On Mar 27, 2010, at 1:13 , Ariel Rokem wrote:
In particular, I am interested in using the plt.cm.RdYlBu_r
colormap. If the data has both negative and positive values, I want
0 to map to the central value of this colormap (a pale whitish
yellow) and I want negative values to be in blue and positive
numbers to be in red.
not sure if this is what you want (I'd never heard of RdYlBu_r...I
need to go read up!), but I've used a similar colormap with the code
posted below. You might be able to modify it for your case.
hope this helps!
bb
from pylab import *
def bluewhitered(a,N=256):
bottom = [0, 0, 0.5]
botmiddle = [0, 0.5, 1]
middle = [1, 1, 1]
topmiddle = [1, 0, 0]
top = [0.5, 0, 0]
lims=[a.min(),a.max()]
if lims[0]<0 and lims[1]>0:
ratio=abs(lims[0])/(abs(lims[0])+lims[1])
cdict={}
cdict['red']=[]
cdict['green']=[]
cdict['blue']=[]
# negative part
red=[(0.0, 0.0, 0.0),
(ratio/2, 0.0, 0.0),
(ratio, 1.0, 1.0)]
green=[(0.0, 0.0, 0.0),
(ratio/2, 0.5, 0.5),
(ratio, 1.0, 1.0)]
blue=[(0.0, 0.5, 0.5),
(ratio/2, 1, 1),
(ratio, 1.0, 1.0)]
cdict['red'].extend(red)
cdict['green'].extend(green)
cdict['blue'].extend(blue)
nratio=1-(1-ratio)/2.0
# positive part
red=[(ratio, 1.0, 1.0),
(nratio, 1.0, 1.0),
(1, 0.5, 0.5)]
green=[(ratio, 1.0, 1.0),
(nratio, 0., 0.),
(1, 0.0, 0.0)]
blue=[(ratio, 1., 1.),
(nratio, 0, 0),
(1, 0, 0)]
cdict['red'].extend(red)
cdict['green'].extend(green)
cdict['blue'].extend(blue)
elif lims[0]>=0: # all positive
cdict={}
cdict['red']=[]
cdict['green']=[]
cdict['blue']=[]
ratio=0.0
nratio=0.5
# positive part
red=[(ratio, 1.0, 1.0),
(nratio, 1.0, 1.0),
(1, 0.5, 0.5)]
green=[(ratio, 1.0, 1.0),
(nratio, 0., 0.),
(1, 0.0, 0.0)]
blue=[(ratio, 1., 1.),
(nratio, 0, 0),
(1, 0, 0)]
cdict['red'].extend(red)
cdict['green'].extend(green)
cdict['blue'].extend(blue)
else: # all negative
cdict={}
cdict['red']=[]
cdict['green']=[]
cdict['blue']=[]
ratio=1.0
# negative part
red=[(0.0, 0.0, 0.0),
(ratio/2, 0.0, 0.0),
(ratio, 1.0, 1.0)]
green=[(0.0, 0.0, 0.0),
(ratio/2, 0.5, 0.5),
(ratio, 1.0, 1.0)]
blue=[(0.0, 0.5, 0.5),
(ratio/2, 1, 1),
(ratio, 1.0, 1.0)]
cdict['red'].extend(red)
cdict['green'].extend(green)
cdict['blue'].extend(blue)
my_cmap = matplotlib.colors.LinearSegmentedColormap
('my_colormap',cdict,N)
return my_cmap
if __name__=="__main__":
a=randn(20,20)
my_cmap=bluewhitered(a,256)
clf()
pcolor(a,cmap=my_cmap)
colorbar()
--
Brian Blais
[email protected]
http://web.bryant.edu/~bblais
http://bblais.blogspot.com/
------------------------------------------------------------------------------
Download Intel® Parallel Studio Eval
Try the new software tools for yourself. Speed compiling, find bugs
proactively, and fine-tune applications for parallel performance.
See why Intel Parallel Studio got high marks during beta.
http://p.sf.net/sfu/intel-sw-dev
_______________________________________________
Matplotlib-users mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/matplotlib-users
