diff --git a/src/backend/cantorWorksheet/CantorWorksheet.h b/src/backend/cantorWorksheet/CantorWorksheet.h index 0208034d9..591663d05 100644 --- a/src/backend/cantorWorksheet/CantorWorksheet.h +++ b/src/backend/cantorWorksheet/CantorWorksheet.h @@ -1,90 +1,90 @@ /*************************************************************************** File : CantorWorksheet.h Project : LabPlot Description : Aspect providing a Cantor Worksheets for Multiple backends -------------------------------------------------------------------- Copyright : (C) 2015 Garvit Khatri (garvitdelhi@gmail.com) Copyright : (C) 2016 by Alexander Semke (alexander.semke@web.de) ***************************************************************************/ /*************************************************************************** * * * 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. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #ifndef CANTORWORKSHEET_H #define CANTORWORKSHEET_H #include #include "backend/core/AbstractScriptingEngine.h" #include namespace Cantor { class PanelPlugin; class WorksheetAccessInterface; } namespace KParts { class ReadWritePart; } class QAbstractItemModel; class Column; class CantorWorksheet : public AbstractPart, public scripted { Q_OBJECT public: CantorWorksheet(AbstractScriptingEngine* engine, const QString& name, bool loading = false); - virtual QWidget* view() const; - virtual QMenu* createContextMenu(); - virtual QIcon icon() const; + virtual QWidget* view() const override; + virtual QMenu* createContextMenu() override; + virtual QIcon icon() const override; - virtual bool exportView() const; - virtual bool printView(); - virtual bool printPreview() const; + virtual bool exportView() const override; + virtual bool printView() override; + virtual bool printPreview() const override; virtual void save(QXmlStreamWriter*) const override; virtual bool load(XmlStreamReader*, bool preview) override; QString backendName(); KParts::ReadWritePart* part(); QList getPlugins(); private: QString m_backendName; Cantor::Session* m_session; KParts::ReadWritePart* m_part; QList m_plugins; QAbstractItemModel* m_variableModel; Cantor::WorksheetAccessInterface* m_worksheetAccess; bool init(QByteArray* content = NULL); private slots: void rowsInserted(const QModelIndex & parent, int first, int last); void rowsAboutToBeRemoved(const QModelIndex & parent, int first, int last); void modelReset(); void sessionChanged(); signals: void requestProjectContextMenu(QMenu*); void statusChanged(Cantor::Session::Status); }; #endif // CANTORWORKSHEET_H diff --git a/src/backend/nsl/nsl_fit.h b/src/backend/nsl/nsl_fit.h index 6be9a5b7f..b822727b9 100644 --- a/src/backend/nsl/nsl_fit.h +++ b/src/backend/nsl/nsl_fit.h @@ -1,130 +1,128 @@ /*************************************************************************** File : nsl_fit.h Project : LabPlot Description : NSL (non)linear fitting functions -------------------------------------------------------------------- Copyright : (C) 2016 by Stefan Gerlach (stefan.gerlach@uni.kn) ***************************************************************************/ /*************************************************************************** * * * 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. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #ifndef NSL_FIT_H #define NSL_FIT_H #define NSL_FIT_MODEL_CATEGORY_COUNT 5 typedef enum {nsl_fit_model_basic, nsl_fit_model_peak, nsl_fit_model_growth, nsl_fit_model_distribution, nsl_fit_model_custom=99} nsl_fit_model_category; #define NSL_FIT_MODEL_BASIC_COUNT 5 typedef enum {nsl_fit_model_polynomial, nsl_fit_model_power, nsl_fit_model_exponential, nsl_fit_model_inverse_exponential, nsl_fit_model_fourier} nsl_fit_model_type_basic; extern const char* nsl_fit_model_basic_pic_name[]; #define NSL_FIT_MODEL_PEAK_COUNT 4 typedef enum {nsl_fit_model_gaussian, nsl_fit_model_lorentz, nsl_fit_model_sech, nsl_fit_model_logistic} nsl_fit_model_type_peak; extern const char* nsl_fit_model_peak_pic_name[]; #define NSL_FIT_MODEL_GROWTH_COUNT 8 typedef enum {nsl_fit_model_atan, nsl_fit_model_tanh, nsl_fit_model_algebraic_sigmoid, nsl_fit_model_sigmoid, nsl_fit_model_erf, nsl_fit_model_hill, nsl_fit_model_gompertz, nsl_fit_model_gudermann} nsl_fit_model_type_growth; extern const char* nsl_fit_model_growth_pic_name[]; extern const char* nsl_fit_model_category_name[]; extern const char* nsl_fit_model_basic_name[]; extern const char* nsl_fit_model_peak_name[]; extern const char* nsl_fit_model_growth_name[]; -extern const char* nsl_fit_model_distribution_name[]; extern const char* nsl_fit_model_basic_equation[]; extern const char* nsl_fit_model_peak_equation[]; extern const char* nsl_fit_model_growth_equation[]; -extern const char* nsl_fit_model_distribution_equation[]; #define NSL_FIT_WEIGHT_TYPE_COUNT 8 typedef enum {nsl_fit_weight_no, /* w = 1 */ nsl_fit_weight_instrumental, /* w = 1/c^2 (Gaussian, Given errors): default */ nsl_fit_weight_direct, /* w = c */ nsl_fit_weight_inverse, /* w = 1/c */ nsl_fit_weight_statistical, /* w = 1/y (Poisson) */ nsl_fit_weight_statistical_fit, /* w = 1/Y (Poisson) */ nsl_fit_weight_relative, /* w = 1/y^2 (Variance) */ nsl_fit_weight_relative_fit, /* w = 1/Y^2 (Variance) */ } nsl_fit_weight_type; extern const char* nsl_fit_weight_type_name[]; /* convert unbounded variable x to bounded variable where bounds are [min, max] */ double nsl_fit_map_bound(double x, double min, double max); /* convert bounded variable x to unbounded variable where bounds are [min, max] */ double nsl_fit_map_unbound(double x, double min, double max); /* model parameter derivatives */ /* basic */ double nsl_fit_model_polynomial_param_deriv(double x, int j, double weight); double nsl_fit_model_power1_param_deriv(int param, double x, double a, double b, double weight); double nsl_fit_model_power2_param_deriv(int param, double x, double b, double c, double weight); double nsl_fit_model_exponential1_param_deriv(int param, double x, double a, double b, double weight); double nsl_fit_model_exponential2_param_deriv(int param, double x, double a, double b, double c, double d, double weight); double nsl_fit_model_exponential3_param_deriv(int param, double x, double a, double b, double c, double d, double e, double f, double weight); double nsl_fit_model_inverse_exponential_param_deriv(int param, double x, double a, double b, double weight); double nsl_fit_model_fourier_param_deriv(int param, int degree, double x, double w, double weight); /* peak */ double nsl_fit_model_gaussian_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_lorentz_param_deriv(int param, double x, double s, double t, double a, double weight); double nsl_fit_model_sech_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_logistic_param_deriv(int param, double x, double s, double mu, double a, double weight); /* growth */ double nsl_fit_model_atan_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_tanh_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_algebraic_sigmoid_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_sigmoid_param_deriv(int param, double x, double k, double mu, double a, double weight); double nsl_fit_model_erf_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_hill_param_deriv(int param, double x, double s, double n, double a, double weight); double nsl_fit_model_gompertz_param_deriv(int param, double x, double a, double b, double c, double weight); double nsl_fit_model_gudermann_param_deriv(int param, double x, double s, double mu, double a, double weight); /* distributions */ double nsl_fit_model_gaussian_tail_param_deriv(int param, double x, double s, double mu, double A, double a, double weight); double nsl_fit_model_exponential_param_deriv(int param, double x, double l, double