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.
nilearn.experimental.surface.SurfaceLabelsMasker¶
- class nilearn.experimental.surface.SurfaceLabelsMasker(labels_img, label_names=None)[source]¶
Extract data from a SurfaceImage, averaging over atlas regions.
- Parameters:
- Attributes:
- labels_data_
numpy.ndarray
- labels_
numpy.ndarray
- label_names_
numpy.ndarray
- labels_data_
- labels_img¶
- label_names¶
- labels_data_¶
- labels_¶
- label_names_¶
- fit(img=None, y=None)[source]¶
Prepare signal extraction from regions.
- Parameters:
- imgSurfaceImage object
Mesh and data for both hemispheres.
- yNone
This parameter is unused. It is solely included for scikit-learn compatibility.
- Returns:
- SurfaceLabelsMasker object
- transform(img)[source]¶
Extract signals from fitted surface object.
- Parameters:
- imgSurfaceImage object
Mesh and data for both hemispheres.
- Returns:
- outputnumpy.ndarray
Signal for each element. shape: (img data shape, total number of vertices)
- fit_transform(img, y=None)[source]¶
Prepare and perform signal extraction from regions.
- Parameters:
- imgSurfaceImage object
Mesh and data for both hemispheres.
- yNone
This parameter is unused. It is solely included for scikit-learn compatibility.
- Returns:
- numpy.ndarray
Signal for each element. shape: (img data shape, total number of vertices)
Examples using nilearn.experimental.surface.SurfaceLabelsMasker
¶
A short demo of the surface images & maskers