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TST: Expand/rewrite the test_allnans tests
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Parametrize w.r.t. the axis, array dimensionality and dtype
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@BvB93
Bas van Beek authored and BvB93 committed Sep 11, 2021
1 parent 95e5d5a commit d944435
Showing 1 changed file with 115 additions and 79 deletions.
194 changes: 115 additions & 79 deletions numpy/lib/tests/test_nanfunctions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,21 +83,23 @@ def test_result_values(self):
res = nf(_ndat, axis=1)
assert_almost_equal(res, tgt)

def test_allnans(self):
mat = np.array([np.nan]*9).reshape(3, 3)
for f in self.nanfuncs:
for axis in [None, 0, 1]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(f(mat, axis=axis)).all())
assert_(len(w) == 1, 'no warning raised')
assert_(issubclass(w[0].category, RuntimeWarning))
# Check scalars
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(f(np.nan)))
assert_(len(w) == 1, 'no warning raised')
assert_(issubclass(w[0].category, RuntimeWarning))
@pytest.mark.parametrize("axis", [None, 0, 1])
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
@pytest.mark.parametrize("array", [
np.full((3, 3), np.nan),
np.array(np.nan),
], ids=["0d", "2d"])
def test_allnans(self, axis, dtype, array):
if axis is not None and array.ndim == 0:
pytest.skip(f"`axis != None` not supported for 0d arrays")

array = array.astype(dtype)
match = "All-NaN slice encountered"
for func in self.nanfuncs:
with pytest.warns(RuntimeWarning, match=match):
out = func(array, axis=axis)
assert np.isnan(out).all()
assert out.dtype == array.dtype

def test_masked(self):
mat = np.ma.fix_invalid(_ndat)
Expand Down Expand Up @@ -193,12 +195,20 @@ def test_result_values(self):
assert_(not fcmp(val, row).any())
assert_(not np.equal(val, row[:ind]).any())

def test_allnans(self):
mat = np.array([np.nan]*9).reshape(3, 3)
for f in self.nanfuncs:
for axis in [None, 0, 1]:
assert_raises(ValueError, f, mat, axis=axis)
assert_raises(ValueError, f, np.nan)
@pytest.mark.parametrize("axis", [None, 0, 1])
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
@pytest.mark.parametrize("array", [
np.full((3, 3), np.nan),
np.array(np.nan),
], ids=["0d", "2d"])
def test_allnans(self, axis, dtype, array):
if axis is not None and array.ndim == 0:
pytest.skip(f"`axis != None` not supported for 0d arrays")

array = array.astype(dtype)
for func in self.nanfuncs:
with pytest.raises(ValueError, match="All-NaN slice encountered"):
func(array, axis=axis)

def test_empty(self):
mat = np.zeros((0, 3))
Expand Down Expand Up @@ -417,20 +427,21 @@ class TestNanFunctions_SumProd(SharedNanFunctionsTestsMixin):
nanfuncs = [np.nansum, np.nanprod]
stdfuncs = [np.sum, np.prod]

def test_allnans(self):
# Check for FutureWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = np.nansum([np.nan]*3, axis=None)
assert_(res == 0, 'result is not 0')
assert_(len(w) == 0, 'warning raised')
# Check scalar
res = np.nansum(np.nan)
assert_(res == 0, 'result is not 0')
assert_(len(w) == 0, 'warning raised')
# Check there is no warning for not all-nan
np.nansum([0]*3, axis=None)
assert_(len(w) == 0, 'unwanted warning raised')
@pytest.mark.parametrize("axis", [None, 0, 1])
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
@pytest.mark.parametrize("array", [
np.full((3, 3), np.nan),
np.array(np.nan),
], ids=["0d", "2d"])
def test_allnans(self, axis, dtype, array):
if axis is not None and array.ndim == 0:
pytest.skip(f"`axis != None` not supported for 0d arrays")

array = array.astype(dtype)
for func, identity in zip(self.nanfuncs, [0, 1]):
out = func(array, axis=axis)
assert np.all(out == identity)
assert out.dtype == array.dtype

def test_empty(self):
for f, tgt_value in zip([np.nansum, np.nanprod], [0, 1]):
Expand All @@ -451,19 +462,21 @@ class TestNanFunctions_CumSumProd(SharedNanFunctionsTestsMixin):
nanfuncs = [np.nancumsum, np.nancumprod]
stdfuncs = [np.cumsum, np.cumprod]

