Keeping several axis scalings in sync?

Hi there,

I often have the case that I want to view different data sets that share one axis. Imagine, for example, a time series of several different observables. Since all observables may have different units, scales and offsets, I would want to display them as separate subplots that have the same x-axis (time) but indepent y scales.

Is there a way to lock the scales of several subplots so that when I zoom into one of the subplots interactively, the scale of the other subplots is automatically adjusted? (Preferably something simple enough to use it in quick-and-dirty scripts or even interactive sessions.)

Thanks for your help!

Greetings,
Norbert Nemec

I think shared_axis_demo.py, in the examples archive, is what you are looking
for. It is well documented, and simple enough to include here in its
entirety:

"""
You can share the x or y axis limits for one axis with another by
passing an axes instance as a sharex or sharey kwarg.

Changing the axis limits on one axes will be reflected automatically
in the other, and vice-versa, so when you navigate with the toolbar
the axes will follow each other on their shared axes. Ditto for
changes in the axis scaling (eg log vs linear). However, it is
possible to have differences in tick labeling, eg you can selectively
turn off the tick labels on one axes.

The example below shows how to customize the tick labels on the
various axes. Shared axes share the tick locator, tick formatter,
view limits, and transformation (eg log, linear). But the ticklabels
themselves do not share properties. This is a feature and not a bug,
because you may want to make the tick labels smaller on the upper
axes, eg in the example below.

If you want to turn off the ticklabels for a given axes (eg on
subplot(211) or subplot(212), you cannot do the standard trick

   setp(ax2, xticklabels=[])

because this changes the tick Formatter, which is shared among all
axes. But you can alter the visibility of the labels, which is a
property

  setp( ax2.get_xticklabels(), visible=False)

"""
from pylab import *

t = arange(0.01, 5.0, 0.01)
s1 = sin(2*pi*t)
s2 = exp(-t)
s3 = sin(4*pi*t)
ax1 = subplot(311)
plot(t,s1)
setp( ax1.get_xticklabels(), fontsize=6)

## share x only
ax2 = subplot(312, sharex=ax1)
plot(t, s2)
# make these tick labels invisible
setp( ax2.get_xticklabels(), visible=False)

# share x and y
ax3 = subplot(313, sharex=ax1, sharey=ax1)
plot(t, s3)
xlim(0.01,5.0)
show()

Darren

···

On Saturday 12 April 2008 7:19:32 am Norbert Nemec wrote:

Hi there,

I often have the case that I want to view different data sets that share
one axis. Imagine, for example, a time series of several different
observables. Since all observables may have different units, scales and
offsets, I would want to display them as separate subplots that have the
same x-axis (time) but indepent y scales.

Is there a way to lock the scales of several subplots so that when I
zoom into one of the subplots interactively, the scale of the other
subplots is automatically adjusted? (Preferably something simple enough
to use it in quick-and-dirty scripts or even interactive sessions.)

Thanks! Perfect! I *love* matplotlib!!!

Darren Dale wrote:

···

On Saturday 12 April 2008 7:19:32 am Norbert Nemec wrote:
  

Hi there,

I often have the case that I want to view different data sets that share
one axis. Imagine, for example, a time series of several different
observables. Since all observables may have different units, scales and
offsets, I would want to display them as separate subplots that have the
same x-axis (time) but indepent y scales.

Is there a way to lock the scales of several subplots so that when I
zoom into one of the subplots interactively, the scale of the other
subplots is automatically adjusted? (Preferably something simple enough
to use it in quick-and-dirty scripts or even interactive sessions.)
    
I think shared_axis_demo.py, in the examples archive, is what you are looking for. It is well documented, and simple enough to include here in its entirety:

"""
You can share the x or y axis limits for one axis with another by
passing an axes instance as a sharex or sharey kwarg.

Changing the axis limits on one axes will be reflected automatically
in the other, and vice-versa, so when you navigate with the toolbar
the axes will follow each other on their shared axes. Ditto for
changes in the axis scaling (eg log vs linear). However, it is
possible to have differences in tick labeling, eg you can selectively
turn off the tick labels on one axes.

The example below shows how to customize the tick labels on the
various axes. Shared axes share the tick locator, tick formatter,
view limits, and transformation (eg log, linear). But the ticklabels
themselves do not share properties. This is a feature and not a bug,
because you may want to make the tick labels smaller on the upper
axes, eg in the example below.

If you want to turn off the ticklabels for a given axes (eg on
subplot(211) or subplot(212), you cannot do the standard trick

   setp(ax2, xticklabels=[])

because this changes the tick Formatter, which is shared among all
axes. But you can alter the visibility of the labels, which is a
property

  setp( ax2.get_xticklabels(), visible=False)

"""
from pylab import *

t = arange(0.01, 5.0, 0.01)
s1 = sin(2*pi*t)
s2 = exp(-t)
s3 = sin(4*pi*t)
ax1 = subplot(311)
plot(t,s1)
setp( ax1.get_xticklabels(), fontsize=6)

## share x only
ax2 = subplot(312, sharex=ax1)
plot(t, s2)
# make these tick labels invisible
setp( ax2.get_xticklabels(), visible=False)

# share x and y
ax3 = subplot(313, sharex=ax1, sharey=ax1)
plot(t, s3)
xlim(0.01,5.0)
show()

Darren

-------------------------------------------------------------------------
This SF.net email is sponsored by the 2008 JavaOne(SM) Conference Don't miss this year's exciting event. There's still time to save $100. Use priority code J8TL2D2. http://ad.doubleclick.net/clk;198757673;13503038;p?http://java.sun.com/javaone
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users