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('Fake 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.'))
x.SetLabelSize(15)
'''
#create ratio plot using TGraphAsymmErrors
gPad.Modified()
ratio = TGraphAsymmErrors()
ratio.Divide(h6,h,"cl=0.683 b(1,1) mode")
ratio.SetMarkerColor(1)
ratio.SetMarkerStyle(21)
ratio.SetLineColor(1)
ratio.GetXaxis().SetTitleSize(0.1)
ratio.GetXaxis().SetLabelSize(0.1)
ratio.GetXaxis().SetRangeUser(200,1000)
ratio.GetXaxis().SetTitle("MET (GeV)")
ratio.GetYaxis().SetTitleSize(0.1)
ratio.GetYaxis().SetLabelSize(0.1)
ratio.GetYaxis().SetTitleOffset(0.4)
ratio.GetYaxis().SetTitle("ratio")
ratio.SetTitle("")
#upper histogram plot is pad1
pad1 = TPad("pad1","pad1", 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0) #joins upper and lower plot
pad1.Draw()
#lower ratio plot is pad2
c1.cd() #returns to main canvas before defining pad2
pad2 = TPad("pad2","pad2", 0, 0.05, 1, 0.3)
pad2.SetTopMargin(0) #joins upper and lower plot
pad2.SetBottomMargin(0.2)
pad2.Draw()
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 main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i", "--inputfile", dest="inputfile")
parser.add_option("-o", "--ouputfile", dest="outputfile")
parser.add_option("-b", "--batch", action="store_true",\
dest="isBatch", default=False)
parser.add_option("--normalization", nargs=2,\
type="float", dest="norm_range")
parser.add_option("--fit", nargs=2,\
type="float", dest="fit_range")
(options, args) = parser.parse_args()
isSaveOutput = options.outputfile is not None
if not (options.inputfile):
parser.error("Please specify inputfiles.")
import configurations as config
if options.fit_range:
fit_range = options.fit_range
norm_range = (fit_range[1] - 200., fit_range[1])
else:
fit_range = config.fit_range
norm_range = config.norm_range
# Override normalization range from input
if options.norm_range:
norm_range = options.norm_range
from Styles import formatST, formatTemplate, formatUncertainty
from ROOT import TFile, TF1, TH1D, TMath, TCanvas, TLegend,\
TGraphAsymmErrors, TVectorD
#input file name
infile = TFile(options.inputfile, "READ")
from HistoStore import HistoStore
store = HistoStore()
canvas = HistoStore()
print "Fit range: %d - %d GeV" % fit_range
print "Normalization range: %d - %d GeV" % norm_range
# Fit
for N in config.exclusive_multiplicities:
hST = infile.Get("plots%dJets/ST" % N)
if not options.isBatch:
c = TCanvas("TemplateN%d" % N,
"TemplateN%d" % N, 500, 500)
canvas.book(c)
formatST(hST)
hST.Draw("e")
hST.GetXaxis().SetRangeUser(fit_range[0], config.maxST)
hST.GetYaxis().SetRangeUser(1e-2, 2e4)
c.SetLogy(1)
for i,formula in enumerate(config.templates):
if N == 2:
f = TF1("templateN%d_%d" % (N, i), formula, 0, 10000)
elif N == 3:
f = store.get("templateN2_%d" % i).Clone("templateN%d_%d" % (N, i))
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
hST.Fit(f, "QN0", "", fit_range[0], fit_range[1])
if i == 0:
hST.Fit(f, "Q0", "", fit_range[0], fit_range[1])
formatTemplate(f, N, i)
store.book(f)
if not options.isBatch:
f.Draw("same")
hTemplate = hST.Clone("histoTemplateN%d_%d" % (N,i))
