gaincal(vis, caltable='', field='', spw='', intent='', selectdata=True, timerange='', uvrange='', antenna='', scan='', observation='', msselect='', solint='inf', combine='', preavg=- 1.0, refant='', refantmode='flex', minblperant=4, minsnr=3.0, solnorm=False, normtype='mean', gaintype='G', smodel=[''], calmode='ap', solmode='', rmsthresh=[''], corrdepflags=False, append=False, splinetime=3600.0, npointaver=3, phasewrap=180.0, docallib=False, callib='', gaintable=[''], gainfield=[''], interp=[''], spwmap=[''], parang=False)[source]

Determine temporal gains from calibrator observations

[Description] [Examples] [Development] [Details]

  • vis (string) - Name of input visibility file

  • caltable (string=’’) - Name of output gain calibration table

  • field (string=’’) - Select field using field id(s) or field name(s)

  • spw (string=’’) - Select spectral window/channels

  • intent (string=’’) - Select observing intent

  • selectdata (bool=True) - Other data selection parameters

    selectdata = True
    • timerange (string=’’) - Select data based on time range

    • uvrange (variant=’’) - Select data by baseline length.

    • antenna (string=’’) - Select data based on antenna/baseline

    • scan (string=’’) - Scan number range

    • observation ({string, int}=’’) - Select by observation ID(s)

    • msselect (string=’’) - Optional complex data selection (ignore for now)

  • solint (variant=’inf’) - Solution interval

  • combine (string=’’) - Data axes which to combine for solve (obs, scan, spw, and/or field)

  • preavg (double=-1.0) - Pre-averaging interval (sec) (rarely needed)

  • refant (string=’’) - Reference antenna name(s)

  • refantmode (string=’flex’) - Reference antenna mode

  • minblperant (int=4) - Minimum baselines _per antenna required for solve

  • minsnr (double=3.0) - Reject solutions below this SNR

  • solnorm (bool=False) - Normalize (squared) solution amplitudes (G, T only)

    solnorm = True
    • normtype (string=’mean’) - Solution normalization calculation type: mean or median

  • gaintype (string=’G’) - Type of gain solution (G,T,GSPLINE,K,KCROSS)

    gaintype = GSPLINE
    • splinetime (double=3600.0) - Spline timescale(sec); All spw's are first averaged.

    • npointaver (int=3) - The phase-unwrapping algorithm

    • phasewrap (double=180.0) - Wrap the phase for jumps greater than this value (degrees)

  • smodel (doubleArray=[‘’]) - Point source Stokes parameters for source model.

  • calmode (string=’ap’) - Type of solution” ('ap', 'p', 'a')

  • solmode (string=’’) - Robust solving mode: ('', 'L1', 'R','L1R')

  • rmsthresh (doubleArray=[‘’]) - RMS Threshold sequence (for solmode='R' or 'L1R'; see help)

  • corrdepflags (bool=False) - Respect correlation-dependent flags

  • append (bool=False) - Append solutions to the (existing) table

  • docallib (bool=False) - Use callib or traditional cal apply parameters

    docallib = False
    • gaintable (stringArray=[‘’]) - Gain calibration table(s) to apply on the fly

    • gainfield (stringArray=[‘’]) - Select a subset of calibrators from gaintable(s)

    • interp (stringArray=[‘’]) - Interpolation parameters for each gaintable, as a list

    • spwmap (intArray=[‘’]) - Spectral window mappings to form for gaintable(s)

    docallib = True
    • callib (string=’’) - Cal Library filename

  • parang (bool=False) - Apply parallactic angle correction


The complex time-dependent gains for each antenna/spwid are determined from the ratio of the data column (raw data), divided by the model column, for the specified data selection. The gains can be obtained for a specified solution interval for each spectral window, or by a spline fit to all spectral windows simultaneously. Any specified prior calibrations (e.g., bandpass) will be applied on the fly.


The fundamental calibration to be done on your interferometer data is to calibrate the antenna-based gains as a function of time, using gaincal. Systematic time-dependent complex gain errors are almost always the dominant calibration effect, and a solution for them is almost always necessary before proceeding with any other calibration solve. Traditionally, this calibration type has been a catch-all for a variety of similar effects, including: the relative amplitude and phase gain for each antenna/polarization, phase and amplitude drifts in the electronics of each antenna, amplitude response as a function of elevation (gain curve), and tropospheric amplitude and phase effects. In CASA, it is possible to handle many of these specific effects separately, as available information, circumstances, and required accuracy warrant, but if accuracy is not paramount it is still possible to solve for the net effect using a quick-and-dirty gaincal. In fact, gaincal is often used for an initial exploration of a dataset, to find data problems, etc. Also, a provisional gaincal solution can be used as prior calibration to optimize bandpass calibration. Such gaincal solutions are typically discarded.

