FarNet-II usage example.
FarNet-II is a deep learning model that improves the detection of activity on the farside of the Sun using farside phase-shift maps as an input.
The reliability of the model is tested by comparing the outputs with binary activity masks extracted from STEREO EUV images of the farside.
For more details, check Broock, E. G. et al. A&A, 2022.
This repository contains a production test for FarNet-II:
· 'input' directory contains two inputs, each one with a batch of sequences of phase-shift maps sections, for dates outside the training set used to train the model. Dates on the name are the dates of the central element of the sequence of the first and last sequence on the file.
· 'masks' contains the associated activity masks, as a proxy of the reliability of the network.
· 'outputs' contains FarNet-II outputs for the given inputs.
· 'farside_to_magnetogram.py' is the script that needs to run in order to produce the outputs.
· 'FarNet-II.py' is the deep learning model.
· 'graphs.py' is a script to display the results.