def normalize(stations):
extract = tb.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": tb.epoch_to_datetime(int(extract(station, "latestupdatetime")), divisor=1000).isoformat(),
}
return [normalized(station) for station in stations.find_all("station")]
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': tb.epoch_to_datetime(station['last_update'],
divisor=1000).isoformat()
}
return [normalized(station) for station in stations]
def normalize(stations):
extract = tb.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': tb.epoch_to_datetime(int(extract(station,
'latestupdatetime')),
divisor=1000).isoformat()
}
return [normalized(station) for station
in stations.find_all('station')]
def choose(situation, target, distance, mode, stationFirst, 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 = tb.convert_time(situation["time"])
# Will 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
stations = [station for station in query.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
forecast = tb.epoch_to_datetime(time + candidate["duration"])
# Extract features from the forecasted time
variables = munging.temporal_features(forecast)
features = [variables["hour"], variables["minute"], variables["weekday"]]
# Check if the prediction is satisfying
settings = tb.read_json("common/settings.json")
method = eval(settings["learning"]["method"])
# Make a prediction
prediction = method.predict(features, target, city, candidate["_id"])
bias = tb.read_json("common/settings.json")["learning"]["bias"]
if target == "bikes":
bias *= -1
if prediction + bias >= people:
stationPosition = list(reversed(candidate["p"]))
if stationFirst is False:
trip = tb.reshape(mode, departure, stationPosition)
else:
trip = tb.reshape(mode, stationPosition, departure)
break
return trip