source: fact/tools/rootmacros/fpeak_discri.C@ 13151

Last change on this file since 13151 was 12262, checked in by neise, 13 years ago
makes use of the new discriminators
  • Property svn:executable set to *
File size: 19.4 KB
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1#include <TROOT.h>
2#include <TCanvas.h>
3#include <TProfile.h>
4#include <TTimer.h>
5#include <TH1F.h>
6#include <TH2F.h>
7#include <Getline.h>
8#include <TLine.h>
9#include <TBox.h>
10#include <TMath.h>
11#include <TFile.h>
12#include <TStyle.h>
13
14#include <stdio.h>
15#include <stdint.h>
16#include <cstdio>
17
18#define NPIX 1440
19#define NCELL 1024
20#define FAD_MAX_SAMPLES 1024
21
22#define HAVE_ZLIB
23#include "fits.h"
24#include "FOpenCalibFile.c"
25
26#include "discriminator.h"
27#include "discriminator.C"
28#include "zerosearch.h"
29#include "zerosearch.C"
30#include "factfir.C"
31
32
33vector<int16_t> AllPixelDataVector;
34vector<int16_t> StartCellVector;
35
36unsigned int CurrentEventID;
37
38bool breakout=false;
39
40size_t ROIxNOP;
41UInt_t NumberOfPixels;
42UInt_t RegionOfInterest;
43int NEvents;
44
45size_t drs_n;
46vector<float> drs_basemean;
47vector<float> drs_gainmean;
48vector<float> drs_triggeroffsetmean;
49
50int FOpenDataFile( fits & );
51
52
53vector<float> Ameas(FAD_MAX_SAMPLES); // copy of the data (measured amplitude
54vector<float> N1mean(FAD_MAX_SAMPLES); // mean of the +1 -1 ch neighbors
55vector<float> Vcorr(FAD_MAX_SAMPLES); // corrected Values
56vector<float> Vdiff(FAD_MAX_SAMPLES); // numerical derivative
57
58vector<float> Vslide(FAD_MAX_SAMPLES); // sliding average result
59vector<float> Vcfd(FAD_MAX_SAMPLES); // CDF result
60vector<float> Vcfd2(FAD_MAX_SAMPLES); // CDF result + 2nd sliding average
61
62// not needed?
63//vector<float> N2mean(FAD_MAX_SAMPLES); // mean of the +2 -2 ch neighbors
64
65
66float getValue( int, int );
67float correctDrsOffset( int slice, int pixel );
68void computeN1mean( int );
69void removeSpikes( int );
70
71// histograms
72const int Ntypes = 7;
73const unsigned int // arranged by Dominik
74 tAmeas = 0,
75 tN1mean = 1,
76 tVcorr = 2,
77 tVtest = 3,
78 tVslide = 4,
79 tVcfd = 5,
80 tVcfd2 = 6;
81
82TH1F* h;
83TH1F *oldh;
84TH2F* hStartCell; // id of the DRS physical pipeline cell where readout starts
85 // x = pixel id, y = DRS cell id
86TH2F hPixelCellData(
87 "PixelPedestal", "PixelPedestal", NCELL, 0., NCELL, 200, -50., 150.);
88TH1F *hBaseline[ NPIX ]; // histograms for baseline extraction
89TH1F *hMeanBsl, *hpltMeanBsl;
90TH1F *hRmsBsl, *hpltRmsBsl;
91TH2F * hAmplSpek_cfd;
92TH2F * hAmplSpek_discri;
93TObjArray hList;
94TObjArray hListBaseline;
95
96void BookHistos( );
97void SaveHistograms( char * );
98
99// Create a canvas
100TCanvas* CW;
