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Editing: test_shape_base.py
import pytest import numpy as np from numpy.core import ( array, arange, atleast_1d, atleast_2d, atleast_3d, block, vstack, hstack, newaxis, concatenate, stack ) from numpy.core.shape_base import (_block_dispatcher, _block_setup, _block_concatenate, _block_slicing) from numpy.testing import ( assert_, assert_raises, assert_array_equal, assert_equal, assert_raises_regex, assert_warns, IS_PYPY ) class TestAtleast1d: def test_0D_array(self): a = array(1) b = array(2) res = [atleast_1d(a), atleast_1d(b)] desired = [array([1]), array([2])] assert_array_equal(res, desired) def test_1D_array(self): a = array([1, 2]) b = array([2, 3]) res = [atleast_1d(a), atleast_1d(b)] desired = [array([1, 2]), array([2, 3])] assert_array_equal(res, desired) def test_2D_array(self): a = array([[1, 2], [1, 2]]) b = array([[2, 3], [2, 3]]) res = [atleast_1d(a), atleast_1d(b)] desired = [a, b] assert_array_equal(res, desired) def test_3D_array(self): a = array([[1, 2], [1, 2]]) b = array([[2, 3], [2, 3]]) a = array([a, a]) b = array([b, b]) res = [atleast_1d(a), atleast_1d(b)] desired = [a, b] assert_array_equal(res, desired) def test_r1array(self): """ Test to make sure equivalent Travis O's r1array function """ assert_(atleast_1d(3).shape == (1,)) assert_(atleast_1d(3j).shape == (1,)) assert_(atleast_1d(3.0).shape == (1,)) assert_(atleast_1d([[2, 3], [4, 5]]).shape == (2, 2)) class TestAtleast2d: def test_0D_array(self): a = array(1) b = array(2) res = [atleast_2d(a), atleast_2d(b)] desired = [array([[1]]), array([[2]])] assert_array_equal(res, desired) def test_1D_array(self): a = array([1, 2]) b = array([2, 3]) res = [atleast_2d(a), atleast_2d(b)] desired = [array([[1, 2]]), array([[2, 3]])] assert_array_equal(res, desired) def test_2D_array(self): a = array([[1, 2], [1, 2]]) b = array([[2, 3], [2, 3]]) res = [atleast_2d(a), atleast_2d(b)] desired = [a, b] assert_array_equal(res, desired) def test_3D_array(self): a = array([[1, 2], [1, 2]]) b = array([[2, 3], [2, 3]]) a = array([a, a]) b = array([b, b]) res = [atleast_2d(a), atleast_2d(b)] desired = [a, b] assert_array_equal(res, desired) def test_r2array(self): """ Test to make sure equivalent Travis O's r2array function """ assert_(atleast_2d(3).shape == (1, 1)) assert_(atleast_2d([3j, 1]).shape == (1, 2)) assert_(atleast_2d([[[3, 1], [4, 5]], [[3, 5], [1, 2]]]).shape == (2, 2, 2)) class TestAtleast3d: def test_0D_array(self): a = array(1) b = array(2) res = [atleast_3d(a), atleast_3d(b)] desired = [array([[[1]]]), array([[[2]]])] assert_array_equal(res, desired) def test_1D_array(self): a = array([1, 2]) b = array([2, 3]) res = [atleast_3d(a), atleast_3d(b)] desired = [array([[[1], [2]]]), array([[[2], [3]]])] assert_array_equal(res, desired) def test_2D_array(self): a = array([[1, 2], [1, 2]]) b = array([[2, 3], [2, 3]]) res = [atleast_3d(a), atleast_3d(b)] desired = [a[:,:, newaxis], b[:,:, newaxis]] assert_array_equal(res, desired) def test_3D_array(self): a = array([[1, 2], [1, 2]]) b = array([[2, 3], [2, 3]]) a = array([a, a]) b = array([b, b]) res = [atleast_3d(a), atleast_3d(b)] desired = [a, b] assert_array_equal(res, desired) class TestHstack: def test_non_iterable(self): assert_raises(TypeError, hstack, 1) def