def test_nan_reductions(reduction, axis, keepdims, fraction):
s = sparse.random((2, 3, 4), data_rvs=random_value_array(np.nan, fraction),
density=.25)
x = s.todense()
expected = getattr(np, reduction)(x, axis=axis, keepdims=keepdims)
actual = getattr(sparse, reduction)(s, axis=axis, keepdims=keepdims)
assert_eq(expected, actual, equal_nan=True, check_nnz=False)
def test_trinary_broadcasting_pathological(shapes, func, value, fraction):
args = [sparse.random(s, density=0.5, data_rvs=random_value_array(value, fraction))
for s in shapes]
dense_args = [arg.todense() for arg in args]
fs = sparse.elemwise(func, *args)
assert isinstance(fs, COO)
assert_eq(fs, func(*dense_args), equal_nan=True)
def test_all_nan_reduction_warning(reduction, axis):
x = random_value_array(np.nan, 1.0)(2 * 3 * 4).reshape(2, 3, 4)
s = COO.from_numpy(x)
with pytest.warns(RuntimeWarning):
getattr(sparse, reduction)(s, axis=axis)
