def normalize(stations):
extract = tools.extract_element
normalized = lambda station: {
'name':
extract(station, 'name'),
'address':
extract(station, 'name'),
'lat':
float(extract(station, 'lat')),
'lon':
float(extract(station, 'long')),
'status':
'OPEN' if extract(station, 'locked') == 'false' else 'CLOSED',
'bikes':
int(extract(station, 'nbbikes')),
'stands':
int(extract(station, 'nbemptydocks')),
'update':
tools.epoch_to_datetime(int(extract(station, 'latestupdatetime')),
divisor=1000).isoformat()
}
return [
normalized(station) for station in stations.find_all('station')
if extract(station, 'latestupdatetime') is not None
]
def normalize(stations):
normalized = lambda station: {
'name': station['name'],
'address': station['address'],
'lat': station['position']['lat'],
'lon': station['position']['lng'],
'status': station['status'],
'bikes': station['available_bikes'],
'stands': station['available_bike_stands'],
'update': tools.epoch_to_datetime(station['last_update'],
divisor=1000).isoformat()
}
return [normalized(station) for station in stations]
def normalize(stations):
extract = tools.extract_element
normalized = lambda station: {
'name': extract(station, 'name'),
'address': extract(station, 'name'),
'lat': float(extract(station, 'lat')),
'lon': float(extract(station, 'long')),
'status': 'OPEN' if extract(station, 'locked') == 'false' else 'CLOSED',
'bikes': int(extract(station, 'nbbikes')),
'stands': int(extract(station, 'nbemptydocks')),
'update': tools.epoch_to_datetime(int(extract(station,
'latestupdatetime')),
divisor=1000).isoformat()
}
return [normalized(station) for station
in stations.find_all('station')]
def generate_path(situation,
target,
distance,
mode,
stationFirst=False,
nbCandidates=5):
'''
Choose the best station around the arrival and then define a trip
between the departure and the station.
'''
city = situation['city']
people = situation['people']
time = tools.convert_time(situation['time'])
# Convert the positions to (lat, lon) if they are textual addresses
departure = geography.convert_to_point(city, situation['departure'])
arrival = geography.convert_to_point(city, situation['arrival'])
# Find the close stations with MongoDB and Hilbert curves
stations = [
station
for station in geo.close_points(city, arrival, number=nbCandidates)
]
# Get the distances to the stations
candidates = geography.compute_distances_manual(arrival, stations,
distance)
# Sort the stations by distance
candidates.sort(key=lambda station: station['duration'])
# Find an appropriate solution through the sorted candidates
trip = False
for candidate in candidates:
# Calculate what time it would be when reaching the candidate station
currentTime = time + candidate['duration']
currentTime = tools.epoch_to_datetime(time)
prediction = wrapper.predict('forest', currentTime, target, city,
candidate['_id'])
# Check if the prediction is satisfying
if prediction >= people:
stationPosition = list(reversed(candidate['p']))
if stationFirst is False:
trip = reshape(mode, departure, stationPosition)
else:
trip = reshape(mode, stationPosition, departure)
break
return trip
def generate_path(situation, target, distance, mode, stationFirst=False,
nbCandidates=5):
'''
Choose the best station around the arrival and then define a trip
between the departure and the station.
'''
city = situation['city']
people = situation['people']
time = tools.convert_time(situation['time'])
# Convert the positions to (lat, lon) if they are textual addresses
departure = geography.convert_to_point(city, situation['departure'])
arrival = geography.convert_to_point(city, situation['arrival'])
# Find the close stations with MongoDB and Hilbert curves
stations = [station for station in
geo.close_points(city, arrival, number=nbCandidates)]
# Get the distances to the stations
candidates = geography.compute_distances_manual(arrival, stations,
distance)
# Sort the stations by distance
candidates.sort(key=lambda station: station['duration'])
# Find an appropriate solution through the sorted candidates
trip = False
for candidate in candidates:
# Calculate what time it would be when reaching the candidate station
currentTime = time + candidate['duration']
currentTime = tools.epoch_to_datetime(time)
prediction = wrapper.predict('forest', currentTime, target,
city, candidate['_id'])
# Check if the prediction is satisfying
if prediction >= people:
stationPosition = list(reversed(candidate['p']))
if stationFirst is False:
trip = reshape(mode, departure, stationPosition)
else:
trip = reshape(mode, stationPosition, departure)
break
return trip