M100 Band3 ACA 4.2.2: Difference between revisions

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This output shows that three sources were observed in each data set: 3c279, Titan, and the M100.
This output shows that three sources were observed in each data set: J1229+0203(3c279), Titan, and the M100.


* The '''M100''' are our science target. Note that the source corresponds to a number of individual fields (see the Field ID column). These are the individual mosaic pointings. There are 23 for M100 Mosaic.
* The '''M100''' are our science target. Note that the source corresponds to a number of individual fields (see the Field ID column). These are the individual mosaic pointings. There are 23 for M100 Mosaic.
* '''Titan''' is observed once and will be used to set the absolute flux scale of the data.
* '''Titan''' is observed once and will be used to set the absolute flux scale of the data. (For these dataset, fluxes are not fixed.....)
* '''3c279''' plays two roles: it will serve as our bandpass calibrator, to characterize the frequency response of the antennas, and because it is fairly close on the sky to the M100, it will serve as our secondary calibrator (also referred to as the "phase calibrator" or "gain calibrator"), to track changes in the phase and amplitude response of the telescopes over time. Observations of 3c279 are interleaved with observations of the M100.
* '''J1229+0203(3c279)''' plays two roles: it will serve as our bandpass calibrator, to characterize the frequency response of the antennas.
* '''J1215+1654''' is "gain calibrator" to track changes in the phase and amplitude response of the telescopes over time. Observations of 3c279 are interleaved with observations of the M100.


The output also shows that the data contain many spectral windows. Using the labeling scheme in the listobs above these are:
The output also shows that the data contain many spectral windows. Using the labeling scheme in the listobs above these are:

Revision as of 04:04, 8 June 2013

  • Details of the ALMA observations are provided at M100Band3
  • This portion of the guide covers calibration of the raw visibility data. To skip to the imaging portion of the guide, see: M100Band3 Imaging 4.1.

Overview

This part of the M100 Band 3 7m CASA guide will step you through the calibration of the visibility data. We will begin by flagging (marking as bad) data known to be useless before any inspection, for example data where one telescope blocks the line of sight of another. Then we will apply telescope-generated calibration tables to partially correct for atmospheric effects. After inspecting the data, we will flag some additional data that exhibit pathologies. Then we will use observations of the calibrators Titan and 3c279 to derive the phase and amplitude response of individual antennas as a function of time and frequency ("phase", "amplitude", and "bandpass" calibrations). We will apply these to the data and then extract the calibrated source data into a file appropriate for imaging.

The general procedure in this guide follows the other ALMA CASA guides: NGC3256Band3 and TWHydraBand7.

Unpack the Data

Once you have downloaded the M100_Band3_7m_CalibratedData.tgz, unpack the file in a terminal outside CASA using

tar -xvzf M100_Band3_7m_CalibratedData.tgz

then change directory to the new directory

cd M100_Band3_7m_CalibratedData

You may wish to type

ls

to look at the files present. You should see a set of files with extension ".ms". These are CASA measurement set (MS) files. The data have already been converted to MS format from the native ALMA format using the CASA task importasdm. In addition to the data, we provide you with calibration tables containing system temperature (Tsys), water vapor radiometer (WVR), and antenna position information. For Early Science, these tables will either be pre-applied or supplied with the data.

This guide requires Python module analysisUtils. If you have not already installed analysisUtils please follow the link to do so.

To begin, start CASA by typing

casapy

Be sure that you are using the version indicated at the top of this page.

Confirm your version of CASA

This guide has been written for CASA release 4.1.0. Please confirm your version before proceeding.

# In CASA
version = casadef.casa_version
print "You are using " + version
if (version < '4.1.0'):
    print "YOUR VERSION OF CASA IS TOO OLD FOR THIS GUIDE."
    print "PLEASE UPDATE IT BEFORE PROCEEDING."
else:
    print "Your version of CASA is appropriate for this guide."

Install Analysis Utilities

Analysis Utilities (or analysisUtils for short) is a small set of Python scripts that provide a number of analysis and plotting utilities for ALMA data reduction. This guide uses a few of these utilities. They are very easy to install (just download and untar). See

http://casaguides.nrao.edu/index.php?title=Analysis_Utilities

for a full description and download instructions. Analysis Utilities are updated frequently so if its been a while since you installed it, its probably worth doing it again. If you are at an ALMA site or ARC, the analysis utilities are probably already installed and up to date.


Initial Inspection

First we will take stock of what we have. If you have not already done so, begin by reviewing the description of the observations here: M100Band3. The 6 data sets each target mosaic, as follows:

  • uid___A002_X5e971a_X124.ms
  • uid___A002_X5e971a_X2e7.ms
  • uid___A002_X5e9ff1_X3f3.ms
  • uid___A002_X5e9ff1_X5b3.ms
  • uid___A002_X60b415_X44.ms
  • uid___A002_X62f759_X4eb.ms

The first step is to get basic information about the data: targets observed, time range, spectral setup, and so on. We do this using the task listobs, which will output a detailed summary of each dataset. Enter the following commands into CASA:

# In CASA

# Define a python list holding the names of all of our data sets
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3',
'uid___A002_X5e9ff1_X5b3', 'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

# Initialize user-input string.  (for Python testing)
dummy_string = ''

# Loop over each element in the list and create summary file using listobs
for asdm in basename:
    os.system('rm '+asdm+'.listobs.txt')
    listobs(vis=asdm+'.ms', listfile=asdm+'.listobs.txt', verbose=True)

Note that after cutting and pasting a 'for' loop like this you often have to press return twice to execute. You may also want to take care to paste a line at a time if you are having trouble copy and pasting. Even better, you can use "cpaste" to paste blocks of code. To do so type "cpaste" at the CASA prompt, paste your commands, and then type "--" and hit return on the final (otherwise empty) line. This should look something like this:


CASA <8>: cpaste
Pasting code; enter '--' alone on the line to stop.
:basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3',
'uid___A002_X5e9ff1_X5b3', 'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']
:
:for asdm in basename:
:    print asdm
:--
uid___A002_X5e971a_X124
uid___A002_X5e971a_X2e7
uid___A002_X5e9ff1_X3f3
uid___A002_X5e9ff1_X5b3
uid___A002_X60b415_X44
uid___A002_X62f759_X4eb

CASA <9>: 

cpaste should be much more robust than copying-and-pasting directly into the shell but if you have trouble, just carefully paste one line at a time directly into CASA and hit return until the desired command executes.

These commands define a python list called "basename", which contains the name of all 6 MS files. The "for" loop executes for each element in basename, calling listobs and directing the output to a file called, e.g., "uid___A002_X5e971a_X124.ms.listobs.txt" for the first measurement set. You can browse through the listobs output as you would normally look at a text file (use emacs, vi, or another editor). You can also send the output to the terminal from inside of CASA. To do so type:

# In CASA
os.system('cat uid___A002_X5e971a_X124.ms.listobs.txt')

or

# In CASA
os.system('more uid___A002_X5e971a_X124.ms.listobs.txt')

CASA knows a few basic shell commands like 'cat', 'ls', and 'rm' but for more complex commands you will need to run them inside 'os.system("command")'. For more information see http://casa.nrao.edu/ .

Here is an example of the (abridged) output from listobs for the first dataset in the list, uid___A002_X5e971a_X124.ms, which targets the Northern Mosaic. You would see this if you had specified verbose to be False in the listobs call:

================================================================================
           MeasurementSet Name:  /lustre/naasc/almauser/M100Band3/uid___A002_X5e971a_X124.ms      MS Version 2
================================================================================
Observer: cvlahakis     Project: uid://A002/X5d9e5c/X5d  
Observation: ALMA
Data records: 940680       Total integration time = 5436.77 seconds
Observed from   17-Mar-2013/04:07:32.8   to   17-Mar-2013/05:38:09.6 (UTC)

ObservationID = 0         ArrayID = 0
Date        Timerange (UTC)          Scan  FldId FieldName           nRows   nUnflRows   SpwIds   Average Interval(s)    ScanIntent
17-Mar-2013/04:07:32.3 - 04:09:35.4     1      0 J1229+0203            43200  43200.00  [0, 1, 2, 3, 4, 5, 6, 7]  [1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01] CALIBRATE_POINTING#ON_SOURCE
              04:10:57.2 - 04:11:57.7     2      0 J1229+0203            45360  45360.00  [8, 9, 10, 11, 12, 13, 14, 15]  [0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48] CALIBRATE_SIDEBAND_RATIO#ON_SOURCE
              04:12:48.5 - 04:14:00.0     3      0 J1229+0203            12960  12960.00  [8, 9, 10, 11, 12, 13, 14, 15]  [0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48] CALIBRATE_ATMOSPHERE#ON_SOURCE
              04:14:44.2 - 04:24:49.9     4      0 J1229+0203           118800  44880.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_BANDPASS#ON_SOURCE
              04:25:53.4 - 04:27:56.4     5      1 J1445-1629            43200  43200.00  [0, 1, 2, 3, 4, 5, 6, 7]  [1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01] CALIBRATE_POINTING#ON_SOURCE
              04:29:02.6 - 04:30:13.9     6      2 Titan                 12960  12960.00  [8, 9, 10, 11, 12, 13, 14, 15]  [0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48] CALIBRATE_ATMOSPHERE#ON_SOURCE
              04:30:57.7 - 04:36:00.5     7      2 Titan                 59400  22468.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_AMPLI#ON_SOURCE
              04:37:10.1 - 04:39:13.2     8      3 J1215+1654            43200  43200.00  [0, 1, 2, 3, 4, 5, 6, 7]  [1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01] CALIBRATE_POINTING#ON_SOURCE
              04:39:58.8 - 04:40:59.3     9      3 J1215+1654            11880   4488.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_PHASE#ON_SOURCE
              04:42:08.6 - 04:43:20.0    10      4 M100                  12960  12960.00  [8, 9, 10, 11, 12, 13, 14, 15]  [0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48] CALIBRATE_ATMOSPHERE#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11      5 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11      6 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11      7 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11      8 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11      9 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11     10 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11     11 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11     12 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11     13 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11     14 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11     15 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11     16 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:44:04.3 - 04:50:43.7    11     17 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:51:04.3 - 04:52:04.8    12      3 J1215+1654            11880   4488.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_PHASE#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13      5 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13      6 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13      7 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     18 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     19 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     20 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     21 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     22 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     23 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     24 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     25 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     26 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     27 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:59:35.7 - 05:00:36.2    14      3 J1215+1654            11880   4488.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_PHASE#ON_SOURCE
              05:01:55.1 - 05:03:06.5    15      4 M100                  12960  12960.00  [8, 9, 10, 11, 12, 13, 14, 15]  [0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48] CALIBRATE_ATMOSPHERE#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16      8 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16      9 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     10 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     11 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     12 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     13 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     14 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     15 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     16 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     17 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     18 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     19 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     20 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:10:50.7 - 05:11:51.2    17      3 J1215+1654            11880   4488.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_PHASE#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18      5 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18      6 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18      7 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18      8 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18      9 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     10 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     21 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     22 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     23 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     24 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     25 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     26 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     27 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:19:22.0 - 05:20:22.6    19      3 J1215+1654            11880   4488.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_PHASE#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     20 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     21 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     22 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     23 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     24 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     25 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     26 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:52:35.8 - 04:59:15.3    13     27 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              04:59:35.7 - 05:00:36.2    14      3 J1215+1654            11880   4488.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_PHASE#ON_SOURCE
              05:01:55.1 - 05:03:06.5    15      4 M100                  12960  12960.00  [8, 9, 10, 11, 12, 13, 14, 15]  [0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48] CALIBRATE_ATMOSPHERE#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16      8 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16      9 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     10 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     11 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     12 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     13 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     14 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     15 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     16 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     17 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     18 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     19 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:03:22.2 - 05:10:01.6    16     20 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:10:50.7 - 05:11:51.2    17      3 J1215+1654            11880   4488.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_PHASE#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18      5 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18      6 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18      7 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18      8 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18      9 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     10 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     21 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     22 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     23 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     24 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     25 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     26 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:12:22.0 - 05:19:01.4    18     27 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:19:22.0 - 05:20:22.6    19      3 J1215+1654            11880   4488.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_PHASE#ON_SOURCE
              05:21:41.7 - 05:22:53.4    20      4 M100                  12960  12960.00  [8, 9, 10, 11, 12, 13, 14, 15]  [0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48] CALIBRATE_ATMOSPHERE#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     11 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     12 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     13 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     14 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     15 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     16 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     17 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     18 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     19 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     20 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     21 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     22 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:23:37.6 - 05:30:17.0    21     23 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:30:37.5 - 05:31:38.1    22      3 J1215+1654            11880   4488.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_PHASE#ON_SOURCE
              05:32:08.4 - 05:36:44.7    23      5 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:32:08.4 - 05:36:44.7    23      6 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:32:08.4 - 05:36:44.7    23      7 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:32:08.4 - 05:36:44.7    23      8 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:32:08.4 - 05:36:44.7    23      9 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:32:08.4 - 05:36:44.7    23     24 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:32:08.4 - 05:36:44.7    23     25 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:32:08.4 - 05:36:44.7    23     26 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:32:08.4 - 05:36:44.7    23     27 M100                   5940   2244.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] OBSERVE_TARGET#ON_SOURCE
              05:37:09.6 - 05:38:10.1    24      3 J1215+1654            11880   4488.00  [16, 17, 18, 19, 20, 21, 22, 23]  [10.1, 1.01, 10.1, 1.01, 10.1, 1.01, 10.1, 1.01] CALIBRATE_PHASE#ON_SOURCE
           (nRows = Total number of rows per scan) 


Fields: 28
  ID   Code Name                RA               Decl           Epoch   SrcId    nRows  nUnflRows
  0    none J1229+0203          12:29:06.699720 +02.03.08.59820 J2000   0       220320  146400.00
  1    none J1445-1629          14:45:53.376290 -16.29.01.61880 J2000   1        43200   43200.00
  2    none Titan               14:36:50.919019 -12.33.38.18202 J2000   1        72360   35428.00
  3    none J1215+1654          12:15:03.979130 +16.54.37.95700 J2000   2       126360   74616.00
  4    none M100                12:22:54.899040 +15.49.20.57160 J2000   2        38880   38880.00
  5    none M100                12:22:57.009007 +15.49.13.60358 J2000   3        23760    8976.00
  6    none M100                12:22:57.009041 +15.49.58.08112 J2000   3        23760    8976.00
  7    none M100                12:22:54.339884 +15.47.22.41016 J2000   3        23760    8976.00
  8    none M100                12:22:54.339918 +15.48.06.88770 J2000   3        23760    8976.00
  9    none M100                12:22:54.339952 +15.48.51.36524 J2000   3        23760    8976.00
  10   none M100                12:22:54.339986 +15.49.35.84278 J2000   3        17820    6732.00
  11   none M100                12:22:54.340020 +15.50.20.32032 J2000   3        17820    6732.00
  12   none M100                12:22:51.670863 +15.47.44.64936 J2000   3        17820    6732.00
  13   none M100                12:22:51.670897 +15.48.29.12690 J2000   3        17820    6732.00
  14   none M100                12:22:51.670931 +15.49.13.60444 J2000   3        17820    6732.00
  15   none M100                12:22:51.670966 +15.49.58.08198 J2000   3        17820    6732.00
  16   none M100                12:22:49.001808 +15.47.22.41102 J2000   3        17820    6732.00
  17   none M100                12:22:49.001842 +15.48.06.88856 J2000   3        17820    6732.00
  18   none M100                12:22:49.001876 +15.48.51.36610 J2000   3        17820    6732.00
  19   none M100                12:22:49.001911 +15.49.35.84364 J2000   3        17820    6732.00
  20   none M100                12:22:49.001945 +15.50.20.32118 J2000   3        17820    6732.00
  21   none M100                12:22:59.677959 +15.47.22.40931 J2000   3        17820    6732.00
  22   none M100                12:22:59.677993 +15.48.06.88685 J2000   3        17820    6732.00
  23   none M100                12:22:59.678027 +15.48.51.36439 J2000   3        17820    6732.00
  24   none M100                12:22:59.678061 +15.49.35.84193 J2000   3        17820    6732.00
  25   none M100                12:22:59.678095 +15.50.20.31947 J2000   3        17820    6732.00
  26   none M100                12:22:57.008938 +15.47.44.64850 J2000   3        17820    6732.00
  27   none M100                12:22:57.008973 +15.48.29.12604 J2000   3        17820    6732.00
Spectral Windows:  (24 unique spectral windows and 1 unique polarization setups)
  SpwID  Name                           #Chans   Frame   Ch1(MHz)  ChanWid(kHz)  TotBW(kHz) BBC Num  Corrs  
  0      ALMA_RB_03#BB_1#SW-01#FULL_RES    124   TOPO   91955.512    -15625.000   1937500.0       1  XX  YY
  1      ALMA_RB_03#BB_1#SW-01#CH_AVG        1   TOPO   90986.762   1937500.000   1937500.0       1  XX  YY
  2      ALMA_RB_03#BB_2#SW-01#FULL_RES    124   TOPO   93893.012    -15625.000   1937500.0       2  XX  YY
  3      ALMA_RB_03#BB_2#SW-01#CH_AVG        1   TOPO   92924.262   1937500.000   1937500.0       2  XX  YY
  4      ALMA_RB_03#BB_3#SW-01#FULL_RES    124   TOPO  102033.637     15625.000   1937500.0       3  XX  YY
  5      ALMA_RB_03#BB_3#SW-01#CH_AVG        1   TOPO  102986.762   1937500.000   1937500.0       3  XX  YY
  6      ALMA_RB_03#BB_4#SW-01#FULL_RES    124   TOPO  104033.637     15625.000   1937500.0       4  XX  YY
  7      ALMA_RB_03#BB_4#SW-01#CH_AVG        1   TOPO  104986.762   1937500.000   1937500.0       4  XX  YY
  8      ALMA_RB_03#BB_1#SW-01#FULL_RES    128   TOPO  101942.187    -15625.000   2000000.0       1  XX  YY
  9      ALMA_RB_03#BB_1#SW-01#CH_AVG        1   TOPO  100926.562   1781250.000   1781250.0       1  XX  YY
  10     ALMA_RB_03#BB_2#SW-01#FULL_RES    128   TOPO  103757.337    -15625.000   2000000.0       2  XX  YY
  11     ALMA_RB_03#BB_2#SW-01#CH_AVG        1   TOPO  102741.712   1781250.000   1781250.0       2  XX  YY
  12     ALMA_RB_03#BB_3#SW-01#FULL_RES    128   TOPO  111814.962     15625.000   2000000.0       3  XX  YY
  13     ALMA_RB_03#BB_3#SW-01#CH_AVG        1   TOPO  112783.712   1781250.000   1781250.0       3  XX  YY
  14     ALMA_RB_03#BB_4#SW-01#FULL_RES    128   TOPO  113689.962     15625.000   2000000.0       4  XX  YY
  15     ALMA_RB_03#BB_4#SW-01#CH_AVG        1   TOPO  114658.712   1781250.000   1781250.0       4  XX  YY
  16     ALMA_RB_03#BB_1#SW-01#FULL_RES   4080   TOPO  101945.850      -488.281   1992187.5       1  XX  YY
  17     ALMA_RB_03#BB_1#SW-01#CH_AVG        1   TOPO  100949.756   1992187.500   1992187.5       1  XX  YY
  18     ALMA_RB_03#BB_2#SW-01#FULL_RES   4080   TOPO  103761.000      -488.281   1992187.5       2  XX  YY
  19     ALMA_RB_03#BB_2#SW-01#CH_AVG        1   TOPO  102764.906   1992187.500   1992187.5       2  XX  YY
  20     ALMA_RB_03#BB_3#SW-01#FULL_RES   4080   TOPO  111811.300       488.281   1992187.5       3  XX  YY
  21     ALMA_RB_03#BB_3#SW-01#CH_AVG        1   TOPO  112806.906   1992187.500   1992187.5       3  XX  YY
  22     ALMA_RB_03#BB_4#SW-01#FULL_RES   4080   TOPO  113686.300       488.281   1992187.5       4  XX  YY
  23     ALMA_RB_03#BB_4#SW-01#CH_AVG        1   TOPO  114681.906   1992187.500   1992187.5       4  XX  YY


Sources: 80
  ID   Name                SpwId RestFreq(MHz)  SysVel(km/s) 
  0    J1229+0203          0     -              -            
  0    J1229+0203          1     -              -            
  0    J1229+0203          2     -              -            
  0    J1229+0203          3     -              -            
  0    J1229+0203          4     -              -            
  0    J1229+0203          5     -              -            
  0    J1229+0203          6     -              -            
  0    J1229+0203          7     -              -            
  0    J1229+0203          8     -              -            
  0    J1229+0203          9     -              -            
  0    J1229+0203          10    -              -            
  0    J1229+0203          11    -              -            
  0    J1229+0203          12    -              -            
  0    J1229+0203          13    -              -            
  0    J1229+0203          14    -              -            
  0    J1229+0203          15    -              -            
  0    J1229+0203          16    100950         0            
  0    J1229+0203          17    100950         0            
  0    J1229+0203          18    102794.1       0            
  0    J1229+0203          19    102794.1       0            
  0    J1229+0203          20    112794.1       0            
  0    J1229+0203          21    112794.1       0            
  0    J1229+0203          22    114669.1       0            
  0    J1229+0203          23    114669.1       0            
  1    J1445-1629          0     -              -            
  1    J1445-1629          1     -              -            
  1    J1445-1629          2     -              -            
  1    J1445-1629          3     -              -            
  1    J1445-1629          4     -              -            
  1    J1445-1629          5     -              -            
  1    J1445-1629          6     -              -            
  1    J1445-1629          7     -              -            
  1    Titan               8     -              -            
  1    Titan               9     -              -            
  1    Titan               10    -              -            
  1    Titan               11    -              -            
  1    Titan               12    -              -            
  1    Titan               13    -              -            
  1    Titan               14    -              -            
  1    Titan               15    -              -            
  1    Titan               16    100950         0            
  1    Titan               17    100950         0            
  1    Titan               18    102794.1       0            
  1    Titan               19    102794.1       0            
  1    Titan               20    112794.1       0            
  1    Titan               21    112794.1       0            
  1    Titan               22    114669.1       0            
  1    Titan               23    114669.1       0            
  2    J1215+1654          0     -              -            
  2    J1215+1654          1     -              -            
  2    J1215+1654          2     -              -            
  2    J1215+1654          3     -              -            
  2    J1215+1654          4     -              -            
  2    J1215+1654          5     -              -            
  2    J1215+1654          6     -              -            
  2    J1215+1654          7     -              -            
  2    J1215+1654          16    100950         0            
  2    J1215+1654          17    100950         0            
  2    J1215+1654          18    102794.1       0            
  2    J1215+1654          19    102794.1       0            
  2    J1215+1654          20    112794.1       0            
  2    J1215+1654          21    112794.1       0            
  2    J1215+1654          22    114669.1       0            
  2    J1215+1654          23    114669.1       0            
  2    M100                8     -              -            
  2    M100                9     -              -            
  2    M100                10    -              -            
  2    M100                11    -              -            
  2    M100                12    -              -            
  2    M100                13    -              -            
  2    M100                14    -              -            
  2    M100                15    -              -            
  3    M100                16    100950         0            
  3    M100                17    100950         0            
  3    M100                18    102794.1       0            
  3    M100                19    102794.1       0            
  3    M100                20    112794.1       0            
  3    M100                21    112794.1       0            
  3    M100                22    114669.1       0            
  3    M100                23    114669.1       0            


Antennas: 9:
  ID   Name  Station   Diam.    Long.         Lat.                Offset from array center (m)                ITRF Geocentric coordinates (m)        
                                                                     East         North     Elevation               x               y               z
  0    CM01  N602      7.0  m   -067.45.17.4  -22.53.22.3          8.8026     -527.8556       22.1988  2225080.352214 -5440132.953723 -2481524.785064
  1    CM02  J502      7.0  m   -067.45.17.7  -22.53.23.0          2.1079     -549.4459       22.1451  2225070.958100 -5440127.669506 -2481544.654450
  2    CM03  J503      7.0  m   -067.45.17.4  -22.53.23.2          9.2488     -555.0633       22.1293  2225076.734603 -5440122.930506 -2481549.823442
  3    CM04  N605      7.0  m   -067.45.17.4  -22.53.23.9          9.6883     -575.8319       22.0821  2225074.066737 -5440115.246896 -2481568.938246
  4    CM05  J506      7.0  m   -067.45.17.9  -22.53.23.2         -4.9539     -555.3433       22.1258  2225063.547041 -5440128.203265 -2481550.079981
  5    CM06  N606      7.0  m   -067.45.17.1  -22.53.23.6         19.1996     -566.5626       22.0993  2225084.240791 -5440114.998068 -2481560.405534
  6    CM07  N601      7.0  m   -067.45.17.0  -22.53.22.5         21.0601     -532.5792       22.2041  2225091.003357 -5440126.617491 -2481529.138855
  7    CM09  N603      7.0  m   -067.45.17.7  -22.53.22.3         -0.0719     -527.8532       22.2212  2225072.146648 -5440136.333195 -2481524.791579
  8    CM12  J504      7.0  m   -067.45.17.0  -22.53.23.0         22.2032     -550.2530       22.1451  2225089.438350 -5440119.771735 -2481545.398029

This output shows that three sources were observed in each data set: J1229+0203(3c279), Titan, and the M100.

  • The M100 are our science target. Note that the source corresponds to a number of individual fields (see the Field ID column). These are the individual mosaic pointings. There are 23 for M100 Mosaic.
  • Titan is observed once and will be used to set the absolute flux scale of the data. (For these dataset, fluxes are not fixed.....)
  • J1229+0203(3c279) plays two roles: it will serve as our bandpass calibrator, to characterize the frequency response of the antennas.
  • J1215+1654 is "gain calibrator" to track changes in the phase and amplitude response of the telescopes over time. Observations of 3c279 are interleaved with observations of the M100.

