/** Construct output
*/
Workspace2D_sptr RefinePowderInstrumentParameters2::genOutputWorkspace(FunctionDomain1DVector domain,
FunctionValues rawvalues)
{
// 1. Create and set up output workspace
size_t lenx = m_dataWS->readX(m_wsIndex).size();
size_t leny = m_dataWS->readY(m_wsIndex).size();
Workspace2D_sptr outws = boost::dynamic_pointer_cast<Workspace2D>
(WorkspaceFactory::Instance().create("Workspace2D", 6, lenx, leny));
outws->getAxis(0)->setUnit("dSpacing");
TextAxis* taxis = new TextAxis(outws->getNumberHistograms());
taxis->setLabel(0, "Data");
taxis->setLabel(1, "Model");
taxis->setLabel(2, "DiffDM");
taxis->setLabel(3, "Start");
taxis->setLabel(4, "DiffDS");
taxis->setLabel(5, "Zdiff");
outws->replaceAxis(1, taxis);
// 3. Re-calculate values
FunctionValues funcvalues(domain);
m_positionFunc->function(domain, funcvalues);
// 4. Add values
// a) X axis
for (size_t iws = 0; iws < outws->getNumberHistograms(); ++iws)
{
MantidVec& vecX = outws->dataX(iws);
for (size_t n = 0; n < lenx; ++n)
vecX[n] = domain[n];
}
// b) Y axis
const MantidVec& dataY = m_dataWS->readY(m_wsIndex);
for (size_t i = 0; i < domain.size(); ++i)
{
outws->dataY(0)[i] = dataY[i];
outws->dataY(1)[i] = funcvalues[i];
outws->dataY(2)[i] = dataY[i] - funcvalues[i];
outws->dataY(3)[i] = rawvalues[i];
outws->dataY(4)[i] = dataY[i] - rawvalues[i];
}
// 5. Zscore
vector<double> zscore = Kernel::getZscore(outws->readY(2));
for (size_t i = 0; i < domain.size(); ++i)
outws->dataY(5)[i] = zscore[i];
return outws;
}
/** Fit function
* Minimizer: "Levenberg-MarquardtMD"/"Simplex"
*/
bool RefinePowderInstrumentParameters2::doFitFunction(IFunction_sptr function, Workspace2D_sptr dataws, int wsindex,
string minimizer, int numiters, double& chi2, string& fitstatus)
{
// 0. Debug output
stringstream outss;
outss << "Fit function: " << m_positionFunc->asString() << endl << "Data To Fit: \n";
for (size_t i = 0; i < dataws->readX(0).size(); ++i)
outss << dataws->readX(wsindex)[i] << "\t\t" << dataws->readY(wsindex)[i] << "\t\t"
<< dataws->readE(wsindex)[i] << "\n";
g_log.information() << outss.str();
// 1. Create and setup fit algorithm
API::IAlgorithm_sptr fitalg = createChildAlgorithm("Fit", 0.0, 0.2, true);
fitalg->initialize();
fitalg->setProperty("Function", function);
fitalg->setProperty("InputWorkspace", dataws);
fitalg->setProperty("WorkspaceIndex", wsindex);
fitalg->setProperty("Minimizer", minimizer);
fitalg->setProperty("CostFunction", "Least squares");
fitalg->setProperty("MaxIterations", numiters);
fitalg->setProperty("CalcErrors", true);
// 2. Fit
bool successfulfit = fitalg->execute();
if (!fitalg->isExecuted() || ! successfulfit)
{
// Early return due to bad fit
g_log.warning("Fitting to instrument geometry function failed. ");
chi2 = DBL_MAX;
fitstatus = "Minimizer throws exception.";
return false;
}
// 3. Understand solution
chi2 = fitalg->getProperty("OutputChi2overDoF");
string tempfitstatus = fitalg->getProperty("OutputStatus");
fitstatus = tempfitstatus;
bool goodfit = fitstatus.compare("success") == 0;
stringstream dbss;
dbss << "Fit Result (GSL): Chi^2 = " << chi2
<< "; Fit Status = " << fitstatus << ", Return Bool = " << goodfit << std::endl;
vector<string> funcparnames = function->getParameterNames();
for (size_t i = 0; i < funcparnames.size(); ++i)
dbss << funcparnames[i] << " = " << setw(20) << function->getParameter(funcparnames[i])
<< " +/- " << function->getError(i) << "\n";
g_log.debug() << dbss.str();
return goodfit;
}
void PoldiPeakSearch::exec() {
g_log.information() << "PoldiPeakSearch:" << std::endl;
Workspace2D_sptr correlationWorkspace = getProperty("InputWorkspace");
MantidVec correlationQValues = correlationWorkspace->readX(0);
MantidVec correlatedCounts = correlationWorkspace->readY(0);
g_log.information() << " Auto-correlation data read." << std::endl;
Unit_sptr xUnit = correlationWorkspace->getAxis(0)->unit();
if (xUnit->caption() == "") {
g_log.information()
<< " Workspace does not have unit, defaulting to MomentumTransfer."
