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| Name: python310-nilearn | Distribution: openSUSE:Factory:zSystems |
| Version: 0.10.1 | Vendor: openSUSE |
| Release: 2.1 | Build date: Thu Aug 10 18:50:15 2023 |
| Group: Development/Languages/Python | Build host: s390zp26 |
| Size: 17142812 | Source RPM: python-nilearn-0.10.1-2.1.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
* 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).
* Tue Oct 11 2022 John Vandenberg <jayvdb@gmail.com>
- Remove fix-test-decoder.patch merged upstream
- Remove use of pytest-xdist which makes failures harder to see
- Update to v0.9.2
* save_glm_to_bids has been added, which writes model outputs to
disk according to BIDS convention
* permuted_ols and non_parametric_inference now support TFCE statistic
* permuted_ols and non_parametric_inference now support cluster-level
Family-wise error correction
* Updated docs with a new theme using furo
* Fix _NEUROVAULT_BASE_URL and _NEUROSYNTH_FETCH_WORDS_URL in
nilearn/datasets/neurovault.py by using https instead of http
* Convert references in nilearn/mass_univariate/permuted_least_squares.py
to use bibtex format
* Update Craddock 2012 parcellation url in nilearn/datasets/atlas.py
* plot_roi failed before when used with the “contours” view type
and passing a list of cut coordinates in display mode “x”, “y” or “z”;
this has been corrected
* plot_markers can now plot a single scatter point
* Fix title display for plot_surf_stat_map. The title argument does
not set the figure title anymore but the axis title.
* load_surf_mesh loaded FreeSurfer specific surface files (e.g. .pial)
with a shift in the coordinates. This is fixed by adding the c_ras
coordinates to the mesh coordinates
* Function nilearn.glm.second_level.second_level._check_second_level_input
now raises an error when flm_object argument is False and
second_level_input is a list of FirstLevelModel
* Function resample_img now warns the user if the provided image has
an sform code equal to 0 or None
* Fix usage of scipy.stats.gamma.pdf in _gamma_difference_hrf function
under nilearn/glm/first_level/hemodynamic_models.py, which resulted
in slight distortion of HRF
* Fix bug introduced due to a fix in the pre-release version of scipy
(1.9.0rc1) which now enforces that elements of a band-pass filter must
meet condition Wn[0] < Wn[1]. Now if band-pass elements are equal
butterworth returns an unfiltered signal with a warning
* The parameter alpha is now correctly passed to plot_glass_brain in
plot_connectome
* Fix plotting of background image in view_img when the background is
not the MNI template
* Fix the typographic error on the page Default Mode Network
extraction of ADHD dataset
* Add sample_masks to fit for censoring time points
* Function run_glm and class FirstLevelModel now accept a random_state
parameter, which allows users to seed the KMeans cluster model used
to estimate AR coefficients
* Conform seeding and docstrings in module _utils.data_gen
* Docstrings of module second_level were improved
* In get_clusters_table, when the center of mass of a binary
cluster falls outside the cluster, report the nearest within-cluster
voxel instead
* Add n_elements_ attribute to masker classes
* Functions expecting string filesystem paths now also accept
path-like objects
* Contributing guidelines now include a recommendation to run flake8
locally on the branch diff with main
* Improvements to permuted_ols and non_parametric_inference with TFCE
statistic runtime
* NiftiLabelsMasker now accepts 1-dimensional arrays in its
inverse_transform method
* Function plot_carpet argument cmap now respects behaviour specified
by docs and changes the color of the carpet_plot. Changing the label
colors is now delegated to a new variable cmap_labels
* Function fetch_surf_fsaverage no longer supports the previously
deprecated option fsaverage5_sphere
* Classes RegressionResults, SimpleRegressionResults, OLSModel,
and LikelihoodModelResults no longer support deprecated shortened
attribute names, including df_resid, wdesign, wresid, norm_resid,
resid, and wY
* Function fetch_openneuro_dataset_index is now deprecated in favor
of the new fetch_ds000030_urls function
* 64-bit integers in Nifti files: some tools such as FSL, SPM and
AFNI cannot handle Nifti images containing 64-bit integers.
To avoid compatibility issues, it is best to avoid writing such
images and in the future trying to create them with nibabel
without explicitly specifying a data type will result in an error.
To avoid this, new_img_like now warns when given int64 arrays
and converts them to int32 when possible (ie when it would not
result in an overflow). Moreover, any atlas fetcher that returned
int64 images now produces images containing smaller ints.
* Refactors fmriprep confound loading such that that the parsing
of the relevant image file and the loading of the confounds are
done in separate steps
* Private submodules, functions, and classes from the decomposition
module now start with a “_” character to make it clear that they
are not part of the public API
* Convert references in nilearn/glm/regression.py and
nilearn/glm/thresholding.py to use footcite/footbibliography
* Boolean input data in new_img_like now defaults to np.uint8
instead of np.int8
* The current behavior of maskers’ transform on 3D niimg inputs,
in which a 2D array is returned, is deprecated, and 1D arrays
will be returned starting in version 0.12
* Fri Sep 16 2022 Guillaume GARDET <guillaume.gardet@opensuse.org>
- Disable multi-thread tests until fixed upstream -- gh#nilearn/nilearn#3359
* Tue Jul 12 2022 Markéta Machová <mmachova@suse.com>
- Update to 0.9.1
* Python 3.6 is deprecated and will be removed in release 0.10
* Nibabel 2.x is no longer supported
* Surface plotting functions can now produce interactive plots
with Plotly
* New module nilearn.interfaces to implement loading and saving
utilities with various interfaces
* New functions load_confounds and load_confounds_strategy to
load confound variables easily from fMRIPrep outputs
* New class HierarchicalKMeans which yields more balanced
clusters than KMeans. It is also callable through Parcellations
using method=hierarchical_kmeans
* Masker objects like NiftiMasker now belong to the new module
nilearn.maskers. The old import style, through the module
input_data, still works but has been deprecated
* Class NiftiMapsMasker can now generate HTML reports in the same
way as NiftiMasker and NiftiLabelsMasker
* The contributing documentation and maintenance pages were improved
* many more changes, see upstream changelog
- Add upstream patch fix-test-decoder.patch
- Delete upstreamed patches:
* nilearn-pr3136-pythoncall.patch
* nilearn-pr3094-py310.patch
* Thu Jan 20 2022 Ben Greiner <code@bnavigator.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 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
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Fabrice Bellet, Sun Feb 23 00:14:44 2025