8000 BUG: Fixed an issue wherein certain `nan<x>` functions could fail for object arrays by BvB93 · Pull Request #19821 · numpy/numpy · GitHub
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BUG: Fixed an issue wherein certain nan<x> functions could fail for object arrays #19821

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Merged
merged 5 commits into from
Sep 5, 2021
Merged

BUG: Fixed an issue wherein certain nan<x> functions could fail for object arrays #19821

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b6d7c46
BUG: Fixed an issue wherein certain `nan<x>` functions could fail for…
Sep 3, 2021
fffcb6e
TST: Expand the old `TestNanFunctions_IntTypes` test with non-integer…
Sep 3, 2021
9ef7783
TST: Add more tests for `nanmedian`, `nanquantile` and `nanpercentile`
Sep 3, 2021
a0ea053
MAINT: Copy the `_methods._std` code-path for handling `nanstd` objec…
BvB93 Sep 4, 2021
ecba713
MAINT: Let `_remove_nan_1d` attempt to identify nan-containing object…
Sep 5, 2021
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TST: Expand the old TestNanFunctions_IntTypes test with non-integer… 
… number types
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Bas van Beek committed Sep 3, 2021
commit fffcb6e2bd45bf423f626ebf77a2e3142de636c4
123 changes: 52 additions & 71 deletions numpy/lib/tests/test_nanfunctions.py
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Expand Up @@ -231,79 +231,60 @@ class MyNDArray(np.ndarray):
assert_(res.shape == ())


class TestNanFunctions_IntTypes:

int_types = (np.int8, np.int16, np.int32, np.int64, np.uint8,
np.uint16, np.uint32, np.uint64)
@pytest.mark.parametrize(
"dtype",
np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O",
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np.typecodes['AllFloat'][1:] would remove 'e', but is not quite as robust.

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No worries, as of fffcb6e the tests seem to work fine with np.float16.

)
class TestNanFunctions_NumberTypes:

mat = np.array([127, 39, 93, 87, 46])

def integer_arrays(self):
for dtype in self.int_types:
yield self.mat.astype(dtype)

def test_nanmin(self):
tgt = np.min(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanmin(mat), tgt)

def test_nanmax(self):
tgt = np.max(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanmax(mat), tgt)

def test_nanargmin(self):
tgt = np.argmin(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmin(mat), tgt)

def test_nanargmax(self):
tgt = np.argmax(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmax(mat), tgt)

def test_nansum(self):
tgt = np.sum(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nansum(mat), tgt)

def test_nanprod(self):
tgt = np.prod(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanprod(mat), tgt)

def test_nancumsum(self):
tgt = np.cumsum(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nancumsum(mat), tgt)

def test_nancumprod(self):
tgt = np.cumprod(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nancumprod(mat), tgt)

def test_nanmean(self):
tgt = np.mean(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanmean(mat), tgt)

def test_nanvar(self):
tgt = np.var(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanvar(mat), tgt)

tgt = np.var(mat, ddof=1)
for mat in self.integer_arrays():
assert_equal(np.nanvar(mat, ddof=1), tgt)

def test_nanstd(self):
tgt = np.std(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanstd(mat), tgt)

tgt = np.std(self.mat, ddof=1)
for mat in self.integer_arrays():
assert_equal(np.nanstd(mat, ddof=1), tgt)
mat.setflags(write=False)

nanfuncs = {
np.nanmin: np.min,
np.nanmax: np.max,
np.nanargmin: np.argmin,
np.nanargmax: np.argmax,
np.nansum: np.sum,
np.nanprod: np.prod,
np.nancumsum: np.cumsum,
np.nancumprod: np.cumprod,
np.nanmean: np.mean,
np.nanvar: np.var,
np.nanstd: np.std,
}
nanfunc_ids = [i.__name__ for i in nanfuncs]

@pytest.mark.parametrize("nanfunc,func", nanfuncs.items(), ids=nanfunc_ids)
def test_nanfunc(self, dtype, nanfunc, func):
if nanfunc is np.nanprod and dtype == "e":
pytest.xfail(reason="overflow encountered in reduce")

mat = self.mat.astype(dtype)
tgt = func(mat)
out = nanfunc(mat)

assert_almost_equal(out, tgt)
if dtype == "O":
assert type(out) is type(tgt)
else:
assert out.dtype == tgt.dtype

@pytest.mark.parametrize(
"nanfunc,func",
[(np.nanvar, np.var), (np.nanstd, np.std)],
ids=["nanvar", "nanstd"],
)
def test_nanfunc_ddof(self, dtype, nanfunc, func):
mat = self.mat.astype(dtype)
tgt = func(mat, ddof=1)
out = nanfunc(mat, ddof=1)

assert_almost_equal(out, tgt)
if dtype == "O":
assert type(out) is type(tgt)
else:
assert out.dtype == tgt.dtype


class SharedNanFunctionsTestsMixin:
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