diff --git a/DataQuality/DataQualityUtils/data/postprocessing/LArMonPostProc.yaml b/DataQuality/DataQualityUtils/data/postprocessing/LArMonPostProc.yaml
index 2f81d98af3fb770e8e6e70bdf5d273a15a714f8d..a25d27c2990503a93a4964485483785d3116b4ca 100644
--- a/DataQuality/DataQualityUtils/data/postprocessing/LArMonPostProc.yaml
+++ b/DataQuality/DataQualityUtils/data/postprocessing/LArMonPostProc.yaml
@@ -145,4 +145,14 @@ Output: [ 'LAr/NoiseCorrelation/FCal{side}/NoiseCorr_FCal{side}ft{ftid}slot{slot
Function: LArMonitoring.LArMonTransforms.computeCorrelations
Description: NoiseCorrelationMon_FCal
+---
+Input: [ 'LAr/PedMonGroupNewAlg/(?P<part>EMB|EMEC|HEC|FCal)/nbOfFebBlocks_(?P=part)(?P<side>A|C)' ]
+Output: [ 'LAr/MeanFeb' ]
+Function: LArMonitoring.LArMonTransforms.Mean
+#Parameters: { useMean : True, useRms : False, useErr: False, useErr_s: False}
+Description: LAr_NbOfFebvsPartition_Mean
+Input: [ 'LAr/PedMonGroupNewAlg/(?P<part>EMB|EMEC|HEC|FCal)/nbOfFebBlocks_(?P=part)(?P<side>A|C)' ]
+Output: [ 'LAr/RMSFeb' ]
+Function: LArMonitoring.LArMonTransforms.RMS
+Description: LAr_NbOfFebvs_RMS
diff --git a/LArCalorimeter/LArMonitoring/python/LArCalibPedMonAlg_REN.py b/LArCalorimeter/LArMonitoring/python/LArCalibPedMonAlg_REN.py
index 3ac1fdd58c3ae64375c19558053446553e53afa7..cc81040a5a809cdc68d147ac5998c5ea3d0551a0 100644
--- a/LArCalorimeter/LArMonitoring/python/LArCalibPedMonAlg_REN.py
+++ b/LArCalorimeter/LArMonitoring/python/LArCalibPedMonAlg_REN.py
@@ -38,6 +38,7 @@ def LArCalibPedMonConfigCore_REN(helper,algoinstance,inputFlags,gain="",doAccDig
larPedMonAlgTest.LArAccumulatedCalibDigitContainerKey=gain
elif doCalibDigit:
larPedMonAlgTest.LArCalibDigitContainerKey=gain
+
# this creates a "Group" called "PedMonGroup" which will put its histograms into the subdirectory "'/LAr/'+GroupName+'/'"
GroupName="PedMonGroup"
@@ -76,7 +77,7 @@ def LArCalibPedMonConfigCore_REN(helper,algoinstance,inputFlags,gain="",doAccDig
xbins=lArDQGlobals.N_FEB_Parttions_Max, xmin=-0.5, xmax=lArDQGlobals.N_FEB_Parttions_Max-0.5,
ybins=lArDQGlobals.N_Partitions, ymin=-0.5, ymax=lArDQGlobals.N_Partitions-0.5,
ylabels=lArDQGlobals.Partitions)
- Group.defineHistogram('febError,part;NbOfLArFEBMonErrors_dE',
+ Group.defineHistogram('febError,part;NbOfFEBMonErrors_dE',
title='# of data corruption errors',
type='TH2I',
path=summary_hist_path,
@@ -93,7 +94,7 @@ def LArCalibPedMonConfigCore_REN(helper,algoinstance,inputFlags,gain="",doAccDig
type='TH1I',
path=summary_hist_path,
xbins=lArDQGlobals.LB_Bins, xmin=lArDQGlobals.LB_Min, xmax=lArDQGlobals.LB_Max)
- Group.defineHistogram('LB0,LArEvSize;eventSizeVsLB',
+ Group.defineHistogram('LB0,LArEvSize;LAreventSizeVsLB',
title='LAr event size (w/o ROS headers);LBs;Megabytes',
type='TProfile',
path=summary_hist_path,
