View source for VLA CASA Bandpass Slope-CASA6.4.1

<b>This CASA Guide is for CASA version 6.4.1</b>

<pre style="background-color: #AAffff;">
If new to CASA, or to VLA data reduction in CASA,
</pre> 
it is '''strongly''' recommended to start with
the [https://casaguides.nrao.edu/index.php?title=Getting_Started_in_CASA Getting Started in CASA] guide,
the [https://casaguides.nrao.edu/index.php/VLA_high_frequency_Spectral_Line_tutorial_-_IRC%2B10216-CASA6.5.2 IRC+10216 spectral line tutorial],
or the [https://casaguides.nrao.edu/index.php/VLA_Continuum_Tutorial_3C391-CASA6.4.1 VLA 3C391 Continuum Tutorial]
<pre style="background-color: #AAffff;">
before proceeding with this tutorial.
</pre> 
Also note that this guide is meant to be used with monolithic CASA and not pip-wheel, because the GUIs have not been validated.
<pre style="background-color: #98FB98;">
This tutorial uses the default calibrator model distributed with this version.
A different CASA version or CASA update may provide a different model than used
  here, in particular in Ka-band (a.k.a. A-band), i.e., in the 28-40 GHz range,
  so be aware when using CASA installed on the NRAO machines that are current.
The plots and results will be different than in this guide in those cases.
</pre>
For the purpose of this tutorial it is also safe to ignore any
annoying messages about the "leap second table" being "out of date"..



== Purpose Background and Method Overview ==

For the standard VLA flux density calibrators 3C138, 3C147, 3C286 and
3C48, the CASA distribution includes angular and spectral models that
are referenced during calibration (but see the note in bright lime
green at the top of this tutorial). The models account for the
observational source characteristics for these standard calibrators,
resulting in improved calibration solutions that therefore more
accurately represent the instrumental and atmospheric
corrections. These VLA standard calibrators, however, exhibit a
negative spectral index (i.e., flux density decreasing with increasing
frequency) and become relatively weak at high frequencies.

Although the standard VLA flux density calibrators are usually still
sufficiently bright enough for absolute flux density calibration even
at the higher frequency bands, a good delay and bandpass determination
&mdash; which is important for spectral line observations or measuring
the spectral index of continuum sources &mdash; may require higher
signal-to-noise ratio bandpass solutions derived from either a long
integration time or a very bright source (refer to the
[https://science.nrao.edu/facilities/vla/docs/manuals/obsguide/modes/line#section-8 Spectral Line (Bandpass Setup) section within the Guide to Observing with the VLA]).
Generally, instead of a long integration on a fainter source,
additional observations of non-standard, but bright bandpass
calibrator alternatives are used. Unfortunately, the spectral
characteristics of these sources will be undetermined, and no
''a-priori'' flux density model is available. If not accounted for,
using undetermined spectral behavior will introduce bias in the delay
and bandpass calibration.

Whereas there are different ways of doing this depending on the
trade-offs one chooses to make, here, instead of using a bandpass
determined from the standard flux density calibrator that might be
noisier and transferring this noise to the target source, a less noisy
bandpass from a different source is applied. To avoid bias to an
undesired and initially unknown spectral index of that source, one
needs to determine and model its spectral behavior so that it can be
taken out during the determination of the bandpass using this brighter
(higher signal-to noise) source.

This tutorial describes how to determine the spectral characteristics
for a point like (!) source and how to correct the delay and bandpass
solutions for such an effect using alternative bandpass calibrator
sources. For that follow these four general steps (below) to obtain a
delay and bandpass calibration as determined from a non-standard
bandpass calibrator source, and avoid including the (higher) noise of
the flux density calibrator in the bandpass:

<pre style="background-color: #fffacd;">
A) After a phase-only self-calibration iteration, determine a good delay and bandpass
   calibration using the alternative bandpass calibrator.
B) Use this first order delay and bandpass solution to calibrate the standard flux densty
   calibrator using the the non-standard bandpass calibrator source. This will bias the
   standard flux density calibrator observations with the spectral features of the
   bandpass calibrator (which contamination will be taken out in the next step, C).
C) Compare the newly self-calibrated and initial bandpass-corrected non-standard bandpass
   calibrator fluxes (as function of frequency) with the self-calibrated and initial
   bandpass-corrected standard flux density calibrator. Fitting these fluxes will yield
   the spectral characteristics, including the spectral index, of the bandpass calibrator. 
D) With this flux density and spectral index model, determine a new, second order delay
   and bandpass calibration using the non-standard bandpass calibrator source.

This method thus does not use the flux density calibrator for bandpass calibration; the
flux density calibrator is only used for obtaining the model spectral behavior of the
alternative bandpass calibrator, which then can be used as if it were a standard.
</pre>

These resulting delay and bandpass solution tables then can be applied
to the rest of the (target) data.


== Obtaining the (Tutorial-Modified) Data and Starting CASA ==

The data used in this guide are taken for a target field in Ka-band
with four 2-GHz basebands paired to two chunks of 4 GHz contiguous
frequency coverage centered at 29 and 36.5 GHz for a total of 8 GHz
frequency coverage. That is, each baseband consists of a series of 16
consecutive 128-MHz subbands "attached" to another baseband for a
total of 64 individual spectral windows.

As this tutorial only concerns bandpass calibration, all scans on
fields other than the flux density and bandpass calibrator fields 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://data.nrao.edu NRAO archive] and has a
raw size of 57.04 GB.

The trimmed measurement set used here, G192-BP.ms, 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]
either by clicking on the link or by using operating system tools like <tt>wget</tt> (dataset size: 3.4 GB).

