Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
{"title":"Sherpa：开源 Python 拟合软件包","authors":"Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell","doi":"arxiv-2409.10400","DOIUrl":null,"url":null,"abstract":"We present an overview of Sherpa, an open source Python project, and discuss\nits development history, broad design concepts and capabilities. Sherpa\ncontains powerful tools for combining parametric models into complex\nexpressions that can be fit to data using a variety of statistics and\noptimization methods. It is easily extensible to include user-defined models,\nstatistics, and optimization methods. It provides a high-level User Interface\nfor interactive data-analysis, such as within a Jupyter notebook, and it can\nalso be used as a library component, providing fitting and modeling\ncapabilities to an application. We include a few examples of Sherpa\napplications to multiwavelength astronomical data. The code is available\nGitHub: https://github.com/sherpa/sherpa","PeriodicalId":501163,"journal":{"name":"arXiv - PHYS - Instrumentation and Methods for Astrophysics","volume":"29 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sherpa: An Open Source Python Fitting Package\",\"authors\":\"Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell\",\"doi\":\"arxiv-2409.10400\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present an overview of Sherpa, an open source Python project, and discuss\\nits development history, broad design concepts and capabilities. Sherpa\\ncontains powerful tools for combining parametric models into complex\\nexpressions that can be fit to data using a variety of statistics and\\noptimization methods. It is easily extensible to include user-defined models,\\nstatistics, and optimization methods. It provides a high-level User Interface\\nfor interactive data-analysis, such as within a Jupyter notebook, and it can\\nalso be used as a library component, providing fitting and modeling\\ncapabilities to an application. We include a few examples of Sherpa\\napplications to multiwavelength astronomical data. The code is available\\nGitHub: https://github.com/sherpa/sherpa\",\"PeriodicalId\":501163,\"journal\":{\"name\":\"arXiv - PHYS - Instrumentation and Methods for Astrophysics\",\"volume\":\"29 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Instrumentation and Methods for Astrophysics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.10400\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Instrumentation and Methods for Astrophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10400","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present an overview of Sherpa, an open source Python project, and discuss
its development history, broad design concepts and capabilities. Sherpa
contains powerful tools for combining parametric models into complex
expressions that can be fit to data using a variety of statistics and
optimization methods. It is easily extensible to include user-defined models,
statistics, and optimization methods. It provides a high-level User Interface
for interactive data-analysis, such as within a Jupyter notebook, and it can
also be used as a library component, providing fitting and modeling
capabilities to an application. We include a few examples of Sherpa
applications to multiwavelength astronomical data. The code is available
GitHub: https://github.com/sherpa/sherpa
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