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image.twopointcorrelation - Function

1.1.1 Compute two point correlation function from the image


Description

This function computes two-point auto-correlation functions from an image.

By default, the auto-correlation function is computed for the Sky axes. If there is no sky in the image, then the first two axes are used. Otherwise you can specify which axes the auto-correlation function lags are computed over with the axes argument (must be of length 2).

Presently, only the Structure Function is implemented. This is defined as :

S (lx,ly) =< (data(i,j)- data(i+ lx,j + ly))2 >

where lx,ly are integer lags in the x (0-axis) and y (1-axis) directions. The ensemble average is over all the values at the same lag pair. This process is extremely compute intensive and so you may have to be patient.

In an auto-correlation function image there are some symmetries. The first and third quadrants are symmetric, and the second and fourth are symmetric. So in principle, all the information is in the top or bottom half of the image. We just write it all out to look nice. The long lags don’t have a lot of contributing values of course.

Arguments





Inputs

outfile

Output image file name. Default is unset.

allowed:

string

Default:

region

Region selection. See ”help par.region” for details. Default is to use the full image.

allowed:

any

Default:

variant

mask

Mask to use. See help par.mask. Default is none.

allowed:

any

Default:

variant

axes

The pixel axes to compute structure function over. The default is sky or first two axes.

allowed:

intArray

Default:

-1

method

The method of computation. String from ’structurefunction’.

allowed:

string

Default:

structurefunction

overwrite

Overwrite (unprompted) pre-existing output file?

allowed:

bool

Default:

false

stretch

Stretch the mask if necessary and possible? See help par.stretch. Default False

allowed:

bool

Default:

false

Returns
bool

Example

 
 
"""  
#  
print "\t----\t twopointcorrelation Ex 1 \t----"  
ia.maketestimage();        # Output image is virtual  
ia.twopointcorrelation()   # Output image is virtual  
#  
"""  
 

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