[Numpy-discussion] Is there a pure numpy recipe for this?

[Slaunger]

Chris Barker - NOAA Federal wrote
> note that  numpy arrays are not re-sizable, so np.append() and np.insert()
> have to make a new array, and copy all the old data over. If you are
> appending one at a time, this can be pretty darn slow.
> 
> I wrote a "grow_array" class once, it was a wrapper around a numpy array
> that pre-allocated extra data to make appending more efficient. It's kind
> of half-baked code now, but let me know if you are interested.

Hi Chris,

Yes, it is a good point and I am aware of it. For some of these functions it
would have been nice if i could have parsed a preallocated, properly sliced
array to the functions, which i could then reuse in each iteration step. 

It is indeed the memory allocation which appear to take more time than the
actual calculations. 

Still it is much faster to create a few arrays than to loop through a
thousand individual elements in pure Python.

Interesting with the grow_array class. I think that what I have for now is
sufficient, but i will keep your offer in mind:)

--Slaunger



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