Mailman 3 July 2010 - SciPy-User

Help required regding Eigenvectors
by Sudheer Phani March 14, 2022

March 14, 2022

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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Linear interpolation in 3D
by Hardock, Andreas June 13, 2013

June 13, 2013

Hi, I'm trying to interpolate a 3D data (from the pic attached) with the interp2d command. What I have, are three vectors f, z, A (x, y, z respectively, A is the percentage data given on the isolines). I first put the f and z in a meshgrid and afterwards in the griddata to get a 3D-grid then started the interpolateion. I plotted the the data after gridding, and I observed that almost all nodes are ignored. Do you have any idea how to prepare data to the interp2d command? Don't hesitate to suggest any other solution. my code so far is: import numpy as np from mpl_toolkits.mplot3d import axes3d from scipy.interpolate import interp2d import matplotlib.pyplot as plt from matplotlib import mlab plt.clf() fig = plt.figure(1) ax = axes3d.Axes3D(fig) #read data (ff,ZZ,A,a) = np.loadtxt("accuracy-map.txt", unpack=True) f=np.log10(ff) z=np.log10(ZZ) ##grid everything fgrid, zgrid=np.meshgrid(f,z) #define grid ef=np.linspace(min(f), max(f), len(f)) ez=np.linspace(min(z), max(z), len(f)) Agrid=mlab.griddata(f,z,A, ef,ez) int2d=interp2d(fgrid, zgrid, Agrid, kind='linear') ax.plot(f, z, A, 'ok', markerfacecolor='w') ax.plot_surface(fgrid, zgrid, Agrid) ax.set_xlim3d((min(f), max(f))) ax.set_ylim3d(min(z), max(z)) ax.set_zlim3d(0,100) plt.show()

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pupynere/scipy.io.netcdf
by Ryan May March 6, 2013

March 6, 2013

Hi, Anyone know if pupynere (a version of which is in scipy.io.netcdf) supports writing files with 64-bit offsets? This allows writing files larger than 2GB. Ryan -- Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma

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Request for usage examples on scipy.stats.rv_continuous and scipy.ndimage.grey_dilate(structure)
by Christoph Deil Dec. 5, 2012

Dec. 5, 2012

Hi, I am having some trouble figuring out how to use the following two aspects of scipy: 1) How can I create a random distribution given by some python formula, e.g. pdf(x) = x**2 in the range x = 1 to 2. The example given in the rv_continuous docstring doesn't work for me: In [1]: import matplotlib.pyplot as plt In [2]: numargs = generic.numargs AttributeError: type object 'numpy.generic' has no attribute 'numargs' Is it also possible to use rv_continuous for multidimensional distributions, say pdf(x,y)=x*y in the range x = 1 to 2 and y = 0 to 3? Or for pdfs that are just given numerically on a grid (say as the result of a gaussian_kde() ), not by a python function? It would be nice if someone could add a few examples that show rv_continuous usage to the docstring or tutorial. 2) How to use the 'structure' argument of scipy.ndimage.grey_dilate() correctly? There are some explanations at http://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html but I think they are inconsistent with the implementation: -------------------------------------------------------------- In [98]: a = zeros((7,7)); a[3,3]=1; a Out[98]: array([[ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 1., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.]]) In [100]: s = generate_binary_structure(2,2); s Out[100]: array([[ True, True, True], [ True, True, True], [ True, True, True]], dtype=bool) In [101]: grey_dilation(a,size=(3,3)) Out[101]: array([[ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.]]) In [102]: grey_dilation(a,size=(3,3),structure=s) Out[102]: array([[ 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 2., 2., 2., 1., 1.], [ 1., 1., 2., 2., 2., 1., 1.], [ 1., 1., 2., 2., 2., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1.]]) In [103]: grey_dilation(a,structure=s) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /Users/deil/<ipython console> in <module>() /opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/ndimage/morphology.pyc in grey_dilation(input, size, footprint, structure, output, mode, cval, origin) 354 sz = footprint.shape[ii] 355 else: --> 356 sz = size[ii] 357 if not sz & 1: 358 origin[ii] -= 1 TypeError: 'NoneType' object is unsubscriptable In [104]: grey_dilation(a,footprint=s) Out[104]: array([[ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 1., 1., 1., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0.]]) -------------------------------------------------------------- Why doesn't grey_dilation(a,structure=s) work? Why doesn't grey_dilation(a,size=(3,3),structure=s) give the same result as grey_dilation(a,size=(3,3),footprint=s)? I am running on numpy version 1.4.0 and scipy version 0.7.1 Thanks! --Christoph

