remove exec
[u/mrichter/AliRoot.git] / EMCAL / AliCaloRawAnalyzerNN.cxx
CommitLineData
e37e3c84 1/**************************************************************************
2 * This file is property of and copyright by the Experimental Nuclear *
3 * Physics Group, Dep. of Physics *
4 * University of Oslo, Norway, 2007 *
5 * *
6 * Author: Per Thomas Hille <perthomas.hille@yale.edu> *
7 * for the ALICE HLT Project. *
8 * Contributors are mentioned in the code where appropriate. *
9 * Please report bugs to perthi@fys.uio.no *
10 * *
11 * Permission to use, copy, modify and distribute this software and its *
12 * documentation strictly for non-commercial purposes is hereby granted *
13 * without fee, provided that the above copyright notice appears in all *
14 * copies and that both the copyright notice and this permission notice *
15 * appear in the supporting documentation. The authors make no claims *
16 * about the suitability of this software for any purpose. It is *
17 * provided "as is" without express or implied warranty. *
18 **************************************************************************/
19
20// Evaluation of peak position
21// and amplitude using Neural Networks (NN)
22// ------------------
23// ------------------
24// ------------------
25
26
27#include "AliCaloRawAnalyzerNN.h"
28#include "AliCaloNeuralFit.h"
29#include "AliCaloFitResults.h"
30#include "AliCaloBunchInfo.h"
e37e3c84 31#include <iostream>
e37e3c84 32using namespace std;
33
168c7b3c 34#include "AliCaloConstants.h"
35
e37e3c84 36ClassImp( AliCaloRawAnalyzerNN )
37
48a2e3eb 38AliCaloRawAnalyzerNN::AliCaloRawAnalyzerNN() : AliCaloRawAnalyzer("Neural Network", "NN"), fNeuralNet(0)
e37e3c84 39{
40 // Comment
168c7b3c 41 fAlgo=Algo::kNeuralNet;
e37e3c84 42
43 fNeuralNet = new AliCaloNeuralFit();
44
45 for(int i=0; i < 5 ; i++)
46 {
47 fNNInput[i] = 0;
48 }
49
50}
51
52
53AliCaloRawAnalyzerNN::~AliCaloRawAnalyzerNN()
54{
55 delete fNeuralNet;
56}
57
58
59AliCaloFitResults
60AliCaloRawAnalyzerNN::Evaluate( const vector<AliCaloBunchInfo> &bunchvector,
61 const UInt_t altrocfg1, const UInt_t altrocfg2 )
62{
63 // The eveluation of Peak position and amplitude using the Neural Network
64 if( bunchvector.size() <= 0 )
65 {
92d9f317 66 // cout << __FILE__ << __LINE__<< " INVALID "<< endl;
67
168c7b3c 68 return AliCaloFitResults( Ret::kInvalid, Ret::kInvalid);
e37e3c84 69 }
70
f57baa2d 71 short maxampindex;
e37e3c84 72 short maxamp;
73
f57baa2d 74 int index = SelectBunch( bunchvector, &maxampindex , &maxamp ) ;
e37e3c84 75
f57baa2d 76 if( index < 0 )
e37e3c84 77 {
92d9f317 78 // cout << __FILE__ << __LINE__<< "INVALID !!!!!!" << endl;
168c7b3c 79 return AliCaloFitResults( Ret::kInvalid, Ret::kInvalid);
e37e3c84 80 }
81
2cd0ffda 82 Float_t ped = ReverseAndSubtractPed( &(bunchvector.at( index ) ) , altrocfg1, altrocfg2, fReversed );
f57baa2d 83 short timebinOffset = maxampindex - (bunchvector.at(index).GetLength()-1);
84 double maxf = maxamp - ped;
85
2cd0ffda 86 if( maxf < fAmpCut || ( maxamp - ped) > fOverflowCut ) // (maxamp - ped) > fOverflowCut = Close to saturation (use low gain then)
87 {
92d9f317 88 // cout << __FILE__ << __LINE__<< ": timebinOffset = " << timebinOffset << " maxf "<< maxf << endl;
168c7b3c 89 return AliCaloFitResults( maxamp, ped, Ret::kCrude, maxf, timebinOffset);
2cd0ffda 90 }
91
92 int first = 0;
93 int last = 0;
94 short maxrev = maxampindex - bunchvector.at(index).GetStartBin();
92d9f317 95 SelectSubarray( fReversed, bunchvector.at(index).GetLength(), maxrev , &first, &last, fFitArrayCut );
e37e3c84 96
2cd0ffda 97 Float_t chi2 = 0;
98 Int_t ndf = 0;
e37e3c84 99 if(maxrev < 1000 )
100 {
101 if ( ( maxrev - first) < 2 && (last - maxrev ) < 2)
102 {
2cd0ffda 103 chi2 = CalculateChi2(maxf, maxrev, first, last);
104 ndf = last - first - 1; // nsamples - 2
92d9f317 105 // cout << __FILE__ << __LINE__<< ": timebinOffset = " << timebinOffset << " maxf\t"<< maxf <<endl;
168c7b3c 106 return AliCaloFitResults( maxamp, ped, Ret::kCrude, maxf, timebinOffset,
107 timebinOffset, chi2, ndf, Ret::kDummy, AliCaloFitSubarray(index, maxrev, first, last) );
e37e3c84 108 }
109 else
110 {
e37e3c84 111
112 for(int i=0; i < 5 ; i++)
113 {
114 fNNInput[i] = fReversed[maxrev-2 +i]/(maxamp -ped);
115 }
116
3b8fd9fe 117
a9ebbc7a 118 double amp = (maxamp - ped)*fNeuralNet->Value( 0, fNNInput[0], fNNInput[1], fNNInput[2], fNNInput[3], fNNInput[4]);
3b8fd9fe 119 double tof = (fNeuralNet->Value( 1, fNNInput[0], fNNInput[1], fNNInput[2], fNNInput[3], fNNInput[4]) + timebinOffset ) ;
e37e3c84 120
2cd0ffda 121 // use local-array time for chi2 estimate
122 chi2 = CalculateChi2(amp, tof-timebinOffset+maxrev, first, last);
123 ndf = last - first - 1; // nsamples - 2
92d9f317 124 //cout << __FILE__ << __LINE__<< ": tof = " << tof << " amp" << amp <<endl;
168c7b3c 125 return AliCaloFitResults( maxamp, ped , Ret::kFitPar, amp , tof, timebinOffset, chi2, ndf,
126 Ret::kDummy, AliCaloFitSubarray(index, maxrev, first, last) );
e37e3c84 127
128 }
129 }
2cd0ffda 130 chi2 = CalculateChi2(maxf, maxrev, first, last);
131 ndf = last - first - 1; // nsamples - 2
92d9f317 132
133 // cout << __FILE__ << __LINE__<< ": timebinOffset = " << timebinOffset << " maxf ="<< maxf << endl;
168c7b3c 134 return AliCaloFitResults( maxamp, ped, Ret::kCrude, maxf, timebinOffset,
135 timebinOffset, chi2, ndf, Ret::kDummy, AliCaloFitSubarray(index, maxrev, first, last) );
2cd0ffda 136
e37e3c84 137}
138
e37e3c84 139