File python-nilearn.changes of Package python-nilearn
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Wed Apr 29 12:53:13 UTC 2020 - Tomáš Chvátal <tchvatal@suse.com>
- Use xdist to speedup the tests to take less than 30 mins
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Thu Jan 30 15:59:31 UTC 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.
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Mon Jan 6 15:55:04 UTC 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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Fri Jul 26 18:00:50 UTC 2019 - Todd R <toddrme2178@gmail.com>
- Initial version
