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
