Function _decompose_outer_indexer

xarray/core/indexing.py:1319–1463

Decompose outer indexer to the successive two indexers, where the first indexer will be used to index backend arrays, while the second one is used to index the loaded on-memory np.ndarray. Parameters ---------- indexer : OuterIndexer or BasicIndexer indexing_support : O

(
    indexer: BasicIndexer | OuterIndexer,
    shape: _Shape,
    indexing_support: IndexingSupport,
)

def _decompose_outer_indexer(
    indexer: BasicIndexer | OuterIndexer,
    shape: _Shape,
    indexing_support: IndexingSupport,
) -> tuple[ExplicitIndexer, ExplicitIndexer]:
    """
    Decompose outer indexer to the successive two indexers, where the
    first indexer will be used to index backend arrays, while the second one
    is used to index the loaded on-memory np.ndarray.

    Parameters
    ----------
    indexer : OuterIndexer or BasicIndexer
    indexing_support : One of the entries of IndexingSupport

    Returns
    -------
    backend_indexer: OuterIndexer or BasicIndexer
    np_indexers: an ExplicitIndexer (OuterIndexer / BasicIndexer)

    Notes
    -----
    This function is used to realize the vectorized indexing for the backend
    arrays that only support basic or outer indexing.

    As an example, let us consider to index a few elements from a backend array
    with an orthogonal indexer ([0, 3, 1], [2, 3, 2]).
    Even if the backend array only supports basic indexing, it is more
    efficient to load a subslice of the array than loading the entire array,

    >>> array = np.arange(36).reshape(6, 6)
    >>> backend_indexer = BasicIndexer((slice(0, 3), slice(2, 4)))
    >>> # load subslice of the array
    ... array = NumpyIndexingAdapter(array)[backend_indexer]
    >>> np_indexer = OuterIndexer((np.array([0, 2, 1]), np.array([0, 1, 0])))
    >>> # outer indexing for on-memory np.ndarray.
    ... NumpyIndexingAdapter(array).oindex[np_indexer]
    array([[ 2,  3,  2],
           [14, 15, 14],
           [ 8,  9,  8]])
    """
    backend_indexer: list[Any] = []
    np_indexer: list[Any] = []

    assert isinstance(indexer, OuterIndexer | BasicIndexer)

    if indexing_support == IndexingSupport.VECTORIZED:
        for k, s in zip(indexer.tuple, shape, strict=False):
            if isinstance(k, slice):
                # If it is a slice, then we will slice it as-is
                # (but make its step positive) in the backend,
                bk_slice, np_slice = _decompose_slice(k, s)
                backend_indexer.append(bk_slice)
                np_indexer.append(np_slice)
            else:
                backend_indexer.append(k)
                if not is_scalar(k):
                    np_indexer.append(slice(None))