applycal – Apply calibrations solutions(s) to data – calibration task

Description

Applycal reads the specified gain calibration tables, applies them to the (raw) data column (with the specified selection), and writes the calibrated results into the corrected column. This is done in one step, so all available calibration must be specified. Applycal will overwrite existing corrected data.

Standard data selection is supported. See help par.selectdata for more information.

One or more calibration tables (both temporal, frequency, polarization calibrations) can be specified in the gaintable parameter. The calibration values associated with a restricted list of fields can also be selected for each table.

See task accum for instructions on forming calibration incrementally. See task split for saving corrected data in another visibility file.

Parameters

Title

Parameter	Default	Description
vis	''
field	''
spw	''
intent	''
selectdata	True
timerange	''
uvrange	''
antenna	''
scan	''
observation	''
msselect	''
docallib	False
callib	''
gaintable	numpy.array( [  ] )
gainfield	numpy.array( [  ] )
interp	numpy.array( [  ] )
spwmap	numpy.array( [  ] )
calwt	numpy.array( [  ] )
parang	False
applymode	''
flagbackup	True

Parameter Explanations

vis

''

Name of input visibility file

field

''

Select field using field id(s) or field name(s)

spw

''

Select spectral window/channels

intent

''

Select observing intent

selectdata

True

Other data selection parameters

timerange

''

Select data based on time range

uvrange

''

Select data within uvrange (default units meters)

antenna

''

Select data based on antenna/baseline

scan

''

Scan number range

observation

''

Select by observation ID(s)

msselect

''

Optional complex data selection (ignore for now)

docallib

False

Use callib or traditional cal apply parameters

callib

''

Cal Library filename

gaintable

numpy.array( [  ] )

Gain calibration table(s) to apply on the fly

gainfield

numpy.array( [  ] )

Select a subset of calibrators from gaintable(s)

interp

numpy.array( [  ] )

Interp type in time[,freq], per gaintable. default==linear,linear

spwmap

numpy.array( [  ] )

Spectral windows combinations to form for gaintables(s)

calwt

numpy.array( [  ] )

Calibrate data weights per gaintable.

parang

False

Apply parallactic angle correction

applymode

''

Calibration mode: “”=”calflag”,”calflagstrict”,”trial”,”flagonly”,”flagonlystrict”, or “calonly”

flagbackup

True

Automatically back up the state of flags before the run?