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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31 | #include "libStructureFactor.h" |
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32 | #include "triaxial_ellipsoid.h" |
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33 | } |
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34 | |
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35 | TriaxialEllipsoidModel :: TriaxialEllipsoidModel() { |
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36 | scale = Parameter(1.0); |
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37 | semi_axisA = Parameter(35.0, true); |
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38 | semi_axisA.set_min(0.0); |
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39 | semi_axisB = Parameter(100.0, true); |
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40 | semi_axisB.set_min(0.0); |
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41 | semi_axisC = Parameter(400.0, true); |
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42 | semi_axisC.set_min(0.0); |
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43 | sldEll = Parameter(1.0e-6); |
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44 | sldSolv = Parameter(6.3e-6); |
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45 | background = Parameter(0.0); |
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46 | axis_theta = Parameter(1.0, true); |
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47 | axis_phi = Parameter(1.0, true); |
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48 | axis_psi = Parameter(0.0, true); |
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49 | } |
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50 | |
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51 | /** |
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52 | * Function to evaluate 1D scattering function |
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53 | * The NIST IGOR library is used for the actual calculation. |
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54 | * @param q: q-value |
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55 | * @return: function value |
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56 | */ |
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57 | double TriaxialEllipsoidModel :: operator()(double q) { |
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58 | double dp[7]; |
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59 | |
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60 | // Fill parameter array for IGOR library |
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61 | // Add the background after averaging |
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62 | dp[0] = scale(); |
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63 | dp[1] = semi_axisA(); |
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64 | dp[2] = semi_axisB(); |
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65 | dp[3] = semi_axisC(); |
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66 | dp[4] = sldEll(); |
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67 | dp[5] = sldSolv(); |
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68 | dp[6] = 0.0; |
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69 | |
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70 | // Get the dispersion points for the semi axis A |
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71 | vector<WeightPoint> weights_semi_axisA; |
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72 | semi_axisA.get_weights(weights_semi_axisA); |
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73 | |
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74 | // Get the dispersion points for the semi axis B |
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75 | vector<WeightPoint> weights_semi_axisB; |
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76 | semi_axisB.get_weights(weights_semi_axisB); |
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77 | |
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78 | // Get the dispersion points for the semi axis C |
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79 | vector<WeightPoint> weights_semi_axisC; |
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80 | semi_axisC.get_weights(weights_semi_axisC); |
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81 | |
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82 | // Perform the computation, with all weight points |
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83 | double sum = 0.0; |
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84 | double norm = 0.0; |
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85 | double vol = 0.0; |
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86 | |
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87 | // Loop over semi axis A weight points |
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88 | for(int i=0; i< (int)weights_semi_axisA.size(); i++) { |
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89 | dp[1] = weights_semi_axisA[i].value; |
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90 | |
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91 | // Loop over semi axis B weight points |
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92 | for(int j=0; j< (int)weights_semi_axisB.size(); j++) { |
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93 | dp[2] = weights_semi_axisB[j].value; |
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94 | |
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95 | // Loop over semi axis C weight points |
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96 | for(int k=0; k< (int)weights_semi_axisC.size(); k++) { |
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97 | dp[3] = weights_semi_axisC[k].value; |
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98 | //Un-normalize by volume |
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99 | sum += weights_semi_axisA[i].weight |
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100 | * weights_semi_axisB[j].weight * weights_semi_axisC[k].weight* TriaxialEllipsoid(dp, q) |
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101 | * weights_semi_axisA[i].value*weights_semi_axisB[j].value*weights_semi_axisC[k].value; |
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102 | //Find average volume |
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103 | vol += weights_semi_axisA[i].weight |
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104 | * weights_semi_axisB[j].weight * weights_semi_axisC[k].weight |
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105 | * weights_semi_axisA[i].value*weights_semi_axisB[j].value*weights_semi_axisC[k].value; |
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106 | |
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107 | norm += weights_semi_axisA[i].weight |
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108 | * weights_semi_axisB[j].weight * weights_semi_axisC[k].weight; |
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109 | } |
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110 | } |
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111 | } |
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112 | if (vol != 0.0 && norm != 0.0) { |
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113 | //Re-normalize by avg volume |
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114 | sum = sum/(vol/norm);} |
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115 | |
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116 | return sum/norm + background(); |
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117 | } |
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118 | |
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119 | /** |
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120 | * Function to evaluate 2D scattering function |
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121 | * @param q_x: value of Q along x |
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122 | * @param q_y: value of Q along y |
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123 | * @return: function value |
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124 | */ |
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125 | double TriaxialEllipsoidModel :: operator()(double qx, double qy) { |
