def __init__(self, adc_east, adc_west):
#input ADC values
self.adc_east = adc_east
self.adc_west = adc_west
#self.adc_east = RooRealVar("adc_east", "adc_east", 10, 1300)
#self.adc_west = RooRealVar("adc_west", "adc_west", 10, 1300)
#east Gaussian
self.gauss_east = Gauss(self.adc_east, "east")
self.gauss_east.mean_1n.setVal(72.9)
self.gauss_east.sigma_1n.setVal(21.4)
#west Gaussian
self.gauss_west = Gauss(self.adc_west, "west")
self.gauss_west.mean_1n.setVal(87.7)
self.gauss_west.sigma_1n.setVal(26.9)
#east Crystal Ball
self.cb_east = CrystalBall(self.adc_east, "east")
self.cb_east.mean_2n.setVal(166.)
self.cb_east.sigma_2n.setVal(42.1)
self.cb_east.alpha_2xn.setVal(-0.7)
self.cb_east.n_2xn.setVal(0.5)
#west Crystal Ball
self.cb_west = CrystalBall(self.adc_west, "west")
self.cb_west.mean_2n.setVal(174.1)
self.cb_west.sigma_2n.setVal(29.3)
self.cb_west.alpha_2xn.setVal(-0.3)
self.cb_west.n_2xn.setVal(0.8)
# (g_e + c_e)*(g_w + c_w) = g_e*g_w + c_e*c_w + g_e*c_w + c_e*g_w
#self.num_max = 3000
self.num_max = 300000
#1n1n 2D Gaussian
self.pdf_1n1n = RooProdPdf("pdf_1n1n", "pdf_1n1n", RooArgList(self.gauss_east.gauss_1n, self.gauss_west.gauss_1n))
self.num_1n1n = RooRealVar("num_1n1n", "num_1n1n", 200, 0, self.num_max) # 1
#1n2xn Gaussian * Crystal Ball
self.pdf_1n2xn = RooProdPdf("pdf_1n2xn", "pdf_1n2xn", RooArgList(self.gauss_east.gauss_1n, self.cb_west.cb_2xn))
self.num_1n2xn = RooRealVar("num_1n2xn", "num_1n2xn", 100, 0, self.num_max) # 1
#2xn1n Crystal Ball * Gaussian
self.pdf_2xn1n = RooProdPdf("pdf_2xn1n", "pdf_2xn1n", RooArgList(self.cb_east.cb_2xn, self.gauss_west.gauss_1n))
self.num_2xn1n = RooRealVar("num_2xn1n", "num_2xn1n", 100, 0, self.num_max) # 1
#2xn2xn 2D Crystal Ball
self.pdf_2xn2xn = RooProdPdf("pdf_2xn2xn", "pdf_2xn2xn", RooArgList(self.cb_east.cb_2xn, self.cb_west.cb_2xn))
self.num_2xn2xn = RooRealVar("num_2xn2xn", "num_2xn2xn", 50, 0, self.num_max) # 1
#fit model
self.model = RooAddPdf("model", "model", RooArgList(self.pdf_1n1n, self.pdf_1n2xn, self.pdf_2xn1n, self.pdf_2xn2xn),
RooArgList(self.num_1n1n, self.num_1n2xn, self.num_2xn1n, self.num_2xn2xn))
def fit(self):
pdfs = RooArgList()
obsvars = RooArgSet('set')
for constraint in self.constraint.values():
pdfs.add(constraint.pdf_constraint)
if hasattr(constraint, 'var_obs'):
obsvars.add(constraint.var_obs)
self.model = RooProdPdf('model', 'model', pdfs)
self.data = RooDataSet('data', 'data', obsvars)
self.data.add(obsvars)
self.data.Print()
self.fit_result = self.model.fitTo(self.data,
ROOT.RooFit.PrintLevel(3),
ROOT.RooFit.Optimize(1),
ROOT.RooFit.Hesse(1),
ROOT.RooFit.Minos(1),
ROOT.RooFit.Strategy(2),
ROOT.RooFit.Save(1))
def fit(self):
fit_variable = RooRealVar("fit_variable", "fit_variable",
self.fit_boundaries[0],
self.fit_boundaries[1])
fit_variable.setBins(self.histograms[self.data_label].nbins())
variables = RooArgList()
variables.add(fit_variable)
variable_set = RooArgSet()
variable_set.add(fit_variable)
roofit_histograms = {}
roofit_pdfs = {}
roofit_variables = {}
N_min = 0.
N_max = self.normalisation[self.data_label] * 2.
pdf_arglist = RooArgList()
variable_arglist = RooArgList()
roofit_histograms[self.data_label] = RooDataHist(
self.data_label, self.data_label, variables,
self.histograms[self.data_label])
for sample in self.samples:
roofit_histogram = RooDataHist(sample, sample, variables,
self.histograms[sample])
roofit_histograms[sample] = roofit_histogram
roofit_pdf = RooHistPdf('pdf' + sample, 'pdf' + sample,
variable_set, roofit_histogram)
roofit_pdfs[sample] = roofit_pdf
roofit_variable = RooRealVar(sample, sample + " events",
self.normalisation[sample], N_min,
N_max)
roofit_variables[sample] = roofit_variable
pdf_arglist.add(roofit_pdf)
variable_arglist.add(roofit_variable)
model = RooAddPdf('model', 'sum of all known', pdf_arglist,
variable_arglist)
use_model = model
if self.constraints:
arg_set = RooArgSet(model)
constraints = self.get_fit_normalisation_constraints(
model, roofit_variables)
for constraint in constraints:
arg_set.add(constraint)
model_with_constraints = RooProdPdf(
"model_with_constraints", "model with gaussian constraints",
arg_set, RooLinkedList())
use_model = model_with_constraints
if self.method == 'TMinuit':
#WARNING: number of cores changes the results!!!
self.saved_result = use_model.fitTo(
roofit_histograms[self.data_label],
RooFit.Minimizer("Minuit2", "Migrad"),
RooFit.NumCPU(1),
RooFit.Extended(),
RooFit.Save(),
)
results = {}
for sample in self.samples:
results[sample] = (roofit_variables[sample].getVal(),
roofit_variables[sample].getError())
self.results = results
def buildTimePdf(config):
"""
build time pdf, return pdf and associated data in dictionary
"""
from B2DXFitters.WS import WS
print 'CONFIGURATION'
for k in sorted(config.keys()):
print ' %32s: %32s' % (k, config[k])
# start building the fit
ws = RooWorkspace('ws_%s' % config['Context'])
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
# start by defining observables
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
qt = WS(ws, RooCategory('qt', 'tagging decision'))
qt.defineType('B+', +1)
qt.defineType('Untagged', 0)
qt.defineType('B-', -1)
# now other settings
Gamma = WS(ws, RooRealVar('Gamma', 'Gamma', 0.661)) # ps^-1
DGamma = WS(ws, RooRealVar('DGamma', 'DGamma', 0.106)) # ps^-1
Dm = WS(ws, RooRealVar('Dm', 'Dm', 17.719)) # ps^-1
# HACK (1/2): be careful about lower bound on eta, since mistagpdf below
# is zero below a certain value - generation in accept/reject would get
# stuck
eta = WS(
ws,
RooRealVar(
'eta', 'eta', 0.35, 0.0 if 'FIT' in config['Context'] else
(1. + 1e-5) * max(0.0, config['TrivialMistagParams']['omega0']),
0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 0.60, 0.0, 1.0))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr', 0.040, 0.001, 0.100))
# now build the PDF
from B2DXFitters.timepdfutils import buildBDecayTimePdf
from B2DXFitters.resmodelutils import getResolutionModel
from B2DXFitters.acceptanceutils import buildSplineAcceptance
obs = [qt, qf, time, eta, timeerr]
acc, accnorm = buildSplineAcceptance(
ws, time, 'Bs2DsPi_accpetance',
config['SplineAcceptance']['KnotPositions'],
config['SplineAcceptance']['KnotCoefficients'][config['Context']],
'FIT' in config['Context']) # float for fitting
if 'GEN' in config['Context']:
acc = accnorm # use normalised acceptance for generation
# get resolution model
resmodel, acc = getResolutionModel(ws, config, time, timeerr, acc)
# build a (mock) mistag distribution
mistagpdfparams = {} # start with parameters of mock distribution
for sfx in ('omega0', 'omegaavg', 'f'):
mistagpdfparams[sfx] = WS(
ws,
RooRealVar('Bs2DsPi_mistagpdf_%s' % sfx,
'Bs2DsPi_mistagpdf_%s' % sfx,
config['TrivialMistagParams'][sfx]))
# build mistag pdf itself
mistagpdf = WS(
ws,
MistagDistribution('Bs2DsPi_mistagpdf', 'Bs2DsPi_mistagpdf', eta,
mistagpdfparams['omega0'],
mistagpdfparams['omegaavg'], mistagpdfparams['f']))
# build mistag calibration
mistagcalibparams = {} # start with parameters of calibration
for sfx in ('p0', 'p1', 'etaavg'):
mistagcalibparams[sfx] = WS(
ws,
RooRealVar('Bs2DsPi_mistagcalib_%s' % sfx,
'Bs2DsPi_mistagpdf_%s' % sfx,
config['MistagCalibParams'][sfx]))
for sfx in ('p0', 'p1'): # float calibration paramters
mistagcalibparams[sfx].setConstant(False)
mistagcalibparams[sfx].setError(0.1)
# build mistag pdf itself
omega = WS(
ws,
MistagCalibration('Bs2DsPi_mistagcalib', 'Bs2DsPi_mistagcalib', eta,
mistagcalibparams['p0'], mistagcalibparams['p1'],
mistagcalibparams['etaavg']))
# build mock decay time error distribution (~ timeerr^6 * exp(-timerr /
# (timerr_av / 7))
terrpdf_shape = WS(
ws,
RooConstVar('timeerr_ac', 'timeerr_ac',
config['DecayTimeResolutionAvg'] / 7.))
terrpdf_truth = WS(
ws, RooTruthModel('terrpdf_truth', 'terrpdf_truth', timeerr))
terrpdf_i0 = WS(
ws,
RooDecay('terrpdf_i0', 'terrpdf_i0', timeerr, terrpdf_shape,
terrpdf_truth, RooDecay.SingleSided))
terrpdf_i1 = WS(
ws,
RooPolynomial('terrpdf_i1', 'terrpdf_i1', timeerr,
RooArgList(zero, zero, zero, zero, zero, zero, one), 0))
terrpdf = WS(ws, RooProdPdf('terrpdf', 'terrpdf', terrpdf_i0, terrpdf_i1))
# build the time pdf
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[omega]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc,
timeerrpdf=terrpdf,
mistagpdf=[mistagpdf],
mistagobs=eta)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
def fitData(fulldata, ibin):
cut = cut_base + '&& (mumuMass*mumuMass > %s && mumuMass*mumuMass < %s)' % (
q2binning[ibin], q2binning[ibin + 1])
data = fulldata.reduce(RooArgSet(tagged_mass, mumuMass, mumuMassE), cut)
fraction = dict_s_rt[ibin] / (dict_s_rt[ibin] + dict_s_wt[ibin])
print 'mistag fraction on MC for bin ', ibin, ' : ', fraction.n, '+/-', fraction.s
### creating RT component
w.loadSnapshot("reference_fit_RT_%s" % ibin)
sigmart1 = w.var("#sigma_{1}^{RT%s}" % ibin)
sigmart2 = w.var("#sigma_{2}^{RT%s}" % ibin)
massrt = w.var("mean^{RT%s}" % ibin)
f1rt = w.var("f^{RT%s}" % ibin)
theRTgauss = w.pdf("doublegaus_RT%s" % ibin)
c_sigma_rt1 = _constrainVar(sigmart1)
c_sigma_rt2 = _constrainVar(sigmart2)
c_mean_rt = _constrainVar(massrt)
c_f1rt = _constrainVar(f1rt)
### creating WT component
w.loadSnapshot("reference_fit_WT_%s" % ibin)
meanwt = w.var("mean^{WT%s}" % ibin)
sigmawt = w.var("#sigma_{CB}^{WT%s}" % ibin)
alphawt1 = w.var("#alpha_{1}^{WT%s}" % ibin)
alphawt2 = w.var("#alpha_{2}^{WT%s}" % ibin)
nwt1 = w.var("n_{1}^{WT%s}" % ibin)
nwt2 = w.var("n_{2}^{WT%s}" % ibin)
theWTgauss = w.pdf("doublecb_%s" % ibin)
c_mean_wt = _constrainVar(meanwt)
c_sigma_wt = _constrainVar(sigmawt)
c_alpha_wt1 = _constrainVar(alphawt1)
c_alpha_wt2 = _constrainVar(alphawt2)
c_n_wt1 = _constrainVar(nwt1)
c_n_wt2 = _constrainVar(nwt2)
### creating constraints for the RT component
c_RTgauss = RooProdPdf(
"c_RTgauss", "c_RTgauss",
RooArgList(theRTgauss, c_sigma_rt1, c_sigma_rt2, c_mean_rt, c_f1rt))
c_vars = RooArgSet(c_sigma_rt1, c_sigma_rt2, c_f1rt, c_mean_rt)
c_vars.add(c_sigma_wt)
c_vars.add(c_mean_wt)
c_vars.add(c_alpha_wt1)
c_vars.add(c_alpha_wt2)
c_vars.add(c_n_wt1)
c_vars.add(c_n_wt2)
### creating constraints for the WT component
c_WTgauss = RooProdPdf(
"c_WTgauss", "c_WTgauss",
RooArgList(theWTgauss, c_alpha_wt1, c_n_wt1, c_sigma_wt, c_mean_wt,
c_alpha_wt2, c_n_wt2))
frt = RooRealVar("F_{RT}", "frt", fraction.n, 0, 1)
signalFunction = RooAddPdf("sumgaus", "rt+wt",
RooArgList(c_RTgauss, c_WTgauss),
RooArgList(frt))
c_frt = RooGaussian("c_frt", "c_frt", frt,
ROOT.RooFit.RooConst(fraction.n),
ROOT.RooFit.RooConst(fraction.s))
c_signalFunction = RooProdPdf("c_signalFunction", "c_signalFunction",
RooArgList(signalFunction, c_frt))
c_vars.add(frt)
### now create background parametrization
slope = RooRealVar("slope", "slope", 0.5, -10, 10)
bkg_exp = RooExponential("bkg_exp", "exponential", slope, tagged_mass)
pol_c1 = RooRealVar("p1", "coeff x^0 term", 0.5, -10, 10)
pol_c2 = RooRealVar("p2", "coeff x^1 term", 0.5, -10, 10)
bkg_pol = RooChebychev("bkg_pol", "2nd order pol", tagged_mass,
RooArgList(pol_c1, pol_c2))
nsig = RooRealVar("Yield", "signal frac", 4000, 0, 1000000)
nbkg = RooRealVar("nbkg", "bkg fraction", 1000, 0, 550000)
# fitFunction = RooAddPdf ("fitfunction" , "fit function" , RooArgList(c_signalFunction, bkg_pol), RooArgList(nsig, nbkg))
fitFunction = RooAddPdf("fitfunction", "fit function",
RooArgList(c_signalFunction, bkg_exp),
RooArgList(nsig, nbkg))
r = fitFunction.fitTo(data, RooFit.Extended(True), RooFit.Save(),
RooFit.Range("full"), RooFit.Verbose(False),
ROOT.RooFit.Constrain(c_vars))
frame = tagged_mass.frame(RooFit.Range("full"))
data.plotOn(frame, RooFit.Binning(35), RooFit.MarkerSize(.7))
fitFunction.plotOn(frame)
drawPdfComponents(fitFunction,
frame,
ROOT.kAzure,
RooFit.NormRange("full"),
RooFit.Range("full"),
isData=True)
parList = RooArgSet(nsig, massrt, sigmart1, sigmart2, f1rt, meanwt,
sigmawt, alphawt1)
parList.add(alphawt2)
parList.add(nwt1)
parList.add(nwt2)
parList.add(frt)
fitFunction.paramOn(frame, RooFit.Parameters(parList),
RooFit.Layout(0.62, 0.86, 0.89))
frame.Draw()
niceFrame(frame, '')
frame.addObject(_writeFitStatus(r))
if not args.year == 'test':
writeCMS(frame, args.year, [q2binning[ibin], q2binning[ibin + 1]])
frame.Draw()
c1.SaveAs('fit_results_mass/save_fit_data_%s_%s_LMNR.pdf' %
(ibin, args.year))
def fitData(fulldata, ibin, n_bkg, w):
cut = cut_base + '&& (mumuMass*mumuMass > %s && mumuMass*mumuMass < %s)' % (
q2binning[ibin], q2binning[ibin + 1])
fulldata_v2 = fulldata.reduce(
RooArgSet(tagged_mass, mumuMass, mumuMassE, randVar), cut)
## reduce to data-like statistics
nDataEntries = fulldata_v2.sumEntries()
nDesired = n_bin[ibin] / nDataEntries
cut = 'rand < %f' % nDesired
signaldata = fulldata_v2.reduce(
RooArgSet(tagged_mass, mumuMass, mumuMassE), cut)
n_realsignal = signaldata.sumEntries()
nrt_mc = _getFittedVar("nRT_%s" % ibin, w)
nwt_mc = _getFittedVar("nWT_%s" % ibin, w)
fraction = nrt_mc / (nrt_mc + nwt_mc)
### creating RT component
w.loadSnapshot("reference_fit_RT_%s" % ibin)
meanrt = w.var("mean^{RT%s}" % ibin)
sigmart = RooRealVar()
sigmart1 = RooRealVar()
sigmart2 = RooRealVar()
alphart1 = RooRealVar()
alphart2 = RooRealVar()
nrt1 = RooRealVar()
nrt2 = RooRealVar()
## double cb fast
if ibin < 5:
sigmart = w.var("#sigma_{CB}^{RT%s}" % ibin)
alphart1 = w.var("#alpha_{1}^{RT%s}" % ibin)
alphart2 = w.var("#alpha_{2}^{RT%s}" % ibin)
nrt1 = w.var("n_{1}^{RT%s}" % ibin)
nrt2 = w.var("n_{2}^{RT%s}" % ibin)
## double cb old
else:
sigmart1 = w.var("#sigma_{CBRT0}^{%s}" % ibin)
sigmart2 = w.var("#sigma_{CBRT1}^{%s}" % ibin)
alphart1 = w.var("#alpha_{RT0}^{%s}" % ibin)
alphart2 = w.var("#alpha_{RT1}^{%s}" % ibin)
nrt1 = w.var("n_{RT0}^{%s}" % ibin)
nrt2 = w.var("n_{RT1}^{%s}" % ibin)
pars_init_vals = {}
theRTgauss = w.pdf("doublecb_RT%s" % ibin)
if ibin < 5:
c_sigma_rt = _constrainVar(sigmart, 1, pars_init_vals)
else:
c_sigma_rt1 = _constrainVar(sigmart1, 1, pars_init_vals)
c_sigma_rt2 = _constrainVar(sigmart2, 1, pars_init_vals)
c_alpha_rt1 = _constrainVar(alphart1, 1, pars_init_vals)
c_alpha_rt2 = _constrainVar(alphart2, 1, pars_init_vals)
c_n_rt1 = _constrainVar(nrt1, 1, pars_init_vals)
c_n_rt2 = _constrainVar(nrt2, 1, pars_init_vals)
### creating WT component
w.loadSnapshot("reference_fit_WT_%s" % ibin)
meanwt = w.var("mean^{WT%s}" % ibin)
sigmawt = w.var("#sigma_{CB}^{WT%s}" % ibin)
alphawt1 = w.var("#alpha_{1}^{WT%s}" % ibin)
alphawt2 = w.var("#alpha_{2}^{WT%s}" % ibin)
nwt1 = w.var("n_{1}^{WT%s}" % ibin)
nwt2 = w.var("n_{2}^{WT%s}" % ibin)
theWTgauss = w.pdf("doublecb_%s" % ibin)
c_sigma_wt = _constrainVar(sigmawt, 1, pars_init_vals)
c_alpha_wt1 = _constrainVar(alphawt1, 1, pars_init_vals)
