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import pandas as pd
import geopandas as gpd
import numpy as np
import os, urllib, json, csv, zipfile, math

INPUT='input_ut.csv'
OUTPUT='output_ut.csv'
OUTPUT_PC='distances_pc4_ut.json'

with zipfile.Zipfile('gtfs-nl.zip') as z:

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URL = 'http://localhost:8080/otp/routers/default/plan?'

outputFile = open(OUTPUT, 'w')
writer = csv.writer(outputFile)
writer.writerow(['StartDate','StartTime','StartLat','StartLon','EndLat',
                 'EndLon', 'DurationMin'])

# Takes CSV input, creates URLs, stores data locally in row array
for c, d in df.iterrows():
    params =  {'date'            : d['Date'],

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pc = gpd.read_file(os.path.join('PC_4-shp','PC4.shp'))
df = pd.read_csv(OUTPUT)
gdf = gpd.GeoDataFrame(df, 
                       geometry=gpd.points_from_xy(df.EndLon, df.EndLat))
                            [['geometry', 'DurationMin']]

def get_avg_traveltime(pc4):
    v = gdf[gdf.within(pc4['geometry'] )].mean(skipna=True)
    if math.isnan(v):
        return np.NaN

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pc.to_file(OUTPUT_PC, driver="GeoJSON")

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# Unreachable? Closest other postal area and add 15 min
for index, pcode in pc.iterrows():   
  if math.isnan(pcode.Traveltime):
    # get 'not disjoint' countries
    dst=pc[pc.geometry.touches(pcode.geometry)]['Traveltime'].mean(skipna=True)
    # add names of neighbors as NEIGHBORS value
    if ~math.isnan(dst) and (dst > 0):
      pc.at[index, "Traveltime"] = int(dst + 15)
    else:
      pc.at[index, "Traveltime"] = pc.Traveltime.max()

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Java -Xmx12G -jar otp-2.0.0-shaded.jar --build --serve .

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java -Xmx12G -jar otp-2.0.0-shaded.jar --build --serve .

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import pandas as pd
import geopandas as gpd
import numpy as np
import urllib, json, csv, zipfile, sys
import matplotlib.pyplot as plt
from geopandas.tools import sjoin
from shapely.geometry import LineString, Point

with zipfile.ZipFile('gtfs-nl.zip') as z:
    with z.open('stops.txt') as f:

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segments_df['line'] = segments_df.apply(lambda x: LineString([Point(x["lon_x"], x["lat_x"]),
                                                              Point(x["lon_y"], x["lat_y"])]), 
                                                              axis=1)

geodata = gpd.GeoDataFrame(segments_df, geometry=segments_df['line'])

geodata['width'] = 5 * (geodata['count']) / (geodata['count'].max())

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geodata.plot(figsize=(15, 15), linewidth=np.minimum(np.maximum(df5['width'], 0.1), 2.75))