def profile_plots(pt_array, ntrk_array, weight, gluon_pt_array,
gluon_ntrk_array, gluon_weight_array, quark_pt_array,
quark_ntrk_array, quark_weight_array, islogpt):
#gStyle.SetOptFit()
print 'total events: ' + str(len(pt_array))
print 'gluons: ' + str(len(gluon_pt_array))
print 'quarks: ' + str(len(quark_pt_array))
c1 = TCanvas('c1', '', 200, 10, 700, 500)
fittype = 'pol1'
if islogpt:
ptmin = 4
ptmax = 7
nbins = 10
else:
ptmin = 0
ptmax = 1300
nbins = 10
hquark = TProfile('hquark', 'Quark Profile of nTrk vs ln(pt)', nbins,
ptmin, ptmax, ntrkmin, ntrkmax)
weighted_hquark = TProfile('weighted_hquark',
'Quark Weighted Profile of nTrk vs ln(pt)',
nbins, ptmin, ptmax, ntrkmin, ntrkmax)
count = 0
for i, j, k in zip(quark_pt_array, quark_ntrk_array, quark_weight_array):
count += 1
if count % 100000 == 0:
print 'processed ' + str(count)
hquark.Fill(i, j)
weighted_hquark.Fill(i, j, k)
draw_hist_with_tag_eff(hquark, "quark", quark_pt_array, quark_ntrk_array,
gluon_pt_array, gluon_ntrk_array)
draw_hist_with_tag_eff(weighted_hquark, "weightedquark", quark_pt_array,
quark_ntrk_array, gluon_pt_array, gluon_ntrk_array)
hgluon = TProfile('hgluon', 'Gluon Profile of nTrk vs ln(pt)', nbins,
ptmin, ptmax, ntrkmin, ntrkmax)
weighted_hgluon = TProfile('weighted_hgluon',
'Gluon Weighted Profile of nTrk vs ln(pt)',
nbins, ptmin, ptmax, ntrkmin, ntrkmax)
for i, j, k in zip(gluon_pt_array, gluon_ntrk_array, gluon_weight_array):
hgluon.Fill(i, j)
weighted_hgluon.Fill(i, j, k)
draw_hist_with_tag_eff(hgluon, "gluon", quark_pt_array, quark_ntrk_array,
gluon_pt_array, gluon_ntrk_array)
draw_hist_with_tag_eff(weighted_hgluon, "weightedgluon", quark_pt_array,
quark_ntrk_array, gluon_pt_array, gluon_ntrk_array)
c3 = TCanvas('c3', '', 200, 10, 700, 500)
weighted_hquark.SetMaximum(30)
weighted_hquark.Draw("HIST")
weighted_hgluon.Draw("HIST SAME")
weighted_hquark.SetLineColor(2)
c3.SaveAs(settings.outputdir + '/overlay.pdf')
def plotPulls(optunf="Bayes",
ntest=10,
leff=True,
loufl=False,
optfun="exp",
opttfun="",
gmean=-1.0,
nrebin=4):
if opttfun == "":
opttfun = optfun
if optfun == "blobel":
gmean = 0.0
funttxt, funtxt = funtxts(opttfun, optfun, leff, loufl)
global histos, canv, canv2
histos = []
canv = TCanvas("canv", "thruth vs reco pulls", 600, 800)
canv.Divide(1, 3)
canv2 = TCanvas("canv2", "P(chi^2)", 600, 800)
canv2.Divide(2, 3)
if optunf == "BasisSplines":
bininfo = BinInfo(nrebin)
unfoldtester = UnfoldTester(optunf, nrebin)
else:
bininfo = BinInfo()
unfoldtester = UnfoldTester(optunf)
trainer = Trainer(bininfo, opttfun, optfun)
tester = Tester(bininfo, optfun)
hbininfo = bininfo.create(optfun)
dx = hbininfo["mhi"]
for sigma, ipad in [[0.01 * dx, 1], [0.03 * dx, 2], [0.1 * dx, 3]]:
txt = optunf + ", smear mu, s.d.= " + str(gmean) + ", " + str(
sigma) + ", train: " + funttxt + ", test: " + funtxt + ", " + str(
ntest) + " tests"
hPulls = TProfile("pulls", txt, hbininfo["tbins"], hbininfo["tlo"],
hbininfo["thi"])
hPulls.SetErrorOption("s")
hPulls.SetYTitle("Thruth reco pull")
histos.append(hPulls)
hChisq = TH1D("chisq", "P(chi^2) rec " + txt, 10, 0.0, 1.0)
hChisqm = TH1D("chisqm", "P(chi^2) mea " + txt, 10, 0.0, 1.0)
histos.append(hChisq)
histos.append(hChisqm)
measurement = createMeasurement(gmean, sigma, leff, optfun)
response = trainer.train(measurement, loufl=loufl)
for itest in range(ntest):
print "Test", itest
unfold, hTrue, hMeas = unfoldtester.rununfoldtest(
tester, measurement, response)
unfold.PrintTable(cout, hTrue, 2)
hReco = unfold.Hreco(2)
nbin = hReco.GetNbinsX()
if hbininfo["nrebin"] > 1:
hTrue = hTrue.Rebin(nrebin)
for ibin in range(nbin + 1):
truevalue = hTrue.GetBinContent(ibin)
recvalue = hReco.GetBinContent(ibin)
error = hReco.GetBinError(ibin)
if error > 0.0:
pull = (recvalue - truevalue) / error
hPulls.Fill(hReco.GetBinCenter(ibin), pull)
chisq = unfold.Chi2(hTrue, 2)
hChisq.Fill(TMath.Prob(chisq, hTrue.GetNbinsX()))
chisqm = unfold.Chi2measured()
pchisqm = TMath.Prob(chisqm, hMeas.GetNbinsX() - hReco.GetNbinsX())
print "Chisq measured=", chisqm, "P(chi^2)=", pchisqm
hChisqm.Fill(pchisqm)
canv.cd(ipad)
gStyle.SetErrorX(0)
hPulls.SetMinimum(-3.0)
hPulls.SetMaximum(3.0)
hPulls.SetMarkerSize(1.0)
hPulls.SetMarkerStyle(20)
hPulls.SetStats(False)
hPulls.Draw()
canv2.cd(ipad * 2 - 1)
hChisq.Draw()
canv2.cd(ipad * 2)
hChisqm.Draw()
fname = "RooUnfoldTestPulls_" + optunf + "_" + opttfun + "_" + optfun
if loufl:
fname += "_oufl"
canv.Print(fname + ".pdf")
fname = "RooUnfoldTestChisq_" + optunf + "_" + opttfun + "_" + optfun
if loufl:
fname += "_oufl"
canv2.Print(fname + ".pdf")
return
if ievent % 100 == 0:
print("Processed %d of %d events..." % (ievent, NEntries))
ntuple.GetEntry(ievent)
n_weight = ntuple.__getattr__('weight')
n_ptPruned = ntuple.__getattr__('ptPruned')
if (170 < n_ptPruned < 1000):
x.append(n_ptPruned)
y.append(n_weight)
print(n_weight)
hprof2d.Fill(n_ptPruned, n_weight)
# hprof2d.LabelsOption("h","pT")
hprof2d.SetMinimum(-0.2)
hprof2d.SetMaximum(0.3)
hprof2d.Draw()
c.SaveAs("old2dhist.png")
# # H = verification
# nbins = 100
# # xedges = 101
# # yedges =4
# H, xedges, yedges = np.histogram2d(x,y,bins=nbins)
#
# # print (xedges)
# print (yedges)
# # H= H.T
# # print (H)
# # verification = H