Mailman 3 May 2009 - NumPy-Discussion

How to convert a list into a structured array?
by Wei Su 05 May '09

05 May '09

Hi,All: My first post! I am very excited to find out structured array (record array) in Python. Since I do data manipulation every day, this is truly great. However, I typically download data using pyodbc, the default output is a big list. So I am wondering how to convert that big list into a structured array? using array() will turn it into a text array, afaik. it is even better if anybody can show me some tricks to download the data directly as a structured array. Thanks a lot for the help. Wei Su BTW: I am also interested in Python's ability to handle large data. Any hints or suggestion is welcome.

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SVD failure
by Philipp K. Janert 05 May '09

05 May '09

The following code: from scipy import * from scipy import linalg m = matrix( [ [1,1,0,0], [1,1,0,0], [0,0,1,1], [0,0,1,1] ] ) u,s,v = linalg.svd( m ) fails with the following message: Traceback (most recent call last): File "boo.py", line 10, in <module> u,s,v = linalg.svd( m ) File "/usr/lib64/python2.6/site-packages/scipy/linalg/decomp.py", line 509, in svd lwork = calc_lwork.gesdd(gesdd.prefix,m,n,compute_uv)[1] RuntimeError: more argument specifiers than keyword list entries (remaining format:'|:calc_lwork.gesdd') On the other hand, calculating la, v = eig( m ) works just fine. If I see this correctly, my SciPy version is 0.6.0; running on 64bit Suse 11. Any thoughts? Best, Ph.

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FaceL - Facile Face Labeling
by David Bolme 05 May '09

05 May '09

I have been working on a open source face recognition demo tool called FaceL for the past few months. FaceL is a simple and fun face processing and labeling tool that labels faces in a live video from an iSight camera or webcam. It uses OpenCV for face detection, ASEF correlation filters for eye localization, and a Support Vector Machine for face classification. FaceL is implemented in python (using PyVision, wxPython, SciPy, OpenCV, libsvm, and PIL libraries) with a binary executable available on Mac OS 10.5 Intel. (Windows and Linux versions expected soon.) FaceL has been fun to work on and I thought that the NumPy community might like to see their software in action. FaceL source code, videos, and executable can be found at: http://pyvision.sourceforge.net/facel

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object arrays and ==
by David Warde-Farley 05 May '09

05 May '09

Hi, Is there a simple way to compare each element of an object array to a single object? objarray == None, for example, gives me a single "False". I couldn't find any reference to it in the documentation, but I'll admit, I wasn't quite sure where to look. David

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loadtxt example problem ?
by bruno Piguet 05 May '09

05 May '09

Hello, I'm new to numpy, and considering using loadtxt() to read a data file. As a starter, I tried the example of the doc page ( http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html) : >>> from StringIO import StringIO # StringIO behaves like a file object >>> c = StringIO("0 1\n2 3") >>> np.loadtxt(c) I didn't get the expectd answer, but : Traceback (moste recent call last): File"(stdin)", line 1, in <module> File "C:\Python25\lib\sire-packages\numpy\core\numeric.py", line 725, in loadtxt X = array(X, dtype) ValueError: setting an array element with a sequence. I'm using verison 1.0.4 of numpy). I got the same problem on a Ms-Windows and a Linux Machine. I could run the example by adding a \n at the end of c : c = StringIO("0 1\n2 3\n") Is it the normal and expected behaviour ? Bruno.

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stop criterion for an alternating signal
by Nils Wagner 04 May '09

04 May '09

Hi all, How can I define a stop criterion for an alternating series ? Any pointer would be appreciated. Nils from numpy import loadtxt, arange from pylab import plot, show A = loadtxt('alternate.dat') m = len(A) x = arange(0,m) plot(x,A) show()

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Really strange result
by Neal Becker 04 May '09

04 May '09

In [16]: (np.linspace (0, len (x)-1, len(x)).astype (np.uint64)*2).dtype Out[16]: dtype('uint64') In [17]: (np.linspace (0, len (x)-1, len(x)).astype (np.uint64)*n).dtype Out[17]: dtype('float64') In [18]: type(n) Out[18]: <type 'int'> Now that's just strange. What's going on?

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Efficient scaling of array
by Johannes Bauer 04 May '09

04 May '09

Hello list, is there a possibility to scale an array by interpolation, automatically? For illustration a 1D-example would be an array of size 5, which is scaled to size 3: before: [ 1, 2, 3, 4, 5 ] 1/1 2/3 1/3 1 1/3 2/3 1 after : [ 2.33, 5, 7.66 ] The same thing should be possible in nD, with the obvious analogy. Is there such a function in numpy? Kind regards, Johannes

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Porting strategy for py3k
by David Cournapeau 04 May '09

04 May '09

Hi, I looked more in detail on what would be needed to port numpy to py3k. In particular, I was interested at the possible strategies to keep one single codebase for both python 2.x and python 3.x. The first step is to remove all py3k warnings reported by python 2.6. A couple of recurrent problems - reduce is removed in py3k - print is removed - some exceptions constructions are not allowed I think a first step would be to add a compatibility module to remove any use of print/reduce in the numpy codebase (should not be too difficult). For reduce, we could import functools from python code (functools is not available in python 2.4). Unless someone has an objection, I will import functools code, make it compile for python 2.4 and above, and remove all builtin reduce calls in numpy. Another problem is related to nose: there is an experimental branch for nose which supports py3k, but nose <= 1.0 will not support py3k. The nose author intend to support py3k in a version > 1.0, at which point he will only support python 2.6 and above. I don't know what to do with this (include our own nose in numpy for version < 2.6 - giving up support for python < 2.6 does not sound like a realistic option in the next few years). cheers, David

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apply table lookup to each element
by Neal Becker 04 May '09

04 May '09

Suggestion for efficient way to apply a table lookup to each element of an integer array? import numpy as np _cos = np.empty ((2**rom_in_bits,), dtype=int) _sin = np.empty ((2**rom_in_bits,), dtype=int) for address in xrange (2**12): _cos[address] = nint ((2.0**(rom_out_bits-1)-1) * cos (2 * pi * address * (2.0**-rom_in_bits))) _sin[address] = nint ((2.0**(rom_out_bits-1)-1) * sin (2 * pi * address * (2.0**-rom_in_bits))) Now _cos, _sin are arrays of integers (quantized sin, cos lookup tables) How to apply _cos lookup to each element of an integer array: phase = np.array (..., dtype =int) cos_out = lookup (phase, _cos) ???

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NumPy-Discussion May 2009