def MakeTH1s(arg, branchname, isMC, binning, category, selection=""):
hs = []
for a in arg:
parse = a.split(',')
filename, label = parse[:2]
if len(parse) > 2: branchname = parse[2]
elif not branchname:
raise Exception("branch not defined for " + filename + ' ' + label)
if len(parse) > 3:
energybranchname = parse[3]
else:
energybranchname = None
print "selection=", selection
chain = ROOT.TFile.Open(filename).Get("selected")
hs.append(
plot.GetTH1(chain,
branchname,
isMC,
binning,
category,
selection,
label=label,
energyBranchName=energybranchname,
usePU=not args.noPU,
useEleIDSF=not args.noEleIDSF,
scale=not args.noScales,
smear=not args.noSmearings))
return hs
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)
def MakeTH1s(arg, branchname, isMC, binning, category):
hs = []
for a in arg:
parse = a.split(',')
filename, label = parse[:2]
if len(parse) >2: branchname = parse[2]
elif not branchname:
raise Exception("branch not defined for " + filename + ' ' + label)
if len(parse) >3:
energybranchname = parse[3]
else:
energybranchname = None
chain = ROOT.TFile.Open(filename).Get("selected")
hs.append( plot.GetTH1(chain, branchname, isMC, binning, category, label=label , energyBranchName = energybranchname))
return hs
hi = 500
nbins = (hi - low) / binwidth
binning = "({n},{l},{h},{n},{l},{h})".format(n=nbins, l=low, h=hi)
branch1 = "energy_ECAL_ele[0]/cosh(etaSCEle[0])"
branch2 = branch1.replace("[0]", "[1]")
branchname = "max({0},{1}):min({0},{1})".format(branch1, branch2)
f = ROOT.TFile.Open("tmp/EtBins.root", "RECREATE")
for region in regions:
if region == "ALL": region = ""
print region
isMC = False
print "Data"
data = plot.GetTH1(data_c, branchname, isMC, binning,
region + '-' + common_cut)
isMC = True
print "Inc"
inc = plot.GetTH1(inc_c,
branchname,
isMC,
binning,
region + '-' + common_cut,
noWeights=True)
print "LT"
lt = plot.GetTH1(lt_c,
branchname,
isMC,
binning,
region + '-' + common_cut,
noWeights=True)
h = None
if args.fromFile:
f = ROOT.TFile.Open("tmp/" + args.name + ".root")
if f:
h = f.Get("hist0")
if not args.fromFile or not h:
nbins, low, hi = eval(args.binning)
binning = "({n},{l},{h},{n},{l},{h})".format(n=nbins, l=low, h=hi)
chain = ROOT.TFile.Open(args.file).Get("selected")
if not args.noSym:
branchpair = args.branch + "," + args.branch.replace("[0]", "[1]")
branchname = "max({0}):min({0})".format(branchpair)
else:
branchname = "{0}:{1}".format(args.branch, args.branch.replace("[0]", "[1]") )
h = plot.GetTH1(chain, branchname, args.isMC, binning, category)
f = ROOT.TFile.Open("tmp/" + args.name + ".root", "RECREATE")
h.Write()
f.Close()
if args.makeDiagBins:
values, integrals = plot.makeDiagBins(h, args.nevents)
with open("data/regions/" +args.name + ".dat","w") as datfile:
for low,hi,integral in zip(values[:-1], values[1:], integrals):
datfile.write(args.category + "-ET_%.2f_%.2f # nevents %d\n" % (low, hi, integral))
else:
values = None
plot.Draw2D(h, args.label, args.name, diagBins = values, logy=True, logx=True)
import sys, os
import argparse
sys.path.insert(0, os.getcwd() + '/python')
import plot
import ROOT
chain = ROOT.TFile.Open("tmp/Moriond17_oldRegr/s1_chain.root").Get("selected")
f = ROOT.TFile.Open("LT_inc.root", "RECREATE")
h = plot.GetTH1(
chain,
"energy_ECAL_ele[0]/cosh(etaSCEle[0])+energy_ECAL_ele[1]/cosh(etaSCEle[1])",
True,
binning="(4000,0,2000)",
category="eleID_loose25nsRun22016Moriond",
label="LT",
usePU=False)
h.Write()
f.Close()
parser.add_argument("binning", help="(nbins,low,hi)")
parser.add_argument("file", help="specify data file")
parser.add_argument("name", help="outfile base name")
parser.add_argument("-c",
"--category",
help="category string",
default=category)
args = parser.parse_args()
sys.path.insert(0, os.getcwd() + '/python')
import plot
import ROOT
#plot.ndraw_entries = 100000
ROOT.gROOT.SetBatch(True)
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
binning = args.binning
chain = ROOT.TFile.Open(args.file).Get("selected")
branchname = "energy_ECAL_ele[0]/cosh(etaSCEle[0]):energy_ECAL_ele[1]/cosh(etaSCEle[1])"
isMC = False
et_h = plot.GetTH1(chain, branchname, isMC, binning, args.category)
f = ROOT.TFile.Open("tmp/" + args.name + ".root", "RECREATE")
et_h.Write()
f.Close()
33.375, 34.125, 34.75, 35.375, 36.0,
36.625, 37.25, 37.875, 38.5, 39.125,
39.625, 40.125, 40.625, 41.125, 41.625,
42.125, 42.625, 43.125, 43.625, 44.125,
44.625, 45.125, 45.625, 46.125, 46.75,
47.5, 48.5, 49.875, 51.625, 53.875,
56.75, 60.5, 65.25, 71.5, 79.875,
91.875, 110.625, 147.625, 2000.0
]
bins = np.array(bins)
ROOT.gStyle.SetOptTitle(1)
branchname = "energy_ECAL_ele[0]/cosh(etaSCEle[0]):energy_ECAL_ele[1]/cosh(etaSCEle)[1]"
inc_et= ROOT.TH2F("inc_et", "", len(bins) - 1, bins, len(bins) - 1, bins)
plot.GetTH1(inc_chain, branchname, isMC, histname="inc_et", category=category, label="Inclusive Madgraph", usePU = False, useEleIDSF = False)
plot.FoldTH2(inc_et)
data_et= ROOT.TH2F("data_et", "", len(bins) - 1, bins, len(bins) - 1, bins)
plot.GetTH1(data_chain, branchname, False, histname="data_et", category=category, label="Data", usePU = False, useEleIDSF = False)
plot.FoldTH2(data_et)
ltbinned_et= ROOT.TH2F("ltbinned_et", "", len(bins) - 1, bins, len(bins) - 1, bins)
plot.GetTH1(lt_binned_all, branchname, isMC, histname="ltbinned_et", category=category, label="LT Binned Madgraph", usePU = False, useLT=True, useEleIDSF = False)
plot.FoldTH2(ltbinned_et)
ltbinned_et_entries = ROOT.TH2F("ltbinned_et_entries", "", len(bins) - 1, bins, len(bins) - 1, bins)
plot.GetTH1(lt_binned_all, branchname, isMC, histname="ltbinned_et_entries", category=category, label="LT Binned Madgraph Unweighted", usePU = False, useLT=False, useEleIDSF = False)
plot.FoldTH2(ltbinned_et_entries)
for h in [inc_et, data_et, ltbinned_et, ltbinned_et_entries]:
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)