test_empty_input(self): assert_raises(ValueError, hstack, ()) def test_0D_array(self): a = array(1) b = array(2) res = hstack([a, b]) desired = array([1, 2]) assert_array_equal(res, desired) def test_1D_array(self): a = array([1]) b = array([2]) res = hstack([a, b]) desired = array([1, 2]) assert_array_equal(res, desired) def test_2D_array(self): a = array([[1], [2]]) b = array([[1], [2]]) res = hstack([a, b]) desired = array([[1, 1], [2, 2]]) assert_array_equal(res, desired) def test_generator(self): with pytest.raises(TypeError, match="arrays to stack must be"): hstack((np.arange(3) for _ in range(2))) with pytest.raises(TypeError, match="arrays to stack must be"): hstack(map(lambda x: x, np.ones((3, 2)))) def test_casting_and_dtype(self): a = np.array([1, 2, 3]) b = np.array([2.5, 3.5, 4.5]) res = np.hstack((a, b), casting="unsafe", dtype=np.int64) expected_res = np.array([1, 2, 3, 2, 3, 4]) assert_array_equal(res, expected_res) def test_casting_and_dtype_type_error(self): a = np.array([1, 2, 3]) b = np.array([2.5, 3.5, 4.5]) with pytest.raises(TypeError): hstack((a, b), casting="safe", dtype=np.int64) class TestVstack: def test_non_iterable(self): assert_raises(TypeError, vstack, 1) def test_empty_input(self): assert_raises(ValueError, vstack, ()) def test_0D_array(self): a = array(1) b = array(2) res = vstack([a, b]) desired = array([[1], [2]]) assert_array_equal(res, desired) def test_1D_array(self): a = array([1]) b = array([2]) res = vstack([a, b]) desired = array([[1], [2]]) assert_array_equal(res, desired) def test_2D_array(self): a = array([[1], [2]]) b = array([[1], [2]]) res = vstack([a, b]) desired = array([[1], [2], [1], [2]]) assert_array_equal(res, desired) def test_2D_array2(self): a = array([1, 2]) b = array([1, 2]) res = vstack([a, b]) desired = array([[1, 2], [1, 2]]) assert_array_equal(res, desired) def test_generator(self): with pytest.raises(TypeError, match="arrays to stack must be"): vstack((np.arange(3) for _ in range(2))) def test_casting_and_dtype(self): a = np.array([1, 2, 3]) b = np.array([2.5, 3.5, 4.5]) res = np.vstack((a, b), casting="unsafe", dtype=np.int64) expected_res = np.array([[1, 2, 3], [2, 3, 4]]) assert_array_equal(res, expected_res) def test_casting_and_dtype_type_error(self): a = np.array([1, 2, 3]) b = np.array([2.5, 3.5, 4.5]) with pytest.raises(TypeError): vstack((a, b), casting="safe", dtype=np.int64) class TestConcatenate: def test_returns_copy(self): a = np.eye(3) b = np.concatenate([a]) b[0, 0] = 2 assert b[0, 0] != a[0, 0] def test_exceptions(self): # test axis must be in bounds for ndim in [1, 2, 3]: a = np.ones((1,)*ndim) np.concatenate((a, a), axis=0) # OK assert_raises(np.AxisError, np.concatenate, (a, a), axis=ndim) assert_raises(np.AxisError, np.concatenate, (a, a), axis=-(ndim + 1)) # Scalars cannot be concatenated assert_raises(ValueError, concatenate, (0,)) assert_raises(ValueError, concatenate, (np.array(0),)) # dimensionality must match assert_raises_regex( ValueError, r"all the input arrays must have same number of dimensions, but " r"the array at index 0 has 1 dimension\(s\) and the array at " r"index 1 has 2 dimension\(s\)", np.concatenate, (np.zeros(1), np.zeros((1, 1)))) # test shapes must match except for concatenation axis a = np.ones((1, 2, 3)) b = np.ones((2, 2, 3)) axis = list(range(3)) for