# [PYTHON MATRIX-SIG] Slice expressions

**Konrad Hinsen
**
hinsen@ibs.ibs.fr

*Wed, 16 Oct 96 17:52:33 +0100*

>* An ignorant question: does this give the functionality of
*>* a Fortran90 array mask (or logical indexes in Splus)?
*
Probably not, although I am not sure what you are talking about ;-)
>* i.e. is there a NumPy equivalent to this Splus expression, which finds
*>* the warm, dark, sea-surface in a cloudy satellite image for which I
*>* have temperature and reflectivity:
*>*
*>* seaSurfaceIndex -> (temp > 280) & (reflec < 0.10)
*>* meanSST -> mean(temp[seaSurfaceIndex])
*
No, that's something else, which you would do using repeat()
in NumPy:
repeat(temp, logical_and(greater(temp, 280), less(reflec, 0.10)))
would give you all elements of temp corresponding to the selected
condition.
My code for index_expression lets you build up multidimensional index
expressions from pieces. You need this to index arrays whose
dimensionality is unknown when you write the code. An example: I have
written a class that represents a function given numerically on a grid
(with operations such as interpolation, integration, differentiation,
and polynomial fitting), which works for functions of any number of
variables. It must sometimes index along a particular axis given
as a number, i.e.
a[::,::,::, ... ,::, 0]
---n times :: ---
Piecing together index expressions is no problem, as they are just
tuples, but *making* them requires a trick, because notations like ::
are syntactically legal only inside brackets. My index_expression code
just allows you to extract the tuple generated by indexing instead
of applying it directly.
--
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Konrad Hinsen | E-Mail: hinsen@ibs.ibs.fr
Laboratoire de Dynamique Moleculaire | Tel.: +33-76.88.99.28
Institut de Biologie Structurale | Fax: +33-76.88.54.94
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