It is best to have determined a (constant or slowly-varying) bandpass from the frequency channels by solving for the bandpass, and to include any other ancillary calibration that may be available via gencal (e.g., gaincurve, antenna position corrections, opacity, etc.).

Common calibration solve parameters

See Solving for Calibration for more information on the task parameters gaincal shares with all solving tasks, including data selection, general solving properties and arranging prior calibration. Also see the rerefant task documentation for the behavior of reference antenna application. Below we describe parameters unique to gaincal, and those common parameters with unique properties.

Gain calibration types: gaintype

The gaintype parameter selects the type of gain solution to compute. For complex gain calibration, the choices are ‘T’, ‘G’, and ‘GSPLINE’. The gaincal task also supports rudimetary delay solutions using ‘K’ and ‘KCROSS’.

Polarization-dependent sampled gain (gaintype=’G’)

Generally speaking, gaintype=’G’ can represent any multiplicative polarization- and time-dependent complex gain effect downstream of the polarizers. (Polarization- and time-independent effects upstream of the polarizers may also be treated implicitly with G.) Multi-channel data (per spectral window) will be averaged in frequency before solving (use calibration type B to solve for frequency-dependent effects within each spectral window).

Polarization-independent sampled gain (gaintype=’T’)

At high radio frequencies (>10 GHz), it is often the case that the most rapid time-dependent gain errors are introduced by the troposphere, and are polarization-independent. It is therefore unnecessary to solve for separate time-dependent solutions for both polarizations, as is the case for gaintype=’G’. Thus gaintype=’T’ is available to calibrate such tropospheric effects, differing from G only in that a single common solution for both polarizations is determined. In cases where only one polarization is observed, gaintype=’T’ is adequate to describe the time-dependent complex multiplicative gain calibration entirely. For the dual-polarization case, it is necessary to ensure that the two polarizations are, in fact, coherent by using a prior G or (unnormalized) bandpass calibration.

Spline gains (gaintype=’GSPLINE’)

At high radio frequencies, where tropospheric phase fluctuates rapidly, it is often the case that there is insufficient signal-to-noise to obtain robust G or T solutions on timescales short enough to track the variation. In this case it is desirable to solve for a best-fit functional form for each antenna using the GSPLINE solver. This fits a time-series of cubic B-splines to the phase and/or amplitude of the calibrator visibilities.

The combine parameter can be used to combine data across spectral windows, scans, and fields. Note that if you want to use combine=’field’, then all fields used to obtain a GSPLINE amplitude solution must have models with accurate relative flux densities. Use of incorrect relative flux densities will introduce spurious variations in the GSPLINE amplitude solution.

The GSPLINE solver requires a number of unique additional parameters, compared to ordinary G and T solving. The sub-parameters are:

gaintype         =  'GSPLINE'   #   Type of solution (G, T, or GSPLINE)
     splinetime  =     3600.0   #   Spline (smooth) timescale (sec), default=1 hours
     npointaver  =          3   #   Points to average for phase wrap
     phasewrap   =        180   #   Wrap phase when greater than this

The duration of each spline segment is controlled by splinetime. The splinetime will be adjusted automatically such that an integral number of equal-length spline segments will fit within the overall range of data.

Phase splines require that cycle ambiguities be resolved prior to the fit; this operation is controlled by npointaver and phasewrap. The npointaver parameter controls how many contiguous points in the time-series are used to predict the cycle ambiguity of the next point in the time-series, and phasewrap sets the threshold phase jump (in degrees) that would indicate a cycle slip. Large values of npointaver improve the SNR of the cycle estimate, but tend to frustrate ambiguity detection if the phase rates are large. The phasewrap parameter may be adjusted to influence when cycles are detected. Generally speaking, large values (>180 degrees) are useful when SNR is high and phase rates are low. Smaller values for phasewrap can force cycle slip detection when low SNR conspires to obscure the jump, but the algorithm becomes significantly less robust. More robust algorithms for phase-tracking are under development (including traditional fringe-fitting).


