def run(self, context, skip_shape_dist=False, **kwargs):
columns = ["trip_id", "arrival_time", "departure_time", "stop_id", "stop_sequence", "stop_headsign", "pickup_type", "drop_off_type", "timepoint"]
if not skip_shape_dist:
columns.append("shape_dist_traveled")
with PrettyCsv("stop_times.txt", columns, **kwargs) as csvout:
ntrips = 0
for trip in context.dao().trips(fltr=context.args.filter, prefetch_stops=False, prefetch_stop_times=True, prefetch_calendars=False, prefetch_routes=False):
if ntrips % 1000 == 0:
print("%d trips..." % (ntrips))
ntrips += 1
for stoptime in trip.stop_times:
row = [ trip.trip_id,
fmttime(stoptime.arrival_time if stoptime.arrival_time else stoptime.departure_time),
fmttime(stoptime.departure_time if stoptime.departure_time else stoptime.arrival_time),
stoptime.stop_id,
stoptime.stop_sequence,
stoptime.stop_headsign,
stoptime.pickup_type,
stoptime.drop_off_type,
stoptime.timepoint ]
if not skip_shape_dist:
row.append(stoptime.shape_dist_traveled)
csvout.writerow(row)
print("Processed %d trips" % (ntrips))
def test_demo(self):
dao = Dao(DAO_URL, sql_logging=False)
dao.load_gtfs(DUMMY_GTFS)
print("List of stops named '...Bordeaux...':")
stops_bordeaux = list(dao.stops(fltr=(Stop.stop_name.ilike('%Bordeaux%')) & (Stop.location_type == Stop.TYPE_STOP)))
for stop in stops_bordeaux:
print(stop.stop_name)
print("List of routes passing by those stops:")
routes_bordeaux = dao.routes(fltr=or_(StopTime.stop == stop for stop in stops_bordeaux))
for route in routes_bordeaux:
print("%s - %s" % (route.route_short_name, route.route_long_name))
july4 = CalendarDate.ymd(2016, 7, 4)
print("All departures from those stops on %s:" % (july4.as_date()))
departures = list(dao.stoptimes(fltr=(or_(StopTime.stop == stop for stop in stops_bordeaux)) & (StopTime.departure_time != None) & (func.date(CalendarDate.date) == july4.date)))
print("There is %d departures" % (len(departures)))
for departure in departures:
print("%30.30s %10.10s %-20.20s > %s" % (departure.stop.stop_name, fmttime(departure.departure_time), departure.trip.route.route_long_name, departure.trip.trip_headsign))
print("Number of departures and time range per stop on %s:" % (july4.as_date()))
departure_by_stop = defaultdict(list)
for departure in departures:
departure_by_stop[departure.stop].append(departure)
for stop, deps in departure_by_stop.items():
min_dep = min(d.departure_time for d in deps)
max_dep = max(d.departure_time for d in deps)
print("%30.30s %3d departures (from %s to %s)" % (stop.stop_name, len(deps), fmttime(min_dep), fmttime(max_dep)))
# Compute the average distance and time to next stop by route type
ntd = [ [0, 0, 0.0] for type in range(0, Route.TYPE_FUNICULAR + 1) ]
for departure in departures:
# The following is guaranteed to succeed as we have departure_time == Null for last stop time in trip
next_arrival = departure.trip.stop_times[departure.stop_sequence + 1]
hop_dist = next_arrival.shape_dist_traveled - departure.shape_dist_traveled
hop_time = next_arrival.arrival_time - departure.departure_time
route_type = departure.trip.route.route_type
ntd[route_type][0] += 1
ntd[route_type][1] += hop_time
ntd[route_type][2] += hop_dist
for route_type in range(0, len(ntd)):
n, t, d = ntd[route_type]
if n > 0:
print("The average distance to the next stop on those departures for route type %d is %.2f meters" % (route_type, d / n))
print("The average time in sec to the next stop on those departures for route type %d is %s" % (route_type, fmttime(t / n)))
def _convert_gtfs_model(feed_id,
gtfs,
dao,
lenient=False,
disable_normalization=False):
feedinfo2 = None
logger.info("Importing feed ID '%s'" % feed_id)
n_feedinfo = 0
for feedinfo in gtfs.feedinfo():
n_feedinfo += 1
if n_feedinfo > 1:
logger.error(
"Feed info should be unique if defined. Taking first one." %
(n_feedinfo))
break
# TODO Automatically compute from calendar range if missing?
feedinfo['feed_start_date'] = _todate(feedinfo.get('feed_start_date'))
feedinfo['feed_end_date'] = _todate(feedinfo.get('feed_end_date'))
feedinfo2 = FeedInfo(feed_id, **feedinfo)
if feedinfo2 is None:
# Optional, generate empty feed info
feedinfo2 = FeedInfo(feed_id)
dao.add(feedinfo2)
dao.flush()
logger.info("Imported %d feedinfo" % n_feedinfo)
logger.info("Importing agencies...")
n_agencies = 0
single_agency = None
agency_ids = set()
for agency in gtfs.agencies():
# agency_id is optional only if we have a single agency
if n_agencies == 0 and agency.get('agency_id') is None:
agency['agency_id'] = ''
agency2 = Agency(feed_id, **agency)
if n_agencies == 0:
single_agency = agency2
else:
single_agency = None
n_agencies += 1
dao.add(agency2)
agency_ids.add(agency2.agency_id)
dao.flush()
logger.info("Imported %d agencies" % n_agencies)
def import_stop(stop, stoptype, zone_ids, item_ids, station_ids=None):
zone_id = stop.get('zone_id')
if zone_id and zone_id not in zone_ids:
# Lazy-creation of zone
zone = Zone(feed_id, zone_id)
zone_ids.add(zone_id)
dao.add(zone)
stop['location_type'] = _toint(stop.get('location_type'),
Stop.TYPE_STOP)
if stop['location_type'] != stoptype:
return 0
stop['wheelchair_boarding'] = _toint(stop.get('wheelchair_boarding'),
Stop.WHEELCHAIR_UNKNOWN)
lat = _tofloat(stop.get('stop_lat'), None)
lon = _tofloat(stop.get('stop_lon'), None)
if lat is None or lon is None:
if lenient:
logger.error("Missing lat/lon for '%s', set to default (0,0)" %
(stop, ))
if lat is None:
lat = 0
if lon is None:
lon = 0
else:
raise ValueError("Missing mandatory lat/lon for '%s'." %
(stop, ))
stop['stop_lat'] = lat
stop['stop_lon'] = lon
# This field has been renamed for consistency
parent_id = stop.get('parent_station')
stop['parent_station_id'] = parent_id if parent_id else None
if parent_id and station_ids and parent_id not in station_ids:
if lenient:
logger.error(
"Parent station ID '%s' in '%s' is invalid, resetting." %
(parent_id, stop))
stop['parent_station_id'] = None
else:
raise KeyError("Parent station ID '%s' in '%s' is invalid." %
(parent_id, stop))
stop.pop('parent_station', None)
stop2 = Stop(feed_id, **stop)
dao.add(stop2)
item_ids.add(stop2.stop_id)
return 1
stop_ids = set()
station_ids = set()
zone_ids = set()
logger.info("Importing zones, stations and stops...")
n_stations = n_stops = 0
for station in gtfs.stops():
n_stations += import_stop(station, Stop.TYPE_STATION, zone_ids,
station_ids)
for stop in gtfs.stops():
n_stops += import_stop(stop, Stop.TYPE_STOP, zone_ids, stop_ids,
station_ids)
dao.flush()
logger.info("Imported %d zones, %d stations and %d stops" %
(len(zone_ids), n_stations, n_stops))
logger.info("Importing transfers...")
n_transfers = 0
for transfer in gtfs.transfers():
from_stop_id = transfer.get('from_stop_id')
to_stop_id = transfer.get('to_stop_id')
transfer['transfer_type'] = _toint(transfer.get('transfer_type'), 0)
for stop_id in (from_stop_id, to_stop_id):
if stop_id not in station_ids and stop_id not in stop_ids:
if lenient:
logger.error("Stop ID '%s' in '%s' is invalid, skipping." %
(stop_id, transfer))
continue
else:
raise KeyError("Stop ID '%s' in '%s' is invalid." %
(stop_id, transfer))
transfer2 = Transfer(feed_id, **transfer)
n_transfers += 1
dao.add(transfer2)
dao.flush()
logger.info("Imported %d transfers" % (n_transfers))
logger.info("Importing routes...")
n_routes = 0
route_ids = set()
for route in gtfs.routes():
route['route_type'] = int(route.get('route_type'))
agency_id = route.get('agency_id')
if (agency_id is None
or len(agency_id) == 0) and single_agency is not None:
# Route.agency is optional if only a single agency exists.
agency_id = route['agency_id'] = single_agency.agency_id
if agency_id not in agency_ids:
if lenient:
logger.error(
"Agency ID '%s' in '%s' is invalid, skipping route." %
(agency_id, route))
continue
else:
raise KeyError("agency ID '%s' in '%s' is invalid." %
(agency_id, route))
route2 = Route(feed_id, **route)
dao.add(route2)
route_ids.add(route2.route_id)
n_routes += 1
dao.flush()
logger.info("Imported %d routes" % n_routes)
logger.info("Importing fares...")
n_fares = 0
for fare_attr in gtfs.fare_attributes():
fare_id = fare_attr.get('fare_id')
fare_price = _tofloat(fare_attr.get('price'))
currency_type = fare_attr.get('currency_type')
payment_method = _toint(fare_attr.get('payment_method'))
n_transfers = None
if fare_attr.get('transfers') is not None:
n_transfers = _toint(fare_attr.get('transfers'))
transfer_duration = None
if fare_attr.get('transfer_duration') is not None:
transfer_duration = _toint(fare_attr.get('transfer_duration'))
fare = FareAttribute(feed_id, fare_id, fare_price, currency_type,
payment_method, n_transfers, transfer_duration)
dao.add(fare)
n_fares += 1
dao.flush()
fare_rules = set()
for fare_rule in gtfs.fare_rules():
fare_rule2 = FareRule(feed_id, **fare_rule)
if fare_rule2 in fare_rules:
if lenient:
logger.error("Duplicated fare rule (%s), skipping." %
(fare_rule2))
continue
else:
raise KeyError("Duplicated fare rule (%s)" % (fare_rule2))
dao.add(fare_rule2)
fare_rules.add(fare_rule2)
dao.flush()
logger.info("Imported %d fare and %d rules" % (n_fares, len(fare_rules)))
logger.info("Importing calendars...")
