Metadata-Version: 2.1
Name: nilearn
Version: 0.7.1
Summary: Statistical learning for neuroimaging in Python
Home-page: http://nilearn.github.io
Maintainer: Gael Varoquaux
Maintainer-email: gael.varoquaux@normalesup.org
License: new BSD
Download-URL: http://nilearn.github.io
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5
Requires-Dist: numpy (>=1.11)
Requires-Dist: scipy (>=0.19)
Requires-Dist: scikit-learn (>=0.19)
Requires-Dist: joblib (>=0.12)
Requires-Dist: nibabel (>=2.0.2)
Requires-Dist: pandas (>=0.18.0)
Requires-Dist: requests (>=2)

.. -*- mode: rst -*-

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nilearn
=======

Nilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive documentation & friendly community.

It supports general linear model (GLM) based analysis and leverages the `scikit-learn <http://scikit-learn.org>`_ Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

Important links
===============

- Official source code repo: https://github.com/nilearn/nilearn/
- HTML documentation (stable release): http://nilearn.github.io/

Dependencies
============

The required dependencies to use the software are:

* Python >= 3.5,
* setuptools
* Numpy >= 1.11
* SciPy >= 0.19
* Scikit-learn >= 0.19
* Joblib >= 0.12
* Nibabel >= 2.0.2

If you are using nilearn plotting functionalities or running the
examples, matplotlib >= 1.5.1 is required.

If you want to run the tests, you need pytest >= 3.9 and pytest-cov for coverage reporting.


Install
=======

First make sure you have installed all the dependencies listed above.
Then you can install nilearn by running the following command in
a command prompt::

    pip install -U --user nilearn

More detailed instructions are available at
http://nilearn.github.io/introduction.html#installation.

Development
===========

Detailed instructions on how to contribute are available at
http://nilearn.github.io/development.html


