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ENH: enhance nansum and nanmean to support complex arrays with torch on cpu #1099

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ENH: enhance nansum and nanmean to support complex arrays with torch on cpu #1099

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c26b752
complete tests with complex arrays
qbarthelemy Nov 10, 2026
334a7e2
update conditions
qbarthelemy Nov 10, 2026
56ffb64
correct expected complex value in nanmean
qbarthelemy Nov 10, 2026
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8 changes: 6 additions & 2 deletions src/array_api_extra/_statistical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -366,8 +366,10 @@
or _compat.is_cupy_namespace(xp)
or _compat.is_dask_namespace(xp)
or _compat.is_jax_namespace(xp)
or _compat.is_torch_namespace(xp)
or (_compat.is_torch_namespace(xp) and
(xp.isdtype(a.dtype, "real floating") or _compat.device(a).type != "cpu")
)
):

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Cannot access attribute "type" for class "Device"
  Attribute "type" is unknown (reportAttributeAccessIssue)
return xp.nanmean(a, axis=axis)

return _agnostic._statistical.nanmean(a, axis=axis, xp=xp)
Expand Down Expand Up @@ -418,8 +420,10 @@
or _compat.is_cupy_namespace(xp)
or _compat.is_dask_namespace(xp)
or _compat.is_jax_namespace(xp)
or _compat.is_torch_namespace(xp)
or (_compat.is_torch_namespace(xp) and
(xp.isdtype(a.dtype, "real floating") or _compat.device(a).type != "cpu")
)
):
return xp.nansum(a, axis=axis)

return _agnostic._statistical.nansum(a, axis=axis, xp=xp)

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Cannot access attribute "type" for class "Device"
  Attribute "type" is unknown (reportAttributeAccessIssue)
34 changes: 34 additions & 0 deletions tests/main/test_statistical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -508,6 +508,23 @@ def test_xp(self, axis: int | None, expected_list: list[float], xp: ArrayNamespa
expected = xp.asarray(expected_list)
assert_equal(res, expected)

def test_complex(self, xp: ArrayNamespace):
a = xp.asarray(
[
[1 + 2j, complex(float("nan"), 0.0), 4 + 1j, 5 - 1j],
[complex(float("nan"), 0.0), -2 + 1j, 3 - 2j, -4 + 2j],
[2 - 1j, 1 + 3j, 3 + 2j, complex(float("nan"), 0.0)],
],
dtype=xp.complex128,
)

res = nansum(a, axis=0)
expected = xp.asarray(
[3 + 1j, -1 + 4j, 10 + 1j, 1 + 1j],
dtype=xp.complex128,
)
assert_equal(res, expected)


class TestNanMean:
def test_simple(self, xp: ArrayNamespace):
Expand Down Expand Up @@ -570,3 +587,20 @@ def test_xp(self, axis: int | None, expected_list: list[float], xp: ArrayNamespa
res = nanmean(a, axis=axis, xp=xp)
expected = xp.asarray(expected_list)
assert_equal(res, expected)

def test_complex(self, xp: ArrayNamespace):
a = xp.asarray(
[
[1 + 2j, complex(float("nan"), 0.0), 4 + 1j, 5 - 1j],
[complex(float("nan"), 0.0), -2 + 1j, 3 - 2j, -4 + 2j],
[2 - 1j, 1 + 3j, 3 + 2j, complex(float("nan"), 0.0)],
],
dtype=xp.complex128,
)

res = nanmean(a, axis=0)
expected = xp.asarray(
[1.5 + 0.5j, -0.5 + 2j, 10 / 3 + 1j / 3, 0.5 + 0.5j],
dtype=xp.complex128,
)
assert_close(res, expected)
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