def conclude(self, pars):
org = self.organizer(pars, verbose=True)
org.scale(toPdf=True)
#org.scale( lumiToUseInAbsenceOfData = pars['lumi'] )
kwargs = {
"detailedCalculables": False,
"blackList": ["lumiHisto", "xsHisto", "nJobsHisto"],
"rowColors": 2 * [13] + 2 * [45],
"rowCycle": 100,
#"omit2D" : True,
}
supy.plotter(org,
pdfFileName=self.pdfFileName(org.tag + "_log"),
doLog=True,
pegMinimum=0.01,
**kwargs).plotAll()
supy.plotter(org,
pdfFileName=self.pdfFileName(org.tag + "_nolog"),
doLog=False,
**kwargs).plotAll()
r.gStyle.SetOptStat(0)
self.resPlots(org)
self.filterPlots(org)
self.fitPlots(org)
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.scale(lumiToUseInAbsenceOfData=1.0e-3) # /pb
supy.plotter(org,
pdfFileName = self.pdfFileName(org.tag),
).plotAll()
def conclude(self, pars):
org = self.organizer(pars, verbose=True)
def gopts(name="", color=1):
return {"name": name, "color": color, "markerStyle": 1, "lineWidth": 2, "goptions": "ehist"}
for new, old, color in [("DY->tt", "dy_tt", r.kBlue),
("DY->mt", "dy_mt", r.kRed),
("DY->et", "dy_et", r.kOrange + 3),
("DY->em", "dy_em", r.kGreen),
]:
org.mergeSamples(targetSpec=gopts(new, color), sources=[old])
# org.scale() # to data
org.scale(lumiToUseInAbsenceOfData=4.0e3) # /pb
# org.scale(toPdf=True)
def yx(h):
if "_prof" in h.GetName():
ax = h.GetXaxis()
f = r.TF1("yx", "x", ax.GetXmin(), ax.GetXmax())
f.SetLineWidth(1)
f.Draw("same")
return f
supy.plotter(org,
pdfFileName=self.pdfFileName(org.tag),
printImperfectCalcPageIfEmpty=False,
printXs=True,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
rowColors=[r.kBlack, r.kViolet+4],
doLog=False,
# pegMinimum=0.1,
fitFunc=yx,
).plotAll()
def conclude(self, pars):
org = self.organizer(pars)
org.scale(lumiToUseInAbsenceOfData=20.0)
supy.plotter(org,
doLog=False,
pdfFileName=self.pdfFileName(org.tag),
).plotAll()
def conclude(self, pars):
org = self.organizer(pars, prefixesNoScale=["efficiency_"])
def gopts(name="", color=1):
return {
"name": name,
"color": color,
"markerStyle": 1,
"lineWidth": 2,
"goptions": "ehist"
}
org.mergeSamples(targetSpec=gopts("QCD", r.kBlue),
sources=["QCD_c4_pu140_Pt_0p3_4"])
org.mergeSamples(targetSpec=gopts("H", r.kBlue), allWithPrefix="H_")
org.scale(1.0 / fb)
supy.plotter(
org,
pdfFileName=self.pdfFileName(org.tag),
printImperfectCalcPageIfEmpty=False,
printXs=True,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
rowColors=[r.kBlack, r.kViolet + 4],
fitFunc=lambda x: self.profFit(x, "eta"),
doLog=False,
drawYx=True,
showStatBox=True,
optStat=1100,
).plotAll()
self.dumpFitResults(fileName="fitResults.py")
def conclude(self, config):
org = self.organizer(config)
supy.utils.io.printSkimResults(org)
supy.plotter(
org, pdfFileName=self.pdfFileName(org.tag), blackList=["lumiHisto", "xsHisto", "nJobsHisto"]
).plotAll()
def conclude(self,pars) :
org = self.organizer(pars)
org.scale(lumiToUseInAbsenceOfData=20.0)
supy.plotter(org,
pdfFileName = self.pdfFileName(org.tag),
doLog = False,
blackList = ['lumiHisto','xsHisto','nJobsHisto'],
).plotAll()
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
supy.plotter( org,
pdfFileName = self.pdfFileName(org.tag),
doLog = False,
blackList = ['num_.*', 'den_.*'],
).plotAll()
def conclude(self, pars):
org = self.organizer(pars)
org.mergeSamples(targetSpec={"name": "qcd_py6", "color": r.kBlue}, allWithPrefix="v12_qcd_py6")
org.scale(100)
supy.plotter(
org,
pdfFileName=self.pdfFileName(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()
supy.plotter( org,
pdfFileName = self.pdfFileName(org.tag),
#samplesForRatios = ("Example_Skimmed_900_GeV_Data","Example_Skimmed_900_GeV_MC"),
#sampleLabelsForRatios = ("data","sim"),
).plotAll()
def conclude(self, pars):
org = self.organizer(pars, verbose=True)
def gopts(name="", color=1):
return {"name":name, "color":color, "markerStyle":1, "lineWidth":2, "goptions":"ehist"}
org.mergeSamples(targetSpec={"name": "Data"}, allWithPrefix="data")
mc = ".".join(["", "LastBinOverBin1", "diTauHadTriggerWeight"])
sig = mc + ".x100"
for sample, color, ws in [("H260_hh_bbtautau", r.kOrange, sig),
("H300_hh_bbtautau", 28, sig),
("H350_hh_bbtautau", 44, sig),
("ZZ_llqq", r.kYellow, mc),
("tt_bblnln", r.kMagenta, mc),
("tt_bblnqq", r.kBlue, mc),
("dy_ll", r.kGreen, mc),
]:
name = sample
if ws.endswith(".x100"):
name = sample[:4] + ".x100"
org.mergeSamples(targetSpec=gopts(name, color), sources=[sample + ws])
org.mergeSamples(targetSpec=gopts("w_ln_123j", r.kMagenta+2), allWithPrefix="w_ln")
org.mergeSamples(targetSpec=gopts("EWK", r.kRed),
keepSources=True,
sources=["ZZ_llqq", "tt_bblnln", "tt_bblnqq", "dy_ll", "w_ln_123j"])
org.scale() # to data
#org.scale(lumiToUseInAbsenceOfData=20.0e3) # /pb
#org.scale(toPdf=True)
supy.plotter(org,
pdfFileName=self.pdfFileName(org.tag),
printImperfectCalcPageIfEmpty=False,
printXs=True,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
rowColors=[r.kBlack, r.kViolet+4],
doLog=True,
pegMinimum=0.1,
showStatBox=False,
latexYieldTable=False,
#samplesForRatios=("H300_hh_bbtautau", "tt_bbll"),
#sampleLabelsForRatios=("hh", "tt"),
#foms=[{"value": lambda x, y: x/y,
# "uncRel": lambda x, y, xUnc, yUnc: ((xUnc/x)**2 + (yUnc/y)**2)**0.5,
# "label": lambda x, y:"%s/%s" % (x, y),
# },
# #{"value": lambda x,y: x/(y**0.5),
# # "uncRel": lambda x, y, xUnc, yUnc: math.sqrt((xUnc/x)**2 + (yUnc/y/2.)**2),
# # "label": lambda x,y: "%s/sqrt(%s)" % (x, y),
# # },
# ],
).plotAll()
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.scale(lumiToUseInAbsenceOfData=1.0e-3) # /pb
supy.plotter(org,
pdfFileName = self.pdfFileName(org.tag),
samplesForRatios = ('SM', ['A2', 'A4', 'A6', 'P3']),
sampleLabelsForRatios = ('SM','BSM'),
).plotAll()
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.scale()
supy.plotter(org,
pdfFileName = self.pdfFileName(org.tag),
doLog=False,
blackList = ['lumiHisto','xsHisto','nJobsHisto','cnt_.*', 'cum_.*'],
).plotAll()
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
mode = self.parameters()['mode']
mode, skim = self.parameters()['mode'], self.parameters()['skim']
supy.plotter( org,
pdfFileName = self.pdfFileName(org.tag+self.parameters()['mode']),
doLog = False,
blackList = ['num_.*', 'den_.*'],
).plotAll()
def conclude(self, conf) :
org = self.organizer(conf)
org.scale(5.0e3)
supy.plotter(org,
pdfFileName = self.pdfFileName(org.tag),
rowColors = [r.kBlack, r.kViolet+4],
#doLog = False,
pegMinimum = 0.1,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
).plotAll()
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.scale(lumiToUseInAbsenceOfData=1.0e-3) # /pb
supy.plotter(org,
pdfFileName = self.pdfFileName(org.tag),
doLog=False,
blackList = ['lumiHisto','xsHisto','nJobsHisto',],
samplesForRatios=('tt-fs',['tt-af','tHu-af','tHu-fs']),
).plotAll()
def conclude(self, pars):
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.scale()
supy.plotter(org,
pdfFileName=self.pdfFileName(org.tag),
samplesForRatios=("Data", "MC"),
sampleLabelsForRatios=("data", "sim"),
detailedCalculables=True,
).plotAll()
def conclude(self,pars) :
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()
supy.plotter( org,
pdfFileName = self.pdfFileName(org.tag),
blackList = ["lumiHisto","xsHisto","xsPostWeightsHisto","nJobsHisto","genpthat"],
detailedCalculables = True,
).plotAll()
def conclude(self,pars) :
org = self.organizer(pars)
org.scale(1.0e3)
supy.plotter(org,
pdfFileName = self.pdfFileName(""),
blackList = ["lumiHisto","xsHisto","nJobsHisto",],
rowColors = [r.kBlack, r.kViolet+4],
pegMinimum = 0.1,
doLog = False,
).plotAll()
def conclude(self,pars) :
org = self.organizer(pars)
org.mergeSamples(sources = ["ttz_8_mg.job269_1"], targetSpec = {"name":"TTZ", "markerStyle":20})
org.scale(10.0e3)
supy.plotter(org,
pdfFileName = self.pdfFileName(""),
blackList = ["lumiHisto","xsHisto","nJobsHisto",],
rowColors = [r.kBlack, r.kViolet+4],
pegMinimum = 0.1,
doLog = False,
).plotAll()
def conclude(self,pars) :
org = self.organizer(pars)
org.scale(toPdf=True)
supy.plotter(org,
pdfFileName = self.pdfFileName(org.tag),
doLog = False,
#noSci = True,
#pegMinimum = 0.1,
detailedCalculables = True,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
).plotAll()
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
# mergeSamples doesn't work with the special step matchingEffVsEt that implements mergeFunc
#org.mergeSamples(targetSpec = {"name":"Period B (part)", "color":r.kBlack}, allWithPrefix="202")
#org.mergeSamples(targetSpec = {"name":"Period B (part)", "color":r.kBlack},
# sources=['202668_L1_4J15', '202712_L1_4J15', '202740_L1_4J15', '202798_L1_4J15'])
#org.scale(lumiToUseInAbsenceOfData=1000.)
