Re: [Matplotlib-users] Bug with setting axis limits

[John H. <jdh...@ac...>]

>>>>> "Srinath" == Srinath Avadhanula <sr...@fa...> writes:

    Srinath> Besides whether or not its a common enough situation is
    Srinath> very application specific. I was planning to use
    Srinath> matplotlib to display a complex layout.  At the original
    Srinath> scale, not much can be seen. There necessarily has to be
    Srinath> a lot of scaling to see the details.

Well, it's not complex scaling that causes troubles.  The case where
you see this failure is when you are drawing a connected line between
two points where one or both of the points is far outside the view
limits.

    Srinath> Could you let me know which files in matplotlib have the
    Srinath> code in question? I'll take a look then.

The two relevant methods are matplotlib.transforms.Transform.positions
and matplotlib.lines.Line2D._draw.  I think the best place to start
would be in the latter function.  Let the transform do it's thing and
then look for negative points.

Note there are two kinds of clipping in matplotlib: data clipping and
view clipping.  data clipping removes data outside the view limits
before transpose and plotting.  view clipping is a renderer operation
that prevents lines from being drawn outside the axes box.  data
clipping would remove a point from a connected line outside the
viewport and prevent any part of that line from being drawn, even the
part in the view port.  view clipping would only clip the part that
extends outside the viewport.

The decision to use one or the other is often motivated by
performance.  Eg, if you want to plot a very large data set with only
a small fraction in the viewport (which I often do), you often want to
use data clipping to reduce the data set before transform and
rendering.  If you normally plot data with limits near the viewport
limits you probably do not want the extra performance overhead and are
happy with just view clipping which gives the "usual" result.  That is
why it's a configurable parameter.

In earlier versions of matplotlib data clipping was on by default so
data outside the view ports was removed.  That is probably why we
didn't see this negative transform error earlier.  But experiments
revealed that in the most common cases, data clipping did not provide
an extra performance benefit, and occasionally surprised users in
cases very similar to yours where one point of a connected line was
outside the viewport and the entire line was removed.

Given that negative display coords are essentially undefined (and
backend dependent) at this point, I'm going to re-enable data clipping
in the default matplotlibrc file, and I suggest most users do the
same.  There may be modulo operations on data points very far outside
the data set that generate negative display values, or display values
far larger than the display limits, each of which could cause artifact
points.

The ideal solution is yet to be found, but I'm open to suggestions and
improvements.

JDH