diff --git a/Code/Mantid/Framework/MDAlgorithms/inc/MantidMDAlgorithms/SXDMDNorm.h b/Code/Mantid/Framework/MDAlgorithms/inc/MantidMDAlgorithms/SXDMDNorm.h
index e70e217d7b0c6d8ac6e8c9be62ffd3e42c3157b6..65513055cf4a976cf8d8e1f6945d2d5510ade4c9 100644
--- a/Code/Mantid/Framework/MDAlgorithms/inc/MantidMDAlgorithms/SXDMDNorm.h
+++ b/Code/Mantid/Framework/MDAlgorithms/inc/MantidMDAlgorithms/SXDMDNorm.h
@@ -46,11 +46,13 @@ namespace Mantid
void init();
void exec();
- std::string inputEnergyMode() const;
void initCaches();
- std::vector<coord_t> getValuesFromOtherDimensions(bool & skipNormalization) const;
+ std::string inputEnergyMode() const;
MDEvents::MDHistoWorkspace_sptr binInputWS();
-
+ void createNormalizationWS(const MDEvents::MDHistoWorkspace & dataWS);
+ std::vector<coord_t> getValuesFromOtherDimensions(bool & skipNormalization) const;
+ Kernel::Matrix<coord_t> findIntergratedDimensions(const std::vector<coord_t> & otherDimValues,
+ bool & skipNormalization);
/// function to calculate intersections of the trajectory with MDBoxes
std::vector<Kernel::VMD> calculateIntersections(const Geometry::IDetector_const_sptr &detector);
diff --git a/Code/Mantid/Framework/MDAlgorithms/src/SXDMDNorm.cpp b/Code/Mantid/Framework/MDAlgorithms/src/SXDMDNorm.cpp
index 25f4504a31b844a301c2b2a9f4c3135693089b64..f3d4c996478380f8ad0603cc954d728f1c365420 100644
--- a/Code/Mantid/Framework/MDAlgorithms/src/SXDMDNorm.cpp
+++ b/Code/Mantid/Framework/MDAlgorithms/src/SXDMDNorm.cpp
@@ -100,90 +100,34 @@ namespace Mantid
void SXDMDNorm::exec()
{
initCaches();
+ auto outputWS = binInputWS();
+ setProperty<Workspace_sptr>("OutputWorkspace", outputWS);
+ createNormalizationWS(*outputWS);
// Check for other dimensions if we could measure anything in the original data
bool skipNormalization = false;
const std::vector<coord_t> otherValues = getValuesFromOtherDimensions(skipNormalization);
- auto outputWS = binInputWS();
- this->setProperty<Workspace_sptr>("OutputWorkspace", outputWS);
-
- // Copy the MDHisto workspace, and change signals and errors to 0.
- m_normWS = boost::make_shared<MDHistoWorkspace>(*outputWS);
- m_normWS->setTo(0.,0.,0.);
-
- //get indices of the original dimensions in the output workspace, and if not found, the corresponding dimension is integrated
- Kernel::Matrix<coord_t> mat = m_normWS->getTransformFromOriginal(0)->makeAffineMatrix();
-
- for (size_t row = 0; row < mat.numRows()-1; row++)//affine matrix, ignore last row
- {
- if(mat[row][0]==1.)
- {
- m_hIntegrated = false;
- m_hIdx = row;
- if(m_hmin<m_normWS->getDimension(row)->getMinimum()) m_hmin = m_normWS->getDimension(row)->getMinimum();
- if(m_hmax>m_normWS->getDimension(row)->getMaximum()) m_hmax = m_normWS->getDimension(row)->getMaximum();
- if((m_hmin>m_normWS->getDimension(row)->getMaximum()) || (m_hmax<m_normWS->getDimension(row)->getMinimum()))
- {
- skipNormalization = true;
- }
- }
- if(mat[row][1] == 1.)
- {
- m_kIntegrated = false;
- m_kIdx = row;
- if(m_kmin<m_normWS->getDimension(row)->getMinimum()) m_kmin = m_normWS->getDimension(row)->getMinimum();
- if(m_kmax>m_normWS->getDimension(row)->getMaximum()) m_kmax = m_normWS->getDimension(row)->getMaximum();
- if((m_kmin>m_normWS->getDimension(row)->getMaximum()) || (m_kmax<m_normWS->getDimension(row)->getMinimum()))
- {
- skipNormalization = true;
- }
- }
- if(mat[row][2] == 1.)