calanddates2 = {}
for calendar in gtfs.calendars():
calid = calendar.get('service_id')
calendar2 = Calendar(feed_id, calid)
dates2 = []
start_date = CalendarDate.fromYYYYMMDD(calendar.get('start_date'))
end_date = CalendarDate.fromYYYYMMDD(calendar.get('end_date'))
for d in CalendarDate.range(start_date, end_date.next_day()):
if int(calendar.get(DOW_NAMES[d.dow()])):
dates2.append(d)
calanddates2[calid] = (calendar2, set(dates2))
logger.info("Normalizing calendar dates...")
for caldate in gtfs.calendar_dates():
calid = caldate.get('service_id')
date2 = CalendarDate.fromYYYYMMDD(caldate.get('date'))
addremove = int(caldate.get('exception_type'))
if calid in calanddates2:
calendar2, dates2 = calanddates2[calid]
else:
calendar2 = Calendar(feed_id, calid)
dates2 = set([])
calanddates2[calid] = (calendar2, dates2)
if addremove == 1:
dates2.add(date2)
elif addremove == 2:
if date2 in dates2:
dates2.remove(date2)
n_calendars = 0
n_caldates = 0
calendar_ids = set()
for (calendar2, dates2) in calanddates2.values():
calendar2.dates = [d for d in dates2]
dao.add(calendar2)
calendar_ids.add(calendar2.service_id)
n_calendars += 1
n_caldates += len(calendar2.dates)
dao.flush()
logger.info("Imported %d calendars and %d dates" %
(n_calendars, n_caldates))
logger.info("Importing shapes...")
n_shape_pts = 0
shape_ids = set()
shapepts_q = []
for shpt in gtfs.shapes():
shape_id = shpt.get('shape_id')
if shape_id not in shape_ids:
dao.add(Shape(feed_id, shape_id))
dao.flush()
shape_ids.add(shape_id)
pt_seq = _toint(shpt.get('shape_pt_sequence'))
# This field is optional
dist_traveled = _tofloat(shpt.get('shape_dist_traveled'), -999999)
lat = _tofloat(shpt.get('shape_pt_lat'))
lon = _tofloat(shpt.get('shape_pt_lon'))
shape_point = ShapePoint(feed_id, shape_id, pt_seq, lat, lon,
dist_traveled)
shapepts_q.append(shape_point)
n_shape_pts += 1
if n_shape_pts % 100000 == 0:
logger.info("%d shape points" % n_shape_pts)
dao.bulk_save_objects(shapepts_q)
dao.flush()
shapepts_q = []
dao.bulk_save_objects(shapepts_q)
dao.flush()
logger.info("Imported %d shapes and %d points" %
(len(shape_ids), n_shape_pts))
logger.info("Importing trips...")
n_trips = 0
trips_q = []
trip_ids = set()
for trip in gtfs.trips():
trip['wheelchair_accessible'] = _toint(
trip.get('wheelchair_accessible'), Trip.WHEELCHAIR_UNKNOWN)
trip['bikes_allowed'] = _toint(trip.get('bikes_allowed'),
Trip.BIKES_UNKNOWN)
cal_id = trip.get('service_id')
if cal_id not in calendar_ids:
if lenient:
logger.error(
"Calendar ID '%s' in '%s' is invalid. Skipping trip." %
(cal_id, trip))
continue
else:
raise KeyError("Calendar ID '%s' in '%s' is invalid." %
(cal_id, trip))
route_id = trip.get('route_id')
if route_id not in route_ids:
if lenient:
logger.error(
"Route ID '%s' in '%s' is invalid. Skipping trip." %
(route_id, trip))
continue
else:
raise KeyError("Route ID '%s' in trip '%s' is invalid." %
(route_id, trip))
trip2 = Trip(feed_id, frequency_generated=False, **trip)
trips_q.append(trip2)
n_trips += 1
if n_trips % 10000 == 0:
dao.bulk_save_objects(trips_q)
dao.flush()
logger.info('%s trips' % n_trips)
trips_q = []
trip_ids.add(trip.get('trip_id'))
dao.bulk_save_objects(trips_q)
dao.flush()
logger.info("Imported %d trips" % n_trips)
logger.info("Importing stop times...")
n_stoptimes = 0
stoptimes_q = []
for stoptime in gtfs.stop_times():
stopseq = _toint(stoptime.get('stop_sequence'))
# Mark times to interpolate later on
arrtime = _timetoint(stoptime.get('arrival_time'), -999999)
deptime = _timetoint(stoptime.get('departure_time'), -999999)
if arrtime == -999999:
arrtime = deptime
if deptime == -999999:
deptime = arrtime
interp = arrtime < 0 and deptime < 0
shpdist = _tofloat(stoptime.get('shape_dist_traveled'), -999999)
pkptype = _toint(stoptime.get('pickup_type'),
StopTime.PICKUP_DROPOFF_REGULAR)
drptype = _toint(stoptime.get('drop_off_type'),
StopTime.PICKUP_DROPOFF_REGULAR)
trip_id = stoptime.get('trip_id')
if trip_id not in trip_ids:
if lenient:
logger.error(
"Trip ID '%s' in '%s' is invalid. Skipping stop time." %
(trip_id, stoptime))
continue
else:
raise KeyError("Trip ID '%s' in '%s' is invalid." %
(trip_id, stoptime))
stop_id = stoptime.get('stop_id')
if stop_id not in stop_ids:
if lenient:
logger.error(
"Stop ID '%s' in '%s' is invalid. Skipping stop time." %
(stop_id, stoptime))
continue
else:
raise KeyError("Trip ID '%s' in stoptime '%s' is invalid." %
(stop_id, stoptime))
stoptime2 = StopTime(feed_id,
trip_id,
stop_id,
stop_sequence=stopseq,
arrival_time=arrtime,
departure_time=deptime,
shape_dist_traveled=shpdist,
interpolated=interp,
pickup_type=pkptype,
drop_off_type=drptype,
stop_headsign=stoptime.get('stop_headsign'))
stoptimes_q.append(stoptime2)
n_stoptimes += 1
# Commit every now and then
if n_stoptimes % 50000 == 0:
logger.info("%d stop times" % n_stoptimes)
dao.bulk_save_objects(stoptimes_q)
dao.flush()
stoptimes_q = []
dao.bulk_save_objects(stoptimes_q)
logger.info("Imported %d stop times" % n_stoptimes)
logger.info("Committing")
dao.flush()
# TODO Add option to enable/disable this commit
# to ensure import is transactionnal
dao.commit()
logger.info("Commit done")
def normalize_trip(trip, odometer):
stopseq = 0
n_stoptimes = len(trip.stop_times)
last_stoptime_with_time = None
to_interpolate = []
odometer.reset()
for stoptime in trip.stop_times:
stoptime.stop_sequence = stopseq
stoptime.shape_dist_traveled = odometer.dist_traveled(
stoptime.stop, stoptime.shape_dist_traveled
if stoptime.shape_dist_traveled != -999999 else None)
if stopseq == 0:
# Force first arrival time to NULL
stoptime.arrival_time = None
if stopseq == n_stoptimes - 1:
# Force last departure time to NULL
stoptime.departure_time = None
if stoptime.interpolated:
to_interpolate.append(stoptime)
else:
if len(to_interpolate) > 0:
# Interpolate
if last_stoptime_with_time is None:
logger.error(
"Cannot interpolate missing time at trip start: %s"
% trip)
for stti in to_interpolate:
# Use first defined time as fallback value.
stti.arrival_time = stoptime.arrival_time
stti.departure_time = stoptime.arrival_time
else:
tdist = stoptime.shape_dist_traveled - last_stoptime_with_time.shape_dist_traveled
ttime = stoptime.arrival_time - last_stoptime_with_time.departure_time
for stti in to_interpolate:
fdist = stti.shape_dist_traveled - last_stoptime_with_time.shape_dist_traveled
t = last_stoptime_with_time.departure_time + ttime * fdist // tdist
stti.arrival_time = t
stti.departure_time = t
to_interpolate = []
last_stoptime_with_time = stoptime
stopseq += 1
if len(to_interpolate) > 0:
# Should not happen, but handle the case, we never know
if last_stoptime_with_time is None:
logger.error(
"Cannot interpolate missing time, no time at all: %s" %
trip)
# Keep times NULL (TODO: or remove the trip?)
else:
logger.error(
"Cannot interpolate missing time at trip end: %s" % trip)
for stti in to_interpolate:
# Use last defined time as fallback value
stti.arrival_time = last_stoptime_with_time.departure_time
stti.departure_time = last_stoptime_with_time.departure_time
if disable_normalization:
logger.info("Skipping shapes and trips normalization")
else:
logger.info("Normalizing shapes and trips...")
nshapes = 0
ntrips = 0
odometer = _Odometer()
# Process shapes and associated trips
for shape in dao.shapes(fltr=Shape.feed_id == feed_id,
prefetch_points=True,
batch_size=50):
# Shape will be registered in the normalize
odometer.normalize_and_register_shape(shape)
for trip in dao.trips(fltr=(Trip.feed_id == feed_id) &
(Trip.shape_id == shape.shape_id),
prefetch_stop_times=False,
prefetch_stops=False,
batch_size=800):
normalize_trip(trip, odometer)
ntrips += 1
if ntrips % 1000 == 0:
logger.info("%d trips, %d shapes" % (ntrips, nshapes))
dao.flush()
nshapes += 1
#odometer._debug_cache()
# Process trips w/o shapes
for trip in dao.trips(fltr=(Trip.feed_id == feed_id) &
(Trip.shape_id == None),
prefetch_stop_times=False,
prefetch_stops=False,
batch_size=800):
odometer.register_noshape()
normalize_trip(trip, odometer)
ntrips += 1
if ntrips % 1000 == 0:
logger.info("%d trips" % ntrips)
dao.flush()
dao.flush()
logger.info("Normalized %d trips and %d shapes" % (ntrips, nshapes))
# Note: we expand frequencies *after* normalization
# for performances purpose only: that minimize the
# number of trips to normalize. We can do that since
# the expansion is neutral trip-normalization-wise.
logger.info("Expanding frequencies...")
