/**
* Search each spectrum for y-values that are less than zero and reset
* them to the supplied value.
*
* @param minWS A workspace of minimum values for each spectra.
* @param value Reset negative values in the spectra to this number.
* @param wksp The workspace to modify.
* @param prog The progress.
*/
void ResetNegatives::changeNegatives(MatrixWorkspace_const_sptr minWS, const double value, MatrixWorkspace_sptr wksp,
Progress &prog)
{
int64_t nHist = wksp->getNumberHistograms();
PARALLEL_FOR2(minWS, wksp)
for (int64_t i = 0; i < nHist; i++)
{
PARALLEL_START_INTERUPT_REGION
if (minWS->readY(i)[0] <= 0.) // quick check to see if there is a reason to bother
{
MantidVec & y = wksp->dataY(i);
for (MantidVec::iterator it = y.begin(); it != y.end(); ++it)
{
if (*it < 0.) {
*it = value;
}
else
*it = fixZero(*it);
}
}
prog.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
}
/** Executes the algorithm
*/
void MultiplyRange::exec()
{
// Get the input workspace and other properties
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
m_startBin = getProperty("StartBin");
m_endBin = getProperty("EndBin");
m_factor = getProperty("Factor");
// A few checks on the input properties
const int specSize = static_cast<int>(inputWS->blocksize());
if ( isEmpty(m_endBin) ) m_endBin = specSize - 1;
if ( m_endBin >= specSize )
{
g_log.error("EndBin out of range!");
throw std::out_of_range("EndBin out of range!");
}
if ( m_endBin < m_startBin )
{
g_log.error("StartBin must be less than or equal to EndBin");
throw std::out_of_range("StartBin must be less than or equal to EndBin");
}
// Only create the output workspace if it's different to the input one
MatrixWorkspace_sptr outputWS = getProperty("OutputWorkspace");
if ( outputWS != inputWS )
{
outputWS = WorkspaceFactory::Instance().create(inputWS);
setProperty("OutputWorkspace",outputWS);
}
// Get the count of histograms in the input workspace
const int histogramCount = static_cast<int>(inputWS->getNumberHistograms());
Progress progress(this,0.0,1.0,histogramCount);
// Loop over spectra
PARALLEL_FOR2(inputWS,outputWS)
for (int i = 0; i < histogramCount; ++i)
{
PARALLEL_START_INTERUPT_REGION
// Copy over the bin boundaries
outputWS->setX(i,inputWS->refX(i));
// Copy over the data
outputWS->dataY(i) = inputWS->readY(i);
outputWS->dataE(i) = inputWS->readE(i);
MantidVec& newY = outputWS->dataY(i);
MantidVec& newE = outputWS->dataE(i);
// Now multiply the requested range
std::transform(newY.begin()+m_startBin,newY.begin()+m_endBin+1,newY.begin()+m_startBin,
std::bind2nd(std::multiplies<double>(),m_factor));
std::transform(newE.begin()+m_startBin,newE.begin()+m_endBin+1,newE.begin()+m_startBin,
std::bind2nd(std::multiplies<double>(),m_factor));
progress.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
}
/**
* Creates QwtData using X and Y values from the workspace spectra.
* @param ws :: Workspace with X and Y values to use
* @param wsIndex :: Workspace index to use
* @return Pointer to created QwtData
*/
boost::shared_ptr<QwtData> curveDataFromWs(MatrixWorkspace_const_sptr ws, size_t wsIndex)
{
const double* x = &ws->readX(wsIndex)[0];
const double* y = &ws->readY(wsIndex)[0];
size_t size = ws->blocksize();
return boost::make_shared<QwtArrayData>(x,y,size);
}
void SumRowColumn::exec()
{
// First task is to integrate the input workspace
MatrixWorkspace_const_sptr integratedWS = integrateWorkspace();
const size_t numSpec = integratedWS->getNumberHistograms();
// Check number of spectra is 128*128 or 192*192. Print warning if not.
if (numSpec != 16384 && numSpec != 36864)
{
g_log.warning() << "The input workspace has " << numSpec << " spectra."
<< "This is not 128*128 or 192*192 - did you make a mistake?\n";
}
// This is the dimension if all rows/columns are included
const int dim = static_cast<int>( std::sqrt(static_cast<double>(numSpec)) );
// Check the column range properties
int start = getProperty("HOverVMin");
int end = getProperty("HOverVMax");
if ( isEmpty(start) ) start = 0;
if ( isEmpty(end) || end > dim-1 ) end = dim-1;
if ( start > end )
{
g_log.error("H/V_Min must be less than H/V_Max");
throw std::out_of_range("H/V_Min must be less than H/V_Max");
}
MatrixWorkspace_sptr outputWS = WorkspaceFactory::Instance().create(integratedWS,1,dim,dim);
// Remove the unit
outputWS->getAxis(0)->unit().reset();
// Get references to the vectors for the results
MantidVec& X = outputWS->dataX(0);
MantidVec& Y = outputWS->dataY(0);
// Get the orientation
const std::string orientation = getProperty("Orientation");
const bool horizontal = ( orientation=="D_H" ? 1 : 0 );
Progress progress(this,0,1,dim);
for (int i = 0; i < dim; ++i)
{
// Copy X values
X[i] = i;
// Now loop over calculating Y's
for (int j = start; j <= end; ++j)
{
const int index = ( horizontal ? ( i + j*dim) : ( i*dim + j) );
Y[i] += integratedWS->readY(index)[0];
}
}
setProperty("OutputWorkspace",outputWS);
}
/**
* Rebin the input quadrilateral to the output grid
* @param inputQ The input polygon
* @param inputWS The input workspace containing the input intensity values
* @param i The index in the vertical axis direction that inputQ references
* @param j The index in the horizontal axis direction that inputQ references
* @param outputWS A pointer to the output workspace that accumulates the data
* @param verticalAxis A vector containing the output vertical axis bin boundaries
*/
void Rebin2D::rebinToFractionalOutput(const Geometry::Quadrilateral & inputQ,
MatrixWorkspace_const_sptr inputWS,
const size_t i, const size_t j,
RebinnedOutput_sptr outputWS,
const std::vector<double> & verticalAxis)
{
const MantidVec & X = outputWS->readX(0);
size_t qstart(0), qend(verticalAxis.size()-1), en_start(0), en_end(X.size() - 1);
if( !getIntersectionRegion(outputWS, verticalAxis, inputQ, qstart, qend, en_start, en_end)) return;
for( size_t qi = qstart; qi < qend; ++qi )
{
const double vlo = verticalAxis[qi];
const double vhi = verticalAxis[qi+1];
for( size_t ei = en_start; ei < en_end; ++ei )
{
const V2D ll(X[ei], vlo);
const V2D lr(X[ei+1], vlo);
const V2D ur(X[ei+1], vhi);
const V2D ul(X[ei], vhi);
const Quadrilateral outputQ(ll, lr, ur, ul);
double yValue = inputWS->readY(i)[j];
if (boost::math::isnan(yValue))
{
continue;
}
try
{
ConvexPolygon overlap = intersectionByLaszlo(outputQ, inputQ);
const double weight = overlap.area()/inputQ.area();
yValue *= weight;
double eValue = inputWS->readE(i)[j] * weight;
const double overlapWidth = overlap.largestX() - overlap.smallestX();
// Don't do the overlap removal if already RebinnedOutput.
// This wreaks havoc on the data.
if(inputWS->isDistribution() && inputWS->id() != "RebinnedOutput")
{
yValue *= overlapWidth;
eValue *= overlapWidth;
}
eValue *= eValue;
PARALLEL_CRITICAL(overlap)
{
outputWS->dataY(qi)[ei] += yValue;
outputWS->dataE(qi)[ei] += eValue;
outputWS->dataF(qi)[ei] += weight;
}
}
catch(Geometry::NoIntersectionException &)
{}
}
}
}
/*
Execute the transformtion. Generates an output IMDEventWorkspace.
@return the constructed IMDEventWorkspace following the transformation.
@param ws: Input MatrixWorkspace const shared pointer
*/
IMDEventWorkspace_sptr ReflectometryTransformQxQz::execute(MatrixWorkspace_const_sptr inputWs) const
{
const size_t nbinsx = 10;
const size_t nbinsz = 10;
auto ws = boost::make_shared<MDEventWorkspace<MDLeanEvent<2>,2> >();
MDHistoDimension_sptr qxDim = MDHistoDimension_sptr(new MDHistoDimension("Qx","qx","(Ang^-1)", static_cast<Mantid::coord_t>(m_qxMin), static_cast<Mantid::coord_t>(m_qxMax), nbinsx));
MDHistoDimension_sptr qzDim = MDHistoDimension_sptr(new MDHistoDimension("Qz","qz","(Ang^-1)", static_cast<Mantid::coord_t>(m_qzMin), static_cast<Mantid::coord_t>(m_qzMax), nbinsz));
ws->addDimension(qxDim);
ws->addDimension(qzDim);
// Set some reasonable values for the box controller
BoxController_sptr bc = ws->getBoxController();
bc->setSplitInto(2);
bc->setSplitThreshold(10);
// Initialize the workspace.
ws->initialize();
// Start with a MDGridBox.
ws->splitBox();
auto spectraAxis = inputWs->getAxis(1);
for(size_t index = 0; index < inputWs->getNumberHistograms(); ++index)
{
auto counts = inputWs->readY(index);
auto wavelengths = inputWs->readX(index);
auto errors = inputWs->readE(index);
const size_t nInputBins = wavelengths.size() -1;
const double theta_final = spectraAxis->getValue(index);
m_QxCalculation.setThetaFinal(theta_final);
m_QzCalculation.setThetaFinal(theta_final);
//Loop over all bins in spectra
for(size_t binIndex = 0; binIndex < nInputBins; ++binIndex)
{
const double& wavelength = 0.5*(wavelengths[binIndex] + wavelengths[binIndex+1]);
double _qx = m_QxCalculation.execute(wavelength);
double _qz = m_QzCalculation.execute(wavelength);
double centers[2] = {_qx, _qz};
ws->addEvent(MDLeanEvent<2>(float(counts[binIndex]), float(errors[binIndex]*errors[binIndex]), centers));
}
ws->splitAllIfNeeded(NULL);
}
return ws;
}
void SaveNISTDAT::exec()
{
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
std::string filename = getPropertyValue("Filename");
// prepare to save to file
std::ofstream out_File(filename.c_str());
if (!out_File)
{
g_log.error("Failed to open file:" + filename);
throw Exception::FileError("Failed to open file:" , filename);
}
out_File << "Data columns Qx - Qy - I(Qx,Qy) - err(I)\r\n";
out_File << "ASCII data\r\n";
// Set up the progress reporting object
Progress progress(this,0.0,1.0,2);
progress.report("Save I(Qx,Qy)");
if ( inputWS->axes() > 1 && inputWS->getAxis(1)->isNumeric() )
{
const Axis& axis = *inputWS->getAxis(1);
for (size_t i = 0; i < axis.length()-1; i++)
{
const double qy = (axis(i)+axis(i+1))/2.0;
const MantidVec& XIn = inputWS->readX(i);
const MantidVec& YIn = inputWS->readY(i);
const MantidVec& EIn = inputWS->readE(i);
for ( size_t j = 0; j < XIn.size()-1; j++)
{
// Exclude NaNs
if (YIn[j]==YIn[j])
{
out_File << (XIn[j]+XIn[j+1])/2.0;
out_File << " " << qy;
out_File << " " << YIn[j];
out_File << " " << EIn[j] << "\r\n";
}
}
}
}
out_File.close();
progress.report("Save I(Qx,Qy)");
}
void ExtractFFTSpectrum::exec()
{
MatrixWorkspace_sptr inputWS = getProperty("InputWorkspace");
MatrixWorkspace_sptr inputImagWS = getProperty("InputImagWorkspace");
const int fftPart = getProperty("FFTPart");
const int numHists = static_cast<int>(inputWS->getNumberHistograms());
MatrixWorkspace_sptr outputWS = WorkspaceFactory::Instance().create(inputWS);
Progress prog(this, 0.0, 1.0, numHists);
PARALLEL_FOR1(outputWS)
for ( int i = 0; i < numHists; i++ )
{
PARALLEL_START_INTERUPT_REGION
IAlgorithm_sptr childFFT = createChildAlgorithm("FFT");
childFFT->setProperty<MatrixWorkspace_sptr>("InputWorkspace", inputWS);
childFFT->setProperty<int>("Real", i);
if( inputImagWS )
{
childFFT->setProperty<MatrixWorkspace_sptr>("InputImagWorkspace", inputImagWS);
childFFT->setProperty<int>("Imaginary", i);
}
childFFT->execute();
MatrixWorkspace_const_sptr fftTemp = childFFT->getProperty("OutputWorkspace");
outputWS->dataE(i) = fftTemp->readE(fftPart);
outputWS->dataY(i) = fftTemp->readY(fftPart);
outputWS->dataX(i) = fftTemp->readX(fftPart);
prog.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
boost::shared_ptr<Kernel::Units::Label> lblUnit = boost::dynamic_pointer_cast<Kernel::Units::Label>(UnitFactory::Instance().create("Label"));
lblUnit->setLabel("Time", "ns");
outputWS->getAxis(0)->unit() = lblUnit;
setProperty("OutputWorkspace", outputWS);
}
void FlatBackground::exec()
{
// Retrieve the input workspace
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
// Copy over all the data
const int numHists = static_cast<int>(inputWS->getNumberHistograms());
const int blocksize = static_cast<int>(inputWS->blocksize());
// Get the required X range
double startX,endX;
this->checkRange(startX,endX);
std::vector<int> specInds = getProperty("WorkspaceIndexList");
// check if the user passed an empty list, if so all of spec will be processed
this->getSpecInds(specInds, numHists);
// Are we removing the background?
