S. Elipot, Philippe Miron, Milan Curcic, Kevin Santana, Rick Lumpkin
{"title":"Clouddrift: a Python package to accelerate the use of\u0000Lagrangian data for atmospheric, oceanic, and climate sciences","authors":"S. Elipot, Philippe Miron, Milan Curcic, Kevin Santana, Rick Lumpkin","doi":"10.21105/joss.06742","DOIUrl":"https://doi.org/10.21105/joss.06742","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"35 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The nonlinear Schrödinger equation (NLSE) is a general nonlinear equation used to model the propagation of light in nonlinear media. This equation is mathematically isomorphic to the Gross-Pitaevskii equation (GPE) (Pitaevskij & Stringari, 2016) describing the evolution of cold atomic ensembles. Recently, the growing field of quantum fluids of light (Carusotto & Ciuti, 2013) has proven a fruitful testbed for several fundamental quantum and classical phenomena such as superfluidity (Michel et al., 2018) or turbulence (Baker-Rasooli et al., 2023). Providing a flexible, modern and performant framework to solve these equations is crucial to model realistic experimental scenarios.
{"title":"NLSE: A Python package to solve the nonlinear\u0000Schrödinger equation","authors":"Tangui Aladjidi, C. Piekarski, Q. Glorieux","doi":"10.21105/joss.06607","DOIUrl":"https://doi.org/10.21105/joss.06607","url":null,"abstract":"The nonlinear Schrödinger equation (NLSE) is a general nonlinear equation used to model the propagation of light in nonlinear media. This equation is mathematically isomorphic to the Gross-Pitaevskii equation (GPE) (Pitaevskij & Stringari, 2016) describing the evolution of cold atomic ensembles. Recently, the growing field of quantum fluids of light (Carusotto & Ciuti, 2013) has proven a fruitful testbed for several fundamental quantum and classical phenomena such as superfluidity (Michel et al., 2018) or turbulence (Baker-Rasooli et al., 2023). Providing a flexible, modern and performant framework to solve these equations is crucial to model realistic experimental scenarios.","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"31 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Stcherbinine, Y. Langevin, John Carter, M. Vincendon, Y. Leseigneur, O. Barraud
{"title":"OMEGA-Py: Python Tools for OMEGA Data","authors":"A. Stcherbinine, Y. Langevin, John Carter, M. Vincendon, Y. Leseigneur, O. Barraud","doi":"10.21105/joss.06566","DOIUrl":"https://doi.org/10.21105/joss.06566","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"29 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Gharib-Nezhad, N. Batalha, Hamed Valizadegan, Miguel J. S. Martinho, M. Habibi, Gopal Nookula
We are on the verge of a revolutionary era in space exploration, thanks to advancements in telescopes such as the James Webb Space Telescope (textit{JWST}). High-resolution, high signal-to-noise spectra from exoplanet and brown dwarf atmospheres have been collected over the past few decades, requiring the development of accurate and reliable pipelines and tools for their analysis. Accurately and swiftly determining the spectroscopic parameters from the observational spectra of these objects is crucial for understanding their atmospheric composition and guiding future follow-up observations. texttt{TelescopeML} is a Python package developed to perform three main tasks: 1. Process the synthetic astronomical datasets for training a CNN model and prepare the observational dataset for later use for prediction; 2. Train a CNN model by implementing the optimal hyperparameters; and 3. Deploy the trained CNN models on the actual observational data to derive the output spectroscopic parameters.
{"title":"TelescopeML – I. An End-to-End Python Package for\u0000Interpreting Telescope Datasets through Training Machine Learning\u0000Models, Generating Statistical Reports, and Visualizing Results","authors":"E. Gharib-Nezhad, N. Batalha, Hamed Valizadegan, Miguel J. S. Martinho, M. Habibi, Gopal Nookula","doi":"10.21105/joss.06346","DOIUrl":"https://doi.org/10.21105/joss.06346","url":null,"abstract":"We are on the verge of a revolutionary era in space exploration, thanks to advancements in telescopes such as the James Webb Space Telescope (textit{JWST}). High-resolution, high signal-to-noise spectra from exoplanet and brown dwarf atmospheres have been collected over the past few decades, requiring the development of accurate and reliable pipelines and tools for their analysis. Accurately and swiftly determining the spectroscopic parameters from the observational spectra of these objects is crucial for understanding their atmospheric composition and guiding future follow-up observations. texttt{TelescopeML} is a Python package developed to perform three main tasks: 1. Process the synthetic astronomical datasets for training a CNN model and prepare the observational dataset for later use for prediction; 2. Train a CNN model by implementing the optimal hyperparameters; and 3. Deploy the trained CNN models on the actual observational data to derive the output spectroscopic parameters.","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":" 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Denis Voskov, Ilshat Saifullin, Aleks Novikov, M. Wapperom, Luisa Orozco, G. S. Seabra, Yuan Chen, M. Khait, Xiaocong Lyu, Xiaoming Tian, Stephan de Hoop, Artur Palha
{"title":"open Delft Advanced Research Terra Simulator\u0000(open-DARTS)","authors":"Denis Voskov, Ilshat Saifullin, Aleks Novikov, M. Wapperom, Luisa Orozco, G. S. Seabra, Yuan Chen, M. Khait, Xiaocong Lyu, Xiaoming Tian, Stephan de Hoop, Artur Palha","doi":"10.21105/joss.06737","DOIUrl":"https://doi.org/10.21105/joss.06737","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Johnny Corbino, Miguel A. Dumett, Jose E. Castillo
{"title":"MOLE: Mimetic Operators Library Enhanced","authors":"Johnny Corbino, Miguel A. Dumett, Jose E. Castillo","doi":"10.21105/joss.06288","DOIUrl":"https://doi.org/10.21105/joss.06288","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"MIRP: A Python package for standardised\u0000radiomics","authors":"A. Zwanenburg, Steffen Löck","doi":"10.21105/joss.06413","DOIUrl":"https://doi.org/10.21105/joss.06413","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amanda Triplett, Georgios Artavanis, W. Hasling, Reed M. Maxwell, Amy Defnet, Amy M. Johnson, William Lytle, Andrew Bennett, Elena Leonarduzzi, Lisa K. Gallagher, L. Condon
{"title":"SubsetTools: A Python package to subset data to build\u0000and run ParFlow hydrologic models","authors":"Amanda Triplett, Georgios Artavanis, W. Hasling, Reed M. Maxwell, Amy Defnet, Amy M. Johnson, William Lytle, Andrew Bennett, Elena Leonarduzzi, Lisa K. Gallagher, L. Condon","doi":"10.21105/joss.06752","DOIUrl":"https://doi.org/10.21105/joss.06752","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"scribl: A system for the semantic capture of\u0000relationships in biological literature","authors":"Gordon D. Webster, Alexander K. Lancaster","doi":"10.21105/joss.06645","DOIUrl":"https://doi.org/10.21105/joss.06645","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":" 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martin Grunnill, Julien Arino, Abbas Ghasemi, Edward W. Thommes, Jianhong Wu
{"title":"MetaCast: A package for broadCASTing epidemiological\u0000and ecological models over META-populations.","authors":"Martin Grunnill, Julien Arino, Abbas Ghasemi, Edward W. Thommes, Jianhong Wu","doi":"10.21105/joss.06851","DOIUrl":"https://doi.org/10.21105/joss.06851","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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