Visualize solar EUV imagery, preprocessing results, and ML outputs with Python.
npx skills add https://github.com/tykimos/ssw-plugin --skill ssw-plugin-ssw-viz
Please help me install this Agent Skill. Skill: ssw-plugin:ssw-viz Source: https://github.com/tykimos/ssw-plugin/tree/main/skills/ssw-viz Command: npx skills add https://github.com/tykimos/ssw-plugin --skill ssw-plugin-ssw-viz
This Skill addresses the need to visualize complex solar observation data, including raw EUV imagery, preprocessed data, and the outputs of machine learning models, making scientific analysis more accessible.
Visualize the solar EUV image from the file 'preprocessed.fits' using the ssw-viz skill.
High-intent search queries and answers about installing and using this skill.
This Skill renders solar EUV imagery from FITS files using Python libraries like SunPy and Matplotlib. It supports single image display with customizable colormaps and normalization for scientific analysis and presentation.
Yes, creating side-by-side multi-wavelength comparison panels for solar flare events is supported. This allows you to compare solar features across different wavelengths to understand their evolution across different atmospheric layers.
To display machine learning predictions against ground truth, this Skill visualizes input data, model predictions, and ground truth side-by-side. This facilitates effective ML model evaluation for solar observation data.
Yes, this Skill supports generating time-lapse animations of solar activity. It also produces pixel intensity distribution plots to help you analyze solar observation data dynamically.
The best way to show before and after comparisons of solar data preprocessing is using the dedicated preprocessing visualization feature. It clearly displays the results of applied data preprocessing steps for scientific evaluation.
tykimosDownload solar observation data from SDO, STEREO, and Solar Orbiter missions.
JunMA98Generate publication-ready CS visualizations with accessible color palettes and export formats.
pur3v4d3rTrain and evaluate classical machine learning models with scikit-learn.
RamanEbrahimiGenerate static, animated, and interactive plots in Python with Matplotlib.
