matplotlib-users Mailing List for matplotlib (Page 5)

Hello -
It seems that in the latest version (0.9.1) the location of the images, such
as home.ppm, has moved to a new directory.
It used to be in ...\mpl-data and now it is in ...\mpl-data\images
This totally breaks my code, as I use my own toolbar that uses these images,
with a hardcoded location of the ppm files. I know, I should from now on
distribute these ppm files with the distribution of my own program, but
there may be others that have this problem.
Not sure what the best solution is. WIll it remain in this new images
directory from now on?
Thanks,
Mark
ps. Cross-posted on the users and development list
ps2. Sorry, it sounds like I am bitching, but I really like matplotlib. Just
needed to point out this backwards incompatibility, which may just be ugly
coding on my part

sorry to be a wet blanket, but...
Barry Wark wrote:
> going "native" for rendering would allow mpl
> to integrate more smoothly with the rest of the 2D and 3D 
MPL is really only a 2D system -- there's been talk of the next 
generation version building 3D in, but we're really not there yet.
 > ability to offload rendering and/or coordinate
> transformation to the graphics card, 
With the multi-backend paradigm, it's really hard to truly take 
advantage of this kind of thing -- optimum performance on any given 
platform is just not going to happen.
> ability to easily produce output
> in any of the CoreImage supported formats,
That's nice, but again, a bit out of sync with the cross platform nature 
of MPL.
 ability to incorporate
> transparency and alpha blending within the view hierarchy, ability to
> combine media (QuickTime, OpenGL, and Quartz) in layers (the real
> purpose of the CoreAnimation engine), 
again -- very platform specific.
In short, CoreGraphics is the obvious choice for a Mac-only tools, but 
the advantages are greatly reduced for a platform and back-end 
independent tool like MPL. Which isn't to say that the Cocoa back-end 
isn't a good idea -- but the primary reason for it that I see is to be 
able to integrate with PyObjC apps.
Really, Agg (or Cairo, if we didn't have those pesky licensing issues) 
is a great way to go for a tool like MPL. It rally keeps things platform 
independent.
If MPL does go 3D in the future, then OpenGL is probably the way to go 
too -- it's more or less the same everywhere.
-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...

mbauer wrote:
> Thanks Jeff,
>
> To clarify, I'm sampling a numpy array (regular lon/lat grid) and 
> extracting a series of same size frames (say 60 longitude grids and 30 
> latitude grids) around a feature of interest, which can be centered 
> somewhere on the map. What I want to do is accumulate statistics with 
> these frames such that the relative size/distances are persevered, 
> which of course means that I can't just add a frame centered on 30N 
> with one centered on 80N. Ideally, I'd like to interpolate each frame 
> to a common point (lon/lat) and display the results either in the 
> common grid space or as radial distances from the common point.
>
> Since you're a meteorologist I can simply say I'm creating an ensemble 
> average of extra tropical cyclones from a dozen or so computer models 
> (each with very different resolutions). I want to see how cloud and 
> precipitation features in each model's cyclones compare to a similar 
> product I'm producing from satellite data using weather model output 
> to locate the cyclones. Much the same thing as the link I provided.
>
> Thanks for your suggests as transform_scalar sounds like a good place 
> to begin.
>
> Mike
Mike: Thanks for the explanation, I get it now - and I think I have 
just the thing for you. First, define a Basemap instance for a Lambert 
Conformal projection centered on each of you frames that is 5000 km wide 
and 5000 km tall.
# for frame centered on lon_0, lat_0.
# resolution=None skips processing of boundary datasets to save time.
m = Basemap(lon_0=lon_0, lat_0=lat_0, projection='lcc', \
 width=5000000, height=5000000, resolution=None) 
Now interpolate your data to a nx by ny grid that is regular in the map 
projection region.
# data is the lat/lon gridded data, lons and lats are 1D arrays in degrees.
data2 = m.transform_scalar(data, lons, lats, nx, ny) 
The data2 grids will be approximately equally spaced on the surface of 
the earth, regardless on lat_0 and lon_0. Therefore, you should be able 
to just ensemble average all the data2 grids and preserve the relative 
shapes and sizes of the features (this is helped by the fact that the 
projection is conformal, or shape-preserving).
HTH,
-Jeff
>
> On Dec 11, 2007, at 4:57 PM, Jeff Whitaker wrote:
>
>> mbauer wrote:
>>> Matplotlib users, I looking to tap your wealth of ideas and 
>>> experience to help solve a problem I'm working on.
>>>
>>> The problem: I have a series of 2d scalar arrays representing a 
>>> fixed width/height lon/lat box centered on an arbitrary lon/lat. I 
>>> need to average these composites on a common basis that 
>>> accommodates the scale changes due to latitude, preferably by 
>>> shifting everything to a common central lon/lat (a polar/radial 
>>> distance basis would work too). I want a plot of the end result too 
>>> and I'm like to do everything with matplotlib and python so that it 
>>> folds into the rest of my program.
>>>
>>> Something similar can be seen at 
>>> http://www.atmos.washington.edu/~robwood/topic_cyclones.htm
>>>
>>> I've been looking at transform_scalar from basemap but I'm not 
>>> quite sure this is what I should use.
>>>
>> Mike:
>>
>> transform_scalar does simple bilinear interpolation from a lat/lon 
>> grid to a regular grid in map projection coordinates. If your map 
>> projection is just a lat/lon projection, then this amounts to 
>> interpolating from one lat/lon grid to another.
>>> If anyone can offer a solution, a point in the right direction, or 
>>> just wave me off this path I'd be most appreciative.
>>>
>> I'm sure numpy/matplotlib can do what you need to do. Matplotlib 
>> can certainly make a plot similar to the one given in your link. I 
>> think you question relates more to the processing of your arrays 
>> though, and not specifically the plotting. Are all your 2d arrays 
>> the same shape (the same number of lats and lons)? Are they just 
>> centered on different regions? If so, I think you can just multiply 
>> each grid point by the cosine of latitude to get the proper area 
>> weighting before summing them together. But perhaps I'm missing the 
>> essence of your question ....
>>
>> -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
>>
>>
>
-- 
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