supy.plotter( org,
pdfFileName = self.pdfFileName(org.tag),
doLog = False,
blackList = ['num_', 'den_'],
).plotAll()
def conclude(self, pars):
org = self.organizer(pars, prefixesNoScale=["efficiency_"])
supy.utils.printSkimResults(org)
org.scale(1.e3)
supy.plotter(org,
pdfFileName=self.pdfFileName(org.tag),
printImperfectCalcPageIfEmpty=False,
printXs=True,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
rowColors=[1, 2],
doLog=True,
optStat=1111,
showStatBox=False,
).plotAll()
def conclude(self, pars):
org = self.organizer(pars)
supy.utils.printSkimResults(org)
def gopts(name="", color=1):
return {
"name": name,
"color": color,
"markerStyle": 1,
"lineWidth": 2,
"goptions": "ehist"
}
org.mergeSamples(targetSpec=gopts("tt_0_6", r.kBlue),
sources=["tt_0_6.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_6_11", r.kGreen),
sources=["tt_6_11.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_11_17", r.kCyan),
sources=["tt_11_17.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_17_25", r.kMagenta),
sources=["tt_17_25.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_25_1k", r.kOrange),
sources=["tt_25_1k.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt", r.kBlack),
allWithPrefix="tt_",
keepSources=True)
org.scale(3.e3 / fb)
supy.plotter(
org,
pdfFileName=self.pdfFileName(org.tag),
printImperfectCalcPageIfEmpty=False,
printXs=True,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
rowColors=[r.kBlack, r.kViolet + 4],
doLog=True,
#optStat=1100,
optStat=1111,
#fitFunc=self.profFit,
showStatBox=False,
latexYieldTable=True,
#samplesForRatios=("hh_bbtt", "tt"),
#sampleLabelsForRatios=("hh", "tt"),
#foms=[{"value": lambda x, y: x/y,
# "uncRel": lambda x, y, xUnc, yUnc: math.sqrt((xUnc/x)**2 + (yUnc/y)**2),
# "label": lambda x, y:"%s/%s" % (x, y),
# },
# ],
).plotAll()
def conclude(self,pars) :
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 ("","")}
supy.plotter(org, pdfFileName = self.pdfFileName(org.tag+'_log'), doLog = True, **kwargs).plotAll()
supy.plotter(org, pdfFileName = self.pdfFileName(org.tag+'_nolog'), doLog = False, **kwargs).plotAll()
def conclude(self, pars):
org = self.organizer(pars, prefixesNoScale=["efficiency_"])
supy.utils.printSkimResults(org)
org.scale(1.e3)
supy.plotter(
org,
pdfFileName=self.pdfFileName(org.tag),
printImperfectCalcPageIfEmpty=False,
printXs=True,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
rowColors=[1, 2],
doLog=True,
optStat=1111,
showStatBox=False,
).plotAll()
def conclude(self,pars) :
org = self.organizer(pars, verbose = True )
org.mergeSamples(targetSpec={"name":"qcd"}, allWithPrefix="qcd")
org.scale( toPdf = True )
names = [ss["name"] for ss in org.samples]
kwargs = {"detailedCalculables": False,
"blackList":["lumiHisto","xsHisto","nJobsHisto"],
"detailedCalculables" : True,
"rowColors" : self.rowcolors,
"rowCycle" : 100,
}
supy.plotter(org, pdfFileName = self.pdfFileName(org.tag+"_log"), doLog = True, pegMinimum = 0.01, **kwargs ).plotAll()
supy.plotter(org, pdfFileName = self.pdfFileName(org.tag+"_nolog"), doLog = False, **kwargs ).plotAll()
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.mergeSamples(targetSpec = {"name":"Standard Model", "color":r.kBlue,"lineWidth":3,"goptions":"hist"}, allWithPrefix = "qcd")
org.mergeSamples(targetSpec = {"name":"Data", "color":r.kBlack, "markerStyle":20}, allWithPrefix = "data")
org.mergeSamples(targetSpec = {"name":"H #rightarrow X #rightarrow q#bar{q}", "color":r.kRed,"lineWidth":3,"goptions":"hist","lineStyle":2}, allWithPrefix = "H")
org.scale()
plotter = supy.plotter( org,
pdfFileName = self.pdfFileName(org.tag),
#samplesForRatios = ("data","qcd"),
#sampleLabelsForRatios = ("data","qcd"),
doLog=True,
anMode=True,
pegMinimum=1,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
)
plotter.plotAll()
plotter.individualPlots(plotSpecs = [{"plotName":"PromptEnergyFrac_h_jetPromptEnergyFrac",
"stepName":"promptness",
"stepDesc":"promptness",
"newTitle":"; Charged Prompt Energy Fraction; jets / bin",
"legendCoords": (0.35, 0.35, 0.9, 0.55),
"stampCoords": (0.6, 0.68)
},
{"plotName":"NPromptTracks_h_jetPromptEnergyFrac",