def test_allnans(self):
for f, tgt_value in zip(self.nanfuncs, [0, 1]):
# Unlike other nan-functions, sum/prod/cumsum/cumprod don't warn on all nan input
with assert_no_warnings():
res = f([np.nan]*3, axis=None)
tgt = tgt_value*np.ones((3))
assert_(np.array_equal(res, tgt), 'result is not %s * np.ones((3))' % (tgt_value))
# Check scalar
res = f(np.nan)
tgt = tgt_value*np.ones((1))
assert_(np.array_equal(res, tgt), 'result is not %s * np.ones((1))' % (tgt_value))
# Check there is no warning for not all-nan
f([0]*3, axis=None)
@pytest.mark.parametrize("axis", [None, 0, 1])
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
@pytest.mark.parametrize("array", [
np.full((3, 3), np.nan),
np.array(np.nan),
], ids=["0d", "2d"])
def test_allnans(self, axis, dtype, array):
if axis is not None and array.ndim == 0:
pytest.skip(f"`axis != None` not supported for 0d arrays")

array = array.astype(dtype)
for func, identity in zip(self.nanfuncs, [0, 1]):
out = func(array)
assert np.all(out == identity)
assert out.dtype == array.dtype

def test_empty(self):
for f, tgt_value in zip(self.nanfuncs, [0, 1]):
Expand Down Expand Up @@ -559,19 +572,29 @@ def test_ddof_too_big(self):
else:
assert_(len(sup.log) == 0)

def test_allnans(self):
mat = np.array([np.nan]*9).reshape(3, 3)
for f in self.nanfuncs:
for axis in [None, 0, 1]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(f(mat, axis=axis)).all())
assert_(len(w) == 1)
assert_(issubclass(w[0].category, RuntimeWarning))
# Check scalar
assert_(np.isnan(f(np.nan)))
assert_(len(w) == 2)
assert_(issubclass(w[0].category, RuntimeWarning))
@pytest.mark.parametrize("axis", [None, 0, 1])
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
@pytest.mark.parametrize("array", [
np.array(np.nan),
np.full((3, 3), np.nan),
], ids=["0d", "2d"])
def test_allnans(self, axis, dtype, array):
if axis is not None and array.ndim == 0:
pytest.skip(f"`axis != None` not supported for 0d arrays")

array = array.astype(dtype)
match = "(Degrees of freedom <= 0 for slice.)|(Mean of empty slice)"
for func in self.nanfuncs:
with pytest.warns(RuntimeWarning, match=match):
out = func(array, axis=axis)
assert np.isnan(out).all()

# `nanvar` and `nanstd` convert complex inputs to their
# corresponding floating dtype
if func is np.nanmean:
assert out.dtype == array.dtype
else:
assert out.dtype == np.abs(array).dtype

def test_empty(self):
mat = np.zeros((0, 3))
Expand Down Expand Up @@ -835,24 +858,21 @@ def test_result_values(self):
res = np.nanpercentile(_ndat, (28, 98), axis=1)
assert_almost_equal(res, tgt)

def test_allnans(self):
mat = np.array([np.nan]*9).reshape(3, 3)
for axis in [None, 0, 1]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(np.nanpercentile(mat, 60, axis=axis)).all())
if axis is None:
assert_(len(w) == 1)
else:
assert_(len(w) == 3)
assert_(issubclass(w[0].category, RuntimeWarning))
# Check scalar
assert_(np.isnan(np.nanpercentile(np.nan, 60)))
if axis is None:
assert_(len(w) == 2)
else:
assert_(len(w) == 4)
assert_(issubclass(w[0].category, RuntimeWarning))
@pytest.mark.parametrize("axis", [None, 0, 1])
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
@pytest.mark.parametrize("array", [
np.array(np.nan),
np.full((3, 3), np.nan),
], ids=["0d", "2d"])
def test_allnans(self, axis, dtype, array):
if axis is not None and array.ndim == 0:
pytest.skip(f"`axis != None` not supported for 0d arrays")

array = array.astype(dtype)
with pytest.warns(RuntimeWarning, match="All-NaN slice encountered"):
out = np.nanpercentile(array, 60, axis=axis)
assert np.isnan(out).all()
assert out.dtype == array.dtype

def test_empty(self):
mat = np.zeros((0, 3))
Expand Down Expand Up @@ -945,6 +965,22 @@ def test_no_p_overwrite(self):
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)

@pytest.mark.parametrize("axis", [None, 0, 1])
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
@pytest.mark.parametrize("array", [
np.array(np.nan),
np.full((3, 3), np.nan),
], ids=["0d", "2d"])
def test_allnans(self, axis, dtype, array):
if axis is not None and array.ndim == 0:
pytest.skip(f"`axis != None` not supported for 0d arrays")

array = array.astype(dtype)
with pytest.warns(RuntimeWarning, match="All-NaN slice encountered"):
out = np.nanquantile(array, 1, axis=axis)
assert np.isnan(out).all()
assert out.dtype == array.dtype

@pytest.mark.parametrize("arr, expected", [
# array of floats with some nans
(np.array([np.nan, 5.0, np.nan, np.inf]),
Expand Down

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