def save(imgdir,
imgname,
redraw_axis=True,
dot_pdf=True,
dot_root=False,
dot_c=False,
additional=[]):
if redraw_axis:
gPad.RedrawAxis()
#gPad.Modified(); gPad.Update()
gPad.Print(imgdir + imgname + ".png")
if dot_pdf:
gPad.Print(imgdir + imgname + ".pdf")
if dot_root:
def save_for_root(p):
if p.ClassName() == "TPad" or p.ClassName() == "TCanvas":
for pp in p.GetListOfPrimitives():
save_for_root(pp)
p.Write()
tfile = TFile.Open(imgdir + imgname + ".root", "RECREATE")
save_for_root(gPad)
if additional:
for a in additional:
a.Write()
tfile.Close()
if dot_c:
gPad.Print(imgdir + imgname + ".C")
def gPadSet():
gPad.SetTickx(1)
gPad.SetTicky(1)
gPad.SetLeftMargin(0.12)
gPad.SetRightMargin(1.4)
gPad.SetBottomMargin(0.10)
gPad.SetFrameLineWidth(2)
gPad.RedrawAxis()
def SaveRatio(hSS, hOS, process, output):
idata = process.split('_')[0]
suffix = process.split('_ss_')[-1]
#print "hSS : " , hSS
#print "hOS : " , hOS
name = '%s_%s' % (idata, suffix)
hratio = hSS.Clone('hratio_%s' % name)
hratio.Divide(hOS)
hratio.SetMarkerStyle(20)
hratio.SetMarkerSize(1.25)
hratio.SetFillColor(418)
hratio.SetFillStyle(1001)
hratio.SetLineColor(1)
hratio.SetLineStyle(1)
hratio.SetLineWidth(2)
c1 = TCanvas('hratio_%s' % name, 'hratio_%s' % name, 800, 600)
if suffix.split('_')[-1] == '2d':
TGaxis.SetMaxDigits(2)
#fout = TFile.Open( 'plots/%s/Object_studies/Ratio_%s_%s_%s.root' %(_token,_token,_isample,_ivar), 'RECREATE' )
c1.SetRightMargin(0.2)
hratio.SetAxisRange(0.00001, 0.01, "Z")
hratio.SetTitle('')
hratio.GetZaxis().SetTitle('N_SS/N_OS (%s)' % idata)
hratio.GetXaxis().SetTitle('Lepton1 eta')
hratio.GetYaxis().SetTitle('Lepton2 eta')
hratio.Draw('colztextE')
else:
hratio.SetTitle('')
hratio.GetYaxis().SetTitle('N_SS/N_OS Ratio (%s)' % idata)
hratio.Draw('PE')
c1.cd()
if '2016' in output: CMS_lumi.lumi_13TeV = "35.87 fb^{-1}"
elif '2017' in output: CMS_lumi.lumi_13TeV = "41.53 fb^{-1}"
elif '2018' in output: CMS_lumi.lumi_13TeV = "59.74 fb^{-1}"
CMS_lumi.writeExtraText = 1
CMS_lumi.extraText = "Preliminary"
CMS_lumi.CMS_lumi(c1, 4, 0)
gPad.RedrawAxis()
#c1.cd()
#if '2016' in _token:
# drawCMS("35.87", "Object Study")
#elif '2017' in _token:
# drawCMS("41.53", "Object Study")
#elif '2018' in _token:
# drawCMS("59.74", "Object Study")
c1.Print('%s/Ratio_%s.png' % (output, name))
#c1.Print( '%s/Ratio_%s.pdf' %(output,name) )
TGaxis.SetMaxDigits(5)
def draw(self):
if not self.histoFinalized:
self.finalizeHistograms()
self.canSup.cd()
self.h1.Draw()
self.h2.Draw('same')
if self.legend == None:
self.setUpLegend()
self.legend.Draw()
gPad.RedrawAxis()
self.canSup.SaveAs(self.canSup.GetName() + '.png')
self.canEff.cd()
# if self.eff == None:
# self.computeEff()
self.eff.GetYaxis().SetRangeUser(0.0001, 2)
self.eff.Draw()
self.canEff.SaveAs(self.canEff.GetName() + '.png')
def Frame(gPad, width=2):
gPad.Update()
gPad.RedrawAxis()
l = ROOT.TLine()
l.SetLineWidth(width)
lm = gPad.GetLeftMargin()
rm = 1. - gPad.GetRightMargin()
tm = 1. - gPad.GetTopMargin()
bm = gPad.GetBottomMargin()
#top
l.DrawLineNDC(lm, tm, rm, tm)
#right
l.DrawLineNDC(rm, bm, rm, tm)
#bottom
l.DrawLineNDC(lm, bm, rm, bm)
#top
l.DrawLineNDC(lm, bm, lm, tm)
def __draw_cut(h, show=True):
if show:
fit = h.GetListOfFunctions()[1]
xmin, xmax = fit.GetParameter(1) - 3 * fit.GetParameter(
2), fit.GetParameter(1) + 3 * fit.GetParameter(2)
b = Draw.box(xmin,
-10,
xmax,
1e7,
line_color=2,
width=2,
fillstyle=3001,
style=7)
h.Draw('same')
b.Draw('l')
Draw.legend([b], ['cut (3 sigma)'],
'lf',
.59,
y2=.656,
margin=.45,
scale=1.25)
gPad.RedrawAxis()
hs.SetMinimum(0.1)
gPad.SetLogy()
hs.Draw("Hist")
h_bkg.Draw("e2 same")
h_data.Draw("same")
for ihist in reversed(templates[5:8]):
print 'overlaying, ', ihist.GetName()
ihist.Draw("ehist same")
xTitle = h_top.GetXaxis().GetTitle()
yTitle = h_top.GetYaxis().GetTitle()
setTitle(hs, xTitle)
gPad.RedrawAxis()
ll = TLatex()
ll.SetNDC(kTRUE)
ll.SetTextSize(0.05)
ll.DrawLatex(0.78, 0.92, "0.53 fb^{-1} (13 TeV)")
#2.2
cms = TLatex()
cms.SetNDC(kTRUE)
cms.SetTextFont(61)
cms.SetTextSize(0.08)
cms.DrawLatex(0.12, 1 - t + 0.2 * t, "CMS")
sel = TLatex()
sel.SetNDC(kTRUE)
sel.SetTextSize(0.065)
def plot(ana, histos, h_legend, minY, maxY, minX, maxX, whatX, whatY, c1=0):
global stack, leg, dum
# print what
# gROOT.SetBatch(True) # suppress root window popping up #! better: sys.argv.append('b')
gROOT.ProcessLine(
"gErrorIgnoreLevel = kWarning;"
) # suppress standard output of the form: "Info in <TCanvas::Print>: ... has been created" and
# "Info in <TCanvas::SaveAs>: ... has been created"
#prepare the layout
if c1 == 0:
c1 = TCanvas('c1', '', 800, 600)
c1.SetLogy()
dum = histo.Clone()
# dum.Reset()
dum.SetMinimum(minY)
dum.SetMaximum(maxY)
dum.GetXaxis().SetRangeUser(minX, maxX)
# last = dum.FindLastBinAbove(0,1)
# last = last + 0.5 * last
# dum.GetXaxis().SetRange(0,last+10)
dum.GetXaxis().SetTitleSize(0.038)
dum.GetYaxis().SetTitleSize(0.04)
dum.GetXaxis().SetLabelSize(0.03)
dum.GetYaxis().SetLabelSize(0.03)
dum.GetXaxis().SetTitleOffset(1.2)
dum.GetYaxis().SetTitleOffset(1.1)
dum.SetXTitle(whatX)
dum.SetYTitle(whatY)
dum.SetTitle('')
dum.SetStats(0)
dum.Draw()
tex = TLatex(
0.89, 0.93,
"#scale[0.8]{CMSSW_7_2_3} #scale[0.6]{ #sqrt{s} = 13 TeV, #int L dt = 4 fb^{-1}, bx = 25 ns, PU = 20}"
)
tex.SetNDC()
tex.SetTextAlign(31)
tex.SetTextFont(42)
tex.SetTextSize(0.047)
tex.SetLineWidth(2)
tex.Draw()
#CMS_lumi_v2( c1, 14, 11 )
# tex1 = TLatex(0.15,0.89,"CMSSW_7_2_3 ")
# tex1.SetNDC()
# tex1.SetTextAlign(13)
# tex1.SetTextFont(61)
# tex1.SetTextSize(0.045)
# tex1.SetLineWidth(2)
# tex1.Draw()
# a legend
# leg = TLegend(0.63,0.525,0.87,0.875) #for 33 TeV
# leg = TLegend(0.65,0.65,0.9,0.9)
leg = TLegend(0.6545226, 0.534965, 0.8944724, 0.8846154) #fits best
leg.SetBorderSize(1)
leg.SetTextFont(62)
leg.SetTextSize(0.035)
leg.SetLineColor(0)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(1001)
if ana == 'TTbar':
title = ' t#bar{t}'
leg.AddEntry(histo, title, 'l')
elif ana == 'WJets':
leg.AddEntry(histo, 'W+jets', 'l')
elif ana == 'DYJets':
leg.AddEntry(histo, 'DY+jets', 'l')
elif ana == 'QCD':
leg.AddEntry(histo, 'QCD', 'l')
elif ana == 'SingleTop':
leg.AddEntry(histo, 't / #bar{t}', 'l')
elif ana == 'TTH':
leg.AddEntry(histo, 'ttH', 'l')
elif ana == 'TTW':
leg.AddEntry(histo, 'ttW', 'l')
elif ana == 'TTZ':
leg.AddEntry(histo, 'ttZ', 'l')
elif ana == 'T1tttt_1500_100':
leg.AddEntry(histo, 'T1t^{4}(1500,100)', 'l')
elif ana == 'T1tttt_1200_800':
leg.AddEntry(histo, 'T1t^{4}(1200,800)', 'l')
elif ana == 'T5tttt_1000_280':
leg.AddEntry(histo, 'T5t^{4}(1000,280)', 'l')
elif ana == 'T5tttt_1300_280':
leg.AddEntry(histo, 'T5t^{4}(1300,280)', 'l')
elif ana == 'T5tttt_1000_285':
leg.AddEntry(histo, 'T5t^{4}(1000,285)', 'l')
elif ana == 'T5tttt_1300_285':
leg.AddEntry(histo, 'T5t^{4}(1300,285)', 'l')
elif ana == 'T1ttbbWW_1300_290':
leg.AddEntry(histo, 'T1t^{2}b^{2}W^{2}(1300,290)', 'l')
elif ana == 'T1ttbbWW_1300_295':
leg.AddEntry(histo, 'T1t^{2}b^{2}W^{2}(1300,295)', 'l')
elif ana == 'T1ttbbWW_1000_715':
leg.AddEntry(histo, 'T1t^{2}b^{2}W^{2}(1000,715)', 'l')
elif ana == 'T1ttbbWW_1000_720':
leg.AddEntry(histo, 'T1t^{2}b^{2}W^{2}(1000,720)', 'l')
elif ana == 'SqGl_1300_100':
leg.AddEntry(histo, 'SqGl(1300,100)', 'l')
# bgrd stack
if histos != 0 and h_legend != 0:
stack = THStack('stack', '')
N = len(histos)
for i in range(N):
h = histos[i][0]
stack.Add(h)
for i in range(N - 1, -1, -1):
h = histos[i][0]
t = h_legend[h]
leg.AddEntry(h, t, 'f')
# draw bgrds + signals
# stack.Draw('samehist')
stack.Draw('same')
#histo.Draw('samehist')
histo.Draw('same')
leg.Draw("same")
gPad.RedrawAxis()
c1.SaveAs(dir_png_noCuts + what + ".png")
c1.SaveAs(dir_pdf_noCuts + what + ".pdf")
c1.SaveAs(dir_root_noCuts + what + ".root")
def plotDistributionComparisonPlot(cfg):
multiGraph = TMultiGraph()
multiGraph.SetName("triggerRateMultiGraph")
tfiles = []
histograms = []
canvas = TCanvas("canvas", "canvas", 800, 800)
'''Contains the legend'''
legend = TLegend(0.3, 0.7, 0.90, 0.9)
'''Maximum value container, used to scale histograms'''
maximumY = float("-inf")
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0.05) # Upper and lower plot are joined
#pad1.SetBottomMargin(0) # Upper and lower plot are joined
pad1.SetGridx() # Vertical grid
pad1.Draw() # Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
for histogramFileNameAndTitle in cfg.plots:
tfile = TFile(histogramFileNameAndTitle[0])
tfiles.append(tfile)
histogram = tfile.Get(histogramFileNameAndTitle[1])
histograms.append(histogram)
if histogram.ClassName() == "TH1F":
histogram.SetStats(0) # No statistics on upper plot
maximumY = histogram.GetMaximum(
) if histogram.GetMaximum() > maximumY else maximumY
legend.AddEntry(histogram, histogramFileNameAndTitle[2], "l")
# histograms[0] settings
histograms[0].SetMarkerColor(4)
histograms[0].SetLineColor(4)
histograms[0].SetLineWidth(1)
# Y axis histograms[0] plot settings
histograms[0].GetYaxis().SetTitleSize(20)
histograms[0].GetYaxis().SetTitleFont(43)
histograms[0].GetYaxis().SetTitleOffset(1.55)
#histograms[0].Scale(1./histograms[0].GetEntries())
if histograms[0].ClassName() == "TH1F":
histograms[0].Draw(
"SAME HIST") # Draw histograms[1] on top of histograms[0]
else:
histograms[0].Draw(
"SAME APE") # Draw histograms[1] on top of histograms[0]
#multiGraph.Add(histograms[0])
if getattr(cfg, "xRange", None) is not None:
histograms[0].GetXaxis().SetRangeUser(cfg.xRange[0], cfg.xRange[1])
gPad.RedrawAxis()
if getattr(cfg, "xAxisLabel", None) is not None:
histograms[0].GetXaxis().SetTitle(cfg.xAxisLabel)
gPad.RedrawAxis()
if getattr(cfg, "yAxisLabel", None) is not None:
histograms[0].GetYaxis().SetTitle(cfg.yAxisLabel)
gPad.RedrawAxis()
if getattr(cfg, "yRange", None) is not None:
histograms[0].GetYaxis().SetRangeUser(cfg.yRange[0], cfg.yRange[1])
gPad.RedrawAxis()
else:
maximumY *= 1.1
histograms[0].GetYaxis().SetRangeUser(1e-6, maximumY)
if getattr(cfg, "logY", False):
canvas.SetLogy()
# histograms[1] settings
histograms[1].SetMarkerColor(2)
histograms[1].SetLineColor(2)
histograms[1].SetLineWidth(1)
#histograms[1].Scale(1./histograms[1].GetEntries())
if histograms[1].ClassName() == "TH1F":
histograms[1].Draw(
"SAME HIST") # Draw histograms[1] on top of histograms[0]
else:
histograms[1].Draw(
"SAME PE") # Draw histograms[1] on top of histograms[0]
#multiGraph.Add(histograms[1])
#if multiGraph.GetListOfGraphs() != None:
# multiGraph.Draw("SAME PE")
# Do not draw the Y axis label on the upper plot and redraw a small
# axis instead, in order to avoid the first label (0) to be clipped.
#histograms[0].GetYaxis().SetLabelSize(0.)
#axis = TGaxis( 0, 20, 0, maximumY, 20, maximumY, 510,"")
#axis.SetLabelFont(43) # Absolute font size in pixel (precision 3)
#axis.SetLabelSize(15)
#axis.Draw()
# Adding a small text with the chi-squared
chiSquared = 0
if (histograms[0].ClassName() == "TGraph") or (histograms[0].ClassName()
== "TGraphErrors"):
numberOfBins = histograms[0].GetN()
numberOfDegreesOfFreedom = numberOfBins
else:
numberOfBins = histograms[0].GetNbinsX()
numberOfDegreesOfFreedom = numberOfBins
for x in xrange(
1, numberOfBins + 1
): # numberOfBins contains last bin, numberOfBins+1 contains the overflow (latter excluded), underflow also excluded
if (histograms[0].ClassName()
== "TGraph") or (histograms[0].ClassName() == "TGraphErrors"):
binContent0 = histograms[0].GetY()[x - 1]
else:
binContent0 = histograms[0].GetBinContent(x)
if (histograms[1].ClassName()
== "TGraph") or (histograms[1].ClassName() == "TGraphErrors"):
binContent1 = histograms[1].GetY()[x - 1]
else:
binContent1 = histograms[1].GetBinContent(x)
bin0ErrorSquared = binContent0
bin1ErrorSquared = binContent1
#bin1ErrorSquared = 0
if (binContent0 == 0) and (binContent1 == 0):
numberOfDegreesOfFreedom -= 1 #No data means one less degree of freedom
else:
binDifferenceSquared = (binContent0 - binContent1)**2
chiSquaredTerm = binDifferenceSquared / (bin0ErrorSquared +
bin1ErrorSquared)
chiSquared += chiSquaredTerm
if chiSquaredTerm > chiSquaredWarningThreshold:
if (histograms[0].ClassName()
== "TGraph") or (histograms[0].ClassName()
== "TGraphErrors"):
print "Bin", x, "-", histograms[0].GetX()[
x - 1], "has a CS=", chiSquaredTerm
else:
print "Bin", x, "-", histograms[0].GetBinCenter(
x), "has a CS=", chiSquaredTerm
chiSquareLabel = TPaveText(0.7, 0.6, 0.9, 0.4)
chiSquareLabel.AddText("#chi^{2}/ndf = " + str(chiSquared) + "/" +
str(numberOfDegreesOfFreedom) + " = " +
str(chiSquared / numberOfDegreesOfFreedom))
chiSquareLabel.Draw()
print "FINAL CS IS", format(
chiSquared,
".2f") + "/" + str(numberOfDegreesOfFreedom) + " = " + format(
chiSquared / numberOfDegreesOfFreedom, ".2f")
legend.SetHeader(
"#chi^{2}/ndf = " + format(chiSquared, ".2f") + "/" +
str(numberOfDegreesOfFreedom) + " = " +
format(chiSquared / numberOfDegreesOfFreedom, ".2f"), "C")
legend.Draw()
# lower plot will be in pad
canvas.cd() # Go back to the main canvas before defining pad2
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetTopMargin(0)
pad2.SetBottomMargin(0.2)
pad2.SetGridx() # vertical grid
pad2.Draw()
pad2.cd() # pad2 becomes the current pad
pad2.SetGridy()
# Define the ratio plot
ratioPlot = TGraphErrors(histograms[0])
ratioPlot.SetName("ratioPlot")
graph_histo0 = TGraphErrors(histograms[0])
graph_histo1 = TGraphErrors(histograms[1])
ratioPlot.SetLineColor(1)
ratioPlot.SetMinimum(0.6) # Define Y ..
ratioPlot.SetMaximum(1.5) # .. range
#ratioPlot.Sumw2()
#ratioPlot.SetStats(0) # No statistics on lower plot
#Dividing point by point
for index in xrange(0, ratioPlot.GetN()):
if graph_histo1.GetY()[index] == 0:
ratioPlot.GetY()[index] = 0
ratioPlot.GetEY()[index] = 0
else:
ratioPlot.GetY()[index] /= graph_histo1.GetY()[index]
ratioPlot.GetEY()[index] = sqrt(
((graph_histo1.GetY()[index])**2 *
(graph_histo0.GetEY()[index])**2 +
(graph_histo0.GetY()[index])**2 *
(graph_histo1.GetEY()[index])**2) /
(graph_histo1.GetY()[index])**4)
ratioPlot.SetMarkerStyle(21)
if getattr(cfg, "xRange", None) is not None:
ratioPlot.GetXaxis().SetRangeUser(cfg.xRange[0], cfg.xRange[1])
gPad.RedrawAxis()
if getattr(cfg, "yRangeRatio", None) is not None:
ratioPlot.GetYaxis().SetRangeUser(cfg.yRangeRatio[0],
cfg.yRangeRatio[1])
gPad.RedrawAxis()
ratioPlot.Draw("APE") # Draw the ratio plot
line0 = TLine(ratioPlot.GetXaxis().GetXmin(), 1,
ratioPlot.GetXaxis().GetXmax(), 1)
line0.SetLineColor(2)
line0.SetLineWidth(2)
line0.SetLineStyle(2)
line0.Draw()
# Ratio plot (ratioPlot) settings
ratioPlot.SetTitle("") # Remove the ratio title
# Y axis ratio plot settings
ratioPlot.GetYaxis().SetTitle("Ratio #frac{blue}{red}")
ratioPlot.GetYaxis().SetNdivisions(505)
ratioPlot.GetYaxis().SetTitleSize(20)
ratioPlot.GetYaxis().SetTitleFont(43)
ratioPlot.GetYaxis().SetTitleOffset(1.55)
ratioPlot.GetYaxis().SetLabelFont(
43) # Absolute font size in pixel (precision 3)
ratioPlot.GetYaxis().SetLabelSize(15)
# X axis ratio plot settings
ratioPlot.GetXaxis().SetTitleSize(20)
ratioPlot.GetXaxis().SetTitleFont(43)
ratioPlot.GetXaxis().SetTitleOffset(4.)