"stepName":"promptness",
"stepDesc":"promptness",
"newTitle":"; Number of Prompt Tracks ; jets / bin",
"legendCoords": (0.55, 0.55, 0.9, 0.75),
"stampCoords": (0.7, 0.88)
},
]
)
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
#org.mergeSamples(targetSpec = {"name":"#tilde{q}(1500)#rightarrow#tilde{#chi}(500) c#tau=18.1cm", "color":r.kRed,"lineWidth":3,"goptions":"hist","lineStyle":1}, allWithPrefix = "SQ_1500_CHI_494")
#org.mergeSamples(targetSpec = {"name":"#tilde{q}(1500)#rightarrow#tilde{#chi}(150) c#tau=4.5cm", "color":r.kYellow,"lineWidth":3,"goptions":"hist","lineStyle":1}, allWithPrefix = "SQ_1500_CHI_150")
#org.mergeSamples(targetSpec = {"name":"#tilde{q}(1000)#rightarrow#tilde{#chi}(150) c#tau=5.9cm", "color":r.kBlack,"lineWidth":3,"goptions":"hist","lineStyle":1}, allWithPrefix = "SQ_1000_CHI_148")
#org.mergeSamples(targetSpec = {"name":"#tilde{q}(1000)#rightarrow#tilde{#chi}(500) c#tau=22.7cm", "color":r.kOrange,"lineWidth":3,"goptions":"hist","lineStyle":1}, allWithPrefix = "SQ_1000_CHI_500")
#org.mergeSamples(targetSpec = {"name":"#tilde{q}(350)#rightarrow#tilde{#chi}(150) c#tau=18.8cm", "color":r.kGreen,"lineWidth":3,"goptions":"hist","lineStyle":1}, allWithPrefix = "SQ_350_CHI_148")
#org.mergeSamples(targetSpec = {"name":"#tilde{q}(700)#rightarrow#tilde{#chi}(150) c#tau=8.1cm", "color":r.kBlue,"lineWidth":3,"goptions":"hist","lineStyle":1}, allWithPrefix = "SQ_700_CHI_150")
#org.mergeSamples(targetSpec = {"name":"#tilde{q}(700)#rightarrow#tilde{#chi}(500) c#tau=27.9cm", "color":r.kMagenta,"lineWidth":3,"goptions":"hist","lineStyle":1}, allWithPrefix = "SQ_700_CHI_500")
org.scale(lumiToUseInAbsenceOfData=18600)
plotter = supy.plotter( org,
pdfFileName = self.pdfFileName(org.tag),
doLog=True,
anMode=True,
showStatBox=True,
pegMinimum=0.1,
shiftUnderOverFlows=False,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
)
plotter.plotAll()
#plotter.doLog=False
plotter.anMode=True
#self.meanLxy(org)
#self.sqsqRatio(org)
org.lumi=None
#self.flavors(org)
#self.effPlots(org,plotter,denName='NE',numName='NEReco',sel='Low',flavor='')
#self.sigPlots(plotter)
self.totEvtEff(org,dir='eff2Neu')
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.mergeSamples(targetSpec = {"name":"QCD", "color":r.kBlue,"lineWidth":3,"goptions":"E2","fillColor":r.kBlue,"fillStyle":3001,"double":True,"markerSize":0}, allWithPrefix = "qcd")
org.mergeSamples(targetSpec = {"name":"Data", "color":r.kBlack, "markerStyle":20}, allWithPrefix = "data")
org.mergeSamples(targetSpec = {"name":"H#rightarrow X #rightarrow q#bar{q}", "color":r.kRed,"lineWidth":3,"goptions":"hist","lineStyle":2}, allWithPrefix = "H")
org.scale(lumiToUseInAbsenceOfData=11)
plotter = supy.plotter( org,
pdfFileName = self.pdfFileName(org.tag),
samplesForRatios = ("Data","QCD"),
sampleLabelsForRatios = ("Data","QCD"),
doLog=True,
pageNumbers=False,
#pegMinimum=1,
anMode=True,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
)
#plotter.plotAll()
#plotABCDscan(self,org,plotter,4,blind=False)
plotter.individualPlots(plotSpecs = [
{"plotName":"Discriminant_h_dijetTrueLxy",
"stepName":"ABCDvars",
"stepDesc":"ABCDvars",
"newTitle":"; Vertex-Cluster discriminant; dijets / bin",
"legendCoords": (0.7, 0.75, 0.9, 0.9),
"stampCoords": (0.45, 0.88)
},
])
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.mergeSamples(targetSpec = {"name":"Simulation", "color":r.kBlue,"lineWidth":3,"goptions":"hist"}, allWithPrefix = "qcd")
org.mergeSamples(targetSpec = {"name":"Data", "color":r.kBlack, "markerStyle":20}, allWithPrefix = "data")
org.scale(lumiToUseInAbsenceOfData=18600)
plotter = supy.plotter( org,
pdfFileName = self.pdfFileName(org.tag),
doLog=True,
samplesForRatios = ("Data","Simulation"),
sampleLabelsForRatios = ("Data","Sim"),
#anMode=True,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
dependence2D=True,
doCorrTable=True,
pegMinimum=5,
anMode=True
)
plotter.plotAll()
org.lumi=None
plotter.individualPlots(plotSpecs = [{"plotName":"NAvgMissHitsAfterVert_h_dijetVtxpt",
"stepName":"cutvars",
"stepDesc":"cutvars",
