class GenericJourneyDataAnalysisPipeline:
def __init__(self):
self.G = GTFS(GTFS_DATA_BASEDIR)
self.day_start_ut = self.G.get_suitable_date_for_daily_extract(ut=True) + 3600
self.start_time = self.day_start_ut + 8 * 3600
self.end_time = self.day_start_ut + 11 * 3600
self.profiles = {}
self.journey_analyzer = None
# self.analysis_start_time
# self.analysis_end_time
def script(self):
journey_analyzer = JourneyDataAnalyzer(JOURNEY_DATA_DIR, GTFS_DATA_BASEDIR)
if False:
gdf = journey_analyzer.get_transfer_stops()
gdf.to_file(shapefile_dir('transfer_stops'), driver='ESRI Shapefile')
gdf = journey_analyzer.get_transfer_walks()
gdf.to_file(shapefile_dir('transfer_walks'), driver='ESRI Shapefile')
gdf = journey_analyzer.journeys_per_section()
gdf.to_file(shapefile_dir('journeys_per_section'), driver='ESRI Shapefile')
gdf = journey_analyzer.journey_alternatives_per_stop()
gdf.to_file(shapefile_dir('journeys_per_stop'), driver='ESRI Shapefile')
journey_analyzer.n_route_alternatives()
class GenericJourneyDataPipeline:
def __init__(self):
self.G = GTFS(GTFS_DATA_BASEDIR)
self.day_start_ut = self.G.get_suitable_date_for_daily_extract(
ut=True) + 3600
self.start_time = self.day_start_ut + 8 * 3600
self.end_time = self.day_start_ut + 11 * 3600
self.profiles = {}
self.journey_analyzer = None
# self.analysis_start_time
# self.analysis_end_time
makedirs(RESULTS_DIRECTORY)
print("Retrieving transit events")
self.connections = []
for e in self.G.generate_routable_transit_events(
start_time_ut=self.start_time, end_time_ut=self.end_time):
self.connections.append(
Connection(int(e.from_stop_I), int(e.to_stop_I),
int(e.dep_time_ut), int(e.arr_time_ut),
int(e.trip_I)))
print("Retrieving walking network")
self.net = self.G.get_walk_transfer_stop_to_stop_network()
def script(self):
self.get_profile_data()
journey_analyzer = JourneyDataManager(TARGET_STOPS,
JOURNEY_DATA_DIR,
GTFS_DATA_BASEDIR,
ROUTING_PARAMS,
track_route=True,
close_connection=False)
journey_analyzer.import_journey_data_for_target_stop(self.profiles)
journey_analyzer.create_indices()
if False:
journey_analyzer.add_fastest_path_column()
"""
all_geoms = journey_analyzer.get_all_geoms()
journey_path = os.path.join(RESULTS_DIRECTORY, "all_routes_to_" + target_list_to_str(TARGET_STOPS) + ".geojson")
with open(journey_path, 'w') as f:
dump(journey_analyzer.extract_geojson(all_geoms), f)
"""
def get_profile_data(self, targets=TARGET_STOPS, recompute=False):
node_profiles_fname = os.path.join(
RESULTS_DIRECTORY,
"node_profile_" + target_list_to_str(targets) + ".pickle")
if not recompute and os.path.exists(node_profiles_fname):
print("Loading precomputed data")
self.profiles = pickle.load(open(node_profiles_fname, 'rb'))
print("Loaded precomputed data")
else:
print("Recomputing profiles")
self._compute_profile_data()
pickle.dump(self.profiles, open(node_profiles_fname, 'wb'), -1)
print("Recomputing profiles")
def _compute_profile_data(self):
csp = MultiObjectivePseudoCSAProfiler(self.connections,
TARGET_STOPS,
walk_network=self.net,
transfer_margin=TRANSFER_MARGIN,
walk_speed=WALK_SPEED,
verbose=True,
track_vehicle_legs=False,
track_time=True,
track_route=True)
print("CSA Profiler running...")
csp.run()
print("CSA profiler finished")
self.profiles = dict(csp.stop_profiles)
def key_measures_as_csv(self, csv_path="stop_data.csv"):
"""
Combines key temporal distance measures for each node with stop data from gtfs and stores in csv format
:return:
"""
node_profiles_list = []
# iterate through all node profiles and add the NodeProfileAnalyzer data to a list of dicts
for node, profile in self.profiles.items():
npa = NodeProfileAnalyzerTimeAndVehLegs.from_profile(
profile, self.start_time, self.end_time)
node_profile_dict = npa.get_node_profile_measures_as_dict()
node_profile_dict["node"] = node
node_profiles_list.append(node_profile_dict)
node_profiles = DataFrame(node_profiles_list)
stops = self.G.stops()
stops.join(node_profiles.set_index("node"),
on='stop_I').to_csv(path_or_buf=csv_path)
