def api_cities():
''' Return the list of cities. '''
cities = list(srv.get_cities(
slug=request.args.get('city_slug'),
country=request.args.get('country'),
provider=request.args.get('provider'),
predictable=request.args.get('predictable'),
active=request.args.get('active')
))
return jsonify(cities)
def init_station_dataframe(city, station, moment, blank):
click.secho('city: {}'.format(city), fg='yellow', bold=True)
click.secho('station: {}'.format(station), fg='yellow', bold=True)
click.secho('moment: {}'.format(moment), fg='yellow')
click.secho('blank: {}'.format(blank), fg='blue', bold=True)
SINCE = moment
FILTERED_DATES = [SINCE + dt.timedelta(**moment) for moment in MOMENTS]
UNTIL = FILTERED_DATES[-1]
util.notify('Querying {city} in database...'.format(city=city), 'cyan')
# Make sure the city exists
try:
CITY = next(srv.get_cities(slug=city, serialized=False))
except CityNotFound as exc:
util.notify(exc, 'red')
sys.exit()
# Make sure the city is predictable
if not CITY.predictable:
util.notify(CityUnpredicable, 'red')
sys.exit()
util.notify('Querying {city} stations in database...'.format(city=city),
'cyan')
# Search city station
STATION = next(
srv.get_stations(slug=station, city_slug=CITY.slug, serialized=False))
# Get bike updates
if not blank:
util.notify('Getting station updates...', 'cyan')
try:
df = STATION.get_updates(SINCE, UNTIL)
except Exception as exc:
util.notify(exc, 'red')
sys.exit()
util.notify('Generating dataframe...', 'cyan')
# Create dataframe
columns = dict(bikes=[
find_tuple_according_moment(df, moment).bikes
for moment in FILTERED_DATES
] if not blank else [''] * len(FILTERED_DATES),
station=[station] * len(FILTERED_DATES),
city=[city] * len(FILTERED_DATES))
index = FILTERED_DATES
column_order = ['city', 'station', 'bikes']
return pd.DataFrame(columns, index)[column_order]
def retrieve_city_weather(city, since, until):
# Make sure the city exists
try:
util.notify('Querying {city} in database...'.format(city=city), 'cyan')
CITY = next(srv.get_cities(slug=city, serialized=False))
except CityNotFound as exc:
util.notify(exc, 'red')
sys.exit()
df = CITY.get_weather_updates(since, until)
df['city'] = [CITY.slug] * len(df.index)
return df
def get_city_stations(city):
click.secho('city: {}'.format(city), fg='yellow', bold=True)
util.notify('Querying {city} in database...'.format(city=city), 'cyan')
# Make sure the city exists
try:
CITY = next(srv.get_cities(name=city, serialized=False))
except CityNotFound as exc:
util.notify(exc, 'red')
sys.exit()
# Make sure the city is predictable
if not CITY.predictable:
util.notify(CityUnpredicable, 'red')
sys.exit()
util.notify('Querying {city} stations in database...'.format(city=city),
'cyan')
# Search city stations
return srv.get_stations(city_slug=CITY.slug, serialized=False)
import pandas as pd
from app import services as srv
PARSER = argparse.ArgumentParser()
PARSER.add_argument('city',
type=str,
help='City for which to run the analysis')
PARAMS = PARSER.parse_args()
SINCE = dt.datetime(year=2015, month=10, day=1)
UNTIL = dt.datetime.now()
# Generate all the dates between SINCE and UNTIL
DAYS = [SINCE + dt.timedelta(days=d) for d in range((UNTIL - SINCE).days + 1)]
city = next(srv.get_cities(name=PARAMS.city, serialized=False))
stations = list(srv.get_stations(city_slug=city.slug, serialized=False))
empty = pd.DataFrame(index=DAYS,
columns=(station.name for station in stations))
full = pd.DataFrame(index=DAYS, columns=(station.name for station in stations))
for station in stations:
df = station.get_updates(SINCE, UNTIL)
df['moment'] = df.index
df['day'] = df['moment'].apply(lambda x: x.date().isoformat())
# Extract the time between each observation
df['duration'] = df['moment'].diff().shift(-1)
for day, group in df.groupby('day'):
empty[station.name][day] = group[group['bikes'] == 0]['duration'].sum()
full[station.name][day] = group[group['spaces'] == 0]['duration'].sum()