Nilearn 0.8.0 release

We just released nilearn 0.8.0! You can get it through pip: $ pip install nilearn.

Highlights

This new release fixes some bugs and adds a bunch of new functionalities, among which:

• nilearn.input_data.NiftiLabelsMasker can now generate HTML reports in the same way as nilearn.input_data.NiftiMasker.

• nilearn.signal.clean accepts new parameter sample_mask. shape: (number of scans - number of volumes removed, ).

• All inherent classes of nilearn.input_data.BaseMasker can use parameter sample_mask for sub-sample masking.

• Fetcher nilearn.datasets.fetch_surf_fsaverage now accepts fsaverage3, fsaverage4 and fsaverage6 as values for parameter mesh, so that all resolutions of fsaverage from 3 to 7 are now available.

• Fetcher nilearn.datasets.fetch_surf_fsaverage now provides attributes {area, curv, sphere, thick}_{left, right} for all fsaverage resolutions.

• nilearn.glm.first_level.run_glm now allows auto regressive noise models of order greater than one.

Warning

• Python 3.5 is no longer supported. We recommend upgrading to Python 3.8.

• Support for Nibabel 2.x is deprecated and will be removed in the 0.9 release. Users with a version of Nibabel < 3.0 will be warned at their first Nilearn import.

• Minimum supported versions of packages have been bumped up:

• Numpy – v1.16
• SciPy – v1.2
• Scikit-learn – v0.21
• Nibabel – v2.5
• Pandas – v0.24