kuibit is a Python package for post-processing simulations. The tool comes with several features for analysis and visualization. For some of the most common operations (e.g., 2D plot of a grid variable, extraction of gravitational waves, ...), users can directly obtain the result without writing any code. The number of such ready-made scripts grows with every release of kuibit. For everything else, kuibit
has a large number of features (https://sbozzolo.github.io/kuibit/features.html)) that can be used in scripts or notebooks.
kuibit is designed to be user-friendly: it has rich documentation (https://sbozzolo.github.io/kuibit/),,) examples ready to be used, and tutorials. kuibit is also designed to be developer- and maintainer- friendly: it is thoroughly commented, and it implements several continuous integration pipelines to test, document, and publish the package automatically. The high quality of kuibit is also recognized by its publication in the Journal of Open Source Software (https://joss.theoj.org/papers/10.21105/joss.03099)..)
kuibit is largely inspired by Wolfgang Kastaun's PyCactus. kuibit shares the same overall design with PyCactus, and in some cases, the implementation details too. kuibit would not exist without PyCactus.
As far as I know, 5-10 people are actively using kuibit as their main tool to interface with the output of simulations. The feedback I got is overwhelmingly positive: users that are new to the Einstein Toolkit praise the simplicity of use and the comfort of working in Python; experienced users claim that kuibit allows them to focus on the science instead of the technical details.
kuibit has already been used for publications, see https://inspirehep.net/literature?sort=mostrecent&size=25&page=1&q=refersto%3Arecid%3A1858070.
kuibit repo: https://github.com/Sbozzolo/kuibit
Comments and feedback are welcomed.