DAOStarFinder

class photutils.detection.DAOStarFinder(threshold, fwhm, ratio=1.0, theta=0.0, sigma_radius=1.5, sharplo=0.2, sharphi=1.0, roundlo=-1.0, roundhi=1.0, sky=0.0, exclude_border=False, brightest=None, peakmax=None, xycoords=None)[source]

Bases: StarFinderBase

Detect stars in an image using the DAOFIND (Stetson 1987) algorithm.

DAOFIND (Stetson 1987; PASP 99, 191) searches images for local density maxima that have a peak amplitude greater than threshold (approximately; threshold is applied to a convolved image) and have a size and shape similar to the defined 2D Gaussian kernel. The Gaussian kernel is defined by the fwhm, ratio, theta, and sigma_radius input parameters.

DAOStarFinder finds the object centroid by fitting the marginal x and y 1D distributions of the Gaussian kernel to the marginal x and y distributions of the input (unconvolved) data image.

DAOStarFinder calculates the object roundness using two methods. The roundlo and roundhi bounds are applied to both measures of roundness. The first method (roundness1; called SROUND in DAOFIND) is based on the source symmetry and is the ratio of a measure of the object’s bilateral (2-fold) to four-fold symmetry. The second roundness statistic (roundness2; called GROUND in DAOFIND) measures the ratio of the difference in the height of the best fitting Gaussian function in x minus the best fitting Gaussian function in y, divided by the average of the best fitting Gaussian functions in x and y. A circular source will have a zero roundness. A source extended in x or y will have a negative or positive roundness, respectively.

The sharpness statistic measures the ratio of the difference between the height of the central pixel and the mean of the surrounding non-bad pixels in the convolved image, to the height of the best fitting Gaussian function at that point.

Parameters:
thresholdfloat

The absolute image value above which to select sources.

fwhmfloat

The full-width half-maximum (FWHM) of the major axis of the Gaussian kernel in units of pixels.

ratiofloat, optional

The ratio of the minor to major axis standard deviations of the Gaussian kernel. ratio must be strictly positive and less than or equal to 1.0. The default is 1.0 (i.e., a circular Gaussian kernel).

thetafloat, optional

The position angle (in degrees) of the major axis of the Gaussian kernel measured counter-clockwise from the positive x axis.

sigma_radiusfloat, optional

The truncation radius of the Gaussian kernel in units of sigma (standard deviation) [1 sigma = FWHM / (2.0*sqrt(2.0*log(2.0)))].

sharplofloat, optional

The lower bound on sharpness for object detection.

sharphifloat, optional

The upper bound on sharpness for object detection.

roundlofloat, optional

The lower bound on roundness for object detection.

roundhifloat, optional

The upper bound on roundness for object detection.

skyfloat, optional

The background sky level of the image. Setting sky affects only the output values of the object peak, flux, and mag values. The default is 0.0, which should be used to replicate the results from DAOFIND.

exclude_borderbool, optional

Set to True to exclude sources found within half the size of the convolution kernel from the image borders. The default is False, which is the mode used by DAOFIND.

brightestint, None, optional

Number of brightest objects to keep after sorting the full object list. If brightest is set to None, all objects will be selected.

peakmaxfloat, None, optional

Maximum peak pixel value in an object. Only objects whose peak pixel values are strictly smaller than peakmax will be selected. This may be used to exclude saturated sources. By default, when peakmax is set to None, all objects will be selected.

Warning

DAOStarFinder automatically excludes objects whose peak pixel values are negative. Therefore, setting peakmax to a non-positive value would result in exclusion of all objects.

xycoordsNone or Nx2 ndarray

The (x, y) pixel coordinates of the approximate centroid positions of identified sources. If xycoords are input, the algorithm will skip the source-finding step.

See also

IRAFStarFinder

Notes

For the convolution step, this routine sets pixels beyond the image borders to 0.0. The equivalent parameters in DAOFIND are boundary='constant' and constant=0.0.

The main differences between DAOStarFinder and IRAFStarFinder are:

  • IRAFStarFinder always uses a 2D circular Gaussian kernel, while DAOStarFinder can use an elliptical Gaussian kernel.

  • IRAFStarFinder calculates the objects’ centroid, roundness, and sharpness using image moments.

References

Methods Summary

__call__(data[, mask])

Call self as a function.

find_stars(data[, mask])

Find stars in an astronomical image.

Methods Documentation

__call__(data, mask=None)

Call self as a function.

find_stars(data, mask=None)[source]

Find stars in an astronomical image.

Parameters:
data2D array_like

The 2D image array.

mask2D bool array, optional

A boolean mask with the same shape as data, where a True value indicates the corresponding element of data is masked. Masked pixels are ignored when searching for stars.

Returns:
tableQTable or None

A table of found stars with the following parameters:

  • id: unique object identification number.

  • xcentroid, ycentroid: object centroid.

  • sharpness: object sharpness.

  • roundness1: object roundness based on symmetry.

  • roundness2: object roundness based on marginal Gaussian fits.

  • npix: the total number of pixels in the Gaussian kernel array.

  • sky: the input sky parameter.

  • peak: the peak, sky-subtracted, pixel value of the object.

  • flux: the object flux calculated as the peak density in the convolved image divided by the detection threshold. This derivation matches that of DAOFIND if sky is 0.0.

  • mag: the object instrumental magnitude calculated as -2.5 * log10(flux). The derivation matches that of DAOFIND if sky is 0.0.

None is returned if no stars are found.