python-nilearn-0.6.2-bp152.1.1

- Use xdist to speedup the tests to take less than 30 mins

- 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.

- 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

- Initial version