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, 0.91960143581348919,         1.1106310149587162, 1.1106310149587162, 1.1527004351293346,         0.87318084435885079, 1.1666132876686799, 1.1666132876686799] 

I want to convert these numbers to colors for display. I want gray scale but when I am using these numbers as it is, it gives me an error:

ValueError: to_rgba: Invalid rgba arg "1.35252299785" to_rgb: Invalid rgb arg "1.35252299785" gray (string) must be in range 0-1  

...which I understand is due to it exceeding 1.

I next tried to divide the items in the list with the highest number in the list to give values less than 1. But this gives a very narrow color scale with hardly any difference between values.

Is there any way in which I can give some min and max range to colors and convert these values to color? I am using matplotlib.

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))