Question

After plotting in Matlab we do caxis(max(caxis()) - [0.5, 0]) to scale the color limits to go from the current max color limit to, say, 0.5 below this max. This works because caxis() in Matlab both gets and sets the color limits. How does one do this in matplotlib?

That is, I want to achieve the following:

import numpy.random, numpy, pylab
arr = numpy.random.randn(100,100)
pylab.figure()
pylab.imshow(arr)
pylab.colorbar()
pylab.clim([numpy.max(arr.ravel())-0.5, numpy.max(arr.ravel())]) # [*]
pylab.show()

without the asterisked call to pylab.clim() having recourse to arr, the array being plotted. In other words, how can I get the current figure’s “clim” in matplotlib?

Answer 1

If you didn’t keep the returned image object, you can use pylab.gci to get the current ScalarMappable (i.e. whatever the current colorbar would be based on).

From there, you just want the get_clim method of the ScalarMappable object.

So, you could do:

vmin, vmax = plt.gci().get_clim()