publication . Preprint . Article . 2012

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

Pat Scott;
Open Access
  • Published: 01 Nov 2012 Journal: The European Physical Journal Plus, volume 127 (eissn: 2190-5444, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
Abstract
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
Subjects
free text keywords: General Physics and Astronomy, Physics - Data Analysis, Statistics and Probability, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Phenomenology, Data Analysis, Statistics and Probability (physics.data-an), Cosmology and Nongalactic Astrophysics (astro-ph.CO), Instrumentation and Methods for Astrophysics (astro-ph.IM), High Energy Physics - Phenomenology (hep-ph), FOS: Physical sciences, Artificial intelligence, business.industry, business, Posterior probability density, Natural language processing, computer.software_genre, computer, Computer science, Parsing, Probability and statistics, Context (language use), Graphics
Communities
  • Digital Humanities and Cultural Heritage
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