- {
- m_lIntegrated = false;
- m_lIdx = row;
- if(m_lmin<m_normWS->getDimension(row)->getMinimum()) m_lmin = m_normWS->getDimension(row)->getMinimum();
- if(m_lmax>m_normWS->getDimension(row)->getMaximum()) m_lmax = m_normWS->getDimension(row)->getMaximum();
- if((m_lmin>m_normWS->getDimension(row)->getMaximum()) || (m_lmax<m_normWS->getDimension(row)->getMinimum()))
- {
- skipNormalization = true;
- }
-
- for(size_t col = 3;col < mat.numCols()-1; col++) //affine matrix, ignore last column
- {
- if(mat[row][col] == 1.)
- {
- double val = otherValues.at(col - 3);
- if((val>m_normWS->getDimension(row)->getMaximum()) || (val<m_normWS->getDimension(row)->getMinimum()))
- {
- skipNormalization = true;
- }
- }
- }
- }
- }
-
+ const auto affineMat = findIntergratedDimensions(otherValues, skipNormalization);
+
auto &hDim = *m_normWS->getDimension(m_hIdx);
m_hX.resize( hDim.getNBins() );
for(size_t i = 0; i < m_hX.size(); ++i)
{
m_hX[i] = hDim.getX(i);
}
-
auto &kDim = *m_normWS->getDimension(m_kIdx);
m_kX.resize( kDim.getNBins() );
for(size_t i = 0; i < m_kX.size(); ++i)
{
m_kX[i] = kDim.getX(i);
}
-
auto &lDim = *m_normWS->getDimension(m_lIdx);
m_lX.resize( lDim.getNBins() );
for(size_t i = 0; i < m_lX.size(); ++i)
{
m_lX[i] = lDim.getX(i);
}
-
+
API::MatrixWorkspace_const_sptr fW = getProperty("FluxWorkspace");
DataObjects::EventWorkspace_const_sptr fluxW = boost::dynamic_pointer_cast<const DataObjects::EventWorkspace>(fW);
m_kiMin = fluxW->getEventXMin();
@@ -265,7 +209,7 @@ namespace Mantid
std::transform( pos.begin(), pos.begin() + sizeOfMVD, pos.begin(), std::bind2nd( std::multiplies<coord_t>(), 0.5 ) );
std::copy( otherValues.begin(), otherValues.end(), pos.begin() + sizeOfMVD );
- std::vector<coord_t> posNew = mat*pos;
+ std::vector<coord_t> posNew = affineMat*pos;
size_t linIndex = m_normWS->getLinearIndexAtCoord(posNew.data());
if(linIndex!=size_t(-1))
@@ -303,7 +247,28 @@ namespace Mantid
this->setProperty("OutputNormalizationWorkspace",m_normWS);
}
-
+
+ /**
+ * Set up starting values for cached variables
+ */
+ void SXDMDNorm::initCaches()
+ {
+ m_inputWS = getProperty("InputWorkspace");
+ if( inputEnergyMode() != "Elastic" )
+ {
+ throw std::invalid_argument("Invalid energy transfer mode. Algorithm currently only supports elastic data.");
+ }
+ // Min/max dimension values
+ const auto hdim(m_inputWS->getDimension(0)), kdim(m_inputWS->getDimension(1)),
+ ldim(m_inputWS->getDimension(2));
+ m_hmin = hdim->getMinimum();
+ m_kmin = kdim->getMinimum();
+ m_lmin = ldim->getMinimum();
+ m_hmax = hdim->getMaximum();
+ m_kmax = kdim->getMaximum();
+ m_lmax = ldim->getMaximum();
+ }
+
/**
* Currently looks for the ConvertToMD algorithm in the history
* @return A string donating the energy transfer mode of the input workspace
@@ -341,24 +306,38 @@ namespace Mantid
}
/**
- * Set up starting values for cached variables
+ * Runs the BinMD algorithm on the input to provide the output workspace
+ * All slicing algorithm properties are passed along
+ * @return MDHistoWorkspace as a result of the binning
*/
- void SXDMDNorm::initCaches()
+ MDHistoWorkspace_sptr SXDMDNorm::binInputWS()
{
- m_inputWS = getProperty("InputWorkspace");
- if( inputEnergyMode() != "Elastic" )
+ const auto & props = getProperties();
+ IAlgorithm_sptr binMD = createChildAlgorithm("BinMD", 0.0, 0.3);
