/**
* Main work portion of algorithm. Calculates mean of standard deviation,
* ignoring
* the detectors marked as "bad", then determines if any of the detectors are
* "bad".
* @param progress :: progress indicator
* @param valid :: eventual output workspace, holding 0 for bad and 1 for good
* @param values :: stddeviations of each spectra (I think)
*/
void IdentifyNoisyDetectors::getStdDev(API::Progress &progress,
MatrixWorkspace_sptr valid,
MatrixWorkspace_sptr values) {
const int nhist = static_cast<int>(valid->getNumberHistograms());
int count = 0;
double mean = 0.0;
double mean2 = 0.0;
for (int i = 0; i < nhist; i++) {
if (valid->readY(i)[0] > 0) {
mean += values->readY(i)[0];
mean2 += std::pow(values->readY(i)[0], 2);
count++;
}
progress.report();
}
if (0 == count) {
// all values are zero, no need to loop
return;
}
mean = mean / count;
double stddev = sqrt((mean2 / count) - std::pow(mean, 2));
double upper = mean + 3 * stddev;
double lower = mean - 3 * stddev;
double min = mean * 0.0001;
for (int i = 0; i < nhist; i++) {
double value = values->readY(i)[0];
if (value > upper) {
valid->dataY(i)[0] = 0.0;
} else if (value < lower) {
valid->dataY(i)[0] = 0.0;
} else if (value < min) {
valid->dataY(i)[0] = 0.0;
}
progress.report("Calculating StdDev...");
}
}
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);
}
/** Creates the output workspace, setting the X vector to the bins boundaries in Qx.
* @return A pointer to the newly-created workspace
*/
API::MatrixWorkspace_sptr Qxy::setUpOutputWorkspace(API::MatrixWorkspace_const_sptr inputWorkspace)
{
const double max = getProperty("MaxQxy");
const double delta = getProperty("DeltaQ");
int bins = static_cast<int>(max/delta);
if ( bins*delta != max ) ++bins; // Stop at first boundary past MaxQxy if max is not a multiple of delta
const double startVal = -1.0*delta*bins;
bins *= 2; // go from -max to +max
bins += 1; // Add 1 - this is a histogram
// Create an output workspace with the same meta-data as the input
MatrixWorkspace_sptr outputWorkspace = WorkspaceFactory::Instance().create(inputWorkspace,bins-1,bins,bins-1);
// ... but clear the masking from the parameter map as we don't want to carry that over since this is essentially
// a 2D rebin
ParameterMap & pmap = outputWorkspace->instrumentParameters();
pmap.clearParametersByName("masked");
// Create a numeric axis to replace the vertical one
Axis* verticalAxis = new NumericAxis(bins);
outputWorkspace->replaceAxis(1,verticalAxis);
// Build up the X values
Kernel::cow_ptr<MantidVec> axis;
MantidVec& horizontalAxisRef = axis.access();
horizontalAxisRef.resize(bins);
for (int i = 0; i < bins; ++i)
{
const double currentVal = startVal + i*delta;
// Set the X value
horizontalAxisRef[i] = currentVal;
// Set the Y value on the axis
verticalAxis->setValue(i,currentVal);
}
// Fill the X vectors in the output workspace
for (int i=0; i < bins-1; ++i)
{
outputWorkspace->setX(i,axis);
for (int j=0; j < bins-j; ++j)
{
outputWorkspace->dataY(i)[j] = std::numeric_limits<double>::quiet_NaN();
outputWorkspace->dataE(i)[j] = std::numeric_limits<double>::quiet_NaN();
}
}
// Set the axis units
outputWorkspace->getAxis(1)->unit() = outputWorkspace->getAxis(0)->unit() = UnitFactory::Instance().create("MomentumTransfer");
// Set the 'Y' unit (gets confusing here...this is probably a Z axis in this case)
outputWorkspace->setYUnitLabel("Cross Section (1/cm)");
setProperty("OutputWorkspace",outputWorkspace);
return outputWorkspace;
}
/// Get the counting time from a workspace
/// @param inputWS :: workspace to read the counting time from
double
HFIRDarkCurrentSubtraction::getCountingTime(MatrixWorkspace_sptr inputWS) {
// First, look whether we have the information in the log
if (inputWS->run().hasProperty("timer")) {
return inputWS->run().getPropertyValueAsType<double>("timer");
} else {
// If we don't have the information in the log, use the default timer
// spectrum
MantidVec &timer = inputWS->dataY(DEFAULT_TIMER_ID);
return timer[0];
}
}
/** Populate output workspace with results
* @param outWS :: [input/output] Output workspace to populate
* @param nplots :: [input] Number of histograms
*/
void PlotAsymmetryByLogValue::populateOutputWorkspace(
MatrixWorkspace_sptr &outWS, int nplots) {
auto tAxis = new TextAxis(nplots);
if (nplots == 1) {
size_t i = 0;
for (auto &value : m_logValue) {
outWS->dataX(0)[i] = value.second;
outWS->dataY(0)[i] = m_redY[value.first];
outWS->dataE(0)[i] = m_redE[value.first];
i++;
}
tAxis->setLabel(0, "Asymmetry");
} else {
size_t i = 0;
for (auto &value : m_logValue) {
outWS->dataX(0)[i] = value.second;
outWS->dataY(0)[i] = m_diffY[value.first];
outWS->dataE(0)[i] = m_diffE[value.first];
outWS->dataX(1)[i] = value.second;
outWS->dataY(1)[i] = m_redY[value.first];
outWS->dataE(1)[i] = m_redE[value.first];
outWS->dataX(2)[i] = value.second;
outWS->dataY(2)[i] = m_greenY[value.first];
outWS->dataE(2)[i] = m_greenE[value.first];
outWS->dataX(3)[i] = value.second;
outWS->dataY(3)[i] = m_sumY[value.first];
outWS->dataE(3)[i] = m_sumE[value.first];
i++;
}
tAxis->setLabel(0, "Red-Green");
tAxis->setLabel(1, "Red");
tAxis->setLabel(2, "Green");
tAxis->setLabel(3, "Red+Green");
}
outWS->replaceAxis(1, tAxis);
outWS->getAxis(0)->title() = m_logName;
outWS->setYUnitLabel("Asymmetry");
}
/** Populate output workspace with results
* @param outWS :: [input/output] Output workspace to populate
* @param nplots :: [input] Number of histograms
*/
void PlotAsymmetryByLogValue::saveResultsToADS(MatrixWorkspace_sptr &outWS,
int nplots) {
if (nplots == 2) {
size_t i = 0;
for (auto &value : m_logValue) {
size_t run = value.first;
outWS->dataX(0)[i] = static_cast<double>(run); // run number
outWS->dataY(0)[i] = value.second; // log value
outWS->dataY(1)[i] = m_redY[run]; // redY
outWS->dataE(1)[i] = m_redE[run]; // redE
i++;
}
} else {
size_t i = 0;
for (auto &value : m_logValue) {
size_t run = value.first;
outWS->dataX(0)[i] = static_cast<double>(run); // run number
outWS->dataY(0)[i] = value.second; // log value
outWS->dataY(1)[i] = m_diffY[run]; // diffY
outWS->dataE(1)[i] = m_diffE[run]; // diffE
outWS->dataY(2)[i] = m_redY[run]; // redY
outWS->dataE(2)[i] = m_redE[run]; // redE
outWS->dataY(3)[i] = m_greenY[run]; // greenY
outWS->dataE(3)[i] = m_greenE[run]; // greenE
outWS->dataY(4)[i] = m_sumY[run]; // sumY
outWS->dataE(4)[i] = m_sumE[run]; // sumE
i++;
}
}
// Set the title!
outWS->setTitle(m_allProperties);
// Save results to ADS
// We can't set an output property to store the results as this algorithm
// is executed as a child algorithm in the Muon ALC interface
// If current results were saved as a property we couln't used
// the functionality to re-use previous results in ALC
AnalysisDataService::Instance().addOrReplace(m_currResName, outWS);
}
/**
* Share the given input counts into the output array bins proportionally
* according to how much the bins overlap the given lambda range.
