File indexing completed on 2024-12-22 04:18:13

 /***************************************************************************
  *                                                                         *
  *   copyright : (C) 2007 The University of Toronto                        *
  *                   netterfield@astro.utoronto.ca                         *
  *   copyright : (C) 2005  University of British Columbia                  *
  *                   dscott@phas.ubc.ca                                    *
  *                                                                         *
  *   This program is free software; you can redistribute it and/or modify  *
  *   it under the terms of the GNU General Public License as published by  *
  *   the Free Software Foundation; either version 2 of the License, or     *
  *   (at your option) any later version.                                   *
  *                                                                         *
  ***************************************************************************/
 
 
 #include "fitlorentzian_unweighted.h"
 #include "objectstore.h"
 #include "ui_fitlorentzian_unweightedconfig.h"
 
 #define NUM_PARAMS 4
 #define MAX_NUM_ITERATIONS 500
 
 #include <gsl/gsl_fit.h>
 #include "../non_linear.h"
 
 static const QString& VECTOR_IN_X = "X Vector";
 static const QString& VECTOR_IN_Y = "Y Vector";
 static const QString& VECTOR_OUT_Y_FITTED = "Fit";
 static const QString& VECTOR_OUT_Y_RESIDUALS = "Residuals";
 static const QString& VECTOR_OUT_Y_PARAMETERS = "Parameters Vector";
 static const QString& VECTOR_OUT_Y_COVARIANCE = "Covariance";
 static const QString& SCALAR_OUT = "chi^2/nu";
 
 class ConfigWidgetFitLorentzianUnweightedPlugin : public Kst::DataObjectConfigWidget, public Ui_FitLorentzian_UnweightedConfig {
   public:
     ConfigWidgetFitLorentzianUnweightedPlugin(QSettings* cfg) : DataObjectConfigWidget(cfg), Ui_FitLorentzian_UnweightedConfig() {
       _store = 0;
       setupUi(this);
     }
 
     ~ConfigWidgetFitLorentzianUnweightedPlugin() {}
 
     void setObjectStore(Kst::ObjectStore* store) { 
       _store = store; 
       _vectorX->setObjectStore(store);
       _vectorY->setObjectStore(store);
     }
 
     void setupSlots(QWidget* dialog) {
       if (dialog) {
         connect(_vectorX, SIGNAL(selectionChanged(QString)), dialog, SIGNAL(modified()));
         connect(_vectorY, SIGNAL(selectionChanged(QString)), dialog, SIGNAL(modified()));
       }
     }
 
 
     void setVectorX(Kst::VectorPtr vector) {
       setSelectedVectorX(vector);
     }
 
     void setVectorY(Kst::VectorPtr vector) {
       setSelectedVectorY(vector);
     }
 
     void setVectorsLocked(bool locked = true) {
       _vectorX->setEnabled(!locked);
       _vectorY->setEnabled(!locked);
     }
 
     Kst::VectorPtr selectedVectorX() { return _vectorX->selectedVector(); };
     void setSelectedVectorX(Kst::VectorPtr vector) { return _vectorX->setSelectedVector(vector); };
 
     Kst::VectorPtr selectedVectorY() { return _vectorY->selectedVector(); };
     void setSelectedVectorY(Kst::VectorPtr vector) { return _vectorY->setSelectedVector(vector); };
 
     virtual void setupFromObject(Kst::Object* dataObject) {
       if (FitLorentzianUnweightedSource* source = static_cast<FitLorentzianUnweightedSource*>(dataObject)) {
         setSelectedVectorX(source->vectorX());
         setSelectedVectorY(source->vectorY());
       }
     }
 
     virtual bool configurePropertiesFromXml(Kst::ObjectStore *store, QXmlStreamAttributes& attrs) {
       Q_UNUSED(store);
       Q_UNUSED(attrs);
 
       bool validTag = true;
 
 //       QStringRef av;
 //       av = attrs.value("value");
 //       if (!av.isNull()) {
 //         _configValue = QVariant(av.toString()).toBool();
 //       }
 
       return validTag;
     }
 
   public slots:
     virtual void save() {
       if (_cfg) {
         _cfg->beginGroup("Fit Lorentzian Plugin");
         _cfg->setValue("Input Vector X", _vectorX->selectedVector()->Name());
         _cfg->setValue("Input Vector Y", _vectorY->selectedVector()->Name());
         _cfg->endGroup();
       }
     }
 
     virtual void load() {
       if (_cfg && _store) {
         _cfg->beginGroup("Fit Lorentzian Plugin");
         QString vectorName = _cfg->value("Input Vector X").toString();
         Kst::Object* object = _store->retrieveObject(vectorName);
         Kst::Vector* vectorx = static_cast<Kst::Vector*>(object);
         if (vectorx) {
           setSelectedVectorX(vectorx);
         }
         vectorName = _cfg->value("Input Vector Y").toString();
         object = _store->retrieveObject(vectorName);
         Kst::Vector* vectory = static_cast<Kst::Vector*>(object);
         if (vectory) {
           setSelectedVectorX(vectory);
         }
         _cfg->endGroup();
       }
     }
 
   private:
     Kst::ObjectStore *_store;
 