ratioPlot.GetXaxis().SetLabelFont(
43) # Absolute font size in pixel (precision 3)
ratioPlot.GetXaxis().SetLabelSize(15)
xRangeBinning = getattr(cfg, "simplifiedRatioPlotXRangeBinning", None)
if xRangeBinning is not None:
simplifiedRatioPlot = TGraphErrors(len(xRangeBinning) - 1)
simplifiedRatioPlot.SetName("simplifiedRatioPlot")
ratioPlotIndex = 0
for idx in xrange(0, simplifiedRatioPlot.GetN()):
yAverage = 0.
yMax = float("-inf")
yMin = float("+inf")
nPoints = 0.
simplifiedRatioPlot.GetX()[idx] = (xRangeBinning[idx] +
xRangeBinning[idx + 1]) / 2.
simplifiedRatioPlot.GetEX()[idx] = (xRangeBinning[idx + 1] -
xRangeBinning[idx]) / 2.
while (ratioPlot.GetX()[ratioPlotIndex] < xRangeBinning[idx]):
ratioPlotIndex += 1
while ((ratioPlotIndex < ratioPlot.GetN()) and
(ratioPlot.GetX()[ratioPlotIndex] < xRangeBinning[idx + 1])
and
(ratioPlot.GetX()[ratioPlotIndex] >= xRangeBinning[idx])):
yAverage += ratioPlot.GetY()[ratioPlotIndex]
if (yMax < ratioPlot.GetY()[ratioPlotIndex] +
ratioPlot.GetEY()[ratioPlotIndex]):
yMax = ratioPlot.GetY()[ratioPlotIndex] + ratioPlot.GetEY(
)[ratioPlotIndex]
if (yMin > ratioPlot.GetY()[ratioPlotIndex] -
ratioPlot.GetEY()[ratioPlotIndex]):
yMin = ratioPlot.GetY()[ratioPlotIndex] - ratioPlot.GetEY(
)[ratioPlotIndex]
nPoints += 1.
ratioPlotIndex += 1
simplifiedRatioPlot.GetY()[idx] = yAverage / nPoints
simplifiedRatioPlot.GetEY()[idx] = (yMax - yMin) / 2.
saveFile = TFile(cfg.saveFileName, "RECREATE")
saveFile.cd()
canvas.Write()
histograms[0].Write()
histograms[1].Write()
if multiGraph.GetListOfGraphs() != None:
multiGraph.Write()
ratioPlot.Write()
if xRangeBinning is not None:
simplifiedRatioPlot.Write()
saveFile.Close()
for tfile in tfiles:
tfile.Close()
def main():
# usage description
usage = "Example: ./scripts/plotLimits.py -M Asymptotic -l logs -f qq --massrange 1200 7000 100"
# input parameters
parser = ArgumentParser(
description='Script that plots limits for specified mass points',
epilog=usage)
parser.add_argument("-M",
"--method",
dest="method",
required=True,
choices=[
'ProfileLikelihood', 'HybridNew', 'Asymptotic',
'MarkovChainMC', 'theta'
],
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')
xs_obs_limits = array('d')
xs_exp_limits = array('d')
masses_exp = array('d')
xs_exp_limits_1sigma = array('d')
xs_exp_limits_1sigma_up = array('d')
xs_exp_limits_2sigma = array('d')
xs_exp_limits_2sigma_up = 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)
if args.method == 'Asymptotic': masses_exp.append(mass)
logName = 'limits_%s_%s_m%i.log' % (args.method, args.final_state,
int(mass))
if args.method == 'theta': logName = logName.replace('limits_', '')
log_file = open(os.path.join(logs_path, logName), 'r')
foundMethod = False
# read the log file
for line in log_file:
if args.method == 'Asymptotic':
if re.search("^Observed Limit: r", line):
xs_obs_limits.append(float(line.split()[-1]))
if re.search("^Expected 50.0%: r", line):
xs_exp_limits.append(float(line.split()[-1]))
if re.search("^Expected 16.0%: r", line):
xs_exp_limits_1sigma.append(float(line.split()[-1]))
if re.search("^Expected 84.0%: r", line):
xs_exp_limits_1sigma_up.append(float(line.split()[-1]))
if re.search("^Expected 2.5%: r", line):
xs_exp_limits_2sigma.append(float(line.split()[-1]))
if re.search("^Expected 97.5%: r", line):
xs_exp_limits_2sigma_up.append(float(line.split()[-1]))
elif args.method == 'theta':
if re.search('^# x; y; yerror', line):
foundMethod = True
if line.split()[0] == '0' and foundMethod:
xs_obs_limits.append(float(line.split()[1]))
else:
if re.search(' -- ' + args.method, line):
foundMethod = True
if re.search("^Limit: r", line) and foundMethod:
xs_obs_limits.append(float(line.split()[3]))
if len(masses) != len(xs_obs_limits):
print "** ERROR: ** Could not find observed limit for m =", int(
mass), "GeV. Aborting."
sys.exit(1)
if args.method == 'Asymptotic':
if len(masses) != len(xs_exp_limits):
print "** ERROR: ** Could not find expected limit for m =", int(
mass), "GeV. Aborting."
sys.exit(1)
if len(masses) != len(xs_exp_limits_1sigma):
print "** ERROR: ** Could not find expected 1 sigma down limit for m =", int(
mass), "GeV. Aborting."
sys.exit(1)
if len(masses) != len(xs_exp_limits_1sigma_up):
print "** ERROR: ** Could not find expected 1 sigma up limit for m =", int(
mass), "GeV. Aborting."
sys.exit(1)
if len(masses) != len(xs_exp_limits_2sigma):
print "** ERROR: ** Could not find expected 2 sigma down limit for m =", int(
mass), "GeV. Aborting."
sys.exit(1)
if len(masses) != len(xs_exp_limits_2sigma_up):
print "** ERROR: ** Could not find expected 2 sigma up limit for m =", int(
mass), "GeV. Aborting."
sys.exit(1)
if args.method == 'Asymptotic':
# complete the expected limit arrays
for i in range(0, len(masses)):
masses_exp.append(masses[len(masses) - i - 1])
xs_exp_limits_1sigma.append(
xs_exp_limits_1sigma_up[len(masses) - i - 1])
xs_exp_limits_2sigma.append(
xs_exp_limits_2sigma_up[len(masses) - i - 1])
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)
all_masses_exp = np.array(results.masses_exp)
indices = []
indices_exp = []
# 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)
if len(all_masses_exp) > 0:
where = np.where(all_masses_exp == 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_exp.extend(where)
# sort indices
indices.sort()
indices_exp.sort()
for i in indices:
masses.append(results.masses[i])
xs_obs_limits.append(results.xs_obs_limits[i])
if len(all_masses_exp) > 0:
xs_exp_limits.append(results.xs_exp_limits[i])
for i in indices_exp:
masses_exp.append(results.masses_exp[i])
xs_exp_limits_1sigma.append(results.xs_exp_limits_1sigma[i])
xs_exp_limits_2sigma.append(results.xs_exp_limits_2sigma[i])
if args.printResults:
print "masses =", masses.tolist()
print "xs_obs_limits =", xs_obs_limits.tolist()
print "xs_exp_limits =", xs_exp_limits.tolist()
print ""
print "masses_exp =", masses_exp.tolist()
print "xs_exp_limits_1sigma =", xs_exp_limits_1sigma.tolist()
print "xs_exp_limits_2sigma =", xs_exp_limits_2sigma.tolist()
# import ROOT stuff
from ROOT import kTRUE, kFALSE, gROOT, gStyle, gPad, TGraph, TCanvas, TLegend
from ROOT import kGreen, kYellow, kWhite
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()
# theory curves: gg
massesS8 = array('d', [
1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0, 1700.0, 1800.0,
1900.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0,
2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0,
3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0,
4600.0, 4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0,
5500.0, 5600.0, 5700.0, 5800.0, 5900.0, 6000.0
])
xsS8 = array('d', [
5.46E+02, 3.12E+02, 1.85E+02, 1.12E+02, 7.19E+01, 4.59E+01, 3.02E+01,
2.01E+01, 1.37E+01, 9.46E+00, 6.55E+00, 4.64E+00, 3.27E+00, 2.36E+00,
1.70E+00, 1.24E+00, 9.11E-01, 6.69E-01, 4.97E-01, 3.71E-01, 2.78E-01,
2.07E-01, 1.55E-01, 1.19E-01, 9.26E-02, 7.08E-02, 5.43E-02, 4.15E-02,
3.22E-02, 2.50E-02, 1.92E-02, 1.51E-02, 1.19E-02, 9.25E-03, 7.35E-03,
5.86E-03, 4.53E-03, 3.66E-03, 2.91E-03, 2.33E-03, 1.86E-03, 1.45E-03,
1.12E-03, 8.75E-04, 6.90E-04, 5.55E-04, 4.47E-04, 3.63E-04, 2.92E-04,
2.37E-04, 1.97E-04
])
graph_xsS8 = TGraph(len(massesS8), massesS8, xsS8)
graph_xsS8.SetLineWidth(3)
graph_xsS8.SetLineStyle(8)
graph_xsS8.SetLineColor(6)
# theory curves: qg
massesString = array('d', [
1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0, 1700.0, 1800.0,
1900.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0,
2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0,
3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0,
4600.0, 4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0,
5500.0, 5600.0, 5700.0, 5800.0, 5900.0, 6000.0, 6100.0, 6200.0, 6300.0,
6400.0, 6500.0, 6600.0, 6700.0, 6800.0, 6900.0, 7000.0, 7100.0, 7200.0,
7300.0, 7400.0, 7500.0, 7600.0, 7700.0, 7800.0, 7900.0, 8000.0, 8100.0,
8200.0, 8300.0, 8400.0, 8500.0, 8600.0, 8700.0, 8800.0, 8900.0, 9000.0,
9100., 9200., 9300., 9400., 9500., 9600., 9700., 9800., 9900., 10000.
])
xsString = array('d', [
8316.184311558545, 5312.93137758767, 3435.0309937336524,
2304.4139502741305, 1569.8115447896687, 1090.9516635659693,
770.901859690924, 551.9206062572061, 399.69535383507633,
293.77957451762086, 218.15126842827823, 162.87634729465125,
123.17685479653694, 93.63530805932386, 71.53697229809124,
55.37491301647483, 42.75271508357369, 33.36378355470234,
26.06619302090876, 20.311817606835643, 16.1180931789545,
12.768644973921226, 10.142660425967444, 8.057990848043234,
6.400465846290908, 5.115134438331436, 4.132099789492928,
3.3193854239538734, 2.6581204529344302, 2.157554604919995,
1.7505176068913348, 1.4049155245498584, 1.140055677916783,
0.9253251132104159, 0.7522038169131606, 0.6119747371392215,
0.49612321727328523, 0.40492020959456737, 0.33091999402250655,
0.27017917021492555, 0.2201693919322846, 0.17830700070267996,
0.14564253802358157, 0.11940534430331146, 0.09694948234356839,
0.0793065371847468, 0.06446186373361917, 0.05282660618352478,
0.0428516302310620888, 0.0348997638039910363, 0.0283334766442618227,
0.0231416918363592127, 0.0187417921340763783, 0.0153501307395115115,
0.0124396534127133717, 0.0100542205744949455, 0.0081744954858627415,
0.0066338099362915941, 0.0053365711503318145, 0.00430912459914657443,
0.00346381039244064343, 0.00278602671711227174, 0.00225154342228859257,
0.0018082930150063248, 0.00143929440338502119, 0.0011581373956044489,
0.00091869589873893118, 0.00073410823691329855, 0.00058669382997948734,
0.0004661568745858897, 0.000368716655469570365,
0.000293168485206959169, 0.000230224535021638668,
0.000182317101888465142, 0.000143263359883433282,
0.000112630538527214965, 0.000088189175598406759,
0.000068708474367442343, 0.000053931726669273556,
0.0000416417855733682702, 0.0000326529676755488658,
0.0000254365480426201587, 0.0000198410151166864761,
0.0000154034425617473576, 0.0000119095554601641413,
9.2537574320108232e-6, 7.2155417437856749e-6, 5.6130924422251982e-6,
4.36634755605624901e-6, 3.39717456406994868e-6, 2.6766018046173896e-6
])
massesQstar = array('d', [
1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0, 1700.0, 1800.0,
1900.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0,
2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0,
3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0,
4600.0, 4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0,
5500.0, 5600.0, 5700.0, 5800.0, 5900.0, 6000.0, 6100.0, 6200.0, 6300.0,
6400.0, 6500.0, 6600.0, 6700.0, 6800.0, 6900.0, 7000.0, 7100.0, 7200.0,
7300.0, 7400.0, 7500.0, 7600.0, 7700.0, 7800.0, 7900.0, 8000.0, 8100.0,
8200.0, 8300.0, 8400.0, 8500.0, 8600.0, 8700.0, 8800.0, 8900.0, 9000.0
])
xsQstar = array('d', [
0.4101E+03, 0.2620E+03, 0.1721E+03, 0.1157E+03, 0.7934E+02, 0.5540E+02,
0.3928E+02, 0.2823E+02, 0.2054E+02, 0.1510E+02, 0.1121E+02, 0.8390E+01,
0.6328E+01, 0.4807E+01, 0.3674E+01, 0.2824E+01, 0.2182E+01, 0.1694E+01,
0.1320E+01, 0.1033E+01, 0.8116E+00, 0.6395E+00, 0.5054E+00, 0.4006E+00,
0.3182E+00, 0.2534E+00, 0.2022E+00, 0.1616E+00, 0.1294E+00, 0.1038E+00,
0.8333E-01, 0.6700E-01, 0.5392E-01, 0.4344E-01, 0.3503E-01, 0.2827E-01,
0.2283E-01, 0.1844E-01, 0.1490E-01, 0.1205E-01, 0.9743E-02, 0.7880E-02,
0.6373E-02, 0.5155E-02, 0.4169E-02, 0.3371E-02, 0.2725E-02, 0.2202E-02,
0.1779E-02, 0.1437E-02, 0.1159E-02, 0.9353E-03, 0.7541E-03, 0.6076E-03,
0.4891E-03, 0.3935E-03, 0.3164E-03, 0.2541E-03, 0.2039E-03, 0.1635E-03,
0.1310E-03, 0.1049E-03, 0.8385E-04, 0.6699E-04, 0.5347E-04, 0.4264E-04,
0.3397E-04, 0.2704E-04, 0.2151E-04, 0.1709E-04, 0.1357E-04, 0.1077E-04,
0.8544E-05, 0.6773E-05, 0.5367E-05, 0.4251E-05, 0.3367E-05, 0.2666E-05,
0.2112E-05, 0.1673E-05, 0.1326E-05
])
graph_xsString = TGraph(len(massesString), massesString, xsString)
graph_xsString.SetLineWidth(3)
graph_xsString.SetLineStyle(8)
graph_xsString.SetLineColor(9)
graph_xsQstar = TGraph(len(massesQstar), massesQstar, xsQstar)
graph_xsQstar.SetLineWidth(3)
graph_xsQstar.SetLineStyle(2)
graph_xsQstar.SetLineColor(1)
# theory curves: qq
massesTh = array('d', [
1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0, 1700.0, 1800.0,
1900.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0,
2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0,
3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0,
4600.0, 4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0,
5500.0, 5600.0, 5700.0, 5800.0, 5900.0, 6000.0, 6100.0, 6200.0, 6300.0,
6400.0, 6500.0, 6600.0, 6700.0, 6800.0, 6900.0, 7000.0, 7100.0, 7200.0,
7300.0, 7400.0, 7500.0, 7600.0, 7700.0, 7800.0, 7900.0, 8000.0, 8100.0,
8200.0, 8300.0, 8400.0, 8500.0, 8600.0, 8700.0, 8800.0, 8900.0, 9000.0
])
xsAxi = array('d', [
0.1849E+03, 0.1236E+03, 0.8473E+02, 0.5937E+02, 0.4235E+02, 0.3069E+02,
0.2257E+02, 0.1680E+02, 0.1263E+02, 0.9577E+01, 0.7317E+01, 0.5641E+01,
0.4374E+01, 0.3411E+01, 0.2672E+01, 0.2103E+01, 0.1658E+01, 0.1312E+01,
0.1041E+01, 0.8284E+00, 0.6610E+00, 0.5294E+00, 0.4250E+00, 0.3417E+00,
0.2752E+00, 0.2220E+00, 0.1792E+00, 0.1449E+00, 0.1172E+00, 0.9487E-01,
0.7686E-01, 0.6219E-01, 0.5033E-01, 0.4074E-01, 0.3298E-01, 0.2671E-01,
0.2165E-01, 0.1755E-01, 0.1422E-01, 0.1152E-01, 0.9322E-02, 0.7539E-02,
0.6092E-02, 0.4917E-02, 0.3965E-02, 0.3193E-02, 0.2568E-02, 0.2062E-02,
0.1653E-02, 0.1323E-02, 0.1057E-02, 0.8442E-03, 0.6728E-03, 0.5349E-03,
0.4242E-03, 0.3357E-03, 0.2644E-03, 0.2077E-03, 0.1627E-03, 0.1271E-03,
0.9891E-04, 0.7686E-04, 0.5951E-04, 0.4592E-04, 0.3530E-04, 0.2704E-04,
0.2059E-04, 0.1562E-04, 0.1180E-04, 0.8882E-05, 0.6657E-05, 0.4968E-05,
0.3693E-05, 0.2734E-05, 0.2016E-05, 0.1481E-05, 0.1084E-05, 0.7903E-06,
0.5744E-06, 0.4160E-06, 0.3007E-06
])
xsDiquark = array('d', [
0.5824E+02, 0.4250E+02, 0.3172E+02, 0.2411E+02, 0.1862E+02, 0.1457E+02,
0.1153E+02, 0.9211E+01, 0.7419E+01, 0.6019E+01, 0.4912E+01, 0.4031E+01,
0.3323E+01, 0.2750E+01, 0.2284E+01, 0.1903E+01, 0.1590E+01, 0.1331E+01,
0.1117E+01, 0.9386E+00, 0.7900E+00, 0.6658E+00, 0.5618E+00, 0.4745E+00,
0.4010E+00, 0.3391E+00, 0.2869E+00, 0.2428E+00, 0.2055E+00, 0.1740E+00,
0.1473E+00, 0.1246E+00, 0.1055E+00, 0.8922E-01, 0.7544E-01, 0.6376E-01,
0.5385E-01, 0.4546E-01, 0.3834E-01, 0.3231E-01, 0.2720E-01, 0.2288E-01,
0.1922E-01, 0.1613E-01, 0.1352E-01, 0.1132E-01, 0.9463E-02, 0.7900E-02,
0.6584E-02, 0.5479E-02, 0.4551E-02, 0.3774E-02, 0.3124E-02, 0.2581E-02,
0.2128E-02, 0.1750E-02, 0.1437E-02, 0.1177E-02, 0.9612E-03, 0.7833E-03,
0.6366E-03, 0.5160E-03, 0.4170E-03, 0.3360E-03, 0.2700E-03, 0.2162E-03,