GSPLINE solutions cannot be used in fluxscale. You should do at least some long-timescale G amplitude solutions to establish the flux scale, then do GSPLINE in phase before or after to fix up the short timescale variations. Note also that the phase tracking algorithm in GSPLINE needs some improvement.

Single- and multi-band delay (gaintype=’K’)

With gaintype=’K’ gaincal solves for simple antenna-based delays via Fourier transforms of the spectra on baselines to (only) the reference antenna. This is not a global fringe fit but will be useful for deriving delays from data of reasonable SNR. If combine includes ‘spw’, multi-band delays solved jointly from all selected spectral windows will be determined, and will be identified with the first spectral window id in the output caltable. When applying a multi-band delay table, a non-trivial spwmap is required to distribute the solutions to all spectral windows (fan-out is not automatic). As of CASA 5.6, multi-band delays can be solved using heterogeneous spws (e.g., with differing bandwidths, channelizations, etc.).

After solving for delays, a subsequent bandpass is recommended to describe higher-order channel-dependent variation in the phase and amplitude.

Cross-hand delays (gaintype=’KCROSS’)

With gaintype=’KCROSS’, gaincal solves for a global cross-hand delay. This is used only when doing polarimetry. Use parang=T to apply prior gain and bandpass solutions. This mode assumes that all cross-hand data (per spw) share the same cross-hand delay residual, which should be the case for a proper gain/bandpass calibration. See sections on polarimetry for more information on use of this mode. Multi-band cross-hand delays are only supported for homogeneous spws (same bandwidths, channelizations, etc.).

Solution normalization: solnorm, normtype

Nominally, gain solution amplitudes are implicitly scaled in amplitude to satisfy the the effective amplitude ratio between the visiibility data and model (as pre-corrected or pre-corrupted, respectively, by specified prior calibrations). If solnorm=True, the solution amplitudes will be normalized so as to achieve an effective time- and antenna-relative gain calibration that will minimally adjust the global amplitude scale of the visibility amplitudes when applied. This is desirable when the model against which the calibration is solved is in some way incomplete w.r.t. the net amplitude scale, but a antenna- and time-relative calibration is desired, e.g., amplitude-sensitive self-calibration when not all of the total flux density has been recovered in the visibility model. The normalization factor is calculated from the power gains (squared solution amplitudes) for all antennas and times (per spw) according to the the setting of normtype. If normtype=’mean’, (the default), the square root of the mean power gain is used to normalize the amplitude gains. If normtype=’median’, the median is used instead, which can be useful to avoid biasing of the normalization by outlier amplitudes. The default for solnorm is solnorm=False, which means no normalization.

Robust solving: solmode, rmsthresh


Robust solving modes in gaincal are considered experimental in CASA 5.5. With more experience and testing in the coming development cycles, we will provide more refined advice for use of these options.

Nominally (solmode=’’), gaincal performs an iterative, steepest-descent chi-squared minimization for its antenna-based gain solution, i.e., minimizaiton of the L2 norm. Visibility outliers (i.e., data not strictly consistent with the assumption of antenna-based gains and the supplied visibility model within the available SNR) can significantly distort the chi-squared gradient calculation, and thereby bias the resulting solution. For an outlier on a single baseline, the solutions for the antennas in that baseline will tend to be biased in the direction of the outlier, and all other antenna solutions in the other direction (by a lesser amount consistent with the fraction of normal, non-outlying baselines to them). It is thus desirable to dampen the influence of such outliers, and solmode/rmshresh provide a mechanism for achieving this. These options apply only to gaintype=’G’ and ‘T’, and will be ignored for other options.

Use of solmode=’L1’ invokes an approximate form of minimization of the aggregate absolute deviation of visibilities with respect to the model, i.e., the L1 norm. This is achieved by accumulating the nominal chi-squared and its gradient using weights divided by (at each iteration of the steepest descent process) the current per-baseline absolute residual (i.e., the square-root of each baseline’s chi-square contribution). (NB: It is not possible to analytically accumulate the gradient of L1 since the absolute value is not differentiable.) To avoid an over-reliance on baselines with atypically small residuals at each interation, the weight adjustments are clamped to a minimum (divided) value, and the steepest descent convergence is repeated three times with increasingly modest clamping. The net effect is to gently but effectively render the weight of relative outliers to appropriately damped influence in the solution.