"newTitle":";Missing Hits per track after Vertex;dijets / bin",
"legendCoords": (0.55, 0.55, 0.9, 0.75),
"stampCoords": (0.67, 0.88)
},
]
)
def conclude(self, pars):
# make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.mergeSamples(
targetSpec={"name": "Simulation", "color": r.kBlue, "markerStyle": 21, "markerSize": 0.6},
allWithPrefix="qcd",
)
org.mergeSamples(
targetSpec={"name": "Data", "color": r.kBlack, "markerStyle": 21, "markerSize": 0.6}, allWithPrefix="data"
)
org.scale(lumiToUseInAbsenceOfData=11)
plotter = supy.plotter(
org,
pdfFileName=self.pdfFileName(org.tag),
samplesForRatios=("Data", "Simulation"),
sampleLabelsForRatios=("Data", "Sim"),
doLog=True,
anMode=True,
pageNumbers=False,
pushLeft=True,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
)
plotter.plotAll()
plotter.doLog = False
self.makeEfficiencyPlots1(org, "jetPt", "jetTrigPrompt1", plotter)
self.makeEfficiencyPlots2(org, "jetPromptEnergyFrac", "jetTrigPrompt2", plotter)
def conclude(self,pars) :
#make a pdf file with plots from the histograms created above
org = self.organizer(pars)
org.mergeSamples(targetSpec = {"name":"H^{0}(1000)#rightarrow 2X^{0}(350) c#tau=35cm", "color":r.kRed,"lineWidth":3,"goptions":"","lineStyle":1}, allWithPrefix = "H_1000_X_350")
org.mergeSamples(targetSpec = {"name":"H^{0}(400)#rightarrow 2X^{0}(150) c#tau=40cm", "color":r.kGreen,"lineWidth":3,"goptions":"","lineStyle":1}, allWithPrefix = "H_400_X_150")
org.mergeSamples(targetSpec = {"name":"H^{0}(200)#rightarrow 2X^{0}(50) c#tau=20cm", "color":r.kBlack,"lineWidth":3,"goptions":"","lineStyle":1}, allWithPrefix = "H_200_X_50")
org.mergeSamples(targetSpec = {"name":"H^{0}(1000)#rightarrow 2X^{0}(150) c#tau=10cm", "color":r.kBlue,"lineWidth":3,"goptions":"","lineStyle":1}, allWithPrefix = "H_1000_X_150")
org.mergeSamples(targetSpec = {"name":"H^{0}(400)#rightarrow 2X^{0}(50) c#tau=8cm", "color":r.kMagenta,"lineWidth":3,"goptions":"","lineStyle":1}, allWithPrefix = "H_400_X_50")
org.scale(lumiToUseInAbsenceOfData=18600)
plotter = supy.plotter( org,
pdfFileName = self.pdfFileName(org.tag),
doLog=True,
anMode=True,
showStatBox=True,
pegMinimum=0.0001,
shiftUnderOverFlows=False,
blackList = ["lumiHisto","xsHisto","nJobsHisto"],
)
plotter.plotAll()
plotter.anMode=True
#self.meanLxy(org)
org.lumi=None
#self.effPlots(org,plotter,denName='NE',numName='NEReco',sel='Low',flavor='')
#self.sigPlots(plotter)
#self.totalEfficiencies(org,dir='eff2',flavor='')
self.totEvtEff(org,dir='eff2HV')
def conclude(self,pars) :
org = self.organizer(pars)
#org.mergeSamples(targetSpec = {"name":"SingleMu", "color":r.kBlack}, allWithPrefix="SingleMu")
#org.scale()
supy.plotter(org,
pdfFileName = self.pdfFileName(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, tagSuffix):
if tagSuffix not in self.orgMelded:
print tagSuffix, "not in", self.orgMelded.keys()
print "run meldScale() before plotMeldScale()"
return
melded = copy.deepcopy(self.orgMelded[tagSuffix])
lname, tt, sn, jn, ptMin = tagSuffix.split('_')
for s in [
'top.ttj_%s.%s.pu' % (tt, s)
for s in ['wQQ', 'wQG', 'wAG', 'wGG']
]:
melded.drop(s)
for log, label in [(False, ""), (True, "_log")][:1]:
pl = supy.plotter(
melded,
pdfFileName=self.pdfFileName(melded.tag + label),
doLog=log,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
rowColors=self.rowcolors,
samplesForRatios=("top.Data 2012", "S.M."),
sampleLabelsForRatios=('data', 's.m.'),
rowCycle=100,
omit2D=True,
pageNumbers=False,
).plotAll()
print
print 'fitTopDPtDPhi indices:'
print melded.indicesOfStepsWithKey('fitTopDPtDPhi')
def conclude(self, pars):
org = self.organizer(pars, prefixesNoScale=["efficiency_"])
def gopts(name="", color=1):
return {
"name": name,
"color": color,
"markerStyle": 1,
"lineWidth": 2,
"goptions": "ehist"
}
#org.mergeSamples(targetSpec=gopts("BB_0_3", r.kGreen), sources=["BB_0_3.GenWeight"])
#org.mergeSamples(targetSpec=gopts("BB_3_7", r.kCyan), sources=["BB_3_7.GenWeight"])
#org.mergeSamples(targetSpec=gopts("BB_7_13", r.kMagenta), sources=["BB_7_13.GenWeight"])
#org.mergeSamples(targetSpec=gopts("BB_13_21", r.kOrange), sources=["BB_13_21.GenWeight"])
