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Only listed segments are highlighted on plots:

Background

Averaged background shape

Background shape evaluated in exp=cxif5315:run=162

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Background shape evaluated with common mode correction - central segments got offset to higher intensity

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Background shape evaluated evaluated in exp=cxif5315:run=162 without common mode correction - common mode offsets averaged out to zero and image represents true background shape.

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Radial background subtraction

For some reason polarization correction does not work well in this experiment for entire image.

Comparison of the 2-d interpolated radial background subtraction 

• nbins (rad:500, phi:1)

Image RemovedImage Removed

• nbins (rad:500, phi:32)

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polarization correction factor map orientation should be consistent with geometry file - in cxif5315 it should be rotated by 90°.

from pyimgalgos.RadialBkgd import RadialBkgd, polarization_factor

    X, Y, Z = geo.get_pixel_coords()
    rb = RadialBkgd(X, Y, mask, nradbins=500, nphibins=1) 
    pf = polarization_factor(rb.pixel_rad(), rb.pixel_phi()+phi_pf, 91.33e3)

# in event loop:
    nda, title = rb.subtract_bkgd_interpol(arr * pf) * mask

For example we apply this algorithm to the water background averaged sample from exp=cxif5315:run=162:

Image AddedImage Added

Radial background fails to work in the region of shadow, where image miss symmetry. This region can be removed by constraining the range of angles phiedges=(40, 325).

In the next plot the radial range is also constrained as radedges=(5000,100000) for the purpose of example:

# the same as in previous case, but
rb = RadialBkgd(X, Y, mask, nradbins=500, nphibins=1, phiedges=(40, 325), radedges=(5000,100000))

Image AddedImage Added

SIngle angular bin still works fine in our ROI defined by both masks.

Background subtraction examples

from pyimgalgos.GlobalUtils import subtract_bkgd
		# once per run:
        sp.nda_peds  = sp.det.pedestals(runnum)
        sp.nda_bkgd  = sp.det.bkgd(runnum)      # get n-d array with averaged background from calib/.../pixel_bkgd
        sp.nda_smask = sp.det.mask(evt, calib=False, status=True, edges=True, central=True, unbond=True, unbondnbrs=True)

	    # in the event loop
        nda_raw = sp.det.raw(evt)
        if nda_raw is not None :
            nda =  np.array(nda_raw, dtype=np.float32, copy=True)
            nda -= sp.nda_peds
 
            # Subtract background shape averaged for pure water
            nda = subtract_bkgd(nda, sp.nda_bkgd, mask=sp.nda_smask, winds=sp.winds_bkgd, pbits=0)

Subtract background shape evaluated in run 192

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