#print '{:<15}'.format('category') + '{:<15}'.format('mean_roch') + '{:<15}'.format('mean_kamu') + '{:<15}'.format('sigma_roch') + '{:<15}'.format('sigma_kamu')
#for i in range(0,len(ggf_ratios)):
# print ('{:<15}'.format(categories[i]) + '{:<15.2f}'.format(ggf_mean_roch[i]) + '{:<15.2f}'.format(ggf_mean_kamu[i]) +
# '{:<15.2f}'.format(ggf_sigma_roch[i]) + '{:<15.2f}'.format(ggf_sigma_kamu[i]) )
#
#print "\n =========== LIST VBF INFO ================ \n"
#
#print '{:<15}'.format('category') + '{:<15}'.format('mean_roch') + '{:<15}'.format('mean_kamu') + '{:<15}'.format('sigma_roch') + '{:<15}'.format('sigma_kamu')
#for i in range(0,len(vbf_ratios)):
# print ('{:<15}'.format(categories[i]) + '{:<15.2f}'.format(vbf_mean_roch[i]) + '{:<15.2f}'.format(vbf_mean_kamu[i]) +
# '{:<15.2f}'.format(vbf_sigma_roch[i]) + '{:<15.2f}'.format(vbf_sigma_kamu[i]) )
print "\n =========== REBIN and RATIO ================ \n"
for i in range(0,len(roch_plots)):
hroch = roch_plots[i]
hkamu = kamu_plots[i]
hroch.SetName(hroch.GetName()+"_Roch")
hroch.SetTitle(hroch.GetTitle()+"_Roch")
hkamu.SetName(hkamu.GetName()+"_KaMu")
hkamu.SetTitle(hkamu.GetTitle()+"_KaMu")
print "\n /// %d /// Stack and Ratio for %s, %s" % (i, hroch.GetName(), hkamu.GetName())
hlist = [hkamu, hroch]
#newBins = tools.getRebinEdges(hroch, hkamu, max_err=0.1)
newBins = np.linspace(110, 160, 50)
#print newBins
rebin_hlist = tools.rebin(hlist, newBins)
hroch = rebin_hlist[1]
hkamu = rebin_hlist[0]
tools.stackAndRatio(rebin_hlist, title=hroch.GetName()+"_vs_KaMu", ytitleratio='Roch/KaMu', log=False, yrange=(0, hroch.GetMaximum()*1.15), yrange_ratio=(0.85,1.15))
h.SetTitle("Inclusive")
inclusive = h
#print "\n =========== OVERLAY ================== \n"
#tools.overlay(hlist, title="Background Shapes", savename="compare_bkg_shapes", xtitle="dimu_mass", ytitle="Events / 0.2 GeV",
# ldim=[0.85, 0.4, 0.98, 0.98], draw='fill-transparent')
print "\n =========== STACKS ================== \n"
list_to_stack = []
for h in hlist:
if 'cAll' in h.GetName():
continue
l = [h, inclusive]
e = tools.getRebinEdges(h, inclusive, max_err=0.05)
lrebin = tools.scaleByBinWidth(tools.rebin(l, e), e)
tools.stackAndRatio(lrebin,
log=False,
name=h.GetTitle(),
savename="bkg_shapes_stack_%s" % h.GetTitle(),
title=h.GetTitle(),
ytitleratio="%s/%s" % ("Inclusive", h.GetTitle()),
errband="Error Band",
ldim=[0.6, 0.55, 0.92, 0.92])
#for l in list_to_stack:
# h = l[0]
# tools.stackAndRatio(l, log=False, name=h.GetName(), savename="bkg_shapes_stack_%s" % h.GetName(), title=h.GetName(),
# ytitleratio="%s/%s" % (h.GetName(), 'inclusive'))
in_list.append( (filename, 'net_histos/%s_%s' % (c, s)) )
