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

 )

fftjetcommon_cfi