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dataCertPlots.py
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dataCertPlots.py
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import ROOT
import sys
import math
import numpy
import argparse
parser = argparse.ArgumentParser(description='Make data certifcation plots')
parser.add_argument('-c', '--certfile', type=str, default="", help='The data certfication tree')
parser.add_argument('-r', '--runlist', type=str, default="", help="Minimum fill number")
parser.add_argument('-o', '--outDir', type=str, default="", help="Path to output directory")
parser.add_argument('-a', '--autogen', action='store_true', default=False, help="Auto generator list of runs from file")
parser.add_argument('-b', '--batch', action='store_true', default=False, help="Batch mode (doesn't make GUI TCanvases)")
parser.add_argument('--csv', type=str, default="", help='The csv file of correction')
parser.add_argument('-d', '--docorr', action='store_true', default=False, help="do PCC aftergolw correction")
args = parser.parse_args()
ROOT.gStyle.SetPadTickY(2)
pcc_dict={}
miss_LS = []
if args.csv!="":
file = open(args.csv)
lines = file.readlines()
for line in lines:
items = line.split(",")
fill = int(items[0])
pcc_dict[fill]=float(items[1])
print float(items[1])
print pcc_dict
rebinLS=1
scale=1
PCCscale=1
def ReStyleHistogram(hist,nRows=3):
hist.GetXaxis().SetTitleSize(0.15*nRows/5)
hist.GetXaxis().SetTitleOffset(-0.4)
#hist.GetXaxis().SetNdivisions(1009)
hist.SetLabelSize(0.14*nRows/5)
hist.GetYaxis().SetTitleSize(0.12*nRows/5)
hist.GetYaxis().SetTitleOffset(0.4)
hist.SetMinimum(0.00001)
hist.SetLineWidth(scale)
def GetMaximum(histList,scale=1.0):
listMax=0
for hist in histList:
listMax=max(listMax,hist.GetMaximum())
return listMax*scale
def GetYAverage(hist,zeroSupress=True):
try:
binsToAve=[]
for ibin in range(1,hist.GetNbinsX()+1):
if hist.GetBinContent(ibin)>0:
binsToAve.append(hist.GetBinContent(ibin))
mean=numpy.mean(binsToAve)
std=numpy.std(binsToAve)
meanError=std/math.sqrt(len(binsToAve))
return mean,meanError
except:
print "Failed to get the average for",hist.GetName()
return -999,-999
if args.batch is True:
ROOT.gROOT.SetBatch(ROOT.kTRUE)
scale=4
if args.outDir!="":
if args.outDir[-1]!="/":
args.outDir=args.outDir+"/"
filename=args.certfile
tfile=ROOT.TFile(filename)
tree=tfile.Get("certtree")
runsToCheck=[]
missingRuns=[]
if args.autogen:
tree.SetBranchStatus("*",0)
tree.SetBranchStatus("run",1)
nentries=tree.GetEntries()
for ient in range(nentries):
tree.GetEntry(ient)
if tree.run not in runsToCheck:
runsToCheck.append(int(tree.run))
missingRuns.append(int(tree.run))
else:
for run in args.runlist.split(","):
runsToCheck.append(int(run))
missingRuns.append(int(run))
runsToCheck.sort()
