def dump_simple_simul():
# idea: take two data sets, both low counts, but same lambda
# and fit said lambda simultanously with both sets
r_x = RooRealVar('x', 'x', 0, bins)
r_lam = RooRealVar('lam', 'lam', 0.5, 0.0, 1.5)
r_lam1 = RooRealVar('lam1', 'lam1', 0.5, 0.0, 1.5)
r_lam2 = RooRealVar('lam2', 'lam2', 0.5, 0.0, 1.5)
model1 = RooPoisson('pois1', 'pois1', r_x, r_lam1)
model2 = RooPoisson('pois2', 'pois2', r_x, r_lam2)
r_index = RooCategory('index', 'index')
r_index.defineType('1')
r_index.defineType('2')
simul_model = RooSimultaneous('model', 'model', r_index)
simul_model.addPdf(model1, '1')
simul_model.addPdf(model2, '2')
data1 = RooDataSet('', '', RooArgSet(r_x))
for val in unbinned_from_binned(xdata, ydata):
r_x.setVal(val)
data1.add(RooArgSet(r_x))
r_index.setLabel('1')
data1.addColumn(r_index)
data2 = RooDataSet('', '', RooArgSet(r_x))
for val in unbinned_from_binned(xdata, ydata2):
r_x.setVal(val)
data2.add(RooArgSet(r_x))
r_index.setLabel('2')
data2.addColumn(r_index)
data1.append(data2)
_result = simul_model.fitTo(data1)
print(r_lam.getVal(), '+-', r_lam.getError(), lam, np.abs(r_lam.getVal() - lam) / lam)
print(r_lam1.getVal(), '+-', r_lam1.getError(), lam, np.abs(r_lam1.getVal() - lam) / lam)
print(r_lam2.getVal(), '+-', r_lam2.getError(), lam, np.abs(r_lam2.getVal() - lam) / lam)
def setupWorkspace(ws,options):
cfg = options.config #for convenience
fit_sections = cfg.sections()
fit_sections.remove('Global') #don't need to iterate over the global configuration
if not isinstance(ws,RooWorkspace):
print "You didn't pass a RooWorkspace!"
exit(1)
cpling_type = cfg.get('Global','couplingType')
par1 = cfg.get('Global','par1Name')
par1bound = [-cfg.getfloat('Global','par1Max'),
cfg.getfloat('Global','par1Max')]
par2 = cfg.get('Global','par2Name')
par2bound = [-cfg.getfloat('Global','par2Max'),
cfg.getfloat('Global','par2Max')]
#create the parameters in the workspace
ws.factory('%s_%s[0,%f,%f]'%(par1,cpling_type,par1bound[0],par1bound[1]))
ws.factory('%s_%s[0,%f,%f]'%(par2,cpling_type,par2bound[0],par2bound[1]))
# since the lumi error is correlated among all channels we only need one penalty term for it
lumi_err = exp(options.config.getfloat('Global','lumi_err')) # exp because we use log normal
ws.factory('luminosityError[%f]'%lumi_err)
ws.factory('RooLognormal::lumiErr(err_gl[1,0.0001,50],1,luminosityError)')
channel_cat = RooCategory('channels','channels')
#first pass: process the backgrounds, signal and data into
# simultaneous counting pdfs over the bins
for section in fit_sections:
#create the basic observable, this is used behind the scenes
#in the background and signal models
channel_cat.defineType(section)
channel_cat.setLabel(section)
print 'Building pdf for configuration section:',section
for it,bkg in getBackgroundsInCfg(section,cfg).iteritems():
ws.factory('backgroundError_%s_%s[%f]'%(section,it,exp(bkg[1])))
ws.factory('selectionError_%s[%f]'%(section,exp(cfg.getfloat(section,'selection_err'))))
processFittingData(ws,cfg,section)
processSignalModel(ws,cfg,section)
processBackgroundModel(ws,cfg,section)
createPdfForChannel(ws,cfg,section)
ws.data('countingdata_%s'%section).addColumn(channel_cat)
getattr(ws,'import')(channel_cat)
top = RooSimultaneous('TopLevelPdf',
'TopLevelPdf',
ws.cat('channels'))
alldatavars = RooArgSet(ws.cat('channels'))
conditionals = RooArgSet()
#second pass: process counting pdfs into simultaneous pdf over channels
for section in fit_sections:
top.addPdf(ws.pdf('countingpdf_%s'%section),section)
alldatavars.add(ws.var('%s_%s'%(cfg.get(section,'obsVar'),section)))
conditionals.add(ws.var('%s_%s'%(cfg.get(section,'obsVar'),section)))
alldatavars.add(ws.var('n_observed_%s'%section))
getattr(ws,'import')(top)
ws.defineSet('condObs',conditionals)
allcountingdata = RooDataSet('allcountingdata',
'allcountingdata',
alldatavars)
getattr(ws,'import')(allcountingdata)
allcountingdata = ws.data('allcountingdata')
#third pass: make the final combined dataset
for section in fit_sections:
current = ws.data('countingdata_%s'%section)
print 'countingdata_%s has %d entries'%(section,current.numEntries())
for i in range(current.numEntries()):
alldatavars = current.get(i)
allcountingdata.add(alldatavars)
def fit_mass(data,
column,
x,
sig_pdf=None,
bkg_pdf=None,
n_sig=None,
n_bkg=None,
blind=False,
nll_profile=False,
second_storage=None,
log_plot=False,
pulls=True,
sPlot=False,
bkg_in_region=False,
importance=3,
plot_importance=3):
"""Fit a given pdf to a variable distribution
Parameter
---------
data : |hepds_type|
The data containing the variable to fit to
column : str
The name of the column to fit the pdf to
sig_pdf : RooFit pdf
The signal Probability Density Function. The variable to fit to has
to be named 'x'.
bkg_pdf : RooFit pdf
The background Probability Density Function. The variable to fit to has
to be named 'x'.
n_sig : None or numeric
The number of signals in the data. If it should be fitted, use None.
n_bkg : None or numeric
The number of background events in the data.
If it should be fitted, use None.
blind : boolean or tuple(numberic, numberic)
If False, the data is fitted. If a tuple is provided, the values are
used as the lower (the first value) and the upper (the second value)
limit of a blinding region, which will be omitted in plots.
