Re: [matplotlib-devel] numeric / numarray

[Todd M. <jm...@st...>]

On Sat, 2004-03-27 at 16:22, John Hunter wrote:
> I just updated the image module to work with Numeric or numarray (now
> that matplotlib is the recommended 2D plotting package on the numarray
> homepage!).  Changes in CVS.
> 
> The code uses the Numeric compatibility layer of numarray, but I am
> unsure how to best handle the decision of which one to build with.
> Whichever arrayobject.h you include (Numeric or numarray), the code
> works with either kind of array, so that is not the issue.  But there
> will be those who want to build with numarray and vice-versa, so my
> question is how to best support this.
> 
> Here is what I am currently doing in setup.py:
> 
>     # build the image support module - requires agg and Numeric or
>     # numarray.  You can build the image module with either Numeric or
>     # numarray.  Whichever way you build it, the code will work with
>     # both Numeric or numarray arrays, but you will get the best
>     # performance if you build with the type of array you use most
>     #BUILD_IMAGE = 0            # no image support
>     #BUILD_IMAGE = 'Numeric'    # use Numeric 
>     BUILD_IMAGE  = 'numarray'   # use numarray 
> 
> and then setting a precompiler macro in setupext.py which determines
> which header is used.
> 
> Another possibility is to look for a matplotlibrc file at build time
> and build according to the numerix setting.  I think this is a nice
> solution but adds an extra later of complexity.
>         
> The additional (and perhaps most important) question is how to handle
> the win32 build vis-a-vis Numeric/numarray?
> 
> I ran some very rough profiling numbers.  image_demo.py and
> image_demo_na.py are identical except the latter defines
> rcParams['numerix'] = 'numarray'.  
> 
>     # build with Numeric
>     hunter:> time python image_demo.py -dAgg
>     0.460u 0.070s 0:00.61 86.8%     0+0k 0+0io 5040pf+0w
>     hunter:> time python image_demo_na.py -dAgg  
>     2.550u 0.080s 0:02.79 94.2%     0+0k 0+0io 5211pf+0w
> 
>     # build with numarray
>     hunter:> time python image_demo.py -dAgg
>     0.560u 0.140s 0:00.78 89.7%     0+0k 0+0io 5226pf+0w
>     hunter:> time python image_demo_na.py -dAgg  
>     0.540u 0.120s 0:00.72 91.6%     0+0k 0+0io 5197pf+0w
> 
> The basic result is that numarray users pay a heavy performance price
> if the image module is built with Numeric, but if built with numarray
> both types of arrays perform comparably (and are performance
> competitive with the pure numeric case)

I'll try to find out why there's a difference here.

> 
> Finally, if any of you stsci folks on the list have a nice color image
> of the heavens you could produce with imshow, that would make a
> stellar screenshot for the website :-)

Perry probably has ideas about more appropriate images for this
application,  but here is one of the coolest images I know of. (check it
out even if you don't want a jpeg):

http://imgsrc.hubblesite.org/hu/db/2004/07/images/m/formats/full_jpg.jpg

I believe the deepest optical image ever taken,  The Deepest.  It's
called the Hubble Ultra Deep Field (HUDF).  Almost everything in the
picture are galaxies.  The picture itself represents a very small region
of sky.  Each little spec is billions of suns. 

Regards,
Todd
> 
> JDH
> 
> 
> 
> 
> 
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-- 
Todd Miller <jm...@st...>