def draw_limits_per_category(nchannels, xmin, xmax, obs, expect, upper1sig,
lower1sig, upper2sig, lower2sig):
channel = np.array(
[3. * nchannels - 1.5 - 3. * i for i in range(0, nchannels)])
ey = np.array([0.8 for i in range(0, nchannels)])
zero = np.zeros(nchannels)
gexpect1sig = TGraphAsymmErrors(nchannels, expect, channel, lower1sig,
upper1sig, ey, ey)
gexpect1sig.SetFillColor(kGreen)
gexpect1sig.SetLineWidth(2)
gexpect1sig.SetLineStyle(2)
gexpect2sig = TGraphAsymmErrors(nchannels, expect, channel, lower2sig,
upper2sig, ey, ey)
gexpect2sig.SetFillColor(kYellow)
gexpect2sig.SetLineWidth(2)
gexpect2sig.SetLineStyle(2)
gexpect2sig.Draw("2")
gexpect1sig.Draw("2")
gobs = TGraphErrors(nchannels, obs, channel, zero, ey)
gobs.SetMarkerStyle(21)
gobs.SetMarkerSize(1.5)
gobs.SetLineWidth(2)
gobs.Draw("pz")
# dashed line at median expected limits
l = TLine()
l.SetLineStyle(2)
l.SetLineWidth(2)
for bin in range(nchannels):
l.DrawLine(expect[bin], channel[bin] - ey[bin], expect[bin],
channel[bin] + ey[bin])
# line to separate individual and combined limits
l.SetLineStyle(1)
l.SetLineWidth(1)
l.DrawLine(xmin, 0, xmax, 0)
# legend
x1 = gStyle.GetPadLeftMargin() + 0.01
y2 = 1 - gStyle.GetPadTopMargin() - 0.01
leg = TLegend(x1, y2 - 0.17, x1 + 0.25, y2)
leg.SetFillColor(4000)
leg.AddEntry(gexpect1sig, "Expected #pm1#sigma", "FL")
leg.AddEntry(gexpect2sig, "Expected #pm2#sigma", "FL")
leg.AddEntry(gobs, "Observed", "pl")
leg.Draw()
return gobs, gexpect1sig, gexpect2sig, leg
def draw_limits_per_category(nchannels, xmin, xmax, obs, expect, upper1sig,
lower1sig, upper2sig, lower2sig):
channel = np.array(
[nchannels - 1.5 - float(i) for i in range(0, nchannels)])
ey = np.full(nchannels, 0.494)
zero = np.zeros(nchannels)
gexpect1sig = TGraphAsymmErrors(nchannels, expect, channel, lower1sig,
upper1sig, ey, ey)
gexpect1sig.SetFillColor(kGreen)
gexpect1sig.SetLineWidth(2)
gexpect1sig.SetLineStyle(2)
gexpect2sig = TGraphAsymmErrors(nchannels, expect, channel, lower2sig,
upper2sig, ey, ey)
gexpect2sig.SetFillColor(kYellow)
gexpect2sig.SetLineWidth(2)
gexpect2sig.SetLineStyle(2)
gexpect2sig.Draw("2")
gexpect1sig.Draw("2")
gobs = TGraphErrors(nchannels, obs, channel, zero, ey)
gobs.SetMarkerStyle(21)
gobs.SetMarkerSize(1.5)
gobs.SetLineWidth(2)
#gobs.Draw("pz")
# dashed line at median expected limits
l = TLine()
l.SetLineStyle(2)
l.SetLineWidth(2)
for bin in range(nchannels):
l.DrawLine(expect[bin], channel[bin] - ey[bin], expect[bin],
channel[bin] + ey[bin])
# line to separate individual and combined limits
l.SetLineStyle(1)
l.SetLineWidth(1)
l.DrawLine(xmin, 0, xmax, 0)
# legend
leg = TLegend(0.75, 0.75, 0.95, 0.9)
leg.SetFillColor(4000)
leg.AddEntry(gexpect1sig, "Expected #pm1#sigma", "FL")
leg.AddEntry(gexpect2sig, "Expected #pm2#sigma", "FL")
#leg.AddEntry( gobs, "Observed", "pl" )
leg.Draw()
return gobs, gexpect1sig, gexpect2sig, leg
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 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 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 limits():
# put real limits here: lepton+jets, dilepton, combined
obs = np.array([1, 1, 1])
expect = np.array([1, 1, 1])
upper1sig = np.array([1.352, 0.736390352249, 0.636])
lower1sig = np.array([0.9153, 0.500572919846, 0.439])
upper2sig = np.array([3.125, 1.66185164452, 1.455])
lower2sig = np.array([1.4717, 0.805953979492, 0.715])
channel = np.array([1.5, 0.5, -0.5])
ey = np.array([0.495, 0.495, 0.495])
zero = np.zeros(nchannels)
xmin = 0.7
xmax = 30
c, h = draw_canvas_histo(
xmin, xmax,
"95% CL limit on #mu = #sigma/#sigma_{SM} at m_{H} = 125 GeV")
c.SetLogx()
gexpect1sig = TGraphAsymmErrors(nchannels, expect, channel, lower1sig,
upper1sig, ey, ey)
gexpect1sig.SetFillColor(kGreen)
gexpect1sig.SetLineWidth(2)
gexpect1sig.SetLineStyle(2)
gexpect2sig = TGraphAsymmErrors(nchannels, expect, channel, lower2sig,
upper2sig, ey, ey)
gexpect2sig.SetFillColor(kYellow)
gexpect2sig.SetLineWidth(2)
gexpect2sig.SetLineStyle(2)
gexpect2sig.Draw("2")
gexpect1sig.Draw("2")
gobs = TGraphErrors(nchannels, obs, channel, zero, ey)
gobs.SetMarkerStyle(21)
gobs.SetMarkerSize(1.5)
gobs.SetLineWidth(2)
#gobs.Draw("pz")
# dashed line at median expected limits
l = TLine()
l.SetLineStyle(2)
l.SetLineWidth(2)
for bin in range(nchannels):
l.DrawLine(expect[bin], channel[bin] - ey[bin], expect[bin],
channel[bin] + ey[bin])
# line to separate individual and combined limits
l.SetLineStyle(1)
l.SetLineWidth(1)
l.DrawLine(xmin, 0, xmax, 0)
# legend
leg = TLegend(0.67, 0.75, 0.95, 0.9)
leg.SetFillColor(4000)
leg.AddEntry(gexpect1sig, "Expected #pm1#sigma", "FL")
leg.AddEntry(gexpect2sig, "Expected #pm2#sigma", "FL")
#leg.AddEntry( gobs, "Observed", "pl" )
leg.Draw()
#draw_disclaimer()
c.RedrawAxis()
c.Modified()
c.Update()
c.SaveAs("limits.pdf")
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 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")
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,
exp68Hv)
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')
ey_hi_ratio = 0.0
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')
extrapol_rate /= 1000
gr_extrapol.SetPoint(i, pu, extrapol_rate)
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
gr_extrapol.SetLineStyle(7)
gr_extrapol.SetLineWidth(1)
gr_extrapol.SetLineColor(920) # kGray
frame = TH1F("frame", "; PU; Trigger rate [kHz]", 100, 0, 350)
frame.SetMinimum(0)
frame.SetMaximum(100)
frame.Draw()
gr_denom.Draw("P")
gr_numer.Draw("P")
gr_extrapol.Draw("C")
for i, x in enumerate(rates):
pu, emtf_rate, emtf_rate_err, emtf2026_rate, emtf2026_rate_err = x
tlatex.DrawLatex(pu - 3, emtf_rate + 4, "%.1f" % emtf_rate)
def limit():
method = ''
channel = "bb"
if INCLUDEACC:
particleP = "X"
else:
particleP = "Z'"
particle = 'b#bar{b}'
multF = ZPTOBB
THEORY = ['A1', 'B3']
if INCLUDEACC: THEORY.append('SSM')
suffix = "_"+BTAGGING
if ISMC: suffix += "_MC"
if SY: suffix += "_comb"
#if method=="cls": suffix="_CLs"
if INCLUDEACC: suffix+="_acc"
if SY:
filename = "./combine/limits/" + BTAGGING + "/combined_run2/"+ YEAR + "_M%d.txt"
else:
filename = "./combine/limits/" + BTAGGING + "/"+ YEAR + "_M%d.txt"
if CATEGORY!="":
if SY:
filename = filename.replace(BTAGGING + "/combined_run2/", BTAGGING + "/single_category/combined_run2/"+CATEGORY+"_")
else:
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
if INCLUDEACC:
acc_factor = ACCEPTANCE[m]
else:
acc_factor = 1.
