def test_pbxpb_BB () :
logger.info ('Non-factorizeable background component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + (expo*P1)**2')
model = Models.Fit2D (
suffix = '_7' ,
signal_x = signal1 ,
signal_y = signal2s ,
bkg_1x = 1 ,
bkg_1y = 1 ,
bkg_2D = Models.ExpoPol2D_pdf ( 'P2D7' , m_x , m_y , nx = 1 , ny = 1 )
)
model.bkg_1x .tau .fix ( 0 )
model.bkg_1y .tau .fix ( 0 )
## 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_p2xp2():
logger.info(
'Simple (factorized) fit model: ( Gauss + P1 ) (x) ( Gauss + P1 ) ')
model = Models.Fit2D(
suffix='_2',
signal_1=signal1,
signal_2=signal2s,
bkg1=-1,
bkg2=-1,
bkgA=-1,
bkgB=-1,
)
## 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_BB():
logger.info(
'Simplest non-factorized fit model: ( Gauss + P1 ) (x) ( Gauss + P1 ) + BB'
)
model = Models.Fit2D(suffix='_3',
signal_1=signal1,
signal_2=signal2s,
bkg1=-1,
bkg2=-1,
bkg2D=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.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_psxps_BBs () :
logger.info ('Non-factorizeable symmetric background component: ( Gauss + P1 ) (x) ( Gauss + P1 ) + (PS*P1)**2')
PS = Ostap.Math.PhaseSpaceNL( 1.0 , 5.0 , 2 , 5 )
model = Models.Fit2D (
suffix = '_11' ,
signal_x = signal1 ,
signal_y = signal2s ,
bkg_1x = -1 ,
bkg_1y = -1 ,
bkg_2D = Models.PSPol2Dsym_pdf ( 'P2D11' , 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] )
logger.info ('B1xS2 : %20s' % result ( model.BS ) [0] )
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)
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)
def test_const():
logger.info(
'Simplest (factorized) fit model: ( Gauss + const ) x ( Gauss + const ) '
)
model = Models.Fit2D(
signal_x=signal1,
signal_y=signal2s,
)
## 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])
logger.info('B1xS2 : %20s' % result(model.BS)[0])
logger.info('B1xB2 : %20s' % result(model.BB)[0])
models.add(model)
def fit_2d():
#variable inizialization
im = ROOT.RooRealVar('im', 'im', 2.42, 2.52)
m23 = ROOT.RooRealVar('m23', 'm23', 0.80, 1.00)
#open and reduce dataset
rfile = ROOT.TFile("../datasets/test_xic_100invpb.root", "READ")
ds = rfile["da_lc"]
ds = ds.reduce("im > 2.42 && im < 2.52 && m23 > 0.8 && m23 < 1.")
dh = (ds.reduce(ROOT.RooArgSet(im, m23), "im>0 && m23>0")).binnedClone()
#model creation
signal_im = Models.Gauss_pdf('Gauss_x',
xvar=im,
mean=(2.45, 2.48),
sigma=(0.001, 0.01))
signal_m23 = Models.Gauss_pdf('Gauss_y',
xvar=m23,
mean=(0.8, 1.),
sigma=(0.001, 0.01))
bkg_poly_im = Models.Bkg_pdf('BkgGauss_x', xvar=im, power=0.)
bkg_poly_m23 = Models.Bkg_pdf('BkgGauss_y', xvar=m23, power=0.)
model_gauss = Models.Fit2D(signal_x=signal_im,
signal_y=signal_m23,
bkg_1x=bkg_poly_im,
bkg_1y=bkg_poly_m23)
#model fitting
result, frame = model_gauss.fitTo(dh)
result, frame = model_gauss.fitTo(ds, draw=True)
result.draw()
def test_model_13():
logger.info(
'Non-factorizeable fit component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + (Expo*PS)**2'
)
PS = Ostap.Math.PhaseSpaceNL(1.0, 5.0, 2, 5)
model = Models.Fit2D(suffix='_13',
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.ExpoPSPol2D_pdf('P2D13',
m_x,
m_y,
psy=PS,
nx=1,
ny=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)
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_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)
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_model_13():
logger = getLogger('test_model_13')
logger.info(
'Non-factorizeable fit component: ( Gauss + P1 ) (x) ( Gauss + P1 ) + (Expo*PS)**2'
)
PS = Ostap.Math.PhaseSpaceNL(1.0, 5.0, 2, 5)
model = Models.Fit2D(suffix='_13',
signal_x=signal1,
signal_y=signal2s,
bkg_1x=-1,
bkg_1y=-1,
bkg_2D=Models.ExpoPSPol2D_pdf('P2D13',
m_x,
m_y,
psy=PS,
nx=1,
ny=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)
result, frame = model.fitTo(dataset)
with use_canvas('test_model_13'):
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_components_2D():
logger = getLogger('test_components_2D')
logger.info('Test multi-component 3d fit')
model = Models.Fit2D(name='fit_comp',
signal_x=signal_x1,
signal_y=signal_y1,
bkg_1x=bkg_x,
bkg_1y=bkg_y,
components=[ss_cmp, sb_cmp, bs_cmp, bb_cmp])
with rooSilent():
## components
model.SS.fix(N_ss)
model.SB.fix(N_sb)
model.SB.fix(N_bs)
model.BB.fix(N_bb)
model.C[0].fix(5000)
model.C[1].fix(500)
model.C[2].fix(500)
model.C[3].fix(1000)
r = model.fitTo(dataset)
model.SS.release()
model.SB.release()
model.BS.release()
model.BB.release()
model.C[0].release()
model.C[1].release()
model.C[2].release()
model.C[3].release()
r = model.fitTo(dataset)
r = model.fitTo(dataset)
r = model.fitTo(dataset)
with use_canvas('test_components_2D'):
model.draw1(dataset)
model.draw2(dataset)
logger.info('Model %s Fit result \n#%s ' % (model.name, r))
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)
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=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.ExpoPol2Dsym_pdf('P2D8', 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)
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_const():
logger.info(
'Simplest (factorized) fit model: ( Gauss + const ) x ( Gauss + const ) '
)
model = Models.Fit2D(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))
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)
model.bkgA.tau.fix(0)
model.bkgB.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_14():
logger = getLogger('test_model_14')
logger.info(
'Non-factorazeable background component (spline): ( Gauss + P1 ) (x) ( Gauss + P1 ) + Spline2D'
)
SPLINE = Ostap.Math.PositiveSpline2D(spline1, spline1)
model = Models.Fit2D(suffix='_14',
signal_x=signal1,
signal_y=signal2s,
bkg_1x=-1,
bkg_1y=-1,
bkg_2D=Models.Spline2D_pdf('P2D14',
m_x,
m_y,
spline=SPLINE))
## 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_14'):
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_p1xp1_BB():
logger = getLogger('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(), use_canvas('test_p1xp1_BB'):
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)
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_comp_2dfit():
logger.info('Test multi-component 3d fit')
model = Models.Fit2D(name='fit_comp',
signal_x=signal_x1,
signal_y=signal_y1,
bkg_1x=bkg_x,
bkg_1y=bkg_y,
components=[ss_cmp, sb_cmp, bs_cmp, bb_cmp])
with rooSilent():
## components
model.SS.fix(5000)
model.SB.fix(500)
model.SB.fix(500)
model.BB.fix(1000)
model.C[0].fix(5000)
model.C[1].fix(500)
model.C[2].fix(500)
model.C[3].fix(1000)
r = model.fitTo(dataset, ncpu=8)
model.SS.release()
model.SB.release()
model.BS.release()
model.BB.release()
model.C[0].release()
model.C[1].release()
model.C[2].release()
model.C[3].release()
r = model.fitTo(dataset, ncpu=8)
model.draw1(dataset, ncpu=8)
model.draw2(dataset, ncpu=8)
logger.info('Model %s Fit result \n#%s ' % (model.name, r))
def 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)
