controller_errors_wind.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "billion-moral",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "earned-boring",
   "metadata": {},
   "source": [
    "### Build the dataset with the six test from the simulation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "disciplinary-devon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1_time_percent</th>\n",
       "      <th>1_error_X</th>\n",
       "      <th>1_error_Y</th>\n",
       "      <th>1_error_Angle</th>\n",
       "      <th>2_time_percent</th>\n",
       "      <th>2_error_X</th>\n",
       "      <th>2_error_Y</th>\n",
       "      <th>2_error_Angle</th>\n",
       "      <th>3_time_percent</th>\n",
       "      <th>3_error_X</th>\n",
       "      <th>3_error_Y</th>\n",
       "      <th>3_error_Angle</th>\n",
       "      <th>4_time_percent</th>\n",
       "      <th>4_error_X</th>\n",
       "      <th>4_error_Y</th>\n",
       "      <th>4_error_Angle</th>\n",
       "      <th>5_time_percent</th>\n",
       "      <th>5_error_X</th>\n",
       "      <th>5_error_Y</th>\n",
       "      <th>5_error_Angle</th>\n",
       "      <th>6_time_percent</th>\n",
       "      <th>6_error_X</th>\n",
       "      <th>6_error_Y</th>\n",
       "      <th>6_error_Angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.817537e+12</td>\n",
       "      <td>165.406860</td>\n",
       "      <td>112.532249</td>\n",
       "      <td>-37.135151</td>\n",
       "      <td>2.792253e+12</td>\n",
       "      <td>136.607895</td>\n",
       "      <td>66.360245</td>\n",
       "      <td>-36.137890</td>\n",
       "      <td>2.795981e+12</td>\n",
       "      <td>166.808792</td>\n",
       "      <td>37.174133</td>\n",
       "      <td>-38.715382</td>\n",
       "      <td>2.793537e+12</td>\n",
       "      <td>153.018341</td>\n",
       "      <td>18.841827</td>\n",
       "      <td>-37.481899</td>\n",
       "      <td>2.795545e+12</td>\n",
       "      <td>145.687134</td>\n",
       "      <td>14.428772</td>\n",
       "      <td>-36.377716</td>\n",
       "      <td>2831453000000</td>\n",
       "      <td>139.788254</td>\n",
       "      <td>-6.930649</td>\n",
       "      <td>-33.987568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.817637e+12</td>\n",
       "      <td>167.445312</td>\n",
       "      <td>113.975128</td>\n",
       "      <td>-37.147095</td>\n",
       "      <td>2.792345e+12</td>\n",
       "      <td>136.552307</td>\n",
       "      <td>66.358673</td>\n",
       "      <td>-36.116817</td>\n",
       "      <td>2.796053e+12</td>\n",
       "      <td>168.134949</td>\n",
       "      <td>37.538925</td>\n",
       "      <td>-38.695744</td>\n",
       "      <td>2.793657e+12</td>\n",
       "      <td>153.671051</td>\n",
       "      <td>18.952454</td>\n",
       "      <td>-37.403046</td>\n",
       "      <td>2.795657e+12</td>\n",
       "      <td>146.841003</td>\n",
       "      <td>14.868301</td>\n",
       "      <td>-36.337376</td>\n",
       "      <td>2831569000000</td>\n",
       "      <td>139.838074</td>\n",
       "      <td>-6.820221</td>\n",
       "      <td>-33.938217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.817733e+12</td>\n",
       "      <td>169.483780</td>\n",
       "      <td>115.417999</td>\n",
       "      <td>-37.159042</td>\n",
       "      <td>2.792441e+12</td>\n",
       "      <td>136.496719</td>\n",
       "      <td>66.357101</td>\n",
       "      <td>-36.095741</td>\n",
       "      <td>2.796261e+12</td>\n",
       "      <td>169.461105</td>\n",
       "      <td>37.903732</td>\n",
       "      <td>-38.676105</td>\n",
       "      <td>2.793745e+12</td>\n",
       "      <td>154.323776</td>\n",
       "      <td>19.063080</td>\n",
       "      <td>-37.324192</td>\n",
       "      <td>2.795745e+12</td>\n",
       "      <td>147.994873</td>\n",
       "      <td>15.307831</td>\n",
       "      <td>-36.297031</td>\n",
       "      <td>2831697000000</td>\n",
       "      <td>139.887909</td>\n",
       "      <td>-6.709793</td>\n",
       "      <td>-33.888863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.817861e+12</td>\n",
       "      <td>171.522247</td>\n",
       "      <td>116.860878</td>\n",
       "      <td>-37.170990</td>\n",
       "      <td>2.792549e+12</td>\n",
       "      <td>136.441132</td>\n",
       "      <td>66.355530</td>\n",
