|
|
| (118 intermediate revisions by 5 users not shown) |
| Line 1: |
Line 1: |
| This is an advanced Jansky VLA data reduction tutorial that calibrates and images a 3-bit dataset.
| | #REDIRECT [[EVLA 3-bit Tutorial G192-CASA4.4]] |
| | |
| <b>This casaguide is for Version 4.1.0 of CASA.</b>
| |
| | |
| == Overview ==
| |
| | |
| This article describes the calibration and imaging of the protostar G192.16-3.84. The data were taken in Ka-band using the 3-bit samplers and widely-spaced basebands centered at 29 and 36.5 GHz, each with 4 GHz of bandwidth (comprised of 32 128-MHz spectral windows). In this tutorial, we will use wideband imaging techniques, as well as corrections for the requantizer gains (which are necessary for 3-bit data calibration and harmless on 8-bit data).
| |
| | |
| This is a more advanced tutorial, so if you are a relative novice (and <em>particularly</em> for EVLA continuum calibration and imaging), it is <em>strongly</em> recommended that you start with the [[EVLA Continuum Tutorial 3C391]] (at least read it through) before tackling this dataset. We will not include basic information on CASA processing in this tutorial.
| |
| | |
| From the [http://casaguides.nrao.edu MainPage] of the CASA Guides you can find helpful information:
| |
| * [[What is CASA?]]
| |
| * [[Getting Started in CASA]]
| |
| * [[CASA Reference Manuals]]
| |
| * [[Hints, Tips, & Tricks]]
| |
| * [[AIPS-to-CASA Cheat Sheet]]
| |
| | |
| In this tutorial we will be invoking the tasks as function calls. You can cut and paste these to your casapy session. We also recommend that you copy all the commands you use, with any relevant commentary, to a text file. This is ''very'' good practice when tackling large datasets. If you wish, you can use the [http://casaguides.nrao.edu/index.php?title=Extracting_scripts_from_these_tutorials Script Extractor] to create a file with the tutorial commands, which can subsequently be edited as desired.
| |
| | |
| Occasionally we will be setting Python variables (e.g. as lists for flags) outside the function call so make sure you set those before running the task command. Note that when you call a CASA task as a function the task parameters which are not set in the function call (assuming there is at least one) will be set to their defaults, and will ''not'' use values
| |
| you set in previous calls or outside the call. See [[Getting_Started_in_CASA#Task_Execution]] for more on calling tasks and setting parameters in the scripting interface.
| |
| | |
| NOTE: If you find that the figures on the right margin of the browser window overlap the text too much and make reading difficult, go ahead and widen the browser window.
| |
| | |
| == Obtaining the Data ==
| |
| | |
| The data for this tutorial were taken with the VLA as part of its commissioning phase as the scheduling block (SB) <tt>TVER0004.sb14459364.eb14492359.56295.26287841435</tt>, which was run on 2013-01-03 from 6:18 to 7:47 UT (raw size is 57.04 GB).
| |
| | |
| The data can be directly downloaded from [http://casa.nrao.edu/Data/EVLA/G192/G192_6s.ms.tar.gz http://casa.nrao.edu/Data/EVLA/G192/G192_6s.ms.tar.gz] (dataset size: GB)
| |
| | |
| Your first step will be to unzip and untar the file in a terminal, before you start CASA:
| |
| | |
| <source lang="bash">
| |
| tar -xzvf G192_6s.ms.tar.gz
| |
| </source>
| |
| | |
| If you are brave enough, you can also get the data straight from the EVLA archive. Go to the [https://archive.nrao.edu/archive/advquery.jsp NRAO Science Data Archive], and search for "TVER0004.sb14459364" in the Archive File ID field. Then select the dataset and choose a time-averaging value of 6 seconds. (Although the data were taken in A-configuration, we will not be imaging outside of the center of the field, so we aren't too worried about time-average smearing and will take advantage of averaging to reduce dataset size.) Also select the "Create tar file" option.
| |
| | |
| In addition, only the fields used for analysis and observation are included in the downloadable file. This can be accomplished using the {{split}} task in CASA:
| |
| <source lang="python">
| |
| # In CASA
| |
| split('TVER0004.sb14459364.eb14492359.56295.26287841435.ms', outputvis='G192_6s.ms', \
| |
| datacolumn='all', field='3,6,7,10', keepflags=False, spw='2~65')
| |
| </source>
| |
| | |
| This will create a file equivalent to what is used at the start of this tutorial.
| |
| | |
| == Starting CASA ==
| |
| | |
| {{CaltechCASAStartup}}
| |
| | |
| == Examining the MS ==
| |
| | |
| We use {{listobs}} to summarize our MS:
| |
| <source lang="python">
| |
| # In CASA
| |
| listobs('G192_6s.ms', listfile='G192_listobs.txt')
| |
| </source>
| |
| | |
| This will write the output to a file called <tt>G192_listobs.txt</tt>, which we can print to the terminal using the <tt>cat</tt> command:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| cat G192_listobs.txt
| |
| </source>
| |
| | |
| <pre>
| |
| ================================================================================
| |
| MeasurementSet Name: /lustre/mkrauss/casa_guides/3bit/G192_6s.ms MS Version 2
| |
| ================================================================================
| |
| Observer: Dr. Debra Shepherd Project: uid://evla/pdb/7303457
| |
| Observation: EVLA
| |
| Data records: 10061248 Total integration time = 4557 seconds
| |
| Observed from 03-Jan-2013/06:31:51.0 to 03-Jan-2013/07:47:48.0 (UTC)
| |
| | |
| ObservationID = 0 ArrayID = 0
| |
| Date Timerange (UTC) Scan FldId FieldName nRows SpwIds Average Interval(s) ScanIntent
| |
| 03-Jan-2013/06:31:48.0 - 06:36:42.0 6 0 3c147-J0542+49 1019200 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5.94, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [CALIBRATE_FLUX#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:46:15.0 - 06:46:54.0 10 1 gcal-J0603+174 145600 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57, 5.57] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:47:09.0 - 06:47:54.0 11 2 G192.16-3.84 163200 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65, 5.65] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:48:06.0 - 06:48:39.0 12 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:48:51.0 - 06:49:39.0 13 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:49:51.0 - 06:50:24.0 14 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:50:36.0 - 06:51:24.0 15 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:51:36.0 - 06:52:09.0 16 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:52:19.5 - 06:53:09.0 17 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:53:21.0 - 06:53:54.0 18 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:54:06.0 - 06:54:54.0 19 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:55:06.0 - 06:55:39.0 20 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:55:51.0 - 06:56:39.0 21 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:56:51.0 - 06:57:24.0 22 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:57:36.0 - 06:58:24.0 23 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:58:36.0 - 06:59:12.0 24 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 06:59:21.0 - 07:00:12.0 25 2 G192.16-3.84 187200 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:00:19.5 - 07:00:57.0 26 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:01:06.0 - 07:01:57.0 27 2 G192.16-3.84 187200 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:02:03.0 - 07:02:42.0 28 1 gcal-J0603+174 125184 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99, 5.99] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:02:48.0 - 07:03:36.0 29 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:03:48.0 - 07:04:21.0 30 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:04:33.0 - 07:05:21.0 31 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:05:33.0 - 07:06:06.0 32 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:06:18.0 - 07:07:06.0 33 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:07:18.0 - 07:07:51.0 34 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:08:03.0 - 07:08:51.0 35 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:09:03.0 - 07:09:36.0 36 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:09:48.0 - 07:10:36.0 37 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:10:46.5 - 07:11:21.0 38 1 gcal-J0603+174 123200 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49, 5.49] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:11:33.0 - 07:12:21.0 39 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:12:33.0 - 07:13:06.0 40 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:13:18.0 - 07:14:06.0 41 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:14:16.5 - 07:14:51.0 42 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:15:01.5 - 07:15:51.0 43 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:16:03.0 - 07:16:36.0 44 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:16:48.0 - 07:17:39.0 45 2 G192.16-3.84 187200 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:17:48.0 - 07:18:24.0 46 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:18:33.0 - 07:19:24.0 47 2 G192.16-3.84 187200 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67, 5.67] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:19:30.0 - 07:20:09.0 48 1 gcal-J0603+174 124864 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:20:18.0 - 07:21:06.0 49 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:21:15.0 - 07:21:48.0 50 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:22:00.0 - 07:22:48.0 51 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:23:00.0 - 07:23:33.0 52 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:23:45.0 - 07:24:33.0 53 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:24:45.0 - 07:25:18.0 54 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:25:30.0 - 07:26:18.0 55 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:26:30.0 - 07:27:03.0 56 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:27:15.0 - 07:28:03.0 57 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:28:15.0 - 07:28:48.0 58 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:29:00.0 - 07:29:48.0 59 2 G192.16-3.84 166400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:30:00.0 - 07:30:33.0 60 1 gcal-J0603+174 124800 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5] [CALIBRATE_AMPLI#UNSPECIFIED, CALIBRATE_PHASE#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| 07:40:27.0 - 07:47:51.0 64 3 3c84-J0319+413 1537600 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [CALIBRATE_BANDPASS#UNSPECIFIED, OBSERVE_TARGET#UNSPECIFIED]
| |
| (nRows = Total number of rows per scan)
| |
| Fields: 4
| |
| ID Code Name RA Decl Epoch SrcId nRows
| |
| 0 E 3c147-J0542+49 05:42:36.137916 +49.51.07.23356 J2000 0 1019200
| |
| 1 D gcal-J0603+174 06:03:09.130269 +17.42.16.81070 J2000 1 3264448
| |
| 2 NONE G192.16-3.84 05:58:13.540000 +16.31.58.30001 J2000 2 4240000
| |
| 3 F 3c84-J0319+413 03:19:48.160102 +41.30.42.10305 J2000 3 1537600
| |
| Spectral Windows: (64 unique spectral windows and 1 unique polarization setups)
| |
| SpwID Name #Chans Frame Ch1(MHz) ChanWid(kHz) TotBW(kHz) BBC Num Corrs
| |
| 0 EVLA_KA#A1C1#2 128 TOPO 34476.000 1000.000 128000.0 10 RR LL
| |
| 1 EVLA_KA#A1C1#3 128 TOPO 34604.000 1000.000 128000.0 10 RR LL
| |
| 2 EVLA_KA#A1C1#4 128 TOPO 34732.000 1000.000 128000.0 10 RR LL
| |
| 3 EVLA_KA#A1C1#5 128 TOPO 34860.000 1000.000 128000.0 10 RR LL
| |
| 4 EVLA_KA#A1C1#6 128 TOPO 34988.000 1000.000 128000.0 10 RR LL
| |
| 5 EVLA_KA#A1C1#7 128 TOPO 35116.000 1000.000 128000.0 10 RR LL
| |
| 6 EVLA_KA#A1C1#8 128 TOPO 35244.000 1000.000 128000.0 10 RR LL
| |
| 7 EVLA_KA#A1C1#9 128 TOPO 35372.000 1000.000 128000.0 10 RR LL
| |
| 8 EVLA_KA#A1C1#10 128 TOPO 35500.000 1000.000 128000.0 10 RR LL
| |
| 9 EVLA_KA#A1C1#11 128 TOPO 35628.000 1000.000 128000.0 10 RR LL
| |
| 10 EVLA_KA#A1C1#12 128 TOPO 35756.000 1000.000 128000.0 10 RR LL
| |
| 11 EVLA_KA#A1C1#13 128 TOPO 35884.000 1000.000 128000.0 10 RR LL
| |
| 12 EVLA_KA#A1C1#14 128 TOPO 36012.000 1000.000 128000.0 10 RR LL
| |
| 13 EVLA_KA#A1C1#15 128 TOPO 36140.000 1000.000 128000.0 10 RR LL
| |
| 14 EVLA_KA#A1C1#16 128 TOPO 36268.000 1000.000 128000.0 10 RR LL
| |
| 15 EVLA_KA#A1C1#17 128 TOPO 36396.000 1000.000 128000.0 10 RR LL
| |
| 16 EVLA_KA#A2C2#18 128 TOPO 36476.000 1000.000 128000.0 11 RR LL
| |
| 17 EVLA_KA#A2C2#19 128 TOPO 36604.000 1000.000 128000.0 11 RR LL
| |
| 18 EVLA_KA#A2C2#20 128 TOPO 36732.000 1000.000 128000.0 11 RR LL
| |
| 19 EVLA_KA#A2C2#21 128 TOPO 36860.000 1000.000 128000.0 11 RR LL
| |
| 20 EVLA_KA#A2C2#22 128 TOPO 36988.000 1000.000 128000.0 11 RR LL
| |
| 21 EVLA_KA#A2C2#23 128 TOPO 37116.000 1000.000 128000.0 11 RR LL
| |
| 22 EVLA_KA#A2C2#24 128 TOPO 37244.000 1000.000 128000.0 11 RR LL
| |
| 23 EVLA_KA#A2C2#25 128 TOPO 37372.000 1000.000 128000.0 11 RR LL
| |
| 24 EVLA_KA#A2C2#26 128 TOPO 37500.000 1000.000 128000.0 11 RR LL
| |
| 25 EVLA_KA#A2C2#27 128 TOPO 37628.000 1000.000 128000.0 11 RR LL
| |
| 26 EVLA_KA#A2C2#28 128 TOPO 37756.000 1000.000 128000.0 11 RR LL
| |
| 27 EVLA_KA#A2C2#29 128 TOPO 37884.000 1000.000 128000.0 11 RR LL
| |
| 28 EVLA_KA#A2C2#30 128 TOPO 38012.000 1000.000 128000.0 11 RR LL
| |
| 29 EVLA_KA#A2C2#31 128 TOPO 38140.000 1000.000 128000.0 11 RR LL
| |
| 30 EVLA_KA#A2C2#32 128 TOPO 38268.000 1000.000 128000.0 11 RR LL
| |
| 31 EVLA_KA#A2C2#33 128 TOPO 38396.000 1000.000 128000.0 11 RR LL
| |
| 32 EVLA_KA#B1D1#34 128 TOPO 26976.000 1000.000 128000.0 13 RR LL
| |
| 33 EVLA_KA#B1D1#35 128 TOPO 27104.000 1000.000 128000.0 13 RR LL
| |
| 34 EVLA_KA#B1D1#36 128 TOPO 27232.000 1000.000 128000.0 13 RR LL
| |
| 35 EVLA_KA#B1D1#37 128 TOPO 27360.000 1000.000 128000.0 13 RR LL
| |
| 36 EVLA_KA#B1D1#38 128 TOPO 27488.000 1000.000 128000.0 13 RR LL
| |
| 37 EVLA_KA#B1D1#39 128 TOPO 27616.000 1000.000 128000.0 13 RR LL
| |
| 38 EVLA_KA#B1D1#40 128 TOPO 27744.000 1000.000 128000.0 13 RR LL
| |
| 39 EVLA_KA#B1D1#41 128 TOPO 27872.000 1000.000 128000.0 13 RR LL
| |
| 40 EVLA_KA#B1D1#42 128 TOPO 28000.000 1000.000 128000.0 13 RR LL
| |
| 41 EVLA_KA#B1D1#43 128 TOPO 28128.000 1000.000 128000.0 13 RR LL
| |
| 42 EVLA_KA#B1D1#44 128 TOPO 28256.000 1000.000 128000.0 13 RR LL
| |
| 43 EVLA_KA#B1D1#45 128 TOPO 28384.000 1000.000 128000.0 13 RR LL
| |
| 44 EVLA_KA#B1D1#46 128 TOPO 28512.000 1000.000 128000.0 13 RR LL
| |
| 45 EVLA_KA#B1D1#47 128 TOPO 28640.000 1000.000 128000.0 13 RR LL
| |
| 46 EVLA_KA#B1D1#48 128 TOPO 28768.000 1000.000 128000.0 13 RR LL
| |
| 47 EVLA_KA#B1D1#49 128 TOPO 28896.000 1000.000 128000.0 13 RR LL
| |
| 48 EVLA_KA#B2D2#50 128 TOPO 28976.000 1000.000 128000.0 14 RR LL
| |
| 49 EVLA_KA#B2D2#51 128 TOPO 29104.000 1000.000 128000.0 14 RR LL
| |
| 50 EVLA_KA#B2D2#52 128 TOPO 29232.000 1000.000 128000.0 14 RR LL
| |
| 51 EVLA_KA#B2D2#53 128 TOPO 29360.000 1000.000 128000.0 14 RR LL
| |
| 52 EVLA_KA#B2D2#54 128 TOPO 29488.000 1000.000 128000.0 14 RR LL
| |
| 53 EVLA_KA#B2D2#55 128 TOPO 29616.000 1000.000 128000.0 14 RR LL
| |
| 54 EVLA_KA#B2D2#56 128 TOPO 29744.000 1000.000 128000.0 14 RR LL
| |
| 55 EVLA_KA#B2D2#57 128 TOPO 29872.000 1000.000 128000.0 14 RR LL
| |
| 56 EVLA_KA#B2D2#58 128 TOPO 30000.000 1000.000 128000.0 14 RR LL
| |
| 57 EVLA_KA#B2D2#59 128 TOPO 30128.000 1000.000 128000.0 14 RR LL
| |
| 58 EVLA_KA#B2D2#60 128 TOPO 30256.000 1000.000 128000.0 14 RR LL
| |
| 59 EVLA_KA#B2D2#61 128 TOPO 30384.000 1000.000 128000.0 14 RR LL
| |
| 60 EVLA_KA#B2D2#62 128 TOPO 30512.000 1000.000 128000.0 14 RR LL
| |
| 61 EVLA_KA#B2D2#63 128 TOPO 30640.000 1000.000 128000.0 14 RR LL
| |
| 62 EVLA_KA#B2D2#64 128 TOPO 30768.000 1000.000 128000.0 14 RR LL
| |
| 63 EVLA_KA#B2D2#65 128 TOPO 30896.000 1000.000 128000.0 14 RR LL
| |
| Sources: 256
| |
| ID Name SpwId RestFreq(MHz) SysVel(km/s)
| |
| 0 3c147-J0542+49 0 - -
| |
| 0 3c147-J0542+49 1 - -
| |
| 0 3c147-J0542+49 2 - -
| |
| 0 3c147-J0542+49 3 - -
| |
| 0 3c147-J0542+49 4 - -
| |
| 0 3c147-J0542+49 5 - -
| |
| 0 3c147-J0542+49 6 - -
| |
| 0 3c147-J0542+49 7 - -
| |
| 0 3c147-J0542+49 8 - -
| |
| 0 3c147-J0542+49 9 - -
| |
| 0 3c147-J0542+49 10 - -
| |
| 0 3c147-J0542+49 11 - -
| |
| 0 3c147-J0542+49 12 - -
| |
| 0 3c147-J0542+49 13 - -
| |
| 0 3c147-J0542+49 14 - -
| |
| 0 3c147-J0542+49 15 - -
| |
| 0 3c147-J0542+49 16 - -
| |
| 0 3c147-J0542+49 17 - -
| |
| 0 3c147-J0542+49 18 - -
| |
| 0 3c147-J0542+49 19 - -
| |
| 0 3c147-J0542+49 20 - -
| |
| 0 3c147-J0542+49 21 - -
| |
| 0 3c147-J0542+49 22 - -
| |
| 0 3c147-J0542+49 23 - -
| |
| 0 3c147-J0542+49 24 - -
| |
| 0 3c147-J0542+49 25 - -
| |
| 0 3c147-J0542+49 26 - -
| |
| 0 3c147-J0542+49 27 - -
| |
| 0 3c147-J0542+49 28 - -
| |
| 0 3c147-J0542+49 29 - -
| |
| 0 3c147-J0542+49 30 - -
| |
| 0 3c147-J0542+49 31 - -
| |
| 0 3c147-J0542+49 32 - -
| |
| 0 3c147-J0542+49 33 - -
| |
| 0 3c147-J0542+49 34 - -
| |
| 0 3c147-J0542+49 35 - -
| |
| 0 3c147-J0542+49 36 - -
| |
| 0 3c147-J0542+49 37 - -
| |
| 0 3c147-J0542+49 38 - -
| |
| 0 3c147-J0542+49 39 - -
| |
| 0 3c147-J0542+49 40 - -
| |
| 0 3c147-J0542+49 41 - -
| |
| 0 3c147-J0542+49 42 - -
| |
| 0 3c147-J0542+49 43 - -
| |
| 0 3c147-J0542+49 44 - -
| |
| 0 3c147-J0542+49 45 - -
| |
| 0 3c147-J0542+49 46 - -
| |
| 0 3c147-J0542+49 47 - -
| |
| 0 3c147-J0542+49 48 - -
| |
| 0 3c147-J0542+49 49 - -
| |
| 0 3c147-J0542+49 50 - -
| |
| 0 3c147-J0542+49 51 - -
| |
| 0 3c147-J0542+49 52 - -
| |
| 0 3c147-J0542+49 53 - -
| |
| 0 3c147-J0542+49 54 - -
| |
| 0 3c147-J0542+49 55 - -
| |
| 0 3c147-J0542+49 56 - -
| |
| 0 3c147-J0542+49 57 - -
| |
| 0 3c147-J0542+49 58 - -
| |
| 0 3c147-J0542+49 59 - -
| |
| 0 3c147-J0542+49 60 - -
| |
| 0 3c147-J0542+49 61 - -
| |
| 0 3c147-J0542+49 62 - -
| |
| 0 3c147-J0542+49 63 - -
| |
| 1 gcal-J0603+174 0 - -
| |
| <snip>
| |
| 2 G192.16-3.84 0 - -
| |
| <snip>
| |
| 3 3c84-J0319+413 63 - -
| |
| Antennas: 26:
| |
| ID Name Station Diam. Long. Lat. Offset from array center (m) ITRF Geocentric coordinates (m)
| |
| East North Elevation x y z
| |
| 0 ea01 N48 25.0 m -107.37.38.1 +33.59.06.2 -855.2759 9405.9595 -25.9351 -1600374.885000 -5036704.201000 3562667.881900
| |
| 1 ea02 N56 25.0 m -107.37.47.9 +34.00.38.4 -1105.2071 12254.3069 -34.2426 -1600128.383400 -5035104.146500 3565024.672100
| |
| 2 ea03 N16 25.0 m -107.37.10.9 +33.54.48.0 -155.8511 1426.6436 -9.3827 -1601061.956000 -5041175.880700 3556058.037600
| |
| 3 ea05 W08 25.0 m -107.37.21.6 +33.53.53.0 -432.1184 -272.1472 -1.5070 -1601614.092200 -5042001.650900 3554652.508900
| |
| 4 ea06 N32 25.0 m -107.37.22.0 +33.56.33.6 -441.7237 4689.9748 -16.9332 -1600781.042100 -5039347.435200 3558761.533000
| |
| 5 ea07 E40 25.0 m -107.32.35.4 +33.52.16.9 6908.8279 -3240.7316 39.0057 -1595124.924100 -5045829.461500 3552210.685200
| |
| 6 ea09 E24 25.0 m -107.35.13.4 +33.53.18.1 2858.1754 -1349.1257 13.7290 -1598663.097500 -5043581.389700 3553767.027800
| |
| 7 ea10 E32 25.0 m -107.34.01.5 +33.52.50.3 4701.6588 -2209.7063 25.2191 -1597053.120700 -5044604.691600 3553059.009300
| |
| 8 ea11 W56 25.0 m -107.44.26.7 +33.49.54.6 -11333.2153 -7637.6824 15.3542 -1613255.404300 -5042613.085000 3548545.901400
| |
| 9 ea12 E08 25.0 m -107.36.48.9 +33.53.55.1 407.8285 -206.0065 -3.2272 -1600801.926000 -5042219.366500 3554706.448200
| |
| 10 ea13 W24 25.0 m -107.38.49.0 +33.53.04.0 -2673.3434 -1784.5870 10.4960 -1604008.742800 -5042135.827600 3553403.728800
| |
| 11 ea14 W16 25.0 m -107.37.57.4 +33.53.33.0 -1348.7083 -890.6269 1.3068 -1602592.853600 -5042055.005300 3554140.703900
| |
| 12 ea15 W72 25.0 m -107.48.24.0 +33.47.41.2 -17419.4730 -11760.2869 14.9578 -1619757.314900 -5042937.673700 3545120.385300
| |
| 13 ea16 N08 25.0 m -107.37.07.5 +33.54.15.8 -68.9252 433.1901 -5.0683 -1601147.956700 -5041733.824100 3555235.952500
| |
| 14 ea17 E48 25.0 m -107.30.56.1 +33.51.38.4 9456.5938 -4431.6366 37.9317 -1592894.088800 -5047229.121000 3551221.221100
| |
| 15 ea18 E72 25.0 m -107.24.42.3 +33.49.18.0 19041.8754 -8769.2059 4.7234 -1584460.867200 -5052385.599300 3547599.997600
| |
| 16 ea19 W64 25.0 m -107.46.20.1 +33.48.50.9 -14240.7600 -9606.2738 17.1055 -1616361.584300 -5042770.519200 3546911.442800
| |
| 17 ea20 N72 25.0 m -107.38.10.5 +34.04.12.2 -1685.6775 18861.8403 -43.4734 -1599557.932000 -5031396.371000 3570494.760600
| |
| 18 ea21 E64 25.0 m -107.27.00.1 +33.50.06.7 15507.6045 -7263.7280 67.1961 -1587600.190400 -5050575.873800 3548885.396600
| |
| 19 ea22 N24 25.0 m -107.37.16.1 +33.55.37.7 -290.3745 2961.8582 -12.2374 -1600930.087700 -5040316.398500 3557330.387000
| |
| 20 ea23 N64 25.0 m -107.37.58.7 +34.02.20.5 -1382.3750 15410.1463 -40.6373 -1599855.675100 -5033332.371000 3567636.622500
| |
| 21 ea24 W40 25.0 m -107.41.13.5 +33.51.43.1 -6377.9740 -4286.7919 8.2191 -1607962.456900 -5042338.214500 3551324.943600
| |
| 22 ea25 W48 25.0 m -107.42.44.3 +33.50.52.1 -8707.9407 -5861.7854 15.5265 -1610451.925400 -5042471.123100 3550021.056800
| |
| 23 ea26 W32 25.0 m -107.39.54.8 +33.52.27.2 -4359.4561 -2923.1223 11.7579 -1605808.647100 -5042230.071500 3552459.203400
| |
| 24 ea27 E16 25.0 m -107.36.09.8 +33.53.40.0 1410.0316 -673.4696 -0.7909 -1599926.110000 -5042772.967300 3554319.791200
| |
| 25 ea28 N40 25.0 m -107.37.29.5 +33.57.44.4 -633.6167 6878.5984 -20.7748 -1600592.764000 -5038121.352000 3560574.847300
| |
| </pre>
| |
| | |
| This task displays a lot of information about the MS. We can see that the observation was performed with the EVLA, and the included integration time is 4557 seconds (1.3 hour). The number of data records (10,061,248) is approximately equal to the number of baselines (N_antenna * [N_antenna - 1] / 2) X the number of integrations (observing time / time-average binning) X the number of spectral windows. For this observation, this is roughly 325 baselines (26X25/2) X 760 integrations (4557s total/6s avg) X 64 spectral windows = 15,808,000. Note that this is high by ~50%; this is because the "total time" reported is simply (start time) - (end time) of the MS, which includes periods of flagged data. Extra exercise: examine the MS using {{browsetable}} to see what a data record looks like (equivalent to a row, as displayed by this task).
| |
| | |
| The most useful parts of the {{listobs}} output are the scan, field, and spectral window listings.
| |
| | |
| From the spectral window information, we can see that there are a total of 64 (0 through 63) spectral windows in this dataset, and that these are in the Ka-band. (Data taken for pointing calibration have already been deleted.)
| |
| | |
| The field listing shows four sources:
| |
| | |
| * 3C147 (Field ID 0), the flux calibration source;
| |
| * J0603+174 (1), used for calibrating the complex gains;
| |
| * G192.16-3.84 (2), the science target; and
| |
| * 3C84 (3), used for calibrating the spectral bandpass.
| |
| | |
| == Flagging the MS ==
| |
| | |
| [[Image:PlotG192_flagcmd_4.1.png|200px|thumb|right|online flags plotted from flagcmd]]
| |
| | |
| The online flags, which are a record of known bad data produced by the EVLA online system, have already been applied by the archive as it generated the MS. However, it's good to have a sense of what was deleted in this process. A record of the flags is also stored in a separate table in the MS, called <tt>FLAG_CMD</tt>. (In fact, the information for this table is actually a subdirectory within the MS; you can see this by listing the contents of <tt>G192_6s.ms</tt>.)
| |
| | |
| You can examine the commands stored in the <tt>FLAG_CMD</tt> table using {{flagcmd}}:
| |
| <source lang="python">
| |
| # In CASA
| |
| flagcmd(vis='G192_6s.ms', inpmode='table', action='list', \
| |
| useapplied=True)
| |
| </source>
| |
| These will go to the logger. Note that we need to set <tt>useapplied</tt> to True, otherwise the flags that have already been applied to the MS (which includes all online flags) will be ignored by the task.
| |
| | |
| You can also plot the commands stored in the <tt>FLAG_CMD</tt> table:
| |
| <source lang="python">
| |
| # In CASA
| |
| myrows = range(2868)
| |
| flagcmd(vis='G192_6s.ms', inpmode='table', action='plot', \
| |
| useapplied=True, tablerows=myrows)
| |
| </source>
| |
| | |
| Note that we are only plotting the first 2868 rows -- this is because the last two are from flagging zeros in the data (caused by correlator errors) and data which have been flagged due to [http://evlaguides.nrao.edu/index.php?title=Observational_Status_Summary#Shadowing_and_Cross-Talk antenna shadowing]. Note that you can omit the <tt>tablerows</tt> selection and plot those too; you will just get lines at the bottom marked as "All" antennas for these flags.
| |
| | |
| This will bring up a <tt>matplotlib</tt> plotter. You can have it plot to a PNG file instead:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| flagcmd(vis='G192_6s.ms', inpmode='table', action='plot', tablerows=myrows,
| |
| useapplied=True, plotfile='PlotG192_flagcmd_4.1.png')
| |
| </source>
| |
| | |
| The flags as plotted in the figure to the above right look normal. They are color-coded by REASON, and you see ANTENNA_NOT_ON_SOURCE flags between scans, some FOCUS_ERROR flags here and there, and the occasional SUBREFLECTOR_ERROR flag also between scans (most likely after band changes when the subreflector rotates to pick up the new feed on the ring, some are slower than others). What you watch for here are long blocks of unexpected flags, which might be false alarms and cause you to flag too much data. In that case, look at the data itself in {{plotms}} (see below for examples) to decide whether or not to apply all flags. (Note: for the dataset in this tutorial, we have already deleted all the flagged data to reduce the file size, so you won't be able to inspect the flagged data within the MS. To do so, you will need to download the original dataset from the [https://archive.nrao.edu/archive/advquery.jsp NRAO Archive].)
