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
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 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 = [ qf, qt, time ]
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 the time pdf
pdf = buildBDecayTimePdf(
config, 'Bs2DsPi', ws,
time, timeerr, qt, qf, [ [ mistag ] ], [ tageff ],
Gamma, DGamma, Dm,
C = one, D = zero, Dbar = zero, S = zero, Sbar = zero,
timeresmodel = resmodel, acceptance = acc)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
def buildTimePdf(config, tupleDataSet, tupleDict):
from B2DXFitters.WS import WS
print 'CONFIGURATION'
for k in sorted(config.keys()):
print ' %32s: %32s' % (k, config[k])
ws = RooWorkspace('ws_%s' % config['Context'])
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
###USE FIT CONTEXT
"""
build time pdf, return pdf and associated data in dictionary
"""
# start by defining observables
time = WS(ws,
tupleDataSet.get().find('ct'))
#qt = WS(ws, tupleDataSet.get().find('ssDecision'));
'''
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
if 'GEN' in config['Context'] or 'FIT' in config['Context']:
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))
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 0.60, 0.0, 1.0))
terrpdf = WS(ws,
tupleDataSet.get().find('cterr'))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr', 0.040, 0.001, 0.100))
#MISTAGPDF
# fit average mistag
# add mistagged
#ge rid of untagged events by putting restriction on qf or something when reduceing ds
# now build the PDF
from B2DXFitters.timepdfutils import buildBDecayTimePdf
from B2DXFitters.resmodelutils import getResolutionModel
from B2DXFitters.acceptanceutils import buildSplineAcceptance
obs = [qf, qt, time]
acc, accnorm = buildSplineAcceptance(
ws, time, 'Bs2DsPi_accpetance',
config['SplineAcceptance']['KnotPositions'],
config['SplineAcceptance']['KnotCoefficients'][config['Context'][0:3]],
'FIT' in config['Context']) # float for fitting
# get resolution model
resmodel, acc = getResolutionModel(ws, config, time, timeerr, acc)
mistagpdf = WS(
ws,
RooArgList(tupleDataSet.get().find('ssMistag'),
tupleDataSet.get().find('osMistag')))
#???
'''
if 'GEN' in config['Context']:
# 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, [tupleDataSet.reduce('ssMistag'), tupleDataSet.reduce('osMistag')]);
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 the time pdf
if 'GEN' in config['Context']:
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,
mistagpdf = [mistagpdf], mistagobs = eta)
else:
pdf = buildBDecayTimePdf(
config, 'Bs2DsPi', ws,
time, timeerr, qt, qf, [ [ eta ] ], [ tageff ],
Gamma, DGamma, Dm,
C = one, D = zero, Dbar = zero, S = zero, Sbar = zero,
timeresmodel = resmodel, acceptance = acc, timeerrpdf = None)
'''
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[eta]], [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
}
# seed the Random number generator
rndm = TRandom3(SEED + 1)
RooRandom.randomGenerator().SetSeed(int(rndm.Uniform(4294967295)))
del rndm
# start building the fit
from B2DXFitters.WS import WS
ws = RooWorkspace('ws')
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
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 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))
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 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
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 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 = [qf, qt, time]
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 the time pdf
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[mistag]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
from B2DXFitters.WS import WS
ws = RooWorkspace('ws')
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
# number of events to generate
nevts = 10000
# define time and its per-event uncertainty
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr', 0.001, 0.100))
# define final state charge
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
# define list of tagging decision categories
qt_os = WS(ws, RooCategory('qt_os', 'os tagging decision'))
qt_os.defineType( 'B+', +1)
qt_os.defineType('Untagged', 0)
qt_os.defineType( 'B-', -1)
qt_ss = WS(ws, RooCategory('qt_ss', 'ss tagging decision'))
qt_ss.defineType( 'B+', +1)
qt_ss.defineType('Untagged', 0)
qt_ss.defineType( 'B-', -1)
qt = [qt_os, qt_ss]
# define list of mistag observable
# HACK (1/2): be careful about lower bound on eta, since mock mistagpdf
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 = [ qf, qt, time, eta ]
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 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 = None,
mistagpdf = [ mistagpdf ], mistagobs = eta)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
from B2DXFitters.WS import WS
ws = RooWorkspace('ws')
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
# number of events to generate
nevts = 10000
# define time and its per-event uncertainty
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr', 0.001, 0.100))
# define final state charge
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
# define list of tagging decision categories
qt_os = WS(ws, RooCategory('qt_os', 'os tagging decision'))
