Hi Brian,

Thanks for the code - this is definitely in the direction of what I want to make!

The RdYlBu_r colormap is one of the built-in colormaps available in matplotlib.pyplot.cm (you can see all of them here:http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps). I think that using the built-in colormaps might give nicer transitions between the colors, so instead of transitioning linearily between red and white and white and blue, it transitions in a slightly non-linear way, along several segments.

Compare:

plot(plt.cm.RdYlBu_r(arange(256)))

with

plot(my_cmap(arange(256)))

I think that the more nonlinear one might look a little bit nicer (and might be less perceptually misleading in interpreting color differences in the result). But I need to figure out how many segments there are in there.

Thanks - Ariel 

On Sat, Mar 27, 2010 at 4:14 AM, Brian Blais <bblais@bryant.edu> wrote:
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






--
Ariel Rokem
Helen Wills Neuroscience Institute
University of California, Berkeley
http://argentum.ucbso.berkeley.edu/ariel