ICALEPCS2023 - List of Authors (Calvo Giraldo, E.)

Paper	Title	Page
THPDP061	Python Expert Applications for Large Beam Instrumentation Systems at CERN	1460
	J. Martínez Samblas, E. Calvo Giraldo, M. Gonzalez-Berges, M. Krupa CERN, Meyrin, Switzerland
	In recent years, beam diagnostics systems with increasingly large numbers of monitors, and systems handling vast amounts of data have been deployed at CERN. Their regular operation and maintenance poses a significant challenge. These systems have to run 24/7 when the accelerators are operating and the quality of the data they produce has to be guaranteed. This paper presents our experience developing applications in Python which are used to assure the readiness and availability of these large systems. The paper will first give a brief introduction to the different functionalities required, before presenting the chosen architectural design. Although the applications work mostly with online data, logged data is also used in some cases. For the implementation, standard Python libraries (e.g. PyQt, pandas, NumPy) have been used, and given the demanding performance requirements of these applications, several optimisations have had to be introduced. Feedback from users, collected during the first year’s run after CERN’s Long Shutdown period and the 2023 LHC commissioning, will also be presented. Finally, several ideas for future work will be described.
	Poster THPDP061 [2.010 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-THPDP061
About •	Received ※ 05 October 2023 — Revised ※ 26 October 2023 — Accepted ※ 13 December 2023 — Issued ※ 21 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Python Expert Applications for Large Beam Instrumentation Systems at CERN

1460

J. Martínez Samblas, E. Calvo Giraldo, M. Gonzalez-Berges, M. Krupa
CERN, Meyrin, Switzerland

In recent years, beam diagnostics systems with increasingly large numbers of monitors, and systems handling vast amounts of data have been deployed at CERN. Their regular operation and maintenance poses a significant challenge. These systems have to run 24/7 when the accelerators are operating and the quality of the data they produce has to be guaranteed. This paper presents our experience developing applications in Python which are used to assure the readiness and availability of these large systems. The paper will first give a brief introduction to the different functionalities required, before presenting the chosen architectural design. Although the applications work mostly with online data, logged data is also used in some cases. For the implementation, standard Python libraries (e.g. PyQt, pandas, NumPy) have been used, and given the demanding performance requirements of these applications, several optimisations have had to be introduced. Feedback from users, collected during the first year’s run after CERN’s Long Shutdown period and the 2023 LHC commissioning, will also be presented. Finally, several ideas for future work will be described.

Poster THPDP061 [2.010 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-THPDP061

About •

Received ※ 05 October 2023 — Revised ※ 26 October 2023 — Accepted ※ 13 December 2023 — Issued ※ 21 December 2023

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)