After making changes to has_data I get the following:
> (0.0, 400.0) (0.0, 400.0) (0.0, 400.0)
> which is not correct it should be:
> (300.0, 400.0) (100.0, 400.0) (100.0, 400.0)
I'm not sure this is incorrect. matplotlib distinguishes between the
data limits and the view limits. The latter are automatically
adjusted to include the data limits but may incorporate more. For
example, the x data limits in [0.1, 10] may produce view limits
[0,10].
If you need the view limits to always equal the data limits, you could
provide a custom ticker, as described in
http://matplotlib.sourceforge.net/matplotlib.ticker.html and
illustrated in the example
http://matplotlib.sourceforge.net/examples/custom_ticker1.py.
You might want to verify that the data limits are correct by printing
ax.dataLim.intervalx().get_bounds() # the x limits
ax.dataLim.intervaly().get_bounds() # the y limits
where ax is your Axes instance. You can compare these with
ax.viewLim.intervalx().get_bounds() # the x limits
ax.viewLim.intervaly().get_bounds() # the y limits
JDH
That's fine. I'll start using
ax.viewLim.intervaly().get_bounds() # the y limits
to get the y limits.
In any case though, the order in which the plot functions are called should
not change the value returned by self.axMiddle.get_ylim. I think that is a
bug.
VJ
···
-----Original Message-----
From: matplotlib-users-admin@lists.sourceforge.net
[mailto:matplotlib-users-admin@lists.sourceforge.net]On Behalf Of John
Hunter
Sent: Monday, August 02, 2004 10:56 AM
To: Vineet Jain
Cc: matplotlib-users
Subject: Re: [Matplotlib-users] Settling y-axis scaling
> After making changes to has_data I get the following:
> (0.0, 400.0) (0.0, 400.0) (0.0, 400.0)
> which is not correct it should be:
> (300.0, 400.0) (100.0, 400.0) (100.0, 400.0)
I'm not sure this is incorrect. matplotlib distinguishes between the
data limits and the view limits. The latter are automatically
adjusted to include the data limits but may incorporate more. For
example, the x data limits in [0.1, 10] may produce view limits
[0,10].
If you need the view limits to always equal the data limits, you could
provide a custom ticker, as described in
http://matplotlib.sourceforge.net/matplotlib.ticker.html and
illustrated in the example
http://matplotlib.sourceforge.net/examples/custom_ticker1.py.
You might want to verify that the data limits are correct by printing
ax.dataLim.intervalx().get_bounds() # the x limits
ax.dataLim.intervaly().get_bounds() # the y limits
where ax is your Axes instance. You can compare these with
ax.viewLim.intervalx().get_bounds() # the x limits
ax.viewLim.intervaly().get_bounds() # the y limits
JDH
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