COTA_Telemetry.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd #To read the dataset\n",
    "import time\n",
    "from IPython import display\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.animation as animation\n",
    "from matplotlib.colors import LogNorm\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Internal Batte</th>\n",
       "      <th>Vertical_acc</th>\n",
       "      <th>Longitudinal_a</th>\n",
       "      <th>Lateral_acc</th>\n",
       "      <th>External Batte</th>\n",
       "      <th>RPM</th>\n",
       "      <th>TPS</th>\n",
       "      <th>BANKING</th>\n",
       "      <th>...</th>\n",
       "      <th>CornerG</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>TotalG</th>\n",
       "      <th>LeanAngleGPS</th>\n",
       "      <th>LeanAngleDataMC</th>\n",
       "      <th>LeanAngleECU</th>\n",
       "      <th>GPS_Latitude</th>\n",
       "      <th>GPS_Longitude</th>\n",
       "      <th>GPS_Elevation</th>\n",
       "      <th>Unnamed: 65</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.169</td>\n",
       "      <td>1.288091</td>\n",
       "      <td>-0.123282</td>\n",
       "      <td>0.003711</td>\n",
       "      <td>14.180549</td>\n",
       "      <td>13313.349609</td>\n",
       "      <td>51.396000</td>\n",
       "      <td>-8.74</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087332</td>\n",
       "      <td>0.121746</td>\n",
       "      <td>0.087332</td>\n",
       "      <td>-1.663606</td>\n",
       "      <td>-4.990935</td>\n",
       "      <td>1.577491</td>\n",
       "      <td>30.131669</td>\n",
       "      <td>-97.639615</td>\n",
       "      <td>151.67</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.022</td>\n",
       "      <td>4.169</td>\n",
       "      <td>1.141163</td>\n",
       "      <td>-0.218517</td>\n",
       "      <td>0.002015</td>\n",
       "      <td>14.180948</td>\n",
       "      <td>12944.599609</td>\n",
       "      <td>27.903999</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098262</td>\n",
       "      <td>0.108590</td>\n",
       "      <td>0.105804</td>\n",
       "      <td>-1.673375</td>\n",
       "      <td>-5.611898</td>\n",
       "      <td>1.940414</td>\n",
       "      <td>30.131631</td>\n",
       "      <td>-97.639559</td>\n",
       "      <td>151.79</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.045</td>\n",
       "      <td>4.169</td>\n",
       "      <td>0.947527</td>\n",
       "      <td>-0.409658</td>\n",
       "      <td>0.006668</td>\n",
       "      <td>14.181349</td>\n",
       "      <td>12568.500000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>-9.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.104591</td>\n",
       "      <td>0.154438</td>\n",
       "      <td>0.154438</td>\n",
       "      <td>-1.641607</td>\n",
       "      <td>-5.970906</td>\n",
       "      <td>2.229481</td>\n",
       "      <td>30.131593</td>\n",
       "      <td>-97.639503</td>\n",
       "      <td>151.92</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.067</td>\n",
       "      <td>4.169</td>\n",
       "      <td>0.800068</td>\n",
       "      <td>-0.573618</td>\n",
       "      <td>0.008480</td>\n",
       "      <td>14.181748</td>\n",
       "      <td>12874.299805</td>\n",
       "      <td>8.648000</td>\n",
       "      <td>-8.63</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102730</td>\n",
       "      <td>0.242858</td>\n",
       "      <td>0.242858</td>\n",
       "      <td>-1.574522</td>\n",
       "      <td>-5.865326</td>\n",
       "      <td>2.674824</td>\n",
       "      <td>30.131555</td>\n",
       "      <td>-97.639447</td>\n",
       "      <td>152.08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.090</td>\n",
       "      <td>4.169</td>\n",
       "      <td>0.737422</td>\n",
       "      <td>-0.566797</td>\n",
       "      <td>0.007098</td>\n",
       "      <td>14.182149</td>\n",
       "      <td>12758.750000</td>\n",
       "      <td>8.752001</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090436</td>\n",
       "      <td>0.343874</td>\n",
       "      <td>0.343874</td>\n",
       "      <td>-1.483905</td>\n",
       "      <td>-5.167118</td>\n",
       "      <td>2.681565</td>\n",
       "      <td>30.131517</td>\n",
       "      <td>-97.639392</td>\n",
       "      <td>152.28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time  Distance  Internal Batte  Vertical_acc  Longitudinal_a  Lateral_acc  \\\n",
       "0   0.0     0.000           4.169      1.288091       -0.123282     0.003711   \n",
       "1   0.1     0.022           4.169      1.141163       -0.218517     0.002015   \n",
       "2   0.2     0.045           4.169      0.947527       -0.409658     0.006668   \n",
       "3   0.3     0.067           4.169      0.800068       -0.573618     0.008480   \n",
       "4   0.4     0.090           4.169      0.737422       -0.566797     0.007098   \n",
       "\n",
       "   External Batte           RPM        TPS  BANKING     ...        CornerG  \\\n",
       "0       14.180549  13313.349609  51.396000    -8.74     ...       0.087332   \n",
       "1       14.180948  12944.599609  27.903999    -9.63     ...       0.098262   \n",
       "2       14.181349  12568.500000   8.500000    -9.00     ...       0.104591   \n",
       "3       14.181748  12874.299805   8.648000    -8.63     ...       0.102730   \n",
       "4       14.182149  12758.750000   8.752001    -8.00     ...       0.090436   \n",
       "\n",
       "   Acceleration    TotalG  LeanAngleGPS  LeanAngleDataMC  LeanAngleECU  \\\n",
       "0      0.121746  0.087332     -1.663606        -4.990935      1.577491   \n",
       "1      0.108590  0.105804     -1.673375        -5.611898      1.940414   \n",
       "2      0.154438  0.154438     -1.641607        -5.970906      2.229481   \n",
       "3      0.242858  0.242858     -1.574522        -5.865326      2.674824   \n",
       "4      0.343874  0.343874     -1.483905        -5.167118      2.681565   \n",
       "\n",
       "   GPS_Latitude  GPS_Longitude  GPS_Elevation  Unnamed: 65  \n",
       "0     30.131669     -97.639615         151.67          NaN  \n",
       "1     30.131631     -97.639559         151.79          NaN  \n",
       "2     30.131593     -97.639503         151.92          NaN  \n",
       "3     30.131555     -97.639447         152.08          NaN  \n",
       "4     30.131517     -97.639392         152.28          NaN  \n",
       "\n",
       "[5 rows x 66 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('KyMa_S1000RR_20171029_011.csv')\n",
    "data = data[:1620]\n",
    "points = data.as_matrix(['Distance', 'GPS_Speed'])\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9eba3d7ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(points[:,0], points[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}