
       "      <td>5.994208</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0       3.708812\n",
       "1       3.968153\n",
       "2       3.88...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.041673</td>\n",
       "      <td>5.998552</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0       3.706942\n",
       "1       3.966149\n",
       "2       3.88...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.032975</td>\n",
       "      <td>5.998842</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0       3.832428\n",
       "1       4.096959\n",
       "2       3.99...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.207059</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0       3.849112\n",
       "1       4.093971\n",
       "2       4.01...</td>\n",
       "      <td>0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.220893</td>\n",
       "      <td>5.999710</td>\n",
       "      <td>IMPALA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    test_id  network_id  network_id_test  trace_id_test  \\\n",
       "0         0           1                1              0   \n",
       "1         1           1               11              0   \n",
       "2         2           1               12              0   \n",
       "3         3           1               13              0   \n",
       "4         4           1               14              0   \n",
       "5         5           1               15              0   \n",
       "6         6           1               16              0   \n",
       "7         7           1               17              0   \n",
       "8         8           1               18              0   \n",
       "9         9           5               19              0   \n",
       "10       10           5               20              0   \n",
       "11       11           5               21              0   \n",
       "12       12           5               22              0   \n",
       "13       13           5               23              0   \n",
       "14       14           5               24              0   \n",
       "15       15           5               25              0   \n",
       "16       16           5               26              0   \n",
       "17       17           5               27              0   \n",
       "18       18           6               28              0   \n",
       "19       19           6               29              0   \n",
       "20       20           6               30              0   \n",
       "21       21           6               31              0   \n",
       "22       22           6               32              0   \n",
       "23       23           6               33              0   \n",
       "24       24           6               34              0   \n",
       "25       25           6               35              0   \n",
       "26       26           6               36              0   \n",
       "\n",
       "                       sample_episode_users_distances  \\\n",
       "0   0       0.685288\n",
       "1       0.635435\n",
       "2       0.64...   \n",
       "1   0       0.873798\n",
       "1       0.891464\n",
       "2       0.87...   \n",
       "2   0       0.869885\n",
       "1       0.872082\n",
       "2       0.86...   \n",
       "3   0       0.947351\n",
       "1       0.892774\n",
       "2       0.96...   \n",
       "4   0       1.425752\n",
       "1       1.502246\n",
       "2       1.42...   \n",
       "5   0       1.473729\n",
       "1       1.549312\n",
       "2       1.47...   \n",
       "6   0       1.473061\n",
       "1       1.548511\n",
       "2       1.47...   \n",
       "7   0       1.418520\n",
       "1       1.493970\n",
       "2       1.41...   \n",
       "8   0       1.454633\n",
       "1       1.585977\n",
       "2       1.50...   \n",
       "9   0       1.298836\n",
       "1       1.320536\n",
       "2       1.27...   \n",
       "10  0       1.530937\n",
       "1       1.575612\n",
       "2       1.55...   \n",
       "11  0       1.544589\n",
       "1       1.589719\n",
       "2       1.56...   \n",
       "12  0       1.710898\n",
       "1       1.667195\n",
       "2       1.78...   \n",
       "13  0       3.098763\n",
       "1       3.412156\n",
       "2       3.19...   \n",
       "14  0       3.388860\n",
       "1       3.605170\n",
       "2       3.37...   \n",
       "15  0       3.387391\n",
       "1       3.603434\n",
       "2       3.36...   \n",
       "16  0       3.381995\n",
       "1       3.598038\n",
       "2       3.36...   \n",
       "17  0       3.617074\n",
       "1       3.868105\n",
       "2       3.61...   \n",
       "18  0       2.072998\n",
       "1       2.126564\n",
       "2       2.15...   \n",
       "19  0       2.579201\n",
       "1       2.560194\n",
       "2       2.68...   \n",
       "20  0       2.639040\n",
       "1       2.616228\n",
       "2       2.79...   \n",
       "21  0       2.635536\n",
       "1       2.480806\n",
       "2       2.78...   \n",
       "22  0       4.112346\n",
       "1       4.411834\n",
       "2       4.34...   \n",
       "23  0       3.708812\n",
       "1       3.968153\n",
       "2       3.88...   \n",
       "24  0       3.706942\n",
       "1       3.966149\n",
       "2       3.88...   \n",
       "25  0       3.832428\n",
       "1       4.096959\n",
       "2       3.99...   \n",
       "26  0       3.849112\n",
       "1       4.093971\n",
       "2       4.01...   \n",
       "\n",
       "                      sample_episode_num_consolidated  penalty_latency  \\\n",
       "0   0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "1   0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "2   0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "3   0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "4   0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "5   0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "6   0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "7   0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "8   0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "9   0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "10  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "11  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "12  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "13  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "14  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "15  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "16  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "17  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "18  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "19  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "20  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "21  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "22  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "23  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "24  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "25  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "26  0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4     ...                4   \n",
       "\n",
       "    penalalty_consolidated  users_distances_test_average  \\\n",
       "0                        4                      0.648368   \n",
       "1                        4                      0.782594   \n",
       "2                        4                      0.817866   \n",
       "3                        4                      0.832932   \n",
       "4                        4                      1.375796   \n",
       "5                        4                      1.480520   \n",
       "6                        4                      1.477645   \n",
       "7                        4                      1.492846   \n",
       "8                        4                      1.583836   \n",
       "9                        4                      1.318361   \n",
       "10                       4                      1.515720   \n",
