1 #ifndef ALIPMDCLUSTERFINDER_H
2 #define ALIPMDCLUSTERFINDER_H
3 /* Copyright(c) 1998-1999, ALICE Experiment at CERN, All rights reserved. *
4 * See cxx source for full Copyright notice */
5 //-----------------------------------------------------//
7 // Date : August 05 2003 //
8 // This reads the file PMD.digits.root(TreeD), //
9 // calls the Clustering algorithm and stores the //
10 // clustering output in PMD.RecPoints.root(TreeR) //
12 //-----------------------------------------------------//
21 class AliPMDCalibData;
23 class AliPMDClusterFinder : public TObject
28 AliPMDClusterFinder();
29 AliPMDClusterFinder(AliRunLoader* runLoader);
30 AliPMDClusterFinder(const AliPMDClusterFinder &finder); // copy constructor
31 AliPMDClusterFinder &operator=(const AliPMDClusterFinder &finder); // assignment op
32 virtual ~AliPMDClusterFinder();
34 void Digits2RecPoints(Int_t ievt);
35 void Digits2RecPoints(AliRawReader *rawReader, TTree *clustersTree);
36 void Digits2RecPoints(Int_t ievt, AliRawReader *rawReader);
37 void SetCellEdepCut(Float_t ecut);
38 void AddRecPoint(Int_t idet, Int_t ismn, Float_t * clusdata);
39 void AddRecHit(Int_t celldataX, Int_t celldataY);
46 void UnLoadClusters();
48 AliPMDCalibData *GetCalibData() const;
51 AliRunLoader *fRunLoader; // Pointer to Run Loader
52 AliLoader *fPMDLoader; // Pointer to specific detector loader
54 AliPMDCalibData *fCalibData; //! calibration data
56 TTree *fTreeD; // Digits tree
57 TTree *fTreeR; // Reconstructed points
59 TClonesArray *fDigits; // List of digits
60 TClonesArray *fRecpoints; // List of reconstructed points
61 TClonesArray *fRechits; // List of cells associated with rec points
65 Int_t fDetNo; // Detector Number (0:PRE, 1:CPV)
66 Float_t fEcut; // Energy/ADC cut per cell
68 static const Int_t fgkRow = 48; // Total number of rows in one unitmodule
69 static const Int_t fgkCol = 96; // Total number of cols in one unitmodule
70 Double_t fCellADC[fgkRow][fgkCol]; // Array containing individual cell ADC
72 ClassDef(AliPMDClusterFinder,10) // To run PMD clustering