magpy-rv 1.0.1

Gaussian Process Regression with affine invariant MCMC algorithm

magpy_rv

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. in review)

Documentation

Documentation Site: MAGPy RV.readthedocs

Installation

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

Examples are hosted here:

1. Simple GP Example shows the most basic code use.

2. Polynomial Model adds a model to the GP and introduces MCMC parameter search.

3. Pegasi 51b walks through the full rv analysis with a GP to model activity and Keplerians to model a planet.

4. Offset: full end-to-end pipeline to calculate 'sun-as-a-star' RVs and magnetic observables