I once had a similar issue. I solved it like this. It takes the minimum and
maximum of the data and returns a colormap: Zero: White, Positive values:
blue, Negative values: red.
def mxcmap(_min,_max):
if _min >= 0 and _max >= 0:
cdict = {'red': ((0.0, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'green': ((0.0, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 1.0, 1.0),
(1.0, 1.0, 1.0))}
elif _min <= 0 and _max <= 0:
cdict = {'red': ((0.0, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0),
(1.0, 1.0, 1.0)),
'blue': ((0.0, 0.0, 0.0),
(1.0, 1.0, 1.0))}
else:
full_red = 1
full_blue = 1
if -_min > _max:
full_blue = -float(_max)/_min
else:
full_red = -float(_min)/_max
zero = 0.5-((_max+_min)/2.)/(_max-_min)
cdict = {'red': ((0.0, 1.0, 1.0),
(zero, 1.0, 1.0),
(1.0, 1-full_blue, 1-full_blue)),
'green': ((0.0, 1-full_red, 1-full_red),
(zero, 1.0, 1.0),
(1.0, 1-full_blue, 1-full_blue)),
'blue': ((0.0, 1-full_red, 1-full_red),
(zero,1.0, 1.0),
(1.0, 1.0, 1.0))}
return
pylab.matplotlib.colors.LinearSegmentedColormap('my_colormap',cdict,256)
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