Thanks for the suggestion Robert. If we ignore the elegance requirement then handling scalars as a special case in pure Python is an adequate approach. However, I am hoping that Numeric3 will allow us to write more elegant functions that perform well on both scalars and arrays. Regards, Duncan Robert Kern wrote:
Duncan Child wrote:
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.
A potential solution to the performance problem, though not the elegance problem, might be Pyrex.
An untested sketch:
cdef extern from "Numeric/arrayobject.h": bool PyArray_Check(object x)
cdef extern from "math.h": double sqrt(double x)
import Numeric
def fast_sqrt(object x): if PyArray_Check(x): return Numeric.sqrt(x) else: return sqrt(x)