On 01/22/2011 05:16 PM, Paul Ivanov wrote: > Paul Ivanov, on 2011-01-22 18:28, wrote: >> 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...
Paul, Your example below is nice, and this question comes up quite often. If we don't already have a gallery example of this, you might want to add one. (Probably better to use deterministic fake data rather than random.) Eric >> >>> 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. > > forgot the attachment, here it is. > > > > > ------------------------------------------------------------------------------ > Special Offer-- Download ArcSight Logger for FREE (a $49 USD value)! > Finally, a world-class log management solution at an even better price-free! > Download using promo code Free_Logger_4_Dev2Dev. Offer expires > February 28th, so secure your free ArcSight Logger TODAY! > http://p.sf.net/sfu/arcsight-sfd2d > > > > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users ------------------------------------------------------------------------------ Special Offer-- Download ArcSight Logger for FREE (a $49 USD value)! Finally, a world-class log management solution at an even better price-free! Download using promo code Free_Logger_4_Dev2Dev. Offer expires February 28th, so secure your free ArcSight Logger TODAY! http://p.sf.net/sfu/arcsight-sfd2d _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