       "11                       4                      1.615163   \n",
       "12                       4                      1.757488   \n",
       "13                       4                      3.094299   \n",
       "14                       4                      3.435251   \n",
       "15                       4                      3.429371   \n",
       "16                       4                      3.490816   \n",
       "17                       4                      3.730679   \n",
       "18                       4                      1.969883   \n",
       "19                       4                      2.335489   \n",
       "20                       4                      2.507059   \n",
       "21                       4                      2.571952   \n",
       "22                       4                      4.235401   \n",
       "23                       4                      4.041673   \n",
       "24                       4                      4.032975   \n",
       "25                       4                      4.207059   \n",
       "26                       4                      4.220893   \n",
       "\n",
       "    num_consolidated_test_average test_types  \n",
       "0                        5.999421     IMPALA  \n",
       "1                        6.000000     IMPALA  \n",
       "2                        6.000000     IMPALA  \n",
       "3                        6.000000     IMPALA  \n",
       "4                        6.000000     IMPALA  \n",
       "5                        5.999710     IMPALA  \n",
       "6                        5.999710     IMPALA  \n",
       "7                        5.999421     IMPALA  \n",
       "8                        5.999421     IMPALA  \n",
       "9                        5.953953     IMPALA  \n",
       "10                       5.959745     IMPALA  \n",
       "11                       5.969881     IMPALA  \n",
       "12                       5.965248     IMPALA  \n",
       "13                       5.965827     IMPALA  \n",
       "14                       5.973646     IMPALA  \n",
       "15                       5.973646     IMPALA  \n",
       "16                       5.961483     IMPALA  \n",
       "17                       5.962352     IMPALA  \n",
       "18                       6.000000     IMPALA  \n",
       "19                       5.998552     IMPALA  \n",
       "20                       5.999131     IMPALA  \n",
       "21                       5.994787     IMPALA  \n",
       "22                       5.994208     IMPALA  \n",
       "23                       5.998552     IMPALA  \n",
       "24                       5.998842     IMPALA  \n",
       "25                       6.000000     IMPALA  \n",
       "26                       5.999710     IMPALA  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming final_stats_df is your DataFrame\n",
    "subset_df = final_stats_df[final_stats_df[\"network_id\"] == 1][[\"test_id\", \"users_distances_test_average\"]]\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(subset_df[\"test_id\"], subset_df[\"users_distances_test_average\"], marker='o', linestyle='-')\n",
    "plt.axvline(x=3.5, color='r', linestyle='--')  # Draw a vertical dashed red line between test_id 3 and 4\n",
    "plt.title(\"16 Users Network\")  # Adjusted title to reflect the updated network ID\n",
    "plt.xlabel(\"Network ID\")\n",
    "plt.ylabel(\"Average Latency\")\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming final_stats_df is your DataFrame\n",
    "subset_df = final_stats_df[final_stats_df[\"network_id\"] == 5][[\"test_id\", \"users_distances_test_average\"]]\n",
    "\n",
    "# Restart network IDs from 0 after filtering\n",
    "subset_df[\"test_id\"] -= subset_df[\"test_id\"].min()\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(subset_df[\"test_id\"], subset_df[\"users_distances_test_average\"], marker='o', linestyle='-')\n",
    "plt.axvline(x=3.5, color='r', linestyle='--')  # Draw a vertical dashed red line between test_id 3 and 4\n",