# Up and Down uncertainty histograms to get for this category, e.g. JES_up, JES_down, ...
if s == 'Net_Data':
continue # Data does not have up/down uncertainty histograms, only MC
for key,value in in_unc_map.iteritems():
value.append( (filename, 'net_histos/%s_%s_%s' % (c, s, key)) )
# list of histograms to make the stack and ratio plots for this category
hlist = tools.get(in_list)
# map to lists of up/down uncertainty histograms for this category
# 'JES_up' -> list of JES_up histos for this category
for in_key, in_value in in_unc_map.iteritems():
unc_hist_map[in_key] = tools.get(in_value)
#hmc = tools.add(hlist[0:-1])
#hdata = hlist[-1]
#newBins = tools.getRebinEdges(hdata, hmc, max_err=0.05)
#print newBins
#rebin_hlist = tools.rebin(hlist, newBins) #rebinned to var bin width
#srebin_hlist = tools.scaleByBinWidth(rebin_hlist, newBins) # rebinned to var bin width and wide bins are scaled down
print "\n =========== STACK AND RATIO ====== \n"
tools.stackAndRatio(hlist, unc_map=unc_hist_map, savename=varname+'_%s' % c, title=('%s '+ vartitle) % c, ldim=[0.6, 0.55, 0.92, 0.92], xtitle=vartitle+units)
#tools.stackAndRatio(srebin_hlist, savename=varname+'_%s' % c, title=('%s '+ vartitle) % c, ldim=[0.6, 0.55, 0.92, 0.92], xtitle=vartitle+units)
#tdrstyle.cmsPrel(35900, 13, False, onLeft=True)
c = TCanvas();
gg_h130 = gg_hlist[2]
gg_h125.SetTitle("GGF_M125");
gg_h120.SetTitle("GGF_M120");
gg_h130.SetTitle("GGF_M130");
gg_l120 = [gg_h120, gg_h125]
gg_l130 = [gg_h130, gg_h125]
# -- vbf -----------------------
vbf_h125 = vbf_hlist[0]
vbf_h120 = vbf_hlist[1]
vbf_h130 = vbf_hlist[2]
vbf_h125.SetTitle("VBF_M125");
vbf_h120.SetTitle("VBF_M120");
vbf_h130.SetTitle("VBF_M130");
vbf_l120 = [vbf_h120, vbf_h125]
vbf_l130 = [vbf_h130, vbf_h125]
print "\n =========== STACK AND RATIO ====== \n"
tools.stackAndRatio(gg_l120, title='BDT_Score_GGF_M125_vs_M120', ytitleratio='M125/M120', log=False, yrange=(0,0.06), xtitle="bdt_score")
tools.stackAndRatio(gg_l130, title='BDT_Score_GGF_M125_vs_M130', ytitleratio='M125/M130', log=False, yrange=(0,0.06), xtitle="bdt_score")
tools.stackAndRatio(vbf_l120, title='BDT_Score_VBF_M125_vs_M120', ytitleratio='M125/M120', log=False, yrange=(0,0.08), xtitle="bdt_score",
ldim=[0.13, 0.67, 0.41, 0.88])
tools.stackAndRatio(vbf_l130, title='BDT_Score_VBF_M125_vs_M130', ytitleratio='M125/M130', log=False, yrange=(0,0.08), xtitle="bdt_score",
ldim=[0.13, 0.67, 0.41, 0.88])
'Drell_Yan', 'Net_Data'
]
filename = dirname + filenames[2]
getlist = []
#for sample in samples:
# getlist.append((filename, 'net_histos/%s_%s' % (category, sample)))
getlist.append((filename, 'Net_DY_HT'))
getlist.append((filename, 'ZJets_AMC'))
print "\n =========== GET ================== \n"
hlist = tools.get(getlist)
print "\n =========== REBIN ================ \n"
hmc = tools.add(hlist[0:-1])
hdata = hlist[-1]
newBins = tools.getRebinEdges(hdata, hmc, max_err=0.1)
print newBins
rebin_hlist = tools.rebin(hlist, newBins) #rebinned to var bin width