* outputX.size() must equal outputY.size() + 1
*
* @param inputCounts [in] :: the input counts to share out
* @param inputErr [in] :: the input errors to share out
* @param bLambda [in] :: the bin width in lambda
* @param lambdaMin [in] :: the start of the range to share counts to
* @param lambdaMax [in] :: the end of the range to share counts to
* @param outSpecIdx [in] :: the spectrum index to be updated in the output
* workspace
* @param IvsLam [in,out] :: the output workspace
* @param outputE [in,out] :: the projected E values
*/
void ReflectometryReductionOne2::sumInQShareCounts(
const double inputCounts, const double inputErr, const double bLambda,
const double lambdaMin, const double lambdaMax, const size_t outSpecIdx,
MatrixWorkspace_sptr IvsLam, std::vector<double> &outputE) {
// Check that we have histogram data
const auto &outputX = IvsLam->dataX(outSpecIdx);
auto &outputY = IvsLam->dataY(outSpecIdx);
if (outputX.size() != outputY.size() + 1) {
throw std::runtime_error(
"Expected output array to be histogram data (got X len=" +
std::to_string(outputX.size()) +
", Y len=" + std::to_string(outputY.size()) + ")");
}
const double totalWidth = lambdaMax - lambdaMin;
// Get the first bin edge in the output X array that is within range.
// There will probably be some overlap, so start from the bin edge before
// this (unless we're already at the first bin edge).
auto startIter = std::lower_bound(outputX.begin(), outputX.end(), lambdaMin);
if (startIter != outputX.begin()) {
--startIter;
}
// Loop through all overlapping output bins. Convert the iterator to an
// index because we need to index both the X and Y arrays.
const int xSize = static_cast<int>(outputX.size());
for (auto outIdx = startIter - outputX.begin(); outIdx < xSize - 1;
++outIdx) {
const double binStart = outputX[outIdx];
const double binEnd = outputX[outIdx + 1];
if (binStart > lambdaMax) {
// No longer in the overlap region so we're finished
break;
}
// Add a share of the input counts to this bin based on the proportion of
// overlap.
if (totalWidth > Tolerance) {
// Share counts out proportionally based on the overlap of this range
const double overlapWidth =
std::min({bLambda, lambdaMax - binStart, binEnd - lambdaMin});
const double fraction = overlapWidth / totalWidth;
outputY[outIdx] += inputCounts * fraction;
outputE[outIdx] += inputErr * fraction;
} else {
// Projection to a single value. Put all counts in the overlapping output
// bin.
outputY[outIdx] += inputCounts;
outputE[outIdx] += inputCounts;
}
}
}
/// Get the counting time from a workspace
/// @param inputWS :: workspace to read the counting time from
double HFIRDarkCurrentSubtraction::getCountingTime(MatrixWorkspace_sptr inputWS)
{
// First, look whether we have the information in the log
if (inputWS->run().hasProperty("timer"))
{
Mantid::Kernel::Property* prop = inputWS->run().getProperty("timer");
Mantid::Kernel::PropertyWithValue<double>* dp = dynamic_cast<Mantid::Kernel::PropertyWithValue<double>* >(prop);
return *dp;
} else {
// If we don't have the information in the log, use the default timer spectrum
MantidVec& timer = inputWS->dataY(DEFAULT_TIMER_ID);
return timer[0];
}
}
/**
* 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::rebinToOutput(const Geometry::Quadrilateral & inputQ, MatrixWorkspace_const_sptr inputWS,
const size_t i, const size_t j, MatrixWorkspace_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();
if(inputWS->isDistribution())
{
yValue *= overlapWidth;
eValue *= overlapWidth;
}
eValue = eValue*eValue;
PARALLEL_CRITICAL(overlap)
{
outputWS->dataY(qi)[ei] += yValue;
outputWS->dataE(qi)[ei] += eValue;
}
}
catch(Geometry::NoIntersectionException &)
{}
}
}
}
/**
* Handles completion of the preview algorithm.
*
* @param error If the algorithm failed
*/
void IndirectSymmetrise::previewAlgDone(bool error) {
if (error)
return;
QString workspaceName = m_uiForm.dsInput->getCurrentDataName();
int spectrumNumber =
static_cast<int>(m_dblManager->value(m_properties["PreviewSpec"]));
MatrixWorkspace_sptr sampleWS =
AnalysisDataService::Instance().retrieveWS<MatrixWorkspace>(
workspaceName.toStdString());
ITableWorkspace_sptr propsTable =
AnalysisDataService::Instance().retrieveWS<ITableWorkspace>(
"__SymmetriseProps_temp");
MatrixWorkspace_sptr symmWS =
AnalysisDataService::Instance().retrieveWS<MatrixWorkspace>(
"__Symmetrise_temp");
// Get the index of XCut on each side of zero
int negativeIndex = propsTable->getColumn("NegativeXMinIndex")->cell<int>(0);
int positiveIndex = propsTable->getColumn("PositiveXMinIndex")->cell<int>(0);
// Get the Y values for each XCut and the difference between them
double negativeY = sampleWS->dataY(0)[negativeIndex];
double positiveY = sampleWS->dataY(0)[positiveIndex];
double deltaY = fabs(negativeY - positiveY);
// Show values in property tree
m_dblManager->setValue(m_properties["NegativeYValue"], negativeY);
m_dblManager->setValue(m_properties["PositiveYValue"], positiveY);
m_dblManager->setValue(m_properties["DeltaY"], deltaY);
// Set indicator positions
m_uiForm.ppRawPlot->getRangeSelector("NegativeEMinYPos")
->setMinimum(negativeY);
m_uiForm.ppRawPlot->getRangeSelector("PositiveEMinYPos")
->setMinimum(positiveY);
// Plot preview plot
size_t spectrumIndex = symmWS->getIndexFromSpectrumNumber(spectrumNumber);
m_uiForm.ppPreviewPlot->clear();
m_uiForm.ppPreviewPlot->addSpectrum("Symmetrised", "__Symmetrise_temp",
spectrumIndex);
// Don't want this to trigger when the algorithm is run for all spectra
disconnect(m_batchAlgoRunner, SIGNAL(batchComplete(bool)), this,
SLOT(previewAlgDone(bool)));
}
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);
}
/** Set up neutron event's TOF correction.
* It can be (1) parsed from TOF-correction table workspace to vectors,
* (2) created according to detector's position in instrument;
* (3) or no correction,i.e., correction value is equal to 1.