mu, double a, double weight); double nsl_fit_model_laplace_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_exp_pow_param_deriv(int param, double x, double s, double mu, double b, double a, double weight); double nsl_fit_model_poisson_param_deriv(int param, double x, double l, double a, double weight); double nsl_fit_model_lognormal_param_deriv(int param, double x, double b, double mu, double a, double weight); double nsl_fit_model_gamma_param_deriv(int param, double x, double t, double k, double a, double weight); double nsl_fit_model_flat_param_deriv(int param, double x, double a, double b, double A, double weight); double nsl_fit_model_rayleigh_param_deriv(int param, double x, double s, double a, double weight); double nsl_fit_model_rayleigh_tail_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_landau_param_deriv(int param, double x, double weight); double nsl_fit_model_chi_square_param_deriv(int param, double x, double n, double a, double weight); double nsl_fit_model_students_t_param_deriv(int param, double x, double n, double a, double weight); double nsl_fit_model_fdist_param_deriv(int param, double x, double n1, double n2, double a, double weight); double nsl_fit_model_beta_param_deriv(int param, double x, double a, double b, double A, double weight); double nsl_fit_model_pareto_param_deriv(int param, double x, double a, double b, double A, double weight); double nsl_fit_model_weibull_param_deriv(int param, double x, double k, double l, double mu, double a, double weight); double nsl_fit_model_gumbel1_param_deriv(int param, double x, double s, double b, double mu, double a, double weight); double nsl_fit_model_gumbel2_param_deriv(int param, double x, double a, double b, double mu, double A, double weight); double nsl_fit_model_binomial_param_deriv(int param, double x, double p, double n, double A, double weight); double nsl_fit_model_negative_binomial_param_deriv(int param, double k, double p, double n, double A, double weight); double nsl_fit_model_pascal_param_deriv(int param, double k, double p, double n, double A, double weight); double nsl_fit_model_geometric_param_deriv(int param, double k, double p, double A, double weight); double nsl_fit_model_hypergeometric_param_deriv(int param, double k, double n1, double n2, double t, double A, double weight); double nsl_fit_model_logarithmic_param_deriv(int param, double k, double p, double A, double weight); double nsl_fit_model_maxwell_param_deriv(int param, double x, double a, double c, double weight); double nsl_fit_model_sech_dist_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_levy_param_deriv(int param, double x, double g, double mu, double a, double weight); double nsl_fit_model_frechet_param_deriv(int param, double x, double a, double mu, double s, double c, double weight); #endif /* NSL_FIT_H */ diff --git a/src/backend/worksheet/plots/cartesian/XYFitCurve.cpp b/src/backend/worksheet/plots/cartesian/XYFitCurve.cpp index 8bf697c5c..a410332a6 100644 --- a/src/backend/worksheet/plots/cartesian/XYFitCurve.cpp +++ b/src/backend/worksheet/plots/cartesian/XYFitCurve.cpp @@ -1,2094 +1,2100 @@ /*************************************************************************** File : XYFitCurve.cpp Project : LabPlot Description : A xy-curve defined by a fit model -------------------------------------------------------------------- Copyright : (C) 2014-2017 Alexander Semke (alexander.semke@web.de) Copyright : (C) 2016-2017 Stefan Gerlach (stefan.gerlach@uni.kn) ***************************************************************************/ /*************************************************************************** * * * 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. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ /*! \class XYFitCurve \brief A xy-curve defined by a fit model \ingroup worksheet */ #include "XYFitCurve.h" #include "XYFitCurvePrivate.h" #include "backend/core/AbstractColumn.h" #include "backend/core/column/Column.h" #include "backend/lib/commandtemplates.h" #include "backend/lib/macros.h" #include "backend/gsl/ExpressionParser.h" extern "C" { #include #include #include #include #include #include #include "backend/gsl/parser.h" -#include "backend/nsl/nsl_fit.h" #include "backend/nsl/nsl_sf_stats.h" } #include #include #include #include #include XYFitCurve::XYFitCurve(const QString& name) : XYCurve(name, new XYFitCurvePrivate(this)) { init(); } XYFitCurve::XYFitCurve(const QString& name, XYFitCurvePrivate* dd) : XYCurve(name, dd) { init(); } XYFitCurve::~XYFitCurve() { //no need to delete the d-pointer here - it inherits from QGraphicsItem //and is deleted during the cleanup in QGraphicsScene } void XYFitCurve::init() { Q_D(XYFitCurve); //TODO: read from the saved settings for XYFitCurve? d->lineType = XYCurve::Line; d->symbolsStyle = Symbol::NoSymbols; } void XYFitCurve::recalculate() { Q_D(XYFitCurve); d->recalculate(); } void XYFitCurve::initFitData(PlotDataDialog::AnalysisAction action) { if (!action) return; Q_D(XYFitCurve); XYFitCurve::FitData& fitData = d->fitData; if (action == PlotDataDialog::FitLinear) { //Linear fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_polynomial; fitData.degree = 1; } else if (action == PlotDataDialog::FitPower) { //Power fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_power; fitData.degree = 1; } else if (action == PlotDataDialog::FitExp1) { //Exponential (degree 1) fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_exponential; fitData.degree = 1; } else if (action == PlotDataDialog::FitExp2) { //Exponential (degree 2) fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_exponential; fitData.degree = 2; } else if (action == PlotDataDialog::FitInvExp) { //Inverse exponential fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_inverse_exponential; } else if (action == PlotDataDialog::FitGauss) { //Gauss fitData.modelCategory = nsl_fit_model_peak; fitData.modelType = nsl_fit_model_gaussian; fitData.degree = 1; } else if (action == PlotDataDialog::FitCauchyLorentz) { //Cauchy-Lorentz fitData.modelCategory = nsl_fit_model_peak; fitData.modelType = nsl_fit_model_lorentz; fitData.degree = 1; } else if (action == PlotDataDialog::FitTan) { //Arc tangent fitData.modelCategory = nsl_fit_model_growth; fitData.modelType = nsl_fit_model_atan; } else if (action == PlotDataDialog::FitTanh) { //Hyperbolic tangent fitData.modelCategory = nsl_fit_model_growth; fitData.modelType = nsl_fit_model_tanh; } else if (action == PlotDataDialog::FitErrFunc) { //Error function fitData.modelCategory = nsl_fit_model_growth; fitData.modelType = nsl_fit_model_erf; } else { //Custom fitData.modelCategory = nsl_fit_model_custom; fitData.modelType = nsl_fit_model_custom; } XYFitCurve::initFitData(fitData); } /*! * sets the model expression and the parameter names for given model category, model type and degree in \c fitData */ void XYFitCurve::initFitData(XYFitCurve::FitData& fitData) { nsl_fit_model_category modelCategory = fitData.modelCategory; unsigned int modelType = fitData.modelType; QString& model = fitData.model; QStringList& paramNames = fitData.paramNames; QStringList& paramNamesUtf8 = fitData.paramNamesUtf8; int num = fitData.degree; QVector& paramStartValues = fitData.paramStartValues; QVector& paramLowerLimits = fitData.paramLowerLimits; QVector& paramUpperLimits = fitData.paramUpperLimits; QVector& paramFixed = fitData.paramFixed; DEBUG("XYFitCurve::initFitData() for model category = " << modelCategory << ", model type = " << modelType << ", degree = " << num); paramNames.clear(); paramNamesUtf8.clear(); // indices used in multi peak parameter models QStringList indices = {QString::fromUtf8("\u2081"), QString::fromUtf8("\u2082"), QString::fromUtf8("\u2083"), QString::fromUtf8("\u2084"), QString::fromUtf8("\u2085"), QString::fromUtf8("\u2086"), QString::fromUtf8("\u2087"), QString::fromUtf8("\u2088"), QString::fromUtf8("\u2089")}; switch (modelCategory) { case nsl_fit_model_basic: + model = nsl_fit_model_basic_equation[fitData.modelType]; switch (modelType) { case nsl_fit_model_polynomial: paramNames << "c0" << "c1"; paramNamesUtf8 << QString::fromUtf8("c\u2080") << QString::fromUtf8("c\u2081"); if (num == 2) { model += " + c2*x^2"; paramNames << "c2"; paramNamesUtf8 << QString::fromUtf8("c\u2082"); } else if (num > 2) { QString numStr = QString::number(num); for (int i = 2; i <= num; ++i) { numStr = QString::number(i); model += "+c" + numStr + "*x^" + numStr; paramNames << "c" + numStr; paramNamesUtf8 << "c" + indices[i-1]; } } break; case nsl_fit_model_power: if (num == 1) paramNames << "a" << "b"; else { paramNames << "a" << "b" << "c"; model = "a + b*x^c"; } break; case nsl_fit_model_exponential: switch (num) { case 1: paramNames << "a" << "b"; break; case 2: model = "a1*exp(b1*x) + a2*exp(b2*x)"; paramNames << "a1" << "b1" << "a2" << "b2"; paramNamesUtf8 << QString::fromUtf8("a\u2081") << QString::fromUtf8("b\u2081") << QString::fromUtf8("a\u2082") << QString::fromUtf8("b\u2082"); break; case 3: model = "a1*exp(b1*x) + a2*exp(b2*x) + a3*exp(b3*x)"; paramNames << "a1" << "b1" << "a2" << "b2" << "a3" << "b3"; paramNamesUtf8 << QString::fromUtf8("a\u2081") << QString::fromUtf8("b\u2081") << QString::fromUtf8("a\u2082") << QString::fromUtf8("b\u2082") << QString::fromUtf8("a\u2083") << QString::fromUtf8("b\u2083"); break; //TODO: up to 9 exponentials } break; case nsl_fit_model_inverse_exponential: num = 1; paramNames << "a" << "b" << "c"; break; case nsl_fit_model_fourier: paramNames << "w" << "a0" << "a1" << "b1"; paramNamesUtf8 << QString::fromUtf8("\u03c9") << QString::fromUtf8("a\u2080") << QString::fromUtf8("a\u2081") << QString::fromUtf8("b\u2081"); if (num > 1) { for (int i = 1; i <= num; ++i) { QString numStr = QString::number(i); model += "+ (a" + numStr + "*cos(" + numStr + "*w*x) + b" + numStr + "*sin(" + numStr + "*w*x))"; paramNames << "a"+numStr << "b"+numStr; paramNamesUtf8 << "a" + indices[i-1] << "b" + indices[i-1]; } } break; } break; case nsl_fit_model_peak: + model = nsl_fit_model_peak_equation[fitData.modelType]; switch ((nsl_fit_model_type_peak)modelType) { case nsl_fit_model_gaussian: switch (num) { case 1: paramNames << "s" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A"; break; case 2: model = "1./sqrt(2*pi) * (a1/s1 * exp(-((x-mu1)/s1)^2/2) + a2/s2 * exp(-((x-mu2)/s2)^2/2))"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082"); break; case 3: model = "1./sqrt(2*pi) * (a1/s1 * exp(-((x-mu1)/s1)^2/2) + a2/s2 * exp(-((x-mu2)/s2)^2/2) + a3/s3 * exp(-((x-mu3)/s3)^2/2))"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2" << "s3" << "mu3" << "a3"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082") << QString::fromUtf8("\u03c3\u2083") << QString::fromUtf8("\u03bc\u2083") << QString::fromUtf8("A\u2083"); break; default: model = "1./sqrt(2*pi) * ("; for (int i = 1; i <= num; ++i) { QString numStr = QString::number(i); if (i > 1) model += " + "; model += "a" + numStr + "/s" + numStr + "* exp(-((x-mu" + numStr + ")/s" + numStr + ")^2/2)"; paramNames << "s" + numStr << "mu" + numStr << "a" + numStr; paramNamesUtf8 << QString::fromUtf8("\u03c3") + indices[i-1] << QString::fromUtf8("\u03bc") + indices[i-1] << QString::fromUtf8("A") + indices[i-1]; } model += ")"; } break; case nsl_fit_model_lorentz: switch (num) { case 1: paramNames << "g" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03b3") << QString::fromUtf8("\u03bc") << "A"; break; case 2: model = "1./pi * (a1 * g1/(g1^2+(x-mu1)^2) + a2 * g2/(g2^2+(x-mu2)^2))"; paramNames << "g1" << "mu1" << "a1" << "g2" << "mu2" << "a2"; paramNamesUtf8 << QString::fromUtf8("\u03b3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03b3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082"); break; case 3: model = "1./pi * (a1 * g1/(g1^2+(x-mu1)^2) + a2 * g2/(g2^2+(x-mu2)^2) + a3 * g3/(g3^2+(x-mu3)^2))"; paramNames << "g1" << "mu1" << "a1" << "g2" << "mu2" << "a2" << "g3" << "mu3" << "a3"; paramNamesUtf8 << QString::fromUtf8("\u03b3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03b3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082") << QString::fromUtf8("\u03b3\u2083") << QString::fromUtf8("\u03bc\u2083") << QString::fromUtf8("A\u2083"); break; default: QString numStr = QString::number(num); model = "1./pi * ("; for (int i = 1; i <= num; ++i) { numStr = QString::number(i); if (i > 1) model += " + "; model += "a" + numStr + " * g" + numStr + "/(g" + numStr + "^2+(x-mu" + numStr + ")^2)"; paramNames << "g" + numStr << "mu" + numStr << "a" + numStr; paramNamesUtf8 << QString::fromUtf8("\u03b3") + indices[i-1] << QString::fromUtf8("\u03bc") + indices[i-1] << QString::fromUtf8("A") + indices[i-1]; } model += ")"; } break; case nsl_fit_model_sech: switch (num) { case 1: paramNames << "s" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A"; break; case 2: model = "1/pi * (a1/s1 * sech((x-mu1)/s1) + a2/s2 * sech((x-mu2)/s2))"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082"); break; case 3: model = "1/pi * (a1/s1 * sech((x-mu1)/s1) + a2/s2 * sech((x-mu2)/s2) + a3/s3 * sech((x-mu3)/s3))"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2" << "s3" << "mu3" << "a3"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082") << QString::fromUtf8("\u03c3\u2083") << QString::fromUtf8("\u03bc\u2083") << QString::fromUtf8("A\u2083"); break; default: QString numStr = QString::number(num); model = "1/pi * ("; for (int i = 1; i <= num; ++i) { numStr = QString::number(i); if (i > 1) model += " + "; model += "a" + numStr + "/s" + numStr + "* sech((x-mu" + numStr + ")/s" + numStr + ")"; paramNames << "s" + numStr << "mu" + numStr << "a" + numStr; paramNamesUtf8 << QString::fromUtf8("\u03c3") + indices[i-1] << QString::fromUtf8("\u03bc") + indices[i-1] << QString::fromUtf8("A") + indices[i-1]; } model += ")"; } break; case nsl_fit_model_logistic: switch (num) { case 1: paramNames << "s" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A"; break; case 2: model = "1/4 * (a1/s1 * sech((x-mu1)/2/s1)**2 + a2/s2 * sech((x-mu2)/2/s2)**2)"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082"); break; case 3: model = "1/4 * (a1/s1 * sech((x-mu1)/2/s1)**2 + a2/s2 * sech((x-mu2)/2/s2)**2 + a3/s3 * sech((x-mu3)/2/s3)**2)"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2" << "s3" << "mu3" << "a3"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082") << QString::fromUtf8("\u03c3\u2083") << QString::fromUtf8("\u03bc\u2083") << QString::fromUtf8("A\u2083"); break; default: QString numStr = QString::number(num); model = "1/4 * ("; for (int i = 1; i <= num; ++i) { numStr = QString::number(i); if (i > 1) model += " + "; model += "a" + numStr + "/s" + numStr + "* sech((x-mu" + numStr + ")/2/s" + numStr + ")**2"; paramNames << "s" + numStr << "mu" + numStr << "a" + numStr; paramNamesUtf8 << QString::fromUtf8("\u03c3") + indices[i-1] << QString::fromUtf8("\u03bc") + indices[i-1] << QString::fromUtf8("A") + indices[i-1]; } model += ")"; } break; } break; case nsl_fit_model_growth: + model = nsl_fit_model_growth_equation[fitData.modelType]; switch ((nsl_fit_model_type_growth)modelType) { case nsl_fit_model_atan: case nsl_fit_model_tanh: case nsl_fit_model_algebraic_sigmoid: case nsl_fit_model_erf: case nsl_fit_model_gudermann: paramNames << "s" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_fit_model_sigmoid: paramNames << "k" << "mu" << "a"; paramNamesUtf8 << "k" << QString::fromUtf8("\u03bc") << "A"; break; case nsl_fit_model_hill: paramNames << "s" << "n" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << "n" << "A"; break; case