c_alpha_wt2 = _constrainVar(alphawt2, 1, pars_init_vals)
c_n_wt1 = _constrainVar(nwt1, 1, pars_init_vals)
c_n_wt2 = _constrainVar(nwt2, 1, pars_init_vals)
### creating constraints for the RT component
c_vars = RooArgSet()
if ibin < 5:
c_RTgauss = RooProdPdf(
"c_RTgauss", "c_RTgauss",
RooArgList(theRTgauss, c_alpha_rt1, c_n_rt1, c_sigma_rt,
c_alpha_rt2, c_n_rt2))
c_vars = RooArgSet(c_sigma_rt, c_alpha_rt1, c_alpha_rt2, c_n_rt1,
c_n_rt2)
else:
c_RTgauss = RooProdPdf(
"c_RTgauss", "c_RTgauss",
RooArgList(theRTgauss, c_alpha_rt1, c_n_rt1, c_sigma_rt1,
c_sigma_rt2, c_alpha_rt2, c_n_rt2))
c_vars = RooArgSet(c_sigma_rt1, c_sigma_rt2, c_alpha_rt1, c_alpha_rt2,
c_n_rt1, c_n_rt2)
### creating constraints for the WT component
c_WTgauss = RooProdPdf(
"c_WTgauss", "c_WTgauss",
RooArgList(theWTgauss, c_alpha_wt1, c_n_wt1, c_sigma_wt, c_alpha_wt2,
c_n_wt2))
c_vars.add(c_sigma_wt)
c_vars.add(c_alpha_wt1)
c_vars.add(c_alpha_wt2)
c_vars.add(c_n_wt1)
c_vars.add(c_n_wt2)
frt = RooRealVar("F_{RT}", "frt", fraction.n, 0, 1)
signalFunction = RooAddPdf("sumgaus", "rt+wt",
RooArgList(c_RTgauss, c_WTgauss),
RooArgList(frt))
c_frt = RooGaussian("c_frt", "c_frt", frt,
ROOT.RooFit.RooConst(fraction.n),
ROOT.RooFit.RooConst(fraction.s))
### creating constraints for the difference between the two peaks
deltaPeaks = RooFormulaVar("deltaPeaks", "@0 - @1",
RooArgList(meanrt, meanwt))
c_deltaPeaks = RooGaussian(
"c_deltaPeaks",
"c_deltaPeaks",
deltaPeaks,
ROOT.RooFit.RooConst(deltaPeaks.getVal()),
ROOT.RooFit.RooConst(0.0005) ## value to be checked
)
c_signalFunction = RooProdPdf(
"c_signalFunction", "c_signalFunction",
RooArgList(signalFunction, c_frt, c_deltaPeaks))
c_vars.add(frt)
c_vars.add(deltaPeaks)
### now create background parametrization
slope = RooRealVar("slope", "slope", 0.5, -10, 10)
bkg_exp = RooExponential("bkg_exp", "exponential", slope, tagged_mass)
pol_c1 = RooRealVar("p1", "coeff x^0 term", 0.5, -10, 10)
pol_c2 = RooRealVar("p2", "coeff x^1 term", 0.5, -10, 10)
bkg_pol = RooChebychev("bkg_pol", "2nd order pol", tagged_mass,
RooArgList(pol_c1, pol_c2))
nsig = RooRealVar("Yield", "signal frac", nrt_mc.n + nwt_mc.n, 0, 1000000)
nbkg = RooRealVar("nbkg", "bkg fraction", 1000, 0, 550000)
print nsig.getVal()
fitFunction = RooAddPdf("fitfunction", "fit function",
RooArgList(c_signalFunction, bkg_pol),
RooArgList(nsig, nbkg))
pars_to_tune = [
sigmawt, alphawt1, alphawt2, nwt1, nwt2, alphart1, alphart2, nrt1, nrt2
]
if ibin < 5:
pars_to_tune.append(sigmart)
else:
pars_to_tune.append(sigmart1)
pars_to_tune.append(sigmart2)
## add toy bkg
for itoy in range(args.ntoys):
data = deepcopy(signaldata)
toy_bkg = generateBkg(tagged_mass, ibin, n_bkg)
data.append(toy_bkg)
print 'toy number', itoy
for ipar in pars_to_tune:
ipar.setVal(pars_init_vals[ipar.GetName()])
# r = fitFunction.fitTo(data,
# RooFit.Extended(True),
# RooFit.Range("full"),
# ROOT.RooFit.Constrain(c_vars),
# # ROOT.RooFit.Minimizer("Minuit2","migrad"),
# ROOT.RooFit.Hesse(True),
# ROOT.RooFit.Strategy(2),
# ROOT.RooFit.Minos(False),
# )
# # print 'fit with Hesse strategy 2 done, now Minos'
r = fitFunction.fitTo(
data,
RooFit.Extended(True),
RooFit.Save(),
RooFit.Range("full"),
RooFit.Verbose(False),
ROOT.RooFit.Constrain(c_vars),
# ROOT.RooFit.Minimizer("Minuit2","migrad"),
# ROOT.RooFit.Hesse(True),
ROOT.RooFit.Strategy(2),
ROOT.RooFit.Minos(True),
)
# # r.Print()
# # r.correlationMatrix().Print()
fitStats['data%s_itoy%s' % (ibin, itoy)] = r.status()
covStats['data%s_itoy%s' % (ibin, itoy)] = r.covQual()
frame = tagged_mass.frame(RooFit.Range("full"))
data.plotOn(frame, RooFit.Binning(nbins), RooFit.MarkerSize(.7))
fitFunction.plotOn(frame, RooFit.NormRange("full"),
RooFit.Range("full"))
## evaluate sort of chi2 and save number of RT/WT events
observables = RooArgSet(tagged_mass)
flparams = fitFunction.getParameters(observables)
nparam = int(
flparams.selectByAttrib("Constant", ROOT.kFALSE).getSize())
pdfstring = "fitfunction_Norm[tagged_mass]_Range[full]_NormRange[full]"
chi2s['data%s_itoy%s' % (ibin, itoy)] = frame.chiSquare(
pdfstring, "h_fulldata", nparam)
frame.addObject(_writeChi2(chi2s['data%s_itoy%s' % (ibin, itoy)]))
## save plot only if 1 toy is run
if args.ntoys == 1:
drawPdfComponents(fitFunction,
frame,
ROOT.kAzure,
RooFit.NormRange("full"),
RooFit.Range("full"),
isData=True)
fitFunction.paramOn(frame, RooFit.Layout(0.62, 0.86, 0.89))
# parList = RooArgSet (nsig, nbkg, meanrt, meanwt, alphart1, alphart2, meanwt, sigmawt)
# if ibin < 5 :
# parList.add(sigmart)
# else:
# parList.add(sigmart1)
# parList.add(sigmart2)
# parList.add(alphawt1)
# parList.add(alphawt2)
# parList.add(nwt1)
# parList.add(nwt2)
# parList.add(frt)
# fitFunction.paramOn(frame, RooFit.Parameters(parList), RooFit.Layout(0.62,0.86,0.89))
frame.Draw()
niceFrame(frame, '')
frame.addObject(_writeFitStatus(r))
c1 = ROOT.TCanvas()
upperPad = ROOT.TPad('upperPad', 'upperPad', 0., 0.35, 1., 1.)
lowerPad = ROOT.TPad('lowerPad', 'lowerPad', 0., 0.0, 1., 0.345)
upperPad.SetBottomMargin(0.012)
lowerPad.SetTopMargin(0)
lowerPad.SetBottomMargin(0.2)
upperPad.Draw()
lowerPad.Draw()
upperPad.cd()
frame.Draw()
if not args.year == 'test':
writeCMS(frame, args.year,
