[0f5bc9f] | 1 | /** |
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| 2 | This software was developed by the University of Tennessee as part of the |
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| 3 | Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 4 | project funded by the US National Science Foundation. |
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| 5 | |
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| 6 | If you use DANSE applications to do scientific research that leads to |
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| 7 | publication, we ask that you acknowledge the use of the software with the |
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| 8 | following sentence: |
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| 9 | |
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| 10 | "This work benefited from DANSE software developed under NSF award DMR-0520547." |
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| 11 | |
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| 12 | copyright 2008, University of Tennessee |
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| 13 | */ |
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| 14 | |
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| 15 | /** |
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| 16 | * Scattering model classes |
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| 17 | * The classes use the IGOR library found in |
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| 18 | * sansmodels/src/libigor |
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| 19 | * |
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| 20 | * TODO: refactor so that we pull in the old sansmodels.c_extensions |
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| 21 | */ |
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| 22 | |
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| 23 | #include <math.h> |
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| 24 | #include "models.hh" |
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| 25 | #include "parameters.hh" |
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| 26 | #include <stdio.h> |
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| 27 | using namespace std; |
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| 28 | |
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| 29 | extern "C" { |
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| 30 | #include "libCylinder.h" |
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[5eb9154] | 31 | #include "libStructureFactor.h" |
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[0f5bc9f] | 32 | #include "ellipsoid.h" |
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| 33 | } |
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| 34 | |
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| 35 | EllipsoidModel :: EllipsoidModel() { |
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| 36 | scale = Parameter(1.0); |
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| 37 | radius_a = Parameter(20.0, true); |
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| 38 | radius_a.set_min(0.0); |
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| 39 | radius_b = Parameter(400.0, true); |
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| 40 | radius_b.set_min(0.0); |
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[f10063e] | 41 | sldEll = Parameter(4.e-6); |
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| 42 | sldSolv = Parameter(1.e-6); |
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[0f5bc9f] | 43 | background = Parameter(0.0); |
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[4628e31] | 44 | axis_theta = Parameter(57.325, true); |
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[0f5bc9f] | 45 | axis_phi = Parameter(0.0, true); |
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| 46 | } |
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| 47 | |
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| 48 | /** |
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| 49 | * Function to evaluate 1D scattering function |
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| 50 | * The NIST IGOR library is used for the actual calculation. |
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| 51 | * @param q: q-value |
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| 52 | * @return: function value |
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| 53 | */ |
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| 54 | double EllipsoidModel :: operator()(double q) { |
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[f10063e] | 55 | double dp[6]; |
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[0f5bc9f] | 56 | |
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| 57 | // Fill parameter array for IGOR library |
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| 58 | // Add the background after averaging |
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| 59 | dp[0] = scale(); |
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| 60 | dp[1] = radius_a(); |
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| 61 | dp[2] = radius_b(); |
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[f10063e] | 62 | dp[3] = sldEll(); |
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| 63 | dp[4] = sldSolv(); |
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| 64 | dp[5] = 0.0; |
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[0f5bc9f] | 65 | |
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| 66 | // Get the dispersion points for the radius_a |
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| 67 | vector<WeightPoint> weights_rad_a; |
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| 68 | radius_a.get_weights(weights_rad_a); |
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| 69 | |
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| 70 | // Get the dispersion points for the radius_b |
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| 71 | vector<WeightPoint> weights_rad_b; |
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| 72 | radius_b.get_weights(weights_rad_b); |
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| 73 | |
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| 74 | // Perform the computation, with all weight points |
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| 75 | double sum = 0.0; |
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| 76 | double norm = 0.0; |
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[c451be9] | 77 | double vol = 0.0; |
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[0f5bc9f] | 78 | |
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| 79 | // Loop over radius_a weight points |
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[34c2649] | 80 | for(size_t i=0; i<weights_rad_a.size(); i++) { |
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[0f5bc9f] | 81 | dp[1] = weights_rad_a[i].value; |
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| 82 | |
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| 83 | // Loop over radius_b weight points |
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[34c2649] | 84 | for(size_t j=0; j<weights_rad_b.size(); j++) { |
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[0f5bc9f] | 85 | dp[2] = weights_rad_b[j].value; |
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[c451be9] | 86 | //Un-normalize by volume |
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[0f5bc9f] | 87 | sum += weights_rad_a[i].weight |
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[c451be9] | 88 | * weights_rad_b[j].weight * EllipsoidForm(dp, q) |
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| 89 | * pow(weights_rad_b[j].value,2) * weights_rad_a[i].value; |
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| 90 | |
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| 91 | //Find average volume |
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| 92 | vol += weights_rad_a[i].weight |
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| 93 | * weights_rad_b[j].weight |
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| 94 | * pow(weights_rad_b[j].value,2) |
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| 95 | * weights_rad_a[i].value; |
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[0f5bc9f] | 96 | norm += weights_rad_a[i].weight |
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| 97 | * weights_rad_b[j].weight; |
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| 98 | } |
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| 99 | } |
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[c451be9] | 100 | |
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| 101 | if (vol != 0.0 && norm != 0.0) { |
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| 102 | //Re-normalize by avg volume |
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| 103 | sum = sum/(vol/norm);} |
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| 104 | |
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[0f5bc9f] | 105 | return sum/norm + background(); |
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| 106 | } |
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| 107 | |
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| 108 | /** |
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| 109 | * Function to evaluate 2D scattering function |
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| 110 | * @param q_x: value of Q along x |
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| 111 | * @param q_y: value of Q along y |
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| 112 | * @return: function value |
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| 113 | */ |
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| 114 | double EllipsoidModel :: operator()(double qx, double qy) { |
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| 115 | EllipsoidParameters dp; |
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| 116 | // Fill parameter array |
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| 117 | dp.scale = scale(); |
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| 118 | dp.radius_a = radius_a(); |
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| 119 | dp.radius_b = radius_b(); |
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[f10063e] | 120 | dp.sldEll = sldEll(); |
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| 121 | dp.sldSolv = sldSolv(); |
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[0f5bc9f] | 122 | dp.background = 0.0; |
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| 123 | dp.axis_theta = axis_theta(); |
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| 124 | dp.axis_phi = axis_phi(); |
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| 125 | |
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| 126 | // Get the dispersion points for the radius_a |
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| 127 | vector<WeightPoint> weights_rad_a; |
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| 128 | radius_a.get_weights(weights_rad_a); |
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| 129 | |
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| 130 | // Get the dispersion points for the radius_b |
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| 131 | vector<WeightPoint> weights_rad_b; |
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| 132 | radius_b.get_weights(weights_rad_b); |
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| 133 | |
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| 134 | // Get angular averaging for theta |
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| 135 | vector<WeightPoint> weights_theta; |
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| 136 | axis_theta.get_weights(weights_theta); |
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| 137 | |
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| 138 | // Get angular averaging for phi |
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| 139 | vector<WeightPoint> weights_phi; |
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| 140 | axis_phi.get_weights(weights_phi); |
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| 141 | |
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| 142 | // Perform the computation, with all weight points |
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| 143 | double sum = 0.0; |
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| 144 | double norm = 0.0; |
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[c451be9] | 145 | double norm_vol = 0.0; |
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| 146 | double vol = 0.0; |
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[4628e31] | 147 | double pi = 4.0*atan(1.0); |
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[0f5bc9f] | 148 | // Loop over radius weight points |
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[34c2649] | 149 | for(size_t i=0; i<weights_rad_a.size(); i++) { |
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[0f5bc9f] | 150 | dp.radius_a = weights_rad_a[i].value; |
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| 151 | |
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| 152 | |
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| 153 | // Loop over length weight points |
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[34c2649] | 154 | for(size_t j=0; j<weights_rad_b.size(); j++) { |
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[0f5bc9f] | 155 | dp.radius_b = weights_rad_b[j].value; |
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| 156 | |
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| 157 | // Average over theta distribution |
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[34c2649] | 158 | for(size_t k=0; k<weights_theta.size(); k++) { |
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[0f5bc9f] | 159 | dp.axis_theta = weights_theta[k].value; |
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| 160 | |
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| 161 | // Average over phi distribution |
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[34c2649] | 162 | for(size_t l=0; l<weights_phi.size(); l++) { |
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[0f5bc9f] | 163 | dp.axis_phi = weights_phi[l].value; |
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[c451be9] | 164 | //Un-normalize by volume |
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[0f5bc9f] | 165 | double _ptvalue = weights_rad_a[i].weight |
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| 166 | * weights_rad_b[j].weight |
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| 167 | * weights_theta[k].weight |
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| 168 | * weights_phi[l].weight |
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[c451be9] | 169 | * ellipsoid_analytical_2DXY(&dp, qx, qy) |
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| 170 | * pow(weights_rad_b[j].value,2) * weights_rad_a[i].value; |
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[0f5bc9f] | 171 | if (weights_theta.size()>1) { |
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[4628e31] | 172 | _ptvalue *= fabs(sin(weights_theta[k].value*pi/180.0)); |
