accor(vis, caltable='', field='', spw='', intent='', selectdata=True, timerange='', antenna='', scan='', observation='', msselect='', solint='inf', combine='', corrdepflags=False, append=False, docallib=False, callib='', gaintable=[''], gainfield=[''], interp=[''], spwmap=[''])[source]

Normalize visibilities based on auto-correlations

[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

    • 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: egs. 'inf', '60s' (see help)

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

  • 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 windows combinations to form for gaintables(s)

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


accor determines the amplitude calibration from auto-correlations.

The accor task determines the amplitude corrections from the apparent normalization of the mean autocorrelation spectra. Mis-normalization of the autocorrelations (and thus also the cross-correlations) is caused by errors in sampler thresholds during an observation. This correction is typically required for data correlated with the DiFX correlator (such as VLBA data). Other correlators (such as the SFXC correlator, which is used to correlate EVN data at JIVE) may already apply this correction at the correlator. In these cases, running this task is not necessary (but shouldn’t hurt).

The accor task should be run with a solution interval (solint) adequate to track variations in effective sampler level optimization (including resets), typically on timescales of seconds to minutes.

See Solving for Calibration for more information on the task parameters accor shares with all calibration solving tasks, including data selection, general solving properties, and arranging prior calibration (i.e., specifying other caltables to pre-apply before solving). In most cases, no prior calibration is required, since the raw mis-normalization of the autocorrelations is essentially the calibration sought from accor.


The following example creates a caltable with accor solutions on a 30s timescale.

accor(vis='', caltable='cal.A', solint='30s')

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
Note: do not forget to include the flux density
calibrator if you have one!
spw (string='') - Select spectral window/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
type ‘help par.selection’ for more examples.
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 (Must set selectdata=True to select
other selection parameters.)
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
antenna (string='') - Select data based on antenna/baseline
Subparameter of selectdata=True
default: ‘’ (all)
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
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 (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 (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
solint-‘-1s’; solint=’inf’ –> ~infinite, up
to boundaries -interacts with combine
combine (string='') - 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)
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.
docallib (bool=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 (string='') - Cal Library filename
Subparameter of callib=True
If docallib=True, specify a file containing cal
library directives
gaintable (stringArray=['']) - Gain calibration table(s) to apply on the fly
Subparameter of callib=False
default: ‘’ (none)
Examples: gaintable=’ngc5921.gcal’
gainfield (stringArray=['']) - 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
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 (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 windows combinations to form for gaintables(s)
Subparameter of callib=False
default: [] (apply solutions from each spw to
that spw only)
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.