/** Fit instrument parameters by non Monte Carlo algorithm
* Requirement: m_positionFunc should have the best fit result;
*/
double RefinePowderInstrumentParameters2::execFitParametersNonMC()
{
// 1. Set up constraints
setFunctionParameterFitSetups(m_positionFunc, m_profileParameters);
// 2. Fit function
// FIXME powerfit should be a user option before freezing this algorithm
bool powerfit = false;
double chi2 = fitFunction(m_positionFunc, m_dataWS, m_wsIndex, powerfit);
// 2. Summary
stringstream sumss;
sumss << "Non-Monte Carlo Results: Best Chi^2 = " << chi2;
g_log.notice(sumss.str());
return chi2;
}
bool PluginFit::load(const QString& pluginName)
{
if (!QFile::exists (pluginName)){
QMessageBox::critical((ApplicationWindow *)parent(), tr("MantidPlot - File not found"),
tr("Plugin file: <p><b> %1 </b> <p>not found. Operation aborted!").arg(pluginName));
return false;
}
QLibrary lib(pluginName);
lib.setAutoUnload(false);
d_fsimplex = (fit_function_simplex) lib.resolve( "function_d" );
if (!d_fsimplex){
QMessageBox::critical((ApplicationWindow *)parent(), tr("MantidPlot - Plugin Error"),
tr("The plugin does not implement a %1 method necessary for simplex fitting.").arg("function_d"));
return false;
}
d_f = (fit_function) lib.resolve( "function_f" );
if (!d_f){
QMessageBox::critical((ApplicationWindow *)parent(), tr("MantidPlot - Plugin Error"),
tr("The plugin does not implement a %1 method necessary for Levenberg-Marquardt fitting.").arg("function_f"));
return false;
}
d_df = (fit_function_df) lib.resolve( "function_df" );
if (!d_df){
QMessageBox::critical((ApplicationWindow *)parent(), tr("MantidPlot - Plugin Error"),
tr("The plugin does not implement a %1 method necessary for Levenberg-Marquardt fitting.").arg("function_df"));
return false;
}
d_fdf = (fit_function_fdf) lib.resolve( "function_fdf" );
if (!d_fdf){
QMessageBox::critical((ApplicationWindow *)parent(), tr("MantidPlot - Plugin Error"),
tr("The plugin does not implement a %1 method necessary for Levenberg-Marquardt fitting.").arg("function_fdf"));
return false;
}
f_eval = (fitFunctionEval) lib.resolve("function_eval");
if (!f_eval)
return false;
typedef char* (*fitFunc)();
fitFunc fitFunction = (fitFunc) lib.resolve("parameters");
if (fitFunction){
d_param_names = QString(fitFunction()).split(",", QString::SkipEmptyParts);
d_p = (int)d_param_names.count();
initWorkspace(d_p);
} else
return false;
fitFunc fitExplain = (fitFunc) lib.resolve("explanations");
if (fitExplain)
d_param_explain = QString(fitExplain()).split(",", QString::SkipEmptyParts);
else
for (int i=0; i<d_p; i++)
d_param_explain << "";
fitFunction = (fitFunc) lib.resolve( "name" );
setObjectName(QString(fitFunction()));
fitFunction = (fitFunc) lib.resolve( "function" );
if (fitFunction)
d_formula = QString(fitFunction());
else
return false;
return true;
}
void fit(TH1* histogram, double xMin, double xMax, double massValue, std::vector<fitParameterType>& fitParameter)
{
// create fit variable
TAxis* xAxis = histogram->GetXaxis();
if ( xMin < 0. ) xMin = xAxis->GetXmin();
if ( xMax < 0. ) xMax = xAxis->GetXmax();
std::string fitVariableName = Form("%s_fitVariable", histogram->GetName());
RooRealVar fitVariable(fitVariableName.data(), fitVariableName.data(), xMin, xMax);
// convert histogram to RooFit format
std::string histogramName_data = Form("%s_data", histogram->GetName());
RooDataHist histogram_data(histogramName_data.data(), histogramName_data.data(), fitVariable, histogram, 1.0);
// create product of linear * Heaviside * Gaussian Error function
