Unified preprocessing for masking and parcellating #103
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added 30 commits
November 6, 2024 01:11
…ta transformation
…line Preprocessor class
…retrieval methods
…parcellation process
added 4 commits
November 12, 2024 23:00
…/testing datasets
…/testing datasets
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Is it ready for review ? |
Apart from some docstrings that need updating, I think it's ready for review. |
bthirion
reviewed
Nov 19, 2024
| from nilearn.masking import apply_mask_fmri, intersect_masks | ||
| from nilearn.regions.parcellations import Parcellations | ||
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| class ParceledData: |
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I'm wondering about the usefulness of this class:
- it does not follow sklearn API: fit method etc.
- it is mostly a container, without internal state: do we need a class ?
How different is it from LabelsMasker ?
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NiftiLabelsMasker extracts and aggregates data in a single ouptut for each parcel. Instead I want to be able to loop over parcels consistently (and without having to carry around to list of labels).
ParceledData enables this by doing lazy parcellations so that we can conviniently write:
transformed_data_list = []
for i in range(len(estimators)):
transformed_data_list.append(estimators[i].transform(parceled_data[i]))
bthirion
reviewed
Nov 26, 2024
added 2 commits
November 27, 2024 16:57
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Waiting for #105 to be merged and then merging it. |
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Solves #89. Creates a common preprocessing backend to be used with
PairwiseAlignmentandTemplateAlignment. Automatically masks and parcellates any number of nifti images in the spirit of scikit-learn'sStandardScaler. Suggestions for better class name are welcome!Goals:
PairwiseAlignmentTemplateAlignmentfastsrm