Mailman 3 March 2009 - SciPy-User

Re. python windows set up
by Karl Young 16 Mar '09

16 Mar '09

Sturla, Gael, Josef, and Sebastian, Many thanks for the sage advice re. windows installation; I will pass the accumulated wisdom to my colleague (and hopefully make some progress re. her conversion to python ! :-)). -- KY

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Has IPython been useful to you? Please let me know...
by Fernando Perez 16 Mar '09

16 Mar '09

Hi all, [ apologies for the semi-spam, I'll keep this brief and expect all replies off-list ] IPython is a project that many of you on this list are likely to use in your daily work, either directly or indirectly (if you've embedded it or used it as a component of some other system). I would simply like to ask you, if IPython has been significantly useful for a project you use, lead, develop, etc., to let me know. For legal/professional reasons, I need to gather information about who has found IPython to be of value. I started IPython as a toy 'afternoon hack' in late 2001, and today it continues to grow, as the nicely summarized Ohloh stats show: https://www.ohloh.net/p/ipython (obviously, this is now the result of the work of many, not just myself, as is true of any healthy open source project as it grows). But I have never systematically tracked its impact, and now I need to do so. So, if you have used IPython and it has made a significant contribution to your project, work, research, company, whatever, I'd be very grateful if you let me know. A short paragraph on what this benefit has been is all I ask. Once I gather any information I get, I would contact directly some of the responders to ask for your authorization before quoting you. I should stress that any information you give me will only go in a documentation packet in support of my legal/residency process here in the USA (think of it as an oversized, obnoxiously detailed CV that goes beyond just publications and regular academic information). To keep traffic off this list, please send your replies directly to me, either at this address or my regular work one: Fernando.Perez(a)berkeley.edu In advance, many thanks to anyone willing to reply. I've never asked for anything in return for working on IPython and the ecosystem of scientific Python tools, but this is actually very important, so any information you can provide me will be very useful. Best regards, Fernando Perez.

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strange behavior of scipy.interpolate.interp2d ?
by Jean-Luc Menut 16 Mar '09

16 Mar '09

Hello All, I'm testing scipy.interpolate.interp2d, and it looks like there is a strange behavior of the 'linear' interpolation. It looks like only a part of the image is interpolated. Maybe I'm mistaking, but the cubic interpolation looks fine to me. I attach 3 examples images to this mail : original.png is the original image, linear.png is the original image linearly interpolated (with twice the number of pixel of original.png) and cubic.png is the same but with a cubic interpolation. here the corresponding few lines of code: a=zeros([5,5]) x=y=arange(5) b=scipy.interpolate.interp2d(x,y,a,'cubic') #or 'linear xx=yy=arange(9)/2. imshow(b(xx,yy),interpolation='nearest') Have you any idea on what's is going on ? Best regards, Jean-Luc

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remezord for scipy; ticket 475
by Günter Dannoritzer 15 Mar '09