0.1725E-03, 0.1372E-03, 0.1087E-03, 0.8577E-04, 0.6742E-04, 0.5278E-04,
0.4114E-04, 0.3192E-04, 0.2465E-04, 0.1894E-04, 0.1448E-04, 0.1101E-04,
0.8322E-05, 0.6253E-05, 0.4670E-05
])
xsWprime = array('d', [
0.8811E+01, 0.6024E+01, 0.4216E+01, 0.3010E+01, 0.2185E+01, 0.1610E+01,
0.1200E+01, 0.9043E+00, 0.6875E+00, 0.5271E+00, 0.4067E+00, 0.3158E+00,
0.2464E+00, 0.1932E+00, 0.1521E+00, 0.1201E+00, 0.9512E-01, 0.7554E-01,
0.6012E-01, 0.4792E-01, 0.3827E-01, 0.3059E-01, 0.2448E-01, 0.1960E-01,
0.1571E-01, 0.1259E-01, 0.1009E-01, 0.8090E-02, 0.6483E-02, 0.5193E-02,
0.4158E-02, 0.3327E-02, 0.2660E-02, 0.2125E-02, 0.1695E-02, 0.1351E-02,
0.1075E-02, 0.8546E-03, 0.6781E-03, 0.5372E-03, 0.4248E-03, 0.3353E-03,
0.2642E-03, 0.2077E-03, 0.1629E-03, 0.1275E-03, 0.9957E-04, 0.7757E-04,
0.6027E-04, 0.4670E-04, 0.3610E-04, 0.2783E-04, 0.2140E-04, 0.1641E-04,
0.1254E-04, 0.9561E-05, 0.7269E-05, 0.5510E-05, 0.4167E-05, 0.3143E-05,
0.2364E-05, 0.1774E-05, 0.1329E-05, 0.9931E-06, 0.7411E-06, 0.5523E-06,
0.4108E-06, 0.3055E-06, 0.2271E-06, 0.1687E-06, 0.1254E-06, 0.9327E-07,
0.6945E-07, 0.5177E-07, 0.3863E-07, 0.2888E-07, 0.2162E-07, 0.1622E-07,
0.1218E-07, 0.9156E-08, 0.6893E-08
])
xsZprime = array('d', [
0.5027E+01, 0.3398E+01, 0.2353E+01, 0.1663E+01, 0.1196E+01, 0.8729E+00,
0.6450E+00, 0.4822E+00, 0.3638E+00, 0.2769E+00, 0.2123E+00, 0.1639E+00,
0.1272E+00, 0.9933E-01, 0.7789E-01, 0.6134E-01, 0.4848E-01, 0.3845E-01,
0.3059E-01, 0.2440E-01, 0.1952E-01, 0.1564E-01, 0.1256E-01, 0.1010E-01,
0.8142E-02, 0.6570E-02, 0.5307E-02, 0.4292E-02, 0.3473E-02, 0.2813E-02,
0.2280E-02, 0.1848E-02, 0.1499E-02, 0.1216E-02, 0.9864E-03, 0.8002E-03,
0.6490E-03, 0.5262E-03, 0.4264E-03, 0.3453E-03, 0.2795E-03, 0.2260E-03,
0.1826E-03, 0.1474E-03, 0.1188E-03, 0.9566E-04, 0.7690E-04, 0.6173E-04,
0.4947E-04, 0.3957E-04, 0.3159E-04, 0.2516E-04, 0.2001E-04, 0.1587E-04,
0.1255E-04, 0.9906E-05, 0.7795E-05, 0.6116E-05, 0.4785E-05, 0.3731E-05,
0.2900E-05, 0.2247E-05, 0.1734E-05, 0.1334E-05, 0.1022E-05, 0.7804E-06,
0.5932E-06, 0.4492E-06, 0.3388E-06, 0.2544E-06, 0.1903E-06, 0.1417E-06,
0.1051E-06, 0.7764E-07, 0.5711E-07, 0.4186E-07, 0.3055E-07, 0.2223E-07,
0.1612E-07, 0.1164E-07, 0.8394E-08
])
graph_xsAxi = TGraph(len(massesTh), massesTh, xsAxi)
graph_xsAxi.SetLineWidth(3)
graph_xsAxi.SetLineStyle(3)
graph_xsAxi.SetLineColor(63)
graph_xsDiquark = TGraph(len(massesTh), massesTh, xsDiquark)
graph_xsDiquark.SetLineWidth(3)
graph_xsDiquark.SetLineStyle(9)
graph_xsDiquark.SetLineColor(8)
graph_xsWprime = TGraph(len(massesTh), massesTh, xsWprime)
graph_xsWprime.SetLineWidth(3)
graph_xsWprime.SetLineStyle(7)
graph_xsWprime.SetLineColor(46)
graph_xsZprime = TGraph(len(massesTh), massesTh, xsZprime)
graph_xsZprime.SetLineWidth(3)
graph_xsZprime.SetLineStyle(5)
graph_xsZprime.SetLineColor(38)
# limits
graph_exp_2sigma = (TGraph(len(masses_exp), masses_exp,
xs_exp_limits_2sigma)
if len(xs_exp_limits_2sigma) > 0 else TGraph(0))
graph_exp_2sigma.SetFillColor(kYellow)
graph_exp_1sigma = (TGraph(len(masses_exp), masses_exp,
xs_exp_limits_1sigma)
if len(xs_exp_limits_2sigma) > 0 else TGraph(0))
graph_exp_1sigma.SetFillColor(kGreen + 1)
graph_exp = (TGraph(len(masses), masses, xs_exp_limits)
if len(xs_exp_limits_2sigma) > 0 else TGraph(0))
#graph_exp.SetMarkerStyle(24)
graph_exp.SetLineWidth(3)
graph_exp.SetLineStyle(2)
graph_exp.SetLineColor(4)
graph_obs = TGraph(len(masses), masses, xs_obs_limits)
graph_obs.SetMarkerStyle(20)
graph_obs.SetLineWidth(3)
#graph_obs.SetLineStyle(1)
graph_obs.SetLineColor(1)
c = TCanvas("c", "", 800, 800)
c.cd()
legend = TLegend(.60, .55, .90, .70)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
legend.SetTextSize(0.03)
legend.SetHeader('95% CL upper limits')
if len(xs_exp_limits_2sigma) > 0:
graph_exp_2sigma.GetXaxis().SetTitle("%s resonance mass [GeV]" %
(args.final_state))
graph_exp_2sigma.GetYaxis().SetTitle(
"#sigma #times #it{B} #times #it{A} [pb]")
graph_exp_2sigma.GetYaxis().SetTitleOffset(1.1)
graph_exp_2sigma.GetYaxis().SetRangeUser(1e-02, 1e+03)
#graph_exp_2sigma.GetXaxis().SetNdivisions(1005)
graph_exp_2sigma.Draw("AF")
graph_exp_1sigma.Draw("F")
graph_exp.Draw("L")
graph_obs.Draw("LP")
legend.AddEntry(graph_obs, "Observed", "lp")
legend.AddEntry(graph_exp, "Expected", "lp")
legend.AddEntry(graph_exp_1sigma, "#pm 1#sigma", "F")
legend.AddEntry(graph_exp_2sigma, "#pm 2#sigma", "F")
else:
graph_obs.GetXaxis().SetTitle("%s resonance mass [GeV]" %
(args.final_state))
graph_obs.GetYaxis().SetTitle(
"#sigma #times #it{B} #times #it{A} [pb]")
graph_obs.GetYaxis().SetTitleOffset(1.1)
graph_obs.GetYaxis().SetRangeUser(1e-02, 1e+03)
#graph_obs.GetXaxis().SetNdivisions(1005)
graph_obs.Draw("ALP")
legend.AddEntry(graph_obs, "Observed", "lp")
if args.final_state == 'gg':
graph_xsS8.Draw("L")
elif args.final_state == 'qg':
graph_xsQstar.Draw("L")
graph_xsString.Draw("L")
elif args.final_state == 'qq':
graph_xsAxi.Draw("L")
graph_xsDiquark.Draw("L")
graph_xsWprime.Draw("L")
graph_xsZprime.Draw("L")
legend.Draw()
if args.final_state == 'gg':
legendTh = TLegend(.60, .80, .90, .84)
legendTh.SetBorderSize(0)
legendTh.SetFillColor(0)
legendTh.SetFillStyle(0)
legendTh.SetTextFont(42)
legendTh.SetTextSize(0.03)
legendTh.AddEntry(graph_xsS8, "S8", "l")
legendTh.Draw()
elif args.final_state == 'qg':
legendTh = TLegend(.60, .80, .90, .88)
legendTh.SetBorderSize(0)
legendTh.SetFillColor(0)
legendTh.SetFillStyle(0)
legendTh.SetTextFont(42)
legendTh.SetTextSize(0.03)
legendTh.AddEntry(graph_xsString, "String", "l")
legendTh.AddEntry(graph_xsQstar, "Excited quark", "l")
legendTh.Draw()
elif args.final_state == 'qq':
legendTh = TLegend(.60, .72, .90, .88)
legendTh.SetBorderSize(0)
legendTh.SetFillColor(0)
legendTh.SetFillStyle(0)
legendTh.SetTextFont(42)
legendTh.SetTextSize(0.03)
legendTh.AddEntry(graph_xsAxi, "Axigluon/coloron", "l")
legendTh.AddEntry(graph_xsDiquark, "Scalar diquark", "l")
legendTh.AddEntry(graph_xsWprime, "W' SSM", "l")
legendTh.AddEntry(graph_xsZprime, "Z' SSM", "l")
legendTh.Draw()
# 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()
fileName = 'xs_limit_%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 cutflow(Region='SignalRegion',
removeEvents=False,
logY=True,
normalise=True,
singlePlots=False,
drawOptions='HIST',
weightedORraw='weighted'):
# removeEvents=True
PreSelectionName = 'PreSelection'
# Region='HIGHSidebandRegion'
PlotBGStack = False
StackBG = False
# singlePlots=False
# normalise=True
# logY=True
# drawOptions='HIST TEXT90'
channel = 'ZZ'
channelTex = {
'WPWP': 'W^{+}W^{+}',
'WPWM': 'W^{+}W^{-}',
'WMWM': 'W^{-}W^{-}',
'WPZ': 'W^{+}Z',
'WMZ': 'W^{-}Z',
'ZZ': 'ZZ'
}
plotstyle = [(1, 1), (1, 2), (2, 1), (2, 2), (4, 1), (4, 2)]
PreSelectionCuts = [
('nocuts', 'All'),
('common', 'CommonModules'),
# ('corrections','JetCorrections'),
# ('cleaner','JetCleaner'),
('AK4pfidfilter', 'AK4 PFID-Filter'),
('AK8pfidfilter', 'AK8 PFID-Filter'),
('AK8N2sel', 'N_{AK8} #geq 2'),
('invMAk8sel', 'M_{jj-AK8} > 1050 GeV'),
('detaAk8sel', '|#Delta#eta_{jj-AK8}|<1.3')
]
SelectionCuts = [ #('preselection','PreSelection-check'),
('softdropAK8sel', '65 GeV <M_{SD}< 105 GeV'),
('tau21sel', '0 #leq #tau_{2}/#tau_{1}<0.35'),
('deltaR48', '#Delta R(AK4,leading AK8) > 1.3'),
# ('VVRegion','VVRegion'),
('AK4N2sel', 'N_{AK4} #geq 2'),
('OpSignsel', '#eta_{1-AK4} #eta_{2-AK4} < 0'),
('detaAk4sel', '|#Delta#eta_{jj-AK4}| > 3.0'),
('invMAk4sel_1p0', 'M_{jj-AK4} > 1.0 TeV')
]
Cuts = PreSelectionCuts + SelectionCuts
# Cuts=SelectionCuts
QCDColor = rt.kAzure + 7
WJetsColor = rt.kRed - 4
ZJetsColor = rt.kOrange - 2
path = '/nfs/dust/cms/user/albrechs/UHH2_Output/'
datasets = [ #(filename, Name for Legend, Linestyle, Color)
('MC.MC_aQGC_%sjj_hadronic.root' % channel, channelTex[channel], 1, 28
) #,
# ('MC.MC_QCD.root','QCD',1,QCDColor),
# ('MC.MC_WJetsToQQ_HT600ToInf.root','W+JetsToQQ',1,WJetsColor),
# ('MC.MC_ZJetsToQQ_HT600ToInf.root','Z+JetsToQQ',1,ZJetsColor),
# ('Data.DATA.root','Data',1,1)
]
gROOT.ProcessLine("gErrorIgnoreLevel = 2001;")
PreSelectionFiles = []
SelectionFiles = []
if removeEvents:
PreSelectionName = PreSelectionName + 'RmEvent'
Region = Region + 'RmEvent'
for dataset in datasets:
PreSelectionFiles.append(
TFile(path + PreSelectionName +
"/uhh2.AnalysisModuleRunner.%s" % dataset[0]))
# SelectionFiles.append(TFile(path+Region+'/backup/'+"/uhh2.AnalysisModuleRunner.%s"%dataset[0]))
SelectionFiles.append(
TFile(path + Region +
"/uhh2.AnalysisModuleRunner.%s" % dataset[0]))
gROOT.ProcessLine("gErrorIgnoreLevel = 0;")
yTitle = 'NEvents_%s' % weightedORraw
if (normalise):
yTitle = yTitle + '/N_%s' % Cuts[0][1]
canv = TCanvas('cutflow_canvas', 'cutflow_canvas', 1000, 500)
if (not singlePlots):
legend = TLegend(0.7, 0.7, 0.9, 0.95)
legend.SetFillStyle(0)
legend.SetTextSize(0.02)
legend.SetMargin(0.4)
NCuts = len(Cuts)
# NMETFilter=0
# if 'common' in Cuts[:][0]:
# NMETFilter=PreSelectionFiles[0].Get('cf_metfilters').GetNbinsX()
# for i in range(1,PreSelectionFiles[0].Get('cf_metfilters').GetNbinsX()):
# Cuts.insert(Cuts.index(('common','CommonModules')),(PreSelectionFiles[0].Get('cf_metfilters').GetXaxis().GetBinLabel(i),PreSelectionFiles[0].Get('cf_metfilters').GetXaxis().GetBinLabel(i)))
# NCuts=NCuts+NMETFilter
print('Plotting Cutflow of', NCuts, 'Cuts.')
# for dataset in datasets:
BGStack = THStack('MCBackground', 'MCBackground')
TotalMinimum = 10**10
NBG = 0
for l in range(len(datasets)):
print('checking', datasets[l][1])
if normalise:
if ((len(Cuts) == len(PreSelectionCuts)) or
(len(Cuts) == (len(PreSelectionCuts) + len(SelectionCuts)))):
Norm = PreSelectionFiles[l].Get(Cuts[0][0] + '/NEvents_%s' %
weightedORraw).GetBinContent(1)
else:
Norm = SelectionFiles[l].Get(Cuts[0][0] + '/NEvents_%s' %
weightedORraw).GetBinContent(1)
else:
Norm = 1
if (len(Cuts) > len(PreSelectionCuts)):
CurrentMinimum = SelectionFiles[l].Get(
SelectionCuts[-1][0] +
'/NEvents_%s' % weightedORraw).GetBinContent(1) / Norm
else:
CurrentMinimum = PreSelectionFiles[l].Get(
PreSelectionCuts[-1][0] +
'/NEvents_%s' % weightedORraw).GetBinContent(1) / Norm
print(CurrentMinimum, TotalMinimum)
if (CurrentMinimum < TotalMinimum):
print('setting ', datasets[l][1], 'as new minimum')
TotalMinimum = CurrentMinimum
if (datasets[l][1] == 'QCD' or datasets[l][1] == 'W+JetsToQQ'
or datasets[l][1] == 'Z+JetsToQQ'):
# NBG= NBG + PreSelectionFiles[l].Get(Cuts[0][0]+'/NEvents_%s'%weightedORraw).GetBinContent(1)
NBG = NBG + Norm
cutflows = []
for i in range(len(datasets)):
if (singlePlots):
legend = TLegend(0.75, 0.6, 1, 0.9)
legend.SetFillStyle(0)
legend.SetTextSize(0.02)
legend.SetMargin(0.4)
print('Drawing', datasets[i][1])
currentCutflow = TH1F(datasets[i][1], datasets[i][1], NCuts, 0, NCuts)
cutflows.append(currentCutflow)
#filling the current cutflow (with NEvents_weighted or NEvents_raw)
if (normalise):
if (StackBG and
(datasets[i][1] == 'QCD' or datasets[i][1] == 'W+JetsToQQ'
or datasets[i][1] == 'Z+JetsToQQ')):
NAll = NBG
else:
if ((len(Cuts) == len(PreSelectionCuts))
or (len(Cuts)
== (len(PreSelectionCuts) + len(SelectionCuts)))):
NAll = PreSelectionFiles[i].Get(
PreSelectionCuts[0][0] +
'/NEvents_%s' % weightedORraw).GetBinContent(1)
else:
NAll = SelectionFiles[i].Get(
SelectionCuts[0][0] +
'/NEvents_%s' % weightedORraw).GetBinContent(1)
else:
NAll = 1
for j in range(NCuts):
cut = Cuts[j]
if ((len(Cuts) == len(PreSelectionCuts)) or
(len(Cuts) == (len(PreSelectionCuts) + len(SelectionCuts)))
and j < len(PreSelectionCuts)):
NEvents = PreSelectionFiles[i].Get(
cut[0] + '/NEvents_%s' % weightedORraw).GetBinContent(1)
else:
NEvents = SelectionFiles[i].Get(cut[0] + '/NEvents_%s' %
weightedORraw).GetBinContent(1)
# NEvents=NAll
currentCutflow.GetXaxis().SetBinLabel(j + 1, cut[1])
currentCutflow.Fill(j + 0.5, NEvents / NAll)
#cosmectics for and draw current cutflow
currentCutflow.SetLineColor(datasets[i][3])
currentCutflow.SetMarkerSize(1.4)
currentCutflow.GetYaxis().SetTitle(yTitle)
HistMax = currentCutflow.GetMaximum()
if (logY):
if (singlePlots):
MAX = float(10**(magnitude(HistMax) + 3))
MIN = float(10**(magnitude(currentCutflow.GetMinimum()) - 1))
else:
MAX = float(10**(magnitude(HistMax) + 2))
MIN = float(10**(magnitude(TotalMinimum) - 1))
MIN += float(10**(magnitude(MIN)))
else:
MAX = 1.1 * HistMax
MIN = 0.
currentCutflow.GetYaxis().SetRangeUser(MIN, MAX)
print('RangeUser=(', MIN, ',', MAX, ')')
print('drawOptions:', drawOptions)
if (datasets[i][1] == 'Data'):
# legend.AddEntry(currentCutflow,datasets[i][1],'lep')
# currentCutflow.SetMarkerStyle(8)
# currentCutflow.Draw(drawOptions+'HP')
legend.AddEntry(currentCutflow, datasets[i][1], 'l')
currentCutflow.Draw(drawOptions)
if (not singlePlots and ('SAME' not in drawOptions)):
drawOptions = drawOptions + 'SAME'
elif (StackBG
and (datasets[i][1] == 'QCD' or datasets[i][1] == 'W+JetsToQQ'
or datasets[i][1] == 'Z+JetsToQQ')):
legend.AddEntry(currentCutflow, datasets[i][1], 'f')
currentCutflow.SetFillColor(datasets[i][3])
BGStack.Add(currentCutflow, drawOptions)
# currentCutflow.Draw(drawOptions)
PlotBGStack = True
else:
legend.AddEntry(currentCutflow, datasets[i][1], 'l')
currentCutflow.Draw(drawOptions)
if (not singlePlots and ('SAME' not in drawOptions)):
drawOptions = drawOptions + 'SAME'
if (singlePlots):
legend.Draw()
if (logY):
canv.SetLogy()
# canv.SetGrid()
canv.SetTicks()
gPad.RedrawAxis()
canv.Print('cutflows/' + Region + '_cutflow_%s_%s.eps' %
(datasets[i][1], weightedORraw))
if (not singlePlots):
if (PlotBGStack):
BGStack.Draw(drawOptions)
legend.Draw()
if (logY):
canv.SetLogy()
# canv.SetGrid()
canv.SetTicks()
gPad.RedrawAxis()
canv.Print('cutflows/' + Region + '_cutflow_%s.eps' % weightedORraw)
def coverlay(hists,
xtitle,
ytitle,
name,
runtype,
tlabel,
xlabel,
xlabel_eta,
sellabel,
comparePerReleaseSuffix="",
norm="",
ndim=1):
gStyle.SetOptStat(111110)
c = TCanvas()
if ndim == 1:
c.SetRightMargin(0.10)
elif ndim == 2:
c.SetRightMargin(0.15)
c.SetLeftMargin(0.12)
c.SetBottomMargin(0.10)
# Upper plot will be in pad1
# pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
# pad1.SetBottomMargin(0.03) # Upper and lower plot are joined
# pad1.Draw() # Draw the upper pad: pad1
# pad1.cd() # pad1 becomes the current pad
# c.SetLogy(0)
# if any(subname in name for subname in ['isoPt', 'outOfConePt', 'IsoRaw', 'deepTau']):
# c.SetLogy()
if ndim == 1:
c.SetLogy()
if ndim == 2:
c.SetLogz()
ymax = max([hist.GetMaximum() for hist in hists])
# leg = TLegend(0.65, 0.65, 0.85, 0.9)
# configureLegend(leg, 1)
hratios = []
for i_hist, hist in enumerate(hists):
hist.SetMaximum(ymax * 1.2)
hist.SetMinimum(0.)
