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I'm looking to plot a matplotlib colormesh on top of another colormesh. The bottom plot is simply grayscale.

The one which sits on top should however draw a transparent square when the value of the passed array is 0, and a different color for each other number in the passed array. These are 2d numpy arrays.

Currently I have:

plt.pcolormesh(array1, vmin = -32, vmax = 32, cmap = plt.cm.binary)
plt.pcolormesh(array2, cmap = plt.cm.spectral)

Obviously this doesn't produce what I'm looking for, and I assume the way to do this is to generate my own colormap, I've read this guide: http://wiki.scipy.org/Cookbook/Matplotlib/ColormapTransformations but this doesn't seem to address transparency, nor how to make specific values map to specific colors.

As a short example of what I'd like, an array:

[[0, 1]
 [2, 3]]

Should produce a grid looking like:

[[transparent, red
 [green, yellow]]

How do I go about doing this? Merging the arrays together isn't an option, as the bottom dataset is a height map, and the values of this will likely always span the values of the second array (these are agent IDs).

1 Answers1

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This code should do something akin to what you require:

Edit using masked_array:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as colors
import numpy.ma as ma

#See http://stackoverflow.com/questions/18926031/how-to-extract-a-subset-of-a-colormap-as-a-new-colormap-in-matplotlib
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
    new_cmap = colors.LinearSegmentedColormap.from_list(
        'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),
        cmap(np.linspace(minval, maxval, n)))
    return new_cmap

#truncate the colourmap
n_colours = 4
new_cmap = truncate_colormap(cm.get_cmap('spectral_r'), 0, 0.4, n=n_colours)

#discretise the colourmap
bounds = np.linspace(0,n_colors,n_colours+1)
norm = colors.BoundaryNorm(bounds, new_cmap.N)

#build array one
array1 = np.random.rand(10,10)

#build array two
array2 = np.random.randint(0,5,100).reshape(10,10)

#mask the array
array2 = ma.masked_array(array2, array2==0)

#plot it
plt.pcolormesh(array1,cmap = plt.cm.binary)
plt.pcolormesh(array2,cmap = new_cmap, norm=norm)
cbar = plt.colorbar()
plt.show()

Here is the new output using a masked array:

enter image description here

dnf0
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  • This is good, and close to the sort of thing I had in mind, but not quite there. Is it possible to apply the alpha channel ONLY to those values of 0? If not, I guess I may need to look into a different way to plot this data. –  Nov 10 '13 at 14:58
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    Yes, using masked arrays will do that. Edited my post to demonstrate. – dnf0 Nov 10 '13 at 15:29
  • Brilliant, thanks. So to increase the number of colors used, I simply change the n parameter in truncate_colormap? - Seems to be the case from the provided link. –  Nov 10 '13 at 15:35
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    Have added a n_colours variable so you can change it. Also to increase differentiability between the colours increase the maxval argument to truncate_colormap – dnf0 Nov 10 '13 at 15:46
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    This is way over complicated, just use `set_under()` See http://stackoverflow.com/questions/16400241/how-to-manually-change-a-colormap-in-python-matplotlib/16401183#16401183 and add the kwarg `alpha=0` to the `set_under` call – tacaswell Nov 10 '13 at 21:44
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    or http://stackoverflow.com/questions/19326526/empty-zero-and-non-zero-pixels-representation-in-matplotlib/19327604#19327604 which has a more complete set of examples – tacaswell Nov 10 '13 at 21:46
  • @tcaswell I do not see how the examples you provided deal with truncating and discretising the colourbar, which is a requirement defined in the question. Could you clarify? – dnf0 Nov 11 '13 at 13:29