       "      <td>-36.074665</td>\n",
       "      <td>2.796345e+12</td>\n",
       "      <td>170.787247</td>\n",
       "      <td>38.268524</td>\n",
       "      <td>-38.656471</td>\n",
       "      <td>2.793861e+12</td>\n",
       "      <td>154.976501</td>\n",
       "      <td>19.173706</td>\n",
       "      <td>-37.245338</td>\n",
       "      <td>2.795837e+12</td>\n",
       "      <td>149.148743</td>\n",
       "      <td>15.747375</td>\n",
       "      <td>-36.256687</td>\n",
       "      <td>2831793000000</td>\n",
       "      <td>139.937729</td>\n",
       "      <td>-6.599365</td>\n",
       "      <td>-33.839508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.817941e+12</td>\n",
       "      <td>173.560699</td>\n",
       "      <td>118.303749</td>\n",
       "      <td>-37.182938</td>\n",
       "      <td>2.792645e+12</td>\n",
       "      <td>136.385544</td>\n",
       "      <td>66.353958</td>\n",
       "      <td>-36.053593</td>\n",
       "      <td>2.796429e+12</td>\n",
       "      <td>172.113403</td>\n",
       "      <td>38.633316</td>\n",
       "      <td>-38.636833</td>\n",
       "      <td>2.793945e+12</td>\n",
       "      <td>155.629227</td>\n",
       "      <td>19.284332</td>\n",
       "      <td>-37.166485</td>\n",
       "      <td>2.795925e+12</td>\n",
       "      <td>150.302612</td>\n",
       "      <td>16.186905</td>\n",
       "      <td>-36.216347</td>\n",
       "      <td>2831873000000</td>\n",
       "      <td>139.987564</td>\n",
       "      <td>-6.488953</td>\n",
       "      <td>-33.790157</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2849997000000</td>\n",
       "      <td>13.608063</td>\n",
       "      <td>6.705078</td>\n",
       "      <td>-41.241192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2850065000000</td>\n",
       "      <td>13.375793</td>\n",
       "      <td>8.201538</td>\n",
       "      <td>-41.115150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2850153000000</td>\n",
       "      <td>11.115021</td>\n",
       "      <td>10.715134</td>\n",
       "      <td>-40.492508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2850265000000</td>\n",
       "      <td>11.390533</td>\n",
       "      <td>10.273819</td>\n",
       "      <td>-41.592968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2850361000000</td>\n",
       "      <td>10.156555</td>\n",
       "      <td>12.096710</td>\n",
       "      <td>-40.773800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     1_time_percent   1_error_X   1_error_Y  1_error_Angle  2_time_percent  \\\n",
       "0      2.817537e+12  165.406860  112.532249     -37.135151    2.792253e+12   \n",
       "1      2.817637e+12  167.445312  113.975128     -37.147095    2.792345e+12   \n",
       "2      2.817733e+12  169.483780  115.417999     -37.159042    2.792441e+12   \n",
       "3      2.817861e+12  171.522247  116.860878     -37.170990    2.792549e+12   \n",
       "4      2.817941e+12  173.560699  118.303749     -37.182938    2.792645e+12   \n",
       "..              ...         ...         ...            ...             ...   \n",
       "187             NaN         NaN         NaN            NaN             NaN   \n",
       "188             NaN         NaN         NaN            NaN             NaN   \n",
       "189             NaN         NaN         NaN            NaN             NaN   \n",
       "190             NaN         NaN         NaN            NaN             NaN   \n",
       "191             NaN         NaN         NaN            NaN             NaN   \n",
       "\n",
       "      2_error_X  2_error_Y  2_error_Angle  3_time_percent   3_error_X  \\\n",
       "0    136.607895  66.360245     -36.137890    2.795981e+12  166.808792   \n",
       "1    136.552307  66.358673     -36.116817    2.796053e+12  168.134949   \n",
       "2    136.496719  66.357101     -36.095741    2.796261e+12  169.461105   \n",
       "3    136.441132  66.355530     -36.074665    2.796345e+12  170.787247   \n",
       "4    136.385544  66.353958     -36.053593    2.796429e+12  172.113403   \n",
       "..          ...        ...            ...             ...         ...   \n",
       "187         NaN        NaN            NaN             NaN         NaN   \n",
       "188         NaN        NaN            NaN             NaN         NaN   \n",
