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  • Writer's pictureJack Orenstein

Marcel and Jupyter

Updated: Feb 10

For years, I kept hearing about iPython, as a vastly improved Python REPL. I played with it a little bit, and yes, I saw that. I found that Object Shell (a forerunner of marcel) ran in iPython with no problem. Fine, interesting, but in my day-to-day work, I really had no place for iPython. iPython looks great, but I didn't find any compelling use cases for it. I don't really need a better REPL.

The reason I wrote marcel (and Object Shell before it) was that I needed two things:

First, I needed a better shell. I was working on a distributed system, running on a cluster. I wanted to be able to submit the same command to all nodes of the cluster, and receive and combine and post-process the results. Often what I needed to do on each node was a database query. So Object Shell, and then marcel, had database access and cluster access. And the combining and post-processing of results gave rise to the idea of piping Python datatypes between operators.

Second, I thought that Python was deficient as a scripting language due to poor shell integration. Bash and Perl were better in this way, and I loathe both of those languages. So I wrote an api module for marcel (as I did for Object Shell), providing shell-like capabilities.

Recently, I decided to go back to school to learn more about machine learning and AI. I am learning a new (to me) batch of tools: numpy, sciki-learn, and Jupyter (formerly iPython).

I think that Jupyter notebooks are fantastic for publishing information in a way that encourages analysis and exploration. A published scientific paper cannot compare. Yes, the data and code can be made available, but it is much harder to get started playing with the ideas compared to what you have in a Jupyter notebook.

My first exposure to Jupyter was in a machine learning programming assignment. Now for development purposes, I don't think that Jupyter is good, at least for me. It doesn't fit my workflow. It's an inferior IDE (compared to Emacs or PyCharm); it is too easy to forget to rerun code in a modified cell; the formatting of cells and output is often quite bad; and if you need to escape to the shell, you're still shelling out from Python, which sucks.

So of course I had to try using marcel from Jupyter. That would fix the shelling out problem, at least. So I tried it, and it just works. My first cell imports the marcel API:

from marcel.api import *

A Hello World command:

who = 'world'
run(map (lambda: (f'Hello {who}')))


Hello world

Compute some factorials:

run(gen(10, 1) | 
    red(r_times, incremental=True) | 
    write(format='{}! = {}'))


1! = 1
2! = 2
3! = 6
4! = 24
5! = 120
6! = 720
7! = 5040
8! = 40320
9! = 362880
10! = 3628800

Now try more shell-like commands. First, define a pipeline that

filters by file extension:

ext = lambda e: select(lambda f: f.suffix[1:] == e)

This defines a pipeline which takes e, a file extension as a

parameter. Files are piped in, bound to the variable f, and then the

select operator passes on those files whose extension is equal to the given extension e.

Using this pipeline, we can search a directory recursively, looking

for .py files that changed in the last ten days:

for f in (ls('~/git/marcel/marcel', file=True, recursive=True) |
          ext('py') |
          select(lambda f: now() - f.mtime < days(10))):

Should you use marcel? Or Jupyter? Why not both?

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