# B mass pdf
from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as Signal_BMass, LP2011_Background_Mass as Background_BMass
sig_m = Signal_BMass(Name = 'sig_m', mass = m, m_sig_mean = dict(Value = 5365, MinMax = (5363,5372)))
# J/psi mass pdf
mpsi_mean = RealVar('mpsi_mean', Unit = 'MeV', Value = 3097, MinMax = (3070, 3110))
mpsi_sigma = RealVar('mpsi_sigma', Unit = 'MeV', Value = 10, MinMax = (5, 20))
mpsi_alpha = RealVar('mpsi_alpha', Unit = '', Value = 1.36, MinMax = (0.5, 3))
mpsi_n = RealVar('mpsi_n', Unit = '', Value = 2, MinMax = (0.1, 4), Constant = True)
psi_m = Pdf(Name = 'psi_m', Type = CrystalBall, Parameters = [mpsi, mpsi_mean, mpsi_sigma, mpsi_alpha, mpsi_n])
# J/psi background
psi_c = RealVar( 'psi_c', Unit = '1/MeV', Value = -0.0004, MinMax = (-0.1, -0.0000001))
bkg_mpsi = Pdf(Name = 'bkg_mpsi', Type = Exponential, Parameters = [mpsi, psi_c])
# Create combinatorical background component
bkg_m = Background_BMass( Name = 'bkg_m', mass = m, m_bkg_exp = dict( Name = 'm_bkg_exp' ) )
background = Component('background', (bkg_m.pdf(), bkg_mpsi), Yield = (100000,100,500000) )
psi_background = Component('psi_background', (bkg_m.pdf(), psi_m), Yield= (200000,500,500000) )
from P2VV.Utilities.DataHandling import readData
tree_name = 'DecayTree'
prefix = '/stuff/PhD' if os.path.exists('/stuff') else '/bfys/raaij'
if dataSample == '2011':
input_file = os.path.join(prefix, 'p2vv/data/Bs2JpsiPhi_ntupleB_for_fitting_20121012_MagDownMagUp.root')
elif dataSample == '2012':
input_file = os.path.join(prefix, '/stuff/PhD/p2vv/data/Bs2JpsiPhi_2012_ntupleB_20121212.root')
else:
raise RuntimeError
cut = 'runNumber > 0 && sel == 1 && sel_cleantail == 1 && (hlt1_biased == 1 || hlt1_unbiased_dec == 1) && hlt2_biased == 1 && '
def __init__(self, time, masses, t_diff = None, MassResult = None,
InputFile = "/bfys/raaij/p2vv/data/Bs2JpsiPhiPrescaled_2011.root",
Workspace = 'Bs2JpsiPhiPrescaled_2011_workspace', Data = 'data',
UseKeysPdf = False, Weights = 'B', Draw = False, Reweigh = {}):
assert(Weights in ShapeBuilder.__weights)
self.__weights = Weights
self.__time = time
self.__t_diff = t_diff
pos = Workspace.find('201')
self.__year = Workspace[pos : pos + 4]
self.__input_ws = None
self.__ws = RooObject().ws()
self.__shapes = {}
self.__diff__shapes = {}
self.__masses = masses
self._sig = Component('wpv_signal', [], Yield = (1000, 10, 5e5))
self._psi = Component('wpv_jpsi', [], Yield = (5000, 10, 5e5))
self._bkg = Component('wpv_bkg', [], Yield = (5000, 10, 5e5))
if 'B' in masses:
## m_sig_mean = RealVar('wpv_m_sig_mean', Unit = 'MeV', Value = 5365, MinMax = (5363, 5372))
## m_sig_sigma = RealVar('wpv_m_sig_sigma', Unit = 'MeV', Value = 10, MinMax = (1, 20))
## from ROOT import RooGaussian as Gaussian
## self._sig_mass = Pdf(Name = 'wpv_sig_m', Type = Gaussian, Parameters = (masses['B'], m_sig_mean, m_sig_sigma ))
self._sig_mass = BMassPdf(masses['B'], Name = 'wpv_sig_mass', ParNamePrefix = "wpv",
AvSigParameterisation = True)
self._bkg_mass = BBkgPdf(masses['B'], Name = 'wpv_bkg_mass', ParNamePrefix = "wpv",
m_bkg_exp = dict(Name = 'm_bkg_exp', Value = -0.0017,
MinMax = (-0.01, -0.00001)))
self._sig[masses['B']] = self._sig_mass.pdf()
self._psi[masses['B']] = self._bkg_mass.pdf()
self._bkg[masses['B']] = self._bkg_mass.pdf()
if 'jpsi' in masses:
self._sig_mpsi = PsiMassPdf(masses['jpsi'], Name = 'wpv_sig_mpsi', ParNamePrefix = "wpv")
self._bkg_mpsi = PsiBkgPdf(masses['jpsi'], Name = 'wpv_bkg_mpsi', ParNamePrefix = "wpv")
self._sig[masses['jpsi']] = self._sig_mpsi.pdf()
self._psi[masses['jpsi']] = self._sig_mpsi.pdf()
self._bkg[masses['jpsi']] = self._bkg_mpsi.pdf()
self.__components = {'jpsi' : dict(jpsi = self._psi, bkg = self._bkg),
'B' : dict(B = self._sig, bkg = self._bkg),