       "189         NaN        NaN            NaN             NaN         NaN   \n",
       "190         NaN        NaN            NaN             NaN         NaN   \n",
       "191         NaN        NaN            NaN             NaN         NaN   \n",
       "\n",
       "     3_error_Y  3_error_Angle  4_time_percent   4_error_X  4_error_Y  \\\n",
       "0    37.174133     -38.715382    2.793537e+12  153.018341  18.841827   \n",
       "1    37.538925     -38.695744    2.793657e+12  153.671051  18.952454   \n",
       "2    37.903732     -38.676105    2.793745e+12  154.323776  19.063080   \n",
       "3    38.268524     -38.656471    2.793861e+12  154.976501  19.173706   \n",
       "4    38.633316     -38.636833    2.793945e+12  155.629227  19.284332   \n",
       "..         ...            ...             ...         ...        ...   \n",
       "187        NaN            NaN             NaN         NaN        NaN   \n",
       "188        NaN            NaN             NaN         NaN        NaN   \n",
       "189        NaN            NaN             NaN         NaN        NaN   \n",
       "190        NaN            NaN             NaN         NaN        NaN   \n",
       "191        NaN            NaN             NaN         NaN        NaN   \n",
       "\n",
       "     4_error_Angle  5_time_percent   5_error_X  5_error_Y  5_error_Angle  \\\n",
       "0       -37.481899    2.795545e+12  145.687134  14.428772     -36.377716   \n",
       "1       -37.403046    2.795657e+12  146.841003  14.868301     -36.337376   \n",
       "2       -37.324192    2.795745e+12  147.994873  15.307831     -36.297031   \n",
       "3       -37.245338    2.795837e+12  149.148743  15.747375     -36.256687   \n",
       "4       -37.166485    2.795925e+12  150.302612  16.186905     -36.216347   \n",
       "..             ...             ...         ...        ...            ...   \n",
       "187            NaN             NaN         NaN        NaN            NaN   \n",
       "188            NaN             NaN         NaN        NaN            NaN   \n",
       "189            NaN             NaN         NaN        NaN            NaN   \n",
       "190            NaN             NaN         NaN        NaN            NaN   \n",
       "191            NaN             NaN         NaN        NaN            NaN   \n",
       "\n",
       "     6_time_percent   6_error_X  6_error_Y  6_error_Angle  \n",
       "0     2831453000000  139.788254  -6.930649     -33.987568  \n",
       "1     2831569000000  139.838074  -6.820221     -33.938217  \n",
       "2     2831697000000  139.887909  -6.709793     -33.888863  \n",
       "3     2831793000000  139.937729  -6.599365     -33.839508  \n",
       "4     2831873000000  139.987564  -6.488953     -33.790157  \n",
       "..              ...         ...        ...            ...  \n",
       "187   2849997000000   13.608063   6.705078     -41.241192  \n",
       "188   2850065000000   13.375793   8.201538     -41.115150  \n",
       "189   2850153000000   11.115021  10.715134     -40.492508  \n",
       "190   2850265000000   11.390533  10.273819     -41.592968  \n",
       "191   2850361000000   10.156555  12.096710     -40.773800  \n",
       "\n",
       "[192 rows x 24 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "for test in range(1, 7):\n",
    "    file = f\"simulations_data/simulation_wind/Wind{test}/error.csv\"\n",
    "    temp_data = pd.read_csv(file)\n",
    "    temp_data = temp_data.rename(columns={\n",
    "        \"%time\": f\"{test}_time_percent\",\n",
    "        \"field.errorX\": f\"{test}_error_X\",\n",
    "        \"field.errorY\": f\"{test}_error_Y\",\n",
    "        \"field.errorT\": f\"{test}_error_Angle\"\n",
    "    })\n",
    "    temp_data = temp_data.drop(columns=[\"field.errorS\"])\n",
    "    df = pd.concat([df, temp_data], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "grateful-wildlife",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"./data/controller_error_wind.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collect-appreciation",
   "metadata": {},
   "source": [
    "### Controllers errors dataset of the simulation\n",
    "\n",
    "This analysis is made to determine the error of the simulation in six different test that were made adding different forces of wind to the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "adjacent-tumor",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_file = \"./data/controller_error_wind.csv\"\n",
