Mailman 3 May 2009 - SciPy-User

Trac down?
by Wes McKinney 04 May '09

04 May '09

Getting a "database is locked error". Thanks

2 1

Restricting the values to be plotted while using contourf
by Mani chandra 04 May '09

04 May '09

Hi, How can I restrict the values of 'z' while using contourf. For example, if my dataset has values of z ranging from say 0 to 10000, I only want the plot of those values of 'z' from 0 to 100 and appropriately set the colormap. Thanks Mani chandra

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Custom tolerance estimate for odeint
by Adam Reichold 04 May '09

04 May '09

I am a physics student and I am currently using scipy for a lecture project. I would like to integrate some ODE using my own "tolerance function," meaning the neccessary stepwidth should not be estimated based on the standard error approximation used in LSODE, but rather using a function that I pass to odeint. Is that possible? I did not find any word on that in the documentation. Maybe someone could give me a hint. Best regards, Adam. P.S.: Maybe this clears it up a bit more: I am integrating an ODE describing a physical problem and I want to use things like conservation of energy or momentum as a tolerance. Meaning I want to use the error of energy conservation to control the stepwidth.

1 0

Additional constraints on optimizing inputs for optimize.fmin_l_bfgs_b
by Leon Adams 04 May '09

04 May '09

Hi, I was wondering if it is possible to place constraints on input variables for the optimize.fmin_l_bfgs_b optimization routine. For example I am optimizing on X1,X2,X3,Y1,Y2,Y3 each with bounds [0,1]. And with the additional constraint that X1+X2+X3==1, Y1+Y2+Y3==1. Thanks in advance. _________________________________________________________________ Hotmail® goes with you. http://windowslive.com/Tutorial/Hotmail/Mobile?ocid=TXT_TAGLM_WL_HM_Tutoria…

2 1

scipy import problem when running from terminal
by David Vine 03 May '09

03 May '09

Hello, when I execute my python scripts from the terminal (ubuntu 9.04) the scipy module cannot be found when i try to import it and i get an ImportError exception. However I am confident that the scipy module is installed correctly because calling the same script from within IPython using execfile has no problem finding the scipy module. To be specific, the following code: #test.py import scipy as sp def main(): m = scipy.zeros((100)) return 0 if __name__ == '__main__': main() will execute to completion under IPython. However, calling it from the command line in a terminal with: > python test.py gives the exception > import scipy as sp > ImportError: No module named scipy I cannot figure out why? Any help would be appreciated. Thanks, David

2 1

Sparse factorisation
by Gael Varoquaux 03 May '09

03 May '09

Hi there, This is a rather general question about sparse factorisation and Scipy. I no nothing about sparse linear algebra, but I happen to need to do a sparse Cholesky. By sparse Cholesky, I don't mean a sparse implementation of Cholesky, my matrices are small, I don't actually care about sparse linear algebra, but I mean finding P, permutation matrix, and L, lower-only matrix, such that the sparse positive definite matrix A can be written: A = P L L' P', where P is a reordering of the rows and column of A used to maximise the sparsity of L. What is really of interest to me is P. P can be computed using the colamd routine, present in UMFPACK. Scipy sparse does make use of the colamd routine (by setting the permc_spec argument of scipy.sparse.splu to 3). However, I cannot figure out a way of extracting P. I have been looking around in the sparse code source, as well as the scikits.umfpack code source, and I must admit I am a bit at loss to what is the best way to achieve my goals. The umfpack.py module, both in the scipy code base and in the scikit, is broken. Scipy seems to hint that it is depreciated, but nothing replaces it to provide the functionality I want. In addition, the tests of the scikit don't run with the svn versions of numpy and scipy. So I am wondering, in what direction is all this going. Where should I invest my efforts to get things working, and get my 'P' in a reasonnably short amount of time? Any help much appreciated, as I am no pro of these problems. By the way, progress on this issue could close ticket 261: http://projects.scipy.org/scipy/ticket/261 Cheers, Gaël

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Re: [SciPy-user] Constrained least-squares fitting routine?
by Adam Ginsburg 03 May '09

03 May '09

Thanks for the replies. Re: Rob - You're right that it's not a simple problem to solve, but I'm pretty sure other people have solved it and I was hoping for a solution that was already implemented. However, your response gives me some insight into how to deal with the problem. Thanks. Re: Alan - That looks potentially very useful, but OpenOpt looks to be in pretty early stages of development, especially in terms of documentation. Is there any chance OpenOpt code will be included in scipy in the (near) future? Re: William - Good call on mpfit.py. Somehow my google searches completely missed it, but once you pointed it out I realized I already had the astrolibpy code. mpfit.py is also at http://cars9.uchicago.edu/software/python/mpfit.html... and even though it's not explicitly compatible with numpy, I haven't run into any problems using that version either. Thanks all! I think I have my solution, though I hope mpfit or some variant will be included in scipy in the future. Adam

3 2

Constrained least-squares fitting routine?
by Adam Ginsburg 02 May '09

02 May '09

Hi Scipy group, Is there a constrained least squares fitting routine available, or can anyone offer me tips on implementing such a beast? I have been using scipy.optimize.leastsq, but I do not know how to constrain parameters. The model I'm looking to emulate is Craig Markwardt's mpfit.pro (http://www.physics.wisc.edu/~craigm/idl/down/mpfit.pro), in particular the parinfo section that allows max/min and fixed parameters. I've tried simply constraining parameters in my fitting function using if statements to set min/max values, but this strategy fails, I think because the algorithm pushes into space outside of the limits and can't get back. I don't think the constrained fitting tools, e.g. fmin_cobyla, are what I'm looking for, but I can't be certain I understand them. Are they likely/likelier to get stuck in local minima than the Levenberg-Marquardt algorithm used in leastsq? Thanks, Adam

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Ignoring pixels with gaussian_filter
by Thomas Robitaille 02 May '09

02 May '09

Hello, I am currently using gaussian_filter to smooth an image stored in a numpy array. My problem is that some pixels have no defined value, and are set to NaN. If gaussian_filter stumbles upon a NaN value, it will set all pixels within a certain radius of that value to NaN. Consider the following example: --- import numpy as np from scipy.ndimage import gaussian_filter a = np.ones((9,9)) a[4,4] = np.nan b = gaussian_filter(a,sigma=2) print "Original:" print a print "Smoothed:" print b --- Of course, I can simply use a[np.where(np.isnan(a))] = 0. to reset all NaN pixels to zero, but then the result depends on the value I replace NaNs by. Is there a way to get gaussian_filter to simply ignore such pixels when smoothing? (apart from writing a gaussian filter algorithm from scratch!) Thanks for any help, Thomas

3 4

fourier transform in numpy/scipy
by Lubos Vrbka 01 May '09

01 May '09

hi guys, are the discrete fourier transform pairs in numpy/scipy self adjoint (np.fft.fft - np.fft.ifft)? i read somewhere, that this property doesn't necessarily have to be fulfilled. i performed some tests and it seems that it indeed is so, but i wanted to be sure... best, -- Lubos _@_" http://www.lubos.vrbka.net

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