bandpass – Calculates a bandpass calibration solution – calibration task

Description

Determines the amplitude and phase as a function of frequency for each spectral window containing more than one channel. Strong sources (or many observations of moderately strong sources) are needed to obtain accurate bandpass functions. The two solution choices are: Individual antenna/based channel solutions ‘B’; and a polynomial fit over the channels ‘BPOLY’. The ‘B’ solutions can determined at any specified time interval, and is recommended if each channel has good signal-to-noise.

Parameters

Title
Parameter	Default	Description
vis	`''`	Name of input visibility file
caltable	`''`	Name of output bandpass 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
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	`''`
solint	`'inf'`	Solution interval in time[,freq]
combine	`'scan'`	Data axes which to combine for solve (obs, scan, spw, and/or field)
refant	`''`	Reference antenna name(s)
minblperant	`int(4)`	Minimum baselines _per antenna required for solve
minsnr	`float(3.0)`	Reject solutions below this SNR (only applies for bandtype = B)
solnorm	`False`	Normalize average solution amplitudes to 1.0
bandtype	`'B'`	Type of bandpass solution (B or BPOLY)
smodel	`numpy.array( [ ] )`	Point source Stokes parameters for source model.
append	`False`	Append solutions to the (existing) table
fillgaps	`int(0)`	Fill flagged solution channels by interpolation
degamp	`int(3)`	Polynomial degree for BPOLY amplitude solution
degphase	`int(3)`	Polynomial degree for BPOLY phase solution
visnorm	`False`	Normalize data prior to BPOLY solution
maskcenter	`int(0)`	Number of channels to avoid in center of each band
maskedge	`int(5)`	Fraction of channels to avoid at each band edge (in %)
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)
parang	`False`	Apply parallactic angle correction

Parameter Explanations

vis

''

Name of input visibility file: default: non

Example: vis=’ngc5921.ms’

caltable

''

Name of output bandpass calibration table: default: none

Example: caltable=’ngc5921.bcal’

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)

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 in time[,freq]: default: ‘inf’ (~infinite, up to boundaries controlled by combine, with no pre-averaging in frequency) Options for time: ‘inf’ (~infinite), ‘int’ (per integration), any float or integer value with or without units Options for freq: an integer with ‘ch’ suffix will enforce pre-averaging by the specified number of channels. A numeric value suffixed with frequency units (e.g., ‘Hz’,’kHz’,’MHz’) will enforce pre-averaging by an integral number of channels amounting to no more than the specified bandwidth.

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 enforced by combine solint=’inf,8Mhz’ –> ~infinite in time, with 8MHz pre-average in freq solint=’int,32ch’ –> per-integration in time, with 32-channel pre-average in freq

combine

'scan'

Data axes to combine for solving: default: ‘scan’ –> solutions will break at obs, field, and spw boundaries but may extend over multiple scans (per obs, field and spw) up to solint. Options: ‘’,’obs’,’scan’,’spw’,field’, or any comma-separated combination in a single string.

Example: combine=’scan,spw’ –> extend solutions over scan boundaries (up to the solint), and combine spws for solving.

refant

''

Reference antenna name(s); a prioritized list may be specified

default: ‘’ (no reference antenna)

Examples: refant=’13’ (antenna with index 13) refant=’VA04’ (VLA antenna #4) refant=’EA02,EA23,EA13’ (EVLA antenna EA02, use EA23 and EA13 as alternates if/when EA02 drops out)

Use ‘go listobs’ for antenna listing

minblperant

int(4)

Minimum baselines _per antenna required for solve

default: 4

Antennas with fewer baselines are excluded from solutions. Amplitude solutions with fewer than 4 baselines, and phase solutions with fewer than 3 baselines are only trivially constrained, and are no better than baseline-based solutions.

example: minblperant=10 –> Antennas participating on 10 or more baselines are included in the solve.

minsnr

float(3.0)

Reject solutions below this SNR (only applies for bandtype = B)

default: 3.0

solnorm

False

Normalize bandpass amplitudes and phase for each spw, pol, ant, and timestamp

default: False (no normalization)

bandtype

'B'

Type of bandpass solution (B or BPOLY)

default: ‘B’

‘B’ does a channel by channel solution for each specified spw. ‘BPOLY’ is somewhat experimental. It will fit an nth order polynomial for the amplitude and phase as a function of frequency. Only one fit is made for all specified spw, and edge channels should be omitted. Use taskname=plotcal in order to compare the results from B and BPOLY.

Example: bandtype=’BPOLY’

smodel

numpy.array( [ ] )

Point source Stokes parameters for source model.

append

False

Append solutions to the (existing) table

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

Append solutions to the (existing) 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.

fillgaps

int(0)

Fill flagged solution channels by interpolation: Subparameter of bandtype=’B’ default: 0 (don’t interpolate)

Example: fillgaps=3 (interpolate gaps 3 channels wide and narrower)

degamp

int(3)

Polynomial degree for BPOLY amplitude solution: Subparameter of bandtype=’BPOLY’ default: 3

Example: degamp=2

degphase

int(3)

Polynomial degree for BPOLY phase solution: Subparameter of bandtype=’BPOLY’ default: 3

Example: degphase=2

visnorm

False

Normalize data prior to BPOLY solution: Subparameter of bandtype=’BPOLY’ default: False

Example: visnorm=True

maskcenter

int(0)

Number of channels to avoid in center of each band: Subparameter of bandtype=’BPOLY’ default: 0

Example: maskcenter=5 (BPOLY only)

maskedge

int(5)

Fraction of channels to avoid at each band edge (in %): Subparameter of bandtype=’BPOLY’ default: 5

Example: maskedge=3 (BPOLY only)

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’] (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)

parang

False

Apply parallactic angle correction

default: False

If True, apply the parallactic angle correction (required for polarization calibration)