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- """Test functions for 1D array set operations.
- """
- import numpy as np
- from numpy.testing import (assert_array_equal, assert_equal,
- assert_raises, assert_raises_regex)
- from numpy.lib.arraysetops import (
- ediff1d, intersect1d, setxor1d, union1d, setdiff1d, unique, in1d, isin
- )
- import pytest
- class TestSetOps:
- def test_intersect1d(self):
- # unique inputs
- a = np.array([5, 7, 1, 2])
- b = np.array([2, 4, 3, 1, 5])
- ec = np.array([1, 2, 5])
- c = intersect1d(a, b, assume_unique=True)
- assert_array_equal(c, ec)
- # non-unique inputs
- a = np.array([5, 5, 7, 1, 2])
- b = np.array([2, 1, 4, 3, 3, 1, 5])
- ed = np.array([1, 2, 5])
- c = intersect1d(a, b)
- assert_array_equal(c, ed)
- assert_array_equal([], intersect1d([], []))
- def test_intersect1d_array_like(self):
- # See gh-11772
- class Test:
- def __array__(self):
- return np.arange(3)
- a = Test()
- res = intersect1d(a, a)
- assert_array_equal(res, a)
- res = intersect1d([1, 2, 3], [1, 2, 3])
- assert_array_equal(res, [1, 2, 3])
- def test_intersect1d_indices(self):
- # unique inputs
- a = np.array([1, 2, 3, 4])
- b = np.array([2, 1, 4, 6])
- c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True)
- ee = np.array([1, 2, 4])
- assert_array_equal(c, ee)
- assert_array_equal(a[i1], ee)
- assert_array_equal(b[i2], ee)
- # non-unique inputs
- a = np.array([1, 2, 2, 3, 4, 3, 2])
- b = np.array([1, 8, 4, 2, 2, 3, 2, 3])
- c, i1, i2 = intersect1d(a, b, return_indices=True)
- ef = np.array([1, 2, 3, 4])
- assert_array_equal(c, ef)
- assert_array_equal(a[i1], ef)
- assert_array_equal(b[i2], ef)
- # non1d, unique inputs
- a = np.array([[2, 4, 5, 6], [7, 8, 1, 15]])
- b = np.array([[3, 2, 7, 6], [10, 12, 8, 9]])
- c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True)
- ui1 = np.unravel_index(i1, a.shape)
- ui2 = np.unravel_index(i2, b.shape)
- ea = np.array([2, 6, 7, 8])
- assert_array_equal(ea, a[ui1])
- assert_array_equal(ea, b[ui2])
- # non1d, not assumed to be uniqueinputs
- a = np.array([[2, 4, 5, 6, 6], [4, 7, 8, 7, 2]])
- b = np.array([[3, 2, 7, 7], [10, 12, 8, 7]])
- c, i1, i2 = intersect1d(a, b, return_indices=True)
- ui1 = np.unravel_index(i1, a.shape)
- ui2 = np.unravel_index(i2, b.shape)
- ea = np.array([2, 7, 8])
- assert_array_equal(ea, a[ui1])
- assert_array_equal(ea, b[ui2])
- def test_setxor1d(self):
- a = np.array([5, 7, 1, 2])
- b = np.array([2, 4, 3, 1, 5])
- ec = np.array([3, 4, 7])
- c = setxor1d(a, b)
- assert_array_equal(c, ec)
- a = np.array([1, 2, 3])
- b = np.array([6, 5, 4])
- ec = np.array([1, 2, 3, 4, 5, 6])
- c = setxor1d(a, b)
- assert_array_equal(c, ec)
- a = np.array([1, 8, 2, 3])
- b = np.array([6, 5, 4, 8])
- ec = np.array([1, 2, 3, 4, 5, 6])
- c = setxor1d(a, b)
- assert_array_equal(c, ec)
- assert_array_equal([], setxor1d([], []))
- def test_ediff1d(self):
- zero_elem = np.array([])
- one_elem = np.array([1])
- two_elem = np.array([1, 2])
- assert_array_equal([], ediff1d(zero_elem))
- assert_array_equal([0], ediff1d(zero_elem, to_begin=0))
- assert_array_equal([0], ediff1d(zero_elem, to_end=0))
- assert_array_equal([-1, 0], ediff1d(zero_elem, to_begin=-1, to_end=0))
- assert_array_equal([], ediff1d(one_elem))
- assert_array_equal([1], ediff1d(two_elem))
- assert_array_equal([7, 1, 9], ediff1d(two_elem, to_begin=7, to_end=9))
- assert_array_equal([5, 6, 1, 7, 8],
