ELH_Presentation+Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'height': 600, 'scroll': True, 'width': 1000}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This cell is used to change parameter of the rise slideshow, \n",
    "# such as the window width/height and enabling a scroll bar\n",
    "\n",
    "from notebook.services.config import ConfigManager\n",
    "cm = ConfigManager()\n",
    "cm.update('livereveal', {\n",
    "              'width': 1000,\n",
    "              'height': 600,\n",
    "              'scroll': True,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# OGTC Hackathon: Data analysis from Tesla vehicle\n",
    "* by: Elon's Little Helpers\n",
    "    * Chinedu Pascal Ezenkwu\n",
    "    * Carlos Moreno-Garcia\n",
    "    * John Guntoro\n",
    "    * Joseph Sheratt\n",
    "    * Darren Nicol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Problem Setting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* By reverse-engineering the Tesla API, Intelligent Plant was able to download the data historian of the usage of a Tesla car (three months)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* $\\approx182$ variables can be obtained."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "*  More information of these variable can be found [here](https://tesla-api.timdorr.com) (unofficial)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Some information that can be obtained:\n",
    "    * Screen status\n",
    "    * Battery charge\n",
    "    * Odometer data\n",
    "    * Heating\n",
    "    * Windshields\n",
    "    * Fan\n",
    "    * And much more!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Gestalt tool API\n",
    "* By querying $*car*$, one can visualise the different variables:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "![Fig 1.](figs/fig1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* You can plot different variables to see the performance across time:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![Fig. 2.](figs/fig2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* The spike-down is a moment where no data was recorded."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![Fig. 3.](figs/fig3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* All data that falls above 0 is considered screen on (i.e. car on)\n",
    "* 8 is a mode called \"dog mode\", which monitors an animal inside the car!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Installing the necessary packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%pip install pandas matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Importing the necessary packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import intelligent_plant.app_store_client as app_store_client\n",
    "import intelligent_plant.utility as utility\n",
    "from os.path import expanduser\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from math import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Getting the data from the repository**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "app_store = app_store_client.AppStoreClient(open(expanduser(\"~/.access_token\"), \"r\").read())\n",
    "data_core = app_store.get_data_core_client()\n",
    "app_store.get_data_core_client()\n",
    "app_store.get_user_info()\n",
    "data_core.get_tags(\"FCBB05262EADC0B147746EE6DFB2B3EA5C272C33C2C5E3FE8F473D85529461CA.Edge Historian\")\n",
    "tags = {\n",
    "    \"FCBB05262EADC0B147746EE6DFB2B3EA5C272C33C2C5E3FE8F473D85529461CA.Edge Historian\": [\n",
    "        \"StevesCar..response.vehicle_state.odometer\",\n",
    "        \"StevesCar..response.charge_state.battery_level\",\n",
    "        \"StevesCar..response.climate_state.outside_temp\",\n",
    "        \"StevesCar..response.vehicle_state.center_display_state\",\n",
    "        \"StevesCar..response.drive_state.speed\",\n",
    "        \"StevesCar..response.climate_state.seat_heater_right\"\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Data filtering**\n",
    "* All interpolated data from 10 days ago in 3-minute intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  'StevesCar..response.climate_state.outside_temp': {'TagName': 'StevesCar..response.climate_state.outside_temp',\n",
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       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.climate_state.outside_temp',\n",
       "     'UtcSampleTime': '2019-11-22T13:56:28.7713825Z',\n",
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       "    {'TagName': 'StevesCar..response.climate_state.outside_temp',\n",
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       "    {'TagName': 'StevesCar..response.climate_state.outside_temp',\n",
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       "     'Notes': None,\n",
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       "     'HasError': False,\n",
       "     'Properties': {}},\n",
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       "     'Properties': {}},\n",
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       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-11-24T15:59:55.9192705Z',\n",
       "     'NumericValue': 72.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '72',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-11-24T16:05:11.0093115Z',\n",
       "     'NumericValue': 71.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '71',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-11-26T09:43:33.3551209Z',\n",
       "     'NumericValue': 46.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '46',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-11-26T10:36:20.1894469Z',\n",
       "     'NumericValue': 90.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '90',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-11-28T20:04:02.0752453Z',\n",
       "     'NumericValue': 56.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '56',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-11-29T07:47:08.6577508Z',\n",
       "     'NumericValue': 55.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '55',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-11-29T12:48:48.4670795Z',\n",
       "     'NumericValue': 44.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '44',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-11-29T13:48:05.7480733Z',\n",
       "     'NumericValue': 90.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '90',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-12-02T13:05:46.9329661Z',\n",
       "     'NumericValue': 79.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '79',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-12-02T13:05:46.9329661Z',\n",
       "     'NumericValue': 79.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '79',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}},\n",
       "    {'TagName': 'StevesCar..response.charge_state.battery_level',\n",
       "     'UtcSampleTime': '2019-12-02T13:07:32.0269072Z',\n",
       "     'NumericValue': 78.0,\n",
       "     'IsNumeric': True,\n",
       "     'TextValue': '78',\n",
       "     'Status': 'Good',\n",
       "     'Unit': '',\n",
       "     'Notes': None,\n",
       "     'Error': None,\n",
       "     'HasError': False,\n",
       "     'Properties': {}}]}}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resp = data_core.get_processed_data(tags, \"*-10d\", \"*\", \"3m\", \"interp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Plotting odometer data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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": [
    "df = utility.query_result_to_data_frame(resp)\n",
    "plt.plot(df[\"StevesCar..response.vehicle_state.odometer\"])\n",
    "plt.xlabel('Data Index')\n",
    "plt.ylabel('Odometer miles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Creation of a *new* differential dataset** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "type(df.loc[0].TimeStamp)\n",
    "df.loc[1].TimeStamp-df.loc[0].TimeStamp\n",
    "df_new=[]\n",
    "for i in range(1,len(df)-1):\n",
    "    df_new.append(df.loc[i]-df.loc[i-1])\n",
    "df_new =pd.DataFrame(df_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* This dataset could be used for time series analysis purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Distance travelled**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Data Index')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(df_new[\"StevesCar..response.vehicle_state.odometer\"])\n",
    "plt.ylabel('Distance travelled in time intervaled (miles)')\n",
    "plt.xlabel('Data Index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* By plotting the differential of the odometer, we can see the distance travelled (in miles) for every entry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Plotting the differential data on the battery level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Data Index')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(df_new[\"StevesCar..response.charge_state.battery_level\"])\n",
    "plt.ylabel('Change in Battery % in time interval')\n",
    "plt.xlabel('Data Index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* Negative entries occur when battery discharges and vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Plotting efficiency**\n",
    "* We created our own efficiency metric as the ratio between battery level differential and odometer differential."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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": [
    "df_new['Efficiency'] = df_new[\"StevesCar..response.charge_state.battery_level\"]/df_new[\"StevesCar..response.vehicle_state.odometer\"]\n",
    "plt.plot(df_new['Efficiency'], 'bo')\n",
    "plt.ylim([-1, 0])\n",
    "plt.ylabel('Battery % drop per mile')\n",
    "plt.xlabel('Data Index') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* Data seems to be random as for each entry, we obtain a very spread distribution.\n",
    "* We only want to see efficiency from 0 to -1, as others would be considered outliers\n",
    "    * Very huge or very little efficiencies don't make sense, as you cannot have more battery level than actual mileage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Another view of the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fde6f5f6b00>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df_new.replace(inf, 0)\n",
    "df_new.replace(-inf, 0)\n",
    "plt.plot(df_new['Efficiency'], 'bo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* Here we can see the presence of outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Histogram of efficiency metric**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: invalid value encountered in greater\n",
      "  import sys\n"
     ]
    },
    {
     "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": [
    "eff=np.array(df_new[\"Efficiency\"])\n",
    "sh=np.array(df_new[\"StevesCar..response.climate_state.seat_heater_right\"])\n",
    "# Limit the efficiency between 0 and -3\n",
    "eff2= eff[np.where(eff>-3)]\n",
    "eff3 = eff2[np.where(eff2<0)]\n",
    "plt.hist(eff3,bins=20)\n",
    "plt.xlabel('Battery % drop per mile')\n",
    "plt.ylabel('Number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Efficiency vs other variables**\n",
    "* Here we compare our efficiency metric against other variables, such as speed or outside temp.\n",
    "* Purpose: Try to find correlations.\n",
    "* Speed may show some correlation, outside temperature does not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in greater\n",
      "  after removing the cwd from sys.path.\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in greater\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "temp = np.array(df[\"StevesCar..response.climate_state.outside_temp\"])[0:-2]\n",
    "speed = np.array(df[\"StevesCar..response.drive_state.speed\"])[0:-2]\n",
    "\n",
    "temp2 = temp[np.where(eff>-3)]\n",
    "temp3 = temp2[np.where(eff2<0)]\n",
    "speed2 = speed[np.where(eff>-3)]\n",
    "speed3 = speed2[np.where(eff2<0)]\n",
    "\n",
    "plt.plot(speed3, eff3, 'bo')\n",
    "plt.xlim([0, 60])\n",
    "plt.ylabel('Battery % drop per mile')\n",
    "plt.xlabel('Speed mph')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(temp3, eff3, 'ro')\n",
    "plt.ylabel('Battery % drop per mile')\n",
    "plt.xlabel('Outside Temp degC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Measuring the effect of heating the driver's seat**\n",
    "* We were also able to isolate this measurement and compare efficiency w.r.t. right seat heated/unheated.\n",
    "* There does not seem to be a change!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2796\n",
      "1922\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: invalid value encountered in greater\n",
      "  import sys\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in greater\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "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": [
    "sh = np.array(df[\"StevesCar..response.climate_state.seat_heater_right\"])[0:-2]\n",
    "\n",
    "eff_without_sh = eff[np.where(sh==0)]\n",
    "eff_with_sh = eff[np.where(sh>0)]\n",
    "\n",
    "print(len(eff_without_sh))\n",
    "print(len(eff_with_sh))\n",
    "\n",
    "eff2_without= eff_without_sh[np.where(eff_without_sh>-2)]\n",
    "eff3_without = eff2_without[np.where(eff2_without<0)]\n",
    "\n",
    "speed_without = speed[np.where(sh==0)]\n",
    "speed2_without = speed_without[np.where(eff_without_sh>-2)]\n",
    "speed3_without = speed2_without[np.where(eff2_without<0)]\n",
    "\n",
    "eff2_with= eff_with_sh[np.where(eff_with_sh>-2)]\n",
    "eff3_with = eff2_with[np.where(eff2_with<0)]\n",
    "\n",
    "speed_with = speed[np.where(sh>0)]\n",
    "speed2_with = speed_with[np.where(eff_with_sh>-2)]\n",
    "speed3_with = speed2_with[np.where(eff2_with<0)]\n",
    "\n",
    "plt.plot(speed3_without, eff3_without, 'bo', label='Without Seat Heater')\n",
    "plt.plot(speed3_with, eff3_with, 'ro',label='With Seat Heater')\n",
    "plt.xlim([0, 60])\n",
    "plt.xlabel('Speed mph')\n",
    "plt.ylabel('Battery % drop per mile')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# If we had more time..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Investigate the dataset in-depth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Discover new relations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Train a regression model to predict efficiency"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "hide_input": false,
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}