Every night, telescopes around the world capture thousands of images containing subtle stellar signals—exoplanets crossing their host stars, variable stars pulsing with cosmic rhythms, and binary systems locked in gravitational dances. But extracting meaningful photometric data from these raw frames requires careful calibration, precise aperture measurements, and statistical analysis that can turn weeks of manual work into an astronomer’s nightmare.
Pyapphot transforms this tedious process into an elegant Python workflow. Built on proven foundations like PyRAF and imexam, it offers three powerful classes: Imexamine for interactive object selection and automatic frame alignment, starPSF for extracting and filtering point spread function data across image sequences, and aperture_phot for both absolute and differential photometry measurements. The toolkit excels at time-series observations, automatically detecting stellar positions across multiple frames and performing the precise aperture photometry needed to measure brightness variations down to millimagnitude precision.
Already battle-tested in published exoplanet research, this package bridges the gap between raw CCD frames and publication-ready light curves. Whether you’re a graduate student analyzing your first transit observation or a seasoned researcher processing survey data, pyapphot handles the computational heavy lifting so you can focus on the science of stellar variability and planetary discovery.
⭐ Stars: 3
💻 Language: Python
🔗 Repository: aritrachakrabarty/pyapphot