def comp(branchname, filename, binning, xlabel, ratio, nele, logx=False, remake=False):
global hist_index
lt_hists = []
lt_sum = 0
if not remake:
print "opening tmp/LT_%s.root" % filename
f = ROOT.TFile.Open("tmp/LT_%s.root" % filename, "READ")
inclusive = f.Get("hist%d" % hist_index)
hist_index += 1
for i in xrange(1,11):
lt_hists.append(f.Get("hist%d" % hist_index ))
hist_index += 1
print lt_hists[-1].GetTitle()
lt_sum += lt_hists[-1].Integral()
else:
inclusive = plot.GetTH1(inc_chain, branchname, isMC, binning, category, label="Inclusive Madgraph", usePU = False, useEleIDSF=nele)
for name, chain in zip(lt_names,lt_binned_chains):
print "Getting", name
h = plot.GetTH1(chain, branchname, isMC, binning, category, label= "L_{T} " + name + " GeV", usePU = False, useEleIDSF=nele)
lt_hists.append(h)
lt_sum += h.Integral()
if remake:
f = ROOT.TFile.Open("tmp/LT_%s.root" % filename, "RECREATE")
for h in lt_hists + [inclusive]:
h.Write()
f.Close()
plot.ColorData(inclusive)
plot.ColorMCs(lt_hists)
#inclusive.Scale(1./inclusive.Integral())
rajdeep_new = [828.72, 129.496, 155.178, 217.287, 175.158, 50.51, 91.48, 3.69, 0.196, 0.0072]
old_xs = [8.670e+02, 1.345e+02, 1.599e+02, 2.295e+02, 1.654e+02, 4.896e+01, 9.401e+01, 3.588e+00, 2.012e-01, 8.329e-03]
fit_new = [ 9.93694e-01, 9.72633e-01, 9.90570e-01, 9.89391e-01, 9.92841e-01, 8.56845e-01, 8.91049e-01, 9.04437e-01, 6.09500e-01,1]
filename = "before_" + filename
#for i, h in enumerate(lt_hists):
#print "### XS RATIO: ", h.GetTitle(), old_xs[i], old_xs[i] * fit_new[i]
#h.Scale(fit_new[i])
#h.Scale(rajdeep_new[i]/old_xs[i])
# h.Scale(1./lt_sum)
inclusive.Scale(1000*4.957e+03/49144274.0)
lt_hists.reverse()
plot.PlotDataMC(inclusive, lt_hists, "plots/validation/LTBinned/", "LT_comparison_" + filename, xlabel=xlabel + " [GeV]", ylabel="Fraction", ylabel_unit="GeV", stack_mc=True, ratio=ratio, logx=logx)
#parser.add_argument("-c", "--category", help="category string", default=category)
##specify the branch for electron [0], second axis will replace [0] with [1]
#parser.add_argument("-b", "--branch", help="branch to plot (yaxis [0] -> [1])", default="energy_ECAL_ele[0]/cosh(etaSCEle[0])")
#parser.add_argument("--isMC", action="store_true", help="isMC for GetTH1()", default=False)
args = parser.parse_args()
sys.path.insert(0, os.getcwd() + '/python')
import plot
import ROOT
infile = ROOT.TFile.Open("LT_LT_hist.root")
stack = [key.ReadObj() for key in infile.GetListOfKeys()]
stack, _ = plot.NormalizeStack(stack)
plot.ColorMCs(stack)
incfile = ROOT.TFile.Open("LT_inc.root")
mc_inc = incfile.Get("hist0")
plot.Normalize([], mc_inc)
plot.ColorMCs(mc_inc)
mc_inc.SetLineColor(ROOT.kBlack)
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
plot.PlotDataMC([], [mc_inc] + list(stack),
"plots/validation/LTBinned/",
"LT_stack",
xlabel="L_{T}",
if args.data:
print "[STATUS] Data: " + branchname
isMC = False
data_hs = MakeTH1s(args.data, branchname, isMC, binning, category,
selection)
plot.ColorData(data_hs)
else:
data_hs = []
if args.mc:
print "[STATUS] MC: " + branchname
isMC = True
mc_hs = MakeTH1s(args.mc, branchname, isMC, binning, category,
selection)
plot.ColorMCs(mc_hs)
else:
mc_hs = []
