A key element to ensure steady state operations in magnetically-confined tokamak devices is the prediction and avoidance of disruptions. These are sudden losses of the thermal and magnetic energy stored within the plasma, which can occur when tokamaks operate near stability boundaries or because of hardware anomalies. The energy stored in the plasma and released during disruptions over milliseconds can cause severe damage to plasma-facing components, limiting experimental operations and the device's lifespan [FST2023]. Disruptions still pose a serious challenge to next-generation fusion devices such as ITER or SPARC, which will have to operate near some of the limits of plasma stability to achieve intended performance and will do so at for long and frequent intervals. Fusion science currently lacks first-principle, theoretical solutions to fully predict and avoid disruptions. However, previous work [NF2019, NF2021] has shown the usefulness of machine-learning (ML) algorithms for disruption prevention for both DIII-D and EAST operations. DisruptionPy provides a standardized analysis pipeline across different fusion devices to build ML-ready datasets.

C-MOD Update C-MOD mdsplus server #513 Add C-MOD attributes #511 DIII-D Use local scratch rather than tmp for DIII-D #494 HBT-EP Add HBT-EP to remote tests #510 Add HBT-EP shotlist #508 MAST Introduce MAST workflows #509 Update MAST configurations #516 Framework Suppress git errors for non-git execution #492 More robust MDSplus fallback logic #499 Suppress warning for dummy DBs #503 Fix FutureWarning when merging xarrays #507 Add metadata to xarray outputs #512 Automation Longer period before stale #515 Documentation Add generic methods to docs #495 Update APS-DPP 2025 IDs #489 Revamp installation readmes #501 Add databases to references #504 Dependencies Update deps to Nov 2025 #491 Bump actions/checkout from 5 to 6 #490 Update deps to Dec 2025 #502 Update deps to Jan 2026 #514

An interoperable Python package for plasma disruption analysis and prediction using ML.