def test_simfit2 ( ) :
# =========================================================================
signal1 = Models.Gauss_pdf ( 'G1' ,
xvar = mass1 ,
mean = (1.5 , 6.5 ) ,
sigma = (0.1 , 2.5 ) )
model1 = Models.Fit1D ( suffix = 'M1' , signal = signal1 , background = -1 )
model1.S = NS1
model1.B = NB1
mean2 = signal1.vars_add ( signal1.mean , 10.0 )
sigma2 = signal1.vars_multiply ( signal1.sigma , 0.5 )
signal2 = Models.Gauss_pdf ( 'G2' ,
xvar = mass2 ,
mean = mean2 ,
sigma = sigma2 )
model2 = Models.Fit1D ( suffix = 'M2' , signal = signal2 , background = -1 )
model2.S = NS2
model2.B = NB2
# =========================================================================
## fit 1
r1 , f1 = model1.fitTo ( dataset1 , draw = True , nbins = 50 , silent = True )
## fit 2
r2 , f2 = model2.fitTo ( dataset2 , draw = True , nbins = 50 , silent = True )
# =========================================================================
## combine data
sample = ROOT.RooCategory ('sample','sample' , 'A' , 'B' )
## combine datasets
from ostap.fitting.simfit import combined_data
vars = ROOT.RooArgSet ( mass1 , mass2 )
dataset = combined_data ( sample , vars , { 'A' : dataset1 , 'B' : dataset2 } )
## combine PDFs
model_sim = Models.SimFit (
sample , { 'A' : model1 , 'B' : model2 } , name = 'X'
)
# =========================================================================
r , f = model_sim.fitTo ( dataset , silent = True )
r , f = model_sim.fitTo ( dataset , silent = True )
fA = model_sim.draw ( 'A' , dataset , nbins = 50 )
fB = model_sim.draw ( 'B' , dataset , nbins = 50 )
fNLL = model_sim.draw_nll ( 'SM2' , dataset , range = (0,1000) )
## significance
wilks = model_sim.wilks ( 'SM2' , dataset )
logger.info ( 'Fit results are: %s ' % r )
def test_simfit2():
logger = getLogger('test_simfit2')
# =========================================================================
signal1 = Models.Gauss_pdf('G1',
xvar=mass1,
mean=(1.5, 6.5),
sigma=(0.1, 2.5))
model1 = Models.Fit1D(suffix='M1', signal=signal1, background=-1)
model1.S = NS1
model1.B = NB1
mean2 = signal1.vars_add(signal1.mean, 10.0)
sigma2 = signal1.vars_multiply(signal1.sigma, 0.5)
signal2 = Models.Gauss_pdf('G2', xvar=mass2, mean=mean2, sigma=sigma2)
model2 = Models.Fit1D(suffix='M2', signal=signal2, background=-1)
model2.S = NS2
model2.B = NB2
with use_canvas('test_simfit2'):
# =========================================================================
## fit 1
with wait(1):
r1, f1 = model1.fitTo(dataset1, draw=True, nbins=50, silent=True)
title = 'Results of fit to dataset1'
logger.info('%s\n%s' % (title, r1.table(title=title, prefix='# ')))
## fit 2
with wait(1):
r2, f2 = model2.fitTo(dataset2, draw=True, nbins=50, silent=True)
title = 'Results of fit to dataset2'
logger.info('%s\n%s' % (title, r2.table(title=title, prefix='# ')))
# =========================================================================
## combine data
sample = ROOT.RooCategory('sample', 'sample', 'A', 'B')
## combine datasets
from ostap.fitting.simfit import combined_data
vars = ROOT.RooArgSet(mass1, mass2)
dataset = combined_data(sample, vars, {'A': dataset1, 'B': dataset2})
## combine PDFs
model_sim = Models.SimFit(sample, {'A': model1, 'B': model2}, name='X')
# =========================================================================
r, f = model_sim.fitTo(dataset, silent=True)
r, f = model_sim.fitTo(dataset, silent=True)
title = 'Results of simultaneous fit'
logger.info('%s\n%s' % (title, r.table(title=title, prefix='# ')))