n_freq = 0
n_exp_trips = 0
trips_to_delete = []
for frequency in gtfs.frequencies():
trip_id = frequency.get('trip_id')
if trip_id not in trip_ids:
if lenient:
logger.error(
"Trip ID '%s' in '%s' is invalid. Skipping frequency." %
(trip_id, frequency))
continue
else:
raise KeyError("Trip ID '%s' in '%s' is invalid." %
(trip_id, frequency))
trip = dao.trip(trip_id, feed_id=feed_id)
start_time = _timetoint(frequency.get('start_time'))
end_time = _timetoint(frequency.get('end_time'))
headway_secs = _toint(frequency.get('headway_secs'))
exact_times = _toint(frequency.get('exact_times'), Trip.TIME_APPROX)
for trip_dep_time in range(start_time, end_time, headway_secs):
# Here we assume departure time are all different.
# That's a requirement in the GTFS specs, but this may break.
# TODO Make the expanded trip ID generation parametrable.
trip_id2 = trip.trip_id + "@" + fmttime(trip_dep_time)
trip2 = Trip(feed_id,
trip_id2,
trip.route_id,
trip.service_id,
wheelchair_accessible=trip.wheelchair_accessible,
bikes_allowed=trip.bikes_allowed,
exact_times=exact_times,
frequency_generated=True,
trip_headsign=trip.trip_headsign,
trip_short_name=trip.trip_short_name,
direction_id=trip.direction_id,
block_id=trip.block_id)
trip2.stop_times = []
base_time = trip.stop_times[0].departure_time
for stoptime in trip.stop_times:
arrtime = None if stoptime.arrival_time is None else stoptime.arrival_time - base_time + trip_dep_time
deptime = None if stoptime.departure_time is None else stoptime.departure_time - base_time + trip_dep_time
stoptime2 = StopTime(
feed_id,
trip_id2,
stoptime.stop_id,
stoptime.stop_sequence,
arrival_time=arrtime,
departure_time=deptime,
shape_dist_traveled=stoptime.shape_dist_traveled,
interpolated=stoptime.interpolated,
timepoint=stoptime.timepoint,
pickup_type=stoptime.pickup_type,
drop_off_type=stoptime.drop_off_type)
trip2.stop_times.append(stoptime2)
n_exp_trips += 1
# This will add the associated stop times
dao.add(trip2)
# Do not delete trip now, as two frequency can refer to same trip
trips_to_delete.append(trip)
n_freq += 1
for trip in trips_to_delete:
# This also delete the associated stop times
dao.delete(trip)
dao.flush()
dao.commit()
logger.info("Expanded %d frequencies to %d trips." % (n_freq, n_exp_trips))
logger.info("Feed '%s': import done." % feed_id)
def decret_2015_1610(trips,
trace=True,
required_distance=500,
required_ratio=2.5):
affiche(trace, "Calcul decret 2015 1610 sur %d voyages." % (len(trips)))
if len(trips) == 0:
affiche(trace, "Aucun voyages, impossible de calculer.")
return None, None, None
affiche(trace, "Calcul de l'espacement moyen des arrêts...")
espacement_moyen = 0
w_esp = 0
for trip in trips:
# Note: on pondère par le nombre de jours chaque voyage est applicable.
# Ceci permet de prendre en compte la fréquence: par exemple, la distance
# d'un intervalle entre deux arrêt actif le lundi uniquement sera pondéré
# 5 fois moins qu'un autre intervalle actif du lundi au vendredi.
n_jours = len(trip.calendar.dates)
for stoptime1, stoptime2 in trip.hops():
espacement_moyen += (stoptime2.shape_dist_traveled -
stoptime1.shape_dist_traveled) * n_jours
w_esp += n_jours
espacement_moyen /= w_esp
affiche(
trace,
"L'espacement moyen entre arrêt du réseau est de %.2f mètres (max %.0fm)."
% (espacement_moyen, float(required_distance)))
affiche(trace,
"Calcul du jour ayant la fréquence en voyage la plus élevée...")
frequences = defaultdict(lambda: 0)
for trip in trips:
for date in trip.calendar.dates:
frequences[date] += 1
date_max = None
freq_max = 0
for date, frequence in frequences.items():
if frequence > freq_max:
freq_max = frequence
date_max = date
affiche(
trace,
"Le jour ayant le nombre de voyage le plus élevé est le %s, avec %d voyages."
% (date_max.as_date(), freq_max))
affiche(trace, "Calcul des fréquences sur la plage horaire 8h - 19h...")
# TODO Est-ce que ce calcul est correct? Le décret est pas clair.
# On calcule le nombre de voyages actifs pendant chaque minute.
frequences = [0 for minute in range(0, 20 * 60)]
for trip in trips:
if date_max not in trip.calendar.dates:
continue
minute_depart = trip.stop_times[0].departure_time // 60
minute_arrivee = trip.stop_times[-1].arrival_time // 60
for minute in range(minute_depart, minute_arrivee + 1):
if minute >= 8 * 60 and minute < 20 * 60:
frequences[minute] += 1
frequence_min = 99999999999
minute_min = 0
frequence_max = 0
minute_max = 0
# La fréquence horaire min/max est calculé en moyenne glissante
# sur une heure en sommant les fréquences par minute.
for minute in range(8 * 60, 19 * 60):
freq = 0
for delta_minute in range(0, 60):
freq += frequences[minute + delta_minute]
if freq > frequence_max:
frequence_max = freq
minute_max = minute
if freq < frequence_min:
frequence_min = freq
minute_min = minute
affiche(
trace,
"La fréquence minimale est de %.2f voyages/heure, entre %s et %s." %
(frequence_min / 60.0, fmttime(
minute_min * 60), fmttime((minute_min + 60) * 60)))
affiche(
trace,
"La fréquence maximale est de %.2f voyages/heure, entre %s et %s." %
(frequence_max / 60.0, fmttime(
minute_max * 60), fmttime((minute_max + 60) * 60)))
if frequence_min == 0:
ratio_frequence = float('inf')
else:
ratio_frequence = frequence_max / float(frequence_min)
affiche(
trace, "Le ratio entre fréquence max et min est de %.3f (max %.2f)." %
(ratio_frequence, float(required_ratio)))
urbain = ratio_frequence < required_ratio and espacement_moyen < required_distance
affiche(
trace, "Ce service est %s au sens du décret n° 2015-1610." %
("URBAIN" if urbain else "NON URBAIN"))
return urbain, espacement_moyen, ratio_frequence
def test_demo(self):
dao = Dao(DAO_URL, sql_logging=False)
dao.load_gtfs(DUMMY_GTFS)
print("List of stops named '...Bordeaux...':")
stops_bordeaux = list(
dao.stops(fltr=(Stop.stop_name.ilike('%Bordeaux%'))
& (Stop.location_type == Stop.TYPE_STOP)))
for stop in stops_bordeaux:
print(stop.stop_name)
print("List of routes passing by those stops:")
routes_bordeaux = dao.routes(fltr=or_(StopTime.stop == stop
for stop in stops_bordeaux))
for route in routes_bordeaux:
print("%s - %s" % (route.route_short_name, route.route_long_name))
july4 = CalendarDate.ymd(2016, 7, 4)
print("All departures from those stops on %s:" % (july4.as_date()))
departures = list(
dao.stoptimes(fltr=(or_(StopTime.stop == stop
for stop in stops_bordeaux))
& (StopTime.departure_time != None)
& (func.date(CalendarDate.date) == july4.date)))
print("There is %d departures" % (len(departures)))
for departure in departures:
print("%30.30s %10.10s %-20.20s > %s" %
(departure.stop.stop_name, fmttime(departure.departure_time),
departure.trip.route.route_long_name,
departure.trip.trip_headsign))
print("Number of departures and time range per stop on %s:" %
(july4.as_date()))
departure_by_stop = defaultdict(list)
for departure in departures:
departure_by_stop[departure.stop].append(departure)
for stop, deps in departure_by_stop.items():
min_dep = min(d.departure_time for d in deps)
max_dep = max(d.departure_time for d in deps)
print("%30.30s %3d departures (from %s to %s)" %
(stop.stop_name, len(deps), fmttime(min_dep),
fmttime(max_dep)))
# Compute the average distance and time to next stop by route type
ntd = [[0, 0, 0.0] for type in range(0, Route.TYPE_FUNICULAR + 1)]
for departure in departures:
# The following is guaranteed to succeed as we have departure_time == Null for last stop time in trip
next_arrival = departure.trip.stop_times[departure.stop_sequence +
1]
hop_dist = next_arrival.shape_dist_traveled - departure.shape_dist_traveled
hop_time = next_arrival.arrival_time - departure.departure_time
route_type = departure.trip.route.route_type
ntd[route_type][0] += 1
ntd[route_type][1] += hop_time
ntd[route_type][2] += hop_dist
for route_type in range(0, len(ntd)):
n, t, d = ntd[route_type]
if n > 0:
print(
"The average distance to the next stop on those departures for route type %d is %.2f meters"
% (route_type, d / n))
print(
"The average time in sec to the next stop on those departures for route type %d is %s"
% (route_type, fmttime(t / n)))
def run(self, context, skip_shape_dist=False, bundle=None, **kwargs):
with PrettyCsv("agency.txt", [
"agency_id", "agency_name", "agency_url", "agency_timezone",
"agency_lang", "agency_phone", "agency_fare_url",
"agency_email"
], **kwargs) as csvout:
nagencies = 0
for agency in context.dao().agencies(fltr=context.args.filter):
nagencies += 1
csvout.writerow([
agency.agency_id, agency.agency_name, agency.agency_url,
agency.agency_timezone, agency.agency_lang,
agency.agency_phone, agency.agency_fare_url,
agency.agency_email
])
print("Exported %d agencies" % (nagencies))
stop_ids = set()
zone_ids = set()
def _output_stop(stop):
csvout.writerow([
stop.stop_id, stop.stop_code, stop.stop_name, stop.stop_desc,
stop.stop_lat, stop.stop_lon, stop.zone_id, stop.stop_url,
stop.location_type, stop.parent_station_id, stop.stop_timezone,
stop.wheelchair_boarding
])
with PrettyCsv("stops.txt", [
"stop_id", "stop_code", "stop_name", "stop_desc", "stop_lat",
"stop_lon", "zone_id", "stop_url", "location_type",
"parent_station", "stop_timezone", "wheelchair_boarding"
], **kwargs) as csvout:
nstops = 0
station_ids = set()
for stop in context.dao().stops(fltr=context.args.filter,
prefetch_parent=False,
prefetch_substops=False):
_output_stop(stop)
stop_ids.add((stop.feed_id, stop.stop_id))
if stop.parent_station_id is not None:
station_ids.add((stop.feed_id, stop.parent_station_id))
if stop.zone_id is not None:
zone_ids.add((stop.feed_id, stop.zone_id))
nstops += 1
# Only export parent station that have not been already seen
station_ids -= stop_ids
for feed_id, st_ids in group_pairs(station_ids, 1000):
for station in context.dao().stops(
fltr=(Stop.feed_id == feed_id)
& (Stop.stop_id.in_(st_ids))):
_output_stop(station)
if station.zone_id is not None:
zone_ids.add((station.feed_id, station.zone_id))
nstops += 1
print("Exported %d stops" % (nstops))
stop_ids |= station_ids
route_ids = set()
with PrettyCsv("routes.txt", [
"route_id", "agency_id", "route_short_name", "route_long_name",
"route_desc", "route_type", "route_url", "route_color",
"route_text_color"
], **kwargs) as csvout:
nroutes = 0
for route in context.dao().routes(fltr=context.args.filter):
nroutes += 1
csvout.writerow([
route.route_id, route.agency_id, route.route_short_name,
route.route_long_name, route.route_desc, route.route_type,
route.route_url, route.route_color, route.route_text_color
])
route_ids.add((route.feed_id, route.route_id))
print("Exported %d routes" % (nroutes))
stop_times_columns = [
"trip_id", "arrival_time", "departure_time", "stop_id",
"stop_sequence", "stop_headsign", "pickup_type", "drop_off_type",
"timepoint"
]
if not skip_shape_dist:
stop_times_columns.append("shape_dist_traveled")
with PrettyCsv("trips.txt", [
"route_id", "service_id", "trip_id", "trip_headsign",
"trip_short_name", "direction_id", "block_id", "shape_id",
"wheelchair_accessible", "bikes_allowed"
], **kwargs) as csvout1:
with PrettyCsv("stop_times.txt", stop_times_columns,
**kwargs) as csvout2:
ntrips = 0
nstoptimes = 0
for trip in context.dao().trips(fltr=context.args.filter,
prefetch_stops=False,
prefetch_stop_times=True,
prefetch_calendars=False,
prefetch_routes=False):
ntrips += 1
if ntrips % 1000 == 0:
print("%d trips..." % (ntrips))
csvout1.writerow([
trip.route_id, trip.service_id, trip.trip_id,
trip.trip_headsign, trip.trip_short_name,
trip.direction_id, trip.block_id, trip.shape_id,
trip.wheelchair_accessible, trip.bikes_allowed
])
for stoptime in trip.stop_times:
nstoptimes += 1
row = [
trip.trip_id,
fmttime(stoptime.arrival_time if stoptime.
