import FWCore.ParameterSet.Config as cms

CSCSegAlgoST = cms.PSet(
    algo_name = cms.string('CSCSegAlgoST'),
    algo_psets = cms.VPSet(
        cms.PSet(
            BPMinImprovement = cms.double(10000.0),
            BrutePruning = cms.bool(True),
            CSCDebug = cms.untracked.bool(False),
            CorrectTheErrors = cms.bool(True),
            Covariance = cms.double(0.0),
            ForceCovariance = cms.bool(False),
            ForceCovarianceAll = cms.bool(False),
            NormChi2Cut2D = cms.double(20.0),
            NormChi2Cut3D = cms.double(10.0),
            Pruning = cms.bool(True),
            SeedBig = cms.double(0.0015),
            SeedSmall = cms.double(0.0002),
            curvePenalty = cms.double(2.0),
            curvePenaltyThreshold = cms.double(0.85),
            dPhiFineMax = cms.double(0.025),
            dRPhiFineMax = cms.double(8.0),
            dXclusBoxMax = cms.double(4.0),
            dYclusBoxMax = cms.double(8.0),
            hitDropLimit4Hits = cms.double(0.6),
            hitDropLimit5Hits = cms.double(0.8),
            hitDropLimit6Hits = cms.double(0.3333),
            maxDPhi = cms.double(999.0),
            maxDTheta = cms.double(999.0),
            maxRatioResidualPrune = cms.double(3),
            maxRecHitsInCluster = cms.int32(20),
            minHitsPerSegment = cms.int32(3),
            onlyBestSegment = cms.bool(False),
            preClustering = cms.bool(True),
            preClusteringUseChaining = cms.bool(True),
            prePrun = cms.bool(True),
            prePrunLimit = cms.double(3.17),
            tanPhiMax = cms.double(0.5),
            tanThetaMax = cms.double(1.2),
            useShowering = cms.bool(False),
            yweightPenalty = cms.double(1.5),
            yweightPenaltyThreshold = cms.double(1.0)
        ),
        cms.PSet(
            BPMinImprovement = cms.double(10000.0),
            BrutePruning = cms.bool(True),
            CSCDebug = cms.untracked.bool(False),
            CorrectTheErrors = cms.bool(True),
            Covariance = cms.double(0.0),
            ForceCovariance = cms.bool(False),
            ForceCovarianceAll = cms.bool(False),
            NormChi2Cut2D = cms.double(20.0),
            NormChi2Cut3D = cms.double(10.0),
            Pruning = cms.bool(True),
            SeedBig = cms.double(0.0015),
            SeedSmall = cms.double(0.0002),
            curvePenalty = cms.double(2.0),
            curvePenaltyThreshold = cms.double(0.85),
            dPhiFineMax = cms.double(0.025),
            dRPhiFineMax = cms.double(8.0),
            dXclusBoxMax = cms.double(4.0),
            dYclusBoxMax = cms.double(8.0),
            hitDropLimit4Hits = cms.double(0.6),
            hitDropLimit5Hits = cms.double(0.8),
            hitDropLimit6Hits = cms.double(0.3333),
            maxDPhi = cms.double(999.0),
            maxDTheta = cms.double(999.0),
            maxRatioResidualPrune = cms.double(3),
            maxRecHitsInCluster = cms.int32(24),
            minHitsPerSegment = cms.int32(3),
            onlyBestSegment = cms.bool(False),
            preClustering = cms.bool(True),
            preClusteringUseChaining = cms.bool(True),
            prePrun = cms.bool(True),
            prePrunLimit = cms.double(3.17),
            tanPhiMax = cms.double(0.5),
            tanThetaMax = cms.double(1.2),
            useShowering = cms.bool(False),
            yweightPenalty = cms.double(1.5),
            yweightPenaltyThreshold = cms.double(1.0)
        )
    ),
    chamber_types = cms.vstring(
        'ME1/a',
        'ME1/b',
        'ME1/2',
        'ME1/3',
        'ME2/1',
        'ME2/2',
        'ME3/1',
        'ME3/2',
        'ME4/1',
        'ME4/2'
    ),
    parameters_per_chamber_type = cms.vint32(
        2, 1, 1, 1, 1,
        1, 1, 1, 1, 1
    )
)
