def main_singleCategory(options, args):
if options.category in [30, 31]:
print 'Error! Too few AF2 stats. Not going to do it.'
return
cans = []
options.outdir = ''
if options.functions:
flist = options.functions.split(',')
options.outdir += '_'.join(flist)
elif options.family == 'official':
#flist = ['Exponential','ExpPoly2','ExpPoly3','Bern4','Bern5','Pow','Pow2','Laurent0','Laurent1','Laurent2']
flist = [
'Exponential', 'ExpPoly2', 'Bern4', 'Bern5', 'Pow', 'Pow2',
'Laurent1', 'Laurent2'
]
options.outdir += 'official_functions'
elif options.family == 'selected':
flist = [Tools.selected[Tools.categories[options.category]]]
options.outdir += 'selected_functions'
print flist
elif options.family == 'ExpPoly':
flist = ['Exponential'] + list('ExpPoly%d' % (d) for d in range(2, 4))
options.outdir += 'exppoly_family'
elif options.family == 'Laurent':
flist = list('Laurent%d' % (d) for d in range(0, 3))
options.outdir += 'Laurent_family'
elif options.family == 'PolyOverX4':
flist = ['1/x^4'] + list('poly%d/x^4' % (d) for d in range(1, 6))
options.outdir += 'PolyOverX4_family'
elif options.family == 'Bernstein':
flist = list('Bern%d' % (d) for d in range(4, 6))
options.outdir += 'Bern_family'
elif options.family == 'PowerSum':
flist = ['Pow', 'Pow2']
options.outdir += 'PowerSum_family'
else:
flist = [
'Exponential', # bad
'ExpPoly2',
'ExpPoly3',
]
options.outdir += '_'.join(flist)
options.outdir += '_c%02d_%s' % (options.category,
Tools.categories[options.category])
functions = []
Tools.PopulateFunctionList(functions, flist)
Tools.LinkFunctionsForFtest(functions)
if len(functions) == 0:
print 'Error! no functions loaded!'
import sys
sys.exit()
for f in functions:
f.SetCategory(options.category)
f.SetFileName(options.file)
f.SetSignalWS(options.signalws)
f.Initialize()
##
## Plot the data and af2 (fit bkg-only just before this.) SpuriousSignal_05_M17_ggH_1J_BSM
##
for f in functions:
f.function.fitTo(f.data, *(Tools.args_bkgonly))
cans.append(
plotfunc.RatioCanvas(
"TemplateFit_%02d_%s" %
(options.category, Tools.categories[options.category]),
"main plot", 600, 500))
functions[0].af2hist.SetMarkerSize(0)
#rebin = Tools.RebinUntilSmallErrors(functions[0].af2hist,0,Tools.lower_range,Tools.upper_range,errormax=0.3)
rebin = 5
functions[0].af2hist.Rebin(rebin)
binwidth = functions[0].af2hist.GetBinWidth(1)
bins = int((Tools.upper_range - Tools.lower_range) / float(binwidth))
functions[0].af2hist.SetTitle('#gamma#gamma MC')
functions[0].af2hist.SetLineWidth(2)
if options.family == 'selected':
functions[0].af2hist.SetLineColor(ROOT.kGray + 2)
functions[0].af2hist.SetFillColor(0)
plotfunc.AddHistogram(cans[-1], functions[0].af2hist, drawopt='')
for i, f in enumerate(functions):
f.obsVar.setBins(int(bins))
f.bins = bins
f.frame = f.obsVar.frame()
if i:
f.af2hist.Rebin(rebin)
f.af2_rebinned = ROOT.RooDataHist('af2_rebinned', '',
ROOT.RooArgList(f.obsVar), f.af2hist,
1.)
#f.af2_rebinned.plotOn(f.frame,ROOT.RooFit.Range("lower,upper"))
#f.function.plotOn(f.frame,ROOT.RooFit.NormRange("lower,upper"),ROOT.RooFit.Range("all"))
f.chisquare = Tools.GetChiSquare_ForSpuriousSignal(
f.frame, f.af2_rebinned, f.function, f.ndof)
f.pvalue_chi2 = ROOT.TMath.Prob(f.chisquare * (f.bins - 1 - f.ndof),
f.bins - 1 - f.ndof)
print 'Original chi2: %2.6f p-value: %2.6f' % (f.chisquare,
f.pvalue_chi2)
f.minNll = 0
curve = f.frame.getCurve()
curve.SetMarkerSize(0)
curve.SetLineWidth(1)
curve.SetTitle(f.name)
curve.SetLineWidth(2)
# resid = f.frame.residHist(); resid.SetMarkerSize(0)
# plotfunc.AddRatioManual(cans[-1],curve,resid,drawopt1='l',drawopt2='l')
pull = f.frame.pullHist()
pull.SetMarkerSize(0.7)
# for special
if options.family == 'selected':
color = {
'Pow': ROOT.kBlack + 0,
'Exponential': ROOT.kRed + 1,
'ExpPoly2': ROOT.kBlue + 1,
'Bern3': ROOT.kGreen + 1,
'Bern4': ROOT.kMagenta + 1,
'Bern5': ROOT.kOrange + 1,
}.get(f.name)
pull.SetMarkerColor(color)
pull.SetLineColor(color)
curve.SetLineColor(color)
curve.SetFillColor(0)
plotfunc.AddRatioManual(cans[-1],
curve,
pull,
drawopt1='l',
drawopt2='p')
f.chisquare_toy = Tools.ChiSquareToys_ForSpuriousSignal(
f, options.outdir)
if not options.family == 'selected':
plotfunc.SetColors(cans[-1])
plotfunc.FormatCanvasAxes(cans[-1])
plotfunc.SetXaxisRanges(cans[-1], Tools.lower_range, Tools.upper_range)
plotfunc.SetAxisLabels(cans[-1], 'm_{#gamma#gamma} [GeV]', 'entries',
'pull')
the_text = [
plotfunc.GetAtlasInternalText(),
plotfunc.GetSqrtsText(13) + ', ' + plotfunc.GetLuminosityText(36.1),
Tools.CategoryNames[Tools.categories[options.category]]
]
plotfunc.DrawText(cans[-1],
the_text,
0.19,
0.70,
0.59,
0.91,
totalentries=3)
plotfunc.MakeLegend(cans[-1], 0.60, 0.70, 0.90, 0.91, totalentries=3)
#plotfunc.GetTopPad(cans[-1]).GetPrimitive('legend').AddEntry(0,'^{ }background-only fit','')
#list(plotfunc.GetTopPad(cans[-1]).GetPrimitive('legend').GetListOfPrimitives())[-1].SetLabel('^{ }background-only fit')
plotfunc.SetYaxisRanges(plotfunc.GetBotPad(cans[-1]), -4, 4)
taxisfunc.AutoFixYaxis(plotfunc.GetTopPad(cans[-1]),
ignorelegend=False,
minzero=True)
for can in cans[:-1]:
plotfunc.FormatCanvasAxes(can)
anaplot.UpdateCanvases(cans, options)
if not options.batch:
raw_input('Press enter to exit')
anaplot.doSaving(options, cans)
return
def main_singleCategory(options, args):
if options.category in [19, 23]: # ggH_0J_Cen is 0
print 'Error! This category has been merged away.'
