/**
* 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;
}
/** 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;
}
std::string FrameworkManagerProxy::createAlgorithmDocs(const std::string& algName, const int version)
{
const std::string EOL="\n";
API::IAlgorithm_sptr algm = API::AlgorithmManager::Instance().createUnmanaged(algName, version);
algm->initialize();
// Put in the quick overview message
std::stringstream buffer;
std::string temp = algm->getOptionalMessage();
if (temp.size() > 0)
buffer << temp << EOL << EOL;
// get a sorted copy of the properties
PropertyVector properties(algm->getProperties());
std::sort(properties.begin(), properties.end(), PropertyOrdering());
// generate the sanitized names
StringVector names(properties.size());
size_t numProps = properties.size();
for ( size_t i = 0; i < numProps; ++i)
{
names[i] = removeCharacters(properties[i]->name(), "");
}
buffer << "Property descriptions: " << EOL << EOL;
// write the actual property descriptions
Mantid::Kernel::Property *prop;
for ( size_t i = 0; i < numProps; ++i)
{
prop = properties[i];
buffer << names[i] << "("
<< Mantid::Kernel::Direction::asText(prop->direction());
if (!prop->isValid().empty())
buffer << ":req";
buffer << ") *" << prop->type() << "* ";
std::set<std::string> allowed = prop->allowedValues();
if (!prop->documentation().empty() || !allowed.empty())
{
buffer << " " << prop->documentation();
if (!allowed.empty())
{
buffer << " [" << Kernel::Strings::join(allowed.begin(), allowed.end(), ", ");
buffer << "]";
}
buffer << EOL;
if( i < numProps - 1 ) buffer << EOL;
}
}
return buffer.str();
}
/**
* Create the concrete instance use for the actual loading.
* @param startProgress :: The percentage progress value of the overall
* algorithm where this child algorithm starts
* @param endProgress :: The percentage progress value of the overall
* algorithm where this child algorithm ends
* @param logging :: Set to false to disable logging from the child algorithm
*/
API::IAlgorithm_sptr Load::createLoader(const double startProgress,
const double endProgress,
const bool logging) const {
std::string name = getPropertyValue("LoaderName");
int version = getProperty("LoaderVersion");
API::IAlgorithm_sptr loader =
API::AlgorithmManager::Instance().createUnmanaged(name, version);
loader->initialize();
if (!loader) {
throw std::runtime_error("Cannot create loader for \"" +
getPropertyValue("Filename") + "\"");
}
setUpLoader(loader, startProgress, endProgress, logging);
return loader;
}
/**
* 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;
}
/**
* Creates a managed version of a specified algorithm.
* @param algName :: The name of the algorithm to execute.
* @param version :: The version number (default=-1=highest version).
* @return Pointer to algorithm.
**/
API::IAlgorithm_sptr FrameworkManagerProxy::createUnmanagedAlgorithm(const std::string& algName, const int version)
{
API::IAlgorithm_sptr alg = API::AlgorithmManager::Instance().createUnmanaged(algName, version);
alg->initialize();
return alg;
}
void GoniometerAnglesFromPhiRotation::exec() {
PeaksWorkspace_sptr PeaksRun1 = getProperty("PeaksWorkspace1");
PeaksWorkspace_sptr PeaksRun2 = getProperty("PeaksWorkspace2");
double Tolerance = getProperty("Tolerance");
Kernel::Matrix<double> Gon1(3, 3);
Kernel::Matrix<double> Gon2(3, 3);
if (!CheckForOneRun(PeaksRun1, Gon1) || !CheckForOneRun(PeaksRun2, Gon2)) {
g_log.error("Each peaks workspace MUST have only one run");
throw std::invalid_argument("Each peaks workspace MUST have only one run");
}
Kernel::Matrix<double> UB1;
bool Run1HasOrientedLattice = true;
if (!PeaksRun1->sample().hasOrientedLattice()) {
Run1HasOrientedLattice = false;
const std::string fft("FindUBUsingFFT");
API::IAlgorithm_sptr findUB = this->createChildAlgorithm(fft);
findUB->initialize();
findUB->setProperty<PeaksWorkspace_sptr>("PeaksWorkspace",
getProperty("PeaksWorkspace1"));
findUB->setProperty("MIND", static_cast<double>(getProperty("MIND")));
findUB->setProperty("MAXD", static_cast<double>(getProperty("MAXD")));
findUB->setProperty("Tolerance", Tolerance);
findUB->executeAsChildAlg();
if (!PeaksRun1->sample().hasOrientedLattice()) {
g_log.notice(std::string("Could not find UB for ") +
std::string(PeaksRun1->getName()));
throw std::invalid_argument(std::string("Could not find UB for ") +
std::string(PeaksRun1->getName()));
}
}
//-------------get UB raw :No goniometer----------------
UB1 = PeaksRun1->sample().getOrientedLattice().getUB();
UB1 = getUBRaw(UB1, Gon1);
int N1;
double avErrIndx, avErrAll;
IndexRaw(PeaksRun1, UB1, N1, avErrIndx, avErrAll, Tolerance);
if (N1 < .6 * PeaksRun1->getNumberPeaks()) {
g_log.notice(std::string("UB did not index well for ") +
std::string(PeaksRun1->getName()));
throw std::invalid_argument(std::string("UB did not index well for ") +
std::string(PeaksRun1->getName()));
}
//----------------------------------------------
Geometry::OrientedLattice lat2 = PeaksRun1->sample().getOrientedLattice();
lat2.setUB(UB1);
PeaksRun2->mutableSample().setOrientedLattice(&lat2);
if (!Run1HasOrientedLattice)
PeaksRun1->mutableSample().setOrientedLattice(nullptr);
double dphi = static_cast<double>(getProperty("Phi2")) -
static_cast<double>(getProperty("Run1Phi"));
Kernel::Matrix<double> Gon22(3, 3, true);
for (int i = 0; i < PeaksRun2->getNumberPeaks(); i++) {
PeaksRun2->getPeak(i).setGoniometerMatrix(Gon22);
}
int RunNum = PeaksRun2->getPeak(0).getRunNumber();
std::string RunNumStr = std::to_string(RunNum);
int Npeaks = PeaksRun2->getNumberPeaks();
// n indexed, av err, phi, chi,omega
std::array<double, 5> MinData = {{0., 0., 0., 0., 0.}};
MinData[0] = 0.0;
std::vector<V3D> directionList = IndexingUtils::MakeHemisphereDirections(50);
API::FrameworkManager::Instance();
for (auto dir : directionList)
for (int sgn = 1; sgn > -2; sgn -= 2) {
dir.normalize();
Quat Q(sgn * dphi, dir);
Q.normalize();
Kernel::Matrix<double> Rot(Q.getRotation());
int Nindexed;
double dummyAvErrIndx, dummyAvErrAll;
IndexRaw(PeaksRun2, Rot * UB1, Nindexed, dummyAvErrIndx, dummyAvErrAll,
Tolerance);
if (Nindexed > MinData[0]) {
MinData[0] = Nindexed;
MinData[1] = sgn;
MinData[2] = dir[0];
MinData[3] = dir[1];
MinData[4] = dir[2];
}
}
g_log.debug() << "Best direction unOptimized is ("
<< (MinData[1] * MinData[2]) << "," << (MinData[1] * MinData[3])
<< "," << (MinData[1] * MinData[4]) << ")\n";
//----------------------- Optimize around best----------------------------
auto ws = createWorkspace<Workspace2D>(1, 3 * Npeaks, 3 * Npeaks);
MantidVec Xvals;
for (int i = 0; i < Npeaks; ++i) {
Xvals.push_back(i);
Xvals.push_back(i);
Xvals.push_back(i);
}
ws->setPoints(0, Xvals);
//--------------------Set up other Fit function arguments------------------
V3D dir(MinData[2], MinData[3], MinData[4]);
