logger.info('Dataset: %s' % dataset)
## various fit components
signal_x1 = Models.Gauss_pdf('G1x',
xvar=m_x,
mean=m1.value(),
sigma=m1.error())
signal_y1 = signal_x1.clone(name='G1y', xvar=m_y)
signal_x2 = Models.Gauss_pdf(name='G2x',
xvar=m_x,
mean=m2.value(),
sigma=m2.error())
signal_y2 = signal_x2.clone(name='G2y', xvar=m_y)
bkg_x = make_bkg(-1, 'Bx', m_x)
bkg_y = bkg_x.clone(name='By', xvar=m_y)
# S(x)*S(y) component
ss_cmp = signal_x2 * signal_y2
# S(x)*B(y) component
sb = signal_x2 * bkg_y
# B(x)*S(y) component
bs = bkg_x * signal_y2
# B(x)*S(y)+ S(x)*B(y) component
sb_cmp = sb + bs
# B(x)*B(y) component
def test_fitting_components_3D():
logger = getLogger('test_fitting_components_3D')
## make simple test mass
m_x = ROOT.RooRealVar('mass_x', 'Some test mass(X)', 0, 10)
m_y = ROOT.RooRealVar('mass_y', 'Some test mass(Y)', 0, 10)
m_z = ROOT.RooRealVar('mass_z', 'Some test mass(z)', 0, 10)
## book very simple data set
varset = ROOT.RooArgSet(m_x, m_y, m_z)
dataset = ROOT.RooDataSet(dsID(), 'Test Data set-1', varset)
m1 = VE(3, 0.10**2)
m2 = VE(7, 0.20**2)
## fill it
N_sss = 5000
N_ssb = 500
N_sbs = 500
N_bss = 1000
N_bbs = 500
N_bsb = 100
N_sbb = 100
N_bbb = 250
random.seed(0)
## fill it : 5000 events Gauss * Gauss *Gauss
for m in (m1, m2):
## S x S x S
for i in range(N_sss):
m_x.value = m.gauss()
m_y.value = m.gauss()
m_z.value = m.gauss()
dataset.add(varset)
## S x S x B
for i in range(0, N_bss):
m_x.value = m.gauss()
m_y.value = m.gauss()
m_z.value = random.uniform(*m_z.minmax())
dataset.add(varset)
## S x B x S
for i in range(N_ssb):
m_x.value = m.gauss()
m_y.value = random.uniform(*m_y.minmax())
m_z.value = m.gauss()
dataset.add(varset)
## B x S x S
for i in range(N_sbs):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = m.gauss()
m_z.value = m.gauss()
dataset.add(varset)
## B x B X S
for i in range(N_sbb):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = random.uniform(*m_y.minmax())
m_z.value = m.gauss()
dataset.add(varset)
## B x S x B
for i in range(N_bsb):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = m.gauss()
m_z.value = random.uniform(*m_y.minmax())
dataset.add(varset)
## S x B x B
for i in range(N_sbb):
m_x.value = m.gauss()
m_y.value = random.uniform(*m_y.minmax())
m_z.value = random.uniform(*m_y.minmax())
dataset.add(varset)
## B x B x B
for i in range(N_bbb):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = random.uniform(*m_y.minmax())
m_z.value = random.uniform(*m_y.minmax())
dataset.add(varset)
logger.info('Dataset:\n%s' % dataset.table(prefix='# '))
## various fit components for main fit model :
signal_x1 = Models.Gauss_pdf('G1x',
xvar=m_x,
mean=m1.value(),
sigma=m1.error())
signal_y1 = signal_x1.clone(name='G1y', xvar=m_y)
signal_z1 = signal_x1.clone(name='G1z', xvar=m_z)
bkg_x = make_bkg(-1, 'Bx', m_x)
bkg_y = bkg_x.clone(name='By', xvar=m_y)
bkg_z = bkg_x.clone(name='Bz', xvar=m_z)
## construct other components
signal_x2 = Models.Gauss_pdf(name='G2x',
xvar=m_x,
mean=m2.value(),
sigma=m2.error())
signal_y2 = signal_x2.clone(name='G2y', xvar=m_y)
signal_z2 = signal_x2.clone(name='G2z', xvar=m_z)
