Published February 22, 2020 | Version v0.1.0
Software Open

PyMODA v0.1.0

Authors/Creators

Contributors

Description

PyMODA is a Python implementation of MODA, a numerical toolbox developed by the Nonlinear & Biomedical Physics group at Lancaster University for analysing real-life time-series.

Algorithms developed by members of:

  • Nonlinear and Biomedical Physics Group, Physics Department, Lancaster
    University, UK from 2006 until present.
  • Nonlinear Dynamics and Synergetic Group at the Faculty of Electrical
    Engineering, University of Ljubljana, Slovenia from 1996 to 2006.

Aneta Stefanosvka extends her personal thanks to Aleš Založnik, and to her PhD students.

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luphysics/PyMODA-v0.1.0.zip

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Additional details

Related works

Is supplement to
https://github.com/luphysics/PyMODA/tree/v0.1.0 (URL)

References

  • J Newman, G Lancaster and A Stefanovska, "Multiscale Oscillatory Dynamics Analysis", v1.01, User Manual, 2018.
  • P Clemson, G Lancaster, A Stefanovska, "Reconstructing time-dependent dynamics", Proc IEEE 104, 223–241 (2016).
  • P Clemson, A Stefanovska, "Discerning non-autonomous dynamics", Phys Rep 542, 297-368 (2014).
  • D Iatsenko, P V E McClintock, A Stefanovska, "Linear and synchrosqueezed time-frequency representations revisited: Overview, standards of use, resolution, reconstruction, concentration, and algorithms", Dig Sig Proc 42, 1–26 (2015).
  • G Lancaster, D Iatsenko, A Pidde, V Ticcinelli, A Stefanovska, "Surrogate data for hypothesis testing of physical systems", Phys Rep 748, 1–60 (2018).
  • Bandrivskyy A, Bernjak A, McClintock P V E, Stefanovska A, "Wavelet phase coherence analysis: Application to skin temperature and blood flow", Cardiovasc Engin 4, 89–93 (2004).
  • Sheppard L W, Stefanovska A, McClintock P V E, "Testing for time-localised coherence in bivariate data", Phys. Rev. E 85, 046205 (2012).
  • D Iatsenko, P V E McClintock, A Stefanovska, "Nonlinear mode decomposition: A noise-robust, adaptive decomposition method", Phys Rev E 92, 032916 (2015).
  • D Iatsenko, P V E McClintock, A Stefanovska, "Extraction of instantaneous frequencies from ridges in time-frequency representations of signals", Sig Process 125, 290–303 (2016).
  • J Jamšek, A Stefanovska, P V E McClintock, "Wavelet bispectral analysis for the study of interactions among oscillators whose basic frequencies are significantly time variable", Phys Rev E 76, 046221 (2007).
  • J Jamšek, M Paluš, A Stefanovska, "Detecting couplings between interacting oscillators with time-varying basic frequencies: Instantaneous wavelet bispectrum and information theoretic approach", Phys Rev E 81, 036207 (2010).
  • J Newman, A Pidde, A Stefanovska, "Defining the wavelet bispectrum", submitted (2019).
  • V N Smelyanskiy, D G Luchinsky, A Stefanovska, P V E McClintock, "Inference of a nonlinear stochastic model of the cardiorespiratory interaction", Phys Rev Lett 94, 098101 (2005).
  • T Stankovski, A Duggento, P V E McClintock, A Stefanovska, "Inference of time-evolving coupled dynamical systems in the presence of noise", Phys Rev Lett 109, 024101 (2012).
  • T Stankovski, A Duggento, P V E McClintock, A Stefanovska, "A tutorial on time-evolving dynamical Bayesian inference", Eur Phys J – Special Topics 223, 2685-2703 (2014).
  • T Stankovski, T Pereira, P V E McClintock, A Stefanovska, "Coupling functions: Universal insights into dynamical interaction mechanisms", Rev Mod Phys 89, 045001 (2017).
  • Special issue of the Philos Trans Royal Soc A (2019) with contributions by Kuramoto and others.