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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