return
cans = []
options.outdir = ''
if options.analysis == 'couplings2017':
category_name = Tools.categories_couplings2017[options.category]
category_title = Tools.CategoryNames_couplings2017[category_name]
fcns['selected'] = [Tools.selected_couplings2017[category_name]]
fcns['ftest'] = ChiSquareTools.ftest[category_name]
background_label = '#gamma#gamma'
lumi = 36.1
elif options.analysis == 'ysy':
category_name = Tools.categories_ysy[options.category]
category_title = Tools.CategoryNames_ysy[category_name]
fcns['selected'] = [Tools.selected_ysy[category_name]]
fcns['ftest'] = ChiSquareTools.ftest[category_name]
background_label = 'll#gamma'
lumi = 139
else:
print('Error - do not understand analysis name %s' %
(options.analysis))
import sys
sys.exit()
family_name = {
'official': 'official_functions',
'selected': 'selected_function',
}.get(options.family, options.family + '_family')
# A list of options is provided
if options.functions:
flist = options.functions.split(',')
options.outdir += '_'.join(flist)
# Otherwise a family must be provided:
else:
flist = fcns.get(options.family)
options.outdir += family_name
options.outdir += '_c%02d_%s' % (options.category, category_name)
functions = []
FunctionsModule.PopulateFunctionList(functions, flist, options.lower,
options.upper)
ChiSquareTools.LinkFunctionsForFtest(functions)
if len(functions) == 0:
print 'Error! no functions loaded!'
import sys
sys.exit()
for f in functions:
f.SetAnalysis(options.analysis)
f.SetCategory(options.category)
f.SetFileName(options.file)
f.Initialize()
# rebin = 5
# if options.category > 16 :
# rebin = 1
# if options.category > 23 : # ttH categories
# rebin = 10
# Rebin to get 1 bin per GeV
rebin = int(functions[0].datahist.GetNbinsX() / 55)
print 'Proceeding with Data histogram:', functions[0].datahist
functions[0].datahist.Rebin(rebin)
for i, f in enumerate(functions):
if i:
f.datahist.Rebin(rebin)
f.datasb_rebinned = ROOT.RooDataHist('data_real_rebinned', '',
ROOT.RooArgList(f.obsVar),
f.datahist, 1.)
if not os.path.exists(options.outdir):
os.makedirs(options.outdir)
ftest_text = ''
cans.append(
plotfunc.RatioCanvas(
"Ftests_%02d_%s" % (options.category, category_name), "main plot",
600, 500))
functions[0].datahist.SetBinErrorOption(ROOT.TH1.kPoisson)
plotfunc.AddHistogram(cans[-1], functions[0].datahist)
##
## Ftests to sideband
##
for f in functions:
if not hasattr(f, 'ftest_function'):
continue
nbins_blind = int(f.datasb_rebinned.numEntries() - 10)
print 'Sideband numEntries:', f.datasb_rebinned.numEntries(
), 'nbins:', nbins_blind
# ndof_bins = nbins_blind-f.ndof-1
# ndof_bins_2 = nbins_blind-f.ftest_function.ndof-1
ndof_bins = nbins_blind - f.ndof
ndof_bins_2 = nbins_blind - f.ftest_function.ndof
#
# Fitting data sidebands - first function
#
print 'Fitting data sidebands'
print f.PrintParameters()
ChiSquareTools.FitForChi2_DataSidebands(f)
print f.PrintParameters()
print 'Fitting data sidebands done'
ftest_text += f.PrintParameters() + '\n'
chi2 = ROOT.GetChiSquare(f.obsVar, f.function_ext, f.datasb_rebinned,
ndof_bins)
pvalue_chi2 = ROOT.TMath.Prob(chi2 * (ndof_bins), ndof_bins)
#
# Plotting stuff - first function
#
Tools.ClearRooPlot(f.frame)
ChiSquareTools.NormalizeToSideband(f)
f.datasb_rebinned.plotOn(
f.frame, ROOT.RooFit.DataError(ROOT.RooAbsData.Poisson))
print 'about to plot'
#f.workspace.var('a1').setVal(-1.6)
print f.PrintParameters()
f.function_ext.plotOn(f.frame, *(Tools.plotOptions_sb_all))
print f.PrintParameters()
print 'about to plot done'
curve = f.frame.getCurve()
curve.SetMarkerSize(0)
curve.SetLineWidth(2)
curve.SetTitle('%s, p(#chi^{2}) = %2.1f%%' %
(f.name, pvalue_chi2 * 100.))
