ArviZ (pronounced "AR-vees") is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, model checking, comparison and diagnostics.
The ArviZ documentation can be found in the official docs. First time users may find the quickstart to be helpful. Additional guidance can be found in the usage documentation.
ArviZ is available for installation from PyPI. The latest stable version can be installed using pip:
pip install arviz
The latest development 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 git and setuptools:
git clone https://github.com/arviz-devs/arviz.git
cd arviz
python setup.py install
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ArviZ is tested on Python 3.5 and 3.6, and depends on NumPy, SciPy, xarray, and Matplotlib.
ArviZ is a community project and welcomes contributions. Additional information can be found in the Contributing Readme
A typical development workflow is:
- Install project requirements:
pip install -r requirements.txt - Install additional testing requirements:
pip install -r requirements-dev.txt - Write helpful code and tests.
- Verify code style:
./scripts/lint.sh - Run test suite:
pytest arviz/tests - Make a pull request.
There is also a Dockerfile which helps for isolating build problems and local development.
- Install Docker for your operating system
- Clone this repo,
- Run
./scripts/container.sh --build
This will build a local image with the tag arviz.
After building the image tests can be executing by running docker run arviz.
An interactive shell can be started by running docker run -it arviz /bin/bash. The correct conda environment will be activated automatically.
ArviZ wishes to maintain a positive community. Additional details can be found in the Code of Conduct