def fit(self, inputFitFile=None):
"""Fits the razor pdf to the data"""
if inputFitFile is None:
self.setDefaultFitParams()
else:
self.loadFitParamsFromFile(inputFitFile)
extRazorPdf = self.workspace.pdf('extRazorPdf')
datahist = self.workspace.data('data_obs')
self.sideband = convertSideband(self.fitRegion, self.workspace,
self.x, self.y, self.z)
result = binnedFit(extRazorPdf, datahist, self.sideband,
box=self.analysis.region, w=self.workspace)
result.Print('v')
self.addToWorkspace(result, tobject=True)
def fit(self, inputFitFile=None):
"""Fits the razor pdf to the data"""
if inputFitFile is None:
self.setDefaultFitParams()
else:
self.loadFitParamsFromFile(inputFitFile)
extRazorPdf = self.workspace.pdf('extRazorPdf')
datahist = self.workspace.data('data_obs')
self.sideband = convertSideband(self.fitRegion, self.workspace, self.x,
self.y, self.z)
result = binnedFit(extRazorPdf,
datahist,
self.sideband,
box=self.analysis.region,
w=self.workspace)
result.Print('v')
self.addToWorkspace(result, tobject=True)
y = array('d', cfg.getBinning(box)[1]) # Rsq binning
z = array('d', cfg.getBinning(box)[2]) # nBtag binning
nBins = (len(x) - 1) * (len(y) - 1) * (len(z) - 1)
xFine = array('d',
[x[0] + i * (x[-1] - x[0]) / 100.
for i in range(0, 101)]) # MR binning fine
yFine = array('d',
[y[0] + i * (y[-1] - y[0]) / 100.
for i in range(0, 101)]) # Rsq binning fine
zFine = array('d', cfg.getBinning(box)[2]) # nBtag binning fine
nBinsFine = (len(xFine) - 1) * (len(yFine) - 1) * (len(zFine) - 1)
th1x = w.var('th1x')
sideband = convertSideband(fitRegion, w, x, y, z)
plotband = convertSideband(plotRegion, w, x, y, z)
myTH1 = convertDataset2TH1(data, cfg, box, w, options.useWeight)
signalDs = None
doSignalInj = (options.signalFileName != "None") and (options.r > -1)
if doSignalInj:
sigRootFile = rt.TFile(options.signalFileName)
#sigWorkspace = sigRootFile.Get('w'+box)
#signalDs = sigWorkspace.data('RMRTree')
model = options.signalFileName.split('.root')[0].split('-')[1].split(
'_')[0]
massPoint = '_'.join(
options.signalFileName.split('.root')[0].split('_')[1:3])
#sigTH1 = convertDataset2TH1(signalDs, cfg, box, w,"signal")
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()
def plot(self, filename=None, unblind=False, toysFile=None, sysFile=None):
"""Plots the fit results"""
options = self.getPlottingOptions()
if unblind:
plotRegion = 'Full'
else:
plotRegion = self.fitRegion
if filename is None:
filename = self.filename.replace('.root', '_Plots.root')
f = rt.TFile(filename, 'UPDATE')
toyTree = self.getToyTree(toysFile)
sysTree = self.getToyTree(sysFile)
if toyTree is not None and sysTree is not None:
dirName = "WithToys"
computeErrors = True
else:
dirName = "BeforeToys"
computeErrors = False
tdirectory = f.GetDirectory(dirName)
if tdirectory == None:
f.mkdir(dirName)
tdirectory = f.GetDirectory(dirName)
c = rt.TCanvas('c', 'c', 500, 400)
rt.SetOwnership(c, False)
rt.TH1D.SetDefaultSumw2()
rt.TH2D.SetDefaultSumw2()
rt.TH3D.SetDefaultSumw2()
plotband = convertSideband(plotRegion, self.workspace, self.x, self.y,
self.z)
# Get the 1D and 2D histograms for each b-tag bin
h_data_nBtagRsqMR, h_nBtagRsqMR = self.get3DFitHistos(plotband)
