{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance wrapper script\n",
    "## Description\n",
    "This notebook computes the distance for the records with respect to the origin point for the selected trips of the route."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from pymongo import     MongoClient\n",
    "con = MongoClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def mydistance(a1,b1,a2,b2):\n",
    "    '''\n",
    "    input: location attributes corresponding to point 1 and 2. (lat1, lon1, lat2, lon2)\n",
    "    output: distance between point 1 and point 2\n",
    "    function: compute distance between two points using haversine formula\n",
    "    '''    \n",
    "    R=6371e3\n",
    "    x1=math.radians(a1)\n",
    "    y1=math.radians(b1)\n",
    "    x2=math.radians(a2)\n",
    "    y2=math.radians(b2)\n",
    "    delx=x2-x1\n",
    "    dely=y2-y1\n",
    "    c=math.sin(delx/2)*math.sin(delx/2)+math.cos(x1)*math.cos(x2)*math.sin(dely/2)*math.sin(dely/2)\n",
    "    d=2*math.atan2(math.sqrt(c),math.sqrt(1-c))\n",
    "    e=R*d\n",
    "    return(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nProjectDataUsed = True\\nUsedPreTrained = True\\nUseMongoDB = False\\n'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#'''\n",
    "ProjectDataUsed = True\n",
    "UsedPreTrained = False\n",
    "UseMongoDB = True\n",
    "#'''\n",
    "'''\n",
    "ProjectDataUsed = True\n",
    "UsedPreTrained = True\n",
    "UseMongoDB = False\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "RouteName = 'Git_ISCON_PDPU'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "TripsInfo=['20_12_2017__18_31_19', '18_01_2018__07_38_10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if UseMongoDB==True:\n",
    "    '''Compute the distance for the trip records of the selected trips'''\n",
    "    for TripIndex in range(len(TripsInfo)):\n",
    "        LocationRecords = [lr for lr in con[RouteName][TripsInfo[TripIndex]+'.Filtered'].find().sort([('epoch',1)])]\n",
    "        i=0\n",
    "        #distanceFromOrigin=0.0\n",
    "        LocationRecordsReference = [lr for lr in con[RouteName][TripsInfo[TripIndex]+'.Filtered'].find().sort([('epoch',1)])]\n",
    "\n",
    "        distanceFromOrigin = mydistance(LocationRecordsReference[0][\"Latitude\"],LocationRecordsReference[0][\"Longitude\"],LocationRecords[0][\"Latitude\"],LocationRecords[0][\"Longitude\"])\n",
    "        normalizedDistanceFromOrigin=0.0\n",
    "        #print(distanceFromOrigin)\n",
    "        #input()\n",
    "        #totalDistance=0\n",
    "        totalDistance=distanceFromOrigin\n",
    "        for index in range(len(LocationRecords)-1):\n",
    "            lt1=LocationRecords[index][\"Latitude\"]\n",
    "            ln1=LocationRecords[index][\"Longitude\"]\n",
    "            lt2=LocationRecords[index+1][\"Latitude\"]\n",
    "            ln2=LocationRecords[index+1][\"Longitude\"]\n",
    "            totalDistance += mydistance(lt1,ln1, lt2,ln2)     \n",
    "\n",
    "        normalizedDistanceFromOrigin = distanceFromOrigin/totalDistance\n",
    "        LocationRecords[i]['distanceFromOrigin']=distanceFromOrigin\n",
    "        LocationRecords[i]['normalizedDistanceFromOrigin']=normalizedDistanceFromOrigin\n",
    "        for i in range(1, len(LocationRecords)):\n",
    "            lt1=LocationRecords[i-1][\"Latitude\"]\n",
    "            ln1=LocationRecords[i-1][\"Longitude\"]\n",
    "            lt2=LocationRecords[i][\"Latitude\"]\n",
    "            ln2=LocationRecords[i][\"Longitude\"]\n",
    "\n",
    "            distanceFromOrigin +=   mydistance(lt1,ln1,lt2,ln2)\n",
    "            normalizedDistanceFromOrigin =  distanceFromOrigin/totalDistance\n",
    "\n",
    "            LocationRecords[i]['distanceFromOrigin']=distanceFromOrigin\n",
    "            LocationRecords[i]['normalizedDistanceFromOrigin']=normalizedDistanceFromOrigin\n",
    "            #print(LocationRecords)\n",
    "            #input()\n",
    "        #con[RouteName].drop_collection(TripsInfo[TripIndex]+'.LocationRecordsWithDistanceFromOrigin')\n",
    "        con[RouteName]['TripInfo'].update_one({'SingleTripInfo':TripsInfo[TripIndex]},{'$set':{'totalDistance':totalDistance}})\n",
    "        con[RouteName][TripsInfo[TripIndex]+'.LocationRecordsWithDistanceFromOrigin'].insert_many(LocationRecords)\n"
   ]
  }
 ],
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