## 1) import dataset and variable
from OstapTutor.TestData2 import m_psi, data
logger.info ( 'Data: %s' % data )
## 2) create the model: signnal + background
import Ostap.FitModels as Models
## 2a) create the signal :
signal = Models.CrystalBall_pdf ( 'CB' ,
mass = m_psi ,
sigma = 0.013 ,
alpha = 1.5 ,
n = 1 ,
mean = 3.096 )
# signal = Models.CB2_pdf ( 'CB' ,
# mass = m_psi ,
# sigma = 0.013 ,
# alphaL = 1.5 ,
# alphaR = 1.5 ,
# nL = 1 ,
# nR = 1 ,
# mean = 3.096 )
# signal = Models.Bukin_pdf ( 'Bk' ,
# mass = m_psi ,
# sigma = 0.013 ,
(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_gauss)
# =============================================================================
## CrystalBall PDF
# =============================================================================
logger.info('Test CrystalBall_pdf')
model_cb = Models.Fit1D(signal=Models.CrystalBall_pdf(name='CB',
mass=mass,
alpha=3,
sigma=signal_gauss.sigma,
mean=signal_gauss.mean),
background=model_gauss.background)
with rooSilent():
result, frame = model_cb.fitTo(dataset0)
model_cb.signal.alpha.release()
result, frame = model_cb.fitTo(dataset0)
result, frame = model_cb.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:
