1 | subroutine clustering(xl,yl,zl,n,xclust,yclust,zclust,fclust,rms, & |
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2 | rmsclust,zrms) |
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3 | ! i i i i o o o o o |
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4 | ! o o |
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5 | !***************************************************************************** |
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6 | ! * |
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7 | ! This routine clusters the particle position into ncluster custers. * |
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8 | ! Input are the longitudes (xl) and latitudes (yl) of the individual * |
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9 | ! points, output are the cluster mean positions (xclust,yclust). * |
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10 | ! Vertical positions are not directly used for the clustering. * |
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11 | ! * |
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12 | ! For clustering, the procedure described in Dorling et al. (1992) is used.* |
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13 | ! * |
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14 | ! Dorling, S.R., Davies, T.D. and Pierce, C.E. (1992): * |
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15 | ! Cluster analysis: a technique for estimating the synoptic meteorological * |
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16 | ! controls on air and precipitation chemistry - method and applications. * |
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17 | ! Atmospheric Environment 26A, 2575-2581. * |
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18 | ! * |
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19 | ! * |
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20 | ! Author: A. Stohl * |
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21 | ! * |
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22 | ! 1 February 2002 * |
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23 | ! * |
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24 | ! Variables: * |
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25 | ! fclust fraction of particles belonging to each cluster * |
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26 | ! ncluster number of clusters to be used * |
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27 | ! rms total horizontal rms distance after clustering * |
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28 | ! rmsclust horizontal rms distance for each individual cluster * |
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29 | ! zrms total vertical rms distance after clustering * |
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30 | ! xclust,yclust, Cluster centroid positions * |
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31 | ! zclust * |
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32 | ! xl,yl,zl particle positions * |
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33 | ! * |
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34 | !***************************************************************************** |
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35 | |
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36 | use par_mod |
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37 | |
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38 | implicit none |
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39 | |
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40 | integer :: n,i,j,l,nclust(maxpart),numb(ncluster),ncl |
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41 | real :: xl(n),yl(n),zl(n),xclust(ncluster),yclust(ncluster),x,y,z |
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42 | real :: zclust(ncluster),distance2,distances,distancemin,rms,rmsold |
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43 | real :: xav(ncluster),yav(ncluster),zav(ncluster),fclust(ncluster) |
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44 | real :: rmsclust(ncluster) |
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45 | real :: zdist,zrms |
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46 | |
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47 | |
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48 | |
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49 | if (n.lt.ncluster) return |
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50 | rmsold=-5. |
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51 | |
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52 | ! Convert longitude and latitude from degrees to radians |
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53 | !******************************************************* |
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54 | |
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55 | do i=1,n |
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56 | nclust(i)=i |
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57 | xl(i)=xl(i)*pi180 |
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58 | yl(i)=yl(i)*pi180 |
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59 | end do |
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60 | |
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61 | |
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62 | ! Generate a seed for each cluster |
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63 | !********************************* |
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64 | |
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65 | do j=1,ncluster |
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66 | zclust(j)=0. |
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67 | xclust(j)=xl(j*n/ncluster) |
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68 | yclust(j)=yl(j*n/ncluster) |
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69 | end do |
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70 | |
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71 | |
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72 | ! Iterative loop to compute the cluster means |
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73 | !******************************************** |
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74 | |
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75 | do l=1,100 |
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76 | |
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77 | ! Assign each particle to a cluster: criterion minimum distance to the |
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78 | ! cluster mean position |
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79 | !********************************************************************* |
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80 | |
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81 | |
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82 | do i=1,n |
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83 | distancemin=10.**10. |
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84 | do j=1,ncluster |
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85 | distances=distance2(yl(i),xl(i),yclust(j),xclust(j)) |
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86 | if (distances.lt.distancemin) then |
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87 | distancemin=distances |
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88 | ncl=j |
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89 | endif |
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90 | end do |
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91 | nclust(i)=ncl |
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92 | end do |
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93 | |
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94 | |
