Page History
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- background shape is evaluated in run 162 WITHOUT common mode correction. See Masks, Background subtraction for detail.
- pfv3r1 - is upgraded PSAS-147
- S/N statistical significance is improved by increasing number of pixels. See Hit /and Peak FindersFinding Algorithms.
Data processing and peak finding is done in
cxif5315/proc-cxif5315-r0169-data-pfvn-2016-05-03.py- pfv3r1 was improved - now in the background avg and rms calculation discards all type of local maximums
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| Code Block |
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nda_data = det.raw(evt)
if nda_data is not None :
nda = np.array(nda_data, dtype=np.float32, copy=True)
nda -= nda_peds
det.common_mode_apply(runnum, nda, cmpars=(5,50)) # applied for pfv3r1 latest processing only
nda = subtract_bkgd(nda, nda_bkgd, mask=nda_smask, winds=winds_bkgd, pbits=0)
nda *= nda_smask |
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alg_arc = PyAlgos(windows=winds_arc, mask=mask_arc, pbits=2) alg_arcequ.set_peak_selection_pars(npix_min=0, npix_max=1e6, amax_thr=0, atot_thr=0, son_min=6)10) # for v3r1 #alg_arc.set_peak_selection_pars(npix_min=0, npix_max=1e6, amax_thr=0, atot_thr=500, son_min=6) # for v2r1 #alg_arc.set_peak_selection_pars(npix_min=0, npix_max=1e6, amax_thr=0, atot_thr=0, son_min=6) # for v4r1 alg_equ = ... the same as above # in the event loop #peaks_arc = alg_arc.peak_finder_v2r1(nda, thr=30, r0=7, dr=2) #peakspeaks_arc = alg_arc.peak_finder_v3r1(nda, rank=5, r0=7, dr=2) peaks#peaks_arc = alg_arc.peak_finder_v4r1(nda, thr_low=10, thr_high=150, rank=5, r0=7, dr=2) peaks_equ = alg_equ... the same as above |
Results
ARC
pre-selection for histograms
| Code Block |
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def procPeakDataArc(pk) :
""" Process peak for ARC region; accumulate peak statistics in histogram arrays.
"""
#===================
sp.lst_arc_atot.append(pk.atot)
if pk.atot<1500 : return
#===================
sp.lst_arc_amax.append(pk.amax)
sp.lst_arc_npix.append(pk.npix)
sp.lst_arc_r .append(pk.r)
sp.lst_arc_phi .append(pk.phi)
... |
plots for pfv2/3/4r1
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================
EQU
pre-selection for histograms
| Code Block |
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def procPeakDataEqu(pk) :
""" Process peak for EQU region; accumulate peak data
"""
# discard small radius peaks from all histograms
#===================
sp.lst_equ_atot.append(pk.atot)
if pk.atot<1500 : return
if pk.r<100 : return
#===================
sp.lst_equ_amax.append(pk.amax)
sp.lst_equ_npix.append(pk.npix)
sp.lst_equ_r .append(pk.r)
sp.lst_equ_phi .append(pk.phi)
... |
plots for pfv2/3/4r1
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================
Fit results
ARC
pre-fit selection
| Code Block |
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def peakIsSelectedArc(pk) :
"""Apply peak selection criteria to each peak from file
"""
if sp.USE_PEAK_SELECTION :
if pk.atot<1500 : return False
if pk.npix>200 : return False
if pk.r<435 : return False
if pk.r>443 : return False
if pk.rms<10 : return False
if pk.rms>80 : return False
if math.fabs(pk.bkgd)>25 : return False
return True |
plots for pfv2/3/4r1
...
================
EQU
pre-fit selection
| Code Block |
|---|
def peakIsSelectedEqu(pk) :
"""Apply peak selection criteria to each peak from file
"""
if sp.USE_PEAK_SELECTION :
if pk.atot<1500 : return False
if pk.npix>200 : return False
if pk.rms>80 : return False
if math.fabs(pk.bkgd)>20 : return False
if pk.r<100 : return False
if pk.r>454 : return False
return True |
plots for pfv2/3/4r1
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================
References
- Masks, Background subtraction
- PSAS-147 pfv3r1 - is upgraded
- Hit /and Peak FindersFinding Algorithms - S/N statistical significance is improved by increasing number of pixels
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