celerite

Scalable 1D Gaussian Processes in C++, Python, and Julia

MIT License

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This project has been superseded by celerite2 <https://github.com/exoplanet-dev/celerite2>_. This project will continue to be maintained at a basic level, but no new features will be added and I have limited capacity so you're encouraged to check out the new version.

celerite: Scalable 1D Gaussian Processes in C++, Python, and Julia

Read the documentation at: celerite.rtfd.io <http://celerite.readthedocs.io>_.

.. image:: https://img.shields.io/badge/GitHub-dfm%2Fcelerite-blue.svg?style=flat :target: https://github.com/dfm/celerite .. image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat&bust :target: https://github.com/dfm/celerite/blob/main/LICENSE .. image:: https://github.com/dfm/celerite/actions/workflows/python.yml/badge.svg :target: https://github.com/dfm/celerite/actions/workflows/python.yml .. image:: https://readthedocs.org/projects/celerite/badge/?version=latest&style=flat&bust=truer :target: http://celerite.readthedocs.io/en/latest/?badge=latest .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.806847.svg?style=flat :target: https://doi.org/10.5281/zenodo.806847 .. image:: https://img.shields.io/badge/PDF-latest-orange.svg?style=flat :target: https://github.com/dfm/celerite/blob/main-pdf/paper/ms.pdf .. image:: https://img.shields.io/badge/ArXiv-1703.09710-orange.svg?style=flat :target: https://arxiv.org/abs/1703.09710

The Julia implementation is being developed in a different repository: ericagol/celerite.jl <https://github.com/ericagol/celerite.jl>_. Issues related to that implementation should be opened there.

If you make use of this code, please cite the following paper:

.. code-block:: tex

@article{celerite,
    author = {{Foreman-Mackey}, D. and {Agol}, E. and {Angus}, R. and
              {Ambikasaran}, S.},
     title = {Fast and scalable Gaussian process modeling
              with applications to astronomical time series},
      year = {2017},
   journal = {AJ},
    volume = {154},
     pages = {220},
       doi = {10.3847/1538-3881/aa9332},
       url = {https://arxiv.org/abs/1703.09710}
}
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