StateMint is a set of software tools that reduce sets of dynamic equations and their constraints to a state-space model and related dynamic system model formulations. These tools are especially useful for students of system dynamics, many of whom can become lost in this algebraic reduction. StateMint includes a Mathematica package, a Python package, and a web interface that is built as a layer on top of the Python package.
{"title":"StateMint: A Set of Tools for Determining Symbolic Dynamic System Models Using Linear Graph Methods","authors":"Cameron Devine, J. Garbini, Rico A. R. Picone","doi":"10.21105/JOSE.00044","DOIUrl":"https://doi.org/10.21105/JOSE.00044","url":null,"abstract":"StateMint is a set of software tools that reduce sets of dynamic equations and their constraints to a state-space model and related dynamic system model formulations. These tools are especially useful for students of system dynamics, many of whom can become lost in this algebraic reduction. StateMint includes a Mathematica package, a Python package, and a web interface that is built as a layer on top of the Python package.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41422194","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}
Python is rapidly emerging as the programming language of choice for data analysis in the atmosphere and ocean sciences. By consulting online tutorials and help pages, most researchers in this community are able to pick up the basic syntax and programming constructs (e.g., loops, lists and conditionals). This self-taught knowledge is sufficient to get work done, but it often involves spending hours to do things that should take minutes, reinventing a lot of wheels, and a nagging uncertainty at the end of it all regarding the reliability and reproducibility of the results. To help address these issues, the Python for Atmosphere and Ocean Scientists educational materials cover a suite of programming and data management best practices that are not so easy to glean from a quick Google search.
Python正迅速成为大气和海洋科学中数据分析的首选编程语言。通过查阅在线教程和帮助页面，该社区的大多数研究人员都能够掌握基本的语法和编程结构（例如循环、列表和条件）。这些自学成才的知识足以完成工作，但它通常需要花费数小时来做一些需要几分钟时间的事情，重新发明很多轮子，以及最后关于结果的可靠性和可重复性的令人不安的不确定性。为了帮助解决这些问题，Python for Atmosphere and Ocean Scientists的教材涵盖了一套编程和数据管理的最佳实践，这些实践不太容易从谷歌快速搜索中收集到。
{"title":"Python for Atmosphere and Ocean Scientists","authors":"Damien B. Irving","doi":"10.21105/JOSE.00037","DOIUrl":"https://doi.org/10.21105/JOSE.00037","url":null,"abstract":"Python is rapidly emerging as the programming language of choice for data analysis in the atmosphere and ocean sciences. By consulting online tutorials and help pages, most researchers in this community are able to pick up the basic syntax and programming constructs (e.g., loops, lists and conditionals). This self-taught knowledge is sufficient to get work done, but it often involves spending hours to do things that should take minutes, reinventing a lot of wheels, and a nagging uncertainty at the end of it all regarding the reliability and reproducibility of the results. To help address these issues, the Python for Atmosphere and Ocean Scientists educational materials cover a suite of programming and data management best practices that are not so easy to glean from a quick Google search.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47331006","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}
treesiftr is a Shiny (Chang, Cheng, Allaire, Xie, & McPherson, 2018) application for visualizing the relationship between phylogenetic trees and the underlying data used to estimate them. It can also be used in RStudio (RStudio Team, 2015) or at the command line as an R package (R Core Team, 2013). treesiftr works by subsetting a phylogenetic matrix according to user-provided input about which characters to visualize. A maximum parsimony tree is then estimated from each data subset. Maximum parsimony was chosen for speed and analytical simplicity. Under the parsimony optimality criterion, the preferred tree is the one that suggests the fewest evolutionary steps, or character changes over evolutionary history. The tree is scored under both parsimony and Lewis’ Mk model (Lewis, 2001), a maximum likelihood model for estimating phylogeny from discrete character data. The data and tree are then visualized using ggtree (Yu, Smith, Zhu, Guan, & Lam, 2017), based upon the ggplot2 (Wickham, 2016) package. Expected outputs are the same whether the learner is interacting via the GUI or the RStudio interface; however, the RStudio interface does have additional options not available in the GUI.
