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        "author": "Yu Feng",
        "author_email": "rainwoodman@gmail.com",
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        "description": "bananaplots\n===========\n\n.. image:: https://travis-ci.org/bccp/bananaplots.svg?branch=master\n       :target: https://travis-ci.org/bccp/bananaplots\n\nA simple tool for making banana plots from several multi-dimensional point datasets.\n\nThe term banana here refers to the usual shape of the contours of two feature projection of the datasets. See the gallery to see the graphical motivation. \n\nWe'd like a tool to drive matplotlib. Declarative.\n\nThe distribution of points are internally modelled with a gaussian mixture model.\n\nGallery (Plots that shall be able to make)\n\nhttps://docs.google.com/document/d/13h6d35Ily_QobpCk3xNivwm4WW_b36pTXpNITnFWPOo/edit?usp=sharing\n\nHere is an example code:\n\nhttps://github.com/bccp/bananaplots/blob/master/bananaplots/tests/test_bananaplots.py#L15\n\nwhich produces the following two figures.\n\n.. image:: https://raw.githubusercontent.com/bccp/bananaplots/master/docs/bananas-upper-right.png\n    \n.. image:: https://raw.githubusercontent.com/bccp/bananaplots/master/docs/bananas-lower-left.png\n\n\nNotes\n-----\n\nThis is an ongoing work. \n\nMartin White suggested the project in August of 2016.\n\nYu Feng discussed the project as an Hack at the AstroHackWeek 2016 with\nBrigitta Sipocz, Dan Foreman-Mackey, and Jake VanderPlas.\n\nThree major outcomes:\n\n- Histograms capture tails much better than mixture of gaussians. We currenlty do not\n  support histograms.\n\n- if we can model the MCMC as a mixture of gaussian, then there\n  probably is a easier way to model the likelihood than running MCMC.\n\n- In addition, we shall explore a declaritive way of describing a plot that\n  supports more complicated modelling. Vega-Lite does not appear to support this.\n\nThe scaffold of the project is roughly based on https://github.com/kbarbary/greeter\nbut we use runtests.py from numpy c.f. https://github.com/kbarbary/greeter/issues/8",
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        "summary": "Plot bananas from combined MCMC (Markov Chain Monte Carlo) chains. For example, Planck or WMAP satellite likelyhood samples.",
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