def getGraph(self):
from array import array
from ROOT import TMultiGraph, TLegend, TGraphAsymmErrors
n = len(self.__x)
if n != len(self.__y) or n != len(self.__yErrLow) or n != len(
self.__yErrHigh):
raise StandardError, "The length of the x(%s), y(%s) and y error(%s,%s) lists does not match" % (
len(self.__x), len(self.__y), len(
self.__yErrLow), len(self.__yErrHigh))
result = TMultiGraph()
legendPosition = [
float(i) for i in self.__getStyleOption("legendPosition").split()
]
legend = TLegend(*legendPosition)
legend.SetFillColor(0)
result.SetTitle("%s;%s;%s" %
(self.__title, self.__xTitle, self.__yTitle))
#(refArrays, refLabel) = self.__getRefernceGraphArrays()
#refGraph = TGraphAsymmErrors(*refArrays)
#refGraph.SetLineWidth(2)
#refGraph.SetLineColor(int(self.__config.get("reference","lineColor")))
#refGraph.SetFillColor(int(self.__config.get("reference","fillColor")))
#result.Add(refGraph,"L3")
#legend.AddEntry(refGraph,self.__config.get("reference","name"))
xErr = array("d", [0 for i in range(n)])
print "__x = ", self.__x
print "__y = ", self.__y
graph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr,
self.__yErrLow, self.__yErrHigh)
graph.SetLineWidth(2)
graph.SetFillColor(0)
graph.SetLineColor(int(self.__getStyleOption("lineColor")))
graph.SetMarkerColor(int(self.__getStyleOption("markerColor")))
graph.SetMarkerStyle(int(self.__getStyleOption("markerStyle")))
graph.SetMarkerSize(float(self.__getStyleOption("markerSize")))
sysGraph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr,
self.__ySysErrLow, self.__ySysErrHigh)
sysGraph.SetLineWidth(1)
sysGraph.SetFillColor(0)
sysGraph.SetLineColor(int(self.__getStyleOption("lineColor")))
sysGraph.SetMarkerColor(int(self.__getStyleOption("markerColor")))
sysGraph.SetMarkerStyle(int(self.__getStyleOption("markerStyle")))
sysGraph.SetMarkerSize(float(self.__getStyleOption("markerSize")))
result.Add(sysGraph, "[]")
result.Add(graph, "P")
# result.SetName("MultiPlots")
# result.SetTitle("%s;%s;%s"%(self.__title,self.__xTitle,self.__yTitle))
result.SetName("MG_%s" % (self.__title))
legend.AddEntry(graph, self.__getStyleOption("name"))
#for (x,y,yErr) in zip(self.__x, self.__y, zip(self.__yErrLow,self.__yErrHigh)):
# self.__addAnnotaion("hallo",x,y,yErr)
return (result, legend)
def bestfit():
xmin = -10
xmax = 6
c, h = draw_canvas_histo(
xmin, xmax, "Best fit #mu = #sigma/#sigma_{SM} at m_{H} = 125 GeV")
# line at SM expectation of mu = 1
l = TLine()
l.SetLineStyle(2)
l.DrawLine(1.0, -1.0, 1.0, -1.0 + nchannels)
mu = np.array([-0.4, -4.734, -2.014])
upper = np.array([2.12, 3.688, 1.80])
lower = np.array([2.08, 3.754, 1.8224])
channel = np.array([1.5, 0.5, -0.5])
zero = np.zeros(nchannels)
gmu = TGraphAsymmErrors(nchannels, mu, channel, lower, upper, zero, zero)
gmu.SetMarkerStyle(21)
gmu.SetMarkerSize(1.5)
gmu.SetLineColor(2)
gmu.SetLineWidth(2)
gmu.Draw("pz")
#draw_disclaimer()
c.RedrawAxis()
c.Modified()
c.Update()
c.SaveAs("bestfit.pdf")
def getGraphSimple(self):
from array import array
from ROOT import TMultiGraph, TLegend, TGraphAsymmErrors
n = len(self.__x)
if n != len(self.__y) or n != len(self.__yErrLow) or n != len(self.__yErrHigh):
raise StandardError, "The length of the x(%s), y(%s) and y error(%s,%s) lists does not match"%(len(self.__x), len(self.__y), len(self.__yErrLow), len(self.__yErrHigh))
legendPosition = [float(i) for i in self.__getStyleOption("legendPosition").split()]
legend = TLegend(*legendPosition)
legend.SetFillColor(0)
xErr = array("d",[0 for i in range(n)])
print "__x = ", self.__x
print "__y = ", self.__y
graph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr, self.__yErrLow,self.__yErrHigh)
graph.SetTitle("%s;%s;%s"%(self.__title,self.__xTitle,self.__yTitle))
graph.SetLineWidth(2)
graph.SetFillColor(0)
graph.SetLineColor(int(self.__getStyleOption("lineColor")))
graph.SetMarkerColor(int(self.__getStyleOption("markerColor")))
graph.SetMarkerStyle(int(self.__getStyleOption("markerStyle")))
graph.SetMarkerSize(float(self.__getStyleOption("markerSize")))
graph.SetDrawOption("AP")
return (graph, legend)
def getEff(name, den_input, num_input, rebin=0, xtitle='', ytitle=''):
c = TCanvas(name + '_Canvas')
legend = TLegend(0.8, 0.1, 0.999, 0.6)
legend.SetFillColor(kWhite)
den = den_input.Clone()
num = num_input.Clone()
if rebin != 0:
den.Rebin(rebin)
num.Rebin(rebin)
error_bars = TGraphAsymmErrors()
error_bars.Divide(num, den, "cl=0.683 b(1,1) mode")
error_bars.SetLineWidth(3)
if xtitle == '':
error_bars.GetXaxis().SetTitle(num.GetXaxis().GetTitle())
else:
error_bars.GetXaxis().SetTitle(xtitle)
if ytitle == '':
error_bars.GetYaxis().SetTitle(num.GetYaxis().GetTitle())
else:
error_bars.GetYaxis().SetTitle(ytitle)
error_bars.GetXaxis().SetRangeUser(400, 2000)
error_bars.SetLineColor(kBlack)
error_bars.SetMaximum(1.01)
error_bars.SetMinimum(0.)
if ytitle == '':
error_bars.GetYaxis().SetTitle("Trigger rate")
else:
error_bars.GetYaxis().SetTitle(ytitle)
error_bars.SetTitle('')
error_bars.Draw('AP')
c.SaveAs('pdf/' + name + '.pdf')
c.Write(name + '_Canvas')
error_bars.Write(name)
def makeResidHist(data, bkg):
pulls = TGraphAsymmErrors(data.GetN())
pulls.SetName("Pulls")
pulls.SetLineWidth(data.GetLineWidth())
pulls.SetLineStyle(data.GetLineStyle())
pulls.SetLineColor(data.GetLineColor())
pulls.SetMarkerSize(data.GetMarkerSize())
pulls.SetMarkerStyle(data.GetMarkerStyle())
pulls.SetMarkerColor(data.GetMarkerColor())
pulls.SetFillStyle(data.GetFillStyle())
pulls.SetFillColor(data.GetFillColor())
# Add histograms, calculate Poisson confidence interval on sum value
for i in range(data.GetN()):
x = data.GetX()[i]
dyl = data.GetErrorYlow(i)
dyh = data.GetErrorYhigh(i)
yy = data.GetY()[i] - bkg.Interpolate(x) #bkg.GetBinContent(i+1)
norm = dyl if yy > 0. else dyh
if norm == 0.:
yy, dyh, dyl = 0., 0., 0.
else:
yy /= norm
dyh /= norm
dyl /= norm
pulls.SetPoint(i, x, yy)
pulls.SetPointEYhigh(i, dyh)
pulls.SetPointEYlow(i, dyl)
return pulls
def convertHistToGraph(hist, useGarwood=False):
alpha = 1 - 0.6827
graph = TGraphAsymmErrors(hist.GetNbinsX())
if useGarwood:
lastEvent = False
for i in reversed(range(hist.GetNbinsX())):
N = hist.GetBinContent(i + 1)
if not lastEvent and N > 0: lastEvent = True
if lastEvent and N <= 0.: N = 1.e-6
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
graph.SetPoint(i,
hist.GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
graph.SetPointError(i, 0., 0., N - L, U - N)
else:
for i in range(hist.GetNbinsX()):
graph.SetPoint(i,
hist.GetXaxis().GetBinCenter(i + 1),
hist.GetBinContent(i + 1))
graph.SetPointError(i,
hist.GetXaxis().GetBinWidth(i + 1) / 2.,
hist.GetXaxis().GetBinWidth(i + 1) / 2.,
hist.GetBinError(i + 1),
hist.GetBinError(i + 1))
graph.SetLineWidth(hist.GetLineWidth())
graph.SetLineStyle(hist.GetLineStyle())
graph.SetLineColor(hist.GetLineColor())
graph.SetMarkerSize(hist.GetMarkerSize())
graph.SetMarkerStyle(hist.GetMarkerStyle())
graph.SetMarkerColor(hist.GetMarkerColor())
graph.SetFillStyle(hist.GetFillStyle())
graph.SetFillColor(hist.GetFillColor())
return graph
def ratioplot():
# create required parts
leg = getLegend()
latex = getLatex()
c = TCanvas()
c.SetLogy()
#Draw input histograms
hists = ['h_frac_recoil_', 'h_full_recoil_']
label = ['recoil with MET triggers', 'recoil without MET triggers']
combineHist(hists, label, leg)
#leg.Draw()
#c.SaveAs("Combinehists_D.pdf")
ratio = []
h1 = f.Get('h_frac_recoil_')
#h1=setHistStyle(h1,bins)
h2 = f.Get('h_full_recoil_')
#h2=setHistStyle(h2,bins)
h3 = createRatio(h1, h2)
gr = TGraphAsymmErrors(h1, h2)
gr.GetXaxis().SetRangeUser(0, 1500)
gr.GetYaxis().SetRangeUser(0, 1.2)
gr.SetMarkerStyle(20)
gr.SetMarkerSize(0.5)
gr.SetLineColor(1)
gr.GetYaxis().SetTitle("Trigger Efficiency")
gr.GetXaxis().SetTitle("Recoil [GeV]")
gr.SetTitle("")
# print ("ratio",ratio )
# c, pad1, pad2 = createCanvasPads()
#
# # draw everything
# pad1.cd()
# h1.Draw()
# h2.Draw("same")
# to avoid clipping the bottom zero, redraw a small axis
# h1.GetYaxis().SetLabelSize(0.0)
# axis = TGaxis(-5, 20, -5, 220, 20, 220, 510, "")
# axis.SetLabelFont(43)
# axis.SetLabelSize(15)
# axis.Draw()
# pad2.cd()
gr.Draw()
latex.DrawLatex(0.41, 0.93, "Trigger Efficincy in MET Run2017E")
xmin = 0.0
line = TLine(max(xmin, gr.GetXaxis().GetXmin()), 1, 1500, 1)
line.SetLineColor(1)
line.SetLineWidth(1)
line.SetLineStyle(7)
line.Draw()
#h3.Draw('pl')
c.SaveAs("test.pdf")
def getDataPoissonErrors(hist,
kPoisson=False,
drawZeroBins=False,
drawXbars=False,
centerBin=True):
'''Make data poisson errors for a histogram with two different methods:
- TH1.kPoisson
- chi-squared quantile
'''
# https://github.com/DESY-CMS-SUS/cmgtools-lite/blob/8_0_25/TTHAnalysis/python/plotter/mcPlots.py#L70-L102
# https://github.com/DESY-CMS-SUS/cmgtools-lite/blob/8_0_25/TTHAnalysis/python/plotter/susy-1lep/RcsDevel/plotDataPredictWithSyst.py#L12-L21
if kPoisson: hist.SetBinErrorOption(TH1D.kPoisson)
Nbins = hist.GetNbinsX()
xaxis = hist.GetXaxis()
alpha = (1 - 0.6827) / 2.
graph = TGraphAsymmErrors(Nbins)
graph.SetName(hist.GetName() + "_graph")
graph.SetTitle(hist.GetTitle())
for i in xrange(1, Nbins + 1):
N = hist.GetBinContent(i)
if N <= 0 and not drawZeroBins: continue
dN = hist.GetBinError(i)
yscale = 1
if centerBin:
x = xaxis.GetBinCenter(i)
else:
x = xaxis.GetBinLowEdge(i)
if N > 0 and dN > 0 and abs(dN**2 / N -
1) > 1e-4: # check is error is Poisson
yscale = (dN**2 / N)
N = (N / dN)**2
if kPoisson:
EYlow = hist.GetBinErrorLow(i)
EYup = hist.GetBinErrorUp(i)
else:
EYlow = (N - Math.chisquared_quantile_c(1 - alpha, 2 * N) /
2.) if N > 0 else 0
EYup = Math.chisquared_quantile_c(alpha, 2 * (N + 1)) / 2. - N
y = yscale * N
EXup = xaxis.GetBinUpEdge(i) - x if drawXbars else 0
EXlow = x - xaxis.GetBinLowEdge(i) if drawXbars else 0
graph.SetPoint(i - 1, x, y)
graph.SetPointError(i - 1, EXlow, EXup, EYlow, EYup)
#print ">>> getDataPoissonErrors - bin %2d: (x,y) = ( %3.1f - %4.2f + %4.2f, %4.2f - %4.2f + %4.2f )"%(i,x,EXlow,EXup,y,EYlow,EYup)
graph.SetLineWidth(hist.GetLineWidth())
graph.SetLineColor(hist.GetLineColor())
graph.SetLineStyle(hist.GetLineStyle())
graph.SetMarkerSize(hist.GetMarkerSize())
graph.SetMarkerColor(hist.GetMarkerColor())
graph.SetMarkerStyle(hist.GetMarkerStyle())
return graph
def makeEffPlotsVars(tree,
varx,
vary,
sel,
nbinx,
xmin,
xmax,
nbiny,
ymin,
ymax,
xtitle,
ytitle,
leglabel=None,
header='',
addon='',
option='pt',
marker=20,
col=1):
binning = [20, 200] if args.onebin else [
20, 30, 40, 50, 60, 70, 80, 100, 150, 200
]
if option == 'pt':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, array('d', binning))
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, array('d', binning))
elif option == 'eta':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, nbinx, xmin, xmax)
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, nbinx, xmin,
xmax)
tree.Draw(varx + ' >> ' + _hist_.GetName(), sel)
tree.Draw(varx + ' >> ' + _ahist_.GetName(), sel + ' && ' + vary)
g_efficiency = TGraphAsymmErrors()
g_efficiency.Divide(_ahist_, _hist_, "cl=0.683 b(1,1) mode")
g_efficiency.GetXaxis().SetTitle(xtitle)
g_efficiency.GetYaxis().SetTitle('efficiency')
g_efficiency.GetYaxis().SetNdivisions(507)
g_efficiency.SetLineWidth(3)
g_efficiency.SetName(header)
g_efficiency.SetMinimum(0.)
g_efficiency.GetYaxis().SetTitleOffset(1.3)
g_efficiency.SetMarkerStyle(marker)
g_efficiency.SetMarkerSize(1)
g_efficiency.SetMarkerColor(col)
g_efficiency.SetLineColor(col)
g_efficiency.Draw('ap')
# save(c, 'plots/' + addon)
return g_efficiency
def convertHistToGraph(hist):
graph = TGraphAsymmErrors(hist.GetNbinsX())
for i in range(hist.GetNbinsX()):
graph.SetPoint(i,
hist.GetXaxis().GetBinCenter(i), hist.GetBinContent(i))
graph.SetPointError(i,
hist.GetXaxis().GetBinWidth(i) / 2.,
hist.GetXaxis().GetBinWidth(i) / 2.,
hist.GetBinError(i), hist.GetBinError(i))
graph.SetLineWidth(hist.GetLineWidth())
graph.SetLineStyle(hist.GetLineStyle())
graph.SetLineColor(hist.GetLineColor())
graph.SetMarkerSize(hist.GetMarkerSize())
graph.SetMarkerStyle(hist.GetMarkerStyle())
graph.SetMarkerColor(hist.GetMarkerColor())
graph.SetFillStyle(hist.GetFillStyle())
graph.SetFillColor(hist.GetFillColor())
return graph
def histToGraph(hist, name='', keepErrors=True, poissonErrors=True):
## Helper method to convert a histogram to a corresponding graph
# @hist TH1 object
# @name name of the graph (default is name of histogram)
# @keepErrors decide if the y-errors should be propagated to the graph
# @poissonErrors decide if the y-errors should be calculated as Poisson errors
# @return graph
if not name:
name = 'g%s' % (hist.GetName())
from ROOT import TGraphAsymmErrors
nBins = hist.GetNbinsX()
graph = TGraphAsymmErrors(nBins)
graph.SetNameTitle(name, hist.GetTitle())
xAxis = hist.GetXaxis()
for i in xrange(nBins):
xVal = xAxis.GetBinCenter(i + 1)
yVal = hist.GetBinContent(i + 1)
graph.SetPoint(i, xVal, yVal)
graph.SetPointEXlow(i, abs(xVal - xAxis.GetBinLowEdge(i + 1)))
graph.SetPointEXhigh(i, abs(xVal - xAxis.GetBinUpEdge(i + 1)))
if keepErrors:
if poissonErrors:
lo, hi = calculatePoissonErrors(yVal)
graph.SetPointEYlow(i, lo)
graph.SetPointEYhigh(i, hi)
else:
graph.SetPointEYlow(i, hist.GetBinErrorLow(i + 1))
graph.SetPointEYhigh(i, hist.GetBinErrorUp(i + 1))
# copy the style
graph.SetMarkerStyle(hist.GetMarkerStyle())
graph.SetMarkerColor(hist.GetMarkerColor())
graph.SetMarkerSize(hist.GetMarkerSize())
graph.SetLineStyle(hist.GetLineStyle())
graph.SetLineColor(hist.GetLineColor())
graph.SetLineWidth(hist.GetLineWidth())
graph.SetFillColor(hist.GetFillColor())
graph.SetFillStyle(hist.GetFillStyle())
return graph
def makeEffPlotsVars(tree,
varx,
numeratorAddSelection,
baseSelection,
binning,
xtitle='',
header='',
addon='',
marker=20,
col=1):
_denomHist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, binning)
_nominatorHist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, binning)
tree.Draw(varx + ' >> ' + _denomHist_.GetName(), baseSelection)
tree.Draw(varx + ' >> ' + _nominatorHist_.GetName(),
baseSelection + ' && ' + numeratorAddSelection)
g_eff = TGraphAsymmErrors()
g_eff.Divide(_nominatorHist_, _denomHist_, "cl=0.683 b(1,1) mode")
g_eff.GetXaxis().SetTitle(xtitle)
g_eff.GetYaxis().SetTitle('efficiency')
g_eff.GetYaxis().SetNdivisions(507)
g_eff.SetLineWidth(3)
g_eff.SetName(header)
g_eff.SetMinimum(0.)
g_eff.GetYaxis().SetTitleOffset(1.3)
g_eff.SetMarkerStyle(marker)
g_eff.SetMarkerSize(1)
g_eff.SetMarkerColor(col)
g_eff.SetLineColor(col)
g_eff.Draw('ap')
return g_eff
def fixPlots_withErrors(middleGraph):
thisBandsGraph = TGraphAsymmErrors()
thisBandsGraph.SetName(middleGraph.GetName())
thisBandsGraph.SetMarkerStyle(middleGraph.GetMarkerStyle())
thisBandsGraph.SetMarkerSize(middleGraph.GetMarkerSize())
thisBandsGraph.SetMarkerColor(middleGraph.GetMarkerColor())
thisBandsGraph.SetLineColor(middleGraph.GetLineColor())
for iPoint in xrange(0, middleGraph.GetN()):
# Retrieve middle graph for (x,y) coordinates
dataPointX = Double(0)
dataPointY = Double(0)
dataErrorX = Double(0)
middleGraph.GetPoint(iPoint, dataPointX, dataPointY)
dataErrorX = middleGraph.GetErrorX(iPoint)
dataErrorY = middleGraph.GetErrorY(iPoint)
dataPointY = fabs(dataPointY - 1)
if (iPoint < middleGraph.GetN()):
#if(dataPointY != 0 and iPoint < middleGraph.GetN()):
print "fixPlots:", dataPointX
thisBandsGraph.SetPoint(iPoint, dataPointX, dataPointY)
thisBandsGraph.SetPointError(iPoint, dataErrorX, dataErrorX,
dataErrorY, dataErrorY)
#if (dataPointY == 0) :
#thisBandsGraph.SetPoint(iPoint, dataPointX, dataPointY)
#thisBandsGraph.SetPointError(iPoint, dataErrorX, dataErrorX, dataErrorY, dataErrorY)
return thisBandsGraph
def doit():
frame = h.ProfileX(hname + "_frame", 1, -1, "s")
gr1 = TGraphAsymmErrors(h.GetNbinsX())
gr2 = TGraphAsymmErrors(h.GetNbinsX())
gr1_aspt = TGraphAsymmErrors(h.GetNbinsX())
gr2_aspt = TGraphAsymmErrors(h.GetNbinsX())
# Apply gaussian fits
for i in xrange(h.GetNbinsX()):
h_py = h.ProjectionY("_py", i + 1, i + 1)
if 50 <= i <= 60: # high pT, not enough entries (300 bins -> 150)
h_py.Rebin(2)
elif i >= 78: # low pT, resolution affected by finite bin width
h_py = h.ProjectionY("_py", i + 1,
i + 2) # merge i & (i+1) entries
if i == 82: # even lower pT, resolution affected by finite bin width
h_py = h.ProjectionY("_py", i + 1,
i + 4) # merge i & (i+4) entries
elif i >= 82:
continue
if h_py.Integral() < 20: continue
r = h_py.Fit("gaus", "SNQ", "", -1, 1.2)
#r = h_py.Fit("gaus", "SNQ", "", h_py.GetMean() - 0.04*5, h_py.GetMean() + 0.04*5)
mean, sigma, meanErr, sigmaErr = r.Parameter(1), r.Parameter(
2), r.ParError(1), r.ParError(2)
gr1.SetPoint(i, h.GetXaxis().GetBinCenter(i + 1), mean)
gr1.SetPointError(i, 0, 0, sigma, sigma)
gr2.SetPoint(i, h.GetXaxis().GetBinCenter(i + 1), sigma)
gr2.SetPointError(i, 0, 0, sigmaErr, sigmaErr)
gr1_aspt.SetPoint(i, 1.0 / h.GetXaxis().GetBinCenter(i + 1), mean)
gr1_aspt.SetPointError(i, 0, 0, sigma, sigma)
gr2_aspt.SetPoint(i, 1.0 / h.GetXaxis().GetBinCenter(i + 1), sigma)
gr2_aspt.SetPointError(i, 0, 0, sigmaErr, sigmaErr)
# Draw
h.Draw("COLZ")
gPad.SetLogx(0)
#draw_cms_lumi()
#gPad.Print("figures_perf/" + hname + "_omtf" + ".png")
#gPad.Print("figures_perf/" + hname + "_omtf" + ".pdf")
#
frame.Reset()
frame.SetBins(50, 0, 50)
frame.GetXaxis().SetTitle("gen p_{T} [GeV]")
frame.GetYaxis().SetTitle("#Delta(p_{T})/p_{T} bias")
frame.SetMaximum(0.5)
frame.SetMinimum(-0.5)
frame.SetStats(0)
frame.Draw()
gr1_aspt.SetLineColor(col)
gr1_aspt.SetMarkerColor(col)
gr1_aspt.Draw("p")
gPad.SetLogx()
draw_cms_lumi()
gPad.Print("figures_perf/" + hname + "_bias" + "_omtf" + ".png")
gPad.Print("figures_perf/" + hname + "_bias" + "_omtf" + ".pdf")
#
frame.GetXaxis().SetTitle("gen p_{T} [GeV]")
frame.GetYaxis().SetTitle("#Delta(p_{T})/p_{T} resolution")
frame.SetMaximum(0.6)
frame.SetMinimum(0.0)
frame.SetStats(0)
frame.Draw()
gr2_aspt.SetLineColor(col)
gr2_aspt.SetMarkerColor(col)
gr2_aspt.Draw("p")
#gr2_aspt.Fit("pol1", "", "", 10, 40)
gPad.SetLogx()
draw_cms_lumi()
gPad.Print("figures_perf/" + hname + "_res" + "_omtf" + ".png")
gPad.Print("figures_perf/" + hname + "_res" + "_omtf" + ".pdf")
#
h.cache = [frame, gr1, gr2, gr1_aspt, gr2_aspt]
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
def makeBand(middleGraph, lowerGraph, upperGraph, noorder):
bandsGraph = TGraphAsymmErrors()
bandsGraph.SetName(middleGraph.GetName())
bandsGraph.SetMarkerStyle(middleGraph.GetMarkerStyle())
bandsGraph.SetMarkerColor(middleGraph.GetMarkerColor())
bandsGraph.SetLineColor(middleGraph.GetLineColor())
x1 = Double(0)
x2 = Double(0)
x3 = Double(0)
y = []
y.append(Double(0))
y.append(Double(0))
y.append(Double(0))
tmp = Double(0)
if lowerGraph.GetN() != upperGraph.GetN() or lowerGraph.GetN(
) != middleGraph.GetN():
print "Plots don't have the same number of points!"
