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Coffea: Columnar Object Framework For Effective Analysis

The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commod...

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Autores principales: Smith, Nicholas, Gray, Lindsey, Cremonesi, Matteo, Jayatilaka, Bo, Gutsche, Oliver, Hall, Allison, Pedro, Kevin, Acosta, Maria, Melo, Andrew, Belforte, Stefano, Pivarski, Jim
Lenguaje:eng
Publicado: 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1051/epjconf/202024506012
http://cds.cern.ch/record/2798133
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author Smith, Nicholas
Gray, Lindsey
Cremonesi, Matteo
Jayatilaka, Bo
Gutsche, Oliver
Hall, Allison
Pedro, Kevin
Acosta, Maria
Melo, Andrew
Belforte, Stefano
Pivarski, Jim
author_facet Smith, Nicholas
Gray, Lindsey
Cremonesi, Matteo
Jayatilaka, Bo
Gutsche, Oliver
Hall, Allison
Pedro, Kevin
Acosta, Maria
Melo, Andrew
Belforte, Stefano
Pivarski, Jim
author_sort Smith, Nicholas
collection CERN
description The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
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spelling cern-27981332023-05-26T02:24:15Zdoi:10.1051/epjconf/202024506012http://cds.cern.ch/record/2798133engSmith, NicholasGray, LindseyCremonesi, MatteoJayatilaka, BoGutsche, OliverHall, AllisonPedro, KevinAcosta, MariaMelo, AndrewBelforte, StefanoPivarski, JimCoffea: Columnar Object Framework For Effective AnalysisDetectors and Experimental TechniquesThe coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions.The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions.arXiv:2008.12712FERMILAB-CONF-20-494-CMS-SCDCMS-CR-2020-069oai:cds.cern.ch:27981332020-02-24
spellingShingle Detectors and Experimental Techniques
Smith, Nicholas
Gray, Lindsey
Cremonesi, Matteo
Jayatilaka, Bo
Gutsche, Oliver
Hall, Allison
Pedro, Kevin
Acosta, Maria
Melo, Andrew
Belforte, Stefano
Pivarski, Jim
Coffea: Columnar Object Framework For Effective Analysis
title Coffea: Columnar Object Framework For Effective Analysis
title_full Coffea: Columnar Object Framework For Effective Analysis
title_fullStr Coffea: Columnar Object Framework For Effective Analysis
title_full_unstemmed Coffea: Columnar Object Framework For Effective Analysis
title_short Coffea: Columnar Object Framework For Effective Analysis
title_sort coffea: columnar object framework for effective analysis
topic Detectors and Experimental Techniques
url https://dx.doi.org/10.1051/epjconf/202024506012
http://cds.cern.ch/record/2798133
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