Thanks Jeff,
To clarify, I'm sampling a numpy array (regular lon/lat grid) and 
extracting a series of same size frames (say 60 longitude grids and 30 
latitude grids) around a feature of interest, which can be centered 
somewhere on the map. What I want to do is accumulate statistics with 
these frames such that the relative size/distances are persevered, 
which of course means that I can't just add a frame centered on 30N 
with one centered on 80N. Ideally, I'd like to interpolate each frame 
to a common point (lon/lat) and display the results either in the 
common grid space or as radial distances from the common point.
Since you're a meteorologist I can simply say I'm creating an ensemble 
average of extra tropical cyclones from a dozen or so computer models 
(each with very different resolutions). I want to see how cloud and 
precipitation features in each model's cyclones compare to a similar 
product I'm producing from satellite data using weather model output 
to locate the cyclones. Much the same thing as the link I provided.
Thanks for your suggests as transform_scalar sounds like a good place 
to begin.
Mike
On Dec 11, 2007, at 4:57 PM, Jeff Whitaker wrote:
> mbauer wrote:
>> Matplotlib users, I looking to tap your wealth of ideas and 
>> experience to help solve a problem I'm working on.
>>
>> The problem: I have a series of 2d scalar arrays representing a 
>> fixed width/height lon/lat box centered on an arbitrary lon/lat. I 
>> need to average these composites on a common basis that 
>> accommodates the scale changes due to latitude, preferably by 
>> shifting everything to a common central lon/lat (a polar/radial 
>> distance basis would work too). I want a plot of the end result 
>> too and I'm like to do everything with matplotlib and python so 
>> that it folds into the rest of my program.
>>
>> Something similar can be seen at http://www.atmos.washington.edu/~robwood/topic_cyclones.htm
>>
>> I've been looking at transform_scalar from basemap but I'm not 
>> quite sure this is what I should use.
>>
> Mike:
>
> transform_scalar does simple bilinear interpolation from a lat/lon 
> grid to a regular grid in map projection coordinates. If your map 
> projection is just a lat/lon projection, then this amounts to 
> interpolating from one lat/lon grid to another.
>> If anyone can offer a solution, a point in the right direction, or 
>> just wave me off this path I'd be most appreciative.
>>
> I'm sure numpy/matplotlib can do what you need to do. Matplotlib 
> can certainly make a plot similar to the one given in your link. I 
> think you question relates more to the processing of your arrays 
> though, and not specifically the plotting. Are all your 2d arrays 
> the same shape (the same number of lats and lons)? Are they just 
> centered on different regions? If so, I think you can just multiply 
> each grid point by the cosine of latitude to get the proper area 
> weighting before summing them together. But perhaps I'm missing the 
> essence of your question ....
>
> -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'm curious about the term 'threading backend'.
Recently I posted a question about how to handle slow plots, suggesting
that the backend canvas have an isabort() method so that the renderer
can stop what it is doing and post the current bitmap as it stands.
This is to support interactive operations such as panning and resizing
on large data collections.
Do you mean something similar when you say 'threading backend', and is
it already supported in IPython?
 - Paul
On Wed, Dec 12, 2007 at 01:36:01AM -0700, Fernando Perez wrote:
> Hi all,
> 
> This was posted to the ipython-dev list, but since it's specifically
> for MPL, I figured the cross-list spam would be forgiven.
> 
> In IPython SVN, I just added the ability to manually control the pylab
> threading backend choice directly from the command line. So for
> example if by default you have:
> 
> tlon[~]> ipython -pylab --nobanner
> 
> In [1]: matplotlib.rcParams['backend']
> Out[1]: 'TkAgg'
> 
> You can now do this as well:
> 
> tlon[~]> ipython -wthread -pylab --nobanner
> 
> In [1]: matplotlib.rcParams['backend']
> Out[1]: 'WXAgg'
> 
> In [2]:
> Closing threads... Done.
> tlon[~]> ipython -gthread -pylab --nobanner
> 
> In [1]: matplotlib.rcParams['backend']
> Out[1]: 'GTKAgg'
> 
> The feature is fairly simplistic: the -Xthread flags map automatically
> to the XAgg backends in MPL, with no more fine-grained choice than
> that. We can later look into allowing explicit backend selection if
> you really scream for it, but I'd rather keep this simple. This means
> that if you don't have the *Agg builds of the GUI backends, you'll
> still need to do the backend selection by hand as before (i.e. by
> modifying your mpl config file).
> 
> This has often been requested and I'd needed it myself on multiple
> occasions, so it's finally in.
> 
> Cheers,
> 
> f
> 
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All this sounds great. There has been some (off-list) discussion 
recently about making it easier to support third-party backends (i.e. 
backends that are maintained separately from the rest of the matplotlib 
tree). This Quartz backend may be a good test candidate for that 
(though I wouldn't worry about the details of that until some progress 
is made on making that possible). It seems like a natural fit for 
something that lives "a bit outside" since it is platform-specific.
Your list of advantages sound interesting. It will be great to see such 
things in action.
One minor comment --->
 > ability to offload rendering and/or coordinate
 > transformation to the graphics card
One of the desired goals of the transforms branch was to offload 
coordinate transformations to external renderers, for example, for PDF 
to offload it to Acrobat Reader via the PDF file. As it turns out, most 
of these systems (at least PDF, PS, SVG) all want to transform the 
stroke width using the same transform as the vertices of the object 
itself. So if you zoom in on the data, the line width becomes enormous. 
 As it turned out, we weren't really able to take advantage of that, 
and end up transforming most things within matplotlib itself anyway (in 
a C extension).
But coordinate transformation is not a significant part of the runtime 
of the current software-only approaches anyway.
 > ability to easily produce output
 > in any of the CoreImage supported formats,
As an alternative, you may want to look at how the GtkAgg backend 
outputs to any of GtkPixbuf's supported formats, while using Agg as the 
renderer. Something analogous is likely possible with Quartz.
Personally, I'm curious to see how Quartz backend performs at producing 
PDF output, particularly with respect to fonts. That was a difficult 
thing to get right (and it still has room for improvement) in mpl's own 
PDF backend.
Cheers,
Mike
Barry Wark wrote:
> Michael,
> 
> I'm sorry. I just realized I hadn't replied to you yet; sorry for the
> delay. John also pointed us to the transforms branch and that's where
> we'll start. Thanks! There's no real advantage to CoreAnimation per se
> (except eye candy), but going "native" for rendering would allow mpl
> to integrate more smoothly with the rest of the 2D and 3D (much of
> Quartz, and CoreImage the 2D rendering and filtering systems in OS X
> are actually rendered on the graphics card as OpenGL) rendering system
> in OS X. Some advantages, off the top of my head are resolution
> independence, ability to offload rendering and/or coordinate
> transformation to the graphics card, ability to easily produce output
> in any of the CoreImage supported formats, ability to incorporate
> transparency and alpha blending within the view hierarchy, ability to
> combine media (QuickTime, OpenGL, and Quartz) in layers (the real
> purpose of the CoreAnimation engine), ability to piggy back on
> improvements in Apple's rendering engine (things like anti-aliasing
> etc.), ColorSync support, and _maybe_ some speed improvements by
> taking a layer or two out of the rendering process. All of these are
> just speculation, at this time... we're just getting started but will
> share our results as soon as they're ready.
> 
> barry
> 
> 
> On 12/5/07, Michael Droettboom <md...@st...> wrote:
>> Barry Wark wrote:
>>> We (at my work) are just starting to think about writing a more direct
>>> Quartz backend for mpl. A native backend would let a matplotlib view
>>> participate in newer Cocoa technologies, such as resolution
>>> independence and CoreAnimation (it's possible with the current backend
>>> method, but not quite as flexible).
>> I'm curious what Cocoa and CoreAnimation might enable...
>>
>> If you are looking into writing a Quartz rendering backend, you may want
>> to start with the matplotlib transforms branch (which should become the
>> trunk shortly, once the 0.91 release bugs get shaken out.) The number
>> of methods that a backend writer must provide has been greatly reduced
>> on that branch.
>>
>> Cheers,
>> Mike
>>
>> --
>> Michael Droettboom
>> Science Software Branch
>> Operations and Engineering Division
>> Space Telescope Science Institute
>> Operated by AURA for NASA
>>
-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

