Mailman 3 August 2004 - NumPy-Discussion

ANN: Python/Scientific Job openings at Enthought
by eric jones 16 Aug '04

16 Aug '04

Hey Folks, Enthought continues to grow, and we're hiring again. There are several positions open, and most of them involve python+scientific computing. A couple of the positions also involve support of SciPy. I'm very interested in finding a person for the "scientist" job posting that is both able to work well with our customers and also has the personality and interest to work with the SciPy community to help push the library forward. More information is available on our web site. http://www.enthought.com/careers.htm Our current development efforts are in the areas of electromagnetics, geophysics, and graphics (2D and 3D). If you have any questions, please feel free to contact me. Please forward the link to anyone you feel might be interested. thanks, eric

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Is there a better way to do this?
by Hee-Seng Kye 16 Aug '04

16 Aug '04

My question is not directly related to NumPy, but since many people here deal with numbers, I was wondering if I could get some help; it would be even better if there is a NumPy (or Numarray) function that takes care of what I want! I'm trying to write a program that computes six-digit numbers, in which the left digit is always smaller than its following digit (i.e., it's always ascending). The best I could do was to have many embedded 'for' statement: c = 1 for p0 in range(0, 7): for p1 in range(1, 12): for p2 in range(2, 12): for p3 in range(3, 12): for p4 in range(4, 12): for p5 in range(5, 12): if p0 < p1 < p2 < p3 < p4 < p5: print repr(c).rjust(3), "\t", print "%X %X %X %X %X %X" % (p0, p1, p2, p3, p4, p5) c += 1 print "...Done" This works, except that it's very slow. I need to get it up to nine-digit numbers, in which case it's significantly slow. I was wondering if there is a more efficient way to do this. I would highly appreciate it if anyone could help. Many thanks. -Kye

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invalid numeric result(s) in divide
by Curzio Basso 13 Aug '04

13 Aug '04

Hi all. I have a matrix tmp a matrix w, both Float32, with >>> tmp.shape (3,512,512) >>> w.shape (512,512) >>> w.max() 1.0 >>> w.min() 0.0 >>> NA.where(NA.and_(w > 0.0, w < 1.0e-2)) (array([]), array([])) What is for me surprising is the following: >>> tmp = NA.where(w > 0.0, tmp / w, 0.0) Warning: Encountered invalid numeric result(s) in divide Ah, both matrices have no 'nan' or 'inf' elements. So, where could the invalid result come from? thanks, curzio

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clear a variable
by Andrew Wilson 12 Aug '04

12 Aug '04

Hello, I can't seem to find this in the documentation, is there a way to clear a variable. For example I have an application that opens displays a bunch of images, they can be big memory hogs. When I'm not showing them I'd like to clear out those variables, instead of having to close down my program and start over. I know matlab has a command "clear /var/" which will do exactly that. Thanks for the help! Andrew

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Re: matrixmultiply problem
by Alan G Isaac 12 Aug '04

12 Aug '04

> The patch is in CVS now. Can the conditions under which this problem occurs be simply stated? Thank you, Alan Isaac

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RE: [Numpy-discussion] Bug in built in SVD?
by Nadav Horesh 12 Aug '04