org.mergeSamples(targetSpec=gopts("BB", r.kBlue), allWithPrefix="BB_")
org.mergeSamples(targetSpec=gopts("H", r.kBlue), allWithPrefix="H_")
org.mergeSamples(targetSpec=gopts("hh_bb#tau#tau", r.kRed),
sources=["hh_bbtt"])
#org.mergeSamples(targetSpec=gopts("H_c0_pu0", r.kBlack), sources=["H_0_3_c0_pu0.GenWeight"])
#org.mergeSamples(targetSpec=gopts("H_c0_pu140", r.kBlue), sources=["H_0_3_c0_pu140.GenWeight"])
#org.mergeSamples(targetSpec=gopts("H_c3_pu140", r.kGreen), sources=["H_0_3_c3_pu140.GenWeight"])
#org.mergeSamples(targetSpec=gopts("H_c4_pu140", r.kCyan), sources=["H_0_3_c4_pu140.GenWeight"])
#org.mergeSamples(targetSpec=gopts("hh_bbtt_c3_pu140", r.kRed), sources=["hh_bbtt_c3_pu140"])
#org.mergeSamples(targetSpec=gopts("hh_bbtt_c4_pu140", r.kMagenta), sources=["hh_bbtt_c4_pu140"])
org.scale(1.0 / fb)
#org.scale(toPdf=True)
supy.plotter(
org,
pdfFileName=self.pdfFileName(org.tag),
printImperfectCalcPageIfEmpty=False,
printXs=True,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
rowColors=[r.kBlack, r.kViolet + 4],
doLog=False,
fitFunc=lambda x: self.profFit(x, "b_v4"),
showStatBox=True,
optStat=1100,
).plotAll()
self.dumpFitResults(fileName="fitResults.py")
def conclude(self, pars):
org = self.organizer(pars)
#org.mergeSamples(targetSpec = {"name":"SingleMu", "color":r.kBlack}, allWithPrefix="SingleMu")
#org.scale()
supy.plotter(
org,
pdfFileName=self.pdfFileName(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 conclude(self,pars) :
rw = pars['reweights']['abbr']
org = self.organizer(pars, verbose = True )
org.mergeSamples(targetSpec = {"name":"Data 2012", "color":r.kBlack, "markerStyle":20},
sources=getattr(self,'muons' if pars['lepton']['name']=='mu' else 'electrons')('.jw') )
org.mergeSamples(targetSpec={"name":"t#bar{t}", "color":r.kViolet}, sources=['ttj_ph.wAG.pu.sf','ttj_ph.wQG.pu.sf','ttj_ph.wQQ.pu.sf','ttj_ph.wGG.pu.sf'], keepSources=True)
org.mergeSamples(targetSpec={"name":"Wbb", "color":r.kRed}, allWithPrefix='wbb')
org.mergeSamples(targetSpec={"name":"W", "color":28}, allWithPrefix='w')
org.mergeSamples(targetSpec={"name":"DY", "color":r.kYellow}, allWithPrefix="dy")
org.mergeSamples(targetSpec={"name":"t", "color":r.kWhite}, sources=['.'.join([s,rw,'sf']) for s in self.single_top() if 'top_t_' in s], keepSources=True)
org.mergeSamples(targetSpec={"name":"t_", "color":r.kWhite}, sources=['.'.join([s,rw,'sf']) for s in self.single_top() if 'tbar_t_' in s], keepSources=True)
org.mergeSamples(targetSpec={"name":"Single", "color":r.kGray}, sources=['.'.join([s,rw,'sf']) for s in self.single_top()])
org.mergeSamples(targetSpec={"name":"St.Model", "color":r.kGreen+2}, sources=["t#bar{t}","W","DY","Single"],
keepSources=True)
try:
self.skimStats(org)
self.printTable(org)
self.skimControl(org)
except: pass
org.scale( lumiToUseInAbsenceOfData = 19590, toPdf=False )
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 2012","St.Model")])), ("","")),
"sampleLabelsForRatios" : ("data","s.m."),
"detailedCalculables" : True,
"rowColors" : self.rowcolors,
"rowCycle" : 100,
"omit2D" : True,
}
#supy.plotter(org, pdfFileName = self.pdfFileName(org.tag+"_log"), doLog=True, pegMinimum=0.01, **kwargs ).plotAll()
supy.plotter(org, pdfFileName = self.pdfFileName(org.tag+"_nolog"), doLog=False, **kwargs ).plotAll()
def conclude(self, pars):
org = self.organizer(pars, prefixesNoScale=["efficiency_"])
supy.utils.printSkimResults(org)
def gopts(name="", color=1):
return {
"name": name,
"color": color,
"markerStyle": 1,
"lineWidth": 2,
"goptions": "ehist"
}
c = pars["tag"]
org.mergeSamples(targetSpec=gopts("hh_bb#tau#tau", r.kRed),
sources=["hh_bbtt_%s_20_skim" % c])
keep = pars["keepSources"]
if keep:
org.mergeSamples(targetSpec=gopts("tt_0_6", r.kBlue),
sources=["tt_%s_0_6_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("tt_6_11", r.kGreen),
sources=["tt_%s_6_11_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("tt_11_17", r.kCyan),
sources=["tt_%s_11_17_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("tt_17_25", r.kMagenta),
sources=["tt_%s_17_25_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("tt_25_1k", r.kOrange),