Using solmode=’R’ invokes the normal L2 solution, but attempts to identify outliers (relative to apparent aggregate rms) upon steepest descent convergence, flag them, and repeat the steepest descent. Since outliers will tend to bias the rms calculation initially (and thus possibly render spuriously large rms residuals for otherwise good data), outlier detection and re-covergence is repeated with increasingly aggressive rms thresholds, a sequence specifiable in rmsthresh. By default (rmsthresh=[]) invokes a sequence of 10 thresholds borrowed from a traditional implementation found in AIPS: [7.0,5.0,4.0,3.5,3.0,2.8,2.6,2.4,2.2,2.5]. Note that the lower threshold values are likely to cull visibilites not formally outliers, but merely with modestly large residuals still consistent with gaussian statistitics, and thereby unnecessarily decrease net effective sensitivity in the gain solution (cf normal L2), especially for larger arrays where the number of baselines likely implies a larger number of visibility residuals falling in the modest wings of the distribution. Thus, it may be desirable to set rmsthresh manually to a more modest sequence of thresholds. Optimization of rmsthresh for modern arrays and conditions is an area of ongoing study.

Use of solmode=’L1R’ combines both the L1 and R modes described above, with the iterative clamped L1 loop occuring inside the R outliner excision threshold sequence loop.


To solve for G on, say, fields 1 & 2, on a 90s timescale, and do so relative to gaincurve and bandpass corrections:

        caltable='cal.G90s',          # Write solutions to disk file 'cal.G'
        field='0,1',                  # Restrict field selection
        solint='90s',                 # Solve for phase and amp on a 90s timescale
        gaintable=['cal.B','cal.gc'], # prior bandpass and gaincurve tables
        refant='3')                   # reference antenna

To solve for more rapid tropopheric gains (3s timescale) using the above G solution, use gaintype=’T’:

        caltable='cal.T',             # Output table name
        gaintype='T',                 # Solve for T (polarization-independent)
        field='0,1',                  # Restrict data selection to calibrators
        solint='3s',                  # Obtain solutions on a 3s timescale
        gaintable=['cal.B','cal.gc','cal.G90s'],   # all prior cal
        refant='3')                   # reference antenna

To solve for GSPLINE phase and amplitudes, with splines of duration 600 seconds:

        gaintype='GSPLINE'       #   Solve for GSPLINE
        calmode='ap'             #   Solve for amp & phase
        field='0,1',             #   Restrict data selection to calibrators
        splinetime=600.)         #   Set spline timescale to 10min

No additional development details

Parameter Details

Detailed descriptions of each function parameter

vis (string) - Name of input visibility file
Default: none
Example: vis=’’
caltable (string='') - Name of output gain calibration table
Default: none
Example: caltable=’ngc5921.gcal’
field (string='') - 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.
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
field = ‘3,4C*’; field id 3, all names
starting with 4C
spw (string='') - Select spectral window/channels
Default: ‘’ (all spectral windows and channels)
spw=’0~2,4’; spectral windows 0,1,2,4 (all
spw=’<2’; spectral windows less than 2
(i.e. 0,1)
spw=’0:5~61’; spw 0, channels 5 to 61,
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
intent (string='') - Select observing intent
Default: ‘’ (no selection by intent)
Example: intent=’BANDPASS’ (selects data
labelled with BANDPASS intent)
selectdata (bool=True) - Other data selection parameters
Default: True
Options: True|False
timerange (string='') - Select data based on time range
Subparameter of selectdata=True
Default = ‘’ (all)
timerange =
(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 (variant='') - Select data by baseline length.
Default = ‘’ (all)
uvrange=’0~1000klambda’; uvrange from 0-1000 kilo-lambda
uvrange=’>4klambda’;uvranges greater than 4 kilo-lambda
uvrange=’0~1000km’; uvrange in kilometers
antenna (string='') - 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

