Changelog for python3-nilearn-0.8.1-2.1.noarch.rpm :

* Thu Jan 20 2022 codeAATTbnavigator.de- Update to 0.8.1
* New atlas fetcher nilearn.datasets.fetch_atlas_juelich to download Juelich atlas from FSL.
* New grey and white-matter template and mask loading functions: nilearn.datasets.load_mni152_gm_template, nilearn.datasets.load_mni152_wm_template, nilearn.datasets.load_mni152_gm_mask, and nilearn.datasets.load_mni152_wm_mask
* Nilearn development process has been reworked. It now provides insights on nilearn organization as a project as well as more explicit Contribution Guidelines.
* nilearn.image.binarize_img binarizes images into 0 and 1.
* 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.
* Support for Nibabel 2.x is deprecated and will be removed in the 0.9 release.- Add nilearn-pr3094-py310.patch -- gh#nilearn/nilearn#3094- Add nilearn-pr3136-pythoncall.patch -- gh#nilearn/nilearn#3136- Only test on 64-bit platforms - - http://nilearn.github.io/introduction.html#installation
* Fri Apr 09 2021 mmachovaAATTsuse.com- Update to 0.7.1
* New atlas fetcher nilearn.datasets.fetch_atlas_difumo to download Dictionaries of Functional Modes, or “DiFuMo”, that can serve as atlases to extract functional signals with different dimensionalities.
* nilearn.decoding.Decoder and nilearn.decoding.DecoderRegressor is now implemented with random predictions to estimate a chance level.
* Some functions are now implemented with new display mode Mosaic. That implies plotting 3D maps in multiple columns and rows in a single axes.- Drop nilearn-fix-aarch64.patch
* Fri Jan 29 2021 codeAATTbnavigator.de- Skip python36 build because Tumbleweed updates to SciPy 1.6.0 which dropped support for Python 3.6 (NEP 29)
* Fri Nov 20 2020 guillaume.gardetAATTopensuse.org- Add runtime deps: python-requests
* Mon Nov 16 2020 guillaume.gardetAATTopensuse.org- Update to 0.7.0- Add patch to fix aarch64 test:
* nilearn-fix-aarch64.patch- Drop upstreamed patches:
* fix-test_save_cmap.patch
* update-numpy-warning.patch- Disable \'test_clean_confounds\' and \'test_reorder_img_mirror \' until we have a fix. See: https://github.com/nilearn/nilearn/issues/2608 https://github.com/nilearn/nilearn/issues/2610
* Wed Oct 14 2020 guillaume.gardetAATTopensuse.org- Backport patches to fix some tests:
* update-numpy-warning.patch - https://github.com/nilearn/nilearn/pull/2530
* fix-test_save_cmap.patch - https://github.com/nilearn/nilearn/pull/2543
* Wed Apr 29 2020 tchvatalAATTsuse.com- Use xdist to speedup the tests to take less than 30 mins
* Thu Jan 30 2020 toddrme2178AATTgmail.com- Update to version 0.6.1 + ENHANCEMENTS
* html pages use the user-provided plot title, if any, as their title + Fixes
* Fetchers for developmental_fmri and localizer datasets resolve URLs correctly.
* Mon Jan 06 2020 toddrme2178AATTgmail.com- Update to version 0.6.0 + HIGHLIGHTS
* Python2 and 3.4 are no longer supported. We recommend upgrading to Python 3.6 minimum.
* Support for Python3.5 wil be removed in the 0.7.x release. Users with a Python3.5 environment will be warned at their first Nilearn import.
* joblib is now a dependency
* Minimum supported versions of packages have been bumped up. > Matplotlib -- v2.0 > Scikit-learn -- v0.19 > Scipy -- v0.19 + NEW
* A new method for :class:`nilearn.input_data.NiftiMasker` instances for generating reports viewable in a web browser, Jupyter Notebook, or VSCode.
* A new function :func:`nilearn.image.get_data` to replace the deprecated nibabel method `Nifti1Image.get_data`. Now use `nilearn.image.get_data(img)` rather than `img.get_data()`. This is because Nibabel is removing the `get_data` method. You may also consider using the Nibabel `Nifti1Image.get_fdata`, which returns the data cast to floating-point. See https://github.com/nipy/nibabel/wiki/BIAP8 . As a benefit, the `get_data` function works on niimg-like objects such as filenames (see http://nilearn.github.io/manipulating_images/input_output.html ).
* Parcellation method ReNA: Fast agglomerative clustering based on recursive nearest neighbor grouping. Yields very fast & accurate models, without creation of giant clusters.
* Plot connectome strength Use :func:`nilearn.plotting.plot_connectome_strength` to plot the strength of a connectome on a glass brain. Strength is absolute sum of the edges at a node.
* Optimization to image resampling
* New brain development fMRI dataset fetcher :func:`nilearn.datasets.fetch_development_fmri` can be used to download movie-watching data in children and adults. A light-weight dataset implemented for teaching and usage in the examples. All the connectivity examples are changed from ADHD to brain development fmri dataset. + ENHANCEMENTS
* :func:`nilearn.plotting.view_img_on_surf`, :func:`nilearn.plotting.view_surf` and :func:`nilearn.plotting.view_connectome` can display a title, and allow disabling the colorbar, and setting its height and the fontsize of its ticklabels.
* Rework of the standardize-options of :func:`nilearn.signal.clean` and the various Maskers in `nilearn.input_data`. You can now set `standardize` to `zscore` or `psc`. `psc` stands for `Percent Signal Change`, which can be a meaningful metric for BOLD.
* Class :class:`nilearn.input_data.NiftiLabelsMasker` now accepts an optional `strategy` parameter which allows it to change the function used to reduce values within each labelled ROI. Available functions include mean, median, minimum, maximum, standard_deviation and variance. This change is also introduced in :func:`nilearn.regions.img_to_signals_labels`.
* :func:`nilearn.plotting.view_surf` now accepts surface data provided as a file path. + CHANGES
* :func:`nilearn.plotting.plot_img` now has explicit keyword arguments `bg_img`, `vmin` and `vmax` to control the background image and the bounds of the colormap. These arguments were already accepted in `kwargs` but not documented before. + FIXES
* :class:`nilearn.input_data.NiftiLabelsMasker` no longer truncates region means to their integral part when input images are of integer type.
* The arg `version=\'det\'` in :func:`nilearn.datasets.fetch_atlas_pauli_2017` now works as expected.
* `pip install nilearn` now installs the necessary dependencies.
* Lots of other fixes in documentation and examples. More detailed change list follows:- Drop python2 support
* Fri Jul 26 2019 toddrme2178AATTgmail.com- Initial version