def ORS_query_directions(query, profile='driving-car', toll_price=True, _id=0, geometry=True):
'''
start (class point)
end (class point)
profile= ["driving-car", "driving-hgv", "foot-walking","foot-hiking", "cycling-regular", "cycling-road","cycling-mountain",
"cycling-electric",]
'''
ORS_client = start_ORS_client()
coord = [query.start_point[::-1], query.end_point[::-1]] # WARNING it seems that [lon,lat] are not in the same order than for other API.
try:
ORS_step = ORS_client.directions(
coord,
profile=profile,
instructions=False,
geometry=geometry,
options={'avoid_features': ['ferries']},
)
except:
return None
geojson = convert.decode_polyline(ORS_step['routes'][0]['geometry'])
local_distance = ORS_step['routes'][0]['summary']['distance']
local_emissions = co2_emissions.calculate_co2_emissions(constants.TYPE_COACH, constants.DEFAULT_CITY,
constants.DEFAULT_FUEL, constants.DEFAULT_NB_SEATS,
constants.DEFAULT_NB_KM) * \
constants.DEFAULT_NB_PASSENGERS * local_distance
step = tmw.Journey_step(_id,
_type=ORS_profile(profile),
label=profile,
distance_m=local_distance,
duration_s=ORS_step['routes'][0]['summary']['duration'],
price_EUR=[ORS_gas_price(ORS_step['routes'][0]['summary']['distance'])],
gCO2=local_emissions,
geojson=geojson,
departure_date=query.departure_date
)
# Correct arrival_date based on departure_date
step.arrival_date = (step.departure_date + timedelta(seconds=step.duration_s))
# Add toll price (optional)
step = ORS_add_toll_price(step) if toll_price else step
ors_journey = tmw.Journey(0,
departure_date=query.departure_date,
arrival_date=step.arrival_date,
steps=[step])
# Add category
category_journey = list()
for step in ors_journey.steps:
if step.type not in [constants.TYPE_TRANSFER, constants.TYPE_WAIT]:
category_journey.append(step.type)
ors_journey.category = list(set(category_journey))
ors_journey.update()
ors_journey.arrival_date = ors_journey.departure_date + timedelta(seconds=ors_journey.total_duration)
return ors_journey
def navitia_journeys_sections_type_street_network(json, _id=0):
mode = json['mode']
mode_to_type = {
'walking': constants.TYPE_WALK,
'bike': constants.TYPE_BIKE,
'car': constants.TYPE_CAR,
}
label = '{} FROM {} TO {}'.format(
mode_to_type[mode],
json['from']['name'],
json['to']['name'],
)
step = tmw.Journey_step(_id,
_type=mode_to_type[mode],
label=label,
distance_m=json['geojson']['properties'][0]['length'],
duration_s=json['duration'],
price_EUR=[0],
gCO2=json['co2_emission']['value'],
departure_point=json['from']['name'],
arrival_point=json['to']['name'],
departure_stop_name=json['from']['name'],
arrival_stop_name=json['to']['name'],
departure_date=datetime.strptime(json['departure_date_time'], '%Y%m%dT%H%M%S'),
arrival_date=datetime.strptime(json['arrival_date_time'], '%Y%m%dT%H%M%S'),
geojson=json['geojson'],
)
return step
def navitia_journeys_sections_type_on_demand(json, _id=0):
display_information = json['display_informations']
label = '{} {} / {} / direction: {}'.format(
display_information['physical_mode'],
display_information['code'],
display_information['name'],
display_information['direction'],
)
step = tmw.Journey_step(
_id,
_type=display_information['network'].lower(),
label=label,
distance_m=json['geojson']['properties'][0]['length'],
duration_s=json['duration'],
price_EUR=[0],
gCO2=json['co2_emission']['value'],
departure_point=json['from']['name'],
arrival_point=json['to']['name'],
departure_stop_name=json['from']['name'],
arrival_stop_name=json['to']['name'],
departure_date=datetime.strptime(json['departure_date_time'],
'%Y%m%dT%H%M%S'),
arrival_date=datetime.strptime(json['arrival_date_time'],
'%Y%m%dT%H%M%S'),
geojson=json['geojson'],
)
return step
def navitia_journeys_sections_type_public_transport(json, _id=0):
display_information = json['display_informations']
label = '{} {} / {} / direction: {}'.format(
display_information['physical_mode'],
display_information['code'],
display_information['name'],
display_information['direction'],
)
switcher_public_transport_type = {
'Métro': constants.TYPE_METRO,
'Bus': constants.TYPE_BUS,
'Tramway': constants.TYPE_TRAM,
'RER': constants.TYPE_METRO,
}
_type = switcher_public_transport_type.get(display_information['commercial_mode'],
"unknown public transport")
# _type = display_information['commercial_mode']
# _type = unicodedata.normalize('NFD', _type).encode('ascii', 'ignore').lower()
step = tmw.Journey_step(_id,
_type=_type,
label=label,
distance_m=json['geojson']['properties'][0]['length'],
duration_s=json['duration'],
price_EUR=[0],
gCO2=json['co2_emission']['value'],
departure_point=json['from']['name'],
arrival_point=json['to']['name'],
departure_stop_name=json['from']['name'],
arrival_stop_name=json['to']['name'],
departure_date=datetime.strptime(json['departure_date_time'], '%Y%m%dT%H%M%S'),
arrival_date=datetime.strptime(json['arrival_date_time'], '%Y%m%dT%H%M%S'),
geojson=json['geojson'],
)
return step
def compute_complete_journey(departure_date='2019-11-25T09:00:00+0200',
geoloc_dep=[48.85, 2.35],
geoloc_arrival=[43.60, 1.44]):
# Let's create the start to finish query
query_start_finish = tmw.query(0, geoloc_dep, geoloc_arrival,
departure_date)
# First we look for intercities journeys
trainline_journeys = Trainline.main(query_start_finish)
skyscanner_journeys = Skyscanner.main(query_start_finish)
ouibus_journeys = OuiBus.main(query_start_finish)
# ors_step = ORS.ORS_query_directions(query_start_finish)
all_journeys = trainline_journeys + skyscanner_journeys + ouibus_journeys
# Then we call Navitia to get
for interurban_journey in all_journeys:
start_to_station_query = tmw.query(
0, geoloc_dep, interurban_journey.steps[0].departure_point,
departure_date)
start_to_station_steps = Navitia.navitia_query_directions(
start_to_station_query)
station_to_arrival_query = tmw.query(
0, interurban_journey.steps[-1].arrival_point, geoloc_arrival,
departure_date)
station_to_arrival_steps = Navitia.navitia_query_directions(
station_to_arrival_query)
if (start_to_station_steps is not None) & (station_to_arrival_steps
is not None):
interurban_journey.add_steps(start_to_station_steps[0].steps,