qt_os.defineType('B+', +1)
qt_os.defineType('Untagged', 0)
qt_os.defineType('B-', -1)
qt_ss = WS(ws, RooCategory('qt_ss', 'ss tagging decision'))
qt_ss.defineType('B+', +1)
qt_ss.defineType('Untagged', 0)
qt_ss.defineType('B-', -1)
qt = [qt_os, qt_ss]
# define list of mistag observable
# HACK (1/2): be careful about lower bound on eta, since mock mistagpdf
# seed the Random number generator
rndm = TRandom3(SEED + 1)
RooRandom.randomGenerator().SetSeed(int(rndm.Uniform(4294967295)))
del rndm
# start building the fit
from B2DXFitters.WS import WS
ws = RooWorkspace('ws')
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
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 0.60, 0.0, 1.0))
# for now, we're in the generation stage
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]', 0.3, 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., 0.5))
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 1.0))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr',
0.039)) #CHANGE THIS LATER
# fit average mistag
# add mistagged
#ge rid of untagged events by putting restriction on qf or something when reduceing ds
# now build the PDF
from B2DXFitters.timepdfutils import buildBDecayTimePdf
from B2DXFitters.resmodelutils import getResolutionModel
from B2DXFitters.acceptanceutils import buildSplineAcceptance
if 'GEN' in config['Context']:
obs = [qf, qt, time, eta]
else:
obs = [qf, qt, time]
acc, accnorm = buildSplineAcceptance(
ws, time, 'Bs2DsPi_accpetance',
config['SplineAcceptance']['KnotPositions'],
config['SplineAcceptance']['KnotCoefficients'][config['Context'][0:3]],
'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)
if 'GEN' in config['Context']:
# 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']))
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 the time pdf
if 'GEN' in config['Context']:
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=None,
mistagpdf=[mistagpdf],
mistagobs=eta)
else:
adet = WS(ws, RooRealVar('adet', 'adet', 0., -.15, .15))
aprod = WS(ws, RooRealVar('aprod', 'aprod', 0., -.15, .15))
adet.setError(0.005)
aprod.setError(0.005)
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[eta]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc,
timeerrpdf=None,
aprod=aprod,
adet=adet)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
def readDataSet(
config, # configuration dictionary
ws, # workspace to which to add data set
observables, # observables
rangeName = None # name of range to clip dataset to
):
"""
read data set from given Ntuple (or a RooDataSet inside a workspace) into
a RooDataSet
arguments:
config -- configuration dictionary (see below for relevant keys)
ws -- workspace into which to import data from tuple
observables -- RooArgSet containing the observables to be read
rangeName -- optional, can be the name of a range of one observable, if
the data read from the tuple needs to be explicitly clipped
to that range for some reason
the routine returns the data set that has been read in and stored inside
ws.
relevant configuration dictionary keys:
'DataFileName' -- file name of data file from which to read ntuple or data
set
'DataSetNames' -- name (TTree or RooDataSet) of the data set to be read;
more than one can be given in a dictionary, providing a
mapping between the sample name and the data set to be
read (see below for an explanation)
'DataWorkSpaceName'
-- name of workspace to read data from (if any - leave None
for reading tuples)
'DataSetCuts' -- cuts to apply to data sets on import - anything that
RooDataSet.reduce will understand is permissible here
(set to None to not apply any cuts on import)
'DataSetVarNameMapping'
-- mapping from variable names in set of observables to
what these variable names are called in the
tuple/workspace to be imported
"special" observable names:
The routine treats some variable names special on import based on their
likely meaning:
'weight' -- this variable name must be used to read in (s)weighted events
'qf' -- final state charge (e.g. +1 for K+ vs -1 for K-); only the
sign is important here, and the import code enforces that
'qt' -- tagging decision; this can be any integer number (positive or
negative); if the tuple should contain a float/double with
that information, it is rounded appropriately
'mistag' -- predicted mistag; the import code makes sure that events with
qt == 0 have mistag = 0.5
'sample' -- if more than one final state is studied (e.g. Ds final states
phipi, kstk, nonres, kpipi, pipipi), the events for these
subsamples often reside in different samples; therefore the
config dictionary entry 'DataSetNames' can contain a
dictionary which maps the category labels (phipi etc) to the
names of the data samples in the ROOT file/workspace
The config dict key 'DataSetVarNameMapping' contains a useful feature:
Instead of providing a one-to-one-mapping of observables to tuple/data set
names, an observable can be calculated from more than one tuple column.
This is useful e.g. to convert a tuple that's stored the tagging decision
as untagged/mixed/unmixed, or to sum up sweights for the different
samples. Most simple formulae should be supported, but constants in
scientific notation (1.0E+00) are not for now (until someone write a
better parser for this).
Example:
@code
seed = 42 # it's easy to modify the filename depending on the seed number
configdict = {
# file to read from
'DataFileName': '/some/path/to/file/with/toy_%04d.root' % seed,
# data set is in a workspace already
'DataWorkSpaceName': 'FitMeToolWS',
# name of data set inside workspace
'DataSetNames': 'combData',
# mapping between observables and variable name in data set
'DataSetVarNameMapping': {
'sample': 'sample',
'mass': 'lab0_MassFitConsD_M',
'pidk': 'lab1_PIDK',
'dsmass': 'lab2_MM',
'time': 'lab0_LifetimeFit_ctau',
'timeerr': 'lab0_LifetimeFit_ctauErr',
'mistag': 'tagOmegaComb',
'qf': 'lab1_ID',
'qt': ' tagDecComb',
# sweights need to be combined from different branches in this
# case, only one of the branches is ever set to a non-zero value,
# depending on which subsample the event is in
'weight': ('nSig_both_nonres_Evts_sw+nSig_both_phipi_Evts_sw+'
'nSig_both_kstk_Evts_sw+nSig_both_kpipi_Evts_sw+'
'nSig_both_pipipi_Evts_sw')
}
}
# define all observables somewhere, and put the into a RooArgSet called obs
# import observables in to a workspace saved in ws
# now read the data set
data = readDataSet(configdict, ws, observables)
@endcode
"""
from ROOT import ( TFile, RooWorkspace, RooRealVar, RooCategory,
RooBinningCategory, RooUniformBinning, RooMappedCategory,
RooDataSet, RooArgSet, RooArgList )
import sys, math
# local little helper routine
def round_to_even(x):
xfl = int(math.floor(x))
rem = x - xfl
if rem < 0.5: return xfl
elif rem > 0.5: return xfl + 1
else:
if xfl % 2: return xfl + 1
else: return xfl
# another small helper routine
def tokenize(s, delims = '+-*/()?:'):
# FIXME: this goes wrong for numerical constants like 1.4e-3
# proposed solution: regexp for general floating point constants,
# replace occurences of matches with empty string
delims = [ c for c in delims ]
delims.insert(0, None)
for delim in delims:
tmp = s.split(delim)
tmp = list(set(( s + ' ' for s in tmp)))
s = ''.join(tmp)
tmp = list(set(s.split(None)))
return tmp
# figure out which names from the mapping we need - look at the observables
names = ()
for n in config['DataSetVarNameMapping'].keys():
if None != observables.find(n):
names += (n,)
# build RooArgSets and maps with source and destination variables
dmap = { }
for k in names: dmap[k] = observables.find(k)
if None in dmap.values():
raise NameError('Some variables not found in destination: %s' % str(dmap))
dset = RooArgSet()
for v in dmap.values(): dset.add(v)
if None != dset.find('weight'):
# RooFit insists on weight variable being first in set
tmpset = RooArgSet()
tmpset.add(dset.find('weight'))
it = dset.fwdIterator()
while True:
obj = it.next()
if None == obj: break
if 'weight' == obj.GetName(): continue
tmpset.add(obj)
dset = tmpset
del tmpset
ddata = RooDataSet('agglomeration', 'of positronic circuits', dset, 'weight')
else:
ddata = RooDataSet('agglomeration', 'of positronic circuits', dset)
# open file with data sets
f = TFile(config['DataFileName'], 'READ')
# get workspace
fws = f.Get(config['DataWorkSpaceName'])
ROOT.SetOwnership(fws, True)
if None == fws or not fws.InheritsFrom('RooWorkspace'):
# ok, no workspace, so try to read a tree of the same name and
# synthesize a workspace
from ROOT import RooWorkspace, RooDataSet, RooArgList
fws = RooWorkspace(config['DataWorkSpaceName'])
iset = RooArgSet()
addiset = RooArgList()
it = observables.fwdIterator()
while True:
obj = it.next()
if None == obj: break
name = config['DataSetVarNameMapping'][obj.GetName()]
vnames = tokenize(name)
if len(vnames) > 1 and not obj.InheritsFrom('RooAbsReal'):
print 'Error: Formulae not supported for categories'
return None
if obj.InheritsFrom('RooAbsReal'):
if 1 == len(vnames):
# simple case, just add variable
var = WS(fws, RooRealVar(name, name, -sys.float_info.max,
sys.float_info.max))
iset.addClone(var)
else:
# complicated case - add a bunch of observables, and
# compute something in a RooFormulaVar
from ROOT import RooFormulaVar
args = RooArgList()
for n in vnames:
try:
# skip simple numerical factors
float(n)
except:
var = iset.find(n)
if None == var:
var = WS(fws, RooRealVar(n, n, -sys.float_info.max,
sys.float_info.max))
iset.addClone(var)
args.add(iset.find(n))
var = WS(fws, RooFormulaVar(name, name, name, args))
addiset.addClone(var)
else:
for dsname in ((config['DataSetNames'], )
if type(config['DataSetNames']) == str else
config['DataSetNames']):
break
leaf = f.Get(dsname).GetLeaf(name)
if None == leaf:
leaf = f.Get(dsname).GetLeaf(name + '_idx')
if leaf.GetTypeName() in (
'char', 'unsigned char', 'Char_t', 'UChar_t',
'short', 'unsigned short', 'Short_t', 'UShort_t',
'int', 'unsigned', 'unsigned int', 'Int_t', 'UInt_t',
'long', 'unsigned long', 'Long_t', 'ULong_t',
'Long64_t', 'ULong64_t', 'long long',
'unsigned long long'):
var = WS(fws, RooCategory(name, name))
tit = obj.typeIterator()
ROOT.SetOwnership(tit, True)
while True:
tobj = tit.Next()
if None == tobj: break
var.defineType(tobj.GetName(), tobj.getVal())
else:
var = WS(fws, RooRealVar(name, name, -sys.float_info.max,
sys.float_info.max))
iset.addClone(var)
for dsname in ((config['DataSetNames'], )
if type(config['DataSetNames']) == str else
config['DataSetNames']):
tmpds = WS(fws, RooDataSet(dsname, dsname,f.Get(dsname), iset), [])
if 0 != addiset.getSize():
# need to add columns with RooFormulaVars
tmpds.addColumns(addiset)
del tmpds
# local data conversion routine
def doIt(config, rangeName, dsname, sname, names, dmap, dset, ddata, fws):
sdata = fws.obj(dsname)
if None == sdata: return 0
if None != config['DataSetCuts']:
# apply any user-supplied cuts
newsdata = sdata.reduce(config['DataSetCuts'])
ROOT.SetOwnership(newsdata, True)
del sdata
sdata = newsdata
del newsdata
sset = sdata.get()
smap = { }
for k in names:
smap[k] = sset.find(config['DataSetVarNameMapping'][k])
if 'sample' in smap.keys() and None == smap['sample'] and None != sname:
smap.pop('sample')
dmap['sample'].setLabel(sname)
if None in smap.values():
raise NameError('Some variables not found in source: %s' % str(smap))