with use_canvas('test_simfit2'):
with wait(1):
fA = model_sim.draw('A', dataset, nbins=50)
with wait(1):
fB = model_sim.draw('B', dataset, nbins=50)
def test_simfit1():
logger = getLogger('test_simfit1')
signal1 = Models.Gauss_pdf('G1',
xvar=mass,
mean=(0.5, 2.5),
sigma=(0.1, 1.0))
model1 = Models.Fit1D(suffix='M1', signal=signal1, background=-1)
model1.S = NS1
model1.B = NB1
mean2 = signal1.vars_add(signal1.mean, 1.0)
sigma2 = signal1.vars_multiply(signal1.sigma, 0.5)
signal2 = Models.Gauss_pdf('G2', xvar=mass, mean=mean2, sigma=sigma2)
model2 = Models.Fit1D(suffix='M2',
signal=signal2,
background=model1.background)
model2.S = NS2
model2.B = NB2
with use_canvas('test_simfit1'):
# =========================================================================
## fit 1
with wait(1):
r1, f1 = model1.fitTo(dataset1, draw=True, nbins=50, silent=True)
## fit 2
with wait(1):
r2, f2 = model2.fitTo(dataset2, draw=True, nbins=50, silent=True)
# =========================================================================
## combine data
sample = ROOT.RooCategory('sample', 'sample', 'A', 'B')
## combine datasets
from ostap.fitting.simfit import combined_data
vars = ROOT.RooArgSet(mass)
dataset = combined_data(sample, vars, {'A': dataset1, 'B': dataset2})
## combine PDFs
model_sim = Models.SimFit(sample, {'A': model1, 'B': model2}, name='X')
# =========================================================================
r, f = model_sim.fitTo(dataset, silent=True)
r, f = model_sim.fitTo(dataset, silent=True)
with use_canvas('test_simfit1'):
with wait(1):
fA = model_sim.draw('A', dataset, nbins=50)
with wait(1):
fB = model_sim.draw('B', dataset, nbins=50)
logger.info('Fit results are: %s ' % r)
def test_simfit4():
# =========================================================================
VARS = MakeVar()
## MC/DATA ratio for signal widths
Rs = ROOT.RooRealVar('Rs', 'MC/DATA ratio for signal widths', 1.0, 0.2,
3.0)
## sigma for normalization peak: sigma from MC multiplied by a factor
sigma_N = VARS.vars_multiply(Rs, sigma1, name='sigma_N')
## sigma for signal peak: sigma from MC multiplied by the same factor
sigma_S = VARS.vars_multiply(Rs, sigma2, name='sigma_S')
## Normalization peak:
signal_N = Models.Gauss_pdf('GN', xvar=mass1, mean=(3, 8), sigma=sigma_N)
## mean of low-statistics signal is constrained to the mean of high statistic signal
mean_S = VARS.vars_sum(signal_N.mean, mean2 - mean1)
## low statistic signal :
signal_S = Models.Gauss_pdf('GS', xvar=mass2, mean=mean_S, sigma=sigma_S)
# =========================================================================
## model for fitting of normalization channel
model_N = Models.Fit1D(suffix='N', signal=signal_N,
background=-2) ## 2nd order positive polynomial
model_N.S = NS1
model_N.B = NB1
## model for fitting of low-statistic signal channel
model_S = Models.Fit1D(suffix='S', signal=signal_S,
background=-2) ## 2nd order positive polynomial
model_S.S = NS2
model_S.B = NB2
# =========================================================================
## make fit for thew normalization channel only
# =========================================================================
rN, fN = model_N.fitTo(dataset1, draw=None, silent=True)
rN, fN = model_N.fitTo(dataset1, draw=None, silent=True)
rN, fN = model_N.fitTo(dataset1, draw=True, nbins=50, silent=True)
logger.info('Fit results for normalization sample only: %s' % rN)
