Modeling Activity with Gaussian process regression in Python
Pipeline to model data with Gaussian Process regression and affine invariant Monte Carlo Markov Chain parameter searching algorith. To use please cite the original publication Rescigno et al. 2023
Main Contributors:
Federica Rescigno
Bryce Dixon
Special Acknowledgements:
Dr. Raphaëlle D. Haywood
Ben S. Lakeland
Documentation Site: MAGPy RV.readthedocs
Build conda environment MAGPy-RV can be run in its own environment. To generate it follow the steps:
Update dependencies in env.yml file
Run the following from the folder containing the .yml file
conda env create -f conda_env.yml
Package installation using pip
Install pip (if Anaconda or miniconda is installe use conda install pip)
Install package
pip install magpy-rv
Examples are hosted here:
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Simple GP Example shows the most basic code use.
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Polynomial Model adds a model to the GP and introduces MCMC parameter search.
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Pegasi 51b walks through the full rv analysis with a GP to model activity and Keplerians to model a planet.
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Offset: full end-to-end pipeline to calculate 'sun-as-a-star' RVs and magnetic observables