Slaunger wrote:
It is always good to ask yourself a question. I had forgooten about the reduce functionI guess this implementation from numpy import * def compl_add_uint16(a, b): c = a + b c += c >> 16 return c & 0xFFFF def compl_one_checksum(uint16s): return reduce(compl_add_uint16, uint16s, 0x0000) is somewhat better? But is it the best way to do it with numpy?
It's not too bad, if you only have 1D arrays to worry about (or you are only concerned with reducing down the first axis). With a Python-implemented function, there isn't much that will get you faster.
My coworker Ilan Schnell came up with a neat way to use PyPy's RPython->C translation scheme and scipy.weave's ad-hoc extension module-building capabilities to generate new numpy ufuncs (which have a .reduce() method) implemented in pure RPython.
http://conference.scipy.org/proceedings/SciPy2008/paper_16/full_text.pdf http://svn.scipy.org/svn/scipy/branches/fast_vectorize/ If you have more numpy questions, please join us on the numpy mailing list. http://www.scipy.org/Mailing_Lists -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