dir.normalize();
Quat Q(MinData[1] * dphi, dir);
Q.normalize();
Kernel::Matrix<double> Rot(Q.getRotation());
Goniometer Gon(Rot);
std::vector<double> omchiphi = Gon.getEulerAngles("yzy");
MinData[2] = omchiphi[2];
MinData[3] = omchiphi[1];
MinData[4] = omchiphi[0];
std::string FunctionArgs =
"name=PeakHKLErrors, PeakWorkspaceName=" + PeaksRun2->getName() +
",OptRuns=" + RunNumStr + ",phi" + RunNumStr + "=" +
boost::lexical_cast<std::string>(MinData[2]) + ",chi" + RunNumStr + "=" +
boost::lexical_cast<std::string>(MinData[3]) + ",omega" + RunNumStr +
"=" + boost::lexical_cast<std::string>(MinData[4]);
std::string Constr = boost::lexical_cast<std::string>(MinData[2] - 5) +
"<phi" + RunNumStr + "<" +
boost::lexical_cast<std::string>(MinData[2] + 5);
Constr += "," + boost::lexical_cast<std::string>(MinData[3] - 5) + "<chi" +
RunNumStr + "<" + boost::lexical_cast<std::string>(MinData[3] + 5) +
",";
Constr += boost::lexical_cast<std::string>(MinData[4] - 5) + "<omega" +
RunNumStr + "<" + boost::lexical_cast<std::string>(MinData[4] + 5);
std::string Ties = "SampleXOffset=0.0,SampleYOffset=0.0,SampleZOffset=0.0,"
"GonRotx=0.0,GonRoty=0.0,GonRotz=0.0";
boost::shared_ptr<Algorithm> Fit = createChildAlgorithm("Fit");
Fit->initialize();
Fit->setProperty("Function", FunctionArgs);
Fit->setProperty("Ties", Ties);
Fit->setProperty("Constraints", Constr);
Fit->setProperty("InputWorkspace", ws);
Fit->setProperty("CreateOutput", true);
std::string outputName = "out";
Fit->setProperty("Output", outputName);
Fit->executeAsChildAlg();
boost::shared_ptr<API::ITableWorkspace> results =
Fit->getProperty("OutputParameters");
double chisq = Fit->getProperty("OutputChi2overDoF");
MinData[0] = chisq;
MinData[2] = results->Double(6, 1);
MinData[3] = results->Double(7, 1);
MinData[4] = results->Double(8, 1);
g_log.debug() << "Best direction Optimized is (" << (MinData[2]) << ","
<< (MinData[3]) << "," << (MinData[4]) << ")\n";
// ---------------------Find number indexed -----------------------
Quat Q1 = Quat(MinData[4], V3D(0, 1, 0)) * Quat(MinData[3], V3D(0, 0, 1)) *
Quat(MinData[2], V3D(0, 1, 0));
int Nindexed;
Kernel::Matrix<double> Mk(Q1.getRotation());
IndexRaw(PeaksRun2, Mk * UB1, Nindexed, avErrIndx, avErrAll, Tolerance);
//------------------------------------ Convert/Save Results
//-----------------------------
double deg, ax1, ax2, ax3;
Q1.getAngleAxis(deg, ax1, ax2, ax3);
if (dphi * deg < 0) {
deg = -deg;
ax1 = -ax1;
ax2 = -ax2;
ax3 = -ax3;
}
double phi2 = static_cast<double>(getProperty("Run1Phi")) + dphi;
double chi2 = acos(ax2) / M_PI * 180;
double omega2 = atan2(ax3, -ax1) / M_PI * 180;
g_log.notice()
<< "============================ Results ============================\n";
g_log.notice() << " phi,chi, and omega= (" << phi2 << "," << chi2 << ","
<< omega2 << ")\n";
g_log.notice() << " #indexed =" << Nindexed << '\n';
g_log.notice()
<< " ==============================================\n";
setProperty("Phi2", phi2);
setProperty("Chi2", chi2);
setProperty("Omega2", omega2);
setProperty("NIndexed", Nindexed);
setProperty("AvErrIndex", avErrIndx);
setProperty("AvErrAll", avErrAll);
Q1 = Quat(omega2, V3D(0, 1, 0)) * Quat(chi2, V3D(0, 0, 1)) *
Quat(phi2, V3D(0, 1, 0));
Kernel::Matrix<double> Gon2a(Q1.getRotation());
for (int i = 0; i < PeaksRun2->getNumberPeaks(); i++) {
PeaksRun2->getPeak(i).setGoniometerMatrix(Gon2a);
