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.mean_img#
- nilearn.image.mean_img(imgs, target_affine=None, target_shape=None, verbose=0, n_jobs=1)[source]#
Compute the mean of the images over time or the 4th dimension.
Note that if list of 4D images are given, the mean of each 4D image is computed separately, and the resulting mean is computed after.
- Parameters
- imgsNiimg-like object or iterable of Niimg-like objects
Images to be averaged over time (see http://nilearn.github.io/manipulating_images/input_output.html for a detailed description of the valid input types).
- target_affine
numpy.ndarray, optional If specified, the image is resampled corresponding to this new affine. target_affine can be a 3x3 or a 4x4 matrix.
- target_shape
tupleorlist, optional If specified, the image will be resized to match this new shape. len(target_shape) must be equal to 3. A target_affine has to be specified jointly with target_shape.
- verbose
int, optional Controls the amount of verbosity: higher numbers give more messages (0 means no messages). Default=0.
- n_jobs
int, optional The number of CPUs to use to do the computation (-1 means ‘all CPUs’). Default=1.
- Returns
Nifti1ImageMean image.
See also
nilearn.image.math_imgFor more general operations on images.
Examples using nilearn.image.mean_img#
Intro to GLM Analysis: a single-session, single-subject fMRI dataset
Decoding with FREM: face vs house object recognition
Decoding of a dataset after GLM fit for signal extraction
Different classifiers in decoding the Haxby dataset
Clustering methods to learn a brain parcellation from fMRI
Example of explicit fixed effects fMRI model fitting
Single-subject data (two sessions) in native space
Computing a Region of Interest (ROI) mask manually
Multivariate decompositions: Independent component analysis of fMRI
Massively univariate analysis of face vs house recognition