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 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(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 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 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 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 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 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
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