 };
 
 
 FitLorentzianUnweightedSource::FitLorentzianUnweightedSource(Kst::ObjectStore *store)
 : Kst::BasicPlugin(store) {
 }
 
 
 FitLorentzianUnweightedSource::~FitLorentzianUnweightedSource() {
 }
 
 
 QString FitLorentzianUnweightedSource::_automaticDescriptiveName() const {
   return tr("%1 Lorentzian").arg(vectorY()->descriptiveName());;
 }
 
 
 void FitLorentzianUnweightedSource::change(Kst::DataObjectConfigWidget *configWidget) {
   if (ConfigWidgetFitLorentzianUnweightedPlugin* config = static_cast<ConfigWidgetFitLorentzianUnweightedPlugin*>(configWidget)) {
     setInputVector(VECTOR_IN_X, config->selectedVectorX());
     setInputVector(VECTOR_IN_Y, config->selectedVectorY());
   }
 }
 
 
 void FitLorentzianUnweightedSource::setupOutputs() {
   setOutputVector(VECTOR_OUT_Y_FITTED, "");
   setOutputVector(VECTOR_OUT_Y_RESIDUALS, "");
   setOutputVector(VECTOR_OUT_Y_PARAMETERS, "");
   setOutputVector(VECTOR_OUT_Y_COVARIANCE, "");
   setOutputScalar(SCALAR_OUT, "");
 }
 
 
 void function_initial_estimate(const double X[], const double Y[], int npts, double P[]) {
   double min_y = 1E300;
   double max_y = -1E300;
   double min_x = 1E300;
   double max_x = -1E300;
   double mean_y = 0.0;
   double x_at_min_y=0;
   double x_at_max_y=0;
 
   double A, B, D;
 
   // find peak, vally, and mean
   for (int i = 0; i<npts; i++) {
     if (min_y > Y[i]) {
       min_y = Y[i];
       x_at_min_y = X[i];
     }
     if (max_y < Y[i]) {
       max_y = Y[i];
       x_at_max_y = X[i];
     }
     mean_y += Y[i];
 
     if (min_x > X[i]) {
       min_x = X[i];
     }
     if (max_x < X[i]) {
       max_x = X[i];
     }
   }
   if (npts>0) {
     mean_y /= double(npts);
   }
 
   // Heuristic for finding the sign : less time is spent in the peak than
   // in background if the range covers more than ~+- 2 sigma.
   // It will fail if you are
   // really zoomed in.  Not sure what happens then :-(
   if (max_y - mean_y > mean_y - min_y) { // positive going gaussian
     A = max_y - min_y;
     D = min_y;
     B = x_at_max_y;
   } else { // negative going gaussian
     A = min_y - mean_y;
     D = max_y;
     B = x_at_min_y;
   }
 
   P[0] = A; // amplitude
   P[1] = B; // x0
   P[2] = (max_x - min_x)*0.1; // Half Width: guess 1/10 the fit range
   P[3] = D; // offset
 }
 
 
 double function_calculate(double x, double P[]) {
   double A = P[0]; // amplitude
   double B = P[1]; // x0
   double C = P[2]; // Half Width
   double D = P[3]; // offset
 
   x -= B;
 
   double Y = A/(1.0 + x*x/(C*C)) + D;
 
   return Y;
 
 }
 
 void function_derivative(double x, double P[], double dFdP[]) {
 
   double A = P[0]; // amplitude
   double B = P[1]; // x0
   double C = P[2]; // Half Width
 
   double C2 = C*C;
   x -= B;
   double x2 = x*x;
   double m = (x2 + C2);
 
 
   dFdP[0] = 1.0/(1.0 + x2/C2); // dF/dA
   dFdP[1] = 2.0*A*C2*x/(m*m);
   dFdP[2] = 2.0*A*C*x2/(m*m);
   dFdP[3] = 1.0;
 }
 
 bool FitLorentzianUnweightedSource::algorithm() {
   Kst::VectorPtr inputVectorX = _inputVectors[VECTOR_IN_X];
   Kst::VectorPtr inputVectorY = _inputVectors[VECTOR_IN_Y];
 
