// Mantid Repository : https://github.com/mantidproject/mantid
//
// Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
// NScD Oak Ridge National Laboratory, European Spallation Source
// & Institut Laue - Langevin
// SPDX - License - Identifier: GPL - 3.0 +
#include "MantidMDAlgorithms/MDNormalization.h"
#include "MantidAPI/IMDEventWorkspace.h"
#include "MantidDataObjects/MDHistoWorkspace.h"
#include "MantidAPI/Run.h"
#include "MantidGeometry/Crystal/OrientedLattice.h"
#include "MantidGeometry/MDGeometry/QSample.h"
#include "MantidGeometry/MDGeometry/HKL.h"
#include "MantidGeometry/MDGeometry/MDFrameFactory.h"
#include "MantidKernel/ArrayLengthValidator.h"
#include "MantidKernel/ArrayProperty.h"
#include "MantidKernel/VisibleWhenProperty.h"
#include "MantidKernel/Strings.h"
#include "MantidKernel/CompositeValidator.h"
#include "MantidAPI/CommonBinsValidator.h"
#include "MantidAPI/InstrumentValidator.h"
#include "MantidAPI/Sample.h"
#include "MantidGeometry/Crystal/OrientedLattice.h"
#include "MantidGeometry/Crystal/SymmetryOperationFactory.h"
#include "MantidGeometry/Crystal/SpaceGroupFactory.h"
#include "MantidGeometry/Crystal/PointGroupFactory.h"
#include <boost/lexical_cast.hpp>
#include "MantidKernel/Exception.h"
namespace Mantid {
namespace MDAlgorithms {
using namespace Mantid::Kernel;
using namespace Mantid::API;
using namespace Mantid::Geometry;
using namespace Mantid::DataObjects;
static bool abs_compare(int a, int b) {
return (std::abs(a) < std::abs(b));
}
// Register the algorithm into the AlgorithmFactory
DECLARE_ALGORITHM(MDNormalization)
//----------------------------------------------------------------------------------------------
/**
* Constructor
*/
MDNormalization::MDNormalization()
:m_normWS(), m_inputWS(), m_isRLU(false), m_UB(3,3), m_W(3,3,true), m_transformation(),
m_numExptInfos(0), m_diffraction(true), m_accumulate(false), m_dEIntegrated(false) {}
/// Algorithms name for identification. @see Algorithm::name
const std::string MDNormalization::name() const { return "MDNormalization"; }
/// Algorithm's version for identification. @see Algorithm::version
int MDNormalization::version() const { return 1; }
/// Algorithm's category for identification. @see Algorithm::category
const std::string MDNormalization::category() const {
return "MDAlgorithms\\Normalisation";
}
/// Algorithm's summary for use in the GUI and help. @see Algorithm::summary
const std::string MDNormalization::summary() const {
return "Bins multidimensional data and calculate the normalization on the same grid";
}
//----------------------------------------------------------------------------------------------
/** Initialize the algorithm's properties.
*/
void MDNormalization::init() {
declareProperty(make_unique<WorkspaceProperty<API::IMDEventWorkspace>>(
"InputWorkspace", "", Kernel::Direction::Input),
"An input MDEventWorkspace. Must be in Q_sample frame.");
// RLU and settings
declareProperty("RLU", true, "Use reciprocal lattice units. If false, use Q_sample");
setPropertyGroup("RLU","Q projections RLU");
auto mustBe3D = boost::make_shared<Kernel::ArrayLengthValidator<double> >(3);
std::vector<double> Q1(3, 0.), Q2(3, 0), Q3(3, 0);
Q1[0] = 1.;
Q2[1] = 1.;
Q3[2] = 1.;
declareProperty(make_unique<ArrayProperty<double>>("QDimension1", Q1, mustBe3D),
"The first Q projection axis - Default is (1,0,0)");
setPropertySettings("QDimension1", make_unique<Kernel::VisibleWhenProperty>("RLU", IS_EQUAL_TO, "1"));
setPropertyGroup("QDimension1","Q projections RLU");
declareProperty(make_unique<ArrayProperty<double>>("QDimension2", Q2, mustBe3D),
"The second Q projection axis - Default is (0,1,0)");
