R (http://cran.r-project.org) might be an alternative, specially if you do a lot of statistics and graphics. (R is probably the most widely used language/system in statistical research).
R. On 16 Nov 2006 13:09:03 -0800, sturlamolden <[EMAIL PROTECTED]> wrote: > Boris wrote: > > Hi, is there any alternative software for Matlab? Although Matlab is > > powerful & popular among mathematical & engineering guys, it still > > costs too much & not publicly open. So I wonder if there's similar > > software/lang that is open & with comparable functionality, at least > > for numerical aspect. Thanks! > > I have used Matlab for years, and has recently changed to Python. In > addition one needs NumPy and Matplotlib, and perhaps SciPy. Other > useful packages are PyGTK for GUI, PyGame for multimedia, PyOpenGL for > 3D graphics, Mpi4Py for parallel computation, etc. You will find python > packages for nearly any conceivable task. Unlike Matlab, Python is a > general purpose programming language, and a distant cousin of Lisp. The > Python language is fare more expressive and productive than Matlab, yet > even more easy to use. > > The NumPy package is the core requirement for numerical work in Python. > It is quite different form Matlab, but I think it is more powerful. > Particularly, arrays are passed by reference (not by value), and > indexing creates view matrices. > > To compare Matlab with NumPy we can e.g. use the D4 discrete wavelet > transform. I have here coded it in Matlab and Python/NumPy using Tim > Swelden's lifting scheme. > > First the Matlab version (D4_Transform.m): > > function x = D4_Transform(x) > % D4 Wavelet transform in Matlab > % (C) Sturla Molden > C1 = 1.7320508075688772; > C2 = 0.4330127018922193; > C3 = -0.066987298107780702; > C4 = 0.51763809020504137; > C5 = 1.9318516525781364; > s1 = zeros(ceil(size(x)/2)); > d1 = zeros(ceil(size(x)/2)); > d2 = zeros(ceil(size(x)/2)); > odd = x(2:2:end); > even = x(1:2:end-1); > d1(:) = odd - C2*even; > s1(1) = even(1) + C2*d1(1) + C3*d1(end); > s1(2:end) = even(2:end) + C2*d1(2:end) + C3*d1(1:end-1); > d2(1) = d1(1) + s1(end); > d2(2:end) = d1(2:end) + s1(1:end-1); > x(1:2:end-1) = C4*s1; > x(2:2:end) = C5*d2; > if (length(x) >2) > x(1:2:end-1) = D4_Transform(x(1:2:end-1)); > end > > > Then the Python version (D4.py): > > import numpy > import time > > def D4_Transform(x, s1=None, d1=None, d2=None): > """ > D4 Wavelet transform in NumPy > (C) Sturla Molden > """ > C1 = 1.7320508075688772 > C2 = 0.4330127018922193 > C3 = -0.066987298107780702 > C4 = 0.51763809020504137 > C5 = 1.9318516525781364 > if d1 == None: > d1 = numpy.zeros(x.size/2) > s1 = numpy.zeros(x.size/2) > d2 = numpy.zeros(x.size/2) > odd = x[1::2] > even = x[:-1:2] > d1[:] = odd[:] - C1*even[:] > s1[0] = even[0] + C2*d1[0] + C3*d1[-1] > s1[1:] = even[1:] + C2*d1[1:] + C3*d1[:-1] > d2[0] = d1[0] + s1[-1] > d2[1:] = d1[1:] + s1[:-1] > even[:] = C4 * s1[:] > odd[:] = C5 * d2[:] > if x.size > 2: > > D4_Transform(even,s1[0:even.size/2],d1[0:even.size/2],d2[0:even.size/2]) > > if __name__ == "__main__": > x = numpy.random.rand(2**23) > t0 = time.clock() > D4_Transform(x) > t = time.clock() > print "Elapsed time is %.6f seconds" % (t-t0) > > > Now let's do benchmark on my laptop (1.73 GHz Pentium M, 0.99 GB RAM). > I have stopped paying for Matlab maintenance (for reasons that will be > obvious), so I only have R14 Service Pack 2 for comparison. > > First we try Matlab (R14 Service Pack 2): > > >> x = rand(2^23,1); > >> tic; D4_Transform(x); toc > Elapsed time is 27.145438 seconds. > > > Then we Python 2.4.4 with NumPy 1.0: > > C:\develop\python\D4>python D4.py > Elapsed time is 3.364887 seconds > > That is quite astonishing. > > If anyone wonders why I think Travis Oliphant and the NumPy team should > be knighted, then this is the answer. The Mathworks' product only > achieved 100% * 3/27 = 11% the speed of Python/NumPy, and is infinitely > more expensive. > > Does anyone wonder why I am not paying for Matlab maintenance anymore? > > Sorry Mathworks, I have used your product for years, but you cannot > compete with NumPy. > > > Cheers, > Sturla Molden > > -- > http://mail.python.org/mailman/listinfo/python-list > -- Ramon Diaz-Uriarte Statistical Computing Team Structural Biology and Biocomputing Programme Spanish National Cancer Centre (CNIO) http://ligarto.org/rdiaz -- http://mail.python.org/mailman/listinfo/python-list