{"title":"treesiftr: An R package and server for viewing phylogenetic trees and data","authors":"A. Wright","doi":"10.21105/JOSE.00035","DOIUrl":"https://doi.org/10.21105/JOSE.00035","url":null,"abstract":"treesiftr is a Shiny (Chang, Cheng, Allaire, Xie, & McPherson, 2018) application for visualizing the relationship between phylogenetic trees and the underlying data used to estimate them. It can also be used in RStudio (RStudio Team, 2015) or at the command line as an R package (R Core Team, 2013). treesiftr works by subsetting a phylogenetic matrix according to user-provided input about which characters to visualize. A maximum parsimony tree is then estimated from each data subset. Maximum parsimony was chosen for speed and analytical simplicity. Under the parsimony optimality criterion, the preferred tree is the one that suggests the fewest evolutionary steps, or character changes over evolutionary history. The tree is scored under both parsimony and Lewis’ Mk model (Lewis, 2001), a maximum likelihood model for estimating phylogeny from discrete character data. The data and tree are then visualized using ggtree (Yu, Smith, Zhu, Guan, & Lam, 2017), based upon the ggplot2 (Wickham, 2016) package. Expected outputs are the same whether the learner is interacting via the GUI or the RStudio interface; however, the RStudio interface does have additional options not available in the GUI.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46575954","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":"Org-Coursepack: A Modular and Reusable Teaching Materials Template in Org-mode","authors":"Joon H. Ro, Jae-Eun Namkoong","doi":"10.21105/JOSE.00034","DOIUrl":"https://doi.org/10.21105/JOSE.00034","url":null,"abstract":"","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45126989","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}
Douglas S. Blank, Jupyter, David D. Bourgin, Alexander Brown, M. Bussonnier, J. Frederic, B. Granger, T. Griffiths, Jessica B. Hamrick, Kyle Kelley, M. Pacer, Logan Page, Fernando Pérez, B. Ragan-Kelley, Jordan W. Suchow, Carol Willing
nbgrader is a flexible tool for creating and grading assignments in the Jupyter Notebook (Kluyver et al., 2016). nbgrader allows instructors to create a single, master copy of an assignment, including tests and canonical solutions. From the master copy, a student version is generated without the solutions, thus obviating the need to maintain two separate versions. nbgrader also automatically grades submitted assignments by executing the notebooks and storing the results of the tests in a database. After auto-grading, instructors can manually grade free responses and provide partial credit using the formgrader Jupyter Notebook extension. Finally, instructors can use nbgrader to leave personalized feedback for each student’s submission, including comments as well as detailed error information.
{"title":"nbgrader: A Tool for Creating and Grading Assignments in the Jupyter Notebook","authors":"Douglas S. Blank, Jupyter, David D. Bourgin, Alexander Brown, M. Bussonnier, J. Frederic, B. Granger, T. Griffiths, Jessica B. Hamrick, Kyle Kelley, M. Pacer, Logan Page, Fernando Pérez, B. Ragan-Kelley, Jordan W. Suchow, Carol Willing","doi":"10.21105/JOSE.00032","DOIUrl":"https://doi.org/10.21105/JOSE.00032","url":null,"abstract":"nbgrader is a flexible tool for creating and grading assignments in the Jupyter Notebook (Kluyver et al., 2016). nbgrader allows instructors to create a single, master copy of an assignment, including tests and canonical solutions. From the master copy, a student version is generated without the solutions, thus obviating the need to maintain two separate versions. nbgrader also automatically grades submitted assignments by executing the notebooks and storing the results of the tests in a database. After auto-grading, instructors can manually grade free responses and provide partial credit using the formgrader Jupyter Notebook extension. Finally, instructors can use nbgrader to leave personalized feedback for each student’s submission, including comments as well as detailed error information.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42590827","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}
• Steps 1–4 are in one dimension: (i) linear convection with a step-function initial condition (IC) and appropriate boundary conditions (BC); with the same IC/BCs: (ii) nonlinear convection, and (iii) diffusion only; (iv) Burgers’ equation, with a saw-tooth IC and periodic BCs. • Steps 5–10 are in two dimensions: (v) linear convection with a square function IC and appropriate BCs; with the same IC/BCs: (vi) nonlinear convection, and (vii) diffusion only; (viii) Burgers’ equation; (ix) Laplace equation, with zero IC and both Neumann and Dirichlet BCs; (x) Poisson equation in 2D. • Steps 11–12 solve the Navier–Stokes equation in 2D: (xi) cavity flow; (xii) channel flow.
{"title":"CFD Python: the 12 steps to Navier-Stokes equations","authors":"L. Barba, G. Forsyth","doi":"10.21105/JOSE.00021","DOIUrl":"https://doi.org/10.21105/JOSE.00021","url":null,"abstract":"• Steps 1–4 are in one dimension: (i) linear convection with a step-function initial condition (IC) and appropriate boundary conditions (BC); with the same IC/BCs: (ii) nonlinear convection, and (iii) diffusion only; (iv) Burgers’ equation, with a saw-tooth IC and periodic BCs. • Steps 5–10 are in two dimensions: (v) linear convection with a square function IC and appropriate BCs; with the same IC/BCs: (vi) nonlinear convection, and (vii) diffusion only; (viii) Burgers’ equation; (ix) Laplace equation, with zero IC and both Neumann and Dirichlet BCs; (x) Poisson equation in 2D. • Steps 11–12 solve the Navier–Stokes equation in 2D: (xi) cavity flow; (xii) channel flow.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48295234","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}
ThermoState is a Python package that provides easy management of thermodynamic states of simple compressible systems. ThermoState relies on CoolProp (Bell, Wronski, Quoilin, & Lemort, 2014, 2016) and Pint (Grecco & others, 2017) to provide the equations of state and units handling, respectively. ThermoState replaces tables that are typically used in engineering courses to evaluate properties when solving for the behavior of systems.
