matplotlib-users Mailing List for matplotlib (Page 10)

I don't think this has anything to do with eggs.  It looks like you
don't have a C++ compiler installed or configured correctly.  On
ubuntu/debian you should make sure "build-essentials" is installed.

On 12/13/06, ch...@se... <ch...@se...> wrote:
> Yes we all know the normal install of Matplotlib is rock solid and reliable.
>
> I'm having trouble doing an "egg" (setuptools) install of matplotlib.
>
> (I'm hoping eggs will be a nice way to have uniform install instructions across
> all OSes.)
>
> I got numpy egg installed but got this when I tried matplotlib egg install....
>
> gcc: installation problem, cannot exec `cc1plus': No such file or directory
> gcc: installation problem, cannot exec `cc1plus': No such file or directory
> error: Setup script exited with error: Command "gcc -pthread
> -fno-strict-aliasin                                               g -DNDEBUG -g
> -O3 -Wall -Wstrict-prototypes -fPIC -Iagg23/include -Isrc -Iswig -
> I/usr/include/python2.4 -c agg23/src/agg_trans_affine.cpp -o
> build/temp.linux-i6
> 86-2.4/agg23/src/agg_trans_affine.o" failed with exit status 1
> Exception exceptions.OSError: (2, 'No such file or directory',
> 'src/_ns_cntr.c')                                                in <bound
> method CleanUpFile.__del__ of <setupext.CleanUpFile instance at 0xb78
> d59ac>> ignored
> Exception exceptions.OSError: (2, 'No such file or directory',
> 'src/_ns_backend_                                               agg.cpp') in
> <bound method CleanUpFile.__del__ of <setupext.CleanUpFile instance
> at 0xb78d53cc>> ignored
> Exception exceptions.OSError: (2, 'No such file or directory',
> 'src/_ns_nxutils.                                               c') in <bound
> method CleanUpFile.__del__ of <setupext.CleanUpFile instance at 0x
> b78d5b8c>> ignored
> Exception exceptions.OSError: (2, 'No such file or directory',
> 'src/_ns_image.cp                                               p') in <bound
> method CleanUpFile.__del__ of <setupext.CleanUpFile instance at 0x
> b78d57ac>> ignored
> Exception exceptions.OSError: (2, 'No such file or directory',
> 'src/_ns_transfor                                               ms.cpp') in
> <bound method CleanUpFile.__del__ of <setupext.CleanUpFile instance
> at 0xb796c12c>> ignored
>
>
> Any help greatly appreciated.
>
> Chris
>
> -------------------------------------------------------------------------
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David Cournapeau wrote:
> But the show case is more interesting:
>
>  ncalls  tottime  percall  cumtime  percall filename:lineno(function)
>       1    0.002    0.002    3.886    3.886 
> slowmatplotlib.py:177(bench_imshow_show)
>       1    0.000    0.000    3.884    3.884 
> slowmatplotlib.py:163(bench_imshow)
>       1    0.698    0.698    3.003    3.003 
> /home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:70(show) 
>
>       2    0.000    0.000    2.266    1.133 
> /home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:275(expose_event) 
>
>       1    0.009    0.009    2.266    2.266 
> /home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtkagg.py:71(_render_figure) 
>
>       1    0.000    0.000    2.256    2.256 
> /home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_agg.py:385(draw) 
>
>       1    0.000    0.000    2.253    2.253 
> /home/david/local/lib/python2.4/site-packages/matplotlib/figure.py:510(draw) 
>
>       1    0.000    0.000    2.251    2.251 
> /home/david/local/lib/python2.4/site-packages/matplotlib/axes.py:994(draw) 
>
>       1    0.005    0.005    1.951    1.951 
> /home/david/local/lib/python2.4/site-packages/matplotlib/image.py:173(draw) 
>
>       1    0.096    0.096    1.946    1.946 
> /home/david/local/lib/python2.4/site-packages/matplotlib/image.py:109(make_image) 
>
>       1    0.002    0.002    1.850    1.850 
> /home/david/local/lib/python2.4/site-packages/matplotlib/cm.py:50(to_rgba) 
>
>       1    0.001    0.001    0.949    0.949 
> /home/david/local/lib/python2.4/site-packages/matplotlib/colors.py:735(__call__) 
>
>       1    0.097    0.097    0.899    0.899 
> /home/david/local/lib/python2.4/site-packages/matplotlib/colors.py:568(__call__) 
>
>     325    0.050    0.000    0.671    0.002 
> /home/david/local/lib/python2.4/site-packages/numpy/core/ma.py:533(__init__) 
>
>       1    0.600    0.600    0.600    0.600 
> /home/david/local/lib/python2.4/site-packages/numpy/core/fromnumeric.py:282(resize) 
>
>       1    0.000    0.000    0.596    0.596 
> /home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:1894(imshow) 
>
>      10    0.570    0.057    0.570    0.057 
> /home/david/local/lib/python2.4/site-packages/numpy/oldnumeric/functions.py:117(where) 
>
>       3    0.000    0.000    0.513    0.171 
> /home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:883(gca) 
>
>       1    0.000    0.000    0.513    0.513 
> /home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:950(ishold) 
>
>       4    0.000    0.000    0.408    0.102 
> /home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:903(gcf) 
>
>
> For more details, see the .kc files which are the in the tbz2 archive, 
> with the script for generating profiles for kcachegrind,
Here is some stuff I tried:

   - first, we can see that in expose_event (one is expensive, the other 
negligeable, from my understanding), two calls are pretty expensive:
the __call__  at line 735 (for normalize functor) and one for __call__ 
at line 568 (for colormap functor).
   - for normalize functor, one line is expensive:  val = 
ma.array(clip(val.filled(vmax), vmin, vmax), mask=mask). If I put a test 
on mask when mask is None (which it is in my case), then the function 
becomes negligeable.
   - for colormap functor, the 3 where calls are expensive. I am not 
sure to understand in which case they are useful; if I understand 
correctly, one tries to avoid
values out of range (0, N), and force out of range values to be clipped. 
Isn't there an easier way than using where ?

   If I remove the where in the colormap functor, I have a 4x speed 
increase for the to_rgba function. After that, it becomes a bit more 
tricky to change things for someone like me who have no knowledge about 
matplotlib internals.

   Cheers,

   David

John Hunter wrote:
> This is where you can help us.  Saying specgram is slow is only
> marginally more useful than saying matplotlib is slow or python is
> slow.  What is helpful is to post a complete, free-standing script
> that we can run, with some attached performance numbers.  For
> starters, just run it with the Agg backend so we can isolate
> matplotlib from the respective GUIs.  Show us how the performance
> scales with the specgram parameters (frames and samples).  specgram is
> divided into two parts (if you look at the Axes.specgram you will see
> that it calls matplotlib.mlab.specgram to do the computation and
> Axes.imshow to visualize it.  Which part is slow: the mlab.specgram
> computation or the visualizion (imshow) part or both?  You can paste
> this function into your own python file and start timing different
> parts.  The most helpful "this is slow" posts come with profiler
> output so we can see where the bottlenecks are.  
(sorry for double posting)

Ok, here we go: I believe that the rendering of the figure returned by 
imshow to be slow.

For example, let's say I have a 2 minutes signal @ 8kHz sampling-rate, 
with windows of 256 samples with 50 % overlap. I have around 64 frames / 
seconds, eg ~ 8000 frames of 256 windows.

So for benchmark purposes, we can just send random data of shape 
8000x256 to imshow. In ipython, this takes a long time (around 2 seconds 
for imshow(data), where data = random(8000, 256)).

Now, on a small script to have a better idea:

import numpy as N
import pylab as P

def generate_data_2d(fr, nwin, hop, len):
    nframes = 1.0 * fr / hop * len
    return N.random.randn(nframes, nwin)

def bench_imshow(fr, nwin, hop, len, show = True):
    data    = generate_data_2d(fr, nwin, hop, 
len)                                        
    P.imshow(data)
    if show:
        
P.show()                                                                         

 
if __name__ == '__main__':
    # 2 minutes (120 sec) of sounds @ 8 kHz with 256 samples with 50 % 
overlap
    bench_imshow(8000, 256, 128, 120, show = False)

Now, I have a problem, because I don't know how to benchmark when using 
show to True (I have to manually close the figure).

If I run the above script with time, I got 1.5 seconds with show = False 
(after several trials to be sure matplotlib files are in the system 
cache: this matters because my home dir is on NFS). If I set show = 
True, and close the figure by hand once the figure is plotted, I have 
4.5 sec instead.

If I run the above script with -dAgg --versbose-helpful (I was looking 
for this one  to check numerix is correctly set to numpy:) ):

with show = False:

matplotlib data path 
/home/david/local/lib/python2.4/site-packages/matplotlib/mpl-data
$HOME=/home/david
CONFIGDIR=/home/david/.matplotlib
loaded rc file /home/david/.matplotlib/matplotlibrc
matplotlib version 0.87.7
verbose.level helpful
interactive is False
platform is linux2
numerix numpy 1.0.2.dev3484
font search path 
['/home/david/local/lib/python2.4/site-packages/matplotlib/mpl-data']
loaded ttfcache file /home/david/.matplotlib/ttffont.cache
backend Agg version v2.2

real    0m1.185s
user    0m0.808s
sys     0m0.224s

with show = True

matplotlib data path 
/home/david/local/lib/python2.4/site-packages/matplotlib/mpl-data
$HOME=/home/david
CONFIGDIR=/home/david/.matplotlib
loaded rc file /home/david/.matplotlib/matplotlibrc
matplotlib version 0.87.7
verbose.level helpful
interactive is False
platform is linux2
numerix numpy 1.0.2.dev3484
font search path 
['/home/david/local/lib/python2.4/site-packages/matplotlib/mpl-data']
loaded ttfcache file /home/david/.matplotlib/ttffont.cache
backend Agg version v2.2

real    0m1.193s
user    0m0.848s
sys     0m0.192s

So the problem is in the rendering, right ? (Not sure to understand 
exactly what Agg backend is doing).

Now, using hotshot (kcache grind profiles attached to the email), for 
the noshow case:

       1    0.001    0.001    0.839    0.839 
slowmatplotlib.py:181(bench_imshow_noshow)
       1    0.000    0.000    0.837    0.837 
slowmatplotlib.py:163(bench_imshow)
       1    0.000    0.000    0.586    0.586 
/home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:1894(imshow) 

       3    0.000    0.000    0.510    0.170 
/home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:883(gca)
       1    0.000    0.000    0.509    0.509 
/home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:950(ishold) 

       4    0.000    0.000    0.409    0.102 
/home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:903(gcf)
       1    0.000    0.000    0.409    0.409 
/home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:818(figure) 

       1    0.000    0.000    0.408    0.408 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtkagg.py:36(new_figure_manager) 

       1    0.003    0.003    0.400    0.400 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:401(__init__) 

       1    0.000    0.000    0.397    0.397 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtkagg.py:25(_get_toolbar) 

       1    0.001    0.001    0.397    0.397 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:496(__init__) 

       1    0.000    0.000    0.396    0.396 
/home/david/local/lib/python2.4/site-packages/matplotlib/backend_bases.py:1112(__init__) 

       1    0.000    0.000    0.396    0.396 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:557(_init_toolbar) 

       1    0.008    0.008    0.396    0.396 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:595(_init_toolbar2_4) 

       1    0.388    0.388    0.388    0.388 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:967(__init__) 

       1    0.251    0.251    0.251    0.251 
slowmatplotlib.py:155(generate_data_2d)
       3    0.000    0.000    0.101    0.034 
/home/david/local/lib/python2.4/site-packages/matplotlib/figure.py:629(gca)
       1    0.000    0.000    0.101    0.101 
/home/david/local/lib/python2.4/site-packages/matplotlib/figure.py:449(add_subplot) 

       1    0.000    0.000    0.100    0.100 
/home/david/local/lib/python2.4/site-packages/matplotlib/axes.py:4523(__init__) 

       1    0.000    0.000    0.100    0.100 
/home/david/local/lib/python2.4/site-packages/matplotlib/axes.py:337(__init__) 


But the show case is more interesting:

  ncalls  tottime  percall  cumtime  percall filename:lineno(function)
       1    0.002    0.002    3.886    3.886 
slowmatplotlib.py:177(bench_imshow_show)
       1    0.000    0.000    3.884    3.884 
slowmatplotlib.py:163(bench_imshow)
       1    0.698    0.698    3.003    3.003 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:70(show) 

