Mailman 3 April 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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Request for usage examples on scipy.stats.rv_continuous and scipy.ndimage.grey_dilate(structure)
by Christoph Deil 05 Dec '12

05 Dec '12

Hi, I am having some trouble figuring out how to use the following two aspects of scipy: 1) How can I create a random distribution given by some python formula, e.g. pdf(x) = x**2 in the range x = 1 to 2. The example given in the rv_continuous docstring doesn't work for me: In [1]: import matplotlib.pyplot as plt In [2]: numargs = generic.numargs AttributeError: type object 'numpy.generic' has no attribute 'numargs' Is it also possible to use rv_continuous for multidimensional distributions, say pdf(x,y)=x*y in the range x = 1 to 2 and y = 0 to 3? Or for pdfs that are just given numerically on a grid (say as the result of a gaussian_kde() ), not by a python function? It would be nice if someone could add a few examples that show rv_continuous usage to the docstring or tutorial. 2) How to use the 'structure' argument of scipy.ndimage.grey_dilate() correctly? There are some explanations at http://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html but I think they are inconsistent with the implementation: -------------------------------------------------------------- In [98]: a = zeros((7,7)); a[3,3]=1; a Out[98]: array([[ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 1., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.]]) In [100]: s = generate_binary_structure(2,2); s Out[100]: array([[ True, True, True], [ True, True, True], [ True, True, True]], dtype=bool) In [101]: grey_dilation(a,size=(3,3)) Out[101]: array([[ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.]]) In [102]: grey_dilation(a,size=(3,3),structure=s) Out[102]: array([[ 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 2., 2., 2., 1., 1.], [ 1., 1., 2., 2., 2., 1., 1.], [ 1., 1., 2., 2., 2., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1.]]) In [103]: grey_dilation(a,structure=s) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /Users/deil/<ipython console> in <module>() /opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/ndimage/morphology.pyc in grey_dilation(input, size, footprint, structure, output, mode, cval, origin) 354 sz = footprint.shape[ii] 355 else: --> 356 sz = size[ii] 357 if not sz & 1: 358 origin[ii] -= 1 TypeError: 'NoneType' object is unsubscriptable In [104]: grey_dilation(a,footprint=s) Out[104]: array([[ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.]]) -------------------------------------------------------------- Why doesn't grey_dilation(a,structure=s) work? Why doesn't grey_dilation(a,size=(3,3),structure=s) give the same result as grey_dilation(a,size=(3,3),footprint=s)? I am running on numpy version 1.4.0 and scipy version 0.7.1 Thanks! --Christoph

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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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Using Pyinstaller or cx_freeze with scipy
by Craig 12 Apr '11

12 Apr '11

Hello: I was attempting to use cx_freeze to create a stand-alone application that uses scipy, but ran into a problem. When I attempt to run the frozen application, I get the error: scipy\interpolate\polyint.py Cannot import name factorial I looked at the scipy package and the factorial function is in scipy.misc.common.py but the file scipy.interpolate.polyint.py has the line: from scipy import factorial So the polyint script is looking for the factorial function in some other place than its actual physical location. I guess scipy somehow inserts the factorial function in the correct namespace so that it runs correctly. Is there a way to get scipy and cx_freeze to get along? Thanks for your help in advance. Regards, Craig Howard

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scipy.stats.invnorm - rename to invgauss?
by Matthew Brett 23 Feb '11

23 Feb '11

Hi, I was caught in the same trap as another innocent, in thinking that scipy.stats.invnorm was the inverse of the normal distribution: http://bytes.com/topic/python/answers/478142-scipy-numpy-inverse-cumulative… In my desire to improve the docstring, I started looking; after a while I found this: http://en.wikipedia.org/wiki/Inverse_Gaussian_distribution - and that seems to fit the invnorm code. A Google search for 'inverse normal distribution' does not include the page above in the first 20 hits, but does find the inverse of the normal distribution, and this: http://en.wikipedia.org/wiki/Normal-inverse_Gaussian_distribution I wonder whether it would be good to rename scipy.stats.invnorm to scipy.stats.invgauss ? Maybe with a deprecation warning to invnorm? Best, Matthew

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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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