I am working on developing algorithms that are usually called with parameters that are Numeric arrays. We have the usual challenge though of trying to craft code that will gracefully accept both floats or arrays. Because of the often discussed problem with the handling of zero length arrays we expend some effort to ensure that we don't make calls on floats that only work on arrays and have ended up with a bunch of code like safe_len() that can be called with either. Today we have a related problem but now it is with performance (see code below). Numeric is faster than NumArray operating on smaller arrays but it is still relatively slow handling regular floats. We could add to the safe_ suite of functions the fast_ series but this still entails a significant performance hit and is not exactly elegant. The problem is larger than just handling sqrt so I would appreciate any feedback or suggestions on how best to proceed. Thanks, Duncan ================================================= def safe_len(a): # Return the length of the input array or 1 if it is a scalar try: safelen = len(a) except: safelen = 1 return safelen ================================================= from scipy import arange, sqrt from math import sqrt as csqrt import time # this is slower ... start_time = time.clock() for i in range(1000): a = sqrt(i) t1 = time.clock() - start_time # this is faster ... start_time = time.clock() for i in range(1000): a = csqrt(i) t2 = time.clock() - start_time print t1, t2, t1 / t2 C:\sqrt.py 0.0537227007132 0.00181048033684 68.6731754663 ================================================= from scipy import sqrt from math import sqrt as csqrt import types def fast_sqrt(arg): if type(arg) == types.FloatType: return csqrt(arg) else: return sqrt(arg)