Re: [Matplotlib-users] Making a data-driven colormap

[Ariel R. <ar...@be...>]

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 <bb...@br...> 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
> bb...@br...
> 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