arrival_time is not None else stoptime.
departure_time),
fmttime(stoptime.departure_time if stoptime.
departure_time is not None else stoptime.
arrival_time), stoptime.stop_id,
stoptime.stop_sequence, stoptime.stop_headsign,
stoptime.pickup_type, stoptime.drop_off_type,
stoptime.timepoint
]
if not skip_shape_dist:
row.append(stoptime.shape_dist_traveled)
csvout2.writerow(row)
print("Exported %d trips with %d stop times" %
(ntrips, nstoptimes))
# Note: GTFS' model does not have calendars objects to export,
# since a calendar is renormalized/expanded to a list of dates.
with PrettyCsv("calendar_dates.txt",
["service_id", "date", "exception_type"],
**kwargs) as csvout:
ncals = ndates = 0
for calendar in context.dao().calendars(fltr=context.args.filter,
prefetch_dates=True):
ncals += 1
if ncals % 1000 == 0:
print("%d calendars, %d dates..." % (ncals, ndates))
for date in calendar.dates:
ndates += 1
csvout.writerow(
[calendar.service_id,
date.toYYYYMMDD(), 1])
print("Exported %d calendars with %d dates" % (ncals, ndates))
fare_attr_ids = set()
nfarerules = [0]
def _output_farerule(farerule):
if farerule.route_id is not None and (
farerule.feed_id, farerule.route_id) not in route_ids:
return False
if farerule.origin_id is not None and (
farerule.feed_id, farerule.origin_id) not in zone_ids:
return False
if farerule.contains_id is not None and (
farerule.feed_id, farerule.contains_id) not in zone_ids:
return False
if farerule.destination_id is not None and (
farerule.feed_id, farerule.destination_id) not in zone_ids:
return False
csvout.writerow([
farerule.fare_id, farerule.route_id, farerule.origin_id,
farerule.destination_id, farerule.contains_id
])
fare_attr_ids.add((farerule.feed_id, farerule.fare_id))
nfarerules[0] += 1
return True
with PrettyCsv("fare_rules.txt", [
"fare_id", "route_id", "origin_id", "destination_id",
"contains_id"
], **kwargs) as csvout:
feed_ids = set()
for feed_id, rt_ids in group_pairs(route_ids, 1000):
feed_ids.add(feed_id)
for farerule in context.dao().fare_rules(
fltr=(FareRule.feed_id == feed_id)
& FareRule.route_id.in_(rt_ids),
prefetch_fare_attributes=False):
if not _output_farerule(farerule):
continue
for feed_id, zn_ids in group_pairs(zone_ids, 1000):
feed_ids.add(feed_id)
for farerule in context.dao().fare_rules(
fltr=(FareRule.feed_id == feed_id) &
(FareRule.origin_id.in_(zn_ids)
| FareRule.contains_id.in_(zn_ids)
| FareRule.destination_id.in_(zn_ids)),
prefetch_fare_attributes=False):
if not _output_farerule(farerule):
continue
# Special code to include all fare rules w/o any relationships
# of any feed_id we've encountered so far
for feed_id in feed_ids:
for farerule in context.dao().fare_rules(
fltr=(FareRule.feed_id == feed_id) &
(FareRule.route_id == None) & (FareRule.origin_id == None)
& (FareRule.contains_id == None) &
(FareRule.destination_id == None),
prefetch_fare_attributes=False):
if not _output_farerule(farerule):
continue
print("Exported %d fare rules" % (nfarerules[0]))
if nfarerules[0] == 0:
os.remove("fare_rules.txt")
with PrettyCsv("fare_attributes.txt", [
"fare_id", "price", "currency_type", "payment_method",
"transfers", "transfer_duration"
], **kwargs) as csvout:
nfareattrs = 0
for feed_id, fa_ids in group_pairs(fare_attr_ids, 1000):
for fareattr in context.dao().fare_attributes(
fltr=(FareAttribute.feed_id == feed_id)
& FareAttribute.fare_id.in_(fa_ids),
prefetch_fare_rules=False):
nfareattrs += 1
csvout.writerow([
fareattr.fare_id, fareattr.price,
fareattr.currency_type, fareattr.payment_method,
fareattr.transfers, fareattr.transfer_duration
])
print("Exported %d fare attributes" % (nfareattrs))
if nfareattrs == 0:
os.remove("fare_attributes.txt")
shapes_columns = [
"shape_id", "shape_pt_lat", "shape_pt_lon", "shape_pt_sequence"
]
if not skip_shape_dist:
shapes_columns.append("shape_dist_traveled")
with PrettyCsv("shapes.txt", shapes_columns, **kwargs) as csvout:
nshapes = nshapepoints = 0
for shape in context.dao().shapes(fltr=context.args.filter,
prefetch_points=True):
nshapes += 1
if nshapes % 100 == 0:
print("%d shapes, %d points..." % (nshapes, nshapepoints))
for point in shape.points:
nshapepoints += 1
row = [
shape.shape_id, point.shape_pt_lat, point.shape_pt_lon,
point.shape_pt_sequence
]
if not skip_shape_dist:
row.append(point.shape_dist_traveled)
csvout.writerow(row)
print("Exported %d shapes with %d points" %
(nshapes, nshapepoints))
if nshapes == 0:
os.remove("shapes.txt")
with PrettyCsv("transfers.txt", [
"from_stop_id", "to_stop_id", "transfer_type",
"min_transfer_time"
], **kwargs) as csvout:
ntransfers = 0
transfer_ids = set()
for feed_id, st_ids in group_pairs(stop_ids, 1000):
# Note: we can't use a & operator below instead of |,
# as we would need to have *all* IDs in one batch.
for transfer in context.dao().transfers(
fltr=(Transfer.feed_id == feed_id) &
(Transfer.from_stop_id.in_(st_ids)
| Transfer.to_stop_id.in_(st_ids)),
prefetch_stops=False):