Additionally, no true number of signal will be returned but only fake.
nll_profile : boolean
If True, a Negative Log-Likelihood Profile will be generated. Does not
work with blind fits.
second_storage : |hepds_type|
A second data-storage that will be concatenated with the first one.
importance : |importance_type|
|importance_docstring|
plot_importance : |plot_importance_type|
|plot_importance_docstring|
Return
------
tuple(numerical, numerical)
Return the number of signals and the number of backgrounds in the
signal-region. If a blind fit is performed, the signal will be a fake
number. If no number of background events is required, -999 will be
returned.
"""
if not (isinstance(column, str) or len(column) == 1):
raise ValueError("Fitting to several columns " + str(column) +
" not supported.")
if type(sig_pdf) == type(bkg_pdf) == None:
raise ValueError("sig_pdf and bkg_pdf are both None-> no fit possible")
if blind is not False:
lower_blind, upper_blind = blind
blind = True
n_bkg_below_sig = -999
# create data
data_name = data.name
data_array, _t1, _t2 = data.make_dataset(second_storage, columns=column)
del _t1, _t2
# double crystalball variables
min_x, max_x = min(data_array[column]), max(data_array[column])
# x = RooRealVar("x", "x variable", min_x, max_x)
# create data
data_array = np.array([i[0] for i in data_array.as_matrix()])
data_array.dtype = [('x', np.float64)]
tree1 = array2tree(data_array, "x")
data = RooDataSet("data", "Data", RooArgSet(x), RooFit.Import(tree1))
# # TODO: export somewhere? does not need to be defined inside...
# mean = RooRealVar("mean", "Mean of Double CB PDF", 5280, 5100, 5600)#, 5300, 5500)
# sigma = RooRealVar("sigma", "Sigma of Double CB PDF", 40, 0.001, 200)
# alpha_0 = RooRealVar("alpha_0", "alpha_0 of one side", 5.715)#, 0, 150)
# alpha_1 = RooRealVar("alpha_1", "alpha_1 of other side", -4.019)#, -200, 0.)
# lambda_0 = RooRealVar("lambda_0", "Exponent of one side", 3.42)#, 0, 150)
# lambda_1 = RooRealVar("lambda_1", "Exponent of other side", 3.7914)#, 0, 500)
#
# # TODO: export somewhere? pdf construction
# frac = RooRealVar("frac", "Fraction of crystal ball pdfs", 0.479, 0.01, 0.99)
#
# crystalball1 = RooCBShape("crystallball1", "First CrystalBall PDF", x,
# mean, sigma, alpha_0, lambda_0)
# crystalball2 = RooCBShape("crystallball2", "Second CrystalBall PDF", x,
# mean, sigma, alpha_1, lambda_1)
# doubleCB = RooAddPdf("doubleCB", "Double CrystalBall PDF",
# crystalball1, crystalball2, frac)
# n_sig = RooRealVar("n_sig", "Number of signals events", 10000, 0, 1000000)
# test input
if n_sig == n_bkg == 0:
raise ValueError("n_sig as well as n_bkg is 0...")
if n_bkg is None:
n_bkg = RooRealVar("n_bkg", "Number of background events", 10000, 0,
500000)
elif n_bkg >= 0:
n_bkg = RooRealVar("n_bkg", "Number of background events", int(n_bkg))
else:
raise ValueError("n_bkg is not >= 0 or None")
if n_sig is None:
n_sig = RooRealVar("n_sig", "Number of signal events", 1050, 0, 200000)
# START BLINDING
blind_cat = RooCategory("blind_cat", "blind state category")
blind_cat.defineType("unblind", 0)
blind_cat.defineType("blind", 1)
if blind:
blind_cat.setLabel("blind")
blind_n_sig = RooUnblindPrecision("blind_n_sig",
"blind number of signals",
"wasistdas", n_sig.getVal(),
10000, n_sig, blind_cat)
else:
# blind_cat.setLabel("unblind")
blind_n_sig = n_sig
print "n_sig value " + str(n_sig.getVal())
# raw_input("blind value " + str(blind_n_sig.getVal()))
# n_sig = blind_n_sig
# END BLINDING
elif n_sig >= 0:
n_sig = RooRealVar("n_sig", "Number of signal events", int(n_sig))
else:
raise ValueError("n_sig is not >= 0")
# if not blind:
# blind_n_sig = n_sig
# # create bkg-pdf
# lambda_exp = RooRealVar("lambda_exp", "lambda exp pdf bkg", -0.00025, -1., 1.)
# bkg_pdf = RooExponential("bkg_pdf", "Background PDF exp", x, lambda_exp)
if blind:
comb_pdf = RooAddPdf("comb_pdf", "Combined DoubleCB and bkg PDF",
RooArgList(sig_pdf, bkg_pdf),
RooArgList(blind_n_sig, n_bkg))
else:
comb_pdf = RooAddPdf("comb_pdf", "Combined DoubleCB and bkg PDF",
RooArgList(sig_pdf, bkg_pdf),
RooArgList(n_sig, n_bkg))
# create test dataset
# mean_gauss = RooRealVar("mean_gauss", "Mean of Gaussian", 5553, -10000, 10000)
# sigma_gauss = RooRealVar("sigma_gauss", "Width of Gaussian", 20, 0.0001, 300)
# gauss1 = RooGaussian("gauss1", "Gaussian test dist", x, mean_gauss, sigma_gauss)
# lambda_data = RooRealVar("lambda_data", "lambda exp data", -.002)
# exp_data = RooExponential("exp_data", "data example exp", x, lambda_data)
# frac_data = RooRealVar("frac_data", "Fraction PDF of data", 0.15)
#
# data_pdf = RooAddPdf("data_pdf", "Data PDF", gauss1, exp_data, frac_data)
# data = data_pdf.generate(RooArgSet(x), 30000)
# data.printValue()
# xframe = x.frame()
# data_pdf.plotOn(xframe)
# print "n_cpu:", meta_config.get_n_cpu()
# input("test")
# comb_pdf.fitTo(data, RooFit.Extended(ROOT.kTRUE), RooFit.NumCPU(meta_config.get_n_cpu()))
# HACK to get 8 cores in testing
c5 = TCanvas("c5", "RooFit pdf not fit vs " + data_name)
c5.cd()
x_frame1 = x.frame()
# data.plotOn(x_frame1)
# comb_pdf.pdfList()[1].plotOn(x_frame1)
if __name__ == "__main__":
n_cpu = 8
else:
n_cpu = meta_config.get_n_cpu()
print "n_cpu = ", n_cpu
