JVLA - Priori Flagging, Auto-Flagging, and Imaging in CASA
- Topical guide, part 1 of 2.
- This CASA guide is designed for CASA 4.5.0
Overview
This CASA guide will cover the importing of a SDM-BDF (Science Data Model-Binary Data File) into CASA (Common Astronomical Science Application), creating a MS (Measurement Set) in the process. It will also cover time averaging, and basic initial data flagging, including shadow, zero clipping, tfcrop, rflag, quacking, and online flagging.
Basic and Advanced Imaging in CASA, Part 2 of the guide covers advanced imaging, including msmfs, multiscale on regular images, spectral indices, w-projection, wide field mode, outlier fields, and interactive mode.
We will be utilizing data taken with the Karl G. Jansy, Very Large Array, of a supernova remnant G055.7+3.4.. The data were taken on August 23, 2010, in the first D-configuration for which the new wide-band capabilities of the WIDAR (Wideband Interferometric Digital ARchitecture) correlator were available. The 8-hour-long observation includes all available 1 GHz of bandwidth in L-band, from 1-2 GHz in frequency.
Obtaining the data
We will be utilizing the original data set (170GB) , which can be downloaded from the NRAO Science Data Archive. We have several search options for finding this observation. We will search for it by using the date of the observation. Set Dates From and To to 2010-Aug-23 and submit the query. The results should display all the observations run on that day. We will look for the archive file name AB1345_sb1800808_1.55431.004049953706, which should be near the top. Check the box for the file.
We want to download the raw data, therefore under Choose download data format :, choose SDM-BDF dataset (all files). Also, let's check the box Create MS or SDM tar file. Now enter your email address at the top, and click on Get My Data. On the next page, click on the Retrieve over internet button. The archive will now create a copy of the data in the default destination directory, and you should receive an email once it is ready to be downloaded.
Once the tar file has been downloaded, we will untar the file:
tar -xvf AB1345_sb1800808_1.55431.004049953706.tar
This should take several minutes, but once it's complete, you will have a directory called AB1345_sb1800808_1.55431.004049953706 which is the raw data.
Start and confirm your version of CASA
Start CASA by typing casa on a terminal command line. If you have not used CASA before, some helpful tips are available on the Getting Started in CASA page.
This guide has been written for CASA release 4.5.0. Please confirm your version before proceeding by checking the message in the command line interface window or the CASA logger after startup.
Importing data into CASA
For this tutorial, we will be running tasks using the task (parameter = value) syntax. When called in this manner, all parameters not explicitly set will use their default values.
# In CASA
importasdm(asdm='AB1345_sb1800808_1.55431.004049953706', vis='SN_G55.ms', process_flags=True,
tbuff=1.5, applyflags=False, outfile='SN_G55.ms.onlineflags.txt', flagbackup=False)
- vis= 'SN_G55.ms': This is the output MS that will be written.
- process_flags=True: This parameter controls the creation of online flags from the Flag.xml SDM table. It will create online flags in the FLAG_CMD sub-table within the MS (more on this later).
- tbuff=1.5: This parameter adds a time "buffer" padding to the flags in both directions to deal with timing mismatches. This is important for JVLA data taken before April 2011. This value should be set to 1.5x integration time. This particular observation had 1 second integrations. (CASA Cookbook section 2.2.2 and 3.5.1.3)
- applyflags=False: We will apply these flags later in the tutorial.
- outfile='SN_G55.ms.onlineflags.txt' : This will create a text file with a list of online flags.
- flagbackup=False: Since we aren't applying any flags at the moment, we'll leave this as False in order to save disk space.
Once the task is completed, you should have a new directory called SN_G55.ms. We can now continue with our initial data flagging using this MS. Also, in order to save disk space, we can now delete the SDM-BDF directory.
Time averaging MS
Due to the large size of the data set, running tasks within CASA may take several minutes. To allow us to run processes more quickly, we will time average the data to every 10-seconds using the mstransform task. This will reduce our MS from 172GB to 17GB.
# In CASA
mstransform(vis='SN_G55.ms', outputvis='SN_G55_10s.ms', datacolumn='data', timeaverage=True, timebin='10s')
Preliminary data evaluation
As a first step, use listobs to have a look at the MS:
# In CASA
listobs(vis='SN_G55_10s.ms', listfile='SN_G55_10s.listobs')
##########################################
##### Begin Task: listobs #####
listobs(vis="SN_G55_10s.ms",selectdata=True,spw="",field="",antenna="",
uvrange="",timerange="",correlation="",scan="",intent="",
feed="",array="",observation="",verbose=True,listfile="SN_G55_10s.listobs",
listunfl=False,cachesize=50,overwrite=False)
================================================================================
MeasurementSet Name: /path/to/your/directory/SN_G55_10s.ms MS Version 2
================================================================================
Observer: Dr. Sanjay Sanjay Bhatnagar Project: uid://evla/pdb/1072564
Observation: EVLA
Data records: 86242968 Total elapsed time = 28722 seconds
Observed from 23-Aug-2010/00:35:39.0 to 23-Aug-2010/08:34:21.0 (UTC)
ObservationID = 0 ArrayID = 0
Date Timerange (UTC) Scan FldId FieldName nRows SpwIds Average Interval(s) ScanIntent
23-Aug-2010/00:35:39.0 - 00:37:39.0 1 0 1331+305=3C286 9072 [0,1] [10, 10] [CALIBRATE_AMPLI#UNSPECIFIED]
00:37:39.0 - 00:40:09.0 2 0 1331+305=3C286 11340 [0,1] [10, 10] [CALIBRATE_AMPLI#UNSPECIFIED]
00:40:09.0 - 00:41:38.0 3 1 J1331+3030 27216 [2,3,4,5,6,7,8,9] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:41:38.0 - 00:43:08.0 4 1 J1331+3030 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:43:08.0 - 00:44:38.0 5 1 J1331+3030 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:44:38.0 - 00:46:08.0 6 1 J1331+3030 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:46:08.0 - 00:47:37.0 7 1 J1331+3030 27216 [2,3,4,5,6,7,8,9] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:47:37.0 - 00:49:07.0 8 1 J1331+3030 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:49:07.0 - 00:50:37.0 9 2 J1407+2827 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:50:37.0 - 00:52:07.0 10 2 J1407+2827 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:52:07.0 - 00:53:36.0 11 2 J1407+2827 27216 [2,3,4,5,6,7,8,9] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:53:36.0 - 00:55:06.0 12 2 J1407+2827 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:55:06.0 - 00:56:36.0 13 2 J1407+2827 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
00:56:36.0 - 00:58:06.0 14 3 J1925+2106 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE#UNSPECIFIED]
00:58:06.0 - 00:59:36.0 15 3 J1925+2106 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE#UNSPECIFIED]
