Metadata-Version: 2.1
Name: autoreject
Version: 0.3
Summary: Automated rejection and repair of epochs in M/EEG.
Home-page: http://autoreject.github.io/
Maintainer: Mainak Jas
Maintainer-email: mainakjas@gmail.com
License: BSD-3-Clause
Download-URL: https://github.com/autoreject/autoreject.git
Project-URL: Documentation, http://autoreject.github.io/
Project-URL: Bug Reports, https://github.com/autoreject/autoreject/issues
Project-URL: Source, https://github.com/autoreject/autoreject
Description: autoreject
        ==========
        
        |CircleCI|_ |GitHub Actions|_ |Codecov|_ |PyPI|_ |Conda-Forge|_
        
        .. |CircleCI| image:: https://circleci.com/gh/autoreject/autoreject/tree/master.svg?style=shield&circle-token=:circle-token
        .. _CircleCI: https://circleci.com/gh/autoreject/autoreject
        
        .. |GitHub Actions| image:: https://github.com/autoreject/autoreject/actions/workflows/test.yml/badge.svg
        .. _GitHub Actions: https://github.com/autoreject/autoreject/actions/workflows/test.yml
        
        .. |Codecov| image:: http://codecov.io/github/autoreject/autoreject/coverage.svg?branch=master
        .. _Codecov: http://codecov.io/github/autoreject/autoreject?branch=master
        
        .. |PyPI| image:: https://badge.fury.io/py/autoreject.svg
        .. _PyPI: https://badge.fury.io/py/autoreject
        
        .. |Conda-Forge| image:: https://img.shields.io/conda/vn/conda-forge/autoreject.svg
        .. _Conda-Forge: https://anaconda.org/conda-forge/autoreject/
        
        This is a library to automatically reject bad trials and repair bad sensors in magneto-/electroencephalography (M/EEG) data.
        
        .. image:: http://autoreject.github.io/_images/sphx_glr_plot_visualize_bad_epochs_002.png
        
        
        The documentation can be found under the following links:
        
        - for the `stable release <https://autoreject.github.io/stable/index.html>`_
        - for the `latest (development) version <https://autoreject.github.io/dev/index.html>`_
        
        .. docs_readme_include_label
        
        Installation
        ------------
        
        We recommend the `Anaconda Python distribution <https://www.anaconda.com/>`_
        and a **Python version >= 3.7**.
        To obtain the stable release of ``autoreject``, you can use ``pip``::
        
            pip install -U autoreject
        
        Or ``conda``::
        
            conda install -c conda-forge autoreject
        
        If you want the latest (development) version of ``autoreject``, use::
        
            pip install https://api.github.com/repos/autoreject/autoreject/zipball/master
        
        If you do not have admin privileges on the computer, use the ``--user`` flag
        with `pip`.
        
        To check if everything worked fine, you can do::
        
            python -c 'import autoreject'
        
        and it should not give any error messages.
        
        Below, we list the dependencies for ``autoreject``.
        All required dependencies are installed automatically when you install ``autoreject``.
        
        * ``mne`` (>=0.24)
        * ``numpy`` (>=1.20)
        * ``scipy`` (>=1.6)
        * ``scikit-learn`` (>=0.24)
        * ``joblib``
        * ``matplotlib`` (>=3.3)
        
        Optional dependencies are:
        
        * ``tqdm`` (for nice progress-bars when setting ``verbose=True``)
        * ``h5py`` (for writing ``autoreject`` objects using the HDF5 format)
        * ``openneuro-py`` (>= 2021.7, for fetching data from OpenNeuro.org)
        
        Quickstart
        ==========
        
        The easiest way to get started is to copy the following three lines of code
        in your script:
        
        .. code:: python
        
        	>>> from autoreject import AutoReject
        	>>> ar = AutoReject()
        	>>> epochs_clean = ar.fit_transform(epochs)  # doctest: +SKIP
        
        This will automatically clean an `epochs` object read in using MNE-Python. To get the
        rejection dictionary, simply do:
        
        .. code:: python
        
        	>>> from autoreject import get_rejection_threshold
        	>>> reject = get_rejection_threshold(epochs)  # doctest: +SKIP
        
        We also implement RANSAC from the `PREP pipeline <https://doi.org/10.3389/fninf.2015.00016>`_.
        The API is the same:
        
        .. code:: python
        
        	>>> from autoreject import Ransac
        	>>> rsc = Ransac()
        	>>> epochs_clean = rsc.fit_transform(epochs)  # doctest: +SKIP
        
        For more details check out the example to
        `automatically detect and repair bad epochs <https://circleci.com/api/v1.1/project/github/autoreject/autoreject/latest/artifacts/0/html/auto_examples/plot_auto_repair.html#sphx-glr-auto-examples-plot-auto-repair-py?branch=master>`_.
        
        Bug reports
        ===========
        
        Please use the `GitHub issue tracker <https://github.com/autoreject/autoreject/issues>`_ to report bugs.
        
        Cite
        ====
        
        [1] Mainak Jas, Denis Engemann, Federico Raimondo, Yousra Bekhti, and Alexandre Gramfort, "`Automated rejection and repair of bad trials in MEG/EEG <https://hal.archives-ouvertes.fr/hal-01313458/document>`_."
        In 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016.
        
        [2] Mainak Jas, Denis Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre Gramfort. 2017.
        "`Autoreject: Automated artifact rejection for MEG and EEG data <http://www.sciencedirect.com/science/article/pii/S1053811917305013>`_".
        NeuroImage, 159, 417-429.
        
Keywords: electroencephalography eeg magnetoencephalography meg preprocessing analysis
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: ~=3.7
Description-Content-Type: text/x-rst
Provides-Extra: full
Provides-Extra: test
Provides-Extra: doc