* Offset should be as F*TOF + B
*/
void FilterEvents::setupDetectorTOFCalibration() {
// Set output correction workspace and set to output
const size_t numhist = m_eventWS->getNumberHistograms();
MatrixWorkspace_sptr corrws = boost::dynamic_pointer_cast<MatrixWorkspace>(
WorkspaceFactory::Instance().create("Workspace2D", numhist, 2, 2));
setProperty("OutputTOFCorrectionWorkspace", corrws);
// Set up the size of correction and output correction workspace
m_detTofOffsets.resize(numhist, 0.0); // unit of TOF
m_detTofFactors.resize(numhist, 1.0); // multiplication factor
// Set up detector values
std::unique_ptr<TimeAtSampleStrategy> strategy;
if (m_tofCorrType == CustomizedCorrect) {
setupCustomizedTOFCorrection();
} else if (m_tofCorrType == ElasticCorrect) {
// Generate TOF correction from instrument's set up
strategy.reset(setupElasticTOFCorrection());
} else if (m_tofCorrType == DirectCorrect) {
// Generate TOF correction for direct inelastic instrument
strategy.reset(setupDirectTOFCorrection());
} else if (m_tofCorrType == IndirectCorrect) {
// Generate TOF correction for indirect elastic instrument
strategy.reset(setupIndirectTOFCorrection());
}
if (strategy) {
for (size_t i = 0; i < numhist; ++i) {
if (!m_vecSkip[i]) {
Correction correction = strategy->calculate(i);
m_detTofOffsets[i] = correction.offset;
m_detTofFactors[i] = correction.factor;
corrws->dataY(i)[0] = correction.factor;
corrws->dataY(i)[1] = correction.offset;
}
}
}
return;
}
/** Create output workspace
* @brief GetSpiceDataRawCountsFromMD::createOutputWorkspace
* @param vecX
* @param vecY
* @param xlabel :: only 'Degrees' can be applied to x-axis
* @param ylabel
* @return
*/
MatrixWorkspace_sptr GetSpiceDataRawCountsFromMD::createOutputWorkspace(
const std::vector<double> &vecX, const std::vector<double> &vecY,
const std::string &xlabel, const std::string &ylabel) {
// Create MatrixWorkspace
size_t sizex = vecX.size();
size_t sizey = vecY.size();
if (sizex != sizey || sizex == 0)
throw std::runtime_error("Unable to create output matrix workspace.");
MatrixWorkspace_sptr outws = boost::dynamic_pointer_cast<MatrixWorkspace>(
WorkspaceFactory::Instance().create("Workspace2D", 1, sizex, sizey));
if (!outws)
throw std::runtime_error("Failed to create output matrix workspace.");
// Set data
MantidVec &dataX = outws->dataX(0);
MantidVec &dataY = outws->dataY(0);
MantidVec &dataE = outws->dataE(0);
for (size_t i = 0; i < sizex; ++i) {
dataX[i] = vecX[i];
dataY[i] = vecY[i];
if (dataY[i] > 1.)
dataE[i] = sqrt(dataY[i]);
else
dataE[i] = 1.;
}
// Set label
outws->setYUnitLabel(ylabel);
if (xlabel.size() != 0) {
try {
outws->getAxis(0)->setUnit(xlabel);
} catch (...) {
g_log.information() << "Label " << xlabel << " for X-axis is not a unit "
"registered."
<< "\n";
}
}
return outws;
}
void ConvertTableToMatrixWorkspace::exec()
{
ITableWorkspace_sptr inputWorkspace = getProperty("InputWorkspace");
std::string columnX = getProperty("ColumnX");
std::string columnY = getProperty("ColumnY");
std::string columnE = getProperty("ColumnE");
size_t nrows = inputWorkspace->rowCount();
std::vector<double> X(nrows);
std::vector<double> Y(nrows);
std::vector<double> E(nrows);
inputWorkspace->getColumn(columnX)->numeric_fill(X);
inputWorkspace->getColumn(columnY)->numeric_fill(Y);
if (!columnE.empty())
{
inputWorkspace->getColumn(columnE)->numeric_fill(E);
}
else
{
E.assign(X.size(),1.0);
}
MatrixWorkspace_sptr outputWorkspace = WorkspaceFactory::Instance().create("Workspace2D",1,X.size(),X.size());
outputWorkspace->dataX(0).assign(X.begin(),X.end());
outputWorkspace->dataY(0).assign(Y.begin(),Y.end());
outputWorkspace->dataE(0).assign(E.begin(),E.end());
outputWorkspace->generateSpectraMap();
boost::shared_ptr<Kernel::Units::Label> labelX = boost::dynamic_pointer_cast<Kernel::Units::Label>(
Kernel::UnitFactory::Instance().create("Label")
);
labelX->setLabel(columnX);
outputWorkspace->getAxis(0)->unit() = labelX;
outputWorkspace->setYUnitLabel(columnY);
setProperty("OutputWorkspace", outputWorkspace);
}
/** Uses 'Linear' as a subalgorithm to fit the log of the exponential curve expected for the transmission.
* @param WS :: The single-spectrum workspace to fit
* @return A workspace containing the fit
*/
API::MatrixWorkspace_sptr CalculateTransmissionBeamSpreader::fitToData(API::MatrixWorkspace_sptr WS)
{
g_log.information("Fitting the experimental transmission curve");
Algorithm_sptr childAlg = createSubAlgorithm("Linear",0.6,1.0);
childAlg->setProperty<MatrixWorkspace_sptr>("InputWorkspace", WS);
const double lambdaMin = getProperty("MinWavelength");
const double lambdaMax = getProperty("MaxWavelength");
childAlg->setProperty<double>("StartX",lambdaMin);
childAlg->setProperty<double>("EndX",lambdaMax);
childAlg->executeAsSubAlg();
std::string fitStatus = childAlg->getProperty("FitStatus");
if ( fitStatus != "success" )
{
g_log.error("Unable to successfully fit the data: " + fitStatus);
throw std::runtime_error("Unable to successfully fit the data");
}
// Only get to here if successful
MatrixWorkspace_sptr result = childAlg->getProperty("OutputWorkspace");
if (logFit)
{
// Need to transform back to 'unlogged'
double b = childAlg->getProperty("FitIntercept");
double m = childAlg->getProperty("FitSlope");
b = std::pow(10,b);
m = std::pow(10,m);
const MantidVec & X = result->readX(0);
MantidVec & Y = result->dataY(0);
MantidVec & E = result->dataE(0);
for (size_t i = 0; i < Y.size(); ++i)
{
Y[i] = b*(std::pow(m,0.5*(X[i]+X[i+1])));
E[i] = std::abs(E[i]*Y[i]);
}
}
return result;
}
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
}
/** Reads from the third line of the input file to the end assuming it contains
* 2D data
* @param firstLine :: the second line in the file
* @return a workspace containing the loaded data
* @throw NotFoundError if there is compulsulary data is missing from the file
* @throw invalid_argument if there is an inconsistency in the header information
*/
const MatrixWorkspace_sptr LoadRKH::read2D(const std::string & firstLine)
{
g_log.information() << "file appears to contain 2D information, reading in 2D data mode\n";
MatrixWorkspace_sptr outWrksp;
MantidVec axis0Data;
Progress prog( read2DHeader(firstLine, outWrksp, axis0Data) );
const size_t nAxis1Values = outWrksp->getNumberHistograms();
for ( size_t i = 0; i < nAxis1Values; ++i )
{
//set the X-values to the common bin values we read above
MantidVecPtr toPass;
toPass.access() = axis0Data;
outWrksp->setX(i, toPass);
//now read in the Y values
MantidVec & YOut = outWrksp->dataY(i);
for (MantidVec::iterator it = YOut.begin(), end=YOut.end(); it != end; ++it)
{
m_fileIn >> *it;
}
prog.report("Loading Y data");
}// loop on to the next spectrum
// the error values form one big block after the Y-values
for ( size_t i = 0; i < nAxis1Values; ++i )
{
MantidVec & EOut = outWrksp->dataE(i);
for (MantidVec::iterator it = EOut.begin(), end=EOut.end(); it != end; ++it)
{
m_fileIn >> *it;
}
prog.report("Loading error estimates");
}// loop on to the next spectrum
return outWrksp;
}
/**
* 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);
}
}
}
/**
* Groups the workspace according to grouping provided.
