mK = 0.49369
mPion = 0.1396
bw = cpp.Gaudi.Math.Phi0(1.0195, 0.0043, mK)
signal = Models.BreitWigner_pdf('BW0',
bw,
mass=m_phi,
gamma=0.0043,
mean=1.0195,
convolution=0.0015)
## Phase space as background:
ps = cpp.Gaudi.Math.PhaseSpaceNL(2 * mK, 10.0, 2, 5)
bkg = Models.PSPol_pdf('PS0', mass=m_phi, phasespace=ps, power=1)
f2 = cpp.Gaudi.Math.Flatte2(0.980, 165, 4.21, mPion, mPion, mK, mK)
flatte = Models.Flatte2_pdf('F20', f2, mass=m_phi, m0_980=1.000, m0g1=0.165)
flatte.mean.fix(0.980)
model = Models.Fit1D(signal=signal, othersignals=[flatte], background=bkg)
model.S.fix(10000)
model.B.fix(5000)
model.S_1.fix(5000)
data = model.pdf.generate(varset, 20000)
f1 = a1 / (a1 + a2)
f2 = a2 / (a1 + a2)
fl_ = cpp.Gaudi.Math.Flatte(m_X, m_X * g_X * f1, f2 / f1, m_Jpsi, m_pipi,
1.86483, 2.00696)
flatte = Models.Flatte_pdf('Flatte',
fl_,
mass=mass,
m0_980=m_X,
m0g1=m_X * g_X * f1,
g2og1=f2 / f1)
flatte2 = Models.Flatte2_pdf('Flatte',
fl_,
mass=mass,
m0_980=m_X,
m0g1=m_X * g_X * f1,
g2og1=f2 / f1)
if not ROOT.gROOT.IsBatch():
with rootWarning():
from Ostap.Canvas import getCanvas
c = getCanvas()
br = breit.draw() # nbins = 500 )
fl = flatte.draw() # nbins = 500 )
##fl2 = flatte2.draw ( nbins = 1000 )
if not ROOT.gROOT.IsBatch():
with rootWarning():
