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]
models.append(model_cbrs)
# =============================================================================
## double sided CrystalBall PDF
# =============================================================================
logger.info('Test CrystalBallDS_pdf')
model_cbds = Models.Fit1D(signal=Models.CB2_pdf(name='CB2',
mass=mass,
sigma=signal_gauss.sigma,
mean=signal_gauss.mean),
background=model_gauss.background)
model_cbds.signal.aL.fix(2)
model_cbds.signal.nL.fix(10)
model_cbds.signal.aR.fix(2.5)
model_cbds.signal.nR.fix(10)
model_cbds.s.setVal(5000)
model_cbds.b.setVal(500)
with rooSilent():
result, frame = model_cbds.fitTo(dataset0)
model_cbds.signal.aL.release()
model_cbds.signal.aR.release()
"""
# =========================================================================================
import ROOT
from Ostap.PyRoUts import *
from AnalysisPython.Logger import getLogger
# ==========================================================================================
if '__main__' == __name__ : logger = getLogger('OstapTutor/yfits')
else : logger = getLogger( __name__ )
# ==========================================================================================
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 )
