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Make an awesome giant triangle confusogram (gtc)!

Project description

What is a Giant Triangle Confusogram?

A Giant-Triangle-Confusogram (GTC, aka triangle plot) is a way of displaying the results of a Monte-Carlo Markov Chain (MCMC) sampling or similar analysis. (For a discussion of MCMC analysis, see the excellent emcee package.) The recovered parameter constraints are displayed on a grid in which the diagonal shows the one-dimensional posteriors (and, optionally, priors) and the lower-left triangle shows the pairwise projections. You might want to look at a plot like this if you are fitting a model to data and want to see the parameter covariances along with the priors.

Here’s an example of a GTC with some random data and arbitrary labels:


But doesn’t this already exist in, distUtils, etc…?

Although several other packages exists to make such a plot, we were unsatisfied with the amount of extra work required to massage the result into something we were happy to publish. With pygtc, we hope to take that extra legwork out of the equation by providing a package that gives a figure that is publication ready on the first try! You should try all the packages and use the one you like most; for us, that is pygtc!


For a quick start, you can just use pip. It will install the required dependencies for you (numpy and matplotlib):

pip install pygtc

For more installation details, see the documentation.


Documentation is hosted at ReadTheDocs. Find an exhaustive set of examples there!


If you use pygtc to generate plots for a publication, please cite as:

  doi = {10.21105/joss.00046},
  url = {},
  year  = {2016},
  month = {oct},
  publisher = {The Open Journal},
  volume = {1},
  number = {6},
  author = {Sebastian Bocquet and Faustin W. Carter},
  title = {pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms)},
  journal = {The Journal of Open Source Software}

Copyright 2016, Sebastian Bocquet and Faustin W. Carter

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