## S(x) * S(y) components
ss_cmp = signal_x2 * signal_y2
## S(x) * B(y) component
sb_cmp = signal_x2 * bkg_y
## B(x) * S(y) component
bs_cmp = bkg_x * signal_y2
## B(x) * B(y) component
bb_cmp = bkg_x * bkg_y
## S(x) * S(y) * S(z) component
sss_cmp = ss_cmp * signal_z2
## S(x) * S(y) * B(z) component
ssb_cmp = ss_cmp * bkg_z
## S(x) * B(y) * S(z) component
sbs_cmp = sb_cmp * signal_z2
## S(x) * B(y) * S(z) component
bss_cmp = bs_cmp * signal_z2
## S(x) * B(y) * B(z) component
sbb_cmp = sb_cmp * bkg_z
## B(x) * S(y) * B(z) component
bsb_cmp = bs_cmp * bkg_z
## B(x) * B(y) * S(z) component
bbs_cmp = bs_cmp * signal_z2
model = Models.Fit3D(
name='fit_comp',
signal_x=signal_x1,
signal_y=signal_y1,
signal_z=signal_z1,
bkg_1x=bkg_x,
bkg_1y=bkg_y,
bkg_1z=bkg_z,
suffix="_1",
##
components=[
sss_cmp, ssb_cmp, sbs_cmp, bss_cmp, sbb_cmp, bsb_cmp, bbs_cmp
])
model.SSS = N_sss
model.SSB = N_ssb
model.SBS = N_sbs
model.BSS = N_bss
model.BBS = N_bbs
model.BSB = N_bsb
model.SBB = N_sbb
model.BBB = N_bbb
model.C = N_sss, N_ssb, N_sbs, N_sbb, N_bss, N_sbs, N_bbs
r = model.fitTo(dataset, silent=True)
r = model.fitTo(dataset, silent=True)
with use_canvas('test_fitting_components_3D'):
with wait(2):
model.draw1(dataset)
with wait(2):
model.draw2(dataset)
with wait(2):
model.draw3(dataset)
logger.info('Model %s Fit result\n%s ' %
(model.name, r.table(prefix='# ')))
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())
signal_z1 = Models.Gauss_pdf(name='G1z',
xvar=m_z,
mean=m3.value(),
sigma=m3.error())
bkg_x = make_bkg(-1, 'Bx', m_x)
bkg_y = make_bkg(-1, name='By', xvar=m_y)
bkg_z = make_bkg(-1, name='Bz', xvar=m_z)
model = Models.Fit3D(
name='fit_comp',
signal_x=signal_x1,
signal_y=signal_y1,
signal_z=signal_z1,
bkg_1x=bkg_x,
bkg_1y=bkg_y,
bkg_1z=bkg_z,
)
with rooSilent():
## components
model.SSS.setVal(5000)
def test_fitting_in_range_3d():
logger = getLogger('test_fitting_in_range_3d')
## 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)
m_z = ROOT.RooRealVar('m_z', 'Some test mass(z)', 10, 15)
## book very simple data set
varset = ROOT.RooArgSet(m_x, m_y, m_z)
dataset = ROOT.RooDataSet(dsID(), 'Test Data set-1', varset)
m1 = VE(3, 0.10**2)
m2 = VE(7, 0.10**2)
m3 = VE(12, 0.10**2)
N_sss = 5000
N_ssb = 5000
N_sbs = 5000
N_sbb = 1000
N_bss = 500
N_bsb = 100
N_bbs = 100
N_bbb = 250
random.seed(0)
## S x S x S
for i in range(N_sss):
m_x.value = m1.gauss()
m_y.value = m2.gauss()
m_z.value = m3.gauss()
dataset.add(varset)
## S x S x B
for i in range(N_ssb):
m_x.value = m1.gauss()
m_y.value = m2.gauss()
m_z.value = random.uniform(*m_z.minmax())
dataset.add(varset)
## S x B x S
for i in range(N_sbs):
m_x.value = m1.gauss()
m_y.value = random.uniform(*m_y.minmax())
m_z.value = m3.gauss()
dataset.add(varset)
## B x S x S
for i in range(N_bss):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = m2.gauss()
m_z.value = m3.gauss()
dataset.add(varset)
## S x B x B
for i in range(N_sbb):
m_x.value = m1.gauss()
m_y.value = random.uniform(*m_y.minmax())
m_z.value = random.uniform(*m_z.minmax())
dataset.add(varset)
## B x S x B
for i in range(N_bsb):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = m2.gauss()
