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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" | |
31 | #include <iostream> | |
32 | using namespace std; | |
33 | ||
34 | #include "AliCaloConstants.h" | |
35 | ||
36 | ClassImp( AliCaloRawAnalyzerNN ) | |
37 | ||
38 | AliCaloRawAnalyzerNN::AliCaloRawAnalyzerNN() : AliCaloRawAnalyzer("Neural Network", "NN"), fNeuralNet(0) | |
39 | { | |
40 | // Ctor | |
41 | ||
42 | fAlgo=Algo::kNeuralNet; | |
43 | ||
44 | fNeuralNet = new AliCaloNeuralFit(); | |
45 | ||
46 | for(int i=0; i < 5 ; i++) | |
47 | { | |
48 | fNNInput[i] = 0; | |
49 | } | |
50 | ||
51 | } | |
52 | ||
53 | ||
54 | AliCaloRawAnalyzerNN::~AliCaloRawAnalyzerNN() | |
55 | { | |
56 | // Dtor | |
57 | delete fNeuralNet; | |
58 | } | |
59 | ||
60 | ||
61 | AliCaloFitResults | |
62 | AliCaloRawAnalyzerNN::Evaluate( const vector<AliCaloBunchInfo> &bunchvector, | |
63 | UInt_t altrocfg1, UInt_t altrocfg2 ) | |
64 | { | |
65 | // The eveluation of Peak position and amplitude using the Neural Network | |
66 | if( bunchvector.size() <= 0 ) | |
67 | { | |
68 | // cout << __FILE__ << __LINE__<< " INVALID "<< endl; | |
69 | ||
70 | return AliCaloFitResults( Ret::kInvalid, Ret::kInvalid); | |
71 | } | |
72 | ||
73 | short maxampindex; | |
74 | short maxamp; | |
75 | ||
76 | int index = SelectBunch( bunchvector, &maxampindex , &maxamp ) ; | |
77 | ||
78 | if( index < 0 ) | |
79 | { | |
80 | // cout << __FILE__ << __LINE__<< "INVALID !!!!!!" << endl; | |
81 | return AliCaloFitResults( Ret::kInvalid, Ret::kInvalid); | |
82 | } | |
83 | ||
84 | Float_t ped = ReverseAndSubtractPed( &(bunchvector.at( index ) ) , altrocfg1, altrocfg2, fReversed ); | |
85 | short timebinOffset = maxampindex - (bunchvector.at(index).GetLength()-1); | |
86 | double maxf = maxamp - ped; | |
87 | ||
88 | if( maxf < fAmpCut || ( maxamp - ped) > fOverflowCut ) // (maxamp - ped) > fOverflowCut = Close to saturation (use low gain then) | |
89 | { | |
90 | // cout << __FILE__ << __LINE__<< ": timebinOffset = " << timebinOffset << " maxf "<< maxf << endl; | |
91 | return AliCaloFitResults( maxamp, ped, Ret::kCrude, maxf, timebinOffset); | |
92 | } | |
93 | ||
94 | int first = 0; | |
95 | int last = 0; | |
96 | short maxrev = maxampindex - bunchvector.at(index).GetStartBin(); | |
97 | SelectSubarray( fReversed, bunchvector.at(index).GetLength(), maxrev , &first, &last, fFitArrayCut ); | |
98 | ||
99 | Float_t chi2 = 0; | |
100 | Int_t ndf = 0; | |
101 | if(maxrev < 1000 ) | |
102 | { | |
103 | if ( ( maxrev - first) < 2 && (last - maxrev ) < 2) | |
104 | { | |
105 | chi2 = CalculateChi2(maxf, maxrev, first, last); | |
106 | ndf = last - first - 1; // nsamples - 2 | |
107 | // cout << __FILE__ << __LINE__<< ": timebinOffset = " << timebinOffset << " maxf\t"<< maxf <<endl; | |
108 | return AliCaloFitResults( maxamp, ped, Ret::kCrude, maxf, timebinOffset, | |
109 | timebinOffset, chi2, ndf, Ret::kDummy, AliCaloFitSubarray(index, maxrev, first, last) ); | |
110 | } | |
111 | else | |
112 | { | |
113 | ||
114 | for(int i=0; i < 5 ; i++) | |
115 | { | |
116 | fNNInput[i] = fReversed[maxrev-2 +i]/(maxamp -ped); | |
117 | } | |
118 | ||
119 | ||
120 | double amp = (maxamp - ped)*fNeuralNet->Value( 0, fNNInput[0], fNNInput[1], fNNInput[2], fNNInput[3], fNNInput[4]); | |
121 | double tof = (fNeuralNet->Value( 1, fNNInput[0], fNNInput[1], fNNInput[2], fNNInput[3], fNNInput[4]) + timebinOffset ) ; | |
122 | ||
123 | // use local-array time for chi2 estimate | |
124 | chi2 = CalculateChi2(amp, tof-timebinOffset+maxrev, first, last); | |
125 | ndf = last - first - 1; // nsamples - 2 | |
126 | //cout << __FILE__ << __LINE__<< ": tof = " << tof << " amp" << amp <<endl; | |
127 | return AliCaloFitResults( maxamp, ped , Ret::kFitPar, amp , tof, timebinOffset, chi2, ndf, | |
128 | Ret::kDummy, AliCaloFitSubarray(index, maxrev, first, last) ); | |
129 | ||
130 | } | |
131 | } | |
132 | chi2 = CalculateChi2(maxf, maxrev, first, last); | |
133 | ndf = last - first - 1; // nsamples - 2 | |
134 | ||
135 | // cout << __FILE__ << __LINE__<< ": timebinOffset = " << timebinOffset << " maxf ="<< maxf << endl; | |
136 | return AliCaloFitResults( maxamp, ped, Ret::kCrude, maxf, timebinOffset, | |
137 | timebinOffset, chi2, ndf, Ret::kDummy, AliCaloFitSubarray(index, maxrev, first, last) ); | |
138 | ||
139 | } | |
140 | ||
141 |