{"title":"PySOFI:用于SOFI的开源Python包。","authors":"Yuting Miao, Shimon Weiss, Xiyu Yi","doi":"10.1016/j.bpr.2022.100052","DOIUrl":null,"url":null,"abstract":"<p><p>Super-resolution optical fluctuation imaging (SOFI) is a highly democratizable technique that provides optical super-resolution without requirement of sophisticated imaging instruments. Easy-to-use open-source packages for SOFI are important to support the utilization and community adoption of the SOFI method, they also encourage the participation and further development of SOFI by new investigators. In this work, we developed <i>PySOFI</i>, an open-source Python package for SOFI analysis that offers the flexibility to inspect, test, modify, improve, and extend the algorithm. We provide complete documentation for the package and a collection of Jupyter Notebooks to demonstrate the usage of the package. We discuss the architecture of <i>PySOFI</i> and illustrate how to use each functional module. A demonstration on how to extend the <i>PySOFI</i> package with additional modules is also included in the <i>PySOFI</i> package. We expect <i>PySOFI</i> to facilitate efficient adoption, testing, modification, dissemination, and prototyping of new SOFI-relevant algorithms.</p>","PeriodicalId":72402,"journal":{"name":"Biophysical reports","volume":" ","pages":"100052"},"PeriodicalIF":2.7000,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680711/pdf/","citationCount":"0","resultStr":"{\"title\":\"<i>PySOFI</i>: an open source Python package for SOFI.\",\"authors\":\"Yuting Miao, Shimon Weiss, Xiyu Yi\",\"doi\":\"10.1016/j.bpr.2022.100052\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Super-resolution optical fluctuation imaging (SOFI) is a highly democratizable technique that provides optical super-resolution without requirement of sophisticated imaging instruments. Easy-to-use open-source packages for SOFI are important to support the utilization and community adoption of the SOFI method, they also encourage the participation and further development of SOFI by new investigators. In this work, we developed <i>PySOFI</i>, an open-source Python package for SOFI analysis that offers the flexibility to inspect, test, modify, improve, and extend the algorithm. We provide complete documentation for the package and a collection of Jupyter Notebooks to demonstrate the usage of the package. We discuss the architecture of <i>PySOFI</i> and illustrate how to use each functional module. A demonstration on how to extend the <i>PySOFI</i> package with additional modules is also included in the <i>PySOFI</i> package. We expect <i>PySOFI</i> to facilitate efficient adoption, testing, modification, dissemination, and prototyping of new SOFI-relevant algorithms.</p>\",\"PeriodicalId\":72402,\"journal\":{\"name\":\"Biophysical reports\",\"volume\":\" \",\"pages\":\"100052\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2022-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680711/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biophysical reports\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.bpr.2022.100052\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/6/8 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"BIOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biophysical reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.bpr.2022.100052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/6/8 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"BIOPHYSICS","Score":null,"Total":0}
Super-resolution optical fluctuation imaging (SOFI) is a highly democratizable technique that provides optical super-resolution without requirement of sophisticated imaging instruments. Easy-to-use open-source packages for SOFI are important to support the utilization and community adoption of the SOFI method, they also encourage the participation and further development of SOFI by new investigators. In this work, we developed PySOFI, an open-source Python package for SOFI analysis that offers the flexibility to inspect, test, modify, improve, and extend the algorithm. We provide complete documentation for the package and a collection of Jupyter Notebooks to demonstrate the usage of the package. We discuss the architecture of PySOFI and illustrate how to use each functional module. A demonstration on how to extend the PySOFI package with additional modules is also included in the PySOFI package. We expect PySOFI to facilitate efficient adoption, testing, modification, dissemination, and prototyping of new SOFI-relevant algorithms.