00:59:36.0 - 01:01:05.0 16 3 J1925+2106 27216 [2,3,4,5,6,7,8,9] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE#UNSPECIFIED]
01:01:05.0 - 01:02:35.0 17 3 J1925+2106 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE#UNSPECIFIED]
01:02:35.0 - 01:04:05.0 18 3 J1925+2106 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE#UNSPECIFIED]
01:04:05.0 - 01:05:34.0 19 3 J1925+2106 27216 [2,3,4,5,6,7,8,9] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE#UNSPECIFIED]
01:05:34.0 - 01:07:04.0 20 3 J1925+2106 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE#UNSPECIFIED]
01:07:04.0 - 01:08:34.0 21 4 G55.7+3.4 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET#UNSPECIFIED]
01:08:34.0 - 01:10:04.0 22 4 G55.7+3.4 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET#UNSPECIFIED]
01:10:04.0 - 01:11:34.0 23 4 G55.7+3.4 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET#UNSPECIFIED]
01:11:34.0 - 01:13:03.0 24 4 G55.7+3.4 27216 [2,3,4,5,6,7,8,9] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET#UNSPECIFIED]
01:13:03.0 - 01:14:33.0 25 4 G55.7+3.4 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET#UNSPECIFIED]
01:14:33.0 - 01:16:03.0 26 4 G55.7+3.4 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET#UNSPECIFIED]
<snip>
08:16:24.0 - 08:17:54.0 308 5 0542+498=3C147 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:17:54.0 - 08:19:23.0 309 5 0542+498=3C147 27216 [2,3,4,5,6,7,8,9] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:19:23.0 - 08:20:53.0 310 5 0542+498=3C147 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:20:53.0 - 08:22:23.0 311 5 0542+498=3C147 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:22:23.0 - 08:23:52.0 312 5 0542+498=3C147 27216 [2,3,4,5,6,7,8,9] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:23:52.0 - 08:25:22.0 313 5 0542+498=3C147 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:25:22.0 - 08:26:52.0 314 6 J0319+4130 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:26:52.0 - 08:28:22.0 315 6 J0319+4130 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:28:22.0 - 08:29:52.0 316 6 J0319+4130 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:29:52.0 - 08:31:21.0 317 6 J0319+4130 27216 [2,3,4,5,6,7,8,9] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:31:21.0 - 08:32:51.0 318 6 J0319+4130 27216 [2,3,4,5,6,7,8,9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
08:32:51.0 - 08:34:21.0 319 6 J0319+4130 27216 [2, 3, 4, 5, 6, 7, 8, 9] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,UNSPECIFIED#UNSPECIFIED]
(nRows = Total number of rows per scan)
Fields: 7
ID Code Name RA Decl Epoch SrcId nRows
0 E 1331+305=3C286 13:31:08.287984 +30.30.32.95886 J2000 0 204120
1 N J1331+3030 13:31:08.287984 +30.30.32.95886 J2000 1 1626912
2 J J1407+2827 14:07:00.394410 +28.27.14.68997 J2000 2 1357776
3 D J1925+2106 19:25:59.605371 +21.06.26.16218 J2000 3 11669616
4 NONE G55.7+3.4 19:21:40.000000 +21.45.00.00000 J2000 4 67855536
5 N 0542+498=3C147 05:42:36.137916 +49.51.07.23356 J2000 5 1899072
6 N J0319+4130 03:19:48.160102 +41.30.42.10305 J2000 6 1629936
Spectral Windows: (10 unique spectral windows and 1 unique polarization setups)
SpwID Name #Chans Frame Ch0(MHz) ChanWid(kHz) TotBW(kHz) CtrFreq(MHz) BBC Num Corrs
0 Subband:0 64 TOPO 1284.000 2000.000 128000.0 1347.0000 4 RR RL LR LL
1 Subband:0 64 TOPO 1796.000 2000.000 128000.0 1859.0000 8 RR RL LR LL
2 Subband:3 64 TOPO 1000.000 2000.000 128000.0 1063.0000 4 RR RL LR LL
3 Subband:1 64 TOPO 1128.000 2000.000 128000.0 1191.0000 4 RR RL LR LL
4 Subband:0 64 TOPO 1256.000 2000.000 128000.0 1319.0000 4 RR RL LR LL
5 Subband:2 64 TOPO 1384.000 2000.000 128000.0 1447.0000 4 RR RL LR LL
6 Subband:3 64 TOPO 1520.000 2000.000 128000.0 1583.0000 8 RR RL LR LL
7 Subband:1 64 TOPO 1648.000 2000.000 128000.0 1711.0000 8 RR RL LR LL
8 Subband:0 64 TOPO 1776.000 2000.000 128000.0 1839.0000 8 RR RL LR LL
9 Subband:2 64 TOPO 1904.000 2000.000 128000.0 1967.0000 8 RR RL LR LL
Sources: 50
ID Name SpwId RestFreq(MHz) SysVel(km/s)
0 1331+305=3C286 0 - -
0 1331+305=3C286 1 - -
1 J1331+3030 2 - -
1 J1331+3030 3 - -
1 J1331+3030 4 - -
1 J1331+3030 5 - -
1 J1331+3030 6 - -
1 J1331+3030 7 - -
1 J1331+3030 8 - -
1 J1331+3030 9 - -
2 J1407+2827 2 - -
2 J1407+2827 3 - -
2 J1407+2827 4 - -
2 J1407+2827 5 - -
2 J1407+2827 6 - -
2 J1407+2827 7 - -
2 J1407+2827 8 - -
2 J1407+2827 9 - -
3 J1925+2106 2 - -
3 J1925+2106 3 - -
3 J1925+2106 4 - -
3 J1925+2106 5 - -
3 J1925+2106 6 - -
3 J1925+2106 7 - -
3 J1925+2106 8 - -
3 J1925+2106 9 - -
4 G55.7+3.4 2 - -
4 G55.7+3.4 3 - -
4 G55.7+3.4 4 - -
4 G55.7+3.4 5 - -
4 G55.7+3.4 6 - -
4 G55.7+3.4 7 - -
4 G55.7+3.4 8 - -
4 G55.7+3.4 9 - -
5 0542+498=3C147 2 - -
5 0542+498=3C147 3 - -
5 0542+498=3C147 4 - -
5 0542+498=3C147 5 - -
5 0542+498=3C147 6 - -
5 0542+498=3C147 7 - -
5 0542+498=3C147 8 - -
5 0542+498=3C147 9 - -
6 J0319+4130 2 - -
6 J0319+4130 3 - -
6 J0319+4130 4 - -
6 J0319+4130 5 - -
6 J0319+4130 6 - -
6 J0319+4130 7 - -
6 J0319+4130 8 - -
6 J0319+4130 9 - -
Antennas: 27:
ID Name Station Diam. Long. Lat. Offset from array center (m) ITRF Geocentric coordinates (m)
East North Elevation x y z
0 ea01 W09 25.0 m -107.37.25.2 +33.53.51.0 -521.9416 -332.7766 -1.2001 -1601710.017000 -5042006.925200 3554602.355600
1 ea02 E02 25.0 m -107.37.04.4 +33.54.01.1 9.8240 -20.4293 -2.7806 -1601150.060300 -5042000.619800 3554860.729400
2 ea03 E09 25.0 m -107.36.45.1 +33.53.53.6 506.0564 -251.8670 -3.5825 -1600715.950800 -5042273.187000 3554668.184500
3 ea04 W01 25.0 m -107.37.05.9 +33.54.00.5 -27.3562 -41.3030 -2.7418 -1601189.030140 -5042000.493300 3554843.425700
4 ea05 W08 25.0 m -107.37.21.6 +33.53.53.0 -432.1167 -272.1478 -1.5054 -1601614.091000 -5042001.652900 3554652.509300
5 ea06 N06 25.0 m -107.37.06.9 +33.54.10.3 -54.0649 263.8778 -4.2273 -1601162.591000 -5041828.999000 3555095.896400
6 ea07 E05 25.0 m -107.36.58.4 +33.53.58.8 164.9788 -92.8032 -2.5268 -1601014.462000 -5042086.252000 3554800.799800
7 ea08 N01 25.0 m -107.37.06.0 +33.54.01.8 -30.8810 -1.4664 -2.8597 -1601185.634945 -5041978.156586 3554876.424700
8 ea09 E06 25.0 m -107.36.55.6 +33.53.57.7 236.9058 -126.3369 -2.4443 -1600951.588000 -5042125.911000 3554773.012300
9 ea10 N03 25.0 m -107.37.06.3 +33.54.04.8 -39.0773 93.0192 -3.3330 -1601177.376760 -5041925.073200 3554954.584100
10 ea11 E04 25.0 m -107.37.00.8 +33.53.59.7 102.8054 -63.7682 -2.6414 -1601068.790300 -5042051.910200 3554824.835300
11 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
12 ea13 N07 25.0 m -107.37.07.2 +33.54.12.9 -61.1037 344.2331 -4.6138 -1601155.635800 -5041783.843800 3555162.374100
13 ea15 W06 25.0 m -107.37.15.6 +33.53.56.4 -275.8288 -166.7451 -2.0590 -1601447.198000 -5041992.502500 3554739.687600
14 ea16 W02 25.0 m -107.37.07.5 +33.54.00.9 -67.9687 -26.5614 -2.7175 -1601225.255200 -5041980.383590 3554855.675000
15 ea17 W07 25.0 m -107.37.18.4 +33.53.54.8 -349.9877 -216.7509 -1.7975 -1601526.387300 -5041996.840100 3554698.327400
16 ea18 N09 25.0 m -107.37.07.8 +33.54.19.0 -77.4346 530.6273 -5.5859 -1601139.485100 -5041679.036800 3555316.533200
17 ea19 W04 25.0 m -107.37.10.8 +33.53.59.1 -152.8599 -83.8054 -2.4614 -1601315.893000 -5041985.320170 3554808.304600
18 ea20 N05 25.0 m -107.37.06.7 +33.54.08.0 -47.8454 192.6015 -3.8723 -1601168.786100 -5041869.054000 3555036.936000
19 ea21 E01 25.0 m -107.37.05.7 +33.53.59.2 -23.8638 -81.1510 -2.5851 -1601192.467800 -5042022.856800 3554810.438800
20 ea22 N04 25.0 m -107.37.06.5 +33.54.06.1 -42.6239 132.8436 -3.5494 -1601173.979400 -5041902.657700 3554987.517500
21 ea23 E07 25.0 m -107.36.52.4 +33.53.56.5 318.0509 -164.1850 -2.6957 -1600880.571400 -5042170.388000 3554741.457400
22 ea24 W05 25.0 m -107.37.13.0 +33.53.57.8 -210.0959 -122.3887 -2.2577 -1601377.009500 -5041988.665500 3554776.393400
23 ea25 N02 25.0 m -107.37.06.2 +33.54.03.5 -35.6245 53.1806 -3.1345 -1601180.861480 -5041947.453400 3554921.628700
24 ea26 W03 25.0 m -107.37.08.9 +33.54.00.1 -105.3447 -51.7177 -2.6037 -1601265.153600 -5041982.533050 3554834.858400
25 ea27 E03 25.0 m -107.37.02.8 +33.54.00.5 50.6641 -39.4835 -2.7273 -1601114.365500 -5042023.151800 3554844.944000
26 ea28 N08 25.0 m -107.37.07.5 +33.54.15.8 -68.9057 433.1889 -5.0602 -1601147.940400 -5041733.837000 3555235.956000
##### End Task: listobs #####
##########################################
We can see that there are numerous sources in this observation:
- 1331+305=3C286, field ID 0: Amplitude Calibrator/Dummy Scans;
- J1331+3030, field ID 1: Amplitude/Bandpass Calibrator;
- J1407+2827, field ID 2: Phase Calibrator;
- J1925+2106, field ID 3: Phase Calibrator;
- G55.7+3.4, field ID 4: The Supernova Remnant;
- 0542+498=3C147, field ID 5: Amplitude/Bandpass Calibrator
- J0319+4130, field ID 6: Amplitude/Bandpass Calibrator
We can also see that these sources have associated "scan intents", which indicate their function in the observation. Note that you can select sources based on their intents in certain CASA tasks. The various scan intents in this data set are:
- CALIBRATE_PHASE indicates that this is a scan to be used for gain calibration;
- OBSERVE_TARGET indicates that this is the science target;
- CALIBRATE_AMPLI indicates that this is to be used for flux calibration; and
- CALIBRATE_BANDPASS indicates that these scans are to be used for bandpass calibration.
Note that 3C147 is to be used for both flux and bandpass calibration.
It's important to also note that the antennas have a name and ID associated with them. For example antenna ID 15 is named ea17 ( The "ea" stemming from the Expanded VLA project). When specifying an antenna within a task parameter, we will mainly reference them by name.
We can see the antenna configuration for this observation by using plotants:
# In CASA
plotants(vis='SN_G55_10s.ms', figfile='SN_G55_10s.plotants.png')
This shows that antennas ea01, ea03, and ea18 were on the extreme ends of the west, east, and north arms, respectively. The antenna position diagram is particularly useful as a guide to help determine which antenna to use as the reference antenna later during calibration. Note that antennas on stations 8 of each arm (N08, E08, W08) do not get moved during array reconfigurations, they can therefore at times be good choices as reference antennas. In this case, we'll probably want to choose something closer to the center of the array.
We may also inspect the raw data using plotms. To start with, lets look at a subset of scans on the supernova remnant:
# In CASA
plotms(vis='SN_G55_10s.ms', scan='30,75,120,165,190,235,303', antenna='ea24', xaxis='freq',
yaxis='amp', coloraxis='spw', iteraxis='scan', correlation='RR,LL')
- coloraxis='spw': Parameter indicates that a different color will be assigned to each spectral window.
- antenna='ea24' : We chose only information for antenna ea24.
- iteraxis='scan': Parameter tells plotms to display a new plot for each scan.
- correlation='RR,LL': We just want to display the right and left circular polarizations, without the cross-hand terms.
Flipping through the scans, we can see that there is significant time and frequency variable RFI present in the observation, as seen by the large spikes in amplitude. In particular, we can see that two spectral windows are quite badly affected. To determine which spectral windows they are, click on the "Mark Regions" tool at the bottom of the plotms GUI (the open box with a green "plus" sign). Use the mouse to select a few of the highest-amplitude points in each of the spectral windows. Click on the "Locate" button (magnifying glass sign). Information about the selected areas should now display in the logger window:
Frequency in [1.22235 1.26797] or [1.58473 1.62123], Amp in [21.7034 24.7039] or [59.9094 62.1097]: Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:20:37.0 BL=ea03@E09 & ea24@W05[2&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=22.5607 (30326/9/374) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:20:47.0 BL=ea03@E09 & ea24@W05[2&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=22.6442 (33654/10/374) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:20:47.0 BL=ea12@E08 & ea24@W05[11&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=22.4441 (34806/10/1526) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:20:57.0 BL=ea03@E09 & ea24@W05[2&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=23.032 (36982/11/374) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:20:57.0 BL=ea12@E08 & ea24@W05[11&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=23.5243 (38134/11/1526) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:21:07.0 BL=ea03@E09 & ea24@W05[2&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=23.6116 (40310/12/374) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:21:07.0 BL=ea12@E08 & ea24@W05[11&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=23.4432 (41462/12/1526) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:21:17.0 BL=ea03@E09 & ea24@W05[2&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=23.1712 (43638/13/374) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:21:17.0 BL=ea12@E08 & ea24@W05[11&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=21.7265 (44790/13/1526) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:21:17.0 BL=ea19@W04 & ea24@W05[17&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=22.3464 (45558/13/2294) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:21:47.0 BL=ea03@E09 & ea24@W05[2&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=22.3503 (53622/16/374) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:21:57.0 BL=ea03@E09 & ea24@W05[2&22] Spw=3 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=23.7536 (56950/17/374) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:21:07.0 BL=ea12@E08 & ea24@W05[11&22] Spw=6 Chan=41 Freq=1.602 Corr=RR X=1.602 Y=61.9097 (131282/39/1490) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:21:17.0 BL=ea12@E08 & ea24@W05[11&22] Spw=6 Chan=41 Freq=1.602 Corr=RR X=1.602 Y=61.1769 (134610/40/1490) Scan=30 Field=G55.7+3.4[4] Time=2010/08/23/01:21:27.0 BL=ea12@E08 & ea24@W05[11&22] Spw=6 Chan=41 Freq=1.602 Corr=RR X=1.602 Y=60.1834 (137938/41/1490) Found 15 points (15 unflagged) among 239616 in 0.01s.
