CosmoHammer is a framework which embeds emcee , an implementation by Foreman-Mackey et al. (2012) of the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler by Goodman and Weare (2010).

It gives the user the possibility to plug in modules for the computation of any desired likelihood. The major goal of the software is to reduce the complexity when one wants to extend or replace the existing computation by modules which fit the user's needs as well as to provide the possibility to easily use large scale computing environments.

We published a paper in the Astronomy and Computing Journal which discusses the advantages and performance of our framework.

This project has been realized in collaboration with the Institute of 4D Technologies of the University of Applied Sciences and Arts Northwest Switzerland (Fachhochschule Nordwestschweiz - FHNW).

The development is coordinated on GitHub and contributions are welcome. The documentation of CosmoHammer is available at readthedocs.org and the package is distributed over PyPI.

For all public modules such as PyCamb, WMAP, Planck and more, see the cosmoHammerPlugins project at http://github.com/cosmo-ethz/CosmoHammerPlugins.