https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2025Matinyan_TRPX2025Matinyan TRPX - Revision history2026-06-13T12:15:45ZRevision history for this page on the wikiMediaWiki 1.44.2https://3demmethods.i2pc.es/index.php?title=2025Matinyan_TRPX&diff=5140&oldid=prevWikiSysop: Created page with "== Citation == Matinyan, S., Filipcik, P., Waterman, D., Owen, C. and Abrahams, J. 2025. TRPXv2. 0: superfast, parallel compression of diffraction patterns and images, with native Python and HDF5 support. Ultramicroscopy. (2025), 114298. == Abstract == Scientific data in structural biology are being produced faster and in larger volumes than can be comfortably stored, processed, or shared. To address this challenge, we introduced the next generation TERSE/PROLIX (TRPX..."2026-01-09T10:16:46Z<p>Created page with "== Citation == Matinyan, S., Filipcik, P., Waterman, D., Owen, C. and Abrahams, J. 2025. TRPXv2. 0: superfast, parallel compression of diffraction patterns and images, with native Python and HDF5 support. Ultramicroscopy. (2025), 114298. == Abstract == Scientific data in structural biology are being produced faster and in larger volumes than can be comfortably stored, processed, or shared. To address this challenge, we introduced the next generation TERSE/PROLIX (TRPX..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
<br />
Matinyan, S., Filipcik, P., Waterman, D., Owen, C. and Abrahams, J. 2025. TRPXv2. 0: superfast, parallel compression of diffraction patterns and images, with native Python and HDF5 support. Ultramicroscopy. (2025), 114298.<br />
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== Abstract ==<br />
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Scientific data in structural biology are being produced faster and in larger volumes than can be comfortably<br />
stored, processed, or shared. To address this challenge, we introduced the next generation TERSE/PROLIX<br />
(TRPX) algorithm for efficient, fast, and lossless compression of integer greyscale data, implemented in C++20.<br />
Here, we report a multithreaded extension with additional options for compressing low-intensity integer images<br />
and for lossless or lossy compression of greyscale float data. This new implementation is accessible through a<br />
dedicated, multithreaded Python library (pyterse) and as an HDF5 filter (terse), allowing seamless integration<br />
into existing scientific workflows.<br />
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Benchmarks show that TRPXv2.0 is at least 2.5 times faster than existing compression schemes for diffraction<br />
data, without increasing file sizes, and often with better compression ratios.<br />
<br />
By combining speed, flexibility, and interoperability, TRPXv2.0 provides a practical and scalable solution for<br />
high-throughput data handling in modern structural biology.<br />
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== Keywords ==<br />
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== Links ==<br />
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https://www.sciencedirect.com/science/article/pii/S0304399125001962<br />
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== Related software ==<br />
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== Related methods ==<br />
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== Comments ==</div>WikiSysop