missingRuns.sort()
print runsToCheck
#246908,246919,246920,246923,246926,246930,246936,246951,246960,247068,247070,247073,247078,247079,247081,247252,247253,247262,247267,247377,247381
tree.SetBranchStatus("*",0)
tree.SetBranchStatus("fill",1)
tree.SetBranchStatus("run",1)
tree.SetBranchStatus("LS",1)
tree.SetBranchStatus("hasBrilData",1)
tree.SetBranchStatus("hasCMSData",1)
tree.SetBranchStatus("nActiveBX",1)
onlyBril=[]
onlyCMS=[]
bothSets=[]
runLSMax={}
nentries=tree.GetEntries()
for ient in range(nentries):
tree.GetEntry(ient)
if tree.run in missingRuns:
missingRuns.remove(tree.run)
if not runLSMax.has_key(tree.run):
runLSMax[tree.run]=tree.LS
elif runLSMax[tree.run]<tree.LS:
runLSMax[tree.run]=tree.LS
if tree.hasBrilData and tree.hasCMSData:
bothSets.append(tree.run)
elif tree.hasBrilData:
onlyBril.append(tree.run)
elif tree.hasCMSData:
onlyCMS.append(tree.run)
print "missingRuns runs: ",missingRuns
print "Not both CMS and Bril: ",list(set(runsToCheck)-set(bothSets))
tree.SetBranchStatus("*",1)
histpix={}
histpix_HF = {}
histpix_BCMF = {}
histpix_PLT = {}
histPCLumiB3p8={}
histbest={}
histHFLumi={}
histBCMFLumi={}
histPLTLumi={}
histlayers={}
PCClayers={}
eventRate={}
layerColors=[616,1,632,600,416]
histPU={}
nActiveBX={}
bestHF={}
bestBCM1f={}
bestPLT={}
nBins={}
for run in runLSMax:
bins=int(runLSMax[run]/rebinLS)+1
runLSMax[run]=bins*rebinLS
nBins[run]=bins
#yLabelPix="PCC/BestLumi*2^18*N_{BX}"
yLabelPix="Pixel Cluster xsec (ub)"
for ient in range(nentries):
tree.GetEntry(ient)
if not nActiveBX.has_key(tree.run):
nActiveBX[tree.run] = tree.nActiveBX
pcc_corr=0.8624
if args.docorr:
if pcc_dict.has_key(tree.fill):
pcc_corr=pcc_dict[tree.fill]
if tree.hasCMSData:
if not eventRate.has_key(tree.run):
eventRate[tree.run]=ROOT.TH1F(str(tree.run)+"_eventrate",";Luminosity Section ;Event Rate (Hz)",nBins[tree.run],0,runLSMax[tree.run])
eventRate[tree.run].Fill(tree.LS,tree.eventRate)
if tree.nCluster>0:
for layer in range(0,5):
layerkey=str(tree.run)+"_PCClayer"+str(layer+1)
if not PCClayers.has_key(layerkey):
PCClayers[layerkey]=ROOT.TH1F(str(tree.run)+"_PCClayer"+str(layer+1),";Luminosity Section ;PCC Ratios in "+str(tree.run),nBins[tree.run],0,runLSMax[tree.run])
PCClayers[layerkey].SetLineColor(layerColors[layer])
ReStyleHistogram(PCClayers[layerkey],2)
PCClayers[str(tree.run)+"_PCClayer"+str(layer+1)].Fill(tree.LS,tree.nPCPerLayer[layer]/tree.nCluster)
else:
print "For run,LS",tree.run,tree.LS,"nCluster is",tree.nCluster
if tree.run in bothSets:
if not histpix.has_key(tree.run):
histpix[tree.run]=ROOT.TH1F(str(tree.run)+"pix",";Luminosity Section ;"+yLabelPix,nBins[tree.run],0,runLSMax[tree.run])
ReStyleHistogram(histpix[tree.run],4)
histpix_HF[tree.run]=ROOT.TH1F(str(tree.run)+"pixHF", ";Luminosity Section ;"+yLabelPix, nBins[tree.run],0,runLSMax[tree.run])
