issue_comments

OK, in it goes. Thanks @fujiisoup !

in case we want to get three diagonal elements (1, 1), (2, 2), (3, 3) from a 1000x1000 array. What we want is array[[1, 2, 3], [1, 2, 3]]. It can be decomposed to array[1: 4, 1:4][[0, 1, 2], [0, 1, 2]]. We only need to load 3 x 3 part of the 1000 x 1000 array, then take its diagonal elements.

OK, this is pretty clever.

There are some obvious fail cases, e.g., if they want to pull out indices array[[1, -1], [1, -1]], in which case the entire array needs to be sliced. I wonder if we should try to detect these with some heuristics, e.g., if the size of the result is much (maybe 10x or 100x) smaller than the size of sliced arrays.

Also, we would want to avoid separating basic/vectorized for backends that support efficient vectorized indexing (scipy and zarr).

I think the design choice here really comes down to whether we want to enable VectorizedIndexing on arbitrary data on disk or not:

Is it better to: 1. Always allow vectorized indexing by means of (lazily) loading all indexed data into memory as a single chunk. This could potentially be very expensive for IO or memory in hard to predict ways. 2. Or to only allow vectorized indexing if a backend supports it directly. This ensures that when vectorized indexing works it works efficiently. Vectorized indexing is still possibly but you have to explicitly write .compute()/.load().

I think I slightly prefer option (2) but I can see the merits in either decision.

Reason 2 is the primary one. We want to load the minimum amount of data possible into memory, mostly because pulling data from disk is slow.

I figured out how to consolidate two vectorized indexers, as long as they don't include any slice objects: ```python import numpy as np

def index_vectorized_indexer(old_indexer, applied_indexer): return tuple(o[applied_indexer] for o in np.broadcast_arrays(*old_indexer))

for x, old, applied in [ (np.arange(10), (np.arange(2, 7),), (np.array([3, 2, 1]),)), (np.arange(10), (np.arange(6).reshape(2, 3),), (np.arange(2), np.arange(1, 3))), (-np.arange(1, 21).reshape(4, 5), (np.arange(3)[:, None], np.arange(4)[None, :]), (np.arange(3), np.arange(3))), ]: new_key = index_vectorized_indexer(old, applied) np.testing.assert_array_equal(x[old][applied], x[new_key]) ```

We could probably make this work with VectorizedIndexer if we converted the slice objects to arrays. I think we might even already have some code to do that conversion somewhere. So another option would be to convert BasicIndexer and OuterIndexer -> VectorizedIndexer if necessary and then use this path.