def main(options, args):
plotfunc.SetupStyle()
for i in options.histformat.keys():
print '\'%s\':' % (i), options.histformat[i]
anaplot.LoadRootCore()
files_b, trees_b, keys_b, cf_b = anaplot.GetTreesFromFiles(options.bkgs)
files_s, trees_s, keys_s, cf_s = anaplot.GetTreesFromFiles(options.susy)
file_d, tree_d, key_d, cf_d = anaplot.GetTreesFromFiles(options.data)
options.fb, lumi_scale_factor = helpers.GetTTreeLumiScaleFactor(
files_b + files_s, options.fb)
dweight = ''
weight = 'TotalWeightNoSF*lepSFRecoIdIso[lep0_index]*lepSFRecoIdIso[lep1_index]*lepSFRecoIdIso[lep2_index]*EvtTrigSF*AntiIDSF_lep3*PassEvent_SMWZsig'
#weight = 'TotalWeightNoPupNoSF'
if ''.join(options.cuts):
weight = weight + '*(%s)' % (' && '.join(options.cuts))
dweight = '(' + ' && '.join(options.cuts) + ')'
if hasattr(options.usermodule, 'weight'):
weight += '*(%s)' % (options.usermodule.weight)
if hasattr(options.usermodule, 'dataweight'):
dweight += '*(%s)' % (options.usermodule.dataweight)
cans = []
# get the histograms from the files
for vi, v1 in enumerate(options.variables.split(',')):
n1, low1, high1 = options.histformat[v1][:3]
xlabel1 = options.histformat[v1][3]
rebin1 = []
if hasattr(options.usermodule,
'rebin') and v1 in options.usermodule.rebin.keys():
rebin1 = options.usermodule.rebin[v1]
for vj, v2 in enumerate(options.variables.split(',')):
if v2 == v1: continue
if vj > vi: continue
n2, low2, high2 = options.histformat[v2][:3]
xlabel2 = options.histformat[v2][3]
rebin2 = []
if hasattr(options.usermodule,
'rebin') and v2 in options.usermodule.rebin.keys():
rebin2 = options.usermodule.rebin[v2]
bkg_hists = []
sig_hists = []
data_hist = None
if options.bkgs:
bkg_hists = anaplot.Get2dVariableHistsFromTrees(
trees_b,
keys_b,
v1,
v2,
weight,
n1,
low1,
high1,
n2,
low2,
high2,
normalize=options.normalize,
rebin1=rebin1,
rebin2=rebin2,
scale=lumi_scale_factor)
anaplot.PrepareBkgHistosForStack(bkg_hists)
for i, h in enumerate(bkg_hists):
canname = '%s_%s_%s' % (v1, v2, keys_b[i])
cans.append(ROOT.TCanvas(canname, canname, 500, 500))
h.SetMinimum(-0.00001)
plotfunc.AddHistogram(cans[-1], h, drawopt='colz')
plotfunc.FormatCanvasAxes(cans[-1])
plotfunc.SetAxisLabels(cans[-1], xlabel1, xlabel2)
if options.susy:
sig_hists = anaplot.Get2dVariableHistsFromTrees(
trees_s,
keys_s,
v1,
v2,
weight,
n1,
low1,
high1,
n2,
low2,
high2,
normalize=options.normalize,
rebin1=rebin1,
rebin2=rebin2,
scale=lumi_scale_factor)
for i, h in enumerate(sig_hists):
canname = '%s_%s_%s' % (v1, v2, keys_s[i])
cans.append(ROOT.TCanvas(canname, canname, 500, 500))
h.SetMinimum(-0.00001)
plotfunc.AddHistogram(cans[-1], h, drawopt='colz')
plotfunc.FormatCanvasAxes(cans[-1])
plotfunc.SetAxisLabels(cans[-1], xlabel1, xlabel2)
if options.data:
data_hist = anaplot.Get2dVariableHistsFromTrees(
tree_d,
key_d,
v1,
v2,
dweight,
n1,
low1,
high1,
n2,
low2,
high2,
normalize=options.normalize,
rebin1=rebin1,
rebin2=rebin2,
scale=lumi_scale_factor)[0]
data_hist.SetLineWidth(2)
data_hist.SetLineColor(1)
data_hist.SetMarkerColor(1)
