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Agree with Eric. I guess if you remove sharex=True, it will work.

Chao

On Wed, Mar 20, 2013 at 10:27 PM, Eric Firing [via matplotlib] <
ml-...@n5...> wrote:

> On 2013/03/20 8:57 AM, Jonathan Slavin wrote:
>
> > Hi all,
> >
> > I've run across a minor but annoying bug.  It can be demonstrated pretty
> > simply:
> >
> > fig, ax = plt.subplots(2,1,sharex=True,figsize=(7.,7.))
> > fig.subplots_adjust(hspace=0.0)
> > x = 4.25*(np.arange(6.) - 2.5)/10.
> > y =  0.6*x/max(x)
> > ax[0].plot(x,y)
> > ax[0].set_xlim(-1.2,1.2)
> > ax[0].set_aspect('equal')
> > ax[1].plot(x,y)
> > ax[0].set_ylim(-0.6,0.6)
> > ax[1].set_ylim(-0.6,0.6)
> > ax[1].set_aspect('equal')
> > plt.show()
> >
> > The problem is that the y limits on the two plots are slightly different
> > from those set:
>
> I think the problem is that you are trying to specify too many things:
> you are specifying the box dimensions when you make the axes, then you
> are specifying xlim, and then you are specifying ylim, but then you are
> asking for a 1:1 aspect ratio.  Something has to give!  The aspect ratio
> handling is designed to provide the specified aspect ratio under a wide
> range of circumstances, including zooming and panning, and to do that,
> it has to be able to change something.  You can choose to let the box
> dimensions be changeable, or the data limits.
>
> If you want to fix the data limits, then you have to make the box
> adjustable.  This can cause problems with shared axes, but you can try
> it with ax[0].set_aspect('equal', adjustable='box-forced').
>
> Eric
>
> > ax[1].get_ylim()
> > (-0.61935483870967734, 0.61935483870967734)
> > and doing a set_ylim doesn't have any effect.  This seems to be caused
> > by the set_aspect('equal'), since removing it results in plots with the
> > correct limits -- but aspect that is not quite equal.  It is affected by
> > the figsize parameter in the call to subplots.  It seems I can get the
> > correct y limits and aspect if I keep the set_aspect('equal') and fiddle
> > with the figsize.  But that certainly doesn't seem to be a desirable
> > behavior.  Ideally, the set_ylim (or set_xlim) would be respected as
> > well as the apect ratio and extra blank space around the figure would be
> > added as needed to fit the figsize.
> >
> > By the way, using no figsize argument to subplots results in y limits
> > even smaller than the data limits.  Also, this problem does not occur
> > for single (non-stacked) plots and the use of subplots_adjust also does
> > not seem to affect the problem.  I'm using matplotlib 1.2.0
> >
> > I did notice that this issue is similar to that discussed in this
> > thread:
> > http://www.mail-archive.com/matplotlib-users@.../msg05783.html<http://www.mail-archive.com/mat...@li.../msg05783.html>
> >
> > Regards,
> > Jon
> >
>
>
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Chao YUE
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Sent from the matplotlib - users mailing list archive at Nabble.com.

On 2013/03/20 8:57 AM, Jonathan Slavin wrote:
> Hi all,
>
> I've run across a minor but annoying bug.  It can be demonstrated pretty
> simply:
>
> fig, ax = plt.subplots(2,1,sharex=True,figsize=(7.,7.))
> fig.subplots_adjust(hspace=0.0)
> x = 4.25*(np.arange(6.) - 2.5)/10.
> y =  0.6*x/max(x)
> ax[0].plot(x,y)
> ax[0].set_xlim(-1.2,1.2)
> ax[0].set_aspect('equal')
> ax[1].plot(x,y)
> ax[0].set_ylim(-0.6,0.6)
> ax[1].set_ylim(-0.6,0.6)
> ax[1].set_aspect('equal')
> plt.show()
>
> The problem is that the y limits on the two plots are slightly different
> from those set:

I think the problem is that you are trying to specify too many things: 
you are specifying the box dimensions when you make the axes, then you 
are specifying xlim, and then you are specifying ylim, but then you are 
asking for a 1:1 aspect ratio.  Something has to give!  The aspect ratio 
handling is designed to provide the specified aspect ratio under a wide 
range of circumstances, including zooming and panning, and to do that, 
it has to be able to change something.  You can choose to let the box 
dimensions be changeable, or the data limits.

If you want to fix the data limits, then you have to make the box 
adjustable.  This can cause problems with shared axes, but you can try 
it with ax[0].set_aspect('equal', adjustable='box-forced').

Eric

> ax[1].get_ylim()
> (-0.61935483870967734, 0.61935483870967734)
> and doing a set_ylim doesn't have any effect.  This seems to be caused
> by the set_aspect('equal'), since removing it results in plots with the
> correct limits -- but aspect that is not quite equal.  It is affected by
> the figsize parameter in the call to subplots.  It seems I can get the
> correct y limits and aspect if I keep the set_aspect('equal') and fiddle
> with the figsize.  But that certainly doesn't seem to be a desirable
> behavior.  Ideally, the set_ylim (or set_xlim) would be respected as
> well as the apect ratio and extra blank space around the figure would be
> added as needed to fit the figsize.
>
> By the way, using no figsize argument to subplots results in y limits
> even smaller than the data limits.  Also, this problem does not occur
> for single (non-stacked) plots and the use of subplots_adjust also does
> not seem to affect the problem.  I'm using matplotlib 1.2.0
>
> I did notice that this issue is similar to that discussed in this
> thread:
> http://www.mail-archive.com/mat...@li.../msg05783.html
>
> Regards,
> Jon
>

Hi all,

I've run across a minor but annoying bug.  It can be demonstrated pretty
simply:

fig, ax = plt.subplots(2,1,sharex=True,figsize=(7.,7.))
fig.subplots_adjust(hspace=0.0)
x = 4.25*(np.arange(6.) - 2.5)/10.
y =  0.6*x/max(x)
ax[0].plot(x,y)
ax[0].set_xlim(-1.2,1.2)
ax[0].set_aspect('equal')
ax[1].plot(x,y)
ax[0].set_ylim(-0.6,0.6)
ax[1].set_ylim(-0.6,0.6)
ax[1].set_aspect('equal')
plt.show()

The problem is that the y limits on the two plots are slightly different
from those set:
ax[1].get_ylim()
(-0.61935483870967734, 0.61935483870967734)
and doing a set_ylim doesn't have any effect.  This seems to be caused
by the set_aspect('equal'), since removing it results in plots with the
correct limits -- but aspect that is not quite equal.  It is affected by
the figsize parameter in the call to subplots.  It seems I can get the
correct y limits and aspect if I keep the set_aspect('equal') and fiddle
with the figsize.  But that certainly doesn't seem to be a desirable
behavior.  Ideally, the set_ylim (or set_xlim) would be respected as
well as the apect ratio and extra blank space around the figure would be
added as needed to fit the figsize.

By the way, using no figsize argument to subplots results in y limits
even smaller than the data limits.  Also, this problem does not occur
for single (non-stacked) plots and the use of subplots_adjust also does
not seem to affect the problem.  I'm using matplotlib 1.2.0

I did notice that this issue is similar to that discussed in this
thread:
http://www.mail-archive.com/mat...@li.../msg05783.html

Regards,
Jon
-- 
______________________________________________________________
Jonathan D. Slavin              Harvard-Smithsonian CfA
js...@cf...         60 Garden Street, MS 83
phone: (617) 496-7981           Cambridge, MA 02138-1516
 cell: (781) 363-0035           USA
______________________________________________________________

On Wed, Mar 13, 2013 at 2:17 AM, Andrew Dawson <da...@at...> wrote:

> You should see that the circle is no longer circular, and also there are
> weird line width issues. What I want it basically exactly like the attached
> without_clipping.png but with paths inside the circle removed.


The reason that circle is no more circle is that simply inverting the
vertices does not always results in a correctly inverted path.
Instead of following line.

interior.vertices = interior.vertices[::-1]

You should use something like below.

interior = mpath.Path(np.concatenate([interior.vertices[-2::-1],
                                      interior.vertices[-1:]]),
                      interior.codes)

It would be good if we have a method to invert a path.

This will give you a circle. But the weird line width issue remains. This
seems to be an Agg issue, and the line width seems to depend on the dpi.
I guess @mdboom nay have some insight on this.

Regards,

-JJ

Thank you Paul,
                          I think the font issue is the mischief of Yahoo. I think I should send mail in text mode rather than html then the issue will not be there I hope. The signature is in normal text mode I saved. Please revert back if my mail shows font issues again so that I can try some thing different. However when I see it in Yahoo there is no issues though..
with best regards,
Sudheer
 
***************************************************************
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Indian National Centre for Ocean Information Services
Ministry of Earth Sciences, Govt. of India
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>________________________________
> From: Paul Hobson <pmh...@gm...>
>To: Sudheer Joseph <sud...@ya...> 
>Cc: "mat...@li..." <mat...@li...> 
>Sent: Tuesday, 19 March 2013 11:30 PM
>Subject: Re: [Matplotlib-users] windrose
> 
>
>On Tue, Mar 19, 2013 at 2:22 AM, Sudheer Joseph <sud...@ya...> wrote:
>
>Dear users,
>>                         Attached is a windrose diagram created by using https://sourceforge.net/project/showfiles.php?group_id=239240&package_id=290902 . Can any one tell me if the numbers displayed in the attached plot is % of wind directions in each category? or are they represent some other numbers?
>>
>>
>>http://3.bp.blogspot.com/_4ZlrnfU7IT8/TPxpftZGzfI/AAAAAAAAADA/uq9cF3PTpR8/s1600/Wind_rose_plot.jpg
>> 
>
>
>Sudheer, 
>
>
>That's correct. The total length of the bars is the percentage of time that the wind is blowing *from* that direction. 
>See my implementation here: https://github.com/phobson/python-metar/blob/master/metar/graphics.py#L135
>
>
>Side note, you're emails are consistently formatted pretty strangely and can be difficult to read. Perhaps stick with the same font that is in your email signature?
>
>