source: sasview/sansmodels/src/sans/models/c_models/parameters.cpp @ fd57185

/**
        This software was developed by the University of Tennessee as part of the
        Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
        project funded by the US National Science Foundation.

        If you use DANSE applications to do scientific research that leads to
        publication, we ask that you acknowledge the use of the software with the
        following sentence:

        "This work benefited from DANSE software developed under NSF award DMR-0520547."

        copyright 2008, University of Tennessee
 */
#include "parameters.hh"
#include <stdio.h>
#include <math.h>
using namespace std;

/**
 * TODO: normalize all dispersion weight lists
 */


/**
 * Weight points
 */
WeightPoint :: WeightPoint() {
        value = 0.0;
        weight = 0.0;
}

WeightPoint :: WeightPoint(double v, double w) {
        value = v;
        weight = w;
}

/**
 * Dispersion models
 */
DispersionModel :: DispersionModel() {
        npts  = 1;
        width = 0.0;
};

void DispersionModel :: accept_as_source(DispersionVisitor* visitor, void* from, void* to) {
        visitor->dispersion_to_dict(from, to);
}
void DispersionModel :: accept_as_destination(DispersionVisitor* visitor, void* from, void* to) {
        visitor->dispersion_from_dict(from, to);
}
void DispersionModel :: operator() (void *param, vector<WeightPoint> &weights){
        // Check against zero width
        if (width<=0) {
                width = 0.0;
                npts  = 1;
        }

        Parameter* par = (Parameter*)param;
        double value = (*par)();

        if (npts<2) {
                weights.insert(weights.end(), WeightPoint(value, 1.0));
        } else {
                for(int i=0; i<npts; i++) {
                        double val = value + width * (1.0*i/float(npts-1) - 0.5);

                        if ( ((*par).has_min==false || val>(*par).min)
                          && ((*par).has_max==false || val<(*par).max)  )
                                weights.insert(weights.end(), WeightPoint(val, 1.0));
                }
        }
}

/**
 * Method to set the weights
 * Not implemented for this class
 */
void DispersionModel :: set_weights(int npoints, double* values, double* weights){}

/**
 * Gaussian dispersion
 */

GaussianDispersion :: GaussianDispersion() {
        npts  = 1;
        width = 0.0;
        nsigmas = 2;
};

void GaussianDispersion :: accept_as_source(DispersionVisitor* visitor, void* from, void* to) {
        visitor->gaussian_to_dict(from, to);
}
void GaussianDispersion :: accept_as_destination(DispersionVisitor* visitor, void* from, void* to) {
        visitor->gaussian_from_dict(from, to);
}

double gaussian_weight(double mean, double sigma, double x) {
        double vary, expo_value;
    vary = x-mean;
    expo_value = -vary*vary/(2*sigma*sigma);
    //return 1.0;
    return exp(expo_value);
}

/**
 * Gaussian dispersion
 * @param mean: mean value of the Gaussian
 * @param sigma: standard deviation of the Gaussian
 * @param x: value at which the Gaussian is evaluated
 * @return: value of the Gaussian
 */
void GaussianDispersion :: operator() (void *param, vector<WeightPoint> &weights){
        // Check against zero width
        if (width<=0) {
                width = 0.0;
                npts  = 1;
                nsigmas = 3;
        }

        Parameter* par = (Parameter*)param;
        double value = (*par)();

        if (npts<2) {
                weights.insert(weights.end(), WeightPoint(value, 1.0));
        } else {
                for(int i=0; i<npts; i++) {
                        // We cover 2 sigmas on each side of the mean
                        double val = value + width * (2.0*nsigmas*i/float(npts-1) - nsigmas);

                        if ( ((*par).has_min==false || val>(*par).min)
                          && ((*par).has_max==false || val<(*par).max)  ) {
                                double _w = gaussian_weight(value, width, val);
                                weights.insert(weights.end(), WeightPoint(val, _w));
                        }
                }
        }
}

/**
 * Array dispersion based on input arrays
 */

void ArrayDispersion :: accept_as_source(DispersionVisitor* visitor, void* from, void* to) {
        visitor->array_to_dict(from, to);
}
void ArrayDispersion :: accept_as_destination(DispersionVisitor* visitor, void* from, void* to) {
        visitor->array_from_dict(from, to);
}

/**
 * Method to get the weights
 */
void ArrayDispersion :: operator() (void *param, vector<WeightPoint> &weights) {
        Parameter* par = (Parameter*)param;
        double value = (*par)();

        if (npts<2) {
                weights.insert(weights.end(), WeightPoint(value, 1.0));
        } else {
                for(int i=0; i<npts; i++) {
                        if ( ((*par).has_min==false || _values[i]>(*par).min)
                          && ((*par).has_max==false || _values[i]<(*par).max)  )
                                weights.insert(weights.end(), WeightPoint(_values[i], _weights[i]));
                }
        }
}


/**
 * Method to set the weights
 */
void ArrayDispersion :: set_weights(int npoints, double* values, double* weights){
        npts = npoints;
        _values = values;
        _weights = weights;
}


/**
 * Parameters
 */
Parameter :: Parameter() {
        value = 0;
        min   = 0.0;
        max   = 0.0;
        has_min = false;
        has_max = false;
        has_dispersion = false;
        dispersion = new GaussianDispersion();
}

Parameter :: Parameter(double _value) {
        value = _value;
        min   = 0.0;
        max   = 0.0;
        has_min = false;
        has_max = false;
        has_dispersion = false;
        dispersion = new GaussianDispersion();
}

Parameter :: Parameter(double _value, bool disp) {
        value = _value;
        min   = 0.0;
        max   = 0.0;
        has_min = false;
        has_max = false;
        has_dispersion = disp;
        dispersion = new GaussianDispersion();
}

void Parameter :: get_weights(vector<WeightPoint> &weights) {
        (*dispersion)((void*)this, weights);
}

void Parameter :: set_min(double value) {
        has_min = true;
        min = value;
}

void Parameter :: set_max(double value) {
        has_max = true;
        max = value;
}

double Parameter :: operator()() {
        return value;
}

double Parameter :: operator=(double _value){
        value = _value;
}