spectralbrain.load_group#

spectralbrain.load_group(files, *, mode='maps', loader=None, n_jobs=1, stack=True, descriptor='hks', k=100, backend=None, descriptor_kwargs=None, template_faces=None)[source]#

Load and stack a cohort for group statistics.

Parameters:

  • files (dict or list) – {subject_id: path} (e.g. from discover_bids() / discover_freesurfer()) or a plain list of paths (subject IDs are then parsed from the filenames).

  • mode ("maps" or "pipeline") – "maps" loads vertex-corresponded overlays/descriptor fields; "pipeline" loads each surface and computes a descriptor.

  • loader (callable, optional) – Custom path -> ndarray loader. Overrides mode.

  • n_jobs (int) – Parallel workers for loading (joblib). 1 = sequential.

  • stack (bool) – Stack into one array when subject shapes match (else keep a list).

  • descriptor (str) – Pipeline-mode options (descriptor name, eigenpairs, compute backend, and extra keyword arguments forwarded to the descriptor).

  • k (int) – Pipeline-mode options (descriptor name, eigenpairs, compute backend, and extra keyword arguments forwarded to the descriptor).

  • backend (Any | None) – Pipeline-mode options (descriptor name, eigenpairs, compute backend, and extra keyword arguments forwarded to the descriptor).

  • descriptor_kwargs (dict[str, Any] | None) – Pipeline-mode options (descriptor name, eigenpairs, compute backend, and extra keyword arguments forwarded to the descriptor).

  • template_faces (ndarray, optional) – Stored on the result for downstream TFCE adjacency.

Returns:

GroupData

Return type:

GroupData