Mailman 3 January 2010 - SciPy-User

Help required regding Eigenvectors
by Sudheer Phani 14 Mar '22

14 Mar '22

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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pupynere/scipy.io.netcdf
by Ryan May 06 Mar '13

06 Mar '13

Hi, Anyone know if pupynere (a version of which is in scipy.io.netcdf) supports writing files with 64-bit offsets? This allows writing files larger than 2GB. Ryan -- Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma

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Re: [SciPy-User] sinc interpolation
by Travis Oliphant 07 Jun '12

07 Jun '12

On Nov 20, 2009, at 2:26 PM, David Trem wrote: > Hello, > > Is sinc interpolation available in Scipy ? Yes, use scipy.signal.resample which uses a Fourier method to downsample or upsample a signal: from scipy.signal import resample from numpy import r_, sin from pylab import plot x = r_[0:10] y = sin(x) yy = resample(x, 100) # This is a bit tricky to get the x-samples right xx = r_[0:10:101j][:-1] plot(x,y,'ro', xx, yy) -Travis

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computing local extrema
by fred 16 Sep '11

16 Sep '11

Hi, I would like to compute local extrema of an array (2D/3D), ie get a list of points (coords + value). How could I do this? Any hint? TIA. Cheers, -- Fred

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Fitting a system of ODEs to data
by C. Campbell 21 Apr '11

21 Apr '11

Hi everyone, I have a system of coupled multivariate ODEs which I would like to fit to experimental data. If I am reading the SciPy documentation correctly, there exist built in functions to handle systems of multivariate nonlinear functions (Broyden's and Anderson's methods), but not systems of ODEs. After reading up on some general methods, it looks like it would be a real bear to write an implementation myself. I posed this issue to an expert Python programmer with whom I am acquainted, and he suggested using Mathematica to address my problem. I have essentially no experience with Mathematica, though, so before biting that particular bullet I thought I'd check with the larger community to see if there is Python/SciPy solution. Thanks very much for any suggestions! Colin _________________________________________________________________ With Windows Live, you can organize, edit, and share your photos. http://www.windowslive.com/Desktop/PhotoGallery

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scipy/numpy on CentOS 5/ Dependency problem
by Matthias Blaicher 09 Dec '10

09 Dec '10

Hello, I want to install SciPy and numpy on a CentOS 5 system. It contains a fresh install with up-to-date packages. I use the "official" Suse Build repos by DavidCournapeau. It fails with a conflict between refblas3 and blas. [root@localhost yum.repos.d]# yum install python-numpy python-scipy Loading "fastestmirror" plugin Loading mirror speeds from cached hostfile * home_ashigabou: download.opensuse.org * extras: mirrors.tummy.com * updates: dds.gina.alaska.edu * base: mirror.hmc.edu * addons: mirrors.tummy.com extras 100% |=========================| 1.1 kB 00:00 updates 100% |=========================| 951 B 00:00 base 100% |=========================| 1.1 kB 00:00 addons 100% |=========================| 951 B 00:00 Setting up Install Process Parsing package install arguments Resolving Dependencies --> Running transaction check ---> Package python-scipy.i386 0:0.6.0-2.1 set to be updated --> Processing Dependency: libblas.so.3 for package: python-scipy --> Processing Dependency: libblas.so.3 for package: python-scipy --> Processing Dependency: liblapack.so.3 for package: python-scipy --> Processing Dependency: libgfortran.so.1 for package: python-scipy --> Processing Dependency: liblapack.so.3 for package: python-scipy ---> Package python-numpy.i386 0:1.2.0-1.1 set to be updated --> Processing Dependency: gcc-gfortran for package: python-numpy --> Processing Dependency: refblas3 for package: python-numpy --> Processing Dependency: lapack3 < 3.1 for package: python-numpy --> Running transaction check ---> Package gcc-gfortran.i386 0:4.1.2-42.el5 set to be updated --> Processing Dependency: gcc = 4.1.2-42.el5 for package: gcc-gfortran ---> Package blas.i386 0:3.0-37.el5 set to be updated ---> Package lapack3.i386 0:3.0-19.1 set to be updated ---> Package lapack.i386 0:3.0-37.el5 set to be updated ---> Package refblas3.i386 0:3.0-11.1 set to be updated ---> Package libgfortran.i386 0:4.1.2-42.el5 set to be updated --> Running transaction check ---> Package gcc.i386 0:4.1.2-42.el5 set to be updated --> Processing Dependency: libgomp.so.1 for package: gcc --> Processing Dependency: glibc-devel >= 2.2.90-12 for package: gcc --> Processing Dependency: libgomp = 4.1.2-42.el5 for package: gcc --> Running transaction check ---> Package glibc-devel.i386 0:2.5-24 set to be updated --> Processing Dependency: glibc-headers for package: glibc-devel --> Processing Dependency: glibc-headers = 2.5-24 for package: glibc-devel ---> Package libgomp.i386 0:4.1.2-42.el5 set to be updated --> Running transaction check ---> Package glibc-headers.i386 0:2.5-24 set to be updated --> Processing Dependency: kernel-headers for package: glibc-headers --> Processing Dependency: kernel-headers >= 2.2.1 for package: glibc-headers --> Running transaction check ---> Package kernel-headers.i386 0:2.6.18-92.1.13.el5 set to be updated --> Processing Conflict: refblas3 conflicts blas --> Finished Dependency Resolution Error: refblas3 conflicts with blas Is there anything I'm doing wrong here, as I don't want to compile all dependencies by myself.. Sincerly, Matthias Blaicher