'both' : dict(B = self._sig, jpsi = self._psi, bkg = self._bkg)}
self.__pdf = buildPdf(self.__components[Weights].values(), Observables = masses.values(),
Name = 'wpv_mass_pdf')
if MassResult:
## Use the provided mass result to set all the parameter values, only float the yields
pdf_params = self.__pdf.getParameters(RooArgSet(*masses.values()))
for p in MassResult.floatParsFinal():
## ignore yields
if any((p.GetName().startswith(n) for n in ['N_', 'mpsi_c'])):
continue
## Find pdf parameter, add "wpv_" prefix
pdf_p = pdf_params.find('wpv_' + p.GetName())
if pdf_p:
pdf_p.setVal(p.getVal())
pdf_p.setError(p.getError())
pdf_p.setConstant(True)
self.__pdf.Print("t")
from ROOT import TFile
input_file = TFile.Open(InputFile)
if not input_file or not input_file.IsOpen():
raise OSError
if Workspace:
self.__input_ws = input_file.Get(Workspace)
if not self.__input_ws:
print 'Cannot find workspace %s in mixing file.' % Workspace
raise RuntimeError
self._data = self.__input_ws.data(Data)
if not self._data:
print 'Cannot find data in workspace %s.' % Workspace
raise RuntimeError
else:
self._data = input_file.Get(Data)
if not self._data.get().find(time.GetName()) and "refit" in time.GetName():
from ROOT import RooFormulaVar, RooArgList
def __add_alias(name, obs):
obs_name = obs.GetName()[:-6]
do = self._data.get().find(obs_name)
rf = RooFormulaVar(name, name, "@0", RooArgList(do))
a = self._data.addColumn(rf)
a.setMin(obs.getMin())
a.setMax(obs.getMax())
return a
## Add refit observables
time = __add_alias("time_refit", time)
self.__time = time
self.__time.Print()
if t_diff:
t_diff = __add_alias("time_diff_refit", t_diff)
self.__t_diff = t_diff
if t_diff:
self._data = self._data.reduce("{0} > {1} && {0} < {2} && {3} > {4} && {3} < {5}".format(time.GetName(), time.getMin(), time.getMax(), t_diff.GetName(), t_diff.getMin(), t_diff.getMax()))
else:
self._data = self._data.reduce("{0} > {1} && {0} < {2}".format(time.GetName(), time.getMin(), time.getMax()))
# self._data = self._data.reduce("mass > 5348 && mass < 5388")
fitOpts = dict(NumCPU = 4, Save = True, Minimizer = 'Minuit2', Optimize = 2)
self.__result = self.__pdf.fitTo(self._data, **fitOpts)
from P2VV.Utilities.SWeights import SData
for p in self.__pdf.Parameters(): p.setConstant(not p.getAttribute('Yield'))
splot = SData(Pdf = self.__pdf, Data = self._data, Name = 'MixingMassSplot')
self.__sdatas = {}
self.__reweigh_weights = {}
for key, c in self.__components[Weights].iteritems():
sdata = splot.data(c.GetName())
if 'Data' in Reweigh and key in Reweigh['Data']:
from array import array
from ROOT import RooBinning
source = Reweigh['Data'][key]
binning = Reweigh['Binning']
if type(binning) == array:
binning = RooBinning(len(binning) - 1, binning)
binning.SetName('reweigh')
Reweigh['DataVar'].setBinning(binning, 'reweigh')
source_obs = source.get().find('nPV')
cat_name = 'nPVs_' + key
source_cat = source.get().find(cat_name)
if source_cat:
# Remove previous weights to make sure we get it right
new_vars = source.get()
new_vars.remove(source_cat)
source = source.reduce(new_vars)
source_obs = source.get().find(source_obs.GetName())
source_obs.setBinning(binning, 'reweigh')
source_cat = BinningCategory(Name = cat_name, Observable = source_obs,
Binning = binning, Data = source, Fundamental = True)
from P2VV.Reweighing import reweigh
sdata, weights = reweigh(sdata, sdata.get().find('nPV'),
source, source_cat)
self.__reweigh_weights[key] = weights
sdata = self.__ws.put(sdata)
self.__sdatas[c] = sdata
rho_keys = dict((v, k) for k, v in self.__components[Weights].iteritems())
self.__shapes = {}
self.__diff_shapes = {}
for c, sdata in self.__sdatas.iteritems():
if UseKeysPdf:
rk = rho_keys[c]
rho = ShapeBuilder.__rho[rk] if rk in ShapeBuilder.__rho else 1.
