def test_resample_iterations_dix_no_squeeze(f_prob):
"""Test _resample_iteration_idx with singular dimension."""
da = f_prob.expand_dims("test_dim")
actual = resample_iterations_idx(da, iterations=ITERATIONS)
other = resample_iterations(da, iterations=ITERATIONS)
assert "test_dim" in actual.dims
assert actual.coords.to_dataset().equals(other.coords.to_dataset())
def test_resampling_roughly_identical_mean(f_prob):
"""check that resampling functions result in allclose iteration mean."""
da = f_prob.isel(lon=0, lat=0, drop=True)
iterations = 1000
r1 = resample_iterations(da, iterations=iterations, replace=True)
r2 = resample_iterations_idx(da, iterations=iterations, replace=True)
xr.testing.assert_allclose(r1.mean("iteration"),
r2.mean("iteration"),
atol=0.05,
rtol=0.05)
def test_resample_iterations_dix_no_squeeze(f_prob):
"""Test _resample_iteration_idx with singular dimension.
Currently this fails for dimensions with just a single index as we use `squeeze` in
the code and not using squeeze doesnt maintain functionality. This means that
``_resample_iteration_idx`` should not be called on singleton dimension inputs
(which is not critical and can be circumvented when using squeeze before.).
"""
da = f_prob.expand_dims("test_dim")
actual = resample_iterations_idx(da, iterations=ITERATIONS)
assert "test_dim" in actual.dims
def test_resampling_identical_dim(f_prob):
"""check that resampling functions have the same ordering of dims and coords."""
da = f_prob
r1 = resample_iterations(da, iterations=ITERATIONS)
r2 = resample_iterations_idx(da, iterations=ITERATIONS)
assert_dim_coords(r1, r2)