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Code location

In LCLS software release code of the class RadialBkgd resides in the package pyimgalgos.

Auto-generated documentation for class RadialBkgd

Initialization

from pyimgalgos.RadialBkgd import RadialBkgd
rb = RadialBkgd(xarr, yarr, mask=None, radedges=None, nradbins=100, phiedges=(0,360), nphibins=32)

See parameters description in Auto-generated documentation for class RadialBkgd.

Algorithm

Intention

This algorithm is intended to subtract background from images with quasi-symmetric radial distribution of intensities. For example, water ring background:

To evaluate background in data, n-d array of data is split for 2-d bins in polar coordinate frame, total intensity and number of involved pixels are counted for each bin and converted to the average bin intensity.

Then this averaged intensity is per-pixel subtracted form data n-d array.

Description

Input per-pixel coordinates passed in numpy arrays xarr, yarr are used to evaluate per-pixel radius and polar angle:

rad = rb.pixel_rad()
phi = rb.pixel_phi()

Binning parameters radedges, nradbins, phiedges, nphibins are used to initialize 2-d bins using class HBins. Initialization with default binning parameters covers entire detector coordinate space.

Non-default binning, for example like

rb = RadialBkgd(X, Y, mask, nradbins=3, nphibins=8, phiedges=(-20, 240), radedges=(10000,80000))

defines angular pixel coordinates with correct offset relative to minimal angle,

and gives 3 bins in radial direction from 10mm to 80mm and 8 bins in angle from -20 to 240 degree:

rad = rb.pixel_rad()
iseq = rb.pixel_iseq()

Pixels masked by the n-d array passed in the parameter mask are excluded from this algorithm and are not corrected.

Averaged background intensity for default and non-default binning cases (nphibins=8, nradbins=500):

bkgd = rb.bkgd_nda(nda)

Background subtraction

Background subtracted data for default (nradbins=100, nphibins=32) and non-default binning cases (nradbins=500, nphibins=1), and (nradbins=500, nphibins=8, phiedges=(-20, 240)):

res = rb.subtract_bkgd(nda)

Polarization correction

  • For good statistical precision of the background averaging 2-d bins should contain large number of pixels. However large bins produces significant binning artifacts whech are seen in resulting image.
  • The main reason for angular bins is a variation of intensity with angle due to polarization effect. The beam polarization effect can be eliminated with appropriate correction.
    Then, radial background can be estimated for a single angular bins (ring-shaped radial bins):
pf = polarization_factor(rb.pixel_rad(), rb.pixel_phi(), z)
res = rb.subtract_bkgd(nda * pf)

For z=1m we get polarization correction factor, corrected data (water-ring) sample, and background subtracted data as follows.

 

Effect of polarization is somehow accounted, but most likely the sample-to-detector distance 1m is not correct.

References

 

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