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PDL 2.4.0: Scientific Computing for the Masses
Dr. Zowie writes "Perl Data Language 2.4.0 was just released;
get it here. This release includes even more powerful array slicing, a complete GIS cartography package, API access to the Gnu Scientific Library, and a host of other goodies. Between PDL and its less-mature siblings Numeric Python and Octave, the established commercial languages'
days appear numbered."
Well, I don't know about how mature/not mature Scientific Python or Octave are with respect to PDL, but I like Python better and I was used to Matlab in the past anyway.
At present, I am using Scipy, a nice more complete version of Numerical Python. Together with IPython, I get a very nice numerical environment. Unfortunately, while Scipy is very nice, it is still a bit of a bleeding edge product. But it is **very** fast for large array computations. I also like the fact that you can link fortran routines easily (yes, people still use fortran, it's useful and easy).
I also use Octave because I miss the ease of generating plots in Matlab (yes, I could do this with scipy, but somehow, I resort to using Octave). It is a very complete program, with many toolboxes. Given that some of the Matlab toolboxes can also be incorporated, there is a vast array of functions for you to play around with.
On the other hand, I think that none of the "established languages" are a good comparison. IDL is extremely powerful for Remote Sensing/Image Processing tasks (my area of research). It is simple to use, and a bit of a standard in the field. From the PDL changelog, the cartographic features in PDL amount to no more than transformations... Mathematica is extremely powerful in symbolic Maths, which as far as I can tell, is not what pdl is about. And Matlab is turning into the VB of scientists (at least, it is multiplatform :D)
Oh well, I'll have to give it a go :-D
Yep, you're right that Mathematica is not a good comparison -- I stuck that in mainly as a reference to the numerical part of Mathematica, but the symbolic stuff is pretty much unmatched (though Maple fans might disagree).
Much of PDL's development has been motivated by a need for something "like IDL, but more powerful", and I think that's really where PDL shines best: in remote sensing and image processing tasks. It helps a lot that all of CPAN is already present, and that the file I/O and indexing have many fewer "gotchas" than those of IDL. The PGPLOT back-end is great, too, for actual device-independent plotting: how many hours have you spent tweaking your IDL plots to actually print right on the PostScript device?
It's (IMHO) a Good Thing that we have all three of numpy/scipy, Octave, and PDL: each has a different set of strengths. Ultimately, each group really should use the tool that suits them best (and it shouldn't cost more than the workstation it runs on...). The reason I've more-or-less committed to perl development rather than Python or Octave is that it has a nice "natural language", expressive feel to it: it's easy to build pipeline-style, imperative-style, or evaluated-style constructs, whichever is most convenient for the current application.
Of course, the open-source languages have the added benefit that results derived using them are actually reproducible, whereas closed-source languages might conceal irreproducible bugs (in the language or the reduction code) that other groups can't identify.