/** Set goniometer to matrix workspace and get its rotation matrix R (from
* Q-sample to Q-lab
* and output 1/R
* @brief ConvertCWSDExpToMomentum::setupTransferMatrix
* @param dataws :: matrix workspace containing sample rotation angles
* @param rotationMatrix :: output as matrix 1/R to convert from Q-lab to
* Q-sample
*/
void ConvertCWSDExpToMomentum::setupTransferMatrix(
API::MatrixWorkspace_sptr dataws, Kernel::DblMatrix &rotationMatrix) {
// Check sample logs
if (!dataws->run().hasProperty("_omega") ||
!dataws->run().hasProperty("_chi") || !dataws->run().hasProperty("_phi"))
throw std::runtime_error(
"Data workspace does not have sample log _phi, _chi or _omega. "
"Unable to set goniometer and calcualte roation matrix R.");
// Call algorithm SetGoniometer
IAlgorithm_sptr setalg = createChildAlgorithm("SetGoniometer");
setalg->initialize();
setalg->setProperty("Workspace", dataws);
setalg->setProperty("Axis0", "_omega,0,1,0,-1");
setalg->setProperty("Axis1", "_chi,0,0,1,-1");
setalg->setProperty("Axis2", "_phi,0,1,0,-1");
setalg->execute();
if (setalg->isExecuted()) {
rotationMatrix = dataws->run().getGoniometer().getR();
g_log.debug() << "Ratation matrix: " << rotationMatrix.str() << "\n";
rotationMatrix.Invert();
g_log.debug() << "Ratation matrix: " << rotationMatrix.str() << "\n";
} else
throw std::runtime_error("Unable to set Goniometer.");
return;
}
/**
* Fills in a column for each active hermite polynomial, starting at the given
* index
* @param cmatrix InOut matrix whose columns should be set to the mass profile
* for each active hermite polynomial
* @param start Index of the column to start on
* @param errors Data errors array
* @returns The number of columns filled
*/
size_t GramCharlierComptonProfile::fillConstraintMatrix(
Kernel::DblMatrix &cmatrix, const size_t start,
const std::vector<double> &errors) const {
std::vector<double> profile(NFINE_Y, 0.0);
const size_t nData(ySpace().size());
std::vector<double> result(nData, 0.0);
// If the FSE term is fixed by user then it's contribution is convoluted with
// Voigt and summed with the first column
// otherwise it gets a column of it's own at the end.
// Either way it needs to be computed, so do this first
std::vector<double> fse(NFINE_Y, 0.0);
std::vector<double> convolvedFSE(nData, 0.0);
addFSETerm(fse);
convoluteVoigt(convolvedFSE.data(), nData, fse);
size_t col(0);
for (unsigned int i = 0; i < m_hermite.size(); ++i) {
if (m_hermite[i] == 0)
continue;
const unsigned int npoly = 2 * i;
addMassProfile(profile.data(), npoly);
convoluteVoigt(result.data(), nData, profile);
if (i == 0 && m_userFixedFSE) {
std::transform(result.begin(), result.end(), convolvedFSE.begin(),
result.begin(), std::plus<double>());
}
std::transform(result.begin(), result.end(), errors.begin(), result.begin(),
std::divides<double>());
cmatrix.setColumn(start + col, result);
std::fill_n(profile.begin(), NFINE_Y, 0.0);
std::fill_n(result.begin(), nData, 0.0);
++col;
}
if (!m_userFixedFSE) // Extra column for He3
{
std::transform(convolvedFSE.begin(), convolvedFSE.end(), errors.begin(),
convolvedFSE.begin(), std::divides<double>());
cmatrix.setColumn(start + col, convolvedFSE);
++col;
}
return col;
}
/**
* This function takes a square matrix and reduces its dimensionality by
* one.
