Christopher Beaumont
University of Hawaii, Harvard University

Thomas Robitaille
Max Planck Institute for Astronomy

Alyssa Goodman
Harvard University

Michelle Borkin
Harvard University

Abstract

Modern research projects incorporate data from several sources, and new insights are increasingly driven by the ability to interpret data in the context of other data. Glue is an interactive environment built on top of the standard Python science stack to visualize relationships within and between datasets. With Glue, users can load and visualize multiple related datasets simultaneously. Users specify the logical connections that exist between data, and Glue transparently uses this information as needed to enable visualization across files. This functionality makes it trivial, for example, to interactively overplot catalogs on top of images.

The central philosophy behind Glue is that the structure of research data is highly customized and problem-specific. Glue aims to accommodate this and simplify the \textquotedbl{}data munging\textquotedbl{} process, so that researchers can more naturally explore what their data have to say. The result is a cleaner scientific workflow, faster interaction with data, and an easier avenue to insight.

Keywords

data visualization, exploratory data analysis, Python

DOI

10.25080/Majora-8b375195-002

Bibtex entry

Full text PDF

Copyright © 2013 Christopher Beaumont et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.