The output also shows that the data contain many spectral windows. Using the labeling scheme in the listobs above these are:

  • spw 0 targets ~185 GHz and holds water vapor radiometer data
  • spw 1 and spw 3 hold our science data. These are "Frequency Domain Mode" (FDM) data with small (0.49 MHz) channel width and wide (1.875 GHz) total bandwidth. As a result these have a lot of channels (3840). spw 1 holds the lower sideband (LSB) data and includes the CO(1-0) line. We will focus on these data. For the CO(1-0) line the channel width corresponds to 0.426 km/s and the bandwidth of spw 1 to 1634 km/s.
  • spw 2 and spw 4 hold frequency-averaged versions of spw 1 and 3 ("Channel 0" for those familiar with AIPS). These are used for some quick automated inspection. We will not use them here but we will carry out an equivalent inspection using spw 1.
  • spw 5 and spw 7 hold lower a resolution processing ("Time Domain Mode", TDM) of the data from the same part of the spectrum (baseband) as spws 1 and 3. These data have only 128 channels across 2 GHz bandwidth and so have a much coarser channel spacing than the FDM data. These were used to generate the calibration tables that we include in the tarball but will not otherwise appear in this guide.

The final column of the listobs output in the logger (not shown above) gives the scan intent. Later we will use this information to flag the pointing scans and the hot and ambient load calibration scans.

We'll now have a look at the configuration of the antennas used to take the data using the task plotants (<xr id="uid___A002_X5e971a_X124.plotants.png"/>).

<figure id="uid___A002_X5e971a_X124.plotants.png">

Position of antennas in dataset uid___A002_X5e971a_X124 obtained using task plotants

</figure>

# In CASA
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3',
'uid___A002_X5e9ff1_X5b3', 'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

for asdm in basename:
    print "Antenna configuration for : "+asdm
    plotants(vis=asdm+'.ms', figfile=asdm+'.plotants.png')
    clearstat()
    dummy_string = raw_input("Hit <Enter> to see the antenna configuration for the next data set.")

This will loop through all 6 data sets, show you the antenna position for each, and save that as a file named, e.g., "uid___A002_X5e971a_X124.plotants.png" for the first data set. The "raw_input" command asks CASA to wait for your input before proceeding. If you would prefer to just browse the .png files after the fact you can remove this. Notice that the antenna setup changes, but only slightly, over the course of the 6 data sets.


  1. In CASA

basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3', 'uid___A002_X5e9ff1_X5b3', 'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

for asdm in basename:

   print "Antenna configuration for : "+asdm
   plotants(vis=asdm+'.ms', figfile=asdm+'.plotants.png')
   clearstat()
   dummy_string = raw_input("Hit <Enter> to see the antenna configuration for the next data set.")

</source>

This will loop through all 6 data sets, show you the antenna position for each, and save that as a file named, e.g., "uid___A002_X5e971a_X124.plotants.png" for the first data set. The "raw_input" command asks CASA to wait for your input before proceeding. If you would prefer to just browse the .png files after the fact you can remove this. Notice that the antenna setup changes, but only slightly, over the course of the 6 data sets.

How to Deal With 6 Measurement Sets

# In CASA
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3',
'uid___A002_X5e9ff1_X5b3', 'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

for asdm in basename:
    print asdm

You only need to define your list of MS files once per CASA session. Then "basename" will be a variable in the casapy shell. You can check if it exists by typing "print basename". In the interests of allowing you to easily exit and restart CASA and pick this guide up at any point we will redefine "basename" in each section of the guide. Feel free to skip this step if you've already defined it in your session.

This page will step you through the reduction of the M100 Band3 SV data set using these 'for' loops. We will not be able to show every diagnostic plot but we give an example of each and the syntax to generate the rest. Also please be aware that even on a very fast machine this whole process can take a while, we are simply dealing with a lot of data.

A Priori Flagging

Even before we look in detail, we know that there are some data that we wish to exclude. We will start by flagging "shadowed" data where one antenna blocks the line of sight of another. We will also flag scans that were used to carry out pointing and atmospheric calibration, identified by their scan intent. Finally, we'll flag the autocorrelation data (the correlation of the signal from an antenna with itself) as we are only interested in cross-correlation data to make an interferometric image.

Start by defining our list of MS files:

# In CASA
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3','uid___A002_X5e9ff1_X5b3', 'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

You may want to reset the flagging if you have tried this step before and are starting over though this is not necessary on your first time through. Do so using flagdata:

# In CASA
for asdm in basename:
    print "Resetting flags for "+asdm
    flagdata(vis=asdm+'.ms', mode='unflag', flagbackup=F)

Then flag shadowed data using the command flagdata:

# In CASA
for asdm in basename:
    print "Flagging shadowed data for "+asdm
    flagdata(vis=asdm+'.ms',mode = 'shadow', flagbackup = F)

In the flagdata task we choose:

  • vis = asdm+'.ms' : each measurement set
  • mode = 'shadow': flag shadowed data
  • flagbackup = F: Do not automatically back up the flag files. We will save all of the a priori flags together using flagmanager at the end of this subsection and save some space and time.

The relevant calibration information has already been extracted from the pointing and atmospheric scans and we will not need them below. Now flag the pointing scans using flagdata in 'manualflag' mode and selecting on 'intent':

# In CASA
for asdm in basename:
    print "Flagging calibration scans for "+asdm
    flagdata(vis=asdm+'.ms', mode='manual', intent='*POINTING*,*SIDEBAND_RATIO*,*ATMOSPHERE*', flagbackup = F)

Note that because the atmospheric calibration scans contain only TDM spectral windows, they will be removed automatically when we separate out the FDM data below.

Now flag the autocorrelation data:

# In CASA
for asdm in basename:
    print "Flagging autocorrelation data for "+asdm
    flagdata(vis=asdm+'.ms',autocorr=True,flagbackup=F)
# In CASA
for asdm in basename:
    print "Flagging shadow for "+asdm
    flagmanager(vis = asdm+'.ms', mode = 'restore', versionname = 'shadow')

Finally store the current flags information using flagmanager:

# In CASA
for asdm in basename:
    print "Backing up 'a priori' flags for "+asdm
    flagmanager(vis = asdm+'.ms', mode = 'save', versionname = 'Apriori')
The flagmanager task will also allow you to view the saved flagging versions,
including those created by running flagdata with flagbackup=T.

For example try 

flagmanager(vis='uid___A002_X5e971a_X124.ms', mode='list')

The output in the logger should list the Apriori flagging that we have applied.
It will also indicate versions associated with any flagdata command where you
did not set flagbackup=F. Other tasks, for example applycal, will also create
flag version tables.


Create and Apply Tsys and Antenna Position Calibration Tables

#In CASA
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3','uid___A002_X5e9ff1_X5b3', 'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

Tsys

The Tsys calibration gives a first-order correction for the atmospheric opacity as a function of time and frequency and associates weights with each visibility that persists through imaging.

Use gencal to create the Tsys calibration tables from the spectral windows with CALIBRATE_ATMOSPHERE intents in listobs. Later in the applycal stage this TDM Tsys table will be interpolated to the FDM.

#In CASA
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3',
'uid___A002_X5e9ff1_X5b3', 'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

for asdm in basename:
  os.system('rm -rf cal_plots/Tsys_plots/'+asdm+'.tdm.tsys*')
  tsysfields=['3c279','Titan','M100']
  caltable=asdm+'.tdm.tsys'
  for field in tsysfields:
    au.plotbandpass(caltable=caltable,yaxis='amp',field=field,xaxis='freq',
                showatm=True,overlay='time',
                figfile='cal_plots/Tsys_plots/'+caltable+'.'+field,buildpdf=False,
                interactive=False,chanrange='8~120',subplot=42)

This sequence loops over all of our files and plots Tsys as a function of time for channel 50 in spectral window 5. In the call to plotcal: The Tsys values in CM01 and CM07 are too high.

  • subplot=421 parameter sets up a 4 x 2 panel grid.
  • iteration tells plotcal to make a separate plot for each antenna.
  • spw '5:50~50' selects spw '5' channel 50-50. This allows us to cleanly separate time variations from frequency variations.

Because 8 panels (2 panels for each antenna - LSB and USB) is not enough to show all antennas on one page, there are two plotcal calls: one for the first 8 antennas (antenna=0~7), and then for the remaining antennas (antenna=8~15). The fontsize needs to be set to a small value or the text overlaps.

The 'raw_input' commands will wait for you to hit Enter before issuing the next plot command. In the example above these are commented out (the leading "#" means that CASA will ignore them). If you would like to interactively cycle through the plots, uncomment them by removing the "#". Otherwise, the figfile parameter directs the output to .png files for later inspection. The easiest way to look at the 20 plots produced here is to simply inspect the .png files using your favorite viewer.

We will also want to look at Tsys as a function of frequency. This will use the analysisutils package mentioned at the beginning of this guide (called by the au. command)

#In CASA
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3','uid___A002_X5e9ff1_X5b3', 
'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']
for name in basename:
    os.system('rm -rf name+'.ms.tsys')
    gencal(vis = name+'.ms',
       caltable=name+'.ms.tsys',
       caltype = 'tsys')

<figure id="uid___A002_X5e971a_X124.spw8.t0.png">

Tsys vs. frequency plot for uid___A002_X5e971a_X124. First 4 antennas. Note the high y-axis values for DV04 and the mesospheric line near 343.2 GHz.

</figure> <figure id="uid___A002_X5e971a_X124.spw10.t3.png">

Tsys vs. frequency plot for uid___A002_X5e971a_X124. Next 4 antennas.

</figure>

<figure id="uid___A002_X5e971a_X124.spw14.t1.png">

Tsys vs. frequency plot for uid___A002_X5e971a_X124. Note the pathological behavior for DV12.

</figure>

Now have a look at the Tsys vs. frequency plots or see <xr id="uid___A002_X5e971a_X124.spw8.t0.png"/>, <xr id="uid___A002_X5e971a_X124.spw10.t3.png"/>, and <xr id="uid___A002_X5e971a_X124.spw14.t1.png"/> for examples on the first data set. You can see the effect of a close pair of atmospheric ozone absorption lines at about *** GHz that makes Tsys larger near that frequency in all antennas. Applying the Tsys calibration tables will minimize the contribution of these atmospheric lines.

Antenna Positions

The antenna position table reflects refinements in the measured positions of the antennas from those stored in the data. gencal will now be used put antenna position data into each observation. Again, gencal will merely append to existing antenna position data, ruining any subsequent results. We start by removing any existing antenna position refinements, followed by defining the antenna names, then their refinements (both as arrays), finally running gencal to create the information CASA can refer to for antenna positions.

basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3', 'uid___A002_X5e9ff1_X5b3', 'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

#In CASA
os.system('rm -rf name+'.ms.antpos')
gencal(vis = 'X2e7.ms',
       caltable = 'X2e7.ms.antpos',
       caltype = 'antpos',
       antenna = 'CM02,CM03,CM04,CM05,CM06,CM07,CM09,CM12',
       parameter = [-1.04341656e-04,4.29447740e-04,4.12447378e-04,1.04838982e-05,4.57765535e-04,3.53057869e-04,
1.38600077e-03,-6.59998506e-04,-4.26100381e-03, -8.91945325e-04,4.27036546e-04,1.40665658e-03,-1.16256997e-04,
2.12460477e-03,-5.08893328e-03,-3.65406508e-03,1.86459431e-02,5.67199755e-03,8.04639747e-03,4.01791865e-02,
1.46969082e-03,-5.75850718e-05,6.55882061e-04,7.88089819e-05])


gencal(vis = 'X3f3.ms',
       caltable = 'X3f3.ms.antpos',
       caltype = 'antpos',
       antenna = 'CM02,CM03,CM04,CM05,CM06,CM07,CM09,CM12',
       parameter = [-1.04341656e-04,4.29447740e-04,4.12447378e-04,1.04838982e-05,4.57765535e-04,3.53057869e-04,
1.38600077e-03,-6.59998506e-04,-4.26100381e-03,-8.91945325e-04,  4.27036546e-04,1.40665658e-03,-1.16256997e-04,
2.12460477e-03,-5.08893328e-03,-3.65406508e-03,1.86459431e-02,5.67199755e-03,8.04639747e-03,4.01791865e-02,
1.46969082e-03,-5.75850718e-05,6.55882061e-04,7.88089819e-05])



gencal(vis = 'X5b3.ms',
       caltable = 'X5b3.ms.antpos',
       caltype = 'antpos',
       antenna = 'CM02,CM03,CM04,CM05,CM06,CM09,CM12',
       parameter = [-1.04341656e-04,4.29447740e-04,4.12447378e-04,1.04838982e-05,4.57765535e-04,3.53057869e-04,
1.38600077e-03,-6.59998506e-04,-4.26100381e-03,-8.91945325e-04,4.27036546e-04,1.40665658e-03,-1.16256997e-04,
2.12460477e-03,-5.08893328e-03,8.04639747e-03,4.01791865e-02,1.46969082e-03,-5.75850718e-05,6.55882061e-04,7.88089819e-05])



gencal(vis = 'X44.ms',
       caltable = 'X44.ms.antpos',
       caltype = 'antpos',
       antenna = 'CM12,CM02,CM03,CM05',
       parameter = [5.70082944295e-05,-0.00065711393962,-7.91980095891e-05,0.000104837426825,-0.000430660050663,
-0.000411915081903,-1.1060689293e-05,-0.000458996548728,-0.00035344706733,
                    0.000982235185802,-0.000412690453231,-0.00145998690277])
gencal(vis = 'X44.ms',
       caltable = 'X44.ms.antpos',
       caltype = 'antpos',
       antenna = 'CM02',
       parameter = [0.0,0.0,0.0])



gencal(vis = 'X124.ms',
       caltable = 'X124.ms.antpos',
       caltype = 'antpos',
       antenna = 'CM02,CM03,CM04,CM05,CM06,CM07,CM09,CM12',
       parameter = [-1.04341656e-04,4.29447740e-04,4.12447378e-04,1.04838982e-05,4.57765535e-04,3.53057869e-04,
1.38600077e-03,-6.59998506e-04,-4.26100381e-03,-8.91945325e-04,4.27036546e-04,1.40665658e-03,-1.16256997e-04,
2.12460477e-03,-5.08893328e-03,-3.65406508e-03,1.86459431e-02,5.67199755e-03,8.04639747e-03,4.01791865e-02,
1.46969082e-03,-5.75850718e-05,6.55882061e-04,   7.88089819e-05])
gencal(vis = 'X124.ms',
       caltable = 'X124.ms.antpos',
       caltype = 'antpos',
       antenna = 'CM02',
       parameter = [0.0,0.0,0.0])


Applycal

We are now ready to apply the Tsys tables to the data with applycal, which reads the specified gain calibration tables, applies them to the (raw) data column, and writes the calibrated results into the corrected column. Again, we loop through all the datasets. It is important to only apply Tsys and WVR corrections obtained close in time to the data being corrected, so in addition to looping over data sets we define the list of unique source names and loop over these. Then by setting gainfield and field to the same value we ensure that Tsys are only applied to the source for which they are measured. The applycal task now has much more flexibility for interpolating and applying calibrations derived in one spectral window to another, even if they do not share the same spectral shape (number of channels and channel width). This new functionality is used below to interpolate the TDM (128 channel) Tsys measurements to the FDM (3840 channel) spectral windows. This is controlled through the spectral window mapping parameter *spwmap*. Because this can be a bit confusing, we've written a "helper" function that will tell you what you should put for the Tsys calibration table part of spwmap. We only need to run it on one of the datasets because they are all the same in this regard

# In CASA

for name in basename:
from recipes.almahelpers import tsysspwmap
tsysspwmap(vis=name+'.ms',tsystable=name+'.tsys')

This will print to your screen: [0, 5, 5, 7, 5, 5, 5, 7]. Recall that the TDM spectral windows are spw=5 and 7 and the FDM spectral windows are 1 and 3. This spwmap tells applycal to use the Tsys information in spectral window 5 for spectral window 1; and to use the information in spectral window 7 for spectral window 3. Beyond that, it is only important that there are placeholders for the other spectral windows, its also useful to note that you need not give placeholders for spectral window ids larger than the largest spectral window you need to use, in this case 7.

You will also need to put in placeholders for other tables that you give the gaintables. So if the Tsys table is 2nd in the list, spwmap=[[],[0,5,5,7,5,5,5,7],[]]

Now run the applycal commands.

# In CASA

# A new list of file names that contain only data from the Northern Mosaic
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3',
'uid___A002_X5e9ff1_X5b3', 'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']
field_names = ['Titan','3c279','M100']

for asdm in basename:
    print "Apply Tsys and Antenna Position calibrations to "+asdm
    for field in field_names:
        applycal(vis=asdm+".ms", spw='1,3', 
            field=field, gainfield=['',field,field],
            interp=['','linear,spline','nearest'], 
            gaintable=[asdm+'.antpos',asdm+'.tdm.tsys'],
            spwmap=[[],[0, 5, 5, 7, 5, 5, 5, 7],[]],
            flagbackup=F, calwt=T)

where:

  • field: the field to which we will apply the calibration,
  • gainfield: the field from which we wish to take the calibration table
  • interp = 'nearest' : apply the nearest solution from the calibration table rather than interpolating.