*
* @param ws :: Workspace to group
* @param g :: The grouping information
* @return Sptr to created grouped workspace
*/
MatrixWorkspace_sptr groupWorkspace(MatrixWorkspace_const_sptr ws, const Grouping& g)
{
// As I couldn't specify multiple groups for GroupDetectors, I am going down quite a complicated
// route - for every group distinct grouped workspace is created using GroupDetectors. These
// workspaces are then merged into the output workspace.
// Create output workspace
MatrixWorkspace_sptr outWs =
WorkspaceFactory::Instance().create(ws, g.groups.size(), ws->readX(0).size(), ws->blocksize());
for(size_t gi = 0; gi < g.groups.size(); gi++)
{
Mantid::API::IAlgorithm_sptr alg = AlgorithmManager::Instance().create("GroupDetectors");
alg->setChild(true); // So Output workspace is not added to the ADS
alg->initialize();
alg->setProperty("InputWorkspace", boost::const_pointer_cast<MatrixWorkspace>(ws));
alg->setPropertyValue("SpectraList", g.groups[gi]);
alg->setPropertyValue("OutputWorkspace", "grouped"); // Is not actually used, just to make validators happy
alg->execute();
MatrixWorkspace_sptr grouped = alg->getProperty("OutputWorkspace");
// Copy the spectrum
*(outWs->getSpectrum(gi)) = *(grouped->getSpectrum(0));
// Update spectrum number
outWs->getSpectrum(gi)->setSpectrumNo(static_cast<specid_t>(gi));
// Copy to the output workspace
outWs->dataY(gi) = grouped->readY(0);
outWs->dataX(gi) = grouped->readX(0);
outWs->dataE(gi) = grouped->readE(0);
}
return outWs;
}
/** Execute the ghost correction on all events in the input workspace **/
void GhostCorrection::exec()
{
// Get the input workspace
this->inputW = getProperty("InputWorkspace");
//Load the grouping
GroupingWorkspace_sptr groupWS = getProperty("GroupingWorkspace");
groupWS->makeDetectorIDToGroupMap(detId_to_group, nGroups);
if (this->nGroups <= 0)
throw std::runtime_error("The # of groups found in the Grouping file is 0.");
// Now the offsets
OffsetsWorkspace_sptr offsetsWS = getProperty("OffsetsWorkspace");
//Make the X axis to bin to.
MantidVecPtr XValues_new;
const int64_t numbins = VectorHelper::createAxisFromRebinParams(getProperty("BinParams"), XValues_new.access());
//Prepare the binfinder class
BinFinder binner( getProperty("BinParams") );
if (binner.lastBinIndex() != static_cast<int>(XValues_new.access().size()-1))
{
std::stringstream msg;
msg << "GhostCorrection: The binner found " << binner.lastBinIndex()+1 << " bins, but the X axis has "
<< XValues_new.access().size() << ". Try different binning parameters.";
throw std::runtime_error(msg.str());
}
//Create an output Workspace2D with group # of output spectra
MatrixWorkspace_sptr outputW = WorkspaceFactory::Instance().create("Workspace2D", this->nGroups-1, numbins, numbins-1);
WorkspaceFactory::Instance().initializeFromParent(inputW, outputW, true);
//Set the X bins in the output WS.
Workspace2D_sptr outputWS2D = boost::dynamic_pointer_cast<Workspace2D>(outputW);
for (std::size_t i=0; i < outputWS2D->getNumberHistograms(); i++)
outputWS2D->setX(i, XValues_new);
//Prepare the maps you need
input_detectorIDToWorkspaceIndexMap = inputW->getDetectorIDToWorkspaceIndexMap(true);
//Load the ghostmapping file
this->loadGhostMap( getProperty("GhostCorrectionFilename") );
//Initialize progress reporting.
int64_t numsteps = 0; //count how many steps
for (int64_t gr=1; gr < this->nGroups; ++gr)
numsteps += this->groupedGhostMaps[gr]->size();
Progress prog(this, 0.0, 1.0, numsteps);
//Set up the tof-to-d_spacing map for all pixel ids.
this->tof_to_d = Mantid::Algorithms::AlignDetectors::calcTofToD_ConversionMap(inputW, offsetsWS);
// Set the final unit that our output workspace will have
outputW->getAxis(0)->unit() = UnitFactory::Instance().create("dSpacing");
//Go through the groups, starting at #1!
PARALLEL_FOR2(inputW, outputW)
for (int64_t gr=1; gr < this->nGroups; ++gr)
{
PARALLEL_START_INTERUPT_REGION
//TODO: Convert between group # and workspace index. Sigh.
//Groups normally start at 1 and so the workspace index will be one below that.
int64_t outputWorkspaceIndex = gr-1;
//Start by making sure the Y and E values are 0.
MantidVec& Y = outputW->dataY(outputWorkspaceIndex);
MantidVec& E = outputW->dataE(outputWorkspaceIndex);
Y.assign(Y.size(), 0.0);
E.assign(E.size(), 0.0);
//Perform the GhostCorrection
//Ok, this map has as keys the source workspace indices
GhostSourcesMap * thisGroupsGhostMap = this->groupedGhostMaps[gr];
GhostSourcesMap::iterator it;
for (it = thisGroupsGhostMap->begin(); it != thisGroupsGhostMap->end(); ++it)
{
//This workspace index is causing 16 ghosts in this group.
int64_t inputWorkspaceIndex = it->first;
int64_t inputDetectorID = it->second;
//This is the events in the pixel CAUSING the ghost.
const EventList & sourceEventList = inputW->getEventList(inputWorkspaceIndex);
//Now get the actual vector of tofevents
const std::vector<TofEvent>& events = sourceEventList.getEvents();
size_t numEvents = events.size();
//Go through all events.
for (size_t i=0; i < numEvents; i++)
{
const TofEvent& event = events[i];
for (int64_t g=0; g < NUM_GHOSTS; g++)
{
//Find the ghost correction
int64_t fileIndex = inputDetectorID * NUM_GHOSTS + g;
GhostDestinationValue ghostVal = (*rawGhostMap)[fileIndex];
//Convert to d-spacing using the factor of the GHOST pixel id
double d_spacing = event.tof() * (*tof_to_d)[ghostVal.pixelId];
//Find the bin for this d-spacing
int64_t binIndex = binner.bin(d_spacing);
if (binIndex >= 0)
{
//Slap it in the Y array for this group; use the weight.
Y[binIndex] += ghostVal.weight;
}
} //for each ghost
} //for each event
//Report progress
prog.report();
}
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
// Assign the workspace to the output workspace property
setProperty("OutputWorkspace", outputW);
}
/**
@ 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);
}
/** Execute the algorithm.