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)
def test_model_14():
logger.info(
'Non-factorazeable background component (spline): ( Gauss + P1 ) (x) ( Gauss + P1 ) + Spline2D'
)
SPLINE = Ostap.Math.Spline2D(spline1, spline1)
model = Models.Fit2D(suffix='_14',
signal_1=signal1,
signal_2=signal2s,
bkg1=-1,
bkg2=-1,
bkg2D=Models.Spline2D_pdf('P2D14',
m_x,
m_y,
spline=SPLINE))
## 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)
# | - bkg_1y : y-background component for S(x)*B(y) term
# | - bkg_2x : x-background component for B(x)*B(y) term, if bkg2D is not specified
# | - bkg_2y : y-background component for B(x)*B(y) term, if bkg2D is not specified
# | - bkg_2D : PDF for 2D-background component for B(x,y) term
# | - sig_2D : PDF for 2D-signal component S(x,y) term
# | - ss : the yield of S(x,y) component
# | - sb : the yield of S(x)*B(y) component
# | - bs : the yield of B(x)*S(y) component
# | - bb : the yield of B(x,y) component
# | - components : the list of other 2D-components
# | - xvar : the x-variable
# | - yvar : the y-variable
# | - name : the name of PDF
model_xy = Models.Fit2D(signal_x=sig_x,
signal_y=sig_y,
bkg_1x=bkg_1x,
bkg_1y=bkg_1y,
bkg_2x=bkg_2x,
bkg_2y=bkg_2y)
small_dh = (small_ds.reduce(ROOT.RooArgSet(im, m12), "im>0")).binnedClone()
# Fit
#r, w = model_x.fitTo( small_ds , draw=True, silent=True)
#r, w = model_y.fitTo( small_ds , draw=True, silent=True)
r, w = model_xy.fitTo(small_dh, draw=True, silent=True)
r, w = model_xy.fitTo(small_ds, draw=True, silent=True)
fx = model_xy.draw1(small_ds, nbins=90)
fx.Draw()
canvas >> "TEMP"
fy = model_xy.draw2(small_ds, nbins=50)
fy.Draw()
def test_fitting_in_range_2d():
logger = getLogger('test_fitting_in_range_2d')
## make simple test mass
m_x = ROOT.RooRealVar('m_x', 'Some test mass(X)', 0, 5)
m_y = ROOT.RooRealVar('m_y', 'Some test mass(Y)', 6, 10)
## book very simple data set
varset = ROOT.RooArgSet(m_x, m_y)
dataset = ROOT.RooDataSet(dsID(), 'Test Data set-1', varset)
m1 = VE(3, 0.10**2)
m2 = VE(7, 0.10**2)
## fill it with three gausissians, 5k events each
N_ss = 5000
N_sb = 1000
N_bs = 500
N_bb = 100
random.seed(0)
## S x S
for i in range(N_ss):
m_x.value = m1.gauss()
m_y.value = m2.gauss()
dataset.add(varset)
## S x B
for i in range(N_sb):
m_x.value = m1.gauss()
m_y.value = random.uniform(*m_y.minmax())
dataset.add(varset)
## B x S
for i in range(N_bs):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = m2.gauss()
dataset.add(varset)
## B x B
for i in range(N_bb):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = random.uniform(*m_y.minmax())
dataset.add(varset)
logger.info('Dataset:\n%s' % dataset.table(prefix='# '))
## various fit components
signal_x1 = Models.Gauss_pdf('G1x',
xvar=m_x,
mean=m1.value(),
sigma=m1.error())
signal_y1 = Models.Gauss_pdf(name='G1y',
xvar=m_y,
mean=m2.value(),
sigma=m2.error())
bkg_x = make_bkg(-1, 'Bx', m_x)
bkg_y = make_bkg(-1, name='By', xvar=m_y)
## build fit model
model = Models.Fit2D(name='fit_comp',
signal_x=signal_x1,
signal_y=signal_y1,
bkg_1x=bkg_x,
bkg_1y=bkg_y)
model.SS = N_ss
model.SB = N_sb
model.BS = N_bs
model.BB = N_bb
r = model.fitTo(dataset, silent=True)
r = model.fitTo(dataset, silent=True)
dataset.m_y.setRange('fit', 8, 10.)
model.yvar.setRange('fit', 8, 10.)
with use_canvas('test_fitting_in_range_2d'):
with wait(2):
model.draw1(dataset, nbins=200, in_range=(6, 8))
with wait(2):
model.draw1(dataset, nbins=200, in_range='fit')
dataset.m_x.setRange('fit2', 0, 2.5)
model.xvar.setRange('fit2', 0, 2.5)
with use_canvas('test_fitting_in_range_2d'):
with wait(2):
model.draw2(dataset, nbins=200, in_range=(2.5, 5))
with wait(2):
model.draw2(dataset, nbins=200, in_range='fit2')
## various fit components
signal_x1 = Models.Gauss_pdf ( 'G1x' , xvar = m_x , mean = m1.value() , sigma = m1.error() )
signal_y1 = Models.Gauss_pdf ( name='G1y' , xvar = m_y , mean = m2.value() , sigma = m2.error() )
bkg_x= make_bkg ( -1 , 'Bx' , m_x )
bkg_y= make_bkg ( -1 , name= 'By' , xvar =m_y )
model = Models.Fit2D (
name = 'fit_comp',
signal_x = signal_x1,
signal_y = signal_y1,
bkg_1x = bkg_x ,
bkg_1y = bkg_y ,
)
with rooSilent() :
## components
model.SS.setVal ( 5000 )
model.SB.setVal ( 1000 )
model.BS.setVal ( 500 )
model.BB.setVal ( 100 )
r = model.fitTo ( dataset , ncpu=8 )
def draw_x() :