On Wed, Dec 12, 2007 at 01:36:01AM -0700, Fernando Perez wrote:
> You can now do this as well:
> tlon[~]> ipython -wthread -pylab --nobanner
> In [1]: matplotlib.rcParams['backend']
> Out[1]: 'WXAgg'
Hurray, mayavi and pylab can now easily live together. Thanks heaps
Fernando.
Gaël

Hi all,
This was posted to the ipython-dev list, but since it's specifically
for MPL, I figured the cross-list spam would be forgiven.
In IPython SVN, I just added the ability to manually control the pylab
threading backend choice directly from the command line. So for
example if by default you have:
tlon[~]> ipython -pylab --nobanner
In [1]: matplotlib.rcParams['backend']
Out[1]: 'TkAgg'
You can now do this as well:
tlon[~]> ipython -wthread -pylab --nobanner
In [1]: matplotlib.rcParams['backend']
Out[1]: 'WXAgg'
In [2]:
Closing threads... Done.
tlon[~]> ipython -gthread -pylab --nobanner
In [1]: matplotlib.rcParams['backend']
Out[1]: 'GTKAgg'
The feature is fairly simplistic: the -Xthread flags map automatically
to the XAgg backends in MPL, with no more fine-grained choice than
that. We can later look into allowing explicit backend selection if
you really scream for it, but I'd rather keep this simple. This means
that if you don't have the *Agg builds of the GUI backends, you'll
still need to do the backend selection by hand as before (i.e. by
modifying your mpl config file).
This has often been requested and I'd needed it myself on multiple
occasions, so it's finally in.
Cheers,
f

I am trying to run scipy in XP on VMWare Workstation. The host OS is 
Ubuntu. When I attempt to launch scipy I get the error:
____________________________________________________________________________ 
C:\Documents and Settings\Administrator>C:\Python25\python.exe 
C:\Python25\scrip
ts\ipython -pylab -p scipy
Traceback (most recent call last):
File "C:\Python25\scripts\ipython", line 27, in <module>
IPython.Shell.start().mainloop()
File "C:\Python25\Lib\site-packages\IPython\Shell.py", line 1111, in start
return shell(user_ns = user_ns)
File "C:\Python25\Lib\site-packages\IPython\Shell.py", line 1008, in 
__init__
shell_class=MatplotlibShell)
File "C:\Python25\Lib\site-packages\IPython\Shell.py", line 74, in __init__
debug=debug,shell_class=shell_class)
File "C:\Python25\Lib\site-packages\IPython\ipmaker.py", line 95, in 
make_IPyt
hon
embedded=embedded,**kw)
File "C:\Python25\Lib\site-packages\IPython\Shell.py", line 562, in 
__init__
user_ns,b2 = self._matplotlib_config(name,user_ns)
File "C:\Python25\Lib\site-packages\IPython\Shell.py", line 463, in 
_matplotli
b_config
from matplotlib import backends
File "C:\Python25\Lib\site-packages\matplotlib\backends\__init__.py", 
line 55,
in <module>
new_figure_manager, draw_if_interactive, show = pylab_setup()
File "C:\Python25\Lib\site-packages\matplotlib\backends\__init__.py", 
line 24,
in pylab_setup
globals(),locals(),[backend_name])
File 
"C:\Python25\Lib\site-packages\matplotlib\backends\backend_tkagg.py", lin
e 8, in <module>
import tkagg # Paint image to Tk photo blitter extension
File "C:\Python25\Lib\site-packages\matplotlib\backends\tkagg.py", line 
1, in
<module>
import _tkagg
ImportError: DLL load failed: The specified module could not be found.
_____________________________________________________________________________________ 
I changed the back end to WxAgg with no luck.
_____________________________________
import wx
ImportError: no module named wx
_______________________________________
Any thoughts?
Thanks,
Bill