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[0f5bc9f] | 173 | } |
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| 174 | sum += _ptvalue; |
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[c451be9] | 175 | //Find average volume |
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| 176 | vol += weights_rad_a[i].weight |
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| 177 | * weights_rad_b[j].weight |
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| 178 | * pow(weights_rad_b[j].value,2) * weights_rad_a[i].value; |
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| 179 | //Find norm for volume |
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| 180 | norm_vol += weights_rad_a[i].weight |
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| 181 | * weights_rad_b[j].weight; |
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[0f5bc9f] | 182 | |
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| 183 | norm += weights_rad_a[i].weight |
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| 184 | * weights_rad_b[j].weight |
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| 185 | * weights_theta[k].weight |
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| 186 | * weights_phi[l].weight; |
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| 187 | |
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| 188 | } |
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| 189 | } |
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| 190 | } |
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| 191 | } |
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| 192 | // Averaging in theta needs an extra normalization |
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| 193 | // factor to account for the sin(theta) term in the |
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| 194 | // integration (see documentation). |
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| 195 | if (weights_theta.size()>1) norm = norm / asin(1.0); |
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[c451be9] | 196 | |
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| 197 | if (vol != 0.0 && norm_vol != 0.0) { |
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| 198 | //Re-normalize by avg volume |
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| 199 | sum = sum/(vol/norm_vol);} |
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| 200 | |
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[0f5bc9f] | 201 | return sum/norm + background(); |
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| 202 | } |
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| 203 | |
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| 204 | /** |
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| 205 | * Function to evaluate 2D scattering function |
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| 206 | * @param pars: parameters of the cylinder |
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| 207 | * @param q: q-value |
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| 208 | * @param phi: angle phi |
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| 209 | * @return: function value |
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| 210 | */ |
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| 211 | double EllipsoidModel :: evaluate_rphi(double q, double phi) { |
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| 212 | double qx = q*cos(phi); |
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| 213 | double qy = q*sin(phi); |
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| 214 | return (*this).operator()(qx, qy); |
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| 215 | } |
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[5eb9154] | 216 | |
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| 217 | /** |
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| 218 | * Function to calculate effective radius |
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| 219 | * @return: effective radius value |
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| 220 | */ |
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| 221 | double EllipsoidModel :: calculate_ER() { |
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| 222 | EllipsoidParameters dp; |
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| 223 | |
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| 224 | dp.radius_a = radius_a(); |
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| 225 | dp.radius_b = radius_b(); |
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| 226 | |
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| 227 | double rad_out = 0.0; |
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| 228 | |
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| 229 | // Perform the computation, with all weight points |
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| 230 | double sum = 0.0; |
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| 231 | double norm = 0.0; |
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| 232 | |
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| 233 | // Get the dispersion points for the major shell |
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| 234 | vector<WeightPoint> weights_radius_a; |
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| 235 | radius_a.get_weights(weights_radius_a); |
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| 236 | |
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| 237 | // Get the dispersion points for the minor shell |
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| 238 | vector<WeightPoint> weights_radius_b; |
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| 239 | radius_b.get_weights(weights_radius_b); |
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| 240 | |
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| 241 | // Loop over major shell weight points |
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| 242 | for(int i=0; i< (int)weights_radius_b.size(); i++) { |
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| 243 | dp.radius_b = weights_radius_b[i].value; |
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| 244 | for(int k=0; k< (int)weights_radius_a.size(); k++) { |
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| 245 | dp.radius_a = weights_radius_a[k].value; |
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| 246 | sum +=weights_radius_b[i].weight |
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| 247 | * weights_radius_a[k].weight*DiamEllip(dp.radius_a,dp.radius_b)/2.0; |
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| 248 | norm += weights_radius_b[i].weight* weights_radius_a[k].weight; |
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| 249 | } |
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| 250 | } |
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| 251 | if (norm != 0){ |
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| 252 | //return the averaged value |
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| 253 | rad_out = sum/norm;} |
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| 254 | else{ |
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| 255 | //return normal value |
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| 256 | rad_out = DiamEllip(dp.radius_a,dp.radius_b)/2.0;} |
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| 257 | |
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| 258 | return rad_out; |
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| 259 | } |
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