def __init__(self, genHist, recHist, hist2d, name):
self.genHist = genHist.Clone()
self.recHist = recHist.Clone()
self.hist2d = hist2d.Clone()
self.response = RooUnfoldResponse(recHist, genHist, hist2d,
"response matrix", "response matrix")
self.response.UseOverflow()
self.name = name
hx = self.hist2d.ProjectionX()
hy = self.hist2d.ProjectionY()
vaild = 0.0001
test1 = 0
test2 = 0
for i in range(hx.GetNbinsX() + 2):
if recHist.GetBinContent(i) != 0:
val = abs(hx.GetBinContent(i) / recHist.GetBinContent(i) - 1)
else:
val = 0
if val > test1:
test1 = val
for i in range(hx.GetNbinsX() + 2):
if genHist.GetBinContent(i) != 0:
val = abs(hy.GetBinContent(i) / genHist.GetBinContent(i) - 1)
else:
val = 0
if val > test2:
test2 = val
if test1 < vaild and test2 < vaild:
print "response matrix is correct"
else:
print "response matrix is wrong"
print test1
print test2
def UnfoldFF(rawFF, detResponse, tag):
padFF.Clear()
padFF.Divide(2)
# Detector response
ResponseNorm(detResponse)
padFF.cd(1)
detResponse.Draw('COLZ')
response = RooUnfoldResponse(rawFF, rawFF, detResponse)
bayes = RooUnfoldBayes(response, rawFF)
# Unfolding - Bayesian
ana_util.COLOR = SelectColor(0)
ana_util.MARKER = SelectMarker(0)
padFF.cd(2)
# Legend
lgd = TLegend(0.12, 0.6, 0.3, 0.85)
lgd.SetName('lgd' + tag)
lgd.SetBorderSize(0)
lgd.SetFillColor(0)
# Z measured
rawFF.SetBins(10, 0, 1.0)
DrawFF(rawFF, 'hRaw_' + tag)
lgd.AddEntry(rawFF, 'Raw')
for nIter in range(4, 5):
bayes.SetIterations(nIter)
hist = DrawFF(bayes.Hreco(0), 'hBayes' + repr(nIter) + '_' + tag)
for ix in range(1, hist.GetNbinsX()):
hist.SetBinError(ix, rawFF.GetBinError(ix))
lgd.AddEntry(hist, 'Bayes (N=%d)' % nIter)
lgd.Draw('same')
padFF.Print(printFile, 'Title:' + tag)
padFF.Write('c' + tag)
def make2Dresponse(responses, jetPt, meas, true, misses=None, fakes=None):
print("++++++++ Create Response from 3D histograms ++++++++")
response2D = RooUnfoldResponse(meas, true)
for r, pT, i in zip(responses, jetPt, range(len(responses))):
nx = r.GetNbinsX()
ny = r.GetNbinsY()
nz = r.GetNbinsZ()
pttrue = (pT[0] + pT[1]) / 2.0
print("{:.0f}%".format(100.0 * i / len(jetPt)))
for ix in range(0, nx):
for iy in range(0, ny):
for iz in range(1, nz):
ib = r.GetBin(ix, iy, iz)
c = r.GetBinContent(ib)
jttrue = r.GetZaxis().GetBinCenter(iz)
# if iy == 0 and ix == 0:
# response2D.Miss(jttrue,pttrue,c)
if ix > 0 and iy > 0:
jtobs = r.GetXaxis().GetBinCenter(ix)
jtobs_meas = meas.GetXaxis().GetBinCenter(ix)
ptobs_meas = meas.GetYaxis().GetBinCenter(iy)
if TMath.Abs(jtobs - jtobs_meas) > 0.01:
print("jtobs: {}, jtobs_meas: {}".format(
jtobs, jtobs_meas))
raise ValueError(
"Incorrect binning in make2Dresponse")
ptobs = r.GetYaxis().GetBinCenter(iy)
if TMath.Abs(ptobs - ptobs_meas) > 0.01:
print("ptobs: {}, ptobs_meas: {}".format(
ptobs, ptobs_meas))
raise ValueError(
"Incorrect binning in make2Dresponse")
jttrue = r.GetZaxis().GetBinCenter(iz)
response2D.Fill(jtobs, ptobs, jttrue, pttrue, c)
print("{:.0f}%".format(100))
if misses != None:
nx = misses.GetNbinsX()
ny = misses.GetNbinsY()
for ix in range(1, nx):
for iy in range(1, ny):
ib = misses.GetBin(ix, iy)
c = misses.GetBinContent(ib)
jttrue = misses.GetXaxis().GetBinCenter(ix)
pttrue = misses.GetYaxis().GetBinCenter(iy)
# print("jtTrue: {}, ptTrue: {}, Misses: {}".format(jttrue,pttrue,c))
response2D.Miss(jttrue, pttrue, c)
if fakes != None:
nx = fakes.GetNbinsX()
ny = fakes.GetNbinsY()
for ix in range(1, nx):
for iy in range(1, ny):
ib = fakes.GetBin(ix, iy)
c = fakes.GetBinContent(ib)
jtobs = fakes.GetXaxis().GetBinCenter(ix)
ptobs = fakes.GetYaxis().GetBinCenter(iy)
# print("jtObs: {}, ptObs: {}, Fakes: {}".format(jtobs,ptobs,c))
response2D.Fake(jtobs, ptobs, c)
return response2D
def createResponse(hMeas, hResponse):
response = RooUnfoldResponse(hMeas, hMeas)
for iby in range(1, hResponse.GetNbinsY() + 1):
jt = hResponse.GetYaxis().GetBinCenter(iby)
ib = hResponse.GetBin(iby, 0)
N = hResponse.GetBinContent(ib)
response.Miss(jt, N)
for ibx in range(1, hResponse.GetNbinsX() + 1):
jtobs = hResponse.GetXaxis().GetBinCenter(ibx)
ib = hResponse.GetBin(ibx, iby)
N = hResponse.GetBinContent(ib)
response.Fill(jtobs, jt, N)
return response
def train(self, measurement, loufl=False):
print "============================ TRAIN ============================="
txt = "Smear mu, s.d.: " + str(measurement.getMean())
txt += ", " + str(measurement.getSigma()) + ", eff.: "
txt += str(measurement.getLeff())
txt += ", o/u-flow: " + str(loufl) + ", function: " + self.opttfun
print txt
# Create response matrix object:
response = RooUnfoldResponse(
self.bininfo["mbins"], self.bininfo["mlo"], self.bininfo["mhi"],
self.bininfo["tbins"] * self.bininfo["nrebin"],
self.bininfo["tlo"], self.bininfo["thi"])
response.UseOverflow(loufl)
for i in xrange(100000):
xt, x = measurement.generate(self.trainfun)
if x != None:
response.Fill(x, xt)
else:
response.Miss(xt)
return response
def makeResponseFromTuple(Ntuple, meas, true):
print("Start makeResponseFromTuple")
start = time.time()
response2D = RooUnfoldResponse(meas, true)
for entry in Ntuple:
jtObs = entry.jtObs
ptObs = entry.ptObs
jtTrue = entry.jtTrue
ptTrue = entry.ptTrue
if ptObs > 0 and ptTrue > 0:
response2D.Fill(jtObs, ptObs, jtTrue, ptTrue)
elif ptObs < 0:
response2D.Miss(jtTrue, ptTrue)
elif ptTrue < 0:
response2D.Fake(jtObs, ptObs)
else:
print("ERROR")
end = time.time()
print("Finished in {}s".format(end - start))
return response2D
def _create_response(reco_norm_data, true_norm_data, data, reco_binning,
true_binning):
"""Create the response object from the signal data."""
logging.debug('Creating response object.')
reco_hist = roounfold._histogram(events=reco_norm_data,
binning=reco_binning,
weights=reco_norm_data['pisa_weight'],
errors=True,
name='reco_signal',
tex=r'\rm{reco_signal}')
true_hist = roounfold._histogram(events=true_norm_data,
binning=true_binning,
weights=true_norm_data['pisa_weight'],
errors=True,
name='true_signal',
tex=r'\rm{true_signal}')
r_flat = roounfold._flatten_to_1d(reco_hist)
t_flat = roounfold._flatten_to_1d(true_hist)
smear_matrix = roounfold._histogram(events=data,
binning=reco_binning +
true_binning,
weights=data['pisa_weight'],
errors=True,
name='smearing_matrix',
tex=r'\rm{smearing_matrix}')
smear_flat = roounfold._flatten_to_2d(smear_matrix)
r_th1d = convert_to_th1d(r_flat, errors=True)
t_th1d = convert_to_th1d(t_flat, errors=True)
smear_th2d = convert_to_th2d(smear_flat, errors=True)
response = RooUnfoldResponse(r_th1d, t_th1d, smear_th2d)
del r_th1d
del smear_th2d
return response, t_th1d
def createToyTraining(self, rootFile, numberEvents):
self._f = root_open(rootFile, "read")
self._hJetPtIn = self._f.Get(
"AliJJetJtTask/AliJJetJtHistManager/JetPt/JetPtNFin{:02d}".format(
2))
self._hZIn = self._f.Get(
"AliJJetJtTask/AliJJetJtHistManager/Z/ZNFin{:02d}".format(2))
LimL = 0.1
LimH = 500
logBW = (TMath.Log(LimH) - TMath.Log(LimL)) / self._NBINS
self._LogBinsX = [
LimL * math.exp(ij * logBW) for ij in range(0, self._NBINS + 1)
]
self._hJetPtMeas = Hist(self._LogBinsX)
self._hJetPtTrue = Hist(self._LogBinsX)
self._myRandom = TRandom3(self._randomSeed)
if self._fEff is None:
self._fEff = TF1("fEff", "1-0.5*exp(-x)")
if self._jetBinBorders is None:
self._jetBinBorders = [5, 10, 20, 30, 40, 60, 80, 100, 150, 500]
self._hJetPtMeasCoarse = Hist(self._jetBinBorders)
self._hJetPtTrueCoarse = Hist(self._jetBinBorders)
low = 0.01
high = 10
BinW = (TMath.Log(high) - TMath.Log(low)) / self._NBINSJt
self._LogBinsJt = [
low * math.exp(i * BinW) for i in range(self._NBINSJt + 1)
]
self._jetBinBorders = self._jetBinBorders
self._jetPtBins = [
(a, b)
for a, b in zip(self._jetBinBorders, self._jetBinBorders[1:])
]
self._hJtTrue2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._hJtMeas2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._hJtFake2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
# Histogram to store jT with respect to the leading hadron
self._hJtTestMeas2D = Hist2D(
self._LogBinsJt, self._jetBinBorders) # Needs a better name
self._hJtTestTrue2D = Hist2D(
self._LogBinsJt, self._jetBinBorders) # Needs a better name
self._responseJetPt = RooUnfoldResponse(self._hJetPtMeas,
self._hJetPtTrue)
self._responseJetPtCoarse = RooUnfoldResponse(self._hJetPtMeasCoarse,
self._hJetPtTrueCoarse)
self._hresponseJetPt = Hist2D(self._jetBinBorders, self._jetBinBorders)
self._hresponseJetPtCoarse = Hist2D(self._jetBinBorders,
self._jetBinBorders)
# Histogram index is jet pT index, Bin 0 is 5-10 GeV
# Histogram X axis is observed jT, Bin 0 is underflow
# Histogram Y axis is observed jet Pt, Bin 0 is underflow
# Histogram Z axis is True jT, Bin 0 is underflow
self._2Dresponses = [
Hist3D(self._LogBinsJt, self._jetBinBorders, self._LogBinsJt)
for i in self._jetPtBins
]
self._misses2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._fakes2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._numberJetsMeasBin = [0 for i in self._jetBinBorders]
self._numberJetsTrueBin = [0 for i in self._jetBinBorders]
self._numberJetsTestMeasBin = [0 for i in self._jetBinBorders]
self._numberJetsTestTrueBin = [0 for i in self._jetBinBorders]
self._numberFakesBin = [0 for i in self._jetBinBorders]
ieout = numberEvents / 10
if ieout > 20000:
ieout = 20000
start_time = datetime.now()
print("Processing MC Training Events")
for ievt in range(numberEvents):
tracksTrue = []
tracksMeas = [0 for x in range(100)]
if ievt % ieout == 0 and ievt > 0:
time_elapsed = datetime.now() - start_time
time_left = timedelta(seconds=time_elapsed.total_seconds() *
1.0 * (numberEvents - ievt) / ievt)
print("Event {} [{:.2f}%] Time Elapsed: {} ETA: {}".format(
ievt,
100.0 * ievt / numberEvents,
fmtDelta(time_elapsed),
fmtDelta(time_left),
))
jetTrue = TVector3(0, 0, 0)
jetMeas = TVector3(0, 0, 0)
jetPt = self._hJetPtIn.GetRandom()
remainder = jetPt
if jetPt < 5:
continue
nt = 0
nt_meas = 0
while remainder > 0:
trackPt = self._hZIn.GetRandom() * jetPt
if trackPt < remainder:
track = TVector3()
remainder = remainder - trackPt
else:
trackPt = remainder
remainder = -1
if trackPt > 0.15:
track.SetPtEtaPhi(
trackPt,
self._myRandom.Gaus(0, 0.1),
self._myRandom.Gaus(math.pi, 0.2),
)
tracksTrue.append(track)
jetTrue += track
if self._fEff.Eval(trackPt) > self._myRandom.Uniform(0, 1):
tracksMeas[nt] = 1
jetMeas += track
nt_meas += 1
else:
tracksMeas[nt] = 0
nt += 1
fakes = []
for it in range(self._fakeRate * 100):
if self._myRandom.Uniform(0, 1) > 0.99:
fake = TVector3()
fake.SetPtEtaPhi(
self._myRandom.Uniform(0.15, 1),
self._myRandom.Gaus(0, 0.1),
self._myRandom.Gaus(math.pi, 0.2),
)
fakes.append(fake)
jetMeas += fake
self._responseJetPt.Fill(jetMeas.Pt(), jetTrue.Pt())
self._responseJetPtCoarse.Fill(jetMeas.Pt(), jetTrue.Pt())
self._hresponseJetPt.Fill(jetMeas.Pt(), jetTrue.Pt())
self._hresponseJetPtCoarse.Fill(jetMeas.Pt(), jetTrue.Pt())
ij_meas = GetBin(self._jetBinBorders, jetMeas.Pt())
ij_true = GetBin(self._jetBinBorders, jetTrue.Pt())
if nt < 5 or nt_meas < 5:
continue
if ij_meas >= 0:
self._numberJetsMeasBin[ij_meas] += 1
if ij_true >= 0:
self._numberJetsTrueBin[ij_true] += 1
for track, it in zip(tracksTrue, range(100)):
zTrue = (track * jetTrue.Unit()) / jetTrue.Mag()
jtTrue = (track - scaleJet(jetTrue, zTrue)).Mag()
if ij_meas >= 0:
if tracksMeas[it] == 1:
zMeas = (track * jetMeas.Unit()) / jetMeas.Mag()
jtMeas = (track - scaleJet(jetMeas, zMeas)).Mag()
self._2Dresponses[ij_true].Fill(
jtMeas, jetMeas.Pt(), jtTrue)
else:
self._misses2D.Fill(jtTrue, jetTrue.Pt())
if ij_meas >= 0:
for fake in fakes:
zFake = (fake * jetMeas.Unit()) / jetMeas.Mag()
jtFake = (fake - scaleJet(jetMeas, zFake)).Mag()
if self._weight:
self._hJtFake2D.Fill(jtFake, jetMeas.Pt(),
1.0 / jtFake)
else:
self._hJtFake2D.Fill(jtFake, jetMeas.Pt())
if self._fillFakes:
self._fakes2D.Fill(jtFake, jetMeas.Pt())
print("Event {} [{:.2f}%] Time Elapsed: {}".format(
numberEvents, 100.0, fmtDelta(time_elapsed)))
self._numberJetsMeasTrain = sum(self._numberJetsMeasBin)
def process_response(self):
list_df_mc_reco = []
list_df_mc_gen = []
for iptskim, _ in enumerate(self.lpt_anbinmin):
df_mc_reco = pickle.load(openfile(self.lpt_recodecmerged[iptskim], "rb"))
if "pt_jet" not in df_mc_reco.columns:
print("Jet variables not found in the dataframe. Skipping process_response.")
