def test_p1xp1_BBsym():
logger.info(
'Symmetric non-factorized fit model: ( Gauss + P1 ) (x) ( Gauss + P1 ) + BBsym'
)
sb = ROOT.RooRealVar('sb', 'SB', 0, 10000)
model = Models.Fit2DSym(
suffix='_6',
signal_x=signal1,
signal_y=signal2s,
bkg_1x=-1,
bkg_2D=Models.PolyPos2Dsym_pdf('P2D5', m_x, m_y, n=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)
model.draw1(dataset)
model.draw2(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] / 2))
logger.info('B1xS2 : %20s' % (result(model.BS)[0] / 2))
logger.info('B1xB2 : %20s' % result(model.BB)[0])
models.add(model)
def test_p1xp1_BBs () :
logger.info ('Non-factorized symmetric background fit model: ( Gauss + P1 ) (x) ( Gauss + P1 ) + BBsym' )
model = Models.Fit2D (
suffix = '_4' ,
signal_x = signal1 ,
signal_y = signal2s ,
bkg_1x = -1 ,
bkg_1y = -1 ,
bkg_2D = Models.PolyPos2Dsym_pdf ( 'P2Ds' , m_x , m_y , n = 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_polypossym2D():
logger = getLogger('test_polypossym2D')
logger.info('Test PolyPos2Dsym_pdf: Symmetric positive polynomial')
model = Models.PolyPos2Dsym_pdf('P2Ds ', m_x, m_y, n=2)
with rooSilent():
result, f = model.fitTo(dataset)
with use_canvas('test_polypossym2D'):
with wait(1):
model.draw1(dataset)
with wait(1):
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_BBss():
logger.info(
'Symmetrised fit model with non-factorized symmetric background: ( Gauss + P1 ) (x) ( Gauss + P1 ) + BBsym'
)
sb = ROOT.RooRealVar('sb', 'SB', 0, 10000)
model = Models.Fit2D(suffix='_5',
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.PolyPos2Dsym_pdf('P2Ds', m_x, m_y, n=2),
sb=sb,
bs=sb)
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)
def test_p1xp1_BBss():
logger = getLogger('test_p1xp1_BBss')
logger.info(
'Symmetrised fit model with non-factorized symmetric background: ( Gauss + P1 ) (x) ( Gauss + P1 ) + BBsym'
)
sb = ROOT.RooRealVar('sb', 'SB', 2500, 0, 10000)
model = Models.Fit2D(suffix='_5',
signal_x=signal1,
signal_y=signal2s,
bkg_1x=-1,
bkg_2D=Models.PolyPos2Dsym_pdf('P2Ds', m_x, m_y, n=1),
sb=sb,
bs=sb)
model.SS = N_ss
model.BB = N_bb
model.SB = 2500
## fit with fixed mass and sigma
with rooSilent(), use_canvas('test_p1xp1_BBss'):
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)
with wait(1):
model.draw1(dataset)
with wait(1):
model.draw2(dataset)
model.draw1(dataset)
model.draw2(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)
