/**
* Add a new entry to the diagnosis table.
* @param runTitle :: Title of the run fitted
* @param fitQuality :: Number representing a goodness of the fit
* @param fittedFunction :: Function containing fitted parameters
*/
void MuonSequentialFitDialog::addDiagnosisEntry(const std::string& runTitle, double fitQuality,
IFunction_sptr fittedFunction)
{
int newRow = m_ui.diagnosisTable->rowCount();
m_ui.diagnosisTable->insertRow(newRow);
QString runTitleDisplay = QString::fromStdString(runTitle);
m_ui.diagnosisTable->setItem( newRow, 0, createTableWidgetItem(runTitleDisplay) );
QString fitQualityDisplay = QString::number(fitQuality);
m_ui.diagnosisTable->setItem( newRow, 1, createTableWidgetItem(fitQualityDisplay) );
for(int i = 2; i < m_ui.diagnosisTable->columnCount(); i += 2)
{
std::string paramName = m_ui.diagnosisTable->horizontalHeaderItem(i)->text().toStdString();
size_t paramIndex = fittedFunction->parameterIndex(paramName);
QString value = QString::number( fittedFunction->getParameter(paramIndex) );
QString error = QString::number( fittedFunction->getError(paramIndex) );
m_ui.diagnosisTable->setItem(newRow, i, createTableWidgetItem(value) );
m_ui.diagnosisTable->setItem(newRow, i + 1, createTableWidgetItem(error) );
}
m_ui.diagnosisTable->scrollToBottom();
}
TEST(FunctionFactoryTest, CreateFitFunctionTest)
{
IFunction_sptr fun = FunctionFactory::instance().createFitFunction("FunctionFactoryTestFunction(A=10,B=20)");
EXPECT_TRUE( fun.get() != nullptr );
EXPECT_EQ(fun->getParameter("A"),10);
EXPECT_EQ(fun->getParameter("B"),20);
//std::cerr << fun->asString() << std::endl;
}
/** 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;
}
/// Returns a TableWorkspace with refined cell parameters and error.
ITableWorkspace_sptr PoldiFitPeaks2D::getRefinedCellParameters(
const IFunction_sptr &fitFunction) const {
Poldi2DFunction_sptr poldi2DFunction =
boost::dynamic_pointer_cast<Poldi2DFunction>(fitFunction);
if (!poldi2DFunction || poldi2DFunction->nFunctions() < 1) {
throw std::invalid_argument(
"Cannot process function that is not a Poldi2DFunction.");
}
// Create a new table for lattice parameters
ITableWorkspace_sptr latticeParameterTable =
WorkspaceFactory::Instance().createTable();
latticeParameterTable->addColumn("str", "Parameter");
latticeParameterTable->addColumn("double", "Value");
latticeParameterTable->addColumn("double", "Error");
// The first function should be PoldiSpectrumPawleyFunction
boost::shared_ptr<PoldiSpectrumPawleyFunction> poldiPawleyFunction =
boost::dynamic_pointer_cast<PoldiSpectrumPawleyFunction>(
poldi2DFunction->getFunction(0));
if (!poldiPawleyFunction) {
throw std::invalid_argument("First function in Poldi2DFunction is not "
"PoldiSpectrumPawleyFunction.");
}
// Get the actual PawleyFunction to extract parameters.
IPawleyFunction_sptr pawleyFunction =
boost::dynamic_pointer_cast<IPawleyFunction>(
poldiPawleyFunction->getDecoratedFunction());
if (pawleyFunction) {
CompositeFunction_sptr pawleyParts =
boost::dynamic_pointer_cast<CompositeFunction>(
pawleyFunction->getDecoratedFunction());
// The first function in PawleyFunction contains the parameters
IFunction_sptr pawleyParameters = pawleyParts->getFunction(0);
for (size_t i = 0; i < pawleyParameters->nParams(); ++i) {
TableRow newRow = latticeParameterTable->appendRow();
newRow << pawleyParameters->parameterName(i)
<< pawleyParameters->getParameter(i)
<< pawleyParameters->getError(i);
}
}
return latticeParameterTable;
}
/** Store function parameter values to a map
*/
void storeFunctionParameterValue(IFunction_sptr function, map<string, pair<double, double> >& parvaluemap)
{
parvaluemap.clear();
vector<string> parnames = function->getParameterNames();
for (size_t i = 0; i < parnames.size(); ++i)
{
string& parname = parnames[i];
double parvalue = function->getParameter(i);
double parerror = function->getError(i);
parvaluemap.insert(make_pair(parname, make_pair(parvalue, parerror)));
}
return;
}
API::MatrixWorkspace_sptr
CreateFloodWorkspace::removeBackground(API::MatrixWorkspace_sptr ws) {
g_log.information() << "Remove background "
<< getPropertyValue(Prop::BACKGROUND) << '\n';
auto fitWS = transpose(ws);
auto const &x = fitWS->x(0);
// Define the fitting interval
double startX = getProperty(Prop::START_X);
double endX = getProperty(Prop::END_X);
std::vector<double> excludeFromFit;
if (isDefault(Prop::START_X)) {
startX = x.front();
} else {
excludeFromFit.push_back(x.front());
excludeFromFit.push_back(startX);
}
if (isDefault(Prop::END_X)) {
endX = x.back();
} else {
excludeFromFit.push_back(endX);
excludeFromFit.push_back(x.back());
}
// Exclude any bad detectors.
