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| 5 | <title>c_timecorrelate.pro (SAXO Documentation Assistant)</title> |
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| 46 | <tr> |
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| 47 | <td width="10%"> |
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| 48 | <a href="a_timecorrelate.html"><img src="./../../prev.gif" border="0" alt="Previous"></a></td> |
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| 49 | <td width="80%" align="center" valign="center"> |
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| 50 | <font size=-1><i>SAXO Documentation Assistant</i>: <a href="./../../home.html">Overview</a></font></td> |
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| 51 | <td width="10%" align="right"> |
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[338] | 52 | <a href="skewness_4d.html"><img src="./../../next.gif" border="0" alt="Next"></a></td> |
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[163] | 53 | </tr> |
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| 54 | </table> |
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| 55 | |
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| 56 | |
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| 57 | <h1><font size="-2">ToBeReviewed/STATISTICS/</font></h1> |
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| 58 | <h2>c_timecorrelate.pro</h2> |
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| 59 | |
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| 60 | <dl> |
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| 61 | </dl> |
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| 62 | |
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| 63 | |
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| 64 | |
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| 65 | |
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| 66 | This function computes the "time cross correlation" Pxy(L) or |
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| 67 | the "time cross covariance" between 2 arrays (this is some |
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[338] | 68 | kind of <a href="/Applications/itt/idl64/help/online_help/C_CORRELATE.html">C_CORRELATE</a> but for multidimensional arrays) as a |
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[163] | 69 | function of the lag (L). |
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| 70 | |
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| 71 | |
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| 72 | |
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| 73 | <h2>Routine summary</h2> |
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| 74 | |
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| 75 | <dl> |
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| 76 | |
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[290] | 77 | <dt><a href="#_timecross_cov"><i>result = </i>timecross_cov(<i>xd, yd, m, nt, ndim</i>, DOUBLE=<i>DOUBLE</i>, ZERO2NAN=<i>ZERO2NAN</i>)</a><dt> |
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[234] | 78 | <dd><font size="-1"> </font></dd> |
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[163] | 79 | |
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[290] | 80 | <dt><a href="#_c_timecorrelate"><i>result = </i>c_timecorrelate(<i>x, y, lag</i>, COVARIANCE=<i>COVARIANCE</i>, DOUBLE=<i>DOUBLE</i>)</a><dt> |
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[242] | 81 | <dd><font size="-1"> </font></dd> |
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[163] | 82 | |
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| 83 | </dl> |
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| 84 | |
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| 85 | <p> </p> |
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| 86 | |
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| 87 | |
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| 88 | |
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| 89 | <a name="#_timecross_cov"></a> |
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| 90 | |
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| 91 | <h2>timecross_cov <font size="-1" color="#006633"> |
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| 92 | Statistics |
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| 93 | </font></h2> |
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| 94 | |
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[290] | 95 | <p><font face="Courier"><i>result = </i>timecross_cov(<i><a href="#_timecross_cov_keyword_xd">xd</a>, <a href="#_timecross_cov_keyword_yd">yd</a>, <a href="#_timecross_cov_keyword_m">m</a>, <a href="#_timecross_cov_keyword_nt">nt</a>, <a href="#_timecross_cov_keyword_ndim">ndim</a></i>, <a href="#_timecross_cov_keyword_DOUBLE">DOUBLE</a>=<i>DOUBLE</i>, <a href="#_timecross_cov_keyword_ZERO2NAN">ZERO2NAN</a>=<i>ZERO2NAN</i>)</font></p> |
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[163] | 96 | |
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[234] | 99 | |
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[163] | 100 | |
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| 101 | |
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| 102 | |
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| 103 | <h3>Parameters</h3> |
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| 104 | |
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| 105 | |
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[290] | 106 | <a name="#_timecross_cov_keyword_xd"></a> |
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| 107 | <h4>xd |
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[163] | 108 | |
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| 116 | </h4> |
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[290] | 122 | <a name="#_timecross_cov_keyword_yd"></a> |
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| 123 | <h4>yd |
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[163] | 124 | |
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| 132 | </h4> |
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[290] | 138 | <a name="#_timecross_cov_keyword_m"></a> |
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| 139 | <h4>m |
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[163] | 140 | |
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| 148 | </h4> |
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[290] | 154 | <a name="#_timecross_cov_keyword_nt"></a> |
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| 155 | <h4>nt |
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[163] | 156 | |
