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.datasets.fetch_mixed_gambles(n_subjects=1, data_dir=None, url=None, resume=True, return_raw_data=False, verbose=1)[source]#

Fetch Jimura “mixed gambles” dataset.

See [1].

n_subjectsint, optional

The number of subjects to load. If None is given, all the subjects are used. Default=1.

data_dirpathlib.Path or str, optional

Path where data should be downloaded. By default, files are downloaded in home directory.

urlstr, optional

URL of file to download. Override download URL. Used for test only (or if you setup a mirror of the data). Default=None.

resumebool, optional

Whether to resume download of a partly-downloaded file. Default=True.

verboseint, optional

Verbosity level (0 means no message). Default=1.

return_raw_databool, optional

If False, then the data will transformed into an (X, y) pair, suitable for machine learning routines. X is a list of n_subjects * 48 Nifti1Image objects (where 48 is the number of trials), and y is an array of shape (n_subjects * 48,). Default=False.


Dictionary-like object, the attributes of interest are:

  • ‘zmaps’: list of str Paths to realigned gain betamaps (one nifti per subject).

  • ‘gain’: list of Nifti1Image or None If make_Xy is True, this is a list of n_subjects * 48 Nifti1Image objects, else it is None.

  • ‘y’: ndarray of shape (n_subjects * 48,) or None If make_Xy is True, then this is a ndarray of shape (n_subjects * 48,), else it is None.


Examples using nilearn.datasets.fetch_mixed_gambles#

FREM on Jimura et al “mixed gambles” dataset

FREM on Jimura et al "mixed gambles" dataset