Metadata-Version: 2.4
Name: pyparrm
Version: 1.1.1
Summary: A Python port of the PARRM algorithm
Project-URL: Bug Tracker, https://github.com/neuromodulation/PyPARRM/issues
Project-URL: Homepage, https://github.com/neuromodulation/PyPARRM
Author-email: "Thomas S. Binns" <t.s.binns@outlook.com>
License-File: LICENSE.txt
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Requires-Dist: matplotlib>=3.7.1
Requires-Dist: numpy
Requires-Dist: pqdm>=0.2.0
Requires-Dist: scipy
Provides-Extra: dev
Requires-Dist: pybispectra[doc]; extra == 'dev'
Requires-Dist: pybispectra[lint]; extra == 'dev'
Requires-Dist: pybispectra[test]; extra == 'dev'
Provides-Extra: doc
Requires-Dist: ipykernel; extra == 'doc'
Requires-Dist: ipython; extra == 'doc'
Requires-Dist: ipywidgets; extra == 'doc'
Requires-Dist: notebook; extra == 'doc'
Requires-Dist: numpydoc; extra == 'doc'
Requires-Dist: pydata-sphinx-theme; extra == 'doc'
Requires-Dist: sphinx; extra == 'doc'
Requires-Dist: sphinx-copybutton; extra == 'doc'
Requires-Dist: sphinx-gallery; extra == 'doc'
Requires-Dist: sphinxcontrib-bibtex; extra == 'doc'
Provides-Extra: lint
Requires-Dist: codespell; extra == 'lint'
Requires-Dist: isort; extra == 'lint'
Requires-Dist: pre-commit; extra == 'lint'
Requires-Dist: pydocstyle; extra == 'lint'
Requires-Dist: pydocstyle[toml]; extra == 'lint'
Requires-Dist: rstcheck; extra == 'lint'
Requires-Dist: ruff; extra == 'lint'
Requires-Dist: toml-sort; extra == 'lint'
Requires-Dist: yamllint; extra == 'lint'
Provides-Extra: test
Requires-Dist: coverage; extra == 'test'
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/markdown

# PyPARRM

A Python signal processing package for identifying and removing stimulation
artefacts from electrophysiological data using the Period-based Artefact
Reconstruction and Removal Method (PARRM) of Dastin-van Rijn *et al.* (2021;
DOI: [10.1016/j.crmeth.2021.100010](https://doi.org/10.1016/j.crmeth.2021.100010)).

### View the documentation here: [pyparrm.readthedocs.io](https://pyparrm.readthedocs.io/en/main/)


All credit for PARRM goes to its original authors. PyPARRM is based on the
original MATLAB implementation of the method ([github.com/neuromotion/PARRM](https://github.com/neuromotion/PARRM)).

If you use this toolbox in your work, please include the following citation:<br/>
Binns, T. S., & Merk, T. (2023). PyPARRM (Version 1.1.0). DOI: [10.5281/zenodo.8360751](https://doi.org/10.5281/zenodo.8360751)