canname = '%s_%s_%s' % (v1, v2, 'data')
cans.append(ROOT.TCanvas(canname, canname, 500, 500))
h.SetMinimum(-0.00001)
plotfunc.AddHistogram(cans[-1], h, drawopt='colz')
plotfunc.FormatCanvasAxes(cans[-1])
plotfunc.SetAxisLabels(cans[-1], xlabel1, xlabel2)
cans.append(
anaplot.DrawHistos(v,
v,
xlabel,
bkg_hists,
sig_hists,
data_hist,
dostack=options.stack,
log=options.log,
ratio=options.ratio,
fb=options.fb))
if not options.batch:
raw_input('Press enter to exit')
if options.save:
for can in cans:
can.Print(can.GetName() + '.pdf')
print 'done.'
return
def main(options, args):
plotfunc.SetupStyle()
for i in options.histformat.keys():
print '\'%s\':' % (i), options.histformat[i]
anaplot.LoadRootCore()
files_b, trees_b, keys_b, cf_b = anaplot.GetTreesFromFiles(options.bkgs)
files_s, trees_s, keys_s, cf_s = anaplot.GetTreesFromFiles(options.susy)
file_d, tree_d, key_d, cf_d = anaplot.GetTreesFromFiles(options.data)
options.fb, lumi_scale_factor = helpers.GetTTreeLumiScaleFactor(
files_b + files_s, options.fb)
dweight = ''
weight = 'TotalWeightNoSF'
if ''.join(options.cuts):
weight = weight + '*(%s)' % (' && '.join(options.cuts))
dweight = '(' + ' && '.join(options.cuts) + ')'
if hasattr(options.usermodule, 'weight'):
weight += '*(%s)' % (options.usermodule.weight)
if hasattr(options.usermodule, 'dataweight'):
dweight += '*(%s)' % (options.usermodule.dataweight)
cans = []
# get the histograms from the files
for v in options.variables.split(','):
n, low, high = options.histformat[v][:3]
xlabel = options.histformat[v][3]
bkg_hists = []
sig_hists = []
data_hist = None
rebin = []
if hasattr(options.usermodule,
'rebin') and v in options.usermodule.rebin.keys():
rebin = options.usermodule.rebin[v]
if options.bkgs:
bkg_hists = anaplot.GetVariableHistsFromTrees(
trees_b,
keys_b,
v,
weight,
n,
low,
high,
normalize=options.normalize,
rebin=rebin,
scale=lumi_scale_factor)
anaplot.PrepareBkgHistosForStack(bkg_hists)
if options.susy:
sig_hists = anaplot.GetVariableHistsFromTrees(
trees_s,
keys_s,
v,
weight,
n,
low,
high,
normalize=options.normalize,
rebin=rebin,
scale=lumi_scale_factor)
if options.data:
data_hist = anaplot.GetVariableHistsFromTrees(
tree_d,
key_d,
v,
dweight,
n,
low,
high,
normalize=options.normalize,
rebin=rebin,
scale=lumi_scale_factor)[0]
data_hist.SetLineWidth(2)
data_hist.SetLineColor(1)
data_hist.SetMarkerColor(1)
nsignal = 0
for si, s in enumerate(keys_s):
mass_map = GetMassesMap()
for i in mass_map.keys():
if str(i) in sig_hists[si].GetName():
title = '%s,%s' % (mass_map[i]['mc1'], mass_map[i]['mn1'])
sig_hists[si].SetTitle(title)
sig_hists[si].SetLineWidth(2)
sig_hists[si].SetLineColor(signal_colors[nsignal])
sig_hists[si].SetMarkerColor(signal_colors[nsignal])
nsignal += 1
cans.append(
anaplot.DrawHistos(v,
v,
xlabel,
bkg_hists,
sig_hists,
data_hist,
dostack=options.stack,
log=options.log,
ratio=options.ratio,
fb=options.fb))
if not options.batch:
raw_input('Press enter to exit')
if options.save:
for can in cans:
can.Print(can.GetName() + '.pdf')
print 'done.'
