def compare_plot(var, plot_range, weight, cut, **kwargs):
histsD = dict()
for samp in samples_mc:
histsD[samp.name] = samp.drawHistogram(var, str(cut), weight=weight, plot_range=plot_range)
Styling.mc_style(histsD[samp.name].hist, samp.name)
for name, hist in histsD.items():
hist.normalize_lumi(lumi_total)
for samp in samples_data:
histsD[samp.name] = samp.drawHistogram(var, str(cut), plot_range=plot_range)
Styling.data_style(histsD[samp.name].hist)
hists_thD = dict()
for (k,v) in histsD.items():
hists_thD[k] = v.hist
merge_cmd = copy.deepcopy(merge_cmds)
merge_cmd["QCD (MC)"] = ["QCDMu"]
merged = merge_hists(hists_thD, merge_cmd)
stack = dict()
stack["mc"] = [merged[name] for name in merged.keys() if name!="single #mu"]
stack["data"] = [merged["single #mu"]]
canv = ROOT.TCanvas()
pl = plot_hists_stacked(canv, stack, **kwargs)
leg = legend(stack["data"] + stack["mc"][::-1], **kwargs)
lb = lumi_textbox(lumi_total)
canv.SaveAs(kwargs.get("filename", "plot") + ".pdf")
return stack, canv, pl, leg, lb
def mc_amount(cut, weight, lumi, ref=None):
histsD = dict()
for samp in samples_mc:
histsD[samp.name] = samp.drawHistogram("eta_lj",
str(cut),
weight=weight,
plot_range=[100, -5, 5])
for name, hist in histsD.items():
hist.normalize_lumi(lumi)
for name, hist in histsD.items():
histsD[name] = hist.hist
merge_cmd = copy.deepcopy(merge_cmds)
merge_cmd["QCD (MC)"] = ["QCDMu"]
merge_cmd["t#bar{t} incl."] = ["TTJets_MassiveBinDECAY"]
merge_cmd.pop("data")
merged_hists = merge_hists(histsD, merge_cmd)
interesting = [
"t#bar{t}", "t#bar{t} incl.", "W(#rightarrow l #nu) + jets",
"QCD (MC)", "t-channel"
]
for i in interesting:
r = 0.0
r0 = 0.0
if ref and i in ref.keys():
r0 = ref[i]
r = 100.0 * merged_hists[i].Integral() / float(r0)
print "%s | %d | %d | %.2f %%" % (i, merged_hists[i].Integral(),
r0, r)
def compare_plot(var, plot_range, weight, cut, **kwargs):
histsD = dict()
for samp in samples_mc:
histsD[samp.name] = samp.drawHistogram(var,
str(cut),
weight=weight,
plot_range=plot_range)
Styling.mc_style(histsD[samp.name].hist, samp.name)
for name, hist in histsD.items():
hist.normalize_lumi(lumi_total)
for samp in samples_data:
histsD[samp.name] = samp.drawHistogram(var,
str(cut),
plot_range=plot_range)
Styling.data_style(histsD[samp.name].hist)
hists_thD = dict()
for (k, v) in histsD.items():
hists_thD[k] = v.hist
merge_cmd = copy.deepcopy(merge_cmds)
merge_cmd["QCD (MC)"] = ["QCDMu"]
merged = merge_hists(hists_thD, merge_cmd)
stack = dict()
stack["mc"] = [
merged[name] for name in merged.keys() if name != "single #mu"
]
stack["data"] = [merged["single #mu"]]
canv = ROOT.TCanvas()
pl = plot_hists_stacked(canv, stack, **kwargs)
leg = legend(stack["data"] + stack["mc"][::-1], **kwargs)
lb = lumi_textbox(lumi_total)
canv.SaveAs(kwargs.get("filename", "plot") + ".pdf")
return stack, canv, pl, leg, lb
def mc_amount(cut, weight, lumi, ref=None):
histsD = dict()
for samp in samples_mc:
histsD[samp.name] = samp.drawHistogram("eta_lj", str(cut), weight=weight, plot_range=[100,-5,5])
for name, hist in histsD.items():
hist.normalize_lumi(lumi)
for name, hist in histsD.items():
histsD[name] = hist.hist
merge_cmd = copy.deepcopy(merge_cmds)
merge_cmd["QCD (MC)"] = ["QCDMu"]
merge_cmd["t#bar{t} incl."] = ["TTJets_MassiveBinDECAY"]
merge_cmd.pop("data")
merged_hists = merge_hists(histsD, merge_cmd)
interesting = ["t#bar{t}", "t#bar{t} incl.", "W(#rightarrow l #nu) + jets", "QCD (MC)", "t-channel"]
for i in interesting:
r = 0.0
r0 = 0.0
if ref and i in ref.keys():
r0 = ref[i]
r = 100.0*merged_hists[i].Integral() / float(r0)
print "%s | %d | %d | %.2f %%" % (i, merged_hists[i].Integral(), r0, r)