const bool removeBackground =
std::string(getProperty("outputMode")) == "Subtract Background";
// Initialise the progress reporting object
m_progress = new Progress(this,0.0,0.2,numHists);
MatrixWorkspace_sptr outputWS = getProperty("OutputWorkspace");
// If input and output workspaces are not the same, create a new workspace for the output
if (outputWS != inputWS )
{
outputWS = WorkspaceFactory::Instance().create(inputWS);
PARALLEL_FOR2(inputWS,outputWS)
for (int i = 0; i < numHists; ++i)
{
PARALLEL_START_INTERUPT_REGION
outputWS->dataX(i) = inputWS->readX(i);
outputWS->dataY(i) = inputWS->readY(i);
outputWS->dataE(i) = inputWS->readE(i);
m_progress->report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
}
/**
* Add -1.*minValue on each spectra.
*
* @param minWS A workspace of minimum values for each spectra. This is calculated in
* the @see exec portion of the algorithm.
* @param wksp The workspace to modify.
* @param prog The progress.
*/
void ResetNegatives::pushMinimum(MatrixWorkspace_const_sptr minWS, MatrixWorkspace_sptr wksp, Progress &prog)
{
int64_t nHist = minWS->getNumberHistograms();
PARALLEL_FOR2(wksp, minWS)
for (int64_t i = 0; i < nHist; i++)
{
PARALLEL_START_INTERUPT_REGION
double minValue = minWS->readY(i)[0];
if (minValue <= 0)
{
minValue *= -1.;
MantidVec & y = wksp->dataY(i);
for (MantidVec::iterator it = y.begin(); it != y.end(); ++it)
{
*it = fixZero(*it + minValue);
}
}
prog.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
}
IMDHistoWorkspace_sptr ReflectometryTransform::executeMDNormPoly(
MatrixWorkspace_const_sptr inputWs) const {
auto input_x_dim = inputWs->getXDimension();
MDHistoDimension_sptr dim0 = MDHistoDimension_sptr(new MDHistoDimension(
input_x_dim->getName(), input_x_dim->getDimensionId(),
input_x_dim->getMDFrame(),
static_cast<Mantid::coord_t>(input_x_dim->getMinimum()),
static_cast<Mantid::coord_t>(input_x_dim->getMaximum()),
input_x_dim->getNBins()));
auto input_y_dim = inputWs->getYDimension();
MDHistoDimension_sptr dim1 = MDHistoDimension_sptr(new MDHistoDimension(
input_y_dim->getName(), input_y_dim->getDimensionId(),
input_y_dim->getMDFrame(),
static_cast<Mantid::coord_t>(input_y_dim->getMinimum()),
static_cast<Mantid::coord_t>(input_y_dim->getMaximum()),
input_y_dim->getNBins()));
auto outWs = boost::make_shared<MDHistoWorkspace>(dim0, dim1);
for (size_t nHistoIndex = 0; nHistoIndex < inputWs->getNumberHistograms();
++nHistoIndex) {
const MantidVec X = inputWs->readX(nHistoIndex);
const MantidVec Y = inputWs->readY(nHistoIndex);
const MantidVec E = inputWs->readE(nHistoIndex);
for (size_t nBinIndex = 0; nBinIndex < inputWs->blocksize(); ++nBinIndex) {
auto value_index = outWs->getLinearIndex(nBinIndex, nHistoIndex);
outWs->setSignalAt(value_index, Y[nBinIndex]);
outWs->setErrorSquaredAt(value_index, E[nBinIndex] * E[nBinIndex]);
}
}
return outWs;
}
/**
* Sets a new preview spectrum for the mini plot.
*
* @param value workspace index
*/
void ResNorm::previewSpecChanged(int value) {
m_previewSpec = value;
// Update vanadium plot
if (m_uiForm.dsVanadium->isValid())
m_uiForm.ppPlot->addSpectrum(
"Vanadium", m_uiForm.dsVanadium->getCurrentDataName(), m_previewSpec);
// Update fit plot
std::string fitWsGroupName(m_pythonExportWsName + "_Fit_Workspaces");
std::string fitParamsName(m_pythonExportWsName + "_Fit");
if (AnalysisDataService::Instance().doesExist(fitWsGroupName)) {
WorkspaceGroup_sptr fitWorkspaces =
AnalysisDataService::Instance().retrieveWS<WorkspaceGroup>(
fitWsGroupName);
ITableWorkspace_sptr fitParams =
AnalysisDataService::Instance().retrieveWS<ITableWorkspace>(
fitParamsName);
if (fitWorkspaces && fitParams) {
Column_const_sptr scaleFactors = fitParams->getColumn("Scaling");
std::string fitWsName(fitWorkspaces->getItem(m_previewSpec)->name());
MatrixWorkspace_const_sptr fitWs =
AnalysisDataService::Instance().retrieveWS<MatrixWorkspace>(
fitWsName);
MatrixWorkspace_sptr fit = WorkspaceFactory::Instance().create(fitWs, 1);
fit->setX(0, fitWs->readX(1));
fit->getSpectrum(0)->setData(fitWs->readY(1), fitWs->readE(1));
for (size_t i = 0; i < fit->blocksize(); i++)
fit->dataY(0)[i] /= scaleFactors->cell<double>(m_previewSpec);
m_uiForm.ppPlot->addSpectrum("Fit", fit, 0, Qt::red);
}
}
}
/**
* Execution path for NormalisedPolygon Rebinning
* @param inputWS : Workspace to be rebinned
* @param vertexes : TableWorkspace for debugging purposes
* @param dumpVertexes : determines whether vertexes will be written to for
* debugging purposes or not
* @param outputDimensions : used for the column headings for Dump Vertexes
*/
MatrixWorkspace_sptr ReflectometryTransform::executeNormPoly(
MatrixWorkspace_const_sptr inputWS,
boost::shared_ptr<Mantid::DataObjects::TableWorkspace> &vertexes,
bool dumpVertexes, std::string outputDimensions) const {
MatrixWorkspace_sptr temp = WorkspaceFactory::Instance().create(
"RebinnedOutput", m_d1NumBins, m_d0NumBins, m_d0NumBins);
RebinnedOutput_sptr outWS = boost::static_pointer_cast<RebinnedOutput>(temp);
const double widthD0 = (m_d0Max - m_d0Min) / double(m_d0NumBins);
const double widthD1 = (m_d1Max - m_d1Min) / double(m_d1NumBins);
std::vector<double> xBinsVec;
std::vector<double> zBinsVec;
VectorHelper::createAxisFromRebinParams({m_d1Min, widthD1, m_d1Max},
zBinsVec);
VectorHelper::createAxisFromRebinParams({m_d0Min, widthD0, m_d0Max},
xBinsVec);
// Put the correct bin boundaries into the workspace
auto verticalAxis = new BinEdgeAxis(zBinsVec);
outWS->replaceAxis(1, verticalAxis);
for (size_t i = 0; i < zBinsVec.size() - 1; ++i)
outWS->setX(i, xBinsVec);
verticalAxis->title() = m_d1Label;
// Prepare the required theta values
DetectorAngularCache cache = initAngularCaches(inputWS.get());
m_theta = cache.thetas;
m_thetaWidths = cache.thetaWidths;
const size_t nHistos = inputWS->getNumberHistograms();
const size_t nBins = inputWS->blocksize();
// Holds the spectrum-detector mapping
std::vector<specnum_t> specNumberMapping;
std::vector<detid_t> detIDMapping;
// Create a table for the output if we want to debug vertex positioning
addColumnHeadings(vertexes, outputDimensions);
for (size_t i = 0; i < nHistos; ++i) {
IDetector_const_sptr detector = inputWS->getDetector(i);
if (!detector || detector->isMasked() || detector->isMonitor()) {
continue;
}
// Compute polygon points
const double theta = m_theta[i];
const double thetaWidth = m_thetaWidths[i];
const double thetaHalfWidth = 0.5 * thetaWidth;
const double thetaLower = theta - thetaHalfWidth;
const double thetaUpper = theta + thetaHalfWidth;
const MantidVec &X = inputWS->readX(i);
const MantidVec &Y = inputWS->readY(i);
const MantidVec &E = inputWS->readE(i);
for (size_t j = 0; j < nBins; ++j) {
const double lamLower = X[j];
const double lamUpper = X[j + 1];
const double signal = Y[j];
const double error = E[j];
auto inputQ =
m_calculator->createQuad(lamUpper, lamLower, thetaUpper, thetaLower);
FractionalRebinning::rebinToFractionalOutput(inputQ, inputWS, i, j, outWS,
zBinsVec);
// Find which qy bin this point lies in
const auto qIndex =
std::upper_bound(zBinsVec.begin(), zBinsVec.end(), inputQ[0].Y()) -
zBinsVec.begin();
if (qIndex != 0 && qIndex < static_cast<int>(zBinsVec.size())) {
// Add this spectra-detector pair to the mapping
specNumberMapping.push_back(
outWS->getSpectrum(qIndex - 1).getSpectrumNo());
detIDMapping.push_back(detector->getID());
}
// Debugging
if (dumpVertexes) {
writeRow(vertexes, inputQ[0], i, j, signal, error);
writeRow(vertexes, inputQ[1], i, j, signal, error);
writeRow(vertexes, inputQ[2], i, j, signal, error);
writeRow(vertexes, inputQ[3], i, j, signal, error);
}
}
}
outWS->finalize();
FractionalRebinning::normaliseOutput(outWS, inputWS);
// Set the output spectrum-detector mapping
SpectrumDetectorMapping outputDetectorMap(specNumberMapping, detIDMapping);
outWS->updateSpectraUsing(outputDetectorMap);
outWS->getAxis(0)->title() = m_d0Label;
outWS->setYUnit("");
outWS->setYUnitLabel("Intensity");
return outWS;
}
/**
@ throw invalid_argument if the workspaces are not mututially compatible
*/
void Q1D2::exec()
{
m_dataWS = getProperty("DetBankWorkspace");
MatrixWorkspace_const_sptr waveAdj = getProperty("WavelengthAdj");
MatrixWorkspace_const_sptr pixelAdj = getProperty("PixelAdj");
MatrixWorkspace_const_sptr wavePixelAdj = getProperty("WavePixelAdj");
const bool doGravity = getProperty("AccountForGravity");
m_doSolidAngle = getProperty("SolidAngleWeighting");
//throws if we don't have common binning or another incompatibility
Qhelper helper;
helper.examineInput(m_dataWS, waveAdj, pixelAdj);
// FIXME: how to examine the wavePixelAdj?
