Note

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.

6.6.6. nilearn.masking.unmask

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.

Parameters:

X: numpy.ndarray (or list of) :

Masked data. shape: (samples #, features #). If X is one-dimensional, it is assumed that samples# == 1.

mask_img: niimg: Niimg-like object :

See http://nilearn.github.io/building_blocks/manipulating_mr_images.html#niimg. Must be 3-dimensional.

Returns:

data: nibabel.Nift1Image object :

Unmasked data. Depending on the shape of X, data can have different shapes:

• X.ndim == 2: Shape: (mask.shape[0], mask.shape[1], mask.shape[2], X.shape[0])
• X.ndim == 1: Shape: (mask.shape[0], mask.shape[1], mask.shape[2])