Like so, not that it couldn't be improved:
import matplotlib.cm as cm
import matplotlib.colors as colors
import pylab as p
def rgb_to_dict(value, cbar):
return dict(zip(('red','green','blue','alpha'), cbar(value)))
def subcolorbar(xmin, xmax, cbar):
'''Returns the part of cbar between xmin, xmax, scaled to 0,1.'''
assert xmin < xmax
assert xmax <=1
cd = cbar._segmentdata.copy()
colornames = ('red','green','blue')
rgbmin, rgbmax = rgb_to_dict(xmin, cbar), rgb_to_dict(xmax, cbar)
for k in cd:
tmp = [x for x in cd[k] if x[0] >= xmin and x[0] <= xmax]
if tmp == [] or tmp[0][0] > xmin:
tmp = [(xmin, rgbmin[k], rgbmin[k])] + tmp
if tmp == [] or tmp[-1][0] < xmax:
tmp = tmp + [ (xmax,rgbmax[k], rgbmax[k])]
#now scale all this to (0,1)
square = zip(*tmp)
xbreaks = [(x - xmin)/(xmax-xmin) for x in square[0]]
square[0] = xbreaks
tmp = zip(*square)
cd[k] = tmp
return colors.LinearSegmentedColormap('local', cd, N=256)
if __name__=="__main__":
subset = [.1, .3, .6]
scb = subcolorbar(min(subset), max(subset), cm.jet)
print 'main segments', cm.jet._segmentdata
print 'smaller', scb._segmentdata
p.subplot(121)
p.scatter([1,2,3],[1,2,3],s=49, c = subset, cmap=scb)
p.colorbar()
p.subplot(122)
p.scatter([2,3,4],[2,3,4],s=49, c =[.001, .5, .99], cmap=cm.jet)
p.colorbar()
p.show()
On Mar 27, 2010, at 11:52 PM, Chloe Lewis wrote:
> To zoom in on the relevant section of a colorbar -- I convinced myself
> once that I'd need an auxiliary function to define a new cdict that
> covers only the current section of the original cdict. (and then
> define a new colorbar from the cdict, and maybe do a little norming of
> the data).
>
> _segmentdata will give you the original cdict for whichever colorbar
> you're using.
>
> Not that I got around to actually doing it! But it would be great for
> paper readability and passing-around of plots.
>
> &C
>
>
>
> On Mar 27, 2010, at 9:24 PM, Ariel Rokem wrote:
>
>> Hi Friedrich,
>>
>> Thanks a lot for your response. I think that you are right - using
>> the vmin/vmax args into imshow (as well as into pcolor) does seem to
>> do what I want. Great!
>>
>> The only thing that remains now is to simultaneously stretch the
>> colormap in the image itself to this range, while also restricting
>> the range of the colorbar which is displayed, to only the part of
>> the colormap which actually has values (in the attached .png, I only
>> want values between 0 and ~0.33 to appear in the colorbar, not from
>> negative -0.33 to +0.33).
>>
>> Does anyone know how to do that?
>>
>> Thanks again -
>>
>> Ariel
>>
>> On Sat, Mar 27, 2010 at 3:29 PM, Friedrich Romstedt
>> <friedrichromst...@gmail.com
>>> wrote:
>> 2010/3/27 Ariel Rokem <aro...@berkeley.edu>:
>>> 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).
>>
>> I might miss something important, but why not use pcolor() with
>> kwargs
>> vmin and vmax,
>> http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.pcolor
>> ,
>> e.g.:
>>
>> maxval = numpy.abs(C).max()
>> pcolor(C, vmin = -maxval, vmax = maxval)
>>
>> As far as I can judge, this should have the desired effect.
>>
>> Friedrich
>>
>>
>>
>> --
>> Ariel Rokem
>> Helen Wills Neuroscience Institute
>> University of California, Berkeley
>> http://argentum.ucbso.berkeley.edu/ariel
>> <
>> colorbar
>> .png
>>>
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