1.2.1.0 ms.split - Function

1.2.1 make a new ms from a subset of an existing ms
Description

This function splits out part of the MS into a new MS. Spectral averaging is available on splitting. More transformations are in the works (like time averaging).

When splitting multiple spectral windows, the parameters nchan, start, step can be vectors, so that each spectral window has its own selection on averaging and number of output channels. But the option of using only one value for each of these parameters means that it will be replicated for all the spectral windows selected.


Inputs
outputms	The name of the measurement split into
	allowed:	string
	Default:
field	Field ids (0-based) or fieldnames to split out
	allowed:	any
	Default:	variant
spw	Spectral windows to split
	allowed:	any
	Default:	variant
nchan	number of channels in output - Vector Int of length 1 or same length as spw
	allowed:	intArray
	Default:	-1
start	Start channels in input data - Vector Int of length 1 or same length as spw
	allowed:	intArray
	Default:	0
step	number of input channels to average to make 1 output channel - Vector Int of length 1 or same as spw
	allowed:	intArray
	Default:	1
baseline	Antenna names or Antenna indices to select
	allowed:	any
	Default:	variant
timebin	Value to average the data to, 0s means keep the data as is sampled, which is the default
	allowed:	any
	Default:	variant -1s
time	Limit data selected to be within a given time range. Syntax is defined in the msselection link
	allowed:	string
	Default:
scan	Limit data selected on scan numbers. Syntax is defined in the msselection link
	allowed:	any
	Default:	variant
uvrange	Limit data selected on uv distance. Syntax is defined in the msselection link
	allowed:	any
	Default:	variant
taql	For the TAQL experts, flexible data selection using the TAQL syntax
	allowed:	string
	Default:
whichcol	’DATA’, ’MODEL_DATA’, ’CORRECTED_DATA’
	allowed:	string
	Default:	DATA

In this example we split out data from the 1st field and 1st spectral window. The output data will have 10 channels which is taken from 50 channels from the input data starting at channel 0 and averaging every 5.

In this example we split out data from the 1st field and four spectral windows. The output data will have 4 spectral windows each of 10 channels which is taken from 50 channels from the input data starting at channel 0 and averaging every 5.

In this example we split out data from the 1st field and four spectral windows. There will be four spectral windows in the output data, with 10, 10, 30 and 40 channels respectively. These are averages of the input spectral windows. The first output spectral window will be formed by picking 10 channels, starting at 0 with no averaging, of the input spwid 0. The second output spectral window will consists of 10 channels and is formed by picking 100 channels from spwid 1 of the input data, starting at channel 4, and every 10 channels to make one output channel.