The first step will be to uncompress and untar the file in a terminal
(before starting CASA):

<pre style="background-color: #cccccc;">
# in a terminal, outside of CASA, prepare the measurement set:
cd <working directory>       (this tutorial uses up to 10GB in this directory)
wget http://casa.nrao.edu/Data/EVLA/G192/G192-BP.ms.tar.gz
tar -xzf G192-BP.ms.tar.gz 
</pre>

Then move to the working directory (if not the same as the location of
the raw data) and optionally move/copy or link the data to a preferred
shortcut name; the ''my-vis'' used below is just an example name and
can be changed to anything more descriptive and less cryptic. This is
particularly useful if the original observation name has it's full
extent like ''TVER0004.sb14459364.eb14492359.56295.26287841435'', for
example:

<pre style="background-color: #cccccc;">
# OPTIONAL !
# in a terminal, outside of CASA, rename (shorten) the MS to a link:
ln -s   <path-to-data>/G192-BP.ms   my-vis.ms  
</pre>

To start CASA, type:

<pre style="background-color: #cccccc;">
# in a terminal, outside of CASA, start the default package:
casa
</pre>

or better, to make sure to be running the CASA version for which this
tutorial was designed (the exact name of the installed versions to
choose from can be obtained by typing ''casa -ls''):

<pre style="background-color: #cccccc;">
# in a terminal, outside of CASA, start the specific package:
casa -r 6.4.1-12-pipeline-2022.2.0.64
</pre>

This will initialize CASA, set the necessary paths appropriately and
start up a logger window on the desktop. Starting CASA will also create
<!-- two files; one is ''ipython-<unique-timestamp>.log'' which contains a record of all the text entered at the CASA prompt, and another as ''casapy-<unique-timestamp>.log'' -->
a file ''casa-<date-time>.log''
which will contain all the messages
that are printed to the CASA logger window. It is recommended to keep
log files intact as a reminder of the steps completed in the data
reduction. The most recent (i.e., current) log file may also be
helpful when submitting a helpdesk ticket.

For convenience, the logger window can be rescaled and the font size
can be changed as desired; see the options under the '''View'''
menu. More easily, remove clutter columns by moving the mouse to the
logger window and press "Ctrl-N", "Ctrl-Y","Ctrl-E", ('''N'''ew
'''Y'''ears '''E'''ve is one way to remember).

== Examining the Measurement Set (MS) and Pre-Defining Variables ==

In order to proceed it is important to know the details of the
observation as captured in the data set. Typical pieces of information
to look for are 1) the calibrator sources with their intents, and 2)
finding a reference antenna, next to the overal structure of the
frequency setup and sequence of observing scans (fields). Furthermore,
at this stage it is also useful to 3) set the flux model for the
standard flux density calibrator, although that can be done at later
stages too; it is done here to finish up the preparations before doing
the actual bandpass calibration procedure.

From here on in this tutorial, the actual ''G192-BP.ms'' name string
of the MS directory will be replaced by the variable value stored in
''id_ms'' (identify-the-MS). This makes the instructions below less
dependent on the actual name of the data root so that if another
measurement set name is used, only the definition here needs to be
edited to the other file name. In order to do that first define these
parameters as variables inside of CASA:

<pre style="background-color: #d8bfd8;">
# In CASA, define the data root and MS:
visbase = 'G192-BP'       (or whatever base name the data set or link to be used has,
                           like "my-vis" above - without the ".ms" !)
id_ms = visbase + '.ms'   (this should match and find the measurement set created earlier)
</pre>

=== Frequency setup, calibrator intents, scan listing, etc ===

Use [https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.information.listobs.html listobs] to summarize the MS (the summary mode of [https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.information.vishead.html vishead] does not reveal calibrator intents by default):

<source lang="python">
# In CASA, dump the MS listing into a file in the working directory:
listobs(vis=id_ms, listfile=visbase+'_list.txt');
</source>
* Note the use of a semicolon (i.e., '';'') ending the command after the parentheses. This will prevent the return dictionary to be printed in the terminal.

This will write the ''listobs'' output of the input MS (stored as the
variable id_ms) to a file called <tt><visbase>_list.txt</tt>, which
can be examined using various operating system tools in a terminal, or
within CASA by using an explanation mark '''(''!'')''' before the
command, such as <tt>cat</tt>, <tt>less</tt>, or
<tt>more</tt>. Preferably use an editor and keep it on the desktop for
frequent consultation with e.g. <tt>emacs</tt>, <tt>vi</tt>, or
<tt>gedit</tt>, etc.:

<source lang="python">
# In CASA, view the file with the MS listing, e.g.:
!emacs <visbase>_list.txt
</source>

<pre style="background-color: #fffacd;">
================================================================================
           MeasurementSet Name:  <path-to-data>/<visbase>.ms      MS Version 2
================================================================================
   Observer: Dr. Observer     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>
Antennas: 22:
  ID   Name  Station   Diam.    Long.         Lat.                Offset from array center (m)                ITRF Geocentric coordinates (m)        
                                                                     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
<snip>
</pre>

This redacted MS contains only scans on the flux density calibrator
3C147 (field 0) and the bandpass calibrator 3C84 (field 1, here named
''3c84-J0319+413''). Both fields have 64 spectral windows (spws); each
spw consists of 128 dual (RR, LL) polarization 1 MHz wide channels,
for a total bandwidth of 128 MHz per spw.

