def test_decompose_indexers(shape, indexer_mode, indexing_support): data = np.random.randn(*shape) indexer = get_indexers(shape, indexer_mode) backend_ind, np_ind = indexing.decompose_indexer(indexer, shape, indexing_support) expected = indexing.NumpyIndexingAdapter(data)[indexer] array = indexing.NumpyIndexingAdapter(data)[backend_ind] if len(np_ind.tuple) > 0: array = indexing.NumpyIndexingAdapter(array)[np_ind] np.testing.assert_array_equal(expected, array) if not all(isinstance(k, indexing.integer_types) for k in np_ind.tuple): combined_ind = indexing._combine_indexers(backend_ind, shape, np_ind) array = indexing.NumpyIndexingAdapter(data)[combined_ind] np.testing.assert_array_equal(expected, array)
def test_decompose_indexers(shape, indexer_mode, indexing_support): data = np.random.randn(*shape) indexer = get_indexers(shape, indexer_mode) backend_ind, np_ind = indexing.decompose_indexer( indexer, shape, indexing_support) expected = indexing.NumpyIndexingAdapter(data)[indexer] array = indexing.NumpyIndexingAdapter(data)[backend_ind] if len(np_ind.tuple) > 0: array = indexing.NumpyIndexingAdapter(array)[np_ind] np.testing.assert_array_equal(expected, array) if not all(isinstance(k, indexing.integer_types) for k in np_ind.tuple): combined_ind = indexing._combine_indexers(backend_ind, shape, np_ind) array = indexing.NumpyIndexingAdapter(data)[combined_ind] np.testing.assert_array_equal(expected, array)