| |
| | |
| [[Image:plotG192_plotants.png|200px|thumb|right|plotants plotter]]
| |
| To plot up the antenna positions in the array:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotants('G192_6s.ms')
| |
| </source>
| |
| | |
| NOTE: if after this point (or any other) you get "table locks", which may occur erroneously and are sometimes triggered by plotting tasks, use {{clearstat}} to clear them:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| clearstat
| |
| </source>
| |
| | |
| Now we examine the MS looking for bad data to flag. We will use {{plotms}} to bring up an interactive GUI that will display 2-D Y vs. X style line plots. <b>NOTE: We do not recommend using the editing/flagging features of {{plotms}}.</b> It is very easy to mess up your data this way. Also, to improve speed we will be restricting the scope of plotting, so most box/flag operations would not get rid of all the bad data -- although they would ''appear'' to delete it, which is misleading.
| |
| | |
| We will instead use {{plotms}} to identify bad data and then use {{flagcmd}} to flag it. This will also allow full scripting of the flagging, which is ultimately the best way to keep track of what's been deleted. Given the large dataset sizes now being generated, reproducibility is extremely important. Imagine spending a day flagging your data, then a disk error corrupts the MS. It's imperative that you have an automated way to regenerate your work. This is also why we encourage you to keep a running file with all the commands you use on a dataset.
| |
| | |
| NOTE: If you need an introduction to {{plotms}}, see:
| |
| * [[Data flagging with plotms]]
| |
| * [[Averaging data in plotms]]
| |
| * [[What's the difference between Antenna1 and Antenna2? Axis definitions in plotms]]
| |
| | |
| WARNING: The '''Flag''' [[Image:FlagThoseData.png]] button on the plotms GUI is close to other buttons you will be using, in particular the one that gets rid of boxes you have drawn. Be careful and don't hit the '''Flag''' button by mistake!
| |
| | |
| To get an idea of the data layout, plot a single baseline (ea02&ea05), and channel (31, for all spectral windows) versus time:
| |
| | |
| [[Image:screenshotPlotG192_plotms_ant02-05_4.1.png|200px|thumb|right|plotms of ea02&ea05 amp vs time]]
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_6s.ms', field='', spw='*:31~31', \
| |
| antenna='ea02&ea05', xaxis='time', yaxis='amp', \
| |
| coloraxis='field')
| |
| </source>
| |
| | |
| Here, we can see the alternating phase calibration and science target scans, as well as the (brighter) flux calibrator at the end of the observation. Feel free to play with ways to view -- for example, you can change the size of the plotted points, if they are too small to see easily, by setting "Unflagged Points Symbol" to "Custom" and increasing the number of pixels under "Style".
| |
| | |
| [[Image:screenshotPlotG192_plotms_baseline_4.1.png|200px|thumb|right|plotms baseline amplitudes for field 3]]
| |
| | |
| Look for bad antennas by picking the last field and plotting baselines. We color the points by "antenna1" to see which antennas might be troublesome:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_6s.ms', field='3', spw='*:31~31', \
| |
| antenna='', xaxis='baseline',\
| |
| yaxis='amp', coloraxis='antenna1')
| |
| </source>
| |
| | |
| You should be able to see that three of the antennas have lower amplitudes than the rest. Boxing with the '''Mark Regions''' [[Image:MarkRegionsButton.png]] tool and using the '''Locate''' [[File:casaplotms-locate-tool.png]] tool will show in the logger that these are antennas ea01, ea10 and ea19; indeed, checking the [https://archive.nrao.edu/archive/ArchiveRouter?OBS_LOGS=EVLA,TVER0004,56295.000000,56296.000000 Operator Log] for this observation shows that these antennas have collimation offsets and that the data have been corrupted. We will delete these antennas.
| |
| | |
| [[Image:plotG192_plotms_field3_ea05_ea13.png|200px|thumb|right|plotms field 3 ea05 and ea13 amp vs frequency]]
| |
| | |
| Now look at the bandpasses of baselines to ea05. It is in the inner core of the array and a prospective reference antenna. Exclude antennas ea01, ea10, and ea19 using negation (represented by "!") in the selection, and iterate by antenna:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_6s.ms', field='3', \
| |
| antenna='ea05;!ea01;!ea10;!ea19', \
| |
| xaxis='frequency', yaxis='amp',
| |
| coloraxis='corr', iteraxis='antenna')
| |
| </source>
| |
| | |
| The plot for ea05 and ea13 shows that ea13's RCP is weak, as noted in the log file as well. We will flag this antenna, since current restrictions do not allow for single-polarization data to be imaged if included in a full-polarization dataset.
| |
| | |
| Also, note that spectral windows 16 through 31 for antenna ea18 look very suspicious. We won't flag these right away, but need to keep an eye out for issues down the line.
| |
| | |
| For antenna ea24, there appear to be some issues with spectral windows 47 and 48, and the RCP of SPW 40 also looks problematic, so we'll flag this preemptively.
| |
| | |
| Now plot the phases, iterating through baselines to ea05:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_6s.ms', field='3', \
| |
| antenna='ea05;!ea01;!ea10;!ea13;!ea19', \
| |
| xaxis='frequency', yaxis='phase', coloraxis='corr', \
| |
| iteraxis='antenna')
| |
| </source>
| |
| | |
| You see the slopes due to residual delays. Mostly a turn or less over a 128MHz subband, but there are some outliers. Step through to ea18. You see that there are jumps between spectral windows for SPW 16-31. This reinforces that something is amiss with these SPWs, and we will flag them as well.
| |
| | |
| To carry out flagging, we again use {{flagcmd}} in the mode where it takes a list of command strings:
| |
| <source lang="python">
| |
| # In CASA
| |
| flaglist = ['antenna="ea01,ea10,ea19,ea13"',
| |
| 'antenna="ea24" spw="40,47~48"',
| |
| 'antenna="ea18" spw="16~31"']
| |
| flagcmd(vis='G192_6s.ms', inpmode='list', inpfile=flaglist, \
| |
| action='apply', flagbackup=True)
| |
| </source>
| |
| These commands will be carried out as well as being added to the FLAG_CMD table (marked as applied). Before applying the flags, a backup version will be stored as <tt>flagcmd_1</tt>, in case you would like to restore the flagged data to the MS (this can be done with {{flagmanager}}).
| |
| | |
| Plot the data again, now that is has been flagged:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_6s.ms', field='3', antenna='ea05', \
| |
| xaxis='frequency', yaxis='amp')
| |
| </source>
| |
| | |
| [[Image:plotG192_plotms_ea02fld3.png|200px|thumb|right|plotms field 3 ea05 amp vs frequency]]
| |
| | |
| Now let's look at our phase calibrator -- it is weaker, and we now start to really see the RFI:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_6s.ms', field='1', antenna='ea05', \
| |
| xaxis='frequency', yaxis='amp', scan='10,20,30,40,50,60')
| |
| </source>
| |
| Note that we've chosen a subset of scans to limit the amount of data being plotted. This will give a sense of whether there was RFI (or other issues) present in the observation, but will obviously not display everything -- later on, when we plot the calibrated data, we will need to again inspect for possible bad data (and may need to iterate and recalibrate).
| |
| | |
| Use the Zoom feature, Mark rectangles and use Locate to identify the frequency/channel of RFI. In particular, we note in our analysis:
| |
| * 27.228 GHz (spw 33 ch 124)
| |
| * 27.707 GHz (spw 37 ch 91)
| |
| * 27.81-27.811 GHz (spw 38 ch 66-67)
| |
| * 27.819-27.821 GHz (spw 38 ch 75-77)
| |
| * 28.894 GHz (spw 46 ch 126)
| |
| * 28.976 GHz (spw 48 ch 0)
| |
| * 29.684-20.685 GHz (spw 53 ch 68-69)
| |
| * 30.976 GHz (spw 63 ch 80) very strong
| |
| * 35.782 GHz (spw 10 ch 26)
| |
| * 36.523 GHz (spw 15 ch 127)
| |
| * 37.946 GHz (spw 27 ch 62)
| |
| * 37.948 GHz (spw 27 ch 64)
| |
| | |
| Flag these channels:
| |
| <source lang="python">
| |
| # In CASA
| |
| flaglist = ['spw="37:91,33:124,38:66~67;75~77,46:126,48:0"', \
| |
| 'spw="53:68~69,63:80,10:26,15:127,27:62,27:64"']
| |
| flagcmd(vis='G192_6s.ms', inpmode='list', inpfile=flaglist, \
| |
| action='apply', flagbackup=True)
| |
| </source>
| |
| When this is finished, it's useful to have a look at the flagged data. To reload the plotms window taking the new flags into account, hold down the "Shift" key while clicking on the "Plot" button.
| |
| | |
| Finally, split off the good data, without retaining the flagged data. This will allow us to work on the data without having to start completely over (if we mess something up badly) as well as letting us do simpler data selections.
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| # Remove any existing split data, otherwise split will not happen
| |
| os.system('rm -rf G192_flagged_6s.ms')
| |
| split(vis='G192_6s.ms', outputvis='G192_flagged_6s.ms', \
| |
| datacolumn='data', keepflags=False)
| |
| </source>
| |
| * keepflags=False: again, to limit the size of the MS, we do not propagate flagged data to the split-off MS.
| |
| | |
| You now have a MS called <tt>G192_flagged_6s.ms</tt> in your working area. This should be 16GB in size, which you can see while still at the CASA command prompt by typing:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| os.system('du -sh G192_flagged_6s.ms')
| |
| </source>
| |
| | |
| Note that the built-in <tt>system</tt> function allows one to execute UNIX shell commands within a CASA session. (Some, like <tt>ls</tt>, don't need this extra wrapper, but most are not automatically understood.)
| |
| | |
| [[Image:PlotG192_plotms_datastream.png|200px|thumb|right|plotms antenna2 vs. time "datastream" plot]]
| |
| At this point it is useful to plot a "datastream" view of the dataset to show what antennas are present at what time. You can do this using
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', xaxis='time', yaxis='antenna2', \
| |
| plotrange=[-1,-1,0,26], coloraxis='field')
| |
| </source>
| |
| This shows the times where data is present on baselines TO a given Antenna2 (which means there is no line for ea01 which is antenna 0). You can pick up ea01 (and drop ea28) by setting <tt>yaxis='antenna1'</tt>. To the right we show this plot. You see that for the most part, the antennas are present for the entire observation. One exception to this is antenna ea16, which comes in a little late on the first scan of G192.
| |
| | |
| == Calibration ==
| |
| | |
| Summarize the split flagged MS:
| |
| <source lang="python">
| |
| # In CASA
| |
| listobs('G192_flagged_6s.ms', listfile='G192_flagged_listobs.txt')
| |
| </source>
| |
| | |
| As before, cat'ing the file we see that there are now 7,121,197 data records present, and 22 antennas remain in the MS.
| |
| | |
| === Setting the flux density scale ===
| |
| | |
| It is now time to begin calibrating the data. The general data reduction strategy is to derive a series of scaling factors or corrections from the calibrators, which are then collectively applied to the science data. For <em>much</em> more discussion of the philosophy, strategy, and implementation of calibration of synthesis data within CASA, see [http://casa.nrao.edu/docs/UserMan/UserManch4.html#x195-1920004 Synthesis Calibration] in the CASA Cookbook and User Reference Manual .
| |
| | |
| Before calibrating, we insert a model for the flux calibration source 3C147 into the MS. In order to do this, we first have to locate the model image on our system with {{setjy}}, which we will also use to set the flux density scale. The {{setjy}} task has an option to list possible model images it knows about:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| setjy(vis='G192_flagged_6s.ms', listmodels=True)
| |
| </source>
| |
| which sends output to your terminal (but not the logger). For example, on an NRAO workstation:
| |
| <pre>
| |
| No candidate modimages matching '*.im* *.mod*' found in .
| |
| | |
| Candidate modimages (*) in /usr/lib64/casapy/release/4.1.0/data/nrao/VLA/CalModels:
| |
| 3C138_A.im 3C138_Q.im 3C147_A.im 3C147_Q.im 3C286_A.im 3C286_Q.im 3C48_A.im 3C48_Q.im README
| |
| 3C138_C.im 3C138_S.im 3C147_C.im 3C147_S.im 3C286_C.im 3C286_S.im 3C48_C.im 3C48_S.im
| |
| 3C138_K.im 3C138_U.im 3C147_K.im 3C147_U.im 3C286_K.im 3C286_U.im 3C48_K.im 3C48_U.im
| |
| 3C138_L.im 3C138_X.im 3C147_L.im 3C147_X.im 3C286_L.im 3C286_X.im 3C48_L.im 3C48_X.im
| |
| </pre>
| |
| | |
| The relevant image for our purposes is <tt>3C147_A.im</tt>, in the directory <tt>/usr/lib64/casapy/release/4.1.0/data/nrao/VLA/CalModels/</tt>. Your system may show a different location (for example <tt>/home/casa/data/nrao/VLA/CalModels/</tt>, or <tt>/Applications/CASA.app/Contents/data/nrao/VLA/CalModels</tt> on a Mac). Since it knows about this image, we only have to give the image name and not the entire path. Otherwise, you will need to give it the entire path.
| |
| | |
| In addition, we need to edit the name of the flux calibrator to be "3C147-J0542+49" rather than its current version, which has a lower-case "c" that will cause {{setjy}} to become confused. We can do this with the CASA Toolkit commands:
| |
| <source lang="python">
| |
| # In CASA
| |
| tb.open('G192_flagged_6s.ms/SOURCE', nomodify=False)
| |
| srcNames = tb.getcol('NAME')
| |
| </source>
| |
| | |
| You can look at the resulting array by typing "srcNames" -- the first 64 entries are '3c147-J0542+49', which we want to modify. We can do this with a little Python:
| |
| <source lang="python">
| |
| # In CASA
| |
| for element in range (0,64):
| |
| srcNames[element] = '3C147'
| |
| | |
| </source>
| |
| | |
| Now, put the modified values back into the MS table:
| |
| <source lang="python">
| |
| # In CASA
| |
| tb.putcol('NAME', srcNames)
| |
| tb.close()
| |
| </source>
| |
| | |
| There's one more place where we need to make this modification -- the "FIELD" table:
| |
| <source lang="python">
| |
| # In CASA
| |
| tb.open('G192_flagged_6s.ms/FIELD', nomodify=False)
| |
| fldNames = tb.getcol('NAME')
| |
| fldNames[0] = '3C147'
| |
| tb.putcol('NAME', fldNames)
| |
| tb.close()
| |
| </source>
| |
| | |
| We can now run the {{setjy}} task using this model:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| setjy(vis='G192_flagged_6s.ms', field='0', scalebychan=True, \
| |
| modimage='3C147_A.im')
| |
| </source>
| |
| | |
| [[Image:screenshotPlotG192_setjy_4.1.png|200px|thumb|right|plotms of model amp vs freq for 3C147]]
| |
| * <tt>scalebychan=True</tt>: will fill the model with per-channel values; otherwise, {{setjy}} would use a single value per spectral window.
| |
| * <tt>usescratch=False</tt>: put the model in the header instead of creating scratch columns in the MS. This will take up considerably less disk space.
