fringefit – Fringe fit delay and rates – calibration task

Description

Phase offsets, groups delays and delay rates are calculated with respect to a specified referance antenna by a two-dimensional FFT and subsequent least-squares optimisation.

Previous calibrations should be applied on the fly.

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)
refant	''	Reference antenna name(s)
minsnr	float(3.0)	Reject solutions below this signal-to-noise ratio (at the FFT stage)
zerorates	False	Zero delay-rates in solution table
globalsolve	True	Refine estimates of delay and rate with global least-squares solver
niter	int(100)	Maximum number of iterations for least-squares solver
delaywindow	numpy.array( [  ] )	Constrain FFT delay search to a window
ratewindow	numpy.array( [  ] )	Constrain FFT rate search to a window
append	False	Append solutions to the (existing) table
corrdepflags	False	Respect correlation-dependent flags
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( [  ] )	Temporal interpolation for each gaintable (‘’=linear)
spwmap	[ ]	Spectral window mappings to form for gaintable(s)
paramactive	numpy.array( [  ] )	Control which parameters are solved for
parang	False	Apply parallactic angle correction on the fly

Parameter Explanations

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)

refant

''

Reference antenna name(s)

minsnr

float(3.0)

Reject solutions below this signal-to-noise ratio (at the FFT stage)

zerorates

False

Zero delay-rates in solution table

Write a solution table with delay-rates zeroed, for the case of

“manual phase calibration”, so that the calibration table can be applied to the full dataset without the extrapolation of a non-zero delay-rate term affecting the data

globalsolve

True

Refine estimates of delay and rate with global least-squares solver

niter

int(100)

Maximum number of iterations for least-squares solver

delaywindow

numpy.array( [  ] )

Constrain FFT delay search to a window specified as a two-element list with units of nanoseconds

Default: [None, None] Examples: [-10, 10]

ratewindow

numpy.array( [  ] )

Constrain FFT rate search to a window specified as a two-element list with units of seconds per second

Default: [None, None] Examples: [-1e-13, 1e-13]

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.

corrdepflags

False

If False (default), if any correlation is flagged, treat all correlations in

the visibility vector as flagged when solving (per channel, per baseline). If True, use unflagged correlations in a visibility vector, even if one or more other correlations are flagged.

Default: False (treat correlation vectors with one or more correlations flagged as entirely flagged)

Traditionally, CASA has observed a strict interpretation of correlation-dependent flags: if one or more correlations (for any baseline and channel) is flagged, then all available correlations for the same baseline and channel are treated as flagged. However, it is desirable in some circumstances to relax this stricture, e.g., to preserve use of data from antennas with only one good polarization (e.g., one polarization is bad or entirely absent). Solutions for the bad or missing polarization will be rendered as flagged.

docallib

False

Control means of specifying the caltables

Default: False (Use gaintable, gainfield, interp, spwmap, calwt) Options: False|True

If True, specify a file containing cal library in callib

callib

''

Specify a file containing cal library directives

Subparameter of docallib=True

gaintable

numpy.array( [  ] )

Gain calibration table(s) to apply on the fly

Default: ‘’ (none) Subparameter of docallib=False Examples: gaintable=’ngc5921.gcal’ gaintable=[‘ngc5921.ampcal’,’ngc5921.phcal’]

gainfield

numpy.array( [  ] )

Select a subset of calibrators from gaintable(s)

Default: ‘’ (all sources on the sky)

‘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

interp

numpy.array( [  ] )

Interpolation parameters (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

[ ]

Spectral window mappings to form for gaintable(s)

Only used if callib=False default: [] (apply solutions from each calibration spw to the same MS spw only) Any available calibration spw can be mechanically mapped to any

MS spw.

Examples:

spwmap=[0,0,1,1] means apply calibration

from cal spw = 0 to MS spw 0,1 and cal spw 1 to MS spws 2,3.

spwmap=[[0,0,1,1],[0,1,0,1]] (use a list of lists for multiple

gaintables)

paramactive

numpy.array( [  ] )

Control which parameters are solved for; a vector of (exactly) three booleans for delay, delay-rate and dispersive delay (in that order)

parang

False

Apply parallactic angle correction on the fly.