class photutils.psf.IterativelySubtractedPSFPhotometry(group_maker, bkg_estimator, psf_model, fitshape, finder, fitter=<astropy.modeling.fitting.LevMarLSQFitter object>, niters=3, aperture_radius=None)[source]

Bases: photutils.psf.BasicPSFPhotometry

This class implements an iterative algorithm to perform point spread function photometry in crowded fields. This consists of applying a loop of find sources, make groups, fit groups, subtract groups, and then repeat until no more stars are detected or a given number of iterations is reached.


group_maker : callable or GroupStarsBase

group_maker should be able to decide whether a given star overlaps with any other and label them as beloging to the same group. group_maker receives as input an Table object with columns named as id, x_0, y_0, in which x_0 and y_0 have the same meaning of xcentroid and ycentroid. This callable must return an Table with columns id, x_0, y_0, and group_id. The column group_id should cotain integers starting from 1 that indicate which group a given source belongs to. See, e.g., DAOGroup.

bkg_estimator : callable, instance of any BackgroundBase subclass, or None

bkg_estimator should be able to compute either a scalar background or a 2D background of a given 2D image. See, e.g., MedianBackground. If None, no background subtraction is performed.

psf_model : astropy.modeling.Fittable2DModel instance

PSF or PRF model to fit the data. Could be one of the models in this package like DiscretePRF, IntegratedGaussianPRF, or any other suitable 2D model. This object needs to identify three parameters (position of center in x and y coordinates and the flux) in order to set them to suitable starting values for each fit. The names of these parameters should be given as x_0, y_0 and flux. prepare_psf_model can be used to prepare any 2D model to match this assumption.

fitshape : int or length-2 array-like

Rectangular shape around the center of a star which will be used to collect the data to do the fitting. Can be an integer to be the same along both axes. E.g., 5 is the same as (5, 5), which means to fit only at the following relative pixel positions: [-2, -1, 0, 1, 2]. Each element of fitshape must be an odd number.

finder : callable or instance of any StarFinderBase subclasses

finder should be able to identify stars, i.e. compute a rough estimate of the centroids, in a given 2D image. finder receives as input a 2D image and returns an Table object which contains columns with names: id, xcentroid, ycentroid, and flux. In which id is an integer-valued column starting from 1, xcentroid and ycentroid are center position estimates of the sources and flux contains flux estimates of the sources. See, e.g., DAOStarFinder or IRAFStarFinder.

fitter : Fitter instance

Fitter object used to compute the optimized centroid positions and/or flux of the identified sources. See fitting for more details on fitters.

aperture_radius : float

The radius (in units of pixels) used to compute initial estimates for the fluxes of sources. If None, one FWHM will be used if it can be determined from the `psf_model.

niters : int or None

Number of iterations to perform of the loop FIND, GROUP, SUBTRACT, NSTAR. If None, iterations will proceed until no more stars remain. Note that in this case it is possible that the loop will never end if the PSF has structure that causes subtraction to create new sources infinitely.


If there are problems with fitting large groups, change the parameters of the grouping algorithm to reduce the number of sources in each group or input a star_groups table that only includes the groups that are relevant (e.g. manually remove all entries that coincide with artifacts).


[1] Stetson, Astronomical Society of the Pacific, Publications,
(ISSN 0004-6280), vol. 99, March 1987, p. 191-222. Available at:…99..191S

Attributes Summary


Methods Summary

do_photometry(*args, **kwargs) Perform PSF photometry in image.

Attributes Documentation


Methods Documentation

do_photometry(*args, **kwargs)[source]

Perform PSF photometry in image.

This method assumes that psf_model has centroids and flux parameters which will be fitted to the data provided in image. A compound model, in fact a sum of psf_model, will be fitted to groups of stars automatically identified by group_maker. Also, image is not assumed to be background subtracted. If init_guesses are not None then this method uses init_guesses as initial guesses for the centroids. If the centroid positions are set as fixed in the PSF model psf_model, then the optimizer will only consider the flux as a variable.


image : 2D array-like, ImageHDU, HDUList

Image to perform photometry.

init_guesses: `~astropy.table.Table`

Table which contains the initial guesses (estimates) for the set of parameters. Columns ‘x_0’ and ‘y_0’ which represent the positions (in pixel coordinates) for each object must be present. ‘flux_0’ can also be provided to set initial fluxes. If ‘flux_0’ is not provided, aperture photometry is used to estimate initial values for the fluxes. Additional columns of the form ‘<parametername>_0’ will be used to set the initial guess for any parameters of the psf_model model that are not fixed.


output_table : Table or None

Table with the photometry results, i.e., centroids and fluxes estimations and the initial estimates used to start the fitting process. Uncertainties on the fitted parameters are reported as columns called <paramname>_unc provided that the fitter object contains a dictionary called fit_info with the key param_cov, which contains the covariance matrix.