

NRAO Home > CASA > CASA Toolkit Reference Manual 

deconvolver.mem  Function
3.1.2 Make the mem image
Description
Makes a mem image using the CornwellEvans algroithm, using either
maximum entropy (entropy) or maxmimum emptiness (emptiness). The
maximum entropy algorithm is the default. You can restart a MEM
deconvolution on an existing model image, but the alpha and beta parameters
are not yet saved.
Mask images can be used to restrict where the algorithm puts flux. A prior, or bias, image can provide a priori information to the algorithm and effectively limit the support as well as a mask. The prior image can be constructed by smoothing an existing estimate for the brightness distribution and clipping. Any pixel values below 1e6 will be clipped to this level, so zero or negative pixels will not cause problems.
Currently, only one Stokes parameter may be deconvolved at a time. Stokes I images can be deconvolved with either maximum entropy or maxmimum emptiness. Stokes Q, U, or V should be deconvolved with maxmimum emptiness, which permits negative pixel values. Joint polarization MEM deconvolution is planned for the future.
The mem entropies possible are:
 entropy
 The smoothness of the image, relative to some prior (also called default or bias) image is maximized. The functional form of the entropy is H = ∑ Iln(I∕M), where I is the mem image brightness and M is the prior image. As the prior image is positive definite, the entropy constrains the mem image pixels to be positive, hence only stokes I can be imaged.
 emptiness
 The number of pixels with absolute value of the flux greater than the noise level is minimized. This treats positive and negative pixel values equally, so it is appropriate for any Stokes image.
This MEM algorithm works in the image plane (ie, is ignorant of visibility data), but performs the convolution by multiplication in the Fourier plane. Not to be confused with this usage of the term ”image plane”, some problems are ”image plane” problems, such as a single dish performing OnTheFly mapping. Independent noise is added at each integration as the beam sweeps over the object (ie, in the image plane). This can lead to a noise signal at nonphysically large spatial frequencies. This nonphysical signal can be removed by convolving the residual image with the PSF. Also key to this problem is that the PSF is of finite extent, permitting the deconvolution of nearly the entire dirty image rather than just the inner quarter. These options are accessed by setting imageplane to T.
Arguments
Inputs 
 
entropy 
 entropy to use
 
 allowed:  string 

 Default:  emptiness entropy 

niter 
 Number of Iterations, set to zero for no MEMing
 
 allowed:  int  
 Default:  20 

sigma 
 Noise level to try to achieve
 
 allowed:  any 

 Default:  variant 0.001Jy 

targetflux 
 Total image flux to try to achieve
 
 allowed:  any 

 Default:  variant 1.0Jy 

constrainflux 
 Use targetflux as a constraint? (or starting flux)
 
 allowed:  bool  
 Default:  false 

displayprogress 
 Display progress
 
 allowed:  bool 

 Default:  false 

model 
 Name of input/output model image
 
 allowed:  string 

 Default: 


prior 
 Name of prior (default) image used for mem
 
 allowed:  string 

 Default: 


mask 
 Mask image restricting emission (all pixels 0 or 1)
 
 allowed:  string 

 Default: 


imageplane 
 Is this an image plane problem (like single dish)?
 
 allowed:  bool 

 Default:  false 

async 
 Run asynchronously in the background?
 
 allowed:  bool 

 Default:  false 

bool
Example
deco.mem(entropy=’entropy’, niter=30, sigma=0.01, targetflux=10.0,
model=’3C273XC1.mem.image’, prior=’3C283XC1.prior’)
__________________________________________________________________
More information about CASA may be found at the
CASA web page
Copyright © 2016 Associated Universities Inc., Washington, D.C.
This code is available under the terms of the GNU General Public Lincense
Home  Contact Us  Directories  Site Map  Help  Privacy Policy  Search