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+145
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‎DQM_SiPM.py‎

Lines changed: 129 additions & 127 deletions
Original file line numberDiff line numberDiff line change
@@ -252,9 +252,9 @@ def define_range_around_peak(h, perc = [0.4, 0.4], m = 0):
252252
h.SetXTitle('Risetime [ns]')
253253
h.SetYTitle('Events / {:.1f} ns'.format(h.GetBinWidth(1)))
254254
if conf['idx_ref'] == -1:#do not apply cut again on reference channels
255-
cut = conf['amp_sel']
255+
cut = selection
256256
else:
257-
cut = conf['amp_sel'] + '&&' + configurations.channel[conf['idx_ref']]['amp_sel']
257+
cut = selection + '&&' + configurations.channel[conf['idx_ref']]['sel']
258258

259259
chain.Project(name, 'risetime['+str(k)+']', cut)
260260

@@ -347,7 +347,8 @@ def define_range_around_peak(h, perc = [0.4, 0.4], m = 0):
347347

348348
selection = conf['sel'] +' && ' + configurations.channel[conf['idx_ref']]['sel'] + ' && {} != 0'.format(time_var)
349349

350-
delta_t = np.concatenate(list(tree2array(chain, var_dT, selection)))
350+
# delta_t = np.concatenate(list(tree2array(chain, var_dT, selection)))
351+
delta_t = tree2array(chain, var_dT, selection).flatten()
351352
if ( len(delta_t) ==0):
352353
print 'Empty delta'
353354
continue
@@ -379,146 +380,147 @@ def define_range_around_peak(h, perc = [0.4, 0.4], m = 0):
379380
delta_t = np.concatenate(list(tree2array(chain, var_dT, selection)))
380381
arr = {}
381382

382-
'''=========================== Time resolution vs impact point ==========================='''
383-
i_s = conf['idx_dut']
383+
if 'TimeCorrected' in configurations.plots:
384+
'''=========================== Time resolution vs impact point ==========================='''
385+
i_s = conf['idx_dut']
384386

385-
name = 'c_space_corr'+str(k)
386-
canvas['space_corr'][k] = rt.TCanvas(name, name, 1000, 600)
387-
canvas['space_corr'][k].Divide(2)
387+
name = 'c_space_corr'+str(k)
388+
canvas['space_corr'][k] = rt.TCanvas(name, name, 1000, 600)
389+
canvas['space_corr'][k].Divide(2)
388390

389-
selection += ' && ntracks == 1 && chi2 < 8'
390-
delta_t = np.concatenate(list(tree2array(chain, var_dT, selection)))
391+
selection += ' && ntracks == 1 && chi2 < 8'
392+
delta_t = np.concatenate(list(tree2array(chain, var_dT, selection)))
393+
394+
add_sel = ''
395+
for i, c in enumerate(['x', 'y']):
396+
conf[c] = {}
397+
pos = np.concatenate(list(tree2array(chain, c+'_dut[0]', selection)))
398+
conf[c]['pl'] = np.percentile(pos, 5)
399+
conf[c]['ph'] = np.percentile(pos, 90)
400+
add_sel += ' && '+c+'_dut['+str(i_s)+'] > ' + str(conf[c]['pl']) + ' && '+c+'_dut['+str(i_s)+'] < ' + str(conf[c]['ph'])
401+
402+
canvas['space_corr'][k].cd(i+1)
403+
h = create_TH2D(np.column_stack((pos, delta_t)), name=c+name, title='Time resolution '+c+' dependence',
404+
binning=[50, conf[c]['pl']-4, conf[c]['ph']+4, 50, median-2*width, median+2*width],
405+
axis_title=[c+' [mm]', '#DeltaT [ns]']
406+
)
407+
408+
h.DrawCopy('COLZ')
409+
410+
prof = h.ProfileX('prof_'+c )
411+
prof.SetLineColor(2)
412+
prof.SetLineWidth(2)
391413

