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| Name: python311-nilearn | Distribution: openSUSE Tumbleweed |
| Version: 0.13.1 | Vendor: openSUSE |
| Release: 1.1 | Build date: Wed Mar 11 14:01:43 2026 |
| Group: Unspecified | Build host: reproducible |
| Size: 23365783 | Source RPM: python-nilearn-0.13.1-1.1.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
* Wed Mar 11 2026 Daniel Garcia <daniel.garcia@suse.com>
- Update to 0.13.1:
This is mostly a release to fix some bugs introduced in 0.13.0 and
to ensure better compatibility with scikit-learn 1.8.0.
[#] Fixes
- Code Use sklearn HTML representation of estimators and their
parameters in notebooks and reports (#5925 by Rémi Gau).
- Code Only throw warning about non-interactive plotting backend
when using nilearn.plotting.show (#5929 by Rémi Gau).
- Code Re-allow use non-multi maskers for Parcellations (#5930 by
Rémi Gau).
- Code Change default slice order of slicers and projectors to be x,
y, z. (#5944 by Hande Gözükan).
- Doc Fix broken scikit-learn contributing anchor in the docs to
avoid linkcheck failures (#5983 by Mohammad Sadeghi Hardengi).
- Plotting Drop background color when using look up table as
colormap (#5936 by Rémi Gau).
- Plotting Add support for dictionary as cut_coords for MosaicSlicer
and image plotting functions. (#5920 by Hande Gözükan).
[#] Changes
- Deprecation The default for the parameter return_masked_atlas of
img_to_signals_labels to True. This deprecation was planned for
0.13.0 but missed. The parameter will be removed in version >=
0.15 (#5942 by Rémi Gau).
- Update to 0.13.0:
* Support for Python 3.9 has been dropped.
* Minimum supported versions of the following packages have been bumped up:
- matplotlib -- 3.8.0
- scipy -- 1.9.0
- requests -- 2.30.0
* A new dependency has been added:
- jinja2 -- 3.1.2
* A dependency has been removed:
- lxml
* Full changelog can be found at: https://nilearn.github.io/stable/changes/whats_new.html
* Thu Aug 14 2025 Steve Kowalik <steven.kowalik@suse.com>
- Update to 0.12.0:
* Use figure_to_svg_base64 instead of figure_to_png_base64
* Fix two-sided image thresholding
* convert labels to string in labels in dataset fetchers
* Convert Schaeffer atlas labels to list of strings
* improve compliance with sklearn checks
* adapt surf_mesh fixture
* Introduce clean_args for nifti maskers to replace paramaters passed via
kwargs
* fix issues regarding using two_sided and negative plotting threshold
during plotting
* ensure datasets do not contain
* Do not set score=0 if all decoder.coef_ ==0
* update thresolding in paired / unpaired t-test example
* Specify output dtype of SurfaceLabelsMasker.transform
* ensure all nifti maskers throw warning when dealing with non finite mask
* return design matrices of FIAC as dataframes
* Update parameters to comply with run time checks
* Assign most frequent value in the neighborhood for atlases via
vol_to_surf with method='nearest_most_frequent'
* set xlim for plot events
* ensure maskers can be refitted
* reduce number of warning during searchligh
* throw warning when meshes are unequal but have compatible number of
vertices
* fix filenames when saving GLM to BIDS
* plot_mask_large_fmri.py oversubscribing threads
* use legacy=False to parse FLM filenames inputs as bids files
* fetch full history to append new results to older results
* use property region_names_ in surface label examples
* rely in __sklearn_tags__ for sklearn > 1.5.2 and on _more_tags otherwise
* Fixes extra resampling when plotting with transparency
* add stacklevel to warnings in plotting
* hide set_output from doc and raise not implemeted error if called
* ensure that multi maskers can clean with confounds and high variance
confounds
* avoid masking out vertices with zero-valued timepoints
* ensure consistent output dimension for inverse_transform of
NiftiMapsMasker and NiftiLabelsMasker
* incorrect dtype.itemsize check in _ensure_float
* labels do not need to be numeric for searlight
* prevent negative threshold for surface plots
* turn _sanitize_cleaning_parameters into a private method
* warn when some attributes passed at first level model init will be
ignored at fit time
* fix input connectivity matrices
* nilearn.plotting.surface function params
- Drop patch sklearn-1.16.1.patch, included upstream.
* Tue Mar 04 2025 Guillaume GARDET <guillaume.gardet@opensuse.org>
- Enable again test_clean_confounds as fixed upstream
https://github.com/nilearn/nilearn/issues/2608
* Thu Feb 06 2025 Markéta Machová <mmachova@suse.com>
- Update to 0.11.1
* Support for Python 3.8 has been dropped.
* We recommend upgrading to Python 3.11 or above.