# if c.GetLogy > 0:
if c.GetLogy > 0:
hist.SetMinimum(0.001)
hist.GetXaxis().SetTitleSize(0.04)
hist.GetXaxis().SetLabelSize(0.04)
hist.GetYaxis().SetTitleSize(0.04)
hist.GetYaxis().SetLabelSize(0.04)
hist.GetXaxis().SetTitle(xtitle)
hist.GetYaxis().SetTitle(ytitle)
if i_hist == 0:
if ndim == 1:
hist.SetMarkerSize(0.)
hist.Draw('h')
# gPad.Update()
hist.SetStats(1)
# st = hist.GetListOfFunctions().FindObject("stats")
# # st.SetOptStat(1110)
# # st = c.GetPrimitive("stats")
# # st.SetNDC()
# st.SetX1NDC(0.7)
# st.SetX2NDC(0.85)
elif ndim == 2:
hist.SetMarkerSize(1.2)
min_bincontent = 0.005
if norm == "abs":
gStyle.SetPaintTextFormat("g")
else:
gStyle.SetPaintTextFormat("4.2f")
hist.SetMaximum(100)
hist.SetMinimum(0.0049)
if norm == "global":
integral = hist.Integral(1, hist.GetNbinsX(), 1,
hist.GetNbinsY())
for i in range(1, hist.GetNbinsX() + 1):
for j in range(1, hist.GetNbinsY() + 1):
bincontent = hist.GetBinContent(
i, j) / integral * 100 if integral > 0 else 0
bincontent = min_bincontent if (
bincontent > 0 and
bincontent < min_bincontent) else bincontent
hist.SetBinContent(i, j, bincontent)
elif norm == "col":
for i in range(1, hist.GetNbinsX() + 1):
integral = hist.Integral(i, i, 1, hist.GetNbinsY())
for j in range(1, hist.GetNbinsY() + 1):
bincontent = hist.GetBinContent(
i, j) / integral * 100 if integral > 0 else 0
bincontent = min_bincontent if (
bincontent > 0 and
bincontent < min_bincontent) else bincontent
hist.SetBinContent(i, j, bincontent)
elif norm == "row":
for j in range(1, hist.GetNbinsY() + 1):
integral = hist.Integral(1, hist.GetNbinsX(), j, j)
for i in range(1, hist.GetNbinsX() + 1):
bincontent = hist.GetBinContent(
i, j) / integral * 100 if integral > 0 else 0
bincontent = min_bincontent if (
bincontent > 0 and
bincontent < min_bincontent) else bincontent
hist.SetBinContent(i, j, bincontent)
hist.Draw("COLZ TEXT")
gPad.RedrawAxis()
# hist_TAR = hist.Clone()
else:
if ndim == 1:
hist.Draw('hsame')
elif ndim == 2:
hist.Draw("TEXTCOLZsame")
# # ihr = hist.Clone()
# # ihr.Sumw2()
# #ihr.Divide(hists[0])
#
# # ihr = hist_TAR.Clone()
# # ihr.Divide(hist)
# ihr = hist.Clone()
# ihr.Divide(hist_TAR)
#
# ihr.SetStats(0)
# ihr.SetLineColor(hist.GetLineColor())
# ihr.SetMarkerColor(hist.GetMarkerColor())
# ihr.SetMarkerStyle(hist.GetMarkerStyle())
# hratios.append(ihr)
#
# leg.AddEntry(hist, hist.GetName(), "l")
# leg.Draw()
#
# xshift = 0.0
# yshift = ymax*1.4
# if tlabel.find('QCD') != -1:
# # pad1.SetTopMargin(0.1)
# xshift = 0.0
# # yshift = ymax*1.45
# if runtype.find('TTbarTau') != -1:
# xshift = 0.78
tex2 = TLatex(0.95, 0.96, tlabel)
tex2.SetNDC()
tex2.SetTextAlign(32)
tex2.SetTextFont(42)
tex2.SetTextSize(0.043)
tex2.Draw()
seltex = TLatex(0.02, 0.96, sellabel)
seltex.SetNDC()
seltex.SetTextAlign(12)
seltex.SetTextFont(42)
seltex.SetTextSize(0.023)
seltex.Draw()
#
# # lower plot will be in pad
# c.cd() # Go back to the main canvas before defining pad2
# pad2 = TPad("pad2", "pad2", 0, 0.0, 1, 0.27)
# pad2.SetTopMargin(0.03)
# pad2.SetBottomMargin(0.35)
# pad2.Draw()
# pad2.cd()
# for ii, hist in enumerate(hratios):
# hist.GetXaxis().SetTitle(xtitle)
# hist.GetXaxis().SetTitleOffset(0.83)
# hist.GetXaxis().SetNdivisions(507)
# hist.GetYaxis().SetTitle('ratio')
# hist.GetYaxis().SetNdivisions(503)
# hist.SetMinimum(0.75)
# hist.SetMaximum(1.25)
# hist.GetYaxis().SetTitleOffset(0.45)
# hist.GetYaxis().SetTitleSize(0.16)
# hist.GetYaxis().SetLabelSize(0.16)
# hist.GetXaxis().SetTitleSize(0.16)
# hist.GetXaxis().SetLabelSize(0.16)
# if ii == 0:
# hist.Draw('ep')
# else:
# hist.Draw('epsame')
c.cd() # Go back to the main canvas
save(
c,
('compare_' + runtype + comparePerReleaseSuffix +
'/taumatching/hist_' + name + '_' + norm + 'norm_' + str(ndim) + 'D'))
c = None
gStyle.SetOptStat(0)
def painter(self, h_data, h_mc_list):
#print(type(h_mc_list[5]))
k = 1
while k < len(h_mc_list):
h_sum_mc = h_mc_list[0].Clone()
h_sum_mc.Add(h_mc_list[k])
k+=1
h_ratio = h_data.Clone()
h_ratio.Divide(h_sum_mc)
mcSumHistWithoutErrors = h_sum_mc.Clone()
h_sum_mc.Sumw2()
mcSumRatioHist = h_sum_mc.Clone()
mcSumRatioHist.Divide(mcSumHistWithoutErrors)
c3 = ROOT.TCanvas ("c3", "ph_et in",600,600)
gStyle.SetOptStat(0)
pad1 = ROOT.TPad("pad1","This is pad1",0.01,0.40,1,1.0)
pad2 = ROOT.TPad("pad2","This is pad2",0.01,0.01,1,0.39)
pad1.SetBorderSize(0)
pad1.SetBottomMargin(0.0)
pad1.Draw()
pad2.SetBorderSize(0)
pad2.SetTopMargin(0.)
pad2.SetBottomMargin(0.40)
pad2.Draw()
pad1.cd(0)
if 'lepmet_dphi' in h_mc_list[0].GetName():
pad1.SetLogy()
self.minYaxispad1 = 1
pt = ROOT.TPaveText(0.13,0.65,0.6,0.88,"NDC")
# pt.SetFillStyle(1)
pt.SetFillColor(0)
#pt.SetFillStyle(1)
pt.SetTextAlign(12)
# pt.SetLineStyle(0)
pt.AddText('#it{ATLAS} #bf{#bf{Work in Progress}}')
pt.AddText('#bf{#bf{3.22fb^{-1}, #sqrt{S}=13 TeV}}')
pt.AddText('#bf{#bf{SR}}')
pt.AddText('#bf{#bf{2015}}')
sizemclist = len(h_mc_list)
for i in reversed(range(sizemclist)):
h_mc_list[i].SetLineColor(36)
h_data.SetLineColor(ROOT.kBlack)
#h_data.SetMarkerSize(1.5)
h_data.SetLineWidth(2)
h_data.SetMarkerStyle(15)
for i in reversed(range(sizemclist)):
h_mc_list[i].SetFillColor(7-i)
h_data.GetYaxis().SetRangeUser(self.minYaxispad1, self.maxYaxispad1)
#self.h_data.GetXaxis().SetRangeUser(40, 140)
h_data.SetLineWidth(2)
######################
h_data.GetYaxis().SetTitle("#scale[2]{%s}"%self.SetTitleY)
h_data.GetYaxis().SetLabelSize(0.05)
#h_data.GetYaxis().SetTitleSize(0.09)
h_data.GetYaxis().SetTitleOffset(1.53)
h_data.SetTitle('')
h_data.Draw('E0')
for i in reversed(range(sizemclist)):
h_mc_list[i].Draw('hist same')
h_data.Draw("E0 same ")
pt.Draw("'NDC' same")
legend1 = ROOT.TLegend(0.70,0.88,0.52,0.7)
legend1.SetLineColor(0)
legend1.SetFillStyle(1)
legend2 = ROOT.TLegend(0.88,0.88,0.70,0.7)
legend2.SetLineColor(0)
legend2.SetFillStyle(1)
legend1.AddEntry( h_data ,"Data","lp")
for i in reversed(range(int(sizemclist/2),sizemclist)):
legend1.AddEntry( h_mc_list[i], str(h_mc_list[i].GetTitle()),"f")
legend1.Draw()
for i in reversed(range(0,int(sizemclist/2))):
legend2.AddEntry( h_mc_list[i], str(h_mc_list[i].GetTitle()),"f")
legend2.Draw()
gr1 = ROOT.TGraph(2)
gr1.SetPoint(0, -1, 1)
gr1.SetPoint(1, 100, 1)
gr1.SetLineStyle(7)
gPad.RedrawAxis()
c3.Update()
mcSumRatioHist.SetFillColor(ROOT.kGreen -9)
#mcSumRatioHist.SetLineColor(ROOT.kGreen -9)
h_ratio.SetTitle('')
pad2.cd(0)
h_ratio.SetMarkerStyle(15)
#h_ratio.SetMarkerSize(1.50)
h_ratio.SetLineColor(ROOT.kBlack)
#h_ratio.GetYaxis().SetLabelOffset(0.01) #сдвиг цифр
#h_ratio.GetYaxis().SetLabelFont(2)
h_ratio.GetXaxis().SetLabelSize(0.07)
h_ratio.GetYaxis().SetTitle("#scale[2.4]{Data / Model }")
h_ratio.GetXaxis().SetTitle("#scale[2.4]{%s}"%self.SetTitleX)
h_ratio.GetXaxis().SetLabelOffset(0.01)
h_ratio.GetYaxis().SetTitleOffset(1.4)
#####################################
h_ratio.GetXaxis().SetTitleOffset(2.4)
#h_ratio.GetXaxis().SetTitleSize(0.1)
# h_ratio.GetXaxis().SetRangeUser(40, 140)
h_ratio.SetLineWidth(2)
h_ratio.Draw("E")
mcSumRatioHist.Draw(' E2 same')
h_ratio.Draw("E same")
gr1.Draw('same')
h_ratio.GetYaxis().SetRangeUser(self.minYaxispad2, self.maxYaxispad2)
gPad.RedrawAxis()
legend2.AddEntry( mcSumRatioHist,"Stat.Uncert.","f")
# c3.cd(1)
# legend1.Draw('same')
pad1.Update()
pad2.Update()
c3.Update()
c3.SaveAs('fff.pdf')
c3.Draw()
return c3
def setaxes(self, *args, **kwargs):
"""Make axis."""
verbosity = LOG.getverbosity(self,kwargs)
hists = [ ]
binning = [ ]
for arg in args[:]:
if hasattr(arg,'GetXaxis'):
hists.append(arg)
elif isinstance(arg,Ratio):
hists.append(arg.frame)
elif isnumber(arg):
binning.append(arg)
if not hists:
LOG.warning("Plot.setaxes: No objects (TH1, TGraph, ...) given in args %s to set axis..."%(args))
return 0, 0, 100, 100
frame = hists[0]
if len(binning)>=2:
xmin, xmax = binning[:2]
else:
xmin, xmax = frame.GetXaxis().GetXmin(), frame.GetXaxis().GetXmax()
nbins = frame.GetXaxis().GetNbins()
binwidth = float(xmax-xmin)/nbins
xmin = kwargs.get('xmin', xmin )
xmax = kwargs.get('xmax', xmax )
ymin = kwargs.get('ymin', None )
ymax = kwargs.get('ymax', None )
ratiorange = kwargs.get('rrange', None )
binlabels = kwargs.get('binlabels', None )
intbins = kwargs.get('intbins', True ) # allow integer binning
logx = kwargs.get('logx', False )
logy = kwargs.get('logy', False )
ymargin = kwargs.get('ymargin', None ) or (1.3 if logy else 1.2) # margin between hist maximum and plot's top
logyrange = kwargs.get('logyrange', None ) or 3 # log(y) range from hist maximum to ymin
negativey = kwargs.get('negativey', True ) # allow negative y values
xtitle = kwargs.get('xtitle', frame.GetTitle() )
ytitle = kwargs.get('ytitle', None )
latex = kwargs.get('latex', True ) # automatically format strings as LaTeX
grid = kwargs.get('grid', False )
ycenter = kwargs.get('center', False )
nxdivisions = kwargs.get('nxdiv', 510 )
nydivisions = kwargs.get('nydiv', 510 )
main = kwargs.get('main', False ) # main panel of ratio plot
scale = 600./min(gPad.GetWh()*gPad.GetHNDC(),gPad.GetWw()*gPad.GetWNDC())
xtitlesize = kwargs.get('xtitlesize', _tsize )*scale
ytitlesize = kwargs.get('ytitlesize', _tsize )*scale
xlabelsize = kwargs.get('xlabelsize', _lsize )*scale
ylabelsize = kwargs.get('ylabelsize', _lsize )*scale
ytitleoffset = kwargs.get('ytitleoffset', 1.0 )*1.27/scale
xtitleoffset = kwargs.get('xtitleoffset', 1.0 )*1.00
xlabeloffset = kwargs.get('xlabeloffset', -0.008*scale if logx else 0.007 )
if main:
xtitlesize = 0.0
xlabelsize = 0.0
if latex:
xtitle = makelatex(xtitle)
LOG.verb("Plot.setaxes: Binning (%s,%.1f,%.1f)"%(nbins,xmin,xmax),verbosity,2)
if ratiorange:
ymin, ymax = 1-ratiorange, 1+ratiorange
if intbins and nbins<15 and int(xmin)==xmin and int(xmax)==xmax and binwidth==1:
LOG.verb("Plot.setaxes: Setting integer binning for (%r,%s,%d,%d)!"%(xtitle,nbins,xmin,xmax),verbosity,1)
binlabels = [str(i) for i in range(int(xmin),int(xmax)+1)]
xlabelsize *= 1.6
xlabeloffset *= 0.88*scale
if logy:
ylabelsize *= 1.08
if logx:
xlabelsize *= 1.08
if binlabels:
nxdivisions = 15
# GET HIST MAX
hmaxs = [ ]
hmins = [ 0 ]
if isinstance(frame,THStack):
hmaxs.append(frame.GetMaximum())
#frame = frame.GetStack().Last()
for hist in hists:
hmaxs.append(getTGraphYRange(hist)[1] if isinstance(hist,TGraph) else hist.GetMaximum())
else:
for hist in hists:
ymin1, ymax1 = getTGraphYRange(hist) if isinstance(hist,TGraph) else hist.GetMinimum(), hist.GetMaximum()
if negativey:
hmins.append(ymin1)
hmaxs.append(ymax1)
if ymin==None:
ymin = min(hmins)*(1.1 if ymin>0 else 0.9)
hmax = max(hmaxs)
hmin = min(hmins)
# SET AXES RANGES
if ymin==None:
ymin = 0
if logy:
if not ymin or ymin<=0: # avoid zero or negative ymin for log plots
ymin = 10**(magnitude(hmax)-logyrange) #max(0.1,10**(magnitude(ymax)-3))
frame.SetMinimum(ymin)
LOG.verb("Plot.setaxes: logy=%s, hmax=%6.6g, magnitude(hmax)=%s, logyrange=%s, ymin=%.6g"%(
logy,hmax,magnitude(hmax),logyrange,ymin),verbosity,2)
if ymax==None:
if hmax>ymin>0:
span = abs(log10(hmax/ymin))*ymargin
ymax = ymin*(10**span)
LOG.verb("Plot.setaxes: log10(hmax/ymin)=%6.6g, span=%6.6g, ymax=%.6g"%(log10(hmax/ymin),span,ymax),verbosity,2)
else:
ymax = hmax*ymargin
gPad.Update(); gPad.SetLogy()
elif ymax==None:
ymax = hmax*ymargin
if logx:
if not xmin: xmin = 0.1
xmax *= 0.9999999999999
gPad.Update(); gPad.SetLogx()
if grid:
gPad.SetGrid()
#frame.GetXaxis().SetLimits(xmin,xmax)
frame.GetXaxis().SetRangeUser(xmin,xmax)
frame.SetMinimum(ymin)
frame.SetMaximum(ymax)
if ytitle==None:
#ytitle = "Events"
if "multiplicity" in xtitle.lower():
ytitle = "Events"
elif hmax<1.:
ytitle = "A.U."
else:
for hist in hists:
if isinstance(hist,TH1) and hist.GetName()!='hframe':
hist0 = hist; break # default frame might have wrong binning
else:
hist0 = frame
binwidth = hist0.GetXaxis().GetBinWidth(0)
binwidstr = ("%.3f"%binwidth).rstrip('0').rstrip('.')
units = re.findall(r' [\[(](.+)[)\]]',xtitle) #+ re.findall(r' (.+)',xtitle)
if hist0.GetXaxis().IsVariableBinSize():
if units:
ytitle = "Events / "+units[-1]
else:
ytitle = "Events / bin size"
elif units:
if binwidth!=1:
ytitle = "Events / %s %s"%(binwidstr,units[-1])
else:
ytitle = "Events / "+units[-1]
elif binwidth!=1:
ytitle = "Events / "+binwidstr
else:
ytitle = "Events"
LOG.verb("Plot.setaxes: ytitle=%r, units=%s, binwidth=%s, binwidstr=%r"%(ytitle,units,binwidth,binwidstr),verbosity,2)
# alphanumerical bin labels
if binlabels:
if len(binlabels)<nbins:
LOG.warning("Plot.setaxes: len(binlabels)=%d < %d=nbins"%(len(binlabels),nbins))
for i, binlabel in zip(range(1,nbins+1),binlabels):
frame.GetXaxis().SetBinLabel(i,binlabel)
#frame.GetXaxis().LabelsOption('h')
# X axis
frame.GetXaxis().SetTitleSize(xtitlesize)
frame.GetXaxis().SetTitleOffset(xtitleoffset)
frame.GetXaxis().SetLabelSize(xlabelsize)
frame.GetXaxis().SetLabelOffset(xlabeloffset)
frame.GetXaxis().SetNdivisions(nxdivisions)
frame.GetXaxis().SetTitle(xtitle)
# Y axis
if ymax>=1e4:
ylabelsize *= 0.95
if ycenter:
frame.GetYaxis().CenterTitle(True)
frame.GetYaxis().SetTitleSize(ytitlesize)
frame.GetYaxis().SetTitleOffset(ytitleoffset)
frame.GetYaxis().SetLabelSize(ylabelsize)
frame.GetYaxis().SetNdivisions(nydivisions)
frame.GetYaxis().SetTitle(ytitle)
gPad.RedrawAxis()
gPad.Update()
if verbosity>=1:
print ">>> Plot.setaxes: xtitle=%r, [hmin,hmax] = [%.6g,%.6g], [xmin,xmax] = [%.6g,%.6g], [ymin,ymax] = [%.6g,%.6g]"%(
xtitle,hmin,hmax,xmin,xmax,ymin,ymax)
elif verbosity>=2:
print ">>> Plot.setaxes: frame=%s"%(frame)
print ">>> Plot.setaxes: hists=%s"%(hists)
print ">>> Plot.setaxes: [hmin,hmax] = [%.6g,%.6g], [xmin,xmax] = [%.6g,%.6g], [ymin,ymax] = [%.6g,%.6g]"%(hmin,hmax,xmin,xmax,ymin,ymax)
print ">>> Plot.setaxes: xtitlesize=%4.4g, xlabelsize=%4.4g, xtitleoffset=%4.4g, xtitle=%r"%(xtitlesize,xlabelsize,xtitleoffset,xtitle)
print ">>> Plot.setaxes: ytitlesize=%4.4g, ylabelsize=%4.4g, ytitleoffset=%4.4g, ytitle=%r"%(ytitlesize,ylabelsize,ytitleoffset,ytitle)
print ">>> Plot.setaxes: scale=%4.4g, nxdivisions=%s, nydivisions=%s, ymargin=%.3f, logyrange=%.3f"%(scale,nxdivisions,nydivisions,ymargin,logyrange)
if main:
#if any(a!=None and a!=b for a, b in [(self.xmin,xmin),(self.xmax,xmax)]):
# LOG.warning("Plot.setaxes: x axis range changed: [xmin,xmax] = [%6.6g,%6.6g] -> [%6.6g,%6.6g]"%(
# self.xmin,self.xmax,xmin,xmax))
#if any(a!=None and a!=b for a, b in [(self.ymin,ymin),(self.ymax,ymax)]):
# LOG.warning("Plot.setaxes: y axis range changed: [ymin,ymax] = [%6.6g,%6.6g] -> [%6.6g,%6.6g]"%(
# self.ymin,self.ymax,ymin,ymax))
self.xmin, self.xmax = xmin, xmax
self.ymin, self.ymax = ymin, ymax
return xmin, xmax, ymin, ymax
def PlotVariables(output_dir,
dataset_name,
variable,
weight_names,
region,
Location,
Bkg_processes,
Lumi,
Lumi_label,
category,
LOG=False):
bkg_names = []
label_names = []
for Bkg_process in Bkg_processes:
if Bkg_process == 9:
continue
bkg_names.append(Bkg_processes[Bkg_process]['folder_name'])
label_names.append([
Bkg_processes[Bkg_process]['folder_name'],
Bkg_processes[Bkg_process]['latex_name'],
Bkg_processes[Bkg_process]['color'], 0
])
signal_name = [Bkg_processes[9]['folder_name']]
Scaling = False
#LOG = False
c = []
p1 = []
p2 = []
plot_count = 0
data_hist = []
data_for_ratio = []
bkg_for_ratio = []
signal_hist = []
bkg_stack = []
legend = []
tex1 = []
tex2 = []
tex3 = []
printing = False
prefix = Location + "output_" + region + "_sh/"
mc_prefix = prefix
DATA = pd.read_csv(prefix + '/' + dataset_name + '/tbl_' +
variable[Properties.Name] + weight_names + '.csv',
comment='#')
tmp1_name, tmp2 = variable[Properties.Name].split('--')
data_hist.append(
CreateHistogramNP(DATA, tmp1_name,
variable[Properties.HistName] + "_data",
variable[Properties.AxisLabels], 'n', 1, 1,
variable[Properties.Rebin]))
data_for_ratio.append(data_hist[plot_count].Clone())
bkg_stack.append(THStack("Background", "Full_Background"))
if printing:
print("Data", data_hist[plot_count].Integral())
if ('W' in region) or ('Z' in region) or ('SR' in region) and not (
"mindphi" in variable[Properties.Name].lower()):
legend_args = (0.7, 0.55, 0.95, 0.91, '', 'NDC')
else:
legend_args = (0.18, 0.55, 0.55, 0.91, '', 'NDC')
if 'mindPhi' in variable[0] or (
'electron' in variable[Properties.Name].lower()
and 'phi' in variable[Properties.Name].lower()):
legend_args = (0.18, 0.55, 0.55, 0.91, '', 'NDC')
#take legend parametrisation out of for loop later
legend.append(TLegend(*legend_args))
legend[plot_count].SetFillStyle(0)
legend[plot_count].AddEntry(data_hist[plot_count], 'Data', 'p')
if not (len(signal_name) == 0):
SIGNAL = pd.read_csv(mc_prefix + '/' + signal_name[0] + '/tbl_' +
variable[Properties.Name] + weight_names + '.csv',
comment='#')
signal_hist.append(
CreateHistogramNP(SIGNAL, tmp1_name,
variable[Properties.HistName] + "_signal",
variable[Properties.AxisLabels],
variable[Properties.Weight], 1, Lumi,
variable[Properties.Rebin]))
if 'SR' in region:
legend[plot_count].AddEntry(signal_hist[plot_count],
'VBF H#rightarrow Inv', 'l')
for bkg_name in bkg_names:
bkg_file = pd.read_csv(mc_prefix + '/' + bkg_name + '/tbl_' +
variable[Properties.Name] + weight_names +
'.csv',
comment='#')
color = 2
addLeg = False
if (groupBy):
bkg_file = bkg_file.dropna().groupby(tmp1_name).sum()
bkg_file.reset_index(inplace=True)
#print bkg_name
index_leg = [['', '', 0, 0]]
for label_name in label_names:
if label_name[0] in bkg_name:
# print "In_check"
color = label_name[2]
if label_name[3] == 0:
#print bkg_name, label_name[0], "in label check"
addLeg = True
label_name[3] = 1
index_leg = label_name
break
bkg_hist = CreateHistogramNP(
bkg_file, tmp1_name,
variable[Properties.HistName] + bkg_name + "_mc" + str(plot_count),
variable[Properties.AxisLabels], variable[Properties.Weight],
color, Lumi, variable[Properties.Rebin])
#Scaling
if Scaling:
if "DY" in bkg_name:
print("Scaling DY")
bkg_hist.Scale(1.15)
if "WJETS" in bkg_name:
bkg_hist.Scale(1.171714)
bkg_hist.GetXaxis().SetRangeUser(
variable[Properties.Axis_low],
variable[Properties.Axis_high]) #- Really strange!
bkg_stack[plot_count].Add(bkg_hist)
if bkg_names.index(bkg_name) == 0:
bkg_for_ratio.append(bkg_hist.Clone())
else:
bkg_for_ratio[plot_count].Add(bkg_hist)
if addLeg:
if not (bkg_hist.Integral() == 0):
# print bkg_name, bkg_hist.Integral()
addLeg = False
legend[plot_count].AddEntry(bkg_hist, index_leg[1], 'f')
else:
for label_name in label_names:
if label_name[0] in bkg_name:
label_name[
3] = 0 # return to the state label_not_drawn, not elegant check. return to it after the EXO workdshop!
break
#WORK IN PROGRESS
printing = False
c.append(ROOT.TCanvas("c" + str(plot_count), "2 Graphs", 900, 1000))
p2.append(ROOT.TPad("p2", "p3", 0., 0., 1., 0.28))
p2[plot_count].Draw()
p2[plot_count].SetTopMargin(0.001)
p2[plot_count].SetBottomMargin(0.3)
p2[plot_count].SetGrid()
p1.append(ROOT.TPad("p1", "p1", 0., 0.3, 1., 1.))
p1[plot_count].Draw()
p1[plot_count].SetBottomMargin(0.001)
p1[plot_count].cd()
data_hist[plot_count].SetLineColor(1)
data_hist[plot_count].GetXaxis().SetRangeUser(
variable[Properties.Axis_low], variable[Properties.Axis_high])
if LOG:
p1[plot_count].SetLogy(1)
data_hist[plot_count].Draw("e1")
bkg_stack[plot_count].Draw("hist same")
bkg_for_ratio[plot_count].SetFillColor(1)
bkg_for_ratio[plot_count].SetMarkerSize(0)
bkg_for_ratio[plot_count].SetFillStyle(3018)
bkg_for_ratio[plot_count].Draw("E2 same")
if LOG:
data_hist[plot_count].GetYaxis().SetRangeUser(
0.2, 1000 * bkg_for_ratio[plot_count].GetMaximum())
else:
data_hist[plot_count].GetYaxis().SetRangeUser(
0, 1.7 * bkg_for_ratio[plot_count].GetMaximum())
if data_hist[plot_count].Integral() > 0:
data_hist[plot_count].GetYaxis().SetRangeUser(
0.1, 1.7 * bkg_for_ratio[plot_count].GetMaximum())
if 'mjj' in variable[Properties.Name] or 'nomu' in variable[
Properties.Name].lower():
data_hist[plot_count].GetYaxis().SetRangeUser(
0.1, 1.3 * bkg_for_ratio[plot_count].GetMaximum())
bkg_stack[plot_count].GetYaxis().SetRangeUser(
0.1, 1.3 * bkg_for_ratio[plot_count].GetMaximum())
data_hist[plot_count].GetXaxis().SetLabelSize(0.1)
data_hist[plot_count].GetYaxis().SetTitleOffset(1.15)
data_hist[plot_count].GetXaxis().SetTitleSize(.12)
data_hist[plot_count].Draw("e1 same")
if not (len(signal_name) == 0):
signal_hist[plot_count].SetLineWidth(4)
signal_hist[plot_count].SetFillStyle(0)
if 'vtr' in category.lower():
signal_hist[plot_count].Scale(1.5)
else:
signal_hist[plot_count].Scale(5)
signal_hist[plot_count].Draw("hist same")
#gPad.Draw()
legend[plot_count].Draw()
tex = Styling(region, "CMS", "Work in Progress", Lumi_label)
tex1.append(tex[0])
tex2.append(tex[1])
tex3.append(tex[2])
tex1[plot_count].Draw("goff")
tex2[plot_count].Draw("goff")
tex3[plot_count].Draw("goff")
#gPad.Update();
gPad.RedrawAxis()
p2[plot_count].cd()
#r = ROOT.TGraph(20);
#r.SetTitle("");
data_for_ratio[plot_count].Divide(bkg_for_ratio[plot_count])
data_for_ratio[plot_count].GetXaxis().SetRangeUser(
variable[Properties.Axis_low], variable[Properties.Axis_high])
data_for_ratio[plot_count].GetYaxis().SetRangeUser(-0.49, 2.49)
data_for_ratio[plot_count].GetYaxis().SetTitle("Data / Pred.")
data_for_ratio[plot_count].GetXaxis().SetTitleSize(.12)
data_for_ratio[plot_count].GetYaxis().SetTitleSize(.12)
data_for_ratio[plot_count].GetYaxis().SetTitleOffset(0.57)
data_for_ratio[plot_count].GetYaxis().SetLabelSize(0.1)
data_for_ratio[plot_count].GetXaxis().SetLabelSize(0.1)
data_for_ratio[plot_count].Draw("P0")
bkg_band = bkg_for_ratio[plot_count].Clone()
bkg_band_d = bkg_for_ratio[plot_count].Clone()
bkg_band.Divide(bkg_band_d)
bkg_band.SetFillColor(1)
bkg_band.SetMarkerSize(0)
bkg_band.SetFillStyle(3018)
bkg_band.Draw("E2 same")
# c[plot_count].Draw()
gPad.RedrawAxis()
if not (LOG):
c[plot_count].SaveAs(output_dir + "/" + tmp1_name + ".pdf")
c[plot_count].SaveAs(output_dir + "/" + tmp1_name + ".png")
c[plot_count].SaveAs(output_dir + "/" + tmp1_name + ".C")
c[plot_count].SaveAs(output_dir + "/" + tmp1_name + ".root")
else:
c[plot_count].SaveAs(output_dir + "/" + tmp1_name + "_log.pdf")
c[plot_count].SaveAs(output_dir + "/" + tmp1_name + "_log.png")
c[plot_count].SaveAs(output_dir + "/" + tmp1_name + "_log.C")
c[plot_count].SaveAs(output_dir + "/" + tmp1_name + "_log.root")
plot_count += 1
return True
def plotPValues(labels, masses0, **kwargs):
print color("plotPValues()", color="magenta", prepend=">>>\n>>> ")
# SIGNAL strength & mass
bins = kwargs.get('bins', [])
ymin = 0.00005
# LOOP over LABELS
for label in labels:
print color("plotPValues - %s" % (label),
color="grey",
prepend="\n>>> ")
masses = array('d', [])
zeros = array('d', [])
limitObs = array('d', [])
limitExps = []
up2s = []
for i, mass in enumerate(masses0):
bin = -1
if bins: bin = bins[i]
filename = getOutputFilename(label,
mass,
method="ProfileLikelihood",
bin=bin,
extralabel=".SignifObs")
limitObs.append(getLimits(filename, brazilian=False))
masses.append(mass)
zeros.append(0.0)
v_masses = TVectorD(len(masses), masses)
v_zeros = TVectorD(len(zeros), zeros)
v_limitObs = TVectorD(len(limitObs), limitObs)
v_limitExps = []
for limitExp in limitExps:
v_limitExps.append(TVectorD(len(limitExp), limitExp))
W = 800
H = 600
T = 0.08 * H
B = 0.12 * H
L = 0.12 * W
R = 0.04 * W
canvas = TCanvas("canvas", "canvas", 100, 100, W, H)
canvas.SetFillColor(0)
canvas.SetBorderMode(0)
canvas.SetFrameFillStyle(0)
canvas.SetFrameBorderMode(0)
canvas.SetLeftMargin(L / W)
canvas.SetRightMargin(R / W)
canvas.SetTopMargin(T / H)
canvas.SetBottomMargin(B / H)
canvas.SetTickx(0)
canvas.SetTicky(0)
canvas.SetGrid()
canvas.SetLogy() # log
canvas.cd()
frame = canvas.DrawFrame(1.4, 0.001, 4.1, 10)
frame.GetYaxis().CenterTitle()
frame.GetYaxis().SetTitleSize(0.05)
frame.GetXaxis().SetTitleSize(0.05)
frame.GetXaxis().SetLabelSize(0.04)
frame.GetYaxis().SetLabelSize(0.04)
frame.GetYaxis().SetTitleOffset(1.14)
frame.GetXaxis().SetNdivisions(508)
frame.GetYaxis().CenterTitle(False)
frame.GetYaxis().SetTitle("local p-value")
#frame.GetYaxis().SetTitle("95% upper limit on #sigma / #sigma_{SM}")
frame.GetXaxis().SetTitle("X#rightarrow#tau#tau mass [GeV]")
frame.SetMinimum(ymin)
frame.SetMaximum(1.5)
frame.GetXaxis().SetLimits(min(masses), max(masses))
latex = TLatex()
latex.SetTextSize(0.4 * canvas.GetTopMargin())
latex.SetTextColor(2)
f1 = TF1("f1", "0.15866", min(masses), max(masses))
f1.SetLineColor(2)
f1.SetLineWidth(2)
f1.Draw("lsame")
latex.DrawLatex(min(masses) + 1, 0.15866 * 1.1, "1#sigma")
f2 = TF1("f2", "0.02275", min(masses), max(masses))
f2.SetLineColor(2)
f2.SetLineWidth(2)
f2.Draw("lsame")
latex.DrawLatex(min(masses) + 1, 0.02275 * 1.1, "2#sigma")
f3 = TF1("f3", "0.0013499", min(masses), max(masses))
f3.SetLineColor(2)
f3.SetLineWidth(2)
f3.Draw("lsame")
latex.DrawLatex(min(masses) + 1, 0.0013499 * 1.1, "3#sigma")
graph_limitExps = []
colors = [4, 2, 3, 6, 7, 8]
for i, v_limitExp in enumerate(v_limitExps):
graph_limitExps.append(
TGraphAsymmErrors(v_masses, v_limitExp, v_zeros, v_zeros,
v_zeros, v_zeros))
graph_limitExps[-1].SetLineColor(colors[i])
graph_limitExps[-1].SetLineWidth(2)
graph_limitExps[-1].SetLineStyle(2)
graph_limitExps[-1].Draw("Lsame")
graph_limitObs = TGraphAsymmErrors(v_masses, v_limitObs, v_zeros,
v_zeros, v_zeros, v_zeros)
graph_limitObs.SetLineColor(1)
graph_limitObs.SetLineWidth(2)
graph_limitObs.Draw("Csame")
CMS_lumi.CMS_lumi(canvas, 13, 0)
gPad.SetTicks(1, 1)
frame.Draw('sameaxis')
x1 = 0.62
x2 = x1 + 0.24
y1 = 0.15
y2 = y1 + 0.20
legend = TLegend(x1, y1, x2, y2)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextSize(0.041)
legend.SetTextFont(42)
legend.SetHeader("%s" % (label_dict[label]))
legend.AddEntry(graph_limitObs, "observed", 'L') #p-value
legend.Draw("same")
gPad.RedrawAxis()
print " "
canvas.SaveAs("%s/p-value-local-%s.png" % (PLOTS_DIR, label))
canvas.SaveAs("%s/p-value-local-%s.pdf" % (PLOTS_DIR, label))
canvas.Close()
def main():
# usage description
usage = "Example: ./scripts/plotLimits.py -M Asymptotic -l logs -f qq --massrange 1200 7000 100"
# input parameters
parser = ArgumentParser(description='Script that plots limits for specified mass points',epilog=usage)
parser.add_argument('analysis', type=str, help='Analysis name')
parser.add_argument('model', type=str, help='Model name')
parser.add_argument("-M", "--method", dest="method", required=True,
choices=['ProfileLikelihood', 'HybridNew', 'Asymptotic', 'MarkovChainMC', 'theta', 'HybridNewGrid'],
help="Method to calculate upper limits",
metavar="METHOD")
parser.add_argument('--fit_function', type=str, default="f4", help="Name of fit function used for background estimate")
parser.add_argument('--timesAE', action='store_true', help="Set y-axis to sigma*BR*A*e, instead of sigma*BR")
parser.add_argument('--fitTrigger', action='store_true', help="Use trigger fit")
parser.add_argument('--correctTrigger', action='store_true', help="Use trigger correction")
parser.add_argument('--useMCTrigger', action='store_true', help="Use MC trigger emulation")
#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("-f2", "--finalstate2", dest="final_state2", required=True, help="hG,lG,hR, or lR", metavar="FINAL_STATE2")
parser.add_argument("--noSyst", action="store_true", help="Make plots for limits without systematics")
parser.add_argument("--freezeNuisances", type=str, help="Make plots for limits with frozen nuisance parameters")
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("--saveObjects", type=str, help="Save plot objects")
parser.add_argument("--extraText", dest="extraText", default='', help="Extra text on the plot (default: %(default)s)")
parser.add_argument("--lumi_sqrtS", dest="lumi_sqrtS", default='19.7 fb^{-1} (8 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()
if args.method == 'HybridNew':
searchmethod = 'Hybrid New'
# 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()
from ROOT import kTRUE, kFALSE, gROOT, gStyle, gPad, TGraph, TCanvas, TLegend, TF1, TFile
from ROOT import kGreen, kYellow, kWhite
# Make acc*eff TGraph
ae_x = array('d',[325, 350, 400, 500, 600, 750, 900, 1200])
if args.timesAE:
ae_y = np.ones(len(ae_x))
else:
ae_y = array('d', [])
for mass in ae_x:
ae_y.append(analysis_config.simulation.get_signal_AE(args.analysis, args.model, int(mass)))
acceptance_times_efficiency = TGraph(len(ae_x), ae_x, ae_y)
#xs = array('d',[250,300,400,500,600,750,900,1200])
#trigger_correctionl = TF1("trigbbl_efficiency", "(1. / (1. + TMath::Exp(-1. * (x - [0]) / [1])))**[2]", 175, 400)
#trigger_correctionl.SetParameter(0, 1.82469e+02)
#trigger_correctionl.SetParameter(1, 2.87768e+01)
#trigger_correctionl.SetParameter(2, 9.11659e-01)
#trigger_correctionh = TF1("trigbbh_efficiency", "(1. / (1. + TMath::Exp(-1. * (x - [0]) / [1])))**[2]", 300, 600)
#trigger_correctionh.SetParameter(0, 3.61785e+02)
#trigger_correctionh.SetParameter(1, 3.16523e+01)
#trigger_correctionh.SetParameter(2, 4.84357e-01)
#if args.timesAE:
# ys = np.ones(len(xs))
#else:
# if args.analysis == "trigbbh_CSVTM" and args.model == "Hbb":
# #ys = array('d',[188./19751.,1304./19993.,2697./49494.,881./19999.,534./19598.])
# ys = array('d',[24./19737.,188./19751.,1171./19984.,1419./19992.,1304./19993.,2697./49494.,881./19999.,534./19598.])
# #graphMod = trigger_correctionh
# elif args.analysis == "trigbbl_CSVTM" and args.model == "Hbb":
# #ys = array('d',[30./2797.,1583./19995.,1295./19996.,999./19996.,528./19999.])
# ys = array('d',[574./19737.,763./39502.,651./19984.,583./19992.,984./39986.,1905./98988.,656./39998.,369./39196.])
# #graphMod = trigger_correctionh
# elif args.analysis == "trigbbh_CSVTM" and args.model == "RSG":
# #ys = array('d',[109./19751.,488./19993.,954./49494.,328./19999.,182./19598.])
# ys = array('d',[40./19977.,30./2797.,1522./19991.,1640./19396.,1583./19995.,1295./19996.,999./19996.,528./19999.])
# #graphMod = trigger_correctionl
# elif args.analysis == "trigbbl_CSVTM" and args.model == "RSG":
# #ys = array('d',[23./2797.,599./19995.,448./19996.,338./19996.,190./19999.])
# ys = array('d',[696./19977.,137./5594.,797./19991.,652./19396.,1206./39990.,891./39992.,675./39992.,379./39998.])
# #graphMod = trigger_correctionl
##ys = array('d',[1,1,1,1,1,1,1,1,])
#acceptance_times_efficiency = TGraph(len(xs),xs,ys)
# arrays holding results
masses = array('d')
xs_obs_limits = array('d')
xs_exp_limits = array('d')
masses_exp = array('d')
xs_exp_limits_1sigma = array('d')
xs_exp_limits_1sigma_up = array('d')
xs_exp_limits_2sigma = array('d')
xs_exp_limits_2sigma_up = array('d')
for mass in input_masses:
print ">> Reading results for %s %s resonance with m = %i GeV..."%(args.analysis, args.model, int(mass))
masses.append(mass)
masses_exp.append(mass)
# For masses above 1100, you scaled down the signal by 10 by hand, to help the limit setting.
#if args.analysis == "trigbbh_CSVTM" and mass >= 1100:
input_xs = 1./100.
