forked from diamondIPP/IPADiamondCCD
-
Notifications
You must be signed in to change notification settings - Fork 0
/
AnalysisAllRunsInFolder.py
executable file
·746 lines (667 loc) · 40.1 KB
/
AnalysisAllRunsInFolder.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
#!/usr/bin/env python
import numpy as np
from struct import unpack
import time, os, sys
from optparse import OptionParser
import ipdb
import cPickle as pickle
from Utils import *
from Settings_Caen import Settings_Caen
from Converter_Caen import Converter_Caen
import subprocess as subp
from AnalysisCaenCCD import AnalysisCaenCCD
import ROOT as ro
fit_method = ('Minuit2', 'Migrad', )
class AnalysisAllRunsInFolder:
def __init__(self, runsdir='', config='', configinput='', overwrite=False, doDebug=False):
self.time0 = time.time()
self.runsdir = Correct_Path(runsdir)
self.config = Correct_Path(config)
self.configInput = Correct_Path(configinput) if configinput != '' else ''
self.overwrite = overwrite
self.doDebug = doDebug
self.are_cal_runs = False if self.configInput == '' else True # this is only true for signal calibration runs
runstemp = glob.glob('{d}/*'.format(d=self.runsdir))
if len(runstemp) < 1:
ExitMessage('The directory does not have any runs', os.EX_USAGE)
# self.num_cores = 1
self.runs = [runi for runi in runstemp if os.path.isdir(runi)]
if self.are_cal_runs:
self.runs.sort(key=lambda x: float(x.split('_')[-1].split('mV')[0].split('V')[0]))
else:
self.runs.sort(key=lambda x: float(x.split('_Pos_')[-1].split('V')[0]) if '_Pos_' in x else -1*float(x.split('_Neg_')[-1].split('V')[0]))
self.num_runs = len(self.runs)
if self.num_runs < 1: ExitMessage('There is not even the required data to convert one run', os.EX_DATAERR)
self.caen_ch = 3
self.voltages = []
self.diaVoltages = {}
self.diaVoltagesSigma = {}
self.signalOut = {}
self.signalOutSigma = {}
self.signalOutVcal = {}
self.signalOutVcalSigma = {}
self.signalOutCharge = {}
self.signalOutChargeSigma = {}
self.signalOutCF = {}
self.signalOutSigmaCF = {}
self.signalOutVcalCF = {}
self.signalOutVcalSigmaCF = {}
self.signalOutChargeCF = {}
self.signalOutChargeSigmaCF = {}
self.signalOutNP = {}
self.signalOutSigmaNP = {}
self.signalOutVcalNP = {}
self.signalOutVcalSigmaNP = {}
self.signalOutChargeNP = {}
self.signalOutChargeSigmaNP = {}
self.signalOutCFNP = {}
self.signalOutSigmaCFNP = {}
self.signalOutVcalCFNP = {}
self.signalOutVcalSigmaCFNP = {}
self.signalOutChargeCFNP = {}
self.signalOutChargeSigmaCFNP = {}
self.signalIn = {}
self.signalInSigma = {}
self.signalPeakTime = {}
self.signalPeakTimeSigma = {}
self.signalPeakTimeCF = {}
self.signalPeakTimeSigmaCF = {}
self.graphPeakTime = ro.TGraphErrors()
self.graphPeakTimeCF = ro.TGraphErrors()
self.graphPH = ro.TGraphErrors()
self.graphVcal = ro.TGraphErrors()
self.graphCharge = ro.TGraphErrors()
self.graphPHCF = ro.TGraphErrors()
self.graphVcalCF = ro.TGraphErrors()
self.graphChargeCF = ro.TGraphErrors()
self.graphPHNP = ro.TGraphErrors()
self.graphVcalNP = ro.TGraphErrors()
self.graphChargeNP = ro.TGraphErrors()
self.graphPHCFNP = ro.TGraphErrors()
self.graphVcalCFNP = ro.TGraphErrors()
self.graphChargeCFNP = ro.TGraphErrors()
self.canvasPeakTime = None
self.canvasPeakTimeCF = None
self.canvasPH = None
self.canvasVcal = None
self.canvasCharge = None
self.canvasPHCF = None
self.canvasVcalCF = None
self.canvasChargeCF = None
self.canvasPHNP = None
self.canvasVcalNP = None
self.canvasChargeNP = None
self.canvasPHCFNP = None
self.canvasVcalCFNP = None
self.canvasChargeCFNP = None
self.fit = None
self.cal_pickle = None
self.LoadPickle()
def LoadPickle(self):
cal_files = glob.glob('{d}/signal_*.cal'.format(d=self.runsdir))
if len(cal_files) > 0:
self.cal_pickle = pickle.load(open(cal_files[0], 'rb'))
self.voltages = self.cal_pickle['voltages']
self.signalIn = self.cal_pickle['signal_in']
self.signalInSigma = self.cal_pickle['signal_in_sigma']
self.signalOut = self.cal_pickle['signal_out']
self.signalOutSigma = self.cal_pickle['signal_out_sigma']
self.caen_ch = self.cal_pickle['caen_ch']
nameVcal = ''
nameCharge = ''
namePeakTime = ''
if 'signal_out_vcal' in self.cal_pickle.keys():
self.signalOutVcal = self.cal_pickle['signal_out_vcal']
self.signalOutVcalSigma = self.cal_pickle['signal_out_vcal_sigma']
nameVcal = '' if self.are_cal_runs else 'SignalVcal_vs_HV'
if 'signal_out_charge' in self.cal_pickle.keys():
self.signalOutCharge = self.cal_pickle['signal_out_charge']
self.signalOutChargeSigma = self.cal_pickle['signal_out_charge_sigma']
nameCharge = '' if self.are_cal_runs else 'SignalCharge_vs_HV'
if 'signal_peak_time' in self.cal_pickle.keys():
self.signalPeakTime = self.cal_pickle['signal_peak_time']
self.signalPeakTimeSigma = self.cal_pickle['signal_peak_time_sigma']
namePeakTime = 'SignalPeakTime_vs_Signal_ch_' + str(self.caen_ch) if self.are_cal_runs else ''
name = 'Signal_vs_CalStep_ch_' + str(self.caen_ch) if self.are_cal_runs else 'Signal_vs_HV'
xpoints = np.array([self.signalIn[volt] for volt in self.voltages], 'f8') if len(self.signalIn.keys()) >= 1 else np.array(self.voltages, 'f8')
xpointserrs = np.array([self.signalInSigma[volt] for volt in self.voltages], 'f8') if len(self.signalInSigma.keys()) >= 1 else np.zeros(len(self.voltages), 'f8')
self.graphPH = ro.TGraphErrors(len(self.voltages), xpoints, np.array([self.signalOut[volt] for volt in self.voltages], 'f8'), xpointserrs, np.array([self.signalOutSigma[volt] for volt in self.voltages], 'f8'))
self.graphPH.SetNameTitle(name, name)
self.graphPH.GetXaxis().SetTitle('vcal step [mV]' if self.are_cal_runs else 'HV [V]')
self.graphPH.GetYaxis().SetTitle('signal [mV]')
self.graphPH.SetMarkerStyle(7)
self.graphPH.SetMarkerColor(ro.kBlack)
self.graphPH.SetLineColor(ro.kBlack)
if self.are_cal_runs:
self.fit = ro.TF1('fit_' + name, 'pol1', -1000, 1000)
self.fit.SetLineColor(ro.kRed)
self.fit.SetNpx(10000)
self.fit.SetParameters(np.array([self.cal_pickle['fit_p0'], self.cal_pickle['fit_p1']], 'f8'))
self.fit.SetParErrors(np.array([self.cal_pickle['fit_p0_error'], self.cal_pickle['fit_p1_error']], 'f8'))
self.fit.SetNDF(self.cal_pickle['fit_ndf'])
self.fit.SetChisquare(self.cal_pickle['fit_chi2'])
if namePeakTime != '':
self.graphPeakTime = ro.TGraphErrors(len(self.signalPeakTime.values()), np.array([self.signalOut[volt] for volt in self.voltages], 'f8'), np.array([self.signalPeakTime[volt] for volt in self.voltages], 'f8'), np.array([self.signalOutSigma[volt] for volt in self.voltages], 'f8'), np.array([self.signalPeakTimeSigma[volt] for volt in self.voltages], 'f8'))
self.graphPeakTime.SetNameTitle(namePeakTime, namePeakTime)
self.graphPeakTime.GetXaxis().SetTitle('signal [mV]')
self.graphPeakTime.GetYaxis().SetTitle('Peak Time [us]')
self.graphPeakTime.SetMarkerStyle(7)
self.graphPeakTime.SetMarkerColor(ro.kBlack)
self.graphPeakTime.SetLineColor(ro.kBlack)
else:
if nameVcal != '':
self.graphVcal = ro.TGraphErrors(len(self.voltages), xpoints, np.array([self.signalOutVcal[volt] for volt in self.voltages], 'f8'), xpointserrs, np.array([self.signalOutVcalSigma[volt] for volt in self.voltages], 'f8'))
self.graphVcal.SetNameTitle(nameVcal, nameVcal)
self.graphVcal.GetXaxis().SetTitle('HV [V]')
self.graphVcal.GetYaxis().SetTitle('signalVcal [mV]')
self.graphVcal.SetMarkerStyle(7)
self.graphVcal.SetMarkerColor(ro.kBlack)
self.graphVcal.SetLineColor(ro.kBlack)
if nameCharge != '':
self.graphCharge = ro.TGraphErrors(len(self.voltages), xpoints, np.array([self.signalOutCharge[volt] for volt in self.voltages], 'f8'), xpointserrs, np.array([self.signalOutChargeSigma[volt] for volt in self.voltages], 'f8'))
self.graphCharge.SetNameTitle(nameVcal, nameVcal)
self.graphCharge.GetXaxis().SetTitle('HV [V]')
self.graphCharge.GetYaxis().SetTitle('signalCharge [e]')
self.graphCharge.SetMarkerStyle(7)
self.graphCharge.SetMarkerColor(ro.kBlack)
self.graphCharge.SetLineColor(ro.kBlack)
self.fit = None
print 'Loaded pickle', cal_files[0]
return
print 'There is no pickle to load yet (or it is not a calibration run)'
def PlotFromPickle(self):
if self.cal_pickle:
if self.graphPH:
self.canvasPH = ro.TCanvas('c_' + self.graphPH.GetName(), 'c_' + self.graphPH.GetName(), 1)
self.graphPH.Draw('AP')
if self.fit:
self.fit.Draw('same')
if self.graphVcal:
self.canvasVcal = ro.TCanvas('c_' + self.graphVcal.GetName(), 'c_' + self.graphVcal.GetName(), 1)
self.graphVcal.Draw('AP')
if self.graphCharge:
self.canvasCharge = ro.TCanvas('c_' + self.graphCharge.GetName(), 'c_' + self.graphCharge.GetName(), 1)
self.graphCharge.Draw('AP')
def DoAll(self, updateHistos=False):
self.time0 = time.time()
if not self.cal_pickle or self.overwrite or updateHistos:
self.LoopRuns()
self.PlotSignals()
if self.are_cal_runs:
self.FitLine()
self.FillPickle()
self.SavePickle()
self.PlotPeakTimes()
self.SaveAllCanvas()
print 'Finished in', time.time() - self.time0, 'seconds'
def LoopRuns(self):
# ro.gROOT.SetBatch(True)
for run in self.runs:
print '\nAnalysing run:', run
if os.path.isdir(run):
root_files = glob.glob('{d}/*.root'.format(d=run))
if len(root_files) > 0:
configfile = self.config if not self.are_cal_runs or '_out_' in run else self.configInput
print 'Using config file:', configfile
print 'Overwriting analysis tree if it exists' if self.overwrite else 'Not overwriting analysis tree if it exists'
anaRun = AnalysisCaenCCD(run, configfile, overw=self.overwrite, doDebug=self.doDebug)
anaRun.DoAll()
self.voltages.append(anaRun.bias)
self.caen_ch = anaRun.ch_caen_signal if self.caen_ch != anaRun.ch_caen_signal else self.caen_ch
peakTime = 0
peakTimeSigma = 0
peakTimeCF = 0
peakTimeSigmaCF = 0
if self.are_cal_runs and '_out_' in run:
if 'peakPosDist' in anaRun.histo.keys():
peakTime = np.double(anaRun.peakTime * 1e6)
peakTimeSigma = np.double(anaRun.histo['peakPosDist'].GetRMS())
if 'peakPosDistCF' in anaRun.histo.keys():
peakTimeCF = np.double(anaRun.peakTimeCF * 1e6)
peakTimeSigmaCF = np.double(anaRun.histo['peakPosDistCF'].GetRMS())
self.diaVoltages[anaRun.bias] = anaRun.voltageDiaMean
self.diaVoltagesSigma[anaRun.bias] = anaRun.voltageDiaSpread
signalRun = 0
signalSigmaRun = 0
signalRunCF = 0
signalSigmaRunCF = 0
signalRunNP = 0
signalSigmaRunNP = 0
signalRunCFNP = 0
signalSigmaRunCFNP = 0
if 'PH' in anaRun.histo.keys():
signalRun = np.double(anaRun.langaus['PH'].GetParameter(1)) if self.are_cal_runs else np.double(anaRun.histo['PH'].GetMean())
signalSigmaRun = np.double(anaRun.langaus['PH'] .GetParameter(2)) if self.are_cal_runs else np.sqrt(np.power(anaRun.histo['PH'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PH'].GetRMS() > anaRun.pedestal_sigma else anaRun.histo['PH'].GetRMS()
signalRunCF = np.double(anaRun.langaus['PH_CF'].GetParameter(1)) if self.are_cal_runs else np.double(anaRun.histo['PH_CF'].GetMean())
signalSigmaRunCF = np.double(anaRun.langaus['PH_CF'] .GetParameter(2)) if self.are_cal_runs else np.sqrt(np.power(anaRun.histo['PH_CF'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PH_CF'].GetRMS() > anaRun.pedestal_sigma else anaRun.histo['PH_CF'].GetRMS()
signalRunNP = signalRun
signalSigmaRunNP = signalSigmaRun
signalRunCFNP = signalRunCF
signalSigmaRunCFNP = signalSigmaRunCF
if anaRun.langaus.has_key('PHNoPedestal') or anaRun.histo.has_key('PHNoPedestal'):
signalRunNP = np.double(anaRun.langaus['PHNoPedestal'].GetParameter(1)) if self.are_cal_runs else np.double(anaRun.histo['PHNoPedestal'].GetMean())
signalSigmaRunNP = np.double(anaRun.langaus['PHNoPedestal'] .GetParameter(2)) if self.are_cal_runs else np.sqrt(np.power(anaRun.histo['PHNoPedestal'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PHNoPedestal'].GetRMS() > anaRun.pedestal_sigma else anaRun.histo['PHNoPedestal'].GetRMS()
if anaRun.langaus.has_key('PH_CFNoPedestal') or anaRun.histo.has_key('PH_CFNoPedestal'):
signalRunCFNP = np.double(anaRun.langaus['PH_CFNoPedestal'].GetParameter(1)) if self.are_cal_runs else np.double(anaRun.histo['PH_CFNoPedestal'].GetMean())
signalSigmaRunCFNP = np.double(anaRun.langaus['PH_CFNoPedestal'] .GetParameter(2)) if self.are_cal_runs else np.sqrt(np.power(anaRun.histo['PH_CFNoPedestal'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PH_CFNoPedestal'].GetRMS() > anaRun.pedestal_sigma else anaRun.histo['PH_CFNoPedestal'].GetRMS()
if not anaRun.is_cal_run:
signalRunVcal = 0
signalSigmaRunVcal = 0
signalRunCharge = 0
signalSigmaRunCharge = 0
signalRunVcalCF = 0
signalSigmaRunVcalCF = 0
signalRunChargeCF = 0
signalSigmaRunChargeCF = 0
if 'PHvcal' in anaRun.histo.keys():
signalRunVcal = np.double(anaRun.histo['PHvcal'].GetMean())
signalSigmaRunVcal = np.sqrt(np.power(anaRun.histo['PHvcal'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_vcal_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PHvcal'].GetRMS() > anaRun.pedestal_vcal_sigma else anaRun.histo['PHvcal'].GetRMS()
if 'PHcharge' in anaRun.histo.keys():
signalRunCharge = np.double(anaRun.histo['PHcharge'].GetMean())
signalSigmaRunCharge = np.sqrt(np.power(anaRun.histo['PHcharge'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_charge_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PHcharge'].GetRMS() > anaRun.pedestal_charge_sigma else anaRun.histo['PHcharge'].GetRMS()
if 'PHvcal_CF' in anaRun.histo.keys():
signalRunVcalCF = np.double(anaRun.histo['PHvcal_CF'].GetMean())
signalSigmaRunVcalCF = np.sqrt(np.power(anaRun.histo['PHvcal_CF'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_vcal_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PHvcal_CF'].GetRMS() > anaRun.pedestal_vcal_sigma else anaRun.histo['PHvcal_CF'].GetRMS()
if 'PHcharge_CF' in anaRun.histo.keys():
signalRunChargeCF = np.double(anaRun.histo['PHcharge_CF'].GetMean())
signalSigmaRunChargeCF = np.sqrt(np.power(anaRun.histo['PHcharge_CF'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_charge_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PHcharge_CF'].GetRMS() > anaRun.pedestal_charge_sigma else anaRun.histo['PHcharge_CF'].GetRMS()
signalRunVcalNP = signalRunVcal
signalSigmaRunVcalNP = signalSigmaRunVcal
signalRunChargeNP = signalRunCharge
signalSigmaRunChargeNP = signalSigmaRunCharge
signalRunVcalCFNP = signalRunVcalCF
signalSigmaRunVcalCFNP = signalSigmaRunVcalCF
signalRunChargeCFNP = signalRunChargeCF
signalSigmaRunChargeCFNP = signalSigmaRunChargeCF
if 'PHvcalNoPedestal' in anaRun.histo.keys():
signalRunVcalNP = np.double(anaRun.histo['PHvcalNoPedestal'].GetMean())