# =========================================================================
## make fit for the low-statistic signal channel only
# =========================================================================
rS, fS = model_S.fitTo(dataset2, draw=None, silent=True)
rS, fS = model_S.fitTo(dataset2, draw=None, silent=True)
rS, fS = model_S.fitTo(dataset2, draw=True, nbins=50, silent=True)
logger.info('Fit results for low-statistic signal only: %s' % rS)
# =========================================================================
## combine data for simultaneous fit
# =========================================================================
sample = ROOT.RooCategory('sample', 'sample', 'S', 'N')
## combine datasets
from ostap.fitting.simfit import combined_data
vars = ROOT.RooArgSet(mass1, mass2)
dataset = combined_data(sample, vars, {'N': dataset1, 'S': dataset2})
## combine PDFs
model_sim = Models.SimFit(sample, {'S': model_S, 'N': model_N}, name='X')
# =========================================================================
rC, fC = model_sim.fitTo(dataset, silent=True)
rC, fC = model_sim.fitTo(dataset, silent=True)
rC, fC = model_sim.fitTo(dataset, silent=True)
fN = model_sim.draw('N', dataset, nbins=50)
fS = model_sim.draw('S', dataset, nbins=50)
logger.info('Combined fit results are: %s ' % rC)
logger.info(' Value | Simple fit | Combined fit ')
logger.info(' #N | %25s | %-25s ' % (rS.SS * 1, rC.SS * 1))
logger.info(' mean | %25s | %-25s ' % (rS.mean_GN * 1, rC.mean_GN * 1))
logger.info(' Rs | %25s | %-25s ' % (rS.Rs * 1, rC.Rs * 1))
def test_toys_simfit_1():
## make simple test mass
mass = ROOT.RooRealVar('test_mass', 'Some test mass', 0, 5)
## book very simple data set:
varset1 = ROOT.RooArgSet(mass)
dataset1 = ROOT.RooDataSet(dsID(), 'Test Data set-1', varset1)
## book very simple data set:
varset2 = ROOT.RooArgSet(mass)
dataset2 = ROOT.RooDataSet(dsID(), 'Test Data set-2', varset2)
## high statistic, low-background "control channel"
mean1 = 2.0
sigma1 = 0.50
NS1 = 1000
NB1 = 250
for i in range(NS1):
v1 = random.gauss(mean1, sigma1)
if v1 in mass:
mass.setVal(v1)
dataset1.add(varset1)
for i in range(NB1):
v1 = random.uniform(0, 5)
if v1 in mass:
mass.setVal(v1)
dataset1.add(varset1)
## low statistic, high-background "control channel"
NS2 = 250
NB2 = 1000
mean2 = mean1 + 1.0
sigma2 = sigma1 * 0.5
for i in range(NS2):
v2 = random.gauss(mean2, sigma2)
if v2 in mass:
mass.setVal(v2)
dataset2.add(varset2)
for i in range(NB2):
v2 = random.uniform(0, 5)
if v2 in mass:
mass.setVal(v2)
dataset2.add(varset2)
signal1 = Models.Gauss_pdf('G1',
xvar=mass,
mean=(0.5, 2.5),
sigma=(0.1, 1.0))
model1 = Models.Fit1D(suffix='M1', signal=signal1, background=-1)
model1.S = NS1
model1.B = NB1
mean2 = signal1.vars_add(signal1.mean, 1.0)
sigma2 = signal1.vars_multiply(signal1.sigma, 0.5)
signal2 = Models.Gauss_pdf('G2', xvar=mass, mean=mean2, sigma=sigma2)
model2 = Models.Fit1D(suffix='M2',
signal=signal2,
background=model1.background)
model2.S = NS2
model2.B = NB2
# =========================================================================
## fit 1
r1, f1 = model1.fitTo(dataset1, draw=True, nbins=50, silent=True)
## fit 2
r2, f2 = model2.fitTo(dataset2, draw=True, nbins=50, silent=True)
# =========================================================================
## combine data
sample = ROOT.RooCategory('sample', 'sample', 'A', 'B')
## combine datasets
from ostap.fitting.simfit import combined_data
vars = ROOT.RooArgSet(mass)
