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Tools to analyze and simulate neural time series, using digital signal processing.
neurodsp is a collection of approaches for applying digital signal processing, and
related algorithms, to neural time series. It also includes simulation tools for generating
plausible simulations of neural time series.
Available modules in NeuroDSP include:
filt : Filter data with bandpass, highpass, lowpass, or notch filterstimefrequency : Estimate instantaneous measures of oscillatory activityspectral : Compute freqeuncy domain features such as power spectraburst : Detect bursting oscillations in neural signalsrhythm : Find and analyze rhythmic and recurrent patterns in time seriesaperiodic : Analyze aperiodic features of neural time seriessim : Simulate time series, including periodic and aperiodic signal componentsplts : Plot neural time series and derived measuresutils : Additional utilities for managing time series dataDocumentation for the NeuroDSP module is available here <https://neurodsp-tools.github.io/neurodsp/>_.
The documentation includes:
Tutorials <https://neurodsp-tools.github.io/neurodsp/auto_tutorials/index.html>_: which describe and work through each module in NeuroDSPExamples <https://neurodsp-tools.github.io/neurodsp/auto_examples/index.html>_: demonstrating example applications and workflowsAPI List <https://neurodsp-tools.github.io/neurodsp/api.html>_: which lists and describes all the code and functionality available in the moduleGlossary <https://neurodsp-tools.github.io/neurodsp/glossary.html>_: which defines all the key terms used in the moduleIf you have a question about using NeuroDSP that doesn't seem to be covered by the documentation, feel free to
open an issue <https://github.com/neurodsp-tools/neurodsp/issues>_ and ask!
NeuroDSP is written in Python, and requires Python >= 3.6 to run.
It has the following dependencies:
numpy <https://github.com/numpy/numpy>_scipy <https://github.com/scipy/scipy>_matplotlib <https://github.com/matplotlib/matplotlib>_Optional dependencies:
pytest <https://github.com/pytest-dev/pytest>_ is needed if you want to run the test suite locallyWe recommend using the Anaconda <https://www.anaconda.com/products/individual>_ distribution to manage these requirements.
The current major release of NeuroDSP is the 2.X.X series.
See the changelog <https://neurodsp-tools.github.io/neurodsp/changelog.html>_ for notes on major version releases.
Stable Release Version
To install the latest stable release, you can use pip:
.. code-block:: shell
$ pip install neurodspNeuroDSP can also be installed with conda, from the conda-forge channel:
.. code-block:: shell
$ conda install -c conda-forge neurodspDevelopment Version
To get the current development version, first clone this repository:
.. code-block:: shell
$ git clone https://github.com/neurodsp-tools/neurodspTo install this cloned copy, move into the directory you just cloned, and run:
.. code-block:: shell
$ pip install .Editable Version
To install an editable version, download the development version as above, and run:
.. code-block:: shell
$ pip install -e .This project welcomes and encourages contributions from the community!
To file bug reports and/or ask questions about this project, please use the
Github issue tracker <https://github.com/neurodsp-tools/neurodsp/issues>_.
To see and get involved in discussions about the module, check out:
issues board <https://github.com/neurodsp-tools/neurodsp/issues>_ for topics relating to code updates, bugs, and fixesdevelopment page <https://github.com/neurodsp-tools/Development>_ for discussion of potential major updates to the moduleWhen interacting with this project, please use the
contribution guidelines <https://github.com/neurodsp-tools/fooof/blob/main/CONTRIBUTING.md>_
and follow the
code of conduct <https://github.com/neurodsp-tools/neurodsp/blob/main/CODE_OF_CONDUCT.md>_.
If you use this code in your project, please cite:
.. code-block:: text
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.
DOI: 10.21105/joss.01272Direct Link: https://doi.org/10.21105/joss.01272
Bibtex:
.. code-block:: text
@article{cole_neurodsp:_2019,
title = {NeuroDSP: A package for neural digital signal processing},
author = {Cole, Scott and Donoghue, Thomas and Gao, Richard and Voytek, Bradley},
journal = {Journal of Open Source Software},
year = {2019},
volume = {4},
number = {36},
issn = {2475-9066},
url = {https://joss.theoj.org/papers/10.21105/joss.01272},
doi = {10.21105/joss.01272},
}Supported by NIH award R01 GM134363 from the
NIGMS <https://www.nigms.nih.gov/>_.
.. image:: https://www.nih.gov/sites/all/themes/nih/images/nih-logo-color.png :width: 400
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