result, f = m_p6.fitTo(dataset2)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
print result
else:
for phi in m_p6.phis:
print "\tPoly6: phi= %s " % phi.ve()
models.append(m_p6)
# =============================================================================
logger.info("Test Poly(4)*Expo -Distribution")
# =============================================================================
m_p4e = Models.Bkg_pdf('P4e', x, power=4)
m_p4e.tau.fix(-1.25)
with rooSilent():
result, f = m_p4e.fitTo(dataset2)
m_p4e.tau.release()
result, f = m_p4e.fitTo(dataset2)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
print result
else:
print "\tPoly4e tau= %s " % result(m_p4e.tau.GetName())[0]
for phi in m_p4e.phis:
print "\tPoly4e: phi= %s " % phi.ve()
# mean = 3.096 )
# signal = Models.BifurcatedStudentT_pdf ( 'Sb' ,
# mass = m_psi ,
# sigma = 0.013 ,
# mean = 3.096 )
# signal = Models.Needham_pdf ( 'Nh' ,
# mass = m_psi ,
# sigma = 0.013 ,
# mean = 3.096 )
## 2b) create the background : exponential times 1st order polymonial
bkg = Models.Bkg_pdf ( 'B' , mass = m_psi , power = 1 )
## 2c) create the model
model = Models.Fit1D ( signal = signal ,
background = bkg )
## 3) try to fit:
r,f = model.fitTo ( data , silence = True , ncpu = 8 )
signal .mean .release()
signal .sigma.release()
# =============================================================================
## gauss PDF
# =============================================================================
logger.info('Test Gauss_pdf')
signal_gauss = Models.Gauss_pdf(name='Gauss', mass=mass)
signal_gauss.mean.setVal(m.value())
signal_gauss.sigma.setVal(m.error())
models.append(signal_gauss)
# =============================================================================
## Gauss PDF
# =============================================================================
model_gauss = Models.Fit1D(signal=signal_gauss,
background=Models.Bkg_pdf('BkgGauss',
mass=mass,
power=0))
model_gauss.background.tau.fix(0)
with rooSilent():
result, frame = model_gauss.fitTo(dataset0)
result, frame = model_gauss.fitTo(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
print result
else:
print 'Signal & Background are: ', result('S')[0], result('B')[0]
print 'Mean & Sigma are: ', result('mean_Gauss')[0], result(
'sigma_Gauss')[0]
signal_1=sigB,
signal_2=sigPhi,
##
bkg2=Models.PSPol_pdf('PSP', m_phi, ps2, power=1),
bkgB=Models.PSPol_pdf('PSB', m_phi, ps2, power=1),
)
model_phi = Models.Fit1D(signal=sigPhi,
background=Models.PSPol_pdf('PSQ',
m_phi,
ps2,
power=1),
suffix='_P')
model_B = Models.Fit1D(signal=sigB,
background=Models.Bkg_pdf('BB', m_b, power=1),
suffix='_B')
varset = ROOT.RooArgSet(m_b, m_phi)
dataset = model0.pdf.generate(varset, 1900)
## 2D fits
with rooSilent():
r, u = model1.fitTo(dataset)
r, u = model1.fitTo(dataset)
logger.info('Results of 2D-fit \n%s' % r)
## B-mass fit
with rooSilent():
rB, u = model_B.fitTo(dataset)
from OstapTutor.TestVars1 import m_psi, m_D0
#
## create data set
#
varset = ROOT.RooArgSet(m_psi, m_D0)
#
## create model
#
import Ostap.FitModels as Models
jpsi = Models.Needham_pdf('N0', mass=m_psi, mean=3.096, sigma=0.013)
D0 = Models.Gauss_pdf('G0', mass=m_D0, mean=1.864, sigma=0.007)
bkg_jpsi = Models.Bkg_pdf('B01', mass=m_psi)
bkg_D0 = Models.Bkg_pdf('B02', mass=m_D0)
bkg_jpsi.tau.fix(-10)
bkg_D0.tau.fix(+5)
model = Models.Fit2D(signal_1=jpsi,
signal_2=D0,
bkg1=bkg_jpsi,
bkgA=bkg_jpsi,
bkg2=bkg_D0,
bkgB=bkg_D0)
model.ss.fix(5000)
model.sb.fix(2500)
model.bs.fix(2500)
model.bb.fix(10000)
# ============================================================================
from OstapTutor.TestVars1 import m_psi
#
## create data set
#
varset = ROOT.RooArgSet(m_psi)
#
## create model
#
import Ostap.FitModels as Models
signal = Models.Needham_pdf('N0', mass=m_psi)
bkg = Models.Bkg_pdf('B0', mass=m_psi)
bkg.tau.setVal(-10.0)
model = Models.Fit1D(signal=signal, background=bkg)
model.s.setVal(10000)
model.B.setVal(5000)
data = model.pdf.generate(varset, 15000)
# ============================================================================
if '__main__' == __name__:
import Ostap.Line
logger.info(__file__ + '\n' + Ostap.Line.line)
logger.info(80 * '*')
from OstapTutor.TestVars1 import m_psi,m_ups
import Ostap.FitModels as Models
signal_psi = Models.CB2_pdf('Jpsi' ,
mass = m_psi ,
sigma = 0.013 ,
mean = 3.096 )
signal_psi.aL.fix(1.8)
signal_psi.aR.fix(1.8)
signal_psi.nL.fix(1.8)
signal_psi.nR.fix(1.8)
## define model for J/psi
model_psi = Models.Fit1D( signal = signal_psi ,
background = Models.Bkg_pdf ( 'BJpsi' , mass = m_psi ) )
## define the model for Y
model_Y = Models.Manca2_pdf ( m_ups , power = 0 ,
m1s = 9.4539e+00 ,
sigma = 4.03195e-02 )
model_Ypsi = Models.MancaX_pdf ( model_Y ,
signal_psi ,
suffix = '' )
# ==========================================================================================
# The END
# ==========================================================================================
