ravest 0.3.0

Radial Velocity fitting, simulation and model comparison

ravest

Ravest is under active development, with lots of new features soon to be released for public use. This website will continue to be populated with new code and examples as they get released, so please check back soon for further updates! If you have any questions or ideas, please don't hesitate to contact me via email.

---

Radial Velocity fitting, simulation and mass precision estimation Features:

• Model exoplanets and host stars, to simulate RV data for given orbital and instrumental parameters
• Fit RV data with MCMC to explore posterior distributions for parameters - including Gaussian Processes for stellar activity
• Visualise/animate the star's orbit (coming soon!)
• Bayesian Model Comparison using harmonic (coming soon!)

Installation

CPU-only

$ pip install ravest

GPU/TPU support

Ravest requires JAX, so you may want to consult the Jax installation docs if you want GPU or TPU support (tl;dr: install JAX first according to those instructions, then install Ravest on top).

Usage

For an introduction to modelling planetary and stellar data, see the example modelling notebook for ravest.model.

For an example of how to fit a model to RV data, see the example fitting notebook where we fit some ELODIE data for 51 Peg b.

For an example of how to use a Gaussian Process to mitigate stellar variability, see the example GP notebook where we use a quasiperiodic kernel on HARPS data for K2-229.

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

Acknowledgements

Ravest makes use of the following open-source packages:

• NumPy for numerical computing
• SciPy for scientific computing algorithms
• Matplotlib for plotting and visualisation
• Astropy for astronomical calculations and utilities
• pandas for data manipulation
• tqdm for progress bars
• emcee for MCMC sampling
• corner for visualising posterior distributions
• tinygp for Gaussian Process modelling, which requires JAX
• harmonic for Bayesian evidence estimation

License

ravest was created by Ross Dobson. It is licensed under the terms of the MIT license.