[WIP] Improve downsampling performance

[daniel-wer]

Description:

• abc

Issues:

• fixes #...

Todos:

Make sure to delete unnecessary points or to check all before merging:

• Updated Changelog
• Updated Documentation
• Added / Updated Tests
• Considered adding this to the Examples

[gemini-code-assist]

Summary of Changes

Hello @daniel-wer, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on a significant performance improvement for downsampling operations within the system. The changes primarily involve integrating Numba for just-in-time compilation of critical data processing functions, adjusting buffer sizes to handle larger data volumes more efficiently, and refining the chunking strategy to optimize data access patterns. These enhancements are designed to reduce the time and resources required to generate lower-resolution representations of datasets.

Highlights

• Performance Optimization with Numba: Introduced the numba library and a JIT-compiled fast_mode function to significantly accelerate mode-based downsampling operations, replacing a potentially slower Python/NumPy implementation.
• Increased Downsampling Buffer Size: The default buffer size used for downsampling has been increased from 256³ to 512³ voxels, allowing larger data chunks to be processed simultaneously, which can improve throughput.
• Optimized Chunking Strategy: Implemented more explicit control over chunking during downsampling by passing calculated source_chunk_shape and target_chunk_shape to the for_zipped_chunks method, aiming for more efficient data processing and I/O.
• Zarr Driver Simplification: The logic for opening Zarr stores was streamlined to directly use the zarr3 driver, removing a previous fallback mechanism and ensuring paths are explicitly converted to strings.

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[gemini-code-assist]

Code Review

The pull request significantly improves downsampling performance by introducing Numba-jitted mode calculation and refining chunking strategies. The change to exclusively use zarr3 for opening files also streamlines the process. However, there are a few areas that warrant further review to ensure correctness and optimal performance, particularly regarding the zarr fallback, the fast_mode implementation, memory implications of increased buffer size, and the chunk shape calculations for zipped chunks.