start_end=True)
print(
f'arrival point is :{interurban_journey.steps[-1].arrival_point}'
)
interurban_journey.add_steps(station_to_arrival_steps[0].steps,
start_end=False)
interurban_journey.update()
else:
interurban_journey.reset()
# all_journeys = all_journeys + ors_step
return all_journeys
def navitia_journeys_sections_type_waiting(json, _id=0):
step = tmw.journey_step(
_id,
_type=constants.TYPE_WAIT,
label='wait',
distance_m=None,
duration_s=json['duration'],
price_EUR=[0],
gCO2=json['co2_emission']['value'],
geojson='',
)
return step
def create_fake_plane_journey(locations, airport_dep, airport_arrival):
"""
We create a fake plane journey with only the approximate eqCO2 to be used in the computation in the front end
:param query:
:return: fake_journey
"""
geoloc_dep = locations['departure'][locations['departure'].city_sky ==
airport_dep].sample().geoloc
geoloc_arrival = locations['arrival'][locations['arrival'].city_sky ==
airport_arrival].sample().geoloc
distance_m = distance(geoloc_dep, geoloc_arrival).m
local_range_km = get_range_km(distance_m)
local_emissions = calculate_co2_emissions(constants.TYPE_PLANE, constants.DEFAULT_CITY,
constants.DEFAULT_FUEL, constants.NB_SEATS_TEST,
local_range_km) * \
constants.DEFAULT_NB_PASSENGERS * distance_m
fake_journey_list = list()
fake_journey_step = tmw.Journey_step(
0,
_type=constants.TYPE_PLANE,
label=
f'Arrive at the airport {format_timespan(_AIRPORT_WAITING_PERIOD)} before departure',
distance_m=0,
duration_s=_AIRPORT_WAITING_PERIOD,
price_EUR=[0],
gCO2=local_emissions,
departure_point=geoloc_dep,
arrival_point=geoloc_arrival,
departure_date=dt.now(),
arrival_date=dt.now(),
geojson=[],
)
fake_journey_list.append(fake_journey_step)
fake_journey = tmw.Journey(0,
steps=fake_journey_list,
departure_date=fake_journey_step.departure_date,
arrival_date=fake_journey_step.arrival_date)
fake_journey.total_gCO2 = local_emissions
fake_journey.is_real_journey = False
return fake_journey
def navitia_journeys_sections_type_waiting(json, _id=0):
step = tmw.Journey_step(_id,
_type=constants.TYPE_WAIT,
label='wait',
distance_m=0,
duration_s=json['duration'],
price_EUR=[0],
gCO2=0,
departure_point=[0,0],
arrival_point=[0,0],
departure_stop_name='',
arrival_stop_name='',
departure_date=datetime.strptime(json['departure_date_time'], '%Y%m%dT%H%M%S'),
arrival_date=datetime.strptime(json['arrival_date_time'], '%Y%m%dT%H%M%S'),
geojson='',
)
return step
def navitia_journeys_sections_type_public_transport(json, _id=0):
display_information = json['display_informations']
label = '{} {} / {} / direction: {}'.format(
display_information['physical_mode'],
display_information['code'],
display_information['name'],
display_information['direction'],
)
step = tmw.journey_step(
_id,
_type=display_information['network'].lower(),
label=label,
distance_m=None,
duration_s=json['duration'],
price_EUR=[0],
gCO2=json['co2_emission']['value'],
geojson=json['geojson'],
)
return step
def navitia_journeys_sections_type_transfer(json, _id=0):
mode = json['transfer_type']
mode_to_type = {
'walking': constants.TYPE_WALK,
'bike': constants.TYPE_BIKE,
'car': constants.TYPE_CAR,
}
label = '{} FROM {} TO {}'.format(mode_to_type[mode], json['from']['name'],
json['to']['name'])
step = tmw.journey_step(
_id,
_type=mode_to_type[mode],
label=label,
distance_m=None,
duration_s=json['duration'],
price_EUR=[0],
gCO2=json['co2_emission']['value'],
geojson=json['geojson'],
)
return step
def navitia_journeys(json, _id=0):
# all journeys loop
lst_journeys = list()
try:
journeys = json['journeys']
except:
logger.warning('ERROR {}'.format(json['error']))
return None
for j in json['journeys']:
i = _id
# journey loop
lst_sections = list()
for section in j['sections']:
try:
lst_sections.append(navitia_journeys_sections_type(section, _id=i))
except:
logger.warning('Navitia ERROR : ')
logger.warning('id: {}'.format(i))
logger.warning(section)
i = i + 1
lst_journeys.append(tmw.Journey(_id, steps=lst_sections))
return lst_journeys
def get_ferries(date_departure, date_return, departure_point, arrival_point):
"""
We create a ferry journey based on the ferry database we scraped
"""
# Find relevant ports
port_deps, port_arrs = get_ports_from_geo_locs(departure_point,
arrival_point)
# Find journeys
journeys = _FERRY_DATA[
(_FERRY_DATA.port_dep.isin(port_deps.port_clean.unique()))
& _FERRY_DATA.port_arr.isin(port_arrs.port_clean.unique())]
journeys['date_dep'] = pd.to_datetime(journeys.date_dep)
journeys = journeys[journeys.date_dep > date_departure]
if len(journeys) == 0:
logger.info(f'No ferry journey was found')
return None
journey_list = list()
for index, row in journeys.iterrows():
distance_m = row.distance_m
local_emissions = calculate_co2_emissions(constants.TYPE_PLANE, constants.DEFAULT_CITY,
constants.DEFAULT_FUEL, constants.NB_SEATS_TEST,
constants.DEFAULT_NB_KM) * \
constants.DEFAULT_NB_PASSENGERS * distance_m
journey_steps = list()
journey_step = tmw.Journey_step(
0,
_type=constants.TYPE_WAIT,
label=
f'Arrive at the port {format_timespan(_PORT_WAITING_PERIOD)} before departure',
distance_m=0,
duration_s=_PORT_WAITING_PERIOD,
price_EUR=[0],
gCO2=0,
departure_point=[row.lat_clean_dep, row.long_clean_dep],
arrival_point=[row.lat_clean_dep, row.long_clean_dep],
departure_date=row.date_dep -
timedelta(seconds=_PORT_WAITING_PERIOD),
arrival_date=row.date_dep,
geojson=[],
)
journey_steps.append(journey_step)
journey_step = tmw.Journey_step(
1,
_type=constants.TYPE_FERRY,
label=f'Sail Ferry from {row.port_dep} to {row.port_arr}',
distance_m=distance_m,
duration_s=(row.date_arr - row.date_dep).seconds,
price_EUR=[row.price_clean_ar_eur / 2],
gCO2=local_emissions,
departure_point=[row.lat_clean_dep, row.long_clean_dep],
arrival_point=[row.lat_clean_arr, row.long_clean_arr],
departure_date=row.date_dep,
arrival_date=row.date_arr,
geojson=[],
)
journey_steps.append(journey_step)
journey = tmw.Journey(
0,
steps=journey_steps,
departure_date=journey_steps[0].departure_date,
arrival_date=journey_steps[1].arrival_date,
)
journey.total_gCO2 = local_emissions
journey.category = constants.CATEGORY_FERRY_JOURNEY