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0]*multF*acc_factor)
Exp0s.SetPoint(n, m, val[m][3]*multF*acc_factor)
Exp1s.SetPoint(n, m, val[m][3]*multF*acc_factor)
Exp1s.SetPointError(n, 0., 0., (val[m][3]-val[m][2])*multF*acc_factor, (val[m][4]-val[m][3])*multF*acc_factor)
Exp2s.SetPoint(n, m, val[m][3]*multF*acc_factor)
Exp2s.SetPointError(n, 0., 0., (val[m][3]-val[m][1])*multF*acc_factor, (val[m][5]-val[m][3])*multF*acc_factor)
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()
Xs_dict = HVT[t]['Z']['XS'] if t!='SSM' else SSM['Z']
for m in sorted(Xs_dict.keys()):
if INCLUDEACC and t!='SSM':
acc_factor = ACCEPTANCE[m]
else:
acc_factor = 1.
if m < SIGNALS[0] or m > SIGNALS[-1]: continue
#if m < mass[0] or m > mass[-1]: continue
#if t!= 'SSM' and m>4500: continue ## I don't have the higher mass xs
if m>4500: continue
XsZ, XsZ_Up, XsZ_Down = 0., 0., 0.
if t!='SSM':
XsZ = 1000.*HVT[t]['Z']['XS'][m]*SSM["BrZ"][m] #assuming the same BR as the SSM Z' one
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]))
else:
XsZ = 1000.*SSM['Z'][m]*SSM["BrZ"][m]
XsZ_Up = XsZ*(1.+math.hypot(HVT['A1']['Z']['QCD'][m][0]-1., HVT['A1']['Z']['PDF'][m][0]-1.))
XsZ_Down = XsZ*(1.-math.hypot(1.-HVT['A1']['Z']['QCD'][m][0], 1.-HVT['A1']['Z']['PDF'][m][0]))
n = Theory[t].GetN()
Theory[t].SetPoint(n, m, XsZ*acc_factor)
Theory[t].SetPointError(n, 0., 0., (XsZ-XsZ_Down)*acc_factor, (XsZ_Up-XsZ)*acc_factor)
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)".format(" #times #Alpha" if INCLUDEACC else ""))
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)
if INCLUDEACC:
Exp2s.GetYaxis().SetRangeUser(0.05, 5.e3)
else:
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
if CATEGORY=="":
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawCMS(LUMI, "", suppressCMS=True)
else:
#drawCMS(LUMI, "Work in Progress, "+CAT_LABELS[CATEGORY], suppressCMS=True)
drawCMS(LUMI, CAT_LABELS[CATEGORY], 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/"+YEAR+suffix+".png")
c1.Print("combine/plotsLimit/ExclusionLimits/"+YEAR+suffix+".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("combine/plotsLimit/ExclusionLimits/"+YEAR+suffix+".C")
c1.Print("combine/plotsLimit/ExclusionLimits/"+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 ##FIXME
# ---------- Significance ----------
c2 = TCanvas("c2", "Significance", 800, 600)
c2.cd()
c2.GetPad(0).SetTopMargin(0.06)
c2.GetPad(0).SetRightMargin(0.05)
c2.GetPad(0).SetTicks(1, 1)
c2.GetPad(0).SetGridx()
c2.GetPad(0).SetGridy()
Sign.GetYaxis().SetRangeUser(0., 5.)
Sign.Draw("AL3")
#drawCMS(LUMI, "Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c2.Print("combine/plotsLimit/Significance/"+YEAR+suffix+".png")
c2.Print("combine/plotsLimit/Significance/"+YEAR+suffix+".pdf")
# c2.Print("plotsLimit/Significance/"+YEAR+suffix+".root")
# c2.Print("plotsLimit/Significance/"+YEAR+suffix+".C")
# ---------- p-Value ----------
c3 = TCanvas("c3", "p-Value", 800, 600)
c3.cd()
c3.GetPad(0).SetTopMargin(0.06)
c3.GetPad(0).SetRightMargin(0.05)
c3.GetPad(0).SetTicks(1, 1)
c3.GetPad(0).SetGridx()
c3.GetPad(0).SetGridy()
c3.GetPad(0).SetLogy()
pVal.Draw("AL3")
pVal.GetYaxis().SetRangeUser(2.e-7, 0.5)
ci = [1., 0.317310508, 0.045500264, 0.002699796, 0.00006334, 0.000000573303, 0.000000001973]
line = TLine()
line.SetLineColor(922)
line.SetLineStyle(7)
text = TLatex()
text.SetTextColor(922)
text.SetTextSize(0.025)
text.SetTextAlign(12)
for i in range(1, len(ci)-1):
line.DrawLine(pVal.GetXaxis().GetXmin(), ci[i]/2, pVal.GetXaxis().GetXmax(), ci[i]/2);
text.DrawLatex(pVal.GetXaxis().GetXmax()*1.01, ci[i]/2, "%d #sigma" % i);
#drawCMS(LUMI, "Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c3.Print("combine/plotsLimit/pValue/"+YEAR+suffix+".png")
c3.Print("combine/plotsLimit/pValue/"+YEAR+suffix+".pdf")
# c3.Print("plotsLimit/pValue/"+YEAR+suffix+".root")
# c3.Print("plotsLimit/pValue/"+YEAR+suffix+".C")
# --------- Best Fit ----------
c4 = TCanvas("c4", "Best Fit", 800, 600)
c4.cd()
c4.GetPad(0).SetTopMargin(0.06)
c4.GetPad(0).SetRightMargin(0.05)
c4.GetPad(0).SetTicks(1, 1)
c4.GetPad(0).SetGridx()
c4.GetPad(0).SetGridy()
Best.Draw("AL3")
#drawCMS(LUMI, "Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c4.Print("combine/plotsLimit/BestFit/"+YEAR+suffix+".png")
c4.Print("combine/plotsLimit/BestFit/"+YEAR+suffix+".pdf")
# c4.Print("plotsLimit/BestFit/"+YEAR+suffix+".root")
# c4.Print("plotsLimit/BestFit/"+YEAR+suffix+".C")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if 'ah' in channel:
outFile = TFile("bands.root", "RECREATE")
outFile.cd()
pVal.Write("graph")
Best.Write("best")
outFile.Close()
def main():
# usage description
usage = "Example: ./scripts/plotSignificance.py -l logs -f qq --massrange 1200 6000 100"
# input parameters
parser = ArgumentParser(description='Script that plots significance for specified mass points',epilog=usage)
parser.add_argument("-M", "--method", dest="method",
choices=['MaxLikelihoodFit'],
default='MaxLikelihoodFit',
help="Method to calculate upper limits",
metavar="METHOD")
results_group = parser.add_mutually_exclusive_group(required=True)
results_group.add_argument("-l", "--logs_path", dest="logs_path",
help="Path to log files",
metavar="LOGS_PATH")
results_group.add_argument("-r", "--results_file", dest="results_file",
help="Path to a file containing results",
metavar="RESULTS_FILE")
parser.add_argument("-f", "--final_state", dest="final_state", required=True,
