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F.2 Jupiter — VLA continuum polarization
This script demonstrates continuum polarization calibration and imaging, including self-calibration. There is also extensive interactive flagging, and image analysis.
The latest version of this script can be found at:
######################################################################
# #
# Use Case Script for Jupiter 6cm VLA #
# Trimmed down from Use Case jupiter6cm_usecase.py #
# #
# Updated STM 2008-05-15 (Beta Patch 2.0) #
# Updated STM 2008-06-11 (Beta Patch 2.0) #
# Updated STM 2008-06-12 (Beta Patch 2.0) for summer school demo #
# Updated STM 2008-06-13 (Beta Patch 2.0) make a bit faster #
# Updated STM 2008-12-24 (Beta Patch 3.0) extendflags #
# Updated STM 2009-05-29 (Beta Patch 4.0) for mcmaster tutorial #
# Updated STM 2009-12-20 (Release 0) for Version 3.0.0 #
# Revised JO 2010-04-13 (Release 3.0.1) edit viewer call #
# Checked JO 2010-10-07 (Release 3.1.0) #
# Updated JO 2012-05-10 (Release 3.4.0) #
# #
# This is a VLA 6cm dataset that was observed in 1999 to set the #
# flux scale for calibration of the VLA. Included in the program #
# were observations of the planets, including Jupiter. #
# #
# This is D-configuration data, with resolution of around 14" #
# #
# Includes polarization imaging and analysis #
# #
######################################################################
import time
import os
print "Jupiter 6cm Interactive Tutorial/Demo Script"
print "Version 2009-12-20 (Version 3.0.0 Release 0)"
print ""
#
#=====================================================================
#
# This script has some interactive commands: scriptmode = True
# if you are running it and want it to stop during interactive parts.
scriptmode = True
#=====================================================================
#
# Set up some useful variables - these will be set during the script
# also, but if you want to restart the script in the middle here
# they are in one place:
# This will prefix all output file names
prefix=’jupiter6cm.demo’
# Clean up old files
os.system(’rm -rf ’+prefix+’*’)
# This is the output MS file name
msfile = prefix + ’.ms’
#
#=====================================================================
# Calibration variables
#
# Use same prefix as rest of script
calprefix = prefix
# spectral windows to process
usespw = ’’
usespwlist = [’0’,’1’]
# prior calibration to apply
usegaincurve = True
gainopacity = 0.0
# reference antenna 11 (11=VLA:N1)
calrefant = ’11’
gtable = calprefix + ’.gcal’
ftable = calprefix + ’.fluxscale’
atable = calprefix + ’.accum’
#
#=====================================================================
# Polarization calibration setup
#
dopolcal = True
ptable = calprefix + ’.pcal’
xtable = calprefix + ’.polx’
# Pol leakage calibrator
poldfield = ’0137+331’
# Pol angle calibrator
polxfield = ’1331+305’
# At Cband the fractional polarization of this source is 0.112 and
# the R-L PhaseDiff = 66deg (EVPA = 33deg)
polxfpol = 0.112
polxrlpd_deg = 66.0
# Dictionary of IPOL in the spw
polxipol = {’0’ : 7.462,
’1’ : 7.510}
# Make Stokes lists for setjy
polxiquv = {}
for spw in [’0’,’1’]:
ipol = polxipol[spw]
fpol = polxfpol
ppol = ipol*fpol
rlpd = polxrlpd_deg*pi/180.0
qpol = ppol*cos(rlpd)
upol = ppol*sin(rlpd)
polxiquv[spw] = [ipol,qpol,upol,0.0]
#
# Split output setup
#
srcname = ’JUPITER’
srcsplitms = calprefix + ’.’ + srcname + ’.split.ms’
calname = ’0137+331’
calsplitms = calprefix + ’.’ + calname + ’.split.ms’
#
#=====================================================================
#
# Intensity imaging parameters
#
# Same prefix for this imaging demo output
#
imprefix = prefix
# This is D-config VLA 6cm (4.85GHz) obs
# Check the observational status summary
# Primary beam FWHM = 45’/f_GHz = 557"
# Synthesized beam FWHM = 14"
# RMS in 10min (600s) = 0.06 mJy (thats now, but close enough)
# Set the output image size and cell size (arcsec)
# 4" will give 3.5x oversampling
clncell = [4.,4.]
# 280 pix will cover to 2xPrimaryBeam
# clean will say to use 288 (a composite integer) for efficiency
clnalg = ’clark’
clnmode = ’’
# For Cotton-Schwab use
clnmode = ’csclean’
clnimsize = [288,288]
# iterations
clniter = 10000
# Also set flux residual threshold (0.04 mJy)
# From our listobs:
# Total integration time = 85133.2 seconds
# With rms of 0.06 mJy in 600s ==> rms = 0.005 mJy
# Set to 10x thermal rms
clnthreshold=0.05
#
# Filenames
#
imname1 = imprefix + ’.clean1’
clnimage1 = imname1+’.image’
clnmodel1 = imname1+’.model’
clnresid1 = imname1+’.residual’
clnmask1 = imname1+’.clean_interactive.mask’
imname2 = imprefix + ’.clean2’
clnimage2 = imname2+’.image’
clnmodel2 = imname2+’.model’
clnresid2 = imname2+’.residual’
clnmask2 = imname2+’.clean_interactive.mask’
imname3 = imprefix + ’.clean3’
clnimage3 = imname3+’.image’
clnmodel3 = imname3+’.model’
clnresid3 = imname3+’.residual’
clnmask3 = imname3+’.clean_interactive.mask’
#
# Selfcal parameters
#
# reference antenna 11 (11=VLA:N1)
calrefant = ’11’
#
# Filenames
#
selfcaltab1 = imprefix + ’.selfcal1.gtable’
selfcaltab2 = imprefix + ’.selfcal2.gtable’
smoothcaltab2 = imprefix + ’.smoothcal2.gtable’
#
#=====================================================================
#
# Polarization imaging parameters
#
# New prefix for polarization imaging output
#
polprefix = prefix + ’.polimg’
# Set up clean slightly differently
polclnalg = ’hogbom’
polclnmode = ’csclean’
polimname = polprefix + ’.clean’
polimage = polimname+’.image’
polmodel = polimname+’.model’
polresid = polimname+’.residual’
polmask = polimname+’.clean_interactive.mask’
#
# Other files
#
ipolimage = polimage+’.I’
qpolimage = polimage+’.Q’
upolimage = polimage+’.U’
poliimage = polimage+’.poli’
polaimage = polimage+’.pola’
#
#=====================================================================
#=====================================================================
# Start processing
#=====================================================================
#
# Get to path to the CASA home and stip off the name
pathname=os.environ.get(’CASAPATH’).split()[0]
# This is where the UVFITS data should be
#fitsdata=pathname+’/data/demo/jupiter6cm.fits’
# Or
#fitsdata=pathname+’/data/nrao/VLA/planets_6cm.fits’
#fitsdata=’/home/ballista/casa/devel/data/nrao/VLA/planets_6cm.fits’
#
# Can also be found online at
#http://casa.nrao.edu/Data/VLA/Planets6cm/planets_6cm.fits
# Use version in current directory
fitsdata=’planets_6cm.fits’
#
#=====================================================================
# Data Import and List
#=====================================================================
#
# Import the data from FITS to MS
#
print ’--Import--’
# Safest to start from task defaults
default(’importuvfits’)
print "Use importuvfits to read UVFITS and make an MS"
# Set up the MS filename and save as new global variable
msfile = prefix + ’.ms’
print "MS will be called "+msfile
# Use task importuvfits
fitsfile = fitsdata
vis = msfile
importuvfits()
#=====================================================================
#
# List a summary of the MS
#
print ’--Listobs--’
# Don’t default this one and make use of the previous setting of
# vis. Remember, the variables are GLOBAL!
print "Use listobs to print verbose summary to logger"
# You may wish to see more detailed information, in this case
# use the verbose = True option
verbose = True
listobs()
# You should get in your logger window and in the casapy.log file
# something like:
#
# Observer: FLUX99 Project:
# Observation: VLA
#
# Data records: 2021424 Total integration time = 85133.2 seconds
# Observed from 23:15:27 to 22:54:20
#
# ObservationID = 0 ArrayID = 0
# Date Timerange Scan FldId FieldName SpwIds
# 15-Apr-1999/23:15:26.7 - 23:16:10.0 1 0 0137+331 [0, 1]
# 23:38:40.0 - 23:48:00.0 2 1 0813+482 [0, 1]
# 23:53:40.0 - 23:55:20.0 3 2 0542+498 [0, 1]
# 16-Apr-1999/00:22:10.1 - 00:23:49.9 4 3 0437+296 [0, 1]
# 00:28:23.3 - 00:30:00.1 5 4 VENUS [0, 1]
# 00:48:40.0 - 00:50:20.0 6 1 0813+482 [0, 1]
# 00:56:13.4 - 00:57:49.9 7 2 0542+498 [0, 1]
# 01:10:20.1 - 01:11:59.9 8 5 0521+166 [0, 1]
# 01:23:29.9 - 01:25:00.1 9 3 0437+296 [0, 1]
# 01:29:33.3 - 01:31:10.0 10 4 VENUS [0, 1]
# 01:49:50.0 - 01:51:30.0 11 6 1411+522 [0, 1]
# 02:03:00.0 - 02:04:30.0 12 7 1331+305 [0, 1]
# 02:17:30.0 - 02:19:10.0 13 1 0813+482 [0, 1]
# 02:24:20.0 - 02:26:00.0 14 2 0542+498 [0, 1]
# 02:37:49.9 - 02:39:30.0 15 5 0521+166 [0, 1]
# 02:50:50.1 - 02:52:20.1 16 3 0437+296 [0, 1]
# 02:59:20.0 - 03:01:00.0 17 6 1411+522 [0, 1]
# 03:12:30.0 - 03:14:10.0 18 7 1331+305 [0, 1]
# 03:27:53.3 - 03:29:39.9 19 1 0813+482 [0, 1]
# 03:35:00.0 - 03:36:40.0 20 2 0542+498 [0, 1]
# 03:49:50.0 - 03:51:30.1 21 6 1411+522 [0, 1]
# 04:03:10.0 - 04:04:50.0 22 7 1331+305 [0, 1]
# 04:18:49.9 - 04:20:40.0 23 1 0813+482 [0, 1]
# 04:25:56.6 - 04:27:39.9 24 2 0542+498 [0, 1]
# 04:42:49.9 - 04:44:40.0 25 8 MARS [0, 1]
# 04:56:50.0 - 04:58:30.1 26 6 1411+522 [0, 1]
# 05:24:03.3 - 05:33:39.9 27 7 1331+305 [0, 1]
# 05:48:00.0 - 05:49:49.9 28 1 0813+482 [0, 1]
# 05:58:36.6 - 06:00:30.0 29 8 MARS [0, 1]
# 06:13:20.1 - 06:14:59.9 30 6 1411+522 [0, 1]
# 06:27:40.0 - 06:29:20.0 31 7 1331+305 [0, 1]
# 06:44:13.4 - 06:46:00.0 32 1 0813+482 [0, 1]
# 06:55:06.6 - 06:57:00.0 33 8 MARS [0, 1]
# 07:10:40.0 - 07:12:20.0 34 6 1411+522 [0, 1]
# 07:28:20.0 - 07:30:10.1 35 7 1331+305 [0, 1]
# 07:42:49.9 - 07:44:30.0 36 8 MARS [0, 1]
# 07:58:43.3 - 08:00:39.9 37 6 1411+522 [0, 1]
# 08:13:30.0 - 08:15:19.9 38 7 1331+305 [0, 1]
# 08:27:53.4 - 08:29:30.0 39 8 MARS [0, 1]
# 08:42:59.9 - 08:44:50.0 40 6 1411+522 [0, 1]
# 08:57:09.9 - 08:58:50.0 41 7 1331+305 [0, 1]
# 09:13:03.3 - 09:14:50.1 42 9 NGC7027 [0, 1]
# 09:26:59.9 - 09:28:40.0 43 6 1411+522 [0, 1]
# 09:40:33.4 - 09:42:09.9 44 7 1331+305 [0, 1]
# 09:56:19.9 - 09:58:10.0 45 9 NGC7027 [0, 1]
# 10:12:59.9 - 10:14:50.0 46 8 MARS [0, 1]
# 10:27:09.9 - 10:28:50.0 47 6 1411+522 [0, 1]
# 10:40:30.0 - 10:42:00.0 48 7 1331+305 [0, 1]
# 10:56:10.0 - 10:57:50.0 49 9 NGC7027 [0, 1]
# 11:28:30.0 - 11:35:30.0 50 10 NEPTUNE [0, 1]
# 11:48:20.0 - 11:50:10.0 51 6 1411+522 [0, 1]
# 12:01:36.7 - 12:03:10.0 52 7 1331+305 [0, 1]
# 12:35:33.3 - 12:37:40.0 53 11 URANUS [0, 1]
# 12:46:30.0 - 12:48:10.0 54 10 NEPTUNE [0, 1]
# 13:00:29.9 - 13:02:10.0 55 6 1411+522 [0, 1]
# 13:15:23.3 - 13:17:10.1 56 9 NGC7027 [0, 1]
# 13:33:43.3 - 13:35:40.0 57 11 URANUS [0, 1]
# 13:44:30.0 - 13:46:10.0 58 10 NEPTUNE [0, 1]
# 14:00:46.7 - 14:01:39.9 59 0 0137+331 [0, 1]
# 14:10:40.0 - 14:12:09.9 60 12 JUPITER [0, 1]
# 14:24:06.6 - 