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)
'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,
phoRes.getError() / t2pdf.r0val),
], position=(0.2, 0.75)).draw()
canvases.update()
t.setRange(*t1range)
xtpdf.fitTo(xtdata, roo.NumCPU(8), roo.Verbose(True), roo.Timer(True),
roo.SumW2Error(True), roo.Minos(ROOT.RooArgSet(phoRes)))
## Plot fXT(t|s,r) fitted to data
## canvases.next('tpdf').SetGrid()
## t.setRange(5, 10)
## t.SetTitle('log(m_{#mu#mu#gamma}^{2} - m_{#mu#mu}^{2})')
## tpdf.fitTo(tdata, roo.Range(5.5, 9.5))
## plot = t.frame(roo.Range(6, 9))
## tdata.plotOn(plot)
## tpdf.plotOn(plot)
## tpdf.paramOn(plot)
## plot.Draw()
canvases.update()
sw.Stop()
print 'CPU time:', sw.CpuTime(), 's, real time:', sw.RealTime(), 's'
unitg = w.factory('Gaussian::unitg(pull[-5,5],zero[0],unit[1])')
pull = w.var('pull')
defaultd = ROOT.RooDataSet('defaultd', 'defaultd', ROOT.RooArgSet(pull))
bind = ROOT.RooDataSet('bind', 'bind', ROOT.RooArgSet(pull))
mediand = ROOT.RooDataSet('mediand', 'mediand', ROOT.RooArgSet(pull))
pdgd = ROOT.RooDataSet('pdgd', 'pdgd', ROOT.RooArgSet(pull))
## Build the model
# model = w.factory('Gaussian::gaus(x[-5,5],mean[0,-6,6],sigma[1,0.5,2])')
model = w.factory('RooSechPdf::sech(x[-5,5],mean[0,-6,6],sigma[1,0.5,2])')
x = w.factory('x')
for itoy in range(ntoys):
## Use the model to generate a toy dataset
data = model.generate(ROOT.RooArgSet(x), nentries, roofit.NumCPU(numCPU))
## Fit the model to the generated data
model.fitTo(data, roofit.NumCPU(numCPU), roofit.Verbose(False))
## Get custom binning
nevents = data.tree().Draw(x.GetName(), '', 'goff')
bins = DataDrivenBinning(nevents,
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()
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')
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)