Abstract
ParaView is one of the most prominent software tools for scientific visualization used by scientists around the world. Color is a primary conduit to visually map data to its representation and, thus, enable investigation and interpretation of the data. Colormap selection has a significant impact on the data revealed; its design and selection is a critical aspect of scientific data visualization. A common choice for a user is the program's default colormap, so careful consideration of this default is consequential. Although the current default colormap in ParaView, a succession of hues from cool blue to warm red, has served the community well, research shows that more nuanced colormap configurations increase discriminability while maintaining other critical metrics. These findings inspire us to revisit and update the default colors in ParaView. Here we present a new ParaView default colormap, the criteria and methods of development, and example visualizations and analytic metrics.
| Original language | English |
|---|---|
| Pages (from-to) | 150-160 |
| Number of pages | 11 |
| Journal | IEEE Computer Graphics and Applications |
| Volume | 44 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2024 |
Funding
This work was supported in part by the U.S. Department of Energy (DOE) RAPIDS SciDAC project under contract number DE-AC05-00OR22725. This article describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the article do not necessarily represent the views of the U.S. Department of Energy or the United States Government. This work was done in part at Sandia National Laboratories, which is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under Contract DE-NA0003525. This work was supported in part by the U.S. Department of Energy (DOE) RAPIDS SciDAC project under contract number DE-AC05-00OR22725. This article describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the article do not necessarily represent the views of the U.S. Department of Energy or the United States Government. This work was done in part at Sandia National Laboratories, which is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under Contract DE-NA0003525. The authors wish to thank the ParaView software development team at Kitware, Inc., for assisting in making these changes to the software. They also wish to thank those in the ParaView community who provided the necessary feedback to guide their designs. They also wish to acknowledge the contributions made by the Data Science at Scale, LANL,22 for their contributions and support for the underlying color research, Gregory Abram for his technical contributions that made this work possible, and Khairi Reda for help with namability evaluations of colormaps. The authors would like to thank all of the scientists who lent their data and provided feedback on the colormaps. The data were provided by scientists at US DOE Laboratories including: M. Petersen, MPAS-Ocean, LANL; R. Linn, LANL, wildfire simulation; W. Daughton, LANL, magnetic reconnection; M. Larsen, LLNL, combustion simulation.
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