14:25:40.1 61 11 URANUS [0, 1]
# 14:34:30.0 - 14:36:10.1 62 10 NEPTUNE [0, 1]
# 14:59:13.4 - 15:00:00.0 63 0 0137+331 [0, 1]
# 15:09:03.3 - 15:10:40.1 64 12 JUPITER [0, 1]
# 15:24:30.0 - 15:26:20.1 65 9 NGC7027 [0, 1]
# 15:40:10.0 - 15:45:00.0 66 11 URANUS [0, 1]
# 15:53:50.0 - 15:55:20.0 67 10 NEPTUNE [0, 1]
# 16:18:53.4 - 16:19:49.9 68 0 0137+331 [0, 1]
# 16:29:10.1 - 16:30:49.9 69 12 JUPITER [0, 1]
# 16:42:53.4 - 16:44:30.0 70 11 URANUS [0, 1]
# 16:54:53.4 - 16:56:40.0 71 9 NGC7027 [0, 1]
# 17:23:06.6 - 17:30:40.0 72 2 0542+498 [0, 1]
# 17:41:50.0 - 17:43:20.0 73 3 0437+296 [0, 1]
# 17:55:36.7 - 17:57:39.9 74 4 VENUS [0, 1]
# 18:19:23.3 - 18:20:09.9 75 0 0137+331 [0, 1]
# 18:30:23.3 - 18:32:00.0 76 12 JUPITER [0, 1]
# 18:44:49.9 - 18:46:30.0 77 9 NGC7027 [0, 1]
# 18:59:13.3 - 19:00:59.9 78 2 0542+498 [0, 1]
# 19:19:10.0 - 19:21:20.1 79 5 0521+166 [0, 1]
# 19:32:50.1 - 19:34:29.9 80 3 0437+296 [0, 1]
# 19:39:03.3 - 19:40:40.1 81 4 VENUS [0, 1]
# 20:08:06.7 - 20:08:59.9 82 0 0137+331 [0, 1]
# 20:18:10.0 - 20:19:50.0 83 12 JUPITER [0, 1]
# 20:33:53.3 - 20:35:40.1 84 1 0813+482 [0, 1]
# 20:40:59.9 - 20:42:40.0 85 2 0542+498 [0, 1]
# 21:00:16.6 - 21:02:20.1 86 5 0521+166 [0, 1]
# 21:13:53.4 - 21:15:29.9 87 3 0437+296 [0, 1]
# 21:20:43.4 - 21:22:30.0 88 4 VENUS [0, 1]
# 21:47:26.7 - 21:48:20.1 89 0 0137+331 [0, 1]
# 21:57:30.0 - 21:59:10.0 90 12 JUPITER [0, 1]
# 22:12:13.3 - 22:14:00.1 91 2 0542+498 [0, 1]
# 22:28:33.3 - 22:30:19.9 92 4 VENUS [0, 1]
# 22:53:33.3 - 22:54:19.9 93 0 0137+331 [0, 1]
#
# Fields: 13
# ID Name Right Ascension Declination Epoch
# 0 0137+331 01:37:41.30 +33.09.35.13 J2000
# 1 0813+482 08:13:36.05 +48.13.02.26 J2000
# 2 0542+498 05:42:36.14 +49.51.07.23 J2000
# 3 0437+296 04:37:04.17 +29.40.15.14 J2000
# 4 VENUS 04:06:54.11 +22.30.35.91 J2000
# 5 0521+166 05:21:09.89 +16.38.22.05 J2000
# 6 1411+522 14:11:20.65 +52.12.09.14 J2000
# 7 1331+305 13:31:08.29 +30.30.32.96 J2000
# 8 MARS 14:21:41.37 -12.21.49.45 J2000
# 9 NGC7027 21:07:01.59 +42.14.10.19 J2000
# 10 NEPTUNE 20:26:01.14 -18.54.54.21 J2000
# 11 URANUS 21:15:42.83 -16.35.05.59 J2000
# 12 JUPITER 00:55:34.04 +04.45.44.71 J2000
#
# Spectral Windows: (2 unique spectral windows and 1 unique polarization setups)
# SpwID #Chans Frame Ch1(MHz) Resoln(kHz) TotBW(kHz) Ref(MHz) Corrs
# 0 1 TOPO 4885.1 50000 50000 4885.1 RR RL LR LL
# 1 1 TOPO 4835.1 50000 50000 4835.1 RR RL LR LL
#
# Feeds: 28: printing first row only
# Antenna Spectral Window # Receptors Polarizations
# 1 -1 2 [ R, L]
#
# Antennas: 27:
# ID Name Station Diam. Long. Lat.
# 0 1 VLA:W9 25.0 m -107.37.25.1 +33.53.51.0
# 1 2 VLA:N9 25.0 m -107.37.07.8 +33.54.19.0
# 2 3 VLA:N3 25.0 m -107.37.06.3 +33.54.04.8
# 3 4 VLA:N5 25.0 m -107.37.06.7 +33.54.08.0
# 4 5 VLA:N2 25.0 m -107.37.06.2 +33.54.03.5
# 5 6 VLA:E1 25.0 m -107.37.05.7 +33.53.59.2
# 6 7 VLA:E2 25.0 m -107.37.04.4 +33.54.01.1
# 7 8 VLA:N8 25.0 m -107.37.07.5 +33.54.15.8
# 8 9 VLA:E8 25.0 m -107.36.48.9 +33.53.55.1
# 9 10 VLA:W3 25.0 m -107.37.08.9 +33.54.00.1
# 10 11 VLA:N1 25.0 m -107.37.06.0 +33.54.01.8
# 11 12 VLA:E6 25.0 m -107.36.55.6 +33.53.57.7
# 12 13 VLA:W7 25.0 m -107.37.18.4 +33.53.54.8
# 13 14 VLA:E4 25.0 m -107.37.00.8 +33.53.59.7
# 14 15 VLA:N7 25.0 m -107.37.07.2 +33.54.12.9
# 15 16 VLA:W4 25.0 m -107.37.10.8 +33.53.59.1
# 16 17 VLA:W5 25.0 m -107.37.13.0 +33.53.57.8
# 17 18 VLA:N6 25.0 m -107.37.06.9 +33.54.10.3
# 18 19 VLA:E7 25.0 m -107.36.52.4 +33.53.56.5
# 19 20 VLA:E9 25.0 m -107.36.45.1 +33.53.53.6
# 21 22 VLA:W8 25.0 m -107.37.21.6 +33.53.53.0
# 22 23 VLA:W6 25.0 m -107.37.15.6 +33.53.56.4
# 23 24 VLA:W1 25.0 m -107.37.05.9 +33.54.00.5
# 24 25 VLA:W2 25.0 m -107.37.07.4 +33.54.00.9
# 25 26 VLA:E5 25.0 m -107.36.58.4 +33.53.58.8
# 26 27 VLA:N4 25.0 m -107.37.06.5 +33.54.06.1
# 27 28 VLA:E3 25.0 m -107.37.02.8 +33.54.00.5
#
# Tables:
# MAIN 2021424 rows
# ANTENNA 28 rows
# DATA_DESCRIPTION 2 rows
# DOPPLER <absent>
# FEED 28 rows
# FIELD 13 rows
# FLAG_CMD <empty>
# FREQ_OFFSET <absent>
# HISTORY 7058 rows
# OBSERVATION 1 row
# POINTING 2604 rows
# POLARIZATION 1 row
# PROCESSOR <empty>
# SOURCE <empty> (see FIELD)
# SPECTRAL_WINDOW 2 rows
# STATE <empty>
# SYSCAL <absent>
# WEATHER <absent>
#
#=====================================================================
# Data Examination and Flagging
#=====================================================================
#
# Use Plotms to interactively flag the data
#
print ’--Plotms--’
default(’plotms’)
print "Now we use plotxy to examine and interactively flag data"
vis = msfile
# The fields we are interested in: 1331+305,JUPITER,0137+331
selectdata = True
# First we do the primary calibrator
field = ’1331+305’
# Plot only the RR and LL for now
correlation = ’RR LL’
# Plot amplitude vs. uvdist
xaxis = ’uvdist’
yaxis = ’amp’
plotms()
print ""
print "-----------------------------------------------------"
print "Plotms"
print "Showing 1331+305 RR LL for all antennas"
print "Use MarkRegion then draw boxes around points to flag"
print "You can use ESC to drop last drawn box"
print "When happy with boxes, hit Flag to flag"
print "You can repeat as necessary"
print ""
#print "NOTE: These flags will extend to the RL LR cross-hands"
#print "Because of this the flagging will be slower than otherwise"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# You can also use flagdata to do this non-interactively
# (see below)
# Now look at the cross-polar products
correlation = ’RL LR’
plotms()
print ""
print "-----------------------------------------------------"
print "Looking at RL LR"
print "Now flag any remaining bad data here"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
#---------------------------------------------------------------------
# Now do calibrater 0137+331
field = ’0137+331’
correlation = ’RR LL’
xaxis = ’uvdist’
spw = ’’
iteration = ’’
antenna = ’’
# As of 2.3.0 (Patch 3) you can extend the flags to the cross-correlations
# But this slows things down immensely
#extendflag = T
#extendcorr = ’all’
title = field+" "
plotms()
# You’ll see a bunch of bad data along the bottom near zero amp
# Draw a box around some of it and use Locate
# Looks like much of it is Antenna 9 (ID=8) in spw=1
print ""
print "-----------------------------------------------------"
print "Plotting 0137+331 RR LL all antennas"
print "You see bad data along bottom"
print "Mark a box around a bit of it and hit Locate"
print "Look in logger to see what it is"
print "You see much is Antenna 9 (ID=8) in spw 1"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
xaxis = ’time’
spw = ’1’
correlation = ’’
extendflag = F
# Note that the strings like antenna=’9’ first try to match the
# NAME which we see in listobs was the number ’9’ for ID=8.
# So be careful here (why naming antennas as numbers is bad).
antenna = ’9’
plotms()
# YES! the last 4 scans are bad. Box ’em and flag.
print ""
print "-----------------------------------------------------"
print "Plotting vs. time antenna=’9’ and spw=’1’ "
print "Box up last 4 scans which are bad and Flag"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# Go back and clean up
xaxis = ’uvdist’
spw = ’’
antenna = ’’
correlation = ’RR LL’
# Note that RL,LR are too weak to clip on.
# As of 2.3.0 (Patch 3) you can extend the flags to the cross-correlations
# But this slows things down immensely
#extendflag = T
#extendcorr = ’all’
plotms()
# Box up the bad low points (basically a clip below 0.52) and flag
print ""
print "-----------------------------------------------------"
print "Back to all data"
print "Clean up remaining bad points"
print ""
#print "NOTE: These flags will extend to the RL LR cross-hands"
#print "Because of this the flagging will be slower than otherwise"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
#---------------------------------------------------------------------
# Finally, do JUPITER
field = ’JUPITER’
correlation = ’RR LL’
iteration = ’’
xaxis = ’uvdist’
plotms()
# Here you will see that the final scan at 22:00:00 UT is bad
# Draw a box around it and flag it!
print ""
print "-----------------------------------------------------"
print "Now plot JUPITER versus uvdist"
print "Lots of bad stuff near bottom"
print "Lets go and find it - try Locate"
print "Looks like lots of different antennas but at same time"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
correlation = ’’
xaxis = ’time’
plotms()
# Here you will see that the final scan at 22:00:00 UT is bad
# Draw a box around it and flag it!
print ""
print "-----------------------------------------------------"
print "Now plotting vs. time"
print "See bad scan at end - flag it!"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# Now look at whats left
correlation = ’RR LL’
xaxis = ’uvdist’
spw = ’1’
antenna = ’’
plotms()
# As you step through, you will see that Antenna 9 (ID=8) is often
# bad in this spw. If you box and do Locate (or remember from
# 0137+331) its probably a bad time.
print ""
print "-----------------------------------------------------"
print "Looking now at SPW 1"
print "Now we set iteration to Antenna"
print "Step through antennas with Next"
print "See bad Antenna 9 (ID 8) as in 0137+331"
print "Do not flag yet, we will isolate this next"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# The easiset way to kill it:
antenna = ’9’
xaxis = ’time’
correlation = ’’
plotms()
# Draw a box around all points in the last bad scans and flag ’em!
print ""
print "-----------------------------------------------------"
print "Now plotting vs. time antenna 9 spw 1"
print "Box up the bad scans and Flag"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# Now clean up the rest
xaxis = ’uvdist’
correlation = ’RR LL’
antenna = ’’
spw = ’’
# You will be drawing many tiny boxes, so remember you can
# use the ESC key to get rid of the most recent box if you
# make a mistake.
plotms()
# Note that the end result is we’ve flagged lots of points
# in RR and LL. We will rely upon imager to ignore the
# RL LR for points with RR LL flagged!
print ""
print "-----------------------------------------------------"
print "Final cleanup of JUPITER data"
print "Back to uvdist plot, see remaining bad data"
print "You can draw little boxes around the outliers and Flag"
print "Depends how patient you are in drawing boxes!"
print "Could also use Locate to find where they come from"
print ""
#print "NOTE: These flags will extend to the RL LR cross-hands"
#print "Because of this the flagging will be slower than otherwise"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
print "Done with plotms!"