sources=["tt_%s_25_1k_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("tt", r.kBlue),
allWithPrefix="tt_",
keepSources=keep)
bj = [
"B_skim", "Bj_0_3_skim", "Bj_3_6_skim", "Bj_6_11_skim",
"Bj_11_18_skim", "Bj_18_27_skim", "Bj_27_37_skim", "Bj_37_1k_skim"
]
org.mergeSamples(targetSpec=gopts("Bj", r.kGreen),
sources=[x + ".GenWeight" for x in bj],
keepSources=keep)
tb = [
"tB_0_5_skim", "tB_5_9_skim", "tB_9_15_skim", "tB_15_22_skim",
"tB_22_1k_skim"
]
org.mergeSamples(targetSpec=gopts("tB", r.kCyan),
sources=[x + ".GenWeight" for x in tb],
keepSources=keep)
ttb = [
"ttB_0_9_skim", "ttB_9_16_skim", "ttB_16_25_skim", "ttB_25_1k_skim"
]
org.mergeSamples(targetSpec=gopts("ttB", r.kBlue - 8),
sources=[x + ".GenWeight" for x in ttb],
keepSources=keep)
h = ["H_0_3_skim", "H_3_8_skim", "H_8_15_skim", "H_15_1k_skim"]
org.mergeSamples(targetSpec=gopts("H", r.kOrange - 3),
sources=[x + ".GenWeight" for x in h],
keepSources=keep)
if keep:
org.mergeSamples(targetSpec=gopts("BB_0_3", r.kBlue),
sources=["BB_%s_0_3_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("BB_3_7", r.kGreen),
sources=["BB_%s_3_7_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("BB_7_13", r.kCyan),
sources=["BB_%s_7_13_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("BB_13_21", r.kMagenta),
sources=["BB_%s_13_21_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("BB_21_1k", r.kOrange),
sources=["BB_%s_21_1k_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("BB", r.kBlack),
allWithPrefix="BB_",
keepSources=keep)
if keep:
org.mergeSamples(targetSpec=gopts("BBB_0_6", r.kBlue),
sources=["BBB_%s_0_6_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("BBB_6_13", r.kGreen),
sources=["BBB_%s_6_13_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("BBB_13_1k", r.kCyan),
sources=["BBB_%s_13_1k_skim.GenWeight" % c])
org.mergeSamples(targetSpec=gopts("BBB", r.kBlack),
allWithPrefix="BBB_",
keepSources=keep)
bjj = [
"Bjj_0_7_skim", "Bjj_7_14_skim", "Bjj_14_23_skim", "Bjj_23_34_skim"
]
org.mergeSamples(targetSpec=gopts("Bjj", r.kYellow),
sources=[x + ".GenWeight" for x in bjj],
keepSources=keep)
ll = [
"LL_0_1_skim", "LL_1_2_skim", "LL_2_5_skim", "LL_5_9_skim",
"LL_9_14_skim", "LL_14_1k_skim"
]
org.mergeSamples(targetSpec=gopts("LL", r.kBlack),
sources=[x + ".GenWeight" for x in ll],
keepSources=keep)
llb = ["LLB_0_4_skim", "LLB_4_9_skim", "LLB_9_1k_skim"]
org.mergeSamples(targetSpec=gopts("LLB", r.kBlack),
sources=[x + ".GenWeight" for x in llb],
keepSources=keep)
tj = [
"tj_0_5_skim", "tj_5_10_skim", "tj_10_16_skim", "tj_16_24_skim",
"tj_24_1k_skim"
]
org.mergeSamples(targetSpec=gopts("tj", r.kPink),
sources=[x + ".GenWeight" for x in tj],
keepSources=keep)
others = ["BB", "BBB", "Bjj", "LL", "LLB", "tj"]
org.mergeSamples(targetSpec=gopts("others", r.kMagenta),
sources=others,
keepSources=False)
sm = ["tt", "Bj", "tB", "ttB", "H", "others"]
org.mergeSamples(targetSpec=gopts("SM", r.kBlack),
sources=sm,
keepSources=True)
org.scale(3.e3 / fb)
#org.scale(toPdf=True)
supy.plotter(
org,
pdfFileName=self.pdfFileName(org.tag),
printImperfectCalcPageIfEmpty=False,
printXs=True,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
rowColors=[r.kBlack, r.kViolet + 4],
doLog=True,
pegMinimum=0.1,
#optStat=1100,
optStat=1111,
#fitFunc=self.profFit,
showStatBox=False,
latexYieldTable=False,
samplesForRatios=("hh_bb#tau#tau", "SM"),
sampleLabelsForRatios=("hh", "SM"),
foms=[
{
"value":
lambda x, y: x / y,
"uncRel":
lambda x, y, xUnc, yUnc: math.sqrt((xUnc / x)**2 +
(yUnc / y)**2),
"label":
lambda x, y: "%s/%s" % (x, y),
},
# #{"value": lambda x,y: x/(y**0.5),
# # "uncRel": lambda x, y, xUnc, yUnc: math.sqrt((xUnc/x)**2 + (yUnc/y/2.)**2),
# # "label": lambda x,y: "%s/sqrt(%s)" % (x, y),
# # },
],
).plotAll()
def conclude(self, pars):
org = self.organizer(pars, prefixesNoScale=["efficiency_"])
def gopts(name="", color=1):
return {"name":name, "color":color, "markerStyle":1, "lineWidth":2, "goptions":"ehist"}
#org.mergeSamples(targetSpec=gopts("tt_0_6", r.kBlue), sources=["tt_0_6.GenWeight"])
#org.mergeSamples(targetSpec=gopts("tt_6_11", r.kGreen), sources=["tt_6_11.GenWeight"])
