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 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 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 _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)
data.addColumn(t1xt2func)
t1xt2func.SetName('t1xt2func')
tfunc.SetName('t')
data.addColumn(tfunc)
tfunc.SetName('tfunc')
t1xt2vtdata = data.reduce(ROOT.RooArgSet(t, t1xt2))
data = data.reduce(ROOT.RooArgSet(mmgMass, mmMass, phoERes, mmgMassPhoGenE))
## Build the model fT1(t1) for log(mmgMassPhoGenE^2 - mmMass^2)
t.setRange(5, 10)
# t.setVal(8.3)
nomirror = ROOT.RooKeysPdf.NoMirror
## t1pdf = ROOT.RooKeysPdf('t1pdf', 't1pdf', t, t1data, nomirror, 1.5)
t1mode = w.factory('t1mode[8.3,5,10]')
t1width = w.factory('t1width[0.2,0.01,5]')
t1pdf = ParametrizedKeysPdf('t1pdf', 't1pdf', t, t1mode, t1width, t1data,
nomirror, 1.5)
t1pdf.fitTo(t1data)
t1mode.setConstant(True)
t1width.setConstant(True)
## TODO: use parametrized KEYS PDF with forced ranges and fit it to data.
## Build the model fT2(t2|s,r) for log(Ereco/Egen) ft2(t2|r,s)
t.setRange(-1, 1)
t.setVal(0)
t2pdf = LogPhoeresKeysPdf('t2pdf',
't2pdf',
phoERes,
t,
phoScale,
phoRes,
data,
sdata.SetName(dataname)
w.Import(sdata)
pdfname = '_'.join(['mmgMassPdf', name, bintag])
pdfname = pdfname.replace('-', 'to')
## KEYS PDF Dilemma: RooKeysPdf is deprecated,
## RooNDKeysPdf cannot be stored in a workspace,
## RooMomentMorph constructor works in workspace factory only
## 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)
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)
canvases.update()
sw.Stop()
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)