Michael,
I'm sorry. I just realized I hadn't replied to you yet; sorry for the
delay. John also pointed us to the transforms branch and that's where
we'll start. Thanks! There's no real advantage to CoreAnimation per se
(except eye candy), but going "native" for rendering would allow mpl
to integrate more smoothly with the rest of the 2D and 3D (much of
Quartz, and CoreImage the 2D rendering and filtering systems in OS X
are actually rendered on the graphics card as OpenGL) rendering system
in OS X. Some advantages, off the top of my head are resolution
independence, ability to offload rendering and/or coordinate
transformation to the graphics card, ability to easily produce output
in any of the CoreImage supported formats, ability to incorporate
transparency and alpha blending within the view hierarchy, ability to
combine media (QuickTime, OpenGL, and Quartz) in layers (the real
purpose of the CoreAnimation engine), ability to piggy back on
improvements in Apple's rendering engine (things like anti-aliasing
etc.), ColorSync support, and _maybe_ some speed improvements by
taking a layer or two out of the rendering process. All of these are
just speculation, at this time... we're just getting started but will
share our results as soon as they're ready.
barry
On 12/5/07, Michael Droettboom <md...@st...> wrote:
> Barry Wark wrote:
> > We (at my work) are just starting to think about writing a more direct
> > Quartz backend for mpl. A native backend would let a matplotlib view
> > participate in newer Cocoa technologies, such as resolution
> > independence and CoreAnimation (it's possible with the current backend
> > method, but not quite as flexible).
>
> I'm curious what Cocoa and CoreAnimation might enable...
>
> If you are looking into writing a Quartz rendering backend, you may want
> to start with the matplotlib transforms branch (which should become the
> trunk shortly, once the 0.91 release bugs get shaken out.) The number
> of methods that a backend writer must provide has been greatly reduced
> on that branch.
>
> Cheers,
> Mike
>
> --
> Michael Droettboom
> Science Software Branch
> Operations and Engineering Division
> Space Telescope Science Institute
> Operated by AURA for NASA
>

Fernando was right on. Here is his response to me:
Laptop - Ok
Windows XP Professional, Service Pack 2
AMD Athlon 64 3400+ (ClawHammer)
1.67 GHz, 768 MB of RAM
Chipset: SiS 755/755FX
Southbridge: SiS LPC Bridge
Instructions: MMX (+), 3DNow! (+), SSE, SSE2, x86-64
Machine 1 - Crashes
Windows XP Professional, Service Pack 2
AMD Athlon XP 2000+ (Thoroughbred)
1.67 GHz, 768 MB of RAM
ASUS A7V8X-X motherboard
Chipset: VIA KT400 (VT8377)
Southbridge: VIA VT8235
Instructions: MMX (+), 3DNow! (+), SSE
Machine 2 - Crashes
Windows XP Professional, Service Pack 2
AMD Athlon XP 2600+ (Barton)
1.92 GHz, 2.0 GB of RAM
ASUS A7V880 motherboard
Chipset: VIA KT880
Southbridge: VIA VT8237
Instructions: MMX (+), 3DNow! (+), SSE
I ran the following statements on both machines which caused it to crash:
import numpy
numpy.test()
Here is the output:
 Numpy is installed in C:\Python25\lib\site-packages\numpy
 Numpy version 1.0.4
 Python version 2.5.1 (r251:54863, Apr 18 2007, 08:51:08) [MSC
v.1310 32 bit (Int
 el)]
 Found 10/10 tests for numpy.core.defmatrix
 Found 36/36 tests for numpy.core.ma
 Found 223/223 tests for numpy.core.multiarray
 Found 65/65 tests for numpy.core.numeric
 Found 31/31 tests for numpy.core.numerictypes
 Found 12/12 tests for numpy.core.records
 Found 6/6 tests for numpy.core.scalarmath
 Found 14/14 tests for numpy.core.umath
 Found 4/4 tests for numpy.ctypeslib
 Found 5/5 tests for numpy.distutils.misc_util
 Found 1/1 tests for numpy.fft.fftpack
 Found 3/3 tests for numpy.fft.helper
 Found 9/9 tests for numpy.lib.arraysetops
 Found 46/46 tests for numpy.lib.function_base
 Found 5/5 tests for numpy.lib.getlimits
 Found 4/4 tests for numpy.lib.index_tricks
 Found 3/3 tests for numpy.lib.polynomial
 Found 49/49 tests for numpy.lib.shape_base
 Found 15/15 tests for numpy.lib.twodim_base
 Found 43/43 tests for numpy.lib.type_check
 Found 1/1 tests for numpy.lib.ufunclike
 Found 40/40 tests for numpy.linalg
 Found 2/2 tests for numpy.random
 Found 0/0 tests for __main__
 ................................................................................
 ................................................................................
 ................................................................................
 ................................................................................
 .....................................
Sounds like the problem is the fact that my desktop computers do not
support SSE2 instructions which are in the latest numpy binaries.
This also explains why it works fine on the laptop which does support
SSE2.
I piggy-backed onto an existing thread on the numpy list (is that bad
listserve etiquette? - probably: I now have the same question on two
lists and I tried to high jack a thread.). Unless someone has a
better idea, I will try and build from source for him. But my windows
building skills are not what they should be.....
Ryan
On Dec 11, 2007 2:06 PM, Fernando Perez <fpe...@gm...> wrote:
>
> On Dec 11, 2007 12:01 PM, Ryan Krauss <rya...@gm...> wrote:
> > I am trying to help a student get started with
> > Python/Scipy/Numpy/Matplotlib in windows. On one of his machines,
> > everything seems to install correctly, we can call figure(1) without a
> > problem, and plotting is fine until we try the show() command. Then
> > python crashes without much in the way of useful information. His
> > laptop is completely fine.
> >
> > We have downloaded a current rc file and set the backend to TkAgg.
> >
> > Any thoughts?
> >
> > How do we get more info to track down the problem?
>
> Go to the windows information screens and fetch out some CPU details.
> If it's a Pentium III, chances are the SSE2 instructions in the latest
> numpy binary are the culprit. If it's a newer chip, we'll need to dig
> deeper.
>
> Cheers,
>
> f
>