ReStyleHistogram(histpix_HF[tree.run],4)
histpix_BCMF[tree.run]=ROOT.TH1F(str(tree.run)+"pixBCMF", ";Luminosity Section ;"+yLabelPix, nBins[tree.run],0,runLSMax[tree.run])
ReStyleHistogram(histpix_BCMF[tree.run],4)
histpix_PLT[tree.run]=ROOT.TH1F(str(tree.run)+"pixPLT", ";Luminosity Section ;"+yLabelPix, nBins[tree.run],0,runLSMax[tree.run])
ReStyleHistogram(histpix_PLT[tree.run],4)
for layer in range(0,5):
layerkey=str(tree.run)+"_layer"+str(layer+1)
histlayers[layerkey]=ROOT.TH1F(str(tree.run)+"_layer"+str(layer+1),";Luminosity Section ;"+yLabelPix,nBins[tree.run],0,runLSMax[tree.run])
ReStyleHistogram(histlayers[layerkey],3)
histpix[tree.run].Fill(tree.LS,tree.PC_xsec*PCCscale*pcc_corr)
if (tree.HFLumi != 0):
histpix_HF[tree.run].Fill(tree.LS,tree.PC_xsec*tree.BestLumi*PCCscale*pcc_corr/tree.HFLumi)
if (tree.PLTLumi != 0):
histpix_PLT[tree.run].Fill(tree.LS,tree.PC_xsec*tree.BestLumi*PCCscale*pcc_corr/tree.PLTLumi)
if (tree.BCMFLumi != 0):
histpix_BCMF[tree.run].Fill(tree.LS,tree.PC_xsec*tree.BestLumi*PCCscale*pcc_corr/tree.BCMFLumi)
for layer in range(0,5):
histlayers[str(tree.run)+"_layer"+str(layer+1)].Fill(tree.LS,tree.PC_xsec_layers[layer]*PCCscale*pcc_corr)
if tree.BestLumi<=0 and tree.HFLumi<=0 and tree.BCMFLumi<=0 and tree.PLTLumi<=0 and tree.PC_lumi_B3p8>0:
miss_LS.append([str(tree.run), str(tree.LS)])
if tree.hasBrilData:
key=tree.run
if not histbest.has_key(key):
histPCLumiB3p8[key]=ROOT.TH1F(str(tree.run)+"PCLumiB3p8",";Luminosity Section ;Inst. Luminosity(Hz/#mub)",nBins[tree.run],0,runLSMax[tree.run])
histbest[key]=ROOT.TH1F(str(tree.run)+"best",";Luminosity Section ;Inst. Luminosity(Hz/#mub)",nBins[tree.run],0,runLSMax[tree.run])
histHFLumi[key]=ROOT.TH1F(str(tree.run)+"HF",";Luminosity Section ;Inst. Luminosity(Hz/#mub)",nBins[tree.run],0,runLSMax[tree.run])
histBCMFLumi[key]=ROOT.TH1F(str(tree.run)+"BCMF",";Luminosity Section ;Inst. Luminosity(Hz/#mub)",nBins[tree.run],0,runLSMax[tree.run])
histPLTLumi[key]=ROOT.TH1F(str(tree.run)+"PLT",";Luminosity Section ;Inst. Luminosity(Hz/#mub)",nBins[tree.run],0,runLSMax[tree.run])
histPU[key]=ROOT.TH1F(str(tree.run)+"bestPU",";Luminosity Section ;Pile-up",nBins[tree.run],0,runLSMax[tree.run])
ReStyleHistogram(histbest[key],3)
ReStyleHistogram(histPCLumiB3p8[key],3)
ReStyleHistogram(histHFLumi[key],3)
ReStyleHistogram(histBCMFLumi[key],3)
ReStyleHistogram(histPLTLumi[key],3)
ReStyleHistogram(histPU[key],3)
if tree.BestLumi>0:#This is where it is fed in
histbest[key].Fill(tree.LS,tree.BestLumi)
histHFLumi[key].Fill(tree.LS,tree.HFLumi)
histBCMFLumi[key].Fill(tree.LS,tree.BCMFLumi)
histPLTLumi[key].Fill(tree.LS,tree.PLTLumi)
if tree.hasCMSData:
histPCLumiB3p8[key].Fill(tree.LS,tree.PC_lumi_B3p8*PCCscale*pcc_corr)
if tree.BestLumi_PU>0:
