def test_breitwigner_rho_more():
"""Rho0 shape from eta' decays
"""
logger = getLogger("test_breitwigner_rho_more")
logger.info("Rho0 shape from eta' decays")
## Rho-profile with Gounaris-Sakurai lineshape
ch4 = Ostap.Math.ChannelGS(g_rho, m_pi)
bw4 = Ostap.Math.BreitWigner(m_rho, ch4)
## Rho-profile from eta' decays
bw5 = Ostap.Math.BW3L(bw4, m_etap, m_pi, m_pi, 0, 1)
mass = ROOT.RooRealVar('mass', 'm(pipi)', 200 * MeV, 1.6 * GeV)
model4 = Models.BreitWigner_pdf('BW4',
bw4,
xvar=mass,
m0=m_rho,
gamma=g_rho)
model5 = Models.BW3L_pdf('BW5', bw5, xvar=mass, m0=m_rho, gamma=g_rho)
f4 = model4.draw(total_fit_options=(ROOT.RooFit.LineColor(4), ))
f5 = model5.draw(total_fit_options=(ROOT.RooFit.LineColor(8), ))
f5.draw()
f4.draw('same')
models.add(bw4)
models.add(bw5)
models.add(model4)
models.add(model5)
time.sleep(2)
def test_breitwigner_rho():
## 1) P-wave Breit-Wigner with Jackson's formfactor
bw1 = Ostap.Math.Rho0(m_rho, g_rho, m_pi)
## 2) P-wave Breit-Wigner with Blatt-Weisskopf formfactor
ff = Ostap.Math.FormFactors.BlattWeisskopf(1, 3.5 / GeV)
ch2 = Ostap.Math.Channel(g_rho, m_pi, m_pi, 1, ff)
bw2 = Ostap.Math.BreitWigner(m_rho, ch2)
## 3) Gounaris-Sakurai lineshape
ch2 = Ostap.Math.ChannelGS(g_rho, m_pi)
bw3 = Ostap.Math.BreitWigner(m_rho, ch2)
bw1.draw(xmin=200 * MeV, xmax=1.6 * GeV, linecolor=2)
bw2.draw('same', xmin=200 * MeV, xmax=1.6 * GeV, linecolor=4)
bw3.draw('same', xmin=200 * MeV, xmax=1.6 * GeV, linecolor=8)
mass = ROOT.RooRealVar('mass', 'm(pipi)', 200 * MeV, 1.6 * GeV)
model1 = Models.BreitWigner_pdf('BW1',
bw1,
xvar=mass,
mean=m_rho,
gamma=g_rho)
model2 = Models.BreitWigner_pdf('BW2',
bw2,
xvar=model1.mass,
mean=model1.mean,
gamma=model1.gamma)
model3 = Models.BreitWigner_pdf('BW3',
bw3,
xvar=model1.mass,
mean=model1.mean,
gamma=model1.gamma)
f1 = model1.draw(total_fit_options=(ROOT.RooFit.LineColor(2), ))
f2 = model2.draw(total_fit_options=(ROOT.RooFit.LineColor(4), ))
f3 = model3.draw(total_fit_options=(ROOT.RooFit.LineColor(8), ))
f1.draw()
f2.draw('same')
f3.draw('same')
def test_bw():
logger.info('Test BreitWigner_pdf')
ff = cpp.Ostap.Math.FormFactors.BlattWeisskopf(1, 3.5) ## formfactor
bw = cpp.Ostap.Math.BreitWigner(
m.value(),
m.error(),
0.150, ## m1
0.150, ## m2
1, ## orbital momentum
ff ## formfactor
)
model = Models.Fit1D(
signal=Models.BreitWigner_pdf(name='BW',
breitwigner=bw,
mass=mass,
mean=signal_gauss.mean,
convolution=0.010), ## CONVOLUTION!
