Python clone Beispiele

Beispiel #1

from RecoEgamma.EgammaElectronProducers.gsfElectrons_cfi import ecalDrivenGsfElectrons

lowPtGsfElectronsPreRegression = ecalDrivenGsfElectrons.clone(gsfElectronCoresTag = "lowPtGsfElectronCores")

from Configuration.Eras.Modifier_fastSim_cff import fastSim
fastSim.toModify(lowPtGsfElectronsPreRegression,ctfTracksTag = "generalTracksBeforeMixing")

Beispiel #2

from RecoEgamma.EgammaElectronProducers.gsfElectrons_cfi import ecalDrivenGsfElectrons

gedGsfElectronsTmp = ecalDrivenGsfElectrons.clone(

    # input collections
    gsfElectronCoresTag="gedGsfElectronCores",

    # steering
    resetMvaValuesUsingPFCandidates=True,
    applyPreselection=True,
    ecalDrivenEcalEnergyFromClassBasedParameterization=False,
    ecalDrivenEcalErrorFromClassBasedParameterization=False,
    useEcalRegression=True,
    useCombinationRegression=True,

    # regression. The labels are needed in all cases.
    ecalRefinedRegressionWeightLabels=[
        "gedelectron_EBCorrection_offline_v1",
        "gedelectron_EECorrection_offline_v1",
        "gedelectron_EBUncertainty_offline_v1",
        "gedelectron_EEUncertainty_offline_v1"
    ],
    combinationRegressionWeightLabels=["gedelectron_p4combination_offline"],
)

from Configuration.Eras.Modifier_pp_on_AA_2018_cff import pp_on_AA_2018
pp_on_AA_2018.toModify(gedGsfElectronsTmp.preselection, minSCEtBarrel=15.0)
pp_on_AA_2018.toModify(gedGsfElectronsTmp.preselection, minSCEtEndcaps=15.0)

from Configuration.ProcessModifiers.egamma_lowPt_exclusive_cff import egamma_lowPt_exclusive
egamma_lowPt_exclusive.toModify(gedGsfElectronsTmp.preselection,

Beispiel #3

Datei: gedGsfElectrons_cfi.py Projekt: waredjeb/cmssw

gedGsfElectronsTmp = ecalDrivenGsfElectrons.clone(

    # input collections
    gsfElectronCoresTag="gedGsfElectronCores",

    # steering
    resetMvaValuesUsingPFCandidates=True,
    applyPreselection=True,
    ecalDrivenEcalEnergyFromClassBasedParameterization=False,
    ecalDrivenEcalErrorFromClassBasedParameterization=False,
    useEcalRegression=True,
    useCombinationRegression=True,

    # regression. The labels are needed in all cases.
    ecalRefinedRegressionWeightLabels=[
        "gedelectron_EBCorrection_offline_v1",
        "gedelectron_EECorrection_offline_v1",
        "gedelectron_EBUncertainty_offline_v1",
        "gedelectron_EEUncertainty_offline_v1"
    ],
    combinationRegressionWeightLabels=["gedelectron_p4combination_offline"],

    #Activate the evaluation of Egamma PFID DNN
    EleDNNPFid=
    dict(modelsFiles=[
        "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/lowpT/lowpT_modelDNN.pb",
        "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/highpTEB/highpTEB_modelDNN.pb",
        "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/highpTEE/highpTEE_modelDNN.pb"
    ],
         scalersFiles=[
             "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/lowpT/lowpT_scaler.txt",
             "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/highpTEB/highpTEB_scaler.txt",
             "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/highpTEE/highpTEE_scaler.txt"
         ]))

Beispiel #4

Datei: lowPtGsfElectrons_cfi.py Projekt: zhwenyu/cmssw

from RecoEgamma.EgammaElectronProducers.gsfElectrons_cfi import ecalDrivenGsfElectrons

lowPtGsfElectrons = ecalDrivenGsfElectrons.clone(
    gsfElectronCoresTag="lowPtGsfElectronCores")

from Configuration.Eras.Modifier_fastSim_cff import fastSim

fastSim.toModify(lowPtGsfElectrons, ctfTracksTag="generalTracksBeforeMixing")

from Configuration.ProcessModifiers.pp_on_AA_cff import pp_on_AA

pp_on_AA.toModify(lowPtGsfElectrons.preselection, minSCEtBarrel=15.0)
pp_on_AA.toModify(lowPtGsfElectrons.preselection, minSCEtEndcaps=15.0)

Beispiel #5

Datei: gedGsfElectrons_cfi.py Projekt: p2l1pfp/cmssw

gedGsfElectronsTmp = ecalDrivenGsfElectrons.clone(

    # input collections
    gsfElectronCoresTag="gedGsfElectronCores",

    # steering
    resetMvaValuesUsingPFCandidates=True,
    applyPreselection=True,
    ecalDrivenEcalEnergyFromClassBasedParameterization=False,
    ecalDrivenEcalErrorFromClassBasedParameterization=False,
    useEcalRegression=True,
    useCombinationRegression=True,

    # regression. The labels are needed in all cases.
    ecalRefinedRegressionWeightLabels=[
        "gedelectron_EBCorrection_offline_v1",
        "gedelectron_EECorrection_offline_v1",
        "gedelectron_EBUncertainty_offline_v1",
        "gedelectron_EEUncertainty_offline_v1"
    ],
    combinationRegressionWeightLabels=["gedelectron_p4combination_offline"],

    #Egamma PFID DNN model configuration
    EleDNNPFid=dict(
        outputTensorName="sequential_1/FinalLayer/Softmax",
        modelsFiles=[
            "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/Run3Summer21_120X/lowpT/lowpT_modelDNN.pb",
            "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/Run3Summer21_120X/EB_highpT/barrel_highpT_modelDNN.pb",
            "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/Run3Summer21_120X/EE_highpT/endcap_highpT_modelDNN.pb"
        ],
        scalersFiles=[
            "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/Run3Summer21_120X/lowpT/lowpT_scaler.txt",
            "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/Run3Summer21_120X/EB_highpT/barrel_highpT_scaler.txt",
            "RecoEgamma/ElectronIdentification/data/Ele_PFID_dnn/Run3Summer21_120X/EE_highpT/endcap_highpT_scaler.txt"
        ]))