draft-02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Data aggregation\n",
    "This script does the data aggregation.\n",
    "\n",
    "Inputs: data in json format\n",
    "\n",
    "Output: Aggregated data in json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Import the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Data info\n",
    "* sensorType\n",
    "* valueLength\n",
    "* values\n",
    "* timestamp\n",
    "* sensorLocation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Data pre processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "allData = []\n",
    "for line in open('./data/trailer-D.json', 'r'):\n",
    "    parsed_json = json.loads(line)\n",
    "    allData.append(parsed_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'GYROSCOPE', 'LIGHT', 'TEMPERATURE', 'MAGNETOMETER', 'ACCELEROMETER', 'BAROMETER'}\n"
     ]
    }
   ],
   "source": [
    "sensors = set()\n",
    "for data in allData:\n",
    "    aa = data['sensorType']\n",
    "    sensors.update(set([aa]))\n",
    "    # print(aa)\n",
    "print(sensors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "class SensorDataALL(object):\n",
    "    sensor = \"\"\n",
    "    # The class \"constructor\" - It's actually an initializer \n",
    "    def __init__(self, sensor):\n",
    "        self.sensor = sensor\n",
    "\n",
    "class SensorDataInfo(object):\n",
    "    valueLength = None\n",
    "    values = 0\n",
    "    # dt = datetime.datetime(2012, 5, 1)\n",
    "    timestamp = datetime.datetime.now()\n",
    "    sensorLocation = \"\"\n",
    "    \n",
    "    # The class \"constructor\" - It's actually an initializer \n",
    "    def __init__(self, valueLength, values, timestamp, sensorLocation):\n",
    "        self.valueLength = valueLength\n",
    "        self.values = values\n",
    "        self.timestamp = timestamp\n",
    "        self.sensorLocation = sensorLocation\n",
    "        \n",
    "    def getvalues(self):\n",
    "        return self.values\n",
    "\n",
    "    def getvalueLength(self):\n",
    "        return self.valueLength\n",
    "    \n",
    "    def gettimestamp(self):\n",
    "        return self.timestamp\n",
    "        \n",
    "    def getsensorLocation(self):\n",
    "        return self.sensorLocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sensor_data = dict()\n",
    "for sensor in sensors:\n",
    "    sensor_data[sensor]=[]\n",
    "    for data in allData:\n",
    "        if data['sensorType'] ==sensor:\n",
    "            timestamp1 =datetime.datetime.fromtimestamp(int(data['timestamp'])/ 1000.0).strftime('%Y-%m-%d %H:%M:%S.%f')\n",
    "            sensr_obj = SensorDataInfo(data['valueLength'],data['values'],timestamp1,data['sensorLocation'])\n",
    "            sensor_data[sensor].append(sensr_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GYROSCOPE\n",
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      "LIGHT\n",
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      "2017-11-30T16:23:49.104000\n",
      "2017-11-30T16:23:51.196000\n",
      "2017-11-30T16:23:53.289000\n",
      "2017-11-30T16:23:55.381000\n",
      "2017-11-30T16:23:57.474000\n",
      "2017-11-30T16:23:59.567000\n",
      "2017-11-30T16:24:01.659000\n",
      "2017-11-30T16:24:03.819000\n",
      "2017-11-30T16:24:05.912000\n",
      "2017-11-30T16:24:08.004000\n",
      "2017-11-30T16:24:10.097000\n",
      "2017-11-30T16:24:12.189000\n",
      "2017-11-30T16:24:14.416000\n",
      "2017-11-30T16:24:16.509000\n",
      "2017-11-30T16:24:18.601000\n",
      "2017-11-30T16:24:20.694000\n",
      "2017-11-30T16:24:22.786000\n",
      "2017-11-30T16:24:25.014000\n",
      "2017-11-30T16:24:27.106000\n",
      "2017-11-30T16:24:29.199000\n",
      "2017-11-30T16:24:31.359000\n",
      "2017-11-30T16:24:33.452000\n",
      "2017-11-30T16:24:35.611000\n",
      "2017-11-30T16:24:37.772000\n",
      "2017-11-30T16:24:39.865000\n",
      "2017-11-30T16:24:41.957000\n",
      "2017-11-30T16:24:44.049000\n",
      "2017-11-30T16:24:46.209000\n",
      "2017-11-30T16:24:48.369000\n",
      "2017-11-30T16:24:50.462000\n",
      "2017-11-30T16:24:52.554000\n",
      "2017-11-30T16:24:54.647000\n",
      "2017-11-30T16:24:56.739000\n",
      "2017-11-30T16:24:58.832000\n",
      "2017-11-30T16:25:00.924000\n",
      "2017-11-30T16:25:03.017000\n",
      "2017-11-30T16:25:05.109000\n",
      "2017-11-30T16:25:07.269000\n",
      "2017-11-30T16:25:09.362000\n",
      "2017-11-30T16:25:11.454000\n",
      "2017-11-30T16:25:13.547000\n",
      "2017-11-30T16:25:15.639000\n",
      "2017-11-30T16:25:17.800000\n",
      "2017-11-30T16:25:19.892000\n",
      "2017-11-30T16:25:21.984000\n",
      "2017-11-30T16:25:24.077000\n",
      "2017-11-30T16:25:26.169000\n",
      "2017-11-30T16:25:28.262000\n",
      "2017-11-30T16:25:30.354000\n",
      "2017-11-30T16:25:32.447000\n",
      "2017-11-30T16:25:34.539000\n",
      "2017-11-30T16:25:36.632000\n",
      "2017-11-30T16:25:38.724000\n",
