/** Creates PoldiPeak-objects from peak position iterators
*
* In this method, PoldiPeak objects are created from the raw peak position
*data and the original x-data. Estimates for peak height and FWHM
* provided along with the position.
*
* @param baseListStart :: Starting iterator of the vector which the peak
*positions refer to.
* @param peakPositions :: List with peakPositions.
* @param xData :: Vector with x-values of the correlation spectrum.
* @return Vector with PoldiPeak objects constructed from the raw peak position
*data.
*/
std::vector<PoldiPeak_sptr>
PoldiPeakSearch::getPeaks(const MantidVec::const_iterator &baseListStart,
const MantidVec::const_iterator &baseListEnd,
std::list<MantidVec::const_iterator> peakPositions,
const MantidVec &xData, const Unit_sptr &unit) const {
std::vector<PoldiPeak_sptr> peakData;
peakData.reserve(peakPositions.size());
for (std::list<MantidVec::const_iterator>::const_iterator peak =
peakPositions.begin();
peak != peakPositions.end(); ++peak) {
size_t index = std::distance(baseListStart, *peak);
double xDataD = getTransformedCenter(xData[index], unit);
double fwhmEstimate =
getFWHMEstimate(baseListStart, baseListEnd, *peak, xData);
UncertainValue fwhm(fwhmEstimate / xData[index]);
PoldiPeak_sptr newPeak = PoldiPeak::create(
MillerIndices(), UncertainValue(xDataD), UncertainValue(**peak), fwhm);
peakData.push_back(newPeak);
}
return peakData;
}
/**
* Fit peak without background i.e, with background removed
* inspired from FitPowderDiffPeaks.cpp
* copied from PoldiPeakDetection2.cpp
*
@param workspaceindex :: indice of the row to use
@param center :: gaussian parameter - center
@param sigma :: gaussian parameter - width
@param height :: gaussian parameter - height
@param startX :: fit range - start X value
@param endX :: fit range - end X value
@returns A boolean status flag, true for fit success, false else
*/
bool ConvertEmptyToTof::doFitGaussianPeak(int workspaceindex, double ¢er,
double &sigma, double &height,
double startX, double endX) {
g_log.debug("Calling doFitGaussianPeak...");
// 1. Estimate
sigma = sigma * 0.5;
// 2. Use factory to generate Gaussian
auto temppeak = API::FunctionFactory::Instance().createFunction("Gaussian");
auto gaussianpeak = boost::dynamic_pointer_cast<API::IPeakFunction>(temppeak);
gaussianpeak->setHeight(height);
gaussianpeak->setCentre(center);
gaussianpeak->setFwhm(sigma);
// 3. Constraint
double centerleftend = center - sigma * 0.5;
double centerrightend = center + sigma * 0.5;
std::ostringstream os;
os << centerleftend << " < PeakCentre < " << centerrightend;
auto *centerbound = API::ConstraintFactory::Instance().createInitialized(
gaussianpeak.get(), os.str(), false);
gaussianpeak->addConstraint(centerbound);
g_log.debug("Calling createChildAlgorithm : Fit...");
// 4. Fit
API::IAlgorithm_sptr fitalg = createChildAlgorithm("Fit", -1, -1, true);
fitalg->initialize();
fitalg->setProperty(
"Function", boost::dynamic_pointer_cast<API::IFunction>(gaussianpeak));
fitalg->setProperty("InputWorkspace", m_inputWS);
fitalg->setProperty("WorkspaceIndex", workspaceindex);
fitalg->setProperty("Minimizer", "Levenberg-MarquardtMD");
fitalg->setProperty("CostFunction", "Least squares");
fitalg->setProperty("MaxIterations", 1000);
fitalg->setProperty("Output", "FitGaussianPeak");
fitalg->setProperty("StartX", startX);
fitalg->setProperty("EndX", endX);
// 5. Result
bool successfulfit = fitalg->execute();
if (!fitalg->isExecuted() || !successfulfit) {
// Early return due to bad fit
g_log.warning() << "Fitting Gaussian peak for peak around "
<< gaussianpeak->centre() << '\n';
return false;
}
// 6. Get result
center = gaussianpeak->centre();
height = gaussianpeak->height();
double fwhm = gaussianpeak->fwhm();
return fwhm > 0.0;
}
/// Returns the integral intensity of the peak function, using the peak radius
/// to determine integration borders.
double IPeakFunction::intensity() const {
double x0 = centre();
double dx = fabs(s_peakRadius * fwhm());
PeakFunctionIntegrator integrator;
IntegrationResult result = integrator.integrate(*this, x0 - dx, x0 + dx);
if (!result.success) {
return 0.0;
}
return result.result;
}
/// Creates a PoldiPeak from the given profile function/hkl pair.
