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#include "MantidQtCustomInterfaces/Muon/ALCBaselineModellingModel.h"
#include "MantidQtCustomInterfaces/Muon/ALCHelper.h"
#include "MantidAPI/AlgorithmManager.h"
#include "MantidAPI/FunctionFactory.h"
#include "MantidAPI/TextAxis.h"
#include "MantidAPI/TableRow.h"
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using namespace Mantid::API;
namespace MantidQt
{
namespace CustomInterfaces
{
void ALCBaselineModellingModel::fit(IFunction_const_sptr function, const std::vector<Section>& sections)
{
// Create a copy of the data
IAlgorithm_sptr clone = AlgorithmManager::Instance().create("CloneWorkspace");
clone->setChild(true);
clone->setProperty("InputWorkspace", boost::const_pointer_cast<MatrixWorkspace>(m_data));
clone->setProperty("OutputWorkspace", "__NotUsed__");
clone->execute();
Workspace_sptr cloned = clone->getProperty("OutputWorkspace");
MatrixWorkspace_sptr dataToFit = boost::dynamic_pointer_cast<MatrixWorkspace>(cloned);
assert(dataToFit); // CloneWorkspace should take care of that
disableUnwantedPoints(dataToFit, sections);
IFunction_sptr funcToFit =
FunctionFactory::Instance().createInitialized(function->asString());
IAlgorithm_sptr fit = AlgorithmManager::Instance().create("Fit");
fit->setChild(true);
fit->setProperty("Function", funcToFit);
fit->setProperty("InputWorkspace", dataToFit);
fit->setProperty("CreateOutput", true);
fit->execute();
MatrixWorkspace_sptr fitOutput = fit->getProperty("OutputWorkspace");
IAlgorithm_sptr extract = AlgorithmManager::Instance().create("ExtractSingleSpectrum");
extract->setChild(true);
extract->setProperty("InputWorkspace", fitOutput);
extract->setProperty("WorkspaceIndex", 2);
extract->setProperty("OutputWorkspace", "__NotUsed__");
extract->execute();
setCorrectedData(extract->getProperty("OutputWorkspace"));
setFittedFunction(funcToFit);
m_sections = sections;
void ALCBaselineModellingModel::setData(MatrixWorkspace_const_sptr data)
{
m_data = data;
emit dataChanged();
}
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/**
* Disable points in the workpsace in the way that points which are not included in any of specified
* sections are not used when fitting given workspace
* @param ws :: Workspace to disable points in
* @param sections :: Section we want to use for fitting
*/
void ALCBaselineModellingModel::disableUnwantedPoints(MatrixWorkspace_sptr ws,
const std::vector<IALCBaselineModellingModel::Section>& sections)
{
// Whether point with particular index should be disabled
std::vector<bool> toDisable(ws->blocksize(), true);
// Find points which are in at least one section, and exclude them from disable list
for (size_t i = 0; i < ws->blocksize(); ++i)
{
for (auto it = sections.begin(); it != sections.end(); ++it)
{
if ( ws->dataX(0)[i] >= it->first && ws->dataX(0)[i] <= it->second )
{
toDisable[i] = false;
break; // No need to check other sections
}
}
}
// XXX: Points are disabled by settings their errors to very high value. This makes those
// points to have very low weights during the fitting, effectively disabling them.
const double DISABLED_ERR = std::numeric_limits<double>::max();
// Disable chosen points
for (size_t i = 0; i < ws->blocksize(); ++i)
{
if (toDisable[i])
{
ws->dataE(0)[i] = DISABLED_ERR;
}
}
}
MatrixWorkspace_sptr ALCBaselineModellingModel::exportWorkspace()
{
if ( m_data && m_fittedFunction && m_correctedData ) {
IAlgorithm_sptr clone = AlgorithmManager::Instance().create("CloneWorkspace");
clone->setChild(true);
clone->setProperty("InputWorkspace", boost::const_pointer_cast<MatrixWorkspace>(m_data));
clone->setProperty("OutputWorkspace", "__NotUsed");
clone->execute();
Workspace_sptr cloneResult = clone->getProperty("OutputWorkspace");
Workspace_sptr baseline = ALCHelper::createWsFromFunction(m_fittedFunction, m_data->readX(0));
IAlgorithm_sptr join1 = AlgorithmManager::Instance().create("ConjoinWorkspaces");
join1->setChild(true);
join1->setProperty("InputWorkspace1", cloneResult);
join1->setProperty("InputWorkspace2", baseline);
join1->setProperty("CheckOverlapping", false);
join1->execute();
MatrixWorkspace_sptr join1Result = join1->getProperty("InputWorkspace1");
IAlgorithm_sptr join2 = AlgorithmManager::Instance().create("ConjoinWorkspaces");
join2->setChild(true);
join2->setProperty("InputWorkspace1", join1Result);
join2->setProperty("InputWorkspace2", boost::const_pointer_cast<MatrixWorkspace>(m_correctedData));
join2->setProperty("CheckOverlapping", false);
join2->execute();
MatrixWorkspace_sptr result = join2->getProperty("InputWorkspace1");
TextAxis* yAxis = new TextAxis(result->getNumberHistograms());
yAxis->setLabel(0,"Data");
yAxis->setLabel(1,"Baseline");
yAxis->setLabel(2,"Corrected");
result->replaceAxis(1,yAxis);
return result;
} else {
return MatrixWorkspace_sptr();
}
}
ITableWorkspace_sptr ALCBaselineModellingModel::exportSections()
{
if ( !m_sections.empty() ) {
ITableWorkspace_sptr table = WorkspaceFactory::Instance().createTable("TableWorkspace");
table->addColumn("double", "Start X");
table->addColumn("double", "End X");
for(auto it = m_sections.begin(); it != m_sections.end(); ++it)
{
TableRow newRow = table->appendRow();
newRow << it->first << it->second;
}
return table;
} else {
return ITableWorkspace_sptr();
}
}
ITableWorkspace_sptr ALCBaselineModellingModel::exportModel()
{
if ( m_fittedFunction ) {
ITableWorkspace_sptr table = WorkspaceFactory::Instance().createTable("TableWorkspace");
table->addColumn("str", "Function");
TableRow newRow = table->appendRow();
newRow << m_fittedFunction->asString();
return table;
} else {
return ITableWorkspace_sptr();
}
void ALCBaselineModellingModel::setCorrectedData(MatrixWorkspace_const_sptr data)
{
m_correctedData = data;
emit correctedDataChanged();
}
void ALCBaselineModellingModel::setFittedFunction(IFunction_const_sptr function)
{
m_fittedFunction = function;
emit fittedFunctionChanged();
}
} // namespace CustomInterfaces
} // namespace Mantid