One of the difficulties I deal with is data that is not evenly distributed across a particular range. Sometimes, I want to highlight a particularly small set of values with great color contrasts and leave other portions of the range to be much less distinguished. For this, I went looking for a way to have a non-linear colormap in matplotlib. To be clear, I didn't want the data itself to be transformed, rather I wanted the color mapping to progress through a normal colormap like cm.jet but expand and contract portions of the map across the entire of values to provide granular resolution at a specific range of values. The graphs below show the difference when using a linear colormap and a transformed colormap:
Image before:
Image after:
I found a script to perform this mapping. It allows the user to set a number of levels that get used to transform the colormap. My modified version of the script is below:
"""
nlcmap - a nonlinear cmap from specified levels
Copyright (c) 2006-2007, Robert Hetland <hetland@tamu.edu>
Release under MIT license.
Some hacks added 2012 noted in code (@MRR)
"""
from pylab import *
from numpy import *
from matplotlib.colors import LinearSegmentedColormap
class nlcmap(LinearSegmentedColormap):
"""A nonlinear colormap"""
name = 'nlcmap'
def __init__(self, cmap, levels):
self.cmap = cmap
# @MRR: Need to add N for backend
self.N = cmap.N
self.monochrome = self.cmap.monochrome
self.levels = asarray(levels, dtype='float64')
self._x = self.levels / self.levels.max()
self._y = linspace(0.0, 1.0, len(self.levels))
#@MRR Need to add **kw for 'bytes'
def __call__(self, xi, alpha=1.0, **kw):
"""docstring for fname"""
# @MRR: Appears broken?
# It appears something's wrong with the
# dimensionality of a calculation intermediate
#yi = stineman_interp(xi, self._x, self._y)
yi = interp(xi, self._x, self._y)
return self.cmap(yi, alpha)
if __name__ == '__main__':
y, x = mgrid[0.0:3.0:100j, 0.0:5.0:100j]
H = 50.0 * exp( -(x**2 + y**2) / 4.0 )
levels = [0, 1, 2, 3, 6, 9, 20, 50]
cmap_lin = cm.jet
cmap_nonlin = nlcmap(cmap_lin, levels)
subplot(2,1,1)
contourf(x, y, H, levels, cmap=cmap_nonlin)
colorbar()
subplot(2,1,2)
contourf(x, y, H, levels, cmap=cmap_lin)
colorbar()
savefig('nlcmap_example.png')
Some of the comments above point to changes that I had to make to get the script to work. Specifically:
- pylab.stineman_interp gave an error relating to the dimensionality of some of the intermediate calculations
- one of the backends requested the colormaps N member, which wasn't set in the original
- the **kw parameter wasn't passed to the __call__ method, causing problems when optional parameters such as bytes were passed.
