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 2009, University of Tennessee |
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13 | */ |
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14 | #include "smearer.hh" |
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15 | #include <stdio.h> |
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16 | #include <math.h> |
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17 | using namespace std; |
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18 | |
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19 | |
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20 | /** |
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21 | * Constructor for BaseSmearer |
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22 | * |
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23 | * @param qmin: minimum Q value |
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24 | * @param qmax: maximum Q value |
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25 | * @param nbins: number of Q bins |
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26 | */ |
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27 | BaseSmearer :: BaseSmearer(double qmin, double qmax, int nbins) { |
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28 | // Number of bins |
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29 | this->nbins = nbins; |
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30 | this->qmin = qmin; |
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31 | this->qmax = qmax; |
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32 | // Flag to keep track of whether we have a smearing matrix or |
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33 | // whether we need to compute one |
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34 | has_matrix = false; |
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35 | even_binning = true; |
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36 | }; |
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37 | |
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38 | /** |
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39 | * Constructor for BaseSmearer |
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40 | * |
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41 | * Used for uneven binning |
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42 | * @param q: array of Q values |
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43 | * @param nbins: number of Q bins |
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44 | */ |
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45 | BaseSmearer :: BaseSmearer(double* q, int nbins) { |
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46 | // Number of bins |
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47 | this->nbins = nbins; |
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48 | this->q_values = q; |
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49 | // Flag to keep track of whether we have a smearing matrix or |
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50 | // whether we need to compute one |
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51 | has_matrix = false; |
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52 | even_binning = false; |
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53 | }; |
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54 | |
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55 | /** |
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56 | * Constructor for SlitSmearer |
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57 | * |
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58 | * @param width: slit width in Q units |
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59 | * @param height: slit height in Q units |
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60 | * @param qmin: minimum Q value |
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61 | * @param qmax: maximum Q value |
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62 | * @param nbins: number of Q bins |
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63 | */ |
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64 | SlitSmearer :: SlitSmearer(double width, double height, double qmin, double qmax, int nbins) : |
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65 | BaseSmearer(qmin, qmax, nbins){ |
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66 | this->height = height; |
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67 | this->width = width; |
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68 | }; |
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69 | |
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70 | /** |
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71 | * Constructor for SlitSmearer |
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72 | * |
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73 | * @param width: slit width in Q units |
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74 | * @param height: slit height in Q units |
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75 | * @param q: array of Q values |
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76 | * @param nbins: number of Q bins |
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77 | */ |
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78 | SlitSmearer :: SlitSmearer(double width, double height, double* q, int nbins) : |
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79 | BaseSmearer(q, nbins){ |
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80 | this->height = height; |
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81 | this->width = width; |
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82 | }; |
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83 | |
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84 | /** |
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85 | * Constructor for QSmearer |
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86 | * |
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87 | * @param width: array slit widths for each Q point, in Q units |
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88 | * @param qmin: minimum Q value |
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89 | * @param qmax: maximum Q value |
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90 | * @param nbins: number of Q bins |
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91 | */ |
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92 | QSmearer :: QSmearer(double* width, double qmin, double qmax, int nbins) : |
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93 | BaseSmearer(qmin, qmax, nbins){ |
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94 | this->width = width; |
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95 | }; |
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96 | |
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97 | /** |
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98 | * Constructor for QSmearer |
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99 | * |
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100 | * @param width: array slit widths for each Q point, in Q units |
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101 | * @param q: array of Q values |
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102 | * @param nbins: number of Q bins |
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103 | */ |
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104 | QSmearer :: QSmearer(double* width, double* q, int nbins) : |
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105 | BaseSmearer(q, nbins){ |
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106 | this->width = width; |
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107 | }; |
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108 | |
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109 | /** |
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110 | * Compute the slit smearing matrix |
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111 | * |
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112 | * For even binning (q_min to q_max with nbins): |
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113 | * |
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114 | * step = (q_max-q_min)/(nbins-1) |
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115 | * first bin goes from q_min to q_min+step |
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116 | * last bin goes from q_max to q_max+step |
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117 | * |
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118 | * For binning according to q array: |
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119 | * |
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120 | * Each q point represents a bin going from half the distance between it |
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121 | * and the previous point to half the distance between it and the next point. |
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122 | * |
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123 | * Example: bin i goes from (q_values[i-1]+q_values[i])/2 to (q_values[i]+q_values[i+1])/2 |
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124 | * |
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125 | * The exceptions are the first and last bins, which are centered at the first and |
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126 | * last q-values, respectively. The width of the first and last bins is the distance between |
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127 | * their respective neighboring q-value. |
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128 | */ |
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129 | void SlitSmearer :: compute_matrix(){ |
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130 | |
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131 | weights = new vector<double>(nbins*nbins,0); |
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132 | |
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133 | // Check the length of the data |
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134 | if (nbins<2) return; |
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135 | |
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136 | // Loop over all q-values |
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137 | for(int i=0; i<nbins; i++) { |
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138 | double q, q_min, q_max; |
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139 | get_bin_range(i, &q, &q_min, &q_max); |
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140 | |
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141 | // For each q-value, compute the weight of each other q-bin |
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142 | // in the I(q) array |
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143 | int npts_h = height>0 ? npts : 1; |
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144 | int npts_w = width>0 ? npts : 1; |
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145 | |
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146 | // If both height and width are great than zero, |
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147 | // modify the number of points in each direction so |
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148 | // that the total number of points is still what |
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149 | // the user would expect (downgrade resolution) |
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150 | if(npts_h>1 && npts_w>1){ |
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151 | npts_h = (int)ceil(sqrt((double)npts)); |
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152 | npts_w = npts_h; |
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153 | } |
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154 | |
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155 | double shift_h, shift_w; |
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156 | for(int k=0; k<npts_h; k++){ |
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157 | if(npts_h==1){ |
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158 | shift_h = 0; |
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159 | } else { |
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160 | shift_h = height/((double)npts_h-1.0) * (double)k; |
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161 | } |
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162 | for(int j=0; j<npts_w; j++){ |
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163 | if(npts_w==1){ |
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164 | shift_w = 0; |
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165 | } else { |
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166 | shift_w = width/((double)npts_w-1.0) * (double)j; |
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167 | } |
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168 | double q_shifted = sqrt( ((q-shift_w)*(q-shift_w) + shift_h*shift_h) ); |
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169 | |
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170 | // Find in which bin this shifted value belongs |
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171 | int q_i=nbins; |
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172 | if (even_binning) { |
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173 | // This is kept for backward compatibility since the binning |
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174 | // was originally defined differently for even bins. |
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175 | q_i = (int)(floor( (q_shifted-qmin) /((qmax-qmin)/((double)nbins -1.0)) )); |
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176 | } else { |
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177 | for(int t=0; t<nbins; t++) { |
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178 | double q_t, q_high, q_low; |
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179 | get_bin_range(t, &q_t, &q_low, &q_high); |
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180 | if(q_shifted>=q_low && q_shifted<q_high) { |
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181 | q_i = t; |
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182 | break; |
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183 | } |
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184 | } |
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185 | } |
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186 | |
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187 | // Skip the entries outside our I(q) range |
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188 | //TODO: be careful with edge effect |
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189 | if(q_i<nbins) |
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190 | (*weights)[i*nbins+q_i]++; |
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191 | } |
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192 | } |
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193 | } |
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194 | }; |
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195 | |
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196 | /** |
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197 | * Compute the point smearing matrix |
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198 | */ |
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199 | void QSmearer :: compute_matrix(){ |
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200 | weights = new vector<double>(nbins*nbins,0); |
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201 | |
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202 | // Loop over all q-values |
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203 | double step = (qmax-qmin)/((double)nbins-1.0); |
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204 | double q, q_min, q_max; |
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205 | double q_j, q_jmax, q_jmin; |
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206 | for(int i=0; i<nbins; i++) { |
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207 | get_bin_range(i, &q, &q_min, &q_max); |
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208 | |
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209 | for(int j=0; j<nbins; j++) { |
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210 | get_bin_range(j, &q_j, &q_jmin, &q_jmax); |
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211 | |
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212 | // Compute the fraction of the Gaussian contributing |
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213 | // to the q_j bin between q_jmin and q_jmax |
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214 | double value = erf( (q_jmax-q)/(sqrt(2.0)*width[i]) ); |
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215 | value -= erf( (q_jmin-q)/(sqrt(2.0)*width[i]) ); |
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216 | (*weights)[i*nbins+j] += value; |
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217 | } |
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218 | } |
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219 | } |
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220 | |
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221 | /** |
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222 | * Computes the Q range of a given bin of the Q distribution. |
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223 | * The range is computed according the the data distribution that |
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224 | * was given to the object at initialization. |
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225 | * |
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226 | * @param i: number of the bin in the distribution |
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227 | * @param q: q-value of bin i |
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228 | * @param q_min: lower bound of the bin |
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229 | * @param q_max: higher bound of the bin |
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230 | * |
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231 | */ |
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232 | int BaseSmearer :: get_bin_range(int i, double* q, double* q_min, double* q_max) { |
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233 | if (even_binning) { |
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234 | double step = (qmax-qmin)/((double)nbins-1.0); |
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235 | *q = qmin + (double)i*step; |
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236 | *q_min = *q - 0.5*step; |
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237 | *q_max = *q + 0.5*step; |
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238 | return 1; |
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239 | } else if (i>=0 && i<nbins) { |
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240 | *q = q_values[i]; |
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241 | if (i==0) { |
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242 | double step = (q_values[1]-q_values[0])/2.0; |
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243 | *q_min = *q - step; |
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244 | *q_max = *q + step; |
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245 | } else if (i==nbins-1) { |
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246 | double step = (q_values[i]-q_values[i-1])/2.0; |
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247 | *q_min = *q - step; |
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248 | *q_max = *q + step; |
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249 | } else { |
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250 | *q_min = *q - (q_values[i]-q_values[i-1])/2.0; |
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251 | *q_max = *q + (q_values[i+1]-q_values[i])/2.0; |
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252 | } |
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253 | return 1; |
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254 | } |
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255 | return -1; |
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256 | } |
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257 | |
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258 | /** |
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259 | * Perform smearing by applying the smearing matrix to the input Q array |
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260 | */ |
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261 | void BaseSmearer :: smear(double *iq_in, double *iq_out, int first_bin, int last_bin){ |
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262 | |
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263 | // If we haven't computed the smearing matrix, do it now |
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264 | if(!has_matrix) { |
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265 | compute_matrix(); |
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266 | has_matrix = true; |
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267 | } |
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268 | |
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269 | // Loop over q-values and multiply apply matrix |
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270 | for(int q_i=first_bin; q_i<=last_bin; q_i++){ |
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271 | double sum = 0.0; |
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272 | double counts = 0.0; |
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273 | |
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274 | for(int i=first_bin; i<=last_bin; i++){ |
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275 | // Skip if weight is less than 1e-04(this value is much smaller than |
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276 | // the weight at the 3*sigma distance |
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277 | // Will speed up a little bit... |
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278 | if ((*weights)[q_i*nbins+i] < 1.0e-004){ |
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279 | continue; |
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280 | } |
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281 | sum += iq_in[i] * (*weights)[q_i*nbins+i]; |
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282 | counts += (*weights)[q_i*nbins+i]; |
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283 | } |
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284 | |
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285 | // Normalize counts |
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286 | iq_out[q_i] = (counts>0.0) ? sum/counts : 0; |
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287 | //printf("\n iii=%g,%g ",iq_out[q_i], q_i); |
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288 | } |
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289 | } |
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