def test_bifstudentT():
logger.info ('Test bifurcated StudentT_pdf: bifurcated Student-t' )
model = Models.Fit1D (
signal = Models.BifurcatedStudentT_pdf ( name = 'BfST' ,
xvar = mass ,
nL = 25 ,
nR = 25 ,
mean = signal_gauss.mean ,
sigma = signal_gauss.sigma ) ,
background = Models.Bkg_pdf ('BkgST2', xvar = mass , power = 0 ))
signal = model.signal
model.S.setVal(5000)
model.B.setVal( 500)
with rooSilent() :
result, frame = model. fitTo ( dataset0 )
signal.nL .release()
signal.nR .release()
signal.sigma.release()
result, frame = model. 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 ( 'Signal & Background are: %-28s & %-28s ' % ( result ( 'S' )[0] , result( 'B' )[0] ) )
logger.info ( 'Mean is: %-28s ' % result ( signal.mean )[0] )
logger.info ( 'Sigma is: %-28s ' % result ( signal.sigma )[0] )
logger.info ( 'Asymmetry is: %-28s ' % result ( signal.asym )[0] )
logger.info ( 'n(L) is: %-28s ' % result ( signal.nL )[0] )
logger.info ( 'n(R) is: %-28s ' % result ( signal.nR )[0] )
models.add ( model )
def test_bifstudentT():
logger.info('Test bifurcated StudentT_pdf: bifurcated Student-t')
model = Models.Fit1D(signal=Models.BifurcatedStudentT_pdf(
name='BfST',
xvar=mass,
nL=25,
nR=25,
mean=signal_gauss.mean,
sigma=signal_gauss.sigma),
background=Models.Bkg_pdf('BkgST2',
xvar=mass,
power=0),
S=S,
B=B)
signal = model.signal
model.S.setVal(5000)
model.B.setVal(500)
with rooSilent():
result, frame = model.fitTo(dataset0)
signal.nL.release()
signal.nR.release()
signal.sigma.release()
result, frame = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("Bifurkated Student's t-function\n%s" %
result.table(prefix="# "))
models.add(model)