def save_templates( templates, outputDir, lead_ptrange=(None,None), namePostfix='' ) :
if outputDir is None :
return
draw_templates = {'lead' : {}, 'subl' : {} }
draw_templates['lead']['real'] = templates['lead']['real']['Data'].Clone( 'draw_%s' %templates['lead']['real']['Data'].GetName())
draw_templates['lead']['fake'] = templates['lead']['fake']['Data'].Clone( 'draw_%s' %templates['lead']['fake']['Data'].GetName())
if templates['lead']['real']['Background'] is not None :
draw_templates['lead']['real'].Add( templates['lead']['real']['Background'])
if templates['lead']['fake']['Background'] is not None :
draw_templates['lead']['fake'].Add( templates['lead']['fake']['Background'])
can_lead_real = ROOT.TCanvas('can_lead_real', '')
can_lead_fake = ROOT.TCanvas('can_lead_fake', '')
pt_label_lead = None
if lead_ptrange[0] is not None :
if lead_ptrange[1] == None :
pt_label_lead = ' p_{T} > %d ' %( lead_ptrange[0] )
else :
pt_label_lead = ' %d < p_{T} < %d ' %( lead_ptrange[0], lead_ptrange[1] )
draw_template( can_lead_real, draw_templates['lead']['real'], sampManFit, normalize=1, label=pt_label_lead, outputName = outputDir+'/template_lead_real%s.pdf' %namePostfix )
draw_template( can_lead_fake, draw_templates['lead']['fake'], sampManFit, normalize=1, label=pt_label_lead, outputName = outputDir+'/template_lead_fake%s.pdf' %namePostfix )
def run_photon_fit( templates, gg_hist, lead_reg, fitvar='sigmaIEIE', pid='Medium', lead_ptrange=(None,None), outputDir=None, outputPrefix='', systematics=None ) :
accept_reg = ['EB', 'EE']
if lead_reg not in accept_reg :
print 'Lead region does not make sense'
return
# get the defaults
samples = get_default_samples()
plotbinning = get_default_binning(var=fitvar)
cuts = get_default_cuts(var=fitvar, pid=pid)
# Find the bins corresponding to the cuts
# lead photon on X axis, subl on Y axis
bins_lead_tight = ( gg_hist.GetXaxis().FindBin( cuts[lead_reg]['tight'][0] ), gg_hist.GetXaxis().FindBin( cuts[lead_reg]['tight'][1] ) )
bins_lead_loose = ( gg_hist.GetXaxis().FindBin( cuts[lead_reg]['loose'][0] ), gg_hist.GetXaxis().FindBin( cuts[lead_reg]['loose'][1] ) )
print 'cuts, bins lead, tight = %f-%f ( %d - %d ) ' %( cuts[lead_reg]['tight'][0], cuts[lead_reg]['tight'][1], bins_lead_tight[0], bins_lead_tight[1] )
print 'cuts, bins lead, loose = %f-%f ( %d - %d ) ' %( cuts[lead_reg]['loose'][0], cuts[lead_reg]['loose'][1], bins_lead_loose[0], bins_lead_loose[1] )
# Integrate to the the data in the four regions
Ndata_T = gg_hist.Integral( bins_lead_tight[0], bins_lead_tight[1])
Ndata_L = gg_hist.Integral( bins_lead_loose[0], bins_lead_loose[1])
# ufloat it!
Ndata = {}
Ndata['T'] = ufloat( Ndata_T, math.sqrt(Ndata_T ), 'Ndata_T' )
Ndata['L'] = ufloat( Ndata_L, math.sqrt(Ndata_L ), 'Ndata_L' )
print 'N data T = ', Ndata['T']
print 'N data L = ', Ndata['L']
# arragnge the cuts by
eff_cuts = {}
eff_cuts['lead'] = {}
eff_cuts['lead']['tight'] = cuts[lead_reg]['tight']
eff_cuts['lead']['loose'] = cuts[lead_reg]['loose']
# get 2-d efficiencies from 1-d inputs
(eff_1d_stat, eff_1d_syst) =generate_1d_efficiencies( templates, eff_cuts, lead_reg, lead_ptrange, systematics=systematics )
print 'eff_1d'
print eff_1d_stat
results_stat = run_fit( {'T': Ndata['T'], 'L' : Ndata['L']}, eff_1d_stat )
results_syst = run_fit( {'T': Ndata['T'], 'L' : Ndata['L']}, eff_1d_syst )
print 'Real Norm = ', results_stat.item(0)
print 'Fake Norm = ', results_stat.item(1)
hist_temp_lead_real = templates['lead']['real']['Data'].Clone( 'hist_temp_lead_real' )
hist_temp_lead_fake = templates['lead']['fake']['Data'].Clone( 'hist_temp_lead_fake' )
if templates['lead']['real']['Background'] is not None :
hist_temp_lead_real.Add( templates['lead']['real']['Background'] )
if templates['lead']['fake']['Background'] is not None :
hist_temp_lead_fake.Add( templates['lead']['fake']['Background'] )
p_R_T = results_stat.item(0)*eff_1d_stat['eff_R_T_lead']
p_R_L = results_stat.item(0)*eff_1d_stat['eff_R_L_lead']
p_F_T = results_stat.item(1)*eff_1d_stat['eff_F_T_lead']
p_F_L = results_stat.item(1)*eff_1d_stat['eff_F_L_lead']
print 'nPred Real Tight = ', p_R_T
print 'nPred Real Loose = ', p_R_L
print 'nPred Fake Tight = ', p_F_T
print 'nPred Fake Loose = ', p_F_L
#normalize lead real template according to fit
hist_temp_lead_real.Scale( ( p_R_T+p_R_L ).n/ hist_temp_lead_real.Integral() )
hist_temp_lead_fake.Scale( ( p_F_T+p_F_L ).n/ hist_temp_lead_fake.Integral() )
hist_temp_lead_real.SetLineColor(ROOT.kMagenta)
hist_temp_lead_real.SetMarkerColor(ROOT.kMagenta)
hist_temp_lead_real.SetLineWidth(2)
hist_temp_lead_fake.SetLineColor(ROOT.kRed)
hist_temp_lead_fake.SetMarkerColor(ROOT.kRed)
hist_temp_lead_fake.SetLineWidth(2)
hist_temp_lead_real.SetStats(0)
hist_temp_lead_fake.SetStats(0)
can_proj_lead = ROOT.TCanvas('proj_lead', '')
namePostfix = '__%s' %( lead_reg )
if lead_ptrange[0] is not None :
if lead_ptrange[1] is None :
namePostfix += '__pt_%d-max' %( lead_ptrange[0])
else :
namePostfix += '__pt_%d-%d' %( lead_ptrange[0], lead_ptrange[1])
outputNamePL=None
if outputDir is not None :
outputNamePL = outputDir + '/fit_with_data_projlead%s%s.pdf' %(outputPrefix, namePostfix)
if lead_reg == 'EE' :
gg_hist.Rebin(10)
hist_temp_lead_real.Rebin(10)
hist_temp_lead_fake.Rebin(10)
draw_template(can_proj_lead, [gg_hist, hist_temp_lead_real, hist_temp_lead_fake], sampManFit, normalize=False, first_hist_is_data=True, legend_entries=['Data', 'real+fake prediction', 'fake+fake prediction' ], outputName=outputNamePL )
text_results_stat = collect_results( results_stat, Ndata, eff_1d_stat, templates, bins_lead_loose, bins_lead_tight )
text_results_syst = collect_results( results_syst, Ndata, eff_1d_stat, templates, bins_lead_loose, bins_lead_tight )
return text_results_stat, text_results_syst
