JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{kim:icalepcs2023-thsdsc03,
author = {K.T. Kim and J.J. Bellister and K.H. Kim and E. Williams and S. Zelazny},
title = {{Integrate EPICS 7 with MATLAB Using PVAccess for Python (P4P) Module}},
% booktitle = {Proc. ICALEPCS'23},
booktitle = {Proc. 19th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'23)},
eventdate = {2023-10-09/2023-10-13},
pages = {1580--1584},
paper = {THSDSC03},
language = {english},
keywords = {EPICS, controls, interface, experiment, status},
venue = {Cape Town, South Africa},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {19},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {02},
year = {2024},
issn = {2226-0358},
isbn = {978-3-95450-238-7},
doi = {10.18429/JACoW-ICALEPCS2023-THSDSC03},
url = {https://jacow.org/icalepcs2023/papers/thsdsc03.pdf},
abstract = {{MATLAB is essential for accelerator scientists engaged in data analysis and processing across diverse fields, including particle physics experiments, synchrotron light sources, XFELs, and telescopes, due to its extensive range of built-in functions and tools. Scientists also depend on EPICS 7 to control and monitor complex systems. Since Python has gained popularity in the scientific community and many facilities have been migrating towards it, SLAC has developed matpva, a Python interface to integrate EPICS 7 with MATLAB. Matpva utilizes the Python P4P module and EPICS 7 to offer a robust and reliable interface for MATLAB users that employ EPICS 7. The EPICS 7 PVAccess API allows higher-level scientific applications to get/set/monitor simple and complex structures from an EPICS 7-based control system. Moreover, matpva simplifies the process by handling the data type conversion from Python to MATLAB, making it easier for researchers to focus on their analyses and innovative ideas instead of technical data conversion. By leveraging matpva, researchers can work more efficiently and make discoveries in diverse fields, including particle physics and astronomy. }},
}