#else:
# input_xs = 1.
if args.method == "HybridNewGrid":
found_limit = {"obs":False, "exp0":False, "exp1":False, "exp2":False, "exp-1":False, "exp-2":False}
for what in found_limit.keys():
log_file_path = limit_config.get_combine_log_path_grid(args.analysis, args.model, mass, args.fit_function, what, systematics=(not args.noSyst), frozen_nps=args.freezeNuisances, fitTrigger=args.fitTrigger, correctTrigger=args.correctTrigger, useMCTrigger=args.useMCTrigger)
print "Reading log file from " + log_file_path
log_file = open(log_file_path, 'r')
for line in log_file:
if re.search("^Limit: r <", line) and re.search("95%", line):
found_limit[what] = True
this_limit = float(line.split()[3])/acceptance_times_efficiency.Eval(mass)
print "Found limit for " + what + " = " + str(this_limit)
if what == "obs":
xs_obs_limits.append(this_limit * input_xs)
elif what == "exp0":
xs_exp_limits.append(this_limit * input_xs)
elif what == "exp1":
xs_exp_limits_1sigma_up.append(this_limit * input_xs)
elif what == "exp2":
xs_exp_limits_2sigma_up.append(this_limit * input_xs)
elif what == "exp-1":
xs_exp_limits_1sigma.append(this_limit * input_xs)
elif what == "exp-2":
xs_exp_limits_2sigma.append(this_limit * input_xs)
if not found_limit["obs"]:
xs_obs_limits.append(0)
if not found_limit["exp0"]:
xs_exp_limits.append(0)
if not found_limit["exp1"]:
xs_exp_limits_1sigma.append(0)
if not found_limit["exp2"]:
xs_exp_limits_1sigma_up.append(0)
if not found_limit["exp-1"]:
xs_exp_limits_2sigma.append(0)
if not found_limit["exp-2"]:
xs_exp_limits_2sigma_up.append(0)
if len(masses) != len(xs_obs_limits):
print "** ERROR: ** Could not find observed limit for m =", int(mass), "GeV. Aborting."
sys.exit(1)
else:
print "Reading log file from " + limit_config.get_combine_log_path(args.analysis, args.model, mass, args.fit_function, args.method, systematics=(not args.noSyst), frozen_nps=args.freezeNuisances, fitTrigger=args.fitTrigger, correctTrigger=args.correctTrigger, useMCTrigger=args.useMCTrigger)
if not os.path.exists((limit_config.get_combine_log_path(args.analysis, args.model, mass, args.fit_function, args.method, systematics=(not args.noSyst), frozen_nps=args.freezeNuisances, fitTrigger=args.fitTrigger, correctTrigger=args.correctTrigger, useMCTrigger=args.useMCTrigger))):
print "[plot_limits] WARNING : Log file not found! Setting limits to zero and skipping this point."
print "[plot_limits] WARNING : \t{}".format(limit_config.get_combine_log_path(args.analysis, args.model, mass, args.fit_function, args.method, systematics=(not args.noSyst), frozen_nps=args.freezeNuisances, fitTrigger=args.fitTrigger, correctTrigger=args.correctTrigger, useMCTrigger=args.useMCTrigger))
xs_obs_limits.append(0)
xs_exp_limits.append(0)
xs_exp_limits_1sigma.append(0)
xs_exp_limits_1sigma_up.append(0)
xs_exp_limits_2sigma.append(0)
xs_exp_limits_2sigma_up.append(0)
continue
log_file = open(limit_config.get_combine_log_path(args.analysis, args.model, mass, args.fit_function, args.method, systematics=(not args.noSyst), frozen_nps=args.freezeNuisances, fitTrigger=args.fitTrigger, correctTrigger=args.correctTrigger, useMCTrigger=args.useMCTrigger))
foundMethod = False
middle = 0
# read the log file
found_limit = {"obs":False, "exp":False, "exp+1":False, "exp+2":False, "exp-1":False, "exp-2":False}
for line in log_file:
if args.method == 'Asymptotic':
if re.search("^Observed Limit: r", line):
xs_obs_limits.append(float(line.split()[-1])/acceptance_times_efficiency.Eval(mass) * input_xs)
found_limit["obs"] = True
if mass == 325 and args.model == "ZPrime":
print "[debug] ZPrime 325 GeV limit = {}".format(xs_obs_limits[-1])
print "[debug] \tA*e={}, input_xs={}".format(acceptance_times_efficiency.Eval(mass), input_xs)
if re.search("^Expected 50.0%: r", line):
middle = float(line.split()[-1])
found_limit["exp"] = True
xs_exp_limits.append(middle/acceptance_times_efficiency.Eval(mass) * input_xs)
if re.search("^Expected 16.0%: r", line):
xs_exp_limits_1sigma.append((float(line.split()[-1]))/acceptance_times_efficiency.Eval(mass) * input_xs)
found_limit["exp-1"] = True
if re.search("^Expected 84.0%: r", line):
xs_exp_limits_1sigma_up.append(float(line.split()[-1])/acceptance_times_efficiency.Eval(mass) * input_xs)
found_limit["exp+1"] = True
if re.search("^Expected 2.5%: r", line):
xs_exp_limits_2sigma.append(float(line.split()[-1])/acceptance_times_efficiency.Eval(mass) * input_xs)
found_limit["exp-2"] = True
if re.search("^Expected 97.5%: r", line):
xs_exp_limits_2sigma_up.append(float(line.split()[-1])/acceptance_times_efficiency.Eval(mass) * input_xs)
found_limit["exp+2"] = True
elif args.method == 'theta':
if re.search('^# x; y; yerror', line):
foundMethod = True
if line.split()[0] == '0' and foundMethod:
xs_obs_limits.append(float(line.split()[1])/acceptance_times_efficiency.Eval(mass) * input_xs)
else:
searchmethod = "Hybrid New"
if re.search(' -- ' + searchmethod, line):
foundMethod = True
if re.search("^Limit: r", line) and foundMethod:
xs_obs_limits.append(float(line.split()[3])/acceptance_times_efficiency.Eval(mass) * input_xs)
found_limit["obs"] = True
print "[debug] Found limit " + str(xs_obs_limits[-1])
if not found_limit["obs"]:
xs_obs_limits.append(0)
if not found_limit["exp"]:
xs_exp_limits.append(0)
if not found_limit["exp+1"]:
xs_exp_limits_1sigma.append(0)
if not found_limit["exp+2"]:
xs_exp_limits_1sigma_up.append(0)
if not found_limit["exp-1"]:
xs_exp_limits_2sigma.append(0)
if not found_limit["exp-2"]:
xs_exp_limits_2sigma_up.append(0)
if len(masses) != len(xs_obs_limits):
print "** ERROR: ** Could not find observed limit for m =", int(mass), "GeV. Aborting."
sys.exit(1)
if args.method == 'Asymptotic' or args.method == 'HybridNewGrid':
if len(masses) != len(xs_exp_limits):
print "** ERROR: ** Could not find expected limit for m =", int(mass), "GeV. Aborting."
print "masses = ",
print masses
print "xs_exp_limits = ",
print xs_exp_limits
sys.exit(1)
if len(masses) != len(xs_exp_limits_1sigma):
print "** ERROR: ** Could not find expected 1 sigma down limit for m =", int(mass), "GeV. Aborting."
print "masses = ",
print masses
print "xs_exp_limits_1sigma = ",
print xs_exp_limits_1sigma
sys.exit(1)
if len(masses) != len(xs_exp_limits_1sigma_up):
print "** ERROR: ** Could not find expected 1 sigma up limit for m =", int(mass), "GeV. Aborting."
print "masses = ",
print masses
print "xs_exp_limits_1sigma_up = ",
print xs_exp_limits_1sigma_up
sys.exit(1)
if len(masses) != len(xs_exp_limits_2sigma):
print "** ERROR: ** Could not find expected 2 sigma down limit for m =", int(mass), "GeV. Aborting."
sys.exit(1)
if len(masses) != len(xs_exp_limits_2sigma_up):
print "** ERROR: ** Could not find expected 2 sigma up limit for m =", int(mass), "GeV. Aborting."
sys.exit(1)
if args.method == 'Asymptotic' or args.method == 'HybridNewGrid':
# complete the expected limit arrays
for i in range(0,len(masses)):
masses_exp.append( masses[len(masses)-i-1] )
xs_exp_limits_1sigma.append( xs_exp_limits_1sigma_up[len(masses)-i-1] )
xs_exp_limits_2sigma.append( xs_exp_limits_2sigma_up[len(masses)-i-1] )
if args.printResults:
print "masses =", masses.tolist()
print "xs_obs_limits =", xs_obs_limits.tolist()
print "xs_exp_limits =", xs_exp_limits.tolist()
print ""
print "masses_exp =", masses_exp.tolist()
print "xs_exp_limits_1sigma =", xs_exp_limits_1sigma.tolist()
print "xs_exp_limits_2sigma =", xs_exp_limits_2sigma.tolist()
gROOT.SetBatch(kTRUE);
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
gStyle.SetTitleFont(42, "XYZ")
gStyle.SetTitleSize(0.05, "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()
# theory curves: gg
massesS8 = array('d', [1000.0,1100.0,1200.0,1300.0,1400.0,1500.0,1600.0,1700.0,1800.0,1900.0,2000.0,2100.0,2200.0,2300.0,2400.0,2500.0,2600.0,2700.0,2800.0,2900.0,3000.0,3100.0,3200.0,3300.0,3400.0,3500.0,3600.0,3700.0,3800.0,3900.0,4000.0,4100.0,4200.0,4300.0,4400.0,4500.0,4600.0,4700.0,4800.0,4900.0,5000.0,5100.0,5200.0,5300.0,5400.0,5500.0,5600.0,5700.0,5800.0,5900.0,6000.0])
xsS8 = array('d', [5.46E+02,3.12E+02,1.85E+02,1.12E+02,7.19E+01,4.59E+01,3.02E+01,2.01E+01,1.37E+01,9.46E+00,6.55E+00,4.64E+00,3.27E+00,2.36E+00,1.70E+00,1.24E+00,9.11E-01,6.69E-01,4.97E-01,3.71E-01,2.78E-01,2.07E-01,1.55E-01,1.19E-01,9.26E-02,7.08E-02,5.43E-02,4.15E-02,3.22E-02,2.50E-02,1.92E-02,1.51E-02,1.19E-02,9.25E-03,7.35E-03,5.86E-03,4.53E-03,3.66E-03,2.91E-03,2.33E-03,1.86E-03,1.45E-03,1.12E-03,8.75E-04,6.90E-04,5.55E-04,4.47E-04,3.63E-04,2.92E-04,2.37E-04,1.97E-04])
graph_xsS8 = TGraph(len(massesS8),massesS8,xsS8)
graph_xsS8.SetLineWidth(3)
graph_xsS8.SetLineStyle(8)
graph_xsS8.SetLineColor(6)
# theory curves: qg
massesString = array('d', [1000.0,1100.0,1200.0,1300.0,1400.0,1500.0,1600.0,1700.0,1800.0,1900.0,2000.0,2100.0,2200.0,2300.0,2400.0,2500.0,2600.0,2700.0,2800.0,2900.0,3000.0,3100.0,3200.0,3300.0,3400.0,3500.0,3600.0,3700.0,3800.0,3900.0,4000.0,4100.0,4200.0,4300.0,4400.0,4500.0,4600.0,4700.0,4800.0,4900.0,5000.0,5100.0,5200.0,5300.0,5400.0,5500.0,5600.0,5700.0,5800.0,5900.0,6000.0,6100.0,6200.0,6300.0,6400.0,6500.0,6600.0,6700.0,6800.0,6900.0,7000.0,7100.0,7200.0,7300.0,7400.0,7500.0,7600.0,7700.0,7800.0,7900.0,8000.0,8100.0,8200.0,8300.0,8400.0,8500.0,8600.0,8700.0,8800.0,8900.0,9000.0,9100.,9200.,9300.,9400.,9500.,9600.,9700.,9800.,9900.,10000.])
xsString = array('d', [8316.184311558545,5312.93137758767,3435.0309937336524,2304.4139502741305,1569.8115447896687,1090.9516635659693,770.901859690924,551.9206062572061,399.69535383507633,293.77957451762086,218.15126842827823,162.87634729465125,123.17685479653694,93.63530805932386,71.53697229809124,55.37491301647483,42.75271508357369,33.36378355470234,26.06619302090876,20.311817606835643,16.1180931789545,12.768644973921226,10.142660425967444,8.057990848043234,6.400465846290908,5.115134438331436,4.132099789492928,3.3193854239538734,2.6581204529344302,2.157554604919995,1.7505176068913348,1.4049155245498584,1.140055677916783,0.9253251132104159,0.7522038169131606,0.6119747371392215,0.49612321727328523,0.40492020959456737,0.33091999402250655,0.27017917021492555,0.2201693919322846,0.17830700070267996,0.14564253802358157,0.11940534430331146,0.09694948234356839,0.0793065371847468,0.06446186373361917,0.05282660618352478,
0.0428516302310620888,0.0348997638039910363,0.0283334766442618227,0.0231416918363592127,0.0187417921340763783,0.0153501307395115115,0.0124396534127133717,0.0100542205744949455,0.0081744954858627415,0.0066338099362915941,0.0053365711503318145,0.00430912459914657443,0.00346381039244064343,0.00278602671711227174,0.00225154342228859257,0.0018082930150063248,0.00143929440338502119,0.0011581373956044489,0.00091869589873893118,0.00073410823691329855,0.00058669382997948734,0.0004661568745858897,0.000368716655469570365,0.000293168485206959169,0.000230224535021638668,0.000182317101888465142,0.000143263359883433282,0.000112630538527214965,0.000088189175598406759,0.000068708474367442343,0.000053931726669273556,0.0000416417855733682702,0.0000326529676755488658,0.0000254365480426201587,0.0000198410151166864761,0.0000154034425617473576,0.0000119095554601641413,9.2537574320108232e-6,7.2155417437856749e-6,5.6130924422251982e-6,4.36634755605624901e-6,3.39717456406994868e-6,2.6766018046173896e-6])
massesQstar = array('d', [1000.0,1100.0,1200.0,1300.0,1400.0,1500.0,1600.0,1700.0,1800.0,1900.0,2000.0,2100.0,2200.0,2300.0,2400.0,2500.0,2600.0,2700.0,2800.0,2900.0,3000.0,3100.0,3200.0,3300.0,3400.0,3500.0,3600.0,3700.0,3800.0,3900.0,4000.0,4100.0,4200.0,4300.0,4400.0,4500.0,4600.0,4700.0,4800.0,4900.0,5000.0,5100.0,5200.0,5300.0,5400.0,5500.0,5600.0,5700.0,5800.0,5900.0,6000.0,6100.0,6200.0,6300.0,6400.0,6500.0,6600.0,6700.0,6800.0,6900.0,7000.0,7100.0,7200.0,7300.0,7400.0,7500.0,7600.0,7700.0,7800.0,7900.0,8000.0,8100.0,8200.0,8300.0,8400.0,8500.0,8600.0,8700.0,8800.0,8900.0,9000.0])
xsQstar = array('d', [0.4101E+03,0.2620E+03,0.1721E+03,0.1157E+03,0.7934E+02,0.5540E+02,0.3928E+02,0.2823E+02,0.2054E+02,0.1510E+02,0.1121E+02,0.8390E+01,0.6328E+01,0.4807E+01,0.3674E+01,0.2824E+01,0.2182E+01,0.1694E+01,0.1320E+01,0.1033E+01,0.8116E+00,0.6395E+00,0.5054E+00,0.4006E+00,0.3182E+00,0.2534E+00,0.2022E+00,0.1616E+00,0.1294E+00,0.1038E+00,0.8333E-01,0.6700E-01,0.5392E-01,0.4344E-01,0.3503E-01,0.2827E-01,0.2283E-01,0.1844E-01,0.1490E-01,0.1205E-01,0.9743E-02,0.7880E-02,0.6373E-02,0.5155E-02,0.4169E-02,0.3371E-02,0.2725E-02,0.2202E-02,0.1779E-02,0.1437E-02,0.1159E-02,0.9353E-03,0.7541E-03,0.6076E-03,0.4891E-03,0.3935E-03,0.3164E-03,0.2541E-03,0.2039E-03,0.1635E-03,0.1310E-03,0.1049E-03,0.8385E-04,0.6699E-04,0.5347E-04,0.4264E-04,0.3397E-04,0.2704E-04,0.2151E-04,0.1709E-04,0.1357E-04,0.1077E-04,0.8544E-05,0.6773E-05,0.5367E-05,0.4251E-05,0.3367E-05,0.2666E-05,0.2112E-05,0.1673E-05,0.1326E-05])
graph_xsString = TGraph(len(massesString),massesString,xsString)
graph_xsString.SetLineWidth(3)
graph_xsString.SetLineStyle(8)
graph_xsString.SetLineColor(9)
graph_xsQstar = TGraph(len(massesQstar),massesQstar,xsQstar)
graph_xsQstar.SetLineWidth(3)
graph_xsQstar.SetLineStyle(2)
graph_xsQstar.SetLineColor(1)
# theory curves: qq
massesTh = array('d', [1000.0,1100.0,1200.0,1300.0,1400.0,1500.0,1600.0,1700.0,1800.0,1900.0,2000.0,2100.0,2200.0,2300.0,2400.0,2500.0,2600.0,2700.0,2800.0,2900.0,3000.0,3100.0,3200.0,3300.0,3400.0,3500.0,3600.0,3700.0,3800.0,3900.0,4000.0,4100.0,4200.0,4300.0,4400.0,4500.0,4600.0,4700.0,4800.0,4900.0,5000.0,5100.0,5200.0,5300.0,5400.0,5500.0,5600.0,5700.0,5800.0,5900.0,6000.0,6100.0,6200.0,6300.0,6400.0,6500.0,6600.0,6700.0,6800.0,6900.0,7000.0,7100.0,7200.0,7300.0,7400.0,7500.0,7600.0,7700.0,7800.0,7900.0,8000.0,8100.0,8200.0,8300.0,8400.0,8500.0,8600.0,8700.0,8800.0,8900.0,9000.0])
xsAxi = array('d', [0.1849E+03,0.1236E+03,0.8473E+02,0.5937E+02,0.4235E+02,0.3069E+02,0.2257E+02,0.1680E+02,0.1263E+02,0.9577E+01,0.7317E+01,0.5641E+01,0.4374E+01,0.3411E+01,0.2672E+01,0.2103E+01,0.1658E+01,0.1312E+01,0.1041E+01,0.8284E+00,0.6610E+00,0.5294E+00,0.4250E+00,0.3417E+00,0.2752E+00,0.2220E+00,0.1792E+00,0.1449E+00,0.1172E+00,0.9487E-01,0.7686E-01,0.6219E-01,0.5033E-01,0.4074E-01,0.3298E-01,0.2671E-01,0.2165E-01,0.1755E-01,0.1422E-01,0.1152E-01,0.9322E-02,0.7539E-02,0.6092E-02,0.4917E-02,0.3965E-02,0.3193E-02,0.2568E-02,0.2062E-02,0.1653E-02,0.1323E-02,0.1057E-02,0.8442E-03,0.6728E-03,0.5349E-03,0.4242E-03,0.3357E-03,0.2644E-03,0.2077E-03,0.1627E-03,0.1271E-03,0.9891E-04,0.7686E-04,0.5951E-04,0.4592E-04,0.3530E-04,0.2704E-04,0.2059E-04,0.1562E-04,0.1180E-04,0.8882E-05,0.6657E-05,0.4968E-05,0.3693E-05,0.2734E-05,0.2016E-05,0.1481E-05,0.1084E-05,0.7903E-06,0.5744E-06,0.4160E-06,0.3007E-06])
xsDiquark = array('d', [0.5824E+02,0.4250E+02,0.3172E+02,0.2411E+02,0.1862E+02,0.1457E+02,0.1153E+02,0.9211E+01,0.7419E+01,0.6019E+01,0.4912E+01,0.4031E+01,0.3323E+01,0.2750E+01,0.2284E+01,0.1903E+01,0.1590E+01,0.1331E+01,0.1117E+01,0.9386E+00,0.7900E+00,0.6658E+00,0.5618E+00,0.4745E+00,0.4010E+00,0.3391E+00,0.2869E+00,0.2428E+00,0.2055E+00,0.1740E+00,0.1473E+00,0.1246E+00,0.1055E+00,0.8922E-01,0.7544E-01,0.6376E-01,0.5385E-01,0.4546E-01,0.3834E-01,0.3231E-01,0.2720E-01,0.2288E-01,0.1922E-01,0.1613E-01,0.1352E-01,0.1132E-01,0.9463E-02,0.7900E-02,0.6584E-02,0.5479E-02,0.4551E-02,0.3774E-02,0.3124E-02,0.2581E-02,0.2128E-02,0.1750E-02,0.1437E-02,0.1177E-02,0.9612E-03,0.7833E-03,0.6366E-03,0.5160E-03,0.4170E-03,0.3360E-03,0.2700E-03,0.2162E-03,0.1725E-03,0.1372E-03,0.1087E-03,0.8577E-04,0.6742E-04,0.5278E-04,0.4114E-04,0.3192E-04,0.2465E-04,0.1894E-04,0.1448E-04,0.1101E-04,0.8322E-05,0.6253E-05,0.4670E-05])
xsWprime = array('d', [0.8811E+01,0.6024E+01,0.4216E+01,0.3010E+01,0.2185E+01,0.1610E+01,0.1200E+01,0.9043E+00,0.6875E+00,0.5271E+00,0.4067E+00,0.3158E+00,0.2464E+00,0.1932E+00,0.1521E+00,0.1201E+00,0.9512E-01,0.7554E-01,0.6012E-01,0.4792E-01,0.3827E-01,0.3059E-01,0.2448E-01,0.1960E-01,0.1571E-01,0.1259E-01,0.1009E-01,0.8090E-02,0.6483E-02,0.5193E-02,0.4158E-02,0.3327E-02,0.2660E-02,0.2125E-02,0.1695E-02,0.1351E-02,0.1075E-02,0.8546E-03,0.6781E-03,0.5372E-03,0.4248E-03,0.3353E-03,0.2642E-03,0.2077E-03,0.1629E-03,0.1275E-03,0.9957E-04,0.7757E-04,0.6027E-04,0.4670E-04,0.3610E-04,0.2783E-04,0.2140E-04,0.1641E-04,0.1254E-04,0.9561E-05,0.7269E-05,0.5510E-05,0.4167E-05,0.3143E-05,0.2364E-05,0.1774E-05,0.1329E-05,0.9931E-06,0.7411E-06,0.5523E-06,0.4108E-06,0.3055E-06,0.2271E-06,0.1687E-06,0.1254E-06,0.9327E-07,0.6945E-07,0.5177E-07,0.3863E-07,0.2888E-07,0.2162E-07,0.1622E-07,0.1218E-07,0.9156E-08,0.6893E-08])