dataset = combined_data(sample, vars, {'A': dataset1, 'B': dataset2})
## combine PDFs
model_sim = Models.SimFit(sample, {'A': model1, 'B': model2}, name='X')
# =========================================================================
r, f = model_sim.fitTo(dataset, silent=True)
r, f = model_sim.fitTo(dataset, silent=True)
fA = model_sim.draw('A', dataset, nbins=50)
fB = model_sim.draw('B', dataset, nbins=50)
logger.info('Fit results are: %s ' % r.table(prefix="# "))
## Make toys
results, stats = Toys.make_toys(pdf=model_sim,
nToys=100,
data=[mass],
gen_config={
'nEvents': (NS1 + NB1, NS2 + NB2),
'sample': True
},
fit_config={'silent': True},
init_pars={
'mean_G1': mean1,
'BM1': NB1,
'BM2': NB2,
'sigma_G1': sigma1,
'SM1': NS1,
'SM2': NS2,
'phi0_Bkg_FitG1_M1': 0
},
silent=True,
progress=True)
def test_simfit4():
logger = getLogger('test_simfit4')
# =========================================================================
VARS = MakeVar()
## MC/DATA ratio for signal widths
Rs = ROOT.RooRealVar('Rs', 'MC/DATA ratio for signal widths', 1.0, 0.2,
3.0)
## sigma for normalization peak: sigma from MC multiplied by a factor
sigma_N = VARS.vars_multiply(Rs, sigma1, name='sigma_N')
## sigma for signal peak: sigma from MC multiplied by the same factor
sigma_S = VARS.vars_multiply(Rs, sigma2, name='sigma_S')
## Normalization peak:
signal_N = Models.Gauss_pdf('GN', xvar=mass1, mean=(3, 8), sigma=sigma_N)
## mean of low-statistics signal is constrained to the mean of high statistic signal
mean_S = VARS.vars_sum(signal_N.mean, mean2 - mean1)
## low statistic signal :
signal_S = Models.Gauss_pdf('GS', xvar=mass2, mean=mean_S, sigma=sigma_S)
# =========================================================================
## 3rd order positive polynomial
bkg_N = Models.make_bkg(-3, 'Bkg_N', mass1)
## model for fitting of normalization channel
model_N = Models.Fit1D(suffix='N', signal=signal_N, background=bkg_N)
model_N.S = NS1
model_N.B = NB1
## 3rd order positive polynomial
bkg_S = Models.make_bkg(-3, 'Bkg_S', mass2)
## model for fitting of low-statistic signal channel
model_S = Models.Fit1D(suffix='S', signal=signal_S, background=bkg_S)
model_S.S = NS2
model_S.B = NB2
# =========================================================================
## make fit for thew normalization channel only
# =========================================================================
with use_canvas('test_simfit4'):
rN, fN = model_N.fitTo(dataset1, draw=None, silent=True)
rN, fN = model_N.fitTo(dataset1, draw=None, silent=True)
rN, fN = model_N.fitTo(dataset1, draw=True, nbins=50, silent=True)
title = 'Fit to high-statistic normalisation sample'
logger.info('Fit results for normalization sample only: %s' %
rN.table(title=title, prefix='# '))
# =========================================================================
## make fit for the low-statistic signal channel only
# =========================================================================
with use_canvas('test_simfit4'):
rS, fS = model_S.fitTo(dataset2, draw=None, silent=True)
rS, fS = model_S.fitTo(dataset2, draw=None, silent=True)
rS, fS = model_S.fitTo(dataset2, draw=True, nbins=50, silent=True)
title = 'Fit to low-statistics sample'
logger.info('Fit results for low-statistic signal only:\n%s' %
rS.table(title=title, prefix='# '))
# =========================================================================
## combine data for simultaneous fit
# =========================================================================
sample = ROOT.RooCategory('sample', 'sample', 'S', 'N')
