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

Abstract 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...

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Autores principales:	Mayank Agarwal, Vishal Deshpande, David Katoshevski, Bimlesh Kumar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/94913bda813f4319b1c987930d4df691
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spelling	oai:doaj.org-article:94913bda813f4319b1c987930d4df6912021-12-02T14:21:42ZA novel Python module for statistical analysis of turbulence (P-SAT) in geophysical flows10.1038/s41598-021-83212-12045-2322https://doaj.org/article/94913bda813f4319b1c987930d4df6912021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83212-1https://doaj.org/toc/2045-2322Abstract 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 .Mayank AgarwalVishal DeshpandeDavid KatoshevskiBimlesh KumarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mayank Agarwal Vishal Deshpande David Katoshevski Bimlesh Kumar A novel Python module for statistical analysis of turbulence (P-SAT) in geophysical flows
description	Abstract 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 .
format	article
author	Mayank Agarwal Vishal Deshpande David Katoshevski Bimlesh Kumar
author_facet	Mayank Agarwal Vishal Deshpande David Katoshevski Bimlesh Kumar
author_sort	Mayank Agarwal
title	A novel Python module for statistical analysis of turbulence (P-SAT) in geophysical flows
title_short	A novel Python module for statistical analysis of turbulence (P-SAT) in geophysical flows
title_full	A novel Python module for statistical analysis of turbulence (P-SAT) in geophysical flows
title_fullStr	A novel Python module for statistical analysis of turbulence (P-SAT) in geophysical flows
title_full_unstemmed	A novel Python module for statistical analysis of turbulence (P-SAT) in geophysical flows
title_sort	novel python module for statistical analysis of turbulence (p-sat) in geophysical flows
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/94913bda813f4319b1c987930d4df691
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A novel Python module for statistical analysis of turbulence (P-SAT) in geophysical flows

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