Metadata-Version: 2.4
Name: arviz-plots
Version: 1.1.0
Summary: ArviZ-plots provides ready to use and composable plots for Bayesian Workflow.
Author-email: ArviZ team <arvizdevs@gmail.com>
Requires-Python: >=3.12
Description-Content-Type: text/markdown
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Framework :: Matplotlib
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
License-File: LICENSE
Requires-Dist: arviz-base>=1.1,<1.2
Requires-Dist: arviz-stats[xarray]>=1.1,<1.2
Requires-Dist: bokeh>=3.4 ; extra == "bokeh"
Requires-Dist: sphinx-book-theme ; extra == "doc"
Requires-Dist: myst-parser[linkify] ; extra == "doc"
Requires-Dist: myst-nb ; extra == "doc"
Requires-Dist: sphinx-copybutton ; extra == "doc"
Requires-Dist: numpydoc ; extra == "doc"
Requires-Dist: sphinx>=6 ; extra == "doc"
Requires-Dist: sphinx-design ; extra == "doc"
Requires-Dist: jupyter-sphinx ; extra == "doc"
Requires-Dist: h5netcdf[h5py] ; extra == "doc"
Requires-Dist: plotly<6 ; extra == "doc"
Requires-Dist: matplotlib>=3.9 ; extra == "matplotlib"
Requires-Dist: plotly>=5.19 ; extra == "plotly"
Requires-Dist: webcolors ; extra == "plotly"
Requires-Dist: hypothesis ; extra == "test"
Requires-Dist: pytest ; extra == "test"
Requires-Dist: pytest-cov ; extra == "test"
Requires-Dist: h5netcdf[h5py] ; extra == "test"
Requires-Dist: kaleido ; extra == "test"
Project-URL: documentation, https://arviz-plots.readthedocs.io
Project-URL: funding, https://opencollective.com/arviz
Project-URL: source, https://github.com/arviz-devs/arviz-plots
Project-URL: tracker, https://github.com/arviz-devs/arviz-plots/issues
Provides-Extra: bokeh
Provides-Extra: doc
Provides-Extra: matplotlib
Provides-Extra: plotly
Provides-Extra: test

# arviz-plots

[![Run tests](https://github.com/arviz-devs/arviz-plots/actions/workflows/test.yml/badge.svg)](https://github.com/arviz-devs/arviz-plots/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/arviz-devs/arviz-plots/graph/badge.svg?token=1VIPLXCOJQ)](https://codecov.io/gh/arviz-devs/arviz-plots)
[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org)

ArviZ (pronounced "AR-_vees_") is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics.

arviz-plots is the subpackage in charge of the visualizations.

### ArviZ in other languages
ArviZ also has a Julia wrapper available [ArviZ.jl](https://julia.arviz.org/).

## Documentation

The ArviZ documentation can be found in the [official docs](https://python.arviz.org).
Here are some quick links for common scenarios:

* First time Bayesian modelers and ArviZ users: [EABM book](https://arviz-devs.github.io/EABM/)
* First time ArviZ users, already familiar with Bayesian modeling: [overview notebook](https://python.arviz.org/projects/plots/en/latest/tutorials/overview.html) or [example gallery](https://python.arviz.org/projects/plots/en/latest/gallery/index.html)
* ArviZ 0.x user: [migration guide](https://python.arviz.org/en/latest/user_guide/migration_guide.html)
* ArviZ-verse documentation:
  - [arviz-base](https://python.arviz.org/projects/base/en/latest/)
  - [arviz-stats](https://python.arviz.org/projects/stats/en/latest/)
  - [arviz-plots](https://python.arviz.org/projects/plots/en/latest/) (this package)


## Installation

### Stable
ArviZ is available for installation from [PyPI](https://pypi.org/project/arviz/).
The latest stable version can be installed using pip:

```
pip install "arviz-plots[backend]"
```
Note that `arviz-plots` is a minimal package, which only depends on xarray, numpy, arviz-base and arviz-stats. None of the possible backends: `matplotlib`, `bokeh` or `plotly` are installed by default.

Consequently, it is not recommended to install arviz-plots but instead to choose which backend to use. For example `arviz-plots[matplotlib]` or `arviz-plots[matplotlib, plotly]`, multiple comma separated values are valid too.


### Development
The latest development version can be installed from the main branch using pip:

```
pip install git+git://github.com/arviz-devs/arviz-plots.git
```

Another option is to clone the repository and install using git and setuptools:

```
git clone https://github.com/arviz-devs/arviz-plots.git
cd arviz-plots
python setup.py install
```

## Citation


If you use ArviZ and want to cite it please use [![DOI](https://joss.theoj.org/papers/10.21105/joss.09889/status.svg)](https://doi.org/10.21105/joss.09889)

Here is the citation in BibTeX format

```
@article{Martin2026,
doi = {10.21105/joss.09889},
url = {https://doi.org/10.21105/joss.09889},
year = {2026},
publisher = {The Open Journal},
volume = {11},
number = {119},
pages = {9889},
author = {Martin, Osvaldo A. and Abril-Pla, Oriol and Deklerk, Jordan and Axen, Seth D. and Carroll, Colin and Hartikainen, Ari and Vehtari, Aki},
title = {ArviZ: a modular and flexible library for exploratory analysis of Bayesian models},
journal = {Journal of Open Source Software}}
```


## Contributions
ArviZ is a community project and welcomes contributions.
Additional information can be found in the [contributing guide](https://python.arviz.org/en/latest/contributing/index.html)


## Code of Conduct
ArviZ wishes to maintain a positive community. Additional details
can be found in the [Code of Conduct](https://www.arviz.org/en/latest/CODE_OF_CONDUCT.html)

## Donations
ArviZ is a non-profit project under NumFOCUS umbrella. If you want to support ArviZ financially, you can donate [here](https://numfocus.org/donate-to-arviz).

## Sponsors and Institutional Partners
[![Aalto University](https://raw.githubusercontent.com/arviz-devs/arviz-project/refs/heads/main/cards/Aalto-black-text.png)](https://www.aalto.fi/en)
[![FCAI](https://raw.githubusercontent.com/arviz-devs/arviz-project/refs/heads/main/cards/FCAI.png)](https://fcai.fi/)
[![NumFOCUS](https://raw.githubusercontent.com/arviz-devs/arviz-project/refs/heads/main/sphinx/NumFocus.png)](https://numfocus.org)

[The ArviZ project website](https://www.arviz.org/en/latest/sponsors_partners.html) has more information about each sponsor and the support they provide.

