Mailman 3 May 2012 - SciPy-User

Re: [SciPy-User] scipy.sparse.linalg.eigs is faster with k=8 than with k=1
by Sergio Rojas 23 May '12

23 May '12

I do not have an answer to your question Alejandro, but I am curious about why the timing changes each time the code is executed. Is there any random allocation in the process of solving your problem? Sergio ---------------------------------------------------------------------- Message: 1 Date: Wed, 23 May 2012 09:33:15 -0600 From: Alejandro Weinstein <alejandro.weinstein(a)gmail.com> Subject: [SciPy-User] scipy.sparse.linalg.eigs is faster with k=8 than with k=1 To: scipy-user(a)scipy.org Message-ID: <CAPFc=oz+ck-_6msNQ7SadNb69RH9zwTgKSj1U0PfdsckH5vydA(a)mail.gmail.com> Content-Type: text/plain; charset=ISO-8859-1 Hi: I am using scipy.sparse.linalg.eigs with a 336x336 sparse matrix with 1144 nonzero entries. I only need the eigenvector corresponding to the larger eigenvalue, so I was running the function with k=1. However, I found that it is about 10 times faster to call the function with k=8. I am testing this with the following code (available here: https://gist.github.com/2775892): ############################################################# import timeit setup = """ import numpy as np import scipy.sparse import scipy.io from scipy.sparse.linalg import eigs P = scipy.io.mmread('P.mtx') """ n = 10 for k in range(1,20): code = 'eigs(P, k=%d)' % k t = timeit.timeit(stmt=code, setup=setup, number=n) / n print 'k: %2d, time: %5.1f ms' % (k, 1000*t) ############################################################# The output is k: 1, time: 301.7 ms k: 2, time: 242.6 ms k: 3, time: 352.0 ms k: 4, time: 168.8 ms k: 5, time: 148.1 ms k: 6, time: 93.2 ms k: 7, time: 70.0 ms k: 8, time: 29.3 ms k: 9, time: 45.2 ms k: 10, time: 63.0 ms k: 11, time: 209.1 ms k: 12, time: 170.8 ms k: 13, time: 120.2 ms k: 14, time: 104.6 ms k: 15, time: 115.0 ms k: 16, time: 97.0 ms k: 17, time: 94.0 ms k: 18, time: 94.4 ms k: 19, time: 74.3 ms Is this behavior typical for eigs? In other words, should I always use k set to values around 6 or 8, or is it matrix dependent? I am also curious about this. I would expect that computing 1 eigenvector should never be slower than computing more eigenvectors. Any about why is this happening? Alejandro. ------------------------------

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scipy.sparse.linalg.eigs is faster with k=8 than with k=1
by Alejandro Weinstein 23 May '12

23 May '12

Hi: I am using scipy.sparse.linalg.eigs with a 336x336 sparse matrix with 1144 nonzero entries. I only need the eigenvector corresponding to the larger eigenvalue, so I was running the function with k=1. However, I found that it is about 10 times faster to call the function with k=8. I am testing this with the following code (available here: https://gist.github.com/2775892): ############################################################# import timeit setup = """ import numpy as np import scipy.sparse import scipy.io from scipy.sparse.linalg import eigs P = scipy.io.mmread('P.mtx') """ n = 10 for k in range(1,20): code = 'eigs(P, k=%d)' % k t = timeit.timeit(stmt=code, setup=setup, number=n) / n print 'k: %2d, time: %5.1f ms' % (k, 1000*t) ############################################################# The output is k: 1, time: 301.7 ms k: 2, time: 242.6 ms k: 3, time: 352.0 ms k: 4, time: 168.8 ms k: 5, time: 148.1 ms k: 6, time: 93.2 ms k: 7, time: 70.0 ms k: 8, time: 29.3 ms k: 9, time: 45.2 ms k: 10, time: 63.0 ms k: 11, time: 209.1 ms k: 12, time: 170.8 ms k: 13, time: 120.2 ms k: 14, time: 104.6 ms k: 15, time: 115.0 ms k: 16, time: 97.0 ms k: 17, time: 94.0 ms k: 18, time: 94.4 ms k: 19, time: 74.3 ms Is this behavior typical for eigs? In other words, should I always use k set to values around 6 or 8, or is it matrix dependent? I am also curious about this. I would expect that computing 1 eigenvector should never be slower than computing more eigenvectors. Any about why is this happening? Alejandro.