    "data = pd.read_csv(error_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "perceived-stamp",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'1': 18.296, '2': 17.76, '3': 20.62, '4': 20.332, '5': 21.076, '6': 22.356}\n"
     ]
    }
   ],
   "source": [
    "controller_times = {}\n",
    "\n",
    "with open(\"./data/controller_time_wind.csv\", \"r\") as csv_file:\n",
    "    csv_reader = csv.reader(csv_file, delimiter=',')\n",
    "    next(csv_reader)\n",
    "    for row in csv_reader:\n",
    "        controller_times[row[0]] = float(row[1])\n",
    "\n",
    "print(controller_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "reasonable-enough",
   "metadata": {},
   "outputs": [],
   "source": [
    "for test, test_time in controller_times.items():    \n",
    "\n",
    "    first_val_tstamp = data[test+\"_time_percent\"][0]\n",
    "    last_val_tstamp = data[test+\"_time_percent\"][data.apply(lambda column: column.dropna().index[-1])[test+\"_time_percent\"]]\n",
    "    \n",
    "    m = test_time / (last_val_tstamp - first_val_tstamp)\n",
    "    b = test_time - m * last_val_tstamp\n",
    "    \n",
    "    data[test+\"_time\"] = m * data[test+\"_time_percent\"] + b\n",
    "\n",
    "data = data.reindex(columns=sorted(data.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "crucial-queue",
   "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",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1_error_Angle</th>\n",
       "      <th>1_error_X</th>\n",
       "      <th>1_error_Y</th>\n",
       "      <th>1_time</th>\n",
       "      <th>1_time_percent</th>\n",
       "      <th>2_error_Angle</th>\n",
       "      <th>2_error_X</th>\n",
       "      <th>2_error_Y</th>\n",
       "      <th>2_time</th>\n",
       "      <th>2_time_percent</th>\n",
       "      <th>3_error_Angle</th>\n",
       "      <th>3_error_X</th>\n",
       "      <th>3_error_Y</th>\n",
       "      <th>3_time</th>\n",
       "      <th>3_time_percent</th>\n",
       "      <th>4_error_Angle</th>\n",
       "      <th>4_error_X</th>\n",
       "      <th>4_error_Y</th>\n",
       "      <th>4_time</th>\n",
       "      <th>4_time_percent</th>\n",
       "      <th>5_error_Angle</th>\n",
       "      <th>5_error_X</th>\n",
       "      <th>5_error_Y</th>\n",
       "      <th>5_time</th>\n",
       "      <th>5_time_percent</th>\n",
       "      <th>6_error_Angle</th>\n",
       "      <th>6_error_X</th>\n",
       "      <th>6_error_Y</th>\n",
       "      <th>6_time</th>\n",
       "      <th>6_time_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-37.135151</td>\n",
       "      <td>165.406860</td>\n",
       "      <td>112.532249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.817537e+12</td>\n",
       "      <td>-36.137890</td>\n",
       "      <td>136.607895</td>\n",
       "      <td>66.360245</td>\n",
       "      <td>4.547474e-13</td>\n",
       "      <td>2.792253e+12</td>\n",
       "      <td>-38.715382</td>\n",
       "      <td>166.808792</td>\n",
       "      <td>37.174133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.795981e+12</td>\n",
       "      <td>-37.481899</td>\n",
       "      <td>153.018341</td>\n",
       "      <td>18.841827</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.793537e+12</td>\n",
       "      <td>-36.377716</td>\n",
       "      <td>145.687134</td>\n",
       "      <td>14.428772</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.795545e+12</td>\n",
       "      <td>-33.987568</td>\n",
       "      <td>139.788254</td>\n",
       "      <td>-6.930649</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2831453000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-37.147095</td>\n",
       "      <td>167.445312</td>\n",
       "      <td>113.975128</td>\n",
       "      <td>0.123488</td>\n",
       "      <td>2.817637e+12</td>\n",
       "      <td>-36.116817</td>\n",
       "      <td>136.552307</td>\n",
       "      <td>66.358673</td>\n",
       "      <td>1.126841e-01</td>\n",
       "      <td>2.792345e+12</td>\n",
       "      <td>-38.695744</td>\n",
       "      <td>168.134949</td>\n",
       "      <td>37.538925</td>\n",
       "      <td>0.088120</td>\n",
       "      <td>2.796053e+12</td>\n",
       "      <td>-37.403046</td>\n",
       "      <td>153.671051</td>\n",