journey.booking_link = 'https://www.ferrysavers.co.uk/ferry-routes.htm'
journey.departure_point = [row.lat_clean_dep, row.long_clean_dep]
journey.arrival_point = [row.lat_clean_arr, row.long_clean_arr]
journey.update()
journey_list.append(journey)
return journey_list
def trainline_journeys(df_response, _id=0):
"""
This function takes in a DF with detailled info about all the Trainline trips
It returns a list of TMW journey objects
"""
# affect a price to each leg (otherwise we would multiply the price by the number of legs
df_response['price_step'] = df_response.cents / (df_response.nb_segments*100)
# Compute distance for each leg
# print(df_response.columns)
df_response['distance_step'] = df_response.apply(lambda x: distance(x.geoloc_depart_seg, x.geoloc_arrival_seg).m,
axis=1)
df_response['trip_code'] = df_response.train_name + ' ' + df_response.train_number
tranportation_mean_to_type = {
'coach': constants.TYPE_COACH,
'train': constants.TYPE_TRAIN,
}
lst_journeys = list()
# all itineraries :
# print(f'nb itinerary : {df_response.id_global.nunique()}')
for itinerary_id in df_response.id_global.unique():
itinerary = df_response[df_response.id_global == itinerary_id].reset_index(drop=True)
# boolean to know whether and when there will be a transfer after the leg
itinerary['next_departure'] = itinerary.departure_date_seg.shift(-1)
itinerary['next_stop_name'] = itinerary.name_depart_seg.shift(-1)
itinerary['next_geoloc'] = itinerary.geoloc_depart_seg.shift(-1)
itinerary['trip_code'] = itinerary.trip_code.fillna('')
# get the slugs to create the booking link
origin_slug = itinerary.origin_slug.unique()[0]
destination_slug = itinerary.destination_slug.unique()[0]
i = _id
lst_sections = list()
# We add a waiting period at the station of 15 minutes
step = tmw.Journey_step(i,
_type=constants.TYPE_WAIT,
label=f'Arrive at the station {format_timespan(_STATION_WAITING_PERIOD)} before departure',
distance_m=0,
duration_s=_STATION_WAITING_PERIOD,
price_EUR=[0],
gCO2=0,
departure_point=[itinerary.latitude.iloc[0], itinerary.longitude.iloc[0]],
arrival_point=[itinerary.latitude.iloc[0], itinerary.longitude.iloc[0]],
departure_date=itinerary.departure_date_seg[0] - timedelta(seconds=_STATION_WAITING_PERIOD),
arrival_date=itinerary.departure_date_seg[0],
bike_friendly=True,
geojson=[],
)
lst_sections.append(step)
i = i + 1
# Go through all steps of the journey
for index, leg in itinerary.iterrows():
local_distance_m = distance(leg.geoloc_depart_seg, leg.geoloc_arrival_seg).m
local_transportation_type = tranportation_mean_to_type[leg.transportation_mean]
local_emissions = co2_emissions.calculate_co2_emissions(local_transportation_type, constants.DEFAULT_CITY,
constants.DEFAULT_FUEL, constants.DEFAULT_NB_SEATS,
constants.DEFAULT_NB_KM) * \
constants.DEFAULT_NB_PASSENGERS * local_distance_m
step = tmw.Journey_step(i,
_type=local_transportation_type,
label=f'{leg.trip_code} to {leg.name_arrival_seg}',
distance_m=local_distance_m,
duration_s=(leg.arrival_date_seg - leg.departure_date_seg).seconds,
price_EUR=[leg.price_step],
gCO2=local_emissions,
departure_point=leg.geoloc_depart_seg,
arrival_point=leg.geoloc_arrival_seg,
departure_stop_name=leg.name_depart_seg,
arrival_stop_name=leg.name_arrival_seg,
departure_date=leg.departure_date_seg,
arrival_date=leg.arrival_date_seg,
trip_code=leg.trip_code,
bike_friendly='bicycle' in leg.bike_friendliness,
geojson=[],
)
lst_sections.append(step)
i = i + 1
# add transfer steps
if not pd.isna(leg.next_departure):
step = tmw.Journey_step(i,
_type=constants.TYPE_TRANSFER,
label=f'Transfer at {leg.name_arrival_seg}',
distance_m=0,
duration_s=(leg['next_departure'] - leg['arrival_date_seg']).seconds,
price_EUR=[0],
departure_point=leg.geoloc_arrival_seg,
arrival_point=leg.next_geoloc,
departure_stop_name=leg.name_depart_seg,
arrival_stop_name=leg.name_arrival_seg,
departure_date=leg.arrival_date_seg,
arrival_date=leg.next_departure,
gCO2=0,
bike_friendly=True,
geojson=[],
)
lst_sections.append(step)
i = i + 1
departure_date_formated = dt.strptime(str(lst_sections[0].departure_date)[0:15], '%Y-%m-%d %H:%M').strftime('%Y-%m-%d %H:00')
journey_train = tmw.Journey(_id, steps=lst_sections,
departure_date= lst_sections[0].departure_date,
arrival_date= lst_sections[-1].arrival_date,
booking_link=f'https://www.trainline.fr/search/{origin_slug}/{destination_slug}/{departure_date_formated}')
# Add category
category_journey = list()
for step in journey_train.steps:
if step.type not in [constants.TYPE_TRANSFER, constants.TYPE_WAIT]:
category_journey.append(step.type)
journey_train.category = list(set(category_journey))
lst_journeys.append(journey_train)
# for journey in lst_journeys:
# journey.update()
return lst_journeys
def create_plane_journey_from_flightradar_data(airports, departure_date):
"""
We create a fake plane journey with only the approximate eqCO2 to be used in the computation in the front end
:param query:
:return: fake_journey
"""
day_of_week = departure_date.weekday()
hour_of_day = departure_date.hour
relevant_flights = _FLIGHTRADAR_DATA[
_FLIGHTRADAR_DATA.city_sky.isin(airports['departure'])
& _FLIGHTRADAR_DATA.city_sky_arr.isin(airports['arrival'])]
relevant_flights = relevant_flights[relevant_flights.day_of_week ==
day_of_week]
relevant_flights['hour_dep'] = relevant_flights.apply(
lambda x: dt.strptime(x.hour_dep, '%H:%M:%S') + timedelta(hours=1),
axis=1)
relevant_flights['hour_dep_int'] = relevant_flights.apply(
lambda x: x.hour_dep.hour, axis=1)
response_flights = pd.DataFrame()
for airport_dep in airports['departure']:
for airport_arr in airports['arrival']:
flights_df = relevant_flights[
(relevant_flights.city_sky == airport_dep)
& (relevant_flights.city_sky_arr == airport_arr) &
(relevant_flights.hour_dep_int >= hour_of_day)]
response_flights = response_flights.append(flights_df)
# distance_m = distance(geoloc_dep, geoloc_arrival).m
response_flights['local_range_km'] = response_flights.apply(
lambda x: get_range_km(x.distance_m), axis=1)
response_flights['local_emissions'] = response_flights.apply(
lambda x: calculate_co2_emissions(
constants.TYPE_PLANE, constants.DEFAULT_CITY, constants.
