def _project(tree,
var,
selection='',
weight=1.0,
bins=None,
includeover=False):
h = None
if var.count(':') == 0:
## Hist (1D)
if bins:
if isinstance(bins, tuple):
assert len(bins) == 3
h = Hist(*bins)
elif isinstance(bins, list):
h = Hist(bins)
else:
assert False
else:
assert False
elif var.count(':') == 1:
## Hist2D
## like rootpy, we use a convention where var=x:y, unlike ROOT
varx, vary = var.split(':')
var = ':'.join([vary, varx])
if bins:
if isinstance(bins, tuple):
assert len(bins) == 6
h = Hist2D(*bins)
elif isinstance(bins, list):
## TODO: support variable bins for Hist2D
h = Hist2D(*bins)
#assert False
else:
assert False
else:
assert False
else:
assert False
assert h
# kwargs['hist'] = h
## add the weight to the selection via a TCut
weighted_selection = str(selection)
if weight and weight != 1.0:
weighted_selection = Cut(weighted_selection)
weighted_selection = weighted_selection * weight
weighted_selection = str(weighted_selection)
tree.Draw('%s>>%s' % (var, h.GetName()), weighted_selection)
## print tree.GetSelectedRows() ## debuging
# for event in t:
# x = getattr(event, var)
# h.fill(x)
if h:
h.SetDirectory(0)
# elist = ROOT.gDirectory.Get('elist')
# elist.Print('all')
if var.count(':') == 0 and includeover:
## include overflow for Hist (1D)
nbins = h.GetNbinsX()
c1 = h.GetBinContent(nbins)
c2 = h.GetBinContent(nbins + 1)
e1 = h.GetBinError(nbins)
e2 = h.GetBinError(nbins + 1)
h.SetBinContent(nbins, c1 + c2)
h.SetBinError(
nbins,
math.sqrt((c1 * e1 * e1 + c2 * e2 * e2) /
(c1 + c2)) if c1 + c2 != 0.0 else 0.0)
h.SetBinContent(nbins + 1, 0.0)
h.SetBinError(nbins + 1, 0.0)
return h
class JtUnfolder(object):
def __init__(self, name, **kwargs):
self._name = name
print("Create Unfolder {}".format(name))
self._Njets = kwargs.get("Njets", 0)
self._fEff = None
self._jetBinBorders = kwargs.get("jetBinBorders", None)
if self._jetBinBorders is not None:
self._jetPtBins = [
(a, b)
for a, b in zip(self._jetBinBorders, self._jetBinBorders[1:])
]
self._fakeRate = kwargs.get("fakeRate", 1)
self._randomSeed = kwargs.get("randomSeed", 123)
self._weight = kwargs.get("weight", True)
self._fillFakes = kwargs.get("fillFakes", False)
self._NBINSJt = kwargs.get("NBINSJt", 64)
self._NBINS = kwargs.get("NBINS", 64)
self._responseJetPt = None
self._responseJetPtCoarse = None
self._IsData = kwargs.get("Data", False)
self._hJtTrue2D = None
self._Niter = kwargs.get("Iterations", 4)
self._NFin = kwargs.get("NFin", 0)
self._hBgJt = None
self._hBgJtNormalized = None
self._numberBackgroundBin = None
def fill_jt_histogram(self, histo, jt, pt):
if self._weight:
histo.Fill(jt, pt, 1.0 / jt)
else:
histo.Fill(jt, pt)
def setTrackMatch(self, hists):
self._matching = hists
def setJtBins(self, bins):
self._LogBinsJt = bins
def setPtBins(self, bins):
self._LogBinsX = bins
def setNbinsJt(self, nbins):
self._NBINSJt = nbins
def setNbinsPt(self, nbins):
self._NBINS = nbins
def setRandomSeed(self, seed):
self._randomSeed = seed
def setJtTestMeas2D(self, hJtMeas):
self._hJtTestMeas2D = hJtMeas
def setJtTestTrue2D(self, hJtTrue):
self._hJtTestTrue2D = hJtTrue
def setJtMeas2D(self, hJtMeas):
self._hJtMeas2D = hJtMeas
def setJtTrue2D(self, hJtTrue):
self._hJtTrue2D = hJtTrue
def setJetPtMeas(self, hPtMeas):
self._hJetPtMeas = hPtMeas
def setJetPtTrue(self, hPtTrue):
self._hJetPtTrue = hPtTrue
def setJetPtMeasCoarse(self, hPtMeas):
self._hJetPtMeasCoarse = hPtMeas
def setJetPtTrueCoarse(self, hPtTrue):
self._hJetPtTrueCoarse = hPtTrue
def setFakeRate(self, rate):
self._fakeRate = rate
def setWeight(self, weight):
self._weight = weight
def setMisses2D(self, misses):
self._misses2D = misses
def setFakes2D(self, fakes):
self._hJtFake2D = fakes
def setJetPtResponseHisto(self, hresponse):
self._hresponseJetPt = hresponse
def setJtBackground(self, background):
self._hBgJt = background
if self._numberBackgroundBin is not None:
self._hBgJtNormalized = normalize(self._hBgJt,
self._numberBackgroundBin)
print("Jt background normalized")
def setJtBackgroundNumbers(self, n):
self._numberBackgroundBin = n
if self._hBgJt is not None:
self._hBgJtNormalized = normalize(self._hBgJt,
self._numberBackgroundBin)
print("Jt background normalized")
def setJetPtResponseCoarseHisto(self, hresponse):
self._hresponseJetPtCoarse = hresponse
def setJetPtResponse(self, response):
self._responseJetPt = response
def setJetPtResponseCoarse(self, response):
self._responseJetPtCoarse = response
def setNumberJetsMeas(self, n):
self._numberJetsMeasBin = n
def setNumberJetsTestMeas(self, n):
self._numberJetsTestMeasBin = n
def setNumberJetsTrue(self, n):
self._numberJetsTrueBin = n
def setNumberJetsTestTrue(self, n):
self._numberJetsTestTrueBin = n
def setNumberJetsMeasTrain(self, n):
self._numberJetsMeasTrain = n
def set2Dresponse(self, responses):
self._2Dresponses = responses
def drawTrackMatch(self, name, option):
drawing.drawMatchHisto(self._matching, self._jetPtBins, name, option)
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 createToyData(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))
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._hMultiTrue = Hist(50, 0, 50)
self._hMultiMeas = Hist(50, 0, 50)
self._hZMeas = Hist(50, 0, 1)
self._hZTrue = Hist(50, 0, 1)
self._hZFake = Hist(50, 0, 1)
self._numberJetsMeasBin = [0 for i in self._jetBinBorders]
self._numberJetsTrueBin = [0 for i in self._jetBinBorders]
print("Create testing data")
start_time = datetime.now()
numberEvents = numberEvents
ieout = numberEvents / 10
jtLeadingMeas = 0
if ieout > 20000:
ieout = 20000
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
leading = None
leadingPt = 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),
)
if track.Pt() > leadingPt:
leading = track
leadingPt = leading.Pt()
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 = []
if leadingPt > 0.25 * jetPt:
doLeading = True
else:
doLeading = False
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._hJetPtMeas.Fill(jetMeas.Pt())
self._hJetPtTrue.Fill(jetTrue.Pt())
self._hMultiTrue.Fill(nt)
self._hMultiMeas.Fill(nt_meas)
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
self._hJetPtMeasCoarse.Fill(jetMeas.Pt())
if doLeading:
self._numberJetsTestMeasBin[ij_meas] += 1
if ij_true >= 0:
self._numberJetsTrueBin[ij_true] += 1
self._hJetPtTrueCoarse.Fill(jetTrue.Pt())
if doLeading:
self._numberJetsTestTrueBin[ij_true] += 1
for track, it in zip(tracksTrue, range(100)):
zTrue = (track * jetTrue.Unit()) / jetTrue.Mag()
jtTrue = (track - scaleJet(jetTrue, zTrue)).Mag()
self._hZTrue.Fill(zTrue)
if track.Pt() < 0.95 * leadingPt and doLeading:
zLeadingTrue = (track * leading.Unit()) / leading.Mag()
jtLeadingTrue = (track -
scaleJet(leading, zLeadingTrue)).Mag()
if ij_true >= 0:
self.fill_jt_histogram(self._hJtTrue2D, jtTrue,
jetTrue.Pt())
if (track.Pt() < 0.95 * leading.Pt()) and doLeading:
self.fill_jt_histogram(self._hJtTestTrue2D,
jtLeadingTrue, jetTrue.Pt())
if ij_meas >= 0 and tracksMeas[it] == 1:
zMeas = (track * jetMeas.Unit()) / jetMeas.Mag()
jtMeas = (track - scaleJet(jetMeas, zMeas)).Mag()
if track.Pt() < 0.95 * leading.Pt():
zLeadingMeas = (track * leading.Unit()) / leading.Mag()
jtLeadingMeas = (
track - scaleJet(leading, zLeadingMeas)).Mag()
self._hZMeas.Fill(zMeas)
self.fill_jt_histogram(self._hJtMeas2D, jtMeas,
jetMeas.Pt())
if (track.Pt() < 0.95 * leadingPt and doLeading
and jtLeadingMeas > 0):
self.fill_jt_histogram(self._hJtTestMeas2D,
jtLeadingMeas, jetMeas.Pt())
if ij_meas < 0:
continue
for fake in fakes:
zFake = (fake * jetMeas.Unit()) / jetMeas.Mag()
jtFake = (fake - scaleJet(jetMeas, zFake)).Mag()
zLeadingFake = (fake * leading.Unit()) / leading.Mag()
jtLeadingFake = (fake - scaleJet(leading, zLeadingFake)).Mag()
self._hZMeas.Fill(zFake)
self._hZFake.Fill(zFake)
self.fill_jt_histogram(self._hJtMeas2D, jtFake, jetMeas.Pt())
self.fill_jt_histogram(self._hJtTestMeas2D, jtLeadingFake,
leadingPt)
# self.fill_jt_histogram(self._hJtFake2D, jtFake,jetMeas.Pt())
time_elapsed = datetime.now() - start_time
print("Event {} [{:.2f}%] Time Elapsed: {}".format(
numberEvents, 100.0, fmtDelta(time_elapsed)))
def unfold(self):
if self._fillFakes:
fakes = self._hJtFake2D
else:
fakes = None
if self._hJtTrue2D:
self._response2D = make2Dresponse(
self._2Dresponses,
self._jetPtBins,
self._hJtMeas2D,
self._hJtTrue2D,
misses=self._misses2D,
fakes=fakes,
)
else:
self._response2D = make2Dresponse(
self._2Dresponses,
self._jetPtBins,
self._hJtMeas2D,
self._hJtMeas2D,
misses=self._misses2D,
fakes=fakes,
)
if not self._fillFakes:
print("Scale hJtFake2D by {}".format(1.0 *
sum(self._numberJetsMeasBin) /
self._numberJetsMeasTrain))
self._hJtFake2D.Scale(1.0 * sum(self._numberJetsMeasBin) /
self._numberJetsMeasTrain)
self._hJtMeas2D.Add(self._hJtFake2D, -1)
self._hJtFakeProjBin = [
makeHist(
self._hJtFake2D.ProjectionX("histFake{}".format(i), i, i),
bins=self._LogBinsJt,
) for i in range(1, len(self._jetBinBorders))
]
for h, nj in zip(self._hJtFakeProjBin, self._numberJetsMeasBin):
if nj > 0:
h.Scale(1.0 / nj, "width")
if self._responseJetPtCoarse is None:
print(self._responseJetPtCoarse)
self._responseJetPtCoarse = createResponse(
self._hJetPtMeasCoarse, self._hresponseJetPtCoarse)
if self._responseJetPt is None:
self._responseJetPt = createResponse(self._hJetPtMeas,
self._hresponseJetPt)
self._hJetPtRecoCoarse = unfoldJetPt(self._hJetPtMeasCoarse,
self._responseJetPtCoarse,
self._jetBinBorders)
self._hJetPtReco = unfoldJetPt(self._hJetPtMeas, self._responseJetPt,
self._LogBinsX)
self._numberJetsMeasFromHist = [
self._hJetPtMeasCoarse.GetBinContent(i)
for i in range(1,
self._hJetPtMeasCoarse.GetNbinsX() + 1)
]
if not self._IsData:
self._numberJetsTrueFromHist = [
self._hJetPtTrueCoarse.GetBinContent(i)
for i in range(1,
self._hJetPtTrueCoarse.GetNbinsX() + 1)
]
self._numberJetsFromReco = [
self._hJetPtRecoCoarse.GetBinContent(i)
for i in range(1, self._hJetPtRecoCoarse.GetNbinsX())
]
self.printJetNumbers()
unfold2D = RooUnfoldBayes(self._response2D, self._hJtMeas2D,
self._Niter)
self._hJtReco2D = make2DHist(unfold2D.Hreco(),
xbins=self._LogBinsJt,
ybins=self._jetBinBorders)
self._hJtMeasBin = [
makeHist(
self._hJtMeas2D.ProjectionX("histMeas{}".format(i), i, i),
bins=self._LogBinsJt,
) for i in range(1, len(self._jetBinBorders))
]
if not self._IsData:
self._hJtTestMeasBin = [
makeHist(
self._hJtTestMeas2D.ProjectionX("histTestMeas{}".format(i),
i, i),
bins=self._LogBinsJt,
) for i in range(1, len(self._jetBinBorders))
]
self._hJtTrueBin = [
makeHist(
self._hJtTrue2D.ProjectionX("histMeas{}".format(i), i, i),
bins=self._LogBinsJt,
) for i in range(1, len(self._jetBinBorders))
]
self._hJtTestTrueBin = [
makeHist(
self._hJtTestTrue2D.ProjectionX("histMeas{}".format(i), i,
i),
bins=self._LogBinsJt,
) for i in range(1, len(self._jetBinBorders))
]
self._hJtRecoBin = [
makeHist(
self._hJtReco2D.ProjectionX("histMeas{}".format(i), i, i),
bins=self._LogBinsJt,
) for i in range(1, len(self._jetBinBorders))
]
for h, n, in zip(
self._hJtMeasBin,
self._numberJetsMeasFromHist,
):
if n > 0:
h.Scale(1.0 / n, "width")
if not self._IsData:
for h, h2, h3, n, n2, n3 in zip(
self._hJtTrueBin,
self._hJtTestTrueBin,
self._hJtTestMeasBin,
self._numberJetsTrueFromHist,
self._numberJetsTestTrueBin,
self._numberJetsTestMeasBin,
):
if n > 0:
h.Scale(1.0 / n, "width")
if n2 > 0:
h2.Scale(1.0 / n2, "width")
if n3 > 0:
h3.Scale(1.0 / n3, "width")
for h, n in zip(self._hJtRecoBin, self._numberJetsFromReco):
if n > 0:
h.Scale(1.0 / n, "width")
def plotJetPt(self):
if self._IsData:
drawing.drawJetPt(
self._hJetPtMeas,
None,
self._hJetPtReco,
filename="JetPtUnfoldedData.pdf",
)
drawing.drawJetPt(
self._hJetPtMeasCoarse,
None,
self._hJetPtRecoCoarse,
filename="JetPtCoarseUnfoldedData.pdf",
)
else:
drawing.drawJetPt(
self._hJetPtMeas,
self._hJetPtTrue,
self._hJetPtReco,
filename="JetPtUnfolded.pdf",
)
drawing.drawJetPt(
self._hJetPtMeasCoarse,
self._hJetPtTrueCoarse,
self._hJetPtRecoCoarse,
filename="JetPtCoarseUnfolded.pdf",
)
def plotJt(self, filename, **kwargs):
nRebin = kwargs.get("Rebin", 1)
histlist = [self._hJtMeasBin, self._hJtRecoBin, self._hJtFakeProjBin]
if not self._IsData:
histlist.append(self._hJtTrueBin)
if self._hBgJtNormalized is not None:
histlist.append(self._hBgJtNormalized)
bg = self._hBgJtNormalized
else:
bg = None
if nRebin > 0:
for hists in histlist:
for h in hists:
h.Rebin(nRebin)
h.Scale(1.0 / nRebin)
if self._IsData:
drawing.draw8gridcomparison(
self._hJtMeasBin,
None,
self._jetPtBins,
xlog=True,
ylog=True,
name="{}.pdf".format(filename),
unf2d=self._hJtRecoBin,
start=4,
stride=1,
backgroundJt=bg,
)
drawing.draw8gridcomparison(
self._hJtMeasBin,
None,
self._jetPtBins,
xlog=True,
ylog=True,
name="{}_Extra.pdf".format(filename),
unf2d=self._hJtRecoBin,
start=0,
stride=1,
backgroundJt=bg,
)
else:
drawing.draw8gridcomparison(
self._hJtMeasBin,
self._hJtTrueBin,
self._jetPtBins,
xlog=True,
ylog=True,
name="{}.pdf".format(filename),
unf2d=self._hJtRecoBin,
fake=self._hJtFakeProjBin,
start=4,
stride=1,
)
drawing.draw8gridcomparison(
self._hJtMeasBin,
self._hJtTrueBin,
self._jetPtBins,
xlog=True,
ylog=True,
name="{}_Extra.pdf".format(filename),
unf2d=self._hJtRecoBin,
fake=self._hJtFakeProjBin,
start=0,
stride=1,
)
def plotLeadingJtComparison(self, filename, **kwargs):
nRebin = kwargs.get("Rebin", 1)
histlist = [self._hJtTestTrueBin, self._hJtTrueBin, self._hJtMeasBin]
if nRebin > 1:
for hists in histlist:
for h in hists:
h.Rebin(nRebin)
h.Scale(1.0 / nRebin)
drawing.draw8gridcomparison(
self._hJtMeasBin,
self._hJtTrueBin,
self._jetPtBins,
xlog=True,
ylog=True,
name="{}.pdf".format(filename),
leadingJt=self._hJtTestTrueBin,
start=2,
stride=1,
)
drawing.draw8gridcomparison(
self._hJtMeasBin,
self._hJtTrueBin,
self._jetPtBins,
xlog=True,
ylog=True,
name="{}_Extra.pdf".format(filename),
leadingJt=self._hJtTestTrueBin,
start=0,
stride=1,
)
def plotResponse(self):
fig, axs = plt.subplots(2, 1, figsize=(10, 10))
axs = axs.reshape(2)
for ax, h in zip(axs, (self._responseJetPt, self._response2D)):
hResponse = h.Hresponse()
xbins = [
hResponse.GetXaxis().GetBinLowEdge(iBin)
for iBin in range(1,
hResponse.GetNbinsX() + 2)
]
ybins = [
hResponse.GetYaxis().GetBinLowEdge(iBin)
for iBin in range(1,
hResponse.GetNbinsY() + 2)
]
drawing.drawResponse(
ax, make2DHist(hResponse, xbins=xbins, ybins=ybins))
plt.show() # Draw figure on screen
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
hResponse = self._responseJetPt.Hresponse()
xbins = [
hResponse.GetXaxis().GetBinLowEdge(iBin)
for iBin in range(1,
hResponse.GetNbinsX() + 2)
]
ybins = [
hResponse.GetYaxis().GetBinLowEdge(iBin)
for iBin in range(1,
hResponse.GetNbinsY() + 2)
]
drawing.drawPtResponse(ax,
make2DHist(hResponse, xbins=xbins, ybins=ybins))
plt.savefig("JetPtResponse", format="pdf") # Save figure
plt.show()
def printJetNumbers(self):
print("Measured jets by bin")
print(self._numberJetsMeasBin)
print(self._numberJetsMeasFromHist)
if not self._IsData:
print("True jets by bin")
print(self._numberJetsTrueBin)
print(self._numberJetsTrueFromHist)
print("Unfolded jet numbers by bin:")
print(self._numberJetsFromReco)
print("Jet bin centers:")
print([
self._hJetPtRecoCoarse.GetBinCenter(i)
for i in range(1, self._hJetPtRecoCoarse.GetNbinsX())
])
def write_files(self, filename, **kwargs):
"""
Write output histograms to file
Args:
filename: Name of output file
"""
print("{}: write results to file {} and folder".format(
self._name, filename))
base_folder = "{}/BayesSubUnfolding/".format(self._name)
print(base_folder)
open_as = "append" if kwargs.get("append", False) else "recreate"
with root_open(filename, open_as) as output_file:
TDir = output_file.mkdir("{}{}".format(base_folder,
"JetConeJtWeightBin"),
title="JetConeJtWeightBin",
recurse=True)
TDir.cd()
# output_file.cd(TDir)
for i, (jt, pt) in enumerate(zip(self._hJtMeasBin,
self._jetPtBins)):
jt.name = "JetConeJtWeightBinNFin{0[NFin]:02d}JetPt{0[pT]:02d}".format(
{
"NFin": self._NFin,
"pT": i
})
jt.title = "Finder:Full_Jets_R04_00 p_{{T,jet}} : {} - {}".format(
pt[0], pt[1])
jt.Write()
if self._hBgJtNormalized is not None:
TDir = output_file.mkdir("{}{}".format(base_folder,
"BgJtWeightBin"),
title="BgJtWeightBin",
recurse=True)
TDir.cd()
for i, (jt, pt) in enumerate(
zip(self._hBgJtNormalized, self._jetPtBins)):
jt.name = "BgJtWeightBinNFin{0[NFin]:02d}JetPt{0[pT]:02d}".format(
{
"NFin": self._NFin,
"pT": i
})
jt.Write()
def unfolding_toy_diagnostics(indir, variable):
plotter = BasePlotter(defaults={
'clone': False,
'name_canvas': True,
'show_title': True,
'save': {
'png': True,
'pdf': False
}
}, )
styles = {
'dots': {
'linestyle': 0,
'markerstyle': 21,
'markercolor': 1
},
'compare': {
'linesstyle': [1, 0],
'markerstyle': [0, 21],
'markercolor': [2, 1],
'linecolor': [2, 1],
'drawstyle': ['hist', 'pe'],
'legendstyle': ['l', 'p']
}
}
xaxislabel = set_pretty_label(variable)
true_distribution = None
curdir = os.getcwd()
os.chdir(indir)
toydirs = get_immediate_subdirectories(".")
methods = []
pulls_lists = {}
pull_means_lists = {}
pull_mean_errors_lists = {}
pull_sums_lists = {}
pull_sigmas_lists = {}
pull_sigma_errors_lists = {}
deltas_lists = {}
delta_means_lists = {}
delta_mean_errors_lists = {}
delta_sigmas_lists = {}
delta_sigma_errors_lists = {}
ratio_sums_lists = {}
nneg_bins_lists = {}
unfoldeds_lists = {}
unfolded_sigmas_lists = {}
taus_lists = {}
histos_created = False
lists_created = False
idir = 0
true_distro = None
#loop over toys
for directory in toydirs:
if not directory.startswith('toy_'): continue
os.chdir(directory)
log.debug('Inspecting toy %s' % directory)
idir = idir + 1
i = 0
if not os.path.isfile("result_unfolding.root"):
raise ValueError('root file not found in %s' % os.getcwd())
with io.root_open("result_unfolding.root") as inputfile:
log.debug('Iteration %s over the file' % i)
i = i + 1
if not methods:
keys = [i.name for i in inputfile.keys()]
for key in keys:
if hasattr(getattr(inputfile, key), "hdata_unfolded"):
methods.append(key)
unfolded_hists = [
inputfile.get('%s/hdata_unfolded' % i) for i in methods
]
unfolded_wps_hists = [
inputfile.get('%s/hdata_unfolded_ps_corrected' % i)
for i in methods
]
for unf, unfps, method in zip(unfolded_hists, unfolded_wps_hists,
methods):
unf.name = method
unfps.name = method
if true_distro is None:
true_distribution = inputfile.true_distribution
ROOT.TH1.AddDirectory(False)
true_distro = true_distribution.Clone()
taus = prettyjson.loads(inputfile.best_taus.GetTitle())
if len(taus_lists) == 0:
taus_lists = dict((i, []) for i in taus)
for i, t in taus.iteritems():
taus_lists[i].append(t)
for histo in unfolded_hists:
#create pull/delta containers during first iteration
name = histo.name
nbins = histo.nbins()
log.debug("name = %s, n bins = %s" % (name, nbins))
if not lists_created:
for ibin in range(1, nbins + 1):
outname = "pull_" + name + "_bin" + str(ibin)
pulls_lists[outname] = []
outname = "delta_" + name + "_bin" + str(ibin)
deltas_lists[outname] = []
outname = "unfolded_" + name + "_bin" + str(ibin)
unfoldeds_lists[outname] = []
unfolded_sigmas_lists[outname] = []
outname = "pull_" + name
pull_means_lists[outname] = {}
pull_mean_errors_lists[outname] = {}
pull_sigmas_lists[outname] = {}
pull_sigma_errors_lists[outname] = {}
outname = "delta_" + name
delta_means_lists[outname] = {}
delta_mean_errors_lists[outname] = {}
delta_sigmas_lists[outname] = {}
delta_sigma_errors_lists[outname] = {}
for ibin in range(1, nbins + 1):
outname = "pull_" + name + "_bin" + str(ibin)
unfolded_bin_content = histo.GetBinContent(ibin)
unfolded_bin_error = histo.GetBinError(ibin)
true_bin_content = true_distro.GetBinContent(ibin)
true_bin_error = true_distro.GetBinError(ibin)
total_bin_error = math.sqrt(unfolded_bin_error**2) #???
if (total_bin_error != 0):
pull = (unfolded_bin_content -
true_bin_content) / total_bin_error
else:
pull = 9999
log.debug(
'unfolded bin content %s +/- %s, true bin content %s, pull %s'
% (unfolded_bin_content, unfolded_bin_error,
true_bin_content, pull))
pulls_lists[outname].append(pull)
outname = "delta_" + name + "_bin" + str(ibin)
delta = unfolded_bin_content - true_bin_content
log.debug(
'unfolded bin content %s +/- %s, true bin content %s, delta %s'
% (unfolded_bin_content, unfolded_bin_error,
true_bin_content, delta))
deltas_lists[outname].append(delta)
outname = "unfolded_" + name + "_bin" + str(ibin)
unfoldeds_lists[outname].append(unfolded_bin_content)
unfolded_sigmas_lists[outname].append(unfolded_bin_error)
nneg_bins_hists = [
i for i in inputfile.keys()
if i.GetName().startswith("nneg_bins")
]
nneg_bins_hists = [asrootpy(i.ReadObj()) for i in nneg_bins_hists]
for histo in nneg_bins_hists:
#create pull/delta containers during first iteration
name = histo.name
nbins = histo.nbins()
log.debug("name = %s, n bins = %s" % (name, nbins))
if not lists_created:
outname = name
nneg_bins_lists[outname] = []
outname = name
nneg_bins_lists[outname].append(histo.GetBinContent(1))
pull_sums_hists = [
i for i in inputfile.keys()
if i.GetName().startswith("sum_of_pulls")
]
pull_sums_hists = [asrootpy(i.ReadObj()) for i in pull_sums_hists]
for histo in pull_sums_hists:
#create pull/delta containers during first iteration
name = histo.name
nbins = histo.nbins()
log.debug("name = %s, n bins = %s" % (name, nbins))
if not lists_created:
outname = name
pull_sums_lists[outname] = []
outname = name
pull_sums_lists[outname].append(histo.GetBinContent(1))
ratio_sums_hists = [
i for i in inputfile.keys()
if i.GetName().startswith("sum_of_ratios")
]
ratio_sums_hists = [
asrootpy(i.ReadObj()) for i in ratio_sums_hists
]
for histo in ratio_sums_hists:
#create ratio/delta containers during first iteration
name = histo.name
nbins = histo.nbins()
log.debug("name = %s, n bins = %s" % (name, nbins))
if not lists_created:
outname = name
ratio_sums_lists[outname] = []
outname = name
ratio_sums_lists[outname].append(histo.GetBinContent(1))
#after the first iteration on the file all the lists are created
lists_created = True
os.chdir("..")
#create histograms
#histo containers
taus = {}
for name, vals in taus_lists.iteritems():
ROOT.TH1.AddDirectory(False) #repeat, you never know
val_min = min(vals)
val_min = 0.8 * val_min if val_min > 0 else 1.2 * val_min
val_max = max(vals)
val_max = 0.8 * val_max if val_max < 0 else 1.2 * val_max
if val_min == val_max:
if tau_nbins % 2: #if odd
val_min, val_max = val_min - 0.01, val_min + 0.01
else:
brange = 0.02
bwidth = brange / tau_nbins
val_min, val_max = val_min - 0.01 + bwidth / 2., val_min + 0.01 + bwidth / 2.
title = '#tau choice - %s ;#tau;N_{toys}' % (name)
histo = Hist(tau_nbins, val_min, val_max, name=name, title=title)
for val in vals:
histo.Fill(val)
taus[name] = histo
pulls = {}
for name, vals in pulls_lists.iteritems():
ROOT.TH1.AddDirectory(False) #repeat, you never know
val_min = min(vals)
val_min = 0.8 * val_min if val_min > 0 else 1.2 * val_min
val_max = max(vals)
val_max = 0.8 * val_max if val_max < 0 else 1.2 * val_max
abs_max = max(abs(val_min), abs(val_max))
if 'L_curve' in name:
method = 'L_curve'
binno = name.split('_')[-1]
else:
_, method, binno = tuple(name.split('_'))
title = 'Pulls - %s - %s ;Pull;N_{toys}' % (binno, method)
histo = Hist(pull_nbins, -abs_max, abs_max, name=name, title=title)
for val in vals:
histo.Fill(val)
pulls[name] = histo
deltas = {}
for name, vals in deltas_lists.iteritems():
ROOT.TH1.AddDirectory(False) #repeat, you never know
val_min = min(vals)
val_min = 0.8 * val_min if val_min > 0 else 1.2 * val_min
val_max = max(vals)
val_max = 0.8 * val_max if val_max < 0 else 1.2 * val_max
if 'L_curve' in name:
method = 'L_curve'
binno = name.split('_')[-1]
else:
_, method, binno = tuple(name.split('_'))
title = 'Deltas - %s - %s ;Delta;N_{toys}' % (binno, method)
histo = Hist(delta_nbins, val_min, val_max, name=name, title=title)
for val in vals:
histo.Fill(val)
deltas[name] = histo
unfoldeds = {}
for name, vals in unfoldeds_lists.iteritems():
ROOT.TH1.AddDirectory(False) #repeat, you never know
val_min = min(vals)
val_min = 0.8 * val_min if val_min > 0 else 1.2 * val_min
val_max = max(vals)
val_max = 0.8 * val_max if val_max < 0 else 1.2 * val_max
if 'L_curve' in name:
method = 'L_curve'
binno = name.split('_')[-1]
else:
_, method, binno = tuple(name.split('_'))
title = 'Unfoldeds - %s - %s ;Unfolded;N_{toys}' % (binno, method)
histo = Hist(unfolded_nbins, val_min, val_max, name=name, title=title)
for val in vals:
histo.Fill(val)
unfoldeds[name] = histo
nneg_bins = {}
for name, vals, in nneg_bins_lists.iteritems():
ROOT.TH1.AddDirectory(False) #repeat, you never know
val_min = min(vals)
val_min = 0 if val_min > 0 else val_min - 1
val_max = max(vals)
val_max = 0 if val_max < 0 else val_max + 1
if 'L_curve' in name:
method = 'L_curve'
else:
set_trace()
_, method, _ = tuple(name.split('_'))
title = 'N of negative bins - %s ;N. neg bins;N_{toys}' % method
histo = Hist(int(val_max - val_min + 1),
val_min,
val_max,
name=name,
title=title)
for val in vals:
histo.Fill(val)
nneg_bins[name] = histo
pull_sums = {}
for name, vals in pull_sums_lists.iteritems():
ROOT.TH1.AddDirectory(False) #repeat, you never know
val_min = min(vals)
val_min = 0.8 * val_min if val_min > 0 else 1.2 * val_min
val_max = max(vals)
val_max = 0.8 * val_max if val_max < 0 else 1.2 * val_max
if 'L_curve' in name:
method = 'L_curve'
else:
set_trace()
_, _, _, _, _, method = tuple(name.split('_'))
title = 'Pull sums - %s ;#Sigma(pull)/N_{bins};N_{toys}' % method
histo = Hist(unfolded_nbins, val_min, val_max, name=name, title=title)
for val in vals:
histo.Fill(val)
pull_sums[name] = histo
ratio_sums = {}
for name, vals in ratio_sums_lists.iteritems():
ROOT.TH1.AddDirectory(False) #repeat, you never know
val_min = min(vals)
val_min = 0.8 * val_min if val_min > 0 else 1.2 * val_min
val_max = max(vals)
val_max = 0.8 * val_max if val_max < 0 else 1.2 * val_max
if 'L_curve' in name:
method = 'L_curve'
binno = name.split('_')[-1]
else:
set_trace()
_, _, _, _, _, method = tuple(name.split('_'))
title = 'Ratio sums - %s;#Sigma(ratio)/N_{bins};N_{toys}' % method
histo = Hist(unfolded_nbins, val_min, val_max, name=name, title=title)
for val in vals:
histo.Fill(val)
ratio_sums[name] = histo
unfolded_sigmas = {}
for name, vals in unfolded_sigmas_lists.iteritems():
ROOT.TH1.AddDirectory(False) #repeat, you never know
val_min = min(vals)
val_min = 0.8 * val_min if val_min > 0 else 1.2 * val_min
val_max = max(vals)
val_max = 0.8 * val_max if val_max < 0 else 1.2 * val_max
if 'L_curve' in name:
method = 'L_curve'
binno = name.split('_')[-1]
else:
_, method, binno = tuple(name.split('_'))
title = 'Unfolded uncertainties - %s - %s ;Uncertainty;N_{toys}' % (
binno, method)
histo = Hist(unfolded_nbins, val_min, val_max, name=name, title=title)
for val in vals:
histo.Fill(val)
unfolded_sigmas[name] = histo
for name, histo in pulls.iteritems():
log.debug("name is %s and object type is %s" % (name, type(histo)))
histo.Fit("gaus", 'Q')
if not histo.GetFunction("gaus"):
log.warning("Function not found for histogram %s" % name)
continue
mean = histo.GetFunction("gaus").GetParameter(1)
meanError = histo.GetFunction("gaus").GetParError(1)
sigma = histo.GetFunction("gaus").GetParameter(2)
sigmaError = histo.GetFunction("gaus").GetParError(2)
general_name, idx = tuple(name.split('_bin'))
idx = int(idx)
pull_means_lists[general_name][idx] = mean
pull_mean_errors_lists[general_name][idx] = meanError
pull_sigmas_lists[general_name][idx] = sigma
pull_sigma_errors_lists[general_name][idx] = sigmaError
for name, histo in deltas.iteritems():
log.debug("name is %s and object type is %s" % (name, type(histo)))
histo.Fit("gaus", 'Q')
if not histo.GetFunction("gaus"):
log.warning("Function not found for histogram %s" % name)
continue
mean = histo.GetFunction("gaus").GetParameter(1)
meanError = histo.GetFunction("gaus").GetParError(1)
sigma = histo.GetFunction("gaus").GetParameter(2)
sigmaError = histo.GetFunction("gaus").GetParError(2)
general_name, idx = tuple(name.split('_bin'))
idx = int(idx)
delta_means_lists[general_name][idx] = mean
delta_mean_errors_lists[general_name][idx] = meanError
delta_sigmas_lists[general_name][idx] = sigma
delta_sigma_errors_lists[general_name][idx] = sigmaError
outfile = rootpy.io.File("unfolding_diagnostics.root", "RECREATE")
outfile.cd()
pull_means = {}
pull_sigmas = {}
pull_means_summary = {}
pull_sigmas_summary = {}
delta_means = {}
delta_sigmas = {}
delta_means_summary = {}
delta_sigmas_summary = {}
for outname, pmeans in pull_means_lists.iteritems():
outname_mean = outname + "_mean"
outtitle = "Pull means - " + outname + ";Pull mean; N_{toys}"
pull_mean_min = min(pmeans.values())
pull_mean_max = max(pmeans.values())
pull_mean_newmin = pull_mean_min - (pull_mean_max -
pull_mean_min) * 0.5
pull_mean_newmax = pull_mean_max + (pull_mean_max -
pull_mean_min) * 0.5
pull_means[outname] = plotting.Hist(pull_mean_nbins,
pull_mean_newmin,
pull_mean_newmax,
name=outname_mean,
title=outtitle)
outname_mean_summary = outname + "_mean_summary"
outtitle_mean_summary = "Pull mean summary - " + outname
histocloned = true_distro.Clone(outname_mean_summary)
histocloned.Reset()
histocloned.xaxis.title = xaxislabel
histocloned.yaxis.title = 'Pull mean'
histocloned.title = outtitle_mean_summary
pull_means_summary[outname] = histocloned
for idx, pmean in pmeans.iteritems():
pull_means[outname].Fill(pmean)
histocloned[idx].value = pmean
histocloned[idx].error = pull_mean_errors_lists[outname][idx]
histocloned.yaxis.SetRangeUser(min(pmeans.values()),
max(pmeans.values()))
for outname, psigmas in pull_sigmas_lists.iteritems():
outname_sigma = outname + "_sigma"
outtitle_sigma = "Pull #sigma's - " + outname + ";Pull #sigma; N_{toys}"
pull_sigma_min = min(psigmas.values())
pull_sigma_max = max(psigmas.values())
pull_sigma_newmin = pull_sigma_min - (pull_sigma_max -
pull_sigma_min) * 0.5
pull_sigma_newmax = pull_sigma_max + (pull_sigma_max -
pull_sigma_min) * 0.5
pull_sigmas[outname] = plotting.Hist(pull_sigma_nbins,
pull_sigma_newmin,
pull_sigma_newmax,
name=outname_sigma,
title=outtitle_sigma)
outname_sigma_summary = outname + "_sigma_summary"
outtitle_sigma_summary = "Pull #sigma summary - " + outname
histocloned = true_distro.Clone(outname_sigma_summary)
histocloned.Reset()
histocloned.xaxis.title = xaxislabel
histocloned.yaxis.title = 'Pull #sigma'
histocloned.title = outtitle_sigma_summary
pull_sigmas_summary[outname] = histocloned
for idx, psigma in psigmas.iteritems():
pull_sigmas[outname].Fill(psigma)
histocloned[idx].value = psigma
histocloned[idx].error = pull_sigma_errors_lists[outname][idx]
histocloned.yaxis.SetRangeUser(min(psigmas.values()),
max(psigmas.values()))
for outname, dmeans in delta_means_lists.iteritems():
outname_mean = outname + "_mean"
outtitle = "Delta means - " + outname + ";Delta mean; N_{toys}"
delta_mean_min = min(dmeans.values())
delta_mean_max = max(dmeans.values())
delta_mean_newmin = delta_mean_min - (delta_mean_max -
delta_mean_min) * 0.5
delta_mean_newmax = delta_mean_max + (delta_mean_max -
delta_mean_min) * 0.5
delta_means[outname] = plotting.Hist(delta_mean_nbins,
delta_mean_newmin,
delta_mean_newmax,
name=outname_mean,
title=outtitle)
outname_mean_summary = outname + "_mean_summary"
outtitle_mean_summary = "Delta mean summary - " + outname
histocloned = true_distro.Clone(outname_mean_summary)
histocloned.Reset()
histocloned.xaxis.title = xaxislabel
histocloned.yaxis.title = 'Delta mean'
histocloned.title = outtitle_mean_summary
delta_means_summary[outname] = histocloned
for idx, dmean in dmeans.iteritems():
delta_means[outname].Fill(dmean)
histocloned[idx].value = dmean
histocloned[idx].error = delta_mean_errors_lists[outname][idx]
histocloned.yaxis.SetRangeUser(min(dmeans.values()),
max(dmeans.values()))
for outname, dsigmas in delta_sigmas_lists.iteritems():
outname_sigma = outname + "_sigma"
outtitle_sigma = "Delta #sigma's - " + outname + ";Delta #sigma; N_{toys}"
delta_sigma_min = min(dsigmas.values())
delta_sigma_max = max(dsigmas.values())
delta_sigma_newmin = delta_sigma_min - (delta_sigma_max -
delta_sigma_min) * 0.5
delta_sigma_newmax = delta_sigma_max + (delta_sigma_max -
delta_sigma_min) * 0.5
delta_sigmas[outname] = plotting.Hist(delta_sigma_nbins,
delta_sigma_newmin,
delta_sigma_newmax,
name=outname_sigma,
title=outtitle_sigma)
outname_sigma_summary = outname + "_sigma_summary"
outtitle_sigma_summary = "Delta #sigma summary - " + outname
histocloned = true_distro.Clone(outname_sigma_summary)
histocloned.Reset()
histocloned.xaxis.title = xaxislabel
histocloned.yaxis.title = 'Delta #sigma'
histocloned.title = outtitle_sigma_summary
delta_sigmas_summary[outname] = histocloned
for idx, dsigma in dsigmas.iteritems():
delta_sigmas[outname].Fill(dsigma)
histocloned[idx].value = dsigma
histocloned[idx].error = delta_sigma_errors_lists[outname][idx]
histocloned.yaxis.SetRangeUser(min(dsigmas.values()),
max(dsigmas.values()))
unfolded_summary = {}
unfolded_average = {}
unfolded_envelope = {}
for name, histo in unfoldeds.iteritems():
log.debug("name is %s and object type is %s" % (name, type(histo)))
histo.Fit("gaus", 'Q')
if not histo.GetFunction("gaus"):
log.warning("Function not found for histogram %s" % name)
continue
mean = histo.GetFunction("gaus").GetParameter(1)
meanError = histo.GetFunction("gaus").GetParError(1)
sigma = histo.GetFunction("gaus").GetParameter(2)
sigmaError = histo.GetFunction("gaus").GetParError(2)
general_name, idx = tuple(name.split('_bin'))
idx = int(idx)
if general_name not in unfolded_summary:
histo = true_distro.Clone("%s_unfolded_summary" % general_name)
outtitle_unfolded_summary = "Unfolded summary - " + general_name
histo.Reset()
histo.xaxis.title = xaxislabel
histo.yaxis.title = 'N_{events}'
histo.title = outtitle_unfolded_summary
unfolded_summary[general_name] = histo
unfolded_envelope[general_name] = histo.Clone(
"%s_unfolded_envelope" % general_name)
unfolded_average[general_name] = histo.Clone(
"%s_unfolded_average" % general_name)
unfolded_summary[general_name][idx].value = mean
unfolded_summary[general_name][idx].error = meanError
unfolded_envelope[general_name][idx].value = mean
unfolded_envelope[general_name][idx].error = sigma
unfolded_average[general_name][idx].value = mean
unfolded_average[general_name][idx].error = \
unfolded_sigmas['%s_bin%i' % (general_name, idx)].GetMean()
plotter.set_subdir('taus')
for name, histo in taus.iteritems():
#canvas = plotter.create_and_write_canvas_single(0, 21, 1, False, False, histo, write=False)
plotter.canvas.cd()
histo = plotter.plot(histo, **styles['dots'])
histo.SetStats(True)
info = plotter.make_text_box(
'mode #tau = %.5f' % histo[histo.GetMaximumBin()].x.center,
position=(plotter.pad.GetLeftMargin(), plotter.pad.GetTopMargin(),
0.3, 0.025))
info.Draw()
plotter.save()
histo.Write()
plotter.canvas.Write()
plotter.set_subdir('pulls')
for name, histo in pulls.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.SetStats(True)
plotter.save()
histo.Write()
plotter.canvas.Write()
for name, histo in pull_means.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.Write()
plotter.save()
for name, histo in pull_sigmas.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.Write()
plotter.save()
plotter.set_subdir('pull_summaries')
for name, histo in pull_means_summary.iteritems():
histo = plotter.plot(histo, **styles['dots'])
#histo.SetStats(True)
line = ROOT.TLine(histo.GetBinLowEdge(1), 0,
histo.GetBinLowEdge(histo.GetNbinsX() + 1), 0)
line.Draw("same")
plotter.save()
histo.Write()
plotter.canvas.Write()
for name, histo in pull_sigmas_summary.iteritems():
histo = plotter.plot(histo, **styles['dots'])
#histo.SetStats(True)
line = ROOT.TLine(histo.GetBinLowEdge(1), 1,
histo.GetBinLowEdge(histo.GetNbinsX() + 1), 1)
line.Draw("same")
plotter.save()
histo.Write()
plotter.canvas.Write()
plotter.set_subdir('deltas')
for name, histo in deltas.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.SetStats(True)
