>> I think this is essential to speed up numpy. Maybe numexpr could handle this
>> in the future? Right now the general use of numexpr is result =
>> numexpr.evaluate("whatever"), so the same problem seems to be there.
>>
>> With this I am not saying that numpy is not worth it, just that for many
>> applications (specially with huge matrices/arrays), pre-allocation does make
>> a huge difference, especially if we want to attract more people to using
>> numpy.
>
> The ufuncs and many scipy functions take a "out" parameter where you
> can specify a pre-allocated array. It can be a little awkward writing
> expressions that way, but the capability is there.
This is a slight digression: is there a way to have a out parameter
like semantics with numexpr. I have always used it as
a[:] = numexpr(expression)
But I dont think numexpr builds the value in place. Is it possible to
have side-effects with numexpr as opposed to obtaining values, for
example
"a= a * b + c"
The documentation is not clear about this. Oh and I do not find the
"out" parameter awkward at all. Its very handy. Furthermore, if I may,
here is a request that the Blitz++ source be updated. Seems like there
is a lot of activity on the Blitz++ repository and weave is very handy
too and can be used as easily as numexpr.
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