from ostap.utils.utils import wait
# =============================================================================
# logging
# =============================================================================
from ostap.logger.logger import getLogger
if '__main__' == __name__ or '__builtin__' == __name__:
logger = getLogger('test_fitting_simfit1')
else:
logger = getLogger(__name__)
# =============================================================================
## make simple test mass
mass = ROOT.RooRealVar('test_mass', 'Some test mass', 0, 5)
## book very simple data set:
varset1 = ROOT.RooArgSet(mass)
dataset1 = ROOT.RooDataSet(dsID(), 'Test Data set-1', varset1)
## book very simple data set:
varset2 = ROOT.RooArgSet(mass)
dataset2 = ROOT.RooDataSet(dsID(), 'Test Data set-2', varset2)
## high statistic, low-background "control channel"
mean1 = 2.0
sigma1 = 0.50
NS1 = 10000
NB1 = 1000
for i in range(NS1):
v1 = random.gauss(mean1, sigma1)
if v1 in mass:
mass.setVal(v1)
# =============================================================================
# logging
# =============================================================================
from ostap.logger.logger import getLogger
if '__main__' == __name__ or '__builtin__' == __name__:
logger = getLogger('test_models2_2D')
else:
logger = getLogger(__name__)
# =============================================================================
## make simple test mass
m_x = ROOT.RooRealVar('mass_x', 'Some test mass(X)', 3, 3.2)
m_y = ROOT.RooRealVar('mass_y', 'Some test mass(Y)', 3, 3.2)
## book very simple data set
varset = ROOT.RooArgSet(m_x, m_y)
dataset = ROOT.RooDataSet(dsID(), 'Test Data set-1', varset)
m = VE(3.100, 0.015**2)
N_ss = 10000
N_sb = 2500
N_bs = 2500
N_bb = 5000
random.seed(0)
## fill it : 5000 events Gauss * Gauss
for i in xrange(0, N_ss):
m_x.value = m.gauss()
m_y.value = m.gauss()
dataset.add(varset)
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')
# =============================================================================
from ostap.logger.logger import getLogger
if '__main__' == __name__ or '__builtin__' == __name__ :
logger = getLogger ( 'test_fitting_efficiency' )
else :
logger = getLogger ( __name__ )
# =============================================================================
## make
x = ROOT.RooRealVar ( 'x', 'test' , 0 , 10 )
xmin , xmax = x.minmax()
acc = ROOT.RooCategory( 'cut','cut')
acc.defineType('accept',1)
acc.defineType('reject',0)
varset = ROOT.RooArgSet ( x , acc )
ds = ROOT.RooDataSet ( dsID() , 'test data' , varset )
eff0 = Models.Monotonic_pdf ( 'E0' , xvar = x , power = 2 , increasing = True )
eff0.phis = 3.1415/2 , 3.1415/2
margin = 1.25
emax = margin * eff0 ( x.getMax() )
for i in range ( 10000 ) :
xv = random.uniform ( xmin , xmax )
x.setVal ( xv )
ev = random.uniform ( 0 , emax )
if eff0( xv ) > ev : acc.setIndex(1)
def test_toys_simfit_1():
logger = getLogger('test_toys_simfit_1')
## make simple test mass
mass = ROOT.RooRealVar('test_mass', 'Some test mass', 0, 5)
## book very simple data set:
varset1 = ROOT.RooArgSet(mass)
dataset1 = ROOT.RooDataSet(dsID(), 'Test Data set-1', varset1)
## book very simple data set:
varset2 = ROOT.RooArgSet(mass)
dataset2 = ROOT.RooDataSet(dsID(), 'Test Data set-2', varset2)
## high statistic, low-background "control channel"
mean1 = 2.0
sigma1 = 0.50
NS1 = 1000
NB1 = 250
for i in range(NS1):
v1 = random.gauss(mean1, sigma1)
if v1 in mass:
mass.setVal(v1)
dataset1.add(varset1)
for i in range(NB1):
v1 = random.uniform(0, 5)
if v1 in mass:
mass.setVal(v1)
dataset1.add(varset1)
## low statistic, high-background "control channel"
NS2 = 250
NB2 = 1000
mean2 = mean1 + 1.0
sigma2 = sigma1 * 0.5
for i in range(NS2):
v2 = random.gauss(mean2, sigma2)
if v2 in mass:
mass.setVal(v2)
dataset2.add(varset2)
for i in range(NB2):
v2 = random.uniform(0, 5)
if v2 in mass:
mass.setVal(v2)
dataset2.add(varset2)
signal1 = Models.Gauss_pdf('G1',
xvar=mass,
mean=(0.5, 2.5),
sigma=(0.1, 1.0))
model1 = Models.Fit1D(suffix='M1', signal=signal1, background=-1)
model1.S = NS1
model1.B = NB1
mean2 = signal1.vars_add(signal1.mean, 1.0)
sigma2 = signal1.vars_multiply(signal1.sigma, 0.5)
signal2 = Models.Gauss_pdf('G2', xvar=mass, mean=mean2, sigma=sigma2)
model2 = Models.Fit1D(suffix='M2',
signal=signal2,
background=model1.background)
model2.S = NS2
model2.B = NB2
with use_canvas('test_toys_simfit_1'):
