On 06/07/2011 01:37 AM, Daniel Mader wrote:
Hi Eric,
2011/6/6 Eric Firing<efir...@hawaii.edu>:
It's not quite clear to me yet, but I assume you want to use a call to
imshow with a different data set in the second subplot, but have the
color scale and colorbar be identical to those in the first subplot. Is
that correct? If so, all you need to do is use the same norm for both
calls to imshow--that is, define a norm, set the limits you want on it,
and supply it as a kwarg.
thanks a lot, you helped me to work around my problem, see code below :)
Also, for this sort of comparison, sometimes it is more efficient to use
a single colorbar for multiple panels, as in this example:
http://matplotlib.sourceforge.net/examples/pylab_examples/multi_image.html
Very nice example! It's a little too complex for me, though, with all
the calculations for the axes layout -- I prefer subplots :) However,
I think I have found a nice compromise:
Attached is a slight modification, much simpler than the example above,
but retaining the single colorbar. Alternatively, if you stick with the
colorbar for each panel (which is sometimes clearer), it illustrates a
slightly clearer way of handling the cmap and norm, explicitly using the
same instance of each for both images.
Eric
import pylab
import matplotlib as mpl
pylab.close('all')
dat = pylab.array([[1,2,3,4],[5,6,7,8]])
datT = dat/2
fig = pylab.figure()
ax1 = fig.add_subplot(211)
ax1.set_title('raw data')
im1 = ax1.imshow(dat, interpolation='nearest',
cmap=mpl.cm.get_cmap('rainbow', 20))
fig.colorbar(im1)
ax2 = fig.add_subplot(212)
ax2.set_title('leveled')
im2 = ax2.imshow(datT, interpolation='nearest',
cmap=mpl.cm.get_cmap('rainbow', 20))
## apply norm:
norm = mpl.colors.Normalize(vmin=dat.min(), vmax=dat.max())
im2.set_norm(norm)
fig.colorbar(im2)
## doesn't really work :/
cax = fig.add_axes([0.25, 0.04, 0.5, 0.02])
fig.colorbar(im2, cax, orientation='horizontal')
pylab.show()
Thanks a lot,
best regards,
Daniel
import pylab
import matplotlib as mpl
dat = pylab.array([[1,2,3,4],[5,6,7,8]])
datT = dat/2
norm = mpl.colors.Normalize(vmin=dat.min(), vmax=dat.max())
cmap = mpl.cm.get_cmap('rainbow', 20)
fig = pylab.figure()
fig.subplots_adjust(top=0.92, bottom=0.15,
hspace=0.25,)
#left=0.07, right=0.95)
ax1 = fig.add_subplot(211)
ax1.set_title('raw data')
im1 = ax1.imshow(dat, interpolation='nearest',
cmap=cmap, norm=norm)
#fig.colorbar(im1)
ax2 = fig.add_subplot(212)
ax2.set_title('leveled')
im2 = ax2.imshow(datT, interpolation='nearest',
cmap=cmap, norm=norm)
#fig.colorbar(im2)
## doesn't really work :/ ## in what way?
cax = fig.add_axes([0.25, 0.06, 0.5, 0.02])
fig.colorbar(im2, cax, orientation='horizontal')
pylab.show()
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