}
OrientedLattice latt2(PeaksRun2->mutableSample().getOrientedLattice());
// Kernel::Matrix<double> UB = latt2.getUB();
Rot.Invert();
Gon2a.Invert();
latt2.setUB(Gon2a * Mk * UB1);
PeaksRun2->mutableSample().setOrientedLattice(&latt2);
}
/**
* Executes the algorithm
*/
void PlotPeakByLogValue::exec()
{
// Create a list of the input workspace
const std::vector<InputData> wsNames = makeNames();
std::string fun = getPropertyValue("Function");
//int wi = getProperty("WorkspaceIndex");
std::string logName = getProperty("LogValue");
bool sequential = getPropertyValue("FitType") == "Sequential";
bool isDataName = false; // if true first output column is of type string and is the data source name
ITableWorkspace_sptr result = WorkspaceFactory::Instance().createTable("TableWorkspace");
if (logName == "SourceName")
{
result->addColumn("str","Source name");
isDataName = true;
}
else if (logName.empty())
{
result->addColumn("double","axis-1");
}
else
{
result->addColumn("double",logName);
}
// Create an instance of the fitting function to obtain the names of fitting parameters
IFitFunction* ifun = FunctionFactory::Instance().createInitialized(fun);
if (!ifun)
{
throw std::invalid_argument("Fitting function failed to initialize");
}
for(size_t iPar=0;iPar<ifun->nParams();++iPar)
{
result->addColumn("double",ifun->parameterName(iPar));
result->addColumn("double",ifun->parameterName(iPar)+"_Err");
}
result->addColumn("double","Chi_squared");
delete ifun;
setProperty("OutputWorkspace",result);
double dProg = 1./static_cast<double>(wsNames.size());
double Prog = 0.;
for(int i=0;i<static_cast<int>(wsNames.size());++i)
{
InputData data = getWorkspace(wsNames[i]);
if (!data.ws)
{
g_log.warning() << "Cannot access workspace " << wsNames[i].name << '\n';
continue;
}
if (data.i < 0 && data.indx.empty())
{
g_log.warning() << "Zero spectra selected for fitting in workspace " << wsNames[i].name << '\n';
continue;
}
int j,jend;
if (data.i >= 0)
{
j = data.i;
jend = j + 1;
}
else
{// no need to check data.indx.empty()
j = data.indx.front();
jend = data.indx.back() + 1;
}
dProg /= abs(jend - j);
for(;j < jend;++j)
{
// Find the log value: it is either a log-file value or simply the workspace number
double logValue;
if (logName.empty())
{
API::Axis* axis = data.ws->getAxis(1);
logValue = (*axis)(j);
}
else if (logName != "SourceName")
{
Kernel::Property* prop = data.ws->run().getLogData(logName);
if (!prop)
{
throw std::invalid_argument("Log value "+logName+" does not exist");
}
TimeSeriesProperty<double>* logp =
dynamic_cast<TimeSeriesProperty<double>*>(prop);
logValue = logp->lastValue();
}
std::string resFun = fun;
std::vector<double> errors;
double chi2;
try
{
// Fit the function
API::IAlgorithm_sptr fit = createSubAlgorithm("Fit");
fit->initialize();
fit->setProperty("InputWorkspace",data.ws);
//fit->setPropertyValue("InputWorkspace",data.ws->getName());
fit->setProperty("WorkspaceIndex",j);
fit->setPropertyValue("Function",fun);
fit->setPropertyValue("StartX",getPropertyValue("StartX"));
fit->setPropertyValue("EndX",getPropertyValue("EndX"));
fit->setPropertyValue("Minimizer",getPropertyValue("Minimizer"));
fit->setPropertyValue("CostFunction",getPropertyValue("CostFunction"));
fit->execute();
resFun = fit->getPropertyValue("Function");
errors = fit->getProperty("Errors");
chi2 = fit->getProperty("OutputChi2overDoF");
}
catch(...)