plot.Normalize(data_hs, mc_hs)
plot.PlotDataMC(data_hs,
mc_hs,
args.plotdir,
args.name,
xlabel=args.xlabel,
ylabel="Events",
ylabel_unit="GeV",
logy=False,
ratio=ratio)
print "[STATUS] Done: " + branchname
def comp(branchname,
filename,
binning,
xlabel,
ratio,
nele,
logx=False,
remake=False):
global hist_index
branchname = "smearerCat[0]:" + branchname
nbins, low, hi = eval(binning)
binning = "({nbins},{low},{hi},{ncat},0,{ncat})".format(nbins=nbins,
low=low,
hi=hi,
ncat=ncat *
(ncat + 1) / 2)
if not remake:
f = ROOT.TFile.Open("tmp/smear_comp_%s.root" % filename, "READ")
histos = [
f.Get("hist%d" % i) for i in range(hist_index, 5 + hist_index)
]
data_step7_hist, data_step2_hist, mc_h, mc_step4_h, mc_step7_h = histos
data_hists = [data_step7_hist, data_step2_hist]
mc_hists = [mc_h, mc_step4_h, mc_step7_h]
hist_index += 5
else:
data_step7_hist = plot.GetTH1(data_step7_chain,
branchname,
not isMC,
binning,
category,
label="Data HggRunEtaR9Et",
usePU=False,
useEleIDSF=nele,
scale=True)
data_step2_hist = plot.GetTH1(data_step2_chain,
branchname,
not isMC,
binning,
category,
label="Data HggRunEtaR9",
usePU=False,
useEleIDSF=nele,
scale=True)
data_hists = [data_step7_hist, data_step2_hist]
mc_h = plot.GetTH1(mc_chain,
branchname,
isMC,
binning,
category,
label="no Correction",
usePU=False,
useEleIDSF=nele,
smear=False)
mc_step4_h = plot.GetTH1(mc_step4_chain,
branchname,
isMC,
binning,
category,
label="Step4 Smear",
usePU=False,
useEleIDSF=nele,
smear=True)
mc_step7_h = plot.GetTH1(mc_step7_chain,
branchname,
isMC,
binning,
category,
label="Step7 Smear",
usePU=False,
useEleIDSF=nele,
smear=True)
mc_hists = [mc_h, mc_step4_h, mc_step7_h]
if remake:
f = ROOT.TFile.Open("tmp/smear_comp_%s.root" % filename, "RECREATE")
for h in data_hists + mc_hists:
h.Write()
f.Close()
c = ROOT.TCanvas()
names = [
"data_HggRunEtaR9Et", "data_HggRunEtaR9", "uncorrected", "step4",
"step7"
]
for name, h in zip(names, data_hists + mc_hists):
h.Draw("colz")
c.SaveAs("plots/scale_smear/%s.png" % name)
data_step7_hs = ROOT.THStack(data_step7_hist, "x")
data_step2_hs = ROOT.THStack(data_step2_hist, "x")
mc_hs = ROOT.THStack(mc_h, "x")
mc_step4_hs = ROOT.THStack(mc_step4_h, "x")
mc_step7_hs = ROOT.THStack(mc_step7_h, "x")
#print "CHI2 Category", " ".join(names[2:])
for i, dicat_name in enumerate(dicat_names):
ds = [data_step7_hs.GetHists()[i], data_step2_hs.GetHists()[i]]
plot.ColorData(ds)
mcs = [
mc_hs.GetHists()[i],
mc_step4_hs.GetHists()[i],
mc_step7_hs.GetHists()[i]
]
plot.ColorMCs(mcs)
plot.Normalize(ds, mcs)
ds[0].GetXaxis().SetRangeUser(80, 100)
#print "CHI2", dicat_name,
#for h in mcs:
#ch2 = ds[0].Chi2Test(h, "UW CHI2/NDF")
#print ch2,
#for h in mcs:
#print h.GetEntries(),
#print
plot.PlotDataMC(ds[0],
mcs,
"plots/smear_comp/",
filename + "_" + dicat_name,
x_range=(80, 100),
xlabel=xlabel + " [GeV]",
ylabel="Events",
ylabel_unit="GeV",
ratio=ratio,
logx=logx)
mcs[1].SetFillStyle(1001)
mcs[1].SetFillColor(ROOT.kAzure + 6)
mcs[1].SetLineColor(ROOT.kAzure + 6)
plot.PlotDataMC(ds,
mcs[1],
"plots/scale_comp/",
filename + "_" + dicat_name,
x_range=(80, 100),
xlabel=xlabel + " [GeV]",
ylabel="Events",
ylabel_unit="GeV",
ratio=False,
logx=logx)