g_log.debug() << "All input workspaces were found to be valid\n";
// normalization as a function of wavelength (i.e. centers of x-value bins)
double const * const binNorms = waveAdj ? &(waveAdj->readY(0)[0]) : NULL;
// error on the wavelength normalization
double const * const binNormEs = waveAdj ? &(waveAdj->readE(0)[0]) : NULL;
//define the (large number of) data objects that are going to be used in all iterations of the loop below
// this will become the output workspace from this algorithm
MatrixWorkspace_sptr outputWS = setUpOutputWorkspace(getProperty("OutputBinning"));
const MantidVec & QOut = outputWS->readX(0);
MantidVec & YOut = outputWS->dataY(0);
MantidVec & EOutTo2 = outputWS->dataE(0);
// normalisation that is applied to counts in each Q bin
MantidVec normSum(YOut.size(), 0.0);
// the error on the normalisation
MantidVec normError2(YOut.size(), 0.0);
const int numSpec = static_cast<int>(m_dataWS->getNumberHistograms());
Progress progress(this, 0.05, 1.0, numSpec+1);
PARALLEL_FOR3(m_dataWS, outputWS, pixelAdj)
for (int i = 0; i < numSpec; ++i)
{
PARALLEL_START_INTERUPT_REGION
// Get the pixel relating to this spectrum
IDetector_const_sptr det;
try {
det = m_dataWS->getDetector(i);
} catch (Exception::NotFoundError&) {
g_log.warning() << "Workspace index " << i << " (SpectrumIndex = " << m_dataWS->getSpectrum(i)->getSpectrumNo() << ") has no detector assigned to it - discarding" << std::endl;
// Catch if no detector. Next line tests whether this happened - test placed
// outside here because Mac Intel compiler doesn't like 'continue' in a catch
// in an openmp block.
}
// If no detector found or if detector is masked shouldn't be included skip onto the next spectrum
if ( !det || det->isMonitor() || det->isMasked() )
{
continue;
}
//get the bins that are included inside the RadiusCut/WaveCutcut off, those to calculate for
//const size_t wavStart = waveLengthCutOff(i);
const size_t wavStart = helper.waveLengthCutOff(m_dataWS, getProperty("RadiusCut"), getProperty("WaveCut"), i);
if (wavStart >= m_dataWS->readY(i).size())
{
// all the spectra in this detector are out of range
continue;
}
const size_t numWavbins = m_dataWS->readY(i).size()-wavStart;
// make just one call to new to reduce CPU overhead on each thread, access to these
// three "arrays" is via iterators
MantidVec _noDirectUseStorage_(3*numWavbins);
//normalization term
MantidVec::iterator norms = _noDirectUseStorage_.begin();
// the error on these weights, it contributes to the error calculation on the output workspace
MantidVec::iterator normETo2s = norms + numWavbins;
// the Q values calculated from input wavelength workspace
MantidVec::iterator QIn = normETo2s + numWavbins;
// the weighting for this input spectrum that is added to the normalization
calculateNormalization(wavStart, i, pixelAdj, wavePixelAdj, binNorms, binNormEs, norms, normETo2s);
// now read the data from the input workspace, calculate Q for each bin
convertWavetoQ(i, doGravity, wavStart, QIn);
// Pointers to the counts data and it's error
MantidVec::const_iterator YIn = m_dataWS->readY(i).begin()+wavStart;
MantidVec::const_iterator EIn = m_dataWS->readE(i).begin()+wavStart;
//when finding the output Q bin remember that the input Q bins (from the convert to wavelength) start high and reduce
MantidVec::const_iterator loc = QOut.end();
// sum the Q contributions from each individual spectrum into the output array
const MantidVec::const_iterator end = m_dataWS->readY(i).end();
for( ; YIn != end; ++YIn, ++EIn, ++QIn, ++norms, ++normETo2s)
{
//find the output bin that each input y-value will fall into, remembering there is one more bin boundary than bins
getQBinPlus1(QOut, *QIn, loc);
// ignore counts that are out of the output range
if ( (loc != QOut.begin()) && (loc != QOut.end()) )
{
// the actual Q-bin to add something to
const size_t bin = loc - QOut.begin() - 1;
PARALLEL_CRITICAL(q1d_counts_sum)
{
YOut[bin] += *YIn;
normSum[bin] += *norms;
//these are the errors squared which will be summed and square rooted at the end
EOutTo2[bin] += (*EIn)*(*EIn);
normError2[bin] += *normETo2s;
}
}
}
PARALLEL_CRITICAL(q1d_spectra_map)
{
progress.report("Computing I(Q)");
// Add up the detector IDs in the output spectrum at workspace index 0
const ISpectrum * inSpec = m_dataWS->getSpectrum(i);
ISpectrum * outSpec = outputWS->getSpectrum(0);
outSpec->addDetectorIDs( inSpec->getDetectorIDs() );
}
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
bool doOutputParts = getProperty("OutputParts");
if (doOutputParts)
{
MatrixWorkspace_sptr ws_sumOfCounts = WorkspaceFactory::Instance().create(outputWS);
ws_sumOfCounts->dataX(0) = outputWS->dataX(0);
ws_sumOfCounts->dataY(0) = outputWS->dataY(0);
for (size_t i = 0; i < outputWS->dataE(0).size(); i++)
{
ws_sumOfCounts->dataE(0)[i] = sqrt(outputWS->dataE(0)[i]);
}
MatrixWorkspace_sptr ws_sumOfNormFactors = WorkspaceFactory::Instance().create(outputWS);
ws_sumOfNormFactors->dataX(0) = outputWS->dataX(0);
for (size_t i = 0; i < ws_sumOfNormFactors->dataY(0).size(); i++)
{
ws_sumOfNormFactors->dataY(0)[i] = normSum[i];
ws_sumOfNormFactors->dataE(0)[i] = sqrt(normError2[i]);
}
helper.outputParts(this, ws_sumOfCounts, ws_sumOfNormFactors);
}
progress.report("Normalizing I(Q)");
//finally divide the number of counts in each output Q bin by its weighting
normalize(normSum, normError2, YOut, EOutTo2);
outputWS->updateSpectraUsingMap();
setProperty("OutputWorkspace",outputWS);
}
/** Executes the algorithm
*
* @throw runtime_error Thrown if algorithm cannot execute
*/
void MaxMin::exec()
{
// Try and retrieve the optional properties
m_MinRange = getProperty("RangeLower");
m_MaxRange = getProperty("RangeUpper");
m_MinSpec = getProperty("StartWorkspaceIndex");
m_MaxSpec = getProperty("EndWorkspaceIndex");
showMin = getProperty("ShowMin");
// Get the input workspace
MatrixWorkspace_const_sptr localworkspace = getProperty("InputWorkspace");
const int numberOfSpectra = static_cast<int>(localworkspace->getNumberHistograms());
// Check 'StartSpectrum' is in range 0-numberOfSpectra
if ( m_MinSpec > numberOfSpectra )
{
g_log.warning("StartSpectrum out of range! Set to 0.");
m_MinSpec = 0;
}
if ( isEmpty(m_MaxSpec) ) m_MaxSpec = numberOfSpectra-1;
if ( m_MaxSpec > numberOfSpectra-1 || m_MaxSpec < m_MinSpec )
{
g_log.warning("EndSpectrum out of range! Set to max detector number");
m_MaxSpec = numberOfSpectra;
}
if ( m_MinRange > m_MaxRange )
{
g_log.warning("Range_upper is less than Range_lower. Will integrate up to frame maximum.");
m_MaxRange = 0.0;
}
// Create the 1D workspace for the output
MatrixWorkspace_sptr outputWorkspace = API::WorkspaceFactory::Instance().create(localworkspace,m_MaxSpec-m_MinSpec+1,2,1);
Progress progress(this,0,1,(m_MaxSpec-m_MinSpec+1));
PARALLEL_FOR2(localworkspace,outputWorkspace)
// Loop over spectra
for (int i = m_MinSpec; i <= m_MaxSpec; ++i)
{
PARALLEL_START_INTERUPT_REGION
int newindex=i-m_MinSpec;
// Copy over spectrum and detector number info
outputWorkspace->getSpectrum(newindex)->copyInfoFrom(*localworkspace->getSpectrum(i));
// Retrieve the spectrum into a vector
const MantidVec& X = localworkspace->readX(i);
const MantidVec& Y = localworkspace->readY(i);
// Find the range [min,max]
MantidVec::const_iterator lowit, highit;
if (m_MinRange == EMPTY_DBL()) lowit=X.begin();
else lowit=std::lower_bound(X.begin(),X.end(),m_MinRange);
if (m_MaxRange == EMPTY_DBL()) highit=X.end();
else highit=std::find_if(lowit,X.end(),std::bind2nd(std::greater<double>(),m_MaxRange));
// If range specified doesn't overlap with this spectrum then bail out
if ( lowit == X.end() || highit == X.begin() ) continue;
--highit; // Upper limit is the bin before, i.e. the last value smaller than MaxRange
MantidVec::difference_type distmin=std::distance(X.begin(),lowit);
MantidVec::difference_type distmax=std::distance(X.begin(),highit);
MantidVec::const_iterator maxY;
// Find the max/min element
if (showMin==true)
{
maxY=std::min_element(Y.begin()+distmin,Y.begin()+distmax);
}
else
{
maxY=std::max_element(Y.begin()+distmin,Y.begin()+distmax);
}
MantidVec::difference_type d=std::distance(Y.begin(),maxY);
// X boundaries for the max/min element
outputWorkspace->dataX(newindex)[0]=*(X.begin()+d);
outputWorkspace->dataX(newindex)[1]=*(X.begin()+d+1); //This is safe since X is of dimension Y+1
outputWorkspace->dataY(newindex)[0]=*maxY;
progress.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
// Assign it to the output workspace property
setProperty("OutputWorkspace",outputWorkspace);
return;
}
void SANSSolidAngleCorrection::exec() {
// Reduction property manager
const std::string reductionManagerName = getProperty("ReductionProperties");
boost::shared_ptr<PropertyManager> reductionManager;
if (PropertyManagerDataService::Instance().doesExist(reductionManagerName)) {
reductionManager =
PropertyManagerDataService::Instance().retrieve(reductionManagerName);
} else {
reductionManager = boost::make_shared<PropertyManager>();
PropertyManagerDataService::Instance().addOrReplace(reductionManagerName,
reductionManager);
}
// If the solid angle algorithm isn't in the reduction properties, add it
if (!reductionManager->existsProperty("SolidAngleAlgorithm")) {
AlgorithmProperty *algProp = new AlgorithmProperty("SolidAngleAlgorithm");
algProp->setValue(toString());
reductionManager->declareProperty(algProp);
}
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
DataObjects::EventWorkspace_const_sptr inputEventWS =
boost::dynamic_pointer_cast<const EventWorkspace>(inputWS);
if (inputEventWS)
return execEvent();
// Now create the output workspace
MatrixWorkspace_sptr outputWS = getProperty("OutputWorkspace");
if (outputWS != inputWS) {
outputWS = WorkspaceFactory::Instance().create(inputWS);
outputWS->isDistribution(true);
outputWS->setYUnit("");
outputWS->setYUnitLabel("Steradian");
setProperty("OutputWorkspace", outputWS);
}
const int numHists = static_cast<int>(inputWS->getNumberHistograms());
Progress progress(this, 0.0, 1.0, numHists);
// Number of X bins
const int xLength = static_cast<int>(inputWS->readY(0).size());
PARALLEL_FOR2(outputWS, inputWS)
for (int i = 0; i < numHists; ++i) {
PARALLEL_START_INTERUPT_REGION
outputWS->dataX(i) = inputWS->readX(i);
IDetector_const_sptr det;
try {
det = inputWS->getDetector(i);
} catch (Exception::NotFoundError &) {
g_log.warning() << "Spectrum index " << i
<< " has no detector assigned to it - discarding"
<< std::endl;
// Catch if no detector. Next line tests whether this happened - test
// placed
// outside here because Mac Intel compiler doesn't like 'continue' in a
// catch
// in an openmp block.
