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import FWCore.ParameterSet.Config as cms
from DQM.EcalMonitorTasks.OccupancyTask_cfi import ecalOccupancyTask
#parameters derived from training
EBThreshold = 0.00017
EEpThreshold = 0.0003009
EEmThreshold = 0.0004360
EB_PUcorr_slope = 9087.286563128135
EB_PUcorr_intercept = 391987.0277612837
EEp_PUcorr_slope = 2.097273231210836457e+03
EEp_PUcorr_intercept= 4.905224959496531665e+04
EEm_PUcorr_slope = 2.029645065864053095e+03
EEm_PUcorr_intercept= 4.874167219924630626e+04
ecalMLClient = cms.untracked.PSet(
params = cms.untracked.PSet(
EBThreshold = cms.untracked.double(EBThreshold),
EEpThreshold = cms.untracked.double(EEpThreshold),
EEmThreshold = cms.untracked.double(EEmThreshold),
EB_PUcorr_slope = cms.untracked.double(EB_PUcorr_slope),
EB_PUcorr_intercept = cms.untracked.double(EB_PUcorr_intercept),
EEp_PUcorr_slope = cms.untracked.double(EEp_PUcorr_slope),
EEp_PUcorr_intercept = cms.untracked.double(EEp_PUcorr_intercept),
EEm_PUcorr_slope = cms.untracked.double(EEm_PUcorr_slope),
EEm_PUcorr_intercept = cms.untracked.double(EEm_PUcorr_intercept)
),
sources = cms.untracked.PSet(
DigiAllByLumi = ecalOccupancyTask.MEs.DigiAllByLumi,
AELoss = ecalOccupancyTask.MEs.AELoss,
AEReco = ecalOccupancyTask.MEs.AEReco,
PU = ecalOccupancyTask.MEs.PU,
NumEvents = ecalOccupancyTask.MEs.NEvents,
BadTowerCount = ecalOccupancyTask.MEs.BadTowerCount,
BadTowerCountNorm = ecalOccupancyTask.MEs.BadTowerCountNorm
),
MEs = cms.untracked.PSet(
MLQualitySummary = cms.untracked.PSet(
path = cms.untracked.string('%(subdet)s/%(prefix)sSummaryClient/%(prefix)sOT%(suffix)s ML quality summary'),
kind = cms.untracked.string('TH2F'),
otype = cms.untracked.string('Ecal3P'),
btype = cms.untracked.string('SuperCrystal'),
description = cms.untracked.string('Quality summary from the ML inference.')
),
EventsperMLImage = cms.untracked.PSet(
path = cms.untracked.string('Ecal/Trends/Number of Events used per ML image'),
kind = cms.untracked.string('TProfile'),
otype = cms.untracked.string('Ecal2P'),
btype = cms.untracked.string('Trend'),
description = cms.untracked.string('Trend of the number of events in an image fed into the ML model')
),
TrendMLBadTower = cms.untracked.PSet(
path = cms.untracked.string('Ecal/Trends/Number of bad towers from MLDQM %(prefix)s'),
kind = cms.untracked.string('TProfile'),
otype = cms.untracked.string('Ecal2P'),
btype = cms.untracked.string('Trend'),
description = cms.untracked.string('Trend of the number of bad towers flagged by the MLDQM model')
)
)
)
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