# mean = 3.096 )
# signal = Models.Needham_pdf ( 'Nh' ,
# mass = m_psi ,
# sigma = 0.013 ,
# mean = 3.096 )
## 2b) create the background : exponential times 1st order polymonial
bkg = Models.Bkg_pdf ( 'B' , mass = m_psi , power = 1 )
## 2c) create the model
model = Models.Fit1D ( signal = signal ,
background = bkg )
## 3) try to fit:
r,f = model.fitTo ( data , silence = True , ncpu = 8 )
signal .mean .release()
signal .sigma.release()
r,f = model.fitTo ( data , ncpu = 8 , draw = True , silence = True )
##
## signal0 = Models.Gauss_pdf ( 'G' ,
def FitHisto(self, fit_methods, histo):
"""Fit Histogram for all fit_methods.
The result is stored in a AnnaResult
Arguments:
fit_methods {list} -- fit configuration string
histo {TH1} -- histo to be fitted
Returns:
list -- contains all the AnnaResults
"""
try:
assert histo is not None
except AssertionError:
error("Cannot get histo")
return None
subresult_list = list()
print(""" \n========= > Fit histo {} < ========= """.format(
histo.GetName()))
for i, fit_method in enumerate(fit_methods):
# Get the fit configuration
for key in self._fit_key.keys():
self._fit_key[key] = None
self.DecodeFitType(fit_method)
# Redefine the fit range to the histo boundaries
hmin = histo.GetXaxis().GetXmin()
hmax = histo.GetXaxis().GetXmax()
if 'range' in self._fit_key.keys():
if self._fit_key['range'] is not None:
print self._fit_key['range']
hmin = float(min(self._fit_key['range'].split(';')))
hmax = float(max(self._fit_key['range'].split(';')))
fit_range = ROOT.RooRealVar(histo.GetXaxis().GetName(),
histo.GetXaxis().GetTitle(), hmin,
hmax)
# get PDF and create fit model
signal = self.CreateSignalPDF(fit_range)
if signal is None:
continue
background = self.CreateBackgroundPDF(fit_range)
if background is None:
continue
# return None
model = Models.Fit1D(signal=signal, background=background)
data = ROOT.RooDataHist(histo.GetName(), histo.GetTitle(),
ROOT.RooArgList(fit_range), histo)
print(" \n------- > with {} + {} (weight = {} ) \n".format(
self._fit_key['signal'], self._fit_key['bkgr'],
self._fit_key['weight']))
result, frame = model.fitTo(data,
silent=False,
draw=True,
refit=True,
ncpu=8)
debug("{}".format(result))
# Create the AnnaResult
sr_name = "{}_{}".format(fit_method, histo.GetName())
sr = AnnaResult(name=sr_name, title=histo.GetTitle(), histo=histo)
sr.weigth = self._fit_key['weight']
sr.frame = frame
for key in result.parameters().keys():
sr.Set(key, result(key)[0].value(), result(key)[0].error())
sr.Set("FitResult", result.status(), 0., 0.)
sr.Set("CovMatrixStatus", result.covQual(), 0., 0.)
subresult_list.append(sr)
return subresult_list
# =============================================================================
## gauss PDF
# =============================================================================
logger.info('Test Gauss_pdf')
signal_gauss = Models.Gauss_pdf(name='Gauss', mass=mass)
signal_gauss.mean.setVal(m.value())
signal_gauss.sigma.setVal(m.error())
models.append(signal_gauss)
# =============================================================================
## Gauss PDF
# =============================================================================
model_gauss = Models.Fit1D(signal=signal_gauss,
background=Models.Bkg_pdf('BkgGauss',
mass=mass,
power=0))
model_gauss.background.tau.fix(0)
with rooSilent():
result, frame = model_gauss.fitTo(dataset0)
result, frame = model_gauss.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:
print 'Signal & Background are: ', result('S')[0], result('B')[0]
print 'Mean & Sigma are: ', result('mean_Gauss')[0], result(
'sigma_Gauss')[0]
mK = 0.49369
mPion = 0.1396
bw = cpp.Gaudi.Math.Phi0(1.0195, 0.0043, mK)
signal = Models.BreitWigner_pdf('BW0',
bw,
mass=m_phi,
gamma=0.0043,
mean=1.0195,
convolution=0.0015)
## Phase space as background:
ps = cpp.Gaudi.Math.PhaseSpaceNL(2 * mK, 10.0, 2, 5)
bkg = Models.PSPol_pdf('PS0', mass=m_phi, phasespace=ps, power=1)
f2 = cpp.Gaudi.Math.Flatte2(0.980, 165, 4.21, mPion, mPion, mK, mK)
flatte = Models.Flatte2_pdf('F20', f2, mass=m_phi, m0_980=1.000, m0g1=0.165)
flatte.mean.fix(0.980)
model = Models.Fit1D(signal=signal, othersignals=[flatte], background=bkg)
model.S.fix(10000)
model.B.fix(5000)
model.S_1.fix(5000)
data = model.pdf.generate(varset, 20000)
model0.ss.fix(1000)
model0.sb.fix(300)
model0.bs.fix(300)
model0.bb.fix(300)
model1 = Models.Fit2D(
signal_1=sigB,
signal_2=sigPhi,
##
bkg2=Models.PSPol_pdf('PSP', m_phi, ps2, power=1),
bkgB=Models.PSPol_pdf('PSB', m_phi, ps2, power=1),
)
model_phi = Models.Fit1D(signal=sigPhi,
background=Models.PSPol_pdf('PSQ',
m_phi,
ps2,
power=1),
suffix='_P')
model_B = Models.Fit1D(signal=sigB,
background=Models.Bkg_pdf('BB', m_b, power=1),
suffix='_B')
varset = ROOT.RooArgSet(m_b, m_phi)
dataset = model0.pdf.generate(varset, 1900)
## 2D fits
with rooSilent():
r, u = model1.fitTo(dataset)
r, u = model1.fitTo(dataset)
from OstapTutor.TestVars1 import m_psi,m_ups
import Ostap.FitModels as Models
signal_psi = Models.CB2_pdf('Jpsi' ,
mass = m_psi ,
sigma = 0.013 ,
mean = 3.096 )
signal_psi.aL.fix(1.8)
signal_psi.aR.fix(1.8)
signal_psi.nL.fix(1.8)
signal_psi.nR.fix(1.8)
## define model for J/psi
model_psi = Models.Fit1D( signal = signal_psi ,
background = Models.Bkg_pdf ( 'BJpsi' , mass = m_psi ) )
## define the model for Y
model_Y = Models.Manca2_pdf ( m_ups , power = 0 ,
m1s = 9.4539e+00 ,
sigma = 4.03195e-02 )
model_Ypsi = Models.MancaX_pdf ( model_Y ,
signal_psi ,
suffix = '' )
# ==========================================================================================
# The END
# ==========================================================================================
signal_Dp = Models.Dp_pdf(mass=m_Dp,
name='Dp',
mean=1.8711e+00,
sigma=7.1183e-03)
signal_Ds = Models.Ds_pdf(mass=m_Ds,
name='Ds',
mean=1.9699e+00,
sigma=6.0178e-03)
signal_Lc = Models.Lc_pdf(mass=m_Lc,
name='Lc',
mean=2.2884e+00,
sigma=5.1856e-03)
model_D0 = Models.Fit1D(signal=signal_D0, bpower=1)
model_Dp = Models.Fit1D(signal=signal_Dp, bpower=1)
model_Ds = Models.Fit1D(signal=signal_Ds, bpower=1)
model_Lc = Models.Fit1D(signal=signal_Lc, bpower=1)
model_Y0 = Models.Manca_pdf(m_ups, power=1, name='Y0')
model_Y1 = Models.Manca2_pdf(m_ups,
power=2,
name='Y1',
m1s=9.4539e+00,
sigma=4.03195e-02)
model_Y2 = Models.Manca2_pdf(m_ups,
power=1,
name='Y2',