       2    0.000    0.000    2.266    1.133 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:275(expose_event) 

       1    0.009    0.009    2.266    2.266 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtkagg.py:71(_render_figure) 

       1    0.000    0.000    2.256    2.256 
/home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_agg.py:385(draw) 

       1    0.000    0.000    2.253    2.253 
/home/david/local/lib/python2.4/site-packages/matplotlib/figure.py:510(draw) 

       1    0.000    0.000    2.251    2.251 
/home/david/local/lib/python2.4/site-packages/matplotlib/axes.py:994(draw)
       1    0.005    0.005    1.951    1.951 
/home/david/local/lib/python2.4/site-packages/matplotlib/image.py:173(draw)
       1    0.096    0.096    1.946    1.946 
/home/david/local/lib/python2.4/site-packages/matplotlib/image.py:109(make_image) 

       1    0.002    0.002    1.850    1.850 
/home/david/local/lib/python2.4/site-packages/matplotlib/cm.py:50(to_rgba)
       1    0.001    0.001    0.949    0.949 
/home/david/local/lib/python2.4/site-packages/matplotlib/colors.py:735(__call__) 

       1    0.097    0.097    0.899    0.899 
/home/david/local/lib/python2.4/site-packages/matplotlib/colors.py:568(__call__) 

     325    0.050    0.000    0.671    0.002 
/home/david/local/lib/python2.4/site-packages/numpy/core/ma.py:533(__init__) 

       1    0.600    0.600    0.600    0.600 
/home/david/local/lib/python2.4/site-packages/numpy/core/fromnumeric.py:282(resize) 

       1    0.000    0.000    0.596    0.596 
/home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:1894(imshow) 

      10    0.570    0.057    0.570    0.057 
/home/david/local/lib/python2.4/site-packages/numpy/oldnumeric/functions.py:117(where) 

       3    0.000    0.000    0.513    0.171 
/home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:883(gca)
       1    0.000    0.000    0.513    0.513 
/home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:950(ishold) 

       4    0.000    0.000    0.408    0.102 
/home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:903(gcf)

For more details, see the .kc files which are the in the tbz2 archive, 
with the script for generating profiles for kcachegrind,

I will post an other email for the other problem (with several subplots)

cheers,

David

Yes we all know the normal install of Matplotlib is rock solid and reliable.

I'm having trouble doing an "egg" (setuptools) install of matplotlib.

(I'm hoping eggs will be a nice way to have uniform install instructions across
all OSes.)

I got numpy egg installed but got this when I tried matplotlib egg install....

gcc: installation problem, cannot exec `cc1plus': No such file or directory
gcc: installation problem, cannot exec `cc1plus': No such file or directory
error: Setup script exited with error: Command "gcc -pthread
-fno-strict-aliasin                                               g -DNDEBUG -g
-O3 -Wall -Wstrict-prototypes -fPIC -Iagg23/include -Isrc -Iswig -
I/usr/include/python2.4 -c agg23/src/agg_trans_affine.cpp -o
build/temp.linux-i6
86-2.4/agg23/src/agg_trans_affine.o" failed with exit status 1
Exception exceptions.OSError: (2, 'No such file or directory',
'src/_ns_cntr.c')                                                in <bound
method CleanUpFile.__del__ of <setupext.CleanUpFile instance at 0xb78
d59ac>> ignored
Exception exceptions.OSError: (2, 'No such file or directory',
'src/_ns_backend_                                               agg.cpp') in
<bound method CleanUpFile.__del__ of <setupext.CleanUpFile instance
at 0xb78d53cc>> ignored
Exception exceptions.OSError: (2, 'No such file or directory',
'src/_ns_nxutils.                                               c') in <bound
method CleanUpFile.__del__ of <setupext.CleanUpFile instance at 0x
b78d5b8c>> ignored
Exception exceptions.OSError: (2, 'No such file or directory',
'src/_ns_image.cp                                               p') in <bound
method CleanUpFile.__del__ of <setupext.CleanUpFile instance at 0x
b78d57ac>> ignored
Exception exceptions.OSError: (2, 'No such file or directory',
'src/_ns_transfor                                               ms.cpp') in
<bound method CleanUpFile.__del__ of <setupext.CleanUpFile instance
at 0xb796c12c>> ignored


Any help greatly appreciated.

Chris

Orion Poplawski wrote:
> Jeff Whitaker wrote:
>> Orion Poplawski wrote:
>>> I've got a 2-D array of values that I'd like to plot onto a map with 
>>> Basemap.  The latitude and longitudes for each point are stored in 
>>> their own 2-D array that matches that of the values.  Can some one 
>>> help with how to do this?  Looks like basemap requires a square and 
>>> uniform set of data to plot?
>>>
>> Orion:  There's an example of this in the examples directory called 
>> 'ccsm_popgrid.py'.  Have a look at that, and if you have questions 
>> let us know.
>>
>
>
> Actually, it looks like I may have been making things harder than I 
> thought they would be.  Is there any reason to believe that this isn't 
> doing what it seems like it should?  ccsm_popgrid.py seems much more 
> complicated, and I'm don't really understand it yet.
>
> tpxyzo has data for 12 different altitudes, and I'm selecting one.  It 
> appears the x,y=m(lon,lat) is unneeded for cylindrical projections, 
> but I've left it in here as a general case.
>
> -- 
> from matplotlib.toolkits.basemap import Basemap
> from pylab import *
>
> lat=load('lat.dat').reshape(270,90)
> lon=load('lon.dat').reshape(270,90)
> tpxyzo=load('tpxyzo.dat').reshape(12,270,90)
>
> m=Basemap(llcrnrlon=-80,llcrnrlat=-75,urcrnrlon=-40,urcrnrlat=-42,projection='cyl',resolution='l') 
>
> m.drawcoastlines()
> x, y = m(lon,lat)
> m.contourf(x,y,tpxyzo[11,:,:],15)
> show()
> -- 
>
> The output is the attached image, which as you can see does not have a 
> rectangular shape.
>
> Thanks!
>

Orion:  If the image were rectangular on a cylindrical projection, then 
you wouldn't need 2d arrays to describe the lats and lons of the grid.  
Are you sure the image should be rectangular?  What kind of a grid is it?

-Jeff

-- 
Jeffrey S. Whitaker         Phone  : (303)497-6313
Meteorologist               FAX    : (303)497-6449
NOAA/OAR/PSD  R/PSD1        Email  : Jef...@no...
325 Broadway                Office : Skaggs Research Cntr 1D-124
Boulder, CO, USA 80303-3328 Web    : http://tinyurl.com/5telg

Orion Poplawski wrote:
> I've got a 2-D array of values that I'd like to plot onto a map with 
> Basemap.  The latitude and longitudes for each point are stored in their 
> own 2-D array that matches that of the values.  Can some one help with 
> how to do this?  Looks like basemap requires a square and uniform set of 
> data to plot?
>
> Thanks!
>
>   
Orion:  There's an example of this in the examples directory called 
'ccsm_popgrid.py'.  Have a look at that, and if you have questions let 
us know.

-Jeff

-- 
Jeffrey S. Whitaker         Phone  : (303)497-6313
Meteorologist               FAX    : (303)497-6449
NOAA/OAR/PSD  R/PSD1        Email  : Jef...@no...
325 Broadway                Office : Skaggs Research Cntr 1D-124
Boulder, CO, USA 80303-3328 Web    : http://tinyurl.com/5telg

I've got a 2-D array of values that I'd like to plot onto a map with 
Basemap.  The latitude and longitudes for each point are stored in their 
own 2-D array that matches that of the values.  Can some one help with 
how to do this?  Looks like basemap requires a square and uniform set of 
data to plot?

Thanks!

-- 
Orion Poplawski
Technical Manager                     303-415-9701 x222
NWRA/CoRA Division                    FAX: 303-415-9702
3380 Mitchell Lane                  or...@co...
Boulder, CO 80301              http://www.cora.nwra.com

Pierre,

I will try to improve it.

Eric

Pierre GM wrote:
> Folks, 
> Would it be possible to switch the current error message
> 
> 
>>>>ValueError: Arguments x, y, z, mask (if present) must be 2D arrays.
> 
> 
> in cntr.c  to something more explicit, when x, y, and z are 2D arrays, but one 
> of them is complex ? Or force a downcast to float if needed ?
> (I know, I should have forced a conversion myself. But I just spent one hour 
> trying to figure what was going wrong before thinking about checking the 
> dtypes...)
> 
> Thx in advance
> 
> -------------------------------------------------------------------------
> Take Surveys. Earn Cash. Influence the Future of IT
> Join SourceForge.net's Techsay panel and you'll get the chance to share your
> opinions on IT & business topics through brief surveys - and earn cash
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> _______________________________________________
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This slightly modified excerpt from the new version of spy may do what 
you want:

nr, nc = Z.shape
extent = [-0.5, nc-0.5, nr-0.5, -0.5]
return self.imshow(Z, interpolation='nearest',
                        extent=extent, origin='upper')

Eric


John Travers wrote:
> Hi, I'm struggling to get an array plotted the way I want with imshow.
> I have an array A which i try to plot with
> 
> e=(tdims[0], tdims[-1], wldims[-1], wldims[0])
> imshow(A,extent=e)
> 
> The image displays fine in the x axis, with tdims marked correctly,
> but in the yaxis the dimensions are incorrect. And no combination of
> extent and origin gives the correect display. To be more clear,
> without extent set I get the array displayed the right way round, but
> my problem is that my y axis should be *decreasing* as it goes up the
> side of the image; if I set extent correctly, the image is scaled
> right with respect to the y axis, but it is flipped so that the y axis
> is increasing upwards. Is there a way to prevent this?
> 
> Many thanks for any help!
> Ans thanks for an excellent plotting library!
> 
> Best regards
> John
> 
> -------------------------------------------------------------------------
> Take Surveys. Earn Cash. Influence the Future of IT
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On 12/12/06, John Hunter <jdh...@ac...> wrote:

> --verbose-helpful will confirm the setting).  A good way to start is
> to write a demonstration script that you find too slow which makes a
> call to savefig, and run it with
>
>   > time myscript.py --verbose-helpful -dAgg

It may be worth mentioning here this little utility (Linux only, unfortunately):

http://amath.colorado.edu/faculty/fperez/python/profiling/

For profiling more complex codes, it's really a godsend.  And note
that the generated cachegrind files are typically small and can be
sent to others for analysis, so you can run it locally (if for example
the run depends on data you can't share) and then send to the list the
generated profile.  Anyone with Kcachegrind will then be able to load
your profile info and study it in detail.

Cheers,

f

>>>>> "David" == David Cournapeau <da...@ar...> writes:

    David> Hi, I am a regular user of matplotlib since I moved from
    David> matlab to python/numpy/scipy. Even if I find matplotlib to
    David> be a real help during the transition from matlab to python,
    David> I must confess I found it the most disappointing compare
    David> other packages ( essentially numpy/scipy/ipython).  This is

   Meatloaf: Now don't be sad, cause two out of three ain't bad

If you consider the fact that matplotlib was originally an ipython
patch that was rejected, you can see why we are such a bastard child
of the scientific python world.  There is a seed of truth in this;
Numeric, scipy and ipython were all mature packages in widespread use
before the first line of matplotlib code was written.  So they are
farther along in terms of maturity, documentation, usability,
etc... than matplotlib is.  

But we've achieved a lot in a comparably short time.  When I started
working on matplotlib there were probably two dozen plotting packages
that people used and recommended.  Now we are down to 5 or 6, with
matplotlib doing most of what most people need.  I've focused on
making something that does most of what people (and I) need rather
than doing it the fastest, so it is too slow for some purposes but
fast enough for most.  When we get a well defined important test case
that is too slow, we typically try and optimize it, sometimes with
dramatic results (eg 25 fold speedups); more on this below.

A consequence of trying to support most of the needs of most users is
this: we run on all major operating systems and all major GUIs with
all major array packages.  Consider the combinatorial problem: 5
graphical user interfaces with two or more versions in the wild across
3 operating systems and you will get a feel for what the support
problem we have.  This is not an academic point.  Most of the GUI
maintainers for *a single backend* burn out in short order.  Most
graphics packages *solve* this problem by supporting a single output
format (PYX) or GUI (chaco) which is a damned fine and admirable
solution.  But the consequence of this is plotting fragmentation:
people who need GTK cannot use Chaco, people who need SVG cannot use
PYX, and so on, and so they'll write their own plotting library for
their own GUI or output format (the situation before matplotlib).  You
can certainly get closer to bare metal speed by reducing choices and
focusing on a single target -- part of the performance price we pay is
in our abstraction layers, part is in trying to support features that
may be rarely used but cost something (masked array support, rotated
text with newlines), part is because we need to get to work and
optimize the slow parts.

