Making custom greyscale colormaps to emphasise parts of the scale

custom_cmaps.py

import numpy as np
import matplotlib.pyplot as plt

# make_cmap function is from: http://schubert.atmos.colostate.edu/~cslocum/custom_cmap.html

def make_cmap(colors, position=None, bit=False):
    '''
    make_cmap takes a list of tuples which contain RGB values. The RGB
    values may either be in 8-bit [0 to 255] (in which bit must be set to
    True when called) or arithmetic [0 to 1] (default). make_cmap returns
    a cmap with equally spaced colors.
    Arrange your tuples so that the first color is the lowest value for the
    colorbar and the last is the highest.
    position contains values from 0 to 1 to dictate the location of each color.
    '''
    import matplotlib as mpl
    import numpy as np
    bit_rgb = np.linspace(0,1,256)
    if position == None:
        position = np.linspace(0,1,len(colors))
    else:
        if len(position) != len(colors):
            sys.exit("position length must be the same as colors")
        elif position[0] != 0 or position[-1] != 1:
            sys.exit("position must start with 0 and end with 1")
    if bit:
        for i in range(len(colors)):
            colors[i] = (bit_rgb[colors[i][0]],
                         bit_rgb[colors[i][1]],
                         bit_rgb[colors[i][2]])
    cdict = {'red':[], 'green':[], 'blue':[]}
    for pos, color in zip(position, colors):
        cdict['red'].append((pos, color[0], color[0]))
        cdict['green'].append((pos, color[1], color[1]))
        cdict['blue'].append((pos, color[2], color[2]))

    cmap = mpl.colors.LinearSegmentedColormap('my_colormap',cdict,256)
    return cmap

colors1 = [
    (255, 255, 255),
    (200, 200, 200),
    (150, 150, 150),
    (100, 100, 100),
    (50, 50, 50),
    (0, 0, 0),
]
colors2 = [
    (255, 255, 255),
    (150, 150, 150),
    (90, 90, 90),
    (50, 50, 50),
    (20, 20, 20),
    (0, 0, 0),
]
my_cmap = make_cmap(colors2, bit=True)

plt.pcolor(np.random.rand(50, 50), cmap=my_cmap)
plt.show()