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RDataFrame enhancements for HEP analyses

In recent years, RDataFrame, ROOT’s high-level interface for data analysis and processing, has seen widespread adoption on the part of HEP physicists. Much of this success is due to RDataFrame’s ergonomic programming model that enables the implementation of common analysis tasks more easily than pre...

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Detalles Bibliográficos
Autores principales: Guiraud, E, Blomer, J, Hageboeck, S, Naumann, A, Padulano, V E, Tejedor, E, Wunsch, S
Lenguaje:eng
Publicado: 2023
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/2438/1/012116
http://cds.cern.ch/record/2871820
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author Guiraud, E
Blomer, J
Hageboeck, S
Naumann, A
Padulano, V E
Tejedor, E
Wunsch, S
author_facet Guiraud, E
Blomer, J
Hageboeck, S
Naumann, A
Padulano, V E
Tejedor, E
Wunsch, S
author_sort Guiraud, E
collection CERN
description In recent years, RDataFrame, ROOT’s high-level interface for data analysis and processing, has seen widespread adoption on the part of HEP physicists. Much of this success is due to RDataFrame’s ergonomic programming model that enables the implementation of common analysis tasks more easily than previous APIs, without compromising on application performance. Nonetheless, RDataFrame’s interfaces have been further improved by the recent addition of several major HEP-oriented features: in this contribution we will introduce for instance a dedicated syntax to define systematic variations, per-data-sample call-backs useful to define quantities that vary on a per-sample basis, simplifications of collection operations and the injection of just-in-time-compiled Python functions in the optimized C++ event loop.
id cern-2871820
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28718202023-09-20T21:01:03Zdoi:10.1088/1742-6596/2438/1/012116http://cds.cern.ch/record/2871820engGuiraud, EBlomer, JHageboeck, SNaumann, APadulano, V ETejedor, EWunsch, SRDataFrame enhancements for HEP analysesComputing and ComputersIn recent years, RDataFrame, ROOT’s high-level interface for data analysis and processing, has seen widespread adoption on the part of HEP physicists. Much of this success is due to RDataFrame’s ergonomic programming model that enables the implementation of common analysis tasks more easily than previous APIs, without compromising on application performance. Nonetheless, RDataFrame’s interfaces have been further improved by the recent addition of several major HEP-oriented features: in this contribution we will introduce for instance a dedicated syntax to define systematic variations, per-data-sample call-backs useful to define quantities that vary on a per-sample basis, simplifications of collection operations and the injection of just-in-time-compiled Python functions in the optimized C++ event loop.oai:cds.cern.ch:28718202023
spellingShingle Computing and Computers
Guiraud, E
Blomer, J
Hageboeck, S
Naumann, A
Padulano, V E
Tejedor, E
Wunsch, S
RDataFrame enhancements for HEP analyses
title RDataFrame enhancements for HEP analyses
title_full RDataFrame enhancements for HEP analyses
title_fullStr RDataFrame enhancements for HEP analyses
title_full_unstemmed RDataFrame enhancements for HEP analyses
title_short RDataFrame enhancements for HEP analyses
title_sort rdataframe enhancements for hep analyses
topic Computing and Computers
url https://dx.doi.org/10.1088/1742-6596/2438/1/012116
http://cds.cern.ch/record/2871820
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AT hageboecks rdataframeenhancementsforhepanalyses
AT naumanna rdataframeenhancementsforhepanalyses
AT padulanove rdataframeenhancementsforhepanalyses
AT tejedore rdataframeenhancementsforhepanalyses
AT wunschs rdataframeenhancementsforhepanalyses