Contourplot with 2 different step sizes in matplotlib

Question

This question has probably a totally simple solution but I just can't find it. I'd like to plot a contourf plot where the one part of my data varies in steps of order 1 and the other part varies with steps of order 100. Now I tried to just give contour levels like this:

contour_levels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 200, 300, 400]

However this leads to the result that the fist 11 levels all have the same color as matplotlib is somehow normalizing this to the maximum value. How can I make every level equally important in terms of my color map?

Thanks a lot HYRY, your answer solved my problem. This is what the plots look like bevore and after the implementation (I adjusted the levels a bit; data from the GOZCARDS team/NASA):

Answer 1

Use colors argument:

import pylab as pl
import numpy as np

x, y = np.mgrid[-1:1:100j, 0:1:100j]

z = ... # your function

contour_levels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 200, 300, 400]

cmap = pl.cm.BuPu
colors = cmap(np.linspace(0, 1, len(contour_levels)))
pl.contour(x, y, z, levels=contour_levels, colors=colors)

Answer 2

I am a little wary of HYRY's solution as the mapping between the colors level can become arbitrary. I would suggest using LogNorm instead which maps your values -> colors with a log .

import pylab as pl
import numpy as np

x, y = np.mgrid[-1:1:100j, 0:1:100j]

z = ... # your function

contour_levels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 200, 300, 400]

cmap = pl.cm.BuPu
pl.contourf(x, y, z, levels=contour_levels, norm=matplotlib.colors.LogNorm)

If you also use vmin and vmax you can explicitly control the limits of the normalization and ensure that the color scales match between graphs independent of what levels you use.