def get_data(zchain=getChains('v11')['z']):
'Get the nominal data that is used for smearing.'
## The TFormula expression defining the data is given in the titles.
weight.SetTitle('pileup.weight')
phoERes.SetTitle('100 * phoERes')
mmgMassPhoGenE.SetTitle('threeBodyMass(mu1Pt, mu1Eta, mu1Phi, 0.106, '
' mu2Pt, mu2Eta, mu2Phi, 0.106, '
' phoGenE * phoPt / phoE, '
' phoEta, phoPhi, 0)')
## Create a preselected tree
tree = zchain.CopyTree('&'.join(cuts))
## Have to copy aliases by hand
for a in zchain.GetListOfAliases():
tree.SetAlias(a.GetName(), a.GetTitle())
## Get the nominal dataset
global data
data = dataset.get(tree=tree,
weight=weight,
cuts=cuts,
variables=[mmgMass, mmMass, phoERes, mmgMassPhoGenE])
## Set units and nice titles
for x, t, u in zip([mmgMass, mmgMassPhoGenE, mmMass, phoERes], [
'reconstructed m_{#mu#mu#gamma}',
'reconstructed m_{#mu#mu#gamma} with E_{gen}^{#gamma}',
'm_{#mu#mu}',
'E_{reco}^{#gamma}/E_{gen}^{#gamma} - 1',
], 'GeV GeV GeV %'.split()):
x.SetTitle(t)
x.setUnit(u)
##-- Get Smeared Data ------------------------------------------------------
global calibrator
calibrator = MonteCarloCalibrator(data)
def get_data(zchain=getChains('v11')['z']):
'Get the nominal data that is used for smearing.'
global data, calibrator
## The TFormula expression defining the data is given in the titles.
weight.SetTitle('pileup.weight')
phoERes.SetTitle('100 * phoERes')
## Create a preselected tree
tree = zchain.CopyTree('&'.join(cuts))
## Have to copy aliases by hand
for a in zchain.GetListOfAliases():
tree.SetAlias(a.GetName(), a.GetTitle())
## Get the nominal dataset
data = dataset.get(tree=tree,
weight=weight,
cuts=cuts,
variables=[
mmgMass,
mmMass,
phoERes,
])
## Set units and nice titles
for x, t, u in zip([mmgMass, mmMass, phoERes], [
'm_{#mu#mu#gamma}',
'm_{#mu#mu}',
'E_{reco}^{#gamma}/E_{gen}^{#gamma} - 1',
], 'GeV GeV %'.split()):
x.SetTitle(t)
x.setUnit(u)
##-- Get Smeared Data ------------------------------------------------------
## Enlarge the range of the observable to get vanishing tails.
# range_save = (phoERes.getMin(), phoERes.getMax())
# phoERes.setRange(-90, 150)
calibrator = MonteCarloCalibrator(data)
# phoERes.setRange(*range_save)
##-- Set the nominal energy scale and resolution targets -------------------
calibrator.w.loadSnapshot('sr0_mctruth')
for i, (s, r) in enumerate(zip(stargets, rtargets)):
if s == 'nominal':
stargets[i] = calibrator.s0.getVal()
if r == 'nominal':
rtargets[i] = calibrator.r0.getVal()
def get_data(zchain=getChains('v11')['z']):
'Get the nominal data that is used for smearing.'
## The TFormula expression defining the data is given in the titles.
weight.SetTitle('pileup.weight')
phoERes.SetTitle('100 * phoERes')
## Create a preselected tree
tree = zchain.CopyTree('&'.join(cuts))
## Have to copy aliases by hand
for a in zchain.GetListOfAliases():
tree.SetAlias(a.GetName(), a.GetTitle())
## Get the nominal dataset
global data
data = dataset.get(tree=tree,
weight=weight,
cuts=cuts,
variables=[
mmgMass,
mmMass,
phoERes,
])
## Set units and nice titles
for x, t, u in zip([mmgMass, mmMass, phoERes, phoRes, phoScale], [
'm_{#mu#mu#gamma}',
'm_{#mu#mu}',
'E_{reco}^{#gamma}/E_{gen}^{#gamma} - 1',
'photon energy resolution',
'photon energy scale',
], 'GeV GeV %'.split()):
x.SetTitle(t)
x.setUnit(u)
##-- Get Smeared Data ------------------------------------------------------
## Enlarge the range of the observable to get vanishing tails.
range_save = (phoERes.getMin(), phoERes.getMax())
phoERes.setRange(-90, 150)
global calibrator
calibrator = MonteCarloCalibrator(data)
phoERes.setRange(*range_save)
def get_data(chains = getChains('v11')):
'''
Get the nominal data that is used for smearing.