| |
| | |
| We can plot the model data using {{plotms}}:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='0', antenna='ea02&ea05', \
| |
| xaxis='freq', yaxis='amp', ydatacolumn='model')
| |
| </source>
| |
| | |
| Inspecting the logger report shows that 3C147 is about 0.98 Jy at the lower end of the band to 0.67 Jy at the upper end.
| |
| | |
| === Deriving pre-determined calibrations ===
| |
| | |
| Some calibration products are carried along throughout the calibration process and used as priors for subsequent calibration steps. These include the antenna position corrections, gain-elevation curves, tropospheric opacity corrections, and requantizer gains.
| |
| | |
| ==== Antenna position corrections ====
| |
| | |
| We use {{gencal}} to determine any antenna-position corrections that need to be applied to the data. This is based on a database of corrections with the time they were determined and when they were applied by the observing system, compared to the times in your observations.
| |
| <source lang="python">
| |
| # In CASA
| |
| gencal('G192_flagged_6s.ms', caltable='calG192.antpos', \
| |
| caltype='antpos', antenna='')
| |
| </source>
| |
| You should see in the logger:
| |
| <pre style="background-color: #fffacd;">
| |
| No offsets found for this MS
| |
| *** Warning *** No offsets found. No caltable created.
| |
| gencal::::casa An error occurred running task gencal.
| |
| </pre>
| |
| Although it looks like the task has failed, reading the warning and error show that in fact, there simply aren't any antenna corrections to apply.
| |
| | |
| ==== Gain-elevation curves ====
| |
| | |
| In CASA 4.1, we now have the option to use {{gencal}} to create calibration tables containing the gain curves and tropospheric opacity corrections for the antennas. Although you can still use the <tt>gaincurve=True</tt> and <tt>opacity</tt> options in the calibration tasks, we will make use of this new feature (note that the <tt>gaincurve=True</tt> will be phased out in future CASA releases):
| |
| <source lang="python">
| |
| # In CASA
| |
| gencal('G192_flagged_6s.ms', caltable='calG192.gaincurve', caltype='gc')
| |
| </source>
| |
| | |
| ==== Tropospheric opacity corrections ====
| |
| | |
| [[Image:G192_plotWX.png|200px|thumb|right|plotweather output]]
| |
| | |
| The opacity of the observation can be computed from a seasonal model and/or weather station information. We are planning to have a task available for this information. We will use the {{plotweather}} task to display the weather information and to calculate the zenith opacities for each spectral window. After the zenith opacities are derived, they will be recomputed for the correct elevation of the data automatically using <math>e^{(-\csc[el]\tau_z)}</math> in {{gaincal}}, {{applycal}}, {{bandpass}} etc.
| |
| | |
| To start, we want to plot the opacity of the atmosphere at the time this observation was taken so it can be corrected for in subsequent calibrations. {{plotweather}} plots the weather conditions at the time of observation and calculates the atmospheric opacities based on these data, in combination with a seasonal model that contains long-term statistics at the VLA site. Using 'seasonal_weight=0.5' gives both equal weights:
| |
| | |
| We will be running '''plotweather''' in a way that will assign the opacity list (one entry for each SPW in ascending order) to the variable myTau:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| myTau = plotweather(vis='G192_flagged_6s.ms', doPlot=T)
| |
| </source>
| |
| | |
| The logger should display:
| |
| <pre style="background-color: #fffacd;">
| |
| ##########################################
| |
| ##### Begin Task: plotweather #####
| |
| plotweather(vis="G192_flagged_6s.ms",seasonal_weight=0.5,doPlot=True,plotName="")
| |
| 2013-06-18 21:47:00 INFO plotweather SPW : Frequency (GHz) : Zenith opacity (nepers)
| |
| 0 : 34.476 : 0.03
| |
| 1 : 34.604 : 0.031
| |
| 2 : 34.732 : 0.031
| |
| 3 : 34.860 : 0.031
| |
| 4 : 34.988 : 0.032
| |
| <snip>
| |
| 61 : 30.640 : 0.024
| |
| 62 : 30.768 : 0.024
| |
| 63 : 30.896 : 0.024
| |
| wrote weather figure: G192_flagged_6s.ms.plotweather.png
| |
| ##### End Task: plotweather #####
| |
| ##########################################
| |
| </pre>
| |
| | |
| It also creates a file '''"G192_flagged_6s.ms.plotweather.png"''' with the elevation of the sun, the wind speed and direction, the temperature, precipitable water vapor (PWV) as functions of time over the observation (view this file with your preferred image viewer like gthumb, xv or Preview), and assigns the myTau variable to the full list of opacities per spectral window.
| |
| | |
| We can now create a calibration table for the opacities via {{gencal}} with the ''calmode='opac' '' parameter. We could input the opacities directly, but it's easier to use the myTau variable with a little Python:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| SPWs = []
| |
| for window in range(0,64):
| |
| SPWs.append(str(window))
| |
| spwString = ','.join(SPWs)
| |
| gencal(vis='G192_flagged_6s.ms', caltable='calG192.opacity',
| |
| caltype='opac', spw=spwString, parameter=myTau)
| |
| </source>
| |
| ''' Note that this method replaces the ''opacity'' option in the calibrations tasks in CASA 3.4 and earlier '''
| |
| | |
| ==== Requantizer gain corrections ====
| |
| | |
| Finally, we will use {{gencal}} to create a calibration table containing corrections for the requantizer gains. Although this is only necessary for 3-bit data, such as our G192 dataset, it can be done for 8-bit datasets without any ill effects. For 3-bit data, this step is needed to account for the small gain changes (~5-10%) that result from resetting the quantizer gains as the correlator changes to a new 3-bit configuration.
| |
| <source lang="python">
| |
| # In CASA
| |
| gencal('G192_flagged_6s.ms', caltable='calG192.requantizer', caltype='rq')
| |
| </source>
| |
| | |
| The caltables we have generated, <tt>calG192.gaincurve</tt>, and <tt>calG192.requantizer</tt>, will need to be pre-applied in subsequent calibration steps.
| |
| | |
| === Calibrating delays and initial bandpass solutions ===
| |
| | |
| [[Image:plotG192_plotcal_G0p1_4.0.png|200px|thumb|right|plotcal G0 phase ant 0~15]]
| |
| [[Image:plotG192_plotcal_G0p2_4.0.png|200px|thumb|right|plotcal G0 phase ant 16~26]]
| |
| | |
| [[Image:plotG192_plotcal_delays.png|200px|thumb|right|plotcal K0 delay vs. antenna]]
| |
| | |
| [[Image:plotG192_plotcal_B0a1_4.0.png|200px|thumb|right|plotcal B0 bandpass amp ant ea06 spw 0-31]]
| |
| [[Image:plotG192_plotcal_B0a2_4.0.png|200px|thumb|right|plotcal B0 bandpass amp ant ea06 spw 32-63]]
| |
| | |
| First, we do a phase-only calibration solution on a narrow range of channels near the center of each SPW on the bandpass calibrator 3C84 to flatten them with respect to time before solving for the bandpass. The range 23~28 should work. Pick a refant near center -- ea05 is a reasonable bet (see above):
| |
| <source lang="python">
| |
| # In CASA
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G0', \
| |
| field='3', spw='*:60~68', \
| |
| gaintable=['calG192.gaincurve', 'calG192.requantizer', \
| |
| 'calG192.opacity'], \
| |
| gaintype='G', refant='ea05', calmode='p', \
| |
| solint='int', minsnr=3)
| |
| </source>
| |
| | |
| * <tt>refant='ea05'</tt> : try to use ea05 as the reference antenna
| |
| * <tt>solint='int'</tt> : do a per-integration solve (every 6 seconds, since we've time-averaged the data)
| |
| * <tt>minsnr=3</tt> : apply a minimum signal-to-noise cutoff. Solutions with less than this value will be flagged
| |
| * <tt>gaintable=['calG192.gaincurve', 'calG192.requantizer', 'calG192.opacity']</tt> : pre-apply the gaincurve, opacity, and requantizer caltables
| |
| | |
| Plot the phase solutions (using full phase range, -180 to 180, instead of autorange):
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| plotcal(caltable='calG192.G0', xaxis='time', yaxis='phase', \
| |
| iteration='antenna', plotrange=[-1,-1,-180,180])
| |
| </source>
| |
| | |
| Step through the antenna-based solutions. They look good (and fairly flat over the scans).
| |
| | |
| NOTE: When you are done plotting and want to use the caltable in another task, use the Quit button on the GUI to dismiss the plotter and free up the lock on the caltable. You should see a message in your terminal window saying "Resetting plotcal" which means you are good to go!
| |
| | |
| If you want to make single-page multipanel plots (like those shown to the right), particularly for a hardcopy (where it only shows the first page), you can do:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotcal(caltable='calG192.G0', xaxis='time', yaxis='phase', \
| |
| antenna='0~10,12~15', subplot=531, iteration='antenna', \
| |
| plotrange=[-1,-1,-180,180], fontsize=8.0, \
| |
| markersize=3.0, figfile='plotG192_plotcal_G0p1.png')
| |
| plotcal(caltable='calG192.G0', xaxis='time', yaxis='phase', \
| |
| antenna='16~26', subplot=531, iteration='antenna', \
| |
| plotrange=[-1,-1,-180,180], fontsize=8.0, \
| |
| markersize=3.0, figfile='plotG192_plotcal_G0p2.png')
| |
| </source>
| |
| | |
| We can now solve for the residual antenna-based delays that we saw in phase vs. frequency. This uses the new gaintype='K' option in gaincal. Note that this currently does not do a "global fringe-fitting" solution for delays, but instead does a baseline-based delay solution to all baselines to the refant, treating these as antenna-based delays. In most cases with high-enough S/N to get baseline-based delay solutions this will suffice. We avoid the edge channels, beginning of spw 0 due to the extreme roll-off (with loss of S/N) at the starting edge. -->
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.K0', \
| |
| gaintable=['calG192.gaincurve', 'calG192.requantizer', \
| |
| 'calG192.G0', 'calG192.opacity'], \
| |
| field='3', spw='*:5~122', gaintype='K', \
| |
| refant='ea05', solint='inf', minsnr=3)
| |
| </source>
| |
| We pre-apply our initial phase table, and produce a new K-type caltable for input to bandpass calibration.
| |
| We can plot the delays, in nanoseconds, as a function of antenna index (you will get one for each subband and polarization):
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| plotcal(caltable='calG192.K0', xaxis='antenna', yaxis='delay')
| |
| </source>
| |
| | |
| The delays range from around -5 to 4 nanoseconds.
| |
| | |
| Now solve for the bandpass using the previous tables:
| |
| <source lang="python">
| |
| # In CASA
| |
| bandpass(vis='G192_flagged_6s.ms', caltable='calG192.B0', \
| |
| gaintable=['calG192.gaincurve', 'calG192.requantizer', \
| |
| 'calG192.G0', 'calG192.K0', 'calG192.opacity'], \
| |
| field='3', refant='ea05', solnorm=False, \
| |
| bandtype='B', solint='inf', gaincurve=False)
| |
| </source>
| |
| '''WARNING''': You must set <tt>solnorm=False</tt> here or later on you will find some offsets
| |
| between spw due to how amplitude scaling adjusts weights internally during solving.
| |
| | |
| [[Image:plotG192_plotcal_B0p1_4.0.png|200px|thumb|right|plotcal B0 bandpass phase ant ea06 spw 0-31]]
| |
| [[Image:plotG192_plotcal_B0p2_4.0.png|200px|thumb|right|plotcal B0 bandpass phase ant ea06 spw 32-63]]
| |
| | |
| You will see in the terminal some reports of solutions failing due to "Insufficient unflagged antennas" -- note that these are for the channels we flagged earlier.
| |
| | |
| This is the first amplitude-scaling calibration that we do, so it is important to have used the <tt>calG192.gaincurve</tt> caltable (or set <tt>gaincurve=True</tt>) as well as the <tt>calG192.opacity</tt> caltable (or set <tt>opacity</tt>).
| |
| | |
| Now plot this, in amplitude then phase:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotcal(caltable='calG192.B0', xaxis='freq', yaxis='amp', \
| |
| spw='0~31', iteration='antenna')
| |
| #
| |
| plotcal(caltable='calG192.B0', xaxis='freq', yaxis='amp', \
| |
| spw='32~63', iteration='antenna')
| |
| #
| |
| plotcal(caltable='calG192.B0', xaxis='freq', yaxis='phase', \
| |
| iteration='antenna', spw='0~31', \
| |
| plotrange=[-1,-1,-180,180])
| |
| #
| |
| plotcal(caltable='calG192.B0', xaxis='freq', yaxis='phase', \
| |
| iteration='antenna', spw='32~63', \
| |
| plotrange=[-1,-1,-180,180])
| |
| </source>
| |
| | |
| In the bandpass phase you no longer see the residual antenna delays (just residual spw phase offsets from the delay solution registration) but there are some band edge effects.
| |
| | |
| === Bootstrapping the bandpass calibrator spectrum ===
| |
| | |
| Unfortunately, our flux density calibrator was not bright enough to use as the bandpass calibrator. Since there is no <i>a priori</i> spectral information for 3C84, in order to avoid including the intrinsic spectral shape of this source in our calibration, we need to bootstrap to find its spectral index, then recalibrate with this information filled in the MODEL column of the MS.
| |
| | |
| First, we use the initial round of bandpass calibration to create gain solutions for the flux and bandpass calibrators:
| |
| <source lang="python">
| |
| # In CASA
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G1', field='0,3', \
| |
| gaintable=['calG192.gaincurve', 'calG192.requantizer', \
| |
| 'calG192.opacity', 'calG192.K0', \
| |
| 'calG192.B0'], \
| |
| gaintype='G', refant='ea05', calmode='ap', solint='30s', minsnr=3)
| |
| </source>
| |
| | |
| Have a look at the phase and amplitude solutions, stepping through antenna. We look at the sources individually since they're fairly widely separated in time:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotcal(caltable='calG192.G1', xaxis='time', yaxis='amp', \
| |
| field='0', iteration='antenna')
| |
| #
| |
| plotcal(caltable='calG192.G1', xaxis='time', yaxis='amp', \
| |
| field='3', iteration='antenna')
| |
| #
| |
| plotcal(caltable='calG192.G1', xaxis='time', yaxis='phase', \
| |
| iteration='antenna', plotrange=[-1,-1,-180,180], \
| |
| field='0')
| |
| #
| |
| plotcal(caltable='calG192.G1', xaxis='time', yaxis='phase', \
| |
| iteration='antenna', plotrange=[-1,-1,-180,180], \
| |
| field='3')
| |
| </source>
| |
| | |
| The solutions all look reasonable and relatively constant with time.
| |
| | |
| Now that we have gain solutions for the flux and bandpass calibrators, we can use {{fluxscale}} to scale the gain amplitudes of the bandpass calibrator:
| |
| <source lang="python">
| |
| # In CASA
| |
| flux1 = fluxscale(vis='G192_flagged_6s.ms', caltable='calG192.G1', \
| |
| fluxtable='calG192.F1', reference='0', \
| |
| transfer='3', listfile='3C84.fluxinfo', fitorder=1)
| |
| </source>
| |
| | |
| * <tt>flux1 = ...</tt>: by providing a variable <tt>flux1</tt>, we allow {{fluxscale}} to use this for the output dictionary it returns with lots of information about the flux scaling.
| |
| * <tt>fluxtable='calG192.F1'</tt>: this is the output scaled gain table. Since we are only using this to find the spectral index of 3C84, we won't be using this table.
