[157] | 1 | ;+ |
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[232] | 2 | ; |
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[157] | 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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[163] | 8 | ; @param X {in}{required}{type=array} |
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[242] | 9 | ; An array which last dimension is the time dimension so |
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[157] | 10 | ; size n. |
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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 | ; @keyword ZERO2NAN |
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| 17 | ; |
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| 18 | ; @keyword DOUBLE |
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| 19 | ; If set to a non-zero value, computations are done in |
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| 20 | ; double precision arithmetic. |
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| 21 | ; |
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| 22 | ; @examples |
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| 23 | ; |
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| 24 | ; @history |
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| 25 | ; |
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| 26 | ; @version |
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| 27 | ; $Id$ |
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| 28 | ; |
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| 29 | ;- |
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[327] | 30 | FUNCTION timeauto_cov, x, m, nt, DOUBLE=double, ZERO2NAN=zero2nan |
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[114] | 31 | ; |
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| 32 | compile_opt idl2, strictarrsubs |
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| 33 | ; |
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[242] | 34 | ;Sample autocovariance function |
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[2] | 35 | TimeDim = size(X, /n_dimensions) |
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| 36 | Xmean = TOTAL(X, TimeDim, Double = Double) / nT |
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| 37 | if double then one = 1.0d ELSE one = 1.0 |
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| 38 | Xmean = Xmean[*]#replicate(one, nT - M) |
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| 39 | ; |
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| 40 | ; |
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| 41 | case TimeDim of |
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| 42 | 1:res = TOTAL((X[0:nT - M - 1L] - Xmean) * (X[M:nT - 1L] - Xmean), $ |
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| 43 | TimeDim, Double = Double) |
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| 44 | 2:res = TOTAL((X[*, 0:nT - M - 1L] - Xmean) * $ |
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| 45 | (X[*, M:nT - 1L] - Xmean) $ |
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| 46 | , TimeDim, Double = Double) |
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| 47 | 3:res = TOTAL((X[*, *, 0:nT - M - 1L] - Xmean) * $ |
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| 48 | (X[*, *, M:nT - 1L] - Xmean) $ |
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| 49 | , TimeDim, Double = Double) |
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| 50 | 4:res = TOTAL((X[*, *, *, 0:nT - M - 1L] - Xmean) * $ |
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| 51 | (X[*, *, *, M:nT - 1L] - Xmean) $ |
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| 52 | , TimeDim, Double = Double) |
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| 53 | ENDCASE |
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| 54 | if keyword_set(zero2nan) then begin |
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| 55 | zero = where(res EQ 0) |
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| 56 | if zero[0] NE -1 then res[zero] = !values.f_nan |
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| 57 | endif |
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| 58 | RETURN, res |
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| 59 | |
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| 60 | END |
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[150] | 61 | ;+ |
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[242] | 62 | ; |
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[150] | 63 | ; @file_comments |
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[327] | 64 | ; Same function as <proidl>A_CORRELATE</proidl> but accept array (until 4 |
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[150] | 65 | ; dimension) for input and do the autocorrelation or the |
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| 66 | ; autocovariance along the time dimension which must be the last |
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| 67 | ; one of the input array. |
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| 68 | ; |
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| 69 | ; This function computes the autocorrelation Px(L) or autocovariance |
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| 70 | ; Rx(L) of a sample population X as a function of the lag (L). |
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| 71 | ; |
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| 72 | ; @categories |
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[157] | 73 | ; Statistics |
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[150] | 74 | ; |
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[163] | 75 | ; @param X {in}{required}{type=array} |
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[242] | 76 | ; An array which last dimension is the time dimension so |
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[150] | 77 | ; size n. |
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| 78 | ; |
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[163] | 79 | ; @param LAG {in}{required}{type=scalar or vector} |
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[242] | 80 | ; A scalar or n-elements vector, in the interval [-(n-2),(n-2)], |
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| 81 | ; of type integer that specifies the absolute distance(s) between |
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[150] | 82 | ; indexed elements of X. |
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| 83 | ; |
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| 84 | ; @keyword COVARIANCE |
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| 85 | ; If set to a non-zero value, the sample autocovariance |
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| 86 | ; is computed. |
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| 87 | ; |
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| 88 | ; @keyword DOUBLE |
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| 89 | ; If set to a non-zero value, computations are done in |
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| 90 | ; double precision arithmetic. |
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| 91 | ; |
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| 92 | ; @examples |
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[242] | 93 | ; Define an n-elements sample population. |
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| 94 | ; 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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[150] | 95 | ; |
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[242] | 96 | ; Compute the autocorrelation of X for LAG = -3, 0, 1, 3, 4, 8 |
