sep.winpos

sep.winpos(data, xinit, yinit, sig, mask=None, maskthresh=0.0, subpix=11, minsig=2.0 / 2.35 * 0.5)

Calculate more accurate object centroids using ‘windowed’ algorithm.

Starting from the supplied initial center position, an iterative algorithm is used to determine a better object centroid. On each iteration, the centroid is calculated using all pixels within a circular aperture of 4*sig from the current position, weighting pixel positions by their flux and the amplitude of a 2-d Gaussian with sigma sig. Iteration stops when the change in position falls under some threshold or a maximum number of iterations is reached. This is equivalent to XWIN_IMAGE and YWIN_IMAGE parameters in Source Extractor (for the correct choice of sigma for each object).

Note: One should be cautious about using windowed positions in crowded fields or for sources with nearby neighbors, as the iterative algorithm can fail catastrophically.

Parameters

datandarray

Data array.

xinit, yinitarray_like

Initial center(s).

sigarray_like

Gaussian sigma used for weighting pixels. Pixels within a circular aperture of radius 4*sig are included.

masknumpy.ndarray, optional

An optional mask.

maskthreshfloat, optional

Pixels with mask > maskthresh will be ignored.

subpixint, optional

Subpixel sampling used to determine pixel overlap with aperture. 11 is used in Source Extractor. For exact overlap calculation, use 0.

minsigfloat, optional

Minimum bound on sig parameter. sig values smaller than this are increased to minsig to replicate Source Extractor behavior. Source Extractor uses a minimum half-light radius of 0.5 pixels, equivalent to a sigma of 0.5 * 2.0 / 2.35.

Returns

x, ynp.ndarray

New x and y position(s).

flagnp.ndarray

Flags.