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FAQ: Frequently Asked Questions

This page addresses common questions and provides troubleshooting tips for using ZarrNii.

General Questions

1. What is ZarrNii?

ZarrNii is a Python library that bridges the gap between OME-Zarr and NIfTI formats, enabling seamless conversion, transformations, and metadata handling for multidimensional biomedical images.

2. What formats does ZarrNii support?

ZarrNii supports: - OME-Zarr: A format for storing chunked, multidimensional microscopy images. - NIfTI: A format commonly used for neuroimaging data.

3. Can ZarrNii handle large datasets?

Yes! ZarrNii uses Dask arrays to handle datasets that don't fit into memory. Most transformations are lazy, meaning computations are only performed when explicitly triggered using .compute().

Installation Issues

1. I installed ZarrNii, but I can't import it.

Ensure that ZarrNii is installed in the correct Python environment. Use poetry show or pip show zarrnii to verify the installation.

If you're still encountering issues, try reinstalling the library:

poetry install

Troubleshooting

Performance Tips

1. How can I speed up transformations on large datasets?

  • Use appropriate chunk sizes with .rechunk() for operations like downsampling or interpolation.
  • Trigger computations only when necessary using .compute().

2. How do I optimize multiscale processing?

For OME-Zarr datasets with multiscale pyramids: 1. Use the appropriate level when loading the dataset.

znimg = ZarrNii.from_ome_zarr("path/to/dataset.zarr", level=2)

Metadata Questions

1. How do I access OME-Zarr metadata?

ZarrNii provides attributes for accessing metadata:

print("Axes:", znimg.axes)
print("Coordinate transformations:", znimg.coordinate_transformations)
print("Omero metadata:", znimg.omero)

2. Does ZarrNii preserve metadata during transformations?

Yes, ZarrNii updates the metadata to remain consistent with transformations like cropping, downsampling, or affine transformations.

Getting Help

If you encounter issues not covered here: 1. Check the API Reference for detailed information about ZarrNii methods. 2. Open an issue on the GitHub repository.

Summary

This FAQ covers common questions about ZarrNii, troubleshooting tips, and best practices for working with large datasets and metadata. For more in-depth information, explore: - Examples - API Reference