return
if self.s_evtsel is not None:
df_mc_reco = df_mc_reco.query(self.s_evtsel)
if self.s_trigger is not None:
df_mc_reco = df_mc_reco.query(self.s_trigger)
df_mc_reco = selectdfrunlist(df_mc_reco, \
self.run_param[self.runlistrigger[self.triggerbit]], "run_number")
if self.doml is True:
df_mc_reco = df_mc_reco.query(self.l_selml[iptskim])
else:
print("Doing std analysis")
list_df_mc_reco.append(df_mc_reco)
df_mc_gen = pickle.load(openfile(self.lpt_gendecmerged[iptskim], "rb"))
df_mc_gen = selectdfrunlist(df_mc_gen, \
self.run_param[self.runlistrigger[self.triggerbit]], "run_number")
df_mc_gen = df_mc_gen.query(self.s_presel_gen_eff)
list_df_mc_gen.append(df_mc_gen)
df_rec = pd.concat(list_df_mc_reco)
df_gen = pd.concat(list_df_mc_gen)
his_njets = TH1F("his_njets_gen", "Number of MC jets", 1, 0, 1)
his_njets.SetBinContent(1, len(df_gen.index)) # total number of generated & selected jets for normalisation
df_rec = df_rec[df_rec.ismcfd == 1] # reconstructed & selected non-prompt jets
df_gen = df_gen[df_gen.ismcfd == 1] # generated & selected non-prompt jets
out_file = TFile.Open(self.n_fileeff, "update")
# Bin arrays
# pt_cand
n_bins_ptc = len(self.lpt_finbinmin)
bins_ptc_temp = self.lpt_finbinmin.copy()
bins_ptc_temp.append(self.lpt_finbinmax[n_bins_ptc - 1])
bins_ptc = array.array('d', bins_ptc_temp)
# pt_jet
n_bins_ptjet = len(self.lvar2_binmin)
bins_ptjet_temp = self.lvar2_binmin.copy()
bins_ptjet_temp.append(self.lvar2_binmax[n_bins_ptjet - 1])
bins_ptjet = array.array('d', bins_ptjet_temp)
# z
bins_z_temp = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1]
n_bins_z = len(bins_z_temp) - 1
bins_z = array.array('d', bins_z_temp)
# Detector response matrix of pt_jet of non-prompt jets
df_resp_jet_fd = df_rec.loc[:, ["pt_gen_jet", "pt_jet"]]
his_resp_jet_fd = TH2F("his_resp_jet_fd", \
"Response matrix of #it{p}_{T}^{jet, ch} of non-prompt jets;#it{p}_{T}^{jet, ch, gen.} (GeV/#it{c});#it{p}_{T}^{jet, ch, rec.} (GeV/#it{c})", \
100, 0, 100, 100, 0, 100)
fill_hist(his_resp_jet_fd, df_resp_jet_fd)
# Simulated pt_cand vs. pt_jet of non-prompt jets
df_ptc_ptjet_fd = df_gen.loc[:, ["pt_cand", "pt_jet"]]
his_ptc_ptjet_fd = TH2F("his_ptc_ptjet_fd", \
"Simulated #it{p}_{T}^{cand.} vs. #it{p}_{T}^{jet} of non-prompt jets;#it{p}_{T}^{cand., gen.} (GeV/#it{c});#it{p}_{T}^{jet, ch, gen.} (GeV/#it{c})", \
n_bins_ptc, bins_ptc, 100, 0, 100)
fill_hist(his_ptc_ptjet_fd, df_ptc_ptjet_fd)
# z_gen of reconstructed feed-down jets (for response)
arr_z_gen_resp = z_gen_calc(df_rec.pt_gen_jet, df_rec.phi_gen_jet, df_rec.eta_gen_jet,
df_rec.pt_gen_cand, df_rec.delta_phi_gen_jet, df_rec.delta_eta_gen_jet)
# z_rec of reconstructed feed-down jets (for response)
arr_z_rec_resp = z_calc(df_rec.pt_jet, df_rec.phi_jet, df_rec.eta_jet,
df_rec.pt_cand, df_rec.phi_cand, df_rec.eta_cand)
# z_gen of simulated feed-down jets
arr_z_gen_sim = z_calc(df_gen.pt_jet, df_gen.phi_jet, df_gen.eta_jet,
df_gen.pt_cand, df_gen.phi_cand, df_gen.eta_cand)
df_rec["z_gen"] = arr_z_gen_resp
df_rec["z"] = arr_z_rec_resp
df_gen["z"] = arr_z_gen_sim
# Simulated pt_cand vs. pt_jet vs z of non-prompt jets
df_ptc_ptjet_z_fd = df_gen.loc[:, ["pt_cand", "pt_jet", "z"]]
his_ptc_ptjet_z_fd = TH3F("his_ptc_ptjet_z_fd", \
"Simulated #it{p}_{T}^{cand.} vs. #it{p}_{T}^{jet} vs. #it{z} of non-prompt jets;"
"#it{p}_{T}^{cand., gen.} (GeV/#it{c});"
"#it{p}_{T}^{jet, ch, gen.} (GeV/#it{c});"
"#it{z}", \
n_bins_ptc, bins_ptc, n_bins_ptjet, bins_ptjet, n_bins_z, bins_z)
fill_hist(his_ptc_ptjet_z_fd, df_ptc_ptjet_z_fd)
# Create response matrix for feed-down smearing
# x axis = z, y axis = pt_jet
his_resp_rec = TH2F("his_resp_rec", "his_resp_rec", n_bins_z, bins_z, n_bins_ptjet, bins_ptjet)
his_resp_gen = TH2F("his_resp_gen", "his_resp_gen", n_bins_z, bins_z, n_bins_ptjet, bins_ptjet)
resp_z = RooUnfoldResponse(his_resp_rec, his_resp_gen)
for row in df_rec.itertuples():
resp_z.Fill(row.z, row.pt_jet, row.z_gen, row.pt_gen_jet)
out_file.cd()
his_resp_jet_fd.Write()
his_ptc_ptjet_fd.Write()
his_ptc_ptjet_z_fd.Write()
his_njets.Write()
resp_z.Write("resp_z")
out_file.Close()
def _makeUnfoldResponse( self ):
if self.fakes:
return RooUnfoldResponse ( self.measured, self.truth, self.fakes, self.response )
else:
return RooUnfoldResponse ( self.measured, self.truth, self.response )
def main():
gROOT.SetBatch()
#range for |t|
ptmin = 0.
ptmax = 0.109 # 0.109 0.01 for interference range
#default binning
ptbin = 0.004 # 0.004 0.0005 for interference range
#long bins at high |t|
ptmid = 0.06 # 0.08, value > ptmax will switch it off 0.06
ptlon = 0.01 # 0.01
#short bins at low |t|
ptlow = 0.01
ptshort = 0.0005
#mass interval
mmin = 2.8
mmax = 3.2
#dy = 2. # rapidity interval, for integrated sigma
dy = 1.
ngg = 131 # number of gamma-gamma from mass fit
lumi = 13871.907 # lumi in inv. ub
#correction to luminosity for ana/triggered events
ratio_ana = 3420950. / 3694000
#scale the lumi for |z| around nominal bunch crossing
ratio_zdc_vtx = 0.502
Reta = 0.503 # pseudorapidity preselection
#Reta = 1.
trg_eff = 0.67 # bemc trigger efficiency
ratio_tof = 1.433 # tof correction to efficiency
bbceff = 0.97 # BBC veto inefficiency
zdc_acc = 0.49 # ZDC acceptance to XnXn 0.7
#zdc_acc = 1.
br = 0.05971 # dielectrons branching ratio
#data
basedir = "../../../star-upc-data/ana/muDst/muDst_run1/sel5"
infile = "ana_muDst_run1_all_sel5z.root"
#MC
basedir_sl = "../../../star-upc-data/ana/starsim/slight14e/sel5"
#infile_sl = "ana_slight14e1x2_s6_sel5z.root"
infile_sl = "ana_slight14e1x3_s6_sel5z.root"
#
basedir_sart = "../../../star-upc-data/ana/starsim/sartre14a/sel5"
infile_sart = "ana_sartre14a1_sel5z_s6_v2.root"
#
basedir_bgen = "../../../star-upc-data/ana/starsim/bgen14a/sel5"
infile_bgen = "ana_bgen14a1_v0_sel5z_s6.root"
#infile_bgen = "ana_bgen14a2_sel5z_s6.root"
#
basedir_gg = "../../../star-upc-data/ana/starsim/slight14e/sel5"
infile_gg = "ana_slight14e2x1_sel5_nzvtx.root"
#model predictions
gSlight = load_starlight(dy)
gSartre = load_sartre()
gFlat = loat_flat_pt2()
gMS = load_ms()
gCCK = load_cck()
#open the inputs
inp = TFile.Open(basedir + "/" + infile)
tree = inp.Get("jRecTree")
#
inp_gg = TFile.Open(basedir_gg + "/" + infile_gg)
tree_gg = inp_gg.Get("jRecTree")
#
inp_sl = TFile.Open(basedir_sl + "/" + infile_sl)
tree_sl_gen = inp_sl.Get("jGenTree")
#
inp_sart = TFile.Open(basedir_sart + "/" + infile_sart)
tree_sart_gen = inp_sart.Get("jGenTree")
#
inp_bgen = TFile.Open(basedir_bgen + "/" + infile_bgen)
tree_bgen_gen = inp_bgen.Get("jGenTree")
#evaluate binning
#print "bins:", ut.get_nbins(ptbin, ptmin, ptmax)
bins = ut.get_bins_vec_2pt(ptbin, ptlon, ptmin, ptmax, ptmid)
#bins = ut.get_bins_vec_3pt(ptshort, ptbin, ptlon, ptmin, ptmax, ptlow, ptmid)
#print "bins2:", bins.size()-1
#load the data
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
hPt = ut.prepare_TH1D_vec("hPt", bins)
tree.Draw("jRecPt*jRecPt >> hPt", strsel)
#distribution for bin centers
hPtCen = hPt.Clone("hPtCen")
#gamma-gamma component
hPtGG = ut.prepare_TH1D_vec("hPtGG", bins)
tree_gg.Draw("jRecPt*jRecPt >> hPtGG", strsel)
#normalize the gamma-gamma component
ut.norm_to_num(hPtGG, ngg, rt.kGreen)
#incoherent functional shape
func_incoh_pt2 = TF1("func_incoh", "[0]*exp(-[1]*x)", 0., 10.)
func_incoh_pt2.SetParameters(873.04, 3.28)
#fill incoherent histogram from functional shape
hPtIncoh = ut.prepare_TH1D_vec("hPtIncoh", bins)
ut.fill_h1_tf(hPtIncoh, func_incoh_pt2, rt.kRed)
#print "Entries before gamma-gamma and incoherent subtraction:", hPt.GetEntries()
#subtract gamma-gamma and incoherent components
hPt.Sumw2()
hPt.Add(hPtGG, -1)
#print "Gamma-gamma entries:", hPtGG.Integral()
#print "Entries after gamma-gamma subtraction:", hPt.Integral()
#print "Incoherent entries:", hPtIncoh.Integral()
hPt.Add(hPtIncoh, -1)
#print "Entries after all subtraction:", hPt.Integral()
#scale the luminosity
lumi_scaled = lumi * ratio_ana * ratio_zdc_vtx
#print "lumi_scaled:", lumi_scaled
#denominator for deconvoluted distribution, conversion ub to mb
den = Reta * br * zdc_acc * trg_eff * bbceff * ratio_tof * lumi_scaled * 1000. * dy
#deconvolution
deconv_min = bins[0]
deconv_max = bins[bins.size() - 1]
deconv_nbin = bins.size() - 1
gROOT.LoadMacro("fill_response_matrix.C")
#Starlight response
#resp_sl = RooUnfoldResponse(deconv_nbin, deconv_min, deconv_max, deconv_nbin, deconv_min, deconv_max)
resp_sl = RooUnfoldResponse(hPt, hPt)
rt.fill_response_matrix(tree_sl_gen, resp_sl)
#
unfold_sl = RooUnfoldBayes(resp_sl, hPt, 15)
#unfold_sl = RooUnfoldSvd(resp_sl, hPt, 15)
hPtSl = unfold_sl.Hreco()
#ut.set_H1D(hPtSl)
#apply the denominator and bin width
ut.norm_to_den_w(hPtSl, den)
#Sartre response
#resp_sart = RooUnfoldResponse(deconv_nbin, deconv_min, deconv_max, deconv_nbin, deconv_min, deconv_max)
#resp_sart = RooUnfoldResponse(hPt, hPt)
#rt.fill_response_matrix(tree_sart_gen, resp_sart)
#
#unfold_sart = RooUnfoldBayes(resp_sart, hPt, 10)
#hPtSart = unfold_sart.Hreco()
#ut.set_H1D(hPtSart)
#hPtSart.SetMarkerStyle(21)
#Flat pT^2 response
#resp_bgen = RooUnfoldResponse(deconv_nbin, deconv_min, deconv_max, deconv_nbin, deconv_min, deconv_max)
resp_bgen = RooUnfoldResponse(hPt, hPt)
rt.fill_response_matrix(tree_bgen_gen, resp_bgen)
#
unfold_bgen = RooUnfoldBayes(resp_bgen, hPt, 14)
hPtFlat = unfold_bgen.Hreco()
#ut.set_H1D(hPtFlat)
#apply the denominator and bin width
ut.norm_to_den_w(hPtFlat, den)
#hPtFlat.SetMarkerStyle(22)
#hPtFlat.SetMarkerSize(1.3)
#systematical errors
err_zdc_acc = 0.1
err_bemc_eff = 0.03
#sys_err = rt.TMath.Sqrt(err_zdc_acc*err_zdc_acc + err_bemc_eff*err_bemc_eff)
sys_err = err_zdc_acc * err_zdc_acc + err_bemc_eff * err_bemc_eff
#print "Total sys err:", sys_err
hSys = ut.prepare_TH1D_vec("hSys", bins)
hSys.SetOption("E2")
hSys.SetFillColor(rt.kOrange + 1)
hSys.SetLineColor(rt.kOrange)
for ibin in xrange(1, hPtFlat.GetNbinsX() + 1):
hSys.SetBinContent(ibin, hPtFlat.GetBinContent(ibin))
sig_sl = hPtSl.GetBinContent(ibin)
sig_fl = hPtFlat.GetBinContent(ibin)
err_deconv = TMath.Abs(sig_fl - sig_sl) / sig_fl
#print "err_deconv", err_deconv
#sys_err += err_deconv*err_deconv
sys_err_sq = sys_err + err_deconv * err_deconv
sys_err_bin = TMath.Sqrt(sys_err_sq)
stat_err = hPtFlat.GetBinError(ibin) / hPtFlat.GetBinContent(ibin)
tot_err = TMath.Sqrt(stat_err * stat_err + sys_err_sq)
#hSys.SetBinError(ibin, hPtFlat.GetBinContent(ibin)*err_deconv)
hSys.SetBinError(ibin, hPtFlat.GetBinContent(ibin) * sys_err_bin)
#hPtFlat.SetBinError(ibin, hPtFlat.GetBinContent(ibin)*tot_err)
#draw the results
gStyle.SetPadTickX(1)
gStyle.SetFrameLineWidth(2)
#frame for models plot only
frame = ut.prepare_TH1D("frame", ptbin, ptmin, ptmax)
can = ut.box_canvas()
#ut.set_margin_lbtr(gPad, 0.1, 0.09, 0.03, 0.03)
ut.set_margin_lbtr(gPad, 0.1, 0.09, 0.055, 0.01)
ytit = "d#it{#sigma}/d#it{t}d#it{y} (mb/(GeV/c)^{2})"
xtit = "|#kern[0.3]{#it{t}}| ((GeV/c)^{2})"
ut.put_yx_tit(frame, ytit, xtit, 1.4, 1.2)
frame.SetMaximum(11)
#frame.SetMinimum(1.e-6)
#frame.SetMinimum(2e-4)
frame.SetMinimum(1e-5) # 3e-5
frame.Draw()
#hSys.Draw("e2same")
#bin center points from data
#gSig = apply_centers(hPtFlat, hPtCen)
gSig = fixed_centers(hPtFlat)
ut.set_graph(gSig)
#hPtSl.Draw("e1same")
#hPtSart.Draw("e1same")
#hPtFlat.Draw("e1same")
#put model predictions
#gSartre.Draw("lsame")
#gFlat.Draw("lsame")
gMS.Draw("lsame")
gCCK.Draw("lsame")
gSlight.Draw("lsame")
gSig.Draw("P")
frame.Draw("same")
gPad.SetLogy()
cleg = ut.prepare_leg(0.1, 0.96, 0.14, 0.01, 0.035)
cleg.AddEntry(
None,
"Au+Au #rightarrow J/#psi + Au+Au + XnXn, #sqrt{#it{s}_{#it{NN}}} = 200 GeV",
"")
cleg.Draw("same")
leg = ut.prepare_leg(0.45, 0.82, 0.18, 0.1, 0.035)
leg.AddEntry(None, "#bf{|#kern[0.3]{#it{y}}| < 1}", "")
hx = ut.prepare_TH1D("hx", 1, 0, 1)
leg.AddEntry(hx, "STAR")
hx.Draw("same")
leg.Draw("same")
#legend for models