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("--postfix", dest="postfix", default='', help="Postfix for the output plot name (default: %(default)s)")
parser.add_argument("--fileFormat", dest="fileFormat", default='pdf', help="Format of the output plot (default: %(default)s)")
parser.add_argument("--extraText", dest="extraText", default='Simulation Preliminary', help="Extra text on the plot (default: %(default)s)")
parser.add_argument("--lumi_sqrtS", dest="lumi_sqrtS", default='1 fb^{-1} (13 TeV)', help="Integrated luminosity and center-of-mass energy (default: %(default)s)")
parser.add_argument("--printResults", dest="printResults", default=False, action="store_true", help="Print results to the screen")
mass_group = parser.add_mutually_exclusive_group(required=True)
mass_group.add_argument("--mass",
type=int,
nargs = '*',
default = 1000,
help="Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
)
mass_group.add_argument("--massrange",
type=int,
nargs = 3,
help="Define a range of masses to be produced. Format: min max step",
metavar = ('MIN', 'MAX', 'STEP')
)
mass_group.add_argument("--masslist",
help = "List containing mass information"
)
args = parser.parse_args()
# mass points for which resonance shapes will be produced
input_masses = []
if args.massrange != None:
MIN, MAX, STEP = args.massrange
input_masses = range(MIN, MAX+STEP, STEP)
elif args.masslist != None:
# A mass list was provided
print "Will create mass list according to", args.masslist
masslist = __import__(args.masslist.replace(".py",""))
input_masses = masslist.masses
else:
input_masses = args.mass
# sort masses
input_masses.sort()
# arrays holding results
masses = array('d')
sig = array('d')
sig_ex = array('d')
sig_eyl = array('d')
sig_eyh = array('d')
if args.logs_path != None:
logs_path = os.path.join(os.getcwd(),args.logs_path)
for mass in input_masses:
print ">> Reading results for %s resonance with m = %i GeV..."%(args.final_state, int(mass))
masses.append(mass)
logName = 'signal_xs_%s_%s_m%i.log'%(args.method,args.final_state,int(mass))
log_file = open(os.path.join(logs_path,logName),'r')
# read the log file
for line in log_file:
if re.search("^Best fit r:", line):
sig.append(float(line.split()[3]))
sig_eyl.append(float(line.split()[4].split('/')[0].lstrip('-')))
sig_eyh.append(float(line.split()[4].split('/')[1].lstrip('+')))
sig_ex.append(0.)
if len(masses) != len(sig):
print "** WARNING: ** Fit failed for m =", int(mass), "GeV. Setting signal cross section to 0."
sig.append(0.)
sig_eyl.append(0.)
sig_eyh.append(0.)
else:
print ">> Importing results..."
sys.path.insert(0, os.path.dirname(args.results_file))
results = __import__(os.path.basename(args.results_file).replace(".py",""))
all_masses = np.array(results.masses)
indices = []
# search for indices of input_masses
for mass in input_masses:
where = np.where(all_masses==mass)[0]
if len(where) == 0:
print "** WARNING: ** Cannot find results for m =", int(mass), "GeV in the provided results file. Skipping this mass point."
indices.extend( where )
# sort indices
indices.sort()
for i in indices:
masses.append( results.masses[i] )
sig.append( results.sig[i] )
sig_ex.append( results.sig_ex[i] )
sig_eyl.append( results.sig_eyl[i] )
sig_eyh.append( results.sig_eyh[i] )
if args.printResults:
print "masses =", masses.tolist()
print "sig =", sig.tolist()
print "sig_ex =", sig_ex.tolist()
print "sig_eyl =", sig_eyl.tolist()
print "sig_eyh =", sig_eyh.tolist()
# create final arrays
sig_pos = array('d')
sig_exl = array('d')
sig_exh = array('d')
# fill final arrays
for i in range(0,len(masses)):
sig_pos.append(sig[i] if sig[i]>0. else 0.)
sig_exl.append(sig_ex[i])
sig_exh.append(sig_ex[i])
sig_eyl.append(sig_eyl[i] if sig[i]>0. else 0.)
sig_eyh.append(sig_eyh[i])
# import ROOT stuff
from ROOT import kTRUE, kFALSE, gROOT, gStyle, gPad, TGraphAsymmErrors, TCanvas, TLegend
from ROOT import kGreen, kYellow, kWhite, kRed, kBlue
gROOT.SetBatch(kTRUE);
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
gStyle.SetTitleFont(42, "XYZ")
gStyle.SetTitleSize(0.06, "XYZ")
gStyle.SetLabelFont(42, "XYZ")
gStyle.SetLabelSize(0.05, "XYZ")
gStyle.SetCanvasBorderMode(0)
gStyle.SetFrameBorderMode(0)
gStyle.SetCanvasColor(kWhite)
gStyle.SetPadTickX(1)
gStyle.SetPadTickY(1)
gStyle.SetPadLeftMargin(0.15)
gStyle.SetPadRightMargin(0.05)
gStyle.SetPadTopMargin(0.05)
gStyle.SetPadBottomMargin(0.15)
gROOT.ForceStyle()
graph_sig = TGraphAsymmErrors(len(masses),masses,sig_pos,sig_exl,sig_exh,sig_eyl,sig_eyh)
graph_sig.GetXaxis().SetTitle("%s resonance mass [GeV]"%(args.final_state))
graph_sig.GetYaxis().SetTitle("Signal cross section [pb]")
graph_sig.GetYaxis().SetTitleOffset(1.2)
graph_sig.GetYaxis().SetRangeUser(1e-4,2e2)
graph_sig.SetMarkerStyle(20)
graph_sig.SetMarkerColor(1)
graph_sig.SetLineWidth(2)
graph_sig.SetLineStyle(1)
graph_sig.SetLineColor(1)
c = TCanvas("c", "",800,800)
c.cd()
graph_sig.Draw("AP")
# draw the lumi text on the canvas
CMS_lumi.extraText = args.extraText
CMS_lumi.lumi_sqrtS = args.lumi_sqrtS # used with iPeriod = 0 (free form)
iPos = 11
iPeriod = 0
CMS_lumi.CMS_lumi(c, iPeriod, iPos)
gPad.RedrawAxis()
c.SetLogy()
c.SetGridx()
c.SetGridy()
fileName = 'signal_xs_%s_%s.%s'%(args.method,args.final_state + ( ('_' + args.postfix) if args.postfix != '' else '' ), args.fileFormat.lower())
c.SaveAs(fileName)
print "Plot saved to '%s'"%(fileName)
def plotMCEffs(input_list, plot_name='fakerate.pdf'):
'''input_list expects tuples with structure:
(hist tight, hist loose, legend line, colour)
'''
graphs = []
