Mailman 3 February 2007 - SciPy-User

Newbie Question about Scipy
by Lorenzo Isella Feb. 14, 2007

Feb. 14, 2007

Dear All, I am pretty new to Python, but it has such a good reputation that I decided to give it a try. I am slightly puzzled about the syntax modulename.function. I am going through the SciPy tutorial by Oliphant (btw, is there anywhere online a free updated version of a document of this kind?). To use SciPy I normally do the following: ~$ ipython Python 2.4.4 (#2, Jan 13 2007, 17:50:26) Type "copyright", "credits" or "license" for more information. IPython 0.7.3 -- An enhanced Interactive Python. ? -> Introduction to IPython's features. %magic -> Information about IPython's 'magic' % functions. help -> Python's own help system. object? -> Details about 'object'. ?object also works, ?? prints more. In [1]: from scipy import * However, statements like the ones in the guide: In [12]: from integrate import quad --------------------------------------------------------------------------- exceptions.ImportError Traceback (most recent call last) /home/iselllo/<ipython console> ImportError: No module named integrate do not work. So I have to use: scipy.integrate. Similarly, the function gamma is not recognized, but special.gamma is. How is this chosen by the system? Then: once I have import everything from scipy, is importing explicitly the gamma function a necessity at all? **************************************************************************** On a more general ground, how does Python compare with e.g. MatLab or Octave for scientific computing? Which are the advantages and drawbacks (sorry if this is not the right forum). Kind Regards Lorenzo

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scipy.optimize.leastsq error estimates
by Ewald Zietsman Feb. 14, 2007

Feb. 14, 2007

Hi all, I want to fit a sinusoid of the form A*cos(2*pi*f*t) + B*sin(2*pi*f*t) to irregularly spaced data so that I can get a wave of the form C*cos(2*pi*f*t + phi) where C**2 = A**2 + B**2 and phi = arctan(-B/A). I have implemented this using the leastsq function but, I'd would like to also know the variances ( or standard errors ) of A,B and f. Is there a way I can get the variance-covariance matrix out from leastsq? or at least get a good estimate of the standard errors of my unknowns? -Ewald Zietsman

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Wriitng a list to an ascii file
by Nils Wagner Feb. 14, 2007

Feb. 14, 2007

Hi, I have a list >>> data[0] [1, 3.98, 3.131435898787629, 146.25144654434166] >>> data[1] [2, 4.0014113157358633, -0.23777483140261779, 169.32304332115922] >>> data[2] [3, 4.0000070483264265, -0.0011815833829018629, 167.64445564164987] >>> data[3] [4, 4.0000000001757536, -2.946728727692971e-08, 167.63609488306861] where the first entry is an integer and the remaining entries are floats. How can I write this list to an ascii file without destroying the integer type of the first entry? Nils io.write_array() yields 1.000000000000000e+00 3.980000000000000e+00 3.131435898787629e+00 1.462514465443417e+02 2.000000000000000e+00 4.001411315735863e+00 -2.377748314026178e-01 1.693230433211592e+02 3.000000000000000e+00 4.000007048326427e+00 -1.181583382901863e-03 1.676444556416499e+02 4.000000000000000e+00 4.000000000175754e+00 -2.946728727692971e-08 1.676360948830686e+02 but I would prefer 1 3.980000000000000e+00 3.131435898787629e+00 1.462514465443417e+02 2 4.001411315735863e+00 -2.377748314026178e-01 1.693230433211592e+02 3 4.000007048326427e+00 -1.181583382901863e-03 1.676444556416499e+02 4 4.000000000175754e+00 -2.946728727692971e-08 1.676360948830686e+02

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Experiences with LAPACK3.1 and scipy
by Nils Wagner Feb. 14, 2007

Feb. 14, 2007

Hi all, has someone tested LAPACK3.1 in connection with scipy ? I am curious about it. Version 3.1 has many improvements http://www.netlib.org/lapack/lapack-3.1.0.changes I am mainly interested in the new MRRR and the Hessenberg QR algorithm with the small bulge multi-shift QR algorithm together with aggressive early deflation. Are there wrappers for these routines ? Nils

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Re: [SciPy-user] Array manipulation
by joris＠ster.kuleuven.ac.be Feb. 14, 2007

