On Sat, 6 Oct 2018 13:06:18 -0400 (EDT)
Aditya Mahajan <adit...@umich.edu> wrote:

> In my opinion, a better long-term option is to write a jupyter client in 
> lua that can be called by context. Then we can easily interface with all 
> languages that provide a jupyter kernel 
> (https://github.com/jupyter/jupyter/wiki/Jupyter-kernels).
> 
> The interface of a jupter-client is available here
> https://jupyter-client.readthedocs.io/en/stable/index.html. It seems 
> relatively straight forward (send a JSON message and receive a JSON 
> message). Translating the JSON messages to ConTeXt should also be easy. 
> Is there anyone who wants to play around trying to implement this?

jupyter runs python code. Have you ever tried doing any real heavy data 
analysis using jupyter? My experience is that it chokes on large data sets... 
So why write lua code to call a jupyter kernel running python? Would it not 
make more sense developing code directly in lua in this case?

The one thing that python (and jupyter) brings, or R for that matter, are 
libraries of calculation routines. These can be quite sophisticated, some 
efficient, and some not so efficient. My approach has always been to write my 
own routines or to adapt algorithms, at least then I know what the calculation 
is actually doing. Of course, this means that I spend time redoing what might 
have been done elsewhere, but the variety of routines that I actually use is 
rather small.

Our experimentation with lua and with MetaPost is that one can achieve HUGE 
differences in efficiencies through efficient programming, factors of 2-3 or 
even orders of magnitude. (Hans can usually succeed in speeding-up my lua 
code.) Sometimes surprising things can make huge differences (never surprising 
once one understands what is happening). One can hope that python and R (and 
other) developers are efficient-minded programmers, but this is not always the 
case.

Alan
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