mleg = ut.prepare_leg(0.68, 0.76, 0.3, 0.16, 0.035)
#mleg = ut.prepare_leg(0.68, 0.8, 0.3, 0.12, 0.035)
mleg.AddEntry(gSlight, "STARLIGHT", "l")
mleg.AddEntry(gMS, "MS", "l")
mleg.AddEntry(gCCK, "CCK-hs", "l")
#mleg.AddEntry(gSartre, "Sartre", "l")
#mleg.AddEntry(gFlat, "Flat #it{p}_{T}^{2}", "l")
mleg.Draw("same")
#legend for deconvolution method
dleg = ut.prepare_leg(0.3, 0.75, 0.2, 0.18, 0.035)
#dleg = ut.prepare_leg(0.3, 0.83, 0.2, 0.1, 0.035)
dleg.AddEntry(None, "Unfolding with:", "")
dleg.AddEntry(hPtSl, "Starlight", "p")
#dleg.AddEntry(hPtSart, "Sartre", "p")
dleg.AddEntry(hPtFlat, "Flat #it{p}_{T}^{2}", "p")
#dleg.Draw("same")
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
#to prevent 'pure virtual method called'
gPad.Close()
#save the cross section to output file
out = TFile("sigma.root", "recreate")
gSig.Write("sigma")
out.Close()
#beep when finished
gSystem.Exec("mplayer ../computerbeep_1.mp3 > /dev/null 2>&1")
from ROOT import RooUnfoldResponse
from ROOT import RooUnfoldBayes
Nbins = len(E_resp_array)
Emin = E_resp_array[0]
Emax = E_resp_array[-1]
matrix_unfolded1 = np.zeros((Nbins, Nbins))
hTrue = TH1D("true", "Test Truth", Nbins, Emin, Emax)
hMeas = TH1D("meas", "Test Measured", Nbins, Emin, Emax)
print(
"==================================== TRAIN ===================================="
)
response = RooUnfoldResponse(hMeas, hTrue)
for i in range(Nbins): # x_true
Ei = E_resp_array[i] # x_true
for j in range(Nbins): # x_measured
Ej = E_resp_array[j] # x_measured
mc = R_2D[i, j]
# response.Fill (x_measured, x_true)
response.Fill(Ej, Ei, mc)
# account for eff < 1
eff_ = R_2D[i, :].sum()
pmisses = 1 - eff_ # probability of misses
response.Miss(Ei, pmisses)
print(
"==================================== TEST ====================================="
reMBBn = f1.Get("reMBBn")
reMBEn = f1.Get("reMBEn")
genBB = f1.Get("genBB")
genBE = f1.Get("genBE")
recBB = f1.Get("recBB")
recBE = f1.Get("recBE")
f2 = ROOT.TFile.Open("unfoldingSample.root")
hmuBE = f2.Get("hist_f26f74ed27824519bcad66a6f539b3e5")
hmuBB = f2.Get("hist_1405a58d67164a8bb28859df616bad3c")
f3 = ROOT.TFile.Open("unfoldingMC.root")
hmuBE_mc = f3.Get("hist_bab6670dc2a946a7af6bdfbc761f87c5")
hmuBB_mc = f3.Get("hist_35313753f6e34206bd2294885ac1096f")
#f1.Close()
#f2.Close()
f = ROOT.TFile("unfoldSample.root", "RECREATE")
responseBB = RooUnfoldResponse(recBB, genBB, reMBB, "reBB", "reBB")
#responseBB.UseOverflow()
print recBB.GetNbinsX()
print reMBB.GetNbinsX()
#responseBB=RooUnfoldResponse(reMBB)
print "Print values of RooUnfold response matrix for BB category"
responseBB.Print()
print "Print values of response matrix for calculated by hand for BB category"
reMBBList = []
for i in range(1, reMBB.GetNbinsX() + 1):
column = []
for j in range(1, reMBB.GetNbinsX() + 1):
column.append(reMBBn.GetBinContent(i, j))
print column
invertBB = RooUnfoldInvert(responseBB, hmuBB, "UnfoldedBB", "UnfoldedBB")
inverthmuBB = invertBB.Hreco()
def MyRooUnfold(matrix_name=args.h_matrix, h_reco_getG0_name=args.h_data, h_ptcl_getG0_name = args.h_particle,h_reco_get_bkg_name = args.h_background,outputname=args.h_data+"_unfolded",nrebin = args.nrebin):
rfile_data = TFile(args.rfile_data, 'read')
rfile_particle = TFile(args.rfile_particle, 'read')
rfile_matrix = TFile(args.rfile_matrix, 'read')
rfile_background = TFile(args.rfile_background, 'read')
myfbu = fbu.PyFBU()
myfbu.verbose = True
#GET DATA
h_reco_get = rfile_data.Get(h_reco_getG0_name)
h_reco_get.Rebin(nrebin)
#GET PARTICLE
h_ptcl_get = rfile_particle.Get(h_ptcl_getG0_name)
h_ptcl_get.Rebin(nrebin)
#GET MATRIX
h_response_unf = rfile_matrix.Get(matrix_name)
h_response_unf.ClearUnderflowAndOverflow()
h_response_unf.GetXaxis().SetRange(1, h_response_unf.GetXaxis().GetNbins() )
h_response_unf.GetYaxis().SetRange(1, h_response_unf.GetYaxis().GetNbins() )
h_response_unf.Rebin2D(nrebin,nrebin)
h_response_unf.SetName("Migration_Matrix_simulation")
########### ACCEPTANCY
h_acc = h_response_unf.ProjectionX("reco_recoandparticleX") # Reco M
h_acc.Divide(h_reco_get)
########### AKCEPTANCE saved in h_acc #############
########### EFFICIENCY
h_eff = h_response_unf.ProjectionY("reco_recoandparticleY") # Ptcl M
h_eff.Divide(h_ptcl_get)
h_reco_get_input = rfile_data.Get(h_reco_getG0_name)
h_reco_get_bkg = rfile_background.Get(h_reco_get_bkg_name)
h_reco_get_bkg.Rebin(nrebin)
h_reco_get_input_clone=h_reco_get_input.Clone("")
h_reco_get_input_clone.Add(h_reco_get_bkg,-1)
h_reco_get_input_clone.Multiply(h_acc)
h_reco_or = rfile_data.Get(h_reco_getG0_name)
h_ptcl_or = rfile_particle.Get(h_ptcl_getG0_name)
h_ptcl_or.SetMaximum(h_ptcl_or.GetMaximum()*1.5)
### ROOUNFOLD METHOD ###
m_RooUnfold = RooUnfoldBayes()
m_RooUnfold.SetRegParm( 4 )
m_RooUnfold.SetNToys( 10000 )
m_RooUnfold.SetVerbose( 0 )
m_RooUnfold.SetSmoothing( 0 )
response = RooUnfoldResponse(None, None, h_response_unf, "response", "methods")
m_RooUnfold.SetResponse( response )
m_RooUnfold.SetMeasured( h_reco_get_input_clone )
### SVD METHOD ###
m_RooUnfold_svd = RooUnfoldSvd (response, h_reco_get_input_clone, int(round(h_reco_get_input_clone.GetNbinsX()/2.0,0))) #8
svd_par = int(round(h_reco_get_input_clone.GetNbinsX()/2.0,0))
m_RooUnfold_T = RooUnfoldTUnfold (response, h_reco_get_input_clone) # OR
m_RooUnfold_Ids= RooUnfoldIds (response, h_reco_get_input_clone,int(round(h_reco_get_input_clone.GetNbinsX()/12.0,0))) ## TO DO, SET PARAMETERS TO THE BINNING
Ids_par = int(round(h_reco_get_input_clone.GetNbinsX()/12.0,0))
### FBU METHOD ###
h_response_unf_fbu = TransposeMatrix(h_response_unf)
h_response_unf_fbu_norm = NormalizeResponse(h_response_unf_fbu)
h_response_unf_fbu_norm.SetName("Migration_Matrix_simulation_transpose")
histograms.append(h_response_unf_fbu_norm)
myfbu.response = MakeListResponse(h_response_unf_fbu_norm)
myfbu.data = MakeListFromHisto(h_reco_get_input_clone)
myfbu.lower = []
myfbu.upper = []
h_det_div_ptcl=h_reco_get_input_clone.Clone("")
h_det_div_ptcl.Divide(h_ptcl_or)
h_det_div_ptcl.Divide(h_eff)
h_det_div_ptcl.SetName("det_div_ptcl")
histograms.append(h_det_div_ptcl)
for l in range(len(myfbu.data)):
if ( args.SplitFromBinLow != 0) and ( l+1 <= args.SplitFromBinLow ):
myfbu.lower.append(h_reco_get_input_clone.GetBinContent(l+1)*(2-args.ParameterSplitFromBinLow)*h_det_div_ptcl.GetBinContent(l+1))
myfbu.upper.append(h_reco_get_input_clone.GetBinContent(l+1)*args.ParameterSplitFromBinLow*h_det_div_ptcl.GetBinContent(l+1))
elif ( args.SplitFromBinHigh != 0 ) and ( l+1 >= args.SplitFromBinHigh ):
myfbu.lower.append(h_reco_get_input_clone.GetBinContent(l+1)*(2-args.ParameterSplitFromBinHigh)*h_det_div_ptcl.GetBinContent(l+1))
myfbu.upper.append(h_reco_get_input_clone.GetBinContent(l+1)*args.ParameterSplitFromBinHigh*h_det_div_ptcl.GetBinContent(l+1))
else:
myfbu.lower.append(h_reco_get_input_clone.GetBinContent(l+1)*(2-args.par)*h_det_div_ptcl.GetBinContent(l+1))
myfbu.upper.append(h_reco_get_input_clone.GetBinContent(l+1)*args.par*h_det_div_ptcl.GetBinContent(l+1))
#myfbu.regularization = Regularization('Tikhonov',parameters=[{'refcurv':0.1,'alpha':0.2}]) works for old FBU package and python2.7 and old pymc
myfbu.run()
trace = myfbu.trace
traceName = 'Posterior_1_iteration'
posteriors_diag = MakeTH1Ds(trace, traceName)
h_reco_unfolded, h_reco_unfolded_Mean = MakeUnfoldedHisto(h_reco_or, posteriors_diag)
PlotPosteriors(posteriors_diag,outputname+'_iteration_1')
# EFFICIENCY AND ACCEPTANCY CORRECTIONS
h_reco_unfolded.Divide(h_eff)
h_reco_unfolded_Mean.Divide(h_eff)
h_reco_unfolded_roof = m_RooUnfold.Hreco()
h_reco_unfolded_roof.Divide(h_eff)
h_reco_unfolded_svd = m_RooUnfold_svd.Hreco()
h_reco_unfolded_svd.Divide(h_eff)
h_reco_unfolded_T = m_RooUnfold_T.Hreco()
h_reco_unfolded_T.Divide(h_eff)
h_reco_unfolded_Ids = m_RooUnfold_Ids.Hreco()
h_reco_unfolded_Ids.Divide(h_eff)
PlotRatio(h_reco_unfolded_Mean, h_ptcl_or, h_reco_unfolded_roof, h_reco_unfolded_svd, h_reco_unfolded_T,h_reco_unfolded_Ids, svd_par, Ids_par, outputname+'_iteration_1')
Repeat = True
j = 2
while Repeat:
print("Runnig iteration number: ",j)
myfbu.lower = []
myfbu.upper = []
for l in range(len(myfbu.data)):
posteriors_diag[l].Fit("gaus")
fit = posteriors_diag[l].GetFunction("gaus")
p1 = fit.GetParameter(1)
p2 = fit.GetParameter(2)
myfbu.lower.append(p1-4*p2)
myfbu.upper.append(p1+4*p2)
myfbu.run()
trace = myfbu.trace
traceName = 'Posterior_'+str(j)+'_iteration'
posteriors_diag = MakeTH1Ds(trace, traceName)
h_reco_unfolded, h_reco_unfolded_Mean = MakeUnfoldedHisto(h_reco_or, posteriors_diag)
Repeat = PlotPosteriors(posteriors_diag,outputname+'_iteration_'+str(j))
# EFFICIENCY AND ACCEPTANCY CORRECTIONS
h_reco_unfolded.Divide(h_eff)
h_reco_unfolded_Mean.Divide(h_eff)
h_reco_unfolded_roof = m_RooUnfold.Hreco()
h_reco_unfolded_roof.Divide(h_eff)
h_reco_unfolded_svd = m_RooUnfold_svd.Hreco()
h_reco_unfolded_svd.Divide(h_eff)
h_reco_unfolded_T = m_RooUnfold_T.Hreco()
h_reco_unfolded_T.Divide(h_eff)
h_reco_unfolded_Ids = m_RooUnfold_Ids.Hreco()
h_reco_unfolded_Ids.Divide(h_eff)
PlotRatio(h_reco_unfolded_Mean, h_ptcl_or, h_reco_unfolded_roof, h_reco_unfolded_svd, h_reco_unfolded_T,h_reco_unfolded_Ids, svd_par, Ids_par, outputname+'_iteration_'+str(j))
if j == args.maxiterations:
break
j = j+1
h_reco_unfolded.SetName("result_fbu_fit")
histograms.append(h_reco_unfolded)
h_reco_unfolded_Mean.SetName("result_fbu_Mean")
histograms.append(h_reco_unfolded_Mean)
h_reco_unfolded_roof.SetName("result_roof")
histograms.append(h_reco_unfolded_roof)
h_reco_unfolded_svd.SetName("result_svd")
histograms.append(h_reco_unfolded_svd)
h_reco_unfolded_T.SetName("result_T")
histograms.append(h_reco_unfolded_T)
h_reco_unfolded_Ids.SetName("result_Ids")
histograms.append(h_reco_unfolded_Ids)
h_eff.SetName("efficiency")
histograms.append(h_eff)
h_acc.SetName("acceptancy")
histograms.append(h_acc)
h_reco_or.SetName("reco")
histograms.append(h_reco_or)
h_ptcl_or.SetName("ptcl_simulation")
histograms.append(h_ptcl_or)
h_ratio = h_reco_unfolded.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_fbu_fit")
histograms.append(h_ratio)
h_ratio = h_reco_unfolded_Mean.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_fbu_Mean")
histograms.append(h_ratio)
h_ratio = h_reco_unfolded_roof.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_roof")
histograms.append(h_ratio)
h_ratio = h_reco_unfolded_svd.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_svd")
histograms.append(h_ratio)
m_RooUnfold_svd.PrintTable (cout, h_ptcl_or)
m_RooUnfold.PrintTable (cout, h_ptcl_or)
# CORRECTIONS TO GET CROSS SECTION
#DivideBinWidth(h_reco_unfolded_Mean)
#DivideBinWidth(h_reco_unfolded_roof)
#DivideBinWidth(h_reco_unfolded_svd)
#DivideBinWidth(h_reco_unfolded_T)
#DivideBinWidth(h_reco_unfolded_Ids)
#DivideBinWidth(h_ptcl_or)
#Lumi = 36.1e3
#for j in range(1,h_reco_unfolded_Mean.GetXaxis().GetNbins()+1):
# h_reco_unfolded_Mean.SetBinContent(j,h_reco_unfolded_Mean.GetBinContent(j)/(Lumi))
# h_reco_unfolded_Mean.SetBinError(j,h_reco_unfolded_Mean.GetBinError(j)/(Lumi))
# h_reco_unfolded_roof.SetBinContent(j,h_reco_unfolded_roof.GetBinContent(j)/(Lumi))
# h_reco_unfolded_roof.SetBinError(j,h_reco_unfolded_roof.GetBinError(j)/(Lumi))
# h_reco_unfolded_svd.SetBinContent(j,h_reco_unfolded_svd.GetBinContent(j)/(Lumi))
# h_reco_unfolded_svd.SetBinError(j,h_reco_unfolded_svd.GetBinError(j)/(Lumi))
# h_reco_unfolded_T.SetBinContent(j,h_reco_unfolded_T.GetBinContent(j)/(Lumi))
# h_reco_unfolded_T.SetBinError(j,h_reco_unfolded_T.GetBinError(j)/(Lumi))
# h_reco_unfolded_Ids.SetBinContent(j,h_reco_unfolded_Ids.GetBinContent(j)/(Lumi))
# h_reco_unfolded_Ids.SetBinError(j,h_reco_unfolded_Ids.GetBinError(j)/(Lumi))
# h_ptcl_or.SetBinContent(j,h_ptcl_or.GetBinContent(j)/(Lumi))
# h_ptcl_or.SetBinError(j,h_ptcl_or.GetBinError(j)/(Lumi))
#h_reco_unfolded_Mean_clone=h_reco_unfolded_Mean.Clone("FBU_cross_section")