As you browse through the whole data set, you will probably note some problems along the same lines as the DV04 issue we saw above. We'll apply these as additional data flagging in just a moment.

Inspect Data

We are not quite done with the original ".ms" data sets yet. Before going further it will be useful to use plotms to show the effects of applying the calibration. In the process we'll take a quick look at each antenna and search for pathologies in the data.

For this basic inspection, we want to compare the phase and amplitude as a function of frequency and time in the DATA and CORRECTED columns of each measurement set. The CORRECTED column has had the Tsys and WVR calibrations applied and so we expect lower phase scatter and flatter amplitude response as a function of time and frequency. We are looking for antenna-based issues, so cycling through a set of baselines that includes each antenna once will be a good start. We'll focus these plots on the phase+bandpass calibrator, 3c279, and on baselines that include antenna CM04, which we will make our reference antenna in just a bit.


Each CASA Measurement Set has up to three "columns" of data: DATA, CORRECTED, and MODEL (though it is possible
for a MS to hold only a DATA column if it has not been processed at all). 

A column entry exists for each data point (baseline, frequency, time). 

The DATA column holds the current raw data, though using split as we just did we can change the definition of
"raw" data. 

The CORRECTED column holds the result of applying one or more calibration tables (e.g., via applycal) to the
DATA column and so represents a corrected version of the DATA column. 

In CASA 4.1: the MODEL column is deprecated (though it is still possible to use them by explicitly setting usescratch=T).
Instead, the model is stored in the header of the ms. This is a great step forward in terms of saving space and time 
to create scratch columns  

To get an intuitive feel for the columns you may wish to explore using plotms (which can plot different data 
columns, as seen below) or the browsetable task.

<figure id="plotms_amp_vs_freq_example-data.png">

Example of Amplitude vs. Frequency before correction for the first Northern Mosaic data set.

</figure> <figure id="plotms_amp_vs_freq_example-corr.png">

Same baseline as <xr id="plotms_amp_vs_freq_example-data.png"/> but now after correction using WVR and Tsys calibrations.

</figure>

First, we plot amplitude as a function of frequency for 3c279. We start by plotting the DATA column, set color to indicate the two correlations (i.e., the XX and YY polarizations), and ask plotms to iterate over baseline. By setting antenna to 'CM04&*' we select only baselines that include CM04. We ask plotms to average all data over a very long timescale, avgtime = 1e8 seconds ~ 3 years or much longer than the time spanned by the whole data set. By setting avgscan = True we allow plotms to average across scan boundaries. The result is a plot of average amplitude per channel vs. frequency.

# In CASA
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3','uid___A002_X5e9ff1_X5b3', 
'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

asdm=basename[0]

plotms(vis=asdm+'.ms', 
       field='3c279',
       xaxis='frequency', yaxis='amp',
       selectdata=T, spw='1', 
       avgtime='1e8',avgscan=T,
       coloraxis='corr',
       iteraxis='baseline',
       antenna='CM04&*',
       ydatacolumn='data')

Notice the green arrows along the bottom of the plotms window. We asked plotms to iterate over baseline. As you click the arrows, the plot will rotate from baseline to baseline, always with CM04 so that each antenna shows up once. To see the effect of the calibration, go to the "Axes" tab along the left of the plotms window and pull down the Data Column menu under the Y Axis. Set this from DATA to CORRECTED and you should see the effects of the calibration. You may need to ensure that the "Force Reload" box is checked before clicking "Plot" (both buttons lie at the bottom of the panel). For the most part things get better (flatter), but as we noted before DV04 is problematic.

<figure id="plotms_amp_vs_time_north.png">

Example of amplitude vs. time for a northern mosaic data set.

</figure> <figure id="plotms_amp_vs_time_south.png">

Example of amplitude vs. time for a southern mosaic data set.

</figure>

You can now make analogous calls to examine the phase vs. frequency, amplitude vs. time, and phase vs. time.

# In CASA
plotms(vis=asdm+'.ms', 
       field='3c279',
       xaxis='frequency', yaxis='phase',
       selectdata=T, spw='1', 
       avgtime='1e8',avgscan=T,
       coloraxis='corr',
       iteraxis='baseline',
       antenna='CM04&*',
       ydatacolumn='data')

plotms(vis=asdm+'.ms', 
       field='3c279',
       xaxis='time', yaxis='amp',
       selectdata=T, spw='1:1200~1300', 
       avgchannel='1000',avgscan=F,
       coloraxis='corr',
       iteraxis='baseline',
       antenna='CM04&*',
       ydatacolumn='data')

plotms(vis=asdm+'.ms', 
       field='3c279',
       xaxis='time', yaxis='phase',
       selectdata=T, spw='1:1200~1300', 
       avgchannel='1000',avgscan=F,
       coloraxis='corr',
       iteraxis='baseline',
       antenna='CM04&*',
       ydatacolumn='data')

Where:

  • spw is set to cover only channels 1200~1300 for the time plots in order to isolate time dependent variations from frequency-dependent behavior. Those 101 channels represent only a small part of the total spw 1 bandpass.
  • avgchannel set to a large number causes the plots of phase and amplitude vs. time to average data at all frequencies into a single point for each measurement.
  • coloraxis corr sets the colors to correspond to the two polarizations of the data.

In each case, you will want to examine each baseline, alternating between the DATA and CORRECTED columns.

This is a lot of data inspection and that's only for one of 10 data sets! You can iterate across the data by hand, updating "asdm" to refer to each data set in order and cycling between baselines and DATA/CORRECTED. It is also possible to script CASA to show you the key plots in succession (see the next block down). However you approach the infrastructure, you are looking for:

  • Improved scatter and lower variability in phase and amplitude vs. frequency and time. This indicates that the WVR and Tsys calibrations helped.
  • Sudden jumps in phase or amplitude as a function of either time or frequency. These may indicate problems with the antenna during that observation.
  • Large gradients, especially full wraps, in phase as a function of frequency. This may indicate a problem in the delays, the signal path length to the telescopes.
  • Unusual magnitude, scatter, or patterns in any plot - though this may be better explored using plots that show all data together, which we'll make in a moment.
  • Missing data. For example, if the phase calibrator drops out for a period of time we will not be able to calibrate and will need to flag the data.

As you look through, note individual potentially problematic antennas. If all antennas in a data set appear problematic it may be that your "reference" antenna, CM04 in the example above, is the source of the problem. In this case swap this reference antenna for another and see whether the problem is isolated to your original reference antenna.


A brief aside on structure:

This section (Data Inspection) and the next (Apply Flags) are closely linked. We will present them as two
separate steps. The idea here is that you look through your data, note problems, and then write commands to
flag problematic data. This mimics one approach to writing data reduction scripts for CASA, where you will
group all flagging together into one convenient place. Other CASA guides take a different approach, interleaving 
flagging and plotting. There is no "right" answer here. Find an approach to data inspection that works for you.

If you do wish to semi-automate the plot generation, the following sequence will cycle between data and corrected plots for each data set in turn. Type "stop" at any input call to break out.

Note: In loops like this involving plotms, hitting enter before the last dataset completes loading may cause plotms to stop refreshing the plot window at each step, or cause the window to disappear altogether. This will be fixed in an upcoming CASA release. Right now, you may need to exit CASA and restart to bring up the plotms window again, but you should be able to continue in the script where you left off.

