accor – Normalize visibilities based on auto-correlations – calibration task

Description

Determines the amplitude corrections neede due to errors in sampler thresholds using measurements of auto-correlation spectra. This correction is typically requiered for data correlated with the DiFX correlator (such as VLBA data). Other correlators (such as the SFXC correlator used to correlate EVN data at JIVE) already apply this correction at the correlator. In this case, running this task is not necessary.

Parameters

Title
Parameter	Default	Description
vis	`''`	Name of input visibility file
caltable	`''`	Name of output gain calibration table
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
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)
solint	`'inf'`	Solution interval: egs. 'inf', '60s' (see help)
combine	`''`	Data axes which to combine for solve (obs, scan, spw, and/or field)
append	`False`	Append solutions to the (existing) table
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)

Parameter Explanations

vis

''

Name of input visibility file: default: none

example: vis=’ngc5921.ms’

caltable

''

Name of output gain calibration table: default: none

example: caltable=’ngc5921.gcal’

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

Note: do not forget to include the flux density calibrator if you have one!

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 (Must set selectdata=True to select other selection parameters.)

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

antenna

''

Select data based on antenna/baseline

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

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

Note: just for antenna selection, an integer (or integer list) is converted to a string and matched against the antenna ‘name’ first. Only if that fails, the integer is matched with the antenna ID. The latter is the case for most observatories, where the antenna name is not strictly an integer.

scan

''

Scan number range

Subparameter of selectdata=True default: ‘’ = all

Check ‘go listobs’ to insure the scan numbers are in order.

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)

solint

'inf'

Solution interval (units optional): default: ‘inf’ (~infinite, up to boundaries controlled by combine) Options: ‘inf’ (~infinite), ‘int’ (per integration), any float or integer value with or without units

Examples: solint=’1min’; solint=’60s’; solint=60 –> 1 minute solint=’0s’; solint=0; solint=’int’ –> per integration solint-‘-1s’; solint=’inf’ –> ~infinite, up to boundaries -interacts with combine

combine

''

Data axes which to combine for solve

default: ‘’ (solutions will break at obs, scan, field, and spw) Options: ‘’,’obs’,’scan’,’spw’,field’, or any comma-separated combination in a single string

For gaintype=’K’, if combine includes ‘spw’, multi-band delays will be determined; otherwise, (per-spw) single-band delays will be determined.

Example: combine=’scan,spw’ (extend solutions over scan boundaries)

append

False

Append solutions to the (existing) table

default: False (overwrite existing table or make new table)

Appended solutions must be derived from the same MS as the existing caltable, and solution spws must have the same meta-info (according to spw selection and solint) or be non-overlapping.

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)

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’] means use field 0 through 3 from first gain file, field 4 through 6 for second.

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]]