Physics validation of parameter methods in DisruptionPy

DisruptionPy is an interoperable open-source library for data access across different magnetic fusion experiment (MFE) devices. This library, developed in Python, contains built-in pipelines for processing and analyzing experimental data, allowing users to quickly, easily, and robustly design machine learning (ML) applications such as for disruption prediction and avoidance. Development of this library will be aligned with Findable, Accessible, Interoperable, Reusable (FAIR) and Open Science (OS) guidelines, and the library will be soon released to the broader community as a publicly accessible Github repository. As a part of the development effort, a thorough review of each of DisruptionPy's physics parameter methods has been carried out. Multiple approaches have been investigated, including comparison with the previous MATLAB disruption_warning database and associated workflows, validation with physics models, and detailed analysis of the data processing schemes. Results from these investigations will be integrated into the continuous integration and continuous deployment (CI/CD) workflows, allowing systematic and automated testing of DisruptionPy's data pipelines as additional parameter methods and features are being incorporated. These protocols will ensure the long-term accuracy and robustness of the framework, facilitating the development and maintenance of downstream ML applications.