i in range(3): np.concatenate((a, b), axis=axis[0]) # OK assert_raises_regex( ValueError, "all the input array dimensions except for the concatenation axis " "must match exactly, but along dimension {}, the array at " "index 0 has size 1 and the array at index 1 has size 2" .format(i), np.concatenate, (a, b), axis=axis[1]) assert_raises(ValueError, np.concatenate, (a, b), axis=axis[2]) a = np.moveaxis(a, -1, 0) b = np.moveaxis(b, -1, 0) axis.append(axis.pop(0)) # No arrays to concatenate raises ValueError assert_raises(ValueError, concatenate, ()) def test_concatenate_axis_None(self): a = np.arange(4, dtype=np.float64).reshape((2, 2)) b = list(range(3)) c = ['x'] r = np.concatenate((a, a), axis=None) assert_equal(r.dtype, a.dtype) assert_equal(r.ndim, 1) r = np.concatenate((a, b), axis=None) assert_equal(r.size, a.size + len(b)) assert_equal(r.dtype, a.dtype) r = np.concatenate((a, b, c), axis=None, dtype="U") d = array(['0.0', '1.0', '2.0', '3.0', '0', '1', '2', 'x']) assert_array_equal(r, d) out = np.zeros(a.size + len(b)) r = np.concatenate((a, b), axis=None) rout = np.concatenate((a, b), axis=None, out=out) assert_(out is rout) assert_equal(r, rout) def test_large_concatenate_axis_None(self): # When no axis is given, concatenate uses flattened versions. # This also had a bug with many arrays (see gh-5979). x = np.arange(1, 100) r = np.concatenate(x, None) assert_array_equal(x, r) # This should probably be deprecated: r = np.concatenate(x, 100) # axis is >= MAXDIMS assert_array_equal(x, r) def test_concatenate(self): # Test concatenate function # One sequence returns unmodified (but as array) r4 = list(range(4)) assert_array_equal(concatenate((r4,)), r4) # Any sequence assert_array_equal(concatenate((tuple(r4),)), r4) assert_array_equal(concatenate((array(r4),)), r4) # 1D default concatenation r3 = list(range(3)) assert_array_equal(concatenate((r4, r3)), r4 + r3) # Mixed sequence types assert_array_equal(concatenate((tuple(r4), r3)), r4 + r3) assert_array_equal(concatenate((array(r4), r3)), r4 + r3) # Explicit axis specification assert_array_equal(concatenate((r4, r3), 0), r4 + r3) # Including negative assert_array_equal(concatenate((r4, r3), -1), r4 + r3) # 2D a23 = array([[10, 11, 12], [13, 14, 15]]) a13 = array([[0, 1, 2]]) res = array([[10, 11, 12], [13, 14, 15], [0, 1, 2]]) assert_array_equal(concatenate((a23, a13)), res) assert_array_equal(concatenate((a23, a13), 0), res) assert_array_equal(concatenate((a23.T, a13.T), 1), res.T) assert_array_equal(concatenate((a23.T, a13.T), -1), res.T) # Arrays much match shape assert_raises(ValueError, concatenate, (a23.T, a13.T), 0) # 3D res = arange(2 * 3 * 7).reshape((2, 3, 7)) a0 = res[..., :4] a1 = res[..., 4:6] a2 = res[..., 6:] assert_array_equal(concatenate((a0, a1, a2), 2), res) assert_array_equal(concatenate((a0, a1, a2), -1), res) assert_array_equal(concatenate((a0.T, a1.T, a2.T), 0), res.T) out = res.copy() rout = concatenate((a0, a1, a2), 2, out=out) assert_(out is rout) assert_equal(res, rout) @pytest.mark.skipif(IS_PYPY, reason="PYPY handles sq_concat, nb_add differently than cpython") def test_operator_concat(self): import operator a = array([1, 2]) b = array([3, 4]) n = [1,2] res = array([1, 2, 3, 4]) assert_raises(TypeError, operator.concat, a, b) assert_raises(TypeError, operator.concat, a, n) assert_raises(TypeError, operator.concat, n, a) assert_raises(TypeError, operator.concat, a, 1) assert_raises(TypeError, operator.concat, 1, a) def test_bad_out_shape(self): a = array([1, 2]) b = array([3, 4]) assert_raises(ValueError, concatenate, (a, b), out=np.empty(5)) assert_raises(ValueError, concatenate, (a, b), out=np.empty((4,1))) assert_raises(ValueError, concatenate, (a, b), out=np.empty((1,4))) concatenate((a, b), out=np.empty(4)) @pytest.mark.parametrize("axis", [None, 0]) @pytest.mark.parametrize("out_dtype", ["c8", "f4", "f8", ">f8", "i8", "S4"]) @pytest.mark.parametrize("casting", ['no', 'equiv', 'safe', 'same_kind', 'unsafe']) def test_out_and_dtype(self, axis, out_dtype, casting): # Compare usage of `out=out` with `dtype=out.dtype` out = np.empty(4, dtype=out_dtype) to_concat = (array([1.1, 2.2]), array([3.3, 4.4])) if not np.can_cast(to_concat[0], out_dtype, casting=casting): with assert_raises(TypeError): concatenate(to_concat, out=out, axis=axis, casting=casting) with assert_raises(TypeError): concatenate(to_concat, dtype=out.dtype, axis=axis, casting=casting) else: res_out = concatenate(to_concat, out=out, axis=axis, casting=casting) res_dtype = concatenate(to_concat, dtype=out.dtype, axis=axis, casting=casting) assert res_out is out assert_array_equal(out, res_dtype) assert res_dtype.dtype == out_dtype with assert_raises(TypeError): concatenate(to_concat, out=out, dtype=out_dtype, axis=axis) @pytest.mark.parametrize("axis", [None, 0]) @pytest.mark.parametrize("string_dt", ["S", "U", "S0", "U0"]) @pytest.mark.parametrize("arrs", [([0.],), ([0.], [1]), ([0], ["string"], [1.])]) def test_dtype_with_promotion(self, arrs, string_dt, axis): # Note that U0 and S0 should be deprecated eventually and changed to # actually give the empty string result (together with `np.array`) res = np.concatenate(arrs, axis=axis, dtype=string_dt, casting="unsafe") # The actual dtype should be identical to a cast (of a double array): assert res.dtype == np.array(1.).astype(string_dt).dtype @pytest.mark.parametrize("axis", [None, 0]) def test_string_dtype_does_not_inspect(self, axis): with pytest.raises(TypeError): np.concatenate(([None], [1]), dtype="S", axis=axis) with pytest.raises(TypeError): np.concatenate(([None], [1]), dtype="U", axis=axis) @pytest.mark.parametrize("axis", [None, 0]) def test_subarray_error(self, axis): with pytest.raises(TypeError, match=".*subarray dtype"): np.concatenate(([1], [1]), dtype="(2,)i", axis=axis) def test_stack(): # non-iterable input assert_raises(TypeError, stack, 1) # 0d input for input_ in [(1, 2, 3), [np.int32(1), np.int32(2), np.int32(3)], [np.array(1), np.array(2), np.array(3)]]: assert_array_equal(stack(input_), [1, 2, 3]) # 1d input examples a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) r1 = array([[1, 2, 3], [4, 5, 6]]) assert_array_equal(np.stack((a, b)), r1) assert_array_equal(np.stack((a, b), axis=1), r1.T) # all input types assert_array_equal(np.stack(list([a, b])), r1) assert_array_equal(np.stack(array([a, b])), r1) # all shapes for 1d input arrays = [np.random.randn(3) for _ in range(10)] axes = [0, 1, -1, -2] expected_shapes = [(10, 3), (3, 10), (3, 10), (10, 3)] for axis, expected_shape in