matplotlib-users Mailing List for matplotlib (Page 3)

On 2014/01/08 11:40 AM, Skip Montanaro wrote:
> Apologies. Gmail (or my fingers) were acting up...
>
> On Wed, Jan 8, 2014 at 3:39 PM, Skip Montanaro <sk...@po...> wrote:
>> I'm happy with the draggable legends, but I have a problem. It seems
>> there are three pointer modes, the initial mode (updates x, y as you
>> move the mouse), zoom rectangle mode, and pan/zoom mode. Once I enter
>> either of those modes, I can't see how to get back to the original
>> mode. I found this navigation documentation:
>
> http://matplotlib.org/users/navigation_toolbar.html
>
> but I didn't see any way to get back to the starting mode (what's that
> mode called?), and the legend can only be moved as far as I can tell
> when that is the current mode.

If you are in pan/zoom or rectangle mode, just unselect it by clicking 
its select button again.

Ideally we would have an obvious radio button setup for this.

Eric

>
> Thx,
>
> Skip

Apologies. Gmail (or my fingers) were acting up...

On Wed, Jan 8, 2014 at 3:39 PM, Skip Montanaro <sk...@po...> wrote:
> I'm happy with the draggable legends, but I have a problem. It seems
> there are three pointer modes, the initial mode (updates x, y as you
> move the mouse), zoom rectangle mode, and pan/zoom mode. Once I enter
> either of those modes, I can't see how to get back to the original
> mode. I found this navigation documentation:

http://matplotlib.org/users/navigation_toolbar.html

but I didn't see any way to get back to the starting mode (what's that
mode called?), and the legend can only be moved as far as I can tell
when that is the current mode.

Thx,

Skip

I'm happy with the draggable legends, but I have a problem. It seems
there are three pointer modes, the initial mode (updates x, y as you
move the mouse), zoom rectangle mode, and pan/zoom mode. Once I enter
either of those modes, I can't see how to get back to the original
mode. I found this navigation documentation:

I am trying to update a figure in a loop:

    import matplotlib.pyplot as plt
    plt.ion()
    plt.figure (1)
    def c2r (z):
        return z.real, z.imag
    
    plt.hexbin (*c2r (run_ofdm (xconst_pred)[:opt.used]), mincnt=1)
    plt.draw()

But no figure appears on the screen.  What am I doing wrong?

This is using using Qt4Agg (I think, that's what's in my matplotlibrc)

Le 07/01/2014 17:51, Paul Hobson a écrit :
> I believe (as of v1.3.1) that after you create the legend you call
> leg.draggable(True)
I had never heard of that nice possibility!

Would it make sense to add a few lines to the Legend Guide/Legend location ?
http://matplotlib.org/users/legend_guide.html#legend-location

and possibly to the legend demo ?
http://matplotlib.org/examples/api/legend_demo.html

(and remove 
http://matplotlib.org/examples/old_animation/draggable_legend.html ?)

best,
Pierre

Sorry, had forgot to reply all:

Thanks Joe, that's perfect.  I appreciate the tip, as I would not have
realized I needed a PathCollection for lines and curves.  PS, do you know
if it is possible to have a background image behind a plot of patches?  I
know it's doable for scatter, but hadn't seen an example for patch plots in
general.

Paul, thanks for you help as well.  I'm actually pretty confident in the
primitives I've chosen.  I was inspired by scikit-image.draw:

http://scikit-image.org/docs/dev/api/skimage.draw.html

Which returns the indicies of an array, such that anytime one ones to draw
the array, they merely pass by index.  For example:

image = np.zeroes( (256, 256) )
rr, cc = draw.circle( center=(128,128), radius=5)
image[rr, cc] = (1, 0, 0)

The code above would generate a red circle.  My library creates primitive
classes that have an rr, cc attribute, with enough metadata to hide
effectively bury this representation.  This can be generated a number of
ways, but the classes take care of all of this, as well as other aspects.
 By keeping only these indicies as primitives, the shapes can be
manipulated and managed outside of any representation (ie the image).  What
I'd like to do is build wrappers to return PatchCollections from my already
storred rr, cc data (and other metadata that is stored).  In this way, I'll
be able to add the very nice patches from matplotlib, but retain the same
api.

