print dataset
# =============================================================================
import Ostap.FitModels as Models
models = []
# =============================================================================
## gauss as signal, const as background
# =============================================================================
logger.info(
'Simplest (factorized) fit model: ( Gauss + const ) x ( Gauss + const ) ')
model = Models.Fit2D(signal_1=Models.Gauss_pdf('Gx',
m_x.getMin(),
m_x.getMax(),
mass=m_x),
signal_2=Models.Gauss_pdf('Gy',
m_y.getMin(),
m_y.getMax(),
mass=m_y))
model.signal1.sigma.fix(m.error())
model.signal2.sigma.fix(m.error())
model.signal1.mean.fix(m.value())
model.signal2.mean.fix(m.value())
model.signal1.mean.fix(m.value())
model.signal2.mean.fix(m.value())
model.bkg1.tau.fix(0)
model.bkg2.tau.fix(0)
model.bkgA.tau.fix(0)
model.bkgB.tau.fix(0)
#
## 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)
data = model.pdf.generate(varset, 20000)
# ============================================================================
if '__main__' == __name__:
import Ostap.Line
logger.info(__file__ + '\n' + Ostap.Line.line)
bw = cpp.Gaudi.Math.Phi0(1.0195, 0.0043, 0.4937)
sigPhi0 = Models.BreitWigner_pdf('Phi0',
bw,
mean=1.0195,
gamma=0.0043,
mass=m_phi,
convolution=0.0005)
sigPhi = sigPhi0
ps2 = cpp.Gaudi.Math.PhaseSpaceNL(2 * 0.4937, 5.278 - 3.096, 2, 3)
model0 = Models.Fit2D(
signal_1=sigB,
signal_2=sigPhi0,
##
bkg2=Models.PSPol_pdf('PSP0', m_phi, ps2, power=1),
bkgB=Models.PSPol_pdf('PSB0', m_phi, ps2, power=1),
suffix='_GEN')
model0.bkg1.tau.fix(0)
model0.bkgA.tau.fix(0)
model0.ss.fix(1000)
model0.sb.fix(300)
model0.bs.fix(300)
model0.bb.fix(300)
model1 = Models.Fit2D(
signal_1=sigB,
signal_2=sigPhi,
##
## 2) create the model: signnal + background
#
## create model
#
import Ostap.FitModels as Models
jpsi = Models.Needham_pdf ( 'N' , mass = m_psi , mean = 3.096 , sigma = 0.013 )
D0 = Models.Gauss_pdf ( 'G' , mass = m_D0 , mean = 1.864 , sigma = 0.007 )
model = Models.Fit2D (
signal_1 = jpsi ,
signal_2 = D0 ,
power1 = 2 ,
power2 = 2 ,
powerA = 2 ,
powerB = 2 )
r,f = model.fitTo ( data , silence = True , ncpu = 8 )
r,f = model.fitTo ( data , ncpu = 8 , silence = True )
model.draw1 ( data , nbins = 50 )
# ============================================================================