Cubehelix Colormap for Python

I have been transitioning to using Python for more and more of my research, which has gone relatively smoothly I'm happy to say! Within the last ~2 years Python's libraries and documentation for things astronomical has reached a "critical mass", and making the transition for most things has never been easier!

However, one little problem still eats at my soul every day I use Python, specifically Matplotlib: the colors in figures are usually terrible!

My absolute favorite color map (at least for now) is cubehelix. I have written about this color map before:
CUBEHELIX, or How I Learn to Love Black & White Printers,
I also wrote the version for IDL used in the Coyote Library, and helped bring a version to the Tableau community last year.

A real cubehelix version for Python

Matplotlib already has the default cubehelix colormap built in, as well as several excellent colormaps that properly desaturate. What makes the cubehelix algorithm so powerful is that it defines a family of colormaps that all desaturate properly. This is what is missing currently in Matplotlib.

The Python community has a strong "put up or shut up" attitude that I love, so I spent a few hours translating my IDL implementation of cubehelix in to Python!

The code is available on github
Try it out, it's dead simple to use!


Some Examples

import numpy as np
import matplotlib.pyplot as plt
import cubehelix 
# set up some simple data to plot
x = np.random.randn(10000)
y = np.random.randn(10000)

# create the default "cubehelix" colormap
cx1 = cubehelix.cmap()
plt.hexbin(x,y,gridsize=50,cmap=cx1)
plt.colorbar()
plt.show()


# Reverse of the default "cubehelix" colormap
# I think this is more appropriate for density maps, 
# as intensity corresponds with density.
cx2 = cubehelix.cmap(reverse=True)
plt.hexbin(x,y,gridsize=50,cmap=cx2)
plt.colorbar()
plt.show()


# My favorite flavor of "cubehelix", 
# mostly blue with a small hue change
cx3 = cubehelix.cmap(reverse=True, start=0.3, rot=-0.5)
plt.hexbin(x,y,gridsize=50,cmap=cx3)
plt.colorbar()
plt.show()


# Another good version, mostly using red/purples
cx4 = cubehelix.cmap(reverse=True, start=0., rot=0.5)
plt.hexbin(x,y,gridsize=50,cmap=cx4)
plt.colorbar()
plt.show() 


update:
Apparently I've just reinvented the cubehelix wheel! I can live with that


update 2:
The creator of D3, Mike Bostock, has made a version available for that language as well. So that's it, people. No more excuses to not use this awesome color scheme! (GitHub README)

5 comments:

  1. Hey Jim, a couple of weeks ago I was playing with mpl._cm.cubehelix Here's the notebook I came up with:
    http://nbviewer.ipython.org/gist/anonymous/a4fa0adb08f9e9ea4f94

    ReplyDelete
  2. Hi Jim. Could you please advise how to apply a stepped cubehelix colormap for use in an IDL contour plot? Any help would be great!

    Joe

    ReplyDelete
    Replies
    1. Hi Joe,

      Assuming you're using the IDL version of cubehelix

      Would it suffice to just specify the levels for the contour plot? You should be able to just use the cubehelix routine and then contour. I'm afraid without some example code or more info I can't answer your Q better!

      Delete
  3. Is there a convenient way to get a diverging cubehelix? For example, black -> reds -> white -> blues -> black?

    ReplyDelete

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