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  <title>IFO/Temperature Example - Pre-processing (LTPDA Toolbox)</title>
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  <h1 class="title"><a name="f3-12899" id="f3-12899"></a>IFO/Temperature Example - Pre-processing</h1>
  <hr>

  <p>
	<p>
  Now we return to the IFO/Temperature example that was started in Topic 1.
</p>
<h2>Loading and checking the calibrated data sets from topic2</h2>
<p>
  In the last topic you should have saved your calibrated data files as<br>
  <tt>ifo_temp_example/ifo_disp.xml</tt> and <br>
  <tt>ifo_temp_example/temp_kelvin.xml</tt>
</p>
<p>
  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>
</p>
<div class="fragment"><pre>
    ifo  = ao(<span class="string">'ifo_temp_example/ifo_disp.xml'</span>);
    temp = ao(<span class="string">'ifo_temp_example/temp_kelvin.xml'</span>);
    ifo.setName;
    temp.setName;
</pre></div>
<p>
  Let's see what kind of pre-processing we have to apply to our data prior to further analysis.
</p>
<p>
  You can have a look at the data by for example displaying the AOs on the terminal and by
  plotting them, of course. Since the two data series have different Y units, we should plot
  them on subplots. To do that with <tt>iplot</tt>, pass the key 'arrangement' in a plist. For
  example:
</p>
<div class="fragment"><pre>
    pl = plist(<span class="string">'arrangement'</span>, <span class="string">'subplots'</span>);
</pre></div>
<p>
  If you plot the two time-series you should see something like the following:<br>
  <img src="images/ltpda_training_1/topic2/ifotempraw.png" alt="ifotempdata" border="1">
  <br>
</p>
<p>
  Some points to note:
  <ol>
    <li>The two data streams:
    <ul>
      <li> do not have the same sampling frequency.</li>
      <li> are not of the same length (nsecs).</li>
    </ul></li>
    <li>The interferometer data has a small transient at the start</li>
  </ol>
</p>
<p>
  To have a closer look at the samples we plot markers at each sample and only plot a zoomed-in section. You can
  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):
</p>
<p>
  <table cellspacing="0" class="body" cellpadding="2" border="0" width="50%">
    <colgroup>
      <col width="35%"/>
      <col width="65%"/>
    </colgroup>
    <thead>
      <tr valign="top">
        <th class="categorylist">Key</th>
        <th class="categorylist">Value</th>
      </tr>
    </thead>
    <tbody>
      <!-- Key 'arrangement' -->
      <tr valign="top">
        <td bgcolor="#f3f4f5">
          <p><tt>ARRANGEMENT</tt></p>
        </td>
        <td bgcolor="#f3f4f5">
          <span class="string">'subplots'</span>
        </td>
      </tr>
      <!-- Key 'arrangement' -->
      <tr valign="top">
        <td bgcolor="#f3f4f5">
          <p><tt>LINESTYLES</tt></p>
        </td>
        <td bgcolor="#f3f4f5">
          {<span class="string">'none'</span>,<span class="string">'none'</span>}
        </td>
      </tr>
      <!-- Key 'markers' -->
      <tr valign="top">
        <td bgcolor="#f3f4f5">
          <p><tt>MARKERS</tt></p>
        </td>
        <td bgcolor="#f3f4f5">
          {<span class="string">'+'</span>,<span class="string">'+'</span>}
        </td>
      </tr>
      <!-- Key 'xranges' -->
      <tr valign="top">
        <td bgcolor="#f3f4f5">
          <p><tt>XRANGES</tt></p>
        </td>
        <td bgcolor="#f3f4f5">
          {<span class="string">'all'</span>, [200 210]}
        </td>
      </tr>
      <!-- Key 'yranges' -->
      <tr valign="top">
        <td bgcolor="#f3f4f5">
          <p><tt>YRANGES</tt></p>
        </td>
        <td bgcolor="#f3f4f5">
          {[2e-7 3e-7], [200 350]}
        </td>
      </tr>
    </tbody>
  </table>
</p>
<p>
  <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
    <tr width="90%">
      <td>
        Notice the use of the keyword 'all' in the value for the 'XRANGES' parameter. Many
        of the <tt>iplot</tt> options support this keyword, which tells <tt>iplot</tt> to use
        the same value for all plots and subplots. For the 'YRANGES' we specify different values
        for each subplot.
