I have the following code:
import matplotlib.pyplot as plt
cdict = {
\'red\' : ( (0.0, 0.25, .25), (0.02, .59, .59), (1., 1., 1.)),
\'green\': ( (0.0
Not sure if this is the most elegant solution (this is what I used), but you could scale your data to the range between 0 to 1 and then modify the colorbar:
import matplotlib as mpl
...
ax, _ = mpl.colorbar.make_axes(plt.gca(), shrink=0.5)
cbar = mpl.colorbar.ColorbarBase(ax, cmap=cm,
norm=mpl.colors.Normalize(vmin=-0.5, vmax=1.5))
cbar.set_clim(-2.0, 2.0)
With the two different limits you can control the range and legend of the colorbar. In this example only the range between -0.5 to 1.5 is show in the bar, while the colormap covers -2 to 2 (so this could be your data range, which you record before the scaling).
So instead of scaling the colormap you scale your data and fit the colorbar to that.