# [PYTHON MATRIX-SIG] Re: The perlDL matrix module

**Jim Hugunin
**
hugunin@mit.edu

*Tue, 29 Oct 1996 17:03:43 -0500*

>* Well, it appears to me that perl has gotten on the bandwagon with regards
*>* to efficient manipulation of matrix objects:
*>*
*>* http://www.aao.gov.au/local/www/kgb/perldl/PDL.html
*....
>* Do any of you perl/python cross-trainers have any insight as to relative
*>* value of each?
*
NumPy is currently in its 1.0alpha5 release. The alpha releases are being
restricted to members of the matrix-sig. The first beta release will be
available publicly sometime in November and I'll announce it here then.
Nonetheless, I just couldn't resist this one. Here's a quick run-down of
some differences (thrown together while reading the perldl announcement).
Note: this is from an extremely biased viewpoint.
I've always said that I thought Perl 5 was the one other language besides
Python in which something like the Numeric Extensions could be done well.
I think that PDL is a reasonable first pass at doing just that. Both PDL
and NumPy provide a way to efficiently work with multi-dimensional arrays
of homogeneous numbers. The most significant difference in my mind between
the two systems is that NumPy uses Python and PDL uses Perl.
There are a lot of other differences between the two extensions, but
without spending a lot of time playing with PDL I'd be hard pressed to
provide a fair comparision. Here's a quick summary of the obvious relative
advantages I noticed for each.
PDL's advantages:
better syntactic sugar
$a < 42 vs. less(a, 42)
a += 1; a x b instead of dot(a,b)
support for FITS and IIS
NumPy's advantages:
supports complex numbers
much more extensive math library
more sophisticated structural operations: repeat, choose
better syntax for multidimensional indexing (and more efficient)
extensive set of modules:
LAPACK, FFTPACK, RANLIB, GIST, PGPLOT, OpenGL, NetCDF, ...
NumPy uses internal storage format compatible with both C and FORTRAN (I
don't know the internal storage format for PDL)
My two cents worth,
Jim Hugunin - author of NumPy
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