'''
global cuts
if Globals.debug:
print '====== CUTS INSIDE phosphorcalculator ======', cuts
## TODO: Break this down into several smaller methods.
## Map of variable names and corresponding TTree expressions to
## calculate it.
expression_map = {
'mmgMass': 'mmgMass',
'mmMass' : 'mmMass' ,
'phoERes' : '100 * phoERes',
'mmgMassPhoGenE': ('threeBodyMass(mu1Pt, mu1Eta, mu1Phi, 0.106, '
' mu2Pt, mu2Eta, mu2Phi, 0.106, '
' phoGenE * phoPt / phoE, '
' phoEta, phoPhi, 0)'),
'weight' : 'pileup.weight',
}
## The TFormula expression defining the data is given in the titles.
# print '+++ DEBUG: before title replacement:', mmgMass.GetTitle()
latex_map = replace_variable_titles(expression_map, w)
# print '+++ DEBUG: after title replacement:', mmgMass.GetTitle()
#global cuts
## Create a preselected tree
tree = {}
tree['z'] = chains['z'].CopyTree('&'.join(cuts))
# dtree = chains['data'].
## Have to copy aliases by hand
for a in chains['z'].GetListOfAliases():
tree['z'].SetAlias(a.GetName(), a.GetTitle())
cuts0 = cuts[:]
cuts1 = cuts[:]
if use_independent_fake_data:
cuts0.append('!(%s)' % fake_data_cut)
cuts1.append(fake_data_cut)
## Get the nominal dataset
global data
data = {}
for xvar in [weight, mmgMass, mmMass, phoERes, mmgMassPhoGenE]:
print xvar.GetName(), ':', xvar.GetTitle()
data['fsr0'] = dataset.get(tree=tree['z'], weight=weight,
cuts = cuts0 + ['isFSR'],
variables=[mmgMass, mmMass, phoERes,
mmgMassPhoGenE])
data['fsr1'] = dataset.get(tree=tree['z'], weight=weight,
cuts=cuts1 + ['isFSR'],
variables=[mmgMass, mmMass, phoERes,
mmgMassPhoGenE])
data['zj0'] = dataset.get(tree=tree['z'], weight=weight,
cuts=cuts0 + ['!isFSR'],
variables=[mmgMass, mmMass])
data['zj1'] = dataset.get(tree=tree['z'], weight=weight,
cuts=cuts1 + ['!isFSR'],
variables=[mmgMass, mmMass])
## Set units and nice titles
replace_variable_titles(latex_map, w)
## Do we want to reduce the data?
if reduce_data:
reduced_entries = int( (1 - fit_data_fraction) *
data['fsr0'].numEntries() )
data['fsr0'] = data['fsr0'].reduce(
roo.EventRange(0, int(reduced_entries))
)
data['zj0'].SetName('zj0_mc')
w.Import(data['zj0'])
##-- Calculate MC Truth Purity ---------------------------------------------
if use_independent_fake_data:
num_fsr_events = data['fsr1'].sumEntries()
num_zj_events = data['zj1'].sumEntries()
else:
num_fsr_events = data['fsr0'].sumEntries()
num_zj_events = data['zj0'].sumEntries()
global fsr_purity
fsr_purity = 100 * num_fsr_events / (num_fsr_events + num_zj_events)
##-- Get Smeared Data ------------------------------------------------------
old_precision = set_default_integrator_precision(2e-9, 2e-9)
global calibrator0, calibrator1, fit_calibrator
calibrator0 = MonteCarloCalibrator(data['fsr0'], printlevel=1, rho=1.5)
if use_independent_fake_data:
calibrator1 = MonteCarloCalibrator(data['fsr1'], printlevel=1, rho=1.5)
fit_calibrator = calibrator1
else:
fit_calibrator = calibrator0
set_default_integrator_precision(*old_precision)
##-- Check the time -------------------------------------------------------
check_timer(
'1. init and get_data (%d entries)' % (
data['fsr0'].numEntries() + data['fsr1'].numEntries() +
data['zj0'].numEntries() + data['zj1'].numEntries()
)
)
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)
