PvaPy streaming framework for real-time data processing

User facility upgrades, new measurement techniques, advances in data analysis algorithms as well as advances in detector capabilities result in an increasing amount of data collected at X-ray beamlines. Some of these data must be analyzed and reconstructed on demand to help execute experiments dynam...

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Vydané v:Journal of synchrotron radiation Ročník 32; číslo 3; s. 823 - 836
Hlavní autori: Veseli, Siniša, Hammonds, John, Henke, Steven, Parraga, Hannah, Frosik, Barbara, Schwarz, Nicholas
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States John Wiley & Sons, Inc 01.05.2025
International Union of Crystallography (IUCr)
International Union of Crystallography
Predmet:
Computation
Computer Programs
computing frameworks
Data analysis
Data processing
data streaming
epics pvaccess
pvapy
python applications
real-time data processing
data streaming
PvaPy
EPICS pvAccess
Python applications
computing frameworks
real-time data processing
ISSN:1600-5775, 0909-0495, 1600-5775
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Popis
Shrnutí:User facility upgrades, new measurement techniques, advances in data analysis algorithms as well as advances in detector capabilities result in an increasing amount of data collected at X-ray beamlines. Some of these data must be analyzed and reconstructed on demand to help execute experiments dynamically and modify them in real time. In turn, this requires a computing framework for real-time processing capable of moving data quickly from the detector to local or remote computing resources, processing data, and returning results to users. In this paper, we discuss the streaming framework built on top of PvaPy , a Python API for the EPICS pvAccess protocol. We describe the framework architecture and capabilities, and discuss scientific use cases and applications that benefit from streaming workflows implemented on top of this framework. We also illustrate the framework's performance in terms of achievable data-processing rates for various detector image sizes.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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USDOE
ISSN:1600-5775
0909-0495
1600-5775
DOI:10.1107/S1600577525002115