setPropertySettings("QDimension2", make_unique<Kernel::VisibleWhenProperty>("RLU", IS_EQUAL_TO, "1"));
setPropertyGroup("QDimension2","Q projections RLU");
declareProperty(make_unique<ArrayProperty<double>>("QDimension3", Q3, mustBe3D),
"The thirdtCalculateCover Q projection axis - Default is (0,0,1)");
setPropertySettings("QDimension3", make_unique<Kernel::VisibleWhenProperty>("RLU", IS_EQUAL_TO, "1"));
setPropertyGroup("QDimension3","Q projections RLU");
// vanadium
auto fluxValidator = boost::make_shared<CompositeValidator>();
fluxValidator->add<InstrumentValidator>();
fluxValidator->add<CommonBinsValidator>();
auto solidAngleValidator = fluxValidator->clone();
declareProperty(make_unique<WorkspaceProperty<>>("SolidAngleWorkspace", "",
Direction::Input, API::PropertyMode::Optional,
solidAngleValidator),
"An input workspace containing integrated vanadium "
"(a measure of the solid angle).\n"
"Mandatory for diffraction, optional for direct geometry inelastic");
declareProperty(make_unique<WorkspaceProperty<>>(
"FluxWorkspace", "", Direction::Input, API::PropertyMode::Optional,
fluxValidator),
"An input workspace containing momentum dependent flux.\n"
"Mandatory for diffraction. No effect on direct geometry inelastic");
setPropertyGroup("SolidAngleWorkspace","Vanadium normalization");
setPropertyGroup("FluxWorkspace","Vanadium normalization");
// Define slicing
for(std::size_t i=0;i<6;i++) {
std::string propName = "Dimension"+Strings::toString(i)+"Name";
std::string propBinning = "Dimension"+Strings::toString(i)+"Binning";
declareProperty(Kernel::make_unique<PropertyWithValue<std::string>>(propName, "", Direction::Input),
"Name for the " + Strings::toString(i) + "th dimension. Leave blank for NONE.");
auto atMost3 = boost::make_shared<ArrayLengthValidator<double> >(0,3);
std::vector<double> temp;
declareProperty(Kernel::make_unique<ArrayProperty<double>>(propBinning,temp,atMost3),
"Binning for the " + Strings::toString(i) + "th dimension.\n"+
"- Leave blank for complete integration\n"+
"- One value is interpreted as step\n"
"- Two values are interpreted integration interval\n"+
"- Three values are interpreted as min, step, max");
setPropertyGroup(propName, "Binning");
setPropertyGroup(propBinning, "Binning");
}
// symmetry operations
declareProperty(Kernel::make_unique<PropertyWithValue<std::string>>("SymmetryOperations", "", Direction::Input),
"If specified the symmetry will be applied, "
"can be space group name, point group name, or list individual symmetries.");
// temporary workspaces
declareProperty(make_unique<WorkspaceProperty<IMDHistoWorkspace>>(
"TemporaryDataWorkspace", "", Direction::Input,
PropertyMode::Optional),
"An input MDHistoWorkspace used to accumulate data from "
"multiple MDEventWorkspaces. If unspecified a blank "
"MDHistoWorkspace will be created.");
declareProperty(make_unique<WorkspaceProperty<IMDHistoWorkspace>>(
"TemporaryNormalizationWorkspace", "", Direction::Input,
PropertyMode::Optional),
"An input MDHistoWorkspace used to accumulate normalization "
"from multiple MDEventWorkspaces. If unspecified a blank "
"MDHistoWorkspace will be created.");
setPropertyGroup("TemporaryDataWorkspace", "Temporary workspaces");
setPropertyGroup("TemporaryNormalizationWorkspace", "Temporary workspaces");
declareProperty(make_unique<WorkspaceProperty<API::Workspace>>(
"OutputWorkspace", "", Kernel::Direction::Output),
"A name for the output data MDHistoWorkspace.");
declareProperty(make_unique<WorkspaceProperty<Workspace>>(
"OutputNormalizationWorkspace", "", Direction::Output),
"A name for the output normalization MDHistoWorkspace.");
}