#h_reco_unfolded_roof_clone=h_reco_unfolded_roof.Clone("DAgostini_cross_section")
#h_reco_unfolded_svd_clone=h_reco_unfolded_svd.Clone("SVD_cross_section")
#h_reco_unfolded_T_clone=h_reco_unfolded_T.Clone("TUnfold_cross_section")
#h_reco_unfolded_Ids_clone=h_reco_unfolded_Ids.Clone("Ids_cross_section")
#h_ptcl_or_clone=h_ptcl_or.Clone("Truth_cross_section")
#
#print("CONTROL*******************************************************************: ",h_reco_unfolded_Mean_clone.GetXaxis().GetNbins(),h_reco_unfolded_roof_clone.GetXaxis().GetNbins(),h_reco_unfolded_svd_clone.GetXaxis().GetNbins(),h_reco_unfolded_T_clone.GetXaxis().GetNbins(),h_reco_unfolded_Ids_clone.GetXaxis().GetNbins(),h_ptcl_or_clone.GetXaxis().GetNbins())
#histograms.append(h_reco_unfolded_Mean_clone)
#histograms.append(h_reco_unfolded_roof_clone)
#histograms.append(h_reco_unfolded_svd_clone)
#histograms.append(h_reco_unfolded_T_clone)
#histograms.append(h_reco_unfolded_Ids_clone)
#histograms.append(h_ptcl_or_clone)
SaveHistograms(outputname)
def process_response(self):
"""
First of all, we load all the mc gen and reco files that are skimmed
in bins of HF candidate ptand we apply the standard selection to all
of them. After this, we merged them all to create a single file of gen
and reco monte carlo sample with all the HF candidate pt. In particular
gen jets are selected according to run trigger, runlist, and gen jet
zbin_recoand pseudorapidity. Reco candidates according to evt selection, eta
jets, trigger and ml probability of the HF hadron
"""
zbin_reco = []
nzbin_reco = self.p_nbinshape_reco
zbin_reco = self.varshaperanges_reco
zbinarray_reco = array.array('d', zbin_reco)
zbin_gen = []
nzbin_gen = self.p_nbinshape_gen
zbin_gen = self.varshaperanges_gen
zbinarray_gen = array.array('d', zbin_gen)
jetptbin_reco = []
njetptbin_reco = self.p_nbin2_reco
jetptbin_reco = self.var2ranges_reco
jetptbinarray_reco = array.array('d', jetptbin_reco)
jetptbin_gen = []
njetptbin_gen = self.p_nbin2_gen
jetptbin_gen = self.var2ranges_gen
jetptbinarray_gen = array.array('d', jetptbin_gen)
candptbin = []
candptbin = self.lpt_finbinmin.copy()
candptbin.append(self.lpt_finbinmax[-1])
candptbinarray = array.array('d', candptbin)
out_file = TFile.Open(self.n_fileeff, "update")
list_df_mc_reco = []
list_df_mc_gen = []
for iptskim, _ in enumerate(self.lpt_anbinmin):
df_mc_gen = pickle.load(openfile(self.lpt_gendecmerged[iptskim], "rb"))
df_mc_gen = selectdfrunlist(df_mc_gen, \
self.run_param[self.runlistrigger[self.triggerbit]], "run_number")
df_mc_gen = df_mc_gen.query(self.s_jetsel_gen)
list_df_mc_gen.append(df_mc_gen)
df_mc_reco = pickle.load(openfile(self.lpt_recodecmerged[iptskim], "rb"))
if self.s_evtsel is not None:
df_mc_reco = df_mc_reco.query(self.s_evtsel)
if self.s_jetsel_reco is not None:
df_mc_reco = df_mc_reco.query(self.s_jetsel_reco)
if self.s_trigger is not None:
df_mc_reco = df_mc_reco.query(self.s_trigger)
if self.doml is True:
df_mc_reco = df_mc_reco.query(self.l_selml[iptskim])
list_df_mc_reco.append(df_mc_reco)
# Here we can merge the dataframes corresponding to different HF pt in a
# single one. In addition we are here selecting only non prompt HF
df_gen = pd.concat(list_df_mc_gen)
df_mc_reco = pd.concat(list_df_mc_reco)
# add the z columns
df_gen["z"] = z_calc(df_gen.pt_jet, df_gen.phi_jet, df_gen.eta_jet,
df_gen.pt_cand, df_gen.phi_cand, df_gen.eta_cand)
df_mc_reco["z"] = z_calc(df_mc_reco.pt_jet, df_mc_reco.phi_jet, df_mc_reco.eta_jet,
df_mc_reco.pt_cand, df_mc_reco.phi_cand, df_mc_reco.eta_cand)
df_mc_reco["z_gen"] = z_gen_calc(df_mc_reco.pt_gen_jet, df_mc_reco.phi_gen_jet,
df_mc_reco.eta_gen_jet, df_mc_reco.pt_gen_cand,
df_mc_reco.delta_phi_gen_jet, df_mc_reco.delta_eta_gen_jet)
df_gen_nonprompt = df_gen[df_gen.ismcfd == 1]
df_gen_prompt = df_gen[df_gen.ismcprompt == 1]
df_mc_reco_merged_nonprompt = df_mc_reco[df_mc_reco.ismcfd == 1]
df_mc_reco_merged_prompt = df_mc_reco[df_mc_reco.ismcprompt == 1]
# The following plots are 3d plots all at generated level of z,
# pt_jet and pt_cand. This was used in the first version of the feeddown
# subtraction, currently is obsolete
hzvsjetpt_gen_unmatched = TH2F("hzvsjetpt_gen_unmatched", "hzvsjetpt_gen_unmatched", \
nzbin_gen, zbinarray_gen, njetptbin_gen, jetptbinarray_gen)
df_zvsjetpt_gen_unmatched = df_gen_prompt.loc[:, [self.v_varshape_binning, "pt_jet"]]
fill_hist(hzvsjetpt_gen_unmatched, df_zvsjetpt_gen_unmatched)
hzvsjetpt_gen_unmatched.Write()
titlehist = "hzvsjetptvscandpt_gen_nonprompt"
hzvsjetptvscandpt_gen_nonprompt = makefill3dhist(df_gen_nonprompt, titlehist, \
zbinarray_gen, jetptbinarray_gen, candptbinarray, self.v_varshape_binning, "pt_jet", "pt_cand")
hzvsjetptvscandpt_gen_nonprompt.Write()
# hz_gen_nocuts is the distribution of generated z values in b in
# bins of gen_jet pt before the reco z and jetpt selection. hz_gen_cuts
# also includes cut on z reco and jet pt reco. These are used for overall
# efficiency correction to estimate the fraction of candidates that are
# in the reco range but outside the gen range and viceversa
for ibin2 in range(self.p_nbin2_gen):
suffix = "%s_%.2f_%.2f" % \
(self.v_var2_binning, self.lvar2_binmin_gen[ibin2], self.lvar2_binmax_gen[ibin2])
hz_gen_nocuts = TH1F("hz_gen_nocuts_nonprompt" + suffix, \
"hz_gen_nocuts_nonprompt" + suffix, nzbin_gen, zbinarray_gen)
hz_gen_nocuts.Sumw2()
hz_gen_cuts = TH1F("hz_gen_cuts_nonprompt" + suffix,
"hz_gen_cuts_nonprompt" + suffix, nzbin_gen, zbinarray_gen)
hz_gen_cuts.Sumw2()
df_tmp = seldf_singlevar(df_mc_reco_merged_nonprompt, "pt_gen_jet", \
self.lvar2_binmin_gen[ibin2], self.lvar2_binmax_gen[ibin2])
df_tmp = seldf_singlevar(df_tmp, self.v_varshape_binning_gen, \
self.lvarshape_binmin_gen[0], self.lvarshape_binmax_gen[-1])
fill_hist(hz_gen_nocuts, df_tmp[self.v_varshape_binning_gen])
df_tmp = seldf_singlevar(df_tmp, "pt_jet",
self.lvar2_binmin_reco[0], self.lvar2_binmax_reco[-1])
df_tmp = seldf_singlevar(df_tmp, self.v_varshape_binning,
self.lvarshape_binmin_reco[0], self.lvarshape_binmax_reco[-1])
fill_hist(hz_gen_cuts, df_tmp[self.v_varshape_binning_gen])
hz_gen_cuts.Write()
hz_gen_nocuts.Write()
# Addendum for unfolding
hz_gen_nocuts_pr = TH1F("hz_gen_nocuts" + suffix, \
"hz_gen_nocuts" + suffix, nzbin_gen, zbinarray_gen)
hz_gen_nocuts_pr.Sumw2()
hz_gen_cuts_pr = TH1F("hz_gen_cuts" + suffix,
"hz_gen_cuts" + suffix, nzbin_gen, zbinarray_gen)
hz_gen_cuts_pr.Sumw2()
df_tmp_pr = seldf_singlevar(df_mc_reco_merged_prompt, "pt_gen_jet", \
self.lvar2_binmin_gen[ibin2], self.lvar2_binmax_gen[ibin2])
df_tmp_pr = seldf_singlevar(df_tmp_pr, self.v_varshape_binning_gen, \
self.lvarshape_binmin_gen[0], self.lvarshape_binmax_gen[-1])
fill_hist(hz_gen_nocuts_pr, df_tmp_pr[self.v_varshape_binning_gen])
df_tmp_pr = seldf_singlevar(df_tmp_pr, "pt_jet",
self.lvar2_binmin_reco[0], self.lvar2_binmax_reco[-1])
df_tmp_pr = seldf_singlevar(df_tmp_pr, self.v_varshape_binning,
self.lvarshape_binmin_reco[0], self.lvarshape_binmax_reco[-1])
fill_hist(hz_gen_cuts_pr, df_tmp_pr[self.v_varshape_binning_gen])
hz_gen_cuts_pr.Write()
hz_gen_nocuts_pr.Write()
# End addendum for unfolding
df_tmp_selgen, df_tmp_selreco, df_tmp_selrecogen = \
self.create_df_closure(df_mc_reco_merged_nonprompt)
df_tmp_selgen_pr, df_tmp_selreco_pr, df_tmp_selrecogen_pr = \
self.create_df_closure(df_mc_reco_merged_prompt)
# histograms for response of feeddown
hzvsjetpt_reco_nocuts = \
build2dhisto("hzvsjetpt_reco_nocuts_nonprompt", zbinarray_reco, jetptbinarray_reco)
hzvsjetpt_reco_cuts = \
build2dhisto("hzvsjetpt_reco_cuts_nonprompt", zbinarray_reco, jetptbinarray_reco)
hzvsjetpt_gen_nocuts = \
build2dhisto("hzvsjetpt_gen_nocuts_nonprompt", zbinarray_gen, jetptbinarray_gen)
hzvsjetpt_gen_cuts = \
build2dhisto("hzvsjetpt_gen_cuts_nonprompt", zbinarray_gen, jetptbinarray_gen)
hzvsjetpt_reco = hzvsjetpt_reco_nocuts.Clone("hzvsjetpt_reco_nonprompt")
hzvsjetpt_gen = hzvsjetpt_gen_nocuts.Clone("hzvsjetpt_genv")
response_matrix = RooUnfoldResponse(hzvsjetpt_reco, hzvsjetpt_gen)
fill2dhist(df_tmp_selreco, hzvsjetpt_reco_nocuts, self.v_varshape_binning, "pt_jet")
fill2dhist(df_tmp_selgen, hzvsjetpt_gen_nocuts, self.v_varshape_binning_gen, "pt_gen_jet")
fill2dhist(df_tmp_selrecogen, hzvsjetpt_reco_cuts, self.v_varshape_binning, "pt_jet")
fill2dhist(df_tmp_selrecogen, hzvsjetpt_gen_cuts, self.v_varshape_binning_gen, "pt_gen_jet")
hzvsjetpt_reco_nocuts.Write()
hzvsjetpt_gen_nocuts.Write()
hzvsjetpt_reco_cuts.Write()
hzvsjetpt_gen_cuts.Write()
# histograms for unfolding
hzvsjetpt_reco_nocuts_pr = \
build2dhisto("hzvsjetpt_reco_nocuts", zbinarray_reco, jetptbinarray_reco)
hzvsjetpt_reco_cuts_pr = \
build2dhisto("hzvsjetpt_reco_cuts", zbinarray_reco, jetptbinarray_reco)
hzvsjetpt_gen_nocuts_pr = \
build2dhisto("hzvsjetpt_gen_nocuts", zbinarray_gen, jetptbinarray_gen)
hzvsjetpt_gen_cuts_pr = \
build2dhisto("hzvsjetpt_gen_cuts", zbinarray_gen, jetptbinarray_gen)
fill2dhist(df_tmp_selreco_pr, hzvsjetpt_reco_nocuts_pr, self.v_varshape_binning, "pt_jet")
fill2dhist(df_tmp_selgen_pr, hzvsjetpt_gen_nocuts_pr, self.v_varshape_binning_gen, "pt_gen_jet")
fill2dhist(df_tmp_selrecogen_pr, hzvsjetpt_reco_cuts_pr, self.v_varshape_binning, "pt_jet")
fill2dhist(df_tmp_selrecogen_pr, hzvsjetpt_gen_cuts_pr, self.v_varshape_binning_gen, "pt_gen_jet")
hzvsjetpt_reco_nocuts_pr.Write()
hzvsjetpt_gen_nocuts_pr.Write()
hzvsjetpt_reco_cuts_pr.Write()
hzvsjetpt_gen_cuts_pr.Write()
hzvsjetpt_reco_closure_pr = \
build2dhisto("hzvsjetpt_reco_closure", zbinarray_reco, jetptbinarray_reco)
hzvsjetpt_gen_closure_pr = \
build2dhisto("hzvsjetpt_gen_closure", zbinarray_reco, jetptbinarray_reco)
hzvsjetpt_reco_pr = \
build2dhisto("hzvsjetpt_reco", zbinarray_reco, jetptbinarray_reco)
hzvsjetpt_gen_pr = \
build2dhisto("hzvsjetpt_gen", zbinarray_gen, jetptbinarray_gen)
response_matrix_pr = RooUnfoldResponse(hzvsjetpt_reco_pr, hzvsjetpt_gen_pr)
response_matrix_closure_pr = RooUnfoldResponse(hzvsjetpt_reco_pr, hzvsjetpt_gen_pr)
fill2dhist(df_tmp_selreco_pr, hzvsjetpt_reco_pr, self.v_varshape_binning, "pt_jet")
fill2dhist(df_tmp_selgen_pr, hzvsjetpt_gen_pr, self.v_varshape_binning_gen, "pt_gen_jet")
hzvsjetpt_reco_pr.Write()
hzvsjetpt_gen_pr.Write()
hjetpt_gen_nocuts_pr = TH1F("hjetpt_gen_nocuts", \
"hjetpt_gen_nocuts", njetptbin_gen, jetptbinarray_gen)
hjetpt_gen_cuts_pr = TH1F("hjetpt_gen_cuts", \
"hjetpt_gen_cuts", njetptbin_gen, jetptbinarray_gen)
hjetpt_gen_nocuts_closure = TH1F("hjetpt_gen_nocuts_closure", \
"hjetpt_gen_nocuts_closure", njetptbin_gen, jetptbinarray_gen)
hjetpt_gen_cuts_closure = TH1F("hjetpt_gen_cuts_closure", \
"hjetpt_gen_cuts_closure", njetptbin_gen, jetptbinarray_gen)
hjetpt_gen_nocuts_pr.Sumw2()
hjetpt_gen_cuts_pr.Sumw2()
hjetpt_gen_nocuts_closure.Sumw2()
hjetpt_gen_nocuts_closure.Sumw2()
fill_hist(hjetpt_gen_nocuts_pr, df_tmp_selgen_pr["pt_gen_jet"])
fill_hist(hjetpt_gen_cuts_pr, df_tmp_selrecogen_pr["pt_gen_jet"])
hjetpt_gen_nocuts_pr.Write()
hjetpt_gen_cuts_pr.Write()
# end of histograms for unfolding
hjetpt_genvsreco_full = \
TH2F("hjetpt_genvsreco_full_nonprompt", "hjetpt_genvsreco_full_nonprompt", \
njetptbin_gen * 100, self.lvar2_binmin_gen[0], self.lvar2_binmax_gen[-1], \
njetptbin_reco * 100, self.lvar2_binmin_reco[0], self.lvar2_binmax_reco[-1])
hz_genvsreco_full = \
TH2F("hz_genvsreco_full_nonprompt", "hz_genvsreco_full_nonprompt", \
nzbin_gen * 100, self.lvarshape_binmin_gen[0], self.lvarshape_binmax_gen[-1],
nzbin_reco * 100, self.lvarshape_binmin_reco[0], self.lvarshape_binmax_reco[-1])
fill2dhist(df_tmp_selrecogen, hjetpt_genvsreco_full, "pt_gen_jet", "pt_jet")
hjetpt_genvsreco_full.Scale(1.0 / hjetpt_genvsreco_full.Integral(1, -1, 1, -1))
hjetpt_genvsreco_full.Write()
fill2dhist(df_tmp_selrecogen, hz_genvsreco_full, self.v_varshape_binning_gen, self.v_varshape_binning)
hz_genvsreco_full.Scale(1.0 / hz_genvsreco_full.Integral(1, -1, 1, -1))
