Jean-Luc R. Stevens
Institute for Adaptive and Neural Computation, University of Edinburgh

Philipp Rudiger
Institute for Adaptive and Neural Computation, University of Edinburgh

James A. Bednar
Institute for Adaptive and Neural Computation, University of Edinburgh

Abstract

Scientific visualization typically requires large amounts of custom coding that obscures the underlying principles of the work and makes it difficult to reproduce the results. Here we describe how the new HoloViews Python package, when combined with the IPython Notebook and a plotting library, provides a rich, interactive interface for flexible and nearly code-free visualization of your results while storing a full record of the process for later reproduction.

HoloViews provides a set of general-purpose data structures that allow you to pair your data with a small amount of metadata. These data structures are then used by a separate plotting system to render your data interactively, e.g. within the IPython Notebook environment, revealing even complex data in publication-quality form without requiring custom plotting code for each figure.

HoloViews also provides powerful containers that allow you to organize this data for analysis, embedding it whatever multidimensional continuous or discrete space best characterizes it. The resulting workflow allows you to focus on exploring, analyzing, and understanding your data and results, while leading directly to an exportable recipe for reproducible research.

Keywords

reproducible, interactive, visualization, notebook

DOI

10.25080/Majora-7b98e3ed-00a

Bibtex entry

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Copyright © 2015 Jean-Luc R. Stevens 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.