//----------------------------------------------------------------------------------------------
/// Validate the input workspace @see Algorithm::validateInputs
std::map<std::string, std::string>
MDNormalization::validateInputs() {
std::map<std::string, std::string> errorMessage;
// Check for input workspace frame
Mantid::API::IMDEventWorkspace_sptr inputWS =
this->getProperty("InputWorkspace");
if (inputWS->getNumDims() < 3) {
errorMessage.emplace("InputWorkspace",
"The input workspace must be at least 3D");
} else {
for (size_t i = 0; i < 3; i++) {
if (inputWS->getDimension(i)->getMDFrame().name() !=
Mantid::Geometry::QSample::QSampleName) {
errorMessage.emplace("InputWorkspace",
"The input workspace must be in Q_sample");
}
}
}
// Check if the vanadium is available for diffraction
bool diffraction=true;
if ((inputWS->getNumDims() >3) && (inputWS->getDimension(3)->getMDFrame().name()=="DeltaE")) {
diffraction = false;
}
if (diffraction) {
API::MatrixWorkspace_const_sptr solidAngleWS = getProperty("SolidAngleWorkspace");
API::MatrixWorkspace_const_sptr fluxWS = getProperty("FluxWorkspace");
if (solidAngleWS == nullptr) {
errorMessage.emplace("SolidAngleWorkspace","SolidAngleWorkspace is required for diffraction");
}
if (fluxWS == nullptr) {
errorMessage.emplace("FluxWorkspace","FluxWorkspace is required for diffraction");
}
}
// Check for property MDNorm_low and MDNorm_high
size_t nExperimentInfos = inputWS->getNumExperimentInfo();
if (nExperimentInfos == 0) {
errorMessage.emplace("InputWorkspace",
"There must be at least one experiment info");
} else {
for (size_t iExpInfo = 0; iExpInfo < nExperimentInfos; iExpInfo++) {
auto ¤tExptInfo =
*(inputWS->getExperimentInfo(static_cast<uint16_t>(iExpInfo)));
if (!currentExptInfo.run().hasProperty("MDNorm_low")) {
errorMessage.emplace("InputWorkspace", "Missing MDNorm_low log. Please "
"use CropWorkspaceForMDNorm "
"before converting to MD");
}
if (!currentExptInfo.run().hasProperty("MDNorm_high")) {
errorMessage.emplace("InputWorkspace",
"Missing MDNorm_high log. Please use "
"CropWorkspaceForMDNorm before converting to MD");
}
}
}
// check projections and UB
if (getProperty("RLU")) {
DblMatrix W = DblMatrix(3, 3);
std::vector<double> Q1Basis = getProperty("QDimension1");
std::vector<double> Q2Basis = getProperty("QDimension2");
std::vector<double> Q3Basis = getProperty("QDimension3");
W.setColumn(0, Q1Basis);
W.setColumn(1, Q2Basis);
W.setColumn(2, Q3Basis);
if (fabs(W.determinant()) < 1e-5) {
errorMessage.emplace("QDimension1",
"The projection dimensions are coplanar or zero");
errorMessage.emplace("QDimension2",
"The projection dimensions are coplanar or zero");
errorMessage.emplace("QDimension3",
"The projection dimensions are coplanar or zero");
}
if (!inputWS->getExperimentInfo(0)->sample().hasOrientedLattice()) {
errorMessage.emplace("InputWorkspace", "There is no oriented lattice "
"associated with the input workspace. "
"Use SetUB algorithm");
}
}
// check dimension names
std::vector<std::string> originalDimensionNames;
for (size_t i=3; i<inputWS->getNumDims(); i++) {
originalDimensionNames.push_back(inputWS->getDimension(i)->getName());
}
originalDimensionNames.push_back("QDimension1");
originalDimensionNames.push_back("QDimension2");
originalDimensionNames.push_back("QDimension3");
std::vector<std::string> selectedDimensions;
for(std::size_t i=0;i<6;i++) {
std::string propName = "Dimension"+Strings::toString(i)+"Name";
std::string dimName = getProperty(propName);
std::string binningName = "Dimension"+Strings::toString(i)+"Binning";
std::vector<double> binning = getProperty(binningName);
if (!dimName.empty()) {
auto it = std::find(originalDimensionNames.begin(),originalDimensionNames.end(),dimName);
