1 | ;+ |
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2 | ; |
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3 | ; @file_comments |
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4 | ; |
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5 | ; @categories |
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6 | ; Statistics |
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7 | ; |
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8 | ; @param XD |
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9 | ; |
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10 | ; @param YD |
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11 | ; |
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12 | ; @param M |
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13 | ; |
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14 | ; @param NT |
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15 | ; |
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16 | ; @param NDIM |
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17 | ; |
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18 | ; @keyword ZERO2NAN |
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19 | ; |
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20 | ; @keyword DOUBLE |
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21 | ; If set to a non-zero value, computations are done in |
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22 | ; double precision arithmetic. |
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23 | ; |
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24 | ; @examples |
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25 | ; |
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26 | ; @history |
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27 | ; |
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28 | ; @version |
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29 | ; $Id$ |
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30 | ; |
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31 | ;- |
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32 | FUNCTION timecross_cov, xd, yd, m, nt, ndim, DOUBLE=double, ZERO2NAN=zero2nan |
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33 | ; |
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34 | compile_opt hidden |
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35 | ; |
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36 | ;Sample cross covariance function. |
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37 | case Ndim OF |
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38 | 1:res = TOTAL(Xd[0:nT - M - 1L] * Yd[M:nT - 1L] $ |
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39 | , Double = Double) |
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40 | 2:res = TOTAL(Xd[*, 0:nT - M - 1L] * Yd[*, M:nT - 1L] $ |
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41 | , Ndim, Double = Double) |
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42 | 3:res = TOTAL(Xd[*, *, 0:nT - M - 1L] * Yd[*, *, M:nT - 1L] $ |
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43 | , Ndim, Double = Double) |
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44 | 4:res = TOTAL(Xd[*, *, *, 0:nT - M - 1L] * Yd[*, *, *, M:nT - 1L] $ |
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45 | , Ndim, Double = Double) |
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46 | ENDCASE |
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47 | if keyword_set(zero2nan) then begin |
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48 | zero = where(res EQ 0) |
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49 | if zero[0] NE -1 then res[zero] = !values.f_nan |
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50 | ENDIF |
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51 | ; |
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52 | RETURN, res |
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53 | |
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54 | END |
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55 | ; |
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56 | ;+ |
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57 | ; |
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58 | ; @file_comments |
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59 | ; This function computes the "time cross correlation" Pxy(L) or |
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60 | ; the "time cross covariance" between 2 arrays (this is some |
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61 | ; kind of <proidl>C_CORRELATE</proidl> but for multidimensional arrays) as a |
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62 | ; function of the lag (L). |
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63 | ; |
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64 | ; @categories |
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65 | ; Statistics |
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66 | ; |
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67 | ; @param X {in}{required} {type=array} |
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68 | ; An array which last dimension is the time dimension of |
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69 | ; size n, float or double. |
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70 | ; |
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71 | ; @param Y {in}{required} {type=array} |
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72 | ; An array which last dimension is the time dimension of |
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73 | ; size n, float or double. |
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74 | ; |
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75 | ; @param LAG {in}{required}{type=scalar or vector} |
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76 | ; A scalar or n-elements vector, in the interval [-(n-2),(n-2)], |
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77 | ; of type integer that specifies the absolute distance(s) between |
