Metadata-Version: 2.1
Name: bayes_jones
Version: 0.0.1
Summary: Bayesian inference of Jones matrices.
Home-page: https://github.com/joshuaalbert/bayes_jones
Author: Joshua G. Albert
Author-email: albert@strw.leidenuniv.nl
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: jax
Requires-Dist: jaxlib
Requires-Dist: chex
Requires-Dist: typing_extensions
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pytest
Requires-Dist: scipy
Requires-Dist: tensorflow_probability
Requires-Dist: tqdm
Requires-Dist: dm-haiku
Requires-Dist: optax
Requires-Dist: jaxns>=2.3.0

[![Python](https://img.shields.io/pypi/pyversions/bayes_jones.svg)](https://badge.fury.io/py/bayes_jones)
[![PyPI](https://badge.fury.io/py/bayes_jones.svg)](https://badge.fury.io/py/bayes_jones)
[![Documentation Status](https://readthedocs.org/projects/bayes_jones/badge/?version=latest)](https://bayes_jones.readthedocs.io/en/latest/?badge=latest)

Main
Status: ![Workflow name](https://github.com/JoshuaAlbert/bayes_jones/actions/workflows/unittests.yml/badge.svg?branch=main)

Develop
Status: ![Workflow name](https://github.com/JoshuaAlbert/bayes_jones/actions/workflows/unittests.yml/badge.svg?branch=develop)

## 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](https://bayes_jones.readthedocs.io/en/latest/#).

# 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

```bash
# 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,

```bash
pip install bayes_jones
```

## For development

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

```bash
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](https://bayes_jones.readthedocs.io/en/latest/#).

# Change Log

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

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