| |
| * <tt>listfile='3C84.fluxinfo'</tt>: an output file that contains the derived flux values and fit information.
| |
| * <tt>fitorder=1</tt>: only find a spectral index, ignoring curvature in the spectrum.
| |
| The last line in the file (and displayed in the logger) show:
| |
| <pre style="background-color: #fffacd;">
| |
| # Fitted spectrum for 3c84-J0319+413 with fitorder=1: Flux density = 29.8756 +/- 0.0381046 (freq=32.4488 GHz) spidx=-0.598929 +/- 0.0105201
| |
| </pre>
| |
| | |
| [[Image:screenshotPlotG192_setjy_bp_4.1.png|200px|thumb|right|plotms of model amp vs freq for 3C84]]
| |
| [[Image:plotG192_3C84_fluxspec_4.1.png|200px|thumb|right|3C84 flux values returned by fluxscale]]
| |
| | |
| Using the information in the returned <tt>flux</tt> dictionary, we can plot the derived spectrum:
| |
| <source lang="python">
| |
| # In CASA
| |
| freq = flux1['freq'] / 1e9
| |
| srcFlux = flux1['3']['fluxd']
| |
| srcErr = flux1['3']['fluxdErr']
| |
| pl.clf()
| |
| pl.plot(freq, srcFlux, 'bo')
| |
| </source>
| |
| | |
| Note the bump around 37 GHz -- what is this? We will not be able to account for it with the simple spectral index model, but still, it's better than not modeling the spectrum at all.
| |
| | |
| We can use the model from {{fluxscale}} to fill the MODEL column with 3C84's spectral information using {{}}:
| |
| <source lang="python">
| |
| # In CASA
| |
| setjy(vis='G192_flagged_6s.ms', field='3', scalebychan=True, \
| |
| fluxdensity=[29.8756, 0, 0, 0], spix=-0.598929, \
| |
| reffreq='32.4488GHz')
| |
| </source>
| |
| | |
| Checking with plotms that the data have been appropriately filled:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='3', antenna='ea05&ea02', \
| |
| xaxis='freq', yaxis='amp', ydatacolumn='model')
| |
| </source>
| |
| | |
| [[Image:plotG192_plotcal_B0a1.b_4.1.png|200px|thumb|right|plotcal B0 bootstrapped bandpass amp ant ea06 spw 0-31]]
| |
| [[Image:plotG192_plotcal_B0a2.b_4.1.png|200px|thumb|right|plotcal B0 bootstrapped bandpass amp ant ea06 spw 32-63]]
| |
| [[Image:plotG192_plotcal_B0p1.b_4.1.png|200px|thumb|right|plotcal B0 bootstrapped bandpass phase ant ea06 spw 0-31]]
| |
| [[Image:plotG192_plotcal_B0p2.b_4.1.png|200px|thumb|right|plotcal B0 bootstrapped bandpass phase ant ea06 spw 32-63]]
| |
| | |
| Finally, we redo the previous calibration using this new model information:
| |
| <source lang="python">
| |
| # In CASA
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G0.b', \
| |
| field='3', spw='*:60~68', \
| |
| gaintable=['calG192.gaincurve', \
| |
| 'calG192.requantizer', 'calG192.opacity'], \
| |
| gaintype='G', refant='ea05', calmode='p', \
| |
| solint='int', minsnr=3)
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.K0.b', \
| |
| gaintable=['calG192.gaincurve', 'calG192.requantizer', \
| |
| 'calG192.G0.b', 'calG192.opacity'], \
| |
| field='3', spw='*:5~122', gaintype='K', \
| |
| refant='ea05', solint='inf', minsnr=3)
| |
| #
| |
| bandpass(vis='G192_flagged_6s.ms', caltable='calG192.B0.b', \
| |
| gaintable=['calG192.gaincurve', 'calG192.requantizer', \
| |
| 'calG192.G0.b', 'calG192.K0.b', \
| |
| 'calG192.opacity'], \
| |
| field='3', refant='ea05', solnorm=False, \
| |
| bandtype='B', solint='inf')
| |
| </source>
| |
| | |
| It's a good idea to inspect these solutions as well:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='amp', \
| |
| spw='0~31', iteration='antenna')
| |
| #
| |
| plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='amp', \
| |
| spw='32~63', iteration='antenna')
| |
| #
| |
| plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='phase', \
| |
| iteration='antenna', spw='0~31', \
| |
| plotrange=[-1,-1,-180,180])
| |
| #
| |
| plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='phase', \
| |
| iteration='antenna', spw='32~63', \
| |
| plotrange=[-1,-1,-180,180])
| |
| </source>
| |
| | |
| They look virtually unchanged from the previous solutions, with the exception that the amplitude scaling is corrected for the spectrum of 3C84. Now that we have the final version of our bandpass calibration, we can proceed to the full calibration of the dataset.
| |
| | |
| === Final phase and amplitude calibration ===
| |
| | |
| [[Image:plotG192_plotcal_G1.int_4.1.png|200px|thumb|right|plotcal G1.int per-int phase ea06]]
| |
| | |
| [[Image:plotG192_plotcal_G1.inf_4.1.png|200px|thumb|right|plotcal G1.inf per-scan phase ea06]]
| |
| | |
| Now we will compute calibrators' gain phases using the full bandwidth. We will do the calibrators one at a time and append subsequent solutions, since we will use different solution intervals. For 3C147 and 3C84, we obtain one solution per integration (these are bright enough); for J0603+174, we will use 12 s solution intervals:
| |
| <source lang="python">
| |
| # In CASA
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G1.int', \
| |
| gaintable=['calG192.requantizer','calG192.gaincurve', \
| |
| 'calG192.opacity', \
| |
| 'calG192.K0.b','calG192.B0.b'], \
| |
| field='0', refant='ea05', solnorm=F, \
| |
| solint='int', gaintype='G', calmode='p')
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G1.int', \
| |
| gaintable=['calG192.requantizer','calG192.gaincurve', \
| |
| 'calG192.opacity', \
| |
| 'calG192.K0.b','calG192.B0.b'], \
| |
| field='1', refant='ea05', solnorm=F, \
| |
| solint='12s', gaintype='G', calmode='p', \
| |
| append=True)
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G1.int', \
| |
| gaintable=['calG192.requantizer','calG192.gaincurve', \
| |
| 'calG192.opacity', \
| |
| 'calG192.K0.b','calG192.B0.b'], \
| |
| field='3', refant='ea05', solnorm=F, \
| |
| solint='int', gaintype='G', calmode='p', \
| |
| append=True)
| |
| </source>
| |
| These will get applied when solving for amplitudes, and when calibrating the calibrators themselves.
| |
| | |
| The phases track nicely with time:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotcal(caltable='calG192.G1.int', xaxis='time', yaxis='phase', \
| |
| iteration='antenna', plotrange=[-1,-1,-180,180])
| |
| </source>
| |
| | |
| To apply phase calibration to the target, we make a second table for the gain calibrator (J0603+174) with one solution per scan:
| |
| <source lang="python">
| |
| # In CASA
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G1.inf', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', \
| |
| 'calG192.K0.b', 'calG192.B0.b'], \
| |
| field='1', refant='ea05', solnorm=F, \
| |
| solint='inf', gaintype='G', calmode='p')
| |
| </source>
| |
| | |
| These scan phases will get interpolated by {{applycal}} onto our target. These look good as well:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotcal(caltable='calG192.G1.inf', xaxis='time', yaxis='phase', \
| |
| iteration='antenna', plotrange=[-1,-1,-180,180])
| |
| </source>
| |
| | |
| Now solve for amplitudes on a per scan interval, after applying the per-integration phases. Do these separately using <tt>gainfield</tt> so phases don't get transferred across fields. For field 2 (3C286) we use <tt>combine='scan'</tt> as there are two scans
| |
| on this source, with the first one having much less data (and will thus give a noisy solution on its own).
| |
| Note that {{gaincal}} uses linear interpolation of the previously determined phases by default, so
| |
| set this to "nearest" if you want to override this.
| |
| <source lang="python">
| |
| # In CASA
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G2', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b', \
| |
| 'calG192.B0.b', 'calG192.G1.int'], \
| |
| gainfield=['', '', '', '3', '3', '0'], \
| |
| interp=['', '', '', 'nearest', 'nearest', 'nearest'], \
| |
| field='0', refant='ea05', solnorm=F, \
| |
| solint='inf', gaintype='G', calmode='a')
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G2', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b', \
| |
| 'calG192.B0.b', 'calG192.G1.int'], \
| |
| gainfield=['', '', '', '3', '3', '1'], \
| |
| interp=['', '', '', 'nearest', 'nearest', 'nearest'], \
| |
| field='1', refant='ea05', solnorm=F, \
| |
| solint='inf', gaintype='G', calmode='a', append=True)
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G2', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b', \
| |
| 'calG192.B0.b', 'calG192.G1.int'], \
| |
| gainfield=['', '', '', '3', '3', '3'], \
| |
| interp=['', '', '', 'nearest', 'nearest', 'nearest'], \
| |
| field='3', refant='ea05', solnorm=F, \
| |
| solint='inf', gaintype='G', calmode='a', append=True)
| |
| #
| |
| </source>
| |
| | |
| [[Image:plotG192_plotcal_G2.inf_4.1.png|200px|thumb|right|plotcal G2 per-scan amp ant ea06]]
| |
| | |
| Have a look at the amplitudes:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotcal(caltable='calG192.G2', xaxis='time', yaxis='amp', \
| |
| iteration='antenna')
| |
| </source>
| |
| | |
| This is the table we will apply to the data.
| |
| | |
| Now, we need to use {{fluxscale}} to transfer the amplitude gains from 3C147:
| |
| <source lang="python">
| |
| # In CASA
| |
| flux2 = fluxscale(vis='G192_flagged_6s.ms', caltable='calG192.G2', \
| |
| fluxtable='calG192.F2', reference='0')
| |
| </source>
| |
| where we have now captured the returned dictionary in the Python variable <tt>flux2</tt>.
| |
| | |
| The logger output gives:
| |
| <pre>
| |
| Found reference field(s): 3C147
| |
| Found transfer field(s): gcal-J0603+174 3c84-J0319+413
| |
| Flux density for gcal-J0603+174 in SpW=0 is: 0.252043 +/- 0.00779693 (SNR = 32.3259, N = 44)
| |
| Flux density for gcal-J0603+174 in SpW=1 is: 0.250608 +/- 0.00785259 (SNR = 31.9141, N = 44)
| |
| Flux density for gcal-J0603+174 in SpW=2 is: 0.250149 +/- 0.00783195 (SNR = 31.9395, N = 44)
| |
| Flux density for gcal-J0603+174 in SpW=3 is: 0.249326 +/- 0.00870076 (SNR = 28.6556, N = 44)
| |
| Flux density for gcal-J0603+174 in SpW=4 is: 0.24779 +/- 0.00860759 (SNR = 28.7873, N = 44)
| |
| <snip>
| |
| Flux density for gcal-J0603+174 in SpW=60 is: 0.280642 +/- 0.00884987 (SNR = 31.7115, N = 44)
| |
| Flux density for gcal-J0603+174 in SpW=61 is: 0.279742 +/- 0.00874457 (SNR = 31.9904, N = 44)
| |
| Flux density for gcal-J0603+174 in SpW=62 is: 0.278071 +/- 0.00910153 (SNR = 30.5521, N = 44)
| |
| Flux density for gcal-J0603+174 in SpW=63 is: 0.277588 +/- 0.00955455 (SNR = 29.0529, N = 44)
| |
| Flux density for 3c84-J0319+413 in SpW=0 is: 1.01141 +/- 0.0316725 (SNR = 31.9333, N = 44)
| |
| Flux density for 3c84-J0319+413 in SpW=1 is: 0.994812 +/- 0.0326974 (SNR = 30.4248, N = 44)
| |
| Flux density for 3c84-J0319+413 in SpW=2 is: 1.00473 +/- 0.0314246 (SNR = 31.9729, N = 44)
| |
| Flux density for 3c84-J0319+413 in SpW=3 is: 1.0042 +/- 0.0325531 (SNR = 30.8479, N = 44)
| |
| <snip>
| |
| Flux density for 3c84-J0319+413 in SpW=60 is: 1.00232 +/- 0.0243617 (SNR = 41.1434, N = 44)
| |
| Flux density for 3c84-J0319+413 in SpW=61 is: 1.00589 +/- 0.0248197 (SNR = 40.5277, N = 44)
| |
| Flux density for 3c84-J0319+413 in SpW=62 is: 1.01762 +/- 0.0240088 (SNR = 42.3855, N = 44)
| |
| Flux density for 3c84-J0319+413 in SpW=63 is: 1.01145 +/- 0.0249814 (SNR = 40.488, N = 44)
| |
| Fitted spectrum for gcal-J0603+174 with fitorder=1: Flux density = 0.264382 +/- 0.000149793 (freq=32.4488 GHz) spidx=-0.834342 +/- 0.00458913
| |
| Fitted spectrum for 3c84-J0319+413 with fitorder=1: Flux density = 1.00101 +/- 0.00121263 (freq=32.4488 GHz) spidx=0.00866148 +/- 0.0100409
| |
| Storing result in calG192.F2
| |
| Writing solutions to table: calG192.F2
| |
| </pre>
| |
| You may see slightly different numbers on your machine. Note that "N" here is the number of antennas x the number of polarizations used for the calculations; in this case, there are 22 unflagged antennas and 2 polarizations.
| |
| | |
| Also, note that the flux-scaled amplitudes for 3C84 are all almost exactly 1 Jy. This is not because the actual flux of 3C84 is 1 Jy, of course. Rather, remember that the spectrum and flux information is now included in the bandpass table. When we apply the calibration, in the next section, you will see that 3C84's flux does indeed come out as expected.
| |
| | |
| == Applying the Calibration and Final Editing ==
| |
| | |
| Next we apply all our accumulated calibration tables to the flagged MS. We apply these to the calibration fields individually, using the appropriate gainfields and interpolation for each:
| |
| * For 3C147 (field 0) we did per-integration phase solutions and a single scan amplitude, so use "linear" and "nearest" interpolation, respectively;
| |
| * for the nearby gain calibrator (field 1) we did 12-s phase and per-scan amplitude solutions, for which we will use "linear" and "nearest" interpolation, respectively;
| |
| * for G192 (field 2), we will calibrate with field 1, using the per-scan solutions and "linear" interpolation; and finally,
| |
| * for the bandpass calibrator 3C84 (field 3), we did per-integration phase solutions and a single scan amplitude, so use "linear" and "nearest" interpolation respectively.
