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.

nilearn.image.get_data#

nilearn.image.get_data(img)[source]#

Get the image data as a numpy.ndarray.

Parameters:
imgNiimg-like object or iterable of Niimg-like objects

See Input and output: neuroimaging data representation.

Returns:
numpy.ndarray

3D or 4D numpy array depending on the shape of img. This function preserves the type of the image data. If img is an in-memory Nifti image it returns the image data array itself – not a copy.

Examples using nilearn.image.get_data#

Searchlight analysis of face vs house recognition

Searchlight analysis of face vs house recognition

Voxel-Based Morphometry on Oasis dataset

Voxel-Based Morphometry on Oasis dataset

Different classifiers in decoding the Haxby dataset

Different classifiers in decoding the Haxby dataset

Clustering methods to learn a brain parcellation from fMRI

Clustering methods to learn a brain parcellation from fMRI

Second-level fMRI model: one sample test

Second-level fMRI model: one sample test

Example of generic design in second-level models

Example of generic design in second-level models

Visualization of affine resamplings

Visualization of affine resamplings

Computing a Region of Interest (ROI) mask manually

Computing a Region of Interest (ROI) mask manually

Massively univariate analysis of a calculation task from the Localizer dataset

Massively univariate analysis of a calculation task from the Localizer dataset

NeuroVault meta-analysis of stop-go paradigm studies

NeuroVault meta-analysis of stop-go paradigm studies

Massively univariate analysis of a motor task from the Localizer dataset

Massively univariate analysis of a motor task from the Localizer dataset

Massively univariate analysis of face vs house recognition

Massively univariate analysis of face vs house recognition