time_shape = KeysPdf(Name = 'wpv_%s_pdf' % c.GetName(), Observable = time, Data = sdata, Rho = rho)
if t_diff:
diff_shape = KeysPdf(Name = 'wpv_%s_diff_pdf' % c.GetName(), Observable = t_diff, Data = sdata)
else:
time_shape = HistPdf(Name = 'wpv_%s_pdf' % c.GetName(), Observables = [time],
Data = sdata, Binning = {time : 35})
if t_diff:
diff_shape = HistPdf(Name = 'wpv_%s_diff_pdf' % c.GetName(), Observables = [t_diff],
Data = sdata, Binning = {time : 35})
self.__shapes[c] = time_shape
if t_diff:
self.__diff_shapes[c] = diff_shape
if Draw:
self.__draw()
class ShapeBuilder(object):
__weights = set(('jpsi', 'B', 'both'))
__rho = dict(B = 2., jpsi = 2.)
def __init__(self, time, masses, t_diff = None, MassResult = None,
InputFile = "/bfys/raaij/p2vv/data/Bs2JpsiPhiPrescaled_2011.root",
Workspace = 'Bs2JpsiPhiPrescaled_2011_workspace', Data = 'data',
UseKeysPdf = False, Weights = 'B', Draw = False, Reweigh = {}):
assert(Weights in ShapeBuilder.__weights)
self.__weights = Weights
self.__time = time
self.__t_diff = t_diff
pos = Workspace.find('201')
self.__year = Workspace[pos : pos + 4]
self.__input_ws = None
self.__ws = RooObject().ws()
self.__shapes = {}
self.__diff__shapes = {}
self.__masses = masses
self._sig = Component('wpv_signal', [], Yield = (1000, 10, 5e5))
self._psi = Component('wpv_jpsi', [], Yield = (5000, 10, 5e5))
self._bkg = Component('wpv_bkg', [], Yield = (5000, 10, 5e5))
if 'B' in masses:
## m_sig_mean = RealVar('wpv_m_sig_mean', Unit = 'MeV', Value = 5365, MinMax = (5363, 5372))
## m_sig_sigma = RealVar('wpv_m_sig_sigma', Unit = 'MeV', Value = 10, MinMax = (1, 20))
## from ROOT import RooGaussian as Gaussian
## self._sig_mass = Pdf(Name = 'wpv_sig_m', Type = Gaussian, Parameters = (masses['B'], m_sig_mean, m_sig_sigma ))
self._sig_mass = BMassPdf(masses['B'], Name = 'wpv_sig_mass', ParNamePrefix = "wpv",
AvSigParameterisation = True)
self._bkg_mass = BBkgPdf(masses['B'], Name = 'wpv_bkg_mass', ParNamePrefix = "wpv",
m_bkg_exp = dict(Name = 'm_bkg_exp', Value = -0.0017,
MinMax = (-0.01, -0.00001)))
self._sig[masses['B']] = self._sig_mass.pdf()
self._psi[masses['B']] = self._bkg_mass.pdf()
self._bkg[masses['B']] = self._bkg_mass.pdf()
if 'jpsi' in masses:
self._sig_mpsi = PsiMassPdf(masses['jpsi'], Name = 'wpv_sig_mpsi', ParNamePrefix = "wpv")
self._bkg_mpsi = PsiBkgPdf(masses['jpsi'], Name = 'wpv_bkg_mpsi', ParNamePrefix = "wpv")
self._sig[masses['jpsi']] = self._sig_mpsi.pdf()
self._psi[masses['jpsi']] = self._sig_mpsi.pdf()
self._bkg[masses['jpsi']] = self._bkg_mpsi.pdf()
self.__components = {'jpsi' : dict(jpsi = self._psi, bkg = self._bkg),
'B' : dict(B = self._sig, bkg = self._bkg),
'both' : dict(B = self._sig, jpsi = self._psi, bkg = self._bkg)}
self.__pdf = buildPdf(self.__components[Weights].values(), Observables = masses.values(),
Name = 'wpv_mass_pdf')
if MassResult:
## Use the provided mass result to set all the parameter values, only float the yields
pdf_params = self.__pdf.getParameters(RooArgSet(*masses.values()))
for p in MassResult.floatParsFinal():
## ignore yields
if any((p.GetName().startswith(n) for n in ['N_', 'mpsi_c'])):
continue
## Find pdf parameter, add "wpv_" prefix
pdf_p = pdf_params.find('wpv_' + p.GetName())
if pdf_p:
pdf_p.setVal(p.getVal())
pdf_p.setError(p.getError())
pdf_p.setConstant(True)
self.__pdf.Print("t")
from ROOT import TFile
input_file = TFile.Open(InputFile)
if not input_file or not input_file.IsOpen():
raise OSError
if Workspace:
self.__input_ws = input_file.Get(Workspace)
if not self.__input_ws:
print 'Cannot find workspace %s in mixing file.' % Workspace
raise RuntimeError
self._data = self.__input_ws.data(Data)
if not self._data:
print 'Cannot find data in workspace %s.' % Workspace
raise RuntimeError
else:
self._data = input_file.Get(Data)
if not self._data.get().find(time.GetName()) and "refit" in time.GetName():
from ROOT import RooFormulaVar, RooArgList
def __add_alias(name, obs):
obs_name = obs.GetName()[:-6]
do = self._data.get().find(obs_name)
rf = RooFormulaVar(name, name, "@0", RooArgList(do))
a = self._data.addColumn(rf)
a.setMin(obs.getMin())
a.setMax(obs.getMax())
return a
## Add refit observables
time = __add_alias("time_refit", time)
self.__time = time
self.__time.Print()
if t_diff:
t_diff = __add_alias("time_diff_refit", t_diff)
self.__t_diff = t_diff
if t_diff:
self._data = self._data.reduce("{0} > {1} && {0} < {2} && {3} > {4} && {3} < {5}".format(time.GetName(), time.getMin(), time.getMax(), t_diff.GetName(), t_diff.getMin(), t_diff.getMax()))
else:
self._data = self._data.reduce("{0} > {1} && {0} < {2}".format(time.GetName(), time.getMin(), time.getMax()))
# self._data = self._data.reduce("mass > 5348 && mass < 5388")
fitOpts = dict(NumCPU = 4, Save = True, Minimizer = 'Minuit2', Optimize = 2)
self.__result = self.__pdf.fitTo(self._data, **fitOpts)
from P2VV.Utilities.SWeights import SData
for p in self.__pdf.Parameters(): p.setConstant(not p.getAttribute('Yield'))
splot = SData(Pdf = self.__pdf, Data = self._data, Name = 'MixingMassSplot')
self.__sdatas = {}
self.__reweigh_weights = {}
for key, c in self.__components[Weights].iteritems():
sdata = splot.data(c.GetName())
if 'Data' in Reweigh and key in Reweigh['Data']:
from array import array
from ROOT import RooBinning
source = Reweigh['Data'][key]
binning = Reweigh['Binning']
if type(binning) == array:
binning = RooBinning(len(binning) - 1, binning)
binning.SetName('reweigh')
Reweigh['DataVar'].setBinning(binning, 'reweigh')
source_obs = source.get().find('nPV')
cat_name = 'nPVs_' + key
source_cat = source.get().find(cat_name)
if source_cat:
# Remove previous weights to make sure we get it right
new_vars = source.get()
new_vars.remove(source_cat)
source = source.reduce(new_vars)
source_obs = source.get().find(source_obs.GetName())
source_obs.setBinning(binning, 'reweigh')
source_cat = BinningCategory(Name = cat_name, Observable = source_obs,
Binning = binning, Data = source, Fundamental = True)
from P2VV.Reweighing import reweigh
sdata, weights = reweigh(sdata, sdata.get().find('nPV'),
source, source_cat)
self.__reweigh_weights[key] = weights
sdata = self.__ws.put(sdata)
self.__sdatas[c] = sdata
rho_keys = dict((v, k) for k, v in self.__components[Weights].iteritems())
self.__shapes = {}
self.__diff_shapes = {}
for c, sdata in self.__sdatas.iteritems():
if UseKeysPdf:
rk = rho_keys[c]
rho = ShapeBuilder.__rho[rk] if rk in ShapeBuilder.__rho else 1.