As the intents for these fields are clearly included in the scan
listing (''CALIBRATE_FLUX'' and ''CALIBRATE_BANDPASS''), for
convenience now introduce new variables to recall the general use of
these intents without having to remember their details like field ID
numbers or name strings &mdash; variable names aren't strings so
therefore using them also avoids typing the quotes when filling in
task parameters (as will be seen in most of the below):

<pre style="background-color: #d8bfd8;">
# In CASA, define field identifiers using standard variable names:
id_flx = '3C147' (or '0' for using the field name or field ID respectively)
id_bp  = '3c84-J0319+413' (or '1')
</pre>


=== Selecting a reference antenna form the array configuration ===

As a first step, a reference antenna for all phase calibrations needs
to be specified. Since observations were performed with the VLA in the
most extended A-configuration, it is desirable to use an antenna that
is near the center of the array (for the most compact D-configuration
select an antenna that is unlikely to get shadowed, i.e., in the
outskirts of the array). Futhermore, it needs to have valid calibrator
data on all baselines with the least amount of data flagged. The array
can be mapped with
[https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.visualization.plotants.html plotants]:

<source lang="python">
# In CASA, plot antenna locations:
plotants(vis=id_ms, antindex=True, logpos=True)
</source>
* <tt>antindex=True</tt> also labels the antennas with their ID number (in parenteses).
* <tt>logpos=True</tt> shows a logarithmic plot to emphasize the center distribution, useful for picking a reference antenna in extended configurations - use the default (blank) for compact configurations to emphasize the outlyer stations. 
 
{|
| [[Image:Fig_01a.c641.png|200px|thumb|left|Figure 1a: Antenna locations logarithmic-scale plotted with plotants() task.]]
| [[Image:Fig_01b.c641.png|200px|thumb|right|Figure 1b: Antenna locations linear-scale plotted with plotants() task.]]
|}

As can be seen (Figure 1), either with the logarithmic-scale or
possibly after zooming in with the magnifying glass icon on the
linear-scale, for example antenna "ea05" (with ID 3) is located close
to the center. Using the
[https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.visualization.plotants.html flagdata]
task with mode='summary' it can be seen that "ea05" has a low
percentage of flags. Therefore, this antenna can be used relatively
safely as the reference antenna; for convenience (and for people with
memory issues) introduce a standard descriptive variable name for it:

<pre style="background-color: #d8bfd8;">
# In CASA, define the reference antenna identifier using a standard name:
id_ref = 'ea05' (or '3' or 'W08' which are the antenna ID number or pad name for ea05)
</pre>

=== Setting the Model of the Flux Density Calibrator ===


To start preparing for a flux comparison later (below, in step C
mentioned above), insert the spectral and angular models for the flux
density calibrator, here 3C147 (field 0), with the

[https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.imaging.setjy.html setjy] task. Use the 'Perley-Butler 2017' standard and 3C147_A.im for
Ka-band (which can be written as ''id_flx+'_A.im'' because here
fortunately id_flx and the model name agree):

<source lang="python">
# In CASA, insert the the flux density calibrator model in the MS model column:
setjy(vis=id_ms, field=id_flx, scalebychan=True,
      standard='Perley-Butler 2017', model=id_flx+'_A.im');
</source>
* <tt>scalebychan=True</tt>: If ''scalebychan=False'' [https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.imaging.setjy.html setjy] would use a single value per spectral window.

[[Image:Fig_02.c641.png|200px|thumb|right|Figure 2: plotms() output for model amplitudes as function of freqency for the flux density calibrator 3C147]]

Inspecting the logger report shows that the flux density calibrator
(3C147 here) has amplitudes ranging from about 1.0 to about 1.5 Jy
across the spws. (Note the output sorting in increasing spw number,
not in frequency.)

The model data can be plotted, e.g. for antenna 'ea03', using
[https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.visualization.plotms.html plotms] (Figure 2):

<source lang="python">
# In CASA, plot the model amplitude as function of frequency
#   for baselines to antenna ea03:
plotms(vis=id_ms, field=id_flx, antenna='ea03', 
       xaxis='freq', yaxis='amp', ydatacolumn='model',coloraxis='ant2')
</source>

This plot shows baselines to antenna "ea03". Since both a spectral and
an angular model for this well resolved calibrator was provided, each
baseline has a somewhat different amplitude and phase behavior as
function of frequency for baselines to this antenna. Selecting another
antenna for the plot will slightly change the curves. Also, a
different CASA version or other update (or picking the wrong
sourceName_frequencyBand model) would show a different behavior.

== A) Calibrating Initial Delays and Bandpass Solutions ==

Start this entire bandpass calibration procedure with a phase-only,
time-dependent (self-)calibration solution for the bandpass
calibrator using the parameter ''gaintype='G' ''option in
[https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.calibration.gaincal.html gaincal], which should be easy for these supposedly bright
sources. Note that the calibration solutions are written to a
calibration table, <tt>cal.ign</tt>, where 'ign' after the
standardized calibration table prefix name (''cal'')stands for
per-integration gain. This table is not a variable but a
file/directory name in the working directory so it needs to be quoted
in the input parameter. Solutions for each integration will remove
most of the (phase) decorrelation of the signal allowing for the
delay/bandpass calibration to succeed. For best results, the phase
variations will be derived from a narrow range of channels near the
centers of each spw (say 60~68 here, out of the 128 available per
spw), narrow enough to not be affected by uncorrected delay (i.e.,
reduce possible decorrelation by excluding any large phase differences
due to a phase slope over the frequency range of the spw):

<source lang="python">
# In CASA, do a phase-only self-calibration on the bandpass calibrator
#   (self-calibrating amplitudes is premature before determining a
#   preliminary bandpass calibration and will be done in a later step):
gaincal(vis=id_ms, caltable='cal.ign', 
        field=id_bp, spw='*:60~68',
        gaintype='G', refant=id_ref, calmode='p', 
        solint='int', minsnr=3)
</source>
* <tt>refant=id_ref</tt> : Use the variable identifying the reference antenna (ea05 here) as the reference antenna.
* <tt>solint='int'</tt> : Do a per-integration solve (every 6 seconds, which is the time-averaging applied to the data for this tutorial).
* <tt>minsnr=3</tt> : Apply a minimum signal-to-noise cutoff of three. Solutions with less signal-to-noise than this value will be flagged.
* <tt>gaintable</tt> is not set here as earlier calibrations (to prepare for this tutorial have already been applied.


Check that the phase solutions, colored by spw, are smoothly varying,
supposedly reflecting the changing phases due to atmospheric effects
(i.e., for these high frequencies due to the troposphere):

<source lang="python">
# In CASA, plot phase solutions as function of time, displaying one-for-one
#   a plot for baselines to each antena:
plotms(vis='cal.ign', xaxis='time', yaxis='phase', iteraxis='antenna', coloraxis='spw')
</source>

Note that the first plot showing the results for antenna ea01 is empty
as ea01 previously was fully flagged, and so were ea10, ea13 and ea19
(ea04 was not part of the array configuration). Click on the green
arrows to pass through the plots for the other antennas. Also note
that the reference antenna (ea05 here) has all phases equal to zero
degrees, which they should be by definition for the reference antenna.

Multipanel plots of the phase solutions can be written to output files
as well as to the screen display (Figures 3a, 3b, & 3c). The output
files that are generated are in the format of their extention in the
<tt>plotfile</tt> parameter, in this case PNG files, and if the
parameter is set to create output in the working directory, can be
viewed at any occasion using CASA by executing an external viewer
program, e.g., <tt>!xv <visbase>_cal.ign_p1*.png</tt>; or by running
an image viewing application such as xv, Preview, Gimp, Photoshop,
etc., external to CASA at the operating system level.  Note that the
hardcopy only shows the first page, requiring three <tt>plotms</tt>
instances of 3x3 plots to cover all antennas in the MS.

<source lang="python">
# In CASA, dump images of multi-panel plots for different antenna-ID ranges
#   (antenna-ID 1 to 10, not 1-9 as the antenna with index 7 is not present
#   as can be noticed in the MS summary <visbase>_list.txt.
#   Unlike anything else in CASA-python indexing, antenna IDs start at one
#   instead of zero):
plotms(vis='cal.ign', xaxis='time', yaxis='phase',
       antenna='1~10', gridrows=3, gridcols=3, iteraxis='antenna',
       coloraxis='spw', plotfile=visbase+'_cal.ign_p1.png')
</source>

<pre style="background-color: #98FB98;">
Note that recently the name of the output file gets appended with the selection of
the iteraxis and there unfortunately seems to be no possibility to switch that off;
to get the file name as specified in the plotfile-parameter one would edit the file
name using the operating system tools.
</pre>

<source lang="python">
# second page batch (next nine antennas)
plotms(vis='cal.ign', xaxis='time', yaxis='phase', 
       antenna='11~19', gridrows=3, gridcols=3, iteraxis='antenna',
       coloraxis='spw', plotfile=visbase+'_cal.ign_p2.png')

# third page batch (remaining six antennas)
plotms(vis='cal.ign', xaxis='time', yaxis='phase', 
       antenna='20~26', gridrows=2, gridcols=3, iteraxis='antenna',
       coloraxis='spw', plotfile=visbase+'_cal.ign_p3.png')

</source>

{|
| [[Image:Fig_03a.c641.png|200px|thumb|left|Figure 3a: Gain phase calibration solutions for antennas ea01 to ea11]]
| [[Image:Fig_03b.c641.png|200px|thumb|center|Figure 3b: Gain phase calibration solutions for antennas ea12 to ea20]]
| [[Image:Fig_03c.c641.png|200px|thumb|right|Figure 3c: Gain phase calibration solutions for antennas ea21 to ea26]]
|}


The next step is to solve for the residual delays using the parameter
''gaintype='K' ''option in
[https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.calibration.gaincal.html gaincal]
and store the solutions in the <tt>cal.dly</tt> table (''dly'' for
''delay''). Note that this currently does not do a global
fringe-fitting solution for delays. Instead it determines 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.  Avoid the edge channels of each spectral window by
de-selecting them (say, select channels 5~122 here, from the 128
available per spw):

<source lang="python">
# In CASA, determine residual delays from the bandpass calibrator scans:
gaincal(vis=id_ms, caltable='cal.dly', 
        field=id_bp, spw='*:5~122', gaintype='K', 
        gaintable=['cal.ign'],
        refant=id_ref, solint='inf', minsnr=3)
</source>

Note that this applies the initial phase table <tt>cal.ign</tt>
to reduce decorrelation due to short-term phase fluctuations (as they
will be corrected by the solutions in ''cal.ign'').

Alternatively, one can derive a delay across all spws of a
baseband. If this is desired, use parameter ''combine='spw' '' in
[https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.calibration.gaincal.html gaincal]
and run the task for each baseband separately (here, in four groups of
16 spw). The solutions from the second and following runs can be
appended to the same calibration table via parameter ''append=True''.

[[Image:Fig_04.c641.png|200px|thumb|right|Figure 4: Antenna based delays after running gaincal().]]

Now plot the delays as a function of antenna index (using one colored
dot for each spw and polarization):

<source lang="python">
# In CASA, display the delay solutions for each spw as function of antenna-ID:
plotms(vis='cal.dly', xaxis='ant1', yaxis='delay', coloraxis='spw')
</source>

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

Now solve for the antenna bandpasses using the previously generated
tables <tt>cal.ign</tt> and <tt>cal.dly</tt> correcting for possible
decorrelation due to phase and delay inaccuracies, and store the
solutions in <tt>cal.bp</tt> (''bp'' for ''bandpass''):

<source lang="python">
# In CASA, determine the bandpass from the bandpass calibrator scans:
bandpass(vis=id_ms, caltable='cal.bp', 
         gaintable=['cal.ign', 'cal.dly'], 
         field=id_bp, refant=id_ref, solnorm=False, 
         bandtype='B', solint='inf')
</source>

'''WARNING''': Set <tt>solnorm=False</tt> here; otherwise there will
be some offsets seen later between spws due to the way the amplitude
scaling adjusts weights internally during solving.


Expect to see solutions failing due to "Insufficient unflagged
antennas" &mdash; these are for bad channels that have been
pre-flagged.

Plot the amplitude and phase soutions of this new bandpass. 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:

<source lang="python">
# In CASA, display the bandpass solutions for amplitude and phase for
#   the different (2x 4GHz) spw ranges; first range, amplitude:
plotms(vis='cal.bp', xaxis='freq', yaxis='amp', 
       spw='0~31', iteraxis='antenna', coloraxis='spw')

# second range, amplitude:
plotms(vis='cal.bp', xaxis='freq', yaxis='amp', 
       spw='32~63', iteraxis='antenna', coloraxis='spw')

# first range, phase:
plotms(vis='cal.bp', xaxis='freq', yaxis='phase', 
       spw='0~31', iteraxis='antenna', coloraxis='spw', 
       plotrange=[-1,-1,-80,80])

# second range, phase:
plotms(vis='cal.bp', xaxis='freq', yaxis='phase', 
       spw='32~63', iteraxis='antenna', coloraxis='spw', 
       plotrange=[-1,-1,-80,80])
</source>

{|
| [[Image:Fig_05a.c641.png|200px|thumb|left|Figure 5a: Gain amplitude bandpass calibration solutions for antenna ea06 in spectral windows from 0 to 31.]]
| [[Image:Fig_05b.c641.png|200px|thumb|center|Figure 5b: Gain amplitude bandpass calibration solutions for antenna ea06 in spectral windows from 32 to 63.]]
| [[Image:Fig_06a.c641.png|200px|thumb|center|Figure 6a: Gain phase bandpass calibration solutions for antenna ea06 in spectral windows from 0 to 31]]
| [[Image:Fig_06b.c641.png|200px|thumb|right|Figure 6b: Gain phase bandpass calibration solutions for antenna ea06 in spectral windows from 32 to 63.]]
|}

== B) Applying the Initial Bandpass Solutions to the Flux Density Calibrator ==


Since there is no ''a priori'' spectral information for the chosen
bandpass calibrator (here 3C84), bootstrapping it is needed to
determine its spectral behavior. This information is then used to
re-determine the bandpass in order to avoid folding the intrinsic
spectral shape of the bandpass calibrator into the bandpass
calibration.

First, repeat the phase-only self-calibration solution with the
preliminary determined delay and bandpass calibration in the previous
step, A), this time for both the bandpass and the flux density
calibrator (which need to be separated by a comma) and store the
solutions in <tt>cal.ign2pha</tt> (use the ''2pha'' to distinguish it
as the second stage for phase-only). This will correct for short-term
phase decorrelation of the signals. Similarly to avoid decorrelation
due to a wide frequency range, use the narrow center channel range
(which was channel 60~68):

<source lang="python">
# In CASA, phase-only self-calibrate the flux density and bandpass calibrators
#   the same way by using the obtained first-order bandpass calibration above:
gaincal(vis=id_ms, caltable='cal.ign2pha', field=id_flx+','+id_bp, 
        gaintable=['cal.dly', 'cal.bp'], 
        gaintype='G', refant=id_ref, calmode='p', solint='int', minsnr=3)
</source>

This again allows to calibrate both phase and gain for each scan, now
corrected (biased) with the delay and bandpass of the bandpass
calibrator. Store the solutions in <tt>cal.ign2</tt> (use the ''2'' to
distinguish it as the second pass gain calibration):

<source lang="python">
# In CASA, now self-calibrate the phase-corrected amplitudes as well:
gaincal(vis=id_ms, caltable='cal.ign2', field=id_flx+','+id_bp, 
        gaintable=['cal.dly', 'cal.bp','cal.ign2pha'], 
        gaintype='G', refant=id_ref, calmode='ap', solint='inf', minsnr=3)
</source>

<!--
Let's have a look at the phase and amplitude solutions, iterating over antenna.  We will look at the flux density calibrator (3C147, id_flx) and bandpass calibrator (3C84, id_bp) individually since they're widely separated in time:
<source lang="python">
## In CASA
plotcal(caltable='cal.ign2', xaxis='time', yaxis='amp', 
        field=id_flx, iteration='antenna')
##
plotcal(caltable='cal.ign2', xaxis='time', yaxis='amp', 
        field=id_bp, iteration='antenna')
##
plotcal(caltable='cal.ign2', xaxis='time', yaxis='phase', 
        iteration='antenna', plotrange=[-1,-1,-180,180], 
        field=id_flx)
##
plotcal(caltable='cal.ign2', xaxis='time', yaxis='phase', 
        iteration='antenna', plotrange=[-1,-1,-180,180], 
        field=id_bp)
</source>

The solutions all look reasonable and relatively constant with time.  
-->

== C) Bootstrapping the Bandpass Calibrator Field Spectrum ==

With full, phase '''and amplitude''' gain solutions for both the flux
density and bandpass calibrators, now use
[https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.calibration.fluxscale.html fluxscale]
to scale the gain amplitudes of the bandpass calibrator using those of
the flux density calibrator. Store the solutions in <tt>cal.bpflx</tt>
and the flux density results for the bandpass calibrator in
<tt><visbase>+'_'+id_bp+'_fluxinfo.txt'</tt>:


<source lang="python">
# In CASA, determine the bandpass calibrator fluxes as function of frequency by
#   using the fluxes set for the flux density calibrator with setjy earlier and
#   store the output dictionary with these values in the variable flux_bp:
flux_bp = fluxscale(vis=id_ms, caltable='cal.ign2', 
                  fluxtable='cal.bpflx', reference=id_flx, transfer=id_bp, 
                  listfile=visbase+'_'+id_bp+'_fluxinfo.txt', fitorder=1)
</source>
* <tt>flux_bp = fluxscale(...)</tt>: the python dictionary returned by the [https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.calibration.fluxscale.html fluxscale] task will be stored in the <tt>flux_bp</tt> variable. Inspect the dictionary flux_bp by typing "print flux_bp" at the CASA command line. Alternatively, when not interested in the dictionary type ''fluxscale(...);'' with a semicolon after the parentheses and not assigning it to a variable. This will prevent the dictionary to be printed in the terminal. Here however the dictionary is captured in flux_bp as it is going to be used below
* <tt>fluxtable='cal.bpflx'</tt>: this is the output scaled gain table containing the flux density of the bandpass calibrator as function of frequency. Since this is only used to find the spectral index of the bandpass calibrator source field, this table is not used further.
* <tt>listfile=visbase+'_'+id_bp+'_fluxinfo.txt'</tt>: an output file that contains the derived flux values and fit information.
* <tt>fitorder=1</tt>: only find a (linear) spectral index, ignoring curvature in the spectrum.
* <tt>reference=id_flx</tt>: the reference field ''from'' which the flux scaling is transferred (here: the flux density calibrator 3C147, field 0, assigned by id_flx)
* <tt>transfer=id_bp</tt>: the target field ''to'' which the flux scaling is transferred (here: the bandpass calibrator 3C84, field 1, assigned by id_bp)


The last line in the file (and displayed in the logger) shows, using the default model supplied with this CASA version:
<pre style="background-color: #fffacd;">
<!-- Fitted spectrum for 3c84-J0319+413 with fitorder=1: Flux density = 29.0286 +/- 0.0308709 (freq=32.5128 GHz) spidx=-0.538803 +/- 0.00883078 -->
Fitted spectrum for 3c84-J0319+413 with fitorder=1: Flux density = 29.0286 +/- 0.0308677 (freq=32.5128 GHz) spidx: a_1 (spectral index) =-0.538798 +/- 0.00882989

</pre>

[[Image:Fig_07.c641.png|200px|thumb|right|Figure 7: Bootstrapped 3C84 flux density spectrum.]]

Using the information in the returned <tt>flux_bp</tt> dictionary, the derived spectrum can be plotted (Figure 7); for that the two loops below fill the parameter variables used in the plotting:

<pre style="background-color: #AAffff;">
Note that the use of ipython in the command line requires the use of "%cpaste" to allow
copy/pasting of loops, conditions, etc..  Press "Enter" (or "Return") after "--" at the
end. This stops (exits) the copy/paste prompt and will return the CASA command line prompt.
</pre>

<source lang="python">
# In CASA, use python code to plot the model spectrum of the bandpass calibrator:

import matplotlib.pyplot as pl
%cpaste

# Press Enter or Return, then copy/paste the following:
freq = flux_bp['freq'] / 1e9
spw_list = range(0,63)
spw_str = []
for i in spw_list:
   thisspw = str(i)
   spw_str.append(thisspw)
bootstrapped_fluxes = []
for j in spw_str:
    thisflux = flux_bp['1'][j]['fluxd'][0]
    if thisflux == None:
        continue
    else:
        bootstrapped_fluxes.append(thisflux)
pl.clf()
pl.plot(freq, bootstrapped_fluxes, 'bo')
pl.xlabel('Frequency (GHz)')
pl.ylabel('Flux Density (Jy)')
pl.title(id_bp)
pl.show()
--
</source>

The model from [https://casadocs.readthedocs.io/en/v6.4.1/api/tt/casatasks.calibration.fluxscale.html fluxscale]
is used to fill the ''<tt>MODEL</tt>'' column with the bandpass
source's spectral information using
[https://casadocs.readthedocs.io/en/v6./4.1api/tt/casatasks.imaging.setjy.html setjy].
With ''standard='fluxscale' '', we can directly use the
<tt>flux_bp</tt> python dictionary as input via ''fluxdict'':

[[Image:Fig_08.c641.png|200px|thumb|right|Figure 8: Model amplitude of 3C84 as a function of frequency.]]