101TCanvas* cFilter;
102
103
104int searchSinglesPeaks = 0;
105
106int fpeak_discri(
107 char *datafilename = "../../20111011_055.fits.gz",
108 const char *drsfilename = "../../20111011_054.drs.fits.gz",
109 int PixelID = -1,
110 int firstevent = 0,
111 int nevents = -1,
112 bool spikeDebug = false,
113 int verbosityLevel = 1 // different verbosity levels can be implemented here
114 )
115{
116
117gStyle->SetPalette(1,0);
118gROOT->SetStyle("Plain");
119// read FACT raw data
120// * remove spikes
121// * calculate baseline
122// * find peaks (CFD and normal discriminator)
123// * compute pulse height and pulse integral spektrum of the peaks
124
125// sliding window filter settings
126 int k_slide = 16;
127 vector<double> a_slide(k_slide, 1);
128 double b_slide = k_slide;
129
130// CFD filter settings
131 int k_cfd = 10;
132 vector<double> a_cfd(k_cfd, 0);
133 double b_cfd = 1.;
134 a_cfd[0]=-0.75;
135 a_cfd[k_cfd-1]=1.;
136
137// 2nd slinding window filter
138 int ks2 = 16;
139 vector<double> as2(ks2, 1);
140 double bs2 = ks2;
141
142// Open the data file
143 fits *datafile = new fits( datafilename );
144 if (!datafile) {
145 printf( "Could not open the file: %s\n", datafilename );
146 return 1;
147 }
148
149// access data
150 NEvents = FOpenDataFile( *datafile );
151 printf("number of events in file: %d\n", NEvents);
152 if ( nevents == -1 || nevents > NEvents ) nevents = NEvents; // -1 means all!
153
154//Get the DRS calibration
155 FOpenCalibFile( drsfilename,
156 drs_basemean,
157 drs_gainmean,
158 drs_triggeroffsetmean,
159 drs_n);
160
161//Check the sizes of the data columns
162 if (drs_n != 1474560) {
163 cerr << "error: DRS calib file has wrong ...erm...size ... drs_n is: "
164 << drs_n << endl;
165 cerr << " Aborting." << endl;
166 return 1;
167 }
168
169 if(ROIxNOP != 1474560)
170 {
171 cout << "warning: data_n should better be 1440x1024=1474560, but is "
172 << ROIxNOP << endl;
173 cout << "this script is not guaranteed to run under these "
174 <<" circumstances....any way ... it is never guaranteed." << endl;
175 }
176// Book the histograms
177
178 BookHistos( );
179 TCanvas * cSpektrum = new TCanvas();
180 cSpektrum->Divide(1,2);
181 cSpektrum->cd(1);
182 hAmplSpek_discri->Draw("COLZ");
183 cSpektrum->cd(2);
184 hAmplSpek_cfd->Draw("COLZ");
185// TCanvas * cStartCell = new TCanvas();
186// cStartCell->cd();
187// hStartCell->Draw();
188// hPixelCellData.Draw();
189
190 float calibratedVoltage;
191 for ( int ev = firstevent; ev < firstevent + nevents; ev++) {
192 datafile->GetRow( ev );
193
194
195 for ( int pix = 0; pix < 1440; pix++ ){
196 // This means: GetEvent
197
198 // this is a stupid hack ... these is more code at the
199 // end of this loop to complete this hack ...