print "Lower: ", lowerGraph.GetN()
print "Upper: ", upperGraph.GetN()
print "Middle: ", middleGraph.GetN()
return 0
#again a hack to forget about the 1st point
for iPoint in xrange(1, lowerGraph.GetN()):
middleGraph.GetPoint(iPoint, x1, y[0])
lowerGraph.GetPoint(iPoint, x1, y[1])
upperGraph.GetPoint(iPoint, x1, y[2])
if (iPoint == lowerGraph.GetN() - 1): x2 = x1
else: upperGraph.GetPoint(iPoint + 1, x2, buf)
if (iPoint == 0): x3 = x1
else: upperGraph.GetPoint(iPoint - 1, x3, buf)
tmp = Double(0)
yup = Double(0)
yce = Double(0)
ydn = Double(0)
if noorder == 0:
yce = y[0]
ydn = y[1]
yup = y[2]
elif noorder == 1:
tmp = y[2]
if y[1] < y[2]:
y[2] = y[1]
y[1] = tmp
yce = y[0]
ydn = y[1]
yup = y[2]
else:
for p in [0, 1, 2]:
for q in [0, 1, 2]:
if y[q] > y[q + 1]:
tmp = y[q + 1]
y[q + 1] = y[q]
y[q] = tmp
ydn = y[0]
yce = y[1]
yup = y[2]
bandsGraph.SetPoint(iPoint, x1, yce)
ex0 = Double(lowerGraph.GetErrorX(iPoint))
binwl = Double(0)
binwh = Double(0)
if (ex0 == 0):
binwl = (x1 - x3) / 2.
binwh = (x2 - x1) / 2.
if binwl == 0.: binwl = binwh
if binwh == 0.: binwh = binwl
else:
binwl = ex0
binwh = ex0
dxl = Double(yce - ydn)
dxh = Double(yup - yce)
if noorder == 0:
if dxl < 0:
tmp = -dxl
dxl = dxh
dxh = tmp
bandsGraph.SetPointError(iPoint, binwl, binwh, dxl, dxh)
return bandsGraph
def makePlot(finname,foutname,plottitle='',masstitle='',scale=False):
xsecs = resonantXsecs if 'resonant' in finname else fcncXsecs
points = {}
if BLIND:
cls = [2.5, 16, 50, 84, 97.5]
else:
cls = [2.5, 16, 50, 84, 97.5,'Observed']
xaxis = []
for cl in cls:
points[cl] = []
xsec=1
for l in open(finname):
try:
if l.strip()[0]=='#':
continue
if 'MASS' in l:
if scale:
xsec = xsecs[int(l.split()[1])]
if VERBOSE:
print ''
stdout.write('$%6s$ & $%7.3g$'%(l.split()[1],xsec/(0.667)))
xaxis.append(float(l.split()[1]))
else:
cl,val = parseLine(l)
points[cl].append(val/xsec)
if VERBOSE and (cl==50 or cl=='Observed'):
stdout.write(' & $%10.4g$'%(val/xsec))
except:
pass
if VERBOSE:
print ''
N = len(xaxis)
up1Sigma=[]; up2Sigma=[]
down1Sigma=[]; down2Sigma=[]
for iM in xrange(N):
up1Sigma.append(points[84][iM]-points[50][iM])
up2Sigma.append(points[97.5][iM]-points[50][iM])
down1Sigma.append(-points[16][iM]+points[50][iM])
down2Sigma.append(-points[2.5][iM]+points[50][iM])
up1Sigma = array('f',up1Sigma)
up2Sigma = array('f',up2Sigma)
down1Sigma = array('f',down1Sigma)
down2Sigma = array('f',down2Sigma)
cent = array('f',points[50])
if not BLIND:
obs = array('f',points['Observed'])
xarray = array('f',xaxis)
xsecarray = array('f',[xsecs[xx] for xx in xaxis])
xsecarrayLow = array('f',[0.0625*xsecs[xx] for xx in xaxis])
onearray = array('f',[1 for xx in xaxis])
graphXsec = TGraph(N,xarray,xsecarray)
graphXsecLow = TGraph(N,xarray,xsecarrayLow)
graphOne = TGraph(N,xarray,onearray)
zeros = array('f',[0 for i in xrange(N)])
graphCent = TGraph(N,xarray,cent)
if not BLIND:
graphObs = TGraph(N,xarray,obs)
graph1Sigma = TGraphAsymmErrors(N,xarray,cent,zeros,zeros,down1Sigma,up1Sigma)
graph2Sigma = TGraphAsymmErrors(N,xarray,cent,zeros,zeros,down2Sigma,up2Sigma)
c = TCanvas('c','c',700,600)
c.SetLogy()
c.SetLeftMargin(.15)
graph2Sigma.GetXaxis().SetTitle(masstitle+' [GeV]')
if scale:
graph2Sigma.GetYaxis().SetTitle('Upper limit [#sigma/#sigma_{theory}]')
else:
graph2Sigma.GetYaxis().SetTitle("Upper limit [#sigma] [pb]")
graph2Sigma.SetLineColor(5)
graph1Sigma.SetLineColor(3)
graph2Sigma.SetFillColor(5)
graph1Sigma.SetFillColor(3)
graph2Sigma.SetMinimum(0.5*min(points[2.5]))
if scale:
graph2Sigma.SetMaximum(10*max(max(points[97.5]),max(xsecarray),4))
else:
graph2Sigma.SetMaximum(10*max(max(points[97.5]),max(xsecarray)))
graphCent.SetLineWidth(2)
graphCent.SetLineStyle(2)
if not BLIND:
graphObs.SetLineColor(1)
graphObs.SetLineWidth(3)
graph1Sigma.SetLineStyle(0)
graph2Sigma.SetLineStyle(0)
leg = TLegend(0.55,0.7,0.9,0.9)
leg.AddEntry(graphCent,'Expected','L')
if not BLIND:
leg.AddEntry(graphObs,'Observed','L')
leg.AddEntry(graph1Sigma,'1 #sigma','F')
leg.AddEntry(graph2Sigma,'2 #sigma','F')
leg.SetFillStyle(0)
leg.SetBorderSize(0)
graph2Sigma.Draw('A3')
graph1Sigma.Draw('3 same')
graphCent.Draw('same L')
if not BLIND:
graphObs.Draw('same L')
if scale:
graphOne.SetLineColor(2)
graphOne.SetLineWidth(2)
graphOne.SetLineStyle(2)
graphOne.Draw('same L')
else:
graphXsec.SetLineColor(2)
graphXsecLow.SetLineColor(4)
subscript = 'SR' if 'Resonant' in plottitle else 'FC'
if 'Resonant' in plottitle:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=0.1}{m_{#chi}=100 GeV}'%(subscript,subscript),'l')
else:
leg.AddEntry(graphXsec,'Theory a_{%s}=b_{%s}=0.1'%(subscript,subscript),'l')
# leg.AddEntry(graphXsecLow,'Theory a_{%s}=b_{%s}=0.025'%(subscript,subscript),'l')
for g in [graphXsec]:
g.SetLineWidth(2)
g.SetLineStyle(2)
g.Draw('same L')
leg.Draw()
label = TLatex()
label.SetNDC()
label.SetTextFont(62)
label.SetTextAlign(11)
label.DrawLatex(0.19,0.85,"CMS")
label.SetTextFont(52)
label.DrawLatex(0.28,0.85,"Preliminary")
label.SetTextFont(42)
label.SetTextSize(0.6*c.GetTopMargin())
label.DrawLatex(0.19,0.77,plottitle)
if scale:
if 'Resonant' in plottitle:
label.DrawLatex(0.19,0.7,"a_{SR} = b_{SR} = 0.1")
label.DrawLatex(0.19,0.64,"m_{#chi}=100 GeV")
else:
label.DrawLatex(0.19,0.7,"a_{FC} = b_{FC} = 0.1")
label.SetTextSize(0.5*c.GetTopMargin())
label.SetTextFont(42)
label.SetTextAlign(31) # align right
label.DrawLatex(0.9, 0.94,"%.1f fb^{-1} (13 TeV)"%(plotConfig.lumi))
c.SaveAs(foutname+'.pdf')
c.SaveAs(foutname+'.png')
def limit():
method = ''
channel = "bb"
particleP = "Z'"
particle = channel
multF = ZPTOBB
THEORY = ['A1', 'B3']
suffix = "_" + BTAGGING
if ISMC: suffix += "_MC"
if SY: suffix += "_comb"
#if method=="cls": suffix="_CLs"
if SY:
filename = "./combine/limits/MANtag_study/" + BTAGGING + "/combined_run2/" + YEAR + "_M%d.txt"
else:
filename = "./combine/limits/MANtag_study/" + BTAGGING + "/" + YEAR + "_M%d.txt"
if CATEGORY != "":
filename = filename.replace(
BTAGGING + "/", BTAGGING + "/single_category/" + CATEGORY + "_")
suffix += "_" + CATEGORY
if ISMC: filename = filename.replace(".txt", "_MC.txt")
mass, val = fillValues(filename)
#print "mass =",mass
#print "val =", val
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
Sign = TGraph()
pVal = TGraph()
Best = TGraphAsymmErrors()
Theory = {}
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF)
Exp0s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][2] * multF,
val[m][4] * multF - val[m][3] * multF)
Exp2s.SetPoint(n, m, val[m][3] * multF)
Exp2s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][1] * multF,
val[m][5] * multF - val[m][3] * multF)
if len(val[m]) > 6: Sign.SetPoint(n, m, val[m][6])
if len(val[m]) > 7: pVal.SetPoint(n, m, val[m][7])
if len(val[m]) > 8: Best.SetPoint(n, m, val[m][8])
if len(val[m]) > 10:
Best.SetPointError(n, 0., 0., abs(val[m][9]), val[m][10])
for t in THEORY:
Theory[t] = TGraphAsymmErrors()
addXZH = True
for m in sorted(HVT[t]['W']['XS'].keys()):
if m < mass[0] or m > mass[-1]: continue
if m > 4500:
continue ## for now because I don't have the higher mass xs FIXME
XsZ, XsZ_Up, XsZ_Down = 0., 0., 0.
if addXZH:
XsZ = 1000. * HVT[t]['Z']['XS'][
m] * 0.12 #temporary BR value set to 0.12 FIXME
XsZ_Up = XsZ * (1. + math.hypot(HVT[t]['Z']['QCD'][m][0] - 1.,
HVT[t]['Z']['PDF'][m][0] - 1.))
