使用P - late - T构造工具和GP - late的py - GP - late深时时空数据分析

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Geoscience Data Journal Pub Date : 2023-03-14 DOI:10.1002/gdj3.185
Ben R. Mather, R. Dietmar Müller, Sabin Zahirovic, John Cannon, Michael Chin, Lauren Ilano, Nicky M. Wright, Christopher Alfonso, Simon Williams, Michael Tetley, Andrew Merdith
{"title":"使用P - late - T构造工具和GP - late的py - GP - late深时时空数据分析","authors":"Ben R. Mather,&nbsp;R. Dietmar Müller,&nbsp;Sabin Zahirovic,&nbsp;John Cannon,&nbsp;Michael Chin,&nbsp;Lauren Ilano,&nbsp;Nicky M. Wright,&nbsp;Christopher Alfonso,&nbsp;Simon Williams,&nbsp;Michael Tetley,&nbsp;Andrew Merdith","doi":"10.1002/gdj3.185","DOIUrl":null,"url":null,"abstract":"<p>PyGPlates is an open-source Python library to visualize and edit plate tectonic reconstructions created using GPlates. The Python API affords a greater level of flexibility than GPlates to interrogate plate reconstructions and integrate with other Python workflows. GPlately was created to accelerate spatio-temporal data analysis leveraging pyGPlates and PlateTectonicTools within a simplified Python interface. This object-oriented package enables the reconstruction of data through deep geologic time (points, lines, polygons and rasters), the interrogation of plate kinematic information (plate velocities, rates of subduction and seafloor spreading), the rapid comparison between multiple plate motion models, and the plotting of reconstructed output data on maps. All tools are designed to be parallel-safe to accelerate spatio-temporal analysis over multiple CPU processors.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 1","pages":"3-10"},"PeriodicalIF":3.3000,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.185","citationCount":"0","resultStr":"{\"title\":\"Deep time spatio-temporal data analysis using pyGPlates with PlateTectonicTools and GPlately\",\"authors\":\"Ben R. Mather,&nbsp;R. Dietmar Müller,&nbsp;Sabin Zahirovic,&nbsp;John Cannon,&nbsp;Michael Chin,&nbsp;Lauren Ilano,&nbsp;Nicky M. Wright,&nbsp;Christopher Alfonso,&nbsp;Simon Williams,&nbsp;Michael Tetley,&nbsp;Andrew Merdith\",\"doi\":\"10.1002/gdj3.185\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>PyGPlates is an open-source Python library to visualize and edit plate tectonic reconstructions created using GPlates. The Python API affords a greater level of flexibility than GPlates to interrogate plate reconstructions and integrate with other Python workflows. GPlately was created to accelerate spatio-temporal data analysis leveraging pyGPlates and PlateTectonicTools within a simplified Python interface. This object-oriented package enables the reconstruction of data through deep geologic time (points, lines, polygons and rasters), the interrogation of plate kinematic information (plate velocities, rates of subduction and seafloor spreading), the rapid comparison between multiple plate motion models, and the plotting of reconstructed output data on maps. All tools are designed to be parallel-safe to accelerate spatio-temporal analysis over multiple CPU processors.</p>\",\"PeriodicalId\":54351,\"journal\":{\"name\":\"Geoscience Data Journal\",\"volume\":\"11 1\",\"pages\":\"3-10\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2023-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.185\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoscience Data Journal\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.185\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscience Data Journal","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.185","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

PyGPlates 是一个开源 Python 库,用于可视化和编辑使用 GPlates 创建的板块构造重建。Python API 提供了比 GPlates 更大的灵活性,可用于查询板块重建并与其他 Python 工作流集成。创建 GPlately 的目的是在简化的 Python 界面中利用 pyGPlates 和 PlateTectonicTools 加速时空数据分析。这个面向对象的软件包可以重建深地质年代的数据(点、线、多边形和栅格),查询板块运动信息(板块速度、俯冲和海底扩张速率),快速比较多个板块运动模型,并在地图上绘制重建的输出数据。所有工具的设计都是并行安全的,以便在多个 CPU 处理器上加速时空分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Deep time spatio-temporal data analysis using pyGPlates with PlateTectonicTools and GPlately

PyGPlates is an open-source Python library to visualize and edit plate tectonic reconstructions created using GPlates. The Python API affords a greater level of flexibility than GPlates to interrogate plate reconstructions and integrate with other Python workflows. GPlately was created to accelerate spatio-temporal data analysis leveraging pyGPlates and PlateTectonicTools within a simplified Python interface. This object-oriented package enables the reconstruction of data through deep geologic time (points, lines, polygons and rasters), the interrogation of plate kinematic information (plate velocities, rates of subduction and seafloor spreading), the rapid comparison between multiple plate motion models, and the plotting of reconstructed output data on maps. All tools are designed to be parallel-safe to accelerate spatio-temporal analysis over multiple CPU processors.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
期刊最新文献
Issue Information Issue Information Exploring Jalisco's water quality: A comprehensive web tool for limnological and phytoplankton data HSPEI: A 1-km spatial resolution SPEI dataset across the Chinese mainland from 2001 to 2022 High-resolution atmospheric CO2 concentration data simulated in WRF-Chem over East Asia for 10 years
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1