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Daniele Nicolodi <nicolodi@science.unitn.it> |
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Mon, 05 Dec 2011 16:20:06 +0100 (2011-12-05) |
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<h1 class="title"><a name="f3-12899" id="f3-12899"></a>Split and join AOs</h1>
<hr>
<p>
<p>
You can split the data inside an AO to produce one or more output AOs. The <tt>ao/split</tt> method
splits an AO by samples, times (if the AO contains time series data), frequencies (if the AO contains
frequency data), intervals, or a number of pieces. We can control this as usual by defining our parameters.
</p>
<h2>Split by times</h2>
<p>
Let us create a new time series AO for these examples.
</p>
<div class="fragment"><pre>
pl = plist(<span class="string">'name'</span>, <span class="string">'None'</span>, <span class="string">'nsecs'</span>, 10, <span class="string">'fs'</span>, 1000, <span class="string">'tsfcn'</span>, <span class="string">'sin(2*pi*7.433*t) + randn(size(t))'</span>,<span class="string">'yunits'</span>,<span class="string">'V'</span>);
a = ao(pl);
</pre></div>
<p>
For splitting in time we need to define a time vector for the parameter list and pass it to <tt>ao/split</tt>
</p>
<div class="fragment"><pre>
pl_time = plist(<span class="string">'times'</span>, [2 3]);
a_time = split(a, pl_time);
iplot(a, a_time)
</pre></div>
<img src="images/ltpda_training_1/topic2/split_times.png" alt="split" border="1">
<h2>Split by frequencies</h2>
<p>
For this we need a frequency data AO. One easy way to get this is by computing the power spectrum using <tt>ao/psd</tt>.
</p>
<div class="fragment"><pre>
axx = a.psd;
</pre></div>
<p>
Again we need a vector for the parameter list and pass it to <tt>ao/split</tt>
</p>
<div class="fragment"><pre>
pl_freq = plist(<span class="string">'frequencies'</span>, [10 100]);
axx_freq = split(axx, pl_freq);
iplot(axx, axx_freq)
</pre></div>
<img src="images/ltpda_training_1/topic2/split_freq.png" alt="split" border="1">
<h2>Split by intervals</h2>
<p>
We can also split the AO by passing a time interval to the <tt>ao/split</tt> method.
</p>
<div class="fragment"><pre>
pl_interv = plist(<span class="string">'start_time'</span>, 4, <span class="string">'end_time'</span>, 6);
a_interv= split(a, pl_interv);
iplot(a, a_interv)
</pre></div>
<img src="images/ltpda_training_1/topic2/split_interv.png" alt="split_interv" border="1">
<h2>Split by samples</h2>
<p>
This type of splitting method we can use on any type of data. Let us use the frequency type, <tt>axx</tt>.
</p>
<p>
Again we need a vector for the parameter list and pass it to <tt>ao/split</tt>,
only that this time we will split our AO in to two parts.
</p>
<div class="fragment"><pre>
pl_samp = plist(<span class="string">'samples'</span>, [50 100 101 300]);
[axx_samp1 axx_samp2]= split(axx, pl_samp)
iplot(axx, axx_samp1, axx_samp2)
</pre></div>
<img src="images/ltpda_training_1/topic2/split_samp.png" alt="split_samp" border="1">
<p>
Although in this example the two resulting AOs are contiguous, they need not to be.
</p>
<h2>Join AOs</h2>
<p>
We can join our two AOs back together using <tt>ao/join</tt>
</p>
<div class="fragment"><pre>
axx_join = join(axx_samp1, axx_samp2);
iplot(axx, axx_join)
</pre></div>
<img src="images/ltpda_training_1/topic2/split_join.png" alt="split_join" border="1">
<p>
If we look at the history for <tt>axx_join</tt> (for instance, by entering <tt>dotview(axx_join.hist)</tt>), we will see the following:
</p>
<img src="images/ltpda_training_1/topic2/split_join_hist.png" alt="Split/join history" width="800px" border="1">
<p>
Since the two AOs that are output from the 'split by samples' stage are independent, the history
tree reflects this, showing two independent branches leading to the <tt>join</tt> step.
</p>
</p>
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<td align="left">Select and find data from an AO</td>
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<td align="right">IFO/Temperature Example - Pre-processing</td>
<td align="right" width="20"><a href=
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