m_z.value = random.uniform(*m_z.minmax())
dataset.add(varset)
## B x B x S
for i in range(N_bbs):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = random.uniform(*m_y.minmax())
m_z.value = m3.gauss()
dataset.add(varset)
## B x B x B
for i in range(N_bbb):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = random.uniform(*m_y.minmax())
m_z.value = random.uniform(*m_z.minmax())
dataset.add(varset)
logger.info('Dataset:\n%s' % dataset.table(prefix='# '))
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())
signal_z1 = Models.Gauss_pdf(name='G1z',
xvar=m_z,
mean=m3.value(),
sigma=m3.error())
bkg_x = make_bkg(-1, 'Bx', m_x)
bkg_y = make_bkg(-1, name='By', xvar=m_y)
bkg_z = make_bkg(-1, name='Bz', xvar=m_z)
model = Models.Fit3D(
name='fit_comp',
signal_x=signal_x1,
signal_y=signal_y1,
signal_z=signal_z1,
bkg_1x=bkg_x,
bkg_1y=bkg_y,
bkg_1z=bkg_z,
)
model.SSS = N_sss
model.SSB = N_ssb
model.SBS = N_sbs
model.BSS = N_bss
model.SBB = N_sbb
model.BSB = N_bsb
model.BBS = N_bbs
model.BBB = N_bbb
r = model.fitTo(dataset, silent=True)
r = model.fitTo(dataset, silent=True)
dataset.m_y.setRange('fit', 6, 8.)
model.yvar.setRange('fit', 6, 8.)
t = 1.0
with use_canvas('test_fitting_in_range_3d'):
with wait(t):
model.draw1(dataset,
nbins=200,
in_range3=(11, 12),
in_range2=(8, 10))
with wait(t):
model.draw1(dataset,
nbins=200,
in_range3=(11, 12),
in_range2='fit')
with wait(t):
model.draw1(dataset, nbins=200, in_range3=(11, 12))
with wait(t):
model.draw1(dataset, nbins=200, in_range2='fit')
dataset.m_x.setRange('fit2', 2.5, 3.)
model.xvar.setRange('fit2', 2.5, 3.)
with use_canvas('test_fitting_in_range_3d'):
with wait(t):
model.draw2(dataset,
nbins=200,
in_range3=(11, 12),
in_range1=(0, 3))
with wait(t):
model.draw2(dataset,
nbins=200,
in_range3=(11, 12),
in_range1='fit2')
with wait(t):
model.draw2(dataset, nbins=200, in_range3=(11, 12))
with wait(t):
model.draw2(dataset, nbins=200, in_range1='fit2')
dataset.m_x.setRange('fit3', 2.5, 3.)
model.xvar.setRange('fit3', 2.5, 3.)
with use_canvas('test_fitting_in_range_3d'):
with wait(t):
model.draw3(dataset, nbins=200, in_range2=(6, 8), in_range1=(0, 3))
with wait(t):
model.draw3(dataset, nbins=200, in_range2=(6, 8), in_range1='fit3')
with wait(t):
model.draw3(dataset, nbins=200, in_range2=(6, 8))
with wait(t):
model.draw3(dataset, nbins=200, in_range1='fit3')
def test_fitting_components3_3D () :
logger = getLogger( 'test_fitting_components3_3D' )
## make simple test mass
m_x = ROOT.RooRealVar ( 'mass_x' , 'Some test mass(X)' , 0 , 10 )
m_y = ROOT.RooRealVar ( 'mass_y' , 'Some test mass(Y)' , 0 , 10 )
m_z = ROOT.RooRealVar ( 'mass_z' , 'Some test mass(z)' , 0 , 10 )
## book very simple data set
varset = ROOT.RooArgSet ( m_x , m_y,m_z )
dataset = ROOT.RooDataSet ( dsID() , 'Test Data set-1' , varset )
m1 = VE ( 3 , 0.10**2 )
m2 = VE ( 7 , 0.20**2 )
## fill it with three gausissians, 5k events each
N_sss = 5000
N_ssb = 500
N_sbs = N_ssb
N_bss = N_ssb
N_bbs = 100
N_bsb = N_bbs
N_sbb = N_bbs
N_bbb = 500
random.seed(0)
## fill it : 5000 events Gauss * Gauss *Gauss
for m in (m1,m2) :
## S x S x S
for i in range ( N_sss ) :
m_x.value = m.gauss()