nsl_fit_model_gompertz: paramNames << "a" << "b" << "c"; break; } break; case nsl_fit_model_distribution: + model = nsl_sf_stats_distribution_equation[fitData.modelType]; switch ((nsl_sf_stats_distribution)modelType) { case nsl_sf_stats_gaussian: case nsl_sf_stats_laplace: case nsl_sf_stats_rayleigh_tail: case nsl_sf_stats_lognormal: case nsl_sf_stats_logistic: case nsl_sf_stats_sech: paramNames << "s" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_gaussian_tail: paramNames << "s" << "mu" << "A" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A" << "a"; break; case nsl_sf_stats_exponential: paramNames << "l" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03bb") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_exponential_power: paramNames << "s" << "mu" << "b" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "b" << "A"; break; case nsl_sf_stats_cauchy_lorentz: case nsl_sf_stats_levy: paramNames << "g" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03b3") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_rayleigh: paramNames << "s" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << "A"; break; case nsl_sf_stats_landau: paramNames << "a"; paramNamesUtf8 << "A"; break; case nsl_sf_stats_levy_alpha_stable: // unused distributions case nsl_sf_stats_levy_skew_alpha_stable: case nsl_sf_stats_bernoulli: break; case nsl_sf_stats_gamma: paramNames << "t" << "k" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03b8") << "k" << "A"; break; case nsl_sf_stats_flat: paramNames << "a" << "b" << "A"; break; case nsl_sf_stats_chi_squared: paramNames << "n" << "a"; paramNamesUtf8 << "n" << "A"; break; case nsl_sf_stats_fdist: paramNames << "n1" << "n2" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03bd") + QString::fromUtf8("\u2081") << QString::fromUtf8("\u03bd") + QString::fromUtf8("\u2082") << "A"; break; case nsl_sf_stats_tdist: paramNames << "n" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03bd") << "A"; break; case nsl_sf_stats_beta: case nsl_sf_stats_pareto: paramNames << "a" << "b" << "A"; break; case nsl_sf_stats_weibull: paramNames << "k" << "l" << "mu" << "a"; paramNamesUtf8 << "k" << QString::fromUtf8("\u03bb") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_gumbel1: paramNames << "s" << "b" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03b2") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_gumbel2: paramNames << "a" << "b" << "mu" << "A"; paramNamesUtf8 << "a" << "b" << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_poisson: paramNames << "l" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03bb") << "A"; break; case nsl_sf_stats_binomial: case nsl_sf_stats_negative_binomial: case nsl_sf_stats_pascal: paramNames << "p" << "n" << "a"; paramNamesUtf8 << "p" << "n" << "A"; break; case nsl_sf_stats_geometric: case nsl_sf_stats_logarithmic: paramNames << "p" << "a"; paramNamesUtf8 << "p" << "A"; break; case nsl_sf_stats_hypergeometric: paramNames << "n1" << "n2" << "t" << "a"; paramNamesUtf8 << "n" + QString::fromUtf8("\u2081") << "n" + QString::fromUtf8("\u2082") << "t" << "A"; break; case nsl_sf_stats_maxwell_boltzmann: paramNames << "s" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << "A"; break; case nsl_sf_stats_frechet: paramNames << "g" << "mu" << "s" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03b3") << QString::fromUtf8("\u03bc") << QString::fromUtf8("\u03c3") << "A"; break; } break; case nsl_fit_model_custom: break; } if (paramNamesUtf8.isEmpty()) paramNamesUtf8 << paramNames; //resize the vector for the start values and set the elements to 1.0 //in case a custom model is used, do nothing, we take over the previous values if (modelCategory != nsl_fit_model_custom) { const int np = paramNames.size(); paramStartValues.resize(np); paramFixed.resize(np); paramLowerLimits.resize(np); paramUpperLimits.resize(np); for (int i = 0; i < np; ++i) { paramStartValues[i] = 1.0; paramFixed[i] = false; paramLowerLimits[i] = -DBL_MAX; paramUpperLimits[i] = DBL_MAX; } // set some model-dependent start values if (modelCategory == nsl_fit_model_distribution) { nsl_sf_stats_distribution type = (nsl_sf_stats_distribution)modelType; if (type == nsl_sf_stats_flat) paramStartValues[0] = -1.0; else if (type == nsl_sf_stats_frechet || type == nsl_sf_stats_levy || type == nsl_sf_stats_exponential_power) paramStartValues[1] = 0.0; else if (type == nsl_sf_stats_weibull || type == nsl_sf_stats_gumbel2) paramStartValues[2] = 0.0; else if (type == nsl_sf_stats_binomial || type == nsl_sf_stats_negative_binomial || type == nsl_sf_stats_pascal || type == nsl_sf_stats_geometric || type == nsl_sf_stats_logarithmic) paramStartValues[0] = 0.5; } } } /*! Returns an icon to be used in the project explorer. */ QIcon XYFitCurve::icon() const { return QIcon::fromTheme("labplot-xy-fit-curve"); } //############################################################################## //########################## getter methods ################################## //############################################################################## BASIC_SHARED_D_READER_IMPL(XYFitCurve, const AbstractColumn*, xDataColumn, xDataColumn) BASIC_SHARED_D_READER_IMPL(XYFitCurve, const AbstractColumn*, yDataColumn, yDataColumn) BASIC_SHARED_D_READER_IMPL(XYFitCurve, const AbstractColumn*, xErrorColumn, xErrorColumn) BASIC_SHARED_D_READER_IMPL(XYFitCurve, const AbstractColumn*, yErrorColumn, yErrorColumn) const QString& XYFitCurve::xDataColumnPath() const { Q_D(const XYFitCurve); return d->xDataColumnPath; } const QString& XYFitCurve::yDataColumnPath() const { Q_D(const XYFitCurve); return d->yDataColumnPath; } const QString& XYFitCurve::xErrorColumnPath() const { Q_D(const XYFitCurve);return d->xErrorColumnPath; } const QString& XYFitCurve::yErrorColumnPath() const { Q_D(const XYFitCurve);return d->yErrorColumnPath; } BASIC_SHARED_D_READER_IMPL(XYFitCurve, XYFitCurve::FitData, fitData, fitData) const XYFitCurve::FitResult& XYFitCurve::fitResult() const { Q_D(const XYFitCurve); return d->fitResult; } - //############################################################################## //################# setter methods and undo commands ########################## //############################################################################## STD_SETTER_CMD_IMPL_S(XYFitCurve, SetXDataColumn, const AbstractColumn*, xDataColumn) void XYFitCurve::setXDataColumn(const AbstractColumn* column) { Q_D(XYFitCurve); if (column != d->xDataColumn) { exec(new XYFitCurveSetXDataColumnCmd(d, column, i18n("%1: assign x-data"))); handleSourceDataChanged(); if (column) { connect(column, SIGNAL(dataChanged(const AbstractColumn*)), this, SLOT(handleSourceDataChanged())); //TODO disconnect on undo } } } STD_SETTER_CMD_IMPL_S(XYFitCurve, SetYDataColumn, const AbstractColumn*, yDataColumn) void XYFitCurve::setYDataColumn(const AbstractColumn* column) { Q_D(XYFitCurve); if (column != d->yDataColumn) { exec(new XYFitCurveSetYDataColumnCmd(d, column, i18n("%1: assign y-data"))); handleSourceDataChanged(); if (column) { connect(column, SIGNAL(dataChanged(const AbstractColumn*)), this, SLOT(handleSourceDataChanged())); //TODO disconnect on undo } } } STD_SETTER_CMD_IMPL_S(XYFitCurve, SetXErrorColumn, const AbstractColumn*, xErrorColumn) void XYFitCurve::setXErrorColumn(const AbstractColumn* column) { Q_D(XYFitCurve); if (column != d->xErrorColumn) { exec(new XYFitCurveSetXErrorColumnCmd(d, column, i18n("%1: assign x-error"))); handleSourceDataChanged(); if (column) { connect(column, SIGNAL(dataChanged(const AbstractColumn*)), this, SLOT(handleSourceDataChanged())); //TODO disconnect on undo } } } STD_SETTER_CMD_IMPL_S(XYFitCurve, SetYErrorColumn, const AbstractColumn*, yErrorColumn) void XYFitCurve::setYErrorColumn(const