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)