hydrogen VS jupyter

Hydrogen got an update to fix this issue, but it was never published on Pulsar's backend. You can install it with pulsar -p https://github.com/nteract/hydrogen.git -t v2.16.5

Folks who know Pulsar and Zed internals: which one of them is more likely to gain support for Atom packages? I find Hydrogen[1] invaluable as a data science scratchpad since it supports python/r/julia/etc under a common interface via jupyter, and have been unable to construct a comparable workflow in vscode or any other editor [using scripts as opposed to notebooks, which i find far too bloated].

[1]: https://github.com/nteract/hydrogen

Finally, newer Pulsar versions (from the master branch on the CI) allow you to install ppm and pulsar from the command-line. We also fixed some of the issues on installing a package directly from github - for example, you can run "pulsar -p https://github.com/nteract/hydrogen.git -t v2.16.5" to install hydrogen on tag v2.16.5 now (tested on Linux and Silicon mac). Package publication is an ongoing process - we fixed lots of issues of the first version, and now it's working for some people, but we are still aware that we have some bugs too... it's hard because we need to "reverse engineer" the old API :(

But with Hydrogen you could do that in a regular Python script without needing to create a notebook or think in terms of cells. There's an example of this in the Hydrogen readme: https://github.com/nteract/hydrogen#hydrogen-

I personally found Atom + Hydrogen [0] to be the most productive interactive Python environment I've ever used. I really want to see VSCode adopt some way to run a Jupyter kernel for a Python file (with a notebook UI) and have rich results in line with the code (i.e. not a terminal output off to the right side of the screen).

[0] https://github.com/nteract/hydrogen

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