
.. currentmodule:: nilearn

.. include:: names.rst

0.3.1
=====

**Released June 2017**

This is a minor release for BrainHack.

Highlights
----------

- **Dropped support for scikit-learn older than 0.14.1** Minimum supported version is now ``0.15``.

Changes
-------

- The function ``sym_to_vec`` is deprecated and will be removed in release ``0.4``. Use function :func:`~connectome.sym_matrix_to_vec` instead.

- Added argument ``smoothing_fwhm`` to :class:`~regions.RegionExtractor` to control smoothing according to the resolution of atlas images.

Fixes
-----

- The helper function ``largest_connected_component`` should now work with inputs of non-native data ``dtypes``.

- Fix plotting issues when non-finite values are present in background anatomical image.

- A workaround to handle non-native endianness in :class:`~nibabel.nifti1.Nifti1Image` passed to resampling the image.

Enhancements
------------

- New functions :func:`~datasets.fetch_neurovault` and :func:`~datasets.fetch_neurovault_ids` help you download statistical maps from the `Neurovault <http://neurovault.org>`_ platform.

- New function :func:`~connectome.vec_to_sym_matrix` reshapes vectors to symmetric matrices. It acts as the reverse of function :func:`~connectome.sym_matrix_to_vec`.

- Add an option allowing to vectorize connectivity matrices returned by the ``transform`` method of class :class:`~connectome.ConnectivityMeasure`.

- Class :class:`~connectome.ConnectivityMeasure` now exposes an ``inverse_transform`` method, useful for going back from vectorized connectivity coefficients to connectivity matrices. Also, it allows to recover the covariance matrices for the "tangent" kind.

- Reworking and renaming of connectivity measures example. Renamed from ``plot_connectivity_measures`` to ``plot_group_level_connectivity``.

- Tighter bounding boxes when using ``add_contours`` for plotting.

- Function :func:`~image.largest_connected_component_img` to directly extract the largest connected component from :class:`~nibabel.nifti1.Nifti1Image`.

- Improvements in plotting, decoding and functional connectivity examples.