plotter.save()
histo.Write()
plotter.canvas.Write()
for name, histo in delta_means.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.Write()
plotter.save()
for name, histo in delta_sigmas.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.Write()
plotter.save()
plotter.set_subdir('delta_summaries')
for name, histo in delta_means_summary.iteritems():
histo = plotter.plot(histo, **styles['dots'])
#histo.SetStats(True)
plotter.save()
histo.Write()
plotter.canvas.Write()
for name, histo in delta_sigmas_summary.iteritems():
histo = plotter.plot(histo, **styles['dots'])
#histo.SetStats(True)
plotter.save()
histo.Write()
plotter.canvas.Write()
plotter.set_subdir('unfolding_unc')
for name, histo in unfolded_sigmas.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.SetStats(True)
plotter.save()
histo.Write()
plotter.canvas.Write()
plotter.set_subdir('unfolded')
for name, histo in unfoldeds.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.SetStats(True)
plotter.save()
histo.Write()
plotter.canvas.Write()
plotter.set_subdir('unfolded_summaries')
for name, histo in unfolded_summary.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.SetStats(True)
plotter.save()
histo.Write()
plotter.canvas.Write()
for name, histo in unfolded_summary.iteritems():
leg = LegendDefinition("Unfolding comparison",
'NE',
labels=['Truth', 'Unfolded'])
plotter.overlay_and_compare([true_distro],
histo,
legend_def=leg,
**styles['compare'])
plotter.canvas.name = 'Pull_' + name
plotter.save()
plotter.canvas.Write()
plotter.overlay_and_compare([true_distro],
histo,
legend_def=leg,
method='ratio',
**styles['compare'])
plotter.canvas.name = 'Ratio_' + name
plotter.save()
plotter.canvas.Write()
plotter.set_subdir('unfolded_average')
for name, histo in unfolded_average.iteritems():
leg = LegendDefinition("Unfolding comparison",
'NE',
labels=['Truth', 'Unfolded'])
#set_trace()
plotter.overlay_and_compare([true_distro],
histo,
legend_def=leg,
**styles['compare'])
plotter.canvas.name = 'Pull_' + name
plotter.save()
plotter.canvas.Write()
plotter.overlay_and_compare([true_distro],
histo,
legend_def=leg,
method='ratio',
**styles['compare'])
plotter.canvas.name = 'Ratio_' + name
plotter.save()
plotter.canvas.Write()
plotter.set_subdir('unfolded_envelope')
for name, histo in unfolded_envelope.iteritems():
leg = LegendDefinition("Unfolding comparison",
'NE',
labels=['Truth', 'Unfolded'])
plotter.overlay_and_compare([true_distro],
histo,
legend_def=leg,
**styles['compare'])
plotter.canvas.name = 'Pull_' + name
plotter.save()
plotter.canvas.Write()
plotter.overlay_and_compare([true_distro],
histo,
legend_def=leg,
method='ratio',
**styles['compare'])
plotter.canvas.name = 'Ratio_' + name
plotter.save()
plotter.canvas.Write()
plotter.set_subdir('figures_of_merit')
for name, histo in nneg_bins.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.SetStats(True)
plotter.save()
histo.Write()
plotter.canvas.Write()
for name, histo in pull_sums.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.SetStats(True)
plotter.save()
histo.Write()
plotter.canvas.Write()
for name, histo in ratio_sums.iteritems():
histo = plotter.plot(histo, **styles['dots'])
histo.SetStats(True)
plotter.save()
histo.Write()
plotter.canvas.Write()
outfile.close()
os.chdir(curdir)
def make_plot(raw_data, binning, title_text, outfn):
h = Hist(*binning,
drawstyle='hist e1',
color=sigCOLORS[0],
linewidth=2,
title=';Percent difference[%];Events')
for sample, value in raw_data.items():
h.Fill(value['variation'], value['count'])
hc = asrootpy(h.GetCumulative())
hc.linecolor = 'gold'
hc.fillcolor = 'lightyellow'
hc.fillstyle = 'solid'
hc.scale(h.max(include_error=True) / hc.max())
xmin_, xmax_, ymin_, ymax_ = get_limits([h, hc])
draw([hc, h], ylimits=(0, ymax_))
x95, x99 = None, None
cumsum = 0
for i in range(1, h.GetNbinsX() + 1):
cumsum += h.GetBinContent(i)
if x95 is None and cumsum / h.Integral() > 0.95:
x95 = h.GetXaxis().GetBinUpEdge(i)
if x99 is None and cumsum / h.Integral() > 0.99:
x99 = h.GetXaxis().GetBinUpEdge(i)
title = TitleAsLatex(title_text)
title.Draw()
# print(title_text, ROOT.gPad.GetUymax(), hc.max())
# draw a second axis on the right.
ROOT.gPad.SetTicks(
1, 0
) # Draw top ticks but not right ticks (https://root.cern.ch/root/roottalk/roottalk99/2908.html)
low, high = 0, ROOT.gPad.GetUymax() / hc.max()
raxis = ROOT.TGaxis(ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymin(),
ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymax(),
low, high, 510, "+L")
raxis.SetLabelSize(0.03)
raxis.SetLabelFont(42)
raxis.SetLabelColor(convert_color('gold', 'root'))
raxis.Draw()
frame = canvas.FindObject('TFrame')
lo, hi = frame.GetY1(), frame.GetY2()
l95 = ROOT.TLine(x95, lo, x95, hi)
l95.SetLineStyle(2)
l95.SetLineColor(convert_color(sigCOLORS[1], 'root'))
l95.SetLineWidth(2)
l95.Draw()
l99 = ROOT.TLine(x99, lo, x99, hi)
l99.SetLineStyle(3)
l99.SetLineColor(convert_color(sigCOLORS[2], 'root'))
l99.Draw()
leg = Legend(3,
margin=0.25,
leftmargin=0.45,
topmargin=0.02,
entrysep=0.01,
entryheight=0.02,
textsize=10)
leg.AddEntry(hc, label='cumulative (norm.)', style='LF')
leg.AddEntry(l95, label='95% @ {}'.format(x95), style='L')
leg.AddEntry(l99, label='99% @ {}'.format(x99), style='L')
leg.Draw()
canvas.SaveAs(outfn)
canvas.clear()
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()
