source: sasview/sansmodels/src/sans/models/c_models/parallelepiped.cpp @ c146f092

/**
        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
 */

/**
 * Scattering model classes
 * The classes use the IGOR library found in
 *   sansmodels/src/libigor
 *
 *      TODO: refactor so that we pull in the old sansmodels.c_extensions
 *      TODO: add 2D function
 */

#include <math.h>
#include "models.hh"
#include "parameters.hh"
#include <stdio.h>
using namespace std;

extern "C" {
        #include "libCylinder.h"
        #include "libStructureFactor.h"
        #include "parallelepiped.h"
}

ParallelepipedModel :: ParallelepipedModel() {
        scale      = Parameter(1.0);
        short_a     = Parameter(35.0, true);
        short_a.set_min(1.0);
        short_b     = Parameter(75.0, true);
        short_b.set_min(1.0);
        long_c     = Parameter(400.0, true);
        long_c.set_min(1.0);
        contrast   = Parameter(53.e-7);
        background = Parameter(0.0);
        parallel_theta  = Parameter(0.0, true);
        parallel_phi    = Parameter(0.0, true);
        parallel_psi    = Parameter(0.0, true);
}

/**
 * Function to evaluate 1D scattering function
 * The NIST IGOR library is used for the actual calculation.
 * @param q: q-value
 * @return: function value
 */
double ParallelepipedModel :: operator()(double q) {
        double dp[6];

        // Fill parameter array for IGOR library
        // Add the background after averaging
        dp[0] = scale();
        dp[1] = short_a();
        dp[2] = short_b();
        dp[3] = long_c();
        dp[4] = contrast();
        dp[5] = 0.0;

        // Get the dispersion points for the short_edgeA
        vector<WeightPoint> weights_short_a;
        short_a.get_weights(weights_short_a);

        // Get the dispersion points for the longer_edgeB
        vector<WeightPoint> weights_short_b;
        short_b.get_weights(weights_short_b);

        // Get the dispersion points for the longuest_edgeC
        vector<WeightPoint> weights_long_c;
        long_c.get_weights(weights_long_c);



        // Perform the computation, with all weight points
        double sum = 0.0;
        double norm = 0.0;

        // Loop over short_edgeA weight points
        for(int i=0; i< (int)weights_short_a.size(); i++) {
                dp[1] = weights_short_a[i].value;

                // Loop over longer_edgeB weight points
                for(int j=0; j< (int)weights_short_b.size(); j++) {
                        dp[2] = weights_short_b[j].value;

                        // Loop over longuest_edgeC weight points
                        for(int k=0; k< (int)weights_long_c.size(); k++) {
                                dp[3] = weights_long_c[k].value;
                                sum += weights_short_a[i].weight * weights_short_b[j].weight
                                        * weights_long_c[k].weight * Parallelepiped(dp, q);

                                norm += weights_short_a[i].weight
                                         * weights_short_b[j].weight * weights_long_c[k].weight;
                        }
                }
        }

        return sum/norm + background();
}
/**
 * Function to evaluate 2D scattering function
 * @param q_x: value of Q along x
 * @param q_y: value of Q along y
 * @return: function value
 */
double ParallelepipedModel :: operator()(double qx, double qy) {
        ParallelepipedParameters dp;
        // Fill parameter array
        dp.scale      = scale();
        dp.short_a   = short_a();
        dp.short_b   = short_b();
        dp.long_c  = long_c();
        dp.contrast   = contrast();
        dp.background = 0.0;
        //dp.background = background();
        dp.parallel_theta  = parallel_theta();
        dp.parallel_phi    = parallel_phi();
        dp.parallel_psi    = parallel_psi();


        // Get the dispersion points for the short_edgeA
        vector<WeightPoint> weights_short_a;
        short_a.get_weights(weights_short_a);

        // Get the dispersion points for the longer_edgeB
        vector<WeightPoint> weights_short_b;
        short_b.get_weights(weights_short_b);

        // Get angular averaging for the longuest_edgeC
        vector<WeightPoint> weights_long_c;
        long_c.get_weights(weights_long_c);

        // Get angular averaging for theta
        vector<WeightPoint> weights_parallel_theta;
        parallel_theta.get_weights(weights_parallel_theta);

        // Get angular averaging for phi
        vector<WeightPoint> weights_parallel_phi;
        parallel_phi.get_weights(weights_parallel_phi);

        // Get angular averaging for psi
        vector<WeightPoint> weights_parallel_psi;
        parallel_psi.get_weights(weights_parallel_psi);

        // Perform the computation, with all weight points
        double sum = 0.0;
        double norm = 0.0;

        // Loop over radius weight points
        for(int i=0; i< (int)weights_short_a.size(); i++) {
                dp.short_a = weights_short_a[i].value;

