Comments (3)
Actually, downsampling and subsampling may be different tasks. We can still make the subsampling function work on images though.
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How do you all feel about using
nilearn.image.resample_img()
forutils.subsampling()
? I am working on #29, and I think it would be easier have a function that downsamples and returns images instead of arrays.
I don't think nilearn.image.resample_img()
is what we're looking for.
Actually, downsampling and subsampling may be different tasks. We can still make the subsampling function work on images though.
I don't see why we would need to do this. The subsampling happens inside the fitting function, where images are already in matrix format. Is there any other case where we would want to subsample images?
from mapca.
Agreed that resample_img
is not the right tool. However, I will say that having subsampling
operate on images will make it easier to stick with nilearn
's masking and unmasking functions internally. At that point, though, the results will be the same and the changes will be buried deep in the code, so it's not the big change that using resample_img
would be.
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Related Issues (20)
- Set up continuous integration HOT 7
- Codecov not working
- [BUG] Dimensions of masked data not considered for fit_transform HOT 3
- Replacing utils._kurtn with scipy.stats.kurtosis HOT 5
- maPCA in tedana does not currently z-score data before performing PCA HOT 3
- Cite original maPCA paper and GIFT package
- Make class accept images instead of arrays HOT 3
- Scaler parameters overridden within MovingAveragePCA HOT 2
- Use nilearn's masking/unmasking functions internally
- Zenodo release
- Remove commented code
- Documentation missing HOT 4
- Tests fail due to make command not being installed
- Document typical results for different datasets/acquisition parameters HOT 1
- Remove DueCredit dependency
- Identified part of the issue with component misestimation
- `n_features_` from sklearn's PCA is deprecated
- Update package installation and minimum Python version
- Black and isort need to be rerun
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