We can see that Spw 3 and 6 are the worst affected by RFI.
A priori calibration and flagging
Before we proceed with further processing, we should check the operator log for the observation to see if there were any issues noted during the run that need to be addressed. The observing log files can be found here. We will change the start and stop dates to search for all logs created on August 23, 2010. Click on PDF next to AB1345 and review the log.
The log has various information, including the start/end times for the observation, frequency bands used, weather, baseline information for recently moved antennas, and any outages or issues that may have occurred during the run. We can see that antenna ea07 may need position corrections, and several antennas are missing an L-Band receiver, including ea06, ea17, ea20, and ea26. We will work on applying position corrections and flagging the affected antennas from the MS.
Antenna position corrections
The observed visibilities are a function of (u,v), therefore correcting a known position error is important in creating images derived from the data. We will do this with the task gencal.
The gencal task will query the JVLA Baseline Corrections database to determine what baseline corrections to apply to the data set. If you wish to double-check this by hand, refer to the EVLA/VLA Baseline Corrections page.
# In CASA
gencal(vis='SN_G55_10s.ms', caltable='SN_G55_10s.pos', caltype='antpos')
As reported by the CASA logger, gencal found a position correction for antenna ea07 of (x, y, z) = (0.00870, 0.01370, 0.00000) and recorded this in our specified calibration table.
The position corrections can be applied later using the task gaincal.
Flagging non-operational antennas
In addition to updating the position for antenna ea07, we have to flag antennas ea06, ea17, ea20, and ea26, since these did not have working L-band receivers at the time of observation. We do this with the task flagdata:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='manual', antenna='ea06,ea17,ea20,ea26')
Note that the first thing flagdata does is create a backup flag file, in this case named "flagdata_1". This flag file contains a copy of the flags present in the MS prior to the requested flagging operation, and can be found inside the SN_G55.ms.flagversions directory, along with any other backed up flag files. Keep in mind that these backup files may take up a fair amount of space, so it may be a good idea to set flagbackup=False, use flagdata to apply flags, and once you feel confident all priori flags have been applied, use flagmanager to create a backup.
Applying online flags
At the time of importing from the SDM-BDF raw data to a MS, we chose to process the online flags from the Flags.xml file to the FLAG_CMD sub-table within the MS. We also created a txt document which includes a list of online flags.
The Flags.xml file holds information of flags created during the observation, such as subreflector issues, and antennas not being on source.
We will now want to apply these online flags to the data, but first, let's create a plot of the flags we are about to apply.
# In CASA
flagcmd(vis='SN_G55_10s.ms', inpmode='table', reason='any', action='plot', plotfile='flaggingreason_vs_time.png')
We can see several instances of online flagging in the created image. Most notably, ea28 and ea08 had some subreflector issues througout the observation. Online flags are instances of possible missing data, including:
- ANTENNA_NOT_ON_SOURCE
The JVLA antennas have slewing speeds of 20 degrees per minute in azimuth, and 40 degrees per minute in elevation. Some antennas are slower than others, and may take a few more seconds to reach the next source. The antennas can also take a few seconds to settle down due to small oscillations after having slewed.
- SUBREFLECTOR_ERROR
The FRM (Focus Rotation Mount) located at the apex of the antennas, is responsible for focusing the incoming radio signal to the corresponding receiver. They can at times have issues with their focus and/or rotation axes. Being off target, so much as a few fractions of a degree can result in loss of data, depending on the frequency being observed.
Now that we've plotted the online flags, we will apply them to the MS.
# In CASA
flagcmd(vis='SN_G55_10s.ms', inpmode='table', reason='any', action='apply', flagbackup=False)
The CASA logger should report the progress as the task applies these flags in chunks. Once it has finished, it will report on the percentage of data that has been flagged.
Flagging shadowed antennas, zero-amplitude data, and quacking
Since this is the most compact EVLA configuration, there may be instances where one antenna blocks, or "shadows" another. Therefore, we will run flagdata to remove these data (CASA Cookbook 3.4.2):
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='shadow', tolerance=0.0, flagbackup=False)
In this particular observation, there does not appear to be much data affected by shadowing, as can be seen in the logger report. One reason why this may be the case, is the antennas were pointed at sources high in elevation. To show this, we can create a plot of elevation vs. time by using the plotms task.
# In CASA
plotms(vis='SN_G55_10s.ms', xaxis='time', yaxis='elevation', antenna='0&1;2&3', spw='*:31',
coloraxis='field', title='Elevation vs. Time', plotfile='Elevation_vs_Time.png', showgui=False)
We can see from the plot that most of the antennas were pointed at or above 40 degrees in elevation. This helped reduce the amount of shadowing on the antennas during the run of this observation.
In addition to shadowing, there may be times during which the correlator writes out pure zero-valued data. In order to remove this bad data, we run flagdata to remove any pure zeroes:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='clip', clipzeros=True, flagbackup=False)
Inspecting the logger output which is generated by flagdata shows that there is a small quantity of zero-valued data (3.47%) present in this MS.
Now we will utilize the flagdata task one more time in order to run it in quaking mode.
It's common for the array to "settle down" at the start of a scan. Quacking is used to remove data at scan boundaries, and it can apply the same edit to all scans for all baselines.
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='quack', quackinterval=5.0, quackmode='beg', flagbackup=False)
- quackmode='beg' : Data from the start of each scan will be flagged.
- quackinterval=5.0: Flag the first 5 seconds of every scan.
Backup data with flagmanager
Now that we've applied online flags, clipped zero amplitude data, removed shadowed data, and quacked, we will create a backup of the MS using flagmanager.
# In CASA
flagmanager(vis='SN_G55_10s.ms', mode='save', versionname='after_priori_calflag')
From here on forward, if we make a mistake, we can always revert back to this version of the MS by setting mode='restore' within flagmanager.
Automatic RFI excision
Hanning-smoothing data
Prior to flagging any data which is affect by strong RFI, one should Hanning-smooth the data to remove Gibbs ringing. This is done with the task hanningsmooth, which can either write a new, Hanning-smoothed MS or directly operate on the requested column of the input MS. To conserve space, we will request the latter. Note that if you wish to make your own "before" and "after" plots, you should make the first plot prior to running hanningsmooth, since you will be overwriting the non-Hanning-smoothed data in the process -- and this is not reversible (CASA Cookbook 4.7.3).
