# Matplotlib: colormap transformations¶

## Operating on color vectors¶

Ever wanted to reverse a colormap, or to desaturate one ? Here is a routine to apply a function to the look up table of a colormap:

In [ ]:
def cmap_map(function,cmap):
""" Applies function (which should operate on vectors of shape 3:
[r, g, b], on colormap cmap. This routine will break any discontinuous     points in a colormap.
"""
cdict = cmap._segmentdata
step_dict = {}
# Firt get the list of points where the segments start or end
for key in ('red','green','blue'):         step_dict[key] = map(lambda x: x[0], cdict[key])
step_list = sum(step_dict.values(), [])
step_list = array(list(set(step_list)))
# Then compute the LUT, and apply the function to the LUT
reduced_cmap = lambda step : array(cmap(step)[0:3])
old_LUT = array(map( reduced_cmap, step_list))
new_LUT = array(map( function, old_LUT))
# Now try to make a minimal segment definition of the new LUT
cdict = {}
for i,key in enumerate(('red','green','blue')):
this_cdict = {}
for j,step in enumerate(step_list):
if step in step_dict[key]:
this_cdict[step] = new_LUT[j,i]
elif new_LUT[j,i]!=old_LUT[j,i]:
this_cdict[step] = new_LUT[j,i]
colorvector=  map(lambda x: x + (x[1], ), this_cdict.items())
colorvector.sort()
cdict[key] = colorvector

return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)


Lets try it out: I want a jet colormap, but lighter, so that I can plot things on top of it:

In [ ]:
light_jet = cmap_map(lambda x: x/2+0.5, cm.jet)
x,y=mgrid[1:2,1:10:0.1]
imshow(y, cmap=light_jet)


As a comparison, this is what the original jet looks like: ![](files/../_downloads/jet.png

## Operating on indices¶

OK, but what if you want to change the indices of a colormap, but not its colors.

In [ ]:
def cmap_xmap(function,cmap):
""" Applies function, on the indices of colormap cmap. Beware, function
should map the [0, 1] segment to itself, or you are in for surprises.

"""
cdict = cmap._segmentdata
function_to_map = lambda x : (function(x[0]), x[1], x[2])
for key in ('red','green','blue'):         cdict[key] = map(function_to_map, cdict[key])
cdict[key].sort()
assert (cdict[key][0]<0 or cdict[key][-1]>1), "Resulting indices extend out of the [0, 1] segment."

return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)


## Discrete colormap¶

Here is how you can discretize a continuous colormap.

In [ ]:
def cmap_discretize(cmap, N):
"""Return a discrete colormap from the continuous colormap cmap.

cmap: colormap instance, eg. cm.jet.
N: number of colors.

Example
x = resize(arange(100), (5,100))
djet = cmap_discretize(cm.jet, 5)
imshow(x, cmap=djet)
"""

if type(cmap) == str:
cmap = get_cmap(cmap)
colors_i = concatenate((linspace(0, 1., N), (0.,0.,0.,0.)))
colors_rgba = cmap(colors_i)
indices = linspace(0, 1., N+1)
cdict = {}
for ki,key in enumerate(('red','green','blue')):
cdict[key] = [ (indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1) ]
# Return colormap object.
return matplotlib.colors.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)


So for instance, this is what you would get by doing {{{cmap_discretize(cm.jet, 6)}}}.

Section author: GaelVaroquaux, DavidHuard, newacct, Unknown[88]

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