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)