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I am using matplotlib version 1.3.1 with numpy 1.7.1 on a win32 platform for the 
following code:


import matplotlib
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
import matplotlib.colors as col
import matplotlib.cm     as cm
from matplotlib.patches import Wedge
import numpy as np


def register_cmap(cmapName):
     """
      Purpose: define colormap using the from_List() method as a
               segmented list and register it.
     """
     startcolor = '#00AF33'   # truegreen
     midcolor   = '#FFE600'   # yolk (a medium dark yellow)
     endcolor   = '#FF0033'   # bright red
     cmap2      = col.LinearSegmentedColormap.from_list(cmapName,
[startcolor,midcolor,endcolor])
     cm.register_cmap(cmap=cmap2)
     return cmap2

# Define my colormap red->yellow->green for curvature
my_cmap = register_cmap('reylgr')


# Modified matplotlib gallery example
radii = np.arange(0.0,1.05,.05) # inner radius of rings
width = 0.05  # width of rings
patches = []
for r in radii:
     wedge = Wedge((0.0,0.0), r+width, 0.0,360.0, width = width)
     patches.append(wedge)

fig = plt.figure()
ax = fig.add_subplot(111)

colors = radii
p = PatchCollection(patches, cmap=my_cmap)
p.set_array(colors)
ax.add_collection(p)
ax.set_xlim(-1.5,+1.5)
ax.set_ylim(-1.5,+1.5)
plt.colorbar(p)

plt.show()

When I execute this code I do get concentric circles (wedge rings) as expected. 
However,
I do not want "edges" along the boundaries of the rings. I would like the color 
changes
between rings to look like the colorbar.

I have tried several options with Wedge (e.g. edgecolor = "none"); but, this had 
no observable effect.

Help on this would be appreciated.

Thanks for pointing it to a packaging issue, as matplotlib works very well
after installing the missing packages.
I don't know really the the issue, but I hope it gets sorted out. The
earlier binaries had everything it needed on windows, so very convenient to
users. I think problems like this could really discourage new users to try,
particularly the inexperienced.

In my case, the matplotlib install was a clean install on a new machine,
though I had used it often on other computers. It was installed after Python
2.7.8, numpy, scipy, and Vpython, so the problem had to be packaging rather
than transitional, it'd would seem.


One possibility is that with v1.3, we changed how packaging was done.
Unfortunately, this did cause some transitional issues. The best bet is to
uninstall *all* versions of matplotlib, pylab, and mpl_toolkits first, then
re-install v1.3.1. Note that waiting for the v1.4 release wouldn't
necessarily solve anything as it is the transition *from* older versions of
matplotlib that is the issue rather than transitioning *to* newer versions.

Hopefully this helps,
Ben Root





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