curve.SetLineColor(ROOT.kRed + 1)
curve.SetFillColor(0)
pull = f.frame.pullHist()
pull.SetMarkerSize(0.7)
pull.SetLineColor(ROOT.kRed + 1)
pull.SetMarkerColor(ROOT.kRed + 1)
plotfunc.AddRatioManual(cans[-1],
curve,
pull,
drawopt1='l',
drawopt2='p')
#
# Fitting data sidebands - first function
#
print 'Fitting data sidebands (other function)'
print f.ftest_function.PrintParameters()
ChiSquareTools.FitForChi2_DataSidebands(f.ftest_function)
print f.ftest_function.PrintParameters()
print 'Fitting data sidebands (other function) done'
ftest_text += f.ftest_function.PrintParameters() + '\n'
chi2_2 = ROOT.GetChiSquare(f.obsVar, f.ftest_function.function_ext,
f.datasb_rebinned, ndof_bins_2)
pvalue_chi2_2 = ROOT.TMath.Prob(chi2_2 * (ndof_bins_2), ndof_bins_2)
#
# Plotting stuff - other function
#
Tools.ClearRooPlot(f.frame)
ChiSquareTools.NormalizeToSideband(f.ftest_function)
f.datasb_rebinned.plotOn(f.frame)
f.ftest_function.function_ext.plotOn(f.frame,
*(Tools.plotOptions_sb_all))
curve = f.frame.getCurve()
curve.SetMarkerSize(0)
curve.SetLineWidth(2)
curve.SetTitle('%s, p(#chi^{2}) = %2.1f%%' %
(f.ftest_function.name, pvalue_chi2_2 * 100.))
curve.SetLineColor(ROOT.kAzure - 2)
curve.SetFillColor(0)
pull = f.frame.pullHist()
pull.SetMarkerSize(0.7)
pull.SetLineColor(ROOT.kAzure - 2)
pull.SetMarkerColor(ROOT.kAzure - 2)
plotfunc.AddRatioManual(cans[-1],
curve,
pull,
drawopt1='l',
drawopt2='p')
ftest = ChiSquareTools.GetF(chi2, chi2_2, ndof_bins, ndof_bins_2)
ftest_text += 'chi2/ndf: %2.5f\n' % (chi2)
ftest_text += 'chi2_2/ndf: %2.5f\n' % (chi2_2)
ftest_text += 'chi2: %2.5f\n' % (chi2 * ndof_bins)
ftest_text += 'chi2_2: %2.5f\n' % (chi2_2 * ndof_bins_2)
ftest_text += 'ftest: %2.5f\n' % (ftest)
#p_ftest = 1.0 - ROOT.TMath.FDistI(ftest,1,ndof_bins_2)
p_ftest = 1.0 - ROOT.TMath.FDistI(ftest, ndof_bins - ndof_bins_2,
ndof_bins_2)
ftest_text += 'p_ftest: %2.5f\n' % (p_ftest)
#
# Throw Toys
#
print 'Running toy Ftests'
fisher_dist = ChiSquareTools.ToyFtest(f, f.ftest_function, ftest,
options.outdir, options.ntoys)
print 'Running toy Ftests done'
if fisher_dist.Integral(0, 100000):
ftest_text += 'p_ftest_toys: %2.5f\n' % (
fisher_dist.Integral(fisher_dist.FindBin(ftest), 100000) /
float(fisher_dist.Integral(0, 100000)))
# chi2_lowerSideBand = ROOT.RooChi2Var("chi2_low","chi2_low",f.function_ext,f.data_realdata,ROOT.RooFit.DataError(ROOT.RooAbsData.Poisson),ROOT.RooFit.Range("lower"));
# chi2_upperSideBand = ROOT.RooChi2Var("chi2_upp","chi2_upp",f.function_ext,f.data_realdata,ROOT.RooFit.DataError(ROOT.RooAbsData.Poisson),ROOT.RooFit.Range("upper"));
# chi2_all = ROOT.RooChi2Var("chi2_all","chi2_all",f.function_ext,f.data_realdata,ROOT.RooFit.Range("lower,upper"));
# chi2_lowerSideBand = ROOT.RooChi2Var("chi2_low","chi2_low",f.function_ext,f.data_realdata,ROOT.RooFit.Range("lower"));
# chi2_upperSideBand = ROOT.RooChi2Var("chi2_upp","chi2_upp",f.function_ext,f.data_realdata,ROOT.RooFit.Range("upper"));