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| 97 | ; IDL> lag = [-3, 0, 1, 3, 4, 8] |
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| 98 | ; IDL> result = a_correlate(x, lag) |
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[150] | 99 | ; |
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[242] | 100 | ; The result should be: |
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| 101 | ; [0.0146185, 1.00000, 0.810879, 0.0146185, -0.325279, -0.151684] |
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[150] | 102 | ; |
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| 103 | ; @history |
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[157] | 104 | ; 24/2/2000 Sebastien Masson (smasson\@lodyc.jussieu.fr) |
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[150] | 105 | ; |
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| 106 | ; Based on the A_CORRELATE procedure of IDL |
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| 107 | ; INTRODUCTION TO STATISTICAL TIME SERIES |
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| 108 | ; Wayne A. Fuller |
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| 109 | ; ISBN 0-471-28715-6 |
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| 110 | ; |
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| 111 | ; @version |
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| 112 | ; $Id$ |
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| 113 | ; |
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| 114 | ;- |
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[327] | 115 | FUNCTION a_timecorrelate, x, lag, COVARIANCE=covariance, DOUBLE=double |
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[114] | 116 | ; |
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| 117 | compile_opt idl2, strictarrsubs |
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| 118 | ; |
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[2] | 119 | |
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[327] | 120 | ; Compute the sample-autocorrelation or autocovariance of (Xt, Xt+l) |
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| 121 | ; as a function of the lag (l). |
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[2] | 122 | |
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| 123 | ON_ERROR, 2 |
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| 124 | |
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| 125 | XDim = SIZE(X, /dimensions) |
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| 126 | XNDim = SIZE(X, /n_dimensions) |
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| 127 | nT = XDim[XNDim-1] |
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| 128 | ;Check length. |
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| 129 | if nT lt 2 then $ |
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[242] | 130 | ras= report("Time axis of X array must contain 2 or more elements.") |
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| 131 | |
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[2] | 132 | ;If the DOUBLE keyword is not set then the internal precision and |
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| 133 | ;result are identical to the type of input. |
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| 134 | if N_ELEMENTS(Double) eq 0 then $ |
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| 135 | Double = (SIZE(X, /type) eq 5) |
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| 136 | |
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[242] | 137 | |
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[2] | 138 | if n_elements(lag) EQ 0 then lag = 0 |
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| 139 | nLag = N_ELEMENTS(Lag) |
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| 140 | |
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| 141 | if nLag eq 1 then Lag = [Lag] ;Create a 1-element vector. |
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| 142 | |
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| 143 | case XNDim of |
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| 144 | 1:if Double eq 0 then Auto = FLTARR(nLag) else Auto = DBLARR(nLag) |
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| 145 | 2:if Double eq 0 then Auto = FLTARR(XDim[0], nLag) else Auto = DBLARR(XDim[0], nLag) |
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| 146 | 3:if Double eq 0 then Auto = FLTARR(XDim[0], XDim[1], nLag) $ |
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| 147 | else Auto = DBLARR(XDim[0], XDim[1], nLag) |
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| 148 | 4:if Double eq 0 then Auto = FLTARR(XDim[0], XDim[1], XDim[2], nLag) $ |
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| 149 | else Auto = DBLARR(XDim[0], XDim[1], XDim[2], nLag) |
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| 150 | endcase |
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| 151 | |
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| 152 | if KEYWORD_SET(Covariance) eq 0 then begin ;Compute Autocorrelation. |
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| 153 | for k = 0, nLag-1 do $ |
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| 154 | case XNDim of |
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| 155 | 1:Auto[k] = TimeAuto_Cov(X, ABS(Lag[k]), nT, Double = Double) / $ |
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| 156 | TimeAuto_Cov(X, 0L, nT, Double = Double, /zero2nan) |
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| 157 | 2:Auto[*, k] = TimeAuto_Cov(X, ABS(Lag[k]), nT, Double = Double) / $ |
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| 158 | TimeAuto_Cov(X, 0L, nT, Double = Double, /zero2nan) |
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| 159 | 3:Auto[*, *, k] = TimeAuto_Cov(X, ABS(Lag[k]), nT, Double = Double) / $ |
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| 160 | TimeAuto_Cov(X, 0L, nT, Double = Double, /zero2nan) |
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| 161 | 4:Auto[*, *, *, k] = TimeAuto_Cov(X, ABS(Lag[k]), nT, Double = Double) / $ |
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| 162 | TimeAuto_Cov(X, 0L, nT, Double = Double, /zero2nan) |
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| 163 | endcase |
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| 164 | endif else begin ;Compute Autocovariance. |
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[242] | 165 | for k = 0, nLag-1 do $ |
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[2] | 166 | case XNDim of |
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| 167 | 1:Auto[k] = TimeAuto_Cov(X, ABS(Lag[k]), nT, Double = Double) / nT |
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| 168 | 2:Auto[*, k] = TimeAuto_Cov(X, ABS(Lag[k]), nT, Double = Double) / nT |
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| 169 | 3:Auto[*, *, k] = TimeAuto_Cov(X, ABS(Lag[k]), nT, Double = Double) / nT |
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| 170 | 4:Auto[*, *, *, k] = TimeAuto_Cov(X, ABS(Lag[k]), nT, Double = Double) / nT |
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| 171 | endcase |
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| 172 | endelse |
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| 173 | |
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| 174 | if Double eq 0 then RETURN, FLOAT(Auto) else $ |
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| 175 | RETURN, Auto |
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| 176 | |
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| 177 | END |
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