Feb. 14, 2007

On Wednesday 14 February 2007 11:13, Nils Wagner wrote: >BTW, "insert" is missing in http://www.scipy.org/Numpy_Example_List. >I forgot my password to access the wiki page. Joris, please can you >add an example for insert. I saw that you have just edited that page. Done. Ciao, Joris Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

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Array manipulation
by Nils Wagner Feb. 14, 2007

Feb. 14, 2007

Hi, I would like to remove the i-th column and row from a two-dimensional array A. The remaining array should be kept and stored in B A = random.rand(n,n) This task is very easy if i is the first or last row/column. In that case one can use B = A[1:,1:] or B=A[:-1,:-1] But, what is the best way to get B if 0 < i < n-1 ? Nils

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Re: [SciPy-user] Array manipulation
by joris＠ster.kuleuven.ac.be Feb. 14, 2007

Feb. 14, 2007

Hi Nils, It's not a one-liner, but it may serve you: In [40]: n = 4 In [41]: i = 2 In [42]: In [42]: A = arange(n*n).reshape(n,n) In [43]: A Out[43]: array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) In [44]: I = range(n) In [45]: I.remove(i) In [46]: B = A[ix_(I,I)] In [47]: B Out[47]: array([[ 0, 1, 3], [ 4, 5, 7], [12, 13, 15]]) Cheers, Joris On Wednesday 14 February 2007 09:02, Nils Wagner wrote: >Hi, > >I would like to remove the i-th column and row from a two-dimensional >array A. The remaining array >should be kept and stored in B > >A = random.rand(n,n) > >This task is very easy if i is the first or last row/column. In that >case one can use > >B = A[1:,1:] > >or > >B=A[:-1,:-1] > >But, what is the best way to get B if 0 < i < n-1 ? > >Nils Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

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Mittag-Leffler function
by Nils Wagner Feb. 13, 2007

Feb. 13, 2007

Hi, has someone implemented the Mittag-Leffler function with the aid of scipy ? A Matlab code is available here http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=87… Nils References: http://mathworld.wolfram.com/Mittag-LefflerFunction.html http://en.wikipedia.org/wiki/Mittag-Leffler_function

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Fit statistics for sum of squared relative error
by zunzun＠zunzun.com Feb. 13, 2007

Feb. 13, 2007

I've been doing fits to lowest sum of squared relative error for a while now. These are useful when a data set exhibits increasing heteroscedasticity as the values of the independent variable increase, i.e., data scatter proportional to distance along the x axis. You can test this at http://zunzun.com by selecting a fitting target of "Lowest sum of squared relative errors" when fitting, and this is also in the Python Equations package at http://sf.net/ptojects/pythonequations. Having investigated fit statistics for some time now, it seems *everything* is geared toward absolute error, with not the slightest drop that I can find regarding relative error. These statistics are needed when performing SSQREL rather than SSQABS fitting. Can I safely use existing routines for covariance matrices and parameter standard errors simply by substituting dy(relative)/dx and relative error whenever dy(absolute)/dx and absolute error are used? I apologize, but this is over my head and I would like to report the fit statistics properly. James Phillips http;//zunzun.com P.S. I don't yet have much in the way of fit statistics on the web site, this is what I'm currently working on. I found much of what I need in the BSD-style licensed MPFIT.py at http://cars9.uchicago.edu/software/python/mpfit.html written by Mark Rivers.

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Re: [SciPy-user] Vectorization question
by Anand Patil Feb. 12, 2007

Feb. 12, 2007

>Anand Patil wrote: >>/Hi all, />>/ />>/I want to make array A from array B like so: />>/ />>/A[t, j, k] = \sum_i B[t, j, i] B[t, i, k] />>/ />>/That is, for each t />>/ />>/A[t,] = dot(B[t,], B[t,]) />>/ />>/There's no loopless way to do this in numpy, right? />>/ />>/ />You should be able to do this just using > >A = dot(B,B) > >Because the dot function returns the sum of products over the last >dimension of the first argument and the second-to-last dimension of the >second argument. > >-Travis The output array I'm looking for is rank-3, but as I understand them dot and tensordot can only ever return even-rank arrays: In [1]: from numpy import zeros, dot In [2]: B=zeros((4,3,3)) In [3]: dot(B,B).shape Out[3]: (4, 3, 4, 3) In [4]: Thanks, Anand

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