def converting_data_to_graph(folder_path_data_filtered):
'''
Transforms GTFS data to a graph using the peartree package
Parameters
----------
folder_path_data_filtered : string
Path of the folder containing the zip file comprising
the cleaned GTFS data.
Returns
-------
G : Graph
Graph containing all the data from the GTFS files.
'''
path = folder_path_data_filtered+'filtered_dfs.zip'
# Automatically identify the busiest day and
# read that in as a Partridge feed
feed = pt.get_representative_feed(path)
# Set a target time period to
# use to summarize impedance
start = 1*60*60 # 1:00 AM
end = 23*60*60 # 11:00 PM
# Converts feed subset into a directed
# network multigraph
G = pt.load_feed_as_graph(feed, start, end, name='RATP',
use_multiprocessing=True,
impute_walk_transfers=True,
connection_threshold=150)
return G
def load_data(name):
# Load the streets
G_s = ox.load_graphml('{}/{}_walk.graphml'.format(name, name))
# Load the area
area = gpd.read_file('../Data/{}/{}_shape'.format(name, name))
area = ox.project_gdf(area,
to_crs={
'datum': 'WGS84',
'ellps': 'WGS84',
'proj': 'utm',
'zone': 34,
'units': 'm'
})
path = '../Data/{}/budapest_gtfs.zip'.format(name)
# Automatically identify the busiest day and read that in as a Partidge feed
#G = ox.load_graphml('{}/{}_bike.graphml'.format(name, name))
# Automatically identify the busiest day and
# read that in as a Partidge feed
feed = pt.get_representative_feed(path)
# Set a target time period to
# use to summarize impedance
start = 7 * 60 * 60 # 7:00 AM
end = 10 * 60 * 60 # 10:00 AM
# Converts feed subset into a directed
# network multigraph
G = pt.load_feed_as_graph(feed, start, end)
return G_s, area, G
def main():
# tl_query = 'https://transit.land/api/v1/feeds?bbox=-73.97339,40.649778,-73.946532,40.670353'
# resp = requests.get(tl_query)
# rj = resp.json()
# zip_url = None
# for f in rj['feeds']:
# if 'brooklyn' in f['onestop_id']:
# zip_url = f['url']
# td = tempfile.mkdtemp()
# path = os.path.join(td, 'mta_bk.zip')
# resp = requests.get(zip_url)
# open(path, 'wb').write(resp.content)
feed = pt.get_representative_feed('reduced_stops/updatedv1.zip')
start = 7*60*60 # 7:00 AM
end = 10*60*60 # 10:00 AM
G = pt.load_feed_as_graph(feed, start, end)
pt.generate_plot(G)
nodes = nx.betweenness_centrality(G)
nids = []
vals = []
for k in nodes.keys():
nids.append(k)
vals.append(nodes[k])
vals_adj = []
m = max(vals)
for v in vals:
if v == 0:
vals_adj.append(0)
else:
r = (v/m)
vals_adj.append(r * 0.01)
fig, ax = pt.generate_plot(G)
ps = []
for nid, buff_dist in zip(nids, vals_adj):
n = G.node[nid]
if buff_dist > 0:
p = Point(n['x'], n['y']).buffer(buff_dist)
ps.append(p)
gpd.GeoSeries(ps).plot(ax=ax, color='r', alpha=0.75)
import peartree as pt
pt.utilities.config(log_console=True)
if __name__ == '__main__':
'''
Run from command line via:
python -m cProfile -o profile/cprof-output.py performance/run_etl.py
And visualize via:
snakeviz profile/cprof-output.py
'''
url_path = 'http://www.actransit.org/wp-content/uploads/GTFS_Fall17.zip'
tempdir = tempfile.mkdtemp()
filepath = os.path.join(tempdir, 'perf_gtfs.zip')
response = urlopen(url_path)
zipcontent = response.read()
with open(filepath, 'wb') as f:
f.write(zipcontent)
feed = pt.get_representative_feed(filepath)
start = 7 * 60 * 60 # 7:00 AM
end = 10 * 60 * 60 # 10:00 AM
G = pt.load_feed_as_graph(feed,
start,
end,
interpolate_times=True,
use_multiprocessing=False)
import peartree as pt
import os
from shapely.geometry import Point, LineString
import psycopg2
from cordon.tabular.records import PresenceFieldsMapping
from cordon.analysis.operation import LocalOperation
import folium
#read transportation network from gtfs file
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(BASE_DIR,'gtfs.zip')
feed = pt.get_representative_feed(path)
start = 0
end = 24*60*60
G = pt.load_feed_as_graph(feed,start,end)
#constructe cordon network based on transportation network
cordon = CordonNetwork()
for n, d in G.nodes(data=True):
name = n.split('_')[-1]
cordon.add_node(name)
cordon.set_node_geometry(name,Point(d['x'],d['y']))
for a, b, d in G.edges(data=True):
from_name = a.split('_')[-1]
to_name = b.split('_')[-1]
segment_name = '''{}-{}'''.format(from_name,to_name)
from_point = cordon.get_node_geometry(from_name)
to_point = cordon.get_node_geometry(to_name)
line = LineString([from_point, to_point])
def __init__(self, path, the_date):
self.feed = self.get_representative_feed(path,the_date)
self.G = pt.load_feed_as_graph(self.feed, 0, 24*60*60)
self.G_add_walk=self.add_edge_walk()
import numpy as np import osmnx as ox import pandas as pd import peartree as pt path = '/Users/valenti/py_work/romatpl/rome_static_gtfs.zip' # Automatically identify the busiest day and # read that in as a Partidge feed feed = pt.get_representative_feed(path) # Set a target time period to # use to summarize impedance start = 7*60*60 # 7:00 AM end = 10*60*60 # 10:00 AM # Converts feed subset into a directed # network multigraph #if there are two bus stops that are close together #the connection_threshold is defaulted to 50 meters. #walk_speed_kmph is default to 4.5 kilometers per hour. G = pt.load_feed_as_graph(feed, start, end, connection_threshold=200,walk_speed_kmph=4.0) #G_projected = ox.project_graph(G) ox.plot_graph(G) origin = [41.867876, 12.519082] destination = [41.889517, 12.506139] point1 = (42.891310, -78.871355) print(ox.get_nearest_node(G, origin)) #G_projected = ox.project_graph(G) # save street network as GraphML file #ox.save_graphml(G_projected, filename='networkRM.graphml')