arXiv subjects

Xiao-Bao Deng

Publications and source records attributed to Xiao-Bao Deng.

3 recordsLinked to original sources

arXiv2026

Beyond the EPICS: comprehensive Python IOC development with QueueIOC

Background and Purpose: Architectural deficiencies in EPICS lead to inefficiency in the development and application of EPICS IOCs. An unintrusive solution is replacing EPICS IOCs with more maintainable and flexible Python IOCs, only reusing the CA protocol of EPICS. While there are libraries like caproto and PCASPy that help to create Python IOCs, they still feel insufficient for more complex requirements. Methods: Noticing caput, caget and camonitor are just specialised combinations of requests/replies and notifications in client-server communication, by combining barebone caproto and event loops like those in server-like programs, the QueueIOC framework for Python IOCs is created, which has the potential to systematically reduce the development and maintenance cost of IOCs. Results: Examples based on QueueIOC are first given for workalikes of StreamDevice and asyn; also given are examples for "sequencer" applications, like those based on seq, include monochromators, motor anti-bumping and motor multiplexing. A QueueIOC-based framework for detector integration is presented in an accompanying paper. Also reported is a simple but expressive architecture for GUIs, as well as software to use with the ~/iocBoot convention which addresses some issues we find with a similar solution based on procServ.

physics.ins-det↗

arXiv2024

A versatile framework for attitude tuning of beamlines at advanced light sources

Aside from regular beamline experiments at light sources, the preparation steps before these experiments are also worth systematic consideration in terms of automation; a representative category in these steps is attitude tuning, which typically appears in names like beam focusing, sample alignment etc. With the goal of saving time and manpower in both writing and using in mind, a Mamba-based attitude-tuning framework is created. It supports flexible input/output ports, easy integration of diverse evaluation functions, and free selection of optimisation algorithms; with the help from Mamba's infrastructure, machine learning (ML) and artificial intelligence (AI) technologies can also be readily integrated. The tuning of a polycapillary lens and of an X-ray emission spectrometer are given as examples for the general use of this framework, featuring powerful command-line interfaces (CLIs) and friendly graphical user interfaces (GUIs) that allow comfortable human-in-the-loop control. The tuning of a Raman spectrometer demonstrates more specialised use of the framework with customised optimisation algorithms. With similar applications in mind, our framework is estimated to be capable of fulfilling a majority of attitude-tuning needs. Also reported is a virtual-beamline mechanism based on easily customisable simulated detectors and motors, which facilitates both testing for developers and training for users.

physics.ins-det↗

arXiv2023

Progress and outlook on advanced fly scans based on Mamba

Development related to PandABox-based fly scans is an important part of the active work on Mamba, the software framework for beamline experiments at the High Energy Photon Source (HEPS); presented in this paper is the progress of our development, and some outlook for advanced fly scans based on knowledge learned during the process. By treating fly scans as a collaboration between a few loosely coupled subsystems - motors / mechanics, detectors / data processing, sequencer devices like PandABox - systematic analyses of issues in fly scans are conducted. Interesting products of these analyses include a general-purpose software-based fly-scan mechanism, a general way to design undulator-monochromator fly scans, a sketch of how to practically implement online tuning of fly-scan behaviours based on processing of the data acquired, and many more. Based on the results above, an architectural discussion on >=10kHz fly scans is given.

physics.ins-det↗