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Merged
ushnah merged 1 commit into from
Aug 4, 2026
Merged

test: adjust nansum regression coverage #3

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126 changes: 63 additions & 63 deletions tests/test_funcs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2315,69 +2315,6 @@ def test_xp(self, axis: int | None, expected_list: list[float], xp: ArrayNamespa
assert_equal(res, expected)


class TestNanSum:
def test_simple(self, xp: ArrayNamespace):
a = xp.asarray([[1.0, 2.0], [3.0, xp.nan]])

res = nansum(a)
expected = 6.0
assert res == expected

res = nansum(a, axis=0)
expected = xp.asarray([4.0, 2.0])
assert_equal(res, expected)

res = nansum(a, axis=1)
expected = xp.asarray([3.0, 3.0])
assert_equal(res, expected)

def test_bigger(self, xp: ArrayNamespace):
a = xp.asarray(
[
[1.0, xp.nan, 4.0, 5.0],
[xp.nan, -2.0, xp.nan, -4.0],
[2.0, 1.0, 3.0, xp.nan],
]
)

res = nansum(a, axis=0)
expected = xp.asarray([3.0, -1.0, 7.0, 1.0])
assert_equal(res, expected)

res = nansum(a, axis=1)
expected = xp.asarray([10.0, -6.0, 6.0])
assert_equal(res, expected)

def test_all_nan_slice(self, xp: ArrayNamespace):
a = xp.asarray([[xp.nan, 1.0], [xp.nan, xp.nan]])

res = nansum(a, axis=0)
expected = xp.asarray([0.0, 1.0])
assert_equal(res, expected)

def test_scalar(self, xp: ArrayNamespace):
a = xp.asarray(1.0)
assert nansum(a) == 1.0

@pytest.mark.skip_xp_backend(
Backend.TORCH, reason="torch.nansum does not support tensors on meta device"
)
@pytest.mark.parametrize("axis", [None, 0, 1])
def test_device(self, axis: int | None, xp: ArrayNamespace, device: Device):
a = xp.asarray([[4.0, xp.nan, 1.0], [2.0, 5.0, xp.nan]], device=device)
res = nansum(a, axis=axis)
assert get_device(res) == device

@pytest.mark.parametrize(
("axis", "expected_list"), [(0, [6.0, 3.0, 1.0]), (1, [5.0, 5.0])]
)
def test_xp(self, axis: int | None, expected_list: list[float], xp: ArrayNamespace):
a = xp.asarray([[4.0, xp.nan, 1.0], [2.0, 3.0, xp.nan]])
res = nansum(a, axis=axis, xp=xp)
expected = xp.asarray(expected_list)
assert_equal(res, expected)


class TestNanMax:
def test_simple(self, xp: ArrayNamespace):
a = xp.asarray([[5, 3], [6, xp.nan]])
Expand Down Expand Up @@ -2457,3 +2394,66 @@ def test_xp(self, axis: int | None, expected_list: list[float], xp: ArrayNamespa
res = nanmax(a, axis=axis, xp=xp)
expected = xp.asarray(expected_list)
assert_equal(res, expected)


class TestNanSum:
def test_simple(self, xp: ArrayNamespace):
a = xp.asarray([[1.0, 2.0], [3.0, xp.nan]])

res = nansum(a)
expected = 6.0
assert res == expected

res = nansum(a, axis=0)
expected = xp.asarray([4.0, 2.0])
assert_equal(res, expected)

res = nansum(a, axis=1)
expected = xp.asarray([3.0, 3.0])
assert_equal(res, expected)

def test_bigger(self, xp: ArrayNamespace):
a = xp.asarray(
[
[1.0, xp.nan, 4.0, 5.0],
[xp.nan, -2.0, xp.nan, -4.0],
[2.0, 1.0, 3.0, xp.nan],
]
)

res = nansum(a, axis=0)
expected = xp.asarray([3.0, -1.0, 7.0, 1.0])
assert_equal(res, expected)

res = nansum(a, axis=1)
expected = xp.asarray([10.0, -6.0, 6.0])
assert_equal(res, expected)

def test_all_nan_slice(self, xp: ArrayNamespace):
a = xp.asarray([[xp.nan, 1.0], [xp.nan, xp.nan]])

res = nansum(a, axis=0)
expected = xp.asarray([0.0, 1.0])
assert_equal(res, expected)

def test_scalar(self, xp: ArrayNamespace):
a = xp.asarray(1.0)
assert nansum(a) == 1.0

@pytest.mark.skip_xp_backend(
Backend.TORCH, reason="torch.nansum does not support tensors on meta device"
)
@pytest.mark.parametrize("axis", [None, 0, 1])
def test_device(self, axis: int | None, xp: ArrayNamespace, device: Device):
a = xp.asarray([[4.0, xp.nan, 1.0], [2.0, 5.0, xp.nan]], device=device)
res = nansum(a, axis=axis)
assert get_device(res) == device

@pytest.mark.parametrize(
("axis", "expected_list"), [(0, [6.0, 3.0, 1.0]), (1, [5.0, 5.0])]
)
def test_xp(self, axis: int | None, expected_list: list[float], xp: ArrayNamespace):
a = xp.asarray([[4.0, xp.nan, 1.0], [2.0, 3.0, xp.nan]])
res = nansum(a, axis=axis, xp=xp)
expected = xp.asarray(expected_list)
assert_equal(res, expected)