# In CASA

user_input = ""
for asdm in basename:
    # check if a stop has been requested
    if user_input == "stop":
        break
    # Extract antenna list for this data set.
    tb.open(asdm+'.ms/ANTENNA', nomodify=True)
    ants = tb.getcol('NAME')
    tb.close
    # Define the reference antenna to make baselines with
    ref_ant = 'CM04'
    # Loop over antennas
    for ant in ants:
        # Check if the user wants to stop
        if user_input == "stop":
            break
        # Skip correlation of reference antenna with itself (autocorrelations are flagged anyhow)        
        if ant == ref_ant:
            continue
        # Define the baseline with the reference antenna for current antenna
        ant_str = ref_ant+'&'+ant
        print "Showing baseline "+ant_str+" for data set "+asdm
        print "Use this to inspect effect of applying wvrcal and Tsys calibrations."
        # Loop over phase and amplitude as axes
        for y_axis in ["amp", "phase"]:
            # Make 'before' plot for frequency x-axis
            print "... "+y_axis+" vs. frequency for DATA:"    
            plotms(vis=asdm+'.ms', spw='1', field='3c279',
               antenna=ant_str, xaxis="frequency", yaxis=y_axis,
               avgtime="1e8", avgscan=T, coloraxis="corr",
               ydatacolumn="data")
            user_input = raw_input("Hit <ENTER> to see CORRECTED data [type 'stop'+<Enter> to break out].")
            if user_input == "stop":
                break
            # Make 'after' plot for frequency x-axis
            print "... "+y_axis+" vs. frequency for CORRECTED:"
            plotms(vis=asdm+'.ms', spw='1', field='3c279',
               antenna=ant_str, xaxis="frequency", yaxis=y_axis,
               avgtime="1e8", avgscan=T, coloraxis="corr",
               ydatacolumn="corrected")
            user_input = raw_input("Hit <ENTER> to proceed to next plot [type 'stop'+<Enter> to break out].")
            if user_input == "stop":
                break
            # Make 'before' plot for time x-axis
            print "... "+y_axis+" vs. time for DATA:"    
            plotms(vis=asdm+'.ms', spw='1:1200~1300', field='3c279',
               antenna=ant_str, xaxis="time", yaxis=y_axis,
               avgchannel="1000", coloraxis="corr",
               ydatacolumn="data")
            user_input = raw_input("Hit <ENTER> to see CORRECTED data [type 'stop'+<Enter> to break out].")
            if user_input == "stop":
                break
            # Make 'after' plot for time y-axis
            print "... "+y_axis+" vs. time for CORRECTED:"
            plotms(vis=asdm+'.ms', spw='1:1200~1300', field='3c279',
               antenna=ant_str, xaxis="time", yaxis=y_axis,
               avgchannel="1000", coloraxis="corr",
               ydatacolumn="corrected")
            user_input = raw_input("Hit <ENTER> to proceed to next plot [type 'stop'+<Enter> to break out].")
            if user_input == "stop":
                break

<figure id="plotms_amp_vs_uvdist_north.png">

Example of amplitude vs. uv-distance for 3c279 in the first northern mosaic data set.

</figure>

<figure id="plotms_amp_vs_freq_3c279.png">

Example of amplitude vs. frequency for 3c279 in the second northern mosaic data set.

</figure> <figure id="plotms_amp_vs_freq_Titan.png">

Example of amplitude vs. frequency for Titan in the second northern mosaic data set. Note the strong line (this is CO 3-2)!

</figure> <figure id="plotms_amp_vs_freq_Antennae.png">

Example of amplitude vs. frequency for the Antennae in the second northern mosaic data set. The CO(3-2) line is visible.

</figure>

A detailed explanation of the procedure is a bit outside the scope of this guide (for more on python see http://www.python.org/ and for more on the CASA toolkit see http://casa.nrao.edu/), but the basic process is to loop over each data set, baseline with the reference antenna (here CM04), and y-axis of interest (phase or amplitude) then plot the effect of the calibration vs. frequency and time for each combination. Running this to step through the data will give you about 200 "before and after" plots from which you could note a subset of problematic cases to be followed up by hand. Many other strategies to inspect the data are also viable.

With the Tsys and WVR calibrations applied successfully and the a priori flagging taken care of we will now split out the corrected data. We will keep only the corrected data, specified via datacolumn, and only spectral window 1, which contains the FDM (high spectral resolution) observations of the CO(3-2) line. Setting keepflags=F tells split not to carry over any fully flagged rows from the original data set to the new MS. We give the new MS files the extension ".wvrtsys.ms" to indicate that they have been corrected for WVR and Tsys effects. Because split will not overwrite existing files, we remove any previous versions of the new MS before beginning.

# In CASA
for asdm in basename:
    os.system('rm -rf '+asdm+'.wvrtsys.ms')
    print "Splitting out corrected data for "+asdm
    split(vis=asdm+'.ms', outputvis=asdm+'.wvrtsys.ms', 
        datacolumn='corrected', spw='1', keepflags=F)

Be patient, split may take awhile. The WVR and Tsys-corrected data now sit in the DATA column of the new measurement sets, which have only one spectral window (now labeled spectral window 0 though it was spectral window 1 in the original data). You may wish to run listobs to illustrate the changes:

# In CASA
for asdm in basename:
    os.system('rm '+asdm+'.wvrtsys.listobs.txt')
    listobs(vis=asdm+'.wvrtsys.ms', listfile=asdm+'.wvrtsys.listobs.txt', verbose=True)

Note the new spectral window information:


2011-08-05 01:07:08 INFO listobs	Spectral Windows:  (1 unique spectral windows and 1 unique polarization setups)
2011-08-05 01:07:08 INFO listobs	  SpwID  #Chans Frame Ch1(MHz)    ChanWid(kHz)TotBW(kHz)  Ref(MHz)    Corrs   
2011-08-05 01:07:08 INFO listobs	  0        3840 TOPO  344845.586  488.28125   1875000     344908.33   XX  YY  

Next we will do a bit more inspection using plotms to look at whole data sets. This will help us identify missing data or look for egregious outliers.

First we plot amplitude versus time (see <xr id="plotms_amp_vs_time_north.png"/> and <xr id="plotms_amp_vs_time_south.png"/>), averaging over all channels (by setting avgchannel to the very large value 10,000). We colorize by field so that scans on Titan are red, the bandpass and phase calibrator 3c279 is black (and orange in the Southern Mosaic where it has two field IDs), and the Antennae mosaic appears as a range of colors (one per pointing).

# In CASA
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3','uid___A002_X5e9ff1_X5b3', 
'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

for asdm in basename:
    plotms(vis=asdm+'.wvrtsys.ms', 
            xaxis='time', yaxis='amp', 
            avgchannel='3840',coloraxis='field')
    dummy_string = raw_input("Examining amplitude vs. time for "+asdm+" . Hit <Enter> to proceed.")

Here look for:

  • Missing data. The source needs to be flanked by phase calibrator scans, if those are missing for any reason we need to flag the appropriate time range.
  • Dramatic outliers. Does the source suddenly get very bright or the otherwise bright calibrator appear anomalously faint for a brief time? This likely indicates problematic data that should be identified and flagged. You can use the "select" (box with green plus along the bottom row in plotms) and "locate" (magnifying glass) buttons in plotms to isolate and identify problem data (it will print to the log).
  • Smooth variation with time. A sudden jump may indicate a problem and often the safest approach is to flag data near a discontinuity.

Look through the amplitudes vs. time for each data set (remember that we've already examined the phases vs. time and amplitude vs. time for individual baselines above).

There are two other very useful "averaging" plots worth making. First, we plot amplitude as a function of u-v distance (projected antenna separation). Discontinuities and spikes in this plot are often from non-astrophysical sources. In the phase analog to the plot, the effects of atmospheric decorrelation can be assessed from increased scatter at longer u-v distances. While using the moon Titan as our flux calibrator, we may want to watch for flaring amplitudes at short u-v distances. These may indicate that Saturn is contaminating our beam. For a perfect, bright point source, we expect flat amplitudes as a function of u-v distance at the source amplitudes. <xr id="plotms_amp_vs_uvdist_north.png"/> shows an example of this plot, generated via:

# In CASA
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3','uid___A002_X5e9ff1_X5b3', 
'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

for asdm in basename:
    plotms(vis=asdm+'.wvrtsys.ms', 
        field='3c279',
        xaxis='uvdist', yaxis='amp', 
        avgchannel='4096',coloraxis='corr')
    dummy_string = raw_input("Examining amplitude vs. UV Distance for 3c279 for "+asdm+" . Hit <Enter> to proceed.")
    plotms(vis=asdm+'.wvrtsys.ms', 
        field='Titan',
        xaxis='uvdist', yaxis='amp', 
        avgchannel='4096',coloraxis='corr')
    dummy_string = raw_input("Examining amplitude vs. UV Distance for Titan for "+asdm+" . Hit <Enter> to proceed [type 'stop'+<Enter> to break out].")
    # check if a stop has been requested
    if dummy_string == "stop":
        break

For this command notice that we can see the CO(3-2) line in the Antennae even before calibration (see <xr id="plotms_amp_vs_freq_Antennae.png"/>) and that Titan also shows evidence of a strong line (also <xr id="plotms_amp_vs_freq_Titan.png"/>)! This will need to be flagged before we can use Titan to calibrate the flux scale of our data.

This suite of plots (along with the earlier inspection of the Tsys tables) gives us the tools we need to identify problematic data through the data sets. We use this to generate a set of inspection-driven flagdata commands for each data set. We apply these before the bandpass and gain calibration.


Apply Flagging

Based on this inspection and the other plots we have made, we now flag problematic portions of the data. We break up the flags by reason for flagging in order to illustrate the process. As you reduce your own data it may be more efficient to group flags by data set and make use of the flagcmd command. Except for the "post-calibration" flagging, the inspection plots that we just looked through have already revealed all of the problems that we flag. We structure the guide so that the flagging is grouped in one place.

