casa-synthetic-observations.html

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    <h2 class="entry-title">
      <a href="https://keflavich.github.io/blog/casa-synthetic-observations.html" rel="bookmark"
         title="Permalink to CASA synthetic observations">CASA synthetic observations</a></h2>
 
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    <abbr class="published" title="2016-08-15T09:36:00-06:00">
      2016/08/15
    </abbr>
    <address class="vcard author">
      By <a class="url fn" href="https://keflavich.github.io/blog/author/adam-ginsburg-adamgginsburggmailcom.html">Adam Ginsburg (Adam.G.Ginsburg@gmail.com)</a>
    </address>
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  <div class="entry-content">
    <p>To evaluate imaging robustness &amp; quality, it is necessary to do some sort of
synthetic observation.  These synthetic observations can be done on real
images, e.g. Herschel data shifted to greater distances, or on simulated data.</p>
<p>Some examples:</p>
<blockquote>
<ul class="simple">
<li><a class="reference external" href="https://github.com/keflavich/W51_ALMA_2013.1.00308.S/blob/master/analysis/synth_imaging_perseus.py">https://github.com/keflavich/W51_ALMA_2013.1.00308.S/blob/master/analysis/synth_imaging_perseus.py</a></li>
<li><a class="reference external" href="https://github.com/keflavich/W51_ALMA_2013.1.00308.S/blob/master/analysis/synth_imaging_perseus_2.py">https://github.com/keflavich/W51_ALMA_2013.1.00308.S/blob/master/analysis/synth_imaging_perseus_2.py</a></li>
<li><a class="reference external" href="https://github.com/keflavich/W51_ALMA_2013.1.00308.S/blob/master/analysis/synth_imaging_aquila.py">https://github.com/keflavich/W51_ALMA_2013.1.00308.S/blob/master/analysis/synth_imaging_aquila.py</a></li>
</ul>
</blockquote>
<p>General process:</p>
<blockquote>
<ol class="arabic simple">
<li>Import file into CASA .image form (e.g., <tt class="docutils literal">importfits</tt>)</li>
<li>Load the .ms file and replace visibilities w/fourier transform of image</li>
<li>Clean.</li>
</ol>
</blockquote>
<p>The hard part is setting up the files to be imported.</p>
<p>For example, you can import a FITS file, which to me seems to be the most
straightforward approach:</p>
<pre class="literal-block">
importfits(fitsimage=perseus_rescaled,
           imagename=perseus_casa_image,
           overwrite=True,
           defaultaxes=True,#['RA---TAN','DEC--TAN','FREQUENCY','STOKES'],
           defaultaxesvalues=['2.909234541667E+02deg',
                              '1.451177222222E+01deg',
                              '233.9468GHz','I'],
           # 18&quot; = 1.22 lambda/D
           beam=[&quot;{0}deg&quot;.format(18/3600.*distance_scaling),
                 &quot;{0}deg&quot;.format(18/3600.*distance_scaling),
                 &quot;0deg&quot;],
          )
</pre>
<p>The beam probably matters for Jy-&gt;K, or Jy/beam-&gt;Jy conversion.  The
<tt class="docutils literal">defaultaxesvalues</tt> should correspond to the pointing center of the
interferometer pointing or mosaic.</p>
<p>Next is bringing this image - which we now pray is in the correct units - into UV space.:</p>
<pre class="literal-block">
sm.openfromms(&quot;continuum_7m12m_noflag.ms&quot;)
sm.setvp()
success = sm.predict(perseus_casa_image)
assert success
# TODO: get these from ASDM_CALWVR and WEATHER
success2 = sm.setnoise(mode='tsys-atm', relhum=60.0, pwv='2mm', tatmos=265.0, )
success3 = sm.corrupt()
sm.done()
sm.close()
</pre>
<p>This snippet loads the .ms file (the visibilities), then overwrites them with
the fourier transform of the image using <tt class="docutils literal">sm.predict</tt>.  It's not clear whether
<tt class="docutils literal">setvp</tt> is necessary.  <tt class="docutils literal">setnoise</tt> + <tt class="docutils literal">corrupt</tt> just adds &quot;appropriate&quot;
atmospheric noise to the visibilities.</p>
<p>From there, you should just use the same <tt class="docutils literal">clean</tt> parameters as for the
original data, or try to optimize <tt class="docutils literal">clean</tt> here and use these parameters on
the real data.</p>
<p>The result will be the 'perfect' (no phase error, no amplitude error)
interferometric image.</p>

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