Preprocess SDO/AIA Level 1 FITS files into ML-ready float32 format.
npx skills add https://github.com/tykimos/ssw-plugin --skill ssw-plugin-ssw-prep
Please help me install this Agent Skill. Skill: ssw-plugin:ssw-prep Source: https://github.com/tykimos/ssw-plugin/tree/main/skills/ssw-prep Command: npx skills add https://github.com/tykimos/ssw-plugin --skill ssw-plugin-ssw-prep
This Skill automates the complex preprocessing of SDO/AIA solar data, transforming raw FITS files into a standardized, machine-learning-ready format.
Use the ssw-prep skill to preprocess the AIA Level 1 FITS file 'aia_lev1_file.fits' into an ML-ready format.
High-intent search queries and answers about installing and using this skill.
Preprocess SDO/AIA FITS files by calibrating pointing, degradation, and exposure, then normalizing the solar disk to a standardized resolution and orientation for ML training.
Solar data calibration corrects Level 1 FITS images for pointing errors, sensor degradation, and exposure variations to ensure consistent input data for machine learning models.
Convert raw FITS solar data into float32 format with units of DN/s by applying calibration, registering the solar disk, and normalizing the images to a consistent resolution and north-up orientation.
Yes, you can batch convert SDO/AIA FITS files by calibrating and normalizing multiple raw images into a standardized ML-ready format suitable for training solar flare prediction models.
Yes, preparing SDO/AIA data requires sunpy, astropy, and aiapy to handle FITS file processing, perform solar physics calibrations, and register the solar disk for machine learning workflows.
tykimosDownload solar observation data from SDO, STEREO, and Solar Orbiter missions.
tykimosTrain deep learning models for solar flare prediction and image-to-image translation.
AMGrobelnikEnforce Python coding standards for experiment and evaluation scripts.
tykimosVisualize solar EUV imagery, preprocessing results, and ML outputs with Python.