time_shape = KeysPdf(Name = 'wpv_%s_pdf' % c.GetName(), Observable = time, Data = sdata, Rho = rho)
if t_diff:
diff_shape = KeysPdf(Name = 'wpv_%s_diff_pdf' % c.GetName(), Observable = t_diff, Data = sdata)
else:
time_shape = HistPdf(Name = 'wpv_%s_pdf' % c.GetName(), Observables = [time],
Data = sdata, Binning = {time : 35})
if t_diff:
diff_shape = HistPdf(Name = 'wpv_%s_diff_pdf' % c.GetName(), Observables = [t_diff],
Data = sdata, Binning = {time : 35})
self.__shapes[c] = time_shape
if t_diff:
self.__diff_shapes[c] = diff_shape
if Draw:
self.__draw()
def __draw(self):
from ROOT import kDashed, kRed, kGreen, kBlue, kBlack
from ROOT import TCanvas
from ROOT import TPaveText
self.__year_label = TPaveText(0.71, 0.72, 0.89, 0.85, "NDC")
self.__year_label.SetFillColor(0)
self.__year_label.AddText(self.__year)
self.__year_label.SetBorderSize(0)
obs = self.__masses.values()
self.__canvas = TCanvas('wpv_canvas', 'wpv_canvas', len(obs) * 600, 533)
for (p,o) in zip(self.__canvas.pads(len(obs)), obs):
from P2VV.Utilities.Plotting import plot
plot(p, o, pdf = self.__pdf, data = self._data
, dataOpts = dict(MarkerSize = 0.8, MarkerColor = kBlack, Binning = 50)
, pdfOpts = dict(LineWidth = 2)
, plotResidHist = 'BX'
, xTitle = 'M_{#mu^{+}#mu^{-}} [MeV/c^{2}]'
, yTitle = 'Candidates / (%2.1f MeV/c^{2})' % ((o.getMax() - o.getMin()) / float(50))
, components = { 'wpv_bkg_*' : dict( LineColor = kRed, LineStyle = kDashed )
, 'wpv_psi_*' : dict( LineColor = kGreen, LineStyle = kDashed )
, 'wpv_sig_*' : dict( LineColor = kBlue, LineStyle = kDashed )
}
)
self.__canvas.cd(1)
self.__year_label.Draw()
self.__canvas.Update()
self.__draw_time()
if self.__t_diff:
self.__draw_t_diff()
def __draw_time(self):
from ROOT import kDashed, kRed, kGreen, kBlue, kBlack
from ROOT import TCanvas
from itertools import product
self.__time_canvases = []
t = self.__time
from P2VV.Utilities.Plotting import plot
from ROOT import RooArgSet
for logy, (c, shape) in product((True, False), self.__shapes.items()):
cn = 'wpv_time_%s' % c.GetName()
if logy: cn += '_log'
p = TCanvas(cn, cn, 600, 400)
self.__time_canvases.append(p)
nBins = 80
frame = plot(p, t, pdf = shape, data = self.__sdatas[c]
, frameOpts = dict(Title = c.GetName())
, dataOpts = dict(MarkerSize = 0.8, Binning = nBins, MarkerColor = kBlack)
, pdfOpts = dict(LineWidth = 2)
, logy = logy, yScale = (1 if logy else 0, None)
, plotResidHist = False)[0]
frame.GetXaxis().SetTitle('t [ps]')
if logy:
frame.GetYaxis().SetRangeUser(1, 7000 if self.__year == '2012' else 5000)
frame.GetYaxis().SetTitle('Candidates / (%3.2f ps)' % ((t.getMax() - t.getMin()) / float(nBins)))
self.__year_label.Draw()
from P2VV.Utilities.Resolution import plot_dir
p.Print(os.path.join(plot_dir, 'wpv_time_%s_%s_%s.pdf' % (c.GetName()[4:], self.__year, 'log' if logy else 'linear')), EmbedFonts = True)
def __draw_t_diff(self):
from ROOT import kDashed, kRed, kGreen, kBlue, kBlack
from ROOT import TCanvas
self.__diff_canvases = []
t_diff = self.__t_diff
from ROOT import RooArgSet
for c, shape in self.__diff_shapes.iteritems():
name = 'wpv_diff_%s' % c.GetName()
canvas = TCanvas(name, name, 600, 400)
p = canvas.cd()
self.__diff_canvases.append(canvas)
from P2VV.Utilities.Plotting import plot
plot(p, t_diff, pdf = shape, data = self.__sdatas[c]
, dataOpts = dict(MarkerSize = 0.8, Binning = 80, MarkerColor = kBlack)
, pdfOpts = dict(LineWidth = 2)
, xTitle = 't_{rec} - t_{true} [ps]'
, yTitle = 'Candidates / (12.5 fs)'
, logy = False
, plotResidHist = False)
self.__year_label.Draw()