xsZprime = array('d', [0.5027E+01,0.3398E+01,0.2353E+01,0.1663E+01,0.1196E+01,0.8729E+00,0.6450E+00,0.4822E+00,0.3638E+00,0.2769E+00,0.2123E+00,0.1639E+00,0.1272E+00,0.9933E-01,0.7789E-01,0.6134E-01,0.4848E-01,0.3845E-01,0.3059E-01,0.2440E-01,0.1952E-01,0.1564E-01,0.1256E-01,0.1010E-01,0.8142E-02,0.6570E-02,0.5307E-02,0.4292E-02,0.3473E-02,0.2813E-02,0.2280E-02,0.1848E-02,0.1499E-02,0.1216E-02,0.9864E-03,0.8002E-03,0.6490E-03,0.5262E-03,0.4264E-03,0.3453E-03,0.2795E-03,0.2260E-03,0.1826E-03,0.1474E-03,0.1188E-03,0.9566E-04,0.7690E-04,0.6173E-04,0.4947E-04,0.3957E-04,0.3159E-04,0.2516E-04,0.2001E-04,0.1587E-04,0.1255E-04,0.9906E-05,0.7795E-05,0.6116E-05,0.4785E-05,0.3731E-05,0.2900E-05,0.2247E-05,0.1734E-05,0.1334E-05,0.1022E-05,0.7804E-06,0.5932E-06,0.4492E-06,0.3388E-06,0.2544E-06,0.1903E-06,0.1417E-06,0.1051E-06,0.7764E-07,0.5711E-07,0.4186E-07,0.3055E-07,0.2223E-07,0.1612E-07,0.1164E-07,0.8394E-08])
graph_xsAxi = TGraph(len(massesTh),massesTh,xsAxi)
graph_xsAxi.SetLineWidth(3)
graph_xsAxi.SetLineStyle(3)
graph_xsAxi.SetLineColor(63)
graph_xsDiquark = TGraph(len(massesTh),massesTh,xsDiquark)
graph_xsDiquark.SetLineWidth(3)
graph_xsDiquark.SetLineStyle(9)
graph_xsDiquark.SetLineColor(8)
graph_xsWprime = TGraph(len(massesTh),massesTh,xsWprime)
graph_xsWprime.SetLineWidth(3)
graph_xsWprime.SetLineStyle(7)
graph_xsWprime.SetLineColor(46)
graph_xsZprime = TGraph(len(massesTh),massesTh,xsZprime)
graph_xsZprime.SetLineWidth(3)
graph_xsZprime.SetLineStyle(5)
graph_xsZprime.SetLineColor(38)
# limits
if args.method == "Asymptotic" or args.method == "HybridNewGrid":
graph_exp_2sigma = ( TGraph(len(masses_exp),masses_exp,xs_exp_limits_2sigma) if len(xs_exp_limits_2sigma) > 0 else TGraph(0) )
graph_exp_2sigma.SetFillColor(kYellow)
graph_exp_1sigma = ( TGraph(len(masses_exp),masses_exp,xs_exp_limits_1sigma) if len(xs_exp_limits_2sigma) > 0 else TGraph(0) )
graph_exp_1sigma.SetFillColor(kGreen+1)
graph_exp = ( TGraph(len(masses),masses,xs_exp_limits) if len(xs_exp_limits_2sigma) > 0 else TGraph(0) )
#graph_exp.SetMarkerStyle(24)
graph_exp.SetLineWidth(3)
graph_exp.SetLineStyle(2)
graph_exp.SetLineColor(4)
graph_obs = TGraph(len(masses),masses,xs_obs_limits)
graph_obs.SetMarkerStyle(20)
graph_obs.SetLineWidth(3)
#graph_obs.SetLineStyle(1)
graph_obs.SetLineColor(1)
c = TCanvas("c", "",800,800)
c.cd()
legend = TLegend(.58,.72,.90,.90)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
legend.SetTextSize(0.03)
legend.SetHeader('95% CL upper limits')
if len(xs_exp_limits_2sigma) > 0 and (args.method == "Asymptotic" or args.method == "HybridNewGrid"):
frame = graph_exp_2sigma.GetHistogram().Clone()
else:
frame = graph_obs.GetHistogram().Clone()
frame.Reset()
frame.GetXaxis().SetTitle("Resonance mass [GeV]")
frame.GetXaxis().SetTitleOffset(1.05)
if args.timesAE:
#if args.model == "ZPrime":
# frame.GetYaxis().SetTitle("#sigma #times BR(c#bar{c},b#bar{b}) #times #it{A} #times #epsilon [pb]")
#else:
frame.GetYaxis().SetTitle("#sigma #times BR(b#bar{b}) #times #it{A} #times #epsilon [pb]")
else:
#if args.model == "ZPrime":
# frame.GetYaxis().SetTitle("#sigma #times BR(c#bar{c},b#bar{b}) [pb]")
#else:
frame.GetYaxis().SetTitle("#sigma #times BR(b#bar{b}) [pb]")
frame.GetYaxis().SetTitleOffset(1.2)
if args.timesAE:
frame.GetYaxis().SetRangeUser(1e-03,1e+01)
else:
frame.GetYaxis().SetRangeUser(1e-01,5e+02)
frame.Draw("axis")
if len(xs_exp_limits_2sigma) > 0 and (args.method == "Asymptotic" or args.method == "HybridNewGrid"):
graph_exp_2sigma.GetXaxis().SetTitle("Resonance mass [GeV]")
graph_exp_2sigma.GetXaxis().SetTitleOffset(1.1)
graph_exp_2sigma.GetYaxis().SetTitle("#sigma #times #it{B} [pb]")
graph_exp_2sigma.GetYaxis().SetTitleOffset(1.1)
#graph_exp_2sigma.GetYaxis().SetRangeUser(1e-03,1e+02)
#graph_exp_2sigma.GetXaxis().SetNdivisions(1005)
graph_exp_2sigma.Draw("F")
graph_exp_1sigma.Draw("F")
graph_exp.Draw("L")
graph_obs.Draw("LP")
legend.AddEntry(graph_obs,"Observed","lp")
legend.AddEntry(graph_exp,"Expected","lp")
legend.AddEntry(graph_exp_1sigma,"#pm 1#sigma","F")
legend.AddEntry(graph_exp_2sigma,"#pm 2#sigma","F")
else:
graph_obs.GetXaxis().SetTitle("Resonance mass [GeV]")
graph_obs.GetYaxis().SetTitle("#sigma #times #it{B} [pb]")
graph_obs.GetYaxis().SetTitleOffset(1.1)
graph_obs.GetYaxis().SetRangeUser(1e-02,1e+03)
#graph_obs.GetXaxis().SetNdivisions(1005)
graph_obs.Draw("LP")
legend.AddEntry(graph_obs,"Observed","lp")
#if args.final_state == 'gg' :
# graph_xsS8.Draw("L")
#elif args.final_state == 'qg' :
# graph_xsQstar.Draw("L")
# graph_xsString.Draw("L")
#elif args.final_state == 'qq' :
# graph_xsAxi.Draw("L")
# graph_xsDiquark.Draw("L")
# graph_xsWprime.Draw("L")
# graph_xsZprime.Draw("L")
legend.Draw()
#legendTh = TLegend(.60,.72,.90,.88)
#legendTh.SetBorderSize(0)
#legendTh.SetFillColor(0)
#legendTh.SetFillStyle(0)
#legendTh.SetTextFont(42)
#legendTh.SetTextSize(0.03)
#legendTh.AddEntry(graph_xsAxi,"Axigluon/coloron","l")
#legendTh.AddEntry(graph_xsDiquark,"Scalar diquark","l")
#legendTh.AddEntry(graph_xsWprime,"W' SSM","l")
#legendTh.AddEntry(graph_xsZprime,"Z' SSM","l")
#legendTh.Draw()
# 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()
postfix = ( ('_' + args.postfix) if args.postfix != '' else '' )
if args.noSyst:
postfix += "_noSyst"
if args.freezeNuisances:
postfix += "_" + args.freezeNuisances.replace(",", "_")
if args.fitTrigger:
postfix += "_fitTrigger"
elif args.correctTrigger:
postfix += "_correctTrigger"
if args.useMCTrigger:
postfix += "_useMCTrigger"
fileName = limit_config.paths["limit_plots"] + '/xs_limit_%s_%s_%s_%s.%s'%(args.method,args.analysis, args.model + postfix, args.fit_function, args.fileFormat.lower())
if args.timesAE:
fileName = fileName.replace("xs_limit", "xsAE_limit")
c.SaveAs(fileName)
print "Plot saved to '%s'"%(fileName)
graph_obs.Print("all")
if args.saveObjects:
output_file = args.saveObjects
if args.timesAE:
output_file = output_file.replace(".root", "_timesAE.root")
f = TFile(output_file, "RECREATE")
if args.method == "Asymptotic" or args.method == "HybridNewGrid":
graph_exp_2sigma.SetName("graph_exp_2sigma")
graph_exp_2sigma.Write()
graph_exp_1sigma.SetName("graph_exp_1sigma")
graph_exp_1sigma.Write()
graph_exp.SetName("graph_exp")
graph_exp.Write()
graph_obs.SetName("graph_obs")
graph_obs.Write()
f.Close()
def fixOverlay(): gPad.RedrawAxis()
def exportPlot(self,logY=True,path="./output/pT_plots",rebin=True):
plottitle="transverse momentum of AK%i Jets (%s-Operator) in %s - %s"%(self.jetRadius,self.OpName,self.channel,self.LastCut)
canv = TCanvas(plottitle,plottitle,600,600)
#turning off the standard Statistic-Box
gStyle.SetOptStat(0)
gStyle.SetOptStat(0)
legend = TLegend(0.5,0.7,0.9,0.9)
drawOptions="Hist"
if(logY):
canv.SetLogy()
stack=THStack(plottitle,plottitle)
if(rebin):
BGHist=self.BHist.Rebin(len(self.dijetbinning)-1,"new binning",self.dijetbinning)
else:
BGHist=self.BHist
#Cosmetics:
BGHist.SetFillColor(867)
BGHist.SetLineColor(867)
BGHist.SetTitle(plottitle)
BGHist.GetXaxis().SetTitle('pT_{leading-AK8} [GeV/c^{2}]')
BGHist.GetYaxis().SetTitle('Events')
BGHist.GetXaxis().SetRangeUser(0,9000)
BGHist.GetYaxis().SetRangeUser(10**(-5),10**2)
BGHist.GetYaxis().SetTitleOffset(1.3)
legend.AddEntry(BGHist,"QCD","f")
BGHistErr=BGHist.Clone()
BGHistErr.SetFillColor(rt.kGray+2)
BGHistErr.SetFillStyle(3204)
BGHistErr.Draw('E2')
BGHist.Draw("SAME"+drawOptions)
BGHistErr.Draw('E2SAME')
stack=THStack('stack',plottitle)
# stack.Add(BGHist)
histcounter=1
#number of Parameters to plot:
n=6
med=(sets[self.OpName][0]-1)/2 -1
for i in range(n):
index= i * med/n
if(rebin):
hist=self.SHists[index].Rebin(len(self.dijetbinning)-1,"new binning",self.dijetbinning)
else:
hist=self.SHists[index]
# hist.Add(BGHist)
hist.SetLineColor(histcounter)
hist.SetMarkerStyle(8)
hist.SetMarkerSize(0.6)
hist.SetMarkerColor(histcounter)
# hist.Draw("SAME"+drawOptions)
stack.Add(hist)
legend.AddEntry(hist,"%sjj (%s=%.1fTeV^{-4})"%(self.channel,self.OpName,self.getPoint(index)))#maybe to -6?
histcounter+=1
canv.SetTitle(plottitle)
# stack.Draw('nostack'+drawOptions)
# stack.GetXaxis().SetRangeUser(0,9000)
# stack.Draw('nostack'+drawOptions)
# stack.Draw('nostackep'+SAME)
# stack.GetXaxis().SetRangeUser(0,9000)
stack.Draw('nostackep'+'SAME')
legend.Draw()
gPad.RedrawAxis()
canv.Update()
canv.Print("%s/%s_AK%i_%s.eps"%(path,self.channel,self.jetRadius,self.OpName))
del stack
def plotter(plotdir,
plot,
xTitle,
logY,
channels=['VV'],
includeData=False,
scaleSignal=0,
UserRange=[None, None, None, None],
initPath=''):
# channelTex={'WPWP':'W^{+}W^{+}', 'WPWM':'W^{+}W^{-}','WMWM':'W^{-}W^{-}','WPZ':'W^{+}Z','WMZ':'W^{-}Z','ZZ':'ZZ'}
channelTex = {'ZZ': 'ZZ'}
# plotstyle=[(1,1),(1,2),(2,1),(2,2),(4,1),(4,2)]
plotstyle = [(1, 1)]
# 0 1 2 3 4 5 6
# Backgrounds=['QCD', 'WJetsToQQ_HT600ToInf', 'ZJetsToQQ_HT600ToInf', 'TT', 'WW', 'WZ', 'ZZ']
# BGColors= [rt.kAzure+7, rt.kRed-4, rt.kOrange-2, rt.kGreen+2, rt.kOrange+7, rt.kBlue+1, rt.kMagenta+2]
# BGTeX= ['QCD', 'W+JetsToQQ', 'Z+JetsToQQ', 'TTbar'], 'WW', 'WZ', 'ZZ']
#stackOrder= [4,5,6,2,1,3,0]
Backgrounds = [
'QCD'
] # 'WJetsToQQ_HT600ToInf', 'ZJetsToQQ_HT600ToInf', 'ZZ'] 'TT', 'WW', 'WZ', 'ZZ']
BGColors = [
rt.kAzure + 7
] #, rt.kOrange-2, rt.kGreen+2], rt.kOrange+7, rt.kBlue+1, rt.kMagenta+2]
BGTeX = ['QCD'] #
stackOrder = [0]
PreSelection = [
'nocuts', 'common', 'corrections', 'cleaner', 'softdropmassCorr',
'AK4pfidfilter', 'AK8pfidfilter', 'invMAk8sel', 'detaAk8sel',
'AK8N2sel'
]
Selection = [
'preselection', 'softdropAK8sel', 'tau21sel', 'deltaR48', 'VVRegion',
'AK4N2sel', 'OpSignsel', 'detaAk4sel', 'invMAk4sel_1p0', 'Kin_AK8'
]
cutnames = [
'cleaner', 'AK8N2sel', 'invMAk8sel', 'detaAk8sel', 'softdropAK8sel',
'tau21sel', 'AK4cleaner', 'AK4N2sel', 'OpSignsel', 'detaAk4sel',
'invMAk4sel_1p0'
]
cuts = {
'cleaner':
'#splitline{p_{T-AK8} > 200 GeV, |#eta_{AK8}| < 2.5}{p_{T-AK4} > 30 GeV, |#eta_{AK4}| < 5.0}',
'AK8N2sel': 'N_{AK8} #geq 2',
'invMAk8sel': 'M_{jj-AK8} > 1050 GeV',
'detaAk8sel': '|#Delta#eta_{jj-AK8}|<1.3',
'softdropAK8sel': '65 GeV <M_{SD}< 105 GeV',
'tau21sel': '0 #leq #tau_{2}/#tau_{1}<0.45',
# 'AK4cleaner':'p_{T-AK4} > 30 GeV, |#eta_{AK4}| < 5.0',
'AK4cleaner': '',
'AK4N2sel': 'N_{AK4} #geq 2',
'OpSignsel': '#eta_{1-AK4} #eta_{2-AK4} < 0',
'detaAk4sel': '|#Delta#eta_{jj-AK4}| > 3.0',
'invMAk4sel_1p0': 'M_{jj-AK4} > 1.0 TeV'
}
VV = ('VV' in channels)
seperate = (not VV)
if VV:
#channels=["WPWP","WPWM","WMWM","WPZ","WMZ","ZZ"]
channels = ["ZZ"]
plottitle = plotdir + '_' + plot
lumi = 36.814
xLabelSize = 18.
yLabelSize = 18.
xTitleSize = 20.
yTitleSize = 22.
xTitleOffset = 4.
yTitleOffset = 1.3
printout = False
Portrait = True
cutname = False
ratio = includeData
if ('highbin' in plot):
binning = 'dijetbinning'
else:
binning = 'default'
if (Portrait):
canvX = 600
canvY = 600
else:
canvX = 900
canvY = 675
if (plotdir in PreSelection):
region = 'PreSelection'
initPath = ''
referenceHistPath = 'detaAk8sel/N_pv'
if (PreSelection.index(plotdir) < 4 and ('softdrop' in plot)):
return 'SofdropMass not filled yet!'
else:
region = 'SignalRegion'
referenceHistPath = 'tau21sel/N_pv'
referenceHistPath = plotdir + '/' + plot
if (initPath == ''):
path = '/nfs/dust/cms/user/loemkerj/bachelor/CMSSW_10_2_16/src/UHH2/aQGCVVjjhadronic/%s' % region
else:
path = initPath
outputPath = path.replace(
'/nfs/dust/cms/user/loemkerj/bachelor/CMSSW_10_2_16/src/UHH2/aQGCVVjjhadronic/SignalRegion',
'plots/')
if (plotdir in PreSelection):
CutNumber = PreSelection.index(plotdir)
else:
CutNumber = Selection.index(plotdir)
outputPath = outputPath + '/%02i_%s' % (CutNumber, plotdir) + '/'
if (printout):
print('InputPath:', path)
print('OutputPath:', outputPath)
#check if OutputPath exists - and if not create it!
if not os.path.exists(outputPath):
os.makedirs(outputPath)
# path='/home/albrec/Master/signal/'
scaleVV = (scaleSignal != 0)
VVScale = scaleSignal
if (UserRange[2] == None or UserRange[3] == None):
YRangeUser = False
Ymin = UserRange[2]
Ymax = UserRange[3]
else:
YRangeUser = True
Ymin = UserRange[2]
Ymax = UserRange[3]
if (UserRange[0] == None or UserRange[1] == None):
XRangeUser = False
Xmin = UserRange[0]
Xmax = UserRange[1]
else:
XRangeUser = True
Xmin = UserRange[0]
Xmax = UserRange[1]
# YRangeUser=False
# Ymin=0.11
# Ymax=9*10**3
# XRangeUser=False
# Xmin=0
# Xmax=6000.
gROOT.ProcessLine("gErrorIgnoreLevel = 2001;")
SFiles = []
for i in range(len(channels)):
SFiles.append(
TFile(
path +
"/uhh2.AnalysisModuleRunner.MC.MC_aQGC_%sjj_hadronic_2016v3.root"
% channels[i]))
#uhh2.AnalysisModuleRunner.MC.MC_aQGC_ZZjj_hadronic_2016v3.root
##Open Files to get BackgroundHist:
BFiles = []
for i in range(len(Backgrounds)):
BFiles.append(
TFile(path +
"/uhh2.AnalysisModuleRunner.MC.MC_%s.root" % Backgrounds[i]))
#Open File to get DataHist:
# DataFile = TFile(path+"/uhh2.AnalysisModuleRunner.Data.DATA.root")
#gROOT.ProcessLine( "gErrorIgnoreLevel = 0;")
if (includeData == True):
#calculate QCDscale with Integrals from the following Histogram:
# referenceHistPath = 'tau21sel/N_AK4'
# referenceHistPath = 'detaAk8sel/N_pv'
# referenceHistPath = 'tau21sel/met_pt_over_sumptAK8_2'
QCDscale = float(DataFile.Get(referenceHistPath).Integral())
QCDNorm = 1
for i in range(len(BFiles)):
if ('QCD' in BFiles[i].GetName()):
QCDNorm = float(BFiles[i].Get(referenceHistPath).Integral())
else:
QCDscale -= float(BFiles[i].Get(referenceHistPath).Integral())
QCDscale /= QCDNorm
else:
QCDscale = 1.0
if (printout):
print('using QCDscale:', QCDscale)
SHists = []
for i in range(len(channels)):
SHists.append(SFiles[i].Get(plotdir + '/' + plot))
BHists = []
for i in range(len(BFiles)):
BHists.append(BFiles[i].Get(plotdir + '/' + plot))
if ('QCD' in BFiles[i].GetName()):
BHists[-1].Scale(QCDscale)
#if(includeData):
# DataHist=DataFile.Get(plotdir+'/'+plot)
if (binning == 'dijetbinning'):
fitbinning = array('d')
binwidth = 200
NBins = (14000 / binwidth) - ((1040 / binwidth) + 1)
NBins = int(NBins)
for i in range(NBins + 1):
fitbinning.append(1050 + i * binwidth)
for i in range(len(channels)):
SHists[i] = SHists[i].Rebin(NBins, "new binning", fitbinning)
for i in range(len(Backgrounds)):
BHists[i] = BHists[i].Rebin(NBins, "new binning", fitbinning)
if (includeData):
DataHist = DataHist.Rebin(NBins, "new binning", fitbinning)
canv = TCanvas(plottitle, plottitle, canvX, canvY)
yplot = 0.7
yratio = 0.3
ymax = 1.0
xmax = 1.0
xmin = 0.0
if (ratio):
plotpad = TPad("plotpad", "Plot", xmin, ymax - yplot, xmax, ymax)
ratiopad = TPad("ratiopad", "Ratio", xmin, ymax - yplot - yratio, xmax,
ymax - yplot)
else:
plotpad = TPad("plotpad", "Plot", xmin, ymax - yplot - yratio, xmax,
ymax)
plotpad.SetTopMargin(0.08)
plotpad.SetLeftMargin(0.1)
plotpad.SetRightMargin(0.05)
plotpad.SetTicks()
plotpad.Draw()
if (ratio):
plotpad.SetBottomMargin(0.016)
ratiopad.SetTopMargin(0.016)
ratiopad.SetBottomMargin(0.35)
ratiopad.SetLeftMargin(0.1)
ratiopad.SetRightMargin(0.05)
ratiopad.SetTicks()
ratiopad.Draw()
else:
plotpad.SetBottomMargin(0.1)
if (logY):
plotpad.SetLogy()
canv.SetLogy()
if ('-logX' in xTitle):
plotpad.SetLogx()
if (ratio):
ratiopad.SetLogx()
canv.SetLogx()
drawOptions = "HE"
stack = THStack(plottitle, plottitle)
BHist = THStack(plottitle, plottitle)
# for i in range(len(Backgrounds)):
for i in stackOrder:
# BHists[i].SetFillColor(BGColors[i])
BHists[i].SetLineColor(BGColors[i])
BHist.Add(BHists[i], 'Hist')
BHist.SetTitle(plottitle)
BHistErr = BHists[0].Clone()
for i in range(1, len(Backgrounds)):
BHistErr.Add(BHists[i])
BHistErr.SetFillStyle(3204)
BHistErr.SetFillColor(rt.kGray + 2)
BHistErr.SetLineColor(1)
BGMax = BHist.GetMaximum()
SIGMax = 0
#if(VV): #new
# SIGMax=VVsum.GetMaximum()
#else:
for i in range(len(channels)):
tmpmax = SHists[i].GetMaximum()
if (tmpmax > SIGMax):
SIGMax = tmpmax
if (scaleVV):
SIGMax = SIGMax * VVScale
if (logY):
MAX = 0.9 * float(10**(magnitude(max(BGMax, SIGMax)) + 1))
MIN = float(10**(magnitude(max(BGMax, SIGMax)) - 5))
MIN += float(10**(magnitude(MIN)))
legendMIN = math.log(max(BGMax, SIGMax)) / math.log(MAX)
else:
MAX = (1.0 / 0.8) * max(BGMax, SIGMax)
legendMIN = 0.7
MIN = 0.