       "      <td>18.952454</td>\n",
       "      <td>0.141293</td>\n",
       "      <td>2.793657e+12</td>\n",
       "      <td>-36.337376</td>\n",
       "      <td>146.841003</td>\n",
       "      <td>14.868301</td>\n",
       "      <td>0.141687</td>\n",
       "      <td>2.795657e+12</td>\n",
       "      <td>-33.938217</td>\n",
       "      <td>139.838074</td>\n",
       "      <td>-6.820221</td>\n",
       "      <td>0.137153</td>\n",
       "      <td>2831569000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-37.159042</td>\n",
       "      <td>169.483780</td>\n",
       "      <td>115.417999</td>\n",
       "      <td>0.242037</td>\n",
       "      <td>2.817733e+12</td>\n",
       "      <td>-36.095741</td>\n",
       "      <td>136.496719</td>\n",
       "      <td>66.357101</td>\n",
       "      <td>2.302676e-01</td>\n",
       "      <td>2.792441e+12</td>\n",
       "      <td>-38.676105</td>\n",
       "      <td>169.461105</td>\n",
       "      <td>37.903732</td>\n",
       "      <td>0.342688</td>\n",
       "      <td>2.796261e+12</td>\n",
       "      <td>-37.324192</td>\n",
       "      <td>154.323776</td>\n",
       "      <td>19.063080</td>\n",
       "      <td>0.244907</td>\n",
       "      <td>2.793745e+12</td>\n",
       "      <td>-36.297031</td>\n",
       "      <td>147.994873</td>\n",
       "      <td>15.307831</td>\n",
       "      <td>0.253013</td>\n",
       "      <td>2.795745e+12</td>\n",
       "      <td>-33.888863</td>\n",
       "      <td>139.887909</td>\n",
       "      <td>-6.709793</td>\n",
       "      <td>0.288495</td>\n",
       "      <td>2831697000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-37.170990</td>\n",
       "      <td>171.522247</td>\n",
       "      <td>116.860878</td>\n",
       "      <td>0.400102</td>\n",
       "      <td>2.817861e+12</td>\n",
       "      <td>-36.074665</td>\n",
       "      <td>136.441132</td>\n",
       "      <td>66.355530</td>\n",
       "      <td>3.625490e-01</td>\n",
       "      <td>2.792549e+12</td>\n",
       "      <td>-38.656471</td>\n",
       "      <td>170.787247</td>\n",
       "      <td>38.268524</td>\n",
       "      <td>0.445494</td>\n",
       "      <td>2.796345e+12</td>\n",
       "      <td>-37.245338</td>\n",
       "      <td>154.976501</td>\n",
       "      <td>19.173706</td>\n",
       "      <td>0.381490</td>\n",
       "      <td>2.793861e+12</td>\n",
       "      <td>-36.256687</td>\n",
       "      <td>149.148743</td>\n",
       "      <td>15.747375</td>\n",
       "      <td>0.369399</td>\n",
       "      <td>2.795837e+12</td>\n",
       "      <td>-33.839508</td>\n",
       "      <td>139.937729</td>\n",
       "      <td>-6.599365</td>\n",
       "      <td>0.402001</td>\n",
       "      <td>2831793000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-37.182938</td>\n",
       "      <td>173.560699</td>\n",
       "      <td>118.303749</td>\n",
       "      <td>0.498892</td>\n",
       "      <td>2.817941e+12</td>\n",
       "      <td>-36.053593</td>\n",
       "      <td>136.385544</td>\n",
       "      <td>66.353958</td>\n",
       "      <td>4.801324e-01</td>\n",
       "      <td>2.792645e+12</td>\n",
       "      <td>-38.636833</td>\n",
       "      <td>172.113403</td>\n",
       "      <td>38.633316</td>\n",
       "      <td>0.548300</td>\n",
       "      <td>2.796429e+12</td>\n",
       "      <td>-37.166485</td>\n",
       "      <td>155.629227</td>\n",
       "      <td>19.284332</td>\n",
       "      <td>0.480395</td>\n",
       "      <td>2.793945e+12</td>\n",
       "      <td>-36.216347</td>\n",
       "      <td>150.302612</td>\n",
       "      <td>16.186905</td>\n",
       "      <td>0.480725</td>\n",
       "      <td>2.795925e+12</td>\n",
       "      <td>-33.790157</td>\n",
       "      <td>139.987564</td>\n",
       "      <td>-6.488953</td>\n",
       "      <td>0.496590</td>\n",
       "      <td>2831873000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-41.241192</td>\n",
       "      <td>13.608063</td>\n",
       "      <td>6.705078</td>\n",
       "      <td>21.925622</td>\n",
       "      <td>2849997000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-41.115150</td>\n",
       "      <td>13.375793</td>\n",
       "      <td>8.201538</td>\n",
       "      <td>22.006022</td>\n",
       "      <td>2850065000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-40.492508</td>\n",