AbstractColumn* column) { Q_D(XYFitCurve); if (column != d->yErrorColumn) { exec(new XYFitCurveSetYErrorColumnCmd(d, column, i18n("%1: assign y-error"))); handleSourceDataChanged(); if (column) { connect(column, SIGNAL(dataChanged(const AbstractColumn*)), this, SLOT(handleSourceDataChanged())); //TODO disconnect on undo } } } STD_SETTER_CMD_IMPL_F_S(XYFitCurve, SetFitData, XYFitCurve::FitData, fitData, recalculate); void XYFitCurve::setFitData(const XYFitCurve::FitData& fitData) { Q_D(XYFitCurve); exec(new XYFitCurveSetFitDataCmd(d, fitData, i18n("%1: set fit options and perform the fit"))); } //############################################################################## //######################### Private implementation ############################# //############################################################################## XYFitCurvePrivate::XYFitCurvePrivate(XYFitCurve* owner) : XYCurvePrivate(owner), xDataColumn(0), yDataColumn(0), xErrorColumn(0), yErrorColumn(0), xColumn(0), yColumn(0), residualsColumn(0), xVector(0), yVector(0), residualsVector(0), q(owner) { } XYFitCurvePrivate::~XYFitCurvePrivate() { //no need to delete xColumn and yColumn, they are deleted //when the parent aspect is removed } -/* data structure to pass parameter to functions */ +// data structure to pass parameter to fit functions struct data { size_t n; //number of data points double* x; //pointer to the vector with x-data values double* y; //pointer to the vector with y-data values double* weight; //pointer to the vector with weight values nsl_fit_model_category modelCategory; unsigned int modelType; int degree; QString* func; // string containing the definition of the model/function QStringList* paramNames; double* paramMin; // lower parameter limits double* paramMax; // upper parameter limits bool* paramFixed; // parameter fixed? }; /*! * \param paramValues vector containing current values of the fit parameters * \param params * \param f vector with the weighted residuals weight[i]*(Yi - y[i]) */ int func_f(const gsl_vector* paramValues, void* params, gsl_vector* f) { size_t n = ((struct data*)params)->n; double* x = ((struct data*)params)->x; double* y = ((struct data*)params)->y; double* weight = ((struct data*)params)->weight; nsl_fit_model_category modelCategory = ((struct data*)params)->modelCategory; unsigned int modelType = ((struct data*)params)->modelType; QByteArray funcba = ((struct data*)params)->func->toLocal8Bit(); // a local byte array is needed! QStringList* paramNames = ((struct data*)params)->paramNames; double *min = ((struct data*)params)->paramMin; double *max = ((struct data*)params)->paramMax; // set current values of the parameters for (int i = 0; i < paramNames->size(); i++) { double x = gsl_vector_get(paramValues, i); // bound values if limits are set assign_variable(paramNames->at(i).toLocal8Bit().data(), nsl_fit_map_bound(x, min[i], max[i])); QDEBUG("Parameter"<at(i).toLocal8Bit().data()<<"\")"<<'['<at(k).toLocal8Bit(); value = nsl_fit_map_bound(gsl_vector_get(paramValues, k), min[k], max[k]); assign_variable(nameba.data(), value); } } nameba = paramNames->at(j).toLocal8Bit(); const char *name = nameba.data(); value = nsl_fit_map_bound(gsl_vector_get(paramValues, j), min[j], max[j]); assign_variable(name, value); const double f_p = parse(func); const double eps = 1.e-9 * fabs(f_p); // adapt step size to value value += eps; assign_variable(name, value); const double f_pdp = parse(func); // qDebug()<<"evaluate deriv"<* >(xColumn->data()); yVector = static_cast* >(yColumn->data()); residualsVector = static_cast* >(residualsColumn->data()); xColumn->setHidden(true); q->addChild(xColumn); yColumn->setHidden(true); q->addChild(yColumn); q->addChild(residualsColumn); q->setUndoAware(false); q->setXColumn(xColumn); q->setYColumn(yColumn); q->setUndoAware(true); } else { xVector->clear(); yVector->clear(); residualsVector->clear(); } // clear the previous result fitResult = XYFitCurve::FitResult(); //fit settings const unsigned int maxIters = fitData.maxIterations; //maximal number of iterations const double delta = fitData.eps; //fit tolerance const unsigned int np = fitData.paramNames.size(); //number of fit parameters if (np == 0) { fitResult.available = true; fitResult.valid = false; fitResult.status = i18n("Model has no parameters."); emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } //determine the data source columns const AbstractColumn* tmpXDataColumn = 0; const AbstractColumn* tmpYDataColumn = 0; if (dataSourceType == XYCurve::DataSourceSpreadsheet) { //spreadsheet columns as data source tmpXDataColumn = xDataColumn; tmpYDataColumn = yDataColumn; } else { //curve columns as data source tmpXDataColumn = dataSourceCurve->xColumn(); tmpYDataColumn = dataSourceCurve->yColumn(); } if (!tmpXDataColumn || !tmpYDataColumn) { emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } //check column sizes if (tmpXDataColumn->rowCount() != tmpYDataColumn->rowCount()) { fitResult.available = true; fitResult.valid = false; fitResult.status = i18n("Number of x and y data points must be equal."); emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } if (yErrorColumn) { if (yErrorColumn->rowCount() < xDataColumn->rowCount()) { fitResult.available = true; fitResult.valid = false; fitResult.status = i18n("Not sufficient weight data points provided."); emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } } //copy all valid data point for the fit to temporary vectors QVector xdataVector; QVector ydataVector; QVector xerrorVector; QVector yerrorVector; double xmin, xmax; if (fitData.autoRange) { xmin = tmpXDataColumn->minimum(); xmax = tmpXDataColumn->maximum(); } else { xmin = fitData.xRange.first(); xmax = fitData.xRange.last(); } for (int row = 0; row < tmpXDataColumn->rowCount(); ++row) { //only copy those data where _all_ values (for x and y and errors, if given) are valid if (!std::isnan(tmpXDataColumn->valueAt(row)) && !std::isnan(tmpYDataColumn->valueAt(row)) && !tmpXDataColumn->isMasked(row) && !tmpYDataColumn->isMasked(row)) { // only when inside given range if (tmpXDataColumn->valueAt(row) >= xmin && tmpXDataColumn->valueAt(row) <= xmax) { if (dataSourceType == XYCurve::DataSourceCurve || (!xErrorColumn && !yErrorColumn) || !fitData.useDataErrors) { // x-y xdataVector.append(tmpXDataColumn->valueAt(row)); ydataVector.append(tmpYDataColumn->valueAt(row)); } else if (!xErrorColumn) { // x-y-dy if (!std::isnan(yErrorColumn->valueAt(row))) { xdataVector.append(tmpXDataColumn->valueAt(row)); ydataVector.append(tmpYDataColumn->valueAt(row)); yerrorVector.append(yErrorColumn->valueAt(row)); } } else { // x-y-dx-dy if (!std::isnan(xErrorColumn->valueAt(row)) && !std::isnan(yErrorColumn->valueAt(row))) { xdataVector.append(tmpXDataColumn->valueAt(row)); ydataVector.append(tmpYDataColumn->valueAt(row)); xerrorVector.append(xErrorColumn->valueAt(row)); yerrorVector.append(yErrorColumn->valueAt(row)); } } } } } //number of data points to fit const size_t n = xdataVector.size(); DEBUG("number of data points: " << n); if (n == 0) { fitResult.available = true; fitResult.valid = false; fitResult.status = i18n("No data points available."); emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } if (n < np) { fitResult.available = true; fitResult.valid = false; fitResult.status = i18n("The number of data points (%1) must be greater than or equal to the number of parameters (%2).", n, np); emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } double* xdata = xdataVector.data(); double* ydata = ydataVector.data(); double* xerror = xerrorVector.data(); // size may be 0 double* yerror = yerrorVector.data(); // size may be 0 - DEBUG("x errors " << xerrorVector.size()); - DEBUG("y errors " << yerrorVector.size()); + DEBUG("x errors: " << xerrorVector.size()); + DEBUG("y errors: " << yerrorVector.size()); double* weight = new double[n]; for (size_t i = 0; i < n; i++) weight[i] = 1.; switch (fitData.weightsType) { case nsl_fit_weight_no: break; case nsl_fit_weight_instrumental: if (yerrorVector.size() > 0) for(size_t i = 0; i < n; i++) weight[i] = 1./gsl_pow_2(yerror[i]); break; case nsl_fit_weight_direct: if (yerrorVector.size() > 0) for(size_t i = 0; i < n; i++) weight[i] = yerror[i]; break; case nsl_fit_weight_inverse: if (yerrorVector.size() > 0) for(size_t i = 0; i < n; i++) weight[i] = 1./yerror[i]; break; case nsl_fit_weight_statistical: for (size_t i = 0; i < n; i++) weight[i] = 1./ydata[i]; break; case nsl_fit_weight_relative: for (size_t i = 0; i < n; i++) weight[i] = 1./gsl_pow_2(ydata[i]); break; case nsl_fit_weight_statistical_fit: case nsl_fit_weight_relative_fit: break; } /////////////////////// GSL >= 2 has a complete new interface! But the old one is still supported. /////////////////////////// // GSL >= 2 : "the 'fdf' field of gsl_multifit_function_fdf is now deprecated and does not need to be specified for nonlinear least squares problems" for (unsigned int i = 0; i < np; i++) - DEBUG("fixed parameter " << i << ' ' << fitData.paramFixed.data()[i]); + DEBUG("parameter " << i << " fixed: " << fitData.paramFixed.data()[i]); //function to fit gsl_multifit_function_fdf f; - struct data params = {n, xdata, ydata, weight, fitData.modelCategory, fitData.modelType, fitData.degree, &fitData.model, &fitData.paramNames, - fitData.paramLowerLimits.data(), fitData.paramUpperLimits.data(), fitData.paramFixed.data()}; + DEBUG("model = " << fitData.model.toStdString()); + struct data params = {n, xdata, ydata, weight, fitData.modelCategory, fitData.modelType, fitData.degree, &fitData.model, &fitData.paramNames, fitData.paramLowerLimits.data(), fitData.paramUpperLimits.data(), fitData.paramFixed.data()}; f.f = &func_f; f.df = &func_df; f.fdf = &func_fdf; f.n = n; f.p = np; f.params = ¶ms; // initialize the derivative solver (using Levenberg-Marquardt robust solver) const gsl_multifit_fdfsolver_type* T = gsl_multifit_fdfsolver_lmsder; gsl_multifit_fdfsolver* s = gsl_multifit_fdfsolver_alloc(T, n, np); // set start values double* x_init = fitData.paramStartValues.data(); double* x_min = fitData.paramLowerLimits.data(); double* x_max = fitData.paramUpperLimits.data(); // scale start values if limits are set for (unsigned int i = 0; i < np; i++) x_init[i] = nsl_fit_map_unbound(x_init[i], x_min[i], x_max[i]); gsl_vector_view x = gsl_vector_view_array(x_init, np); // initialize solver with function f and initial guess x gsl_multifit_fdfsolver_set(s, &f, &x.vector); // iterate int status; unsigned int iter = 0; fitResult.solverOutput.clear(); writeSolverState(s); do { iter++; // update weights for Y-depending weights if (fitData.weightsType == nsl_fit_weight_statistical_fit) { for (size_t i = 0; i < n; i++) weight[i] = 1./(gsl_vector_get(s->f, i) + ydata[i]); // 1/Y_i } else if (fitData.weightsType == nsl_fit_weight_relative_fit) { for (size_t i = 0; i < n; i++) weight[i] = 1./gsl_pow_2(gsl_vector_get(s->f, i) + ydata[i]); // 1/Y_i^2 } status = gsl_multifit_fdfsolver_iterate(s); writeSolverState(s); - if (status) break; + if (status) { + DEBUG("iter " << iter << ", status = " << gsl_strerror(status)); + break; + } status = gsl_multifit_test_delta(s->dx, s->x, delta, delta); } while (status == GSL_CONTINUE && iter < maxIters); // second run for x-error fitting if (xerrorVector.size() > 0) { DEBUG("Rerun fit with x errors"); // y'(x) double *yd = new double[n]; for (size_t i = 0; i < n; i++) { size_t index = i; if (index == n-1) index = n-2; yd[i] = gsl_vector_get(s->f, index+1) + ydata[index+1] - gsl_vector_get(s->f, index) - ydata[index]; yd[i] /= (xdata[index+1] - xdata[index]); } switch (fitData.weightsType) { case nsl_fit_weight_no: break; case nsl_fit_weight_instrumental: for (size_t i = 0; i < n; i++) { double sigma; if (yerrorVector.size() > 0) // x- and y-error // sigma = sqrt(sigma_y^2 + (y'(x)*sigma_x)^2) sigma = sqrt(gsl_pow_2(yerror[i]) + gsl_pow_2(yd[i] * xerror[i])); else // only x-error sigma = yd[i] * xerror[i]; weight[i] = 1./gsl_pow_2(sigma); } break; // other weight types: y'(x) considered correctly? case nsl_fit_weight_direct: for (size_t i = 0; i < n; i++) { weight[i] = xerror[i]/yd[i]; if (yerrorVector.size() > 0) weight[i] += yerror[i]; } break; case nsl_fit_weight_inverse: for (size_t i = 0; i < n; i++) { weight[i] = yd[i]/xerror[i]; if (yerrorVector.size() > 0) weight[i] += 1./yerror[i]; } break; case nsl_fit_weight_statistical: case nsl_fit_weight_relative: break; case nsl_fit_weight_statistical_fit: for (size_t i = 0; i < n; i++) weight[i] = 1./(gsl_vector_get(s->f, i) + ydata[i]); // 1/Y_i break; case nsl_fit_weight_relative_fit: for (size_t i = 0; i < n; i++) weight[i] = 1./gsl_pow_2(gsl_vector_get(s->f, i) + ydata[i]); // 1/Y_i^2 break; } delete[] yd; do { iter++; status = gsl_multifit_fdfsolver_iterate(s); writeSolverState(s); if (status) break; status = gsl_multifit_test_delta(s->dx, s->x, delta, delta); } while (status == GSL_CONTINUE && iter < maxIters); } delete[] weight; // unscale start values for (unsigned int i = 0; i < np; i++) x_init[i] = nsl_fit_map_bound(x_init[i], x_min[i], x_max[i]); //get the covariance matrix //TODO: scale the Jacobian when limits are used before constructing the covar matrix? gsl_matrix* covar = gsl_matrix_alloc(np, np); #if GSL_MAJOR_VERSION >= 2 // the Jacobian is not part of the solver anymore gsl_matrix *J = gsl_matrix_alloc(s->fdf->n, s->fdf->p); gsl_multifit_fdfsolver_jac(s, J); gsl_multifit_covar(J, 0.0, covar); #else gsl_multifit_covar(s->J, 0.0, covar); #endif //write the result fitResult.available = true; fitResult.valid = true; fitResult.status = QString(gsl_strerror(status)); // i18n? GSL does not support translations fitResult.iterations = iter; fitResult.dof = n - np; //gsl_blas_dnrm2() - computes the Euclidian norm (||r||_2 = \sqrt {\sum r_i^2}) of the vector with the elements weight[i]*(Yi - y[i]) //gsl_blas_dasum() - computes the absolute sum \sum |r_i| of the elements of the vector with the elements weight[i]*(Yi - y[i]) fitResult.sse = gsl_pow_2(gsl_blas_dnrm2(s->f)); if (fitResult.dof != 0) { fitResult.rms = fitResult.sse/fitResult.dof; fitResult.rsd = sqrt(fitResult.rms); } fitResult.mse = fitResult.sse/n; fitResult.rmse = sqrt(fitResult.mse); fitResult.mae = gsl_blas_dasum(s->f)/n; //needed for coefficient of determination, R-squared fitResult.sst = gsl_stats_tss(ydata, 1, n); //parameter values const double c = GSL_MIN_DBL(1., sqrt(fitResult.rms)); //limit error for poor fit fitResult.paramValues.resize(np); fitResult.errorValues.resize(np); for (unsigned int i = 0; i < np; i++) { // scale resulting values if they are bounded fitResult.paramValues[i] = nsl_fit_map_bound(gsl_vector_get(s->x, i), x_min[i], x_max[i]); // use results as start values if desired if (fitData.useResults) { fitData.paramStartValues.data()[i] = fitResult.paramValues[i]; DEBUG("saving parameter " << i << ": " << fitResult.paramValues[i] << ' ' << fitData.paramStartValues.data()[i]); } fitResult.errorValues[i] = c*sqrt(gsl_matrix_get(covar, i, i)); } // fill residuals vector. To get residuals on the correct x values, fill the rest with zeros. residualsVector->resize(tmpXDataColumn->rowCount()); if (fitData.evaluateFullRange) { // evaluate full range of residuals xVector->resize(tmpXDataColumn->rowCount()); for (int i = 0; i < tmpXDataColumn->rowCount(); i++) (*xVector)[i] = tmpXDataColumn->valueAt(i); ExpressionParser* parser = ExpressionParser::getInstance(); bool rc = parser->evaluateCartesian(fitData.model, xVector, residualsVector, fitData.paramNames, fitResult.paramValues); for (int i = 0; i < tmpXDataColumn->rowCount(); i++) (*residualsVector)[i] = tmpYDataColumn->valueAt(i) - (*residualsVector)[i]; if (!rc) residualsVector->clear(); } else { // only selected range size_t j = 0; for (int i = 0; i < tmpXDataColumn->rowCount(); i++) { if (tmpXDataColumn->valueAt(i) >= xmin && tmpXDataColumn->valueAt(i) <= xmax) residualsVector->data()[i] = - gsl_vector_get(s->f, j++); else // outside range residualsVector->data()[i] = 0; } } residualsColumn->setChanged(); //free resources gsl_multifit_fdfsolver_free(s); gsl_matrix_free(covar); //calculate the fit function (vectors) ExpressionParser* parser = ExpressionParser::getInstance(); if (fitData.evaluateFullRange) { // evaluate fit on full data range if selected xmin = tmpXDataColumn->minimum(); xmax = tmpXDataColumn->maximum(); } xVector->resize(fitData.evaluatedPoints); yVector->resize(fitData.evaluatedPoints); bool rc = parser->evaluateCartesian(fitData.model, QString::number(xmin), QString::number(xmax), fitData.evaluatedPoints, xVector, yVector, fitData.paramNames, fitResult.paramValues); if (!rc) { xVector->clear(); yVector->clear(); } fitResult.elapsedTime = timer.elapsed(); //redraw the curve emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; } /*! * writes out the current state of the solver \c s */ void XYFitCurvePrivate::writeSolverState(gsl_multifit_fdfsolver* s) { QString state; //current parameter values, semicolon separated double* min = fitData.paramLowerLimits.data(); double* max = fitData.paramUpperLimits.data(); for (int i = 0; i < fitData.paramNames.size(); ++i) { const double x = gsl_vector_get(s->x, i); // map parameter if bounded state += QString::number(nsl_fit_map_bound(x, min[i], max[i])) + '\t'; } //current value of the chi2-function state += QString::number(gsl_pow_2(gsl_blas_dnrm2(s->f))); state += ';'; fitResult.solverOutput += state; } //############################################################################## //################## Serialization/Deserialization ########################### //############################################################################## //! Save as XML void XYFitCurve::save(QXmlStreamWriter* writer) const { Q_D(const XYFitCurve); writer->writeStartElement("xyFitCurve"); //write xy-curve information XYCurve::save(writer); //write xy-fit-curve specific information //fit data - no need to save model expression and parameter names, they are set in XYFitCurve::initFitData() writer->writeStartElement("fitData"); WRITE_COLUMN(d->xDataColumn, xDataColumn); WRITE_COLUMN(d->yDataColumn, yDataColumn); WRITE_COLUMN(d->xErrorColumn, xErrorColumn); WRITE_COLUMN(d->yErrorColumn, yErrorColumn); writer->writeAttribute("autoRange", QString::number(d->fitData.autoRange)); writer->writeAttribute("xRangeMin", QString::number(d->fitData.xRange.first(), 'g', 15)); writer->writeAttribute("xRangeMax", QString::number(d->fitData.xRange.last(), 'g', 15)); writer->writeAttribute("modelCategory", QString::number(d->fitData.modelCategory)); writer->writeAttribute("modelType", QString::number(d->fitData.modelType)); writer->writeAttribute("weightsType", QString::number(d->fitData.weightsType)); writer->writeAttribute("degree", QString::number(d->fitData.degree)); writer->writeAttribute("maxIterations", QString::number(d->fitData.maxIterations)); writer->writeAttribute("eps", QString::number(d->fitData.eps, 'g', 15)); writer->writeAttribute("evaluatedPoints", QString::number(d->fitData.evaluatedPoints)); writer->writeAttribute("evaluateFullRange", QString::number(d->fitData.evaluateFullRange)); writer->writeAttribute("useDataErrors", QString::number(d->fitData.useDataErrors)); writer->writeAttribute("useResults", QString::number(d->fitData.useResults)); writer->writeStartElement("paramStartValues"); foreach (const double &value, d->fitData.paramStartValues) writer->writeTextElement("startValue", QString::number(value, 'g', 15)); writer->writeEndElement(); // use 16 digits to handle -DBL_MAX writer->writeStartElement("paramLowerLimits"); foreach (const double &limit, d->fitData.paramLowerLimits) writer->writeTextElement("lowerLimit", QString::number(limit, 'g', 16)); writer->writeEndElement(); // use 16 digits to handle DBL_MAX writer->writeStartElement("paramUpperLimits"); foreach (const double &limit, d->fitData.paramUpperLimits) writer->writeTextElement("upperLimit", QString::number(limit, 'g', 16)); writer->writeEndElement(); writer->writeStartElement("paramFixed"); foreach (const double &fixed, d->fitData.paramFixed) writer->writeTextElement("fixed", QString::number(fixed)); writer->writeEndElement(); writer->writeEndElement(); //fit results (generated columns and goodness of the fit) writer->writeStartElement("fitResult"); writer->writeAttribute("available", QString::number(d->fitResult.available)); writer->writeAttribute("valid", QString::number(d->fitResult.valid)); writer->writeAttribute("status", d->fitResult.status); writer->writeAttribute("iterations", QString::number(d->fitResult.iterations)); writer->writeAttribute("time", QString::number(d->fitResult.elapsedTime)); writer->writeAttribute("dof", QString::number(d->fitResult.dof)); writer->writeAttribute("sse", QString::number(d->fitResult.sse, 'g', 15)); writer->writeAttribute("sst", QString::number(d->fitResult.sst, 'g', 15)); writer->writeAttribute("rms", QString::number(d->fitResult.rms, 'g', 15)); writer->writeAttribute("rsd", QString::number(d->fitResult.rsd, 'g', 15)); writer->writeAttribute("mse", QString::number(d->fitResult.mse, 'g', 15)); writer->writeAttribute("rmse", QString::number(d->fitResult.rmse, 'g', 15)); writer->writeAttribute("mae", QString::number(d->fitResult.mae, 'g', 15)); writer->writeAttribute("solverOutput", d->fitResult.solverOutput); writer->writeStartElement("paramValues"); foreach (const double &value, d->fitResult.paramValues) writer->writeTextElement("value", QString::number(value, 'g', 15)); writer->writeEndElement(); writer->writeStartElement("errorValues"); foreach (const double &value, d->fitResult.errorValues) writer->writeTextElement("error", QString::number(value, 'g', 15)); writer->writeEndElement(); //save calculated columns if available if (d->xColumn && d->yColumn && d->residualsColumn) { d->xColumn->save(writer); d->yColumn->save(writer); d->residualsColumn->save(writer); } writer->writeEndElement(); //"fitResult" writer->writeEndElement(); //"xyFitCurve" } //! Load from XML bool XYFitCurve::load(XmlStreamReader* reader, bool preview) { Q_D(XYFitCurve); if (!reader->isStartElement() || reader->name() != "xyFitCurve") { reader->raiseError(i18n("no xy fit curve element found")); return false; } QString attributeWarning = i18n("Attribute '%1' missing or empty, default value is used"); QXmlStreamAttributes attribs; QString str; while (!reader->atEnd()) { reader->readNext(); if (reader->isEndElement() && reader->name() == "xyFitCurve") break; if (!reader->isStartElement()) continue; if (reader->name() == "xyCurve") { if ( !XYCurve::load(reader, preview) ) return false; if (preview) return true; } else if (reader->name() == "fitData") { attribs = reader->attributes(); READ_COLUMN(xDataColumn); READ_COLUMN(yDataColumn); READ_COLUMN(xErrorColumn); READ_COLUMN(yErrorColumn); READ_INT_VALUE("autoRange", fitData.autoRange, bool); READ_DOUBLE_VALUE("xRangeMin", fitData.xRange.first()); READ_DOUBLE_VALUE("xRangeMax", fitData.xRange.last()); READ_INT_VALUE("modelCategory", fitData.modelCategory, nsl_fit_model_category); READ_INT_VALUE("modelType", fitData.modelType, unsigned int); READ_INT_VALUE("weightsType", fitData.weightsType, nsl_fit_weight_type); READ_INT_VALUE("degree", fitData.degree, int); READ_INT_VALUE("maxIterations", fitData.maxIterations, int); READ_DOUBLE_VALUE("eps", fitData.eps); READ_INT_VALUE("fittedPoints", fitData.evaluatedPoints, size_t); // old