def customized_preFitSteps_preFit(self, var, val):
""" Set proper initial value to reasonable region."""
unboundAfb = self.args.find("unboundAfb")
unboundFl = self.args.find("unboundFl")
if var == "afb":
for iFl in range(1, 20):
unboundAfb.setVal(
StdFitter.afbToUnboundAfb(
val, (1. - 1.33 * math.fabs(val)) / 20. *
(20. - iFl)))
unboundFl.setVal(
StdFitter.flToUnboundFl(
(1. - 1.33 * math.fabs(val)) / 20. *
(20. - iFl)))
self.FitMigrad()
if math.fabs(self._nll.getVal()) < 1e20:
break
elif var == "fl":
unboundFl.setVal(StdFitter.flToUnboundFl(val))
unboundFl.setConstant(True)
for iAfb in list(
itertools.chain(*[(i, -i) for i in range(-9, 0)])):
unboundAfb.setVal(
StdFitter.afbToUnboundAfb(
0.75 * (1. - val) / 10. * iAfb, val))
self.FitMigrad()
if math.fabs(self._nll.getVal()) < 1e20:
break
def _postRunFitSteps(self, setIdx):
""" Fill information to otree """
if math.fabs(self.fitter._nll.getVal()) < 1e20:
unboundAfb = self.fitter.args.find('unboundAfb').getVal()
unboundFl = self.fitter.args.find('unboundFl').getVal()
self.treeContent.fl = StdFitter.unboundFlToFl(unboundFl)
self.treeContent.afb = StdFitter.unboundAfbToAfb(unboundAfb, self.treeContent.fl)
self.treeContent.fs = self.fitter.args.find('fs').getVal()
self.treeContent.transAs = self.fitter.args.find('transAs').getVal()
self.treeContent.nSig = self.fitter.args.find('nSig').getVal()
self.treeContent.nBkgComb = self.fitter.args.find('nBkgComb').getVal()
self.treeContent.nll = self.fitter._nll.getVal()
self.otree.Fill()
def _postRunFitSteps(self, setIdx):
""" Fill information to otree """
if math.fabs(self.fitter._nll.getVal()) < 1e20:
unboundAfb = self.fitter.args.find('unboundAfb').getVal()
unboundFl = self.fitter.args.find('unboundFl').getVal()
self.treeContent.fl = StdFitter.unboundFlToFl(unboundFl)
self.treeContent.afb = StdFitter.unboundAfbToAfb(
unboundAfb, self.treeContent.fl)
self.treeContent.entries = self.fitter.data.numEntries(
) #self.fitter.args.find('fs').getVal()
self.treeContent.status = self.fitter.fitResult[
'finalFitter.migrad'][
'status'] #self.fitter.args.find('transAs').getVal()
self.treeContent.nSig = self.fitter.args.find('nSig').getVal()
self.treeContent.nBkgComb = self.fitter.args.find(
'nBkgComb').getVal()
self.treeContent.nll = self.fitter._nll.getVal()
self.otree.Fill()
def _postRunFitSteps(self, setIdx):
SimFit = self.process.cfg['args'].SimFit
if not SimFit: self.fitter._nll = self.fitter._nll.getVal()
if math.fabs(self.fitter._nll) < 1e20:
SimFit = self.process.cfg['args'].SimFit
unboundAfb = self.fitter.minimizer.getParameters(
self.fitter.dataWithCategories).find('unboundAfb').getVal(
) if SimFit else self.fitter.args.find('unboundAfb').getVal()
unboundFl = self.fitter.minimizer.getParameters(
self.fitter.dataWithCategories).find('unboundFl').getVal(
) if SimFit else self.fitter.args.find('unboundFl').getVal()
self.treeContent.fl = StdFitter.unboundFlToFl(unboundFl)
self.treeContent.afb = StdFitter.unboundAfbToAfb(
unboundAfb, self.treeContent.fl)
#self.treeContent.fl = self.process.sourcemanager.get('fl.{}'.format(self.process.cfg['args'].Year)).getVal()
