def test_rank_hist_tied():
"""Test that rank_histogram handles tied ranks."""
a = xr.DataArray(np.zeros(100), dims="a")
b = xr.DataArray(np.zeros((100, 10)), dims=["a", "member"])
rh = rank_histogram(a, b)
assert rh.min() > 3
assert rh.max() < 30
def test_rank_histogram_sum(o, f_prob, dim, obj):
"""Test that the number of samples in the rank histogram is correct"""
if "ds" in obj:
name = "var"
o = o.to_dataset(name=name)
f_prob = f_prob.to_dataset(name=name)
if "chunked" in obj:
o = o.chunk()
f_prob = f_prob.chunk()
if dim == []:
with pytest.raises(ValueError):
rank_histogram(o, f_prob, dim=dim)
else:
rank_hist = rank_histogram(o, f_prob, dim=dim)
if "ds" in obj:
rank_hist = rank_hist[name]
o = o[name]
assert_allclose(rank_hist.sum(), o.count())
def test_rank_histogram_sum(o, f_prob, dim, chunk_bool, input_type):
"""Test that the number of samples in the rank histogram is correct"""
o, f_prob = modify_inputs(o, f_prob, input_type, chunk_bool)
if dim == []:
with pytest.raises(ValueError):
rank_histogram(o, f_prob, dim=dim)
else:
rank_hist = rank_histogram(o, f_prob, dim=dim)
if "Dataset" in input_type:
rank_hist = rank_hist[list(o.data_vars)[0]]
o = o[list(o.data_vars)[0]]
assert_allclose(rank_hist.sum(), o.count())
assert_allclose(rank_hist.sum(), o.count())
# test that returns chunks
assert_chunk(rank_hist, chunk_bool)
# test that attributes are kept # TODO: add
# assert_keep_attrs(rank_hist, o, keep_attrs)
# test that input types equal output types
assign_type_input_output(rank_hist, o)
def test_rank_histogram_accessor(o, f_prob, outer_bool):
actual = rank_histogram(o, f_prob)
ds = xr.Dataset()
ds["o"] = o
ds["f_prob"] = f_prob
if outer_bool:
ds = ds.drop_vars("f_prob")
expected = ds.xs.rank_histogram("o", f_prob)
else:
expected = ds.xs.rank_histogram("o", "f_prob")
assert_allclose(actual, expected)
def test_rank_histogram_values(o, f_prob):
"""Test values in extreme cases that observations \
all smaller/larger than forecasts"""
assert rank_histogram((f_prob.min() - 1) + 0 * o, f_prob)[0] == o.size
assert rank_histogram((f_prob.max() + 1) + 0 * o, f_prob)[-1] == o.size
def test_rank_histogram_dask(o_dask, f_prob_dask):
"""Test that rank_histogram returns dask array if provided dask array"""
actual = rank_histogram(o_dask, f_prob_dask)
assert actual.chunks is not None
def test_rank_histogram_values(o, f_prob):
"""Test values in extreme cases that observations \
all smaller/larger than forecasts"""
assert rank_histogram(o - 10, f_prob)[0] == o.size
assert rank_histogram(o + 10, f_prob)[-1] == o.size