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Re: [SciPy-User] sinc interpolation
by Travis Oliphant June 7, 2012

June 7, 2012

On Nov 20, 2009, at 2:26 PM, David Trem wrote: > Hello, > > Is sinc interpolation available in Scipy ? Yes, use scipy.signal.resample which uses a Fourier method to downsample or upsample a signal: from scipy.signal import resample from numpy import r_, sin from pylab import plot x = r_[0:10] y = sin(x) yy = resample(x, 100) # This is a bit tricky to get the x-samples right xx = r_[0:10:101j][:-1] plot(x,y,'ro', xx, yy) -Travis

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inverse function of a spline
by josef.pktd＠gmail.com Oct. 2, 2011

Oct. 2, 2011

I have a function y = f(x) which is monotonically increasing (a cumulative distribution function) f is defined by piecewise polynomial interpolation, an interpolating spline on some points I would like to get the inverse function (ppf) x = f^{-1} (y) if the spline is of higher order than linear In the linear case it's trivial, because the inverse function is also just a piecewise linear interpolation. If I have a cubic spline, or any other smooth interpolator in scipy, is there a way to get the inverse function directly? I don't know much about general properties of splines, and would appreciate any hints, so I can avoid numerical inversion (fsolve or similar) Thanks, Josef

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computing local extrema
by fred Sept. 16, 2011

Sept. 16, 2011

Hi, I would like to compute local extrema of an array (2D/3D), ie get a list of points (coords + value). How could I do this? Any hint? TIA. Cheers, -- Fred

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get best few of many: argsort( few= ) using std::partial_sort ?
by denis May 3, 2011

May 3, 2011

Folks, to get the best few of a large number of objects, e.g. vectors near a given one, or small distances in spatial.distance.cdist or .pdist, argsort( bigArray )[: a few ] is not so hot. It would be nice if argsort( bigArray, few= ) did this -- faster, save mem too. Would anyone else find this useful ? I recently stumbled across partial_sort in stl; fwiw, std:partial_sort( A, A + sqrt(N), A + N ) is ~ 10 times faster than std:sort on my old mac ppc, even for N 100. Also fwiw, nth_element alone is ~ twice as slow as partial_sort -- odd. cheers -- denis

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Fitting a system of ODEs to data
by C. Campbell April 21, 2011

April 21, 2011

Hi everyone, I have a system of coupled multivariate ODEs which I would like to fit to experimental data. If I am reading the SciPy documentation correctly, there exist built in functions to handle systems of multivariate nonlinear functions (Broyden's and Anderson's methods), but not systems of ODEs. After reading up on some general methods, it looks like it would be a real bear to write an implementation myself. I posed this issue to an expert Python programmer with whom I am acquainted, and he suggested using Mathematica to address my problem. I have essentially no experience with Mathematica, though, so before biting that particular bullet I thought I'd check with the larger community to see if there is Python/SciPy solution. Thanks very much for any suggestions! Colin _________________________________________________________________ With Windows Live, you can organize, edit, and share your photos. http://www.windowslive.com/Desktop/PhotoGallery

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Using Pyinstaller or cx_freeze with scipy
by Craig April 12, 2011

April 12, 2011

Hello: I was attempting to use cx_freeze to create a stand-alone application that uses scipy, but ran into a problem. When I attempt to run the frozen application, I get the error: scipy\interpolate\polyint.py Cannot import name factorial I looked at the scipy package and the factorial function is in scipy.misc.common.py but the file scipy.interpolate.polyint.py has the line: from scipy import factorial So the polyint script is looking for the factorial function in some other place than its actual physical location. I guess scipy somehow inserts the factorial function in the correct namespace so that it runs correctly. Is there a way to get scipy and cx_freeze to get along? Thanks for your help in advance. Regards, Craig Howard

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