hz_genvsreco_full.Write()
for row in df_tmp_selrecogen.itertuples():
response_matrix.Fill(getattr(row, self.v_varshape_binning), row.pt_jet, getattr(row, self.v_varshape_binning_gen), row.pt_gen_jet)
response_matrix.Write("response_matrix_nonprompt")
# histograms for unfolding
hjetpt_genvsreco_full_pr = \
TH2F("hjetpt_genvsreco_full", "hjetpt_genvsreco_full", \
njetptbin_gen * 100, self.lvar2_binmin_gen[0], self.lvar2_binmax_gen[-1], \
njetptbin_reco * 100, self.lvar2_binmin_reco[0], self.lvar2_binmax_reco[-1])
hz_genvsreco_full_pr = \
TH2F("hz_genvsreco_full", "hz_genvsreco_full", \
nzbin_gen * 100, self.lvarshape_binmin_gen[0], self.lvarshape_binmax_gen[-1],
nzbin_reco * 100, self.lvarshape_binmin_reco[0], self.lvarshape_binmax_reco[-1])
fill2dhist(df_tmp_selrecogen_pr, hjetpt_genvsreco_full_pr, "pt_gen_jet", "pt_jet")
hjetpt_genvsreco_full_pr.Scale(1.0 / hjetpt_genvsreco_full_pr.Integral(1, -1, 1, -1))
hjetpt_genvsreco_full_pr.Write()
fill2dhist(df_tmp_selrecogen_pr, hz_genvsreco_full_pr, self.v_varshape_binning_gen, self.v_varshape_binning)
hz_genvsreco_full_pr.Scale(1.0 / hz_genvsreco_full_pr.Integral(1, -1, 1, -1))
hz_genvsreco_full_pr.Write()
hzvsjetpt_prior_weights = build2dhisto("hzvsjetpt_prior_weights", \
zbinarray_gen, jetptbinarray_gen)
fill2dhist(df_tmp_selrecogen_pr, hzvsjetpt_prior_weights, self.v_varshape_binning_gen, "pt_gen_jet")
# end of histograms for unfolding
for ibin2 in range(self.p_nbin2_reco):
df_tmp_selrecogen_jetbin = seldf_singlevar(df_tmp_selrecogen, "pt_jet", \
self.lvar2_binmin_reco[ibin2], self.lvar2_binmax_reco[ibin2])
suffix = "%s_%.2f_%.2f" % (self.v_var2_binning, \
self.lvar2_binmin_reco[ibin2], self.lvar2_binmax_reco[ibin2])
hz_genvsreco = TH2F("hz_genvsreco_nonprompt" + suffix, "hz_genvsreco_nonprompt" + suffix, \
nzbin_gen * 100, self.lvarshape_binmin_gen[0], self.lvarshape_binmax_gen[-1], \
nzbin_reco*100, self.lvarshape_binmin_reco[0], self.lvarshape_binmax_reco[-1])
fill2dhist(df_tmp_selrecogen_jetbin, hz_genvsreco, self.v_varshape_binning_gen, self.v_varshape_binning)
norm = hz_genvsreco.Integral(1, -1, 1, -1)
if norm > 0:
hz_genvsreco.Scale(1.0/norm)
hz_genvsreco.Write()
df_tmp_selrecogen_pr_jetbin = seldf_singlevar(df_tmp_selrecogen_pr, "pt_jet", \
self.lvar2_binmin_reco[ibin2], self.lvar2_binmax_reco[ibin2])
suffix = "%s_%.2f_%.2f" % (self.v_var2_binning, \
self.lvar2_binmin_reco[ibin2], self.lvar2_binmax_reco[ibin2])
hz_genvsreco_pr = TH2F("hz_genvsreco" + suffix, "hz_genvsreco" + suffix, \
nzbin_gen * 100, self.lvarshape_binmin_gen[0], self.lvarshape_binmax_gen[-1], \
nzbin_reco*100, self.lvarshape_binmin_reco[0], self.lvarshape_binmax_reco[-1])
fill2dhist(df_tmp_selrecogen_pr_jetbin, hz_genvsreco_pr, self.v_varshape_binning_gen, self.v_varshape_binning)
norm_pr = hz_genvsreco_pr.Integral(1, -1, 1, -1)
if norm_pr > 0:
hz_genvsreco_pr.Scale(1.0/norm_pr)
hz_genvsreco_pr.Write()
for ibinshape in range(len(self.lvarshape_binmin_reco)):
df_tmp_selrecogen_zbin = seldf_singlevar(df_tmp_selrecogen, self.v_varshape_binning, \
self.lvarshape_binmin_reco[ibinshape], self.lvarshape_binmax_reco[ibinshape])
suffix = "%s_%.2f_%.2f" % \
(self.v_varshape_binning, self.lvarshape_binmin_reco[ibinshape], self.lvarshape_binmax_reco[ibinshape])
hjetpt_genvsreco = TH2F("hjetpt_genvsreco_nonprompt" + suffix, \
"hjetpt_genvsreco_nonprompt" + suffix, njetptbin_gen * 100, self.lvar2_binmin_gen[0], \
self.lvar2_binmax_gen[-1], njetptbin_reco * 100, self.lvar2_binmin_reco[0], \
self.lvar2_binmax_reco[-1])
fill2dhist(df_tmp_selrecogen_zbin, hjetpt_genvsreco, "pt_gen_jet", "pt_jet")
norm = hjetpt_genvsreco.Integral(1, -1, 1, -1)
if norm > 0:
hjetpt_genvsreco.Scale(1.0/norm)
hjetpt_genvsreco.Write()
df_tmp_selrecogen_pr_zbin = seldf_singlevar(df_tmp_selrecogen_pr, self.v_varshape_binning, \
self.lvarshape_binmin_reco[ibinshape], self.lvarshape_binmax_reco[ibinshape])
suffix = "%s_%.2f_%.2f" % \
(self.v_varshape_binning, self.lvarshape_binmin_reco[ibinshape], self.lvarshape_binmax_reco[ibinshape])
hjetpt_genvsreco_pr = TH2F("hjetpt_genvsreco" + suffix, \
"hjetpt_genvsreco" + suffix, njetptbin_gen * 100, self.lvar2_binmin_gen[0], \
self.lvar2_binmax_gen[-1], njetptbin_reco * 100, self.lvar2_binmin_reco[0], \
self.lvar2_binmax_reco[-1])
fill2dhist(df_tmp_selrecogen_pr_zbin, hjetpt_genvsreco_pr, "pt_gen_jet", "pt_jet")
norm_pr = hjetpt_genvsreco_pr.Integral(1, -1, 1, -1)
if norm_pr > 0:
hjetpt_genvsreco_pr.Scale(1.0/norm_pr)
hjetpt_genvsreco_pr.Write()
for ibinshape in range(len(self.lvarshape_binmin_gen)):
dtmp_nonprompt_zgen = seldf_singlevar(df_mc_reco_merged_nonprompt, \
self.v_varshape_binning_gen, self.lvarshape_binmin_gen[ibinshape], self.lvarshape_binmax_gen[ibinshape])
suffix = "%s_%.2f_%.2f" % \
(self.v_varshape_binning, self.lvarshape_binmin_gen[ibinshape], self.lvarshape_binmax_gen[ibinshape])
hz_fracdiff = TH1F("hz_fracdiff_nonprompt" + suffix,
"hz_fracdiff_nonprompt" + suffix, 100, -2, 2)
fill_hist(hz_fracdiff, (dtmp_nonprompt_zgen[self.v_varshape_binning] - \
dtmp_nonprompt_zgen[self.v_varshape_binning_gen])/dtmp_nonprompt_zgen[self.v_varshape_binning_gen])
norm = hz_fracdiff.Integral(1, -1)
if norm:
hz_fracdiff.Scale(1.0 / norm)
hz_fracdiff.Write()
dtmp_prompt_zgen = seldf_singlevar(df_mc_reco_merged_prompt, \
self.v_varshape_binning_gen, self.lvarshape_binmin_gen[ibinshape], self.lvarshape_binmax_gen[ibinshape])
suffix = "%s_%.2f_%.2f" % \
(self.v_varshape_binning, self.lvarshape_binmin_gen[ibinshape], self.lvarshape_binmax_gen[ibinshape])
hz_fracdiff_pr = TH1F("hz_fracdiff_prompt" + suffix,
"hz_fracdiff_prompt" + suffix, 100, -2, 2)
fill_hist(hz_fracdiff_pr, (dtmp_prompt_zgen[self.v_varshape_binning] - \
dtmp_prompt_zgen[self.v_varshape_binning_gen])/dtmp_prompt_zgen[self.v_varshape_binning_gen])
norm_pr = hz_fracdiff_pr.Integral(1, -1)
if norm_pr:
hz_fracdiff_pr.Scale(1.0 / norm_pr)
hz_fracdiff_pr.Write()
for ibin2 in range(self.p_nbin2_gen):
dtmp_nonprompt_jetptgen = seldf_singlevar(df_mc_reco_merged_nonprompt, \
"pt_gen_jet", self.lvar2_binmin_gen[ibin2], self.lvar2_binmax_gen[ibin2])
suffix = "%s_%.2f_%.2f" % (self.v_var2_binning,
self.lvar2_binmin_gen[ibin2], self.lvar2_binmax_gen[ibin2])
hjetpt_fracdiff = TH1F("hjetpt_fracdiff_nonprompt" + suffix,
"hjetpt_fracdiff_nonprompt" + suffix, 100, -2, 2)
fill_hist(hjetpt_fracdiff, (dtmp_nonprompt_jetptgen["pt_jet"] - \
dtmp_nonprompt_jetptgen["pt_gen_jet"])/dtmp_nonprompt_jetptgen["pt_gen_jet"])
norm = hjetpt_fracdiff.Integral(1, -1)
if norm:
hjetpt_fracdiff.Scale(1.0 / norm)
hjetpt_fracdiff.Write()
dtmp_prompt_jetptgen = seldf_singlevar(df_mc_reco_merged_prompt, \
"pt_gen_jet", self.lvar2_binmin_gen[ibin2], self.lvar2_binmax_gen[ibin2])
suffix = "%s_%.2f_%.2f" % (self.v_var2_binning,
self.lvar2_binmin_gen[ibin2], self.lvar2_binmax_gen[ibin2])
hjetpt_fracdiff_pr = TH1F("hjetpt_fracdiff_prompt" + suffix,
"hjetpt_fracdiff_prompt" + suffix, 100, -2, 2)
fill_hist(hjetpt_fracdiff_pr, (dtmp_prompt_jetptgen["pt_jet"] - \
dtmp_prompt_jetptgen["pt_gen_jet"])/dtmp_prompt_jetptgen["pt_gen_jet"])
norm_pr = hjetpt_fracdiff_pr.Integral(1, -1)
if norm_pr:
hjetpt_fracdiff_pr.Scale(1.0 / norm_pr)
hjetpt_fracdiff_pr.Write()
df_mc_reco_merged_prompt_train, df_mc_reco_merged_prompt_test = \
train_test_split(df_mc_reco_merged_prompt, test_size=self.closure_frac)
df_tmp_selgen_pr_test, df_tmp_selreco_pr_test, df_tmp_selrecogen_pr_test = \
self.create_df_closure(df_mc_reco_merged_prompt_test)
_, _, df_tmp_selrecogen_pr_train = \
self.create_df_closure(df_mc_reco_merged_prompt_train)
fill2dhist(df_tmp_selreco_pr_test, hzvsjetpt_reco_closure_pr, self.v_varshape_binning, "pt_jet")
fill2dhist(df_tmp_selgen_pr_test, hzvsjetpt_gen_closure_pr, self.v_varshape_binning_gen, "pt_gen_jet")
hzvsjetpt_reco_closure_pr.Write("input_closure_reco")
hzvsjetpt_gen_closure_pr.Write("input_closure_gen")
for ibin2 in range(self.p_nbin2_gen):
suffix = "%s_%.2f_%.2f" % \
(self.v_var2_binning, self.lvar2_binmin_gen[ibin2], self.lvar2_binmax_gen[ibin2])
hz_gen_nocuts_closure = TH1F("hz_gen_nocuts_closure" + suffix,
"hz_gen_nocuts_closure" + suffix,
nzbin_gen, zbinarray_gen)
hz_gen_nocuts_closure.Sumw2()
hz_gen_cuts_closure = TH1F("hz_gen_cuts_closure" + suffix,
"hz_gen_cuts_closure" + suffix,
nzbin_gen, zbinarray_gen)
hz_gen_cuts_closure.Sumw2()
df_tmp_selgen_pr_test_bin = seldf_singlevar(df_tmp_selgen_pr_test, \
"pt_gen_jet", self.lvar2_binmin_gen[ibin2], self.lvar2_binmax_gen[ibin2])
df_tmp_selrecogen_pr_test_bin = seldf_singlevar(df_tmp_selrecogen_pr_test, \
"pt_gen_jet", self.lvar2_binmin_gen[ibin2], self.lvar2_binmax_gen[ibin2])
fill_hist(hz_gen_nocuts_closure, df_tmp_selgen_pr_test_bin[self.v_varshape_binning_gen])
fill_hist(hz_gen_cuts_closure, df_tmp_selrecogen_pr_test_bin[self.v_varshape_binning_gen])
hz_gen_cuts_closure.Write()
hz_gen_nocuts_closure.Write()
fill_hist(hjetpt_gen_nocuts_closure, df_tmp_selgen_pr_test["pt_gen_jet"])
fill_hist(hjetpt_gen_cuts_closure, df_tmp_selrecogen_pr_test["pt_gen_jet"])
hjetpt_gen_nocuts_closure.Write()
hjetpt_gen_cuts_closure.Write()
hzvsjetpt_reco_nocuts_closure = TH2F("hzvsjetpt_reco_nocuts_closure",
"hzvsjetpt_reco_nocuts_closure",
nzbin_reco, zbinarray_reco,
njetptbin_reco, jetptbinarray_reco)
hzvsjetpt_reco_nocuts_closure.Sumw2()
hzvsjetpt_reco_cuts_closure = TH2F("hzvsjetpt_reco_cuts_closure",
"hzvsjetpt_reco_cuts_closure",
nzbin_reco, zbinarray_reco,
njetptbin_reco, jetptbinarray_reco)
hzvsjetpt_reco_cuts_closure.Sumw2()
fill2dhist(df_tmp_selreco_pr_test, hzvsjetpt_reco_nocuts_closure, self.v_varshape_binning, "pt_jet")
fill2dhist(df_tmp_selrecogen_pr_test, hzvsjetpt_reco_cuts_closure, self.v_varshape_binning, "pt_jet")
hzvsjetpt_reco_nocuts_closure.Write()
hzvsjetpt_reco_cuts_closure.Write()
for row in df_tmp_selrecogen_pr.itertuples():
response_matrix_weight = 1.0
if self.doprior is True:
binx = hzvsjetpt_prior_weights.GetXaxis().FindBin(getattr(row, self.v_varshape_binning_gen))
biny = hzvsjetpt_prior_weights.GetYaxis().FindBin(row.pt_gen_jet)
weight = hzvsjetpt_prior_weights.GetBinContent(binx, biny)
if weight > 0.0:
response_matrix_weight = 1.0/weight
response_matrix_pr.Fill(getattr(row, self.v_varshape_binning), row.pt_jet,\
getattr(row, self.v_varshape_binning_gen), row.pt_gen_jet, response_matrix_weight)
for row in df_tmp_selrecogen_pr_train.itertuples():
response_matrix_weight = 1.0
if self.doprior is True:
binx = hzvsjetpt_prior_weights.GetXaxis().FindBin(getattr(row, self.v_varshape_binning_gen))
biny = hzvsjetpt_prior_weights.GetYaxis().FindBin(row.pt_gen_jet)
weight = hzvsjetpt_prior_weights.GetBinContent(binx, biny)
if weight > 0.0:
response_matrix_weight = 1.0/weight
response_matrix_closure_pr.Fill(getattr(row, self.v_varshape_binning), row.pt_jet,\
getattr(row, self.v_varshape_binning_gen), row.pt_gen_jet, response_matrix_weight)
response_matrix_pr.Write("response_matrix")
response_matrix_closure_pr.Write("response_matrix_closure")
out_file.Close()
def smear(xt):
xeff = 0.3 + (1.0 - 0.3) / 20 * (xt + 10.0)
# efficiency
x = gRandom.Rndm()
if x > xeff: return None
xsmear = gRandom.Gaus(-2.5, 0.2)
# bias and smear
return xt + xsmear
# ==============================================================================
# Example Unfolding
# ==============================================================================
print "==================================== TRAIN ===================================="
response = RooUnfoldResponse(40, -10.0, 10.0)
# Train with a Breit-Wigner, mean 0.3 and width 2.5.
for i in xrange(100000):
xt = gRandom.BreitWigner(0.3, 2.5)
x = smear(xt)
if x != None:
response.Fill(x, xt)
else:
response.Miss(xt)
print "==================================== TEST ====================================="
hTrue = TH1D("true", "Test Truth", 40, -10.0, 10.0)
hMeas = TH1D("meas", "Test Measured", 40, -10.0, 10.0)
