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Paper: Compression of Smooth One-dimensional Data Series Using Polycomp
Volume: 521, Astronomical Data Analysis Software and Systems XXVI
Page: 560
Authors: Tomasi, M.
Abstract: Data compression is increasingly important in astrophysics, as the amount of data acquired by modern experiments often needs hundreds of terabytes for the storage of raw data. In this talk I will present a few usage cases of the C/Python library polycomp, a library to compress smooth one-dimensional data whose error is either zero or negligible. One of the algorithms implemented by polycomp combines the advantages of polynomial least-squares fitting and the properties of the discrete Chebyshev transform. This algorithm can lead to compression ratios larger than 10 in a number of realistic cases. I will show a few examples of datasets that can be easily compressed using this approach, namely (1) spacecraft attitude information, and (2) timelines of pointing information for a realistic all-sky survey experiment.
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