srebin_hlist = tools.scaleByBinWidth(
rebin_hlist,
newBins) # rebinned to var bin width and wide bins are scaled down
srebin_hlist[0].SetTitle('ZJets_MG')
print "\n =========== STACK AND RATIO ====== \n"
tools.stackAndRatio(srebin_hlist,
title='Drell_Yan_MC_AMC_vs_Madgraph',
ytitleratio='AMC/MG',
yrange=(1e3, 1e7))
'c_01_Jet_Loose_OO',
'c_01_Jet_Loose_OE',
'c_01_Jet_Loose_EE'
]
category = 'c_01_Jet_Tight_BB'
samples = ['VH', 'H2Mu_VBF', 'H2Mu_gg', 'Diboson_plus', 'TTbar_Plus_SingleTop', 'Drell_Yan', 'Net_Data']
filename = dirname+filenames[2]
getlist = []
#for sample in samples:
# getlist.append((filename, 'net_histos/%s_%s' % (category, sample)))
getlist.append( (filename, 'Net_DY_HT') )
getlist.append( (filename, 'ZJets_AMC') )
print "\n =========== GET ================== \n"
hlist = tools.get(getlist)
print "\n =========== REBIN ================ \n"
hmc = tools.add(hlist[0:-1])
hdata = hlist[-1]
newBins = tools.getRebinEdges(hdata, hmc, max_err=0.1)
print newBins
rebin_hlist = tools.rebin(hlist, newBins) #rebinned to var bin width
srebin_hlist = tools.scaleByBinWidth(rebin_hlist, newBins) # rebinned to var bin width and wide bins are scaled down
srebin_hlist[0].SetTitle('ZJets_MG');
print "\n =========== STACK AND RATIO ====== \n"
tools.stackAndRatio(srebin_hlist, title='Drell_Yan_MC_AMC_vs_Madgraph', ytitleratio='AMC/MG', yrange=(1e3, 1e7))
# Normalize all of the backgrounds to the same scale
for i,h in enumerate(hlist):
h.Scale(1/h.Integral())
#h.SetName(bdt_categories[i])
h.SetTitle(bdt_categories[i])
if 'cAll' in h.GetName():
h.SetTitle("Inclusive")
inclusive = h
#print "\n =========== OVERLAY ================== \n"
#tools.overlay(hlist, title="Background Shapes", savename="compare_bkg_shapes", xtitle="dimu_mass", ytitle="Events / 0.2 GeV",
# ldim=[0.85, 0.4, 0.98, 0.98], draw='fill-transparent')
print "\n =========== STACKS ================== \n"
list_to_stack = []
for h in hlist:
if 'cAll' in h.GetName():
continue
l = [h,inclusive]
e = tools.getRebinEdges(h, inclusive, max_err=0.05)
lrebin = tools.scaleByBinWidth(tools.rebin(l, e), e)
tools.stackAndRatio(lrebin, log=False, name=h.GetTitle(), savename="bkg_shapes_stack_%s" % h.GetTitle(), title=h.GetTitle(),
ytitleratio="%s/%s" % ("Inclusive",h.GetTitle()), errband="Error Band", ldim=[0.6, 0.55, 0.92, 0.92])
#for l in list_to_stack:
# h = l[0]
# tools.stackAndRatio(l, log=False, name=h.GetName(), savename="bkg_shapes_stack_%s" % h.GetName(), title=h.GetName(),
# ytitleratio="%s/%s" % (h.GetName(), 'inclusive'))
for key, value in in_unc_map.iteritems():
value.append((filename, 'net_histos/%s_%s_%s' % (c, s, key)))
# list of histograms to make the stack and ratio plots for this category
hlist = tools.get(in_list)
# map to lists of up/down uncertainty histograms for this category
# 'JES_up' -> list of JES_up histos for this category
for in_key, in_value in in_unc_map.iteritems():
unc_hist_map[in_key] = tools.get(in_value)
#hmc = tools.add(hlist[0:-1])
#hdata = hlist[-1]