# In CASA
plotms(vis='SN_G55_10s.ms', scan='30', antenna='ea24', spw='2~4', xaxis='freq', yaxis='amp', coloraxis='spw',
correlation='RR,LL', plotrange=[1.0,1.27,-0.1,5], plotfile='amp_v_freq.beforeHanning.png')
hanningsmooth(vis='SN_G55_10s.ms', datacolumn='data')
plotms(vis='SN_G55_10s.ms', scan='30', antenna='ea24', spw='2~5', xaxis='freq', yaxis='amp', coloraxis='spw',
correlation='RR,LL', plotrange=[1.0,1.27,-0.1,5], plotfile='amp_v_freq.afterHanning.png')
Task hanningsmooth will take a few minutes to run. Note that the 2nd plotms command above contains a trivial change in the spw selection (trivial because the 5th spw is outside of the specified plotrange). This forces plotms to reload the plot since by default, plotms will not redraw a plot if the input parameters are unchanged. In this case, since the data column was changed between calls to plotms, a redraw is necessary. When using the GUI, you can simply check "force reload" in the bottom left corner of the side bar before clicking "Plot."
We can compare the Hanning-smoothed data with the raw data by plotting a subset of data to show the result of Hanning-smoothing (see plots to the left and right). As you can see, the smoothing has spread the single-channel RFI into three channels, but has also removed the effects of some of the worst RFI from a number of channels. Overall, this will improve our ability to flag RFI from the data and retain as much good data as possible.
Using the phase calibration source for preliminary bandpass calibration
In order to get the best possible result from the automatic RFI excision, we will first apply bandpass calibration to the MS. Since the RFI is time-variable, using the phase calibration source to make an average bandpass over the entire observation will mitigate the amount of RFI present in the calculated bandpass. (For the final calibration, we will use the designated bandpass source 3C147; however, since this object was only observed in the last set of scans, it doesn't sample the time variability and would not provide a good average bandpass.)
Since there are likely to be gain variations over the course of the observation, we will run gaincal to solve for an initial set of antenna-based phases over a narrow range of channels. These will be used to create the bandpass solutions. While amplitude variations will have little effect on the bandpass solutions, it is important to solve for these phase variations with sufficient time resolution to prevent decorrelation when vector averaging the data in computing the bandpass solutions.
In order to choose a narrow range of channels for each spectral window which are relatively RFI-free over the course of the observation, we can look at the data with plotms. Note that it's important to only solve for phase using a narrow channel range, since an antenna-specific delay will cause the phase to vary with respect to frequency over the spectral window, perhaps by a substantial amount.
# In CASA
plotms(vis='SN_G55_10s.ms', scan='30,75,120,165,190,235,303', antenna='ea24',
xaxis='channel', yaxis='amp', iteraxis='spw', yselfscale=True, correlation='RR,LL')
- yselfscale=True: sets the y-scaling to be for the currently displayed spectral window, since some spectral windows have much worse RFI and will skew the scale for others.
Looking at these plots, we can choose appropriate channel ranges for each SPW:
SPW 2: 10-13 SPW 3: 30-33 SPW 4: 32-35 SPW 5: 30-33 SPW 6: 35-38 SPW 7: 30-33 SPW 8: 30-33 SPW 9: 46-49
Using these channel ranges, we run gaincal to calculate phase-only solutions that will be used as input during our initial bandpass calibration. Remember - the calibration tables we are creating now are so that we can use automatic RFI flagging algorithms. Our final calibration tables will be generated later, after automated flagging. Here are the inputs for our initial pre-bandpass phase calibration:
# In CASA
gaincal(vis='SN_G55_10s.ms', caltable='SN_G55_10s.initPh', field='J1925+2106', solint='int',
spw='2:10~13,3;5;7~8:30~33,4:32~35,6:35~38,9:46~49', refant='ea24', minblperant=3,
minsnr=3.0, calmode='p', gaintable='SN_G55_10s.pos')
- caltable='SN_G55_10s.initPh': this is the output calibration table that will be written.
- field='J1925+2106': this is the phase calibrator we will use to calibrate the phase.
- solint='int': we request a solution for each 10-second integration.
- spw='2:10~13,3;5;7~8:30~33,4:32~35,6:35~38,9:46~49': note the syntax of this selection: a ":" is used to separate the SPW from channel selection, ";" is used to separate within this selection, and "~" is used to indicate an inclusive range.
- refant='ea24': we have chosen ea24 as the reference antenna after inspecting the antenna position diagram (see above). It is relatively close to, but not directly in, the center of the array, which could be important in D-configuration, since you don't want the reference antenna to have a high probability of being shadowed by nearby antennas.
- minblperant=3: the minimum number of baselines which must be present to attempt a phase solution.
- minsnr=3.0: the minimum signal-to-noise a solution must have to be considered acceptable. Note that solutions which fail this test will cause these data to be flagged downstream of this calibration step.
- calmode='p': perform phase-only solutions.
- gaintable='SN_G55_10s.pos': use the antenna position correction for ea07 that we created earlier.
Note that a number of solutions do not pass the requirements of the minimum 3 baselines (generating the terminal message "Insufficient unflagged antennas to proceed with this solve.") or minimum signal-to-noise ratio (outputting "n of x solutions rejected due to SNR < 3 ..."). A particularly large number of solutions are rejected in SPW 6, where the RFI is most severe.
We can inspect the resulting calibration table with plotcal:
# In CASA
plotcal(caltable='SN_G55_10s.initPh', xaxis='time', yaxis='phase',
iteration='antenna', spw='2', plotrange=[-1,-1,-180,180])
We may also iterate over spectral window for a subset of antennas:
# In CASA
plotcal(caltable='SN_G55_10s.initPh', xaxis='time', yaxis='phase',
iteration='spw', antenna='ea01,ea05,ea24', plotrange=[-1,-1,-180,180])
Clearly, the phases are affected by RFI in some places, especially in SPW 6.
Using this phase information, we create time-averaged bandpass solutions for the phase calibration source:
# In CASA
bandpass(vis='SN_G55_10s.ms', caltable='SN_G55_10s.initBP', field='J1925+2106', solint='inf', combine='scan',
refant='ea24', minblperant=3, minsnr=10.0, gaintable=['SN_G55_10s.pos', 'SN_G55_10s.initPh'],
interp=['', 'nearest'], solnorm=False)
- solint='inf', combine='scan': the solution interval of 'inf' will automatically break by scans; this requests that the solution intervals be combined over scans, so that we will get one solution per antenna.
- gaintable=['SN_G55_10s.pos', 'SN_G55_10s.initPh']: we will pre-apply both the antenna position corrections as well as the initial phase solutions.
- interp=['', 'nearest']: by default, gaincal will use linear interpolation for pre-applied calibration. However, we want the nearest phase solution to be used for a given time.
Again, we can see that a number of solutions have been rejected by our choices of minblperant and minsnr.
We may plot the bandpasses with plotcal; first looking at the amplitudes:
# In CASA
plotcal(caltable='SN_G55_10s.initBP', xaxis='freq', yaxis='amp',
iteration='antenna', subplot=331)
- subplot=331: displays 3x3 plots per screen
Now lets iterate through spw. This will give us a better idea of where RFI is most present.
# In CASA
plotcal(caltable='SN_G55_10s.initBP', xaxis='freq', yaxis='amp',
iteration='spw', subplot=331)
Also, we can look at the phase solutions:
# In CASA
plotcal(caltable='SN_G55_10s.initBP', xaxis='freq', yaxis='phase',
iteration='antenna', subplot=331)
We can see that SPW 6 is virtually wiped-out by RFI; furthermore, there are channels in SPW 3 that are consistently badly affected. Prior to running any automatic flagging, we will flag these manually. In addition, we will flag the first 9 channels of SPW 2, since this is affected by an issue which causes the noise to be substantially higher. Also, we will want to flag spw's 0 and 1, since they only cover scans 1 and 2 for 3C286, which we won't be using as a calibrator.
