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dragon.image - Function



Package synthesis
Module imager
Tool dragon


Clean and self-calibrate


Synopsis
image(levels, amplitudelevels, timescales, niter, gain, threshold, model, complist, image, residual, statsregion, statsout, algorithm, maskmodification)


Description
Makes a clean, self-calibrated image using the wide-field algorithms.

For cleaning, the deconvolution is performed on the residual image calculated from the visibility data currently selected. Hence the first step performed in clean is to transform the current model or models (optionally including a componentlist) to fill in the MODEL_DATA column, and then inverse transform the residual visibilities to get residual image for all facets. These residual image are then cleaned using the corresponding point spread function. This means that if the initial model is used as the starting point for the deconvolution. Thus if you want to restart a clean, simply set the model to the model that was previously produced by clean.

The CLEAN deconvolution is joint in whatever Stokes parameters are present. Thus it searchs for peaks in either I or I + | V| or I + $ \sqrt{Q^2 + U^2 + V^2}$, the rationale for the latter two forms being to be biased towards finding strongly polarized pixels first (these forms are also the maximum eigenvalue of the coherency matrix). The PSF is constrained to be the same in all polarizations (a feature of this implementation, not of the Hamaker-Bregman-Sault formalism).

The clean algorithm is split into minor and major cycles. In the minor cycles only the brightest points are cleaned, using a subset of the point spread function. In the major cycle, the points thus found are subtracted from the original visibilities. Note that aliasing can be reduced by using the padding argument in setoptions.

In the self-calibration phase, the data are self-calibrated to reduce the rms difference between the current model and the observed data. The antenna gain model may be either phase-only via the T-Jones or amplitude and phase via G-Jones (see calibrater for more details).

Self-calibration is invoked whenever the peak residual (in any field) drops below the current level (as specified in the levels argument). The best strategy is usually to set levels to initiate the first self-calibration when close to but above the worst calibration errors in the image, and thereafter down in steps of a factor of about 3. Usually at the high flux levels, only phase self-calibration is required. One can switch to amplitude and phase self-calibration by setting the argument amplitudelevel appropriately.

Note that the processing can be restarted from the existing images. Note also that the number of facets may be changed on restart.

There are 2 options of masking, for defining regions in which to look for CLEAN components, available. If the maskmodification argument is set to 'auto', when the image is cleaned to the flux level of a calibration stage, an automatic mask is made by thresholding the restored image to the flux level reached. However if maskmodification is set to 'interactive' a viewer tool is started prompting the user to draw the mask (See interactivemask).



Arguments

levels Flux levels at which to self-calibrate e.g. 0.3Jy 0.1Jy 0.03Jy
Allowed: Vector of strings
Default: '0Jy'
amplitudelevels Flux level below which amplitude self-calibration is used
Allowed: Quantity
Default: '0Jy'
timescales Time scales for the self-calibrations e.g (60s 10s 10s)
Allowed: Vector of strings
Default: '10s'
niter Number of Iterations, set to zero for no CLEANing
Allowed: Int
Default: 1000
gain Loop Gain for CLEANing
Allowed: Double
Default: 0.1
threshold Flux level below which cleaning will stop
Allowed: Quantity
Default: '0Jy'
model Name of images
Allowed: Vector of strings
complist Name of component list
Allowed: String
image Names of restored images
Allowed: Vector of strings
residual Names of restored images
Allowed: Vector of strings
statsregion Region in which to calculate statistics
Allowed: Any valid region
Default: unset
statsout Statistics record
Allowed: Record
Default: [=]
algorithm deconvolving algorithm
Allowed: String
Default: 'wfclark'
maskmodification Masking update at every cal cycle from 'none', 'auto', 'interactive'
Allowed: String
Default: 'none'



Returns
Bool


Example
drag.image(levels='0.3Jy 0.1Jy 0.03Jy 0.01Jy', amplitudelevel='0.02Jy', model='BF7.clean.model',
image='BF7.clean.restored', niter=1000, gain=0.25)





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2006-10-15