jupytext VS jupyter

Jupytext (https://github.com/mwouts/jupytext) has been designed exactly for this

jupytext is your friend.

So if you run your own jupyter server, -jupy+text can be a great workflow : it takes your notebook synchronized with other formats (python file, makdown, ...), so you can edit your py/md file with neovim, and refresh the browser to execute the notebook.

Automatically convert ipynb files to py when saving them on JupyterLab

I don't use Jupyter so I don't know for sure, but on a quick glance you might want to look at https://github.com/mwouts/jupytext to see if that could help at all.

The format is only partially invented, it follows Jupytext [0], but adds support for cell metadata. There is no obvious way to get that in fenced codeblocks, especially with the ability to spread it over multiple lines so it plays well with version control.

One more consideration is that it's not "Markdown with code blocks interspersed", one might as well use plaintext or AsciiDoc.

Of course there are tradeoffs.. I wish I had more time to work on it.

[0]: https://github.com/gzuidhof/starboard-notebook/blob/master/d...

[1]: https://github.com/mwouts/jupytext

Using jupytext (allows you to open .md files as notebooks) + jupyter gives you pretty much the same experience. The main issue is that the cell's output will be discarded. To fix it, you can use ploomber to generate an output HTML, so the workflow goes like this:

First, the format. The ipynb format does not play nicely with git since it stores the cell's source code and output in the same file. But Jupyter has built-in mechanisms to allow other formats to look like notebooks. For example, here's a library that allows you to store notebooks on a postgres database (I know this isn't practical, but it's a great example). To give more practical advice, jupytext allows you to open .py files as notebooks. So you can develop interactively but in the backend, you're storing .py files.

For ipython, you'd better use some more specialized package like https://github.com/emacs-jupyter/jupyter, not the generic python support.

I have seen this thread, but I don't want to have to put an extra source block to set the renderers in every org file where I use plotly. Does anyone have a good solution for the moment? Any help is appreciated.

Should no one take you up on the bounty, I suggest trying emacs-jupyter instead. I've had better luck with it in the past.

For anybody following along with the examples, a few points/tips that might help newcomers:

1. (By default) before you can use Python source blocks, you need to have the Org Babel Python functionality loaded which is most easily done by evaluating the elisp (require 'ob-babel), but there are other ways also [1].

2. The first example, which uses the print function, will not output anything because the Python blocks by default are evaluated inside a function body and the return value is returned to Org [2]. To return the printed output instead, you need the header argument ":results output". There is an example of this syntax later in TFA.

3. If you are serious about replacing (or complementing) other Jupyter tools with Org mode, you might want to eventually look at emacs-jupyter [3], which provides a more advanced handling of outputs and also supports other (i.e. non-Python) kernels.

Also, I don't think I've ever seen anything like the debugging example and when I tried to replicate it out of curiosity, the block simply failed with a bdb.BdbQuit exception. Am I missing something? What is supposed to happen?

[1] https://orgmode.org/manual/Languages.html

[2] https://orgmode.org/worg/org-contrib/babel/languages/ob-doc-...

[3] https://github.com/nnicandro/emacs-jupyter

Emacs, Sublime Text 3 and Atom Pulsar can all do this with arbitrary Jupyter kernels with the emacs-jupyter/code-cells, helium and hydrogen packages, respectively.

You can use emacs as a jupyter client if that would help in your case https://github.com/nnicandro/emacs-jupyter

I've been using code-cells together with emacs-jupyter, the combination of the two lets you work pretty much identically as you would in Atom with Hydrogen, Sublime with Helium, or VSCode with the Jupyter Python extension; you just delimit code cells with #%% and execute in a separate Jupyter REPL buffer. It does require some getting used to the key bindings though (or some tweaking to make it more similar to what you're used to).

For writing source blocks: https://github.com/nnicandro/emacs-jupyter