Metadata-Version: 1.2
Name: pactools
Version: 0.3
Summary: Estimation of phase-amplitude coupling (PAC) in neural time series,
           including with driven auto-regressive (DAR) models.
Home-page: http://github.com/pactools/pactools
Maintainer: Tom Dupre la Tour
Maintainer-email: tom.dupre-la-tour@m4x.org
License: BSD (3-clause)
Download-URL: https://github.com/pactools/pactools.git
Description: =============================
        Getting Started with pactools
        =============================
        
        .. image:: https://travis-ci.org/pactools/pactools.svg?branch=master
            :target: https://travis-ci.org/pactools/pactools
            :alt: Build Status
        
        .. image:: https://codecov.io/gh/pactools/pactools/branch/master/graph/badge.svg
            :target: https://codecov.io/gh/pactools/pactools
            :alt: Test coverage
        
        .. image:: https://img.shields.io/badge/python-2.7-blue.svg
            :target: https://github.com/pactools/pactools
            :alt: Python27
        
        .. image:: https://img.shields.io/badge/python-3.6-blue.svg
            :target: https://github.com/pactools/pactools
            :alt: Python36
        
        This package provides tools to estimate **phase-amplitude coupling (PAC)**
        in neural time series.
        
        In particular, it implements the **driven auto-regressive (DAR)**
        models presented in the reference below [`Dupre la Tour et al. 2017`_].
        
        Read more in the `documentation <https://pactools.github.io>`_.
        
        Installation
        ============
        
        To install ``pactools``, you first need to install its dependencies::
        
        	pip install numpy scipy matplotlib scikit-learn
        
        To enable all features, you will also need to install optional packages::
        
            pip install mne h5py
        
        Then install ``pactools`` with one of the following two commands:
        
        - Development version::
        
            pip install git+https://github.com/pactools/pactools.git#egg=pactools
        
        - Latest stable version::
        
            pip install pactools
        
        To upgrade, use the ``--upgrade`` flag provided by ``pip``.
        
        To check if everything worked fine, you can do::
        
        	python -c 'import pactools'
        
        and it should not give any error messages.
        
        Phase-amplitude coupling (PAC)
        ==============================
        Among the different classes of cross-frequency couplings,
        phase-amplitude coupling (PAC) - i.e. high frequency activity time-locked
        to a specific phase of slow frequency oscillations - is by far the most
        acknowledged.
        PAC is typically represented with a comodulogram, which shows the strenght of
        the coupling over a grid of frequencies.
        Comodulograms can be computed in `pactools` with more
        than 10 different methods.
        
        
        Driven auto-regressive (DAR) models
        ===================================
        One of the method is based on driven auto-regressive (DAR) models.
        As this method models the entire spectrum simultaneously, it avoids the
        pitfalls related to incorrect filtering or the use of the Hilbert transform
        on wide-band signals. As the model is probabilistic, it also provides a
        score of the model **goodness of fit** via the likelihood, enabling easy
        and legitimate model selection and parameter comparison;
        this data-driven feature is unique to such model-based approach.
        
        We recommend using DAR models to estimate PAC in neural time-series.
        More detail in [`Dupre la Tour et al. 2017`_].
        
        
        Acknowledgment
        ==============
        
        This work was supported by the ERC Starting Grant SLAB ERC-YStG-676943 to
        Alexandre Gramfort, the ERC Starting Grant MindTime ERC-YStG-263584 to Virginie
        van Wassenhove, the ANR-16-CE37-0004-04 AutoTime to Valerie Doyere and Virginie
        van Wassenhove, and the Paris-Saclay IDEX NoTime to Valerie Doyere, Alexandre
        Gramfort and Virginie van Wassenhove,
        
        Cite this work
        ==============
        
        If you use this code in your project, please cite
        [`Dupre la Tour et al. 2017`_]:
        
        
        .. code-block::
        
            @article{duprelatour2017nonlinear,
                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},
                journal = {PLOS Computational Biology},
                publisher = {Public Library of Science},
                title = {Non-linear auto-regressive models for cross-frequency coupling in neural time series},
                year = {2017},
                month = {12},
                volume = {13},
                url = {https://doi.org/10.1371/journal.pcbi.1005893},
                pages = {1-32},
                number = {12},
                doi = {10.1371/journal.pcbi.1005893}
            }
        
        
        .. _Dupre la Tour et al. 2017: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005893
        
Platform: UNKNOWN