# Test with a Gaussian, mean 0 and width 2.
for i in xrange(10000):
from ROOT import RooUnfoldTUnfold
import sys
unfold = "unfold" in sys.argv
f = ROOT.TFile("~/Documents/stpol/trees/noSkim/T_t.root")
tree = f.Get("treesCands").Get("eventTree")
N = tree.GetEntries()
firstHalf = N / 2
hMeas_test = ROOT.TH1D("hMeas_test", "measured", 20, -5, 5)
hTrue_test = ROOT.TH1D("hTrue_test", "true", 20, -5, 5)
if unfold:
response = RooUnfoldResponse(20, -5, 5)
for i in range(N / 2):
tree.GetEntry(i)
xm = tree._recoTop_0_Eta
xt = tree.trueTop_genParticleSelector_0_Eta
if xm == xm and xt == xt:
response.Fill(xm, xt)
elif xm == xm and xt != xt:
response.Fake(xm)
elif xt == xt and xm != xm:
response.Miss(xt)
for i in range(N / 2, N):
tree.GetEntry(i)
xm = tree._recoTop_0_Eta
xt = tree.trueTop_genParticleSelector_0_Eta
def main(optunf="Bayes"):
optunfs = ["Bayes", "SVD", "TUnfold", "Invert", "Reverse"]
if not optunf in optunfs:
txt = "Unfolding option " + optunf + " not recognised"
raise ValueError(txt)
global hReco, hMeas, hTrue, hTrueEE, hTrueMM, hMeasMM, hMeasEE
#c2 = TCanvas("c2")
print "==================================== TRAIN ===================================="
# Create response matrix object
inputdirZZ = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/UNFOLDacc/HADD/UnfoldInput/" #ZZ
# inputdirWZ = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/UNFOLDWZ/HADD/UnfoldInput/" #WZ
# inputdirNoPrep = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/ZZoutput/" #data, NRB
inputdirDY = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/DYbkgd/" #gamma
#inputGEN = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/Unfolding/PT.root"
inputdir = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/EWKSET/HADD/UnfoldInput/" #ZZ
inputdirWZ = inputdir #WZ
inputdirNoPrep = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/EWKSET/" #data, NRB
#inputdirNoPrep2 = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/" #data, NRB
inputdir2 = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/NOSET/HADD/UnfoldInput/"
# inputdirWZ = inputdir
fw = '/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/Unfolding/MCFM_withAcceptance/ZZlept_tota_MSTW200_90__90__test.root'
fwo = '/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/Unfolding/MCFM_withoutAcceptance/ZZlept_tota_MSTW200_90__90__test.root'
#inputdirOTH = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/NOSET/HADD/UnfoldInput/"
#inputdirWZOTH = inputdirOTH
#inputdirEWK = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/EWKSET/HADD/UnfoldInput/" #ZZ
PAIR = ['zptG', 'zpt']
outputnameGR = PAIR[0] + '_vs_' + PAIR[1] + '.png'
outputnameG = PAIR[0] + '.png'
outputnameR = PAIR[1] + '.png'
cutData = "(zpt>45)*(preco>0)" #"preco"
cutGMM = "((L1GId*L2GId)==-13*13)"
cutRMM = "((l1id*l2id)==-13*13)"
cutGEE = "((L1GId*L2GId)==-11*11)"
cutREE = "((l1id*l2id)==-11*11)"
cutQQ = "(abs(l1id*l2id) == 11*13)"
hsWW = makehistos("DataFused.root", inputdirNoPrep)
hsDY = makehistosDY('gamma_out_8_MoreBins_ll.root', inputdirDY)
print "MEASURED DATA " + "=" * 30
hMeasMM = Make1DPlot(inputdirNoPrep + "DataFused.root", "finalTree",
PAIR[1], "Data.png", cutRMM + "*" + cutData)
hMeasEE = Make1DPlot(inputdirNoPrep + "DataFused.root", "finalTree",
PAIR[1], "Data.png", cutREE + "*" + cutData)
#hMeasQQ = Make1DPlot(inputdirNoPrep+"DataFused.root","finalTree",PAIR[1],"Data.png",cutQQ + "*" + cutData)
print "MEASURED DATA " + "=" * 30
print hMeasMM.Integral(), hMeasEE.Integral(), "Measured"
print "NRB and DY"
print hsWW[0].Integral(), hsWW[1].Integral(), "WW"
print hsDY[0].Integral(), hsDY[1].Integral(), "DY"
#MCFM rescaling and plotting
#
hMCFM = GetMCFMHisto(fwo, True)
hMCFM.Scale(143.2 / hMCFM.Integral())
hMCFM = REBIN(hMCFM)
print hMCFM.Integral()
hMCFM.Print("range")
#sys.exit("donesies")
MWO = GetMCFMHisto(fwo, True)
MW = GetMCFMHisto(fw, False)
MWO.Divide(MW)
MWO.Print("range")
MWO = REBIN(MWO)
MWO.Print("range")
# MWO = REBIN(GetMCFMHisto(fwo,True))
# MW = REBIN(GetMCFMHisto(fw, False))
# MWO.Divide(MW)
# MWO.Print("range")
#sys.exit("donesies")
systematic = [
'_jer', '_jes', '_umet', '_les', '_pu', '_btag', '_qcd', '_pdf'
] #'_jer','_jes','_umet','_les','_pu','_btag','_sherpa','_qcd','_pdf']
# systematic = ['_jer','_jes','_qcd','_pdf']
UD = ['up', 'down']
SYST_UD = ['']
for syst in systematic:
for ud in UD:
SYST_UD.append(syst + ud)
MCGenHistos = []
MCRecoHistos = []
hTrues = []
hDiffs = []
hCloses = []
hunfolds = []
########
MCGenHistosMM = []
MCRecoHistosMM = []
hTruesMM = []
hDiffsMM = []
hClosesMM = []
hunfoldsMM = []
########
MCGenHistosEE = []
MCRecoHistosEE = []
hTruesEE = []
hDiffsEE = []
hClosesEE = []
hunfoldsEE = []
######
MCGenHistosMM2 = []
MCGenHistosEE2 = []
MCGenHistos2 = []
for syst_ud in SYST_UD:
print "=O" * 40
print syst_ud
print "O=" * 40
SYS = "(sys" + syst_ud + ">0)"
cutR = "Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(preco>0)*" + SYS
# cutG = "Eweight*BR*(B2/B3)*(pgen>0)*"+SYS #Accstring
# [5718.8469039797783, 1737.4891278286839, 1737.4891278286839] fb
#SSS = 2.1299863918*(3.0)
#SSS = 2.1299863918*(3.0/2.0)
#SSS = (3.0/2.0)
#SSS = (3.0)
#cutG = str(SSS)+"*Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(pgen>0)*"+SYS #Accstring
cutG = "Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(pgen>0)*" + SYS #Accstring
# cutG = str(SSS)+"*Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(1/Acc2)*(pgen>0)*"+SYS #Accstring
#cutG = str(SSS)+"*Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(pgen>0)*"+SYS #Accstring
cutGR = "Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(pgen>0)*(preco>0)*" + SYS #+Accstring
cutWZ = "Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(preco>0)*" + SYS
cutGEN = "XS*LUM*(1/NGE)*(B2/B3)*(g_pt>45.0)"
cutee = "(ee>0.0)"
cutmm = "(mumu>0.0)"
# MCRecoHistoMM = REBIN(Make1DPlot(inputdir+"ZZ.root","tmvatree",PAIR[1],outputnameR,cutR+"*"+cutRMM))
# MCRecoHistoEE = REBIN(Make1DPlot(inputdir+"ZZ.root","tmvatree",PAIR[1],outputnameR,cutR+"*"+cutREE))
# MCGenHistoMM = REBIN(Make1DPlot(inputdir+"ZZ.root","tmvatree",PAIR[0],outputnameG,cutG+"*"+cutGMM))
# MCGenHistoEE = REBIN(Make1DPlot(inputdir+"ZZ.root","tmvatree",PAIR[0],outputnameG,cutG+"*"+cutGEE))
MCRecoHistoMM = Make1DPlot(inputdir + "ZZ.root", "tmvatree", PAIR[1],
outputnameR, cutR + "*" + cutRMM)
MCRecoHistoEE = Make1DPlot(inputdir + "ZZ.root", "tmvatree", PAIR[1],
outputnameR, cutR + "*" + cutREE)
MCGenHistoMM = Make1DPlotGEN(inputdir + "ZZ.root", "tmvatree", PAIR[0],
outputnameG, cutG + "*" + cutGMM)
MCGenHistoEE = Make1DPlotGEN(inputdir + "ZZ.root", "tmvatree", PAIR[0],
outputnameG, cutG + "*" + cutGEE)
# MCGenHistoMM = Make1DPlotGEN(inputGEN,"GPT","g_pt",outputnameG,cutGEN+"*"+cutmm)
# MCGenHistoEE = Make1DPlotGEN(inputGEN,"GPT","g_pt",outputnameG,cutGEN+"*"+cutee)
# MCGenHistoMM.Print("range")
# sys.exit("done")
GenVsReco2DHistoMM = Make2DPlot(inputdir + "ZZ.root", "tmvatree", PAIR,
'MM' + outputnameGR,
cutGR + "*" + cutGMM + "*" + cutRMM,
syst_ud)
GenVsReco2DHistoEE = Make2DPlot(inputdir + "ZZ.root", "tmvatree", PAIR,
'EE' + outputnameGR,
cutGR + "*" + cutGEE + "*" + cutREE,
syst_ud)
# sys.exit("done")
#
MCGenHistoMM2 = Make1DPlotGEN(inputdir2 + "ZZ.root", "tmvatree",
PAIR[0], outputnameG,
cutG + "*" + cutGMM)
MCGenHistoEE2 = Make1DPlotGEN(inputdir2 + "ZZ.root", "tmvatree",
PAIR[0], outputnameG,
cutG + "*" + cutGEE)
#MCGenHistoMM2 = Make1DPlotMCFM(1)
#MCGenHistoEE2 = Make1DPlotMCFM(1)
hWZMM = Make1DPlot(inputdirWZ + "WZ.root", "tmvatree", PAIR[1],
"WZ.png", cutWZ + "*" + cutRMM) #MC
hWZEE = Make1DPlot(inputdirWZ + "WZ.root", "tmvatree", PAIR[1],
"WZ.png", cutWZ + "*" + cutREE) #MC
print hWZMM.Integral(), hWZEE.Integral(), "WZ MM EE"
print MCRecoHistoMM.Integral(), MCRecoHistoEE.Integral(), "ZZ MM EE"
BKGDSMM = [REBIN(hsWW[0]), REBIN(hsDY[0]), hWZMM]
BKGDSEE = [REBIN(hsWW[1]), REBIN(hsDY[1]), hWZEE]
for bk in (BKGDSMM + BKGDSEE):
print bk.Integral(), "subtract"
print hMeasMM.Integral()
print hMeasEE.Integral()
hDiffMM = SubtractMany1DPlots(hMeasMM, BKGDSMM)
hDiffEE = SubtractMany1DPlots(hMeasEE, BKGDSEE)
# hDiffMM = hMeasMM
# hDiffEE = hMeasEE
print hDiffMM.Integral(), quadadd(BinError(hDiffMM))
print hDiffEE.Integral(), quadadd(BinError(hDiffEE))
print "Difference between Data and Background^"
print hMeasMM.Integral(), quadadd(BinError(hMeasMM))
print hMeasEE.Integral(), quadadd(BinError(hMeasEE))
print "DATA yield^"
print REBIN(hsWW[0]).Integral(), quadadd(BinError(REBIN(hsWW[0])))
print REBIN(hsWW[1]).Integral(), quadadd(BinError(REBIN(hsWW[1])))
print "NRB yield^"
print REBIN(hsDY[0]).Integral(), quadadd(BinError(REBIN(hsDY[0])))
print REBIN(hsDY[1]).Integral(), quadadd(BinError(REBIN(hsDY[1])))
print "DY yield^"
print hWZMM.Integral(), quadadd(BinError(hWZMM))
print hWZEE.Integral(), quadadd(BinError(hWZEE))
print "WZ yield^"
print MCRecoHistoMM.Integral(), quadadd(BinError(MCRecoHistoMM))
print MCRecoHistoEE.Integral(), quadadd(BinError(MCRecoHistoEE))
print "ZZ yield^"
#sys.exit("donesies")
responseMM = RooUnfoldResponse(MCRecoHistoMM, MCGenHistoMM,
GenVsReco2DHistoMM)
responseEE = RooUnfoldResponse(MCRecoHistoEE, MCGenHistoEE,
GenVsReco2DHistoEE)
# MCRecoHistoMM.Print("range")
# MCGenHistoMM.Print("range")
# GenVsReco2DHistoMM.Print("range")
#sys.exit("DONE")
#sys.exit("done")
# print "==================================== TEST ====================================="
# #hTrue= TH1D( "true", "Test Truth", 20, -40.0, 40.0 )
# #hMeas= TH1D( "meas", "Test Measured", 40, -10.0, 10.0 )
# #hTrue= TH1D( "true", "Test Truth", 10, 40.0, 760.0 )
# # hMeas= TH1D( "meas", "Test Measured", 10, 0.0, 800.0 )
print "==================================== UNFOLD ==================================="
print "Unfolding method:", optunf
if "Bayes" in optunf:
# Bayes unfoldung with 4 iterations
#unfoldMM= RooUnfoldBayes( responseMM, hDiffMM, 4)
closureMM = RooUnfoldBayes(responseMM, MCRecoHistoMM, 4)
#unfoldEE= RooUnfoldBayes( responseEE, hDiffEE, 4)
closureEE = RooUnfoldBayes(responseEE, MCRecoHistoEE, 4)
unfoldsysMM = RooUnfoldBayes(responseMM, hDiffMM, 4)
unfoldsysEE = RooUnfoldBayes(responseEE, hDiffEE, 4)
# if syst_ud == "":
# unfoldsysMM.SetNToys(100)
# unfoldsysMM.IncludeSystematics()
# unfoldsysEE.SetNToys(100)
# unfoldsysEE.IncludeSystematics()
print "Bayes " * 20
#closure= RooUnfoldBayes( response, MCRecoHisto , 4 )
#unfold= RooUnfoldBayes( response, hMeas, 10, False, True )
elif "SVD" in optunf:
# SVD unfoding with free regularisation
# unfold= RooUnfoldSvd( response, hMeas, 20 )
unfold = RooUnfoldSvd(response, hMeas)
elif "TUnfold" in optunf:
# TUnfold with fixed regularisation tau=0.002
# unfold= RooUnfoldTUnfold( response, hMeas )
unfold = RooUnfoldTUnfold(response, hMeas, 0.002)
elif "Invert" in optunf:
unfold = RooUnfoldInvert(response, hMeas)
elif "Reverse" in optunf:
unfold = RooUnfoldBayes(response, hMeas, 1)
#hTrueMM= unfoldMM.Hreco()
hCloseMM = closureMM.Hreco()
#hTrueEE= unfoldEE.Hreco()
hCloseEE = closureEE.Hreco()
if syst_ud == "":
#unfoldsysMM.SetNToys(500)
# unfoldsysMM.IncludeSystematics(0) # 0 DATA ONLY
unfoldsysMM.IncludeSystematics(1) # 1 EVERYTHING
# unfoldsysMM.IncludeSystematics(2) # 2 MC RESPONSE
#unfoldsysEE.SetNToys(500)
unfoldsysEE.IncludeSystematics(1)
# hunfoldMM = unfoldsysMM.Hreco(2) # 2 DIRECT ERROR PROAGATION, ONLY WITH IncludeSystematics(0) -- data only. Can't directly propagate MC response unc.
hunfoldMM = unfoldsysMM.Hreco(2)
hunfoldEE = unfoldsysEE.Hreco(
2
) # 3 Toy MC error evaluation. Would apply to both data-MC uncertainty, and MC response uncertainty.
