How do I use matplotlibs colormap alpha-values to make tripcolor plot fade to transparency?

Question

I want to plot a probability distribution over a map using tripcolor and want the distribution to fade to transparency where the probability is low/zero. However tripcolor doesn't seem to accept local alpha-values provided by the colormap.

I set up a custom colormap that transitions from a transparent (alpha=0.) white to some blueish color (alpha=1.), as described in the matplotlib docs.

cdict = {'red': ((0., 1., 1.), (1., 0., 0.)), 'green': ((0., 1., 1.), (1., 0.5, 0.5)), 'blue': ((0., 1., 1.), (1., 1., 1.)), 'alpha': ((0., 0., 0.), (1., 1., 1.))} testcmap = colors.LinearSegmentedColormap('test', cdict) plt.register_cmap(cmap=testcmap)

If I apply this to a line, as described here everything works fine.

However if I want to use tripcolor to draw the distribution, it seems to ignore the colormap alpha values...

It works for a scatter plot.

A minimal working example can be found below.

import numpy as np from matplotlib import pyplot as plt, colors, cm # quick and dirty test data ext = np.linspace(0., 1., 21) coords, _ = np.meshgrid(ext, ext) x = coords.flatten() y = coords.T.flatten() vals = 1. - np.sin(coords * np.pi / 2).flatten() # color dict cdict = {'red': ((0., 1., 1.), (1., 0., 0.)), 'green': ((0., 1., 1.), (1., 0.5, 0.5)), 'blue': ((0., 1., 1.), (1., 1., 1.)), 'alpha': ((0., 0., 0.), (1., 1., 1.))} # colormap from dict testcmap = colors.LinearSegmentedColormap('test', cdict) plt.register_cmap(cmap=testcmap) # plotting fig, ax = plt.subplots(1) ax.set_facecolor('black') ax.tripcolor(x, y, vals, cmap='test') fig2, ax2 = plt.subplots(1) ax2.set_facecolor('black') ax2.scatter(x, y, c=vals, cmap='test') plt.show()

Edit: Looking at the sourcecode line 118 seems to set a global alpha for the triangulation. Copy/pasting the tripcolor function and omitting this line worked. However it would still be nice to use matplotlibs built-in functions...

Edit2: Changed the data generation function from cos to 1-sin to get a more suggestive transition. For the first edit to give a nice result I also hat to use shading='gouraud'.

Hmmm... when increasing ext = np.linspace(0., 1., 21) to ext = np.linspace(0., 1., 200) tripcolor does not look bad at all... — Sosel
– Sosel, Commented Sep 17, 2019 at 12:34
@Sosel tripcolor still doesn't show any transparency to me. The scatter plot appear more tripcolor-like, yeah, but using scatters as a workaround is not what I want... — Arthus701
– Arthus701, Commented Sep 17, 2019 at 12:40
With fine granularity both, scatter and tripcolor nearly look identical here, so probably I just do not fully understand... — Sosel
– Sosel, Commented Sep 17, 2019 at 12:49
@Sosel Can you look at the right edge in the plots? For me there is a sharp jump-like boundary between white and black in the tripcolor plot, while the scatter plot fades to black smoothly. This is rather different, even if I increase the granularity to 200... Edit: It appears the cosine was not the best choice to indicate the behaviour I want, see the updated question. — Arthus701
– Arthus701, Commented Sep 17, 2019 at 12:54

Aguy · Accepted Answer · 2019-09-17 14:28:45Z

2

Is this closer to what you are looking for?

ax.tripcolor(x, y, vals, cmap='test', alpha=None)

Not sure why, but my guess is that setting alpha=None allows each triangle to get the alpha color from the colormap.

edited Sep 17, 2019 at 14:28

answered Sep 17, 2019 at 14:20

Aguy

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1 Comment

ImportanceOfBeingErnest Over a year ago

Correct, for most other artists alpha is already None, but for the tripcolor it defaults to 1.0 so one will need to set it to None manually.

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