--On 5 janvier 2015 08:43:45 +0000 Sturla Molden <sturla.mol...@gmail.com> wrote:
> To me it seems that algorithms in scientific papers and books are > described in various forms of pseudo-code. That's indeed what people do when they write a paper about an algorithm. But many if not most algorithms in computational science are never published in a specific article. Very often, a scientific article gives only an outline of a method in plain English. The only full documentation of the method is the implementation. > Perhaps we need a notation > which is universal and ethernal like the language mathematics. But I am > not sure Python could or should try to be that "scripting" language. Neither Python nor any other programming was designed for that task, and none of them is really a good fit. But today's de facto situation is that programming languages fulfill the role of algorithmic specification languages in computational science. And I don't expect this to change rapidly, in particular because to the best of my knowledge there is no better choice available at the moment. I wrote an article on this topic that will appear in the March 2015 issue of "Computing in Science and Engineering". It concludes that for now, a simple Python script is probably the best you can do for an executable specification of an algorithm. However, I also recommend not using big libraries such as NumPy in such scripts. > I also think it is reasonable to ask if journals should require code as > algorithmic documentation to be written in some ISO standard language like > C or Fortran 90. The behavior of Python and NumPy are not dictated by > standards, and as such is not better than pseudo-code. True, but the ISO specifications of C and Fortran have so many holes ("undefined behavior") that they are not really much better for the job. And again, we can't ignore the reality of the de facto use today: there are no such requirements or even guidelines, so Python scripts are often the best we have as algorithmic documentation. Konrad. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion