Difference between revisions of "VLA CASA Bandpass Slope-CASA4.5.2"

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== Overview ==
 
== Overview ==
  
For the standard VLA flux calibrators 3C138, 3C147, 3C286, and 3C48, CASA includes a spatial and spectral model that is being applied for the bandpass calibration. This model takes out the source characteristics and calibration solution, then represents the instrument and atmospheric corrections. These VLA standards, however, have a relatively steep spectral index and are relatively weak at high frequencies.
+
For the standard VLA flux density calibrators 3C138, 3C147, 3C286 and 3C48, CASA includes spatial and spectral models that are applied during calibration. The models account for the source characteristics, resulting in calibration solutions that represent the instrumental and atmospheric corrections. These VLA standard calibrators, however, exhibit a negative spectral index and are relatively weak at high frequencies.
Although this is usually not a problem for absolute flux calibration, a good bandpass determination, which is very important for spectral line observations,  requires a very strong source (see e.g. [https://science.nrao.edu/facilities/vla/docs/manuals/obsguide/modes/line#section-8 Spectral Line Guide for Observing]). Therefore it is advisable to observe a different, but very strong source to correct for the bandpass. Such sources typically are variable and no a-priory model is available. With no a-priory model at hand, any spectral slope the bandpass calibrator has would create an error in the calibration.  This tutorial describes how to model such a slope and correct the bandpass solution for it.  
 
  
 +
Although the standard VLA flux density calibrators are usually still bright enough for absolute flux density calibration, a good bandpass determination—which is very important for spectral line observations—requires large signal-to-noise ratios derived from either a long integration time or a very strong source (see the [https://science.nrao.edu/facilities/vla/docs/manuals/obsguide/modes/line#section-8 Spectral Line Guide for Observing]). Observations of non-standard, but strong, bandpass calibrators are therefore common at high frequencies. Unfortunately, such sources are likely variable and no ''a priori'' flux density model is available. In particular, these sources exhibit an unknown and maybe variable spectral slope, which, if not accounted for, will create an error in the bandpass calibration. This tutorial describes how to model a spectral slope and how to correct the bandpass solution for this effect.
  
Data used in this guide are taken in wide, 3-bit mode for the protostar G192.16-3.84 in Ka-band with basebands centered at 29 and 36.5 GHz. Each baseband has over 4 GHz of bandwidth comprised of 32 128-MHz spectral windows each.
+
Data used in this guide are taken in wide 3-bit mode for the protostar G192.16-3.84 in Ka-band with basebands centered at 29 and 36.5 GHz. Each baseband has over 4 GHz of bandwidth comprising thirty-two 128-MHz spectral windows.
  
This is an advanced tutorial; if you are new to CASA or VLA data reduction in CASA, it is <em>strongly</em> recommended that you start with the [[EVLA Continuum Tutorial 3C391]] (at least read it through), or even [[Getting Started in CASA]] before proceeding with this tutorial.
+
If you are new to CASA, or with VLA data reduction in CASA, it is '''strongly''' recommended that you start with the [[Getting Started in CASA]] guide, the [https://casaguides.nrao.edu/index.php?title=VLA_high_frequency_Spectral_Line_tutorial_-_IRC%2B10216 IRC+10216 spectral line tutorial], or the [[VLA Continuum Tutorial 3C391]] before proceeding with this tutorial.
  
 
== Obtaining the Data ==
 
== Obtaining the Data ==
  
As this tutorial concerns bandpass solutions only, we removed all other sources from the MS and only keep the flux and bandpass calibrator scans. We also applied flagging and all pre-calibration steps including antenna position offsets, requantizer gains, opacity corrections, and gain-elevation curves. The original data (<tt>TVER0004.sb14459364.eb14492359.56295.26287841435</tt>) can be obtained through the [[https://archive.nrao.edu NRAO archive]] and has a raw size of 57.04 GB.
+
As this tutorial concerns bandpass calibration, all sources other than the flux density and bandpass calibrator scans were removed from the measurement set (MS). All pre-calibration steps including flagging, antenna position offsets, requantizer gains, opacity corrections, and gain-elevation curves were applied. The original data (<tt>TVER0004.sb14459364.eb14492359.56295.26287841435</tt>) can be obtained through the [https://archive.nrao.edu NRAO archive] and has a raw size of 57.04 GB.
 
 
  
 
  The trimmed measurement set can be downloaded directly from [http://casa.nrao.edu/Data/EVLA/G192/G192-BP.ms.tar.gz http://casa.nrao.edu/Data/EVLA/G192/G192-BP.ms.tar.gz] (dataset size: 3.4 GB)
 
  The trimmed measurement set can be downloaded directly from [http://casa.nrao.edu/Data/EVLA/G192/G192-BP.ms.tar.gz http://casa.nrao.edu/Data/EVLA/G192/G192-BP.ms.tar.gz] (dataset size: 3.4 GB)
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== Starting CASA ==
 
== Starting CASA ==
  
As usual, to start CASA, type:
+
To start CASA, type:
  
 
<source lang="bash">
 
<source lang="bash">
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</source>
 
</source>
  
This will run a script to initialize CASA, setting paths appropriately. It will also start writing to a file called ipython-<unique-stamp>.log, which will contain a record of all the text you enter at the CASA prompt, as well as casapy-<unique-stamp>.log, which will contain all the messages that are printed to the CASA logger window. It is recommended that you keep your log files in tact - you may need them to remind you of the last step you completed in your data reduction(It is also a good idea to include your log files when submitting a help desk ticket).
+
This will run a script initializing CASA and setting paths appropriately. The script will also create two files called ''ipython-<unique-stamp>.log'' (which contains a record of all the text you enter at the CASA prompt) as well as ''casapy-<unique-stamp>.log'' (which will contain all the messages that are printed to the CASA logger window). It is recommended that you keep your log files intact&#151;you may need them to remind you of the last step you completed in your data reduction. (It is also a good idea to include your log files when submitting a help desk ticket).
  
Once CASA has started, a logger window will appear. Note that you can rescale this window or change the font size as desired (the latter is under "View").
+
Once CASA has started, a logger window will appear. Note that you can rescale this window or change the font size (under the '''View''' menu option) as desired.
  
 
== Examining the Measurement Set (MS) ==
 
== Examining the Measurement Set (MS) ==
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</source>
 
</source>
  
This will write the output to a file called <tt>G192_listobs.txt</tt>, which we can print to the terminal using the <tt>cat</tt> command:
+
This will write the output to a file called <tt>G192_listobs.txt</tt>, which we can print to the terminal using various Unix/Linux commands such as <tt>cat</tt>, <tt>less</tt>, or <tt>more</tt>:
  
 
<source lang="python">
 
<source lang="python">
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   2      EVLA_KA#A1C1#4    128  TOPO  34732.000      1000.000    128000.0  34795.5000      10  RR  LL
 
   2      EVLA_KA#A1C1#4    128  TOPO  34732.000      1000.000    128000.0  34795.5000      10  RR  LL
 
   3      EVLA_KA#A1C1#5    128  TOPO  34860.000      1000.000    128000.0  34923.5000      10  RR  LL
 
   3      EVLA_KA#A1C1#5    128  TOPO  34860.000      1000.000    128000.0  34923.5000      10  RR  LL
  4      EVLA_KA#A1C1#6    128  TOPO  34988.000      1000.000    128000.0  35051.5000      10  RR  LL
+
 
  5      EVLA_KA#A1C1#7    128  TOPO  35116.000      1000.000    128000.0  35179.5000      10  RR  LL
+
<snip>
  6      EVLA_KA#A1C1#8    128  TOPO  35244.000      1000.000    128000.0  35307.5000      10  RR  LL
+
 
  7      EVLA_KA#A1C1#9    128  TOPO  35372.000      1000.000    128000.0  35435.5000      10  RR  LL
 
  8      EVLA_KA#A1C1#10    128  TOPO  35500.000      1000.000    128000.0  35563.5000      10  RR  LL
 
  9      EVLA_KA#A1C1#11    128  TOPO  35628.000      1000.000    128000.0  35691.5000      10  RR  LL
 
  10    EVLA_KA#A1C1#12    128  TOPO  35756.000      1000.000    128000.0  35819.5000      10  RR  LL
 
  11    EVLA_KA#A1C1#13    128  TOPO  35884.000      1000.000    128000.0  35947.5000      10  RR  LL
 
  12    EVLA_KA#A1C1#14    128  TOPO  36012.000      1000.000    128000.0  36075.5000      10  RR  LL
 
 
   13    EVLA_KA#A1C1#15    128  TOPO  36140.000      1000.000    128000.0  36203.5000      10  RR  LL
 
   13    EVLA_KA#A1C1#15    128  TOPO  36140.000      1000.000    128000.0  36203.5000      10  RR  LL
 
   14    EVLA_KA#A1C1#16    128  TOPO  36268.000      1000.000    128000.0  36331.5000      10  RR  LL
 
   14    EVLA_KA#A1C1#16    128  TOPO  36268.000      1000.000    128000.0  36331.5000      10  RR  LL
Line 92: Line 85:
 
   17    EVLA_KA#A2C2#19    128  TOPO  36604.000      1000.000    128000.0  36667.5000      11  RR  LL
 
   17    EVLA_KA#A2C2#19    128  TOPO  36604.000      1000.000    128000.0  36667.5000      11  RR  LL
 
   18    EVLA_KA#A2C2#20    128  TOPO  36732.000      1000.000    128000.0  36795.5000      11  RR  LL
 
   18    EVLA_KA#A2C2#20    128  TOPO  36732.000      1000.000    128000.0  36795.5000      11  RR  LL
  19    EVLA_KA#A2C2#21    128  TOPO  36860.000      1000.000    128000.0 36923.5000      11  RR  LL
+
   
  20    EVLA_KA#A2C2#22    128  TOPO  36988.000      1000.000    128000.0  37051.5000      11  RR  LL
+
<snip>
  21    EVLA_KA#A2C2#23    128  TOPO  37116.000      1000.000    128000.0  37179.5000      11  RR  LL
+
 
  22    EVLA_KA#A2C2#24    128  TOPO  37244.000      1000.000    128000.0  37307.5000      11  RR  LL
 
  23    EVLA_KA#A2C2#25    128  TOPO  37372.000      1000.000    128000.0  37435.5000      11  RR  LL
 
  24    EVLA_KA#A2C2#26    128  TOPO  37500.000      1000.000    128000.0  37563.5000      11  RR  LL
 
  25    EVLA_KA#A2C2#27    128  TOPO  37628.000      1000.000    128000.0  37691.5000      11  RR  LL
 
  26    EVLA_KA#A2C2#28    128  TOPO  37756.000      1000.000    128000.0  37819.5000      11  RR  LL
 
  27    EVLA_KA#A2C2#29    128  TOPO  37884.000      1000.000    128000.0  37947.5000      11  RR  LL
 
  28    EVLA_KA#A2C2#30    128  TOPO  38012.000      1000.000    128000.0  38075.5000      11  RR  LL
 
 
   29    EVLA_KA#A2C2#31    128  TOPO  38140.000      1000.000    128000.0  38203.5000      11  RR  LL
 
   29    EVLA_KA#A2C2#31    128  TOPO  38140.000      1000.000    128000.0  38203.5000      11  RR  LL
 
   30    EVLA_KA#A2C2#32    128  TOPO  38268.000      1000.000    128000.0  38331.5000      11  RR  LL
 
   30    EVLA_KA#A2C2#32    128  TOPO  38268.000      1000.000    128000.0  38331.5000      11  RR  LL
Line 108: Line 94:
 
   33    EVLA_KA#B1D1#35    128  TOPO  27104.000      1000.000    128000.0  27167.5000      13  RR  LL
 
   33    EVLA_KA#B1D1#35    128  TOPO  27104.000      1000.000    128000.0  27167.5000      13  RR  LL
 
   34    EVLA_KA#B1D1#36    128  TOPO  27232.000      1000.000    128000.0  27295.5000      13  RR  LL
 
   34    EVLA_KA#B1D1#36    128  TOPO  27232.000      1000.000    128000.0  27295.5000      13  RR  LL
  35    EVLA_KA#B1D1#37    128  TOPO  27360.000      1000.000    128000.0  27423.5000      13  RR  LL
+
 
  36    EVLA_KA#B1D1#38    128  TOPO  27488.000      1000.000    128000.0  27551.5000      13  RR  LL
+
<snip>
  37    EVLA_KA#B1D1#39    128  TOPO  27616.000      1000.000    128000.0  27679.5000      13  RR  LL
+
 
  38    EVLA_KA#B1D1#40    128  TOPO  27744.000      1000.000    128000.0  27807.5000      13  RR  LL
 
  39    EVLA_KA#B1D1#41    128  TOPO  27872.000      1000.000    128000.0  27935.5000      13  RR  LL
 
  40    EVLA_KA#B1D1#42    128  TOPO  28000.000      1000.000    128000.0  28063.5000      13  RR  LL
 
  41    EVLA_KA#B1D1#43    128  TOPO  28128.000      1000.000    128000.0  28191.5000      13  RR  LL
 
  42    EVLA_KA#B1D1#44    128  TOPO  28256.000      1000.000    128000.0  28319.5000      13  RR  LL
 
  43    EVLA_KA#B1D1#45    128  TOPO  28384.000      1000.000    128000.0  28447.5000      13  RR  LL
 
  44    EVLA_KA#B1D1#46    128  TOPO  28512.000      1000.000    128000.0  28575.5000      13  RR  LL
 
 
   45    EVLA_KA#B1D1#47    128  TOPO  28640.000      1000.000    128000.0  28703.5000      13  RR  LL
 
   45    EVLA_KA#B1D1#47    128  TOPO  28640.000      1000.000    128000.0  28703.5000      13  RR  LL
 
   46    EVLA_KA#B1D1#48    128  TOPO  28768.000      1000.000    128000.0  28831.5000      13  RR  LL
 
   46    EVLA_KA#B1D1#48    128  TOPO  28768.000      1000.000    128000.0  28831.5000      13  RR  LL
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   49    EVLA_KA#B2D2#51    128  TOPO  29104.000      1000.000    128000.0  29167.5000      14  RR  LL
 
   49    EVLA_KA#B2D2#51    128  TOPO  29104.000      1000.000    128000.0  29167.5000      14  RR  LL
 
   50    EVLA_KA#B2D2#52    128  TOPO  29232.000      1000.000    128000.0  29295.5000      14  RR  LL
 
   50    EVLA_KA#B2D2#52    128  TOPO  29232.000      1000.000    128000.0  29295.5000      14  RR  LL
  51    EVLA_KA#B2D2#53    128  TOPO  29360.000      1000.000    128000.0  29423.5000      14  RR  LL
+
 
  52    EVLA_KA#B2D2#54    128  TOPO  29488.000      1000.000    128000.0  29551.5000      14  RR  LL
+
<snip>
  53    EVLA_KA#B2D2#55    128  TOPO  29616.000      1000.000    128000.0  29679.5000      14  RR  LL
+
 
  54    EVLA_KA#B2D2#56    128  TOPO  29744.000      1000.000    128000.0  29807.5000      14  RR  LL
 
  55    EVLA_KA#B2D2#57    128  TOPO  29872.000      1000.000    128000.0  29935.5000      14  RR  LL
 
  56    EVLA_KA#B2D2#58    128  TOPO  30000.000      1000.000    128000.0  30063.5000      14  RR  LL
 
  57    EVLA_KA#B2D2#59    128  TOPO  30128.000      1000.000    128000.0  30191.5000      14  RR  LL
 
  58    EVLA_KA#B2D2#60    128  TOPO  30256.000      1000.000    128000.0  30319.5000      14  RR  LL
 
  59    EVLA_KA#B2D2#61    128  TOPO  30384.000      1000.000    128000.0  30447.5000      14  RR  LL
 
  60    EVLA_KA#B2D2#62    128  TOPO  30512.000      1000.000    128000.0  30575.5000      14  RR  LL
 
 
   61    EVLA_KA#B2D2#63    128  TOPO  30640.000      1000.000    128000.0  30703.5000      14  RR  LL
 
   61    EVLA_KA#B2D2#63    128  TOPO  30640.000      1000.000    128000.0  30703.5000      14  RR  LL
 
   62    EVLA_KA#B2D2#64    128  TOPO  30768.000      1000.000    128000.0  30831.5000      14  RR  LL
 
   62    EVLA_KA#B2D2#64    128  TOPO  30768.000      1000.000    128000.0  30831.5000      14  RR  LL
Line 142: Line 114:
 
   0    3C147              1    -              -             
 
   0    3C147              1    -              -             
 
   0    3C147              2    -              -             
 
   0    3C147              2    -              -             
  0    3C147              3    -              -           
+
 
  0    3C147              4    -              -           
+
<snip>
  0    3C147              5    -              -           
+
 
  0    3C147              6    -              -           
 
  0    3C147              7    -              -           
 
  0    3C147              8    -              -           
 
  0    3C147              9    -              -           
 
  0    3C147              10    -              -           
 
  0    3C147              11    -              -           
 
  0    3C147              12    -              -           
 
  0    3C147              13    -              -           
 
  0    3C147              14    -              -           
 
  0    3C147              15    -              -           
 
  0    3C147              16    -              -           
 
  0    3C147              17    -              -           
 
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  0    3C147              45    -              -           
 
  0    3C147              46    -              -           
 
  0    3C147              47    -              -           
 
  0    3C147              48    -              -           
 
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  0    3C147              50    -              -           
 
  0    3C147              51    -              -           
 
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  0    3C147              54    -              -           
 
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  0    3C147              60    -              -           
 
 
   0    3C147              61    -              -             
 
   0    3C147              61    -              -             
 
   0    3C147              62    -              -             
 
   0    3C147              62    -              -             
Line 206: Line 123:
 
   1    3c84-J0319+413      1    -              -             
 
   1    3c84-J0319+413      1    -              -             
 
   1    3c84-J0319+413      2    -              -             
 
   1    3c84-J0319+413      2    -              -             
  1    3c84-J0319+413      3    -              -           
+
 
  1    3c84-J0319+413      4    -              -           
+
<snip>
  1    3c84-J0319+413      5    -              -           
+
 
  1    3c84-J0319+413      6    -              -           
 
  1    3c84-J0319+413      7    -              -           
 
  1    3c84-J0319+413      8    -              -           
 
  1    3c84-J0319+413      9    -              -           
 
  1    3c84-J0319+413      10    -              -           
 
  1    3c84-J0319+413      11    -              -           
 
  1    3c84-J0319+413      12    -              -           
 
  1    3c84-J0319+413      13    -              -           
 
  1    3c84-J0319+413      14    -              -           
 
  1    3c84-J0319+413      15    -              -           
 
  1    3c84-J0319+413      16    -              -           
 
  1    3c84-J0319+413      17    -              -           
 
  1    3c84-J0319+413      18    -              -           
 
  1    3c84-J0319+413      19    -              -           
 
  1    3c84-J0319+413      20    -              -           
 
  1    3c84-J0319+413      21    -              -           
 
  1    3c84-J0319+413      22    -              -           
 
  1    3c84-J0319+413      23    -              -           
 
  1    3c84-J0319+413      24    -              -           
 
  1    3c84-J0319+413      25    -              -           
 
  1    3c84-J0319+413      26    -              -           
 
  1    3c84-J0319+413      27    -              -           
 
  1    3c84-J0319+413      28    -              -           
 
  1    3c84-J0319+413      29    -              -           
 
  1    3c84-J0319+413      30    -              -           
 
  1    3c84-J0319+413      31    -              -           
 
  1    3c84-J0319+413      32    -              -           
 
  1    3c84-J0319+413      33    -              -           
 
  1    3c84-J0319+413      34    -              -           
 
  1    3c84-J0319+413      35    -              -           
 
  1    3c84-J0319+413      36    -              -           
 
  1    3c84-J0319+413      37    -              -           
 
  1    3c84-J0319+413      38    -              -           
 
  1    3c84-J0319+413      39    -              -           
 
  1    3c84-J0319+413      40    -              -           
 
  1    3c84-J0319+413      41    -              -           
 
  1    3c84-J0319+413      42    -              -           
 
  1    3c84-J0319+413      43    -              -           
 
  1    3c84-J0319+413      44    -              -           
 
  1    3c84-J0319+413      45    -              -           
 
  1    3c84-J0319+413      46    -              -           
 
  1    3c84-J0319+413      47    -              -           
 
  1    3c84-J0319+413      48    -              -           
 
  1    3c84-J0319+413      49    -              -           
 
  1    3c84-J0319+413      50    -              -           
 
  1    3c84-J0319+413      51    -              -           
 
  1    3c84-J0319+413      52    -              -           
 
  1    3c84-J0319+413      53    -              -           
 
  1    3c84-J0319+413      54    -              -           
 
  1    3c84-J0319+413      55    -              -           
 
  1    3c84-J0319+413      56    -              -           
 
  1    3c84-J0319+413      57    -              -           
 
  1    3c84-J0319+413      58    -              -           
 
  1    3c84-J0319+413      59    -              -           
 
  1    3c84-J0319+413      60    -              -           
 
 
   1    3c84-J0319+413      61    -              -             
 
   1    3c84-J0319+413      61    -              -             
 
   1    3c84-J0319+413      62    -              -             
 
   1    3c84-J0319+413      62    -              -             
 
   1    3c84-J0319+413      63    -              -             
 
   1    3c84-J0319+413      63    -              -             
Antennas: 22:
+
 
  ID  Name  Station  Diam.    Long.        Lat.                Offset from array center (m)                ITRF Geocentric coordinates (m)       
+
<snip>
                                                                    East        North    Elevation              x              y              z
 
  1    ea02  N56      25.0 m  -107.37.47.9  +34.00.38.4      -1105.2071    12254.3069      -34.2426 -1600128.383400 -5035104.146500  3565024.672100
 
  2    ea03  N16      25.0 m  -107.37.10.9  +33.54.48.0      -155.8511    1426.6436      -9.3827 -1601061.956000 -5041175.880700  3556058.037600
 
  3    ea05  W08      25.0 m  -107.37.21.6  +33.53.53.0      -432.1184    -272.1472      -1.5070 -1601614.092200 -5042001.650900  3554652.508900
 
  4    ea06  N32      25.0 m  -107.37.22.0  +33.56.33.6      -441.7237    4689.9748      -16.9332 -1600781.042100 -5039347.435200  3558761.533000
 
  5    ea07  E40      25.0 m  -107.32.35.4  +33.52.16.9      6908.8279    -3240.7316      39.0057 -1595124.924100 -5045829.461500  3552210.685200
 
  6    ea09  E24      25.0 m  -107.35.13.4  +33.53.18.1      2858.1754    -1349.1257      13.7290 -1598663.097500 -5043581.389700  3553767.027800
 
  8    ea11  W56      25.0 m  -107.44.26.7  +33.49.54.6    -11333.2153    -7637.6824      15.3542 -1613255.404300 -5042613.085000  3548545.901400
 
  9    ea12  E08      25.0 m  -107.36.48.9  +33.53.55.1        407.8285    -206.0065      -3.2272 -1600801.926000 -5042219.366500  3554706.448200
 
  11  ea14  W16      25.0 m  -107.37.57.4  +33.53.33.0      -1348.7083    -890.6269        1.3068 -1602592.853600 -5042055.005300  3554140.703900
 
  12  ea15  W72      25.0 m  -107.48.24.0  +33.47.41.2    -17419.4730  -11760.2869      14.9578 -1619757.314900 -5042937.673700  3545120.385300
 
  13  ea16  N08      25.0 m  -107.37.07.5  +33.54.15.8        -68.9252      433.1901      -5.0683 -1601147.956700 -5041733.824100  3555235.952500
 
  14  ea17  E48      25.0 m  -107.30.56.1  +33.51.38.4      9456.5938    -4431.6366      37.9317 -1592894.088800 -5047229.121000  3551221.221100
 
  15  ea18  E72      25.0 m  -107.24.42.3  +33.49.18.0      19041.8754    -8769.2059        4.7234 -1584460.867200 -5052385.599300  3547599.997600
 
  17  ea20  N72      25.0 m  -107.38.10.5  +34.04.12.2      -1685.6775    18861.8403      -43.4734 -1599557.932000 -5031396.371000  3570494.760600
 
  18  ea21  E64      25.0 m  -107.27.00.1  +33.50.06.7      15507.6045    -7263.7280      67.1961 -1587600.190400 -5050575.873800  3548885.396600
 
  19  ea22  N24      25.0 m  -107.37.16.1  +33.55.37.7      -290.3745    2961.8582      -12.2374 -1600930.087700 -5040316.398500  3557330.387000
 
  20  ea23  N64      25.0 m  -107.37.58.7  +34.02.20.5      -1382.3750    15410.1463      -40.6373 -1599855.675100 -5033332.371000  3567636.622500
 
  21  ea24  W40      25.0 m  -107.41.13.5  +33.51.43.1      -6377.9740    -4286.7919        8.2191 -1607962.456900 -5042338.214500  3551324.943600
 
  22  ea25  W48      25.0 m  -107.42.44.3  +33.50.52.1      -8707.9407    -5861.7854      15.5265 -1610451.925400 -5042471.123100  3550021.056800
 
  23  ea26  W32      25.0 m  -107.39.54.8  +33.52.27.2      -4359.4561    -2923.1223      11.7579 -1605808.647100 -5042230.071500  3552459.203400
 
  24  ea27  E16      25.0 m  -107.36.09.8  +33.53.40.0      1410.0316    -673.4696      -0.7909 -1599926.110000 -5042772.967300  3554319.791200
 
  25  ea28  N40      25.0 m  -107.37.29.5  +33.57.44.4      -633.6167    6878.5984      -20.7748 -1600592.764000 -5038121.352000  3560574.847300
 
 
</pre>
 
</pre>
 +
This small MS contains only scans on the flux density calibrator 3C147 (field 0) and the bandpass calibrator 3C84 (field 1). Both fields have 64 spectral windows (spws); each spw is comprised of 128 channels, each channel being 1 MHz wide, for a total bandwidth of 128 MHz / spw.
  
We have trimmed the MS to contain only scans on the flux calibrator 3C147 and the bandpass calibrator 3C84, but retained all 64 spectral windows, each 128MHz wide and containing 128 1MHz channels.
+
== Setting the Model of the Flux Density Calibrator ==
 
 
== Setting the flux model for the Flux Calibrator ==
 
  
  
We first run the {{setjy}} task on the flux calibrator (field 0, 3C147) using the appropriate model (3C147_A.im for Ka-band):
+
To start, we insert the spectral (using the 'Perley-Butler 2013' standard) and spatial (3C147_A.im for Ka-band) models for the flux density calibrator 3C147 (field 0) with the {{setjy}} task:
  
 
<source lang="python">
 
<source lang="python">
# In CASA: model for the flux calibrator
+
# In CASA: model for the flux density calibrator
 
setjy(vis='G192-BP.ms', field='0', scalebychan=True, \
 
setjy(vis='G192-BP.ms', field='0', scalebychan=True, \
 
       standard='Perley-Butler 2013', model='3C147_A.im')
 
       standard='Perley-Butler 2013', model='3C147_A.im')
 
</source>
 
</source>
  
[[Image:ScreenshotPlotG192-setjy-4.5.png|200px|thumb|right|plotms of model amp vs freq for 3C147]]
+
[[Image:ScreenshotPlotG192-setjy-4.5.png|200px|thumb|right|Figure 1: plotms of model amp vs freq for 3C147]]
* <tt>scalebychan=True</tt>: will fill the model with per-channel values; otherwise, {{setjy}} would use a single value per spectral window.
+
* <tt>scalebychan=True</tt>: If ''scalebychan=False'' {{setjy}} would use a single value per spectral window.
  
Inspecting the logger report shows that 3C147 has a flux density of 1.3002 Jy at the lower end of the band (spw 63; ~31 GHz) and 1.1768 Jy at the upper end (spw 0; ~35 GHz).
+
Inspecting the logger report shows that 3C147 has amplitudes ranging from ~1.0-1.47 Jy across all spws.  
  
We can plot the model data using {{plotms}}:
+
We can plot the model data using {{plotms}} (Figure 1):
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
plotms(vis='G192-BP.ms', field='0', antenna='ea02&ea05', \
+
plotms(vis='G192-BP.ms', field='0', antenna='ea03', \
       xaxis='freq', yaxis='amp', ydatacolumn='model')
+
       xaxis='freq', yaxis='amp', ydatacolumn='model',coloraxis='ant2')
 
</source>
 
</source>
 +
 +
This plot shows baselines to antenna ea03. Since we provided both a spectral and a spatial model for this well resolved calibrator, each baseline has a somewhat different behavior.
  
 
== Calibrating delays and initial bandpass solutions ==
 
== Calibrating delays and initial bandpass solutions ==
  
As a first step, we use an antenna that is near the center of the array and has a minimum of flags. The array can be mapped with {{plotants}}:
+
As a first step, we need to specify a reference antenna for all phase calibrations. It is desirable to use an antenna that is near the center of the array and has the least amount of calibrator data flagged. The array can be mapped with {{plotants}}:
  
 
<source lang="python">
 
<source lang="python">
# In CASA: phase only calibration
+
# In CASA: plotting antenna locations
 
plotants(vis='G192-BP.ms')
 
plotants(vis='G192-BP.ms')
 
</source>
 
</source>
although the plot is a bit crowded, a zoom in shows that ea05 sits close to the center and appears to be a good choice.  
+
Although the plot is a bit crowded (Figure 2), a zooming in (with the magnifying glass icon) shows that ea05 is located close to the center. It also has a comparably small number of flags and we will use this antenna as our reference.
[[Image:plotG192_plotants.png|200px|thumb|right|plotants plotter]]
+
 
+
[[Image:plotG192_plotants.png|200px|thumb|right|Figure 2: plotants plotter]]
 
 
[[Image:plotG192_plotcal_G0p1_4.0.png|200px|thumb|right|plotcal G0 phase ant 0~15]]
 
[[Image:plotG192_plotcal_G0p2_4.0.png|200px|thumb|right|plotcal G0 phase ant 16~26]]
 
 
 
[[Image:plotG192_plotcal_delays.png|200px|thumb|right|plotcal K0 delay vs. antenna]]
 
  
[[Image:plotG192_plotcal_B0a1_4.0.png|200px|thumb|right|plotcal B0 bandpass amp ant ea06 spw 0-31]]
+
We start with a phase-only, time-dependent calibration solution for the bandpass calibrator. Solutions for each integration will remove most of the decorrelation of the signal. For best results, we will derive the phase variations from a narrow range of channels (60~68) near the centers of each spws:
[[Image:plotG192_plotcal_B0a2_4.0.png|200px|thumb|right|plotcal B0 bandpass amp ant ea06 spw 32-63]]
 
  
First, we do a phase-only calibration solution on a narrow range of channels near the center of each spectral window on the bandpass calibrator 3C84 to flatten them with respect to time before solving for the bandpass. The range 60~68 should work:
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA: phase only calibration
 
# In CASA: phase only calibration
Line 346: Line 179:
 
         gaintype='G', refant='ea05', calmode='p', \
 
         gaintype='G', refant='ea05', calmode='p', \
 
         solint='int', minsnr=3)
 
         solint='int', minsnr=3)
 
 
</source>
 
</source>
  
Line 353: Line 185:
 
* <tt>minsnr=3</tt> : Apply a minimum signal-to-noise cutoff.  Solutions with less than this value will be flagged.
 
* <tt>minsnr=3</tt> : Apply a minimum signal-to-noise cutoff.  Solutions with less than this value will be flagged.
 
* <tt>gaintable</tt> is not set here as we have already applied pre-calibrations.
 
* <tt>gaintable</tt> is not set here as we have already applied pre-calibrations.
Plot the phase solutions (using full phase range, -180 to 180, instead of autorange):
+
 
 +
Plot the phase solutions (using full phase range -180 to 180 instead of autorange):
  
 
<source lang="python">
 
<source lang="python">
Line 361: Line 194:
 
</source>
 
</source>
  
The first panel is blanked as ea01 is completely flagged. Step through the antenna-based solutions, here they look good (and fairly flat over the scans).
+
Click on the Next button to navigate through the antennas. Click on the Quit button to exit the viewer.
  
NOTE: When you are done plotting and want to use the calibration table in another task (e.g., for subsequent calibration or viewing with plotms), use the Quit button on the GUI to dismiss the plotter and free-up the lock on the calibration table. You should see a message in your terminal window saying "Resetting plotcal" which means you are good to go!
+
We will now produce multipanel plots of the phase solutions, writing the plots to output files as well as on the screen (Figures 3a & 3b). The output files generated are PNG files and can be viewed within CASA by executing an external viewer program, e.g., <tt>!xv plotG192_plotcal_G0p1.png</tt>; or by running an image viewing application such as xv, Preview, Gimp, Photoshop, etc., external to CASA at the OS level. <!-- (Note that the hardcopy only shows the first page): -->
  
If you want to make single-page, multipanel plots (like those shown to the right), particularly for a hardcopy (where it only shows the first page), you can do:
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
Line 378: Line 210:
 
</source>
 
</source>
  
We can now solve for the residual delays that we saw in plotms when we plotted phase vs. frequency. This uses the <tt>gaintype='K'</tt> option in gaincal. Note that this currently does not do a "global fringe-fitting" solution for delays, but instead does a baseline-based delay solution for all baselines to the reference antenna, treating these as antenna-based delays. In most cases with high-enough S/N to get baseline-based delay solutions, this will suffice.  We avoid the edge channels of each spectral window by selecting channels 5~122:
+
{|
 +
| [[Image:plotG192_plotcal_G0p1_4.0.png|200px|thumb|left|Figure 3a: plotcal G0 phase ant 0~15]]
 +
| [[Image:plotG192_plotcal_G0p2_4.0.png|200px|thumb|center|Figure 3b: plotcal G0 phase ant 16~26]]
 +
|}
 +
 
 +
<!--
 +
NOTE: When you are done plotting and want to use the calibration table in another task (e.g., for subsequent calibration or viewing with plotms), use the Quit button on the GUI to dismiss the plotter and free-up the lock on the calibration table. You should see a message in your terminal window saying "Resetting plotcal" which means you are good to go!
 +
-->
 +
 
 +
We can now solve for the residual delays using the parameter ''gaintype='K' ''option in {{gaincal}}. Note that this currently does not do a global fringe-fitting solution for delays, but instead does a baseline-based delay solution per spw for all baselines to the reference antenna, treating these as antenna-based delays. In most cases, with high enough S/N to get baseline-based delay solutions, this will suffice.  We avoid the edge channels of each spectral window by selecting channels 5~122:
 +
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA: residual delays
 
# In CASA: residual delays
Line 387: Line 229:
 
</source>
 
</source>
  
Note that we have also pre-applied our initial phase table, calG192.G0.  We can plot the delays, in nanoseconds, as a function of antenna index (you will get one for each spw and polarization):
+
Note that we have also pre-applied our initial phase table <tt>calG192.G0</tt>.   
 +
 
 +
Alternatively, you can also derive a delay across all spws of a baseband. If this is desired, use parameter ''combine='spw''' in {{gaincal}} and run the task for each baseband separately. The solutions from the second and following runs can be appended to the same calibration table via parameter ''append=T''.
 +
 
 +
[[Image:plotG192_plotcal_delays.png|200px|thumb|right|Figure 4: plotcal K0 delay vs. antenna]]
 +
 
 +
Now plot the delays, in nanoseconds, as a function of antenna index (you will get one for each spw and polarization):
 +
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
Line 393: Line 242:
 
</source>
 
</source>
  
The delays range from around -5 to 4 nanoseconds.
+
The delays range from around -5 to 4 nanoseconds (Figure 4).
 +
 
 +
Now solve for the antenna bandpasses using the previously generated tables <tt>calG192.G0</tt> and <tt>calG192.K0</tt>:
  
Now we solve for the antenna bandpasses using the previous tables:
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA: antenna bandpasses
 
# In CASA: antenna bandpasses
Line 403: Line 253:
 
         bandtype='B', solint='inf')
 
         bandtype='B', solint='inf')
 
</source>
 
</source>
 +
 
'''WARNING''': You must set <tt>solnorm=False</tt> here or later on you will find some offsets
 
'''WARNING''': You must set <tt>solnorm=False</tt> here or later on you will find some offsets
 
among spws due to the way the amplitude scaling adjusts weights internally during solving.
 
among spws due to the way the amplitude scaling adjusts weights internally during solving.
  
[[Image:plotG192_plotcal_B0p1_4.0.png|200px|thumb|right|plotcal B0 bandpass phase ant ea06 spw 0-31]]
 
[[Image:plotG192_plotcal_B0p2_4.0.png|200px|thumb|right|plotcal B0 bandpass phase ant ea06 spw 32-63]]
 
  
You will see in the terminal some reports of solutions failing due to "Insufficient unflagged antennas" -- note that these are for the channels we flagged earlier.
+
You will see in the terminal window some reports of solutions failing due to "Insufficient unflagged antennas"&#151;note that these are for bad channels that have been pre-flagged.
 +
 
 +
Plot the resulting bandpasses in amplitude and phase. Note that the first panel with ea01 is empty as it is completely flagged. Proceed to ea06 to see the plots as shown in Figures 5a, 5b, 6a, and 6b:
  
Plot the resulting bandpasses in amplitude and phase:
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
Line 429: Line 279:
 
</source>
 
</source>
  
In the bandpass phases you no longer see the residual antenna delays (just residual spw phase offsets from the delay solution registration), but there are some band edge effects apparent.
+
{|
 +
| [[Image:plotG192_plotcal_B0a1_4.0.png|200px|thumb|left|Figure 5a: plotcal B0 bandpass amp ant ea06 spw 0-31]]
 +
| [[Image:plotG192_plotcal_B0a2_4.0.png|200px|thumb|center|Figure 5b: plotcal B0 bandpass amp ant ea06 spw 32-63]]
 +
| [[Image:plotG192_plotcal_B0p1_4.0.png|200px|thumb|center|Figure 6a: plotcal B0 bandpass phase ant ea06 spw 0-31]]
 +
| [[Image:plotG192_plotcal_B0p2_4.0.png|200px|thumb|right|Figure 6b: plotcal B0 bandpass phase ant ea06 spw 32-63]]
 +
|}
  
 
== Bootstrapping the bandpass calibrator spectrum ==
 
== Bootstrapping the bandpass calibrator spectrum ==
  
  
Since there is no <i>a priori</i> spectral information for our chosen bandpass calibrator, 3C84, we need to bootstrap to find its spectral index, then recalibrate with this information in order to avoid folding the intrinsic spectral shape of 3C84 into our calibration.
+
Since there is no ''a priori'' spectral information for our chosen bandpass calibrator of 3C84, we need to bootstrap to find its spectral index, then recalibrate with this information in order to avoid folding the intrinsic spectral shape of 3C84 into our calibration.
 +
 
 +
First, we again do a phase-only calibration solution, this time for both the bandpass and the flux density calibrator. This will correct for decorrelation of the signals. We again use the channel range 60~68 and apply the bandpass and delay calibration tables:
 +
 
 +
<source lang="python">
 +
# In CASA: flux and bandpass calibrators gain
 +
gaincal(vis='G192-BP.ms', caltable='calG192.G1p', field='0,1', \
 +
        gaintable=['calG192.K0', 'calG192.B0'], \
 +
        gaintype='G', refant='ea05', calmode='p', solint='int', minsnr=3)
 +
</source>
 +
 
 +
Now we are ready to solve for both phase and gain for each scan:
  
First, we use the initial round of bandpass calibration to create gain solutions for the flux and bandpass calibrators:
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA: flux and bandpass calibrators gain
 
# In CASA: flux and bandpass calibrators gain
 
gaincal(vis='G192-BP.ms', caltable='calG192.G1', field='0,1', \
 
gaincal(vis='G192-BP.ms', caltable='calG192.G1', field='0,1', \
         gaintable=['calG192.K0', 'calG192.B0'], \
+
         gaintable=['calG192.K0', 'calG192.B0','calG192.G1p'], \
         gaintype='G', refant='ea05', calmode='ap', solint='30s', minsnr=3)
+
         gaintype='G', refant='ea05', calmode='ap', solint='inf', minsnr=3)
 
</source>
 
</source>
  
Now let's have a look at the phase and amplitude solutions, iterating over antenna.  We will look at the flux calibrator (3C147) and bandpass calibrator (3C84) individually since they're widely separated in time:
+
<!--
 +
Let's have a look at the phase and amplitude solutions, iterating over antenna.  We will look at the flux density calibrator (3C147) and bandpass calibrator (3C84) individually since they're widely separated in time:
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
Line 463: Line 329:
  
 
The solutions all look reasonable and relatively constant with time.   
 
The solutions all look reasonable and relatively constant with time.   
 +
-->
 +
 +
With gain solutions for the flux density and bandpass calibrators, we can now use {{fluxscale}} to scale the gain amplitudes of the bandpass calibrator using those of the flux density calibrator:
 +
  
Now that we have gain solutions for the flux and bandpass calibrators, we can use {{fluxscale}} to scale the gain amplitudes of the bandpass calibrator:
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA: bandpass calibrator gain amplitudes scaling
 
# In CASA: bandpass calibrator gain amplitudes scaling
Line 471: Line 340:
 
                   transfer='1', listfile='3C84.fluxinfo', fitorder=1)
 
                   transfer='1', listfile='3C84.fluxinfo', fitorder=1)
 
</source>
 
</source>
* <tt>flux1 = fluxscale(...)</tt>: by providing a variable <tt>flux1</tt>, we allow {{fluxscale}} to use this for the output Python dictionary it returns with lots of information about the flux scaling. You can inspect the output dictionary flux1 by typing "print flux1" at the CASA command line.
+
* <tt>flux1 = fluxscale(...)</tt>: we allow {{fluxscale}} to use the variable <tt>flux1</tt> for the Python dictionary that is returned, which has information about the flux scaling. You can inspect the dictionary flux1 by typing "print flux1" at the CASA command line.
* <tt>fluxtable='calG192.F1'</tt>: this is the output scaled gain table. Since we are only using this to find the spectral index of 3C84, we won't be using this table.
+
* <tt>fluxtable='calG192.F1'</tt>: this is the output scaled gain table. Since we are only using this to find the spectral index of 3C84, we won't be using this table.
 
* <tt>listfile='3C84.fluxinfo'</tt>: an output file that contains the derived flux values and fit information.
 
* <tt>listfile='3C84.fluxinfo'</tt>: an output file that contains the derived flux values and fit information.
 
* <tt>fitorder=1</tt>: only find a spectral index, ignoring curvature in the spectrum.
 
* <tt>fitorder=1</tt>: only find a spectral index, ignoring curvature in the spectrum.
 +
* <tt>reference='0'</tt>: the reference field ''from'' which the flux scaling is transferred (here: the flux density calibrator 3C147, field 0)
 +
* <tt>transfer='1'</tt>: the target field ''to'' which the flux scaling is transferred (here: the bandpass calibrator 3C84, field 1)
 +
  
 
The last line in the file (and displayed in the logger) shows:
 
The last line in the file (and displayed in the logger) shows:
 
<pre style="background-color: #fffacd;">
 
<pre style="background-color: #fffacd;">
Fitted spectrum for 3c84-J0319+413 with fitorder=1: Flux density = 29.0299 +/- 0.0309108 (freq=32.5128 GHz) spidx=-0.53974 +/- 0.00884919
+
Fitted spectrum for 3c84-J0319+413 with fitorder=1: Flux density = 29.0282 +/- 0.0308648 (freq=32.5128 GHz) spidx=-0.538758 +/- 0.00882913
 
</pre>
 
</pre>
  
[[Image:PlotG192-3C84-fluxspec-4.5.png|200px|thumb|right|3C84 flux values returned by fluxscale]]
+
[[Image:PlotG192-3C84-fluxspec-4.5.png|200px|thumb|right|Figure 7: 3C84 flux values returned by fluxscale]]
[[Image:ScreenshotPlotG192-setjy-bp-4.5.png|200px|thumb|right|plotms of model amp vs freq for 3C84]]
 
  
Using the information in the returned <tt>flux</tt> dictionary, we can plot the derived spectrum:
+
Using the information in the returned <tt>flux1</tt> dictionary, we can plot the derived spectrum (Figure 7):
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
Line 493: Line 364:
 
   thisspw = str(i)
 
   thisspw = str(i)
 
   spw_str.append(thisspw)
 
   spw_str.append(thisspw)
 +
</source>
 +
 +
(Note that in order to close indented python loops, conditions etc. you will have to press ''Enter'' again to execute the indented commands and to return to the CASA prompt.)
  
 +
<source lang="python">
 +
# In CASA
 
bootstrapped_fluxes = []
 
bootstrapped_fluxes = []
 
for j in spw_str:
 
for j in spw_str:
Line 501: Line 377:
 
     else:
 
     else:
 
         bootstrapped_fluxes.append(thisflux)
 
         bootstrapped_fluxes.append(thisflux)
 +
</source>
 +
  
 +
<source lang="python">
 +
# In CASA - this section creates the plot seen in Figure 7
 
pl.clf()
 
pl.clf()
 
pl.plot(freq, bootstrapped_fluxes, 'bo')
 
pl.plot(freq, bootstrapped_fluxes, 'bo')
Line 510: Line 390:
 
</source>
 
</source>
  
 +
We can use the model from {{fluxscale}} to fill the MODEL column with 3C84's spectral information using {{setjy}}. With ''standard='fluxscale''', we can directly use the <tt>flux1</tt> Python dictionary as input via ''fluxdict'':
 +
 +
[[Image:ScreenshotPlotG192-setjy-bp-4.5.png|200px|thumb|right|Figure 8: plotms of model amp vs freq for 3C84]]
  
We can use the model from {{fluxscale}} to fill the MODEL column with 3C84's spectral information using {{setjy}}. With ''standard='fluxscale''', we can directly use the flux1 python dictionary as input for ''fluxdict'':
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA: spectral information
 
# In CASA: spectral information
Line 518: Line 400:
 
</source>
 
</source>
  
 +
Check with plotms that the data have been appropriately filled (Figure 8):
  
 
Checking with plotms that the data have been appropriately filled:
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
Line 527: Line 408:
 
</source>
 
</source>
  
[[Image:plotG192_plotcal_B0a1.b_4.1.png|200px|thumb|right|plotcal B0 bootstrapped bandpass amp ant ea06 spw 0-31]]
+
Next, we redo the previous calibration using this new model information. Although the commands are the same as issued earlier, keep in mind that the model values for the bandpass calibrator have changed and, therefore, the results of these calibration calculations will differ:
[[Image:plotG192_plotcal_B0a2.b_4.1.png|200px|thumb|right|plotcal B0 bootstrapped bandpass amp ant ea06 spw 32-63]]
 
[[Image:plotG192_plotcal_B0p1.b_4.1.png|200px|thumb|right|plotcal B0 bootstrapped bandpass phase ant ea06 spw 0-31]]
 
[[Image:plotG192_plotcal_B0p2.b_4.1.png|200px|thumb|right|plotcal B0 bootstrapped bandpass phase ant ea06 spw 32-63]]
 
  
Finally, we redo the previous calibration using this new model information.  Although the commands are the same as what we issued earlier, keep in mind that the model values for the bandpass calibrator have changed, and therefore the results of these calibration calculations will differ:
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA: phase only recalibration
 
# In CASA: phase only recalibration
Line 551: Line 428:
 
</source>
 
</source>
  
It's a good idea to inspect these solutions as well:
+
Finally, we inspect these solutions (Figures 9a, 9b, 10a, and 10b):
 +
 
 
<source lang="python">
 
<source lang="python">
# In CASA
+
# In CASA - Figure 9a
 
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='amp', \
 
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='amp', \
 
         spw='0~31', iteration='antenna')
 
         spw='0~31', iteration='antenna')
#
+
# Figure 9b
 
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='amp', \
 
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='amp', \
 
         spw='32~63', iteration='antenna')
 
         spw='32~63', iteration='antenna')
#
+
# Figure 10a
 
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='phase', \
 
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='phase', \
 
         iteration='antenna', spw='0~31', \
 
         iteration='antenna', spw='0~31', \
 
         plotrange=[-1,-1,-180,180])
 
         plotrange=[-1,-1,-180,180])
#
+
# Figure 10b
 
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='phase', \
 
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='phase', \
 
         iteration='antenna', spw='32~63', \
 
         iteration='antenna', spw='32~63', \
Line 569: Line 447:
 
</source>
 
</source>
  
They look virtually unchanged from the previous solutions, with the exception that the amplitude scaling is corrected for the spectrum of 3C84.  Now that we have the final version of our delay and bandpass calibration, calG192.K0.b and calG192.B0.b will be used for all subsequent calibration steps.  
+
{|
 +
| [[Image:plotG192_plotcal_B0a1.b_4.1.png|200px|thumb|left|Figure 9a: plotcal B0 bootstrapped bandpass amp ant ea06 spw 0-31]]
 +
| [[Image:plotG192_plotcal_B0a2.b_4.1.png|200px|thumb|center|Figure 9b: plotcal B0 bootstrapped bandpass amp ant ea06 spw 32-63]]
 +
| [[Image:plotG192_plotcal_B0p1.b_4.1.png|200px|thumb|center|Figure 10a: plotcal B0 bootstrapped bandpass phase ant ea06 spw 0-31]]
 +
| [[Image:plotG192_plotcal_B0p2.b_4.1.png|200px|thumb|right|Figure 10b: plotcal B0 bootstrapped bandpass phase ant ea06 spw 32-63]]
 +
|}
 +
 
 +
They look virtually unchanged from the previous solutions with the exception that the amplitude scaling is corrected for the spectrum of 3C84.  We have the final version of our delay and bandpass calibration tables, '''calG192.K0.b''' and '''calG192.B0.b''', which can be used for all subsequent calibration steps.  
  
 
{{Checked 4.5.2}}
 
{{Checked 4.5.2}}
 
+
<!--
 
-- Original: Miriam Hartman (full G192 guide) <br />
 
-- Original: Miriam Hartman (full G192 guide) <br />
 
-- Modifications: Jose Salcido (4.5.2. 2016/04/14) <br />
 
-- Modifications: Jose Salcido (4.5.2. 2016/04/14) <br />
 
-- Topical Guide: Juergen Ott (4.5.2, 2016/04/14) <br />
 
-- Topical Guide: Juergen Ott (4.5.2, 2016/04/14) <br />
 +
-- Edits to Guide: Tony Perreault (4.5.2, 2016/05/18) <br />
 +
-->

Latest revision as of 09:41, 14 June 2016

This CASA Guide is for CASA version 4.5.2


Overview

For the standard VLA flux density calibrators 3C138, 3C147, 3C286 and 3C48, CASA includes spatial and spectral models that are applied during calibration. The models account for the source characteristics, resulting in calibration solutions that represent the instrumental and atmospheric corrections. These VLA standard calibrators, however, exhibit a negative spectral index and are relatively weak at high frequencies.

Although the standard VLA flux density calibrators are usually still bright enough for absolute flux density calibration, a good bandpass determination&#151;which is very important for spectral line observations&#151;requires large signal-to-noise ratios derived from either a long integration time or a very strong source (see the Spectral Line Guide for Observing). Observations of non-standard, but strong, bandpass calibrators are therefore common at high frequencies. Unfortunately, such sources are likely variable and no a priori flux density model is available. In particular, these sources exhibit an unknown and maybe variable spectral slope, which, if not accounted for, will create an error in the bandpass calibration. This tutorial describes how to model a spectral slope and how to correct the bandpass solution for this effect.

Data used in this guide are taken in wide 3-bit mode for the protostar G192.16-3.84 in Ka-band with basebands centered at 29 and 36.5 GHz. Each baseband has over 4 GHz of bandwidth comprising thirty-two 128-MHz spectral windows.

If you are new to CASA, or with VLA data reduction in CASA, it is strongly recommended that you start with the Getting Started in CASA guide, the IRC+10216 spectral line tutorial, or the VLA Continuum Tutorial 3C391 before proceeding with this tutorial.

Obtaining the Data

As this tutorial concerns bandpass calibration, all sources other than the flux density and bandpass calibrator scans were removed from the measurement set (MS). All pre-calibration steps including flagging, antenna position offsets, requantizer gains, opacity corrections, and gain-elevation curves were applied. The original data (TVER0004.sb14459364.eb14492359.56295.26287841435) can be obtained through the NRAO archive and has a raw size of 57.04 GB.

The trimmed measurement set can be downloaded directly from http://casa.nrao.edu/Data/EVLA/G192/G192-BP.ms.tar.gz (dataset size: 3.4 GB)

Your first step will be to unzip and untar the file in a terminal (before you start CASA):

tar -xzvf G192-BP.ms.tar.gz

Starting CASA

To start CASA, type:

casa

This will run a script initializing CASA and setting paths appropriately. The script will also create two files called ipython-<unique-stamp>.log (which contains a record of all the text you enter at the CASA prompt) as well as casapy-<unique-stamp>.log (which will contain all the messages that are printed to the CASA logger window). It is recommended that you keep your log files intact&#151;you may need them to remind you of the last step you completed in your data reduction. (It is also a good idea to include your log files when submitting a help desk ticket).

Once CASA has started, a logger window will appear. Note that you can rescale this window or change the font size (under the View menu option) as desired.

Examining the Measurement Set (MS)

We use listobs to summarize our MS:

# In CASA: listobs on the initial data set
listobs('G192-BP.ms', listfile='G192_listobs.txt')

This will write the output to a file called G192_listobs.txt, which we can print to the terminal using various Unix/Linux commands such as cat, less, or more:

# In CASA
cat G192_listobs.txt
================================================================================
           MeasurementSet Name:  /lustre/aoc/sciops/jott/casa/topicalguide/bandpass/new/G192-BP.ms      MS Version 2
================================================================================
   Observer: Dr. Debra Shepherd     Project: uid://evla/pdb/7303457  
Observation: EVLA
Data records: 1769355       Total elapsed time = 4563 seconds
   Observed from   03-Jan-2013/06:31:48.0   to   03-Jan-2013/07:47:51.0 (UTC)

   ObservationID = 0         ArrayID = 0
  Date        Timerange (UTC)          Scan  FldId FieldName             nRows     SpwIds   Average Interval(s)    ScanIntent
  03-Jan-2013/06:31:48.0 - 06:36:42.0     6      0 3C147                   704865  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63]  [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5.94, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [CALIBRATE_FLUX#UNSPECIFIED,OBSERVE_TARGET#UNSPECIFIED]
              07:40:27.0 - 07:47:51.0    64      1 3c84-J0319+413         1064490  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]  [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [CALIBRATE_BANDPASS#UNSPECIFIED,OBSERVE_TARGET#UNSPECIFIED]
           (nRows = Total number of rows per scan) 
Fields: 2
  ID   Code Name                RA               Decl           Epoch   SrcId      nRows
  0    E    3C147               05:42:36.137916 +49.51.07.23356 J2000   0         704865
  1    F    3c84-J0319+413      03:19:48.160102 +41.30.42.10305 J2000   1        1064490
Spectral Windows:  (64 unique spectral windows and 1 unique polarization setups)
  SpwID  Name            #Chans   Frame   Ch0(MHz)  ChanWid(kHz)  TotBW(kHz) CtrFreq(MHz) BBC Num  Corrs  
  0      EVLA_KA#A1C1#2     128   TOPO   34476.000      1000.000    128000.0  34539.5000       10  RR  LL
  1      EVLA_KA#A1C1#3     128   TOPO   34604.000      1000.000    128000.0  34667.5000       10  RR  LL
  2      EVLA_KA#A1C1#4     128   TOPO   34732.000      1000.000    128000.0  34795.5000       10  RR  LL
  3      EVLA_KA#A1C1#5     128   TOPO   34860.000      1000.000    128000.0  34923.5000       10  RR  LL

<snip>

  13     EVLA_KA#A1C1#15    128   TOPO   36140.000      1000.000    128000.0  36203.5000       10  RR  LL
  14     EVLA_KA#A1C1#16    128   TOPO   36268.000      1000.000    128000.0  36331.5000       10  RR  LL
  15     EVLA_KA#A1C1#17    128   TOPO   36396.000      1000.000    128000.0  36459.5000       10  RR  LL
  16     EVLA_KA#A2C2#18    128   TOPO   36476.000      1000.000    128000.0  36539.5000       11  RR  LL
  17     EVLA_KA#A2C2#19    128   TOPO   36604.000      1000.000    128000.0  36667.5000       11  RR  LL
  18     EVLA_KA#A2C2#20    128   TOPO   36732.000      1000.000    128000.0  36795.5000       11  RR  LL
 
<snip>

  29     EVLA_KA#A2C2#31    128   TOPO   38140.000      1000.000    128000.0  38203.5000       11  RR  LL
  30     EVLA_KA#A2C2#32    128   TOPO   38268.000      1000.000    128000.0  38331.5000       11  RR  LL
  31     EVLA_KA#A2C2#33    128   TOPO   38396.000      1000.000    128000.0  38459.5000       11  RR  LL
  32     EVLA_KA#B1D1#34    128   TOPO   26976.000      1000.000    128000.0  27039.5000       13  RR  LL
  33     EVLA_KA#B1D1#35    128   TOPO   27104.000      1000.000    128000.0  27167.5000       13  RR  LL
  34     EVLA_KA#B1D1#36    128   TOPO   27232.000      1000.000    128000.0  27295.5000       13  RR  LL

<snip>

  45     EVLA_KA#B1D1#47    128   TOPO   28640.000      1000.000    128000.0  28703.5000       13  RR  LL
  46     EVLA_KA#B1D1#48    128   TOPO   28768.000      1000.000    128000.0  28831.5000       13  RR  LL
  47     EVLA_KA#B1D1#49    128   TOPO   28896.000      1000.000    128000.0  28959.5000       13  RR  LL
  48     EVLA_KA#B2D2#50    128   TOPO   28976.000      1000.000    128000.0  29039.5000       14  RR  LL
  49     EVLA_KA#B2D2#51    128   TOPO   29104.000      1000.000    128000.0  29167.5000       14  RR  LL
  50     EVLA_KA#B2D2#52    128   TOPO   29232.000      1000.000    128000.0  29295.5000       14  RR  LL

<snip>

  61     EVLA_KA#B2D2#63    128   TOPO   30640.000      1000.000    128000.0  30703.5000       14  RR  LL
  62     EVLA_KA#B2D2#64    128   TOPO   30768.000      1000.000    128000.0  30831.5000       14  RR  LL
  63     EVLA_KA#B2D2#65    128   TOPO   30896.000      1000.000    128000.0  30959.5000       14  RR  LL
Sources: 128
  ID   Name                SpwId RestFreq(MHz)  SysVel(km/s) 
  0    3C147               0     -              -            
  0    3C147               1     -              -            
  0    3C147               2     -              -            

<snip>

  0    3C147               61    -              -            
  0    3C147               62    -              -            
  0    3C147               63    -              -            
  1    3c84-J0319+413      0     -              -            
  1    3c84-J0319+413      1     -              -            
  1    3c84-J0319+413      2     -              -            

<snip>

  1    3c84-J0319+413      61    -              -            
  1    3c84-J0319+413      62    -              -            
  1    3c84-J0319+413      63    -              -            

<snip>

This small MS contains only scans on the flux density calibrator 3C147 (field 0) and the bandpass calibrator 3C84 (field 1). Both fields have 64 spectral windows (spws); each spw is comprised of 128 channels, each channel being 1 MHz wide, for a total bandwidth of 128 MHz / spw.

Setting the Model of the Flux Density Calibrator

To start, we insert the spectral (using the 'Perley-Butler 2013' standard) and spatial (3C147_A.im for Ka-band) models for the flux density calibrator 3C147 (field 0) with the setjy task:

# In CASA: model for the flux density calibrator
setjy(vis='G192-BP.ms', field='0', scalebychan=True, \
      standard='Perley-Butler 2013', model='3C147_A.im')
Figure 1: plotms of model amp vs freq for 3C147
  • scalebychan=True: If scalebychan=False setjy would use a single value per spectral window.

Inspecting the logger report shows that 3C147 has amplitudes ranging from ~1.0-1.47 Jy across all spws.

We can plot the model data using plotms (Figure 1):

# In CASA
plotms(vis='G192-BP.ms', field='0', antenna='ea03', \
       xaxis='freq', yaxis='amp', ydatacolumn='model',coloraxis='ant2')

This plot shows baselines to antenna ea03. Since we provided both a spectral and a spatial model for this well resolved calibrator, each baseline has a somewhat different behavior.

Calibrating delays and initial bandpass solutions

As a first step, we need to specify a reference antenna for all phase calibrations. It is desirable to use an antenna that is near the center of the array and has the least amount of calibrator data flagged. The array can be mapped with plotants:

# In CASA: plotting antenna locations
plotants(vis='G192-BP.ms')

Although the plot is a bit crowded (Figure 2), a zooming in (with the magnifying glass icon) shows that ea05 is located close to the center. It also has a comparably small number of flags and we will use this antenna as our reference.

Figure 2: plotants plotter

We start with a phase-only, time-dependent calibration solution for the bandpass calibrator. Solutions for each integration will remove most of the decorrelation of the signal. For best results, we will derive the phase variations from a narrow range of channels (60~68) near the centers of each spws:

# In CASA: phase only calibration
gaincal(vis='G192-BP.ms', caltable='calG192.G0', \
        field='1', spw='*:60~68', \
        gaintype='G', refant='ea05', calmode='p', \
        solint='int', minsnr=3)
  • refant='ea05' : Use ea05 as the reference antenna
  • solint='int' : Do a per-integration solve (every 6 seconds, since we've time-averaged the data).
  • minsnr=3 : Apply a minimum signal-to-noise cutoff. Solutions with less than this value will be flagged.
  • gaintable is not set here as we have already applied pre-calibrations.

Plot the phase solutions (using full phase range -180 to 180 instead of autorange):

# In CASA
plotcal(caltable='calG192.G0', xaxis='time', yaxis='phase', \
        iteration='antenna', plotrange=[-1,-1,-180,180])

Click on the Next button to navigate through the antennas. Click on the Quit button to exit the viewer.

We will now produce multipanel plots of the phase solutions, writing the plots to output files as well as on the screen (Figures 3a & 3b). The output files generated are PNG files and can be viewed within CASA by executing an external viewer program, e.g., !xv plotG192_plotcal_G0p1.png; or by running an image viewing application such as xv, Preview, Gimp, Photoshop, etc., external to CASA at the OS level.

# In CASA
plotcal(caltable='calG192.G0', xaxis='time', yaxis='phase', \
        antenna='0~10,12~15', subplot=531, iteration='antenna', \
        plotrange=[-1,-1,-180,180], fontsize=8.0, \
        markersize=3.0, figfile='plotG192_plotcal_G0p1.png')
plotcal(caltable='calG192.G0', xaxis='time', yaxis='phase', \
        antenna='16~26', subplot=531, iteration='antenna', \
        plotrange=[-1,-1,-180,180], fontsize=8.0, \
        markersize=3.0, figfile='plotG192_plotcal_G0p2.png')
Figure 3a: plotcal G0 phase ant 0~15
Figure 3b: plotcal G0 phase ant 16~26


We can now solve for the residual delays using the parameter gaintype='K' option in gaincal. Note that this currently does not do a global fringe-fitting solution for delays, but instead does a baseline-based delay solution per spw for all baselines to the reference antenna, treating these as antenna-based delays. In most cases, with high enough S/N to get baseline-based delay solutions, this will suffice. We avoid the edge channels of each spectral window by selecting channels 5~122:

# In CASA: residual delays
gaincal(vis='G192-BP.ms', caltable='calG192.K0', \
        field='1', spw='*:5~122', gaintype='K', \
        gaintable=['calG192.G0'],
        refant='ea05', solint='inf', minsnr=3)

Note that we have also pre-applied our initial phase table calG192.G0.

Alternatively, you can also derive a delay across all spws of a baseband. If this is desired, use parameter combine='spw' in gaincal and run the task for each baseband separately. The solutions from the second and following runs can be appended to the same calibration table via parameter append=T.

Figure 4: plotcal K0 delay vs. antenna

Now plot the delays, in nanoseconds, as a function of antenna index (you will get one for each spw and polarization):

# In CASA
plotcal(caltable='calG192.K0', xaxis='antenna', yaxis='delay')

The delays range from around -5 to 4 nanoseconds (Figure 4).

Now solve for the antenna bandpasses using the previously generated tables calG192.G0 and calG192.K0:

# In CASA: antenna bandpasses
bandpass(vis='G192-BP.ms', caltable='calG192.B0', \
         gaintable=['calG192.G0', 'calG192.K0'], \
         field='1', refant='ea05', solnorm=False, \
         bandtype='B', solint='inf')

WARNING: You must set solnorm=False here or later on you will find some offsets among spws due to the way the amplitude scaling adjusts weights internally during solving.


You will see in the terminal window some reports of solutions failing due to "Insufficient unflagged antennas"&#151;note that these are for bad channels that have been pre-flagged.

Plot the resulting bandpasses in amplitude and phase. Note that the first panel with ea01 is empty as it is completely flagged. Proceed to ea06 to see the plots as shown in Figures 5a, 5b, 6a, and 6b:

# In CASA
plotcal(caltable='calG192.B0', xaxis='freq', yaxis='amp', \
        spw='0~31', iteration='antenna')
#
plotcal(caltable='calG192.B0', xaxis='freq', yaxis='amp', \
        spw='32~63', iteration='antenna')
#
plotcal(caltable='calG192.B0', xaxis='freq', yaxis='phase', \
        iteration='antenna', spw='0~31', \
        plotrange=[-1,-1,-180,180])
#
plotcal(caltable='calG192.B0', xaxis='freq', yaxis='phase', \
        iteration='antenna', spw='32~63', \
        plotrange=[-1,-1,-180,180])
Figure 5a: plotcal B0 bandpass amp ant ea06 spw 0-31
Figure 5b: plotcal B0 bandpass amp ant ea06 spw 32-63
Figure 6a: plotcal B0 bandpass phase ant ea06 spw 0-31
Figure 6b: plotcal B0 bandpass phase ant ea06 spw 32-63

Bootstrapping the bandpass calibrator spectrum

Since there is no a priori spectral information for our chosen bandpass calibrator of 3C84, we need to bootstrap to find its spectral index, then recalibrate with this information in order to avoid folding the intrinsic spectral shape of 3C84 into our calibration.

First, we again do a phase-only calibration solution, this time for both the bandpass and the flux density calibrator. This will correct for decorrelation of the signals. We again use the channel range 60~68 and apply the bandpass and delay calibration tables:

# In CASA: flux and bandpass calibrators gain
gaincal(vis='G192-BP.ms', caltable='calG192.G1p', field='0,1', \
        gaintable=['calG192.K0', 'calG192.B0'], \
        gaintype='G', refant='ea05', calmode='p', solint='int', minsnr=3)

Now we are ready to solve for both phase and gain for each scan:

# In CASA: flux and bandpass calibrators gain
gaincal(vis='G192-BP.ms', caltable='calG192.G1', field='0,1', \
        gaintable=['calG192.K0', 'calG192.B0','calG192.G1p'], \
        gaintype='G', refant='ea05', calmode='ap', solint='inf', minsnr=3)


With gain solutions for the flux density and bandpass calibrators, we can now use fluxscale to scale the gain amplitudes of the bandpass calibrator using those of the flux density calibrator:


# In CASA: bandpass calibrator gain amplitudes scaling
flux1 = fluxscale(vis='G192-BP.ms', caltable='calG192.G1', \
                  fluxtable='calG192.F1', reference='0', \
                  transfer='1', listfile='3C84.fluxinfo', fitorder=1)
  • flux1 = fluxscale(...): we allow fluxscale to use the variable flux1 for the Python dictionary that is returned, which has information about the flux scaling. You can inspect the dictionary flux1 by typing "print flux1" at the CASA command line.
  • fluxtable='calG192.F1': this is the output scaled gain table. Since we are only using this to find the spectral index of 3C84, we won't be using this table.
  • listfile='3C84.fluxinfo': an output file that contains the derived flux values and fit information.
  • fitorder=1: only find a spectral index, ignoring curvature in the spectrum.
  • reference='0': the reference field from which the flux scaling is transferred (here: the flux density calibrator 3C147, field 0)
  • transfer='1': the target field to which the flux scaling is transferred (here: the bandpass calibrator 3C84, field 1)


The last line in the file (and displayed in the logger) shows:

Fitted spectrum for 3c84-J0319+413 with fitorder=1: Flux density = 29.0282 +/- 0.0308648 (freq=32.5128 GHz) spidx=-0.538758 +/- 0.00882913
Figure 7: 3C84 flux values returned by fluxscale

Using the information in the returned flux1 dictionary, we can plot the derived spectrum (Figure 7):

# In CASA
freq = flux1['freq'] / 1e9
spw_list = range(0,64)
spw_str = []
for i in spw_list:
   thisspw = str(i)
   spw_str.append(thisspw)

(Note that in order to close indented python loops, conditions etc. you will have to press Enter again to execute the indented commands and to return to the CASA prompt.)

# In CASA
bootstrapped_fluxes = []
for j in spw_str:
    thisflux = flux1['1'][j]['fluxd'][0]
    if thisflux ==None:
        continue
    else:
        bootstrapped_fluxes.append(thisflux)


# In CASA - this section creates the plot seen in Figure 7
pl.clf()
pl.plot(freq, bootstrapped_fluxes, 'bo')
pl.xlabel('Frequency (GHz)')
pl.ylabel('Flux Density (Jy)')
pl.title('3C84')
pl.show()

We can use the model from fluxscale to fill the MODEL column with 3C84's spectral information using setjy. With standard='fluxscale', we can directly use the flux1 Python dictionary as input via fluxdict:

Figure 8: plotms of model amp vs freq for 3C84
# In CASA: spectral information
setjy(vis='G192-BP.ms', field='1', scalebychan=True, \
      standard = 'fluxscale', fluxdict=flux1)

Check with plotms that the data have been appropriately filled (Figure 8):

# In CASA
plotms(vis='G192-BP.ms', field='1', antenna='ea05&ea02', \
       xaxis='freq', yaxis='amp', ydatacolumn='model')

Next, we redo the previous calibration using this new model information. Although the commands are the same as issued earlier, keep in mind that the model values for the bandpass calibrator have changed and, therefore, the results of these calibration calculations will differ:

# In CASA: phase only recalibration
gaincal(vis='G192-BP.ms', caltable='calG192.G0.b', \
        field='1', spw='*:60~68', \
        gaintype='G', refant='ea05', calmode='p', \
        solint='int', minsnr=3) 
# In CASA: residual delays recalibration
gaincal(vis='G192-BP.ms', caltable='calG192.K0.b', \
        gaintable=['calG192.G0.b'], \
        field='1', spw='*:5~122', gaintype='K', \
        refant='ea05', solint='inf', minsnr=3)
# In CASA: antenna bandpasses recalibration
bandpass(vis='G192-BP.ms', caltable='calG192.B0.b', \
         gaintable=['calG192.G0.b', 'calG192.K0.b'], \
         field='1', refant='ea05', solnorm=False, \
         bandtype='B', solint='inf')

Finally, we inspect these solutions (Figures 9a, 9b, 10a, and 10b):

# In CASA - Figure 9a
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='amp', \
        spw='0~31', iteration='antenna')
# Figure 9b
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='amp', \
        spw='32~63', iteration='antenna')
# Figure 10a
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='phase', \
        iteration='antenna', spw='0~31', \
        plotrange=[-1,-1,-180,180])
# Figure 10b
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='phase', \
        iteration='antenna', spw='32~63', \
        plotrange=[-1,-1,-180,180])
Figure 9a: plotcal B0 bootstrapped bandpass amp ant ea06 spw 0-31
Figure 9b: plotcal B0 bootstrapped bandpass amp ant ea06 spw 32-63
Figure 10a: plotcal B0 bootstrapped bandpass phase ant ea06 spw 0-31
Figure 10b: plotcal B0 bootstrapped bandpass phase ant ea06 spw 32-63

They look virtually unchanged from the previous solutions with the exception that the amplitude scaling is corrected for the spectrum of 3C84. We have the final version of our delay and bandpass calibration tables, calG192.K0.b and calG192.B0.b, which can be used for all subsequent calibration steps.

Last checked on CASA Version 4.5.2