signalSigmaRunVcalNP = np.sqrt(np.power(anaRun.histo['PHvcalNoPedestal'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_vcal_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PHvcalNoPedestal'].GetRMS() > anaRun.pedestal_vcal_sigma else anaRun.histo['PHvcalNoPedestal'].GetRMS()
if 'PHchargeNoPedestal' in anaRun.histo.keys():
signalRunChargeNP = np.double(anaRun.histo['PHchargeNoPedestal'].GetMean())
signalSigmaRunChargeNP = np.sqrt(np.power(anaRun.histo['PHchargeNoPedestal'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_charge_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PHchargeNoPedestal'].GetRMS() > anaRun.pedestal_charge_sigma else anaRun.histo['PHchargeNoPedestal'].GetRMS()
if 'PHvcal_CFNoPedestal' in anaRun.histo.keys():
signalRunVcalCFNP = np.double(anaRun.histo['PHvcal_CFNoPedestal'].GetMean())
signalSigmaRunVcalCFNP = np.sqrt(np.power(anaRun.histo['PHvcal_CFNoPedestal'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_vcal_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PHvcal_CFNoPedestal'].GetRMS() > anaRun.pedestal_vcal_sigma else anaRun.histo['PHvcal_CFNoPedestal'].GetRMS()
if 'PHcharge_CFNoPedestal' in anaRun.histo.keys():
signalRunChargeCFNP = np.double(anaRun.histo['PHcharge_CFNoPedestal'].GetMean())
signalSigmaRunChargeCFNP = np.sqrt(np.power(anaRun.histo['PHcharge_CFNoPedestal'].GetRMS(), 2, dtype='f8') - np.power(anaRun.pedestal_charge_sigma, 2, dtype='f8'), dtype='f8') if anaRun.histo['PHcharge_CFNoPedestal'].GetRMS() > anaRun.pedestal_charge_sigma else anaRun.histo['PHcharge_CFNoPedestal'].GetRMS()
if not self.are_cal_runs or '_out_' in run:
self.signalOut[anaRun.bias] = signalRun if anaRun.bias < 0 else -signalRun
self.signalOutSigma[anaRun.bias] = signalSigmaRun
self.signalOutCF[anaRun.bias] = signalRunCF if anaRun.bias < 0 else -signalRunCF
self.signalOutSigmaCF[anaRun.bias] = signalSigmaRunCF
self.signalOutNP[anaRun.bias] = signalRunNP if anaRun.bias < 0 else -signalRunNP
self.signalOutSigmaNP[anaRun.bias] = signalSigmaRunNP
self.signalOutCFNP[anaRun.bias] = signalRunCFNP if anaRun.bias < 0 else -signalRunCFNP
self.signalOutSigmaCFNP[anaRun.bias] = signalSigmaRunCFNP
if not self.are_cal_runs:
self.signalOutVcal[anaRun.bias] = -signalRunVcal if anaRun.bias < 0 else signalRunVcal
self.signalOutVcalSigma[anaRun.bias] = signalSigmaRunVcal
self.signalOutCharge[anaRun.bias] = -signalRunCharge if anaRun.bias < 0 else signalRunCharge
self.signalOutChargeSigma[anaRun.bias] = signalSigmaRunCharge
self.signalOutVcalCF[anaRun.bias] = -signalRunVcalCF if anaRun.bias < 0 else signalRunVcalCF
self.signalOutVcalSigmaCF[anaRun.bias] = signalSigmaRunVcalCF
self.signalOutChargeCF[anaRun.bias] = -signalRunChargeCF if anaRun.bias < 0 else signalRunChargeCF
self.signalOutChargeSigmaCF[anaRun.bias] = signalSigmaRunChargeCF
self.signalOutVcalNP[anaRun.bias] = -signalRunVcalNP if anaRun.bias < 0 else signalRunVcalNP
self.signalOutVcalSigmaNP[anaRun.bias] = signalSigmaRunVcalNP
self.signalOutChargeNP[anaRun.bias] = -signalRunChargeNP if anaRun.bias < 0 else signalRunChargeNP
self.signalOutChargeSigmaNP[anaRun.bias] = signalSigmaRunChargeNP
self.signalOutVcalCFNP[anaRun.bias] = -signalRunVcalCFNP if anaRun.bias < 0 else signalRunVcalCFNP
self.signalOutVcalSigmaCFNP[anaRun.bias] = signalSigmaRunVcalCFNP
self.signalOutChargeCFNP[anaRun.bias] = -signalRunChargeCFNP if anaRun.bias < 0 else signalRunChargeCFNP
self.signalOutChargeSigmaCFNP[anaRun.bias] = signalSigmaRunChargeCFNP
if '_out_' in run:
self.signalPeakTime[anaRun.bias] = peakTime
self.signalPeakTimeSigma[anaRun.bias] = peakTimeSigma
self.signalPeakTimeCF[anaRun.bias] = peakTimeCF
self.signalPeakTimeSigmaCF[anaRun.bias] = peakTimeSigmaCF
else:
self.signalIn[anaRun.bias] = -signalRun if anaRun.bias < 0 else signalRun
self.signalInSigma[anaRun.bias] = signalSigmaRun
del anaRun
self.voltages = sorted(set(self.voltages))
# ro.gROOT.SetBatch(False)
def PlotSignals(self):
if len(self.voltages) > 0:
if self.are_cal_runs:
voltages2 = []
for volt in self.voltages:
if volt in self.signalOut.keys() and volt in self.signalIn.keys():
voltages2.append(volt)
self.voltages = voltages2
stepsIn = np.array([self.signalIn[volt] for volt in self.voltages], dtype='f8')
stepsInErrs = np.array([self.signalInSigma[volt] for volt in self.voltages], dtype='f8')
signalO = np.array([self.signalOut[volt] for volt in self.voltages], dtype='f8')
signalOutErrs = np.array([self.signalOutSigma[volt] for volt in self.voltages], dtype='f8')
signalOCF = np.array([self.signalOutCF[volt] for volt in self.voltages], dtype='f8')
signalOutErrsCF = np.array([self.signalOutSigmaCF[volt] for volt in self.voltages], dtype='f8')
self.graphPH = ro.TGraphErrors(len(self.voltages), stepsIn, signalO, stepsInErrs, signalOutErrs)
self.graphPH.SetNameTitle('Signal_vs_CalStep_ch_' + str(self.caen_ch), 'Signal_vs_CalStep_ch_' + str(self.caen_ch))