*/
void LoadNXSPE::exec() {
std::string filename = getProperty("Filename");
// quicly check if it's really nxspe
try {
::NeXus::File file(filename);
std::string mainEntry = (*(file.getEntries().begin())).first;
file.openGroup(mainEntry, "NXentry");
file.openData("definition");
if (identiferConfidence(file.getStrData()) < 1) {
throw std::invalid_argument("Not NXSPE");
}
file.close();
} catch (...) {
throw std::invalid_argument("Not NeXus or not NXSPE");
}
// Load the data
::NeXus::File file(filename);
std::string mainEntry = (*(file.getEntries().begin())).first;
file.openGroup(mainEntry, "NXentry");
file.openGroup("NXSPE_info", "NXcollection");
std::map<std::string, std::string> entries = file.getEntries();
std::vector<double> temporary;
double fixed_energy, psi = 0.;
if (!entries.count("fixed_energy")) {
throw std::invalid_argument("fixed_energy field was not found");
}
file.openData("fixed_energy");
file.getData(temporary);
fixed_energy = temporary.at(0);
file.closeData();
if (entries.count("psi")) {
file.openData("psi");
file.getData(temporary);
psi = temporary.at(0);
file.closeData();
}
int kikfscaling = 0;
if (entries.count("ki_over_kf_scaling")) {
file.openData("ki_over_kf_scaling");
std::vector<int> temporaryint;
file.getData(temporaryint);
kikfscaling = temporaryint.at(0);
file.closeData();
}
file.closeGroup(); // NXSPE_Info
file.openGroup("data", "NXdata");
entries = file.getEntries();
if (!entries.count("data")) {
throw std::invalid_argument("data field was not found");
}
file.openData("data");
::NeXus::Info info = file.getInfo();
std::size_t numSpectra = static_cast<std::size_t>(info.dims.at(0));
std::size_t numBins = static_cast<std::size_t>(info.dims.at(1));
std::vector<double> data;
file.getData(data);
file.closeData();
if (!entries.count("error")) {
throw std::invalid_argument("error field was not found");
}
file.openData("error");
std::vector<double> error;
file.getData(error);
file.closeData();
if (!entries.count("energy")) {
throw std::invalid_argument("energy field was not found");
}
file.openData("energy");
std::vector<double> energies;
file.getData(energies);
file.closeData();
if (!entries.count("azimuthal")) {
throw std::invalid_argument("azimuthal field was not found");
}
file.openData("azimuthal");
std::vector<double> azimuthal;
file.getData(azimuthal);
file.closeData();
if (!entries.count("azimuthal_width")) {
throw std::invalid_argument("azimuthal_width field was not found");
}
file.openData("azimuthal_width");
std::vector<double> azimuthal_width;
file.getData(azimuthal_width);
file.closeData();
if (!entries.count("polar")) {
throw std::invalid_argument("polar field was not found");
}
file.openData("polar");
std::vector<double> polar;
file.getData(polar);
file.closeData();
if (!entries.count("polar_width")) {
throw std::invalid_argument("polar_width field was not found");
}
file.openData("polar_width");
std::vector<double> polar_width;
file.getData(polar_width);
file.closeData();
// distance might not have been saved in all NXSPE files
std::vector<double> distance;
if (entries.count("distance")) {
file.openData("distance");
file.getData(distance);
file.closeData();
}
file.closeGroup(); // data group
file.closeGroup(); // Main entry
file.close();
// check if dimensions of the vectors are correct
if ((error.size() != data.size()) || (azimuthal.size() != numSpectra) ||
(azimuthal_width.size() != numSpectra) || (polar.size() != numSpectra) ||
(polar_width.size() != numSpectra) ||
((energies.size() != numBins) && (energies.size() != numBins + 1))) {
throw std::invalid_argument(
"incompatible sizes of fields in the NXSPE file");
}
MatrixWorkspace_sptr outputWS = boost::dynamic_pointer_cast<MatrixWorkspace>(
WorkspaceFactory::Instance().create("Workspace2D", numSpectra,
energies.size(), numBins));
// Need to get hold of the parameter map
outputWS->getAxis(0)->unit() = UnitFactory::Instance().create("DeltaE");
outputWS->setYUnit("SpectraNumber");
// add logs
outputWS->mutableRun().addLogData(
new PropertyWithValue<double>("Ei", fixed_energy));
outputWS->mutableRun().addLogData(new PropertyWithValue<double>("psi", psi));
outputWS->mutableRun().addLogData(new PropertyWithValue<std::string>(
"ki_over_kf_scaling", kikfscaling == 1 ? "true" : "false"));
// Set Goniometer
Geometry::Goniometer gm;
gm.pushAxis("psi", 0, 1, 0, psi);
outputWS->mutableRun().setGoniometer(gm, true);
// generate instrument
Geometry::Instrument_sptr instrument(new Geometry::Instrument("NXSPE"));
outputWS->setInstrument(instrument);
Geometry::ObjComponent *source = new Geometry::ObjComponent("source");
source->setPos(0.0, 0.0, -10.0);
instrument->add(source);
instrument->markAsSource(source);
Geometry::ObjComponent *sample = new Geometry::ObjComponent("sample");
instrument->add(sample);
instrument->markAsSamplePos(sample);
Geometry::Object_const_sptr cuboid(
createCuboid(0.1, 0.1, 0.1)); // FIXME: memory hog on rendering. Also,
// make each detector separate size
for (std::size_t i = 0; i < numSpectra; ++i) {
double r = 1.0;
if (!distance.empty()) {
r = distance.at(i);
}
Kernel::V3D pos;
pos.spherical(r, polar.at(i), azimuthal.at(i));
Geometry::Detector *det =
new Geometry::Detector("pixel", static_cast<int>(i + 1), sample);
det->setPos(pos);
det->setShape(cuboid);
instrument->add(det);
instrument->markAsDetector(det);
}
Geometry::ParameterMap &pmap = outputWS->instrumentParameters();
std::vector<double>::iterator itdata = data.begin(), iterror = error.begin(),
itdataend, iterrorend;
API::Progress prog = API::Progress(this, 0.0, 0.9, numSpectra);
for (std::size_t i = 0; i < numSpectra; ++i) {
itdataend = itdata + numBins;
iterrorend = iterror + numBins;
outputWS->dataX(i) = energies;
if ((!boost::math::isfinite(*itdata)) || (*itdata <= -1e10)) // masked bin
{
outputWS->dataY(i) = std::vector<double>(numBins, 0);
outputWS->dataE(i) = std::vector<double>(numBins, 0);
