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| Name: python3-nilearn | Distribution: SUSE Linux Enterprise 15 SP6 |
| Version: 0.7.1 | Vendor: openSUSE |
| Release: bp156.3.2 | Build date: Tue May 21 03:31:33 2024 |
| Group: Development/Languages/Python | Build host: i04-ch1a |
| Size: 10757254 | Source RPM: python-nilearn-0.7.1-bp156.3.2.src.rpm |
| Packager: https://bugs.opensuse.org | |
| Url: https://github.com/nilearn/nilearn | |
| Summary: Statistical learning tool for neuroimaging | |
Nilearn is a Python module for statistical learning on NeuroImaging data.
BSD-3-Clause
* Fri Apr 09 2021 Markéta Machová <mmachova@suse.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 Ben Greiner <code@bnavigator.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 GARDET <guillaume.gardet@opensuse.org>
- Add runtime deps: python-requests
* Mon Nov 16 2020 Guillaume GARDET <guillaume.gardet@opensuse.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 GARDET <guillaume.gardet@opensuse.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 Tomáš Chvátal <tchvatal@suse.com>
- Use xdist to speedup the tests to take less than 30 mins
* Thu Jan 30 2020 Todd R <toddrme2178@gmail.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 Todd R <toddrme2178@gmail.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 Todd R <toddrme2178@gmail.com>
- Initial version
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Fabrice Bellet, Sun Mar 9 14:56:22 2025