Ryan May wrote:
> Hi,
>
> Does anyone know why set_aspect('equal', 'box') isn't accepted on shared axes?
> I'm trying to make the following type of scenario work:
Ryan,
Mark Bakker keeps asking about this, too.
I have never been able to figure out an algorithm, consistent with the
present mpl architecture, that would cleanly handle shared axes with
fixed aspect ratio when the box is adjustable. If you can specify
exactly what should happen under all such conditions that might
arise--including single or dual shared axes, and fixed aspect ratio on
one or more of the plots with shared axes, and any kind of zooming or
panning--then we should be able to come up with the logic to implement it.
Maybe the problem is not that there is no such specification or
algorithm, but that it requires a global solution, involving figuring
out the dimensions of all the boxes at the same time, whereas with
adjustable datalim the calculation can be done on any subplot
individually--hence it can be in Axes.draw, as at present. I suspect
this is the crux of it--fixed aspect ratio, shared axes, adjustable box
would require a level of code that does not exist at present; a method
invoked at the Figure.draw() level that would calculate and freeze all
of the axes positions in one whack before any of them are drawn.
Actually, even this is not right, because IIRC axes can be shared across
figures, so this calculation would need to be done at the highest
level--before the Figure.draw() method.
If we go this route--which sounds like going to full-fledged sizer/pack
type algorithms--we need to be sure it does not slow down interactive
responsiveness. Or burden us with bugs and unmaintainable code.
Sometimes it is worthwhile to accept some limitations and keep things
simple.
Note that the present implementation of shared axes, unlike an earlier
implementation, has no notion of master and slaves; all are equivalent,
and can be calculated and drawn in any order.
Eric
>
> import numpy as np
> from matplotlib.pyplot import figure, show
>
> fig1 = figure()
> fig2 = figure()
>
> ax1 = fig1.add_subplot(1, 1, 1)
> ax1.set_aspect('equal', 'datalim')
>
> ax2 = fig2.add_subplot(1, 2, 1, sharex=ax1, sharey=ax1)
> ax2.set_aspect('equal', 'datalim')
> ax3 = fig2.add_subplot(1, 2, 2, sharex=ax2, sharey=ax2)
>
> data = np.random.rand(50,50)
> ax1.pcolormesh(data)
> ax2.pcolormesh(data)
> ax3.pcolormesh(data)
>
> show()
>
> Basically, I have multiple figures with multiple subplots, all of which
> should be
> displaying the same range. However, the different figures have different
> numbers
> of subplots. The example above doesn't work, because once you zoom into one
> of
> the figures, it iteratively zooms out, adjusting data limits until both
> figures
> have their aspect ratio properly set again. I thought using 'box' might
> alleviate the problem, but that's throwing an exception.
>
> I realize making the figures have the same layout would solve the problem, I
> just
> wasn't sure if there was another way.
>
> Ryan
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