pmap.addBool(outputWS->getDetector(i)->getComponentID(), "masked", true);
} else {
outputWS->dataY(i) = std::vector<double>(itdata, itdataend);
outputWS->dataE(i) = std::vector<double>(iterror, iterrorend);
}
itdata = (itdataend);
iterror = (iterrorend);
prog.report();
}
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 TOFSANSResolution::exec()
{
Workspace2D_sptr iqWS = getProperty("InputWorkspace");
MatrixWorkspace_sptr reducedWS = getProperty("ReducedWorkspace");
EventWorkspace_sptr reducedEventWS = boost::dynamic_pointer_cast<EventWorkspace>(reducedWS);
const double min_wl = getProperty("MinWavelength");
const double max_wl = getProperty("MaxWavelength");
double pixel_size_x = getProperty("PixelSizeX");
double pixel_size_y = getProperty("PixelSizeY");
double R1 = getProperty("SourceApertureRadius");
double R2 = getProperty("SampleApertureRadius");
// Convert to meters
pixel_size_x /= 1000.0;
pixel_size_y /= 1000.0;
R1 /= 1000.0;
R2 /= 1000.0;
wl_resolution = getProperty("DeltaT");
// Although we want the 'ReducedWorkspace' to be an event workspace for this algorithm to do
// anything, we don't want the algorithm to 'fail' if it isn't
if (!reducedEventWS)
{
g_log.warning() << "An Event Workspace is needed to compute dQ. Calculation skipped." << std::endl;
return;
}
// Calculate the output binning
const std::vector<double> binParams = getProperty("OutputBinning");
// Count histogram for normalization
const int xLength = static_cast<int>(iqWS->readX(0).size());
std::vector<double> XNorm(xLength-1, 0.0);
// Create workspaces with each component of the resolution for debugging purposes
MatrixWorkspace_sptr thetaWS = WorkspaceFactory::Instance().create(iqWS);
declareProperty(new WorkspaceProperty<>("ThetaError","",Direction::Output));
setPropertyValue("ThetaError","__"+iqWS->getName()+"_theta_error");
setProperty("ThetaError",thetaWS);
thetaWS->setX(0,iqWS->readX(0));
MantidVec& ThetaY = thetaWS->dataY(0);
MatrixWorkspace_sptr tofWS = WorkspaceFactory::Instance().create(iqWS);
declareProperty(new WorkspaceProperty<>("TOFError","",Direction::Output));
setPropertyValue("TOFError","__"+iqWS->getName()+"_tof_error");
setProperty("TOFError",tofWS);
tofWS->setX(0,iqWS->readX(0));
MantidVec& TOFY = tofWS->dataY(0);
// Initialize Dq
MantidVec& DxOut = iqWS->dataDx(0);
for ( int i = 0; i<xLength-1; i++ ) DxOut[i] = 0.0;
const V3D samplePos = reducedWS->getInstrument()->getSample()->getPos();
const V3D sourcePos = reducedWS->getInstrument()->getSource()->getPos();
const V3D SSD = samplePos - sourcePos;
const double L1 = SSD.norm();
const int numberOfSpectra = static_cast<int>(reducedWS->getNumberHistograms());
Progress progress(this,0.0,1.0,numberOfSpectra);
PARALLEL_FOR2(reducedEventWS, iqWS)
for (int i = 0; i < numberOfSpectra; i++)
{
PARALLEL_START_INTERUPT_REGION
IDetector_const_sptr det;
try {
det = reducedEventWS->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 flight path from the sample to the detector pixel
const V3D scattered_flight_path = det->getPos() - samplePos;
// Multiplicative factor to go from lambda to Q
// Don't get fooled by the function name...
const double theta = reducedEventWS->detectorTwoTheta(det);
const double factor = 4.0 * M_PI * sin( theta/2.0 );
EventList& el = reducedEventWS->getEventList(i);
el.switchTo(WEIGHTED);
std::vector<WeightedEvent>::iterator itev;
std::vector<WeightedEvent>::iterator itev_end = el.getWeightedEvents().end();
for (itev = el.getWeightedEvents().begin(); itev != itev_end; ++itev)
{
if ( itev->m_weight != itev->m_weight ) continue;
if (std::abs(itev->m_weight) == std::numeric_limits<double>::infinity()) continue;
if ( !isEmpty(min_wl) && itev->m_tof < min_wl ) continue;
if ( !isEmpty(max_wl) && itev->m_tof > max_wl ) continue;
const double q = factor/itev->m_tof;
int iq = 0;
// Bin assignment depends on whether we have log or linear bins
if(binParams[1]>0.0)
{
iq = (int)floor( (q-binParams[0])/ binParams[1] );
} else {
iq = (int)floor(log(q/binParams[0])/log(1.0-binParams[1]));
}
const double L2 = scattered_flight_path.norm();
const double src_to_pixel = L1+L2;
const double dTheta2 = ( 3.0*R1*R1/(L1*L1) + 3.0*R2*R2*src_to_pixel*src_to_pixel/(L1*L1*L2*L2)
+ 2.0*(pixel_size_x*pixel_size_x+pixel_size_y*pixel_size_y)/(L2*L2) )/12.0;
const double dwl_over_wl = 3.9560*getTOFResolution(itev->m_tof)/(1000.0*(L1+L2)*itev->m_tof);
const double dq_over_q = std::sqrt(dTheta2/(theta*theta)+dwl_over_wl*dwl_over_wl);
PARALLEL_CRITICAL(iq) /* Write to shared memory - must protect */
if (iq>=0 && iq < xLength-1 && !dq_over_q!=dq_over_q && dq_over_q>0)
{
DxOut[iq] += q*dq_over_q*itev->m_weight;
XNorm[iq] += itev->m_weight;
TOFY[iq] += q*std::fabs(dwl_over_wl)*itev->m_weight;
ThetaY[iq] += q*std::sqrt(dTheta2)/theta*itev->m_weight;
}
}
progress.report("Computing Q resolution");
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
// Normalize according to the chosen weighting scheme
for ( int i = 0; i<xLength-1; i++ )
{
if (XNorm[i]>0)
{
DxOut[i] /= XNorm[i];
TOFY[i] /= XNorm[i];
ThetaY[i] /= XNorm[i];
}
}
}
/** Read a data file that contains only one spectrum into a workspace
* @return the new workspace
*/
const API::MatrixWorkspace_sptr LoadRKH::read1D()
{
g_log.information() << "file appears to contain 1D information, reading in 1D data mode\n";
//The 3rd line contains information regarding the number of points in the file and
// start and end reading points
int totalPoints(0), readStart(0), readEnd(0), buried(0);
std::string fileline;
getline(m_fileIn, fileline);
std::istringstream is(fileline);
//Get data information
for( int counter = 1; counter < 8; ++counter )
{
switch( counter )
{
case 1:
is >> totalPoints;
break;
case 5:
is >> readStart;
break;
case 6:
is >> readEnd;
break;
default:
is >> buried;
break;
}
}
g_log.information() << "Total number of data points declared to be in the data file: "
<< totalPoints << "\n";
//What are we reading?
