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 https://nilearn.github.io/stable/manipulating_images/input_output.html # noqa:E501 Niimgs to concatenate.
- dtypenumpy dtype, optional
The dtype of the returned image. Default=np.float32.
- ensure_ndiminteger, optional
Indicate the dimensionality of the expected niimg. An error is raised if the niimg is of another dimensionality.
- auto_resampleboolean, optional
Converts all images to the space of the first one. Default=False.
- verboseint, optional
Controls the amount of verbosity (0 means no messages). Default=0.
- memoryinstance of joblib.Memory or string, optional
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. Default=Memory(location=None).
- memory_levelinteger, optional
Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Default=0.
- Returns
- concatenatednibabel.Nifti1Image
A single image.
See also
Examples using nilearn.image.concat_imgs#
Intro to GLM Analysis: a single-session, single-subject fMRI dataset
Single-subject data (two sessions) in native space
Beta-Series Modeling for Task-Based Functional Connectivity and Decoding