- ediff1d(two_elem, to_begin=[5, 6], to_end=[7, 8]))
- assert_array_equal([1, 9], ediff1d(two_elem, to_end=9))
- assert_array_equal([1, 7, 8], ediff1d(two_elem, to_end=[7, 8]))
- assert_array_equal([7, 1], ediff1d(two_elem, to_begin=7))
- assert_array_equal([5, 6, 1], ediff1d(two_elem, to_begin=[5, 6]))
- @pytest.mark.parametrize("ary, prepend, append, expected", [
- # should fail because trying to cast
- # np.nan standard floating point value
- # into an integer array:
- (np.array([1, 2, 3], dtype=np.int64),
- None,
- np.nan,
- 'to_end'),
- # should fail because attempting
- # to downcast to int type:
- (np.array([1, 2, 3], dtype=np.int64),
- np.array([5, 7, 2], dtype=np.float32),
- None,
- 'to_begin'),
- # should fail because attempting to cast
- # two special floating point values
- # to integers (on both sides of ary),
- # `to_begin` is in the error message as the impl checks this first:
- (np.array([1., 3., 9.], dtype=np.int8),
- np.nan,
- np.nan,
- 'to_begin'),
- ])
- def test_ediff1d_forbidden_type_casts(self, ary, prepend, append, expected):
- # verify resolution of gh-11490
- # specifically, raise an appropriate
- # Exception when attempting to append or
- # prepend with an incompatible type
- msg = 'dtype of `{}` must be compatible'.format(expected)
- with assert_raises_regex(TypeError, msg):
- ediff1d(ary=ary,
- to_end=append,
- to_begin=prepend)
- @pytest.mark.parametrize(
- "ary,prepend,append,expected",
- [
- (np.array([1, 2, 3], dtype=np.int16),
- 2**16, # will be cast to int16 under same kind rule.
- 2**16 + 4,
- np.array([0, 1, 1, 4], dtype=np.int16)),
- (np.array([1, 2, 3], dtype=np.float32),
- np.array([5], dtype=np.float64),
- None,
- np.array([5, 1, 1], dtype=np.float32)),
- (np.array([1, 2, 3], dtype=np.int32),
- 0,
- 0,
- np.array([0, 1, 1, 0], dtype=np.int32)),
- (np.array([1, 2, 3], dtype=np.int64),
- 3,
- -9,
- np.array([3, 1, 1, -9], dtype=np.int64)),
- ]
- )
- def test_ediff1d_scalar_handling(self,
- ary,
- prepend,
- append,
- expected):
- # maintain backwards-compatibility
- # of scalar prepend / append behavior
- # in ediff1d following fix for gh-11490
- actual = np.ediff1d(ary=ary,
- to_end=append,
- to_begin=prepend)
- assert_equal(actual, expected)
- assert actual.dtype == expected.dtype
- def test_isin(self):
- # the tests for in1d cover most of isin's behavior
- # if in1d is removed, would need to change those tests to test
- # isin instead.
- def _isin_slow(a, b):
- b = np.asarray(b).flatten().tolist()
- return a in b
- isin_slow = np.vectorize(_isin_slow, otypes=[bool], excluded={1})
- def assert_isin_equal(a, b):
- x = isin(a, b)
- y = isin_slow(a, b)
- assert_array_equal(x, y)
- # multidimensional arrays in both arguments
- a = np.arange(24).reshape([2, 3, 4])
- b = np.array([[10, 20, 30], [0, 1, 3], [11, 22, 33]])
- assert_isin_equal(a, b)
- # array-likes as both arguments
- c = [(9, 8), (7, 6)]
- d = (9, 7)
- assert_isin_equal(c, d)
- # zero-d array:
- f = np.array(3)
- assert_isin_equal(f, b)
- assert_isin_equal(a, f)
- assert_isin_equal(f, f)
- # scalar:
- assert_isin_equal(5, b)
- assert_isin_equal(a, 6)
- assert_isin_equal(5, 6)
- # empty array-like:
- x = []
- assert_isin_equal(x, b)
- assert_isin_equal(a, x)
- assert_isin_equal(x, x)
- def test_in1d(self):
- # we use two different sizes for the b array here to test the
- # two different paths in in1d().
