{"id":16510435,"url":"https://github.com/arm61/uravu","last_synced_at":"2025-03-21T08:31:22.364Z","repository":{"id":49406876,"uuid":"241184437","full_name":"arm61/uravu","owner":"arm61","description":"A straightforward Bayesian data fitting library","archived":false,"fork":false,"pushed_at":"2023-10-23T08:31:52.000Z","size":1747,"stargazers_count":23,"open_issues_count":1,"forks_count":5,"subscribers_count":3,"default_branch":"master","last_synced_at":"2024-04-23T23:55:25.113Z","etag":null,"topics":["bayesian-inference","bayesian-statistics","data-analysis","fitting","markov-chain-monte-carlo","nested-sampling"],"latest_commit_sha":null,"homepage":"https://uravu.readthedocs.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/arm61.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-02-17T18:58:43.000Z","updated_at":"2023-09-28T08:23:50.000Z","dependencies_parsed_at":"2024-10-11T15:55:09.517Z","dependency_job_id":"1181d543-3c1f-4058-8980-49edf695ce93","html_url":"https://github.com/arm61/uravu","commit_stats":null,"previous_names":[],"tags_count":13,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arm61%2Furavu","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arm61%2Furavu/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arm61%2Furavu/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arm61%2Furavu/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arm61","download_url":"https://codeload.github.com/arm61/uravu/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244765017,"owners_count":20506747,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-inference","bayesian-statistics","data-analysis","fitting","markov-chain-monte-carlo","nested-sampling"],"created_at":"2024-10-11T15:55:07.218Z","updated_at":"2025-03-21T08:31:21.299Z","avatar_url":"https://github.com/arm61.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"![uravu logo](https://github.com/arm61/uravu/raw/master/docs/source/logo/uravu_logo.png)\n\n**making Bayesian modelling easy(er)**\n\n[![status](https://joss.theoj.org/papers/e9047e48bf024589e0765f955b3e4c76/status.svg)](https://joss.theoj.org/papers/e9047e48bf024589e0765f955b3e4c76)\n[![DOI](https://zenodo.org/badge/241184437.svg)](https://zenodo.org/badge/latestdoi/241184437)\n\n[![PyPI version](https://badge.fury.io/py/uravu.svg)](https://badge.fury.io/py/uravu)\n[![Documentation Status](https://readthedocs.org/projects/uravu/badge/?version=latest)](https://uravu.readthedocs.io/en/latest/?badge=latest)\n[![Coverage Status](https://coveralls.io/repos/github/arm61/uravu/badge.svg?branch=master)](https://coveralls.io/github/arm61/uravu?branch=master)\n[![Build Status](https://github.com/arm61/uravu/workflows/python-ci/badge.svg)](https://github.com/arm61/uravu/actions?query=workflow%3Apython-ci)\n[![Build status](https://ci.appveyor.com/api/projects/status/eo426m99lmkbh5rx?svg=true)](https://ci.appveyor.com/project/arm61/uravu)\n\n``uravu`` (from the Tamil for relationship) is about the relationship between some data and a function that may be used to describe the data.\n\nThe 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).\nTherefore enabling many more to make use of these exciting tools and powerful libraries.\nPlus, we have some nice plotting functionalities available in the `plotting` module, capable of generating publication quality figures.\n\n![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)\n\nIn an effort to make the ``uravu`` API friendly to those new to Bayesian inference, ``uravu`` is *opinionated*, making assumptions about priors among other things.\nHowever, we have endevoured to make it straightforward to ignore these opinions.\n\nIn 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.\n\n## Bayesian inference in Python\n\nThere are a couple of fantastic Bayesian inference libraries available in Python that `uravu` makes use of:\n\n- [emcee](https://emcee.readthedocs.io/): enables the use of the [Goodman \u0026 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,\n- [dynesty](https://dynesty.readthedocs.io/): implements the [nested sampling](https://doi.org/10.1063/1.1835238) algorithm for evidence estimation.\n\n## Problems\n\nIf you discover any issues with `uravu` please feel free to submit an issue to our issue tracker on [Github](https://github.com/arm61/uravu).\nAlternatively, 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).\n\n## Installation\n\n`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.\n\n```\npip install -r requirements.txt\npython setup.py build\npython setup.py install\npytest\n```\n\n## [Contributors](https://github.com/arm61/uravu/graphs/contributors)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farm61%2Furavu","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farm61%2Furavu","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farm61%2Furavu/lists"}