<source lang="python">
# In CASA, insert the spectral information for the bandpass calibrator field:
setjy(vis=id_ms, field=id_bp, scalebychan=True, 
      standard = 'fluxscale', fluxdict=flux_bp);
</source>

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

<source lang="python">
# In CASA, display the spectral model of the bandpass calibrator field, in this
#   case for a specific baseline (which should be identical for all baselines
#   for a point-like source, otherwise it will depend on baseline length):
plotms(vis=id_ms, field=id_bp, antenna='ea05&ea02', 
       xaxis='freq', yaxis='amp', ydatacolumn='model')
</source>

== D) Creating a New Bandpass using the Bandpass Calibrator Spectral Model ==

Next, redo the previous calibration using this new model information
as if it were a standard flux density calibrator. 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 accordingly will differ if
the specral index differs significanlty from the spectral index of the
flux density calibrator:

<source lang="python">
# In CASA, redo a phase-only self-calibration on the bandpass calibrator:
gaincal(vis=id_ms, caltable='cal.ign.redo', 
        field=id_bp, spw='*:60~68', 
        gaintype='G', refant=id_ref, calmode='p', 
        solint='int', minsnr=3) 

# In CASA, re-determine residual delays from the bandpass calibrator scans:
gaincal(vis=id_ms, caltable='cal.dly.redo', 
        gaintable=['cal.ign.redo'], 
        field=id_bp, spw='*:5~122', gaintype='K', 
        refant=id_ref, solint='inf', minsnr=3)

# In CASA, re-determine the bandpass from the bandpass calibrator scans:
bandpass(vis=id_ms, caltable='cal.bp.redo', 
         gaintable=['cal.ign.redo', 'cal.dly.redo'], 
         field=id_bp, refant=id_ref, solnorm=False, 
         bandtype='B', solint='inf')
</source>

Finally, inspect these solutions (Figures 9a, 9b, 10a, and 10b; again note that antenna ea01 is fully flagged):

<source lang="python">
# In CASA, display and check the bandpass solutions; first range, amplitude:
plotms(vis='cal.bp.redo', xaxis='freq', yaxis='amp', 
       spw='0~31', iteraxis='antenna', coloraxis='spw')

# second range, amplitude:
plotms(vis='cal.bp.redo', xaxis='freq', yaxis='amp', 
       spw='32~63', iteraxis='antenna', coloraxis='spw')

# first range, phase:
plotms(vis='cal.bp.redo', xaxis='freq', yaxis='phase', 
       spw='0~31', iteraxis='antenna', coloraxis='spw', 
       plotrange=[-1,-1,-80,80])

# second range, phase:
plotms(vis='cal.bp.redo', xaxis='freq', yaxis='phase', 
       spw='32~63', iteraxis='antenna', coloraxis='spw', 
       plotrange=[-1,-1,-80,80])
</source>

{|
| [[Image:Fig_09a.c641.png|200px|thumb|left|Figure 9a: Calibration solutions for the bootstrapped bandpass amplitudes for 3C84. Shown is antenna ea06 and spectral windows spw=0~31.]]
| [[Image:Fig_09b.c641.png|200px|thumb|center|Figure9b: Calibration solutions for the bootstrapped bandpass amplitudes for 3C84. Shown is antenna ea06 and spectral windows spw=32~63.]]
| [[Image:Fig_10a.c641.png|200px|thumb|center|Figure 10a: Calibration solutions for the bootstrapped bandpass phases for 3C84. Shown is antenna ea06 and spectral windows spw=0~31.]]
| [[Image:Fig_10b.c641.png|200px|thumb|right|Figure 10b: Calibration solutions for the bootstrapped bandpass phases for 3C84. Shown is antenna ea06 and spectral windows spw=32-63.]]
|}

As in this particualr observation the spectral indices for the flux
density calibrator (3C147: -0.696, standard) and the bandpass
calibrator (3C84: -0.626, derived here) are very similar, the
solutions look virtually unchanged from the previous solutions with
the exception that the amplitude scaling is corrected for the spectrum
of the bandpass calibrator. These steps yielded the final version of
the delay and bandpass calibration tables, '''cal.dly.redo''' and
'''cal.bp.redo''', which now would be used for all subsequent
calibration steps.

{{Checked 6.4.1}}
<!--
-- Original: Miriam Hartman (full G192 guide) <br />
-- Modifications: Jose Salcido (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 />
-- Edits to Guide: Heidi Medlin (5.0.0, 2017/07/20) <br />
-- Edits to Guide: Alex Sobotka (5.4.0, 2018/10/10) <br />
-- Edits to Guide: Anna Kapinska (5.5.0, 2019/06/28) <br />
-- Edits to Guide: Trent Seelig (6.2.0, 2021/07/08) <br />
-- Version for CASA 6.4.1, Lorant (Jan 2023), restructured in steps
        including more background explanation and now also defining
	standard variables for parameters: id_* for identifiers and
	cal.* for calibration tables. Added a cheat sheet too.<br />
-->

== Cheat Sheet - All Commands Summarized ==

In the tutorial above, these commands were used. Apart from the
''%cpaste'' section (that requires typing the double dash '''--''' to
end the section), all commands are intended to be single-line commands
and the space-indented line commands are informational (mostly plots)
rather than essential. Obviously, lines starting with a hash-tag, the
octothorp '''#''', are comment lines and need not be issued.

Note that the automatic formatting by the browser may introduce some
weirdness in displaying the pre-formatted text and single line
commands; be aware when copy/pasting that the line is complete, i.e.,
the task commands must be ending with the closing parenthesis and/or
semicolon.