- for mult in (1, 10):
- # One check without np.array to make sure lists are handled correct
- a = [5, 7, 1, 2]
- b = [2, 4, 3, 1, 5] * mult
- ec = np.array([True, False, True, True])
- c = in1d(a, b, assume_unique=True)
- assert_array_equal(c, ec)
- a[0] = 8
- ec = np.array([False, False, True, True])
- c = in1d(a, b, assume_unique=True)
- assert_array_equal(c, ec)
- a[0], a[3] = 4, 8
- ec = np.array([True, False, True, False])
- c = in1d(a, b, assume_unique=True)
- assert_array_equal(c, ec)
- a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5])
- b = [2, 3, 4] * mult
- ec = [False, True, False, True, True, True, True, True, True,
- False, True, False, False, False]
- c = in1d(a, b)
- assert_array_equal(c, ec)
- b = b + [5, 5, 4] * mult
- ec = [True, True, True, True, True, True, True, True, True, True,
- True, False, True, True]
- c = in1d(a, b)
- assert_array_equal(c, ec)
- a = np.array([5, 7, 1, 2])
- b = np.array([2, 4, 3, 1, 5] * mult)
- ec = np.array([True, False, True, True])
- c = in1d(a, b)
- assert_array_equal(c, ec)
- a = np.array([5, 7, 1, 1, 2])
- b = np.array([2, 4, 3, 3, 1, 5] * mult)
- ec = np.array([True, False, True, True, True])
- c = in1d(a, b)
- assert_array_equal(c, ec)
- a = np.array([5, 5])
- b = np.array([2, 2] * mult)
- ec = np.array([False, False])
- c = in1d(a, b)
- assert_array_equal(c, ec)
- a = np.array([5])
- b = np.array([2])
- ec = np.array([False])
- c = in1d(a, b)
- assert_array_equal(c, ec)
- assert_array_equal(in1d([], []), [])
- def test_in1d_char_array(self):
- a = np.array(['a', 'b', 'c', 'd', 'e', 'c', 'e', 'b'])
- b = np.array(['a', 'c'])
- ec = np.array([True, False, True, False, False, True, False, False])
- c = in1d(a, b)
- assert_array_equal(c, ec)
- def test_in1d_invert(self):
- "Test in1d's invert parameter"
- # We use two different sizes for the b array here to test the
- # two different paths in in1d().
- for mult in (1, 10):
- a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5])
- b = [2, 3, 4] * mult
- assert_array_equal(np.invert(in1d(a, b)), in1d(a, b, invert=True))
- def test_in1d_ravel(self):
- # Test that in1d ravels its input arrays. This is not documented
- # behavior however. The test is to ensure consistentency.
- a = np.arange(6).reshape(2, 3)
- b = np.arange(3, 9).reshape(3, 2)
- long_b = np.arange(3, 63).reshape(30, 2)
- ec = np.array([False, False, False, True, True, True])
- assert_array_equal(in1d(a, b, assume_unique=True), ec)
- assert_array_equal(in1d(a, b, assume_unique=False), ec)
- assert_array_equal(in1d(a, long_b, assume_unique=True), ec)
- assert_array_equal(in1d(a, long_b, assume_unique=False), ec)
- def test_in1d_first_array_is_object(self):
- ar1 = [None]
- ar2 = np.array([1]*10)
- expected = np.array([False])
- result = np.in1d(ar1, ar2)
- assert_array_equal(result, expected)
- def test_in1d_second_array_is_object(self):
- ar1 = 1
- ar2 = np.array([None]*10)
- expected = np.array([False])
- result = np.in1d(ar1, ar2)
- assert_array_equal(result, expected)
- def test_in1d_both_arrays_are_object(self):
- ar1 = [None]
- ar2 = np.array([None]*10)
- expected = np.array([True])
- result = np.in1d(ar1, ar2)
- assert_array_equal(result, expected)
- def test_in1d_both_arrays_have_structured_dtype(self):
- # Test arrays of a structured data type containing an integer field
- # and a field of dtype `object` allowing for arbitrary Python objects