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78 | ; indexed elements of X. |
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79 | ; |
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80 | ; @keyword COVARIANCE |
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81 | ; If set to a non-zero value, the sample cross |
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82 | ; covariance is computed. |
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83 | ; |
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84 | ; @keyword DOUBLE |
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85 | ; If set to a non-zero value, computations are done in |
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86 | ; double precision arithmetic. |
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87 | ; |
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88 | ; @examples |
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89 | ; |
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90 | ; Define two n-elements sample populations. |
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91 | ; IDL> x = [3.73, 3.67, 3.77, 3.83, 4.67, 5.87, 6.70, 6.97, 6.40, 5.57] |
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92 | ; IDL> y = [2.31, 2.76, 3.02, 3.13, 3.72, 3.88, 3.97, 4.39, 4.34, 3.95] |
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93 | ; |
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94 | ; Compute the cross correlation of X and Y for LAG = -5, 0, 1, 5, 6, 7 |
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95 | ; IDL> lag = [-5, 0, 1, 5, 6, 7] |
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96 | ; IDL> result = c_timecorrelate(x, y, lag) |
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97 | ; |
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98 | ; The result should be: |
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99 | ; [-0.428246, 0.914755, 0.674547, -0.405140, -0.403100, -0.339685] |
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100 | ; |
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101 | ; @history |
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102 | ; - 01/03/2000 Sebastien Masson (smasson\@lodyc.jussieu.fr) |
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103 | ; Based on the C_CORRELATE procedure of IDL |
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104 | ; - August 2003 Sebastien Masson |
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105 | ; update according to the update made in C_CORRELATE by |
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106 | ; W. Biagiotti and available in IDL 5.5 |
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107 | ; |
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108 | ; INTRODUCTION TO STATISTICAL TIME SERIES |
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109 | ; Wayne A. Fuller |
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110 | ; ISBN 0-471-28715-6 |
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111 | ; |
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112 | ; @version |
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113 | ; $Id$ |
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114 | ; |
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115 | ;- |
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116 | FUNCTION c_timecorrelate, x, y, lag, COVARIANCE=covariance, DOUBLE=double |
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117 | ; |
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118 | |
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119 | ;Compute the sample cross correlation or cross covariance of |
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120 | ;(Xt, Xt+l) and (Yt, Yt+l) as a function of the lag (l). |
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121 | |
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122 | ON_ERROR, 2 |
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123 | |
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124 | xsize = SIZE(X) |
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125 | ysize = SIZE(Y) |
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126 | nt = float(xsize[xsize[0]]) |
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127 | NDim = xsize[0] |
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128 | |
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129 | if total(xsize[0:xsize[0]] NE ysize[0:ysize[0]]) NE 0 then $ |
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130 | ras = report("X and Y arrays must have the same size and the same dimensions") |
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131 | |
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132 | ;Check length. |
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133 | if nt lt 2 then $ |
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134 | ras = report("Time dimension of X and Y arrays must contain 2 or more elements.") |
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135 | |
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136 | ;If the DOUBLE keyword is not set then the internal precision and |
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137 | ;result are identical to the type of input. |
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138 | if N_ELEMENTS(Double) eq 0 then $ |
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139 | Double = (Xsize[Xsize[0]+1] eq 5 or ysize[ysize[0]+1] eq 5) |
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140 | |
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141 | if n_elements(lag) EQ 0 then lag = 0 |
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142 | nLag = N_ELEMENTS(Lag) |
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143 | |
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144 | ;Deviations |
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145 | if double then one = 1.0d ELSE one = 1.0 |
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146 | Ndim = size(X, /n_dimensions) |
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147 | Xd = TOTAL(X, Ndim, Double = Double) / nT |