| |
| | |
| [[Image:plotG192_plotms_applied_fld0.png|200px|thumb|right|3C147 with calibration applied]]
| |
| [[Image:plotG192_plotms_fld0_bybaseline.png|200px|thumb|right|3C147 with calibration applied, amp vs. baseline]]
| |
| <source lang="python">
| |
| # In CASA
| |
| applycal(vis='G192_flagged_6s.ms', field='0', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b', \
| |
| 'calG192.B0.b', 'calG192.G1.int', \
| |
| 'calG192.G2'], \
| |
| gainfield=['', '', '', '', '', '0', '0'],
| |
| interp=['', '', '', 'nearest', 'nearest', 'linear', \
| |
| 'nearest'], calwt=False)
| |
| #
| |
| applycal(vis='G192_flagged_6s.ms', field='1', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b', \
| |
| 'calG192.B0.b', 'calG192.G1.int', \
| |
| 'calG192.G2'], \
| |
| gainfield=['', '', '', '', '', '1', '1'],
| |
| interp=['', '', '', 'nearest', 'nearest', 'linear', \
| |
| 'nearest'], calwt=False)
| |
| #
| |
| applycal(vis='G192_flagged_6s.ms', field='2', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b', \
| |
| 'calG192.B0.b', 'calG192.G1.inf', \
| |
| 'calG192.G2'], \
| |
| gainfield=['', '', '', '', '', '1', '1'],
| |
| interp=['', '', '', 'nearest', 'nearest', 'linear', \
| |
| 'linear'], calwt=False)
| |
| #
| |
| applycal(vis='G192_flagged_6s.ms', field='3', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b', \
| |
| 'calG192.B0.b', 'calG192.G1.int', \
| |
| 'calG192.G2'], \
| |
| gainfield=['', '', '', '', '', '3', '3'],
| |
| interp=['', '', '', 'nearest', 'nearest', 'linear', \
| |
| 'nearest'], calwt=False)
| |
| </source>
| |
| Because we used <tt>usesratch=False</tt> in {{setjy}}, the <tt>CORRECTED_DATA</tt> scratch column will be created the first time you run {{applycal}}. This will take a few minutes to write, increasing the size of the MS to 30 GB, and will store the calibrated data.
| |
| | |
| We can examine the corrected data for 3C147, avoiding SPW edges, and binning the data in time and frequency:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='0', \
| |
| xaxis='frequency', yaxis='amp', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='8', \
| |
| avgtime='1000s', coloraxis='baseline')
| |
| </source>
| |
| | |
| See figure above right. There is some suspicious data in the frequency range 38.15-38.26 GHz (SPW 29). We can plot around this frequency range with respect to time, to see if it's isolated RFI or something we should flag from the whole dataset:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='0', \
| |
| xaxis='time', yaxis='amp', \
| |
| ydatacolumn='corrected', spw='29:5~122', \
| |
| averagedata=True, avgchannel='16', \
| |
| avgtime='', coloraxis='baseline')
| |
| </source>
| |
| | |
| Indeed, something looks wrong for the time interval 6:35:00-6:36:40 for this SPW. Flag this data:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| flagdata(vis='G192_flagged_6s.ms', field='0', \
| |
| spw='29', timerange='6:35:00~6:36:40')
| |
| </source>
| |
| | |
| It's also instructive to plot the corrected amplitude as a function of baseline:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='0', \
| |
| xaxis='baseline', yaxis='amp', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='8', \
| |
| avgtime='1000s', coloraxis='antenna1')
| |
| </source>
| |
| Looks good now!
| |
| | |
| Next, we examine the corrected data for the gain calibrator, J0603+174, again avoiding SPW edges. This time, we will bin the data even more in frequency, since it's a fainter source:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='1', \
| |
| xaxis='frequency', yaxis='amp', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='32', \
| |
| avgtime='6000s', coloraxis='baseline')
| |
| </source>
| |
| | |
| This generally looks quite good. Plot with respect to baseline as well:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='1', \
| |
| xaxis='baseline', yaxis='amp', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='32', \
| |
| avgtime='6000s', coloraxis='antenna1')
| |
| </source>
| |
| A few antennas look a little noisier, but nothing bad enough to flag for now.
| |
| | |
| Finally, we examine the corrected data for 3C84:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='3', \
| |
| xaxis='frequency', yaxis='amp', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='8', \
| |
| avgtime='1000s', coloraxis='baseline')
| |
| </source>
| |
| In general, it looks good, though there is one rather suspicious baseline dropping below the rest of the data. Box a few data points and use the "Locate" button to find that this is ea03&ea07. Plotting the same baseline for 3C147, we see that it doesn't look the best there either -- so we will flag this baseline:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| flagdata(vis='G192_flagged_6s.ms', antenna='ea03&ea07')
| |
| </source>
| |
| | |
| Now, let's plot amplitude vs. baseline:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='3', \
| |
| xaxis='baseline', yaxis='amp', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='8', \
| |
| avgtime='1000s', coloraxis='antenna1')
| |
| </source>
| |
| Looks good!
| |
| | |
| Finally, let's plot amplitude vs. phase:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='3', \
| |
| xaxis='phase', yaxis='amp', \
| |
| xdatacolumn='corrected', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='8', \
| |
| avgtime='1000s', coloraxis='baseline')
| |
| </source>
| |
| There is less than 2 degrees of phase scatter, and a plot vs. frequency shows that this is mostly in the highest frequencies. We will keep all these data.
| |
| | |
| == A Quick Recalibration ==
| |
| | |
| Since we flagged additional data, we will now go back and recalibrate:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| # Clear the corrected data and model from header
| |
| clearcal('G192_flagged_6s.ms', addmodel=False)
| |
| #
| |
| setjy(vis='G192_flagged_6s.ms', field='0', scalebychan=True, \
| |
| modimage='3C147_A.im')
| |
| #
| |
| setjy(vis='G192_flagged_6s.ms', field='3', scalebychan=True, \
| |
| fluxdensity=[29.8756, 0, 0, 0], spix=-0.598929, \
| |
| reffreq='32.4488GHz')
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G0.b.2', field='3', \
| |
| spw='*:60~68', gaintable=['calG192.gaincurve', 'calG192.requantizer', \
| |
| 'calG192.opacity'], \
| |
| gaintype='G', refant='ea05', calmode='p', solint='int', minsnr=3)
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.K0.b.2', \
| |
| field='3', spw='*:5~122', gaintype='K', \
| |
| gaintable=['calG192.gaincurve', 'calG192.requantizer', \
| |
| 'calG192.G0.b.2', 'calG192.opacity'], \
| |
| refant='ea05', solint='inf', minsnr=3)
| |
| #
| |
| bandpass(vis='G192_flagged_6s.ms', caltable='calG192.B0.b.2', \
| |
| field='3', refant='ea05', solnorm=False, \
| |
| gaintable=['calG192.gaincurve', 'calG192.requantizer', \
| |
| 'calG192.G0.b.2', 'calG192.K0.b.2', 'calG192.opacity'], \
| |
| bandtype='B', solint='inf')
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G1.int.2', \
| |
| field='0', refant='ea05', solnorm=F, \
| |
| gaintable=['calG192.requantizer','calG192.gaincurve', \
| |
| 'calG192.opacity', \
| |
| 'calG192.K0.b.2','calG192.B0.b.2'], \
| |
| solint='int', gaintype='G', calmode='p')
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G1.int.2', \
| |
| field='1', refant='ea05', solnorm=F, \
| |
| gaintable=['calG192.requantizer','calG192.gaincurve', \
| |
| 'calG192.opacity', \
| |
| 'calG192.K0.b.2','calG192.B0.b.2'], \
| |
| solint='12s', gaintype='G', calmode='p', \
| |
| append=True)
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G1.int.2', \
| |
| field='3', refant='ea05', solnorm=F, \
| |
| gaintable=['calG192.requantizer','calG192.gaincurve', \
| |
| 'calG192.opacity', \
| |
| 'calG192.K0.b.2','calG192.B0.b.2'], \
| |
| solint='int', gaintype='G', calmode='p', \
| |
| append=True)
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G1.inf.2', \
| |
| field='1', refant='ea05', solnorm=F, \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', \
| |
| 'calG192.K0.b.2', 'calG192.B0.b.2'], \
| |
| solint='inf', gaintype='G', calmode='p')
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G2.2', \
| |
| field='0', refant='ea05', solnorm=F, \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b.2', \
| |
| 'calG192.B0.b.2', 'calG192.G1.int.2'], \
| |
| gainfield=['', '', '', '3', '3', '0'], \
| |
| interp=['', '', '', 'nearest', 'nearest', 'nearest'], \
| |
| solint='inf', gaintype='G', calmode='a')
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G2.2', \
| |
| field='1', refant='ea05', solnorm=F, \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b.2', \
| |
| 'calG192.B0.b.2', 'calG192.G1.int.2'], \
| |
| gainfield=['', '', '', '3', '3', '1'], \
| |
| interp=['', '', '', 'nearest', 'nearest', 'nearest'], \
| |
| solint='inf', gaintype='G', calmode='a', append=True)
| |
| #
| |
| gaincal(vis='G192_flagged_6s.ms', caltable='calG192.G2.2', \
| |
| field='3', refant='ea05', solnorm=F, \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b.2', \
| |
| 'calG192.B0.b.2', 'calG192.G1.int.2'], \
| |
| gainfield=['', '', '', '3', '3', '3'], \
| |
| interp=['', '', '', 'nearest', 'nearest', 'nearest'], \
| |
| solint='inf', gaintype='G', calmode='a', append=True)
| |
| #
| |
| flux3 = fluxscale(vis='G192_flagged_6s.ms', caltable='calG192.G2.2', \
| |
| fluxtable='calG192.F2.2', reference='0')
| |
| #
| |
| applycal(vis='G192_flagged_6s.ms', field='0', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b.2', \
| |
| 'calG192.B0.b.2', 'calG192.G1.int.2', \
| |
| 'calG192.G2.2'], \
| |
| gainfield=['', '', '', '', '', '0', '0'], \
| |
| interp=['', '', '', 'nearest', 'nearest', 'linear', \
| |
| 'nearest'], calwt=False)
| |
| #
| |
| applycal(vis='G192_flagged_6s.ms', field='1', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b.2', \
| |
| 'calG192.B0.b.2', 'calG192.G1.int.2', \
| |
| 'calG192.G2.2'], \
| |
| gainfield=['', '', '', '', '', '1', '1'], \
| |
| interp=['', '', '', 'nearest', 'nearest', 'linear', \
| |
| 'nearest'], calwt=False)
| |
| #
| |
| applycal(vis='G192_flagged_6s.ms', field='2', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b.2', \
| |
| 'calG192.B0.b.2', 'calG192.G1.inf.2', \
| |
| 'calG192.G2.2'], \
| |
| gainfield=['', '', '', '', '', '1', '1'], \
| |
| interp=['', '', '', 'nearest', 'nearest', 'linear', \
| |
| 'linear'], calwt=False)
| |
| #
| |
| applycal(vis='G192_flagged_6s.ms', field='3', \
| |
| gaintable=['calG192.requantizer', 'calG192.gaincurve', \
| |
| 'calG192.opacity', 'calG192.K0.b.2', \
| |
| 'calG192.B0.b.2', 'calG192.G1.int.2', \
| |
| 'calG192.G2.2'], \
| |
| gainfield=['', '', '', '', '', '3', '3'], \
| |
| interp=['', '', '', 'nearest', 'nearest', 'linear', \
| |
| 'nearest'], calwt=False)
| |
| </source>
| |
| | |
| The recalibration will take a little while -- it was over 1.5 hours on our system -- so this is a good time to work on a different project or grab lunch!
| |
| | |
| The {{fluxscale}} output this time around is slightly different to the last:
| |
| <pre>
| |
| </pre>
| |
| | |
| | |
| Plots of corrected amplitude vs. baseline:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='0', \
| |
| xaxis='baseline', yaxis='amp', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='8', \
| |
| avgtime='1000s', coloraxis='antenna1')
| |
| #
| |
| plotms(vis='G192_flagged_6s.ms', field='1', \
| |
| xaxis='baseline', yaxis='amp', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='32', \
| |
| avgtime='6000s', coloraxis='antenna1')
| |
| #
| |
| plotms(vis='G192_flagged_6s.ms', field='3', \
| |
| xaxis='baseline', yaxis='amp', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='8', \
| |
| avgtime='1000s', coloraxis='antenna1')
| |
| </source>
| |
| | |
| And, finally, corrected amplitude vs. corrected phase:
| |
| <source lang="python">
| |
| # In CASA
| |
| plotms(vis='G192_flagged_6s.ms', field='0', \
| |
| xaxis='phase', yaxis='amp', \
| |
| xdatacolumn='corrected', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='8', \
| |
| avgtime='1000s', coloraxis='baseline')
| |
| #
| |
| plotms(vis='G192_flagged_6s.ms', field='1', \
| |
| xaxis='phase', yaxis='amp', \
| |
| xdatacolumn='corrected', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='32', \
| |
| avgtime='6000s', coloraxis='baseline')
| |
| #
| |
| plotms(vis='G192_flagged_6s.ms', field='3', \
| |
| xaxis='phase', yaxis='amp', \
| |
| xdatacolumn='corrected', \
| |
| ydatacolumn='corrected', spw='*:5~122', \
| |
| averagedata=True, avgchannel='8', \
| |
| avgtime='1000s', coloraxis='baseline')
| |
| </source>
| |
| | |
| | |
| | |
| The inability of this run to get a spectral index and curvature is due to the flagged/missing solutions for spw 12 and 13. The code will be fixed in an upcoming release to not give this error.
| |
| | |
| == Imaging ==
| |
| | |
| This is VLA A-configuration data at Ka-band. To determine the best parameters for imaging, it helps to start with the relevant information in the [http://evlaguides.nrao.edu/index.php?title=Observational_Status_Summary_-_Current Observational Status Summary]:
| |
| | |
| * Synthesized beam should be 0.059" at 33 GHz with a primary beam field of view of 1.4 arcmin (82")
| |
| | |
| Our data spans 27.0-38.4 GHz: this is a relatively large fractional bandwidth, resulting in substantial variation of the field of view over the entire frequency range. FOV = 45 arcmin / Frequency (GHz), giving 10 arcmin at 4.5 GHz, and 6 arcmin at 7.5 GHz. Likewise, the synthesized beam ranges from 16" at 4.5 GHz to 9.6" at 7.5 GHz. We want to subsample the synthesized beam by a factor of 3-4, so will use a cellsize of 3". To cover the full FOV (keeping it at the inner part of the image) at the lowest frequencies, we will want an image size of >400 pixels, or >20 arcmin.
| |
| | |
| We will also use the Briggs robust (with <tt>robust=0.5</tt>) weighting, which is a compromise between uniform and natural weighting,
| |
| and will give reasonable resolution but will allow us to still see larger scale structure.
| |
| | |
| Due to the numerology of [http://www.fftw.org/ FFTW's] (which {{clean}} uses under the hood for FFTs) optimal sizes,
| |
| <tt>imsize</tt> should be composite number with two and only two prime factors chosen from
| |
| 2, 3, and 5. Taking into account the x1.2 padding that clean uses internally to the imsize
| |
| you give it (and 1.2 = 2*3/5), we choose 640 or 1280 as our imsize (640 = 2^7*5). Other
| |
| reasonable sets would be 405, 1215, etc. (405 = 3^4*5) or 432, 648, 1296 (these are 2^n*3^m*5).
| |
| In practice, if you give it non-optimal values for imsize, you may find that the transforms
| |
| take a bit longer, which is noticeable if you are doing interactive clean.
| |
| | |
| WARNING: By default, a single-field nterms=1 clean does NOT use Cotton-Schwab (CS) clean to break
| |
| into major cycles going back to data residuals, it just does cleaning in a bunch of minor
| |
| cycles in the image plane. This can give much poorer imaging quality in cases with poor
| |
| uv coverage (snapshots) or in the case of complex emission structure (like ours) -- clean tends to
| |
| diverge in this case. You should explicitly set <tt>imagermode='csclean'</tt> in your
| |
| call to clean. Also, in our case the psf is very good using mfs, so by default it will not
| |
| take many major cycle breaks. We use the <tt>cyclefactor</tt> parameter to control this, which
| |
| sets the break threshold to be cyclefactor times the max psf sidelobe level (outside the main
| |
| peak). We start at <tt>cyclefactor=1.5</tt> in a single spw, and ratchet it up to 4.5 when we
| |
| clean all the spw. This seems to work ok. Rule of thumb is if it is gobbling up many hundreds of
| |
| clean iterations in the minor cycles early on, increase cyclefactor. Conversely, if your psf is poor
| |
| but you source structure is simple, you can reduce cyclefactor (e.g. below 1) to stop it from taking
| |
| lots of extra major cycles.
