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in reply to Re: Putting Perl Back on Top in the Fields of Scientific and Financial Computing
in thread Putting Perl Back on Top in the Fields of Scientific and Financial Computing

Might I suggest the best of both worlds given your situation. Use the Python as "pseudocode" to write the algorithms in Perl. You keep your expertise in the language domain while benefiting from the work in another (and benefiting the Perl community in general with the new tools).

Perl really can make this kind of thing easy and having sample code before to redraft/translate can sometimes even lead to better code as you won't be boxed in or painted into a corner the way so many code bases end up, you'll just be guided.

Compare your project to the Perl effort to port Python's WSGI. And don't stop there! There is more to be learned from others. Why not mix in Ruby's Rack? We arrive at Plack. And it was a pretty short trip.

Caveat: if your team was reticent to hit up the CPAN for help in the past, trying to keep this kind of thing together on your own might not be a good match for your culture. "Modern" has more to do with approach than the language and if you're rooted in old-school Perl it might be harder to change that than to change languages.

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Re^3: Putting Perl Back on Top in the Fields of Scientific and Financial Computing
by Anonymous Monk on Mar 25, 2011 at 03:10 UTC

    And how do you plan to convince people to repeatedly rewrite a piece of code that works fine in Python to Perl?

    Unless a Person is Perl fan, the moment he sees Python looking for cleaner and getting the job done. He will switch to Python.

      I sort of explained why. This is a situation where there is an expertise in Perl already and none in Python. You have a chance to do it better, using the original as a prototype. And you still have the huge eco-system of Perl supporting you. Plack is the ideal example of this. Another is KinoSearch, a rewrite of Java’s Lucene which outperforms it in several metrics and is having some elements of its design back ported to Java because they are so good.

      The attitude that it’s already done so why bother, is a losing proposition and not just in software. For example, Assembly can solve every software problem, we shouldn’t create new languages. Soap and good diet can prevent most disease, we shouldn’t worry about medical research.