def normalizeSignal(self,S_exp,**kwargs):
"""Calculates normalization for a given expected signal yield."""
verbosity = kwargs.get('verbosity',0)
cuts = [ ("%s && %s" % (baseline, category1)),
("%s && %s" % (baseline, category2)), ]
cuts = kwargs.get('cuts',cuts)
weight = kwargs.get('weight',"")
(aa,bb) = kwargs.get('signalregion',(0,40))
N = 0; MC = 0
scale = 1
for i,cut in enumerate(cuts):
cut = combineCuts("m_sv>0", cut)
name = "m_sv_for_signal_renormalization_%d" % i
hist = self.hist("m_sv",100,aa,bb,name=name,cuts=cut,weight=weight)
N += hist.GetSumOfWeights()
MC += hist.GetEntries()
gDirectory.Delete(name)
if N:
scale = S_exp / N * self.scale
printVerbose(">>> normalizeSignal: S_exp=%.4f, N=%.4f, MC=%.1f, old scale=%.4f, scale=%.4f" % (S_exp, N, MC, self.scale, scale), verbosity)
printVerbose(">>> normalizeSignal: signalregion=(%.1f,%.1f)" % (aa,bb),verbosity)
else: print warning("Could not find normalization for signal: no MC events in given signal region after cuts (%s)!" % cuts)
self.setAllScales(scale)
return scale
def hist2D(self, var1, nBins1, a1, b1, var2, nBins2, a2, b2, **kwargs):
"""Make a 2D histogram with a tree."""
scale = kwargs.get('scale', 1.0) * self.scale
tree = self.file.Get(self.treeName)
name = kwargs.get('name', makeHistName(self.label, "%s_vs_%s" % (var1,var2)))
title = kwargs.get('title', self.label)
verbosity = kwargs.get('verbosity', 0)
blindcuts = ""
if var1 in self.blind: blindcuts += self.blind[var1]
if var2 in self.blind: blindcuts += self.blind[var2]
weight = combineWeights(self.weight, kwargs.get('weight', ""))
cuts = combineCuts(self.cuts, kwargs.get('cuts', ""), blindcuts, weight=weight)
printVerbose(">>>>\n>>> Sample - %s, %s vs. %s: %s" % (color(name,color="grey"), var1, var2, self.filenameshort),verbosity)
printVerbose(">>> scale: %.4f" % (scale),verbosity)
printVerbose(">>> weight: %s" % (weight),verbosity)
printVerbose(">>> %s" % (cuts),verbosity)
hist2D = TH2F(name, title, nBins2, a2, b2, nBins1, a1, b1)
out = tree.Draw("%s:%s >> %s" % (var1,var2,name), cuts, "gOff")
if out < 0: print error("Drawing histogram for %s sample failed!" % (title))
#if scale is not 1.0: hist.Scale(scale)
#if scale is 0.0: print warning("Scale of %s is 0!" % self.label)
return hist2D
def hist(self, var, nBins, a, b, **kwargs):
"""Make a histogram with a tree."""
scale = kwargs.get('scale', 1.0) * self.scale
tree = self.file.Get(self.treeName)
name = kwargs.get('name', makeHistName(self.label, var))
title = kwargs.get('title', self.label)
shift = kwargs.get('shift', 0)
smear = kwargs.get('smear', 0)
verbosity = kwargs.get('verbosity', 0)
if self.isSignal and self.scale is not self.scaleBU and self.scaleBU:
title += " (#times%d)" % (self.scale/self.scaleBU)
blindcuts = ""
if var in self.blind: blindcuts = self.blind[var]
weight = combineWeights(self.weight, kwargs.get('weight', ""))
cuts = combineCuts(self.cuts, kwargs.get('cuts', ""), blindcuts, weight=weight)
hist = TH1F(name, title, nBins, a, b)
out = tree.Draw("%s >> %s" % (var,name), cuts, "gOff")
if shift or smear:
mean0 = hist.GetMean()
#smear = min(1,smear)
#smear = sqrt(smear*smear-1) #*sigma
#tree.SetAlias("rng","sin(2*pi*rndm)*sqrt(-2*log(rndm))")
var2 = "%s*%s + %s + %s*%s" % (var,smear,shift,(1-smear),mean0)
tree.Draw("%s >> %s" % (var2,name), cuts, "gOff")
if out < 0: print error("Drawing histogram for %s sample failed!" % (title))
if scale is not 1.0: hist.Scale(scale)
if scale is 0.0: print warning("Scale of %s is 0!" % self.label)
#print hist.GetEntries()
#gDirectory.Delete(label)
printVerbose(">>>\n>>> Sample - %s, %s: %s" % (color(name,color="grey"), var, self.filenameshort),verbosity)
printVerbose(">>> scale: %.4f (%.4f)" % (scale,self.scale),verbosity)
printVerbose(">>> weight: %s" % (("\n>>>%s*("%(' '*18)).join(weight.rsplit('*(',max(0,weight.count("*(")-1)))),verbosity)
printVerbose(">>> %s" % (cuts.replace("*(","\n>>>%s*("%(' '*18))),verbosity)
return hist
def histAndColor(self, var, nBins, a, b, **kwargs):
'''Return a list of tuples containing a histogram and a color.
Return multiple ntuples if a sample need to be split.'''
split = kwargs.get('split', False) and len(self.split)
verbosity = kwargs.get('verbosity', 0)
if split:
printVerbose(">>> histAndColor: splitting %s" % (self.label),
verbosity)
histsAndColors = []
cuts0 = kwargs.get('cuts', "")
for key, (splitlabel, splitcut,
splitcolor) in self.split.iteritems():
kwargs['cuts'] = combineCuts(cuts0, splitcut)
kwargs['title'] = splitlabel
kwargs['append_name'] = "_%s" % (key)
hist = self.hist(var, nBins, a, b, **kwargs)
histsAndColors.append((hist, splitcolor))
return histsAndColors
else:
printVerbose(">>> histAndColor: not splitting", verbosity, level=2)
hist = self.hist(var, nBins, a, b, **kwargs)
return [(hist, self.color)]
def hist(self, var, nBins, a, b, **kwargs):
"""Make a histogram with a tree."""
scale = kwargs.get('scale', 1.0) * self.scale
treeName = kwargs.get('treeName', self.treeName)
name = kwargs.get('name', makeHistName(self.label, var))
name += kwargs.get('append_name', "")
title = kwargs.get('title', self.label)
shift = kwargs.get('shift', 0)
smear = kwargs.get('smear', 0)
blind = kwargs.get('blind', self.blind)
verbosity = kwargs.get('verbosity', 0)
if self.isSignal and self.scale is not self.scaleBU and self.scaleBU:
title += " (#times%d)" % (self.scale / self.scaleBU)
blindcuts = ""
if var in blind and "SS" not in name:
blindcuts = blind[
var] # TODO: blind by removing bins from hist or rounding? FindBin(a), SetBinContent
weight = combineWeights(self.weight, kwargs.get('weight', ""))
cuts = combineCuts(self.cuts,
kwargs.get('cuts', ""),
blindcuts,
weight=weight)
tree = self.file.Get(treeName)
if not tree or not isinstance(tree, TTree):
print error("Could not find tree \"%s\" for %s! Check %s" %
(treeName, self.label, self.filenameshort))
hist = TH1D(name, title, nBins, a, b)
hist.Sumw2()
out = tree.Draw("%s >> %s" % (var, name), cuts, "gOff")
if shift or (smear and smear != 1):
mean0 = hist.GetMean()
#smear = min(1,smear)
#smear = sqrt(smear*smear-1) #*sigma
#tree.SetAlias("rng","sin(2*pi*rndm)*sqrt(-2*log(rndm))")
var2 = "%s*%s + %s + %s*%s" % (var, smear, shift,
(1 - smear), mean0)
tree.Draw("%s >> %s" % (var2, name), cuts, "gOff")
if out < 0:
print error("Drawing histogram for %s sample failed!" % (title))
if scale is not 1.0: hist.Scale(scale)
if scale is 0.0: print warning("Scale of %s is 0!" % self.label)
if verbosity > 2: printBinError(hist)
#print hist.GetEntries()
#gDirectory.Delete(label)
printVerbose(
">>>\n>>> Sample - %s, %s: %s (%s)" % (color(
name, color="grey"), var, self.filenameshort, self.treeName),
verbosity)
printVerbose(">>> scale: %.4f (%.4f)" % (scale, self.scale),
verbosity)
printVerbose(
">>> weight: %s" % (("\n>>>%s*(" % (' ' * 18)).join(
weight.rsplit('*(', max(0,
weight.count("*(") - 1)))), verbosity)
printVerbose(
">>> entries: %d (%.2f integral)" %
(hist.GetEntries(), hist.Integral()), verbosity)
printVerbose(
">>> %s" % (cuts.replace("*(", "\n>>>%s*(" % (' ' * 18))),
verbosity)
return hist
def renormalizeTT(samples, **kwargs):
"""Helpfunction to renormalize TT."""
var = kwargs.get('var', "pfmt_1")
label = kwargs.get('label', "baseline")
QCD = kwargs.get('QCD', True)
channel = kwargs.get('channel', "mutau")
cuts = kwargs.get('cuts', baseline)
reset = kwargs.get('reset', True)
shift_QCD = kwargs.get('shift_QCD', 0) # e.g. 0.30
prepend = kwargs.get('prepend', "")
verbosity = kwargs.get('verbosity', 0)
savedscale = 0
category = ""
#print header("%s: TT renormalization" % (channel))
print ">>>\n>>> renormalizing TT..."
# GET sample
sampleTT = getSample(samples, "TT", unique=True)
if not sampleTT:
print warning("renormalizeTT: Could not renormalize TT: no TT sample.")
print ">>>"
return
# CHECK category
if "category 1" in label:
category = "category 1"
cuts = combineCuts(cuts,
category1TT) # "%s && %s" % (baseline, category1TT)
elif "category 2" in label:
category = "category 2"
cuts = combineCuts(cuts,
category2TT) # "%s && %s" % (baseline, category2TT)
else:
print ">>> category does not apply for TT renormalization"
if sampleTT.scale != sampleTT.scaleBU:
print ">>> resetting TT scale %.3f back to %.3f" % (
sampleTT.scale, sampleTT.scaleBU)
sampleTT.scale = sampleTT.scaleBU
print ">>>"
return
# CHECK is scale is already saved
savedscale = TTscales[channel][category]
if savedscale > 0:
sampleTT.scale = sampleTT.scaleBU * savedscale
print ">>> using old TT bar renormalizaion scale %.2f from %s" % (
savedscale, category)
print ">>> TT renormalization scale = %.3f (new total scale = %.3f)" % (
savedscale, sampleTT.scale)
print ">>>"
return
# CALCULATE and SAVE scale
name = "%s/%s%s/%s_%s_TTrenormalization.png" % (PLOTS_DIR, channel,
mylabel, var, label)
title = "%s: %s" % (channel.replace("tau", "#tau").replace("mu",
"#mu"), label)
plot = Plot(samples,
var,
200,
0,
400,
cuts=cuts,
QCD=QCD,
reset=True,
shift_QCD=shift_QCD,
verbosity=verbosity)
#plot.plot(stack=True, title=title, staterror=True, ratio=True)
scale = plot.renormalizeTT(prepend=prepend, verbosity=verbosity)
plot.close()
#plot.saveAs(name,save=True)
# SAVE
TTscales[channel][category] = scale
print ">>> "