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exoplanet is a toolkit for probabilistic modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series using PyMC3. PyMC3 is a flexible and high-performance model building language and inference engine that scales well to problems with a large number of parameters. exoplanet extends PyMC3’s language to support many of the custom functions and distributions required when fitting exoplanet datasets. These features include:

All of these functions and distributions include methods for efficiently calculating their gradients so that they can be used with gradient-based inference methods like Hamiltonian Monte Carlo, No U-Turns Sampling, and variational inference. These methods tend to be more robust than the methods more commonly used in astronomy (like ensemble samplers and nested sampling) especially when the model has more than a few parameters. For many exoplanet applications, exoplanet (the code) can improve the typical performance by orders of magnitude.

exoplanet is being actively developed in a public repository on GitHub so if you have any trouble, open an issue there.

License & attribution

Copyright 2018, 2019 Daniel Foreman-Mackey.

The source code is made available under the terms of the MIT license.

If you make use of this code, please cite this package and its dependencies. You can find more information about how and what to cite in the citation documentation.


0.1.4 (2019-02-10)

  • Improves the reliability of the PyMC3Sampler
  • Adds a new optimize function since the find_MAP method in PyMC3 is deprecated
  • Adds cronjob script for automatically updating tutorials.

0.1.3 (2019-01-09)

  • Adds a more robust and faster Kepler solver (ref)
  • Fixes minor behavioral bugs in PyMC3 sampler wrapper

0.1.2 (2018-12-13)

  • Adds regular grid interpolation Op for Theano
  • Fixes major bug in handling of the stellar radius for transits
  • Fixes small bugs in packaging and installation
  • Fixes handling of diagonal covariances in PyMC3Sampler

0.1.1 (IPO; 2018-12-06)

  • Initial public release