#newBins = tools.getRebinEdges(hdata, hmc, max_err=0.05)
#print newBins
#rebin_hlist = tools.rebin(hlist, newBins) #rebinned to var bin width
#srebin_hlist = tools.scaleByBinWidth(rebin_hlist, newBins) # rebinned to var bin width and wide bins are scaled down
print "\n =========== STACK AND RATIO ====== \n"
tools.stackAndRatio(hlist,
unc_map=unc_hist_map,
savename=varname + '_%s' % c,
title=('%s ' + vartitle) % c,
ldim=[0.6, 0.55, 0.92, 0.92],
xtitle=vartitle + units)
#tools.stackAndRatio(srebin_hlist, savename=varname+'_%s' % c, title=('%s '+ vartitle) % c, ldim=[0.6, 0.55, 0.92, 0.92], xtitle=vartitle+units)
#tdrstyle.cmsPrel(35900, 13, False, onLeft=True)
c = TCanvas()
gg_h125.SetTitle("GGF_M125");
gg_h120.SetTitle("GGF_M120");
gg_h130.SetTitle("GGF_M130");
gg_l120 = [gg_h120, gg_h125]
gg_l130 = [gg_h130, gg_h125]
# -- vbf -----------------------
vbf_h125 = vbf_hlist[0]
vbf_h120 = vbf_hlist[1]
vbf_h130 = vbf_hlist[2]
vbf_h125.SetTitle("VBF_M125");
vbf_h120.SetTitle("VBF_M120");
vbf_h130.SetTitle("VBF_M130");
vbf_l120 = [vbf_h120, vbf_h125]
vbf_l130 = [vbf_h130, vbf_h125]
print "\n =========== STACK AND RATIO ====== \n"
tools.stackAndRatio(gg_l120, title='BDT_Score_GGF_M125_vs_M120', ytitleratio='M125/M120', log=False, yrange=(0,0.06), xtitle="bdt_score",
ldim=[0.23, 0.65, 0.55, 0.83])
tools.stackAndRatio(gg_l130, title='BDT_Score_GGF_M125_vs_M130', ytitleratio='M125/M130', log=False, yrange=(0,0.06), xtitle="bdt_score",
ldim=[0.23, 0.65, 0.55, 0.83])
tools.stackAndRatio(vbf_l120, title='BDT_Score_VBF_M125_vs_M120', ytitleratio='M125/M120', log=False, yrange=(0,0.08), xtitle="bdt_score",
ldim=[0.23, 0.65, 0.55, 0.83])
tools.stackAndRatio(vbf_l130, title='BDT_Score_VBF_M125_vs_M130', ytitleratio='M125/M130', log=False, yrange=(0,0.08), xtitle="bdt_score",
ldim=[0.23, 0.65, 0.55, 0.83])
#
#print '{:<15}'.format('category') + '{:<15}'.format('mean_roch') + '{:<15}'.format('mean_kamu') + '{:<15}'.format('sigma_roch') + '{:<15}'.format('sigma_kamu')
#for i in range(0,len(vbf_ratios)):
# print ('{:<15}'.format(categories[i]) + '{:<15.2f}'.format(vbf_mean_roch[i]) + '{:<15.2f}'.format(vbf_mean_kamu[i]) +
# '{:<15.2f}'.format(vbf_sigma_roch[i]) + '{:<15.2f}'.format(vbf_sigma_kamu[i]) )
print "\n =========== REBIN and RATIO ================ \n"
for i in range(0, len(roch_plots)):
hroch = roch_plots[i]
hkamu = kamu_plots[i]
hroch.SetName(hroch.GetName() + "_Roch")
hroch.SetTitle(hroch.GetTitle() + "_Roch")
hkamu.SetName(hkamu.GetName() + "_KaMu")
hkamu.SetTitle(hkamu.GetTitle() + "_KaMu")
print "\n /// %d /// Stack and Ratio for %s, %s" % (i, hroch.GetName(),
hkamu.GetName())
hlist = [hkamu, hroch]
#newBins = tools.getRebinEdges(hroch, hkamu, max_err=0.1)
newBins = np.linspace(110, 160, 50)
#print newBins
rebin_hlist = tools.rebin(hlist, newBins)
hroch = rebin_hlist[1]
hkamu = rebin_hlist[0]
tools.stackAndRatio(rebin_hlist,
title=hroch.GetName() + "_vs_KaMu",
ytitleratio='Roch/KaMu',
log=False,
yrange=(0, hroch.GetMaximum() * 1.15),
yrange_ratio=(0.85, 1.15))