Science Score: 33.0%
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✓DOI references
Found 4 DOI reference(s) in README -
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Low similarity (14.5%) to scientific vocabulary
Keywords
auto-regressive-model
cfc
cross-frequency
dar
models
pac
phase-amplitude
Keywords from Contributors
eeg
meg
neuroimaging
electroencephalography
magnetoencephalography
neuroscience
ecog
electrocorticography
Last synced: 6 months ago
·
JSON representation
Repository
Phase-amplitude coupling (PAC) toolbox
Basic Info
- Host: GitHub
- Owner: pactools
- License: bsd-3-clause
- Language: Python
- Default Branch: master
- Homepage: https://pactools.github.io
- Size: 480 KB
Statistics
- Stars: 98
- Watchers: 10
- Forks: 38
- Open Issues: 15
- Releases: 0
Topics
auto-regressive-model
cfc
cross-frequency
dar
models
pac
phase-amplitude
Created almost 9 years ago
· Last pushed almost 5 years ago
Metadata Files
Readme
License
README.rst
=============================
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 `_.
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.
.. include:: generated/backreferences/pactools.Comodulogram.examples
.. raw:: html
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`_].
.. include:: generated/backreferences/pactools.dar_model.DAR.examples
.. raw:: html
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
GitHub Events
Total
- Watch event: 12
- Pull request event: 1
- Fork event: 3
Last Year
- Watch event: 12
- Pull request event: 1
- Fork event: 3
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Tom Dupré la Tour | t****r@m****g | 231 |
| LGrabot | l****7@i****r | 7 |
| Tom Dupré la Tour | T****T@u****m | 3 |
| Alex Rockhill | a****l@m****g | 2 |
| Fede Raimondo | f****o@d****r | 2 |
| LaetitiaG | l****t@g****m | 2 |
| Alexandre Gramfort | a****t@m****g | 1 |
| Mainak Jas | j****k@u****m | 1 |
| Mikolaj Magnuski | m****i@s****l | 1 |
| mathurinm | m****m@u****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 21
- Total pull requests: 17
- Average time to close issues: about 2 months
- Average time to close pull requests: 14 days
- Total issue authors: 11
- Total pull request authors: 9
- Average comments per issue: 3.19
- Average comments per pull request: 2.06
- Merged pull requests: 11
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- alexrockhill (5)
- lepmik (4)
- mgm248 (3)
- akatav (2)
- mehdikuchi (1)
- jasmainak (1)
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- apoorva6262 (1)
- fraimondo (1)
- TomDLT (1)
- neurofractal (1)
Pull Request Authors
- alexrockhill (6)
- larsoner (2)
- fraimondo (2)
- TomDLT (2)
- jasmainak (1)
- mathurinm (1)
- agramfort (1)
- mmagnuski (1)
- LaetitiaG (1)
Top Labels
Issue Labels
question (6)
enhancement (5)
bug (3)
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Packages
- Total packages: 2
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Total downloads:
- pypi 494 last-month
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Total dependent packages: 1
(may contain duplicates) -
Total dependent repositories: 11
(may contain duplicates) - Total versions: 4
- Total maintainers: 1
pypi.org: pactools
Estimation of phase-amplitude coupling (PAC) in neural time series, including with driven auto-regressive (DAR) models.
- Homepage: http://github.com/pactools/pactools
- Documentation: https://pactools.readthedocs.io/
- License: BSD (3-clause)
-
Latest release: 0.3.1
published over 5 years ago
Rankings
Dependent repos count: 4.4%
Forks count: 7.1%
Stargazers count: 7.9%
Average: 8.2%
Dependent packages count: 10.0%
Downloads: 11.8%
Maintainers (1)
Last synced:
7 months ago
conda-forge.org: pactools
- Homepage: https://pactools.github.io
- License: BSD-3-Clause
-
Latest release: 0.3.1
published about 4 years ago
Rankings
Forks count: 29.0%
Average: 31.9%
Stargazers count: 34.7%
Last synced:
6 months ago
Dependencies
requirements.txt
pypi
- h5py *
- matplotlib *
- mne *
- numpy *
- scikit-learn *
- scipy *
setup.py
pypi
- h5py *
- matplotlib *
- mne *
- numpy *
- scikit-learn *
- scipy *