The Astrophysical Journal Supplement Series · 2010 · 1.2K citations · 32 references
ComputingHigh-performance ComputingVisualizationAstrophysicsData VisualizationScientific VisualizationAdaptive Mesh RefinementAstrophysical SimulationsComputational AstrophysicsNumerical AstrophysicsSimulation VisualizationVolumetric VisualizationComplexityHigh-performance Computing In AstrophysicsHistogramsLight Cone StructureMesh RefinementParallel VisualizationScientific Volume Data ExplorationSimulation Data Visualization
Astrophysical simulations increasingly face challenges of reproducibility, parallelization, and ever‑growing data size and complexity. To address these challenges and foster collaboration across simulation platforms, the authors introduce yt, an open‑source, community‑developed analysis and visualization toolkit. yt centers on physically relevant quantities, supports multiple codes (Enzo, Orion, RAMSES, FLASH), and provides data reading, handling, and visualization (projections, volume rendering, histograms, halo finding, light‑cone generation, isocontour identification) with parallelized processing algorithms.
The analysis of complex multiphysics astrophysical simulations presents a unique and rapidly growing set of challenges: reproducibility, parallelization, and vast increases in data size and complexity chief among them. In order to meet these challenges, and in order to open up new avenues for collaboration between users of multiple simulation platforms, we present yt (available at http://yt.enzotools.org/), an open source, community-developed astrophysical analysis and visualization toolkit. Analysis and visualization with yt are oriented around physically relevant quantities rather than quantities native to astrophysical simulation codes. While originally designed for handling Enzo's structure adaptive mesh refinement (AMR) data, yt has been extended to work with several different simulation methods and simulation codes including Orion, RAMSES, and FLASH. We report on its methods for reading, handling, and visualizing data, including projections, multivariate volume rendering, multi-dimensional histograms, halo finding, light cone generation and topologically-connected isocontour identification. Furthermore, we discuss the underlying algorithms yt uses for processing and visualizing data, and its mechanisms for parallelization of analysis tasks.
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