from P2VV.Utilities.Resolution import plot_dir
canvas.Print(os.path.join(plot_dir, 'wpv_tdiff_%s_%s_linear.pdf' % (c.GetName()[4:], self.__year)), EmbedFonts = True)
def sdata(self, key):
c = self.__components[self.__weights][key]
return self.__sdatas[c]
def shape(self, key):
c = self.__components[self.__weights][key]
return self.__shapes[c]
def diff_shape(self, key):
c = self.__components[self.__weights][key]
return self.__diff_shapes[c]
def input_ws(self):
return self.__input_ws
def reweigh_weights(self, key):
try:
return self.__reweigh_weights[key]
except AttributeError:
return {}
else :
dataFile = TFile.Open(dataFilePath)
dataSets = dict( [ ( key, dataFile.Get( 'data_' + key ) ) for key in cutKeys ] )
dataFile.Close()
print 'build mass PDF'
from P2VV.RooFitWrappers import Component
sigMassComps = Component( 'sigMass', [ ], Yield = ( 85.e3, 0., 2.e5 ) )
bkgMassComps = Component( 'bkgMass', [ ], Yield = ( 1.e5, 0., 2.e5 ) )
yields = [ comp.getYield() for comp in [ sigMassComps, bkgMassComps ] ]
from P2VV.Parameterizations.MassPDFs import DoubleGauss_Signal_Mass as SignalBMass
sigMass = SignalBMass( mass = mass )
sigMassComps += sigMass.pdf()
from P2VV.Parameterizations.MassPDFs import LP2011_Background_Mass as BackgroundBMass
bkgMass = BackgroundBMass( mass = mass )
bkgMassComps += bkgMass.pdf()
from P2VV.RooFitWrappers import buildPdf
massPdf = buildPdf( [ sigMassComps, bkgMassComps ], Observables = [ mass ], Name = 'JpsiKKMass' )
ws['N_sigMass'].setVal(123.e3)
ws['m_sig_mean'].setVal(5368.2)
ws['m_sig_sigma_1'].setVal(5.6)
ws['m_sig_sigma_2'].setVal(14.)
ws['m_sig_frac'].setVal(0.65)
ws['m_bkg_exp'].setVal(-0.0015)
massPdf.getVariables().Print('v')
print 'draw mass plots'
from P2VV.Load import LHCbStyle
from ROOT import gStyle, TCanvas, kFullDotLarge, kBlack, kBlue, kGreen, kRed, kYellow
if model == 'ipatia2' :
from P2VV.Parameterizations.MassPDFs import Ipatia2_Signal_Mass as SignalBMass
sigMassArgs['m_sig_lambda'] = -2.5
sigMassArgs['m_sig_zeta'] = 0.01
sigMassArgs['m_sig_alpha_1'] = 3.0
sigMassArgs['m_sig_alpha_2'] = 2.5
sigMassArgs['m_sig_n_1'] = 1.5
sigMassArgs['m_sig_n_2'] = 2.0
else :
from P2VV.Parameterizations.MassPDFs import DoubleGauss_Signal_Mass as SignalBMass
signalBMass = SignalBMass( **sigMassArgs )
sigMassComps += signalBMass.pdf()
# build the combinatorial background mass PDF
from P2VV.Parameterizations.MassPDFs import LP2011_Background_Mass as BackgroundBMass
backgroundBMass = BackgroundBMass( Name = 'cbkg_m', mass = observables['mass'] )
cbkgMassComps += backgroundBMass.pdf()
# build total mass PDF
from P2VV.RooFitWrappers import buildPdf
massPdf = buildPdf( massComps, Observables = [ observables['mass'] ], Name = 'JpsiKKMass' )
# get splitting categories and parameters
yieldNames = [ par.GetName() for par in massPdf.Parameters() if par.getAttribute('Yield') ]
allParNames = [ par.GetName() for par in massPdf.Parameters() if not par.getAttribute('Yield') if not par.isConstant() ]
sigParNames = [ par.GetName() for par in signalBMass.parameters() if not par.isConstant() ]
bkgParNames = [ par.GetName() for par in backgroundBMass.parameters() if not par.isConstant() ]
splitCats = { observables['runPeriod'].GetName() : set( yieldNames + bkgParNames )
, observables['KKMassCat'].GetName() : set( yieldNames )
}
if not biasedOnly :