      "2017-11-30T16:25:40.817000\n",
      "2017-11-30T16:25:42.909000\n",
      "2017-11-30T16:25:45.002000\n",
      "2017-11-30T16:25:47.162000\n",
      "2017-11-30T16:25:49.254000\n",
      "2017-11-30T16:25:51.549000\n",
      "2017-11-30T16:25:53.709000\n",
      "2017-11-30T16:25:55.802000\n",
      "2017-11-30T16:25:57.894000\n",
      "2017-11-30T16:25:59.987000\n",
      "2017-11-30T16:26:02.079000\n",
      "2017-11-30T16:26:04.172000\n",
      "2017-11-30T16:26:06.264000\n",
      "2017-11-30T16:26:08.357000\n",
      "2017-11-30T16:26:10.449000\n",
      "2017-11-30T16:26:12.542000\n",
      "2017-11-30T16:26:14.634000\n",
      "2017-11-30T16:26:16.727000\n",
      "2017-11-30T16:26:18.820000\n",
      "2017-11-30T16:26:20.912000\n",
      "2017-11-30T16:26:23.004000\n",
      "2017-11-30T16:26:25.097000\n",
      "2017-11-30T16:26:27.324000\n",
      "2017-11-30T16:26:29.417000\n",
      "2017-11-30T16:26:31.509000\n",
      "ACCELEROMETER\n",
      "2017-11-30T16:19:51.639000\n",
      "2017-11-30T16:19:53.799000\n",
      "2017-11-30T16:19:55.892000\n",
      "2017-11-30T16:19:57.984000\n",
      "2017-11-30T16:20:00.212000\n",
      "2017-11-30T16:20:02.304000\n",
      "2017-11-30T16:20:04.397000\n",
      "2017-11-30T16:20:06.489000\n",
      "2017-11-30T16:20:08.582000\n",
      "2017-11-30T16:20:10.742000\n",
      "2017-11-30T16:20:12.834000\n",
      "2017-11-30T16:20:14.927000\n",
      "2017-11-30T16:20:17.019000\n",
      "2017-11-30T16:20:19.112000\n",
      "2017-11-30T16:20:21.204000\n",
      "2017-11-30T16:20:23.297000\n",
      "2017-11-30T16:20:25.389000\n",
      "2017-11-30T16:20:27.482000\n",
      "2017-11-30T16:20:29.574000\n",
      "2017-11-30T16:20:31.667000\n",
      "2017-11-30T16:20:33.759000\n",
      "2017-11-30T16:20:35.919000\n",
      "2017-11-30T16:20:38.012000\n",
      "2017-11-30T16:20:40.104000\n",
      "2017-11-30T16:20:42.264000\n",
      "2017-11-30T16:20:44.357000\n",
      "2017-11-30T16:20:46.584000\n",
      "2017-11-30T16:20:48.812000\n",
      "2017-11-30T16:20:50.972000\n",
      "2017-11-30T16:20:53.132000\n",
      "2017-11-30T16:20:55.224000\n",
      "2017-11-30T16:20:57.317000\n",
      "2017-11-30T16:20:59.409000\n",
      "2017-11-30T16:21:01.502000\n",
      "2017-11-30T16:21:03.662000\n",
      "2017-11-30T16:21:05.754000\n",
      "2017-11-30T16:21:07.847000\n",
      "2017-11-30T16:21:09.939000\n",
      "2017-11-30T16:21:12.032000\n",
      "2017-11-30T16:21:14.124000\n",
      "2017-11-30T16:21:16.217000\n",
      "2017-11-30T16:21:18.444000\n",
      "2017-11-30T16:21:20.537000\n",
      "2017-11-30T16:21:22.629000\n",
      "2017-11-30T16:21:24.722000\n",
      "2017-11-30T16:21:26.814000\n",
      "2017-11-30T16:21:28.907000\n",
      "2017-11-30T16:21:31.067000\n",
      "2017-11-30T16:21:33.159000\n",
      "2017-11-30T16:21:35.387000\n",
      "2017-11-30T16:21:37.479000\n",
      "2017-11-30T16:21:39.707000\n",
      "2017-11-30T16:21:41.799000\n",
      "2017-11-30T16:21:43.892000\n",
      "2017-11-30T16:21:45.985000\n",
      "2017-11-30T16:21:48.077000\n",
      "2017-11-30T16:21:50.237000\n",
      "2017-11-30T16:21:52.397000\n",
      "2017-11-30T16:21:54.490000\n",
      "2017-11-30T16:21:56.582000\n",
      "2017-11-30T16:21:58.675000\n",
      "2017-11-30T16:22:00.835000\n",
      "2017-11-30T16:22:02.927000\n",
      "2017-11-30T16:22:05.020000\n",
      "2017-11-30T16:22:07.180000\n",
      "2017-11-30T16:22:09.272000\n",
      "2017-11-30T16:22:11.365000\n",
      "2017-11-30T16:22:13.457000\n",
      "2017-11-30T16:22:15.550000\n",
      "2017-11-30T16:22:17.710000\n",
      "2017-11-30T16:22:19.802000\n",
      "2017-11-30T16:22:21.895000\n",
      "2017-11-30T16:22:23.987000\n",
      "2017-11-30T16:22:26.080000\n",
      "2017-11-30T16:22:28.172000\n",
      "2017-11-30T16:22:30.265000\n",
      "2017-11-30T16:22:32.357000\n",
      "2017-11-30T16:22:34.450000\n",
      "2017-11-30T16:22:36.610000\n",
      "2017-11-30T16:22:38.702000\n",
      "2017-11-30T16:22:40.795000\n",
      "2017-11-30T16:22:42.887000\n",
      "2017-11-30T16:22:44.980000\n",
      "2017-11-30T16:22:47.072000\n",
      "2017-11-30T16:22:49.165000\n",
      "2017-11-30T16:22:51.257000\n",
      "2017-11-30T16:22:53.417000\n",
      "2017-11-30T16:22:55.510000\n",
      "2017-11-30T16:22:57.602000\n",
      "2017-11-30T16:22:59.762000\n",
      "2017-11-30T16:23:01.855000\n",
      "2017-11-30T16:23:03.947000\n",
      "2017-11-30T16:23:06.040000\n",
      "2017-11-30T16:23:08.132000\n",
      "2017-11-30T16:23:10.292000\n",
      "2017-11-30T16:23:12.385000\n",
      "2017-11-30T16:23:14.477000\n",
      "2017-11-30T16:23:16.637000\n",
      "2017-11-30T16:23:18.730000\n",
      "2017-11-30T16:23:20.823000\n",
      "2017-11-30T16:23:22.915000\n",
      "2017-11-30T16:23:25.075000\n",
      "2017-11-30T16:23:27.235000\n",
      "2017-11-30T16:23:29.395000\n",
      "2017-11-30T16:23:31.487000\n",
      "2017-11-30T16:23:33.580000\n",
      "2017-11-30T16:23:35.673000\n",
      "2017-11-30T16:23:37.832000\n",
      "2017-11-30T16:23:39.925000\n",
      "2017-11-30T16:23:42.017000\n",
      "2017-11-30T16:23:44.110000\n",
      "2017-11-30T16:23:46.203000\n",
      "2017-11-30T16:23:48.295000\n",
      "2017-11-30T16:23:50.387000\n",
      "2017-11-30T16:23:52.480000\n",
      "2017-11-30T16:23:54.572000\n",
      "2017-11-30T16:23:56.665000\n",
      "2017-11-30T16:23:58.757000\n",
      "2017-11-30T16:24:00.850000\n",
      "2017-11-30T16:24:03.010000\n",
      "2017-11-30T16:24:05.103000\n",
      "2017-11-30T16:24:07.195000\n",
      "2017-11-30T16:24:09.287000\n",
      "2017-11-30T16:24:11.380000\n",
      "2017-11-30T16:24:13.607000\n",
      "2017-11-30T16:24:15.700000\n",
      "2017-11-30T16:24:17.792000\n",
      "2017-11-30T16:24:19.885000\n",
      "2017-11-30T16:24:21.977000\n",
      "2017-11-30T16:24:24.205000\n",
      "2017-11-30T16:24:26.297000\n",
      "2017-11-30T16:24:28.390000\n",
      "2017-11-30T16:24:30.550000\n",
      "2017-11-30T16:24:32.642000\n",
      "2017-11-30T16:24:34.802000\n",
      "2017-11-30T16:24:36.895000\n",
      "2017-11-30T16:24:39.055000\n",
      "2017-11-30T16:24:41.148000\n",
      "2017-11-30T16:24:43.240000\n",
      "2017-11-30T16:24:45.400000\n",
      "2017-11-30T16:24:47.560000\n",
      "2017-11-30T16:24:49.652000\n",
      "2017-11-30T16:24:51.745000\n",
      "2017-11-30T16:24:53.838000\n",
      "2017-11-30T16:24:55.930000\n",
      "2017-11-30T16:24:58.023000\n",
      "2017-11-30T16:25:00.115000\n",
      "2017-11-30T16:25:02.208000\n",
      "2017-11-30T16:25:04.300000\n",
      "2017-11-30T16:25:06.460000\n",
      "2017-11-30T16:25:08.553000\n",
      "2017-11-30T16:25:10.645000\n",
      "2017-11-30T16:25:12.738000\n",
      "2017-11-30T16:25:14.830000\n",
      "2017-11-30T16:25:16.990000\n",
      "2017-11-30T16:25:19.083000\n",
      "2017-11-30T16:25:21.175000\n",
      "2017-11-30T16:25:23.268000\n",
      "2017-11-30T16:25:25.360000\n",
      "2017-11-30T16:25:27.453000\n",
      "2017-11-30T16:25:29.545000\n",
      "2017-11-30T16:25:31.638000\n",
      "2017-11-30T16:25:33.730000\n",
      "2017-11-30T16:25:35.823000\n",
      "2017-11-30T16:25:37.915000\n",
      "2017-11-30T16:25:40.008000\n",
      "2017-11-30T16:25:42.100000\n",
      "2017-11-30T16:25:44.193000\n",
      "2017-11-30T16:25:46.353000\n",
      "2017-11-30T16:25:48.445000\n",
      "2017-11-30T16:25:50.673000\n",
      "2017-11-30T16:25:52.833000\n",
      "2017-11-30T16:25:54.993000\n",
      "2017-11-30T16:25:57.085000\n",
      "2017-11-30T16:25:59.178000\n",
      "2017-11-30T16:26:01.270000\n",
      "2017-11-30T16:26:03.363000\n",
      "2017-11-30T16:26:05.455000\n",
      "2017-11-30T16:26:07.548000\n",
      "2017-11-30T16:26:09.640000\n",
      "2017-11-30T16:26:11.733000\n",
      "2017-11-30T16:26:13.825000\n",
      "2017-11-30T16:26:15.918000\n",
      "2017-11-30T16:26:18.010000\n",
      "2017-11-30T16:26:20.103000\n",
      "2017-11-30T16:26:22.195000\n",
      "2017-11-30T16:26:24.288000\n",
      "2017-11-30T16:26:26.448000\n",
      "2017-11-30T16:26:28.608000\n",
      "2017-11-30T16:26:30.700000\n",
      "2017-11-30T16:26:32.860000\n",
      "MAGNETOMETER\n",
      "2017-11-30T16:19:51.909000\n",
      "2017-11-30T16:19:54.069000\n",
      "2017-11-30T16:19:56.162000\n",
      "2017-11-30T16:19:58.254000\n",
      "2017-11-30T16:20:00.482000\n",
      "2017-11-30T16:20:02.574000\n",
      "2017-11-30T16:20:04.667000\n",
      "2017-11-30T16:20:06.759000\n",
      "2017-11-30T16:20:08.852000\n",
      "2017-11-30T16:20:11.012000\n",
      "2017-11-30T16:20:13.104000\n",
      "2017-11-30T16:20:15.197000\n",
      "2017-11-30T16:20:17.289000\n",
      "2017-11-30T16:20:19.382000\n",
      "2017-11-30T16:20:21.474000\n",
      "2017-11-30T16:20:23.567000\n",
      "2017-11-30T16:20:25.659000\n",
      "2017-11-30T16:20:27.752000\n",
      "2017-11-30T16:20:29.844000\n",
      "2017-11-30T16:20:31.937000\n",
      "2017-11-30T16:20:34.029000\n",
      "2017-11-30T16:20:36.189000\n",
      "2017-11-30T16:20:38.282000\n",
      "2017-11-30T16:20:40.374000\n",
      "2017-11-30T16:20:42.534000\n",
      "2017-11-30T16:20:44.627000\n",
      "2017-11-30T16:20:46.854000\n",
      "2017-11-30T16:20:49.082000\n",
      "2017-11-30T16:20:51.309000\n",
      "2017-11-30T16:20:53.402000\n",
      "2017-11-30T16:20:55.494000\n",
      "2017-11-30T16:20:57.587000\n",
      "2017-11-30T16:20:59.679000\n",
      "2017-11-30T16:21:01.772000\n",
      "2017-11-30T16:21:03.932000\n",
      "2017-11-30T16:21:06.024000\n",
      "2017-11-30T16:21:08.117000\n",
      "2017-11-30T16:21:10.209000\n",
      "2017-11-30T16:21:12.302000\n",
      "2017-11-30T16:21:14.394000\n",
      "2017-11-30T16:21:16.487000\n",
      "2017-11-30T16:21:18.714000\n",
      "2017-11-30T16:21:20.807000\n",
      "2017-11-30T16:21:22.899000\n",
      "2017-11-30T16:21:24.992000\n",
      "2017-11-30T16:21:27.084000\n",
      "2017-11-30T16:21:29.177000\n",
      "2017-11-30T16:21:31.337000\n",
      "2017-11-30T16:21:33.497000\n",
      "2017-11-30T16:21:35.657000\n",
      "2017-11-30T16:21:37.749000\n",
      "2017-11-30T16:21:39.977000\n",
      "2017-11-30T16:21:42.070000\n",
      "2017-11-30T16:21:44.162000\n",
      "2017-11-30T16:21:46.254000\n",
      "2017-11-30T16:21:48.347000\n",
      "2017-11-30T16:21:50.507000\n",
      "2017-11-30T16:21:52.667000\n",
      "2017-11-30T16:21:54.760000\n",
      "2017-11-30T16:21:56.852000\n",
      "2017-11-30T16:21:58.945000\n",
      "2017-11-30T16:22:01.105000\n",
      "2017-11-30T16:22:03.197000\n",
      "2017-11-30T16:22:05.357000\n",
      "2017-11-30T16:22:07.450000\n",
      "2017-11-30T16:22:09.542000\n",
      "2017-11-30T16:22:11.635000\n",
      "2017-11-30T16:22:13.727000\n",
      "2017-11-30T16:22:15.820000\n",
      "2017-11-30T16:22:17.980000\n",
      "2017-11-30T16:22:20.072000\n",
      "2017-11-30T16:22:22.165000\n",
      "2017-11-30T16:22:24.257000\n",
      "2017-11-30T16:22:26.350000\n",
      "2017-11-30T16:22:28.442000\n",
      "2017-11-30T16:22:30.535000\n",
      "2017-11-30T16:22:32.627000\n",
      "2017-11-30T16:22:34.720000\n",
      "2017-11-30T16:22:36.880000\n",
      "2017-11-30T16:22:38.972000\n",
      "2017-11-30T16:22:41.065000\n",
      "2017-11-30T16:22:43.157000\n",
      "2017-11-30T16:22:45.250000\n",
      "2017-11-30T16:22:47.343000\n",
      "2017-11-30T16:22:49.435000\n",
      "2017-11-30T16:22:51.527000\n",
      "2017-11-30T16:22:53.687000\n",
      "2017-11-30T16:22:55.780000\n",
      "2017-11-30T16:22:57.872000\n",
      "2017-11-30T16:23:00.032000\n",
      "2017-11-30T16:23:02.125000\n",
      "2017-11-30T16:23:04.217000\n",
      "2017-11-30T16:23:06.310000\n",
      "2017-11-30T16:23:08.402000\n",
      "2017-11-30T16:23:10.562000\n",
      "2017-11-30T16:23:12.655000\n",
      "2017-11-30T16:23:14.747000\n",
      "2017-11-30T16:23:16.907000\n",
      "2017-11-30T16:23:19.000000\n",
      "2017-11-30T16:23:21.092000\n",
      "2017-11-30T16:23:23.185000\n",
      "2017-11-30T16:23:25.345000\n",
      "2017-11-30T16:23:27.572000\n",
      "2017-11-30T16:23:29.665000\n",
      "2017-11-30T16:23:31.757000\n",
      "2017-11-30T16:23:33.850000\n",
      "2017-11-30T16:23:35.942000\n",
      "2017-11-30T16:23:38.102000\n",
      "2017-11-30T16:23:40.195000\n",
      "2017-11-30T16:23:42.287000\n",
      "2017-11-30T16:23:44.380000\n",
      "2017-11-30T16:23:46.472000\n",
      "2017-11-30T16:23:48.565000\n",
      "2017-11-30T16:23:50.658000\n",
      "2017-11-30T16:23:52.750000\n",
      "2017-11-30T16:23:54.842000\n",
      "2017-11-30T16:23:56.935000\n",
      "2017-11-30T16:23:59.027000\n",
      "2017-11-30T16:24:01.120000\n",
      "2017-11-30T16:24:03.280000\n",
      "2017-11-30T16:24:05.372000\n",
      "2017-11-30T16:24:07.465000\n",
      "2017-11-30T16:24:09.557000\n",
      "2017-11-30T16:24:11.650000\n",
      "2017-11-30T16:24:13.877000\n",
      "2017-11-30T16:24:15.970000\n",
      "2017-11-30T16:24:18.062000\n",
      "2017-11-30T16:24:20.155000\n",
      "2017-11-30T16:24:22.248000\n",
      "2017-11-30T16:24:24.475000\n",
      "2017-11-30T16:24:26.567000\n",
      "2017-11-30T16:24:28.660000\n",
      "2017-11-30T16:24:30.820000\n",
      "2017-11-30T16:24:32.912000\n",
      "2017-11-30T16:24:35.072000\n",
      "2017-11-30T16:24:37.165000\n",
      "2017-11-30T16:24:39.325000\n",
      "2017-11-30T16:24:41.417000\n",
      "2017-11-30T16:24:43.510000\n",
      "2017-11-30T16:24:45.670000\n",
      "2017-11-30T16:24:47.830000\n",
      "2017-11-30T16:24:49.923000\n",
      "2017-11-30T16:24:52.015000\n",
      "2017-11-30T16:24:54.108000\n",
      "2017-11-30T16:24:56.200000\n",
      "2017-11-30T16:24:58.293000\n",
      "2017-11-30T16:25:00.385000\n",
      "2017-11-30T16:25:02.478000\n",
      "2017-11-30T16:25:04.570000\n",
      "2017-11-30T16:25:06.730000\n",
      "2017-11-30T16:25:08.823000\n",
      "2017-11-30T16:25:10.915000\n",
      "2017-11-30T16:25:13.008000\n",
      "2017-11-30T16:25:15.100000\n",
      "2017-11-30T16:25:17.260000\n",
      "2017-11-30T16:25:19.353000\n",
      "2017-11-30T16:25:21.445000\n",
      "2017-11-30T16:25:23.538000\n",
      "2017-11-30T16:25:25.630000\n",
      "2017-11-30T16:25:27.723000\n",
      "2017-11-30T16:25:29.815000\n",
      "2017-11-30T16:25:31.908000\n",
      "2017-11-30T16:25:34.000000\n",
      "2017-11-30T16:25:36.093000\n",
      "2017-11-30T16:25:38.185000\n",
      "2017-11-30T16:25:40.278000\n",
      "2017-11-30T16:25:42.370000\n",
      "2017-11-30T16:25:44.463000\n",
      "2017-11-30T16:25:46.623000\n",
      "2017-11-30T16:25:48.715000\n",
      "2017-11-30T16:25:50.943000\n",
      "2017-11-30T16:25:53.103000\n",
      "2017-11-30T16:25:55.263000\n",
      "2017-11-30T16:25:57.355000\n",
      "2017-11-30T16:25:59.448000\n",
      "2017-11-30T16:26:01.540000\n",
      "2017-11-30T16:26:03.633000\n",
      "2017-11-30T16:26:05.725000\n",
      "2017-11-30T16:26:07.818000\n",
      "2017-11-30T16:26:09.910000\n",
      "2017-11-30T16:26:12.003000\n",