    David> not a rant; I want to know if this slowness is coming from
    David> my lack of matplotlib knowledge or not; I apologize in
    David> advance if the following hurts anyone feelings :)

    Meatloaf: But -- there ain't no way I'm ever gonna love you

OK, I'll stop now.

    David>     First, I must admit that whereas I took a significant
    David> amount of time to study numpy and scipy, I didn't take that
    David> same time for matplotlib.  So this disappointment may just
    David> be a consequences of this laziness.

I suspect this is partly true; see below.

    David>     My main problem with matplotlib is speed: I find it
    David> really annoying to use in an interactive manner. For
    David> example, when I need to display some 2d information, such
    David> as spectrogramm or correlogram, this take 1 or 2 seconds
    David> for a small signal (~4500 frames of 256 samples). My
    David> function correlogram (similar to specgram, but compute
    David> correlation instead of log spectrum) uses imshow, and this
    David> function takes 20 times more time than imagesc of matlab
    David> for the same size.  Also, I found changing the size of the

This is where you can help us.  Saying specgram is slow is only
marginally more useful than saying matplotlib is slow or python is
slow.  What is helpful is to post a complete, free-standing script
that we can run, with some attached performance numbers.  For
starters, just run it with the Agg backend so we can isolate
matplotlib from the respective GUIs.  Show us how the performance
scales with the specgram parameters (frames and samples).  specgram is
divided into two parts (if you look at the Axes.specgram you will see
that it calls matplotlib.mlab.specgram to do the computation and
Axes.imshow to visualize it.  Which part is slow: the mlab.specgram
computation or the visualizion (imshow) part or both?  You can paste
this function into your own python file and start timing different
parts.  The most helpful "this is slow" posts come with profiler
output so we can see where the bottlenecks are.  

Such a post by Fernando Perez on "plot" with markers yielded
performance boosts of 25x for large numbers of points when he showed
we were making about one hundred thousand function calls per plot.

    David> matplotlib window really 'annoying to the eye': I compared
    David> to matlab, and this may be due to the fact that the whole
    David> window is redrawn with matplotlib, including the toolbar,
    David> whereas in matlab, the top toolbar is not redrawn.

It would be nice if we exposed the underlying GTK widgets to you so
you could customize the "expand" and "fill" properties of the gtk
toolbar, but this gets us into the multiple GUI, multiple version
problem discussed above.  Providing an abstract interface to such
details that works across the mpl backends is a lot of work that takes
us away from our core incompetency -- plotting.  What we do is enable
you to write your own widgets and embed mpl in them; see
examples/embedding_in_gtk2.py which shows you how to do this for
GTK/GTKAgg.  You can then customize the toolbar to your heart's
content.

    David> Finally, plotting many data (using plot(X, Y) with X and Y
    David> around 1000/10000 samples) is 'slow' (the '' are because I
    David> don't know much about computer graphics, and I understand
    David> that slow in the rendering is often just a perception)

This shouldn't be slow -- again a test script with some performance
numbers would help so we can compare what we are getting.  One
thought: make sure you are using the numerix layer properly -- ie, if
you are creating arrays with numpy make sure you have numerix set to
numpy ( i see below that you set numerix to numpy but
--verbose-helpful will confirm the setting).  A good way to start is
to write a demonstration script that you find too slow which makes a
call to savefig, and run it with

  > time myscript.py --verbose-helpful -dAgg

and post the output and script.  Then we might be able to help.

    David> So, is this a current limitation of matplotlib, is
    David> matplotlib optimized for good rendering for publication,
    David> and not for interactive use, or I am just misguided in my
    David> use of matplotlib ?

Many people use it interactively, but a number of power users find it
slow.

JDH

On 12/12/06, Pierre GM <pgm...@gm...> wrote:
> On Tuesday 12 December 2006 06:57, John Travers wrote:
> > Hi, I'm struggling to get an array plotted the way I want with imshow.
> > I have an array A which i try to plot with
> >
> > e=(tdims[0], tdims[-1], wldims[-1], wldims[0])
> > imshow(A,extent=e)
> >
> >  if I set extent correctly, the image is scaled
> > right with respect to the y axis, but it is flipped so that the y axis
> > is increasing upwards. Is there a way to prevent this?
>
> MMh. You can always play with the limits on the y axis:
>
> gca().set_ylim(N.asarray(gca().get_ylim())[[-1,0]])
>
> will invert (ymin, ymax) on the current axis (gca())
>

Thanks for your answers!
Pierre's method worked (I didn't try Scott's).
Cheers,
John

On Tuesday 12 December 2006 06:57, John Travers wrote:
> Hi, I'm struggling to get an array plotted the way I want with imshow.
> I have an array A which i try to plot with
>
> e=(tdims[0], tdims[-1], wldims[-1], wldims[0])
> imshow(A,extent=e)
>
>  if I set extent correctly, the image is scaled
> right with respect to the y axis, but it is flipped so that the y axis
> is increasing upwards. Is there a way to prevent this?

MMh. You can always play with the limits on the y axis:

gca().set_ylim(N.asarray(gca().get_ylim())[[-1,0]])

will invert (ymin, ymax) on the current axis (gca())

Hi,

    I am a regular user of matplotlib since I moved from matlab to 
python/numpy/scipy. Even if I find matplotlib to be a real help during 
the transition from matlab to python, I must confess I found it the most 
disappointing compare other packages ( essentially numpy/scipy/ipython). 
This is not a rant; I want to know if this slowness is coming from my 
lack of matplotlib knowledge or not; I apologize in advance if the 
following hurts anyone feelings :)

    First, I must admit that whereas I took a significant amount of time 
to study numpy and scipy, I didn't take that same time for matplotlib. 
So this disappointment may just be a consequences of this laziness.