#org.mergeSamples(targetSpec=gopts("tt_11_17", r.kCyan), sources=["tt_11_17.GenWeight"])
#org.mergeSamples(targetSpec=gopts("tt_17_25", r.kMagenta), sources=["tt_17_25.GenWeight"])
#org.mergeSamples(targetSpec=gopts("tt_25_1k", r.kOrange), sources=["tt_25_1k.GenWeight"])
#org.mergeSamples(targetSpec=gopts("tt", r.kBlack), allWithPrefix="tt_")
#org.mergeSamples(targetSpec=gopts("BB_0_3", r.kGreen), sources=["BB_0_3.GenWeight"])
#org.mergeSamples(targetSpec=gopts("BB_3_7", r.kCyan), sources=["BB_3_7.GenWeight"])
#org.mergeSamples(targetSpec=gopts("BB_7_13", r.kMagenta), sources=["BB_7_13.GenWeight"])
#org.mergeSamples(targetSpec=gopts("BB_13_21", r.kOrange), sources=["BB_13_21.GenWeight"])
org.mergeSamples(targetSpec=gopts("BB", r.kBlue), allWithPrefix="BB_")
#org.mergeSamples(targetSpec=gopts("BBB_0_6", r.kBlue), sources=["BBB_0_6.GenWeight"])
#org.mergeSamples(targetSpec=gopts("BBB_6_13", r.kCyan), sources=["BBB_6_13.GenWeight"])
#org.mergeSamples(targetSpec=gopts("BBB_13_1k", r.kMagenta), sources=["BBB_13_1k.GenWeight"])
org.mergeSamples(targetSpec=gopts("BBB", r.kGreen), allWithPrefix="BBB_")
org.mergeSamples(targetSpec=gopts("hh_bb#tau#tau", r.kRed), sources=["hh_bbtt"])
#org.mergeSamples(targetSpec=gopts("tt*w", r.kBlue), sources=["tt0_600.GenWeight"])
#org.mergeSamples(targetSpec=gopts("tt*1.0", r.kBlack) sources=["tt0_600"])
org.mergeSamples(targetSpec=gopts("H_c0_pu0", r.kBlack), sources=["H_0_3_c0_pu0.GenWeight"])
org.mergeSamples(targetSpec=gopts("H_c0_pu140", r.kBlue), sources=["H_0_3_c0_pu140.GenWeight"])
org.mergeSamples(targetSpec=gopts("H_c3_pu140", r.kGreen), sources=["H_0_3_c3_pu140.GenWeight"])
org.mergeSamples(targetSpec=gopts("H_c4_pu140", r.kCyan), sources=["H_0_3_c4_pu140.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_pu0", r.kBlack), sources=["tt_0_6_pu0.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_pu50", r.kOrange+7), sources=["tt_0_6_pu50.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_pu140", r.kBlue), sources=["tt_0_6_pu140.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_c0_pu0", r.kBlack), sources=["tt_0_6_c0_pu0.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_c0_pu140", r.kBlue), sources=["tt_0_6_c0_pu140.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_c3_pu140", r.kGreen), sources=["tt_0_6_c3_pu140.GenWeight"])
org.mergeSamples(targetSpec=gopts("tt_c4_pu140", r.kCyan), sources=["tt_0_6_c4_pu140.GenWeight"])
org.mergeSamples(targetSpec=gopts("hh_bbtt_c3_pu140", r.kRed), sources=["hh_bbtt_c3_pu140"])
org.mergeSamples(targetSpec=gopts("hh_bbtt_c4_pu140", r.kMagenta), sources=["hh_bbtt_c4_pu140"])
#org.mergeSamples(targetSpec=gopts("hh_bbtt_c4_pu140", r.kGreen), sources=["hh_bbtt_c4_pu140"])
#org.mergeSamples(targetSpec=gopts("rho<80", r.kBlack), sources=["hh_bbtt_c4_pu140.rho.le.80"])
#org.mergeSamples(targetSpec=gopts("90<rho<100", r.kRed), sources=["hh_bbtt_c4_pu140.90.le.rho.le.100"])
#org.mergeSamples(targetSpec=gopts("110<rho", r.kBlue), sources=["hh_bbtt_c4_pu140.110.le.rho"])
org.scale(1.0/fb)
#org.scale(toPdf=True)
supy.plotter(org,
pdfFileName=self.pdfFileName(org.tag),
printImperfectCalcPageIfEmpty=False,
printXs=True,
blackList=["lumiHisto", "xsHisto", "nJobsHisto"],
rowColors=[r.kBlack, r.kViolet+4],
doLog=False,
showStatBox=False,
latexYieldTable=False,
#samplesForRatios=("hh_bb#tau#tau", "tt"),
sampleLabelsForRatios=("hh", "tt"),
foms=[{"value": lambda x, y: x/y,
"uncRel": lambda x, y, xUnc, yUnc: math.sqrt((xUnc/x)**2 + (yUnc/y)**2),
"label": lambda x, y:"%s/%s" % (x, y),
},
#{"value": lambda x,y: x/(y**0.5),
# "uncRel": lambda x, y, xUnc, yUnc: math.sqrt((xUnc/x)**2 + (yUnc/y/2.)**2),
# "label": lambda x,y: "%s/sqrt(%s)" % (x, y),
# },
#{"value": lambda x, y: x/(((a*y)**2+y)**0.5),
# "uncRel": lambda x, y, xUnc, yUnc: math.sqrt((xUnc/x)**2 +\
# (yUnc*(2*a*a*y+1)/(a*a*y*y+y)/2.)**2\
# ),
# "label": lambda x,y: "%s/r(%s+%s^2/%3.0f)" % (x, y, y, 1/a/a),
# },
],
).plotAll()
def conclude(self,pars):
org = self.organizer(pars, verbose = True )
supy.plotter(org, pdfFileName = self.pdfFileName(org.tag), doLog=False ).plotAll()