15 Mar '09

Hi, A while back I asked about the remezord() function here on the mailing list and got the reply that unit tests will be needed in order to add it to the SciPy. I had sent some questions concerning the functionality to Lev who provided the function and he agreed on moving this discussion to the mailing list. So below are my questions and Lev's reply. Guenter Lev Givon wrote: > Received from Günter Dannoritzer on Fri, Feb 20, 2009 at 05:33:16PM EST: >> Hi Lev, >> >> I saw you provided a patch to support remezord() in scipy with ticket #475: >> >> http://scipy.org/scipy/scipy/ticket/475 >> > > Hi, Günter. Glad to see that someone besides myself finds the above > function useful :-) > >> Unfortunately this patch is not scheduled for any release yet due to the >> missing test cases. Now I am trying to figure out how difficult it would >> be to add test cases and was wondering whether you could give me some >> hints for them. >> >> Basically what I see is that remezord() actually is based on three >> functions, using methods from Kaiser, Herrmann, and Ichige papers to >> compute the required order for a filter. >> >> What I am having a hard time with is how to verify the proper function >> of those algorithms in self-checking tests. I can see that some simple >> parameter checking can be done, but then, how to determine that the >> results are correct? One way would be to calculate the actual filter >> after the order estimation and make sure that the transfer function is >> within the specified window. Maybe calculate further transfer functions >> with reduced order and make sure that the specified transfer function is >> not met anymore within a specified error range? > > Rather than checking whether the transfer functions determined by the > remez() function actually satisfy the filter design parameters passed > to remezord(), wouldn't it be preferrable (and possibly more feasible) > to check that the my implementations of the various filter order > estimation functions actually produce the output values described in > the respective papers where they are described? As far as I can > gather, numpy tests should check whether an algorithm implementation > is correct on the assumption that the underlying algorithm is correct. > >> Like allow the calculated order to be +/- 1 or 2 within the actual >> minimum order that can be calculated with the remez() function and meets >> the required transfer function? > > Not exactly sure what you mean. The Remez algorithm computes a filter's > coefficients given a specified filter order; it cannot calculate the > minimum order itself. > >> Next question would be how complex to do the transfer function? Just >> low, high, band pass filter and stop band filter? Then vary the corner >> frequencies? >> >> I was not sure whether you are still subscribed to the scipy user >> mailing list and send you this mail direct. If you are still subscribed >> we could just move the discussion to the mailing list. > > Yes, I am subscribed to scipy-user; feel free to continue the > discussion there. I'm interested in feedback from others so that I can > learn a bit about writing numpy tests myself in the future. > >> Thanks for your ideas. >> >> Cheers, >> >> Guenter > > L.G. >

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scipy.sparse.linalg.eigen
by Nils Wagner 15 Mar '09

15 Mar '09

Hi all, I am interested in the eigenvalues of a product of matrices: M_m M_{m-1} \cdots M_1 x_0 = \lambda x_0 (1) The problem can be rewritten as a generalized eigenvalue problem A z = \lambda B z, (2) where B is singular. I am only interested in the f i n i t e eigenvalues of (2). So far I have solved the problem by scipy.linalg.eigvals. Is it possible to compute these eigenvalues with scipy.sparse.linalg.eigen ? Nils

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numpy installation
by Avi Purkayastha 15 Mar '09

15 Mar '09

Hi, I installed numpy fine in ~/softwares/numpy. However when I went to test it.. >>> import numpy Traceback (most recent call last): File "<stdin>", line 1, in ? ImportError: No module named numpy How do I "load" the numpy module in python? I have the numpy install directory in my $PATH, do I need to do anything else? Thanks -- Avi

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Mac OS X 10.5 PPC Python 2.5.1 vecLib.framework error
by Rodrigo Guerra 14 Mar '09

14 Mar '09

Hi, I am trying to compile SciPy from SVN r5623 on a Mac PPC G5 running Leopard, Python 2.5.1 (that came bundled with the system), gcc 4.0.1, gfortran 4.2.3, NumPy 1.2.1 from sources. Here is the snippet of the output at the point the first error occurs: creating build/temp.macosx-10.5-ppc-2.5/scipy/sparse/linalg/eigen creating build/temp.macosx-10.5-ppc-2.5/scipy/sparse/linalg/eigen/arpack creating build/temp.macosx-10.5-ppc-2.5/scipy/sparse/linalg/eigen/arpack/ARPACK creating build/temp.macosx-10.5-ppc-2.5/scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS compile options: '-Iscipy/sparse/linalg/eigen/arpack/ARPACK/SRC -c' gcc: scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/veclib_cabi_c.c In file included from /System/Library/Frameworks/vecLib.framework/Headers/vecLib.h:61, from scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/veclib_cabi_c.c:1: /System/Library/Frameworks/vecLib.framework/Headers/vForce.h:33: error: syntax error before 'float' /System/Library/Frameworks/vecLib.framework/Headers/vForce.h:34: error: syntax error before 'double' In file included from /System/Library/Frameworks/vecLib.framework/Headers/vecLib.h:61, from scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/veclib_cabi_c.c:1: /System/Library/Frameworks/vecLib.framework/Headers/vForce.h:145: error: syntax error before '*' token /System/Library/Frameworks/vecLib.framework/Headers/vForce.h:146: error: syntax error before '*' token Any help appreciated. Cheers, Guerra

