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PointSourceExtractor

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user29A edited this page Dec 13, 2021 · 36 revisions

Overview

PointSourceExtractor provides functionality for identifying and characterizing point sources in an image. The source extraction algorithm is described here:

https://iopscience.iop.org/article/10.1088/1538-3873/ab7ee8

Constructors

PointSourceExtractor() The default constructor for the class object, used when an image is to be examined for sources.

PointSourceExtractor(double[] XCoords, double[] YCoords) The constructor for the class object used when an image already has a given list of coordinate locations for sources in the image. Use overload of Extract_Sources referencing the objects own centroids to determine source characteristics at the coordinate locations.

PointSourceExtractor(JPFITS.FITSBinTable BinTablePSE) The constructor for the class object based on a PointSourceExtractor saved from another session.

Methods

Extract_Sources(double[,] image, double pix_saturation, double pix_min, double pix_max, double kernel_min, double kernel_max, bool threshholds_as_SN, int kernel_radius, int source_separation, bool auto_background, string kernel_filename_template, bool[,]? ROI_region, bool show_waitbar) Searches for sources within a 2D image array.

  • image The 2D image array to find sources in.
  • pix_saturation The saturation threshold of the image pixels, for finding saturation islands. Set equal to zero (0) if not needed.
  • pix_min The minimum pixel threshold value (or SN) to consider a potential source.
  • pix_max The maximum pixel threshold value (or SN) to consider a potential source.
  • kernel_min The minimum kernel pixel sum threshold value (or SN) to consider a potential source.
  • kernel_max The maximum kernel pixel sum threshold value (or SN) to consider a potential source.
  • threshholds_as_SN Treat the thresholds as Signal to Noise instead of pixel values.
  • kernel_radius The radius (pixels) of the kernel to find sources within. Secondary sources within the radius will be ignored.
  • source_separation The separation (pixels) between sources. Only the brightest source within the separation radius is kept.
  • auto_background Automatically determine the local background for potential sources. Not required if background is known to be zeroed, but should have no effect if used in this case.
  • kernel_filename_template The template full file name for the kernels to be saved. Sources will be numbered sequentially. Pass empty string for no saving.
  • ROI_region A boolean array of valid area to examine. Pass null or array of equal dimension to source image all true for entire image search.
  • show_waitbar Show a cancellable wait bar.

Extract_Sources(double[,] image, double[] XCoords, double[] YCoords, int kernel_radius, bool auto_background, string kernel_filename_template) Determines centroids and other kernel information for known sources at given coordinates.

  • image The 2D image array containing the known sources to extract.
  • XCoords The x-axis coordinates of the sources. These can be PSE.Centroids_X.
  • YCoords The y-axis coordinates of the sources. These can be PSE.Centroids_Y.
  • kernel_radius The radius (pixels) of the kernel to centroid.
  • auto_background Automatically determine the local background for potential sources. Not required if background is known to be zeroed, but should have no effect if used in this case.
  • kernel_filename_template The template full file name for the kernels to be saved. Sources will be numbered sequentially. Pass empty string for no saving.

Extract_Attempt_N_Sources(int N, double[,] image, double pix_saturation, double pix_min, double pix_max, double kernel_min, double kernel_max, bool threshholds_as_SN, int kernel_radius, int source_separation, bool auto_background, string kernel_filename_template, bool[,]? ROI_region, bool show_waitbar) Attempt to find N strongest sources in an image.

  • N The number of strongest sources to try to find.
  • Other parameters as above.

Fit_Sources_Gaussian_Circular(double[] Pinit, double[] LBnds, double[] UBnds) Performs a least-squares fit on all sources of the form

G(x,y|P) = P(0) * exp( -((x - P(1)).^2 + (y - P(2)).^2 ) / (2*P(3)^2)) + P(4)

  • Pinit Initial guesses for the fit parameters. Only P(3) and P(4) are used, all other parameter initial estimates are determined locally.
  • LBnds Lower bounds for the fit parameters. Only LBnds(3) and LBnds(4) are used, all other parameter bound estimates are determined locally.
  • UBnds Upper bounds for the fit parameters. Only UBnds(3) and LBnds(4) are used, all other parameter bound estimates are determined locally.

Fit_Sources_Gaussian_Elliptical(double[] Pinit, double[] LBnds, double[] UBnds) Performs a least-squares fit on all sources of the form

G(x,y|P) = P(0) * exp( -((x - P(1))*cosd(P(3)) + (y - P(2))sind(P(3))).^2 / (2P(4)^2) - ( -(x - P(1))*sind(P(3)) + (y - P(2))cosd(P(3))).^2 / (2P(5)^2) ) + P(6).

  • Pinit Initial guesses for the fit parameters. Only P(3), P(4), P(5) and P(6) are used, all other parameter initial estimates are determined locally.
  • LBnds Lower bounds for the fit parameters. Same restrictions as above.
  • UBnds Upper bounds for the fit parameters. Same restrictions as above.

Fit_Sources_Moffat_Circular(double[] Pinit, double[] LBnds, double[] UBnds) Performs a least-squares fit on all sources of the form

M(x,y|P) = P(0) * ( 1 + { (x - P(1))^2 + (y - P(2))^2 } / P(3)^2 ) ^ (-P(4)) + P(5).

  • Pinit Initial guesses for the fit parameters. Only P(3), P(4), P(5) are used, all other parameter initial estimates are determined locally.
  • LBnds Lower bounds for the fit parameters. Same restrictions as above.
  • UBnds Upper bounds for the fit parameters. Same restrictions as above.
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