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=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=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. If None is passed will default to Memory(location=None).
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

Visualizing global patterns with a carpet plot

Single-subject data (two runs) in native space

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

nilearn.image.concat_imgs¶

Examples using nilearn.image.concat_imgs¶

Examples using `nilearn.image.concat_imgs`¶