background=Models.Bkg_pdf('BkgBW', mass=mass, power=0))
signal = model.signal
model.s.setVal(5000)
model.b.setVal(500)
signal.mean.fix(m.value())
with rooSilent():
result, frame = model.fitTo(dataset0)
signal.mean.release()
signal.gamma.release()
result, frame = model.fitTo(dataset0)
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('Signal & Background are: %-28s & %-28s ' %
(result('S')[0], result('B')[0]))
logger.info('Mean is: %-28s ' % result(signal.mean)[0])
logger.info('Gamma is: %-28s ' %
result(signal.gamma)[0])
models.add(model)
def fit_bw():
im = ROOT.RooRealVar('im', 'im', 2.42, 2.52)
pk = ROOT.RooRealVar('pk', 'pk', 2.47, 2.46, 2.48)
bw = cpp.Ostap.Math.BreitWigner(pk)
sig_ap = Models.BreitWigner_pdf('sig_bw', breitwigner=bw, xvar=im)
bkg0 = Models.Bkg_pdf('bkg0', xvar=im, power=2.)
model = Models.Fit1D(signal=sig_ap, background=bkg0)
rfile = ROOT.TFile("../datasets/test_xic_100invpb.root", "READ")
ds = rfile["da_lc"]
ds = ds.reduce("im > 2.42 && im < 2.52")
dh = (ds.reduce(ROOT.RooArgSet(im), "im>0")).binnedClone()
with timing():
r, w = model.fitTo(dh)
r, w = model.fitTo(ds, draw=True, nbins=200)
r.draw()
def test_bw():
logger.info('Test BreitWigner_pdf')
ff = cpp.Ostap.Math.FormFactors.BlattWeisskopf(1, 3.5) ## formfactor
bw = cpp.Ostap.Math.BreitWigner(
m.value(),
m.error(),
0.150, ## m1
0.150, ## m2
1, ## orbital momentum
ff ## formfactor
)
model = Models.Fit1D(signal=Models.BreitWigner_pdf(name='BW',
breitwigner=bw,
xvar=mass,
m0=signal_gauss.mean),
background=Models.Bkg_pdf('BkgBW', xvar=mass,
power=0),
S=S,
B=B)
signal = model.signal
model.S.setVal(5000)
model.B.setVal(500)
signal.mean.fix(m.value())
with rooSilent():
result, frame = model.fitTo(dataset0)
signal.m0.release()
signal.gamma.release()
result, frame = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("Breit-Wigner function\n%s" % result.table(prefix="# "))
models.add(model)
m23 = ROOT.RooRealVar("m23", "m23", 1.006, 1.065)
phi_ds = ds.reduce(ROOT.RooArgSet(im, m23),
"im>2.24 && im<2.33 && m23>1.006 && m23<1.065")
#--- Prepare a composite model
import ostap.fitting.models as Models
sig = Models.Voigt_pdf("sig",
xvar=m23,
mean=(1.02, 1.015, 1.025),
gamma=(0.0042, 0.0004, 0.0044),
sigma=(0.001, 0.0001, 0.003))
bkg = Models.PS2_pdf("bkg", xvar=m23, m1=0.493272, m2=0.493272)
bw = Ostap.Math.BreitWigner(1.019, 0.0042, 0.493272, 0.493272, 1)
breit = Models.BreitWigner_pdf('BW',
bw,
xvar=m23,
mean=(1.019, 1.016, 1.022),
gamma=0.0042)
reso = ROOT.RooRealVar("reso", "reso", 0.001, 0.0001, 0.003)
cnv_pdf = Models.Convolution_pdf(pdf=breit,
xvar=m23,
resolution=reso,
useFFT=True)
model1 = Models.Fit1D(signal=sig, background=bkg)
model2 = Models.Fit1D(signal=breit, background=bkg)
model3 = Models.Fit1D(signal=cnv_pdf, background=bkg)
# ---- Fit and Draw
r, w = model3.fitTo(phi_ds, draw=True, silent=True)
canv = ROOT.TCanvas("canv", "canv", 900, 900)
def test_breitwigner_rho():
"""Different rho0 parameterizations
"""
logger = getLogger("test_breitwigner_rho")
logger.info("Rho0 shapes")
## 1) P-wave Breit-Wigner with Jackson's formfactor
bw1 = Ostap.Math.Rho0(m_rho, g_rho, m_pi)
## 2) P-wave Breit-Wigner with Blatt-Weisskopf formfactor
ff = Ostap.Math.FormFactors.BlattWeisskopf(1, 3.5 / GeV)
ch2 = Ostap.Math.Channel(g_rho, m_pi, m_pi, 1, ff)
bw2 = Ostap.Math.BreitWigner(m_rho, ch2)
## 3) Gounaris-Sakurai lineshape
ch2 = Ostap.Math.ChannelGS(g_rho, m_pi)
bw3 = Ostap.Math.BreitWigner(m_rho, ch2)
## 3) P-wave Breit-Wigner with no formfactors
bw4 = Ostap.Math.BreitWigner(m_rho, g_rho, m_pi, m_pi, 1)
bw1.draw(xmin=200 * MeV, xmax=1.6 * GeV, linecolor=2)
bw2.draw('same', xmin=200 * MeV, xmax=1.6 * GeV, linecolor=4)
bw3.draw('same', xmin=200 * MeV, xmax=1.6 * GeV, linecolor=8)
bw4.draw('same', xmin=200 * MeV, xmax=1.6 * GeV, linecolor=5)
mass = ROOT.RooRealVar('mass', 'm(pipi)', 200 * MeV, 1.6 * GeV)
model1 = Models.BreitWigner_pdf('BW1',
bw1,
xvar=mass,
m0=m_rho,
gamma=g_rho)
model2 = Models.BreitWigner_pdf('BW2',
bw2,
xvar=model1.mass,
m0=model1.mean,
gamma=model1.gamma)
model3 = Models.BreitWigner_pdf('BW3',
bw3,
xvar=model1.mass,
m0=model1.mean,
gamma=model1.gamma)
f1 = model1.draw(total_fit_options=(ROOT.RooFit.LineColor(2), ))
f2 = model2.draw(total_fit_options=(ROOT.RooFit.LineColor(4), ))
f3 = model3.draw(total_fit_options=(ROOT.RooFit.LineColor(8), ))
f1.draw()
f2.draw('same')
f3.draw('same')
models.add(bw1)
models.add(bw2)
models.add(bw3)
models.add(model1)
models.add(model2)
models.add(model3)
time.sleep(2)
import ROOT
import ostap.fitting.models as Models
from ostap.core.core import cpp
#variables
imc = ROOT.RooRealVar('imc', 'imc', 1., 3.)
pk = ROOT.RooRealVar('pk', 'peak', 0.)
#signal
bw = cpp.Ostap.Math.BreitWigner(pk)
sig_bw = Models.BreitWigner_pdf('sig_bw', breitwigner=bw, xvar=imc)
sig_bw.gamma.setVal(0.20)
sig_bw.gamma.setError(0.01)
sig_bw.mean.setVal(2.10)
sig_bw.mean.setError(0.10)
#background
bkg0 = Models.PolyPos_pdf('bkg', imc, power=1)
bkg0.phis[0].setVal(2.72)
bkg0.phis[0].setError(0.02)
#compound model
model = Models.Fit1D(signal=sig_bw, background=bkg0)
model.draw()
## 1b: prepare DATA dataset
# ===============================================================================
gen_reso_data = Models.ResoCB2(
'R',
xvar=mass,
sigma=1.15 * MeV, ## 15% wider
alpha=1.5, ## slightly different alpha
n=10, ## slightly different N
kappaN=0.05, ## small asymmetry
kappaA=0.05) ## small asymmetry
ff = Ostap.Math.FormFactors.BlattWeisskopf(1, 3.5 / GeV)
phi = Ostap.Math.BreitWigner(m_phi, g_phi, m_K, m_K, 1, ff)
gen_phi = Models.BreitWigner_pdf('BW0',
xvar=mass,
breitwigner=phi,
m0=(m_phi, m_phi - 10 * MeV,
m_phi + 10 * MeV),
gamma=(g_phi, 1 * MeV, g_phi + 10 * MeV))
gen_cnv_conf = {
'resolution': gen_reso_data,
'nbins': 5000,
'buffer': 0.25,
'bufstrat': 2
}
## Signals:
signal = Models.Convolution_pdf(gen_phi, **gen_cnv_conf)
ps = Ostap.Math.PhaseSpace2(m_K, m_K)
bkg = Models.PSLeftExpoPol_pdf('B0',
xvar=mass,