[q2binning[ibin], q2binning[ibin + 1]], 1)
frame.Draw()
## add plot of pulls
lowerPad.cd()
hpull = frame.pullHist("h_fulldata", pdfstring)
frame2 = tagged_mass.frame(RooFit.Range("full"), RooFit.Title(''))
frame2.addPlotable(hpull, "P")
niceFrameLowerPad(frame2, 'pull')
frame2.Draw()
line = ROOT.TLine(5.0, 1, 5.6, 1)
line.SetLineColor(ROOT.kGreen + 3)
line.Draw()
for ilog in [True, False]:
upperPad.SetLogy(ilog)
c1.SaveAs(
'fit_results_mass_checkOnMC/toybkg/save_fit_data_%s_%s_nbkg%s_LMNR_Final%s_%s_update_pol2bkg.pdf'
% (ibin, args.year, n_bkg, '_logScale' * ilog, itoy))
out_f.cd()
# r.Write('results_data_%s_ntoy%s'%(ibin,itoy))
## compare nkbg fitted w/ original value
nbkgs['data%s_itoy%s' %
(ibin, itoy)] = (toy_bkg.sumEntries() -
nbkg.getVal()) / toy_bkg.sumEntries()
nsigs['data%s_itoy%s' %
(ibin, itoy)] = (n_realsignal - nsig.getVal()) / n_realsignal
def fitData(fulldata, ibin, w):
cut = cut_base + '&& (mumuMass*mumuMass > %s && mumuMass*mumuMass < %s)'%(q2binning[ibin], q2binning[ibin+1])
data = fulldata.reduce(RooArgSet(tagged_mass,mumuMass,mumuMassE), cut)
nrt_mc = _getFittedVar("nRT_%s"%ibin, w)
nwt_mc = _getFittedVar("nWT_%s"%ibin, w)
fraction = nrt_mc / (nrt_mc + nwt_mc)
print 'mistag fraction on MC for bin ', ibin , ' : ' , fraction.n , '+/-', fraction.s
### creating RT component
w.loadSnapshot("reference_fit_RT_%s"%ibin)
mean_rt = w.var("mean^{RT%s}"%ibin)
sigma_rt1 = w.var("#sigma_{RT1}^{%s}"%ibin)
sigma_rt2 = RooRealVar()
alpha_rt1 = RooRealVar()
alpha_rt2 = RooRealVar()
n_rt1 = RooRealVar()
n_rt2 = RooRealVar()
f1rt = RooRealVar()
## double cb fast
if ibin < 4:
alpha_rt1 = w.var("#alpha_{RT1}^{%s}"%ibin)
alpha_rt2 = w.var("#alpha_{RT2}^{%s}"%ibin)
n_rt1 = w.var("n_{RT1}^{%s}"%ibin)
n_rt2 = w.var("n_{RT2}^{%s}"%ibin)
## double cb old
else:
sigma_rt2 = w.var("#sigma_{RT2}^{%s}"%ibin)
alpha_rt1 = w.var("#alpha_{RT1}^{%s}"%ibin)
alpha_rt2 = w.var("#alpha_{RT2}^{%s}"%ibin)
n_rt1 = w.var("n_{RT1}^{%s}"%ibin)
n_rt2 = w.var("n_{RT2}^{%s}"%ibin)
f1rt = w.var("f^{RT%s}"%ibin)
theRTgauss = w.pdf("doublecb_RT%s"%ibin)
### creating WT component
w.loadSnapshot("reference_fit_WT_%s"%ibin)
mean_wt = w.var("mean_{WT}^{%s}"%ibin)
sigma_wt = w.var("#sigma_{WT1}^{%s}"%ibin)
alpha_wt1 = w.var("#alpha_{WT1}^{%s}"%ibin)
alpha_wt2 = w.var("#alpha_{WT2}^{%s}"%ibin)
n_wt1 = w.var("n_{WT1}^{%s}"%ibin)
n_wt2 = w.var("n_{WT2}^{%s}"%ibin)
theWTgauss = w.pdf("doublecb_%s"%ibin)
### creating variable for the difference between the two peaks
deltaPeaks = RooFormulaVar("deltaPeaks%s"%ibin, "@0 - @1", RooArgList(mean_rt, mean_wt))
frt = RooRealVar ("F_{RT}%s"%ibin , "frt" , fraction.n , 0, 1)
signalFunction = RooAddPdf ("sumgaus%s"%ibin , "rt+wt" , RooArgList(theRTgauss,theWTgauss), RooArgList(frt))
### now create background parametrization
slope = RooRealVar ("slope_%s"%ibin , "slope" , 0.5, -10, 10);
bkg_exp = RooExponential("bkg_exp%s"%ibin , "exponential" , slope, tagged_mass );
pol_c1 = RooRealVar ("p1_%s"%ibin , "coeff x^0 term" , 0.5, -10, 10);
pol_c2 = RooRealVar ("p2_%s"%ibin , "coeff x^1 term" , 0.5, -10, 10);
bkg_pol = RooPolynomial ("bkg_pol%s"%ibin , "2nd order pol" , tagged_mass, RooArgList(pol_c1, pol_c2));
fsig = RooRealVar("fsig%s"%ibin , "fsig" , 0.9, 0, 1);
# nsig = RooRealVar("Yield%s"%ibin , "signal frac" , 1000, 0, 10000);
# nbkg = RooRealVar("nbkg%s"%ibin , "bkg fraction" , 1000, 0, 500000);
nsig = RooRealVar("Yield" , "signal frac" , 600000, 0, 5000000);
nbkg = RooRealVar("nbkg" , "bkg fraction" , 100000, 0, 2000000);
# if ibin==4:
# nsig.setRange(500000,1500000)
# nsig.setVal(900000)
# nbkg.setRange(80000,1000000)
# nbkg.setVal(100000)
### creating constraints
c_vars = RooArgSet()
c_pdfs = RooArgSet()
c_sigma_rt1 = _constrainVar(sigma_rt1, 1)
c_alpha_rt1 = _constrainVar(alpha_rt1, 1)
c_alpha_rt2 = _constrainVar(alpha_rt2, 1)
c_n_rt1 = _constrainVar(n_rt1, 1)
c_n_rt2 = _constrainVar(n_rt2, 1)
c_sigma_wt = _constrainVar(sigma_wt, 1)
c_alpha_wt1 = _constrainVar(alpha_wt1, 1)
c_alpha_wt2 = _constrainVar(alpha_wt2, 1)
c_n_wt1 = _constrainVar(n_wt1, 1)
c_n_wt2 = _constrainVar(n_wt2, 1)
if ibin < 4:
c_pdfs = RooArgSet(c_sigma_rt1, c_alpha_rt1, c_alpha_rt2, c_n_rt1, c_n_rt2)
c_vars = RooArgSet(sigma_rt1, alpha_rt1, alpha_rt2, n_rt1, n_rt2)
else:
c_sigma_rt2 = _constrainVar(sigma_rt2, 1)
c_pdfs = RooArgSet(c_sigma_rt1, c_sigma_rt2, c_alpha_rt1, c_alpha_rt2, c_n_rt1, c_n_rt2)
c_vars = RooArgSet( sigma_rt1, sigma_rt2, alpha_rt1, alpha_rt2, n_rt1, n_rt2)
c_pdfs.add(c_sigma_wt); c_vars.add(sigma_wt)
c_pdfs.add(c_alpha_wt1); c_vars.add(alpha_wt1)
c_pdfs.add(c_alpha_wt2); c_vars.add(alpha_wt2)
c_pdfs.add(c_n_wt1); c_vars.add(n_wt1)
c_pdfs.add(c_n_wt2); c_vars.add(n_wt2)
c_deltaPeaks = RooGaussian("c_deltaPeaks%s"%ibin , "c_deltaPeaks", deltaPeaks, ROOT.RooFit.RooConst( deltaPeaks.getVal() ),
ROOT.RooFit.RooConst( 0.0005 ) ## value to be checked
)
c_pdfs.add(c_deltaPeaks)
c_vars.add(deltaPeaks)
c_frt = RooGaussian("c_frt%s"%ibin , "c_frt" , frt, ROOT.RooFit.RooConst(fraction.n) , ROOT.RooFit.RooConst(frt_sigma[ibin]) )
c_pdfs.add(c_frt)
c_vars.add(frt)
constr_list = RooArgList(c_pdfs)
constr_list.add(signalFunction)
c_signalFunction = RooProdPdf ("c_signalFunction", "c_signalFunction", constr_list)
