def test_pspolsym2D():
logger = getLogger('test_pspolsym2D')
logger.info(
'Test PSPol2Dsym_pdf: *SYMMETRIC* product of phase space factors, modulated by positive polynomial in X and Y '
)
## "fictive phase space"
ps = Ostap.Math.PhaseSpaceNL(0, 10, 2, 10)
model = Models.PSPol2Dsym_pdf('PS2Ds', m_x, m_y, ps, n=2)
with rooSilent():
result, f = model.fitTo(dataset)
with use_canvas('test_pspolsym2D'):
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_psxps_BBsym () :
logger.info ('Simmetric fit model with non-factorizeable background component: ( Gauss + P1 ) (x) ( Gauss + P1 ) + (PS*P1)**2')
PS = Ostap.Math.PhaseSpaceNL( 1.0 , 5.0 , 2 , 5 )
model = Models.Fit2DSym (
suffix = '_12' ,
signal_x = signal1 ,
signal_y = signal2s ,
bkg_1x = -1 ,
bkg_2D = Models.PSPol2Dsym_pdf ( 'P2D12' , m_x , m_y , ps = PS , n = 1 )
)
## 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] /2 ) )
logger.info ('B1xS2 : %20s' % ( result ( model.BS ) [0] /2 ) )
logger.info ('B1xB2 : %20s' % result ( model.BB ) [0] )
models.add ( model )
def test_psxps_BBs():
logger.info(
'Non-factorizeable symmetric background component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + (PS*P1)**2'
)
PS = Ostap.Math.PhaseSpaceNL(1.0, 5.0, 2, 5)
model = Models.Fit2D(suffix='_11',
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.PSPol2Dsym_pdf('P2D11',
m_x,
m_y,
ps=PS,
n=1))
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)