# # additional complication: toys save decay time in ps, data is in nm
# # figure out which time conversion factor to use
# timeConvFactor = 1e9 / 2.99792458e8
# meantime = sdata.mean(smap['time'])
# if ((dmap['time'].getMin() <= meantime and
# meantime <= dmap['time'].getMax() and config['IsToy']) or
# not config['IsToy']):
# timeConvFactor = 1.
# print 'DEBUG: Importing data sample meantime = %f, timeConvFactor = %f' % (
# meantime, timeConvFactor)
timeConvFactor = 1.
# loop over all entries of data set
ninwindow = 0
if None != sname:
sys.stdout.write('Dataset conversion and fixup: %s: progress: ' % sname)
else:
sys.stdout.write('Dataset conversion and fixup: progress: ')
for i in xrange(0, sdata.numEntries()):
sdata.get(i)
if 0 == i % 128:
sys.stdout.write('*')
vals = { }
for vname in smap.keys():
obj = smap[vname]
if obj.InheritsFrom('RooAbsReal'):
val = obj.getVal()
vals[vname] = val
else:
val = obj.getIndex()
vals[vname] = val
# first fixup: apply time/timeerr conversion factor
if 'time' in dmap.keys():
vals['time'] *= timeConvFactor
if 'timeerr' in dmap.keys():
vals['timeerr'] *= timeConvFactor
# second fixup: only sign of qf is important
if 'qf' in dmap.keys():
vals['qf'] = 1 if vals['qf'] > 0.5 else (-1 if vals['qf'] <
-0.5 else 0.)
# third fixup: untagged events are forced to 0.5 mistag
if ('qt' in dmap.keys() and 'mistag' in dmap.keys() and 0 ==
vals['qt']):
vals['mistag'] = 0.5
# apply cuts
inrange = True
for vname in dmap.keys():
if not dmap[vname].InheritsFrom('RooAbsReal'): continue
# no need to cut on untagged events
if 'mistag' == vname and 0 == vals['qt']: continue
if None != rangeName and dmap[vname].hasRange(rangeName):
if (dmap[vname].getMin(rangeName) > vals[vname] or
vals[vname] >= dmap[vname].getMax(rangeName)):
inrange = False
break
else:
if (dmap[vname].getMin() > vals[vname] or
vals[vname] >= dmap[vname].getMax()):
inrange = False
break
# skip cuts which are not within the allowed range
if not inrange: continue
# copy values over, doing real-category conversions as needed
for vname in smap.keys():
dvar, svar = dmap[vname], vals[vname]
if dvar.InheritsFrom('RooAbsRealLValue'):
if float == type(svar): dvar.setVal(svar)
elif int == type(svar): dvar.setVal(svar)
elif dvar.InheritsFrom('RooAbsCategoryLValue'):
if int == type(svar): dvar.setIndex(svar)
elif float == type(svar):
dvar.setIndex(round_to_even(svar))
if 'weight' in dmap:
ddata.add(dset, vals['weight'])
else:
ddata.add(dset)
ninwindow = ninwindow + 1
del sdata
sys.stdout.write(', done - %d events\n' % ninwindow)
return ninwindow
ninwindow = 0
if type(config['DataSetNames']) == str:
ninwindow += doIt(config, rangeName, config['DataSetNames'],
None, names, dmap, dset, ddata, fws)
else:
for sname in config['DataSetNames'].keys():
ninwindow += doIt(config, rangeName, config['DataSetNames'][sname],
sname, names, dmap, dset, ddata, fws)
# free workspace and close file
del fws
f.Close()
del f
# put the new dataset into our proper workspace
ddata = WS(ws, ddata, [])
# for debugging
if config['Debug']:
ddata.Print('v')
if 'qt' in dmap.keys():
data.table(dmap['qt']).Print('v')
if 'qf' in dmap.keys():
data.table(dmap['qf']).Print('v')
if 'qf' in dmap.keys() and 'qt' in dmap.keys():
data.table(RooArgSet(dmap['qt'], dmap['qf'])).Print('v')
if 'sample' in dmap.keys():
data.table(dmap['sample']).Print('v')
# all done, return Data to the bridge
return ddata
def runBdGammaFitterOnToys(debug, wsname,
pereventterr, year,
toys,pathName,
treeName, fileNamePull, configName, configNameMD,
sWeightsCorr,
noresolution, noacceptance, notagging,
noprodasymmetry, nodetasymmetry, notagasymmetries,
nosWeights, noUntagged,
singletagger) :
if not Blinding and not toys :
print "RUNNING UNBLINDED!"
really = input('Do you really want to unblind? ')
if really != "yes" :
exit(-1)
if sWeightsCorr and nosWeights:
print "ERROR: cannot have sWeightsCorr and nosWeights at the same time!"
exit(-1)
if notagging and not singletagger:
print "ERROR: having more perfect taggers is meaningless! Please check your options"
exit(-1)
# Get the configuration file
myconfigfilegrabber = __import__(configName,fromlist=['getconfig']).getconfig
myconfigfile = myconfigfilegrabber()
print "=========================================================="
print "FITTER IS RUNNING WITH THE FOLLOWING CONFIGURATION OPTIONS"
for option in myconfigfile :
if option == "constParams" :
for param in myconfigfile[option] :
print param, "is constant in the fit"
else :
print option, " = ", myconfigfile[option]
print "=========================================================="
if debug:
myconfigfile['Debug'] = True
else:
myconfigfile['Debug'] = False
#Choosing fitting context
myconfigfile['Context'] = 'FIT'
# tune integrator configuration
print "---> Setting integrator configuration"
RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-7)
RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-7)
RooAbsReal.defaultIntegratorConfig().getConfigSection('RooIntegrator1D').setCatLabel('extrapolation','Wynn-Epsilon')
RooAbsReal.defaultIntegratorConfig().getConfigSection('RooIntegrator1D').setCatLabel('maxSteps','1000')
RooAbsReal.defaultIntegratorConfig().getConfigSection('RooIntegrator1D').setCatLabel('minSteps','0')
RooAbsReal.defaultIntegratorConfig().getConfigSection('RooAdaptiveGaussKronrodIntegrator1D').setCatLabel('method','21Points')
RooAbsReal.defaultIntegratorConfig().getConfigSection('RooAdaptiveGaussKronrodIntegrator1D').setRealValue('maxSeg', 1000)
# since we have finite ranges, the RooIntegrator1D is best suited to the job
RooAbsReal.defaultIntegratorConfig().method1D().setLabel('RooIntegrator1D')
# Reading data set
#-----------------------
lumRatio = RooRealVar("lumRatio","lumRatio", myconfigfile["LumRatio"][year])
print "=========================================================="
print "Getting configuration"
print "=========================================================="
config = TString("../data/")+TString(configName)+TString(".py")
from B2DXFitters.MDFitSettingTranslator import Translator
mdt = Translator(myconfigfile,"MDSettings",False)
MDSettings = mdt.getConfig()
MDSettings.Print("v")
bound = 1
Bin = [TString("BDTGA")]
from B2DXFitters.WS import WS as WS
ws = RooWorkspace("intWork","intWork")
workspace =[]
workspaceW = []
mode = "Bd2DPi"
print "=========================================================="
print "Getting sWeights"
print "=========================================================="
for i in range (0,bound):
workspace.append(SFitUtils.ReadDataFromSWeights(TString(pathName),
TString(treeName),
MDSettings,
TString(mode),
TString(year),
TString(""),
TString("both"),
False, toys, False, sWeightsCorr, singletagger, debug))
workspaceW.append(SFitUtils.ReadDataFromSWeights(TString(pathName),
TString(treeName),
MDSettings,
TString(mode),
TString(year),
TString(""),
TString("both"),
True, toys, False, sWeightsCorr, singletagger, debug))
if nosWeights:
workspace[0].Print("v")
else:
workspaceW[0].Print("v")
zero = RooConstVar('zero', '0', 0.)
half = RooConstVar('half','0.5',0.5)
one = RooConstVar('one', '1', 1.)
minusone = RooConstVar('minusone', '-1', -1.)
two = RooConstVar('two', '2', 2.)