## combine datasets
from ostap.fitting.simfit import combined_data
vars = ROOT.RooArgSet(mass1, mass2)
dataset = combined_data(sample, vars, {'N': dataset1, 'S': dataset2})
## combine PDFs
model_sim = Models.SimFit(sample, {'S': model_S, 'N': model_N}, name='X')
# =========================================================================
rC, fC = model_sim.fitTo(dataset, silent=True)
rC, fC = model_sim.fitTo(dataset, silent=True)
rC, fC = model_sim.fitTo(dataset, silent=True)
rC, fC = model_sim.fitTo(dataset, silent=True)
with use_canvas('test_simfit4'):
with wait(1):
fN = model_sim.draw('N', dataset, nbins=50)
with wait(1):
fS = model_sim.draw('S', dataset, nbins=50)
title = 'Simultaneous fit'
logger.info('Combined fit results are:\n%s ' %
rC.table(title=title, prefix='#'))
## combine data
sample = ROOT.RooCategory ('sample','sample' , 'A' , 'B' )
## combine datasets
from ostap.fitting.simfit import combined_data
vars = ROOT.RooArgSet ( mass )
dataset = combined_data ( sample , vars , { 'A' : dataset1 , 'B' : dataset2 } )
## combine PDFs
model_sim = Models.SimFit (
sample , { 'A' : model1 , 'B' : model2 } , name = 'X'
)
# =============================================================================
r , f = model_sim.fitTo ( dataset , silent = True )
r , f = model_sim.fitTo ( dataset , silent = True )
fA = model_sim.draw ( 'A' , dataset , nbins = 50 )
fB = model_sim.draw ( 'B' , dataset , nbins = 50 )
logger.info ( 'Fit results are: %s ' % r )
# =============================================================================
## The END
# =============================================================================
def test_simfit3():
logger = getLogger('test_simfit3')
## low statistic, high-background "signal channel"
signal2 = Models.Gauss_pdf('G2',
xvar=mass2,
mean=(15, 20),
sigma=(0.1, 5.0))
signal3 = Models.Gauss_pdf('G3',
xvar=mass3,
mean=signal2.mean,
sigma=signal2.sigma)
model_S = Models.Fit2DSym(suffix='M2',
yvar=mass3,
signal_x=signal2,
signal_y=signal3,
bkg_1x=None,
bkg_2x=None)
model_S.BB = NB2
model_S.SS = NS2
model_S.SB = NC2
model_S.SB.setMin(-500)
## fit 2
rS, fx = model_S.fitTo(dataset2, draw=None, nbins=50, silent=True)
rS, fx = model_S.fitTo(dataset2, draw=None, nbins=50, silent=True)
title = 'Results of fit to signal sample only'
logger.info('%s\n%s' % (title, rS.table(title=title, prefix='# ')))
with use_canvas('test_simfit3'):
with wait(1):
rS, fx = model_S.fitTo(dataset2, draw='X', nbins=50, silent=True)
with wait(1):
rS, fy = model_S.fitTo(dataset2, draw='Y', nbins=50, silent=True)
# =========================================================================
## high statistic, low-background "control/normalization channel"
mean_N = signal2.vars_subtract(signal2.mean, 10.0)
sigma_N = signal2.vars_multiply(signal2.sigma, 0.5)
signal_N = Models.Gauss_pdf('G1', xvar=mass1, mean=mean_N, sigma=sigma_N)
model_N = Models.Fit1D(suffix='M1', signal=signal_N, background=-1)
model_N.S = NS1
model_N.B = NB1
## fit 1
rN, fN = model_N.fitTo(dataset1, draw=False, nbins=50, silent=True)
with use_canvas('test_simfit3'), wait(1):
rN, fN = model_N.fitTo(dataset1, draw=True, nbins=50, silent=True)
title = 'Results of fit to normalization sample only'
logger.info('%s\n%s' % (title, rN.table(title=title, prefix='# ')))
# =========================================================================
## combine data
sample = ROOT.RooCategory('sample', 'sample', 'S', 'N')