{"title":"ThermoState: A state manager for thermodynamics courses","authors":"B. Weber","doi":"10.21105/JOSE.00033","DOIUrl":"https://doi.org/10.21105/JOSE.00033","url":null,"abstract":"ThermoState is a Python package that provides easy management of thermodynamic states of simple compressible systems. ThermoState relies on CoolProp (Bell, Wronski, Quoilin, & Lemort, 2014, 2016) and Pint (Grecco & others, 2017) to provide the equations of state and units handling, respectively. ThermoState replaces tables that are typically used in engineering courses to evaluate properties when solving for the behavior of systems.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48263353","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}
Mikrokosmos is an educational untyped and simply typed lambda-calculus interpreter. For students, it is a tool to learn lambda-calculus and intuitionistic logic by coding. For educators, it is a didactic resource, grounded in the theoretical implementation of a functional programming language, so that they can integrate it with other learning materials. Mikrokosmos can be used on three different environments: (1) as a command line executable, implementing a read-eval-print loop interpreter; (2) as a Jupyter kernel, executing code blocks from a Jupyter notebook (Jupyter Development Team, 2016); and (3) as a Javascript web application, that can be used in conjunction with web text editors such as Codemirror1 to create an online programming environment.
{"title":"Mikrokosmos: an educational lambda calculus interpreter","authors":"Mario Román","doi":"10.21105/JOSE.00029","DOIUrl":"https://doi.org/10.21105/JOSE.00029","url":null,"abstract":"Mikrokosmos is an educational untyped and simply typed lambda-calculus interpreter. For students, it is a tool to learn lambda-calculus and intuitionistic logic by coding. For educators, it is a didactic resource, grounded in the theoretical implementation of a functional programming language, so that they can integrate it with other learning materials. Mikrokosmos can be used on three different environments: (1) as a command line executable, implementing a read-eval-print loop interpreter; (2) as a Jupyter kernel, executing code blocks from a Jupyter notebook (Jupyter Development Team, 2016); and (3) as a Javascript web application, that can be used in conjunction with web text editors such as Codemirror1 to create an online programming environment.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43185736","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}
Andrew R. McCluskey, Benjamin J. Morgan, Karen J. Edler, Stephen C. Parker
{"title":"pylj: A teaching tool for classical atomistic simulation","authors":"Andrew R. McCluskey, Benjamin J. Morgan, Karen J. Edler, Stephen C. Parker","doi":"10.21105/jose.00019","DOIUrl":"https://doi.org/10.21105/jose.00019","url":null,"abstract":"","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67736229","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}
Fitting models and testing the match of the models to the measured data is a fundamental activity in many fields of science. This short (approximately 3-hour) course (available at: https://github.com/arokem/scipy-optimize) aims to teach participants to use the Scipy library’s optimize module to fit models to data (Jones et al. 2001). Using data from a psychology experiment (Rokem and Landau 2016) as an example, the course motivates the use of explicit mathematical models to explain and predict data and compares linear models and non-linear models. The core of the lesson focuses on fitting a curve with the curve_fit function. The course also introduces the idea of model comparison with cross-validation for evaluation and selection between non-nested non-linear models.
{"title":"A short course about fitting models with the scipy.optimize module","authors":"A. Rokem","doi":"10.21105/JOSE.00016","DOIUrl":"https://doi.org/10.21105/JOSE.00016","url":null,"abstract":"Fitting models and testing the match of the models to the measured data is a fundamental activity in many fields of science. This short (approximately 3-hour) course (available at: https://github.com/arokem/scipy-optimize) aims to teach participants to use the Scipy library’s optimize module to fit models to data (Jones et al. 2001). Using data from a psychology experiment (Rokem and Landau 2016) as an example, the course motivates the use of explicit mathematical models to explain and predict data and compares linear models and non-linear models. The core of the lesson focuses on fitting a curve with the curve_fit function. The course also introduces the idea of model comparison with cross-validation for evaluation and selection between non-nested non-linear models.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48441944","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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