New Python-based methods for data processing
Current pixel‐array detectors produce diffraction images at extreme data rates (of up to 2 TB h−1) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to gu...
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| Veröffentlicht in: | Acta crystallographica. Section D, Biological crystallography. Jg. 69; H. 7; S. 1274 - 1282 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
5 Abbey Square, Chester, Cheshire CH1 2HU, England
International Union of Crystallography
01.07.2013
Wiley Subscription Services, Inc |
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| ISSN: | 1399-0047, 0907-4449, 1399-0047 |
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| Abstract | Current pixel‐array detectors produce diffraction images at extreme data rates (of up to 2 TB h−1) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web‐serving tools that interact with the Python scripting language, it was possible to implement a high‐throughput Bragg‐spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron‐radiation beamlines. Similarly, Python interoperability enabled the production of a new data‐reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data‐reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi‐crystal specimens. In these challenging cases, accurate modeling of close‐lying Bragg spots could benefit from the high‐performance computing capabilities of graphics‐processing units. |
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| AbstractList | Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h(-1)) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units. Current pixel‐array detectors produce diffraction images at extreme data rates (of up to 2 TB h−1) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web‐serving tools that interact with the Python scripting language, it was possible to implement a high‐throughput Bragg‐spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron‐radiation beamlines. Similarly, Python interoperability enabled the production of a new data‐reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data‐reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi‐crystal specimens. In these challenging cases, accurate modeling of close‐lying Bragg spots could benefit from the high‐performance computing capabilities of graphics‐processing units. Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h −1 ) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer ( cctbx.spotfinder ) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package ( cctbx.xfel ) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units. The Computational Crystallography Toolbox (cctbx) is a flexible software platform that has been used to develop high-throughput crystal-screening tools for both synchrotron sources and X-ray free-electron lasers. Plans for data-processing and visualization applications are discussed, and the benefits and limitations of using graphics-processing units are evaluated. Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h−1) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units. Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h(-1)) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units.Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h(-1)) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units. Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2TBh-1) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units. [PUBLICATION ABSTRACT] Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2TBh-1) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units. |
| Author | Hattne, Johan Sauter, Nicholas K. Grosse-Kunstleve, Ralf W. Echols, Nathaniel |
| Author_xml | – sequence: 1 givenname: Nicholas K. surname: Sauter fullname: Sauter, Nicholas K. organization: Physical Biosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA – sequence: 2 givenname: Johan surname: Hattne fullname: Hattne, Johan organization: Physical Biosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA – sequence: 3 givenname: Ralf W. surname: Grosse-Kunstleve fullname: Grosse-Kunstleve, Ralf W. organization: Physical Biosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA – sequence: 4 givenname: Nathaniel surname: Echols fullname: Echols, Nathaniel organization: Physical Biosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23793153$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/1129466$$D View this record in Osti.gov |
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| Snippet | Current pixel‐array detectors produce diffraction images at extreme data rates (of up to 2 TB h−1) that make severe demands on computational resources. New... Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h −1 ) that make severe demands on computational resources. New... Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h(-1)) that make severe demands on computational resources. New... Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2TBh-1) that make severe demands on computational resources. New... The Computational Crystallography Toolbox (cctbx) is a flexible software platform that has been used to develop high-throughput crystal-screening tools for... |
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| SubjectTerms | Algorithms cctbx Coherent light Computation Crystallography Crystallography, X-Ray Data Interpretation, Statistical Data processing Diffraction Electronic Data Processing - methods Electrons Humans Lasers Methods multiprocessing Multiprocessing (computers) Muramidase - chemistry Research Papers reusable code Software Software reviews Spots Synchrotrons - instrumentation XFEL |
| Title | New Python-based methods for data processing |
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