PyMFR is intended as a library gathering open-source implementations of tools to analyze time-series data related to magnetic flux ropes (MFRs).
- Grad-Shafranov (GS)-based automated detection algorithm (Hu et al. 2018, see also fluxrope.info). PyMFR's implementation is very fast, using GPU computation and improvements to the algorithm for performance. It can process a month's worth of data at a wide range of trial axes and durations in mere seconds. For a demo of the detection algorithm, see the demo folder or try it yourself at https://colab.research.google.com/drive/1RbExzbcDsqmo60izQZH_FpenYrYzk9wD?usp=sharing
- Grad-Shafranov (GS) reconstruction (see e.g. Hu & Sonnerup 2002). For an example, see demo/example_reconstruction.ipynb.
- Notebooks with demos of various analytical models