Function _outer_to_vectorized_indexer

xarray/core/indexing.py:1027–1061

Convert an OuterIndexer into a vectorized indexer. Parameters ---------- indexer : Outer/Basic Indexer An indexer to convert. shape : tuple Shape of the array subject to the indexing. Returns ------- VectorizedIndexer Tuple suitable for use to in

(
    indexer: BasicIndexer | OuterIndexer, shape: _Shape
)

def _outer_to_vectorized_indexer(
    indexer: BasicIndexer | OuterIndexer, shape: _Shape
) -> VectorizedIndexer:
    """Convert an OuterIndexer into a vectorized indexer.

    Parameters
    ----------
    indexer : Outer/Basic Indexer
        An indexer to convert.
    shape : tuple
        Shape of the array subject to the indexing.

    Returns
    -------
    VectorizedIndexer
        Tuple suitable for use to index a NumPy array with vectorized indexing.
        Each element is an array: broadcasting them together gives the shape
        of the result.
    """
    key = indexer.tuple

    n_dim = len([k for k in key if not isinstance(k, integer_types)])
    i_dim = 0
    new_key = []
    for k, size in zip(key, shape, strict=True):
        if isinstance(k, integer_types):
            new_key.append(np.array(k).reshape((1,) * n_dim))
        else:  # np.ndarray or slice
            if isinstance(k, slice):
                k = np.arange(*k.indices(size))
            assert k.dtype.kind in {"i", "u"}
            new_shape = [(1,) * i_dim + (k.size,) + (1,) * (n_dim - i_dim - 1)]
            new_key.append(k.reshape(*new_shape))
            i_dim += 1
    return VectorizedIndexer(tuple(new_key))


def _outer_to_numpy_indexer(indexer: BasicIndexer | OuterIndexer, shape: _Shape):