#
#=====================================================================
#
# Use Flagmanager to save a copy of the flags so far
#
print ’--Flagmanager--’
default(’flagmanager’)
print "Now will use flagmanager to save a copy of the flags we just made"
print "These are named xyflags"
vis = msfile
mode = ’save’
versionname = ’xyflags’
comment = ’Plotms flags’
merge = ’replace’
flagmanager()
#=====================================================================
#
# Use Flagmanager to list all saved versions
#
print ’--Flagmanager--’
default(’flagmanager’)
print "Now will use flagmanager to list all the versions we saved"
vis = msfile
mode = ’list’
flagmanager()
#
# Done Flagging
print ’--Done with flagging--’
#
#=====================================================================
# Calibration
#=====================================================================
#
# Set the fluxes of the primary calibrator(s)
#
print ’--Setjy--’
default(’setjy’)
print "Use setjy to set flux of 1331+305 (3C286)"
vis = msfile
#
# 1331+305 = 3C286 is our primary calibrator
field = ’1331+305’
# Setjy knows about this source so we dont need anything more
setjy()
#
# You should see something like this in the logger and casapy.log file:
#
# 1331+305 spwid= 0 [I=7.462, Q=0, U=0, V=0] Jy, (Perley-Taylor 99)
# 1331+305 spwid= 1 [I=7.51, Q=0, U=0, V=0] Jy, (Perley-Taylor 99)
#
print "Look in logger for the fluxes (should be 7.462 and 7.510 Jy)"
#
#=====================================================================
#
# Initial gain calibration
#
print ’--Gaincal--’
default(’gaincal’)
print "Solve for antenna gains on 1331+305 and 0137+331"
print "We have 2 single-channel continuum spw"
print "Do not want bandpass calibration"
vis = msfile
# set the name for the output gain caltable
caltable = gtable
print "Output gain cal table will be "+gtable
# Gain calibrators are 1331+305 and 0137+331 (FIELD_ID 7 and 0)
# We have 2 IFs (SPW 0,1) with one channel each
# selection is via the field and spw strings
field = ’1331+305,0137+331’
spw = ’’
# a-priori calibration application
gaincurve = usegaincurve
opacity = gainopacity
# scan-based G solutions for both amplitude and phase
gaintype = ’G’
calmode = ’ap’
# one solution per scan
solint = ’inf’
combine = ’’
# do not apply parallactic angle correction (yet)
parang = False
# reference antenna
refant = calrefant
# minimum SNR 3
minsnr = 3
gaincal()
#
#=====================================================================
#
# Bootstrap flux scale
#
print ’--Fluxscale--’
default(’fluxscale’)
print "Use fluxscale to rescale gain table to make new one"
vis = msfile
# set the name for the output rescaled caltable
fluxtable = ftable
print "Output scaled gain cal table is "+ftable
# point to our first gain cal table
caltable = gtable
# we will be using 1331+305 (the source we did setjy on) as
# our flux standard reference
reference = ’1331+305’
# we want to transfer the flux to our other gain cal source 0137+331
# to bring its gain amplitues in line with the absolute scale
transfer = ’0137+331’
fluxscale()
# You should see in the logger something like:
#Flux density for 0137+331 in SpW=0 is:
# 5.42575 +/- 0.00285011 (SNR = 1903.7, nAnt= 27)
#Flux density for 0137+331 in SpW=1 is:
# 5.46569 +/- 0.00301326 (SNR = 1813.88, nAnt= 27)
#
#---------------------------------------------------------------------
# Plot calibration
#
print ’--PlotCal--’
default(’plotcal’)
showgui = True
caltable = ftable
multiplot = True
yaxis = ’amp’
showgui = True
plotcal()
print ""
print "-------------------------------------------------"
print "Plotcal"
print "Looking at amplitude in cal-table "+caltable
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
#
# Now go back and plot to file
#
showgui = False
yaxis = ’amp’
#figfile = caltable + ’.plotcal.amp.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable.plotcal.amp.saved’)
#plotcal()
yaxis = ’phase’
#figfile = caltable + ’.plotcal.phase.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable.plotcal.phase.saved’)
#plotcal()
#
#=====================================================================
# Polarization Calibration
#=====================================================================
#
if (dopolcal):
print ’--Polcal (D)--’
default(’polcal’)
print "Solve for polarization leakage on 0137+331"
print "Pretend it has unknown polarization"
vis = msfile
# Start with the un-fluxscaled gain table
gaintable = gtable
# use settings from gaincal
gaincurve = usegaincurve
opacity = gainopacity
# Output table
caltable = ptable
# Use a 3C48 tracked through a range of PA
field = ’0137+331’
spw = ’’
# No need for further selection
selectdata=False
# Polcal mode (D+QU = unknown pol for D)
poltype = ’D+QU’
# One solution for entire dataset
solint = ’inf’
combine = ’scan’
# reference antenna
refant = calrefant
# minimum SNR 3
minsnr = 3
#saveinputs(’polcal’,calprefix+’.polcal.saved’)
polcal()
#=====================================================================
#
# List polcal solutions
#
print ’--Listcal (PolD)--’
listfile = caltable + ’.list’
print "Listing calibration to file "+listfile
listcal()
#=====================================================================
#
# Plot polcal solutions
#
print ’--Plotcal (PolD)--’
iteration = ’’
showgui = False
xaxis = ’antenna’
yaxis = ’amp’
showgui = True
figfile = ’’
plotcal()
print "These are the amplitudes of D-terms versus antenna"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# Now plot to files
showgui = False
#figfile = caltable + ’.plotcal.antamp.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable+’.plotcal.antamp.saved’)
#plotcal()
xaxis = ’antenna’
yaxis = ’phase’
#figfile = caltable + ’.plotcal.antphase.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable+’.plotcal.antphase.saved’)
#plotcal()
xaxis = ’antenna’
yaxis = ’snr’
#figfile = caltable + ’.plotcal.antsnr.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable+’.plotcal.antsnr.saved’)
#plotcal()
xaxis = ’real’
yaxis = ’imag’
#figfile = caltable + ’.plotcal.reim.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable+’.plotcal.reim.saved’)
#plotcal()
#=====================================================================
# Do Chi (X) pol angle calibration
#=====================================================================
# First set the model
print ’--Setjy--’
default(’setjy’)
vis = msfile
print "Use setjy to set IQU fluxes of "+polxfield
field = polxfield
for spw in usespwlist:
fluxdensity = polxiquv[spw]
#saveinputs(’setjy’,calprefix+’.setjy.polspw.’+spw+’.saved’)
setjy()
#
# Polarization (X-term) calibration
#
print ’--PolCal (X)--’
default(’polcal’)
print "Polarization R-L Phase Calibration (linear approx)"
vis = msfile
# Start with the G and D tables
gaintable = [gtable,ptable]
# use settings from gaincal
gaincurve = usegaincurve
opacity = gainopacity
# Output table
caltable = xtable
# previously set with setjy
field = polxfield
spw = ’’
selectdata=False
# Solve for Chi
poltype = ’X’
solint = ’inf’
combine = ’scan’
# reference antenna
refant = calrefant
# minimum SNR 3
minsnr = 3
#saveinputs(’polcal’,calprefix+’.polcal.X.saved’)
polcal()
#=====================================================================
# Apply the Calibration
#=====================================================================
#
# Interpolate the gains onto Jupiter (and others)
#
# print ’--Accum--’
# default(’accum’)
#
# print "This will interpolate the gains onto Jupiter"
#
# vis = msfile
#
# tablein = ’’
# incrtable = ftable
# calfield = ’1331+305, 0137+331’
#
# # set the name for the output interpolated caltable
# caltable = atable
#
# print "Output cumulative gain table will be "+atable
#
# # linear interpolation
# interp = ’linear’
#
# # make 10s entries
# accumtime = 10.0
#
# accum()
#
# NOTE: bypassing this during testing
atable = ftable
# #=====================================================================
#
# Correct the data
# (This will put calibrated data into the CORRECTED_DATA column)
#
print ’--ApplyCal--’
default(’applycal’)
print "This will apply the calibration to the DATA"
print "Fills CORRECTED_DATA"
vis = msfile
# Start with the interpolated fluxscale/gain table
gaintable = [atable,ptable,xtable]
# use settings from gaincal
gaincurve = usegaincurve
opacity = gainopacity
# select the fields
field = ’1331+305,0137+331,JUPITER’
spw = ’’
selectdata = False
# IMPORTANT set parang=True for polarization
parang = True
# do not need to select subset since we did accum
# (note that correct only does ’nearest’ interp)
gainfield = ’’
applycal()
#
#=====================================================================
#
# Now split the Jupiter target data
#
print ’--Split Jupiter--’
default(’split’)
vis = msfile
# Now we write out the corrected data to a new MS
# Select the Jupiter field
field = srcname
spw = ’’
# pick off the CORRECTED_DATA column
datacolumn = ’corrected’
# Make an output vis file
outputvis = srcsplitms
print "Split "+field+" data into new ms "+srcsplitms
split()
# Also split out 0137+331 as a check
field = calname
outputvis = calsplitms
print "Split "+field+" data into new ms "+calsplitms
split()
#=====================================================================
# Use Plotms to look at the split calibrated data
#
print ’Calibration completed’
#
#=====================================================================
#
# Intensity Imaging/Selfcal
#
#=====================================================================
# FIRST CLEAN / SELFCAL CYCLE
#=====================================================================
#
# Now clean an image of Jupiter
# NOTE: this uses the new combined invert/clean/mosaic task Patch 2
#
print ’--Clean 1--’
default(’clean’)
# Pick up our split source data
vis = srcsplitms
# Make an image root file name
imagename = imname1
print "Output images will be prefixed with "+imname1
# Set up the output continuum image (single plane mfs)
mode = ’mfs’
stokes = ’I’
print "Will be a single MFS continuum image"
# NOTE: current version field=’’ doesnt work
field = ’*’
# Combine all spw
spw = ’’
# Imaging mode params
psfmode = clnalg
imagermode = clnmode
# Imsize and cell
imsize = clnimsize
cell = clncell
# NOTE: will eventually have an imadvise task to give you this
# information
# Standard gain factor 0.1
gain = 0.1
# Fix maximum number of iterations and threshold
niter = clniter
threshold = clnthreshold
# Note - we can change niter and threshold interactively
# during clean
# Set up the weighting
# Use Briggs weighting (a moderate value, on the uniform side)
weighting = ’briggs’
robust = 0.5
# No clean mask or box
mask = ’’
# Use interactive clean mode
interactive = True
# We will be doing selfcal
calready = True
# Moderate number of iter per interactive cycle
npercycle = 100
saveinputs(’clean’,imagename+’.clean.saved’)
clean()
# When the interactive clean window comes up, use the right-mouse
# to draw rectangles around obvious emission double-right-clicking
# inside them to add to the flag region. You can also assign the
# right-mouse to polygon region drawing by right-clicking on the
# polygon drawing icon in the toolbar. When you are happy with
# the region, click ’Done Flagging’ and it will go and clean another
# 100 iterations. When done, click ’Stop’.
print ""
print "----------------------------------------------------"
print "Clean"
print "Final clean model is "+clnmodel1
print "Final restored clean image is "+clnimage1
print "The clean residual image is "+clnresid1
print "Your final clean mask is "+clnmask1
print ""
print "This is the final restored clean image in the viewer"
print "Zoom in and set levels to see faint emission"
print "Use rectangle drawing tool to box off source"
print "Double-click inside to print statistics"
print "Move box on-source and get the max"
print "Calcualte DynRange = MAXon/RMSoff"
print "I got 1.060/0.004 = 270"
print "Still not as good as it can be - lets selfcal"
print "Close viewer panel when done"
#
#---------------------------------------------------------------------
#
# If you did not do interactive clean, bring up viewer manually
viewer(clnimage1)
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# You can use the right-mouse to draw a box in the lower right
