A novel Python module for statistical analysis of turbulence (P-SAT) in geophysical flows

Mayank Agarwal, Vishal Deshpande, David Katoshevski, Bimlesh Kumar

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We present Python Statistical Analysis of Turbulence (P-SAT), a lightweight, Python framework that can automate the process of parsing, filtering, computation of various turbulent statistics, spectra computation for steady flows. P-SAT framework is capable to work with single as well as on batch inputs. The framework quickly filters the raw velocity data using various methods like velocity correlation, signal-to-noise ratio (SNR), and acceleration thresholding method in order to de-spike the velocity signal of steady flows. It is flexible enough to provide default threshold values in methods like correlation, SNR, acceleration thresholding and also provide the end user with an option to provide a user defined value. The framework generates a.csv file at the end of the execution, which contains various turbulent parameters mentioned earlier. The P-SAT framework can handle velocity time series of steady flows as well as unsteady flows. The P-SAT framework is capable to obtain mean velocities from instantaneous velocities of unsteady flows by using Fourier-component based averaging method. Since P-SAT framework is developed using Python, it can be deployed and executed across the widely used operating systems. The GitHub link for the P-SAT framework is: https://github.com/mayank265/flume.git.

Original languageEnglish
Article number3998
JournalScientific Reports
Issue number1
StatePublished - 1 Dec 2021

ASJC Scopus subject areas

  • General


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