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Array Selection Help -Part2-
by Lothar Ulferts 23 May '12

23 May '12

Dear list, based upon the thread "Array Selection Help" http://thread.gmane.org/gmane.comp.python.scientific.user/19412 I would like to modifiy the task a little bit: The arr1 contains numbers (label) , arr2 floating points (values). But, here it is different, zones shouldn't be 'Multiparts'. A zone should be split into its untouched parts. For example: label: 100 100 100 -99 -99 -99 100 100 100 -99 -99 -99 100 100 -99 -99 200 200 100 100 -99 -99 200 200 -99 -99 -99 -99 200 200 => -99 -99 -99 -99 200 200 300 300 300 300 300 300 300 300 300 300 300 300 200 200 200 -99 100 100 300 400 400 -99 500 500 200 200 200 -99 100 100 400 400 400 -99 500 500 values: 1.5 1.9 1.8 0.3 0.1 0.1 1.5 1.7 0.6 0.3 2.5 2.9 0.6 0.6 0.8 0.4 2.1 2.1 3.1 3.2 3.3 3.4 3.5 3.6 4.7 4.7 4.0 0.1 1.0 1.4 4.3 4.0 4.9 0.3 1.2 1.1 Result of zonal min should be: 1.7 1.7 1.7 -99 -99 -99 1.7 1.7 -99 0.3 2.9 2.9 -99 -99 -99 -99 2.9 2.9 3.6 3.6 3.6 3.6 3.6 3.6 4.0 4.0 4.0 -99 1.0 1.0 4.0 4.0 4.0 -99 1.0 1.0 The question is, how do I tranform the left array to the right? beste regards Lothar

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Wrong Step Response
by otti 23 May '12

23 May '12

Hello everyone, after reading some threads about step response problems in the mailing list i couldnt come up with a proper solution for my problem. I've got the following transfer function: G = Vr/(sT1*(1+sT2)) Vr = 2/15 T1 = 0.2 T2 = 1 For this i like to plot the step response so i made the following script: *---Begin Script* # some imports import numpy as np import matplotlib.pyplot as plt import scipy as sp from scipy import signal # Constants T1 = 0.2 T2 = 1 Vr = float(2)/15 Tsim = 50 # Denumerator N0 = np.array([T1*T2 ,T1 , 0]) # numerator Z0 = Vr # create tfcn sys = sp.signal.lti(Z0, N0) # create the step response t = np.linspace(0, Tsim, 1000) u = np.arange(len(t)) u = np.ones_like(u) yout = sp.signal.lsim2(sys, T=t, U=u)[1] plt.figure(1) plt.plot(t, u, t, yout/yout.max()) plt.grid("on") plt.xlabel("t") plt.ylabel("h(t)") plt.title("Sprungantwort") plt.show() *---End Script* This gives me the following response, which i know is not the rigth one because i made this one already in a lab at university with MATLAB but i don't have MATLAB at home because i prefer to use Numpy+Scipy+Matplotlib. wrong step response Here is the correct response: correct step response After several hours of reading an trying different approaches im kind of frustrated ^^. I would be really thankfull for any help so that i can keep on going the Scipy track. Greetings, William

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scipy.weave on windows vista compiling error
by Tobias 23 May '12

23 May '12

Hello! I'm trying to bring scipy.weave to work on Windows Vista with MinGw. I use Python 2.7.1, Scipy 0.10.1 and MinGw 4.6.2 My code looks like the following: from scipy import weave weave.inline("""print('Hello World!');""", [], compiler = 'mingw32-gcc') I get the following error message: DistutilsPlatformError: don't know how to compile C/C++ code on platform 'nt' with 'mingw32-gcc' compiler With MinGw come a lot of different compiler. If I choose not 'mingw32-gcc' but for example 'gcc' weave complains ValueError: invalid version number '4.' So I also downloaded the olf MinGW 2.9.5 Version. Then I also get the Error "don't know how to compile C/C++ code on platform 'nt'" The PATH-Variables are set and I also made shure, that there is write access to the MinGw and Python folders. Searching for this error message gave no progress, but maybe you could help me. Would be great! All the best Tobi

