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

Description

Applycal reads the specified gain calibration tables or cal library, 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 tables must be specified.

Applycal will overwrite existing corrected data, and will flag data for which there is no calibration available.

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

Parameters

Title
Parameter	Default	Description
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( [ ] )`	Interpolation parameters for each gaintable, as a list
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?

Parameter Explanations

vis

''

Name of input visibility file: default: non

Example: vis=’ngc5921.ms’

field

''

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

default: ‘’ –> all fields

Use ‘go listobs’ to obtain the list id’s or names. If field string is a non-negative integer, it is assumed a field index, otherwise, it is assumed a field name.

Examples: field=’0~2’; field ids 0,1,2 field=’0,4,5~7’; field ids 0,4,5,6,7 field=’3C286,3C295’; field named 3C286 and 3C295 field = ‘3,4C*’; field id 3, all names starting with 4C

spw

''

Select spectral window/channels

Examples: spw=’0~2,4’; spectral windows 0,1,2,4 (all channels) spw=’<2’; spectral windows less than 2 (i.e. 0,1) spw=’0:5~61’; spw 0, channels 5 to 61, INCLUSIVE spw=’*:5~61’; all spw with channels 5 to 61 spw=’0,10,3:3~45’; spw 0,10 all channels, spw 3, channels 3 to 45. spw=’0~2:2~6’; spw 0,1,2 with channels 2 through 6 in each. spw=’0:0~10;15~60’; spectral window 0 with channels 0-10,15-60. (NOTE ‘;’ to separate channel selections) spw=’0:0~10^2,1:20~30^5’; spw 0, channels 0,2,4,6,8,10, spw 1, channels 20,25,30 type ‘help par.selection’ for more examples.

intent

''

Select observing intent: default: ‘’ (no selection by intent)

Example: intent=’BANDPASS’ (selects data labelled with BANDPASS intent)

selectdata

True

Other data selection parameters: default: True

timerange

''

Select data based on time range: Subparameter of selectdata=True default = ‘’ (all)

Examples: timerange = ‘YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss’ (Note: if YYYY/MM/DD is missing date defaults to first day in data set.) timerange=’09:14:0~09:54:0’ picks 40 min on first day timerange= ‘25:00:00~27:30:00’ picks 1 hr to 3 hr 30min on NEXT day timerange=’09:44:00’ pick data within one integration of time timerange=’>10:24:00’ data after this time

uvrange

''

Select data within uvrange (default units meters): Subparameter of selectdata=True default: ‘’ (all)

Examples: uvrange=’0~1000klambda’; uvrange from 0-1000 kilo-lambda uvrange=’>4klambda’;uvranges greater than 4 kilolambda

antenna

''

Select data based on antenna/baseline

Subparameter of selectdata=True default: ‘’ (all)

If antenna string is a non-negative integer, it is assumed an antenna index, otherwise, it is assumed as an antenna name

Examples: antenna=’5&6’; baseline between antenna index 5 and index 6. antenna=’VA05&VA06’; baseline between VLA antenna 5 and 6. antenna=’5&6;7&8’; baselines with indices 5-6 and 7-8 antenna=’5’; all baselines with antenna index 5 antenna=’05’; all baselines with antenna number 05 (VLA old name) antenna=’5,6,10’; all baselines with antennas 5,6,10 index numbers

scan

''

Scan number range: Subparameter of selectdata=True default: ‘’ = all

observation

''

Select by observation ID(s): Subparameter of selectdata=True default: ‘’ = all

Example: observation=’0~2,4’

msselect

''

Optional complex data selection (ignore for now)

docallib

False

Control means of specifying the caltables

default: False –> Use gaintable, gainfield, interp, spwmap, calwt.

