w2 = VE(6.0, 4**2)
n1 = VE(2.0, 1**2)
n2 = VE(4.0, 1**2)
for i in xrange(1000):
for w in (w1, w2, n1, n2):
v = w.gauss()
if v in mass:
mass.value = v
dataset.add(varset)
logger.info('Dataset: %s' % dataset)
## various fit components
signal_1 = Models.Gauss_pdf('G1', xvar=mass, mean=m1.value(), sigma=m1.error())
signal_2 = Models.Gauss_pdf('G2', xvar=mass, mean=m2.value(), sigma=m2.error())
signal_3 = Models.Gauss_pdf('G3', xvar=mass, mean=m3.value(), sigma=m3.error())
wide_1 = Models.Gauss_pdf('GW1', xvar=mass, mean=4.0, sigma=4)
wide_2 = Models.Gauss_pdf('GW2', xvar=mass, mean=6.0, sigma=4)
narrow_1 = Models.Gauss_pdf('GN1', xvar=mass, mean=2.0, sigma=1)
narrow_2 = Models.Gauss_pdf('GN2', xvar=mass, mean=4.0, sigma=1)
# =============================================================================
## Test extended multi-component fit'
def test_extended1():
logger.info('Test extended multi-component fit')
m_x.value = random.uniform(*m_x.minmax())
m_y.value = random.uniform(*m_y.minmax())
dataset.add(varset)
logger.info('Dataset:%s ' % dataset)
models = set()
# =============================================================================
signal1 = Models.Gauss_pdf('Gx', xvar=m_x)
signal2 = Models.Gauss_pdf('Gy', xvar=m_y)
signal2s = signal1.clone(name='GyS', xvar=m_y)
signal1.mean = m.value()
signal1.sigma = m.error()
signal2.mean = m.value()
signal2.sigma = m.error()
knots = std.vector('double')()
knots.push_back(m_x.xmin())
knots.push_back(0.5 * (m_x.xmin() + m_x.xmax()))
knots.push_back(m_x.xmax())
spline1 = Ostap.Math.BSpline(knots, 2)
# =============================================================================
## gauss as signal, expo times 1st order polynomial as background
# =============================================================================
def test_model_14():
def print_bootstrap(fitresult, stats, morevars={}, logger=logger, title=''):
"""print Bootstrap statistics
"""
header = ('Parameter', 'theta', 'theta_boot', 'bias/sigma [%]',
'error [%]')
table = []
n = 0
for name in sorted(stats):
if name in fitresult:
p = fitresult[name]
theta = p * 1.0
if not isinstance(theta, VE) or theta.cov2() <= 0:
logger.warning(
'print_bootstrap: parameter "%s" is invalid in ``fitresult'
', skip %s' % (name, theta))
continue
elif name in morevars:
theta = morevars[name]
if not isinstance(theta, VE) or theta.cov2() <= 0:
logger.warning(
'print_bootstrap: parameter "%s" is invalid in ``morevars'
', skip %s' % (name, theta))
continue
else:
continue
statistics = stats[name]
n = max(n, statistics.nEntries())
theta_boot = VE(statistics.mean().value(), statistics.mu2())
bias = theta_boot.value() - theta.value()
scale = theta.error() / theta_boot.error()
row = (name, "%+13.6g +/- %-13.6g" % (theta.value(), theta.error()),
"%+13.6g +/- %-13.6g" %
(theta_boot.value(), theta_boot.error()), '%+6.2f' %
(bias / theta.error() * 100), '%+6.2f' % (scale * 100 - 100))
table.append(row)
for name in sorted(stats):
if name in fitresult: continue
if name in morevars: continue
statistics = stats[name]
theta_boot = VE(statistics.mean().value(), statistics.mu2())
row = name, '', "%+13.6g +/- %-13.6g" % (theta_boot.value(),
theta_boot.error()), '', ''
table.append(row)
table = [header] + table
title = title if title else "Bootstrapping with #%d samples" % n
import ostap.logger.table as Table