for (hist_tight, hist_loose, legend, colour, style) in input_list:
g = TGraphAsymmErrors(hist_tight)
g.Divide(hist_tight, hist_loose)
g.GetYaxis().SetTitle('Misidentification rate')
g.GetXaxis().SetTitle(hist_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(colour)
g.SetLineStyle(style)
g.SetMarkerColor(colour)
g.SetMarkerStyle(19+style)
g.legend = legend
graphs.append(g)
ratio_graphs = []
base_graph = graphs[0]
for graph in graphs[1:]:
g_vals = base_graph.GetY()
g_data_vals = graph.GetY()
g_ratio = graph.Clone('ratio')
for i in xrange(graph.GetN()):
ratio = g_data_vals[i]/g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, base_graph.GetX()[i], ratio)
rel_y_low = math.sqrt((graph.GetErrorYlow(i)/g_data_vals[i])**2 + (base_graph.GetErrorYlow(i)/g_vals[i])**2) if g_data_vals[i] > 0.0000001 and g_vals[i] > 0.0000001 else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt((graph.GetErrorYhigh(i)/g_data_vals[i])**2 + (base_graph.GetErrorYhigh(i)/g_vals[i])**2) if g_data_vals[i] > 0.0000001 and g_vals[i] > 0.0000001 else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
ratio_graphs.append(g_ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
for g_ratio in ratio_graphs:
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_ratio.GetXaxis().SetTitle(base_graph.GetXaxis().GetTitle())
for graph in graphs:
graph.GetXaxis().SetLabelColor(0)
graph.GetXaxis().SetLabelSize(0)
maxy = 1.3 * max([gr.GetMaximum() for gr in graphs] + [1./1.25])
for g in graphs:
g.GetYaxis().SetRangeUser(0.0011, maxy)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
base_graph.Draw('AP')
for graph in graphs[1:]:
graph.Draw('P')
legend = TLegend(0.63, 0.63, 0.93, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend2 = TLegend(0.78, 0.63, 0.93, 0.91)
legend2.SetFillColor(0)
legend2.SetFillStyle(0)
legend2.SetLineColor(0)
legend2.SetLineWidth(0)
for graph in graphs:
if 'opp' in graph.legend:
legend2.AddEntry(graph.GetName(), graph.legend, 'lep')
else:
legend.AddEntry(graph.GetName(), graph.legend, 'lep')
legend.Draw()
legend2.Draw()
padr.cd()
for g_ratio in ratio_graphs:
g_ratio.GetYaxis().SetRangeUser(0.01, 1.99)
g_ratio.GetYaxis().SetTitle('Ratio to '+base_graph.legend)
g_ratio.Draw('AP' if g_ratio == ratio_graphs[0] else 'P')
drawRatioLines(g_ratio)
cv.Print(plot_name)
g.GetYaxis().SetRangeUser(0.0001, 1)
pad.SetLogy(True)
cv.Print(plot_name.replace('.', '_log.'))
f = ROOT.TFile(plot_name.replace('.', '_log.').replace('.pdf', '.root'), 'RECREATE')
g.Write()
# g_data.Write()
cv.Write()
f.Close()
graph_HV_L1 = TGraphAsymmErrors()
n = 0
for a in sorted(HV_scan_L1):
#for a in sorted(run_parameters):
##Fill the TGraph with threshold (x-axis) and rate (y-axis)
#######graph.SetPoint(n,int(run_parameters[a]['VTHR']),float(run_parameters[a]['RATE_SL1_L1']))
graph_HV_L1.SetPoint(n, int(a), float(HV_scan_L1[a]))
n = n + 1
graph_HV_L1.SetMarkerSize(1.)
graph_HV_L1.SetMarkerStyle(21)
graph_HV_L1.SetMarkerColor(418)
graph_HV_L1.SetFillColor(868)
graph_HV_L1.SetFillStyle(3844)
graph_HV_L1.SetLineColor(418 - 1)
graph_HV_L1.SetLineWidth(2)
graph_HV_L1.SetLineStyle(2)
graph_HV_L1.GetXaxis().SetTitle("HV [V]")
graph_HV_L1.GetYaxis().SetTitleOffset(1.2)
graph_HV_L1.GetYaxis().SetTitle("efficiency")
graph_HV_L1.GetYaxis().SetRangeUser(0, 1.01)
graph_HV_L1.Draw("APL")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextColor(1)
latex.SetTextFont(42)
latex.SetTextAlign(33)
latex.SetTextSize(0.04)
latex.SetTextFont(62)
latex.DrawLatex(0.30, 0.96, "GIF++")
etichetta = TLatex()
def PlotLimits(Type):
theory_x = array('d', [
800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1900, 2100,
2300, 2500
])
if Type == 'Right':
theory_y = array('d', [
1.2 * 1.6997, 1.2 * 0.97609, 1.2 * 0.58782, 1.2 * 0.36266,
1.2 * 0.22815, 1.2 * 0.14584, 1.2 * 0.09445, 1.2 * 0.06195,
1.2 * 0.04102, 1.2 * 0.027453, 1.2 * 0.012585, 1.2 * 0.005984,
1.2 * 0.0029719, 1.2 * 0.0015585
])
theory_xv = TVectorD(len(theory_x), theory_x)
theory_yv = TVectorD(len(theory_y), theory_y)
theory = TGraph(theory_xv, theory_yv)
theory.SetLineColor(2)
theory.SetLineWidth(2)
Nentries = Trees[Type].GetEntriesFast()
mass = array('d', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
exp = array('d', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
obs = array('d', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
exp68H = array('d', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
exp68L = array('d', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
exp95H = array('d', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
exp95L = array('d', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
masserr = array('d', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
obserr = array('d', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
experr = array('d', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
m = 0
for entry in xrange(Nentries):
Trees[Type].GetEntry(entry)
thismass = Trees[Type].mh
thisquantile = Trees[Type].quantileExpected
thislimit = Trees[Type].limit
thislimiterr = Trees[Type].limitErr
if thismass == 800.0: i = 0
elif thismass == 900.0: i = 1
elif thismass == 1000.0: i = 2
elif thismass == 1100.0: i = 3
elif thismass == 1200.0: i = 4
elif thismass == 1300.0: i = 5
elif thismass == 1400.0: i = 6
elif thismass == 1500.0: i = 7
elif thismass == 1600.0: i = 8
elif thismass == 1700.0: i = 9
elif thismass == 1900.0: i = 10
elif thismass == 2100.0: i = 11
elif thismass == 2300.0: i = 12
elif thismass == 2500.0: i = 13
