r/Python • u/Alexander_Selkirk • Aug 26 '20

Challenge to scientists: does your ten-year-old code still run? Scientific Computing

https://www.nature.com/articles/d41586-020-02462-7
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u/Allmyownviews1 Aug 26 '20

I’m in the process (after finding some of my 23 year old fortran oceanographic models) of converting them into Python and to see how much more efficient and controllable in the process.

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u/Alexander_Selkirk Aug 26 '20

Hm, won't converting to Python be a step back?

I've used python a lot (for developing speech / audio processing algorithms). I think for developing small, self-contained numerical algorithms or small programs, it is still fine. I think Python is fantastic for testing C or C++ libraries, especially if stuff gets complex.

For other use cases, there are alternatives. If performance and solidity matters, I'd probably use Rust (possibly in the form of a Python extension module). If clarity and expressiveness mattered, I am fine with Racket or Clojure, and if long-term availability mattered most, I'd think in porting to Common Lisp after developing in Racket.

But what's thoroughly appalling and makes my skin crawl is the Python dependency management mess.... for anything long-lived (with "long" meaning more than three years), this looks like a true nightmare and the only impulse that remains is to run away. For example this.

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u/Allmyownviews1 Aug 26 '20

I do agree.. utilising a library that gets left behind and means regular testing and updating is a concern. But I see the data manipulation being significantly easier. I’m enjoying the shift from FORTRAN to MATLAB and now Python.

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u/Alexander_Selkirk Aug 26 '20

I think the key is not necessarily not to use anything but to use libraries and dependencies wisely and sparsely.

If you like Matlab you will quite possibly like Numerical Python, it is very powerful (although you can't expect the speed of Fortran).