      </td>
    </tr>
  </table>
</p>
<p>
  <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
    <tr width="90%">
      <td>
        Please store your parameter lists in 2 different variables. We can reuse them for
        plotting our results later. If you are working on a pipeline insteaad of a script, you
        can use two <tt>plist</tt> constructor blocks and pass these as an input to an
        <tt>iplot</tt> block.
      </td>
    </tr>
  </table>
</p>
<p>
  Passing such a parameter list to <tt>iplot</tt> together with the two AO time-series should yield
  a plot something like:
</p>
<br>
<img src="images/ltpda_training_1/topic2/samples.png" alt="samples" border="1">
<br>
<p>
  From this plot you may be able to see that the temperature data is <bb>unevenly sampled</bb>.
</p>
<p>
  To confirm this, enter the following (standard) MATLAB commands on the terminal:
</p>
<div class="fragment"><pre>
    dt = diff(temp.x);
    min(dt)
    max(dt)
</pre></div>
<p>
  <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
    <tr width="90%">
      <td>
        Don't forget the semicolon at the end of the <tt>diff</tt> calculation; this is a long data series
        and will be printed to the terminal if you do forget.
      </td>
    </tr>
  </table>
  You see that the minimum and maximum difference in the time-stamps of the data is different, showing
  that the data are not evenly sampled.
</p>
<p>
  Before we proceed with the later analysis of this data, we need to
  <ul>
    <li>Fix the uneven sampling of the temperature data</li>
    <li>Resample both data streams to the same rate</li>
    <li>Resample both data streams on to the same timing grid</li>
    <li>Select the segment of interferometer data that matches the temperature data</li>
  </ul>
</p>
<p>
  Each of these steps can, in principle, be done by hand. However, LTPDA provides
  a 'data fixer' method called <tt>ao/consolidate</tt> which attempts to automate this
  process. The call to <tt>consolidate</tt> is shown below:
</p>
<div class="fragment"><pre>
    [temp_fixed ifo_fixed] = consolidate(temp, ifo, plist(<span class="string">'fs'</span>,1));
</pre></div>
<p>
  We tell <tt>consolidate</tt> that we want to have our data resampled to 1 Hz by specifying the
  parameter key 'fs'.
</p>
<p>
  Now we can inspect the time-series of these data. The result should look something like
  the figure below:
</p>
<br>
<img src="images/ltpda_training_1/topic2/ifo_temp_consolidated.png" alt="consolidated" border="1">
<br>
<p>
  <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
    <tr width="90%">
      <td>
        Note that the time origin above the plots has now changed from zero to 13.105 which
        was the time of the first sample in the temperature measurement.
      </td>
    </tr>
  </table>
</p>
<p>
  If we also plot the zoomed-in view again, we should see something like:
</p>
<img src="images/ltpda_training_1/topic2/samples_conso.png" alt="consolidated samples" border="1">
<p>
  As you can see <tt>consolidate</tt> solved all our issues with these two data streams.
  They now start at the same time and are evenly sampled at the same sampling frequency.
</p>
<p>
  In the next topic, we will look at the
  spectral content and coherence of the data before and after the pre-processing. For now,
  finish by saving the consolidated data ready for the next topic.
</p>
<div class="fragment"><pre>
    save(temp_fixed,<span class="string">'ifo_temp_example/temp_fixed.xml'</span>);
    save(ifo_fixed,<span class="string">'ifo_temp_example/ifo_fixed.xml'</span>);
</pre></div>


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  <p class="copy">&copy;LTP Team</p>
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author	Daniele Nicolodi <nicolodi@science.unitn.it>
date	Tue, 06 Dec 2011 19:07:27 +0100
parents	f0afece42f48
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