table = Table.table(table, title=title, alignment='lcccc', prefix="# ")
logger.info('%s:\n%s' % (title, table))
dataset0.add(varset0)
for i in range(0, 500):
mass.value = random.uniform(mmin, mmax)
dataset0.add(varset0)
logger.info('DATASET\n%s' % dataset0)
models = set()
## signal component
signal_gauss = Models.Gauss_pdf(
name='Gauss', ## the name
xvar=mass, ## the variable
mean=m.value(), ## mean value (fixed)
sigma=m.error()) ## sigma (fixed)
## construct composite model: signal + background
model_gauss = Models.Fit1D(
signal=signal_gauss,
background=Models.Bkg_pdf('BkgGauss', xvar=mass, power=0),
)
S = model_gauss.S
B = model_gauss.B
# =============================================================================
## gauss PDF
# =============================================================================
def test_gauss():
dataset.add(varset)
## fill it : 1000 events const * const *Gauss
for i in range(0, N_bb):
m_x.value = random.uniform(*m_x.minmax())
m_y.value = random.uniform(*m_y.minmax())
dataset.add(varset)
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
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 getLumi(data, *args):
"""Get lumi :
>>> l1 = getLumi ( 'myfile.root' )
>>> l2 = getLumi ( tree )
>>> l3 = getLumi ( chain )
>>> l4 = getLumi ( file )
>>> l5 = getLumi ( [ any sequence of above ] )
"""
#
if args:
data = [data]
for a in args:
data.append(a)
return getLumi(data)
if isinstance(data, str):
## expand the actual file name
data = os.path.expandvars(data)
data = os.path.expanduser(data)
data = os.path.expandvars(data)
data = os.path.expandvars(data)
try:
tree = ROOT.TChain(lumi_tree)
tree.Add(data)
return getLumi(tree)
except:
logger.error('Unable to get lumi/1 for %s' % data)
return VE()
#
if isinstance(data, ROOT.TFile):
try:
tree = data.Get(lumi_tree)
return getLumi(tree)
except:
logger.error('Unable to get lumi/2 for %s' % data.GetName())
return VE()
if isinstance(data, ROOT.TTree):
## try :
with rootError(): ## suppress errors from ROOT
## if hasattr ( data , 'pstatVar' ) :
## stat = data.pstatVar ( [ lumi , lumi_err ] , lumi_cuts , chunk_size = -1 , max_files = 10 )
## else :
stat = data.statVar([lumi, lumi_err], lumi_cuts)
##
s1 = stat[lumi]
s2 = stat[lumi_err]
##
return VE(s1.sum(), s2.sum()**2)
##except :
## logger.error('Unable to get lumi/3 for %s' % data.GetName() )
## return VE()
l = VE()
for i in data:
k = getLumi(i)
## @attention: linear addition of uncertainties:
l = VE(l.value() + k.value(), (l.error() + k.error())**2)
return l
dataset.add ( varset )
## fill it : 1000 events const * const *Gauss
for i in range(0,N_bb) :
m_x.value = random.uniform ( *m_x.minmax() )
m_y.value = random.uniform ( *m_y.minmax() )
dataset.add ( varset )
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
def test_splot():
logger = getLogger('test_splot')
## make simple test mass
mass = ROOT.RooRealVar('test_mass', 'Some test mass', 0, 10)
tau = ROOT.RooRealVar('test_tau', 'Some test tau', 0, 10)
## book very simple data set
varset = ROOT.RooArgSet(mass, tau)
dataset = ROOT.RooDataSet(dsID(), 'Test Data set', varset)
mmin, mmax = mass.minmax()
tmin, tmax = tau.minmax()
m0 = VE(3, 0.2**2)
taus = 6
taub = 0.4
NS = 5000
NB = 5000
## generate signal
for i in range(0, NS):
m = m0.gauss()
while not mmin < m < mmax:
m = m0.gauss()
t = random.expovariate(1. / taus)
while not tmin < t < tmax:
t = random.expovariate(1. / taus)
mass.value = m
tau.value = t
dataset.add(varset)
## generate background
for i in range(0, NB):
m = random.expovariate(1. / 5)
while not mmin < m < mmax:
m = random.expovariate(1. / 3)
t = random.expovariate(1. / taub)
while not tmin < t < tmax:
t = random.expovariate(1. / taus)
mass.value = m
tau.value = t
dataset.add(varset)
logger.info("Original dataset\n%s" % dataset.table(prefix='# '))
signal = Models.Gauss_pdf('G',
xvar=mass,
mean=(3, 2, 4),
sigma=(0.2, 0.1, 0.5))
model = Models.Fit1D(signal=signal, background=1)
model.S = NS
model.B = NB
signal.mean.fix(m0.value())
signal.sigma.fix(m0.error())
model.fitTo(dataset, silent=True)
signal.mean.release()
signal.sigma.release()
model.fitTo(dataset, silent=True)
with use_canvas('test_splot'):
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='# '))
## make splot analysis
model.sPlot(dataset)
logger.info("Dataset after sPlot\n%s" % dataset.table(prefix='# '))
## make signal-weighted dataset
ds_signal = dataset.makeWeighted('S_sw')
## make background-weighted dataset
ds_bkg = dataset.makeWeighted('B_sw')
logger.info("Signal-weighted dataset\n%s" % ds_signal.table(prefix='# '))
logger.info("Background-weighted dataset\n%s" % ds_bkg.table(prefix='# '))
## make exponential fits fot signal and background samples
TS = Models.Bkg_pdf('TS', xvar=tau, power=0)
TB = Models.Bkg_pdf('TB', xvar=tau, power=0)
TS.fitTo(ds_signal, silent=True)
TS.fitTo(ds_signal, silent=True)
with use_canvas('test_splot'):
rS, f = TS.fitTo(ds_signal, silent=True, draw=True, nbins=100)
logger.info("Tau/signal fit : fit results\n%s" %
rS.table(title='Tau signal fit', prefix='# '))
TB.fitTo(ds_bkg, silent=True)
TB.fitTo(ds_bkg, silent=True)
with use_canvas('test_splot'):
rB, f = TB.fitTo(ds_bkg, silent=True, draw=True, nbins=100)
logger.info("Tau/bkg fit : fit results\n%s" %
rB.table(title='Tau bkg fit', prefix='# '))
logger.info("Tau/signal : %28s vs %s" % (abs(1.0 / rS.tau_TS), taus))
logger.info("Tau/bkg : %28s vs %s" % (abs(1.0 / rB.tau_TB), taub))
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')
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_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 getLumi ( data , *args ) :
"""Get lumi :
>>> l1 = getLumi ( 'myfile.root' )
>>> l2 = getLumi ( tree )
>>> l3 = getLumi ( chain )
>>> l4 = getLumi ( file )
>>> l5 = getLumi ( [ any sequence of above ] )
"""
tree_name = 'GetIntegratedLuminosity/LumiTuple'
#
from ostap.core.core import VE, hID
import ostap.io.root_file
import ostap.trees.trees
#
if args :
data = [ data ]
for a in args : data.append ( a )
return getLumi ( data )
if isinstance ( data , str ) :
## expand the actual file name
import os
data = os.path.expandvars ( data )
data = os.path.expanduser ( data )
data = os.path.expandvars ( data )
data = os.path.expandvars ( data )
try :
tree = ROOT.TChain ( tree_name )
tree.Add ( data )
lumi = getLumi ( tree )
return lumi
except :
logger.error('Unable to get lumi(1) for %s' % data )
return VE()
#
if isinstance ( data , ROOT.TFile ) :
try :
tree = data.Get( tree_name )
return getLumi ( tree )
except:
logger.error('Unable to get lumi(2) for %s' % data.GetName() )
return VE()
if isinstance ( data , ROOT.TTree ) :
## print data
from ostap.logger.utils import rootError
try:
#
with rootError() : ## suppress errors from ROOT
## @attention here we are using sumVar!
l1 = data.sumVar ( '1.0*IntegratedLuminosity+0.0*IntegratedLuminosityErr' , '0<=IntegratedLuminosity' )
l2 = data.sumVar ( '1.0*IntegratedLuminosity+1.0*IntegratedLuminosityErr' , '0<=IntegratedLuminosity' )
l3 = data.sumVar ( '1.0*IntegratedLuminosity-1.0*IntegratedLuminosityErr' , '0<=IntegratedLuminosity' )
#
l1.setError ( 0.5 * abs ( l2.value () - l3.value () ) )
#
l0 = data.sumVar ( 'IntegratedLuminosity' , '0 >IntegratedLuminosity' )
if 0 != l0.value() : logger.error( 'Something weird happens with Lumi/1: %s' % l0 )
l0 = data.sumVar ( 'IntegratedLuminosity' , 'IntegratedLuminosity>100000' )
if 0 != l0.value() : logger.error( 'Something weird happens with Lumi/2: %s' % l0 )
l0 = data.sumVar ( 'IntegratedLuminosity' , '0>IntegratedLuminosityErr' )
if 0 != l0.value() : logger.error( 'Something weird happens with Lumi/3: %s' % l0 )
#
return l1
except :
logger.error('Unable to get lumi(3) for %s' % data.GetName() )
return VE()
l = VE()
for i in data :
k = getLumi ( i )
## @attention: linear addition of uncertainties:
l = VE ( l.value() + k.value() , ( l.error() + k.error () ) ** 2 )
return l
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 = '# ') ) )
def getLumi(data, *args):
"""Get lumi :
>>> l1 = getLumi ( 'myfile.root' )
>>> l2 = getLumi ( tree )
>>> l3 = getLumi ( chain )
>>> l4 = getLumi ( file )
>>> l5 = getLumi ( [ any sequence of above ] )
"""
#
if args:
data = [data]
for a in args:
data.append(a)
return getLumi(data)
if isinstance(data, str):
## expand the actual file name
data = os.path.expandvars(data)
data = os.path.expanduser(data)
data = os.path.expandvars(data)
data = os.path.expandvars(data)
try:
tree = ROOT.TChain(lumi_tree)
tree.Add(data)
return getLumi(tree)
except:
logger.error('Unable to get lumi/1 for %s' % data)
return VE()
#
if isinstance(data, ROOT.TFile):
try:
tree = data.Get(lumi_tree)
return getLumi(tree)
except:
logger.error('Unable to get lumi/2 for %s' % data.GetName())
return VE()
if isinstance(data, ROOT.TTree):
## try :
with rootError(): ## suppress errors from ROOT
## if ( 6 , 25 ) < root_info :
## ## use DataFrames. It is faster here? not sure....
## from ostap.frame.frames import DataFrame , frame_statVars
## frame = DataFrame ( data )
## stat = frame_statVars ( frame , [ lumi , lumi_err ] , lumi_cuts )
## else :
stat = data.statVar([lumi, lumi_err], lumi_cuts)
##
s1 = stat[lumi]
s2 = stat[lumi_err]
##
return VE(s1.sum(), s2.sum()**2)
##except :
## logger.error('Unable to get lumi/3 for %s' % data.GetName() )
## return VE()
l = VE()
for i in data:
k = getLumi(i)
## @attention: linear addition of uncertainties:
l = VE(l.value() + k.value(), (l.error() + k.error())**2)
return l
n2 = VE(12.0, 1**2)
for i in range(N3):
for w in (w1, w2, n1, n2):
v = w.gauss()
if v in mass:
mass.value = v
dataset.add(varset)
logger.info('Dataset:\n%s' % dataset.table(prefix='# '))
NT = 3.0 * N1 + N2 + 4 * N3
## various fit components
signal_1 = Models.Gauss_pdf('G1', xvar=mass, mean=m1.value(), sigma=m1.error())
signal_2 = Models.Gauss_pdf('G2', xvar=mass, mean=m2.value(), sigma=m2.error())
signal_3 = Models.Gauss_pdf('G3', xvar=mass, mean=m3.value(), sigma=m3.error())
wide_1 = Models.Gauss_pdf('GW1', xvar=mass, mean=w1.value(), sigma=w1.error())
wide_2 = Models.Gauss_pdf('GW2', xvar=mass, mean=w2.value(), sigma=w2.error())
narrow_1 = Models.Gauss_pdf('GN1',
xvar=mass,
mean=n1.value(),
sigma=n1.error())
narrow_2 = Models.Gauss_pdf('GN2',
xvar=mass,
mean=n2.value(),
sigma=n2.error())
