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Mask
To create mask use Mask Editor command med or launch it through the calibman.
Geometry center
ix_cent, iy_cent = det.point_indexes(runnum)
879, 871 (then x and y are swapped to Cartesian system).
Equatorial mask
Mask parameters
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Wedge 871 879 454 387 148 196 1 k False False Wedge 871 879 454 387 -27 21 1 k False False |
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t = pars[0] # figure type, ex. 'Wedge' x = float(pars[1]) # x coordinate of the wedge center y = float(pars[2]) # y coordinate of the wedge center r = float(pars[3]) # 1-st radius of the wedge w = float(pars[4]) # radial width of the wedge t1 = float(pars[5]) # 1-st angle t2 = float(pars[6]) # 2-nd angle lw = int(pars[7]) # line width col = str(pars[8]) # color s = self.dicBool[pars[9].lower()] # isSelected boolean parameter rem = self.dicBool[pars[10].lower()] # isRemoved - helper parameter |
Involved sensors in adopted cxif5315 geometry: 0, 1, 9,15, 16,17, 25,31
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--------- | 1 | 0 | ----+---- | 2 | 3 | --------- |
Arc mask
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Wedge 871 879 454 26 -174 186 1 k False False |
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Involved sensors in the top part of the image in adopted cxif5315 geometry: 0, (1), 7, 8, (9), 15
Equatorial and arc combined mask
Radial background subtraction
For some reason polarization correction does not work well in this experiment for entire image.
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SIngle angular bin still works fine in our ROI defined by both masks.
Comparison of the pfv2 with Cheetah list of peaks
Code for comparison: cxif5315/peak-list-comp-cheetah.py
Peak lists for comparison:
- peakfinder:
pfv2-cxif5315-r0169-2016-03-28T15:02:47.txt - cheetah: r0169-cheetah-peaks.txt - with unknown origin...
Comparison algorithm and conditions
Use pyimgalgos.TDFileContainer for both peakfinder and cheetah
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fc_ch = TDFileContainer(fncheet, indhdr='frameNumber', objtype=TDCheetahPeakRecord) # , pbits=256)
fc_pf = TDFileContainer(fnpeaks, indhdr='Evnum', objtype=TDPeakRecord) #, pbits=256) |
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References
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Statistics for number of peaks:
- total in Cheetah: 14002
- matched: 10593
- not-matched: 3409
- fraction of matched: 0.757
Plots for number of peaks in peak-finder, Cheetah, and number of non-matched:matched Cheetah peaks.
Peak-finder number of peaks accounts for arc region.
Peak finding
Try to work with versions > v1 of peak-finders:
Parameters:
Peak-finder and selector parameters in cxif5315/proc-cxif5315-r0169-data-pfvn-2016-03-28.py
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alg_arc = PyAlgos(windows=winds_arc, mask=mask_arc, pbits=2)
alg_equ.set_peak_selection_pars(npix_min=4, npix_max=500, amax_thr=0, atot_thr=600, son_min=5) # for pfv2,4
#alg_equ.set_peak_selection_pars(npix_min=4, npix_max=500, amax_thr=0, atot_thr=600, son_min=10) # for pfv3
alg_equ = # all parameters are the same as for alg_arc
peaks_arc = alg_arc.peak_finder_v2(nda, thr=10, r0=6, dr=0.5) # Flood-filling
#peaks_arc = alg_arc.peak_finder_v3(nda, rank=6, r0=6, dr=0.5) # Ranker
#peaks_arc = alg_arc.peak_finder_v4(nda, thr_low=10, thr_high=150, rank=5, r0=6, dr=0.5) # Droplet-finder |
Peak selection parameters
peak_finder_v2, v3, v4: ARC
- Amax is only used in pfv4 as
thr_high=150
that helps to reduce a number of seed peaks
- Atot is used in all peak-finders as
atot_thr=600most likely can be harmlessly increased to 1500-1800ADU
- rms is used in all peak-finders at processing in peak selector
if pk.rms>60 : return False
- rms is used in all peak-finders at processing in peak selector
if math.fabs(pk.bkgd)>20 : return False
- npix is used in all peak-finders in peak selector
npix_min=4, npix_max=500it does not cut anything
- S/N is used as
son_min=5 # for pfv2,4son_min=10 # for pfv3 - Ranker - to eliminate large number of purely statistical seed peaks
peak_finder_v2, v3, v4: EQU
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