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Code location
This algorithm is intended to subtract background from "non-assembled" images with approximately angular-symmetric radial distribution of intensities.
For example, pure water ring background from exp=cxij4716:run=22 for single event has an angular symmetry as shown in the plot:
Code location
Class In LCLS software release code of the class RadialBkgd resides in the package pyimgalgos.
Auto-generated documentation for class RadialBkgd
Initialization
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from pyimgalgos.RadialBkgd import RadialBkgd, polarization_factor
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:
Input n-d arrays can be obtained through the Detector (AreaDetector) interface or directly through the class working with geometry. For example,
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from PSCalib.GeometryAccess import GeometryAccess
geo = GeometryAccess(fname_geo)
xarr, yarr, zarr = geo.get_pixel_coords()
iX, iY = geo.get_pixel_coord_indexes()
mask = geo.get_pixel_mask(mbits=0377) # mask for 2x1 edges, two central columns, and unbound pixels with their neighbours
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Algorithm
To evaluate background To evaluate background in data, n-d array of data is split for 2-d bins in polar coordinate frame, total . Total intensity and number of involved pixels are counted for each bin and converted to the average bin intensity.Then Then this averaged intensity is per-pixel subtracted form from data n-d array.
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Input per-pixel coordinates passed in as numpy n-d arrays xarr , and yarr are used to evaluate per-pixel radius radial and polar angle coordinate arrays:
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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.
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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 mask are excluded from this algorithm and are not corrected.
Averaged background intensity for default and (nradbins=200, nphibins=32) and non-default binning cases (nphibins=8, nradbins=500) binning cases :
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bkgd = rb.bkgd_nda(nda) |
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Background subtraction
Background subtracted data for default (nradbins=100200, 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 precision of the background averaging 2-d bins should contain large number of pixels. Binning artifact in some places However large bins produces significant binning distortion which 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
- 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.
Method for polarization correction factor:
Code Block collapse true def polarization_factor(rad, phi_deg, z) : """Returns per-pixel polarization factors, assuming that detector is perpendicular to Z. """ phi = np.deg2rad(phi_deg) ones = np.ones_like(rad) theta = np.arctan2(rad, z) sxc = np.sin(theta)*np.cos(phi) pol = 1 - sxc*sxc return divide_protected(ones, pol, vsub_zero=0)
Then, radial background can be estimated using ring-shaped radial bins, single bin in angle:
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arr = load_txt(fname_nda)
rb = RadialBkgd(X, Y, mask, nradbins=500, nphibins=1)
pf = polarization_factor(rb.pixel_rad(), rb.pixel_phi(), 94e3)
nda = rb.subtract_bkgd(arr * pf) * mask.flatten() |
For exp=cxij4716:run=22 z=94mm
- averaged over all 14636 events calibrated (pedestal, common mode) data
- polarization correction factor
- polarization-corrected data
- radial-background subtracted data using single angular bin
This image indicates on in-correct geometry; background center consistent with beam intersection does not coincide with detector origin (0,0). - radial-background subtracted data using 8 angular bins
dynamically apply some geometry correction
Code Block geo = GeometryAccess(fname_geo) geo.move_geo('CSPAD:V1', 0, 1600, 0, 0) geo.move_geo('QUAD:V1', 2, -100, 0, 0)and plot again radial-background subtracted data using single angular bin
we get that "dish"-like shape disappears.Interpolation (linear) between centers of the background bins could also help:
Code Block bkgd = rb.bkgd_nda_interpol(nda, method='linear') # method='nearest' 'cubic' cdata = rb.subtract_interpol(nda, method='linear')Interpolation for entire detector and for part of the image:
Image is not as impressive as for polarization-corrected sample, but the residual intensity spread shrink down to RMS~3 ADU.
- Single angular bin interpolated background, and its subtraction from data:
CSPAD "dopping" artifacts
- zoomed-in regions of four quads
- Shows some "doping" artifacts.
- A few smooth curves earlier interpreted as "scratches" apparently become a nice "chart" probably on the detector shield. This "chart" can be used for alignment.
References
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