Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python

Scipp is heavily inspired by the Python library xarray. It enriches raw NumPy-like multi-dimensional arrays of data by adding named dimensions and associated coordinates. Multiple arrays are combined into datasets. On top of this, scipp introduces (i) implicit handling of physical units, (ii) implic...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2020-10
Hauptverfasser: Heybrock, Simon, Owen, Arnold, Gudich, Igor, Nixon, Daniel, Vaytet, Neil
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Heybrock, Simon
Owen, Arnold
Gudich, Igor
Nixon, Daniel
Vaytet, Neil
description Scipp is heavily inspired by the Python library xarray. It enriches raw NumPy-like multi-dimensional arrays of data by adding named dimensions and associated coordinates. Multiple arrays are combined into datasets. On top of this, scipp introduces (i) implicit handling of physical units, (ii) implicit propagation of uncertainties, (iii) support for histograms, i.e., bin-edge coordinate axes, which exceed the data's dimension extent by one, and (iv) support for event data. In conjunction these features enable a more natural and more concise user experience. The combination of named dimensions, coordinates, and units helps to drastically reduce the risk for programming errors. The core of scipp is written in C++ to open opportunities for performance improvements that a Python-based solution would not allow for. On top of the C++ core, scipp's Python components provide functionality for plotting and content representations, e.g., for use in Jupyter Notebooks. While none of scipp's concepts in isolation is novel per-se, we are not aware of any project combining all of these aspects in a single coherent software package.
doi_str_mv 10.48550/arxiv.2010.00257
format Article
fullrecord <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_2010_00257</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2448036882</sourcerecordid><originalsourceid>FETCH-LOGICAL-a522-46503660a77a22310e39c1f6d52c25dd87f9903fc30086bf50cd5930daa0f00b3</originalsourceid><addsrcrecordid>eNotj1FLwzAUhYMgOOZ-gE8GfBydtzdNm_omQ6cwUHAPvpW7JnEZXVuTTt2_N24-HTice8_5GLtKYZYpKeGW_I_7miFEAwBlccZGKESaqAzxgk1C2EL08wKlFCP2_la7vr_jUUw7OOtqrmkgvqFWN6794N9u2PCG1qYxmu_2zeAS7XamDa5rqeHkPR0Ct53n8-mUxyv-ehg2XXvJzi01wUz-dcxWjw-r-VOyfFk8z--XCUnEJMsliDwHKgpCFCkYUdapzbXEGqXWqrBlCcLWAkDlayuh1rIUoInAAqzFmF2f3h6xq967HflD9YdfHfFj4uaU6H33uTdhqLbd3sftocIsU7FeKRS_wgBcCg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2448036882</pqid></control><display><type>article</type><title>Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python</title><source>arXiv.org</source><source>Open Access: Freely Accessible Journals by multiple vendors</source><creator>Heybrock, Simon ; Owen, Arnold ; Gudich, Igor ; Nixon, Daniel ; Vaytet, Neil</creator><creatorcontrib>Heybrock, Simon ; Owen, Arnold ; Gudich, Igor ; Nixon, Daniel ; Vaytet, Neil</creatorcontrib><description>Scipp is heavily inspired by the Python library xarray. It enriches raw NumPy-like multi-dimensional arrays of data by adding named dimensions and associated coordinates. Multiple arrays are combined into datasets. On top of this, scipp introduces (i) implicit handling of physical units, (ii) implicit propagation of uncertainties, (iii) support for histograms, i.e., bin-edge coordinate axes, which exceed the data's dimension extent by one, and (iv) support for event data. In conjunction these features enable a more natural and more concise user experience. The combination of named dimensions, coordinates, and units helps to drastically reduce the risk for programming errors. The core of scipp is written in C++ to open opportunities for performance improvements that a Python-based solution would not allow for. On top of the C++ core, scipp's Python components provide functionality for plotting and content representations, e.g., for use in Jupyter Notebooks. While none of scipp's concepts in isolation is novel per-se, we are not aware of any project combining all of these aspects in a single coherent software package.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2010.00257</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Arrays ; C plus plus ; C++ (programming language) ; Computer Science - Mathematical Software ; Histograms ; Physics - Data Analysis, Statistics and Probability ; Physics - Instrumentation and Detectors ; Python</subject><ispartof>arXiv.org, 2020-10</ispartof><rights>2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,784,885,27925</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.2010.00257$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.3233/JNR-190131$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Heybrock, Simon</creatorcontrib><creatorcontrib>Owen, Arnold</creatorcontrib><creatorcontrib>Gudich, Igor</creatorcontrib><creatorcontrib>Nixon, Daniel</creatorcontrib><creatorcontrib>Vaytet, Neil</creatorcontrib><title>Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python</title><title>arXiv.org</title><description>Scipp is heavily inspired by the Python library xarray. It enriches raw NumPy-like multi-dimensional arrays of data by adding named dimensions and associated coordinates. Multiple arrays are combined into datasets. On top of this, scipp introduces (i) implicit handling of physical