nameData = TString("dataSet_time_")+part
data = []
dataW = []
print "=========================================================="
print "Getting input dataset"
print "=========================================================="
for i in range(0, bound):
data.append(GeneralUtils.GetDataSet(workspace[i], nameData, debug))
dataW.append(GeneralUtils.GetDataSet(workspaceW[i], nameData, debug))
dataWA = dataW[0]
dataA = data[0]
if nosWeights:
nEntries = dataA.numEntries()
dataA.Print("v")
else:
nEntries = dataWA.numEntries()
dataWA.Print("v")
print "=========================================================="
print "Getting observables"
print "=========================================================="
if nosWeights:
obs = dataA.get()
else:
obs = dataWA.get()
obs.Print("v")
print "=========================================================="
print "Creating variables"
print "=========================================================="
time = WS(ws,obs.find(MDSettings.GetTimeVarOutName().Data()))
time.setRange(myconfigfile["BasicVariables"]["BeautyTime"]["Range"][0],
myconfigfile["BasicVariables"]["BeautyTime"]["Range"][1])
print "==> Time"
time.Print("v")
terr = WS(ws,obs.find(MDSettings.GetTerrVarOutName().Data()))
terr.setRange(myconfigfile["BasicVariables"]["BeautyTimeErr"]["Range"][0],
myconfigfile["BasicVariables"]["BeautyTimeErr"]["Range"][1])
print "==> Time error"
terr.Print("v")
if noresolution:
terr.setMin(0.0)
terr.setVal(0.0)
terr.setConstant(True)
id = WS(ws,obs.find(MDSettings.GetIDVarOutName().Data()))
print "==> Bachelor charge (to create categories; not really a variable!)"
id.Print("v")
if singletagger:
tag = WS(ws,RooCategory("tagDecComb","tagDecComb"))
tag.defineType("Bbar_1",-1)
tag.defineType("Untagged",0)
tag.defineType("B_1",+1)
else:
tag = WS(ws,obs.find("tagDecComb"))
print "==> Tagging decision"
tag.Print("v")
mistag = WS(ws,obs.find("tagOmegaComb"))
mistag.setRange(0,0.5)
if notagging:
mistag.setVal(0.0)
mistag.setConstant(True)
print "==> Mistag"
mistag.Print("v")
observables = RooArgSet(time,tag)
# Physical parameters
#-----------------------
print "=========================================================="
print "Setting physical parameters"
print "=========================================================="
gammad = WS(ws,RooRealVar('Gammad', '%s average lifetime' % bName, myconfigfile["DecayRate"]["Gammad"], 0., 5., 'ps^{-1}'))
#setConstantIfSoConfigured(ws.obj('Gammad'),myconfigfile)
deltaGammad = WS(ws,RooRealVar('deltaGammad', 'Lifetime difference', myconfigfile["DecayRate"]["DeltaGammad"], -1., 1., 'ps^{-1}'))
#setConstantIfSoConfigured(ws.obj('deltaGammad'),myconfigfile)
deltaMd = WS(ws,RooRealVar('deltaMd', '#Delta m_{d}', myconfigfile["DecayRate"]["DeltaMd"], 0.0, 1.0, 'ps^{-1}'))
#setConstantIfSoConfigured(ws.obj('deltaMd'),myconfigfile)
# Decay time acceptance model
# ---------------------------
print "=========================================================="
print "Defining decay time acceptance model"
print "=========================================================="
if noacceptance:
print '==> Perfect acceptance ("straight line")'
tacc = None
taccNorm = None
else:
print '==> Time-dependent acceptance'
tacc, taccNorm = buildSplineAcceptance(ws,
ws.obj('BeautyTime'),
"splinePDF",
myconfigfile["AcceptanceKnots"],
myconfigfile["AcceptanceValues"],
False,
debug)
print tacc
print taccNorm
# Decay time resolution model
# ---------------------------
print "=========================================================="
print "Defining decay time resolution model"
print "=========================================================="
if noresolution:
print '===> Using perfect resolution'
trm = None
terrpdf = None
else:
if not pereventterr:
print '===> Using a mean resolution model'
myconfigfile["DecayTimeResolutionModel"] = myconfigfile["DecayTimeResolutionMeanModel"]
terrpdf = None
else:
print '===> Using a per-event time resolution'
myconfigfile["DecayTimeResolutionModel"] = myconfigfile["DecayTimeResolutionPEDTE"]
observables.add( terr )
terrWork = GeneralUtils.LoadWorkspace(TString(myconfigfile["Toys"]["fileNameTerr"]), TString(myconfigfile["Toys"]["Workspace"]), debug)
terrpdf = []
for i in range(0,bound):
