{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SRUP Experiment – Combined Results Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "MQTT_EXPERIMENTS = [1,2,3,5,9]\n",
    "SRUP_EXPERIMENTS = list(range(1,11))\n",
    "DEVICES = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Experiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.328480</td>\n",
       "      <td>2.287217</td>\n",
       "      <td>2.367920</td>\n",
       "      <td>2.259036</td>\n",
       "      <td>2.295755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.293944</td>\n",
       "      <td>49.334731</td>\n",
       "      <td>47.332237</td>\n",
       "      <td>49.574488</td>\n",
       "      <td>48.804452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.810112</td>\n",
       "      <td>96.575703</td>\n",
       "      <td>96.495300</td>\n",
       "      <td>96.319972</td>\n",
       "      <td>95.644064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>127.700608</td>\n",
       "      <td>127.738265</td>\n",
       "      <td>131.494173</td>\n",
       "      <td>125.442380</td>\n",
       "      <td>126.971284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>154.159200</td>\n",
       "      <td>152.587232</td>\n",
       "      <td>146.957924</td>\n",
       "      <td>141.882181</td>\n",
       "      <td>146.866044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1           2           3           4           5\n",
       "Experiment                                                            \n",
       "1             2.328480    2.287217    2.367920    2.259036    2.295755\n",
       "2            49.293944   49.334731   47.332237   49.574488   48.804452\n",
       "3            95.810112   96.575703   96.495300   96.319972   95.644064\n",
       "5           127.700608  127.738265  131.494173  125.442380  126.971284\n",
       "9           154.159200  152.587232  146.957924  141.882181  146.866044"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mqtt_df = pd.read_csv(\"mqtt_summary.csv\")\n",
    "mqtt_df.set_index(\"Experiment\", inplace=True)\n",
    "mqtt_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Experiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58.778940</td>\n",
       "      <td>58.455221</td>\n",
       "      <td>58.536856</td>\n",
       "      <td>58.487908</td>\n",
       "      <td>57.941856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103.118228</td>\n",
       "      <td>104.624200</td>\n",
       "      <td>102.651864</td>\n",
       "      <td>104.075241</td>\n",
       "      <td>104.311508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>147.072711</td>\n",
