KerrGeoPy is a Python package which computes both stable and plunging timelike geodesics in Kerr spacetime using analytic solutions to the geodesic equation that are written in terms of Legendre elliptic integrals. It mirrors and builds upon much of the functionality of the KerrGeodesics Mathematica library. Users can construct a geodesic by providing the initial position and four-velocity, or by providing either the constants of motion or a generalized version of the parameters defining a Keplerian orbit. The package provides methods for computing the four-velocity, fundamental frequencies, and constants of motion associated with a given geodesic along with the location of the separatrix. It also includes several methods for visualizing and animating geodesics.
{"title":"KerrGeoPy: A Python Package for Computing Timelike\u0000Geodesics in Kerr Spacetime","authors":"Seyong Park, Zachary Nasipak","doi":"10.21105/joss.06587","DOIUrl":"https://doi.org/10.21105/joss.06587","url":null,"abstract":"KerrGeoPy is a Python package which computes both stable and plunging timelike geodesics in Kerr spacetime using analytic solutions to the geodesic equation that are written in terms of Legendre elliptic integrals. It mirrors and builds upon much of the functionality of the KerrGeodesics Mathematica library. Users can construct a geodesic by providing the initial position and four-velocity, or by providing either the constants of motion or a generalized version of the parameters defining a Keplerian orbit. The package provides methods for computing the four-velocity, fundamental frequencies, and constants of motion associated with a given geodesic along with the location of the separatrix. It also includes several methods for visualizing and animating geodesics.","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141277654","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}
Bryna Hazelton, Matthew Kolopanis, Adam Lanman, Jonathan Pober
{"title":"pyradiosky: A Python package for Radio Sky\u0000Models","authors":"Bryna Hazelton, Matthew Kolopanis, Adam Lanman, Jonathan Pober","doi":"10.21105/joss.06503","DOIUrl":"https://doi.org/10.21105/joss.06503","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140962362","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}
Lars Hellemo, Espen Flo Bødal, S. Holm, Dimitri Pinel, Julian Straus
{"title":"EnergyModelsX: Flexible Energy Systems Modelling with\u0000Multiple Dispatch","authors":"Lars Hellemo, Espen Flo Bødal, S. Holm, Dimitri Pinel, Julian Straus","doi":"10.21105/joss.06619","DOIUrl":"https://doi.org/10.21105/joss.06619","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140964172","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}
Thomas Walzthoeni, Bom Bahadur Singiali, N. W. Rayner, Francesco Paolo Casale, Christoph Feest, Carsten Marr, Alf Wachsmann
{"title":"SCAS dashboard: A tool to intuitively and interactively\u0000analyze Slurm cluster usage","authors":"Thomas Walzthoeni, Bom Bahadur Singiali, N. W. Rayner, Francesco Paolo Casale, Christoph Feest, Carsten Marr, Alf Wachsmann","doi":"10.21105/joss.06017","DOIUrl":"https://doi.org/10.21105/joss.06017","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140991849","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}
Long term observations and space missions have generated a wealth of data on the magnetic fields of the Earth and other solar system planets. planetMagfields is a Python package designed to have all the planetary magnetic field data currently available in one place and to provide an easy interface to access the data. planetMagfields focuses on planetary bodies that generate their own magnetic field, namely Mercury, Earth, Jupiter, Saturn, Uranus, Neptune and Ganymede. planetMagfields provides functions to compute as well as plot the magnetic field on the planetary surface or at a distance above or under the surface. It also provides functions to filter out the field to large or small scales as well as to produce .vts files to visualize the field in 3D using Paraview, VisIt or similar rendering software. Lastly, the planetMagfields repository also provides a Jupyter notebook for easy interactive visualizations.