histPU[key].Fill(tree.LS,tree.BestLumi_PU*PCCscale*pcc_corr)
#print tree.BestLumi_PU
if tree.HFLumi>0 and tree.BCMFLumi>0 and tree.BestLumi>0:
# FIXME USE ACTUAL BEST LUMI STRING FROM TREE (NOT IN TREE YET)
if tree.run not in bestHF.keys():
bestHF[tree.run]=0
bestBCM1f[tree.run]=0
bestPLT[tree.run]=0
diffBestHF=math.fabs(tree.HFLumi-tree.BestLumi)
diffBestBCMF=math.fabs(tree.BCMFLumi-tree.BestLumi)
diffBestPLT=math.fabs(tree.PLTLumi-tree.BestLumi)
minLumi=min(diffBestHF,min(diffBestBCMF,diffBestPLT))
if minLumi==diffBestHF:
bestHF[tree.run]=bestHF[tree.run]+1
elif minLumi==diffBestBCMF:
bestBCM1f[tree.run]=bestBCM1f[tree.run]+1
elif minLumi==diffBestPLT:
bestPLT[tree.run]=bestPLT[tree.run]+1
tcan=ROOT.TCanvas("tcan","",1200*scale,700*scale)
padlumis =ROOT.TPad("padlumis", "",0.0,0.0,0.5,1.0)
pad2 =ROOT.TPad("pad2","",0.5,0.0,1.0,1.0)
padlumis.Draw()
pad2.Draw()
PC_calib_xsec_B0=9.4e6
PC_calib_xsec_B3p8=9.4e6
for run in runsToCheck:
padlumis.Divide(1,3)
pad2.Divide(1,3)
#pad2.Divide(1,4)
lineB0=ROOT.TLine(0,PC_calib_xsec_B0,runLSMax[run],PC_calib_xsec_B0)
lineB0.SetLineColor(634)
lineB0.SetLineStyle(ROOT.kDashed)
lineB0.SetLineWidth(2*scale)
lineB3p8=ROOT.TLine(0,PC_calib_xsec_B3p8,runLSMax[run],PC_calib_xsec_B3p8)
lineB3p8.SetLineColor(418)
lineB3p8.SetLineStyle(ROOT.kDashed)
lineB3p8.SetLineWidth(2*scale)
legPixXSec=ROOT.TLegend(0.8,0.7,0.9,0.9)
legPixXSec.AddEntry(lineB3p8,"B=3.8","l")
legPixXSec.AddEntry(lineB0,"B=0","l")
if run in histpix.keys():
pixhistmax=histpix[run].GetMaximum()*1.4
if pixhistmax>PC_calib_xsec_B3p8*1.5 or pixhistmax<PC_calib_xsec_B3p8:
pixhistmax=PC_calib_xsec_B3p8*1.5
pad2.cd(1)
histpix[run].SetMaximum(pixhistmax)
label=ROOT.TText(0,histpix[run].GetMaximum()*0.88," Pixel Cluster Cross Section - Run="+str(run))
label.SetTextSize(.1)
histpix[run].Draw("hist")
label.Draw("same")
lineB0.Draw("same")
lineB3p8.Draw("same")
legPixXSec.Draw("same")
pad2.Update()
pad2.cd(2)
eventRate[run].SetLineWidth(2)
meanRate,meanRateError=GetYAverage(eventRate[run],True)
ReStyleHistogram(eventRate[run],3)
eventRate[run].Draw("hist")
labelRate=ROOT.TText(0,eventRate[run].GetMaximum()*0.4," Average Rate in Run="+str(run)+" = "+"{0:.1f}".format(meanRate)+" Hz")
labelRate.SetTextSize(.1)
labelRate.Draw("same")
pad2.Update()
# pad2.cd(3)
# #add my code right here.
# histpix_HF[run].SetMaximum(pixhistmax)
#set the maximum
# label3=ROOT.TText(0,histpix_HF[run].GetMaximum()*0.88," Pixel Cluster Cross Section HF - Run="+str(run))
# label3.SetTextSize(.1)
# histpix_HF[run].Draw("hist")
# label3.Draw("same")
# lineB0.Draw("same")
# lineB3p8.Draw("same")
# legPixXSec.Draw("same")
# histpix_BCMF[run].SetMaximum(pixhistmax)
# label4=ROOT.TText(0,histpix_BCMF[run].GetMaximum()*0.88," Pixel Cluster Cross Section BCM1f - Run="+str(run))
# label4.SetTextSize(.1)
# histpix_BCMF[run].Draw("hist")
# label4.Draw("same")
# lineB0.Draw("same")
# lineB3p8.Draw("same")
# legPixXSec.Draw("same")
# pad2.Update()
#pad2.cd(3)