self.graphPH.GetXaxis().SetTitle('vcal step [mV]')
self.graphPH.GetYaxis().SetTitle('signal [mV]')
self.graphPHCF = ro.TGraphErrors(len(self.voltages), stepsIn, signalOCF, stepsInErrs, signalOutErrsCF)
self.graphPHCF.SetNameTitle('Signal_CF_vs_CalStep_ch_' + str(self.caen_ch), 'Signal_CF_vs_CalStep_ch_' + str(self.caen_ch))
self.graphPHCF.GetXaxis().SetTitle('vcal step [mV]')
self.graphPHCF.GetYaxis().SetTitle('signal_CF [mV]')
signalONP = np.array([self.signalOutNP[volt] for volt in self.voltages], dtype='f8')
signalOutErrsNP = np.array([self.signalOutSigmaNP[volt] for volt in self.voltages], dtype='f8')
signalOCFNP = np.array([self.signalOutCFNP[volt] for volt in self.voltages], dtype='f8')
signalOutErrsCFNP = np.array([self.signalOutSigmaCFNP[volt] for volt in self.voltages], dtype='f8')
self.graphPHNP = ro.TGraphErrors(len(self.voltages), stepsIn, signalONP, stepsInErrs, signalOutErrsNP)
self.graphPHNP.SetNameTitle('Signal_NoPedestal_vs_CalStep_ch_' + str(self.caen_ch), 'Signal_NoPedestal_vs_CalStep_ch_' + str(self.caen_ch))
self.graphPHNP.GetXaxis().SetTitle('vcal step [mV]')
self.graphPHNP.GetYaxis().SetTitle('signal [mV]')
self.graphPHCFNP = ro.TGraphErrors(len(self.voltages), stepsIn, signalOCFNP, stepsInErrs, signalOutErrsCFNP)
self.graphPHCFNP.SetNameTitle('Signal_NoPedestal_CF_vs_CalStep_ch_' + str(self.caen_ch), 'Signal_NoPedestal_CF_vs_CalStep_ch_' + str(self.caen_ch))
self.graphPHCFNP.GetXaxis().SetTitle('vcal step [mV]')
self.graphPHCFNP.GetYaxis().SetTitle('signal_CF [mV]')
else:
diaVoltages = np.array([self.diaVoltages[volt] for volt in self.voltages], dtype='f8')
diaVoltagesSigma = np.array([self.diaVoltagesSigma[volt] for volt in self.voltages], dtype='f8')
signalO = np.array([self.signalOut[volt] for volt in self.voltages], dtype='f8')
signalOutErrs = np.array([self.signalOutSigma[volt] for volt in self.voltages], dtype='f8')
signalOVcal = np.array([self.signalOutVcal[volt] for volt in self.voltages], dtype='f8')
signalOutVcalErrs = np.array([self.signalOutVcalSigma[volt] for volt in self.voltages], dtype='f8')
signalOCharge = np.array([self.signalOutCharge[volt] for volt in self.voltages], dtype='f8')
signalOutChargeErrs = np.array([self.signalOutChargeSigma[volt] for volt in self.voltages], dtype='f8')
signalOCF = np.array([self.signalOutCF[volt] for volt in self.voltages], dtype='f8')
signalOutErrsCF = np.array([self.signalOutSigmaCF[volt] for volt in self.voltages], dtype='f8')
signalOVcalCF = np.array([self.signalOutVcalCF[volt] for volt in self.voltages], dtype='f8')
signalOutVcalErrsCF = np.array([self.signalOutVcalSigmaCF[volt] for volt in self.voltages], dtype='f8')
signalOChargeCF = np.array([self.signalOutChargeCF[volt] for volt in self.voltages], dtype='f8')
signalOutChargeErrsCF = np.array([self.signalOutChargeSigmaCF[volt] for volt in self.voltages], dtype='f8')
signalONP = np.array([self.signalOutNP[volt] for volt in self.voltages], dtype='f8')
signalOutErrsNP = np.array([self.signalOutSigmaNP[volt] for volt in self.voltages], dtype='f8')
signalOVcalNP = np.array([self.signalOutVcalNP[volt] for volt in self.voltages], dtype='f8')
signalOutVcalErrsNP = np.array([self.signalOutVcalSigmaNP[volt] for volt in self.voltages], dtype='f8')
signalOChargeNP = np.array([self.signalOutChargeNP[volt] for volt in self.voltages], dtype='f8')
signalOutChargeErrsNP = np.array([self.signalOutChargeSigmaNP[volt] for volt in self.voltages], dtype='f8')
signalOCFNP = np.array([self.signalOutCFNP[volt] for volt in self.voltages], dtype='f8')
signalOutErrsCFNP = np.array([self.signalOutSigmaCFNP[volt] for volt in self.voltages], dtype='f8')
signalOVcalCFNP = np.array([self.signalOutVcalCFNP[volt] for volt in self.voltages], dtype='f8')
signalOutVcalErrsCFNP = np.array([self.signalOutVcalSigmaCFNP[volt] for volt in self.voltages], dtype='f8')
signalOChargeCFNP = np.array([self.signalOutChargeCFNP[volt] for volt in self.voltages], dtype='f8')
signalOutChargeErrsCFNP = np.array([self.signalOutChargeSigmaCFNP[volt] for volt in self.voltages], dtype='f8')
self.graphPH = ro.TGraphErrors(len(self.voltages), diaVoltages, signalO, diaVoltagesSigma, signalOutErrs)
self.graphPH.SetNameTitle('Signal_vs_HV', 'Signal_vs_HV')
self.graphPH.GetXaxis().SetTitle('HV [V]')
self.graphPH.GetYaxis().SetTitle('signal [mV]')
self.graphPHCF = ro.TGraphErrors(len(self.voltages), diaVoltages, signalOCF, diaVoltagesSigma, signalOutErrsCF)
self.graphPHCF.SetNameTitle('Signal_CF_vs_HV', 'Signal_CF_vs_HV')
self.graphPHCF.GetXaxis().SetTitle('HV [V]')
self.graphPHCF.GetYaxis().SetTitle('signal_CF [mV]')
self.graphVcal = ro.TGraphErrors(len(self.voltages), diaVoltages, signalOVcal, diaVoltagesSigma, signalOutVcalErrs)
self.graphVcal.SetNameTitle('SignalVcal_vs_HV', 'SignalVcal_vs_HV')
self.graphVcal.GetXaxis().SetTitle('HV [V]')
