def conclude(self,pars) :
from core import plotter
org = self.organizer(pars)
org.mergeSamples(targetSpec = {"name":"qcd_py6", "color":r.kBlue}, allWithPrefix="v12_qcd_py6")
org.scale(100)
plotter.plotter( org,
psFileName = self.psFileName(org.tag),
blackList = ["lumiHisto","xsHisto","xsPostWeightsHisto","nJobsHisto"],
).plotAll()
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.scale()
plotter.plotter( org,
psFileName = self.psFileName(org.tag),
samplesForRatios = ("Example_Skimmed_900_GeV_Data","Example_Skimmed_900_GeV_MC"),
sampleLabelsForRatios = ("data","sim"),
).plotAll()
def conclude(self,pars) :
from core import plotter
org = self.organizer(pars)
org.mergeSamples(targetSpec = {"name":"qcd_mg", "color":r.kBlue}, allWithPrefix="qcd_mg")
org.mergeSamples(targetSpec = {"name":"qcd_py6", "color":r.kRed}, allWithPrefix="qcd_py6")
org.scale()
plotter.plotter( org,
psFileName = self.psFileName(org.tag),
blackList = ["lumiHisto","xsHisto","xsPostWeightsHisto","nJobsHisto","genpthat"],
detailedCalculables = True,
).plotAll()
def conclude(self, conf) :
org = self.organizer(conf)
##for skimming only
#utils.printSkimResults(org)
lineWidth = 3; goptions = "hist"
org.mergeSamples(targetSpec = {"name":"2011 Data", "color":r.kBlack, "markerStyle":20}, allWithPrefix="DoubleMu.Run2011")
org.mergeSamples(targetSpec = {"name":"t#bar{t}", "color":r.kOrange, "lineWidth":lineWidth, "goptions":goptions}, allWithPrefix="tt")
org.mergeSamples(targetSpec = {"name":"DY->ll", "color":r.kBlue, "lineWidth":lineWidth, "goptions":goptions}, allWithPrefix="dyll")
org.mergeSamples(targetSpec = {"name":"W + jets", "color":r.kRed, "lineWidth":lineWidth, "goptions":goptions}, allWithPrefix="w_jets")
org.mergeSamples(targetSpec = {"name":"s.m.", "color":r.kGreen, "lineWidth":lineWidth, "goptions":goptions},
sources = ["t#bar{t}", "DY->ll", "W + jets"], keepSources = True)
org.scale() if not self.parameters()["tanBeta"] else org.scale(100.0)
#plot
pl = plotter.plotter(org,
psFileName = self.psFileName(org.tag),
samplesForRatios = ("2011 Data","DY->ll"),
sampleLabelsForRatios = ("data","DY"),
showStatBox = True,
#doLog = False,
linYAfter = ("value", "0.40<=xcak5JetMhtOverHtPat"),
pegMinimum = 0.1,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
)
pl.plotAll()
def conclude(self,pars) :
from core.plotter import plotter
org = self.organizer(pars)
org.mergeSamples(targetSpec = {"name":"SingleMu","color":r.kBlack,"markerStyle":20}, allWithPrefix="SingleMu")
org.mergeSamples(targetSpec = {"name":"qcd", "color":r.kBlue}, allWithPrefix="qcd")
org.mergeSamples(targetSpec = {"name":"w_jets","color":r.kRed}, allWithPrefix="w_jets")
org.mergeSamples(targetSpec = {"name":"t#bar{t}","color":r.kViolet}, allWithPrefix="tt_tauola_fj_mg")
#org.scaleOneRaw([ss['name'] for ss in org.samples].index('w_jets'), 0.6)
org.mergeSamples(targetSpec = {"name":"s.m.", "color":r.kGreen+3}, keepSources = True, sources = ['qcd','w_jets','t#bar{t}'], force = True)
org.scale()
kwargs = { "blackList":["lumiHisto","xsHisto","nJobsHisto","muonTriggerWeightPF"],
"samplesForRatios":("SingleMu","s.m.") if "s.m." in [ss['name'] for ss in org.samples] else ("","")}
plotter(org, psFileName = self.psFileName(org.tag+'_log'), doLog = True, **kwargs).plotAll()
plotter(org, psFileName = self.psFileName(org.tag+'_nolog'), doLog = False, **kwargs).plotAll()
def conclude(self,conf) :
org = self.organizer(conf)
#org.mergeSamples(targetSpec = {"name":"SingleMu", "color":r.kBlack}, allWithPrefix="SingleMu")
#org.scale()
from core.plotter import plotter
plotter(org,
psFileName = self.psFileName(org.tag),
#samplesForRatios = ("2010 Data","standard_model"),
#sampleLabelsForRatios = ("data","s.m."),
#whiteList = ["lowestUnPrescaledTrigger"],
#doLog = False,
#compactOutput = True,
#noSci = True,
#pegMinimum = 0.1,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
).plotAll()
def plotMeldScale(self) :
if not hasattr(self,"orgMelded") : print "run meldScale() before plotMeldScale()"; return
melded = copy.deepcopy(self.orgMelded)
for ss in filter(lambda ss: 'tt_tauola_fj' in ss['name'], melded.samples) : melded.drop(ss['name'])
pl = plotter.plotter(melded, psFileName = self.psFileName(melded.tag),
doLog = False,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
rowColors = self.rowcolors,
samplesForRatios = ("top.Data 2011","S.M."),
sampleLabelsForRatios = ('data','s.m.')