* This version is the first one to support Python 3.13.
* This version includes a new “surface” API that allow to represent
surface meshes and data.
* PolyMesh (and PolyData) can be used to instantiate mesh (and
respectively data) objects with both hemisphere.
* SurfaceImage are objects that hold meshes and data for both hemispheres.
* Surface plotting functions have been adapted to work with SurfaceImage
object.
* Remove the unused argument url from fetch_localizer_contrasts,
fetch_localizer_calculation_task and fetch_localizer_button_task
* Remove the unused argument rank from the constructor of LikelihoodModelResults
* Remove the unused arguments upper_cutoff and exclude_zeros for
compute_multi_background_mask
* The parameter tr for Repetition time will be replaced by t_r in the “HRF”
functions in version 0.13.0.
* The parameter ax will be replaced by axes in plot_contrast_matrix and
plot_design_matrix in release 0.13.0.
* For more changes see upstream changelog.
- Drop support-new-scipy.patch, not needed anymore.
- Add upstream patch sklearn-1.16.1.patch to fix tests
* Wed Oct 16 2024 Steve Kowalik <steven.kowalik@suse.com>
- Update to 0.10.4:
[#]# Highlights
* Use the appropriate HTML templates for nilearn.maskers.MultiNiftiMapsMasker
and nilearn.maskers.MultiNiftiLabelsMasker reports that would lead to
empty reports for MultiNiftiMapsMasker.
* Fix color bar handling with color map with only 1 level.
* Improve colorbar size and labels in mosaic display.
[#]# Fixes
* Fix plotting of carpet plot due to a change in the coming version of
matplolib (3.9.0).
* Fix errant warning when using stat_type in nilearn.glm.compute_contrast.
* Fix when thresholding is applied to images by GLM reports.
* Make masker report tables horizontally scrollable.
* Masker reports tables do not overlap with report image.
* Ensure that region_names_ can be generated by
nilearn.maskers.NiftiSpheresMasker.transform_single_imgs for deterministic
atlases that use floats or non-consecutive values as region_ids whether
they include background in their labels or not.
* Ensure that nilearn.interfaces.bids.save_glm_to_bids serializes numpy
arrays to list used as GLM parameters before saving them to JSON.
* Check that the view parameter in surface plotting functions is a pair of
int or float when it is not a string.
* Fix positions of the markers on the images on the sphere masker reports.
* Fix cut position in nifti maps maskers to match displayed map maximum.
* Make sure that nilearn.maskers.NiftiSpheresMasker reports displays
properly when it contains only 1 sphere.
[#]# Enhancements
* Add an avg_method parameter to nilearn.plotting.plot_surf_stat_map.
- Add patch support-new-scipy.patch, support new scipy changes.
* Tue Feb 20 2024 Ben Greiner <code@bnavigator.de>
- Update to 0.10.3
[#]# Highlights
* API Allow passing arguments to first_level_from_bids to build
first level models that include specific set of confounds by
relying on the strategies from load_confounds (#4103 by Rémi
Gau).
* Support passing t and F contrasts to compute_contrast that that
have fewer columns than the number of estimated parameters.
Remaining columns are padded with zero (#4067 by Rémi Gau).
* NiftiSpheresMasker now has generate_report method (#3102 by
Yasmin Mzayek and Nicolas Gensollen).
* Update the CompCor strategy in load_confounds and
load_confounds_strategy to support fmriprep 21.x series and
above. (#3285 by Hao-Ting Wang).
* Allow setting vmin in plot_glass_brain and plot_stat_map (#3993
by Michelle Wang).
* When plotting thresholded statistical maps with a colorbar, the
threshold value(s) will now be displayed as tick labels on the
colorbar (##2833 by Nicolas Gensollen).
* Combine GLM examples plot_fixed_effect and plot_fiac_analysis
into a a single example plot_two_runs_model (#3191 by Taylor
Salo and Rémi Gau).
[#]# New
* The temp_file_lifetime parameter of interactive plots’
open_in_browser method is deprecated and has no effect (#4180
by Jerome Dockes)..
[#]# Fixes
* MultiNiftiMasker can now call generate_report which will
generate a report for the first subject in the list of subjects
(#4001 by Yasmin Mzayek).
* Make sure that all atlases and datasets fetchers return a
description (#4215 Rémi Gau).
* Update the CompCor strategy in load_confounds and
load_confounds_strategy to support fmriprep 21.x series and
above. (#3285 by Hao-Ting Wang).
* Fix SimpleRegressionResults to accommodate for the lack of a
model attribute (#4071 Rémi Gau)
* clean_img can now use kwargs clean__sample_mask argument to
correctly reshape the nifti image to the dimensions of the mask
in the output (#4051 by Mia Zwally).