}
// If no detector found, skip onto the next spectrum
if (!det)
continue;
// Skip if we have a monitor or if the detector is masked.
if (det->isMonitor() || det->isMasked())
continue;
const MantidVec &YIn = inputWS->readY(i);
const MantidVec &EIn = inputWS->readE(i);
MantidVec &YOut = outputWS->dataY(i);
MantidVec &EOut = outputWS->dataE(i);
// Compute solid angle correction factor
const bool is_tube = getProperty("DetectorTubes");
const double tanTheta = tan(inputWS->detectorTwoTheta(det));
const double theta_term = sqrt(tanTheta * tanTheta + 1.0);
double corr;
if (is_tube) {
const double tanAlpha = tan(getYTubeAngle(det, inputWS));
const double alpha_term = sqrt(tanAlpha * tanAlpha + 1.0);
corr = alpha_term * theta_term * theta_term;
} else {
corr = theta_term * theta_term * theta_term;
}
// Correct data for all X bins
for (int j = 0; j < xLength; j++) {
YOut[j] = YIn[j] * corr;
EOut[j] = fabs(EIn[j] * corr);
}
progress.report("Solid Angle Correction");
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
setProperty("OutputMessage", "Solid angle correction applied");
}
/** Executes the algorithm
*
* @throw runtime_error Thrown if algorithm cannot execute
*/
void Fit1D::exec() {
// Custom initialization
prepare();
// check if derivative defined in derived class
bool isDerivDefined = true;
gsl_matrix *M = NULL;
try {
const std::vector<double> inTest(m_parameterNames.size(), 1.0);
std::vector<double> outTest(m_parameterNames.size());
const double xValuesTest = 0;
JacobianImpl J;
M = gsl_matrix_alloc(m_parameterNames.size(), 1);
J.setJ(M);
// note nData set to zero (last argument) hence this should avoid further
// memory problems
functionDeriv(&(inTest.front()), &J, &xValuesTest, 0);
} catch (Exception::NotImplementedError &) {
isDerivDefined = false;
}
gsl_matrix_free(M);
// Try to retrieve optional properties
int histNumber = getProperty("WorkspaceIndex");
const int maxInterations = getProperty("MaxIterations");
// Get the input workspace
MatrixWorkspace_const_sptr localworkspace = getProperty("InputWorkspace");
// number of histogram is equal to the number of spectra
const size_t numberOfSpectra = localworkspace->getNumberHistograms();
// Check that the index given is valid
if (histNumber >= static_cast<int>(numberOfSpectra)) {
g_log.warning("Invalid Workspace index given, using first Workspace");
histNumber = 0;
}
// Retrieve the spectrum into a vector
const MantidVec &XValues = localworkspace->readX(histNumber);
const MantidVec &YValues = localworkspace->readY(histNumber);
const MantidVec &YErrors = localworkspace->readE(histNumber);
// Read in the fitting range data that we were sent
double startX = getProperty("StartX");
double endX = getProperty("EndX");
// check if the values had been set, otherwise use defaults
if (isEmpty(startX)) {
startX = XValues.front();
modifyStartOfRange(startX); // does nothing by default but derived class may
// provide a more intelligent value
}
if (isEmpty(endX)) {
endX = XValues.back();
modifyEndOfRange(endX); // does nothing by default but derived class may
// previde a more intelligent value
}
int m_minX;
int m_maxX;
// Check the validity of startX
if (startX < XValues.front()) {
g_log.warning("StartX out of range! Set to start of frame.");
startX = XValues.front();
}
// Get the corresponding bin boundary that comes before (or coincides with)
// this value
for (m_minX = 0; XValues[m_minX + 1] < startX; ++m_minX) {
}
// Check the validity of endX and get the bin boundary that come after (or
// coincides with) it
if (endX >= XValues.back() || endX < startX) {
g_log.warning("EndX out of range! Set to end of frame");
endX = XValues.back();
m_maxX = static_cast<int>(YValues.size());
} else {
for (m_maxX = m_minX; XValues[m_maxX] < endX; ++m_maxX) {
}
}
afterDataRangedDetermined(m_minX, m_maxX);
// create and populate GSL data container warn user if l_data.n < l_data.p
// since as a rule of thumb this is required as a minimum to obtained
// 'accurate'
// fitting parameter values.
FitData l_data(this, getProperty("Fix"));
l_data.n =
m_maxX -
m_minX; // m_minX and m_maxX are array index markers. I.e. e.g. 0 & 19.
if (l_data.n == 0) {
g_log.error("The data set is empty.");
throw std::runtime_error("The data set is empty.");
}
if (l_data.n < l_data.p) {
g_log.error(
"Number of data points less than number of parameters to be fitted.");
throw std::runtime_error(
"Number of data points less than number of parameters to be fitted.");
}
l_data.X = new double[l_data.n];
l_data.sigmaData = new double[l_data.n];
l_data.forSimplexLSwrap = new double[l_data.n];
l_data.parameters = new double[nParams()];
// check if histogram data in which case use mid points of histogram bins
const bool isHistogram = localworkspace->isHistogramData();
for (unsigned int i = 0; i < l_data.n; ++i) {
if (isHistogram)
l_data.X[i] =
0.5 * (XValues[m_minX + i] +
XValues[m_minX + i + 1]); // take mid-point if histogram bin
else
l_data.X[i] = XValues[m_minX + i];
}
l_data.Y = &YValues[m_minX];
// check that no error is negative or zero
for (unsigned int i = 0; i < l_data.n; ++i) {
if (YErrors[m_minX + i] <= 0.0) {
l_data.sigmaData[i] = 1.0;
} else
l_data.sigmaData[i] = YErrors[m_minX + i];
}
// create array of fitted parameter. Take these to those input by the user.
// However, for doing the
// underlying fitting it might be more efficient to actually perform the
// fitting on some of other
// form of the fitted parameters. For instance, take the Gaussian sigma
// parameter. In practice it
// in fact more efficient to perform the fitting not on sigma but 1/sigma^2.
// The methods
// modifyInitialFittedParameters() and modifyFinalFittedParameters() are used
// to allow for this;
// by default these function do nothing.
m_fittedParameter.clear();
for (size_t i = 0; i < nParams(); i++) {
m_fittedParameter.push_back(getProperty(m_parameterNames[i]));
}
modifyInitialFittedParameters(
m_fittedParameter); // does nothing except if overwritten by derived class
for (size_t i = 0; i < nParams(); i++) {
l_data.parameters[i] = m_fittedParameter[i];
}
// set-up initial guess for fit parameters
gsl_vector *initFuncArg;
initFuncArg = gsl_vector_alloc(l_data.p);
for (size_t i = 0, j = 0; i < nParams(); i++) {
if (l_data.active[i])
gsl_vector_set(initFuncArg, j++, m_fittedParameter[i]);
}
// set-up GSL container to be used with GSL simplex algorithm
gsl_multimin_function gslSimplexContainer;
gslSimplexContainer.n = l_data.p; // n here refers to number of parameters
gslSimplexContainer.f = &gsl_costFunction;
gslSimplexContainer.params = &l_data;
// set-up GSL least squares container
gsl_multifit_function_fdf f;
f.f = &gsl_f;
f.df = &gsl_df;
f.fdf = &gsl_fdf;
f.n = l_data.n;
f.p = l_data.p;
f.params = &l_data;
// set-up remaining GSL machinery for least squared
const gsl_multifit_fdfsolver_type *T = gsl_multifit_fdfsolver_lmsder;
gsl_multifit_fdfsolver *s = NULL;
if (isDerivDefined) {
s = gsl_multifit_fdfsolver_alloc(T, l_data.n, l_data.p);
gsl_multifit_fdfsolver_set(s, &f, initFuncArg);
}
// set-up remaining GSL machinery to use simplex algorithm
const gsl_multimin_fminimizer_type *simplexType =
gsl_multimin_fminimizer_nmsimplex;
gsl_multimin_fminimizer *simplexMinimizer = NULL;
gsl_vector *simplexStepSize = NULL;
if (!isDerivDefined) {
simplexMinimizer = gsl_multimin_fminimizer_alloc(simplexType, l_data.p);
simplexStepSize = gsl_vector_alloc(l_data.p);
gsl_vector_set_all(simplexStepSize,
1.0); // is this always a sensible starting step size?
