This is a quick post to show you how to import my perceptual color palettes – or any other color palette – into Python and convert them into Matplotlib colormaps. We will use as an example the CIE Lab linear L* palette, which was my adaptation to Matlab of the luminance controlled colormap by Kindlmann et al.
Introduction
You can use the code snippets in here or download the iPython notebook from here (*** please see update ***). You will need NumPy in addition to Matplotlib.
*** UPDATE ***
Fellow Pythonista Matt Hall of Agile Geoscience extended this work – see comment below to include more flexible import of the data and formatting routines, and code to reverse the colormap. Please use Matt’s expanded iPython notebook.
Preliminaries
First of all, get the color palettes in plain ASCII format rom this page. Download the .odt file for the RGB range 0-1 colors, change the file extension to .zip, and unzip it. Next, open the file Linear_L_0-1, which contains comma separated values, replace commas with tabs, and save as .txt file. That’s it, the rest is in Python.
Code snippets – importing data
Let’s bring in numpy and matplotlib:
%pylab inline
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
Now we load the data. We get RGB triplets from tabs delimited text file. Values are expected in the 0-1 range, not 0-255 range.
LinL = np.loadtxt('Linear_L_0-1.txt')
A quick QC of the data:
LinL[:3]
gives us:
array
([[ 0.0143, 0.0143, 0.0143],
[ 0.0404, 0.0125, 0.0325],
[ 0.0516, 0.0154, 0.0443]])
looking good!
Code snippets – creating colormap
Now we want to go from an array of values for blue in this format:
b1=[0.0000,0.1670,0.3330,0.5000,0.6670,0.8330,1.0000]
to a list that has this format:
[(0.0000,1.0000,1.0000),
(0.1670,1.0000,1.0000),
(0.3330,1.0000,1.0000),
(0.5000,0.0000,0.0000),
(0.6670,0.0000,0.0000),
(0.8330,0.0000,0.0000),
(1.0000,0.0000,0.0000)]
# to a dictionary entry that has this format:
{
'blue': [
(0.0000,1.0000,1.0000),
(0.1670,1.0000,1.0000),
(0.3330,1.0000,1.0000),
(0.5000,0.0000,0.0000),
(0.6670,0.0000,0.0000),
(0.8330,0.0000,0.0000),
(1.0000,0.0000,0.0000)],
...
...
}
which is the format required to make the colormap using matplotlib.colors. Here’s the code:
b3=LinL[:,2] # value of blue at sample n
b2=LinL[:,2] # value of blue at sample n
b1=linspace(0,1,len(b2)) # position of sample n - ranges from 0 to 1
# setting up columns for list
g3=LinL[:,1]
g2=LinL[:,1]
g1=linspace(0,1,len(g2))
r3=LinL[:,0]
r2=LinL[:,0]
r1=linspace(0,1,len(r2))
# creating list
R=zip(r1,r2,r3)
G=zip(g1,g2,g3)
B=zip(b1,b2,b3)
# transposing list
RGB=zip(R,G,B)
rgb=zip(*RGB)
# print rgb
# creating dictionary
k=['red', 'green', 'blue']
LinearL=dict(zip(k,rgb)) # makes a dictionary from 2 lists
Code snippets – testing colormap
That’s it, with the next 3 lines we are done:
my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap',LinearL)
pcolor(rand(10,10),cmap=my_cmap)
colorbar()
which gives you this result: 
Notice that if the original file hadn’t had 256 samples, and you wanted a 256 sample color palette, you’d use the line below instead:
my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap',LinearL,256)
Have fun!
Related Posts (MyCarta)
The rainbow is dead…long live the rainbow! – Part 5 – CIE Lab linear L* rainbow
Visualize Mt S Helens with Python and a custom color palette
A bunch of links on colors in Python
http://bl.ocks.org/mbostock/5577023
http://matplotlib.org/api/cm_api.html
http://matplotlib.org/api/colors_api.html
http://matplotlib.org/examples/color/colormaps_reference.html
http://matplotlib.org/api/colors_api.html#matplotlib.colors.ListedColormap
http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
http://stackoverflow.com/questions/21094288/convert-list-of-rgb-codes-to-matplotlib-colormap
http://stackoverflow.com/questions/16834861/create-own-colormap-using-matplotlib-and-plot-color-scale
http://stackoverflow.com/questions/11647261/create-a-colormap-with-white-centered-around-zero
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