      "2017-11-30T16:26:14.095000\n",
      "2017-11-30T16:26:16.188000\n",
      "2017-11-30T16:26:18.280000\n",
      "2017-11-30T16:26:20.373000\n",
      "2017-11-30T16:26:22.465000\n",
      "2017-11-30T16:26:24.558000\n",
      "2017-11-30T16:26:26.785000\n",
      "2017-11-30T16:26:28.878000\n",
      "2017-11-30T16:26:30.970000\n",
      "2017-11-30T16:26:33.130000\n",
      "BAROMETER\n",
      "2017-11-30T16:19:51.368000\n",
      "2017-11-30T16:19:53.461000\n",
      "2017-11-30T16:19:55.621000\n",
      "2017-11-30T16:19:57.714000\n",
      "2017-11-30T16:19:59.941000\n",
      "2017-11-30T16:20:02.033000\n",
      "2017-11-30T16:20:04.126000\n",
      "2017-11-30T16:20:06.218000\n",
      "2017-11-30T16:20:08.311000\n",
      "2017-11-30T16:20:10.403000\n",
      "2017-11-30T16:20:12.563000\n",
      "2017-11-30T16:20:14.656000\n",
      "2017-11-30T16:20:16.748000\n",
      "2017-11-30T16:20:18.841000\n",
      "2017-11-30T16:20:20.934000\n",
      "2017-11-30T16:20:23.026000\n",
      "2017-11-30T16:20:25.118000\n",
      "2017-11-30T16:20:27.211000\n",
      "2017-11-30T16:20:29.303000\n",
      "2017-11-30T16:20:31.396000\n",
      "2017-11-30T16:20:33.488000\n",
      "2017-11-30T16:20:35.648000\n",
      "2017-11-30T16:20:37.741000\n",
      "2017-11-30T16:20:39.833000\n",
      "2017-11-30T16:20:41.993000\n",
      "2017-11-30T16:20:44.086000\n",
      "2017-11-30T16:20:46.314000\n",
      "2017-11-30T16:20:48.474000\n",
      "2017-11-30T16:20:50.633000\n",
      "2017-11-30T16:20:52.861000\n",
      "2017-11-30T16:20:54.954000\n",
      "2017-11-30T16:20:57.046000\n",
      "2017-11-30T16:20:59.139000\n",
      "2017-11-30T16:21:01.231000\n",
      "2017-11-30T16:21:03.391000\n",
      "2017-11-30T16:21:05.484000\n",
      "2017-11-30T16:21:07.576000\n",
      "2017-11-30T16:21:09.669000\n",
      "2017-11-30T16:21:11.761000\n",
      "2017-11-30T16:21:13.854000\n",
      "2017-11-30T16:21:15.946000\n",
      "2017-11-30T16:21:18.174000\n",
      "2017-11-30T16:21:20.266000\n",
      "2017-11-30T16:21:22.359000\n",
      "2017-11-30T16:21:24.451000\n",
      "2017-11-30T16:21:26.544000\n",
      "2017-11-30T16:21:28.636000\n",
      "2017-11-30T16:21:30.796000\n",
      "2017-11-30T16:21:32.889000\n",
      "2017-11-30T16:21:35.116000\n",
      "2017-11-30T16:21:37.209000\n",
      "2017-11-30T16:21:39.436000\n",
      "2017-11-30T16:21:41.529000\n",
      "2017-11-30T16:21:43.621000\n",
      "2017-11-30T16:21:45.714000\n",
      "2017-11-30T16:21:47.806000\n",
      "2017-11-30T16:21:49.966000\n",
      "2017-11-30T16:21:52.126000\n",
      "2017-11-30T16:21:54.219000\n",
      "2017-11-30T16:21:56.311000\n",
      "2017-11-30T16:21:58.404000\n",
      "2017-11-30T16:22:00.564000\n",
      "2017-11-30T16:22:02.656000\n",
      "2017-11-30T16:22:04.749000\n",
      "2017-11-30T16:22:06.909000\n",
      "2017-11-30T16:22:09.001000\n",
      "2017-11-30T16:22:11.094000\n",
      "2017-11-30T16:22:13.186000\n",
      "2017-11-30T16:22:15.279000\n",
      "2017-11-30T16:22:17.439000\n",
      "2017-11-30T16:22:19.531000\n",
      "2017-11-30T16:22:21.624000\n",
      "2017-11-30T16:22:23.716000\n",
      "2017-11-30T16:22:25.809000\n",
      "2017-11-30T16:22:27.901000\n",
      "2017-11-30T16:22:29.994000\n",
      "2017-11-30T16:22:32.086000\n",
      "2017-11-30T16:22:34.179000\n",
      "2017-11-30T16:22:36.271000\n",
      "2017-11-30T16:22:38.431000\n",
      "2017-11-30T16:22:40.524000\n",
      "2017-11-30T16:22:42.616000\n",
      "2017-11-30T16:22:44.709000\n",
      "2017-11-30T16:22:46.801000\n",
      "2017-11-30T16:22:48.894000\n",
      "2017-11-30T16:22:50.986000\n",
      "2017-11-30T16:22:53.146000\n",
      "2017-11-30T16:22:55.239000\n",
      "2017-11-30T16:22:57.331000\n",
      "2017-11-30T16:22:59.491000\n",
      "2017-11-30T16:23:01.584000\n",
      "2017-11-30T16:23:03.676000\n",
      "2017-11-30T16:23:05.769000\n",
      "2017-11-30T16:23:07.861000\n",
      "2017-11-30T16:23:10.021000\n",
      "2017-11-30T16:23:12.114000\n",
      "2017-11-30T16:23:14.206000\n",
      "2017-11-30T16:23:16.366000\n",
      "2017-11-30T16:23:18.459000\n",
      "2017-11-30T16:23:20.552000\n",
      "2017-11-30T16:23:22.644000\n",
      "2017-11-30T16:23:24.804000\n",
      "2017-11-30T16:23:26.964000\n",
      "2017-11-30T16:23:29.124000\n",
      "2017-11-30T16:23:31.217000\n",
      "2017-11-30T16:23:33.309000\n",
      "2017-11-30T16:23:35.401000\n",
      "2017-11-30T16:23:37.494000\n",
      "2017-11-30T16:23:39.654000\n",
      "2017-11-30T16:23:41.747000\n",
      "2017-11-30T16:23:43.839000\n",
      "2017-11-30T16:23:45.932000\n",
      "2017-11-30T16:23:48.024000\n",
      "2017-11-30T16:23:50.117000\n",
      "2017-11-30T16:23:52.209000\n",
      "2017-11-30T16:23:54.302000\n",
      "2017-11-30T16:23:56.394000\n",
      "2017-11-30T16:23:58.487000\n",
      "2017-11-30T16:24:00.579000\n",
      "2017-11-30T16:24:02.672000\n",
      "2017-11-30T16:24:04.832000\n",
      "2017-11-30T16:24:06.924000\n",
      "2017-11-30T16:24:09.017000\n",
      "2017-11-30T16:24:11.109000\n",
      "2017-11-30T16:24:13.337000\n",
      "2017-11-30T16:24:15.429000\n",
      "2017-11-30T16:24:17.522000\n",
      "2017-11-30T16:24:19.614000\n",
      "2017-11-30T16:24:21.707000\n",
      "2017-11-30T16:24:23.934000\n",
      "2017-11-30T16:24:26.027000\n",
      "2017-11-30T16:24:28.119000\n",
      "2017-11-30T16:24:30.279000\n",
      "2017-11-30T16:24:32.372000\n",
      "2017-11-30T16:24:34.532000\n",
      "2017-11-30T16:24:36.624000\n",
      "2017-11-30T16:24:38.784000\n",
      "2017-11-30T16:24:40.877000\n",
      "2017-11-30T16:24:42.969000\n",
      "2017-11-30T16:24:45.129000\n",
      "2017-11-30T16:24:47.289000\n",
      "2017-11-30T16:24:49.382000\n",
      "2017-11-30T16:24:51.474000\n",
      "2017-11-30T16:24:53.567000\n",
      "2017-11-30T16:24:55.659000\n",
      "2017-11-30T16:24:57.752000\n",
      "2017-11-30T16:24:59.844000\n",
      "2017-11-30T16:25:01.937000\n",
      "2017-11-30T16:25:04.029000\n",
      "2017-11-30T16:25:06.189000\n",
      "2017-11-30T16:25:08.282000\n",
      "2017-11-30T16:25:10.374000\n",
      "2017-11-30T16:25:12.467000\n",
      "2017-11-30T16:25:14.559000\n",
      "2017-11-30T16:25:16.719000\n",
      "2017-11-30T16:25:18.812000\n",
      "2017-11-30T16:25:20.904000\n",
      "2017-11-30T16:25:22.997000\n",
      "2017-11-30T16:25:25.089000\n",
      "2017-11-30T16:25:27.182000\n",
      "2017-11-30T16:25:29.274000\n",
      "2017-11-30T16:25:31.367000\n",
      "2017-11-30T16:25:33.459000\n",
      "2017-11-30T16:25:35.552000\n",
      "2017-11-30T16:25:37.645000\n",
      "2017-11-30T16:25:39.737000\n",
      "2017-11-30T16:25:41.829000\n",
      "2017-11-30T16:25:43.922000\n",
      "2017-11-30T16:25:46.082000\n",
      "2017-11-30T16:25:48.174000\n",
      "2017-11-30T16:25:50.402000\n",
      "2017-11-30T16:25:52.562000\n",
      "2017-11-30T16:25:54.722000\n",
      "2017-11-30T16:25:56.814000\n",
      "2017-11-30T16:25:58.907000\n",
      "2017-11-30T16:26:00.999000\n",
      "2017-11-30T16:26:03.092000\n",
      "2017-11-30T16:26:05.184000\n",
      "2017-11-30T16:26:07.277000\n",
      "2017-11-30T16:26:09.369000\n",
      "2017-11-30T16:26:11.462000\n",
      "2017-11-30T16:26:13.554000\n",
      "2017-11-30T16:26:15.647000\n",
      "2017-11-30T16:26:17.740000\n",
      "2017-11-30T16:26:19.832000\n",
      "2017-11-30T16:26:21.924000\n",
      "2017-11-30T16:26:24.017000\n",
      "2017-11-30T16:26:26.177000\n",
      "2017-11-30T16:26:28.337000\n",
      "2017-11-30T16:26:30.430000\n",
      "2017-11-30T16:26:32.589000\n",
      "TEMPERATURE\n",
      "2017-11-30T16:19:50.963000\n",
      "2017-11-30T16:19:53.056000\n",
      "2017-11-30T16:19:55.216000\n",
      "2017-11-30T16:19:57.308000\n",
      "2017-11-30T16:19:59.468000\n",
      "2017-11-30T16:20:01.628000\n",
      "2017-11-30T16:20:03.721000\n",
      "2017-11-30T16:20:05.813000\n",
      "2017-11-30T16:20:07.906000\n",
      "2017-11-30T16:20:09.998000\n",
      "2017-11-30T16:20:12.158000\n",
      "2017-11-30T16:20:14.251000\n",
      "2017-11-30T16:20:16.343000\n",
      "2017-11-30T16:20:18.436000\n",
      "2017-11-30T16:20:20.528000\n",
      "2017-11-30T16:20:22.621000\n",
      "2017-11-30T16:20:24.713000\n",
      "2017-11-30T16:20:26.806000\n",
      "2017-11-30T16:20:28.898000\n",
      "2017-11-30T16:20:30.991000\n",
      "2017-11-30T16:20:33.083000\n",
      "2017-11-30T16:20:35.176000\n",
      "2017-11-30T16:20:37.336000\n",
      "2017-11-30T16:20:39.428000\n",
      "2017-11-30T16:20:41.588000\n",
      "2017-11-30T16:20:43.681000\n",
      "2017-11-30T16:20:45.909000\n",
      "2017-11-30T16:20:48.068000\n",
      "2017-11-30T16:20:50.229000\n",
      "2017-11-30T16:20:52.456000\n",
      "2017-11-30T16:20:54.548000\n",
      "2017-11-30T16:20:56.641000\n",
      "2017-11-30T16:20:58.733000\n",
      "2017-11-30T16:21:00.826000\n",
      "2017-11-30T16:21:02.986000\n",
      "2017-11-30T16:21:05.078000\n",
      "2017-11-30T16:21:07.171000\n",
      "2017-11-30T16:21:09.264000\n",
      "2017-11-30T16:21:11.356000\n",
      "2017-11-30T16:21:13.449000\n",
      "2017-11-30T16:21:15.541000\n",
      "2017-11-30T16:21:17.769000\n",
      "2017-11-30T16:21:19.861000\n",
      "2017-11-30T16:21:21.954000\n",
      "2017-11-30T16:21:24.046000\n",
      "2017-11-30T16:21:26.139000\n",
      "2017-11-30T16:21:28.231000\n",
      "2017-11-30T16:21:30.391000\n",
      "2017-11-30T16:21:32.484000\n",
      "2017-11-30T16:21:34.711000\n",
      "2017-11-30T16:21:36.804000\n",
      "2017-11-30T16:21:38.896000\n",
      "2017-11-30T16:21:41.124000\n",
      "2017-11-30T16:21:43.216000\n",
      "2017-11-30T16:21:45.309000\n",
      "2017-11-30T16:21:47.401000\n",
      "2017-11-30T16:21:49.561000\n",
      "2017-11-30T16:21:51.654000\n",
      "2017-11-30T16:21:53.814000\n",
      "2017-11-30T16:21:55.906000\n",
      "2017-11-30T16:21:57.999000\n",
      "2017-11-30T16:22:00.159000\n",
