Jupyter has a built-in templating system based on Pandoc (nbconvert) which is actually quite powerful. I have used it to generate print-ready documents via LaTeX templates.
For longer documents, one can use Pandoc JSON as an intermediary format, which can be assembled together before being output as LaTeX/PDF. For this one has to add a build tool like Make and not just the nbconvert command.
My biggest issue is that the nbconvert stuff is generally very hard to discover. It has very limited documentation which is further harmed by the considerable churn in the templating system, how they are discovered and how to plug them in to Jupyter lab. I suspect most people try "Export->PDF", see an ugly result with no obvious way to change it and give up. It's unfortunate because it's really a very powerful system under the hood.
Generating a paper or a technical note -- complete with beautiful typeset maths and figures -- from a computational document is really nice. Being able to take some data, generate a plot, calculate some values and put those values into prose such that the prose and the plot and the data are never out of sync is great! To me it's the next step up from having a referencing system so your references and bibliography are never out of sync.
Mainly just needs wiki style links and backlinks and a search function.
As for the source of that page, look at the Worg Maintenance section at https://orgmode.org/worg/
I haven't edited them yet, but I believe Worg is mostly org files on a Git repository - so you can clone it and see the source of all articles there.
LEARN theme applied to a HUGO blog
Or is this a more common pattern that is implemented multiple ways?
Is there a simple worklfow to convert Markdown documents to a Hugo or JupyterBook site. I want to build some simple step-wise guides or workshop in this format -- and want to spend most of my time on content and less on the publishing tools and pipeline. Any advise appreciated -Thanks!
> To turn off notebook execution, change the above configuration value to:
Not the cleanest one, but better than nothing.
It’s what I used with a nice weighty 5 gigabyte Tex installation.
Some parts of it are quite flexible, like using Mathematica stylesheets to separate style and content for particular mathematical forms and change them globally to find the most ergonomic thing, with Katex that matches the mathematica (though I have to manually maintain that correspondence).
We took a lot of inspiration from Jupyter Book (and use Jupyter kernels under the hood), so nothing but respect for all things Jupyter.
Email email@example.com if you want us to look over a draft (the same offer goes for anyone) - just please realize that we can't necessarily respond quickly; it depends on how brutal the inbox is that week/month.
"Derivative of sigmoid function"
I write a lot of Jupyter notebooks at my main job and I also lecture at a university thus Pathbird seems relevant.
You can demo the student experience with code JULIACON2020 as well.
It’s not the Jupyter notebook (or lab) frontend, it’s a custom webapp built for learning and specifically around exercise based, guided learning. So a student will go through exercises (mostly multiple choice and code-based “autograded” exercises) to check their understanding and guide them through a lesson.
This style of learning tends to work best with interactive languages (Julia and Python and R at present). Theoretically we could support other languages with Jupyter kernels (including Go and C++, etc) as well. I wonder how well those languages would work in this context considering it’s a bit hard to be “iterative” with those (but consider than a challenge rather than a limitation!).
Feel free to reach out with any questions/comments/concerns and I can answer in more long form!