m_y.value = m.gauss()
m_z.value = m.gauss()
dataset.add ( varset )
## S x S x B
for i in range(0,N_bss) :
m_x.value = m.gauss()
m_y.value = m.gauss()
m_z.value = random.uniform ( *m_z.minmax() )
dataset.add ( varset )
## S x B x S
for i in range ( N_ssb ) :
m_x.value = m.gauss()
m_y.value = random.uniform ( *m_y.minmax() )
m_z.value = m.gauss()
dataset.add ( varset )
## B x S x S
for i in range ( N_sbs ) :
m_x.value = random.uniform ( *m_x.minmax() )
m_y.value = m.gauss()
m_z.value = m.gauss()
dataset.add ( varset )
## B x B X S
for i in range ( N_sbb ) :
m_x.value = random.uniform ( *m_x.minmax() )
m_y.value = random.uniform ( *m_y.minmax() )
m_z.value = m.gauss()
dataset.add ( varset )
## B x S x B
for i in range ( N_bsb ) :
m_x.value = random.uniform ( *m_x.minmax() )
m_y.value = m.gauss()
m_z.value = random.uniform ( *m_y.minmax() )
dataset.add ( varset )
## S x B x B
for i in range ( N_sbb ) :
m_x.value = m.gauss()
m_y.value = random.uniform ( *m_y.minmax() )
m_z.value = random.uniform ( *m_y.minmax() )
dataset.add ( varset )
## B x B x B
for i in range ( N_bbb ) :
m_x.value = random.uniform ( *m_x.minmax() )
m_y.value = random.uniform ( *m_y.minmax() )
m_z.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 = signal_x1.clone ( name='G1y' , xvar = m_y )
signal_z1 = signal_x1.clone ( name='G1z' , xvar = m_z )
bkg_x = make_bkg ( -1 , 'Bx' , m_x )
bkg_y = bkg_x.clone ( name= 'By' , xvar =m_y )
bkg_z = bkg_x.clone ( name='Bz' , xvar =m_z )
signal_x2 = Models.Gauss_pdf ( name='G2x' , xvar = m_x , mean = m2.value() , sigma = m2.error() )
signal_y2 = signal_x2.clone ( name='G2y' , xvar = m_y )
signal_z2 = signal_x2.clone ( name='G2z' , xvar = m_z )
# S(x) * S(y) component
ss_cmp = signal_x2 * signal_y2
# S(x) * B(y) component
sb_cmp = signal_x2 * bkg_y
# B(x) * S(y) component
bs_cmp = bkg_x * signal_y2
# B(x) * B(y) component
bb_cmp = bkg_x * bkg_y
# S(x)*S(y)*S(z) component
sss_cmp = ss_cmp * signal_z2
# S(x) * B(y) * S(z) + B(x) * S(y) * S(z) + S(x) * S(y) * B(z) component
ssb_ = ss_cmp * bkg_z
sbs_ = sb_cmp * signal_z2
bss_ = bs_cmp * signal_z2
ssb_cmp = ssb_ + ( sbs_ , bss_ )
# S(x)*B(y)*B(z) + B(x)*S(y)*B(z) + B(x)*B(y)*S(z) component
sbb_ = sb_cmp * bkg_z
bsb_ = bs_cmp * bkg_z
bbs_ = bb_cmp * signal_z2
bbs_cmp = sbb_ + ( bsb_ , bbs_ )
logger.info ('Test multi-component 3d Sym fit')
model = Models.Fit3DMix (
name = 'fitSym_comp',
signal_x = signal_x1,
signal_y = signal_y1,
signal_z = signal_z1,
bkg_1x = bkg_x ,
bkg_1y= bkg_y,
bkg_2x = 'clone' ,
bkg_2y= 'clone',
components = [ sss_cmp , ssb_cmp , bbs_cmp ]
)
## components
model.SSS = N_sss
model.SSB = N_ssb * 3
model.SBB = N_sbb * 3
model.BBB = N_bbb
model.C = N_sss, N_ssb * 3 , N_sbb * 3
r = model.fitTo ( dataset , silent = True )
r = model.fitTo ( dataset , silent = True )
r = model.fitTo ( dataset , silent = True )
with use_canvas ( 'test_fitting_components3_3D' ) :
with wait ( after = 2 ) : model.draw1 ( dataset )
with wait ( after = 2 ) : model.draw2 ( dataset )
with wait ( after = 2 ) : model.draw3 ( dataset )
logger.info ( 'Model %s Fit result\n%s ' % ( model.name , r.table (prefix = '# ') ) )
