wvrgcal – Generate a gain table based on Water Vapour Radiometer data – calibration task

Description

Information about the observation and the performance of WVRGCAL is written to the CASA logger and also returned in a dictionary; see the CASA cookbook for a more detailed description of these parameters. The dictionary element ‘success’ is True if no errors occured.

Of particular note is the discrepancy parameter (Disc): high values (> a few hundred microns) may indicate some levels of cloud contamination and the effect of applying the WVRGCAL correction should be checked; values > 1000 um in all antennas have currently been found to indicate that WVRGCAL correction should not be used.

vis – Name of input visibility file

default: none; example: vis=’ngc5921.ms’

caltable – Name of output gain calibration table

default: none; example: caltable=’ngc5921.wvr’

toffset – Time offset (sec) between interferometric and WVR data

default: 0 (ALMA default for cycle 1, for cycle 0, i.e. up to Jan 2013 it was -1)

segsource – Do a new coefficient calculation for each source

default: True

tie – Prioritise tieing the phase of these sources as well as possible

(requires segsource=True) default: [] example: [‘3C273,NGC253’, ‘IC433,3C279’]

sourceflag – Flag the WVR data for these source(s) as bad and do not produce corrections for it

(requires segsource=True) default: [] (none) example: [‘3C273’]

nsol – Number of solutions for phase correction coefficients during this observation.

By default only one set of coefficients is generated for the entire observation. If more sets are requested, then they will be evenly distributed in time throughout the observation. Values > 1 require segsource=False. default: 1

disperse – Apply correction for dispersion

default: False

wvrflag – Regard the WVR data for these antenna(s) as bad and use interpolated values instead

default: [] (none) example: [‘DV03’,’DA05’,’PM02’]

statfield – Compute the statistics (Phase RMS, Disc) on this field only

default: ‘’ (all)

statsource – Compute the statistics (Phase RMS, Disc) on this source only

default: ‘’ (all)

smooth – Smooth the calibration solution on the given timescale

default: ‘’ (no smoothing), example: ‘3s’ smooth on a timescale of 3 seconds

scale – Scale the entire phase correction by this factor

default: 1. (no scaling)

spw – List of the spectral window IDs for which solutions should be saved into the caltable

default: [] (all spectral windows), example [17,19,21,23]

wvrspw – List of the spectral window IDs from which the WVR data should be taken

default: [] (all WVR spectral windows), example [0]

reversespw – Reverse the sign of the correction for the listed SPWs

(only neede for early ALMA data before Cycle 0) default: ‘’ (none), example: reversespw=’0~2,4’; spectral windows 0,1,2,4

cont – Estimate the continuum (e.g., due to clouds)

default: False

maxdistm – maximum distance (m) an antenna may have to be considered for being part

of the antenna set (minnumants to 3 antennas) for the interpolation of a solution for a flagged antenna default: 500.

minnumants – minimum number of near antennas required for interpolation

default: 2

mingoodfrac – If the fraction of unflagged data for an antenna is below this value (0. to 1.),

the antenna is flagged. default: 0.8

usefieldtab – derive the antenna AZ/EL values from the FIELD rather than the POINTING table

default: False

refant – use the WVR data from this antenna for calculating the dT/dL parameters (can give ranked list)

default: ‘’ (use the first good or interpolatable antenna), examples: ‘DA45’ - use DA45

[‘DA45’,’DV51’] - use DA45 and if that is not good, use DV51 instead

offsetstable – (experimental) subtract the temperature offsets in this table from the WVR measurements before

using them to calculate the phase corrections

default: ‘’ (do not apply any offsets) examples: ‘uid___A002_Xabd867_X2277.cloud_offsets’ use the given table

Parameters

Title

Parameter	Default	Description
vis	''
caltable	''
toffset	float(0)
segsource	True
sourceflag	numpy.array( [  ] )
tie	numpy.array( [  ] )
nsol	int(1)
disperse	False
wvrflag	numpy.array( [  ] )
statfield	''
statsource	''
smooth	''
scale	float(1.)
spw	numpy.array( [  ] )
wvrspw	numpy.array( [  ] )
reversespw	''
cont	False
maxdistm	float(500.)
minnumants	int(2)
mingoodfrac	float(0.8)
usefieldtab	False
refant	numpy.array( [  ] )
offsetstable	''

Parameter Explanations

vis

''

Name of input visibility file

caltable

''

Name of output gain calibration table

toffset

float(0)

Time offset (sec) between interferometric and WVR data

segsource

True

Do a new coefficient calculation for each source

sourceflag

numpy.array( [  ] )

Regard the WVR data for these source(s) as bad and do not produce corrections for it (requires segsource=True)

tie

numpy.array( [  ] )

Prioritise tieing the phase of these sources as well as possible (requires segsource=True)

nsol

int(1)

Number of solutions for phase correction coefficients (nsol>1 requires segsource=False)

disperse

False

Apply correction for dispersion

wvrflag

numpy.array( [  ] )

Regard the WVR data for these antenna(s) as bad and replace its data with interpolated values from neighbouring antennas

statfield

''

Compute the statistics (Phase RMS, Disc) on this field only

statsource

''

Compute the statistics (Phase RMS, Disc) on this source only

smooth

''

Smooth calibration solution on the given timescale

scale

float(1.)

Scale the entire phase correction by this factor

spw

numpy.array( [  ] )

List of the spectral window IDs for which solutions should be saved into the caltable

wvrspw

numpy.array( [  ] )

List of the spectral window IDs from which the WVR data should be taken

reversespw

''

Reverse the sign of the correction for the listed SPWs (only needed for early ALMA data before Cycle 0)

cont

False

Estimate the continuum (e.g., due to clouds) (experimental)

maxdistm

float(500.)

maximum distance (m) of an antenna used for interpolation for a flagged antenna

minnumants

int(2)

minimum number of near antennas (up to 3) required for interpolation

mingoodfrac

float(0.8)

If the fraction of unflagged data for an antenna is below this value (0. to 1.), the antenna is flagged.

usefieldtab

False

derive the antenna AZ/EL values from the FIELD rather than the POINTING table

refant

numpy.array( [  ] )

use the WVR data from this antenna for calculating the dT/dL parameters (can give ranked list)

offsetstable

''

(experimental) subtract the temperature offsets in this table from the WVR measurements before calculating the phase corrections