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148 | Xd = X - Xd[*]#replicate(one, nT) |
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149 | Yd = TOTAL(Y, Ndim, Double = Double) / nT |
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150 | Yd = Y - Yd[*]#replicate(one, nT) |
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151 | |
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152 | if nLag eq 1 then Lag = [Lag] ;Create a 1-element vector. |
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153 | |
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154 | case NDim of |
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155 | 1:if Double eq 0 then Cross = FLTARR(nLag) else Cross = DBLARR(nLag) |
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156 | 2:if Double eq 0 then Cross = FLTARR(Xsize[1], nLag) else Cross = DBLARR(Xsize[1], nLag) |
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157 | 3:if Double eq 0 then Cross = FLTARR(Xsize[1], Xsize[2], nLag) $ |
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158 | else Cross = DBLARR(Xsize[1], Xsize[2], nLag) |
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159 | 4:if Double eq 0 then Cross = FLTARR(Xsize[1], Xsize[2], Xsize[3], nLag) $ |
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160 | else Cross = DBLARR(Xsize[1], Xsize[2], Xsize[3], nLag) |
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161 | endcase |
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162 | |
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163 | if KEYWORD_SET(Covariance) eq 0 then begin ;Compute Cross Crossation. |
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164 | for k = 0, nLag-1 do begin |
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165 | if Lag[k] ge 0 then BEGIN |
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166 | case NDim of |
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167 | 1: Cross[k] = TimeCross_Cov(Xd, Yd, Lag[k], nT, Ndim, Double = Double) |
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168 | 2: Cross[*, k] = TimeCross_Cov(Xd, Yd, Lag[k], nT, Ndim, Double = Double) |
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169 | 3: Cross[*, *, k] = TimeCross_Cov(Xd, Yd, Lag[k], nT, Ndim, Double = Double) |
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170 | 4: Cross[*, *, *, k] = TimeCross_Cov(Xd, Yd, Lag[k], nT, Ndim, Double = Double) |
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171 | endcase |
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172 | ENDIF else BEGIN |
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173 | case NDim of |
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174 | 1: Cross[k] = TimeCross_Cov(Yd, Xd, ABS(Lag[k]), nT, Ndim, Double = Double) |
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175 | 2: Cross[*, k] = TimeCross_Cov(Yd, Xd, ABS(Lag[k]), nT, Ndim, Double = Double) |
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176 | 3: Cross[*, *, k] = TimeCross_Cov(Yd, Xd, ABS(Lag[k]), nT, Ndim, Double = Double) |
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177 | 4: Cross[*, *, *, k] = TimeCross_Cov(Yd, Xd, ABS(Lag[k]), nT, Ndim, Double = Double) |
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178 | endcase |
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179 | ENDELSE |
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180 | ENDFOR |
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181 | div = sqrt(TimeCross_Cov(Xd, Xd, 0L, nT, Ndim, Double = Double, /zero2nan) * $ |
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182 | TimeCross_Cov(Yd, Yd, 0L, nT, Ndim, Double = Double, /zero2nan)) |
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183 | Cross = temporary(Cross)/((temporary(div))[*]#replicate(one, nLag)) |
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184 | endif else begin ;Compute Cross Covariance. |
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185 | for k = 0, nLag-1 do begin |
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186 | if Lag[k] ge 0 then BEGIN |
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187 | case NDim of |
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188 | 1: Cross[k] = TimeCross_Cov(Xd, Yd, Lag[k], nT, Ndim, Double = Double) / nT |
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189 | 2: Cross[*, k] = TimeCross_Cov(Xd, Yd, Lag[k], nT, Ndim, Double = Double) / nT |
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190 | 3: Cross[*, *, k] = TimeCross_Cov(Xd, Yd, Lag[k], nT, Ndim, Double = Double) / nT |
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191 | 4: Cross[*, *, *, k] = TimeCross_Cov(Xd, Yd, Lag[k], nT, Ndim, Double = Double) / nT |
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192 | ENDCASE |
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193 | ENDIF else BEGIN |
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194 | case NDim of |
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195 | 1: Cross[k] = TimeCross_Cov(yd, xd, ABS(Lag[k]), nT, Ndim, Double = Double) / nT |
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196 | 2: Cross[*, k] = TimeCross_Cov(yd, xd, ABS(Lag[k]), nT, Ndim, Double = Double) / nT |
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197 | 3: Cross[*, *, k] = TimeCross_Cov(yd, xd, ABS(Lag[k]), nT, Ndim, Double = Double) / nT |
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198 | 4: Cross[*, *, *, k] = TimeCross_Cov(yd, xd, ABS(Lag[k]), nT, Ndim, Double = Double) / nT |
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199 | ENDCASE |
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200 | ENDELSE |
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201 | endfor |
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202 | endelse |
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203 | |
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204 | if Double eq 0 then RETURN, FLOAT(Cross) else RETURN, Cross |
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205 | |
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206 | END |
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