sigma = RooRealVar( 'sigma', 'width of gaussian', 5.0 )
# Load reference ROOT tree
full_ch = TChain("DecayTree")
full_ch.Add("/mnt/home/kklimaszewski/LHCb/Bs2JpsieePhi/M_Bs_Fits/mc/DVNtuples_Bs2JpsieePhi_13154001_MCfull_R14ac_TupleBsDetached_PIDCorr_S21_NewSel_tupleB.root")
# Run selection
ch = full_ch.CopyTree("sigmam>0. && sigmam<250. && time>0.3 && time<14. && sigmat<0.12 && BDT_response_NewSel>0.2 && eminus_bremmult==0 && eplus_bremmult==0")
#fit.mass.setBins(_comp['config']['bins'])
# Create hist PDF
data = RooDataSet(
'data_set',
'',
RooArgSet(x),
RooFit.Import(ch)
)
hist = data.binnedClone()
p1 = RooHistPdf(
'histpdf',
'',
RooArgSet(x),
hist,
2 # Order of interpolation function
)
#p2 = RooGaussian(
p2 = RooGaussModel(
"comp_2",
"",
x,
mean,
sigma
)
## Phi fitting
kcanvas = TCanvas("kcanvas", "kcanvas", 1400, 800)
pullpad = TPad("pullpad", "pullpad", 0.0, 0.05, 1.0, 0.33)
plotpad = TPad("histopad", "histopad", 0.0, 0.35, 1.0, 1.0)
plotpad.SetFillStyle(4004)
pullpad.SetFillStyle(4004)
plotpad.Draw()
pullpad.Draw()
traKFitData = theData.Clone("fitTrakData")
if binnedfit:
tt_mass.setBins(30)
traKFitData = theData.binnedClone("binnedTrakData")
phimean = 1.019
gammavalue = 0.0012
fitphimin = 1.00
fitphimax = 1.04
kkSigma = RooRealVar("#sigma", "#sigma", 0.0013)
kkGamma = RooRealVar("#Gamma", "#Gamma", gammavalue, 0.001, 0.015)
kkMean = RooRealVar("m_{kk}", "m_{kk}", phimean, phimean - 0.005,
phimean + 0.005)
# B_1 = RooRealVar ( "B_1" , "B_1 " , 0.3 , -20 , 100 )
# B_2 = RooRealVar ( "B_2" , "B_2" , 0.3 , -20 , 100 )
# B_3 = RooRealVar ( "B_3" , "B_3" , 0.3 , -20 , 100 )
n_regions= len(regions)
#Create index for RooFit to distinguish regions
regionIndex = RooCategory("regionIndex", "regionIndex")
for i in range(n_regions) :
regionIndex.defineType(regions[i])
lo_mass_cut = 1845
hi_mass_cut = 1885
lo_mid_dataset = mid_dataset_raw.reduce("D0_mass < "+str(lo_mass_cut))
mid_dataset = mid_dataset_raw.reduce("D0_mass >= "+str(lo_mass_cut)+" && D0_mass <= "+str(hi_mass_cut))
hi_mid_dataset = mid_dataset_raw.reduce("D0_mass > "+str(hi_mass_cut))
#Create new dataset separated by region
dataset = RooDataSet("dataset_"+mag, "dataset_"+mag, RooArgSet(deltam), Index(regionIndex), Import("lo_mid", lo_mid_dataset), Import("mid", mid_dataset), Import("hi_mid", hi_mid_dataset))
datahist = dataset.binnedClone()
##############################################
print "Dataset binned successfully!\n"
########### Fitting Pure Signal ##############
draw_p_sig = True
#Initial parameters for signal components
init_m = [145.4, 145.1, 148.3]
init_w = [0.37, 1, 5]
w_min = 0
w_max = 10
#Number of separate pdfs which make up the signal
def init_pdfs(fit, params, prefix, signal):
'''
Initialize RooFit PDFs from parameter dictionary
:param MassFit fit: - MassFit instance
:param dict params: - parameter dictionary
:param str prefix: - pdf name prefix
:param bool signal: - signal pdf
:return: (pdf, components) with pdf - Composit pdf, components - list of component pdfs
:rtype: tuple
'''
# Build recursive composit pdf:
# M(x) = f_sig*S(x) + (1-f_sig){f_peak*P(x) + (1-f_peak)*B(x)}
#
# This avoids problems with sum(fractions) > 1
# See chapter 3 of RooFit Users Manual
#
# Store all auto generated obejcts in TLists. Otherwise python will destroy
# them as soon as this function ends
_pdf = None #: Composit PDF to return
_components = TList() #: List of component PDFs
_components_all = TList() #: List of component PDFs
_fracs = TList() #: List of fractions
_funcs = TList() #: List of formula variables
_params = { prefix+"_"+fit.mass_name: fit.mass }
_i = 1
_n_comps = len(params["components"])
for _comp in params["components"]:
# Create parameters
_comp_params = TList()
_comp_constrs = TList()
for _par in _comp["params"]:
_name = prefix + "_" + _par["name"]
if _name in _params:
_comp_params.append(_params[_name])
else:
if "random" in _par and _par["random"] == True:
# Generate starting value and validity range
_min = random.uniform(_par["min"], _par["value"])
_max = random.uniform(_par["value"], _par["max"])
_value = random.uniform(_min, _max)
_par["random"] = False
# Store obtained values
_par["min"] = _min
_par["max"] = _max
_par["value"] = _value
_var = RooRealVar(
_name, _par["title"], _value,
_min, _max, _par["units"]
)
else:
_var = RooRealVar(
_name, _par["title"], _par["value"],
_par["min"], _par["max"], _par["units"]
)
fit.parameters.push_back(_var)
_comp_params.Add(_var)
_params[_name] = _var
if "constraint" in _par:
_constr = RooGaussian(
_name+"_constr",
_par["title"]+"_constr",
_var,
RooFit.RooConst(_par["constraint"]["mean"]),
RooFit.RooConst(_par["constraint"]["sigma"])
)
fit.constraints.push_back(_constr)
_comp_constrs.Add(_constr)
# Store mean and sigma of core part of the signal PDF
if _i == 1 and signal == True:
fit.sig_mass = _comp_params.At(0)
fit.sig_sigma = _comp_params.At(1)
_name = prefix+"_"+_comp["name"]
# Initialize component
if _comp["type"] not in supported_pdfs:
raise Exception("Unknown PDF type: %s" % _comp["type"])
if _comp["type"] == "RooAddPdf":
(_sum_pdf, _sum_components, _sum_params) = init_pdfs(fit, _comp, _comp['name'], signal)
_components_all.AddAll(_sum_components)
_components.Add(_sum_pdf)
_params.update(_sum_params)
elif _comp["type"] == "RooGaussian":
_components.Add(RooGaussian(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1))
)
elif _comp["type"] == "RooParamGaussian":
_v = RooFormulaVar(
_name+"_variable",
"",
"@0*@1",
RooArgList(
_comp_params.At(1),
_comp_params.At(2))
)
_funcs.Add(_v)
_components.Add(RooGaussian(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_v)
)
elif _comp["type"] == "RooParamGaussianOffset":
_m = RooFormulaVar(
_name+"_var_mean",
"",
"@0+@1",
RooArgList(
_comp_params.At(0),
_comp_params.At(1))
)
_s = RooFormulaVar(
_name+"_var_sigma",
"",
"@0*@1",
RooArgList(
_comp_params.At(2),
_comp_params.At(3))
)
_funcs.Add(_m)
_funcs.Add(_s)
_components.Add(RooGaussian(
_name,
_comp["title"],
fit.mass,
_m,
_s)
)
elif _comp["type"] == "RooPolynomial":
if len(_comp_params):
_components.Add(RooPolynomial(
_name,
_comp["title"],
fit.mass,
RooArgList(_comp_params))
)
else:
_components.Add(RooPolynomial(
_name,
_comp["title"],
fit.mass)
)
elif _comp["type"] == "RooChebychev":
_components.Add(RooChebychev(
_name,
_comp["title"],
fit.mass,
RooArgList(_comp_params))
)
elif _comp["type"] == "RooExponential":
_components.Add(RooExponential(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0))
)
elif _comp["type"] == "RooVoigtian":
_components.Add(RooVoigtian(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2))
)
elif _comp["type"] == "RooCBShape":
_components.Add(RooCBShape(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3))
)
elif _comp["type"] == "RooParamCBShape":
_v = RooFormulaVar(
_name+"_variable",
"",
"@0*@1",
RooArgList(
_comp_params.At(1),
_comp_params.At(2))
)
_funcs.Add(_v)
_components.Add(RooCBShape(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_v,
_comp_params.At(3),
_comp_params.At(4))
)
elif _comp["type"] == "RooDoubleCBShape":
_components.Add(RooDoubleCBShape(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3),
_comp_params.At(4),
_comp_params.At(5))
)
elif _comp["type"] == "RooCutCBShape":
_components.Add(RooCutCBShape(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3),
_comp_params.At(4))
)
elif _comp["type"] == "RooArgusBG":
_components.Add(RooArgusBG(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1))
)
elif _comp["type"] == "RooRArgusBG":
_v = RooFormulaVar(
_name+"_variable",
"",
"2*@0 - @1",
RooArgList(
_comp_params.At(0),
fit.mass)
)
_funcs.Add(_v)
_components.Add(RooArgusBG(
_name,
_comp["title"],
_v,
_comp_params.At(0),
_comp_params.At(1))
)
elif _comp["type"] == "RooIpatia":
_components.Add(RooIpatia(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3),
_comp_params.At(4),
_comp_params.At(5),
_comp_params.At(6))
)
elif _comp["type"] == "RooIpatia2":
_components.Add(RooIpatia2(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3),
_comp_params.At(4),
_comp_params.At(5),
_comp_params.At(6),
_comp_params.At(7),
_comp_params.At(8))
)
elif _comp["type"] == "RooCassandra":
_components.Add(RooCassandra(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3))
)
elif _comp["type"] == "RooAsymCassandra":
_components.Add(RooAsymCassandra(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3),
_comp_params.At(4))
)
elif _comp["type"] == "RooDecay":
_r = RooGaussModel(
_name+"_resolution",
_comp["title"]+"_resolution",
fit.mass,
_comp_params.At(0),
_comp_params.At(2)
)
_components.Add(RooDecay(
_name,
_comp["title"],
fit.mass,
_comp_params.At(1),
_r,
RooDecay.DoubleSided)
)
_funcs.Add(_r)
elif _comp["type"] == "RooDecayLeft":
_r = RooGaussModel(
_name+"_resolution",
_comp["title"]+"_resolution",
fit.mass,
_comp_params.At(0),
_comp_params.At(2)
)
_components.Add(RooDecay(
_name,
_comp["title"],
fit.mass,
_comp_params.At(1),
_r,
RooDecay.Flipped)
)
_funcs.Add(_r)
elif _comp["type"] == "RooDecayRight":
_r = RooGaussModel(
_name+"_resolution",
_comp["title"]+"_resolution",
fit.mass,
_comp_params.At(0),
_comp_params.At(2)
)
_components.Add(RooDecay(
_name,
_comp["title"],
fit.mass,
_comp_params.At(1),
_r,
RooDecay.SingleSided)
)
_funcs.Add(_r)
elif _comp["type"] == "RooExpGaussExp":
_components.Add(RooExpGaussExp(
_name,
_comp["title"],
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3))
)
elif _comp["type"] == "RooHistPdf":
if 'config' not in _comp:
raise Exception("Missing 'config' section for RooHistPdf.")
if 'file' not in _comp['config']:
raise Exception("Missing 'file' key in the 'config' section for RooHistPdf.")
if 'name' not in _comp['config']:
raise Exception("Missing 'name' key in the 'config' section for RooHistPdf.")
_f = TFile(_comp['config']['file'])
_pdf = _f.Get(_comp['config']['name'])
_components.Add(_pdf)
elif _comp["type"] == "Conv_RooDecay_RooBreitWigner":
_r1 = RooGaussModel(
_name+"_resolution_1",
_comp["title"]+"_resolution_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(2)
)
_f1 = RooDecay(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(1),
_r1,
RooDecay.DoubleSided
)
_f2 = RooBreitWigner(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(3),
_comp_params.At(4)
)
_funcs.Add(_r1)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooNumConvPdf(
_name,
_comp["title"],
fit.mass,
_f1,
_f2
)
_components.Add(_pdf)
elif _comp["type"] == "FFT_RooDecay_RooBreitWigner":
# Define sampling frequency
fit.mass.setBins(10000,"fft") ;
_r1 = RooGaussModel(
_name+"_resolution_1",
_comp["title"]+"_resolution_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(2)
)
_f1 = RooDecay(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(1),
_r1,
RooDecay.DoubleSided
)
_f2 = RooBreitWigner(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(3),
_comp_params.At(4)
)
_funcs.Add(_r1)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f1,
_f2
)
_pdf.setBufferFraction(50.0)
_components.Add(_pdf)
elif _comp["type"] == "FFT_RooDecay_RooCBShape":
# Define sampling frequency
fit.mass.setBins(10000,"fft") ;
_r1 = RooGaussModel(
_name+"_resolution_1",
_comp["title"]+"_resolution_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(2)
)
_f1 = RooDecay(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(1),
_r1,
RooDecay.DoubleSided
)
_f2 = RooCBShape(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(3),
_comp_params.At(4),
_comp_params.At(5),
_comp_params.At(6)
)
_funcs.Add(_r1)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f1,
_f2
)
_pdf.setBufferFraction(50.0)
_components.Add(_pdf)
elif _comp["type"] == "FFT_RooCassandra_RooGaussian":
# Define sampling frequency
fit.mass.setBins(10000,"fft") ;
_f1 = RooCassandra(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2)
)
_f2 = RooGaussian(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(3),
_comp_params.At(4)
)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f1,
_f2
)
_pdf.setBufferFraction(50.0)
_components.Add(_pdf)
elif _comp["type"] == "FFT_RooAsymCassandra_RooGaussian":
# Define sampling frequency
fit.mass.setBins(10000,"fft") ;
_f1 = RooAsymCassandra(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3),
_comp_params.At(4)
)
_f2 = RooGaussian(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(5),
_comp_params.At(6)
)
_funcs.Add(_f1)
_funcs.Add(_f2)
#_pdf = RooFFTConvPdf(
# _name,
# _comp["title"],
# fit.mass,
# _f1,
# _f2
# )
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f2,
_f1
)
_pdf.setBufferFraction(50.0)
_components.Add(_pdf)
elif _comp["type"] == "FFT_RooAsymCassandra_RooDoubleGaussian":
# Define sampling frequency
fit.mass.setBins(10000,"fft") ;
_v = RooFormulaVar(
_name+"_variable",
"",
"@0*@1",
RooArgList(
_comp_params.At(6),
_comp_params.At(7))
)
_funcs.Add(_v)
_f1 = RooAsymCassandra(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3),
_comp_params.At(4)
)
_f2 = RooGaussian(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(5),
_comp_params.At(6)
)
_f3 = RooGaussian(
_name+"_comp_3",
_comp["title"]+"_comp_3",
fit.mass,
_comp_params.At(5),
_v
)
_f4 = RooAddPdf(
_name,
_comp["title"],
RooArgList(_f2, _f3),
RooArgList(_comp_params.At(8))
)
_funcs.Add(_f1)
_funcs.Add(_f2)
_funcs.Add(_f3)
_funcs.Add(_f4)
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f4,
_f1
)
_pdf.setBufferFraction(50.0)
_components.Add(_pdf)
elif _comp["type"] == "Conv_RooAsymCassandra_RooGaussian":
_f1 = RooAsymCassandra(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3),
_comp_params.At(4)
)
_f2 = RooGaussian(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(5),
_comp_params.At(6)
)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooNumConvPdf(
_name,
_comp["title"],
fit.mass,
_f2,
_f1
)
# Does not work
#_pdf.setConvolutionWindow(_comp_params.At(5), _comp_params.At(6), 6.0)
_components.Add(_pdf)
elif _comp["type"] == "FFT_RooAsymCassandra3_RooGaussian":
# Define sampling frequency
fit.mass.setBins(10000,"fft") ;
_f1 = RooAsymCassandra3(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3),
_comp_params.At(4),
_comp_params.At(5),
_comp_params.At(6)
)
_f2 = RooGaussian(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(7),
_comp_params.At(8)
)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f2,
_f1
)
_pdf.setBufferFraction(50.0)
_components.Add(_pdf)
elif _comp["type"] == "FFT_RooCassandra_RooCBShape":
# Define sampling frequency
fit.mass.setBins(50000,"fft") ;
_f1 = RooCassandra(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2)
)
_f2 = RooCBShape(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(3),
_comp_params.At(4),
_comp_params.At(5),
_comp_params.At(6)
)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f1,
_f2
)
_pdf.setBufferFraction(100.0)
_components.Add(_pdf)
elif _comp["type"] == "FFT_RooCassandra3_RooCBShape":
# Define sampling frequency
fit.mass.setBins(50000,"fft") ;
_f1 = RooCassandra3(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3)
)
_f2 = RooCBShape(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(4),
_comp_params.At(5),
_comp_params.At(6),
_comp_params.At(7)
)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f1,
_f2
)
_pdf.setBufferFraction(100.0)
_components.Add(_pdf)
elif _comp["type"] == "FFT_RooHistPdf_RooGaussian":
# Load reference ROOT tree
_ref_full_ch = TChain(_comp['config']['tree'])
_ref_full_ch.Add(_comp['config']['files'])
# Run selection
_ref_ch = _ref_full_ch.CopyTree(_comp['config']['selection'])
_bins = fit.mass.getBins()
fit.mass.setBins(_comp['config']['bins'])
# Create hist PDF
_data = RooDataSet(
'ds_' + _name,
'',
RooArgSet(fit.mass),
RooFit.Import(_ref_ch)
)
_hist = _data.binnedClone()
_f1 = RooHistPdf(
name+'_histpdf',
_comp['title']+'_histpdf',
RooArgSet(fit.mass),
_hist,
2 # Order of interpolation function
)
fit.mass.setBins(_bins)
# Define sampling frequency
fit.mass.setBins(10000,"fft") ;
_f2 = RooGaussModel(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(0),
_comp_params.At(1)
)
_funcs.Add(_hist)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f2,
_f1
)
_pdf.setBufferFraction(5.0)
_components.Add(_pdf)
elif _comp["type"] == "Conv_RooHistPdf_RooGaussian":
if 'config' not in _comp:
raise Exception("Missing 'config' section for RooHistPdf.")
if 'file' not in _comp['config']:
raise Exception("Missing 'file' key in the 'config' section for RooHistPdf.")
if 'name' not in _comp['config']:
raise Exception("Missing 'name' key in the 'config' section for RooHistPdf.")
_file = TFile(_comp['config']['file'])
_f1 = _file.Get(_comp['config']['name'])
_f2 = RooGaussian(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(0),
_comp_params.At(1)
)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooNumConvPdf(
_name,
_comp["title"],
fit.mass,
_f2,
_f1
)
_components.Add(_pdf)
elif _comp["type"] == "FFT_RooDoubleCBShape_RooGaussian":
# Define sampling frequency
fit.mass.setBins(10000,"fft") ;
_f1 = RooDoubleCBShape(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3),
_comp_params.At(4),
_comp_params.At(5)
)
_f2 = RooGaussian(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(6),
_comp_params.At(7)
)
_funcs.Add(_f1)
_funcs.Add(_f2)
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f1,
_f2
)
_pdf.setBufferFraction(50.0)
_components.Add(_pdf)
elif _comp["type"] == "FFT_2xCBShape_RooGaussian":
# Define sampling frequency
fit.mass.setBins(10000,"fft") ;
_cb_frac = RooRealVar(
_name+"_cb_frac",
_name+"_cb_frac",
0.1, 0.0, 1.0)
_f1 = RooCBShape(
_name+"_comp_1",
_comp["title"]+"_comp_1",
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(2),
_comp_params.At(3)
)
_f2 = RooCBShape(
_name+"_comp_2",
_comp["title"]+"_comp_2",
fit.mass,
_comp_params.At(0),
_comp_params.At(1),
_comp_params.At(4),
_comp_params.At(5)
)
_f3 = RooAddPdf(
_name+"_comp_3",
_comp["title"]+"_comp_3",
RooArgList(_f1, _f2),
RooArgList(_cb_frac),
kTRUE)
_f4 = RooGaussian(
_name+"_comp_4",
_comp["title"]+"_comp_4",
fit.mass,
_comp_params.At(6),
_comp_params.At(7)
)
_funcs.Add(_f1)
_funcs.Add(_f2)
_funcs.Add(_f3)
_funcs.Add(_f4)
_funcs.Add(_cb_frac)
_pdf = RooFFTConvPdf(
_name,
_comp["title"],
fit.mass,
_f3,
_f4
)
_pdf.setBufferFraction(50.0)
_components.Add(_pdf)
else:
raise Exception("Unknown PDF type: "+_comp["type"])
if _i < _n_comps:
_f_v = 0.1
_f_min = 0.0
_f_max = 1.0
if "fraction" in _comp:
_f_v = _comp["fraction"]["value"]
_f_min = _comp["fraction"]["min"]
_f_max = _comp["fraction"]["max"]
_f = RooRealVar(
prefix+"_frac_"+_comp["name"],
prefix+" component "+_comp["name"]+" fraction",
_f_v, _f_min, _f_max)
if "fraction" in _comp and "constraint" in _comp["fraction"]:
_constr = RooGaussian(
_name+"_frac_constr",
_name+"_frac_constr",
_f,
RooFit.RooConst(_comp["fraction"]["constraint"]["mean"]),
RooFit.RooConst(_comp["fraction"]["constraint"]["sigma"])
)
fit.constraints.push_back(_constr)
_comp_constrs.Add(_constr)
_fracs.Add(_f)
_i += 1
if _n_comps == 1:
_pdf = _components.At(0)
else:
# Make sure we are creating a recursive composit PDF
_pdf = RooAddPdf(
prefix+"_pdf",
prefix+" PDF",
RooArgList(_components),
RooArgList(_fracs),
kTRUE)
_components_all.AddAll(_components)
return (_pdf, _components_all, _params)
resmodel, RooDecay.SingleSided)
decay = RooAddPdf('decay', 'Decay function for the B_{s}',
decayH, decayL, RooRealConstant.value(0.5))
decayargset = RooArgSet(decay)
# Get tree
rfile = get_file('data/smalltree-new-MC.root', 'read')
ftree = get_object('ftree', rfile)
# dt dataset for denom
trigger = 'HLT2Topo3BodyTOS'
triggerVar = RooRealVar(trigger, trigger, 0, 2)
cut = trigger+'>0'
tmpdtdataset = RooDataSet('dtdataset', 'dt dataset', RooArgSet(dt, triggerVar),
RooFit.Import(ftree), RooFit.Cut(cut))
dtdatahist = tmpdtdataset.binnedClone('dtdatahist', 'Binned dt')
dtdatahist = dtdatahist.reduce(dtargset)
errorPdf = RooHistPdf('errorPdf', 'Time error Hist PDF', dtargset, dtdatahist)
decaywdt = RooProdPdf('decaywdt', 'Decay function with dt distribution',
RooArgSet(errorPdf),
RooFit.Conditional(decayargset, timeargset))
del tmpdtdataset, dtdatahist
# Variable width binning
nbins1 = 50
nbins2 = 24
binedges1 = numpy.zeros(nbins1+1 , dtype=float)
binedges2 = numpy.zeros(nbins2+1 , dtype=float)
logwidth = log10((1E-2 + 2E-4) / 2E-4)
c.SaveAs("mm_mass_histos.root")
if args.nofit:
sys.exit()
#### #### Done plotting
#### #### Fitting
### Trak trak
#
traKFitData = trakData.Clone("fitTrakData")
if binnedfit:
tt_mass.setBins(100)
traKFitData = trakData.binnedClone("binnedTrakData")
phimean = 1.019
gammavalue = 0.01
fitphimin = 1.0
fitphimax = 1.04
if args.nofitkk and args.noreco:
## RECO
kkSigma = RooRealVar("#sigma", "#sigma", 0.0013)
kkGamma = RooRealVar("#Gamma", "#Gamma", gammavalue, 0.001, 0.015)
kkMean = RooRealVar("mean", "mean", phimean, phimean - 0.007,
phimean + 0.007)
