add support for non-uniformity correction

[pbellec]

this would be based on AFNI.

TODO

• [ ] update niak docker container to add afni.
• [ ] create bricks for distortion correction
• [ ] run tests
• [ ] add brick to the preprocessing pipeline

Resources:

Example of docker file for AFNI nipype interfaces for Qwarp and QwarpPlusMinus.

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[chrisholder]

One key change worth documenting is in the cache script there is this step:

      - name: Install CPU version of pytorch on linux
        uses: nick-fields/retry@v3
        if: steps.cache.outputs.cache-hit != 'true' && runner.os == 'Linux'
        with:
          timeout_minutes: 30
          max_attempts: 3
          command: python -m pip install torch --index-url https://download.pytorch.org/whl/cpu

I found the default pip install torch to be multiple gbs in size because it installs all the GPU dependencies (which we dont use in the CI). I instead opt to only install the CPU dependencies for the cache which is only 200mbs of size. This allowed us to stay under the 10gb limit

[TonyBagnall]

is this sped up?

[chrisholder]

The caching doesn't exist until merged onto main (as the cache's need to be made on main).

This is a run on my fork using the pip cache:

[chrisholder]

This is the usage on this branch that doesn't have any pip caching:

Overall the caching saves a hour over all tests

[MatthewMiddlehurst]

Even with the comments and questions in the review, I really like the idea of this and hope it works out. Thanks for the work.