# As we used from_stop_id.in(...) OR to_stop_id.in(...),
# we need to filter out the potential superfluous results.
from_stop_id = (transfer.feed_id, transfer.from_stop_id)
to_stop_id = (transfer.feed_id, transfer.to_stop_id)
if from_stop_id not in stop_ids or to_stop_id not in stop_ids:
continue
transfer_id = (from_stop_id, to_stop_id)
if transfer_id in transfer_ids:
# Prevent duplicates (can happen from grouping)
continue
transfer_ids.add(transfer_id)
ntransfers += 1
csvout.writerow([
transfer.from_stop_id, transfer.to_stop_id,
transfer.transfer_type, transfer.min_transfer_time
])
print("Exported %d transfers" % (ntransfers))
if ntransfers == 0:
os.remove("transfers.txt")
if bundle:
if not isinstance(bundle, six.string_types):
# Allow the use of "--bundle" option only
bundle = "gtfs.zip"
if not bundle.endswith('.zip'):
bundle = bundle + '.zip'
print("Zipping result to %s (removing .txt files)" % (bundle))
with zipfile.ZipFile(bundle, 'w', zipfile.ZIP_DEFLATED) as zipf:
for f in [
"agency.txt", "stops.txt", "routes.txt", "trips.txt",
"stop_times.txt", "calendar_dates.txt",
"fare_rules.txt", "fare_attributes.txt", "shapes.txt",
"transfers.txt"
]:
if os.path.isfile(f):
zipf.write(f)
os.remove(f)
def run(self, context, skip_shape_dist=False, bundle=None, **kwargs):
with PrettyCsv("agency.txt", ["agency_id", "agency_name", "agency_url", "agency_timezone", "agency_lang", "agency_phone", "agency_fare_url", "agency_email" ], **kwargs) as csvout:
nagencies = 0
for agency in context.dao().agencies(fltr=context.args.filter):
nagencies += 1
csvout.writerow([ agency.agency_id, agency.agency_name, agency.agency_url, agency.agency_timezone, agency.agency_lang, agency.agency_phone, agency.agency_fare_url, agency.agency_email ])
print("Exported %d agencies" % (nagencies))
stop_ids = set()
zone_ids = set()
def _output_stop(stop):
csvout.writerow([ stop.stop_id, stop.stop_code, stop.stop_name, stop.stop_desc, stop.stop_lat, stop.stop_lon, stop.zone_id, stop.stop_url, stop.location_type, stop.parent_station_id, stop.stop_timezone, stop.wheelchair_boarding ])
with PrettyCsv("stops.txt", ["stop_id", "stop_code", "stop_name", "stop_desc", "stop_lat", "stop_lon", "zone_id", "stop_url", "location_type", "parent_station", "stop_timezone", "wheelchair_boarding" ], **kwargs) as csvout:
nstops = 0
station_ids = set()
for stop in context.dao().stops(fltr=context.args.filter, prefetch_parent=False, prefetch_substops=False):
_output_stop(stop)
stop_ids.add((stop.feed_id, stop.stop_id))
if stop.parent_station_id is not None:
station_ids.add((stop.feed_id, stop.parent_station_id))
if stop.zone_id is not None:
zone_ids.add((stop.feed_id, stop.zone_id))
nstops += 1
# Only export parent station that have not been already seen
station_ids -= stop_ids
for feed_id, st_ids in group_pairs(station_ids, 1000):
for station in context.dao().stops(fltr=(Stop.feed_id == feed_id) & (Stop.stop_id.in_(st_ids))):
_output_stop(station)
if station.zone_id is not None:
zone_ids.add((station.feed_id, station.zone_id))
nstops += 1
print("Exported %d stops" % (nstops))
stop_ids |= station_ids
route_ids = set()
with PrettyCsv("routes.txt", ["route_id", "agency_id", "route_short_name", "route_long_name", "route_desc", "route_type", "route_url", "route_color", "route_text_color" ], **kwargs) as csvout:
nroutes = 0
for route in context.dao().routes(fltr=context.args.filter):
nroutes += 1
csvout.writerow([ route.route_id, route.agency_id, route.route_short_name, route.route_long_name, route.route_desc, route.route_type, route.route_url, route.route_color, route.route_text_color ])
route_ids.add((route.feed_id, route.route_id))
print("Exported %d routes" % (nroutes))
stop_times_columns = ["trip_id", "arrival_time", "departure_time", "stop_id", "stop_sequence", "stop_headsign", "pickup_type", "drop_off_type", "timepoint"]
if not skip_shape_dist:
stop_times_columns.append("shape_dist_traveled")
with PrettyCsv("trips.txt", ["route_id", "service_id", "trip_id", "trip_headsign", "trip_short_name", "direction_id", "block_id", "shape_id", "wheelchair_accessible", "bikes_allowed" ], **kwargs) as csvout1:
with PrettyCsv("stop_times.txt", stop_times_columns, **kwargs) as csvout2:
ntrips = 0
nstoptimes = 0
for trip in context.dao().trips(fltr=context.args.filter, prefetch_stops=False, prefetch_stop_times=True, prefetch_calendars=False, prefetch_routes=False):
ntrips += 1
if ntrips % 1000 == 0:
print("%d trips..." % (ntrips))
csvout1.writerow([ trip.route_id, trip.service_id, trip.trip_id, trip.trip_headsign, trip.trip_short_name, trip.direction_id, trip.block_id, trip.shape_id, trip.wheelchair_accessible, trip.bikes_allowed])
for stoptime in trip.stop_times:
nstoptimes += 1
row = [ trip.trip_id,
fmttime(stoptime.arrival_time if stoptime.arrival_time is not None else stoptime.departure_time),
fmttime(stoptime.departure_time if stoptime.departure_time is not None else stoptime.arrival_time),
stoptime.stop_id,
stoptime.stop_sequence,
stoptime.stop_headsign,
stoptime.pickup_type,
stoptime.drop_off_type,
stoptime.timepoint ]
if not skip_shape_dist:
row.append(stoptime.shape_dist_traveled)
csvout2.writerow(row)
print("Exported %d trips with %d stop times" % (ntrips, nstoptimes))
# Note: GTFS' model does not have calendars objects to export,
# since a calendar is renormalized/expanded to a list of dates.
with PrettyCsv("calendar_dates.txt", ["service_id", "date", "exception_type"], **kwargs) as csvout:
ncals = ndates = 0
for calendar in context.dao().calendars(fltr=context.args.filter, prefetch_dates=True):
ncals += 1
if ncals % 1000 == 0:
print("%d calendars, %d dates..." % (ncals, ndates))
for date in calendar.dates:
ndates += 1
csvout.writerow([calendar.service_id, date.toYYYYMMDD(), 1])
print("Exported %d calendars with %d dates" % (ncals, ndates))
fare_attr_ids = set()
nfarerules = [0]
def _output_farerule(farerule):
if farerule.route_id is not None and (farerule.feed_id, farerule.route_id) not in route_ids:
return False
if farerule.origin_id is not None and (farerule.feed_id, farerule.origin_id) not in zone_ids:
return False
if farerule.contains_id is not None and (farerule.feed_id, farerule.contains_id) not in zone_ids:
return False
if farerule.destination_id is not None and (farerule.feed_id, farerule.destination_id) not in zone_ids:
return False
csvout.writerow([ farerule.fare_id, farerule.route_id, farerule.origin_id, farerule.destination_id, farerule.contains_id ])
fare_attr_ids.add((farerule.feed_id, farerule.fare_id))
nfarerules[0] += 1
return True
with PrettyCsv("fare_rules.txt", ["fare_id", "route_id", "origin_id", "destination_id", "contains_id"], **kwargs) as csvout:
feed_ids = set()
for feed_id, rt_ids in group_pairs(route_ids, 1000):
feed_ids.add(feed_id)
for farerule in context.dao().fare_rules(fltr=(FareRule.feed_id==feed_id) & FareRule.route_id.in_(rt_ids), prefetch_fare_attributes=False):
if not _output_farerule(farerule):
continue
for feed_id, zn_ids in group_pairs(zone_ids, 1000):
feed_ids.add(feed_id)
for farerule in context.dao().fare_rules(fltr=(FareRule.feed_id==feed_id) & (FareRule.origin_id.in_(zn_ids) | FareRule.contains_id.in_(zn_ids) | FareRule.destination_id.in_(zn_ids)), prefetch_fare_attributes=False):
if not _output_farerule(farerule):
continue
# Special code to include all fare rules w/o any relationships
# of any feed_id we've encountered so far
for feed_id in feed_ids:
for farerule in context.dao().fare_rules(fltr=(FareRule.feed_id==feed_id) & (FareRule.route_id==None) & (FareRule.origin_id==None) & (FareRule.contains_id==None) & (FareRule.destination_id==None), prefetch_fare_attributes=False):
if not _output_farerule(farerule):
continue
print("Exported %d fare rules" % (nfarerules[0]))
if nfarerules[0] == 0:
os.remove("fare_rules.txt")
with PrettyCsv("fare_attributes.txt", ["fare_id", "price", "currency_type", "payment_method", "transfers", "transfer_duration"], **kwargs) as csvout:
nfareattrs = 0
for feed_id, fa_ids in group_pairs(fare_attr_ids, 1000):
for fareattr in context.dao().fare_attributes(fltr=(FareAttribute.feed_id==feed_id) & FareAttribute.fare_id.in_(fa_ids), prefetch_fare_rules=False):
nfareattrs += 1
csvout.writerow([ fareattr.fare_id, fareattr.price, fareattr.currency_type, fareattr.payment_method, fareattr.transfers, fareattr.transfer_duration ])
print("Exported %d fare attributes" % (nfareattrs))
if nfareattrs == 0:
os.remove("fare_attributes.txt")
shapes_columns = ["shape_id", "shape_pt_lat", "shape_pt_lon", "shape_pt_sequence"]
if not skip_shape_dist:
shapes_columns.append("shape_dist_traveled")
with PrettyCsv("shapes.txt", shapes_columns, **kwargs) as csvout:
nshapes = nshapepoints = 0
for shape in context.dao().shapes(fltr=context.args.filter, prefetch_points=True):
nshapes += 1
if nshapes % 100 == 0:
print("%d shapes, %d points..." % (nshapes, nshapepoints))
for point in shape.points:
nshapepoints += 1
row = [shape.shape_id, point.shape_pt_lat, point.shape_pt_lon, point.shape_pt_sequence]
if not skip_shape_dist:
row.append(point.shape_dist_traveled)
csvout.writerow(row)
print("Exported %d shapes with %d points" % (nshapes, nshapepoints))
if nshapes == 0:
os.remove("shapes.txt")
with PrettyCsv("transfers.txt", ["from_stop_id", "to_stop_id", "transfer_type", "min_transfer_time"], **kwargs) as csvout:
ntransfers = 0
transfer_ids = set()
for feed_id, st_ids in group_pairs(stop_ids, 1000):
# Note: we can't use a & operator below instead of |,
# as we would need to have *all* IDs in one batch.
for transfer in context.dao().transfers(fltr=(Transfer.feed_id == feed_id) & (Transfer.from_stop_id.in_(st_ids) | Transfer.to_stop_id.in_(st_ids)), prefetch_stops=False):
# As we used from_stop_id.in(...) OR to_stop_id.in(...),
# we need to filter out the potential superfluous results.
from_stop_id = (transfer.feed_id, transfer.from_stop_id)
to_stop_id = (transfer.feed_id, transfer.to_stop_id)
if from_stop_id not in stop_ids or to_stop_id not in stop_ids:
continue
transfer_id = (from_stop_id, to_stop_id)
if transfer_id in transfer_ids:
# Prevent duplicates (can happen from grouping)
continue
transfer_ids.add(transfer_id)
ntransfers += 1
csvout.writerow([ transfer.from_stop_id, transfer.to_stop_id, transfer.transfer_type, transfer.min_transfer_time ])
print("Exported %d transfers" % (ntransfers))
if ntransfers == 0:
os.remove("transfers.txt")
if bundle:
if not isinstance(bundle, six.string_types):
# Allow the use of "--bundle" option only
bundle = "gtfs.zip"
if not bundle.endswith('.zip'):
bundle = bundle + '.zip'
print("Zipping result to %s (removing .txt files)" % (bundle))
with zipfile.ZipFile(bundle, 'w', zipfile.ZIP_DEFLATED) as zipf:
for f in [ "agency.txt", "stops.txt", "routes.txt", "trips.txt", "stop_times.txt", "calendar_dates.txt", "fare_rules.txt", "fare_attributes.txt", "shapes.txt", "transfers.txt" ]:
if os.path.isfile(f):
zipf.write(f)
os.remove(f)
def _convert_gtfs_model(feed_id, gtfs, dao, lenient=False, disable_normalization=False):
feedinfo2 = None
logger.info("Importing feed ID '%s'" % feed_id)
n_feedinfo = 0
for feedinfo in gtfs.feedinfo():
n_feedinfo += 1
if n_feedinfo > 1:
logger.error("Feed info should be unique if defined. Taking first one." % (n_feedinfo))
break
# TODO Automatically compute from calendar range if missing?
feedinfo['feed_start_date'] = _todate(feedinfo.get('feed_start_date'))
feedinfo['feed_end_date'] = _todate(feedinfo.get('feed_end_date'))
feedinfo2 = FeedInfo(feed_id, **feedinfo)
if feedinfo2 is None:
# Optional, generate empty feed info
feedinfo2 = FeedInfo(feed_id)
dao.add(feedinfo2)
dao.flush()
logger.info("Imported %d feedinfo" % n_feedinfo)
logger.info("Importing agencies...")