200 if (PixelID != -1) {
201 pix = PixelID;
202 if (verbosityLevel > 0){
203 cout << "Processing Event number: " << CurrentEventID << "\t"
204 << "Pixel number: "<< pix << endl;
205
206 }
207 }
208
209 if (verbosityLevel > 0){
210 if (pix % 20 ==0){
211 cout << "Processing Event number: " << CurrentEventID << "\t"
212 << "Pixel number: "<< pix << endl;
213 }
214 }
215
216 //hStartCell->Fill( pix, StartCellVector[pix] );
217
218 for ( unsigned int sl = 0; sl < RegionOfInterest; sl++){
219 //calibratedVoltage = getValue( sl, pix);
220 calibratedVoltage = correctDrsOffset( sl, pix);
221 if (verbosityLevel > 10){
222 printf("calibratedVoltage = %f\n", calibratedVoltage);
223 }
224 Ameas[ sl ] = calibratedVoltage;
225 if (spikeDebug){
226 oldh[tAmeas].SetBinContent(sl, calibratedVoltage);
227 }
228 }
229
230 computeN1mean( RegionOfInterest );
231 // operates on Ameas[] and writes to N1mean[]
232 removeSpikes( RegionOfInterest );
233 // operates on Ameas[] and N1mean[], then writes to Vcorr[]
234
235 if (spikeDebug){
236 for ( unsigned int sl = 0; sl < RegionOfInterest; sl++){
237 hPixelCellData.Fill(sl, Vcorr[ sl ]);
238 }
239 }
240
241 // filter Vcorr with sliding average using FIR filter function
242 factfir(b_slide , a_slide, k_slide, Vcorr, Vslide);
243 // filter Vslide with CFD using FIR filter function
244 factfir(b_cfd , a_cfd, k_cfd, Vslide, Vcfd);
245 // filter Vcfd with sliding average using FIR filter function
246 factfir(bs2 , as2, ks2, Vcfd, Vcfd2);
247
248 float myTHR = 3.5;
249 vector<Region> *Regions = discriminator( Vslide, myTHR,true , 120);
250 for (unsigned int p=0; p<Regions->size(); p++ ){
251 hAmplSpek_discri->Fill(pix , Regions->at(p).maxVal);
252 }
253
254 // peaks in Ameas[] are found by searching for zero crossings
255 // in Vcfd2
256 // first Argument 1 means ... *rising* edge
257 // second Argument 1 means ... search with stepsize 1 ... 10 is okay as well
258 vector<Region> * zXings = zerosearch( Vcfd2 , 1 , 1);
259
260 // zXings means "zero cross ings"
261 ShiftRegionBy(*zXings, -ks2/2);
262 EnlargeRegion(*zXings, 10, 10);
263 findAbsMaxInRegions(*zXings, Vslide);
264
265 if (zXings->size() != 0 ){
266 for (unsigned int i=0; i<zXings->size(); i++){
267 if (verbosityLevel > 1){
268 cout << zXings->at(i).maxPos << ":\t"
269 << zXings->at(i).maxVal <<endl;
270 }
271 hAmplSpek_cfd->Fill(pix, zXings->at(i).maxVal);
272 }
273 }
274
275 if ( spikeDebug ){
276 for ( unsigned int sl = 0; sl < RegionOfInterest; sl++){
277 oldh[tVslide].SetBinContent( sl, Vslide[sl] );
278 oldh[tVcfd].SetBinContent( sl, Vcfd[sl] );
279 oldh[tVcfd2].SetBinContent( sl, Vcfd2[sl] );
280 }
281
282 CW->cd( 1);
283 gPad->SetGrid();
284 oldh[tAmeas].Draw();
285
286// CW->cd( tN1mean + 1);
287// gPad->SetGrid();
288// oldh[tN1mean].Draw();
289
290 CW->cd( 2);
291 gPad->SetGrid();
292 oldh[tVcorr].Draw();
293
294 //cFilter->cd( Ntypes - tVslide );
295 cFilter->cd(1);
296 gPad->SetGrid();
297 oldh[tVslide].Draw();
298 TLine thrLine(0, myTHR, 1024, myTHR);
299 thrLine.SetLineColor(kRed);
300 thrLine.SetLineWidth(2);
301 thrLine.Draw();
302 TLine * OneLine;
303 vector<TLine*> MyLines;
304 for (unsigned int p=0; p<Regions->size(); p++ ){
305 OneLine = new TLine(
306 Regions->at(p).begin ,
307 Regions->at(p).maxVal,
308 Regions->at(p).end,
309 Regions->at(p).maxVal);
310 OneLine->SetLineColor(kRed);
311 OneLine->SetLineWidth(2);
312 MyLines.push_back(OneLine);
