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.concat_imgs#

nilearn.image.concat_imgs(niimgs, dtype=<class 'numpy.float32'>, ensure_ndim=None, memory=Memory(location=None), memory_level=0, auto_resample=False, verbose=0)[source]#

Concatenate a list of 3D/4D niimgs of varying lengths.

The niimgs list can contain niftis/paths to images of varying dimensions (i.e., 3D or 4D) as well as different 3D shapes and affines, as they will be matched to the first image in the list if auto_resample=True.

Parameters:
niimgsiterable of Niimg-like objects or glob pattern

See Input and output: neuroimaging data representation. Niimgs to concatenate.

dtypenumpy dtype, default=np.float32

The dtype of the returned image.

ensure_ndiminteger, optional

Indicate the dimensionality of the expected niimg. An error is raised if the niimg is of another dimensionality.

auto_resampleboolean, default=False

Converts all images to the space of the first one.

verboseint, default=0

Controls the amount of verbosity (0 means no messages).

memoryinstance of joblib.Memory or string, default=Memory(location=None)

Used to cache the resampling process. By default, no caching is done. If a string is given, it is the path to the caching directory.

memory_levelinteger, default=0

Rough estimator of the amount of memory used by caching. Higher value means more memory for caching.

Returns:
concatenatednibabel.Nifti1Image

A single image.

Examples using nilearn.image.concat_imgs#

3D and 4D niimgs: handling and visualizing

3D and 4D niimgs: handling and visualizing

Intro to GLM Analysis: a single-run, single-subject fMRI dataset

Intro to GLM Analysis: a single-run, single-subject fMRI dataset

Visualizing global patterns with a carpet plot

Visualizing global patterns with a carpet plot

Single-subject data (two runs) in native space

Single-subject data (two runs) in native space

Predicted time series and residuals

Predicted time series and residuals

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding