matplotlib-users Mailing List for matplotlib

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 However, this seems out of date, some examples work, some don't. Are 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

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

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However, this seems out of date, some examples work, some don't. Are 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

On Fri, Jul 25, 2008 at 10:22 AM, Peter Wesbdell
<fly...@go...> 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 0-th
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.
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On Fri, Jul 25, 2008 at 10:08 AM, Ben Axelrod <bax...@co...> 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/matplotlib-0.98.3rc1.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

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.

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