self.graphVcal.GetYaxis().SetTitle('signalVcal [mV]')
self.graphVcalCF = ro.TGraphErrors(len(self.voltages), diaVoltages, signalOVcalCF, diaVoltagesSigma, signalOutVcalErrsCF)
self.graphVcalCF.SetNameTitle('SignalVcal_CF_vs_HV', 'SignalVcal_CF_vs_HV')
self.graphVcalCF.GetXaxis().SetTitle('HV [V]')
self.graphVcalCF.GetYaxis().SetTitle('signalVcal_CF [mV]')
self.graphCharge = ro.TGraphErrors(len(self.voltages), diaVoltages, signalOCharge, diaVoltagesSigma, signalOutChargeErrs)
self.graphCharge.SetNameTitle('SignalCharge_vs_HV', 'SignalCharge_vs_HV')
self.graphCharge.GetXaxis().SetTitle('HV [V]')
self.graphCharge.GetYaxis().SetTitle('signalCharge [e]')
self.graphChargeCF = ro.TGraphErrors(len(self.voltages), diaVoltages, signalOChargeCF, diaVoltagesSigma, signalOutChargeErrsCF)
self.graphChargeCF.SetNameTitle('SignalCharge_CF_vs_HV', 'SignalCharge_CF_vs_HV')
self.graphChargeCF.GetXaxis().SetTitle('HV [V]')
self.graphChargeCF.GetYaxis().SetTitle('signalCharge_CF [e]')
self.graphPHNP = ro.TGraphErrors(len(self.voltages), diaVoltages, signalONP, diaVoltagesSigma, signalOutErrsNP)
self.graphPHNP.SetNameTitle('Signal_NoPedestal_vs_HV', 'Signal_NoPedestal_vs_HV')
self.graphPHNP.GetXaxis().SetTitle('HV [V]')
self.graphPHNP.GetYaxis().SetTitle('signal [mV]')
self.graphPHCFNP = ro.TGraphErrors(len(self.voltages), diaVoltages, signalOCFNP, diaVoltagesSigma, signalOutErrsCFNP)
self.graphPHCFNP.SetNameTitle('Signal_NoPedestal_CF_vs_HV', 'Signal_NoPedestal_CF_vs_HV')
self.graphPHCFNP.GetXaxis().SetTitle('HV [V]')
self.graphPHCFNP.GetYaxis().SetTitle('signal_CF [mV]')
self.graphVcalNP = ro.TGraphErrors(len(self.voltages), diaVoltages, signalOVcalNP, diaVoltagesSigma, signalOutVcalErrsNP)
self.graphVcalNP.SetNameTitle('SignalVcal_NoPedestal_vs_HV', 'SignalVcal_NoPedestal_vs_HV')
self.graphVcalNP.GetXaxis().SetTitle('HV [V]')
self.graphVcalNP.GetYaxis().SetTitle('signalVcal [mV]')
self.graphVcalCFNP = ro.TGraphErrors(len(self.voltages), diaVoltages, signalOVcalCFNP, diaVoltagesSigma, signalOutVcalErrsCFNP)
self.graphVcalCFNP.SetNameTitle('SignalVcal_NoPedestal_CF_vs_HV', 'SignalVcal_NoPedestal_CF_vs_HV')
self.graphVcalCFNP.GetXaxis().SetTitle('HV [V]')
self.graphVcalCFNP.GetYaxis().SetTitle('signalVcal_CF [mV]')
self.graphChargeNP = ro.TGraphErrors(len(self.voltages), diaVoltages, signalOChargeNP, diaVoltagesSigma, signalOutChargeErrsNP)
self.graphChargeNP.SetNameTitle('SignalCharge_NoPedestal_vs_HV', 'SignalCharge_NoPedestal_vs_HV')
self.graphChargeNP.GetXaxis().SetTitle('HV [V]')
self.graphChargeNP.GetYaxis().SetTitle('signalCharge [e]')
self.graphChargeCFNP = ro.TGraphErrors(len(self.voltages), diaVoltages, signalOChargeCFNP, diaVoltagesSigma, signalOutChargeErrsCFNP)
self.graphChargeCFNP.SetNameTitle('SignalCharge_NoPedestal_CF_vs_HV', 'SignalCharge_NoPedestal_CF_vs_HV')
self.graphChargeCFNP.GetXaxis().SetTitle('HV [V]')
self.graphChargeCFNP.GetYaxis().SetTitle('signalCharge_CF [e]')
self.graphPH.SetMarkerStyle(7)
self.graphPH.SetMarkerColor(ro.kBlack)
self.graphPH.SetLineColor(ro.kBlack)
self.graphPHCF.SetMarkerStyle(7)
self.graphPHCF.SetMarkerColor(ro.kBlack)
self.graphPHCF.SetLineColor(ro.kBlack)
self.canvasPH = ro.TCanvas('c_' + self.graphPH.GetName(), 'c_' + self.graphPH.GetName(), 1)
self.graphPH.Draw('AP')
self.canvasPH.SetGridx()
self.canvasPH.SetGridy()
self.canvasPHCF = ro.TCanvas('c_' + self.graphPHCF.GetName(), 'c_' + self.graphPHCF.GetName(), 1)
self.graphPHCF.Draw('AP')
self.canvasPHCF.SetGridx()
self.canvasPHCF.SetGridy()
self.graphPHNP.SetMarkerStyle(7)
self.graphPHNP.SetMarkerColor(ro.kBlack)
self.graphPHNP.SetLineColor(ro.kBlack)
self.graphPHCFNP.SetMarkerStyle(7)
self.graphPHCFNP.SetMarkerColor(ro.kBlack)
self.graphPHCFNP.SetLineColor(ro.kBlack)
self.canvasPHNP = ro.TCanvas('c_' + self.graphPHNP.GetName(), 'c_' + self.graphPHNP.GetName(), 1)
self.graphPHNP.Draw('AP')
self.canvasPHNP.SetGridx()
self.canvasPHNP.SetGridy()
self.canvasPHCFNP = ro.TCanvas('c_' + self.graphPHCFNP.GetName(), 'c_' + self.graphPHCFNP.GetName(), 1)
self.graphPHCFNP.Draw('AP')
self.canvasPHCFNP.SetGridx()
self.canvasPHCFNP.SetGridy()
if not self.are_cal_runs:
self.graphVcal.SetMarkerStyle(7)
self.graphVcal.SetMarkerColor(ro.kBlack)
self.graphVcal.SetLineColor(ro.kBlack)
self.graphVcalCF.SetMarkerStyle(7)
self.graphVcalCF.SetMarkerColor(ro.kBlack)
self.graphVcalCF.SetLineColor(ro.kBlack)
self.canvasVcal = ro.TCanvas('c_' + self.graphVcal.GetName(), 'c_' + self.graphVcal.GetName(), 1)
self.graphVcal.Draw('AP')
self.canvasVcal.SetGridx()
self.canvasVcal.SetGridy()
self.canvasVcalCF = ro.TCanvas('c_' + self.graphVcalCF.GetName(), 'c_' + self.graphVcalCF.GetName(), 1)
self.graphVcalCF.Draw('AP')
self.canvasVcalCF.SetGridx()
self.canvasVcalCF.SetGridy()
self.graphCharge.SetMarkerStyle(7)
self.graphCharge.SetMarkerColor(ro.kBlack)
