The James Webb Space Telescope captures the universe in unprecedented detail, but transforming its raw FITS files into meaningful scientific imagery requires specialized processing workflows. This challenge becomes even more complex when tracking the constant stream of new observations across multiple wavelengths and instruments, from NIRCam’s infrared snapshots to MIRI’s thermal signatures.
This Python toolkit automates the entire pipeline from NASA’s MAST archive to publication-ready astronomical images. The system automatically downloads Level 3 science data, handles multi-wavelength coregistration challenges, and generates both grayscale and color composite previews. It maintains curated galleries of NGC objects, tracks recent releases through automated social media bots, and even creates wallpaper-formatted crops of cosmic highlights. The toolkit handles the intricate technical details like filling detector gaps and aligning images across different filters, while providing clean web interfaces for browsing results.
Whether you’re a researcher monitoring specific targets, an educator showcasing cosmic beauty, or a developer building astronomical applications, this repository provides both the processing infrastructure and the visual outputs needed to work with cutting-edge space telescope data. The automated news feeds and organized galleries make it particularly valuable for staying current with JWST’s rapidly expanding catalog of cosmic discoveries.
⭐ Stars: 7
💻 Language: Python
🔗 Repository: yuval-harpaz/astro