PoldiPeak_sptr
PoldiFitPeaks2D::getPeakFromPeakFunction(IPeakFunction_sptr profileFunction,
const V3D &hkl) {
// Use EstimatePeakErrors to calculate errors of FWHM and so on
IAlgorithm_sptr errorAlg = createChildAlgorithm("EstimatePeakErrors");
errorAlg->setProperty(
"Function", boost::dynamic_pointer_cast<IFunction>(profileFunction));
errorAlg->setPropertyValue("OutputWorkspace", "Errors");
errorAlg->execute();
double centre = profileFunction->centre();
double fwhmValue = profileFunction->fwhm();
ITableWorkspace_sptr errorTable = errorAlg->getProperty("OutputWorkspace");
double centreError = errorTable->cell<double>(0, 2);
double fwhmError = errorTable->cell<double>(2, 2);
UncertainValue d(centre, centreError);
UncertainValue fwhm(fwhmValue, fwhmError);
UncertainValue intensity;
bool useIntegratedIntensities = getProperty("OutputIntegratedIntensities");
if (useIntegratedIntensities) {
double integratedIntensity = profileFunction->intensity();
double integratedIntensityError = errorTable->cell<double>(3, 2);
intensity = UncertainValue(integratedIntensity, integratedIntensityError);
} else {
double height = profileFunction->height();
double heightError = errorTable->cell<double>(1, 2);
intensity = UncertainValue(height, heightError);
}
// Create peak with extracted parameters and supplied hkl
PoldiPeak_sptr peak =
PoldiPeak::create(MillerIndices(hkl), d, intensity, UncertainValue(1.0));
peak->setFwhm(fwhm, PoldiPeak::FwhmRelation::AbsoluteD);
return peak;
}
/**
* Gaussian fit to determine peak position if no user position given.
*
* @return :: detector position of the peak: Gaussian fit and position
* of the maximum (serves as start value for the optimization)
*/
double LoadILLReflectometry::reflectometryPeak() {
if (!isDefault("BeamCentre")) {
return getProperty("BeamCentre");
}
size_t startIndex;
size_t endIndex;
std::tie(startIndex, endIndex) =
fitIntegrationWSIndexRange(*m_localWorkspace);
IAlgorithm_sptr integration = createChildAlgorithm("Integration");
integration->initialize();
integration->setProperty("InputWorkspace", m_localWorkspace);
integration->setProperty("OutputWorkspace", "__unused_for_child");
integration->setProperty("StartWorkspaceIndex", static_cast<int>(startIndex));
integration->setProperty("EndWorkspaceIndex", static_cast<int>(endIndex));
integration->execute();
MatrixWorkspace_sptr integralWS = integration->getProperty("OutputWorkspace");
IAlgorithm_sptr transpose = createChildAlgorithm("Transpose");
transpose->initialize();
transpose->setProperty("InputWorkspace", integralWS);
transpose->setProperty("OutputWorkspace", "__unused_for_child");
transpose->execute();
integralWS = transpose->getProperty("OutputWorkspace");
rebinIntegralWorkspace(*integralWS);
// determine initial height: maximum value
const auto maxValueIt =
std::max_element(integralWS->y(0).cbegin(), integralWS->y(0).cend());
const double height = *maxValueIt;
// determine initial centre: index of the maximum value
const size_t maxIndex = std::distance(integralWS->y(0).cbegin(), maxValueIt);
const double centreByMax = static_cast<double>(maxIndex);
g_log.debug() << "Peak maximum position: " << centreByMax << '\n';
// determine sigma
const auto &ys = integralWS->y(0);
auto lessThanHalfMax = [height](const double x) { return x < 0.5 * height; };
using IterType = HistogramData::HistogramY::const_iterator;
std::reverse_iterator<IterType> revMaxValueIt{maxValueIt};
auto revMinFwhmIt = std::find_if(revMaxValueIt, ys.crend(), lessThanHalfMax);
auto maxFwhmIt = std::find_if(maxValueIt, ys.cend(), lessThanHalfMax);
std::reverse_iterator<IterType> revMaxFwhmIt{maxFwhmIt};
if (revMinFwhmIt == ys.crend() || maxFwhmIt == ys.cend()) {
g_log.warning() << "Couldn't determine fwhm of beam, using position of max "
"value as beam center.\n";
return centreByMax;
}
const double fwhm =
static_cast<double>(std::distance(revMaxFwhmIt, revMinFwhmIt) + 1);
g_log.debug() << "Initial fwhm (full width at half maximum): " << fwhm
<< '\n';
// generate Gaussian
auto func =
API::FunctionFactory::Instance().createFunction("CompositeFunction");
auto sum = boost::dynamic_pointer_cast<API::CompositeFunction>(func);
func = API::FunctionFactory::Instance().createFunction("Gaussian");
auto gaussian = boost::dynamic_pointer_cast<API::IPeakFunction>(func);
gaussian->setHeight(height);
gaussian->setCentre(centreByMax);
gaussian->setFwhm(fwhm);
sum->addFunction(gaussian);
func = API::FunctionFactory::Instance().createFunction("LinearBackground");
func->setParameter("A0", 0.);
func->setParameter("A1", 0.);
sum->addFunction(func);
// call Fit child algorithm
API::IAlgorithm_sptr fit = createChildAlgorithm("Fit");
fit->initialize();
fit->setProperty("Function",
boost::dynamic_pointer_cast<API::IFunction>(sum));
fit->setProperty("InputWorkspace", integralWS);
fit->setProperty("StartX", centreByMax - 3 * fwhm);
fit->setProperty("EndX", centreByMax + 3 * fwhm);
fit->execute();
const std::string fitStatus = fit->getProperty("OutputStatus");
if (fitStatus != "success") {
g_log.warning("Fit not successful, using position of max value.\n");
return centreByMax;
}
const auto centre = gaussian->centre();
g_log.debug() << "Sigma: " << gaussian->fwhm() << '\n';
g_log.debug() << "Estimated peak position: " << centre << '\n';
return centre;
}