print "STUFF" * 40
hunfoldMM.Print("range")
print "UNFOLDED NUMBERS :--:" * 10
print hunfoldMM.Integral(), quadadd(BinError(hunfoldMM))
print hunfoldEE.Integral(), quadadd(BinError(hunfoldEE))
print "UNFOLDED NUMBERS :--:" * 10
#############################
#IMPORTANT!!! Includesys&Hreco: 0&2, 1&2 (some errors), N&2 (bayesian)*, 1&3, 2&3
#############################
else:
hunfoldMM = unfoldsysMM.Hreco()
hunfoldEE = unfoldsysEE.Hreco()
print " ---- TEST @@@@@@@@@@@@@@@@@@@@@@@@@ "
hunfoldEE.Print("range")
hunfoldMM.Print("range")
# print hTrueMM.Integral(), hTrueEE.Integral(), "True"
print MCGenHistoMM.Integral(), MCGenHistoEE.Integral(), "Gen"
print hunfoldMM.Integral(), hunfoldEE.Integral(), "unfold"
#makeComparison(MCGenHistoMM,MCRecoHistoMM,hTrueMM,hDiffMM,hCloseMM,"MM"+syst_ud)
#makeComparison(MCGenHistoEE,MCRecoHistoEE,hTrueEE,hDiffEE,hCloseEE,"EE"+syst_ud)
#RESCALE TO MCFM with kinematic cut ratios
hunfoldEE.Multiply(MWO)
hunfoldMM.Multiply(MWO)
MCGenHistoEE.Multiply(MWO)
MCGenHistoMM.Multiply(MWO)
MCGenHistoEE2.Multiply(MWO)
MCGenHistoMM2.Multiply(MWO)
SSS = 3.0 #3.0 for single channel, 3.0/2.0 for both
hunfoldMM.Scale(SSS)
hunfoldEE.Scale(SSS)
MCGenHistoMM.Scale(SSS)
MCGenHistoEE.Scale(SSS)
MCGenHistoMM2.Scale(SSS)
MCGenHistoEE2.Scale(SSS)
print MCGenHistoMM.Integral(), MCGenHistoEE.Integral(), "Gen"
print hunfoldMM.Integral(), hunfoldEE.Integral(), "unfold"
#sys.exit("donesies")
#For mumu channel
hDiffsEE.append([hDiffEE, syst_ud])
hunfoldsEE.append([hunfoldEE, syst_ud])
if syst_ud == "":
hClosesEE.append([hCloseEE, syst_ud])
MCGenHistosEE.append([MCGenHistoEE, syst_ud])
MCRecoHistosEE.append([MCRecoHistoEE, syst_ud])
#MCGenHistosEE2.append([hMCFM,syst_ud])
#For mumu channel
hDiffsMM.append([hDiffMM, syst_ud])
hunfoldsMM.append([hunfoldMM, syst_ud])
if syst_ud == "":
hClosesMM.append([hCloseMM, syst_ud])
MCGenHistosMM.append([MCGenHistoMM, syst_ud])
MCRecoHistosMM.append([MCRecoHistoMM, syst_ud])
MCGenHistosMM2.append([hMCFM, syst_ud])
#For combined channel
hDiffs.append([ADDER(hDiffMM, hDiffEE, 1), syst_ud])
hunfolds.append([ADDER(hunfoldMM, hunfoldEE, 1), syst_ud])
if syst_ud == "":
hCloses.append([ADDER(hCloseMM, hCloseEE, 1), syst_ud])
MCGenHistos.append([ADDER(MCGenHistoMM, MCGenHistoEE, 1), syst_ud])
MCRecoHistos.append(
[ADDER(MCRecoHistoMM, MCRecoHistoEE, 1), syst_ud])
MCGenHistos2.append(
[ADDER(MCGenHistoMM2, MCGenHistoEE2, 1), syst_ud])
print "=|" * 20
print syst_ud * 20
print "unfold yield:"
print(hunfoldMM.Integral() + hunfoldEE.Integral()) / 19.7
print(MCGenHistoMM.Integral() + MCGenHistoEE.Integral()) / 19.7, "Gen"
hunfoldMM.Print("range")
hunfoldEE.Print("range")
print ">GEN<" * 40
MCGenHistoMM.Print("range")
MCGenHistoEE.Print("range")
print "=|" * 20
# print "=|"*20
# print "unfold yield:"
# print (hunfoldMM.Integral()+hunfoldEE.Integral())/19.7
# print "=|"*20
# hDiffs.append([hDiffEE,syst_ud])
# hunfolds.append([hunfoldEE,syst_ud])
# if syst_ud == "":
# hCloses.append([hCloseEE,syst_ud])
# MCGenHistos.append([MCGenHistoEE,syst_ud])
# MCRecoHistos.append([MCRecoHistoEE,syst_ud])
#QQQ = makeComparison(LUMscale(MCGenHistos),LUMscale(MCRecoHistos),LUMscale(hunfolds),LUMscale(hDiffs),LUMscale(MCGenHistos2),"comb")
#QQQ= makeComparison(LUMscale(MCGenHistosEE),LUMscale(MCRecoHistosEE),LUMscale(hunfoldsEE),LUMscale(hDiffsEE),MCGenHistosMM2,"ee")
#QQQ = makeComparison(LUMscale(MCGenHistosMM),LUMscale(MCRecoHistosMM),LUMscale(hunfoldsMM),LUMscale(hDiffsMM),MCGenHistosMM2,"mm")
#QQQ = makeComparison(LUMscale(MCGenHistos),LUMscale(MCRecoHistos),LUMscale(hunfolds),LUMscale(hDiffs),MCGenHistosMM2,"comb")
QQQ = makeComparison(LUMscale(MCGenHistosEE), LUMscale(MCRecoHistosEE),
LUMscale(hunfoldsEE), LUMscale(hDiffsEE),
MCGenHistosMM2, "ee")
#QQQ = makeComparison(LUMscale(MCGenHistosMM),LUMscale(MCRecoHistosMM),LUMscale(hunfoldsMM),LUMscale(hDiffsMM),MCGenHistosMM2,"mm")
Neemm = QQQ[0]
#Nee = makeComparison(LUMscale(MCGenHistosEE),LUMscale(MCRecoHistosEE),LUMscale(hunfoldsEE),LUMscale(hDiffsEE),LUMscale(hClosesEE),"ee")
#Nmm = makeComparison(LUMscale(MCGenHistosMM),LUMscale(MCRecoHistosMM),LUMscale(hunfoldsMM),LUMscale(hDiffsMM),LUMscale(hClosesMM),"mm")
# #makeComparison(MCGenHistos,MCRecoHistos,hunfolds,hDiffs,hCloses)
# print len(MCRecoHistos)
# print len(hunfolds)
# print "Bin Content Check"
# print "#"*30
# print "#"*10, "DIMUON CHANNEL", "#"*10
# print "MCReco", MCRecoHistosMM[0][0].Integral(), BinContent(MCRecoHistosMM[0][0])
# print "MCGen", MCGenHistosMM[0][0].Integral(), BinContent(MCGenHistosMM[0][0])
# print "True", hunfoldsMM[0][0].Integral(), BinContent(hunfoldsMM[0][0])
# print "Diff", hDiffsMM[0][0].Integral(), BinContent(hDiffsMM[0][0])
# print "#"*30
# print "#"*10, "DIELECTRON CHANNEL", "#"*10
# print "MCReco", MCRecoHistosEE[0][0].Integral(), BinContent(MCRecoHistosEE[0][0])
# print "MCGen", MCGenHistosEE[0][0].Integral(), BinContent(MCGenHistosEE[0][0])
# print "True", hunfoldsEE[0][0].Integral(), BinContent(hunfoldsEE[0][0])
# print "Diff", hDiffsEE[0][0].Integral(), BinContent(hDiffsEE[0][0])
# print "#"*30
# print "#"*10, "COMBINED CHANNEL", "#"*10
# print "MCReco", MCRecoHistos[0][0].Integral(), BinContent(MCRecoHistos[0][0])
# print "MCGen", MCGenHistos[0][0].Integral(), BinContent(MCGenHistos[0][0])
# print "True", hunfolds[0][0].Integral(), BinContent(hunfolds[0][0])
# print "Diff", hDiffs[0][0].Integral(), BinContent(hDiffs[0][0])
# print "#"*30
# print "#"*30
# print "#"*30
# print hunfoldsMM[0][0].Integral()*(3.0)*2.13, "ZZ->2l2nu cross section, muon"
# print hunfoldsEE[0][0].Integral()*(3.0)*2.13, "ZZ->2l2nu cross section, electron"
# print hunfolds[0][0].Integral()*(3.0/2.0)*2.13, "ZZ->2l2nu cross section, combined"
# print "#"*30
# print Nmm
# print Nee
print Neemm
print QQQ[1]
#print Nmm
#print Nee
# print "@"*30
# print "@"*30
# print "@"*30
# S=2.1299863918
# dS=0.0451194907919
# print numpy.multiply(Nmm,S*3)
# print numpy.multiply(Nee,S*3)
# print numpy.multiply(Neemm,S*3.0/2.0)
# print "#"*30
# print "#"*30
# print "#"*30
# print numpy.multiply(RescaleToPreZpt45(Nmm,S,dS),3.0), "muons"
# print numpy.multiply(RescaleToPreZpt45(Nee,S,dS),3.0), "electrons"
# print numpy.multiply(RescaleToPreZpt45(Neemm,S,dS),3.0/2.0), "combined"
# Just unfolding
# [ 317.58667967 124.62000891 124.62000891] muons
# [ 204.44743361 93.817413 93.817413 ] electrons
# [ 261.01705568 78.68129176 78.68129176] combined
#all else
# Bin Content Check
# ##############################
# ########## DIMUON CHANNEL ##########
# MCReco 5.48821856594 [1.6566265821456909, 1.5307177305221558, 2.0848486423492432, 0.21176119148731232, 0.0042644194327294827]
# MCGen 50.3628177345 [28.911533355712891, 8.1944065093994141, 11.555961608886719, 1.5890809297561646, 0.11183533072471619]
# True 49.7008933779 [27.718360900878906, 7.7494301795959473, 13.04161262512207, 1.2007522583007812, -0.0092625860124826431]
# Diff 5.54152165353 [1.6073417663574219, 1.4832497835159302, 2.371938943862915, 0.16530285775661469, -0.086311697959899902]
# ##############################
# ########## DIELECTRON CHANNEL ##########
# MCReco 3.674643751 [1.0442177057266235, 0.9972614049911499, 1.4791457653045654, 0.15098364651203156, 0.0030352284666150808]
# MCGen 33.3869778588 [18.415399551391602, 5.4404187202453613, 8.2116708755493164, 1.2221240997314453, 0.097364611923694611]
# True 31.9951079488 [22.219881057739258, 3.5517585277557373, 5.7815923690795898, 0.44187599420547485, 0.0]
# Diff 3.02480854467 [1.2913563251495361, 0.70207256078720093, 1.0841209888458252, 0.059399198740720749, -0.11214052885770798]
# ##############################
# ########## COMBINED CHANNEL ##########
# MCReco 9.16539874254 [2.7004456520080566, 2.5281918048858643, 3.5664489269256592, 0.36300751566886902, 0.0073048430494964123]
# MCGen 83.7497940361 [47.326930999755859, 13.634824752807617, 19.767633438110352, 2.8112049102783203, 0.2091999351978302]
# True 81.6960010286 [49.938240051269531, 11.301189422607422, 18.823205947875977, 1.6426281929016113, -0.0092625860124826431]
# Diff 8.56633037329 [2.8986983299255371, 2.1853222846984863, 3.4560599327087402, 0.22470206022262573, -0.19845223426818848]
# ##############################
# ##############################
# ##############################
# 317.588708685 ZZ->2l2nu cross section, muon
# 204.448739793 ZZ->2l2nu cross section, electron
# 261.018723287 ZZ->2l2nu cross section, combined
# ##############################
# [49.700893377885222, 24.356035601246898, 24.954409212519852]
# [31.99510794878006, 17.444453802993618, 19.251802135458732]
# [81.696001028642058, 34.234254955780528, 37.056285190785381]
# ##############################
# ##############################
# ##############################
# [ 317.58667967 155.77940538 159.59950658] muons 93.47233023637472982875, 99.70885557387058841952 quaddifference to just unfolding
# [ 204.44743361 111.55344624 123.09443832] electrons 60.35283246052335146028, 79.69023631100434516015
# [ 261.01705568 109.51740717 118.52311228] combined 76.17950380658385143785, 88.63962134122213690629
# os.system(str(Npm)+' > output.txt')
# SystematicErrors(MCRecoHistos,MCGenHistos,hTrues,hDiffs,hCloses)
#
#testtest
return
def main(optunf="Bayes"):
optunfs = ["Bayes", "SVD", "TUnfold", "Invert", "Reverse"]
if not optunf in optunfs:
txt = "Unfolding option " + optunf + " not recognised"
raise ValueError(txt)
global hReco, hMeas, hTrue
print "==================================== TRAIN ===================================="
# Create response matrix object for 40 measured and 20
# unfolded bins:
response = RooUnfoldResponse(40, -10.0, 10.0, 20, -10.0, 10.0)
# Train with a Breit-Wigner, mean 0.3 and width 2.5.
for i in xrange(100000):
# xt= gRandom.BreitWigner( 0.3, 2.5 )
xt = gRandom.Gaus(0.0, 5.0)
x = smear(xt)
if x != None:
response.Fill(x, xt)
else:
response.Miss(xt)
print "==================================== TEST ====================================="
hTrue = TH1D("true", "Test Truth", 20, -10.0, 10.0)
hMeas = TH1D("meas", "Test Measured", 40, -10.0, 10.0)
# Test with a Gaussian, mean 0 and width 2.
for i in xrange(10000):
# xt= gRandom.Gaus( 0.0, 2.0 )
xt = gRandom.BreitWigner(0.3, 2.5)
x = smear(xt)
hTrue.Fill(xt)
if x != None:
hMeas.Fill(x)
print "==================================== UNFOLD ==================================="
print "Unfolding method:", optunf
if "Bayes" in optunf:
# Bayes unfoldung with 4 iterations
# unfold= RooUnfoldBayes( response, hMeas, 4 )
unfold = RooUnfoldBayes(response, hMeas, 10, False, True)
elif "SVD" in optunf:
# SVD unfoding with free regularisation
# unfold= RooUnfoldSvd( response, hMeas, 20 )
unfold = RooUnfoldSvd(response, hMeas)
elif "TUnfold" in optunf:
# TUnfold with fixed regularisation tau=0.002
# unfold= RooUnfoldTUnfold( response, hMeas )
unfold = RooUnfoldTUnfold(response, hMeas, 0.002)
elif "Invert" in optunf:
unfold = RooUnfoldInvert(response, hMeas)
elif "Reverse" in optunf:
unfold = RooUnfoldBayes(response, hMeas, 1)
hReco = unfold.Hreco()
# unfold.PrintTable( cout, hTrue )
unfold.PrintTable(cout, hTrue, 2)
hReco.Draw()
hMeas.Draw("SAME")
hTrue.SetLineColor(8)
hTrue.Draw("SAME")
return
binError = h_data_toUse.GetBinError(bin)
newContent = gauss(binContent, sqrt(binContent))
if newContent < 0: newContent = 0
h_data_toUse.SetBinContent(bin, newContent)
h_data_toUse.SetBinError(bin, sqrt(newContent))
h_measured_toUse = asrootpy(h_measured)
# Remove fakes before unfolding
h_fakes = h_measured_toUse - h_response.ProjectionX()
nonFakeRatio = 1 - h_fakes / h_measured_toUse
h_measured_toUse *= nonFakeRatio / nonFakeRatio
h_data_toUse *= nonFakeRatio / nonFakeRatio
# Unfold with SVD
response = RooUnfoldResponse(h_measured_toUse, h_truth, h_response)
svdUnfolding = RooUnfoldSvd(response, h_data_toUse, 0.7)
svdUnfolding.SetVerbose(0)
h_unfolded_data_svd = svdUnfolding.Hreco(3)
# Unfold with Bayes
response_bayes = RooUnfoldResponse(h_measured_toUse, h_truth, h_response)
bayesUnfolding = RooUnfoldBayes(response_bayes, h_data_toUse, 4)
h_unfolded_data_bayes = bayesUnfolding.Hreco(3)
# Store result of first bin
h_svdFirstBin.Fill(h_unfolded_data_svd.GetBinContent(1))
h_bayesFirstBin.Fill(h_unfolded_data_bayes.GetBinContent(1))
# Draw histograms
h_svdFirstBin.Draw()
def main():
if len(sys.argv) < 3:
print("Usage: ToyMC [numberEvents] [randomSeed]")
return
numberEvents = int(sys.argv[1])
seed = int(sys.argv[2])
print(
"==================================== TRAIN ===================================="
)
f = root_open(
"legotrain_350_20161117-2106_LHCb4_fix_CF_pPb_MC_ptHardMerged.root", "read"
)
hJetPt = f.Get("AliJJetJtTask/AliJJetJtHistManager/JetPt/JetPtNFin{:02d}".format(2))
hZ = f.Get("AliJJetJtTask/AliJJetJtHistManager/Z/ZNFin{:02d}".format(2))
FillFakes = False
dummy_variable = True
weight = True
NBINS = 50
LimL = 0.1
LimH = 500
logBW = (TMath.Log(LimH) - TMath.Log(LimL)) / NBINS
LogBinsX = [LimL * math.exp(ij * logBW) for ij in range(0, NBINS + 1)]
hJetPtMeas = Hist(LogBinsX)
hJetPtTrue = Hist(LogBinsX)
myRandom = TRandom3(seed)
fEff = TF1("fEff", "1-0.5*exp(-x)")
jetBinBorders = [5, 10, 20, 30, 40, 60, 80, 100, 150, 500]
hJetPtMeasCoarse = Hist(jetBinBorders)
hJetPtTrueCoarse = Hist(jetBinBorders)
NBINSJt = 64
low = 0.01
high = 10
BinW = (TMath.Log(high) - TMath.Log(low)) / NBINSJt
LogBinsJt = [low * math.exp(i * BinW) for i in range(NBINSJt + 1)]
hJtTrue = Hist(LogBinsJt)
hJtMeas = Hist(LogBinsJt)
hJtFake = Hist(LogBinsJt)
LogBinsPt = jetBinBorders
jetPtBins = [(a, b) for a, b in zip(jetBinBorders, jetBinBorders[1:])]
hJtTrue2D = Hist2D(LogBinsJt, LogBinsPt)
hJtMeas2D = Hist2D(LogBinsJt, LogBinsPt)
