emcee

emcee is an MIT licensed pure-Python implementation of Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler and these pages will show you how to use it.

This documentation won’t teach you too much about MCMC but there are a lot of resources available for that (try this one). We also published a paper explaining the emcee algorithm and implementation in detail.

emcee has been used in quite a few projects in the astrophysical literature and it is being actively developed on GitHub.

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Basic Usage

If you wanted to draw samples from a 5 dimensional Gaussian, you would do something like:

import numpy as np
import emcee

def log_prob(x, ivar):
    return -0.5 * np.sum(ivar * x ** 2)

ndim, nwalkers = 5, 100
ivar = 1. / np.random.rand(ndim)
p0 = np.random.randn(nwalkers, ndim)

sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar])
sampler.run_mcmc(p0, 10000)

A more complete example is available in the Quickstart tutorial.

How to Use This Guide

To start, you’re probably going to need to follow the Installation guide to get emcee installed on your computer. After you finish that, you can probably learn most of what you need from the from the tutorials listed below (you might want to start with Quickstart and go form there). If you need more details about specific functionality, the User Guide below should have what you need. If you run into any issues, please don’t hesitate to open an issue on GitHub.

License & Attribution

Copyright 2010-2017 Dan Foreman-Mackey and contributors.

emcee is free software made available under the MIT License. For details see the LICENSE.

If you make use of emcee in your work, please cite our paper (arXiv, ADS, BibTeX) and consider adding your paper to the Testimonials list.

Changelog

3.0.0 (upcoming)

  • Added progress bars using tqdm.
  • Added HDF5 backend using h5py.
  • Improved autocorrelation time estimation algorithm.
  • Switched documentation to using Jupyter notebooks for tutorials.

2.2.0 (2016-07-12)

  • Improved autocorrelation time computation.
  • Numpy compatibility issues.
  • Fixed deprecated integer division behavior in PTSampler.

2.1.0 (2014-05-22)

  • Removing dependence on acor extension.
  • Added arguments to PTSampler function.
  • Added automatic load-balancing for MPI runs.
  • Added custom load-balancing for MPI and multiprocessing.
  • New default multiprocessing pool that supports ^C.

2.0.0 (2013-11-17)

  • Re-licensed under the MIT license!
  • Clearer less verbose documentation.
  • Added checks for parameters becoming infinite or NaN.
  • Added checks for log-probability becoming NaN.
  • Improved parallelization and various other tweaks in PTSampler.

1.2.0 (2013-01-30)

  • Added a parallel tempering sampler PTSampler.
  • Added instructions and utilities for using emcee with MPI.
  • Added flatlnprobability property to the EnsembleSampler object to be consistent with the flatchain property.
  • Updated document for publication in PASP.
  • Various bug fixes.

1.1.3 (2012-11-22)

  • Made the packaging system more robust even when numpy is not installed.

1.1.2 (2012-08-06)

  • Another bug fix related to metadata blobs: the shape of the final blobs object was incorrect and all of the entries would generally be identical because we needed to copy the list that was appended at each step. Thanks goes to Jacqueline Chen (MIT) for catching this problem.

1.1.1 (2012-07-30)

  • Fixed bug related to metadata blobs. The sample function was yielding the blobs object even when it wasn’t expected.

1.1.0 (2012-07-28)

  • Allow the lnprobfn to return arbitrary “blobs” of data as well as the log-probability.
  • Python 3 compatible (thanks Alex Conley)!
  • Various speed ups and clean ups in the core code base.
  • New documentation with better examples and more discussion.

1.0.1 (2012-03-31)

  • Fixed transpose bug in the usage of acor in EnsembleSampler.

1.0.0 (2012-02-15)

  • Initial release.