If True, specify a file containing cal library in callib

callib

''

Cal Library filename

Subparameter of callib=True

If docallib=True, specify a file containing cal library directives

gaintable

numpy.array( [ ] )

Gain calibration table(s) to apply on the fly

Subparameter of callib=False default: ‘’ (none)

All gain table types: ‘G’, GSPLINE, ‘T’, ‘B’, ‘BPOLY’, ‘D’s’ can be applied.

Examples: gaintable=’ngc5921.gcal’ gaintable=[‘ngc5921.ampcal’,’ngc5921.phcal’]

gainfield

numpy.array( [ ] )

Select a subset of calibrators from gaintable(s)

Subparameter of callib=False default:’’ –> all sources in table

gaintable=’nearest’ –> nearest (on sky) available field in table. Otherwise, same syntax as field

Examples: gainfield=’0~2,5’ means use fields 0,1,2,5 from gaintable gainfield=[‘0~3’,’4~6’] (for multiple gaintables)

interp

numpy.array( [ ] )

Interpolation parmameters (in time[,freq]) for each gaintable, as a list of strings.

Default: ‘’ –> ‘linear,linear’ for all gaintable(s) Options: Time: ‘nearest’, ‘linear’

Freq: ‘nearest’, ‘linear’, ‘cubic’, ‘spline’

Specify a list of strings, aligned with the list of caltable specified in gaintable, that contain the required interpolation parameters for each caltable. * When frequency interpolation is relevant (B, Df,

Xf), separate time-dependent and freq-dependent interp types with a comma (freq_after_ the comma).

Specifications for frequency are ignored when the calibration table has no channel-dependence.
Time-dependent interp options ending in ‘PD’ enable a “phase delay” correction per spw for non-channel-dependent calibration types.
For multi-obsId datasets, ‘perobs’ can be appended to the time-dependent interpolation specification to enforce obsId boundaries when interpolating in time.
Freq-dependent interp options can have ‘flag’ appended to enforce channel-dependent flagging, and/or ‘rel’ appended to invoke relative frequency interpolation

Examples: interp=’nearest’ (in time, freq-dep will be linear, if relevant) interp=’linear,cubic’ (linear in time, cubic in freq) interp=’linearperobs,splineflag’ (linear in time per obsId, spline in freq with channelized flagging) interp=’nearest,linearflagrel’ (nearest in time, linear in freq with with channelized flagging and relative-frequency interpolation) interp=’,spline’ (spline in freq; linear in time by default) interp=[‘nearest,spline’,’linear’] (for multiple gaintables)

spwmap

numpy.array( [ ] )

Spectral windows combinations to form for gaintables(s): Subparameter of callib=False default: [] (apply solutions from each spw to that spw only)

Examples: spwmap=[0,0,1,1] means apply the caltable solutions from spw = 0 to the spw 0,1 and spw 1 to spw 2,3. spwmap=[[0,0,1,1],[0,1,0,1]] (for multiple gaintables)

calwt

numpy.array( [ ] )

Calibrate data weights per gaintable.: default: True (for all specified gaintables)

Examples: calwt=False (for all specified gaintables) calwt=[True,False,True] (specified per gaintable)

parang

False

Apply parallactic angle correction

default: False

If True, apply the parallactic angle correction. FOR ANY POLARIZATION CALIBRATION AND IMAGING, parang = True

applymode

''

Calibration apply mode

default: ‘calflag’ Options: “calflag”, “calflagstrict”, “trial”, “flagonly”, “flagonlystrict”, “calonly”

– applymode=’calflag’: calibrate data and apply flags from solutions – applymode=’trial’: report on flags from solutions, dataset entirely unchanged – applymode=’flagonly’: apply flags from solutions only, data not calibrated – applymode=’calonly’ calibrate data only, flags from solutions NOT applied (use with extreme caution!) – applymode=’calflagstrict’ or ‘flagonlystrict’ same as above except flag spws for which calibration is unavailable in one or more tables (instead of allowing them to pass uncalibrated and unflagged)

flagbackup

True

Automatically back up the state of flags before the run?: default: True