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Cleanup
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 18:04:34 +0100
parents f0afece42f48
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  <title>IFO/Temperature Example - signal subtraction (LTPDA Toolbox)</title>
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  <h1 class="title"><a name="f3-12899" id="f3-12899"></a>IFO/Temperature Example - signal subtraction</h1>
  <hr>
  
  <p>
	


<p>
  During this exercise we will:
  <ol>
    <li> Load the AOs with the IFO and Temperature data
    <li> Load the transfer function of the data
    <li> Split the TF to select the meaningful region only
    <li> Fit the TF with <tt>zDomainFit</tt>
    <li> Subtract the temperature contribution from the IFO signal
  </ol>
</p>

<p>
  Let us load the test data and split out the 'good' part as we did in Topic 3:
</p>

<div class="fragment"><pre>
    ifo = ao(plist(<span class="string">'filename'</span>, <span class="string">'ifo_temp_example/ifo_fixed.xml'</span>));
    ifo.setName;
    T = ao(plist(<span class="string">'filename'</span>, <span class="string">'ifo_temp_example/temp_fixed.xml'</span>));
    T.setName;

    <span class="comment">% Split out the good part of the data</span>
    pl_split = plist(<span class="string">'split_type'</span>, <span class="string">'interval'</span>, ...
                <span class="string">'start_time'</span>, ifo.t0 + 40800, ...
                <span class="string">'end_time'</span>, ifo.t0 + 193500);

    ifo_red = split(ifo, pl_split);
    T_red = split(T, pl_split);
</pre></div>

<p>
  These data are already preprocessed with <tt>ao/consolidate</tt> in order to set
  the sampling frequency to 1Hz. <br/>
  We could look at the data...
</p>
<div class="fragment"><pre>
    iplot(ifo_red,T_red,plist(<span class="string">'arrangement'</span>, <span class="string">'subplots'</span>))
</pre></div>

<p>
  <div align="center">
    <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_5_1.png" align="center" border="0">
  </div>
</p>

<p>
  Let us load the transfer function estimate we made in Topic 3.
</p>

<div class="fragment"><pre>
  tf = ao(<span class="string">'ifo_temp_example/T_ifo_tf.xml'</span>);
</pre></div>

<p>
  The meaningful frequency region is in the range 2e-5 <i>Hz</i> - 1e-3 <i>Hz</i>.
  Therefore we split the transfer function to extract only meaningful data.
</p>

<div class="fragment"><pre>
    tfsp = split(tf,plist(<span class="string">'frequencies'</span>, [2e-5 1e-3]));
    iplot(tf,tfsp)
</pre></div>

<p>
  The plot compares full range TF with splitted TF
  <div align="center">
    <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_5_4.png" align="center" border="0">
  </div>
  Once we have the proper transfer function, we could start the fitting process.
  A rapid look to the TF data should convince us that we need a very simple object
  to fit our data so we could try a fitting session "by hand". In other words,
  it is more convenient to skip the automathic functionality of <tt>zDomainFit</tt>.
  Moreover, we force <tt> zDomainFit </tt> to fit a stable model to data because
  we want to output a stable filter.
</p>

<div class="fragment"><pre>
    plfit = plist(<span class="string">'FS'</span>,1,...
        <span class="string">'AutoSearch'</span>,<span class="string">'off'</span>,...
        <span class="string">'StartPolesOpt'</span>,<span class="string">'clog'</span>,...
        <span class="string">'maxiter'</span>,20,...
        <span class="string">'minorder'</span>,3,...
        <span class="string">'maxorder'</span>,3,...
        <span class="string">'weightparam'</span>,<span class="string">'abs'</span>,...
        <span class="string">'Plot'</span>,<span class="string">'on'</span>,...
        <span class="string">'ForceStability'</span>,<span class="string">'on'</span>,...
        <span class="string">'CheckProgress'</span>,<span class="string">'off'</span>);

    fobj = zDomainFit(tfsp,plfit);
    fobj.filters.setIunits(<span class="string">'K'</span>);
    fobj.filters.setOunits(<span class="string">'m'</span>);
</pre></div>

<p>
  <div align="center">
    <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_5_5.png" align="center" border="0">
  </div>
</p>

<p>
  It is time to filter temperature data with the fit output in order to
  extract temperature contribution to interferometer output. Detrend
  after the filtering is performed to subtract mean to data (bias subtraction).
</p>

<div class="fragment"><pre>
    ifoT = filter(T_red,fobj,plist(<span class="string">'bank'</span>,<span class="string">'parallel'</span>));
    ifoT.detrend(plist(<span class="string">'order'</span>,0));
    ifoT.simplifyYunits;
    ifoT.setName;
</pre></div>

<p>
  Then we subtract temperature contribution from measured interferometer data
</p>

<div class="fragment"><pre>
  ifonT = ifo_red - ifoT;
  ifonT.setName;
</pre></div>

<p>
  The figure reports measured interferometer data, temperature contribution
  to interferometer output and interferometer output without thermal drifts.
</p>

<div class="fragment"><pre>
  iplot(ifo_red,ifoT,ifonT)
</pre></div>

<p>
  <div align="center">
    <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_5_6.png" align="center" border="0">
  </div>
</p>

<p>
  If you now compare spectra of the original IFO signal and the one with the temperature contribution
  removed, you should see something like the figure below:
</p>
<div class="fragment"><pre>
  ifoxx = ifo_red.lpsd;
  ifonTxx = ifonT.lpsd;
  iplot(ifoxx,ifonTxx)
</pre></div>
<p>
  <div align="center">
    <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_5_7.png" align="center" border="0">
  </div>
</p>















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