https://github.com/SebastianBocquet/pygtc

Make a sweet giant triangle confusogram (GTC) plot
https://github.com/SebastianBocquet/pygtc

bayesian-data-analysis data-visualization mcmc

Last synced: about 2 months ago
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Make a sweet giant triangle confusogram (GTC) plot

• Host: GitHub
• URL: https://github.com/SebastianBocquet/pygtc
• Owner: SebastianBocquet
• License: mit
• Created: 2016-07-20T23:27:33.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2023-10-12T06:29:32.000Z (about 1 year ago)
• Last Synced: 2024-08-19T08:52:24.221Z (4 months ago)
• Topics: bayesian-data-analysis, data-visualization, mcmc
• Language: Jupyter Notebook
• Homepage: http://pygtc.readthedocs.io/
• Size: 26.1 MB
• Stars: 33
• Watchers: 6
• Forks: 9
• Open Issues: 6
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • License: LICENSE.txt

Awesome Lists containing this project

README

        
pygtc.py

=========

**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::

  pygtc.plotGTC(chains=[samples1,samples2],

                paramNames=names,

                chainLabels=chainLabels,

                truths=truths,

                truthLabels=truthLabels,

                priors=priors,

                paramRanges=paramRanges,

                figureSize='MNRAS_page')

.. image:: https://raw.githubusercontent.com/SebastianBocquet/pygtc/master/docs/_static/demo_files/demo_9_1.png

**But doesn't this already exist in corner.py, 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``!

Installation

------------

For a quick start, you can install with either ``pip`` or ``conda``. Either will install the required

dependencies for you (``packaging``, ``numpy``, and ``matplotlib``)::

  $ pip install pygtc

or, if you use ``conda``::

  $ conda install pygtc -c conda-forge

For more installation details, see the `documentation `_.

Documentation

-------------

Documentation is hosted at `ReadTheDocs `_. Find

an exhaustive set of examples there!

Citation

--------

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

  @article{Bocquet2016,

    doi = {10.21105/joss.00046},

    url = {http://dx.doi.org/10.21105/joss.00046},

    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

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.159091.svg

   :target: https://doi.org/10.5281/zenodo.159091