       "      <td>149.890420</td>\n",
       "      <td>147.119080</td>\n",
       "      <td>147.996508</td>\n",
       "      <td>146.240472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>152.934552</td>\n",
       "      <td>163.394008</td>\n",
       "      <td>153.887815</td>\n",
       "      <td>158.206960</td>\n",
       "      <td>155.585476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>169.181652</td>\n",
       "      <td>178.415176</td>\n",
       "      <td>178.912554</td>\n",
       "      <td>177.977600</td>\n",
       "      <td>167.744532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1           2           3           4           5\n",
       "Experiment                                                            \n",
       "1            58.778940   58.455221   58.536856   58.487908   57.941856\n",
       "2           103.118228  104.624200  102.651864  104.075241  104.311508\n",
       "3           147.072711  149.890420  147.119080  147.996508  146.240472\n",
       "4           152.934552  163.394008  153.887815  158.206960  155.585476\n",
       "5           169.181652  178.415176  178.912554  177.977600  167.744532"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srup_df = pd.read_csv(\"srup_summary.csv\")\n",
    "srup_df.set_index(\"Experiment\", inplace=True)\n",
    "srup_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = mqtt_df.plot(kind='bar', figsize=(15,10), width=0.8)\n",
    "ax.legend(['Device 1', 'Device 2', 'Device 3', 'Device 4', 'Device 5'])\n",
    "plt.ylabel(\"Mean Duration (ms)\")\n",
    "plt.title('Mean network & processing delay by device for MQTT Experiments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = srup_df.plot(kind='bar', figsize=(15,10), width=0.8)\n",
    "ax.legend(['Device 1', 'Device 2', 'Device 3', 'Device 4', 'Device 5'])\n",
    "plt.ylabel(\"Mean Duration (ms)\")\n",
    "plt.title('Mean network & processing delay by device for SRUP Experiments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vZpo45YHz6/tapslkjs90nF0wb3/VJdSfJH+e6Xh9Y1V9LdNn/5Pr3Hb7H1q+UFXvza5/PoCDTE2nIgDA7s09c3/TK76u2Waae0zOy3Q5gX/aD+r5tyT/ubs3u5dsr9R0KYhfSPLvu/vLKy7nO2rta/wBDEXPHgDsYB4+et15SOnjM503t1ZP26aqql/M1Du1qwl09is9XQripEzn7AGwiQ7d/SoAcNC5faYhflfJNKTy3ru6JMFmqKq3ZBpS+qAlDYdcmu7e1YQ/ACyJYZwAAAADMowTAABgQMIeAADAgA7oc/YOP/zw3rp166rLAAAAWIn3vOc9n+/uLWstO6DD3tatW3P66Tu79BYAAMDYqurcnS0zjBMAAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADCgQ1ddAAAAHAi2nnjqqktgRc556j1WXcJe0bMHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwoKWFvap6blV9rqrOWGPZY6qqq+rw+XFV1dOr6qyq+tequs2y6gIAADgYLLNn73lJjt2xsapumOTnknxyofluSW463x6R5JlLrAsAAGB4Swt73f3WJF9cY9HTkjw2SS+0HZfklJ68I8l1q+qIZdUGAAAwuk09Z