Session JM10: Mini-Conference on Growing an Open Source Software Ecosystem for Plasma Science I

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The PlasmaPy Project is an ambitious effort centered around the open source Python package PlasmaPy. The PlasmaPy package is a community-driven and community-developed package that provides common functionality required for plasma physics research and education. PlasmaPy prioritizes code readability, consistency, and maintainability while using best practices for scientific computing such as version control, continuous integration testing, and code review. PlasmaPy has a code of conduct and is available under a BSD 3-clause license with explicit protections against software patents. We will describe current capabilities of PlasmaPy, as well as our development roadmap. We will discuss how members of the plasma physics community can become contributors to this project.   

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Software has been a crucial contributor to scientific progress in astronomy for decades, but practices that enable machine-actionable citations have not been consistently applied to software itself. Instead, software citation behaviors developed independently from standard publication mechanisms and policies, resulting in human- readable citations that remain hidden over time and that cannot represent the influence software has had in the field. These historical software citation behaviors need to be understood in order to improve software citation guidance and develop relevant publishing practices that fully support software authors. To this end, a 23 year retrospective analysis of software citation practices in astronomy was developed. Astronomy publications were mined for 410 aliases associated with nine software packages and analyzed to identify past practices and trends that prevent software citations from benefiting software authors.   

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Although plasma physics is more commonly known for high temperatures, many plasma experiments involve various cryogenic systems. Measurement and logging of temperatures in these systems may be fairly mundane (e.g., an indication that a cryopump needs to be serviced) or may be an essential element of experimental success/reproducibility. As an alternative to pricey commercial options, one can assemble from only a handful of low-cost electronics an open-source, flexible, modular solution for the read-out, logging, and monitoring of, e.g., standard silicon cryo-diodes. In our example system, a multi-channel Arduino-based controller unit uses a three-component current source to excite each diode; the resulting voltage is read via a differential-mode 16-bit ADC and converted to a real-time temperature output. A Linux-based single-board computer (SBC) running a pair of Python scripts records long-term temperature data (SQLite) as well as recent data at higher time resolution (Redis) and makes the data available via a unified web API for simple integration with other services, such as browser-based monitoring or cloud storage. Together with the Arduino and SBC, the open-source microcontroller firmware, Python scripts, and circuit designs constitute a fully open hardware/software stack.   

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The bapsflib package is an open-source python package designed to assist users of the Basic Plasma Science Facility (BaPSF) in their workflow from preparing for experiments to analyzing data collected from experiments. BaPSF is a jointly funded DOE/NSF collaborative user facility at the University of California, Los Angeles (UCLA) that is best known for work done on its Large Plasma Device (LaPD). As with any user facility, there are common software pain-points experienced by users and the bapsflib package is initially addressing these points by focusing on three crucial areas: (1) providing functionality to prepare and test machine configurations for desired plasma parameters; (2) building an open library of machine physical parameters; and (3) providing a clean, intuitive interface to the datasets generated during a campaign. Development of the package will also include interoperability with PlasmaPy to seamlessly connect the data wrangling capabilities of bapsflib with the analysis techniques of PlasmaPy. [Work supported by the US DOE and NSF, and performed at the Basic Plasma Science Facility, UCLA.]   

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Swept Langmuir probes are used to deduce temperature, density, and electric potential in laboratory plasmas. Traces from swept probe measurements can be difficult to interpret using existing hand-tuned heuristics, and are restricted to using only analytical probe models. Using an unsupervised hybrid model of neural networks and analytical theory, I constructed an automated sweep analysis routine that is robust to noise and provides plasma parameters for a semi-infinite planar probe and a Maxwellian plasma. The model was trained on over a million swept Langmuir probe measurements from the Large Plasma Device (LAPD) and the Small Plasma Device (SMPD), and was validated on data from a smaller device. This model can be easily expanded to accommodate any theoretical probe model and an arbitrary velocity distribution function. An overview of the model and a demonstration of its capabilities will be presented. The source code will also be provided.   

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Testing the sensitivity of microturbulence to the variation of pedestal profiles within uncertainties is of great importance for understanding the transport mechanisms that govern pedestal evolution and structure. CheasePy is an Open Source/Access Python script that works as a wrapper to the CHEASE (Cubic Hermite Element Axisymmetric Static Equilibrium) code developed in FORTRAN at Ecole Polytechnique Fédérale de Lausanne, Switzerland, to solve the Grad-Shafranov equation for toroidal MHD equilibria using pressure and current profiles at fixed plasma boundaries that is defined by a set of data points (R,Z)1. The CheasePy script allows an iterative running of the CHEASE code either to check the preservation of MHD equilibria or converge to an experimentally defined total toroidal current by modifying the bootstrap or parallel currents. CheasePy also adopts a capability for fitting and modifying the experimental profiles of the electron and ion density, temperature, and pressure, in addition to scaling the geometry coefficients to any Tokamak-like machine, then reconstructing a new MHD equilibrium under the new plasma conditions. 1 Lütjens, Hinrich, Anders Bondeson, and Olivier Sauter. "The CHEASE code for toroidal MHD equilibria." Computer physics communications 97.3 (1996)   

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The Python package turboPy implements the most common aspects of computational physics problems and provides an object oriented, yet accessible approach for novices and experts alike to manage data, implement numerical algorithms, track simulation flow, and create models describing dynamics of quantities on a grid. The framework was the centerpiece for a virtual internship during the summer, enabling a valuable experience to interns and useful contributions to turboPy. The intern project included development of example applications for solving common physics problems, development and automation of documentation, implementation and automation of unit and integration testing, and implementation of a Continuous Integration (CI) pipeline, thus providing interns with exposure to the full lifecycle of physics simulations development using good software engineering practices in a team environment. In addition to reviewing the goals, execution, and outcomes of the project, some thoughts on replication and scaling of the experience for future internships will be provided.   

SunPy : The community-developed, free and open source solar data analysis environment for Python.

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the SunPy core package, supporting an ecosystem of affiliated packages, and educating the solar physics community about the Python scientific software stack. In the last year, the SunPy project released the first official stable release (version 1.0) of the core package, won a grant from NASA, published a paper about the project in The Astrophysical Journal (The SunPy Community et. al, 2020), published a paper about the software in The Journal of Open Source Software (Mumford et al., 2020), and surveyed the solar physics community about software and hardware usage published these results in Solar Physics (Bobra et al., 2020). This talk will present how the sunpy package can be used for solar physics data analysis and discuss the current status and roadmap for the package.   

OMAS: A Python Library to Interface with the ITER Integrated Modeling and Analysis Suite (IMAS)

The ITER IMAS effort is driving the worldwide adoption of the Physics Data Model (PDM) as the standard data structure (ie. ontology) for managing tokamak fusion data. OMAS is a numerical library designed to facilitate the adoption of the PDM within Python codes, and ease their interface with IMAS. In OMAS the data strictly adheres to the PDM, and it is presented to the developers as objects behaving like familiar Python dictionaries and lists. Conveniently, these objects can perform automatic COordinate COnventionS (COCOS) transformations, grid interpolation, and units conversions. In addition to IMAS, OMAS supports saving/loading data in universal data formats such HDF5, JSON files, and MongoDB. We will present a series of diverse examples that illustrate how OMAS has been adopted across the fusion community to: 1) interface with IMAS; 2) support the integration of physics codes in complex workflows; and 3) curate databases of both experimental and modeling fusion data.