- dt = np.dtype([('field1', int), ('field2', object)])
- ar1 = np.array([(1, None)], dtype=dt)
- ar2 = np.array([(1, None)]*10, dtype=dt)
- expected = np.array([True])
- result = np.in1d(ar1, ar2)
- assert_array_equal(result, expected)
- def test_union1d(self):
- a = np.array([5, 4, 7, 1, 2])
- b = np.array([2, 4, 3, 3, 2, 1, 5])
- ec = np.array([1, 2, 3, 4, 5, 7])
- c = union1d(a, b)
- assert_array_equal(c, ec)
- # Tests gh-10340, arguments to union1d should be
- # flattened if they are not already 1D
- x = np.array([[0, 1, 2], [3, 4, 5]])
- y = np.array([0, 1, 2, 3, 4])
- ez = np.array([0, 1, 2, 3, 4, 5])
- z = union1d(x, y)
- assert_array_equal(z, ez)
- assert_array_equal([], union1d([], []))
- def test_setdiff1d(self):
- a = np.array([6, 5, 4, 7, 1, 2, 7, 4])
- b = np.array([2, 4, 3, 3, 2, 1, 5])
- ec = np.array([6, 7])
- c = setdiff1d(a, b)
- assert_array_equal(c, ec)
- a = np.arange(21)
- b = np.arange(19)
- ec = np.array([19, 20])
- c = setdiff1d(a, b)
- assert_array_equal(c, ec)
- assert_array_equal([], setdiff1d([], []))
- a = np.array((), np.uint32)
- assert_equal(setdiff1d(a, []).dtype, np.uint32)
- def test_setdiff1d_unique(self):
- a = np.array([3, 2, 1])
- b = np.array([7, 5, 2])
- expected = np.array([3, 1])
- actual = setdiff1d(a, b, assume_unique=True)
- assert_equal(actual, expected)
- def test_setdiff1d_char_array(self):
- a = np.array(['a', 'b', 'c'])
- b = np.array(['a', 'b', 's'])
- assert_array_equal(setdiff1d(a, b), np.array(['c']))
- def test_manyways(self):
- a = np.array([5, 7, 1, 2, 8])
- b = np.array([9, 8, 2, 4, 3, 1, 5])
- c1 = setxor1d(a, b)
- aux1 = intersect1d(a, b)
- aux2 = union1d(a, b)
- c2 = setdiff1d(aux2, aux1)
- assert_array_equal(c1, c2)
- class TestUnique:
- def test_unique_1d(self):
- def check_all(a, b, i1, i2, c, dt):
- base_msg = 'check {0} failed for type {1}'
- msg = base_msg.format('values', dt)
- v = unique(a)
- assert_array_equal(v, b, msg)
- msg = base_msg.format('return_index', dt)
- v, j = unique(a, True, False, False)
- assert_array_equal(v, b, msg)
- assert_array_equal(j, i1, msg)
- msg = base_msg.format('return_inverse', dt)
- v, j = unique(a, False, True, False)
- assert_array_equal(v, b, msg)
- assert_array_equal(j, i2, msg)
- msg = base_msg.format('return_counts', dt)
- v, j = unique(a, False, False, True)
- assert_array_equal(v, b, msg)
- assert_array_equal(j, c, msg)
- msg = base_msg.format('return_index and return_inverse', dt)
- v, j1, j2 = unique(a, True, True, False)
- assert_array_equal(v, b, msg)
- assert_array_equal(j1, i1, msg)
- assert_array_equal(j2, i2, msg)
- msg = base_msg.format('return_index and return_counts', dt)
- v, j1, j2 = unique(a, True, False, True)
- assert_array_equal(v, b, msg)
- assert_array_equal(j1, i1, msg)
- assert_array_equal(j2, c, msg)
- msg = base_msg.format('return_inverse and return_counts', dt)
- v, j1, j2 = unique(a, False, True, True)
- assert_array_equal(v, b, msg)
- assert_array_equal(j1, i2, msg)
- assert_array_equal(j2, c, msg)
- msg = base_msg.format(('return_index, return_inverse '
- 'and return_counts'), dt)
- v, j1, j2, j3 = unique(a, True, True, True)
- assert_array_equal(v, b, msg)
- assert_array_equal(j1, i1, msg)
- assert_array_equal(j2, i2, msg)
- assert_array_equal(j3, c, msg)
- a = [5, 7, 1, 2, 1, 5, 7]*10
- b = [1, 2, 5, 7]
- i1 = [2, 3, 0, 1]
- i2 = [2, 3, 0, 1, 0, 2, 3]*10
- c = np.multiply([2, 1, 2, 2], 10)
