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        "author": "Tom Dupre la Tour",
        "author_email": "tom.dupre-la-tour@m4x.org",
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        "description": "=============================\nGetting Started with pactools\n=============================\n\n.. image:: https://travis-ci.org/pactools/pactools.svg?branch=master\n    :target: https://travis-ci.org/pactools/pactools\n    :alt: Build Status\n\n.. image:: https://codecov.io/gh/pactools/pactools/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/pactools/pactools\n    :alt: Test coverage\n\n.. image:: https://img.shields.io/badge/python-2.7-blue.svg\n    :target: https://github.com/pactools/pactools\n    :alt: Python27\n\n.. image:: https://img.shields.io/badge/python-3.6-blue.svg\n    :target: https://github.com/pactools/pactools\n    :alt: Python36\n\nThis package provides tools to estimate **phase-amplitude coupling (PAC)**\nin neural time series.\n\nIn particular, it implements the **driven auto-regressive (DAR)**\nmodels presented in the reference below [`Dupre la Tour et al. 2017`_].\n\nRead more in the `documentation <https://pactools.github.io>`_.\n\nInstallation\n============\n\nTo install ``pactools``, you first need to install its dependencies::\n\n\tpip install numpy scipy matplotlib scikit-learn\n\nTo enable all features, you will also need to install optional packages::\n\n    pip install mne h5py\n\nThen install ``pactools`` with one of the following two commands:\n\n- Development version::\n\n    pip install git+https://github.com/pactools/pactools.git#egg=pactools\n\n- Latest stable version::\n\n    pip install pactools\n\nTo upgrade, use the ``--upgrade`` flag provided by ``pip``.\n\nTo check if everything worked fine, you can do::\n\n\tpython -c 'import pactools'\n\nand it should not give any error messages.\n\nPhase-amplitude coupling (PAC)\n==============================\nAmong the different classes of cross-frequency couplings,\nphase-amplitude coupling (PAC) - i.e. high frequency activity time-locked\nto a specific phase of slow frequency oscillations - is by far the most\nacknowledged.\nPAC is typically represented with a comodulogram, which shows the strenght of\nthe coupling over a grid of frequencies.\nComodulograms can be computed in `pactools` with more\nthan 10 different methods.\n\n\nDriven auto-regressive (DAR) models\n===================================\nOne of the method is based on driven auto-regressive (DAR) models.\nAs this method models the entire spectrum simultaneously, it avoids the\npitfalls related to incorrect filtering or the use of the Hilbert transform\non wide-band signals. As the model is probabilistic, it also provides a\nscore of the model **goodness of fit** via the likelihood, enabling easy\nand legitimate model selection and parameter comparison;\nthis data-driven feature is unique to such model-based approach.\n\nWe recommend using DAR models to estimate PAC in neural time-series.\nMore detail in [`Dupre la Tour et al. 2017`_].\n\n\nAcknowledgment\n==============\n\nThis work was supported by the ERC Starting Grant SLAB ERC-YStG-676943 to\nAlexandre Gramfort, the ERC Starting Grant MindTime ERC-YStG-263584 to Virginie\nvan Wassenhove, the ANR-16-CE37-0004-04 AutoTime to Valerie Doyere and Virginie\nvan Wassenhove, and the Paris-Saclay IDEX NoTime to Valerie Doyere, Alexandre\nGramfort and Virginie van Wassenhove,\n\nCite this work\n==============\n\nIf you use this code in your project, please cite\n[`Dupre la Tour et al. 2017`_]:\n\n\n.. code-block::\n\n    @article{duprelatour2017nonlinear,\n        author = {Dupr{\\'e} la Tour, Tom and Tallot, Lucille and Grabot, Laetitia and Doy{\\`e}re, Val{\\'e}rie and van Wassenhove, Virginie and Grenier, Yves and Gramfort, Alexandre},\n        journal = {PLOS Computational Biology},\n        publisher = {Public Library of Science},\n        title = {Non-linear auto-regressive models for cross-frequency coupling in neural time series},\n        year = {2017},\n        month = {12},\n        volume = {13},\n        url = {https://doi.org/10.1371/journal.pcbi.1005893},\n        pages = {1-32},\n        number = {12},\n        doi = {10.1371/journal.pcbi.1005893}\n    }\n\n\n.. _Dupre la Tour et al. 2017: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005893",
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