      "2017-11-30T16:22:02.251000\n",
      "2017-11-30T16:22:04.344000\n",
      "2017-11-30T16:22:06.504000\n",
      "2017-11-30T16:22:08.596000\n",
      "2017-11-30T16:22:10.689000\n",
      "2017-11-30T16:22:12.781000\n",
      "2017-11-30T16:22:14.874000\n",
      "2017-11-30T16:22:16.966000\n",
      "2017-11-30T16:22:19.126000\n",
      "2017-11-30T16:22:21.219000\n",
      "2017-11-30T16:22:23.311000\n",
      "2017-11-30T16:22:25.404000\n",
      "2017-11-30T16:22:27.496000\n",
      "2017-11-30T16:22:29.589000\n",
      "2017-11-30T16:22:31.681000\n",
      "2017-11-30T16:22:33.774000\n",
      "2017-11-30T16:22:35.866000\n",
      "2017-11-30T16:22:38.026000\n",
      "2017-11-30T16:22:40.119000\n",
      "2017-11-30T16:22:42.211000\n",
      "2017-11-30T16:22:44.304000\n",
      "2017-11-30T16:22:46.396000\n",
      "2017-11-30T16:22:48.489000\n",
      "2017-11-30T16:22:50.581000\n",
      "2017-11-30T16:22:52.674000\n",
      "2017-11-30T16:22:54.834000\n",
      "2017-11-30T16:22:56.926000\n",
      "2017-11-30T16:22:59.086000\n",
      "2017-11-30T16:23:01.179000\n",
      "2017-11-30T16:23:03.271000\n",
      "2017-11-30T16:23:05.364000\n",
      "2017-11-30T16:23:07.456000\n",
      "2017-11-30T16:23:09.616000\n",
      "2017-11-30T16:23:11.709000\n",
      "2017-11-30T16:23:13.801000\n",
      "2017-11-30T16:23:15.961000\n",
      "2017-11-30T16:23:18.054000\n",
      "2017-11-30T16:23:20.146000\n",
      "2017-11-30T16:23:22.239000\n",
      "2017-11-30T16:23:24.331000\n",
      "2017-11-30T16:23:26.559000\n",
      "2017-11-30T16:23:28.719000\n",
      "2017-11-30T16:23:30.811000\n",
      "2017-11-30T16:23:32.904000\n",
      "2017-11-30T16:23:34.997000\n",
      "2017-11-30T16:23:37.089000\n",
      "2017-11-30T16:23:39.249000\n",
      "2017-11-30T16:23:41.342000\n",
      "2017-11-30T16:23:43.434000\n",
      "2017-11-30T16:23:45.527000\n",
      "2017-11-30T16:23:47.619000\n",
      "2017-11-30T16:23:49.712000\n",
      "2017-11-30T16:23:51.804000\n",
      "2017-11-30T16:23:53.897000\n",
      "2017-11-30T16:23:55.989000\n",
      "2017-11-30T16:23:58.082000\n",
      "2017-11-30T16:24:00.174000\n",
      "2017-11-30T16:24:02.267000\n",
      "2017-11-30T16:24:04.427000\n",
      "2017-11-30T16:24:06.519000\n",
      "2017-11-30T16:24:08.612000\n",
      "2017-11-30T16:24:10.704000\n",
      "2017-11-30T16:24:12.932000\n",
      "2017-11-30T16:24:15.024000\n",
      "2017-11-30T16:24:17.117000\n",
      "2017-11-30T16:24:19.209000\n",
      "2017-11-30T16:24:21.302000\n",
      "2017-11-30T16:24:23.529000\n",
      "2017-11-30T16:24:25.622000\n",
      "2017-11-30T16:24:27.714000\n",
      "2017-11-30T16:24:29.874000\n",
      "2017-11-30T16:24:31.967000\n",
      "2017-11-30T16:24:34.127000\n",
      "2017-11-30T16:24:36.219000\n",
      "2017-11-30T16:24:38.379000\n",
      "2017-11-30T16:24:40.472000\n",
      "2017-11-30T16:24:42.564000\n",
      "2017-11-30T16:24:44.724000\n",
      "2017-11-30T16:24:46.817000\n",
      "2017-11-30T16:24:48.977000\n",
      "2017-11-30T16:24:51.069000\n",
      "2017-11-30T16:24:53.162000\n",
      "2017-11-30T16:24:55.254000\n",
      "2017-11-30T16:24:57.347000\n",
      "2017-11-30T16:24:59.439000\n",
      "2017-11-30T16:25:01.532000\n",
      "2017-11-30T16:25:03.624000\n",
      "2017-11-30T16:25:05.784000\n",
      "2017-11-30T16:25:07.877000\n",
      "2017-11-30T16:25:09.969000\n",
      "2017-11-30T16:25:12.062000\n",
      "2017-11-30T16:25:14.154000\n",
      "2017-11-30T16:25:16.247000\n",
      "2017-11-30T16:25:18.407000\n",
      "2017-11-30T16:25:20.499000\n",
      "2017-11-30T16:25:22.592000\n",
      "2017-11-30T16:25:24.684000\n",
      "2017-11-30T16:25:26.777000\n",
      "2017-11-30T16:25:28.869000\n",
      "2017-11-30T16:25:30.962000\n",
      "2017-11-30T16:25:33.054000\n",
      "2017-11-30T16:25:35.147000\n",
      "2017-11-30T16:25:37.239000\n",
      "2017-11-30T16:25:39.332000\n",
      "2017-11-30T16:25:41.424000\n",
      "2017-11-30T16:25:43.517000\n",
      "2017-11-30T16:25:45.609000\n",
      "2017-11-30T16:25:47.771000\n",
      "2017-11-30T16:25:49.997000\n",
      "2017-11-30T16:25:52.157000\n",
      "2017-11-30T16:25:54.317000\n",
      "2017-11-30T16:25:56.409000\n",
      "2017-11-30T16:25:58.502000\n",
      "2017-11-30T16:26:00.594000\n",
      "2017-11-30T16:26:02.687000\n",
      "2017-11-30T16:26:04.779000\n",
      "2017-11-30T16:26:06.872000\n",
      "2017-11-30T16:26:08.964000\n",
      "2017-11-30T16:26:11.057000\n",
      "2017-11-30T16:26:13.149000\n",
      "2017-11-30T16:26:15.242000\n",
      "2017-11-30T16:26:17.334000\n",
      "2017-11-30T16:26:19.427000\n",
      "2017-11-30T16:26:21.519000\n",
      "2017-11-30T16:26:23.612000\n",
      "2017-11-30T16:26:25.772000\n",
      "2017-11-30T16:26:27.932000\n",
      "2017-11-30T16:26:30.024000\n",
      "2017-11-30T16:26:32.184000\n"
     ]
    }
   ],
   "source": [
    "for sens,data in sensor_data.items():\n",
    "    print(sens)\n",
    "    # print(data.gettimestamp())\n",
