{"title":"两个世界中最好的","authors":"F. Westad, L. Gidskehaug, Chuck Miller","doi":"10.1177/0960336020944008","DOIUrl":null,"url":null,"abstract":"With the latest release of Unscrambler, Camo Analytics introduced support for Python scripting, giving users the best of two worlds. This Python extension allows users to tap into the vast ecosystem of Data Science tools that are continually being produced in the Python community, while still leveraging the familiar data handling, validation and visualization features of Unscrambler – all contained within a fully compliant framework. This paper discusses the value propositions that the Python extension can provide to Unscrambler users, and follows this up with some specific examples of common workflows that are enabled by this extension: Data Importing, Spectral Preprocessing and Innovative Modeling methods.","PeriodicalId":113081,"journal":{"name":"NIR News","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The best of two worlds\",\"authors\":\"F. Westad, L. Gidskehaug, Chuck Miller\",\"doi\":\"10.1177/0960336020944008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the latest release of Unscrambler, Camo Analytics introduced support for Python scripting, giving users the best of two worlds. This Python extension allows users to tap into the vast ecosystem of Data Science tools that are continually being produced in the Python community, while still leveraging the familiar data handling, validation and visualization features of Unscrambler – all contained within a fully compliant framework. This paper discusses the value propositions that the Python extension can provide to Unscrambler users, and follows this up with some specific examples of common workflows that are enabled by this extension: Data Importing, Spectral Preprocessing and Innovative Modeling methods.\",\"PeriodicalId\":113081,\"journal\":{\"name\":\"NIR News\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NIR News\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/0960336020944008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NIR News","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/0960336020944008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
With the latest release of Unscrambler, Camo Analytics introduced support for Python scripting, giving users the best of two worlds. This Python extension allows users to tap into the vast ecosystem of Data Science tools that are continually being produced in the Python community, while still leveraging the familiar data handling, validation and visualization features of Unscrambler – all contained within a fully compliant framework. This paper discusses the value propositions that the Python extension can provide to Unscrambler users, and follows this up with some specific examples of common workflows that are enabled by this extension: Data Importing, Spectral Preprocessing and Innovative Modeling methods.
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