I found by timing measurements that a faster scheme with less penalty for the case of sqrt(array) looks like this: def sqrt(x): if type(x) is float: return mathsqrt(x) return numpysqrt(x) Bruce Sherwood wrote:
Roman Yakovenko wrote:
On Dec 29, 2007 7:47 AM, Bruce Sherwood
wrote: I realized belatedly that I should upgrade from Boost 1.33 to 1.34. Alas, that didn't cure my problem.
Can you post small and complete example of what you are trying to achieve?
I don't have a "small and complete" example available, but I'll summarize from earlier posts. VPython (vpython.org) has its own vector class to mimic the properties of 3D vectors used in physics, in the service of easy creation of 3D animations. There is a beta version which imports numpy and uses it internally; the older production version uses Numeric. Boost python and thread libraries are used to connect the C++ VPython code to Python.
There is operator overloading that includes scalar*vector and vector*scalar, both producing vector. With Numeric, sqrt produced a float, which was a scalar for the operator overloading. With numpy, sqrt produces a numpy.float64 which is caught by vector*scalar but not by scalar*vector, which means that scalar*vector produces an ndarray rather than a vector, which leads to a big performance hit in existing VPython programs. The overloading and Boost code is the same in the VPython/Numeric and VPython/numpy versions. I don't know whether the problem is with numpy or with Boost or with the combination of the two.
Here is the relevant part of the vector class:
inline vector operator*( const double s) const throw() { return vector( s*x, s*y, s*z); }
and here is the free function for right multiplication:
inline vector operator*( const double& s, const vector& v) { return vector( s*v.x, s*v.y, s*v.z); }
Maybe the problem is in the Boost definitions:
py::class_<vector>("vector", py::init< py::optional
>()) .def( self * double()) .def( double() * self) Left multiplication is fine, but right multiplication isn't.
A colleague suggested the following Boost declarations but cautioned that he wasn't sure of the syntax for referring to operator, and indeed this doesn't compile:
.def( "__mul__", &vector::operator*(double), "Multiply vector times scalar") .def( "__rmul__", &operator*(const double&, const vector&), "Multiply scalar times vector")
I would really appreciate a Boost or numpy expert being able to tell me what's wrong (if anything) with these forms. However, I may have a useful workaround as I described in a post to the numpy discussion list. A colleague suggested that I do something like this for sqrt and other such mathematical functions:
def sqrt(x): try: return mathsqrt(x) except TypeError: return numpysqrt(x)
That is, first try the simple case of a scalar argument, handled by the math module sqrt, and only use the numpy sqrt routine in the case of an array argument. Even with the overhead of the try/except machinery, one gets must faster square roots for scalar arguments this way than with the numpy sqrt.
Bruce Sherwood