mbauer wrote:
> Matplotlib users, I looking to tap your wealth of ideas and experience 
> to help solve a problem I'm working on.
>
> The problem: I have a series of 2d scalar arrays representing a fixed 
> width/height lon/lat box centered on an arbitrary lon/lat. I need to 
> average these composites on a common basis that accommodates the scale 
> changes due to latitude, preferably by shifting everything to a common 
> central lon/lat (a polar/radial distance basis would work too). I want 
> a plot of the end result too and I'm like to do everything with 
> matplotlib and python so that it folds into the rest of my program.
>
> Something similar can be seen at http://www.atmos.washington.edu/~robwood/topic_cyclones.htm
>
> I've been looking at transform_scalar from basemap but I'm not quite 
> sure this is what I should use.
> 
Mike:
transform_scalar does simple bilinear interpolation from a lat/lon grid 
to a regular grid in map projection coordinates. If your map projection 
is just a lat/lon projection, then this amounts to interpolating from 
one lat/lon grid to another.
> If anyone can offer a solution, a point in the right direction, or 
> just wave me off this path I'd be most appreciative.
> 
I'm sure numpy/matplotlib can do what you need to do. Matplotlib can 
certainly make a plot similar to the one given in your link. I think 
you question relates more to the processing of your arrays though, and 
not specifically the plotting. Are all your 2d arrays the same shape 
(the same number of lats and lons)? Are they just centered on different 
regions? If so, I think you can just multiply each grid point by the 
cosine of latitude to get the proper area weighting before summing them 
together. But perhaps I'm missing the essence of your question ....
-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

Matplotlib users, I looking to tap your wealth of ideas and experience 
to help solve a problem I'm working on.
The problem: I have a series of 2d scalar arrays representing a fixed 
width/height lon/lat box centered on an arbitrary lon/lat. I need to 
average these composites on a common basis that accommodates the scale 
changes due to latitude, preferably by shifting everything to a common 
central lon/lat (a polar/radial distance basis would work too). I want 
a plot of the end result too and I'm like to do everything with 
matplotlib and python so that it folds into the rest of my program.
Something similar can be seen at http://www.atmos.washington.edu/~robwood/topic_cyclones.htm
I've been looking at transform_scalar from basemap but I'm not quite 
sure this is what I should use.
If anyone can offer a solution, a point in the right direction, or 
just wave me off this path I'd be most appreciative.
Mike

Pretty sure it's a newer chip, but I will find out.
On Dec 11, 2007 2:06 PM, Fernando Perez <fpe...@gm...> wrote:
>
> On Dec 11, 2007 12:01 PM, Ryan Krauss <rya...@gm...> wrote:
> > I am trying to help a student get started with
> > Python/Scipy/Numpy/Matplotlib in windows. On one of his machines,
> > everything seems to install correctly, we can call figure(1) without a
> > problem, and plotting is fine until we try the show() command. Then
> > python crashes without much in the way of useful information. His
> > laptop is completely fine.
> >
> > We have downloaded a current rc file and set the backend to TkAgg.
> >
> > Any thoughts?
> >
> > How do we get more info to track down the problem?
>
> Go to the windows information screens and fetch out some CPU details.
> If it's a Pentium III, chances are the SSE2 instructions in the latest
> numpy binary are the culprit. If it's a newer chip, we'll need to dig
> deeper.
>
> Cheers,
>
> f
>

On Dec 11, 2007 12:01 PM, Ryan Krauss <rya...@gm...> wrote:
> I am trying to help a student get started with
> Python/Scipy/Numpy/Matplotlib in windows. On one of his machines,
> everything seems to install correctly, we can call figure(1) without a
> problem, and plotting is fine until we try the show() command. Then
> python crashes without much in the way of useful information. His
> laptop is completely fine.
>
> We have downloaded a current rc file and set the backend to TkAgg.
>
> Any thoughts?
>
> How do we get more info to track down the problem?
Go to the windows information screens and fetch out some CPU details.
If it's a Pentium III, chances are the SSE2 instructions in the latest
numpy binary are the culprit. If it's a newer chip, we'll need to dig
deeper.
Cheers,
f

I should add that in 0.91.1, you'll be hit by this bug:
http://sourceforge.net/mailarchive/message.php?msg_name=475ED00A.400%40stsci.edu
You can wait for 0.91.2, or patch your cbook.py as suggested by Brandon 
in the above thread.
Cheers,
Mike
Michael Droettboom wrote:
> [I only seem to be getting this message from the list today, despite the 
> timestamp of 2007年12月05日. Sorry for the delay.]
> 
> As of matplotlib 0.91.1, savefig() supports saving to a file-like object 
> for most backends (excluding Gtk and Wx). You can pass in a StringIO 
> object to savefig for instance, and then pass its content to the XML 
> parser of your choice. (There is no way to get DOM objects directly, 
> since the SVG backend actually writes the XML content directly as a 
> stream of bytes).
> 
> You can specify SVG as your output by either setting the backend 
> appropriately or passing a "format" kwarg to savfig, e.g.
> 
> savefig(fileobj, format="svg")
> 
> Cheers,
> Mike
> 
> bplewe wrote:
>> [Fairly new at matplotlib, but very happy with it so far]
>>
>> Is it possible to retrieve images rendered by one of the backends as an
>> object, rather than just saving to a file? 
>>
>> Specifically, I need to render graphs to SVG code, that I can turn into a
>> DOM object for further manipulation. I can save to a temp file and
>> immediately reload it into a DOM, but that is cumbersome in a single
>> program.
>>
>> The only place I can see to generate rendered output is savefig(). There is
>> a reference in the documentation to using a file-like object with the Cairo
>> backend. Is that the only possibility?
>>
>> If so, any ideas on a workaround other than temp files?
> 
-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