   Kst::VectorPtr outputVectorYFitted = _outputVectors[VECTOR_OUT_Y_FITTED];
   Kst::VectorPtr outputVectorYResiduals = _outputVectors[VECTOR_OUT_Y_RESIDUALS];
   Kst::VectorPtr outputVectorYParameters = _outputVectors[VECTOR_OUT_Y_PARAMETERS];
   Kst::VectorPtr outputVectorYCovariance = _outputVectors[VECTOR_OUT_Y_COVARIANCE];
   Kst::ScalarPtr outputScalar = _outputScalars[SCALAR_OUT];
 
 
   Kst::LabelInfo label_info = inputVectorY->labelInfo();
   label_info.name = tr("Lorentzian Fit to %1").arg(label_info.name);
   outputVectorYFitted->setLabelInfo(label_info);
 
   label_info.name = tr("Lorentzian Fit Residuals");
   outputVectorYResiduals->setLabelInfo(label_info);
 
   bool bReturn = false;
 
   bReturn = kstfit_nonlinear( inputVectorX, inputVectorY,
                               outputVectorYFitted, outputVectorYResiduals, outputVectorYParameters,
                               outputVectorYCovariance, outputScalar );
   return bReturn;
 }
 
 
 Kst::VectorPtr FitLorentzianUnweightedSource::vectorX() const {
   return _inputVectors[VECTOR_IN_X];
 }
 
 
 Kst::VectorPtr FitLorentzianUnweightedSource::vectorY() const {
   return _inputVectors[VECTOR_IN_Y];
 }
 
 
 QStringList FitLorentzianUnweightedSource::inputVectorList() const {
   QStringList vectors(VECTOR_IN_X);
   vectors += VECTOR_IN_Y;
   return vectors;
 }
 
 
 QStringList FitLorentzianUnweightedSource::inputScalarList() const {
   return QStringList();
 }
 
 
 QStringList FitLorentzianUnweightedSource::inputStringList() const {
   return QStringList( /*STRING_IN*/ );
 }
 
 
 QStringList FitLorentzianUnweightedSource::outputVectorList() const {
   QStringList vectors(VECTOR_OUT_Y_FITTED);
   vectors += VECTOR_OUT_Y_RESIDUALS;
   vectors += VECTOR_OUT_Y_PARAMETERS;
   vectors += VECTOR_OUT_Y_COVARIANCE;
   vectors += VECTOR_OUT_Y_PARAMETERS;
   return vectors;
 }
 
 
 QStringList FitLorentzianUnweightedSource::outputScalarList() const {
   return QStringList( SCALAR_OUT );
 }
 
 
 QStringList FitLorentzianUnweightedSource::outputStringList() const {
   return QStringList( /*STRING_OUT*/ );
 }
 
 
 void FitLorentzianUnweightedSource::saveProperties(QXmlStreamWriter &s) {
   Q_UNUSED(s);
 //   s.writeAttribute("value", _configValue);
 }
 
 
 QString FitLorentzianUnweightedSource::parameterName(int index) const {
   QString parameter;
   switch (index) {
     case 0:
       parameter = "Amplitide";
       break;
     case 1:
       parameter = "x_o";
       break;
     case 2:
       parameter = "Half Width";
       break;
     case 3:
       parameter = "Offset";
       break;
   }
 
   return parameter;
 }
 
 
 // Name used to identify the plugin.  Used when loading the plugin.
 QString FitLorentzianUnweightedPlugin::pluginName() const { return tr("Lorentzian Fit"); }
 QString FitLorentzianUnweightedPlugin::pluginDescription() const { return tr("Generates a lorentzian fit for a set of data."); }
 
 
 Kst::DataObject *FitLorentzianUnweightedPlugin::create(Kst::ObjectStore *store, Kst::DataObjectConfigWidget *configWidget, bool setupInputsOutputs) const {
 
   if (ConfigWidgetFitLorentzianUnweightedPlugin* config = static_cast<ConfigWidgetFitLorentzianUnweightedPlugin*>(configWidget)) {
 
     FitLorentzianUnweightedSource* object = store->createObject<FitLorentzianUnweightedSource>();
 
     if (setupInputsOutputs) {
       object->setupOutputs();
       object->setInputVector(VECTOR_IN_X, config->selectedVectorX());
       object->setInputVector(VECTOR_IN_Y, config->selectedVectorY());
     }
 
     object->setPluginName(pluginName());
 
     object->writeLock();
     object->registerChange();
     object->unlock();
 
     return object;
   }
   return 0;
 }
 
 
 Kst::DataObjectConfigWidget *FitLorentzianUnweightedPlugin::configWidget(QSettings *settingsObject) const {
   ConfigWidgetFitLorentzianUnweightedPlugin *widget = new ConfigWidgetFitLorentzianUnweightedPlugin(settingsObject);
   return widget;
 }
 
 #ifndef QT5
 Q_EXPORT_PLUGIN2(kstplugin_FitLorentzianUnweightedPlugin, FitLorentzianUnweightedPlugin)
 #endif
 
 // vim: ts=2 sw=2 et