NGC3256Band3 for CASA 3.3
Overview
[To be written by Eric]
Retrieving the Data
The data were taken in six different datasets over two consecutive nights: April 16-17, 2011. There are three datasets for April 16th and three for April 17th. Here we provide you with "starter" datasets, where we have taken the raw data in ALMA Science Data Model (ASDM) format and converted them to CASA Measurement Sets (MS). We did this using the importasdm task in CASA.
[What else are we going to do to the data we provide?]
Along with the Measurement Sets, we also provide the Tsys tables...[more]
You can download the data here: [Provide link to the raw .ms files in tar'd, gzip'd format]
Once the download has finished, unpack the file:
# In a terminal outside CASA
tar -xvf ngc3256band3.tgz
[Also provide links to the calibrated data (but maybe not here?)]
Initial inspection and a priori flagging
We start by defining an array 'basename' that includes the names of the six files used here. This will simplify the following steps by allowing us to loop through the files using a simple for-loop in python. The parameter numms is simply the number of measurement sets.
# In CASA
basename=["uid___A002_X1d54a1_X5","uid___A002_X1d54a1_X174","uid___A002_X1d54a1_X2e3","uid___A002_X1d5a20_X5","uid___A002_X1d5a20_X174","uid___A002_X1d5a20_X330"]
numms=len(basename)
The usual first step is then to get some basic information about the data. We do this using the task listobs, which will output a detailed summary of each dataset supplied.
# In CASA
for name in basename:
listobs(vis=name+'.ms')
The output will be sent to the CASA logger. You will have to scroll up to see the individual output for each of the six datasets. Here is an example of the most relevant output for the first file in the list.
Fields: 3 ID Code Name RA Decl Epoch SrcId nVis 0 none 1037-295 10:37:16.0790 -29.34.02.8130 J2000 0 38759 1 none Titan 00:00:00.0000 +00.00.00.0000 J2000 1 16016 2 none NGC3256 10:27:51.6000 -43.54.18.0000 J2000 2 151249 (nVis = Total number of time/baseline visibilities per field) Spectral Windows: (9 unique spectral windows and 2 unique polarization setups) SpwID #Chans Frame Ch1(MHz) ChanWid(kHz)TotBW(kHz) Ref(MHz) Corrs 0 4 TOPO 184550 1500000 7500000 183300 I 1 128 TOPO 113211.988 15625 2000000 113204.175 XX YY 2 1 TOPO 114188.55 1796875 1796875 113204.175 XX YY 3 128 TOPO 111450.813 15625 2000000 111443 XX YY 4 1 TOPO 112427.375 1796875 1796875 111443 XX YY 5 128 TOPO 101506.187 15625 2000000 101514 XX YY 6 1 TOPO 100498.375 1796875 1796875 101514 XX YY 7 128 TOPO 103050.863 15625 2000000 103058.675 XX YY 8 1 TOPO 102043.05 1796875 1796875 103058.675 XX YY Sources: 48 ID Name SpwId RestFreq(MHz) SysVel(km/s) 0 1037-295 0 - - 0 1037-295 9 - - 0 1037-295 10 - - 0 1037-295 11 - - 0 1037-295 12 - - 0 1037-295 13 - - 0 1037-295 14 - - 0 1037-295 15 - - 0 1037-295 1 - - 0 1037-295 2 - - 0 1037-295 3 - - 0 1037-295 4 - - 0 1037-295 5 - - 0 1037-295 6 - - 0 1037-295 7 - - 0 1037-295 8 - - 1 Titan 0 - - 1 Titan 9 - - 1 Titan 10 - - 1 Titan 11 - - 1 Titan 12 - - 1 Titan 13 - - 1 Titan 14 - - 1 Titan 15 - - 1 Titan 1 - - 1 Titan 2 - - 1 Titan 3 - - 1 Titan 4 - - 1 Titan 5 - - 1 Titan 6 - - 1 Titan 7 - - 1 Titan 8 - - 2 NGC3256 0 - - 2 NGC3256 9 - - 2 NGC3256 10 - - 2 NGC3256 11 - - 2 NGC3256 12 - - 2 NGC3256 13 - - 2 NGC3256 14 - - 2 NGC3256 15 - - 2 NGC3256 1 - - 2 NGC3256 2 - - 2 NGC3256 3 - - 2 NGC3256 4 - - 2 NGC3256 5 - - 2 NGC3256 6 - - 2 NGC3256 7 - - 2 NGC3256 8 - - Antennas: 7: ID Name Station Diam. Long. Lat. 0 DV04 J505 12.0 m -067.45.18.0 -22.53.22.8 1 DV06 T704 12.0 m -067.45.16.2 -22.53.22.1 2 DV07 J510 12.0 m -067.45.17.8 -22.53.23.5 3 DV08 T703 12.0 m -067.45.16.2 -22.53.23.9 4 DV09 N602 12.0 m -067.45.17.4 -22.53.22.3 5 PM02 T701 12.0 m -067.45.18.8 -22.53.22.2 6 PM03 J504 12.0 m -067.45.17.0 -22.53.23.0
[Talk about the structure of the dataset, including spw 0, and 2,4,6. Also point out that the position of Titan is 0,0, and we will have to fix this below.]
The first editing we will do is some a priori flagging. We will start by flagging the shadowed data and the autocorrelation data:
# In CASA
for name in basename:
flagdata(vis=name+".ms",flagbackup = F, mode = 'shadow')
flagautocorr(vis=name+".ms")
There are a number of scans in the data that were used by the online system for pointing calibration. These scans are no longer needed, and we can flag them easily by selecting on 'intent':
# In CASA
for name in basename:
flagdata(vis='ngc3256_line.ms', mode='manualflag', flagbackup = F, intent="*POINTING*")
Similarly, we can flag the scans corresponding to atmospheric calibration:
# In CASA
for name in basename:
flagdata(vis='ngc3256_line.ms', mode='manualflag', flagbackup = F, intent="*ATMOSPHERE*")
We will then store the current flagging state for each dataset using the flagmanager: [NOTE: I changed the versionname from 'Original' since that is created on import...]
# In CASA
for name in basename:
flagmanager(vis = name+'.ms', mode = 'save', versionname = 'Apriori')
[Martin: Is there any reason you sometimes use double quotes and sometimes single?]
Tsys calibration and WVR Correction
[Talk about what each thing does and how we currently must use the un-concatenated data for each.]
First we will inspect the Tsys tables:
# In CASA
for name in basename:
plotcal(caltable="tsys_"+name+".cal", xaxis="freq", yaxis="amp", spw="1,3,5,7", timerange="<2020", subplot=221, overplot=False, iteration="spw", plotrange=[0, 0, 40, 180], plotsymbol=".", figfile="tsys_per_spw"+name+".png")
Apply the Tsys values and WVR tables
# In CASA
for name in basename:
for msnumber in range (0,numms):
asdm=basename[msnumber]
for field in ['Titan','1037*','NGC*']:
applycal(vis=asdm+".ms", flagbackup=F, field=field, gainfield=field, spw='1,3,5,7',
interp='nearest', gaintable=['tsys_'+asdm+'.cal',asdm+'.W'])
Split out spectral windows 1,3,5,7
This will get rid of the channel average spws, and spw 0, which is
the one for the WVR data. Most importantly, it will remove the "WVR placeholder spws" that do not
show up in listobs, but are in the SPECTRAL_WINDOW table and can cause problems in concat and split
The WVR and Tsys tables are now applied in the DATA column:
# In CASA
for name in basename:
asdm=name
os.system('rm -rf '+asdm+'.ms*')
split(
vis=asdm+".ms",
outputvis=asdm+".ms",
datacolumn='corrected',
spw='1,3,5,7')
---More to come here---
[THESE FLAGGING COMMANDS WERE HIGHER UP, BUT I THINK THEY SHOULD BE MOVED BELOW CONCAT, MAYBE AFTER A BASIC PLOTMS] Antenna DV07 shows low amplitudes and strong phase wraps on the first day of observations. We flag this antenna for the data taken on April 16:
# In CASA
flagdata(
vis='ngc3256_line.ms',
mode='manualflag',
flagbackup = F,
antenna='DV07',
timerange='<2011/04/16/15:00:00')
Remove the noisy edge channels
# In CASA
flagdata(vis = 'ngc3256_line.ms', flagbackup = F, spw = ['*:0~10','*:125~127'])
Bandpass calibration
Before we do the bandpass calibration, we use gaincal to determine phase-only gaincal solutions for the bandpass calibrator, to correct for any phase variations with time. In these data, the phase calibrator and bandpass calibrator is the same source, so we just run this on 1037. For the solution interval we use solint='inf', which means that one gain solution will be determined for every scan. For our reference antenna, we choose PM03. The average of channels 40 to 80 is used to determine the antenna based phase solutions. The output calibration table is named "ngc3256.G1".
# In CASA
gaincal(
vis = 'ngc3256_line.ms', caltable = 'ngc3256.G1', spw = '*:40~80', field = '1037*',
selectdata=T, solint= 'inf', refant = 'PM03', calmode = 'p')
We check the time variations of the phases with plotcal. We make plot of the XX and YY polarization products separately and make different subplots for each of the spectal windows. This is donw by selecting iteration of 'spw' and subplot=221. and generate png plots
# In CASA
plotcal(
caltable = 'ngc3256.G1', xaxis = 'time', yaxis = 'phase',
poln='X', plotsymbol='o', plotrange = [0,0,-180,180], iteration = 'spw',
figfile='phase_vs_time_XX.G1.png', subplot = 221)
# In CASA
plotcal(
caltable = 'ngc3256.G1', xaxis = 'time', yaxis = 'phase',
poln='Y', plotsymbol='o', plotrange = [0,0,-180,180], iteration = 'spw',
figfile='phase_vs_time_YY.G1.png', subplot = 221)
Now that we have a first measuremnt of the phase variations as function of time, we can determine the bandpass solutions with bandpass, using the phase calibration table 'on-the-fly'.
First, plot the phase as a function of frequency for 1037. We use avgscan=T and avgtime='1E6' to average in time over all scans, and coloraxis='baseline' is used to colorize by baseline.
# In CASA
plotms(
vis='ngc3256_line.ms', xaxis='freq', yaxis='phase', selectdata=True,
field='1037*', avgtime='1E6', avgscan=T, coloraxis='baseline', iteraxis='antenna')
and the amplitudes
# In CASA
plotms(
vis='ngc3256_line.ms', xaxis='freq', yaxis='amp', selectdata=True, spw='*:10~120',
field='1037*', avgtime='1E6', avgscan=T, coloraxis='baseline', iteraxis='antenna')
# In CASA
bandpass(
vis = 'ngc3256_line.ms', caltable = 'ngc3256.B1', gaintable = 'ngc3256.G1',
field = '0', minblperant=3, minsnr=1, solint='inf',
bandtype='B', fillgaps=1, refant = 'DV10', solnorm = F)
