def plot_training_phoeres_with_shape_and_fit():
"""Plot the nominal MC photon energy smearing overlayed with the pdf shape
and fit."""
canvases.next('TrainingPhoEResWithShapeAndFit')
plot = phoERes.frame(roo.Range(-7.5, 5))
plot.SetTitle("Photon energy smearing overlayed with PDF shape (blue) "
"and it's parametrized fit (dashed red)")
data.plotOn(plot)
## Define model for the photon energy smearing function Ereco/Etrue - 1.
phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf', phoERes,
phoScale, phoRes, data,
ROOT.RooKeysPdf.NoMirror, 1.5)
## PDF shape
phoEResPdf.shape.plotOn(plot)
## Parametrized fit of the PDF shape
phoEResPdf.fitTo(data, roo.Range(-50, 50), roo.PrintLevel(-1))
phoEResPdf.plotOn(plot, roo.LineColor(ROOT.kRed),
roo.LineStyle(ROOT.kDashed))
plot.Draw()
Latex([
's_{shape}: %.3f %%' % phoEResPdf.shapemode,
's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' %
(phoScale.getVal() - phoEResPdf.shapemode, phoScale.getError()),
'r_{shape}: %.3f %%' % phoEResPdf.shapewidth,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
'r_{fit} - r_{shape}: %.4f #pm %.4f %%' %
(phoRes.getVal() - phoEResPdf.shapewidth, phoRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' %
(phoRes.getVal() / phoEResPdf.shapewidth,
phoRes.getError() / phoEResPdf.shapewidth),
],
position=(0.2, 0.8)).draw()
def plot_nominal_mmgmass_with_shape_and_fit():
"""Plot the nominal MC mmg mass data overlayed with the pdf shape and
fit."""
canvases.next('NominalMmgMassWithShapeAndFit')
plot = mmgMass.frame(roo.Range(75, 105))
plot.SetTitle("m(#mu#mu#gamma) overlayed with PDF shape (blue) "
"and it's parametrized fit (dashed red)")
data.plotOn(plot)
## Define the mmg mass model.
mmgMassPdf = ParametrizedKeysPdf('mmgMassPdf', 'mmgMassPdf', mmgMass,
massPeak, massWidth, data,
ROOT.RooKeysPdf.NoMirror, 1.5)
## PDF shape
mmgMassPdf.shape.plotOn(plot)
## Parametrized fit of the PDF shape
mmgMassPdf.fitTo(data, roo.Range(60, 120), roo.PrintLevel(-1))
mmgMassPdf.plotOn(plot, roo.LineColor(ROOT.kRed),
roo.LineStyle(ROOT.kDashed))
plot.Draw()
sshape = 100 * (mmgMassPdf.shapemode / mZ.getVal() - 1)
rshape = 100 * mmgMassPdf.shapewidth / mmgMassPdf.shapemode
Latex([
's_{shape}: %.3f %%' % sshape,
's_{fit}: %.3f #pm %.3f %%' %
(massScale.getVal(), massScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' %
(massScale.getVal() - sshape, massScale.getError()),
'r_{shape}: %.3f %%' % rshape,
'r_{fit}: %.3f #pm %.3f %%' % (massRes.getVal(), massRes.getError()),
'r_{fit} - r_{shape}: %.4f #pm %.4f %%' %
(massRes.getVal() - rshape, massRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' %
(massRes.getVal() / rshape, massRes.getError() / rshape),
],
position=(0.2, 0.8)).draw()
def loop_over_smearings():
"Extract scale and resolution for mass and energy."
## Set initial value for the mode calculation of the phoERes shape.
phoERes.setVal(0)
## Set initial value for the mode calculation of the mmgMass shape.
mmgMass.setVal(91.2)
## Define then nominal model for the photon energy smearing function.
phoEResPdf = ParametrizedKeysPdf('phoEResPdf_nominal',
'phoEResPdf_nominal', phoERes, phoScale,
phoRes, data, ROOT.RooKeysPdf.NoMirror,
1.5)
## Define the nominal mmg mass model.
mmgMassPdf = ParametrizedKeysPdf('mmgMassPdf_nominal',
'mmgMassPdf_nominal', mmgMass, massPeak,
massWidth, data, ROOT.RooKeysPdf.NoMirror,
1.5)
w.Import(phoEResPdf)
w.Import(mmgMassPdf)
w.Import(phoEResPdf.shape)
w.Import(mmgMassPdf.shape)
for i, (s, r) in enumerate(zip(stargets, rtargets)):
## Get the smeared data.
phoScaleTarget.setVal(s)
phoResTarget.setVal(r)
sdata = calibrator.get_smeared_data(s, r)
## Save the smeared data in the workspace.
sdata.SetName('sdata_%d' % i)
sdata.SetTitle('smeared mmg data %d' % i)
w.Import(sdata)
## Build the energy and mass models for each smearing since the
## shapes may have changed.
## Guess the right values of the fit parameters.
phoScale.setVal(s)
phoRes.setVal(phoEResPdf.shapewidth)
massScale.setVal(100 * (mmgMassPdf.shapemode / mZ.getVal() - 1))
massRes.setVal(100 * mmgMassPdf.shapewidth / mmgMassPdf.shapemode)
## Fit the models.
phoFit = phoEResPdf.fitTo(
sdata,
roo.Range(-50, 50),
# roo.Range(s - 5*r, s + 5*r),
# roo.PrintLevel(-1),
roo.Save())
massFit = mmgMassPdf.fitTo(
sdata,
roo.Range(60, 120),
# roo.PrintLevel(-1),
roo.Save())
## Store results in the workspace.
w.Import(phoFit)
w.Import(massFit)
w.saveSnapshot('smear_%d' % i, params, True)
def _get_mctruth_scale_and_resolution(self):
## Enlarge the range of the observable to get vanishing tails for
## the photon energy scale resolution
# savrange = (self.phoERes.getMin(), self.phoERes.getMax())
# self.phoERes.setRange(savrange[0] - 10, savrange[1] + 10)
## Build the model for the photon energy resolution.
self.phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf',
self.phoERes, self.s, self.r,
self.data,
ROOT.RooKeysPdf.NoMirror,
self.rho)
# self.phoERes.setRange(*savrange)
## Set sensible initial values
self.s.setVal(self.phoEResPdf.shapemode)
self.r.setVal(self.phoEResPdf.shapewidth)
## Extract the MC truth scale and resolution from MC
self.fitresult_mctruth = self.phoEResPdf.fitTo(
self.data, roo.PrintLevel(self.printlevel), roo.SumW2Error(False),
roo.Range(-50, 50), roo.Save(), roo.Strategy(2))
self.w.Import(self.fitresult_mctruth)
## Store the MC truth scale and resolution
for source, target in zip([self.s, self.r], [self.s0, self.r0]):
target.setVal(source.getVal())
target.setError(source.getError())
target.setAsymError(source.getErrorLo(), source.getErrorHi())
# self.s0.setVal(self.s.getVal())
# self.r0.setVal(self.r.getVal())
self.w.saveSnapshot('sr_mctruth', self.sr)
self.w.saveSnapshot('sr0_mctruth', self.sr0)
self.s0.setConstant(True)
self.r0.setConstant(True)
def _fit_smeared_data(self, name):
'Fit the current smeared data to get the smeared s and r.'
self.fitresult_sdata = self.phoEResPdf.fitTo(
self.sdata, roo.PrintLevel(self.printlevel), roo.SumW2Error(False),
roo.Range(-50, 50), roo.Save(), roo.Strategy(2))
self.w.saveSnapshot(name + '_sr', self.sr)
self.w.Import(self.fitresult_sdata, name + '_fitresult')
def plot_fit_to_real_data(label):
'''
Plot fit to real data for a dataset specified by the label:
"data" (full 2011A+B), "2011A" or "2011B".
'''
# mmgMass.setRange('plot', 70, 110)
mmgMass.setBins(80)
plot = mmgMass.frame(roo.Range('plot'))
if label == 'data':
title_start = '2011A+B'
else:
title_start = label
plot.SetTitle('%s, %s' % (title_start, latex_title))
data[label].plotOn(plot)
pm.plotOn(plot, roo.Range('plot'), roo.NormRange('plot'))
pm.plotOn(plot, roo.Range('plot'), roo.NormRange('plot'),
roo.Components('*zj*'), roo.LineStyle(ROOT.kDashed))
canvases.next(name + '_' + label).SetGrid()
plot.Draw()
def fit_real_data(label):
'''
Fit dataset specified by the label: "data" (full 2011A+B),
"2011A" or "2011B".
