aion is designed to provide a consistent framework for representing archaeological time series that can extend very far in the past. aion provides a system of classes and methods to represent and work with such time series. This package does not provide tools for temporal analysis or modeling. Instead, it offers a system of classes and methods to represent and work with archaeological time series. This API can be extended and used by other specialized packages (see kairos v2.0 as an example)
{"title":"aion: An R Package to Represent Archaeological Time\u0000Series","authors":"Nicolas Frerebeau","doi":"10.21105/joss.06210","DOIUrl":"https://doi.org/10.21105/joss.06210","url":null,"abstract":"aion is designed to provide a consistent framework for representing archaeological time series that can extend very far in the past. aion provides a system of classes and methods to represent and work with such time series. This package does not provide tools for temporal analysis or modeling. Instead, it offers a system of classes and methods to represent and work with archaeological time series. This API can be extended and used by other specialized packages (see kairos v2.0 as an example)","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140715016","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}
Shuang Song, Shuai Wang, Chentai Jiao, Elías José Mantilla Ibarra
ABSESpy is a novel agent-based modeling (ABM) framework that facilitates socio-ecological systems (SES) research. It serves as an extension package of Mesa , the most popular ABM framework
{"title":"ABSESpy: An agent-based modeling framework for\u0000social-ecological systems","authors":"Shuang Song, Shuai Wang, Chentai Jiao, Elías José Mantilla Ibarra","doi":"10.21105/joss.06298","DOIUrl":"https://doi.org/10.21105/joss.06298","url":null,"abstract":"ABSESpy is a novel agent-based modeling (ABM) framework that facilitates socio-ecological systems (SES) research. It serves as an extension package of Mesa , the most popular ABM framework","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140716988","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}
Joanna Piper Morgan, Ilham Variansyah, Sam Pasmann, Kayla B. Clements, Braxton Cuneo, Alexander Mote, Charles Goodman, Caleb Shaw, Jordan Northrop, Rohan Pankaj, Ethan Lame, Benjamin Whewell, Ryan G. McClarren, Todd S. Palmer, Lizhong Chen, Dmitriy Anistratov, C. T. Kelley, Camille J. Palmer, Kyle E. Niemeyer
pseudo-random
伪随机
{"title":"Monte Carlo / Dynamic Code (MC/DC): An accelerated\u0000Python package for fully transient neutron transport and rapid methods\u0000development","authors":"Joanna Piper Morgan, Ilham Variansyah, Sam Pasmann, Kayla B. Clements, Braxton Cuneo, Alexander Mote, Charles Goodman, Caleb Shaw, Jordan Northrop, Rohan Pankaj, Ethan Lame, Benjamin Whewell, Ryan G. McClarren, Todd S. Palmer, Lizhong Chen, Dmitriy Anistratov, C. T. Kelley, Camille J. Palmer, Kyle E. Niemeyer","doi":"10.21105/joss.06415","DOIUrl":"https://doi.org/10.21105/joss.06415","url":null,"abstract":"pseudo-random","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140722598","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}
Gamma-ray burst (GRBs) are the brightest events in the universe. For decades, astrophysicists have known about their cosmological nature. Every year, space missions such as Fermi and SWIFT detect hundreds of them. In spite of this large sample, GRBs show a complex taxonomy in the first seconds after their appearance, which makes it very difficult to find similarities between them using conventional techniques. It is known that GRBs originate from the death of a massive star or from the merger of two compact objects. GRB classification is typically based on the duration of the burst (Kouveliotou et al., 1993). Nevertheless, events such as GRB 211211A (Yang et al., 2022), whose duration of about 50 seconds lies in the group of long GRBs, has challenged this categorization by the evidence of features related with the short GRB population (the kilonova emission and the properties of its host galaxy). Therefore, a classification based only on their gamma-ray duration does not provide a completely reliable determination of the progenitor. Motivated by this problem, Jespersen et al. (2020) and Steinhardt et al. (2023) carried out analysis of GRB light curves by using the t-SNE algorithm, showing that Swift/BAT GRBs database, consisting of light curves in four energy bands (15-25 keV, 25-50 keV, 50-100 keV, 100-350 keV), clusters into two groups corresponding with the typical long/short classification. However, in this case, this classification is based on the information provided by their gamma-ray emission light curves. ClassiPyGRB is a Python 3 package to download, process, visualize and classify GRBs database from the Swift/BAT Instrument (up to July 2022). It is distributed over the GNU General Public License Version 2 (1991). We also included a noise-reduction and an interpolation tools for achieving a deeper analysis of the data.