On Dec 11, 2007 1:01 PM, Ryan Krauss <rya...@gm...> wrote:
> I am trying to help a student get started with
> Python/Scipy/Numpy/Matplotlib in windows. On one of his machines,
> everything seems to install correctly, we can call figure(1) without a
> problem, and plotting is fine until we try the show() command. Then
> python crashes without much in the way of useful information. His
> laptop is completely fine.
>
> We have downloaded a current rc file and set the backend to TkAgg.
>
> Any thoughts?
First try
 >>> import numpy
 >>> numpy.test()
there is a numpy problem on windows that affects older machines.
If that works, create a simple test file that generates a plot and
calls savefig and run it with
 > python myscript.py --verbose-debug-annoying
try different backends by adding -dPS or -dAgg to the verbose flag
from the command line and see which backends crash and report back
with the verbose output
JDH

I am trying to help a student get started with
Python/Scipy/Numpy/Matplotlib in windows. On one of his machines,
everything seems to install correctly, we can call figure(1) without a
problem, and plotting is fine until we try the show() command. Then
python crashes without much in the way of useful information. His
laptop is completely fine.
We have downloaded a current rc file and set the backend to TkAgg.
Any thoughts?
How do we get more info to track down the problem?
Thanks,
Ryan

David.Goldsmith wrote:
> You say you tried wx but don't mention wxmpl explicitly - did you try 
> wxmpl (http://agni.phys.iit.edu/~kmcivor/wxmpl/)?
good choice -- the other is to follow the "embedded_in_***" examples -- 
where are they? A quick googling didn't find them!
>> all attempts to control or modify input to
>> Matplotlib from a GUI (Tkinter, Wx, Jython, PyGTK etc.) have proved
>> fruitless due to seeming incompatibility between these modules,
I'm guessing that you're using the pylab interface -- pylab manages the 
GUI for you -- which is great if you're doing simple interactive 
plotting, but will not work if you are embedding MPL in a gui -- you 
need to use the OO interface instead -- see the embedded_in_*** examples.
>> particularly
>> when one distributes any finished product to another platform.
This is no harder than with JAVA -- easier I think -- you can completely 
control the environment if you want -- see py2exe, pyInstaller, py2app, etc.
 >> I return to my Java roots where I can easily solve all GUI
>> problems 
Once you get the hang of Python, I think you'll find that you can solve 
your GUI problems even more easily! I'm partial to wxPython, but pyQT 
and pyGTK have their strengths also.
As a JAVA coder, I recommend you read:
http://dirtsimple.org/2004/12/python-is-not-java.html
-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...

You say you tried wx but don't mention wxmpl explicitly - did you try 
wxmpl (http://agni.phys.iit.edu/~kmcivor/wxmpl/)?
DG
adamski246 wrote:
> I am part of a team trying to create interactive GUI scientific
> visualisations and would like some advice regarding the best way to proceed. 
>
> We are trying to output mathematical functions (Fourier transforms, ray
> tracing etc.) in graphical form and have been very impressed with the ease
> Matplotlib can do this. However, all attempts to control or modify input to
> Matplotlib from a GUI (Tkinter, Wx, Jython, PyGTK etc.) have proved
> fruitless due to seeming incompatibility between these modules, particularly
> when one distributes any finished product to another platform.
>
> I am an experienced Java programmer who needs the portability and free
> technologies provided by Java (or Python) to distribute our applications and
> would like to know of the best way to mesh Matplotlib to a GUI creating
> system. We have experimented with the GUI creation possibilities of
> Matplotlib itself but these are inadequate for our needs.
>
> Does anyone know of (or has examples of) Matplotlib applications controlled
> by a GUI or must I return to my Java roots where I can easily solve all GUI
> problems but do not have access to a powerful maths library such as
> Matplotlib.
>
> Thanks
> adam 
> 

[I only seem to be getting this message from the list today, despite the 
timestamp of 2007年12月05日. Sorry for the delay.]
As of matplotlib 0.91.1, savefig() supports saving to a file-like object 
for most backends (excluding Gtk and Wx). You can pass in a StringIO 
object to savefig for instance, and then pass its content to the XML 
parser of your choice. (There is no way to get DOM objects directly, 
since the SVG backend actually writes the XML content directly as a 
stream of bytes).
You can specify SVG as your output by either setting the backend 
appropriately or passing a "format" kwarg to savfig, e.g.
 savefig(fileobj, format="svg")
Cheers,
Mike
bplewe wrote:
> [Fairly new at matplotlib, but very happy with it so far]
> 
> Is it possible to retrieve images rendered by one of the backends as an
> object, rather than just saving to a file? 
> 
> Specifically, I need to render graphs to SVG code, that I can turn into a
> DOM object for further manipulation. I can save to a temp file and
> immediately reload it into a DOM, but that is cumbersome in a single
> program.
> 
> The only place I can see to generate rendered output is savefig(). There is
> a reference in the documentation to using a file-like object with the Cairo
> backend. Is that the only possibility?
> 
> If so, any ideas on a workaround other than temp files?
-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