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Re: [SciPy-User] scipy.weave on windows vista compiling error
by federico vaggi 22 May '12

22 May '12

> Message: 2 > Date: Mon, 21 May 2012 11:36:13 +0000 (UTC) > From: Tobias <tobias(a)ebockwurst.de> > Subject: [SciPy-User] scipy.weave on windows vista compiling error > To: scipy-user(a)scipy.org > Message-ID: <loom.20120521T131903-9(a)post.gmane.org> > Content-Type: text/plain; charset=us-ascii > > Hello! > > I'm trying to bring scipy.weave to work on Windows Vista with MinGw. > I use Python 2.7.1, Scipy 0.10.1 and MinGw 4.6.2 > My code looks like the following: > > from scipy import weave > weave.inline("""print('Hello World!');""", [], compiler = 'mingw32-gcc') > > I get the following error message: > > DistutilsPlatformError: don't know how to compile C/C++ code on platform > 'nt' > with 'mingw32-gcc' compiler > > With MinGw come a lot of different compiler. If I choose not 'mingw32-gcc' > but > for example 'gcc' weave complains > > ValueError: invalid version number '4.' > > So I also downloaded the olf MinGW 2.9.5 Version. Then I also get the Error > "don't know how to compile C/C++ code on platform 'nt'" > > The PATH-Variables are set and I also made shure, that there is write > access to > the MinGw and Python folders. Searching for this error message gave no > progress, but maybe you could help me. Would be great! > > All the best > Tobi > > Your C++ compiler has to match the C++ compiler used to compile Python - which, I think, by default, on Vista is Visual Studio 2008. However, the weave module is not actively maintained, and there are much better options right now for interfacing with C/C++. I would highly suggest that if you have some performance-critical part of your code, you either use Cython, or, if it is a simple numerical expression, use the amazing numexpr package. I warn you though - while pure Python code is very portable across platforms, interfacing Python with other libraries (mostly, C and C++) is a huge pain on Windows. If you really want to continue, I strongly advise you take advantage of Christoph Gohlke's work: http://www.lfd.uci.edu/~gohlke/pythonlibs/ who has done all the hard work has made binary installers for most of the hard-to-compile Python libraries with C and C++ dependencies. Federico

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subclassing ndarray : i want slice to return ndarray, not subclass
by pierre puiseux UPPA 21 May '12

21 May '12

Hello, all my question is in the title. More precisely, like in scipy doc i try this : ====================================== import sys import numpy as np class ArrayChild(np.ndarray): def __new__(cls, array, info=None): obj = np.asarray(array).view(cls) obj.info = info return obj def __array_finalize__(self, obj): print>>sys.stderr, "__array_finalize__" if obj is None: return self.info = getattr(obj, 'info', None) if __name__=='__main__': a = np.arange(6) a.shape=2,3 a_child = ArrayChild(a) for x in a_child : print>>sys.stderr, x, type(x) ===================================== and i have the answer : ===================================== __array_finalize__ __array_finalize__ [0 1 2] <class '__main__.ArrayChild'> __array_finalize__ [3 4 5] <class '__main__.ArrayChild'> ===================================== but i want ===================================== __array_finalize__ __array_finalize__ [0 1 2] <class '__main__.np.ndarray'> __array_finalize__ [3 4 5] <class '__main__.np.ndarray'> ===================================== I've tried to redefine : __getslice__ like this, but it does not work. ===================================== def __getslice__(self, *args, **kwargs): return np.ndarray.__getslice__(self, *args, **kwargs) ===================================== Some idea ? Thanks to numpy/scipy team for that great job. ===================================== Pierre Puiseux Laboratoire de Mathématiques Appliquées Université de Pau et des Pays de l'Adour pierre.puiseux(a)univ-pau.fr http://www.univ-pau.fr/~puiseux