DEFAULT_FUEL, constants.NB_SEATS_TEST, x.local_range_km) *
constants.DEFAULT_NB_PASSENGERS * x.distance_m,
axis=1)
# merge global departure date and flight time to create flight actual departure datetime
response_flights['flight_departure_date'] = response_flights.apply(
lambda x: dt.combine(departure_date,
dt_time(x.hour_dep.hour, x.hour_dep.minute)),
axis=1)
response_flights['flight_arrival_date'] = response_flights.apply(
lambda x: x.flight_departure_date + timedelta(seconds=x.flight_time_s),
axis=1)
journey_list = list()
for index, flight in response_flights.iterrows():
lst_sections = list()
# We add a waiting period at the airport of x hours
step = tmw.Journey_step(
0,
_type=constants.TYPE_WAIT,
label=
f'Arrive at the airport {format_timespan(_AIRPORT_WAITING_PERIOD)} before departure',
distance_m=0,
duration_s=_AIRPORT_WAITING_PERIOD,
price_EUR=[],
gCO2=0,
departure_point=[flight.latitude, flight.longitude],
arrival_point=[flight.latitude, flight.longitude],
departure_date=flight.flight_departure_date -
timedelta(seconds=_AIRPORT_WAITING_PERIOD),
arrival_date=flight.flight_departure_date,
geojson=[],
)
lst_sections.append(step)
step = tmw.Journey_step(
1,
_type=constants.TYPE_PLANE,
label=f'Flight {flight.flight_number} to {flight.airport_to_code}',
distance_m=flight.distance_m,
duration_s=flight.flight_time_s,
price_EUR=[],
gCO2=flight.local_emissions,
departure_point=[flight.latitude, flight.longitude],
arrival_point=[flight.latitude_arr, flight.longitude_arr],
departure_stop_name=flight.airport_from,
arrival_stop_name=flight.airport_to_code,
departure_date=flight.flight_departure_date,
arrival_date=flight.flight_arrival_date,
trip_code=flight.flight_number,
geojson=[],
)
lst_sections.append(step)
departure_date_formated = dt.strptime(
str(lst_sections[0].departure_date)[0:10], '%Y-%m-%d')
departure_date_formated = str(departure_date_formated.year)[2:4]+\
('0'+str(departure_date_formated.month))[-2:]+\
('0'+str(departure_date_formated.day))[-2:]
journey_flightradar = tmw.Journey(
0,
steps=lst_sections,
departure_date=lst_sections[0].departure_date,
arrival_date=lst_sections[-1].arrival_date,
booking_link=
f'https://www.skyscanner.fr/transport/vols/{flight.airport_from}/{flight.airport_to_code}/{departure_date_formated}/'
)
journey_flightradar.category = [constants.TYPE_PLANE]
journey_flightradar.update()
journey_flightradar.is_real_journey = False
journey_list.append(journey_flightradar)
return journey_list
def ouibus_journeys(df_response, _id=0):
"""
This function takes in a DF with detailled info about all the OuiBus trips
It returns a list of TMW journey objects
"""
# affect a price to each leg
df_response['price_step'] = df_response.price_cents / (
df_response.nb_segments * 100)
# Compute distance for each leg
# print(df_response.columns)
df_response['distance_step'] = df_response.apply(
lambda x: distance(x.geoloc_origin_seg, x.geoloc_destination_seg).m,
axis=1)
lst_journeys = list()
# all itineraries :
# logger.info(f'nb itinerary : {df_response.id.nunique()}')
for itinerary_id in df_response.id.unique():
itinerary = df_response[df_response.id == itinerary_id].reset_index(
drop=True)
# boolean to know whether and when there will be a transfer after the leg
itinerary['next_departure'] = itinerary.departure_seg.shift(-1)
itinerary['next_stop_name'] = itinerary.short_name_origin_seg.shift(1)
itinerary['next_geoloc'] = itinerary.geoloc_origin_seg.shift(-1)
# get the slugs to create the booking link
origin_slug = itinerary.origin_slug.unique()[0]
destination_slug = itinerary.destination_slug.unique()[0]
i = _id
lst_sections = list()
# We add a waiting period at the station of 15 minutes
step = tmw.Journey_step(
i,
_type=constants.TYPE_WAIT,
label=
f'Arrive at the station {format_timespan(_STATION_WAITING_PERIOD)} before departure',
distance_m=0,
duration_s=_STATION_WAITING_PERIOD,
price_EUR=[0],
gCO2=0,
departure_point=itinerary.geoloc.iloc[0],
arrival_point=itinerary.geoloc.iloc[0],
departure_date=itinerary.departure_seg[0] -
timedelta(seconds=_STATION_WAITING_PERIOD),
arrival_date=itinerary.departure_seg[0],
geojson=[],
)
lst_sections.append(step)
i = i + 1
for index, leg in itinerary.iterrows():
local_distance_m = leg.distance_step
local_emissions = calculate_co2_emissions(constants.TYPE_COACH, constants.DEFAULT_CITY,
constants.DEFAULT_FUEL, constants.DEFAULT_NB_SEATS,
constants.DEFAULT_NB_KM) *\
constants.DEFAULT_NB_PASSENGERS*local_distance_m
step = tmw.Journey_step(
i,
_type=constants.TYPE_COACH,
label=
f'Coach OuiBus {leg.bus_number} to {leg.short_name_destination_seg}',
distance_m=local_distance_m,
duration_s=(leg.arrival_seg - leg.departure_seg).seconds,
price_EUR=[leg.price_step],
gCO2=local_emissions,
departure_point=leg.geoloc_origin_seg,
arrival_point=leg.geoloc_destination_seg,
departure_stop_name=leg.short_name_origin_seg,
arrival_stop_name=leg.short_name_destination_seg,
departure_date=leg.departure_seg,
arrival_date=leg.arrival_seg,
trip_code='OuiBus ' + leg.bus_number,
geojson=[],
)
lst_sections.append(step)
i = i + 1
# add transfer steps
if not pd.isna(leg.next_departure):
step = tmw.Journey_step(
i,
_type=constants.TYPE_TRANSFER,
label=f'Transfer at {leg.short_name_destination_seg}',
distance_m=distance(leg.geoloc_destination_seg,
leg.next_geoloc).m,
duration_s=(leg['next_departure'] -
leg['arrival_seg']).seconds,
price_EUR=[0],
departure_point=leg.geoloc_destination_seg,
arrival_point=leg.next_geoloc,
departure_stop_name=leg.short_name_destination_seg,
arrival_stop_name=leg.next_stop_name,
gCO2=0,
geojson=[],
)
lst_sections.append(step)
i = i + 1
departure_date_formated = dt.strptime(
str(lst_sections[0].departure_date)[0:15],
'%Y-%m-%d %H:%M').strftime('%Y-%m-%d %H:00')
journey_ouibus = tmw.Journey(
_id,
steps=lst_sections,
booking_link=