#self.treeContent.afb = self.process.sourcemanager.get('afb.{}'.format(self.process.cfg['args'].Year)).getVal()
self.treeContent.index = setIdx
self.treeContent.status = self.fitter.migradResult if SimFit else self.fitter.fitResult[
'{}.migrad'.format(self.fitter.name)]['status']
self.treeContent.hesse = self.fitter.hesseResult if SimFit else self.fitter.fitResult[
'{}.hesse'.format(self.fitter.name)]['status']
#self.treeContent.minos = self.fitter.minosResult.status() if SimFit else self.fitter.fitResult['{}.minos'.format(self.fitter.name)]['status']
if self.process.cfg['args'].SimFit:
simargs = self.fitter.minimizer.getParameters(
self.fitter.dataWithCategories)
self.treeContent.events16 = self.fitter.data[0].numEntries()
self.treeContent.events17 = self.fitter.data[1].numEntries()
self.treeContent.events18 = self.fitter.data[2].numEntries()
self.treeContent.Wevents16 = self.fitter.data[0].sumEntries()
self.treeContent.Wevents17 = self.fitter.data[1].sumEntries()
self.treeContent.Wevents18 = self.fitter.data[2].sumEntries()
else:
self.treeContent.events = self.fitter.data.numEntries()
self.treeContent.Wevents = self.fitter.data.sumEntries()
self.treeContent.nll = self.fitter._nll
self.otree.Fill()
setupProfiledFCToyStudier = deepcopy(
AbsToyStudier.AbsToyStudier.templateConfig())
setupProfiledFCToyStudier.update({
'name': "profiledFCToyStudier",
'data': "ToyGenerator.mixedToy",
'fitter': fitCollection.finalFitter,
'nSetOfToys':
500, # Typically 100 Toys * 5 submissions for acceptable precision, in proportion to generating time.
})
profiledFCToyStudier = ProfiledFCToyStudier(setupProfiledFCToyStudier)
setupProfiler = deepcopy(fitCollection.setupFinalFitter)
setupProfiler.update({
'FitMinos': [True, ('nSig', 'unboundAfb', 'unboundFl', 'nBkgComb')],
})
profiler = StdFitter.StdFitter(setupProfiler)
profiler.name = "profiler"
# Customize batch task
class BatchTaskWrapper(AbsBatchTaskWrapper.AbsBatchTaskWrapper):
def createJdl(self, parser_args):
jdl = self.createJdlBase()
for BinKey in parser_args.binKey:
jdl += """
JobBatchName = "{JobBatchName}_{binKey}"
arguments = --binKey {binKey} run $(Process)
queue {nJobs}
"""
jdl = jdl.format(
def func_postproc(args):
""" Fit to fit result and make plots """
GenResult = []
RecoResult = []
os.chdir(args.wrapper.task_dir)
args.process.addService(
"dbplayer",
FitDBPlayer(absInputDir=os.path.join(modulePath, "plots_{}".format(
args.Year))))
binKeys = args.process.cfg['allBins'] if args.process.cfg[
'args'].forall else args.process.cfg[
'bins'] #Run over all bins if no binkey arg is supplied
for binKey in binKeys:
ifilename = "setSummary_{}_{}.root".format(
"Simult" if args.SimFit else args.Year, q2bins[binKey]['label'])
if not os.path.exists(ifilename) or args.forceHadd:
call(["hadd", "-f", ifilename] +
glob.glob('*/setSummary_{}_{}.root'.format(
"Simult" if args.SimFit else args.Year, q2bins[binKey]
['label'])))
ifile = ROOT.TFile(ifilename)
tree = ifile.Get("tree")
Cuts = "covQual==3&&status==0&&hesse==0"
binWidth = 0.01
h_setSummary_afb = ROOT.TH1F("h_setSummary_afb", "",
int(1.5 / binWidth), -0.75, 0.75)
h_setSummary_fl = ROOT.TH1F("h_setSummary_fl", "", int(1. / binWidth),
0., 1.)
tree.Draw("afb>>h_setSummary_afb", Cuts)
tree.Draw("fl>>h_setSummary_fl", Cuts)
f_setSummary_afb = ROOT.TF1("f_setSummary_afb", "gaus",
-0.75 + binWidth, 0.75 - binWidth)
h_setSummary_afb.Fit("f_setSummary_afb")
h_setSummary_afb.GetFunction("f_setSummary_afb").SetLineColor(4)
f_setSummary_fl = ROOT.TF1("f_setSummary_fl", "gaus", binWidth,
1 - binWidth)
if binKey == "betweenPeaks":
r = h_setSummary_fl.Fit("f_setSummary_fl", "S", "", 0.39, .8)
else:
r = h_setSummary_fl.Fit("f_setSummary_fl", "S")
h_setSummary_fl.GetFunction("f_setSummary_fl").SetLineColor(4)
# Draw
db = shelve.open(
os.path.join(args.process.dbplayer.absInputDir,
"fitResults_{0}.db".format(q2bins[binKey]['label'])),
'r')
fl_GEN = StdFitter.unboundFlToFl(db['unboundFl_GEN']['getVal'])
afb_GEN = StdFitter.unboundAfbToAfb(db['unboundAfb_GEN']['getVal'],
fl_GEN)
line = ROOT.TLine()
line.SetLineWidth(2)
line.SetLineColor(2)
line.SetLineStyle(9)
plotCollection.Plotter.canvas.cd()
h_setSummary_afb.SetXTitle("A_{6}")
h_setSummary_afb.SetYTitle("Number of test samples")
h_setSummary_afb.SetFillColor(ROOT.kGreen - 10)
h_setSummary_afb.GetYaxis().SetRangeUser(
0., 1.5 * h_setSummary_afb.GetMaximum())
h_setSummary_afb.Draw()
GetParams(f_setSummary_afb, h_setSummary_afb, binKey, afb_GEN, "a6")
if args.drawGEN:
line.DrawLine(afb_GEN, 0, afb_GEN, h_setSummary_afb.GetMaximum())
plotCollection.Plotter.latexDataMarks(['mix'])
plotCollection.Plotter.canvas.Print(
"h_setSummary_mixedToyValidation_a6_{}_{}.pdf".format(
"Simult" if args.SimFit else args.Year,
q2bins[binKey]['label']))
h_setSummary_fl.SetXTitle("F_{L}")
h_setSummary_fl.SetYTitle("Number of test samples")
h_setSummary_fl.SetFillColor(ROOT.kGreen - 10)
h_setSummary_fl.GetYaxis().SetRangeUser(
0., 1.5 * h_setSummary_fl.GetMaximum())
h_setSummary_fl.Draw()
GetParams(f_setSummary_fl, h_setSummary_fl, binKey, fl_GEN, "fl")
if args.drawGEN:
line.DrawLine(fl_GEN, 0, fl_GEN, h_setSummary_fl.GetMaximum())
plotCollection.Plotter.latexDataMarks(['mix'])
plotCollection.Plotter.canvas.Print(
"h_setSummary_mixedToyValidation_fl_{}_{}.pdf".format(
"Simult" if args.SimFit else args.Year,
q2bins[binKey]['label']))
Fl_GENErr = abs(
StdFitter.unboundFlToFl(db['unboundFl_GEN']['getVal'] +
db['unboundFl_GEN']['getError']) - fl_GEN)
A6_GENErr = abs(
StdFitter.unboundAfbToAfb(
db['unboundAfb_GEN']['getVal'] +
db['unboundAfb_GEN']['getError'], fl_GEN) - afb_GEN)
GenResult.append((fl_GEN, Fl_GENErr, afb_GEN, A6_GENErr))
RecoResult.append(
(f_setSummary_fl.GetParameter(1), f_setSummary_fl.GetParameter(2),
f_setSummary_afb.GetParameter(1),
f_setSummary_afb.GetParameter(2)))
if not args.process.cfg['args'].SimFit:
from copy import deepcopy
tree.Draw("nSig", Cuts)
nSigHist = deepcopy(ROOT.gPad.GetPrimitive("htemp"))
tree.Draw("nBkgComb", Cuts)
nBkgCombHist = deepcopy(ROOT.gPad.GetPrimitive("htemp"))
tree.Draw("nBkgPeak", Cuts)
nBkgPeakHist = deepcopy(ROOT.gPad.GetPrimitive("htemp"))
tree.Draw("entries", Cuts)
nTotalHist = deepcopy(ROOT.gPad.GetPrimitive("htemp"))
# Signal Event Distributions
nSigHist.Draw()
nSigHist.SetFillColor(ROOT.kBlue - 10)
#nSigHist.SetLineColor(ROOT.kBlue)
ROOT.TLatex().DrawLatexNDC(
.73, .77,
r"#scale[0.7]{{#color[4]{{Mean}} : {param}}}".format(
param=round(nSigHist.GetMean(), 5)))
ROOT.TLatex().DrawLatexNDC(
.73, .73,
r"#scale[0.7]{{#color[1]{{Samples}} : {param}}}".format(
param=round(nSigHist.GetEntries(), 0)))
ROOT.TLatex().DrawLatexNDC(
.73, .82, r"#scale[0.7]{{#color[2]{{nSig}} : {param}}}".format(
param=round(db['nSig']['getVal'], 0)))
plotCollection.Plotter.canvas.Update()
line.DrawLine(db['nSig']['getVal'], 0, db['nSig']['getVal'],
ROOT.gPad.GetUymax())
ROOT.TLatex().DrawLatexNDC(
.45, .89, r"#scale[0.8]{{{latexLabel}}}".format(
latexLabel=q2bins[binKey]['latexLabel']))
plotCollection.Plotter.canvas.Print("h_nSig_{}_{}.pdf".format(
args.Year, q2bins[binKey]['label']))
#Bkg yield distribution
nBkgCombHist.Draw()
nBkgCombHist.SetFillColor(ROOT.kRed - 10)
#nBkgCombHist.SetLineColor(ROOT.kRed)
ROOT.TLatex().DrawLatexNDC(
.73, .77,
r"#scale[0.7]{{#color[2]{{Mean}} : {param}}}".format(
param=round(nBkgCombHist.GetMean(), 5)))
ROOT.TLatex().DrawLatexNDC(
.73, .73,
r"#scale[0.7]{{#color[1]{{Samples}} : {param}}}".format(
param=round(nBkgCombHist.GetEntries(), 0)))
ROOT.TLatex().DrawLatexNDC(
.73, .82, r"#scale[0.7]{{#color[2]{{nBkg}} : {param}}}".format(
param=round(db['nBkgComb']['getVal'], 0)))
plotCollection.Plotter.canvas.Update()
line.DrawLine(db['nBkgComb']['getVal'], 0,
db['nBkgComb']['getVal'], ROOT.gPad.GetUymax())
ROOT.TLatex().DrawLatexNDC(
.45, .89, r"#scale[0.8]{{{latexLabel}}}".format(
latexLabel=q2bins[binKey]['latexLabel']))
plotCollection.Plotter.canvas.Print("h_nBkgComb_{}_{}.pdf".format(
args.Year, q2bins[binKey]['label']))
#Peaking Bkg yield distribution
nBkgPeakHist.Draw()
nBkgPeakHist.SetFillColor(ROOT.kGreen - 10)
#nBkgPeakHist.SetLineColor(ROOT.kGreen)
ROOT.TLatex().DrawLatexNDC(
.73, .77,
r"#scale[0.7]{{#color[3]{{Mean}} : {param}}}".format(
param=round(nBkgPeakHist.GetMean(), 5)))
ROOT.TLatex().DrawLatexNDC(
.73, .73,
r"#scale[0.7]{{#color[1]{{Samples}} : {param}}}".format(
param=round(nBkgPeakHist.GetEntries(), 0)))
nPeak = db['PeakFrac']['getVal'] * db['nSig']['getVal']
ROOT.TLatex().DrawLatexNDC(
.73, .82,
r"#scale[0.7]{{#color[2]{{nPeak}} : {param}}}".format(
param=round(nPeak, 0)))
plotCollection.Plotter.canvas.Update()
line.DrawLine(nPeak, 0, nPeak, ROOT.gPad.GetUymax())
ROOT.TLatex().DrawLatexNDC(
.45, .89, r"#scale[0.8]{{{latexLabel}}}".format(
latexLabel=q2bins[binKey]['latexLabel']))
plotCollection.Plotter.canvas.Print("h_nBkgPeak_{}_{}.pdf".format(
args.Year, q2bins[binKey]['label']))
# Total yield distributions
nTotalHist.Draw()
nTotalHist.SetFillColor(ROOT.kBlue - 10)
#nTotalHist.SetLineColor(ROOT.kBlue)
ROOT.TLatex().DrawLatexNDC(
.73, .77,
r"#scale[0.7]{{#color[4]{{Mean}} : {param}}}".format(
param=round(nTotalHist.GetMean(), 5)))
ROOT.TLatex().DrawLatexNDC(
.73, .73,
r"#scale[0.7]{{#color[1]{{Samples}} : {param}}}".format(
param=round(nTotalHist.GetEntries(), 0)))
nTotal = db['nBkgComb']['getVal'] + db['nSig']['getVal'] + nPeak
ROOT.TLatex().DrawLatexNDC(
.73, .82,
r"#scale[0.7]{{#color[2]{{nTotal}} : {param}}}".format(
param=round(nTotal, 0)))
plotCollection.Plotter.canvas.Update()
line.DrawLine(nTotal, 0, nTotal, ROOT.gPad.GetUymax())
ROOT.TLatex().DrawLatexNDC(
.45, .89, r"#scale[0.8]{{{latexLabel}}}".format(
latexLabel=q2bins[binKey]['latexLabel']))
plotCollection.Plotter.canvas.Print("h_nTotal_{}_{}.pdf".format(
args.Year, q2bins[binKey]['label']))
Hist2D = ROOT.TH2F("Hist2D", "title", 100, -1, 1, 100, 0., 1.05)
tree.Draw("fl:afb>>Hist2D", Cuts) #+"&&(fl-abs(afb))<=0.95")
Hist2D.Draw("COLZ")
Hist2D.GetXaxis().SetTitle("A_{6}")
Hist2D.GetYaxis().SetTitle("F_{L}")
ROOT.gPad.SetRightMargin(0.115)
#ROOT.TColor.InvertPalette()
#ROOT.TLine().DrawLine(-1, 0, 0, 1); ROOT.TLine().DrawLine(0, 1, 1, 0); ROOT.TLine().DrawLine(-1, 0, 1, 0)
plotCollection.Plotter.canvas.Print("h2_{}_{}.pdf".format(
"Simult" if args.SimFit else args.Year, q2bins[binKey]['label']))
db.close()
if args.process.cfg['args'].forall:
GetSummaryGraph(binKeys,
RecoResult,
GenResult,
args,
pltName="mixedToy")
def _postRunFitSteps(self, setIdx):
SimFit = self.process.cfg['args'].SimFit
if not SimFit: self.fitter._nll = self.fitter._nll.getVal()
if math.fabs(self.fitter._nll) < 1e20:
unboundAfb = self.fitter.minimizer.getParameters(
self.fitter.dataWithCategories).find('unboundAfb').getVal(
) if SimFit else self.fitter.args.find('unboundAfb').getVal()
unboundFl = self.fitter.minimizer.getParameters(
self.fitter.dataWithCategories).find('unboundFl').getVal(
) if SimFit else self.fitter.args.find('unboundFl').getVal()
self.treeContent.fl = StdFitter.unboundFlToFl(unboundFl)
self.treeContent.afb = StdFitter.unboundAfbToAfb(
unboundAfb, self.treeContent.fl)
self.treeContent.index = setIdx
self.treeContent.status = self.fitter.fitter.fitResult[
'{}.StdFitter'.format(
self.fitter.name
)]['MIGRAD'] if SimFit else self.fitter.fitResult[
'{}.StdFitter'.format(self.fitter.name)]['MIGRAD']
self.treeContent.hesse = self.fitter.fitter.fitResult[
'{}.StdFitter'.format(
self.fitter.name
)]['HESSE'] if SimFit else self.fitter.fitResult[
'{}.StdFitter'.format(self.fitter.name)]['HESSE']
self.treeContent.covQual = self.fitter.fitter.fitResult[
'{}.StdFitter'.format(
self.fitter.name
)]['covQual'] if SimFit else self.fitter.fitResult[
