stack-to-chunk

Convert stacks of images to a chunked zarr dataset. Specifically this code is written to:

• Take stacks of 2D images (e.g., TIFF, JPEG files) that represent a 3D spatial volume as input.

• Convert them to an OME Next Generation File Format (NGFF) zarr dataset.

• Tutorial
  • Generating sample data
  • Setting up input
  • Running stack-to-chunk
  • Cleanup
• Guide
  • Parallelisation strategy
  • Sharding
  • Third-party multi-threading
  • Zarr group layout
• API Reference
  • MultiScaleGroup
  • open_multiscale_group()
  • SPATIAL_UNIT

Installing

stack-to-chunk is designed to be used as a tool, which means its dependencies are pinned to exact versions. For this reason, it’s recommended to install stack-to-chunk in its own virtual environment. This is easy to do with virtual environments using the uv Python package and project manager.

stack-to-chunk can be installed using pip:

pip install stack-to-chunk

Changelog

See https://github.com/HiPCTProject/stack-to-chunk/releases for the list of tags and a changelog for each one.

Version 2

Version 2 of stack-to-chunk is a major breaking release to add support for OME-Zarr version 0.5 (and therefore Zarr version 3). The following are major changes to the library:

• stack-to-chunk automatically adds sharding to the resulting data.

  See the guide page for more information on how this works.

• memory_per_process has been renamed memory_per_slab_process, as it calculates the memory required to copy a single slab of data.

• A new function, memory_per_downsample_process has also been added to calculate the minimum memory required in a downsampling process.