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126 | TriaxialEllipsoidParameters dp; |
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127 | // Fill parameter array |
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128 | dp.scale = scale(); |
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129 | dp.semi_axisA = semi_axisA(); |
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130 | dp.semi_axisB = semi_axisB(); |
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131 | dp.semi_axisC = semi_axisC(); |
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132 | dp.sldEll = sldEll(); |
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133 | dp.sldSolv = sldSolv(); |
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134 | dp.background = 0.0; |
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135 | dp.axis_theta = axis_theta(); |
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136 | dp.axis_phi = axis_phi(); |
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137 | dp.axis_psi = axis_psi(); |
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138 | |
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139 | // Get the dispersion points for the semi_axis A |
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140 | vector<WeightPoint> weights_semi_axisA; |
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141 | semi_axisA.get_weights(weights_semi_axisA); |
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142 | |
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143 | // Get the dispersion points for the semi_axis B |
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144 | vector<WeightPoint> weights_semi_axisB; |
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145 | semi_axisB.get_weights(weights_semi_axisB); |
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146 | |
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147 | // Get the dispersion points for the semi_axis C |
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148 | vector<WeightPoint> weights_semi_axisC; |
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149 | semi_axisC.get_weights(weights_semi_axisC); |
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150 | |
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151 | // Get angular averaging for theta |
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152 | vector<WeightPoint> weights_theta; |
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153 | axis_theta.get_weights(weights_theta); |
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154 | |
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155 | // Get angular averaging for phi |
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156 | vector<WeightPoint> weights_phi; |
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157 | axis_phi.get_weights(weights_phi); |
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158 | |
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159 | // Get angular averaging for psi |
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160 | vector<WeightPoint> weights_psi; |
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161 | axis_psi.get_weights(weights_psi); |
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162 | |
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163 | // Perform the computation, with all weight points |
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164 | double sum = 0.0; |
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165 | double norm = 0.0; |
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166 | double norm_vol = 0.0; |
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167 | double vol = 0.0; |
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168 | |
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169 | // Loop over semi axis A weight points |
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170 | for(int i=0; i< (int)weights_semi_axisA.size(); i++) { |
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171 | dp.semi_axisA = weights_semi_axisA[i].value; |
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172 | |
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173 | // Loop over semi axis B weight points |
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174 | for(int j=0; j< (int)weights_semi_axisB.size(); j++) { |
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175 | dp.semi_axisB = weights_semi_axisB[j].value; |
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176 | |
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177 | // Loop over semi axis C weight points |
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178 | for(int k=0; k < (int)weights_semi_axisC.size(); k++) { |
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179 | dp.semi_axisC = weights_semi_axisC[k].value; |
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180 | |
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181 | // Average over theta distribution |
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182 | for(int l=0; l< (int)weights_theta.size(); l++) { |
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183 | dp.axis_theta = weights_theta[l].value; |
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184 | |
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185 | // Average over phi distribution |
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186 | for(int m=0; m <(int)weights_phi.size(); m++) { |
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187 | dp.axis_phi = weights_phi[m].value; |
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188 | // Average over psi distribution |
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189 | for(int n=0; n <(int)weights_psi.size(); n++) { |
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190 | dp.axis_psi = weights_psi[n].value; |
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191 | //Un-normalize by volume |
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192 | double _ptvalue = weights_semi_axisA[i].weight |
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193 | * weights_semi_axisB[j].weight |
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194 | * weights_semi_axisC[k].weight |
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195 | * weights_theta[l].weight |
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196 | * weights_phi[m].weight |
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197 | * weights_psi[n].weight |
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198 | * triaxial_ellipsoid_analytical_2DXY(&dp, qx, qy) |
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199 | * weights_semi_axisA[i].value*weights_semi_axisB[j].value*weights_semi_axisC[k].value; |
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200 | if (weights_theta.size()>1) { |
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201 | _ptvalue *= sin(weights_theta[k].value); |
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202 | } |
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203 | sum += _ptvalue; |
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204 | //Find average volume |
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205 | vol += weights_semi_axisA[i].weight |
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206 | * weights_semi_axisB[j].weight |
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207 | * weights_semi_axisC[k].weight |
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208 | * weights_semi_axisA[i].value*weights_semi_axisB[j].value*weights_semi_axisC[k].value; |
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209 | //Find norm for volume |
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210 | norm_vol += weights_semi_axisA[i].weight |
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211 | * weights_semi_axisB[j].weight |
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212 | * weights_semi_axisC[k].weight; |
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213 | |
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214 | norm += weights_semi_axisA[i].weight |
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215 | * weights_semi_axisB[j].weight |
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216 | * weights_semi_axisC[k].weight |
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217 | * weights_theta[l].weight |
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218 | * weights_phi[m].weight |
