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Update unit tests
| author |
Daniele Nicolodi <nicolodi@science.unitn.it> |
| date |
Wed, 07 Dec 2011 17:24:37 +0100 (2011-12-07) |
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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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