Hi everyone,
I am trying to make a color-map which will respond to the range of values in
the data itself. That is - I want to take one of the mpl colormaps and use
parts of it, depending on the range of the data.
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. Also - I would want to use the
parts of the colormap that represent how far away the smallest and largest
values in the data are from 0. So - if my data is in the range [x1,x2] I
would want to use the part of the colormap in indices
127-127*abs(x1)/(x2-x1) through 127+127*x2/(x2-x1). If the data only
includes positive numbers, I would want to only use the blue part of the
colormap and if there are negative numbers, I would want to only use the red
part of the colormap (in these cases, I would also want to take only a
portion of the colormap which represents the size of the interval [x1,x2]
relative to the interval [0,x1] or [x2,0], as the case may be).
I think that this might be useful when comparing matrices generated from
different data, but with the same computation, such as correlation or
coherence (see http://nipy.sourceforge.net/nitime/examples/fmri.html to get
an idea of what I mean).
First of all - is this a good idea? Or in other words - is there any reason
I am not thinking of why this idea is a really bad idea?
Second - the technical questions. I think that I can make this happen by
using matplotlib.colors.LinearSegmentedColormap, after fiddling with the
values of the color-map a bit (as described above), but in order to do
that, I need to know what segmentdata was used in order to generate the
original colormap (for example, how many lines did each of the entries in
the cdict have? Looking at a plot of the cmap it looks like there must have
been 8 or 9 for RdYlBu_r, but I can't be sure). I could analyze it in more
detail to get that out empirically, but I am guessing that someone around
here might be able to spare me that lunacy (if not others...).
Thanks in advance,
Ariel
--
Ariel Rokem
Helen Wills Neuroscience Institute
University of California, Berkeley
http://argentum.ucbso.berkeley.edu/ariel
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