Training tool for SUVI thematic map machine learning
The Solar Ultraviolet Imager (SUVI) aboard the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental R-Series satellites is used in machine learning applications to create thematic maps, images showing where different features are on the Sun. This tool allows domain experts to load images, manipulate them, and create labeled maps, which are used in training solar image classifiers.
These instructions will get you a copy of the project up and running on your local machine
for development and testing purposes. At the moment, it is advised to run this code in a fresh virtual environment with
Python 3.5. Installation should only require running the
pip3 install suvitrainer
Please note that the extra scripts may require packages that are not automatically installed. They are auxiliary and not fundamental for the annotation tool to run. Everything needed for run_suvitrainer.py and the main annotation tool should be automatically installed. If you want to use an auxiliary script download the repo.
The run_suvitrainer.py script should provide all needed functionality for the average user.
It takes a couple optional arguments: verbosity and dates.
--verbose argument will print helpful status information while running.
date option allows three methods of specifying which date to run on:
simply a date string (2018-08-05T17:52), a path to a local file that contains a list of date
strings where each one is on a different line, or a url to an online list of dates.
The default is to pull using the url for dates.txt stored in this repository. This is preferred to create
a large curated data-set with some repeats for validation.
The list of options for input images:
- any of the AIA EUV channels listed as
aia-[WAVELENGTH]with the wavelength in angstroms, e.g.
- any of the SUVI EUV L1B channels listed as products, e.g.
suvi-l1b-fe131(see the FTP site for a list)
- any locally saved SUVI l2 composite images (only available internally to NOAA employees at this time), e.g.
For more detailed usage, see the user guide.
The output of training is saved as a FITS file which is later converted into a labeled png. Labeled data will be available shortly. A couple examples are available here. This labeled data is then used in machine learning classification of solar images.
- J. Marcus Hughes - Initial work
This project is licensed under the MIT License - see the LICENSE file for details
- Guidance from Dan Seaton, Jon Darnel, and Vicki Hsu
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