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Re: [SciPy-User] SciPy installation troubles on CentOS 6.2
by Sergio Rojas 21 May '12

21 May '12

Magician, since I did not see any mention of it, I think you are missing to include in your build up a "site.cfg" file (see for instance http://www.scipy.org/Installing_SciPy/Linux Step 4: Build numpy(1.5.0) ). In that file you need to specify where your libraries are. That file needs to be used in building both Numpy and Scipy. Sergio ---------------------------------------------------------------------- Message: 1 Date: Sun, 20 May 2012 23:16:48 +0900 From: Magician <f_magician(a)mac.com> Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2 To: ralf.gommers(a)googlemail.com Cc: scipy-user(a)scipy.org Message-ID: <C3368B14-EED1-4E87-A7E9-80F76990A0E2(a)mac.com> Content-Type: text/plain; CHARSET=US-ASCII Hi Ralf, Thanks for your advice. I tried to install BLAS/Lapack/ATLAS as below: > yum install blas-devel lapack-devel atlas-devel Next I installed NumPy as below: > tar xzvf numpy-1.6.1.tar.gz > cd numpy-1.6.1 > export CFLAGS="-L/usr/local/python-2.7.3/lib" > python setup.py build But then I got those errors: > building 'numpy.linalg.lapack_lite' extension > compiling C sources > C compiler: gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -L/usr/local/python-2.7.3/lib -fPIC > > creating build/temp.linux-x86_64-2.7/numpy/linalg > compile options: '-DATLAS_INFO="\"3.8.4\"" -I/usr/include -Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c' > gcc: numpy/linalg/lapack_litemodule.c > gcc: numpy/linalg/python_xerbla.c > /usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so > /usr/bin/ld: cannot find -lpython2.7 > collect2: ld returned 1 exit status > /usr/bin/ld: cannot find -lpython2.7 > collect2: ld returned 1 exit status > error: Command "/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so" failed with exit status 1 If I haven't install BLAS/Lapack/ATLAS, NumPy will be successfully built and installed. Magician On 2012/05/20, at 19:40, scipy-user-request(a)scipy.org wrote: > Message: 1 > Date: Sun, 20 May 2012 10:21:12 +0200 > From: Ralf Gommers <ralf.gommers(a)googlemail.com> > Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2 > To: SciPy Users List <scipy-user(a)scipy.org> > Message-ID: > <CABL7CQh+WPwpiMtO0UDKo9G01Gj38kTaWV=bSvq9eCK_qihAfQ(a)mail.gmail.com> > Content-Type: text/plain; charset="iso-8859-1" > > On Sat, May 19, 2012 at 5:09 PM, Magician <f_magician(a)mac.com> wrote: > >> Hi All, >> >> >> I'm trying to build SciPy from source code, >> but I have some troubles. >> >> My environment is below: >>> CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software >> Development WS) >>> Python 2.7.3 (already built from sources, installed at >> /usr/local/python-2.7.3) >>> NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to >> build) >> I and my colleagues (other users) want to use recent Python, >> so I installed Python from sources, and I can't install SciPy >> by using yum command. >> >> Now I'm facing ATLAS compiling errors. >> Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg -fPIC". >> I tried to build it for several times, and always I got errors as below: >>> res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS. >>> >>> ATL_gemvN_mm.c : 1257.99 >>> ATL_gemvN_1x1_1.c : 581.74 >>> ATL_gemvN_1x1_1a.c : 1589.45 >>> ATL_gemvN_4x2_0.c : 813.13 >>> ATL_gemvN_4x4_1.c : 755.54 >>> make[3]: *** [res/dMVRES] Error 255 >>> make[3]: Leaving directory >> `/home/magician/Desktop/ATLAS/build/tune/blas/gemv' >>> make[2]: *** >> [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2 >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' >>> ERROR 734 DURING MVTUNE!!. CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS. >>> make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin' >>> cd /home/magician/Desktop/ATLAS/build ; make error_report >>> make[3]: Entering directory `/home/magician/Desktop/ATLAS/build' >>> make -f Make.top error_report >>> make[4]: Entering directory `/home/magician/Desktop/ATLAS/build' >>> uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> gcc -v 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> Using built-in specs. >>> Target: x86_64-redhat-linux >>> Configured with: ../configure --prefix=/usr --mandir=/usr/share/man >> --infodir=/usr/share/info --with-bugurl= >> http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared >> --enable-threads=posix --enable-checking=release --with-system-zlib >> --enable-__cxa_atexit --disable-libunwind-exceptions >> --enable-gnu-unique-object >> --enable-languages=c,c++,objc,obj-c++,java,fortran,ada >> --enable-java-awt=gtk --disable-dssi >> --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre >> --enable-libgcj-multifile --enable-java-maintainer-mode >> --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib >> --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686 >> --build=x86_64-redhat-linux >>> Thread model: posix >>> gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC) >>> gcc -V 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> gcc: '-V' option must have argument >>> make[4]: [error_report] Error 1 (ignored) >>> gcc --version 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/* >>> gzip --best error_Corei264SSE3.tar >>> mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz >>> make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build' >>> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build' >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' >>> Error report error_<ARCH>.tgz has been created in your top-level ATLAS >>> directory. Be sure to include this file in any help request. >>> cat: ../../CONFIG/error.txt: No such file or directory >>> cat: ../../CONFIG/error.txt: No such file or directory >>> make[1]: *** [build] Error 255 >>> make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build' >>> make: *** [build] Error 2 >> >> It's very troublesome for me to build ATLAS by myself. >> My purpose is just using SciPy on my Python. >> Even if it's optimized not so good for my environment, it's OK. >> >> Is there an easy or a sure way to build and install SciPy? > > > Building ATLAS is much harder than building scipy, so you should try to > find some rpm's for it, like > http://linuxtoolkit.blogspot.com/2010/09/installing-lapack-blas-and-atlas-o…. > There's no problem building scipy against ATLAS from a binary install. > > Ralf -