gsl_multimin_fminimizer_set(simplexMinimizer, &gslSimplexContainer,
initFuncArg, simplexStepSize);
}
// finally do the fitting
int iter = 0;
int status;
double finalCostFuncVal;
double dof = static_cast<double>(
l_data.n - l_data.p); // dof stands for degrees of freedom
// Standard least-squares used if derivative function defined otherwise
// simplex
Progress prog(this, 0.0, 1.0, maxInterations);
if (isDerivDefined) {
do {
iter++;
status = gsl_multifit_fdfsolver_iterate(s);
if (status) // break if error
break;
status = gsl_multifit_test_delta(s->dx, s->x, 1e-4, 1e-4);
prog.report();
} while (status == GSL_CONTINUE && iter < maxInterations);
double chi = gsl_blas_dnrm2(s->f);
finalCostFuncVal = chi * chi / dof;
// put final converged fitting values back into m_fittedParameter
for (size_t i = 0, j = 0; i < nParams(); i++)
if (l_data.active[i])
m_fittedParameter[i] = gsl_vector_get(s->x, j++);
} else {
do {
iter++;
status = gsl_multimin_fminimizer_iterate(simplexMinimizer);
if (status) // break if error
break;
double size = gsl_multimin_fminimizer_size(simplexMinimizer);
status = gsl_multimin_test_size(size, 1e-2);
prog.report();
} while (status == GSL_CONTINUE && iter < maxInterations);
finalCostFuncVal = simplexMinimizer->fval / dof;
// put final converged fitting values back into m_fittedParameter
for (unsigned int i = 0, j = 0; i < m_fittedParameter.size(); i++)
if (l_data.active[i])
m_fittedParameter[i] = gsl_vector_get(simplexMinimizer->x, j++);
}
modifyFinalFittedParameters(
m_fittedParameter); // do nothing except if overwritten by derived class
// Output summary to log file
std::string reportOfFit = gsl_strerror(status);
g_log.information() << "Iteration = " << iter << "\n"
<< "Status = " << reportOfFit << "\n"
<< "Chi^2/DoF = " << finalCostFuncVal << "\n";
for (size_t i = 0; i < m_fittedParameter.size(); i++)
g_log.information() << m_parameterNames[i] << " = " << m_fittedParameter[i]
<< " \n";
// also output summary to properties
setProperty("OutputStatus", reportOfFit);
setProperty("OutputChi2overDoF", finalCostFuncVal);
for (size_t i = 0; i < m_fittedParameter.size(); i++)
setProperty(m_parameterNames[i], m_fittedParameter[i]);
std::string output = getProperty("Output");
if (!output.empty()) {
// calculate covariance matrix if derivatives available
gsl_matrix *covar(NULL);
std::vector<double> standardDeviations;
std::vector<double> sdExtended;
if (isDerivDefined) {
covar = gsl_matrix_alloc(l_data.p, l_data.p);
gsl_multifit_covar(s->J, 0.0, covar);
int iPNotFixed = 0;
for (size_t i = 0; i < nParams(); i++) {
sdExtended.push_back(1.0);
if (l_data.active[i]) {
sdExtended[i] = sqrt(gsl_matrix_get(covar, iPNotFixed, iPNotFixed));
iPNotFixed++;
}
}
modifyFinalFittedParameters(sdExtended);
for (size_t i = 0; i < nParams(); i++)
if (l_data.active[i])
standardDeviations.push_back(sdExtended[i]);
declareProperty(
new WorkspaceProperty<API::ITableWorkspace>(
"OutputNormalisedCovarianceMatrix", "", Direction::Output),
"The name of the TableWorkspace in which to store the final "
"covariance matrix");
setPropertyValue("OutputNormalisedCovarianceMatrix",
output + "_NormalisedCovarianceMatrix");
Mantid::API::ITableWorkspace_sptr m_covariance =
Mantid::API::WorkspaceFactory::Instance().createTable(
"TableWorkspace");
m_covariance->addColumn("str", "Name");
std::vector<std::string>
paramThatAreFitted; // used for populating 1st "name" column
for (size_t i = 0; i < nParams(); i++) {
if (l_data.active[i]) {
m_covariance->addColumn("double", m_parameterNames[i]);
paramThatAreFitted.push_back(m_parameterNames[i]);
}
}
for (size_t i = 0; i < l_data.p; i++) {
Mantid::API::TableRow row = m_covariance->appendRow();
row << paramThatAreFitted[i];
for (size_t j = 0; j < l_data.p; j++) {
if (j == i)
row << 1.0;
else {
row << 100.0 * gsl_matrix_get(covar, i, j) /
sqrt(gsl_matrix_get(covar, i, i) *
gsl_matrix_get(covar, j, j));
}
}
}
setProperty("OutputNormalisedCovarianceMatrix", m_covariance);
}
declareProperty(new WorkspaceProperty<API::ITableWorkspace>(
"OutputParameters", "", Direction::Output),
"The name of the TableWorkspace in which to store the "
"final fit parameters");
declareProperty(
new WorkspaceProperty<MatrixWorkspace>("OutputWorkspace", "",
Direction::Output),
"Name of the output Workspace holding resulting simlated spectrum");
setPropertyValue("OutputParameters", output + "_Parameters");
setPropertyValue("OutputWorkspace", output + "_Workspace");
// Save the final fit parameters in the output table workspace
Mantid::API::ITableWorkspace_sptr m_result =
Mantid::API::WorkspaceFactory::Instance().createTable("TableWorkspace");
m_result->addColumn("str", "Name");
m_result->addColumn("double", "Value");
if (isDerivDefined)
m_result->addColumn("double", "Error");
Mantid::API::TableRow row = m_result->appendRow();
row << "Chi^2/DoF" << finalCostFuncVal;
for (size_t i = 0; i < nParams(); i++) {
Mantid::API::TableRow row = m_result->appendRow();
row << m_parameterNames[i] << m_fittedParameter[i];
if (isDerivDefined && l_data.active[i]) {
// perhaps want to scale standard deviations with sqrt(finalCostFuncVal)
row << sdExtended[i];
}
}
setProperty("OutputParameters", m_result);
// Save the fitted and simulated spectra in the output workspace
MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
int iSpec = getProperty("WorkspaceIndex");
const MantidVec &inputX = inputWorkspace->readX(iSpec);
const MantidVec &inputY = inputWorkspace->readY(iSpec);
int histN = isHistogram ? 1 : 0;
Mantid::DataObjects::Workspace2D_sptr ws =
boost::dynamic_pointer_cast<Mantid::DataObjects::Workspace2D>(
Mantid::API::WorkspaceFactory::Instance().create(
"Workspace2D", 3, l_data.n + histN, l_data.n));
ws->setTitle("");
ws->getAxis(0)->unit() =
inputWorkspace->getAxis(0)
->unit(); // UnitFactory::Instance().create("TOF");
for (int i = 0; i < 3; i++)
ws->dataX(i)
.assign(inputX.begin() + m_minX, inputX.begin() + m_maxX + histN);
ws->dataY(0).assign(inputY.begin() + m_minX, inputY.begin() + m_maxX);
MantidVec &Y = ws->dataY(1);
MantidVec &E = ws->dataY(2);
double *lOut =
new double[l_data.n]; // to capture output from call to function()
modifyInitialFittedParameters(m_fittedParameter); // does nothing except if
// overwritten by derived
// class
function(&m_fittedParameter[0], lOut, l_data.X, l_data.n);
modifyInitialFittedParameters(m_fittedParameter); // reverse the effect of
// modifyInitialFittedParameters - if any
for (unsigned int i = 0; i < l_data.n; i++) {
Y[i] = lOut[i];
E[i] = l_data.Y[i] - Y[i];
}
delete[] lOut;
setProperty("OutputWorkspace",
boost::dynamic_pointer_cast<MatrixWorkspace>(ws));
if (isDerivDefined)
gsl_matrix_free(covar);
}
// clean up dynamically allocated gsl stuff
if (isDerivDefined)
gsl_multifit_fdfsolver_free(s);
else {
gsl_vector_free(simplexStepSize);
gsl_multimin_fminimizer_free(simplexMinimizer);
}
delete[] l_data.X;
delete[] l_data.sigmaData;
delete[] l_data.forSimplexLSwrap;
delete[] l_data.parameters;
gsl_vector_free(initFuncArg);
return;
}
/**
* This function deals with the logic necessary for summing a Workspace2D.
* @param localworkspace The input workspace for summing.
* @param outSpec The spectrum for the summed output.
* @param progress The progress indicator.
* @param numSpectra The number of spectra contributed to the sum.
* @param numMasked The spectra dropped from the summations because they are
* masked.
* @param numZeros The number of zero bins in histogram workspace or empty
* spectra for event workspace.
*/
void SumSpectra::doWorkspace2D(MatrixWorkspace_const_sptr localworkspace,
ISpectrum *outSpec, Progress &progress,
size_t &numSpectra, size_t &numMasked,
size_t &numZeros) {
// Get references to the output workspaces's data vectors
MantidVec &YSum = outSpec->dataY();
MantidVec &YError = outSpec->dataE();
MantidVec Weight;
std::vector<size_t> nZeros;
if (m_calculateWeightedSum) {
Weight.assign(YSum.size(), 0);
nZeros.assign(YSum.size(), 0);
}
numSpectra = 0;
numMasked = 0;
numZeros = 0;
// Loop over spectra
std::set<int>::iterator it;
// for (int i = m_minSpec; i <= m_maxSpec; ++i)
for (it = this->m_indices.begin(); it != this->m_indices.end(); ++it) {
int i = *it;
// Don't go outside the range.
if ((i >= this->m_numberOfSpectra) || (i < 0)) {
g_log.error() << "Invalid index " << i
<< " was specified. Sum was aborted.\n";
break;
}
try {
// Get the detector object for this spectrum
Geometry::IDetector_const_sptr det = localworkspace->getDetector(i);
// Skip monitors, if the property is set to do so
if (!m_keepMonitors && det->isMonitor())
continue;
// Skip masked detectors
if (det->isMasked()) {
numMasked++;
continue;
}
} catch (...) {
// if the detector not found just carry on
}
numSpectra++;
// Retrieve the spectrum into a vector
const MantidVec &YValues = localworkspace->readY(i);
const MantidVec &YErrors = localworkspace->readE(i);
if (m_calculateWeightedSum) {
for (int k = 0; k < this->m_yLength; ++k) {
if (YErrors[k] != 0) {
double errsq = YErrors[k] * YErrors[k];
YError[k] += errsq;
Weight[k] += 1. / errsq;
YSum[k] += YValues[k] / errsq;
} else {
nZeros[k]++;
}
}
} else {
for (int k = 0; k < this->m_yLength; ++k) {
YSum[k] += YValues[k];
YError[k] += YErrors[k] * YErrors[k];
}
}
// Map all the detectors onto the spectrum of the output
outSpec->addDetectorIDs(localworkspace->getSpectrum(i)->getDetectorIDs());
progress.report();
}
if (m_calculateWeightedSum) {
numZeros = 0;
for (size_t i = 0; i < Weight.size(); i++) {
if (nZeros[i] == 0)
YSum[i] *= double(numSpectra) / Weight[i];
else
numZeros += nZeros[i];
}
}
}
void SANSDirectBeamScaling::exec()
{
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
const double beamRadius = getProperty("BeamRadius");
const double attTrans = getProperty("AttenuatorTransmission");
const double attTransErr = getProperty("AttenuatorTransmissionError");
// Extract the required spectra into separate workspaces
std::vector<detid_t> udet;
std::vector<size_t> index;
udet.push_back(getProperty("BeamMonitor"));
// Convert UDETs to workspace indices
inputWS->getIndicesFromDetectorIDs(udet,index);
if (index.size() < 1)
{
g_log.debug() << "inputWS->getIndicesFromDetectorIDs() returned empty\n";
throw std::invalid_argument("Could not find the incident beam monitor spectra\n");
}
const int64_t numHists = inputWS->getNumberHistograms();
Progress progress(this,0.0,1.0,numHists);
// Number of X bins
const int64_t xLength = inputWS->readY(0).size();
// Monitor counts
double monitor = 0.0;
const MantidVec& MonIn = inputWS->readY(index[0]);
for (int64_t j = 0; j < xLength; j++)
monitor += MonIn[j];
const V3D sourcePos = inputWS->getInstrument()->getSource()->getPos();
double counts = 0.0;
double error = 0.0;
int nPixels = 0;
// Sample-detector distance for the contributing pixels
double sdd = 0.0;
for (int64_t i = 0; i < int64_t(numHists); ++i)
{
IDetector_const_sptr det;
try {
det = inputWS->getDetector(i);
} catch (Exception::NotFoundError&) {
g_log.warning() << "Spectrum index " << i << " has no detector assigned to it - discarding" << std::endl;
continue;
}
// Skip if we have a monitor or if the detector is masked.
if ( det->isMonitor() || det->isMasked() ) continue;
const MantidVec& YIn = inputWS->readY(i);
const MantidVec& EIn = inputWS->readE(i);
// Sum up all the counts
V3D pos = det->getPos() - V3D(sourcePos.X(), sourcePos.Y(), 0.0);
const double pixelDistance = pos.Z();
pos.setZ(0.0);
if (pos.norm() <= beamRadius) {
// Correct data for all X bins
for (int64_t j = 0; j < xLength; j++)
{
counts += YIn[j];
error += EIn[j]*EIn[j];
}
nPixels += 1;
sdd += pixelDistance;
}
progress.report("Absolute Scaling");
}
// Get the average SDD for the counted pixels, and transform to mm.