    "plt.title(\"32 Users Network\")  # Adjusted title to reflect the updated network ID\n",
    "plt.xlabel(\"Network ID\")\n",
    "plt.ylabel(\"Average Latency\")\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VtdzwWDU19Y0Uv7iQq+eUMHd6cdjlp4RlH2zh/ooaFlTUULtpO/36ZDPnoJF8Ynoxx+xfRLYG2Ms+UggTkYywsCpCQZ9sjpgwJOxSkqqgTw7nHT6W8w4fu3PvWEUNIwb0ZcO2ZlqiDkBNfSPz7lsKkBFBbH5FzUcC6NH7D+WBylrur6jhzTWbyc4yTphcxHdOm8rHDxhBQR/9b1N6jv42iUj4pkxJ6OHdnbKqOo6dVJTRd6l17B37j/lLdwSwNo0tUa78xxL+e+FycrOyyM4ycrON7CwjJzsreJ9FbpYF27KCbRbfP9uCbfF9c9rvH+yTk23kBMdrO35OVtaOdTnB8XLa7xu8bzv/Tvu0nb+tlizb7eXB+RU1zLtvKY0tUSAeQL99dyWx4I/j0LGFXHvWAZx56GiK+vdNyM9DRCFMRMJ3000JPfzySAM19Y18bfakhJ4nXbT1jn3n3tc63R6NOQeMGkhr1GmNOa2xWPA+RlNLjNZYtN06pzUaC16Dfdu/D/ZJtrYwlpvdIUgG4XD1xsaP1BVzGJCXw4LLj2XisP5Jr1kyj0KYiPR6ZVXxqSlKS4aFXElqGV2YT01940fWFxfm87uLDuux87g70VhboNs5tLVEY8G2j65rica/1xKEuWgs9uG6tn2C4324f3yf9sdr2799qHxv/bZOa23Y3qoAJkmT8BBmZtnAIqDG3c/ssK0vcAswA1gPnO/u7yW6JhFJMZddFn9NUI/YwqoIHxs1kFGD8hNy/HR19ZySnS7JAeTnZnP1nJIePY9ZcOkwha4EL3pvY6cBdHSh/o5I8mQl4RzfBN7qYtsXgI3uPgn4NfCfSahHRFLNsmXxJQE2b29h0fsbmaVesI+YO72Y6z95MMVB8CguzOf6Tx6cEYPyr55TQn7uzqkwEQFUZFcS2hNmZmOAM4CfAt/uZJdzgGuD9/cAvzMzc/fkDyAQkV7pmWXriMY8Y6am2FNzpxczd3ox5eXllJaWhl1O0rQFzR13Rxbma3oOSTpLZN4xs3uA64EBwFWdXI58HTjV3VcHn1cAR7r7ug77XQZcBjBixIgZd955Z8JqbtPQ0ED//pk5LkBtz8y2Q3jtnxY8tqgyAY8t+t/Xmqisa+W/ZxXscl4n/ewzt/1qe2a2HZLT/lmzZi1295mdbUtYT5iZnQlE3H2xmZXuy7Hc/SbgJoCZM2d6Mn5by7TfCttT20vDLiM0obW/sBCgx88dizlXPfsEJx0wmpNmT9/lvvrZZ2771fbSsMsITdjtT+TlyGOBs83sdCAPGGhmt7n7xe32qQHGAqvNLAcYRHyAvohkkmnTEnLYpTWbWNfQzKypGg8mIqknYSHM3ecB8wCCnrCrOgQwgAeAzwIvAOcCCzUeTCQDJeAyJEBZdQQzOHGKxoOJSOpJ+jxhZnYdsMjdHwBuBm41s7eBDcAFya5HRHqvsqoI08cWMqRfn7BLERH5iKSEMHcvB8qD9z9ot3478Olk1CAiKezioJP8ttt67JB1W5pYsnoTV348sY9EEhHZW5oxX0TCt3p1jx/yqWV1AMzS1BQikqKSMVmriEjSlVVHGD6gLweOHhh2KSIinVIIE5FepyUa4+lldcwqGY5Z13ODiYiESSFMRHqdxe9vZMv2Vk1NISIpTWPCRCR8Rx/do4crq46Qm20cO6moR48rItKTFMJEJHzXX9+jhyurinD4+CEMyMvt0eOKiPQkXY4UkV5l9cZtLPugQQ/sFpGUpxAmIuH71KfiSw8oq9bUFCKSHnQ5UkTCt77nHhlbXhVh3JACJhb167FjiogkgnrCRKTX2N4S5bkV65g9VVNTiEjqUwgTkV7jhXfWs70lRmmJpqYQkdSnECYivUZ5VYS83CyOmjg07FJERHZLY8JEJHwnnbTPh3B3FlZHOHb/IvJys3ugKBGRxFIIE5HwXXPNPh9iRd1WVm1o5Esn7N8DBYmIJJ4uR4pIr1BWFQE0NYWIpA+FMBEJ32mnxZd9UFYdoWTEAIoL83uoKBGRxFIIE5HwNTbGl720ZXsLL7+7Qb1gIpJWFMJEJO099/Y6WmPOLE1NISJpRCFMRNLewqoIA/JymLHf4LBLERHpNoUwEUlrsZhTVl3HCVOGkZOtf9JEJH1oigoRCd+ZZ+71V99cs5m6LU3MLtF4MBFJLwphIhK+q67a668urIpgBidqPJiIpBn13YtIWiurjnDImEKK+vcNuxQRkT2iECYi4SstjS97aH1DE5Wr6nVXpIikJYUwEUlbTy+vwx1ma34wEUlDCmEikrYWVtVR1L8vB40eFHYpIiJ7TCFMRNJSazTGU9URSkuGkZVlYZcjIrLHFMJEJC1VrKpn8/ZWXYoUkbSlKSpEJHznnbfHX1lYFSEnyzhuclECChIRSTyFMBEJ31e/usdfKauKMHP8YAbm5SagIBGRxNPlSBEJ37Zt8aWbausbqVq7hVmaJV9E0ph6wkQkfKefHn8tL+/W7uXVdYCmphCR9KaeMBFJOwurIowZnM+k4f3DLkVEZK8phIlIWmlqjfLc2+uYVTIcM01NISLpSyFMRNLKS+9soLElqkuRIpL2FMJEJK0srIrQNyeLoyYODbsUEZF9ooH5IhK+Sy/t9q7l1RGO2X8o+X2yE1ePiEgSKISJSPi6GcLeqWvgvfXb+PxxExJbj4hIEuhypIiEb926+LIbZcHUFJofTER6A/WEiUj4zj03/rqbecLKqiJMGt6fsUMKEl+TiEiCqSdMRNLC1qZWXnp3ve6KFJFeQyFMRNLCs2+voyXquhQpIr2GQpiIpIWyqggD+uYwc/zgsEsREekRCmEikvLcnbLqCMdPKSI3W/9siUjvoIH5IhK+r3xll5vfXLOZDzY3UapLkSLSiyiEiUj4zj9/l5vLg6kpSkuGJaMaEZGkUL++iIRv1ar40oWFVREOLh7E8AF5SSxKRCSxFMJEJHyXXBJfOrFxazMVKzcyS1NTiEgvoxAmIint6eV1xBxm6VKkiPQyCmEiktLKqiIM7deHQ8cUhl2KiEiPUggTkZQVjTlPLavjxJJhZGVZ2OWIiPQohTARSVmVqzaycVuLZskXkV5JU1SISPiuvLLT1WVVdWRnGSdM1ngwEel9FMJEJHxnndXp6oVVEWaMG8yggtwkFyQikni6HCki4auuji/trN20nTfXbNbUFCLSa6knTETC96UvxV/Ly3esKq+OADBrqi5FikjvlLCeMDPLM7OXzWyJmb1hZj/qZJ9LzazOzCqD5d8SVY+IpJey6gijB+VRMmJA2KWIiCREInvCmoDZ7t5gZrnAs2b2iLu/2GG/u9z9awmsQ0TSTFNrlGeXr+Oc6cWYaWoKEemdEhbC3N2BhuBjbrB4os4nIr3HK+9uZGtzlNmamkJEerGEDsw3s2wzqwQiwOPu/lInu33KzF4zs3vMbGwi6xGR9FBWHaFPThbHTBoadikiIglj8Q6rBJ/ErBC4H/i6u7/ebv1QoMHdm8zsS8D57j67k+9fBlwGMGLEiBl33nlnwmtuaGigf//+CT9PKlLbM7PtEF77By9eDMDGGTMA+O7T2ygqyOKqmXlJq0E/+8xtv9qemW2H5LR/1qxZi919ZmfbkhLCAMzsB8A2d/9lF9uzgQ3uPmhXx5k5c6YvWrQoESXupLy8nNLS0oSfJxWp7aVhlxGaVGj/e+u2UvrLcq496wAuPXZC0s6bCm0PUya3X20vDbuM0CSj/WbWZQhL5N2Rw4IeMMwsH/g4UNVhn1HtPp4NvJWoekQkhVVWxhfilyIBzQ8mIr1eIu+OHAX8LejhygLudveHzOw6YJG7PwB8w8zOBlqBDcClCaxHRFLVFVfEX8vLKauuY+Kwfuw3tF+oJYmIJFoi7458DZjeyfoftHs/D5iXqBpEJL1sa27lxXfWc8lR+4VdiohIwumxRSKSMp5/ez3NrTFm61KkiGQAhTARSRkLqyP065PN4eOHhF2KiEjCKYSJSEpwoKwqwnGTi+iTo3+aRKT30wO8RSR8P/sZK9dvZc1z27ni5MlhVyMikhT6dVNEwnfMMfxzQHxOsFI9qkhEMoRCmIiE7/nnqf3nQg4cPZARA5M3S76ISJh2eznSzIa6+/pkFCMiman1u/M48/2NFN70j7BLERFJmu70hL1oZv8ws9PNzBJekYhknPptLeCuWfJFJKN0J4RNAW4CLgGWm9nPzGxKYssSkUxSv62ZnOwspo0tDLsUEZGk2W0I87jH3f1C4IvAZ4GXzewpMzs64RWKSK8WjTn1jS0U5ueSnaXOdhHJHN0aEwZcTLwn7APg68ADwDTgH8CEBNYnIr3ca6vraY3GKCzIDbsUEZGk6s48YS8AtwJz3X11u/WLzOyPiSlLRDJFWVWEJ0++jDu/eFTYpYiIJFV3QliJu3tnG9z9P3u4HhHJMGXVdeQfPoMBRx8edikiIknVnYH5/zKzwrYPZjbYzB5LXEkikikim7eztGYTl2xdDk88EXY5IiJJ1Z2esGHuXt/2wd03mpnuIxeRfVa+rA6Ak+/7MzyUDSefHHJFIiLJ052esKiZjWv7YGb7EX/WrojIPimrijByYB4FfbLDLkVEJOm60xP2H8CzZvYUYMDxwGUJrUpEer2WaIxnlq/jrENHYf8KuxoRkeTbbQhz90fN7DCg7dalK9x9XWLLEpHe7pX3NtDQ1KoHdotIxupOTxhAX2BDsP8BZoa7P524skSktyuritAnO4vjJhWFXYqISCi6M1nrfwLnA28AsWC1AwphIrLXyqrrOHLiEPr1zYE//SnsckREkq47PWFzic8V1pTgWkQkQ6zasI23Iw1ceERwz09JSbgFiYiEoDt3R74D6HkiItJjyqojAMyeGowHe/DB+CIikkG60xO2Dag0syeBHb1h7v6NhFUlIr3awqoI44cWMKGoX3zFr34Vfz3rrPCKEhFJsu6EsAeCRURknzU2R3lhxXouOnLc7ncWEenFujNFxd/MLB8Y5+7VSahJRHqxF95ZR1NrjFmamkJEMtxux4SZ2VlAJfBo8HmamalnTET2SllVHfm52Rw5cUjYpYiIhKo7A/OvBY4A6gHcvRKYmLCKRKTXcncWVkU4dlIRfXP0qCIRyWzdGRPW4u6bzKz9ulhXO4uIdGV5pIGa+kYunzVp5w233hpOQSIiIepOCHvDzC4Css1sMvAN4PnEliUivVFZVXxqillTh+28YezYEKoREQlXdy5Hfh04kPj0FLcDm4BvJrIoEemdFlZFmDpyAKMG5e+84a674ouISAbpTgg7w93/w90PD5bvA2cnujAR6V02b29h0fsbP5ygtb0//CG+iIhkkO6EsHndXCci0qVnlq0jGnNmdRbCREQyUJdjwszsNOB0oNjM/rvdpoFAa6ILE5Hepaw6wqD8XKaPLQy7FBGRlLCrgfm1wCLilx4Xt1u/BfhWIosSkd4lFnPKqyOcMGUYOdnd6YAXEen9ugxh7r4EWGJmt7t7SxJrEpFeZmnNJtY1NDO7412RIiIZrDtTVIw3s+uBA4C8tpXurglbRaRbyqojmMEJk7sIYffck9yCRERSQHeuC/wf8Afi48BmAbcAtyWyKBHpXcqqIkwbW8jQ/n0736GoKL6IiGSQ7oSwfHd/EjB3f9/drwXOSGxZItJb1G1pYsnqTcze1QO7//rX+CIikkG6czmyycyygOVm9jWgBuif2LJEpLd4alkdwK6npmgLYJdemvB6RERSRXd6wr4JFBB/XNEM4GLg/yWyKBHpPcqqIwwf0JcDRw8MuxQRkZSy254wd38leNsAfA7AzH4JvJTAukSkF2iJxnh6WR2nHTQSMwu7HBGRlLK3E/ac16NViEiv9Or7G9myvbXzRxWJiGS4vQ1h+pVWRHZrYXWE3Gzj2Em681FEpKNdPbZoSFebUAgTkW4oq4pw+PghDMjL3fWODz+cnIJERFLIrsaELQaczgNXc2LKEZHeYvXGbSz7oIFPzxi7+50LChJfkIhIitnVY4smJLMQEeldyqq7MTVFm9//Pv761a8msCIRkdSiJ+mKSEKUV0UYN6SA/Yf12/3Od98dX0REMohCmIj0uO0tUZ5bsY5ZJcM0NYWISBcUwkSkx734znq2t8S6dylSRCRDdSuEmdlxZtY2UeswM9N4MRHpUllVhLzcLI6aODTsUkREUtZuQ5iZ/RD4DjAvWJUL3JbIokQkfbk7C6sjHLt/EXm52WGXIyKSsrrzAO9PANOBVwHcvdbMBiS0KhFJWyvqtrJqQyOXnbB/979UXp6wekREUlV3Lkc2u7sTnzMMM+vGrU4ikqnKqiIAelSRiMhudCeE3W1mfwIKzeyLwBPA/ya2LBFJV2XVEUpGDKC4ML/7X/rlL+OLiEgG2W0Ic/dfAvcA9wIlwA/c/beJLkxE0s+W7S28/O4GSqcO27MvPvRQfBERySDdGROGuz8OPJ7gWkQkzT339jpaY87sEl2KFBHZnd2GMDPbQjAerJ1NwCLgSnd/JxGFiUj6WVgVYUBeDoftNzjsUkREUl53esJuBFYDtxN/mPcFwP7E75b8C1Da2ZfMLA94GugbnOced/9hh336ArcAM4D1wPnu/t6eN0NEwubulFXXccKUYeRmax5oEZHd6c6/lGe7+5/cfYu7b3b3m4A57n4XsKtfd5uA2e5+KDANONXMjuqwzxeAje4+Cfg18J973gQRSQVv1G6mbksTs/bmUmR+fnwREckg3ekJ22Zm5xEfnA9wLrA9eN/xMuUOwbQWDcHH3GDpuP85wLXB+3uA35mZBd8VkTSyMJiaorRkDwflAzzySA9XIyKS+mx3ecfMJgK/AY4mHqJeBL4F1AAz3P3ZXXw3G1gMTAL+x92/02H768Cp7r46+LwCONLd13XY7zLgMoARI0bMuPPOO/ekjXuloaGB/v37J/w8qUhtz8y2w761/8cvNBIDfnh0evZo6Wefue1X2zOz7ZCc9s+aNWuxu8/sdKO7J3wBCoEy4KAO618HxrT7vAIo2tWxZsyY4clQVlaWlPOkIrU9c+1t+9dt2e7jv/uQ//rx6r078XXXxZcQ6WdfFnYJoVHbM1cy2g8s8i4yTXfujswjPnbrQCCvXXj7fHdToLvXm1kZcGoQvNrUAGOB1WaWAwwiPkBfRNLI08vrcN+HWfKffDL+es01PVeUiEiK687A/FuBkcAc4ClgDLBld18ys2FmVhi8zwc+DlR12O0B4LPB+3OBhUFqFJE0srCqjqL+fTlo9KCwSxERSRvdCWGT3P0aYKu7/w04AziyG98bBZSZ2WvAK8Dj7v6QmV1nZmcH+9wMDDWzt4FvA9/d8yaISJhaozGeXlZHackwsrIs7HJERNJGd+6ObAle683sIGAtsNtrDu7+GjC9k/U/aPd+O/Dp7pUqIqmoYlU9mxpb9m5qChGRDNadEHaTmQ0Gvk/88mF/QAM3RASIT02RnWUcP6Vo7w8ydGjPFSQikiZ2GcLMLAvY7O4bic9+PzEpVYlI2iirijBzv8EMzMvd+4Pce2/PFSQikiZ2OSbM3WPAvyepFhFJM7X1jVSt3bL3d0WKiGSw7gzMf8LMrjKzsWY2pG1JeGUikvLKq+sAmLWvIWzevPgiIpJBujMm7Pzg9fJ26xxdmhTJeAurIhQX5jN5+D7OOP3CCz1TkIhIGtltCHP3CckoRETSS1NrlOfeXse5M8ZgpqkpRET21G4vR5pZgZl938xuCj5PNrMzE1+aiKSyl97ZQGNLlFlT9+KB3SIi0q0xYf8HNAPHBJ9rgJ8krCIRSQtl1RH65mRx9MR9mJpCRCSDdSeE7e/uvyCYtNXdtwG69iCS4cqqIhy9/1Dy+2Tv+8HGjIkvIiIZpDsD85uDZz86gJntDzQltCoRSWnv1DXw3vptfP64HhoyetttPXMcEZE00p0Qdi3wKDDWzP4OHAtcmsCaRCTFlbVNTaFHFYmI7LXu3B35LzNbDBxF/DLkN919XcIrE5GUVVYVYdLw/owdUtAzB7ziivjrjTf2zPFERNLAbkOYmT0I3