Academic Journal
Development of a python interface for a framework of cross-reference solar-terrestrial physics simulation.
| Title: | Development of a python interface for a framework of cross-reference solar-terrestrial physics simulation. |
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| Authors: | Fukazawa, Keiichiro1 (AUTHOR) fukazawa@chikyu.ac.jp, Zhao, Haichao2 (AUTHOR) zhao.haichao.48n@st.kyoto-u.ac.jp, Nanri, Takeshi3 (AUTHOR) nanri.takeshi.995@m.kyushu-u.ac.jp, Miyake, Yohei4 (AUTHOR) y-miyake@eagle.kobe-u.ac.jp, Katoh, Yuto5 (AUTHOR) yuto.katoh@tohoku.ac.jp |
| Source: | Earth, Planets & Space. 9/20/2026, Vol. 78 Issue 1, p1-15. 15p. |
| Subject Terms: | *Python programming language, *Simulation software, *Software frameworks, *Scientific visualization, *Knowledge transfer, *Solar-terrestrial physics |
| Abstract: | Cross-reference simulation combines multiple simulation codes when one code requires data produced by another. To provide an efficient and convenient solution for cross-reference Solar-Terrestrial Physics (STP) simulation, a framework called Code-To-Code Adapter (CoToCoA) Library has been developed. CoToCoA was designed to implement cross-reference simulation with minimal modifications to the simulation codes while maintaining low overhead and currently supports only C and Fortran. To further enhance its usability by integrating CoToCoA with Python, we developed a Python interface. Two development approaches were conducted: (1) direct development of CoToCoA in Python and (2) the use of a library to call C functions of CoToCoA from Python. To evaluate the performance of the Python interfaces, experiments of data transfer methods in CoToCoA were performed. As a result of the evaluation, the library-based implementation showed performance comparable to the native C implementation, while the direct implementation required more additional time. The results show that the Python interface using the library enables efficient data transfer from both C- and Fortran-based STP simulation programs to Python for advanced data analysis and processing. Furthermore, the developed Python interface was applied to low-latency online visualization of magnetohydrodynamics simulation results. The framework has the potential of extension to integration with other Python tools, such as machine-learning or artificial intelligence models. [ABSTRACT FROM AUTHOR] |
| Database: | Academic Search Index |
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