f'https://fr.ouibus.com/recherche?origin={origin_slug}&destination={destination_slug}&outboundDate={departure_date_formated}'
)
# Add category
category_journey = list()
for step in journey_ouibus.steps:
if step.type not in [constants.TYPE_TRANSFER, constants.TYPE_WAIT]:
category_journey.append(step.type)
journey_ouibus.category = list(set(category_journey))
lst_journeys.append(journey_ouibus)
# for journey in lst_journeys:
# journey.update()
return lst_journeys
def skyscanner_journeys(df_response, _id=0):
"""
This function takes in a dataframe with detailled information on the plane journeys returned by Skyscanner API
and returns a list containing one TMW journey object for each of those plane journey
"""
# affect a price to each leg
df_response[
'price_step'] = df_response.PriceTotal_AR / df_response.nb_segments
df_response['DepartureDateTime'] = pd.to_datetime(
df_response['DepartureDateTime'])
df_response['ArrivalDateTime'] = pd.to_datetime(
df_response['ArrivalDateTime'])
# Compute distance for each leg
df_response['distance_step'] = df_response.apply(
lambda x: distance(x.geoloc_origin_seg, x.geoloc_destination_seg).m,
axis=1)
lst_journeys = list()
# all itineraries :
for itinerary_id in df_response.itinerary_id.unique():
itinerary = df_response[df_response.itinerary_id ==
itinerary_id].reset_index(drop=True)
i = _id
# boolean to know whether and when there will be a transfer after the leg
itinerary['next_departure'] = itinerary.DepartureDateTime.shift(-1)
itinerary['next_stop_name'] = itinerary.Name_origin_seg.shift(-1)
itinerary['next_geoloc'] = itinerary.geoloc_origin_seg.shift(-1)
# get the slugs to create the booking link
departure_slug = itinerary.departure_slug.unique()[0].lower()[0:4]
arrival_slug = itinerary.arrival_slug.unique()[0].lower()[0:4]
lst_sections = list()
# We add a waiting period at the airport of x hours
step = tmw.Journey_step(
i,
_type=constants.TYPE_WAIT,
label=
f'Arrive at the airport {format_timespan(_AIRPORT_WAITING_PERIOD)} before departure',
distance_m=0,
duration_s=_AIRPORT_WAITING_PERIOD,
price_EUR=[0],
gCO2=0,
departure_point=itinerary.geoloc.iloc[0],
arrival_point=itinerary.geoloc.iloc[0],
departure_date=itinerary.DepartureDateTime[0] -
timedelta(seconds=_AIRPORT_WAITING_PERIOD),
arrival_date=itinerary.DepartureDateTime[0],
geojson=[],
)
lst_sections.append(step)
i = i + 1
for index, leg in itinerary.sort_values(
by='DepartureDateTime').iterrows():
local_distance_m = leg.distance_step
local_range_km = get_range_km(local_distance_m)
local_emissions = calculate_co2_emissions(constants.TYPE_PLANE, constants.DEFAULT_CITY,
constants.DEFAULT_FUEL, constants.NB_SEATS_TEST,
local_range_km) * \
constants.DEFAULT_NB_PASSENGERS * local_distance_m
step = tmw.Journey_step(
i,
_type=constants.TYPE_PLANE,
label=f'Flight {leg.FlightNumber_rich} to {leg.Name}',
distance_m=leg.distance_step,
duration_s=leg.Duration_seg * 60,
price_EUR=[leg.price_step],
gCO2=local_emissions,
departure_point=leg.geoloc_origin_seg,
arrival_point=leg.geoloc_destination_seg,
departure_stop_name=leg.Name_origin_seg,
arrival_stop_name=leg.Name,
departure_date=leg.DepartureDateTime,
arrival_date=leg.ArrivalDateTime,
trip_code=leg.FlightNumber_rich,
geojson=[],
)
lst_sections.append(step)
i = i + 1
# add transfer steps
if not pd.isna(leg.next_departure):
#duration = dt.strptime(leg['next_departure'], '%Y-%m-%dT%H:%M:%S') - \
# dt.strptime(leg['ArrivalDateTime'], '%Y-%m-%dT%H:%M:%S')
step = tmw.Journey_step(
i,
_type=constants.TYPE_TRANSFER,
label=f'Transfer at {leg.Name}',
distance_m=0,
duration_s=(leg.next_departure -
leg.ArrivalDateTime).seconds,
price_EUR=[0],
departure_point=leg.geoloc_destination_seg,
arrival_point=leg.next_geoloc,
departure_date=leg.ArrivalDateTime,
arrival_date=leg.next_departure,
departure_stop_name=leg.Name,
arrival_stop_name=leg.next_stop_name,
gCO2=0,
geojson=[],
)
lst_sections.append(step)
i = i + 1
departure_date_formated = dt.strptime(
str(lst_sections[0].departure_date)[0:10], '%Y-%m-%d')
departure_date_formated = str(departure_date_formated.year)[2:4]+\
('0'+str(departure_date_formated.month))[-2:]+\
('0'+str(departure_date_formated.day))[-2:]
journey_sky = tmw.Journey(
_id,
steps=lst_sections,
departure_date=lst_sections[0].departure_date,
arrival_date=lst_sections[-1].arrival_date,
booking_link=
f'https://www.skyscanner.fr/transport/vols/{departure_slug}/{arrival_slug}/{departure_date_formated}/'
)
# journey_sky = tmw.Journey(_id, steps=lst_sections)
# Add category
category_journey = list()
for step in journey_sky.steps:
if step.type not in [constants.TYPE_TRANSFER, constants.TYPE_WAIT]:
category_journey.append(step.type)
journey_sky.category = list(set(category_journey))
lst_journeys.append(journey_sky)
for journey in lst_journeys:
journey.update()
return lst_journeys
def trainline_journeys(df_response, _id=0):
# affect a price to each leg
df_response['price_step'] = df_response.cents / 100
# Compute distance for each leg
# print(df_response.columns)
df_response['distance_step'] = df_response.apply(
lambda x: distance(x.geoloc_depart_seg, x.geoloc_arrival_seg).m,
axis=1)
df_response[
'trip_code'] = df_response.train_name + ' ' + df_response.train_number
tranportation_mean_to_type = {
'coach': constants.TYPE_COACH,
'train': constants.TYPE_TRAIN,
}
lst_journeys = list()
# all itineraries :
print(f'nb itinerary : {df_response.id_global.nunique()}')
for itinerary_id in df_response.id_global.unique():
itinerary = df_response[df_response.id_global == itinerary_id]
# boolean to know whether and when there will be a transfer after the leg
itinerary['next_departure'] = itinerary.departure_date_seg.shift(-1)
itinerary['next_stop_name'] = itinerary.name_depart_seg.shift(1)
itinerary['next_geoloc'] = itinerary.geoloc_depart_seg.shift(-1)
i = _id
lst_sections = list()
# We add a waiting period at the station of 15 minutes
step = tmw.journey_step(
i,
