I have a simple image that I’m showing with imshow in matplotlib. I’d like to apply a custom colormap so that values between 0-5 are white, 5-10 are red (very simple colors), etc. I’ve tried following this tutorial:

http://assorted-experience.blogspot.com/2007/07/custom-colormaps.html with the following code:

cdict = {
'red'  :  ((0., 0., 0.), (0.5, 0.25, 0.25), (1., 1., 1.)),
'green':  ((0., 1., 1.), (0.7, 0.0, 0.5), (1., 1., 1.)),
'blue' :  ((0., 1., 1.), (0.5, 0.0, 0.0), (1., 1., 1.))

my_cmap = mpl.colors.LinearSegmentedColormap('my_colormap', cdict, 3)

plt.imshow(num_stars, extent=(min(x), max(x), min(y), max(y)), cmap=my_cmap)

But this ends up showing strange colors, and I only need 3-4 colors that I want to define. How do I do this?

You can use a ListedColormap to specify the white and red as the only colors in the color map, and the bounds determine where the transition is from one color to the next:

import matplotlib.pyplot as plt
from matplotlib import colors
import numpy as np

zvals = np.random.rand(100, 100) * 10

# make a color map of fixed colors
cmap = colors.ListedColormap(['white', 'red'])
norm = colors.BoundaryNorm(bounds, cmap.N)

# tell imshow about color map so that only set colors are used
img = plt.imshow(zvals, interpolation='nearest', origin='lower',
                    cmap=cmap, norm=norm)

# make a color bar
plt.colorbar(img, cmap=cmap, norm=norm, boundaries=bounds, ticks=[0, 5, 10])


The resulting figure has only two colors:

enter image description here

I proposed essentially the same thing for a somewhat different question: 2D grid data visualization in Python

The solution is inspired by a matplotlib example. The example explains that the bounds must be one more than the number of colors used.

The BoundaryNorm is a normalization that maps a series of values to integers, which are then used to assign the corresponding colors. cmap.N, in the example above, just defines the number of colors.