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2 "http://www.w3.org/TR/1999/REC-html401-19991224/loose.dtd">
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4 <html lang="en">
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5 <head>
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7 "HTML Tidy for Mac OS X (vers 1st December 2004), see www.w3.org">
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11 <title>IFO/Temperature Example - Pre-processing (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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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;"> </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_2_8.html"><img src="b_prev.gif" border="0" align=
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30 "bottom" alt="Split and join AOs"></a> <a href=
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31 "ltpda_training_topic_3.html"><img src="b_next.gif" border="0" align=
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32 "bottom" alt="Topic 3 - Spectral Analysis"></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 - Pre-processing</h1>
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37 <hr>
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38
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39 <p>
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40 <p>
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41 Now we return to the IFO/Temperature example that was started in Topic 1.
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42 </p>
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43 <h2>Loading and checking the calibrated data sets from topic2</h2>
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44 <p>
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45 In the last topic you should have saved your calibrated data files as<br>
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46 <tt>ifo_temp_example/ifo_disp.xml</tt> and <br>
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47 <tt>ifo_temp_example/temp_kelvin.xml</tt>
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48 </p>
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49 <p>
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50 Now load each file into an AO, and simplify the name of the objects by assigning them the name of the Matlab workspace variable containing them:<br>
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51 </p>
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52 <div class="fragment"><pre>
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53 ifo = ao(<span class="string">'ifo_temp_example/ifo_disp.xml'</span>);
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54 temp = ao(<span class="string">'ifo_temp_example/temp_kelvin.xml'</span>);
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55 ifo.setName;
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56 temp.setName;
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57 </pre></div>
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58 <p>
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59 Let's see what kind of pre-processing we have to apply to our data prior to further analysis.
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60 </p>
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61 <p>
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62 You can have a look at the data by for example displaying the AOs on the terminal and by
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63 plotting them, of course. Since the two data series have different Y units, we should plot
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64 them on subplots. To do that with <tt>iplot</tt>, pass the key 'arrangement' in a plist. For
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65 example:
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66 </p>
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67 <div class="fragment"><pre>
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68 pl = plist(<span class="string">'arrangement'</span>, <span class="string">'subplots'</span>);
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69 </pre></div>
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70 <p>
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71 If you plot the two time-series you should see something like the following:<br>
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72 <img src="images/ltpda_training_1/topic2/ifotempraw.png" alt="ifotempdata" border="1">
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73 <br>
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74 </p>
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75 <p>
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76 Some points to note:
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77 <ol>
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78 <li>The two data streams:
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79 <ul>
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80 <li> do not have the same sampling frequency.</li>
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81 <li> are not of the same length (nsecs).</li>
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82 </ul></li>
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83 <li>The interferometer data has a small transient at the start</li>
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84 </ol>
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85 </p>
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86 <p>
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87 To have a closer look at the samples we plot markers at each sample and only plot a zoomed-in section. You can
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88 do this by creating a parameter list with the following key/value pairs (remember the 'key' properties for <tt>plist</tt> entries is not case sensitive):
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89 </p>
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90 <p>
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91 <table cellspacing="0" class="body" cellpadding="2" border="0" width="50%">
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92 <colgroup>
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93 <col width="35%"/>
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94 <col width="65%"/>
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95 </colgroup>
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96 <thead>
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97 <tr valign="top">
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98 <th class="categorylist">Key</th>
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99 <th class="categorylist">Value</th>
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100 </tr>
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101 </thead>
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102 <tbody>
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103 <!-- Key 'arrangement' -->
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104 <tr valign="top">
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105 <td bgcolor="#f3f4f5">
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106 <p><tt>ARRANGEMENT</tt></p>
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107 </td>
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108 <td bgcolor="#f3f4f5">
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109 <span class="string">'subplots'</span>
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110 </td>
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111 </tr>
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112 <!-- Key 'arrangement' -->
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113 <tr valign="top">
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114 <td bgcolor="#f3f4f5">
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115 <p><tt>LINESTYLES</tt></p>
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116 </td>
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117 <td bgcolor="#f3f4f5">
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118 {<span class="string">'none'</span>,<span class="string">'none'</span>}
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119 </td>
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120 </tr>
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121 <!-- Key 'markers' -->
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122 <tr valign="top">
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123 <td bgcolor="#f3f4f5">
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124 <p><tt>MARKERS</tt></p>
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125 </td>
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126 <td bgcolor="#f3f4f5">
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127 {<span class="string">'+'</span>,<span class="string">'+'</span>}
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128 </td>
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129 </tr>
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130 <!-- Key 'xranges' -->
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131 <tr valign="top">
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132 <td bgcolor="#f3f4f5">
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133 <p><tt>XRANGES</tt></p>
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134 </td>
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135 <td bgcolor="#f3f4f5">
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136 {<span class="string">'all'</span>, [200 210]}
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137 </td>
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138 </tr>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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139 <!-- Key 'yranges' -->
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140 <tr valign="top">
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141 <td bgcolor="#f3f4f5">
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142 <p><tt>YRANGES</tt></p>
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143 </td>
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144 <td bgcolor="#f3f4f5">
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145 {[2e-7 3e-7], [200 350]}
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146 </td>
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147 </tr>
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148 </tbody>
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149 </table>
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150 </p>
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151 <p>
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152 <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
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153 <tr width="90%">
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154 <td>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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155 Notice the use of the keyword 'all' in the value for the 'XRANGES' parameter. Many
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156 of the <tt>iplot</tt> options support this keyword, which tells <tt>iplot</tt> to use
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157 the same value for all plots and subplots. For the 'YRANGES' we specify different values
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158 for each subplot.