XsZ_Down = XsZ * (1. -
math.hypot(1. - HVT[t]['Z']['QCD'][m][0],
1. - HVT[t]['Z']['PDF'][m][0]))
n = Theory[t].GetN()
Theory[t].SetPoint(n, m, XsZ)
Theory[t].SetPointError(n, 0., 0., (XsZ - XsZ_Down),
(XsZ_Up - XsZ))
Theory[t].SetLineColor(theoryLineColor[t])
Theory[t].SetFillColor(theoryFillColor[t])
Theory[t].SetFillStyle(theoryFillStyle[t])
Theory[t].SetLineWidth(2)
#Theory[t].SetLineStyle(7)
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp1s.SetFillColor(417) #kGreen+1
Exp1s.SetLineColor(417) #kGreen+1
Exp2s.SetFillColor(800) #kOrange
Exp2s.SetLineColor(800) #kOrange
Exp2s.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle("#sigma(" + particleP + ") #bf{#it{#Beta}}(" +
particleP + " #rightarrow " + particle +
") (fb)")
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Sign.SetLineWidth(2)
Sign.SetLineColor(629)
Sign.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Sign.GetXaxis().SetTitleSize(Sign.GetXaxis().GetTitleSize() * 1.1)
Sign.GetYaxis().SetTitle("Significance")
pVal.SetLineWidth(2)
pVal.SetLineColor(629)
pVal.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
pVal.GetXaxis().SetTitleSize(pVal.GetXaxis().GetTitleSize() * 1.1)
pVal.GetYaxis().SetTitle("local p-Value")
Best.SetLineWidth(2)
Best.SetLineColor(629)
Best.SetFillColor(629)
Best.SetFillStyle(3003)
Best.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Best.GetXaxis().SetTitleSize(Best.GetXaxis().GetTitleSize() * 1.1)
Best.GetYaxis().SetTitle("Best Fit (pb)")
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
#c1.GetPad(0).SetGridx()
#c1.GetPad(0).SetGridy()
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
Exp0s.Draw("SAME, L")
if not options.blind: Obs0s.Draw("SAME, L")
#setHistStyle(Exp2s)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.1, 5.e3)
#else: Exp2s.GetYaxis().SetRangeUser(0.1, 1.e2)
#Exp2s.GetXaxis().SetRangeUser(mass[0], min(mass[-1], MAXIMUM[channel] if channel in MAXIMUM else 1.e6))
Exp2s.GetXaxis().SetRangeUser(SIGNALS[0], SIGNALS[-1])
#drawAnalysis(channel)
drawAnalysis("")
#drawRegion(channel, True)
drawRegion("", True)
#drawCMS(LUMI, "Simulation Preliminary") #Preliminary
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
# legend
top = 0.9
nitems = 4 + len(THEORY)
leg = TLegend(0.55, top - nitems * 0.3 / 5., 0.98, top)
#leg = TLegend(0.45, top-nitems*0.3/5., 0.98, top)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
for t in THEORY:
leg.AddEntry(Theory[t], theoryLabel[t], "fl")
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.045)
latex.SetTextFont(42)
#latex.DrawLatex(0.66, leg.GetY1()-0.045, particleP+" #rightarrow "+particle+"h")
leg2 = TLegend(0.12, 0.225 - 2 * 0.25 / 5., 0.65, 0.225)
leg2.SetBorderSize(0)
leg2.SetFillStyle(0) #1001
leg2.SetFillColor(0)
c1.GetPad(0).RedrawAxis()
leg2.Draw()
if not options.blind: Obs0s.Draw("SAME, L")
c1.GetPad(0).Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".png")
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".C")
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".root")
for t in THEORY:
print "Model", t, ":",
for m in range(mass[0], mass[-1], 1):
if not (Theory[t].Eval(m) > Obs0s.Eval(m)) == (
Theory[t].Eval(m + 1) > Obs0s.Eval(m + 1)):
print m,
print ""
return
def makeplot_single(
h1_sig=None,
h1_bkg=None,
h1_data=None,
sig_legends_=None,
bkg_legends_=None,
sig_colors_=None,
bkg_colors_=None,
hist_name_=None,
sig_scale_=1.0,
dir_name_="plots",
output_name_=None,
extraoptions=None
):
if h1_sig == None or h1_bkg == None:
print("nothing to plot...")
return
os.system("mkdir -p "+dir_name_)
os.system("cp index.php "+dir_name_)
s_color = [632, 617, 839, 800, 1]
b_color = [920, 2007, 2005, 2003, 2001, 2011]
if sig_colors_:
s_color = sig_colors_
if bkg_colors_:
b_color = bkg_colors_
for idx in range(len(h1_sig)):
h1_sig[idx].SetLineWidth(3)
h1_sig[idx].SetLineColor(s_color[idx])
for idx in range(len(h1_bkg)):
h1_bkg[idx].SetLineWidth(2)
h1_bkg[idx].SetLineColor(b_color[idx])
h1_bkg[idx].SetFillColorAlpha(b_color[idx], 1)
if h1_data:
h1_data.SetBinErrorOption(1)
h1_data.SetLineColor(1)
h1_data.SetLineWidth(2)
h1_data.SetMarkerColor(1)
h1_data.SetMarkerStyle(20)
myC = r.TCanvas("myC","myC", 600, 600)
myC.SetTicky(1)
pad1 = r.TPad("pad1","pad1", 0.05, 0.33,0.95, 0.97)
pad1.SetBottomMargin(0.027)
pad1.SetRightMargin( rightMargin )
pad1.SetLeftMargin( leftMargin )
pad2 = r.TPad("pad2","pad2", 0.05, 0.04, 0.95, 0.31)
pad2.SetBottomMargin(0.4)
pad2.SetTopMargin(0.05)
pad2.SetRightMargin( rightMargin )
pad2.SetLeftMargin( leftMargin )
pad2.Draw()
pad1.Draw()
pad1.cd()
for idx in range(len(h1_sig)):
print("before signal scaling",h1_sig[idx].Integral())
h1_sig[idx].Scale(sig_scale_)
print("after signal scaling",h1_sig[idx].Integral())
stack = r.THStack("stack", "stack")
nS = np.zeros(h1_bkg[0].GetNbinsX())
eS = np.zeros(h1_bkg[0].GetNbinsX())
#hist_all is used to make the data/mc ratio. remove signal for the moment due to signal is scaled right now
hist_all = h1_sig[0].Clone("hist_all")
hist_all.Scale(0.0)
hist_s = h1_sig[0].Clone("hist_s")
hist_b = h1_bkg[0].Clone("hist_b")
for idx in range(len(h1_bkg)):
stack.Add(h1_bkg[idx])
for ib in range(h1_bkg[0].GetNbinsX()):
nS[ib] += h1_bkg[idx].GetBinContent(ib+1)
eS[ib] = math.sqrt(eS[ib]*eS[ib] + h1_bkg[idx].GetBinError(ib+1)*h1_bkg[idx].GetBinError(ib+1))
hist_all.Add(h1_bkg[idx])
if idx > 0:
hist_b.Add(h1_bkg[idx])
for idx in range(len(h1_sig)):
print("ggH signal yield: ", hist_s.Integral())
if idx > 0:
hist_temp = h1_sig[idx].Clone(h1_sig[idx].GetName()+"_temp")
#hist_all.Add(hist_temp)
hist_s.Add(h1_sig[idx])
print("all signal yield: ", hist_s.Integral())
stack.SetTitle("")
maxY = 0.0
if "stack_signal" in extraoptions and extraoptions["stack_signal"]:
for idx in range(len(h1_sig)):
h1_sig[idx].SetFillColorAlpha(s_color[idx], 1)
stack.Add(h1_sig[idx])
for ib in range(h1_bkg[0].GetNbinsX()):
nS[ib] += h1_sig[idx].GetBinContent(ib+1)
eS[ib] = math.sqrt(eS[ib]*eS[ib] + h1_sig[idx].GetBinError(ib+1)*h1_sig[idx].GetBinError(ib+1))
if stack.GetMaximum() > maxY:
maxY = stack.GetMaximum()
#if "SR" in h.GetTitle():
stack.Draw("hist")
else:
stack.Draw("hist")
if stack.GetMaximum() > maxY:
maxY = stack.GetMaximum()
for idx in range(len(h1_sig)):
if h1_sig[idx].GetMaximum() > maxY:
maxY = h1_sig[idx].GetMaximum()
if "SR" in h1_bkg[0].GetTitle():
#h1_sig[idx].Draw("samehist")
hist_s.Draw("samehist")
##draw stack total unc on top of total histogram
box = r.TBox(0,0,1,1,)
box.SetFillStyle(3002)
box.SetLineWidth(0)
box.SetFillColor(r.kBlack)
for idx in range(h1_bkg[0].GetNbinsX()):
box.DrawBox(h1_bkg[0].GetBinCenter(idx+1)-0.5*h1_bkg[0].GetBinWidth(idx+1), nS[idx]-eS[idx], h1_bkg[0].GetBinCenter(idx+1)+0.5*h1_bkg[0].GetBinWidth(idx+1), nS[idx]+eS[idx])
if h1_data:
if h1_data.GetMaximum() > maxY:
maxY = h1_data.GetMaximum()+np.sqrt(h1_data.GetMaximum())
#if not "SR" in h1_data.GetTitle() or "fail" in h1_data.GetTitle():
if True:
#print("debug h1_data.GetName()",h1_data.GetName(), h1_data.GetTitle())
TGraph_data = TGraphAsymmErrors(h1_data)
for i in range(TGraph_data.GetN()):
#data point
var_x, var_y = Double(0.), Double(0.)
TGraph_data.GetPoint(i,var_x,var_y)
if np.fabs(var_y) < 1e-5:
TGraph_data.SetPoint(i,var_x,-1.0)
TGraph_data.SetPointEYlow(i,-1)
TGraph_data.SetPointEYhigh(i,-1)
#print("zero bins in the data TGraph: bin",i+1)
else:
TGraph_data.SetPoint(i,var_x,var_y)
err_low = var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y))
TGraph_data.SetPointEYlow(i, var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y)))
TGraph_data.SetPointEYhigh(i, (0.5*TMath.ChisquareQuantile(1.-0.1586555,2.*(var_y+1))) - var_y)
TGraph_data.SetMarkerColor(1)
TGraph_data.SetMarkerSize(1)
TGraph_data.SetMarkerStyle(20)
TGraph_data.Draw("same P")
stack.GetYaxis().SetTitle("Events")
stack.GetYaxis().SetTitleOffset(1.05)
stack.GetYaxis().SetTitleSize(0.08)
stack.GetYaxis().SetLabelSize(0.06)
#stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetLabelSize(0.)
