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

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# kuibit

`kuibit` is a Python library to analyze simulations performed with the Einstein
Toolkit largely inspired by
[PostCactus](https://github.com/wokast/PyCactus/tree/master/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](https://sbozzolo.github.io/kuibit).

### Quick links

- [Official documentation](https://sbozzolo.github.io/kuibit)
- [Tutorials](https://sbozzolo.github.io/kuibit/#tutorials)
- [Examples](https://sbozzolo.github.io/kuibit/#examples)
- [Archive with most recent examples and tools](https://github.com/sbozzolo/kuibit/releases/latest/download/examples.tar.gz)
- [Changelog](https://github.com/Sbozzolo/kuibit/releases)
- [What people say about kuibit](https://sbozzolo.github.io/kuibit/testimonials.html)
- [Users/developers group chat](https://t.me/kuibit)

## Installation

``kuibit`` is available in PyPI. To install it with `pip`
``` bash
pip3 install kuibit
```
If they are not already available, `pip` will install all the necessary dependencies.

``kuibit`` follows
[NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) and requires
Python >= 3.9.

If you intend to develop ``kuibit``, see [CONTRIBUTING.md](CONTRIBUTING.md) and
follow the instruction below.

### Development

For development, we use [poetry](https://python-poetry.org/). Poetry simplifies
dependency management, building, and publishing the package.

To install `kuibit` with poetry, clone this repo, move into the folder, and run:
``` sh
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
[sbozzolo.github.io/kuibit/dev](https://sbozzolo.github.io/kuibit/dev).

## Help!

Users and developers of ``kuibit`` meet in the [Telegram
group](https://t.me/kuibit). 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.

## Documentation

`kuibit` uses Sphinx to generate the documentation. To produce the documentation
```sh
cd docs && make html
```
Documentation is automatically generated after each commit by GitHub Actions.

We use [nbsphinx](https://nbsphinx.readthedocs.io/) to translate Jupyter
notebooks to the examples. The extension is required. Note: Jupyter notebooks
have to be un-evaluated. `nbsphinx` requires [pandoc](https://pandoc.org/). If
don't have `pandoc`, you should comment out `nbsphinx` in `docs/conf.py`, or
compiling the documentation will fail.

## Videos

Here is a list of videos describing `kuibit` and how to use it:
- [Tutorial: Post-processing Cactus simulations with Python](https://youtu.be/14YOQ6SqTUU)
- [Introduction on kuibit - Einstein Toolkit Seminar, 2021](https://www.youtube.com/watch?v=7-F2xh-m31A)
- [Using kuibit](https://www.youtube.com/playlist?list=PLIVVtc6RlFxpi3BiF6KTWd3z4TTQ5hY06)
- [kuibit - Einstein Toolkit Summer School, 2021](https://youtu.be/g-JlXfLvUZU)
- [Tutorial: Post-processing Cactus simulations with Python - Einstein Toolkit
  Summer School, 2022](https://www.youtube.com/watch?v=14YOQ6SqTUU)

The [Using
kuibit](https://www.youtube.com/playlist?list=PLIVVtc6RlFxpi3BiF6KTWd3z4TTQ5hY06)
series is a great place where to get started with `kuibit`.

## Tests

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

## Credits

`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 [freepik.com](freepik.com). We thank
``kuibit`` first users, Stamatis Vretinaris and Pedro Espino, for providing
comments to improve the code and the documentation.

## Citation

`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).
``` bibtex
@article{kuibit,
       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 = {https://ui.adsabs.harvard.edu/abs/2021JOSS....6.3099B},
      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.

## Disclaimer

`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.

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