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fftjetdijetfilter_cfi.py
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1 import math
2 import FWCore.ParameterSet.Config as cms
3 
5 
6 # Dijet pattern recognition filter configuration
7 fftjetDijetFilter = cms.EDFilter(
8  "FFTJetDijetFilter",
9  #
10  # Label for the input clustering tree (sparse or dense)
11  treeLabel = cms.InputTag("fftjetpatreco", "FFTJetPatternRecognition"),
12  #
13  # Do we have the complete event at the lowest clustering tree scale?
14  # Note that sparse clustering tree removes it by default, even if
15  # it is inserted by the pattern recognition module.
16  insertCompleteEvent = cms.bool(fftjet_insert_complete_event),
17  completeEventScale = cms.double(fftjet_complete_event_scale),
18  #
19  # The initial set of scales used by the pattern recognition stage.
20  # This is also the final set unless clustering tree construction
21  # is adaptive. Needed here for reading back non-adaptive trees.
22  InitialScales = fftjet_patreco_scales_3_at_050,
23  #
24  # Clustering tree distance functor
25  TreeDistanceCalculator = fftjet_fixed_bandwidth_distance,
26  #
27  # The scale to work with. Clustering tree will find the closest
28  # level whose scale is equal to or above the one given here.
29  fixedScale = cms.double(0.5/1.0001),
30  #
31  # Conversion factor from scale squared times peak magnitude to Pt.
32  # Note that this factor depends on the grid used for pattern resolution.
33  # The default value given here is correct for the default grid only.
34  ptConversionFactor = cms.double(128*256/(4.0*math.pi)),
35  #
36  # The cuts which must be passed for this event to continue along
37  # the data processing chain
38  min1to0PtRatio = cms.double(0.6),
39  minDeltaPhi = cms.double(2.7),
40  maxThirdJetFraction = cms.double(0.1),
41  minPt0 = cms.double(1.0),
42  minPt1 = cms.double(1.0),
43  maxPeakEta = cms.double(fftjet_standard_eta_range)
44 )