#stack.GetXaxis().SetLabelOffset(0.013)
#if "xaxis_range" in extraoptions:
# stack.GetXaxis().SetRangeUser(float(extraoptions["xaxis_range"][0]),float(extraoptions["xaxis_range"][1]))
leg = r.TLegend(0.2, 0.60, 0.9, 0.88)
leg.SetNColumns(3)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.SetTextSize(0.05)
for idx in range(len(h1_bkg)):
leg.AddEntry(h1_bkg[idx], bkg_legends_[idx], "F")
if "SR" in hist_s.GetTitle():
leg.AddEntry(hist_s, 'HH #times {:1.2}'.format(sig_scale_), "L")
leg.AddEntry(box, "Total unc", "F")
if h1_data:
leg.AddEntry(h1_data, "Data", "ep")
leg.Draw()
pad2.cd()
pad2.SetGridy(1)
ratio = None
ratio_Low = 0.0
ratio_High = 4
if h1_data:
ratio = TGraphAsymmErrors(h1_data)
for i in range(ratio.GetN()):
#bkg prediction
imc = Double(hist_all.GetBinContent(i+1))
#data point
var_x, var_y = Double(0.), Double(0.)
if not ("SR" in h1_data.GetTitle() and (i>5 and i<9)):
ratio.GetPoint(i,var_x,var_y)
if var_y == 0.:
ratio.SetPoint(i,var_x,-1.0)
ratio.SetPointEYlow(i,-1)
ratio.SetPointEYhigh(i,-1)
continue
ratio.SetPoint(i,var_x,var_y/imc)
err_low = (var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y)))/imc
err_high = ((0.5*TMath.ChisquareQuantile(1.-0.1586555,2.*(var_y+1))) - var_y)/imc
ratio.SetPointEYlow(i, err_low)
ratio.SetPointEYhigh(i, err_high)
ratio.SetMarkerColor(1)
ratio.SetMarkerSize(1)
ratio.SetMarkerStyle(20)
ratio.GetXaxis().SetTitle("j_{2} regressed mass [GeV]")
#myC.Update()
if "ratio_range" in extraoptions:
ratio_Low = extraoptions["ratio_range"][0]
ratio_High = extraoptions["ratio_range"][1]
ratio.GetYaxis().SetTitle("data/mc")
ratio.GetYaxis().SetRangeUser(ratio_Low, ratio_High)
ratio.GetXaxis().SetRangeUser(50, 220)
ratio.SetTitle("")
ratio.Draw("same AP")
pad2.Update()
print(ratio.GetTitle(),ratio.GetName(),"debug")
else:
ratio = h1_sig[0].Clone("ratio")
ratio_High = 0.0
for ibin in range(1,ratio.GetNbinsX()+1):
s = hist_s.GetBinContent(ibin)
b = hist_b.GetBinContent(ibin)
L = 0.0
if b > 0.0:
L = s/math.sqrt(b)
if L > ratio_High:
ratio_High = L
ratio.SetBinContent(ibin, L)
if ratio_High > 1.0:
ratio_High = 1.0
ratio.GetYaxis().SetRangeUser(ratio_Low, ratio_High*1.2)
ratio.GetYaxis().SetTitle("S/#sqrt{B}")
ratio.Draw("samehist")
ratio.SetLineColor(1)
ratio.SetLineWidth(2)
ratio.SetMarkerStyle(20)
ratio.SetMarkerColor(1)
ratio.SetFillColorAlpha(1, 0)
ratio.GetXaxis().SetTitleOffset(0.94)
ratio.GetXaxis().SetTitleSize(0.18)
ratio.GetXaxis().SetLabelSize(0.12)
ratio.GetXaxis().SetLabelOffset(0.013)
ratio.GetYaxis().SetTitleOffset(0.40)
ratio.GetYaxis().SetTitleSize(0.17)
ratio.GetYaxis().SetLabelSize(0.13)
ratio.GetYaxis().SetTickLength(0.01)
ratio.GetYaxis().SetNdivisions(505)
#if "xaxis_range" in extraoptions:
# ratio.GetXaxis().SetRangeUser(float(extraoptions["xaxis_range"][0]),float(extraoptions["xaxis_range"][1]))
#draw stack total unc on the ratio plot to present the background uncertainty
box_ratio = r.TBox(0,0,1,1,)
box_ratio.SetFillStyle(3002)
box_ratio.SetLineWidth(0)
box_ratio.SetFillColor(r.kBlack)
for idx in range(h1_bkg[0].GetNbinsX()):
if np.fabs(nS[idx])> 1e-06:
box_ratio.DrawBox(h1_bkg[0].GetBinCenter(idx+1)-0.5*h1_bkg[0].GetBinWidth(idx+1), (nS[idx]-eS[idx])/nS[idx], h1_bkg[0].GetBinCenter(idx+1)+0.5*h1_bkg[0].GetBinWidth(idx+1), (nS[idx]+eS[idx])/nS[idx])
else:
print("blinded Higgs peak region")
if "xaxis_label" in extraoptions and extraoptions["xaxis_label"] != None:
x_title = extraoptions["xaxis_label"]
ratio.GetXaxis().SetTitle(x_title)
ratio.GetYaxis().CenterTitle()
##########draw CMS preliminary
pad1.cd()
tex1 = r.TLatex(leftMargin, 0.91, "CMS")
tex1.SetNDC()
tex1.SetTextFont(61)
tex1.SetTextSize(0.070)
tex1.SetLineWidth(2)
tex1.Draw()
tex2 = r.TLatex(leftMargin+0.12,0.912,"Internal")
tex2.SetNDC()
tex2.SetTextFont(52)
tex2.SetTextSize(0.055)
tex2.SetLineWidth(2)
tex2.Draw()
lumi_value = 137
if "lumi_value" in extraoptions:
lumi_value = extraoptions["lumi_value"]
tex3 = r.TLatex(0.72,0.912,"%d"%lumi_value+" fb^{-1} (13 TeV)")
tex3.SetNDC()
tex3.SetTextFont(42)
tex3.SetTextSize(0.055)
tex3.SetLineWidth(2)
tex3.Draw()
outFile = dir_name_
if output_name_:
outFile = outFile + "/" +output_name_
else:
outFile = outFile + "/" + hist_name_
#print("maxY = "+str(maxY))
stack.SetMaximum(maxY*1.7)
#print everything into txt file
text_file = open(outFile+"_linY.txt", "w")
text_file.write("bin | x ")
for idx in range(len(h1_bkg)):
text_file.write(" | %21s"%bkg_legends_[idx])
text_file.write(" | %21s"%("total B"))
for idx in range(len(sig_legends_)):
text_file.write(" | %25s"%sig_legends_[idx])
if h1_data:
text_file.write(" | data | data/mc")
text_file.write("\n-------------")
for idx in range(24*(len(h1_bkg) + 1)+ 29*len(sig_legends_)):
text_file.write("-")
if h1_data:
text_file.write("-------")
text_file.write("\n")
for ibin in range(0,h1_sig[0].GetNbinsX()+1):
text_file.write("%3d"%ibin+" ")
text_file.write(" | %6.3f"%h1_data.GetBinCenter(ibin)+" ")
for idx in range(len(h1_bkg)):
text_file.write(" | %7.3f "%h1_bkg[idx].GetBinContent(ibin)+"$\\pm$"+ " %7.3f"%h1_bkg[idx].GetBinError(ibin))
text_file.write(" | %7.3f "%hist_b.GetBinContent(ibin)+"$\\pm$"+ " %7.3f"%hist_b.GetBinError(ibin))
for idx in range(len(sig_legends_)):
text_file.write(" | %9.3f "%h1_sig[idx].GetBinContent(ibin)+"$\\pm$"+ " %9.3f"%h1_sig[idx].GetBinError(ibin))
if h1_data:
text_file.write(" | %d"%h1_data.GetBinContent(ibin) + " | %7.3f "%h1_data.GetBinContent(ibin) +"$\\pm$"+ " %7.3f"%h1_data.GetBinError(ibin))
text_file.write("\n\n")
#print yield table for AN
text_file.write("print yield table for AN\n")
bkg_all = 0
bkg_all_errsq = 0
for idx in range(len(h1_bkg)):
bkg_tmp = h1_bkg[idx].GetBinContent(7)+h1_bkg[idx].GetBinContent(8)+h1_bkg[idx].GetBinContent(9)
bkg_errsq_tmp = h1_bkg[idx].GetBinError(7)*h1_bkg[idx].GetBinError(7)+h1_bkg[idx].GetBinError(8)*h1_bkg[idx].GetBinError(8)+h1_bkg[idx].GetBinError(9)*h1_bkg[idx].GetBinError(9)
bkg_all += bkg_tmp
bkg_all_errsq += bkg_errsq_tmp
text_file.write("%s"%(bkg_legends_[idx])+"& %7.2f"%(bkg_tmp)+"$\\pm$"+ "%7.2f"%np.sqrt(bkg_errsq_tmp)+"\n")
text_file.write("total background & %7.2f"%(bkg_all)+"$\\pm$"+ "%7.2f"%np.sqrt(bkg_all_errsq)+"\n")
text_file.write("\ggHH SM ($\kapl=1$) & %7.2f"%((h1_sig[0].GetBinContent(7)+h1_sig[0].GetBinContent(8)+h1_sig[0].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.1f"%(sig_scale_*np.sqrt(h1_sig[0].GetBinError(7)*h1_sig[0].GetBinError(7)+h1_sig[0].GetBinError(8)*h1_sig[0].GetBinError(8)+h1_sig[0].GetBinError(9)*h1_sig[0].GetBinError(9)))+"\n")
text_file.write("\VBFHH SM ($\kapl=1$) & %7.2f"%((h1_sig[1].GetBinContent(7)+h1_sig[1].GetBinContent(8)+h1_sig[1].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.1f"%(sig_scale_*np.sqrt(h1_sig[1].GetBinError(7)*h1_sig[1].GetBinError(7)+h1_sig[1].GetBinError(8)*h1_sig[1].GetBinError(8)+h1_sig[1].GetBinError(9)*h1_sig[1].GetBinError(9)))+"\n")
text_file.write("HH bin 8 value %s"%h1_sig[0].GetBinContent(8)+"\n")
text_file.write("HH bin 9 value %s"%h1_sig[0].GetBinContent(9)+"\n")
text_file.write("HH bin 7 value %s"%h1_sig[0].GetBinContent(7)+"\n")
text_file.write("HH bin 8 error %s"%h1_sig[0].GetBinError(8)+"\n")
text_file.write("HH bin 9 error %s"%h1_sig[0].GetBinError(9)+"\n")
text_file.write("HH bin 7 error %s"%h1_sig[0].GetBinError(7)+"\n")
text_file.write("total & %7.2f"%(bkg_all+(h1_sig[0].GetBinContent(7)+h1_sig[0].GetBinContent(8)+h1_sig[0].GetBinContent(9)+h1_sig[1].GetBinContent(7)+h1_sig[1].GetBinContent(8)+h1_sig[1].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.2f"%(np.sqrt((h1_sig[0].GetBinError(7)*h1_sig[0].GetBinError(7)+h1_sig[0].GetBinError(8)*h1_sig[0].GetBinError(8)+h1_sig[0].GetBinError(9)*h1_sig[0].GetBinError(9))/(sig_scale_*sig_scale_)+(h1_sig[1].GetBinError(7)*h1_sig[1].GetBinError(7)+h1_sig[1].GetBinError(8)*h1_sig[1].GetBinError(8)+h1_sig[1].GetBinError(9)*h1_sig[1].GetBinError(9))/(sig_scale_*sig_scale_)+bkg_all_errsq))+"\n")
text_file.close()
os.system("cp "+outFile+"_linY.txt "+outFile+"_logY.txt")
pad1.RedrawAxis()
myC.SaveAs(outFile+"_linY.png")
myC.SaveAs(outFile+"_linY.pdf")
myC.SaveAs(outFile+"_linY.C")
pad1.cd()
stack.SetMaximum(maxY*100.0)
stack.SetMinimum(0.5)
pad1.SetLogy()
pad1.RedrawAxis()
myC.SaveAs(outFile+"_logY.png")
myC.SaveAs(outFile+"_logY.pdf")
myC.SaveAs(outFile+"_logY.C")
#save histogram and ratio to root file
outFile_root = r.TFile(outFile+".root", "recreate")
outFile_root.cd()
for idx in range(len(h1_bkg)):
h1_bkg[idx].Write()
for idx in range(len(sig_legends_)):
h1_sig[idx].Write()
if h1_data:
h1_data.Write()
