Mailman 3 July 2009 - 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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scipy/numpy on CentOS 5/ Dependency problem
by Matthias Blaicher 08 Dec '10

08 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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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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3D plotting
by Robert Cimrman 10 Sep '09

10 Sep '09

Hi, I need to do some simple 3D plots, so I tried the two Cookbook recipes (xplt, mplot3d) and failed: * xplt: does not work when using a custom installation directory such as python setup.py install --root=/home/share/software apparently the gistdata directory path is wrong: gist.error: failed to create drawing -- Gist work.gs style sheet missing * mplot3d: does not work with my version of matplotlib (0.80). I have made the changes mentioned in the Cookbook to no avail. (Axes.__init__() args apparently changed, as well as some other matplotlib object attributes) Any ideas? mplot3d looks great, I would really like to use it! thanks, r.

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Solve an arbitrary function
by Permafacture 25 Aug '09

25 Aug '09

I'm new to this package, and am used to mathematica. I've been reading through the tutorials and numpy lists of functions, yet i cannot find the answer to this, though it would be trivial in mathematica. Can someone show me an example of how to find the intersection of a line and a polynomial? for instance: y1=x1^2, y2=x2+1 Solving a system of linear equations is pretty straight forward, but it isn't clear to me how to approach this in scipy. The most general approach to this would probably be the most useful to me. thanks.

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Memory usage of scipy.io.loadmat
by Ariel Rokem 17 Aug '09

17 Aug '09

Hi everyone, I have stumbled on some interesting behavior of scipy.io.loadmat. The short of it - it looks like loadmat is gobbling up memory in some unjustified manner and releasing it under some strange circumstances. Here's the long story. This is all happening on a Mac OS 10.5.7, running EPD 4.0.30001 (Python 2.5.2), but with a relatively new version of scipy (see below). I start ipython -pylab in one terminal and run 'top' in another, in order to monitor the memory usage. Here's what I get initially: PhysMem: 417M wired, 449M active, 183M inactive, 1055M used, 3041M free. Then: In [1]: import scipy In [2]: scipy.__version__ Out[2]: '0.8.0.dev5606' In [3]: import scipy.io as sio Here's what it looks like now: PhysMem: 418M wired, 450M active, 183M inactive, 1058M used, 3038M free. So far, so good. I read in a large matfile with tons of data in it: In [4]: a = sio.loadmat('/Users/arokem/Projects/SchizoSpread/Scans/SMR033109_MC/Gray/Original/TSeries/Scan1/tSeries1.mat') PhysMem: 419M wired, 1024M active, 183M inactive, 1632M used, 2464M free. So - about 600 MB of memory is taken up by this new variable. Now to the wierdness: In [5]: b --------------------------------------------------------------------------- NameError Traceback (most recent call last) /Users/arokem/<ipython console> in <module>() NameError: name 'b' is not defined Of course - 'b' doesn't exist! But now, the memory usage has dramatically gone down: PhysMem: 420M wired, 740M active, 183M inactive, 1350M used, 2746M free. So - just invoking an error in the ipython command line has freed up 300 MB. Where did they come from? I tried different things - assigning other variables doesn't seem to free up this memory. Neither do calls to other functions. Except "plot()", which does seem to do the trick for some reason. Interestingly, when I run all this in a python interactive session (and not ipython), I get a similar memory usage initially. Calling a non-existent variable does not free up the memory, but other things do. For example, import matplotlib.pylab into the namespace did the trick. Does anyone have any idea what is going on? Thanks -- Ariel -- Ariel Rokem Helen Wills Neuroscience Institute University of California, Berkeley http://argentum.ucbso.berkeley.edu/ariel

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Ubuntu vs Fedora for scientific work?
by Dharhas Pothina 05 Aug '09

05 Aug '09

Hi All, This is slightly off topic but I felt that this lists membership would have good input on this question. I am presently running fedora 8 on my main workstation and it is getting a bit long in the tooth. Back when I set this machine up, RHEL was too much of a pain to use because scientific packages were always extremely outdated and difficult to install. Ubuntu was nice to use at home but installing scientific packages like the Intel Fortran compiler etc was complicated (not impossible, just more work than I wanted). So I went with Fedora which has worked pretty well so far. Recently, I've noticed that a lot of scientific packages now have ubuntu repositories and even the intel compiler has an ubuntu option. So I'm trying to decide whether to go with Fedora 11 or Ubuntu Jaunty. I'm not trying to start a flame war but I'm interested in what people's experience has been. thanks, - dharhas

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book on scipy
by Bala subramanian 04 Aug '09

04 Aug '09

Friends, Someone please suggest me some good books on scipy/numpy for a beginner. Thanks, Bala

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