# print 'chi2_all .getValV()',chi2_all.getValV()
# print 'chi2_lowerSideBand.getValV()',chi2_lowerSideBand.getValV()
# print 'chi2_upperSideBand.getValV()',chi2_upperSideBand.getValV()
# print 'total:',(chi2_lowerSideBand.getValV()+chi2_upperSideBand.getValV()) / float(f.bins-1-f.ndof)
# Get the chi2 from the background-only fit
# print 'f.frame.chiSquare(1+f.ndof-10)',f.frame.chiSquare(1+f.ndof)*(f.bins-1-f.ndof)
# f.chisquare_sb = f.frame.chiSquare(1+f.ndof-10) # n-1 bins MINUS 10 BINS
# f.pvalue_chi2_sb = ROOT.TMath.Prob(f.chisquare_sb*(f.bins-1-f.ndof-10),f.bins-1-f.ndof-10)
# print 'chisquare:',f.chisquare_sb,f.pvalue_chi2_sb
continue
curve = f.frame.getCurve()
curve.SetMarkerSize(0)
curve.SetLineWidth(1)
curve.SetTitle(f.name)
curve.SetLineWidth(2)
# resid = f.frame.residHist(); resid.SetMarkerSize(0)
# plotfunc.AddRatioManual(cans[-1],curve,resid,drawopt1='l',drawopt2='l')
pull = f.frame.pullHist()
pull.SetMarkerSize(0.7)
# for special
if options.family == 'selected':
color = {
'Pow': ROOT.kBlack + 0,
'Exponential': ROOT.kRed + 1,
'ExpPoly2': ROOT.kBlue + 1,
'Bern3': ROOT.kGreen + 1,
'Bern4': ROOT.kMagenta + 1,
'Bern5': ROOT.kOrange + 1,
}.get(f.name)
pull.SetMarkerColor(color)
pull.SetLineColor(color)
curve.SetLineColor(color)
curve.SetFillColor(0)
plotfunc.AddRatioManual(cans[-1],
curve,
pull,
drawopt1='l',
drawopt2='p')
plotfunc.SetAxisLabels(cans[-1], 'm_{#gamma#gamma} [GeV]', 'entries',
'pull')
the_text = [
plotfunc.GetAtlasInternalText(),
plotfunc.GetSqrtsText(13) + ', ' + plotfunc.GetLuminosityText(36.1),
category_title,
'1-p(F_{%d%d}) = %2.1f%%' %
(functions[0].ndof, functions[0].ftest_function.ndof, p_ftest * 100)
]
plotfunc.DrawText(cans[-1],
the_text,
0.19,
0.63,
0.59,
0.91,
totalentries=4)
plotfunc.MakeLegend(cans[-1], 0.57, 0.63, 0.90, 0.91, totalentries=4)
plotfunc.SetYaxisRanges(plotfunc.GetBotPad(cans[-1]), -4, 4)
plotfunc.SetXaxisRanges(cans[-1], functions[0].lower_range,
functions[0].upper_range)
taxisfunc.AutoFixYaxis(plotfunc.GetTopPad(cans[-1]), forcemin=0.001)
print ftest_text
a = open('%s/ftests.txt' % (options.outdir), 'w')
a.write(options.file + '\n')
a.write(ftest_text + '\n')
a.close()
for can in cans:
plotfunc.FormatCanvasAxes(can)
anaplot.UpdateCanvases(cans, options)
if not options.batch:
raw_input('Press enter to exit')
anaplot.doSaving(options, cans)
return
def main(options,args) :
jj_file = ROOT.TFile(options.jj,'read')
yj_file = ROOT.TFile(options.yj,'read')
af2_file = ROOT.TFile(options.af2,'read')
outfile = ROOT.TFile('out.root','RECREATE')
cans = []
for i,c in enumerate(['Inclusive']+Tools.categories) :
if c == 'M17_VHMET_LOW' :
break
#print 'HGamEventInfoAuxDyn_m_yy_over_1000_c%d_%s_AF2'%(i+offset,c)
tmp = []
af2 = af2_file.Get('HGamEventInfoAuxDyn_m_yy_over_1000_c%d_%s_AF2' %(i+offset,c)).Clone()
yj = yj_file .Get('HGamEventInfoAuxDyn_m_yy_over_1000_c%d_%s_data'%(i+offset,c))
