nilearn.experimental: Experimental Modules

The nilearn.experimental module provides importable modules that enable the use of experimental features.

Warning

All features in the nilearn.experimental module are experimental and subject to change.

They are included in the nilearn package to gather early feedback from users about prototypes of new features. Changes may break backwards compatibility without prior notice or a deprecation cycle. Moreover, some features may be incomplete or may have been tested less thoroughly than the rest of the library.

Use those features at your own risks!

nilearn.experimental.surface

The nilearn.experimental.surface module.

Classes:

FileMesh(file_path)	A surface mesh stored in a Gifti or Freesurfer file.
InMemoryMesh(coordinates, faces)	A surface mesh stored as in-memory numpy arrays.
Mesh()	A surface mesh having vertex, coordinates and faces (triangles).
PolyMesh
SurfaceImage(mesh, data)	Surface image, usually containing meshes & data for both hemispheres.
SurfaceLabelsMasker(labels_img[, label_names])	Extract data from a SurfaceImage, averaging over atlas regions.
SurfaceMasker([mask_img, standardize, ...])	Extract data from a SurfaceImage.

Functions:

fetch_destrieux()	Load Destrieux surface atlas into a surface object.
fetch_nki([n_subjects])	Load NKI enhanced surface data into a surface object.
load_fsaverage([mesh_name])	Load several fsaverage mesh types for both hemispheres.