/**
* Uses linear algorithm to do the fitting.
* @param histogram the histogram to fit
* @param background an output variable for the calculated background
* @param variance an output variable for background's variance, currently always
* zero.
* @param startX an X value in the first bin to be included in the fit
* @param endX an X value in the last bin to be included in the fit
*/
void CalculateFlatBackground::LinearFit(
const HistogramData::Histogram &histogram, double &background,
double &variance, const double startX, const double endX) {
MatrixWorkspace_sptr WS = WorkspaceFactory::Instance().create(
"Workspace2D", 1, histogram.x().size(), histogram.y().size());
WS->setHistogram(0, histogram);
IAlgorithm_sptr childAlg = createChildAlgorithm("Fit");
IFunction_sptr func =
API::FunctionFactory::Instance().createFunction("LinearBackground");
childAlg->setProperty<IFunction_sptr>("Function", func);
childAlg->setProperty<MatrixWorkspace_sptr>("InputWorkspace", WS);
childAlg->setProperty<bool>("CreateOutput", true);
childAlg->setProperty<int>("WorkspaceIndex", 0);
childAlg->setProperty<double>("StartX", startX);
childAlg->setProperty<double>("EndX", endX);
// Default minimizer doesn't work properly even on the easiest cases,
// so Levenberg-MarquardtMD is used instead
childAlg->setProperty<std::string>("Minimizer", "Levenberg-MarquardtMD");
childAlg->executeAsChildAlg();
std::string outputStatus = childAlg->getProperty("OutputStatus");
if (outputStatus != "success") {
g_log.warning("Unable to successfully fit the data: " + outputStatus);
background = -1;
return;
}
Mantid::API::ITableWorkspace_sptr output =
childAlg->getProperty("OutputParameters");
// Find rows with parameters we are after
size_t rowA0, rowA1;
output->find(static_cast<std::string>("A0"), rowA0, 0);
output->find(static_cast<std::string>("A1"), rowA1, 0);
// Linear function is defined as A0 + A1*x
const double intercept = output->cell<double>(rowA0, 1);
const double slope = output->cell<double>(rowA1, 1);
const double centre = (startX + endX) / 2.0;
// Calculate the value of the flat background by taking the value at the
// centre point of the fit
background = slope * centre + intercept;
// ATM we don't calculate the error here.
variance = 0;
}
