Set Colorbar Range in matplotlib

Question

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         


        

Answer 1

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 ).

Answer 2

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()

Answer 3

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()

Answer 4

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.