Whilst this is great for one off pages, ThebeLab is not built into simple publishing workflow.
#Interactive jupyter notebook online code#
A preferable approach is to run interactives within the HTML textbook environment one way of doing this is to use something like the early demonstrated ThebeLab (see also Juniper, which also lets users “edit and execute code snippets in the browser using Jupyter kernels” and the voila “interactive renderer for Jupyter notebooks”). One of the problems with separating the static HTML site from the runnable notebooks is that the user is forced to move from one environment – the static HTML site – to another – the Binderhub environment – in order to run any interactive elements. It looks like there was an early attempt at this here: Jupyter WebBook. There’s also a handy deployment guide.Īnother possible approach would be to use a gitbook workflow. A template repo from which you can bootstrap your own textbook is available here: choldgraf/textbooks-with-jupyter. It’d be nice to see this in the form of a minimal repo that can be downloaded and used to bootstrap a new book published to the same workflow. Another nice feature is that the generated book can link to Binder run versions of the source notebooks too.įrom the textbook repo, it looks as if the recipe could quite easily be separated from the actual book.
Image paths required by Jekyll are set automatically. Another script ( generate_summary_from_folders.py) can be used to create a summary.md page which scaffolds the notebook/chapter order if one is not provided. A switch allows notebooks to be executed – or not – before generating the markdown. This uses Jupyter notebooks for the chapter source documents, and Jekyll to build the book from markdown files generated from the notebooks a python script / command line utility generates the markdown files from theoriginal Jupyter notebooks: generate_textbook.py. Ish via Simon Willison (again?!), I note a recipe used to publish the Computational and Inferential Thinking: The Foundations of Data Science online textbook for the Berkeley course Data 8: The Foundations of Data Science. One of the things on my to do list is try out a workflow for publishing something like a web-book along the lines of Gitbook from Jupyter notebook source documents.
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