{
g_log.error("Error in Fit subalgorithm");
throw;
}
if (sequential)
{
fun = resFun;
}
// Extract the fitted parameters and put them into the result table
TableRow row = result->appendRow();
if (isDataName)
{
row << wsNames[i].name;
}
else
{
row << logValue;
}
ifun = FunctionFactory::Instance().createInitialized(resFun);
for(size_t iPar=0;iPar<ifun->nParams();++iPar)
{
row << ifun->getParameter(iPar) << errors[iPar];
}
row << chi2;
delete ifun;
Prog += dProg;
progress(Prog);
interruption_point();
} // for(;j < jend;++j)
}
}
/** Get a workspace identified by an InputData structure.
* @param data :: InputData with name and either spec or i fields defined.
* @return InputData structure with the ws field set if everything was OK.
*/
PlotPeakByLogValue::InputData PlotPeakByLogValue::getWorkspace(const InputData& data)
{
InputData out(data);
if (API::AnalysisDataService::Instance().doesExist(data.name))
{
DataObjects::Workspace2D_sptr ws = boost::dynamic_pointer_cast<DataObjects::Workspace2D>(
API::AnalysisDataService::Instance().retrieve(data.name));
if (ws)
{
out.ws = ws;
}
else
{
return data;
}
}
else
{
std::ifstream fil(data.name.c_str());
if (!fil)
{
g_log.warning() << "File "<<data.name<<" does not exist\n";
return data;
}
fil.close();
std::string::size_type i = data.name.find_last_of('.');
if (i == std::string::npos)
{
g_log.warning() << "Cannot open file "<<data.name<<"\n";
return data;
}
std::string ext = data.name.substr(i);
try
{
API::IAlgorithm_sptr load = createSubAlgorithm("Load");
load->initialize();
load->setPropertyValue("FileName",data.name);
load->execute();
if (load->isExecuted())
{
API::Workspace_sptr rws = load->getProperty("OutputWorkspace");
if (rws)
{
DataObjects::Workspace2D_sptr ws = boost::dynamic_pointer_cast<DataObjects::Workspace2D>(rws);
if (ws)
{
out.ws = ws;
}
else
{
API::WorkspaceGroup_sptr gws = boost::dynamic_pointer_cast<API::WorkspaceGroup>(rws);
if (gws)
{
std::vector<std::string> wsNames = gws->getNames();
std::string propName = "OUTPUTWORKSPACE_" + boost::lexical_cast<std::string>(data.period);
if (load->existsProperty(propName))
{
Workspace_sptr rws1 = load->getProperty(propName);
out.ws = boost::dynamic_pointer_cast<DataObjects::Workspace2D>(rws1);
}
}
}
}
}
}
catch(std::exception& e)
{
g_log.error(e.what());
return data;
}
}
if (!out.ws) return data;
API::Axis* axis = out.ws->getAxis(1);
if (axis->isSpectra())
{// spectra axis
if (out.spec < 0)
{
if (out.i >= 0)
{
out.spec = axis->spectraNo(out.i);
}
else
{// i < 0 && spec < 0 => use start and end
for(size_t i=0;i<axis->length();++i)
{
double s = double(axis->spectraNo(i));
if (s >= out.start && s <= out.end)
{
out.indx.push_back(static_cast<int>(i));
}
}
}
}
else
{
for(size_t i=0;i<axis->length();++i)
{
int j = axis->spectraNo(i);
if (j == out.spec)
{
out.i = static_cast<int>(i);
break;
}
}
}
if (out.i < 0 && out.indx.empty())
{
return data;
}
}
else
{// numeric axis
out.spec = -1;
if (out.i >= 0)
{
out.indx.clear();
}
else
{
if (out.i < -1)
{
out.start = (*axis)(0);
out.end = (*axis)(axis->length()-1);
}
for(size_t i=0;i<axis->length();++i)
{
double s = (*axis)(i);
if (s >= out.start && s <= out.end)