'{}.StdFitter'.format(self.fitter.name)]['covQual']
if SimFit:
simargs = self.fitter.minimizer.getParameters(
self.fitter.dataWithCategories)
self.treeContent.events16 = self.fitter.data[0].numEntries()
self.treeContent.events17 = self.fitter.data[1].numEntries()
self.treeContent.events18 = self.fitter.data[2].numEntries()
self.treeContent.Wevents16 = self.fitter.data[0].sumEntries()
self.treeContent.Wevents17 = self.fitter.data[1].sumEntries()
self.treeContent.Wevents18 = self.fitter.data[2].sumEntries()
self.treeContent.nSig16 = simargs.find('nSig_2016').getVal()
self.treeContent.nSig17 = simargs.find('nSig_2017').getVal()
self.treeContent.nSig18 = simargs.find('nSig_2018').getVal()
self.treeContent.nBkgComb16 = simargs.find(
'nBkgComb_2016').getVal()
self.treeContent.nBkgComb17 = simargs.find(
'nBkgComb_2017').getVal()
self.treeContent.nBkgComb18 = simargs.find(
'nBkgComb_2018').getVal()
self.treeContent.nBkgPeak16 = self.fitter.pdf[0].servers(
).findByName("nBkgPeak").getVal()
self.treeContent.nBkgPeak17 = self.fitter.pdf[1].servers(
).findByName("nBkgPeak").getVal()
self.treeContent.nBkgPeak18 = self.fitter.pdf[2].servers(
).findByName("nBkgPeak").getVal()
else:
self.treeContent.entries = self.fitter.data.sumEntries()
self.treeContent.sigEntries = self.sigEntries
self.treeContent.nSig = self.fitter.args.find('nSig').getVal()
self.treeContent.nBkgComb = self.fitter.args.find(
'nBkgComb').getVal()
self.treeContent.nBkgPeak = self.fitter.pdf.servers(
).findByName("nBkgPeak").getVal()
self.treeContent.nll = self.fitter._nll
self.otree.Fill()
if not SimFit:
if (self.treeContent.fl > 0.97
and abs(self.treeContent.afb) < 0.05
and self.plotflag):
self._postRunFitPlotter(setIdx)
print("Status: ", self.treeContent.status, self.treeContent.hesse,
self.treeContent.covQual)
def func_postproc(args):
""" Fit to fit result and make plots """
GenResult = []
RecoResult = []
os.chdir(args.wrapper.task_dir)
args.process.addService(
"dbplayer",
FitDBPlayer(absInputDir=os.path.join(modulePath, "plots_{}".format(
args.Year))))
binKeys = args.process.cfg['allBins'] if args.process.cfg[
'args'].forall else args.process.cfg[
'bins'] #Run over all bins if no binkey arg is supplied
for binKey in binKeys:
ifilename = "setSummary_{}_{}.root".format(
"Simult" if args.SimFit else args.Year, q2bins[binKey]['label'])
if not os.path.exists(ifilename) or args.forceHadd:
call(["hadd", "-f", ifilename] +
glob.glob('*/setSummary_{}_{}.root'.format(
"Simult" if args.SimFit else args.Year, q2bins[binKey]
['label'])))
ifile = ROOT.TFile(ifilename)
tree = ifile.Get("tree")
binWidth = 0.01
h_setSummary_afb = ROOT.TH1F("h_setSummary_afb", "",
int(1.5 / binWidth), -0.75, 0.75)
h_setSummary_fl = ROOT.TH1F("h_setSummary_fl", "", int(1. / binWidth),
0., 1.)
