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:

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:

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)

[](files/attachments/Matplotlib_ColormapTransformations/light_jet4.png

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

Operating on indices

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

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.

    See also cmap_xmap.
    """
    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.

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)}}}.

Attachments

• dicrete_jet1.png
• discrete_jet.png
• jet.png
• light_jet.png
• light_jet2.png
• light_jet3.png
• light_jet4.png