'''
fit_result = pm.fitTo(data[label], roo.Range('fit'), roo.NumCPU(8),
roo.Timer(), # roo.Verbose()
roo.InitialHesse(True), roo.Minos(),
roo.Save(),
)
w.Import(fit_result, 'fitresult_' + label)
def plot_sanity_checks(data):
pdfname = '_'.join(['bananaPdf', data.GetName()])
pdf = ROOT.RooNDKeysPdf(pdfname, pdfname, ROOT.RooArgList(mmMass, mmgMass),
data, "a", 1.5)
canvases.next(pdf.GetName() + '_mmgMassProj')
plot = mmgMass.frame(roo.Range(60, 120))
data.plotOn(plot)
pdf.plotOn(plot)
plot.Draw()
canvases.next(pdf.GetName() + '_mmMassProj')
plot = mmMass.frame(roo.Range(10, 120))
data.plotOn(plot)
pdf.plotOn(plot)
plot.Draw()
canvases.next(pdf.GetName()).SetGrid()
h_pdf = pdf.createHistogram('h_' + pdf.GetName(), mmMass,
roo.Binning(40, 40, 80),
roo.YVar(mmgMass, roo.Binning(40, 70, 110)))
h_pdf.Draw("cont1")
canvases.next(data.GetName()).SetGrid()
h_data = data.createHistogram(mmMass, mmgMass, 130, 100, '',
'h_' + data.GetName())
h_data.GetXaxis().SetRangeUser(40, 80)
h_data.GetYaxis().SetRangeUser(70, 110)
h_data.Draw("cont1")
canvases.next('_'.join([pdf.GetName(), 'mmgMassSlices']))
plot = mmgMass.frame(roo.Range(60, 120))
for mmmassval, color in zip([55, 60, 65, 70],
'Red Orange Green Blue'.split()):
mmMass.setVal(mmmassval)
color = getattr(ROOT, 'k' + color)
pdf.plotOn(plot, roo.LineColor(color))
plot.Draw()
canvases.update()
def plot_nominal_and_smeared_mmgmass():
canvases.next('SmearedMMGMass').SetGrid()
plot = mmgMass.frame(roo.Range(76, 106))
plot.SetTitle("Nominal (black) and smeared (red) mmg mass")
data.plotOn(plot)
datasmeared.plotOn(plot, roo.MarkerColor(ROOT.kRed),
roo.LineColor(ROOT.kRed))
plot.Draw()
Latex([
's_{0}: %.2g %%, s: %.2g %%' % (phoScaleRef, targets),
'r_{0}: %.2g %%, r: %.2g %%' % (phoResRef, targetr)
],
position=(0.2, 0.8)).draw()
def plot_smeared_phoeres_with_fit():
phoEResPdf.fitTo(datasmeared, roo.PrintLevel(-1), roo.SumW2Error(False))
canvases.next('SmearedSampleWithFit')
plot = phoERes.frame(roo.Range(-30, 30))
plot.SetTitle("Smeared MC with paremetrized fit")
datasmeared.plotOn(plot)
phoEResPdf.plotOn(plot)
phoEResPdf.paramOn(plot)
plot.Draw()
Latex([
'target s: %.3g %%' % targets,
'target r: %.3g %%' % targetr,
],
position=(0.2, 0.75)).draw()
def plot_training_phoeres_with_shape_and_fit():
canvases.next('TrainingSampleWithShapeAndFit')
plot = phoERes.frame(roo.Range(-7.5, 7.5))
plot.SetTitle(
"MC overlayed with PDF shape (blue) and it's parametrized fit"
"(dashed red)")
data.plotOn(plot)
phoEResPdf.shape.plotOn(plot)
phoEResPdf.plotOn(plot, roo.LineColor(ROOT.kRed),
roo.LineStyle(ROOT.kDashed))
plot.Draw()
Latex([
's_{shape}: %.3f %%' % phoEResPdf.shapemode,
's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f' %
(phoScale.getVal() - phoEResPdf.shapemode, phoScale.getError()),
'r_{shape}: %.3f %%' % phoEResPdf.shapewidth,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' %
(phoRes.getVal() / phoEResPdf.shapewidth,
phoRes.getError() / phoEResPdf.shapewidth),
],
position=(0.2, 0.75)).draw()
data.tree().GetV1(), minBinContent, maxBinContent)
binning = bins.binning(ROOT.RooBinning())
ubinning = bins.uniformBinning(ROOT.RooUniformBinning())
## Make sure that there are at least 1./epsilonPerBin curve points
## in each bin. The RooChi2Calculator fails if there is none perhaps
## due to a bug in RooCurve::average? Reason is not understood yet.
epsilon = epsilonPerBin * ubinning.binWidth(0) / bins.length()
## Make frames
log_plot = x.frame()
log_plot_bins = x.frame()
log_plot_medians = x.frame()
bins.setSigmaLevel(3)
lin_plot = x.frame(roofit.Range(-3, 3))
lin_plot_bins = x.frame(*bins.bounds())
lin_plot_medians = x.frame(*bins.bounds())
bins.setFraction(1)
log_plot.SetTitle('Default RooFit')
lin_plot.SetTitle(log_plot.GetTitle())
log_plot_bins.SetTitle("New - Bin Content > %d" % minBinContent)
lin_plot_bins.SetTitle(log_plot_bins.GetTitle())
log_plot_medians.SetTitle(
"New - Bin Content > %d and Data at Bin Medians" % minBinContent)
lin_plot_medians.SetTitle(log_plot_medians.GetTitle())
## Add the data and fit to the plots
for plot in [log_plot, lin_plot]:
data.plotOn(plot)
canvases.next('xt1_proj_t1')
t.setRange(*t1range)
t.SetTitle('log(m_{#mu#mu#gamma,E_{gen}^{#gamma}}^{2} - m_{#mu#mu}^{2})')
plot = t.frame()
xt1data.plotOn(plot)
xt1pdf.plotOn(plot)
plot.Draw()
## Plot fT2(t2|s,r) fitted to training data.
canvases.next('t2pdf').SetGrid()
t.setRange(*t2range)
t.setVal(0)
t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})')
phoScale.setVal(t2pdf.s0val)
phoRes.setVal(t2pdf.r0val)
t2pdf.fitTo(t2data, roo.Range(ROOT.TMath.Log(0.5), ROOT.TMath.Log(1.5)))
plot = t.frame(roo.Range(-0.3, 0.3))
t2data.plotOn(plot)
t2pdf.plotOn(plot)
plot.Draw()
Latex([
's_{shape}: %.3f %%' % t2pdf.s0val,
's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' % (
phoScale.getVal() - t2pdf.s0val,
phoScale.getError()
),
'r_{shape}: %.3f %%' % t2pdf.r0val,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' % (
phoRes.getVal() / t2pdf.r0val,
print 'CPU time:', sw.CpuTime(), 's, real time:', sw.RealTime(), 's'
## End of main()
##------------------------------------------------------------------------------
sw = ROOT.TStopwatch()
sw.Start()
init()
get_data()
phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf', phoERes, phoScale,
phoRes, data, ROOT.RooKeysPdf.NoMirror, 1.5)
phoEResPdf.fitTo(data, roo.Range(-50, 50))
canvases.next('phoEResPdf').SetGrid()
plot = phoERes.frame(roo.Range(-10, 10))
data.plotOn(plot)
phoEResPdf.plotOn(plot)
phoEResPdf.paramOn(plot)
plot.Draw()
t = w.factory('t[0,-1,1]')
t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})')
tfunc = w.factory('expr::tfunc("log(0.01 * phoERes + 1)", {phoERes})')
tfunc.SetName('t')
data.addColumn(tfunc)
## Build the model for log(Ereco/Egen) ft2(t2|r,s)
def process_monte_carlo():
'''
Get, fit and plot monte carlo.
'''
global fitdata1
fitdata1 = fit_calibrator.get_smeared_data(
sfit, rfit, 'fitdata1', 'fitdata1', True
)
fitdata1.reduce(ROOT.RooArgSet(mmgMass, mmMass))
fitdata1.append(data['zj1'])
fitdata1.SetName('fitdata1')
data['fit1'] = fitdata1
if reduce_data == True:
fitdata1 = fitdata1.reduce(roo.Range(reduced_entries,
fitdata1.numEntries()))
check_timer('3. get fit data (%d entries)' % fitdata1.numEntries())
nll = pm.createNLL(fitdata1, roo.Range('fit'), roo.NumCPU(8))
minuit = ROOT.RooMinuit(nll)
minuit.setProfile()
minuit.setVerbose()
phoScale.setError(1)
phoRes.setError(1)
## Initial HESSE
status = minuit.hesse()
fitres = minuit.save(name + '_fitres1_inithesse')
w.Import(fitres, fitres.GetName())
check_timer('4. initial hesse (status: %d)' % status)
## Minimization
minuit.setStrategy(2)
status = minuit.migrad()
fitres = minuit.save(name + '_fitres2_migrad')
w.Import(fitres, fitres.GetName())
check_timer('5. migrad (status: %d)' % status)
## Parabolic errors
status = minuit.hesse()
fitres = minuit.save(name + '_fitres3_hesse')
w.Import(fitres, fitres.GetName())
check_timer('6. hesse (status: %d)' % status)
## Minos errors
status = minuit.minos()
fitres = minuit.save(name + '_fitres4_minos')
w.Import(fitres, fitres.GetName())
check_timer('7. minos (status: %d)' % status)
#fres = pm.fitTo(fitdata1, roo.SumW2Error(True),
#roo.Range('fit'),
## roo.Strategy(2),
#roo.InitialHesse(True),
#roo.Minos(),
#roo.Verbose(True),
#roo.NumCPU(8), roo.Save(), roo.Timer())
signal_model._phorhist.GetXaxis().SetRangeUser(75, 105)
signal_model._phorhist.GetYaxis().SetRangeUser(0, 15)
signal_model._phorhist.GetXaxis().SetTitle('%s (%s)' % (mmgMass.GetTitle(),
mmgMass.getUnit()))
signal_model._phorhist.GetYaxis().SetTitle('E^{#gamma} Resolution (%)')
signal_model._phorhist.GetZaxis().SetTitle('Probability Density (1/GeV/%)')
signal_model._phorhist.SetTitle(latex_title)
signal_model._phorhist.GetXaxis().SetTitleOffset(1.5)
signal_model._phorhist.GetYaxis().SetTitleOffset(1.5)
signal_model._phorhist.GetZaxis().SetTitleOffset(1.5)
signal_model._phorhist.SetStats(False)
canvases.next(name + '_phorhist')
signal_model._phorhist.Draw('surf1')
global graph
graph = signal_model.make_mctrue_graph()
graph.GetXaxis().SetTitle('E^{#gamma} resolution (%)')
graph.GetYaxis().SetTitle('m_{#mu^{+}#mu^{-}#gamma} effective #sigma (GeV)')
graph.SetTitle(latex_title)
canvases.next(name + '_mwidth_vs_phor').SetGrid()
graph.Draw('ap')
mmgMass.setBins(80)
plot = mmgMass.frame(roo.Range('plot'))
plot.SetTitle('Fall11 MC, ' + latex_title)
fitdata1.plotOn(plot)
pm.plotOn(plot, roo.Range('plot'), roo.NormRange('plot'))
pm.plotOn(plot, roo.Range('plot'), roo.NormRange('plot'),
roo.Components('*zj*'), roo.LineStyle(ROOT.kDashed))
canvases.next(name + '_fit').SetGrid()
plot.Draw()
## Estimate the MC truth phos and phor:
old_precision = set_default_integrator_precision(2e-9, 2e-9)
calibrator0.s.setRange(-15, 15)
calibrator0.r.setRange(0,25)
calibrator0.phoEResPdf.fitTo(fitdata1, roo.Range(-50, 50), roo.Strategy(2))
set_default_integrator_precision(*old_precision)
set_mc_truth(calibrator0.s, calibrator0.r)
## Store the result in the workspace:
w.saveSnapshot('mc_fit', ROOT.RooArgSet(phoScale, phoRes,
phoScaleTrue, phoResTrue))
## Draw the results on the canvas:
Latex([
'E^{#gamma} Scale (%)',
' MC Truth: %.2f #pm %.2f' % (calibrator0.s.getVal(),
calibrator0.s.getError()),
' MC Fit: %.2f #pm %.2f ^{+%.2f}_{%.2f}' % (
phoScale.getVal(), phoScale.getError(), phoScale.getErrorHi(),
phoScale.getErrorLo()
),
'',
'E^{#gamma} Resolution (%)',
' MC Truth: %.2f #pm %.2f' % (calibrator0.r.getVal(),
calibrator0.r.getError()),
' MC Fit: %.2f #pm %.2f ^{+%.2f}_{%.2f}' % (
phoRes.getVal(), phoRes.getError(), phoRes.getErrorHi(),
phoRes.getErrorLo()
),
'',
'Signal Purity (%)',
' MC Truth: %.2f' % fsr_purity,
' MC Fit: %.2f #pm %.2f' % (
100 * w.var('signal_f').getVal(),
100 * w.var('signal_f').getError()
)
],
position=(0.2, 0.8)
).draw()
check_timer('8. fast plots')
## pdf = ROOT.RooNDKeysPdf(pdfname, pdfname,
## ROOT.RooArgList(mmgMass), sdata, "a", 1.5)
## -> Stick to deprecated RooKeysPdf for now.
## mmgMass.setRange(40, 140)
peak = w.factory('%s_mode[91.2, 60, 120]' % pdfname)
width = w.factory('%s_effsigma[3, 0.1, 20]' % pdfname)
pdf = ParametrizedKeysPdf(pdfname + '_pkeys', pdfname + '_pkyes', mmgMass,
peak, width, sdata, ROOT.RooKeysPdf.NoMirror,
1.5)
savrange = (mmgMass.getMin(), mmgMass.getMax())
normrange = (40, 130)
fitrange = (60, 120)
mmgMass.setRange(*fitrange)
peak.setVal(pdf.shapemode)
width.setVal(pdf.shapewidth)
pdf.fitTo(sdata, roo.Range(*fitrange), roo.Strategy(2))
w.Import(pdf)
## peak.setConstant()
## width.setConstant()
hist = pdf.createHistogram(pdfname + '_hist', mmgMass, roo.Binning(1000))
dhist = ROOT.RooDataHist(pdfname + '_dhist', pdfname + '_hist',
ROOT.RooArgList(mmgMass), hist)
w.Import(dhist)
hpdf = w.factory(
'HistPdf::{name}({{mmgMass}}, {name}_dhist, 1)'.format(name=pdfname))
## bdata = data.reduce(ROOT.RooArgSet(mmgMass))
## bdata = bdata.binnedClone(data.GetName() + '_binned', data.GetTitle())
## pdf = ROOT.RooHistPdf(pdfname, pdfname, ROOT.RooArgSet(mmgMass), bdata, 2)
pdf.Print()
# w.Import(pdf)
m1gOplusM2g.SetTitle('sqrt(mmgMass^2-mmMass^2)')
isrData = dataset.get(tree=chains['z'],
variable=mmMass,
weight=weight,
cuts=[
'!isFSR', 'mmgMass < 200', 'mmMass < 200',
'phoPt > 15', 'Entry$ < 500000'
])
mmgMassIsrData = dataset.get(variable=mmgMass)
m1gOplusM2gIsrData = dataset.get(variable=m1gOplusM2g)
isrData.merge(mmgMassIsrData, m1gOplusM2gIsrData)
## Fit the model to the data
mmMassPdf.fitTo(isrData, roofit.Range(60, 120))
## Plot the data and the fit
mmPlot = mmMass.frame(roofit.Range(60, 120))
isrData.plotOn(mmPlot)
mmMassPdf.plotOn(mmPlot)
canvases.next('mmMass')
mmPlot.Draw()
## Model for the reconstructed mmg mass of the ISR through transformation
# mmgMassIsrPdf = ROOT.RooFFTConvPdf('mmgMassIsrPdf', 'mmgMassIsrPdf', mmMassFunc,
# mmMass, zmmGenShape, mmMassRes)
mmgMassPdf = w.factory('Voigtian::mmgMassPdf(mmMassFunc, mmMean, GZ, mmRes)')
