matplotlib-devel Mailing List for matplotlib

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

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:
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
-- 
Ryan May
Graduate Research Assistant
School of Meteorology
University of Oklahoma