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qimager.weight - Function

	Package	synthesis
	Module	qimager
	Tool	qimager

Apply additional weighting to the visibility weights

Synopsis
weight(type, rmode, noise, robust, fieldofview, npixels, async)

Description
Apply visibility weighting to correct for the local density of sampling in the uv plane. The imaging weights are written into a Table column called IMAGING_WEIGHT, which may be plotted using plotweights. In addition this columns may be accessed directly using either the table or ms modules.

To correct for visibility sampling effects, natural, uniform (the default), radial, and Briggs weighting are supported. These work as follows. Then:

natural

: minimizes the noise in the dirty image. The weight of the i-th sample is set to the inverse variance:

$${1\over{\sigma_i^2}}$$ (1.25)

where $\sigma_{i}^{}$ is the noise of the i'th sample.

radial

: approximately minimizes rms sidelobes for an east-west synthesis array. The weight of the i-th sample is multiplied by the radial distance from the center of the u, v plane:

$$\sqrt{u_i^2+v_i^2}$$ (1.26)

uniform

: For Briggs and uniform weighting, we first grid the inverse variance w_i for all selected data onto a grid of size given by the argument npixels (default to nx) and u,v cell-size given by 2/fieldofview where fieldofview is the specified field of view (defaults to the image field of view). This forms the gridded weights W_k. The weight of the i-th sample is then changed:

$${w_i\over{W_k}}$$ (1.27)

where W_k is the gridded weight of the relevant cell. It may be shown that this minimizes rms sidelobes over the field of view. By changing the field of view, one may suppress the sidelobes over a region different (usually smaller) than the image size.

briggs: rmode='norm'

: The weights are changed:

$${w_i\over{1 + W_k f^2}}$$ (1.28)

where:

$${{(5*10^{-R})^2}\over{{\sum_k W_k^2}\over{\sum_i w_i}}}$$ (1.29)

and R is the robust parameter. The scaling of R is such that R = 0 gives a good tradeoff between resolution and sensitivity. R takes value between -2.0 (close to uniform weighting) to 2.0 (close to natural).

briggs: rmode='abs'

: The weights are changed:

$${w_i\over{W_k*R^2+2*\sigma_R^2}}$$ (1.30)

where R is the robust parameter and $\sigma_{R}^{}$ is the noise parameter.

For more details about Briggs (aka robust) weighting, see the Briggs thesis.

Note that this weighting is not cumulative since the imaging weights are calculated from the specified sigma (expected noise) per visibility (actually stored in the SIGMA column).

Arguments

type		Type of weighting
		Allowed:	String: 'uniform'|'natural'|'briggs'|'radial'
		Default:	uniform
rmode		Mode of briggs weighting
		Allowed:	String: 'norm'|'abs'|'none'
		Default:	none
noise		Noise used in absolute briggs weighting
		Allowed:	Quantity
		Default:	'0.0Jy'
robust		Parameter in briggs weighting
		Allowed:	Double: range -2.0 to 2.0
		Default:	0.0
fieldofview		Field of view for uniform weighting
		Allowed:	Quantity
		Default:	'0arcsec'
npixels		Number of pixels in the u and v directions
		Allowed:	Int
		Default:	0
async		Run asynchronously in the background
		Allowed:	Bool
		Default:	!dowait

Returns
Bool

Example

imgr.weight(type='briggs', rmode='norm', robust=0.5)

Applies Briggs (robust) weighting.