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uravu

uravu: Making Bayesian modelling easy(er) - Published in JOSS (2020)

https://github.com/arm61/uravu

Science Score: 100.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 5 DOI reference(s) in README and JOSS metadata
  • ✓
    Academic publication links
    Links to: joss.theoj.org, zenodo.org
  • ✓
    Committers with academic emails
    5 of 6 committers (83.3%) from academic institutions
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

bayesian-inference bayesian-statistics data-analysis fitting markov-chain-monte-carlo nested-sampling

Scientific Fields

Engineering Computer Science - 40% confidence
Last synced: 4 months ago · JSON representation ·

Repository

A straightforward Bayesian data fitting library

Basic Info
Statistics
  • Stars: 27
  • Watchers: 2
  • Forks: 4
  • Open Issues: 1
  • Releases: 13
Topics
bayesian-inference bayesian-statistics data-analysis fitting markov-chain-monte-carlo nested-sampling
Created almost 6 years ago · Last pushed about 1 year ago
Metadata Files
Readme Contributing License Code of conduct Citation Zenodo

README.md

uravu logo

making Bayesian modelling easy(er)

status DOI

PyPI version Documentation Status Coverage Status Build Status Build status

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. 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.

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 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:

Problems

If you discover any issues with uravu please feel free to submit an issue to our issue tracker on Github. 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 first).

Installation

uravu is available from the PyPI repository so can be installed using pip 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

Owner

  • Name: Andrew McCluskey
  • Login: arm61
  • Kind: user
  • Location: Copenhagen
  • Company: European Spallation Source

instrument data scientist @essneutron (he/him)

JOSS Publication

uravu: Making Bayesian modelling easy(er)
Published
June 05, 2020
DOI
Volume 5, Issue 50, Page 2214
Authors
Andrew R. McCluskey ORCID
Diamond Light Source, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK, Department of Chemistry, University of Bath, Claverton Down, Bath, BA2 7AY, UK
Tim Snow ORCID
Diamond Light Source, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK, School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
Editor
Dan Foreman-Mackey ORCID
Tags
Bayesian analysis evidence estimation nested sampling mcmc
View PDF Review Thread Software Archive

Citation (CITATION.cff) 

abstract: "making Bayesian modelling easy(er)"
authors: 
  - affiliation: "European Spallation Source ERIC; Department of Chemistry, University of Bath"
    family-names: McCluskey
    given-names: "Andrew R."
    orcid: "https://orcid.org/0000-0003-3381-5911"
  - affiliation: "Geollect Ltd."
    family-names: Symington
    given-names: "Adam R."
    orcid: "https://orcid.org/0000-0001-6059-497X"
  - affiliation: "Department of Chemistry, University of Bath"
    family-names: Dean
    given-names: "Jacob M."
    orcid: "https://orcid.org/0000-0003-3363-4256"
cff-version: "1.1.0"
date-released: 2021-12-03
license: MIT
message: "If you use this software, please cite it using these metadata."
repository-code: "https://github.com/arm61/uravu"
title: uravu
version: "1.2.7"

GitHub Events

Total
  • Watch event: 2
  • Pull request review event: 1
  • Pull request event: 2
Last Year
  • Watch event: 2
  • Pull request review event: 1
  • Pull request event: 2

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 348
  • Total Committers: 6
  • Avg Commits per committer: 58.0
  • Development Distribution Score (DDS): 0.348
Past Year
  • Commits: 2
  • Committers: 1
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Andrew McCluskey a****y@d****k 227
Andrew McCluskey a****1 111
Andrew McCluskey a****y@b****k 6
ab5424 a****i@r****e 2
j-m-dean j****0@b****k 1
Adam Symington a****4@b****k 1
Committer Domains (Top 20 + Academic)
bath.ac.uk: 3 rwth-aachen.de: 1 diamond.ac.uk: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 17
  • Total pull requests: 13
  • Average time to close issues: 9 days
  • Average time to close pull requests: about 11 hours
  • Total issue authors: 5
  • Total pull request authors: 4
  • Average comments per issue: 2.76
  • Average comments per pull request: 0.08
  • Merged pull requests: 13
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 6 days
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 1.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • arm61 (11)
  • ff995 (3)
  • nvaytet (1)
  • JohannesBuchner (1)
  • ff9955 (1)
Pull Request Authors
  • arm61 (10)
  • ab5424 (2)
  • symmy596 (1)
  • j-m-dean (1)
Top Labels
Issue Labels
enhancement (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 189 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 1
  • Total versions: 21
  • Total maintainers: 1
pypi.org: uravu

Bayesian methods for analytical relationships

  • Versions: 21
  • Dependent Packages: 1
  • Dependent Repositories: 1
  • Downloads: 189 Last month
Rankings
Dependent packages count: 4.7%
Downloads: 9.6%
Average: 12.0%
Dependent repos count: 21.7%
Maintainers (1)
arm61
Last synced: 4 months ago

Dependencies

docs/requirements.txt pypi
  • corner *
  • coverage *
  • coveralls *
  • dynesty *
  • emcee *
  • jupyter-sphinx ==0.2.4
  • matplotlib *
  • nbsphinx *
  • numpy *
  • scipy >=1.5.4
  • seaborn *
  • sphinx_rtd_theme *
  • tqdm *
  • uncertainties *
requirements.txt pypi
  • dynesty ==1.0.1
  • emcee *
  • matplotlib *
  • numpy *
  • pytest *
  • scipy >=1.5.4
  • seaborn *
  • uncertainties *
.github/workflows/ci.yml actions
  • AndreMiras/coveralls-python-action develop composite
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/release.yml actions
  • actions/checkout master composite
  • actions/setup-python v2 composite
  • pypa/gh-action-pypi-publish release/v1 composite
setup.py pypi