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
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 (string='') - Scan number range
Subparameter of selectdata=True
Default: ‘’ = all
Check ‘go listobs’ to insure the scan numbers are
in order.
observation ({string, int}='') - Select by observation ID(s)
Subparameter of selectdata=True
Default: ‘’ = all
Example: observation=’0~2,4’
msselect (string='') - Optional complex data selection (ignore for now)
solint (variant='inf') - Solution interval
Default: ‘inf’ (infinite, up to boundaries
controlled by combine);
Options: ‘inf’ (~infinite), ‘int’ (per
integration), any float or integer value with or
without units
solint=’1min’; solint=’60s’, solint=60 (i.e.,
1 minute); solint=’0s’; solint=0; solint=’int’
(i.e., per integration); solint-‘-1s’;
solint=’inf’ (i.e., ~infinite, up to
boundaries enforced by combine)
combine (string='') - Data axes which to combine for solve
Default: ‘scan’ (solutions will break at obs,
field, and spw boundaries)
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
preavg (double=-1.0) - Pre-averaging interval (sec)
Default: -1.0 (none)
Rarely needed. Will average data over periods
shorter than the solution interval first.
refant (string='') - Reference antenna name(s); a prioritized list may be
Default: ‘’ (No refant applied)
refant=’4’ (antenna with index 4)
refant=’VA04’ (VLA antenna #4)
refant=’EA02,EA23,EA13’ (EVLA antenna EA02,
use EA23 and EA13 as alternates if/when EA02
drops out)
Use taskname=listobs for antenna listing
refantmode (string='flex') - Reference antenna mode
minblperant (int=4) - Minimum number of baselines required per antenna for each
Default: 4
Antennas with fewer baselines are excluded from
Example: minblperant=10 –> Antennas
participating on 10 or more baselines are
included in the solve
minblperant = 1 will solve for all baseline
pairs, even if only one is present in the data
set. Unless closure errors are expected, use
taskname=gaincal rather than taskname=blcal to
obtain more options in data analysis.
minsnr (double=3.0) - Reject solutions below this SNR
Default: 3.0
solnorm (bool=False) - Normalize (squared) solution amplitudes (G, T only)
Default: False (no normalization)
normtype (string='mean') - Solution normalization calculation type: mean or median
Default: ‘mean’
gaintype (string='G') - Type of gain solution (G,T,GSPLINE,K,KCROSS)
Default: ‘G’
Example: gaintype=’GSPLINE’
* ‘G’ means determine gains for each polarization and sp_wid
* ‘T’ obtains one solution for both polarizations;
Hence. their phase offset must be first removed
using a prior G.
* ‘GSPLINE’ makes a spline fit to the calibrator
data. It is useful for noisy data and fits a
smooth curve through the calibrated amplitude and
phase. However, at present GSPLINE is somewhat
experimental. Use with caution and check
* ‘K’ solves for simple antenna-based delays via
FFTs of the spectra on baselines to the reference
antenna. (This is not global fringe-fitting.)
If combine includes ‘spw’, multi-band delays are
determined; otherwise, per-spw single-band delays
will be determined.
* ‘KCROSS’ solves for a global cross-hand delay.
Use parang=T and apply prior gain and bandpass
solutions. Multi-band delay solves
(combine=’spw’) not yet supported for KCROSS.
smodel (doubleArray=['']) - Point source Stokes parameters for source model
Default: [] (use MODEL_DATA column)
Example: [1,0,0,0] (I=1, unpolarized)
calmode (string='ap') - Type of solution” (‘ap’, ‘p’, ‘a’)
Default: ‘ap’ (amp and phase)
Options: ‘p’ (phase) ,’a’ (amplitude), ‘ap’
(amplitude and phase)
Example: calmode=’p’
solmode (string='') - Robust solving mode:
Options: ‘’, ‘L1’, ‘R’, ‘L1R’
rmsthresh (doubleArray=['']) - RMS Threshold sequence
Subparameter of solmode=’R’ or ‘L1R’
See CASA Docs for more information
corrdepflags (bool=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.
append (bool=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.
splinetime (double=3600.0) - Spline timescale(sec); All spw's are first averaged.
Subparameter of gaintype=’GSPLINE’
Default: 3600 (1 hour)
Example: splinetime=1000
Typical splinetime should cover about 3 to 5
calibrator scans.
npointaver (int=3) - Tune phase-unwrapping algorithm
Subparameter of gaintype=’GSPLINE’
Default: 3; Keep at this value
phasewrap (double=180.0) - Wrap the phase for jumps greater than this value
Subparameter of gaintype=’GSPLINE’
Default: 180; Keep at this value
docallib (bool=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 (string='') - Specify a file containing cal library directives
Subparameter of docallib=True
gaintable (stringArray=['']) - Gain calibration table(s) to apply on the fly
Default: ‘’ (none)
Subparameter of docallib=False
gainfield (stringArray=['']) - 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
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 (stringArray=['']) - 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’,
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
* 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
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 (intArray=['']) - 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.
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
parang (bool=False) - Apply parallactic angle correction
Default: False
If True, apply the parallactic angle correction
(required for polarization calibration)