sdd = sdd/nPixels*1000.0;
error = std::sqrt(error);
// Transform from m to mm
double sourceAperture = getProperty("SourceApertureRadius");
sourceAperture *= 1000.0;
// Transform from m to mm
double sampleAperture = getProperty("SampleApertureRadius");
sampleAperture *= 1000.0;
//TODO: replace this by some meaningful value
const double KCG2FluxPerMon_SUGAR = 1.0;
// Solid angle correction scale in 1/(cm^2)/steradian
double solidAngleCorrScale = sdd/(M_PI*sourceAperture*sampleAperture);
solidAngleCorrScale = solidAngleCorrScale*solidAngleCorrScale*100.0;
// Scaling factor in n/(monitor count)/(cm^2)/steradian
double scale = counts/monitor*solidAngleCorrScale/KCG2FluxPerMon_SUGAR;
double scaleErr = std::abs(error/monitor)*solidAngleCorrScale/KCG2FluxPerMon_SUGAR;
scaleErr = std::abs(scale/attTrans)*sqrt( (scaleErr/scale)*(scaleErr/scale) +(attTransErr/attTrans)*(attTransErr/attTrans) );
scale /= attTrans;
std::vector<double> output;
output.push_back(scale);
output.push_back(scaleErr);
setProperty("ScaleFactor", output);
}
/** Executes the rebin algorithm
*
* @throw runtime_error Thrown if
*/
void Rebunch::exec()
{
// retrieve the properties
int n_bunch=getProperty("NBunch");
// Get the input workspace
MatrixWorkspace_const_sptr inputW = getProperty("InputWorkspace");
bool dist = inputW->isDistribution();
// workspace independent determination of length
int histnumber = static_cast<int>(inputW->size()/inputW->blocksize());
/*
const std::vector<double>& Xold = inputW->readX(0);
const std::vector<double>& Yold = inputW->readY(0);
int size_x=Xold.size();
int size_y=Yold.size();
*/
int size_x = static_cast<int>(inputW->readX(0).size());
int size_y = static_cast<int>(inputW->readY(0).size());
//signal is the same length for histogram and point data
int ny=(size_y/n_bunch);
if(size_y%n_bunch >0)ny+=1;
// default is for hist
int nx=ny+1;
bool point=false;
if (size_x==size_y)
{
point=true;
nx=ny;
}
// make output Workspace the same type is the input, but with new length of signal array
API::MatrixWorkspace_sptr outputW = API::WorkspaceFactory::Instance().create(inputW,histnumber,nx,ny);
int progress_step = histnumber / 100;
if (progress_step == 0) progress_step = 1;
PARALLEL_FOR2(inputW,outputW)
for (int hist=0; hist < histnumber;hist++)
{
PARALLEL_START_INTERUPT_REGION
// Ensure that axis information are copied to the output workspace if the axis exists
try
{
outputW->getAxis(1)->spectraNo(hist)=inputW->getAxis(1)->spectraNo(hist);
}
catch( Exception::IndexError& )
{
// Not a Workspace2D
}
// get const references to input Workspace arrays (no copying)
const MantidVec& XValues = inputW->readX(hist);
const MantidVec& YValues = inputW->readY(hist);
const MantidVec& YErrors = inputW->readE(hist);
//get references to output workspace data (no copying)
MantidVec& XValues_new=outputW->dataX(hist);
MantidVec& YValues_new=outputW->dataY(hist);
MantidVec& YErrors_new=outputW->dataE(hist);
// output data arrays are implicitly filled by function
if(point)
{
rebunch_point(XValues,YValues,YErrors,XValues_new,YValues_new,YErrors_new,n_bunch);
}
else
{
rebunch_hist(XValues,YValues,YErrors,XValues_new,YValues_new,YErrors_new,n_bunch, dist);
}
if (hist % progress_step == 0)
{
progress(double(hist)/histnumber);
interruption_point();
}
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
outputW->isDistribution(dist);
// Copy units
if (outputW->getAxis(0)->unit().get())
outputW->getAxis(0)->unit() = inputW->getAxis(0)->unit();
try
{
if (inputW->getAxis(1)->unit().get())
outputW->getAxis(1)->unit() = inputW->getAxis(1)->unit();
}
catch(Exception::IndexError&) {
// OK, so this isn't a Workspace2D
}
// Assign it to the output workspace property
setProperty("OutputWorkspace",outputW);
return;
}
//----------------------------------------------------------------------------------------------
/// @copydoc Mantid::API::Algorithm::exec()
void ResetNegatives::exec()
{
MatrixWorkspace_sptr inputWS = this->getProperty("InputWorkspace");
MatrixWorkspace_sptr outputWS = this->getProperty("OutputWorkspace");
// get the minimum for each spectrum
IAlgorithm_sptr alg = this->createChildAlgorithm("Min", 0., .1);
alg->setProperty<MatrixWorkspace_sptr>("InputWorkspace", inputWS);
alg->executeAsChildAlg();
MatrixWorkspace_const_sptr minWS = alg->getProperty("OutputWorkspace");
// determine if there is anything to do
int64_t nHist = static_cast<int64_t>(minWS->getNumberHistograms());
bool hasNegative = false;
for (int64_t i = 0; i < nHist; i++)
{
if (minWS->readY(i)[0] < 0)
{
hasNegative = true;
}
break;
}
// get out early if there is nothing to do
if (!hasNegative)
{
g_log.information() << "No values are negative. Copying InputWorkspace to OutputWorkspace\n";
if (inputWS != outputWS)
{
IAlgorithm_sptr alg = this->createChildAlgorithm("CloneWorkspace", .1, 1.);
alg->setProperty<Workspace_sptr>("InputWorkspace", inputWS);
alg->executeAsChildAlg();
Workspace_sptr temp = alg->getProperty("OutputWorkspace");
setProperty("OutputWorkspace", boost::dynamic_pointer_cast<MatrixWorkspace>(temp));
}
return;
}
// sort the event list to make it fast and thread safe
DataObjects::EventWorkspace_const_sptr eventWS
= boost::dynamic_pointer_cast<const DataObjects::EventWorkspace>( inputWS );
if (eventWS)
eventWS->sortAll(DataObjects::TOF_SORT, NULL);
Progress prog(this, .1, 1., 2*nHist);
// generate output workspace - copy X and dY
outputWS = API::WorkspaceFactory::Instance().create(inputWS);
PARALLEL_FOR2(inputWS,outputWS)
for (int64_t i = 0; i < nHist; i++)
{
PARALLEL_START_INTERUPT_REGION
outputWS->dataY(i) = inputWS->readY(i);
outputWS->dataE(i) = inputWS->readE(i);
outputWS->setX(i, inputWS->refX(i)); // share the pointer more
prog.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
// do the actual work
if (this->getProperty("AddMinimum"))
{
this->pushMinimum(minWS, outputWS, prog);
}
else
{
this->changeNegatives(minWS, this->getProperty("ResetValue"), outputWS, prog);
}
setProperty("OutputWorkspace",outputWS);
}
void Qxy::exec()
{
MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
MatrixWorkspace_const_sptr waveAdj = getProperty("WavelengthAdj");
MatrixWorkspace_const_sptr pixelAdj = getProperty("PixelAdj");
const bool doGravity = getProperty("AccountForGravity");
const bool doSolidAngle = getProperty("SolidAngleWeighting");
//throws if we don't have common binning or another incompatibility
Qhelper helper;
helper.examineInput(inputWorkspace, waveAdj, pixelAdj);
g_log.debug() << "All input workspaces were found to be valid\n";
// Create the output Qx-Qy grid
MatrixWorkspace_sptr outputWorkspace = this->setUpOutputWorkspace(inputWorkspace);
// Will also need an identically-sized workspace to hold the solid angle/time bin masked weight
MatrixWorkspace_sptr weights = WorkspaceFactory::Instance().create(outputWorkspace);
// Copy the X values from the output workspace to the solidAngles one
cow_ptr<MantidVec> axis;
axis.access() = outputWorkspace->readX(0);
for ( size_t i = 0; i < weights->getNumberHistograms(); ++i ) weights->setX(i,axis);
const size_t numSpec = inputWorkspace->getNumberHistograms();
const size_t numBins = inputWorkspace->blocksize();
// the samplePos is often not (0, 0, 0) because the instruments components are moved to account for the beam centre
const V3D samplePos = inputWorkspace->getInstrument()->getSample()->getPos();
// Set the progress bar (1 update for every one percent increase in progress)
Progress prog(this, 0.05, 1.0, numSpec);
// PARALLEL_FOR2(inputWorkspace,outputWorkspace)
for (int64_t i = 0; i < int64_t(numSpec); ++i)
{
// PARALLEL_START_INTERUPT_REGION
// Get the pixel relating to this spectrum
IDetector_const_sptr det;
try {
det = inputWorkspace->getDetector(i);
} catch (Exception::NotFoundError&) {
g_log.warning() << "Spectrum index " << i << " has no detector assigned to it - discarding" << std::endl;
// Catch if no detector. Next line tests whether this happened - test placed
// outside here because Mac Intel compiler doesn't like 'continue' in a catch
// in an openmp block.
}
// If no detector found or if it's masked or a monitor, skip onto the next spectrum
if ( !det || det->isMonitor() || det->isMasked() ) continue;
//get the bins that are included inside the RadiusCut/WaveCutcut off, those to calculate for
const size_t wavStart = helper.waveLengthCutOff(inputWorkspace, getProperty("RadiusCut"), getProperty("WaveCut"), i);
if (wavStart >= inputWorkspace->readY(i).size())
{
// all the spectra in this detector are out of range
continue;
}
V3D detPos = det->getPos()-samplePos;
// these will be re-calculated if gravity is on but without gravity there is no need
double phi = atan2(detPos.Y(),detPos.X());
double a = cos(phi);
double b = sin(phi);
double sinTheta = sin( inputWorkspace->detectorTwoTheta(det)/2.0 );
// Get references to the data for this spectrum
const MantidVec& X = inputWorkspace->readX(i);
const MantidVec& Y = inputWorkspace->readY(i);
const MantidVec& E = inputWorkspace->readE(i);
const MantidVec& axis = outputWorkspace->readX(0);
// the solid angle of the detector as seen by the sample is used for normalisation later on
double angle = det->solidAngle(samplePos);
// some bins are masked completely or partially, the following vector will contain the fractions
MantidVec maskFractions;
if ( inputWorkspace->hasMaskedBins(i) )
{
// go through the set and convert it to a vector
const MatrixWorkspace::MaskList& mask = inputWorkspace->maskedBins(i);
maskFractions.resize(numBins, 1.0);
MatrixWorkspace::MaskList::const_iterator it, itEnd(mask.end());
for (it = mask.begin(); it != itEnd; ++it)
{
// The weight for this masked bin is 1 minus the degree to which this bin is masked
maskFractions[it->first] -= it->second;
}
}
double maskFraction(1);
// this object is not used if gravity correction is off, but it is only constructed once per spectrum
GravitySANSHelper grav;
if (doGravity)
{
grav = GravitySANSHelper(inputWorkspace, det);
}
for (int j = static_cast<int>(numBins)-1; j >= static_cast<int>(wavStart); --j)
{
if( j < 0 ) break; // Be careful with counting down. Need a better fix but this will work for now
const double binWidth = X[j+1]-X[j];
// Calculate the wavelength at the mid-point of this bin
const double wavLength = X[j]+(binWidth)/2.0;
if (doGravity)
{
// SANS instruments must have their y-axis pointing up, show the detector position as where the neutron would be without gravity
sinTheta = grav.calcComponents(wavLength, a, b);
}
// Calculate |Q| for this bin
const double Q = 4.0*M_PI*sinTheta/wavLength;
// Now get the x & y components of Q.