392-
add_sel = ''
393-
for i, c in enumerate(['x', 'y']):
394-
conf[c] = {}
395-
pos = np.concatenate(list(tree2array(chain, c+'_dut[0]', selection)))
396-
conf[c]['pl'] = np.percentile(pos, 5)
397-
conf[c]['ph'] = np.percentile(pos, 90)
398-
add_sel += ' && '+c+'_dut['+str(i_s)+'] > ' + str(conf[c]['pl']) + ' && '+c+'_dut['+str(i_s)+'] < ' + str(conf[c]['ph'])
414+
f = rt.TF1(c+'_fit','[0]+[1]*x+[2]*x^2',conf[c]['pl'], conf[c]['ph'])
399415

400-
canvas['space_corr'][k].cd(i+1)
401-
h = create_TH2D(np.column_stack((pos, delta_t)), name=c+name, title='Time resolution '+c+' dependence',
402-
binning=[50, conf[c]['pl']-4, conf[c]['ph']+4, 50, median-2*width, median+2*width],
403-
axis_title=[c+' [mm]', '#DeltaT [ns]']
404-
)
416+
aux_t = delta_t[np.logical_and(pos>conf[c]['pl'], pos<conf[c]['ph'])]
417+
pos = pos[np.logical_and(pos>conf[c]['pl'], pos<conf[c]['ph'])]
418+
coeff, r, rank, s = np.linalg.lstsq(np.column_stack((0*pos+1, pos, pos**2)), aux_t)
419+
for j,a in enumerate(coeff):
420+
f.SetParameter(j, a)
421+
conf[c]['coeff'] = np.flipud(coeff)
422+
f.SetLineColor(6)
423+
f.DrawCopy('SAMEL')
405424

406-
h.DrawCopy('COLZ')
425+
prof.DrawCopy('SAMEE')
426+
427+
canvas['space_corr'][k].Update()
428+
canvas['space_corr'][k].SaveAs(out_dir + '/TimeResolution_Position_dependece_ch'+str(k)+'.png')
429+
430+
431+
line = rt.TLine()
432+
line.SetLineColor(6)
433+
line.SetLineStyle(7)
434+
line.SetLineWidth(3)
435+
for can in [canvas['pos'][k], canvas['w_pos'][k]]:
436+
can.cd()
437+
line.DrawLine(conf['x']['pl'], conf['y']['pl'], conf['x']['pl'], conf['y']['ph'])
438+
line.DrawLine(conf['x']['ph'], conf['y']['pl'], conf['x']['ph'], conf['y']['ph'])
439+
line.DrawLine(conf['x']['pl'], conf['y']['pl'], conf['x']['ph'], conf['y']['pl'])
440+
line.DrawLine(conf['x']['pl'], conf['y']['ph'], conf['x']['ph'], conf['y']['ph'])
441+
canvas['w_pos'][k].SaveAs(out_dir + '/PositionXY_amp_weight_ch'+str(k)+'.png')
442+
canvas['pos'][k].SaveAs(out_dir + '/PositionXY_raw_ch'+str(k)+'.png')
407443

408-
prof = h.ProfileX('prof_'+c )
409-
prof.SetLineColor(2)
444+
selection += add_sel
445+
arr['x'] = np.concatenate(list(tree2array(chain, 'x_dut[0]', selection)))
446+
arr['y'] = np.concatenate(list(tree2array(chain, 'y_dut[0]', selection)))
447+
delta_t = np.concatenate(list(tree2array(chain, var_dT, selection)))
448+
449+
dt_space_corrected = np.copy(delta_t)
450+
for c in ['x', 'y']:
451+
dt_space_corrected -= np.polyval(conf[c]['coeff'], arr[c])
452+
453+
h = create_TH1D(dt_space_corrected, 'h_delta_space_corr'+str(k), 'Time resolution space corrected ch '+str(k),
454+
binning = [ None, np.min(dt_space_corrected), np.max(dt_space_corrected)],
455+
axis_title = [var_dT+' [ns]', 'Events'])
456+
457+
canvas['t_res_space'][k] = rt.TCanvas('c_t_res_space'+str(k), 'c_t_res_raw'+str(k), 700, 500)
458+
h.DrawCopy('E1')
459+
canvas['t_res_space'][k].Update()
460+
canvas['t_res_space'][k].SaveAs(out_dir + '/TimeResolution_space_ch'+str(k)+'.png')
461+
462+
'''=========================== Time resolution vs amplitude ==========================='''
463+
conf['amp'] = {}
464+
print selection
465+
arr['amp'] = np.concatenate(list(tree2array(chain, 'amp['+str(k)+']', selection)))
466+
canvas['dt_vs_amp'][k] = rt.TCanvas('dt_vs_amp'+str(k), 'dt_vs_amp'+str(k), 1200, 600)
467+
canvas['dt_vs_amp'][k].Divide(2)
468+
469+
h = create_TH2D(np.column_stack((arr['amp'], delta_t)), name='h_amp_dip', title='h_amp_dip',
470+
binning=[50, np.min(arr['amp']), np.max(arr['amp']), 50, np.min(delta_t), np.max(delta_t)],
471+
axis_title=['Amp [mV]', '#DeltaT [ns]']
472+
)
473+
canvas['dt_vs_amp'][k].cd(1)
474+
h.DrawCopy('colz')
475+
prof = h.ProfileX('prof_amp')
476+
prof.SetLineColor(6)
410477
prof.SetLineWidth(2)
478+
prof.DrawCopy('SAMEE1')
411479

