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
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| Parameters | ||
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| data: np.ndarray |
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2D ? 3D ? meaning of the dimensions ?
| 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 ?
| + "Its intersection with the clustering was used instead." | ||
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| def _one_parcellation(self, imgs): |
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Please add 1-line docstrings for private methods, full dosctrings for public methods.
| from fmralign.tests.utils import random_niimg | ||
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| def test_init_default_params(): |
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1-line docstrings for tests is very helpful for test mainetnance...
| _make_parcellation(img, clustering_method, n_pieces, masker) | ||
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| def test_initialization(): |
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1-line docsrtings welcome too...
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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