The project is called "pyparty" and I'll share it pretty soon with the
scikit image mailing list.  If I am able to get the patches built it, would
anyone mind if I share it with the matplotlib list as well?

Thanks


On Tue, Jan 7, 2014 at 4:10 PM, Joe Kington <jof...@gm...> wrote:

>
>
>
> On Tue, Jan 7, 2014 at 2:29 PM, Adam Hughes <hug...@gm...>wrote:
>
>> Sorry, quick followup.  I did find the gallery example to plot multiple
>> patches together:
>>
>> http://matplotlib.org/examples/api/patch_collection.html
>>
>> That's excellent.  Now I guess my question is how best to generalize the
>> process of turning my objects into patches.  I think I will just try to
>> keep the geometry (ie line --> mpatch.Line) unless anyone has any better
>> suggestions.
>>
>
> As you've already found out, it sounds like you want a PatchCollection.
>
> There is one catch, though.  Your lines/curves will need to be converted
> to PathPatches (which is trivial), which can then have a facecolor.
> Because all items in a collection will have the same facecolor by default,
> this means that your lines will become "filled" polygons, unless you
> specify otherwise.
>
> Therefore, you'll need to do something like this:
>
> import matplotlib.pyplot as plt
> from matplotlib.path import Path
> import matplotlib.patches as mpatches
> from matplotlib.collections import PatchCollection
>
> # Just a simple line, but it could be a bezier curve, etc.
> line = Path([(-20, -20), (-10, 10), (20, 20)])
>
> # We'll need to convert the line to a PathPatch, and we'll throw in a
> circle, too
> line = mpatches.PathPatch(line)
> circle = mpatches.Circle([0, 0])
>
> # If we don't specify facecolor='none' for the line, it will be filled!
> col = PatchCollection([line, circle], facecolors=['none', 'red'])
>
> fig, ax = plt.subplots()
> ax.add_collection(col)
> ax.autoscale()
> plt.show()
>
> Alternatively, you can just put the lines/curves in a PathCollection and
> the patches/polygons/etc in a PatchCollection.
>
> Hope that helps!
> -Joe
>
>
>>
>> Thanks!
>>
>>
>> On Tue, Jan 7, 2014 at 3:08 PM, Adam Hughes <hug...@gm...>wrote:
>>
>>> Hi,
>>>
>>> I am working on a library for image analysis which stores particles as
>>> indexed numpy arrays and provides functionality for managing the particles
>>> beyond merely image masking or altering the arrays directly.  I've already
>>> designed classes for many common shapes including Lines/Curves,
>>> Circles/Ellipses, Polygons, Multi-shapes (eg 4 circles with variable
>>> overlap).
>>>
>>> What I'd really LOVE to do would be able to generate a
>>> matplotlib.Collection instance from these objects as generally as possible.
>>>  Then, I'd be able to show data as a masked image, but also get a really
>>> nice looking plot from the objects in their Collection representation.
>>>
>>> So my question really is in the implementation.  First, is there a
>>> general collection object that could work with ANY shape, or am I better
>>> off matching my shape to that collection?  For example:
>>>
>>>     line --> LineCollection       *vs.*    line --> GeneralCollection
>>>     circle --> CircleCollection           circle ---> GeneralCollection
>>>
>>> And then, is the Collections plotting API flexible enough to mix all of
>>> these types together?  Or would I have to settle for only being able to
>>> plot a collection of any 1 shape type at at time?
>>>
>>> I will delve into the API further, but ascertaining this information
>>> would really help me get started.
>>>
>>> Thanks
>>>
>>
>>
>>
>> ------------------------------------------------------------------------------
>> Rapidly troubleshoot problems before they affect your business. Most IT
>> organizations don't have a clear picture of how application performance
>> affects their revenue. With AppDynamics, you get 100% visibility into your
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>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>

On Tue, Jan 7, 2014 at 2:29 PM, Adam Hughes <hug...@gm...> wrote:

> Sorry, quick followup.  I did find the gallery example to plot multiple
> patches together:
>
> http://matplotlib.org/examples/api/patch_collection.html
>
> That's excellent.  Now I guess my question is how best to generalize the
> process of turning my objects into patches.  I think I will just try to
> keep the geometry (ie line --> mpatch.Line) unless anyone has any better
> suggestions.
>

As you've already found out, it sounds like you want a PatchCollection.

