Driving simulation and data analysis of magnetic nanostructures through Jupyter Notebook
We present ongoing work from a project that makes a particular computer simulation (implemented in C++ and Tk/Tcl) accessible through a Python interface, and through the Jupyter Notebook. The talk describes the motivation, benefits and current status of the project.
Tags: Data Science, Jupyter, Programming, Python, Science
Scheduled on thursday 16:35 in room media
Speaker
Hans Fangohr is a Data Analysis Scientist at the European XFEL GmbH (which hosts the worlds most brilliant X-Ray Free Electron Laser, http://xfel.eu) and Professor of Computational Modelling at the University of Southampton. He received his undergraduate degree "Diplomphysiker" in physics from the University of Hamburg (Germany) and completed his PhD studies in the High Performance Computing Group at the department of Computer Science in Southampton. He is a full professor since 2010, and specialised in computational science, data analysis and software engineering for science.
Description
We present ongoing work from a project that makes a particular computer simulation (implemented in C++ and Tk/Tcl) accessible through a Python interface, and through the Jupyter Notebook. The talk describes the motivation and current status of the project.
In more detail, the computer simulation in question is the Object Oriented Micromagnetic Modelling Framework (OOMMF) which is likely the most widely used micromagnetic simulation package. It can be driven through a graphical (Tk) user interface or through a configuration file that defines a simulation run.
In this talk, we first show a Python interface to OOMMF that allows the driving of OOMMF simulations from a Python program or interpreter prompt. This way we embed a widely used scientific code from 1990s in a general purpose programming language [1] and enable the full use of the ecosystem of scientific libraries available for Python. For example, design optimisation, specialised post-processing, and the creation of figures can all be carried out using a single script; making the work more easily reproducible.
Second, we integrate the Python interface to OOMMF into a Jupyter notebook, so that all existing benefits of using Jupyter are inherited for the use in computational micromagnetics, which is the reason we named our code Jupyter-OOMMF (JOOMMF). A JupyterHub installation of the tool reduces barriers in uptake, and all the code is on github.
We discuss the benefits of driving computer simulation and data analysis through Jupyter Notebooks.
This project is a part of the Jupyter-OOMMF (JOOMMF) activity in the OpenDreamKit project and we acknowledge financial support from Horizon 2020 European Research Infrastructures project (676541). The work is also supported by the EPSRC CDT in Next Generation Computational Modelling EP/L015382/1, and the EPSRC grants EP/M022668/1 and EP/N032128/1.
For additional context: micromagnetic modelling is a key research method in academia and industry to support development of high-capacity magnetic storage devices that are cheap, fast, and reliable, and to enable research into future alternative storage and processing technologies such as spintronics. The OOMMF modelling package has been used in over 2500 publications since 1999.
[1] Beg, M., Pepper, R. A., and Fangohr, H. User interfaces for computational science: A domain specific language for OOMMF embedded in Python. AIP Advances 7, 056025 (2017), https://doi.org/10.1063/1.4977225