| |
| | |
| For more information on using {{clean}}, in particular on using the interactive GUI, see
| |
| [[EVLA_Continuum_Tutorial_3C391#Imaging]]. WARNING: In CASA 4.0 the GUI interface for clean
| |
| and the viewer has changed slightly. Some of the screenshots shown below may differ slightly
| |
| from what you see.
| |
| | |
| NOTE: If you are pressed for time, then you might want to jump ahead to
| |
| [[EVLA_6-cm_Wideband_Tutorial_G192_(Caltech)#Cleaning_the_lower_baseband_using_two_MFS_Taylor_terms]]
| |
| and while it is cleaning you can read the other Imaging descriptions.
| |
| | |
| === Cleaning a single spectral window ===
| |
| | |
| Let us start by interactively cleaning one of the lower baseband spw (spw 5 in this example).
| |
| NOTE: this first time will take a few minutes at start to create scratch columns
| |
| in the MS in case we want to do self-calibration later.
| |
| | |
| '''Note that interrupting {{clean}} by Ctrl+C may corrupt your visibilities -- you may be better off choosing to let {{clean}} finish. We are currently implementing a command that will nicely exit to prevent this from happening, but for the moment try to avoid Ctrl+C.'''
| |
| | |
| [[Image:viewG192_spw5_clean640_4.0.png|200px|thumb|right|interactive clean spw5 640x640 after around 1000 iterations]]
| |
| [[Image:viewG192_spw5_clean1280.png|200px|thumb|right|2nd interactive clean spw5 1280x1280 before cleaning]]
| |
| [[Image:viewG192_spw5_clean1280final_4.0.png|200px|thumb|right|viewer showing clean spw5 1280x1280 restored image]]
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| # Removing any previous cleaning information
| |
| # This assumes you want to start this clean from scratch
| |
| # If you want to continue this from a previous clean run,
| |
| # the rm -rf system command should be be skipped
| |
| os.system ('rm -rf imgG192_6s_spw5_clean640*')
| |
| clean(vis='G192_split10s.ms',spw='5:4~59', \
| |
| imagename='imgG192_6s_spw5_clean640', \
| |
| mode='mfs',nterms=1,niter=10000,gain=0.1,threshold='0.0mJy', \
| |
| psfmode='clark',imsize=[640,640],cell=['3.0arcsec'],stokes='I', \
| |
| imagermode='csclean', cyclefactor=1.5, \
| |
| weighting='briggs',robust=0.5,interactive=True)
| |
| </source>
| |
| | |
| * Start carefully by boxing the bright source and setting iterations to 10 at first
| |
| * Gradually add more boxes and increase the number of iterations
| |
| * Since this is not much more than a snapshot you see the six-fold sidelobe pattern
| |
| of the extended emission in the center of the map. This decreases as you clean
| |
| out this emission.
| |
| * Stop cleaning when the residuals look like noise (and you cannot clearly see sources).
| |
| * To stop, click the red [[File:clean-stop.png]] button.
| |
| | |
| The top figure to the right shows a zoom in on the end state of the clean, where
| |
| we have marked a number of boxes and cleaned them out.
| |
| | |
| Note that there are some strange sidelobe patterns in lower left, possibly
| |
| from a source outside the image area. We can make a bigger image starting from
| |
| our current model:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| # Removing any previous cleaning information
| |
| # This assumes you want to start this clean from scratch
| |
| # If you want to continue this from a previous clean run,
| |
| # the rm -rf system command should be skipped
| |
| os.system ('rm -rf imgG192_6s_spw5_clean1280*')
| |
| clean(vis='G192_split10s.ms',spw='5:4~59', \
| |
| imagename='imgG192_6s_spw5_clean1280', \
| |
| mode='mfs',nterms=1,niter=10000,gain=0.1,threshold='0.0mJy', \
| |
| psfmode='clark',imsize=[1280,1280],cell=['3.0arcsec'],stokes='I', \
| |
| imagermode='csclean', cyclefactor=1.5, \
| |
| modelimage='imgG192_6s_spw5_clean640.model', \
| |
| weighting='briggs',robust=0.5,interactive=True)
| |
| </source>
| |
| | |
| Sure enough, there is a bright source near the lower left (see middle panel at right).
| |
| Box it, clean it a bit, and look again. There is a second source in the mid-left (track
| |
| it down by its sidelobes). Box this one, clean it a bit, and when satisfied stop.
| |
| | |
| You can use the CASA {{viewer}} to display the images that {{clean}} creates. If you need more guidance
| |
| on using the viewer, see the [http://casa.nrao.edu/CasaViewerDemo/casaViewerDemo.html CASA Viewer Demo] video
| |
| (note that this is for a much earlier version of the viewer, and the interface has changed since then).
| |
| | |
| Bring up your restored image directly:
| |
| <source lang="python">
| |
| # In CASA
| |
| viewer('imgG192_6s_spw5_clean1280.image')
| |
| </source>
| |
| | |
| The restored image is shown in the bottom panel to the right. I have chosen the Grayscale1 instead of default color
| |
| map as I prefer "Grayscale" to false color "Rainbow" for assessing image quality. Also, you can change the scaling of the image using the "scaling power cycles" slider under "basic settings".
| |
| | |
| Check the rms of the residuals using the {{imstat}} task:
| |
| <source lang="python">
| |
| # In CASA
| |
| mystat = imstat('imgG192_6s_spw5_clean1280.residual')
| |
| print 'Residual standard deviation = '+str(mystat['sigma'][0])
| |
| </source>
| |
| In this particular case, it's 31.8 uJy; yours will likely be slightly different.
| |
| | |
| === Cleaning the lower baseband ===
| |
| | |
| [[Image:viewG192_spw0to7_clean1280final.png|200px|thumb|right|clean spw0-7 restored image center]]
| |
| Now, image the entire lower baseband (spw 0-7).
| |
| Follow same iterative procedure as before, and get the best
| |
| residuals you can without "cleaning the noise".
| |
| | |
| * Because of the bandwidth and frequency synthesis, the sidelobe pattern is different than before and it is much easier to see fainter emission.
| |
| * Be careful cleaning sources that lie near or on sidelobe splotches.
| |
| * Clean the central emission region way down first to reduce the sidelobe level before adding components in the sidelobe areas.
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| # Removing any previous cleaning information
| |
| # This assumes you want to start this clean from scratch
| |
| # If you want to continue this from a previous clean run,
| |
| # the rm -rf system command should be be skipped
| |
| os.system ('rm -rf imgG192_6s_spw0to7_clean1280*')
| |
| clean(vis='G192_split10s.ms',spw='0:16~59,1~6:4~59,7:4~54', \
| |
| imagename='imgG192_6s_spw0to7_clean1280', \
| |
| mode='mfs',nterms=1,niter=10000,gain=0.1,threshold='0.0mJy', \
| |
| psfmode='clark',imsize=[1280,1280],cell=['3.0arcsec'],stokes='I', \
| |
| imagermode='csclean', cyclefactor=1.5, \
| |
| weighting='briggs',robust=0.5,interactive=True)
| |
| #
| |
| mystat = imstat('imgG192_6s_spw0to7_clean1280.residual')
| |
| print 'Residual standard deviation = '+str(mystat['sigma'][0])
| |
| </source>
| |
| | |
| For this run, the rms is 11.3 uJy (and there is clearly some structure left in the residual). To the right is a zoom-in on the center of the restored image.
| |
| | |
| ==== Cleaning the lower baseband using two MFS Taylor terms ====
| |
| | |
| The mfs nterms=2 option creates two "Taylor Term" images - an average intensity image (with suffix <tt>.image.tt0</tt>)
| |
| and a spectral slope image (with suffix <tt>.image.tt1</tt>) which is intensity x alpha (where alpha is spectral index).
| |
| For convenience there is a spectral index image (with suffix <tt>.image.alpha</tt>). These Taylor expansions are with respect to the "Reference Frequency" of the image (by default the center frequency of the spw selected, but can be specified using the <tt>reffreq</tt> parameter in {{clean}}). The convention for spectral index alpha is that
| |
| | |
| <math>
| |
| S \propto \nu^\alpha
| |
| </math>
| |
| | |
| so negative spectral indexes indicate a "steep" spectrum (falling with frequency).
| |
| | |
| [[Image:viewG192_spw0to7_mfs2clean.png|200px|thumb|right|clean spw0-7 mfs nterms=2 in progress]]
| |
| Let's try using multi-frequency synthesis with nterms=2 on the lower baseband.
| |
| The dirty beam will have lower sidelobes so we turn up <tt>cyclefactor</tt> for <tt>csclean</tt> a bit. Note: if you're feeling a bit lazy, and trust your previous set of clean boxes, you can also set <tt>mask='imgG192_6s_spw0to7_clean1280.mask'</tt> to use these as a starting point:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| # Removing any previous cleaning information
| |
| # This assumes you want to start this clean from scratch
| |
| # If you want to continue this from a previous clean run,
| |
| # the rm -rf system command should be be skipped
| |
| os.system ('rm -rf imgG192_6s_spw0to7_mfs2_clean1280*')
| |
| clean(vis='G192_split10s.ms',spw='0:16~59,1~6:4~59,7:4~54', \
| |
| imagename='imgG192_6s_spw0to7_mfs2_clean1280', \
| |
| mode='mfs',nterms=2,niter=10000,gain=0.1,threshold='0.0mJy', \
| |
| psfmode='clark',imsize=[1280,1280],cell=['3.0arcsec'],stokes='I', \
| |
| imagermode='csclean', cyclefactor=4.5, \
| |
| weighting='briggs',robust=0.5,interactive=True,mask=[])
| |
| #
| |
| mystat = imstat('imgG192_6s_spw0to7_mfs2_clean1280.residual.tt0')
| |
| print 'Residual standard deviation = '+str(mystat['sigma'][0])
| |
| </source>
| |
| | |
| For this run, the rms is 10.5 uJy (somewhat better-looking than the nterms=1).
| |
| The top screenshot to the right shows an intermediate but early stage of cleaning where we are looking at
| |
| the central emission and cleaning it out slowly.
| |
| | |
| You can use the {{viewer}} to load the average intensity image:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| viewer('imgG192_6s_spw0to7_mfs2_clean1280.image.tt0')
| |
| </source>
| |
| | |
| and then use the Open Data panel to load the spectral index image <tt>imgG192_6s_spw0to7_mfs2_clean1280.image.alpha</tt>
| |
| which can then be blinked (optionally plotted side-by-side using the Panel Display Options panel to set 2 panels in the x direction).
| |
| | |
| [[Image:viewG192_spw0to7_mfs2loadalpha_4.0.png|200px|thumb|right|clean spw0-7 mfs nterms=2 load alpha with LEL]]
| |
| [[Image:viewG192_spw0to7_mfs2panelalpha_4.0.png|200px|thumb|right|clean spw0-7 mfs nterms=2 tt0 and alpha (filtered at 0.1mJy in tt0)]]
| |
| | |
| Note there is a lot of noise in alpha in the low-intensity regions, and thus filtering the alpha image based on the values in the tt0 image is desirable. You can use the {{immath}} task to make this filtered alpha image explicitly, using a
| |
| Lattice Expression Language (LEL) expression:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| immath(imagename=['imgG192_6s_spw0to7_mfs2_clean1280.image.alpha',
| |
| 'imgG192_6s_spw0to7_mfs2_clean1280.image.tt0'],
| |
| mode='evalexpr',
| |
| expr='IM0[IM1>1.0E-4]',
| |
| outfile='imgG192_6s_spw0to7_mfs2_clean1280.image.alpha.filtered')
| |
| </source>
| |
| | |
| This will use 0.1 mJy (or 10 x the sigma we found) as the cutoff.
| |
| You can then view or manipulate the filtered alpha image as normal.
| |
| | |
| We can also use LEL to filter the alpha image on the intensity on-the-fly when we load this raster in the Open Data panel
| |
| by specifying a LEL string in the LEL box instead of selecting the image from the directory
| |
| listing. The LEL string:
| |
| | |
| <pre>
| |
| 'imgG192_6s_spw0to7_mfs2_clean1280.image.alpha'['imgG192_6s_spw0to7_mfs2_clean1280.image.tt0'>1.0E-04]
| |
| </pre>
| |
| | |
| will replicate what we did above. The middle figure to the right shows the Open Data panel
| |
| with our LEL string in it. Just click the Raster button to load this.
| |
| | |
| The lower panel to the right shows the intensity and LEL-filtered alpha images side-by-side in the viewer, zoomed
| |
| in on the galaxy emission. Mousing over the alpha shows spectral indexes ranging from -1 to +1 in the center, with
| |
| the brightest emission with alpha -0.7 in the knots in the disk.
| |
| | |
| === Cleaning using both basebands combined ===
| |
| | |
| For the ultimate image, use the "clean" part of the upper baseband in addition
| |
| to the lower (use spw 0-11). We will use mfs with nterms=2 (if you try nterms=1
| |
| on this wide bandwidth you will get much poorer residuals). Because of the added
| |
| work and extra data involved, this will take much longer than our other runs of
| |
| clean. Therefore, we will get a head start by doing a non-interactive clean using
| |
| the mask left from the previous clean (spw 0-7). We will insert a clean threshold
| |
| to limit runaway cleaning too far beneath the noise level.
| |
| | |
| This will take a while, especially if there are other processes running on your machine (with nothing else running, expect ~30-40 minutes).