* Fix plotting of an image with color bar when maximum value is
exactly zero (#4204 by Rémi Gau).
* Code Fix PSC normalisation method applied by standardize_signal
in signal (#4116 by Hao-Ting Wang and Yasmin Mzayek).
* Fixed inheritance of decoder classes to keep compatibility with
scikit-learn>v1.3 (#4188 by Yasmin Mzayek).
* Make sure that all the fetchers for atlas and func from
nilearn.datasets return a sklearn.utils.Bunch. Add new behavior
to fetch_language_localizer_demo_dataset to return output as
Bunch. This behavior will become the default in version 0.13.0
(#4233 by Rémi Gau).
[#]# Enhancements
* NiftiSpheresMasker now has generate_report method (#3102 by
Yasmin Mzayek and Nicolas Gensollen).
* Allow passing Pandas Series of image filenames to
SecondLevelModel (#4070 by Rémi Gau).
* Allow passing arguments to first_level_from_bids to build first
level models that include specific set of confounds by relying
on the strategies from load_confounds (#4103 by Rémi Gau).
* Support passing t and F contrasts to compute_contrast that that
have fewer columns than the number of estimated parameters.
Remaining columns are padded with zero (#4067 by Rémi Gau).
* Multi-subject maskers’ generate_report method no longer fails
with 5D data but instead shows report of first subject. User
can index input list to show report for different subjects
(#3935 by Yasmin Mzayek).
* Allow global_signal parameter in load_confounds_strategy in
denoise_strategy='compcor' (#4225 by Hao-Ting Wang).
* Add two_sided option for binarize_img (#4121 by Steven
Meisler).
* generate_report now uses appropriate cut coordinates when
functional image is provided (#4099 by Yasmin Mzayek and
Nicolas Gensollen).
* Mention the classification type (all-vs-one) in Decoding of a
dataset after GLM fit for signal extraction (#4122 by Tamer
Gezici).
* Add backslash to homogenize Parcellations
- Release 0.10.2
[#]# Higlights
* Volume plotting functions like plotting.plot_img now have an
optional radiological parameter, defaulting to False. If True,
this will invert the x-axis and L and R annotations to confirm
to radiological conventional view..
* Update Decoder objects to use the more efficient
LogisticRegressionCV.
* Add LassoCV as a new estimator option for Decoder objects
* Add vmin and symmetric_cbar arguments to
nilearn.plotting.plot_img_on_surf.
* Improve contrasts allowing fixed effects on F contrasts
* New experimental surface API to facilitate working with surface
data in downstream surface-based analyses. We provide this API
as a nilearn.experimental.surface module as it is still
incomplete and subject to change without a deprecation cycle..
[#]# NEW
* Volume plotting functions like plotting.plot_img now have an
optional radiological parameter, defaulting to False. If True,
this will invert the x-axis and L and R annotations to confirm
to radiological conventional view..
* New experimental surface API to facilitate working with surface
data in downstream surface-based analyses. We provide this API
as a nilearn.experimental.surface module as it is still
incomplete and subject to change without a deprecation cycle..
[#]# Fixes
* Fix bug in method transform_imgs of
maskers.MultiNiftiMapsMasker and maskers.MultiNiftiLabelsMasker
that would raise an error if a list of sample_mask was
specified to fit_transform.
* Fix bug in nilearn.plotting.surf_plotting._plot_surf_matplotlib
that would make vertices transparent when saving in PDF or SVG
format.
* Fix bug that would prevent using symmetric_cmap=True or the
avg_method argument with plotting.plot_surf_roi.
* Fixes a bug that would lead to an error when loading a
fsaverage surface while relying on numpy >= 1.24.x.
* Fix bug that would prevent loading the confounds of a gifti
file in actual fmriprep datasets.
* Fix bug that prevented using dataframes as input for second
level GLM when computing contrasts.
* Fix bug in glm.first_level.first_level_from_bids that returned
no confound files if the corresponding bold files contained
derivatives BIDS entities.
* Fix bug in glm.first_level.first_level_from_bids that would
throw a warning about slice_time_ref not being provided even
when it was.
* Fix bug where the cv_params_ attribute of fitter Decoder
objects sometimes had missing entries if grid_param is a
sequence of dicts with different keys.
* Make the nilearn.interfaces.fmriprep.load_confounds confounds
file selection more generic.
* Change default figure sizes to prevent titles from overlapping
figure content
* Relax the nilearn.interfaces.fmriprep.load_confounds confounds
selection on cosine as not all confound files contained the
variables.
* Fix pathlib.Path not being counted as Niimg-like object in
image.new_img_like.
