def test_compute_persistence_lead0_lead1(pm_da_ds1d, pm_da_ds1d_lead0,
pm_da_control1d, metric):
"""
Checks that persistence forecast results are identical for a lead 0 and lead 1 setup
"""
res1 = compute_persistence(pm_da_ds1d, pm_da_control1d, metric=metric)
res2 = compute_persistence(pm_da_ds1d_lead0,
pm_da_control1d,
metric=metric)
assert (res1.values == res2.values).all()
def test_persistence_lead0_lead1(
initialized_ds, initialized_ds_lead0, reconstruction_ds, metric
):
"""
Checks that compute persistence returns the same results with a lead-0 and lead-1
framework.
"""
res1 = compute_persistence(initialized_ds, reconstruction_ds, metric=metric)
res2 = compute_persistence(initialized_ds_lead0, reconstruction_ds, metric=metric)
assert (res1.SST.values == res2.SST.values).all()
def test_persistence(initialized, reconstruction, metric):
"""
Checks that compute persistence works without breaking.
"""
res = compute_persistence(initialized, reconstruction,
metric=metric).isnull().any()
assert not res
def test_compute_persistence_ds1d_not_nan(pm_ds_ds1d, pm_ds_control1d, metric):
"""
Checks that there are no NaNs on persistence forecast of 1D time series.
"""
actual = (compute_persistence(pm_ds_ds1d, pm_ds_control1d,
metric=metric).isnull().any())
for var in actual.data_vars:
assert not actual[var]