def test_verify_no_need_for_control(PM_da_initialized_1d, PM_da_control_1d):
"""Tests that no error is thrown when no control present
when calling verify(reference=['uninitialized'])."""
v = "tos"
comparison = "m2e"
pm = PerfectModelEnsemble(PM_da_initialized_1d).load()
# verify needs to control
skill = pm.verify(metric="mse", comparison=comparison, dim="init")
assert not skill[v].isnull().any()
# control not needed for normalized metrics as normalized
# with verif which is the verification member in PM and
# not the control simulation.
assert (not pm.verify(metric="nmse", comparison=comparison,
dim="init")[v].isnull().any())
with pytest.raises(DatasetError) as e:
pm.verify(metric="mse",
comparison=comparison,
dim="init",
reference=["persistence"])
assert "at least one control dataset" in str(e.value)
# unlikely case that control gets deleted after generating uninitialized
pm = pm.add_control(PM_da_control_1d).generate_uninitialized()
pm._datasets["control"] = {}
assert (not pm._compute_uninitialized(
metric="mse", comparison=comparison, dim="init")[v].isnull().any())
assert (not pm.verify(metric="mse",
comparison=comparison,
dim="init",
reference=["uninitialized"])[v].isnull().any())
def test_HindcastEnsemble_as_PerfectModelEnsemble(hindcast_recon_1d_mm):
"""Test that initialized dataset for HindcastEnsemble can also be used for
PerfectModelEnsemble."""
v = "SST"
alignment = "maximize"
hindcast = hindcast_recon_1d_mm
assert (not hindcast.verify(
metric="acc", comparison="e2o", dim="init",
alignment=alignment)[v].isnull().any())
# try PerfectModelEnsemble predictability
init = hindcast.get_initialized()
pm = PerfectModelEnsemble(init)
assert (not pm.verify(metric="acc",
comparison="m2e",
dim=["member", "init"])[v].isnull().any())
pm = pm.add_control(
init.isel(member=0, lead=0, drop=True).rename({
"init": "time"
}).resample(time="1MS").interpolate("linear"))
pm = pm.generate_uninitialized()
assert (not pm.verify(
metric="acc",
comparison="m2e",
dim=["member", "init"],
reference=["uninitialized"],
)[v].isnull().any())
pm.bootstrap(iterations=2,
metric="acc",
comparison="m2e",
dim=["member", "init"])
def test_HindcastEnsemble_as_PerfectModelEnsemble(hindcast_recon_1d_mm):
"""Test that initialized dataset for HindcastEnsemble can also be used for
PerfectModelEnsemble."""
v = "SST"
alignment = "maximize"
hindcast = hindcast_recon_1d_mm.isel(lead=[0, 1])
assert (
not hindcast.verify(
metric="acc", comparison="e2o", dim="init", alignment=alignment
)[v]
.isnull()
.any()
)
# try PerfectModelEnsemble predictability
init = hindcast.get_initialized()
pm = PerfectModelEnsemble(init)
assert (
not pm.verify(metric="acc", comparison="m2e", dim=["member", "init"])[v]
.isnull()
.any()
)
def test_PerfectModelEnsemble_time_resolution_verify(
HindcastEnsemble_time_resolution):
"""Test that PerfectModelEnsemble.verify() in any lead time resolution works."""
pm = PerfectModelEnsemble(
HindcastEnsemble_time_resolution.get_initialized())
assert pm.verify(**PerfectModelEnsemble_verify_kw).notnull().any()