# corner of the image away from emission, the double-click inside
# to bring up statistics. Use the right-mouse to grab this box
# and move it up over Jupiter and double-click again. You should
# see stuff like this in the terminal:
#
# jupiter6cm.demo.clean1.image (Jy/beam)
#
# n Std Dev RMS Mean Variance Sum
# 4712 0.003914 0.003927 0.0003205 1.532e-05 1.510
#
# Flux Med |Dev| IntQtlRng Median Min Max
# 0.09417 0.002646 0.005294 0.0001885 -0.01125 0.01503
#
#
# On Jupiter:
#
# n Std Dev RMS Mean Variance Sum
# 3640 0.1007 0.1027 0.02023 0.01015 73.63
#
# Flux Med |Dev| IntQtlRng Median Min Max
# 4.592 0.003239 0.007120 0.0001329 -0.01396 1.060
#
# Estimated dynamic range = 1.060 / 0.003927 = 270 (poor)
#
# Note that the exact numbers you get will depend on how deep you
# take the interactive clean and how you draw the box for the stats.
#=====================================================================
#
# Do some non-interactive image statistics
print ’--Imstat--’
default(’imstat’)
imagename = clnimage1
on_statistics1 = imstat()
# Now do stats in the lower right corner of the image
# remember clnimsize = [288,288]
box = ’216,1,287,72’
off_statistics1 = imstat()
# Pull the max and rms from the clean image
thistest_immax=on_statistics1[’max’][0]
print ’ Found : Max in image = ’,thistest_immax
thistest_imrms=off_statistics1[’rms’][0]
print ’ Found : rms in image = ’,thistest_imrms
print ’ Clean image Dynamic Range = ’,thistest_immax/thistest_imrms
print ’’
#
#---------------------------------------------------------------------
#
# Self-cal using clean model
#
# Note: clean will have left FT of model in the MODEL_DATA column
# If you’ve done something in between, can use the ft task to
# do this manually.
#
print ’--SelfCal 1--’
default(’gaincal’)
vis = srcsplitms
print "Will self-cal using MODEL_DATA left in MS by clean"
# New gain table
caltable = selfcaltab1
print "Will write gain table "+selfcaltab1
# Don’t need a-priori cals
selectdata = False
gaincurve = False
opacity = 0.0
# This choice seemed to work
refant = calrefant
# Do amp and phase
gaintype = ’G’
calmode = ’ap’
# Do 30s solutions with SNR>1
solint = 30.0
minsnr = 1.0
print "Calibrating amplitudes and phases on 30s timescale"
# Do not need to normalize (let gains float)
solnorm = False
gaincal()
#
#---------------------------------------------------------------------
# It is useful to put this up in plotcal
#
#
print ’--PlotCal--’
default(’plotcal’)
caltable = selfcaltab1
multiplot = True
yaxis = ’amp’
plotcal()
print ""
print "-------------------------------------------------"
print "Plotcal"
print "Looking at amplitude in self-cal table "+caltable
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
yaxis = ’phase’
plotcal()
print ""
print "-------------------------------------------------"
print "Plotcal"
print "Looking at phases in self-cal table "+caltable
#
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
#
#---------------------------------------------------------------------
#
# Correct the data (no need for interpolation this stage)
#
print ’--ApplyCal--’
default(’applycal’)
vis = srcsplitms
print "Will apply self-cal table to over-write CORRECTED_DATA in MS"
gaintable = selfcaltab1
gaincurve = False
opacity = 0.0
field = ’’
spw = ’’
selectdata = False
calwt = True
applycal()
# Self-cal is now in CORRECTED_DATA column of split ms
#
#=====================================================================
# SECOND CLEAN / SELFCAL CYCLE
#=====================================================================
#
print ’--Clean 2--’
default(’clean’)
print "Now clean on self-calibrated data"
vis = srcsplitms
imagename = imname2
field = ’*’
spw = ’’
mode = ’mfs’
gain = 0.1
# Imaging mode params
psfmode = clnalg
imagermode = clnmode
imsize = clnimsize
cell = clncell
niter = clniter
threshold = clnthreshold
weighting = ’briggs’
robust = 0.5
mask = ’’
interactive = True
npercycle = 100
calready = True
saveinputs(’clean’,imagename+’.clean.saved’)
clean()
print ""
print "----------------------------------------------------"
print "Clean"
print "Final clean model is "+clnmodel2
print "Final restored clean image is "+clnimage2
print "The clean residual image is "+clnresid2
print "Your final clean mask is "+clnmask2
print ""
print "This is the final restored clean image in the viewer"
print "Zoom in and set levels to see faint emission"
print "Use rectangle drawing tool to box off source"
print "Double-click inside to print statistics"
print "Move box on-source and get the max"
print "Calculate DynRange = MAXon/RMSoff"
print "This time I got 1.050 / 0.001 = 1050 (better)"
print "Still not as good as it can be - you can try selfcal again"
print "We will stop here"
print "Close viewer panel when done"
#
#---------------------------------------------------------------------
#
# If you did not do interactive clean, bring up viewer manually
viewer(clnimage2)
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# jupiter6cm.demo.clean2.image (Jy/beam)
#
# n Std Dev RMS Mean Variance Sum
# 5236 0.001389 0.001390 3.244e-05 1.930e-06 0.1699
#
# Flux Med |Dev| IntQtlRng Median Min Max
# 0.01060 0.0009064 0.001823 -1.884e-05 -0.004015 0.004892
#
#
# On Jupiter:
#
# n Std Dev RMS Mean Variance Sum
# 5304 0.08512 0.08629 0.01418 0.007245 75.21
#
# Flux Med |Dev| IntQtlRng Median Min Max
# 4.695 0.0008142 0.001657 0.0001557 -0.004526 1.076
#
# Estimated dynamic range = 1.076 / 0.001389 = 775 (better)
#
# Note that the exact numbers you get will depend on how deep you
# take the interactive clean and how you draw the box for the stats.
#
print ""
print "--------------------------------------------------"
print "After this script is done you can continue on with"
print "more self-cal, or try different cleaning options"
#
#=====================================================================
# Image Analysis
#=====================================================================
#
# Can do some image statistics if you wish
print ’--Imstat (Cycle 2)--’
default(’imstat’)
imagename = clnimage2
on_statistics2 = imstat()
# Now do stats in the lower right corner of the image
# remember clnimsize = [288,288]
box = ’216,1,287,72’
off_statistics2 = imstat()
# Pull the max and rms from the clean image
thistest_immax=on_statistics2[’max’][0]
print ’ Found : Max in image = ’,thistest_immax
thistest_imrms=off_statistics2[’rms’][0]
print ’ Found : rms in image = ’,thistest_imrms
print ’ Clean image Dynamic Range = ’,thistest_immax/thistest_imrms
print ’’
#=====================================================================
#
# Print results and regression versus previous runs
#
print ""
print ’ Final Jupiter results ’
print ’ ===================== ’
print ’’
# Pull the max and rms from the clean image
thistest_immax=on_statistics2[’max’][0]
oldtest_immax = 1.07732224464
print ’ Clean image ON-SRC max = ’,thistest_immax
print ’ Previously found to be = ’,oldtest_immax
diff_immax = abs((oldtest_immax-thistest_immax)/oldtest_immax)
print ’ Difference (fractional) = ’,diff_immax
print ’’
thistest_imrms=off_statistics2[’rms’][0]
oldtest_imrms = 0.0010449
print ’ Clean image OFF-SRC rms = ’,thistest_imrms
print ’ Previously found to be = ’,oldtest_imrms
diff_imrms = abs((oldtest_imrms-thistest_imrms)/oldtest_imrms)
print ’ Difference (fractional) = ’,diff_imrms
print ’’
print ’ Final Clean image Dynamic Range = ’,thistest_immax/thistest_imrms
print ’’
print ’--- Done with I Imaging and Selfcal---’
#
#=====================================================================
# Polarization Imaging
#=====================================================================
#
print ’--Clean (Polarization)--’
default(’clean’)
print "Now clean polarized data"
vis = srcsplitms
imagename = polimname
field = ’*’
spw = ’’
mode = ’mfs’
gain = 0.1
# Polarization
stokes = ’IQUV’
psfmode = polclnalg
imagermode = polclnmode
niter = clniter
threshold = clnthreshold
imsize = clnimsize
cell = clncell
weighting = ’briggs’
robust = 0.5
interactive = True
npercycle = 100
saveinputs(’clean’,imagename+’.clean.saved’)
clean()
print ""
print "----------------------------------------------------"
print "Clean"
print "Final restored clean image is "+polimage
print "Final clean model is "+polmodel
print "The clean residual image is "+polresid
print "Your final clean mask is "+polmask
#
#=====================================================================
# Image Analysis
#=====================================================================
#
# Polarization statistics
print ’--Final Pol Imstat--’
default(’imstat’)
imagename = polimage
on_statistics = {}
off_statistics = {}
# lower right corner of the image (clnimsize = [288,288])
onbox = ’’
# lower right corner of the image (clnimsize = [288,288])
offbox = ’216,1,287,72’
for stokes in [’I’,’Q’,’U’,’V’]:
box = onbox
on_statistics[stokes] = imstat()
box = offbox
off_statistics[stokes] = imstat()
#
# Peel off some Q and U planes
#
print ’--Immath--’
default(’immath’)
mode = ’evalexpr’
stokes = ’I’
outfile = ipolimage
imagename = polimage
expr = ’IM0’
immath()
print "Created I image "+outfile
stokes = ’Q’
outfile = qpolimage
immath()
print "Created Q image "+outfile
stokes = ’U’
outfile = upolimage
immath()
print "Created U image "+outfile
#
#---------------------------------------------------------------------
# Now make POLI and POLA images
#
# Use our rms above for debiasing
mysigma = 0.5*( off_statistics[’Q’][’rms’][0] + off_statistics[’U’][’rms’][0] )
#sigma = str(mysigma)+’Jy/beam’
# This does not work well yet
sigma = ’0.0Jy/beam’
#stokes = ’’
#imagename = [qpolimage,upolimage]
#outfile = poliimage
#mode = ’poli’
#immath()
#
#outfile = polaimage
#mode = ’pola’
#immath()
#Call as function instead:
immath(imagename=[qpolimage,upolimage],mode=’poli’,outfile=poliimage,sigma=’0.0Jy/beam’)
print "Created POLI image "+poliimage
immath(imagename=[qpolimage,upolimage],mode=’pola’,outfile=polaimage,sigma=’0.0Jy/beam’)
print "Created POLA image "+polaimage
#
#---------------------------------------------------------------------
# Save statistics of these images
default(’imstat’)
imagename = poliimage
stokes = ’’
box = onbox
on_statistics[’POLI’] = imstat()
box = offbox
off_statistics[’POLI’] = imstat()
#
#
#---------------------------------------------------------------------
# Display clean I image in viewer but with polarization vectors
#
# If you did not do interactive clean, bring up viewer manually
viewer(polimage)
print "Displaying pol I now. You should overlay pola vectors"
print "Bring up the Load Data panel:"
print ""
print "Use LEL for POLA VECTOR with cut above 6*mysigma in POLI = "+str(6*mysigma)
print "For example:"
print "\’"+polaimage+"\’[\’"+poliimage+"\’>0.0048]"
print ""
print "In the Data Display Options for the vector plot:"
print " Set the x,y increments to 2 (default is 3)"
print " Use an extra rotation this 90deg to get B field"
print "Note the lengths are all equal. You can fiddle these."
print ""
print "You can also load the poli image as contours"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# NOTE: the LEL will be something like
# ’jupiter6cm.demo.polimg.clean.image.pola’[’jupiter6cm.demo.polimg.clean.image.poli’>0.005]
#
# NOTE: The viewer can take complex images to make Vector plots, although
# the image analysis tasks (and ia tool) cannot yet handle these. But we
# can use the imagepol tool (which is not imported by default) to make
# a complex image of the linear polarized intensity for display.
# See CASA User Reference Manual:
# http://casa.nrao.edu/docs/casaref/imagepol-Tool.html
#
# Make an imagepol tool and open the clean image
potool = casac.homefinder.find_home_by_name(’imagepolHome’)
po = potool.create()
po.open(polimage)
# Use complexlinpol to make a Q+iU image
complexlinpolimage = polimname + ’.cmplxlinpol’
po.complexlinpol(complexlinpolimage)
po.close()
# You can now display this in the viewer, in particular overlay this
# over the intensity raster with the poli contours. The vector lengths
# will be proportional to the polarized intensity. You can play with
# the Data Display Options panel for vector spacing and length.
# You will want to have this masked, like the pola image above, on
# the polarized intensity. When you load the image, use the LEL:
# ’jupiter6cm.demo.polimg.clean.cmplxlinpol’[’jupiter6cm.demo.polimg.clean.image.poli’>0.005]
#=====================================================================
#
# Print results
#
print ""
print ’ Jupiter polarization results ’
print ’ ============================ ’
print ’’
for stokes in [’I’,’Q’,’U’,’V’,’POLI’]:
print ’’
print ’ =============== ’
print ’’
print ’ Polarization (Stokes ’+stokes+’):’
mymax = on_statistics[stokes][’max’][0]
mymin = on_statistics[stokes][’min’][0]
myrms = off_statistics[stokes][’rms’][0]
absmax = max(mymax,mymin)
mydra = absmax/myrms
print ’ Clean image ON-SRC max = ’,mymax
print ’ Clean image ON-SRC min = ’,mymin
print ’ Clean image OFF-SRC rms = ’,myrms
print ’ Clean image dynamic rng = ’,mydra
print ’--- Done ---’
#
#=====================================================================
# #
# Use Case Script for Jupiter 6cm VLA #
# Trimmed down from Use Case jupiter6cm_usecase.py #
# #
# Updated STM 2008-05-15 (Beta Patch 2.0) #
# Updated STM 2008-06-11 (Beta Patch 2.0) #
# Updated STM 2008-06-12 (Beta Patch 2.0) for summer school demo #
# Updated STM 2008-06-13 (Beta Patch 2.0) make a bit faster #
# Updated STM 2008-12-24 (Beta Patch 3.0) extendflags #
# Updated STM 2009-05-29 (Beta Patch 4.0) for mcmaster tutorial #
# Updated STM 2009-12-20 (Release 0) for Version 3.0.0 #
# Revised JO 2010-04-13 (Release 3.0.1) edit viewer call #
# Checked JO 2010-10-07 (Release 3.1.0) #
# Updated JO 2012-05-10 (Release 3.4.0) #
# #
# This is a VLA 6cm dataset that was observed in 1999 to set the #
# flux scale for calibration of the VLA. Included in the program #
# were observations of the planets, including Jupiter. #
# #
# This is D-configuration data, with resolution of around 14" #
# #
# Includes polarization imaging and analysis #
# #
######################################################################
import time
import os
print "Jupiter 6cm Interactive Tutorial/Demo Script"
print "Version 2009-12-20 (Version 3.0.0 Release 0)"
print ""
#
#=====================================================================
#
# This script has some interactive commands: scriptmode = True
# if you are running it and want it to stop during interactive parts.
scriptmode = True
#=====================================================================
#
# Set up some useful variables - these will be set during the script
# also, but if you want to restart the script in the middle here
# they are in one place:
# This will prefix all output file names
prefix=’jupiter6cm.demo’
# Clean up old files
os.system(’rm -rf ’+prefix+’*’)
# This is the output MS file name
msfile = prefix + ’.ms’
#
#=====================================================================
# Calibration variables
#
# Use same prefix as rest of script
calprefix = prefix
# spectral windows to process
usespw = ’’
usespwlist = [’0’,’1’]
# prior calibration to apply
usegaincurve = True
gainopacity = 0.0
# reference antenna 11 (11=VLA:N1)
calrefant = ’11’
gtable = calprefix + ’.gcal’
ftable = calprefix + ’.fluxscale’
atable = calprefix + ’.accum’
#
#=====================================================================
# Polarization calibration setup
#
dopolcal = True
ptable = calprefix + ’.pcal’
xtable = calprefix + ’.polx’
# Pol leakage calibrator
poldfield = ’0137+331’
# Pol angle calibrator
polxfield = ’1331+305’
# At Cband the fractional polarization of this source is 0.112 and
# the R-L PhaseDiff = 66deg (EVPA = 33deg)
polxfpol = 0.112
polxrlpd_deg = 66.0
# Dictionary of IPOL in the spw
polxipol = {’0’ : 7.462,
’1’ : 7.510}
# Make Stokes lists for setjy
polxiquv = {}
for spw in [’0’,’1’]:
ipol = polxipol[spw]
fpol = polxfpol
ppol = ipol*fpol
rlpd = polxrlpd_deg*pi/180.0
qpol = ppol*cos(rlpd)
upol = ppol*sin(rlpd)
polxiquv[spw] = [ipol,qpol,upol,0.0]
#
# Split output setup
#
srcname = ’JUPITER’
srcsplitms = calprefix + ’.’ + srcname + ’.split.ms’
calname = ’0137+331’
calsplitms = calprefix + ’.’ + calname + ’.split.ms’
#
#=====================================================================
#
# Intensity imaging parameters
#
# Same prefix for this imaging demo output
#
imprefix = prefix
# This is D-config VLA 6cm (4.85GHz) obs
# Check the observational status summary
# Primary beam FWHM = 45’/f_GHz = 557"
# Synthesized beam FWHM = 14"
# RMS in 10min (600s) = 0.06 mJy (thats now, but close enough)
# Set the output image size and cell size (arcsec)
# 4" will give 3.5x oversampling
clncell = [4.,4.]
# 280 pix will cover to 2xPrimaryBeam
# clean will say to use 288 (a composite integer) for efficiency
clnalg = ’clark’
clnmode = ’’
# For Cotton-Schwab use
clnmode = ’csclean’
clnimsize = [288,288]
# iterations
clniter = 10000
# Also set flux residual threshold (0.04 mJy)
# From our listobs:
# Total integration time = 85133.2 seconds
# With rms of 0.06 mJy in 600s ==> rms = 0.005 mJy
# Set to 10x thermal rms
clnthreshold=0.05
#
# Filenames
#
imname1 = imprefix + ’.clean1’
clnimage1 = imname1+’.image’
clnmodel1 = imname1+’.model’
clnresid1 = imname1+’.residual’
clnmask1 = imname1+’.clean_interactive.mask’
imname2 = imprefix + ’.clean2’
clnimage2 = imname2+’.image’
clnmodel2 = imname2+’.model’
clnresid2 = imname2+’.residual’
clnmask2 = imname2+’.clean_interactive.mask’
imname3 = imprefix + ’.clean3’
clnimage3 = imname3+’.image’
clnmodel3 = imname3+’.model’
clnresid3 = imname3+’.residual’
clnmask3 = imname3+’.clean_interactive.mask’
#
# Selfcal parameters
#
# reference antenna 11 (11=VLA:N1)
calrefant = ’11’
#
# Filenames
#
selfcaltab1 = imprefix + ’.selfcal1.gtable’
selfcaltab2 = imprefix + ’.selfcal2.gtable’
smoothcaltab2 = imprefix + ’.smoothcal2.gtable’
#
#=====================================================================
#
# Polarization imaging parameters
#
# New prefix for polarization imaging output
#
polprefix = prefix + ’.polimg’
# Set up clean slightly differently
polclnalg = ’hogbom’
polclnmode = ’csclean’
polimname = polprefix + ’.clean’
polimage = polimname+’.image’
polmodel = polimname+’.model’
polresid = polimname+’.residual’
polmask = polimname+’.clean_interactive.mask’
#
# Other files
#
ipolimage = polimage+’.I’
qpolimage = polimage+’.Q’
upolimage = polimage+’.U’
poliimage = polimage+’.poli’
polaimage = polimage+’.pola’
#
#=====================================================================
#=====================================================================
# Start processing
#=====================================================================
#
# Get to path to the CASA home and stip off the name
pathname=os.environ.get(’CASAPATH’).split()[0]
# This is where the UVFITS data should be
#fitsdata=pathname+’/data/demo/jupiter6cm.fits’
# Or
#fitsdata=pathname+’/data/nrao/VLA/planets_6cm.fits’
#fitsdata=’/home/ballista/casa/devel/data/nrao/VLA/planets_6cm.fits’
#
# Can also be found online at
#http://casa.nrao.edu/Data/VLA/Planets6cm/planets_6cm.fits
# Use version in current directory
fitsdata=’planets_6cm.fits’
#
#=====================================================================
# Data Import and List
#=====================================================================
#
# Import the data from FITS to MS
#
print ’--Import--’
# Safest to start from task defaults
default(’importuvfits’)
print "Use importuvfits to read UVFITS and make an MS"
# Set up the MS filename and save as new global variable
msfile = prefix + ’.ms’
print "MS will be called "+msfile
# Use task importuvfits
fitsfile = fitsdata
vis = msfile
importuvfits()
#=====================================================================
#
# List a summary of the MS
#
print ’--Listobs--’
# Don’t default this one and make use of the previous setting of
# vis. Remember, the variables are GLOBAL!
print "Use listobs to print verbose summary to logger"
# You may wish to see more detailed information, in this case
# use the verbose = True option
verbose = True
listobs()
# You should get in your logger window and in the casapy.log file
# something like:
#
# Observer: FLUX99 Project:
# Observation: VLA
#
# Data records: 2021424 Total integration time = 85133.2 seconds
# Observed from 23:15:27 to 22:54:20
#
# ObservationID = 0 ArrayID = 0
# Date Timerange Scan FldId FieldName SpwIds
# 15-Apr-1999/23:15:26.7 - 23:16:10.0 1 0 0137+331 [0, 1]
# 23:38:40.0 - 23:48:00.0 2 1 0813+482 [0, 1]
# 23:53:40.0 - 23:55:20.0 3 2 0542+498 [0, 1]
# 16-Apr-1999/00:22:10.1 - 00:23:49.9 4 3 0437+296 [0, 1]
# 00:28:23.3 - 00:30:00.1 5 4 VENUS [0, 1]
# 00:48:40.0 - 00:50:20.0 6 1 0813+482 [0, 1]
# 00:56:13.4 - 00:57:49.9 7 2 0542+498 [0, 1]
# 01:10:20.1 - 01:11:59.9 8 5 0521+166 [0, 1]
# 01:23:29.9 - 01:25:00.1 9 3 0437+296 [0, 1]
# 01:29:33.3 - 01:31:10.0 10 4 VENUS [0, 1]
# 01:49:50.0 - 01:51:30.0 11 6 1411+522 [0, 1]
# 02:03:00.0 - 02:04:30.0 12 7 1331+305 [0, 1]
# 02:17:30.0 - 02:19:10.0 13 1 0813+482 [0, 1]
# 02:24:20.0 - 02:26:00.0 14 2 0542+498 [0, 1]
# 02:37:49.9 - 02:39:30.0 15 5 0521+166 [0, 1]
# 02:50:50.1 - 02:52:20.1 16 3 0437+296 [0, 1]
# 02:59:20.0 - 03:01:00.0 17 6 1411+522 [0, 1]
# 03:12:30.0 - 03:14:10.0 18 7 1331+305 [0, 1]
# 03:27:53.3 - 03:29:39.9 19 1 0813+482 [0, 1]
# 03:35:00.0 - 03:36:40.0 20 2 0542+498 [0, 1]
# 03:49:50.0 - 03:51:30.1 21 6 1411+522 [0, 1]
# 04:03:10.0 - 04:04:50.0 22 7 1331+305 [0, 1]
# 04:18:49.9 - 04:20:40.0 23 1 0813+482 [0, 1]
# 04:25:56.6 - 04:27:39.9 24 2 0542+498 [0, 1]
# 04:42:49.9 - 04:44:40.0 25 8 MARS [0, 1]
# 04:56:50.0 - 04:58:30.1 26 6 1411+522 [0, 1]
# 05:24:03.3 - 05:33:39.9 27 7 1331+305 [0, 1]
# 05:48:00.0 - 05:49:49.9 28 1 0813+482 [0, 1]
# 05:58:36.6 - 06:00:30.0 29 8 MARS [0, 1]
# 06:13:20.1 - 06:14:59.9 30 6 1411+522 [0, 1]
# 06:27:40.0 - 06:29:20.0 31 7 1331+305 [0, 1]
# 06:44:13.4 - 06:46:00.0 32 1 0813+482 [0, 1]
# 06:55:06.6 - 06:57:00.0 33 8 MARS [0, 1]
# 07:10:40.0 - 07:12:20.0 34 6 1411+522 [0, 1]
# 07:28:20.0 - 07:30:10.1 35 7 1331+305 [0, 1]
# 07:42:49.9 - 07:44:30.0 36 8 MARS [0, 1]
# 07:58:43.3 - 08:00:39.9 37 6 1411+522 [0, 1]
# 08:13:30.0 - 08:15:19.9 38 7 1331+305 [0, 1]
# 08:27:53.4 - 08:29:30.0 39 8 MARS [0, 1]
# 08:42:59.9 - 08:44:50.0 40 6 1411+522 [0, 1]
# 08:57:09.9 - 08:58:50.0 41 7 1331+305 [0, 1]
# 09:13:03.3 - 09:14:50.1 42 9 NGC7027 [0, 1]
# 09:26:59.9 - 09:28:40.0 43 6 1411+522 [0, 1]
# 09:40:33.4 - 09:42:09.9 44 7 1331+305 [0, 1]
# 09:56:19.9 - 09:58:10.0 45 9 NGC7027 [0, 1]
# 10:12:59.9 - 10:14:50.0 46 8 MARS [0, 1]
# 10:27:09.9 - 10:28:50.0 47 6 1411+522 [0, 1]
# 10:40:30.0 - 10:42:00.0 48 7 1331+305 [0, 1]
# 10:56:10.0 - 10:57:50.0 49 9 NGC7027 [0, 1]
# 11:28:30.0 - 11:35:30.0 50 10 NEPTUNE [0, 1]
# 11:48:20.0 - 11:50:10.0 51 6 1411+522 [0, 1]
# 12:01:36.7 - 12:03:10.0 52 7 1331+305 [0, 1]
# 12:35:33.3 - 12:37:40.0 53 11 URANUS [0, 1]
# 12:46:30.0 - 12:48:10.0 54 10 NEPTUNE [0, 1]
# 13:00:29.9 - 13:02:10.0 55 6 1411+522 [0, 1]
# 13:15:23.3 - 13:17:10.1 56 9 NGC7027 [0, 1]
# 13:33:43.3 - 13:35:40.0 57 11 URANUS [0, 1]
# 13:44:30.0 - 13:46:10.0 58 10 NEPTUNE [0, 1]
# 14:00:46.7 - 14:01:39.9 59 0 0137+331 [0, 1]
# 14:10:40.0 - 14:12:09.9 60 12 JUPITER [0, 1]
# 14:24:06.6 - 14:25:40.1 61 11 URANUS [0, 1]
# 14:34:30.0 - 14:36:10.1 62 10 NEPTUNE [0, 1]
# 14:59:13.4 - 15:00:00.0 63 0 0137+331 [0, 1]
# 15:09:03.3 - 15:10:40.1 64 12 JUPITER [0, 1]
# 15:24:30.0 - 15:26:20.1 65 9 NGC7027 [0, 1]
# 15:40:10.0 - 15:45:00.0 66 11 URANUS [0, 1]
# 15:53:50.0 - 15:55:20.0 67 10 NEPTUNE [0, 1]
# 16:18:53.4 - 16:19:49.9 68 0 0137+331 [0, 1]
# 16:29:10.1 - 16:30:49.9 69 12 JUPITER [0, 1]
# 16:42:53.4 - 16:44:30.0 70 11 URANUS [0, 1]
# 16:54:53.4 - 16:56:40.0 71 9 NGC7027 [0, 1]
# 17:23:06.6 - 17:30:40.0 72 2 0542+498 [0, 1]
# 17:41:50.0 - 17:43:20.0 73 3 0437+296 [0, 1]
# 17:55:36.7 - 17:57:39.9 74 4 VENUS [0, 1]
# 18:19:23.3 - 18:20:09.9 75 0 0137+331 [0, 1]
# 18:30:23.3 - 18:32:00.0 76 12 JUPITER [0, 1]
# 18:44:49.9 - 18:46:30.0 77 9 NGC7027 [0, 1]
# 18:59:13.3 - 19:00:59.9 78 2 0542+498 [0, 1]
# 19:19:10.0 - 19:21:20.1 79 5 0521+166 [0, 1]
# 19:32:50.1 - 19:34:29.9 80 3 0437+296 [0, 1]
# 19:39:03.3 - 19:40:40.1 81 4 VENUS [0, 1]
# 20:08:06.7 - 20:08:59.9 82 0 0137+331 [0, 1]
# 20:18:10.0 - 20:19:50.0 83 12 JUPITER [0, 1]
# 20:33:53.3 - 20:35:40.1 84 1 0813+482 [0, 1]
# 20:40:59.9 - 20:42:40.0 85 2 0542+498 [0, 1]
# 21:00:16.6 - 21:02:20.1 86 5 0521+166 [0, 1]
# 21:13:53.4 - 21:15:29.9 87 3 0437+296 [0, 1]
# 21:20:43.4 - 21:22:30.0 88 4 VENUS [0, 1]
# 21:47:26.7 - 21:48:20.1 89 0 0137+331 [0, 1]
# 21:57:30.0 - 21:59:10.0 90 12 JUPITER [0, 1]
# 22:12:13.3 - 22:14:00.1 91 2 0542+498 [0, 1]
# 22:28:33.3 - 22:30:19.9 92 4 VENUS [0, 1]
# 22:53:33.3 - 22:54:19.9 93 0 0137+331 [0, 1]
#
# Fields: 13
# ID Name Right Ascension Declination Epoch
# 0 0137+331 01:37:41.30 +33.09.35.13 J2000
# 1 0813+482 08:13:36.05 +48.13.02.26 J2000
# 2 0542+498 05:42:36.14 +49.51.07.23 J2000
# 3 0437+296 04:37:04.17 +29.40.15.14 J2000
# 4 VENUS 04:06:54.11 +22.30.35.91 J2000
# 5 0521+166 05:21:09.89 +16.38.22.05 J2000
# 6 1411+522 14:11:20.65 +52.12.09.14 J2000
# 7 1331+305 13:31:08.29 +30.30.32.96 J2000
# 8 MARS 14:21:41.37 -12.21.49.45 J2000
# 9 NGC7027 21:07:01.59 +42.14.10.19 J2000
# 10 NEPTUNE 20:26:01.14 -18.54.54.21 J2000
# 11 URANUS 21:15:42.83 -16.35.05.59 J2000
# 12 JUPITER 00:55:34.04 +04.45.44.71 J2000
#
# Spectral Windows: (2 unique spectral windows and 1 unique polarization setups)
# SpwID #Chans Frame Ch1(MHz) Resoln(kHz) TotBW(kHz) Ref(MHz) Corrs
# 0 1 TOPO 4885.1 50000 50000 4885.1 RR RL LR LL
# 1 1 TOPO 4835.1 50000 50000 4835.1 RR RL LR LL
#
# Feeds: 28: printing first row only
# Antenna Spectral Window # Receptors Polarizations
# 1 -1 2 [ R, L]
#
# Antennas: 27:
# ID Name Station Diam. Long. Lat.
# 0 1 VLA:W9 25.0 m -107.37.25.1 +33.53.51.0
# 1 2 VLA:N9 25.0 m -107.37.07.8 +33.54.19.0
# 2 3 VLA:N3 25.0 m -107.37.06.3 +33.54.04.8
# 3 4 VLA:N5 25.0 m -107.37.06.7 +33.54.08.0
# 4 5 VLA:N2 25.0 m -107.37.06.2 +33.54.03.5
# 5 6 VLA:E1 25.0 m -107.37.05.7 +33.53.59.2
# 6 7 VLA:E2 25.0 m -107.37.04.4 +33.54.01.1
# 7 8 VLA:N8 25.0 m -107.37.07.5 +33.54.15.8
# 8 9 VLA:E8 25.0 m -107.36.48.9 +33.53.55.1
# 9 10 VLA:W3 25.0 m -107.37.08.9 +33.54.00.1
# 10 11 VLA:N1 25.0 m -107.37.06.0 +33.54.01.8
# 11 12 VLA:E6 25.0 m -107.36.55.6 +33.53.57.7
# 12 13 VLA:W7 25.0 m -107.37.18.4 +33.53.54.8
# 13 14 VLA:E4 25.0 m -107.37.00.8 +33.53.59.7
# 14 15 VLA:N7 25.0 m -107.37.07.2 +33.54.12.9
# 15 16 VLA:W4 25.0 m -107.37.10.8 +33.53.59.1
# 16 17 VLA:W5 25.0 m -107.37.13.0 +33.53.57.8
# 17 18 VLA:N6 25.0 m -107.37.06.9 +33.54.10.3
# 18 19 VLA:E7 25.0 m -107.36.52.4 +33.53.56.5
# 19 20 VLA:E9 25.0 m -107.36.45.1 +33.53.53.6
# 21 22 VLA:W8 25.0 m -107.37.21.6 +33.53.53.0
# 22 23 VLA:W6 25.0 m -107.37.15.6 +33.53.56.4
# 23 24 VLA:W1 25.0 m -107.37.05.9 +33.54.00.5
# 24 25 VLA:W2 25.0 m -107.37.07.4 +33.54.00.9
# 25 26 VLA:E5 25.0 m -107.36.58.4 +33.53.58.8
# 26 27 VLA:N4 25.0 m -107.37.06.5 +33.54.06.1
# 27 28 VLA:E3 25.0 m -107.37.02.8 +33.54.00.5
#
# Tables:
# MAIN 2021424 rows
# ANTENNA 28 rows
# DATA_DESCRIPTION 2 rows
# DOPPLER <absent>
# FEED 28 rows
# FIELD 13 rows
# FLAG_CMD <empty>
# FREQ_OFFSET <absent>
# HISTORY 7058 rows
# OBSERVATION 1 row
# POINTING 2604 rows
# POLARIZATION 1 row
# PROCESSOR <empty>
# SOURCE <empty> (see FIELD)
# SPECTRAL_WINDOW 2 rows
# STATE <empty>
# SYSCAL <absent>
# WEATHER <absent>
#
#=====================================================================
# Data Examination and Flagging
#=====================================================================
#
# Use Plotms to interactively flag the data
#
print ’--Plotms--’
default(’plotms’)
print "Now we use plotxy to examine and interactively flag data"
vis = msfile
# The fields we are interested in: 1331+305,JUPITER,0137+331
selectdata = True
# First we do the primary calibrator
field = ’1331+305’
# Plot only the RR and LL for now
correlation = ’RR LL’
# Plot amplitude vs. uvdist
xaxis = ’uvdist’
yaxis = ’amp’
plotms()
print ""
print "-----------------------------------------------------"
print "Plotms"
print "Showing 1331+305 RR LL for all antennas"
print "Use MarkRegion then draw boxes around points to flag"
print "You can use ESC to drop last drawn box"
print "When happy with boxes, hit Flag to flag"
print "You can repeat as necessary"
print ""
#print "NOTE: These flags will extend to the RL LR cross-hands"
#print "Because of this the flagging will be slower than otherwise"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# You can also use flagdata to do this non-interactively
# (see below)
# Now look at the cross-polar products
correlation = ’RL LR’
plotms()
print ""
print "-----------------------------------------------------"
print "Looking at RL LR"
print "Now flag any remaining bad data here"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
#---------------------------------------------------------------------
# Now do calibrater 0137+331
field = ’0137+331’
correlation = ’RR LL’
xaxis = ’uvdist’
spw = ’’
iteration = ’’
antenna = ’’
# As of 2.3.0 (Patch 3) you can extend the flags to the cross-correlations
# But this slows things down immensely
#extendflag = T
#extendcorr = ’all’
title = field+" "
plotms()
# You’ll see a bunch of bad data along the bottom near zero amp
# Draw a box around some of it and use Locate
# Looks like much of it is Antenna 9 (ID=8) in spw=1
print ""
print "-----------------------------------------------------"
print "Plotting 0137+331 RR LL all antennas"
print "You see bad data along bottom"
print "Mark a box around a bit of it and hit Locate"
print "Look in logger to see what it is"
print "You see much is Antenna 9 (ID=8) in spw 1"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
xaxis = ’time’
spw = ’1’
correlation = ’’
extendflag = F
# Note that the strings like antenna=’9’ first try to match the
# NAME which we see in listobs was the number ’9’ for ID=8.
# So be careful here (why naming antennas as numbers is bad).
antenna = ’9’
plotms()
# YES! the last 4 scans are bad. Box ’em and flag.
print ""
print "-----------------------------------------------------"
print "Plotting vs. time antenna=’9’ and spw=’1’ "
print "Box up last 4 scans which are bad and Flag"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# Go back and clean up
xaxis = ’uvdist’
spw = ’’
antenna = ’’
correlation = ’RR LL’
# Note that RL,LR are too weak to clip on.
# As of 2.3.0 (Patch 3) you can extend the flags to the cross-correlations
# But this slows things down immensely
#extendflag = T
#extendcorr = ’all’
plotms()
# Box up the bad low points (basically a clip below 0.52) and flag
print ""
print "-----------------------------------------------------"
print "Back to all data"
print "Clean up remaining bad points"
print ""
#print "NOTE: These flags will extend to the RL LR cross-hands"
#print "Because of this the flagging will be slower than otherwise"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
#---------------------------------------------------------------------
# Finally, do JUPITER
field = ’JUPITER’
correlation = ’RR LL’
iteration = ’’
xaxis = ’uvdist’
plotms()
# Here you will see that the final scan at 22:00:00 UT is bad
# Draw a box around it and flag it!
print ""
print "-----------------------------------------------------"
print "Now plot JUPITER versus uvdist"
print "Lots of bad stuff near bottom"
print "Lets go and find it - try Locate"
print "Looks like lots of different antennas but at same time"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
correlation = ’’
xaxis = ’time’
plotms()
# Here you will see that the final scan at 22:00:00 UT is bad
# Draw a box around it and flag it!
print ""
print "-----------------------------------------------------"
print "Now plotting vs. time"
print "See bad scan at end - flag it!"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# Now look at whats left
correlation = ’RR LL’
xaxis = ’uvdist’
spw = ’1’
antenna = ’’
plotms()
# As you step through, you will see that Antenna 9 (ID=8) is often
# bad in this spw. If you box and do Locate (or remember from
# 0137+331) its probably a bad time.
print ""
print "-----------------------------------------------------"
print "Looking now at SPW 1"
print "Now we set iteration to Antenna"
print "Step through antennas with Next"
print "See bad Antenna 9 (ID 8) as in 0137+331"
print "Do not flag yet, we will isolate this next"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# The easiset way to kill it:
antenna = ’9’
xaxis = ’time’
correlation = ’’
plotms()
# Draw a box around all points in the last bad scans and flag ’em!
print ""
print "-----------------------------------------------------"
print "Now plotting vs. time antenna 9 spw 1"
print "Box up the bad scans and Flag"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# Now clean up the rest
xaxis = ’uvdist’
correlation = ’RR LL’
antenna = ’’
spw = ’’
# You will be drawing many tiny boxes, so remember you can
# use the ESC key to get rid of the most recent box if you
# make a mistake.
plotms()
# Note that the end result is we’ve flagged lots of points
# in RR and LL. We will rely upon imager to ignore the
# RL LR for points with RR LL flagged!
print ""
print "-----------------------------------------------------"
print "Final cleanup of JUPITER data"
print "Back to uvdist plot, see remaining bad data"
print "You can draw little boxes around the outliers and Flag"
print "Depends how patient you are in drawing boxes!"
print "Could also use Locate to find where they come from"
print ""
#print "NOTE: These flags will extend to the RL LR cross-hands"
#print "Because of this the flagging will be slower than otherwise"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
print "Done with plotms!"
#
#=====================================================================
#
# Use Flagmanager to save a copy of the flags so far
#
print ’--Flagmanager--’
default(’flagmanager’)
print "Now will use flagmanager to save a copy of the flags we just made"
print "These are named xyflags"
vis = msfile
mode = ’save’
versionname = ’xyflags’
comment = ’Plotms flags’
merge = ’replace’
flagmanager()
#=====================================================================
#
# Use Flagmanager to list all saved versions
#
print ’--Flagmanager--’
default(’flagmanager’)
print "Now will use flagmanager to list all the versions we saved"
vis = msfile
mode = ’list’
flagmanager()
#
# Done Flagging
print ’--Done with flagging--’
#
#=====================================================================
# Calibration
#=====================================================================
#
# Set the fluxes of the primary calibrator(s)
#
print ’--Setjy--’
default(’setjy’)
print "Use setjy to set flux of 1331+305 (3C286)"
vis = msfile
#
# 1331+305 = 3C286 is our primary calibrator
field = ’1331+305’
# Setjy knows about this source so we dont need anything more
setjy()
#
# You should see something like this in the logger and casapy.log file:
#
# 1331+305 spwid= 0 [I=7.462, Q=0, U=0, V=0] Jy, (Perley-Taylor 99)
# 1331+305 spwid= 1 [I=7.51, Q=0, U=0, V=0] Jy, (Perley-Taylor 99)
#
print "Look in logger for the fluxes (should be 7.462 and 7.510 Jy)"
#
#=====================================================================
#
# Initial gain calibration
#
print ’--Gaincal--’
default(’gaincal’)
print "Solve for antenna gains on 1331+305 and 0137+331"
print "We have 2 single-channel continuum spw"
print "Do not want bandpass calibration"
vis = msfile
# set the name for the output gain caltable
caltable = gtable
print "Output gain cal table will be "+gtable
# Gain calibrators are 1331+305 and 0137+331 (FIELD_ID 7 and 0)
# We have 2 IFs (SPW 0,1) with one channel each
# selection is via the field and spw strings
field = ’1331+305,0137+331’
spw = ’’
# a-priori calibration application
gaincurve = usegaincurve
opacity = gainopacity
# scan-based G solutions for both amplitude and phase
gaintype = ’G’
calmode = ’ap’
# one solution per scan
solint = ’inf’
combine = ’’
# do not apply parallactic angle correction (yet)
parang = False
# reference antenna
refant = calrefant
# minimum SNR 3
minsnr = 3
gaincal()
#
#=====================================================================
#
# Bootstrap flux scale
#
print ’--Fluxscale--’
default(’fluxscale’)
print "Use fluxscale to rescale gain table to make new one"
vis = msfile
# set the name for the output rescaled caltable
fluxtable = ftable
print "Output scaled gain cal table is "+ftable
# point to our first gain cal table
caltable = gtable
# we will be using 1331+305 (the source we did setjy on) as
# our flux standard reference
reference = ’1331+305’
# we want to transfer the flux to our other gain cal source 0137+331
# to bring its gain amplitues in line with the absolute scale
transfer = ’0137+331’
fluxscale()
# You should see in the logger something like:
#Flux density for 0137+331 in SpW=0 is:
# 5.42575 +/- 0.00285011 (SNR = 1903.7, nAnt= 27)
#Flux density for 0137+331 in SpW=1 is:
# 5.46569 +/- 0.00301326 (SNR = 1813.88, nAnt= 27)
#
#---------------------------------------------------------------------
# Plot calibration
#
print ’--PlotCal--’
default(’plotcal’)
showgui = True
caltable = ftable
multiplot = True
yaxis = ’amp’
showgui = True
plotcal()
print ""
print "-------------------------------------------------"
print "Plotcal"
print "Looking at amplitude in cal-table "+caltable
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
#
# Now go back and plot to file
#
showgui = False
yaxis = ’amp’
#figfile = caltable + ’.plotcal.amp.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable.plotcal.amp.saved’)
#plotcal()
yaxis = ’phase’
#figfile = caltable + ’.plotcal.phase.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable.plotcal.phase.saved’)
#plotcal()
#
#=====================================================================
# Polarization Calibration
#=====================================================================
#
if (dopolcal):
print ’--Polcal (D)--’
default(’polcal’)
print "Solve for polarization leakage on 0137+331"
print "Pretend it has unknown polarization"
vis = msfile
# Start with the un-fluxscaled gain table
gaintable = gtable
# use settings from gaincal
gaincurve = usegaincurve
opacity = gainopacity
# Output table
caltable = ptable
# Use a 3C48 tracked through a range of PA
field = ’0137+331’
spw = ’’
# No need for further selection
selectdata=False
# Polcal mode (D+QU = unknown pol for D)
poltype = ’D+QU’
# One solution for entire dataset
solint = ’inf’
combine = ’scan’
# reference antenna
refant = calrefant
# minimum SNR 3
minsnr = 3
#saveinputs(’polcal’,calprefix+’.polcal.saved’)
polcal()
#=====================================================================
#
# List polcal solutions
#
print ’--Listcal (PolD)--’
listfile = caltable + ’.list’
print "Listing calibration to file "+listfile
listcal()
#=====================================================================
#
# Plot polcal solutions
#
print ’--Plotcal (PolD)--’
iteration = ’’
showgui = False
xaxis = ’antenna’
yaxis = ’amp’
showgui = True
figfile = ’’
plotcal()
print "These are the amplitudes of D-terms versus antenna"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# Now plot to files
showgui = False
#figfile = caltable + ’.plotcal.antamp.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable+’.plotcal.antamp.saved’)
#plotcal()
xaxis = ’antenna’
yaxis = ’phase’
#figfile = caltable + ’.plotcal.antphase.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable+’.plotcal.antphase.saved’)
#plotcal()
xaxis = ’antenna’
yaxis = ’snr’
#figfile = caltable + ’.plotcal.antsnr.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable+’.plotcal.antsnr.saved’)
#plotcal()
xaxis = ’real’
yaxis = ’imag’
#figfile = caltable + ’.plotcal.reim.png’
#print "Plotting calibration to file "+figfile
#saveinputs(’plotcal’,caltable+’.plotcal.reim.saved’)
#plotcal()
#=====================================================================
# Do Chi (X) pol angle calibration
#=====================================================================
# First set the model
print ’--Setjy--’
default(’setjy’)
vis = msfile
print "Use setjy to set IQU fluxes of "+polxfield
field = polxfield
for spw in usespwlist:
fluxdensity = polxiquv[spw]
#saveinputs(’setjy’,calprefix+’.setjy.polspw.’+spw+’.saved’)
setjy()
#
# Polarization (X-term) calibration
#
print ’--PolCal (X)--’
default(’polcal’)
print "Polarization R-L Phase Calibration (linear approx)"
vis = msfile
# Start with the G and D tables
gaintable = [gtable,ptable]
# use settings from gaincal
gaincurve = usegaincurve
opacity = gainopacity
# Output table
caltable = xtable
# previously set with setjy
field = polxfield
spw = ’’
selectdata=False
# Solve for Chi
poltype = ’X’
solint = ’inf’
combine = ’scan’
# reference antenna
refant = calrefant
# minimum SNR 3
minsnr = 3
#saveinputs(’polcal’,calprefix+’.polcal.X.saved’)
polcal()
#=====================================================================
# Apply the Calibration
#=====================================================================
#
# Interpolate the gains onto Jupiter (and others)
#
# print ’--Accum--’
# default(’accum’)
#
# print "This will interpolate the gains onto Jupiter"
#
# vis = msfile
#
# tablein = ’’
# incrtable = ftable
# calfield = ’1331+305, 0137+331’
#
# # set the name for the output interpolated caltable
# caltable = atable
#
# print "Output cumulative gain table will be "+atable
#
# # linear interpolation
# interp = ’linear’
#
# # make 10s entries
# accumtime = 10.0
#
# accum()
#
# NOTE: bypassing this during testing
atable = ftable
# #=====================================================================
#
# Correct the data
# (This will put calibrated data into the CORRECTED_DATA column)
#
print ’--ApplyCal--’
default(’applycal’)
print "This will apply the calibration to the DATA"
print "Fills CORRECTED_DATA"
vis = msfile
# Start with the interpolated fluxscale/gain table
gaintable = [atable,ptable,xtable]
# use settings from gaincal
gaincurve = usegaincurve
opacity = gainopacity
# select the fields
field = ’1331+305,0137+331,JUPITER’
spw = ’’
selectdata = False
# IMPORTANT set parang=True for polarization
parang = True
# do not need to select subset since we did accum
# (note that correct only does ’nearest’ interp)
gainfield = ’’
applycal()
#
#=====================================================================
#
# Now split the Jupiter target data
#
print ’--Split Jupiter--’
default(’split’)
vis = msfile
# Now we write out the corrected data to a new MS
# Select the Jupiter field
field = srcname
spw = ’’
# pick off the CORRECTED_DATA column
datacolumn = ’corrected’
# Make an output vis file
outputvis = srcsplitms
print "Split "+field+" data into new ms "+srcsplitms
split()
# Also split out 0137+331 as a check
field = calname
outputvis = calsplitms
print "Split "+field+" data into new ms "+calsplitms
split()
#=====================================================================
# Use Plotms to look at the split calibrated data
#
print ’Calibration completed’
#
#=====================================================================
#
# Intensity Imaging/Selfcal
#
#=====================================================================
# FIRST CLEAN / SELFCAL CYCLE
#=====================================================================
#
# Now clean an image of Jupiter
# NOTE: this uses the new combined invert/clean/mosaic task Patch 2
#
print ’--Clean 1--’
default(’clean’)
# Pick up our split source data
vis = srcsplitms
# Make an image root file name
imagename = imname1
print "Output images will be prefixed with "+imname1
# Set up the output continuum image (single plane mfs)
mode = ’mfs’
stokes = ’I’
print "Will be a single MFS continuum image"
# NOTE: current version field=’’ doesnt work
field = ’*’
# Combine all spw
spw = ’’
# Imaging mode params
psfmode = clnalg
imagermode = clnmode
# Imsize and cell
imsize = clnimsize
cell = clncell
# NOTE: will eventually have an imadvise task to give you this
# information
# Standard gain factor 0.1
gain = 0.1
# Fix maximum number of iterations and threshold
niter = clniter
threshold = clnthreshold
# Note - we can change niter and threshold interactively
# during clean
# Set up the weighting
# Use Briggs weighting (a moderate value, on the uniform side)
weighting = ’briggs’
robust = 0.5
# No clean mask or box
mask = ’’
# Use interactive clean mode
interactive = True
# We will be doing selfcal
calready = True
# Moderate number of iter per interactive cycle
npercycle = 100
saveinputs(’clean’,imagename+’.clean.saved’)
clean()
# When the interactive clean window comes up, use the right-mouse
# to draw rectangles around obvious emission double-right-clicking
# inside them to add to the flag region. You can also assign the
# right-mouse to polygon region drawing by right-clicking on the
# polygon drawing icon in the toolbar. When you are happy with
# the region, click ’Done Flagging’ and it will go and clean another
# 100 iterations. When done, click ’Stop’.
print ""
print "----------------------------------------------------"
print "Clean"
print "Final clean model is "+clnmodel1
print "Final restored clean image is "+clnimage1
print "The clean residual image is "+clnresid1
print "Your final clean mask is "+clnmask1
print ""
print "This is the final restored clean image in the viewer"
print "Zoom in and set levels to see faint emission"
print "Use rectangle drawing tool to box off source"
print "Double-click inside to print statistics"
print "Move box on-source and get the max"
print "Calcualte DynRange = MAXon/RMSoff"
print "I got 1.060/0.004 = 270"
print "Still not as good as it can be - lets selfcal"
print "Close viewer panel when done"
#
#---------------------------------------------------------------------
#
# If you did not do interactive clean, bring up viewer manually
viewer(clnimage1)
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# You can use the right-mouse to draw a box in the lower right