6+qjkvy6e7+wA6LjkryqYXH581taz3HI6rq9Ko6fdu2bUuqFAAA4MC2aWGvqq6e5PFJ/ue+PE93n9Tdx3T3MVu2bNmY4gAAAAZz6Cbu6weT3DjJB6oqSY5O8t6qum2STye54cK6R89tAAAA7IVN69nr7g929/d299bu3pppqOZtuvuCJK9O8uB5Vs7bJflKd5+/WbUBAACMZpmXXnhRkn9JcrOqOq+qHr6L1V+X5OwkZyV5dpL/sqy6AAAADgZLG8bZ3ffbzfKtC/c7ya8vqxYAAICDzabOxgkAAMDmEPYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMKBDV10AAMCe2nriqasugRU556n3WHUJcMDQswcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAa0tLBXVc+tqs9V1RkLbX9cVR+pqn+tqldW1XUXlj2uqs6qqo9W1V2XVRcAAMDBYJk9e89LcuwObW9K8iPd/WNJPpbkcUlSVbdIcnySW87bPKOqDllibQAAAENbWtjr7rcm+eIObW/s7kvnh+9IcvR8/7gkL+7ub3b3J5KcleS2y6oNAABgdKs8Z+9hSV4/3z8qyacWlp03twEAALAXVhL2quoJSS5N8sK92PYRVXV6VZ2+bdu2jS8OAABgAJse9qrqIUl+PskDurvn5k8nueHCakfPbVfQ3Sd19zHdfcyWLVuWWisAAMCBalPDXlUdm+SxSe7V3RctLHp1kuOr6qpVdeMkN03yrs2sDQAAYCSHLuuJq+pFSe6U5PCqOi/JEzPNvnnVJG+qqiR5R3f/WnefWVUvSfKhTMM7f727L1tWbQAAAKNbWtjr7vut0fycXaz/lCRPWVY9AAAAB5NVzsYJAADAkgh7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABjQ0sJeVT23qj5XVWcstF2/qt5UVR+f/73e3F5V9fSqOquq/rWqbrOsugAAAA4Gy+zZe16SY3doOzHJm7v7pknePD9Okrsluel8e0SSZy6xLgAAgOEtLex191uTfHGH5uOSPH++//wk915oP6Un70hy3ao6Ylm1AQAAjG6zz9m7QXefP9+/IMkN5vtHJfnUwnrnzW0AAADshZVN0NLdnaT3dLuqekRVnV5Vp2/btm0JlQEAABz4NjvsfXb78Mz538/N7Z9OcsOF9Y6e266gu0/q7mO6+5gtW7YstVgAAIAD1WaHvVcnOWG+f0KSVy20P3ielfN2Sb6yMNwTAACAPXTosp64ql6U5E5JDq+q85I8MclTk7ykqh6e5Nwk951Xf12Suyc5K8lFSR66rLoAAAAOBksLe919v50sussa63aSX19WLQAAAAeblU3QAgAAwPIIewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAlnZRdQAOHltPPHXVJbAC5zz1HqsuAYBd0LMHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAzo0N2tUFVHJzk+yc8kOTLJxUnOSHJqktd397eXWiEAAAB7bJdhr6pOTnJUktcm+cMkn0tytSQ/lOTYJE+oqhO7+63LLhQAAID1213P3v/u7jPWaD8jySuq6ipJvn/jywIAAGBf7PKcvbWCXlVdr6p+bF5+SXeftaziAAAA2DvrmqClqt5SVdeuqusneW+SZ1fV05ZbGgAAAHtrvbNxXqe7v5rkPklO6e6fTHKX5ZUFAADAvlhv2Du0qo5Ict9Mk7UAAACwH1tv2PvdJP+Q5KzufndV/UCSjy+vLAAAAPbFbq+zlyTd/dIkL114fHaSX1xWUQAAAOybdYW9qrpxkkcl2bq4TXffazllAQAAsC/WFfaS/H2S5yR5TZJvL60aAAAANsR6w943uvvpS60EAACADbPesPfnVfXEJG9M8s3tjd393qVUBQAAwD5Zb9j70SQPSnLnXD6Ms+fHAAAA7GfWG/Z+OckPdPclyywGAACAjbHe6+ydkeS6S6wDAACADbTenr3rJvlIVb07333OnksvAAAA7IfWG/aeuNQqAAAA2FC7DHtVVT05bXfrbHxpAAAA7K3dnbP3T1X1qKr6/sXGqrpKVd25qp6f5ITllQcAAMDe2N0wzmOTPCzJi6rqxkm+nOSwTCHxjUn+rLvft9QKAQAA2GO7DHvd/Y0kz0jyjKq6cpLDk1zc3V/ehNoAAADYS+udoCXd/a0k5y+xFgAAADbIeq+zBwAAwAFE2AMAABiQsAcAADCgdYW9qrpPVX28qr5SVV+tqq9V1VeXXRwAAAB7Z70TtPxRknt294eXWQwAAAAbY73DOD8r6AEAABw41tuzd3pV/V2Sv0/yze2N3f2KZRQFAADAvllv2Lt2kouS/NxCWycR9gAAAPZD6wp73f3QZRcCAADAxlnvbJxHV9Urq+pz8+3lVXX0sosDAABg76x3gpaTk7w6yZHz7TVzGwAAAPuh9Ya9Ld19cndfOt+el2TLEusCAABgH6w37H2hqh5YVYfMtwcm+cIyCwMAAGDvrTfsPSzJfZNckOT8JL+UxKQtAAAA+6n1zsZ5bpJ7bdROq+o3k/xqpss3fDBTcDwiyYuTfE+S9yR5UHdfslH7BAAAOJjsMuxV1WO7+4+q6i8yBbPv0t2P3tMdVtVRSR6d5BbdfXFVvSTJ8UnunuRp3f3iqnpWkocneeaePj8AAAC779n78Pzv6UvY72FV9a0kV880NPTOSe4/L39+kidF2AMAANgruwx73f2a+e5F3f3SxWVV9ct7s8Pu/nRV/UmSTya5OMkbMw3b/HJ3Xzqvdl6So/bm+QEAAFj/BC2PW2fbblXV9ZIcl+TGma7Zd40kx+7B9o+oqtOr6vRt27btTQkAAADD2905e3fLdC7dUVX19IVF105y6dpb7dbPJvlEd2+b9/GKJHdIct2qOnTu3Ts6yafX2ri7T0pyUpIcc8wxVziPEAAAgN337H0m0/l638g01HL77dVJ7rqX+/xkkttV1dWrqpLcJcmHkvxTpks6JMkJSV61l88PAABw0NvdOXsfSPKBqvrb7v7WRuywu99ZVS9L8t5MvYPvy9RTd2qSF1fV789tz9mI/QEAAByM1nWdvSRbq+p/JblFkqttb+zuH9ibnXb3E5M8cYfms5Pcdm+eDwAAgO+23glaTs50GYRLk/yHJKck+ZtlFQUAAMC+WW/YO6y735ykuvvc7n5SknssrywAAAD2xXqHcX6zqq6U5ONV9chMM2Vec3llAQAAsC/W27P3G0munuTRSX48yQMzzZgJAADAfmi3PXtVdUiSX+nu305yYZKHLr0qAAAA9slue/a6+7IkP70JtQAAALBB1nvO3vuq6tVJXprk69sbu/sVS6kKAACAfbLesHe1JF9IcueFtk4i7AEAAOyH1hX2utt5egAAAAeQdYW9qjo5U0/ed+nuh214RQAAAOyz9Q7jfO3C/asl+YUkn9n4cgAAANgI6x3G+fLFx1X1oiRvW0pFAAAA7LP1XlR9RzdN8r0bWQgAAAAbZ73n7H0t0zl7Nf97QZLfWWJdAAAA7IP1DuO81rILAQAAYOPsNuxV1aFJ7pbk5nPTh5L8Q3dfuszCAAAA2Hu7PGevqo5KcmaSxyQ5MslRSR6b5MyqOnL55QEAALA3dtez95Qkz+zuP1tsrKpHJ/lfSU5YUl0AAADsg92Fvdt190N2bOzup1fVR5dTEgAAAPtqd5deuHgXyy7ayEIAAADYOLvr2btOVd1njfZKcu0l1AMAAMAG2F3YOy3JPXey7K0bXAsAAAAbZJdhr7sfulmFAAAAsHF2d84eAAAAByBhDwAAYEDCHgAAwIB2N0HLd1TVTyXZurhNd5+yhJoAAADYR+sKe1X1giQ/mOT9SS6bmzuJsAcAALAfWm/P3jFJbtHdvcxiAAAA2BjrPWfvjCTft8xCAAAA2Djr7dk7PMmHqupdSb65vbG777WUqgAAANgn6w17T1pmEQAAAGysdYW97j5t2YUAAACwcdZ1zl5V3a6q3l1VF1bVJVV1WVV9ddnFAQAAsHfWO0HLXya5X5KPJzksya8m+atlFQUAAMC+WW/YS3efleSQ7r6su09OcuzyygIAAGBfrHeClouq6ipJ3l9Vf5Tk/OxBUAQAAGBzrTewPWhe95FJvp7khkl+cVlFAQAAsG/WOxvnuVV1WJIjuvvJS64JAACAfbTe2TjvmeT9Sd4wP751Vb16iXUBAACwD9Y7jPNJSW6b5MtJ0t3vT3LjpVQEAADAPltv2PtWd39lh7be6GIAAADYGOudjfPMqrp/kkOq6qZJHp3k7csrCwAAgH2x3p69RyW5ZZJvJnlRkq8m+a9LqgkAAIB9tN7ZOC9K8oT5BgAAwH5ul2FvdzNudve9NrYcAAAANsLuevZun+RTmYZuvjNJLb0iAAAA9tnuwt73JfmPSe6X5P5JTk3you4+c9mFAQAAsPd2OUFLd1/W3W/o7hOS3C7JWUneUlWP3JTqAAAA2Cu7naClqq6a5B6Zeve2Jnl6klcutywAAAD2xe4maDklyY8keV2SJ3f3GZtSFQAAAPtkdz17D0zy9SS/keTRVd+Zn6WSdHdfe4m1AQAAsJd2Gfa6e70XXQcAAGA/IswBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABrSSsFdV162ql1XVR6rqw1V1+6q6flW9qao+Pv97vVXUBgAAMIJV9ez9eZI3dPfNk9wqyYeTnJjkzd190yRvnh8DAACwFzY97FXVdZL8+yTPSZLuvqS7v5zkuCTPn1d7fpJ7b3ZtAAAAo1hFz96Nk2xLcnJVva+q/rqqrpHkBt19/rzOBUlusNbGVfWIqjq9qk7ftm3bJpUMAABwYFlF2Ds0yW2SPLO7/12Sr2eHIZvd3Ul6rY27+6TuPqa7j9myZcvSiwUAADgQrSLsnZfkvO5+5/z4ZZnC32er6ogkmf/93ApqAwAAGMKmh73uviDJp6rqZnPTXZJ8KMmrk5wwt52Q5FWbXRsAAMAoDl3Rfh+V5IVVdZUkZyd5aKbg+ZKqeniSc5Pcd0W1AQAAHPBWEva6+/1Jjllj0V02uRQAAIAhreo6ewAAACyRsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAY0KGrLgDYWFtPPHXVJbAC5zz1HqsuAQDYz+jZAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMKCVhb2qOqSq3ldVr50f37iq3llVZ1XV31XVVVZVGwAAwIFulT17v5HkwwuP/zDJ07r7Jkm+lOThK6kKAABgACsJe1V1dJJ7JPnr+XEluXOSl82rPD/JvVdRGwAAwAhW1bP3Z0kem+Tb8+PvSfLl7r50fnxekqNWUBcAAMAQNj3sVdXPJ/lcd79nL7d/RFWdXlWnb9u2bYOrAwAAGMMqevbukOReVXVOkhdnGr7550muW1WHzuscneTTa23c3Sd19zHdfcyWLVs2o14AAIADzqaHve5+XHcf3d1bkxyf5B+7+wFJ/inJL82rnZDkVZtdGwAAwCj2p+vs/U6S36qqszKdw/ecFdcDAABwwDp096ssT3e/Jclb5vtnJ7ntKusBAAAYxf7UswcAAMAGEfYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMKBND3tVdcOq+qeq+lBVnVlVvzG3X7+q3lRVH5//vd5m1wYAADCKVfTsXZrkMd19iyS3S/LrVXWLJCcmeXN33zTJm+fHAAAA7IVND3vdfX53v3e+/7UkH05yVJLjkjx/Xu35Se692bUBAACMYqXn7FXV1iT/Lsk7k9ygu8+fF12Q5AarqgsAAOBAt7KwV1XXTPLyJP+1u7+6uKy7O0nvZLtHVNXpVXX6tm3bNqFSAACAA89Kwl5VXTlT0Hthd79ibv5sVR0xLz8iyefW2ra7T+ruY7r7mC1btmxOwQAAAAeYVczGWUmek+TD3f2nC4teneSE+f4JSV612bUBAACM4tAV7PMOSR6U5INV9f657fFJnprkJVX18CTnJrnvCmoDAAAYwqaHve5+W5LayeK7bGYtAAAAo1rpbJwAAAAsh7AHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADEjYAwAAGJCwBwAAMCBhDwAAYEDCHgAAwICEPQAAgAEJewAAAAMS9gAAAAYk7AEAAAxI2AMAABiQsAcAADAgYQ8AAGBAwh4AAMCAhD0AAIABCXsAAAADEvYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgIQ9AACAAQl7AAAAAxL2AAAABiTsAQAADOjQVRcwsq0nnrrqEliBc556j1WXAAAAevYAAABGJOwBAAAMSNgDAAAYkLAHAAAwoP0u7FXVsVX10ao6q6pOXHU9AAAAB6L9KuxV1SFJ/irJ3ZLcIsn9quoWq60KAADgwLNfhb0kt01yVnef3d2XJHlxkuNWXBMAAMABp7p71TV8R1X9UpJju/tX58cPSvKT3f3IhXUekeQR88ObJfnophfKehye5POrLoKDjuOOVXDcsQqOO1bBcbd/ulF3b1lrwQF3UfXuPinJSauug12rqtO7+5hV18HBxXHHKjjuWAXHHavguDvw7G/DOD+d5IYLj4+e2wAAANgD+1vYe3eSm1bVjavqKkmOT/LqFdcEAABwwNmvhnF296VV9cgk/5DkkCTP7e4zV1wWe8dQW1bBcccqOO5YBccdq+C4O8DsVxO0AAAAsDH2t2GcAAAAbABhDwAAYEDCHgAAwID2qwlaAPZEVd08yVFJ3tndFy60H9vdb1hdZYyqqm6bpLv73VV1iyTHJvlId79uxaVxkKiqn05y2yRndPcbV10P46qqH0hyn0yXRbssyceS/G13f3WlhbFH9OyxVFX10FXXwJiq6tFJXpXkUUnOqKrjFhb/wWqqYmRV9cQkT0/yzKr6X0n+Msk1kpxYVU9YaXEMq6retXD/P2U67q6V5IlVdeLKCmNo8/+xz0pytSQ/keSqmULfO6rqTqurjD1lNk6Wqqo+2d3fv+o6GE9VfTDJ7bv7wqramuRlSV7Q3X9eVe/r7n+32goZzXzM3TrTl54Lkhzd3V+tqsMy9S7/2CrrY0yLv8+q6t1J7t7d26rqGkne0d0/utoKGdH233fdfVlVXT3J67r7TlX1/Ule5f/YA4dhnOyzqvrXnS1KcoPNrIWDypW2D93s7nPmvzS+rKpulOnYg412aXdfluSiqvq37UOZuvviqvr2imtjXFeqqutlGo1V3b0tSbr761V16WpLY3CHZhq+edUk10yS7v5kVV15pVWxR4Q9NsINktw1yZd2aK8kb9/8cjhIfLaqbt3d70+SuYfv55M8N4m/dLMMl1TV1bv7oiQ/vr2xqq6TRNhjWa6T5D2Z/k/tqjqiu8+vqmvGH7ZYnr9O8u6qemeSn0nyh0lSVVuSfHGVhbFnDONkn1XVc5Kc3N1vW2PZ33b3/VdQFoOrqqMz9bRcsMayO3T3P6+gLAZWVVft7m+u0X54kiO6+4MrKIuD1Dy07gbd/YlV18KYquqWSX4402RAH1l1PewdYQ8AAGBAZuMEAAAYkLAHAAAwIGEPgKFU1WVV9f6F21KvRVZV99qEfdypqn5qmfsAYDzO2QNgKFV1YXdfc5P2dWh3L336+6p6UpILu/tPlr0vAMahZw+A4VXVdarqo1V1s/nxi6rqP833L6yqp1XVmVX15nlq8VTVD1bVG6rqPVX1f6vq5nP786rqWfOU5H9UVQ+pqr9cWPbMqnpHVZ0998g9t6o+XFXPW6jn56rqX6rqvVX10nka/VTVOVX15Ln9g1V186ramuTXkvzm3FP5M5v41gFwABP2ABjNYTsM4/yV7v5KkkcmeV5VHZ/ket397Hn9ayQ5vbtvmeS0JE+c209K8qju/vEkv53kGQv7ODrJT3X3b62x/+sluX2S30zy6iRPS3LLJD9aVbeeL9Xw35P8bHffJsnpSRaf5/Nz+zOT/HZ3n5PkWUme1t237u7/uy9vDgAHDxdVB2A0F3f3rXds7O43VdUvJ/mrJLdaWPTtJH833/+bJK+Ye9p+KslLq75z3eqrLmzz0u6+bCf7f013d1V9MMlnt19/r6rOTLI1U1C8RZJ/np/7Kkn+ZWH7V8z/vifJfXb7agFgJ4Q9AA4KVXWlTBcIvihT79t5O1m1M418+fJaoXH29V3savuF17+9cH/740OTXJbkTd19v91sf1n8Pw3APjCME4CDxW8m+XCS+yc5uaquPLdfKckvzffvn+Rt3f3VJJ+YewJTk1vt+IR76R1J7lBVN5mf+xpV9UO72eZrSa61QfsH4CAh7AEwmh3P2XvqPDHLryZ5zHzO21sznTeXTL10t62qM5LcOcnvzu0PSPLwqvpAkjOTHLcRxXX3tiQPSfKiqvrXTEM4b76bzV6T5BdM0ALAnnDpBQAOapt5qQYA2Ex69gAAAAakZw8AAGBAevYAAAAGJOwBAAAMSNgDAAAYkLAHAAAwIGEPAABgQMIeAADAgP4fL5aXr/1sdhwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = mqtt_df.stack().reset_index()\n",
    "t.columns = ['Experiment', 'Device', 'Mean']\n",
    "mqtt_ave_df = t.groupby(['Experiment'])['Mean'].agg([np.average])\n",
    "mqtt_ave_df.plot(kind='bar', figsize=(15,10), width=0.8, legend=False)\n",
    "plt.ylabel(\"Mean Duration (ms)\")\n",
    "plt.title('Mean network & processing delay for MQTT Experiments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = srup_df.stack().reset_index()\n",
    "t.columns = ['Experiment', 'Device', 'Mean']\n",
    "srup_ave_df = t.groupby(['Experiment'])['Mean'].agg([np.average])\n",
    "srup_ave_df.plot(kind='bar', figsize=(15,10), width=0.8, legend=False)\n",
    "plt.ylabel(\"Mean Duration (ms)\")\n",
    "plt.title('Mean network & processing delay for SRUP Experiments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mqtt_ave_df['Type'] = pd.Series(['MQTT' for _ in range(len(mqtt_ave_df.index))], index=mqtt_ave_df.index)\n",
    "srup_ave_df['Type'] = pd.Series(['SRUP' for _ in range(len(srup_ave_df.index))], index=srup_ave_df.index)\n",
    "\n",
    "comb_ave_df = pd.merge(mqtt_ave_df, srup_ave_df, on='Experiment', how='inner')\n",
    "\n",
    "ax = comb_ave_df.plot(kind='bar', figsize=(15,10), width=0.8)\n",
    "ax.legend(['MQTT', 'SRUP'])\n",
    "plt.ylabel(\"Mean Duration (ms)\")\n",
    "plt.title('Comparison of mean network & processing delay')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment 1:\n",
      "=============\n",
      "Total MQTT Delay: 2.3077\n",
      "Total SRUP Delay: 58.4402\n",
      "Difference = 56.1325\n",
      "Overhead Cost: 2432.42%\n",
      "\n",
      "Experiment 3:\n",
      "=============\n",
      "Total MQTT Delay: 96.1690\n",
      "Total SRUP Delay: 147.6638\n",
      "Difference = 51.4948\n",
      "Overhead Cost: 53.55%\n"
     ]
    }
   ],
   "source": [
    "r1m = comb_ave_df.iloc[[0]].average_x\n",
    "r1s = comb_ave_df.iloc[[0]].average_y\n",
    "d1m = r1m.loc[1]\n",
    "d1s = r1s.loc[1]\n",