{"title":"planetMagFields: A Python package for analyzing and\u0000plotting planetary magnetic field data","authors":"Ankit Barik, R. Angappan","doi":"10.21105/joss.06677","DOIUrl":"https://doi.org/10.21105/joss.06677","url":null,"abstract":"Long term observations and space missions have generated a wealth of data on the magnetic fields of the Earth and other solar system planets. planetMagfields is a Python package designed to have all the planetary magnetic field data currently available in one place and to provide an easy interface to access the data. planetMagfields focuses on planetary bodies that generate their own magnetic field, namely Mercury, Earth, Jupiter, Saturn, Uranus, Neptune and Ganymede. planetMagfields provides functions to compute as well as plot the magnetic field on the planetary surface or at a distance above or under the surface. It also provides functions to filter out the field to large or small scales as well as to produce .vts files to visualize the field in 3D using Paraview, VisIt or similar rendering software. Lastly, the planetMagfields repository also provides a Jupyter notebook for easy interactive visualizations.","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997239","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":"21cmSense v2: A modular, open-source 21 cm sensitivity\u0000calculator","authors":"Steven G. Murray, Jonathan Pober, M. Kolopanis","doi":"10.21105/joss.06501","DOIUrl":"https://doi.org/10.21105/joss.06501","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997016","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}
Abigail Snyder, K. Dorheim, Claudia Tebaldi, Chris R. Vernon
{"title":"STITCHES: a Python package to amalgamate existing Earth\u0000system model output into new scenario realizations","authors":"Abigail Snyder, K. Dorheim, Claudia Tebaldi, Chris R. Vernon","doi":"10.21105/joss.05525","DOIUrl":"https://doi.org/10.21105/joss.05525","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994267","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}
Interferometric observations sample the visibility space of the targeted source. Thus, procedures for image reconstruction are needed to obtain images from those observations. Several factors are involved in the detectability of physical features in a reconstructed image, such as the angular resolution of the observation, point spread function, noise level, and the assumptions of the reconstruction algorithm. The most robust way to test the observability of simulated emission features is by taking a simulated image and reconstructing it with the same algorithms. SIMIO-continuum takes simulated images of continuum emission in millimeter wavelengths and returns the synthetic observation and reconstructed images as if the input image had been observed in the sky with an existing interferometric observation. The documentation and tutorials give a detailed description of the code functionalities and syntax, with publicly available datasets for reproducibility.
{"title":"SIMIO-continuum: Connecting simulations to ALMA\u0000observations","authors":"N. Kurtovic","doi":"10.21105/joss.04942","DOIUrl":"https://doi.org/10.21105/joss.04942","url":null,"abstract":"Interferometric observations sample the visibility space of the targeted source. Thus, procedures for image reconstruction are needed to obtain images from those observations. Several factors are involved in the detectability of physical features in a reconstructed image, such as the angular resolution of the observation, point spread function, noise level, and the assumptions of the reconstruction algorithm. The most robust way to test the observability of simulated emission features is by taking a simulated image and reconstructing it with the same algorithms. SIMIO-continuum takes simulated images of continuum emission in millimeter wavelengths and returns the synthetic observation and reconstructed images as if the input image had been observed in the sky with an existing interferometric observation. The documentation and tutorials give a detailed description of the code functionalities and syntax, with publicly available datasets for reproducibility.","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140998259","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 James Webb Space Telescope produces some of the highest sensitivity imaging of the cosmos across all instruments. One of them, the NIRISS Fine Guidance Sensor, provides guide star imaging with a passband of 0.6 to 5 microns through two separate channels, each with a 2.3' x 2.3' field of view (FOV) and a sampling rate of 64 ms$-$data that is taken in parallel and is thus available for every JWST observing program. While the onboard system uses guide stars to provide information to the attitude control system (ACS), which stabilizes the observatory, the astronomical community can also use the data products associated with these 64 ms cadence images as science products. Usages range from studying guide star photometry in search of transient phenomena to using these data to identify and investigate technical anomalies that might occur during scientific observations with the rest of the JWST instruments. Despite this wide range of possible usages, these data products are not straightforward to manipulate and analyze, and there is no publicly available package to download, investigate, and research guide star data. Spelunker is a Python library that was developed to enable access to these guide star data products and their analysis.
{"title":"Spelunker: A quick-look Python pipeline for JWST NIRISS\u0000FGS Guide Star Data","authors":"Derod Deal, N'estor Espinoza","doi":"10.21105/joss.06202","DOIUrl":"https://doi.org/10.21105/joss.06202","url":null,"abstract":"The James Webb Space Telescope produces some of the highest sensitivity imaging of the cosmos across all instruments. One of them, the NIRISS Fine Guidance Sensor, provides guide star imaging with a passband of 0.6 to 5 microns through two separate channels, each with a 2.3' x 2.3' field of view (FOV) and a sampling rate of 64 ms$-$data that is taken in parallel and is thus available for every JWST observing program. While the onboard system uses guide stars to provide information to the attitude control system (ACS), which stabilizes the observatory, the astronomical community can also use the data products associated with these 64 ms cadence images as science products. Usages range from studying guide star photometry in search of transient phenomena to using these data to identify and investigate technical anomalies that might occur during scientific observations with the rest of the JWST instruments. Despite this wide range of possible usages, these data products are not straightforward to manipulate and analyze, and there is no publicly available package to download, investigate, and research guide star data. Spelunker is a Python library that was developed to enable access to these guide star data products and their analysis.","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141000027","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}
Caleb N. Ellington, Ben Lengerich, Wesley Lo, Aaron Alvarez, Andrea Rubbi, M. Kellis, Eric P. Xing
{"title":"Contextualized: Heterogeneous Modeling Toolbox","authors":"Caleb N. Ellington, Ben Lengerich, Wesley Lo, Aaron Alvarez, Andrea Rubbi, M. Kellis, Eric P. Xing","doi":"10.21105/joss.06469","DOIUrl":"https://doi.org/10.21105/joss.06469","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141000782","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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