A toolbox for working with observations of star clusters.
In the long-running tradition of astronomy software, ocelot is not a good acronym for this project. It's the Open-source star ClustEr muLti-purpOse Toolkit. (We hope the results you get from this package are better than this acronym)
For the time being, ocelot is a collection of code that emilyhunt wrote during her PhD, but the eventual goal will be to make a package usable by the entire star cluster community. If you'd like to see a feature added, then please consider opening an issue and proposing it!
Install from PyPI with:
pip install ocelot
Currently, using ocelot.simulate also requires manually downloading data from here. Place it at a directory of your choosing, and set the environment variable OCELOT_DATA to this location.
If you're just working with a local dev copy of ocelot (i.e. you installed it via git clone), then you could put the data at the default location - /data in this folder.
We recommend using uv to manage Python dependencies when developing a local copy of the project. Here's everything you need to do:
- Clone the repo:
git clone https://github.com/emilyhunt/ocelot
-
Install uv, if you haven't already. (This won't mess with any of your other Python installations.)
-
Navigate to the new ocelot directory, and sync the project dependences including dev and docs ones with:
uv sync --all-extras
After installing development dependencies, you can also make and view edits to the package's documentation. To view a local copy of the documentation, do mkdocs serve. You can do a test build with mkdocs build.
There is currently no paper associated with ocelot. For now, please at least mention the package and add a footnote to your mention, linking to this repository - in LaTeX, that would be:
\footnote{\url{https://github.com/emilyhunt/ocelot}}
You can also cite Hunt & Reffert 2021, which was the paper for which development of this module began:
@ARTICLE{2021A&A...646A.104H,
author = {{Hunt}, Emily L. and {Reffert}, Sabine},
title = "{Improving the open cluster census. I. Comparison of clustering algorithms applied to Gaia DR2 data}",
journal = {\aap},
keywords = {methods: data analysis, open clusters and associations: general, astrometry, Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics},
year = 2021,
month = feb,
volume = {646},
eid = {A104},
pages = {A104},
doi = {10.1051/0004-6361/202039341},
archivePrefix = {arXiv},
eprint = {2012.04267},
primaryClass = {astro-ph.GA},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021A&A...646A.104H},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}