zip(axes, expected_shapes): assert_equal(np.stack(arrays, axis).shape, expected_shape) assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=2) assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=-3) # all shapes for 2d input arrays = [np.random.randn(3, 4) for _ in range(10)] axes = [0, 1, 2, -1, -2, -3] expected_shapes = [(10, 3, 4), (3, 10, 4), (3, 4, 10), (3, 4, 10), (3, 10, 4), (10, 3, 4)] for axis, expected_shape in zip(axes, expected_shapes): assert_equal(np.stack(arrays, axis).shape, expected_shape) # empty arrays assert_(stack([[], [], []]).shape == (3, 0)) assert_(stack([[], [], []], axis=1).shape == (0, 3)) # out out = np.zeros_like(r1) np.stack((a, b), out=out) assert_array_equal(out, r1) # edge cases assert_raises_regex(ValueError, 'need at least one array', stack, []) assert_raises_regex(ValueError, 'must have the same shape', stack, [1, np.arange(3)]) assert_raises_regex(ValueError, 'must have the same shape', stack, [np.arange(3), 1]) assert_raises_regex(ValueError, 'must have the same shape', stack, [np.arange(3), 1], axis=1) assert_raises_regex(ValueError, 'must have the same shape', stack, [np.zeros((3, 3)), np.zeros(3)], axis=1) assert_raises_regex(ValueError, 'must have the same shape', stack, [np.arange(2), np.arange(3)]) # do not accept generators with pytest.raises(TypeError, match="arrays to stack must be"): stack((x for x in range(3))) #casting and dtype test a = np.array([1, 2, 3]) b = np.array([2.5, 3.5, 4.5]) res = np.stack((a, b), axis=1, casting="unsafe", dtype=np.int64) expected_res = np.array([[1, 2], [2, 3], [3, 4]]) assert_array_equal(res, expected_res) #casting and dtype with TypeError with assert_raises(TypeError): stack((a, b), dtype=np.int64, axis=1, casting="safe") @pytest.mark.parametrize("axis", [0]) @pytest.mark.parametrize("out_dtype", ["c8", "f4", "f8", ">f8", "i8"]) @pytest.mark.parametrize("casting", ['no', 'equiv', 'safe', 'same_kind', 'unsafe']) def test_stack_out_and_dtype(axis, out_dtype, casting): to_concat = (array([1, 2]), array([3, 4])) res = array([[1, 2], [3, 4]]) out = np.zeros_like(res) if not np.can_cast(to_concat[0], out_dtype, casting=casting): with assert_raises(TypeError): stack(to_concat, dtype=out_dtype, axis=axis, casting=casting) else: res_out = stack(to_concat, out=out, axis=axis, casting=casting) res_dtype = stack(to_concat, dtype=out_dtype, axis=axis, casting=casting) assert res_out is out assert_array_equal(out, res_dtype) assert res_dtype.dtype == out_dtype with assert_raises(TypeError): stack(to_concat, out=out, dtype=out_dtype, axis=axis) class TestBlock: @pytest.fixture(params=['block', 'force_concatenate', 'force_slicing']) def block(self, request): # blocking small arrays and large arrays go through different paths. # the algorithm is triggered depending on the number of element # copies required. # We define a test fixture that forces most tests to go through # both code paths. # Ultimately, this should be removed if a single algorithm is found # to be faster for both small and large arrays. def _block_force_concatenate(arrays): arrays, list_ndim, result_ndim, _ = _block_setup(arrays) return _block_concatenate(arrays, list_ndim, result_ndim) def _block_force_slicing(arrays): arrays, list_ndim, result_ndim, _ = _block_setup(arrays) return _block_slicing(arrays, list_ndim, result_ndim) if request.param == 'force_concatenate': return _block_force_concatenate