n_agencies = 0
single_agency = None
agency_ids = set()
for agency in gtfs.agencies():
# agency_id is optional only if we have a single agency
if n_agencies == 0 and agency.get('agency_id') is None:
agency['agency_id'] = ''
agency2 = Agency(feed_id, **agency)
if n_agencies == 0:
single_agency = agency2
else:
single_agency = None
n_agencies += 1
dao.add(agency2)
agency_ids.add(agency2.agency_id)
dao.flush()
logger.info("Imported %d agencies" % n_agencies)
def import_stop(stop, stoptype, zone_ids, item_ids, station_ids=None):
zone_id = stop.get('zone_id')
if zone_id and zone_id not in zone_ids:
# Lazy-creation of zone
zone = Zone(feed_id, zone_id)
zone_ids.add(zone_id)
dao.add(zone)
stop['location_type'] = _toint(stop.get('location_type'), Stop.TYPE_STOP)
if stop['location_type'] != stoptype:
return 0
stop['wheelchair_boarding'] = _toint(stop.get('wheelchair_boarding'), Stop.WHEELCHAIR_UNKNOWN)
lat = _tofloat(stop.get('stop_lat'), None)
lon = _tofloat(stop.get('stop_lon'), None)
if lat is None or lon is None:
if lenient:
logger.error("Missing lat/lon for '%s', set to default (0,0)" % (stop,))
if lat is None:
lat = 0
if lon is None:
lon = 0
else:
raise ValueError("Missing mandatory lat/lon for '%s'." % (stop,))
stop['stop_lat'] = lat
stop['stop_lon'] = lon
# This field has been renamed for consistency
parent_id = stop.get('parent_station')
stop['parent_station_id'] = parent_id if parent_id else None
if parent_id and station_ids and parent_id not in station_ids:
if lenient:
logger.error("Parent station ID '%s' in '%s' is invalid, resetting." % (parent_id, stop))
stop['parent_station_id'] = None
else:
raise KeyError("Parent station ID '%s' in '%s' is invalid." % (parent_id, stop))
stop.pop('parent_station', None)
stop2 = Stop(feed_id, **stop)
dao.add(stop2)
item_ids.add(stop2.stop_id)
return 1
stop_ids = set()
station_ids = set()
zone_ids = set()
logger.info("Importing zones, stations and stops...")
n_stations = n_stops = 0
for station in gtfs.stops():
n_stations += import_stop(station, Stop.TYPE_STATION, zone_ids, station_ids)
for stop in gtfs.stops():
n_stops += import_stop(stop, Stop.TYPE_STOP, zone_ids, stop_ids, station_ids)
dao.flush()
logger.info("Imported %d zones, %d stations and %d stops" % (len(zone_ids), n_stations, n_stops))
logger.info("Importing transfers...")
n_transfers = 0
for transfer in gtfs.transfers():
from_stop_id = transfer.get('from_stop_id')
to_stop_id = transfer.get('to_stop_id')
transfer['transfer_type'] = _toint(transfer.get('transfer_type'), 0)
for stop_id in (from_stop_id, to_stop_id):
if stop_id not in station_ids and stop_id not in stop_ids:
if lenient:
logger.error("Stop ID '%s' in '%s' is invalid, skipping." % (stop_id, transfer))
continue
else:
raise KeyError("Stop ID '%s' in '%s' is invalid." % (stop_id, transfer))
transfer2 = Transfer(feed_id, **transfer)
n_transfers += 1
dao.add(transfer2)
dao.flush()
logger.info("Imported %d transfers" % (n_transfers))
logger.info("Importing routes...")
n_routes = 0
route_ids = set()
for route in gtfs.routes():
route['route_type'] = int(route.get('route_type'))
agency_id = route.get('agency_id')
if (agency_id is None or len(agency_id) == 0) and single_agency is not None:
# Route.agency is optional if only a single agency exists.
agency_id = route['agency_id'] = single_agency.agency_id
if agency_id not in agency_ids:
if lenient:
logger.error("Agency ID '%s' in '%s' is invalid, skipping route." % (agency_id, route))
continue
else:
raise KeyError("agency ID '%s' in '%s' is invalid." % (agency_id, route))
route2 = Route(feed_id, **route)
dao.add(route2)
route_ids.add(route2.route_id)
n_routes += 1
dao.flush()
logger.info("Imported %d routes" % n_routes)
logger.info("Importing fares...")
n_fares = 0
for fare_attr in gtfs.fare_attributes():
fare_id = fare_attr.get('fare_id')
fare_price = _tofloat(fare_attr.get('price'))
currency_type = fare_attr.get('currency_type')
payment_method = _toint(fare_attr.get('payment_method'))
n_transfers = None
if fare_attr.get('transfers') is not None:
n_transfers = _toint(fare_attr.get('transfers'))
transfer_duration = None
if fare_attr.get('transfer_duration') is not None:
transfer_duration = _toint(fare_attr.get('transfer_duration'))
fare = FareAttribute(feed_id, fare_id, fare_price, currency_type,
payment_method, n_transfers, transfer_duration)
dao.add(fare)
n_fares += 1
dao.flush()
fare_rules = set()
for fare_rule in gtfs.fare_rules():
fare_rule2 = FareRule(feed_id, **fare_rule)
if fare_rule2 in fare_rules:
if lenient:
logger.error("Duplicated fare rule (%s), skipping." % (fare_rule2))
continue
else:
raise KeyError("Duplicated fare rule (%s)" % (fare_rule2))
dao.add(fare_rule2)
fare_rules.add(fare_rule2)
dao.flush()
logger.info("Imported %d fare and %d rules" % (n_fares, len(fare_rules)))
logger.info("Importing calendars...")
calanddates2 = {}
for calendar in gtfs.calendars():
calid = calendar.get('service_id')
calendar2 = Calendar(feed_id, calid)
dates2 = []
start_date = CalendarDate.fromYYYYMMDD(calendar.get('start_date'))
end_date = CalendarDate.fromYYYYMMDD(calendar.get('end_date'))
for d in CalendarDate.range(start_date, end_date.next_day()):
if int(calendar.get(DOW_NAMES[d.dow()])):
dates2.append(d)
calanddates2[calid] = (calendar2, set(dates2))
logger.info("Normalizing calendar dates...")
for caldate in gtfs.calendar_dates():
calid = caldate.get('service_id')
date2 = CalendarDate.fromYYYYMMDD(caldate.get('date'))
addremove = int(caldate.get('exception_type'))
if calid in calanddates2:
calendar2, dates2 = calanddates2[calid]
else:
calendar2 = Calendar(feed_id, calid)
dates2 = set([])
calanddates2[calid] = (calendar2, dates2)
if addremove == 1:
dates2.add(date2)
elif addremove == 2:
if date2 in dates2:
dates2.remove(date2)
n_calendars = 0
n_caldates = 0
calendar_ids = set()
for (calendar2, dates2) in calanddates2.values():
calendar2.dates = [ d for d in dates2 ]
dao.add(calendar2)
calendar_ids.add(calendar2.service_id)
n_calendars += 1
n_caldates += len(calendar2.dates)
dao.flush()
logger.info("Imported %d calendars and %d dates" % (n_calendars, n_caldates))
logger.info("Importing shapes...")
n_shape_pts = 0
shape_ids = set()
shapepts_q = []
for shpt in gtfs.shapes():
shape_id = shpt.get('shape_id')
if shape_id not in shape_ids:
dao.add(Shape(feed_id, shape_id))
dao.flush()
shape_ids.add(shape_id)
pt_seq = _toint(shpt.get('shape_pt_sequence'))
# This field is optional
dist_traveled = _tofloat(shpt.get('shape_dist_traveled'), -999999)
lat = _tofloat(shpt.get('shape_pt_lat'))
lon = _tofloat(shpt.get('shape_pt_lon'))
shape_point = ShapePoint(feed_id, shape_id, pt_seq, lat, lon, dist_traveled)
shapepts_q.append(shape_point)
n_shape_pts += 1
if n_shape_pts % 100000 == 0:
logger.info("%d shape points" % n_shape_pts)
dao.bulk_save_objects(shapepts_q)
dao.flush()
shapepts_q = []
dao.bulk_save_objects(shapepts_q)
dao.flush()
logger.info("Imported %d shapes and %d points" % (len(shape_ids), n_shape_pts))
logger.info("Importing trips...")
n_trips = 0
trips_q = []
trip_ids = set()
for trip in gtfs.trips():
trip['wheelchair_accessible'] = _toint(trip.get('wheelchair_accessible'), Trip.WHEELCHAIR_UNKNOWN)
trip['bikes_allowed'] = _toint(trip.get('bikes_allowed'), Trip.BIKES_UNKNOWN)
cal_id = trip.get('service_id')
if cal_id not in calendar_ids:
if lenient:
logger.error("Calendar ID '%s' in '%s' is invalid. Skipping trip." % (cal_id, trip))
continue
else:
raise KeyError("Calendar ID '%s' in '%s' is invalid." % (cal_id, trip))
route_id = trip.get('route_id')
if route_id not in route_ids:
if lenient:
logger.error("Route ID '%s' in '%s' is invalid. Skipping trip." % (route_id, trip))
continue
else:
raise KeyError("Route ID '%s' in trip '%s' is invalid." % (route_id, trip))
trip2 = Trip(feed_id, frequency_generated=False, **trip)
trips_q.append(trip2)
n_trips += 1
if n_trips % 10000 == 0:
dao.bulk_save_objects(trips_q)
dao.flush()
logger.info('%s trips' % n_trips)
trips_q = []
trip_ids.add(trip.get('trip_id'))
dao.bulk_save_objects(trips_q)
dao.flush()
logger.info("Imported %d trips" % n_trips)
logger.info("Importing stop times...")