## Plot the mmg mass data and model overlaid without fitting (!)
mmgPlot = mmgMass.frame(roofit.Range(60, 200))
phoRes,
data,
rho=1.5)
## Build the model for fT(t|s,r) = fT1(t1) * fT2(t2|s,r)
t.setRange(5, 10)
t.setBins(1000, "cache")
tpdf = ROOT.RooFFTConvPdf('tpdf', 'tpdf', t, t1pdf, t2pdf)
tpdf.setBufferFraction(0.1)
##------------------------------------------------------------------------------
## Plot fT1(t1) with training data.
canvases.next('t1pdf').SetGrid()
t.setRange(5, 10)
t.SetTitle('log(m_{#mu#mu#gamma,E_{gen}^{#gamma}}^{2} - m_{#mu#mu}^{2})')
plot = t.frame(roo.Range(6, 9))
t1data.plotOn(plot)
t1pdf.shape.plotOn(plot)
t1pdf.plotOn(plot, roo.LineColor(ROOT.kRed), roo.LineStyle(ROOT.kDashed))
plot.Draw()
Latex([
's_{shape}: %.3f' % t1pdf.shapemode,
's_{fit}: %.3f #pm %.3f' % (t1mode.getVal(), t1mode.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f' %
(t1mode.getVal() - t1pdf.shapemode, t1mode.getError()),
'r_{shape}: %.3f' % t1pdf.shapewidth,
'r_{fit}: %.3f #pm %.3f' % (t1width.getVal(), t1width.getError()),
'r_{fit} - r_{shape}: %.4f #pm %.4f' %
(t1width.getVal() - t1pdf.shapewidth, t1width.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' %
(t1width.getVal() / t1pdf.shapewidth,
def __init__(self,
name,
title,
mass,
phos,
phor,
data,
workspace,
phostarget,
phortargets,
rho=1.5,
mirror=ROOT.RooKeysPdf.NoMirror,
mrangetrain=(40, 140),
mrangenorm=(50, 130),
mrangefit=(60, 120)):
'''PhosphorModel4(str name, str title, RooRealVar mass, RooRealVar phos,
RooRealVar phor, RooDataSet data, float phostarget,
[float] phortargets, float rho=1.5,
int mirror=ROOT.RooKeysPdf.NoMirror)
name - PDF name
title - PDF title
mass - mumugamma invariant mass (GeV), observable
phos - photon energy scale (%), parameter
phor - photon energy resolution (%), paramter
data - (mmMass, mmgMass, phoERes = 100*(phoEreco/phoEgen - 1), dataset
on which the shapes of reference PDFs are trained.
phostarget - target reference value of the photon energy scale at which
the model is being trained
phortargetss - a list of target photon energy resolution values for the
moment morphing
rho - passed to trained RooKeysPdfs
mirror - passed to trained RooKeysPdfs
'''
## Attach args.
self._name = name
self._title = title
self._mass = mass
self._phos = phos
self._phor = phor
self._data = data
self._phostarget = phostarget
self._phortargets = phortargets[:]
self._rho = rho
self._mirror = mirror
## Attach other attributes.
self._massrange = (mass.getMin(), mass.getMax())
self._sdata_list = []
self._phostrue_list = []
self._phortrue_list = []
self._dm_dphos_list = []
self._msubs_list = []
self._keys_pdfs = []
self._keys_modes = []
self._keys_effsigmas = []
self._keys_fitresults = []
self._pdfs = []
self._custs = []
# self._pdfrefs = []
self._mrefs = []
self._phormorphs = []
self._phorhists = []