#histpix_HF[run].SetMaximum(pixhistmax)
#label3=ROOT.TText(0,histpix_HF[run].GetMaximum()*0.88," Pixel Cluster Cross Section HF - Run="+str(run))
#label3.SetTextSize(.1)
#histpix_HF[run].Draw("hist")
#label3.Draw("same")
#lineB0.Draw("same")
#lineB3p8.Draw("same")
#legPixXSec.Draw("same")
#histpix_BCMF[run].SetMaximum(pixhistmax)
#label4=ROOT.TText(0,histpix_BCMF[run].GetMaximum()*0.88," Pixel Cluster Cross Section BCM1f - Run="+str(run))
#label4.SetTextSize(.1)
#histpix_BCMF[run].Draw("hist")
#label4.Draw("same")
#lineB0.Draw("same")
#lineB3p8.Draw("same")
#legPixXSec.Draw("same")
#pad2.Update()
#pad2.cd(4)
#histpix_PLT[run].SetMaximum(pixhistmax)
#label5=ROOT.TText(0,histpix_PLT[run].GetMaximum()*0.88," Pixel Cluster Cross Section PLT - Run="+str(run))
#label5.SetTextSize(.1)
#histpix_PLT[run].Draw("hist")
#label5.Draw("same")
#lineB0.Draw("same")
#lineB3p8.Draw("same")
#legPixXSec.Draw("same")
#pad2.Update()
if run in histbest.keys():
padlumis.cd(1)
maxHist=GetMaximum([histbest[run],histHFLumi[run],histBCMFLumi[run],histPLTLumi[run],histPCLumiB3p8[run]],1.1)
if maxHist>2*histbest[run].GetMaximum():
print "There is a serious outlier; setting max to 1.5*bestmax."
maxHist=histbest[run].GetMaximum()*1.5
histbest[run].SetLineColor(ROOT.kBlack)
histbest[run].SetLineWidth(2*scale)
histbest[run].SetMinimum(0)
histbest[run].SetMaximum(maxHist)
histbest[run].Draw("hist")
print "intL for best in run",run,"is",histbest[run].Integral()
histHFLumi[run].SetLineColor(633)
histHFLumi[run].Draw("histsame")
histBCMFLumi[run].SetLineColor(417)
histBCMFLumi[run].Draw("histsame")
histPLTLumi[run].SetLineColor(601)
histPLTLumi[run].Draw("histsame")
histPCLumiB3p8[run].SetLineColor(802)
histPCLumiB3p8[run].Draw("histsame")
intLPCC=histPCLumiB3p8[run].Integral()*23.31*1.e-6
intLHF=histHFLumi[run].Integral()*23.31*1.e-6
intLBCM=histBCMFLumi[run].Integral()*23.31*1.e-6
intLPLT=histPLTLumi[run].Integral()*23.31*1.e-6
leg=ROOT.TLegend(0.1,0.1,0.7,0.4)
tot=1
try:
tot=float(bestHF[run]+bestBCM1f[run]+bestPLT[run])
leg.AddEntry(histbest[run],"Best Lumi","l")
leg.AddEntry(histHFLumi[run],"HF: "+"{0:.1f}".format((bestHF[run]/tot)*100)+"%, Integrated Lumi = "+"{:2.2f}".format(intLHF)+" pb^{-1}","l")
leg.AddEntry(histBCMFLumi[run],"BCM1f: "+"{0:.1f}".format((bestBCM1f[run]/tot)*100)+"%, Integrated Lumi = "+"{:2.2f}".format(intLBCM)+" pb^{-1}","l")
leg.AddEntry(histPLTLumi[run],"PLT: "+"{0:.1f}".format((bestPLT[run]/tot)*100)+"%, Integrated Lumi = "+"{:2.2f}".format(intLPLT)+" pb^{-1}","l")
leg.AddEntry(histPCLumiB3p8[run],"PCC - B=3.8, Integrated Lumi = "+"{:2.2f}".format(intLPCC)+" pb^{-1}","l")
