Metadata-Version: 2.1
Name: uravu
Version: 1.2.4
Summary: Bayesian methods for analytical relationships
Home-page: UNKNOWN
Author: Andrew R. McCluskey
Author-email: andrew.mccluskey@diamond.ac.uk
License: MIT
Description: ![uravu logo](https://github.com/arm61/uravu/raw/master/docs/source/logo/uravu_logo.png)
        
        **making Bayesian modelling easy(er)**
        
        [![status](https://joss.theoj.org/papers/e9047e48bf024589e0765f955b3e4c76/status.svg)](https://joss.theoj.org/papers/e9047e48bf024589e0765f955b3e4c76)
        [![DOI](https://zenodo.org/badge/241184437.svg)](https://zenodo.org/badge/latestdoi/241184437)
        
        [![PyPI version](https://badge.fury.io/py/uravu.svg)](https://badge.fury.io/py/uravu)
        [![Documentation Status](https://readthedocs.org/projects/uravu/badge/?version=latest)](https://uravu.readthedocs.io/en/latest/?badge=latest)
        [![Coverage Status](https://coveralls.io/repos/github/arm61/uravu/badge.svg?branch=master)](https://coveralls.io/github/arm61/uravu?branch=master)
        [![Build Status](https://github.com/arm61/uravu/workflows/python-ci/badge.svg)](https://github.com/arm61/uravu/actions?query=workflow%3Apython-ci)
        [![Build status](https://ci.appveyor.com/api/projects/status/eo426m99lmkbh5rx?svg=true)](https://ci.appveyor.com/project/arm61/uravu)
        
        ``uravu`` (from the Tamil for relationship) is about the relationship between some data and a function that may be used to describe the data.
        
        The aim of ``uravu`` is to make using the **amazing** Bayesian inference libraries that are available in Python as easy as [`scipy.optimize.curve_fit`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html).
        Therefore enabling many more to make use of these exciting tools and powerful libraries.
        Plus, we have some nice plotting functionalities available in the `plotting` module, capable of generating publication quality figures.
        
        ![An example of the type of figures that uravu can produce. Showing straight line distribution with increasing uncertainty.](https://github.com/arm61/uravu/raw/master/docs/source/sample_fig.png)
        
        In an effort to make the ``uravu`` API friendly to those new to Bayesian inference, ``uravu`` is *opinionated*, making assumptions about priors among other things.
        However, we have endevoured to make it straightforward to ignore these opinions.
        
        In addition to the library and API, we also have some [basic tutorials](https://uravu.readthedocs.io/en/latest/tutorials.html) discussing how Bayesian inference methods can be used in the analysis of data.
        
        ## Bayesian inference in Python
        
        There are a couple of fantastic Bayesian inference libraries available in Python that `uravu` makes use of:
        
        - [emcee](https://emcee.readthedocs.io/): enables the use of the [Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler](https://doi.org/10.2140/camcos.2010.5.65) to evaluate the structure of the model parameter posterior distributions,
        - [dynesty](https://dynesty.readthedocs.io/): implements the [nested sampling](https://doi.org/10.1063/1.1835238) algorithm for evidence estimation.
        
        ## Problems
        
        If you discover any issues with `uravu` please feel free to submit an issue to our issue tracker on [Github](https://github.com/arm61/uravu).
        Alternatively, if you are feeling confident, fix the bug yourself and make a pull request to the main codebase (be sure to check out our [contributing guidelines](https://github.com/arm61/uravu/blob/master/CONTRIBUTING.md) first).
        
        ## Installation
        
        `uravu` is available from the [PyPI](https://pypi.org/project/uravu/) repository so can be [installed using `pip`](https://uravu.readthedocs.io/en/latest/installation.html) or alternatively `clone` this repository and install the latest development build with the commands below.
        
        ```
        pip install -r requirements.txt
        python setup.py build
        python setup.py install
        pytest
        ```
        
        ## [Contributors](https://github.com/arm61/uravu/graphs/contributors)
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Description-Content-Type: text/markdown