self.graphCharge.SetLineColor(ro.kBlack)
self.graphChargeCF.SetMarkerStyle(7)
self.graphChargeCF.SetMarkerColor(ro.kBlack)
self.graphChargeCF.SetLineColor(ro.kBlack)
self.canvasCharge = ro.TCanvas('c_' + self.graphCharge.GetName(), 'c_' + self.graphCharge.GetName(), 1)
self.graphCharge.Draw('AP')
self.canvasCharge.SetGridx()
self.canvasCharge.SetGridy()
self.canvasChargeCF = ro.TCanvas('c_' + self.graphChargeCF.GetName(), 'c_' + self.graphChargeCF.GetName(), 1)
self.graphChargeCF.Draw('AP')
self.canvasChargeCF.SetGridx()
self.canvasChargeCF.SetGridy()
self.graphVcalNP.SetMarkerStyle(7)
self.graphVcalNP.SetMarkerColor(ro.kBlack)
self.graphVcalNP.SetLineColor(ro.kBlack)
self.graphVcalCFNP.SetMarkerStyle(7)
self.graphVcalCFNP.SetMarkerColor(ro.kBlack)
self.graphVcalCFNP.SetLineColor(ro.kBlack)
self.canvasVcalNP = ro.TCanvas('c_' + self.graphVcalNP.GetName(), 'c_' + self.graphVcalNP.GetName(), 1)
self.graphVcalNP.Draw('AP')
self.canvasVcalNP.SetGridx()
self.canvasVcalNP.SetGridy()
self.canvasVcalCFNP = ro.TCanvas('c_' + self.graphVcalCFNP.GetName(), 'c_' + self.graphVcalCFNP.GetName(), 1)
self.graphVcalCFNP.Draw('AP')
self.canvasVcalCFNP.SetGridx()
self.canvasVcalCFNP.SetGridy()
self.graphChargeNP.SetMarkerStyle(7)
self.graphChargeNP.SetMarkerColor(ro.kBlack)
self.graphChargeNP.SetLineColor(ro.kBlack)
self.graphChargeCFNP.SetMarkerStyle(7)
self.graphChargeCFNP.SetMarkerColor(ro.kBlack)
self.graphChargeCFNP.SetLineColor(ro.kBlack)
self.canvasChargeNP = ro.TCanvas('c_' + self.graphChargeNP.GetName(), 'c_' + self.graphChargeNP.GetName(), 1)
self.graphChargeNP.Draw('AP')
self.canvasChargeNP.SetGridx()
self.canvasChargeNP.SetGridy()
self.canvasChargeCFNP = ro.TCanvas('c_' + self.graphChargeCFNP.GetName(), 'c_' + self.graphChargeCFNP.GetName(), 1)
self.graphChargeCFNP.Draw('AP')
self.canvasChargeCFNP.SetGridx()
self.canvasChargeCFNP.SetGridy()
def PlotPeakTimes(self):
if len(self.voltages) > 0:
if self.are_cal_runs:
if len(self.signalPeakTime.values()) < 1:
for run in self.runs:
print 'Analysing run:', run
if os.path.isdir(run):
root_files = glob.glob('{d}/*.root'.format(d=run))
if len(root_files) > 0:
if '_out_' in run:
configfile = self.config
print 'Using config file:', configfile
print 'Loading analysis tree if it exists'
anaRun = AnalysisCaenCCD(run, configfile, overw=False)
anaRun.AnalysisWaves()
peakTime = 0
peakTimeSigma = 0
peakTimeCF = 0
peakTimeSigmaCF = 0
if 'peakPosDist' in anaRun.histo.keys():
peakTime = np.double(anaRun.peakTime * 1e6)
peakTimeSigma = np.double(anaRun.histo['peakPosDist'].GetRMS())
if 'peakPosDistCF' in anaRun.histo.keys():
peakTimeCF = np.double(anaRun.peakTime * 1e6)
peakTimeSigmaCF = np.double(anaRun.histo['peakPosDistCF'].GetRMS())
self.signalPeakTime[anaRun.bias] = peakTime
self.signalPeakTimeSigma[anaRun.bias] = peakTimeSigma
self.signalPeakTimeCF[anaRun.bias] = peakTimeCF
self.signalPeakTimeSigmaCF[anaRun.bias] = peakTimeSigmaCF
signalO = np.array([self.signalOut[volt] for volt in self.voltages], dtype='f8')
signalOutErrs = np.array([self.signalOutSigma[volt] for volt in self.voltages], dtype='f8')
signalPeaks = np.array([self.signalPeakTime[volt] for volt in self.voltages], 'f8')
signalPeaksSigma = np.array([self.signalPeakTimeSigma[volt] for volt in self.voltages], 'f8')
signalOCF = np.array([self.signalOutCF[volt] for volt in self.voltages], dtype='f8')
signalOutErrsCF = np.array([self.signalOutSigmaCF[volt] for volt in self.voltages], dtype='f8')
signalPeaksCF = np.array([self.signalPeakTimeCF[volt] for volt in self.voltages], 'f8')
signalPeaksSigmaCF = np.array([self.signalPeakTimeSigmaCF[volt] for volt in self.voltages], 'f8')
self.graphPeakTime = ro.TGraphErrors(len(self.voltages), signalO, signalPeaks, signalOutErrs, signalPeaksSigma)
self.graphPeakTime.SetNameTitle('SignalPeakTime_vs_Signal_ch_' + str(self.caen_ch), 'SignalPeakTime_vs_Signal_ch_' + str(self.caen_ch))
self.graphPeakTime.GetXaxis().SetTitle('signal [mV]')
self.graphPeakTime.GetYaxis().SetTitle('Peak Time [us]')
self.graphPeakTime.SetMarkerStyle(7)
self.graphPeakTime.SetMarkerColor(ro.kBlack)
self.graphPeakTime.SetLineColor(ro.kBlack)
self.canvasPeakTime = ro.TCanvas('c_' + self.graphPeakTime.GetName(), 'c_' + self.graphPeakTime.GetName(), 1)
self.graphPeakTime.Draw('AP')
self.canvasPeakTime.SetGridx()
self.canvasPeakTime.SetGridy()
self.graphPeakTimeCF = ro.TGraphErrors(len(self.voltages), signalOCF, signalPeaksCF, signalOutErrsCF, signalPeaksSigmaCF)
self.graphPeakTimeCF.SetNameTitle('SignalPeakTime_CF_vs_Signal_CF_ch_' + str(self.caen_ch), 'SignalPeakTime_CF_vs_Signal_CF_ch_' + str(self.caen_ch))
self.graphPeakTimeCF.GetXaxis().SetTitle('signal [mV]')
self.graphPeakTimeCF.GetYaxis().SetTitle('Peak Time [us]')
self.graphPeakTimeCF.SetMarkerStyle(7)
self.graphPeakTimeCF.SetMarkerColor(ro.kBlack)