_type=constants.TYPE_WAIT,
label='',
distance_m=0,
duration_s=_STATION_WAITING_PERIOD,
price_EUR=[0],
gCO2=0,
departure_point=[
itinerary.latitude.iloc[0], itinerary.longitude.iloc[0]
],
arrival_point=[
itinerary.latitude.iloc[0], itinerary.longitude.iloc[0]
],
geojson=[],
)
lst_sections.append(step)
i = i + 1
for index, leg in itinerary.iterrows():
local_distance_m = distance(leg.geoloc_depart_seg,
leg.geoloc_arrival_seg).m
local_emissions = calculate_co2_emissions(constants.TYPE_TRAIN, '', '', '', '') * \
constants.DEFAULT_NB_PASSENGERS * local_distance_m
step = tmw.journey_step(
i,
_type=tranportation_mean_to_type[leg.transportation_mean],
label='',
distance_m=local_distance_m,
duration_s=(leg.arrival_date_seg -
leg.departure_date_seg).seconds,
price_EUR=[leg.price_step],
gCO2=local_emissions,
departure_point=leg.geoloc_depart_seg,
arrival_point=leg.geoloc_arrival_seg,
departure_stop_name=leg.name_depart_seg,
arrival_stop_name=leg.name_arrival_seg,
departure_date=leg.departure_date_seg,
arrival_date=leg.arrival_date_seg,
trip_code=leg.trip_code,
geojson=[],
)
lst_sections.append(step)
i = i + 1
# add transfer steps
if not pd.isna(leg.next_departure):
step = tmw.journey_step(
i,
_type=constants.TYPE_TRANSFER,
label='',
distance_m=0,
duration_s=(leg['next_departure'] -
leg['arrival_date_seg']).seconds,
price_EUR=[0],
departure_point=leg.geoloc_arrival_seg,
arrival_point=leg.next_geoloc,
departure_stop_name=leg.name_depart_seg,
arrival_stop_name=leg.name_arrival_seg,
departure_date=leg.arrival_date_seg,
arrival_date=leg.next_departure,
gCO2=0,
geojson=[],
)
lst_sections.append(step)
i = i + 1
journey_sky = tmw.journey(_id, steps=lst_sections)
lst_journeys.append(journey_sky)
# for journey in lst_journeys:
# journey.update()
return lst_journeys
def compute_complete_journey(departure_date='2019-11-28',
geoloc_dep=[48.85, 2.35],
geoloc_arrival=[43.60, 1.44]):
"""
Build a multi-modal journey:
First we call each API to get a few journeys for each type of transportation
Then we create a multi-modal trip by calling NAvitia between the departure point and departure station
and between arrival station and arrival point.
To limit the nb of Navitia calls, we first create all the necessary Navitia queries, and deduplicate them
to make sure we call Navitia only once for each query
Finally we call the filter function to choose which journeys we keep
"""
# format date from %Y-%m-%dT%H:%M:%S.xxxZ without considering ms
departure_date = datetime.datetime.strptime(
str(departure_date)[0:19], "%Y-%m-%dT%H:%M:%S")
# We only accept date up to 9 month in the future
date_within_range = (datetime.datetime.today() + datetime.timedelta(days=9 * 30)) \
> departure_date
if not date_within_range:
raise Exception('Date out of range')
# Let's create the start to finish query
query_start_finish = tmw.Query(0, geoloc_dep, geoloc_arrival,
departure_date)
# logger.info(f'query_start_finish{query_start_finish.to_json()}')
# Start the stopwatch / counter
t1_start = perf_counter()
# First we look for intercities journeys
# Création des threads
thread_skyscanner = tmw.ThreadComputeJourney(api='Skyscanner',
query=query_start_finish)
thread_ouibus = tmw.ThreadComputeJourney(api='OuiBus',
query=query_start_finish)
thread_trainline = tmw.ThreadComputeJourney(api='Trainline',
query=query_start_finish)
thread_ors = tmw.ThreadComputeJourney(api='ORS', query=query_start_finish)
# Lancement des threads
thread_skyscanner.start()
thread_ouibus.start()
thread_trainline.start()
thread_ors.start()
# Attendre que les threads se terminent
skyscanner_journeys, time_skyscanner = thread_skyscanner.join()
ouibus_journeys, time_ouibus = thread_ouibus.join()
trainline_journeys, time_trainline = thread_trainline.join()
ors_journey, time_or = thread_ors.join()
if skyscanner_journeys is None:
skyscanner_journeys = list()
if trainline_journeys is None:
trainline_journeys = list()
if ouibus_journeys is None:
ouibus_journeys = list()
all_journeys = trainline_journeys + skyscanner_journeys + ouibus_journeys
# all_journeys = trainline_journeys
i = 0
logger.info(f'we found {len(all_journeys)} inter urban journeys')
logger.info(
f'it took for computing the interrurban journeys {perf_counter() - t1_start}'
)
# Then we call Navitia to get the beginning and the end of the journey
# Let's record all the query we need to send to Navitia, deduplicate them and call NAvitia only once
navitia_queries = list()
for interurban_journey in all_journeys:
# if fake journey no call to Navitia
if not interurban_journey.is_real_journey:
continue
interurban_journey.id = i
i = i + 1
navitia_queries.append(
tmw.Query(0, geoloc_dep,
interurban_journey.steps[0].departure_point,
departure_date))
navitia_queries.append(
tmw.Query(0, interurban_journey.steps[-1].arrival_point,
geoloc_arrival, departure_date))
nav_start = perf_counter()
# Call Navitia only once each time:
navitia_dict = {}
navitia_query_done = list()
navitia_thread_list = list()
i = 0
for navitia_query in navitia_queries:
if navitia_query.to_json() in navitia_query_done:
# if query has been called then skip
continue
# logger.info(f'call Navitia with {navitia_query.to_json()}')
navitia_thread_list.append(tmw.ThreadNavitiaCall(navitia_query))
navitia_thread_list[i].start()
i = i + 1
# navitia_steps = Navitia.navitia_query_directions(navitia_query)
# navitia_dict[str(navitia_query.to_json())] = navitia_steps
navitia_query_done.append(navitia_query.to_json())
# navitia_dict_list.append(navitia_dict)
for navitia_thread in navitia_thread_list:
navitia_steps, navitia_query = navitia_thread.join()
navitia_dict[str(navitia_query.to_json())] = navitia_steps
logger.info(f'navitia_dict is {navitia_dict}')
# Reconsiliate between navitia queries and interrurban journeys
for interurban_journey in all_journeys:
# if fake journey no call to Navitia
# if not interurban_journey.is_real_journey:
# continue
# Get start to station query
start_to_station_query = tmw.Query(
0, geoloc_dep, interurban_journey.steps[0].departure_point,
departure_date)
start_to_station_steps = navitia_dict[str(
start_to_station_query.to_json())]
station_to_arrival_query = tmw.Query(
0, interurban_journey.steps[-1].arrival_point, geoloc_arrival,
departure_date)
station_to_arrival_steps = navitia_dict[str(
station_to_arrival_query.to_json())]
if (start_to_station_steps is not None) & (station_to_arrival_steps
is not None):
interurban_journey.add_steps(start_to_station_steps[0].steps,
start_end=True)
interurban_journey.add_steps(station_to_arrival_steps[0].steps,
start_end=False)
interurban_journey.update()
else:
logger.info(f'remove category {interurban_journey.category}')
# logger.info(f'remove price {interurban_journey.total_price_EUR}')#
# logger.info(f'remove price {interurban_journey.total_distance}')
# logger.info(f'remove legs nb {len(interurban_journey.steps)}')
# logger.info(f'last leg departs from {interurban_journey.steps[-1].departure_stop_name}')
# logger.info(f'last leg arrives in {interurban_journey.steps[-1].arrival_stop_name}')
all_journeys.remove(interurban_journey)
nav_stop = perf_counter()
if ors_journey is not None:
all_journeys.append(ors_journey)
if len(all_journeys) > 0:
# Filter most relevant Journeys
filtered_journeys = filter_and_label_relevant_journey(all_journeys)
filtered_journeys = [
filtered_journey.to_json()
for filtered_journey in filtered_journeys
]
else:
filtered_journeys = all_journeys
t1_stop = perf_counter()
logger.info(f'Elapsed time during computation: {t1_stop-t1_start} s')
logger.info(f'including: {time_trainline}s for trainline ')
logger.info(f'including: {time_skyscanner}s for skyscanner ')
logger.info(f'including: {time_ouibus}s for ouibus ')
logger.info(f'including: {time_or}s for ors ')
logger.info(f'including: {nav_stop - nav_start}s for navitia ')
return filtered_journeys
def skyscanner_journeys(df_response, _id=0):
# affect a price to each leg
df_response[
'price_step'] = df_response.PriceTotal_AR / df_response.nb_segments
# Compute distance for each leg
print(df_response.columns)
df_response['distance_step'] = df_response.apply(
lambda x: distance(x.geoloc_origin_seg, x.geoloc_destination_seg).m,
axis=1)
lst_journeys = list()
# all itineraries :
for itinerary_id in df_response.itinerary_id.unique():
itinerary = df_response[df_response.itinerary_id == itinerary_id]
i = _id
# boolean to know whether and when there will be a transfert after the leg
itinerary['next_departure'] = itinerary.DepartureDateTime.shift(1)
itinerary['next_stop_name'] = itinerary.Name_origin_seg.shift(1)
itinerary['next_geoloc'] = itinerary.geoloc_origin_seg.shift(-1)
lst_sections = list()
# We add a waiting period at the airport of 2 hours
step = tmw.journey_step(
i,
_type=constants.TYPE_WAIT,
label='',
distance_m=0,
duration_s=_AIRPORT_WAITING_PERIOD,
price_EUR=[0],
gCO2=0,
departure_point=itinerary.geoloc.iloc[0],
arrival_point=itinerary.geoloc.iloc[0],
geojson=[],
)
lst_sections.append(step)
i = i + 1
for index, leg in itinerary.sort_values(
by='DepartureDateTime').iterrows():
local_distance_m = leg.distance_step
local_range_km = get_range_km(local_distance_m)
local_emissions = calculate_co2_emissions(constants.TYPE_PLANE, '', constants.DEFAULT_PLANE_FUEL,
constants.DEFAULT_NB_SEATS, local_range_km) * \
constants.DEFAULT_NB_PASSENGERS * local_distance_m
step = tmw.journey_step(
i,
_type=constants.TYPE_PLANE,
label='',
distance_m=leg.distance_step,
duration_s=leg.Duration_seg * 60,
price_EUR=[leg.price_step],
gCO2=local_emissions,
departure_point=leg.geoloc_origin_seg,
arrival_point=leg.geoloc_destination_seg,
departure_stop_name=leg.Name_origin_seg,
arrival_stop_name=leg.Name,
departure_date=leg.DepartureDateTime,
arrival_date=leg.ArrivalDateTime,
trip_code=leg.FlightNumber_rich,
geojson=[],
)
lst_sections.append(step)
i = i + 1
# add transfer steps
if not pd.isna(leg.next_departure):
duration = dt.strptime(leg['next_departure'], '%Y-%m-%dT%H:%M:%S') - \
dt.strptime(leg['ArrivalDateTime'], '%Y-%m-%dT%H:%M:%S')
step = tmw.journey_step(
i,
_type=constants.TYPE_TRANSFER,
label='',
distance_m=0,
duration_s=duration.seconds,
price_EUR=[0],
departure_point=leg.geoloc_destination_seg,
arrival_point=leg.next_geoloc,
departure_date=leg.ArrivalDateTime,
arrival_date=leg.next_departure,
departure_stop_name=leg.Name,
arrival_stop_name=leg.next_stop_name,
gCO2=0,
geojson=[],
)
lst_sections.append(step)
i = i + 1
journey_sky = tmw.journey(_id, steps=lst_sections)
lst_journeys.append(journey_sky)
for journey in lst_journeys:
journey.update()
return lst_journeys
def ouibus_journeys(df_response, _id=0):
# affect a price to each leg
df_response['price_step'] = df_response.price_cents / (
df_response.nb_segments * 100)
# Compute distance for each leg
# print(df_response.columns)
df_response['distance_step'] = df_response.apply(
lambda x: distance(x.geoloc_origin_seg, x.geoloc_destination_seg).m,
axis=1)
lst_journeys = list()
# all itineraries :
print(f'nb itinerary : {df_response.id.nunique()}')
for itinerary_id in df_response.id.unique():
itinerary = df_response[df_response.id == itinerary_id]
# boolean to know whether and when there will be a transfer after the leg
itinerary['next_departure'] = itinerary.departure_seg.shift(-1)
itinerary['next_stop_name'] = itinerary.short_name_origin_seg.shift(1)
itinerary['next_geoloc'] = itinerary.geoloc_origin_seg.shift(-1)
i = _id
lst_sections = list()
# We add a waiting period at the station of 15 minutes
step = tmw.journey_step(
i,
_type=constants.TYPE_WAIT,
label='',
distance_m=0,
duration_s=_STATION_WAITING_PERIOD,
price_EUR=[0],
gCO2=0,
departure_point=itinerary.geoloc.iloc[0],
arrival_point=itinerary.geoloc.iloc[0],
geojson=[],
)
lst_sections.append(step)
i = i + 1
