DQM
SiStripCommissioningDbClients
python
Namespaces
|
Variables
OfflineDbClient_cff.py File Reference
Go to the source code of this file.
Namespaces
OfflineDbClient_cff
Variables
OfflineDbClient_cff.AddBadStrips
OfflineDbClient_cff.AdProbabCut
if a strip has a noise significance larger than N, mark it as bad
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OfflineDbClient_cff.AmplitudeRatio
to flag double peaked strips
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OfflineDbClient_cff.ApvTimingParameters
OfflineDbClient_cff.AshmanDistance
selection on the N-sigma integral
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OfflineDbClient_cff.CalibrationParameters
OfflineDbClient_cff.Chi2ProbabCut
this is 3 sigma quantile selection on the jacque-Bera p-value
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OfflineDbClient_cff.cosmic
OfflineDbClient_cff.DaqScopeModeParameters
OfflineDbClient_cff.db_client
OfflineDbClient_cff.DeadStripMax
OfflineDbClient_cff.DisableBadStrips
analysis-wide low threshold for the fed zero suppression Flags on bad strips
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OfflineDbClient_cff.DisableDevices
OfflineDbClient_cff.FastFedCablingParameters
OfflineDbClient_cff.FedCablingParameters
OfflineDbClient_cff.FedTimingParameters
OfflineDbClient_cff.FilePath
OfflineDbClient_cff.FineDelayParameters
OfflineDbClient_cff.GenerateRandomHisto
this is 3 sigma quantile selection on the Kolmogorov Smirnov p-value
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OfflineDbClient_cff.HighThreshold
to flag double peaked strips Zero suppression information
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OfflineDbClient_cff.IntegralNsigma
max value of kurtosis to identify strips with long tails
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OfflineDbClient_cff.IntegralTailCut
this is expressed in terms of number of gaussian quantiles .
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OfflineDbClient_cff.JbProbabCut
random sampling of the gaussian fit or not while computing p-values
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OfflineDbClient_cff.KeepsStripsDisabled
OfflineDbClient_cff.KeepStripsDisabled
When the upload is performed, strips are masked in case they are flagged by the analysis.
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OfflineDbClient_cff.KsProbabCut
this is 3 sigma quantile selection on the AndersonDarling p-value
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OfflineDbClient_cff.KurtosisCut
this is 3 sigma quantile selection on the chi2 p-value (from a Gaussian fit)
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OfflineDbClient_cff.LatencyParamameters
OfflineDbClient_cff.LowThreshold
analysis-wide high threshold for the fed zero suppression
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OfflineDbClient_cff.MaxDriftResidualCut
selections used to define a bad strip
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OfflineDbClient_cff.MaxStripNoiseCut
if a strip has a noise value less the N
ADC
, mark as low noisy i.e.
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OfflineDbClient_cff.MaxStripNoiseSignificanceCut
if a strip has a noise value larger than N
ADC
, mark strip has high noisy i.e.
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OfflineDbClient_cff.MinStripNoiseCut
the strip baseline can drift during run .
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OfflineDbClient_cff.NoiseParameters
OfflineDbClient_cff.NoisyStripMin
OfflineDbClient_cff.OptimizePerPartition
OfflineDbClient_cff.OptoScanParameters
OfflineDbClient_cff.PedestalsParameters
OfflineDbClient_cff.PedsFullNoiseParameters
Bad channel analysis
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OfflineDbClient_cff.PedsOnlyParameters
OfflineDbClient_cff.RunNumber
OfflineDbClient_cff.SamplingParameters
Tell whether the PedsFullNoise DB table needs to be uploaded --> for the time being this can be done ONLY on the oracle test account.
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OfflineDbClient_cff.SaveClientFile
OfflineDbClient_cff.SkipEmptyStrips
True: means that pedestal and noise values are not changed in the FED version --> use old values.
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OfflineDbClient_cff.SkipFecUpdate
OfflineDbClient_cff.SkipFedUpdate
OfflineDbClient_cff.SkipGainUpdate
OfflineDbClient_cff.SkipPedestalUpdate
OfflineDbClient_cff.SkipTickUpdate
OfflineDbClient_cff.SummaryXmlFile
OfflineDbClient_cff.targetDecayTime
OfflineDbClient_cff.TargetDelay
OfflineDbClient_cff.TargetGain
OfflineDbClient_cff.targetRiseTime
OfflineDbClient_cff.tuneSimultaneously
OfflineDbClient_cff.UploadAnalyses
OfflineDbClient_cff.UploadHwConfig
OfflineDbClient_cff.UploadOnlyStripBadChannelBit
True: if a strip is already flagged bad in the db, it will be kept bad; False: if a strip was bad, now the analysis will tell us if it's bad or not.
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OfflineDbClient_cff.UploadPedsFullNoiseDBTable
In the analysis, if true strips with no data are not marked as bad but as dead --> could be mis-configured at the time of the run, not masked.
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OfflineDbClient_cff.UseClientFile
OfflineDbClient_cff.VpspScanParameters
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