legendMIN = (legendMIN * 0.7) + 0.3 - 0.016
legend = TLegend(0.5, 0.75, 0.85, 0.89)
legend.SetFillStyle(0)
legend.SetTextSize(0.02)
legend.SetMargin(0.4)
legend.SetNColumns(2)
legend.SetColumnSeparation(0.3)
if (includeData):
DataHist.SetMarkerStyle(8)
DataHist.SetLineColor(1)
DataHist.SetTitle(plottitle)
if VV:
for i in range(len(channels)):
if (i == 0):
VVsum = SHists[i].Clone()
else:
VVsum.Add(SHists[i])
legentry = 'VVjj'
if (scaleVV):
VVsum.Scale(VVScale)
legentry += ' *%0.f' % VVScale
VVsum.SetLineColor(1)
VVsum.SetLineStyle(plotstyle[0][1])
VVsum.SetLineWidth(2)
legend.AddEntry(VVsum, legentry)
else:
for i in range(len(channels)):
SHists[i].SetLineColor(plotstyle[i][0])
SHists[i].SetLineStyle(plotstyle[i][1])
SHists[i].SetLineWidth(2)
legentry = "%sjj" % channelTex[channels[i]]
if (scaleVV):
SHists[i].Scale(VVScale)
legentry += ' *%.2E' % VVScale
legend.AddEntry(SHists[i], legentry)
for i in stackOrder:
legend.AddEntry(BHists[i], BGTeX[i], "f")
legend.AddEntry(BHistErr, "stat. Uncertainty", "f")
if (includeData):
legend.AddEntry(DataHist, "Data", "lep")
canv.SetTitle(plottitle)
BHistErr.GetYaxis().SetTitle('Events')
BHistErr.GetYaxis().SetRangeUser(MIN, MAX)
BHistErr.GetYaxis().SetTitleFont(43)
BHistErr.GetYaxis().SetTitleSize(yTitleSize)
BHistErr.GetYaxis().SetTitleOffset(yTitleOffset)
BHistErr.GetYaxis().SetLabelFont(43)
BHistErr.GetYaxis().SetLabelSize(yLabelSize)
if (ratio):
BHistErr.GetXaxis().SetTitleSize(0.0)
BHistErr.GetXaxis().SetLabelSize(0.0)
else:
BHistErr.GetXaxis().SetTitle(xTitle)
BHistErr.GetXaxis().SetTitleFont(43)
BHistErr.GetXaxis().SetTitleSize(xTitleSize)
# BHistErr.GetXaxis().SetTitleOffset(xTitleOffset)
BHistErr.GetXaxis().SetTitleOffset(1.2)
BHistErr.GetXaxis().SetLabelFont(43)
BHistErr.GetXaxis().SetLabelSize(xLabelSize)
# BHistErr.GetXaxis().SetTickLength(0.08)
# BHistErr.GetXaxis().SetNdivisions(506)
if (YRangeUser):
BHistErr.GetYaxis().SetRangeUser(Ymin, Ymax)
if (XRangeUser):
BHistErr.GetXaxis().SetRangeUser(Xmin, Xmax)
plotpad.cd()
BHistErr.Draw("E2")
BHist.Draw("HistSAME")
BHistErr.Draw("E2SAME")
if (VV):
VVsum.Draw("SAME" + drawOptions)
elif ('-noSig' not in xTitle):
for i in range(len(channels)):
SHists[i].Draw("SAME" + drawOptions)
if (includeData):
DataHist.Draw("APE1SAME")
plotpad.RedrawAxis()
if (ratio):
ratiopad.cd()
if (includeData):
ratioHist = DataHist.Clone()
else:
ratioHist = BHistErr.Clone()
ratioHist.SetLineColor(rt.kBlack)
# ratioHist.Sumw2()
ratioHist.SetStats(0)
ratioHist.Divide(BHistErr)
ratioHist.SetMarkerStyle(21)
ratioHist.SetMarkerSize(0.7)
#Yaxis
ratioHist.GetYaxis().SetRangeUser(0.3, 1.7)
ratioHist.GetYaxis().SetTitle("Data/BG")
ratioHist.GetYaxis().CenterTitle()
ratioHist.GetYaxis().SetTitleFont(43)
ratioHist.GetYaxis().SetTitleSize(yTitleSize)
ratioHist.GetYaxis().SetTitleOffset(yTitleOffset)
ratioHist.GetYaxis().SetLabelFont(43)
ratioHist.GetYaxis().SetLabelSize(yLabelSize)
ratioHist.GetYaxis().SetNdivisions(506)
#Xaxis
ratioHist.GetXaxis().SetTitle(xTitle)
ratioHist.GetXaxis().SetTitleFont(43)
ratioHist.GetXaxis().SetTitleSize(xTitleSize)
ratioHist.GetXaxis().SetTitleOffset(xTitleOffset)
ratioHist.GetXaxis().SetLabelFont(43)
ratioHist.GetXaxis().SetLabelSize(xLabelSize)
ratioHist.GetXaxis().SetTickLength(0.08)
ratioHist.GetXaxis().SetNdivisions(506)
# if(YRangeUser):
# ratioHist.GetYaxis().SetRangeUser(Ymin,Ymax)
if (XRangeUser):
ratioHist.GetXaxis().SetRangeUser(Xmin, Xmax)
ratioXMin = Xmin
ratioXMax = Xmax
else:
ratioXMin = ratioHist.GetXaxis().GetXmin()
ratioXMax = ratioHist.GetXaxis().GetXmax()
ratioHist.Draw("ep")
zeropercent = TLine(ratioXMin, 1, ratioXMax, 1)
zeropercent.Draw()
plus10percent = TLine(ratioXMin, 1.1, ratioXMax, 1.1)
plus10percent.SetLineStyle(rt.kDashed)
plus10percent.Draw()
minus10percent = TLine(ratioXMin, 0.9, ratioXMax, 0.9)
minus10percent.SetLineStyle(rt.kDashed)
minus10percent.Draw()
canv.cd()
gPad.RedrawAxis()
legend.Draw()
latex = TLatex()
latex.SetNDC(kTRUE)
latex.SetTextSize(20)
latex.DrawLatex(0.69, 0.953, "%.2f fb^{-1} (13 TeV)" % lumi)
latex.DrawLatex(0.1, 0.953, "private work")
lastcut = 'nocuts'
for cut in cutnames:
if cut in plotdir:
lastcut = cut
if (not (lastcut == 'nocuts') and cutname):
# latex.SetTextSize(0.03)
latex.SetTextSize(15)
for l in range(cutnames.index(lastcut) + 1):
latex.DrawLatex(0.12, 0.8 - l * 0.04, cuts[cutnames[l]])
canv.Update()
canv.Print(outputPath + '/%s_%s.pdf' % (plotdir, plot))
#prevents memory leak in Canvas Creation/Deletion
#see: https://root.cern.ch/root/roottalk/roottalk04/2484.html
gSystem.ProcessEvents()
if (ratio):
del ratiopad
del plotpad, canv
# gc.collect()
return 'done!'
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 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('analysis', type=str, help='Analysis name')
parser.add_argument('model', type=str, help='Model name')
parser.add_argument("-M",
"--method",
dest="method",
choices=['ProfileLikelihood', 'HybridNew', 'theta'],
default='ProfileLikelihood',
help="Method to calculate upper limits",
metavar="METHOD")
parser.add_argument(
'--fit_function',
type=str,
default="dijet4",
help="Name of fit function used for background estimate")
parser.add_argument(
"--sigRange",
dest="sigRange",
type=float,
default=2.5,
help="Significance range to plot (default: %(default)f)")
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='Preliminary',
help="Extra text on the plot (default: %(default)s)")
parser.add_argument(
"--lumi_sqrtS",
dest="lumi_sqrtS",
default='19.7 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")
parser.add_argument('--fitTrigger',
action='store_true',
help="Use trigger fit")
parser.add_argument('--correctTrigger',
action='store_true',
help="Use trigger correction")
parser.add_argument('--useMCTrigger',
action='store_true',
help="Use MC trigger emulation")
parser.add_argument("--noSyst",
action="store_true",
help="Make plots for limits without systematics")
parser.add_argument(
"--freezeNuisances",
type=str,
help="Make plots for limits with frozen nuisance parameters")
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')
significances = array('d')
p0_values = array('d')
for mass in input_masses:
masses.append(mass)
#print ">> Reading results for resonance with m = %i GeV..."%(int(mass))
log_file = open(
limit_config.get_combine_log_path(
args.analysis,
args.model,
mass,
args.fit_function,
args.method,
what="significance",
systematics=(not args.noSyst),
frozen_nps=args.freezeNuisances,
fitTrigger=args.fitTrigger,
correctTrigger=args.correctTrigger,
useMCTrigger=args.useMCTrigger), 'r')
if args.method == 'theta':
logName = logName.replace('significance_', '')
# read the log file
for line in log_file:
if args.method == 'theta':
if re.search("^{'signal': {'Z':", line):
significances.append(
float(line.split()[-1].lstrip('[').rstrip('}').rstrip(
']')))
else:
if re.search("^Significance:", line):
significances.append(float(line.split()[1]))
elif re.search("^Null p-value:", line):
p0_values.append(float(line.split()[2]))
if len(masses) != len(significances):
print "** ERROR: ** Could not find significance for m =", int(
mass), "GeV. Aborting."
sys.exit(1)
if len(masses) != len(p0_values):
print "** ERROR: ** Could not find p0 value for m =", int(
mass), "GeV. Aborting."
sys.exit(1)
# Allow only positive fluctuations
if significances[-1] < 0:
significances[-1] = 0.
if args.printResults:
print "masses =", masses.tolist()
print "significances =", significances.tolist()
print "p0_values =", p0_values.tolist()
# import ROOT stuff
from ROOT import kTRUE, kFALSE, gROOT, gStyle, gPad, TGraph, 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 = TGraph(len(masses), masses, significances)
graph_sig.GetXaxis().SetTitle("Resonance mass [GeV]")
graph_sig.GetYaxis().SetTitle("p_0")
graph_sig.GetYaxis().SetTitleOffset(1.2)
graph_sig.GetYaxis().SetRangeUser(0., args.sigRange)
graph_sig.SetLineWidth(2)
graph_sig.SetLineColor(kRed)
graph_sig.SetMarkerStyle(21)
graph_sig.SetMarkerSize(1)
graph_sig.SetMarkerColor(kBlue)
c = TCanvas("c", "", 800, 800)
c.cd()
graph_sig.Draw("ALP")
# 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.SetGridx()
c.SetGridy()
fileName = limit_config.paths[
"limit_plots"] + '/significance_%s_%s_%s_%s.%s' % (
args.method, args.analysis, args.model + args.postfix,
args.fit_function, args.fileFormat.lower())
c.SaveAs(fileName)
#print "Significance plot saved to '%s'"%(fileName)
graph_p0 = TGraph(len(masses), masses, p0_values)
graph_p0.GetXaxis().SetTitle("Resonance mass [GeV]")
graph_p0.GetYaxis().SetTitle("p_{0} (local)")
graph_p0.GetYaxis().SetTitleOffset(1.2)
graph_p0.GetYaxis().SetRangeUser(1.e-3, 1.)
graph_p0.SetLineWidth(2)
graph_p0.SetLineColor(kRed)
graph_p0.SetMarkerStyle(21)
graph_p0.SetMarkerSize(1)
graph_p0.SetMarkerColor(kBlue)
c_p0 = TCanvas("c_p0", "", 800, 800)
c_p0.SetLogy()
c_p0.cd()
graph_p0.Draw("ALP")
# draw the lumi text on the canvas
# 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)
#Root.CMSLabel(0.2, 0.2, "Internal", 1, 0.65)
gPad.RedrawAxis()
c_p0.SetGridx()
c_p0.SetGridy()
fileName = limit_config.paths["limit_plots"] + '/p0_%s_%s_%s_%s.%s' % (
args.method, args.analysis, args.model + args.postfix,
args.fit_function, args.fileFormat.lower())
c_p0.SaveAs(fileName)
print "p0 plot saved to '%s'" % (fileName)
def makePlot(hName, CR, isQCDMC, isData, isLog, isRatio):
'''
We first draw stacked histograms then data then unc band.
The ratio of data and background is drawn next in a separate
pad.
'''
#Divide canvas for the ratio plot
canvas = TCanvas()
if isRatio and isData:
canvas.Divide(1,2)
canvas.cd(1)
gPad.SetRightMargin(0.03);
gPad.SetPad(xPadRange[0],yPadRange[2],xPadRange[1],yPadRange[3]);
gPad.SetTopMargin(0.09);
gPad.SetBottomMargin(padGap);
#gPad.SetTickx(0);
gPad.RedrawAxis();
if isLog:
gPad.SetLogy(True);
#Get nominal histograms
#dataHist, bkgHists, qcdMCHist, qcdDDHist = getBaseHists(fileDict, hName, CR)
dataHist, bkgHists, qcdMCHist = getBaseHists(fileDict, hName, CR)
hSumOtherBkg = bkgHists[0].Clone("hSumOtherBkg")
hSumOtherBkg.Reset()
hAllBkgs = bkgHists
for bkgHist in bkgHists:
hColor = Samples[bkgHist.GetName().split("_")[0]][1]
hSumOtherBkg.Add(bkgHist)
hSumAllBkg = hSumOtherBkg.Clone("hSumAllBkg")
if isQCDMC:
hSumAllBkg.Add(qcdMCHist[0])
hAllBkgs.append(qcdMCHist[0])
#else:
# hSumAllBkg.Add(qcdDDHist[0])
# hAllBkgs.append(qcdDDHist[0])
#Stack nominal hists
xTitle = histograms[hName][0]
yTitle = histograms[hName][1]
hStack = THStack(hName,hName)
hForStack = sortHists(hAllBkgs, False)
for h in hForStack:
sampleName = h.GetName().split("_")[0]
decoHist(h, xTitle, yTitle, Samples[sampleName][1])
hStack.Add(h)
hStack.SetMinimum(1.0)
if isLog:
hStack.SetMaximum(100*hStack.GetMaximum())
else:
hStack.SetMaximum(1.3*hStack.GetMaximum())
hStack.Draw("HIST")
decoHistStack(hStack, xTitle, yTitle)
#Get histograms for the difference between nominal and syst up/down
hSumOtherBkgUps, hQCDUps = getSystHists(fileDict, hName, CR, "Up")
hSumOtherBkgDowns, hQCDDowns = getSystHists(fileDict, hName, CR, "Down")
hDiffUp = hSumOtherBkg.Clone("hDiffUp")
hDiffUp.Reset()
hDiffDown = hSumOtherBkg.Clone("hDiffDown")
hDiffDown.Reset()
for hUp in hSumOtherBkgUps:
hDiff = hUp.Clone("hDiff")
hDiff.Add(hSumOtherBkg, -1)
hDiffUp.Add(hDiff)
print "hDiffUp = ", hDiffUp.Integral()
for hDown in hSumOtherBkgDowns:
hDiff = hSumOtherBkg.Clone("hDiff")
hDiff.Add(hDown, -1)
hDiffDown.Add(hDiff)
if isQCDMC:
for hUp in hQCDUps:
hDiff = hUp.Clone("hDiff")
hDiff.Add(qcdMCHist[0], -1)
hDiffUp.Add(hDiff)
for hDown in hQCDDowns:
hDiff = qcdMCHist[0].Clone("hDiff")
hDiff.Add(hDown, -1)
hDiffDown.Add(hDiff)
#Get unc band for the top plot
uncGraphTop = getUncBand(hSumAllBkg, hDiffUp, hDiffDown,False)
uncGraphTop.SetFillColor(2);
uncGraphTop.SetFillStyle(3001);
uncGraphTop.Draw(" E2 same ");
#Draw data
decoHist(dataHist[0], xTitle, yTitle, Samples["Data"][1])
dataHist[0].SetMarkerStyle(20)
if isData:
dataHist[0].Draw("EPsame")
#Draw legend
hForLegend = sortHists(hAllBkgs, True)
plotLegend = getLegend(dataHist, hForLegend, uncGraphTop)
plotLegend.Draw()
#Draw CMS, Lumi, channel
if channel in ["mu", "Mu", "m"]:
chName = "#mu + jets"
else:
chName = "e + jets"
crName = formatCRString(CR)
if CR=="":
chName = "%s, SR"%chName
else:
chName = "%s, CR"%chName
chCRName = "#splitline{#font[42]{%s}}{#font[42]{%s}}"%(chName, crName)
extraText = "#splitline{Preliminary}{%s}"%chCRName
#CMS_lumi(canvas, iPeriod, iPosX, extraText)
lumi_13TeV = "35.9 fb^{-1}"
if "16" in year:
lumi_13TeV = "35.9 fb^{-1} (2016)"
if "17" in year:
lumi_13TeV = "41.5 fb^{-1} (2017)"
if "18" in year:
lumi_13TeV = "59.7 fb^{-1} (2018)"
CMS_lumi(lumi_13TeV, canvas, iPeriod, iPosX, extraText)
#Draw the ratio of data and all background
if isData and isRatio:
canvas.cd(2)
gPad.SetTopMargin(padGap);
gPad.SetBottomMargin(0.30);
gPad.SetRightMargin(0.03);
#gPad.SetTickx(0);
gPad.SetPad(xPadRange[0],yPadRange[0],xPadRange[1],yPadRange[2]);
gPad.RedrawAxis();
hRatio = dataHist[0].Clone("hRatio")
hRatio.Divide(hSumAllBkg)
decoHistRatio(hRatio, xTitle, "Obs./Exp.", 1)
hRatio.Draw()
uncGraphRatio = getUncBand(hSumAllBkg, hDiffUp, hDiffDown,True)
uncGraphRatio.SetFillColor(2);
uncGraphRatio.SetFillStyle(3001);
uncGraphRatio.Draw("E2same");
baseLine = TF1("baseLine","1", -100, 2000);
#baseLine.SetLineColor(kRed+1);
baseLine.SetLineColor(3);
baseLine.Draw("SAME");
hRatio.Draw("same")
#canvas.SaveAs("%s/%s.pdf"%(outPlotFullDir, hName))
canvas.SaveAs("%s/%s_%s_%s.png"%(outPlotFullDir, hName, year, channel))