2.3.1.0 imager.pixon

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imager.pixon - Function

2.3.1 DEPRECATED-to be removed as is not working and do not have license-Calculate a deconvolved image with the pixon algorithm (experimental)
Description

Makes a image using the Pixon algorithm. According to its developers, the pixon method is:

a new way of viewing the problem of modeling the underlying, unblurred, noise-free image. The goal of the [new] Pixon method model was to construct the simplest, i.e. smoothest, model for the image that would be consistent with the data, i.e. have an acceptable chi-square fit. Being the simplest model, the derived image would be artifact free, i.e. there would be no spurious sources, since by construction the simplest model eliminates unneeded structures. In addition, the model would necessarily be a ”critical” model, i.e. most tightly constrained by the data, and consequently have the most accurately determined parameters.

In the simplest terms, the pixon method smooths a model locally as much as is allowed by the specified noise level. This, like all high performance estimation methods, the pixon approach works best when the noise level is known and well-characterized.

The Pixon algorithm is available via an IDL library courtesy of the Pixon LLC. This means that you must have the library and IDL installed on your computer. The Pixon library is available free of charge for your personal scientific use direct from Pixon LLC. IDL is available commercially from RSI.

Before pixon can be run, you must run setdata and setimage.

The algorithms available are:

singledish: The single dish observations are fed directly to the pixon algorithm. Make the image cellsize considerably finer than Nyquist sampling. The errors are those attached to each data point. It is particularly important that the estimated sigmas attached to the data are accurate.
synthesis: A dirty image and dirty point spread function are fed to the pixon algorithm. The error is assumed to be constant across the image. This approach is not formally correct and gives results that are not optimum. Development of pixon processing for synthesis observations is proceeding.
test: The standard Pixon LLC test is run.

Arguments


Inputs
algorithm		Algorithm to use
		allowed:	string
		Default:	singledish synthesis test singledish
sigma	Jy	Image sigma to try to achieve
		allowed:	doubleJy
		Default:	0.001
model		Name of model image
		allowed:	string
		Default:
async		Run asynchronously in the background?
		allowed:	bool
		Default:	false

Returns
bool

Example

im.pixon(model=’3C273XC1.pixon.model’, algorithm=’synthesis’,
sigma=’0.001Jy’)

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