def test_BasicSeasonalMetrics():
"""
Test BasicSeasonalMetrics.
"""
df = make_some_data()
data = df[['ref', 'k1']]
metriccalc = MonthsMetricsAdapter(BasicMetrics(other_name='k1'))
res = metriccalc.calc_metrics(data, gpi_info=(0, 0, 0))
should = dict(ALL_n_obs=np.array([366]), dtype='float32')
assert res['ALL_n_obs'] == should['ALL_n_obs']
assert np.isnan(res['ALL_rho'])
def test_BasicSeasonalMetrics_metadata():
"""
Test BasicSeasonalMetrics with metadata.
"""
df = make_some_data()
data = df[['ref', 'k1']]
metadata_dict_template = {'network': np.array(['None'], dtype='U256')}
metriccalc = MonthsMetricsAdapter(BasicMetrics(
other_name='k1', metadata_template=metadata_dict_template))
res = metriccalc.calc_metrics(
data, gpi_info=(0, 0, 0, {'network': 'SOILSCAPE'}))
assert res['network'] == np.array(['SOILSCAPE'], dtype='U256')
def test_BasicSeasonalMetrics():
"""
Test BasicSeasonalMetrics.
"""
df = make_some_data()
data = df[['ref', 'k1']]
with warnings.catch_warnings():
warnings.simplefilter("ignore") # many warnings due to test data
metriccalc = MonthsMetricsAdapter(BasicMetrics(other_name='k1'))
res = metriccalc.calc_metrics(data, gpi_info=(0, 0, 0))
should = dict(ALL_n_obs=np.array([366]), dtype='float32')
assert res['ALL_n_obs'] == should['ALL_n_obs']
assert np.isnan(res['ALL_rho'])