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_bootstrap(pm_ds_ds1d, pm_ds_control1d):
"""Test that perfect model ensemble object can be bootstrapped"""
pm = PerfectModelEnsemble(pm_ds_ds1d)
pm.add_control(pm_ds_control1d)
pm.bootstrap(bootstrap=2)
