Re: [Numpy-discussion] How do I make numpy raise exceptions instead of print warnings?

2008-03-29 Thread Robert Kern
On Sat, Mar 29, 2008 at 4:25 PM, Roy H. Han <[EMAIL PROTECTED]> wrote: > Is there a way to have numpy raise exceptions instead of printing > warnings? The printed warnings make debugging hard. numpy.seterr() Read the docstring for the various options. -- Robert Kern "I have come to believe t

Re: [Numpy-discussion] OSX 10.4 installation problems

2008-03-29 Thread mhgreen
I am not sure where my version of gcc is from or how it was installed. I installed Xcode from the CD I got with the computer (in 2005). I will try updating it and see if everything works better. Thanks. Original message >Date: Sat, 29 Mar 2008 16:01:59 -0500 >From: "Robert Kern" <[E

[Numpy-discussion] How do I make numpy raise exceptions instead of print warnings?

2008-03-29 Thread Roy H. Han
Is there a way to have numpy raise exceptions instead of printing warnings? The printed warnings make debugging hard. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] OSX 10.4 installation problems

2008-03-29 Thread Robert Kern
On Sat, Mar 29, 2008 at 1:04 PM, <[EMAIL PROTECTED]> wrote: > Hi, > > I cannot seem to install numpy on my mac. Here is some > relevant info: > > I have the following installed on my PPC G4 powerbook: > > MacOSX 10.4.10 > gcc version 4.0.0 > gfortran version 4.2.1 > fftw version 3.1.2 > M

[Numpy-discussion] OSX 10.4 installation problems

2008-03-29 Thread mhgreen
Hi, I cannot seem to install numpy on my mac. Here is some relevant info: I have the following installed on my PPC G4 powerbook: MacOSX 10.4.10 gcc version 4.0.0 gfortran version 4.2.1 fftw version 3.1.2 MacPython version 2.5.2 Xcode version 2.0 I have the unzipped numpy directory placed in /

Re: [Numpy-discussion] confusion about eigenvector

2008-03-29 Thread harryos
> - > from scipy import linalg > facearray-=facearray.mean(0) #mean centering > u, s, vt = linalg.svd(facearray, 0) > scores = u*s > facespace = vt.T > # reconstruction: facearray ~= dot(scores, facespace.T) > explained_variance = 100*s.cumsum()/s.sum() hi i am a newbie in this area o