       "      <td>11.115021</td>\n",
       "      <td>10.715134</td>\n",
       "      <td>22.110070</td>\n",
       "      <td>2850153000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-41.592968</td>\n",
       "      <td>11.390533</td>\n",
       "      <td>10.273819</td>\n",
       "      <td>22.242494</td>\n",
       "      <td>2850265000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-40.773800</td>\n",
       "      <td>10.156555</td>\n",
       "      <td>12.096710</td>\n",
       "      <td>22.356000</td>\n",
       "      <td>2850361000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     1_error_Angle   1_error_X   1_error_Y    1_time  1_time_percent  \\\n",
       "0       -37.135151  165.406860  112.532249  0.000000    2.817537e+12   \n",
       "1       -37.147095  167.445312  113.975128  0.123488    2.817637e+12   \n",
       "2       -37.159042  169.483780  115.417999  0.242037    2.817733e+12   \n",
       "3       -37.170990  171.522247  116.860878  0.400102    2.817861e+12   \n",
       "4       -37.182938  173.560699  118.303749  0.498892    2.817941e+12   \n",
       "..             ...         ...         ...       ...             ...   \n",
       "187            NaN         NaN         NaN       NaN             NaN   \n",
       "188            NaN         NaN         NaN       NaN             NaN   \n",
       "189            NaN         NaN         NaN       NaN             NaN   \n",
       "190            NaN         NaN         NaN       NaN             NaN   \n",
       "191            NaN         NaN         NaN       NaN             NaN   \n",
       "\n",
       "     2_error_Angle   2_error_X  2_error_Y        2_time  2_time_percent  \\\n",
       "0       -36.137890  136.607895  66.360245  4.547474e-13    2.792253e+12   \n",
       "1       -36.116817  136.552307  66.358673  1.126841e-01    2.792345e+12   \n",
       "2       -36.095741  136.496719  66.357101  2.302676e-01    2.792441e+12   \n",
       "3       -36.074665  136.441132  66.355530  3.625490e-01    2.792549e+12   \n",
       "4       -36.053593  136.385544  66.353958  4.801324e-01    2.792645e+12   \n",
       "..             ...         ...        ...           ...             ...   \n",
       "187            NaN         NaN        NaN           NaN             NaN   \n",
       "188            NaN         NaN        NaN           NaN             NaN   \n",
       "189            NaN         NaN        NaN           NaN             NaN   \n",
       "190            NaN         NaN        NaN           NaN             NaN   \n",
       "191            NaN         NaN        NaN           NaN             NaN   \n",
       "\n",
       "     3_error_Angle   3_error_X  3_error_Y    3_time  3_time_percent  \\\n",
       "0       -38.715382  166.808792  37.174133  0.000000    2.795981e+12   \n",
       "1       -38.695744  168.134949  37.538925  0.088120    2.796053e+12   \n",
       "2       -38.676105  169.461105  37.903732  0.342688    2.796261e+12   \n",
       "3       -38.656471  170.787247  38.268524  0.445494    2.796345e+12   \n",
       "4       -38.636833  172.113403  38.633316  0.548300    2.796429e+12   \n",
       "..             ...         ...        ...       ...             ...   \n",
       "187            NaN         NaN        NaN       NaN             NaN   \n",
       "188            NaN         NaN        NaN       NaN             NaN   \n",
       "189            NaN         NaN        NaN       NaN             NaN   \n",
       "190            NaN         NaN        NaN       NaN             NaN   \n",
       "191            NaN         NaN        NaN       NaN             NaN   \n",
       "\n",
       "     4_error_Angle   4_error_X  4_error_Y    4_time  4_time_percent  \\\n",
       "0       -37.481899  153.018341  18.841827  0.000000    2.793537e+12   \n",
       "1       -37.403046  153.671051  18.952454  0.141293    2.793657e+12   \n",
       "2       -37.324192  154.323776  19.063080  0.244907    2.793745e+12   \n",
       "3       -37.245338  154.976501  19.173706  0.381490    2.793861e+12   \n",
       "4       -37.166485  155.629227  19.284332  0.480395    2.793945e+12   \n",
       "..             ...         ...        ...       ...             ...   \n",
       "187            NaN         NaN        NaN       NaN             NaN   \n",
       "188            NaN         NaN        NaN       NaN             NaN   \n",
