def test_horizon_bootstrap_1d(perfectModelEnsemble_initialized_control):
"""test horizon for pm.bootstrap for 1d."""
bskill = perfectModelEnsemble_initialized_control.bootstrap(
iterations=201,
metric="rmse",
comparison="m2e",
dim=["member", "init"],
reference="uninitialized",
)
ph = horizon(bskill.sel(results="p", skill="uninitialized") <= 0.05)
assert ph.tos.attrs["units"] == "years", print(ph.tos)
assert int(ph.tos) in [4, 5, 6, 7,
8] # should be 6, testing on the safe side
def test_horizon_smooth(perfectModelEnsemble_initialized_control, smooth):
"""test horizon for pm.smooth(lead).verify."""
pm = perfectModelEnsemble_initialized_control
if smooth:
pm = pm.smooth(
{"lead": 2},
how="mean") # converts lead to '1-2', '2-3', ... after verify
skill = pm.verify(
metric="rmse",
comparison="m2e",
dim=["member", "init"],
reference="persistence",
)
assert skill.lead.attrs["units"] == "years", print(skill.lead.attrs)
# initialized better than persistence if RMSE smaller
cond = skill.sel(skill="initialized", drop=True) < skill.sel(
skill="persistence", drop=True)
ph = horizon(cond)
assert ph.tos.attrs["units"] == "years", print("ph.tos.attrs = ",
ph.tos.attrs)
assert ph.tos.values == skill.lead.isel(lead=-1).values
def test_horizon_3d(hindcast_recon_3d):
he = hindcast_recon_3d
# set one grid to all nan mimicking land
land = he._datasets["initialized"]["SST"][:, :, 0, 0]
he._datasets["initialized"]["SST"][:, :, 0, 0] = land.where(land == 100000)
# verify
skill = he.verify(
metric="acc",
comparison="e2o",
dim="init",
alignment="maximize",
reference=["persistence"],
)
ph = horizon(
skill.sel(skill="initialized") > skill.sel(skill="persistence"))
# test all nan on land
assert ph["SST"][0, 0].isnull()
# test significant everywhere
assert (ph >= 1).all()
assert (ph.isel(nlat=-1) == 2).all()