).plotAll()
def makeIndividualPlots(self, org) :
#plot all
pl = plotter.plotter(org,
psFileName = self.psFileName(org.tag),
showStatBox = False,
doLog = False,
anMode = True,
)
pl.individualPlots(plotSpecs = [{"plotName":"xcak5JetAlphaTFewBinsPat",
"stepName" :"alphaHistogrammer",
"stepDesc" :"xcak5JetPat",
"newTitle":";#alpha_{T};events / bin"},
#{"plotName":"xcak5JetAlphaTRoughPat",
# "stepName" :"variablePtGreaterFilter",
# "stepDesc" :"xcak5JetSumP4Pat.pt()>=140.0 GeV",
# "newTitle":";#alpha_{T};events / bin / 35 pb^{-1}"},
{"plotName":"xcak5JetIndicesPat",
"stepName" :"histogrammer",
"stepDesc" :"(lambda x:len(x))(xcak5JetIndicesPat)",
"newTitle":";N_{jets};events / bin"},
#{"plotName":"photonPat1MinDRToJet",
# "stepName" :"passFilter",
# "stepDesc" :"singlePhotonPlots2",
# "newTitle":";#DeltaR(photon, nearest jet);events / bin / 35 pb^{-1}",
# "reBinFactor":3},
#
#{"plotName":"photonPat1SeedTime",
# "stepName" :"passFilter",
# "stepDesc" :"singlePhotonPlots2",
# "newTitle":";time of photon seed crystal hit (ns);events / bin / 35 pb^{-1}",
# "sampleWhiteList": ["2010 Data"]},
#
#{"plotName":"photonPat1sigmaIetaIetaBarrel",
# "stepName" :"passFilter",
# "stepDesc" :"singlePhotonPlots2",
# "newTitle":";#sigma_{i#eta i#eta};events / bin / 35 pb^{-1}"},
#{"plotName":"photonPat1combinedIsolation",
# "stepName" :"passFilter",
# "stepDesc" :"singlePhotonPlots2",
# "onlyDumpToFile":True},
],
newSampleNames = {"qcd_mg_nVtx": "Madgraph QCD",
"g_jets_mg_nVtx": "Madgraph #gamma + jets",
"2011 Data": "Data",
"standard_model_nVtx": "Standard Model",
},
preliminary = True,
)
def conclude(self,pars) :
org = self.organizer(pars)
org.mergeSamples(targetSpec = {"name":"Data 2011", "color":r.kBlack, "markerStyle":20}, allWithPrefix="SingleMu")
org.mergeSamples(targetSpec = {"name":"qcd_py6", "color":r.kBlue}, allWithPrefix="qcd_py6")
org.mergeSamples(targetSpec = {"name":"t#bar{t}", "color":r.kViolet}, sources=["tt_tauola_fj.wNonQQbar.tw.nvr","tt_tauola_fj.wTopAsymP00.tw.nvr"], keepSources = True)
org.mergeSamples(targetSpec = {"name":"t#bar{t}.q#bar{q}.N30", "color":r.kRed}, sources = ["tt_tauola_fj.wTopAsymN30.tw.nvr","tt_tauola_fj.wNonQQbar.tw.nvr"][:1])
org.mergeSamples(targetSpec = {"name":"t#bar{t}.q#bar{q}.P30", "color":r.kGreen}, sources = ["tt_tauola_fj.wTopAsymP30.tw.nvr","tt_tauola_fj.wNonQQbar.tw.nvr"][:1])
org.mergeSamples(targetSpec = {"name":"standard_model", "color":r.kGreen+2}, sources = ["qcd_py6","t#bar{t}","w_jets_fj_mg.tw.nvr"], keepSources = True)
#for ss in filter(lambda ss: 'tt_tauola' in ss['name'],org.samples) : org.drop(ss['name'])
orgpdf = copy.deepcopy(org)
orgpdf.scale( toPdf = True )
org.scale( lumiToUseInAbsenceOfData = 1.1e3 )
names = [ss["name"] for ss in org.samples]
kwargs = {"detailedCalculables": False,
"blackList":["lumiHisto","xsHisto","nJobsHisto"],
"samplesForRatios" : next(iter(filter(lambda x: x[0] in names and x[1] in names, [("Data 2011","standard_model")])), ("","")),