from P2VV.RooFitWrappers import Component sigMassComps = Component( 'sigMass', [ ], Yield = ( nSignal, 0., nEvents ) ) # signal cbkgMassComps = Component( 'cbkgMass', [ ], Yield = ( nBackground, 0., nEvents ) ) # combinatorial background massComps = [ sigMassComps, cbkgMassComps ] yieldNames = [ comp.getYield().GetName() for comp in massComps ] # build the signal mass PDF sigMassArgs = dict( Name = 'sig_m', mass = observables['mass'] ) from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as SignalBMass signalBMass = SignalBMass( **sigMassArgs ) # build the combinatorial background mass PDF cbkgMassArgs = dict( Name = 'cbkg_m', mass = observables['mass'] ) from P2VV.Parameterizations.MassPDFs import LP2011_Background_Mass as BackgroundBMass backgroundBMass = BackgroundBMass( **cbkgMassArgs ) # build mass PDF from P2VV.RooFitWrappers import buildPdf sigMassComps += signalBMass.pdf() cbkgMassComps += backgroundBMass.pdf() massPdf = buildPdf( massComps, Observables = [ observables['mass'] ], Name = 'JpsiKKMass' ) ########################################################################################################################################### ## fit J/psiKK mass distributions ## #################################### # determine mass parameters with a fit print 120 * '=' print 'P2VV - INFO: createB2CCFitNTuple: fitting with mass PDF'
nEvents = data.sumEntries()
nSignal = nEvents * sigFrac
nBackground = nEvents * (1.0 - sigFrac)
## from P2VV.Parameterizations.MassPDFs import Ipatia2_Signal_Mass as SignalBMass
## sig_m = SignalBMass(Name = 'sig_m', mass = mass, m_sig_lambda = -2.5, m_sig_zeta = 0.01,
## m_sig_alpha_1 = 3.0, m_sig_alpha_2 = 2.5, m_sig_n_1 = 1.5, m_sig_n_2 = 2.0)
from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as SignalBMass
sig_m = SignalBMass(Name="sig_m", mass=mass)
signal = Component("signal", [sig_m.pdf()], Yield=(nSignal, 0.0, nEvents))
from P2VV.Parameterizations.MassPDFs import LP2011_Background_Mass as BackgroundBMass
bkg_m = BackgroundBMass(Name="bkg_m", mass=mass)
background = Component("background", [bkg_m.pdf()], Yield=(nBackground, 0.0, nEvents))
mass_pdf = buildPdf([signal, background], Observables=[mass], Name="mass_pdf")
fitOpts = dict(NumCPU=4, Optimize=2, Save=True, Timer=True, Minimizer="Minuit2", Offset=True)
mass_result = mass_pdf.fitTo(data, **fitOpts)
# categories for splitting the PDF
if not options.prescaled:
split_cats = [[excl_biased]]
# get mass parameters that are split
split_params = [[par for par in mass_pdf.Parameters() if par.getAttribute("Yield")]]
# build simultaneous mass PDF
from P2VV.Utilities.DataHandling import readData
data = readData(filePath = input_file, dataSetName = 'DecayTree', NTuple = True,
observables = [mass, t, st, excl_biased], Rename = 'JpsiphiData', ntupleCuts = cut)
sigFrac = 0.504
nEvents = data.sumEntries()
nSignal = nEvents * sigFrac
nBackground = nEvents * ( 1. - sigFrac )
from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as SignalBMass
sig_m = SignalBMass(Name = 'sig_m', mass = mass)
signal = Component( 'signal', [sig_m.pdf()], Yield = ( nSignal, 0., nEvents ) )
from P2VV.Parameterizations.MassPDFs import LP2011_Background_Mass as BackgroundBMass
bkg_m = BackgroundBMass(Name = 'bkg_m', mass = mass)
background = Component( 'background', [bkg_m.pdf()], Yield = ( nBackground, 0., nEvents ) )
mass_pdf = buildPdf( [ signal, background ], Observables = [ mass ], Name = 'mass_pdf')
fitOpts = dict(NumCPU = 4, Optimize = 2, Save = True, Timer = True, Minimizer = 'Minuit2',
Offset = True)
mass_result = mass_pdf.fitTo(data, **fitOpts)
# categories for splitting the PDF
if data_type.endswith("signal"):
split_cats = [[excl_biased]]
# get mass parameters that are split
split_params = [[par for par in mass_pdf.Parameters() if par.getAttribute('Yield')]]