for (auto i : m_excludedSpectra) {
excludeFromFit.push_back(i);
excludeFromFit.push_back(i);
}
std::string const function = getBackgroundFunction();
// Fit the data to determine unwanted background
auto alg = createChildAlgorithm("Fit", 0.9, 0.99);
alg->setProperty("Function", function);
alg->setProperty("InputWorkspace", fitWS);
alg->setProperty("WorkspaceIndex", 0);
if (!excludeFromFit.empty()) {
alg->setProperty("Exclude", excludeFromFit);
}
alg->setProperty("Output", "fit");
alg->execute();
IFunction_sptr func = alg->getProperty("Function");
g_log.information() << "Background function parameters:\n";
for (size_t i = 0; i < func->nParams(); ++i) {
g_log.information() << " " << func->parameterName(i) << ": "
<< func->getParameter(i) << '\n';
}
// Divide the workspace by the fitted curve to remove the background
// and scale to values around 1
MatrixWorkspace_sptr bkgWS = alg->getProperty("OutputWorkspace");
auto const &bkg = bkgWS->y(1);
auto const nHisto = static_cast<int>(ws->getNumberHistograms());
PARALLEL_FOR_IF(Kernel::threadSafe(*ws, *bkgWS))
for (int i = 0; i < nHisto; ++i) {
PARALLEL_START_INTERUPT_REGION
auto const xVal = x[i];
if (isExcludedSpectrum(xVal)) {
ws->mutableY(i)[0] = VERY_BIG_VALUE;
ws->mutableE(i)[0] = 0.0;
} else if (xVal >= startX && xVal <= endX) {
auto const background = bkg[i];
if (background <= 0.0) {
throw std::runtime_error(
"Background is expected to be positive, found value " +
std::to_string(background) + " at spectrum with workspace index " +
std::to_string(i));
}
ws->mutableY(i)[0] /= background;
ws->mutableE(i)[0] /= background;
} else {
ws->mutableY(i)[0] = 1.0;
ws->mutableE(i)[0] = 0.0;
}
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
// Remove the logs
ws->setSharedRun(make_cow<Run>());
return ws;
}
void IqtFit::singleFitComplete(bool error) {
disconnect(m_batchAlgoRunner, SIGNAL(batchComplete(bool)), this,
SLOT(singleFitComplete(bool)));
if (error) {
QString msg =
"There was an error executing the fitting algorithm. Please see the "
"Results Log pane for more details.";
showMessageBox(msg);
return;
}
IFunction_sptr outputFunc = m_singleFitAlg->getProperty("Function");
// Get params.
QMap<QString, double> parameters;
std::vector<std::string> parNames = outputFunc->getParameterNames();
std::vector<double> parVals;
for (size_t i = 0; i < parNames.size(); ++i)
parVals.push_back(outputFunc->getParameter(parNames[i]));
for (size_t i = 0; i < parNames.size(); ++i)
parameters[QString(parNames[i].c_str())] = parVals[i];
m_ffRangeManager->setValue(m_properties["BackgroundA0"], parameters["f0.A0"]);
const int fitType = m_uiForm.cbFitType->currentIndex();
if (fitType != 2) {
// Exp 1
m_dblManager->setValue(m_properties["Exponential1.Intensity"],
parameters["f1.Intensity"]);
m_dblManager->setValue(m_properties["Exponential1.Tau"],
parameters["f1.Tau"]);
if (fitType == 1) {
// Exp 2
m_dblManager->setValue(m_properties["Exponential2.Intensity"],
parameters["f2.Intensity"]);
m_dblManager->setValue(m_properties["Exponential2.Tau"],
parameters["f2.Tau"]);
}
}
if (fitType > 1) {
// Str
QString fval;
if (fitType == 2) {
fval = "f1.";
} else {
fval = "f2.";
}
m_dblManager->setValue(m_properties["StretchedExp.Intensity"],
parameters[fval + "Intensity"]);
m_dblManager->setValue(m_properties["StretchedExp.Tau"],
parameters[fval + "Tau"]);
m_dblManager->setValue(m_properties["StretchedExp.Beta"],
parameters[fval + "Beta"]);
}
// Can start upddating the guess curve again
connect(m_dblManager, SIGNAL(propertyChanged(QtProperty *)), this,
SLOT(plotGuess(QtProperty *)));
// Plot the guess first so that it is under the fit
plotGuess(NULL);
// Now show the fitted curve of the mini plot
m_uiForm.ppPlot->addSpectrum("Fit", m_singleFitOutputName + "_Workspace", 1,
Qt::red);
m_pythonExportWsName = "";
}
