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This talk explores how researchers can use the scalable, self-describing HDF5 data format together with the Python programming language to improve the analysis pipeline, easily archive and share large datasets, and improve confidence in scientific results. The discussion will focus on real-world applications of HDF5 in experimental physics at two multimillion-dollar research facilities: the Large Plasma Device at UCLA, and the NASA-funded hypervelocity dust accelerator at CU Boulder. This event coincides with the launch of a new O'Reilly O’Reilly book, Python and HDF5: Unlocking Scientific Data, complimentary copies of which will be available for attendees.
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Over the past several years, Python has emerged as a credible alternative to scientific analysis environments like IDL or MATLAB. In addition to stable core packages for handling numerical arrays, analysis, and plotting, the Python ecosystem provides a huge selection of more specialized software, reducing the amount of work necessary to write scientific code while also increasing the quality of results. Python's Python’s excellent support for standard data formats allows scientists to interact seamlessly with colleagues using other platforms.
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