    My main problem with matplotlib is speed: I find it really annoying 
to use in an interactive manner. For example, when I need to display 
some 2d information, such as spectrogramm or correlogram, this take 1 or 
2 seconds for a small signal (~4500 frames of 256 samples). My function 
correlogram (similar to specgram, but compute correlation instead of log 
spectrum) uses imshow, and this function takes 20 times more time than 
imagesc of matlab for the same size.
    Also, I found changing the size of the matplotlib window really 
'annoying to the eye': I compared to matlab, and this may be due to the 
fact that the whole window is redrawn with matplotlib, including the 
toolbar, whereas in matlab, the top toolbar is not redrawn.
    Finally, plotting many data (using plot(X, Y) with X and Y around 
1000/10000 samples) is 'slow' (the '' are because I don't know much 
about computer graphics, and I understand that slow in the rendering is 
often just a perception)

    So, is this a current limitation of matplotlib, is matplotlib 
optimized for good rendering for publication, and not for interactive 
use, or I am just misguided in my use of matplotlib ?

    Config info:

    - ubuntu edgy on a bi xeon 3.2 Ghz with 2 Gb of Ram
    - numpy SVN (post 1.0)
    - matplotlib 0.87.7
    - matplotlibrc: uses numpy for numeric, Gtk as a backend (or GtkAdd 
for anti aliasing, but this makes the problem worse).

    Cheers,

    David

Hi, I'm struggling to get an array plotted the way I want with imshow.
I have an array A which i try to plot with

e=(tdims[0], tdims[-1], wldims[-1], wldims[0])
imshow(A,extent=e)

The image displays fine in the x axis, with tdims marked correctly,
but in the yaxis the dimensions are incorrect. And no combination of
extent and origin gives the correect display. To be more clear,
without extent set I get the array displayed the right way round, but
my problem is that my y axis should be *decreasing* as it goes up the
side of the image; if I set extent correctly, the image is scaled
right with respect to the y axis, but it is flipped so that the y axis
is increasing upwards. Is there a way to prevent this?

Many thanks for any help!
Ans thanks for an excellent plotting library!

Best regards
John

Eric Firing wrote:
> Robert Cimrman wrote:
> [...]
>> What could be done, though, is to raise an exception explaining that 
>> sparse matrices and the image mode don't like each other; as it is, 
>> the function spy3 just dies on asarray (should be st. like asarray( 
>> Z.todense() ))
> 
> (I think it would be Z.toarray().)

Yes.

> Good point.  Alternatives would be to automatically switch it to plot 
> mode, with a warning, or to automatically convert it.  Probably the 
> former is the better of these two, and maybe your originally suggested 
> exception is best of all.  What do you think?

I am slightly more for raising the exception: IMHO, people really 
needing and using sparse matrices convert never to dense ones (they 
would not fit into memory in most cases); people happy with automatic 
conversion should use dense matrices from the very beginning - there is 
no gain in exploiting sparsity for them; people knowing what they are 
doing can convert explicitly prior to calling spy().

So:
+1 raise an exception
+0 switch to plot mode (ok, but inconsistent - why I got a plot when I 
wanted an image?)
-10 automatic conversion

r.

(sorry for my English)
The following code in Python causes leakage of memory:

################################################################
from pylab import *
import pygtk
pygtk.require('2.0')
import gtk
import matplotlib
matplotlib.use('GTKAgg')  # or 'GTK'
from matplotlib.backends.backend_gtk import FigureCanvasGTK as FigureCanvas
from matplotlib.figure import Figure

for i in range(200):
	fig = Figure(figsize=(1,1))
	canvas = FigureCanvas(fig)
	axes = fig.add_subplot(111)
	axes.grid(True)
	ind = arange(200)
	xx = rand(len(ind))
	line= axes.plot(ind, xx)
	canvas.show_all()

	line[0].set_data(ind, xx)
	canvas.draw()
		
	line = None
	axes.clear()
	fig.delaxes(axes)
	fig.clear()
	fig = None
	axes = None
	canvas.destroy()
	canvas = None
#####################################################
 
 
 And next lines too (but smaller):
 
 ######################################################
 fig = Figure(figsize=(1,1))
 canvas = FigureCanvas(fig)
 axes = fig.add_subplot(111)
 axes.grid(True)
 ind = arange(200)
 xx = rand(len(ind))
 line= axes.plot(ind, xx)
 canvas.show_all()
 
 for i in range(200):
 	canvas.draw()
 #######################################################
 
 How can I correct it?
 
 Yours faithfully,
 Andrey
 

Folks, 
Would it be possible to switch the current error message

>>> ValueError: Arguments x, y, z, mask (if present) must be 2D arrays.

in cntr.c  to something more explicit, when x, y, and z are 2D arrays, but one 
of them is complex ? Or force a downcast to float if needed ?
(I know, I should have forced a conversion myself. But I just spent one hour 
trying to figure what was going wrong before thinking about checking the 
dtypes...)

Thx in advance

In article <E87...@cs...>,
 belinda thom <bt...@cs...> wrote:

> Hi again,
> 
> At http://matplotlib.sourceforge.net/installing.html under topic OS X:
> 
>    "All of the backends run on OS X. Chris Barker has built a binary  
> package (fink users see below) for matplotlib which is hosted on  
> pythonmac, and works with Agg, Wx and Tk; see the step-by-step  
> instructions kindly provided by Michael Tobis."
> 
> note that the step-by-step link leads to:
> 
>    Under revision. Please come back soon.

That is indeed outdated (Charlie Moad has done most of the recent binary 
builds, though I got in first on a few). One source of "how to build" is:
Building Matplotlib for MacOS 
<http://www.astro.washington.edu/rowen/BuildingMatplotlibForMac.html>

-- Russell

Robert Cimrman wrote:
[...]
> What could be done, though, is to raise an exception explaining that 
> sparse matrices and the image mode don't like each other; as it is, the 
> function spy3 just dies on asarray (should be st. like asarray( 
> Z.todense() ))

(I think it would be Z.toarray().)

Good point.  Alternatives would be to automatically switch it to plot 
mode, with a warning, or to automatically convert it.  Probably the 
former is the better of these two, and maybe your originally suggested 
exception is best of all.  What do you think?

