Metadata-Version: 2.1
Name: mne-features
Version: 0.2
Summary: MNE-Features software for extracting features from multivariate time series
Home-page: UNKNOWN
Maintainer: Jean-Baptiste Schiratti
Maintainer-email: jean.baptiste.schiratti@gmail.com
License: BSD (3-clause)
Download-URL: https://github.com/mne-tools/mne-features.git
Description: MNE-Features
        =========================================
        
        |GitHub Actions|_ |Codecov|_
        
        .. |GitHub Actions| image:: https://github.com/mne-tools/mne-features/actions/workflows/main.yml/badge.svg
        .. _GitHub Actions: https://github.com/mne-tools/mne-features/actions/workflows/main.yml
        
        .. |Codecov| image:: http://codecov.io/github/mne-tools/mne-features/coverage.svg?branch=master
        .. _Codecov: http://codecov.io/github/mne-tools/mne-features?branch=master
        
        This repository provides code for feature extraction with M/EEG data.
        The documentation of the MNE-Features module is available at: `documentation <https://mne-tools.github.io/mne-features/index.html>`_.
        
        Installation
        ------------
        
        To install the package, the simplest way is to use pip to get the latest release::
        
          $ pip install mne-features
        
        or to get the latest version of the code::
        
          $ pip install git+https://github.com/mne-tools/mne-features.git#egg=mne_features
        
        
        Dependencies
        ------------
        
        These are the dependencies to use MNE-Features:
        
        * numpy (>=1.17)
        * matplotlib (>=1.5)
        * scipy (>=1.0)
        * numba (>=0.46.0)
        * llvmlite (>=0.30)
        * scikit-learn (>=0.21)
        * mne (>=0.18.2)
        * PyWavelets (>=0.5.2)
        * pandas (>=0.25)
        
        
        Cite
        ----
        
        If you use this code in your project, please cite::
        
            Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT,
            "An ensemble learning approach to detect epileptic seizures from long intracranial EEG recordings"
            Proc. IEEE ICASSP Conf. 2018
        
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
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
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
