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
Parameter |
Default |
Description |
|---|---|---|
vis |
|
|
caltable |
|
|
field |
|
|
spw |
|
|
intent |
|
|
selectdata |
|
|
timerange |
|
|
antenna |
|
|
scan |
|
|
observation |
|
|
msselect |
|
|
solint |
|
|
combine |
|
|
append |
|
|
docallib |
|
|
callib |
|
|
gaintable |
|
|
gainfield |
|
|
interp |
|
|
spwmap |
|
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)
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( [ ] )
Temporal interpolation for each gaintable (‘’=linear)
spwmap
numpy.array( [ ] )
Spectral windows combinations to form for gaintables(s)