Metadata-Version: 1.1
Name: pyGTC
Version: 0.4.0
Summary: Make an awesome giant triangle confusogram (gtc)!
Home-page: http://github.com/sebastianbocquet/pygtc
Author: Sebastian Bocquet and Faustin Carter
Author-email: sebastian.bocquet@gmail.com
License: MIT
Description: 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 just use ``pip``. It will install the required
        dependencies for you (``numpy`` and ``matplotlib``)::
        
          pip install pygtc
        
        For more installation details, see the `documentation <http://pygtc.readthedocs.io/>`_.
        
        Documentation
        -------------
        Documentation is hosted at `ReadTheDocs <http://pygtc.readthedocs.io/>`_. 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
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Visualization
