| Name | pactools JSON |
| Version |
0.3.1
JSON |
| download |
| home_page | http://github.com/pactools/pactools |
| Summary | Estimation of phase-amplitude coupling (PAC) in neural time series, including with driven auto-regressive (DAR) models. |
| upload_time | 2020-11-03 18:43:38 |
| maintainer | Tom Dupre la Tour |
| docs_url | None |
| author | |
| requires_python | |
| license | BSD (3-clause) |
| keywords |
|
| VCS |
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| requirements |
No requirements were recorded.
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=============================
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``, use one of the following two commands:
- Latest stable version::
pip install pactools
- Development version::
pip install git+https://github.com/pactools/pactools.git#egg=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
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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``, use one of the following two commands:\n\n- Latest stable version::\n\n pip install pactools\n\n- Development version::\n\n pip install git+https://github.com/pactools/pactools.git#egg=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\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\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\n\n\n",
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