n_stoptimes = 0
stoptimes_q = []
for stoptime in gtfs.stop_times():
stopseq = _toint(stoptime.get('stop_sequence'))
# Mark times to interpolate later on
arrtime = _timetoint(stoptime.get('arrival_time'), -999999)
deptime = _timetoint(stoptime.get('departure_time'), -999999)
if arrtime == -999999:
arrtime = deptime
if deptime == -999999:
deptime = arrtime
interp = arrtime < 0 and deptime < 0
shpdist = _tofloat(stoptime.get('shape_dist_traveled'), -999999)
pkptype = _toint(stoptime.get('pickup_type'), StopTime.PICKUP_DROPOFF_REGULAR)
drptype = _toint(stoptime.get('drop_off_type'), StopTime.PICKUP_DROPOFF_REGULAR)
trip_id = stoptime.get('trip_id')
if trip_id not in trip_ids:
if lenient:
logger.error("Trip ID '%s' in '%s' is invalid. Skipping stop time." % (trip_id, stoptime))
continue
else:
raise KeyError("Trip ID '%s' in '%s' is invalid." % (trip_id, stoptime))
stop_id = stoptime.get('stop_id')
if stop_id not in stop_ids:
if lenient:
logger.error("Stop ID '%s' in '%s' is invalid. Skipping stop time." % (stop_id, stoptime))
continue
else:
raise KeyError("Trip ID '%s' in stoptime '%s' is invalid." % (stop_id, stoptime))
stoptime2 = StopTime(feed_id, trip_id, stop_id,
stop_sequence=stopseq, arrival_time=arrtime, departure_time=deptime,
shape_dist_traveled=shpdist, interpolated=interp,
pickup_type=pkptype, drop_off_type=drptype,
stop_headsign=stoptime.get('stop_headsign'))
stoptimes_q.append(stoptime2)
n_stoptimes += 1
# Commit every now and then
if n_stoptimes % 50000 == 0:
logger.info("%d stop times" % n_stoptimes)
dao.bulk_save_objects(stoptimes_q)
dao.flush()
stoptimes_q = []
dao.bulk_save_objects(stoptimes_q)
logger.info("Imported %d stop times" % n_stoptimes)
logger.info("Committing")
dao.flush()
# TODO Add option to enable/disable this commit
# to ensure import is transactionnal
dao.commit()
logger.info("Commit done")
def normalize_trip(trip, odometer):
stopseq = 0
n_stoptimes = len(trip.stop_times)
last_stoptime_with_time = None
to_interpolate = []
odometer.reset()
for stoptime in trip.stop_times:
stoptime.stop_sequence = stopseq
stoptime.shape_dist_traveled = odometer.dist_traveled(stoptime.stop,
stoptime.shape_dist_traveled if stoptime.shape_dist_traveled != -999999 else None)
if stopseq == 0:
# Force first arrival time to NULL
stoptime.arrival_time = None
if stopseq == n_stoptimes - 1:
# Force last departure time to NULL
stoptime.departure_time = None
if stoptime.interpolated:
to_interpolate.append(stoptime)
else:
if len(to_interpolate) > 0:
# Interpolate
if last_stoptime_with_time is None:
logger.error("Cannot interpolate missing time at trip start: %s" % trip)
for stti in to_interpolate:
# Use first defined time as fallback value.
stti.arrival_time = stoptime.arrival_time
stti.departure_time = stoptime.arrival_time
else:
tdist = stoptime.shape_dist_traveled - last_stoptime_with_time.shape_dist_traveled
ttime = stoptime.arrival_time - last_stoptime_with_time.departure_time
for stti in to_interpolate:
fdist = stti.shape_dist_traveled - last_stoptime_with_time.shape_dist_traveled
t = last_stoptime_with_time.departure_time + ttime * fdist // tdist
stti.arrival_time = t
stti.departure_time = t
to_interpolate = []
last_stoptime_with_time = stoptime
stopseq += 1
if len(to_interpolate) > 0:
# Should not happen, but handle the case, we never know
if last_stoptime_with_time is None:
logger.error("Cannot interpolate missing time, no time at all: %s" % trip)
# Keep times NULL (TODO: or remove the trip?)
else:
logger.error("Cannot interpolate missing time at trip end: %s" % trip)
for stti in to_interpolate:
# Use last defined time as fallback value
stti.arrival_time = last_stoptime_with_time.departure_time
stti.departure_time = last_stoptime_with_time.departure_time
if disable_normalization:
logger.info("Skipping shapes and trips normalization")
else:
logger.info("Normalizing shapes and trips...")
nshapes = 0
ntrips = 0
odometer = _Odometer()
# Process shapes and associated trips
for shape in dao.shapes(fltr=Shape.feed_id == feed_id, prefetch_points=True, batch_size=50):
# Shape will be registered in the normalize
odometer.normalize_and_register_shape(shape)
for trip in dao.trips(fltr=(Trip.feed_id == feed_id) & (Trip.shape_id == shape.shape_id), prefetch_stop_times=True, prefetch_stops=True, batch_size=800):
normalize_trip(trip, odometer)
ntrips += 1
if ntrips % 1000 == 0:
logger.info("%d trips, %d shapes" % (ntrips, nshapes))
dao.flush()
nshapes += 1
#odometer._debug_cache()
# Process trips w/o shapes
for trip in dao.trips(fltr=(Trip.feed_id == feed_id) & (Trip.shape_id == None), prefetch_stop_times=True, prefetch_stops=True, batch_size=800):
odometer.register_noshape()
normalize_trip(trip, odometer)
ntrips += 1
if ntrips % 1000 == 0:
logger.info("%d trips" % ntrips)
dao.flush()
dao.flush()
logger.info("Normalized %d trips and %d shapes" % (ntrips, nshapes))
# Note: we expand frequencies *after* normalization
# for performances purpose only: that minimize the
# number of trips to normalize. We can do that since
# the expansion is neutral trip-normalization-wise.
logger.info("Expanding frequencies...")
n_freq = 0
n_exp_trips = 0
trips_to_delete = []
for frequency in gtfs.frequencies():
trip_id = frequency.get('trip_id')
if trip_id not in trip_ids:
if lenient:
logger.error("Trip ID '%s' in '%s' is invalid. Skipping frequency." % (trip_id, frequency))
continue
else:
raise KeyError("Trip ID '%s' in '%s' is invalid." % (trip_id, frequency))
trip = dao.trip(trip_id, feed_id=feed_id)
start_time = _timetoint(frequency.get('start_time'))
end_time = _timetoint(frequency.get('end_time'))
headway_secs = _toint(frequency.get('headway_secs'))
exact_times = _toint(frequency.get('exact_times'), Trip.TIME_APPROX)
for trip_dep_time in range(start_time, end_time, headway_secs):
# Here we assume departure time are all different.
# That's a requirement in the GTFS specs, but this may break.
# TODO Make the expanded trip ID generation parametrable.
trip_id2 = trip.trip_id + "@" + fmttime(trip_dep_time)
trip2 = Trip(feed_id, trip_id2, trip.route_id, trip.service_id,
wheelchair_accessible=trip.wheelchair_accessible,
bikes_allowed=trip.bikes_allowed,
exact_times=exact_times,
frequency_generated=True,
trip_headsign=trip.trip_headsign,
trip_short_name=trip.trip_short_name,
direction_id=trip.direction_id,
block_id=trip.block_id)
trip2.stop_times = []
base_time = trip.stop_times[0].departure_time
for stoptime in trip.stop_times:
arrtime = None if stoptime.arrival_time is None else stoptime.arrival_time - base_time + trip_dep_time
deptime = None if stoptime.departure_time is None else stoptime.departure_time - base_time + trip_dep_time
stoptime2 = StopTime(feed_id, trip_id2, stoptime.stop_id, stoptime.stop_sequence,
arrival_time=arrtime,
departure_time=deptime,
shape_dist_traveled=stoptime.shape_dist_traveled,
interpolated=stoptime.interpolated,
timepoint=stoptime.timepoint,
pickup_type=stoptime.pickup_type,
drop_off_type=stoptime.drop_off_type)
trip2.stop_times.append(stoptime2)
n_exp_trips += 1
# This will add the associated stop times
dao.add(trip2)
# Do not delete trip now, as two frequency can refer to same trip
trips_to_delete.append(trip)
n_freq += 1
for trip in trips_to_delete:
# This also delete the associated stop times
dao.delete(trip)
dao.flush()
dao.commit()
logger.info("Expanded %d frequencies to %d trips." % (n_freq, n_exp_trips))
logger.info("Feed '%s': import done." % feed_id)
def run(self, context, skip_shape_dist=False, bundle=None, **kwargs):
with PrettyCsv("agency.txt", ["agency_id", "agency_name", "agency_url", "agency_timezone", "agency_lang", "agency_phone", "agency_fare_url", "agency_email" ], **kwargs) as csvout:
nagencies = 0
for agency in context.dao().agencies(fltr=context.args.filter):
nagencies += 1
csvout.writerow([ agency.agency_id, agency.agency_name, agency.agency_url, agency.agency_timezone, agency.agency_lang, agency.agency_phone, agency.agency_fare_url, agency.agency_email ])
print("Exported %d agencies" % (nagencies))
with PrettyCsv("stops.txt", ["stop_id", "stop_code", "stop_name", "stop_desc", "stop_lat", "stop_lon", "zone_id", "stop_url", "location_type", "parent_station", "stop_timezone", "wheelchair_boarding" ], **kwargs) as csvout:
nstops = 0
for stop in context.dao().stops(fltr=context.args.filter, prefetch_parent=False, prefetch_substops=False):
nstops += 1
csvout.writerow([ stop.stop_id, stop.stop_code, stop.stop_name, stop.stop_desc, stop.stop_lat, stop.stop_lon, stop.zone_id, stop.stop_url, stop.location_type, stop.parent_station_id, stop.stop_timezone, stop.wheelchair_boarding ])
print("Exported %d stops" % (nstops))
with PrettyCsv("routes.txt", ["route_id", "agency_id", "route_short_name", "route_long_name", "route_desc", "route_type", "route_url", "route_color", "route_text_color" ], **kwargs) as csvout:
nroutes = 0
for route in context.dao().routes(fltr=context.args.filter):
nroutes += 1
csvout.writerow([ route.route_id, route.agency_id, route.route_short_name, route.route_long_name, route.route_desc, route.route_type, route.route_url, route.route_color, route.route_text_color ])
print("Exported %d routes" % (nroutes))
stop_times_columns = ["trip_id", "arrival_time", "departure_time", "stop_id", "stop_sequence", "stop_headsign", "pickup_type", "drop_off_type", "timepoint"]
if not skip_shape_dist:
stop_times_columns.append("shape_dist_traveled")
with PrettyCsv("trips.txt", ["route_id", "service_id", "trip_id", "trip_headsign", "trip_short_name", "direction_id", "block_id", "shape_id", "wheelchair_accessible", "bikes_allowed" ], **kwargs) as csvout1:
with PrettyCsv("stop_times.txt", stop_times_columns, **kwargs) as csvout2:
ntrips = 0
nstoptimes = 0
for trip in context.dao().trips(fltr=context.args.filter, prefetch_stops=False, prefetch_stop_times=True, prefetch_calendars=False, prefetch_routes=False):
ntrips += 1
if ntrips % 1000 == 0:
print("%d trips..." % (ntrips))
csvout1.writerow([ trip.route_id, trip.service_id, trip.trip_id, trip.trip_headsign, trip.trip_short_name, trip.direction_id, trip.block_id, trip.shape_id, trip.wheelchair_accessible, trip.bikes_allowed])
for stoptime in trip.stop_times:
nstoptimes += 1
row = [ trip.trip_id,
fmttime(stoptime.arrival_time if stoptime.arrival_time else stoptime.departure_time),
fmttime(stoptime.departure_time if stoptime.departure_time else stoptime.arrival_time),
stoptime.stop_id,
stoptime.stop_sequence,
stoptime.stop_headsign,
stoptime.pickup_type,
stoptime.drop_off_type,
stoptime.timepoint ]
if not skip_shape_dist:
row.append(stoptime.shape_dist_traveled)
csvout2.writerow(row)
print("Exported %d trips with %d stop times" % (ntrips, nstoptimes))
