Ilya Shlyakhter, on 2011-01-22 19:06, wrote: > Is it possible to create a "break" in the y-axis so that it has ticks > for value 0-.2, then ticks for values .8-1.0, but devotes only a token > amount of space to the area 0.2-0.8? > I have a dataset with most datapoints in 0-.2 and a couple in .8-1.0, > and none in .2-.8 . The default scaling wastes a lot of space and > compresses the data in the 0-.2 range > such that it is hard to distinguish. Hi Ilya,
this... > p.s. I know I could use two y-axes with different scales; but this > would require splitting the data into two different datasets as well, > and would not allow connecting all points > with one line. ... is the way I'd proceed, because it's clean, and requires the least amount of work. Connecting your lines across such breaks is misleading - since the magnitude of the slope of the connecting line segment arbitrary relative to all other line segments. You don't actually have to divide your data, you can just replot *all* data on the secondary plot, and then set the x and y lims to break up your views on the data. I'm attaching a quick sketch of what that would look like. (Note how different the outlier line segments would look if we connected them in the same manner that all other points are connected). import numpy as np import matplotlib.pylab as plt pts = np.random.rand(30)*.2 pts[[7,11]] += .8 f,(ax,ax2) = plt.subplots(2,1,sharex=True) ax.plot(pts) ax2.plot(pts) ax.set_ylim(.78,1.) ax2.set_ylim(0,.22) ax.xaxis.tick_top() ax.spines['bottom'].set_visible(False) ax.tick_params(labeltop='off') ax2.xaxis.tick_bottom() ax2.spines['top'].set_visible(False) If this is something you really want, though, you can achieve it by making your own projection/scale: http://matplotlib.sourceforge.net/devel/add_new_projection.html Yet another way would be to re-label the tick lines (e.g. make .6 label be 1.0 and subtract that offset from your two outliers. best, -- Paul Ivanov 314 address only used for lists, off-list direct email at: http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7
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