<< std::endl;
xUnit = UnitFactory::Instance().create("MomentumTransfer");
} else {
g_log.information() << " Unit of workspace is " << xUnit->caption() << "."
<< std::endl;
}
setMinimumDistance(getProperty("MinimumPeakSeparation"));
setMinimumPeakHeight(getProperty("MinimumPeakHeight"));
setMaximumPeakNumber(getProperty("MaximumPeakNumber"));
if (m_doubleMinimumDistance > static_cast<int>(correlatedCounts.size())) {
throw(std::runtime_error("MinimumPeakSeparation is smaller than number of "
"spectrum points - no peaks possible."));
}
g_log.information() << " Parameters set." << std::endl;
MantidVec summedNeighborCounts = getNeighborSums(correlatedCounts);
g_log.information() << " Neighboring counts summed, contains "
<< summedNeighborCounts.size() << " data points."
<< std::endl;
std::list<MantidVec::const_iterator> peakPositionsSummed =
findPeaks(summedNeighborCounts.begin(), summedNeighborCounts.end());
g_log.information() << " Peaks detected in summed spectrum: "
<< peakPositionsSummed.size() << std::endl;
/* This step is required because peaks are actually searched in the
* "sum-of-neighbors"-spectrum.
* The mapping removes the offset from the peak position which results from
* different beginning
* of this vector compared to the original correlation counts.
*/
std::list<MantidVec::const_iterator> peakPositionsCorrelation =
mapPeakPositionsToCorrelationData(peakPositionsSummed,
summedNeighborCounts.begin(),
correlatedCounts.begin());
g_log.information() << " Peak positions transformed to original spectrum."
<< std::endl;
/* Since intensities are required for filtering, they are extracted from the
* original count data,
* along with the Q-values.
*/
std::vector<PoldiPeak_sptr> peakCoordinates =
getPeaks(correlatedCounts.begin(), correlatedCounts.end(),
peakPositionsCorrelation, correlationQValues, xUnit);
g_log.information()
<< " Extracted peak positions in Q and intensity guesses." << std::endl;
UncertainValue backgroundWithSigma =
getBackgroundWithSigma(peakPositionsCorrelation, correlatedCounts);
g_log.information() << " Calculated average background and deviation: "
<< UncertainValueIO::toString(backgroundWithSigma)
<< std::endl;
if ((*getProperty("MinimumPeakHeight")).isDefault()) {
setMinimumPeakHeight(minimumPeakHeightFromBackground(backgroundWithSigma));
}
std::vector<PoldiPeak_sptr> intensityFilteredPeaks(peakCoordinates.size());
auto newEnd = std::remove_copy_if(
peakCoordinates.begin(), peakCoordinates.end(),
intensityFilteredPeaks.begin(),
boost::bind(&PoldiPeakSearch::isLessThanMinimum, this, _1));
intensityFilteredPeaks.resize(
std::distance(intensityFilteredPeaks.begin(), newEnd));
g_log.information() << " Peaks above minimum intensity ("
<< m_minimumPeakHeight
<< "): " << intensityFilteredPeaks.size() << std::endl;
std::sort(intensityFilteredPeaks.begin(), intensityFilteredPeaks.end(),
boost::bind<bool>(&PoldiPeak::greaterThan, _1, _2,
&PoldiPeak::intensity));
for (std::vector<PoldiPeak_sptr>::const_iterator peak =
intensityFilteredPeaks.begin();
peak != intensityFilteredPeaks.end(); ++peak) {
m_peaks->addPeak(*peak);
}
/* The derived background error is set as error in the workspace containing
* correlation data, so it may be used as weights for peak fitting later on.
*/
setErrorsOnWorkspace(correlationWorkspace, backgroundWithSigma.error());
setProperty("OutputWorkspace", m_peaks->asTableWorkspace());
}