terrtemp = WS(ws,Bs2Dsh2011TDAnaModels.GetRooHistPdfFromWorkspace(terrWork, TString(myconfigfile["Toys"]["TerrTempName"]), debug))
#Dirty, nasty but temporary workaround to cheat RooFit strict requirements (changing dependent RooRealVar)
lab0_LifetimeFit_ctauErr = WS(ws,RooRealVar("lab0_LifetimeFit_ctauErr",
"lab0_LifetimeFit_ctauErr",
myconfigfile["BasicVariables"]["BeautyTimeErr"]["Range"][0],
myconfigfile["BasicVariables"]["BeautyTimeErr"]["Range"][1]))
terrHist = WS(ws,terrtemp.createHistogram("terrHist",lab0_LifetimeFit_ctauErr))
terrDataHist = WS(ws,RooDataHist("terrHist","terrHist",RooArgList(terr),terrHist))
terrpdf.append(WS(ws,RooHistPdf(terrtemp.GetName(),terrtemp.GetTitle(),RooArgSet(terr),terrDataHist)))
print terrpdf[i]
trm, tacc = getResolutionModel(ws, myconfigfile, time, terr, tacc)
print trm
print tacc
# Per-event mistag
# ---------------------------
print "=========================================================="
print "Defining tagging and mistag"
print "=========================================================="
p0B = []
p0Bbar = []
p1B = []
p1Bbar = []
avB = []
avBbar = []
constList = RooArgSet()
mistagCalibB = []
mistagCalibBbar = []
tagOmegaList = []
if notagging:
print '==> No tagging: <eta>=0'
mistag.setVal(0.0)
mistag.setConstant(True)
tagOmegaList += [ [mistag] ]
else:
print '==> Non-trivial tagging'
if singletagger:
print '==> Single tagger'
p0B.append(WS(ws,RooRealVar('p0_B_OS', 'p0_B_OS', myconfigfile["TaggingCalibration"]["OS"]["p0"], 0.0, 0.5)))
p1B.append(WS(ws,RooRealVar('p1_B_OS', 'p1_B_OS', myconfigfile["TaggingCalibration"]["OS"]["p1"], 0.5, 1.5)))
avB.append(WS(ws,RooRealVar('av_B_OS', 'av_B_OS', myconfigfile["TaggingCalibration"]["OS"]["average"])))
#setConstantIfSoConfigured(p0B[0],myconfigfile)
#setConstantIfSoConfigured(p1B[0],myconfigfile)
mistagCalibB.append(WS(ws,MistagCalibration("mistagCalib_B_OS", "mistagCalib_B_OS", mistag, p0B[0], p1B[0], avB[0])))
p0Bbar.append(WS(ws,RooRealVar('p0_Bbar_OS', 'p0_B_OS', myconfigfile["TaggingCalibration"]["OS"]["p0Bar"], 0.0, 0.5)))
p1Bbar.append(WS(ws,RooRealVar('p1_Bbar_OS', 'p1_B_OS', myconfigfile["TaggingCalibration"]["OS"]["p1Bar"], 0.5, 1.5)))
avBbar.append(WS(ws,RooRealVar('av_Bbar_OS', 'av_B_OS', myconfigfile["TaggingCalibration"]["OS"]["averageBar"])))
#setConstantIfSoConfigured(p0Bbar[0],myconfigfile)
#setConstantIfSoConfigured(p1Bbar[0],myconfigfile)
mistagCalibBbar.append(WS(ws,MistagCalibration("mistagCalib_Bbar_OS", "mistagCalib_Bbar_OS", mistag, p0Bbar[0], p1Bbar[0], avBbar[0])))
tagOmegaList += [ [mistagCalibB[0],mistagCalibBbar[0]] ]
else:
print '==> Combining more taggers'
i=0
for tg in ["OS","SS","OS+SS"]:
p0B.append(WS(ws,RooRealVar('p0_B_'+tg, 'p0_B_'+tg, myconfigfile["TaggingCalibration"][tg]["p0"], 0., 0.5 )))
p1B.append(WS(ws,RooRealVar('p1_B_'+tg, 'p1_B_'+tg, myconfigfile["TaggingCalibration"][tg]["p1"], 0.5, 1.5 )))
avB.append(WS(ws,RooRealVar('av_B_'+tg, 'av_B_'+tg, myconfigfile["TaggingCalibration"][tg]["average"])))
#setConstantIfSoConfigured(p0B[i],myconfigfile)
#setConstantIfSoConfigured(p1B[i],myconfigfile)
mistagCalibB.append(WS(ws,MistagCalibration("mistagCalib_B_"+tg, "mistagCalib_B_"+tg, mistag, p0B[i], p1B[i], avB[i])))
p0Bbar.append(WS(ws,RooRealVar('p0_Bbar_'+tg, 'p0_Bbar_'+tg, myconfigfile["TaggingCalibration"][tg]["p0Bar"], 0., 0.5 )))
p1Bbar.append(WS(ws,RooRealVar('p1_Bbar_'+tg, 'p1_Bbar_'+tg, myconfigfile["TaggingCalibration"][tg]["p1Bar"], 0.5, 1.5 )))
avBbar.append(WS(ws,RooRealVar('av_Bbar_'+tg, 'av_Bbar_'+tg, myconfigfile["TaggingCalibration"][tg]["averageBar"])))
#setConstantIfSoConfigured(p0Bbar[i],myconfigfile)
#setConstantIfSoConfigured(p1Bbar[i],myconfigfile)
mistagCalibBbar.append(WS(ws,MistagCalibration("mistagCalib_Bbar_"+tg, "mistagCalib_Bbar_"+tg, mistag, p0Bbar[i], p1Bbar[i], avBbar[i])))
tagOmegaList += [ [mistagCalibB[i],mistagCalibBbar[i]] ]
i = i+1
print '==> Tagging calibration lists:'
print tagOmegaList
mistagWork = GeneralUtils.LoadWorkspace(TString(myconfigfile["Toys"]["fileNameMistag"]), TString(myconfigfile["Toys"]["Workspace"]), debug)
mistagPDF = []
mistagPDFList = []
if notagging:
mistagPDFList = None
else:
for i in range(0,3):
mistagPDF.append(WS(ws,Bs2Dsh2011TDAnaModels.GetRooHistPdfFromWorkspace(mistagWork, TString(myconfigfile["Toys"]["MistagTempName"][i]), debug)))
