@app.route('/rundsgenerator')
def processards ():
    import dsgenerator
    import experiments

temp = csv.writer(open("dataanalysis/temp.csv", "wb"))
index = csv.writer(open("dataanalysis/index.csv", "wb"))
index.writerow(['p', 'latitude', 'longitude'])
print("Writing temp.csv with real distance and similarity valuesnnPoints ignored:")
with open('dataanalysis/arquivo.csv') as f1:
    for row in csv.reader(iter(f1.readline, '')):
        countlines += 1
        if(firstline == False): # Ignore header line
            loopfirstline = True
            loopcountlines = -1
            firstloop = True
            ignoreIndex = 0
            if(ignorePoint(float(row[latpick]), float(row[longpick]), float(row[latdrop]), float(row[longdrop]))):
                ignoreIndex += 2
                print(float(row[latpick]), float(row[longpick]), float(row[latdrop]), float(row[longdrop]))
            else:
                index.writerow([(countlines*2) - 1 - ignoreIndex, row[latpick], row[longpick]])
                index.writerow([(countlines*2) - ignoreIndex, row[latdrop], row[longdrop]])
                with open('dataanalysis/arquivo.csv') as f2:
                    for looprow in csv.reader(iter(f2.readline, '')):
                        loopcountlines += 1
                        ignoreIndexloop = 0
                        if(loopfirstline == False):
                            if(countlines < loopcountlines):
                                if(ignorePoint(float(looprow[latpick]), float(looprow[longpick]), float(looprow[latdrop]), float(looprow[longdrop]))):
                                    ignoreIndexloop += 2
                                else:
                                    columnsid = [(countlines*2) - 1 - ignoreIndex, (countlines*2)  - ignoreIndex, (loopcountlines*2) - 1 - ignoreIndexloop, (loopcountlines*2) - ignoreIndexloop]
                                    # Column 1 actual X Column 2 actual
                                    # partsimilarity to iguals rows
                                    if(firstloop == True):
                                        partsimilarity =  (   (int(row[Passenger]) * 2) + 1  )
                                        distance = harvestine_distance(float(row[latpick]), float(row[longpick]), float(row[latdrop]), float(row[longdrop]))
                                        similarity = distance * partsimilarity
                                        temp.writerow([columnsid[0], columnsid[1], distance, similarity])
                                        setBiggerValue(distance, similarity)
                                        # partsimilarity to diferent rows
                                        firstloop = False
                                    try:
                                        partsimilarity =  (  (int(row[Passenger]) + int(looprow[Passenger])) + (getHour(row[Hour]) / getHour(looprow[Hour]))  )
                                    except:
                                        partsimilarity =  (  (int(row[Passenger]) + int(looprow[Passenger]))  )
                                    # Column 1 actual X Column 2 below
                                    distance = harvestine_distance(float(row[latpick]), float(row[longpick]), float(looprow[latdrop]), float(looprow[longdrop]))
                                    similarity = distance * partsimilarity
                                    temp.writerow([columnsid[0], columnsid[3], distance, similarity])
                                    setBiggerValue(distance, similarity)
                                    # Column 1 actual X Column 1 below
                                    distance = harvestine_distance(float(row[latpick]), float(row[longpick]), float(looprow[latpick]), float(looprow[longpick]))
                                    similarity = distance * partsimilarity
                                    temp.writerow([columnsid[0], columnsid[2], distance, similarity])
                                    setBiggerValue(distance, similarity)
                                    # Column 2 actual X Column 2 below
                                    distance = harvestine_distance(float(row[latdrop]), float(row[longdrop]), float(looprow[latpick]), float(looprow[longpick]))
                                    similarity = distance * partsimilarity
                                    temp.writerow([columnsid[1], columnsid[3], distance, similarity])
                                    setBiggerValue(distance, similarity)
                                    # Column 2 actual X Column 1 below
                                    distance = harvestine_distance(float(row[latdrop]), float(row[longdrop]), float(looprow[latdrop]), float(looprow[longdrop]))
                                    similarity = distance * partsimilarity
                                    temp.writerow([columnsid[1], columnsid[2], distance, similarity])
                                    setBiggerValue(distance, similarity)
                        else:
                            loopfirstline = False

                loopfirstline = True
        else:
            firstline = False