def test_BasicMetrics_calculator_metadata():
"""
Test BasicMetrics with metadata.
"""
df = make_some_data()
data = df[['ref', 'k1']]
metadata_dict_template = {'network': np.array(['None'], dtype='U256')}
with warnings.catch_warnings():
warnings.simplefilter("ignore") # many warnings due to test data
metriccalc = BasicMetrics(other_name='k1',
calc_tau=False,
metadata_template=metadata_dict_template)
res = metriccalc.calc_metrics(data,
gpi_info=(0, 0, 0, {
'network': 'SOILSCAPE'
}))
should = dict(network=np.array(['SOILSCAPE'], dtype='U256'),
n_obs=np.array([366]),
RMSD=np.array([0.2], dtype='float32'),
BIAS=np.array([-0.2], dtype='float32'),
dtype='float32')
assert res['n_obs'] == should['n_obs']
assert np.isnan(res['rho'])
assert res['RMSD'] == should['RMSD']
assert res['BIAS'] == should['BIAS']
assert res['network'] == should['network']
assert np.isnan(res['R'])
# depends on scipy version changed after v1.2.1
assert res['p_R'] == np.array([1.]) or np.isnan(res['R'])
def test_BasicMetrics_calculator():
"""
Test BasicMetrics.
"""
df = make_some_data()
data = df[['ref', 'k1']]
with warnings.catch_warnings():
warnings.simplefilter("ignore") # many warnings due to test data
metriccalc = BasicMetrics(other_name='k1', calc_tau=False)
res = metriccalc.calc_metrics(data, gpi_info=(0, 0, 0))
should = dict(n_obs=np.array([366]),
RMSD=np.array([0.2], dtype='float32'),
BIAS=np.array([-0.2], dtype='float32'))
assert res['n_obs'] == should['n_obs']
assert np.isnan(res['rho'])
assert res['RMSD'] == should['RMSD']
assert res['BIAS'] == should['BIAS']
assert np.isnan(res['R'])
# scipy 1.3.0 is not built for python 2.7 so we allow both for now
assert (np.isnan(res['p_R']) or res['p_R'] == 1.0)
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 pytest.warns(UserWarning):
metriccalc = MonthsMetricsAdapter(BasicMetrics(other_name="k1"))
res = metriccalc.calc_metrics(data, gpi_info=(0, 0, 0))
should = {("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_BasicMetrics_calculator_metadata():
"""
Test BasicMetrics with metadata.
"""
df = make_some_data()
data = df[["ref", "k1"]]
metadata_dict_template = {"network": np.array(["None"], dtype="U256")}
metriccalc = BasicMetrics(
other_name="k1",
calc_tau=False,
metadata_template=metadata_dict_template,
)
res = metriccalc.calc_metrics(data,
gpi_info=(0, 0, 0, {
"network": "SOILSCAPE"
}))
should = dict(
network=np.array(["SOILSCAPE"], dtype="U256"),
n_obs=np.array([366]),
RMSD=np.array([0.2], dtype="float32"),
BIAS=np.array([-0.2], dtype="float32"),
dtype="float32",
)
assert res["n_obs"] == should["n_obs"]
assert np.isnan(res["rho"])
assert res["RMSD"] == should["RMSD"]
assert res["BIAS"] == should["BIAS"]
assert res["network"] == should["network"]
assert np.isnan(res["R"])
# depends on scipy version changed after v1.2.1
assert res["p_R"] == np.array([1.0]) or np.isnan(res["R"])
def test_BasicMetrics_calculator():
"""
Test BasicMetrics.
"""
df = make_some_data()
data = df[["ref", "k1"]]
metriccalc = BasicMetrics(other_name="k1", calc_tau=False)
res = metriccalc.calc_metrics(data, gpi_info=(0, 0, 0))
should = dict(
n_obs=np.array([366]),
RMSD=np.array([0.2], dtype="float32"),
BIAS=np.array([-0.2], dtype="float32"),
)
assert res["n_obs"] == should["n_obs"]
assert np.isnan(res["rho"])
assert res["RMSD"] == should["RMSD"]
assert res["BIAS"] == should["BIAS"]
assert np.isnan(res["R"])
# scipy 1.3.0 is not built for python 2.7 so we allow both for now
assert np.isnan(res["p_R"]) or res["p_R"] == 1.0
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'])
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")}
with pytest.warns(UserWarning):
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")