std::string firstColVal = getProperty("FirstColumnValue");
bool colIsUnit(true);
if( m_RKHKeys.find( firstColVal ) != m_RKHKeys.end() )
{
colIsUnit = false;
readStart = 1;
readEnd = totalPoints;
}
if( readStart < 1 || readEnd < 1 || readEnd < readStart ||
readStart > totalPoints || readEnd > totalPoints )
{
g_log.error("Invalid data range specfied.");
m_fileIn.close();
throw std::invalid_argument("Invalid data range specfied.");
}
g_log.information() << "Reading started on data line: " << readStart << "\n";
g_log.information() << "Reading finished on data line: " << readEnd << "\n";
//The 4th and 5th line do not contain useful information either
skipLines(m_fileIn, 2);
int pointsToRead = readEnd - readStart + 1;
//Now stream sits at the first line of data
fileline = "";
std::vector<double> columnOne, ydata, errdata;
columnOne.reserve(readEnd);
ydata.reserve(readEnd);
errdata.reserve(readEnd);
Progress prog(this,0.0,1.0,readEnd);
for( int index = 1; index <= readEnd; ++index )
{
getline(m_fileIn, fileline);
if( index < readStart ) continue;
double x(0.), y(0.), yerr(0.);
std::istringstream datastr(fileline);
datastr >> x >> y >> yerr;
columnOne.push_back(x);
ydata.push_back(y);
errdata.push_back(yerr);
prog.report();
}
m_fileIn.close();
assert( pointsToRead == static_cast<int>(columnOne.size()) );
assert( pointsToRead == static_cast<int>(ydata.size()) );
assert( pointsToRead == static_cast<int>(errdata.size()) );
if( colIsUnit )
{
MatrixWorkspace_sptr localworkspace =
WorkspaceFactory::Instance().create("Workspace2D", 1, pointsToRead, pointsToRead);
localworkspace->getAxis(0)->unit() = UnitFactory::Instance().create(firstColVal);
localworkspace->dataX(0) = columnOne;
localworkspace->dataY(0) = ydata;
localworkspace->dataE(0) = errdata;
return localworkspace;
}
else
{
MatrixWorkspace_sptr localworkspace =
WorkspaceFactory::Instance().create("Workspace2D", pointsToRead, 1, 1);
//Set the appropriate values
for( int index = 0; index < pointsToRead; ++index )
{
localworkspace->getAxis(1)->setValue(index, static_cast<int>(columnOne[index]));
localworkspace->dataY(index)[0] = ydata[index];
localworkspace->dataE(index)[0] = errdata[index];
}
return localworkspace;
}
}
void Linear::exec()
{
// Get the input workspace
MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
// Get the spectrum to fit
const int histNumber = getProperty("WorkspaceIndex");
// Check validity
if ( histNumber >= static_cast<int>(inputWorkspace->getNumberHistograms()) )
{
g_log.error() << "WorkspaceIndex set to an invalid value of " << histNumber << std::endl;
throw Exception::IndexError(histNumber,inputWorkspace->getNumberHistograms(),"Linear WorkspaceIndex property");
}
// Get references to the data in the chosen spectrum
const MantidVec& X = inputWorkspace->dataX(histNumber);
const MantidVec& Y = inputWorkspace->dataY(histNumber);
const MantidVec& E = inputWorkspace->dataE(histNumber);
// Check if this spectrum has errors
double errorsCount = 0.0;
// Retrieve the Start/EndX properties, if set
this->setRange(X,Y);
const bool isHistogram = inputWorkspace->isHistogramData();
// If the spectrum to be fitted has masked bins, we want to exclude them (even if only partially masked)
const MatrixWorkspace::MaskList * const maskedBins =
( inputWorkspace->hasMaskedBins(histNumber) ? &(inputWorkspace->maskedBins(histNumber)) : NULL );
// Put indices of masked bins into a set for easy searching later
std::set<size_t> maskedIndices;
if (maskedBins)
{
MatrixWorkspace::MaskList::const_iterator it;
for (it = maskedBins->begin(); it != maskedBins->end(); ++it)
maskedIndices.insert(it->first);
}
progress(0);
// Declare temporary vectors and reserve enough space if they're going to be used
std::vector<double> XCen, unmaskedY, weights;
int numPoints = m_maxX - m_minX;
if (isHistogram) XCen.reserve(numPoints);
if (maskedBins) unmaskedY.reserve(numPoints);
weights.reserve(numPoints);
for (int i = 0; i < numPoints; ++i)
{
// If the current bin is masked, skip it
if ( maskedBins && maskedIndices.count(m_minX+i) ) continue;
// Need to adjust X to centre of bin, if a histogram
if (isHistogram) XCen.push_back( 0.5*(X[m_minX+i]+X[m_minX+i+1]) );
// If there are masked bins present, we need to copy the unmasked Y values
if (maskedBins) unmaskedY.push_back(Y[m_minX+i]);
// GSL wants the errors as weights, i.e. 1/sigma^2
// We need to be careful if E is zero because that would naively lead to an infinite weight on the point.
// Solution taken here is to zero weight if error is zero, which typically means Y is zero
// (so it is effectively excluded from the fit).
const double& currentE = E[m_minX+i];
weights.push_back( currentE ? 1.0/(currentE*currentE) : 0.0 );
// However, if the spectrum given has all errors of zero, then we should use the gsl function that
// doesn't take account of the errors.
if ( currentE ) ++errorsCount;
}
progress(0.3);
// If masked bins present, need to recalculate numPoints here
if (maskedBins) numPoints = static_cast<int>(unmaskedY.size());
// If no points left for any reason, bail out
if (numPoints == 0)
{
g_log.error("No points in this range to fit");
throw std::runtime_error("No points in this range to fit");
}
// Set up pointer variables to pass to gsl, pointing them to the right place
const double * const xVals = ( isHistogram ? &XCen[0] : &X[m_minX] );
const double * const yVals = ( maskedBins ? &unmaskedY[0] : &Y[m_minX] );
// Call the gsl fitting function
// The stride value of 1 reflects that fact that we want every element of our input vectors
const int stride = 1;
double *c0(new double),*c1(new double),*cov00(new double),*cov01(new double),*cov11(new double),*chisq(new double);
int status;
// Unless our spectrum has error values for vast majority of points,
// call the gsl function that doesn't use errors
if ( errorsCount/numPoints < 0.9 )
{
g_log.debug("Calling gsl_fit_linear (doesn't use errors in fit)");
status = gsl_fit_linear(xVals,stride,yVals,stride,numPoints,c0,c1,cov00,cov01,cov11,chisq);
}
// Otherwise, call the one that does account for errors on the data points
else
{
g_log.debug("Calling gsl_fit_wlinear (uses errors in fit)");
status = gsl_fit_wlinear(xVals,stride,&weights[0],stride,yVals,stride,numPoints,c0,c1,cov00,cov01,cov11,chisq);
}
progress(0.8);
// Check that the fit succeeded
std::string fitStatus = gsl_strerror(status);
// For some reason, a fit where c0,c1 & chisq are all infinity doesn't report as a
// failure, so check explicitly.
