astronomy

The Siril image processing software for amateur astronomy

A simple planetary orbit simulator, written in the C language

Pyxel is a general detector simulation framework (https://esa.gitlab.io/pyxel). An easy-to-use framework that can simulate a variety of imaging detector effects combined on images (e.g. radiation and optical effects, noises) made by CCD or CMOS-based detectors.

Python-Gimp Astronomy Plugin by M27

A two-channel deconvolution method with Starlet regularization. The code also features a PSF reconstruction algorithm.

OSSR curation process for records to be accepted in the escape2020 community

Mirror of the main Gaia Sky repository hosted on Codeberg: https://codeberg.org/gaiasky/gaiasky

LensMC - Weak lensing shear measurement with forward modelling and MCMC sampling

SiriLic (Siril's Interactive Companion) is a software that prepares acquisition files (RAW, Offset, Flat and Dark) for processing with the SiriL software.

This library provides a set of high precision date and time classes covering a large time span. The library is implemented in C++, Python and Java

Astrophotography software written in Rust

mirror of https://github.com/dreamalligator/vondrak

This is a copy of the CIAO threads, http://cxc.cfa.harvard.edu/ciao/threads , converted to jupyter notebooks using the bash kernel.

Rust implementation of Sky-Watcher's SnyScan protocol over Wi-Fi.

Cross-platform open source software for astrophotography based on python-gphoto2 and PySide6, oriented to Raspberry devices.

pySiriL ( python SiriL) is a python library to interface python to a SiriL script.

pySiriL ( python SiriL) est une librairie python pour interfacer python au langage de script de SiriL.

Open Source Photometer

Tracking utilities for F4KLO project

Spatial-kinematic modelling of a flat subsystem of the Milky Way by the method of maximum likelihood

CASCADe: Calibration of trAnsit Spectroscopy using CAusal Data

This package provides the functionality to calibrate and fit the light-curves of transiting exoplanets, using the causal connections within the data to remove systematic noise.