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219 | * weights_psi[n].weight; |
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220 | } |
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221 | } |
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222 | |
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223 | } |
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224 | } |
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225 | } |
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226 | } |
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227 | // Averaging in theta needs an extra normalization |
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228 | // factor to account for the sin(theta) term in the |
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229 | // integration (see documentation). |
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230 | if (weights_theta.size()>1) norm = norm / asin(1.0); |
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231 | |
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232 | if (vol != 0.0 && norm_vol != 0.0) { |
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233 | //Re-normalize by avg volume |
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234 | sum = sum/(vol/norm_vol);} |
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235 | |
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236 | return sum/norm + background(); |
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237 | } |
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238 | |
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239 | /** |
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240 | * Function to evaluate 2D scattering function |
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241 | * @param pars: parameters of the triaxial ellipsoid |
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242 | * @param q: q-value |
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243 | * @param phi: angle phi |
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244 | * @return: function value |
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245 | */ |
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246 | double TriaxialEllipsoidModel :: evaluate_rphi(double q, double phi) { |
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247 | double qx = q*cos(phi); |
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248 | double qy = q*sin(phi); |
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249 | return (*this).operator()(qx, qy); |
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250 | } |
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251 | /** |
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252 | * Function to calculate effective radius |
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253 | * @return: effective radius value |
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254 | */ |
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255 | double TriaxialEllipsoidModel :: calculate_ER() { |
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256 | TriaxialEllipsoidParameters dp; |
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257 | |
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258 | dp.semi_axisA = semi_axisA(); |
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259 | dp.semi_axisB = semi_axisB(); |
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260 | //polar axis C |
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261 | dp.semi_axisC = semi_axisC(); |
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262 | |
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263 | double rad_out = 0.0; |
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264 | //Surface average radius at the equat. cross section. |
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265 | double suf_rad = sqrt(dp.semi_axisA * dp.semi_axisB); |
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266 | |
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267 | // Perform the computation, with all weight points |
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268 | double sum = 0.0; |
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269 | double norm = 0.0; |
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270 | |
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271 | // Get the dispersion points for the semi_axis A |
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272 | vector<WeightPoint> weights_semi_axisA; |
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273 | semi_axisA.get_weights(weights_semi_axisA); |
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274 | |
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275 | // Get the dispersion points for the semi_axis B |
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276 | vector<WeightPoint> weights_semi_axisB; |
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277 | semi_axisB.get_weights(weights_semi_axisB); |
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278 | |
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279 | // Get the dispersion points for the semi_axis C |
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280 | vector<WeightPoint> weights_semi_axisC; |
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281 | semi_axisC.get_weights(weights_semi_axisC); |
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282 | |
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283 | // Loop over semi axis A weight points |
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284 | for(int i=0; i< (int)weights_semi_axisA.size(); i++) { |
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285 | dp.semi_axisA = weights_semi_axisA[i].value; |
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286 | |
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287 | // Loop over semi axis B weight points |
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288 | for(int j=0; j< (int)weights_semi_axisB.size(); j++) { |
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289 | dp.semi_axisB = weights_semi_axisB[j].value; |
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290 | |
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291 | // Loop over semi axis C weight points |
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292 | for(int k=0; k < (int)weights_semi_axisC.size(); k++) { |
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293 | dp.semi_axisC = weights_semi_axisC[k].value; |
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294 | |
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295 | //Calculate surface averaged radius |
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296 | suf_rad = sqrt(dp.semi_axisA * dp.semi_axisB); |
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297 | |
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298 | //Sum |
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299 | sum += weights_semi_axisA[i].weight |
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300 | * weights_semi_axisB[j].weight |
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301 | * weights_semi_axisC[k].weight * DiamEllip(dp.semi_axisC, suf_rad)/2.0; |
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302 | //Norm |
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303 | norm += weights_semi_axisA[i].weight* weights_semi_axisB[j].weight |
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304 | * weights_semi_axisC[k].weight; |
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305 | } |
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306 | } |
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307 | } |
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308 | if (norm != 0){ |
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309 | //return the averaged value |
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310 | rad_out = sum/norm;} |
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311 | else{ |
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312 | //return normal value |
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313 | rad_out = DiamEllip(dp.semi_axisC, suf_rad)/2.0;} |
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314 | |
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315 | return rad_out; |
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316 | } |
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