#if thismass < 1000: continue
if (abs(thisquantile + 1.000) < 0.01):
mass[i] = thismass
obs[i] = thislimit
obserr[i] = 0
masserr[i] = 0
elif (abs(thisquantile - 0.025) < 0.01):
exp95L[i] = thislimit
print '95L ', thislimit
elif (abs(thisquantile - 0.160) < 0.01):
exp68L[i] = thislimit
elif (abs(thisquantile - 0.500) < 0.01):
exp[i] = thislimit
experr[i] = thislimiterr
elif (abs(thisquantile - 0.840) < 0.01):
exp68H[i] = thislimit
elif (abs(thisquantile - 0.975) < 0.01):
exp95H[i] = thislimit
print str(thismass) + ' ' + str(thisquantile) + ' ' + str(thislimit)
print 'exp95L', exp95L
print 'exp95H', exp95H
print 'exp68L', exp68L
print 'exp68H', exp68H
massv = TVectorD(len(mass), mass)
expv = TVectorD(len(mass), exp)
obsv = TVectorD(len(mass), obs)
exp68Hv = TVectorD(len(mass), exp68H)
exp68Lv = TVectorD(len(mass), exp68L)
exp95Hv = TVectorD(len(mass), exp95H)
exp95Lv = TVectorD(len(mass), exp95L)
#exp95Lv = TVectorD(len(mass),exp68L)
masserrv = TVectorD(len(mass), masserr)
obserrv = TVectorD(len(mass), obserr)
experrv = TVectorD(len(mass), experr)
observed = TGraphAsymmErrors(massv, obsv, masserrv, masserrv, obserrv,
obserrv)
observed.SetLineColor(ROOT.kBlack)
observed.SetLineWidth(2)
observed.SetMarkerStyle(20)
expected = TGraphAsymmErrors(massv, expv, masserrv, masserrv, experrv,
experrv)
expected.SetLineColor(ROOT.kBlack)
expected.SetLineWidth(2)
expected.SetLineStyle(2)
## I'm confused, somehow this is the way that works
expected68 = TGraphAsymmErrors(massv, expv, masserrv, masserrv, exp95Lv,
exp68Hv)
#expected68 = TGraphAsymmErrors(massv,expv,masserrv,masserrv,exp68Lv,exp68Hv)
expected68.SetFillColor(ROOT.kGreen)
expected95 = TGraphAsymmErrors(massv, expv, masserrv, masserrv, exp68Lv,
exp95Hv)
#expected95 = TGraphAsymmErrors(massv,expv,masserrv,masserrv,exp95Lv,exp95Hv)
expected95.SetFillColor(ROOT.kYellow)
c4 = TCanvas("c4", "W' tb Limits", 1000, 800)
c4.SetBottomMargin(0.15)
c4.SetRightMargin(0.06)
expected95.Draw("a3")
expected95.GetXaxis().SetTitle("W'_{" + Type + "} mass [GeV/c^{2}]")
expected95.GetYaxis().SetTitle("#sigma(pp#rightarrowW/W'_{" + str(Type) +
"}#rightarrowtb) [pb]")
expected68.Draw("3same")
expected.Draw("csame")
observed.Draw("cpsame")
theory.Draw("csame")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextAlign(31) # align right
latex.DrawLatex(0.45, 0.95, "CMS Preliminary")
latex2 = TLatex()
latex2.SetNDC()
latex2.SetTextSize(0.04)
latex2.SetTextAlign(31) # align right
latex2.DrawLatex(0.87, 0.95,
str(lumiPlot) + " fb^{-1} at #sqrt{s} = 7 TeV")
latex4 = TLatex()
latex4.SetNDC()
latex4.SetTextSize(0.04)
latex4.SetTextAlign(31) # align right
latex4.DrawLatex(0.80, 0.87, "e+jets N_{b tags} #geq 1 ")
legend = TLegend(.566, .684, .84, .84)
legend.AddEntry(observed, '95% C.L. Observed', "lp")
legend.AddEntry(expected, '95% C.L. Expected', "l")
legend.AddEntry(expected68, '#pm 1#sigma Expected', "f")
legend.AddEntry(expected95, '#pm 2#sigma Expected', "f")
legend.SetShadowColor(0)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.Draw()
c4.SaveAs('Wprime_' + Type + '_Limits.root')
c4.SaveAs('Wprime_' + Type + '_Limits.png')
def limit(method, channel):
particle = channel[1:2]
particleP = particle + "'" if channel.startswith('X') else channel[0]
THEORY = ['A1', 'B3'] if channel.startswith('X') else []
suffix = ""
if method == "hvt": suffix = "_HVT"
if method == "cls": suffix = "_CLs"
if method == "monoH": suffix = "_monoH"
filename = "./combine/" + method + "/" + channel + "_M%d.txt"
mass, val = fillValues(filename)
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])
Exp0s.SetPoint(n, m, val[m][3])
Exp1s.SetPoint(n, m, val[m][3])
Exp1s.SetPointError(n, 0., 0., val[m][3] - val[m][2],
val[m][4] - val[m][3])
Exp2s.SetPoint(n, m, val[m][3])
Exp2s.SetPointError(n, 0., 0., val[m][3] - val[m][1],
val[m][5] - val[m][3])
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()
for m in sorted(HVT[t]['Z']['XS'].keys()):
if m < mass[0] or m > mass[-1]: continue
XsW, XsW_Up, XsW_Down, XsZ, XsZ_Up, XsZ_Down = 0., 0., 0., 0., 0., 0.
XsZ = 1000. * HVT[t]['Z']['XS'][m] * HVT[t]['Z']['BR'][m]
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, XsW + XsZ)
Theory[t].SetPointError(n, 0., 0.,
(XsW - XsW_Down) + (XsZ - XsZ_Down),
(XsW_Up - XsW) + (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 +
"h) (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(0.01, 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))
drawAnalysis(channel)
drawRegion(channel, True)
drawCMS(LUMI, YEAR, "Preliminary") #Preliminary
# legend
top = 0.9
nitems = 4 + len(THEORY)
leg = TLegend(0.55, 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()
"""
if True and channel.endswith('sl'):
mass, val = fillValues("./combine/alpha/X"+particle+"Hbonly_M%d.txt")
Exp, Obs = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(mass):
if not m in val: continue
Exp.SetPoint(Exp.GetN(), m, val[m][3])
Obs.SetPoint(Obs.GetN(), m, val[m][0])
Exp.SetLineWidth(3)
Exp.SetLineColor(602) #602
Exp.SetLineStyle(5)
Obs.SetLineWidth(3)
Obs.SetLineColor(602)
Exp.Draw("SAME, L")
Obs.Draw("SAME, L")
mass15, val15 = fillValues("./combine/Vh_2015/X"+particle+"h_M%d.txt")
Exp15, Obs15 = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(mass15):
if not m in val: continue
Exp15.SetPoint(Exp15.GetN(), m, val15[m][3]*multF*Theory['B3'].GetY()[i]*(0.625 if particle=='V' and m>3000 else 1.))
Obs15.SetPoint(Obs15.GetN(), m, val15[m][0]*multF*Theory['B3'].GetY()[i]*(0.625 if particle=='V' and m>3000 else 1.))