# In CASA
flagdata(vis='SN_G55_10s.ms', spw='0,1,2:0~8,3:41~63,6')
Note that this has created a backup flag file called "flagdata_2". Now we apply the antenna position corrections and the bandpass calibration table to the data:
# In CASA
applycal(vis='SN_G55_10s.ms', gaintable=['SN_G55_10s.pos', 'SN_G55_10s.initBP'], calwt=False)
This operation will flag data that correspond to flagged solutions, so applycal makes a backup version of the flags prior to operating on the data. Note that running applycal might take a little while.
To see the corrected data, we can plot the data as we did before, choosing ydatacolumn='corrected' this time:
# In CASA
plotms(vis='SN_G55_10s.ms', scan='30,75,120,165,190,235,303',
antenna='ea24', xaxis='freq', yaxis='amp', coloraxis='spw',
iteraxis='scan', ydatacolumn='corrected')
Note that some of the worst RFI is no longer there, and that the amplitude scale has changed, since the bandpass solutions include amplitude scaling. SPW 6 has been completely flagged, and we can see the flagged portion in SPW 3. Scrolling through the other scans, we can see there is still RFI present. We will now continue the process of RFI excision with the use of auto flagging algorithms.
Automatic flagging
Now that we have bandpass-corrected data with some of the worst RFI flagged out, we will run flagdata in rflag mode (CASA Cookbook 3.4.2.8). Note that there are many parameters which may be modified:
# In CASA
default flagdata
mode='rflag'
inp
# flagdata :: All-purpose flagging task based on data-selections and flagging modes/algorithms
vis = '' # Name of MS file to flag
mode = 'rflag' # Flagging mode
# (list/manual/clip/shadow/quack/elevation/tfcrop/rflag/extend/unflag/summary)
field = '' # Field names or field index numbers: '' ==> all, field='0~2,3C286'
spw = '' # Spectral-window/frequency/channel: '' ==> all, spw='0:17~19'
antenna = '' # Antenna/baselines: '' ==> all, antenna ='3,VA04'
timerange = '' # Time range: '' ==> all,timerange='09:14:0~09:54:0'
correlation = '' # Correlation: '' ==> all, correlation='XX,YY'
scan = '' # Scan numbers: '' ==> all
intent = '' # Observation intent: '' ==> all, intent='CAL*POINT*'
array = '' # (Sub)array numbers: '' ==> all
uvrange = '' # UV range: '' ==> all; uvrange ='0~100klambda', default units=meters
observation = '' # Observation ID: '' ==> all
feed = '' # Multi-feed numbers: Not yet implemented
ntime = 'scan' # Time-range to use for each chunk (in seconds or minutes)
combinescans = False # Accumulate data across scans.
datacolumn = 'DATA' # Data column on which to operate (data,corrected,model,residual)
winsize = 3 # Number of timesteps in the sliding time window [aips:fparm(1)]
timedev = '' # Time-series noise estimate [aips:noise]
freqdev = '' # Spectral noise estimate [aips:scutoff]
timedevscale = 5.0 # Threshold scaling for timedev [aips:fparm(9)]
freqdevscale = 5.0 # Threshold scaling for freqdev [aips:fparm(10)]
spectralmax = 1000000.0 # Flag whole spectrum if freqdev is greater than spectralmax [aips:fparm(6)]
spectralmin = 0.0 # Flag whole spectrum if freqdev is less than spectralmin [aips:fparm(5)]
action = 'apply' # Action to perform in MS and/or in inpfile (none/apply/calculate)
display = '' # Display data and/or end-of-MS reports at runtime (data/report/both).
flagbackup = True # Back up the state of flags before the run
savepars = False # Save the current parameters to the FLAG_CMD table or to a file
Additional information on the algorithm used in RFlag, as well as the other available automatic flagging algorithm in flagdata ("TFCrop"), can be found on this webpage (sections 2.1.6 and 2.1.7).
Following are a set of flagdata commands which have been found to work reasonably well with these data. Please take some time to play with the parameters and the plotting capabilities. Since these runs set display='both' and action='calculate', the flags are displayed but not actually written to the MS. This allows one to try different sets of parameters before actually applying the flags to the data.
Some representative plots are also displayed. Each column displays an individual polarization product; since we're using all four, from left to right are RR, RL, LR, and LL. The first row shows the data with current flags applied, and the second includes the flags generated by flagdata. The x-axis is channel number (the spectral window ID is displayed in the top title) and the y-axis of the first two rows is all integrations included in a time "chunk", set by the ntime parameter. These are the data considered by the RFlag algorithm during its flagging process, and changes in ntime will have some (relatively small) affect on what data are flagged.
Each plot page displays data for a single baseline and time chunk. The buttons at the bottom allow one to step through baseline (backward as well as forward), SPW, scan, and field; "Stop Display" will continue the flagging operation without the GUI, and "Quit" aborts the run.
SPW 2
First, we will run flagdata with mode='rflag', using the default parameter values, for just one source (the supernova remnant) and spectral window (2):
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag', field='4', spw='2', datacolumn='corrected',
action='calculate', display='both', flagbackup=False)
While this is clearly picking up some RFI, much is being left untouched (see figure to left, below). After stepping through a few baselines and scans, hit "Quit" to stop the flagger.
Let's try making it more sensitive to deviations from the calculated RMS in frequency, setting both timedevscale and freqdevscale=1.5 (the default is 5.0):
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag', field='4', spw='2', datacolumn='corrected', freqdevscale=1.5,
timedevscale=1.5, action='calculate', display='both', flagbackup=False)
Using a cutoff value of 1.5 sigma may seem a bit extreme, but as you can see from the figure on the right, it does a substantially better job of getting rid of the RFI in the badly affected SPW 2.
We now run flagdata to calculate and apply these flags for all data in SPW 2. Note that this will take a little while.
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag', spw='2', datacolumn='corrected',
freqdevscale=1.5, timedevscale=1.5, action='apply', display='', flagbackup=False)
Although RFlag has done a pretty good job of finding the bad data, some still remains. One way to delete it is to use the mode='extend' feature in flagdata, which can extend flags along a chosen axis. First, we will extend the flags across polarization, so if any one polarization is flagged, all data for that time / channel will be flagged:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='extend', spw='2', extendpols=True,
action='apply', display='', flagbackup=False)
Now, we will extend the flags in time and frequency, using the "growtime" and "growfreq" parameters. For the data here, the RFlag algorithm seems most likely to miss RFI which should be flagged along more of the time axis, so we will try with growtime=50.0, which will flag all data for a given channel if more than 50% of that channel's time is already flagged, and growfreq=90.0, which will flag the entire spectrum for an integration if more than 90% of the channels in that integration are already flagged.
Again, first just have a look at the flags that will be generated before applying them:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='extend', spw='2', growtime=50.0,
growfreq=90.0, action='calculate', display='data', flagbackup=False)
It still appears to be missing some RFI, but this is also a very badly-affected SPW, so leave it as is for now and run to apply the flags:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='extend', spw='2', growtime=50.0,
growfreq=90.0, action='apply', display='', flagbackup=False)
SPW 3
Now, let's work on SPW 3, flipping through time, baseline, and fields to get a sense of how the flagging will go with these parameters:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag', spw='3', datacolumn='corrected', freqdevscale=2.0,
timedevscale=2.0, action='calculate', display='both', flagbackup=False)
Unfortunately, this SPW is very badly affected by RFI, and it does not seem possible to flag adequately with the automated task (and probably not by hand, either). In this case, we choose to manually flag the entire SPW:
# In CASA
flagdata(vis='SN_G55_10s.ms', spw='3', flagbackup=False)
SPW 4
Moving on to SPW 4:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag', spw='4', datacolumn='corrected', freqdevscale=5.0,
timedevscale=4.0, action='calculate', display='both', flagbackup=False)
Since the RFI is narrower and more pronounced in this frequency range, we have increased the RMS cutoff for both the time and frequency calculations to avoid over-flagging and deleting good data.
