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kuibit

kuibit: Analyzing Einstein Toolkit simulations with Python - Published in JOSS (2021)

https://github.com/sbozzolo/kuibit

Science Score: 100.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 5 DOI reference(s) in README and JOSS metadata
  • ✓
    Academic publication links
    Links to: joss.theoj.org
  • ✓
    Committers with academic emails
    2 of 8 committers (25.0%) from academic institutions
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Scientific Fields

Mathematics Computer Science - 84% confidence
Last synced: 6 months ago · JSON representation ·

Repository

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

Basic Info
Statistics
  • Stars: 40
  • Watchers: 4
  • Forks: 13
  • Open Issues: 9
  • Releases: 16
Created over 5 years ago · Last pushed 8 months ago
Metadata Files
Readme Changelog Contributing License Citation Zenodo

README.md

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

kuibit

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

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 and requires Python >= 3.7.

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

Development

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: 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.

Help!

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.

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 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/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 - Introduction on kuibit - Einstein Toolkit Seminar, 2021 - Using kuibit - kuibit - Einstein Toolkit Summer School, 2021 - Tutorial: Post-processing Cactus simulations with Python - Einstein Toolkit Summer School, 2022

The Using kuibit 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. 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.

Owner

  • Name: Gabriele Bozzola
  • Login: Sbozzolo
  • Kind: user
  • Location: San Francisco, CA
  • Company: Amazon Web Services, Center for Quantum Computing

Applied Scientist at the Amazon Center for Quantum Computing || Passionate about building reliable and user-friendly scientific software (and Emacs, of course)

JOSS Publication

kuibit: Analyzing Einstein Toolkit simulations with Python
Published
April 13, 2021
DOI
Volume 6, Issue 60, Page 3099
Authors
Gabriele Bozzola ORCID
Steward Observatory and Astronomy Department, University of Arizona
Editor
Eloisa Bentivegna ORCID
Tags
Python Einstein Toolkit Cactus numerical relativity astrophysics
View PDF Review Thread Software Archive

Citation (CITATION.cff) 

# YAML 1.2
---
authors:
  -
    affiliation: "Department of Astronomy, University of Arizona, Tucson"
    family-names: Bozzola
    given-names: Gabriele
    orcid: "https://orcid.org/0000-0003-3696-6408"
cff-version: "1.1.0"
date-released: 2021-04-12
doi: "10.21105/joss.03099"
keywords:
  - "Numerical Relativity"
  - Python
  - "Einstein Toolkit"
  - Astrophysics
  - Cactus
  - "General Relativity"
  - "Gravitational-wave Astronomy"
license: "GPL-3.0+"
message: "If you use this software, please cite it using these metadata."
repository-code: "https://github.com/Sbozzolo/kuibit"
title: kuibit
version: "1.0.0"
...

GitHub Events

Total
  • Create event: 6
  • Release event: 1
  • Issues event: 1
  • Watch event: 2
  • Issue comment event: 3
  • Push event: 14
  • Pull request event: 3
  • Fork event: 2
Last Year
  • Create event: 6
  • Release event: 1
  • Issues event: 1
  • Watch event: 2
  • Issue comment event: 3
  • Push event: 14
  • Pull request event: 3
  • Fork event: 2

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 726
  • Total Committers: 8
  • Avg Commits per committer: 90.75
  • Development Distribution Score (DDS): 0.011
Past Year
  • Commits: 12
  • Committers: 3
  • Avg Commits per committer: 4.0
  • Development Distribution Score (DDS): 0.167
Top Committers
Name Email Commits
Gabriele Bozzola s****r@g****m 718
Eloisa Bentivegna e****a@p****m 2
deepsource-autofix[bot] 6****] 1
Yumeng Xu x****x@g****m 1
Krishiv Bhatia f****4@g****m 1
Jonah Miller j****m@l****v 1
Cheng-Hsin Cheng c****3@i****u 1
Benjamin Meyers m****s 1
Committer Domains (Top 20 + Academic)
illinois.edu: 1 lanl.gov: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 23
  • Total pull requests: 20
  • Average time to close issues: 11 days
  • Average time to close pull requests: 26 days
  • Total issue authors: 12
  • Total pull request authors: 11
  • Average comments per issue: 2.48
  • Average comments per pull request: 2.05
  • Merged pull requests: 10
  • Bot issues: 0
  • Bot pull requests: 6
Past Year
  • Issues: 1
  • Pull requests: 2
  • Average time to close issues: about 9 hours
  • Average time to close pull requests: 4 days
  • Issue authors: 1
  • Pull request authors: 2
  • Average comments per issue: 2.0
  • Average comments per pull request: 2.5
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Sbozzolo (6)
  • Yurlungur (5)
  • AuroraDysis (2)
  • irrationalnumbers (2)
  • Brookluo (1)
  • pdiener (1)
  • assumpcaothiago (1)
  • stevenrbrandt (1)
  • svretina (1)
  • bgiacoma (1)
  • eloisabentivegna (1)
  • ekwessel (1)
Pull Request Authors
  • krishivbhatia (8)
  • deepsource-autofix[bot] (6)
  • eloisabentivegna (2)
  • xuyumeng (2)
  • meyersbs (2)
  • konrad-topolski (1)
  • chcheng3 (1)
  • Yurlungur (1)
  • irrationalnumbers (1)
  • samueltootle (1)
  • ekwessel (1)
Top Labels
Issue Labels
enhancement (5) help wanted (2) good first issue (2) bug (1) documentation (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 226 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 17
  • Total maintainers: 1
pypi.org: kuibit

Read and analyze Einstein Toolkit simulations.

  • Versions: 17
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 226 Last month
Rankings
Dependent packages count: 10.1%
Forks count: 10.5%
Stargazers count: 10.6%
Average: 13.7%
Downloads: 15.7%
Dependent repos count: 21.6%
Maintainers (1)
sbozzolo
Last synced: 6 months ago

Dependencies

poetry.lock pypi
  • 148 dependencies
pyproject.toml pypi
  • Sphinx ^3.2.1 develop
  • black ^20.8b1 develop
  • coverage ^5.3 develop
  • flake8 ^3.9.2 develop
  • isort ^5.0.0 develop
  • jedi ^0.17.2 develop
  • jupyter ^1.0.0 develop
  • mayavi ^4.7.4 develop
  • nbsphinx ^0.8.6 develop
  • snakeviz ^2.1.0 develop
  • ConfigArgParse ^1.2.3
  • argcomplete ^1.12.3
  • h5py >=2.9.0
  • lalsuite ^6.77
  • matplotlib ^3.3.2
  • motionpicture ^0.2.0
  • numba ^0.53.1
  • numpy --- - !ruby/hash:ActiveSupport::HashWithIndifferentAccess version: "^1.18.5" python: ">=3.6, <3.10" - !ruby/hash:ActiveSupport::HashWithIndifferentAccess version: "^1.21.4" python: ">=3.10"
  • py-expression-eval ^0.3.14
  • pycbc ^1.16.10
  • python ^3.6.1, <3.11
  • scipy ^1.5.2
  • tikzplotlib --- - !ruby/hash:ActiveSupport::HashWithIndifferentAccess version: ">=0.9.5, <=0.9.12" python: ">=3.6, <3.7" - !ruby/hash:ActiveSupport::HashWithIndifferentAccess version: "^0.9.5" python: ">=3.7"
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