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159 </td>
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160 </tr>
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161 </table>
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162 </p>
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163 <p>
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164 <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
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165 <tr width="90%">
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166 <td>
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167 Please store your parameter lists in 2 different variables. We can reuse them for
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168 plotting our results later. If you are working on a pipeline insteaad of a script, you
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169 can use two <tt>plist</tt> constructor blocks and pass these as an input to an
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170 <tt>iplot</tt> block.
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171 </td>
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172 </tr>
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173 </table>
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174 </p>
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175 <p>
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176 Passing such a parameter list to <tt>iplot</tt> together with the two AO time-series should yield
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177 a plot something like:
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178 </p>
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179 <br>
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180 <img src="images/ltpda_training_1/topic2/samples.png" alt="samples" border="1">
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181 <br>
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182 <p>
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183 From this plot you may be able to see that the temperature data is <bb>unevenly sampled</bb>.
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184 </p>
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185 <p>
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186 To confirm this, enter the following (standard) MATLAB commands on the terminal:
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187 </p>
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188 <div class="fragment"><pre>
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189 dt = diff(temp.x);
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190 min(dt)
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191 max(dt)
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192 </pre></div>
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193 <p>
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194 <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
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195 <tr width="90%">
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196 <td>
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197 Don't forget the semicolon at the end of the <tt>diff</tt> calculation; this is a long data series
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198 and will be printed to the terminal if you do forget.
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199 </td>
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200 </tr>
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201 </table>
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202 You see that the minimum and maximum difference in the time-stamps of the data is different, showing
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203 that the data are not evenly sampled.
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204 </p>
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205 <p>
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206 Before we proceed with the later analysis of this data, we need to
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207 <ul>
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208 <li>Fix the uneven sampling of the temperature data</li>
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209 <li>Resample both data streams to the same rate</li>
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210 <li>Resample both data streams on to the same timing grid</li>
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211 <li>Select the segment of interferometer data that matches the temperature data</li>
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212 </ul>
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213 </p>
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214 <p>
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215 Each of these steps can, in principle, be done by hand. However, LTPDA provides
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216 a 'data fixer' method called <tt>ao/consolidate</tt> which attempts to automate this
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217 process. The call to <tt>consolidate</tt> is shown below:
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218 </p>
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219 <div class="fragment"><pre>
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220 [temp_fixed ifo_fixed] = consolidate(temp, ifo, plist(<span class="string">'fs'</span>,1));
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221 </pre></div>
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222 <p>
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223 We tell <tt>consolidate</tt> that we want to have our data resampled to 1 Hz by specifying the
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224 parameter key 'fs'.
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225 </p>
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226 <p>
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227 Now we can inspect the time-series of these data. The result should look something like
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228 the figure below:
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229 </p>
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230 <br>
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231 <img src="images/ltpda_training_1/topic2/ifo_temp_consolidated.png" alt="consolidated" border="1">
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232 <br>
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233 <p>
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234 <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
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235 <tr width="90%">
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236 <td>
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237 Note that the time origin above the plots has now changed from zero to 13.105 which
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238 was the time of the first sample in the temperature measurement.
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239 </td>
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240 </tr>
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241 </table>
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242 </p>
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243 <p>
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244 If we also plot the zoomed-in view again, we should see something like:
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245 </p>
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246 <img src="images/ltpda_training_1/topic2/samples_conso.png" alt="consolidated samples" border="1">
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247 <p>
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248 As you can see <tt>consolidate</tt> solved all our issues with these two data streams.
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249 They now start at the same time and are evenly sampled at the same sampling frequency.
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250 </p>
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251 <p>
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252 In the next topic, we will look at the
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253 spectral content and coherence of the data before and after the pre-processing. For now,
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254 finish by saving the consolidated data ready for the next topic.
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255 </p>
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256 <div class="fragment"><pre>
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257 save(temp_fixed,<span class="string">'ifo_temp_example/temp_fixed.xml'</span>);
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258 save(ifo_fixed,<span class="string">'ifo_temp_example/ifo_fixed.xml'</span>);
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259 </pre></div>
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260
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261
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262
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263 </p>
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264
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265 <br>
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266 <br>
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267 <table class="nav" summary="Navigation aid" border="0" width=
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268 "100%" cellpadding="0" cellspacing="0">
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269 <tr valign="top">
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270 <td align="left" width="20"><a href="ltpda_training_topic_2_8.html"><img src=
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271 "b_prev.gif" border="0" align="bottom" alt=
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272 "Split and join AOs"></a> </td>
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273
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274 <td align="left">Split and join AOs</td>
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275
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276 <td> </td>
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277
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278 <td align="right">Topic 3 - Spectral Analysis</td>
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279
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280 <td align="right" width="20"><a href=
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281 "ltpda_training_topic_3.html"><img src="b_next.gif" border="0" align=
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282 "bottom" alt="Topic 3 - Spectral Analysis"></a></td>
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283 </tr>
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284 </table><br>
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285
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286 <p class="copy">©LTP Team</p>
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287 </body>
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288 </html>
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