There is one catch, though.  Your lines/curves will need to be converted to
PathPatches (which is trivial), which can then have a facecolor.  Because
all items in a collection will have the same facecolor by default, this
means that your lines will become "filled" polygons, unless you specify
otherwise.

Therefore, you'll need to do something like this:

import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as mpatches
from matplotlib.collections import PatchCollection

# Just a simple line, but it could be a bezier curve, etc.
line = Path([(-20, -20), (-10, 10), (20, 20)])

# We'll need to convert the line to a PathPatch, and we'll throw in a
circle, too
line = mpatches.PathPatch(line)
circle = mpatches.Circle([0, 0])

# If we don't specify facecolor='none' for the line, it will be filled!
col = PatchCollection([line, circle], facecolors=['none', 'red'])

fig, ax = plt.subplots()
ax.add_collection(col)
ax.autoscale()
plt.show()

Alternatively, you can just put the lines/curves in a PathCollection and
the patches/polygons/etc in a PatchCollection.

Hope that helps!
-Joe


>
> Thanks!
>
>
> On Tue, Jan 7, 2014 at 3:08 PM, Adam Hughes <hug...@gm...>wrote:
>
>> Hi,
>>
>> I am working on a library for image analysis which stores particles as
>> indexed numpy arrays and provides functionality for managing the particles
>> beyond merely image masking or altering the arrays directly.  I've already
>> designed classes for many common shapes including Lines/Curves,
>> Circles/Ellipses, Polygons, Multi-shapes (eg 4 circles with variable
>> overlap).
>>
>> What I'd really LOVE to do would be able to generate a
>> matplotlib.Collection instance from these objects as generally as possible.
>>  Then, I'd be able to show data as a masked image, but also get a really
>> nice looking plot from the objects in their Collection representation.
>>
>> So my question really is in the implementation.  First, is there a
>> general collection object that could work with ANY shape, or am I better
>> off matching my shape to that collection?  For example:
>>
>>     line --> LineCollection       *vs.*    line --> GeneralCollection
>>     circle --> CircleCollection           circle ---> GeneralCollection
>>
>> And then, is the Collections plotting API flexible enough to mix all of
>> these types together?  Or would I have to settle for only being able to
>> plot a collection of any 1 shape type at at time?
>>
>> I will delve into the API further, but ascertaining this information
>> would really help me get started.
>>
>> Thanks
>>
>
>
>
> ------------------------------------------------------------------------------
> Rapidly troubleshoot problems before they affect your business. Most IT
> organizations don't have a clear picture of how application performance
> affects their revenue. With AppDynamics, you get 100% visibility into your
> Java,.NET, & PHP application. Start your 15-day FREE TRIAL of AppDynamics
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> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

Adam,

Not sure if this is the try you're trying to bark up, but I've used a total
hack to do what I think you're describing:
 1) store lists of coordinate pairs in a pandas DataFrame
 2) use df.apply() to turn each list of coords in to a patch and add to an
axes object

I'm sure you know this, but for posterity's sake, I'll mention that you
*really* should only store primitives in pandas DataFrames. For that reason
alone, I would describe the method above as the death-throes of a failing
project trying to meet deadlines.

Perhaps a more robust way would be to store the coordinates in a "long"
format, i.e.,

shapeid, vertexid, x, y
1,1,0,0
1,2,1,1
1,3,2,2
2,1,10,10
2,2,11,11
3,3,12,12
...

And the group that DataFrame by `shapeid` and use `apply` on the pandas
GroupBy object to construct a patch and add it to an axes object.

Just a thought.


On Tue, Jan 7, 2014 at 12:29 PM, Adam Hughes <hug...@gm...> wrote:

> Sorry, quick followup.  I did find the gallery example to plot multiple
> patches together:
>
> http://matplotlib.org/examples/api/patch_collection.html
>
> That's excellent.  Now I guess my question is how best to generalize the
> process of turning my objects into patches.  I think I will just try to
> keep the geometry (ie line --> mpatch.Line) unless anyone has any better
> suggestions.
>
> Thanks!
>
>
> On Tue, Jan 7, 2014 at 3:08 PM, Adam Hughes <hug...@gm...>wrote:
>
>> Hi,
>>
>> I am working on a library for image analysis which stores particles as
>> indexed numpy arrays and provides functionality for managing the particles
>> beyond merely image masking or altering the arrays directly.  I've already
>> designed classes for many common shapes including Lines/Curves,
>> Circles/Ellipses, Polygons, Multi-shapes (eg 4 circles with variable
>> overlap).
>>
>> What I'd really LOVE to do would be able to generate a
>> matplotlib.Collection instance from these objects as generally as possible.
>>  Then, I'd be able to show data as a masked image, but also get a really
>> nice looking plot from the objects in their Collection representation.
>>
>> So my question really is in the implementation.  First, is there a
>> general collection object that could work with ANY shape, or am I better
>> off matching my shape to that collection?  For example:
>>
>>     line --> LineCollection       *vs.*    line --> GeneralCollection
>>     circle --> CircleCollection           circle ---> GeneralCollection
>>
>> And then, is the Collections plotting API flexible enough to mix all of
>> these types together?  Or would I have to settle for only being able to
>> plot a collection of any 1 shape type at at time?
>>
>> I will delve into the API further, but ascertaining this information
>> would really help me get started.
>>
>> Thanks
>>
>
>
>
> ------------------------------------------------------------------------------
> Rapidly troubleshoot problems before they affect your business. Most IT
> organizations don't have a clear picture of how application performance
> affects their revenue. With AppDynamics, you get 100% visibility into your
> Java,.NET, & PHP application. Start your 15-day FREE TRIAL of AppDynamics
> Pro!
> http://pubads.g.doubleclick.net/gampad/clk?id=84349831&iu=/4140/ostg.clktrk
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

Sorry, quick followup.  I did find the gallery example to plot multiple
patches together:

http://matplotlib.org/examples/api/patch_collection.html

That's excellent.  Now I guess my question is how best to generalize the
process of turning my objects into patches.  I think I will just try to
keep the geometry (ie line --> mpatch.Line) unless anyone has any better
suggestions.

Thanks!


On Tue, Jan 7, 2014 at 3:08 PM, Adam Hughes <hug...@gm...> wrote:

> Hi,
>
> I am working on a library for image analysis which stores particles as
> indexed numpy arrays and provides functionality for managing the particles
> beyond merely image masking or altering the arrays directly.  I've already
> designed classes for many common shapes including Lines/Curves,
> Circles/Ellipses, Polygons, Multi-shapes (eg 4 circles with variable
> overlap).
>
> What I'd really LOVE to do would be able to generate a
> matplotlib.Collection instance from these objects as generally as possible.
>  Then, I'd be able to show data as a masked image, but also get a really
> nice looking plot from the objects in their Collection representation.
>
> So my question really is in the implementation.  First, is there a general
> collection object that could work with ANY shape, or am I better off
> matching my shape to that collection?  For example:
>
>     line --> LineCollection       *vs.*    line --> GeneralCollection
>     circle --> CircleCollection           circle ---> GeneralCollection
>
> And then, is the Collections plotting API flexible enough to mix all of
> these types together?  Or would I have to settle for only being able to
> plot a collection of any 1 shape type at at time?
>
> I will delve into the API further, but ascertaining this information would
> really help me get started.
>
> Thanks
>

Hi,

I am working on a library for image analysis which stores particles as
indexed numpy arrays and provides functionality for managing the particles
beyond merely image masking or altering the arrays directly.  I've already
designed classes for many common shapes including Lines/Curves,
Circles/Ellipses, Polygons, Multi-shapes (eg 4 circles with variable
overlap).

What I'd really LOVE to do would be able to generate a
matplotlib.Collection instance from these objects as generally as possible.
 Then, I'd be able to show data as a masked image, but also get a really
nice looking plot from the objects in their Collection representation.

So my question really is in the implementation.  First, is there a general
collection object that could work with ANY shape, or am I better off
matching my shape to that collection?  For example:

    line --> LineCollection       *vs.*    line --> GeneralCollection
    circle --> CircleCollection           circle ---> GeneralCollection

And then, is the Collections plotting API flexible enough to mix all of
these types together?  Or would I have to settle for only being able to
plot a collection of any 1 shape type at at time?

I will delve into the API further, but ascertaining this information would
really help me get started.