As before, we may wish to reset our flags before beginning (particularly if one iterates this process) via:

# In CASA

basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3','uid___A002_X5e9ff1_X5b3', 
'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']
for asdm in basename:
    flagdata(vis = asdm+'.wvrtsys.ms',mode='unflag', flagbackup = F)

Remember that we dropped the flagged data when splitting out after the WVR and Tsys calibration, so this should not undo your "A Priori" flagging of shadowed data, autocorrelations, etc.. In any case this unflagging step is not necessary during the first iteration.

  • Edge Channels

ALMA's sensitivity decreases near the edge of the baseband and it is often useful to check for a 'roll-off' in sensitivity near the edge of the band. This will appear as a downturn in amplitude as a function of channel near the edge of the window in the uncalibrated data, as a flaring due to increased noise at the spw edges in the calibrated data. It will also be visible in the amplitude part of the bandpass calibration table. Because our FDM spw does not cover the full baseband, we do not see a strong roll off in our data (see <xr id="amp_vs_channel_example.png"/>), where there is only a mild hint of a roll-off at the high end) but we do flag a (very) few channels at the high and low-frequency edge of the data set to be safe.

<figure id="amp_vs_channel_example.png">

Amplitude vs. channel for one uncalibrated antenna pair. This kind of plot can be inspected to get an idea of the presence or magnitude of any roll-off in sensitivity near the edges of the spectral window.

</figure>

# In CASA

for asdm in basename:
    flagdata(vis = asdm+'.wvrtsys.ms', mode='manual', spw = '0:0~7,0:3831~3839', flagbackup = F)

for asdm in basename:
    flagdata(vis = asdm+'.wvrtsys.ms', mode='manual', spw = '0:3260~3320', flagbackup = F)
  • Problematic Tsys measurements

Above we noted issues with the Tsys measurements for both DV04 and DV12. We flag the affected data. Each of these issues should be visible in the Tsys plots you made above (e.g., see <xr id="Uid___A002_X1ff7b0_Xb.tdm.tsys.3c279.DV02.spw5.CASA3_4.png"/> and <xr id="Uid___A002_X1ff7b0_Xb.tdm.tsys.3c279.PM01.spw5.CASA3_4.png"/>).

# In CASA

asdm="uid___A002_X1ff7b0_Xb"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV04', flagbackup=F)

asdm="uid___A002_X207fe4_X3a"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV12',correlation='YY', flagbackup=F)

asdm="uid___A002_X207fe4_X3b9"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV12',correlation='YY', flagbackup=F)

asdm="uid___A002_X2181fb_X49"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV12',correlation='YY', flagbackup=F)

asdm="uid___A002_X1ff7b0_X1c8"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV04',flagbackup=F)

asdm="uid___A002_X207fe4_X1f7" 
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV12',correlation='YY', flagbackup=F)

asdm="uid___A002_X207fe4_X4d7" 
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV12',correlation='YY', flagbackup=F)

asdm="uid___A002_X215db8_X18"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV12',correlation='YY', flagbackup=F)

asdm="uid___A002_X215db8_X1d5"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV12',correlation='YY', flagbackup=F)

asdm="uid___A002_X215db8_X392"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV12',correlation='YY', flagbackup=F)
  • Unreliable Short-Spacing Measurements on Titan

<figure id="amp_vs_uv_titan_obs.png">

Observed amplitude vs. uv-distance for observations of Titan in the first data set. Note the scatter for low projected antenna separations. We will flag these short-spacing data, which may reflect contamination by Saturn, and use only the more extended baselines for flux calibration.

</figure>

Saturn may contaminate the short u-v spacings from Titan. In any case these often show significant scatter (<xr id="amp_vs_uv_titan_obs.png"/>), so we flag them. There are still enough baselines to determine a good amplitude calibration for each antenna.

# In CASA

asdm="uid___A002_X1ff7b0_Xb"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', field='Titan', uvrange='0~50', flagbackup = F)

asdm="uid___A002_X207fe4_X3a"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', field='Titan', uvrange='0~40', flagbackup = F)

asdm="uid___A002_X207fe4_X3b9"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', field='Titan', uvrange='0~50', flagbackup = F)

asdm="uid___A002_X2181fb_X49"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', field='Titan', uvrange='0~30', flagbackup = F)

asdm="uid___A002_X1ff7b0_X1c8"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', field='Titan', uvrange='0~50', flagbackup = F)

asdm="uid___A002_X207fe4_X1f7" 
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', field='Titan', uvrange='0~30', flagbackup = F)

asdm="uid___A002_X207fe4_X4d7" 
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', field='Titan', uvrange='0~30', flagbackup = F)
  • Delay Issues

<figure id="phase_vs_freq_DV13.png">

Phase vs. frequency for DV13 in a data set where this antenna shows evidence of imperfect delay calibration.

</figure>

DV13 and a few other antennas show signatures of an imperfect delay calibration. This is most easily identified via strong "wrapping" of phase as a function of frequency (see <xr id="phase_vs_freq_DV13.png"/>). Such effects can be calibrated out with mild delay issues largely accounted for by the bandpass solution. The phase wrapping in DV13 seems weak enough that we will trust the calibrations to remove it. For a more extreme example see the CASA guide describing the calibration of NGC3256Band3.

  • Missing Phase Calibrator Observations

<figure id="amp_vs_time_missing_scans.png">

Amplitude vs. time colored by field for a data set where the final visit to the phase calibrator is missing. We will flag the last set of source data to ensure that each visit to the source is flanked in time by visits to the phase calibrator.

</figure>

As a general rule, we want to be sure that observations of the phase calibrator (3c279) bracket each source observation. Two of the data sets do not include a final phase calibrator observation (see <xr id="amp_vs_time_missing_scans.png"/>) and for those two cases we flag the affected source observations.

# In CASA

asdm="uid___A002_X207fe4_X3b9"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', timerange='00:53:47~01:08:00',flagbackup = F)

asdm="uid___A002_X215db8_X18"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV10',timerange='19:46:20~20:34:40',flagbackup=F)
  • Unexpected Scatter or Discontinuity in the Data

For several antennas we find sudden jumps in the phase of the phase calibrator as a function of time. These are visible in the plots of uncalibrated phase vs. time for single baselines above, and we show an example in <xr id="phase_vs_time_DV09.png"/>. It will not be possible to effectively interpolate the phase between measurements when we see these discontinuities. The safest approach is to flag the source data across these jumps. We do so here (though note that the last two flaggings are borderline cases).

<figure id="phase_vs_time_DV09.png">

Phase vs. time for DV09 on a problematic day. It may prove problematic to calibrate the data near this discontinuity so we flag data near this time.

</figure>

# In CASA

asdm="uid___A002_X207fe4_X3a"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV09', timerange='21:24:09~21:35:35', flagbackup = F)

asdm="uid___A002_X207fe4_X1f7" 
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='DV09',timerange='23:30:52~24:10:00',flagbackup=F)
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='PM01',timerange='23:16:50~24:10:00',flagbackup=F)
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='PM03',timerange='23:16:50~24:10:00',flagbackup=F)
  • Outliers Visible After Calibration

<figure id="amp_vs_uvdist_outliers.png">

Amplitude vs. u-v distance after calibration. Note the outlying data. We track these down using the select (box with green "+") and locate (magnifying glass) features inside plotms and identify several problematic baselines across our data. Note that you cannot make this plot at this point in the guide, these pathologies become evident after calibration, requiring an iterative approach to reduction.

</figure>

Often issues with the data may become evident after calibration (i.e., after the next few steps that we apply). These data can appear as outliers in diagnostic plots for the calibrated data or even show up in the imaging stages. Once these are identified, best practice is to apply this new flagging then redo the calibration (if the issue is very minor, then re-calibrating may not be necessary).

# In CASA
asdm="uid___A002_X5e971a_X124"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', timerange='21:18:00~21:22:15', flagbackup=F)

asdm="uid___A002_X5e971a_X124"
flagdata(vis=asdm+'.wvrtsys.ms', mode='manual', antenna='CM04&CM08', flagbackup=F)

Now that we've applied our flagging, back up the flags as version "User" using flagmanager:

# In CASA
basename=['uid___A002_X5e971a_X124','uid___A002_X5e971a_X2e7','uid___A002_X5e9ff1_X3f3','uid___A002_X5e9ff1_X5b3', 
'uid___A002_X60b415_X44','uid___A002_X62f759_X4eb']

for asdm in basename:
    flagmanager(vis=asdm+'.wvrtsys.ms',mode='save',versionname ='User')

Applying this flagging will remove the most egregious pathologies from the data. We are now ready to calibrate the