# HACK
# n_cpu = 8
result_fit = comb_pdf.fitTo(data, RooFit.Minos(ROOT.kTRUE),
RooFit.Extended(ROOT.kTRUE),
RooFit.NumCPU(n_cpu))
# HACK end
if bkg_in_region:
x.setRange("signal", bkg_in_region[0], bkg_in_region[1])
bkg_pdf_fitted = comb_pdf.pdfList()[1]
int_argset = RooArgSet(x)
# int_argset = x
# int_argset.setRange("signal", bkg_in_region[0], bkg_in_region[1])
integral = bkg_pdf_fitted.createIntegral(int_argset,
RooFit.NormSet(int_argset),
RooFit.Range("signal"))
bkg_cdf = bkg_pdf_fitted.createCdf(int_argset, RooFit.Range("signal"))
bkg_cdf.plotOn(x_frame1)
# integral.plotOn(x_frame1)
n_bkg_below_sig = integral.getVal(int_argset) * n_bkg.getVal()
x_frame1.Draw()
if plot_importance >= 3:
c2 = TCanvas("c2", "RooFit pdf fit vs " + data_name)
c2.cd()
x_frame = x.frame()
# if log_plot:
# c2.SetLogy()
# x_frame.SetTitle("RooFit pdf vs " + data_name)
x_frame.SetTitle(data_name)
if pulls:
pad_data = ROOT.TPad("pad_data", "Pad with data and fit", 0, 0.33,
1, 1)
pad_pulls = ROOT.TPad("pad_pulls", "Pad with data and fit", 0, 0,
1, 0.33)
pad_data.SetBottomMargin(0.00001)
pad_data.SetBorderMode(0)
if log_plot:
pad_data.SetLogy()
pad_pulls.SetTopMargin(0.00001)
pad_pulls.SetBottomMargin(0.2)
pad_pulls.SetBorderMode(0)
pad_data.Draw()
pad_pulls.Draw()
pad_data.cd()
else:
if log_plot:
c2.SetLogy()
if blind:
# HACK
column = 'x'
# END HACK
x.setRange("lower", min_x, lower_blind)
x.setRange("upper", upper_blind, max_x)
range_str = "lower,upper"
lower_cut_str = str(
min_x) + "<=" + column + "&&" + column + "<=" + str(lower_blind)
upper_cut_str = str(
upper_blind) + "<=" + column + "&&" + column + "<=" + str(max_x)
sideband_cut_str = "(" + lower_cut_str + ")" + "||" + "(" + upper_cut_str + ")"
n_entries = data.reduce(
sideband_cut_str).numEntries() / data.numEntries()
# raw_input("n_entries: " + str(n_entries))
if plot_importance >= 3:
data.plotOn(x_frame, RooFit.CutRange(range_str),
RooFit.NormRange(range_str))
comb_pdf.plotOn(
x_frame, RooFit.Range(range_str),
RooFit.Normalization(n_entries, RooAbsReal.Relative),
RooFit.NormRange(range_str))
if pulls:
# pull_hist(pull_frame=x_frame, pad_data=pad_data, pad_pulls=pad_pulls)
x_frame_pullhist = x_frame.pullHist()
else:
if plot_importance >= 3:
data.plotOn(x_frame)
comb_pdf.plotOn(x_frame)
if pulls:
pad_pulls.cd()
x_frame_pullhist = x_frame.pullHist()
pad_data.cd()
comb_pdf.plotOn(x_frame,
RooFit.Components(sig_pdf.namePtr().GetName()),
RooFit.LineStyle(ROOT.kDashed))
comb_pdf.plotOn(x_frame,
RooFit.Components(bkg_pdf.namePtr().GetName()),
RooFit.LineStyle(ROOT.kDotted))
# comb_pdf.plotPull(n_sig)
if plot_importance >= 3:
x_frame.Draw()
if pulls:
pad_pulls.cd()
x_frame.SetTitleSize(0.05, 'Y')
x_frame.SetTitleOffset(0.7, 'Y')
x_frame.SetLabelSize(0.04, 'Y')
# c11 = TCanvas("c11", "RooFit\ pulls" + data_name)
# c11.cd()
# frame_tmp = x_frame
frame_tmp = x.frame()
# frame_tmp.SetTitle("significance")
frame_tmp.SetTitle("Roofit\ pulls\ " + data_name)
frame_tmp.addObject(x_frame_pullhist)
frame_tmp.SetMinimum(-5)
frame_tmp.SetMaximum(5)
# frame_tmp.GetYaxis().SetTitle("significance")
frame_tmp.GetYaxis().SetNdivisions(5)
frame_tmp.SetTitleSize(0.1, 'X')
frame_tmp.SetTitleOffset(1, 'X')
frame_tmp.SetLabelSize(0.1, 'X')
frame_tmp.SetTitleSize(0.1, 'Y')
frame_tmp.SetTitleOffset(0.5, 'Y')
frame_tmp.SetLabelSize(0.1, 'Y')
frame_tmp.Draw()
# raw_input("")
if not blind and nll_profile:
# nll_range = RooRealVar("nll_range", "Signal for nLL", n_sig.getVal(),
# -10, 2 * n_sig.getVal())
sframe = n_sig.frame(RooFit.Bins(20), RooFit.Range(1, 1000))
# HACK for best n_cpu
lnL = comb_pdf.createNLL(data, RooFit.NumCPU(8))
# HACK end
lnProfileL = lnL.createProfile(ROOT.RooArgSet(n_sig))
lnProfileL.plotOn(sframe, RooFit.ShiftToZero())
c4 = TCanvas("c4", "NLL Profile")
c4.cd()
# input("press ENTER to show plot")
sframe.Draw()
if plot_importance >= 3:
pass
params = comb_pdf.getVariables()
params.Print("v")
# print bkg_cdf.getVal()
if sPlot:
sPlotData = ROOT.RooStats.SPlot(
"sPlotData",
"sPlotData",
data, # variable fitted to, RooDataSet
comb_pdf, # fitted pdf
ROOT.RooArgList(
n_sig,
n_bkg,
# NSigB0s
))
sweights = np.array([
sPlotData.GetSWeight(i, 'n_sig') for i in range(data.numEntries())
])
return n_sig.getVal(), n_bkg_below_sig, sweights
if blind:
return blind_n_sig.getVal(), n_bkg_below_sig, comb_pdf
else:
return n_sig.getVal(), n_bkg_below_sig, comb_pdf
# dataset = fill_dataset(RooArgSet(time, wtvar), ftree, wt, wtvar, cutstr)
dataset = get_dataset(RooArgSet(time, wtvar), ftree, cutstr, wt, scale)
name_title = '{}_{}'.format(dataset.GetName(), mode)
dataset.SetNameTitle(name_title, name_title)
print '%s is weighted: %s' % (dataset.GetName(), dataset.isWeighted())
dsetlist += [dataset]
decaycat = RooCategory('decaycat', 'Decay mode category')
decaycat.defineType('DsPi')
decaycat.defineType('DsK')
varlist += [decaycat]
for idx, mode in enumerate(['DsPi', 'DsK']):
decaycat.setLabel(mode)
dsetlist[idx].addColumn(decaycat)
dataset = RooDataSet('dataset', 'Combined dataset (DsK + DsPi)',
RooArgSet(time, decaycat),
RooFit.Import(dsetlist[0]))
dataset.append(dsetlist[1])
for dset in dsetlist:
dset.Print()
dataset.Print()
## Basic B decay pdf with time resolution
# Resolution model
mean = RooRealVar('mean', 'Mean', 0.)
# scale = RooRealVar('scale', 'Per-event time error scale factor', 1.19)
class SimultaneousFit():
''' A fit that performs a simultaneous fit in more than one variable.
It expects the input of fit_data which is a dictionary of the form
{variable_name: FitData()}'''
def __init__(self, fit_data):
MapStrRootPtr = stl.map(stl.string, "TH1*")
StrHist = stl.pair(stl.string, "TH1*")
self.fit_data = fit_data
self.models = {}
self.sample = RooCategory('sample', 'sample')
self.roofit_variables = []
input_hists = MapStrRootPtr()
# first create observables
# Since we are looking for normalisation in equivalent regions
# the number of events in each sample has to be identical
# Hence, pick one fit_data to create the set of observables
fit_data_1 = fit_data.itervalues().next()
samples = fit_data_1.samples
self.observables = {}
N_min = 0
N_max = fit_data_1.n_data() * 2
for sample in samples:
self.observables[sample] = Observable(
'n_' + sample, 'number of ' + sample + " events",
fit_data_1.normalisation[sample], N_min, N_max, "events")
# next create the models
for variable, fit_input in fit_data.iteritems():
self.models[variable] = fit_input.get_roofit_model(
variable, self.observables)
self.sample.defineType(variable)
self.sample.setLabel(variable)
data = deepcopy(fit_input.real_data_histogram())
input_hists.insert(StrHist(variable, data))
self.roofit_variables.append(fit_input.fit_variable)
self.comb_data = RooDataHist(
"combData",
"combined data",
RooArgList(self.roofit_variables[0]),
self.sample,
input_hists,
)
def fit(self):
sim_pdf = RooSimultaneous("simPdf", "simultaneous pdf", self.sample)
self.individual_results = {}
for name, model in self.models.iteritems():
fit_input = self.fit_data[name]
model.fitTo(fit_input.real_data_roofit_histogram())
self.individual_results[name] = fit_input.get_results()
sim_pdf.addPdf(model, name)
argument_list = RooLinkedList()
argument_list.Add(RooFit.Minimizer("Minuit2", "Migrad"))
argument_list.Add(RooFit.NumCPU(1))
argument_list.Add(RooFit.Extended())
argument_list.Add(RooFit.Save())
sim_pdf.fitTo(self.comb_data,
# argument_list
)
# sim_pdf.fitTo( self.combined_data,
# RooFit.Minimizer( "Minuit2", "Migrad" ) )
# sim_pdf.fitTo( data = self.combined_data,
# arg1 = RooFit.Minimizer( "Minuit2", "Migrad" ),
# arg2 = RooFit.NumCPU( 1 ),
# arg3 = RooFit.Extended(),
# arg4 = RooFit.Save() )
# sim_pdf.fitTo( self.combined_data,
# argument_list )
# get fit results
results = {}
for variable, fit_input in self.fit_data.iteritems():
results[variable] = fit_input.get_results()
self.results = results
return results
def mbc_single_3s(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa,
fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2,
title1, title2, epsfile, txtfile, ymin=0.5,
cuts=None, err_type='SYMM', test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous,
RooArgusBG, RooFormulaVar, RooDLineShape, RooAbsData,
RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta,
TPaveText)
set_root_style(stat=1, grid=0)
# // sp1 = sigma of signal
# // sp2 = ratio of sigmas betwwen sigma2 sigma 1
# // sp3 = ratio of sigmas betwwen sigma3 sigma 2
# // fa, fb, - fractions
# // xi_side - slope of argus
# // p_side - power of argus
# mc = 1 Monte Carlo Model: EvtGenModels/Class/EvtVPHOtoVISR.cc
# mc = 3 Data Model: with BES 2007 paper (BES2006 lineshape hepex/0612056)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav','D flavor')
dflav.defineType('dflav', 1)
dflav.defineType('dbarflav', -1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
elif 'kpimass' in cuts:
kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4, 'GeV')
ras = RooArgSet(mbc, ebeam, kpimass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' %cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
res = RooRealVar("datares", "datares", mc)
mres = RooRealVar("mres","mres", setMres)
gamma = RooRealVar('gamma', 'gamma', setGamma)
r = RooRealVar('r', 'r', setR)
sigmaE = RooRealVar("sigmaE","sigmaE", 0.0021)
sigmap1 = RooRealVar("sigmap1","sigmap1", sp1, 0.002, 0.040)
scalep2 = RooRealVar("scalep2","scalep2",2.00,1.500,5.500)
scalep3 = RooRealVar("scalep3","scalep3",5.00,3.00,10.000)
scalep2.setVal(sp2)
scalep2.setConstant(1)
scalep3.setVal(sp3)
scalep3.setConstant(1)
as12 = RooArgList(sigmap1,scalep2)
sigmap2 = RooFormulaVar("sigmap2","sigma2","sigmap1*scalep2", as12)
as123 = RooArgList(sigmap1,scalep2,scalep3)
sigmap3 = RooFormulaVar("sigmap3","sigma3","sigmap1*scalep2*scalep3",
as123)
md = RooRealVar("md","md", setmd,1.863,1.875)
f2 = RooRealVar("f2","f2", fa)
f3 = RooRealVar("f3","f3", fb)
al23 = RooArgList(f2,f3)
f1 = RooFormulaVar("f1","f1","1.0-f2-f3", al23)
# Construct signal shape
fcn1_1 = RooDLineShape("DLineshape1_1","DLineShape1_1",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap1,md,res)
fcn1_2 = RooDLineShape("DLineshape1_2","DLineShape1_2",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap2,md,res)