# =========================================================================
## fit 1
with wait(1):
r1, f1 = model1.fitTo(dataset1, draw=True, nbins=50, silent=True)
## fit 2
with wait(1):
r2, f2 = model2.fitTo(dataset2, draw=True, nbins=50, silent=True)
# =========================================================================
## combine data
sample = ROOT.RooCategory('sample', 'sample', 'A', 'B')
## combine datasets
from ostap.fitting.simfit import combined_data
vars = ROOT.RooArgSet(mass)
dataset = combined_data(sample, vars, {'A': dataset1, 'B': dataset2})
## combine PDFs
model_sim = Models.SimFit(sample, {'A': model1, 'B': model2}, name='X')
# =========================================================================
r, f = model_sim.fitTo(dataset, silent=True)
r, f = model_sim.fitTo(dataset, silent=True)
with use_canvas('test_toys_simfit_1'):
with wait(1):
fA = model_sim.draw('A', dataset, nbins=50)
with wait(1):
fB = model_sim.draw('B', dataset, nbins=50)
logger.info('Fit results are: %s ' % r.table(prefix="# "))
## Make toys
results, stats = Toys.make_toys(pdf=model_sim,
nToys=100,
data=[mass],
gen_config={
'nEvents': (NS1 + NB1, NS2 + NB2),
'sample': True
},
fit_config={'silent': True},
init_pars={
'mean_G1': mean1,
'BM1': NB1,
'BM2': NB2,
'sigma_G1': sigma1,
'SM1': NS1,
'SM2': NS2,
'phi0_Bkg_FitG1_M1': 0
},
silent=True,
progress=True)
from ostap.logger.utils import rooSilent
# =============================================================================
# logging
# =============================================================================
from ostap.logger.logger import getLogger
if '__main__' == __name__ or '__builtin__' == __name__:
logger = getLogger('test_models')
else:
logger = getLogger(__name__)
# =============================================================================
## make simple test mass
mass = ROOT.RooRealVar('test_mass', 'Some test mass', 3.0, 3.2)
## book very simple data set
varset0 = ROOT.RooArgSet(mass)
dataset0 = ROOT.RooDataSet(dsID(), 'Test Data set-0', varset0)
mmin, mmax = mass.minmax()
## fill it
m = VE(3.100, 0.015**2)
for i in xrange(0, 5000):
mass.value = m.gauss()
dataset0.add(varset0)
for i in xrange(0, 500):
mass.value = random.uniform(mmin, mmax)
dataset0.add(varset0)
logger.info('DATASET %s' % dataset0)
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_components_2():
## make simple test mass
mass = ROOT.RooRealVar('test_mass', 'Some test mass', 0, 10)
## book very simple data set
varset = ROOT.RooArgSet(mass)
dataset = ROOT.RooDataSet(dsID(), 'Test Data set', varset)
mmin, mmax = mass.minmax()
## two gaussinan signal cmppnent
N1 = 5000
m1 = VE(5, 0.2**2)
N2 = 5000
m2 = VE(3, 0.3**2)
## background exponential components
taub = 5.0
NB = 5000
## generate 1st signal
for i in range(0, N1):
m = m1.gauss()
while not mmin < m < mmax:
m = m1.gauss()
mass.value = m
dataset.add(varset)
## generate 2nd signal
for i in range(0, N2):
m = m2.gauss()
while not mmin < m < mmax:
m = m2.gauss()
mass.value = m
dataset.add(varset)
## generate background
for i in range(0, NB):
m = random.expovariate(1. / taub)
while not mmin < m < mmax:
m = random.expovariate(1. / taub)
mass.value = m
dataset.add(varset)
logger.info("Original dataset\n%s" % dataset.table(prefix='# '))
signal1 = Models.Gauss_pdf('G1',
xvar=mass,
mean=(5, 4, 6),
sigma=(0.2, 0.1, 0.3))
signal2 = Models.Gauss_pdf('G2',
xvar=mass,
mean=(3, 2, 4),
sigma=(0.3, 0.2, 0.4))
S1 = ROOT.RooRealVar('S1', '1st signal yeild', N1, 1, 10000)
ratio = ROOT.RooRealVar('R', 'ratio of S2 to S1', 1.0 * N2 / N1, 0.05, 100)
S2 = signal1.vars_multiply(S1, ratio, name='S2', title='2ns signal yield')
model = Models.Fit1D(signals=[signal1, signal2], background=1, S=(S1, S2))
model.B = NB
signal1.mean.fix(m1.value())
signal1.sigma.fix(m1.error())
signal2.mean.fix(m2.value())
signal2.sigma.fix(m2.error())
model.fitTo(dataset, silent=True)
signal1.mean.release()
signal1.sigma.release()
signal2.mean.release()
signal2.sigma.release()
model.fitTo(dataset, silent=True)
r, f = model.fitTo(dataset, silent=True, draw=True, nbins=50)
logger.info("Mass fit : fit results\n%s" %
r.table(title='Mass fit', prefix='# '))
r, f = model.fitTo(dataset,
silent=True,
draw=True,
nbins=50,
minos=('R', 'S1'))
logger.info("Mass fit : fit results\n%s" %
r.table(title='Mass fit', prefix='# '))
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 = '# ') ) )
