1.1.1.0 image.decompose

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image.decompose - Function

1.1.1 Separate a complex image into individual components

Description

This function is an image decomposition tool that performs several tasks, with the end result being that a strongly blended image is separated into components - both in the sense that it determines the parameters for each component (assuming a Gaussian model) and that it physically assigns each pixel in the image to an individual object. The products of these two operations are called the component list and the component map, respectively. The fitting process (which determines the component list) and the pixel-decomposition process (which determines the component map) are designed to work cooperatively to increase the efficiency and accuracy of both.

The algorithm behind the decomposition is based on the function clfind, described in Williams et al 1994, which uses a contouring procedure whereby a closed contour designates a separate component. The program first separates the image into clearly distint ’regions’ of blended emission, then contours each region to determine the areas constituting each component and passes this information on to the fitter, which determines the component list.

The contour deblending can optionally be replaced with a simpler local maximum scan, and the fitting can be replaced with a moment-based estimation method to speed up calculations on very large images or if either primary method causes trouble, but in general this will impede the accuracy of the fit.

The function works with both two and three dimensional images.

The return value is a record (or dictionary) that has 3 keys: ’components’, ’blc’, ’trc’.
The ’components’ element is a matrix each row of which contains the gaussian parameters of the component fitted.
The ’blc’ element is a matrix of the bottom left corners (blc) of the regions found. Each row correspond to a region blc.
The ’trc’ element is a matrix of the top right corners (trc) of the regions found. Each row correspond to a region trc.
Please Note that the returned blc’s and trc’s are relative to region defined by the user. A blc of [0,0] implies the bottom left of the region selected and not the bottom left of the image. Obviously if no region is defined then it is the bottom left of the image. Arguments


Inputs
region	Region selection. Default is to use the full image.
	allowed:	variant
	Default:	variant
mask	Mask to use. Default is none.
	allowed:	any
	Default:	variant
simple	Skip contour deblending and scan for local maxima
	allowed:	bool
	Default:	false
threshold	Value of minimum positive contour. Must be set and nonnegative.
	allowed:	double
	Default:	-1
ncontour	Number of contours to use in deblending (>= 2)
	allowed:	int
	Default:	11
minrange	Minimum number of closed contours in a component (> 0)
	allowed:	int
	Default:	1
naxis	Max number of perpendicular steps between contiguous pixels. Values of 1, 2 or 3 are allowed.
	allowed:	int
	Default:	2
fit	Fit to the components after deblending?
	allowed:	bool
	Default:	true
maxrms	Maximum RMS of fit residuals to not retry fit (> 0). Default is unset.
	allowed:	double
	Default:	-1
maxretry	Maximum number of times to retry the fit (>= 0). Default is unset.
	allowed:	int
	Default:	-1
maxiter	Maximum number of iterations allowed in a single fit (> 0)
	allowed:	int
	Default:	256
convcriteria	Criterion to establish convergence (>=0)
	allowed:	double
	Default:	0.0001
stretch	Stretch the mask if necessary and possible?
	allowed:	bool
	Default:	false

Returns
record

Example

"""
#
print "\t----\t decompose Ex 1 \t----"
ia.maketestimage()
out=ia.decompose(threshold=2.5, maxrms=1.0)
#Attempt 1: Converged after 21 iterations
#Attempt 1: Converged after 15 iterations
#1: Peak: 17.955  Mu: [0.000327928, 8.62573e-05]
#               Axes: [0.00175981, 0.00142841]  Rotation: 1.29539
#2: Peak: 19.8093  Mu: [1.67927e-06, -0.000374393]
#                Axes: [0.00179054, 0.00132541]  Rotation: 1.78404
#3: Peak: 10.1155  Mu: [6.28252, -7.09688e-05]
#                Axes: [0.00180877, 0.00104523]  Rotation: 1.78847
print out[’components’]
#[[  1.79549522e+01   3.27928370e-04   8.62573434e-05   1.75980886e-03
#    8.11686337e-01   1.29538655e+00]
# [  1.98093319e+01   1.67927124e-06  -3.74393392e-04   1.79054437e-03
#    7.40229547e-01   1.78403902e+00]
# [  1.01155214e+01   6.28252172e+00  -7.09688029e-05   1.80877140e-03
#    5.77867746e-01   1.78847444e+00]]
print out[’blc’]
#[[37 31]
# [47 25]
# [67 33]]
print out[’trc’]
#[[54 47]
# [66 38]
# [78 40]]
ia.close()
#
"""

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