<pre>
# in a terminal, outside of CASA
wget http://casa.nrao.edu/Data/EVLA/G192/G192-BP.ms.tar.gz
tar -xzf G192-BP.ms.tar.gz 
casa -r 6.4.1-12-pipeline-2022.2.0.64

# inside CASA, on command line
visbase='G192-BP'
id_ms=visbase+'.ms'
  listobs(vis=id_ms,listfile=visbase+'_list.txt');
id_flx='3C147'
id_bp='3c84-J0319+413'
  plotants(vis=id_ms,antindex=True,logpos=True)
id_ref='ea05'
setjy(vis=id_ms,field=id_flx,scalebychan=True,standard='Perley-Butler 2017',model=id_flx+'_A.im');
  plotms(vis=id_ms,field=id_flx,antenna='ea03',xaxis='freq',yaxis='amp',ydatacolumn='model',coloraxis='ant2')

# *** Step A) ***
gaincal(vis=id_ms,caltable='cal.ign',field=id_bp,spw='*:60~68',gaintype='G',refant=id_ref,calmode='p',solint='int',minsnr=3)
  plotms(vis='cal.ign',xaxis='time',yaxis='phase',iteraxis='antenna',coloraxis='spw')
  plotms(vis='cal.ign',xaxis='time',yaxis='phase',antenna='1~11',gridrows=3,gridcols=3,iteraxis='antenna',coloraxis='spw',plotfile=visbase+'_cal.ign_p1.png')
  plotms(vis='cal.ign',xaxis='time',yaxis='phase',antenna='12~20',gridrows=3,gridcols=3,iteraxis='antenna',coloraxis='spw',plotfile=visbase+'_cal.ign_p2.png')
  plotms(vis='cal.ign',xaxis='time',yaxis='phase',antenna='21~26',gridrows=2,gridcols=3,iteraxis='antenna',coloraxis='spw',plotfile=visbase+'_cal.ign_p3.png')
gaincal(vis=id_ms,caltable='cal.dly',field=id_bp,spw='*:5~122',gaintype='K',gaintable=['cal.ign'],refant=id_ref,solint='inf',minsnr=3)
  plotms(vis='cal.dly',xaxis='ant1',yaxis='delay',coloraxis='spw')
bandpass(vis=id_ms,caltable='cal.bp',gaintable=['cal.ign','cal.dly'],field=id_bp,refant=id_ref,solnorm=False,bandtype='B',solint='inf')
  plotms(vis='cal.bp',xaxis='freq',yaxis='amp',spw='0~31',iteraxis='antenna',coloraxis='spw',plotrange=[-1,-1,0,80])
  plotms(vis='cal.bp',xaxis='freq',yaxis='amp',spw='32~63',iteraxis='antenna',coloraxis='spw',plotrange=[-1,-1,0,80])
  plotms(vis='cal.bp',xaxis='freq',yaxis='phase',spw='0~31',iteraxis='antenna',coloraxis='spw',plotrange=[-1,-1,-80,80])
  plotms(vis='cal.bp',xaxis='freq',yaxis='phase',spw='32~63',iteraxis='antenna',coloraxis='spw',plotrange=[-1,-1,-80,80])

# *** Step B) ***
gaincal(vis=id_ms,caltable='cal.ign2pha',field=id_flx+','+id_bp,gaintable=['cal.dly','cal.bp'],gaintype='G',refant=id_ref,calmode='p',solint='int',minsnr=3)
gaincal(vis=id_ms,caltable='cal.ign2',field=id_flx+','+id_bp,gaintable=['cal.dly','cal.bp','cal.ign2pha'],gaintype='G',refant=id_ref,calmode='ap',solint='inf',minsnr=3)

# *** Step C) ***
flux_bp=fluxscale(vis=id_ms,caltable='cal.ign2',fluxtable='cal.bpflx',reference=id_flx,transfer=id_bp,listfile=visbase+'_'+id_bp+'_fluxinfo.txt',fitorder=1)

import matplotlib.pyplot as pl
%cpaste

freq = flux_bp['freq'] / 1e9
spw_list = range(0,63)
spw_str = []
for i in spw_list:
   thisspw = str(i)
   spw_str.append(thisspw)
bootstrapped_fluxes = []
for j in spw_str:
    thisflux = flux_bp['1'][j]['fluxd'][0]
    if thisflux ==None:
        continue
    else:
        bootstrapped_fluxes.append(thisflux)
pl.clf()
pl.plot(freq,bootstrapped_fluxes,'bo')
pl.xlabel('Frequency (GHz)')
pl.ylabel('Flux Density (Jy)')
pl.title(id_bp)
pl.show()
--

setjy(vis=id_ms,field=id_bp,scalebychan=True,standard='fluxscale',fluxdict=flux_bp);
  plotms(vis=id_ms,field=id_bp,antenna='ea05&ea02',xaxis='freq',yaxis='amp',ydatacolumn='model')

# *** Step D) ***
gaincal(vis=id_ms,caltable='cal.ign.redo',field=id_bp,spw='*:60~68',gaintype='G',refant=id_ref,calmode='p',solint='int',minsnr=3) 
gaincal(vis=id_ms,caltable='cal.dly.redo',gaintable=['cal.ign.redo'],field=id_bp,spw='*:5~122',gaintype='K',refant=id_ref,solint='inf',minsnr=3)
bandpass(vis=id_ms,caltable='cal.bp.redo',gaintable=['cal.ign.redo','cal.dly.redo'],field=id_bp,refant=id_ref,solnorm=False,bandtype='B',solint='inf')
  plotms(vis='cal.bp.redo',xaxis='freq',yaxis='amp',spw='0~31',iteraxis='antenna',coloraxis='spw',plotrange=[-1,-1,0,15])
  plotms(vis='cal.bp.redo',xaxis='freq',yaxis='amp',spw='32~63',iteraxis='antenna',coloraxis='spw',plotrange=[-1,-1,0,15])
  plotms(vis='cal.bp.redo',xaxis='freq',yaxis='phase',spw='0~31',iteraxis='antenna',coloraxis='spw',plotrange=[-1,-1,-80,80])
  plotms(vis='cal.bp.redo',xaxis='freq',yaxis='phase',spw='32~63',iteraxis='antenna',coloraxis='spw',plotrange=[-1,-1,-80,80])


###
</pre>