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Install newest version of scipy to Ubuntu 8.04
by Joseph Johnson 14 Mar '09

14 Mar '09

Hi, I was wondering if there is an apt source for Ubuntu 8.04? I did find: https://edge.launchpad.net/~scipy/+archive/ppa but its for 8.10 Thanks for the help. _________________________________________________________________ Windows Live™: Life without walls. http://windowslive.com/explore?ocid=TXT_TAGLM_WL_allup_1a_explore_032009

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storing numpy arrays
by Michael Hearne 13 Mar '09

13 Mar '09

I have a MySQL database, in which I would like to store a numpy array as a BLOB. I'm trying to find out the easiest/most efficient way to store/extract this data. I looked at scipy.io.write_array, which looked somewhat promising, until I saw the notice that this method is deprecated in favor of numpy.savetxt. I could potentially use scipy.io.savemat/loadmat, but that would require writing to a binary (.mat file), then reading it back in again just to stuff it into the database. I could also use pickle, but I found a bug report somewhere that pickle may do strange things to the array in the process or reading or writing. Does anyone have any suggestions? Thanks, Mike

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[Fwd: Re: storing numpy arrays]
by Michael Hearne 13 Mar '09

13 Mar '09

All: Never mind - I figured out I needed to seek to the beginning of the file-like object I created! --Mike -------- Original Message -------- Subject: Re: [SciPy-user] storing numpy arrays Date: Fri, 13 Mar 2009 10:33:30 -0600 From: Michael Hearne <mhearne(a)usgs.gov> Reply-To: SciPy Users List <scipy-user(a)scipy.org> To: SciPy Users List <scipy-user(a)scipy.org> References: <49B9802E.9050308(a)usgs.gov> <49B991A0.7060409(a)usgs.gov> <3d375d730903121557q573b1d39ud0d631324bc99b18(a)mail.gmail.com> Robert - Thanks. I hadn't run across those methods. However... I tried the script below (using numpy version "1.1.0.dev5077"), and got the following output: ####output#### [ 0.05867671 0.71670412 0.99391546 0.02065827] ?NUMPYF{'descr': '<f8', 'fortran_order': False, 'shape': (4,)} pm??? ??җ{=????=??'??? 9m?p'?? Traceback (most recent call last): File "./testme.py", line 13, in <module> y = numpy.lib.format.read_array(output) File "/Library/Python/2.5/site-packages/numpy-1.1.0.dev5077-py2.5-macosx-10.3-i386.egg/numpy/lib/format.py", line 302, in read_array version = read_magic(fp) File "/Library/Python/2.5/site-packages/numpy-1.1.0.dev5077-py2.5-macosx-10.3-i386.egg/numpy/lib/format.py", line 105, in read_magic raise ValueError("could not read %d characters for the magic string; got %r" % (MAGIC_LEN, magic_str)) ValueError: could not read 8 characters for the magic string; got '' ####output#### ######script##### #!/usr/bin/python from numpy import array from numpy.random import random import numpy.lib.format import StringIO output = StringIO.StringIO() x = random(4) print x numpy.lib.format.write_array(output, x) print output.getvalue() y = numpy.lib.format.read_array(output) print y ######script##### Robert Kern wrote: > On Thu, Mar 12, 2009 at 17:50, Michael Hearne <mhearne(a)usgs.gov> wrote: > >> Conor - Thank you for your suggestion. I am in fact already using >> pytables in another part of my application. However, using PyTables for >> this portion is not a desirable option. >> >> Perhaps I should rephrase the question: >> What is the the most efficient way to serialize a numpy array into a >> structure that _could_ be written to a disk, or stuffed into a database, >> etc? >> > > Use numpy.lib.format.{read,write}_array() to use the NPY format, which > should be the most robust and efficient way to do what you want. I > have used this to store numpy arrays as MySQL BLOBs before, and it > worked quite well. > > _______________________________________________ SciPy-user mailing list SciPy-user(a)scipy.org http://mail.scipy.org/mailman/listinfo/scipy-user

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