Exp15.SetLineWidth(3)
Exp15.SetLineColor(856) #602
Exp15.SetLineStyle(6)
Obs15.SetLineWidth(3)
Obs15.SetLineColor(856)
Exp15.Draw("SAME, L")
#Obs15.Draw("SAME, L")
leg2.AddEntry(Exp, "1+2 b-tag", "l")
"""
if True and channel == 'AZh':
massLL, valLL = fillValues("./combine/AZh/AZhll_M%d.txt")
ExpLL, ObsLL = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massLL):
if not m in val: continue
ExpLL.SetPoint(ExpLL.GetN(), m, valLL[m][3] * multF)
ObsLL.SetPoint(ObsLL.GetN(), m, valLL[m][0] * multF)
ExpLL.SetLineWidth(3)
ExpLL.SetLineColor(833) #602
ExpLL.SetLineStyle(5)
ObsLL.SetLineWidth(3)
ObsLL.SetLineColor(833)
ExpLL.Draw("SAME, L")
#ObsLL.Draw("SAME, L")
massNN, valNN = fillValues("./combine/AZh/AZhnn_M%d.txt")
ExpNN, ObsNN = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massNN):
if not m in val: continue
ExpNN.SetPoint(ExpNN.GetN(), m, valNN[m][3] * multF)
ObsNN.SetPoint(ObsNN.GetN(), m, valNN[m][0] * multF)
ExpNN.SetLineWidth(3)
ExpNN.SetLineColor(855) #602
ExpNN.SetLineStyle(6)
ObsNN.SetLineWidth(3)
ObsNN.SetLineColor(855)
ExpNN.Draw("SAME, L")
#ObsNN.Draw("SAME, L")
leg2.AddEntry(ExpLL,
"Expected, A #rightarrow Zh #rightarrow llb#bar{b}", "l")
leg2.AddEntry(ExpNN,
"Expected, A #rightarrow Zh #rightarrow #nu#nub#bar{b}",
"l")
if method == 'combo':
massAH, valAH = fillValues("./combine/dijet/X" + particle +
"Hah_M%d.txt")
ExpAH = TGraphAsymmErrors()
for i, m in enumerate(massAH):
if not m in val: continue
ExpAH.SetPoint(ExpAH.GetN(), m, valAH[m][3] * multF)
ExpAH.SetLineWidth(2)
ExpAH.SetLineColor(602) #602
ExpAH.SetLineStyle(4)
ExpAH.Draw("SAME, L")
massSL, valSL = fillValues("./combine/alpha/X" + particle +
"Hsl_M%d.txt")
ExpSL = TGraphAsymmErrors()
for i, m in enumerate(massSL):
if not m in val: continue
ExpSL.SetPoint(ExpSL.GetN(), m, valSL[m][3] * multF)
ExpSL.SetLineWidth(3)
ExpSL.SetLineColor(860 - 9) #602
ExpSL.SetLineStyle(7)
ExpSL.Draw("SAME, L")
leg2.AddEntry(ExpAH, "B2G-17-002", "l")
leg2.AddEntry(ExpSL, "B2G-17-004", "l")
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("plotsLimit/Exclusion/" + channel + suffix + ".png")
c1.Print("plotsLimit/Exclusion/" + channel + suffix + ".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("plotsLimit/Exclusion/" + channel + suffix + ".C")
c1.Print("plotsLimit/Exclusion/" + channel + 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 ""
print "p1s[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i] + Exp1s.GetErrorYhigh(i), ",",
print "],"
print "m1s[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i] - Exp1s.GetErrorYlow(i), ",",
print "],"
print "[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i], ",",
print "]"
#if not 'ah' in channel and not 'sl' in channel: return
# ---------- Significance ----------
c2 = TCanvas("c2", "Significance", 800, 600)
c2.cd()
c2.GetPad(0).SetTopMargin(0.06)
c2.GetPad(0).SetRightMargin(0.05)
c2.GetPad(0).SetTicks(1, 1)
c2.GetPad(0).SetGridx()
c2.GetPad(0).SetGridy()
Sign.GetYaxis().SetRangeUser(0., 5.)
Sign.Draw("AL3")
drawCMS(LUMI, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c2.Print("plotsLimit/Significance/" + channel + suffix + ".png")
c2.Print("plotsLimit/Significance/" + channel + suffix + ".pdf")
# c2.Print("plotsLimit/Significance/"+channel+suffix+".root")
# c2.Print("plotsLimit/Significance/"+channel+suffix+".C")
# ---------- p-Value ----------
c3 = TCanvas("c3", "p-Value", 800, 600)
c3.cd()
c3.GetPad(0).SetTopMargin(0.06)
c3.GetPad(0).SetRightMargin(0.05)
c3.GetPad(0).SetTicks(1, 1)
c3.GetPad(0).SetGridx()
c3.GetPad(0).SetGridy()
c3.GetPad(0).SetLogy()
pVal.Draw("AL3")
pVal.GetYaxis().SetRangeUser(2.e-7, 0.5)
ci = [
1., 0.317310508, 0.045500264, 0.002699796, 0.00006334, 0.000000573303,
0.000000001973
]
line = TLine()
line.SetLineColor(922)
line.SetLineStyle(7)
text = TLatex()
text.SetTextColor(922)
text.SetTextSize(0.025)
text.SetTextAlign(12)
for i in range(1, len(ci) - 1):
line.DrawLine(pVal.GetXaxis().GetXmin(), ci[i] / 2,
pVal.GetXaxis().GetXmax(), ci[i] / 2)
text.DrawLatex(pVal.GetXaxis().GetXmax() * 1.01, ci[i] / 2,
"%d #sigma" % i)
drawCMS(LUMI, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c3.Print("plotsLimit/pValue/" + channel + suffix + ".png")
c3.Print("plotsLimit/pValue/" + channel + suffix + ".pdf")
# c3.Print("plotsLimit/pValue/"+channel+suffix+".root")
# c3.Print("plotsLimit/pValue/"+channel+suffix+".C")
# --------- Best Fit ----------
c4 = TCanvas("c4", "Best Fit", 800, 600)
c4.cd()
c4.GetPad(0).SetTopMargin(0.06)
c4.GetPad(0).SetRightMargin(0.05)
c4.GetPad(0).SetTicks(1, 1)
c4.GetPad(0).SetGridx()
c4.GetPad(0).SetGridy()
Best.Draw("AL3")
drawCMS(LUMI, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c4.Print("plotsLimit/BestFit/" + channel + suffix + ".png")
c4.Print("plotsLimit/BestFit/" + channel + suffix + ".pdf")
# c4.Print("plotsLimit/BestFit/"+channel+suffix+".root")
# c4.Print("plotsLimit/BestFit/"+channel+suffix+".C")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if 'ah' in channel:
outFile = TFile("bands.root", "RECREATE")
outFile.cd()
pVal.Write("graph")
Best.Write("best")
outFile.Close()
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 nuisance_plot(input_file_, orien_, sticker_, oname_):
pars = get_parameters_from_file(input_file_)
#adjust_parameters(pars)
N = len(pars)
g68 = TGraph(2 * N + 7)
g95 = TGraph(2 * N + 7)
gPR = TGraphAsymmErrors(N)
for i in range(0, N):
x = pars[i].value
y = i + 1.5
xerr = pars[i].error
yerr = 0.