# mean = RooRealVar ("mass" , "mean" , B0Mass_, 3, 7, "GeV")
# sigma = RooRealVar ("#sigma_{1}" , "sigma" , 0.028, 0, 10, "GeV")
# signalGauss = RooGaussian("signalGauss" , "signal gauss" , tagged_mass, mean,sigma)
#
# sigma2 = RooRealVar ("#sigma_{2}" , "sigma2" , 0.048, 0, 0.07, "GeV")
# signalGauss2 = RooGaussian("signalGauss2" , "signal gauss2" , tagged_mass, mean,sigma2)
# f1 = RooRealVar ("f1" , "f1" , 0.8 , 0., 1.)
# gaus = RooAddPdf ("gaus" , "gaus1+gaus2" , RooArgList(signalGauss,signalGauss2), RooArgList(f1))
# pol_c1 = RooRealVar ("p1" , "coeff x^0 term", 0.5, -10, 10);
# pol_c2 = RooRealVar ("p2" , "coeff x^1 term", 0.5, -10, 10);
# pol_c3 = RooRealVar ("p3" , "coeff x^2 term", 0.5, -10, 10);
# slope = RooRealVar ("slope" , "slope" , 0.5, -10, 10);
# bkg_exp = RooExponential("bkg_exp" , "exponential" , slope, tagged_mass );
# bkg_pol = RooChebychev("bkg_pol" , "2nd order pol" , tagged_mass, RooArgList(pol_c1,pol_c2));
fitFunction = RooAddPdf ("fitfunction%s"%ibin , "fit function" , RooArgList(c_signalFunction, bkg_exp), RooArgList(nsig, nbkg))
# r = fitFunction.fitTo(data,
# # RooFit.Extended(True),
# RooFit.Range("full"),
# ROOT.RooFit.Constrain(c_vars),
# ROOT.RooFit.Minimizer("Minuit2","migrad"),
# ROOT.RooFit.Hesse(True),
# ROOT.RooFit.Strategy(2),
# ROOT.RooFit.Minos(False),
# )
print 'fit with Hesse strategy 2 done, now Minos'
r = fitFunction.fitTo(data,
RooFit.Extended(True),
RooFit.Save(),
RooFit.Range("full"),
RooFit.Verbose(False),
ROOT.RooFit.Constrain(c_vars),
# ROOT.RooFit.Minimizer("Minuit2","migrad"),
# ROOT.RooFit.Hesse(True),
# ROOT.RooFit.Strategy(2),
# ROOT.RooFit.Minos(False),
)
r.Print()
r.correlationMatrix().Print()
fitStats['data%s'%(ibin)] = r.status()
covStats['data%s'%(ibin)] = r.covQual()
frame = tagged_mass.frame( RooFit.Range("full") )
data.plotOn(frame, RooFit.Binning(nbins), RooFit.MarkerSize(.7))
fitFunction.plotOn(frame, RooFit.NormRange("full"), RooFit.Range("full"))
## evaluate sort of chi2 and save number of RT/WT events
observables = RooArgSet(tagged_mass)
flparams = fitFunction.getParameters(observables)
nparam = int(flparams.selectByAttrib("Constant",ROOT.kFALSE).getSize())
pdfstring = "fitfunction%s_Norm[tagged_mass]_Range[full]_NormRange[full]"%ibin
chi2s['data%s'%ibin] = frame.chiSquare(pdfstring, "h_fulldata", nparam)
frame. addObject(_writeChi2( chi2s['data%s'%ibin] ))
drawPdfComponents(fitFunction, frame, ROOT.kAzure, RooFit.NormRange("full"), RooFit.Range("full"), isData = True)
# fitFunction.paramOn(frame, RooFit.Layout(0.62,0.86,0.89))
parList = RooArgSet (nsig, mean_rt, sigma_rt, alpha_rt1, alpha_rt2, n_rt1, n_rt2, mean_wt, sigma_wt)
# parList.add(alphawt1)
# parList.add(alphawt2)
# parList.add(nwt1)
# parList.add(nwt2)
parList.add(frt)
fitFunction.paramOn(frame, RooFit.Parameters(parList), RooFit.Layout(0.62,0.86,0.89))
frame.Draw()
niceFrame(frame, '')
frame. addObject(_writeFitStatus(r))
c1 = ROOT.TCanvas()
upperPad = ROOT.TPad('upperPad' , 'upperPad' , 0., 0.35 , 1., 1. )
lowerPad = ROOT.TPad('lowerPad' , 'lowerPad' , 0., 0.0 , 1., 0.345 )
upperPad.SetBottomMargin(0.012)
lowerPad.SetTopMargin(0)
lowerPad.SetBottomMargin(0.2)
upperPad.Draw(); lowerPad.Draw()
upperPad.cd()
frame.Draw()
if not args.year=='test': writeCMS(frame, args.year, [ q2binning[ibin], q2binning[ibin+1] ], 0)
frame.Draw()
## add plot of pulls
lowerPad.cd()
hpull = frame.pullHist("h_fulldata", pdfstring)
frame2 = tagged_mass.frame(RooFit.Range("full"), RooFit.Title(''))
frame2.addPlotable(hpull,"P")
niceFrameLowerPad(frame2, 'pull')
frame2.Draw()
line = ROOT.TLine(5.0,1,5.6,1)
line.SetLineColor(ROOT.kGreen+3)
line.Draw()
for ilog in [True,False]:
upperPad.SetLogy(ilog)
c1.SaveAs('fit_results_mass/save_fit_data_%s_%s_LMNR_Update%s_newSigmaFRT_pars_Jpsi.pdf'%(ibin, args.year, '_logScale'*ilog))
out_f.cd()
r.Write('results_data_%s'%(ibin))
params = fitFunction.getParameters(RooArgSet(tagged_mass))
out_w.saveSnapshot("reference_fit_data_%s"%(ibin),params,ROOT.kTRUE)
getattr(out_w, 'import')(fitFunction)
def get_fitted_normalisation_from_ROOT(channel, input_files, variable,
met_type, b_tag_bin):
results = {}
initial_values = {}
templates = {}
for variable_bin in variable_bins_ROOT[variable]:
histograms = get_histograms(channel,
input_files,
variable=variable,
met_type=met_type,
variable_bin=variable_bin,
b_tag_bin=b_tag_bin,
rebin=measurement_config.rebin)
# create signal histograms
h_eta_signal = histograms['TTJet'] + histograms['SingleTop']
N_ttbar_before_fit = histograms['TTJet'].Integral()
N_SingleTop_before_fit = histograms['SingleTop'].Integral()
N_vjets_before_fit = histograms['V+Jets'].Integral()
N_qcd_before_fit = histograms['QCD'].Integral()
N_signal_before_fit = N_ttbar_before_fit + N_SingleTop_before_fit
N_ttbar_error_before_fit = sum(histograms['TTJet'].errors())
N_SingleTop_error_before_fit = sum(histograms['SingleTop'].errors())
N_vjets_error_before_fit = sum(histograms['V+Jets'].errors())
N_QCD_error_before_fit = sum(histograms['QCD'].errors())
if (N_SingleTop_before_fit != 0):
TTJet_SingleTop_ratio = N_ttbar_before_fit / N_SingleTop_before_fit
else:
print 'Bin ', variable_bin, ': ttbar/singleTop ratio undefined for %s channel! Setting to 0.' % channel
TTJet_SingleTop_ratio = 0
leptonAbsEta = RooRealVar("leptonAbsEta", "leptonAbsEta", 0., 2.4)
# this has to move to dps.utils.Fitting.py
vars = RooArgList()
vars.add(leptonAbsEta)
vars_set = RooArgSet()
vars_set.add(leptonAbsEta)
n_event_obs = histograms['data'].Integral()
lowerBound = 0.
upperBound = n_event_obs + 10 * sqrt(n_event_obs)
n_init = n_event_obs / 2.
data = RooDataHist("data", "dataset with leptonAbsEta", vars,
histograms['data'])
rh_vj = RooDataHist("rh_vj", "vj", vars, histograms['V+Jets'])
rh_qcd = RooDataHist("rh_qcd", "qcd", vars, histograms['QCD'])
rh_signal = RooDataHist("rh_signal", "signal", vars, h_eta_signal)
pdf_vj = RooHistPdf("pdf_vj", "V+Jets pdf", vars_set, rh_vj, 0)
pdf_qcd = RooHistPdf("pdf_qcd", "QCD pdf ", vars_set, rh_qcd, 0)
pdf_signal = RooHistPdf("pdf_signal", "single top pdf", vars_set,
rh_signal, 0)
# RooRealVar(const char *name, const char *title, Double_t value, Double_t minValue, Double_t maxValue, const char *unit) :
nSignal = RooRealVar("nSignal", "number of single top + ttbar events",
N_signal_before_fit, lowerBound, upperBound,
"event")
nvj = RooRealVar("nvj", "number of V+Jets bgnd events",
N_vjets_before_fit, lowerBound, upperBound, "event")
nqcd = RooRealVar("nqcd", "number of QCD bgnd events",
N_QCD_error_before_fit, lowerBound, upperBound,
"event")
model = RooAddPdf("model", "sig+vj+qcd",
RooArgList(pdf_signal, pdf_vj, pdf_qcd),
RooArgList(nSignal, nvj, nqcd))
vj_constraint = RooGaussian("nvj_constraint", "nvj_constraint", nvj,
RooFit.RooConst(N_vjets_before_fit),
RooFit.RooConst(0.5 * N_vjets_before_fit))
qcd_constraint = RooGaussian("nqcd_constraint", "nqcd_constraint",
nqcd, RooFit.RooConst(N_qcd_before_fit),
RooFit.RooConst(2 * N_qcd_before_fit))
model_with_constraints = RooProdPdf(
"model_with_constraints", "model with gaussian constraints",
RooArgSet(model, vj_constraint, qcd_constraint), RooLinkedList())
model_with_constraints.fitTo(data, RooFit.Minimizer(
"Minuit2",
"Migrad")) #WARNING: number of cores changes the results!!!