hTemplate.Reset()
hTemplate.Eval(f)
formatTemplate(hTemplate, N, i)
store.book(hTemplate)
if i == 0:
hRef = hTemplate.Clone("ReferenceTemplateN%d_0" % N)
store.book(hRef)
# Print Chi-squre/Ndof
print "N = %d, Chi^2/Ndof = %0.2f/%d" %\
(N, f.GetChisquare(), f.GetNDF())
if not options.isBatch:
c.Update()
# Calculate scale/error
from OptimizationTools import OptimizeScale
for histoN, templateN in [[2,3]]:
hST = store.get("ReferenceTemplateN%d_0" % histoN)
hTemplate = store.get("ReferenceTemplateN%d_0" % templateN)
hlnL, scale, err = OptimizeScale(hST, hTemplate, norm_range)
hlnL.SetName("LogLikelihood_%dto%d" % (templateN, histoN))
store.book(hlnL)
for i in range(len(config.templates)):
hTemplate = store.get("histoTemplateN%d_%d" % (templateN, i))
hTemplate_ = hTemplate.Clone("histoTemplateN%d_%d__RescaledToN%d"
% (templateN, i, histoN))
hTemplate_.Scale(scale)
store.book(hTemplate_)
# Shape Uncertainty
hBkgTemplate = store.get("histoTemplateN2_0")
hBkgTemplate.Rebin(config.rebin)
nbins = hBkgTemplate.GetNbinsX()
vST = TVectorD(nbins)
vBkg = TVectorD(nbins)
vexl = TVectorD(nbins)
vexh = TVectorD(nbins)
shape_el = TVectorD(nbins)
shape_eh = TVectorD(nbins)
rel_shape_el = TVectorD(nbins)
rel_shape_eh = TVectorD(nbins)
for i in range(nbins):
vST[i] = hBkgTemplate.GetBinCenter(i+1)
if (vST[i] < config.com):
vBkg[i] = hBkgTemplate.GetBinContent(i+1)
else:
vBkg[i] = 0.0
vexl[i] = 0.0
vexh[i] = 0.0
shape_el[i] = 0.0
shape_eh[i] = 0.0
rel_shape_el[i] = 0.0
rel_shape_eh[i] = 0.0
for i in range(len(config.templates)):
for label in ["histoTemplateN2_%d", "histoTemplateN3_%d__RescaledToN2"]:
if label % i == "histoTemplateN2_0":
continue
h = store.get(label % i)
h.Rebin(config.rebin)
for ibin in range(nbins):
diff = h.GetBinContent(ibin+1) - vBkg[ibin]
if diff > 0 and diff > shape_eh[ibin]:
shape_eh[ibin] = diff
elif diff < 0 and abs(diff) > shape_el[ibin]:
shape_el[ibin] = abs(diff)
# Relative Shape Uncertaincy
for i in range(nbins):
if vBkg[i] > 0:
#rel_shape_el[i] = rel_shape_el[i] / vBkg[i]
#hape_eh[i] = rel_shape_eh[i] / vBkg[i]
max_err = max(shape_el[i], shape_eh[i])
shape_el[i] = max_err
shape_eh[i] = max_err
rel_shape_el[i] = max_err /vBkg[i]
rel_shape_eh[i] = max_err /vBkg[i]
else:
rel_shape_el[i] = 0.0
rel_shape_eh[i] = 0.0
#print vST[i], vBkg[i], rel_shape_el[i], rel_shape_eh[i]
gShapeUncertainty = TGraphAsymmErrors(vST, vBkg,
vexl, vexh, shape_el, shape_eh)
gShapeUncertainty.SetName("Shape_Uncertainty")
formatUncertainty(gShapeUncertainty)
store.book(gShapeUncertainty)
gRelShapeUncertainty = TGraphAsymmErrors(vST, vexl,
vexl, vexh, rel_shape_el, rel_shape_eh)
gRelShapeUncertainty.SetName("Relative_Shape_Uncertainty")
formatUncertainty(gRelShapeUncertainty)
store.book(gRelShapeUncertainty)
# Generate Backgrouds
for N in config.label_for_data:
hST = infile.Get("plotsN%s/ST" % N)
rel_scale_err2 = 0.0
scale_factor = 1.0
for Nref in config.label_for_ref:
if N == Nref:
continue
template = store.get("ReferenceTemplateN%s_0" % Nref)
hlnL, scale, err = OptimizeScale(hST, template, norm_range)
