Treffer: An Open‐Source Python Version of Power Spectral Density Periodicity Detection and Background Estimation Using the Adaptive Multitaper Method.

The adaptive Multitaper Method (aMTM) is a Fourier analysis‐based technique that produces a power spectral density (PSD) with a user‐defined frequency resolution, reduces variance associated with standard digital Fourier Transform (FT) algorithms, and minimizes spectral power leakage. This technique is the backbone of our periodic signal detection procedure, which models the background PSD and determines if a discrete enhancement in the PSD is statistically significant. Due to the utility of this Fourier analysis tool, we created an open‐source Python version and compared its functionality to the IDL procedure that is already well‐tested and widely used. Differences from the IDL code emerged due to how Python estimated the aMTM PSD and modeled the background spectrum. In computing the relative error estimates between the Python and IDL‐produced aMTM PSD, we observed discrepancies of less than 2% at each frequency. In generating 100 artificial red noise time series from a defined PSD ground truth model, we observed that Python reproduced the PSD with a better accuracy than IDL, and that both saw an improvement in precision as the length of the time series increased. As such, we consider the Python version to be an improvement in performing spectral analysis of identifying discrete periodic signal(s) with continued support to make the code more user‐friendly. Finally, we demonstrate an application of the routine on high resolution magnetic field data from NASA's Magnetospheric Multiscale (MMS) mission and demonstrate that our new Python code has the computational efficiency to analyze data intervals much longer than IDL is capable of. Key Points: This describes an open‐source python version for periodic signal detection in data series' power spectral densityThis method uses a combination of spectral and harmonic statistical tests to identify discrete periodic signalsThis routine has multiple options for modeling the background Power Spectral Density [ABSTRACT FROM AUTHOR]

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