self._workspace = workspace
## self._workspace = ROOT.RooWorkspace(name + '_workspace',
## title + ' workspace')
w = self._workspace
self.w = w
## Import important args in workspace.
# w.Import(ROOT.RooArgSet(mass, phos, phor))
w.Import(mass)
w.Import(phos)
w.Import(phor)
w.Import(self._data)
w.factory('ConstVar::{name}_phostarget({value})'.format(
name=name, value=self._phostarget))
## Define the morphing parameter. This an identity with the
## photon resolution for now.
mpar = self._mpar = w.factory(
'expr::{name}_mpar("{phor}", {{{phor}}})'.format(
## 'expr::{name}_mpar("2 + sqrt(0.5^2 + 0.05 * {phor}^2)", {{{phor}}})'.format(
## 'expr::{name}_mpar("2.325+sqrt(0.4571 + (0.1608*{phor})^2)", {{{phor}}})'.format(
name=name,
phor=phor.GetName()))
## Define the formula for dlog(m)/dphos.
self._dm_dphos_func = w.factory('''
expr::dm_dphos_func("0.5 * (1 - mmMass^2 / mmgMass^2) * mmgMass",
{mmMass, mmgMass})
''')
## Get the calibrator.
self._calibrator = MonteCarloCalibrator(self._data)
## Loop over target reference photon energy resolutions in phortargets.
for index, phortarget in enumerate(self._phortargets):
## Store the target photon resolution value.
w.factory('ConstVar::{name}_phortarget_{index}({value})'.format(
name=name, index=index, value=phortarget))
## Get the corresponding smeared RooDataSet sdata named
## {name}_sdata_{index} and attach it to self._sdata
sdata = self._calibrator.get_smeared_data(
self._phostarget,
phortarget,
name + '_sdata_%d' % index,
title + ' sdata %d' % index,
## Get the true scale and resolution with errors. (This can be
## added to the calibrator and snapshots of
## self._calibrator.s and self._calibrator.r stored as
## {name}_sdata_{index}_sr in self._calibrator.w)
dofit=True)
self._sdata_list.append(sdata)
w.Import(sdata)
phostrue = ROOT.RooRealVar(self._calibrator.s,
name + '_phostrue_%d' % index)
phortrue = ROOT.RooRealVar(self._calibrator.r,
name + '_phortrue_%d' % index)
phostrue.setConstant(True)
phortrue.setConstant(True)
self._phostrue_list.append(phostrue)
self._phortrue_list.append(phortrue)
w.Import(phostrue)
w.Import(phortrue)
## Calculate the dlogm/dphos {name}_dm_dphos_{index}
## for the smeared dataset.
sdata.addColumn(self._dm_dphos_func)
dm_dphos = w.factory('''
{name}_dm_dphos_{index}[{mean}, 0, 1]
'''.format(name=name,
index=index,
mean=sdata.mean(sdata.get()['dm_dphos_func'])))
dm_dphos.setConstant(True)
self._dm_dphos_list.append(dm_dphos)
## Define the mass scaling {name}_msubs{i} introducing
## the dependence on phos. This needs self._calibrator.s and
## dm_dphos.
## msubs = w.factory(
## '''
## LinearVar::{msubs}(
## {mass}, 1,
## expr::{offset}(
## "- 0.01 * {dm_dphos} * ({phos} - {phostrue})",
## {{ {dm_dphos}, {phos}, {phostrue} }}
## )
## )
## '''.format(msubs = name + '_msubs_%d' % index,
## offset = name + '_msubs_offset_%d' % index,
## mass = self._mass.GetName(),
## dm_dphos = dm_dphos.GetName(),
## phos = self._phos.GetName(),
## phostrue = phostrue.GetName())
## )
## LinearVar cannot be persisted.
msubs = w.factory('''
expr::{msubs}(
"{mass} - 0.01 * {dm_dphos} * ({phos} - {phostrue})",
{{ {mass}, {dm_dphos}, {phos}, {phostrue} }}
)
'''.format(msubs=name + '_msubs_%d' % index,
mass=self._mass.GetName(),
dm_dphos=dm_dphos.GetName(),
phos=self._phos.GetName(),
phostrue=phostrue.GetName()))
self._msubs_list.append(msubs)
