bayes-jones 0.0.1

Bayesian inference of Jones matrices.

Main Status:

Develop Status:

Mission: To make ionospheric calibration faster, easier, and more powerful

What is it?

Bayes is:

1. a set of tools for Bayesian inference of Jones matrices using JAXNS as the engine;
2. coded in JAX in a manner that allows lowering the entire inference algorithm to XLA primitives, which are JIT-compiled for high performance

Documentation

You can read the documentation here.

Install

Notes:

1. BayesJones requires >= Python 3.8.
2. It is always highly recommended to use a unique virtual environment for each project. To use miniconda, have it installed, and run

# To create a new env, if necessary
conda create -n bayes_jones_py python=3.11
conda activate bayes_jones_py

For end users

Install directly from PyPi,

pip install bayes_jones

For development

Clone repo git clone https://www.github.com/JoshuaAlbert/bayes_jones.git, and install:

cd bayes_jones
pip install -r requirements.txt
pip install -r requirements-tests.txt
pip install -r requirements-examples.txt
pip install .

Quick start

Checkout the examples here.

Change Log

14 Dec, 2023 -- BayesJones 0.0.1 released for SaLF 9 conference.

Star History