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Import.
| author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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| date | Wed, 23 Nov 2011 19:22:13 +0100 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/m-toolbox/html_help/help/ug/ltpda_training_topic_5_5.html Wed Nov 23 19:22:13 2011 +0100 @@ -0,0 +1,233 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" + "http://www.w3.org/TR/1999/REC-html401-19991224/loose.dtd"> + +<html lang="en"> +<head> + <meta name="generator" content= + "HTML Tidy for Mac OS X (vers 1st December 2004), see www.w3.org"> + <meta http-equiv="Content-Type" content= + "text/html; charset=us-ascii"> + + <title>IFO/Temperature Example - signal subtraction (LTPDA Toolbox)</title> + <link rel="stylesheet" href="docstyle.css" type="text/css"> + <meta name="generator" content="DocBook XSL Stylesheets V1.52.2"> + <meta name="description" content= + "Presents an overview of the features, system requirements, and starting the toolbox."> + </head> + +<body> + <a name="top_of_page" id="top_of_page"></a> + + <p style="font-size:1px;"> </p> + + <table class="nav" summary="Navigation aid" border="0" width= + "100%" cellpadding="0" cellspacing="0"> + <tr> + <td valign="baseline"><b>LTPDA Toolbox</b></td><td><a href="../helptoc.html">contents</a></td> + + <td valign="baseline" align="right"><a href= + "ltpda_training_topic_5_4.html"><img src="b_prev.gif" border="0" align= + "bottom" alt="Non-linear least squares fitting of time series"></a> <a href= + "examplesindex.html"><img src="b_next.gif" border="0" align= + "bottom" alt="Examples"></a></td> + </tr> + </table> + + <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> + + + + + + + + + + + + + + + + </p> + + <br> + <br> + <table class="nav" summary="Navigation aid" border="0" width= + "100%" cellpadding="0" cellspacing="0"> + <tr valign="top"> + <td align="left" width="20"><a href="ltpda_training_topic_5_4.html"><img src= + "b_prev.gif" border="0" align="bottom" alt= + "Non-linear least squares fitting of time series"></a> </td> + + <td align="left">Non-linear least squares fitting of time series</td> + + <td> </td> + + <td align="right">Examples</td> + + <td align="right" width="20"><a href= + "examplesindex.html"><img src="b_next.gif" border="0" align= + "bottom" alt="Examples"></a></td> + </tr> + </table><br> + + <p class="copy">©LTP Team</p> +</body> +</html>