Skip to content


Repository files navigation

DOI codecov Test Lint Documentation GPLv3 license Get help on Telegram PyPI version DeepSource


kuibit is a Python library to analyze simulations performed with the Einstein Toolkit largely inspired by PostCactus. kuibit can read simulation data and represent it with high-level classes. This page is mainly intended for developers. Documentation for users is available here.

Quick links


kuibit is available in PyPI. To install it with pip

pip3 install kuibit

If they are not already available, pip will install all the necessary dependencies.

kuibit follows NEP29 and requires Python >= 3.7.

If you intend to develop kuibit, see and follow the instruction below.


For development, we use poetry. Poetry simplifies dependency management, building, and publishing the package.

To install kuibit with poetry, clone this repo, move into the folder, and run:

poetry install -E full

This will download all the needed dependencies in a sandboxed environment (the -E full flag is for the optional dependencies). When you want to use kuibit, just run poetry shell from within the kuibit directory. This will drop you in a shell in which you have full access to kuibit in "development" version, and its dependencies (including the one needed only for development). Alternatively, you can activate the virtual environment directly. You can find where the environment in installed running the command poetry env info --path in the kuibit directory. This is a standard virtual environment, which can be activated with the activate scripts in the bin folder. Once you do that, you will be able to use kuibit for anywhere.

As of version 1.3.0, we adopt the following philosophy for git branches:

  • master always corresponds to the latest stable version, the one available on PyPI. Hotfixes are applied directly on master, and a new release is tagged.
  • next is where most of the development occurs. This corresponds to the next version of kuibit. next often experiences rebasing.
  • Specific features that can be developed on their separate feature branch. This will be merged into next.

The documentation of the development version is served at


Users and developers of kuibit meet in the Telegram group. If you have any problem or suggestion, that's a good place where to discuss it. Alternatively, you can also open an issue on GitHub.


kuibit uses Sphinx to generate the documentation. To produce the documentation

cd docs && make html

Documentation is automatically generated after each commit by GitHub Actions.

We use nbsphinx to translate Jupyter notebooks to the examples. The extension is required. Note: Jupyter notebooks have to be un-evaluated. nbsphinx requires pandoc. If don't have pandoc, you should comment out nbsphinx in docs/, or compiling the documentation will fail.


Here is a list of videos describing kuibit and how to use it:

The Using kuibit series is a great place where to get started with kuibit.


kuibit comes with a suite of unit tests. To run the tests, (in a poetry shell),

poetry run python -m unittest

Tests are automatically run after each commit by GitHub Actions.

If you want to look at the coverage of your tests, run (in a poetry shell)

coverage run -m unittest
coverage html

This will produce a directory with the html files containing the analysis of the coverage of the tests.

What is a kuibit?

A kuibit (also known as kukuipad, meaning harvest pole) is the tool traditionally used by the Tohono O'odham people to reach the fruit of the Saguaro cacti during the harvesting season. In the same way, this package is a tool that you can use to collect the fruit of your Cactus simulations.


kuibit follows the same design and part of the implementation details of PostCactus, code developed by Wolfgang Kastaun. This fork completely rewrites the original code, adding emphasis on documentation, testing, and extensibility. The logo contains elements designed by We thank kuibit first users, Stamatis Vretinaris and Pedro Espino, for providing comments to improve the code and the documentation.


kuibit is built and maintained by the dedication of one graduate student. Please, consider citing kuibit if you find the software useful. You can use the following bibtex key (as provided by ADSABS).

       author = {{Bozzola}, Gabriele},
        title = "{kuibit: Analyzing Einstein Toolkit simulations with Python}",
      journal = {The Journal of Open Source Software},
     keywords = {numerical relativity, Python, Einstein Toolkit, astrophysics, Cactus, General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena},
         year = 2021,
        month = apr,
       volume = {6},
       number = {60},
          eid = {3099},
        pages = {3099},
          doi = {10.21105/joss.03099},
archivePrefix = {arXiv},
       eprint = {2104.06376},
 primaryClass = {gr-qc},
       adsurl = {},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}

You can find this entry in Python with from kuibit import __bibtex__.

kuibit is built with NumPy, SciPy, and h5py, and optionally uses matplotlib, mayavi, and numba. Consider citing these packages too.


kuibit is developed as professional tool that can be used for research to be published in peer-reviewed journals. As such, kuibit is tested to ensure that results are scientifically sound. However, we do not guarantee that the entirety of the software is correct and does what it is intended to do. Hence, users are strongly recommended to perform their independent validations and to report any problem.


Python 3 post-processing tools for simulations performed with the Einstein Toolkit







No packages published