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.Draw("same")
except:
try:
print "HF,BCM1f,PLT",
print bestHF.has_key[run]
print bestBCM1f.has_key[run]
print bestPLT.has_key[run]
except:
print "failed to count best lumi"
padlumis.Update()
##best
padlumis.cd(2)
BestOPCLumi=histbest[run].Clone("best_o_PCLumi")
BestOPCLumi.Divide(histPCLumiB3p8[run])
BestOPCLumi.SetMaximum(2.0)
BestOPCLumi.SetTitle(";Luminosity Section ;Ratios")
BestOPCLumi.SetLineColor(802)
mean1,meanError1=GetYAverage(BestOPCLumi,True)
##hf
HFOPCLumi=histHFLumi[run].Clone("HF_o_PCLumi")
HFOPCLumi.Divide(histPCLumiB3p8[run])
HFOPCLumi.SetMaximum(2.0)
HFOPCLumi.SetTitle(";Luminosity Section ;HF/PCLumi")
HFOPCLumi.SetLineColor(800)
mean2,meanError2=GetYAverage(HFOPCLumi,True)
##BCM1F
BCM1FOPCLumi=histBCMFLumi[run].Clone("BCM1F_o_PCLumi")
BCM1FOPCLumi.Divide(histPCLumiB3p8[run])
BCM1FOPCLumi.SetMaximum(2.0)
BCM1FOPCLumi.SetTitle(";Luminosity Section ;BCM1F/PCLumi")
BCM1FOPCLumi.SetLineColor(617)
mean3,meanError3=GetYAverage(BCM1FOPCLumi,True)
##PLT
PLTOPCLumi=histPLTLumi[run].Clone("PLT_o_PCLumi")
PLTOPCLumi.Divide(histPCLumiB3p8[run])
PLTOPCLumi.SetMaximum(2.0)
PLTOPCLumi.SetTitle(";Luminosity Section ;PLT/PCLumi")
mean4,meanError4=GetYAverage(PLTOPCLumi,True)
##HF
hfOverPLT=histHFLumi[run].Clone("HF_o_PLT")
hfOverPLT.Divide(histPLTLumi[run])
hfOverPLT.SetMaximum(2.0)
hfOverPLT.SetTitle(";Luminosity Section ;HF/PLTLumi")
mean5,meanError5=GetYAverage(hfOverPLT,True)
##BCM1F
bcmfOverPLT=histBCMFLumi[run].Clone("BCM1F_o_PLT")
bcmfOverPLT.Divide(histPLTLumi[run])
bcmfOverPLT.SetMaximum(2.0)
bcmfOverPLT.SetTitle(";Luminosity Section ;BCM1F/PLTLumi")
mean6,meanError6=GetYAverage(bcmfOverPLT,True)
leg2=ROOT.TLegend(0.1,0.7,0.5,0.9)
print mean1,meanError1
leg2.AddEntry(BestOPCLumi, "Best/PCLumi = "+"{:10.4f}".format(mean1)+" #pm "+"{:10.4f}".format(meanError1),"l")
leg2.AddEntry(HFOPCLumi, "HF/PCLumi = "+"{:10.4f}".format(mean2)+" #pm "+"{:10.4f}".format(meanError2),"l")
leg2.AddEntry(BCM1FOPCLumi,"BCM1F/PCLumi = "+"{:10.4f}".format(mean3)+" #pm "+"{:10.4f}".format(meanError3),"l")
leg2.AddEntry(PLTOPCLumi, "PLT/PCLumi = "+"{:10.4f}".format(mean4)+" #pm "+"{:10.4f}".format(meanError4),"l")
leg2.AddEntry(hfOverPLT, "HF/PLT = "+"{:10.4f}".format(mean5)+" #pm "+"{:10.4f}".format(meanError5),"l")
leg2.AddEntry(bcmfOverPLT, "BCM1F/PLT = "+"{:10.4f}".format(mean6)+" #pm "+"{:10.4f}".format(meanError6),"l")
BestOPCLumi.Draw("hist")
#take these histograms
HFOPCLumi.Draw("histsame")
BCM1FOPCLumi.Draw("histsame")
PLTOPCLumi.Draw("histsame")
hfOverPLT.Draw("histsame")
bcmfOverPLT.Draw("histsame")
leg2.Draw("sames")
padlumis.Update()
if run in bothSets:
pad2.cd(3)
histPU[run].Draw("hist")
pad2.Update()
#move this to the right
#dictionary, linearity plots
linPlots = {}
#TProfile (const char *name, const char *title, Int_t nbinsx, const Double_t *xbins, Option_t *option="")
linPlots["HFoverPCC"]=ROOT.TProfile("",";PCC Average SBIL Hz/ub ;Online/PCC",50,0,8,0,2)
linPlots["HFoverPCC"].SetMaximum(2.0)
iGraph=0
for iBin in range(HFOPCLumi.GetNbinsX()): #divide lumi by number of active bunches in fill
lumi = histPCLumiB3p8[run].GetBinContent(iBin)/nActiveBX[run]
ratio = HFOPCLumi.GetBinContent(iBin)
if(ratio>0.1 and ratio<5 and lumi>0.0): #x_val = pcc inst. lumi (see histogram)
linPlots["HFoverPCC"].Fill(lumi,ratio,iGraph)
print str(lumi)+" "+str(ratio)
iGraph = iGraph+1
#add titles and stuff
padlumis.cd(3)
linPlots["HFoverPCC"].SetLineColor(ROOT.kRed)
linPlots["HFoverPCC"].SetLineWidth(50)
ReStyleHistogram(linPlots["HFoverPCC"],3)
linPlots["HFoverPCC"].Draw("PE")
fun_hfoverpcc=ROOT.TF1("fun_hfoverpcc", "[0]+[1]*x", 0, 20)
linPlots["HFoverPCC"].Fit(fun_hfoverpcc, "N")
hf_p0 = fun_hfoverpcc.GetParameter(0)
hf_e0 = fun_hfoverpcc.GetParError(0)
hf_p1 = fun_hfoverpcc.GetParameter(1)
hf_e1 = fun_hfoverpcc.GetParError(1)
padlumis.Update()
linPlots["PLToverPCC"]=ROOT.TProfile("",";Hz/ub ;Online/PCC",50,0,8,0,2)
linPlots["PLToverPCC"].SetMaximum(2.0)
iGraph=0
for iBin in range(HFOPCLumi.GetNbinsX()):
lumi = histPCLumiB3p8[run].GetBinContent(iBin)/nActiveBX[run]
ratio = PLTOPCLumi.GetBinContent(iBin)
if(ratio>0.1 and ratio<5 and lumi>0.0):
linPlots["PLToverPCC"].Fill(lumi,ratio,iGraph)
iGraph = iGraph+1
padlumis.cd(3)
linPlots["PLToverPCC"].SetLineColor(ROOT.kBlue)
linPlots["PLToverPCC"].SetLineWidth(50)
ReStyleHistogram(linPlots["PLToverPCC"],3)
linPlots["PLToverPCC"].Draw("pe same")
padlumis.Update()
fun_pltoverpcc=ROOT.TF1("fun_pltoverpcc", "[0]+[1]*x", 0, 20)
linPlots["PLToverPCC"].Fit(fun_pltoverpcc, "N")
plt_p0 = fun_pltoverpcc.GetParameter(0)
plt_e0 = fun_pltoverpcc.GetParError(0)
plt_p1 = fun_pltoverpcc.GetParameter(1)
plt_e1 = fun_pltoverpcc.GetParError(1)
linPlots["BCM1FoverPCC"]=ROOT.TProfile("",";Hz/ub ;Online/PCC",50,0,8,0,2)
linPlots["BCM1FoverPCC"].SetMaximum(2.0)
iGraph=0
for iBin in range(HFOPCLumi.GetNbinsX()):
lumi = histPCLumiB3p8[run].GetBinContent(iBin)/nActiveBX[run]
ratio = BCM1FOPCLumi.GetBinContent(iBin)
if(ratio>0.1 and ratio<5 and lumi>0.0):
linPlots["BCM1FoverPCC"].Fill(lumi,ratio,iGraph)
iGraph = iGraph+1
padlumis.cd(3)
linPlots["BCM1FoverPCC"].SetLineColor(ROOT.kGreen)
linPlots["BCM1FoverPCC"].SetLineWidth(50)
ReStyleHistogram(linPlots["BCM1FoverPCC"],3)
linPlots["BCM1FoverPCC"].Draw("pe same")
padlumis.Update()
fun_bcm1foverpcc=ROOT.TF1("fun_bcm1foverpcc", "[0]+[1]*x", 0, 20)
linPlots["BCM1FoverPCC"].Fit(fun_bcm1foverpcc, "N")
bcm1f_p0 = fun_bcm1foverpcc.GetParameter(0)
bcm1f_e0 = fun_bcm1foverpcc.GetParError(0)
bcm1f_p1 = fun_bcm1foverpcc.GetParameter(1)