# nll = model.createNLL(data, RooFit.NumCPU(2))
# RooMinuit(nll).migrad()
# frame1 = nSignal.frame(RooFit.Bins(100), RooFit.Range(lowerBound, n_event_obs), RooFit.Title("LL and profileLL in nSignal"))
# nll.plotOn(frame1, RooFit.ShiftToZero())
# frame2 = nvj.frame(RooFit.Bins(100), RooFit.Range(lowerBound, n_event_obs), RooFit.Title("LL and profileLL in nvj"))
# nll.plotOn(frame2, RooFit.ShiftToZero())
# frame3 = nqcd.frame(RooFit.Bins(100), RooFit.Range(lowerBound, n_event_obs), RooFit.Title("LL and profileLL in nqcd"))
# nll.plotOn(frame3, RooFit.ShiftToZero())
#
# pll_nSignal = nll.createProfile(nSignal)
# pll_nSignal.plotOn(frame1, RooFit.LineColor(2))
# frame1.SetMinimum(0)
# frame1.SetMaximum(3)
#
# pll_nvj = nll.createProfile(nvj)
# pll_nvj.plotOn(frame2, RooFit.LineColor(2))
# frame2.SetMinimum(0)
# frame2.SetMaximum(3)
#
# pll_nqcd = nll.createProfile(nqcd)
# pll_nqcd.plotOn(frame3, RooFit.LineColor(2))
# frame3.SetMinimum(0)
# frame3.SetMaximum(3)
# c = TCanvas("profilell","profilell",1200, 400)
# c.Divide(3)
# c.cd(1)
# frame1.Draw()
# c.cd(2)
# frame2.Draw()
# c.cd(3)
# frame3.Draw()
# c.SaveAs('profileLL.png')
# model.fitTo(data, RooFit.Minimizer("Minuit2", "Migrad"), RooFit.NumCPU(1))#WARNING: number of cores changes the results!!!
fit_results = {}
fit_results['signal'] = (nSignal.getVal(), nSignal.getError())
fit_results['QCD'] = ufloat(nqcd.getVal(), nqcd.getError())
fit_results['V+Jets'] = ufloat(nvj.getVal(), nvj.getError())
N_ttbar, N_SingleTop = decombine_result(fit_results['signal'],
TTJet_SingleTop_ratio)
fit_results['signal'] = ufloat(nSignal.getVal(), nSignal.getError())
fit_results['TTJet'] = ufloat(N_ttbar)
fit_results['SingleTop'] = ufloat(N_SingleTop)
if results == {}: # empty
for sample in fit_results.keys():
results[sample] = [fit_results[sample]]
else:
for sample in fit_results.keys():
results[sample].append(fit_results[sample])
return results, None, None
def fitData(fulldata, ibin, nRT_fromMC, nWT_fromMC):
cut = cut_base + '&& (mumuMass*mumuMass > %s && mumuMass*mumuMass < %s)' % (
q2binning[ibin], q2binning[ibin + 1])
fulldata_v2 = fulldata.reduce(
RooArgSet(tagged_mass, mumuMass, mumuMassE, randVar), cut)
## reduce to data-like statistics
nDataEntries = fulldata_v2.sumEntries()
nDesired = n_bin[ibin] / nDataEntries
cut = 'rand < %f' % nDesired
data = fulldata_v2.reduce(RooArgSet(tagged_mass, mumuMass, mumuMassE), cut)
fraction = dict_s_rt[ibin] / (dict_s_rt[ibin] + dict_s_wt[ibin])
print 'mistag fraction on MC for bin ', ibin, ' : ', fraction.n, '+/-', fraction.s
### creating RT component
w.loadSnapshot("reference_fit_RT_%s" % ibin)
mean_rt = w.var("mean_{RT}^{%s}" % ibin)
sigma_rt1 = w.var("#sigma_{RT1}^{%s}" % ibin)
sigma_rt2 = RooRealVar()
alpha_rt1 = RooRealVar()
alpha_rt2 = RooRealVar()
n_rt1 = RooRealVar()
n_rt2 = RooRealVar()
f1rt = RooRealVar()
## double cb fast
if ibin < 4:
alpha_rt1 = w.var("#alpha_{RT1}^{%s}" % ibin)
alpha_rt2 = w.var("#alpha_{RT2}^{%s}" % ibin)
n_rt1 = w.var("n_{RT1}^{%s}" % ibin)
n_rt2 = w.var("n_{RT2}^{%s}" % ibin)
## double cb old
else:
sigma_rt2 = w.var("#sigma_{RT2}^{%s}" % ibin)
alpha_rt1 = w.var("#alpha_{RT1}^{%s}" % ibin)
alpha_rt2 = w.var("#alpha_{RT2}^{%s}" % ibin)
n_rt1 = w.var("n_{RT1}^{%s}" % ibin)
n_rt2 = w.var("n_{RT2}^{%s}" % ibin)
f1rt = w.var("f^{RT%s}" % ibin)
theRTgauss = w.pdf("doublecb_RT%s" % ibin)
### creating WT component
w.loadSnapshot("reference_fit_WT_%s" % ibin)
mean_wt = w.var("mean_{WT}^{%s}" % ibin)
sigma_wt = w.var("#sigma_{WT1}^{%s}" % ibin)
alpha_wt1 = w.var("#alpha_{WT1}^{%s}" % ibin)
alpha_wt2 = w.var("#alpha_{WT2}^{%s}" % ibin)
n_wt1 = w.var("n_{WT1}^{%s}" % ibin)
n_wt2 = w.var("n_{WT2}^{%s}" % ibin)
theWTgauss = w.pdf("doublecb_%s" % ibin)
### creating variable for the difference between the two peaks
deltaPeaks = RooFormulaVar("deltaPeaks", "@0 - @1",
RooArgList(mean_rt, mean_wt))
frt = RooRealVar("F_{RT}", "frt", fraction.n, 0, 1)
signalFunction = RooAddPdf("sumgaus", "rt+wt",
RooArgList(theRTgauss, theWTgauss),
RooArgList(frt))
### now create background parametrization
slope = RooRealVar("slope", "slope", 0.5, -10, 10)
bkg_exp = RooExponential("bkg_exp", "exponential", slope, tagged_mass)
pol_c1 = RooRealVar("p1", "coeff x^0 term", 0.5, -10, 10)
pol_c2 = RooRealVar("p2", "coeff x^1 term", 0.5, -10, 10)
bkg_pol = RooChebychev("bkg_pol", "2nd order pol", tagged_mass,
RooArgList(pol_c1))
nsig = RooRealVar("Yield", "signal frac", nRT_fromMC + nWT_fromMC, 0,
1000000)
nbkg = RooRealVar("nbkg", "bkg fraction", 1000, 0, 550000)
print nsig.getVal()
### creating constraints
c_vars = RooArgSet()
c_pdfs = RooArgSet()
c_sigma_rt1 = _constrainVar(sigma_rt1, 1)
c_alpha_rt1 = _constrainVar(alpha_rt1, 1)
c_alpha_rt2 = _constrainVar(alpha_rt2, 1)
c_n_rt1 = _constrainVar(n_rt1, 1)
c_n_rt2 = _constrainVar(n_rt2, 1)
c_sigma_wt = _constrainVar(sigma_wt, 1)
c_alpha_wt1 = _constrainVar(alpha_wt1, 1)
c_alpha_wt2 = _constrainVar(alpha_wt2, 1)
c_n_wt1 = _constrainVar(n_wt1, 1)
c_n_wt2 = _constrainVar(n_wt2, 1)
if ibin < 4:
c_pdfs = RooArgSet(c_sigma_rt1, c_alpha_rt1, c_alpha_rt2, c_n_rt1,