@@ -142,9 +143,11 @@ if __name__=='__main__':
from LArMonitoring.LArMonConfigFlags import createLArMonConfigFlags
createLArMonConfigFlags()
+#/eos/atlas/atlastier0/rucio/data21_calib/calibration_LArElec-Pedestal-32s-High-All/00395761/data21_calib.00395761.calibration_LArElec-Pedestal-32s-High-All.daq.RAW/data21_calib.00395761.calibration_LArElec-Pedestal-32s-High-All.daq.RAW_lb0000._SFO-2._0001.data
- run="00395761"
- ConfigFlags.Input.Files = ["/eos/atlas/atlastier0/rucio/data21_calib/calibration_LArElec-Pedestal-32s-High-All/00395761/data21_calib.00395761.calibration_LArElec-Pedestal-32s-High-All.daq.RAW/data21_calib.00395761.calibration_LArElec-Pedestal-32s-High-All.daq.RAW._lb0000._SFO-3._0001.data"]
+#/eos/atlas/atlastier0/rucio/data20_calib/calibration_LArElec-Pedestal-32s-High-All/00384597/data20_calib.00384597.calibration_LArElec-Pedestal-32s-High-All.daq.RAW/data20_calib.00384597.calibration_LArElec-Pedestal-32s-High-All.daq.RAW._lb0000._SFO-2._0001.data
+ run="00404637"
+ ConfigFlags.Input.Files = ["/eos/atlas/atlastier0/rucio/data21_calib/calibration_LArElec-Pedestal-32s-High-All/00404637/data21_calib.00404637.calibration_LArElec-Pedestal-32s-High-All.daq.RAW/data21_calib.00404637.calibration_LArElec-Pedestal-32s-High-All.daq.RAW._lb0000._SFO-1._0001.data"]
ConfigFlags.Output.HISTFileName = 'LArCalibPedMonOutput_'+run+'.root'
ConfigFlags.DQ.enableLumiAccess = False
ConfigFlags.Input.isMC = False
@@ -170,7 +173,7 @@ if __name__=='__main__':
# If you want to turn on more detailed messages ...
# LArCalibPedMonConfig_REN.getEventAlgo('larPedMonAlgTest').OutputLevel = 2 # DEBUG
- cfg.printConfig(withDetails=True) #set True for exhaustive info
+ cfg.printConfig(withDetails=False) #set True for exhaustive info
ConfigFlags.dump()
f=open("LArCalibPedMon_"+run+".pkl","wb")
diff --git a/LArCalorimeter/LArMonitoring/python/LArMonTransforms.py b/LArCalorimeter/LArMonitoring/python/LArMonTransforms.py
index f8cbaccdb8641bd907cac97ec06614501898270b..e6d68a4d026bdd7e1e731aaefd129acaf377432c 100644
--- a/LArCalorimeter/LArMonitoring/python/LArMonTransforms.py
+++ b/LArCalorimeter/LArMonitoring/python/LArMonTransforms.py
@@ -10,7 +10,7 @@
#
-from ROOT import TMath, TH2F
+from ROOT import TMath, TH2F, TH1F, TProfile
import cppyy
def setMaxMin(inputs,maxVal=0,minVal=0,useMax=True,useMin=True):
@@ -349,5 +349,134 @@ def computeCorrelations(inputs):
return [cl]
+def Mean(inputs):
+ cl = TProfile("summary","Summary;Partition;Mean",8,0.,8.,0.,10., "s")
+ cl.SetDirectory(0)
+ cl.GetXaxis().SetBinLabel(1,"EMBA")
+ cl.GetXaxis().SetBinLabel(2,"EMBC")
+ cl.GetXaxis().SetBinLabel(3,"EMECA")
+ cl.GetXaxis().SetBinLabel(4,"EMECC")
+ cl.GetXaxis().SetBinLabel(5,"HECA")
+ cl.GetXaxis().SetBinLabel(6,"HECC")
+ cl.GetXaxis().SetBinLabel(7,"FCalA")
+ cl.GetXaxis().SetBinLabel(8,"FCalC")