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why do the discrete distributions not have a `fit`?
by josef.pktd＠gmail.com 20 May '12

20 May '12

Why do the discrete distributions not have a `fit` method like the continuous distributions? currently it's a bug in the documentation http://projects.scipy.org/scipy/ticket/1659 in statsmodels, we fit several of the discrete distributions. How about discrete parameters? (in analogy to the erlang discussion) hypergeom is based on a story about marbles or balls http://en.wikipedia.org/wiki/Hypergeometric_distribution#Application_and_ex… but why should we care, it's just a discrete distribution with 3 shape parameters, isn't it? fractional marbles ? >>> nn = np.linspace(4.5, 8, 101) >>> pmf = [stats.hypergeom.pmf(5, 10.8, n, 8.5) for n in nn] >>> plt.plot(nn, pmf, '-o') >>> plt.title("pmf of hypergeom as function of parameter n") Doesn't look like there are any problems, and the likelihood function is nicely concave. conclusion: scipy.stats doesn't have a hypergeometric distribution, but a generalized version that is defined on a real parameter space. Josef (so what's the point? Sorry, I was just getting distracted while looking for `fit`.)

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Re: [SciPy-User] SciPy installation troubles on CentOS 6.2
by Magician 20 May '12

20 May '12

Hi Ralf, Thanks for your advice. I tried to install BLAS/Lapack/ATLAS as below: > yum install blas-devel lapack-devel atlas-devel Next I installed NumPy as below: > tar xzvf numpy-1.6.1.tar.gz > cd numpy-1.6.1 > export CFLAGS="-L/usr/local/python-2.7.3/lib" > python setup.py build But then I got those errors: > building 'numpy.linalg.lapack_lite' extension > compiling C sources > C compiler: gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -L/usr/local/python-2.7.3/lib -fPIC > > creating build/temp.linux-x86_64-2.7/numpy/linalg > compile options: '-DATLAS_INFO="\"3.8.4\"" -I/usr/include -Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c' > gcc: numpy/linalg/lapack_litemodule.c > gcc: numpy/linalg/python_xerbla.c > /usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so > /usr/bin/ld: cannot find -lpython2.7 > collect2: ld returned 1 exit status > /usr/bin/ld: cannot find -lpython2.7 > collect2: ld returned 1 exit status > error: Command "/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so" failed with exit status 1 If I haven't install BLAS/Lapack/ATLAS, NumPy will be successfully built and installed. Magician On 2012/05/20, at 19:40, scipy-user-request(a)scipy.org wrote: > Message: 1 > Date: Sun, 20 May 2012 10:21:12 +0200 > From: Ralf Gommers <ralf.gommers(a)googlemail.com> > Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2 > To: SciPy Users List <scipy-user(a)scipy.org> > Message-ID: > <CABL7CQh+WPwpiMtO0UDKo9G01Gj38kTaWV=bSvq9eCK_qihAfQ(a)mail.gmail.com> > Content-Type: text/plain; charset="iso-8859-1" > > On Sat, May 19, 2012 at 5:09 PM, Magician <f_magician(a)mac.com> wrote: > >> Hi All, >> >> >> I'm trying to build SciPy from source code, >> but I have some troubles. >> >> My environment is below: >>> CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software >> Development WS) >>> Python 2.7.3 (already built from sources, installed at >> /usr/local/python-2.7.3) >>> NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to >> build) >> I and my colleagues (other users) want to use recent Python, >> so I installed Python from sources, and I can't install SciPy >> by using