|
| tuple | efficiency_map.parser = argparse.ArgumentParser() |
| |
| tuple | efficiency_map.args = parser.parse_args() |
| |
| | efficiency_map.topo = args.topology |
| |
| | efficiency_map.outdir = args.babydir |
| |
| tuple | efficiency_map.minnum = int(args.first) |
| |
| tuple | efficiency_map.maxnum = int(args.last) |
| |
| | efficiency_map.skipcalc = True |
| |
| | efficiency_map.infile = args.input |
| |
| | efficiency_map.outfile = args.outfilename |
| |
| tuple | efficiency_map.bdir = os.getcwd() |
| |
| float | efficiency_map.lumi = 2.3 |
| |
| tuple | efficiency_map.masspoints = set([x.split(topo+"_").pop().split("_Tune")[0] for x in glob.glob(outdir+"/baby_SMS-"+topo+"*.root")]) |
| | With the fake dataset names: More...
|
| |
| tuple | efficiency_map.npoints = len(masspoints) |
| |
| list | efficiency_map.bg = [3.5,0.5,2.7,0.5,0.8,0.1,1.3,0.3,0.5,0.1] |
| |
| list | efficiency_map.bindefs = ["njets<=8&&nbm==1&&met<=400","njets>=9&&nbm==1&&met<=400","njets<=8&&nbm==2&&met<=400","njets>=9&&nbm==2&&met<=400","njets<=8&&nbm>=3&&met<=400","njets>=9&&nbm>=3&&met<=400","njets<=8&&nbm==1&&met>400","njets>=9&&nbm==1&&met>400","njets<=8&&nbm>=2&&met>400","njets>=9&&nbm>=2&&met>400"] |
| |
| list | efficiency_map.cosmeticbindefs = [cut.replace("<="," #leq ").replace(">="," #geq ").replace(">"," > ").replace("=="," = ").replace("&&",", ").replace("met", "MET").replace("ht", "H_{T}").replace("njets","n_{jets}").replace("nleps","n_{lep}").replace("nbm","n_{b}") for cut in bindefs] |
| |
| list | efficiency_map.glumass = [] |
| |
| list | efficiency_map.lspmass = [] |
| |
| list | efficiency_map.effs = [[] for i in range(2+len(bindefs))] |
| |
| list | efficiency_map.yields = [[] for i in range(2+len(bindefs))] |
| |
| int | efficiency_map.sofar = 0 |
| |
| tuple | efficiency_map.ch = ROOT.TChain("tree") |
| | if sofar>10: continue More...
|
| |
| tuple | efficiency_map.glu = float(m.split("mGluino-").pop().split("_")[0]) |
| |
| tuple | efficiency_map.lsp = float(m.split("mLSP-").pop().split("_")[0]) |
| |
| int | efficiency_map.tot = 1000 |
| |
| tuple | efficiency_map.reg4 = float(get_weighted_entries(ch,"mj>400&&mt>140")) |
| |
| tuple | efficiency_map.reg3 = float(get_weighted_entries(ch,"mj<=400&&mt>140")) |
| |
| tuple | efficiency_map.num = float(get_weighted_entries(ch,"mj>400&&mt>140&&"+cut)) |
| |
| tuple | efficiency_map.outputFile = ROOT.TFile("plots/maps/"+outfile+".root","recreate") |
| |
| list | efficiency_map.graphs = [] |
| |
| list | efficiency_map.hist = graphs[-1] |
| |
| tuple | efficiency_map.val1 = hist.GetBinContent(hist.FindBin(700,0)) |
| |
| tuple | efficiency_map.val2 = hist.GetBinContent(hist.FindBin(600,100)) |
| |
| list | efficiency_map.graphTitles = [] |
| | Get histograms from root file to allow reading of input to use same code for original calculation and cosmetic re-runs. More...
|
| |
| int | efficiency_map.maxfrac = 0 |
| |
| int | efficiency_map.maxyield = 0 |
| |
| tuple | efficiency_map.graph = outputFile.Get(name) |
| |
| tuple | efficiency_map.frame = ROOT.TH2F("frame","",100,700,1950,152,0,1450) |
| |
| tuple | efficiency_map.npx = int((1950-700)/17.68) |
| |
| tuple | efficiency_map.npy = int(1450/17.68) |
| |
| tuple | efficiency_map.c = ROOT.TCanvas() |
| |
| tuple | efficiency_map.tla = ROOT.TLatex() |
| |
| string | efficiency_map.outname = outfile+"efficiency_" |
| |
1.8.11