units, (ii) implicit propagation of uncertainties, (iii) support for histograms, i.e., bin-edge coordinate axes, which exceed the data's dimension extent by one, and (iv) support for event data. In conjunction these features enable a more natural and more concise user experience. The combination of named dimensions, coordinates, and units helps to drastically reduce the risk for programming errors. The core of scipp is written in C++ to open opportunities for performance improvements that a Python-based solution would not allow for. On top of the C++ core, scipp's Python components provide functionality for plotting and content representations, e.g., for use in Jupyter Notebooks. While none of scipp's concepts in isolation is novel per-se, we are not aware of any project combining all of these aspects in a single coherent software package.</description><subject>Arrays</subject><subject>C plus plus</subject><subject>C++ (programming language)</subject><subject>Computer Science - Mathematical Software</subject><subject>Histograms</subject><subject>Physics - Data Analysis, Statistics and Probability</subject><subject>Physics - Instrumentation and Detectors</subject><subject>Python</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj1FLwzAUhYMgOOZ-gE8GfBydtzdNm_omQ6cwUHAPvpW7JnEZXVuTTt2_N24-HTice8_5GLtKYZYpKeGW_I_7miFEAwBlccZGKESaqAzxgk1C2EL08wKlFCP2_la7vr_jUUw7OOtqrmkgvqFWN6794N9u2PCG1qYxmu_2zeAS7XamDa5rqeHkPR0Ct53n8-mUxyv-ehg2XXvJzi01wUz-dcxWjw-r-VOyfFk8z--XCUnEJMsliDwHKgpCFCkYUdapzbXEGqXWqrBlCcLWAkDlayuh1rIUoInAAqzFmF2f3h6xq967HflD9YdfHfFj4uaU6H33uTdhqLbd3sftocIsU7FeKRS_wgBcCg</recordid><startdate>20201001</startdate><enddate>20201001</enddate><creator>Heybrock, Simon</creator><creator>Owen, Arnold</creator><creator>Gudich, Igor</creator><creator>Nixon, Daniel</creator><creator>Vaytet, Neil</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20201001</creationdate><title>Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python</title><author>Heybrock, Simon ; Owen, Arnold ; Gudich, Igor ; Nixon, Daniel ; Vaytet, Neil</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a522-46503660a77a22310e39c1f6d52c25dd87f9903fc30086bf50cd5930daa0f00b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Arrays</topic><topic>C plus plus</topic><topic>C++ (programming language)</topic><topic>Computer Science - Mathematical Software</topic><topic>Histograms</topic><topic>Physics - Data Analysis, Statistics and Probability</topic><topic>Physics - Instrumentation and Detectors</topic><topic>Python</topic><toplevel>online_resources</toplevel><creatorcontrib>Heybrock, Simon</creatorcontrib><creatorcontrib>Owen, Arnold</creatorcontrib><creatorcontrib>Gudich, Igor</creatorcontrib><creatorcontrib>Nixon, Daniel</creatorcontrib><creatorcontrib>Vaytet, Neil</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest Publicly Available Content database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Computer Science</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Heybrock, Simon</au><au>Owen, Arnold</au><au>Gudich, Igor</au><au>Nixon, Daniel</au><au>Vaytet, Neil</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python</atitle><jtitle>arXiv.org</jtitle><date>2020-10-01</date><risdate>2020</risdate><eissn>2331-8422</eissn><abstract>Scipp is heavily inspired by the Python library xarray. It enriches raw NumPy-like multi-dimensional arrays of data by adding named dimensions and associated coordinates. Multiple arrays are combined into datasets. On top of this, scipp introduces (i) implicit handling of physical units, (ii) implicit propagation of uncertainties, (iii) support for histograms, i.e., bin-edge coordinate axes, which exceed the data's dimension extent by one, and (iv) support for event data. In conjunction these features enable a more natural and more concise user experience. The combination of named dimensions, coordinates, and units helps to drastically reduce the risk for programming errors. The core of scipp is written in C++ to open opportunities for performance improvements that a Python-based solution would not allow for. On top of the C++ core, scipp's Python components provide functionality for plotting and content representations, e.g., for use in Jupyter Notebooks. While none of scipp's concepts in isolation is novel per-se, we are not aware of any project combining all of these aspects in a single coherent software package.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2010.00257</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2020-10
issn 2331-8422
language eng
recordid cdi_arxiv_primary_2010_00257
source arXiv.org; Open Access: Freely Accessible Journals by multiple vendors
subjects Arrays
C plus plus
C++ (programming language)
Computer Science - Mathematical Software
Histograms
Physics - Data Analysis, Statistics and Probability
Physics - Instrumentation and Detectors
Python
title Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T05%3A47%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Scipp:%20Scientific%20data%20handling%20with%20labeled%20multi-dimensional%20arrays%20for%20C++%20and%20Python&rft.jtitle=arXiv.org&rft.au=Heybrock,%20Simon&rft.date=2020-10-01&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2010.00257&rft_dat=%3Cproquest_arxiv%3E2448036882%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2448036882&rft_id=info:pmid/&rfr_iscdi=true