Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.

3.2.1. fmralign.alignment.group_alignment.GroupAlignment

class fmralign.alignment.group_alignment.GroupAlignment(method='identity', labels=None, n_jobs=1, verbose=0, n_iter=2, scale_template=False)

Performs group-level alignment of various subject data.

This class aligns multiple subjects’ data either to a computed template or to a specific target subject’s data. It supports various alignment methods and can process data in parallel.

Parameters:

methodstr or a BaseAlignment instance, default=”identity”

The alignment method to use. It can be a string representing the method name or an instance of a class derived from BaseAlignment. Available methods include: [“identity”, “procrustes”, “ot”, “sparseuot”, “ridge”].

labelsarray-like or None, default=None

Describes each voxel label’s in the case of non-overlapping parcels. If provided, local alignments can be performed in parallel. If None, global alignment is performed across all features.

n_jobsint, default=1

Number of parallel jobs to run. -1 means using all processors.

verboseint, default=0

Verbosity level. Higher values provide more detailed output.

n_iterint, default=2

Number of iterations for the template alignment algorithm.

scale_templatebool, default=False

Whether to scale the features during template learning. If True, features are rescaled to the updating Euclidean mean.

Attributes:

labels_array-like

Validated labels used during fitting.

method_str

Validated alignment method used during fitting.

fit_list

List of fitted alignment estimators, one per subject.

templatearray-like or None

Computed template for template alignment. None for pairwise alignment.

Examples

>>> import numpy as np
>>> from fmralign import GroupAlignment
>>> n_voxels = 5

>>> # Template alignment
>>> alignment_dict = {
...     "sub-01": np.random.rand(10, n_voxels),
...     "sub-02": np.random.rand(10, n_voxels),
... }
>>> testing_dict = {
...     "sub-01": np.random.rand(8, n_voxels),
...     "sub-02": np.random.rand(8, n_voxels),
... }
>>> aligner = GroupAlignment(method="procrustes", n_iter=3)
>>> aligner.fit(alignment_dict, y="template")
>>> aligned_data = aligner.transform(testing_dict)

>>> # Pairwise alignment to target
>>> target_data = np.random.rand(10, n_voxels)
>>> aligner = GroupAlignment(method="procrustes")
>>> aligner.fit(alignment_dict, y=target_data)
>>> aligned_data = aligner.transform(testing_dict)

__init__(method='identity', labels=None, n_jobs=1, verbose=0, n_iter=2, scale_template=False)

3.2.1.1. Examples using fmralign.alignment.group_alignment.GroupAlignment

Template-based prediction.

Template-based prediction.

Alignment methods benchmark (template-based ROI case)

Alignment methods benchmark (template-based ROI case)