Exploratory analysis of Bayesian models
Project description
<img src="https://arviz-devs.github.io/arviz/_static/logo.png" height=100></img>
[![Build Status](https://travis-ci.org/arviz-devs/arviz.svg?branch=master)](https://travis-ci.org/arviz-devs/arviz) [![Coverage Status](https://coveralls.io/repos/github/arviz-devs/arviz/badge.svg?branch=master)](https://coveralls.io/github/arviz-devs/arviz?branch=master)
# ArviZ
ArviZ (pronounced "AR-_vees_") is a Python package for exploratory analysis of Bayesian models.
Includes functions for posterior analysis, model checking, comparison and diagnostics.
## Documentation
The official Arviz documentation can be found here
https://arviz-devs.github.io/arviz/index.html
## Installation
The latest version can be installed from the master branch using pip:
```
pip install git+git://github.com/arviz-devs/arviz.git
```
Another option is to clone the repository and install using `python setup.py install`.
-------------------------------------------------------------------------------
## [Gallery](https://arviz-devs.github.io/arviz/examples/index.html)
<p>
<table>
<tr>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_forest_ridge.html">
<img alt="Ridge plot"
src="https://arviz-devs.github.io/arviz/_static/plot_forest_ridge_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_parallel.html">
<img alt="Parallel plot"
src="https://arviz-devs.github.io/arviz/_static/plot_parallel_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_trace.html">
<img alt="Trace plot"
src="https://arviz-devs.github.io/arviz/_static/plot_trace_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_density.html">
<img alt="Density plot"
src="https://arviz-devs.github.io/arviz/_static/plot_density_thumb.png" />
</a>
</td>
</tr>
<tr>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_posterior.html">
<img alt="Posterior plot"
src="https://arviz-devs.github.io/arviz/_static/plot_posterior_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_joint.html">
<img alt="Joint plot"
src="https://arviz-devs.github.io/arviz/_static/plot_joint_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_ppc.html">
<img alt="Posterior predictive plot"
src="https://arviz-devs.github.io/arviz/_static/plot_ppc_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_pair.html">
<img alt="Pair plot"
src="https://arviz-devs.github.io/arviz/_static/plot_pair_thumb.png" />
</a>
</td>
</tr>
<tr>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_energy.html">
<img alt="Energy Plot"
src="https://arviz-devs.github.io/arviz/_static/plot_energy_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_violin.html">
<img alt="Violin Plot"
src="https://arviz-devs.github.io/arviz/_static/plot_violin_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_forest.html">
<img alt="Forest Plot"
src="https://arviz-devs.github.io/arviz/_static/plot_forest_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_autocorr.html">
<img alt="Autocorrelation Plot"
src="https://arviz-devs.github.io/arviz/_static/plot_autocorr_thumb.png" />
</a>
</td>
</tr>
</table>
## Dependencies
Arviz is tested on Python 3.5 and 3.6, and depends on NumPy, SciPy, xarray, and Matplotlib.
## Developing
A typical development workflow is:
1. Install project requirements: `pip install requirements.txt`
2. Install additional testing requirements: `pip install requirements-dev.txt`
3. Write helpful code and tests.
4. Verify code style: `./scripts/lint.sh`
5. Run test suite: `pytest arviz/tests`
6. Make a pull request.
There is also a Dockerfile which helps for isolating build problems and local development.
1. Install Docker for your operating system
2. Clone this repo,
3. Run `./scripts/start_container.sh`
This should start a local docker container called arviz, as well as a Jupyter notebook server running on port 8888. The notebook should be opened in your browser automatically (you can disable this by passing --no-browser). The container will be running the code from your local copy of arviz, so you can test your changes.
[![Build Status](https://travis-ci.org/arviz-devs/arviz.svg?branch=master)](https://travis-ci.org/arviz-devs/arviz) [![Coverage Status](https://coveralls.io/repos/github/arviz-devs/arviz/badge.svg?branch=master)](https://coveralls.io/github/arviz-devs/arviz?branch=master)
# ArviZ
ArviZ (pronounced "AR-_vees_") is a Python package for exploratory analysis of Bayesian models.
Includes functions for posterior analysis, model checking, comparison and diagnostics.
## Documentation
The official Arviz documentation can be found here
https://arviz-devs.github.io/arviz/index.html
## Installation
The latest version can be installed from the master branch using pip:
```
pip install git+git://github.com/arviz-devs/arviz.git
```
Another option is to clone the repository and install using `python setup.py install`.
-------------------------------------------------------------------------------
## [Gallery](https://arviz-devs.github.io/arviz/examples/index.html)
<p>
<table>
<tr>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_forest_ridge.html">
<img alt="Ridge plot"
src="https://arviz-devs.github.io/arviz/_static/plot_forest_ridge_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_parallel.html">
<img alt="Parallel plot"
src="https://arviz-devs.github.io/arviz/_static/plot_parallel_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_trace.html">
<img alt="Trace plot"
src="https://arviz-devs.github.io/arviz/_static/plot_trace_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_density.html">
<img alt="Density plot"
src="https://arviz-devs.github.io/arviz/_static/plot_density_thumb.png" />
</a>
</td>
</tr>
<tr>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_posterior.html">
<img alt="Posterior plot"
src="https://arviz-devs.github.io/arviz/_static/plot_posterior_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_joint.html">
<img alt="Joint plot"
src="https://arviz-devs.github.io/arviz/_static/plot_joint_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_ppc.html">
<img alt="Posterior predictive plot"
src="https://arviz-devs.github.io/arviz/_static/plot_ppc_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_pair.html">
<img alt="Pair plot"
src="https://arviz-devs.github.io/arviz/_static/plot_pair_thumb.png" />
</a>
</td>
</tr>
<tr>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_energy.html">
<img alt="Energy Plot"
src="https://arviz-devs.github.io/arviz/_static/plot_energy_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_violin.html">
<img alt="Violin Plot"
src="https://arviz-devs.github.io/arviz/_static/plot_violin_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_forest.html">
<img alt="Forest Plot"
src="https://arviz-devs.github.io/arviz/_static/plot_forest_thumb.png" />
</a>
</td>
<td>
<a href="https://arviz-devs.github.io/arviz/examples/plot_autocorr.html">
<img alt="Autocorrelation Plot"
src="https://arviz-devs.github.io/arviz/_static/plot_autocorr_thumb.png" />
</a>
</td>
</tr>
</table>
## Dependencies
Arviz is tested on Python 3.5 and 3.6, and depends on NumPy, SciPy, xarray, and Matplotlib.
## Developing
A typical development workflow is:
1. Install project requirements: `pip install requirements.txt`
2. Install additional testing requirements: `pip install requirements-dev.txt`
3. Write helpful code and tests.
4. Verify code style: `./scripts/lint.sh`
5. Run test suite: `pytest arviz/tests`
6. Make a pull request.
There is also a Dockerfile which helps for isolating build problems and local development.
1. Install Docker for your operating system
2. Clone this repo,
3. Run `./scripts/start_container.sh`
This should start a local docker container called arviz, as well as a Jupyter notebook server running on port 8888. The notebook should be opened in your browser automatically (you can disable this by passing --no-browser). The container will be running the code from your local copy of arviz, so you can test your changes.
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