hlnL.SetName("LogLikelihood_%sto%s" % (Nref, N))
store.book(hlnL)
if Nref == "2":
scale_factor = scale
rel_scale_err2 += err/scale * err/scale
print "%s/%s %.3f +/- %.3f" % (N, Nref, scale, err)
vy = TVectorD(nbins)
veyh = TVectorD(nbins)
veyl = TVectorD(nbins)
for i in range(nbins):
vy[i] = vBkg[i] * scale_factor
veyh[i] = vy[i] * TMath.Sqrt(rel_scale_err2
+ rel_shape_eh[i]*rel_shape_eh[i])
veyl[i] = vy[i] * TMath.Sqrt(rel_scale_err2
+ rel_shape_el[i]*rel_shape_el[i])
print "Scaling uncertainty (%s): %.2f" %\
(N, TMath.sqrt(rel_scale_err2) * 100.0)
gBkg = TGraphAsymmErrors(vST, vy, vexl, vexh, veyl, veyh)
gBkg.SetName("BackgroundGraph_N%s" % N)
formatUncertainty(gBkg)
store.book(gBkg)
hST.Rebin(config.rebin)
hST.SetName("Data_N%s" % N)
formatST(hST)
store.book(hST)
hBkg = hST.Clone("Background_N%s" % N)
hBkg.Reset()
store.book(hBkg)
for i in range(nbins):
ibin = hBkg.FindBin(vST[i])
hBkg.SetBinContent(ibin, vy[i])
hBkg.SetBinError(ibin, max(veyh[i], vexl[i]))
from OptimizationTools import Integral
hIntBkg = hBkg.Clone("IntegralBackground_N%s" % N)
Integral(hIntBkg)
store.book(hIntBkg)
hIntData = hST.Clone("IntegralData_N%s" % N)
Integral(hIntData)
store.book(hIntData)
# Plot Shape Uncertainty
if not options.isBatch:
legend_shape = TLegend(0.5544355,0.5741525,0.9495968,0.9152542)
legend_shape.SetTextFont(42)
legend_shape.SetFillColor(0)
c = TCanvas("ShapeUncertaintyN2", "ShapeUncertaintyN2", 500, 500)
canvas.book(c)
gShapeUncertainty.Draw("AC3")
gShapeUncertainty.GetXaxis().SetRangeUser(fit_range[0], config.maxST)
gShapeUncertainty.GetYaxis().SetRangeUser(5e-2, 1.2e6)
legend_shape.AddEntry(store.get("Data_N2"), "Data (N = 2)", "p")
legend_shape.AddEntry(gShapeUncertainty, "Shape Uncertainty", "f")
for i in range(len(config.templates)):
for label in ["histoTemplateN2_%d", "histoTemplateN3_%d__RescaledToN2"]:
h = store.get(label % i)
h.GetXaxis().SetRangeUser(fit_range[0], config.maxST)
h.Draw("histcsame")
if label == "histoTemplateN2_%d":
N = 2
else:
N = 3
legend_shape.AddEntry(h, "Parametrization %d (N = %d)" % (i, N), "l")
store.get("Data_N2").Draw("esame")
c.SetLogy(1)
legend_shape.Draw("plain")
c.Update()
if isSaveOutput:
store.saveAs(options.outputfile)
if not options.isBatch:
raw_input("Press Enter to continue...")
def limit():
method = ''
channel = "bb"
if INCLUDEACC:
particleP = "X"
else:
particleP = "Z'"
particle = 'b#bar{b}'
multF = ZPTOBB
THEORY = ['bstar']
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/bstar/" + BTAGGING + "/combined_run2/" + YEAR + "_M%d.txt"
else:
filename = "./combine/limits/bstar/" + 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()
if 'bstar' == t:
x = []
y = []
with open('bstar_deta1p1_lhc13TeV2.txt') as fin:
for line in fin.readlines():
x.append(float(line.split()[0]))
y.append(float(line.split()[1]) * 1000)
for ind in range(len(x)):
Theory[t].SetPoint(ind + 1, x[ind], y[ind])
Theory[t].SetLineColor(theoryLineColor[t])
Theory[t].SetFillColor(theoryFillColor[t])
Theory[t].SetFillStyle(theoryFillStyle[t])
Theory[t].SetLineWidth(2)
continue
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()