伽马射线暴（GRB）是宇宙中最亮的事件。几十年来，天体物理学家已经知道它们的宇宙学性质。每年，费米和 SWIFT 等太空任务都会探测到数百个伽马射线暴。尽管样本如此之多，但在出现后的最初几秒钟内，GRB 呈现出复杂的分类，这使得使用传统技术找到它们之间的相似性变得非常困难。众所周知，GRB 起源于一颗大质量恒星的死亡或两个紧凑天体的合并。对 GRB 的分类通常是基于爆发的持续时间（Kouveliotou 等人，1993 年）。然而，GRB 211211A（Yang 等人，2022 年）等事件（其持续时间约为 50 秒，属于长 GRB）通过证明与短 GRB 群体有关的特征（千新星发射及其宿主星系的特性），对这种分类方法提出了挑战。因此，仅仅根据伽马射线的持续时间进行分类并不能完全可靠地确定原生体。受这一问题的启发，Jespersen 等人（2020 年）和 Steinhardt 等人（2023 年）利用 t-SNE 算法对 GRB 光变曲线进行了分析，结果表明 Swift/BAT GRB 数据库由四个能段（15-25 千伏、25-50 千伏、50-100 千伏、100-350 千伏）的光变曲线组成，按照典型的长/短分类法可将其分为两组。不过，在这种情况下，这种分类是基于它们的伽马射线发射光曲线所提供的信息。ClassiPyGRB是一个Python 3软件包，用于下载、处理、可视化和分类来自Swift/BAT仪器（截至2022年7月）的伽马射线暴数据库。它采用 GNU 通用公共许可证第 2 版（1991 年）发布。我们还加入了降噪和插值工具，以便对数据进行更深入的分析。
{"title":"ClassiPyGRB: Machine Learning-Based Classification and\u0000Visualization of Gamma Ray Bursts using t-SNE","authors":"K. Garcia-Cifuentes, R. L. Becerra, F. Colle","doi":"10.21105/joss.05923","DOIUrl":"https://doi.org/10.21105/joss.05923","url":null,"abstract":"Gamma-ray burst (GRBs) are the brightest events in the universe. For decades, astrophysicists have known about their cosmological nature. Every year, space missions such as Fermi and SWIFT detect hundreds of them. In spite of this large sample, GRBs show a complex taxonomy in the first seconds after their appearance, which makes it very difficult to find similarities between them using conventional techniques. It is known that GRBs originate from the death of a massive star or from the merger of two compact objects. GRB classification is typically based on the duration of the burst (Kouveliotou et al., 1993). Nevertheless, events such as GRB 211211A (Yang et al., 2022), whose duration of about 50 seconds lies in the group of long GRBs, has challenged this categorization by the evidence of features related with the short GRB population (the kilonova emission and the properties of its host galaxy). Therefore, a classification based only on their gamma-ray duration does not provide a completely reliable determination of the progenitor. Motivated by this problem, Jespersen et al. (2020) and Steinhardt et al. (2023) carried out analysis of GRB light curves by using the t-SNE algorithm, showing that Swift/BAT GRBs database, consisting of light curves in four energy bands (15-25 keV, 25-50 keV, 50-100 keV, 100-350 keV), clusters into two groups corresponding with the typical long/short classification. However, in this case, this classification is based on the information provided by their gamma-ray emission light curves. ClassiPyGRB is a Python 3 package to download, process, visualize and classify GRBs database from the Swift/BAT Instrument (up to July 2022). It is distributed over the GNU General Public License Version 2 (1991). We also included a noise-reduction and an interpolation tools for achieving a deeper analysis of the data.","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729499","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":"seesus: a social, environmental, and economic\u0000sustainability classifier for Python","authors":"Meng Cai, Yingjie Li, Dirk Colbry, Veronica F. Frans, Yuqian Zhang","doi":"10.21105/joss.06244","DOIUrl":"https://doi.org/10.21105/joss.06244","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729613","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":"HiddenMarkovModels.jl: generic, fast and reliable state\u0000space modeling","authors":"Guillaume Dalle","doi":"10.21105/joss.06436","DOIUrl":"https://doi.org/10.21105/joss.06436","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740462","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}
Gabriele Ottino, Daniele Panico, D. Tomatis, Pierre-Alexandre Pantel