return
def main(options, args):
anaplot.LoadRootCore()
files_b, trees_b, keys_b, cf_b = anaplot.GetTreesFromFiles(options.bkgs)
files_s, trees_s, keys_s, cf_s = anaplot.GetTreesFromFiles(options.susy)
file_d, tree_d, key_d, cf_d = anaplot.GetTreesFromFiles(options.data)
all_cutflows = cf_b + cf_s + cf_d
options.fb, lumi_scale_factor = helpers.GetTTreeLumiScaleFactor(
files_b + files_s, options.fb)
for i in all_cutflows:
for j in i:
j.Scale(lumi_scale_factor)
dweight = ''
weight = 'TotalWeightNoSF'
# if ''.join(options.cuts) :
# weight = weight+'*(%s)'%(' && '.join(options.cuts))
# dweight = '('+' && '.join(options.cuts)+')'
if hasattr(options.usermodule, 'weight'):
weight += '*(%s)' % (options.usermodule.weight)
if hasattr(options.usermodule, 'dataweight'):
dweight += '*(%s)' % (options.usermodule.dataweight)
cuts = []
samples = []
data_index = -1
if options.bkgs:
samples += list(
a.replace('.root', '') for a in options.bkgs.split(','))
samples += ['Total Bkg.']
totbkg_index = len(samples) - 1
if options.susy:
samples += list(
a.replace('.root', '') for a in options.susy.split(','))
if options.data:
samples += ['data']
data_index = len(samples) - 1
samples += ['s/b']
s_over_b_index = len(samples) - 1
samples += ['purity']
purity_index = len(samples) - 1
print samples
#
# Figure out what all the cuts were
#
firsthistlist = all_cutflows[0]
for i in firsthistlist:
for y in range(i.GetNbinsY(), 0, -1):
cuts.append(i.GetYaxis().GetBinLabel(y))
if hasattr(options.usermodule, 'cutflow'):
for i in options.usermodule.cutflow:
cuts.insert(0, i[0])
cuts.reverse()
print cuts
#
# Make a histogram to fill
#
cutflow_hist = ROOT.TH2F('Cutflow', 'Cutflow', len(samples), 0,
len(samples), len(cuts), 0, len(cuts))
for i in range(len(samples)):
cutflow_hist.GetXaxis().SetBinLabel(i + 1, samples[i])
for i in range(len(cuts)):
cutflow_hist.GetYaxis().SetBinLabel(i + 1, cuts[i])
#
# Grab the bin content from the "all" histograms that were saved in each file. (bkgs)
#
current_sample = 1
for i, histlist in enumerate(cf_b):
current_cut = len(cuts)
if hasattr(options.usermodule, 'cutflow'):
tree_cuts_list = list('(' + a[1] + ')'
for a in options.usermodule.cutflow)
for j in options.usermodule.cutflow:
tmp_weight = weight + '*(%s)' % (' && '.join(tree_cuts_list))
trees_b[keys_b[i]].Draw('GRL>>h1(2,0,2)', tmp_weight, 'egoff')
tree_cuts_list.pop()
hist = ROOT.gDirectory.Get('h1')
hist.Scale(lumi_scale_factor)
cutflow_hist.SetBinContent(current_sample, current_cut,
hist.GetBinContent(2))
cutflow_hist.SetBinError(current_sample, current_cut,
hist.GetBinError(2))
totbkg_content = cutflow_hist.GetBinContent(
totbkg_index + 1, current_cut) + hist.GetBinContent(2)
totbkg_error = math.sqrt(
cutflow_hist.GetBinError(totbkg_index +
1, current_cut)**2 +
