Mailman 3 March 2009 - SciPy-User

Equivalent in Scipy of Matlab resize
by Nicolas ROUX 25 Mar '09

25 Mar '09

Hi, I am looking for the equivalent of Matlab resize in Scipy. Matlab's resize is able to upscale/downscale an array with 'linear', 'bilinear', 'bicubic'. Giving a scale factor or the required output shape. I can't find the equivalent function in Scipy. Does it exist ? Thanks for your help ;-) Nicolas.

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Strange Warning When Running Simple Script
by Lorenzo Isella 25 Mar '09

25 Mar '09

Dear All, I am running debian testing (for the amd64 architecture) on my machine and I have Num(Sci)Py and matplotlib installed from standard repositories. Today, I have run again a simple code to create arrays by stitching together several copies of the same array #! /usr/bin/env python import scipy as s import numpy as n import pylab as p def replica(pos_arr, N_rep): #temp=pos_arr dim=s.shape(pos_arr) n_row=dim[0] if (len(dim)>1): n_col=dim[1] print "n_row and n_col are, ", n_row, n_col else: print "n_row is, ", n_row if (len(dim)>1): copy=s.zeros(((n_row*N_rep),n_col)) else: copy=s.zeros((n_row*N_rep)) for i in xrange(N_rep): if (len(dim)>1): copy[(i*n_row):((i+1)*n_row),:]=pos_arr else: copy[(i*n_row):((i+1)*n_row)]=pos_arr return copy eta_0=s.array([0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389]) eta_1=s.sqrt(eta_0) my_copy=replica(eta_0,200) p.save("eta_0_8_200", my_copy) my_copy=replica(eta_1,200) p.save("eta_1_8_200", my_copy) print "So far so good" However, when I run this code (replica.py) now (I have not touched this script in a while) I get the following output $ ./replica.py /usr/lib/python2.5/site-packages/scipy/misc/__init__.py:25: DeprecationWarning: NumpyTest will be removed in the next release; please update your code to use nose or unittest test = NumpyTest().test n_row is, 8 n_row is, 8 So far so good I never had that warning before. Is it anything I should be worried about? Cheers Lorenzo

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Create n-dim array from axis
by Paul Hilscher 25 Mar '09

25 Mar '09

Dear all, I am looking for a function which takes some axes as an argument and produce the correspond n-dimensional array. So let's say we have two dimensions with following axis x = linspace(0.0,1.0,3) y = linspace(2.0,3.0,3) and we want to create the array by providing only the axis arr = create_array(x,y) and "create_array" produces then arr = array([[0.0,2.0], [0.5,2.0], [1.0,2.0]], [[0.0,2.5], [0.5,2.5], [1.0,2.5]] [[0.0,3.0], [0.5,3.0], [1.0,3.0]]) I've been looking for quite a while now for such a function, but I did not found any versatile and clean one. Any hints ? Thanks for all help, Paul

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scipy conference 2009?
by Andrew Straw 24 Mar '09

24 Mar '09

As summer plans are starting to get made, and I don't want to miss the SciPy Conference (again) this year -- what's the plan for SciPy 09? -Andrew

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coo_matrix, lapack, cvxopt
by Eric Friedman 24 Mar '09

24 Mar '09

Hi, a couple of questions: 1) how do I look inside a coo_matrix -- I'd like to extract the i,j, data? 2) I need to solve large sparse rectangular matrix equation which doesn't have full rank, but is solvable. spsolve (dsolve) doesn't work. Can I do my own LUP decomposition using scipy.sparse? 3) How does lapack compare to umfpack? (I'm thinking about using cvxopt which has lapack.) Also more generally, how does cvxopt's sparse routines do compared to scipy? thanks, Eric

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umfpack installation (and dsolve error)
by Eric Friedman 23 Mar '09

23 Mar '09

Hi, sorry for the newbie question. I'd like to install umfpack, but am confused on what to do. I downloaded easy_installer, but where do I get the full umfpack etc. btw, the reason for this is that I'm having a problem with dsolve which I think is a bug, but may just be my error. for example: a=matrix([[1,1],[1,1]]) b= matrix([.5,.5]).T dsolve(a,b) yields [.5,.5] which is not a solution of ax=b - I think... (I also entered the matrix as sparse) any ideas what's going on here? thanks, Eric

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1.3.0b1 howto test
by Neal Becker 23 Mar '09

23 Mar '09

howto test without installing? PYTHONPATH=~/numpy-1.3.0b1/build/lib.linux-x86_64-2.5/ python -c 'import numpy; numpy.test()' Running unit tests for numpy NumPy version 1.3.0b1 NumPy is installed in /home/nbecker/numpy-1.3.0b1/build/lib.linux- x86_64-2.5/numpy Python version 2.5.2 (r252:60911, Sep 30 2008, 15:42:03) [GCC 4.3.2 20080917 (Red Hat 4.3.2-4)] nose version 0.10.3 ---------------------------------------------------------------------- Ran 0 tests in 0.004s OK Well, found numpy 1.3.0 OK, but what about the tests?

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sparse.lil_matrix accepts array assignments to elements
by Andrew Hawryluk 21 Mar '09

21 Mar '09

Hi, we noticed an unexpected behaviour in sparse.lil_matrix today. The following runs without errors: import numpy as np from scipy import sparse a = sparse.lil_matrix((10,10)) a[0,0] = np.array([3.1415926,3.1415926]) I would have expected some error ('angry red text' in IDLE) when I tried to assign a 1D array to a single element of the sparse array, but no error occurs. (This assignment was a bug in our code.) We found the error later when we tried a.tocsr(). Should there be a type-check of some sort in sparse.lil_matrix to prevent this? Andrew

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Monotone Interpolation
by Darcoux Christine 21 Mar '09

21 Mar '09

Hi, I would like to interpolate 1D array. My data are monotonic and the interpolation method should also be monotonic if I want stability guaranteed in my application. I would like to know if there is a simple way to get third order monotone interpolation with scipy (for example monotone cubic spline interpolation like this http://en.wikipedia.org/wiki/Monotone_cubic_interpolation) ? Regards, Christine

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1.3.0b1 OK
by Neal Becker 20 Mar '09

20 Mar '09

Tested using virtualenv fedora f10 x86_64 ~/python/bin/python -c 'import numpy; numpy.test()' Running unit tests for numpy NumPy version 1.2.0 NumPy is installed in /usr/lib64/python2.5/site-packages/numpy Python version 2.5.2 (r252:60911, Sep 30 2008, 15:42:03) [GCC 4.3.2 20080917 (Red Hat 4.3.2-4)] nose version 0.10.3 ...........................................................................................................................................................................................................................................................................................................................................................................................................................................................K.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................. ---------------------------------------------------------------------- Ran 1726 tests in 5.921s OK (KNOWNFAIL=1)

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