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Related Experiment Video

Updated: May 15, 2025

Fabrication, Operation and Flow Visualization in Surface-acoustic-wave-driven Acoustic-counterflow Microfluidics

Fabrication, Operation and Flow Visualization in Surface-acoustic-wave-driven Acoustic-counterflow Microfluidics

Published on: August 27, 2013

PvaPy streaming framework for real-time data processing.

Siniša Veseli¹, John Hammonds¹, Steven Henke¹

¹Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA.

Journal of Synchrotron Radiation

|April 25, 2025

Summary

A new streaming framework enhances real-time data analysis for X-ray beamlines. This enables dynamic experiment modification and faster scientific discovery by processing large datasets efficiently.

Keywords:

EPICS pvAccess PvaPy Python applications computing frameworks data streaming real-time data processing

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Last Updated: May 15, 2025

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Area of Science:

* Scientific computing
* Data science
* X-ray science

Background:

* Increasing data volumes from X-ray beamlines necessitate advanced analysis.
* Real-time data processing is crucial for dynamic experiment execution and modification.
* Existing systems struggle with the speed and volume of experimental data.

Purpose of the Study:

* To introduce a novel streaming framework for real-time data processing at X-ray beamlines.
* To demonstrate the framework's architecture, capabilities, and performance.
* To highlight scientific use cases benefiting from real-time streaming workflows.

Main Methods:

* Development of a streaming framework utilizing PvaPy, a Python API for EPICS pvAccess.
* Implementation of a system for rapid data transfer from detectors to computing resources.
* Integration of data processing and result return for immediate user feedback.

Main Results:

* The framework supports on-demand analysis and reconstruction of experimental data.
* Achievable data-processing rates were quantified for various detector image sizes.
* Scientific applications demonstrate the benefits of real-time streaming workflows.

Conclusions:

* The developed streaming framework effectively addresses the need for real-time data processing in X-ray science.
* It facilitates dynamic experimental adjustments and accelerates scientific discovery.
* The framework shows promise for enhancing user facility capabilities and data throughput.