How to make the color of one end of colorbar darker in matplotlib?

Question

Say I have the following plot:

import numpy as np
import matplotlib.pyplot as plt    

np.random.seed(1)
data =  np.sort(np.random.rand(8,12))
plt.figure()
c = plt.pcolor(data, edgecolors='k', linewidths=4, cmap='Blues', vmin=0.0, vmax=1.0)
plt.colorbar(c)
plt.show()

The colorbar has the (almost) white color assigned to the lowest values. How do I make it slightly darker? I want that instead of the colorbar ranging from white to blue, it should range from light blue to dark blue. Like, the color for the value 0 should be something like what it is for the value 0.4 in the plot above.

I found this when searching about it, but the question (and the solutions) is about making all the colors darker, which is not what I am looking for.

Answer 1

Although the suggestion of @user3483203 is very good, you do re-interpolate the colormap. You could avoid this by first getting the colormap as a matrix of colors (based on the original interpolation) and then select a part of this matrix as your new colormap:

import matplotlib as mpl

cmap = mpl.cm.Blues(np.linspace(0,1,20))
cmap = mpl.colors.ListedColormap(cmap[10:,:-1])

Your example then becomes

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

cmap = mpl.cm.Blues(np.linspace(0,1,20))
cmap = mpl.colors.ListedColormap(cmap[10:,:-1])

np.random.seed(1)
data = np.sort(np.random.rand(8,12))

plt.figure()
c = plt.pcolor(data, edgecolors='k', linewidths=4, cmap=cmap, vmin=0.0, vmax=1.0)
plt.colorbar(c)
plt.show()

which gives

which is in this case probably equivalent to re-interpolated colormap, as Blues itself comes from some interpolation.

For other colormaps the results may be quite different. For example, for jet:

• No new interpolation, but just a subset of the original colormap (i.e. current solution):

  

• Using re-interpolation (i.e. @user3483203's solution):

  

Answer 2

Simply define your own custom colormap:

from matplotlib.colors import LinearSegmentedColormap

colors = [(0.6, 0.76, 0.98), (0, 0.21, 0.46)] # Experiment with this
cm = LinearSegmentedColormap.from_list('test', colors, N=10)

Then just plug it in for the cmap parameter:

import numpy as np
import matplotlib.pyplot as plt    

np.random.seed(1)
data =  np.sort(np.random.rand(8,12))
plt.figure()
c = plt.pcolor(data, edgecolors='k', linewidths=4, cmap=cm, vmin=0.0, vmax=1.0)
plt.colorbar(c)
plt.show()

And the result: