Metadata-Version: 2.1
Name: uravu
Version: 0.0.7
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)**
        
        [![DOI](https://zenodo.org/badge/241184437.svg)](https://zenodo.org/badge/latestdoi/241184437)
        [![Documentation Status](https://readthedocs.org/projects/uravu/badge/?version=latest)](https://uravu.readthedocs.io/en/latest/?badge=latest)
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        [![Build Status](https://travis-ci.org/arm61/uravu.svg?branch=master)](https://travis-ci.org/arm61/uravu)
        [![Build status](https://ci.appveyor.com/api/projects/status/eo426m99lmkbh5rx?svg=true)](https://ci.appveyor.com/project/arm61/uravu)
        [![Gitter](https://badges.gitter.im/uravu/community.svg)](https://gitter.im/uravu/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
        
        ``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 amoung 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/CONTRIBUTING.md) first). 
        Finally, if you are just wanting to ask a question and cannot find the information elsewhere, we have a [gitter chat room](https://gitter.im/uravu/community?utm_source=share-link&utm_medium=link&utm_campaign=share-link) as another way to seek support. 
        
        ## Installation
        
        ```
        pip install -r requirements.txt
        python setup.py build
        python setup.py install 
        pytest
        ```
        
        ## Contributors 
        
        - [Andrew R. McCluskey](https://armccluskey.com)
        
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