hist.GetBinError(2)**2)
cutflow_hist.SetBinContent(totbkg_index + 1, current_cut,
totbkg_content)
cutflow_hist.SetBinError(totbkg_index + 1, current_cut,
totbkg_error)
ROOT.gROOT.ProcessLine('delete h1')
current_cut -= 1
for hist in histlist:
for x in range(hist.GetNbinsX()):
if hist.GetXaxis().GetBinLabel(x + 1) == 'all':
allbin = x + 1
break
for y in range(hist.GetNbinsY(), 0, -1):
cutflow_hist.SetBinContent(current_sample, current_cut,
hist.GetBinContent(allbin, y))
cutflow_hist.SetBinError(current_sample, current_cut,
hist.GetBinError(allbin, y))
totbkg_content = cutflow_hist.GetBinContent(
totbkg_index + 1, current_cut) + hist.GetBinContent(
allbin, y)
totbkg_error = math.sqrt(
cutflow_hist.GetBinError(totbkg_index +
1, current_cut)**2 +
hist.GetBinError(allbin, y)**2)
cutflow_hist.SetBinContent(totbkg_index + 1, current_cut,
totbkg_content)
cutflow_hist.SetBinError(totbkg_index + 1, current_cut,
totbkg_error)
current_cut -= 1
current_sample += 1
current_sample += 1
#
# susy (or wz!)
#
for i, histlist in enumerate(cf_s):
current_cut = len(cuts)
if hasattr(options.usermodule, 'cutflow'):
tree_cuts_list = list('(' + a[1] + ')'
for a in options.usermodule.cutflow)
for j in options.usermodule.cutflow:
#print tree_cuts_list
tmp_weight = weight + '*(%s)' % (' && '.join(tree_cuts_list))
trees_s[keys_s[i]].Draw('GRL>>h1(2,0,2)', tmp_weight, 'egoff')
tree_cuts_list.pop()
hist = ROOT.gDirectory.Get('h1')
hist.Scale(lumi_scale_factor)
signal_n = hist.GetBinContent(2)
cutflow_hist.SetBinContent(current_sample, current_cut,
signal_n)
if signal_n > 0:
cutflow_hist.SetBinError(current_sample, current_cut,
100. / float(math.sqrt(signal_n)))
tot_bkg = cutflow_hist.GetBinContent(totbkg_index + 1,
current_cut)
if tot_bkg:
cutflow_hist.SetBinContent(s_over_b_index + 1, current_cut,
signal_n / float(tot_bkg))
if tot_bkg + signal_n:
cutflow_hist.SetBinContent(
purity_index + 1, current_cut,
100. * signal_n / float(tot_bkg + signal_n))
ROOT.gROOT.ProcessLine('delete h1')
current_cut -= 1
for hist in histlist:
for x in range(hist.GetNbinsX()):
if hist.GetXaxis().GetBinLabel(x + 1) == 'all':
allbin = x + 1
break
for y in range(hist.GetNbinsY(), 0, -1):
signal_n = hist.GetBinContent(allbin, y)
cutflow_hist.SetBinContent(current_sample, current_cut,
signal_n)
cutflow_hist.SetBinError(current_sample, current_cut,
100. / float(math.sqrt(signal_n)))
tot_bkg = cutflow_hist.GetBinContent(totbkg_index + 1,
current_cut)
if tot_bkg:
cutflow_hist.SetBinContent(s_over_b_index + 1, current_cut,
signal_n / float(tot_bkg))
if tot_bkg + signal_n:
cutflow_hist.SetBinContent(
purity_index + 1, current_cut,
100. * signal_n / float(tot_bkg + signal_n))
current_cut -= 1
current_sample += 1
#
# data
#
for i, histlist in enumerate(cf_d):
