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)
rpdflist.append(pdfname)
## Store the target parameters in the workspaces
phoScaleTarget.setVal(starget)
phoResTarget.setVal(rtarget)
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._dlogm_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._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("sqrt(3^2 + {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._dlogm_dphos_func = w.factory('''
expr::dlogm_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}_dlogm_dphos_{index}
## for the smeared dataset.
sdata.addColumn(self._dlogm_dphos_func)
dlogm_dphos = w.factory(
'''
{name}_dlogm_dphos_{index}[{mean}, 0, 1]
'''.format(name=name, index=index,
mean=sdata.mean(sdata.get()['dlogm_dphos_func']))
)
dlogm_dphos.setConstant(True)
self._dlogm_dphos_list.append(dlogm_dphos)
## Define the mass scaling {name}_msubs{i} introducing
## the dependence on phos. This needs self._calibrator.s and
## dlogm_dphos.
## msubs = w.factory(
## '''
## cexpr::{msubs}(
## "{mass}*(1 - 0.01 * {dlogm_dphos} * ({phos} - {phostrue}))",
## {{ {mass}, {dlogm_dphos}, {phos}, {phostrue} }}
## )
## '''.format(msubs = name + '_msubs_%d' % index,
## mass = self._mass.GetName(),
## dlogm_dphos = dlogm_dphos.GetName(),
## phos = self._phos.GetName(),
## phostrue = phostrue.GetName())
## )
## msubs = w.factory(
## '''
## LinearVar::{msubs}(
## {mass},
## expr::{slope}(
## "(1 - 0.01 * {dlogm_dphos} * ({phos} - {phostrue}))",
## {{ {dlogm_dphos}, {phos}, {phostrue} }}
## ),
## 0
## )
## '''.format(msubs = name + '_msubs_%d' % index,
## slope = name + '_msubs_slope_%d' % index,
## mass = self._mass.GetName(),
## dlogm_dphos = dlogm_dphos.GetName(),
## phos = self._phos.GetName(),
## phostrue = phostrue.GetName())
## )
msubs = w.factory(
'''
LinearVar::{msubs}(
{mass}, 1,
expr::{offset}(
"- 0.01 * {dlogm_dphos} * ({phos} - {phostrue})",
{{ {dlogm_dphos}, {phos}, {phostrue} }}
)
)
'''.format(msubs = name + '_msubs_%d' % index,
offset = name + '_msubs_offset_%d' % index,
mass = self._mass.GetName(),
dlogm_dphos = dlogm_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)
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(1000))
## 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(
## 'HistPdf::{name}({{{msubs}}}, {{{mass}}}, {dhist}, 1)'.format(
## name = name + '_pdf_%d' % index, msubs = msubs.GetName(),
## mass = mass.GetName(), dhist = dhist.GetName()
## )
## )
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)
## Supstitute for mass using customizer.
cust = ROOT.RooCustomizer(pdf, 'msubs_%d' % index)
self._custs.append(cust)
cust.replaceArg(mass, msubs)
pdfref = cust.build()
pdfref.addOwnedComponents(ROOT.RooArgSet(msubs))
pdfref.SetName(name + '_pdfref_%d' % index)
pdfref.SetTitle(name + '_pdfref_%d' % index)
w.Import(pdfref)
self._pdfrefs.append(pdfref)
## 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
## Define the RooMomentMorph model.
model = w.factory(
'''
MomentMorph::{name}({mpar}, {{{mass}}}, {{{pdfs}}}, {{{mrefs}}})
'''.format(name=name, mpar=mpar.GetName(), mass=mass.GetName(),
pdfs=','.join([f.GetName() for f in self._pdfs]),
# pdfs=','.join([f.GetName() for f in self._pdfrefs]),
mrefs=','.join([str(m) for m in self._mrefs]))
)
## Quick hack to make things work.
cust = ROOT.RooCustomizer(model, 'msub')
cust.replaceArg(mass, self._msubs_list[0])
model2 = cust.build()
ROOT.RooMomentMorph.__init__(self, model2)
self.SetName(name)
self.SetTitle(title)
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)
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)
rpdflist.append(pdfname)
## Store the target parameters in the workspaces
phoScaleTarget.setVal(starget)
phoResTarget.setVal(rtarget)
w.saveSnapshot(pdfname, params, True)
mode4 = w.factory('mode4[91,60,120]')
mode8 = w.factory('mode8[91,60,120]')
seff4 = w.factory('seff4[4,0.1,50]')
seff8 = w.factory('seff8[4,0.1,50]')
pdf4 = ParametrizedKeysPdf('pdf4', 'pdf4', mmgMass, mode4, seff4,
sdata4, mirror, 1.5)
pdf8 = ParametrizedKeysPdf('pdf8', 'pdf8', mmgMass, mode8, seff8,
sdata8, mirror, 1.5)
mmgMass.setRange(50, 130)
pdf4.fitTo(sdata4, roo.Range(60, 120))
h4 = pdf4.createHistogram('h4', mmgMass, roo.Binning(1000))
pdf8.fitTo(sdata8, roo.Range(60, 120))
h8 = pdf8.createHistogram('h8', mmgMass, roo.Binning(1000))
d4 = ROOT.RooDataHist('d4', 'd4', ROOT.RooArgList(mmgMass), h4)
d8 = ROOT.RooDataHist('d8', 'd8', ROOT.RooArgList(mmgMass), h8)
f4 = ROOT.RooHistPdf('f4', 'f4', ROOT.RooArgSet(mmgMass), d4, 1)
f8 = ROOT.RooHistPdf('f8', 'f8', ROOT.RooArgSet(mmgMass), d8, 1)
w.Import(f4)
w.Import(f8)
fm = w.factory('MomentMorph::fm(phoRes, {mmgMass}, {f4, f8}, {4, 6})')
