def __init__(self, box, tag="Razor2016_MoriondRereco",
weights=None, isData=True, configFile="config/run2_2016.config",
full=False):
self.sideband = None
self.weights = weights
self.isData = isData
if full:
self.fitRegion = 'Full'
else:
self.fitRegion = 'LowMR,LowRsq'
self.analysis = Analysis(box, tag=tag)
self.filename = self.getFilename()
self.dirname = os.path.dirname(self.filename)
self.toysFile = self.getToysFilename(noStat=False, doFreq=False)
self.sysFile = self.getToysFilename(noStat=True, doFreq=False)
self.freqToysFile = self.getToysFilename(noStat=False, doFreq=True)
self.freqSysFile = self.getToysFilename(noStat=True, doFreq=True)
if not os.path.isdir(self.dirname):
os.makedirs(self.dirname)
self.config = Config.Config(configFile)
self.x = array('d', self.config.getBinning(box)[0]) # MR binning
self.y = array('d', self.config.getBinning(box)[1]) # Rsq binning
self.z = array('d', self.config.getBinning(box)[2]) # nBtag binning
self.workspace = rt.RooWorkspace("w"+box)
initializeWorkspace(self.workspace, self.config, box)
setStyle()
def __init__(self,
box,
tag="Razor2016_MoriondRereco",
weights=None,
isData=True,
configFile="config/run2_2016.config",
full=False):
self.sideband = None
self.weights = weights
self.isData = isData
if full:
self.fitRegion = 'Full'
else:
self.fitRegion = 'LowMR,LowRsq'
self.analysis = Analysis(box, tag=tag)
self.filename = self.getFilename()
self.dirname = os.path.dirname(self.filename)
self.toysFile = self.getToysFilename(noStat=False, doFreq=False)
self.sysFile = self.getToysFilename(noStat=True, doFreq=False)
self.freqToysFile = self.getToysFilename(noStat=False, doFreq=True)
self.freqSysFile = self.getToysFilename(noStat=True, doFreq=True)
if not os.path.isdir(self.dirname):
os.makedirs(self.dirname)
self.config = Config.Config(configFile)
self.x = array('d', self.config.getBinning(box)[0]) # MR binning
self.y = array('d', self.config.getBinning(box)[1]) # Rsq binning
self.z = array('d', self.config.getBinning(box)[2]) # nBtag binning
self.workspace = rt.RooWorkspace("w" + box)
initializeWorkspace(self.workspace, self.config, box)
setStyle()
nBins = (len(x) - 1) * (len(y) - 1) * (len(z) - 1)
ixMin = 3
iyMin = 3
if box in ['MuMultiJet', 'EleMultiJet']:
if x[2] == 500:
ixMin = 3
else:
ixMin = 2
iyMin = 3
binSumDict = getBinSumDicts("z", ixMin,
len(x) - 1, iyMin,
len(y) - 1, 0,
len(z) - 1, x, y, z)
setStyle()
if options.bayesToyFile is not None:
bayesFiles = options.bayesToyFile.split(',')
bayesTree = rt.TChain("myTree")
for bayesFile in bayesFiles:
bayesTree.Add(bayesFile)
bayesFileOpen = rt.TFile.Open(bayesFile)
d = rt.TCanvas('d', 'd', 500, 400)
d.SetLeftMargin(0.15)
d.SetRightMargin(0.1)
d.SetTopMargin(0.12)
d.SetBottomMargin(0.15)
bestFitTot = 0
nObsTot = 0
def runToys(w, options, cfg, seed):
setStyle()
rt.RooRandom.randomGenerator().SetSeed(seed)
extRazorPdf = w.pdf('extRazorPdf')
dataHist = w.data("data_obs")
if w.obj("fitresult_extRazorPdf_data_obs") != None:
fr = w.obj("fitresult_extRazorPdf_data_obs")
elif w.obj("nll_extRazorPdf_data_obs") != None:
fr = w.obj("nll_extRazorPdf_data_obs")
elif w.obj("fitresult_extRazorPdf_data_obs_with_constr") != None:
fr = w.obj("fitresult_extRazorPdf_data_obs_with_constr")
elif w.obj("nll_extRazorPdf_data_obs_with_constr") != None:
fr = w.obj("nll_extRazorPdf_data_obs_with_constr")
fr.Print("V")
if options.r > -1:
extSpBPdf = w.pdf('extSpBPdf')
#nll = w.function('nll_extRazorPdf_data_obs')
th1x = w.var("th1x")
params = extRazorPdf.getParameters(dataHist)
paramsToRemove = []
for p in rootTools.RootIterator.RootIterator(params):
if p.isConstant(): paramsToRemove.append(p)
[params.remove(p) for p in paramsToRemove]
paramNames = [
p.GetName() for p in rootTools.RootIterator.RootIterator(params)
]
paramNames.sort()
if options.r > -1: paramNames.append('r')
x = array('d', cfg.getBinning(options.box)[0]) # MR binning
y = array('d', cfg.getBinning(options.box)[1]) # Rsq binning
z = array('d', cfg.getBinning(options.box)[2]) # nBtag binning
nBins = (len(x) - 1) * (len(y) - 1) * (len(z) - 1)
th1x.setBins(nBins)
if seed > -1:
output = rt.TFile.Open(
options.outDir + '/genfittoys_Freq_s%i_%s.root' %
(seed, options.box), 'recreate')
else:
output = rt.TFile.Open(
options.outDir + '/genfittoys_Freq_%s.root' % (options.box),
'recreate')
output.cd()
myTree = rt.TTree("myTree", "myTree")
s1 = getTree(myTree, paramNames, nBins, options.box, z)
pSet = fr.floatParsFinal()
for p in rootTools.RootIterator.RootIterator(pSet):
w.var(p.GetName()).setVal(p.getVal())
w.var(p.GetName()).setError(p.getError())
asimov = extRazorPdf.generateBinned(rt.RooArgSet(th1x),
rt.RooFit.Name('true'),
rt.RooFit.Asimov())
value = setattr(
s1, 'toy_num',
-1) #save the toy number (for differentiating from true pdf)
iBinX = -1
for i in range(1, len(x)):
for j in range(1, len(y)):
for k in range(1, len(z)):
iBinX += 1
th1x.setVal(iBinX + 0.5)
expected = extRazorPdf.getValV(
rt.RooArgSet(th1x)) * extRazorPdf.expectedEvents(
rt.RooArgSet(th1x))
toy = float(asimov.weight(rt.RooArgSet(th1x)))
value = setattr(s1, 'b%i_ff' % iBinX,
expected) #save predicted fit yield
value = setattr(s1, 'b%i_sf' % iBinX,
expected) #save predicted fit yield
value = setattr(s1, 'b%i_toy' % iBinX,
toy) #save toy yield
myTree.Fill()
iToy = 0
widgets = [
'Running Freq toys ',
Percentage(), ' ',
Bar(marker=RotatingMarker()), ' ',
ETA(), ' ',
FileTransferSpeed()
]
pbar = ProgressBar(widgets=widgets, max_value=options.nToys).start()
while iToy < options.nToys:
pSet = fr.floatParsFinal()
for p in rootTools.RootIterator.RootIterator(pSet):
w.var(p.GetName()).setVal(p.getVal())
w.var(p.GetName()).setError(p.getError())
if 'Ntot' in p.GetName():
w.var(p.GetName()).setVal(options.scaleFactor * p.getVal())
#print "%s = %f +- %f"%(p.GetName(),p.getVal(),p.getError())
#print "good pars"
errorCountBefore = rt.RooMsgService.instance().errorCount()
asimov = extRazorPdf.generateBinned(rt.RooArgSet(th1x),
rt.RooFit.Name('toy'))
errorCountAfter = rt.RooMsgService.instance().errorCount()
if errorCountAfter > errorCountBefore:
#print "can't generate toy=%i"%iToy
continue
#print "SUCCESS: generated toy=%i"%iToy
pSetSave = pSet
migrad_status_sf = -1
hesse_status_sf = -1
minos_status_sf = -1
migrad_status_ff = -1
hesse_status_ff = -1
minos_status_ff = -1
sideband = convertSideband('LowMR,LowRsq', w, x, y, z)
nll_func_toy_ff = extRazorPdf.createNLL(asimov,
rt.RooFit.Extended(True))
nll_func_toy_sf = extRazorPdf.createNLL(asimov,
rt.RooFit.Extended(True),
rt.RooFit.Range(sideband))
m = rt.RooMinimizer(nll_func_toy_ff)
m.setStrategy(0)
m.setPrintLevel(-1)
m.setPrintEvalErrors(-1)
migrad_status_ff = m.minimize('Minuit2', 'migrad')
#hesse_status_ff = m.minimize('Minuit2','hesse')
fr_ff = m.save()
covQual_ff = fr_ff.covQual()
value = setattr(
s1, 'toy_num',
iToy) #save the toy number (for differentiating from true pdf)
value = setattr(s1, 'migrad_ff',
migrad_status_ff) #save migrad status full fit
value = setattr(s1, 'hesse_ff',
hesse_status_ff) #save hesse status full fit
value = setattr(s1, 'covQual_ff', covQual_ff) #save cov qual full fit
iBinX = -1
for i in range(1, len(x)):
for j in range(1, len(y)):
for k in range(1, len(z)):
iBinX += 1
th1x.setVal(iBinX + 0.5)
expected = extRazorPdf.getValV(
rt.RooArgSet(th1x)) * extRazorPdf.expectedEvents(
rt.RooArgSet(th1x))
toy = float(asimov.weight(rt.RooArgSet(th1x)))
value = setattr(s1, 'b%i_ff' % iBinX,
expected) #save predicted full fit yield
value = setattr(s1, 'b%i_toy' % iBinX,
toy) #save toy yield
# save full fit parameters
for p in rootTools.RootIterator.RootIterator(fr_ff.floatParsFinal()):
value = setattr(s1, p.GetName() + "_ff", p.getVal())
value = setattr(s1, p.GetName() + "_ff_error", p.getError())
m = rt.RooMinimizer(nll_func_toy_sf)
m.setStrategy(0)
m.setPrintLevel(-1)
m.setPrintEvalErrors(-1)
migrad_status_sf = m.minimize('Minuit2', 'migrad')
#hesse_status_sf = m.minimize('Minuit2','hesse')
fr_sf = m.save()
covQual_sf = fr_sf.covQual()
value = setattr(s1, 'migrad_sf',
migrad_status_sf) #save migrad status sideband fit
value = setattr(s1, 'hesse_sf',
hesse_status_sf) #save hesse status sideband fit
value = setattr(s1, 'covQual_sf',
covQual_sf) #save cov qual sideband fit
iBinX = -1
for i in range(1, len(x)):
for j in range(1, len(y)):
for k in range(1, len(z)):
iBinX += 1
th1x.setVal(iBinX + 0.5)
expected = extRazorPdf.getValV(
rt.RooArgSet(th1x)) * extRazorPdf.expectedEvents(
rt.RooArgSet(th1x))
value = setattr(
s1, 'b%i_sf' % iBinX,
expected) #save predicted sideband fit yield
# save sideband fit parameters
for p in rootTools.RootIterator.RootIterator(fr_sf.floatParsFinal()):
value = setattr(s1, p.GetName() + "_sf", p.getVal())
value = setattr(s1, p.GetName() + "_sf_error", p.getError())
pbar.update(iToy)
myTree.Fill()
iToy += 1
rt.RooMsgService.instance().reset()
pbar.finish()
w.Print('v')
output.cd()
myTree.Write()
w.Write()
output.Close()
return output.GetName()
#gendata = extRazorPdf.generateBinned(rt.RooArgSet(th1x),rt.RooFit.Name('forsignalinj'),rt.RooFit.Asimov()) #weighted approach
#rt.RooRandom.randomGenerator().SetSeed(1989) # for r = 1, new
rt.RooRandom.randomGenerator().SetSeed(1988) # for r = 1, new
#gendata = extRazorPdf.generateBinned(rt.RooArgSet(th1x),rt.RooFit.Name('forsignalinj')) # real unweighted dataset
gendata = w.pdf('extSpBPdf').generateBinned(rt.RooArgSet(th1x),rt.RooFit.Name('forsignalinj')) # real unweighted dataset
myTH1 = gendata.createHistogram('gendata',th1x)
#Npois = rt.RooRandom.randomGenerator().Poisson(options.r*sigTH1.Integral())
#myTH1.Add(sigTH1,options.r) # "weighted approach"
#myTH1.FillRandom(sigTH1,Npois) # "unweighted approach" - generating a real dataset
dataHist = rt.RooDataHist("data_obs","data_obs",rt.RooArgList(th1x), rt.RooFit.Import(myTH1))
dataHist.Print('v')
rootTools.Utils.importToWS(w,dataHist)
setStyle()
if noFit and options.inputFitFile is not None:
fr = frIn
fr.Print('v')
rootTools.Utils.importToWS(w,fr)
elif noFit:
fr = rt.RooFitResult()
else:
fr = binnedFit(extRazorPdf,dataHist,sideband,options.useWeight)
total = extRazorPdf.expectedEvents(rt.RooArgSet(th1x))
nll = 0
iBinX = -1
observed_counts = []
for i in range(1,len(x)):
for j in range(1,len(y)):