Public Member Functions
	NNTrackWordSelector (tensorflow::Session *AssociationSesh, const double AssociationThreshold, const std::vector< double > &AssociationNetworkZ0binning, const std::vector< double > &AssociationNetworkEtaBounds, const std::vector< double > &AssociationNetworkZ0ResBins)

bool	operator() (const TTTrackType &t, const l1t::VertexWord &v) const

Private Attributes
tensorflow::Session *	AssociationSesh_

double	AssociationThreshold_

std::vector< double >	eta_bins_

std::vector< double >	res_bins_

std::vector< double >	z0_binning_

Detailed Description

Definition at line 155 of file L1TrackVertexAssociationProducer.cc.

Constructor & Destructor Documentation

◆ NNTrackWordSelector()

L1TrackVertexAssociationProducer::NNTrackWordSelector::NNTrackWordSelector	(	tensorflow::Session *	AssociationSesh,
		const double	AssociationThreshold,
		const std::vector< double > &	AssociationNetworkZ0binning,
		const std::vector< double > &	AssociationNetworkEtaBounds,
		const std::vector< double > &	AssociationNetworkZ0ResBins
	)

inline

Definition at line 156 of file L1TrackVertexAssociationProducer.cc.

         : AssociationSesh_(AssociationSesh),
           AssociationThreshold_(AssociationThreshold),
           z0_binning_(AssociationNetworkZ0binning),
           eta_bins_(AssociationNetworkEtaBounds),
           res_bins_(AssociationNetworkZ0ResBins) {}

Member Function Documentation

◆ operator()()

bool L1TrackVertexAssociationProducer::NNTrackWordSelector::operator()	(	const TTTrackType &	t,
		const l1t::VertexWord &	v
	)		const

inline

Definition at line 167 of file L1TrackVertexAssociationProducer.cc.

References funct::abs(), AssociationSesh_, AssociationThreshold_, HLT_2024v14_cff::distance, l1ctLayer1_cff::dZ, eta_bins_, JetChargeProducer_cfi::exp, nano_mu_digi_cff::float, pfDeepBoostedJetPreprocessParams_cfi::lower_bound, res_bins_, tensorflow::run(), submitPVValidationJobs::t, findQualityFiles::v, and z0_binning_.

                                                                         {
       tensorflow::Tensor inputAssoc(tensorflow::DT_FLOAT, {1, 4});
       std::vector<tensorflow::Tensor> outputAssoc;
 
       TTTrack_TrackWord::tanl_t etaEmulationBits = t.getTanlWord();
       ap_fixed<16, 3> etaEmulation;
       etaEmulation.V = (etaEmulationBits.range());
 
       auto lower = std::lower_bound(eta_bins_.begin(), eta_bins_.end(), etaEmulation.to_double());
 
       int resbin = std::distance(eta_bins_.begin(), lower);
       float binWidth = z0_binning_[2];
       // Calculate integer dZ from track z0 and vertex z0 (use floating point version and convert internally allowing use of both emulator and simulator vertex and track)
       float dZ =
           abs(floor(((t.getZ0() + z0_binning_[1]) / (binWidth))) - floor(((v.z0() + z0_binning_[1]) / (binWidth))));
 
       // The following constants <14, 9>, <22, 9> are defined by the quantisation of the Neural Network
       ap_uint<14> ptEmulationBits = t.getTrackWord()(TTTrack_TrackWord::TrackBitLocations::kRinvMSB - 1,
                                                      TTTrack_TrackWord::TrackBitLocations::kRinvLSB);
       ap_ufixed<14, 9> ptEmulation;
       ptEmulation.V = (ptEmulationBits.range());
 
       ap_ufixed<22, 9> ptEmulation_rescale;
       ptEmulation_rescale = ptEmulation.to_double();
 
       ap_ufixed<22, 9> resBinEmulation_rescale;
       resBinEmulation_rescale = res_bins_[resbin];
 
       ap_ufixed<22, 9> MVAEmulation_rescale;
       MVAEmulation_rescale = t.getMVAQualityBits();
 
       ap_ufixed<22, 9> dZEmulation_rescale;
       dZEmulation_rescale = dZ;
 
       inputAssoc.tensor<float, 2>()(0, 0) = ptEmulation_rescale.to_double();
       inputAssoc.tensor<float, 2>()(0, 1) = MVAEmulation_rescale.to_double();
       inputAssoc.tensor<float, 2>()(0, 2) = resBinEmulation_rescale.to_double() / 16.0;
       inputAssoc.tensor<float, 2>()(0, 3) = dZEmulation_rescale.to_double();
 
       // Run Association Network:
       tensorflow::run(AssociationSesh_, {{"assoc:0", inputAssoc}}, {"Identity:0"}, &outputAssoc);
 
       double NNOutput = (double)outputAssoc[0].tensor<float, 2>()(0, 0);
 
       double NNOutput_exp = 1.0 / (1.0 + exp(-1.0 * (NNOutput)));
 
       return NNOutput_exp >= AssociationThreshold_;
     }