name READ_INT_VALUE("evaluatedPoints", fitData.evaluatedPoints, size_t); READ_INT_VALUE("evaluateFullRange", fitData.evaluateFullRange, bool); READ_INT_VALUE("useDataErrors", fitData.useDataErrors, bool); READ_INT_VALUE("useResults", fitData.useResults, bool); //set the model expression and the parameter names (can be derived from the saved values for category, type and degree) XYFitCurve::initFitData(d->fitData); } else if (reader->name() == "startValue") { d->fitData.paramStartValues << reader->readElementText().toDouble(); } else if (reader->name() == "fixed") { d->fitData.paramFixed << (bool)reader->readElementText().toInt(); } else if (reader->name() == "lowerLimit") { bool ok; double x = reader->readElementText().toDouble(&ok); if (ok) // -DBL_MAX results in conversion error d->fitData.paramLowerLimits << x; else d->fitData.paramLowerLimits << -DBL_MAX; } else if (reader->name() == "upperLimit") { bool ok; double x = reader->readElementText().toDouble(&ok); if (ok) // DBL_MAX results in conversion error d->fitData.paramUpperLimits << x; else d->fitData.paramUpperLimits << DBL_MAX; } else if (reader->name() == "value") { d->fitResult.paramValues << reader->readElementText().toDouble(); } else if (reader->name() == "error") { d->fitResult.errorValues << reader->readElementText().toDouble(); } else if (reader->name() == "fitResult") { attribs = reader->attributes(); READ_INT_VALUE("available", fitResult.available, int); READ_INT_VALUE("valid", fitResult.valid, int); READ_STRING_VALUE("status", fitResult.status); READ_INT_VALUE("iterations", fitResult.iterations, int); READ_INT_VALUE("time", fitResult.elapsedTime, int); READ_DOUBLE_VALUE("dof", fitResult.dof); READ_DOUBLE_VALUE("sse", fitResult.sse); READ_DOUBLE_VALUE("sst", fitResult.sst); READ_DOUBLE_VALUE("rms", fitResult.rms); READ_DOUBLE_VALUE("rsd", fitResult.rsd); READ_DOUBLE_VALUE("mse", fitResult.mse); READ_DOUBLE_VALUE("rmse", fitResult.rmse); READ_DOUBLE_VALUE("mae", fitResult.mae); READ_STRING_VALUE("solverOutput", fitResult.solverOutput); } else if (reader->name() == "column") { Column* column = new Column("", AbstractColumn::Numeric); if (!column->load(reader, preview)) { delete column; return false; } if (column->name() == "x") d->xColumn = column; else if (column->name() == "y") d->yColumn = column; else if (column->name() == "residuals") d->residualsColumn = column; } } // new fit model style if (d->fitData.modelCategory == nsl_fit_model_basic && d->fitData.modelType >= NSL_FIT_MODEL_BASIC_COUNT) d->fitData.modelType = 0; // wait for data to be read before using the pointers QThreadPool::globalInstance()->waitForDone(); if (d->xColumn && d->yColumn && d->residualsColumn) { d->xColumn->setHidden(true); addChild(d->xColumn); d->yColumn->setHidden(true); addChild(d->yColumn); addChild(d->residualsColumn); d->xVector = static_cast* >(d->xColumn->data()); d->yVector = static_cast* >(d->yColumn->data()); d->residualsVector = static_cast* >(d->residualsColumn->data()); XYCurve::d_ptr->xColumn = d->xColumn; XYCurve::d_ptr->yColumn = d->yColumn; } return true; } diff --git a/src/backend/worksheet/plots/cartesian/XYFitCurve.h b/src/backend/worksheet/plots/cartesian/XYFitCurve.h index 576395f13..b760ffc49 100644 --- a/src/backend/worksheet/plots/cartesian/XYFitCurve.h +++ b/src/backend/worksheet/plots/cartesian/XYFitCurve.h @@ -1,151 +1,151 @@ /*************************************************************************** File : XYFitCurve.h Project : LabPlot Description : A xy-curve defined by a fit model -------------------------------------------------------------------- Copyright : (C) 2014-2016 Alexander Semke (alexander.semke@web.de) Copyright : (C) 2016 Stefan Gerlach (stefan.gerlach@uni.kn) ***************************************************************************/ /*************************************************************************** * * * 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. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #ifndef XYFITCURVE_H #define XYFITCURVE_H #include "backend/worksheet/plots/cartesian/XYCurve.h" #include "kdefrontend/spreadsheet/PlotDataDialog.h" //for PlotDataDialog::AnalysisAction. TODO: find a better place for this enum. extern "C" { #include "backend/nsl/nsl_fit.h" } class XYFitCurvePrivate; class XYFitCurve : public XYCurve { Q_OBJECT public: struct FitData { FitData() : modelCategory(nsl_fit_model_basic), modelType(0), weightsType(nsl_fit_weight_no), degree(1), maxIterations(500), eps(1e-4), evaluatedPoints(100), evaluateFullRange(true), useDataErrors(true), useResults(true), autoRange(true), xRange(2) {}; nsl_fit_model_category modelCategory; unsigned int modelType; nsl_fit_weight_type weightsType; int degree; QString model; QStringList paramNames; QStringList paramNamesUtf8; // Utf8 version of paramNames QVector paramStartValues; QVector paramLowerLimits; QVector paramUpperLimits; QVector paramFixed; int maxIterations; double eps; size_t evaluatedPoints; bool evaluateFullRange; // evaluate fit function on full data range (default) bool useDataErrors; // use given data errors when fitting (default) bool useResults; // use results as new start values (default) - bool autoRange; // use all data? + bool autoRange; // use all data? QVector xRange; // x range for integration }; struct FitResult { FitResult() : available(false), valid(false), iterations(0), elapsedTime(0), dof(0), sse(0), sst(0), rms(0), rsd(0), mse(0), rmse(0), mae(0) {}; bool available; bool valid; QString status; int iterations; qint64 elapsedTime; double dof; //degrees of freedom // residuals: r_i = y_i - Y_i double sse; // sum of squared errors (SSE) / residual sum of squares (RSS) / sum of sq. residuals (SSR) / S = chi^2 = \sum_i^n r_i^2 double sst; // total sum of squares (SST) = \sum_i^n (y_i - )^2 double rms; // residual mean square / reduced chi^2 = SSE/dof double rsd; // residual standard deviation = sqrt(SSE/dof) double mse; // mean squared error = SSE/n double rmse; // root-mean squared error = \sqrt(mse) double mae; // mean absolute error = \sum_i^n |r_i| // see also http://www.originlab.com/doc/Origin-Help/NLFit-Algorithm QVector paramValues; QVector errorValues; QString solverOutput; }; explicit XYFitCurve(const QString& name); virtual ~XYFitCurve(); void recalculate(); void initFitData(PlotDataDialog::AnalysisAction); static void initFitData(XYFitCurve::FitData&); virtual QIcon icon() const override; virtual void save(QXmlStreamWriter*) const override; virtual bool load(XmlStreamReader*, bool preview) override; POINTER_D_ACCESSOR_DECL(const AbstractColumn, xDataColumn, XDataColumn) POINTER_D_ACCESSOR_DECL(const AbstractColumn, yDataColumn, YDataColumn) POINTER_D_ACCESSOR_DECL(const AbstractColumn, xErrorColumn, XErrorColumn) POINTER_D_ACCESSOR_DECL(const AbstractColumn, yErrorColumn, YErrorColumn) const QString& xDataColumnPath() const; const QString& yDataColumnPath() const; const QString& xErrorColumnPath() const; const QString& yErrorColumnPath() const; CLASS_D_ACCESSOR_DECL(FitData, fitData, FitData) const FitResult& fitResult() const; typedef XYFitCurvePrivate Private; protected: XYFitCurve(const QString& name, XYFitCurvePrivate* dd); private: Q_DECLARE_PRIVATE(XYFitCurve) void init(); signals: friend class XYFitCurveSetXDataColumnCmd; friend class XYFitCurveSetYDataColumnCmd; friend class XYFitCurveSetXErrorColumnCmd; friend class XYFitCurveSetYErrorColumnCmd; void xDataColumnChanged(const AbstractColumn*); void yDataColumnChanged(const AbstractColumn*); void xErrorColumnChanged(const AbstractColumn*); void yErrorColumnChanged(const AbstractColumn*); friend class XYFitCurveSetFitDataCmd; void fitDataChanged(const XYFitCurve::FitData&); }; #endif