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[MAINT] Refactor test nifti label maskers #4333
[MAINT] Refactor test nifti label maskers #4333
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👋 @Remi-Gau Thanks for creating a PR! Until this PR is ready for review, you can include the [WIP] tag in its title, or leave it as a github draft. Please make sure it is compliant with our contributing guidelines. In particular, be sure it checks the boxes listed below.
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #4333 +/- ##
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+ Coverage 91.85% 92.07% +0.21%
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Files 144 143 -1
Lines 16419 16496 +77
Branches 3434 3463 +29
==========================================
+ Hits 15082 15188 +106
+ Misses 792 760 -32
- Partials 545 548 +3
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Sentry. |
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LGTM overall, thx !
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LGTM then.
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# Test with data and atlas of different shape: | ||
# the atlas should be resampled to the data |
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IIUC, both atlas and mask would be resampled?
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From the doc:
For example, if resampling_target is “data”, the atlas is resampled to the shape of the data if needed.
If it is “labels” then mask_img and images provided to fit() are resampled to the shape and affine of maps_img.
“None” means no resampling: if shapes and affines do not match, a ValueError is raised.
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When the target is labels
there is a check in the test to make sure the mask is resampled.
I don't see a test for the None
case though so actually worth adding it.
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I see! So mask_img does not need to be resampled to data because it is applied to the labels_img (not the data). As mentioned here:
"Mask to apply to regions before extracting signals."
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Thanks!
Merging! |
Just realized that the test for some of the errors did not have a Will make a mini follow up PR |
Oops, did I merge too soon? |
no worries, I will send a follow up PR that should be smaller and also easier to review |
Changes proposed in this pull request: