def test_p1xp1_BB () :
logger.info ('Simplest non-factorized fit model: ( Gauss + P1 ) (x) ( Gauss + P1 ) + BB' )
model = Models.Fit2D (
suffix = '_3' ,
signal_x = signal1 ,
signal_y = signal2s ,
bkg_1x = -1 ,
bkg_1y = -1 ,
bkg_2D = Models.PolyPos2D_pdf ( 'P2D' , m_x , m_y , nx = 2 , ny = 2 )
)
## fit with fixed mass and sigma
with rooSilent() :
result, frame = model. fitTo ( dataset )
model.signal_x.sigma.release ()
model.signal_y.sigma.release ()
model.signal_x.mean .release ()
model.signal_y.mean .release ()
result, frame = model. fitTo ( dataset )
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 ('S1xS2 : %20s' % result ( model.SS ) [0] )
logger.info ('S1xB2 : %20s' % result ( model.SB ) [0] )
logger.info ('B1xS2 : %20s' % result ( model.BS ) [0] )
logger.info ('B1xB2 : %20s' % result ( model.BB ) [0] )
models.add ( model )
def test_polypos2D() :
logger.info ('Test PolyPos2D_pdf: positive polynomial in 2D' )
model = Models.PolyPos2D_pdf ( 'P2D' ,
m_x ,
m_y ,
nx = 2 ,
ny = 2 )
with rooSilent() :
result, f = model.fitTo ( dataset )
model.draw1 ( dataset )
model.draw2 ( dataset )
result, f = model.fitTo ( dataset , silent = True )
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 ( 'Bernstein Coefficients:\n%s' % model.pars() )
models.add ( model )
def test_p1xp1_BB():
logger.info(
'Simplest non-factorized fit model: ( Gauss + P1 ) (x) ( Gauss + P1 ) + BB'
)
model = Models.Fit2D(suffix='_3',
signal_1=Models.Gauss_pdf('Gx',
m_x.getMin(),
m_x.getMax(),
mass=m_x),
signal_2=Models.Gauss_pdf('Gy',
m_y.getMin(),
m_y.getMax(),
mass=m_y),
power1=1,
power2=1,
bkg2D=Models.PolyPos2D_pdf('P2D',
m_x,
m_y,
nx=2,
ny=2))
model.signal1.sigma.fix(m.error())
model.signal2.sigma.fix(m.error())
model.signal1.mean.fix(m.value())
model.signal2.mean.fix(m.value())
model.signal1.mean.fix(m.value())
model.signal2.mean.fix(m.value())
model.bkg1.tau.fix(0)
model.bkg2.tau.fix(0)
## fit with fixed mass and sigma
with rooSilent():
result, frame = model.fitTo(dataset)
model.signal1.sigma.release()
model.signal2.sigma.release()
model.signal1.mean.release()
model.signal2.mean.release()
result, frame = model.fitTo(dataset)
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('S1xS2 : %20s' % result(model.ss)[0])
logger.info('S1xB2 : %20s' % result(model.sb)[0])
logger.info('B1xS2 : %20s' % result(model.bs)[0])
logger.info('B1xB2 : %20s' % result(model.bb)[0])
models.add(model)