if not singletagger:
mistagPDFList.append(mistagPDF[i])
if singletagger:
mistagPDFList.append(mistagPDF[0])
observables.add( mistag )
print "=========================================================="
print "Summary of observables"
print "=========================================================="
observables.Print("v")
# Total time PDF
# ---------------------------
print "=========================================================="
print "Creating time PDF"
print "=========================================================="
timePDFplus = []
timePDFminus = []
timePDF = []
adet_plus = WS(ws,RooConstVar('adet_plus','+1',1.0))
id_plus = WS(ws, RooCategory('id_plus','Pi+'))
id_plus.defineType('h+',1)
adet_minus = WS(ws,RooConstVar('adet_minus','-1',-1.0))
id_minus = WS(ws, RooCategory('id_minus','Pi-'))
id_minus.defineType('h-',-1)
for i in range(0,bound):
utils = GenTimePdfUtils(myconfigfile,
ws,
gammad,
deltaGammad,
deltaMd,
singletagger,
notagging,
noprodasymmetry,
notagasymmetries,
debug)
timePDFplus.append(buildBDecayTimePdf(myconfigfile,
"Signal_DmPip",
ws,
time, terr, tag, id_plus,
tagOmegaList,
utils['tagEff'],
gammad,
deltaGammad,
deltaMd,
utils['C'],
utils['D'],
utils['Dbar'],
utils['S'],
utils['Sbar'],
trm,
tacc,
terrpdf[i] if terrpdf != None else terrpdf,
mistagPDFList,
mistag,
None,
None,
utils['aProd'],
adet_plus,
utils['aTagEff']))
timePDFminus.append(buildBDecayTimePdf(myconfigfile,
"Signal_DpPim",
ws,
time, terr, tag, id_minus,
tagOmegaList,
utils['tagEff'],
gammad,
deltaGammad,
deltaMd,
utils['C'],
utils['D'],
utils['Dbar'],
utils['S'],
utils['Sbar'],
trm,
tacc,
terrpdf[i] if terrpdf != None else terrpdf,
mistagPDFList,
mistag,
None,
None,
utils['aProd'],
adet_minus,
utils['aTagEff']))
timePDF.append(WS(ws,RooSimultaneous("Signal", "Signal", id)))
timePDF[i].addPdf(timePDFplus[i],"h+")
timePDF[i].addPdf(timePDFminus[i],"h-")
totPDF = []
for i in range(0,bound):
totPDF.append(timePDF[i])
# Fitting
# ---------------------------
print "=========================================================="
print "Fixing what is required for the fit"
print "=========================================================="
from B2DXFitters.utils import setConstantIfSoConfigured
setConstantIfSoConfigured(myconfigfile, totPDF[0])
print "=========================================================="
print "Fitting"
print "=========================================================="
if not Blinding and toys: #Unblind yourself
if nosWeights:
myfitresult = totPDF[0].fitTo(dataA, RooFit.Save(1), RooFit.Optimize(2), RooFit.Strategy(2),\
RooFit.Verbose(True), RooFit.SumW2Error(False), RooFit.Timer(True), RooFit.Offset(True))#,
#RooFit.ExternalConstraints(constList))
else:
myfitresult = totPDF[0].fitTo(dataWA, RooFit.Save(1), RooFit.Optimize(2), RooFit.Strategy(2), RooFit.Timer(True),\
RooFit.Verbose(True), RooFit.SumW2Error(True), RooFit.Timer(True), RooFit.Offset(True))#,
#RooFit.ExternalConstraints(constList))
qual = myfitresult.covQual()
status = myfitresult.status()
print 'MINUIT status is ', myfitresult.status()
print "---> Fit done; printing results"
myfitresult.Print("v")
myfitresult.correlationMatrix().Print()
myfitresult.covarianceMatrix().Print()
floatpar = myfitresult.floatParsFinal()
initpar = myfitresult.floatParsInit()
else : #Don't
myfitresult = totPDF[0].fitTo(dataWA, RooFit.Save(1), RooFit.Optimize(2), RooFit.Strategy(2),\
RooFit.SumW2Error(True), RooFit.PrintLevel(-1), RooFit.Offset(True), #RooFit.ExternalConstraints(constList),
RooFit.Timer(True))
print "=========================================================="
print "Fit done; saving output workspace"
print "=========================================================="
workout = RooWorkspace("workspace","workspace")
if nosWeights:
getattr(workout,'import')(dataWA)
else:
getattr(workout,'import')(dataWA)
getattr(workout,'import')(totPDF[0])
getattr(workout,'import')(myfitresult)
saveNameTS = TString(wsname)
workout.Print()
GeneralUtils.SaveWorkspace(workout,saveNameTS, debug)
#Save fit results for pull plots
if not Blinding and toys:
from B2DXFitters.FitResultGrabberUtils import CreatePullTree as CreatePullTree
CreatePullTree(fileNamePull, myfitresult, 'status')