       "189            NaN         NaN        NaN       NaN             NaN   \n",
       "190            NaN         NaN        NaN       NaN             NaN   \n",
       "191            NaN         NaN        NaN       NaN             NaN   \n",
       "\n",
       "     5_error_Angle   5_error_X  5_error_Y    5_time  5_time_percent  \\\n",
       "0       -36.377716  145.687134  14.428772  0.000000    2.795545e+12   \n",
       "1       -36.337376  146.841003  14.868301  0.141687    2.795657e+12   \n",
       "2       -36.297031  147.994873  15.307831  0.253013    2.795745e+12   \n",
       "3       -36.256687  149.148743  15.747375  0.369399    2.795837e+12   \n",
       "4       -36.216347  150.302612  16.186905  0.480725    2.795925e+12   \n",
       "..             ...         ...        ...       ...             ...   \n",
       "187            NaN         NaN        NaN       NaN             NaN   \n",
       "188            NaN         NaN        NaN       NaN             NaN   \n",
       "189            NaN         NaN        NaN       NaN             NaN   \n",
       "190            NaN         NaN        NaN       NaN             NaN   \n",
       "191            NaN         NaN        NaN       NaN             NaN   \n",
       "\n",
       "     6_error_Angle   6_error_X  6_error_Y     6_time  6_time_percent  \n",
       "0       -33.987568  139.788254  -6.930649   0.000000   2831453000000  \n",
       "1       -33.938217  139.838074  -6.820221   0.137153   2831569000000  \n",
       "2       -33.888863  139.887909  -6.709793   0.288495   2831697000000  \n",
       "3       -33.839508  139.937729  -6.599365   0.402001   2831793000000  \n",
       "4       -33.790157  139.987564  -6.488953   0.496590   2831873000000  \n",
       "..             ...         ...        ...        ...             ...  \n",
       "187     -41.241192   13.608063   6.705078  21.925622   2849997000000  \n",
       "188     -41.115150   13.375793   8.201538  22.006022   2850065000000  \n",
       "189     -40.492508   11.115021  10.715134  22.110070   2850153000000  \n",
       "190     -41.592968   11.390533  10.273819  22.242494   2850265000000  \n",
       "191     -40.773800   10.156555  12.096710  22.356000   2850361000000  \n",
       "\n",
       "[192 rows x 30 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "happy-october",
   "metadata": {
    "scrolled": true
   },
   "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>Time</th>\n",
       "      <th>Force [N]</th>\n",
       "      <th>Error_X</th>\n",
       "      <th>Error_Y</th>\n",
       "      <th>Error_Angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>165.406860</td>\n",
       "      <td>112.532249</td>\n",
       "      <td>-37.135151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.123488</td>\n",
       "      <td>1</td>\n",
       "      <td>167.445312</td>\n",
       "      <td>113.975128</td>\n",
       "      <td>-37.147095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.242037</td>\n",
       "      <td>1</td>\n",
       "      <td>169.483780</td>\n",
       "      <td>115.417999</td>\n",
       "      <td>-37.159042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.400102</td>\n",
       "      <td>1</td>\n",
       "      <td>171.522247</td>\n",
       "      <td>116.860878</td>\n",
       "      <td>-37.170990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.498892</td>\n",
       "      <td>1</td>\n",
       "      <td>173.560699</td>\n",
       "      <td>118.303749</td>\n",
       "      <td>-37.182938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>21.925622</td>\n",
       "      <td>6</td>\n",
       "      <td>13.608063</td>\n",
       "      <td>6.705078</td>\n",
       "      <td>-41.241192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>22.006022</td>\n",
       "      <td>6</td>\n",
       "      <td>13.375793</td>\n",
       "      <td>8.201538</td>\n",
       "      <td>-41.115150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>22.110070</td>\n",
       "      <td>6</td>\n",
       "      <td>11.115021</td>\n",
       "      <td>10.715134</td>\n",
       "      <td>-40.492508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>22.242494</td>\n",
       "      <td>6</td>\n",
       "      <td>11.390533</td>\n",
       "      <td>10.273819</td>\n",