    "print(f\"Experiment 1:\\n=============\\nTotal MQTT Delay: {d1m:.4f}\\nTotal SRUP Delay: {d1s:.4f}\")\n",
    "print(f\"Difference = {(d1s - d1m):.4f}\\nOverhead Cost: {(((d1s - d1m)/d1m) * 100):.2f}%\")\n",
    "print()\n",
    "\n",
    "r3m = comb_ave_df.iloc[[2]].average_x\n",
    "r3s = comb_ave_df.iloc[[2]].average_y\n",
    "d3m = r3m.loc[3]\n",
    "d3s = r3s.loc[3]\n",
    "print(f\"Experiment 3:\\n=============\\nTotal MQTT Delay: {d3m:.4f}\\nTotal SRUP Delay: {d3s:.4f}\")\n",
    "print(f\"Difference = {(d3s - d3m):.4f}\\nOverhead Cost: {(((d3s - d3m)/d3m) * 100):.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comb_ave_df['Difference'] = comb_ave_df.apply(lambda row: (row.average_y-row.average_x), axis=1)\n",
    "comb_ave_df.head()\n",
    "ax = comb_ave_df['Difference'].plot(kind='bar', figsize=(15,10), width=0.8, legend=False)\n",
    "plt.ylabel(\"Mean Difference (ms)\")\n",
    "plt.title('Addtional processing overhead')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm = mqtt_df.stack().reset_index()\n",
    "pm.columns = ['Experiment', 'Device', 'Mean']\n",
    "pm = pm.pivot(index = \"Device\", columns = 'Experiment', values = 'Mean')\n",
    "#pm.head()\n",
    "\n",
    "pm.boxplot(figsize=(15,10))\n",
    "plt.ylabel(\"Mean Duration (ms)\")\n",
    "plt.xlabel(\"Experiment\")\n",
    "plt.title(\"Mean network & processing delay by device for MQTT Experiments'\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps = srup_df.stack().reset_index()\n",
    "ps.columns = ['Experiment', 'Device', 'Mean']\n",
    "ps = ps.pivot(index = \"Device\", columns = 'Experiment', values = 'Mean')\n",
    "ps.head()\n",
    "\n",
    "ps.boxplot(figsize=(15,10))\n",
    "plt.ylabel(\"Mean Duration (ms)\")\n",
    "plt.xlabel(\"Experiment\")\n",
    "plt.title(\"Mean network & processing delay by device for SRUP Experiments'\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = plt.boxplot(comb_ave_df['Difference'], labels=['SRUP - MQTT'], patch_artist=True)\n",
    "plt.grid(b=True, which='major', color='#AAAAAA', linestyle='-')\n",
    "for median in box['medians']: \n",
    "    median.set(color ='red', linewidth = 1) \n",
    "    \n",
    "colors = ['tab:blue']\n",
    "for patch, color in zip(box['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "    \n",
    "plt.title(\"Distribution of differences\")\n",
    "plt.show(block=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean processing overhead for SRUP = 51.600805633413664ms\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean processing overhead for SRUP = {}ms\".format(np.average(comb_ave_df['Difference'])))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}