def test_spline2D() :
logger.info ('Test Spline2D_pdf : 2D-spline')
s1 = Ostap.Math.BSpline ( m_x.xmin(), m_x.xmax() , 1 , 2 )
s2 = Ostap.Math.BSpline ( m_y.xmin(), m_y.xmax() , 1 , 2 )
s3 = Ostap.Math.PositiveSpline2D ( s1 , s2 )
model = Models.Spline2D_pdf ( 'S2D' , m_x , m_y, s3 )
model = Models.ExpoPol2Dsym_pdf ( 'S2DS',
m_x ,
m_y ,
n = 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)
models.add ( model )
def test_pbxpb_BBsym () :
logger.info ('Symmetric fit model with non-factorizeable background component: ( Gauss + P1 ) (x) ( Gauss + P1 ) + Sym(expo*P1)**2')
model = Models.Fit2DSym (
suffix = '_9' ,
signal_x = signal1 ,
signal_y = signal2s ,
bkg_1x = -1 ,
bkg_2D = Models.ExpoPol2Dsym_pdf ( 'P2D9' , m_x , m_y , 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_expopolsym2D():
logger = getLogger('test_expopolsym2D')
logger.info(
'Test ExpoPol2Dsym_pdf: symmetric exponential times exponential modulated by positive polynomial in X and Y'
)
model = Models.ExpoPol2Dsym_pdf('EPs', m_x, m_y, n=2)
with rooSilent():
result, f = model.fitTo(dataset)
with use_canvas('test_expopolsym2D'):
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_pbxpb_BBs():
logger.info(
'Non-factorizeable background component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + Sym(expo*P1)**2'
)
model = Models.Fit2D(suffix='_8',
signal_1=signal1,
signal_2=signal2s,
bkg1=1,
bkg2=1,
bkg2D=Models.ExpoPol2Dsym_pdf('P2D8', m_x, m_y, n=1))
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_pbxpb_BBsym():
logger.info(
'Symmetric fit model with non-factorizeable background component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + Sym(expo*P1)**2'
)
model = Models.Fit2DSym(suffix='_9',
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),
bkg1=1,
bkg2D=Models.ExpoPol2Dsym_pdf('P2D9',
m_x,
m_y,
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)
## 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] / 2))
logger.info('B1xS2 : %20s' % (result(model.bs)[0] / 2))
logger.info('B1xB2 : %20s' % result(model.bb)[0])
models.add(model)
def test_pbxpb_BBs():
logger = getLogger('test_pbxpb_BBs')
logger.info(
'Non-factorizeable background component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + Sym(expo*P1)**2'
)
model = Models.Fit2D(suffix='_8',
signal_x=signal1,
signal_y=signal2s,
bkg_1x=1,
bkg_1y=1,
bkg_2D=Models.ExpoPol2Dsym_pdf('P2D8', m_x, m_y, n=1))
model.bkg_1x.tau.fix(0)
model.bkg_1y.tau.fix(0)
## fit with fixed mass and sigma
with rooSilent(), use_canvas('test_pbxpb_BBs'):
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)