Thanks

On 07.01.2014 18:18, Fabrice Silva wrote:
> Le mardi 07 janvier 2014 à 17:57 +0100, V. Armando Sole a écrit :
>> > What about using axvline with the picker argument?
>> > see http://matplotlib.org/users/event_handling.html
>> >
>>
>> I think axvline is part of the pyplot interface that I am not using.
>>
>> However your link is going to help me a lot. I thought picking was
>> restricted to patches, and I had missed Line2D is an Artist too with 
>> the
>> same capabilities.
>
> axvline is a pyplot function, but it is also a method of the Axes 
> class.
> So if you have an Axes in your Figure, everything is ok
> http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.axvline
>

Even better :-)

Thanks a lot,

Armando

Le mardi 07 janvier 2014 à 17:57 +0100, V. Armando Sole a écrit :
> > What about using axvline with the picker argument?
> > see http://matplotlib.org/users/event_handling.html
> >
> 
> I think axvline is part of the pyplot interface that I am not using.
> 
> However your link is going to help me a lot. I thought picking was 
> restricted to patches, and I had missed Line2D is an Artist too with the 
> same capabilities.

axvline is a pyplot function, but it is also a method of the Axes class.
So if you have an Axes in your Figure, everything is ok
http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.axvline

regards

> I believe (as of v1.3.1) that after you create the legend you call
> leg.draggable(True)
> http://matplotlib.org/api/legend_api.html#matplotlib.legend.Legend.draggable

Outstanding! (Google was not my friend here. I wasn't searching for
"draggable.")

Skip

On 07.01.2014 16:27, Fabrice Silva wrote:
> Le mardi 07 janvier 2014 à 15:19 +0100, "V. Armando Solé" a écrit :
>> Hello,
>>
>> I am trying to add some vertical lines into a matplotlib figure 
>> axes.
>>
>> The idea is to detect when the mouse passes over those lines in 
>> order to
>> displace them following the mouse if the left button is pressed.
>>
>> I need some help to know the simplest way to proceed. Currently I am
>> looking into matplotlib.patches and considering to use a Polygon or 
>> a
>> Rectangle but perhaps that solution is overkill for a simple line. 
>> Is
>> there any example about how to do it? I am not using the pyplot
>> interface but instantiating myself the Figure,  the FigureCanvas and
>> adding the axes to the figure.
>
> What about using axvline with the picker argument?
> see http://matplotlib.org/users/event_handling.html
>

I think axvline is part of the pyplot interface that I am not using.

However your link is going to help me a lot. I thought picking was 
restricted to patches, and I had missed Line2D is an Artist too with the 
same capabilities.

Armando

I believe (as of v1.3.1) that after you create the legend you call
leg.draggable(True)
http://matplotlib.org/api/legend_api.html#matplotlib.legend.Legend.draggable



On Tue, Jan 7, 2014 at 6:37 AM, Skip Montanaro <sk...@po...> wrote:

> Sometimes the legend simply gets in the way. You can't always guess
> the correct placement (think generic tool which processes lots of
> different input data sets), or zooming/panning makes it obscure a
> chunk of the plot you want to look at. Is it possible to move it
> interactively? I'm using mpl 1.3.1.
>
> Thx,
>
> Skip
>
>
> ------------------------------------------------------------------------------
> Rapidly troubleshoot problems before they affect your business. Most IT
> organizations don't have a clear picture of how application performance
> affects their revenue. With AppDynamics, you get 100% visibility into your
> Java,.NET, & PHP application. Start your 15-day FREE TRIAL of AppDynamics
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> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

Le mardi 07 janvier 2014 à 15:19 +0100, "V. Armando Solé" a écrit :
> Hello,
> 
> I am trying to add some vertical lines into a matplotlib figure axes.
> 
> The idea is to detect when the mouse passes over those lines in order to 
> displace them following the mouse if the left button is pressed.
> 
> I need some help to know the simplest way to proceed. Currently I am 
> looking into matplotlib.patches and considering to use a Polygon or a 
> Rectangle but perhaps that solution is overkill for a simple line. Is 
> there any example about how to do it? I am not using the pyplot 
> interface but instantiating myself the Figure,  the FigureCanvas and 
> adding the axes to the figure.

What about using axvline with the picker argument?
see http://matplotlib.org/users/event_handling.html

Sometimes the legend simply gets in the way. You can't always guess
the correct placement (think generic tool which processes lots of
different input data sets), or zooming/panning makes it obscure a
chunk of the plot you want to look at. Is it possible to move it
interactively? I'm using mpl 1.3.1.

