issue_comments: 125974756

Exactly.

I've used something like this to generate the discrete colormap:

``` python def cmap_discretize(cmap, n_colors): """Return a discrete colormap from the continuous colormap cmap.

Parameters
----------
cmap : str or colormap object
    Colormap to discretize.
n_colors : int
    Number of discrete colors to divide `cmap` into.

Returns
----------
discrete_cmap : LinearSegmentedColormap
    Discretized colormap.
"""
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt

if type(cmap) == basestring:
    cmap = plt.get_cmap(cmap)
colors_i = np.concatenate((np.linspace(0, 1., n_colors), (0., 0., 0., 0.)))
colors_rgba = cmap(colors_i)
indices = np.linspace(0, 1., n_colors + 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 range(n_colors + 1)]
# Return colormap object.
return LinearSegmentedColormap(cmap.name + "_%d" % n_colors, cdict, 1024)

```

Typically, I've manually constructed the colorbar by hand with the discrete colormap:

``` python cmap = cmap_discretize(cmap, n_colors=10) cnorm = mpl.colors.Normalize(vmin=vmin, vmax=vmax) cticks = np.linspace(vmin, vmax, num=cn + 1)

cb = mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=cnorm, orientation='vertical', extend=cbar_extend, ticks=cticks) ```

I'm sure this later part can be improved. Possibly by using matplotlib.ticker.MaxNLocator to find/set the ticks and intervals.