Metadata-Version: 2.1
Name: fooof
Version: 1.0.0
Summary: fitting oscillations & one-over f
Home-page: https://github.com/fooof-tools/fooof
Author: The Voytek Lab
Author-email: voyteklab@gmail.com
Maintainer: Thomas Donoghue
Maintainer-email: tdonoghue.research@gmail.com
License: Apache License, 2.0
Download-URL: https://github.com/fooof-tools/fooof/releases
Project-URL: Documentation, https://fooof-tools.github.io/fooof/
Project-URL: Bug Reports, https://github.com/fooof-tools/fooof/issues
Project-URL: Source, https://github.com/fooof-tools/fooof
Keywords: neuroscience,neural oscillations,power spectra,1/f,electrophysiology
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Requires-Dist: numpy
Requires-Dist: scipy (>=0.19.0)
Provides-Extra: all
Requires-Dist: matplotlib ; extra == 'all'
Requires-Dist: tqdm ; extra == 'all'
Requires-Dist: pytest ; extra == 'all'
Provides-Extra: plot
Requires-Dist: matplotlib ; extra == 'plot'
Provides-Extra: tests
Requires-Dist: pytest ; extra == 'tests'


FOOOF: Fitting Oscillations & One-Over F

FOOOF is a fast, efficient, physiologically-informed model to parameterize neural
power spectra, characterizing both the aperiodic & periodic components.

The model conceives of the neural power spectrum as consisting of two distinct components:

1) an aperiodic component, reflecting 1/f like characteristics, modeled with an exponential fit, with
2) band-limited peaks, reflecting putative oscillations, and modeled as Gaussians

The module includes:

- Code for applying models to parameterize neural power spectra
- Plotting functions for visualizing power spectra, model fits, and model parameters
- Analysis functions for examining model components and parameters
- Utilities for Input/Output management, data management and analysis reports
- Simulation code for simulating power spectra for methods testing

More details are available on the documentation site.

Documentation: https://fooof-tools.github.io/

If you use this code in your project, please cite:

Haller M, Donoghue T, Peterson E, Varma P, Sebastian P, Gao R, Noto T, Knight RT, Shestyuk A,
Voytek B (2018) Parameterizing Neural Power Spectra. bioRxiv, 299859. doi: https://doi.org/10.1101/299859

A full description of the method and approach is available in this paper.

Direct Paper Link: https://www.biorxiv.org/content/10.1101/299859v1


