perf: Vectorize get_chunk_slice for faster sharded writes

[mkitti]

Summary

This PR adds vectorized methods to _ShardIndex and _ShardReader for batch chunk slice lookups, significantly reducing per-chunk function call overhead when writing to shards.

Changes

New Methods

_ShardIndex.get_chunk_slices_vectorized: Batch lookup of chunk slices using NumPy vectorized operations instead of per-chunk Python calls.

_ShardReader.to_dict_vectorized: Build a chunk dictionary using vectorized lookup instead of iterating with individual get() calls.

Modified Code Path

In _encode_partial_single, replaced:

shard_dict = {k: shard_reader.get(k) for k in morton_order_iter(chunks_per_shard)}

With vectorized approach:

morton_coords = _morton_order(chunks_per_shard)
chunk_coords_array = np.array(morton_coords, dtype=np.uint64)
shard_dict = shard_reader.to_dict_vectorized(chunk_coords_array, morton_coords)

Benchmark Results

Single Chunk Write to Large Shard

Writing a single 1x1x1 chunk to a shard with 32³ chunks (using test_sharded_morton_write_single_chunk from PR #3712):

Optimization	Time	Speedup vs Main
Main branch (original)	422ms	-
+ Morton optimization (PR #3708)	261ms	1.6x
+ Vectorized get_chunk_slice	95ms	4.4x

Profile Breakdown

Function	Before	After
get_chunk_slice + _localize_chunk	215ms	3ms
to_dict_vectorized loop	81ms	9ms
Total function calls	299k	37k

Checklist

• Add unit tests and/or doctests in docstrings
• Add docstrings and API docs for any new/modified user-facing classes and functions
• New/modified features documented in docs/user-guide/*.md
• Changes documented as a new file in changes/
• GitHub Actions have all passed
• Test coverage is 100% (Codecov passes)

[mkitti]

@d-v-b add the benchmark tag here as well please.

[codspeed-hq]

Merging this PR will improve performance by ×7.2

⚡ 4 improved benchmarks
✅ 52 untouched benchmarks
⏩ 6 skipped benchmarks¹

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
⚡	WallTime	test_morton_order_iter[(8, 8, 8)]	6.2 ms	1.1 ms	×5.5
⚡	WallTime	test_morton_order_iter[(16, 16, 16)]	56.2 ms	8.1 ms	×6.9
⚡	WallTime	test_morton_order_iter[(32, 32, 32)]	502.6 ms	70.2 ms	×7.2
⚡	WallTime	test_sharded_morton_write_single_chunk[(32, 32, 32)-memory]	953.3 ms	164.4 ms	×5.8

---

_{Comparing mkitti:mkitti-get-chunk-slice-vectorization (157d283) with main (36caf1f)}

Footnotes

1. 6 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩

[d-v-b]

why does to_dict_vectorized take the same values as 2 different types? Can't we pass in morton_coords and have the array version created internally as an implementation detail?