# corner of the image away from emission, the double-click inside
# to bring up statistics. Use the right-mouse to grab this box
# and move it up over Jupiter and double-click again. You should
# see stuff like this in the terminal:
#
# jupiter6cm.demo.clean1.image (Jy/beam)
#
# n Std Dev RMS Mean Variance Sum
# 4712 0.003914 0.003927 0.0003205 1.532e-05 1.510
#
# Flux Med |Dev| IntQtlRng Median Min Max
# 0.09417 0.002646 0.005294 0.0001885 -0.01125 0.01503
#
#
# On Jupiter:
#
# n Std Dev RMS Mean Variance Sum
# 3640 0.1007 0.1027 0.02023 0.01015 73.63
#
# Flux Med |Dev| IntQtlRng Median Min Max
# 4.592 0.003239 0.007120 0.0001329 -0.01396 1.060
#
# Estimated dynamic range = 1.060 / 0.003927 = 270 (poor)
#
# Note that the exact numbers you get will depend on how deep you
# take the interactive clean and how you draw the box for the stats.
#=====================================================================
#
# Do some non-interactive image statistics
print ’--Imstat--’
default(’imstat’)
imagename = clnimage1
on_statistics1 = imstat()
# Now do stats in the lower right corner of the image
# remember clnimsize = [288,288]
box = ’216,1,287,72’
off_statistics1 = imstat()
# Pull the max and rms from the clean image
thistest_immax=on_statistics1[’max’][0]
print ’ Found : Max in image = ’,thistest_immax
thistest_imrms=off_statistics1[’rms’][0]
print ’ Found : rms in image = ’,thistest_imrms
print ’ Clean image Dynamic Range = ’,thistest_immax/thistest_imrms
print ’’
#
#---------------------------------------------------------------------
#
# Self-cal using clean model
#
# Note: clean will have left FT of model in the MODEL_DATA column
# If you’ve done something in between, can use the ft task to
# do this manually.
#
print ’--SelfCal 1--’
default(’gaincal’)
vis = srcsplitms
print "Will self-cal using MODEL_DATA left in MS by clean"
# New gain table
caltable = selfcaltab1
print "Will write gain table "+selfcaltab1
# Don’t need a-priori cals
selectdata = False
gaincurve = False
opacity = 0.0
# This choice seemed to work
refant = calrefant
# Do amp and phase
gaintype = ’G’
calmode = ’ap’
# Do 30s solutions with SNR>1
solint = 30.0
minsnr = 1.0
print "Calibrating amplitudes and phases on 30s timescale"
# Do not need to normalize (let gains float)
solnorm = False
gaincal()
#
#---------------------------------------------------------------------
# It is useful to put this up in plotcal
#
#
print ’--PlotCal--’
default(’plotcal’)
caltable = selfcaltab1
multiplot = True
yaxis = ’amp’
plotcal()
print ""
print "-------------------------------------------------"
print "Plotcal"
print "Looking at amplitude in self-cal table "+caltable
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
yaxis = ’phase’
plotcal()
print ""
print "-------------------------------------------------"
print "Plotcal"
print "Looking at phases in self-cal table "+caltable
#
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
#
#---------------------------------------------------------------------
#
# Correct the data (no need for interpolation this stage)
#
print ’--ApplyCal--’
default(’applycal’)
vis = srcsplitms
print "Will apply self-cal table to over-write CORRECTED_DATA in MS"
gaintable = selfcaltab1
gaincurve = False
opacity = 0.0
field = ’’
spw = ’’
selectdata = False
calwt = True
applycal()
# Self-cal is now in CORRECTED_DATA column of split ms
#
#=====================================================================
# SECOND CLEAN / SELFCAL CYCLE
#=====================================================================
#
print ’--Clean 2--’
default(’clean’)
print "Now clean on self-calibrated data"
vis = srcsplitms
imagename = imname2
field = ’*’
spw = ’’
mode = ’mfs’
gain = 0.1
# Imaging mode params
psfmode = clnalg
imagermode = clnmode
imsize = clnimsize
cell = clncell
niter = clniter
threshold = clnthreshold
weighting = ’briggs’
robust = 0.5
mask = ’’
interactive = True
npercycle = 100
calready = True
saveinputs(’clean’,imagename+’.clean.saved’)
clean()
print ""
print "----------------------------------------------------"
print "Clean"
print "Final clean model is "+clnmodel2
print "Final restored clean image is "+clnimage2
print "The clean residual image is "+clnresid2
print "Your final clean mask is "+clnmask2
print ""
print "This is the final restored clean image in the viewer"
print "Zoom in and set levels to see faint emission"
print "Use rectangle drawing tool to box off source"
print "Double-click inside to print statistics"
print "Move box on-source and get the max"
print "Calculate DynRange = MAXon/RMSoff"
print "This time I got 1.050 / 0.001 = 1050 (better)"
print "Still not as good as it can be - you can try selfcal again"
print "We will stop here"
print "Close viewer panel when done"
#
#---------------------------------------------------------------------
#
# If you did not do interactive clean, bring up viewer manually
viewer(clnimage2)
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# jupiter6cm.demo.clean2.image (Jy/beam)
#
# n Std Dev RMS Mean Variance Sum
# 5236 0.001389 0.001390 3.244e-05 1.930e-06 0.1699
#
# Flux Med |Dev| IntQtlRng Median Min Max
# 0.01060 0.0009064 0.001823 -1.884e-05 -0.004015 0.004892
#
#
# On Jupiter:
#
# n Std Dev RMS Mean Variance Sum
# 5304 0.08512 0.08629 0.01418 0.007245 75.21
#
# Flux Med |Dev| IntQtlRng Median Min Max
# 4.695 0.0008142 0.001657 0.0001557 -0.004526 1.076
#
# Estimated dynamic range = 1.076 / 0.001389 = 775 (better)
#
# Note that the exact numbers you get will depend on how deep you
# take the interactive clean and how you draw the box for the stats.
#
print ""
print "--------------------------------------------------"
print "After this script is done you can continue on with"
print "more self-cal, or try different cleaning options"
#
#=====================================================================
# Image Analysis
#=====================================================================
#
# Can do some image statistics if you wish
print ’--Imstat (Cycle 2)--’
default(’imstat’)
imagename = clnimage2
on_statistics2 = imstat()
# Now do stats in the lower right corner of the image
# remember clnimsize = [288,288]
box = ’216,1,287,72’
off_statistics2 = imstat()
# Pull the max and rms from the clean image
thistest_immax=on_statistics2[’max’][0]
print ’ Found : Max in image = ’,thistest_immax
thistest_imrms=off_statistics2[’rms’][0]
print ’ Found : rms in image = ’,thistest_imrms
print ’ Clean image Dynamic Range = ’,thistest_immax/thistest_imrms
print ’’
#=====================================================================
#
# Print results and regression versus previous runs
#
print ""
print ’ Final Jupiter results ’
print ’ ===================== ’
print ’’
# Pull the max and rms from the clean image
thistest_immax=on_statistics2[’max’][0]
oldtest_immax = 1.07732224464
print ’ Clean image ON-SRC max = ’,thistest_immax
print ’ Previously found to be = ’,oldtest_immax
diff_immax = abs((oldtest_immax-thistest_immax)/oldtest_immax)
print ’ Difference (fractional) = ’,diff_immax
print ’’
thistest_imrms=off_statistics2[’rms’][0]
oldtest_imrms = 0.0010449
print ’ Clean image OFF-SRC rms = ’,thistest_imrms
print ’ Previously found to be = ’,oldtest_imrms
diff_imrms = abs((oldtest_imrms-thistest_imrms)/oldtest_imrms)
print ’ Difference (fractional) = ’,diff_imrms
print ’’
print ’ Final Clean image Dynamic Range = ’,thistest_immax/thistest_imrms
print ’’
print ’--- Done with I Imaging and Selfcal---’
#
#=====================================================================
# Polarization Imaging
#=====================================================================
#
print ’--Clean (Polarization)--’
default(’clean’)
print "Now clean polarized data"
vis = srcsplitms
imagename = polimname
field = ’*’
spw = ’’
mode = ’mfs’
gain = 0.1
# Polarization
stokes = ’IQUV’
psfmode = polclnalg
imagermode = polclnmode
niter = clniter
threshold = clnthreshold
imsize = clnimsize
cell = clncell
weighting = ’briggs’
robust = 0.5
interactive = True
npercycle = 100
saveinputs(’clean’,imagename+’.clean.saved’)
clean()
print ""
print "----------------------------------------------------"
print "Clean"
print "Final restored clean image is "+polimage
print "Final clean model is "+polmodel
print "The clean residual image is "+polresid
print "Your final clean mask is "+polmask
#
#=====================================================================
# Image Analysis
#=====================================================================
#
# Polarization statistics
print ’--Final Pol Imstat--’
default(’imstat’)
imagename = polimage
on_statistics = {}
off_statistics = {}
# lower right corner of the image (clnimsize = [288,288])
onbox = ’’
# lower right corner of the image (clnimsize = [288,288])
offbox = ’216,1,287,72’
for stokes in [’I’,’Q’,’U’,’V’]:
box = onbox
on_statistics[stokes] = imstat()
box = offbox
off_statistics[stokes] = imstat()
#
# Peel off some Q and U planes
#
print ’--Immath--’
default(’immath’)
mode = ’evalexpr’
stokes = ’I’
outfile = ipolimage
imagename = polimage
expr = ’IM0’
immath()
print "Created I image "+outfile
stokes = ’Q’
outfile = qpolimage
immath()
print "Created Q image "+outfile
stokes = ’U’
outfile = upolimage
immath()
print "Created U image "+outfile
#
#---------------------------------------------------------------------
# Now make POLI and POLA images
#
# Use our rms above for debiasing
mysigma = 0.5*( off_statistics[’Q’][’rms’][0] + off_statistics[’U’][’rms’][0] )
#sigma = str(mysigma)+’Jy/beam’
# This does not work well yet
sigma = ’0.0Jy/beam’
#stokes = ’’
#imagename = [qpolimage,upolimage]
#outfile = poliimage
#mode = ’poli’
#immath()
#
#outfile = polaimage
#mode = ’pola’
#immath()
#Call as function instead:
immath(imagename=[qpolimage,upolimage],mode=’poli’,outfile=poliimage,sigma=’0.0Jy/beam’)
print "Created POLI image "+poliimage
immath(imagename=[qpolimage,upolimage],mode=’pola’,outfile=polaimage,sigma=’0.0Jy/beam’)
print "Created POLA image "+polaimage
#
#---------------------------------------------------------------------
# Save statistics of these images
default(’imstat’)
imagename = poliimage
stokes = ’’
box = onbox
on_statistics[’POLI’] = imstat()
box = offbox
off_statistics[’POLI’] = imstat()
#
#
#---------------------------------------------------------------------
# Display clean I image in viewer but with polarization vectors
#
# If you did not do interactive clean, bring up viewer manually
viewer(polimage)
print "Displaying pol I now. You should overlay pola vectors"
print "Bring up the Load Data panel:"
print ""
print "Use LEL for POLA VECTOR with cut above 6*mysigma in POLI = "+str(6*mysigma)
print "For example:"
print "\’"+polaimage+"\’[\’"+poliimage+"\’>0.0048]"
print ""
print "In the Data Display Options for the vector plot:"
print " Set the x,y increments to 2 (default is 3)"
print " Use an extra rotation this 90deg to get B field"
print "Note the lengths are all equal. You can fiddle these."
print ""
print "You can also load the poli image as contours"
# Pause script if you are running in scriptmode
if scriptmode:
user_check=raw_input(’Return to continue script\n’)
# NOTE: the LEL will be something like
# ’jupiter6cm.demo.polimg.clean.image.pola’[’jupiter6cm.demo.polimg.clean.image.poli’>0.005]
#
# NOTE: The viewer can take complex images to make Vector plots, although
# the image analysis tasks (and ia tool) cannot yet handle these. But we
# can use the imagepol tool (which is not imported by default) to make
# a complex image of the linear polarized intensity for display.
# See CASA User Reference Manual:
# http://casa.nrao.edu/docs/casaref/imagepol-Tool.html
#
# Make an imagepol tool and open the clean image
potool = casac.homefinder.find_home_by_name(’imagepolHome’)
po = potool.create()
po.open(polimage)
# Use complexlinpol to make a Q+iU image
complexlinpolimage = polimname + ’.cmplxlinpol’
po.complexlinpol(complexlinpolimage)
po.close()
# You can now display this in the viewer, in particular overlay this
# over the intensity raster with the poli contours. The vector lengths
# will be proportional to the polarized intensity. You can play with
# the Data Display Options panel for vector spacing and length.
# You will want to have this masked, like the pola image above, on
# the polarized intensity. When you load the image, use the LEL:
# ’jupiter6cm.demo.polimg.clean.cmplxlinpol’[’jupiter6cm.demo.polimg.clean.image.poli’>0.005]
#=====================================================================
#
# Print results
#
print ""
print ’ Jupiter polarization results ’
print ’ ============================ ’
print ’’
for stokes in [’I’,’Q’,’U’,’V’,’POLI’]:
print ’’
print ’ =============== ’
print ’’
print ’ Polarization (Stokes ’+stokes+’):’
mymax = on_statistics[stokes][’max’][0]
mymin = on_statistics[stokes][’min’][0]
myrms = off_statistics[stokes][’rms’][0]
absmax = max(mymax,mymin)
mydra = absmax/myrms
print ’ Clean image ON-SRC max = ’,mymax
print ’ Clean image ON-SRC min = ’,mymin
print ’ Clean image OFF-SRC rms = ’,myrms
print ’ Clean image dynamic rng = ’,mydra
print ’--- Done ---’
#
#=====================================================================
More information about CASA may be found at the
CASA web page
Copyright © 2010 Associated Universities Inc., Washington, D.C.
This code is available under the terms of the GNU General Public Lincense
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