def calc_precipitation_stats(self, months=None, avg_stats=True, percentile=50):
"""
Calculates precipitation statistics for the cascade model while aggregating hourly observations
Parameters
----------
months : Months for each seasons to be used for statistics (array of numpy array, default=1-12, e.g., [np.arange(12) + 1])
avg_stats : average statistics for all levels True/False (default=True)
percentile : percentil for splitting the dataset in small and high intensities (default=50)
"""
if months is None:
months = [np.arange(12) + 1]
self.precip.months = months
self.precip.stats = melodist.build_casc(self.data, months=months, avg_stats=avg_stats, percentile=percentile)
def test_cascade(self):
data = pd.read_csv(
'examples/testdata_precip5min.csv.gz',
names=['time', 'precip'],
parse_dates=True,
index_col=0,
)
precip_daily = data.precip.resample('D').sum()
cascopt = melodist.build_casc(data,
hourly=False,
level=9,
percentile=90)
precip_disagg = melodist.disagg_prec_cascade(precip_daily,
cascopt[0],
hourly=False,
level=9)
assert np.allclose(precip_daily,
precip_disagg.resample('D').sum(),
atol=1e-3,
equal_nan=True)
def calc_precipitation_stats(self,
months=None,
avg_stats=True,
percentile=50):
"""
Calculates precipitation statistics for the cascade model while aggregating hourly observations
Parameters
----------
months : Months for each seasons to be used for statistics (array of numpy array, default=1-12, e.g., [np.arange(12) + 1])
avg_stats : average statistics for all levels True/False (default=True)
percentile : percentil for splitting the dataset in small and high intensities (default=50)
"""
if months is None:
months = [np.arange(12) + 1]
self.precip.months = months
self.precip.stats = melodist.build_casc(self.data,
months=months,
avg_stats=avg_stats,
percentile=percentile)