autoreject
==========
|CircleCI|_ |GitHub Actions|_ |Codecov|_ |PyPI|_ |Conda-Forge|_
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.. _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:: https://autoreject.github.io/stable/_images/sphx_glr_plot_auto_repair_001.png
:width: 400
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 >= 3.8**.
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`` (>=1.0)
* ``numpy`` (>=1.20.2)
* ``scipy`` (>=1.6.3)
* ``scikit-learn`` (>=0.24.2)
* ``joblib``
* ``matplotlib`` (>=3.4.0)
Optional dependencies are:
* ``openneuro-py`` (>= 2021.10.1, for fetching data from `OpenNeuro.org <https://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>`_
(see `PyPREP <https://github.com/sappelhoff/pyprep>`_ for a full implementation of the PREP pipeline).
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://autoreject.github.io/stable/_images/sphx_glr_plot_auto_repair_001.png>`_.
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.
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"description": "autoreject\n==========\n\n|CircleCI|_ |GitHub Actions|_ |Codecov|_ |PyPI|_ |Conda-Forge|_\n\n.. |CircleCI| image:: https://circleci.com/gh/autoreject/autoreject/tree/master.svg?style=shield&circle-token=:circle-token\n.. _CircleCI: https://circleci.com/gh/autoreject/autoreject\n\n.. |GitHub Actions| image:: https://github.com/autoreject/autoreject/actions/workflows/test.yml/badge.svg\n.. _GitHub Actions: https://github.com/autoreject/autoreject/actions/workflows/test.yml\n\n.. |Codecov| image:: http://codecov.io/github/autoreject/autoreject/coverage.svg?branch=master\n.. _Codecov: http://codecov.io/github/autoreject/autoreject?branch=master\n\n.. |PyPI| image:: https://badge.fury.io/py/autoreject.svg\n.. _PyPI: https://badge.fury.io/py/autoreject\n\n.. |Conda-Forge| image:: https://img.shields.io/conda/vn/conda-forge/autoreject.svg\n.. _Conda-Forge: https://anaconda.org/conda-forge/autoreject/\n\nThis is a library to automatically reject bad trials and repair bad sensors in magneto-/electroencephalography (M/EEG) data.\n\n.. image:: https://autoreject.github.io/stable/_images/sphx_glr_plot_auto_repair_001.png\n :width: 400\n\n\nThe documentation can be found under the following links:\n\n- for the `stable release <https://autoreject.github.io/stable/index.html>`_\n- for the `latest (development) version <https://autoreject.github.io/dev/index.html>`_\n\n.. docs_readme_include_label\n\nInstallation\n------------\n\nWe recommend the `Anaconda Python distribution <https://www.anaconda.com/>`_\nand a **Python version >= 3.8**.\nTo obtain the stable release of ``autoreject``, you can use ``pip``::\n\n pip install -U autoreject\n\nOr ``conda``::\n\n conda install -c conda-forge autoreject\n\nIf you want the latest (development) version of ``autoreject``, use::\n\n pip install https://api.github.com/repos/autoreject/autoreject/zipball/master\n\nIf you do not have admin privileges on the computer, use the ``--user`` flag\nwith `pip`.\n\nTo check if everything worked fine, you can do::\n\n python -c 'import autoreject'\n\nand it should not give any error messages.\n\nBelow, we list the dependencies for ``autoreject``.\nAll required dependencies are installed automatically when you install ``autoreject``.\n\n* ``mne`` (>=1.0)\n* ``numpy`` (>=1.20.2)\n* ``scipy`` (>=1.6.3)\n* ``scikit-learn`` (>=0.24.2)\n* ``joblib``\n* ``matplotlib`` (>=3.4.0)\n\nOptional dependencies are:\n\n* ``openneuro-py`` (>= 2021.10.1, for fetching data from `OpenNeuro.org <https://openneuro.org>`_)\n\nQuickstart\n==========\n\nThe easiest way to get started is to copy the following three lines of code\nin your script:\n\n.. code:: python\n\n\t>>> from autoreject import AutoReject\n\t>>> ar = AutoReject()\n\t>>> epochs_clean = ar.fit_transform(epochs) # doctest: +SKIP\n\nThis will automatically clean an `epochs` object read in using MNE-Python. To get the\nrejection dictionary, simply do:\n\n.. code:: python\n\n\t>>> from autoreject import get_rejection_threshold\n\t>>> reject = get_rejection_threshold(epochs) # doctest: +SKIP\n\nWe also implement RANSAC from the `PREP pipeline <https://doi.org/10.3389/fninf.2015.00016>`_\n(see `PyPREP <https://github.com/sappelhoff/pyprep>`_ for a full implementation of the PREP pipeline).\nThe API is the same:\n\n.. code:: python\n\n\t>>> from autoreject import Ransac\n\t>>> rsc = Ransac()\n\t>>> epochs_clean = rsc.fit_transform(epochs) # doctest: +SKIP\n\nFor more details check out the example to\n`automatically detect and repair bad epochs <https://autoreject.github.io/stable/_images/sphx_glr_plot_auto_repair_001.png>`_.\n\nBug reports\n===========\n\nPlease use the `GitHub issue tracker <https://github.com/autoreject/autoreject/issues>`_ to report bugs.\n\nCite\n====\n\n[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>`_.\"\nIn 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016.\n\n[2] Mainak Jas, Denis Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre Gramfort. 2017.\n\"`Autoreject: Automated artifact rejection for MEG and EEG data <http://www.sciencedirect.com/science/article/pii/S1053811917305013>`_\".\nNeuroImage, 159, 417-429.\n",
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