current_cut = len(cuts)
if hasattr(options.usermodule, 'cutflow'):
tree_cuts_list = list('(' + a[1] + ')'
for a in options.usermodule.cutflow)
for j in options.usermodule.cutflow:
tmp_weight = weight + '*(%s)' % (' && '.join(tree_cuts_list))
tree_d[key_d[i]].Draw('GRL>>h1(2,0,2)', tmp_weight, 'egoff')
tree_cuts_list.pop()
hist = ROOT.gDirectory.Get('h1')
hist.Scale(lumi_scale_factor)
cutflow_hist.SetBinContent(current_sample, current_cut,
hist.GetBinContent(2))
cutflow_hist.SetBinError(current_sample, current_cut,
hist.GetBinError(2))
ROOT.gROOT.ProcessLine('delete h1')
current_cut -= 1
for hist in histlist:
for x in range(hist.GetNbinsX()):
if (hist.GetXaxis().GetBinLabel(x + 1) == 'data'):
allbin = x + 1
break
for y in range(hist.GetNbinsY(), 0, -1):
cutflow_hist.SetBinContent(current_sample, current_cut,
hist.GetBinContent(allbin, y))
cutflow_hist.SetBinError(current_sample, current_cut,
hist.GetBinError(allbin, y))
current_cut -= 1
current_sample += 1
PrintCutflow('Cutflow',
cutflow_hist,
samp_list=','.join(samples),
latex=options.latex)
return
def main(options, args):
plotfunc.SetupStyle()
for i in options.histformat.keys():
print '\'%s\':' % (i), options.histformat[i]
anaplot.LoadRootCore()
files_mc1, trees_mc1, keys_mc1, cf_mc1 = anaplot.GetTreesFromFiles(
options.mc1)
files_mc2, trees_mc2, keys_mc2, cf_mc2 = anaplot.GetTreesFromFiles(
options.mc2)
weight = 'TotalWeightNoSF'
if ''.join(options.cuts):
weight = weight + '*(%s)' % (' && '.join(options.cuts))
if hasattr(options.usermodule, 'weight'):
weight += '*(%s)' % (options.usermodule.weight)
cans = []
# get the histograms from the files
for v in options.variables.split(','):
n, low, high = options.histformat[v][:3]
xlabel = options.histformat[v][3]
mc_hists = []
rebin = []
if hasattr(options.usermodule,
'rebin') and v in options.usermodule.rebin.keys():
rebin = options.usermodule.rebin[v]
if options.mc1:
mc_hists.append(
anaplot.GetVariableHistsFromTrees(trees_mc1,
keys_mc1,
v,
weight,
n,
low,
high,
normalize=options.normalize,
rebin=rebin)[0])
mc_hists[-1].SetTitle(options.label1)
mc_hists[-1].SetLineWidth(2)
mc_hists[-1].SetLineColor(1)
mc_hists[-1].SetMarkerColor(1)
if options.mc2:
mc_hists.append(
anaplot.GetVariableHistsFromTrees(trees_mc2,
keys_mc2,
v,
weight,
n,
low,
high,
normalize=options.normalize,
rebin=rebin)[0])
mc_hists[-1].SetTitle(options.label2)
mc_hists[-1].SetLineWidth(ROOT.kRed + 1)
mc_hists[-1].SetLineColor(ROOT.kRed + 1)
mc_hists[-1].SetMarkerColor(ROOT.kRed + 1)
cans.append(
anaplot.DrawHistos(v,
v,
xlabel,
mc_hists,
dostack=False,
log=options.log,
ratio=options.ratio,
fb=options.fb))
if not options.batch:
raw_input('Press enter to exit')
if options.save:
for can in cans:
can.Print(can.GetName() + '.pdf')
print 'done.'
return