const double Qx = a*Q;
// Test whether they're in range, if not go to next spectrum.
if ( Qx < axis.front() || Qx >= axis.back() ) break;
const double Qy = b*Q;
if ( Qy < axis.front() || Qy >= axis.back() ) break;
// Find the indices pointing to the place in the 2D array where this bin's contents should go
const MantidVec::difference_type xIndex = std::upper_bound(axis.begin(),axis.end(),Qx) - axis.begin() - 1;
const int yIndex = static_cast<int>(
std::upper_bound(axis.begin(),axis.end(),Qy) - axis.begin() - 1);
// PARALLEL_CRITICAL(qxy) /* Write to shared memory - must protect */
{
// the data will be copied to this bin in the output array
double & outputBinY = outputWorkspace->dataY(yIndex)[xIndex];
double & outputBinE = outputWorkspace->dataE(yIndex)[xIndex];
if ( boost::math::isnan(outputBinY))
{
outputBinY = outputBinE = 0;
}
// Add the contents of the current bin to the 2D array.
outputBinY += Y[j];
// add the errors in quadranture
outputBinE = std::sqrt( (outputBinE*outputBinE) + (E[j]*E[j]) );
// account for masked bins
if ( ! maskFractions.empty() )
{
maskFraction = maskFractions[j];
}
// add the total weight for this bin in the weights workspace,
// in an equivalent bin to where the data was stored
// first take into account the product of contributions to the weight which have
// no errors
double weight = 0.0;
if(doSolidAngle)
weight = maskFraction*angle;
else
weight = maskFraction;
// then the product of contributions which have errors, i.e. optional
// pixelAdj and waveAdj contributions
if (pixelAdj && waveAdj)
{
weights->dataY(yIndex)[xIndex] += weight*pixelAdj->readY(i)[0]*waveAdj->readY(0)[j];
const double pixelYSq = pixelAdj->readY(i)[0]*pixelAdj->readY(i)[0];
const double pixelESq = pixelAdj->readE(i)[0]*pixelAdj->readE(i)[0];
const double waveYSq = waveAdj->readY(0)[j]*waveAdj->readY(0)[j];
const double waveESq = waveAdj->readE(0)[j]*waveAdj->readE(0)[j];
// add product of errors from pixelAdj and waveAdj (note no error on weight is assumed)
weights->dataE(yIndex)[xIndex] += weight*weight*(waveESq*pixelYSq + pixelESq*waveYSq);
}
else if (pixelAdj)
{
weights->dataY(yIndex)[xIndex] += weight*pixelAdj->readY(i)[0];
const double pixelE = weight*pixelAdj->readE(i)[0];
// add error from pixelAdj
weights->dataE(yIndex)[xIndex] += pixelE*pixelE;
}
else if(waveAdj)
{
weights->dataY(yIndex)[xIndex] += weight*waveAdj->readY(0)[j];
const double waveE = weight*waveAdj->readE(0)[j];
// add error from waveAdj
weights->dataE(yIndex)[xIndex] += waveE*waveE;
}
else
weights->dataY(yIndex)[xIndex] += weight;
}
} // loop over single spectrum
prog.report("Calculating Q");
// PARALLEL_END_INTERUPT_REGION
} // loop over all spectra
// PARALLEL_CHECK_INTERUPT_REGION
// take sqrt of error weight values
// left to be executed here for computational efficiency
size_t numHist = weights->getNumberHistograms();
for (size_t i = 0; i < numHist; i++)
{
for (size_t j = 0; j < weights->dataE(i).size(); j++)
{
weights->dataE(i)[j] = sqrt(weights->dataE(i)[j]);
}
}
bool doOutputParts = getProperty("OutputParts");
if (doOutputParts)
{
// copy outputworkspace before it gets further modified
MatrixWorkspace_sptr ws_sumOfCounts = WorkspaceFactory::Instance().create(outputWorkspace);
for (size_t i = 0; i < ws_sumOfCounts->getNumberHistograms(); i++)
{
ws_sumOfCounts->dataX(i) = outputWorkspace->dataX(i);
ws_sumOfCounts->dataY(i) = outputWorkspace->dataY(i);
ws_sumOfCounts->dataE(i) = outputWorkspace->dataE(i);
}
helper.outputParts(this, ws_sumOfCounts, weights);
}
// Divide the output data by the solid angles
outputWorkspace /= weights;
outputWorkspace->isDistribution(true);
// Count of the number of empty cells
MatrixWorkspace::const_iterator wsIt(*outputWorkspace);
int emptyBins = 0;
for (;wsIt != wsIt.end(); ++wsIt)
{
if (wsIt->Y() < 1.0e-12) ++emptyBins;
}
// Log the number of empty bins
g_log.notice() << "There are a total of " << emptyBins << " ("
<< (100*emptyBins)/(outputWorkspace->size()) << "%) empty Q bins.\n";
}
void EQSANSMonitorTOF::exec()
{
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
// Now create the output workspace
MatrixWorkspace_sptr outputWS = getProperty("OutputWorkspace");
if ( outputWS != inputWS )
{
outputWS = WorkspaceFactory::Instance().create(inputWS);
setProperty("OutputWorkspace",outputWS);
}
// Get the nominal sample-to-detector distance (in mm)
// const double MD = MONITORPOS/1000.0;
// Get the monitor
const std::vector<detid_t> monitor_list = inputWS->getInstrument()->getMonitors();
if (monitor_list.size() != 1) {
g_log.error() << "EQSANS workspace does not have exactly ones monitor! This should not happen" << std::endl;
}
IDetector_const_sptr mon;
try {
mon = inputWS->getInstrument()->getDetector(monitor_list[0]);
} catch (Exception::NotFoundError&) {
g_log.error() << "Spectrum index " << monitor_list[0] << " has no detector assigned to it - discarding" << std::endl;
return;
}
// Get the source to monitor distance in mm
double source_z = inputWS->getInstrument()->getSource()->getPos().Z();
double monitor_z = mon->getPos().Z();
double source_to_monitor = (monitor_z - source_z)*1000.0;
// Calculate the frame width
double frequency = dynamic_cast<TimeSeriesProperty<double>*>(inputWS->run().getLogData("frequency"))->getStatistics().mean;
double tof_frame_width = 1.0e6/frequency;
// Determine whether we need frame skipping or not by checking the chopper speed
bool frame_skipping = false;
const double chopper_speed = dynamic_cast<TimeSeriesProperty<double>*>(inputWS->run().getLogData("Speed1"))->getStatistics().mean;
if (std::fabs(chopper_speed-frequency/2.0)<1.0) frame_skipping = true;
// Get TOF offset
// this is the call to the chopper code to say where
// the start of the data frame is relative to the native facility frame
double frame_tof0 = getTofOffset(inputWS, frame_skipping, source_to_monitor);
// Calculate the frame width
// none of this changes in response to just looking at the monitor
double tmp_frame_width = frame_skipping ? tof_frame_width * 2.0 : tof_frame_width;
double frame_offset=0.0;
if (frame_tof0 >= tmp_frame_width) frame_offset = tmp_frame_width * ( (int)( frame_tof0/tmp_frame_width ) );
// Find the new binning first
const MantidVec XIn = inputWS->readX(0); // Copy here to avoid holding on to reference for too long (problem with managed workspaces)
// Since we are swapping the low-TOF and high-TOF regions around the cutoff value,
// there is the potential for having an overlap between the two regions. We exclude
// the region beyond a single frame by considering only the first 1/60 sec of the
// TOF histogram. (Bins 1 to 1666, as opposed to 1 to 2000)
const int nTOF = static_cast<int>(XIn.size());
// Loop through each bin to find the cutoff where the TOF distribution wraps around
int cutoff = 0;
double threshold = frame_tof0-frame_offset;
int tof_bin_range = 0;
double frame = 1000000.0/frequency;
for (int i=0; i<nTOF; i++)
{
if (XIn[i] < threshold) cutoff = i;
if (XIn[i] < frame) tof_bin_range = i;
}
g_log.information() << "Cutoff=" << cutoff << "; Threshold=" << threshold << std::endl;
g_log.information() << "Low TOFs: old = [" << (cutoff+1) << ", " << (tof_bin_range-2) << "] -> new = [0, " << (tof_bin_range-3-cutoff) << "]" << std::endl;
g_log.information() << "High bin boundary of the Low TOFs: old = " << tof_bin_range-1 << "; new = " << (tof_bin_range-2-cutoff) << std::endl;
g_log.information() << "High TOFs: old = [0, " << (cutoff-1) << "] -> new = [" << (tof_bin_range-1-cutoff) << ", " << (tof_bin_range-2) << "]" << std::endl;
g_log.information() << "Overlap: new = [" << (tof_bin_range-1) << ", " << (nTOF-2) << "]" << std::endl;
// Keep a copy of the input data since we may end up overwriting it
// if the input workspace is equal to the output workspace.
// This is necessary since we are shuffling around the TOF bins.
MantidVec YCopy = MantidVec(inputWS->readY(0));
MantidVec& YIn = YCopy;
MantidVec ECopy = MantidVec(inputWS->readE(0));
MantidVec& EIn = ECopy;
MantidVec& XOut = outputWS->dataX(0);
MantidVec& YOut = outputWS->dataY(0);
MantidVec& EOut = outputWS->dataE(0);
// Here we modify the TOF according to the offset we calculated.
// Since this correction will change the order of the TOF bins,
// we do it in sequence so that we obtain a valid distribution
// as our result (with increasing TOF values).
// Move up the low TOFs
for (int i=0; i<cutoff; i++)
{
XOut[i+tof_bin_range-1-cutoff] = XIn[i] + frame_offset + tmp_frame_width;
YOut[i+tof_bin_range-1-cutoff] = YIn[i];
EOut[i+tof_bin_range-1-cutoff] = EIn[i];
}
// Get rid of extra bins
for (int i=tof_bin_range-1; i<nTOF-1; i++)
{
XOut[i] = XOut[i-1]+10.0;
YOut[i] = 0.0;
EOut[i] = 0.0;
}
XOut[nTOF-1] = XOut[nTOF-2]+10.0;
// Move down the high TOFs
for (int i=cutoff+1; i<tof_bin_range-1; i++)
{
XOut[i-cutoff-1] = XIn[i] + frame_offset;
YOut[i-cutoff-1] = YIn[i];
EOut[i-cutoff-1] = EIn[i];
}
// Don't forget the low boundary
XOut[tof_bin_range-2-cutoff] = XIn[tof_bin_range] + frame_offset;
// Zero out the cutoff bin, which no longer makes sense because
// len(x) = len(y)+1
YOut[tof_bin_range-2-cutoff] = 0.0;
EOut[tof_bin_range-2-cutoff] = 0.0;
setProperty("OutputWorkspace",outputWS);
}
void SofQWCentre::exec() {
using namespace Geometry;
MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
// Do the full check for common binning
if (!WorkspaceHelpers::commonBoundaries(inputWorkspace)) {
g_log.error(
"The input workspace must have common binning across all spectra");
throw std::invalid_argument(
"The input workspace must have common binning across all spectra");
}
std::vector<double> verticalAxis;
MatrixWorkspace_sptr outputWorkspace = setUpOutputWorkspace(
inputWorkspace, getProperty("QAxisBinning"), verticalAxis);
setProperty("OutputWorkspace", outputWorkspace);
// Holds the spectrum-detector mapping
std::vector<specnum_t> specNumberMapping;
std::vector<detid_t> detIDMapping;
m_EmodeProperties.initCachedValues(*inputWorkspace, this);
int emode = m_EmodeProperties.m_emode;
// Get a pointer to the instrument contained in the workspace
Instrument_const_sptr instrument = inputWorkspace->getInstrument();
// Get the distance between the source and the sample (assume in metres)
IComponent_const_sptr source = instrument->getSource();
IComponent_const_sptr sample = instrument->getSample();
V3D beamDir = sample->getPos() - source->getPos();
beamDir.normalize();
try {
double l1 = source->getDistance(*sample);
g_log.debug() << "Source-sample distance: " << l1 << '\n';
} catch (Exception::NotFoundError &) {
g_log.error("Unable to calculate source-sample distance");
throw Exception::InstrumentDefinitionError(
"Unable to calculate source-sample distance",
inputWorkspace->getTitle());
}
// Conversion constant for E->k. k(A^-1) = sqrt(energyToK*E(meV))
const double energyToK = 8.0 * M_PI * M_PI * PhysicalConstants::NeutronMass *
PhysicalConstants::meV * 1e-20 /
(PhysicalConstants::h * PhysicalConstants::h);
// Loop over input workspace bins, reassigning data to correct bin in output
// qw workspace
const size_t numHists = inputWorkspace->getNumberHistograms();
const size_t numBins = inputWorkspace->blocksize();
Progress prog(this, 0.0, 1.0, numHists);
for (int64_t i = 0; i < int64_t(numHists); ++i) {
try {
// Now get the detector object for this histogram
IDetector_const_sptr spectrumDet = inputWorkspace->getDetector(i);
if (spectrumDet->isMonitor())
continue;
const double efixed = m_EmodeProperties.getEFixed(*spectrumDet);
// For inelastic scattering the simple relationship q=4*pi*sinTheta/lambda
// does not hold. In order to
// be completely general we must calculate the momentum transfer by
// calculating the incident and final
// wave vectors and then use |q| = sqrt[(ki - kf)*(ki - kf)]
DetectorGroup_const_sptr detGroup =
boost::dynamic_pointer_cast<const DetectorGroup>(spectrumDet);
std::vector<IDetector_const_sptr> detectors;
if (detGroup) {
detectors = detGroup->getDetectors();
} else {
detectors.push_back(spectrumDet);
}
const size_t numDets = detectors.size();
const double numDets_d = static_cast<double>(
numDets); // cache to reduce number of static casts
const MantidVec &Y = inputWorkspace->readY(i);
const MantidVec &E = inputWorkspace->readE(i);
const MantidVec &X = inputWorkspace->readX(i);
// Loop over the detectors and for each bin calculate Q
for (size_t idet = 0; idet < numDets; ++idet) {
IDetector_const_sptr det = detectors[idet];
// Calculate kf vector direction and then Q for each energy bin
V3D scatterDir = (det->getPos() - sample->getPos());
scatterDir.normalize();
for (size_t j = 0; j < numBins; ++j) {
const double deltaE = 0.5 * (X[j] + X[j + 1]);
// Compute ki and kf wave vectors and therefore q = ki - kf
double ei(0.0), ef(0.0);
if (emode == 1) {
ei = efixed;
ef = efixed - deltaE;
if (ef < 0) {
std::string mess =
"Energy transfer requested in Direct mode exceeds incident "
"energy.\n Found for det ID: " +
std::to_string(idet) + " bin No " + std::to_string(j) +
" with Ei=" + boost::lexical_cast<std::string>(efixed) +
" and energy transfer: " +
boost::lexical_cast<std::string>(deltaE);
throw std::runtime_error(mess);
}
} else {
ei = efixed + deltaE;
ef = efixed;
if (ef < 0) {
std::string mess =
"Incident energy of a neutron is negative. Are you trying to "
"process Direct data in Indirect mode?\n Found for det ID: " +
std::to_string(idet) + " bin No " + std::to_string(j) +
" with efied=" + boost::lexical_cast<std::string>(efixed) +
" and energy transfer: " +
boost::lexical_cast<std::string>(deltaE);
throw std::runtime_error(mess);
}
}
if (ei < 0)
throw std::runtime_error(
"Negative incident energy. Check binning.");
const V3D ki = beamDir * sqrt(energyToK * ei);
const V3D kf = scatterDir * (sqrt(energyToK * (ef)));
const double q = (ki - kf).norm();
// Test whether it's in range of the Q axis
if (q < verticalAxis.front() || q > verticalAxis.back())
continue;
// Find which q bin this point lies in
const MantidVec::difference_type qIndex =
std::upper_bound(verticalAxis.begin(), verticalAxis.end(), q) -
verticalAxis.begin() - 1;
// Add this spectra-detector pair to the mapping
specNumberMapping.push_back(
outputWorkspace->getSpectrum(qIndex).getSpectrumNo());
detIDMapping.push_back(det->getID());
// And add the data and it's error to that bin, taking into account
// the number of detectors contributing to this bin
outputWorkspace->dataY(qIndex)[j] += Y[j] / numDets_d;
// Standard error on the average
outputWorkspace->dataE(qIndex)[j] =
sqrt((pow(outputWorkspace->readE(qIndex)[j], 2) + pow(E[j], 2)) /
numDets_d);
}
}
} catch (Exception::NotFoundError &) {
// Get to here if exception thrown when calculating distance to detector
// Presumably, if we get to here the spectrum will be all zeroes anyway
// (from conversion to E)
continue;
}
prog.report();
}
// If the input workspace was a distribution, need to divide by q bin width
if (inputWorkspace->isDistribution())
this->makeDistribution(outputWorkspace, verticalAxis);
// Set the output spectrum-detector mapping
SpectrumDetectorMapping outputDetectorMap(specNumberMapping, detIDMapping);
outputWorkspace->updateSpectraUsing(outputDetectorMap);
}
/** Executes the regroup algorithm
*
* @throw runtime_error Thrown if
*/
void Regroup::exec()
{
// retrieve the properties
std::vector<double> rb_params=getProperty("Params");
// Get the input workspace
MatrixWorkspace_const_sptr inputW = getProperty("InputWorkspace");
// can work only if all histograms have the same boundaries
if (!API::WorkspaceHelpers::commonBoundaries(inputW))
{
g_log.error("Histograms with different boundaries");
throw std::runtime_error("Histograms with different boundaries");
}
bool dist = inputW->isDistribution();
int histnumber = static_cast<int>(inputW->getNumberHistograms());
MantidVecPtr XValues_new;
const MantidVec & XValues_old = inputW->readX(0);
std::vector<int> xoldIndex;// indeces of new x in XValues_old
// create new output X axis
int ntcnew = newAxis(rb_params,XValues_old,XValues_new.access(),xoldIndex);
// make output Workspace the same type is the input, but with new length of signal array
API::MatrixWorkspace_sptr outputW = API::WorkspaceFactory::Instance().create(inputW,histnumber,ntcnew,ntcnew-1);
int progress_step = histnumber / 100;
if (progress_step == 0) progress_step = 1;
for (int hist=0; hist < histnumber;hist++)
{
// get const references to input Workspace arrays (no copying)
const MantidVec& XValues = inputW->readX(hist);
const MantidVec& YValues = inputW->readY(hist);
const MantidVec& YErrors = inputW->readE(hist);
//get references to output workspace data (no copying)
MantidVec& YValues_new=outputW->dataY(hist);
MantidVec& YErrors_new=outputW->dataE(hist);
// output data arrays are implicitly filled by function
rebin(XValues,YValues,YErrors,xoldIndex,YValues_new,YErrors_new, dist);
outputW->setX(hist,XValues_new);
if (hist % progress_step == 0)
{
progress(double(hist)/histnumber);
interruption_point();
}
}
outputW->isDistribution(dist);
// Copy units
if (outputW->getAxis(0)->unit().get())
outputW->getAxis(0)->unit() = inputW->getAxis(0)->unit();
try
{
if (inputW->getAxis(1)->unit().get())
outputW->getAxis(1)->unit() = inputW->getAxis(1)->unit();
}
catch(Exception::IndexError) {
// OK, so this isn't a Workspace2D
}
// Assign it to the output workspace property
setProperty("OutputWorkspace",outputW);
return;
}
void SmoothData::exec()
{
// Get the input properties
MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
int npts = getProperty("NPoints");
// Number of smoothing points must always be an odd number, so add 1 if it isn't.
if (!(npts%2))
{
g_log.information("Adding 1 to number of smoothing points, since it must always be odd");
++npts;
}
// Check that the number of points in the smoothing isn't larger than the spectrum length
const int vecSize = static_cast<int>(inputWorkspace->blocksize());
if ( npts >= vecSize )
{
g_log.error("The number of averaging points requested is larger than the spectrum length");
throw std::out_of_range("The number of averaging points requested is larger than the spectrum length");
}
const int halfWidth = (npts-1)/2;
// Create the output workspace
MatrixWorkspace_sptr outputWorkspace = WorkspaceFactory::Instance().create(inputWorkspace);
Progress progress(this,0.0,1.0,inputWorkspace->getNumberHistograms());
PARALLEL_FOR2(inputWorkspace,outputWorkspace)
// Loop over all the spectra in the workspace
for (int i = 0; i < static_cast<int>(inputWorkspace->getNumberHistograms()); ++i)
{
PARALLEL_START_INTERUPT_REGION
// Copy the X data over. Preserves data sharing if present in input workspace.
outputWorkspace->setX(i,inputWorkspace->refX(i));
// Now get references to the Y & E vectors in the input and output workspaces
const MantidVec &Y = inputWorkspace->readY(i);
const MantidVec &E = inputWorkspace->readE(i);
MantidVec &newY = outputWorkspace->dataY(i);
MantidVec &newE = outputWorkspace->dataE(i);
// Use total to help hold our moving average
double total = 0.0, totalE = 0.0;
// First push the values ahead of the current point onto total
for (int i = 0; i < halfWidth; ++i)
{
if ( Y[i] == Y[i] ) total += Y[i]; // Exclude if NaN
totalE += E[i]*E[i];
}
// Now calculate the smoothed values for the 'end' points, where the number contributing
// to the smoothing will be less than NPoints
for (int j = 0; j <= halfWidth; ++j)
{
const int index = j+halfWidth;
if ( Y[index] == Y[index] ) total += Y[index]; // Exclude if NaN
newY[j] = total/(index+1);
totalE += E[index]*E[index];
newE[j] = sqrt(totalE)/(index+1);
}
// This is the main part, where each data point is the average of NPoints points centred on the
// current point. Note that the statistical error will be reduced by sqrt(npts) because more
// data is now contributing to each point.
for (int k = halfWidth+1; k < vecSize-halfWidth; ++k)
{
const int kp = k+halfWidth;
const int km = k-halfWidth-1;
total += (Y[kp]!=Y[kp] ? 0.0 : Y[kp]) - (Y[km]!=Y[km] ? 0.0 : Y[km]); // Exclude if NaN
newY[k] = total/npts;
totalE += E[kp]*E[kp] - E[km]*E[km];
// Use of a moving average can lead to rounding error where what should be
// zero actually comes out as a tiny negative number - bad news for sqrt so protect
newE[k] = std::sqrt(std::abs(totalE))/npts;
}
// This deals with the 'end' at the tail of each spectrum
for (int l = vecSize-halfWidth; l < vecSize; ++l)
{
const int index = l-halfWidth;
total -= (Y[index-1]!=Y[index-1] ? 0.0 : Y[index-1]); // Exclude if NaN
newY[l] = total/(vecSize-index);
totalE -= E[index-1]*E[index-1];
newE[l] = std::sqrt(std::abs(totalE))/(vecSize-index);
}
progress.report();
PARALLEL_END_INTERUPT_REGION
} // Loop over spectra
PARALLEL_CHECK_INTERUPT_REGION
// Set the output workspace to its property
setProperty("OutputWorkspace", outputWorkspace);
}
