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The usage of the high-level scripting language Python has enabled new mechanisms for data interrogation, discovery and visualization of scientific data. We present yt, an open source, community-developed astrophysical analysis and visualization toolkit for data generated by high-performance computing (HPC) simulations of astrophysical phenomena. Through a separation of responsibilities in the underlying Python code, yt allows data generated by incompatible, and sometimes even directly competing, astrophysical simulation platforms to be analyzed in a consistent manner, focusing on physically relevant quantities rather than quantities native to astrophysical simulation codes. We present on its mechanisms for data access, capabilities for MPI-parallel analysis, and its implementation as an in situ analysis and visualization tool.
The scientific community is presently witnessing an unprecedented growth in the quality and quantity of data sets coming from simulations and real-world experiments. To access effectively and extract the scientific content of such large-scale data se
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