313 OneLine->Draw();
314 }
315 TBox *OneBox;
316 vector<TBox*> MyBoxes;
317 for (unsigned int i=0; i<zXings->size(); i++){
318 OneBox = new TBox(
319 zXings->at(i).maxPos -10 ,
320 zXings->at(i).maxVal -0.5,
321 zXings->at(i).maxPos +10 ,
322 zXings->at(i).maxVal +0.5);
323 OneBox->SetLineColor(kBlue);
324 OneBox->SetLineWidth(1);
325 OneBox->SetFillStyle(0);
326 OneBox->SetFillColor(kRed);
327 MyBoxes.push_back(OneBox);
328 OneBox->Draw();
329 }
330
331// cFilter->cd( Ntypes - tVcfd );
332// cFilter->cd(2);
333// gPad->SetGrid();
334// oldh[tVcfd].Draw();
335// TLine zeroline(0, 0, 1024, 0);
336// zeroline.SetLineColor(kBlue);
337// zeroline.Draw();
338
339 //cFilter->cd( Ntypes - tVcfd2 );
340 cFilter->cd(2);
341 gPad->SetGrid();
342 oldh[tVcfd2].Draw();
343 TLine zeroline(0, 0, 1024, 0);
344 zeroline.SetLineColor(kBlue);
345 zeroline.Draw();
346
347
348
349
350
351 CW->Update();
352 cFilter->Update();
353
354 //Process gui events asynchronously during input
355 TTimer timer("gSystem->ProcessEvents();", 50, kFALSE);
356 timer.TurnOn();
357 TString input = Getline("Type 'q' to exit, <return> to go on: ");
358 timer.TurnOff();
359 if (input=="q\n") {
360 breakout=true;
361 delete OneLine;
362// for (unsigned int i=0; i<MyLines.size(); i++){
363// delete MyLines[i];
364// }
365// for (unsigned int i=0; i<MyBoxes.size(); i++){
366// delete MyBoxes[i];
367// }
368 }
369 }// end of if(spikeDebug)
370 delete Regions;
371 delete zXings;
372
373 if (PixelID != -1){
374 pix = 2000;
375 }
376
377 if (breakout)
378 break;
379
380 } // end of loop over pixels
381
382 if (ev % 10 ==0){
383 cSpektrum->Modified();
384 cSpektrum->Update();
385// cStartCell->Modified();
386// cStartCell->Update();
387 }
388 if (breakout)
389 break;
390 } // end of loop over pixels
391
392
393// delete cStartCell;
394 return( 0 );
395}
396
397void removeSpikes(int Samples){
398
399 const float fract = 0.8;
400 float x, xp, xpp, x3p;
401
402 // assume that there are no spikes
403 for ( int i = 0; i < Samples; i++) Vcorr[i] = Ameas[i];
404
405// find the spike and replace it by mean value of neighbors
406 for ( int i = 0; i < Samples; i++) {
407
408 // printf("Vcorr[%d] = %f, Ameas[%d] = %f\n", i, Vcorr[ i ], i, Ameas[ i ] );
409
410 x = Ameas[i] - N1mean[i];
411
412 if ( x < -5. ){ // a spike candidate
413 // check consistency with a single channel spike
414 xp = Ameas[i+1] - N1mean[i+1];
415 xpp = Ameas[i+2] - N1mean[i+2];
416 x3p = Ameas[i+3] - N1mean[i+3];
417
418 // printf("candidates x[%d] = %f; xp = %f; xpp = %f, x3p = %f\n", i, x, xp, xpp, x3p);
419
420 if ( Ameas[i+2] - ( Ameas[i] + Ameas[i+3] )/2. > 10. ){
421 // printf("double spike candidate\n");
422 Vcorr[i+1] = ( Ameas[i] + Ameas[i+3] )/2.;
423 Vcorr[i+2] = ( Ameas[i] + Ameas[i+3] )/2.;
424 // printf("Vcorr[%d] = %f %f %f %f\n", i, Vcorr[i], Vcorr[i+1], Vcorr[i+2], Vcorr[ i+3 ]);
425 // printf("Ameas[%d] = %f %f %f %f\n", i, Ameas[ i ], Ameas[ i+1 ], Ameas[ i+2 ], Ameas[i+3]);
426 i = i + 3;
427 }
428 else{
429
430 if ( ( xp > -2.*x*fract ) && ( xpp < -10. ) ){
431 Vcorr[i+1] = N1mean[i+1];
432 // printf("Vcorr[%d] = %f %f %f\n", i, Vcorr[i], Vcorr[i+1], Vcorr[i+2]);
433 // N1mean[i+1] = (Ameas[i] - Ameas[i+2] / 2.);
434 N1mean[i+2] = (Ameas[i+1] - Ameas[i+3] / 2.);
435 i = i + 2;//do not care about the next sample it was the spike
436 }
437 // treatment for the end of the pipeline must be added !!!
