2.3.1.0 imager.setdata

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imager.setdata - Function

2.3.1 DEPRECATED...use selectvis instead..Set the data parameters selection for subsequent processing
Description

This setup tool function selects which data are to be used subsequently. After invocation of setdata, only the selected data are operated on. Thus, for example, in imaging, only the selected data are gridded into an image, and in plotting, only the selected data are plotted.

Data can be selected by field and spectral window ids. Note that all data thus selected are passed to the imaging, and may or may not be imaged, depending on how the image was constructed using setimage. For example, in mosaicing, use fieldid in setimage to control what pointing is used to define the field center, and use fieldid in setdata to control what pointings are used in the imaging.

For spectral processing, it is possible to make cubes out multi-spectral window selections but the selection is terse till a better selection scheme is devised.

The selection is controlled by the mode argument:

none: Selection ignores channel parameters but selects all channels from spectral window ids and field ids selected.
channel: Selection in channels using the nchan, start and step arguments

For channel mode, the other fields have the following meaning:

nchan: is the number of output channels selected. It defaults to 1 (i.e., the first channel).
start: is the first channel from input dataset that is to be used. It defaults to 1 (i.e. first channel).
step: gives the increment between selected input channels.

The channels are centered on velocities: start, start+step, start+2*step, etc.

By choosing the parameters for setdata and setimage correctly, one may obtain various mappings of visibility channels to image channels. For example, to average 512 visibility channels into 64 image channels (producing image channels consisting of 8 visibility channels):

im.setimage(mode=’channel’, nchan=64, start=1, step=8);
im.setdata(mode=’channel’, nchan=512, start=1, step=1)
im.clean();

This averages the spectral channels during the gridding process. If one wanted to only include every 8th channel in the deconvolution, one would do:

im.setdata(mode=’channel’, nchan=64, start=1, step=8)
im.setimage(mode=’channel’, nchan=64, start=1, step=8);
im.clean();

For velocity and opticalvelocity modes, the mstart and mstep are the start and step velocities as strings.

im.setimage(mode=’velocity’, nchan=64, mstart=’20 km/s’, mstep=’-100m/s’);
im.setdata(mode=’velocity’, nchan=64, mstart=’20km/s’, mstep=’-100m/s’);
im.clean();

If the image and data selections differ, then averaging is done during the gridding and degridding process in the image deconvolution.

im.setimage(mode=’channel’, nchan=64, start=1, step=8);
im.setdata(mode=’channel’, nchan=512, start=1, step=1)
im.clean()

Note: The channels numbers used in setimage and setdata refers to the same channel. So if a channel is not selected in setdata but is selected in setimage, then blank channels image are made. The example below will result in the having the first 5 channels in the image to be blank.

im.setdata(mode=’channel’, nchan=50, start=6, step=1) #selected chan 6-55
im.setimage(mode=’channel’, nchan=50, start=1, step=1);

# will try to image channel 1-50. But as previously only channel 6-55
# was selected only channel 6-50 will have data; images of channels
# 1-5 are blank
im.clean()

For multi-spectral window cube imaging the selection of the data can be done as follows

im.setdata(mode=’channel’, nchan=[50,60], start=[1,1], step=[1,1],
spwid=[1,2])
im.setimage(mode=’channel’, nchan=110, start=1, step=1, spwid=[1,2]);

The above means that you would make a data selection of 50 channels (starting from 1 steping 1) from the first spectral window and 60 channels (starting from 1 steping 1). The setimage defines the image to be a cube of 110 channels. The caveat is the step size in the frequency direction is the step size of the first spectral window. If the step size of channels of the two spectral windows are different then one is better off defining the image cube in velocities (e.g. as below).

im.setdata(mode=’channel’, nchan=[50,60], start=[1,1], step=[1,1],
spwid=[1,2])
im.setimage(mode=’velocity’, nchan=200, mstart=’20km/s’,
mstep=’-100m/s’);

Arguments


Inputs
mode		Type of processing: channel or velocity
		allowed:	string
		Default:	channel none none
nchan		Number of channels to select
		allowed:	intArray
		Default:	1
start		Start channels (0-relative)
		allowed:	intArray
		Default:	0
step		Step in channel number
		allowed:	intArray
		Default:	1
mstart	km/s	Start velocity (e.g. ’20Km/s’)
		allowed:	doublekm/s
		Default:	0.0
mstep	km/s	Step in velocity (e.g. ’100m/s’
		allowed:	doublekm/s
		Default:	0.0
spwid		Spectral Window Ids (0 relative) to select
		allowed:	intArray
		Default:	0
fieldid		Field Ids (0 relative) to select
		allowed:	intArray
		Default:	0
msselect		TQL select string applied as a logical ”and” with the other selections
		allowed:	string
		Default:
async		Run asynchronously in the background?
		allowed:	bool
		Default:	false

Returns
bool

Example

im = imager(’3C273XC1.MS’);
im.setdata(nchan=512,start=1,step=1, msselect=’SCAN_NUMBER > 10 && FIELD_ID==2)

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