bcm1f_e1 = fun_bcm1foverpcc.GetParError(1)
leg6=ROOT.TLegend(0.1,0.7,0.5,0.9)
print mean1,meanError1
# mean4,meanError4=GetYAverage(PLTOPCLumi,True)
ratio_mean1, ratio_meanError1=GetYAverage(linPlots["HFoverPCC"],True)
ratio_mean2, ratio_meanError2=GetYAverage(linPlots["PLToverPCC"],True)
ratio_mean3, ratio_meanError3=GetYAverage(linPlots["BCM1FoverPCC"],True)
leg6.AddEntry(linPlots["HFoverPCC"], "HF/PCC Linearity Fit: "+"p0="+"{:10.4f}".format(hf_p0)+"#pm"+"{:4.4f}".format(hf_e0)+", p1="+"{:10.4f}".format(hf_p1)+"#pm"+"{:4.4f}".format(hf_e1),"l")
leg6.AddEntry(linPlots["PLToverPCC"], "PLT/PCC Linearity Fit: "+"p0="+"{:10.4f}".format(plt_p0)+"#pm"+"{:4.4f}".format(plt_e0)+", p1="+"{:10.4f}".format(plt_p1)+"#pm"+"{:4.4f}".format(plt_e1),"l")
leg6.AddEntry(linPlots["BCM1FoverPCC"],"BCM1F/PCC Linearity Fit: "+"p0="+"{:10.4f}".format(bcm1f_p0)+"#pm"+"{:4.4f}".format(bcm1f_e0)+", p1="+"{:10.4f}".format(bcm1f_p1)+"#pm"+"{:4.4f}".format(bcm1f_e1), "l")
leg6.SetFillStyle(0)
leg6.SetBorderSize(0)
leg6.Draw("same")
padlumis.Update()
tcan.Update()
tcan.SaveAs(args.outDir+str(run)+".png")
#raw_input()
padlumis.Clear()
pad2.Clear()
tcan.Update()
if run in bothSets:
layertexts={}
lines={}
canlayers=ROOT.TCanvas("canlayers","",1200,700)
canlayers.Divide(2,3)
canlayers.cd(1)
ReStyleHistogram(histpix[run],3)
histpix[run].Draw("hist")
label.Draw("same")
lineB0.Draw("same")
lineB3p8.Draw("same")
layermax=PC_calib_xsec_B3p8*1.15
for layer in range(5):
key=str(run)+"_layer"+str(layer+1)
if histlayers[key].GetMaximum()*1.15 > layermax:
layermax=histlayers[key].GetMaximum()*1.15
layermax=min(layermax,PC_calib_xsec_B3p8*1.5)
for layer in range(5):
lines[layer]=ROOT.TF1("pol1_"+str(layer+1),"pol1",70,175)
canlayers.cd(layer+2)
key=str(run)+"_layer"+str(layer+1)
layertexts[layer]=ROOT.TText(0,layermax*1.05," Pixel Cluster Cross Section - Layer="+str(layer+1))
layertexts[layer].SetTextSize(.1)
histlayers[key].SetMaximum(layermax)
histlayers[key].Draw("hist")
layertexts[layer].Draw("same")
canlayers.Update()
canlayers.SaveAs(args.outDir+str(run)+"_layers.png")
if run in bothSets or run in onlyCMS:
layertexts={}
lines={}
stability=ROOT.TCanvas("stability","",1200,700)
stabLeg=ROOT.TLegend(0.5,0.20,0.9,0.43)
for layer in range(5):
key=str(run)+"_PCClayer"+str(layer+1)
mean,meanError=GetYAverage(PCClayers[key],True)
print mean, meanError
stabLeg.AddEntry(PCClayers[key],"Layer "+str(layer+1)+" / Layer 2-5 "+"{:10.4f}".format(mean)+" #pm "+"{:10.4f}".format(meanError),"l")
if layer==0:
PCClayers[key].Draw("hist")
PCClayers[key].SetMinimum(0.18)
PCClayers[key].GetYaxis().SetTitleOffset(0.7)
else:
PCClayers[key].Draw("histsame")
stabLeg.Draw("same")
stability.Update()
stability.SaveAs(args.outDir+str(run)+"_stability.png")
print "Missing LSs: ",miss_LS