SunFLAAR is an open-source Python package for comprehensive solar flare analysis, integrating deep learning and statistical methods. It provides automated tools for multivariate time-series forecasting, active region classification, and imbalanced data metrics to aid space weather research.
Please remember to acknowledge and cite the use of SunFLAAR in your publications.
Interface Options
Web Portal
Climatography Researchers
An interactive, visual interface to map and analyze correlations between solar activity and long-term climatological data without requiring coding expertise.
Python Package
Solar Physicists
Direct programmatic integration of deep learning models, robust evaluation metrics, and MVTS preprocessing pipelines into existing Python workflows.
Institutional Collaboration
SunFLAAR is in its foundational stages and is being adopted by researchers studying heliophysics and space weather phenomena. We welcome institutional collaborations to help integrate real-time observatory streams (like HMI/AIA). Contact us.
Command line installation
$ pip install sunflaar
Report Bugs and Contribute
Handling imbalanced solar data (minor vs major flares) is challenging. If you encounter mistakes, errors, or bugs—or if you have a proposal for a new predictive architecture—please report it on the GitHub issue tracker. If you know how to fix a problem, we strongly encourage contributions.