yum command. >> >> Now I'm facing ATLAS compiling errors. >> Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg -fPIC". >> I tried to build it for several times, and always I got errors as below: >>> res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS. >>> >>> ATL_gemvN_mm.c : 1257.99 >>> ATL_gemvN_1x1_1.c : 581.74 >>> ATL_gemvN_1x1_1a.c : 1589.45 >>> ATL_gemvN_4x2_0.c : 813.13 >>> ATL_gemvN_4x4_1.c : 755.54 >>> make[3]: *** [res/dMVRES] Error 255 >>> make[3]: Leaving directory >> `/home/magician/Desktop/ATLAS/build/tune/blas/gemv' >>> make[2]: *** >> [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2 >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' >>> ERROR 734 DURING MVTUNE!!. CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS. >>> make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin' >>> cd /home/magician/Desktop/ATLAS/build ; make error_report >>> make[3]: Entering directory `/home/magician/Desktop/ATLAS/build' >>> make -f Make.top error_report >>> make[4]: Entering directory `/home/magician/Desktop/ATLAS/build' >>> uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> gcc -v 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> Using built-in specs. >>> Target: x86_64-redhat-linux >>> Configured with: ../configure --prefix=/usr --mandir=/usr/share/man >> --infodir=/usr/share/info --with-bugurl= >> http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared >> --enable-threads=posix --enable-checking=release --with-system-zlib >> --enable-__cxa_atexit --disable-libunwind-exceptions >> --enable-gnu-unique-object >> --enable-languages=c,c++,objc,obj-c++,java,fortran,ada >> --enable-java-awt=gtk --disable-dssi >> --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre >> --enable-libgcj-multifile --enable-java-maintainer-mode >> --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib >> --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686 >> --build=x86_64-redhat-linux >>> Thread model: posix >>> gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC) >>> gcc -V 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> gcc: '-V' option must have argument >>> make[4]: [error_report] Error 1 (ignored) >>> gcc --version 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/* >>> gzip --best error_Corei264SSE3.tar >>> mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz >>> make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build' >>> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build' >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' >>> Error report error_<ARCH>.tgz has been created in your top-level ATLAS >>> directory. Be sure to include this file in any help request. >>> cat: ../../CONFIG/error.txt: No such file or directory >>> cat: ../../CONFIG/error.txt: No such file or directory >>> make[1]: *** [build] Error 255 >>> make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build' >>> make: *** [build] Error 2 >> >> It's very troublesome for me to build ATLAS by myself. >> My purpose is just using SciPy on my Python. >> Even if it's optimized not so good for my environment, it's OK. >> >> Is there an easy or a sure way to build and install SciPy? > > > Building ATLAS is much harder than building scipy, so you should try to > find some rpm's for it, like > http://linuxtoolkit.blogspot.com/2010/09/installing-lapack-blas-and-atlas-o…. > There's no problem building scipy against ATLAS from a binary install. > > Ralf

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