Mailman 3 November 2012 - SciPy-User

How to display a RGB image (MFISH)?
by jp 03 Apr '23

03 Apr '23

Hi, I have written a script to try to combine five images into a three channels RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…> . The resulting RGB image looks like a grey scale image instead of a color one. I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array: *rgb = np.zeros((shape[0],shape[1],3),dtype=float) mxr=np.max(R) mxg=np.max(G) mxb=np.max(B) Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array rgb[:,:,0]=Rnorm rgb[:,:,1]=Gnorm rgb[:,:,2]=Bnorm* *pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[]) pylab.imshow(rgb) pylab.show()* Any advice? Thank you Jean-Patrick

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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn 28 Jul '22

28 Jul '22

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Help required regding Eigenvectors
by Sudheer Phani 14 Mar '22

14 Mar '22

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 13 Jun '13

13 Jun '13

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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efficiency of the simplex routine: R (optim) vs scipy.optimize.fmin
by servant mathieu 10 May '13

10 May '13

Dear scipy users, Again a question about optimization. I've just compared the efficiency of the simplex routine in R (optim) vs scipy (fmin), when minimizing a chi-square. fmin is faster than optim, but appears to be less efficient. In R, the value of the function is always minimized step by step (there are of course some exceptions) while there is lot of fluctuations in python. Given that the underlying simplex algorithm is supposed to be the same, which mechanism is responsible for this difference? Is it possible to constrain fmin so it could be more rigorous? Cheers, Mathieu

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scipy sprint @ EuroSciPy '12?
by Ralf Gommers 01 May '13

01 May '13

Hi all, Would people be interested in having a scipy sprint at EuroSciPy this year? Last year we tried to do a last minute mini-sprint and ended up hunting for wifi access for half the time, so it would be good to organize things better this time around. I'm thinking a one-day sprint on Wed Aug 22nd would be good. If there's interest, I'm happy to organize (contact the conference organizers for a room, create a wiki page, etc.). Ralf

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Re: [SciPy-User] cobyla
by The Helmbolds 26 Mar '13

26 Mar '13

On my system (Windows 7, Python 2.7.x and IDLE, latest SciPy), I observe the following behavior with fmin_cobyla and minimize's COBYLA method. Case 1: When run either in the IDLE interactive shell or within an enclosing Python program: 1.1. The fmin_cobyla function never returns the Results dictionary, and never displays it to Python's stdout. This is true regardless of the function call's disp setting. 1.2. The 'minimize' function always returns the Results dictionary but never displays it to Python's stdout. Again, this is true regardless of the function call's disp setting. Case 2: When run interactively in Window's Command Prompt box: 2.1 The fmin_cobyla function never returns the Result dictionary, regardless of the function call's disp setting. Setting disp to True or False either displays the Results dictionary in the command box or not (respectively). I don't think the Results dictionary gets to the command box via stdout. 2.2 The 'minimize' function always returns the Result dictionary, regardless of the function call's disp setting. Setting disp to True or False either displays the Results dictionary in the command box or not (respectively). I don't think the Results dictionary gets to the command box via stdout. My thanks to all who helped clarify this situation. Bob H

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pupynere/scipy.io.netcdf
by Ryan May 06 Mar '13

06 Mar '13

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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Reading TDM/TDMS Files with scipy
by Nils Wagner 20 Feb '13

20 Feb '13

Hi all, Is it possible to read TDM/TDMS files with scipy ? I found a tool for Matlab http://zone.ni.com/devzone/cda/epd/p/id/5957 Nils

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Weighted KDE
by Zachary Pincus 13 Jan '13

13 Jan '13

Hello all, A while ago, someone asked on this list about whether it would be simple to modify scipy.stats.kde.gaussian_kde to deal with weighted data: http://mail.scipy.org/pipermail/scipy-user/2008-November/018578.html Anne and Robert assured the writer that this was pretty simple (modulo bandwidth selection), though I couldn't find any code that the original author may have generated based on that advice. I've got a problem that could (perhaps) be solved neatly with weighed KDE, so I'd like to give this a go. I assume that at a minimum, to get basic gaussian_kde.evaluate() functionality: (1) The covariance calculation would need to be replaced by a weighted-covariance calculation. (Simple enough.) (2) In evaluate(), the critical part looks like this (and a similar stanza that loops over the points instead): # if there are more points than data, so loop over data for i in range(self.n): diff = self.dataset[:, i, newaxis] - points tdiff = dot(self.inv_cov, diff) energy = sum(diff*tdiff,axis=0) / 2.0 result = result + exp(-energy) I assume that, further, the 'diff' values ought to be scaled by the weights, too. Is this all that would need to be done? (For the integration and resampling, obviously, there would be a bit of other work...) Thanks, Zach

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