# Note: GTFS' model does not have calendars objects to export,
# since a calendar is renormalized/expanded to a list of dates.
with PrettyCsv("calendar_dates.txt", ["service_id", "date", "exception_type"], **kwargs) as csvout:
ncals = ndates = 0
for calendar in context.dao().calendars(fltr=context.args.filter, prefetch_dates=True):
ncals += 1
if ncals % 1000 == 0:
print("%d calendars, %d dates..." % (ncals, ndates))
for date in calendar.dates:
ndates += 1
csvout.writerow([calendar.service_id, date.toYYYYMMDD(), 1])
print("Exported %d calendars with %d dates" % (ncals, ndates))
with PrettyCsv("fare_attributes.txt", ["fare_id", "price", "currency_type", "payment_method", "transfers", "transfer_duration"], **kwargs) as csvout:
nfareattrs = 0
for fareattr in context.dao().fare_attributes(fltr=context.args.filter, prefetch_fare_rules=False):
nfareattrs += 1
csvout.writerow([ fareattr.fare_id, fareattr.price, fareattr.currency_type, fareattr.payment_method, fareattr.transfers, fareattr.transfer_duration ])
print("Exported %d fare attributes" % (nfareattrs))
with PrettyCsv("fare_rules.txt", ["fare_id", "route_id", "origin_id", "destination_id", "contains_id"], **kwargs) as csvout:
nfarerules = 0
for farerule in context.dao().fare_rules(fltr=context.args.filter, prefetch_fare_attributes=False):
nfarerules += 1
csvout.writerow([ farerule.fare_id, farerule.route_id, farerule.origin_id, farerule.destination_id, farerule.contains_id ])
print("Exported %d fare rules" % (nfarerules))
shapes_columns = ["shape_id", "shape_pt_lat", "shape_pt_lon", "shape_pt_sequence"]
if not skip_shape_dist:
shapes_columns.append("shape_dist_traveled")
with PrettyCsv("shapes.txt", shapes_columns, **kwargs) as csvout:
nshapes = nshapepoints = 0
for shape in context.dao().shapes(fltr=context.args.filter, prefetch_points=True):
nshapes += 1
if nshapes % 100 == 0:
print("%d shapes, %d points..." % (nshapes, nshapepoints))
for point in shape.points:
nshapepoints += 1
row = [shape.shape_id, point.shape_pt_lat, point.shape_pt_lon, point.shape_pt_sequence]
if not skip_shape_dist:
row.append(point.shape_dist_traveled)
csvout.writerow(row)
print("Exported %d shapes with %d points" % (nshapes, nshapepoints))
with PrettyCsv("transfers.txt", ["from_stop_id", "to_stop_id", "transfer_type", "min_transfer_time"], **kwargs) as csvout:
ntransfers = 0
for transfer in context.dao().transfers(fltr=context.args.filter, prefetch_stops=False):
ntransfers += 1
csvout.writerow([ transfer.from_stop_id, transfer.to_stop_id, transfer.transfer_type, transfer.min_transfer_time ])
print("Exported %d transfers" % (ntransfers))
if bundle:
if not isinstance(bundle, six.string_types):
# Allow the use of "--bundle" option only
bundle = "gtfs.zip"
if not bundle.endswith('.zip'):
bundle = bundle + '.zip'
print("Zipping result to %s (removing .txt files)" % (bundle))
with zipfile.ZipFile(bundle, 'w', zipfile.ZIP_DEFLATED) as zipf:
for f in [ "agency.txt", "stops.txt", "routes.txt", "trips.txt", "stop_times.txt", "calendar_dates.txt", "fare_rules.txt", "fare_attributes.txt", "shapes.txt", "transfers.txt" ]:
zipf.write(f)
os.remove(f)
def run(self, context, csv=None, cluster=0, dstp=0.5, samename=False, alldates=False, **kwargs):
cluster_meters = float(cluster)
dstp = float(dstp)
print("Loading stops...")
stops = set()
sc = SpatialClusterizer(cluster_meters)
for stop in context.dao().stops(fltr=context.args.filter):
sc.add_point(stop)
stops.add(stop)
print("Loaded %d stops. Clusterize..." % (len(stops)))
sc.clusterize(comparator=sc.make_comparator(samename, dstp))
print("Aggregated in %d clusters" % (len(sc.clusters())))
print("Loading calendar dates...")
dates = set(context.dao().calendar_dates_date(fltr=context.args.filter))
print("Loaded %d dates" % (len(dates)))
print("Processing trips...")
departures_by_clusters = defaultdict(lambda : defaultdict(list))
ntrips = 0
for trip in context.dao().trips(fltr=context.args.filter, prefetch_stops=True, prefetch_stop_times=True, prefetch_calendars=True):
for stop_time in trip.stop_times:
if not stop_time.departure_time:
continue
if not stop_time.stop in stops:
continue
cluster = sc.cluster_of(stop_time.stop)
departures_by_dates = departures_by_clusters[cluster]
for date in trip.calendar.dates:
if date.as_date() not in dates:
continue
departures_by_dates[date.as_date()].append(stop_time)
if ntrips % 1000 == 0:
print("%d trips..." % (ntrips))
ntrips += 1
with PrettyCsv(csv, ["cluster", "stop_id", "stop_name", "date", "departures", "min_time", "max_time", "dep_hour" ], **kwargs) as csvout:
for cluster, departures_by_dates in departures_by_clusters.items():
for stop in cluster.items:
csvout.writerow([ cluster.id, stop.stop_id, stop.stop_name ])
if alldates:
# Print departure count for all dates
dates_to_print = list(departures_by_dates.keys())
dates_to_print.sort()
else:
# Compute the max only
date_max = None
dep_max = 0
for date, departures in departures_by_dates.items():
ndep = len(departures)
if ndep >= dep_max:
dep_max = ndep
date_max = date
if date_max is None:
continue
dates_to_print = [ date_max ]
for date in dates_to_print:
dep_times = [dep.departure_time for dep in departures_by_dates.get(date)]
max_hour = max(dep_times)
min_hour = min(dep_times)
delta_hour = max_hour - min_hour
avg_dep = float('inf') if delta_hour == 0 else len(dep_times) * 3600. / (max_hour - min_hour)
csvout.writerow([ cluster.id, None, None, date, len(dep_times), fmttime(min_hour), fmttime(max_hour), "%.3f" % avg_dep ])
def decret_2015_1610(trips, trace=True, required_distance=500, required_ratio=2.5):
affiche(trace, "Calcul decret 2015 1610 sur %d voyages." % (len(trips)))
if len(trips) == 0:
affiche(trace, "Aucun voyages, impossible de calculer.")
return None, None, None
affiche(trace, "Calcul de l'espacement moyen des arrêts...")
espacement_moyen = 0
w_esp = 0
for trip in trips:
# Note: on pondère par le nombre de jours chaque voyage est applicable.
# Ceci permet de prendre en compte la fréquence: par exemple, la distance
# d'un intervalle entre deux arrêt actif le lundi uniquement sera pondéré
# 5 fois moins qu'un autre intervalle actif du lundi au vendredi.
n_jours = len(trip.calendar.dates)
for stoptime1, stoptime2 in trip.hops():
espacement_moyen += (stoptime2.shape_dist_traveled - stoptime1.shape_dist_traveled) * n_jours
w_esp += n_jours
espacement_moyen /= w_esp
affiche(trace, "L'espacement moyen entre arrêt du réseau est de %.2f mètres (max 500m)." % espacement_moyen)
affiche(trace, "Calcul du jour ayant la fréquence en voyage la plus élevée...")
frequences = defaultdict(lambda: 0)
for trip in trips:
for date in trip.calendar.dates:
frequences[date] += 1
date_max = None
freq_max = 0
for date, frequence in frequences.items():
if frequence > freq_max:
freq_max = frequence
date_max = date
affiche(trace, "Le jour ayant le nombre de voyage le plus élevé est le %s, avec %d voyages." % (date_max.as_date(), freq_max))
affiche(trace, "Calcul des fréquences sur la plage horaire 8h - 19h...")
# TODO Est-ce que ce calcul est correct? Le décret est pas clair.
# On calcule le nombre de voyages actifs pendant chaque minute.
frequences = [ 0 for minute in range(0, 20 * 60) ]
for trip in trips:
if date_max not in trip.calendar.dates:
continue
minute_depart = trip.stop_times[0].departure_time // 60
minute_arrivee = trip.stop_times[-1].arrival_time // 60
for minute in range(minute_depart, minute_arrivee + 1):
if minute >= 8 * 60 and minute < 20 * 60:
frequences[minute] += 1
frequence_min = 99999999999
minute_min = 0
frequence_max = 0
minute_max = 0
# La fréquence horaire min/max est calculé en moyenne glissante
# sur une heure en sommant les fréquences par minute.
for minute in range(8 * 60, 19 * 60):
freq = 0
for delta_minute in range(0, 60):
freq += frequences[minute + delta_minute]
if freq > frequence_max:
frequence_max = freq
minute_max = minute
if freq < frequence_min:
frequence_min = freq
minute_min = minute
affiche(trace, "La fréquence minimale est de %.2f voyages/heure, entre %s et %s." % (frequence_min / 60.0, fmttime(minute_min * 60), fmttime((minute_min + 60) * 60)))
affiche(trace, "La fréquence maximale est de %.2f voyages/heure, entre %s et %s." % (frequence_max / 60.0, fmttime(minute_max * 60), fmttime((minute_max + 60) * 60)))
if frequence_min == 0:
ratio_frequence = float('inf')
else:
ratio_frequence = frequence_max / float(frequence_min)
affiche(trace, "Le ratio entre fréquence max et min est de %.3f (max 2.5)." % ratio_frequence)
urbain = ratio_frequence < required_ratio and espacement_moyen < required_distance
affiche(trace, "Ce service est %s au sens du décret n° 2015-1610."
% ("URBAIN" if urbain else "NON URBAIN"))
return urbain, espacement_moyen, ratio_frequence
