[AL-1713] Dicom

[farizrahman4u]

🚀 🚀 Pull Request

Checklist:

• My code follows the style guidelines of this project and the Contributing document
• I have commented my code, particularly in hard-to-understand areas
• I have kept the coverage-rate up
• I have performed a self-review of my own code and resolved any problems
• I have checked to ensure there aren't any other open Pull Requests for the same change
• I have described and made corresponding changes to the relevant documentation
• New and existing unit tests pass locally with my changes

Changes

[codecov]

Codecov Report

Merging #1572 (a4566b9) into main (0955356) will decrease coverage by 0.12%.
The diff coverage is 84.55%.

❗ Current head a4566b9 differs from pull request most recent head 7c7aa5d. Consider uploading reports for the commit 7c7aa5d to get more accurate results

@@            Coverage Diff             @@
##             main    #1572      +/-   ##
==========================================
- Coverage   92.47%   92.34%   -0.13%     
==========================================
  Files         201      202       +1     
  Lines       18611    18612       +1     
==========================================
- Hits        17210    17188      -22     
- Misses       1401     1424      +23     

Flag	Coverage Δ
unittests	92.34% <84.55%> (-0.13%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
hub/api/read.py	100.00% <ø> (ø)
hub/api/tests/test_api.py	100.00% <ø> (ø)
setup.py	0.00% <ø> (ø)
hub/core/sample.py	84.89% <56.41%> (-5.03%)	⬇️
hub/core/compression.py	87.88% <88.57%> (-0.57%)	⬇️
hub/api/tests/test_dicom.py	100.00% <100.00%> (ø)
hub/api/tests/test_sample_info.py	85.54% <100.00%> (ø)
hub/compression.py	97.36% <100.00%> (+0.07%)	⬆️
hub/core/chunk_engine.py	94.79% <100.00%> (+<0.01%)	⬆️
hub/core/dataset/dataset.py	92.96% <100.00%> (-0.07%)	⬇️
... and 18 more

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[davidbuniat]

Few remarks and questions

• DICOM itself is a container who has multiple types of lossless compression methods currently under cover. What if the user is going to adjust?
• Do we apply any compression to the metadata in sample_info?
• Have you tried to train a model on dicom file? (the exercise of doing it will help to truly understand user experience)
  Other than that overall looked good to me.

[farizrahman4u]

@davidbuniat

Few remarks and questions

• DICOM itself is a container who has multiple types of lossless compression methods currently under cover. What if the user is going to adjust?
• Do we apply any compression to the metadata in sample_info?
• Have you tried to train a model on dicom file? (the exercise of doing it will help to truly understand user experience)
  Other than that overall looked good to me.

• User can't.
• Meta data has no compression (for faster query)
• TODO :)