Pippi — Painless parsing, post-processing and plotting of posterior and likelihood samples

Interpreting samples from likelihood or posterior probability density functions is rarely as straightforward as it seems it should be. Producing publication-quality graphics of these distributions is often similarly painful. In this short note I describe pippi, a simple, publicly-available package for parsing and post-processing such samples, as well as generating high-quality PDF graphics of the results. Pippi is easily and extensively configurable and customisable, both in its options for parsing and post-processing samples, and in the visual aspects of the figures it produces. I illustrate some of these using an existing supersymmetric global fit, performed in the context of a gamma-ray search for dark matter. Pippi can be downloaded and followed at http://github.com/patscott/pippi .

4 pages, 1 figure. v3: Updated for pippi 2.0. New features include hdf5 support, out-of-core processing, inline post-processing with arbitrary Python code in the input file, and observable-specific data cuts. Pippi can be downloaded from http://github.com/patscott/pippi