Thanks, that is all good info to know. I change my data to log and
normalize it so the logNorm is just linear actually so specifying only
levels is fine. I'll let you know if that doesn't work properly for some
Ok, yeah I looked at pcolormesh quickly and can't remember why I chose
originally when I wrote this to go with contourf but I use to only do
like 10 levels. I think it might be because use a log yaxis and think it
used to be a bit funky or couldn't get it working properly but seemed
No, I don't want to modify the ticks but the black lines around that
like how they are removed on the major axis in this example:
I want to remove the black lines also around the colorbar. Not the tick
marks. Does that make sense?
One more quick question out of curiosity noticing from saving plots to
.pdf from contourf and pcolormesh vs specgram. Specgram seems to output
the lines and text as vector graphics. Then imbeds the image. When
outputting from pcolormesh or contourf this isn't the case. It tries to
write the lines or something else weird happens. Can you output to .pdf
from these and make the lines and text be vectors. Then the image output
as an image in the pdf like in specgram. Or is there a setting to do
this and specify the .dpi of the image in the .pdf.
Lines and text are output to pdf exactly the same by specgram, pcolormesh, and contourf. The difference should be only in the image part of the plot, which is rasterized for a specgram image and for the "quadmesh" produced by pcolormesh, but is a set of patches (vector specification, not rasterized) for contourf. Are you seeing results that are inconsistent with this expectation?
On 2012/07/26 10:26 PM, Jeffrey Spencer wrote:
Thanks a lot,
On Fri, Jul 27, 2012 at 5:51 PM, Eric Firing <efiring@…202… > <mailto:efiring@…202…>> wrote:
On 2012/07/26 9:20 PM, Jeffrey Spencer wrote:
import numpy as np
import matplotlib as mpl
X, Y = np.meshgrid(arange(20),arange(__20))
Z = np.arange(20*20)
Z = Z.reshape(20,20)
logNorm = mpl.colors.Normalize(vmin=0,__vmax=200)
fig = mpl.pyplot.figure(10)
ax = fig.add_subplot(111)
surf = ax.contourf(X,Y,Z, 100, cmap=matplotlib.cm.jet, norm =
cbar = fig.colorbar(surf, shrink=0.70, norm=logNorm)
OK, the basic problem here is that you are specifying 100 levels,
which are being auto-selected to cover the actual data range; and
the colorbar is doing what it is supposed to do, which is show the
levels you actually have. Try leaving out the norm, and just
specify the levels to cover what you want, more like this:
surf = ax.contourf(X, Y, Z, np.arange(0, 200.1, 2), cmap=mpl.cm.jet,
cbar = fig.colorbar(surf, shrink=0.7)
If you actually do want a log norm, you can pass that in to contourf
and it will be passed on to colorbar; but most likely you should
still specify the levels you want as an array, and not specify vmin
and vmax in the norm. If you want log scaling, it may work better
to simply plot the log of Z, and use the colorbar label to indicate
that this is what you are doing.
Note that with a recent change, you can use the set_under and
set_over methods of the cmap to specify arbitrary colors, or no
color, for the extended regions; or you can leave out the "extend"
kwarg and not color the regions outside the range of your contour
In general, contourf is most appropriate when there is a moderate
number of levels, well under 100; if you want that many gradations,
then you might do better with pcolormesh or ax.pcolorfast or imshow.
For those image-like methods, it is appropriate to use vmin and
vmax, either directly, or in a norm.