self.graphPeakTimeCF.SetLineColor(ro.kBlack)
self.canvasPeakTimeCF = ro.TCanvas('c_' + self.graphPeakTimeCF.GetName(), 'c_' + self.graphPeakTimeCF.GetName(), 1)
self.graphPeakTimeCF.Draw('AP')
self.canvasPeakTimeCF.SetGridx()
self.canvasPeakTimeCF.SetGridy()
def FitLine(self):
ro.Math.MinimizerOptions.SetDefaultMinimizer(*fit_method)
ro.Math.MinimizerOptions.SetDefaultMaxFunctionCalls(1000000)
ro.Math.MinimizerOptions.SetDefaultTolerance(0.00001)
ro.gStyle.SetOptFit(1111)
func = ro.TF1('fit_' + self.graphPH.GetName(), 'pol1', -1000, 1000)
func.SetLineColor(ro.kRed)
func.SetNpx(10000)
tfit = self.graphPH.Fit('fit_' + self.graphPH.GetName(), 'QM0S', '', -1000, 1000)
if tfit.Prob() < 0.9:
tfit = self.graphPH.Fit('fit_' + self.graphPH.GetName(), 'QM0S', '', -1000, 1000)
if tfit.Prob() < 0.9:
tfit = self.graphPH.Fit('fit_' + self.graphPH.GetName(), 'QM0S', '', -1000, 1000)
# func.SetParameters(np.array([tfit.Parameter(0), tfit.Parameter(1)], 'f8'))
# func.SetChisquare(tfit.Chi2())
# func.SetNDF(tfit.Ndf())
func = tfit.FittedFunction().GetFunction()
self.fit = func.Clone()
self.canvasPH.cd()
self.fit.Draw('same')
self.canvasPH.Modified()
ro.gPad.Update()
def FillPickle(self):
self.cal_pickle = {'voltages': self.voltages,
'signal_in': self.signalIn,
'signal_in_sigma': self.signalInSigma,
'signal_out': self.signalOut,
'signal_out_sigma': self.signalOutSigma,
'signal_out_vcal': self.signalOutVcal,
'signal_out_vcal_sigma': self.signalOutVcalSigma,
'signal_out_charge': self.signalOutCharge,
'signal_out_charge_sigma': self.signalOutChargeSigma,
'signal_peak_time': self.signalPeakTime,
'signal_peak_time_sigma': self.signalPeakTimeSigma,
'caen_ch': self.caen_ch,
'fit_p0': self.fit.GetParameter(0) if self.fit else 0,
'fit_p0_error': self.fit.GetParError(0) if self.fit else 0,
'fit_p1': self.fit.GetParameter(1) if self.fit else 0,
'fit_p1_error': self.fit.GetParError(1) if self.fit else 0,
'fit_prob': self.fit.GetProb() if self.fit else 0,
'fit_chi2': self.fit.GetChisquare() if self.fit else 0,
'fit_ndf': self.fit.GetNDF() if self.fit else 0
}
def SavePickle(self, overWritePickle=False):
pickleName = 'signal_cal_{c}.cal'.format(c=self.caen_ch) if self.are_cal_runs else 'signal_{c}.cal'.format(c=self.caen_ch)
if not self.cal_pickle:
self.FillPickle()
if os.path.isfile('{d}/{f}'.format(d=self.runsdir, f=pickleName)):
if not self.overwrite and not overWritePickle:
print 'The file', pickleName, 'already exists in', self.runsdir, '. Not saving!'
return
with open('{d}/{f}'.format(d=self.runsdir, f=pickleName), 'wb') as fpickle:
pickle.dump(self.cal_pickle, fpickle, pickle.HIGHEST_PROTOCOL)
print 'Saved pickle', pickleName, 'in', self.runsdir
def SaveCanvas(self, canvas, graph):
if canvas and graph:
canvas.SaveAs('{d}/{n}.png'.format(d=self.runsdir, n=graph.GetName()))
canvas.SaveAs('{d}/{n}.root'.format(d=self.runsdir, n=graph.GetName()))
def SaveAllCanvas(self):
self.SaveCanvas(self.canvasPH, self.graphPH)
self.SaveCanvas(self.canvasPHCF, self.graphPHCF)
self.SaveCanvas(self.canvasPHNP, self.graphPHNP)
self.SaveCanvas(self.canvasPHCFNP, self.graphPHCFNP)
self.SaveCanvas(self.canvasVcal, self.graphVcal)
self.SaveCanvas(self.canvasVcalCF, self.graphVcalCF)
self.SaveCanvas(self.canvasVcalNP, self.graphVcalNP)
self.SaveCanvas(self.canvasVcalCFNP, self.graphVcalCFNP)
self.SaveCanvas(self.canvasCharge, self.graphCharge)
self.SaveCanvas(self.canvasChargeCF, self.graphChargeCF)
self.SaveCanvas(self.canvasChargeNP, self.graphChargeNP)
self.SaveCanvas(self.canvasChargeCFNP, self.graphChargeCFNP)
self.SaveCanvas(self.canvasPeakTime, self.graphPeakTime)
self.SaveCanvas(self.canvasPeakTimeCF, self.graphPeakTimeCF)
def main():
parser = OptionParser()
parser.add_option('-d', '--runsdir', dest='runsdir', type='str', default='', help='path to folder containing all the run folders to modify')
parser.add_option('-c', '--config', dest='config', type='str', default='', help='path to analysis config file used for all runs inside the folder')
parser.add_option('-o', '--overwrite', dest='overwrite', default=False, action='store_true', help='Sets overwrite of analysis tree for the run if it exists')
parser.add_option('--configinput', dest='configinput', type='str', default='', help='path to analysis config file used for all runs inside the folder that are of calibration type "in". Only necessary when doing signal calibration')
parser.add_option('--debug', dest='dodebug', default=False, help='Toggles debug mode', action='store_true')
(options, args) = parser.parse_args()
runsdir = str(options.runsdir)
config = str(options.config)
configinput = str(options.configinput)
overwrite = bool(options.overwrite)
dodebug = bool(options.dodebug)
arif = AnalysisAllRunsInFolder(runsdir, config, configinput, overwrite, dodebug)
return arif
if __name__ == '__main__':
arif = main()