elif request.param == 'force_slicing': return _block_force_slicing elif request.param == 'block': return block else: raise ValueError('Unknown blocking request. There is a typo in the tests.') def test_returns_copy(self, block): a = np.eye(3) b = block(a) b[0, 0] = 2 assert b[0, 0] != a[0, 0] def test_block_total_size_estimate(self, block): _, _, _, total_size = _block_setup([1]) assert total_size == 1 _, _, _, total_size = _block_setup([[1]]) assert total_size == 1 _, _, _, total_size = _block_setup([[1, 1]]) assert total_size == 2 _, _, _, total_size = _block_setup([[1], [1]]) assert total_size == 2 _, _, _, total_size = _block_setup([[1, 2], [3, 4]]) assert total_size == 4 def test_block_simple_row_wise(self, block): a_2d = np.ones((2, 2)) b_2d = 2 * a_2d desired = np.array([[1, 1, 2, 2], [1, 1, 2, 2]]) result = block([a_2d, b_2d]) assert_equal(desired, result) def test_block_simple_column_wise(self, block): a_2d = np.ones((2, 2)) b_2d = 2 * a_2d expected = np.array([[1, 1], [1, 1], [2, 2], [2, 2]]) result = block([[a_2d], [b_2d]]) assert_equal(expected, result) def test_block_with_1d_arrays_row_wise(self, block): # # # 1-D vectors are treated as row arrays a = np.array([1, 2, 3]) b = np.array([2, 3, 4]) expected = np.array([1, 2, 3, 2, 3, 4]) result = block([a, b]) assert_equal(expected, result) def test_block_with_1d_arrays_multiple_rows(self, block): a = np.array([1, 2, 3]) b = np.array([2, 3, 4]) expected = np.array([[1, 2, 3, 2, 3, 4], [1, 2, 3, 2, 3, 4]]) result = block([[a, b], [a, b]]) assert_equal(expected, result) def test_block_with_1d_arrays_column_wise(self, block): # # # 1-D vectors are treated as row arrays a_1d = np.array([1, 2, 3]) b_1d = np.array([2, 3, 4]) expected = np.array([[1, 2, 3], [2, 3, 4]]) result = block([[a_1d], [b_1d]]) assert_equal(expected, result) def test_block_mixed_1d_and_2d(self, block): a_2d = np.ones((2, 2)) b_1d = np.array([2, 2]) result = block([[a_2d], [b_1d]]) expected = np.array([[1, 1], [1, 1], [2, 2]]) assert_equal(expected, result) def test_block_complicated(self, block): # a bit more complicated one_2d = np.array([[1, 1, 1]]) two_2d = np.array([[2, 2, 2]]) three_2d = np.array([[3, 3, 3, 3, 3, 3]]) four_1d = np.array([4, 4, 4, 4, 4, 4]) five_0d = np.array(5) six_1d = np.array([6, 6, 6, 6, 6]) zero_2d = np.zeros((2, 6)) expected = np.array([[1, 1, 1, 2, 2, 2], [3, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4], [5, 6, 6, 6, 6, 6], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) result = block([[one_2d, two_2d], [three_2d], [four_1d], [five_0d, six_1d], [zero_2d]]) assert_equal(result, expected) def test_nested(self, block): one = np.array([1, 1, 1]) two = np.array([[2, 2, 2], [2, 2, 2], [2, 2, 2]]) three = np.array([3, 3, 3]) four = np.array([4, 4, 4]) five = np.array(5) six = np.array([6, 6, 6, 6, 6]) zero = np.zeros((2, 6)) result = block([ [ block([ [one], [three], [four] ]), two ], [five, six], [zero] ]) expected = np.array([[1, 1, 1, 2, 2, 2], [3, 3, 3, 2, 2, 2], [4, 4, 4, 2, 2, 2], [5, 6, 6, 6, 6, 6], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) assert_equal(result, expected) def test_3d(self, block): a000 = np.ones((2, 2, 2), int) * 1 a100 = np.ones((3, 2, 2), int) * 2 a010 = np.ones((2, 3, 2), int) * 3 a001 = np.ones((2, 2, 3), int) * 4 a011 = np.ones((2, 