PyNumDiff: Practical Numerical Differentiation for Noisy Data

Jupyter Notebook Python Submitted 02 April 2026 • Published 02 November 2026
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Editor: @prashjha (all papers)
Reviewers: @alexzerg (all reviews), @sidxz (all reviews)

Authors

Pavel Komarov (0009-0007-7482-2807), Floris van Breugel (0000-0001-6538-7179), Maria Protogerou, J. Nathan Kutz (0000-0002-6004-2275)

Citation

Komarov et al., (2026). PyNumDiff: Practical Numerical Differentiation for Noisy Data. Journal of Open Source Software, 11(127), 11172, https://doi.org/10.21105/joss.11172

@article{Komarov2026, doi = {10.21105/joss.11172}, url = {https://doi.org/10.21105/joss.11172}, year = {2026}, publisher = {The Open Journal}, volume = {11}, number = {127}, pages = {11172}, author = {Komarov, Pavel and van Breugel, Floris and Protogerou, Maria and Kutz, J. Nathan}, title = {PyNumDiff: Practical Numerical Differentiation for Noisy Data}, journal = {Journal of Open Source Software} }
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numerical differentiation time series denoising dynamics signal processing

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