jj = jj_file .Get('HGamEventInfoAuxDyn_m_yy_over_1000_c%d_%s_data'%(i+offset,c))
# print c, af2.Integral(), yj.Integral(), jj.Integral()
# continue
data_blinded = af2_file.Get('HGamEventInfoAuxDyn_m_yy_over_1000_c%d_%s_data'%(i+offset,c))
higgs = af2_file.Get('HGamEventInfoAuxDyn_m_yy_over_1000_c%d_%s_Higgs'%(i+offset,c))
data_integral = integral(data_blinded,105,160)
outfile.cd()
data_blinded.Write()
higgs.Write()
af2.Sumw2(); yj.Sumw2(); jj.Sumw2();
# Get the parameters for the reweighting procedure
yj_newcans,yj_par0,yj_par1,yj_integral_factor = DoRescaleProcedure(af2,yj,'yj',i+offset,c)
jj_newcans,jj_par0,jj_par1,jj_integral_factor = DoRescaleProcedure(af2,jj,'jj',i+offset,c)
tmp += yj_newcans
tmp += jj_newcans
# Get the fractions of yy, yj, jj
fractions_file = open('fractions.txt','read')
for l,line in enumerate(fractions_file) :
if (l != i+offset) :
continue
#print l,line
yy_frac = float(line.split()[-3])/100.
yj_frac = float(line.split()[-2])/100.
jj_frac = float(line.split()[-1])/100.
fractions_file.close()
print yy_frac,yj_frac,jj_frac
# The full reweighting function
# yj jj
function = ROOT.TF1('%d_%s'%(i+offset,c),'([0]*(x-132.5)/(160-105) + [1])*[2]*[3] + ([4]*(x-132.5)/(160-105) + [5])*[6]*[7] + [8]',105,160)
function.SetParameters(yj_par0,yj_par1,yj_frac,yj_integral_factor,jj_par0,jj_par1,jj_frac,jj_integral_factor,yy_frac)
data_integral = float(integral(data_blinded,105,120) + integral(data_blinded,130,160) )
af2_integral = float(integral(af2,105,120)+integral(af2,130,160) )
if af2_integral == 0 :
print 'ERROR! AF2 Integral for category %s is 0!'%(c)
return
rebin = 10
# yy for stack
af2_yy_stack = af2.Clone(); af2_yy_stack.SetName(af2_yy_stack.GetName()+'_yy_forStack')
af2_yy_stack.Scale( yy_frac * data_integral / af2_integral )
af2_yy_stack.Rebin(rebin)
# yj for stack
af2_yj_stack = af2.Clone(); af2_yj_stack.SetName(af2_yj_stack.GetName()+'_yj_forStack')
af2_yj_stack.SetTitle('#gamma^{}#font[12]{j}')
function_yj = ROOT.TF1('%d_%s'%(i+offset,c),'([0]*(x-132.5)/(160-105) + [1])*[2]*[3]',105,160)
function_yj.SetParameters(yj_par0,yj_par1,yj_frac,yj_integral_factor)
function_yj.SetRange(af2.GetBinLowEdge(1),af2.GetBinLowEdge(af2.GetNbinsX()+1))
af2_yj_stack.Multiply(function_yj)
af2_yj_stack.Scale( data_integral / af2_integral )
af2_yj_stack.Rebin(rebin)
# jj for stack
af2_jj_stack = af2.Clone(); af2_jj_stack.SetName(af2_jj_stack.GetName()+'_jj_forStack')
af2_jj_stack.SetTitle('#font[12]{jj}')
function_jj = ROOT.TF1('%d_%s'%(i+offset,c),'([0]*(x-132.5)/(160-105) + [1])*[2]*[3]',105,160)
function_jj.SetParameters(jj_par0,jj_par1,jj_frac,jj_integral_factor)
function_jj.SetRange(af2.GetBinLowEdge(1),af2.GetBinLowEdge(af2.GetNbinsX()+1))
af2_jj_stack.Multiply(function_jj)
af2_jj_stack.Scale( data_integral / af2_integral )
af2_jj_stack.Rebin(rebin)
anaplot.PrepareBkgHistosForStack([af2_jj_stack,af2_yj_stack,af2_yy_stack],'')