## combine datasets
from ostap.fitting.simfit import combined_data
vars = ROOT.RooArgSet(mass1, mass2, mass3)
dataset = combined_data(sample, vars, {'N': dataset1, 'S': dataset2})
## combine PDFs
model_sim = Models.SimFit(sample, {'S': model_S, 'N': model_N}, name='X')
# =========================================================================
rC, fC = model_sim.fitTo(dataset, silent=True)
rC, fC = model_sim.fitTo(dataset, silent=True)
title = 'Results of simultaneous fit'
logger.info('%s\n%s' % (title, rC.table(title=title, prefix='# ')))
with use_canvas('test_simfit3'), wait(1):
with wait(1):
fN = model_sim.draw('N', dataset, nbins=50)
with wait(1):
fSx = model_sim.draw('S/x', dataset, nbins=50)
with wait(1):
fSy = model_sim.draw('S/y', dataset, nbins=50)
logger.info(' Value | Simple fit | Combined fit ')
logger.info(' #N | %25s | %-25s ' % (rS.SSM2 * 1, rC.SSM2 * 1))
logger.info(' mean | %25s | %-25s ' % (rS.mean_G2 * 1, rC.mean_G2 * 1))
logger.info(' sigma | %25s | %-25s ' % (rS.sigma_G2 * 1, rC.sigma_G2 * 1))
# =============================================================================
## combine data
sample = ROOT.RooCategory ('sample','sample' , 'S' , 'N' )
## combine datasets
from ostap.fitting.simfit import combined_data
vars = ROOT.RooArgSet ( mass1 , mass2 , mass3 )
dataset = combined_data ( sample , vars , { 'N' : dataset1 , 'S' : dataset2 } )
## combine PDFs
model_sim = Models.SimFit (
sample , { 'S' : model_S , 'N' : model_N } , name = 'X'
)
# =============================================================================
rC , fC = model_sim.fitTo ( dataset , silent = True )
rC , fC = model_sim.fitTo ( dataset , silent = True )
fN = model_sim.draw ( 'N' , dataset , nbins = 50 )
fSx = model_sim.draw ( 'S/x' , dataset , nbins = 50 )
fSy = model_sim.draw ( 'S/y' , dataset , nbins = 50 )
logger.info ( 'Combined fit results are: %s ' % rC )
logger.info ( ' Value | Simple fit | Combined fit ' )
logger.info ( ' #N | %25s | %-25s ' % ( rS.SSM2 * 1 , rC.SSM2 * 1 ) )
logger.info ( ' mean | %25s | %-25s ' % ( rS.mean_G2 * 1 , rC.mean_G2 * 1 ) )
def test_simfit5():
logger = getLogger('test_simfit5')
# =========================================================================
## resoltuion for the left peak
# =========================================================================
reso1 = Models.ResoGauss('RG1',
xvar=dm1,
sigma=(sigma1, sigma1 / 2, sigma1 * 2),
mean=(0, -1, 1))
# fit DM1 dataset for resolution
r1, f1 = reso1.fitTo(dset1, silent=True)
r1, f1 = reso1.fitTo(dset1, silent=True, draw=True, nbins=100)
# =========================================================================
## resoltuion for the central peak
# =========================================================================
reso2 = Models.ResoGauss('RG2',
xvar=dm2,
sigma=(sigma2, sigma2 / 2, sigma2 * 2),
mean=(0, -1, 1))
# fit DM2 dataset for resolution
r2, f2 = reso2.fitTo(dset2, silent=True)
r2, f2 = reso2.fitTo(dset2, silent=True, draw=True, nbins=100)
# =========================================================================
## resoltuion for the right peak
# =========================================================================
reso3 = Models.ResoGauss('RG3',
xvar=dm3,
sigma=(sigma3, sigma3 / 2, sigma3 * 2),
mean=(0, -1, 1))
# fit DM2 dataset for resolution
r3, f3 = reso3.fitTo(dset3, silent=True)
r3, f3 = reso3.fitTo(dset3, silent=True, draw=True, nbins=100)
