C = TCanvas()
C.Print("PUWeights.pdf[")
# Loop over json content #
for name, path in dict_files.items():
print("Looking at %s" % name)
# Load json content #
with open(path, "r") as handle:
data = json.load(handle)
# Get binning content #
bins = data['data'] # List of dict (1 dict per bin)
g = TGraphAsymmErrors(len(bins))
# Loop over bins #
for i, b in enumerate(bins):
val = b['value']
up = b['error_high']
down = b['error_low']
bincenter = (b['bin'][0] + b['bin'][1]) / 2
g.SetPoint(i, bincenter, val)
g.SetPointError(i, 0, 0, up, down)
g.GetHistogram().SetMinimum(0.)
g.GetHistogram().SetMaximum(2.)
g.SetTitle("Sample : %s" % name)
g.GetXaxis().SetTitle("nTrueInt")
g.GetYaxis().SetTitle("PU weight")
C.Clear()
g.Draw()
C.Print("PUWeights.pdf", "Title:%s" % name)
C.Print("PUWeights.pdf]")
def significanceSB(cutlist, labellist):
basecut = labellist[0]
dim = len(cutlist)
significance = [0] * (dim + 1)
file = {}
tree = {}
effs = {}
hist = {}
GrAsym = {}
yErrorUp = {}
yErrorDown = {}
totEve = 0
GrAsym = TGraphAsymmErrors()
cuts = ""
for j, c in enumerate(cutlist):
s = 0.
b = 0.
cuts += cutlist[0] if j == 0 else " && " + cutlist[j]
print "cuts = ", cuts
for num1, v in enumerate(signals):
#print "Signal = ", v
for num2, filename in enumerate(samples[v]['files']):
#print "Signal rootfile read = ", filename
file[filename] = TFile(NTUPLESIG + filename + ".root",
"READ") # Read TFile
tree[filename] = file[filename].Get("Events") # Read TTree
nevents = float(sample[filename]['nevents'])
xs = float(sample[filename]['xsec']) * float(
sample[filename]['kfactor'])
LumiMC = nevents / xs
Weight = float(LUMI) / float(LumiMC)
sig_entries = tree[filename].GetEntries(cuts)
#print "s = ", float(sig_entries) * float(Weight)
s += float(sig_entries) * float(Weight)
print "TOT SIG = ", s
for num1, k in enumerate(back):
#print "backgrounds = ", k
for num2, filename in enumerate(samples[k]['files']):
#print "backgrounds rootfile read = ", filename
file[filename] = TFile(NTUPLEDIR + filename + ".root",
"READ") # Read TFile
tree[filename] = file[filename].Get("Events") # Read TTree
nevents = float(sample[filename]['nevents'])
xs = float(sample[filename]['xsec']) * float(
sample[filename]['kfactor'])
LumiMC = nevents / xs
Weight = float(LUMI) / float(LumiMC)
bkg_entries = tree[filename].GetEntries(cuts)
#print "b = ", float(bkg_entries) * float(Weight)
b += float(bkg_entries) * float(Weight)
print "TOT BKG = ", b
##End of cutlist
#COMPUTE
#print "s = ", s
#print "b = ", b
#print "sqrt(b) = ", math.sqrt(b)
#print "significance = ", float(s/math.sqrt(b))
significance[j] = float(s / math.sqrt(b))
yErrorUp[j] = float(
TEfficiency.ClopperPearson(math.sqrt(b), s, 0.68, True) -
significance[j])
yErrorDown[j] = float(
significance[j] -
TEfficiency.ClopperPearson(math.sqrt(b), s, 0.68, False))
GrAsym.SetPoint(j, j + 0.5, significance[j])
GrAsym.SetPointError(j, 0, 0, yErrorUp[j], yErrorDown[j])
for k, cs in enumerate(labellist):
GrAsym.GetHistogram().GetXaxis().Set(dim, 0, dim)
GrAsym.GetHistogram().GetXaxis().SetBinLabel(k + 1,
"%s" % labellist[k])
GrAsym.SetLineColor(2)
GrAsym.SetLineWidth(3)
GrAsym.SetMarkerStyle(8)
GrAsym.SetMarkerColor(2)
c1 = TCanvas("c1", "Signals Acceptance", 800, 600)
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
gStyle.SetOptStat(0)
#GrAsym.SetMaximum(1.3)
#GrAsym.SetMinimum(0.)
GrAsym.GetHistogram().GetXaxis().SetTitle("")
GrAsym.GetHistogram().GetYaxis().SetTitle("Significance (S/#sqrt{B})")
GrAsym.Draw("pa")
drawCMS(LUMI, "Work In Progress")
drawRegion(basecut)
if not os.path.exists('plots/Signal/Significance/'):
os.system('mkdir -p plots/Signal/Significance/')
c1.Print("plots/Signal/Significance/Sigf_SB_" + basecut + ".png")
c1.Print("plots/Signal/Significance/Sigf_SB_" + basecut + ".pdf")
#if not options.runBash: raw_input("Press Enter to continue...")
pass
print(x, x_low, x_high)
graph = TGraphAsymmErrors(x.size, x,y,x_low,x_high, y_error, y_error)
graph.SetMarkerStyle(8)
c = ROOT.TCanvas("c","c",700,600)
c.SetLeftMargin(0.17)
c.Draw()
graph.GetYaxis().SetTitle("#sigma(#Delta t) [ns]")
graph.GetXaxis().SetTitle("A_{eff}")
graph.Draw("ap")
if args.logx:
c.SetLogx(1)
graph.GetXaxis().SetLimits(x[0]-2*x_low[0], x[-1]+2*x_low[-1])
if args.logy:
c.SetLogy(1)
graph.GetHistogram().SetMinimum(y.min()/2)
graph.GetHistogram().SetMaximum(y.max()*3)
graph.SetTitle("")
Frame(gPad)
yrlabel = TextAuto(gPad, args.year, align = 31)
last = histo.GetNbinsX() - 1
func = TF1("func","sqrt(([0]/(x))^2 + 2*([1]^2))", x[0]-x_low[0], x[last]+x_high[last])
func.SetParameter(0, N)
func.SetParameter(1, C)
graph.Fit("func","R")
Nlabel = Text(gPad, 0.6, 0.7, "N = {:.3f} #pm {:.3f} ns".format(func.GetParameter(0), func.GetParError(0)))
Clabel = Text(gPad, 0.6, 0.65,"C = {:.3f} #pm {:.3f} ns".format(func.GetParameter(1), func.GetParError(1)))
