def get_indexers(shape, mode):
if mode == "vectorized":
indexed_shape = (3, 4)
indexer = tuple(
np.random.randint(0, s, size=indexed_shape) for s in shape)
return indexing.VectorizedIndexer(indexer)
elif mode == "outer":
indexer = tuple(np.random.randint(0, s, s + 2) for s in shape)
return indexing.OuterIndexer(indexer)
elif mode == "outer_scalar":
indexer = (np.random.randint(0, 3, 4), 0, slice(None, None, 2))
return indexing.OuterIndexer(indexer[:len(shape)])
elif mode == "outer_scalar2":
indexer = (np.random.randint(0, 3, 4), -2, slice(None, None, 2))
return indexing.OuterIndexer(indexer[:len(shape)])
elif mode == "outer1vec":
indexer = [slice(2, -3) for s in shape]
indexer[1] = np.random.randint(0, shape[1], shape[1] + 2)
return indexing.OuterIndexer(tuple(indexer))
elif mode == "basic": # basic indexer
indexer = [slice(2, -3) for s in shape]
indexer[0] = 3
return indexing.BasicIndexer(tuple(indexer))
elif mode == "basic1": # basic indexer
return indexing.BasicIndexer((3, ))
elif mode == "basic2": # basic indexer
indexer = [0, 2, 4]
return indexing.BasicIndexer(tuple(indexer[:len(shape)]))
elif mode == "basic3": # basic indexer
indexer = [slice(None) for s in shape]
indexer[0] = slice(-2, 2, -2)
indexer[1] = slice(1, -1, 2)
return indexing.BasicIndexer(tuple(indexer[:len(shape)]))
def test_create_mask_dask() -> None:
da = pytest.importorskip("dask.array")
indexer = indexing.OuterIndexer((1, slice(2), np.array([0, -1, 2])))
expected = np.array(2 * [[False, True, False]])
actual = indexing.create_mask(indexer, (5, 5, 5),
da.empty((2, 3), chunks=((1, 1), (2, 1))))
assert actual.chunks == ((1, 1), (2, 1))
np.testing.assert_array_equal(expected, actual)
indexer_vec = indexing.VectorizedIndexer(
(np.array([0, -1, 2]), slice(None), np.array([0, 1, -1])))
expected = np.array([[False, True, True]] * 2).T
actual = indexing.create_mask(indexer_vec, (5, 2),
da.empty((3, 2), chunks=((3, ), (2, ))))
assert isinstance(actual, da.Array)
np.testing.assert_array_equal(expected, actual)
with pytest.raises(ValueError):
indexing.create_mask(indexer_vec, (5, 2), da.empty((5, ),
chunks=(1, )))