accum – Accumulate incremental calibration solutions into a calibration table – calibration task

Description

Accum will interpolate and extrapolate a calibration table onto a new table that has a regularly-space time grid.

The first run of accum defines the time grid and fills this table with the results from the input table.

Subsequent use of accum will combine additional calibration tables onto the same grid of the initial accum table to obtain an output accum table. See below for concrete examples.

Accum tables are similar to CL tables in AIPS. Incremental tables are similar to SN tables in AIPS.

Parameters

Title
Parameter	Default	Description
vis	`''`	Name of input visibility file
tablein	`''`	Input cumulative calibration table; use '' on first run
incrtable	`''`	Input incremental calibration table to add
caltable	`''`	Output (cumulative) calibration table
field	`numpy.array( [ ] )`
calfield	`numpy.array( [ ] )`	List of field names to use from incrtable.
interp	`'linear'`	Interpolation mode to use for resampling incrtable solutions
accumtime	`float(1.0)`	Time-interval when create cumulative table
spwmap	`numpy.array( [ ] )`	Spectral window combinations to apply

Parameter Explanations

vis

''

Name of input visibility file: Default: none

Example: vis=’ngc5921.ms’

tablein

''

Input cumulative calibration table

Default: ‘’ (none)

On first execution of accum, tablein=’’ and accumtime is used to generate tablein with the specified time gridding.

incrtable

''

The calibration data to be interpolated onto the tablein file.

Default: ‘’ (must be specified)

caltable

''

The output cumulative calibration table: Default: ‘’ (use tablein as the output file)

field

numpy.array( [ ] )

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

calfield

numpy.array( [ ] )

Select field(s) from incrtable to process.: Default: ‘’ (all fields)

interp

'linear'

Interpolation type (in time[,freq]) to use for each gaintable.

Default: ‘’ (‘linear,linear’ for all gaintable(s)) Options: Time: ‘nearest’, ‘linear’

Freq: ‘nearest’, ‘linear’, ‘cubic’, ‘spline’

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.

Examples: interp=’nearest’ (in time, freq-dep will be linear, if relevant) interp=’linear,cubic’ (linear in time, cubic in freq) interp=’linearperobs,spline’ (linear in time per obsId, spline in freq) interp=’,spline’ (spline in freq; linear in time by default) interp=[‘nearest,spline’,’linear’] (for multiple gaintables)

accumtime

float(1.0)

The time separation when making tablein.

Subparameter of tablein Default: 1.0 (1 second)

Note: This time should not be less than the visibility sampling time, but should be less than about 30% of a typical scan length.

spwmap

numpy.array( [ ] )

Spectral windows combinations to form for gaintable(s)

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)