ICALEPCS2023 - List of Authors (Martínez Samblas, J.)

Paper	Title	Page
THPDP060	Beam Instrumentation Simulation in Python	1454
	M. Gonzalez-Berges, D. Alves, A. Boccardi, V. Chariton, I. Degl’Innocenti, S. Jackson, J. Martínez Samblas CERN, Meyrin, Switzerland
	The design of acquisition electronics for particle accelerator systems relies on simulations in various domains. System level simulation frameworks can integrate the results of specific tools with analytical models and stochastic analysis. This allows the designer to estimate the performance of different architectures, compare the results, and ultimately optimize the design. These simulation frameworks are often made of custom scripts for specific designs, which are hard to share or reuse. Adopting a standard interface for modular components can address these issues. Also, providing a graphical interface where these components can be easily configured, connected and the results visualised, eases the creation of simulations. This paper identifies which characteristics ISPy (Instrumentation Simulation in Python) should fulfill as a simulation framework. It subsequently proposes a standard format for signal-processing simulation modules. Existing environments which allow script integration and an intuitive graphical interface have then been evaluated and the KNIME Analytics Platform was the proposed solution. Additionally, the need to handle parameter sweeps for any parameter of the simulation, and the need for a bespoke visualisation tool will be discussed. Python has been chosen for all of these developments due to its flexibility and its wide adoption in the scientific community. The ensuing performance of the tool will also be discussed.
	Poster THPDP060 [2.931 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-THPDP060
About •	Received ※ 07 October 2023 — Accepted ※ 08 December 2023 — Issued ※ 12 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

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

Beam Instrumentation Simulation in Python

1454

M. Gonzalez-Berges, D. Alves, A. Boccardi, V. Chariton, I. Degl’Innocenti, S. Jackson, J. Martínez Samblas
CERN, Meyrin, Switzerland

The design of acquisition electronics for particle accelerator systems relies on simulations in various domains. System level simulation frameworks can integrate the results of specific tools with analytical models and stochastic analysis. This allows the designer to estimate the performance of different architectures, compare the results, and ultimately optimize the design. These simulation frameworks are often made of custom scripts for specific designs, which are hard to share or reuse. Adopting a standard interface for modular components can address these issues. Also, providing a graphical interface where these components can be easily configured, connected and the results visualised, eases the creation of simulations. This paper identifies which characteristics ISPy (Instrumentation Simulation in Python) should fulfill as a simulation framework. It subsequently proposes a standard format for signal-processing simulation modules. Existing environments which allow script integration and an intuitive graphical interface have then been evaluated and the KNIME Analytics Platform was the proposed solution. Additionally, the need to handle parameter sweeps for any parameter of the simulation, and the need for a bespoke visualisation tool will be discussed. Python has been chosen for all of these developments due to its flexibility and its wide adoption in the scientific community. The ensuing performance of the tool will also be discussed.

Poster THPDP060 [2.931 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-THPDP060

About •

Received ※ 07 October 2023 — Accepted ※ 08 December 2023 — Issued ※ 12 December 2023

Cite •

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

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)