I think the problem might be related to using numarray and not numpy. 
matplotlib is not heavily tested (if at all) on anything but numpy anymore.
See this from your output log:
D:\Program 
Files\Python-2.5.1\Lib\site-packages\matplotlib\numerix\__init__.py:65:
 DeprecationWarning: numarray use as a numerix backed for matplotlib is
 deprecated
 DeprecationWarning, stacklevel=1)
Indeed, using "float" as a type specifier works with numpy but not with 
numarray.
Try setting your "numerix" setting to "numpy" in your matplotlibrc file.
If there are good reasons that you need to keep using numarray (for 
compatibility with your own code for instance), please mention that on 
this list -- there may be workarounds.
Cheers,
Mike
Thorsten.G wrote:
> Hello i' am an newbie in matplotlib and python!
> I've installed python 2.5.1 + numpy 1.0.4 + numarray 1.5.2 but have many
> problems !
> I've written an easy plot script
> import matplotlib
> import pylab
> from pylab import arange,sin,pi,plot,xlabel,ylabel,title,grid,show,axis
> 
> ymax = max([1,4,9,16,12,5,7,13,7,1,33])
> ymin = min([1,-4,9,16,12,5,7,13,7,1,33])
> print 'ymax = %s' % ymax
> print 'ymin = %s' % ymin
> plot([1,2,3,4,5,6,7,8,9,10,11], [1,4,9,16,12,5,7,13,7,1,33], 'ro')
> axis([0, 15, ymin-5, ymax+5])
> xlabel('time (s)')
> ylabel('voltage (mV)')
> title('About as simple as it gets, folks')
> grid(True)
> show()
> 
> --> AND i get some problems
> 
> D:\Program
> Files\Python-2.5.1\Lib\site-packages\matplotlib\numerix\__init__.py:65:
> DeprecationWarning: numarray use as a numerix backed for matplotlib is
> deprecated
> DeprecationWarning, stacklevel=1)
> Traceback (most recent call last):
> File "D:\workspace-py\GDOParser\PlotTest.py", line 12, in <module>
> plot([1,2,3,4], [1,4,9,16], 'ro')
> File "d:\Program
> Files\Python-2.5.1\Lib\site-packages\matplotlib\pyplot.py", line 1775, in
> plot
> b = ishold()
> File "d:\Program
> Files\Python-2.5.1\Lib\site-packages\matplotlib\pyplot.py", line 340, in
> ishold
> return gca().ishold()
> File "d:\Program
> Files\Python-2.5.1\Lib\site-packages\matplotlib\pyplot.py", line 433, in gca
> ax = gcf().gca(**kwargs)
> File "d:\Program
> Files\Python-2.5.1\Lib\site-packages\matplotlib\figure.py", line 722, in gca
> return self.add_subplot(111, **kwargs)
> File "d:\Program
> Files\Python-2.5.1\Lib\site-packages\matplotlib\figure.py", line 542, in
> add_subplot
> a = Subplot(self, *args, **kwargs)
> File "d:\Program Files\Python-2.5.1\Lib\site-packages\matplotlib\axes.py",
> line 5561, in __init__
> self.figW, self.figH], **kwargs)
> File "d:\Program Files\Python-2.5.1\Lib\site-packages\matplotlib\axes.py",
> line 507, in __init__
> self._init_axis()
> File "d:\Program Files\Python-2.5.1\Lib\site-packages\matplotlib\axes.py",
> line 545, in _init_axis
> self.xaxis = maxis.XAxis(self)
> File "d:\Program Files\Python-2.5.1\Lib\site-packages\matplotlib\axis.py",
> line 518, in __init__
> self.cla()
> File "d:\Program Files\Python-2.5.1\Lib\site-packages\matplotlib\axis.py",
> line 553, in cla
> self.majorTicks.extend([self._get_tick(major=True) for i in range(1)])
> File "d:\Program Files\Python-2.5.1\Lib\site-packages\matplotlib\axis.py",
> line 1033, in _get_tick
> return XTick(self.axes, 0, '', major=major)
> File "d:\Program Files\Python-2.5.1\Lib\site-packages\matplotlib\axis.py",
> line 96, in __init__
> self.tick1line = self._get_tick1line(loc)
> File "d:\Program Files\Python-2.5.1\Lib\site-packages\matplotlib\axis.py",
> line 285, in _get_tick1line
> markersize=self._size,
> File "d:\Program
> Files\Python-2.5.1\Lib\site-packages\matplotlib\lines.py", line 284, in
> __init__
> self.set_data(xdata, ydata)
> File "d:\Program
> Files\Python-2.5.1\Lib\site-packages\matplotlib\lines.py", line 405, in
> set_data
> self.recache()
> File "d:\Program
> Files\Python-2.5.1\Lib\site-packages\matplotlib\lines.py", line 410, in
> recache
> x = ma.asarray(self.convert_xunits(self._xorig), float)
> File "D:\Program Files\Python-2.5.1\Lib\site-packages\numarray\ma\MA.py",
> line 2164, in asarray
> return array(data, typecode=typecode, copy=0)
> File "D:\Program Files\Python-2.5.1\Lib\site-packages\numarray\ma\MA.py",
> line 628, in __init__
> c = Numeric.array(data, tc, savespace=ss)
> File "D:\Program
> Files\Python-2.5.1\Lib\site-packages\numarray\numarraycore.py", line 334, in
> array
> type=_nt._typeFromKeywords(type,typecode,dtype)
> File "d:\Program
> Files\Python-2.5.1\Lib\site-packages\numarray\numerictypes.py", line 474, in
> _typeFromKeywords
> return getType(typecode)
> File "d:\Program
> Files\Python-2.5.1\Lib\site-packages\numarray\numerictypes.py", line 450, in
> getType
> raise TypeError("Not a numeric type")
> TypeError: Not a numeric type
> 
> Has anyone an idea?
> Thanks Thorsten
> 
> 
> 
-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

On Dec 5, 2007, at 11:58 AM, Christopher Barker wrote:
>
> or use wxmpl:
>
> http://agni.phys.iit.edu/~kmcivor/wxmpl/
>
> By the way, couldn't that be distributed with Matplotlib? Maybe in
> toolkits, if not the main distro.
I'd be all for having wxmpl distributed as part of the matplotlib 
toolkits. I had originally hoped to merge it into the main 
distribution after unifying the event handling so that matplotlib 
events worked robustly, but I don't see myself finding the time to 
work on it any time soon.
Ken

On Dec 7, 2007 10:38 AM, adamski246 <a...@go...> wrote:
> Does anyone know of (or has examples of) Matplotlib applications controlled
> by a GUI or must I return to my Java roots where I can easily solve all GUI
> problems but do not have access to a powerful maths library such as
> Matplotlib.
matplotlib can be easily embedded in tk, gtk, wx, qt or fltk. See the
API FAQ at http://matplotlib.sf.net/faq.html#OO and the
embedding_in_*.py examples at http://matplotlib.sf.net/examples