fcn1_3 = RooDLineShape("DLineshape1_3","DLineShape1_3",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap3,md,res)
fcn2_1 = RooDLineShape("DLineshape2_1","DLineShape2_1",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap1,md,res)
fcn2_2 = RooDLineShape("DLineshape2_2","DLineShape2_2",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap2,md,res)
fcn2_3 = RooDLineShape("DLineshape2_3","DLineShape2_3",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap3,md,res)
alf1_123 = RooArgList(fcn1_1,fcn1_2,fcn1_3)
af12 = RooArgList(f1,f2)
signal1_3 = RooAddPdf("signal1_3","signal1_3", alf1_123, af12)
alf2_123 = RooArgList(fcn2_1,fcn2_2,fcn2_3)
signal2_3 = RooAddPdf("signal2_3","signal2_3", alf2_123, af12)
p = RooRealVar("p","p", setp, 0.1, 1.5)
xi= RooRealVar("xi","xi",setxi,-100.0,-0.1)
Bkgd1 = RooArgusBG("argus1","argus1",mbc,ebeam,xi,p)
Bkgd2 = RooArgusBG("argus2","argus2",mbc,ebeam,xi,p)
shapes1 = RooArgList(signal1_3)
shapes1.add(signal1_3)
shapes1.add(Bkgd1)
shapes2 = RooArgList(signal2_3)
shapes2.add(signal2_3)
shapes2.add(Bkgd2)
N1 = RooRealVar("N1","N1",setN1,0.0,200000000.0)
N2 = RooRealVar("N2","N2",setN2,0.0,200000000.0)
Nbkgd1 = RooRealVar("Nbkgd1","Nbkgd1",setNbkgd1, 0.0, 200000000.0)
Nbkgd2 = RooRealVar("Nbkgd2","Nbkgd2",setNbkgd2, 0.0, 200000000.0)
yields1 = RooArgList(N1)
yields1.add(N1)
yields1.add(Nbkgd1)
yields2 = RooArgList(N2)
yields2.add(N2)
yields2.add(Nbkgd2)
totalPdf1 = RooAddPdf("totalPdf1","totalPdf1", shapes1,yields1)
totalPdf2 = RooAddPdf("totalPdf2","totalPdf2", shapes2,yields2)
totalPdf = RooSimultaneous("totalPdf","totalPdf",dflav)
totalPdf.addPdf(totalPdf1,"dflav")
totalPdf.addPdf(totalPdf2,"dbarflav")
# Check fitTo options at:
# http://root.cern.ch/root/html512/RooAbsPdf.html#RooAbsPdf:fitTo
#
# Available fit options:
# "m" = MIGRAD only, i.e. no MINOS
# "s" = estimate step size with HESSE before starting MIGRAD
# "h" = run HESSE after MIGRAD
# "e" = Perform extended MLL fit
# "0" = Run MIGRAD with strategy MINUIT 0
# (no correlation matrix calculation at end)
# Does not apply to HESSE or MINOS, if run afterwards.
# "q" = Switch off verbose mode
# "l" = Save log file with parameter values at each MINUIT step
# "v" = Show changed parameters at each MINUIT step
# "t" = Time fit
# "r" = Save fit output in RooFitResult object
# Available optimizer options
# "c" = Cache and precalculate components of PDF that exclusively
# depend on constant parameters
# "2" = Do NLL calculation in multi-processor mode on 2 processors
# "3" = Do NLL calculation in multi-processor mode on 3 processors
# "4" = Do NLL calculation in multi-processor mode on 4 processors
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' %epsfile)
sys.stdout.write('Will save txtfile as: %s \n' %txtfile)
return
if dataset.numEntries() == 0:
N1.setVal(0)
N2.setVal(0)
else:
# Start Fitting
fitres = totalPdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas','mbc', 1200, 400);
canvas.Divide(3,1)
canvas_1 = canvas.GetListOfPrimitives().FindObject('canvas_1')
canvas_2 = canvas.GetListOfPrimitives().FindObject('canvas_2')
canvas_1.SetLogy(1)
canvas_2.SetLogy(1)
LineColorRed = RooFit.LineColor(kRed)
LineColorBlue = RooFit.LineColor(kBlue)
LineWidth = RooFit.LineWidth(1) #0.6)
# Plot the D
canvas.cd(1)
mbcFrame=mbc.frame()
mbcFrame=mbc.frame(60)
dflav.setLabel('dflav')
ebas = RooArgSet(ebeam, dflav)
ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset)
dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dflav"))
mbcFrame.getAttMarker().SetMarkerSize(0.6)
mbcFrame.Draw()
Slice = RooFit.Slice(dflav)
ProjWData = RooFit.ProjWData(ebas, ebeamdata)
totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData)
chisq1 = mbcFrame.chiSquare()*mbcFrame.GetNbinsX()
mbcFrame.Draw()
as_bkg1 = RooArgSet(Bkgd1)
cp_bkg1 = RooFit.Components(as_bkg1)
totalPdf.plotOn(mbcFrame, cp_bkg1, Slice, LineColorBlue, LineWidth, ProjWData)
mbcFrame.SetTitle(title1)
mbcFrame.SetMinimum(ymin)
mbcFrame.Draw()
# Plot the D bar
canvas.cd(2)
mbcFrame=mbc.frame()
mbcFrame=mbc.frame(60)
dflav.setLabel('dbarflav')
ebas = RooArgSet(ebeam, dflav)
ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset)
dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dbarflav"))
mbcFrame.getAttMarker().SetMarkerSize(0.6)
mbcFrame.Draw()
Slice = RooFit.Slice(dflav)
ProjWData = RooFit.ProjWData(ebas, ebeamdata)
totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData)
chisq2 = mbcFrame.chiSquare()*mbcFrame.GetNbinsX()
mbcFrame.Draw()
as_bkg2 = RooArgSet(Bkgd2)
cp_bkg2 = RooFit.Components(as_bkg2)
totalPdf.plotOn(mbcFrame, cp_bkg2, Slice, LineColorBlue, LineWidth, ProjWData)
mbcFrame.SetTitle(title2)
mbcFrame.SetMinimum(ymin)
mbcFrame.Draw()
# Plot Statistics Box
canvas.cd(3)
mbcFrame = mbc.frame()
paramWin1 = totalPdf.paramOn(mbcFrame,dataset, "",2,"NELU",0.1,0.9,0.9)
mbcFrame.GetXaxis().SetLabelSize(0)
mbcFrame.GetXaxis().SetTickLength(0)
mbcFrame.GetXaxis().SetLabelSize(0)
