Welcome to the GitHub repo for Taweret, the state of the art Python package for applying Bayesian Model Mixing!

Taweret is a new generalized package to help with applying Bayesian model mixing methods, developed by members of the BAND collaboration, to a wide variety of problems in physics.

At present, this package possesses the following BMM methods:

• Linear model mixing ( With simultaneous model mixing and calibration)
• Multivariate BMM
• Bayesian Trees

See Taweret's docs webpage here.

We have produced a Jupyter Book that contains all of our Jupyter notebook tutorials and further information about Bayesian model mixing to get users started on understanding the techniques included in Taweret. The Book contains the most current version of the tutorial notebooks, and these can be downloaded directly from the Book website or from the most current version of this repository.

This repository uses submodules. To clone this repository and automatically checkout all the submodules, use

git clone --recursive https://github.com/bandframework/Taweret.git 

If you want to limit the size of the repository (this or the submodules), you can use the depth flag

git clone --depth=1 https://github.com/bandframework/Taweret.git

Inside the directory containing the cloned repository, you then run

git submodule update --init --depth=1

GitHub's Codespaces is a great place to test using Taweret. Right now, you can try out Taweret's Bivariate Linear BMM and Multivariate BMM methods there, by following the instructions below.

1. Click the dropdown arrow on the green 'code' button found at the top of this page.
2. Click on the tab there that says 'codespaces'.
3. Click the button for 'create Codespace on main'.
4. Wait for the terminal to be finish spinning up a virtual environment and loading all needed variables (this can take a few minutes).
5. Once that is done, navigate on the file tree to a notebook you wish to run. To run a file, you need to set a kernel for the Jupyter notebook, so click on 'choose a kernel' in the upper right hand corner of the notebook. If you haven't gotten this message already, a message will pop up that says 'install preferred Python extension?', and you should click 'yes'.
6. When you click 'choose a kernel' it will offer a preferred Python version or a base version (usually a newer Python version). Choose whichever you prefer, and then you can run the notebook!

If you have benefited from Taweret, please cite our software using the following format:

@inproceedings{Taweret,
    author = "Liyanage, Dan and Semposki, Alexandra and Yannotty, John and Ingles, Kevin",
    title  = "{{Taweret: A Python Package for Bayesian Model Mixing}}",
    year   = "2023",
    url    = {https://github.com/bandframework/Taweret}
}

and our explanatory paper:

@article{Ingles:2023nha,
    author = "Ingles, Kevin and Liyanage, Dananjaya and Semposki, Alexandra C. and Yannotty, John C.",
    title = "{Taweret: a Python package for Bayesian model mixing}",
    eprint = "2310.20549",
    archivePrefix = "arXiv",
    primaryClass = "nucl-th",
    doi = "10.21105/joss.06175",
    journal = "J. Open Source Softw.",
    volume = "9",
    number = "97",
    pages = "6175",
    year = "2024"
}

Please also cite the BAND collaboration software suite using the format here.

Check out our SDK form here.

To contact the Taweret team, please submit an issue through the Issues page.

Authors: Kevin Ingles, Dan Liyanage, Alexandra Semposki, and John Yannotty.