A484O5bE1+SiKSyrU2tvPTuei49ZnzPHbSysueOJSKSJrozMP+XwPHAm2Z2j5mda2Z5Ca5LRFLUs2+voyXq+z5LvohIhuvO5cingKfMLBuYDXwR+AswMMG1iUgKKq+O0L9vDoeP19PLRET2RXcG5hPcHXkW8UcYHQb8LZFFiUhqcnfKquo4fnIRudnd6UgXEZGudGdM2N3AEcTvkPwd8JS7xxJdmIiknjfXbGbt5u09fylyypSePZ6ISBroTk/YzcCF7h4FMLPjzOxCd798N98TkV6mPJiaorSkhx9VdNNNPXs8EZE00J0xYY+Z2XQzuxA4D3gXuC/hlYlIyllYFeHg4kEMH6B7c0RE9lWXIczMpgAXBss64C7A3H1WkmoTkRSycWszFSs38rVZk3r+4JddFn9Vj5iIZJBd9YRVAc8AZ7r72wBm9q2kVCUiKefp5XXEnMRMTbFsWc8fU0Qkxe3q9qZPAmuAMjP7XzM7CT24WyRjlVVFGNKvD4eMKQy7FBGRXqHLEObu8939AmAqUAZcAQw3sz+Y2SlJqk9EUkA05jy1rI7SKcPIztLvYiIiPWG3E/24+1Z3v93dzwLGABXAdxJemYikjMpV9Wzc1qJZ8kVEelC3Jmtt4+4bgZuCRUQyRFlVhOws44TJPTw1RZtp0xJzXBGRFLZHIUxEMtPCqggzxg1mUEFuYk5w442JOa6ISArTc0dEZJfWbtrOm2s2Uzo1Qb1gIiIZSiFMRHapvDoCwOxEjge7+OL4IiKSQXQ5UkR2qaw6wqhBeZSMGJC4k6xenbhji4ikKPWEiUiXmlqjPLt8HbOmDsdMU1OIiPQkhTAR6dKi9zaytTnK7BJNTSEi0tMUwkSkSwurIvTJyeKYSUPDLkVEpNfRmDAR6VJZVYSjJg6loE+C/6k4+ujEHl9EJAUphIlIp95bt5V31m3lkqP3S/zJrr8+8ecQEUkxuhwpIp0qC6ammKXxYCIiCaEQJiKdKquuY2JRP8YX9Uv8yT71qfgiIpJBFMJE5CO2Nbfy4jvrk/fA7vXr44uISAZRCBORj3j+7fU0t8Z0KVJEJIEUwkTkIxZWR+jXJ5sjJgwJuxQRkV5LIUxEduLulFdFOG5yEX1y9E+EiEiiaIoKEdlJ9QdbqN20nW+cNDl5Jz3ppOSdS0QkRSiEichOFlYFU1Mka1A+wDXXJO9cIiIpQtcaRGQn5VV1HDBqICMG5oVdiohIr6YQJiI7bG1xFq/cyOxk9oIBnHZafBERySC6HCkiO7y+Lko05syaOiy5J25sTO75RERSgHrCRGSHJXVRCgtymTZ2cNiliIj0egphIgJALOYsrWvlxCnDyM6ysMsREen1FMJEhPkVNRz5syfZ0gJPL6tjfkVN2CWJiPR6GhMmkuHmV9Qw776lNLZEAdi4rYV59y0FYO704uQUceaZyTmPiEgKUQgTyVDNrTFq6hv58UNv7ghgbRpbotzwWHXyQthVVyXnPCIiKUQhTKSXcnc2Nbbw/vptrNwQLO3er9nUSMy7/n5tve5YFBFJJIUwkTTWEo2xpn4772/Y2mnQ2rK9daf9i/r3ZdyQfI6YMISxQwoYN6SAnz/yFusamj9y7NGF+clqBpSWxl/Ly5N3ThGRkCmEiaS4TY0trNqwbecerSB01dZvJ9quO6tPdhZjhuQzbkgBM/YbzLggaI0bGn8t6PPR/+RzsmynMWEA+bnZXD2nJCntExHJVAphIiFrjcZYs2l7PGht+DBotQWvTY0tO+0/tF8fxg4pYPrYwZxz6IcBa9yQAkYOzCNrD6eXaBv3dcNj1dTUN1JcmM/Vc0qSNx5MRCRDKYSJJMGW7S07Bav2QWv1xkZa2/Vm5WQZYwbnM25oPw4ZMygIWP0YN6SAsUPyGZCX2+P1zZ1ezNzpxZSXl1PadmlQREQSSiFMpAdEY87azdtZuT4erFa269VatWEbG7buPOaqsCCXcUMKOKh4EKcfPGqnS4ajBuVrslQRkQyQsBBmZmOBW4ARgAM3uftvOuxTCiwA3g1W3efu1yWqJpGuzK+o+fBy3IsLO70ct7WpdacerJVBr1Zbb1ZzNLZj3+wso7gwPjbr1INGfjg2a0gBY4cUMCi/53uz0tp554VdgYhI0iWyJ6wVuNLdXzWzAcBiM3vc3d/ssN8z7q6ZGiU0HScrralv5Op7lvDI0lry++TwfhC6Ot5BOCAvh/2GFjB11ABOOXDnoDW6MI+cbD2Qotu++tWwKxARSbqEhTB3XwOsCd5vMbO3gGKgYwgTCdX1j7z1kclKW6LOY29G4mOzhhRw8sdG7DQAftyQAgoL+oRUcS+0bVv8taAg3DpERJIoKWPCzGw8MB14qZPNR5vZEqAWuMrd30hGTZLZ6rc188+la1hQUcsHm5s63ceAZ78zO7mFZarTT4+/ap4wEckg5r6LKbN74gRm/YGngJ+6+30dtg0EYu7eYGanA79x98mdHOMy4DKAESNGzLjzzjsTWjNAQ0MD/fv3T/h5UlFvbXtT1KmMRHmhtpWl66JEHUb1M+qbnMbWj+4/NM/4VWlm9cyE9bOfdsUVAFTeeGPSz92mt/69765Mbr/anplth+S0f9asWYvdfWZn2xIawswsF3gIeMzd/6sb+78HzHT3dV3tM3PmTF+0aFHPFdmFTL5Vvze1vTUa47kV61lQUcNjb6xla3OUEQP7cvahozlnWjEHjh7IgsraTicrvf6TB2fcXFmh/exTYMb83vT3fm9kcvvV9tKwywhNMtpvZl2GsETeHWnAzcBbXQUwMxsJfODubmZHAFnA+kTVJJnB3alcVc+Cyloeeq2WdQ3NDMjL4cxDRnPO9NEcOWHoTlNAaLJSEREJQyLHhB0LXAIsNbPKYN33gHEA7v5H4FzgK2bWCjQCF3iir49Kr7WiroEFFTUsWFLL++u30Scni5OmDuecacWUlgwjLze7y+9qslIREUm2RN4d+Szxsc272ud3wO8SVYP0fh9s3s6DS2pZUFnL0ppNmMEx+w/l8lmTOPWgkQxMwOzykgCXXhp2BSIiSacZ8yXtbN7ewqNL17JgSQ3Pr1iPOxxcPIjvn/Exzjp0NCMG5oVdouwphTARyUAKYZIWtrdEKa+OsKCylierIjS3xthvaAFfnz2Zc6aNZv9hmXt3T6+wLrgXp6go3DpERJJIIUxSVjTmvPTOehZU1vLw62vYsr2Vov59uOiIccydXsyhYwYRv/9D0t6558ZfNU+YiGQQhTBJKe7OG7WbWVBZwwNL4hOp9uuTzZyDRjJ3WjHH7D9UjwMSEZFeQSFMUsL767fyQGUt8ytrWFG3ldxs48Qpw7nmzNGcNHUE+X26vrNRREQkHSmESWjWNTTxz9fWML+yhoqV9QAcMWEInz9uAqcfNIrB/fRsRhER6b0UwiSpGppa+dcba1lQWcuzb68jGnOmjhzAd0+bylmHjqa4MD/sEkVERJJCIUwSrrk1xjPL65hfWcvjb65le0uM4sJ8vnTCRM6ZVkzJyAFhlyhh+8pXwq5ARCTpFMIkIWIxZ/HKjcyvqOHhpWvYuK2FwQW5nDtjDOdMK2bGuMFkZenORgmcf37YFYiIJJ1CmPSo6rVbmF9ZwwOVtdTUN5KXm8UpB4zknGmjOX7yMPrk6M5G6cSqVfHXsWPDrUNEJIkUwmSf1dQ38kBlLQsqa6hau4XsLOP4yUVcNWcKpxwwkn599ddMduOSS+KvmidMRDKI/u8oe2Xj1mYefn0NCypqefm9DQAcNq6Q6845kNMPHkVR/74hVygiIpLaFMKk2xqbozzx1gcsqKzhqWV1tESdScP7c9UpUzj70GLGDS0Iu0QREZG0oRAmO8yvqOGGx6qpqW+k+MWFXD2nhDMPGcVzK9azoKKGx95Yy9bmKCMG9uXSY8ZzzrRiDhw9UI8OEhER2QsKYQLEA9i8+5bS2BIF4uO8rvzHEr4/fykNTVEG5OVw5iGjOWf6aI6cMJRs3dkoIiKyTxTCBIAbHqveEcDaRGNOa8z548UzmDV1GH1z9OggSZArrwy7AhGRpFMIEwBq6xs7Xd/UEuPUg0YmuRrJOGedFXYFIiJJp0mbhFdXbqSrq4uj9RghSYbq6vgiIpJB1BOWwdydm599l58/UsWggly2NkVpao3t2J6fm83Vc0pCrFAyxpe+FH/VPGEikkEUwjLUpm0tXHXPEh5/8wNOOWAEN3z6UMqqIh/eHVmYz9VzSpg7vTjsUkVERHolhbAMtGRVPZff/iprN23nmjMP4PPHjsfMmDu9mLnTiykvL6e0tDTsMkVERHo1hbAM4u787fn3+OnDbzF8QB7/+PLRTB83OOyyREREMpJCWIbYvL2F7977Gg8vXctJU4fzq/MOpbCgT9hliYiIZCyFsAzwes0mLr/9VVZvbGTeaVP54vETydJkq5JKvv/9sCsQEUk6hbBezN35+0srue7BNxnSrw93XXYUM8cPCbsskY86+eSwKxARSTqFsF6qoamVefct5cEltZw4ZRi/Pn8aQ/rp8qOkqMrK+Ou0aWFWISKSVAphvdBbazZz+d9f5b31W7l6TglfOXF/XX6U1HbFFfFXzRMmIhlEIawXcXfuemUVP3zgDQbl53L7F4/iqIlDwy5LREREOqEQ1ktsa27l+/e/zn0VNRw3qYhfnz+NYQP6hl2WiIiIdEEhrBdY9sEWvvr3V1lR18C3Tp7C12ZPIluXH0VERFKaQliau2fxaq6Z/zr9+mZz2xeO5NhJRWGXJCIiIt2gEJamGpuj/GDB6/xj8WqOmjiE/75gOsMH5oVdlsje+dnPwq5ARCTpFMLS0NuRBi7/+6ssi2zh67Mn8c2TJpOTnRV2WSJ775hjwq5ARCTpFMLSzILKGubdt5S83Gz+9rkjOGHKsLBLEtl3zz8ff1UYE5EMohCWJra3RPnRg29yx8srOXz8YH574WGMHKTLj9JLfO978VfNEyYiGUQhLA28u24rX/37q7y1ZjNfPnF/rjplii4/ioiIpDmFsBT30Gu1fPfepeRkG3+5dCazp44IuyQRERHpAQphKaqpNcpP//kWt7zwPoeNK+S3Fx1GcWF+2GWJiIhID1EIS0Er12/j8ttfZWnNJr54/AT+/dSp5Oryo4iISK+iEJZiHn19LVffswQDbrpkBqccODLskkQS78Ybw65ARCTpFMJSRHNrjOsfeYv/e+49Dh0ziN9ddBhjhxSEXZZIckybFnYFIiJJpxCWAlZv3Mblt1ewZFU9lx4znu+d/jH65Ojyo2SQJ56Iv558crh1iIgkkUJYyJ548wOu/McSYjHnD585jNMOHhV2SSLJ95OfxF8VwkQkgyiEhaQlGuOGx6q56el3OHD0QH7/mcPYb2i/sMsSERGRJFEIC0FtfSNfv6OCxe9v5OKjxvH9Mw4gLzc77LJEREQkiRTCkqysOsK376qkuTXGby+czlmHjg67JBEREQmBQliStEZj/Nfjy/h9+QqmjhzA7z9zGBOH9Q+7LBEREQmJQlgSfLB5O1+/o4KX393ABYeP5dqzD9TlR5H2/vSnsCsQEUk6hbAEe2Z5HVfcWcm25ii/Pv9QPjF9TNgliaSekpKwKxARSTqFsASJxpzfPLmc3y5czuTh/bnrM4cxafiAsMsSSU0PPhh/PeuscOsQEUkihbAEiGzZzhV3VvL8ivWcO2MM151zIAV99Ect0qVf/Sr+qhAmIhlEyaCHPb9iHd+4o5KGphZ+ce4hnDdzbNgliYiISApSCOshsZjzu7K3ufGJZUwo6sff/+1ISkbq8qOIiIh0TiGsB6xvaOKKuyp5Zvk65k4bzU8/cTD9+uqPVkRERLqmpLCPXn53A1+/41U2bmvh+k8ezAWHj8XMwi5LREREUpxC2F6KxZw/Pr2CX/1rGWMH5/OXrx7OgaMHhV2WSHq69dawKxARSTqFsL2wcWsz3767krLqOs44ZBQ//+TBDMjLDbsskfQ1VjewiEjmSVgIM7OxwC3ACMCBm9z9Nx32MeA3wOnANuBSd381UTX1hMXvb+Brt1ewvqGZH59zIBcftZ8uP4rsq7vuir+ef364dYiIJFEie8JagSvd/VUzGwAsNrPH3f3NdvucBkwOliOBPwSvKcfd+fMz7/Kfj1YxujCfe79yDAeP0eVHkR7xhz/EXxXCRCSDJCyEufsaYE3wfouZvQUUA+1D2DnALe7uwItmVmhmo4LvpoxN21q48h9LeOKtD5hz4Ah+ce6hDMrX5UcRERHZexbPPwk+idl44GngIHff3G79Q8DP3f3Z4POTwHfcfVGH718GXAYwYsSIGXfeeWfCa25oaKB///68Ux/lfyqbqG9yzi/pw8f3y+n1lx/b2p6JMrntEF77p11xBQCVN96Y9HO30c8+c9uvtmdm2yE57Z81a9Zid5/Z2baED8w3s/7AvcAV7QPYnnD3m4CbAGbOnOmlpaU9V2AH8ytquOGxamrqjUH5TWzZ3sqoQfnc+4XDmDa2MGHnTSXl5eUk8s84lWVy2yHE9hcWAoT6Z6+ffea2X20vDbuM0ITd/oSGMDPLJR7A/u7u93WySw3Q/raoMcG6UMyvqGHefUtpbIkCsKmxlSyDy2ftnzEBTERERJIjK1EHDu58vBl4y93/q4vdHgD+n8UdBWwKczzYDY9V7whgbWIO/1O2IqSKRDLEPffEFxGRDJLInrBjgUuApWZWGaz7HjAOwN3/CDxMfHqKt4lPUfG5BNazW7X1jXu0XkR6SFFR2BWIiCRdIu+OfBbY5Qj24K7IyxNVw54aXZhPTSeBa3RhfgjViGSQv/41/nrppWFWISKSVAm7HJmOrp5TQn5u9k7r8nOzuXpOSUgViWSIv/71wyAmIpIh9NiiduZOLwYI7o5spLgwn6vnlOxYLyIiItJTFMI6mDu9mLnTi0O/bVVERER6N12OFBEREQmBQpiIiIhICHQ5UkTC9/DDYVcgIpJ0CmEiEr6CgrArEBFJOl2OFJHw/f738UVEJIMohIlI+O6+O76IiGQQhTARERGRECiEiYiIiIRAIUxEREQkBAphIiIiIiEwdw+7hj1iZnXA+0k4VRGwLgnnSUVqe+bK5PZnctshs9uvtmeuZLR/P3cf1tmGtAthyWJmi9x9Zth1hEFtz8y2Q2a3P5PbDpndfrU9M9sO4bdflyNFREREQqAQJiIiIhIChbCu3RR2ASFS2zNXJrc/k9sOmd1+tT1zhdp+jQkTERERCYF6wkRERERCoBDWgZmdambVZva2mX037HqSycz+YmYRM3s97FqSzczGmlmZmb1pZm+Y2TfDrilZzCzPzF42syVB238Udk1hMLNsM6sws4fCriWZzOw9M1tqZpVmtijsepLNzArN7B4zqzKzt8zs6LBrSgYzKwl+5m3LZjO7Iuy6ksXMvhX8e/e6md1hZnmh1KHLkR8ys2xgGfBxYDXwCnChu78ZamFJYmYnAA3ALe5+UNj1JJOZjQJGufurZjYAWAzMzYSfvZkZ0M/dG8wsF3gW+Ka7vxhyaUllZt8GZgID3f3MsOtJFjN7D5jp7hk5V5SZ/Q14xt3/bGZ9gAJ3rw+5rKQK/t9XAxzp7smYhzNUZlZM/N+5A9y90czuBh52978muxb1hO3sCOBtd3/H3ZuBO4FzQq4padz9aWBD2HWEwd3XuPurwfstwFtAcbhVJYfHNQQfc4Mlo347M7MxwBnAn8OuRZLHzAYBJwA3A7h7c6YFsMBJwIpMCGDt5AD5ZpYDFAC1YRShELazYmBVu8+ryZD/EcuHzGw8MB14KeRSkia4FFcJRIDH3T1j2h64Efh3IBZyHWFw4F9mttjMLgu7mCSbANQB/xdciv6zmfULu6gQXADcEXYRyeLuNcAvgZXAGmCTu/8rjFoUwkTaMbP+wL3AFe6+Oex6ksXdo+4+DRgDHGFmGXM52szOBCLuvjjsWkJynLsfBpwGXB4MS8gUOcBhwB/cfTqwFci0scB9gLOBf4RdS7KY2WDiV7kmAKOBfmZ2cRi1KITtrAYY2+7zmGCdZIBgPNS9wN/d/b6w6wlDcCmmDDg15FKS6Vjg7GBs1J3AbDO7LdySkifoFcDdI8D9xIdlZIrVwOp2Pb/3EA9lmeQ04FV3/yDsQpLoZOBdd69z9xbgPuCYMApRCNvZK8BkM5sQ/HZwAfBAyDVJEgSD028G3nL3/wq7nmQys2FmVhi8zyd+Y