"sampleLabelsForRatios" : ("data","s.m."),
"detailedCalculables" : True,
"rowColors" : self.rowcolors,
}
plotter.plotter(org, psFileName = self.psFileName(org.tag+"_log"), doLog = True, pegMinimum = 0.01, **kwargs ).plotAll()
plotter.plotter(org, psFileName = self.psFileName(org.tag+"_nolog"), doLog = False, **kwargs ).plotAll()
kwargs["samplesForRatios"] = ("","")
kwargs["dependence2D"] = True
plotter.plotter(orgpdf, psFileName = self.psFileName(org.tag+"_pdf"), doLog = False, **kwargs ).plotAll()
def conclude(self,pars) :
org = self.organizer(pars)
org.scale(toPdf=True)
self.signalChi2(org,("tt_tauola_fj_mg.wTopAsymN30","tt_tauola_fj_mg.wTopAsymP30"), org.keysMatching(["genTopBeta",
"genTopDeltaAbsYttbar",
"genTopTrue",
"genTopMez"
]))
from core import plotter
pl = plotter.plotter(org,
psFileName = self.psFileName(org.tag),
doLog = False,
pegMinimum = 0.1,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
).plotAll()
def meldQCDpartitions(self) :
samples = {"top_muon_pf" : ["qcd_py6fjmu"],
"Wlv_muon_pf" : [],
"QCD_muon_pf" : ["qcd_py6fjmu","SingleMu"]}
organizers = [organizer.organizer(tag, [s for s in self.sampleSpecs(tag) if any(item in s['name'] for item in samples[tag])])
for tag in [p['tag'] for p in self.readyConfs]]
if not organizers : return
for org in organizers :
org.mergeSamples(targetSpec = {"name":"Data 2011", "color":r.kBlack, "markerStyle":20}, allWithPrefix="SingleMu")
org.mergeSamples(targetSpec = {"name":"qcd_py6mu", "color":r.kRed if "QCD" in org.tag else r.kBlue, "markerStyle": 22}, allWithPrefix="qcd_py6fjmu")
org.scale(toPdf=True)
melded = organizer.organizer.meld("qcdPartitions",filter(lambda o: o.samples, organizers))
pl = plotter.plotter(melded,
psFileName = self.psFileName(melded.tag),
doLog = False,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
).plotAll()
def makeStandardPlots(self, org) :
#plot
pl = plotter.plotter(org,
psFileName = self.psFileName(org.tag),
samplesForRatios = ("2011 Data","Standard Model "),
sampleLabelsForRatios = ("data","s.m."),
printRatios = True,
showStatBox = not self.ra1Cosmetics(),
rowColors = [r.kBlack, r.kViolet+4],
#whiteList = ["lowestUnPrescaledTrigger"],
#doLog = False,
#compactOutput = True,
#noSci = True,
#latexYieldTable = True,
linYAfter = ("variableGreaterFilter", "xcak5JetAlphaTEtPat>=0.550 "),
pegMinimum = 0.1,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
)
pl.plotAll()
def makeStandardPlots(self, org) :
#plot
pl = plotter.plotter(org,
psFileName = self.psFileName(org.tag),
samplesForRatios = ("2011 Data","standard_model" if not self.ra1Cosmetics() else "Standard Model "),
sampleLabelsForRatios = ("data","s.m."),
#samplesForRatios = ("calo_325_scaled.xcak5JetnJetsWeightPat", "calo_325_scaled"),
#sampleLabelsForRatios = ("3jet","Njet"),
showStatBox = not self.ra1Cosmetics(),
#whiteList = ["lowestUnPrescaledTrigger"],
#doLog = False,
#compactOutput = True,
#noSci = True,
#latexYieldTable = True,
linYAfter = ("variableGreaterFilter", "xcak5JetAlphaTEtPat>=0.550 "),
pegMinimum = 0.1,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
)
pl.plotAll()
def conclude(self,pars) :
import re
org = self.organizer(pars)
black = sum([[hist for hist in step if re.match(r"HLT_Mu\d*_v\d*_p\d*",hist)] for step in org.steps],[])
args = {"blackList":["lumiHisto","xsHisto","nJobsHisto"] + black,
"detailedCalculables" : True }
from core.plotter import plotter
plotter(org, psFileName = self.psFileName(org.tag+"unmerged"), **args ).plotAll()
plotter(org, psFileName = self.psFileName(org.tag+"unmerged_nolog"), doLog=False, **args ).plotAll()
org.mergeSamples(targetSpec = {"name":"SingleMu","color":r.kRed}, allWithPrefix="SingleMu")
plotter(org, psFileName = self.psFileName(org.tag), **args ).plotAll()
self.printPrescales(org)
def makeStandardPlots(self, org) :
names = [ss["name"] for ss in org.samples]
samplesForRatios = filter(lambda x: x[0] in names and x[1] in names,
[("2011 Data","standard_model_nVtx"),
(".2011 Data",".standard_model_nVtx"),
("g.2011 Data","g.standard_model_nVtx"),
("2011 Data","standard_model"),
("2011 Data","standard_model_py6"),
("2010 Data","sm_2010")])
#plot all
pl = plotter.plotter(org,
psFileName = self.psFileName(org.tag),
sampleLabelsForRatios = ("data","s.m."),
samplesForRatios = next(iter(samplesForRatios), ("","")),
blackList = ["lumiHisto","xsHisto","nJobsHisto",
"deltaRGenReco",
"photonMothergenPt", "photonMotherrecoPt", "photonMothermht",
"quarkMothergenPt", "quarkMotherrecoPt", "quarkMothermht",
"otherMothergenPt", "otherMotherrecoPt", "otherMothermht",
"nGenPhotonsStatus1Photon","photonEtaStatus1Photon","photonPtStatus1Photon",
"photonPhiVsEtaStatus1Photon", "photonIsoStatus1Photon",
"nJetsStatus1Photon", "jetHtStatus1Photon",
"nJetsPlusnPhotonsStatus1Photon", "jetHtPlusPhotonHtStatus1Photon",
],
doLog = False,
#latexYieldTable = True,
rowColors = [r.kBlack, r.kViolet+4],
showErrorsOnDataYields = False,
#whiteList = ["xcak5JetIndicesPat",
# #"photonPat1Pt",
# #"photonPat1mhtVsPhotonPt",
# "xcak5JetAlphaTFewBinsPat",
# "xcak5JetAlphaTRoughPat",
# "xcak5JetAlphaTWithPhoton1PtRatherThanMhtPat",
# ],
)
pl.plotAll()
def meldNorm(self) :
meldSamples = {"top_muon_pf" : ["SingleMu","P00","NonQQbar","w_jets"],
#"Wlv_muon_pf" : ["w_jets"],
"QCD_muon_pf" : ["SingleMu"]}
organizers = [organizer.organizer(tag, [s for s in self.sampleSpecs(tag) if any(item in s['name'] for item in meldSamples[tag])])
for tag in [p['tag'] for p in self.readyConfs if p["tag"] in meldSamples]]
if not organizers : return
for org in organizers :
org.mergeSamples(targetSpec = {"name":"t#bar{t}", "color":r.kViolet}, sources=["tt_tauola_fj.wNonQQbar.nvr","tt_tauola_fj.wTopAsymP00.nvr"])
org.mergeSamples(targetSpec = {"name":"w_jets", "color":r.kRed}, allWithPrefix = "w_jets")
org.mergeSamples(targetSpec = {"name":"Data 2011",
"color":r.kBlue if "qcd_" in org.tag else r.kBlack,
"markerStyle":(20 if "top" in org.tag else 1)}, allWithPrefix="SingleMu")
if True :
templates = []
dist = "TriDiscriminant"
for org in organizers :
before = next(org.indicesOfStep("label","selection complete"))
distTup = org.steps[next(iter(filter(lambda i: before<i, org.indicesOfStepsWithKey(dist))))][dist]
for ss,hist in zip(org.samples,distTup) :
contents = [hist.GetBinContent(i) for i in range(hist.GetNbinsX()+2)]
if "top" in org.tag and ss["name"] is "Data 2011": signal = contents
else : templates.append(contents)
print org.tag, ss["name"]
from core import fractions
cs = fractions.componentSolver(signal, templates, 1e4)
with open("measuredFractions.txt","w") as file : print >> file, cs
stuff = fractions.drawComponentSolver(cs)
stuff[0].Print("measuredFractions.eps")
for org in organizers : org.scale(toPdf=True)
melded = organizer.organizer.meld(organizers = organizers)
pl = plotter.plotter(melded, psFileName = self.psFileName(melded.tag),
doLog = False, blackList = ["lumiHisto","xsHisto","nJobsHisto"] ).plotAll()
def conclude(self, conf) :
org = self.organizer(conf)
pl = plotter.plotter(org, psFileName = self.psFileName(org.tag), blackList = ["lumiHisto","xsHisto","nJobsHisto"])
pl.plotAll()
def makeIndividualPlots(self, org) :
#plot all
pl = plotter.plotter(org,
psFileName = self.psFileName(org.tag),
showStatBox = False,
doLog = True,
pegMinimum = 0.1,
anMode = True,
)
pl.individualPlots(plotSpecs = [{"plotName":"xcak5JetAlphaTRoughPat",
"stepName" :"alphaHistogrammer",
"stepDesc" :"xcak5JetPat",
"newTitle":";#alpha_{T};events / bin",
"legendCoords": (0.55, 0.60, 0.85, 0.90),
"stampCoords": (0.75, 0.55)
},
{"plotName":"jetMultiplicity",
"stepName":"singleJetHistogrammer",
"stepDesc":"xcak5JetPat through index 2",
"newTitle":";N_{jets};events / bin",
"legendCoords": (0.7, 0.7, 0.92, 0.92),
"stampCoords": (0.5, 0.28),
},
{"plotName":"xcak5JetHtPat",
"stepName":"cleanJetHtMhtHistogrammer",
"stepDesc":"xcak5JetPat",
"newTitle":";H_{T} (GeV);events / bin",
"legendCoords": (0.6, 0.60, 0.92, 0.92),
"stampCoords": (0.45, 0.88)
},
##after alphaT
{"plotName":"xcak5JetDeltaPhiStarPat",
"stepName":"histogrammer",
"stepDesc" :"(lambda x:x[0][0])(xcak5JetDeltaPhiStarPat)",
"index" : -1,
"newTitle":";#Delta#phi*;events / bin",
"legendCoords": (0.6, 0.6, 0.92, 0.92),
"stampCoords": (0.33, 0.88),
},
{"plotName":"xcak5JetHtPlusMhtRoughPat",
"stepName":"cleanJetHtMhtHistogrammer",
"stepDesc":"xcak5JetPat",
"index":-1,
"newTitle":";M_{eff} (GeV);events / bin",
"legendCoords": (0.7, 0.7, 0.92, 0.92),
"stampCoords": (0.75, 0.4),
},
{"plotName":"xcak5JetIndicesPat",
"stepName":"histogrammer",
"stepDesc" :"(lambda x:len(x))(xcak5JetIndicesPat)",
"index" : -1,
"newTitle":";N_{jets};events / bin",
"legendCoords": (0.6, 0.6, 0.92, 0.92),
"stampCoords": (0.6, 0.38),
}
],
newSampleNames = None,
#newSampleNames = {"qcd_mg_nVtx": "Madgraph QCD",
# "g_jets_mg_nVtx": "Madgraph #gamma + jets",
# "2011 Data": "Data",
# "standard_model_nVtx": "Standard Model",
# },
preliminary = True,
)