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

Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases

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