0pVqEUlkbvPc/cx7j6e+H/zC909lN+Kk83M+gU3ohBchjsFyJi7o919LbDKzEqCVScBvf5mnA4uJIMuRQZWAkeZWUHwb/9JxMcBJ11OGCdNVe7eamZfAx4DsoG/uPsbIZeVNGZ2B1AKFJnZauCH7n5zuFUlzbHAJcDSYGwUwPfc/eHwSkqaUcDfgjuksoC73T2jpmnIYCOA++P/HyIHuN3dHw23pKT7OvD34Bfvd4DPhVxP0gTB++PAl8KuJZnc/SUzuwd4FWgFKghp5nxNUSEiIiISAl2OFBEREQmBQpiIiIhICBTCREREREKgECYiIiISAoUwERERkRAohIlISjMzN7Nftft8lZldu5vvlJpZj0++aGaXmtnvuruPmV1rZjVmVmlmy83sPjM7oKfrEpH0pBAmIqmuCfikmRXtwXdK6eEZsIMH/e6NX7v7NHefDNwFLDSzYT1YmoikKYUwEUl1rcQnUvxWxw3BbP/3mtkrwXJs8AD2LwPfCnqgTjSzdy2u0Myibc9HNLOnzWyymQ0xs/lm9pqZvWhmhwTbrzWzW83sOeDWDuc+w8xe2JNw6O53Af8CLtrbPwwR6T00Y76IpIP/AV4zs190WP8b4j1Nz5rZOOAxd/+Ymf0RaHD3XwKYWTVwAPEH9r4KHG9mLwFj3X25mf0WqHD3uWY2G7gFmBac4wDiD7luNLNLg+N9Avg2cLq7b9zDtrwKTN3D74hIL6QQJiIpz903m9ktwDeAxnabTgYOCB67AzDQzPp3cohngBOIh7DrgS8CTxF/XizAccCngnMtNLOhZjYw2PaAu7c/52xgJnCKu2/ei+bY7ncRkUygy5Eiki5uBL4A9Gu3Lgs4KhhzNc3di929oZPvPg0cDxwBPAwUEh839kw3zru1w+cVwABgyp4U3850QnpYsIikFoUwEUkL7r4BuJt4EGvzL+IPYAbAzKYFb7cQD0ptXiY+UD/m7tuBSuIPLX462P4M8JngGKXAul30cr1PvNfsFjM7cE/aYGafAk4B7tiT74lI76QQJiLp5FdA+4Hw3wBmBgPq3yQ+IB/gQeATwcD84929CVgFvBhsf4Z4SFsafL4WmGFmrwE/Bz67qyLcvYp4aPuHme2/m5rbbhBYDlwMzHb3um60VUR6OXP3sGsQERERyTjqCRMREREJgUKYiIiISAgUwkRERERCoBAmIiIiEgKFMBEREZEQKISJiIiIhEAhTERERCQECmEiIiIiIfj/9uC94VPSnAQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming final_stats_df is your DataFrame\n",
    "subset_df = final_stats_df[final_stats_df[\"network_id\"] == 6][[\"test_id\", \"users_distances_test_average\"]]\n",
    "\n",
    "# Restart network IDs from 0 after filtering\n",
    "subset_df[\"test_id\"] -= subset_df[\"test_id\"].min()\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(subset_df[\"test_id\"], subset_df[\"users_distances_test_average\"], marker='o', linestyle='-')\n",
    "plt.axvline(x=3.5, color='r', linestyle='--')  # Draw a vertical dashed red line between test_id 3 and 4\n",
    "plt.title(\"48 Users Network\")  # Adjusted title to reflect the updated network ID\n",
    "plt.xlabel(\"Network ID\")\n",
    "plt.ylabel(\"Average Latency\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set up subplots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "# Plotting for the first network (16 Users Network)\n",
    "subset_df = final_stats_df[final_stats_df[\"network_id\"] == 1][[\"test_id\", \"users_distances_test_average\"]]\n",
    "axs[0].plot(subset_df[\"test_id\"], subset_df[\"users_distances_test_average\"], marker='o', linestyle='-')\n",
    "axs[0].axvline(x=3.5, color='r', linestyle='--')\n",
    "axs[0].set_title(\"# users 16\")\n",
    "axs[0].set_xlabel(\"Network ID\")\n",
    "axs[0].set_ylabel(\"Average Latency\")\n",
    "axs[0].grid(True)\n",
    "\n",
    "# Plotting for the second network (32 Users Network)\n",
    "subset_df = final_stats_df[final_stats_df[\"network_id\"] == 5][[\"test_id\", \"users_distances_test_average\"]]\n",
    "subset_df[\"test_id\"] -= subset_df[\"test_id\"].min()  # Restart test IDs from 0\n",
    "axs[1].plot(subset_df[\"test_id\"], subset_df[\"users_distances_test_average\"], marker='o', linestyle='-')\n",
    "axs[1].axvline(x=3.5, color='r', linestyle='--')\n",
    "axs[1].set_title(\"# users 32\")\n",
    "axs[1].set_xlabel(\"Network ID\")\n",
    "axs[1].set_ylabel(\"Average Latency\")\n",
    "axs[1].grid(True)\n",
    "\n",
    "# Plotting for the third network (48 Users Network)\n",
    "subset_df = final_stats_df[final_stats_df[\"network_id\"] == 6][[\"test_id\", \"users_distances_test_average\"]]\n",
    "subset_df[\"test_id\"] -= subset_df[\"test_id\"].min()  # Restart test IDs from 0\n",
    "axs[2].plot(subset_df[\"test_id\"], subset_df[\"users_distances_test_average\"], marker='o', linestyle='-')\n",
    "axs[2].axvline(x=3.5, color='r', linestyle='--')\n",
    "axs[2].set_title(\"# users 48 \")\n",
    "axs[2].set_xlabel(\"Network ID\")\n",
    "axs[2].set_ylabel(\"Average Latency\")\n",
    "axs[2].grid(True)\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.savefig('../../paper-figs/limitation.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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