{
out.indx.push_back(static_cast<int>(i));
}
}
}
}
return out;
}
void MaskBinsFromTable::exec()
{
MatrixWorkspace_sptr inputWS = getProperty("InputWorkspace");
DataObjects::TableWorkspace_sptr paramWS = getProperty("MaskingInformation");
// 1. Check input table workspace and column order
g_log.debug() << "Lines of parameters workspace = " << paramWS->rowCount() << std::endl;
bool colname_specx = false;
if (!paramWS)
{
throw std::invalid_argument("Input table workspace is not accepted.");
}
else
{
std::vector<std::string> colnames = paramWS->getColumnNames();
// check colum name order
if (colnames.size() < 3)
{
g_log.error() << "Input MaskingInformation table workspace has fewer than 3 columns. " << colnames.size()
<< " columns indeed" << std::endl;
throw std::invalid_argument("MaskingInformation (TableWorkspace) has too few columns.");
}
if (colnames[0].compare("XMin") == 0)
{
// 1. Style XMin, XMax, SpectraList. Check rest
if (colnames[1].compare("XMax") != 0 || colnames[2].compare("SpectraList") != 0)
{
g_log.error() << "INput MaskingInformation table workspace has wrong column order. " << std::endl;
throw std::invalid_argument("MaskingInformation (TableWorkspace) has too few columns.");
}
}
else if (colnames[0].compare("SpectraList") == 0)
{
// 2. Style SpectraList, XMin, XMax
colname_specx = true;
if (colnames[1].compare("XMin") != 0 || colnames[2].compare("XMax") != 0)
{
g_log.error() << "INput MaskingInformation table workspace has wrong column order. " << std::endl;
throw std::invalid_argument("MaskingInformation (TableWorkspace) has too few columns.");
}
}
else
{
g_log.error() << "INput MaskingInformation table workspace has wrong column order. " << std::endl;
throw std::invalid_argument("MaskingInformation (TableWorkspace) has too few columns.");
}
}
// 2. Loop over all rows
bool firstloop = true;
API::MatrixWorkspace_sptr outputws = this->getProperty("OutputWorkspace");
for (size_t ib = 0; ib < paramWS->rowCount(); ++ib)
{
API::TableRow therow = paramWS->getRow(ib);
double xmin, xmax;
std::string speclist;
if (colname_specx)
{
therow >> speclist >> xmin >> xmax;
}
else
{
therow >> xmin >> xmax >> speclist;
}
g_log.debug() << "Row " << ib << " XMin = " << xmin << " XMax = " << xmax << " SpectraList = " << speclist << std::endl;
API::IAlgorithm_sptr maskbins = this->createChildAlgorithm("MaskBins", 0, 0.3, true);
maskbins->initialize();
if (firstloop)
{
maskbins->setProperty("InputWorkspace", inputWS);
firstloop = false;
}
else
{
maskbins->setProperty("InputWorkspace", outputws);
}
maskbins->setProperty("OutputWorkspace", outputws);
maskbins->setPropertyValue("SpectraList", speclist);
maskbins->setProperty("XMin", xmin);
maskbins->setProperty("XMax", xmax);
bool isexec = maskbins->execute();
if (!isexec)
{
g_log.error() << "MaskBins() is not executed for row " << ib << std::endl;
throw std::runtime_error("MaskBins() is not executed");
}
outputws = maskbins->getProperty("OutputWorkspace");
if (!outputws)
{
g_log.error() << "OutputWorkspace is not retrieved for row " << ib << ". " << std::endl;
throw std::runtime_error("OutputWorkspace is not got from MaskBins");
}
}