+ cl.GetXaxis().SetLabelSize(0.055)
+ cl.GetYaxis().SetLabelSize(0.055)
+
+ for i in inputs:
+ assert len(i[1])==1, len(i[1])
+
+ #x bin
+ pr=i[0]['part']
+ if "EMB" in pr:
+ ix=1
+ elif "EMEC" in pr:
+ ix=3
+ elif "HEC" in pr:
+ ix=5
+ elif "FCal" in pr:
+ ix=7
+ else:
+ ix=-10
+ pass
+ if i[0]['side'] == "C":
+ ix=ix+1
+ pass
+
+ #y bin
+ if i[1][0].GetEntries()==0:
+ continue
+ else:
+ if ix>=0:
+ #filling the profile histogram for each x_bin separately
+ for xbin in range(0,i[1][0].GetNbinsX()):
+ #cl.Fill (ix-1, i[1][0].GetBinContent(xbin))
+ if i[1][0].GetBinContent(xbin) > 0.: #only for non-zero bins
+ cl.Fill (ix-1, i[1][0].GetBinContent(xbin))
+ ##mean=i[1][0].GetEntries()/float(i[1][0].GetNbinsX())
+ ##pass
+
+ ##get mean and rms from c2 histogram
+ #mean = c2.GetBinContent(ix) #mean of Y
+ #err = c2.GetBinError(ix) #(default), error of the mean of all y values
+ #c2.SetErrorOption("i")
+ #err_s = c2.GetBinError(ix) #standard error on the mean
+ #c2.SetErrorOption("s")
+ #rms = c2.GetBinError(ix) #standard deviation of Y
+ #pass
+
+ #if ix>0:
+ #if useMean:
+ #cl.GetYaxis().SetLabel("Mean Events")
+ #mean = c2.GetBinContent(ix) #mean of Y
+ #cl.Fill(ix, mean, 1)
+ #pass
+ #if useRms:
+ #cl.GetYaxis().SetLabel("RMS")
+ #c2.SetErrorOption("s")
+ #rms = c2.GetBinError(ix) #standard deviation of Y
+ #cl.Fill(ix, rms, 1)
+ #pass
+ #if useErr:
+ #cl.GetYaxis().SetLabel("Error of the mean")
+ #err = c2.GetBinError(ix) #(default), error of the mean of all y values
+ #cl.Fill(ix, err, 1)
+ #pass
+ #if useErr_s:
+ #cl.GetYaxis().SetLabel("Standard error on the mean")
+ #c2.SetErrorOption("i")
+ #err_s = c2.GetBinError(ix) #standard error on the mean
+ #cl.Fill(ix, err_s, 1)
+ #pass
+
+ pass #end of input loop
+ return [cl]
+
+def RMS(inputs):
+
+ #cl = TH1F('Mean', 'FEBs of all partition; N_FEB', 450, 0, 450)
+ cl = TH2F("Mean","Mean of each partition's FEB hist;Partition;Mean",8,0.,8.,450,0,450)
+ cl.SetDirectory(0)
+ cl.GetXaxis().SetBinLabel(1,"EMBA")
+ cl.GetXaxis().SetBinLabel(2,"EMBC")
+ cl.GetXaxis().SetBinLabel(3,"EMECA")
+ cl.GetXaxis().SetBinLabel(4,"EMECC")
+ cl.GetXaxis().SetBinLabel(5,"HECA")
+ cl.GetXaxis().SetBinLabel(6,"HECC")
+ cl.GetXaxis().SetBinLabel(7,"FCalA")
+ cl.GetXaxis().SetBinLabel(8,"FCalC")
+ cl.GetXaxis().SetLabelSize(0.055)
+ cl.GetYaxis().SetLabelSize(0.055)
+
+ for i in inputs:
+ assert len(i[1])==1, len(i[1])
+
+ #x bin
+ pr=i[0]['part']
+ if "EMB" in pr:
+ ix=1
+ elif "EMEC" in pr:
+ ix=3
+ elif "HEC" in pr:
+ ix=5
+ elif "FCal" in pr:
+ ix=7
+ else:
+ ix=-10
+ pass
+ if i[0]['side'] == "C":
+ ix=ix+1
+ pass
+
+ j = [_[1][0] for _ in inputs] # all plots passed as first element of list
+
+ content = 0.
+ for plot in j:
+ mean=plot.GetMean()
+ if mean > 0.:
+ content = content + 1
+ cl.Fill(ix, mean, content)
+
+ return [cl]