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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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