matplotlib-users Mailing List for matplotlib

fill(x, y) returns an error like:


/Users/rob/Projects/Merrimack/Grid/landfill.py in <module>()
      24 for filename in filenames:
      25     x, y, = pl.load(filename).T
---> 26     pl.fill(x, y, facecolor=fillcolor, alpha=fillalpha)
      27
      28

/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site- 
packages/matplotlib/pylab.py in fill(*args, **kwargs)
    1869         hold(h)
    1870     try:
-> 1871         ret =  gca().fill(*args, **kwargs)
    1872         draw_if_interactive()
    1873     except:

/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site- 
packages/matplotlib/axes.py in fill(self, *args, **kwargs)
    3677         patches = []
    3678         for poly in self._get_patches_for_fill(*args,  
**kwargs):
-> 3679             self.add_patch( poly )
    3680             patches.append( poly )
    3681         self.autoscale_view()

/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site- 
packages/matplotlib/axes.py in add_patch(self, p)
     951         xys = self._get_verts_in_data_coords(
     952             p.get_transform(), p.get_verts())
--> 953         self.update_datalim(xys)
     954         self.patches.append(p)
     955

/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site- 
packages/matplotlib/axes.py in update_datalim(self, xys)
     966         # and the data in xydata
     967         xys = asarray(xys)
--> 968         self.dataLim.update_numerix_xy(xys, -1)
     969
     970

<type 'exceptions.TypeError'>: Bbox::update_numerix_xy expected  
numerix array
WARNING: Failure executing file: <landfill.py>


----
Rob Hetland, Associate Professor
Dept. of Oceanography, Texas A&M University
http://pong.tamu.edu/~rob
phone: 979-458-0096, fax: 979-845-6331

ch...@se... wrote:
> Thanks for help.  Now it freezes always here...
> 
> 
> GTK requires pygtk
> GTKAgg requires pygtk
> TKAgg requires TkInter

I never used eggs, but I guess you need to install these libs by yourself.

apt-cache search for this stuff and make sure you install the *-dev 
versions of the packages.

Seems that you need (list may not exhaustive) python-gtk2-dev (pygtk) 
and tk8.4-dev, python-tk, ... (TkInter, this is a little tricky to 
search for, check the dependencies)

-- 
cheers,
steve

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

Thanks for help.  Now it freezes always here...


GTK requires pygtk
GTKAgg requires pygtk
TKAgg requires TkInter
warning: no files found matching 'MANIFEST'
warning: no files found matching 'lib/matplotlib/toolkits'
no previously-included directories found matching 'examples/_tmp_*'
In file included from /usr/include/python2.4/Python.h:8,
                 from CXX/Objects.hxx:9,
                 from CXX/Extensions.hxx:19,
                 from src/_transforms.h:12,
                 from src/_ns_transforms.cpp:5:
/usr/include/python2.4/pyconfig.h:832:1: warning: "_POSIX_C_SOURCE" redefined
In file included from /usr/include/c++/3.3/i486-linux/bits/os_defines.h:39,
                 from /usr/include/c++/3.3/i486-linux/bits/c++config.h:35,
                 from /usr/include/c++/3.3/functional:53,
                 from src/_ns_transforms.cpp:1:
/usr/include/features.h:131:1: warning: this is the location of the previous
definition


Chris

Eric Firing wrote:

> Regarding the clip line, I think that your test for mask is None is not 
> the right solution because it knocks out the clipping operation, but the 
> clipping is intended regardless of the state of the mask.  I had 
> expected it to be a very fast operation,

for what it's worth, a few years ago a wrote a "fast_clip" c extension 
that did clip without making nearly as many temporary arrays as the 
Numeric one -- I don't know what numpy does , I haven't needed a fast 
clip recently. I'd be glad to send the code to anyone interested.

> Now I recall very recent discussion explaining why "where" is slow 
> compared to indexing with a boolean, so I know I can speed it up with 
> numpy.  Unfortunately Numeric does not support this, so maybe what will 
> be needed is numerix functions that take advantage of numpy when 
> available.

good idea.

> This is one of those times when I really wish we could drop 
> Numeric and numarray support *now* and start taking full advantage of numpy.

I'd love that too. Maybe your proposal is a good one, though -- make 
numeric functions that are optimized for numpy. I think that's a good 
way to transition.

-Chris



-- 
Christopher Barker, Ph.D.
Oceanographer

Emergency Response Division
NOAA/NOS/OR&R            (206) 526-6959   voice
7600 Sand Point Way NE   (206) 526-6329   fax
Seattle, WA  98115       (206) 526-6317   main reception

Chr...@no...

When I look at http://matplotlib.sourceforge.net/tutorial.html with  
Safari, I see a lot of broken images. Any ideas?

David,


>    - 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.

The things you have identified were added by me to support masked array 
bad values and special colors for regions above or below the mapped 
range of values.  I will be happy to make changes to speed them up.

Regarding the clip line, I think that your test for mask is None is not 
the right solution because it knocks out the clipping operation, but the 
clipping is intended regardless of the state of the mask.  I had 
expected it to be a very fast operation, so I am surprised it is a 
bottleneck; in any case I can take a look to see how it can be sped up, 
or whether it can be bypassed in some cases.  Maybe it is also using 
"where" internally.

Now I recall very recent discussion explaining why "where" is slow 
compared to indexing with a boolean, so I know I can speed it up with 
numpy.  Unfortunately Numeric does not support this, so maybe what will 
be needed is numerix functions that take advantage of numpy when 
available.  This is one of those times when I really wish we could drop 
Numeric and numarray support *now* and start taking full advantage of numpy.

In any case, thanks for pointing out the slowdowns--I will fix them as 
best I can--and keep at it.  I share your interest in speeding up 
interactive use of matplotlib, along with fixing bugs, filling holes in 
functionalisy, and smoothing rough edges. There is a lot to be done. As 
John noted, though, there will always be tradeoffs among flexibility, 
code simplicity, generality, and speed.

Eric

I have received reports from clients with the following traceback or 
similar.

This happens when application is packaged with py2exe.

Traceback (most recent call last):
  File "appwine.pyo", line 1341, in OnToolbarChart
  File "frameplotmpl.pyo", line 19, in ?
  File "matplotlib\backends\__init__.pyo", line 19, in ?
  File "matplotlib\backends\backend_wxagg.pyo", line 18, in ?
  File "matplotlib\backends\backend_agg.pyo", line 82, in ?
  File "matplotlib\figure.pyo", line 3, in ?
  File "matplotlib\axes.pyo", line 14, in ?
  File "matplotlib\axis.pyo", line 21, in ?
  File "matplotlib\font_manager.pyo", line 982, in ?
  File "matplotlib\font_manager.pyo", line 826, in __init__
  File "matplotlib\font_manager.pyo", line 819, in rebuild
  File "matplotlib\font_manager.pyo", line 454, in createFontDict
SystemError: error return without exception set

Any hints of what might cause this would be very welcome.

Werner

P.S.
I am still on matplotlib version  '0.82' (plan to upgrade to newer 
version but need to upgrade to Unicode wxPython first), with Python 2.4 
and wxPython 2.6.x

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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> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

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