c_n_rt2)
c_vars = RooArgSet(sigma_rt1, alpha_rt1, alpha_rt2, n_rt1, n_rt2)
else:
c_sigma_rt2 = _constrainVar(sigma_rt2, 1)
c_f1rt = _constrainVar(f1rt, 1)
c_pdfs = RooArgSet(c_sigma_rt1, c_sigma_rt2, c_alpha_rt1, c_alpha_rt2,
c_n_rt1, c_n_rt2, c_f1rt)
c_vars = RooArgSet(sigma_rt1, sigma_rt2, alpha_rt1, alpha_rt2, n_rt1,
n_rt2, f1rt)
c_pdfs.add(c_sigma_wt)
c_vars.add(sigma_wt)
c_pdfs.add(c_alpha_wt1)
c_vars.add(alpha_wt1)
c_pdfs.add(c_alpha_wt2)
c_vars.add(alpha_wt2)
c_pdfs.add(c_n_wt1)
c_vars.add(n_wt1)
c_pdfs.add(c_n_wt2)
c_vars.add(n_wt2)
### creating constraints for the difference between the two peaks
c_deltaPeaks = RooGaussian(
"c_deltaPeaks",
"c_deltaPeaks",
deltaPeaks,
ROOT.RooFit.RooConst(deltaPeaks.getVal()),
ROOT.RooFit.RooConst(0.0005) ## value to be checked
)
c_pdfs.add(c_deltaPeaks)
c_vars.add(deltaPeaks)
c_frt = RooGaussian("c_frt%s" % ibin, "c_frt", frt,
ROOT.RooFit.RooConst(fraction.n),
ROOT.RooFit.RooConst(frt_sigma[ibin]))
c_pdfs.add(c_frt)
c_vars.add(frt)
constr_list = RooArgList(c_pdfs)
constr_list.add(signalFunction)
c_signalFunction = RooProdPdf("c_signalFunction", "c_signalFunction",
constr_list)
fitFunction = c_signalFunction
r = fitFunction.fitTo(
data,
# RooFit.Extended(True),
RooFit.Range("full"),
ROOT.RooFit.Constrain(c_vars),
ROOT.RooFit.Minimizer("Minuit2", "migrad"),
ROOT.RooFit.Hesse(True),
ROOT.RooFit.Strategy(2),
ROOT.RooFit.Minos(False),
)
print 'fit with Hesse strategy 2 done, now Minos'
r = fitFunction.fitTo(
data,
# RooFit.Extended(True),
RooFit.Save(),
RooFit.Range("full"),
RooFit.Verbose(False),
ROOT.RooFit.Constrain(c_vars),
# ROOT.RooFit.Minimizer("Minuit2","migrad"),
# ROOT.RooFit.Hesse(True),
ROOT.RooFit.Strategy(2),
ROOT.RooFit.Minos(True),
)
r.Print()
r.correlationMatrix().Print()
fitStats['data%s' % (ibin)] = r.status()
covStats['data%s' % (ibin)] = r.covQual()
frame = tagged_mass.frame(RooFit.Range("full"))
data.plotOn(frame, RooFit.Binning(nbins), RooFit.MarkerSize(.7))
fitFunction.plotOn(frame, RooFit.NormRange("full"), RooFit.Range("full"))
## evaluate sort of chi2 and save number of RT/WT events
observables = RooArgSet(tagged_mass)
flparams = fitFunction.getParameters(observables)
nparam = int(flparams.selectByAttrib("Constant", ROOT.kFALSE).getSize())
pdfstring = "c_signalFunction_Norm[tagged_mass]_Range[full]_NormRange[full]"
chi2s['data%s' % ibin] = frame.chiSquare(pdfstring, "h_fulldata", nparam)
frame.addObject(_writeChi2(chi2s['data%s' % ibin]))
drawPdfComponents(fitFunction,
frame,
ROOT.kAzure,
RooFit.NormRange("full"),
RooFit.Range("full"),
isData=True)
parList = RooArgSet(nsig, mean_rt, mean_wt, sigma_rt1, sigma_rt2,
alpha_rt1, alpha_rt2, mean_wt, sigma_wt)
parList.add(alpha_wt1)
parList.add(alpha_wt2)
parList.add(n_wt1)
parList.add(n_wt2)
parList.add(frt)
fitFunction.paramOn(frame, RooFit.Parameters(parList),
RooFit.Layout(0.62, 0.86, 0.89))
frame.Draw()
niceFrame(frame, '')
frame.addObject(_writeFitStatus(r))
c1 = ROOT.TCanvas()
upperPad = ROOT.TPad('upperPad', 'upperPad', 0., 0.35, 1., 1.)
lowerPad = ROOT.TPad('lowerPad', 'lowerPad', 0., 0.0, 1., 0.345)
upperPad.SetBottomMargin(0.012)
lowerPad.SetTopMargin(0)
lowerPad.SetBottomMargin(0.2)
upperPad.Draw()
lowerPad.Draw()
upperPad.cd()
frame.Draw()
if not args.year == 'test':
writeCMS(frame, args.year, [q2binning[ibin], q2binning[ibin + 1]], 1)
frame.Draw()
## add plot of pulls
lowerPad.cd()
hpull = frame.pullHist("h_fulldata", pdfstring)
frame2 = tagged_mass.frame(RooFit.Range("full"), RooFit.Title(''))
frame2.addPlotable(hpull, "P")
niceFrameLowerPad(frame2, 'pull')
frame2.Draw()
line = ROOT.TLine(5.0, 1, 5.6, 1)
line.SetLineColor(ROOT.kGreen + 3)
line.Draw()
for ilog in [True, False]:
upperPad.SetLogy(ilog)
c1.SaveAs(
'fit_results_mass_checkOnMC/save_fit_data_%s_%s_LMNR_newSigmaFRT_%s.pdf'
% (ibin, args.year, '_logScale' * ilog))
out_f.cd()
r.Write('results_data_%s' % (ibin))
params = fitFunction.getParameters(RooArgSet(tagged_mass))
w.saveSnapshot("reference_fit_data_%s" % (ibin), params, ROOT.kTRUE)
getattr(w, 'import')(fitFunction)
# Get tree
rfile = get_file('data/smalltree-new-MC.root', 'read')
ftree = get_object('ftree', rfile)
# dt dataset for denom
trigger = 'HLT2Topo3BodyTOS'
triggerVar = RooRealVar(trigger, trigger, 0, 2)
cut = trigger+'>0'
tmpdtdataset = RooDataSet('dtdataset', 'dt dataset', RooArgSet(dt, triggerVar),
RooFit.Import(ftree), RooFit.Cut(cut))
dtdatahist = tmpdtdataset.binnedClone('dtdatahist', 'Binned dt')
dtdatahist = dtdatahist.reduce(dtargset)
errorPdf = RooHistPdf('errorPdf', 'Time error Hist PDF', dtargset, dtdatahist)
decaywdt = RooProdPdf('decaywdt', 'Decay function with dt distribution',
RooArgSet(errorPdf),
RooFit.Conditional(decayargset, timeargset))
del tmpdtdataset, dtdatahist
# Variable width binning
nbins1 = 50
nbins2 = 24
binedges1 = numpy.zeros(nbins1+1 , dtype=float)
binedges2 = numpy.zeros(nbins2+1 , dtype=float)
logwidth = log10((1E-2 + 2E-4) / 2E-4)
for i in range(nbins1+1):
# binedges1[i] = 10**(log10(2E-4) + i*logwidth/nbins1)
binedges1[i] = epsilon + i*1E-2/nbins1
# print '%2d: %e' % (i, binedges1[i])