3, 3), int) * 5 a101 = np.ones((3, 2, 3), int) * 6 a110 = np.ones((3, 3, 2), int) * 7 a111 = np.ones((3, 3, 3), int) * 8 result = block([ [ [a000, a001], [a010, a011], ], [ [a100, a101], [a110, a111], ] ]) expected = array([[[1, 1, 4, 4, 4], [1, 1, 4, 4, 4], [3, 3, 5, 5, 5], [3, 3, 5, 5, 5], [3, 3, 5, 5, 5]], [[1, 1, 4, 4, 4], [1, 1, 4, 4, 4], [3, 3, 5, 5, 5], [3, 3, 5, 5, 5], [3, 3, 5, 5, 5]], [[2, 2, 6, 6, 6], [2, 2, 6, 6, 6], [7, 7, 8, 8, 8], [7, 7, 8, 8, 8], [7, 7, 8, 8, 8]], [[2, 2, 6, 6, 6], [2, 2, 6, 6, 6], [7, 7, 8, 8, 8], [7, 7, 8, 8, 8], [7, 7, 8, 8, 8]], [[2, 2, 6, 6, 6], [2, 2, 6, 6, 6], [7, 7, 8, 8, 8], [7, 7, 8, 8, 8], [7, 7, 8, 8, 8]]]) assert_array_equal(result, expected) def test_block_with_mismatched_shape(self, block): a = np.array([0, 0]) b = np.eye(2) assert_raises(ValueError, block, [a, b]) assert_raises(ValueError, block, [b, a]) to_block = [[np.ones((2,3)), np.ones((2,2))], [np.ones((2,2)), np.ones((2,2))]] assert_raises(ValueError, block, to_block) def test_no_lists(self, block): assert_equal(block(1), np.array(1)) assert_equal(block(np.eye(3)), np.eye(3)) def test_invalid_nesting(self, block): msg = 'depths are mismatched' assert_raises_regex(ValueError, msg, block, [1, [2]]) assert_raises_regex(ValueError, msg, block, [1, []]) assert_raises_regex(ValueError, msg, block, [[1], 2]) assert_raises_regex(ValueError, msg, block, [[], 2]) assert_raises_regex(ValueError, msg, block, [ [[1], [2]], [[3, 4]], [5] # missing brackets ]) def test_empty_lists(self, block): assert_raises_regex(ValueError, 'empty', block, []) assert_raises_regex(ValueError, 'empty', block, [[]]) assert_raises_regex(ValueError, 'empty', block, [[1], []]) def test_tuple(self, block): assert_raises_regex(TypeError, 'tuple', block, ([1, 2], [3, 4])) assert_raises_regex(TypeError, 'tuple', block, [(1, 2), (3, 4)]) def test_different_ndims(self, block): a = 1. b = 2 * np.ones((1, 2)) c = 3 * np.ones((1, 1, 3)) result = block([a, b, c]) expected = np.array([[[1., 2., 2., 3., 3., 3.]]]) assert_equal(result, expected) def test_different_ndims_depths(self, block): a = 1. b = 2 * np.ones((1, 2)) c = 3 * np.ones((1, 2, 3)) result = block([[a, b], [c]]) expected = np.array([[[1., 2., 2.], [3., 3., 3.], [3., 3., 3.]]]) assert_equal(result, expected) def test_block_memory_order(self, block): # 3D arr_c = np.zeros((3,)*3, order='C') arr_f = np.zeros((3,)*3, order='F') b_c = [[[arr_c, arr_c], [arr_c, arr_c]], [[arr_c, arr_c], [arr_c, arr_c]]] b_f = [[[arr_f, arr_f], [arr_f, arr_f]], [[arr_f, arr_f], [arr_f, arr_f]]] assert block(b_c).flags['C_CONTIGUOUS'] assert block(b_f).flags['F_CONTIGUOUS'] arr_c = np.zeros((3, 3), order='C') arr_f = np.zeros((3, 3), order='F') # 2D b_c = [[arr_c, arr_c], [arr_c, arr_c]] b_f = [[arr_f, arr_f], [arr_f, arr_f]] assert block(b_c).flags['C_CONTIGUOUS'] assert block(b_f).flags['F_CONTIGUOUS'] def test_block_dispatcher(): class ArrayLike: pass a = ArrayLike() b = ArrayLike() c = ArrayLike() assert_equal(list(_block_dispatcher(a)), [a]) assert_equal(list(_block_dispatcher([a])), [a]) assert_equal(list(_block_dispatcher([a, b])), [a, b]) assert_equal(list(_block_dispatcher([[a], [b, [c]]])), [a, b, c]) # don't recurse into non-lists assert_equal(list(_block_dispatcher((a, b))), [(a, b)])
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