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Ah, thank you very much, that helped! It works nicely!
Best regards,
Daniel

On 01/22/2011 05:16 PM, Paul Ivanov wrote:
> Paul Ivanov, on 2011年01月22日 18:28, wrote:
>> Ilya Shlyakhter, on 2011年01月22日 19:06, wrote:
>>> Is it possible to create a "break" in the y-axis so that it has ticks
>>> for value 0-.2, then ticks for values .8-1.0, but devotes only a token
>>> amount of space to the area 0.2-0.8?
>>> I have a dataset with most datapoints in 0-.2 and a couple in .8-1.0,
>>> and none in .2-.8 . The default scaling wastes a lot of space and
>>> compresses the data in the 0-.2 range
>>> such that it is hard to distinguish.
>>
>> Hi Ilya,
>>
>> this...
Paul,
Your example below is nice, and this question comes up quite often. If 
we don't already have a gallery example of this, you might want to add 
one. (Probably better to use deterministic fake data rather than random.)
Eric
>>
>>> p.s. I know I could use two y-axes with different scales; but this
>>> would require splitting the data into two different datasets as well,
>>> and would not allow connecting all points
>>> with one line.
>>
>> ... is the way I'd proceed, because it's clean, and requires the
>> least amount of work. Connecting your lines across such breaks
>> is misleading - since the magnitude of the slope of the
>> connecting line segment arbitrary relative to all other line
>> segments. You don't actually have to divide your data, you can
>> just replot *all* data on the secondary plot, and then set the x
>> and y lims to break up your views on the data. I'm attaching a
>> quick sketch of what that would look like. (Note how different
>> the outlier line segments would look if we connected them in the
>> same manner that all other points are connected).
>>
>> import numpy as np
>> import matplotlib.pylab as plt
>> pts = np.random.rand(30)*.2
>> pts[[7,11]] += .8
>> f,(ax,ax2) = plt.subplots(2,1,sharex=True)
>>
>> ax.plot(pts)
>> ax2.plot(pts)
>> ax.set_ylim(.78,1.)
>> ax2.set_ylim(0,.22)
>>
>> ax.xaxis.tick_top()
>> ax.spines['bottom'].set_visible(False)
>> ax.tick_params(labeltop='off')
>> ax2.xaxis.tick_bottom()
>> ax2.spines['top'].set_visible(False)
>>
>> If this is something you really want, though, you can achieve it
>> by making your own projection/scale:
>> http://matplotlib.sourceforge.net/devel/add_new_projection.html
>>
>> Yet another way would be to re-label the tick lines (e.g. make .6
>> label be 1.0 and subtract that offset from your two outliers.
>
> forgot the attachment, here it is.
>
>
>
>
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Paul Ivanov, on 2011年01月22日 18:28, wrote:
> Ilya Shlyakhter, on 2011年01月22日 19:06, wrote:
> > Is it possible to create a "break" in the y-axis so that it has ticks
> > for value 0-.2, then ticks for values .8-1.0, but devotes only a token
> > amount of space to the area 0.2-0.8?
> > I have a dataset with most datapoints in 0-.2 and a couple in .8-1.0,
> > and none in .2-.8 . The default scaling wastes a lot of space and
> > compresses the data in the 0-.2 range
> > such that it is hard to distinguish.
> 
> Hi Ilya,
> 
> this...
> 
> > p.s. I know I could use two y-axes with different scales; but this
> > would require splitting the data into two different datasets as well,
> > and would not allow connecting all points
> > with one line.
> 
> ... is the way I'd proceed, because it's clean, and requires the
> least amount of work. Connecting your lines across such breaks
> is misleading - since the magnitude of the slope of the
> connecting line segment arbitrary relative to all other line
> segments. You don't actually have to divide your data, you can
> just replot *all* data on the secondary plot, and then set the x
> and y lims to break up your views on the data. I'm attaching a
> quick sketch of what that would look like. (Note how different
> the outlier line segments would look if we connected them in the
> same manner that all other points are connected).
> 
> import numpy as np
> import matplotlib.pylab as plt
> pts = np.random.rand(30)*.2
> pts[[7,11]] += .8
> f,(ax,ax2) = plt.subplots(2,1,sharex=True)
> 
> ax.plot(pts)
> ax2.plot(pts)
> ax.set_ylim(.78,1.)
> ax2.set_ylim(0,.22)
> 
> ax.xaxis.tick_top()
> ax.spines['bottom'].set_visible(False)
> ax.tick_params(labeltop='off')
> ax2.xaxis.tick_bottom()
> ax2.spines['top'].set_visible(False)
> 
> If this is something you really want, though, you can achieve it
> by making your own projection/scale:
> http://matplotlib.sourceforge.net/devel/add_new_projection.html
> 
> Yet another way would be to re-label the tick lines (e.g. make .6
> label be 1.0 and subtract that offset from your two outliers.
forgot the attachment, here it is.
-- 
Paul Ivanov
314 address only used for lists, off-list direct email at:
http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7 