After checking the data and changing the parameters until you're happy, apply these flags:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag', spw='4', datacolumn='corrected', freqdevscale=5.0,
timedevscale=4.0, action='apply', flagbackup=False)
Again, extend the flags along polarization:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='extend', spw='4', extendpols=True,
action='apply', display='', flagbackup=False)
Try extending in frequency and time, as before:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='extend', spw='4', growtime=50.0, growfreq=90.0,
action='calculate', display='data', flagbackup=False)
This looks pretty good, so let's apply it and have a look in plotms:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='extend', spw='4', growtime=50.0, growfreq=90.0, action='apply', flagbackup=False)
plotms(vis='SN_G55_10s.ms', scan='30,75,120,165,190,235,303', xaxis='baseline', yaxis='amp',
spw='4', iteraxis='scan', correlation='RR,LL', coloraxis = 'baseline')
Although we're trying to avoid doing this too much, it appears that there is one baseline which is consistently higher-amplitude than the others, indicating that it's probably contaminated by RFI. Use the plotms tools to identify this baseline, which turns out to be ea04 and ea16, and flag it:
# In CASA
flagdata(vis='SN_G55_10s.ms', antenna='ea04&ea16', spw='4', flagbackup=False)
SPW 5
We could have narrowed this further by channel and perhaps time, but remember: this tutorial is about the quick-and-dirty way of flagging data! With this in mind, we move on to SPW 5. Note that this time, we only look at data from the supernova remnant (target) field.
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag', spw='5', datacolumn='corrected', field='4', freqdevscale=5.0,
timedevscale=4.0, action='calculate', display='data', flagbackup=False)
The parameters we used for SPW 4 seem to work well for SPW 5 also. Go ahead and flag, then extend as before:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag',
spw='5', datacolumn='corrected',
freqdevscale=5.0, timedevscale=4.0,
action='apply', flagbackup=False)
flagdata(vis='SN_G55_10s.ms', mode='extend',
spw='5', extendpols=True,
action='apply', flagbackup=False)
flagdata(vis='SN_G55_10s.ms', mode='extend',
spw='5', growtime=50.0, growfreq=90.0,
action='apply', flagbackup=False)
SPW 7 & 8
Recall that we already deleted SPW 6 due to bad RFI, so we only have 7-9 remaining. SPWs 7 and 8 have similar RFI properties to 4 and 5, so let's use the same RFlag parameters for these (feel free to play with this a bit yourself, if you like, to try to optimize):
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag',
spw='7~8', datacolumn='corrected',
freqdevscale=5.0, timedevscale=4.0,
action='apply', flagbackup=False)
flagdata(vis='SN_G55_10s.ms', mode='extend',
spw='7~8', extendpols=True,
action='apply', flagbackup=False)
flagdata(vis='SN_G55_10s.ms', mode='extend',
spw='7~8', growtime=50.0, growfreq=90.0,
action='apply', flagbackup=False)
SPW 9
However, SPW 9 is a bit more affected, and we may wish to use a somewhat lower threshold to catch all the RFI. First, try with the same parameters:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag',
spw='9', datacolumn='corrected', field='4',
freqdevscale=5.0, timedevscale=4.0,
action='calculate', display='data',
flagbackup=False)
Indeed, this seems to be missing a lot of the RFI. Try less stringent limits:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag',
spw='9', datacolumn='corrected', field='4',
freqdevscale=1.0, timedevscale=1.0,
action='calculate', display='data',
flagbackup=False)
This looks pretty good. Check the calibrator sources to be sure it works for them as well:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag',
spw='9', datacolumn='corrected', field='3',
freqdevscale=1.0, timedevscale=1.0,
action='calculate', display='data',
flagbackup=False)
flagdata(vis='SN_G55_10s.ms', mode='rflag',
spw='9', datacolumn='corrected', field='5',
freqdevscale=1.0, timedevscale=1.0,
action='calculate', display='data',
flagbackup=False)
These seem reasonable as well, though it's apparent that 3C147 was very affected, possibly because of its low elevation at the time of the observation. Apply and extend the flags:
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='rflag',
spw='9', datacolumn='corrected',
freqdevscale=1.0, timedevscale=1.0,
action='apply', flagbackup=False)
flagdata(vis='SN_G55_10s.ms', mode='extend',
spw='9', extendpols=True,
action='apply', flagbackup=False)
flagdata(vis='SN_G55_10s.ms', mode='extend',
spw='9', growtime=50.0, growfreq=90.0,
action='apply', flagbackup=False)
Evaluating results & further manual flagging
Now, we will use flagdata to see a summary of how much data we have flagged:
# In CASA
flagInfo = flagdata(vis='SN_G55_10s.ms', mode='summary')
Using the information stored in the flagInfo Python dictionary, we can calculate and print out some interesting statistics:
# In CASA
print("\n %2.1f%% of G55.7+3.4, %2.1f%% of 3C147, and %2.1f%% of J1925+2106 are flagged. \n" % (100.0 * flagInfo['field']['G55.7+3.4']['flagged'] / flagInfo['field']['G55.7+3.4']['total'], 100.0 * flagInfo['field']['0542+498=3C147']['flagged'] / flagInfo['field']['0542+498=3C147']['total'], 100.0 * flagInfo['field']['J1925+2106']['flagged'] / flagInfo['field']['J1925+2106']['total'])) <br />
print("Spectral windows are flagged as follows:")
for spw in range(2,10):
print("SPW %s: %2.1f%%" % (spw, 100.0 * flagInfo['spw'][str(spw)]['flagged'] / flagInfo['spw'][str(spw)]['total']))
So, as a result of the flagging thus far, we have sacrificed a bit over half of all the data. Let's see how well it has been cleaned up, using plotms:
# In CASA
plotms(vis='SN_G55_10s.ms', scan='165', spw='2,4~5,7~9',
antenna='ea24', xaxis='freq', yaxis='amp',
correlation='RR,LL', coloraxis='spw')
Unfortunately, despite our best autoflagging efforts, SPW 2 still looks pretty bad. (Take heart -- even the by-hand flagging did not work out well for this one.) So, we will flag the rest of SPW 2:
# In CASA
flagdata(vis='SN_G55_10s.ms', spw='2', flagbackup=False)
After this is complete, refresh the plotms window using Shift + Plot. This generates the plot to the left. Just to compare with the unflagged data, we will restore the original flags, and have a look at the same slice. Be sure to save the current flags first!
# In CASA
flagmanager(vis='SN_G55_10s.ms', mode='save',
versionname='after_autoflagging_rflag_1')
flagmanager(vis='SN_G55_10s.ms', mode='restore',
versionname='flagdata_1')
plotms(vis='SN_G55_10s.ms', scan='165', spw='4~5,7~9',
antenna='ea24', xaxis='freq', yaxis='amp',
correlation='RR,LL', coloraxis='spw')
The pre-flagging plot is shown on the right. Clearly, a lot of the RFI has been excised. Restore the flags:
# In CASA
flagmanager(vis='SN_G55_10s.ms', mode='restore',
versionname='after_autoflagging_rflag_1')
Other instructive ways to view the data are by baseline and uv-distance. Note that we're plotting all baselines in these plots, rather than just baselines to ea24 as before.
# In CASA
plotms(vis='SN_G55_10s.ms', scan='30,75,120,165,190,235,303',
xaxis='baseline', yaxis='amp', spw='4~5,7~9', iteraxis='spw',
correlation='RR,LL', coloraxis='antenna1')
No particular baselines look bad enough to flag outright, so we will leave this as is. Feel free to do some more flagging if you like. Now, let's plot as a function of uv-distance:
# In CASA
plotms(vis='SN_G55_10s.ms', scan='30,75,120,165,190,235,303',
xaxis='uvdist', yaxis='amp', spw='4~5,7~9', iteraxis='spw',
correlation='RR,LL', coloraxis='antenna1')
Again, nothing really sticks out as obviously in need of flagging. Clearly, there is still some residual RFI left here and there -- however, for the purposes of this tutorial, we will accept the current rflag results and and move on to a little more flagging with tfcrop.
tfcrop
Let's now move on to a different form of auto-flagging with the flagdata task called tfcrop (CASA Cookbook 3.4.2.7)
# In CASA
default flagdata
mode='tfcrop'
inp
# flagdata :: All-purpose flagging task based on data-selections and flagging modes/algorithms.
vis = '' # Name of MS file or calibration table to flag
mode = 'tfcrop' # Flagging mode
field = '' # Field names or field index numbers: '' ==> all,
# field='0~2,3C286'
spw = '' # Spectral-window/frequency/channel: '' ==> all, spw='0:17~19'
antenna = '' # Antenna/baselines: '' ==> all, antenna ='3,VA04'
timerange = '' # Time range: '' ==> all,timerange='09:14:0~09:54:0'
correlation = '' # Correlation: '' ==> all, correlation='XX,YY'
scan = '' # Scan numbers: '' ==> all
intent = '' # Scan intent: '' ==> all, intent='CAL*POINT*'
array = '' # (Sub)array numbers: '' ==> all
uvrange = '' # UV range: '' ==> all; uvrange ='0~100klambda', default
# units=meters
observation = '' # Observation ID: '' ==> all
feed = '' # Multi-feed numbers: Not yet implemented
ntime = 'scan' # Time-range to use for each chunk (in seconds or minutes)
combinescans = False # Accumulate data across scans depending on the value of ntime.
datacolumn = 'DATA' # Data column on which to operate
# (data,corrected,model,weight,etc.)
timecutoff = 4.0 # Flagging thresholds in units of deviation from the fit
freqcutoff = 3.0 # Flagging thresholds in units of deviation from the fit
timefit = 'line' # Fitting function for the time direction (poly/line)
freqfit = 'poly' # Fitting function for the frequency direction (poly/line)
maxnpieces = 7 # Number of pieces in the polynomial-fits (for 'freqfit' or
# 'timefit' = 'poly')
flagdimension = 'freqtime' # Dimensions along which to calculate fits
# (freq/time/freqtime/timefreq)
usewindowstats = 'none' # Calculate additional flags using sliding window statistics
# (none,sum,std,both)
halfwin = 1 # Half-width of sliding window to use with 'usewindowstats'
# (1,2,3).
extendflags = True # Extend flags along time, frequency and correlation.
action = 'apply' # Action to perform in MS and/or in inpfile (none/apply/calculate)
display = '' # Display data and/or end-of-MS reports at runtime
# (data/report/both).
flagbackup = True # Back up the state of flags before the run
savepars = False # Save the current parameters to the FLAG_CMD table or to a file
Tfcrop is an algorithm that detects outliers in the 2D time-frequency plane, and can operate on un-calibrated data (non bandpass-corrected). Much like rflag, tfcrop will iterate through chunks of time, and undergo several steps in order to find and excise different types of RFI.
Step 1 will look for and find short-duration RFI spikes (narrow-band and broad-band).
Step 2 looks for time-persistent RFI.
Step 3 will look for time-persistent, narrow-band RFI.
Step 4 will look for low-level wings of very strong RFI.
More details on the algorithm steps can be found on this webpage
We will apply the auto-flagging tfcrop algorithm to the most affected spectral windows 4,7, and 9. First, lets plot the corrected data, so we can compare the data before and after tfcrop.
# In CASA
plotms(vis='SN_G55_10s.ms', scan='30,75,120,165,190,235,303',
spw='4~5,7~9', antenna='ea24', xaxis='freq', yaxis='amp',
ydatacolumn='corrected', correlation='RR,LL', coloraxis='spw')
SPW 4
Calculate how much of the RFI in spw 4 will be detected and flagged.
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='tfcrop', spw='4', datacolumn='corrected', action='calculate', display='both', flagbackup=False)
We can see that tfcrop is still finding and flagging RFI in this spectral window. Let's now apply the flags.
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='tfcrop', spw='4', datacolumn='corrected', action='apply', display='', flagbackup=False)
We can now move on to spw 7 and 9.
SPW 7
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='tfcrop', spw='7',
datacolumn='corrected', action='calculate',
display='both', flagbackup=False)
flagdata(vis='SN_G55_10s.ms', mode='tfcrop', spw='7',
datacolumn='corrected', action='apply',
display='', flagbackup=False)
SPW 9
# In CASA
flagdata(vis='SN_G55_10s.ms', mode='tfcrop', spw='9',
datacolumn='corrected', action='calculate',
display='both', flagbackup=False)
flagdata(vis='SN_G55_10s.ms', mode='tfcrop', spw='9',
datacolumn='corrected', action='apply',
display='', flagbackup=False)
We can now use plotms to review the effects of using tfcrop and compare.
# In CASA
plotms(vis='SN_G55_10s.ms', scan='30,75,120,165,190,235,303', spw='4~5,7~9',
antenna='ea24', xaxis='freq', yaxis='amp', plotrange=[-1,-1,0,4],
ydatacolumn='corrected', correlation='RR,LL', coloraxis='spw')
We can see that there are improvements, especially for SPW 9 which had some of the worst RFI remaining.
Now, let's inspect just scan 165 for all spectral windows.
# In CASA
plotms(vis='SN_G55_10s.ms', scan='165', spw='4~5,7~9', antenna='ea24',
xaxis='freq', yaxis='amp', plotrange=[-1,-1,-0.005, 0.03],
correlation='RR,LL', coloraxis='spw')
We can compare this post-tfcrop plot to the post-rflag plot, and see that there are slight improvements. We can also do as before, and calculate the percentage of flagged data.
# In CASA
flagInfo = flagdata(vis='SN_G55_10s.ms', mode='summary')
print("\n %2.1f%% of G55.7+3.4, %2.1f%% of 3C147, and %2.1f%% of J1925+2106 are flagged. \n" % (100.0 * flagInfo['field']['G55.7+3.4']['flagged'] / flagInfo['field']['G55.7+3.4']['total'], 100.0 * flagInfo['field']['0542+498=3C147']['flagged'] / flagInfo['field']['0542+498=3C147']['total'], 100.0 * flagInfo['field']['J1925+2106']['flagged'] / flagInfo['field']['J1925+2106']['total'])) <br />
print("Spectral windows are flagged as follows:")
for spw in range(2,10):
print("SPW %s: %2.1f%%" % (spw, 100.0 * flagInfo['spw'][str(spw)]['flagged'] / flagInfo['spw'][str(spw)]['total']))
We have flagged a little over 66% of the data from G66.7+3.4.
Before moving on, let's create a backup of our data using the flagmanager task as we did before.
# In CASA
flagmanager(vis='SN_G55_10s.ms', mode='save',
versionname='after_autoflagging_tfcrop_1')
We can now begin the task of calibrating our data, and imaging the supernova remnant in part 2.
-- original: ??
--modifications: Lorant Sjouwerman (4.4.0, 2015/07/07)
--modifications: Jose Salcido (4.5.1, 2016/02/16)
Last checked on CASA Version 4.5.1.
