d8/da0/fftjetpatrecoproducer__cfi_8py_source.html

import math

import FWCore.ParameterSet.Config as cms


from RecoJets.FFTJetProducers.fftjetcommon_cfi import *


# FFTJet pattern recognition module configuration

fftjetPatrecoProducer = cms.EDProducer(

    "FFTJetPatRecoProducer",

    #

    # The main eta and phi scale factors for the pattern recognition kernel

    kernelEtaScale = cms.double(math.sqrt(1.0/fftjet_phi_to_eta_bw_ratio)),

    kernelPhiScale = cms.double(math.sqrt(fftjet_phi_to_eta_bw_ratio)),

    #

    # Make the clustering trees? If you do not make the trees,

    # you should at least turn on the "storeDiscretizationGrid"

    # flag, otherwise this module will not produce anything at all.

    makeClusteringTree = cms.bool(True),

    #

    # Verify data conversion? For trees, this is only meaningful with

    # double precision storage. Grids, however, will always be verified

    # if this flag is set.

    verifyDataConversion = cms.untracked.bool(False),

    #

    # Are we going to produce sparse or full clustering trees

    sparsify = cms.bool(True),

    #

    # Are we going to store the discretized energy flow?

    storeDiscretizationGrid = cms.bool(False),

    #

    # Are we going to dump discretized energy flow into an external file?

    # Empty file name means "no".

    externalGridFile = cms.string(""),

    #

    # Configuration for the preliminary peak finder.

    # Its main purpose is to reject peaks produced by the FFT round-off noise.

    peakFinderMaxEta = cms.double(fftjet_standard_eta_range),

    peakFinderMaxMagnitude = cms.double(1.e-8),

    #

    # Attempt to correct the jet finding efficiency near detector eta limits?

    fixEfficiency = cms.bool(False),

    #

    # Minimum and maximum eta bin number for 1d convolver. Also used

    # to indicate detector limits for 2d convolvers in case "fixEfficiency"

    # is True.

    convolverMinBin = cms.uint32(0),

    convolverMaxBin = cms.uint32(fftjet_large_int),

    #

    # Insert complete event at the end when the clustering tree is constructed?

    insertCompleteEvent = cms.bool(fftjet_insert_complete_event),

    #

    # The scale variable for the complete event. Should be smaller than

    # any other pattern recognition scale but not too small so that the

    # tree can be nicely visualized in the ln(scale) space.

    completeEventScale = cms.double(fftjet_complete_event_scale),

    #

    # The grid data cutoff for the complete event

    completeEventDataCutoff = cms.double(0.0),

    #

    # Label for the produced objects

    outputLabel = cms.string("FFTJetPatternRecognition"),

    #

    # Label for the input collection of Candidate objects

    src = cms.InputTag("towerMaker"),

    #

    # Label for the jets which will be produced. The algorithm might do

    # different things depending on the type. In particular, vertex

    # correction may be done for "CaloJet"

    jetType = cms.string("CaloJet"),

    #

    # Perform vertex correction?

    doPVCorrection = cms.bool(False),

    #

    # Label for the input collection of vertex objects. Meaningful

    # only when "doPVCorrection" is True

    srcPVs = cms.InputTag("offlinePrimaryVertices"),

    #

    # Are we going to perform adaptive clustering? Setting the maximum

    # number of adaptive scales to 0 turns adaptive clustering off.

    maxAdaptiveScales = cms.uint32(0),

    #

    # Minimum distance between the scales (in the ln(scale) space)

    # for adaptive clustering. Meaningful only when the "maxAdaptiveScales"

    # parameter is not 0.

    minAdaptiveRatioLog = cms.double(0.01),

    #

    # Eta-dependent scale factors for the sequential 1d convolver.

    # If this vector is empty, 2d convolver will be used.

    etaDependentScaleFactors = cms.vdouble(),

    #

    # Eta-dependent magnitude factors for the data. These can be used

    # to correct for various things (including the eta-dependent scale

    # factors above).

    etaDependentMagnutideFactors = cms.vdouble(),

    #

    # Configuration for the energy discretization grid

    GridConfiguration = fftjet_grid_256_128,

    #

    # Configuration for the peak selector determining which peaks

    # are kept when the clustering tree is constructed

    PeakSelectorConfiguration = fftjet_peak_selector_allpass,

    #

    # The initial set of scales used by the pattern recognition stage.

    # This is also the final set unless clustering tree construction

    # is adaptive.

    InitialScales = fftjet_patreco_scales_50,

    #

    # Configuration for the clustering tree sparsification.

    #

    # Do not write the last tree level (the complete event) into the sparse

    # tree. This is done by setting the "maxLevelNumber" parameter to -1

    # in which case the counting for the max level is performed backwards

    # from the last level. Counting backwards is especially useful in the

    # adaptive clustering mode when the number of clustering tree levels

    # is not known in advance.

    SparsifierConfiguration = cms.PSet(

        maxLevelNumber = cms.int32(-1),

        filterMask = cms.uint32(fftjet_large_int),

        userScales = cms.vdouble()

    ),

    #

    # Clustering tree distance functor

    TreeDistanceCalculator = fftjet_fixed_bandwidth_distance,

    #

    # Anomalous calo tower definition (comes from JetProducers default)

    anomalous = fftjet_anomalous_tower_default

)