*
* @param mat : The matrix to strip dimensionality
*/
void vtkDataSetToNonOrthogonalDataSet::stripMatrix(Kernel::DblMatrix &mat) {
std::size_t dim = mat.Ssize() - 1;
Kernel::DblMatrix temp(dim, dim);
for (std::size_t i = 0; i < dim; i++) {
for (std::size_t j = 0; j < dim; j++) {
temp[i][j] = mat[i][j];
}
}
mat = temp;
}
/**
* Fills in a column of the matrix with this mass profile, starting at the given
* index
* @param cmatrix InOut matrix whose column should be set to the mass profile
* for each active hermite polynomial
* @param start Index of the column to start on
* @param errors The data errors
* @returns The number of columns filled
*/
size_t MultivariateGaussianComptonProfile::fillConstraintMatrix(
Kernel::DblMatrix &cmatrix, const size_t start,
const std::vector<double> &errors) const {
std::vector<double> result(ySpace().size());
this->massProfile(result.data(), ySpace().size());
std::transform(result.begin(), result.end(), errors.begin(), result.begin(),
std::divides<double>());
cmatrix.setColumn(start, result);
return 1;
}
/**
* This function will create the skew matrix and basis for a non-orthogonal
* representation.
*
* @param ol : The oriented lattice containing B matrix and crystal basis
*vectors
* @param w : The tranform requested when MDworkspace was created
* @param aff : The affine matrix taking care of coordinate transformations
*/
void vtkDataSetToNonOrthogonalDataSet::createSkewInformation(
Geometry::OrientedLattice &ol, Kernel::DblMatrix &w,
Kernel::Matrix<coord_t> &aff) {
// Get the B matrix
Kernel::DblMatrix bMat = ol.getB();
// Apply the W tranform matrix
bMat *= w;
// Create G*
Kernel::DblMatrix gStar = bMat.Tprime() * bMat;
Geometry::UnitCell uc(ol);
uc.recalculateFromGstar(gStar);
m_skewMat = uc.getB();
// Calculate the column normalisation
std::vector<double> bNorm;
for (std::size_t i = 0; i < m_skewMat.numCols(); i++) {
double sum = 0.0;
for (std::size_t j = 0; j < m_skewMat.numRows(); j++) {
sum += m_skewMat[j][i] * m_skewMat[j][i];
}
bNorm.push_back(std::sqrt(sum));
}
// Apply column normalisation to skew matrix
Kernel::DblMatrix scaleMat(3, 3, true);
scaleMat[0][0] /= bNorm[0];
scaleMat[1][1] /= bNorm[1];
scaleMat[2][2] /= bNorm[2];
m_skewMat *= scaleMat;
// Setup basis normalisation array
// Intel and MSBuild can't handle this
// m_basisNorm = {ol.astar(), ol.bstar(), ol.cstar()};
m_basisNorm.push_back(ol.astar());
m_basisNorm.push_back(ol.bstar());
m_basisNorm.push_back(ol.cstar());
// Expand matrix to 4 dimensions if necessary
if (4 == m_numDims) {
m_basisNorm.push_back(1.0);
Kernel::DblMatrix temp(4, 4, true);
for (std::size_t i = 0; i < 3; i++) {
for (std::size_t j = 0; j < 3; j++) {
temp[i][j] = m_skewMat[i][j];
}
}
m_skewMat = temp;
}
// Convert affine matrix to similar type as others
Kernel::DblMatrix affMat(aff.numRows(), aff.numCols());
for (std::size_t i = 0; i < aff.numRows(); i++) {
for (std::size_t j = 0; j < aff.numCols(); j++) {
affMat[i][j] = aff[i][j];
}
}
// Strip affine matrix down to correct dimensions
this->stripMatrix(affMat);
// Perform similarity transform to get coordinate orientation correct
m_skewMat = affMat.Tprime() * (m_skewMat * affMat);
m_basisNorm = affMat * m_basisNorm;
if (4 == m_numDims) {
this->stripMatrix(m_skewMat);
}
this->findSkewBasis(m_basisX, m_basisNorm[0]);
this->findSkewBasis(m_basisY, m_basisNorm[1]);
this->findSkewBasis(m_basisZ, m_basisNorm[2]);
}