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95 | ! Recalculate the cluster centroid position: convert to 3D Cartesian coordinates, |
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96 | ! calculate mean position, and re-project this point onto the Earth's surface |
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97 | !***************************************************************************** |
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98 | |
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99 | do j=1,ncluster |
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100 | xav(j)=0. |
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101 | yav(j)=0. |
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102 | zav(j)=0. |
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103 | rmsclust(j)=0. |
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104 | numb(j)=0 |
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105 | end do |
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106 | rms=0. |
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107 | |
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108 | do i=1,n |
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109 | numb(nclust(i))=numb(nclust(i))+1 |
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110 | distances=distance2(yl(i),xl(i), & |
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111 | yclust(nclust(i)),xclust(nclust(i))) |
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112 | |
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113 | ! rms is the total rms of all particles |
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114 | ! rmsclust is the rms for a particular cluster |
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115 | !********************************************* |
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116 | |
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117 | rms=rms+distances*distances |
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118 | rmsclust(nclust(i))=rmsclust(nclust(i))+distances*distances |
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119 | |
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120 | ! Calculate Cartesian 3D coordinates from longitude and latitude |
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121 | !*************************************************************** |
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122 | |
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123 | x = cos(yl(i))*sin(xl(i)) |
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124 | y = -1.*cos(yl(i))*cos(xl(i)) |
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125 | z = sin(yl(i)) |
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126 | xav(nclust(i))=xav(nclust(i))+x |
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127 | yav(nclust(i))=yav(nclust(i))+y |
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128 | zav(nclust(i))=zav(nclust(i))+z |
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129 | end do |
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130 | |
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131 | rms=sqrt(rms/real(n)) |
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132 | |
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133 | |
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134 | ! Find the mean location in Cartesian coordinates |
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135 | !************************************************ |
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136 | |
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137 | do j=1,ncluster |
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138 | if (numb(j).gt.0) then |
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139 | rmsclust(j)=sqrt(rmsclust(j)/real(numb(j))) |
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140 | xav(j)=xav(j)/real(numb(j)) |
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141 | yav(j)=yav(j)/real(numb(j)) |
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142 | zav(j)=zav(j)/real(numb(j)) |
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143 | |
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144 | ! Project the point back onto Earth's surface |
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145 | !******************************************** |
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146 | |
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147 | xclust(j)=atan2(xav(j),-1.*yav(j)) |
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148 | yclust(j)=atan2(zav(j),sqrt(xav(j)*xav(j)+yav(j)*yav(j))) |
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149 | endif |
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150 | end do |
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151 | |
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152 | |
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153 | ! Leave the loop if the RMS distance decreases only slightly between 2 iterations |
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154 | !***************************************************************************** |
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155 | |
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156 | if ((l.gt.1).and.(abs(rms-rmsold)/rmsold.lt.0.005)) goto 99 |
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157 | rmsold=rms |
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158 | |
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159 | end do |
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160 | |
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161 | 99 continue |
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162 | |
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163 | ! Convert longitude and latitude from radians to degrees |
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164 | !******************************************************* |
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165 | |
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166 | do i=1,n |
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167 | xl(i)=xl(i)/pi180 |
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168 | yl(i)=yl(i)/pi180 |
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169 | zclust(nclust(i))=zclust(nclust(i))+zl(i) |
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170 | end do |
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171 | |
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172 | do j=1,ncluster |
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173 | xclust(j)=xclust(j)/pi180 |
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174 | yclust(j)=yclust(j)/pi180 |
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175 | if (numb(j).gt.0) zclust(j)=zclust(j)/real(numb(j)) |
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176 | fclust(j)=100.*real(numb(j))/real(n) |
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177 | end do |
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178 | |
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179 | ! Determine total vertical RMS deviation |
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180 | !*************************************** |
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181 | |
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182 | zrms=0. |
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183 | do i=1,n |
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184 | zdist=zl(i)-zclust(nclust(i)) |
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185 | zrms=zrms+zdist*zdist |
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186 | end do |
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187 | if (zrms.gt.0.) zrms=sqrt(zrms/real(n)) |
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188 | |
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189 | end subroutine clustering |
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