438 }
439 }
440 else{
441 // do nothing
442 }
443 } // end of spike search and correction
444 for ( int i = 0; i < Samples; i++ ) oldh[ tVcorr ].SetBinContent( i, Vcorr[i] );
445}
446/*
447void computeN1mean( int Samples ){
448cout << "In compute N1mean" << endl;
449// compute the mean of the left and right neighbors of a channel
450
451 for( int i = 0; i < Samples; i++){
452 if (i == 0){ // use right sample es mean
453 N1mean[i] = Ameas[i+1];
454 }
455 else if ( i == Samples-1 ){ //use left sample as mean
456 N1mean[i] = Ameas[i-1];
457 }
458 else{
459 N1mean[i] = ( Ameas[i-1] + Ameas[i+1] ) / 2.;
460 }
461 h[tN1mean].SetBinContent(i, Ameas[i] - N1mean[i]);
462 }
463} // end of computeN1mean computation
464*/
465
466void computeN1mean( int Samples ){
467// compute the mean of the left and right neighbors of a channel
468
469 for( int i = 2; i < Samples - 2; i++){
470/* if (i == 0){ // use right sample es mean
471 N1mean[i] = Ameas[i+1];
472 }
473 else if ( i == Samples-1 ){ //use left sample as mean
474 N1mean[i] = Ameas[i-1];
475 }
476 else{
477 N1mean[i] = ( Ameas[i-1] + Ameas[i+1] ) / 2.;
478 }
479*/
480 N1mean[i] = ( Ameas[i-1] + Ameas[i+1] ) / 2.;
481 }
482} // end of computeN1mean computation
483
484float getValue( int slice, int pixel ){
485 const float dconv = 2000/4096.0;
486
487 float vraw, vcal;
488
489 unsigned int pixel_pt;
490 unsigned int slice_pt;
491 unsigned int cal_pt;
492 unsigned int drs_cal_offset;
493
494 // printf("pixel = %d, slice = %d\n", slice, pixel);
495
496 pixel_pt = pixel * RegionOfInterest;
497 slice_pt = pixel_pt + slice;
498 drs_cal_offset = ( slice + StartCellVector[ pixel ] )%RegionOfInterest;
499 cal_pt = pixel_pt + drs_cal_offset;
500
501 vraw = AllPixelDataVector[ slice_pt ] * dconv;
502 vcal = ( vraw - drs_basemean[ cal_pt ] - drs_triggeroffsetmean[ slice_pt ] ) / drs_gainmean[ cal_pt ]*1907.35;
503
504 return( vcal );
505}
506float correctDrsOffset( int slice, int pixel ){
507
508 const float dconv = 2000/4096.0;
509
510 // here 1024 is not the RegionOfInterest, but really the lenth of the pipeline
511 unsigned int physical_slice = ( slice + StartCellVector[ pixel ] ) % 1024;
512
513 unsigned int slice_pt;
514 unsigned int physical_slice_pt;
515 slice_pt = pixel * RegionOfInterest + slice;
516 physical_slice_pt = pixel * RegionOfInterest + physical_slice;
517
518 float vcal = AllPixelDataVector[ slice_pt ] *
519 dconv - drs_basemean[ physical_slice_pt ];
520 return( vcal );
521}
522
523void BookHistos( ){
524// booking and parameter settings for all histos
525
526 // histograms for baseline extraction
527 char hName[500];
528 char hTitle[500];
529
530 TH1F *h;
531
532 for( int i = 0; i < NPIX; i++ ) {
533 sprintf(&hTitle[0],"all events all slices of pixel %d", i);
534 sprintf(&hName[0],"base%d", i);
535
