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From: Ryan May <rmay31@gm...>  20080725 19:04:28

David Arnold wrote: > All, > > I am aware of the 3d examples at: http://scipy.org/Cookbook/ > Matplotlib/mplot3D > > However, this seems out of date, some examples work, some don't. Are > there other pointers that show how I can use matplotlib to draw three > dimensional surfaces similar to the ones drawn in Matlab with mesh, > surf, and friends? Unfortunately, the 3d plotting routine, axes3d, is unmaintained and unsupported. It has actually been removed from SVN and won't be in future releases. Volunteers are welcome to try and see if they can fix it. :) Other options would be Vtk (with python bindings) (http://www.vtk.org) or Mayavi2 (https://svn.enthought.com/enthought/wiki/MayaVi). Ryan  Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma 
From: Ryan May <rmay31@gm...>  20080725 18:56:49

Ben Axelrod wrote: > 3. Both of the above mentioned bandaid fixes suffer from some bug (I > think in numpy). Where the min() and max() of a numpy array where the > first value is NaN, bugs out: > > > > x = np.asarray([None, 1, 2, 3, 4, 5, 6, 7, 8, 9], float) > > y = np.asarray([0, 1, 2, 3, 4, 5, 6, 7, 8, None], float) > > z = np.asarray([0, 1, 2, 3, 4, 5, None, 7, 8, 9], float) > > > > print min(x), max(x) #prints 1.#QNAN 1.#QNAN > > print min(y), max(y) #prints 0.0 8.0 > > print min(z), max(z) #pritns 0.0 9.0 It's actually pure luck that min/max worked at all. What you want is numpy.nanmax() and numpy.nanmin() which properly handle NaN's in your array. Ryan  Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma 
From: David Arnold <dwarnold45@su...>  20080725 17:51:31

All, I am aware of the 3d examples at: http://scipy.org/Cookbook/ Matplotlib/mplot3D However, this seems out of date, some examples work, some don't. Are there other pointers that show how I can use matplotlib to draw three dimensional surfaces similar to the ones drawn in Matlab with mesh, surf, and friends? Any url's appreciated. Thanks. David 
From: John Hunter <jdh2358@gm...>  20080725 17:00:05

On Fri, Jul 25, 2008 at 10:22 AM, Peter Wesbdell <flyingdeckchair@...> wrote: > Hello all, my first post here. > > I am moving from using scilab to Pylab, can anyone tell me why the two > following snippets of code produce very different results? BTW. The scilab > code produces the expected result. I don't know what scilab does, but the filling of the z array looks wrong, since python indexing starts at 0, not 1, so you would want to do for i in range(0,100): for j in range(0,100): but you rarely want to loop over arrays. My snippet below shows how to use meshgrid to use numpy's elementwise operations and avoid loops. By only filling starting at 1, you are using the memory in the 0th row and column unintialized (np.zeros can be safer than np.empty in this regard) Also, be careful when defining constants as integers, since integer division produces integers (3/2=1) Here is my script  does it produce what you are expecting? import numpy as np import matplotlib.pyplot as plt Lx = 1. Ly = 1. n = 2. m = 2. f = 100. w = 2*np.pi*f t = 1. A = 2. Kx = n*np.pi/Lx Ky = m*np.pi/Ly x = np.arange(100.) y = np.arange(100.) X, Y = np.meshgrid(x, y) Z = A * np.sin(Kx*X) * np.sin(Ky*Y) * np.exp(1j*w*t) plt.contourf(Z) plt.show() > > Scilab: > Lx=1; > Ly=1; > n=2; > m=2; > f=100; > w=2*%pi*f; > t=1; > A=2; > Kx=n*%pi/Lx; > Ky=m*%pi/Ly; > x=linspace(0,100); > y=linspace(0,100); > z=zeros(100,100); > for i = 1:100 > for j = 1:100 > z(i,j) = A * sin(Kx*x(i)) * sin(Ky*y(j)) * %e^(%i*w*t); > end > end > contour(z) > > > Pylab: > from pylab import * > Lx=1 > Ly=1 > n=2 > m=2 > f=100 > w=2*pi*f > t=1 > A=2 > Kx=n*pi/Lx > Ky=m*pi/Ly > x=arange(0,100) > y=arange(0,100) > z=empty((100,100)) > for i in range(1,100): > for j in range(1,100): > z[i, j] = A * sin(Kx*x[i]) * sin(Ky*y[j]) * e**(1j*w*t) > contourf(z) > show() > > Cheers, > Pete. >  > This SF.Net email is sponsored by the Moblin Your Move Developer's challenge > Build the coolest Linux based applications with Moblin SDK & win great > prizes > Grand prize is a trip for two to an Open Source event anywhere in the world > http://moblincontest.org/redirect.php?banner_id=100&url=/ > _______________________________________________ > Matplotlibusers mailing list > Matplotlibusers@... > https://lists.sourceforge.net/lists/listinfo/matplotlibusers > > 
From: John Hunter <jdh2358@gm...>  20080725 16:39:13

On Fri, Jul 25, 2008 at 10:08 AM, Ben Axelrod <baxelrod@...> wrote: > I have noticed 2 bugs having to do with NaN handling in the scatter() I believe this is fixed in svn (0.98 branch)  I tested your first example and it behaved as expected. I f you have a build environment, please test the release candidate http://matplotlib.sourceforge.net/tmp/matplotlib0.98.3rc1.tar.gz Any other users who would like to test the release candidate, we would be much obliged. We do not have any binaries for testing unfortunately. JDH 
From: Peter Wesbdell <flyingdeckchair@go...>  20080725 15:39:48

Hello all, my first post here. I am moving from using scilab to Pylab, can anyone tell me why the two following snippets of code produce very different results? BTW. The scilab code produces the expected result. Scilab: Lx=1; Ly=1; n=2; m=2; f=100; w=2*%pi*f; t=1; A=2; Kx=n*%pi/Lx; Ky=m*%pi/Ly; x=linspace(0,100); y=linspace(0,100); z=zeros(100,100); for i = 1:100 for j = 1:100 z(i,j) = A * sin(Kx*x(i)) * sin(Ky*y(j)) * %e^(%i*w*t); end end contour(z) Pylab: from pylab import * Lx=1 Ly=1 n=2 m=2 f=100 w=2*pi*f t=1 A=2 Kx=n*pi/Lx Ky=m*pi/Ly x=arange(0,100) y=arange(0,100) z=empty((100,100)) for i in range(1,100): for j in range(1,100): z[i, j] = A * sin(Kx*x[i]) * sin(Ky*y[j]) * e**(1j*w*t) contourf(z) show() Cheers, Pete. 
From: Ben Axelrod <baxelrod@co...>  20080725 15:09:01

I have noticed 2 bugs having to do with NaN handling in the scatter() function. And one other bug that seems to be in numpy. 1. The min and max for the axes are not computed properly when there are NaNs in the data. Example: import pylab as pl import numpy as np x = np.asarray([0, 1, 2, 3, None, 5, 6, 7, 8, 9], float) y = np.asarray([0, None, 2, 3, 4, 5, 6, 7, 8, 9], float) ax = pl.subplot(111) ax.scatter(x, y) pl.show() The points with NaN values are left out of the plot as expected, but you will see that everything before the NaN is ignored when computing the axis ranges. (The X axis goes from 4 to 10, cutting off some data, when it should be from 1 to 10. The Y axis goes from 1 to 10 when it should be also be from 1 to 10.) This is rather annoying since these simple calls fix the issue: ax.set_xlim(min(x), max(y)) ax.set_ylim(min(y), max(y)) 2. We see the same behavior for the 'c' axis. Example: import pylab as pl import numpy as np x = np.asarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], float) y = np.asarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], float) z = np.asarray([0, 1, 2, 3, 4, 5, None, 7, 8, 9], float) ax = pl.subplot(111) ax.scatter(x, y, c=z) pl.show() We see that everything before point 7 has zero color. And we can bandaid fix it by adding: ax.scatter(x, y, c=z, vmin=min(z), vmax=max(z)) Then only the one NaN point has zero color. 3. Both of the above mentioned bandaid fixes suffer from some bug (I think in numpy). Where the min() and max() of a numpy array where the first value is NaN, bugs out: x = np.asarray([None, 1, 2, 3, 4, 5, 6, 7, 8, 9], float) y = np.asarray([0, 1, 2, 3, 4, 5, 6, 7, 8, None], float) z = np.asarray([0, 1, 2, 3, 4, 5, None, 7, 8, 9], float) print min(x), max(x) #prints 1.#QNAN 1.#QNAN print min(y), max(y) #prints 0.0 8.0 print min(z), max(z) #pritns 0.0 9.0 FYI, I am using MatPlotLib version 0.91.4 and NumPy 1.1.0 on windows and Debian Linux. Thanks, Ben 