hJtFake2D = Hist2D(LogBinsJt, LogBinsPt)
hJtMeasBin = [Hist(LogBinsJt) for i in jetBinBorders]
hJtTrueBin = [Hist(LogBinsJt) for i in jetBinBorders]
response = RooUnfoldResponse(hJtMeas, hJtTrue)
response2D = RooUnfoldResponse(hJtMeas2D, hJtTrue2D)
responseBin = [RooUnfoldResponse(hJtMeas, hJtTrue) for i in jetBinBorders]
responseJetPt = RooUnfoldResponse(hJetPtMeas, hJetPtTrue)
responseJetPtCoarse = RooUnfoldResponse(hJetPtMeasCoarse, hJetPtTrueCoarse)
# Histogram index is jet pT index, Bin 0 is 5-10 GeV
# Histogram X axis is observed jT, Bin 0 is underflow
# Histogram Y axis is observed jet Pt, Bin 0 is underflow
# Histogram Z axis is True jT, Bin 0 is underflow
responses = [Hist3D(LogBinsJt, LogBinsPt, LogBinsJt) for i in jetPtBins]
misses = Hist2D(LogBinsJt, LogBinsPt)
fakes2D = Hist2D(LogBinsJt, LogBinsPt)
outFile = TFile("tuple.root", "recreate")
responseTuple = TNtuple(
"responseTuple", "responseTuple", "jtObs:ptObs:jtTrue:ptTrue"
)
hMultiTrue = Hist(50, 0, 50)
hMultiMeas = Hist(50, 0, 50)
hZMeas = Hist(50, 0, 1)
hZTrue = Hist(50, 0, 1)
hZFake = Hist(50, 0, 1)
responseMatrix = Hist2D(LogBinsJt, LogBinsJt)
numberJets = 0
numberFakes = 0
numberJetsMeasBin = [0 for i in jetBinBorders]
numberJetsTrueBin = [0 for i in jetBinBorders]
numberFakesBin = [0 for i in jetBinBorders]
ieout = numberEvents / 10
if ieout > 10000:
ieout = 10000
fakeRate = 1
start_time = datetime.now()
print("Processing Training Events")
for ievt in range(numberEvents):
tracksTrue = []
tracksMeas = [0 for x in range(100)]
if ievt % ieout == 0 and ievt > 0:
time_elapsed = datetime.now() - start_time
time_left = timedelta(
seconds=time_elapsed.total_seconds()
* 1.0
* (numberEvents - ievt)
/ ievt
)
print(
"Event {} [{:.2f}%] Time Elapsed: {} ETA: {}".format(
ievt,
100.0 * ievt / numberEvents,
fmtDelta(time_elapsed),
fmtDelta(time_left),
)
)
jetTrue = TVector3(0, 0, 0)
jetMeas = TVector3(0, 0, 0)
jetPt = hJetPt.GetRandom()
remainder = jetPt
if jetPt < 5:
continue
nt = 0
nt_meas = 0
while remainder > 0:
trackPt = hZ.GetRandom() * jetPt
if trackPt < remainder:
track = TVector3()
remainder = remainder - trackPt
else:
trackPt = remainder
remainder = -1
if trackPt > 0.15:
track.SetPtEtaPhi(
trackPt, myRandom.Gaus(0, 0.1), myRandom.Gaus(math.pi, 0.2)
)
tracksTrue.append(track)
jetTrue += track
if fEff.Eval(trackPt) > myRandom.Uniform(0, 1):
tracksMeas[nt] = 1
jetMeas += track
nt_meas += 1
else:
tracksMeas[nt] = 0
nt += 1
fakes = []
for it in range(fakeRate * 100):
if myRandom.Uniform(0, 1) > 0.99:
fake = TVector3()
fake.SetPtEtaPhi(
myRandom.Uniform(0.15, 1),
myRandom.Gaus(0, 0.1),
myRandom.Gaus(math.pi, 0.2),
)
fakes.append(fake)
jetMeas += fake
hJetPtMeas.Fill(jetMeas.Pt())
hJetPtTrue.Fill(jetTrue.Pt())
responseJetPt.Fill(jetMeas.Pt(), jetTrue.Pt())
responseJetPtCoarse.Fill(jetMeas.Pt(), jetTrue.Pt())
hMultiTrue.Fill(nt)
hMultiMeas.Fill(nt_meas)
ij_meas = GetBin(jetBinBorders, jetMeas.Pt())
ij_true = GetBin(jetBinBorders, jetTrue.Pt())
if nt < 5 or nt_meas < 5:
continue
numberJets += 1
if ij_meas >= 0:
numberJetsMeasBin[ij_meas] += 1
hJetPtMeasCoarse.Fill(jetMeas.Pt())
if ij_true >= 0:
numberJetsTrueBin[ij_true] += 1
hJetPtTrueCoarse.Fill(jetTrue.Pt())
for track, it in zip(tracksTrue, range(100)):
zTrue = (track * jetTrue.Unit()) / jetTrue.Mag()
jtTrue = (track - scaleJet(jetTrue, zTrue)).Mag()
hZTrue.Fill(zTrue)
if ij_true >= 0:
if weight:
hJtTrue.Fill(jtTrue, 1.0 / jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue, 1.0 / jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt(), 1.0 / jtTrue)
else:
hJtTrue.Fill(jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt())
if ij_meas >= 0:
if tracksMeas[it] == 1:
zMeas = (track * jetMeas.Unit()) / jetMeas.Mag()
jtMeas = (track - scaleJet(jetMeas, zMeas)).Mag()
hZMeas.Fill(zMeas)
if weight:
hJtMeasBin[ij_meas].Fill(jtMeas, 1.0 / jtMeas)
hJtMeas.Fill(jtMeas, 1.0 / jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt(), 1.0 / jtMeas)
else:
hJtMeas.Fill(jtMeas)
hJtMeasBin[ij_meas].Fill(jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt())
response.Fill(jtMeas, jtTrue)
responseBin[ij_true].Fill(jtMeas, jtTrue)
response2D.Fill(jtMeas, jetMeas.Pt(), jtTrue, jetTrue.Pt())
responseMatrix.Fill(jtMeas, jtTrue)
responses[ij_true].Fill(jtMeas, jetMeas.Pt(), jtTrue)
responseTuple.Fill(jtMeas, jetMeas.Pt(), jtTrue, jetTrue.Pt())
else:
response.Miss(jtTrue)
responseBin[ij_true].Miss(jtTrue)
response2D.Miss(jtTrue, jetTrue.Pt())
misses.Fill(jtTrue, jetTrue.Pt())
responseTuple.Fill(-1, -1, jtTrue, jetTrue.Pt())
if ij_meas >= 0:
for fake in fakes:
zFake = (fake * jetMeas.Unit()) / jetMeas.Mag()
jtFake = (fake - scaleJet(jetMeas, zFake)).Mag()
hZMeas.Fill(zFake)
hZFake.Fill(zFake)
if weight:
hJtMeas.Fill(jtFake, 1.0 / jtFake)
hJtMeasBin[ij_meas].Fill(jtFake, 1.0 / jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake.Fill(jtFake, 1.0 / jtFake)
else:
hJtMeas.Fill(jtFake)
hJtMeasBin[ij_meas].Fill(jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt())
hJtFake2D.Fill(jtFake, jetMeas.Pt())
hJtFake.Fill(jtFake)
if FillFakes:
response.Fake(jtFake)
responseBin[ij_true].Fake(jtFake)
response2D.Fake(jtFake, jetMeas.Pt())
fakes2D.Fill(jtFake, jetMeas.Pt())
responseTuple.Fill(jtFake, jetMeas.Pt(), -1, -1)
numberFakes += 1
numberFakesBin[ij_true] += 1
response2Dtest = make2Dresponse(
responses, jetPtBins, hJtMeas2D, hJtTrue2D, misses=misses, fakes=fakes2D
)
if dummy_variable:
hJetPtMeas.Reset()
hJetPtTrue.Reset()
hMultiTrue.Reset()
hMultiMeas.Reset()
hJetPtMeasCoarse.Reset()
hJetPtTrueCoarse.Reset()
hZTrue.Reset()
hZMeas.Reset()
hJtTrue.Reset()
hJtTrue2D.Reset()
hJtMeas.Reset()
hJtMeas2D.Reset()
hJtFake.Reset()
hJtFake2D.Reset()
for h, h2 in zip(hJtTrueBin, hJtMeasBin):
h.Reset()
h2.Reset()
numberJetsMeasBin = [0 for i in jetBinBorders]
numberJetsTrueBin = [0 for i in jetBinBorders]
numberJets = 0
print("Create testing data")
start_time = datetime.now()
numberEvents = numberEvents / 2
for ievt in range(numberEvents):
tracksTrue = []
tracksMeas = [0 for x in range(100)]
if ievt % ieout == 0 and ievt > 0:
time_elapsed = datetime.now() - start_time
time_left = timedelta(
seconds=time_elapsed.total_seconds()
* 1.0
* (numberEvents - ievt)
/ ievt
)
print(
"Event {} [{:.2f}%] Time Elapsed: {} ETA: {}".format(
ievt,
100.0 * ievt / numberEvents,
fmtDelta(time_elapsed),
fmtDelta(time_left),
)
)
jetTrue = TVector3(0, 0, 0)
jetMeas = TVector3(0, 0, 0)
jetPt = hJetPt.GetRandom()
remainder = jetPt
if jetPt < 5:
continue
nt = 0
nt_meas = 0
while remainder > 0:
trackPt = hZ.GetRandom() * jetPt
if trackPt < remainder:
track = TVector3()
remainder = remainder - trackPt
else:
trackPt = remainder
remainder = -1
if trackPt > 0.15:
track.SetPtEtaPhi(
trackPt, myRandom.Gaus(0, 0.1), myRandom.Gaus(math.pi, 0.2)
)
tracksTrue.append(track)
jetTrue += track
if fEff.Eval(trackPt) > myRandom.Uniform(0, 1):
tracksMeas[nt] = 1
jetMeas += track
nt_meas += 1
else:
tracksMeas[nt] = 0
nt += 1
fakes = []
for it in range(fakeRate * 100):
if myRandom.Uniform(0, 1) > 0.99:
fake = TVector3()
fake.SetPtEtaPhi(
myRandom.Uniform(0.15, 1),
myRandom.Gaus(0, 0.1),
myRandom.Gaus(math.pi, 0.2),
)
fakes.append(fake)
jetMeas += fake
hJetPtMeas.Fill(jetMeas.Pt())
hJetPtTrue.Fill(jetTrue.Pt())
hMultiTrue.Fill(nt)
hMultiMeas.Fill(nt_meas)
ij_meas = GetBin(jetBinBorders, jetMeas.Pt())
ij_true = GetBin(jetBinBorders, jetTrue.Pt())
if nt < 5 or nt_meas < 5:
continue
numberJets += 1
if ij_meas >= 0:
numberJetsMeasBin[ij_meas] += 1
hJetPtMeasCoarse.Fill(jetMeas.Pt())
if ij_true >= 0:
numberJetsTrueBin[ij_true] += 1
hJetPtTrueCoarse.Fill(jetTrue.Pt())
for track, it in zip(tracksTrue, range(100)):
zTrue = (track * jetTrue.Unit()) / jetTrue.Mag()
jtTrue = (track - scaleJet(jetTrue, zTrue)).Mag()
hZTrue.Fill(zTrue)
if ij_true >= 0:
if weight:
hJtTrue.Fill(jtTrue, 1.0 / jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue, 1.0 / jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt(), 1.0 / jtTrue)
else:
hJtTrue.Fill(jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt())
if ij_meas >= 0:
if tracksMeas[it] == 1:
zMeas = (track * jetMeas.Unit()) / jetMeas.Mag()
jtMeas = (track - scaleJet(jetMeas, zMeas)).Mag()
hZMeas.Fill(zMeas)
if weight:
hJtMeasBin[ij_meas].Fill(jtMeas, 1.0 / jtMeas)
hJtMeas.Fill(jtMeas, 1.0 / jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt(), 1.0 / jtMeas)
else:
hJtMeas.Fill(jtMeas)
hJtMeasBin[ij_meas].Fill(jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt())
if ij_meas >= 0:
for fake in fakes:
zFake = (fake * jetMeas.Unit()) / jetMeas.Mag()
jtFake = (fake - scaleJet(jetMeas, zFake)).Mag()
hZMeas.Fill(zFake)
hZFake.Fill(zFake)
if weight:
hJtMeas.Fill(jtFake, 1.0 / jtFake)
hJtMeasBin[ij_meas].Fill(jtFake, 1.0 / jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake.Fill(jtFake, 1.0 / jtFake)
else:
hJtMeas.Fill(jtFake)
hJtMeasBin[ij_meas].Fill(jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt())
hJtFake2D.Fill(jtFake, jetMeas.Pt())
hJtFake.Fill(jtFake)
time_elapsed = datetime.now() - start_time
print(
"Event {} [{:.2f}%] Time Elapsed: {}".format(
numberEvents, 100.0, fmtDelta(time_elapsed)
)
)
if not FillFakes:
hJtMeas.Add(hJtFake, -1)
hJtMeas2D.Add(hJtFake2D, -1)
responseTuple.Print()
outFile.Write()
# printTuple(responseTuple)
hJtMeasProjBin = [
makeHist(hJtMeas2D.ProjectionX("histMeas{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
hJtMeasProj = makeHist(hJtMeas2D.ProjectionX("histMeas"), bins=LogBinsJt)
hJtTrueProjBin = [
makeHist(hJtTrue2D.ProjectionX("histTrue{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
hJtTrueProj = makeHist(hJtTrue2D.ProjectionX("histTrue"), bins=LogBinsJt)
hJtFakeProjBin = [
makeHist(hJtFake2D.ProjectionX("histFake{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
if not FillFakes:
for h, h2 in zip(hJtMeasBin, hJtFakeProjBin):
h.Add(h2, -1)
for h in (
hJtMeasProj,
hJtTrueProj,
hJtMeas,
hJtTrue,
hJtFake,
hZFake,
hZMeas,
hZTrue,
):
h.Scale(1.0 / numberJets, "width")
for meas, true, n_meas, n_true in zip(
hJtMeasBin, hJtTrueBin, numberJetsMeasBin, numberJetsTrueBin
):
if n_meas > 0:
meas.Scale(1.0 / n_meas, "width")
if n_true > 0:
true.Scale(1.0 / n_true, "width")
numberJetsMeasFromHist = [
hJetPtMeasCoarse.GetBinContent(i)
for i in range(1, hJetPtMeasCoarse.GetNbinsX() + 1)
]
numberJetsTrueFromHist = [
hJetPtTrueCoarse.GetBinContent(i)
for i in range(1, hJetPtTrueCoarse.GetNbinsX() + 1)
]
print("Total number of jets: {}".format(numberJets))
print("Total number of fakes: {}".format(numberFakes))
print("Measured jets by bin")
print(numberJetsMeasBin)
print(numberJetsMeasFromHist)
print("True jets by bin")
print(numberJetsTrueBin)
print(numberJetsTrueFromHist)
hRecoJetPtCoarse = unfoldJetPt(hJetPtMeasCoarse, responseJetPtCoarse, jetBinBorders)
numberJetsFromReco = [
hRecoJetPtCoarse.GetBinContent(i)
for i in range(1, hRecoJetPtCoarse.GetNbinsX())
]
print("Unfolded jet numbers by bin:")
print(numberJetsFromReco)
print("Fakes by bin")
print(numberFakesBin)
print(
"==================================== UNFOLD ==================================="
)
unfold = RooUnfoldBayes(response, hJtMeas, 4) # OR
unfoldSVD = RooUnfoldSvd(response, hJtMeas, 20) # OR
unfold2D = RooUnfoldBayes(response2D, hJtMeas2D, 4)
for u in (unfold, unfoldSVD, unfold2D):
u.SetVerbose(0)
# response2Dtest = makeResponseFromTuple(responseTuple,hJtMeas2D,hJtTrue2D)
unfold2Dtest = RooUnfoldBayes(response2Dtest, hJtMeas2D, 4)
unfoldBin = [
RooUnfoldBayes(responseBin[i], hJtMeasBin[i]) for i in range(len(jetBinBorders))
]
for u in unfoldBin:
u.SetVerbose(0)
hRecoBayes = makeHist(unfold.Hreco(), bins=LogBinsJt)
hRecoSVD = makeHist(unfoldSVD.Hreco(), bins=LogBinsJt)
hRecoBin = [
makeHist(unfoldBin[i].Hreco(), bins=LogBinsJt)
for i in range(len(jetBinBorders))
]
hReco2D = make2DHist(unfold2D.Hreco(), xbins=LogBinsJt, ybins=LogBinsPt)
hReco2Dtest = make2DHist(unfold2Dtest.Hreco(), xbins=LogBinsJt, ybins=LogBinsPt)
hRecoJetPt = unfoldJetPt(hJetPtMeas, responseJetPt, LogBinsX)
hReco2DProjBin = [
makeHist(hReco2D.ProjectionX("histReco{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
hReco2DTestProjBin = [
makeHist(
hReco2Dtest.ProjectionX("histRecoTest{}".format(i), i, i), bins=LogBinsJt
)
for i in range(1, len(jetBinBorders))
]
hReco2DProj = makeHist(hReco2D.ProjectionX("histReco"), bins=LogBinsJt)
hReco2DProj.Scale(1.0 / numberJets, "width")
for h, h2, n in zip(hReco2DProjBin, hReco2DTestProjBin, numberJetsFromReco):
if n > 0:
h.Scale(1.0 / n, "width")
h2.Scale(1.0 / n, "width")
# unfold.PrintTable (cout, hJtTrue)
for h, h2, nj in zip(hJtMeasProjBin, hJtFakeProjBin, numberJetsMeasBin):
if nj > 0:
h.Scale(1.0 / nj, "width")
h2.Scale(1.0 / nj, "width")
# else:
# print("nj is 0 for {}".format(h.GetName()))
for h, nj in zip(hJtTrueProjBin, numberJetsTrueBin):
if nj > 0:
h.Scale(1.0 / nj, "width")
# draw8grid(hJtMeasBin[1:],hJtTrueBin[1:],jetPtBins[1:],xlog = True,ylog = True,name="newfile.pdf",proj = hJtMeasProjBin[2:], unf2d = hReco2DProjBin[2:], unf=hRecoBin[1:])
if numberEvents > 1000:
if numberEvents > 1000000:
filename = "ToyMC_{}M_events.pdf".format(numberEvents / 1000000)
else:
filename = "ToyMC_{}k_events.pdf".format(numberEvents / 1000)
else:
filename = "ToyMC_{}_events.pdf".format(numberEvents)
draw8gridcomparison(
hJtMeasBin,
hJtTrueBin,
jetPtBins,
xlog=True,
ylog=True,
name=filename,
proj=None,
unf2d=hReco2DProjBin,
unf2dtest=hReco2DTestProjBin,
unf=hRecoBin,
fake=hJtFakeProjBin,
start=1,
stride=1,
)
drawQA(
hJtMeas,
hJtTrue,
hJtFake,
hRecoBayes,
hRecoSVD,
hReco2DProj,
hZ,
hZTrue,
hZMeas,
hZFake,
hMultiMeas,
hMultiTrue,
hJetPt,
hJetPtTrue,
hJetPtMeas,
hRecoJetPt,
responseMatrix,
)
outFile.Close()