{"title":"lbh15: a Python package for standard use and\u0000implementation of physical data of heavy liquid metals used in nuclear\u0000reactors","authors":"Gabriele Ottino, Daniele Panico, D. Tomatis, Pierre-Alexandre Pantel","doi":"10.21105/joss.06383","DOIUrl":"https://doi.org/10.21105/joss.06383","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738458","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}
Pierre J. Walker, Andrés Riedemann, Zhen-Gang Wang
{"title":"GCIdentifier.jl: A Julia package for identifying\u0000molecular fragments from SMILES","authors":"Pierre J. Walker, Andrés Riedemann, Zhen-Gang Wang","doi":"10.21105/joss.06453","DOIUrl":"https://doi.org/10.21105/joss.06453","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740712","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 R package goFlux has been developed for calculating greenhouse gas (GHG) flux estimates from static chamber measurements. Compared to previous software developed for the same purpose, the goFlux package is not limited to the linear regression approach (LM), but also estimates GHG fluxes from a non-linear regression approach, i.e., the Hutchinson and Mosier model (HM). An automatic selection procedure has been implemented in the package to help users select the best flux estimate (LM or HM) based on objective criteria. In addition, this package can be used to import raw data directly downloaded from a broad selection of instruments (LI-COR, LGR, GAIA2TECH, Gasmet, Picarro, Aeris and PP-Systems). The package is divided into five steps: 1. import raw data into R; 2. define the start and end points of each measurement and assign a UniqueID; 3. calculate GHG flux estimates (LM and HM); 4. automatically select the best flux estimate (LM or HM) based on our default choices of objective criteria; 5. visually inspect the results on plots that can be saved as pdf. For a detailed protocol on how to use this package, visit the webpage https://qepanna.quarto.pub/
{"title":"goFlux: A user-friendly way to calculate GHG fluxes\u0000yourself, regardless of user experience","authors":"Karelle Rheault, J. Christiansen, K. Larsen","doi":"10.21105/joss.06393","DOIUrl":"https://doi.org/10.21105/joss.06393","url":null,"abstract":"The R package goFlux has been developed for calculating greenhouse gas (GHG) flux estimates from static chamber measurements. Compared to previous software developed for the same purpose, the goFlux package is not limited to the linear regression approach (LM), but also estimates GHG fluxes from a non-linear regression approach, i.e., the Hutchinson and Mosier model (HM). An automatic selection procedure has been implemented in the package to help users select the best flux estimate (LM or HM) based on objective criteria. In addition, this package can be used to import raw data directly downloaded from a broad selection of instruments (LI-COR, LGR, GAIA2TECH, Gasmet, Picarro, Aeris and PP-Systems). The package is divided into five steps: 1. import raw data into R; 2. define the start and end points of each measurement and assign a UniqueID; 3. calculate GHG flux estimates (LM and HM); 4. automatically select the best flux estimate (LM or HM) based on our default choices of objective criteria; 5. visually inspect the results on plots that can be saved as pdf. For a detailed protocol on how to use this package, visit the webpage https://qepanna.quarto.pub/","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140742401","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}
Luc van Vught, Corné Haasjes, Jan-Willem M. Beenakker
{"title":"ZOSPy: optical ray tracing in Python through\u0000OpticStudio","authors":"Luc van Vught, Corné Haasjes, Jan-Willem M. Beenakker","doi":"10.21105/joss.05756","DOIUrl":"https://doi.org/10.21105/joss.05756","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140743747","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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