Colormaps are basically just interpolation functions which you can call. They map values from the interval [0,1] to colors. So you can just sample colors from both maps and then combine them:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
data = np.random.rand(10,10) * 2 - 1
# sample the colormaps that you want to use. Use 128 from each so we get 256
# colors in total
colors1 = plt.cm.binary(np.linspace(0., 1, 128))
colors2 = plt.cm.gist_heat_r(np.linspace(0, 1, 128))
# combine them and build a new colormap
colors = np.vstack((colors1, colors2))
mymap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors)
plt.pcolor(data, cmap=mymap)
plt.colorbar()
plt.show()
Result:
NOTE: I understand that you might have specific needs for this, but in my opinion this is not a good approach: How will you distinguish -0.1 from 0.9? -0.9 from 0.1?
One way to prevent this is to sample the maps only from ~0.2 to ~0.8 (e.g.: colors1 = plt.cm.binary(np.linspace(0.2, 0.8, 128))) so they wont go all the way up to black:
