logger.info('Create 1D dataset for Ostap Tutorial ')
# ============================================================================
from OstapTutor.TestVars1 import m_psi, m_D0
#
## create data set
#
varset = ROOT.RooArgSet(m_psi, m_D0)
#
## 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)
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_cbds)
# =============================================================================
## Needham PDF
# =============================================================================
logger.info('Test Needham_pdf')
model_matt = Models.Fit1D(signal=Models.Needham_pdf(name='Matt',
mass=mass,
sigma=signal_gauss.sigma,
mean=signal_gauss.mean),
background=model_gauss.background)
with rooSilent():
result, frame = model_matt.fitTo(dataset0)
model_matt.signal.mean.release()
model_matt.signal.sigma.release()
result, frame = model_matt.fitTo(dataset0)
result, frame = model_matt.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:
logger.info('Create 1D dataset for Ostap Tutorial ')
# ============================================================================
from OstapTutor.TestVars1 import m_psi
#
## create data set
#
varset = ROOT.RooArgSet(m_psi)
#
## create model
#
import Ostap.FitModels as Models
signal = Models.Needham_pdf('N0', mass=m_psi)
bkg = Models.Bkg_pdf('B0', mass=m_psi)
bkg.tau.setVal(-10.0)
model = Models.Fit1D(signal=signal, background=bkg)
model.s.setVal(10000)
model.B.setVal(5000)
data = model.pdf.generate(varset, 15000)
# ============================================================================
if '__main__' == __name__:
import Ostap.Line
logger.info(__file__ + '\n' + Ostap.Line.line)
