def test_crps_gaussian_dask_b_int(o_dask, keep_attrs):
mu = 0
sig = 1
actual = crps_gaussian(o_dask, mu, sig, keep_attrs=keep_attrs)
assert actual is not None
if keep_attrs:
assert actual.attrs == o_dask.attrs
else:
assert actual.attrs == {}
def test_crps_gaussian_dask(o_dask, f_prob_dask, keep_attrs):
mu = f_prob_dask.mean("member")
sig = f_prob_dask.std("member")
actual = crps_gaussian(o_dask, mu, sig, keep_attrs=keep_attrs)
expected = properscoring.crps_gaussian(o_dask, mu, sig)
expected = xr.DataArray(expected, coords=o_dask.coords).mean()
# test for numerical identity of xskillscore crps and properscoring 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
def test_crps_gaussian_accessor(o, f_prob, outer_bool):
mu = f_prob.mean("member")
sig = f_prob.std("member")
actual = crps_gaussian(o, mu, sig)
ds = xr.Dataset()
ds["o"] = o
ds["mu"] = mu
ds["sig"] = sig
if outer_bool:
ds = ds.drop_vars("mu")
expected = ds.xs.crps_gaussian("o", mu, sig)
else:
expected = ds.xs.crps_gaussian("o", "mu", "sig")
assert_allclose(actual, expected)
def test_crps_gaussian_args(o, keep_attrs, input_type):
"""Test that crps_gaussian accepts various data types as args."""
mu = 0
sig = 1
if "input_type" == "float":
mu = float(mu)
sig = float(sig)
elif "input_type" == "xr.DataArray":
mu = xr.DataArray(mu)
sig = xr.DataArray(sig)
elif "input_type" == "np.array":
mu = np.array(mu)
sig = np.array(sig)
actual = crps_gaussian(o, mu, sig, keep_attrs=keep_attrs)
assert_keep_attrs(actual, o, keep_attrs)
def test_crps_gaussian_accessor(o, f, dask_bool, outer_bool):
if dask_bool:
o = o.chunk()
f = f.chunk()
mu = f.mean('member')
sig = f.std('member')
actual = crps_gaussian(o, mu, sig)
ds = xr.Dataset()
ds['o'] = o
ds['mu'] = mu
ds['sig'] = sig
if outer_bool:
ds = ds.drop_vars('mu')
expected = ds.xs.crps_gaussian('o', mu, sig)
else:
expected = ds.xs.crps_gaussian('o', 'mu', 'sig')
assert_allclose(actual, expected)
def test_crps_gaussian_api_and_inputs(o, f_prob, keep_attrs, input_type, chunk_bool):
"""Test that crps_gaussian keeps attributes, chunking, input types and equals
properscoring.crps_gaussian."""
o, f_prob = modify_inputs(o, f_prob, input_type, chunk_bool)
mu = f_prob.mean("member")
sig = f_prob.std("member")
actual = crps_gaussian(o, mu, sig, keep_attrs=keep_attrs)
if input_type == "DataArray": # properscoring allows only DataArrays
expected = properscoring.crps_gaussian(o, mu, sig)
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)
def test_crps_gaussian_dim(o, f_prob, dim):
"""Check that crps_gaussian reduces only dim."""
mu = f_prob.mean("member")
sig = f_prob.std("member")
actual = crps_gaussian(o, mu, sig, dim=dim)
assert_only_dim_reduced(dim, actual, o)