# =========================================================================
# model for data fit
# =========================================================================
signal1 = Models.Gauss_pdf('G1',
xvar=mass,
sigma=reso1.sigma,
mean=(mean1, mean1 - 1, mean1 + 1))
signal2 = Models.Gauss_pdf('G2',
xvar=mass,
sigma=reso2.sigma,
mean=(mean2, mean2 - 1, mean2 + 1))
signal3 = Models.Gauss_pdf('G3',
xvar=mass,
sigma=reso3.sigma,
mean=(mean3, mean3 - 1, mean3 + 1))
background = Models.PolyPos_pdf('Bkg', xvar=mass, power=1)
model = Models.Fit1D(signals=[signal1, signal2, signal3],
background=background)
model.S = NS1, NS2, NS3
model.B = NB
# =========================================================================
## Fit data (without resolution samples)
# =========================================================================
r0, f0 = model.fitTo(dataset, silent=True)
r0, f0 = model.fitTo(dataset, silent=True, draw=True, nbins=100)
logger.info('Fit result (only data):\n%s' % r0.table(prefix="# "))
## combine PDFs
model_sim = Models.SimFit(category, {
'data': model,
'dm1': reso1,
'dm2': reso2,
'dm3': reso3
},
name='X')
# =========================================================================
## Simultanegous fit data with resolution samples)
# =========================================================================
r_1, f_1 = model_sim.fitTo(cdataset1, silent=True)
r_1, f_1 = model_sim.fitTo(cdataset1, silent=True)
logger.info('Simultaneous fit result (unbinned reso):\n%s' %
r_1.table(prefix="# "))
with use_canvas('test_simfit5'), wait(1):
with wait(1):
fdm1 = model_sim.draw('dm1', cdataset1, nbins=100)
with wait(1):
fdm2 = model_sim.draw('dm2', cdataset1, nbins=100)
with wait(1):
fdm3 = model_sim.draw('dm3', cdataset1, nbins=100)
with wait(1):
fd = model_sim.draw('data', cdataset1, nbins=100)
# =========================================================================
## Simultanegous fit data with resolution samples)
# =========================================================================
r_2, f_2 = model_sim.fitTo(cdataset2, silent=True, sumw2=True)
r_2, f_2 = model_sim.fitTo(cdataset2, silent=True, sumw2=True)
logger.info('Simultaneous fit result (binned reso):\n%s' %
r_2.table(prefix="# "))
with use_canvas('test_simfit5'), wait(1):
with wait(1):
fdm1 = model_sim.draw('dm1', cdataset2, nbins=100)
with wait(1):
fdm2 = model_sim.draw('dm2', cdataset2, nbins=100)
with wait(1):
fdm3 = model_sim.draw('dm3', cdataset2, nbins=100)
with wait(1):
fd = model_sim.draw('data', cdataset2, nbins=100)
# ===============================================================================
## 3a: Construction for simultaneous fit
# ===============================================================================
## combine data
sample = ROOT.RooCategory('sample', 'sample', 'DATA', 'MC')
## combine datasets
vars = ROOT.RooArgSet(mass, dm)
dataset = combined_data(sample, vars, {'DATA': ds_DATA, 'MC': ds_MC})
## combine PDFs
model_sim = Models.SimFit(sample, {
'DATA': model_DATA,
'MC': model_MC
},
name='X')
## fit it!
rSIM, fSIM = model_sim.fitTo(dataset, silent=True)
rSIM, fSIM = model_sim.fitTo(dataset, silent=True)
title = 'Simultaneous fit(1) to DATA&MC'
logger.info('%s\n%s' % (title, rSIM.table(title=title, prefix='# ')))
phi_DATA.m0.release()
reso_MC.sigma.release()
## fit it!
rSIM, fSIM = model_sim.fitTo(dataset, silent=True)
rSIM, fSIM = model_sim.fitTo(dataset, silent=True)
