def test_PredictionEnsemble_smooth_None(
perfectModelEnsemble_initialized_control_1d_ym_cftime,
):
"""Test that PredictionEnsemble.smooth(None) does nothing."""
pm = perfectModelEnsemble_initialized_control_1d_ym_cftime
pm_smoothed = pm.smooth(None)
assert_PredictionEnsemble(pm, pm_smoothed)
def test_PerfectModelEnsemble_plus_broadcast(PM_ds_initialized_3d, operator):
"""Test that PerfectModelEnsemble math operator (+-*/) other also broadcasts
correctly."""
he = PerfectModelEnsemble(PM_ds_initialized_3d)
operator = eval(operator)
# minimal adding an offset or like multiplying area
he2 = operator(
he, xr.ones_like(PM_ds_initialized_3d.isel(init=1, lead=1, drop=True)))
he3 = operator(he, 1)
assert_PredictionEnsemble(he2, he3)
def test_PredictionEnsemble_map_dim_or(hindcast_hist_obs_1d):
"""Tests that PredictionEnsemble allows dim0_or_dim1 as kwargs without UserWarning."""
with pytest.warns(None): # no warnings
he_or = hindcast_hist_obs_1d.map(rm_poly, dim="init_or_time", deg=2)
assert he_or != hindcast_hist_obs_1d
with pytest.warns(UserWarning) as record: # triggers warnings
he_chained = hindcast_hist_obs_1d.map(rm_poly, dim="init", deg=2).map(
rm_poly, dim="time", deg=2
)
assert he_chained != hindcast_hist_obs_1d
assert len(record) == 3 # for init, uninit and obs
for r in record:
assert "Error due to " in str(r.message)
assert_PredictionEnsemble(he_or, he_chained)
def test_PerfectModelEnsemble_area_weighted_mean(PM_ds_initialized_3d):
"""Test area weighted mean PerfectModelEnsemble."""
he = PerfectModelEnsemble(PM_ds_initialized_3d)
# fake area
area = np.cos(PM_ds_initialized_3d.lat) + 1
spatial_dims = [
d for d in PM_ds_initialized_3d.dims if d not in CLIMPRED_DIMS
]
# PredictionEnsemble doesnt like other data_vars
he_self_spatial_mean = (he * area).sum(spatial_dims) / area.sum()
# weighted requires Dataset
area = area.to_dataset(name="area")
he_xr_spatial_mean = he.weighted(area).mean(spatial_dims)
assert_PredictionEnsemble(he_self_spatial_mean,
he_xr_spatial_mean,
how="allclose",
rtol=0.03,
atol=0.05)
def test_HindcastEnsemble_area_weighted_mean(hind_ds_initialized_3d):
"""Test area weighted mean HindcastEnsemble."""
he = HindcastEnsemble(hind_ds_initialized_3d)
# fake area
area = hind_ds_initialized_3d["TAREA"]
spatial_dims = [
d for d in hind_ds_initialized_3d.dims if d not in CLIMPRED_DIMS
]
# PredictionEnsemble doesnt like other data_vars
he_self_spatial_mean = (he * area).sum(spatial_dims) / area.sum()
# weighted requires Dataset
area = area.to_dataset(name="area")
he_xr_spatial_mean = he.weighted(area).mean(spatial_dims)
assert_PredictionEnsemble(he_self_spatial_mean,
he_xr_spatial_mean,
how="allclose",
rtol=0.03,
atol=0.05)