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         title="Permalink to A workaround for individual maps?">A workaround for individual maps?</a></h2>
 
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    <abbr class="published" title="2011-03-24T00:33:00-06:00">
      2011/03/24
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      By <a class="url fn" href="https://keflavich.github.io/blog/author/adam-adamgginsburggmailcom.html">Adam (adam.g.ginsburg@gmail.com)</a>
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    <p>I closely examined the timestreams of 101208_ob7 as I said I would
yesterday. Unfortunately, all I can do is describe the symptoms: the
first deconvolution model looks good, though it isn't quite as wide as
the true source (this should be OK; it is an iterative method, after
all). In the second iteration, though, the deconvolution model is even
smaller and lower amplitude... and it goes on like that.</p>
<p>Not deconvolving results in a healthy-looking clean map - pretty much
what you expect and want to see.</p>
<p>This implies that somehow removing an incomplete deconvolved model leads
to more of the source being included in the 'atmosphere' than would have
been included with no model subtraction at all. I'm not sure how this is
possible. In fact... I'm really quite sure that it is not.
The workaround is to only add positive changes to the model. This should
'definitely work' but may be non-convergent and assumes that the model
never has anything wrong with it at any iteration. I have demonstrated
that this works nicely for the two Uranus observations I tested on, but
now I have to run the gamut of tests.... the first (very obvious)
problem is that the background is now positive, which is dead wrong.
This workaround is not viable.
Alright, so what next? I've described the symptoms and that I think they
can't occur...
A closer look shows that new_astro is not being incorporated into
astro_model at the second iteration. Why?
AHA! Pyflagger + find_all_points reveals the problem!</p>
<pre class="literal-block">
Map value: 16.939728   Weighted average: 17.476323   Unweighted Average: 524.573136
scan,bolo,time:       mapped       astro       flags      weight       scale
3,  22,  12:     8.380408   13.561113    0.000000    0.025132    1.000000
4, 124,  23:   822.005327   13.561113    0.000000    0.000038    1.118012
4,  21,  38:   719.408983   13.561113    0.000000    0.000037    0.946721
5,  20,   7:     4.470616   13.561113    0.000000    0.013303    1.400000
5, 119,  23:   882.508303   13.561113    0.000000    0.000033    0.926887
5, 100,  35:   327.007750   13.561113    0.000000    0.000074    1.184397
5, 106,  38:   162.562098   13.561113    0.000000    0.000704    0.970000
6, 116,  27:   779.075640   13.561113    0.000000    0.000033    0.891768
8, 112,   3:   235.557390   13.561113    0.000000    0.000147    0.947130
9,   3,  14:   966.721773   13.561113    0.000000    0.000032    1.166292
9, 109,  41:   139.753656   13.561113    0.000000    0.000753    1.075269
10, 104,   8:   641.121935   13.561113    0.000000    0.000050    0.927827
10, 105,  24:     4.323228   13.561113    0.000000    0.032759    0.019022
10,  32,  36:   847.646990   13.561113    0.000000    0.000034    1.099406
11,  36,   9:   834.757586   13.561113    0.000000    0.000038    1.184751
11,  76,  37:   566.851891   13.561113    0.000000    0.000040    1.111000
12,  77,  13:   834.603090   13.561113    0.000000    0.000034    1.128464
12,  44,  44:   335.465654   13.561113    0.000000    0.000195    2.165775
13,  26,  17:    50.423143   13.561113    0.000000    0.004826    0.829932
13,  75,  29:   724.884676   13.561113    0.000000    0.000042    0.923077
14,  49,  21:   797.618990   13.561113    0.000000    0.000038    1.091918
14,  29,  33:   743.856012   13.561113    0.000000    0.000035    1.050360
15,  33,  13:   660.670099   13.561113    0.000000    0.000031    0.832180
15,  53,  25:   604.174286   13.561113    0.000000    0.000047    0.889922
15,  88,  40:     4.626476   13.561113    0.000000    0.008241    0.191489
17,  64,  20:   778.950533   13.561113    0.000000    0.000037    1.233108
18,  68,  30:   686.048136   13.561113    0.000000    0.000040    1.387283
</pre>
<p>Note that the lowest points have the highest weights. They DEFINITELY
shouldn't. What's wrong with them?
Apparently they have NO sensitivity to the sky! What?! There were a
bunch of bad bolos in Dec2010 that weren't flagged out... I wonder if
that problem persists to other epochs. Still, why does it only affect
pointing observations? Looking at the power spectra... the
large-timescale stuff becomes less dominant when scans are longer, but
the noisy spectra are still clearly noise-only. How odd.
Dropped to 112 good bolos from 134. That is much more believable. Have
to go back and fix Dec09 data too...
Even after fixing the bad bolos, the model drops with iteration number.
Why why why?
Well, looking at deconv_map, I've always returned the truly deconvolved
version, not the reconvolved... maybe the reconvolved really is better?
Again, this will have to be extensively tested, but it certainly gets
rid of the obvious/dominant error that the model kept dropping off.
However, FINALLY, based on how ridiculously good the reconv-deconvolved
map looks, I think I'm ready to do the extensive pipeline tests. So,
10dec_caltest has been started up with all of the new bolo_params
applied and the changes in place to deconv_map... let's see what
happens.</p>
<p>After that runs, I'll have to re-run the fit_and_plot routines</p>

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