* Fix fit_transform behavior to match when fit method is passed
image data
* Allow using both vmin and threshold with "plotly" engine to be
consistent with "matplotlib" behavior
* Set edgecolors to be the same as facecolors in
plotting.plot_surf_contours so that ROI contours are rendered
clearly
* Refactor deprecation of behavior of
datasets.fetch_atlas_craddock_2012,
datasets.fetch_atlas_smith_2009 and
datasets.fetch_atlas_basc_multiscale_2015 fetchers including
using API consistent maps key
[#]# Enhancements
* Add cross-reference links to type definitions in public surface
functions.
* Update Decoder objects to use the more efficient
LogisticRegressionCV.
* Throw warning in glm.first_level.first_level_from_bids when
using event.tsv files with no trial type column.
* Make return key names in the description file of destrieux
surface consistent with datasets.fetch_atlas_surf_destrieux.
* Add LassoCV as a new estimator option for Decoder objects
* Add vmin and symmetric_cbar arguments to
nilearn.plotting.plot_img_on_surf.
* Improve generate_report method of maskers by allowing users to
pass a cmap argument for plotting image
* Improve contrasts allowing fixed effects on F contrasts
[#]# Changes
* Validate the content of events files before plotting them.
* nilearn.glm.first_level.experimental_paradigm.check_events will
now throw a warning if some events have a 0 second duration and
will throw an error if an event has NaN onset or duration.
* Removed old files and test code from deprecated datasets COBRE
and NYU resting state.
* PEP8 and isort compliance extended to the whole nilearn
codebase..
* Finish applying black formatting to most of the codebase..
* Empty region signals resulting from applying mask_img in
maskers.NiftiLabelsMasker will no longer be kept in release
0.15. Meanwhile, use keep_masked_labels parameter when
initializing the maskers.NiftiLabelsMasker object to
enable/disable this behavior..
* Empty region signals resulting from applying mask_img in
maskers.NiftiMapsMasker will no longer be kept in release 0.15.
Meanwhile, use keep_masked_maps parameter when initializing the
maskers.NiftiMapsMasker object to enable/disable this
behavior..
* Removed mention of license in "header".
* Configure plots in example gallery for better rendering
* Make one_mesh_info and full_brain_info into private functions
_one_mesh_info and _full_brain_info
* Refactor error raising tests using context managers
* Added warning to deprecate darkness in
surf_plotting._compute_facecolors_matplotlib and
html_surface._get_vertexcolor
* Replace skipped doctests with default code-blocks
* Move the ~nilearn.plotting.html_surface._mix_colormaps to cm.py
in nilearn.plotting
* Remove deprecation decorator from ~nilearn.glm.regression
* Expose standardize in plotting.plot_carpet and
connectome.ConnectivityMeasure to handle "zscore" deprecation.
* Example
:ref:sphx_glr_auto_examples_01_plotting_plot_prob_atlas.py is
shortened to speed up build time
- Drop numpy-1.25.patch
- Drop warning-based-sklearn-version.patch
* Thu Aug 10 2023 Daniel Garcia <daniel.garcia@suse.com>
- Add numpy-1.25.patch, upstream patch gh#nilearn/nilearn#3746
- Add warning-based-sklearn-version.patch, upstream patch gh#nilearn/nilearn#3763
* Tue Jun 27 2023 Markéta Machová <mmachova@suse.com>
- Update to 0.10.1
* New function load_sample_motor_activation_image to load example
contrast map
* fsaverage meshes accessed through fetch_surf_fsaverage now come
with flat maps for all resolutions
* Improve how first_level_from_bids handles fetching slice timing
metadata and add additional input validation. In release 0.12
the default slice_time_ref will be None instead of 0
* Fixes several bugs in first_level_from_bids.
* Add correct “zscore_sample” strategy to signal._standardize
which will replace the default “zscore” strategy in release 0.13
* Moved packaging from setup.py and setuptools build backend to
pyproject.toml and hatchling backend.
* Restore resample_img compatibility with all nibabel.spatialimages.SpatialImage objects
* Fri Apr 14 2023 Steve Kowalik <steven.kowalik@suse.com>
- Skip a failing array test.
* Mon Feb 06 2023 Daniel Garcia <daniel.garcia@suse.com>
- Update to 0.10.0
* New classes MultiNiftiLabelsMasker and MultiNiftiMapsMasker create maskers
to extract signals from a list of subjects with 4D images using
parallelization (#3237 by Yasmin Mzayek).
* Updated docs with a new theme using furo (#3125 by Alexis Thual).
* permuted_ols and non_parametric_inference now support TFCE statistic (#3196
by Taylor Salo).
* permuted_ols and non_parametric_inference now support cluster-level
Family-wise error correction (#3181 by Taylor Salo).
* save_glm_to_bids has been added, which writes model outputs to disk
according to BIDS convention (#2715 by Taylor Salo).
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Fabrice Bellet, Sun Mar 15 22:19:25 2026