536 h = new TH1F( hName, hTitle, 400, -99.5 ,100.5 );
537
538 h->GetXaxis()->SetTitle( "Sample value (mV)" );
539 h->GetYaxis()->SetTitle( "Entries / 0.5 mV" );
540 hListBaseline.Add( h );
541 hBaseline[i] = h;
542 }
543
544 hMeanBsl = new TH1F("histo_mean","Value of maximal probability",400,-99.5,100.5);
545 hMeanBsl->GetXaxis()->SetTitle( "max value (mV)" );
546 hMeanBsl->GetYaxis()->SetTitle( "Entries / 0.5 mV" );
547 hList.Add( hMeanBsl );
548
549 hpltMeanBsl = new TH1F("hplt_mean","Value of maximal probability",1440,-0.5,1439.5);
550 hpltMeanBsl->GetXaxis()->SetTitle( "pixel" );
551 hpltMeanBsl->GetYaxis()->SetTitle( "max value in mV" );
552 hList.Add( hpltMeanBsl );
553
554 hRmsBsl = new TH1F("histo_rms","RMS in mV",2000,-99.5,100.5);
555 hRmsBsl->GetXaxis()->SetTitle( "RMS (mV)" );
556 hRmsBsl->GetYaxis()->SetTitle( "Entries / 0.5 mV" );
557 hList.Add( hRmsBsl );
558
559 hpltRmsBsl = new TH1F("hplt_rms","Value of maximal probability",1440,-0.5,1439.5);
560 hpltRmsBsl->GetXaxis()->SetTitle( "pixel" );
561 hpltRmsBsl->GetYaxis()->SetTitle( "RMS in mV" );
562 hList.Add( hpltRmsBsl );
563
564 hAmplSpek_cfd = new TH2F("hAmplSpek_cfd","amplitude spektrum - CFD",1440,-0.5,1439.5, 256, -27.5, 100.5);
565 hAmplSpek_cfd->GetXaxis()->SetTitle( "pixel" );
566 hAmplSpek_cfd->GetYaxis()->SetTitle( "amplitude in mV" );
567 hList.Add( hAmplSpek_cfd );
568
569 hAmplSpek_discri = new TH2F("hAmplSpek_discri","amplitude spektrum - std discriminator",1440,-0.5,1439.5, 256, -27.5, 100.5);
570 hAmplSpek_discri->GetXaxis()->SetTitle( "pixel" );
571 hAmplSpek_discri->GetXaxis()->SetTitle( "amplitude in mV" );
572 hList.Add( hAmplSpek_discri );
573
574 hStartCell = new TH2F("StartCell", "StartCell", 1440, 0., 1440., 1024, 0., 1024);
575 hStartCell->GetXaxis()->SetTitle( "pixel" );
576 hStartCell->GetXaxis()->SetTitle( "slice" );
577 hList.Add( hStartCell );
578
579 oldh = new TH1F[ Ntypes ];
580
581 for ( int type = 0; type < Ntypes; type++){
582
583 oldh[ type ].SetBins(1024, 0, 1024);
584 oldh[ type ].SetLineColor(1);
585 oldh[ type ].SetLineWidth(2);
586
587 // set X axis paras
588 oldh[ type ].GetXaxis()->SetLabelSize(0.1);
589 oldh[ type ].GetXaxis()->SetTitleSize(0.1);
590 oldh[ type ].GetXaxis()->SetTitleOffset(1.2);
591 oldh[ type ].GetXaxis()->SetTitle(Form("Time slice (%.1f ns/slice)", 1./2.));
592
593 // set Y axis paras
594 oldh[ type ].GetYaxis()->SetLabelSize(0.1);
595 oldh[ type ].GetYaxis()->SetTitleSize(0.1);
596 oldh[ type ].GetYaxis()->SetTitleOffset(0.3);
597 oldh[ type ].GetYaxis()->SetTitle("Amplitude (a.u.)");
598 }
599
600 CW = new TCanvas("CW","DRS Waveform",10,10,800,600);
601 CW->Divide(1, 2);
602 cFilter = new TCanvas("cFilter","filtered DRS Waveforms",10,10,800,600);
603 cFilter->Divide(1, 2);
604}
605
606void SaveHistograms( char * loc_fname ){
607
608 TString fName; // name of the histogram file