ratio.Write()
#outFile_root.Write()
outFile_root.Close()
def getGraph(self,dset):
from array import array
from ROOT import TMultiGraph, TLegend, TGraphAsymmErrors
n = len(self.__x)
if n != len(self.__y) or n != len(self.__yErrLow) or n != len(self.__yErrHigh):
raise StandardError, "The length of the x(%s), y(%s) and y error(%s,%s) lists does not match"%(len(self.__x), len(self.__y), len(self.__yErrLow), len(self.__yErrHigh))
result = TMultiGraph()
legendPosition = [float(i) for i in self.__getStyleOption("legendPosition").split()]
legend = TLegend(*legendPosition)
legend.SetFillColor(0)
result.SetTitle("%s;%s;%s"%(self.__title,self.__xTitle,self.__yTitle))
#(refArrays, refLabel) = self.__getRefernceGraphArrays()
#refGraph = TGraphAsymmErrors(*refArrays)
#refGraph.SetLineWidth(2)
#refGraph.SetLineColor(int(self.__config.get("reference","lineColor")))
#refGraph.SetFillColor(int(self.__config.get("reference","fillColor")))
#result.Add(refGraph,"L3")
#legend.AddEntry(refGraph,self.__config.get("reference","name"))
xErr = array("d",[0 for i in range(n)])
print "__x = ", self.__x
print "__y = ", self.__y
lst = []
for inc in range (0,n):
d={}
d['run']=self.__runs[inc]
d['x']=self.__x[inc]
d['y']=self.__y[inc]
d['yErr']=self.__yErrLow[inc]
d['yTitle']=self.__yTitle
if self.__config.has_option(self.__section,"yMin") and self.__config.has_option(self.__section,"yMax") :
d['ymin']=float(self.__config.get(self.__section,"yMin"))
d['ymax']=float(self.__config.get(self.__section,"yMax"))
else:
d['ymin']=0
d['ymax']=0
lst.append(d)
obj ={}
obj[self.__title]=lst
#finalObj[self.__title]=lst
#finalList.append(finalObj)
# save_path = './JSON_A/'
#completeName = os.path.join(save_path, self.__title+".json")
if not os.path.exists("JSON_RECO"):
os.makedirs("JSON_RECO")
if not os.path.exists("JSON_RECO/"+dset):
os.makedirs("JSON_RECO/"+dset)
with open("./JSON_RECO/"+dset+"/"+self.__title+".json", 'w') as outfile:
json.dump(obj, outfile,indent=4)
print json.dumps(obj,indent=2)
graph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr, self.__yErrLow,self.__yErrHigh)
graph.SetLineWidth(2)
graph.SetFillColor(0)
graph.SetLineColor(int(self.__getStyleOption("lineColor")))
graph.SetMarkerColor(int(self.__getStyleOption("markerColor")))
graph.SetMarkerStyle(int(self.__getStyleOption("markerStyle")))
graph.SetMarkerSize(float(self.__getStyleOption("markerSize")))
sysGraph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr, self.__ySysErrLow,self.__ySysErrHigh)
sysGraph.SetLineWidth(1)
sysGraph.SetFillColor(0)
sysGraph.SetLineColor(int(self.__getStyleOption("lineColor")))
sysGraph.SetMarkerColor(int(self.__getStyleOption("markerColor")))
sysGraph.SetMarkerStyle(int(self.__getStyleOption("markerStyle")))
sysGraph.SetMarkerSize(float(self.__getStyleOption("markerSize")))
#TOMAS removed sys error from the plot
#result.Add(sysGraph,"[]")
result.Add(graph,"P")
# result.SetName("MultiPlots")
# result.SetTitle("%s;%s;%s"%(self.__title,self.__xTitle,self.__yTitle))
result.SetName("MG_%s"%(self.__title))
legend.AddEntry(graph, self.__getStyleOption("name"))
#for (x,y,yErr) in zip(self.__x, self.__y, zip(self.__yErrLow,self.__yErrHigh)):
# self.__addAnnotaion("hallo",x,y,yErr)
return (result, legend)
gr_denom = TGraphAsymmErrors(len(rates))
gr_numer = TGraphAsymmErrors(len(rates))
for i, x in enumerate(rates):
pu, emtf_rate, emtf_rate_err, emtf2026_rate, emtf2026_rate_err = x
gr_denom.SetPoint(i, pu, emtf_rate)
gr_denom.SetPointError(i, 0, 0, emtf_rate_err, emtf_rate_err)
gr_numer.SetPoint(i, pu, emtf2026_rate)
gr_numer.SetPointError(i, 0, 0, emtf2026_rate_err, emtf2026_rate_err)
gr_denom.SetMarkerStyle(20)
gr_denom.SetMarkerSize(1.4)
gr_denom.SetMarkerColor(632) # kRed
gr_denom.SetLineWidth(2)
gr_denom.SetLineColor(632) # kRed
gr_numer.SetMarkerStyle(20)
gr_numer.SetMarkerSize(1.4)
gr_numer.SetMarkerColor(600) # kBlue
gr_numer.SetLineWidth(2)
gr_numer.SetLineColor(600) # kBlue
frame = TH1F("frame", "; PU; Trigger rate [kHz]", 100, 0, 350)
frame.SetMinimum(0)
frame.SetMaximum(25)
frame.Draw()
#gr_denom.Draw("P")
gr_numer.Draw("P")
def makePlot1D(filepath, foutname, plottitle='', masstitle=''):
br = 1 if 'Resonant' in plottitle else 0.68
limits = parseLimitFiles2D(filepath, br)
xaxis = []
xseclist = []
xsecerr = []
cent = []
obs = []
up1 = []
up2 = []
down1 = []
down2 = []
maxval = 0
minval = 999
for m in sorted(limits):
l = limits[m]
xaxis.append(m)
xseclist.append(l.xsec)
xsecerr.append(l.xsec * .2)
cent.append(l.cent)
up1.append(l.up1 - l.cent)
up2.append(l.up2 - l.cent)
down1.append(l.cent - l.down1)
down2.append(l.cent - l.down2)
obs.append(l.obs)
maxval = max(maxval, l.up2)
minval = min(minval, l.down2)
N = len(xaxis)
up1Sigma = array('f', up1)
up2Sigma = array('f', up2)
down1Sigma = array('f', down1)
down2Sigma = array('f', down2)
cent = array('f', cent)
obs = array('f', obs)
xarray = array('f', xaxis)
xsecarray = array('f', xseclist)
xsecerrarray = array('f', xsecerr)
zeros = array('f', [0 for i in xrange(N)])
graphXsec = TGraphErrors(N, xarray, xsecarray, zeros, xsecerrarray)
graphCent = TGraph(N, xarray, cent)
graphObs = TGraph(N, xarray, obs)
graph1Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down1Sigma,
up1Sigma)
graph2Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down2Sigma,
up2Sigma)
c = TCanvas('c', 'c', 700, 600)
c.SetLogy()
c.SetLeftMargin(.15)
graph2Sigma.GetXaxis().SetTitle(masstitle + ' [GeV]')
graph2Sigma.GetYaxis().SetTitle(
'95% C.L. upper limit [#sigma/#sigma_{theory}]')
c2 = root.kOrange
c1 = root.kGreen + 1
graph2Sigma.SetLineColor(c2)
graph1Sigma.SetLineColor(c1)
graph2Sigma.SetFillColor(c2)
graph1Sigma.SetFillColor(c1)
graph2Sigma.SetMinimum(0.5 * minval)
graph2Sigma.SetMaximum(10 * maxval)
graphCent.SetLineWidth(2)
graphCent.SetLineStyle(2)
graphObs.SetLineColor(1)
graphObs.SetLineWidth(3)
graphObs.SetMarkerStyle(20)
graphObs.SetMarkerSize(1)
graphObs.SetMarkerColor(1)
graph1Sigma.SetLineStyle(0)
graph2Sigma.SetLineStyle(0)
leg = TLegend(0.55, 0.7, 0.9, 0.9)
leg.AddEntry(graphCent, 'Expected', 'L')
if not BLIND:
leg.AddEntry(graphObs, 'Observed', 'Lp')
leg.AddEntry(graph1Sigma, '1 std. dev.', 'F')
leg.AddEntry(graph2Sigma, '2 std. dev.', 'F')
leg.SetFillStyle(0)
leg.SetBorderSize(0)
graph2Sigma.Draw('A3')
graph1Sigma.Draw('3 same')
graphCent.Draw('same L')
if not BLIND:
graphObs.Draw('same Lp')
subscript = 'SR' if 'Resonant' in plottitle else 'FC'
coupling = '0.1' if 'Resonant' in plottitle else '0.25'
graphXsec.SetLineColor(2)
graphXsec.SetLineWidth(2)
graphXsec.SetLineStyle(2)
graphXsec.SetFillColor(2)
graphXsec.SetFillStyle(3005)
graphXsec.Draw('same L3')
'''
if not scale:
if 'Resonant' in plottitle:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=100 GeV}'%(subscript,subscript,coupling),'l')
else:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=10 GeV}'%(subscript,subscript,coupling),'l')
'''
if not BLIND:
findIntersect1D(graphObs, graphXsec, xaxis[0], xaxis[-1])
findIntersect1D(graphCent, graphXsec, xaxis[0], xaxis[-1])
leg.Draw()
label = TLatex()
label.SetNDC()
label.SetTextSize(0.8 * c.GetTopMargin())
label.SetTextFont(62)
label.SetTextAlign(11)
label.DrawLatex(0.15, 0.94, "CMS")
label.SetTextFont(52)
label.SetTextSize(0.6 * c.GetTopMargin())
# label.DrawLatex(0.25,0.94,"Preliminary")
label.SetTextFont(42)
label.SetTextSize(0.7 * c.GetTopMargin())
label.DrawLatex(0.19, 0.83, plottitle)
if 'Resonant' in plottitle:
label.DrawLatex(0.19, 0.75, "a_{SR} = b_{SR} = %s" % coupling)
label.DrawLatex(0.19, 0.68, "m_{#chi}=100 GeV")
else:
label.DrawLatex(0.19, 0.75, "g_{DM}^{V}=1,g_{q}^{V}=0.25")
label.DrawLatex(0.19, 0.68, "m_{#chi}=1 GeV")
label.SetTextSize(0.6 * c.GetTopMargin())
label.SetTextFont(42)
label.SetTextAlign(31) # align right
label.DrawLatex(0.95, 0.94, "%.1f fb^{-1} (13 TeV)" % (plotConfig.lumi))
c.SaveAs(foutname + '.pdf')
c.SaveAs(foutname + '.png')
def pullsVertical_noBonly(fileName):
content = filterPullFile(fileName)
nbins, off = len(content), 0.10
# Graphs
h_pulls = TH2F("pulls", "", 6, -3., 3., nbins, 0, nbins)
S_pulls = TGraphAsymmErrors(nbins)
boxes = []
canvas = TCanvas("canvas", "Pulls", 720, 300 + nbins * 18) #nbins*20)
canvas.cd()
canvas.SetGrid(0, 1)
canvas.SetTopMargin(0.01)
canvas.SetRightMargin(0.01)
canvas.SetBottomMargin(0.10)
canvas.SetLeftMargin(0.40)
canvas.SetTicks(1, 1)
for i, s in enumerate(content):
l = s.split()
h_pulls.GetYaxis().SetBinLabel(i + 1, l[0])
S_pulls.SetPoint(i, float(l[3]), float(i + 1) - 0.5)
S_pulls.SetPointError(i, float(l[4]), float(l[4]), 0., 0.)
h_pulls.GetXaxis().SetTitle("(#hat{#theta} - #theta_{0}) / #Delta#theta")
h_pulls.GetXaxis().SetLabelOffset(0.0)
h_pulls.GetXaxis().SetTitleOffset(0.8)
h_pulls.GetXaxis().SetLabelSize(0.045)
h_pulls.GetXaxis().SetTitleSize(0.050)
h_pulls.GetYaxis().SetLabelSize(0.046)
h_pulls.GetYaxis().SetNdivisions(nbins, 0, 0)
S_pulls.SetFillColor(kBlack)
S_pulls.SetLineColor(kBlack)
S_pulls.SetMarkerColor(kBlack)
S_pulls.SetLineWidth(2)
S_pulls.SetMarkerStyle(20)
S_pulls.SetMarkerSize(1)
box1 = TBox(-1., 0., 1., nbins)
#box1.SetFillStyle(3001) # 3001 checkered
#box1.SetFillStyle(0)
box1.SetFillColor(kGreen + 1) # 417
box1.SetLineWidth(2)
box1.SetLineStyle(2)
box1.SetLineColor(kGreen + 1) # 417
box2 = TBox(-2., 0., 2., nbins)
#box2.SetFillStyle(3001) # 3001 checkered
#box2.SetFillStyle(0)
box2.SetFillColor(kOrange) # 800
box2.SetLineWidth(2)
box2.SetLineStyle(2)
box2.SetLineColor(kOrange) # 800
leg = TLegend(0.01, 0.01, 0.3, 0.15)
leg.SetTextSize(0.05)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
#leg.SetNColumns(2)
leg.AddEntry(S_pulls, "S+B fit", "lp")
if text: leg.AddEntry(0, text, "")
h_pulls.Draw("")
box2.Draw()
box1.Draw()
S_pulls.Draw("P6SAME")
leg.Draw()
canvas.RedrawAxis()
canvas.Print(outName + ".png")
canvas.Print(outName + ".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
else:
ey_hi_ratio = y_ratio * math.hypot(ey_hi_mu / y_mu, ey_hi_ele / y_ele)
grRatio.SetPoint(i, x, y_ratio)
grRatio.SetPointEXlow(i, ex_lo)
grRatio.SetPointEXhigh(i, ex_hi)
grRatio.SetPointEYlow(i, ey_lo_ratio)
grRatio.SetPointEYhigh(i, ey_hi_ratio)
if True:
grRatio.SetMarkerStyle(20)
grRatio.SetMarkerSize(1.5)
grRatio.SetMarkerColor(1)
grRatio.SetLineStyle(1)
grRatio.SetLineColor(1)
grRatio.SetLineWidth(1)
grRatio.GetXaxis().SetLabelSize(0.04)
grRatio.GetXaxis().SetTitleSize(0.04)
grRatio.GetXaxis().SetTitleOffset(1.25)
grRatio.GetYaxis().SetLabelSize(0.04)
grRatio.GetYaxis().SetTitleSize(0.04)
grRatio.GetYaxis().SetTitleOffset(1.5)
# grRatio.GetYaxis().SetRangeUser(ylo, yhi)
grRatio.Draw("ALP")
fOutput = TFile('durp.root', 'recreate')
grRatio.Write('durp')
vey1l = array('d',ey1l)
vey1h = array('d',ey1h)
vey2l = array('d',ey2l)
vey2h = array('d',ey2h)
g1 = TGraphAsymmErrors(len(vx),vx,vy1,vexl,vexh,vey1l,vey1h)
g2 = TGraphAsymmErrors(len(vx),vx,vy2,vexl,vexh,vey2l,vey2h)
gObs = TGraph(len(vx),vx,vyObs)
gExp = TGraph(len(vx),vx,vyExp)
gxs = TGraph(len(vx),vx,vxs)
gExpDinko = TGraph(len(massesTeV_v),massesTeV_v,xs_exp_limits)
for i in range(0,len(massesTeV_v)):
print "exp limit Dinko: "+str(xs_exp_limits[i])
g2.SetFillColor(ROOT.kYellow)
g2.SetLineColor(ROOT.kYellow)
g1.SetFillColor(ROOT.kGreen)
g1.SetLineColor(ROOT.kGreen)
gExp.SetLineWidth(2)
gExp.SetLineStyle(9)
gExpDinko.SetLineWidth(2)
gExpDinko.SetLineStyle(2)
gObs.SetLineWidth(2)
gObs.SetLineStyle(1)
gObs.SetLineColor(ROOT.kBlue+1)
gObs.SetMarkerColor(ROOT.kBlue+1)
gObs.SetMarkerStyle(21)
gObs.SetMarkerSize(1.5)
gxs.SetLineStyle(5)
gxs.SetLineWidth(3)
gxs.SetLineColor(ROOT.kRed)
def plotDataOverMCEff(hist_mc_tight,
hist_mc_loose,
hist_data_tight,
hist_data_loose,
plot_name='fakerate.pdf'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Fake rate')
g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(2)
g.SetMarkerColor(2)
g_data = TGraphAsymmErrors(hist_data_tight)
g_data.Divide(hist_data_tight, hist_data_loose)
g_data.GetYaxis().SetTitle('Fake rate')
g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle())
g_data.GetYaxis().SetTitleOffset(1.2)
g_data.GetYaxis().SetTitleOffset(1.3)
g_data.SetMarkerColor(1)
g_vals = g.GetY()
g_data_vals = g_data.GetY()
g_ratio = g_data.Clone('ratio')
for i in xrange(g_data.GetN()):
ratio = g_data_vals[i] / g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, g.GetX()[i], ratio)
rel_y_low = math.sqrt((g_data.GetErrorYlow(i) / g_data_vals[i])**2 + (
g.GetErrorYlow(i) /
g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt(
(g_data.GetErrorYhigh(i) / g_data_vals[i])**2 +
(g.GetErrorYhigh(i) /
g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() /
xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(
g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_data.GetXaxis().SetLabelColor(0)
g_data.GetXaxis().SetLabelSize(0)
g.GetXaxis().SetLabelColor(0)
g.GetXaxis().SetLabelSize(0)
g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
# maxy = 1.1 * min(g.GetMaximum(), g_data.GetMaximum(), 0.2)
g.GetYaxis().SetRangeUser(0.001, 0.2)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
g.Draw('AP')
g_data.Draw('P')
legend = TLegend(0.23, 0.73, 0.43, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend.AddEntry(g.GetName(), 'MC', 'lep')
legend.AddEntry(g_data.GetName(), 'Observed', 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.51, 1.49)
g_ratio.GetYaxis().SetTitle('Obs/MC')
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
def pullsVertical(fileName):
content = filterPullFile(fileName)
nbins, off = len(content), 0.10
b_pulls = TH1F("b_pulls", ";;Pulls", nbins, 0. - off, nbins - off)
s_pulls = TH1F("s_pulls", ";;Pulls", nbins, 0. + off, nbins + off) #
for i, s in enumerate(content):
l = s.split()
b_pulls.GetXaxis().SetBinLabel(i + 1, l[0])
s_pulls.GetXaxis().SetBinLabel(i + 1, l[0])
b_pulls.SetBinContent(i + 1, float(l[1]))
b_pulls.SetBinError(i + 1, float(l[2]))
s_pulls.SetBinContent(i + 1, float(l[3]))
s_pulls.SetBinError(i + 1, float(l[4]))
b_pulls.SetFillStyle(3005)
b_pulls.SetFillColor(923)
b_pulls.SetLineColor(923)
b_pulls.SetLineWidth(1)
b_pulls.SetMarkerStyle(20)
b_pulls.SetMarkerSize(1.25)
s_pulls.SetLineColor(602)
s_pulls.SetMarkerColor(602)
s_pulls.SetMarkerStyle(24) #24
s_pulls.SetLineWidth(1)
b_pulls.GetYaxis().SetRangeUser(-2.5, 2.5)
# Graphs
h_pulls = TH2F("pulls", "", 6, -3., 3., nbins, 0, nbins)
B_pulls = TGraphAsymmErrors(nbins)
S_pulls = TGraphAsymmErrors(nbins)
boxes = []
canvas = TCanvas("canvas", "Pulls", 600, 150 + nbins * 10) #nbins*20)
canvas.cd()
canvas.SetGrid(0, 1)
canvas.GetPad(0).SetTopMargin(0.01)
canvas.GetPad(0).SetRightMargin(0.01)
canvas.GetPad(0).SetBottomMargin(0.05)
canvas.GetPad(0).SetLeftMargin(0.25) #(0.25)#(0.065)
canvas.GetPad(0).SetTicks(1, 1)
for i, s in enumerate(content):
l = s.split()
if "1034h" in l[0]: l[0] = "CMS_PDF_13TeV"
h_pulls.GetYaxis().SetBinLabel(i + 1, l[0].replace('CMS2016_', '')) #C
#y1 = gStyle.GetPadBottomMargin()
#y2 = 1. - gStyle.GetPadTopMargin()
#h = (y2 - y1) / float(nbins)
#y1 = y1 + float(i) * h
#y2 = y1 + h
#box = TPaveText(0, y1, 1, y2, 'NDC')
#box.SetFillColor(0)
#box.SetTextSize(0.02)
#box.SetBorderSize(0)
#box.SetTextAlign(12)
#box.SetMargin(0.005)
#if i % 2 == 0:
# box.SetFillColor(18)
#box.Draw()
#boxes.append(box)
B_pulls.SetPoint(i + 1, float(l[1]), float(i + 1) - 0.3) #C
B_pulls.SetPointError(i + 1, float(l[2]), float(l[2]), 0., 0.) #C
for i, s in enumerate(content):
l = s.split()
S_pulls.SetPoint(i + 1, float(l[3]), float(i + 1) - 0.7) #C
S_pulls.SetPointError(i + 1, float(l[4]), float(l[4]), 0., 0.) #C
h_pulls.GetXaxis().SetTitle("(#hat{#theta} - #theta_{0}) / #Delta#theta")
h_pulls.GetXaxis().SetLabelOffset(-0.01)
h_pulls.GetXaxis().SetTitleOffset(.6)
h_pulls.GetYaxis().SetNdivisions(nbins, 0, 0)
B_pulls.SetFillColor(1)
B_pulls.SetLineColor(1)
B_pulls.SetLineStyle(1)
B_pulls.SetLineWidth(2)
B_pulls.SetMarkerColor(1)
B_pulls.SetMarkerStyle(20)
B_pulls.SetMarkerSize(1) #(0.75)
S_pulls.SetFillColor(629)
S_pulls.SetLineColor(629)
S_pulls.SetMarkerColor(629)
S_pulls.SetLineWidth(2)
S_pulls.SetMarkerStyle(20)
S_pulls.SetMarkerSize(1)
box1 = TBox(-1., 0., 1., nbins)
box1.SetFillStyle(3001)
#box1.SetFillStyle(0)
box1.SetFillColor(417)
box1.SetLineWidth(2)
box1.SetLineStyle(2)
box1.SetLineColor(417)
box2 = TBox(-2., 0., 2., nbins)
box2.SetFillStyle(3001)
#box2.SetFillStyle(0)
box2.SetFillColor(800)
box2.SetLineWidth(2)
box2.SetLineStyle(2)
box2.SetLineColor(800)
leg = TLegend(0.1, -0.05, 0.7, 0.08)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetNColumns(2)
leg.AddEntry(B_pulls, "B-only fit", "lp")
leg.AddEntry(S_pulls, "S+B fit", "lp")
if text: leg.AddEntry(0, text, "")
h_pulls.Draw("")
box2.Draw()
box1.Draw()
B_pulls.Draw("P6SAME")
S_pulls.Draw("P6SAME")
leg.Draw()
# drawCMS(35867, "Preliminary")
# drawAnalysis("VH")
# drawRegion(outName)
canvas.Print(outName + ".png")
canvas.Print(outName + ".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
def limit2HDM():
global signals
signals = range(800, 2000 + 1, 50)
multF = HTOBB
THEORY = ['T1', 'T2']
mass, val = fillValues("./combine/AZh/AZh_M%d.txt")
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
massB, valB = fillValues("./combine/BBAZh/BBAZh_M%d.txt")
Obs0sB = TGraph()
Exp0sB = TGraph()
Exp1sB = TGraphAsymmErrors()
Exp2sB = TGraphAsymmErrors()
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF)
Exp0s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][2] * multF,
val[m][4] * multF - val[m][3] * multF)
Exp2s.SetPoint(n, m, val[m][3] * multF)
Exp2s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][1] * multF,
val[m][5] * multF - val[m][3] * multF)
Obs0sB.SetPoint(n, m, valB[m][0] * multF)
Exp0sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][2] * multF,
valB[m][4] * multF - valB[m][3] * multF)
Exp2sB.SetPoint(n, m, valB[m][3] * multF)
Exp2sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][1] * multF,
valB[m][5] * multF - valB[m][3] * multF)
col = 629
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp0s.SetLineColor(1)
# Exp1s.SetFillColorAlpha(col, 0.4) #kGreen+1
# Exp1s.SetLineColorAlpha(col, 0.4)
# Exp2s.SetFillColorAlpha(col, 0.2) #kOrange
# Exp2s.SetLineColorAlpha(col, 0.2)
Exp1s.SetFillColor(417)
Exp1s.SetLineColor(417)
Exp2s.SetFillColor(800)
Exp2s.SetLineColor(800)
colB = 922
Exp2sB.SetLineWidth(2)
Obs0sB.SetLineStyle(9)
Obs0sB.SetLineWidth(3)
Obs0sB.SetMarkerStyle(0)
Obs0sB.SetLineColor(colB)
Exp0sB.SetLineStyle(8)
Exp0sB.SetLineWidth(3)
Exp0sB.SetLineColor(colB)
Exp1sB.SetFillColorAlpha(colB, 0.4) #kGreen+1
Exp1sB.SetLineColorAlpha(colB, 0.4)
Exp2sB.SetFillColorAlpha(colB, 0.2) #kOrange
Exp2sB.SetLineColorAlpha(colB, 0.2)
Exp2s.GetXaxis().SetTitle("m_{A} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle(
"#sigma(A) #bf{#it{#Beta}}(A #rightarrow Zh) #bf{#it{#Beta}}(h #rightarrow bb) (fb)"
)
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Theory = {}
#for t in THEORY:
# Theory[t] = TGraphAsymmErrors()
# for m in sorted(THDM[t]['ggA'].keys()):
# if m < mass[0] or m > mass[-1]: continue
# Xs, Xs_Up, Xs_Down = 0., 0., 0.
# Xs = THDM[t]['ggA'][m]
# Xs_Up = Xs*(1.+math.sqrt((THDM['PDF']['ggA'][m][0]-1.)**2 + (THDM['QCD']['ggA'][m][0]-1.)**2))
# Xs_Down = Xs*(1.-math.sqrt((1.-THDM['PDF']['ggA'][m][1])**2 + (1.-THDM['QCD']['ggA'][m][1])**2))
# n = Theory[t].GetN()
# Theory[t].SetPoint(n, m, Xs)
# Theory[t].SetPointError(n, 0., 0., (Xs-Xs_Down), (Xs_Up-Xs))
# Theory[t].SetLineColor(theoryLineColor[t])
# Theory[t].SetFillColor(theoryFillColor[t])
# Theory[t].SetFillStyle(theoryFillStyle[t])
# Theory[t].SetLineWidth(2)
# #Theory[t].SetLineStyle(7)
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
Exp0s.Draw("SAME, L")
# Exp2sB.Draw("SAME, 3")
# Exp1sB.Draw("SAME, 3")
Exp0sB.Draw("SAME, L")
if not options.blind:
Obs0s.Draw("SAME, L")
Obs0sB.Draw("SAME, L")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
#setHistStyle(Exp2s)
# Exp2s.GetXaxis().SetTitleSize(0.045)
# Exp2s.GetYaxis().SetTitleSize(0.04)
# Exp2s.GetXaxis().SetLabelSize(0.04)
# Exp2s.GetYaxis().SetLabelSize(0.04)
# Exp2s.GetXaxis().SetTitleOffset(1)
# Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.5, 1.e3)
Exp2s.GetXaxis().SetRangeUser(mass[0], mass[-1])
drawAnalysis('AZh')
drawRegion('AZHsl', True)
drawCMS(LUMI, "") #Preliminary
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
# legend
leg = TLegend(0.6, 0.90, 0.99, 0.90)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(None, "gg #rightarrow A #rightarrow Zh",
"") #"95% CL upper limits"
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
leg.AddEntry(None, "", "")
leg.AddEntry(None, "bbA #rightarrow Zh", "")
leg.AddEntry(Obs0sB, "Observed", "l")
leg.AddEntry(Exp0sB, "Expected", "l")
leg.SetY1(leg.GetY2() - leg.GetNRows() * 0.045)
leg.Draw()
# latex = TLatex()
# latex.SetNDC()
# latex.SetTextSize(0.040)
# latex.SetTextFont(42)
# latex.DrawLatex(0.65, leg.GetY1()-0.045, "cos(#beta-#alpha)=0.25, tan(#beta)=1")
# legB = TLegend(0.12, 0.4-4*0.3/5., 0.65, 0.4)
legB = TLegend(0.15, 0.27, 0.68, 0.27)
legB.SetBorderSize(0)
legB.SetFillStyle(0) #1001
legB.SetFillColor(0)
for t in THEORY:
legB.AddEntry(Theory[t], theoryLabel[t], "fl")
legB.AddEntry(None, "cos(#beta-#alpha)=0.25, tan(#beta)=1", "")
legB.SetY1(legB.GetY2() - legB.GetNRows() * 0.045)
legB.Draw()
c1.GetPad(0).RedrawAxis()
leg.Draw()
c1.Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("plotsLimit/Exclusion/THDM.png")
c1.Print("plotsLimit/Exclusion/THDM.pdf")
def ratioplot():
# create required parts
leg = getLegend()
latex = getLatex()
c = SetCanvas()
#c.SetLogy()
#c = TCanvas()
#c.SetLogy()
h1 = f.Get('h_num_calo_') #'calo',pf
h1 = setHistStyle(h1, bins)
h2 = f.Get('h_den_calo_')
h2 = setHistStyle(h2, bins)
h11 = f2.Get('h_num_calo_')
h11 = setHistStyle(h11, bins)
h21 = f2.Get('h_den_calo_')
h21 = setHistStyle(h21, bins)
gr = TGraphAsymmErrors(30)
#gr.Divide(h1,h2)
gr = TGraphAsymmErrors(h1, h2)
gr2 = TGraphAsymmErrors(h11, h21)
gr2.SetMarkerStyle(20)
gr2.GetXaxis().SetRangeUser(0, 1000)
gr2.SetMarkerSize(1.5)
gr2.SetLineColor(2)
gr2.SetLineWidth(1)
gr2.SetMarkerColor(2)
gr.GetXaxis().SetRangeUser(0, 1000)
# gr.GetYaxis().SetRangeUser(0.0001,1.2)
gr.SetMarkerStyle(20)
gr.SetMarkerSize(1.5)
gr.SetLineColor(1)
gr.SetLineWidth(1)
gr.SetMarkerColor(1)
gr.GetYaxis().SetTitle("Trigger Efficiency")
gr.GetXaxis().SetTitle("MET [GeV]")
gr.SetTitle("")
#base histogram
histogram_base = TH1F("histogram_base", "", 1000, 0, 1000.)
histogram_base.SetTitle("")
histogram_base.SetStats(0)
histogram_base.SetMarkerSize(2)
#histogram_base.SetMinimum(0.0)
histogram_base.SetMaximum(1.2)
histogram_base.GetXaxis().SetTitle("Online E_{T}^{miss} (GeV)")
histogram_base.GetYaxis().SetTitle("Efficiency")
histogram_base = setHistStyle(histogram_base, bins)
histogram_base.Draw("HIST")
# c.SaveAs()
gr.Draw('P same')
gr2.Draw('P same')
latex.DrawLatex(0.49, 0.93, " EGamma Run2018C, 13 TeV")
xmin = 0.0
line = TLine(max(xmin, gr.GetXaxis().GetXmin()), 1, 1000, 1)
line.SetLineColor(1)
line.SetLineWidth(1)
line.SetLineStyle(7)
line.Draw()
leg.AddEntry(gr, 'With HBHENoise filter', 'P')
leg.AddEntry(gr2, 'Without HBHENoise filter', 'P')
leg.Draw()
txt = 'Path: HLT_PFMETTypeOne200_HBHE_BeamHaloCleaned'
texcms = AddText(txt)
texcms.Draw("same")
c.SaveAs('testTurnOn_EGamma.png')
])
theory_v = TVectorD(7, theory_a)
mass_a = array("d", [1500, 1750, 2000, 2250, 2500, 3000, 3250, 3500])
mass_v = TVectorD(7, mass_a)
err_a = array("d", [0, 0, 0, 0, 0, 0, 0, 0])
err_v = TVectorD(7, err_a)
## theory_a = array("d",[0.0006434760289,0.0005751010031,0.0005231099785,0.0005344069796,0.0005340549978])
## theory_v = TVectorD(5,theory_a)
## mass_a = array("d",[1750,2000,2500,2750,3000])
## mass_v = TVectorD(5,mass_a)
## err_a = array("d",[0,0,0,0,0,0])
## err_v = TVectorD(5,err_a)
theory = TGraphAsymmErrors(mass_v, theory_v, err_v, err_v, err_v, err_v)
theory.SetLineColor(4)
theory.SetLineStyle(9)
theory.SetLineWidth(3)
observed_p = TGraphAsymmErrors(massv, obsv, masserrv, masserrv, obserrv,
obserrv)
observed_p.SetLineColor(ROOT.kBlack)
observed_p.SetLineWidth(2)
observed_p.SetMarkerStyle(20)
expected_p = TGraphAsymmErrors(massv, expv, masserrv, masserrv, experrv,
experrv)
expected_p.SetLineColor(ROOT.kBlack)
expected_p.SetLineWidth(2)
expected_p.SetLineStyle(2)
expected68 = TGraphAsymmErrors(massv, expv, masserrv, masserrv, exp68Lv,