if ( !gsl_finite(*chisq) || !gsl_finite(*c0) || !gsl_finite(*c1) )
fitStatus = "Fit gives infinities";
if (fitStatus != "success") g_log.error() << "The fit failed: " << fitStatus << "\n";
else
g_log.information() << "The fit succeeded, giving y = " << *c0 << " + " << *c1 << "*x, with a Chi^2 of " << *chisq << "\n";
// Set the fit result output properties
setProperty("FitStatus",fitStatus);
setProperty("FitIntercept",*c0);
setProperty("FitSlope",*c1);
setProperty("Cov00",*cov00);
setProperty("Cov11",*cov11);
setProperty("Cov01",*cov01);
setProperty("Chi2",*chisq);
// Create and fill a workspace2D with the same bins as the fitted spectrum and the value of the fit for the centre of each bin
const size_t YSize = Y.size();
MatrixWorkspace_sptr outputWorkspace = WorkspaceFactory::Instance().create(inputWorkspace,1,X.size(),YSize);
// Copy over the X bins
outputWorkspace->dataX(0).assign(X.begin(),X.end());
// Now loop over the spectrum and use gsl function to calculate the Y & E values for the function
for (size_t i = 0; i < YSize; ++i)
{
const double x = ( isHistogram ? 0.5*(X[i]+X[i+1]) : X[i] );
const int err = gsl_fit_linear_est(x,*c0,*c1,*cov00,*cov01,*cov11,&(outputWorkspace->dataY(0)[i]),&(outputWorkspace->dataE(0)[i]));
if (err) g_log.warning() << "Problem in filling the output workspace: " << gsl_strerror(err) << std::endl;
}
setProperty("OutputWorkspace",outputWorkspace);
progress(1);
// Clean up
delete c0;
delete c1;
delete cov00;
delete cov01;
delete cov11;
delete chisq;
}
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");
}
/** Filter non-background data points out and create a background workspace
*/
Workspace2D_sptr
ProcessBackground::filterForBackground(BackgroundFunction_sptr bkgdfunction) {
double posnoisetolerance = getProperty("NoiseTolerance");
double negnoisetolerance = getProperty("NegativeNoiseTolerance");
if (isEmpty(negnoisetolerance))
negnoisetolerance = posnoisetolerance;
// Calcualte theoretical values
const std::vector<double> x = m_dataWS->readX(m_wsIndex);
API::FunctionDomain1DVector domain(x);
API::FunctionValues values(domain);
bkgdfunction->function(domain, values);
g_log.information() << "Function used to select background points : "
<< bkgdfunction->asString() << "\n";
// Optional output
string userbkgdwsname = getPropertyValue("UserBackgroundWorkspace");
if (userbkgdwsname.size() == 0)
throw runtime_error("In mode SelectBackgroundPoints, "
"UserBackgroundWorkspace must be given!");
size_t sizex = domain.size();
size_t sizey = values.size();
MatrixWorkspace_sptr visualws = boost::dynamic_pointer_cast<MatrixWorkspace>(
WorkspaceFactory::Instance().create("Workspace2D", 4, sizex, sizey));
for (size_t i = 0; i < sizex; ++i) {
for (size_t j = 0; j < 4; ++j) {
visualws->dataX(j)[i] = domain[i];
}
}
for (size_t i = 0; i < sizey; ++i) {
visualws->dataY(0)[i] = values[i];
visualws->dataY(1)[i] = m_dataWS->readY(m_wsIndex)[i] - values[i];
visualws->dataY(2)[i] = posnoisetolerance;
visualws->dataY(3)[i] = -negnoisetolerance;
}
setProperty("UserBackgroundWorkspace", visualws);
// Filter for background
std::vector<double> vecx, vecy, vece;
for (size_t i = 0; i < domain.size(); ++i) {
// double y = m_dataWS->readY(m_wsIndex)[i];
// double theoryy = values[i]; y-theoryy
double purey = visualws->readY(1)[i];
if (purey < posnoisetolerance && purey > -negnoisetolerance) {
// Selected
double x = domain[i];
double y = m_dataWS->readY(m_wsIndex)[i];
double e = m_dataWS->readE(m_wsIndex)[i];
vecx.push_back(x);
vecy.push_back(y);
vece.push_back(e);
}
}
g_log.information() << "Found " << vecx.size() << " background points out of "
<< m_dataWS->readX(m_wsIndex).size()
<< " total data points. "
<< "\n";
// Build new workspace for OutputWorkspace
size_t nspec = 3;
Workspace2D_sptr outws =
boost::dynamic_pointer_cast<DataObjects::Workspace2D>(
API::WorkspaceFactory::Instance().create("Workspace2D", nspec,
vecx.size(), vecy.size()));
for (size_t i = 0; i < vecx.size(); ++i) {
for (size_t j = 0; j < nspec; ++j)
outws->dataX(j)[i] = vecx[i];
outws->dataY(0)[i] = vecy[i];
outws->dataE(0)[i] = vece[i];
}
return outws;
}
void TOFSANSResolutionByPixel::exec() {
MatrixWorkspace_sptr inOutWS = getProperty("Workspace");
double deltaR = getProperty("DeltaR");
double R1 = getProperty("SourceApertureRadius");
double R2 = getProperty("SampleApertureRadius");
// Convert to meters
deltaR /= 1000.0;
R1 /= 1000.0;
R2 /= 1000.0;
const MatrixWorkspace_sptr sigmaModeratorVSwavelength =
getProperty("SigmaModerator");
// create interpolation table from sigmaModeratorVSwavelength
Kernel::Interpolation lookUpTable;
const MantidVec xInterpolate = sigmaModeratorVSwavelength->readX(0);
const MantidVec yInterpolate = sigmaModeratorVSwavelength->readY(0);
// prefer the input to be a pointworkspace and create interpolation function
if (sigmaModeratorVSwavelength->isHistogramData()) {
g_log.notice() << "mid-points of SigmaModerator histogram bins will be "
"used for interpolation.";
for (size_t i = 0; i < xInterpolate.size() - 1; ++i) {
const double midpoint = xInterpolate[i + 1] - xInterpolate[i];
lookUpTable.addPoint(midpoint, yInterpolate[i]);
}
} else {
for (size_t i = 0; i < xInterpolate.size(); ++i) {
lookUpTable.addPoint(xInterpolate[i], yInterpolate[i]);
}
}
const V3D samplePos = inOutWS->getInstrument()->getSample()->getPos();
const V3D sourcePos = inOutWS->getInstrument()->getSource()->getPos();
const V3D SSD = samplePos - sourcePos;
const double L1 = SSD.norm();
const int numberOfSpectra = static_cast<int>(inOutWS->getNumberHistograms());
Progress progress(this, 0.0, 1.0, numberOfSpectra);
for (int i = 0; i < numberOfSpectra; i++) {
IDetector_const_sptr det;
try {
det = inOutWS->getDetector(i);
} catch (Exception::NotFoundError &) {
g_log.information() << "Spectrum index " << i
<< " has no detector assigned to it - discarding"
<< std::endl;
}
// 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 flight path from the sample to the detector pixel
const V3D scatteredFlightPathV3D = det->getPos() - samplePos;
const double L2 = scatteredFlightPathV3D.norm();
const double Lsum = L1 + L2;
// calculate part that is wavelenght independent
const double dTheta2 = (4.0 * M_PI * M_PI / 12.0) *
(3.0 * R1 * R1 / (L1 * L1) +
3.0 * R2 * R2 * Lsum * Lsum / (L1 * L1 * L2 * L2) +
(deltaR * deltaR) / (L2 * L2));
// Multiplicative factor to go from lambda to Q
// Don't get fooled by the function name...
const double theta = inOutWS->detectorTwoTheta(det);
const double factor = 4.0 * M_PI * sin(theta / 2.0);
const MantidVec &xIn = inOutWS->readX(i);
MantidVec &yIn = inOutWS->dataY(i);
const size_t xLength = xIn.size();
// for each wavelenght bin of each pixel calculate a q-resolution
for (size_t j = 0; j < xLength - 1; j++) {
// use the midpoint of each bin
const double wl = (xIn[j + 1] + xIn[j]) / 2.0;
// Calculate q. Alternatively q could be calculated using ConvertUnit
const double q = factor / wl;
// wavelenght spread from bin assumed to be
const double sigmaSpreadFromBin = xIn[j + 1] - xIn[j];
// wavelenght spread from moderatorm, converted from microseconds to
// wavelengths
const double sigmaModerator =
lookUpTable.value(wl) * 3.9560 / (1000.0 * Lsum);
// calculate wavelenght resolution from moderator and histogram time bin
const double sigmaLambda =
std::sqrt(sigmaSpreadFromBin * sigmaSpreadFromBin / 12.0 +
sigmaModerator * sigmaModerator);
// calculate sigmaQ for a given lambda and pixel
const double sigmaOverLambdaTimesQ = q * sigmaLambda / wl;
const double sigmaQ = std::sqrt(
dTheta2 / (wl * wl) + sigmaOverLambdaTimesQ * sigmaOverLambdaTimesQ);
// update inout workspace with this sigmaQ
yIn[j] = sigmaQ;
}
progress.report("Computing Q resolution");
}
}