609
610 // create the filename for the histogram file
611 fName = loc_fname; // use the name of the tree file
612 // TODO ... next statement doesn't work for ".fits.gz"
613 fName.Remove(fName.Length() - 5); // remove the extension .fits
614 fName += "_discri.root"; // add the new extension
615
616 // create the histogram file (replace if already existing)
617 TFile tf( fName, "RECREATE");
618
619 hList.Write(); // write the major histograms into the top level directory
620 tf.mkdir("BaselineHisto");
621 tf.cd("BaselineHisto"); // go to new subdirectory
622 hListBaseline.Write(); // write histos into subdirectory
623
624 tf.Close(); // close the file
625} // end of SaveHistograms( char * loc_fname )
626
627int FOpenDataFile(fits &datafile){
628
629// cout << "-------------------- Data Header -------------------" << endl;
630// datafile.PrintKeys();
631// cout << "------------------- Data Columns -------------------" << endl;
632// datafile.PrintColumns();
633
634 //Get the size of the data column
635 RegionOfInterest = datafile.GetUInt("NROI");
636 NumberOfPixels = datafile.GetUInt("NPIX");
637 //Size of column "Data" = #Pixel x ROI
638 ROIxNOP = datafile.GetN("Data");
639
640 //Set the sizes of the data vectors
641 AllPixelDataVector.resize(ROIxNOP,0);
642 StartCellVector.resize(NumberOfPixels,0);
643
644 //Link the data to variables
645 datafile.SetRefAddress("EventNum", CurrentEventID);
646 datafile.SetVecAddress("Data", AllPixelDataVector);
647 datafile.SetVecAddress("StartCellData", StartCellVector);
648 datafile.GetRow(0);
649
650 return datafile.GetNumRows() ;
651}
652
653
654/////////////////////////////////////////////////////////////////////////////
655/// old BookHistos
656/*
657void BookHistos( int Samples ){
658// booking and parameter settings for all histos
659
660 h = new TH1F[ Ntypes ];
661
662 for ( int type = 0; type < Ntypes; type++){
663
664 h[ type ].SetBins(Samples, 0, Samples);
665 h[ type ].SetLineColor(1);
666 h[ type ].SetLineWidth(2);
667
668 // set X axis paras
669 h[ type ].GetXaxis()->SetLabelSize(0.1);
670 h[ type ].GetXaxis()->SetTitleSize(0.1);
671 h[ type ].GetXaxis()->SetTitleOffset(1.2);
672 h[ type ].GetXaxis()->SetTitle(Form("Time slice (%.1f ns/slice)", 1./2.));
673
674 // set Y axis paras
675 h[ type ].GetYaxis()->SetLabelSize(0.1);
676 h[ type ].GetYaxis()->SetTitleSize(0.1);
677 h[ type ].GetYaxis()->SetTitleOffset(0.3);
678 h[ type ].GetYaxis()->SetTitle("Amplitude (a.u.)");
679 }
680 CW = new TCanvas("CW","DRS Waveform",10,10,800,600);
681 CW->Divide(1, 3);
682 cFilter = new TCanvas("cFilter","filtered DRS Waveforms",10,10,800,600);
683 cFilter->Divide(1, 3);
684
685 hStartCell = new TH2F("StartCell", "StartCell", 1440, 0., 1440., 1024, 0., 1024);
686
687}
688*/
689
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