Question

I use the colormap in python to plot and analyse values in a matrix. I need to associate the white color to each element equal to 0.0 while for others I'd like to have a "traditional" color map. Looking at Python Matplotlib Colormap I modified the dictionary used by pcolor as:

dic = {'red': ((0., 1, 1), 
               (0.00000000001, 0, 0), 
               (0.66, 1, 1), 
               (0.89,1, 1), 
               (1, 0.5, 0.5)), 
       'green': ((0., 1, 1), 
                (0.00000000001, 0, 0), 
                (0.375,1, 1), 
                (0.64,1, 1), 
                (0.91,0,0), 
                (1, 0, 0)), 
       'blue': ((0., 1, 1), 
               (0.00000000001, 1, 1), 
               (0.34, 1, 1), 
               (0.65,0, 0), 
               (1, 0, 0))}

The result is:enter image description here

I set:

matrix[0][0]=0 matrix[0][1]=0.002

But as you can see they are both associated with the white color, even if I set 0.00000000001 as the starting point for the blue. How is this possible? How can I change it in order to obtain what I'd like?

Was it helpful?

Solution

Although not ideal, masking the zero value works. You can control the display of it with the cmap.set_bad().

from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
import numpy as np

dic = {'red': ((0., 1, 0), 
               (0.66, 1, 1), 
               (0.89,1, 1), 
               (1, 0.5, 0.5)), 
       'green': ((0., 1, 0), 
                (0.375,1, 1), 
                (0.64,1, 1), 
                (0.91,0,0), 
                (1, 0, 0)), 
       'blue': ((0., 1, 1), 
               (0.34, 1, 1), 
               (0.65,0, 0), 
               (1, 0, 0))}

a = np.random.rand(10,10)
a[0,:2] = 0
a[0,2:4] = 0.0001

fig, ax = plt.subplots(1,1, figsize=(6,6))

cmap = LinearSegmentedColormap('custom_cmap', dic)
cmap.set_bad('white')

ax.imshow(np.ma.masked_values(a, 0), interpolation='none', cmap=cmap)

enter image description here

Licensed under: CC-BY-SA with attribution
Not affiliated with StackOverflow
scroll top