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| 164 | </h4> |
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[290] | 170 | <a name="#_timecross_cov_keyword_ndim"></a> |
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| 171 | <h4>ndim |
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[163] | 172 | |
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| 180 | </h4> |
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| 187 | |
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| 188 | <h3>Keywords</h3> |
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| 189 | |
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| 190 | |
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[290] | 191 | <a name="#_timecross_cov_keyword_DOUBLE"></a> |
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| 192 | <h4>DOUBLE |
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[163] | 193 | |
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| 201 | </h4> |
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| 202 | |
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| 204 | If set to a non-zero value, computations are done in |
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| 205 | double precision arithmetic. |
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| 206 | |
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| 207 | |
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| 208 | <a name="#_timecross_cov_keyword_ZERO2NAN"></a> |
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| 209 | <h4>ZERO2NAN |
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| 218 | </h4> |
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[234] | 225 | <h3>Examples</h3><pre> |
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[163] | 226 | |
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[234] | 227 | </pre><h3>Version history</h3> |
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[163] | 228 | |
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| 229 | <h4>Version</h4> |
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[402] | 230 | $Id: c_timecorrelate.pro 371 2008-08-07 09:32:02Z pinsard $ |
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[163] | 231 | |
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| 232 | <h4>History</h4> |
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| 233 | |
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| 250 | <font size="-3"><p> </p></font> |
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| 251 | <hr size="1" color="#CCCCCC"/> |
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| 252 | |
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| 253 | <a name="#_c_timecorrelate"></a> |
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| 254 | |
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| 255 | <h2>c_timecorrelate <font size="-1" color="#006633"> |
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| 256 | Statistics |
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| 257 | </font></h2> |
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| 258 | |
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[290] | 259 | <p><font face="Courier"><i>result = </i>c_timecorrelate(<i><a href="#_c_timecorrelate_keyword_x">x</a>, <a href="#_c_timecorrelate_keyword_y">y</a>, <a href="#_c_timecorrelate_keyword_lag">lag</a></i>, <a href="#_c_timecorrelate_keyword_COVARIANCE">COVARIANCE</a>=<i>COVARIANCE</i>, <a href="#_c_timecorrelate_keyword_DOUBLE">DOUBLE</a>=<i>DOUBLE</i>)</font></p> |
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[163] | 260 | |
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[242] | 263 | |
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[163] | 264 | |
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| 266 | |
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| 267 | <h3>Parameters</h3> |
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| 268 | |
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| 269 | |
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[290] | 270 | <a name="#_c_timecorrelate_keyword_x"></a> |
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| 271 | <h4>x |
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[163] | 272 | <font size="-1" color="#006633">in</font> |
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| 273 | |
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| 274 | |
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| 275 | <font size="-1" color="#006633">required</font> |
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| 276 | |
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| 277 | <font size="-1" color="#006633">type:</font> <font size="-1" color="#006633"><i>array</i></font> |
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| 278 | |
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| 279 | |
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| 280 | </h4> |
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| 281 | |
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| 282 | |
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[242] | 283 | An array which last dimension is the time dimension of |
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[163] | 284 | size n, float or double. |
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| 285 | |
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| 286 | |
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| 287 | |
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[290] | 288 | <a name="#_c_timecorrelate_keyword_y"></a> |
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| 289 | <h4>y |
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[163] | 290 | <font size="-1" color="#006633">in</font> |
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| 291 | |
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| 292 | |
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| 293 | <font size="-1" color="#006633">required</font> |
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| 294 | |
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| 295 | <font size="-1" color="#006633">type:</font> <font size="-1" color="#006633"><i>array</i></font> |
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| 296 | |
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| 297 | |
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| 298 | </h4> |
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| 299 | |
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| 300 | |
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[242] | 301 | An array which last dimension is the time dimension of |