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| # Removing any previous cleaning information
| |
| # This assumes you want to start this clean from scratch
| |
| # If you want to continue this from a previous clean run,
| |
| # the rm -rf system command should be be skipped
| |
| os.system ('rm -rf imgG192_6s_spw0to11_mfs2_clean1280*')
| |
| clean(vis='G192_split10s.ms', \
| |
| spw='0:16~59,1~6:4~59,7:4~54,8:30~59,9~10:4~59,11:4~19;21~59', \
| |
| imagename='imgG192_6s_spw0to11_mfs2_clean1280', \
| |
| mode='mfs',nterms=2,niter=3000,gain=0.1,threshold='0.002mJy', \
| |
| psfmode='clark',imsize=[1280,1280],cell=['3.0arcsec'],stokes='I', \
| |
| imagermode='csclean', cyclefactor=4.5, \
| |
| mask=['imgG192_6s_spw0to7_mfs2_clean1280.mask'], \
| |
| weighting='briggs',robust=0.5,interactive=False)
| |
| #
| |
| mystat = imstat('imgG192_6s_spw0to11_mfs2_clean1280.residual.tt0')
| |
| print 'Residual standard deviation = '+str(mystat['sigma'][0])
| |
| </source>
| |
| | |
| For this particular run, the rms was 8.9 uJy (noticeably better than the lower baseband only results).
| |
| | |
| [[Image:viewG192 spw0to11_mfs2resid.png|200px|thumb|right|final residual and mask]]
| |
| | |
| Let us see if there is more to clean. Bring this up in interactive mode:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| clean(vis='G192_split10s.ms', \
| |
| spw='0:16~59,1~6:4~59,7:4~54,8:30~59,9~10:4~59,11:4~19;21~59', \
| |
| imagename='imgG192_6s_spw0to11_mfs2_clean1280', \
| |
| mode='mfs',nterms=2,niter=3000,gain=0.1,threshold='0.001mJy', \
| |
| psfmode='clark',imsize=[1280,1280],cell=['3.0arcsec'],stokes='I', \
| |
| imagermode='csclean', cyclefactor=4.5, \
| |
| weighting='briggs',robust=0.5,interactive=True)
| |
| </source>
| |
| | |
| You might find a few more sources revealed in the outer parts of the image, and also more
| |
| emission around the galaxy disk in the center. Try drawing new boxes, perhaps extend the box
| |
| in the center, and do ~100-1000 more iterations. At the end, what is left should be dominated
| |
| by the error patterns from mis-calibration. Only self-calibration will get rid of
| |
| these. Stop cleaning for now. See the figure to the right for the interactive display panel
| |
| showing final residuals and mask (changing the colormap to <tt>Greyscale 1</tt>).
| |
| | |
| Check the residual levels:
| |
| <source lang="python">
| |
| # In CASA
| |
| mystat = imstat('imgG192_6s_spw0to11_mfs2_clean1280.residual.tt0')
| |
| sigma = mystat['sigma'][0]
| |
| print 'Residual standard deviation = '+str(mystat['sigma'][0])
| |
| </source>
| |
| | |
| The final rms achieved here is 8.6 uJy; slightly better.
| |
| | |
| == Analyzing the image ==
| |
| | |
| Let's see how close we got to expected noise and dynamic range:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| mystat = imstat('imgG192_6s_spw0to11_mfs2_clean1280.image.tt0')
| |
| peak = mystat['max'][0]
| |
| print 'Image max flux = '+str(mystat['max'][0])
| |
| #
| |
| mystat = imstat('imgG192_6s_spw0to11_mfs2_clean1280.model.tt0')
| |
| total = mystat['sum'][0]
| |
| print 'Model total flux = '+str(mystat['sum'][0])
| |
| #
| |
| snr = peak/sigma
| |
| print 'G192 peak S/N = '+str(snr)
| |
| #
| |
| snr = total/sigma
| |
| print 'G192 total S/N = '+str(snr)
| |
| </source>
| |
| The output gives:
| |
| <pre>
| |
| Residual standard deviation = 8.60710739215e-06
| |
| Image max flux = 0.00995589420199
| |
| Model total flux = 0.0371581438531
| |
| G192 peak S/N = 1156.70616717
| |
| G192 total S/N = 4317.14653485
| |
| </pre>
| |
| | |
| What do we expect? If we do {{listobs}} on this MS we see the scans:
| |
| <pre>
| |
| Date Timerange (UTC) Scan FldId FieldName nRows Int(s)
| |
| 11-Jul-2010/21:38:44.0 - 21:39:51.0 9 0 G192 33696 9.16
| |
| 21:40:01.0 - 21:41:20.5 10 0 G192 37908 9.89
| |
| 21:41:30.0 - 21:42:50.0 11 0 G192 37908 10
| |
| 21:43:00.0 - 21:44:20.0 12 0 G192 37908 10
| |
| 21:44:30.0 - 21:45:50.0 13 0 G192 37908 10
| |
| 21:46:00.0 - 21:47:19.5 14 0 G192 37908 9.89
| |
| 21:47:29.0 - 21:47:49.0 15 0 G192 12636 9.67
| |
| 21:49:42.0 - 21:50:49.0 17 0 G192 33696 9.17
| |
| 21:50:59.0 - 21:52:19.0 18 0 G192 37908 10
| |
| 21:52:29.0 - 21:53:48.5 19 0 G192 37908 9.89
| |
| 21:53:58.0 - 21:55:18.0 20 0 G192 37908 10
| |
| 21:55:28.0 - 21:56:48.0 21 0 G192 37908 10
| |
| 21:56:58.0 - 21:58:18.0 22 0 G192 37908 10
| |
| 21:58:28.0 - 21:58:47.5 23 0 G192 12636 9.67
| |
| 22:00:39.5 - 22:01:47.0 25 0 G192 33696 9.18
| |
| 22:01:57.0 - 22:03:17.0 26 0 G192 37908 10
| |
| 22:03:27.0 - 22:04:47.0 27 0 G192 37908 10
| |
| 22:04:57.0 - 22:06:16.5 28 0 G192 37908 9.89
| |
| 22:06:26.0 - 22:07:46.0 29 0 G192 37908 10
| |
| 22:07:56.0 - 22:09:16.0 30 0 G192 37908 10
| |
| 22:09:26.0 - 22:09:45.5 31 0 G192 12636 9.67
| |
| 22:11:38.0 - 22:12:45.5 33 0 G192 33696 9.19
| |
| 22:12:55.0 - 22:14:15.0 34 0 G192 37908 10
| |
| 22:14:25.0 - 22:15:45.0 35 0 G192 37908 10
| |
| 22:15:55.0 - 22:17:15.0 36 0 G192 37908 10
| |
| 22:17:25.0 - 22:18:44.5 37 0 G192 37908 9.89
| |
| 22:18:54.0 - 22:20:14.0 38 0 G192 37908 10
| |
| 22:20:24.0 - 22:20:43.5 39 0 G192 12636 9.67
| |
| (nVis = Total number of time/baseline visibilities per scan)
| |
| </pre>
| |
| (listing columns truncated) and we estimate about 37 minutes on target. We had about 25 antennas on average, and our spw selection picked out 610 channels (2 MHz each) for a total of 1220 MHz bandwidth. If we plug this
| |
| into the
| |
| [https://science.nrao.edu/facilities/evla/calibration-and-tools/exposure EVLA exposure calculator], at 5 GHz, we find that we expect a rms thermal noise level of 8.7 uJy, and at 7 GHz, 7.0 uJy. So, our values are within the expected range (a bit higher than theoretical, but that's expected).
| |
| | |
| [[Image:plotG192_viewerfinal.png|200px|thumb|right|final image]]
| |
| Look at this in the viewer:
| |
| <source lang="python">
| |
| # In CASA
| |
| viewer('imgG192_6s_spw0to11_mfs2_clean1280.image.tt0')
| |
| </source>
| |
| Zoom in on the center (see figure to the right).
| |
| | |
| [[Image:viewG192_spw0to11_mfs2tt1.png|200px|thumb|right|final tt1 image with box]]
| |
| In the previous section we demonstrated how to process and display the spectral index image. You can do
| |
| the same for this final image. Here, we will do some rough analysis on the spectral index to determine
| |
| an intensity-weighted mean spectral index over the core region.
| |
| The <tt>.image.tt1</tt> from our mfs is an intensity times alpha image. See the figure to the right.
| |
| Let's gate the Taylor-term images on intensity:
| |
| <source lang="python">
| |
| # In CASA
| |
| # Removing any file output from previous runs, so immath will proceed
| |
| os.system('rm -rf imgG192_6s_spw0to11_mfs2_clean1280.image.tt1.filtered')
| |
| immath(imagename=['imgG192_6s_spw0to11_mfs2_clean1280.image.tt1',
| |
| 'imgG192_6s_spw0to11_mfs2_clean1280.image.tt0'],
| |
| mode='evalexpr',
| |
| expr='IM0[IM1>5.0E-5]',
| |
| outfile='imgG192_6s_spw0to11_mfs2_clean1280.image.tt1.filtered')
| |
| #
| |
| # Removing any file output from previous runs, so immath will proceed
| |
| os.system('rm -rf imgG192_6s_spw0to11_mfs2_clean1280.image.tt0.filtered')
| |
| immath(imagename=['imgG192_6s_spw0to11_mfs2_clean1280.image.tt0'],
| |
| mode='evalexpr',
| |
| expr='IM0[IM0>5.0E-5]',
| |
| outfile='imgG192_6s_spw0to11_mfs2_clean1280.image.tt0.filtered')
| |
| </source>
| |
| | |
| We can identify a box containing the central emission (see figure of tt1 in viewer) and note the corners.
| |
| (We could also use the region tools from the viewer, but that is for another exercise.)
| |
| Let us compute the intensity-weighted spectral index over this box by averaging
| |
| these masked images using {{imstat}} and computing the ratio:
| |
| <source lang="python">
| |
| # In CASA
| |
| mystat = imstat('imgG192_6s_spw0to11_mfs2_clean1280.image.tt1.filtered',
| |
| box='503,533,756,762')
| |
| avgtt0alpha = mystat['mean'][0]
| |
| #
| |
| mystat = imstat('imgG192_6s_spw0to11_mfs2_clean1280.image.tt0.filtered',
| |
| box='503,533,756,762')
| |
| avgtt0 = mystat['mean'][0]
| |
| avgalpha = avgtt0alpha/avgtt0
| |
| print 'G192 I-weighted Alpha = '+str(avgalpha)
| |
| </source>
| |
| We get
| |
| <pre>
| |
| G192 I-weighted Alpha = -1.38157453384
| |
| </pre>
| |
| | |
| The emission in this source is on the steep side. At this point we do not know how reliable this is or
| |
| what we expect (though our calibrators come out with correct spectral indexes if we image them the
| |
| same way). But this illustrates a way to extract spectral information from our wideband mfs images.
| |
| | |
| == Comparing with the Optical/Infrared ==
| |
| | |
| As a final comparison, we turn to the Sloan Digital Sky Survey (SDSS) and a cutout image of our galaxy:
| |
| [[Image:NGC_2967_UGC_5180_IRAS_09394+0033_irg.jpg|400px|thumb|center|]]
| |
| from their [http://cosmo.nyu.edu/hogg/rc3/NGC_2967_UGC_5180_IRAS_09394+0033_irg.jpg RC3]
| |
| album (courtesy D.Hogg, M.Blanton, SDSS collaboration - see [[#Credits]]). This looks like a nice nearby
| |
| face-on spiral galaxy. How does our 6cm continuum emission line up with the optical?
| |
| | |
| Here is the EVLA 6cm image side by side with a i-band image from the Sloan Digital Sky Survey (SDSS) registered to our image:
| |
| | |
| [[Image:plotG192 viewerfinalandSDSS.png|600px|thumb|center|final and sdss image]]
| |
| | |
| You can also find this image, named <tt>NGC_2967_UGC_5180_IRAS_09394+0033-i.fits</tt>, on the web at <tt>http://casa.nrao.edu/Data/EVLA/G192/NGC_2967_UGC_5180_IRAS_09394+0033-i.fits</tt> (at the CASA workshop, it's in <tt>/data/casa/evla/</tt> or a similar location that will be given to you in the instructions). Load it into your viewer, and blink against our 6cm image.
| |
| | |
| We can also plot one as a raster and the other overlaid as contours. You can load the SDSS image
| |
| from the viewer Load Data panel and fiddle with contours. Once you know contour levels, you can
| |
| also use the imview task to load a raster and contour image:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| imview(raster={ 'file' : 'imgG192_6s_spw0to11_mfs2_clean1280.image.tt0'},
| |
| contour = { 'file' : 'NGC_2967_UGC_5180_IRAS_09394+0033-i.fits',
| |
| 'levels' : [0.2, 0.5, 1, 1.5, 3],
| |
| 'base' : 0.0,
| |
| 'unit' : 1.0 } )
| |
| </source>
| |
| | |
| The figure below shows the SDSS contours overlaid on our 6cm image (after fiddling with the
| |
| colormap shift/slope for the EVLA raster image).
| |
| | |
| [[Image:viewG192_spw0to11_mfs2tt0plusSDSS.png|400px|thumb|center|6cm EVLA raster plus SDSS i-band contours]]
| |
| | |
| Likewise, we can plot the SDSS image as a raster and overlay EVLA 6cm contours:
| |
| | |
| <source lang="python">
| |
| # In CASA
| |
| imview(raster={ 'file' : 'NGC_2967_UGC_5180_IRAS_09394+0033-i.fits',
| |
| 'scaling' : -2.0,
| |
| 'range' : [0,10] },
| |
| contour = { 'file' : 'imgG192_6s_spw0to11_mfs2_clean1280.image.tt0',
| |
| 'levels' : [0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56],
| |
| 'base' : 0.0,
| |
| 'unit' : 0.001 },
| |
| zoom = { 'blc' : [397,300],
| |
| 'trc' : [1567,1231] } )
| |
| </source>
| |
| | |
| This is shown in the figure below. Is the compact 6cm emission in upper left associated with a
| |
| spiral arm?
| |
| | |
| [[Image:viewG192_spw0to11_SDSSiplusEVLA6cm.png|400px|thumb|center|SDSS i-band raster plus EVLA 6cm contours]]
| |
| | |
| == What to do next: some exercises for the user ==
| |
| | |
| Here are a number of things you can try after completing this tutorial:
| |
| | |
| # Use self-calibration to improve the data and re-clean to make a better image. See [http://casaguides.nrao.edu/index.php?title=WorkshopSelfcal_(Caltech) this tutorial] for more information on self-calibration.
| |
| # Use multi-scale clean by adding non-zero scales to the <tt>multiscale</tt> parameter.
| |
| # Image the calibrators. What sort of dynamic range can you get on them? Is self-calibration needed (and if so what dynamic range do you get when you use it)?
| |
| # Try the <tt>testautoflag</tt> task (in 3.3.0 and later) to automatically flag RFI from the upper sideband. There is more information on running <tt>testautoflag</tt> in [http://casaguides.nrao.edu/index.php?title=EVLA_Wide-Band_Wide-Field_Imaging:_G55.7_3.4_(Caltech) this tutorial].
| |
| | |
| == Credits ==
| |
| | |
| The EVLA data was taken by A. Soderberg et al. as part of project AS1015. See
| |
| [https://science.nrao.edu/enews/3.8/index.shtml#evlanoise NRAO eNews 3.8] (1-Sep-2010) for more on this result.
| |
| | |
| <blockquote><i>
| |
| The Expanded Very Large Array (EVLA) is a partnership of the United States, Canada, and Mexico. The EVLA is funded in the United States by the National Science Foundation, in Canada by the National Research Council, and in Mexico by the Comisión Nacional de Investigación Científica y Tecnológica (CONICyT).
| |
| </i></blockquote>
| |
| | |
| <blockquote><i>
| |
| The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
| |
| </i></blockquote>
| |
| | |
| SDSS image courtesy David Hogg & Michael Blanton, private communication. Data comes from
| |
| SDSS DR7, see [http://adsabs.harvard.edu/abs/2009ApJS..182..543A Abazajian et. al 2009].
| |
| | |
| <blockquote><i>
| |
| Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS Web Site is [http://www.sdss.org/].
| |
| </i></blockquote>
| |
| | |
| <blockquote><i>
| |
| The SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions. The Participating Institutions are the American Museum of Natural History, Astrophysical Institute Potsdam, University of Basel, University of Cambridge, Case Western Reserve University, University of Chicago, Drexel University, Fermilab, the Institute for Advanced Study, the Japan Participation Group, Johns Hopkins University, the Joint Institute for Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and Cosmology, the Korean Scientist Group, the Chinese Academy of Sciences (LAMOST), Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, Ohio State University, University of Pittsburgh, University of Portsmouth, Princeton University, the United States Naval Observatory, and the University of Washington.
| |
| </i></blockquote>
| |
| | |
| {{Checked 4.1.0}}
| |