# now build the actual signal PDF...
from ROOT import RooGaussian as Gaussian
from ROOT import RooExponential as Exponential
from ROOT import RooDecay as Decay
from P2VV.Parameterizations.TimeResolution import Gaussian_TimeResolution as TimeResolution
tres = TimeResolution(Name = 'resolution', time = t, BiasScaleFactor = False,
timeResSigma = dict(Value = 0.045, Constant = True),
timeResMu = dict(Value = 0, Constant = True), Cache = False)
# B mass pdf
from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as Signal_BMass, LP2011_Background_Mass as Background_BMass
sig_m = Signal_BMass(Name = 'sig_m', mass = m, m_sig_mean = dict( Value = 5365, MinMax = (5363,5372)))
# Create combinatorical background component
bkg_m = Background_BMass( Name = 'bkg_m', mass = m, m_bkg_exp = dict( Name = 'm_bkg_exp' ) )
# Create components
signal_mass = Component('signal', (sig_m.pdf(),), Yield = (30000,100,100000))
background_mass = Component('background', (bkg_m.pdf(),), Yield = (100000,100,300000) )
## Build mass PDF
mass_pdf = buildPdf(Components = (signal_mass, background_mass), Observables = (m, ), Name='mass_pdf')
mass_pdf.Print("t")
## base_location = '/home/raaij'
base_location = '/stuff/PhD/p2vv'
# Build the acceptance using the histogram as starting values
input_file = os.path.join(base_location, 'data/start_values.root')
from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as Signal_BMass, LP2011_Background_Mass as Background_BMass
sig_m = Signal_BMass( Name = 'sig_m', mass = m, m_sig_mean = dict( Value = 5365, MinMax = (5363,5372) ) )
# J/psi mass pdf
mpsi_mean = RealVar('mpsi_mean', Unit = 'MeV', Value = 3097, MinMax = (3070, 3110))
mpsi_sigma = RealVar('mpsi_sigma', Unit = 'MeV', Value = 10, MinMax = (5, 20))
mpsi_alpha = RealVar('mpsi_alpha', Unit = '', Value = 1.8, MinMax = (0.5, 3), Constant = True)
mpsi_n = RealVar('mpsi_n', Unit = '', Value = 2, MinMax = (0.1, 4), Constant = True)
psi_m = Pdf(Name = 'psi_m', Type = CrystalBall, Parameters = [mpsi, mpsi_mean, mpsi_sigma, mpsi_alpha, mpsi_n])
# J/psi background
psi_c = RealVar( 'psi_c', Unit = '1/MeV', Value = -0.0004, MinMax = (-0.1, -0.0000001))
bkg_mpsi = Pdf(Name = 'bkg_mpsi', Type = Exponential, Parameters = [mpsi, psi_c])
# Create combinatorical background component
bkg_m = Background_BMass( Name = 'bkg_m', mass = m, m_bkg_exp = dict( Name = 'm_bkg_exp' ) )
# Create components
signal_mass = Component('signal', (sig_m.pdf(), psi_m), Yield = (30000,100,100000))
psi_background_mass = Component('psi_background', (bkg_m.pdf(), psi_m), Yield= (100000,500,200000) )
background_mass = Component('background', (bkg_m.pdf(), bkg_mpsi), Yield = (100000,100,300000) )
## Build mass PDF
mass_pdf = buildPdf(Components = (signal_mass, background_mass), Observables = (m, ), Name='mass_pdf')
mass_pdf.Print("t")
## base_location = '/home/raaij'
base_location = '/stuff/PhD/p2vv'
# Build the acceptance using the histogram as starting values
input_file = os.path.join(base_location, 'data/start_values.root')