for index, leg in itinerary.iterrows():
local_distance_m = leg.distance_step
local_emissions = calculate_co2_emissions(constants.TYPE_COACH, constants.BIG_CITY, '', '', '')*\
constants.DEFAULT_NB_PASSENGERS*local_distance_m
step = tmw.journey_step(
i,
_type=constants.TYPE_COACH,
label='',
distance_m=local_distance_m,
duration_s=(leg.arrival_seg - leg.departure_seg).seconds,
price_EUR=[leg.price_step],
gCO2=local_emissions,
departure_point=leg.geoloc_origin_seg,
arrival_point=leg.geoloc_destination_seg,
departure_stop_name=leg.short_name_origin_seg,
arrival_stop_name=leg.short_name_destination_seg,
departure_date=leg.departure_seg,
arrival_date=leg.arrival_seg,
trip_code='OuiBus ' + leg.bus_number,
geojson=[],
)
lst_sections.append(step)
i = i + 1
# add transfer steps
if not pd.isna(leg.next_departure):
step = tmw.journey_step(
i,
_type=constants.TYPE_TRANSFER,
label='',
distance_m=distance(leg.geoloc_destination_seg,
leg.next_geoloc).m,
duration_s=(leg['next_departure'] -
leg['arrival_seg']).seconds,
price_EUR=[0],
departure_point=leg.geoloc_destination_seg,
arrival_point=leg.next_geoloc,
departure_stop_name=leg.short_name_destination_seg,
arrival_stop_name=leg.next_stop_name,
gCO2=0,
geojson=[],
)
lst_sections.append(step)
i = i + 1
journey_ouibus = tmw.journey(_id, steps=lst_sections)
lst_journeys.append(journey_ouibus)
# for journey in lst_journeys:
# journey.update()
return lst_journeys
def blablacar_journey(df_response, departure_date, start_point, end_point):
"""
This function takes in a DF with detailled info about all the BlaBlaCar trips
It returns a list of TMW journey objects """
lst_journeys = list()
# all itineraries :
# print(f'nb itinerary : {df_response.id_global.nunique()}')
_id = 0
for trip_id in df_response.trip_id.unique():
itinerary = df_response[df_response.trip_id == trip_id]
# Get the arrival info on the same line
itinerary['date_time_arrival'] = itinerary.date_time.shift(-1)
itinerary['city_arrival'] = itinerary.city.shift(-1)
itinerary['address_arrival'] = itinerary.address.shift(-1)
itinerary['latitude_arrival'] = itinerary.latitude.shift(-1)
itinerary['longitude_arrival'] = itinerary.longitude.shift(-1)
# boolean to know whether and when there will be a transfer after the leg
itinerary['next_departure'] = itinerary.date_time.shift(1)
# Get rid of the "last line" for the last leg of the blablacar trip
itinerary = itinerary[~pd.isna(itinerary.city_arrival)]
# Divide price between legs weighted by distance and distance
itinerary['total_distance'] = itinerary.distance_in_meters.sum()
itinerary['price'] = float(itinerary['price'])
itinerary['price_leg'] = itinerary.apply(
lambda x: x.distance_in_meters / x.total_distance * x.price,
axis=1)
i = _id
lst_sections = list()
# We add a waiting period at the pick up point of 15 minutes
#print(itinerary.date_time.get_values())
#print(type(itinerary.date_time.get_value(0)))
#print(type(timedelta(seconds=_BLABLACAR_WAITING_PERIOD)))
#print(itinerary.date_time.get_value(0)-timedelta(seconds=_BLABLACAR_WAITING_PERIOD))
step = tmw.Journey_step(
i,
_type=constants.TYPE_WAIT,
label=
f'Arrive at pick up point {format_timespan(_BLABLACAR_WAITING_PERIOD)} before departure',
distance_m=0,
duration_s=_BLABLACAR_WAITING_PERIOD,
price_EUR=[0],
gCO2=0,
departure_point=[
itinerary.latitude.iloc[0], itinerary.longitude.iloc[0]
],
arrival_point=[
itinerary.latitude.iloc[0], itinerary.longitude.iloc[0]
],
departure_date=itinerary.date_time.iat[0] -
timedelta(seconds=_BLABLACAR_WAITING_PERIOD),
arrival_date=itinerary.date_time.iat[0],
bike_friendly=True,
geojson=[],
)
lst_sections.append(step)
i = i + 1
# Go through all steps of the journey
for index, leg in itinerary.iterrows():
local_distance_m = leg.distance_in_meters
local_transportation_type = constants.TYPE_CAR
local_emissions = co2_emissions.calculate_co2_emissions(local_transportation_type, constants.DEFAULT_CITY,
constants.DEFAULT_FUEL, constants.DEFAULT_NB_SEATS,
constants.DEFAULT_NB_KM) * \
constants.DEFAULT_NB_PASSENGERS * local_distance_m
step = tmw.Journey_step(
i,
_type=constants.TYPE_CARPOOOLING,
label=f'BlablaCar trip from {leg.city} to {leg.city_arrival}',
distance_m=local_distance_m,
duration_s=leg.duration_in_seconds,
price_EUR=[leg.price_leg],
gCO2=local_emissions,
departure_point=[leg.latitude, leg.longitude],
arrival_point=[leg.latitude_arrival, leg.longitude_arrival],
departure_stop_name=leg.address + ' ' + leg.city,
arrival_stop_name=leg.address_arrival + ' ' + leg.city_arrival,
departure_date=leg.date_time,
arrival_date=leg.date_time_arrival,
trip_code='BlaBlaCar_' + str(leg.trip_id),
bike_friendly=False,
geojson=[],
)
lst_sections.append(step)
i = i + 1
# add transfer steps
if not pd.isna(leg.next_departure):
step = tmw.Journey_step(
i,
_type=constants.TYPE_TRANSFER,
label=f'Transfer at {leg.name_arrival_seg}',
distance_m=0,
duration_s=(leg['next_departure'] -
leg['arrival_date_seg']).seconds,
price_EUR=[0],
departure_point=[
leg.latitude_arrival, leg.longitude_arrival
],
arrival_point=[
leg.latitude_arrival, leg.longitude_arrival
],
departure_stop_name=leg.address_arrival + ' ' +
leg.city_arrival,
arrival_stop_name=leg.address_arrival + ' ' +
leg.city_arrival,
departure_date=leg.date_time_arrival,
arrival_date=leg.next_departure,
gCO2=0,
bike_friendly=False,
geojson=[],
)
lst_sections.append(step)
i = i + 1
journey_blablacar = tmw.Journey(
_id,
steps=lst_sections,
departure_date=lst_sections[0].departure_date,
arrival_date=lst_sections[-1].arrival_date,
booking_link=leg.link)
# Add category
category_journey = list()
for step in journey_blablacar.steps:
if step.type not in [constants.TYPE_TRANSFER, constants.TYPE_WAIT]:
category_journey.append(step.type)
journey_blablacar.category = list(set(category_journey))
lst_journeys.append(journey_blablacar)
# for journey in lst_journeys:
# journey.update()
return lst_journeys