- # test for numeric arrays
- types = []
- types.extend(np.typecodes['AllInteger'])
- types.extend(np.typecodes['AllFloat'])
- types.append('datetime64[D]')
- types.append('timedelta64[D]')
- for dt in types:
- aa = np.array(a, dt)
- bb = np.array(b, dt)
- check_all(aa, bb, i1, i2, c, dt)
- # test for object arrays
- dt = 'O'
- aa = np.empty(len(a), dt)
- aa[:] = a
- bb = np.empty(len(b), dt)
- bb[:] = b
- check_all(aa, bb, i1, i2, c, dt)
- # test for structured arrays
- dt = [('', 'i'), ('', 'i')]
- aa = np.array(list(zip(a, a)), dt)
- bb = np.array(list(zip(b, b)), dt)
- check_all(aa, bb, i1, i2, c, dt)
- # test for ticket #2799
- aa = [1. + 0.j, 1 - 1.j, 1]
- assert_array_equal(np.unique(aa), [1. - 1.j, 1. + 0.j])
- # test for ticket #4785
- a = [(1, 2), (1, 2), (2, 3)]
- unq = [1, 2, 3]
- inv = [0, 1, 0, 1, 1, 2]
- a1 = unique(a)
- assert_array_equal(a1, unq)
- a2, a2_inv = unique(a, return_inverse=True)
- assert_array_equal(a2, unq)
- assert_array_equal(a2_inv, inv)
- # test for chararrays with return_inverse (gh-5099)
- a = np.chararray(5)
- a[...] = ''
- a2, a2_inv = np.unique(a, return_inverse=True)
- assert_array_equal(a2_inv, np.zeros(5))
- # test for ticket #9137
- a = []
- a1_idx = np.unique(a, return_index=True)[1]
- a2_inv = np.unique(a, return_inverse=True)[1]
- a3_idx, a3_inv = np.unique(a, return_index=True,
- return_inverse=True)[1:]
- assert_equal(a1_idx.dtype, np.intp)
- assert_equal(a2_inv.dtype, np.intp)
- assert_equal(a3_idx.dtype, np.intp)
- assert_equal(a3_inv.dtype, np.intp)
- def test_unique_axis_errors(self):
- assert_raises(TypeError, self._run_axis_tests, object)
- assert_raises(TypeError, self._run_axis_tests,
- [('a', int), ('b', object)])
- assert_raises(np.AxisError, unique, np.arange(10), axis=2)
- assert_raises(np.AxisError, unique, np.arange(10), axis=-2)
- def test_unique_axis_list(self):
- msg = "Unique failed on list of lists"
- inp = [[0, 1, 0], [0, 1, 0]]
- inp_arr = np.asarray(inp)
- assert_array_equal(unique(inp, axis=0), unique(inp_arr, axis=0), msg)
- assert_array_equal(unique(inp, axis=1), unique(inp_arr, axis=1), msg)
- def test_unique_axis(self):
- types = []
- types.extend(np.typecodes['AllInteger'])
- types.extend(np.typecodes['AllFloat'])
- types.append('datetime64[D]')
- types.append('timedelta64[D]')
- types.append([('a', int), ('b', int)])
- types.append([('a', int), ('b', float)])
- for dtype in types:
- self._run_axis_tests(dtype)
- msg = 'Non-bitwise-equal booleans test failed'
- data = np.arange(10, dtype=np.uint8).reshape(-1, 2).view(bool)
- result = np.array([[False, True], [True, True]], dtype=bool)
- assert_array_equal(unique(data, axis=0), result, msg)
- msg = 'Negative zero equality test failed'
- data = np.array([[-0.0, 0.0], [0.0, -0.0], [-0.0, 0.0], [0.0, -0.0]])
- result = np.array([[-0.0, 0.0]])
- assert_array_equal(unique(data, axis=0), result, msg)
- @pytest.mark.parametrize("axis", [0, -1])
- def test_unique_1d_with_axis(self, axis):
- x = np.array([4, 3, 2, 3, 2, 1, 2, 2])
- uniq = unique(x, axis=axis)
- assert_array_equal(uniq, [1, 2, 3, 4])
- def test_unique_axis_zeros(self):
- # issue 15559
- single_zero = np.empty(shape=(2, 0), dtype=np.int8)
- uniq, idx, inv, cnt = unique(single_zero, axis=0, return_index=True,
- return_inverse=True, return_counts=True)
- # there's 1 element of shape (0,) along axis 0