    "    for dat in data:\n",
    "        print(np.datetime64(dat.gettimestamp()))\n",
    "        #time_stamp = datetime.datetime.fromtimestamp().strftime('%Y-%m-%d %H:%M:%S')\n",
    "        # print(dat.getvalues()[0], dat.gettimestamp())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191 191\n"
     ]
    },
    {
     "data": {
      "image/png": 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X+1k1sdBMX8tyFMHPuz/PZBxCjF8kaQJX3RLEmrarGWOPM8b2Mcb2Msb+u3d/K2PsEcbY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\nO47j1Aie8B3HcWqEP8iKiEdddH/eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba080d4d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "time_stp = []\n",
    "sens_val = []\n",
    "\n",
    "## Generate the data list\n",
    "for sens,data in sensor_data.items():\n",
    "    if sens==\"GYROSCOPE\":\n",
    "        for dat in data:\n",
    "            sens_val.append(dat.getvalues()[0])\n",
    "            time_stp.append(dat.gettimestamp())\n",
    "\n",
    "x = [datetime.datetime.strptime(elem, '%Y-%m-%d %H:%M:%S.%f') for elem in time_stp]\n",
    "            \n",
    "print(len(x),len(sens_val))\n",
    "# Plot the data\n",
    "plt.plot(x,sens_val, label='linear')\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190 191 190\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9fc6a3278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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v4XDeH3yJ1Pqq4Mf44hP5Huf/Lmzzm2k8DO/8XH6XsrXwzDdattLceHg4gg6+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\nJAkq6IqiKEnC/wdXjDwUH9XHfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9fc5ac6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "time_stp = []\n",
    "sens_val = []\n",
    "\n",
    "## Generate the data list\n",
    "for sens,data in sensor_data.items():\n",
    "    if sens==\"LIGHT\":\n",
    "        for dat in data:\n",
    "            sens_val.append(dat.getvalues())\n",
    "            time_stp.append(dat.gettimestamp())\n",
    "\n",
    "            \n",
    "## Generate the data list\n",
    "sens_val1 = []\n",
    "sens_val2 = []\n",
    "for sens,data in sensor_data.items():\n",
    "    if sens==\"TEMPERATURE\":\n",
    "        for dat in data:\n",
    "            sens_val1.append(dat.getvalues()[0])\n",
    "            sens_val2.append(dat.getvalues()[1])\n",
    "            \n",
    "            \n",
    "timeStampfinal = [datetime.datetime.strptime(elem, '%Y-%m-%d %H:%M:%S.%f') for elem in time_stp]\n",
    "\n",
    "print(len(sens_val),len(sens_val1),len(timeStampfinal))\n",
    "\n",
    "# Plot the data\n",
    "plt.figure(1)\n",
    "plt.plot(timeStampfinal[:190],sens_val[:190], label='linear')\n",
    "\n",
    "plt.figure(2)\n",
    "plt.plot(timeStampfinal[:190],sens_val1[:190], label='linear')\n",
    "plt.plot(timeStampfinal[:190],sens_val2[:190], label='linear')\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Compute Veclocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "## Get difference in time \n",
    "# np.diff(index)/np.timedelta64(1, 's')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "## Define vel matrix\n",
    "vel_x = []\n",
    "vel_y = []\n",
    "vel_z = []\n",
    "\n",
    "acc_x = []\n",
    "acc_y = []\n",
    "acc_z = []\n",
    "time_stp = []\n",
    "vel_abs = []\n",
    "\n",
    "## Get accelerometer data\n",
    "for sens,data in sensor_data.items():\n",
    "    if sens==\"ACCELEROMETER\":\n",
    "        for dat in data:\n",
    "            acc_x.append(dat.getvalues()[0])\n",
    "            acc_y.append(dat.getvalues()[1])\n",
    "            acc_z.append(dat.getvalues()[2])\n",
    "            time_stp.append(np.datetime64(dat.gettimestamp()))\n",
    "\n",
    "try:\n",
    "    assert len(acc_x) == len(acc_y) \n",
    "    assert len(acc_z) == len(time_stp)\n",
    "    \n",
    "    time_diff = np.diff(time_stp)/np.timedelta64(1, 's')\n",
    "\n",
    "    vel_x.append(0)\n",
    "    vel_y.append(0)\n",
    "    vel_z.append(0)\n",
    "    vel_abs.append(0)\n",
    "    \n",
    "    ## Generate velocities\n",
    "    for i in range(len(acc_x)):\n",
    "        # Integrate over the acceleration data:\n",
    "        vx = (acc_x[i])*(time_diff[i]) + vel_x[i]\n",
    "        vy = (acc_y[i])*(time_diff[i]) + vel_y[i]\n",
    "        vz = (acc_z[i])*(time_diff[i]) + vel_z[i]\n",
    "        vel_x.append(vx)\n",
    "        vel_y.append(vy)\n",
    "        vel_z.append(vz) \n",
    "        vel_abs.append(np.sqrt(vx**2+vy**2+vz**2))\n",
    "        \n",
    "except:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from web3 import Web3, HTTPProvider, IPCProvider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "web3 = Web3(HTTPProvider('http://localhost:8545'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "320"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "web3.eth.blockNumber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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