+ binMD->setPropertyValue("AxisAligned","1");
+ for(auto it = props.begin(); it != props.end(); ++it)
{
- throw std::invalid_argument("Invalid energy transfer mode. Algorithm currently only supports elastic data.");
+ const auto & propName = (*it)->name();
+ if(propName != "FluxWorkspace" && propName != "SolidAngleWorkspace" &&
+ propName != "OutputNormalizationWorkspace")
+ {
+ binMD->setPropertyValue(propName,(*it)->value());
+ }
}
- // Min/max dimension values
- const auto hdim(m_inputWS->getDimension(0)), kdim(m_inputWS->getDimension(1)),
- ldim(m_inputWS->getDimension(2));
- m_hmin = hdim->getMinimum();
- m_kmin = kdim->getMinimum();
- m_lmin = ldim->getMinimum();
- m_hmax = hdim->getMaximum();
- m_kmax = kdim->getMaximum();
- m_lmax = ldim->getMaximum();
+ binMD->executeAsChildAlg();
+ Workspace_sptr outputWS = binMD->getProperty("OutputWorkspace");
+ return boost::dynamic_pointer_cast<MDHistoWorkspace>(outputWS);
+ }
+
+ /**
+ * Create & cached the normalization workspace
+ * @param dataWS The binned workspace that will be used for the data
+ */
+ void SXDMDNorm::createNormalizationWS(const MDHistoWorkspace &dataWS)
+ {
+ // Copy the MDHisto workspace, and change signals and errors to 0.
+ m_normWS = boost::make_shared<MDHistoWorkspace>(dataWS);
+ m_normWS->setTo(0.,0.,0.);
}
/**
@@ -393,27 +372,73 @@ namespace Mantid
}
/**
- * Runs the BinMD algorithm on the input to provide the output workspace
- * All slicing algorithm properties are passed along
- * @return MDHistoWorkspace as a result of the binning
+ * 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
*/
- MDHistoWorkspace_sptr SXDMDNorm::binInputWS()
+ Kernel::Matrix<coord_t> SXDMDNorm::findIntergratedDimensions(const std::vector<coord_t> &otherDimValues,
+ bool &skipNormalization)
{
- const auto & props = getProperties();
- IAlgorithm_sptr binMD = createChildAlgorithm("BinMD", 0.0, 0.3);
- binMD->setPropertyValue("AxisAligned","1");
- for(auto it = props.begin(); it != props.end(); ++it)
+ // 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 & propName = (*it)->name();
- if(propName != "FluxWorkspace" && propName != "SolidAngleWorkspace" &&
- propName != "OutputNormalizationWorkspace")
+ const auto dimen = m_normWS->getDimension(row);
+ const auto dimMin(dimen->getMinimum()), dimMax(dimen->getMaximum());
+ if(affineMat[row][0] == 1.)
{
- binMD->setPropertyValue(propName,(*it)->value());
+ m_hIntegrated = false;
+ m_hIdx = row;
+ if( m_hmin < dimMin ) m_hmin = dimMin;
+ if( m_hmax > dimMax ) m_hmax = dimMax;
+ if( m_hmin > dimMax || m_hmax < dimMin)
+ {
+ skipNormalization = true;
+ }
+ }
+ if(affineMat[row][1] == 1.)
+ {
+ m_kIntegrated = false;
+ m_kIdx = row;
+ if( m_kmin < dimMin ) m_kmin = dimMin;
+ if( m_kmax > dimMax ) m_kmax = dimMax;
+ if( m_kmin > dimMax || m_kmax < dimMin )
+ {
+ skipNormalization = true;
+ }
+ }
+ if(affineMat[row][2] == 1.)
+ {
+ m_lIntegrated = false;
+ m_lIdx = row;
+ if( m_lmin < dimMin ) m_lmin = dimMin;
+ if( m_lmax > dimMax ) m_lmax = dimMax;
+ if( m_lmin > dimMax || m_lmax < dimMin )
+ {
+ skipNormalization = true;
+ }
+
+ for(size_t col = 3; 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;
+ }
+ }
+ }
}
}
- binMD->executeAsChildAlg();
- Workspace_sptr outputWS = binMD->getProperty("OutputWorkspace");
- return boost::dynamic_pointer_cast<MDHistoWorkspace>(outputWS);
+
+ return affineMat;
}
/**