radvel 1.6.1

General Toolkit for Modeling Radial Velocities

RadVel

General Toolkit for Modeling Radial Velocities.

Attribution

Written by BJ Fulton, Erik Petigura, Sarah Blunt, and Evan Sinukoff. Fulton et al. (2018)

Please cite the original publication and the following DOI if you make use of this software in your research.

Installation

Quick Install

pip install radvel

Development Install

git clone https://github.com/California-Planet-Search/radvel.git
cd radvel

# Install problematic dependencies via conda (recommended for macOS)
conda install pytables h5py

# Install in development mode
pip install -e .

System Requirements

• Python: 3.8, 3.9, 3.11, 3.12
• LaTeX: Required for report generation (install TexLive or similar)
• macOS users: Consider using conda for pytables and h5py to avoid compilation issues

Documentation

Documentation is available here

Run as a service (v1.6+)

RadVel 1.6 ships an HTTP service that wraps the entire CLI workflow behind a JSON API plus an optional browser UI. Useful for non-Python clients, long-running MCMC jobs, and reproducible deployments.

docker run --rm -p 8000:8000 -v "$PWD/.runs:/data" \
    ghcr.io/california-planet-search/radvel-api:latest
open http://localhost:8000/ui

See the service guide and the UI walkthrough for details. The same image is what runs in production.

Features

With RadVel you can

• Optimize
  • leverages the suite of minimizers in scipy.optimize
• Run MCMC
  • leverages the emcee package for MCMC exploration of the posterior probability distribution
• Visualize
  • creates quicklook summary plots and statistics

RadVel is

• Flexible
  • fix/float parameters that are indexed as strings (emulates lmfit API)
  • convert between different parameterizations e.g. e omega <-> sqrtecosw sqrtesinw
  • incorporate RVs from multiple telescopes
• Extensible
  • Object-oriented programing makes adding new likelihoods, priors, etc. easy
• Scriptable
  • Code can be run through a convenient Command-line Interface (CLI)
• Fast
  • Kepler's equation solved in C (slower Python solver also included)
  • MCMC is multi-threaded

Tutorials

Follow examples in

• radvel/docs/tutorials/SyntheticData.ipynb
• radvel/docs/tutorials/K2-24_Fitting+MCMC.ipynb
• radvel/docs/tutorials/164922_Fitting+MCMC.ipynb
• radvel/docs/tutorials/GaussianProcess-tutorial.ipynb