Great. The forthcoming 0.91.2 release sounds like it will be very 
important to Windows users.
Apologies for introducing this bug in the first place!
Cheers,
Mike
Bri...@ub... wrote:
> Michael et al.
> 
> The r4633 patch fixes the problem indeed. Thanks for your help!!
> 
> 
> Brian
> 
> 
> -----Original Message-----
> From: Michael Droettboom [mailto:md...@st...] 
> Sent: Tuesday, December 11, 2007 10:03 AM
> To: Boonstra, Brian
> Cc: mat...@li...
> Subject: Re: [Matplotlib-users] Repeated calls to set_text using TeX
> formatting results in RuntimeError
> 
> The patch for r4633 is pretty simple, so you could test it without
> needing to check out from SVN or build your own matplotlib etc.
> 
> Open the file "font_manager.py", which should live in
> "%PYTHONPATH%/Lib/site-packages/matplotlib". Around line 681, you'll
> find the function:
> 
> def __hash__(self):
> return hash(repr(self.__props))
> 
> Change it to:
> 
> def __hash__(self):
> return hash(repr(self.__props.__dict__))
> 
> (Obviously, back up the file first...)
> 
> Then try your script again. If that doesn't work, I'll have to fire up
> Windows some time to have a look -- I'm not able to reproduce this bug
> on Linux.
> 
> Cheers,
> Mike
> 
> Bri...@ub... wrote:
>>> I believe this is a known bug with 0.90.1. Are you able to run
>> 0.91.1?
>>
>>
>> I just upgraded and checked -- the bug still exists in 0.91.1. I'm 
>> afraid I don't know whether it has been fixed by r4633 or not.
>>
>> Best,
>> Brian
>>
>>
>> Bri...@ub... wrote:
>>> I'm doing a parameter fitting exercise, and plotting the progress as 
>>> I
>>> do so. I have found that repeated calls to set_text() on a text 
>>> object will result in an error opening a font file iff the text uses 
>>> TeX formatting. (I am not using the experimental usetex feature).
>>>
>>> I speculate that matplotlib is opening the font file anew with each 
>>> call to set_text and never closing it, resulting ultimately in having
> 
>>> too many files open. Here is a brief program to reproduce this 
>>> behavior (WinXP, Py2.5, matplotlib 0.90.1):
>>>
>>>
>>> from pylab import figure, axes, draw, ion from numpy import array, 
>>> cos, abs
>>> ion()
>>> fig=figure()
>>> axs=axes()
>>> x=array(range(100))/10.0
>>> cosPlot=axs.plot( x, cos(x)**2, 'r' ) powText = 
>>> axs.text(0.9,0.02,r'$\alpha=$',
>>>
>> horizontalalignment='left',verticalalignment='bottom',
>>> transform = axs.transAxes)
>>> draw()
>>> for alpha in array(range(10,400))/100.0:
>>> axs.lines[-1].set_ydata( abs(cos(x))**alpha)
>>> powText.set_text(r'$\alpha=%.4g$'%alpha)
>>> print alpha
>>> draw()
>>> 
>>>
>>>
>>>
>>>
>>> Traceback (most recent call last):
>>> File "delme.py", line 16, in <module>
>>> File
>>> "C:\Python25\Lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matp
>>> l
>>> otlib\pylab.py",
>>> line 754, in draw
>>> File
>>> "C:\Python25\Lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matp
>>> l
>>> otlib\backends\backend_tkagg.py",
>>> line 154, in draw
>>>
>>> File
>>> "C:\Python25\Lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matp
>>> l
>>> otlib\backends\backend_agg.py",
>>> line 392, in draw
>>>
>>> File
>>> "C:\Python25\Lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matp
>>> l
>>> otlib\figure.py",
>>> line 601, in draw
>>> File
>>> "C:\Python25\Lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matp
>>> l
>>> otlib\axes.py",
>>> line 1286, in draw
>>> File
>>> "C:\Python25\Lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matp
>>> l
>>> otlib\text.py",
>>> line 410, in draw
>>> File
>>> "C:\Python25\Lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matp
>>> l
>>> otlib\text.py",
>>> line 255, in _get_layout
>>> File
>>> "C:\Python25\Lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matp
>>> l
>>> otlib\backends\backend_agg.py",
>>> line 246, in get_text_width_height
>>>
>>> File
>>> "C:\Python25\Lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matp
>>> l
>>> otlib\mathtext.py",
>>> line 1569, in __call__
>>> File
>>> "C:\Python25\Lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matp
>>> l
>>> otlib\mathtext.py",
>>> line 578, in __init__
>>> RuntimeError: Could not open facefile 
>>> c:\Python25\lib\site-packages\matplotlib-0.90.1-py2.5-win32.egg\matpl
>>> o
>>> tlib\mpl-data\fonts\ttf\cmtt10.ttf;
>>> Cannot_Open_Resource
>>>
>>>
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>>>
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>>> ---
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>>>
>>> _______________________________________________
>>> Matplotlib-users mailing list
>>> Mat...@li...
>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>> --
>> Michael Droettboom
>> Science Software Branch
>> Operations and Engineering Division
>> Space Telescope Science Institute
>> Operated by AURA for NASA
>>
>> This message contains confidential information and is intended only 
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>> should not disseminate, distribute or copy this e-mail. Please notify
> 
>> the sender immediately by e-mail if you have received this e-mail by 
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>> information could be intercepted, corrupted, lost, destroyed, arrive 
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>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> 
> --
> Michael Droettboom
> Science Software Branch
> Operations and Engineering Division
> Space Telescope Science Institute
> Operated by AURA for NASA
> 
> This message contains confidential information and is intended only
> for the individual named. If you are not the named addressee you
> should not disseminate, distribute or copy this e-mail. Please
> notify the sender immediately by e-mail if you have received this
> e-mail by mistake and delete this e-mail from your system and destroy 
> any copies thereof.
> 
> E-mail transmission cannot be guaranteed to be secure or error-free
> as information could be intercepted, corrupted, lost, destroyed,
> arrive late or incomplete, or contain viruses. The sender therefore
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Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
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