if (it==originalDimensionNames.end()) {
errorMessage.emplace(propName, "Name '"+dimName+"' is not one of the "
"original workspace names or a Q dimension");
} else {
//make sure dimension is unique
auto itSel = std::find(selectedDimensions.begin(),selectedDimensions.end(),dimName);
if (itSel==selectedDimensions.end()){
selectedDimensions.push_back(dimName);
} else{
errorMessage.emplace(propName, "Name '"+dimName+"' was already selected");
}
}
} else {
if (!binning.empty()) {
errorMessage.emplace(binningName, "There should be no binning if the dimension name is empty");
}
}
}
// since Q dimensions can be non - orthogonal, all must be present
if ((std::find(selectedDimensions.begin(),selectedDimensions.end(),"QDimension1") == selectedDimensions.end())||
(std::find(selectedDimensions.begin(),selectedDimensions.end(),"QDimension2") == selectedDimensions.end())||
(std::find(selectedDimensions.begin(),selectedDimensions.end(),"QDimension3") == selectedDimensions.end())) {
for(std::size_t i=0;i<6;i++) {
std::string propName = "Dimension"+Strings::toString(i)+"Name";
errorMessage.emplace(propName, "All of QDimension1, QDimension2, QDimension3 must be present");
}
}
// symmetry operations
std::string symOps = this->getProperty("SymmetryOperations");
if(!symOps.empty()) {
bool isSpaceGroup =Geometry::SpaceGroupFactory::Instance().isSubscribed(symOps);
bool isPointGroup = Geometry::PointGroupFactory::Instance().isSubscribed(symOps);
if(!isSpaceGroup &&!isPointGroup) {
try {
Geometry::SymmetryOperationFactory::Instance().createSymOps(symOps);
}
catch (const Mantid::Kernel::Exception::ParseError&) {
errorMessage.emplace("SymmetryOperations", "The input is not a space group, a point group, or a list of symmetry operations");
}
}
}
return errorMessage;
}
//----------------------------------------------------------------------------------------------
/** Execute the algorithm.
*/
void MDNormalization::exec() {
// symmetry operations
std::string symOps = this->getProperty("SymmetryOperations");
std::vector<Geometry::SymmetryOperation> symmetryOps;
if (symOps.empty()){
symOps="x,y,z";
}
if(Geometry::SpaceGroupFactory::Instance().isSubscribed(symOps)){
auto spaceGroup = Geometry::SpaceGroupFactory::Instance().createSpaceGroup(symOps);
auto pointGroup = spaceGroup->getPointGroup();
symmetryOps = pointGroup->getSymmetryOperations();
} else if (Geometry::PointGroupFactory::Instance().isSubscribed(symOps)) {
auto pointGroup = Geometry::PointGroupFactory::Instance().createPointGroup(symOps);
symmetryOps = pointGroup->getSymmetryOperations();
} else {
symmetryOps = Geometry::SymmetryOperationFactory::Instance().createSymOps(symOps);
}
g_log.debug()<<"Symmetry operations\n";
for(auto so:symmetryOps){
g_log.debug()<<so.identifier()<<"\n";
}
m_isRLU = getProperty("RLU");
//get the workspaces
m_inputWS = this->getProperty("InputWorkspace");
if ((m_inputWS->getNumDims() >3) && (m_inputWS->getDimension(3)->getMDFrame().name()=="DeltaE")) {
m_diffraction = false;
}
auto outputWS = binInputWS(symmetryOps);
createNormalizationWS(*outputWS);
this->setProperty("OutputNormalizationWorkspace",m_normWS);
this->setProperty("OutputWorkspace",outputWS);
m_numExptInfos = outputWS->getNumExperimentInfo();
// loop over all experiment infos
for (uint16_t expInfoIndex = 0; expInfoIndex < m_numExptInfos;
expInfoIndex++) {
// Check for other dimensions if we could measure anything in the original
// data
bool skipNormalization = false;
const std::vector<coord_t> otherValues =
getValuesFromOtherDimensions(skipNormalization, expInfoIndex);
g_log.warning()<<skipNormalization<<"\n";
/*
const auto affineTrans =