Thx,

Skip

Hello,

I am trying to add some vertical lines into a matplotlib figure axes.

The idea is to detect when the mouse passes over those lines in order to 
displace them following the mouse if the left button is pressed.

I need some help to know the simplest way to proceed. Currently I am 
looking into matplotlib.patches and considering to use a Polygon or a 
Rectangle but perhaps that solution is overkill for a simple line. Is 
there any example about how to do it? I am not using the pyplot 
interface but instantiating myself the Figure,  the FigureCanvas and 
adding the axes to the figure.

Thanks for your time,

Armando

Hi all,

I am running a script that cranks out multiple plots in a loop.  The script
has plt.show() as the very last line as I think you are supposed to do.
All plots show up, but I get a seg fault (sometimes a bus error - I haven't
figured why it occasionally does that) when I close the last plot and the
script exits.  I was wondering if someone could help me figure out what
info to grab to determine what is causing it.  I use gentoo and recently
did an upgrade world, so I am guessing some new library is not playing
nicely as this always seemed to work before.  Also, when I switch to Qt4Agg
backend,
everything works as expected, but if I can help iron out a bug, I would
like to - I really think that matplotlib is an excellent piece of software.

Pertinent info:

uname -a:
Linux dayd 3.10.15-gentoo #6 SMP Sat Dec 14 15:53:47 MST 2013 x86_64
Intel(R) Xeon(R) CPU E5-2620 0 @ 2.00GHz GenuineIntel GNU/Linux

Matplotlib version - 1.3.1

matplotlibrc:
backend: TkAgg (Qt4Agg works as expected)
interactive: False

because it segfaults
 python3.3 testplot.py --verbose-helpful > output.txt
outputs nothing

gcc --version:
gcc (Gentoo 4.8.2 p1.0, pie-0.5.8) 4.8.2
Copyright (C) 2013 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

script to reproduce the problem:

import sys
import matplotlib.pyplot as plt

def main():
    for x in range(6):
        plt.figure()
        plt.title(x)
        plt.plot([1,1],[1,1],'r.')
    plt.show()

if __name__ == "__main__":
    sys.exit(main())

I found some instructions on how to get debug info when you install a
package in gentoo.  If anyone else can recreate this or need some more info
from me - please let me know.  I will do what I can.

Thanks!

Hi all,
after upgrading to matplotlib 1.3.1, I noticed some display errors on
the plots with regard to accented characters (such as carons etc.).
As I recall, I had similar problem in the past and could work around
them by modifying rcParams, however, this fix doesn't work as expected
in 1.3.1. (with python 2.7.6, 32bit on Win 7, Czech - with both WXAgg
and TKAgg backends).
>From the usual Czech diacritics   áčďéěíňóřšťúůýž some are not
displayed  (ďěňřťů) - replacement squares are shown instead.

Simply prepending a suitable font at the beginning of the list
rcParams['font.sans-serif'] doesn't help in 1.3.1.
I eventually found out, that "Bitstream Vera Sans" (which is not
installed on this computer) is somehow offending - as long as this
item is in the list (even at the end), the mentioned characters aren't
displayed.

The problem can be observed in the following simple pylab script:
==============
#! Python
# -*- coding: utf-8 -*-

# with implicit fonts "ďěňřťů" are not displayed properly in the plot title
from matplotlib import rcParams
rcParams['font.family'] = 'sans-serif'
if "Bitstream Vera Sans" in rcParams['font.sans-serif']:
    rcParams['font.sans-serif'].remove("Bitstream Vera Sans")

# after appending the "offending" font even at the end of the list (by
uncommenting the following line), ďěňřťů are not displayed again
# rcParams['font.sans-serif'].append("Bitstream Vera Sans")

import pylab
pylab.title(u"abcd áčďéěíňóřšťúůýž äöüß ê xyz")
pylab.show()
==============

Is there something special in the resolution of the font items in  rcParams?
This individual issue seems to be fixed with removing the single font,
but I'd like to understand this more generally, as the installed fonts
on different computers differ.

Thanks in advance
              Vlastimil Brom

Hi,

Does somebody knows how can I remove a backgroundimage. I set the image with
imshow.

regards,
    flambert



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