Distance wrapper script

Description

This notebook computes the distance for the records with respect to the origin point for the selected trips of the route.

import json
import math
import matplotlib.pyplot as plt

from pymongo import     MongoClient
con = MongoClient()

def mydistance(a1,b1,a2,b2):
    '''
    input: location attributes corresponding to point 1 and 2. (lat1, lon1, lat2, lon2)
    output: distance between point 1 and point 2
    function: compute distance between two points using haversine formula
    '''
    R=6371e3
    x1=math.radians(a1)
    y1=math.radians(b1)
    x2=math.radians(a2)
    y2=math.radians(b2)
    delx=x2-x1
    dely=y2-y1
    c=math.sin(delx/2)*math.sin(delx/2)+math.cos(x1)*math.cos(x2)*math.sin(dely/2)*math.sin(dely/2)
    d=2*math.atan2(math.sqrt(c),math.sqrt(1-c))
    e=R*d
    return(e)

#'''
ProjectDataUsed = True
UsedPreTrained = False
UseMongoDB = True
#'''
'''
ProjectDataUsed = True
UsedPreTrained = True
UseMongoDB = False
'''

'\nProjectDataUsed = True\nUsedPreTrained = True\nUseMongoDB = False\n'

RouteName = 'Git_ISCON_PDPU'

TripsInfo=['20_12_2017__18_31_19', '18_01_2018__07_38_10']

if UseMongoDB==True:
    '''Compute the distance for the trip records of the selected trips'''
    for TripIndex in range(len(TripsInfo)):
        LocationRecords = [lr for lr in con[RouteName][TripsInfo[TripIndex]+'.Filtered'].find().sort([('epoch',1)])]
        i=0
        #distanceFromOrigin=0.0
        LocationRecordsReference = [lr for lr in con[RouteName][TripsInfo[TripIndex]+'.Filtered'].find().sort([('epoch',1)])]

        distanceFromOrigin = mydistance(LocationRecordsReference[0]["Latitude"],LocationRecordsReference[0]["Longitude"],LocationRecords[0]["Latitude"],LocationRecords[0]["Longitude"])
        normalizedDistanceFromOrigin=0.0
        #print(distanceFromOrigin)
        #input()
        #totalDistance=0
        totalDistance=distanceFromOrigin
        for index in range(len(LocationRecords)-1):
            lt1=LocationRecords[index]["Latitude"]
            ln1=LocationRecords[index]["Longitude"]
            lt2=LocationRecords[index+1]["Latitude"]
            ln2=LocationRecords[index+1]["Longitude"]
            totalDistance += mydistance(lt1,ln1, lt2,ln2)

        normalizedDistanceFromOrigin = distanceFromOrigin/totalDistance
        LocationRecords[i]['distanceFromOrigin']=distanceFromOrigin
        LocationRecords[i]['normalizedDistanceFromOrigin']=normalizedDistanceFromOrigin
        for i in range(1, len(LocationRecords)):
            lt1=LocationRecords[i-1]["Latitude"]
            ln1=LocationRecords[i-1]["Longitude"]
            lt2=LocationRecords[i]["Latitude"]
            ln2=LocationRecords[i]["Longitude"]

            distanceFromOrigin +=   mydistance(lt1,ln1,lt2,ln2)
            normalizedDistanceFromOrigin =  distanceFromOrigin/totalDistance

            LocationRecords[i]['distanceFromOrigin']=distanceFromOrigin
            LocationRecords[i]['normalizedDistanceFromOrigin']=normalizedDistanceFromOrigin
            #print(LocationRecords)
            #input()
        #con[RouteName].drop_collection(TripsInfo[TripIndex]+'.LocationRecordsWithDistanceFromOrigin')
        con[RouteName]['TripInfo'].update_one({'SingleTripInfo':TripsInfo[TripIndex]},{'$set':{'totalDistance':totalDistance}})
        con[RouteName][TripsInfo[TripIndex]+'.LocationRecordsWithDistanceFromOrigin'].insert_many(LocationRecords)