def get_fill(self, _from, to):
constants.validate_date(_from)
constants.validate_date(to)
# waarden zijn in cm onder overstortbuis
ts = self._get_timeseries_as_pd_series(
constants.fill_filter_id,
constants.fill_location_id,
constants.fill_parameter_id,
_from, to, 'fill'
)
# deal with NaN values
#if np.NaN in ts:
# raise Exception('Found NaN in results')
#ts = ts.fillna(0)
# zet om in cm vanaf bodem bak
ts += constants.hoogte_niveaumeter_cm
# zet om naar fractie totale bak
ts /= constants.bovenkant_bak_cm
# zet om naar m3
ts *= constants.max_berging_m3
return ts
def get_demand(self, _from, to):
constants.validate_date(_from)
constants.validate_date(to)
# ensure we deal with values for _from which are mid-week
from_adj = _from - 2 * one_week
to_adj = to + 2 * one_week
weekly = pd.date_range(from_adj, to_adj, freq='W-MON', tz=pytz.utc) # week changes on monday
values = [self.get_week_demand_on(date) for date in weekly]
ts = pd.Series(values, weekly, name='demand')
ts = ts.resample('15min')
ts = ts.interpolate()
# divide by amount of quarter hours in a week
ts /= 7 * 24 * 4
#import pdb; pdb.set_trace()
return ts[_from:to]
def get_rain(self, which, _from, to):
constants.validate_date(_from)
constants.validate_date(to)
rain = self._get_timeseries_as_pd_series(
constants.rain_filter_id,
constants.rain_location_id,
constants.rain_parameter_ids[which],
_from, to, 'rain_' + which
)
# convert to quarterly figures
rain = rain.resample('15min', fill_method='ffill')
# 4 quarters in an hour
rain /= 4
# deal with NaN values
#if np.nan in rain:
# raise Exception('Found NaN in results')
#rain = rain.fillna(0)
return rain
