--- Begin Message ---
Source: python-skbio
Version: 0.5.8-1
Severity: serious
User: debian...@lists.debian.org
Usertags: needs-update
Hi Maintainer
The autopkgtests of python-skbio fail with NumPy 1.24 [1]. I've
copied what I hope is the relevant part of the log below.
Regards
Graham
[1] https://ci.debian.net/packages/p/python-skbio/testing/amd64/
=================================== FAILURES ===================================
___________________ TestLoc.test_multiindex_complicated_axis ___________________
self = <skbio.alignment.tests.test_tabular_msa.TestLoc
testMethod=test_multiindex_complicated_axis>
def test_multiindex_complicated_axis(self):
a = RNA("UUAG", metadata={0: 0}, positional_metadata={0: [1, 2, 3, 4]})
b = RNA("UAAG", metadata={1: 0}, positional_metadata={1: [1, 2, 3, 4]})
c = RNA("UAA-", metadata={2: 0}, positional_metadata={2: [1, 2, 3, 4]})
d = RNA("UA-G", metadata={3: 0}, positional_metadata={3: [1, 2, 3, 4]})
msa = TabularMSA([a, b, c, d], metadata={'x': 'y'},
positional_metadata={'c': ['a', 'b', 'c', 'd']},
index=[('a', 'x', 0), ('a', 'x', 1), ('a', 'y', 2),
('b', 'x', 0)])
> self.assertEqual(self.get(msa, (([False, True, False, True],
'x', 0), Ellipsis)),
TabularMSA([d], metadata={'x': 'y'},
positional_metadata={'c': ['a', 'b', 'c',
'd']},
index=[('b', 'x', 0)]))
skbio/alignment/tests/test_tabular_msa.py:1373:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
skbio/alignment/tests/test_tabular_msa.py:1271: in get
return obj.loc[indexable]
skbio/alignment/_indexing.py:39: in __getitem__
return self._handle_both_axes(*indexable)
skbio/alignment/_indexing.py:53: in _handle_both_axes
r = self._slice_on_first_axis(self._obj, seq_index)
skbio/alignment/_indexing.py:77: in _slice_on_first_axis
return self._slice_sequences(obj, indexable)
skbio/alignment/_indexing.py:202: in _slice_sequences
self._assert_bool_vector_right_size(indexable, axis=0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <skbio.alignment._indexing.TabularMSALoc object at 0x7f9cc59ece80>
indexable = ([False, True, False, True], 'x', 0), axis = 0
def _assert_bool_vector_right_size(self, indexable, axis):
if isinstance(indexable, np.ndarray):
pass
elif hasattr(indexable, '__iter__'):
> indexable = np.asarray(list(indexable))
E ValueError: setting an array element with a sequence. The
requested array has an inhomogeneous shape after 1 dimensions. The
detected shape was (3,) + inhomogeneous part.
skbio/alignment/_indexing.py:116: ValueError
___________ TestLoc.test_multiindex_complicated_axis_empty_selection ___________
self = <skbio.alignment.tests.test_tabular_msa.TestLoc
testMethod=test_multiindex_complicated_axis_empty_selection>
@unittest.skipIf(tuple(map(int, pd.__version__.split('.'))) < (1, 2, 5),
"Old pandas will return empty frame")
def test_multiindex_complicated_axis_empty_selection(self):
a = RNA("UUAG", metadata={0: 0}, positional_metadata={0: [1, 2, 3, 4]})
b = RNA("UAAG", metadata={1: 0}, positional_metadata={1: [1, 2, 3, 4]})
c = RNA("UAA-", metadata={2: 0}, positional_metadata={2: [1, 2, 3, 4]})
d = RNA("UA-G", metadata={3: 0}, positional_metadata={3: [1, 2, 3, 4]})
msa = TabularMSA([a, b, c, d], metadata={'x': 'y'},
positional_metadata={'c': ['a', 'b', 'c', 'd']},
index=[('a', 'x', 0), ('a', 'x', 1), ('a', 'y', 2),
('b', 'x', 0)])
# Pandas will KeyError when the intersection is empty
# change appears to have happened in:
# https://github.com/pandas-dev/pandas/pull/42245
# but this was not bisected to confirm
with self.assertRaises(KeyError):
> self.get(msa, (([False, True, False, True], 'x', 2), Ellipsis))
skbio/alignment/tests/test_tabular_msa.py:1396:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
skbio/alignment/tests/test_tabular_msa.py:1271: in get
return obj.loc[indexable]
skbio/alignment/_indexing.py:39: in __getitem__
return self._handle_both_axes(*indexable)
skbio/alignment/_indexing.py:53: in _handle_both_axes
r = self._slice_on_first_axis(self._obj, seq_index)
skbio/alignment/_indexing.py:77: in _slice_on_first_axis
return self._slice_sequences(obj, indexable)
skbio/alignment/_indexing.py:202: in _slice_sequences
self._assert_bool_vector_right_size(indexable, axis=0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
def _assert_bool_vector_right_size(self, indexable, axis):
if isinstance(indexable, np.ndarray):
pass
elif hasattr(indexable, '__iter__'):
> indexable = np.asarray(list(indexable))
E ValueError: setting an array element with a sequence. The
requested array has an inhomogeneous shape after 1 dimensions. The
detected shape was (3,) + inhomogeneous part.
skbio/alignment/_indexing.py:116: ValueError
___________________ TestSequence.test_init_invalid_sequence ____________________
self = <skbio.sequence.tests.test_sequence.TestSequence
testMethod=test_init_invalid_sequence>
def test_init_invalid_sequence(self):
# invalid dtype (numpy.ndarray input)
with self.assertRaises(TypeError):
# int64
Sequence(np.array([1, 2, 3]))
with self.assertRaises(TypeError):
# |S21
Sequence(np.array([1, "23", 3]))
with self.assertRaises(TypeError):
# object
> Sequence(np.array([1, {}, ()]))
E ValueError: setting an array element with a sequence. The
requested array has an inhomogeneous shape after 1 dimensions. The
detected shape was (3,) + inhomogeneous part.
skbio/sequence/tests/test_sequence.py:462: ValueError
____________ TestSequence.test_munge_to_index_array_valid_iterable _____________
self = <skbio.sequence.tests.test_sequence.TestSequence
testMethod=test_munge_to_index_array_valid_iterable>
def test_munge_to_index_array_valid_iterable(self):
s = Sequence('')
def slices_only():
return (slice(i, i+1) for i in range(0, 10, 2))
def mixed():
return (slice(i, i+1) if i % 2 == 0 else i for i in range(10))
def unthinkable():
for i in range(10):
if i % 3 == 0:
yield slice(i, i+1)
elif i % 3 == 1:
yield i
else:
yield np.array([i], dtype=int)
for c in (lambda x: x, list, tuple, lambda x: np.array(tuple(x)),
lambda x: pd.Series(tuple(x))):
exp = np.arange(10, dtype=int)
obs = s._munge_to_index_array(c(mixed()))
npt.assert_equal(obs, exp)
exp = np.arange(10, dtype=int)
> obs = s._munge_to_index_array(c(unthinkable()))
skbio/sequence/tests/test_sequence.py:2222:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
x = <generator object
TestSequence.test_munge_to_index_array_valid_iterable.<locals>.unthinkable
at 0x7f9cb7346420>
> for c in (lambda x: x, list, tuple, lambda x: np.array(tuple(x)),
lambda x: pd.Series(tuple(x))):
E ValueError: setting an array element with a sequence.
The requested array has an inhomogeneous shape after 1 dimensions. The
detected shape was (10,) + inhomogeneous part.
skbio/sequence/tests/test_sequence.py:2215: ValueError
--- End Message ---
