BicAn: An integrated, open-source framework for polyspectral analysis
ORAL
Abstract
We present a novel platform for higher-order spectral analysis of time series data in Python. The theory and utility of such analyses are summarized. Direct estimation of coherence (n = 2), bicoherence (n = 3), and tricoherence (n = 4) spectra are presented for test signals; higher-order (n > 4) spectra are inferred at single points in polyfrequency space. Quantification of uncertainty for nonstationary processes is considered, and applications to nonlinear plasma physics are given.
*Partial funding for this work from DE-SC-0018036 and DE-NA-0003874 is gratefully acknowledged.
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Publication: We plan to submit a manuscript to Computer Physics Communications (CPC).
Presenters
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Gregory Allen Riggs
- West Virginia University