Pynamical is an educational Python package for introducing the modeling, simulation, and visualization of discrete nonlinear dynamical systems and chaos, focusing on one-dimensional maps (such as the logistic map and the cubic map). Pynamical facilitates defining discrete one-dimensional nonlinear models as Python functions with just-in-time compilation for fast simulation. It comes packaged with the logistic map, the Singer map, and the cubic map predefined. The models may be run with a range of parameter values over a set of time steps, and the resulting numerical output is returned as a pandas DataFrame. Pynamical can then visualize this output in various ways, including with bifurcation diagrams, two-dimensional phase diagrams, three-dimensional phase diagrams, and cobweb plots. These visualizations enable simple qualitative assessments of system behavior including phase transitions, bifurcation points, attractors and limit cycles, basins of attraction, and fractals.
{"title":"Pynamical: Model and visualize discrete nonlinear dynamical systems, chaos, and fractals","authors":"G. Boeing","doi":"10.21105/JOSE.00015","DOIUrl":"https://doi.org/10.21105/JOSE.00015","url":null,"abstract":"Pynamical is an educational Python package for introducing the modeling, simulation, and visualization of discrete nonlinear dynamical systems and chaos, focusing on one-dimensional maps (such as the logistic map and the cubic map). Pynamical facilitates defining discrete one-dimensional nonlinear models as Python functions with just-in-time compilation for fast simulation. It comes packaged with the logistic map, the Singer map, and the cubic map predefined. The models may be run with a range of parameter values over a set of time steps, and the resulting numerical output is returned as a pandas DataFrame. Pynamical can then visualize this output in various ways, including with bifurcation diagrams, two-dimensional phase diagrams, three-dimensional phase diagrams, and cobweb plots. These visualizations enable simple qualitative assessments of system behavior including phase transitions, bifurcation points, attractors and limit cycles, basins of attraction, and fractals.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49365139","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}
Pub Date : 2018-01-01Epub Date: 2018-08-30DOI: 10.21105/jose.00013
Patrick D Schloss
{"title":"The Riffomonas Reproducible Research Tutorial Series.","authors":"Patrick D Schloss","doi":"10.21105/jose.00013","DOIUrl":"https://doi.org/10.21105/jose.00013","url":null,"abstract":"","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39307018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01Epub Date: 2018-10-02DOI: 10.21105/jose.00027
Evan Bolyen, Jai Ram Rideout, John Chase, T Anders Pitman, Arron Shiffer, Willow Mercurio, Matthew R Dillon, J Gregory Caporaso
Statement of need: Due to the increasing rate of biological data generation, bioinformatics is rapidly growing as a field and is now an essential part of scientific advances in human health and environmental sciences. Online and publicly accessible resources for learning bioinformatics exist (e.g., Rosalind, (Searls, 2012, 2014)), and there are excellent textbooks and courses in the area, some focused heavily on theory (Durbin, Eddy, Krogh, & Mitchison, 1998; Felsenstein, 2003), and others geared toward learning specific skills such as Python programming or the Unix shell (Dunn & Haddock, 2010; Wilson, 2016). An Introduction to Applied Bioinformatics (IAB) is a free, online bioinformatics text that bridges the gap between theory and application by teaching fundamentals of bioinformatics in the context of their implementation, using an interactive framework based on highly relevant tools including Python 3, Jupyter Notebooks, and GitHub. IAB is geared toward students who are completely new to bioinformatics, though having completed an introductory course (or book) in both Computer Science and Biology are useful prerequisites. IAB readers begin on the project website. While it is possible to view the content statically from this page, we recommend that readers work interactively by installing IAB. Readers progress through chapters that introduce fundamental topics, such as sequence homology searching and multiple sequence alignment, and presents their Python 3 implementation. Because the content is presented in Jupyter Notebooks, students can edit and execute the code, for example to explore how changing k-word size or an alignment gap penalty might impact the results of a database search. The Python code that readers interact with is intended for educational purposes, where the implementation is made as simple as possible, sometimes at the cost of computational efficiency. Chapters therefore also include examples of performing the same analyses with scikit-bio, a production-quality bioinformatics Python 3 library. This enables a rapid transition from learning theory, or how an algorithm works, to applying techniques in a real-world setting. IAB additionally contains Wikipedia-style “Edit” links in each section of the text. When one of these links is followed, the reader is taken to the GitHub online editor where they can submit a pull request to modify content or code. Readers are therefore introduced to GitHub through a user-friendly web interface, and can begin building their GitHub activity history (commonly reviewed by bioinformatics hiring managers). Finally, every time a change is proposed via GitHub, all of the executable content of IAB is automatically tested. This continuous integration testing ensures that IAB example code remains functional as changes are introduced, solving an issue that plagues printed applied computational texts (for example because they describe an outdated software interface). IAB evolved from lecture mat
{"title":"An Introduction to Applied Bioinformatics: a free, open, and interactive text.","authors":"Evan Bolyen, Jai Ram Rideout, John Chase, T Anders Pitman, Arron Shiffer, Willow Mercurio, Matthew R Dillon, J Gregory Caporaso","doi":"10.21105/jose.00027","DOIUrl":"https://doi.org/10.21105/jose.00027","url":null,"abstract":"Statement of need: \u0000Due to the increasing rate of biological data generation, bioinformatics is rapidly growing as a field and is now an essential part of scientific advances in human health and environmental sciences. Online and publicly accessible resources for learning bioinformatics exist (e.g., Rosalind, (Searls, 2012, 2014)), and there are excellent textbooks and courses in the area, some focused heavily on theory (Durbin, Eddy, Krogh, & Mitchison, 1998; Felsenstein, 2003), and others geared toward learning specific skills such as Python programming or the Unix shell (Dunn & Haddock, 2010; Wilson, 2016). An Introduction to Applied Bioinformatics (IAB) is a free, online bioinformatics text that bridges the gap between theory and application by teaching fundamentals of bioinformatics in the context of their implementation, using an interactive framework based on highly relevant tools including Python 3, Jupyter Notebooks, and GitHub. \u0000 \u0000IAB is geared toward students who are completely new to bioinformatics, though having completed an introductory course (or book) in both Computer Science and Biology are useful prerequisites. IAB readers begin on the project website. While it is possible to view the content statically from this page, we recommend that readers work interactively by installing IAB. Readers progress through chapters that introduce fundamental topics, such as sequence homology searching and multiple sequence alignment, and presents their Python 3 implementation. Because the content is presented in Jupyter Notebooks, students can edit and execute the code, for example to explore how changing k-word size or an alignment gap penalty might impact the results of a database search. The Python code that readers interact with is intended for educational purposes, where the implementation is made as simple as possible, sometimes at the cost of computational efficiency. Chapters therefore also include examples of performing the same analyses with scikit-bio, a production-quality bioinformatics Python 3 library. This enables a rapid transition from learning theory, or how an algorithm works, to applying techniques in a real-world setting. \u0000 \u0000IAB additionally contains Wikipedia-style “Edit” links in each section of the text. When one of these links is followed, the reader is taken to the GitHub online editor where they can submit a pull request to modify content or code. Readers are therefore introduced to GitHub through a user-friendly web interface, and can begin building their GitHub activity history (commonly reviewed by bioinformatics hiring managers). Finally, every time a change is proposed via GitHub, all of the executable content of IAB is automatically tested. This continuous integration testing ensures that IAB example code remains functional as changes are introduced, solving an issue that plagues printed applied computational texts (for example because they describe an outdated software interface). \u0000 \u0000IAB evolved from lecture mat","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6343836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36903218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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