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():