///////////////////////////////////////////////////////////////////////////
//
// Copyright (c) 1998-2011, Industrial Light & Magic, a division of Lucas
// Digital Ltd. LLC
//
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
// * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above
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// in the documentation and/or other materials provided with the
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///////////////////////////////////////////////////////////////////////////
#ifndef _PyImathMatrix_h_
#define _PyImathMatrix_h_
#include <Python.h>
#include <boost/python.hpp>
#include <PyImath.h>
#include <ImathMatrix.h>
#include <ImathMatrixAlgo.h>
#include <PyImath.h>
namespace PyImath {
template <class T> boost::python::class_<IMATH_NAMESPACE::Matrix33<T> > register_Matrix33();
template <class T> boost::python::class_<IMATH_NAMESPACE::Matrix44<T> > register_Matrix44();
template <class T> boost::python::class_<FixedArray<IMATH_NAMESPACE::Matrix44<T> > > register_M44Array();
template <class T> boost::python::class_<FixedArray<IMATH_NAMESPACE::Matrix33<T> > > register_M33Array();
typedef FixedArray<IMATH_NAMESPACE::Matrix33<float> > M33fArray;
typedef FixedArray<IMATH_NAMESPACE::Matrix33<double> > M33dArray;
typedef FixedArray<IMATH_NAMESPACE::Matrix44<float> > M44fArray;
typedef FixedArray<IMATH_NAMESPACE::Matrix44<double> > M44dArray;
//
// Other code in the Zeno code base assumes the existance of a class with the
// same name as the Imath class, and with static functions wrap() and
// convert() to produce a PyImath object from an Imath object and vice-versa,
// respectively. The class Boost generates from the Imath class does not
// have these properties, so we define a companion class here.
// The template argument, T, is the element type (e.g.,float, double).
template <class T>
class M33 {
public:
static PyObject * wrap (const IMATH_NAMESPACE::Matrix33<T> &m);
static int convert (PyObject *p, IMATH_NAMESPACE::Matrix33<T> *m);
};
template <class T>
class M44 {
public:
static PyObject * wrap (const IMATH_NAMESPACE::Matrix44<T> &m);
static int convert (PyObject *p, IMATH_NAMESPACE::Matrix44<T> *m);
};
template <class T>
PyObject *
M33<T>::wrap (const IMATH_NAMESPACE::Matrix33<T> &m)
{
typename boost::python::return_by_value::apply < IMATH_NAMESPACE::Matrix33<T> >::type converter;
PyObject *p = converter (m);
return p;
}
template <class T>
PyObject *
M44<T>::wrap (const IMATH_NAMESPACE::Matrix44<T> &m)
{
typename boost::python::return_by_value::apply < IMATH_NAMESPACE::Matrix44<T> >::type converter;
PyObject *p = converter (m);
return p;
}
template <class T>
int
M33<T>::convert (PyObject *p, IMATH_NAMESPACE::Matrix33<T> *m)
{
boost::python::extract <IMATH_NAMESPACE::M33f> extractorMf (p);
if (extractorMf.check())
{
IMATH_NAMESPACE::M33f e = extractorMf();
m->setValue (e);
return 1;
}
boost::python::extract <IMATH_NAMESPACE::M33d> extractorMd (p);
if (extractorMd.check())
{
IMATH_NAMESPACE::M33d e = extractorMd();
m->setValue (e);
return 1;
}
return 0;
}
template <class T>
int
M44<T>::convert (PyObject *p, IMATH_NAMESPACE::Matrix44<T> *m)
{
boost::python::extract <IMATH_NAMESPACE::M44f> extractorMf (p);
if (extractorMf.check())
{
IMATH_NAMESPACE::M44f e = extractorMf();
m->setValue (e);
return 1;
}
boost::python::extract <IMATH_NAMESPACE::M44d> extractorMd (p);
if (extractorMd.check())
{
IMATH_NAMESPACE::M44d e = extractorMd();
m->setValue (e);
return 1;
}
return 0;
}
template <class Matrix>
boost::python::tuple
jacobiEigensolve(const Matrix& m)
{
typedef typename Matrix::BaseType T;
typedef typename Matrix::BaseVecType Vec;
// For the C++ version, we just assume that the passed-in matrix is
// symmetric, but we assume that many of our script users are less
// sophisticated and might get tripped up by this. Also, the cost
// of doing this check is likely miniscule compared to the Pythonic
// overhead.
// Give a fairly generous tolerance to account for possible epsilon drift:
const int d = Matrix::dimensions();
const T tol = std::sqrt(IMATH_NAMESPACE::limits<T>::epsilon());
for (int i = 0; i < d; ++i)
{
for (int j = i+1; j < d; ++j)
{
const T Aij = m[i][j],
Aji = m[j][i];
ASSERT (std::abs(Aij - Aji) < tol,
IEX_NAMESPACE::ArgExc,
"Symmetric eigensolve requires a symmetric matrix (matrix[i][j] == matrix[j][i]).");
}
}
Matrix tmp = m;
Matrix Q;
Vec S;
IMATH_NAMESPACE::jacobiEigenSolver (tmp, S, Q);
return boost::python::make_tuple (Q, S);
}
typedef M33<float> M33f;
typedef M33<double> M33d;
typedef M44<float> M44f;
typedef M44<double> M44d;
}
#endif