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0.1.1 accum

Requires:

Synopsis
Accumulate incremental calibration solutions into a calibration table

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



Arguments





Inputs

vis

Name of input visibility file

allowed:

string

Default:

tablein

Input cumulative calibration table; use ” on first run

allowed:

string

Default:

incrtable

Input incremental calibration table to add

allowed:

string

Default:

caltable

Output (cumulative) calibration table

allowed:

string

Default:

field

List of field names to process from tablein

allowed:

stringArray

Default:

calfield

List of field names to use from incrtable.

allowed:

stringArray

Default:

interp

Interpolation mode to use for resampling incrtable solutions

allowed:

string

Default:

linear

accumtime

Time-interval when create cumulative table

allowed:

any

Default:

variant 1.0

spwmap

Spectral window combinations to apply

allowed:

intArray

Default:

-1

Returns
void

Example

 
 
       Accum will interpolate and extrapolate a temporal 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 a concrete example.  
 
 
     Keyword arguments:  
 
     vis -- Name of input visibility file  
             default: none.  example: vis=’ngc5921.ms’  
     tablein -- Input cumulative calibration table.  
             default: ’’  means none  
             On first execution of accum, tablein=’’  
             and accumtime is used to generate tablein with  
             the specified time gridding.  
     accumtime -- The time separation when making tablein.  
             default: 1.0  (1 second).  This time should not be  
             less than the visibiility sampling time, but should  
             be less than about 30% of a typical scan length.  
     incrtable -- The calibration data to be interpolated onto the  
             tablein file.  
             default: ’’.  Must be specified  
     caltable -- The output cumulated calibration file.  
             default: ’’  means use tablein as the output file  
 
     field -- Select field(s) from tablein to process.  
              [’go listobs’ to obtain the list id’s or names]  
            default: ’’= all fields  
            If field string is a non-negative integer, it is assumed to  
               be a field index otherwise, it is assumed to be 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 3C295  
            field = ’3,4C*’; field id 3, all names starting with 4C  
     calfield -- Select field(s) from incrtable to process.  
            default: ’’ = all fields  
     interp -- Interpolation type (in time[,freq]) to use for each gaintable.  
                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.  
                default: ’’ --> ’linear,linear’ for all gaintable(s)  
                example: 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)  
                Options: Time: ’nearest’, ’linear’  
                         Freq: ’nearest’, ’linear’, ’cubic’, ’spline’  
     spwmap -- Spectral windows combinations to form for gaintable(s)  
            default: [] (apply solutions from each spw to that spw only)  
            Example:  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)  
     async -- Run task in a separate process  
             default: False; example: async=True  
 
     Examples:  
 
       Create an accum table with 10-sec sampling, filling it with the calibration  
          in ’first_cal’ with the desired interpolation.  
 
           taskname = ’accum’  
             default()  
             vis = ’mydata.ms’  
             tablein = ’’  
             accumtime = 10  
             incrtable = ’first_cal’  
             caltable = ’accum1_cal’  
             accum()  
 
       If you plot ’accum1_cal’ with plotcal, you can see how the incrtable was  
             interpolated.  
 
       Continue accumulating calibrations in accum1_cal from ’second_cal’  
 
           taskname = ’accum’  
             default()  
             vis = ’mydata.ms’  
             tablein = ’accum1_cal’  
             incrtable = ’second_cal’  
             caltable = ’accum1_cal’  
             accum()  
 
 


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