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and gives 3 bins in radial direction from 10mm to 80mm and 8 bins in angle from -20 to 240 degree:
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iradrad = rb.pixel_iradrad() iphi = rb.pixel_iphi() 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.
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bkgd = rb.bkgd_nda(nda) |
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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)):
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res = rb.subtract_bkgd(nda) |
Polarization correction
- For good statistical background averaging 2-d bins should contain large number of pixels. Binning artifact in some places are seen in resulting image.
- Photon beam polarization effect is observed in the image with single angular bin (ring-shaped radial bins). One could eliminate this effect by modulation of the background sample using polarization correction:
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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:
References
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