socca¶
/’sɔ.ka/ noun
A JAX-accelerated Python library for efficiently modelling image-space astronomical data using Bayesian inference.
A delicious flatbread made from chickpea flour, water, olive oil, and salt from Nice, France.
socca (Source Characterization using a Composable Analysis) is a minimal library for efficiently modelling image-space astronomical data. It is intended to be fast and flexible, taking advantage of the JAX framework for performing just-in-time compilation and of state-of-the-art sampling algorithms (dynesty, nautilus, pocomc, emcee) for the posterior exploration.
This code is broadly inspired by the excellent pysersic and astrophot libraries. Their use is recommended if you require a more mature and thoroughly tested solution. socca is still in its infancy and many experimental features may undergo significant changes in the future.
Where to start?¶
⚙️ Clearly, first things first: head to the installation guide to get the package installed on your system.
🚀 To start using socca, you can go through the quickstart tutorial, which will guide you through the main features of the library step by step. For a more in-depth overview of the different functionalities, check out the other guides in the “Using socca” section or the API reference.
🐛 Found a bug or have a question? Check out the contribution guide for more information on how to get involved and to contribute to the code development.
📚 Using socca in your research? Please see the citation page for how to cite it in your publications.