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 <bbl...@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
> bbl...@bryant.edu
> http://web.bryant.edu/~bblais <http://web.bryant.edu/%7Ebblais>
> http://bblais.blogspot.com/
>
>
>
>
--
Ariel Rokem
Helen Wills Neuroscience Institute
University of California, Berkeley
http://argentum.ucbso.berkeley.edu/ariel
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