PlasmaPy: an open source community-developed Python package for plasma physics
Creators
- 1. Harvard-Smithsonian Center for Astrophysics
- 2. Aperio Software
- 3. West Virginia University
- 4. Los Alamos National Laboratory
- 5. University of Chicago
- 6. University of Sheffield
- 7. University of Delaware
- 8. Princeton University
Description
PlasmaPy is a community-developed and community-driven open source core Python package for plasma physics. This package is being developed to provide the core functionality that is needed to support a fully open source Python ecosystem for plasma physics. PlasmaPy prioritizes code readability, consistency, and maintainability while using best practices for scientific computing such as version control, continuous integration testing, embedding documentation in code, and code review. PlasmaPy has a code of conduct and is being developed under a BSD 3-clause license with explicit protections against software patents. PlasmaPy's first year of active development culminated in the release of version 0.1.0. This version includes subpackages that provide functional and object-oriented interfaces to access particle data; tools to calculate plasma parameters, dielectric tensor components, and transport coefficients; mathematical functions commonly needed in plasma physics; tools to analyze diagnostic data including Langmuir probes; and prototype base data structures for PlasmaPy. Users may request new features by raising an issue in PlasmaPy's GitHub repository. PlasmaPy's first development release serves as an invitation to plasma students and scientists to collaboratively develop a community-wide shared software package for our field.
Notes
Files
PlasmaPy_20180430.pdf
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Additional details
Related works
- Is new version of
- 10.5281/zenodo.163752 (DOI)
- 10.5281/zenodo.1238125 (DOI)
References
- Astropy Collaboration et al. (2018), arXiv:1801.02634
- B. D. Fried and S. D. Conte (1961), The Plasma Dispersion Function: The Hilbert Transform of the Gaussian, Academic Press, doi:10.1016/C2013-0-12176-9
- D. O. Gericke, M. S. Murillo, & M. Schlanges (2002) Phys. Rev. E, 65, 036418, doi:10.1103/PhysRevE.65.036418
- D. Muna et al. (2016), arXiv:1610.03159
- SunPy Community et al. (2015), Computational Science and Discovery, 8, 014009, doi:10.1088/1749-4699/8/1/014009
- G. Wilson et al. (2014), PLOS Biology, 12, e1001745, doi:10.1371/journal.pbio.1001745