Mailman 3 November 2008 - SciPy-User

adding cookbook entry
by Brian Blais 07 Nov '08

07 Nov '08

Hello, I was wondering if there are some directions/tutorial for adding a cookbook entry. I'd like to add one under Scientific Scripts, but I don't want to mess up the Scipy Cookbook in the process of my bumbling around. :) thanks, Brian Blais -- Brian Blais bblais(a)bryant.edu http://web.bryant.edu/~bblais

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Calculating a lot of (squared) Mahalanobis distances
by David Warde-Farley 07 Nov '08

07 Nov '08

Hi folks, I'm trying to calculate a lot of Mahalanobis distances (in essence, applying a positive definite quadratic x.T * A * x to a lot of vectors x) and trying to think of the fastest way to do it with numpy. If I've got a single vector x and a 2D array sigmainv, then I've got something like this. import numpy as np ... xmmu = x - mu dist = np.dot(xmmu, np.dot(sigmainv, xmmu)) However if I've got a DxN 2d array of N different vectors for which I want this quantity, it seems I can either use a loop or do something like xmmu = x - mu[:,np.newaxis] dist = np.diag(xmmu, np.dot(sigmainv, xmmu))) It seems like a lot of wasted computation to throw out the off- diagonals. One thought I've had would be to diagonalize sigmainv and then do something tricky with scalar products and broadcasting the diagonal, but I am not sure whether that would save me much. Does anyone have any other tricks up their sleeve? Thanks, David

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syntax for indexing
by Michael Hearne 07 Nov '08

07 Nov '08

Assume that I have a setup like this: from pylab import * x = random((4,4)) I know how to get the indices of the values that are (for example), greater than 0.5: i = (x > 0.5).nonzero() How do I get the indices for those values in x that are greater than 0.5 AND less than 0.8? I tried: i = (x > 0.5 && x < 0.8).nonzero() i = (x > 0.5 & x < 0.8).nonzero() i = (x > 0.5 and x < 0.8).nonzero() to no avail. Is this the wrong approach? For Matlab users, the functionality which I am trying to replicate is: x = rand(4,4); i = find(x > 0.5 & x < 0.8); Thanks, Mike -- ------------------------------------------------------ Michael Hearne mhearne(a)usgs.gov (303) 273-8620 USGS National Earthquake Information Center 1711 Illinois St. Golden CO 80401 Senior Software Engineer Synergetics, Inc. ------------------------------------------------------

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Where did the Numpy-discussion group go?
by SimonPalmer 06 Nov '08

06 Nov '08

The Numpy-discussion group seems to have disappeared. I'm still receiving emails from the distribution list but I can't find the group. I doubt I am the first person to ask, so sorry for the repeat.

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subscribe
by Brendon Lloyd 06 Nov '08

06 Nov '08

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Distributed testing?
by Gardner Pomper 06 Nov '08

06 Nov '08

I have a c library, hooked to python with swig, that I want to test on my condor cluster. I have not found any testing frameworks that seem to have any hooks for running on a cluster. I was wondering if anyone in this group knows of something. I am currently using nose to test on a single machine, but I want to run many data file through the tests, and would like to use my cluster to do that. Thanks, - Gardner

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solving linear equations ?
by Stef Mientki 06 Nov '08

06 Nov '08

hello, (forgive my math is a bit rusty, so I don't know the right terms anymore) If I want to solve a set of linear equations, I use in MatLab: a \ b this works also if I have too many equations, so more columns than rows. In Numpy for Matlab users http://www.scipy.org/NumPy_for_Matlab_Users I read this: linalg.solve(a,b) if a is square linalg.lstsq(a,b) otherwise I find the name already suspicious, sound like least square, which is confirmed by the help. So I guess the translation from MatLab to Numpy is not correct. Is there a function to reduce the number of columns / remove the redundancy, so I end up with a square matrix ? thanks, Stef Mientki

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Understanding numpy array operations
by Evan Wilson 04 Nov '08

04 Nov '08

Hello, I am a novice python/numpy user, using the language for computational physical science research. I have been modifying scripts developed by another student who worked on the project before me. However, I require a good understanding of what is actually happening in the code for when I go back to modify them. I was wondering if anyone could tell me what the bit of code does: n = 5 A = reshape(zeros(5*3*n), (n*n,3))*1.0 for j in range(n): for i in range(n): A[i+n*j] = i*u + j*v I understand there are some matrix operations happening but I cannot tell what they do. I would appreciate any help you could offer. Thanks, BT

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F2PY: Problems after upgrading to Python2.6
by Benjamin Kern 04 Nov '08

04 Nov '08

Hello, i'm experiencing strange problems after upgrading to python2.6. I'm also using numpy-svn and scipy-svn. So here is the problem. When i try to wrap the following simple fortran code, C File hello.f subroutine foo (a) integer a print*, "Hello from Fortran!" print*, "a=",a end I have problems executing this from python, i.e. >>> import hello >>> print hello.__doc__ This module 'hello' is auto-generated with f2py (version:2_5968). Functions: foo(a) . >>> print hello.foo.__doc__ foo - Function signature: foo(a) Required arguments: a : input int >>> hello.foo(4) Traceback (most recent call last): File "<stdin>", line 1, in <module> RuntimeError: more argument specifiers than keyword list entries (remaining format:'|:hello.foo') Thanks for the help in advance

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Explanation of different edge modes in scipy.ndimage
by Kurt Smith 04 Nov '08

04 Nov '08

Hello, I'm doing some gaussian filtering of periodic 2D arrays using scipy.ndimage.gaussian_filter. There is a 'mode' argument that is set to 'reflect' by default. In _ni_support.py:34 there is a conversion function, '_extend_mode_to_code' that gives the different modes available. For periodic data I believe I should use 'wrap', but I'm interested to know what the other modes mean, esp the difference between 'reflect' and 'mirror'. For the record, the modes defined are 'nearest', 'wrap', 'reflect', 'mirror', and 'constant'. For future reference, is there a place where these arguments are documented? Thanks, Kurt

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