mbcFrame.GetXaxis().SetTitle("")
mbcFrame.GetXaxis().CenterTitle()
mbcFrame.GetYaxis().SetLabelSize(0)
mbcFrame.GetYaxis().SetTitleSize(0.03)
mbcFrame.GetYaxis().SetTickLength(0)
paramWin1.getAttText().SetTextSize(0.06)
mbcFrame.Draw()
mbcFrame.SetTitle("Fit Parameters")
ATextBox = TPaveText(.1, .1, .8, .2,"BRNDC")
tempString = "#chi^{2}_{1} = %.1f, #chi^{2}_{2} = %.1f" % (chisq1,chisq2)
ATextBox.AddText(tempString)
ATextBox.SetFillColor(0)
ATextBox.SetBorderSize(1)
mbcFrame.addObject(ATextBox)
mbcFrame.Draw()
canvas.Print(epsfile)
rootfile = epsfile.replace('.eps', '.root')
canvas.Print(rootfile)
# Save fitting parameters
pars = [N1, N2, Nbkgd1, Nbkgd2, md, p, sigmap1, xi]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def mbc_single_3s(evtfile,
mc,
setMres,
setGamma,
setR,
sp1,
sp2,
sp3,
fa,
fb,
setmd,
setp,
setxi,
setN1,
setN2,
setNbkgd1,
setNbkgd2,
title1,
title2,
epsfile,
txtfile,
ymin=0.5,
cuts=None,
err_type='SYMM',
test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf,
RooSimultaneous, RooArgusBG, RooFormulaVar,
RooDLineShape, RooAbsData, RooDataHist, TCanvas, kRed,
kBlue, kGreen, kMagenta, TPaveText)
set_root_style(stat=1, grid=0)
# // sp1 = sigma of signal
# // sp2 = ratio of sigmas betwwen sigma2 sigma 1
# // sp3 = ratio of sigmas betwwen sigma3 sigma 2
# // fa, fb, - fractions
# // xi_side - slope of argus
# // p_side - power of argus
# mc = 1 Monte Carlo Model: EvtGenModels/Class/EvtVPHOtoVISR.cc
# mc = 3 Data Model: with BES 2007 paper (BES2006 lineshape hepex/0612056)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav', 'D flavor')
dflav.defineType('dflav', 1)
dflav.defineType('dbarflav', -1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90,
'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
elif 'kpimass' in cuts:
kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4,
'GeV')
ras = RooArgSet(mbc, ebeam, kpimass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' % cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
res = RooRealVar("datares", "datares", mc)
mres = RooRealVar("mres", "mres", setMres)
gamma = RooRealVar('gamma', 'gamma', setGamma)
r = RooRealVar('r', 'r', setR)
sigmaE = RooRealVar("sigmaE", "sigmaE", 0.0021)
sigmap1 = RooRealVar("sigmap1", "sigmap1", sp1, 0.002, 0.040)
scalep2 = RooRealVar("scalep2", "scalep2", 2.00, 1.500, 5.500)
scalep3 = RooRealVar("scalep3", "scalep3", 5.00, 3.00, 10.000)
scalep2.setVal(sp2)
scalep2.setConstant(1)
scalep3.setVal(sp3)
scalep3.setConstant(1)
as12 = RooArgList(sigmap1, scalep2)
sigmap2 = RooFormulaVar("sigmap2", "sigma2", "sigmap1*scalep2", as12)
as123 = RooArgList(sigmap1, scalep2, scalep3)
sigmap3 = RooFormulaVar("sigmap3", "sigma3", "sigmap1*scalep2*scalep3",
as123)
md = RooRealVar("md", "md", setmd, 1.863, 1.875)
f2 = RooRealVar("f2", "f2", fa)
f3 = RooRealVar("f3", "f3", fb)
al23 = RooArgList(f2, f3)
f1 = RooFormulaVar("f1", "f1", "1.0-f2-f3", al23)
# Construct signal shape
fcn1_1 = RooDLineShape("DLineshape1_1", "DLineShape1_1", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap1, md, res)
fcn1_2 = RooDLineShape("DLineshape1_2", "DLineShape1_2", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap2, md, res)
fcn1_3 = RooDLineShape("DLineshape1_3", "DLineShape1_3", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap3, md, res)
fcn2_1 = RooDLineShape("DLineshape2_1", "DLineShape2_1", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap1, md, res)
fcn2_2 = RooDLineShape("DLineshape2_2", "DLineShape2_2", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap2, md, res)
fcn2_3 = RooDLineShape("DLineshape2_3", "DLineShape2_3", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap3, md, res)
alf1_123 = RooArgList(fcn1_1, fcn1_2, fcn1_3)
af12 = RooArgList(f1, f2)
signal1_3 = RooAddPdf("signal1_3", "signal1_3", alf1_123, af12)
alf2_123 = RooArgList(fcn2_1, fcn2_2, fcn2_3)
signal2_3 = RooAddPdf("signal2_3", "signal2_3", alf2_123, af12)
p = RooRealVar("p", "p", setp, 0.1, 1.5)
xi = RooRealVar("xi", "xi", setxi, -100.0, -0.1)
Bkgd1 = RooArgusBG("argus1", "argus1", mbc, ebeam, xi, p)
Bkgd2 = RooArgusBG("argus2", "argus2", mbc, ebeam, xi, p)
shapes1 = RooArgList(signal1_3)
shapes1.add(signal1_3)
shapes1.add(Bkgd1)
shapes2 = RooArgList(signal2_3)
shapes2.add(signal2_3)
shapes2.add(Bkgd2)
N1 = RooRealVar("N1", "N1", setN1, 0.0, 200000000.0)
N2 = RooRealVar("N2", "N2", setN2, 0.0, 200000000.0)
Nbkgd1 = RooRealVar("Nbkgd1", "Nbkgd1", setNbkgd1, 0.0, 200000000.0)
Nbkgd2 = RooRealVar("Nbkgd2", "Nbkgd2", setNbkgd2, 0.0, 200000000.0)
yields1 = RooArgList(N1)
yields1.add(N1)
yields1.add(Nbkgd1)
yields2 = RooArgList(N2)
yields2.add(N2)
yields2.add(Nbkgd2)
totalPdf1 = RooAddPdf("totalPdf1", "totalPdf1", shapes1, yields1)
totalPdf2 = RooAddPdf("totalPdf2", "totalPdf2", shapes2, yields2)
totalPdf = RooSimultaneous("totalPdf", "totalPdf", dflav)
totalPdf.addPdf(totalPdf1, "dflav")
totalPdf.addPdf(totalPdf2, "dbarflav")