412-
f = rt.TF1(c+'_fit','[0]+[1]*x+[2]*x^2',conf[c]['pl'], conf[c]['ph'])
480+
f = rt.TF1('amp_fit'+str(k),'[0]+[1]*x+[2]*x^2', np.min(arr['amp']), np.max(arr['amp']))
481+
f.DrawCopy('SAMEL')
413482

414-
aux_t = delta_t[np.logical_and(pos>conf[c]['pl'], pos<conf[c]['ph'])]
415-
pos = pos[np.logical_and(pos>conf[c]['pl'], pos<conf[c]['ph'])]
416-
coeff, r, rank, s = np.linalg.lstsq(np.column_stack((0*pos+1, pos, pos**2)), aux_t)
483+
coeff, r, rank, s = np.linalg.lstsq(np.column_stack((0*arr['amp']+1, arr['amp'], arr['amp']**2)), delta_t)
417484
for j,a in enumerate(coeff):
418485
f.SetParameter(j, a)
419-
conf[c]['coeff'] = np.flipud(coeff)
486+
conf['amp']['coeff'] = np.flipud(coeff)
420487
f.SetLineColor(6)
488+
f.SetLineStyle(9)
421489
f.DrawCopy('SAMEL')
422490

423-
prof.DrawCopy('SAMEE')
424491

425-
canvas['space_corr'][k].Update()
426-
canvas['space_corr'][k].SaveAs(out_dir + '/TimeResolution_Position_dependece_ch'+str(k)+'.png')
492+
dt_amp_corrected = np.copy(delta_t) - np.polyval(conf['amp']['coeff'], arr['amp'])
427493

494+
h = create_TH1D(dt_amp_corrected, 'h_delta_amp_corr'+str(k), 'Time resolution amp corrected',
495+
binning = [ None, np.min(dt_amp_corrected), np.max(dt_amp_corrected)],
496+
axis_title = [var_dT+' [ns]', 'Events'])
497+
canvas['dt_vs_amp'][k].cd(2)
498+
h.Fit('gaus', 'LQR','', np.percentile(dt_amp_corrected, 1), np.percentile(dt_amp_corrected, 99))
499+
h.DrawCopy('E1')
428500

429-
line = rt.TLine()
430-
line.SetLineColor(6)
431-
line.SetLineStyle(7)
432-
line.SetLineWidth(3)
433-
for can in [canvas['pos'][k], canvas['w_pos'][k]]:
434-
can.cd()
435-
line.DrawLine(conf['x']['pl'], conf['y']['pl'], conf['x']['pl'], conf['y']['ph'])
436-
line.DrawLine(conf['x']['ph'], conf['y']['pl'], conf['x']['ph'], conf['y']['ph'])
437-
line.DrawLine(conf['x']['pl'], conf['y']['pl'], conf['x']['ph'], conf['y']['pl'])
438-
line.DrawLine(conf['x']['pl'], conf['y']['ph'], conf['x']['ph'], conf['y']['ph'])
439-
canvas['w_pos'][k].SaveAs(out_dir + '/PositionXY_amp_weight_ch'+str(k)+'.png')
440-
canvas['pos'][k].SaveAs(out_dir + '/PositionXY_raw_ch'+str(k)+'.png')
501+
canvas['dt_vs_amp'][k].Update()
502+
canvas['dt_vs_amp'][k].SaveAs(out_dir + '/TimeResolution_amp_ch'+str(k)+'.png')
441503