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[163] | 302 | size n, float or double. |
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| 303 | |
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| 304 | |
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| 305 | |
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[290] | 306 | <a name="#_c_timecorrelate_keyword_lag"></a> |
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| 307 | <h4>lag |
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[163] | 308 | <font size="-1" color="#006633">in</font> |
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| 309 | |
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| 310 | |
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| 311 | <font size="-1" color="#006633">required</font> |
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| 312 | |
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| 313 | <font size="-1" color="#006633">type:</font> <font size="-1" color="#006633"><i>scalar or vector</i></font> |
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| 314 | |
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| 315 | |
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| 316 | </h4> |
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| 317 | |
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| 318 | |
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[242] | 319 | A scalar or n-elements vector, in the interval [-(n-2),(n-2)], |
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[163] | 320 | of type integer that specifies the absolute distance(s) between |
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| 321 | indexed elements of X. |
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| 322 | |
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| 326 | |
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| 327 | <h3>Keywords</h3> |
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| 328 | |
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| 329 | |
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[290] | 330 | <a name="#_c_timecorrelate_keyword_COVARIANCE"></a> |
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| 331 | <h4>COVARIANCE |
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[163] | 332 | |
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| 334 | |
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| 339 | |
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| 340 | </h4> |
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| 341 | |
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[234] | 343 | If set to a non-zero value, the sample cross |
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[163] | 344 | covariance is computed. |
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| 345 | |
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| 346 | |
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[290] | 347 | <a name="#_c_timecorrelate_keyword_DOUBLE"></a> |
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| 348 | <h4>DOUBLE |
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[163] | 349 | |
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| 356 | |
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| 357 | </h4> |
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| 358 | |
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| 359 | |
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| 360 | If set to a non-zero value, computations are done in |
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| 361 | double precision arithmetic. |
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| 362 | |
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| 363 | |
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[234] | 366 | <h3>Examples</h3><pre> |
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| 367 | |
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[242] | 368 | Define two n-elements sample populations. |
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[402] | 369 | 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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| 370 | 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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[163] | 371 | |
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[242] | 372 | Compute the cross correlation of X and Y for LAG = -5, 0, 1, 5, 6, 7 |
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[402] | 373 | IDL> lag = [-5, 0, 1, 5, 6, 7] |
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| 374 | IDL> result = c_timecorrelate(x, y, lag) |
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[163] | 375 | |
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[242] | 376 | The result should be: |
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| 377 | [-0.428246, 0.914755, 0.674547, -0.405140, -0.403100, -0.339685] |
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[163] | 378 | |
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[234] | 379 | </pre><h3>Version history</h3> |
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[163] | 380 | |
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| 381 | <h4>Version</h4> |
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[402] | 382 | $Id: c_timecorrelate.pro 371 2008-08-07 09:32:02Z pinsard $ |
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[163] | 383 | |
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| 384 | <h4>History</h4> |
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| 385 | - 01/03/2000 Sebastien Masson (smasson@lodyc.jussieu.fr) |
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| 386 | Based on the C_CORRELATE procedure of IDL |
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[234] | 387 | - August 2003 Sebastien Masson |
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[163] | 388 | update according to the update made in C_CORRELATE by |
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| 389 | W. Biagiotti and available in IDL 5.5 |
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| 390 | |
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| 391 | INTRODUCTION TO STATISTICAL TIME SERIES |
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| 392 | Wayne A. Fuller |
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| 393 | ISBN 0-471-28715-6 |
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[187] | 417 | <p><font color="gray" size="-3"> Produced by IDLdoc 2.0.</font></p> |
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[249] | 420 | </html> |
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