std::string svFitLineShapeName_slope = Form("%s_svFitLineShape_slope", histogram->GetName());
RooRealVar svFitLineShape_slope(svFitLineShapeName_slope.data(), svFitLineShapeName_slope.data(), 2./(massValue*massValue), 0., 1.);
std::string svFitLineShapeName_offset = Form("%s_svFitLineShape_offset", histogram->GetName());
RooRealVar svFitLineShape_offset(svFitLineShapeName_offset.data(), svFitLineShapeName_offset.data(), 0., -1., +1.);
std::string heavisideName_threshold = Form("%s_heaviside_threshold", histogram->GetName());
RooRealVar heaviside_threshold(heavisideName_threshold.data(), heavisideName_threshold.data(), 0.2*massValue, 1.e-2*massValue, 0.5*massValue);
std::string sculptingName_bias = Form("%s_sculpting_bias", histogram->GetName());
RooRealVar sculpting_bias(sculptingName_bias.data(), sculptingName_bias.data(), 0.25*massValue, 0.*massValue, 0.9*massValue);
std::string sculptingName_width = Form("%s_sculpting_width", histogram->GetName());
RooRealVar sculpting_width(sculptingName_width.data(), sculptingName_width.data(), 0.1*massValue, 1.e-2*massValue, 1.0*massValue);
std::string svFitLineShapeName = Form("%s_svFitLineShape", histogram->GetName());
std::string svFitLineShapeFormula = "(@0*@1 + @2)*0.5*(1.0 + TMath::Sign(+1, @0 - @3))*0.5*(1.0 + TMath::Erf((@0 - @4)/@5))";
RooArgList svFitLineShapeArgs(fitVariable,
svFitLineShape_slope,
svFitLineShape_offset,
heaviside_threshold,
sculpting_bias,
sculpting_width);
RooGenericPdf svFitLineShape(svFitLineShapeName.data(), svFitLineShapeName.data(), svFitLineShapeFormula.data(), svFitLineShapeArgs);
// create Gaussian
std::string gaussianName_mean = Form("%s_gaussian_mean", histogram->GetName());
RooConstVar gaussian_mean(gaussianName_mean.data(), gaussianName_mean.data(), 0.);
std::string gaussianName_sigma = Form("%s_gaussian_sigma", histogram->GetName());
RooRealVar gaussian_sigma(gaussianName_sigma.data(), gaussianName_sigma.data(), 0.2*massValue, 1.e-2*massValue, 0.5*massValue);
std::string gaussianName = Form("%s_gaussian", histogram->GetName());
RooGaussian gaussian(gaussianName.data(), gaussianName.data(), fitVariable, gaussian_mean, gaussian_sigma);
// numerically convolute both PDFs using fast Fourier transform
std::string fitFunctionName = Form("%s_fitFunction", histogram->GetName());
RooFFTConvPdf fitFunction(fitFunctionName.data(), fitFunctionName.data(), fitVariable, svFitLineShape, gaussian);
// fit histogram
fitFunction.fitTo(histogram_data);
// save fit parameter
fitParameter.resize(6);
fitParameter[0] = fitParameterType(&svFitLineShape_slope);
fitParameter[1] = fitParameterType(&svFitLineShape_offset);
fitParameter[2] = fitParameterType(&gaussian_sigma);
fitParameter[3] = fitParameterType(&heaviside_threshold);
fitParameter[4] = fitParameterType(&sculpting_bias);
fitParameter[5] = fitParameterType(&sculpting_width);
// create control plot
std::string frameTitle = Form("%s_frame", histogram->GetName());
RooPlot* frame = fitVariable.frame(RooFit::Title(frameTitle.data()));
histogram_data.plotOn(frame);
fitFunction.plotOn(frame, RooFit::LineColor(kRed));
std::string canvasName = Form("%s_canvas", histogram->GetName());
TCanvas* canvas = new TCanvas(canvasName.data(), canvasName.data(), 800, 600);
gPad->SetLeftMargin(0.15);
frame->GetYaxis()->SetTitleOffset(1.4);
frame->Draw();
std::string outputFileName_plot = Form("svFitPerformance_%s_fit", histogram->GetName());
canvas->Print(std::string(outputFileName_plot).append(".eps").data());
canvas->Print(std::string(outputFileName_plot).append(".png").data());
canvas->Print(std::string(outputFileName_plot).append(".pdf").data());
delete canvas;
}
