Nice work, Halldor! I've spent a bit of time on data
> interpolation recently, but this Stineman interpolation
> method beats everything I came up with in quality and
> simplicity.
Wow, you two make a potent team!
Norbert, I took your revision and made some minor modifications to
support numeric and numarray and added it to matplotlib.mlab. It was
a crime to take your nice code utilizing numpy/numarray array indexing
and backport it to the Numeric.take ugly equivalents, but that's the
price we pay for supporting Numeric.
See examples/interp_demo.py in svn revision 2244
JDH
I just wanted to start this reply with the words "Very nice!", when I
realized that "nice" probably is the last thing one would say about the
"uglyfication" necessary for numerix compatibility. 
So lets instead say: "Good work!" and thank you for taking in the code.
Greetings,
Norbert
John Hunter wrote:
···
> Nice work, Halldor! I've spent a bit of time on data
> interpolation recently, but this Stineman interpolation
> method beats everything I came up with in quality and
> simplicity.
Wow, you two make a potent team!
Norbert, I took your revision and made some minor modifications to
support numeric and numarray and added it to matplotlib.mlab. It was
a crime to take your nice code utilizing numpy/numarray array indexing
and backport it to the Numeric.take ugly equivalents, but that's the
price we pay for supporting Numeric.
See examples/interp_demo.py in svn revision 2244
JDH
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