12 Aug '04

It can be a bit more complicate, the story is this: One my gentoo system I use ATLAS, and gentoo instaltion generates the ATLAS version of blas and lapack which omit some of the original blas/lapack routines. Till now I used the numarray/Numeric internal lapack_lite package, since every time I tried to compile against the external lapck, I could not load the linear-algebra package because I got an error complaining about a missing "dgesdd_". In order to solve it I downloaded the original lapack library from netlib and compiled it with gcc3.4.1 and full optimization (by tweaking the makefile). I copied the generated lapack.a to /usr/local/lob/lapack_original.a and updated the link list in addon.py/setup.py (numarray/Numeric). That worked fine --- I have numarray/Numeric linked against highly optimized lapack without this infinite loop problem. That leads me to the other issue: Assuming ATLAS routines runs faster then their equivalent lapack's. Why not replace dgesdd with the one which has an ATLAS equivalent? Nadav -----Original Message----- From: Michiel Jan Laurens de Hoon [mailto:mdehoon@ims.u-tokyo.ac.jp] Sent: Thu 12-Aug-04 10:29 To: Nadav Horesh Cc: Subject: Re: [Numpy-discussion] Bug in built in SVD? This may also be related to patch 732520 for Numerical Python. Some parts of lapack need to be compiled without optimization. Numerical Python (and I assume numarray) use optimization anyway, and usually gets away with it. So gcc 3.4.1 may be doing the right thing after all. The external Lapack that you mentioned may have been compiled correctly, i.e. without optimization for the specific Lapack files. --Michiel, U Tokyo. Nadav Horesh wrote: > Sorry it is a gcc3.4.1 optimization bug. Either lowering to optimization to -O1 or stepping back to gcc3.3.4 (with all optimization guns on) fix it. > > Nadav. > > -----Original Message----- > From: Nadav Horesh > Sent: Wed 11-Aug-04 15:48 > To: numpy-discussion > Cc: > Subject: [Numpy-discussion] Bug in built in SVD? > When numarray is compiled with the buit-in linear-algebra routine the svd routine runs into an infinite loop. > > Platform2: python 2.3.4 and 2.4a2; numarray 1.1; gcc 3.4.1; RH9 and gentoo 1.4 > > example: > > >>>from numarray.linear_algebra.mlab import * >>> >>>>svd(arange(20.0, shape = (5,4))) > > > ... infine loop ... > > > -------------------------------------------------------------------------- > > svd runs OK when compiled againts external LAPACK. > > BTW: Why numarray used dgesdd routine for SVD, and not the svd routine that has the ATLAS equivalent? > > Nadav. > > > > ------------------------------------------------------- > SF.Net email is sponsored by Shop4tech.com-Lowest price on Blank Media > 100pk Sonic DVD-R 4x for only $29 -100pk Sonic DVD+R for only $33 > Save 50% off Retail on Ink & Toner - Free Shipping and Free Gift. > http://www.shop4tech.com/z/Inkjet_Cartridges/9_108_r285 > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion(a)lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > > > > > > ------------------------------------------------------- > SF.Net email is sponsored by Shop4tech.com-Lowest price on Blank Media > 100pk Sonic DVD-R 4x for only $29 -100pk Sonic DVD+R for only $33 > Save 50% off Retail on Ink & Toner - Free Shipping and Free Gift. > http://www.shop4tech.com/z/Inkjet_Cartridges/9_108_r285 > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion(a)lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > > -- Michiel de Hoon, Assistant Professor University of Tokyo, Institute of Medical Science Human Genome Center 4-6-1 Shirokane-dai, Minato-ku Tokyo 108-8639 Japan http://bonsai.ims.u-tokyo.ac.jp/~mdehoon

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matrixmultiply problem
by Geoffrey Philbrick 12 Aug '04

12 Aug '04

Executing the following code has the side effect of modifying one of the arguments (matrices) I passed to matrixmultiply. Note that the matrix m2 changes after the call to matrixmultiply. This is on Windows with numarray 1.0. Why does this happen? Code: from numarray import array, matrixmultiply m1 = array( [[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]] ) m2 = array( [[ 0.500011, 0.86601905, 0. ], [-0.86601905, 0.500011, 0. ], [ 0., 0., 1. ]] ) print "m1 before\n", m1 print "m2 before\n", m2 matrixmultiply(m1, m2) print "m1 after\n", m1 print "m2 after\n", m2 Output: m1 before [[ 1. 0. 0.] [ 0. 1. 0.] [ 0. 0. 1.]] m2 before [[ 0.500011 0.86601905 0. ] [-0.86601905 0.500011 0. ] [ 0. 0. 1. ]] m1 after [[ 1. 0. 0.] [ 0. 1. 0.] [ 0. 0. 1.]] m2 after [[ 0.500011 -0.86601905 0. ] [ 0.86601905 0.500011 0. ] [ 0. 0. 1. ]]

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RE: [Numpy-discussion] Bug in built in SVD?
by Nadav Horesh 12 Aug '04

12 Aug '04

Sorry it is a gcc3.4.1 optimization bug. Either lowering to optimization to -O1 or stepping back to gcc3.3.4 (with all optimization guns on) fix it. Nadav. -----Original Message----- From: Nadav Horesh Sent: Wed 11-Aug-04 15:48 To: numpy-discussion Cc: Subject: [Numpy-discussion] Bug in built in SVD? When numarray is compiled with the buit-in linear-algebra routine the svd routine runs into an infinite loop. Platform2: python 2.3.4 and 2.4a2; numarray 1.1; gcc 3.4.1; RH9 and gentoo 1.4 example: >> from numarray.linear_algebra.mlab import * >>> svd(arange(20.0, shape = (5,4))) ... infine loop ... -------------------------------------------------------------------------- svd runs OK when compiled againts external LAPACK. BTW: Why numarray used dgesdd routine for SVD, and not the svd routine that has the ATLAS equivalent? Nadav. ------------------------------------------------------- SF.Net email is sponsored by Shop4tech.com-Lowest price on Blank Media 100pk Sonic DVD-R 4x for only $29 -100pk Sonic DVD+R for only $33 Save 50% off Retail on Ink & Toner - Free Shipping and Free Gift. http://www.shop4tech.com/z/Inkjet_Cartridges/9_108_r285 _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

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Repondeur de <baud.ph@ifrance.com>
by baud.ph＠ifrance.com 12 Aug '04

12 Aug '04

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masked arrays and nd_image
by Curzio Basso 11 Aug '04

11 Aug '04

Hi all. I just tried to use a masked array with nd_image, namely to pass it to the convolve1d() function, and I got: error: getShape: couldn't get sequence length. Does someone know if it possible at all to use nd_image on masked arrays? Or if there is a possibility to do operations on masked images without using the ma module? thanks. curzio

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