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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
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1 <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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2 "http://www.w3.org/TR/1999/REC-html401-19991224/loose.dtd">
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3
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4 <html lang="en">
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5 <head>
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6 <meta name="generator" content=
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7 "HTML Tidy for Mac OS X (vers 1st December 2004), see www.w3.org">
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8 <meta http-equiv="Content-Type" content=
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9 "text/html; charset=us-ascii">
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10
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11 <title>IFO/Temperature Example - signal subtraction (LTPDA Toolbox)</title>
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12 <link rel="stylesheet" href="docstyle.css" type="text/css">
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13 <meta name="generator" content="DocBook XSL Stylesheets V1.52.2">
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14 <meta name="description" content=
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15 "Presents an overview of the features, system requirements, and starting the toolbox.">
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16 </head>
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17
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18 <body>
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19 <a name="top_of_page" id="top_of_page"></a>
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20
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21 <p style="font-size:1px;">&nbsp;</p>
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22
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23 <table class="nav" summary="Navigation aid" border="0" width=
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24 "100%" cellpadding="0" cellspacing="0">
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25 <tr>
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26 <td valign="baseline"><b>LTPDA Toolbox</b></td><td><a href="../helptoc.html">contents</a></td>
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27
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28 <td valign="baseline" align="right"><a href=
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29 "ltpda_training_topic_5_4.html"><img src="b_prev.gif" border="0" align=
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30 "bottom" alt="Non-linear least squares fitting of time series"></a>&nbsp;&nbsp;&nbsp;<a href=
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31 "examplesindex.html"><img src="b_next.gif" border="0" align=
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32 "bottom" alt="Examples"></a></td>
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33 </tr>
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34 </table>
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35
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36 <h1 class="title"><a name="f3-12899" id="f3-12899"></a>IFO/Temperature Example - signal subtraction</h1>
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37 <hr>
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38
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39 <p>
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40
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41
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42
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43 <p>
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44 During this exercise we will:
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45 <ol>
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46 <li> Load the AOs with the IFO and Temperature data
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47 <li> Load the transfer function of the data
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48 <li> Split the TF to select the meaningful region only
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49 <li> Fit the TF with <tt>zDomainFit</tt>
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50 <li> Subtract the temperature contribution from the IFO signal
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51 </ol>
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52 </p>
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53
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54 <p>
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55 Let us load the test data and split out the 'good' part as we did in Topic 3:
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56 </p>
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57
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58 <div class="fragment"><pre>
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59 ifo = ao(plist(<span class="string">'filename'</span>, <span class="string">'ifo_temp_example/ifo_fixed.xml'</span>));
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60 ifo.setName;
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61 T = ao(plist(<span class="string">'filename'</span>, <span class="string">'ifo_temp_example/temp_fixed.xml'</span>));
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62 T.setName;
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63
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64 <span class="comment">% Split out the good part of the data</span>
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65 pl_split = plist(<span class="string">'split_type'</span>, <span class="string">'interval'</span>, ...
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66 <span class="string">'start_time'</span>, ifo.t0 + 40800, ...
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67 <span class="string">'end_time'</span>, ifo.t0 + 193500);
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68
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69 ifo_red = split(ifo, pl_split);
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70 T_red = split(T, pl_split);
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71 </pre></div>
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72
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73 <p>
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74 These data are already preprocessed with <tt>ao/consolidate</tt> in order to set
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75 the sampling frequency to 1Hz. <br/>
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76 We could look at the data...
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77 </p>
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78 <div class="fragment"><pre>
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79 iplot(ifo_red,T_red,plist(<span class="string">'arrangement'</span>, <span class="string">'subplots'</span>))
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80 </pre></div>
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81
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82 <p>
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83 <div align="center">
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84 <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_5_1.png" align="center" border="0">
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85 </div>
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86 </p>
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87
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88 <p>
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89 Let us load the transfer function estimate we made in Topic 3.
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90 </p>
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91
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92 <div class="fragment"><pre>
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93 tf = ao(<span class="string">'ifo_temp_example/T_ifo_tf.xml'</span>);
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94 </pre></div>
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95
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96 <p>
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97 The meaningful frequency region is in the range 2e-5 <i>Hz</i> - 1e-3 <i>Hz</i>.
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98 Therefore we split the transfer function to extract only meaningful data.
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99 </p>
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100
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101 <div class="fragment"><pre>
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102 tfsp = split(tf,plist(<span class="string">'frequencies'</span>, [2e-5 1e-3]));
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103 iplot(tf,tfsp)
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104 </pre></div>
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105
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106 <p>
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107 The plot compares full range TF with splitted TF
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108 <div align="center">
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109 <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_5_4.png" align="center" border="0">
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110 </div>
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111 Once we have the proper transfer function, we could start the fitting process.