tree.Draw("afb>>h_setSummary_afb", "status==0")
tree.Draw("fl>>h_setSummary_fl", "status==0")
f_setSummary_afb = ROOT.TF1("f_setSummary_afb", "gaus",
-0.75 + binWidth, 0.75 - binWidth)
#f_setSummary_afb.SetLineColor(ROOT.kBlue)
h_setSummary_afb.Fit("f_setSummary_afb")
h_setSummary_afb.GetFunction("f_setSummary_afb").SetLineColor(4)
f_setSummary_fl = ROOT.TF1("f_setSummary_fl", "gaus", binWidth,
1 - binWidth)
#f_setSummary_fl.SetLineColor(ROOT.kBlue)
h_setSummary_fl.Fit("f_setSummary_fl")
h_setSummary_fl.GetFunction("f_setSummary_fl").SetLineColor(4)
# Draw
db = shelve.open(
os.path.join(args.process.dbplayer.absInputDir,
"fitResults_{0}.db".format(q2bins[binKey]['label'])),
'r')
fl_GEN = StdFitter.unboundFlToFl(db['unboundFl_GEN']['getVal'])
afb_GEN = StdFitter.unboundAfbToAfb(db['unboundAfb_GEN']['getVal'],
fl_GEN)
line = ROOT.TLine()
line.SetLineWidth(2)
line.SetLineColor(2)
line.SetLineStyle(9)
plotCollection.Plotter.canvas.cd()
h_setSummary_afb.SetXTitle("A_{6}")
h_setSummary_afb.SetYTitle("Number of test samples")
h_setSummary_afb.SetFillColor(ROOT.kGreen - 10)
h_setSummary_afb.GetYaxis().SetRangeUser(
0., 1.5 * h_setSummary_afb.GetMaximum())
h_setSummary_afb.Draw()
GetParams(f_setSummary_afb, h_setSummary_afb, binKey, afb_GEN, "a6")
if args.drawGEN:
line.DrawLine(afb_GEN, 0, afb_GEN, h_setSummary_afb.GetMaximum())
plotCollection.Plotter.latexDataMarks(['sim'])
plotCollection.Plotter.canvas.Print(
"h_setSummary_sigMCValidation_a6_{}_{}.pdf".format(
"Simult" if args.SimFit else args.Year,
q2bins[binKey]['label']))
h_setSummary_fl.SetXTitle("F_{L}")
h_setSummary_fl.SetYTitle("Number of test samples")
h_setSummary_fl.SetFillColor(ROOT.kGreen - 10)
h_setSummary_fl.GetYaxis().SetRangeUser(
0., 1.5 * h_setSummary_fl.GetMaximum())
h_setSummary_fl.Draw()
GetParams(f_setSummary_fl, h_setSummary_fl, binKey, fl_GEN, "fl")
if args.drawGEN:
line.DrawLine(fl_GEN, 0, fl_GEN, h_setSummary_fl.GetMaximum())
plotCollection.Plotter.latexDataMarks(['sim'])
plotCollection.Plotter.canvas.Print(
"h_setSummary_sigMCValidation_fl_{}_{}.pdf".format(
"Simult" if args.SimFit else args.Year,
q2bins[binKey]['label']))
Fl_GENErr = abs(
StdFitter.unboundFlToFl(db['unboundFl_GEN']['getVal'] +
db['unboundFl_GEN']['getError']) - fl_GEN)
A6_GENErr = abs(
StdFitter.unboundAfbToAfb(
db['unboundAfb_GEN']['getVal'] +
db['unboundAfb_GEN']['getError'], fl_GEN) - afb_GEN)
GenResult.append((fl_GEN, Fl_GENErr, afb_GEN, A6_GENErr))
RecoResult.append(
(f_setSummary_fl.GetParameter(1), f_setSummary_fl.GetParameter(2),
f_setSummary_afb.GetParameter(1),
f_setSummary_afb.GetParameter(2)))
db.close()
if args.process.cfg['args'].forall:
from BsToPhiMuMuFitter.script.batchTask_mixedToyValidation import GetSummaryGraph as SummaryGraph
SummaryGraph(binKeys,
RecoResult,
GenResult,
args,
pltName="sigMCSubsample")