if orien_ == 'h':
x, y = y, x
xerr, yerr = yerr, xerr
gPR.SetPoint(i, x, y)
gPR.SetPointEXlow(i, xerr)
gPR.SetPointEYlow(i, yerr)
gPR.SetPointEXhigh(i, xerr)
gPR.SetPointEYhigh(i, yerr)
for a in range(0, N + 3):
if orien_ == 'h':
g68.SetPoint(a, a, -1)
g95.SetPoint(a, a, -2)
g68.SetPoint(a + 1 + N + 2, N + 2 - a, 1)
g95.SetPoint(a + 1 + N + 2, N + 2 - a, 2)
else:
g68.SetPoint(a, -1, a)
g95.SetPoint(a, -2, a)
g68.SetPoint(a + 1 + N + 2, 1, N + 2 - a)
g95.SetPoint(a + 1 + N + 2, 2, N + 2 - a)
gPR.SetLineStyle(1)
gPR.SetLineWidth(1)
gPR.SetLineColor(1)
gPR.SetMarkerStyle(21)
gPR.SetMarkerSize(1.25)
g68.SetFillColor(kGreen)
g95.SetFillColor(kYellow)
if orien_ == 'h':
text_TL = TPaveText(0.100, 0.925, 0.440, 0.995, 'NDC')
text_TR = TPaveText(0.587, 0.925, 0.999, 0.993, 'NDC')
else:
text_TL = TPaveText(0.370, 0.945, 0.590, 0.995, 'NDC')
text_TR = TPaveText(0.600, 0.945, 0.995, 0.993, 'NDC')
text_TL.AddText(label_TL)
text_TL.SetFillColor(0)
text_TL.SetTextAlign(12)
text_TL.SetTextSize(0.06)
text_TL.SetTextFont(42)
text_TR.AddText(sticker_)
text_TR.SetFillColor(0)
text_TR.SetTextAlign(32)
text_TR.SetTextSize(0.05)
text_TR.SetTextFont(42)
if orien_ == 'h':
c = TCanvas('c', 'c', 1000, 700)
c.SetTopMargin(0.08)
c.SetRightMargin(0.02)
c.SetBottomMargin(0.16)
c.SetLeftMargin(0.11)
else:
c = TCanvas('c', 'c', 700, 1000)
c.SetTopMargin(0.06)
c.SetRightMargin(0.02)
c.SetBottomMargin(0.12)
c.SetLeftMargin(0.35 * 700 / 650)
c.SetTickx()
c.SetTicky()
c.Update()
g95.Draw('AF')
g68.Draw('F')
gPR.Draw('P')
text_TL.Draw('same')
text_TR.Draw('same')
ax_1 = g95.GetHistogram().GetYaxis()
ax_2 = g95.GetHistogram().GetXaxis()
if orien_ == 'h': ax_1, ax_2 = ax_2, ax_1
ax_1.Set(N + 2, 0, N + 2)
ax_1.SetNdivisions(-414)
for i in range(0, N):
ax_1.SetBinLabel(i + 2, pars[i].name)
g95.SetTitle('')
ax_2.SetTitle('post-fit nuisance parameters values')
#ax_2.SetTitle('deviation in units of #sigma')
ax_1.SetTitleSize(0.050)
ax_2.SetTitleSize(0.050)
ax_1.SetTitleOffset(1.4)
ax_2.SetTitleOffset(1.0)
ax_1.SetLabelSize(0.05)
#ax_2.SetLabelSize(0.05)
ax_1.SetRangeUser(0, N + 2)
ax_2.SetRangeUser(-2.4, 2.4)
g95.GetHistogram().Draw('axis,same')
c.SaveAs(oname_ + '.pdf')
c.Close()
def limit(channel, signal):
multF = 1. # in fb
filename = "./limitOutput_" + options.name + "/" + signal + "_MChi1_MPhi%d_scalar" + options.bjets + "_" + channel + "_AsymptoticLimits_grepOutput.txt"
if (options.mediator == 'SC'):
filename = "./limitOutput_" + options.name + "/" + signal + "_MChi1_MPhi%d_scalar" + options.bjets + "_" + channel + "_AsymptoticLimits_grepOutput.txt"
elif (options.mediator == 'PS'):
filename = "./limitOutput_" + options.name + "/" + signal + "_MChi1_MPhi%d_pseudo" + options.bjets + "_" + channel + "_AsymptoticLimits_grepOutput.txt"
else:
print 'WRONG mediator type'
mass, val = fillValues(filename)
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
Sign = TGraph()
pVal = TGraph()
Best = 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)
#Sign.SetPoint(n, m, val[m][6])
#pVal.SetPoint(n, m, val[m][7])
#Best.SetPoint(n, m, val[m][8])
#Best.SetPointError(m, 0., 0., abs(val[m][9]), abs(val[m][10]))
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_{#phi} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle("#sigma/#sigma_{th}")
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Sign.SetLineWidth(2)
Sign.SetLineColor(629)
Sign.GetXaxis().SetTitle("m_{#phi} (GeV)")
Sign.GetXaxis().SetTitleSize(Sign.GetXaxis().GetTitleSize() * 1.1)
Sign.GetYaxis().SetTitle("Significance")
pVal.SetLineWidth(2)
pVal.SetLineColor(629)
pVal.GetXaxis().SetTitle("m_{#phi} (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_{#phi} (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).SetTicks(1, 1)
#c1.GetPad(0).SetGridx()
#c1.GetPad(0).SetGridy()
c1.GetPad(0).SetLogx()
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
Exp0s.Draw("SAME, L")
if not options.blind: Obs0s.Draw("SAME, L")
#Theory[0].Draw("SAME, L")
#Theory[1].Draw("SAME, L")
#setHistStyle(Exp2s)
Exp2s.GetXaxis().SetTitleSize(0.045)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetXaxis().SetNoExponent(True)
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.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.1, 1000.)
#else: Exp2s.GetYaxis().SetRangeUser(0.1, 1.e2)
Exp2s.GetXaxis().SetRangeUser(mass[0], mass[-1])
drawAnalysis("tDM")
drawRegion(channel, True)
drawCMS(LUMI, "Preliminary")
if True:
if (options.mediator == 'SC'):
massT, valT = fillValues("./limitOutput_" + options.name + "/" +
signal.replace('tttDM', 'tDM') +
"_MChi1_MPhi%d_scalar" + options.bjets +
"_" + channel +
"_AsymptoticLimits_grepOutput.txt")
elif (options.mediator == 'PS'):
massT, valT = fillValues("./limitOutput_" + options.name + "/" +
signal.replace('tttDM', 'tDM') +
"_MChi1_MPhi%d_pseudo" + options.bjets +
"_" + channel +
"_AsymptoticLimits_grepOutput.txt")
ExpT, ObsT = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massT):
if not m in val: continue
ExpT.SetPoint(ExpT.GetN(), m, valT[m][3] * multF)
ObsT.SetPoint(ObsT.GetN(), m, valT[m][0] * multF)
ExpT.SetLineWidth(3)
ExpT.SetLineColor(602) #602
ExpT.SetLineStyle(5)
ObsT.SetLineWidth(3)
ObsT.SetLineColor(602)
ExpT.SetMarkerStyle(21)
ObsT.SetMarkerStyle(22)
ExpT.SetMarkerColor(602)
ObsT.SetMarkerColor(602)
ExpT.Draw("SAME, PC")
#if not options.blind: ObsT.Draw("SAME, P")
if (options.mediator == 'SC'):
massTTT, valTTT = fillValues("./limitOutput_" + options.name +
"/" +
signal.replace('tttDM', 'ttDM') +
"_MChi1_MPhi%d_scalar" +
options.bjets + "_" + channel +
"_AsymptoticLimits_grepOutput.txt")
elif (options.mediator == 'PS'):
massTTT, valTTT = fillValues("./limitOutput_" + options.name +
"/" +
signal.replace('tttDM', 'ttDM') +
"_MChi1_MPhi%d_pseudo" +
options.bjets + "_" + channel +
"_AsymptoticLimits_grepOutput.txt")
ExpTTT, ObsTTT = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massTTT):
if not m in val: continue
ExpTTT.SetPoint(ExpTTT.GetN(), m, valTTT[m][3] * multF)
ObsTTT.SetPoint(ObsTTT.GetN(), m, valTTT[m][0] * multF)
ExpTTT.SetLineWidth(3)
ExpTTT.SetLineColor(634) #602
ExpTTT.SetLineStyle(5)
ObsTTT.SetLineWidth(3)
ObsTTT.SetLineColor(634)
ExpTTT.SetMarkerStyle(21)
ObsTTT.SetMarkerStyle(22)
ExpTTT.SetMarkerColor(634)
ObsTTT.SetMarkerColor(634)
ExpTTT.Draw("SAME, PC")
#if not options.blind: ObsTTT.Draw("SAME, P")
# legend
top = 0.9
nitems = 4 + 2
leg = TLegend(0.55, top - nitems * 0.3 / 5., 0.95, top)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL limits")
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected (t+DM, tt+DM)", "l")
leg.AddEntry(Exp1s, "#pm 1 s. d.", "f")
leg.AddEntry(Exp2s, "#pm 2 s. d.", "f")
if True:
leg.AddEntry(ExpT, "Expected (t+DM)", "p")
leg.AddEntry(ExpTTT, "Expected (tt+DM)", "p")
leg.Draw()
c1.GetPad(0).RedrawAxis()
c1.GetPad(0).Update()
if gROOT.IsBatch():
c1.Print("plotsLimit_" + options.name + "/Exclusion_" + channel + "_" +
options.mediator + "_" + options.bjets + ".png")
c1.Print("plotsLimit_" + options.name + "/Exclusion_" + channel + "_" +
options.mediator + "_" + options.bjets + ".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
# print "p1s[",
# for i in range(Exp0s.GetN()):
# print Exp0s.GetY()[i]+Exp1s.GetErrorYhigh(i), ",",
# print "],"
# print "m1s[",
# for i in range(Exp0s.GetN()):
# print Exp0s.GetY()[i]-Exp1s.GetErrorYlow(i), ",",
# print "],"
# print "[",
# for i in range(Exp0s.GetN()):
# print Exp0s.GetY()[i], ",",
# print "]"
return
# ---------- Significance ----------
c2 = TCanvas("c2", "Significance", 800, 600)
c2.cd()
c2.GetPad(0).SetTopMargin(0.06)
c2.GetPad(0).SetRightMargin(0.05)
c2.GetPad(0).SetTicks(1, 1)
c2.GetPad(0).SetGridx()
c2.GetPad(0).SetGridy()
Sign.GetYaxis().SetRangeUser(0., 5.)
