Thanks for the suggestion. It hadn't occurred to me to try to override numpy as you suggest. However, when I try the code shown below as the start of a test of this scheme, I get the following error: Traceback (most recent call last): File "C:\Documents and Settings\Bruce\My Documents\0VPythonWork\vectors.py", line 24, in <module> numpy.float64.__mul__ = new_mul TypeError: can't set attributes of built-in/extension type 'numpy.float64' I'm copying this to the numpy discussion list, as maybe someone there will see where to go starting from your suggestion. Bruce Sherwood --------------- import numpy class vector(object): def __init__(self,x,y,z): self.data = [x,y,z] def __mul__(self,other): return vector(other*self.data[0], other*self.data[1], other*self.data[2]) __rmul__ = __mul__ def show(self): print self.data old_mul = numpy.float64.__mul__ def new_mul( self, other ): if isinstance( other, vector ): return other*self else: return old_mul( self, other ) numpy.float64.__mul__ = new_mul a = vector(1,2,3) a.show() b = 5*a b.show() c = a*7 c.show() Roman Yakovenko wrote:

On Dec 26, 2007 8:11 AM, Bruce Sherwood <Bruce_Sherwood@ncsu.edu> wrote:

Sorry to repeat myself and be insistent, but could someone please at least comment on whether I'm doing anything obviously wrong, even if you don't immediately have a solution to my serious problem? There was no response to my question (see copy below) which I sent to both the numpy and Boost mailing lists.

To the Boost experts: Is there something wrong, or something I could/should change in how I'm trying to define to Boost the overloaded multiplication of a numpy square root (or other numpy function) times my own "vector" object? I'm seeing a huge performance hit in going from Numeric to numpy because Numeric sqrt returned float whereas numpy sqrt returns numpy.float64, so that the result is not one of my vector objects. I don't have a problem with myvector*sqrt(5.5). Here is what I currently am doing:

py::class_<vector>("vector", py::init< py::optional<double, double, double> >()) .def( self * double()) .def( double() * self)

Desperately,

If I understand you right, than you can do something like this:

replace __mul__ method of numpy.float64 class:

old_mul = numpy.float64.__mul__

def new_mul( self, other ): if other isinstance( vector ): return other*self else: return old_mul( self, other )

numpy.float64.__mul__ = new_mul

HTH