Nmag micromagnetic simulation Tool: Software engineering lessons learned. We review design and development decisions and their impact for the open source code Nmag from a software engineering in computational science point of view. We summarise lessons learned and recommendations for future computational science projects. Key lessons include that encapsulating the simulation functionality in a library of a general purpose language, here Python, provides great flexibility in using the software. The choice of Python for the top-level user interface was very well received by users from the science and engineering community. The from-source installation in which required external libraries and dependencies are compiled from a tarball was remarkably robust. In places, the code is a lot more ambitious than necessary, which introduces unnecessary complexity and reduces maintainability. Tests distributed with the package are useful, although more unit tests and continuous integration would have been desirable. The detailed documentation, together with a tutorial for the usage of the system, was perceived as one of its main strengths by the community.
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References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Bisotti, M.-A., Cortés-Ortuño, D., Pepper, R., Wang, W., Beg, M., Kluyver, T., Fangohr, H.: Fidimag - A Finite Difference Atomistic and Micromagnetic Simulation Package (2018) not zbMATH
- Oliver Laslett, Jonathon Waters, Hans Fangohr, Ondrej Hovorka: Magpy: A C++ accelerated Python package for simulating magnetic nanoparticle stochastic dynamics (2018) arXiv
- Hans Fangohr, Maximilian Albert, Matteo Franchin: Nmag micromagnetic simulation tool: software engineering lessons learned (2016) not zbMATH