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 -- View this message in context: http://numpy-discussion.10968.n7.nabble.com/Is-there-a-pure-numpy-recipe-for-this-tp37077p37102.html Sent from the Numpy-discussion mailing list archive at Nabble.com. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion