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 numpy experts: Is there something wrong, or something I could/should change in how I'm trying to overload 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).

Desperately,

Bruce Sherwood

------------------- I'm not sure whether this is a Numpy problem or a Boost problem, so I'm posting to both communities. (I'm subscribed to both lists, but an attempt to post yesterday to this Boost list seems never have gotten to the archives, so I'm trying again. My apologies if this shows up twice here.)

In old Numeric, type(sqrt(5.5)) was float, but in numpy, type(sqrt(5.5)) is numpy.float64. This leads to a big performance hit in calculations in a beta version of VPython, using the VPython 3D "vector" class, compared with the old version that used Numeric (VPython is a 3D graphics module for Python; see vpython.org).

Operator overloading of the VPython vector class works fine for vector*sqrt(5.5) but not for sqrt(5.5)*vector. The following free function catches 5.5*vector but fails to catch sqrt(5.5)*vector, whose type ends up as numpy.ndarray instead of the desired vector, with concomitant slow conversions in later vector calculations:

inline vector operator*( const double& s, const vector& v) { return vector( s*v.x, s*v.y, s*v.z); }

I've thrashed around on this, including trying to add this:

inline vector operator*( const npy_float64& s, const vector& v) { return vector( s*v.x, s*v.y, s*v.z); }

But the compiler correctly complains that this is in conflict with the version of double*vector, since in fact npy_float64 is actually double.

It's interesting and presumably meaningful to the knowledgeable (not me) that vector*sqrt(5.5) yields a vector, even though the overloading speaks of double, not a specifically numpy name:

inline vector operator*( const double s) const throw() { return vector( s*x, s*y, s*z); }

VPython uses Boost, and the glue concerning vectors includes the following:

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

As far as I can understand from the Boost Python documentation, this is the proper way to specify the left-hand and right-hand overloadings. But do I have to add something like .def( npy_float64() * self)? Help would be much appreciated.

Bruce Sherwood