h_data_MR, h_data_Rsq, h_data_RsqMR = make3DHistProjections(
h_data_nBtagRsqMR)
h_MR, h_Rsq, h_RsqMR = make3DHistProjections(h_nBtagRsqMR)
if computeErrors:
h_MR = getErrors1D(h_MR, h_data_MR, sysTree, options, "x", 0,
len(self.x) - 1, 0,
len(self.y) - 1, 0,
len(self.z) - 1, self.x, self.y, self.z)
h_Rsq = getErrors1D(h_Rsq, h_data_Rsq, sysTree, options, "y", 0,
len(self.x) - 1, 0,
len(self.y) - 1, 0,
len(self.z) - 1, self.x, self.y, self.z)
h_RsqMR = getErrors2D(h_RsqMR, h_data_RsqMR, sysTree, options,
"yx", 0,
len(self.x) - 1, 0,
len(self.y) - 1, 0,
len(self.z) - 1, self.x, self.y, self.z)
h_nBtagRsqMR = getErrors3D(h_nBtagRsqMR, h_data_nBtagRsqMR,
sysTree, options, "zyx", 0,
len(self.x) - 1, 0,
len(self.y) - 1, 0,
len(self.z) - 1, self.x, self.y, self.z)
for h in [
h_data_MR, h_data_Rsq, h_data_RsqMR, h_data_nBtagRsqMR, h_MR,
h_Rsq, h_RsqMR, h_nBtagRsqMR
]:
tdirectory.cd()
h.Write()
if len(self.z) > 1:
h_MR_components = []
h_Rsq_components = []
h_RsqMR_components = []
h_data_RsqMR_components = []
h_sig_RsqMR_components = []
h_th1x_components = []
h_data_th1x_components = []
h_sig_th1x_components = []
h_labels = []
h_colors = [
rt.kOrange, rt.kViolet, rt.kRed, rt.kGreen, rt.kGray + 2
]
for k in range(1, len(self.z)):
h_MR_components.append(
h_nBtagRsqMR.ProjectionX("MR_%ibtag" % self.z[k - 1], 0,
-1, k, k, ""))
h_Rsq_components.append(
h_nBtagRsqMR.ProjectionY("Rsq_%ibtag" % self.z[k - 1], 0,
-1, k, k, ""))
h_nBtagRsqMR.GetZaxis().SetRange(k, k)
h_RsqMR_components.append(
h_nBtagRsqMR.Project3D("%ibtag_yx" % self.z[k - 1]))
h_data_nBtagRsqMR.GetZaxis().SetRange(k, k)
h_data_RsqMR_components.append(
h_data_nBtagRsqMR.Project3D("%ibtag_yx" % self.z[k - 1]))
if computeErrors:
h_RsqMR_components[-1] = getErrors2D(
h_RsqMR_components[-1], h_data_RsqMR_components[-1],
sysTree, options, "yx", 0,
len(self.x) - 1, 0,
len(self.y) - 1, k, k, self.x, self.y, self.z)
h_th1x_components.append(
get1DHistoFrom2D(h_RsqMR_components[-1], self.x, self.y,
'h_th1x_%ibtag' % (self.z[k - 1])))
h_data_th1x_components.append(
get1DHistoFrom2D(h_data_RsqMR_components[-1], self.x,
self.y,
'h_th1x_data_%ibtag' % (self.z[k - 1])))
if self.z[k - 1] == 3 and self.z[-1] == 4:
h_labels.append("#geq %i b-tag" % self.z[k - 1])
elif self.z[k - 1] == 1 and self.z[-1] == 4 and len(
self.z) == 2:
h_labels.append("#geq %i b-tag" % self.z[k - 1])
else:
h_labels.append("%i b-tag" % self.z[k - 1])
# Create the nsigma histograms
h_RsqMR_statnsigma_components = []
h_RsqMR_nsigma_components = []
if len(self.z) > 1:
for k in range(1, len(self.z)):
h_RsqMR_statnsigma_components.append(
getStatNSigmaHist(
h_RsqMR_components[k - 1],
h_data_RsqMR_components[k - 1],
"h_RsqMR_statnsigma_%ibtag" % self.z[k - 1]))
h_RsqMR_nsigma_btag = h_RsqMR.Clone("h_RsqMR_nsigma_%ibtag" %
self.z[k - 1])
if computeErrors:
h_RsqMR_nsigma_btag = getNsigma2D(
h_RsqMR_nsigma_btag, h_data_RsqMR_components[k - 1],
toyTree, options, "yx", 0,
len(self.x) - 1, 0,
len(self.y) - 1, k, k, self.x, self.y, self.z)
self.blindSideband(h_RsqMR_nsigma_btag, plotRegion)
h_RsqMR_nsigma_components.append(h_RsqMR_nsigma_btag)
# Print everything out to pdf files
btagLabel = getBtagLabel(self.z)
lumiLabel = "%.1f fb^{-1} (13 TeV)" % (self.analysis.lumi / 1000.)
boxLabel = "razor %s %s %s Fit" % (self.analysis.region, btagLabel,
self.fitRegion.replace(
'LowMR,LowRsq', 'Sideband'))
plotLabel = ""
eventsLabel = "Events"
sidebandFit = self.getSidebandMax()
for h in h_RsqMR_components:
tdirectory.cd()
h.Write()
print1DProj(c, tdirectory, h_MR, h_data_MR,
self.dirname + "/h_MR_%s.pdf" % self.analysis.region,
"M_{R} [GeV]", eventsLabel, lumiLabel, boxLabel, plotLabel,
self.isData, False, None, None, h_MR_components, h_colors,
h_labels)
print1DProj(c, tdirectory, h_Rsq, h_data_Rsq,
self.dirname + "/h_Rsq_%s.pdf" % self.analysis.region,
"R^{2}", eventsLabel, lumiLabel, boxLabel, plotLabel,
self.isData, False, None, None, h_Rsq_components, h_colors,
h_labels)
#if len(self.z)>2:
if len(self.z) > 1:
for k in range(0, len(self.z) - 1):
newBoxLabel = "razor %s %s %s Fit" % (
self.analysis.region, h_labels[k],
self.fitRegion.replace('LowMR,LowRsq', 'Sideband'))
if computeErrors:
print1DProjNs(c,
tdirectory,
h_th1x_components[k],
h_data_th1x_components[k],
h_RsqMR_nsigma_components[k],
self.dirname + "/h_th1x_ns_%ibtag_%s.pdf" %
(self.z[k], self.analysis.region),
"Bin Number",
eventsLabel,
lumiLabel,
newBoxLabel,
plotLabel,
self.isData,
False,
options,
cfg=self.config)
print2DResiduals(
c, tdirectory, h_RsqMR_nsigma_components[k],
self.dirname + "/h_RsqMR_nsigma_log_%ibtag_%s.pdf" %
(self.z[k], self.analysis.region), "M_{R} [GeV]",
"R^{2}", "Stat.+Sys. n#sigma", lumiLabel, newBoxLabel,
plotLabel, self.x, self.y, self.isData, sidebandFit,
False, options)
else:
print1DProj(
c, tdirectory, h_th1x_components[k],
h_data_th1x_components[k],
self.dirname + "/h_th1x_%ibtag_%s.pdf" %
(self.z[k], self.analysis.region), "Bin Number",
eventsLabel, lumiLabel, newBoxLabel, plotLabel,
self.isData, False, options)
print2DResiduals(
c, tdirectory, h_RsqMR_statnsigma_components[k],
self.dirname +
"/h_RsqMR_statnsigma_log_%ibtag_%s.pdf" %
(self.z[k], self.analysis.region), "M_{R} [GeV]",
"R^{2}", "Stat. n#sigma (Data - Fit)/sqrt(Fit)",
lumiLabel, newBoxLabel, plotLabel, self.x, self.y,
self.isData, sidebandFit, False, options)
f.Close()
def plot(self, filename=None, unblind=False,
toysFile=None, sysFile=None):
"""Plots the fit results"""
options = self.getPlottingOptions()
if unblind:
plotRegion = 'Full'
else:
plotRegion = self.fitRegion
if filename is None:
filename = self.filename.replace('.root','_Plots.root')
f = rt.TFile(filename, 'UPDATE')
toyTree = self.getToyTree(toysFile)
sysTree = self.getToyTree(sysFile)
if toyTree is not None and sysTree is not None:
dirName = "WithToys"
computeErrors = True
else:
dirName = "BeforeToys"
computeErrors = False
tdirectory = f.GetDirectory(dirName)
if tdirectory==None:
f.mkdir(dirName)
tdirectory = f.GetDirectory(dirName)
c = rt.TCanvas('c','c',500,400)
rt.SetOwnership(c, False)
rt.TH1D.SetDefaultSumw2()
rt.TH2D.SetDefaultSumw2()
rt.TH3D.SetDefaultSumw2()
plotband = convertSideband(plotRegion, self.workspace,
self.x, self.y, self.z)
# Get the 1D and 2D histograms for each b-tag bin
h_data_nBtagRsqMR, h_nBtagRsqMR = self.get3DFitHistos(plotband)
h_data_MR,h_data_Rsq,h_data_RsqMR = make3DHistProjections(
h_data_nBtagRsqMR)
h_MR,h_Rsq,h_RsqMR = make3DHistProjections(h_nBtagRsqMR)
if computeErrors:
h_MR = getErrors1D(h_MR,h_data_MR,sysTree,options,"x",0,
len(self.x)-1,0,len(self.y)-1,0,len(self.z)-1,
self.x,self.y,self.z)
h_Rsq = getErrors1D(h_Rsq,h_data_Rsq,sysTree,options,"y",0,
len(self.x)-1,0,len(self.y)-1,0,len(self.z)-1,
self.x,self.y,self.z)
h_RsqMR = getErrors2D(h_RsqMR,h_data_RsqMR,sysTree,options,"yx",0,
len(self.x)-1,0,len(self.y)-1,0,len(self.z)-1,
self.x,self.y,self.z)
h_nBtagRsqMR = getErrors3D(h_nBtagRsqMR,h_data_nBtagRsqMR,sysTree,
options,"zyx",0,len(self.x)-1,0,len(self.y)-1,0,
len(self.z)-1,self.x,self.y,self.z)
for h in [h_data_MR,h_data_Rsq,h_data_RsqMR,h_data_nBtagRsqMR,
h_MR,h_Rsq,h_RsqMR,h_nBtagRsqMR]:
tdirectory.cd()
h.Write()
if len(self.z)>1:
h_MR_components = []
h_Rsq_components = []
h_RsqMR_components = []
h_data_RsqMR_components = []
h_sig_RsqMR_components = []
h_th1x_components = []
h_data_th1x_components = []
h_sig_th1x_components = []
h_labels = []
h_colors = [rt.kOrange,rt.kViolet,rt.kRed,rt.kGreen,rt.kGray+2]
for k in range(1,len(self.z)):
h_MR_components.append(h_nBtagRsqMR.ProjectionX(
"MR_%ibtag"%self.z[k-1],0,-1,k,k,""))
h_Rsq_components.append(h_nBtagRsqMR.ProjectionY(
"Rsq_%ibtag"%self.z[k-1],0,-1,k,k,""))
h_nBtagRsqMR.GetZaxis().SetRange(k,k)
h_RsqMR_components.append(h_nBtagRsqMR.Project3D(
"%ibtag_yx"%self.z[k-1]))
h_data_nBtagRsqMR.GetZaxis().SetRange(k,k)
h_data_RsqMR_components.append(h_data_nBtagRsqMR.Project3D(
"%ibtag_yx"%self.z[k-1]))
if computeErrors:
h_RsqMR_components[-1] = getErrors2D(h_RsqMR_components[-1],
h_data_RsqMR_components[-1],sysTree,options,
"yx",0,len(self.x)-1,0,len(self.y)-1,k,k,
self.x,self.y,self.z)
h_th1x_components.append(get1DHistoFrom2D(
h_RsqMR_components[-1],self.x,self.y,
'h_th1x_%ibtag'%(self.z[k-1])))
h_data_th1x_components.append(get1DHistoFrom2D(
h_data_RsqMR_components[-1],self.x,self.y,
'h_th1x_data_%ibtag'%(self.z[k-1])))
if self.z[k-1]==3 and self.z[-1]==4:
h_labels.append("#geq %i b-tag" % self.z[k-1] )
elif self.z[k-1]==1 and self.z[-1]==4 and len(self.z)==2:
h_labels.append("#geq %i b-tag" % self.z[k-1] )
else:
h_labels.append("%i b-tag" % self.z[k-1] )
# Create the nsigma histograms
h_RsqMR_statnsigma_components = []
h_RsqMR_nsigma_components = []
if len(self.z)>1:
for k in range(1,len(self.z)):
h_RsqMR_statnsigma_components.append(
getStatNSigmaHist(h_RsqMR_components[k-1],
h_data_RsqMR_components[k-1],
"h_RsqMR_statnsigma_%ibtag"%self.z[k-1]))
h_RsqMR_nsigma_btag = h_RsqMR.Clone(
"h_RsqMR_nsigma_%ibtag"%self.z[k-1])
if computeErrors:
h_RsqMR_nsigma_btag = getNsigma2D(h_RsqMR_nsigma_btag,
h_data_RsqMR_components[k-1],toyTree,options,
"yx",0,len(self.x)-1,0,len(self.y)-1,k,k,
self.x,self.y,self.z)
self.blindSideband(h_RsqMR_nsigma_btag, plotRegion)
h_RsqMR_nsigma_components.append(h_RsqMR_nsigma_btag)
# Print everything out to pdf files
btagLabel = getBtagLabel(self.z)
lumiLabel = "%.1f fb^{-1} (13 TeV)" % (self.analysis.lumi/1000.)
boxLabel = "razor %s %s %s Fit" % (self.analysis.region,
btagLabel,self.fitRegion.replace('LowMR,LowRsq','Sideband'))
plotLabel = ""
eventsLabel = "Events"
sidebandFit = self.getSidebandMax()
for h in h_RsqMR_components:
tdirectory.cd()
h.Write()
print1DProj(c,tdirectory,h_MR,h_data_MR,
self.dirname+"/h_MR_%s.pdf"%self.analysis.region,"M_{R} [GeV]",
eventsLabel,lumiLabel,boxLabel,plotLabel,self.isData,False,None,
None,h_MR_components,h_colors,h_labels)
print1DProj(c,tdirectory,h_Rsq,h_data_Rsq,
self.dirname+"/h_Rsq_%s.pdf"%self.analysis.region,"R^{2}",
eventsLabel,lumiLabel,boxLabel,plotLabel,self.isData,False,None,
None,h_Rsq_components,h_colors,h_labels)
#if len(self.z)>2:
if len(self.z)>1:
for k in range(0,len(self.z)-1):
newBoxLabel = "razor %s %s %s Fit"%(self.analysis.region,
h_labels[k],self.fitRegion.replace('LowMR,LowRsq','Sideband'))
if computeErrors:
print1DProjNs(c,tdirectory,h_th1x_components[k],
h_data_th1x_components[k],
h_RsqMR_nsigma_components[k],
self.dirname+"/h_th1x_ns_%ibtag_%s.pdf"%(
self.z[k],self.analysis.region),
"Bin Number",eventsLabel,lumiLabel,newBoxLabel,
plotLabel,self.isData,False,options,cfg=self.config)
print2DResiduals(c,tdirectory,h_RsqMR_nsigma_components[k],
self.dirname+"/h_RsqMR_nsigma_log_%ibtag_%s.pdf"%(
self.z[k],self.analysis.region),"M_{R} [GeV]",
"R^{2}","Stat.+Sys. n#sigma",lumiLabel,newBoxLabel,
plotLabel, self.x,self.y,self.isData,sidebandFit,
False,options)
else:
print1DProj(c,tdirectory,h_th1x_components[k],
h_data_th1x_components[k],
self.dirname+"/h_th1x_%ibtag_%s.pdf"%(self.z[k],
self.analysis.region),"Bin Number",
eventsLabel,lumiLabel,newBoxLabel,plotLabel,self.isData,
False, options)
print2DResiduals(c,tdirectory,h_RsqMR_statnsigma_components[k],
self.dirname+"/h_RsqMR_statnsigma_log_%ibtag_%s.pdf"%(self.z[k],
self.analysis.region), "M_{R} [GeV]", "R^{2}",
"Stat. n#sigma (Data - Fit)/sqrt(Fit)",
lumiLabel,newBoxLabel,plotLabel,self.x,self.y,self.isData,
sidebandFit,False,options)
f.Close()
w.var(p.GetName()).setError(p.getError())
x = array('d', cfg.getBinning(box)[0]) # MR binning
y = array('d', cfg.getBinning(box)[1]) # Rsq binning
z = array('d', cfg.getBinning(box)[2]) # nBtag binning
nBins = (len(x)-1)*(len(y)-1)*(len(z)-1)
xFine = array('d', [x[0]+i*(x[-1]-x[0])/100. for i in range(0,101)]) # MR binning fine
yFine = array('d', [y[0]+i*(y[-1]-y[0])/100. for i in range(0,101)]) # Rsq binning fine
zFine = array('d', cfg.getBinning(box)[2]) # nBtag binning fine
nBinsFine = (len(xFine)-1)*(len(yFine)-1)*(len(zFine)-1)
th1x = w.var('th1x')
sideband = convertSideband(fitRegion,w,x,y,z)
plotband = convertSideband(plotRegion,w,x,y,z)
myTH1 = convertDataset2TH1(data, cfg, box, w, options.useWeight)
signalDs = None
doSignalInj = (options.signalFileName != "None") and (options.r > -1)
if doSignalInj:
sigRootFile = rt.TFile(options.signalFileName)
#sigWorkspace = sigRootFile.Get('w'+box)
#signalDs = sigWorkspace.data('RMRTree')
model = options.signalFileName.split('.root')[0].split('-')[1].split('_')[0]
massPoint = '_'.join(options.signalFileName.split('.root')[0].split('_')[1:3])
#sigTH1 = convertDataset2TH1(signalDs, cfg, box, w,"signal")
#sigTH1.Scale(lumi/lumi_in)
sigTH1 = sigRootFile.Get('%s_%s'%(box,model)).Clone('hist_%s_%s'%(box,model))