/// Constructor
/// @param M :: A matrix to copy.
GSLMatrix::GSLMatrix(const Kernel::Matrix<double> &M) {
m_matrix = gsl_matrix_alloc(M.numRows(), M.numCols());
for (size_t i = 0; i < size1(); ++i)
for (size_t j = 0; j < size2(); ++j) {
set(i, j, M[i][j]);
}
}
std::vector<Kernel::V3D> PeakShapeEllipsoid::getDirectionInSpecificFrame(
Kernel::Matrix<double> &invertedGoniometerMatrix) const {
std::vector<Kernel::V3D> directionsInFrame;
if ((invertedGoniometerMatrix.numCols() != m_directions.size()) ||
(invertedGoniometerMatrix.numRows() != m_directions.size())) {
throw std::invalid_argument("The inverted goniometer matrix is not "
"compatible with the direction vector");
}
for (const auto &direction : m_directions) {
directionsInFrame.push_back(invertedGoniometerMatrix * direction);
}
return directionsInFrame;
}
/**
* Checks the normalization workspace against the indices of the original
* dimensions.
* If not found, the corresponding dimension is integrated
* @param otherDimValues Values from non-HKL dimensions
* @param skipNormalization [InOut] Sets the flag true if normalization values
* are outside of original inputs
* @return Affine trasform matrix
*/
Kernel::Matrix<coord_t> MDNormDirectSC::findIntergratedDimensions(
const std::vector<coord_t> &otherDimValues, bool &skipNormalization) {
// Get indices of the original dimensions in the output workspace,
// and if not found, the corresponding dimension is integrated
Kernel::Matrix<coord_t> affineMat =
m_normWS->getTransformFromOriginal(0)->makeAffineMatrix();
const size_t nrm1 = affineMat.numRows() - 1;
const size_t ncm1 = affineMat.numCols() - 1;
for (size_t row = 0; row < nrm1; row++) // affine matrix, ignore last row
{
const auto dimen = m_normWS->getDimension(row);
const auto dimMin(dimen->getMinimum()), dimMax(dimen->getMaximum());
if (affineMat[row][0] == 1.) {
m_hIntegrated = false;
m_hIdx = row;
m_hmin = std::max(m_hmin, dimMin);
m_hmax = std::min(m_hmax, dimMax);
if (m_hmin > dimMax || m_hmax < dimMin) {
skipNormalization = true;
}
}
if (affineMat[row][1] == 1.) {
m_kIntegrated = false;
m_kIdx = row;
m_kmin = std::max(m_kmin, dimMin);
m_kmax = std::min(m_kmax, dimMax);
if (m_kmin > dimMax || m_kmax < dimMin) {
skipNormalization = true;
}
}
if (affineMat[row][2] == 1.) {
m_lIntegrated = false;
m_lIdx = row;
m_lmin = std::max(m_lmin, dimMin);
m_lmax = std::min(m_lmax, dimMax);
if (m_lmin > dimMax || m_lmax < dimMin) {
skipNormalization = true;
}
}
if (affineMat[row][3] == 1.) {
m_dEIntegrated = false;
m_eIdx = row;
m_dEmin = std::max(m_dEmin, dimMin);
m_dEmax = std::min(m_dEmax, dimMax);
if (m_dEmin > dimMax || m_dEmax < dimMin) {
skipNormalization = true;
}
}
for (size_t col = 4; col < ncm1; col++) // affine matrix, ignore last column
{
if (affineMat[row][col] == 1.) {
double val = otherDimValues.at(col - 3);
if (val > dimMax || val < dimMin) {
skipNormalization = true;
}
}
}
}
return affineMat;
}
/// Constructor
/// @param M :: A matrix to copy.
GSLMatrix::GSLMatrix(const Kernel::Matrix<double> &M)
: m_data(M.getVector()),
m_view(gsl_matrix_view_array(m_data.data(), M.numRows(), M.numCols())) {}
/**
* 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]);
}