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ocelot

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)

Current package status

⚠️ ocelot is currently in alpha and is in active development. Expect breaking API changes ⚠️

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!

Installation

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.

Development

We recommend using uv to manage Python dependencies when developing a local copy of the project. Here's everything you need to do:

  1. Clone the repo:
git clone https://github.com/emilyhunt/ocelot

  1. Install uv, if you haven't already. (This won't mess with any of your other Python installations.)

  2. 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.

Citation

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}
}