- assert_equal(uniq.dtype, single_zero.dtype)
- assert_array_equal(uniq, np.empty(shape=(1, 0)))
- assert_array_equal(idx, np.array([0]))
- assert_array_equal(inv, np.array([0, 0]))
- assert_array_equal(cnt, np.array([2]))
- # there's 0 elements of shape (2,) along axis 1
- uniq, idx, inv, cnt = unique(single_zero, axis=1, return_index=True,
- return_inverse=True, return_counts=True)
- assert_equal(uniq.dtype, single_zero.dtype)
- assert_array_equal(uniq, np.empty(shape=(2, 0)))
- assert_array_equal(idx, np.array([]))
- assert_array_equal(inv, np.array([]))
- assert_array_equal(cnt, np.array([]))
- # test a "complicated" shape
- shape = (0, 2, 0, 3, 0, 4, 0)
- multiple_zeros = np.empty(shape=shape)
- for axis in range(len(shape)):
- expected_shape = list(shape)
- if shape[axis] == 0:
- expected_shape[axis] = 0
- else:
- expected_shape[axis] = 1
- assert_array_equal(unique(multiple_zeros, axis=axis),
- np.empty(shape=expected_shape))
- def test_unique_masked(self):
- # issue 8664
- x = np.array([64, 0, 1, 2, 3, 63, 63, 0, 0, 0, 1, 2, 0, 63, 0],
- dtype='uint8')
- y = np.ma.masked_equal(x, 0)
- v = np.unique(y)
- v2, i, c = np.unique(y, return_index=True, return_counts=True)
- msg = 'Unique returned different results when asked for index'
- assert_array_equal(v.data, v2.data, msg)
- assert_array_equal(v.mask, v2.mask, msg)
- def test_unique_sort_order_with_axis(self):
- # These tests fail if sorting along axis is done by treating subarrays
- # as unsigned byte strings. See gh-10495.
- fmt = "sort order incorrect for integer type '%s'"
- for dt in 'bhilq':
- a = np.array([[-1], [0]], dt)
- b = np.unique(a, axis=0)
- assert_array_equal(a, b, fmt % dt)
- def _run_axis_tests(self, dtype):
- data = np.array([[0, 1, 0, 0],
- [1, 0, 0, 0],
- [0, 1, 0, 0],
- [1, 0, 0, 0]]).astype(dtype)
- msg = 'Unique with 1d array and axis=0 failed'
- result = np.array([0, 1])
- assert_array_equal(unique(data), result.astype(dtype), msg)
- msg = 'Unique with 2d array and axis=0 failed'
- result = np.array([[0, 1, 0, 0], [1, 0, 0, 0]])
- assert_array_equal(unique(data, axis=0), result.astype(dtype), msg)
- msg = 'Unique with 2d array and axis=1 failed'
- result = np.array([[0, 0, 1], [0, 1, 0], [0, 0, 1], [0, 1, 0]])
- assert_array_equal(unique(data, axis=1), result.astype(dtype), msg)
- msg = 'Unique with 3d array and axis=2 failed'
- data3d = np.array([[[1, 1],
- [1, 0]],
- [[0, 1],
- [0, 0]]]).astype(dtype)
- result = np.take(data3d, [1, 0], axis=2)
- assert_array_equal(unique(data3d, axis=2), result, msg)
- uniq, idx, inv, cnt = unique(data, axis=0, return_index=True,
- return_inverse=True, return_counts=True)
- msg = "Unique's return_index=True failed with axis=0"
- assert_array_equal(data[idx], uniq, msg)
- msg = "Unique's return_inverse=True failed with axis=0"
- assert_array_equal(uniq[inv], data)
- msg = "Unique's return_counts=True failed with axis=0"
- assert_array_equal(cnt, np.array([2, 2]), msg)
- uniq, idx, inv, cnt = unique(data, axis=1, return_index=True,
- return_inverse=True, return_counts=True)
- msg = "Unique's return_index=True failed with axis=1"
- assert_array_equal(data[:, idx], uniq)
- msg = "Unique's return_inverse=True failed with axis=1"
- assert_array_equal(uniq[:, inv], data)
- msg = "Unique's return_counts=True failed with axis=1"
- assert_array_equal(cnt, np.array([2, 1, 1]), msg)
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