                // Loop over longer_edgeB weight points
                for(int j=0; j< (int)weights_short_b.size(); j++) {
                        dp.short_b = weights_short_b[j].value;

                        // Average over longuest_edgeC distribution
                        for(int k=0; k< (int)weights_long_c.size(); k++) {
                                dp.long_c = weights_long_c[k].value;

                                // Average over theta distribution
                                for(int l=0; l< (int)weights_parallel_theta.size(); l++) {
                                dp.parallel_theta = weights_parallel_theta[l].value;

                                        // Average over phi distribution
                                        for(int m=0; m< (int)weights_parallel_phi.size(); m++) {
                                                dp.parallel_phi = weights_parallel_phi[m].value;

                                                // Average over phi distribution
                                                for(int n=0; n< (int)weights_parallel_psi.size(); n++) {
                                                        dp.parallel_psi = weights_parallel_psi[n].value;

                                                        double _ptvalue = weights_short_a[i].weight
                                                                * weights_short_b[j].weight
                                                                * weights_long_c[k].weight
                                                                * weights_parallel_theta[l].weight
                                                                * weights_parallel_phi[m].weight
                                                                * weights_parallel_psi[n].weight
                                                                * parallelepiped_analytical_2DXY(&dp, qx, qy);
                                                        if (weights_parallel_theta.size()>1) {
                                                                _ptvalue *= sin(weights_parallel_theta[l].value);
                                                        }
                                                        sum += _ptvalue;

                                                        norm += weights_short_a[i].weight
                                                                * weights_short_b[j].weight
                                                                * weights_long_c[k].weight
                                                                * weights_parallel_theta[l].weight
                                                                * weights_parallel_phi[m].weight
                                                                * weights_parallel_psi[n].weight;
                                                }
                                        }

                                }
                        }
                }
        }
        // Averaging in theta needs an extra normalization
        // factor to account for the sin(theta) term in the
        // integration (see documentation).
        if (weights_parallel_theta.size()>1) norm = norm / asin(1.0);
        return sum/norm + background();
}


/**
 * Function to evaluate 2D scattering function
 * @param pars: parameters of the cylinder
 * @param q: q-value
 * @param phi: angle phi
 * @return: function value
 */
double ParallelepipedModel :: evaluate_rphi(double q, double phi) {
        double qx = q*cos(phi);
        double qy = q*sin(phi);
        return (*this).operator()(qx, qy);
}
/**
 * Function to calculate effective radius
 * @return: effective radius value
 */
double ParallelepipedModel :: calculate_ER() {
        ParallelepipedParameters dp;
        dp.short_a   = short_a();
        dp.short_b   = short_b();
        dp.long_c  = long_c();
        double rad_out = 0.0;
        double pi = 4.0*atan(1.0);
        double suf_rad = sqrt(dp.short_a*dp.short_b/pi);

        // Perform the computation, with all weight points
        double sum = 0.0;
        double norm = 0.0;

        // Get the dispersion points for the short_edgeA
        vector<WeightPoint> weights_short_a;
        short_a.get_weights(weights_short_a);

        // Get the dispersion points for the longer_edgeB
        vector<WeightPoint> weights_short_b;
        short_b.get_weights(weights_short_b);

        // Get angular averaging for the longuest_edgeC
        vector<WeightPoint> weights_long_c;
        long_c.get_weights(weights_long_c);

        // Loop over radius weight points
        for(int i=0; i< (int)weights_short_a.size(); i++) {
                dp.short_a = weights_short_a[i].value;

                // Loop over longer_edgeB weight points
                for(int j=0; j< (int)weights_short_b.size(); j++) {
                        dp.short_b = weights_short_b[j].value;

                        // Average over longuest_edgeC distribution
                        for(int k=0; k< (int)weights_long_c.size(); k++) {
                                dp.long_c = weights_long_c[k].value;
                                //Calculate surface averaged radius
                                //This is rough approximation.
                                suf_rad = sqrt(dp.short_a*dp.short_b/pi);

                                //Note: output of "DiamCyl(dp.length,dp.radius)" is DIAMETER.
                                sum +=weights_short_a[i].weight* weights_short_b[j].weight
                                        * weights_long_c[k].weight*DiamCyl(dp.long_c, suf_rad)/2.0;
                                norm += weights_short_a[i].weight* weights_short_b[j].weight*weights_long_c[k].weight;
                        }
                }
        }

        if (norm != 0){
                //return the averaged value
                rad_out =  sum/norm;}
        else{
                //return normal value
                //Note: output of "DiamCyl(length,radius)" is DIAMETER.
                rad_out = DiamCyl(dp.long_c, suf_rad)/2.0;}
        return rad_out;

}