442-
selection += add_sel
443-
arr['x'] = np.concatenate(list(tree2array(chain, 'x_dut[0]', selection)))
444-
arr['y'] = np.concatenate(list(tree2array(chain, 'y_dut[0]', selection)))
445-
delta_t = np.concatenate(list(tree2array(chain, var_dT, selection)))
504+
'''=========================== Time resolution w/ one-shot corrections ==========================='''
505+
def create_regression_input(x, y, amp):
506+
out = (np.ones_like(x), x, y, amp, x**2, y**2, amp**2, x*y, amp*x, amp*y)
507+
return np.column_stack(out)
446508

447-
dt_space_corrected = np.copy(delta_t)
448-
for c in ['x', 'y']:
449-
dt_space_corrected -= np.polyval(conf[c]['coeff'], arr[c])
450-
451-
h = create_TH1D(dt_space_corrected, 'h_delta_space_corr'+str(k), 'Time resolution space corrected ch '+str(k),
452-
binning = [ None, np.min(dt_space_corrected), np.max(dt_space_corrected)],
453-
axis_title = [var_dT+' [ns]', 'Events'])
454-
455-
canvas['t_res_space'][k] = rt.TCanvas('c_t_res_space'+str(k), 'c_t_res_raw'+str(k), 700, 500)
456-
h.DrawCopy('E1')
457-
canvas['t_res_space'][k].Update()
458-
canvas['t_res_space'][k].SaveAs(out_dir + '/TimeResolution_space_ch'+str(k)+'.png')
459-
460-
'''=========================== Time resolution vs amplitude ==========================='''
461-
conf['amp'] = {}
462-
print selection
463-
arr['amp'] = np.concatenate(list(tree2array(chain, 'amp['+str(k)+']', selection)))
464-
canvas['dt_vs_amp'][k] = rt.TCanvas('dt_vs_amp'+str(k), 'dt_vs_amp'+str(k), 1200, 600)
465-
canvas['dt_vs_amp'][k].Divide(2)
466-
467-
h = create_TH2D(np.column_stack((arr['amp'], delta_t)), name='h_amp_dip', title='h_amp_dip',
468-
binning=[50, np.min(arr['amp']), np.max(arr['amp']), 50, np.min(delta_t), np.max(delta_t)],
469-
axis_title=['Amp [mV]', '#DeltaT [ns]']
470-
)
471-
canvas['dt_vs_amp'][k].cd(1)
472-
h.DrawCopy('colz')
473-
prof = h.ProfileX('prof_amp')
474-
prof.SetLineColor(6)
475-
prof.SetLineWidth(2)
476-
prof.DrawCopy('SAMEE1')
477-
478-
f = rt.TF1('amp_fit'+str(k),'[0]+[1]*x+[2]*x^2', np.min(arr['amp']), np.max(arr['amp']))
479-
f.DrawCopy('SAMEL')
480-
481-
coeff, r, rank, s = np.linalg.lstsq(np.column_stack((0*arr['amp']+1, arr['amp'], arr['amp']**2)), delta_t)
482-
for j,a in enumerate(coeff):
483-
f.SetParameter(j, a)
484-
conf['amp']['coeff'] = np.flipud(coeff)
485-
f.SetLineColor(6)
486-
f.SetLineStyle(9)
487-
f.DrawCopy('SAMEL')
488-
489-
490-
dt_amp_corrected = np.copy(delta_t) - np.polyval(conf['amp']['coeff'], arr['amp'])
491-
492-
h = create_TH1D(dt_amp_corrected, 'h_delta_amp_corr'+str(k), 'Time resolution amp corrected',
493-
binning = [ None, np.min(dt_amp_corrected), np.max(dt_amp_corrected)],
494-
axis_title = [var_dT+' [ns]', 'Events'])
495-
canvas['dt_vs_amp'][k].cd(2)
496-
h.Fit('gaus', 'LQR','', np.percentile(dt_amp_corrected, 1), np.percentile(dt_amp_corrected, 99))
