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dc.contributor.authorRyan M. May
dc.contributor.authorKevin H. Goebbert
dc.contributor.authorJonathan E. Thielen
dc.contributor.authorJohn R. Leeman
dc.contributor.authorM. Drew Camron
dc.contributor.authorZachary Bruick
dc.contributor.authorEric C. Bruning
dc.contributor.authorRussell P. Manser
dc.contributor.authorSean C. Arms
dc.contributor.authorPatrick T. Marsh
dc.date.accessioned2023-04-12T18:50:01Z
dc.date.available2023-04-12T18:50:01Z
dc.date.copyright2022/10/26
dc.date.issued2022
dc.identifier.otherBAMS-D-21-0125.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4290319
dc.description.abstractMetPy is an open-source, Python-based package for meteorology, providing domain-specific functionality built extensively on top of the robust scientific Python software stack, which includes libraries like NumPy, SciPy, Matplotlib, and xarray. The goal of the project is to bring the weather analysis capabilities of GEMPAK (and similar software tools) into a modern computing paradigm. MetPy strives to employ best practices in its development, including software tests, continuous integration, and automated publishing of web-based documentation. As such, MetPy represents a sustainable, long-term project that fills a need for the meteorological community. MetPy’s development is substantially driven by its user community, both through feedback on a variety of open, public forums like Stack Overflow, and through code contributions facilitated by the GitHub collaborative software development platform. MetPy has recently seen the release of version 1.0, with robust functionality for analyzing and visualizing meteorological datasets. While previous versions of MetPy have already seen extensive use, the 1.0 release represents a significant milestone in terms of completeness and a commitment to long-term support for the programming interfaces. This article provides an overview of MetPy’s suite of capabilities, including its use of labeled arrays and physical unit information as its core data model, unit-aware calculations, cross sections, skew
dc.publisherAmerican Meteorological Society
dc.titleMetPy: A Meteorological Python Library for Data Analysis and Visualization
dc.typeJournal Paper
dc.journal.volume103
dc.journal.issue10
dc.journal.titleBulletin of the American Meteorological Society
dc.identifier.doi10.1175/BAMS-D-21-0125.1
dc.journal.fristpageE2273
dc.journal.lastpageE2284
dc.pageE2273–E2284
dc.treeBulletin of the American Meteorological Society:;2022:;volume( 103 ):;issue: 010
dc.contenttypeFulltext


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