# full thing.
function.SetRange(af2.GetBinLowEdge(1),af2.GetBinLowEdge(af2.GetNbinsX()+1))
af2.Multiply(function)
af2.Scale( data_integral / float(integral(af2,105,120)+integral(af2,130,160) ) )
main_can = plotfunc.RatioCanvas('Mimic_Plot_%02d_%s'%(i+offset,c),'Mimic plot',600,500)
plotfunc.AddHistogram(main_can,af2_jj_stack)
plotfunc.AddHistogram(main_can,af2_yj_stack)
plotfunc.AddHistogram(main_can,af2_yy_stack)
plotfunc.Stack(main_can)
outfile.cd()
af2.Write()
# Print the final plots, below.
#print Tools.FindRebinFactors(af2)
af2.Rebin(rebin)
data_blinded.Rebin(rebin)
data_blinded.SetTitle('Data')
#data_blinded.SetBinErrorOption(ROOT.TH1.kPoisson);
af2.SetMarkerSize(0); af2.SetLineColor(1); af2.SetLineWidth(2); af2.SetFillColor(1)
af2.SetFillStyle(3254);
af2.SetTitle('SM')
plotfunc.AddHistogram(main_can,af2,drawopt='E2')
if False :
# RATIO
plotfunc.AddRatio(main_can,data_blinded,af2)
taxisfunc.SetYaxisRanges(plotfunc.GetBotPad(main_can),0.5,1.5)
else :
# PULL
plotfunc.AddRatio(main_can,data_blinded,af2,divide='pull')
taxisfunc.SetYaxisRanges(plotfunc.GetBotPad(main_can),-3.5,4.5)
the_text = [plotfunc.GetAtlasInternalText(),
plotfunc.GetSqrtsText(13)+', '+plotfunc.GetLuminosityText(36.1),
Tools.CategoryNames[c]
]
plotfunc.DrawText(main_can,the_text,.2,.67,.61,.90,totalentries=3)
plotfunc.MakeLegend(main_can,0.70,0.67,0.92,0.90,ncolumns=2,option=['f','f','f','f','p'])
taxisfunc.SetXaxisRanges(main_can,105,160)
taxisfunc.AutoFixYaxis(plotfunc.GetTopPad(main_can),minzero=True)
plotfunc.SetAxisLabels(main_can,'m_{#gamma#gamma} [GeV]','entries','pull')
tmp.append(main_can)
for can in tmp :
plotfunc.FormatCanvasAxes(can)
anaplot.UpdateCanvases(tmp)
os.system('mkdir -p c%02d_%s'%(i,c))
for can in tmp :
can.Print('c%02d_%s/%s.pdf'%(i,c,can.GetName()))
can.Print('c%02d_%s/%s.eps'%(i,c,can.GetName()))
cans += yj_newcans
cans += jj_newcans
if options.nobatch :
raw_input('pause')
return
