This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.
- nilearn.masking.unmask(X, mask_img, order='F')#
Take masked data and bring them back into 3D/4D.
This function can be applied to a list of masked data.
Masked data. shape: (samples #, features #). If X is one-dimensional, it is assumed that samples# == 1.
- mask_imgNiimg-like object
See Input and output: neuroimaging data representation. Must be 3-dimensional.
Unmasked data. Depending on the shape of X, data can have different shapes:
X.ndim == 2: Shape: (mask.shape, mask.shape, mask.shape, X.shape)
X.ndim == 1: Shape: (mask.shape, mask.shape, mask.shape)