findIntergratedDimensions(otherValues, skipNormalization);
cacheDimensionXValues();
if (!skipNormalization) {
calculateNormalization(otherValues, affineTrans, expInfoIndex);
} else {
g_log.warning("Binning limits are outside the limits of the MDWorkspace. "
"Not applying normalization.");
}
*/
// if more than one experiment info, keep accumulating
m_accumulate = true;
}
}
std::string MDNormalization::QDimensionName(std::vector<double> projection) {
std::vector<double>::iterator result;
result = std::max_element(projection.begin(), projection.end(), abs_compare);
std::vector<char> symbol{'H','K','L'};
char character=symbol[std::distance(projection.begin(), result)];
std::stringstream name;
name<<"[";
for(size_t i=0;i<3;i++){
if(projection[i]==0){
name<<"0";
} else if (projection[i]==1){
name<<character;
} else if (projection[i]==-1){
name<<"-"<<character;
} else {
name<<std::defaultfloat<<std::setprecision(3)<<projection[i]<<character;
}
if(i!=2){
name<<",";
}
}
name<<"]";
return name.str();
}
std::map<std::string, std::string> MDNormalization::getBinParameters()
{
std::map<std::string, std::string> parameters;
std::stringstream extents;
std::stringstream bins;
std::vector<std::string> originalDimensionNames;
originalDimensionNames.push_back("QDimension1");
originalDimensionNames.push_back("QDimension2");
originalDimensionNames.push_back("QDimension3");
for (size_t i=3; i<m_inputWS->getNumDims(); i++) {
originalDimensionNames.push_back(m_inputWS->getDimension(i)->getName());
}
if (m_isRLU) {
m_Q1Basis = getProperty("QDimension1");
m_Q2Basis = getProperty("QDimension2");
m_Q3Basis = getProperty("QDimension3");
m_UB = m_inputWS->getExperimentInfo(0)->sample().getOrientedLattice().getUB()*2*M_PI;
}
std::vector<double> W(m_Q1Basis);
W.insert(W.end(),m_Q2Basis.begin(),m_Q2Basis.end());
W.insert(W.end(),m_Q3Basis.begin(),m_Q3Basis.end());
m_W = DblMatrix(W);
m_W.Transpose();
// Find maximum Q
auto &exptInfo0 = *(m_inputWS->getExperimentInfo(static_cast<uint16_t>(0)));
auto upperLimitsVector = (*(dynamic_cast<Kernel::PropertyWithValue<std::vector<double>> *>(
exptInfo0.getLog("MDNorm_high"))))();
double maxQ;
if(m_diffraction){
maxQ=2.*(*std::max_element(upperLimitsVector.begin(),upperLimitsVector.end()));
} else {
double Ei;
double maxDE = *std::max_element(upperLimitsVector.begin(),upperLimitsVector.end());
auto loweLimitsVector = (*(dynamic_cast<Kernel::PropertyWithValue<std::vector<double>> *>(
exptInfo0.getLog("MDNorm_low"))))();
double minDE = *std::min_element(loweLimitsVector.begin(),loweLimitsVector.end());
if (exptInfo0.run().hasProperty("Ei")) {
Kernel::Property *eiprop = exptInfo0.run().getProperty("Ei");
Ei = boost::lexical_cast<double>(eiprop->value());
if (Ei <= 0) {
throw std::invalid_argument("Ei stored in the workspace is not positive");
}
} else {
throw std::invalid_argument("Could not find Ei value in the workspace.");
}
const double energyToK = 8.0 * M_PI * M_PI * PhysicalConstants::NeutronMass *
PhysicalConstants::meV * 1e-20 /
(PhysicalConstants::h * PhysicalConstants::h);
double ki = std::sqrt(energyToK * Ei);
double kfmin = std::sqrt(energyToK * (Ei - minDE));
double kfmax = std::sqrt(energyToK * (Ei - maxDE));
maxQ=ki+std::max(kfmin,kfmax);
}
size_t basisVectorIndex=0;
std::vector<double> transformation;
for(std::size_t i=0;i<6;i++) {
std::string propName = "Dimension"+Strings::toString(i)+"Name";
std::string binningName = "Dimension"+Strings::toString(i)+"Binning";
std::string dimName = getProperty(propName);
std::vector<double> binning = getProperty(binningName);
std::string bv="BasisVector";
if (!dimName.empty()) {
std::string property=bv+Strings::toString(basisVectorIndex);