       "      <td>-41.592968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>22.356000</td>\n",
       "      <td>6</td>\n",
       "      <td>10.156555</td>\n",
       "      <td>12.096710</td>\n",
       "      <td>-40.773800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time Force [N]     Error_X     Error_Y  Error_Angle\n",
       "0      0.000000         1  165.406860  112.532249   -37.135151\n",
       "1      0.123488         1  167.445312  113.975128   -37.147095\n",
       "2      0.242037         1  169.483780  115.417999   -37.159042\n",
       "3      0.400102         1  171.522247  116.860878   -37.170990\n",
       "4      0.498892         1  173.560699  118.303749   -37.182938\n",
       "...         ...       ...         ...         ...          ...\n",
       "1009  21.925622         6   13.608063    6.705078   -41.241192\n",
       "1010  22.006022         6   13.375793    8.201538   -41.115150\n",
       "1011  22.110070         6   11.115021   10.715134   -40.492508\n",
       "1012  22.242494         6   11.390533   10.273819   -41.592968\n",
       "1013  22.356000         6   10.156555   12.096710   -40.773800\n",
       "\n",
       "[1014 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "controllers = pd.DataFrame(columns=[\"Time\", \"Force [N]\", \"Error_X\", \"Error_Y\", \"Error_Angle\"])\n",
    "\n",
    "for test in controller_times:\n",
    "    control = pd.DataFrame(columns=[\"Time\", \"Force [N]\", \"Error_X\", \"Error_Y\", \"Error_Angle\"])\n",
    "    row_num = data.apply(lambda column: column.dropna().index[-1])[test+\"_time\"]\n",
    "    \n",
    "    control[\"Time\"] = data[test+\"_time\"][0:row_num+1]\n",
    "    control[\"Error_X\"] = data[test+\"_error_X\"][0:row_num+1]\n",
    "    control[\"Error_Y\"] = data[test+\"_error_Y\"][0:row_num+1]\n",
    "    control[\"Error_Angle\"] = data[test+\"_error_Angle\"][0:row_num+1]\n",
    "    control[\"Force [N]\"] = test\n",
    "    \n",
    "    controllers = controllers.append(control, ignore_index = True)\n",
    "\n",
    "    del control\n",
    "\n",
    "controllers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "nominated-people",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Controller Errors with Low Illumination and Wind Disturbances')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timesfont = {'fontname':'Times New Roman'}\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "fig = plt.figure(constrained_layout=True, figsize=(10,3))\n",
    "\n",
    "gs = GridSpec(1, 3, figure=fig)\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax2 = fig.add_subplot(gs[0, 1])\n",
    "ax3 = fig.add_subplot(gs[0, 2])\n",
    "\n",
    "x = sns.lineplot(data=controllers, x=\"Time\", y=\"Error_X\",\n",
    "             hue=\"Force [N]\", ax=ax1)\n",
    "y = sns.lineplot(data=controllers, x=\"Time\", y=\"Error_Y\",\n",
    "             hue=\"Force [N]\", ax=ax2)\n",
    "angle = sns.lineplot(data=controllers, x=\"Time\", y=\"Error_Angle\",\n",
    "             hue=\"Force [N]\", ax=ax3)\n",
    "\n",
    "x.axhline(y=0, linewidth=1.5, color='grey', ls='--', alpha=0.75)\n",
    "y.axhline(y=0, linewidth=1.5, color='grey', ls='--', alpha=0.75)\n",
    "angle.axhline(y=0, linewidth=1.5, color='grey', ls='--', alpha=0.75)\n",
    "\n",
    "\n",
    "x.set_title(\"Error in the X distance\", fontsize=12, **timesfont)\n",
    "y.set_title(\"Error in the Y distance\", fontsize=12, **timesfont)\n",
    "angle.set_title(\"Error in the rotation angle\", fontsize=12, **timesfont)\n",
    "\n",
    "x.set_xlabel(\"time [s]\\n(a)\", fontsize=12, **timesfont)\n",
    "y.set_xlabel(\"time [s]\\n(b)\", fontsize=12, **timesfont)\n",
    "angle.set_xlabel(\"time [s]\\n(c)\", fontsize=12, **timesfont)\n",
    "\n",
    "x.set_ylabel(\"X error [px]\", fontsize=12, **timesfont)\n",
    "y.set_ylabel(\"Y error [px]\", fontsize=12, **timesfont)\n",
    "angle.set_ylabel(\"Angle error [deg]\", fontsize=12, **timesfont)\n",
    "\n",
    "\n",
    "fig.suptitle('Controller Errors with Low Illumination and Wind Disturbances', fontsize=16, fontweight='bold', **timesfont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "trying-lambda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "political-pasta",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "broke-edition",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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