def test_crps_quadrature_consistent(self):
def normcdf(*args, **kwdargs):
return stats.norm(*args, **kwdargs).cdf
dists = np.vectorize(normcdf)(loc=self.mu, scale=self.sig)
crps = crps_quadrature(self.obs, dists, xmin=self.mu - 5 * self.sig, xmax=self.mu + 5 * self.sig)
np.testing.assert_allclose(crps, self.expected, rtol=1e-4)
def test_crps_quadrature_args(o_dask, f_prob_dask, keep_attrs):
# to speed things up
o_dask = o_dask.isel(time=0, drop=True)
f_prob_dask = f_prob_dask.isel(time=0, drop=True)
xmin, xmax, tol = -10, 10, 1e-6
cdf_or_dist = norm
actual = crps_quadrature(o_dask,
cdf_or_dist,
xmin,
xmax,
tol,
keep_attrs=keep_attrs)
expected = properscoring.crps_quadrature(o_dask, cdf_or_dist, xmin, xmax,
tol)
expected = xr.DataArray(expected, coords=o_dask.coords).mean()
# test for numerical identity of xskillscore crps and crps
assert_allclose(actual, expected)
# test that xskillscore crps_ensemble returns chunks
assert actual.chunks is not None
# show that properscoring crps_ensemble returns no chunks
assert expected.chunks is None
if keep_attrs:
assert actual.attrs == o_dask.attrs
else:
assert actual.attrs == {}
def test_crps_quadrature_consistent(self):
def normcdf(*args, **kwdargs):
return stats.norm(*args, **kwdargs).cdf
dists = np.vectorize(normcdf)(loc=self.mu, scale=self.sig)
crps = crps_quadrature(self.obs,
dists,
xmin=self.mu - 5 * self.sig,
xmax=self.mu + 5 * self.sig)
np.testing.assert_allclose(crps, self.expected, rtol=1e-4)
def test_xr_crps_quadrature_dask(o_dask):
cdf_or_dist = norm
actual = xr_crps_quadrature(o_dask, cdf_or_dist)
expected = crps_quadrature(o_dask, cdf_or_dist)
expected = xr.DataArray(expected, coords=o_dask.coords)
# test for numerical identity of xr_crps and crps
assert_allclose(actual, expected)
# test that xr_crps_ensemble returns chunks
assert actual.chunks is not None
# show that crps_ensemble returns no chunks
assert expected.chunks is None
def test_crps_quadrature_api_and_inputs(o, f_prob, keep_attrs, input_type, chunk_bool):
"""Test that crps_quadrature keeps attributes, chunking, input types and equals
properscoring.crps_quadrature."""
o, f_prob = modify_inputs(o, f_prob, input_type, chunk_bool)
# to speed things up
o = o.isel(time=0, drop=True)
cdf_or_dist = norm
actual = crps_quadrature(o, cdf_or_dist, keep_attrs=keep_attrs)
if input_type == "DataArray": # properscoring allows only DataArrays
expected = properscoring.crps_quadrature(o, cdf_or_dist)
expected = xr.DataArray(expected, coords=o.coords).mean()
# test for numerical identity of xskillscore crps and properscoring crps
assert_allclose(actual, expected)
# test that returns chunks
assert_chunk(actual, chunk_bool)
# test that attributes are kept
assert_keep_attrs(actual, o, keep_attrs)
# test that input types equal output types
assign_type_input_output(actual, o)