Glen Shennan, on 2011年01月21日 15:41, wrote:
> Hi,
> 
> I'm trying to install matplotlib from the svn source. I can compile
> the code and install it to my desired location but I cannot import it
> into python.
> 
> I did:
> 
> svn co https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/trunk/matplotlib
> matplotlib
> cd matplotlib
> python setup.py install --prefix=/home/glen/local
> 
> I have numpy and scipy installed and working correctly using the above
> prefix and matplotlib compiles and installs the same way but when I
> issue "import matplotlib as mpl" nothing results. There is no error
> but also no library.
> 
> >>> dir(mpl)
> ['__builtins__', '__doc__', '__file__', '__name__', '__package__', '__path__']
> 
> >>> mpl.__path__
> ['matplotlib']
> 
> >>> mpl.__file__
> 'matplotlib/__init__.pyc'
Hi Glen,
what directory are you in when you're doing this?
> >>> mpl.__path__
> ['matplotlib']
suggests that you're importing from some local directory. If
everything worked right, the path should be something like
>>> mpl.__path__
['/home/glen/local/lib/python2.x/site-packages/matplotlib']
best,
-- 
Paul Ivanov
314 address only used for lists, off-list direct email at:
http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7 

Ilya Shlyakhter, on 2011年01月22日 19:06, wrote:
> Is it possible to create a "break" in the y-axis so that it has ticks
> for value 0-.2, then ticks for values .8-1.0, but devotes only a token
> amount of space to the area 0.2-0.8?
> I have a dataset with most datapoints in 0-.2 and a couple in .8-1.0,
> and none in .2-.8 . The default scaling wastes a lot of space and
> compresses the data in the 0-.2 range
> such that it is hard to distinguish.
 
Hi Ilya,
this...
> p.s. I know I could use two y-axes with different scales; but this
> would require splitting the data into two different datasets as well,
> and would not allow connecting all points
> with one line.
... is the way I'd proceed, because it's clean, and requires the
least amount of work. Connecting your lines across such breaks
is misleading - since the magnitude of the slope of the
connecting line segment arbitrary relative to all other line
segments. You don't actually have to divide your data, you can
just replot *all* data on the secondary plot, and then set the x
and y lims to break up your views on the data. I'm attaching a
quick sketch of what that would look like. (Note how different
the outlier line segments would look if we connected them in the
same manner that all other points are connected).
 import numpy as np
 import matplotlib.pylab as plt
 pts = np.random.rand(30)*.2
 pts[[7,11]] += .8
 f,(ax,ax2) = plt.subplots(2,1,sharex=True)
 
 ax.plot(pts)
 ax2.plot(pts)
 ax.set_ylim(.78,1.)
 ax2.set_ylim(0,.22)
 
 ax.xaxis.tick_top()
 ax.spines['bottom'].set_visible(False)
 ax.tick_params(labeltop='off')
 ax2.xaxis.tick_bottom()
 ax2.spines['top'].set_visible(False)
If this is something you really want, though, you can achieve it
by making your own projection/scale:
http://matplotlib.sourceforge.net/devel/add_new_projection.html
Yet another way would be to re-label the tick lines (e.g. make .6
label be 1.0 and subtract that offset from your two outliers.
best,
-- 
Paul Ivanov
314 address only used for lists, off-list direct email at:
http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7 

Is it possible to create a "break" in the y-axis so that it has ticks
for value 0-.2, then ticks for values .8-1.0, but devotes only a token
amount of space to the area 0.2-0.8?
I have a dataset with most datapoints in 0-.2 and a couple in .8-1.0,
and none in .2-.8 . The default scaling wastes a lot of space and
compresses the data in the 0-.2 range
such that it is hard to distinguish.
Thanks for any help!
Ilya
p.s. I know I could use two y-axes with different scales; but this
would require splitting the data into two different datasets as well,
and would not allow connecting all points
with one line.