497-
h.DrawCopy('E1')
498-
499-
canvas['dt_vs_amp'][k].Update()
500-
canvas['dt_vs_amp'][k].SaveAs(out_dir + '/TimeResolution_amp_ch'+str(k)+'.png')
501-
502-
'''=========================== Time resolution w/ one-shot corrections ==========================='''
503-
def create_regression_input(x, y, amp):
504-
out = (np.ones_like(x), x, y, amp, x**2, y**2, amp**2, x*y, amp*x, amp*y)
505-
return np.column_stack(out)
506-
507-
inputs = create_regression_input(arr['x'], arr['y'], arr['amp'])
508-
coeff, r, rank, s = np.linalg.lstsq(inputs, delta_t)
509-
510-
dt_corr = delta_t - np.dot(inputs, coeff)
511-
512-
h = create_TH1D(dt_corr, 'h_dt_corr'+str(k), 'Time resolution one-shot correction',
513-
binning = [ None, np.min(dt_corr), np.max(dt_corr)],
514-
axis_title = [var_dT+' [ns]', 'Events'])
515-
516-
f = rt.TF1('f_corr'+str(k),'gaus', np.percentile(dt_corr, 3), np.percentile(dt_corr, 97))
517-
h.Fit('f_corr'+str(k), 'LQR+')
518-
canvas['dt_corr'][k] = rt.TCanvas('c_dt_corr'+str(k), 'c_dt_corr'+str(k), 800, 600)
519-
h.DrawCopy('E1')
520-
f.SetLineColor(2)
521-
f.DrawCopy('SAMEL')
522-
523-
canvas['dt_corr'][k].Update()
524-
canvas['dt_corr'][k].SaveAs(out_dir + '/TimeResolution_OneShot_ch'+str(k)+'.png')
509+
inputs = create_regression_input(arr['x'], arr['y'], arr['amp'])
510+
coeff, r, rank, s = np.linalg.lstsq(inputs, delta_t)
511+
512+
dt_corr = delta_t - np.dot(inputs, coeff)
513+
514+
h = create_TH1D(dt_corr, 'h_dt_corr'+str(k), 'Time resolution one-shot correction',
515+
binning = [ None, np.min(dt_corr), np.max(dt_corr)],
516+
axis_title = [var_dT+' [ns]', 'Events'])
517+
518+
f = rt.TF1('f_corr'+str(k),'gaus', np.percentile(dt_corr, 3), np.percentile(dt_corr, 97))
519+
h.Fit('f_corr'+str(k), 'LQR+')
520+
canvas['dt_corr'][k] = rt.TCanvas('c_dt_corr'+str(k), 'c_dt_corr'+str(k), 800, 600)
521+
h.DrawCopy('E1')
522+
f.SetLineColor(2)
523+
f.DrawCopy('SAMEL')
524+
525+
canvas['dt_corr'][k].Update()
526+
canvas['dt_corr'][k].SaveAs(out_dir + '/TimeResolution_OneShot_ch'+str(k)+'.png')

‎config/VME_FNALTestbeam_June2018_SiPM_forDQMpy.txt‎

Lines changed: 16 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,16 @@
1+
-->Print: Amp Int Risetime PosRaw PosWeight TimeResRaw
2+
#-->Print: Amp Int Risetime PosRaw PosWeight TimeResRaw TimeCorrected
3+
-->XYcenter-width: 20 20 30
4+
ch_num idx_time idx_dut idx_ref var_ref int_min int_max
5+
18 2 2 -1 gaus_mean 40 240
6+
19 2 2 18 LP2_5 300 450
7+
20 2 2 18 LP2_5 300 450
8+
21 2 2 18 LP2_5 350 510
9+
22 2 2 18 LP2_5 250 540
10+
23 2 2 18 LP2_5 100 400
11+
24 2 2 18 LP2_5 140 500
12+
27 3 3 -1 gaus_mean 50 180
13+
28 3 3 27 LP2_5 200 500
14+
29 3 3 27 LP2_5 200 600
15+
30 3 3 27 LP2_5 320 610
16+
31 3 3 27 LP2_5 200 450

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