Metadata-Version: 1.2
Name: fmralign
Version: 0.0.2a0
Summary: Functional alignment of fMRI data in Python
Home-page: UNKNOWN
Maintainer: Bertrand Thirion, Thomas Bazeille
Maintainer-email: bertrand.thirion@inria.fr
License: new BSD
Description: # fmralign
        [Functional alignment and template estimation library](https://parietal-inria.github.io/fmralign-docs) for functional Magnetic Resonance Imaging (fMRI) data
        
        This library is meant to be a light-weight Python library that handles functional
        alignment tasks. It is compatible with and inspired from [Nilearn](http://nilearn.github.io).
        
        Alternative implementations of these ideas can be found in the
        [pymvpa](http://www.pymvpa.org) or [brainiak](http://brainiak.org) packages.
        
        ## Getting Started
        
        ### Prerequisites
        
        fmralign requires a Python installation and the following dependencies:
        * Python >= 3.6
        * Numpy >= 1.16.2
        * SciPy >= 1.2.2
        * Scikit-learn >= 0.20
        * Matplotlib >= 3.1.1
        * Nibabel >= 2.5.0
        * Nilearn >= 1.5.0
        
        ### Installation
        
        Open a terminal window, go the location where you want to install it. Then run:
        
        ```
        pip install fmralign
        ```
        
        Or if you want the latest version available (for example to develop):
        
        ```
        git clone https://github.com/Parietal-INRIA/fmralign.git
        cd fmralign
        pip install -e .
        ```
        
        Optionally, if you want to use optimal transport based method, you should also run:
        
        ```
        pip install POT
        ```
        
        You're up and running!
        
        ### Documentation
        
        You can found an introduction to functional alignment, a user guide and some examples
        on how to use the package at https://parietal-inria.github.io/fmralign-docs.
        
        ## License
        
        This project is licensed under the Simplified BSD License.
        
        ## Acknowledgments
        
        This project has received funding from the European Union’s Horizon
        2020 Research and Innovation Programme under Grant Agreement No. 785907
        (HBP SGA2).
        This project was supported by [Digiteo](http://www.digiteo.fr).
        
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 :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
