Metadata-Version: 2.1
Name: neurodsp
Version: 2.0.0
Summary: Digital signal processing for neural time series.
Home-page: https://github.com/neurodsp-tools/neurodsp
Author: The Voytek Lab
Author-email: voyteklab@gmail.com
License: Apache License, 2.0
Download-URL: https://github.com/neurodsp-tools/neurodsp/releases
Keywords: neuroscience,neural oscillations,time series analysis,local field potentials,spectral analysis,time frequency analysis,electrophysiology
Platform: UNKNOWN
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 :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
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
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib


========
Neurodsp
========

NeuroDSP is package of tools to analyze and simulate neural time series, using digital signal processing.

Available modules in NeuroDSP include:

- filt : Filter data with bandpass, highpass, lowpass, or notch filters
- burst : Detect bursting oscillations in neural signals
- rhythm : Find and analyze rhythmic and recurrent patterns in time series
- spectral : Compute spectral domain features such as power spectra
- timefrequency : Estimate instantaneous measures of oscillatory activity
- sim : Simulate time series, including periodic and aperiodic signal components
- plts : Plotting functions

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

Cole, S., Donoghue, T., Gao, R., & Voytek, B. (2019). NeuroDSP: A package for
neural digital signal processing. Journal of Open Source Software, 4(36), 1272.
https://doi.org/10.21105/joss.01272

Direct Link: https://doi.org/10.21105/joss.01272


