/** 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;
}
/** Select background automatically
*/
DataObjects::Workspace2D_sptr
ProcessBackground::autoBackgroundSelection(Workspace2D_sptr bkgdWS) {
// Get background type and create bakground function
BackgroundFunction_sptr bkgdfunction = createBackgroundFunction(m_bkgdType);
int bkgdorder = getProperty("BackgroundOrder");
if (bkgdorder == 0)
g_log.warning("(Input) background function order is 0. It might not be "
"able to give a good estimation.");
bkgdfunction->setAttributeValue("n", bkgdorder);
bkgdfunction->initialize();
g_log.information() << "Input background points has "
<< bkgdWS->readX(0).size() << " data points for fit "
<< bkgdorder << "-th order " << bkgdfunction->name()
<< " (background) function" << bkgdfunction->asString()
<< "\n";
// Fit input (a few) background pionts to get initial guess
API::IAlgorithm_sptr fit;
try {
fit = this->createChildAlgorithm("Fit", 0.0, 0.2, true);
} catch (Exception::NotFoundError &) {
g_log.error() << "Requires CurveFitting library." << std::endl;
throw;
}
double startx = m_lowerBound;
double endx = m_upperBound;
fit->setProperty("Function",
boost::dynamic_pointer_cast<API::IFunction>(bkgdfunction));
fit->setProperty("InputWorkspace", bkgdWS);
fit->setProperty("WorkspaceIndex", 0);
fit->setProperty("MaxIterations", 500);
fit->setProperty("StartX", startx);
fit->setProperty("EndX", endx);
fit->setProperty("Minimizer", "Levenberg-Marquardt");
fit->setProperty("CostFunction", "Least squares");
fit->executeAsChildAlg();
// Get fit result
// a) Status
std::string fitStatus = fit->getProperty("OutputStatus");
bool allowedfailure = (fitStatus.find("cannot") < fitStatus.size()) &&
(fitStatus.find("tolerance") < fitStatus.size());
if (fitStatus.compare("success") != 0 && !allowedfailure) {
g_log.error() << "ProcessBackground: Fit Status = " << fitStatus
<< ". Not to update fit result" << std::endl;
throw std::runtime_error("Bad Fit");
}
// b) check that chi2 got better
const double chi2 = fit->getProperty("OutputChi2overDoF");
g_log.information() << "Fit background: Fit Status = " << fitStatus
<< ", chi2 = " << chi2 << "\n";
// Filter and construct for the output workspace
Workspace2D_sptr outws = filterForBackground(bkgdfunction);
return outws;
} // END OF FUNCTION
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());
}
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];
}
}
}
/** Select background automatically
*/
DataObjects::Workspace2D_sptr ProcessBackground::autoBackgroundSelection(Workspace2D_sptr bkgdWS)
{
// Get background type and create bakground function
BackgroundFunction_sptr bkgdfunction = createBackgroundFunction(m_bkgdType);
int bkgdorder = getProperty("BackgroundOrder");
bkgdfunction->setAttributeValue("n", bkgdorder);
g_log.debug() << "DBx622 Background Workspace has " << bkgdWS->readX(0).size()
<< " data points." << std::endl;
// Fit input (a few) background pionts to get initial guess
API::IAlgorithm_sptr fit;
try
{
fit = this->createChildAlgorithm("Fit", 0.0, 0.2, true);
}
catch (Exception::NotFoundError &)
{
g_log.error() << "Requires CurveFitting library." << std::endl;
throw;
}
double startx = m_lowerBound;
double endx = m_upperBound;
fit->setProperty("Function", boost::dynamic_pointer_cast<API::IFunction>(bkgdfunction));
fit->setProperty("InputWorkspace", bkgdWS);
fit->setProperty("WorkspaceIndex", 0);
fit->setProperty("MaxIterations", 500);
fit->setProperty("StartX", startx);
fit->setProperty("EndX", endx);
fit->setProperty("Minimizer", "Levenberg-Marquardt");
fit->setProperty("CostFunction", "Least squares");
fit->executeAsChildAlg();
// Get fit result
// a) Status
std::string fitStatus = fit->getProperty("OutputStatus");
bool allowedfailure = (fitStatus.find("cannot") < fitStatus.size()) &&
(fitStatus.find("tolerance") < fitStatus.size());
if (fitStatus.compare("success") != 0 && !allowedfailure)
{
g_log.error() << "ProcessBackground: Fit Status = " << fitStatus
<< ". Not to update fit result" << std::endl;
throw std::runtime_error("Bad Fit");
}
// b) check that chi2 got better
const double chi2 = fit->getProperty("OutputChi2overDoF");
g_log.information() << "Fit background: Fit Status = " << fitStatus << ", chi2 = "
<< chi2 << "\n";
// c) get out the parameter names
API::IFunction_sptr func = fit->getProperty("Function");
/* Comment out as not being used
std::vector<std::string> parnames = func->getParameterNames();
std::map<std::string, double> parvalues;
for (size_t iname = 0; iname < parnames.size(); ++iname)
{
double value = func->getParameter(parnames[iname]);
parvalues.insert(std::make_pair(parnames[iname], value));
}
DataObject::Workspace2D_const_sptr theorybackground = AnalysisDataService::Instance().retrieve(wsname);
*/
// Filter and construct for the output workspace
Workspace2D_sptr outws = filterForBackground(bkgdfunction);
return outws;
} // END OF FUNCTION
