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dragon.weight - Function



Package synthesis
Module imager
Tool dragon


Apply 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:

wi = $\displaystyle {1\over{\sigma_i^2}}$ (1.12)

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:

wi = wi$\displaystyle \sqrt{u_i^2+v_i^2}$ (1.13)

uniform
: For briggs and uniform weighting, we first grid the inverse variance wi 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 Wk. The weight of the i-th sample is then changed:

wi = $\displaystyle {w_i\over{W_k}}$ (1.14)

where Wk 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:

wi = $\displaystyle {w_i\over{1 + W_k f^2}}$ (1.15)

where:

f2 = $\displaystyle {{(5*10^{-R})^2}\over{{\sum_k W_k^2}\over{\sum_i w_i}}}$ (1.16)

and R is the briggs parameter. The scaling of R is such that R = 0 gives a good tradeoff between resolution and sensitivity.
briggs: rmode='abs'
: The weights are changed:

wi = $\displaystyle {w_i\over{W_k*R^2+2*\sigma_R^2}}$ (1.17)

where R is the briggs parameter and $ \sigma_{R}^{}$ is the noise parameter.
For more details about briggs 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
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
drag.weight(type='briggs', rmode='norm', robust=0.5)

Applies briggs weighting.





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2006-10-15