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64 bit on Mac?
by Keflavich 26 Sep '10

26 Sep '10

Hi folks, I'd really like to work with some of the large images I have; I work on a galactic plane survey that has lots of images with ~10^8 floating point pixels. I'm also using a mac. Unfortunately I can't get a 64-bit version of python compiled and google has been unhelpful in resolving the problem. Is there a workaround to get 64 bit numpy working without 64 bit python installed? I doubt that, but if anyone has tips I'd appreciate them. On a related note, I also use a linux machine with 64 bit python installed, but it crashes with large arrays giving a segfault where my mac spits out the "ValueError: dimensions too large." error. Any ideas there? Thanks, Adam

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specify lognormal distribution with mu and sigma using scipy.stats
by Mark Bakker 21 Jul '10

21 Jul '10

Hello list, I am having trouble creating a lognormal distribution with known mean mu and standard deviation sigma using scipy.stats According to the docs, the programmed function is: lognorm.pdf(x,s) = 1/(s*x*sqrt(2*pi)) * exp(-1/2*(log(x)/s)**2) So s is the standard deviation. But how do I specify the mean? I found some information that when you specify loc and scale, you replace x by (x-loc)/scale But in the lognormal distribution, you want to replace log(x) by log(x)-loc where loc is mu. How do I do that? In addition, would it be a good idea to create some convenience functions that allow you to simply create lognormal (and maybe normal) distributions by specifying the more common mu and sigma? That would surely make things more userfriendly. Thanks, Mark

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problems with splrep,splev
by Christian K. 08 Jun '10

08 Jun '10

Hi, I am having problems with the spline functions from scipy.interpolate. Reading the doc strings I expected the following to work: from scipy.interpolate import splev,splrep import numpy as N x = N.linspace(-4,10,5) y = x**2 t,c,l = splrep(x,y,xb=0,xe=4) Traceback (most recent call last): File "C:\pythonxy\python\lib\site-packages\scipy\interpolate\fitpack.py", line 406, in splrep n,c,fp,ier = dfitpack.curfit(task, x, y, w, t, wrk, iwrk, xb, xe, k, s) error: (xb<=x[0]) failed for 1st keyword xb Second, I want to fit several curves using the same knots, thus I tried the following: y2 = x**2-3*x+4 t2,c2,l2 = splrep(x,y2,t=t) Traceback (most recent call last): File "C:\pythonxy\python\lib\site-packages\scipy\interpolate\fitpack.py", line 418, in splrep raise _iermess[ier][1],_iermess[ier][0] ValueError: Error on input data Are those bugs or am I misunderstanding something? Eventually I would like to morph one spline into another, therefor I want to stick with the knots. If anyone has experiences with splines and morphing I would be very interested to hear about it. Regards, Christian

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[SciPy-user] Maximum entropy distribution for Ising model - setup?
by Jordi Molins Coronado 12 Mar '10

12 Mar '10

Hello, I am new to this forum. I am looking for a numerical solution to the inverse problem of an Ising model (or a model not-unlike the Ising model, see below). I have seen an old discussion, but very interesting, about this subject on this forum (http://mail.scipy.org/pipermail/scipy-user/2006-October/009703.html). I would like to pose my problem (which is quite similar to the problem discussed in the thread above) and kindly ask you your opinion on that: My space is a set of discrete nodes,s_i, where i=1,...,N, which can take two values, {0,1}. Empirically I have the following information: <s_i>_emp and <s_i*s_j>_emp, where i,j=1,...,N with i!=j. It is well known in the literature that the Ising model P(s_1, s_2, ..., s_N) = 1 / Z * exp( sum(h_i*s_i) + 0.5*sum(J_ij*s_i*s_j) ) i i!=jmaximizes entropy with the constraints given above (in fact, this is not the Ising model, because the Ising model assumes only nearest-neigbour interactions, and I have interactions with all other nodes, but I believe it is still true that the above P(s1,...sN)).What I would like is to solve the inverse problem of finding the h_i and J_ij which maximize entropy given my constraints. However, I would like to restrict the number of h_i and J_ij possible, since having complete freedom could become an unwieldly problem. For example, I could restrict h_i = H and J_ij = J for all i,j=1,...N, i!=j, or I could have a partition of my nodes, say nodes from 1 to M having h_i = H1 and J_ij=J1 i,j=1,...,M i!=j, and h_i=H2 and J_ij=J2 i,j=M+1,...,N i!=j.If I understand correctly the discussion in the thread shown above, a numerical solution for the inverse problem would be:hi_{new}=hi_{old} + K * (<si> - <si>_{emp}) Jij_{new}=Jij_{old}+ K' * (<si*sj> - <si*sj>_{emp}) where K and K' are pos. "step size" constants. (On the RHS, <si> and <si*sj> are w.r.t. hi_{old} and Jij_{old}.)Have IHave I understood all this correctly? In particular, for the case h_i = H and J_ij = J for all i,j=1,...N, i!=j could I simplify the previous algorithm by restricting the calculations only to say i=1 (i=2,...,N should be the same?), and for the case h_i = H1 and J_ij=J1 i,j=1,...,M i!=j, and h_i=H2 and J_ij=J2 i,j=M+1,...,N i!=j simplify it by restricting the calculations only to say i=1 and i=M+1?Thank you for your help and sorry if I am new here and I have committed some "ettiquette" mistake.Jordi

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