/**
* 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;
}
/** 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;
}
