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.

Using vmin and vmax forces the range for the colors. Here's an example:

import matplotlib as m
import matplotlib.pyplot as plt
import numpy as np


cdict = {
'red'  :  ( (0.0, 0.25, .25), (0.02, .59, .59), (1., 1., 1.)),
'green':  ( (0.0, 0.0, 0.0), (0.02, .45, .45), (1., .97, .97)),
'blue' :  ( (0.0, 1.0, 1.0), (0.02, .75, .75), (1., 0.45, 0.45))
}


cm = m.colors.LinearSegmentedColormap('my_colormap', cdict, 1024)


x = np.arange(0, 10, .1)
y = np.arange(0, 10, .1)
X, Y = np.meshgrid(x,y)


data = 2*( np.sin(X) + np.sin(3*Y) )


def do_plot(n, f, title):
#plt.clf()
plt.subplot(1, 3, n)
plt.pcolor(X, Y, f(data), cmap=cm, vmin=-4, vmax=4)
plt.title(title)
plt.colorbar()


plt.figure()
do_plot(1, lambda x:x, "all")
do_plot(2, lambda x:np.clip(x, -4, 0), "<0")
do_plot(3, lambda x:np.clip(x, 0, 4), ">0")
plt.show()

Use the CLIM function (equivalent to CAXIS function in MATLAB):

plt.pcolor(X, Y, v, cmap=cm)
plt.clim(-4,4)  # identical to caxis([-4,4]) in MATLAB
plt.show()

Using figure environment and .set_clim()

Could be easier and safer this alternative if you have multiple plots:

import matplotlib as m
import matplotlib.pyplot as plt
import numpy as np


cdict = {
'red'  :  ( (0.0, 0.25, .25), (0.02, .59, .59), (1., 1., 1.)),
'green':  ( (0.0, 0.0, 0.0), (0.02, .45, .45), (1., .97, .97)),
'blue' :  ( (0.0, 1.0, 1.0), (0.02, .75, .75), (1., 0.45, 0.45))
}


cm = m.colors.LinearSegmentedColormap('my_colormap', cdict, 1024)


x = np.arange(0, 10, .1)
y = np.arange(0, 10, .1)
X, Y = np.meshgrid(x,y)


data = 2*( np.sin(X) + np.sin(3*Y) )
data1 = np.clip(data,0,6)
data2 = np.clip(data,-6,0)
vmin = np.min(np.array([data,data1,data2]))
vmax = np.max(np.array([data,data1,data2]))


fig = plt.figure()
ax = fig.add_subplot(131)
mesh = ax.pcolormesh(data, cmap = cm)
mesh.set_clim(vmin,vmax)
ax1 = fig.add_subplot(132)
mesh1 = ax1.pcolormesh(data1, cmap = cm)
mesh1.set_clim(vmin,vmax)
ax2 = fig.add_subplot(133)
mesh2 = ax2.pcolormesh(data2, cmap = cm)
mesh2.set_clim(vmin,vmax)
# Visualizing colorbar part -start
fig.colorbar(mesh,ax=ax)
fig.colorbar(mesh1,ax=ax1)
fig.colorbar(mesh2,ax=ax2)
fig.tight_layout()
# Visualizing colorbar part -end


plt.show()

A single colorbar

The best alternative is then to use a single color bar for the entire plot. There are different ways to do that, this tutorial is very useful for understanding the best option. I prefer this solution that you can simply copy and paste instead of the previous visualizing colorbar part of the code.

fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,
wspace=0.4, hspace=0.1)
cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8])
cbar = fig.colorbar(mesh, cax=cb_ax)

P.S.

I would suggest using pcolormesh instead of pcolor because it is faster (more infos here ).