def plcLimit(obs_, poi_, model, ws, data, CL=0.95, verbose=False):
# obs : observable variable or RooArgSet of observables
# poi : parameter of interest or RooArgSet of parameters
# model : RooAbsPdf of model to consider including any constraints
# data : RooAbsData of the data
# CL : confidence level for interval
# returns a dictionary with the upper and lower limits for the first/only
# parameter in poi_ as well as the interval object and status flag
obs = RooArgSet(obs_)
obs.setName('observables')
poi = RooArgSet(poi_)
poi.setName('poi')
poi.setAttribAll('Constant', False)
nuis = model.getParameters(obs)
nuis.remove(poi)
nuis.remove(nuis.selectByAttrib('Constant', True))
nuis.setName('nuisance')
if verbose:
print 'observables'
obs.Print('v')
print 'parameters of interest'
poi.Print('v')
print 'nuisance parameters'
nuis.Print('v')
mc = RooStats.ModelConfig('mc')
mc.SetWorkspace(ws)
mc.SetPdf(model)
mc.SetObservables(obs)
mc.SetParametersOfInterest(poi)
mc.SetNuisanceParameters(nuis)
plc = RooStats.ProfileLikelihoodCalculator(data, mc)
plc.SetConfidenceLevel(CL)
interval = plc.GetInterval()
upperLimit = Double(999.)
lowerLimit = Double(0.)
Limits = {}
paramIter = poi.createIterator()
param = paramIter.Next()
while param:
ok = interval.FindLimits(param, lowerLimit, upperLimit)
Limits[param.GetName()] = {
'ok': ok,
'upper': float(upperLimit),
'lower': float(lowerLimit)
}
param = paramIter.Next()
if verbose:
print '%.0f%% CL limits' % (interval.ConfidenceLevel() * 100)
print Limits
Limits['interval'] = interval
return Limits
def transferResults(cfg, ws, rfr):
""" Transfer results from the RooFitResult to the workspace, and make relevant snapshots """
mc = ws.obj("ModelConfig")
np = RooArgSet(mc.GetNuisanceParameters(), mc.GetParametersOfInterest())
if logging.getLogger().isEnabledFor(logging.DEBUG):
logging.debug("Expanded RooFit results")
np.Print("v")
# we want to be sure that the snapshot contains all we need, including POI
ws.loadSnapshot("snapshot_paramsVals_initial")
ws.saveSnapshot("vars_initial", np)
fpf = rfr.floatParsFinal().Clone()
cp = rfr.constPars()
fpf.add(
cp
) # add all const parameters of the RooFitResult to the floating ones
if logging.getLogger().isEnabledFor(logging.DEBUG):
logging.debug("Expanded RooFit results")
fpf.Print()
np.assignValueOnly(
fpf
) # only sets the central value. Should be ok as VisualizeError uses errors from the RooFitResult, according to ROOT doc
force_mu, mu_val = cfg.force_mu_value()
if force_mu:
# is_higgs_fit = True
# it = np.createIterator()
# n = it.Next()
# while n:
# if cfg.transferResults_fitName in n.GetName():
# n.setVal(0)
# is_higgs_fit = False
# break
# n = it.Next()
# cfg._muhat = 1
# if is_higgs_fit:
# mc.GetParametersOfInterest().first().setVal(1)
# cfg._muhat = mu_val
mc.GetParametersOfInterest().first().setVal(mu_val)
# else:
# cfg._muhat = mc.GetParametersOfInterest().first().getVal()
cfg._muhat = mc.GetParametersOfInterest().first().getVal()
if logging.getLogger().isEnabledFor(logging.DEBUG):
logging.debug("Expanded RooFit results")
np.Print("v")
ws.saveSnapshot("vars_final", np)
ws.loadSnapshot("vars_final")
