| Autor: |
Farzad Asgari, Seyed Hossein Mohajeri, Mojtaba Mehraein |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
SoftwareX, Vol 27, Iss , Pp 101868- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2352-7110 |
| DOI: |
10.1016/j.softx.2024.101868 |
| Popis: |
The accurate analysis of velocity time series data from Acoustic Doppler Velocimeter (ADV) devices is crucial in various aquatic turbulent flow researches. This paper introduces ProADV, an open-source Python package designed to address the velocity data preparations challenges in this domain. ProADV integrates advanced computational methods and informatics approaches to facilitate efficient and precise analysis of natural turbulent flow large datasets. The software is built on a foundation of robust algorithms for data analysis, ensuring it meets the high demands of modern water and fluid research. ProADV supports a variety of data analysis and advanced signal processing techniques such as Kernel Density Estimation (KDE) and Power Spectral Density (PSD) analysis. Its comprehensive toolkit not only simplifies the analytical workflow but also enhances the quality of research outputs by enabling precise control over data filtering, spike detection, and noise reduction. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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