Map values to colors in matplotlib

Question

I have a list of numbers as follows:

lst = [1.9378076554115014, 1.2084586588892861, 1.2133096565896173, 
       1.2427632053442292, 1.1809971732733273, 0.919         


        

Answer 1

The matplotlib.colors module is what you are looking for. This provides a number of classes to map from values to colourmap values.

import matplotlib
import matplotlib.cm as cm

lst = [1.9378076554115014, 1.2084586588892861, 1.2133096565896173, 1.2427632053442292, 
       1.1809971732733273, 0.91960143581348919, 1.1106310149587162, 1.1106310149587162, 
       1.1527004351293346, 0.87318084435885079, 1.1666132876686799, 1.1666132876686799]

minima = min(lst)
maxima = max(lst)

norm = matplotlib.colors.Normalize(vmin=minima, vmax=maxima, clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap=cm.Greys_r)

for v in lst:
    print(mapper.to_rgba(v))

The general approach is find the minima and maxima in your data. Use these to create a Normalize instance (other normalisation classes are available, e.g. log scale). Next you create a ScalarMappable using the Normalize instance and your chosen colormap. You can then use mapper.to_rgba(v) to map from an input value v, via your normalised scale, to a target color.

for v in sorted(lst):
    print("%.4f: %.4f" % (v, mapper.to_rgba(v)[0]) )

Produces the output:

0.8732: 0.0000
0.9196: 0.0501
1.1106: 0.2842
1.1106: 0.2842
1.1527: 0.3348
1.1666: 0.3469
1.1666: 0.3469
1.1810: 0.3632
1.2085: 0.3875
1.2133: 0.3916
1.2428: 0.4200
1.9378: 1.0000

The matplotlib.colors module documentation has more information if needed.

Answer 2

Colormaps are powerful, but (a) you can often do something simpler and (b) because they're powerful, they sometimes do more than I expect. Extending mfitzp's example:

import matplotlib
import matplotlib.cm as cm

lst = [1.9378076554115014, 1.2084586588892861, 1.2133096565896173, 1.2427632053442292,
   1.1809971732733273, 0.91960143581348919, 1.1106310149587162, 1.1106310149587162,
   1.1527004351293346, 0.87318084435885079, 1.1666132876686799, 1.1666132876686799]

minima = min(lst)
maxima = max(lst)

norm = matplotlib.colors.Normalize(vmin=minima, vmax=maxima, clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap=cm.Greys)

for v in lst:
    print(mapper.to_rgba(v))

# really simple grayscale answer
algebra_list = [(x-minima)/(maxima-minima) for x in lst]
# let's compare the mapper and the algebra
mapper_list = [mapper.to_rgba(x)[0] for x in lst]

matplotlib.pyplot.plot(lst, mapper_list, color='red', label='ScalarMappable')
matplotlib.pyplot.plot(lst, algebra_list, color='blue', label='Algebra')

# I did not expect them to go in opposite directions. Also, interesting how
# Greys uses wider spacing for darker colors.
# You could use Greys_r (reversed)

# Also, you can do the colormapping in a call to scatter (for instance)
# it will do the normalizing itself
matplotlib.pyplot.scatter(lst, lst, c=lst, cmap=cm.Greys, label='Default norm, Greys')
matplotlib.pyplot.scatter(lst, [x-0.25 for x in lst], marker='s', c=lst,
                      cmap=cm.Greys_r, label='Reversed Greys, default norm')
matplotlib.pyplot.legend(bbox_to_anchor=(0.5, 1.05))