MatlabEDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to explore this data set a module in the collector has been created, the idea of the module is to be able to collect store and analyze data properly, to import the module just add the `colector` path to the current `sys.path`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import sys \n",
    "sys.path.append('collector')\n",
    "\n",
    "from collector.matlab import GetData\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case you want to obtain more details on the class and its methods "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GetData??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For usage, provide a `matlab` path and initialize the class with the corresponding path: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matlab path \n",
    "matlab_path = 'data/raw/mat/session_4_D_2018-09-03.mat'\n",
    "veh = GetData(matlab_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check available vehicles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Prius1', 'Prius4', 'Prius7')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veh.vehicle_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data of one of the vehicles via the `transform_vehicle_data` method, this will format data into a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get vehicle from vehicle \n",
    "\n",
    "veh.transform_data_vehicle('Prius1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check all the available subsets as the one indicated in the `Logset_Description.xlsx` file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Ublox_GPS_driver_fix',\n",
       " 'Ublox_GPS_driver_fix_velocity',\n",
       " 'base_link_accel',\n",
       " 'vehicle_gear',\n",
       " 'vehicle_odom',\n",
       " 'vehicle_pedals',\n",
       " 'vehicle_steering_wheel',\n",
       " 'world_model_front_target',\n",
       " 'vehicle_hmi',\n",
       " 'vehicle_ControllerState',\n",
       " 'APU')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veh.datakeys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may query each one of the properties directly as follows: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-09-03 17:51:48.207000</td>\n",
       "      <td>52.366409</td>\n",
       "      <td>4.700660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-09-03 17:51:48.448515</td>\n",
       "      <td>52.366436</td>\n",
       "      <td>4.700699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-09-03 17:51:48.464891</td>\n",
       "      <td>52.366463</td>\n",
       "      <td>4.700738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-09-03 17:51:48.450619</td>\n",
       "      <td>52.366490</td>\n",
       "      <td>4.700776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-03 17:51:48.468762</td>\n",
       "      <td>52.366516</td>\n",
       "      <td>4.700815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5209</th>\n",
       "      <td>2018-09-03 17:51:48.462022</td>\n",
       "      <td>52.367871</td>\n",
       "      <td>4.704871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5210</th>\n",
       "      <td>2018-09-03 17:51:48.459751</td>\n",
       "      <td>52.367889</td>\n",
       "      <td>4.704820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5211</th>\n",
       "      <td>2018-09-03 17:51:48.472907</td>\n",
       "      <td>52.367908</td>\n",
       "      <td>4.704770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5212</th>\n",
       "      <td>2018-09-03 17:51:48.432921</td>\n",
       "      <td>52.367929</td>\n",
       "      <td>4.704722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5213</th>\n",
       "      <td>2018-09-03 17:51:48.465798</td>\n",
       "      <td>52.367952</td>\n",
       "      <td>4.704675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5214 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           time   latitude  longitude\n",
       "0    2018-09-03 17:51:48.207000  52.366409   4.700660\n",
       "1    2018-09-03 17:51:48.448515  52.366436   4.700699\n",
       "2    2018-09-03 17:51:48.464891  52.366463   4.700738\n",
       "3    2018-09-03 17:51:48.450619  52.366490   4.700776\n",
       "4    2018-09-03 17:51:48.468762  52.366516   4.700815\n",
       "...                         ...        ...        ...\n",
       "5209 2018-09-03 17:51:48.462022  52.367871   4.704871\n",
       "5210 2018-09-03 17:51:48.459751  52.367889   4.704820\n",
       "5211 2018-09-03 17:51:48.472907  52.367908   4.704770\n",
       "5212 2018-09-03 17:51:48.432921  52.367929   4.704722\n",
       "5213 2018-09-03 17:51:48.465798  52.367952   4.704675\n",
       "\n",
       "[5214 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veh.Ublox_GPS_driver_fix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to plot and play 😃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "veh.Ublox_GPS_driver_fix.plot(x='longitude',y='latitude');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Transport Findings",
   "language": "python",
   "name": "cacc"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}