Eric

Yannick Copin wrote:
> Hi,
> 
> Eric Firing wrote:
>>> from pylab import *
>>> X, Y = meshgrid(linspace(-3,3,11),linspace(-3,3,11))
>>> Z = randn(*X.shape)
>>> lev = linspace(Z.min(),Z.max(),11)[1:-1]
>>> contourf(X,Y,Z, lev, extend='both')
>>> contour(X,Y,Z, lev, colors='k')
>>> show()
>>
>> Now, you may be wondering why we can't simply use the boundary of the 
>> filled regions for the lines as well, to guarantee they are the same. 
>> The reason is that filled contour boundaries include cuts connecting 
>> inner and outer contours, and also inner boundaries (edges of masked 
>> regions--except when affected by a bug) and the outer boundaries of 
>> the domain).  It might be possible to simply exclude those line 
>> segments from the line contours, but it is not clear to me that the 
>> effort would be well-spent.
> 
> OK, thanks for the explanations. I was using this dual approach 
> contourf+contour to emphasize the contours from contourf. So is there a 
> direct way to set the linewidth and linecolor (and linestyle?) of 
> contours from contourf? (I suspect not, according to contourf 
> documentation:
> 
>     contourf differs from the Matlab (TM) version in that it does not
>         draw the polygon edges, because the contouring engine yields
>         simply connected regions with branch cuts.  To draw the edges,
>         add line contours with calls to contour.
> 
> )
> 

You could turn on coloring of the edges, but I don't think you would 
like the result because the edges would include the boundary lines and 
the cut lines.

The ContourSet object returned by contour and contourf has a 
.collections attribute.  For contourf it is a list of PolyCollections, 
and you can set their attributes.


>> I think that the differences illustrated in your example will occur 
>> almost entirely in pathologically ambiguous cases, but it is also 
> 
> Not necessarily pathological cases, just noisy data :-/ (I agree my 
> randn-based example was a bit extreme!)

You might be able to avoid the problem most of the time by using some 
gridding routine, preferably something that uses a bit of curvature, to 
double the number of points in each dimension.  I haven't tried it, but 
I suspect that this would turn even a very noisy field into something 
that would be contoured the same by contour and contourf.

> 
> Cheers.

Steve Schmerler a =E9crit :
> Darren Dale wrote:
>  =20
>> On Monday 11 December 2006 09:59, Nicolas Champavert wrote:
>>    =20
>>> Steve Schmerler a =E9crit :
>>>      =20
>>>> Nicolas Champavert wrote:
>>>>        =20
>>>>> Hello,
>>>>>
>>>>>   I have some problems when trying to save a figure with usetex=3DT=
rue.
>>>>> Sometimes, it is not possible to save the figure when trying to put=
 an
>>>>> xlabel with LaTeX inside.
>>>>> It works with pylab.xlabel('M$_\odot$') but not with
>>>>> pylab.xlabel('10$^3$ M$_\odot$') (see below). Do you know why ?
>>>>>          =20
>>>> Works fine here, with and w/o raw strings.
>>>>        =20
>> [...]
>>    =20
>>> I had matplotlib revision 2835. I made an upgrade. Now I have revisio=
n
>>> 2905. I still have problem but it is very strange:
>>> - when I plot [0,1] with xlabel(r'(10$^3$ M$_\odot$)'), it works.
>>> - when I plot [1,2] with xlabel(r'Upper mass for the IMF (10$^3$
>>> M$_\odot$)'), it works.
>>> - but it doesn't work if I plot [0,1] with xlabel(r'Upper mass for th=
e
>>> IMF (10$^3$ M$_\odot$)')...
>>>      =20
>> It sounds like an issue with one of the dependencies, probably ghostsc=
ript. I=20
>> have matplotlib-0.87.7, svn revision 2905, and gpl-ghostscript-8.54. I=
 can=20
>> not reproduce the problem here.
>>
>>    =20
>
> I seem to be running ESP Ghostscript 8.15.3 (this is what 'gs --version=
'=20
> and 'apt-cache show gs' tell me). All the examples work fine here ...
>
>  =20
I had Ghostscript 8.15.3 too. I have made an update to ghostscript 8.54=20
and now it works.

Nicolas

Darren Dale wrote:
> On Monday 11 December 2006 09:59, Nicolas Champavert wrote:
>> Steve Schmerler a écrit :
>>> Nicolas Champavert wrote:
>>>> Hello,
>>>>
>>>>   I have some problems when trying to save a figure with usetex=True.
>>>> Sometimes, it is not possible to save the figure when trying to put an
>>>> xlabel with LaTeX inside.
>>>> It works with pylab.xlabel('M$_\odot$') but not with
>>>> pylab.xlabel('10$^3$ M$_\odot$') (see below). Do you know why ?
>>> Works fine here, with and w/o raw strings.
> [...]
>> I had matplotlib revision 2835. I made an upgrade. Now I have revision
>> 2905. I still have problem but it is very strange:
>> - when I plot [0,1] with xlabel(r'(10$^3$ M$_\odot$)'), it works.
>> - when I plot [1,2] with xlabel(r'Upper mass for the IMF (10$^3$
>> M$_\odot$)'), it works.
>> - but it doesn't work if I plot [0,1] with xlabel(r'Upper mass for the
>> IMF (10$^3$ M$_\odot$)')...
> 
> It sounds like an issue with one of the dependencies, probably ghostscript. I 
> have matplotlib-0.87.7, svn revision 2905, and gpl-ghostscript-8.54. I can 
> not reproduce the problem here.
> 

I seem to be running ESP Ghostscript 8.15.3 (this is what 'gs --version' 
and 'apt-cache show gs' tell me). All the examples work fine here ...

-- 
cheers,
steve

Random number generation is the art of producing pure gibberish as 
quickly as possible.

On Monday 11 December 2006 09:59, Nicolas Champavert wrote:
> Steve Schmerler a =E9crit :
> > Nicolas Champavert wrote:
> >> Hello,
> >>
> >>   I have some problems when trying to save a figure with usetex=3DTrue.
> >> Sometimes, it is not possible to save the figure when trying to put an
> >> xlabel with LaTeX inside.
> >> It works with pylab.xlabel('M$_\odot$') but not with
> >> pylab.xlabel('10$^3$ M$_\odot$') (see below). Do you know why ?
> >
> > Works fine here, with and w/o raw strings.
[...]
>
> I had matplotlib revision 2835. I made an upgrade. Now I have revision
> 2905. I still have problem but it is very strange:
> - when I plot [0,1] with xlabel(r'(10$^3$ M$_\odot$)'), it works.
> - when I plot [1,2] with xlabel(r'Upper mass for the IMF (10$^3$
> M$_\odot$)'), it works.
> - but it doesn't work if I plot [0,1] with xlabel(r'Upper mass for the
> IMF (10$^3$ M$_\odot$)')...

