PyDynamic - Analysis of dynamic measurements

PyDynamic is a Python software package developed jointly by mathematicians from Physikalisch-Technische Bundesanstalt (Germany) and National Physical Laboratory (UK) as part of the joint European Research Project EMPIR 14SIP08 Dynamic.

For the PyDynamic homepage go to GitHub.

PyDynamic is written in Python 3 and strives to run with all Python versions with upstream support. Currently it is tested to work with Python 3.5 to 3.8.

Contents:

• Getting started
• Evaluation of uncertainties
• Model estimation
• Design of deconvolution filters
• Fitting filters and transfer functions models
• Miscellaneous

Indices and tables

• Index
• Module Index
• Search Page

References

[Eichst2016]

S. Eichstädt und V. Wilkens GUM2DFT — a software tool for uncertainty evaluation of transient signals in the frequency domain. Meas. Sci. Technol., 27(5), 055001, 2016. https://dx.doi.org/10.1088/0957-0233/27/5/055001

[Eichst2012]

S. Eichstädt, A. Link, P. M. Harris and C. Elster Efficient implementation of a Monte Carlo method for uncertainty evaluation in dynamic measurements Metrologia, vol 49(3), 401 https://dx.doi.org/10.1088/0026-1394/49/3/401

[Eichst2010]

S. Eichstädt, C. Elster, T. J. Esward and J. P. Hessling Deconvolution filters for the analysis of dynamic measurement processes: a tutorial Metrologia, vol. 47, nr. 5 https://stacks.iop.org/0026-1394/47/i=5/a=003?key=crossref.310be1c501bb6b6c2056bc9d22ec93d4

[Elster2008]

C. Elster and A. Link Uncertainty evaluation for dynamic measurements modelled by a linear time-invariant system Metrologia, vol 45 464-473, 2008 https://dx.doi.org/10.1088/0026-1394/45/4/013

[Link2009]

A. Link and C. Elster Uncertainty evaluation for IIR filtering using a state-space approach Meas. Sci. Technol. vol. 20, 2009 https://dx.doi.org/10.1088/0957-0233/20/5/055104

[Vuer1996]

R. Vuerinckx, Y. Rolain, J. Schoukens and R. Pintelon Design of stable IIR filters in the complex domain by automatic delay selection IEEE Trans. Signal Proc., 44, 2339-44, 1996 https://dx.doi.org/10.1109/78.536690

[Smith]

Smith, J.O. Introduction to Digital Filters with Audio Applications, https://ccrma.stanford.edu/~jos/filters/, online book

[Savitzky]

A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639.

[NumRec]

Numerical Recipes 3rd Edition: The Art of Scientific Computing W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery Cambridge University Press ISBN-13: 9780521880688

[White2017]

White, D.R. Int J Thermophys (2017) 38: 39. https://doi.org/10.1007/s10765-016-2174-6