std::stringstream propertyValue;
propertyValue<<dimName;
//get the index in the original workspace
auto dimIndex=std::distance(originalDimensionNames.begin(),std::find(originalDimensionNames.begin(),originalDimensionNames.end(),dimName));
auto dimension=m_inputWS->getDimension(dimIndex);
propertyValue<<","<<dimension->getMDUnits().getUnitLabel().ascii();
for (size_t j=0;j<originalDimensionNames.size();j++){
if(j==static_cast<size_t>(dimIndex)){
propertyValue<<",1";
transformation.push_back(1.);
} else {
propertyValue<<",0";
transformation.push_back(0.);
}
}
parameters.emplace(property,propertyValue.str());
//get the extents an number of bins
coord_t dimMax=dimension->getMaximum();
coord_t dimMin=dimension->getMinimum();
if(m_isRLU){
Mantid::Geometry::OrientedLattice ol;
ol.setUB(m_UB*m_W); //note that this is already multiplied by 2Pi
if(dimIndex==0) {
dimMax=static_cast<coord_t>(ol.a()*maxQ);
dimMin=-dimMax;
} else if (dimIndex==1){
dimMax=static_cast<coord_t>(ol.b()*maxQ);
dimMin=-dimMax;
} else if (dimIndex==2){
dimMax=static_cast<coord_t>(ol.c()*maxQ);
dimMin=-dimMax;
}
}
if (binning.size()==0){
//only one bin, integrating from min to max
extents<<dimMin<<","<<dimMax<<",";
bins<<1<<",";
} else if (binning.size()==2){
//only one bin, integrating from min to max
extents<<binning[0]<<","<<binning[1]<<",";
bins<<1<<",";
} else if (binning.size()==1){
auto step=binning[0];
int nsteps=static_cast<int>(std::ceil((dimMax-dimMin)/step));
bins<<nsteps<<",";
extents<<dimMin<<","<<dimMin+nsteps*step<<",";
} else if (binning.size()==3){
dimMin=static_cast<coord_t>(binning[0]);
auto step=binning[1];
dimMax=static_cast<coord_t>(binning[2]);
int nsteps=static_cast<int>(std::ceil((dimMax-dimMin)/step));
bins<<nsteps<<",";
extents<<dimMin<<","<<dimMin+nsteps*step<<",";
}
basisVectorIndex++;
}
}
parameters.emplace("OutputExtents",extents.str());
parameters.emplace("OutputBins",bins.str());
m_transformation = DblMatrix(transformation,static_cast<size_t>((transformation.size())/m_inputWS->getNumDims()), m_inputWS->getNumDims());
return parameters;
}
void MDNormalization::createNormalizationWS(const DataObjects::MDHistoWorkspace &dataWS)
{
// Copy the MDHisto workspace, and change signals and errors to 0.
boost::shared_ptr<IMDHistoWorkspace> tmp =
this->getProperty("TemporaryNormalizationWorkspace");
m_normWS = boost::dynamic_pointer_cast<MDHistoWorkspace>(tmp);
if (!m_normWS) {
m_normWS = dataWS.clone();
m_normWS->setTo(0., 0., 0.);
} else {
m_accumulate = true;
}
}
DataObjects::MDHistoWorkspace_sptr MDNormalization::binInputWS(std::vector<Geometry::SymmetryOperation> symmetryOps)
{
Mantid::API::IMDHistoWorkspace_sptr tempDataWS =
this->getProperty("TemporaryDataWorkspace");
Mantid::API::Workspace_sptr outputWS;
std::map<std::string, std::string> parameters=getBinParameters();
double soIndex = 0;
std::vector<size_t> qDimensionIndices;
for(auto so:symmetryOps){
// calculate dimensions for binning
V3D Q1,Q2,Q3;
Q1=so.transformHKL(V3D(m_Q1Basis[0],m_Q1Basis[1],m_Q1Basis[2]));
Q2=so.transformHKL(V3D(m_Q2Basis[0],m_Q2Basis[1],m_Q2Basis[2]));
Q3=so.transformHKL(V3D(m_Q3Basis[0],m_Q3Basis[1],m_Q3Basis[2]));
if (m_isRLU) {
Q1 = m_UB * Q1;
Q2 = m_UB * Q2;
Q3 = m_UB * Q3;
}
// bin the data
double fraction=1./static_cast<double>(symmetryOps.size());
IAlgorithm_sptr binMD = createChildAlgorithm("BinMD", soIndex*0.3*fraction, (soIndex+1)*0.3*fraction);
binMD->setPropertyValue("AxisAligned", "0");
binMD->setProperty("InputWorkspace",m_inputWS);
binMD->setProperty("TemporaryDataWorkspace", tempDataWS);
binMD->setPropertyValue("NormalizeBasisVectors","0");
binMD->setPropertyValue("OutputWorkspace",getPropertyValue("OutputWorkspace"));