# Check fitTo options at:
# http://root.cern.ch/root/html512/RooAbsPdf.html#RooAbsPdf:fitTo
#
# Available fit options:
# "m" = MIGRAD only, i.e. no MINOS
# "s" = estimate step size with HESSE before starting MIGRAD
# "h" = run HESSE after MIGRAD
# "e" = Perform extended MLL fit
# "0" = Run MIGRAD with strategy MINUIT 0
# (no correlation matrix calculation at end)
# Does not apply to HESSE or MINOS, if run afterwards.
# "q" = Switch off verbose mode
# "l" = Save log file with parameter values at each MINUIT step
# "v" = Show changed parameters at each MINUIT step
# "t" = Time fit
# "r" = Save fit output in RooFitResult object
# Available optimizer options
# "c" = Cache and precalculate components of PDF that exclusively
# depend on constant parameters
# "2" = Do NLL calculation in multi-processor mode on 2 processors
# "3" = Do NLL calculation in multi-processor mode on 3 processors
# "4" = Do NLL calculation in multi-processor mode on 4 processors
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' % epsfile)
sys.stdout.write('Will save txtfile as: %s \n' % txtfile)
return
if dataset.numEntries() == 0:
N1.setVal(0)
N2.setVal(0)
else:
# Start Fitting
fitres = totalPdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas', 'mbc', 1200, 400)
canvas.Divide(3, 1)
canvas_1 = canvas.GetListOfPrimitives().FindObject('canvas_1')
canvas_2 = canvas.GetListOfPrimitives().FindObject('canvas_2')
canvas_1.SetLogy(1)
canvas_2.SetLogy(1)
LineColorRed = RooFit.LineColor(kRed)
LineColorBlue = RooFit.LineColor(kBlue)
LineWidth = RooFit.LineWidth(1) #0.6)
# Plot the D
canvas.cd(1)
mbcFrame = mbc.frame()
mbcFrame = mbc.frame(60)
dflav.setLabel('dflav')
ebas = RooArgSet(ebeam, dflav)
ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset)
dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dflav"))
mbcFrame.getAttMarker().SetMarkerSize(0.6)
mbcFrame.Draw()
Slice = RooFit.Slice(dflav)
ProjWData = RooFit.ProjWData(ebas, ebeamdata)
totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData)
chisq1 = mbcFrame.chiSquare() * mbcFrame.GetNbinsX()
mbcFrame.Draw()
as_bkg1 = RooArgSet(Bkgd1)
cp_bkg1 = RooFit.Components(as_bkg1)
totalPdf.plotOn(mbcFrame, cp_bkg1, Slice, LineColorBlue, LineWidth,
ProjWData)
mbcFrame.SetTitle(title1)
mbcFrame.SetMinimum(ymin)
mbcFrame.Draw()
# Plot the D bar
canvas.cd(2)
mbcFrame = mbc.frame()
mbcFrame = mbc.frame(60)
dflav.setLabel('dbarflav')
ebas = RooArgSet(ebeam, dflav)
ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset)
dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dbarflav"))
mbcFrame.getAttMarker().SetMarkerSize(0.6)
mbcFrame.Draw()
Slice = RooFit.Slice(dflav)
ProjWData = RooFit.ProjWData(ebas, ebeamdata)
totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData)
chisq2 = mbcFrame.chiSquare() * mbcFrame.GetNbinsX()
mbcFrame.Draw()
as_bkg2 = RooArgSet(Bkgd2)
cp_bkg2 = RooFit.Components(as_bkg2)
totalPdf.plotOn(mbcFrame, cp_bkg2, Slice, LineColorBlue, LineWidth,
ProjWData)
mbcFrame.SetTitle(title2)
mbcFrame.SetMinimum(ymin)
mbcFrame.Draw()
# Plot Statistics Box
canvas.cd(3)
mbcFrame = mbc.frame()
paramWin1 = totalPdf.paramOn(mbcFrame, dataset, "", 2, "NELU", 0.1, 0.9,
0.9)
mbcFrame.GetXaxis().SetLabelSize(0)
mbcFrame.GetXaxis().SetTickLength(0)
mbcFrame.GetXaxis().SetLabelSize(0)
mbcFrame.GetXaxis().SetTitle("")
mbcFrame.GetXaxis().CenterTitle()
mbcFrame.GetYaxis().SetLabelSize(0)
mbcFrame.GetYaxis().SetTitleSize(0.03)
mbcFrame.GetYaxis().SetTickLength(0)
paramWin1.getAttText().SetTextSize(0.06)
mbcFrame.Draw()
mbcFrame.SetTitle("Fit Parameters")
ATextBox = TPaveText(.1, .1, .8, .2, "BRNDC")
tempString = "#chi^{2}_{1} = %.1f, #chi^{2}_{2} = %.1f" % (chisq1, chisq2)
ATextBox.AddText(tempString)
ATextBox.SetFillColor(0)
ATextBox.SetBorderSize(1)
mbcFrame.addObject(ATextBox)
mbcFrame.Draw()
canvas.Print(epsfile)
rootfile = epsfile.replace('.eps', '.root')
canvas.Print(rootfile)
# Save fitting parameters
pars = [N1, N2, Nbkgd1, Nbkgd2, md, p, sigmap1, xi]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
# dataset = fill_dataset(RooArgSet(time, wtvar), ftree, wt, wtvar, cutstr)
dataset = get_dataset(RooArgSet(time, wtvar), ftree, cutstr, wt, scale)
name_title = '{}_{}'.format(dataset.GetName(), mode)
dataset.SetNameTitle(name_title, name_title)
print '%s is weighted: %s' % (dataset.GetName(), dataset.isWeighted())
dsetlist += [dataset]
decaycat = RooCategory('decaycat', 'Decay mode category')
decaycat.defineType('DsPi')
decaycat.defineType('DsK')
varlist += [decaycat]
for idx, mode in enumerate(['DsPi', 'DsK']):
decaycat.setLabel(mode)
dsetlist[idx].addColumn(decaycat)
dataset = RooDataSet('dataset', 'Combined dataset (DsK + DsPi)',
RooArgSet(time, decaycat), RooFit.Import(dsetlist[0]))
dataset.append(dsetlist[1])
for dset in dsetlist:
dset.Print()
dataset.Print()
## Basic B decay pdf with time resolution
# Resolution model
mean = RooRealVar('mean', 'Mean', 0.)
