SimpleSubtraction

class psfsubtraction.fitpsf.SimpleSubtraction(psfbase, image=None)[source] [edit on github]

Bases: psfsubtraction.fitpsf.BasePSFFitter

Simple examples of PSF fitting.

  • The whole (unmasked) image is fit at once
  • using all bases.

Methods Summary

findbase(region) Return all available bases.
fitpsfcoeff(image1d, psfbase) solve a linear algebra system for the best PSF
optregion(region, indpsf) Here we select the maximal region.
regions() Return the unmasked part of an image.

Methods Documentation

findbase(region) [edit on github]

Return all available bases.

fitpsfcoeff(image1d, psfbase) [edit on github]

solve a linear algebra system for the best PSF

Parameters:
image1d : array in 1 dim
psfbase : array in [M,N]

M = number of pixels in flattened image N = number of images that form the space of potential PSFs

Returns:
psf_coeff : array in 1 dim

Coefficients for a linear combination of psfbase elements that that give the optimal PSF.

Raises:
ValueError : If given masked data, because numpy.linalg.solve would silently

use the “values behind the mask”.

optregion(region, indpsf) [edit on github]

Here we select the maximal region.

The region is maximal in the sense that it includes all pixels that are not masked in the data or any of the bases.

Returns:
optregion : np.array of type bool

True for those pixels that should be included in the fit

regions() [edit on github]

Return the unmasked part of an image.

Returns:
regions: list

List of one element (the image)