It sounds like an issue with one of the dependencies, probably ghostscript.=
 I=20
have matplotlib-0.87.7, svn revision 2905, and gpl-ghostscript-8.54. I can=
=20
not reproduce the problem here.

Darren

No the '\' is not the problem. \odot is a LaTeX command. I still have=20
the problem if I don't use it (see below with only pylab.xlabel(r'Upper=20
mass for the IMF (10$^3$)')) and \\odot writes 'odot' and doesn't write=20
the 'odot' symbol.


Gary Ruben a =E9crit :
> I haven't tried it, but my guess is the '\' character is the problem.
>
> > pylab.xlabel('10$^3$ M$_\odot$')
>
> Try
> pylab.xlabel(r'10$^3$ M$_\odot$')
>              ^
>              Add raw string marker.
>
> or maybe
> pylab.xlabel('10$^3$ M$_\\odot$')
>
> Gary R.
>
> Nicolas Champavert wrote:
>> Hello,
>>
>>   I have some problems when trying to save a figure with usetex=3DTrue=
.=20
>> Sometimes, it is not possible to save the figure when trying to put=20
>> an xlabel with LaTeX inside.
>> It works with pylab.xlabel('M$_\odot$') but not with=20
>> pylab.xlabel('10$^3$ M$_\odot$') (see below). Do you know why ?
>

In [1]:import matplotlib

In [2]:import pylab

In [3]:matplotlib.__version__
Out[3]:'0.87.7'

In [4]:matplotlib.__revision__
Out[4]:'$Revision: 2905 $'

In [5]:matplotlib.rc('text',usetex=3DTrue)

In [6]:pylab.plot([0,1])
Out[6]:[<matplotlib.lines.Line2D instance at 0xb67dde4c>]

In [7]:pylab.xlabel(r'Upper mass for the IMF (10$^3$)')
Out[7]:<matplotlib.text.Text instance at 0xb693d62c>

In [8]:pylab.savefig('test.eps')
sh: line 1: 19944 Erreur de segmentation  gs -dBATCH -dNOPAUSE -r6000=20
-sDEVICE=3Dpswrite -sPAPERSIZE=3Dletter=20
-sOutputFile=3D"/tmp/098f6bcd4621d373cade4e832627b4f6.ps"=20
"/tmp/098f6bcd4621d373cade4e832627b4f6"=20
 >"/tmp/098f6bcd4621d373cade4e832627b4f6.output"
-------------------------------------------------------------------------=
--
exceptions.RuntimeError                              Traceback (most=20
recent call last)

/home/champavert/<ipython console>

/usr/local/lib/python2.4/site-packages/matplotlib/pylab.py in=20
savefig(*args, **kwargs)
    811 def savefig(*args, **kwargs):
    812     fig =3D gcf()
--> 813     return fig.savefig(*args, **kwargs)
    814 if Figure.savefig.__doc__ is not None:
    815     savefig.__doc__ =3D _shift_string(Figure.savefig.__doc__)

/usr/local/lib/python2.4/site-packages/matplotlib/figure.py in=20
savefig(self, *args, **kwargs)
    685                 kwargs[key] =3D rcParams['savefig.%s'%key]
    686
--> 687         self.canvas.print_figure(*args, **kwargs)
    688
    689     def colorbar(self, mappable, cax=3DNone, **kw):

/usr/local/lib/python2.4/site-packages/matplotlib/backends/backend_tkagg.=
py=20
in print_figure(self, filename, dpi, facecolor, edgecolor, orientation,=20
**kwargs)
    184         if dpi is None: dpi =3D rcParams['savefig.dpi']
    185         agg =3D self.switch_backends(FigureCanvasAgg)
--> 186         agg.print_figure(filename, dpi, facecolor, edgecolor,=20
orientation,
    187                          **kwargs)
    188         self.figure.set_canvas(self)

/usr/local/lib/python2.4/site-packages/matplotlib/backends/backend_agg.py=
=20
in print_figure(self, filename, dpi, facecolor, edgecolor, orientation,=20
**kwargs)
    486                 ps =3D self.switch_backends(FigureCanvasPS)
    487                 ps.print_figure(filename, dpi, facecolor, edgecol=
or,
--> 488                                 orientation, **kwargs)
    489             elif ext.find('pdf')>=3D0:
    490                 from backend_pdf import FigureCanvasPdf

/usr/local/lib/python2.4/site-packages/matplotlib/backends/backend_ps.py=20
in print_figure(self, outfile, dpi, facecolor, edgecolor, orientation,=20
papertype)
   1009             # Let's keep the usetex stuff seperate from the=20
generic postscript
   1010             self._print_figure_tex(outfile, dpi, facecolor,=20
edgecolor,
-> 1011                                    orientation, papertype)
   1012         else:
   1013             if  isinstance(outfile, file):

/usr/local/lib/python2.4/site-packages/matplotlib/backends/backend_ps.py=20
in _print_figure_tex(self, outfile, dpi, facecolor, edgecolor,=20
orientation, papertype)
   1243         elif rcParams['text.usetex']:
   1244             if False: pass # for debugging
-> 1245             else: gs_distill(tmpfile, ext=3D=3D'.eps',=20
ptype=3Dpapertype, bbox=3Dbbox)
   1246
   1247         if  isinstance(outfile, file):

/usr/local/lib/python2.4/site-packages/matplotlib/backends/backend_ps.py=20
in gs_distill(tmpfile, eps, ptype, bbox)
   1341     exit_status =3D os.system(command)
   1342     fh =3D file(outfile)
-> 1343     if exit_status: raise RuntimeError('ghostscript was not able=20
to process \
   1344 your image.\nHere is the full report generated by=20
ghostscript:\n\n' + fh.read())
   1345     else: verbose.report(fh.read(), 'debug')

RuntimeError: ghostscript was not able to process your image.
Here is the full report generated by ghostscript:

ESP Ghostscript 8.15 (2006-04-19)
Copyright (C) 2004 artofcode LLC, Benicia, CA.  All rights reserved.
This software comes with NO WARRANTY: see the file COPYING for details.