# scale = RooRealVar('scale', 'Per-event time error scale factor', 1.19)
resmodel = RooGaussModel('resmodel', 'Time resolution model', time, mean,
def setupWorkspace(ws, options):
cfg = options.config #for convenience
fit_sections = cfg.sections()
fit_sections.remove(
'Global') #don't need to iterate over the global configuration
if not isinstance(ws, RooWorkspace):
print "You didn't pass a RooWorkspace!"
exit(1)
cpling_type = cfg.get('Global', 'couplingType')
par1 = cfg.get('Global', 'par1Name')
par1bound = [
-cfg.getfloat('Global', 'par1Max'),
cfg.getfloat('Global', 'par1Max')
]
par2 = cfg.get('Global', 'par2Name')
par2bound = [
-cfg.getfloat('Global', 'par2Max'),
cfg.getfloat('Global', 'par2Max')
]
#create the parameters in the workspace
ws.factory('%s_%s[0,%f,%f]' %
(par1, cpling_type, par1bound[0], par1bound[1]))
ws.factory('%s_%s[0,%f,%f]' %
(par2, cpling_type, par2bound[0], par2bound[1]))
# since the lumi error is correlated among all channels we only need one penalty term for it
lumi_err = exp(options.config.getfloat(
'Global', 'lumi_err')) # exp because we use log normal
ws.factory('luminosityError[%f]' % lumi_err)
ws.factory('RooLognormal::lumiErr(err_gl[1,0.0001,50],1,luminosityError)')
channel_cat = RooCategory('channels', 'channels')
#first pass: process the backgrounds, signal and data into
# simultaneous counting pdfs over the bins
for section in fit_sections:
#create the basic observable, this is used behind the scenes
#in the background and signal models
channel_cat.defineType(section)
channel_cat.setLabel(section)
print 'Building pdf for configuration section:', section
for it, bkg in getBackgroundsInCfg(section, cfg).iteritems():
ws.factory('backgroundError_%s_%s[%f]' %
(section, it, exp(bkg[1])))
ws.factory('selectionError_%s[%f]' %
(section, exp(cfg.getfloat(section, 'selection_err'))))
processFittingData(ws, cfg, section)
processSignalModel(ws, cfg, section)
processBackgroundModel(ws, cfg, section)
createPdfForChannel(ws, cfg, section)
ws.data('countingdata_%s' % section).addColumn(channel_cat)
getattr(ws, 'import')(channel_cat)
top = RooSimultaneous('TopLevelPdf', 'TopLevelPdf', ws.cat('channels'))
alldatavars = RooArgSet(ws.cat('channels'))
conditionals = RooArgSet()
#second pass: process counting pdfs into simultaneous pdf over channels
for section in fit_sections:
top.addPdf(ws.pdf('countingpdf_%s' % section), section)
alldatavars.add(ws.var('%s_%s' %
(cfg.get(section, 'obsVar'), section)))
conditionals.add(
ws.var('%s_%s' % (cfg.get(section, 'obsVar'), section)))
alldatavars.add(ws.var('n_observed_%s' % section))
getattr(ws, 'import')(top)
ws.defineSet('condObs', conditionals)
allcountingdata = RooDataSet('allcountingdata', 'allcountingdata',
alldatavars)
getattr(ws, 'import')(allcountingdata)
allcountingdata = ws.data('allcountingdata')
#third pass: make the final combined dataset
for section in fit_sections:
current = ws.data('countingdata_%s' % section)
print 'countingdata_%s has %d entries' % (section,
current.numEntries())
for i in range(current.numEntries()):
alldatavars = current.get(i)
allcountingdata.add(alldatavars)
class SimultaneousFit:
""" A fit that performs a simultaneous fit in more than one variable.
It expects the input of fit_data which is a dictionary of the form
{variable_name: FitData()}"""
def __init__(self, fit_data):
MapStrRootPtr = stl.map(stl.string, "TH1*")
StrHist = stl.pair(stl.string, "TH1*")
self.fit_data = fit_data
self.models = {}
self.sample = RooCategory("sample", "sample")
self.roofit_variables = []
input_hists = MapStrRootPtr()
# first create observables
# Since we are looking for normalisation in equivalent regions
# the number of events in each sample has to be identical
# Hence, pick one fit_data to create the set of observables
fit_data_1 = fit_data.itervalues().next()
samples = fit_data_1.samples
self.observables = {}
N_min = 0
N_max = fit_data_1.n_data() * 2
for sample in samples:
self.observables[sample] = Observable(
"n_" + sample,
"number of " + sample + " events",
fit_data_1.normalisation[sample],
N_min,
N_max,
"events",
)
# next create the models
for variable, fit_input in fit_data.iteritems():
self.models[variable] = fit_input.get_roofit_model(variable, self.observables)
self.sample.defineType(variable)
self.sample.setLabel(variable)
data = deepcopy(fit_input.real_data_histogram())
input_hists.insert(StrHist(variable, data))
self.roofit_variables.append(fit_input.fit_variable)
self.comb_data = RooDataHist(
"combData", "combined data", RooArgList(self.roofit_variables[0]), self.sample, input_hists
)
def fit(self):
sim_pdf = RooSimultaneous("simPdf", "simultaneous pdf", self.sample)
self.individual_results = {}
for name, model in self.models.iteritems():
fit_input = self.fit_data[name]
model.fitTo(fit_input.real_data_roofit_histogram())
self.individual_results[name] = fit_input.get_results()
sim_pdf.addPdf(model, name)
argument_list = RooLinkedList()
argument_list.Add(RooFit.Minimizer("Minuit2", "Migrad"))
argument_list.Add(RooFit.NumCPU(1))
argument_list.Add(RooFit.Extended())
argument_list.Add(RooFit.Save())
sim_pdf.fitTo(
self.comb_data,
# argument_list
)
# sim_pdf.fitTo( self.combined_data,
# RooFit.Minimizer( "Minuit2", "Migrad" ) )
# sim_pdf.fitTo( data = self.combined_data,
# arg1 = RooFit.Minimizer( "Minuit2", "Migrad" ),
# arg2 = RooFit.NumCPU( 1 ),
# arg3 = RooFit.Extended(),
# arg4 = RooFit.Save() )
# sim_pdf.fitTo( self.combined_data,
# argument_list )
# get fit results
results = {}
for variable, fit_input in self.fit_data.iteritems():
results[variable] = fit_input.get_results()
self.results = results
return results