Sign.Draw("AL3")
drawCMS(LUMI, "Preliminary")
drawAnalysis(channel[1:3])
if gROOT.IsBatch():
c2.Print("plotsLimit_" + options.name + "/Significance/" + channel +
"_" + options.mediator + "_" + options.bjets + ".png")
c2.Print("plotsLimit_" + options.name + "/Significance/" + channel +
"_" + options.mediator + "_" + options.bjets + ".pdf")
# c2.Print("plotsLimit/Significance/"+channel+suffix+".root")
# c2.Print("plotsLimit/Significance/"+channel+suffix+".C")
# ---------- p-Value ----------
c3 = TCanvas("c3", "p-Value", 800, 600)
c3.cd()
c3.GetPad(0).SetTopMargin(0.06)
c3.GetPad(0).SetRightMargin(0.05)
c3.GetPad(0).SetTicks(1, 1)
c3.GetPad(0).SetGridx()
c3.GetPad(0).SetGridy()
c3.GetPad(0).SetLogy()
pVal.Draw("AL3")
pVal.GetYaxis().SetRangeUser(2.e-7, 0.5)
ci = [
1., 0.317310508, 0.045500264, 0.002699796, 0.00006334, 0.000000573303,
0.000000001973
]
line = TLine()
line.SetLineColor(922)
line.SetLineStyle(7)
text = TLatex()
text.SetTextColor(922)
text.SetTextSize(0.025)
text.SetTextAlign(12)
for i in range(1, len(ci) - 1):
line.DrawLine(pVal.GetXaxis().GetXmin(), ci[i] / 2,
pVal.GetXaxis().GetXmax(), ci[i] / 2)
text.DrawLatex(pVal.GetXaxis().GetXmax() * 1.01, ci[i] / 2,
"%d #sigma" % i)
drawCMS(LUMI, "Preliminary")
drawAnalysis(channel[1:3])
if gROOT.IsBatch():
c3.Print("plotsLimit_" + options.name + "/pValue/" + channel + suffix +
"_" + options.mediator + "_" + options.bjets + ".png")
c3.Print("plotsLimit_" + options.name + "/pValue/" + channel + suffix +
"_" + options.mediator + "_" + options.bjets + ".pdf")
# c3.Print("plotsLimit/pValue/"+channel+suffix+".root")
# c3.Print("plotsLimit/pValue/"+channel+suffix+".C")
# --------- Best Fit ----------
c4 = TCanvas("c4", "Best Fit", 800, 600)
c4.cd()
c4.GetPad(0).SetTopMargin(0.06)
c4.GetPad(0).SetRightMargin(0.05)
c4.GetPad(0).SetTicks(1, 1)
c4.GetPad(0).SetGridx()
c4.GetPad(0).SetGridy()
Best.Draw("AL3")
drawCMS(LUMI, "Preliminary")
drawAnalysis(channel[1:3])
if gROOT.IsBatch():
c4.Print("plotsLimit_" + options.name + "/BestFit/" + channel +
suffix + "_" + options.mediator + "_" + options.bjets +
".png")
c4.Print("plotsLimit_" + options.name + "/BestFit/" + channel +
suffix + "_" + options.mediator + "_" + options.bjets +
".pdf")
# c4.Print("plotsLimit/BestFit/"+channel+suffix+".root")
# c4.Print("plotsLimit/BestFit/"+channel+suffix+".C")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
g_th_166.SetPoint(bin-1, scales[bin-1], th_xsec_166[bin-1])
g_th_166.SetPointError(bin-1,scales[bin-1]-bins[bin-1], bins[bin]-scales[bin-1], 0, 0)
xsec_err = getXsec(bin)
g_exp.SetPoint(bin-1, scales[bin-1], xsec_err[0])
g_exp.SetPointError(bin-1, 0, 0, xsec_err[2], xsec_err[1])
g_th_164.SetMarkerStyle(0)
g_th_164.SetMarkerColor(ROOT.kRed)
g_th_164.SetLineColor(ROOT.kRed)
g_th_162.SetMarkerStyle(0)
g_th_162.SetMarkerColor(ROOT.kBlue)
g_th_162.SetLineColor(ROOT.kBlue)
g_th_162.SetLineStyle(2)
g_th_166.SetMarkerStyle(0)
g_th_166.SetMarkerColor(ROOT.kGreen+2)
g_th_166.SetLineColor(ROOT.kGreen+2)
g_th_166.SetLineStyle(3)
g_exp.SetMarkerStyle(8)
g_exp.SetLineWidth(1)
latexLabel1 = ROOT.TLatex()
latexLabel1.SetTextSize(0.06)
latexLabel1.SetNDC()
latexLabel2 = ROOT.TLatex()
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...")
graph = TGraphAsymmErrors()
n=0
for a in sorted(threshold_scan):
#for a in sorted(run_parameters):
##Fill the TGraph with threshold (x-axis) and rate (y-axis)
#######graph.SetPoint(n,int(run_parameters[a]['VTHR']),float(run_parameters[a]['RATE_SL1_L1']))
graph.SetPoint(n,int(a),float(threshold_scan[a]))
n = n+1
graph.SetMarkerSize(1.)
graph.SetMarkerStyle(21)
graph.SetMarkerColor(862)
graph.SetFillColor(868)
graph.SetFillStyle(3844)
graph.SetLineColor(868)
graph.SetLineWidth(2)
graph.SetLineStyle(2)
graph.GetXaxis().SetTitle("threshold [mV]")
graph.GetYaxis().SetTitleOffset(1.2)
graph.GetYaxis().SetTitle("rate [kHz]")
graph.Draw("APL")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextColor(1)
latex.SetTextFont(42)
latex.SetTextAlign(33)
latex.SetTextSize(0.04)
latex.SetTextFont(62)
latex.DrawLatex(0.30, 0.96, "GIF++")
etichetta = TLatex()
etichetta.SetNDC()
