def test_splinesym2D():
logger = getLogger('test_splinesym2D')
logger.info('Test Spline2Dsym_pdf: Symetric 2D-spline')
ss = Ostap.Math.BSpline(m_x.xmin(), m_x.xmax(), 1, 2)
ss3 = Ostap.Math.PositiveSpline2DSym(ss)
model = Models.Spline2Dsym_pdf('SS2D', m_x, m_y, ss3)
with rooSilent():
result, f = model.fitTo(dataset)
with use_canvas('test_splinesym2D'):
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)
models.add(model)
def test_model_16():
logger.info(
'Symmetric fit model with non-factorazeable symmetric (spline) background component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + Spline2Dsym'
)
SPLINES = Ostap.Math.PositiveSpline2DSym(spline1)
model = Models.Fit2DSym(suffix='_16',
signal_x=signal1,
signal_y=signal2s,
bkg_1x=1,
bkg_2D=Models.Spline2Dsym_pdf('P2D16',
m_x,
m_y,
spline=SPLINES))
## 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_model_15():
logger.info(
'Non-factorized symmetric background component (spline): ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + Spline2Dsym'
)
SPLINES = Ostap.Math.Spline2DSym(spline1)
model = Models.Fit2D(suffix='_15',
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.Spline2Dsym_pdf('P2D15',
m_x,
m_y,
spline=SPLINES))
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_model_15():
logger = getLogger('test_model_15')
logger.info(
'Non-factorized symmetric background component (spline): ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + Spline2Dsym'
)
SPLINES = Ostap.Math.PositiveSpline2DSym(spline1)
model = Models.Fit2D(suffix='_15',
signal_x=signal1,
signal_y=signal2s,
bkg_1x=-1,
bkg_1y=-1,
bkg_2D=Models.Spline2Dsym_pdf('P2D15',
m_x,
m_y,
spline=SPLINES))
## 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)
result, frame = model.fitTo(dataset)
with use_canvas('test_model_15'):
with wait(1):
model.draw1(dataset)
with wait(1):
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)
def test_model_15():
logger.info(
'Non-factorized symmetric background component (spline): ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + Spline2Dsym'
)
SPLINES = Ostap.Math.Spline2DSym(spline1)
model = Models.Fit2D(suffix='_15',
signal_1=signal1,
signal_2=signal2s,
bkg1=-1,
bkg2=-1,
bkg2D=Models.Spline2Dsym_pdf('P2D15',
m_x,
m_y,
spline=SPLINES))
## 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)