## Build the corresponding parametrized KEYS PDF {name}_kyes_{index}
## with {name}_keys_mode_{index} and
## {name}_keys_effsigma_{index}.
keys_mode = w.factory(
'{name}_keys_mode_{index}[91.2, 60, 120]'.format(name=name,
index=index))
keys_effsigma = w.factory(
'{name}_keys_effsigma_{index}[3, 0.1, 60]'.format(name=name,
index=index))
mass.setRange(*mrangetrain)
keys_pdf = ParametrizedKeysPdf(name + '_keys_pdf_%d' % index,
name + '_keys_pdf_%d' % index,
mass,
keys_mode,
keys_effsigma,
sdata,
rho=self._rho)
self._keys_modes.append(keys_mode)
self._keys_effsigmas.append(keys_effsigma)
self._keys_pdfs.append(keys_pdf)
## Fit the KEYS PDF to the training data and save the result
## {name}_keys_fitresult_{index} and parameter snapshots
## {name}_keys_mctrue_{index}.
mass.setRange(*mrangenorm)
keys_fitresult = keys_pdf.fitTo(sdata, roo.Range(*mrangefit),
roo.Strategy(2), roo.NumCPU(8),
roo.Save(True))
self._keys_fitresults.append(keys_fitresult)
w.Import(keys_fitresult, name + '_keys_fitresult_%d' % index)
w.saveSnapshot(
name + '_mctrue_%d' % index, ','.join([
phostrue.GetName(),
phortrue.GetName(),
keys_mode.GetName(),
keys_effsigma.GetName()
]))
## Sample the fitted KEYS PDF to a histogram {name}_hist_{index}.
mass.setRange(*mrangetrain)
hist = keys_pdf.createHistogram(name + '_hist_%d' % index, mass,
roo.Binning('cache'))
## Build a RooDataHist {name}_dhist{index} of the sampled histogram.
dhist = ROOT.RooDataHist(name + '_dhist_%d' % index,
name + '_dhist_%d' % index,
ROOT.RooArgList(mass), hist)
w.Import(dhist)
## Build a RooHistPdf {name}_pdf_{index} using the dhist and msubs.
## pdf = w.factory(
pdf = w.factory('HistPdf::{name}({{{mass}}}, {dhist}, 1)'.format(
name=name + '_pdf_%d' % index,
msubs=msubs.GetName(),
mass=mass.GetName(),
dhist=dhist.GetName()))
self._pdfs.append(pdf)
mass.setRange(*self._massrange)
## Calculate morphing parameter reference values float mref[index].
phorval = phor.getVal()
phor.setVal(phortrue.getVal())
self._mrefs.append(mpar.getVal())
phor.setVal(phorval)
## End of loop over target phortargets
## Make the reference and cache binnings in phor
self._check_phor_ranges()
partitions = self._partition_binning(self._phor, 'cache', 'reference')
## Loop over phor reference bins and define pairwise RooMomentMorphs.
for ilo in range(len(self._mrefs) - 1):
ihi = ilo + 1
mlo = self._mrefs[ilo]
mhi = self._mrefs[ihi]
pdflo = self._pdfs[ilo]
pdfhi = self._pdfs[ihi]
phormorph = w.factory('''
MomentMorph::{name}_phormorph_{ilo}to{ihi}(
{mpar}, {{{mass}}}, {{{pdfs}}}, {{{mrefs}}}
)
'''.format(name=name,
ilo=ilo,
ihi=ihi,
mpar=mpar.GetName(),
mass=mass.GetName(),
pdfs='%s, %s' % (pdflo.GetName(), pdfhi.GetName()),
mrefs='%f, %f' % (mlo, mhi)))
self._phormorphs.append(phormorph)
## Sample the morph in a 2D histogram in mass and phor.
phorhist = phormorph.createHistogram(
name + '_phorhist_%dto%d' % (ilo, ihi), mass,
roo.Binning('cache'),
roo.YVar(phor, roo.Binning(partitions[ilo])))
self._phorhists.append(phorhist)
## End of loop over phor reference bins.
self._stitch_phorhists()
self._phor_dhist = phor_dhist = ROOT.RooDataHist(
name + '_phor_dhist', name + '_phor_dhist',
ROOT.RooArgList(mass, phor), self._phorhist)
average_index = (len(self._msubs_list) + 1) / 2
msubs = self._msubs_list[average_index]
## self._model = model = ROOT.RooHistPdf(
## name + '_phor_histpdf', name + '_phor_histpdf',
## ROOT.RooArgList(msubs, phor), ROOT.RooArgList(mass, phor), phor_dhist, 2
## )
## self._model = model = ROOT.RooPhosphorPdf(
## name + '_phor_histpdf', name + '_phor_histpdf', mass, msubs, phor, phor_dhist, 2
## )
## ## Quick hack to make things work.
## self._customizer = customizer = ROOT.RooCustomizer(model, 'msub')
## customizer.replaceArg(mass, msubs)
## model = customizer.build()
# ROOT.RooHistPdf.__init__(self, model)
ROOT.RooPhosphorPdf.__init__(self, name, title, mass, msubs, phos,
phor, phor_dhist, 2)
self.SetName(name)
self.SetTitle(title)
