Metadata-Version: 2.1
Name: pyprep
Version: 0.3.0
Summary: A Python implementation of the preprocessing pipeline (PREP) for EEG data.
Home-page: https://github.com/sappelhoff/pyprep
Author: pyprep developers
Maintainer: Stefan Appelhoff
Maintainer-email: stefan.appelhoff@mailbox.org
License: MIT
Project-URL: Documentation, https://pyprep.readthedocs.io/en/latest
Project-URL: Bug Reports, https://github.com/sappelhoff/pyprep/issues
Project-URL: Source, https://github.com/sappelhoff/pyprep
Keywords: EEG artifact preprocessing data
Platform: any
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Requires-Python: >=3.6
Description-Content-Type: text/x-rst; charset=UTF-8
Requires-Dist: numpy (>=1.14.1)
Requires-Dist: scipy (>=1.0.0)
Requires-Dist: statsmodels (>=0.8.0)
Requires-Dist: mne (>=0.20.0)
Requires-Dist: psutil (>=5.4.3)



.. image:: https://github.com/sappelhoff/pyprep/workflows/Python%20tests/badge.svg
   :target: https://github.com/sappelhoff/pyprep/actions?query=workflow%3A%22Python+tests%22
   :alt: Python tests


.. image:: https://codecov.io/gh/sappelhoff/pyprep/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/sappelhoff/pyprep
   :alt: codecov


.. image:: https://readthedocs.org/projects/pyprep/badge/?version=latest
   :target: http://pyprep.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation Status


.. image:: https://badge.fury.io/py/pyprep.svg
   :target: https://badge.fury.io/py/pyprep
   :alt: PyPI version


pyprep
======

For documentation, see the:

- `stable documentation <http://pyprep.readthedocs.io/en/stable/>`_
- `latest (development) documentation <http://pyprep.readthedocs.io/en/latest/>`_

.. docs_readme_include_label

``pyprep`` is a python implementation of the
`Preprocessing Pipeline (PREP) <https://doi.org/10.3389/fninf.2015.00016>`_ for
EEG data, working with `MNE-Python <https://www.martinos.org/mne/stable/index.html>`_
for EEG data processing and analysis. Also contains a function to detect
outlier epochs inspired by the FASTER algorithm.

**ALPHA SOFTWARE.**
**This package is currently in its early stages of iteration.**
**It may change both its internals or its user-facing API in the near future.**
**Any feedback and ideas on how to improve either of these is more than welcome!**
**Use this software at your own risk.**


Installation
============

``pyprep`` requires Python version ``3.6`` or higher to run properly.
We recommend to run ``pyprep`` in a dedicated virtual environment
(using e.g., `conda <https://docs.conda.io/en/latest/miniconda.html>`_).

For installing the **stable** version of ``pyprep``, simply call
``pip install pyprep``.
This should install dependencies automatically, which are defined in the
``setup.cfg`` file in the ``options.install_requires`` section.

For installation of the **development** version use:

.. code-block:: Text

   git clone https://github.com/sappelhoff/pyprep
   cd pyprep
   pip install -r requirements-dev.txt
   pre-commit install
   pip install -e .

Contributions
=============

**We are actively looking for contributors!**

Please chime in with your ideas on how to improve this software by opening
a GitHub issue, or submitting a pull request.

See also our `CONTRIBUTING.md <https://github.com/sappelhoff/pyprep/blob/master/.github/CONTRIBUTING.md>`_
file for help with submitting a pull request.

References
==========

1. Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., & Robbins, K. A.
   (2015). The PREP pipeline: standardized preprocessing for large-scale EEG
   analysis. Frontiers in Neuroinformatics, 9, 16. doi:
   `10.3389/fninf.2015.00016 <https://doi.org/10.3389/fninf.2015.00016>`_

2. Nolan, H., Whelan, R., & Reilly, R. B. (2010). FASTER: fully automated
   statistical thresholding for EEG artifact rejection. Journal of neuroscience
   methods, 192(1), 152-162. doi:
   `10.1016/j.jneumeth.2010.07.015 <https://doi.org/10.1016/j.jneumeth.2010.07.015>`_