//set binning properties
size_t qindex=0;
for( auto const& p : parameters ) {
auto key=p.first;
auto value=p.second;
std::stringstream basisVector;
std::vector<double> projection(m_inputWS->getNumDims(),0.);
if (value.find("QDimension1")!=std::string::npos) {
if (!m_isRLU) {
projection[0]=1.;
basisVector<<"Q_sample_x,A^{-1}";
} else {
qDimensionIndices.push_back(qindex);
projection[0]=Q1.X();
projection[1]=Q1.Y();
projection[2]=Q1.Z();
basisVector<<QDimensionName(m_Q1Basis)<<", r.l.u.";
}
} else if (value.find("QDimension2")!=std::string::npos) {
if (!m_isRLU) {
projection[1]=1.;
basisVector<<"Q_sample_y,A^{-1}";
} else {
qDimensionIndices.push_back(qindex);
projection[0]=Q2.X();
projection[1]=Q2.Y();
projection[2]=Q2.Z();
basisVector<<QDimensionName(m_Q2Basis)<<", r.l.u.";
}
} else if (value.find("QDimension3")!=std::string::npos) {
if (!m_isRLU) {
projection[2]=1.;
basisVector<<"Q_sample_z,A^{-1}";
} else {
qDimensionIndices.push_back(qindex);
projection[0]=Q3.X();
projection[1]=Q3.Y();
projection[2]=Q3.Z();
basisVector<<QDimensionName(m_Q3Basis)<<", r.l.u.";
}
}
if (!basisVector.str().empty()){
for(auto const& proji: projection){
basisVector<<","<<proji;
}
value=basisVector.str();
}
g_log.debug()<<"Binning parameter "<<key<<" value: "<<value<<"\n";
binMD->setPropertyValue(key, value);
qindex++;
}
//execute algorithm
binMD->executeAsChildAlg();
outputWS=binMD->getProperty("OutputWorkspace");
//set the temporary workspace to be the output workspace, so it keeps adding different symmetries
tempDataWS = boost::dynamic_pointer_cast<MDHistoWorkspace>(outputWS);
soIndex+=1;
}
auto outputMDHWS = boost::dynamic_pointer_cast<MDHistoWorkspace>(outputWS);
//set MDUnits for Q dimensions
if(m_isRLU){
Mantid::Geometry::MDFrameArgument argument(Mantid::Geometry::HKL::HKLName,"r.l.u.");
auto mdFrameFactory = Mantid::Geometry::makeMDFrameFactoryChain();
Mantid::Geometry::MDFrame_uptr hklFrame = mdFrameFactory->create(argument);
for(size_t i:qDimensionIndices){
auto mdHistoDimension =
boost::const_pointer_cast<Mantid::Geometry::MDHistoDimension>(
boost::dynamic_pointer_cast<
const Mantid::Geometry::MDHistoDimension>(outputMDHWS->getDimension(i)));
mdHistoDimension->setMDFrame(*hklFrame);
}
}
outputMDHWS->setDisplayNormalization(Mantid::API::NoNormalization);
return outputMDHWS;
}
std::vector<coord_t> MDNormalization::getValuesFromOtherDimensions(bool &skipNormalization, uint16_t expInfoIndex) const
{
const auto ¤tRun = m_inputWS->getExperimentInfo(expInfoIndex)->run();
std::vector<coord_t> otherDimValues;
for (size_t i = 3; i < m_inputWS->getNumDims(); i++) {
const auto dimension = m_inputWS->getDimension(i);
coord_t inputDimMin = static_cast<float>(dimension->getMinimum());
coord_t inputDimMax = static_cast<float>(dimension->getMaximum());
coord_t outputDimMin(0),outputDimMax(0);
bool isIntegrated=true;
for(size_t j=0; j<m_transformation.numRows(); j++) {
if(m_transformation[j][i]==1){
isIntegrated = false;
outputDimMin=m_normWS->getDimension(j)->getMinimum();
outputDimMax=m_normWS->getDimension(j)->getMaximum();
}
}
if(dimension->getName()=="DeltaE") {
if((inputDimMax<outputDimMin) || (inputDimMin>outputDimMax)){
skipNormalization=true;
}
} else {
coord_t value = static_cast<coord_t>(currentRun.getLogAsSingleValue(
dimension->getName(), Mantid::Kernel::Math::TimeAveragedMean));
otherDimValues.push_back(value);
if (value < inputDimMin || value > inputDimMax) {
skipNormalization = true;
}
if ((!isIntegrated) && (value < outputDimMin || value > outputDimMax)) {
skipNormalization = true;
}
}
}
return otherDimValues;
}
} // namespace MDAlgorithms
} // namespace Mantid