On 2015/04/08 11:15 AM, Jody Klymak wrote:
Hi Eric,
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2,sharex=True)
axes[0].set_aspect(1.)
axes[0].plot(np.arange(10),np.arange(10))
axes[0].set_ylim([0,24])
axes[0].set_xlim([0,12])
axes[1].plot(np.arange(10),np.arange(10)*2.)
Hi Eric,
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2,sharex=True)
axes[0].set_aspect(1.)
axes[0].plot(np.arange(10),np.arange(10))
axes[0].set_ylim([0,24])
axes[0].set_xlim([0,12])
axes[1].plot(np.arange(10),np.arange(10)*2.)
plt.show()
does not work as I'd expect.
What are the data limits you are using?
I suspect they you are over constraining the system/order of operations
issue. Try dropping the adjustable setting and pre setting both the data
limits and the approximate size in figure fraction (ex via grid spec) of
the axes.
Tom
On Tue, Apr 7, 2015,
Thanks, Thomas.
That works indeed, but it doesn't make the figure adjustable, which is what
I wanted (that the physical size of the axes changes while the aspect ratio
is fixed to 1). I guess that functionality has been taken out.
Mark
On Wed, Apr 8, 2015 at 12:50 PM, Thomas Caswell
Can you please provide a minimal, but complete and runnable example of what
you are doing?
On Wed, Apr 8, 2015, 08:13 Mark Bakker mark...@gmail.com wrote:
Thanks, Thomas.
That works indeed, but it doesn't make the figure adjustable, which is
what I wanted (that the physical size of the axes
Hi Ryan,
could you write down, as a tutorial, how you built the example with the qt
designer?In the last hours I read all most everything what can be found on the
issue of getting matplotlib running with pyqt5 and the designer but as you
realized yourself, there is little to be found handy.
I'm
Hi Jody,
Thank you very much for your help. You are right, this is what I wanted :-)
Cheers,
Markus
On 2015-04-07 23:33, Jody Klymak wrote:
xerr is +/- relative to the data:
*xerr*/*yerr*: [ scalar | N, Nx1, or 2xN array-like ]
If a scalar number, len(N) array-like object, or an Nx1
Following up on this, I’d like to complain about set_aspect()…
If I do:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2,sharex=True)
axes[0].set_ylim(0,1.)
axes[0].set_aspect(1.)
plt.show()
the x-axis goes from 0. to 1., but axes[0]’s y-axis goes from 0.32 to 0.67.
Swapping
What version had this behavior? I recall some work on how aspect worked,
but not exactly what we did. I see why it is useful, but on the other hand
that sort of coupling seems like could cause some trouble if we are not
careful. This all come back to we need a real layout manager/constraint
On 2015/04/08 7:04 AM, Jody Klymak wrote:
Following up on this, I’d like to complain about set_aspect()…
If I do:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2,sharex=True)
axes[0].set_ylim(0,1.)
axes[0].set_aspect(1.)
plt.show()
the x-axis goes from 0. to 1., but
On 2015/04/08 7:04 AM, Jody Klymak wrote:
Maybe there is a reason for the default, but I really think the data
view should be prioritized over the shape of the axis.
I forgot to include: I was trying to make everything sane (and
reversible) under zoom and pan as well as reshaping and resizing.
On 2015/04/08 8:43 AM, Jody Klymak wrote:
Hi Eric,
On 8 Apr 2015, at 11:02 AM, Eric Firing efir...@hawaii.edu wrote:
I'm the guilty party for most of how set_aspect works. I developed it a
long time ago. Yes, there was a reason--still is, I'm 99% sure--but I
don't remember everything, and
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