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112 A rapid look to the TF data should convince us that we need a very simple object
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113 to fit our data so we could try a fitting session "by hand". In other words,
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114 it is more convenient to skip the automathic functionality of <tt>zDomainFit</tt>.
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115 Moreover, we force <tt> zDomainFit </tt> to fit a stable model to data because
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116 we want to output a stable filter.
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117 </p>
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118
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119 <div class="fragment"><pre>
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120 plfit = plist(<span class="string">'FS'</span>,1,...
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121 <span class="string">'AutoSearch'</span>,<span class="string">'off'</span>,...
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122 <span class="string">'StartPolesOpt'</span>,<span class="string">'clog'</span>,...
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123 <span class="string">'maxiter'</span>,20,...
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124 <span class="string">'minorder'</span>,3,...
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125 <span class="string">'maxorder'</span>,3,...
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126 <span class="string">'weightparam'</span>,<span class="string">'abs'</span>,...
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127 <span class="string">'Plot'</span>,<span class="string">'on'</span>,...
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128 <span class="string">'ForceStability'</span>,<span class="string">'on'</span>,...
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129 <span class="string">'CheckProgress'</span>,<span class="string">'off'</span>);
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130
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131 fobj = zDomainFit(tfsp,plfit);
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132 fobj.filters.setIunits(<span class="string">'K'</span>);
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133 fobj.filters.setOunits(<span class="string">'m'</span>);
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134 </pre></div>
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135
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136 <p>
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137 <div align="center">
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138 <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_5_5.png" align="center" border="0">
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139 </div>
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140 </p>
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141
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142 <p>
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143 It is time to filter temperature data with the fit output in order to
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144 extract temperature contribution to interferometer output. Detrend
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145 after the filtering is performed to subtract mean to data (bias subtraction).
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146 </p>
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147
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148 <div class="fragment"><pre>
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149 ifoT = filter(T_red,fobj,plist(<span class="string">'bank'</span>,<span class="string">'parallel'</span>));
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150 ifoT.detrend(plist(<span class="string">'order'</span>,0));
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151 ifoT.simplifyYunits;
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152 ifoT.setName;
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153 </pre></div>
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154
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155 <p>
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156 Then we subtract temperature contribution from measured interferometer data
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157 </p>
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158
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159 <div class="fragment"><pre>
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160 ifonT = ifo_red - ifoT;
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161 ifonT.setName;
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162 </pre></div>
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163
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164 <p>
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165 The figure reports measured interferometer data, temperature contribution
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166 to interferometer output and interferometer output without thermal drifts.
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167 </p>
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168
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169 <div class="fragment"><pre>
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170 iplot(ifo_red,ifoT,ifonT)
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171 </pre></div>
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172
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173 <p>
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174 <div align="center">
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175 <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_5_6.png" align="center" border="0">
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176 </div>
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177 </p>
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178
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179 <p>
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180 If you now compare spectra of the original IFO signal and the one with the temperature contribution
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181 removed, you should see something like the figure below:
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182 </p>
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183 <div class="fragment"><pre>
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184 ifoxx = ifo_red.lpsd;
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185 ifonTxx = ifonT.lpsd;
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186 iplot(ifoxx,ifonTxx)
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187 </pre></div>
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188 <p>
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189 <div align="center">
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190 <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_5_7.png" align="center" border="0">
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191 </div>
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192 </p>
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193
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194
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195
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199
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207
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208 </p>
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209
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210 <br>
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211 <br>
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212 <table class="nav" summary="Navigation aid" border="0" width=
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213 "100%" cellpadding="0" cellspacing="0">
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214 <tr valign="top">
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215 <td align="left" width="20"><a href="ltpda_training_topic_5_4.html"><img src=
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216 "b_prev.gif" border="0" align="bottom" alt=
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217 "Non-linear least squares fitting of time series"></a>&nbsp;</td>
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218
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219 <td align="left">Non-linear least squares fitting of time series</td>
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220
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221 <td>&nbsp;</td>
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222
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223 <td align="right">Examples</td>
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224
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225 <td align="right" width="20"><a href=
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226 "examplesindex.html"><img src="b_next.gif" border="0" align=
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227 "bottom" alt="Examples"></a></td>
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228 </tr>
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229 </table><br>
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230
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231 <p class="copy">&copy;LTP Team</p>
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232 </body>
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233 </html>