split_cat = split_cats[0][0]
if hasattr(split_cat, '_target_'):
split_cat = split_cat._target_()
means = [sig_sdata.mean(split_obs._target_(), '{0} == {0}::{1}'.format(split_cat.GetName(), s.GetName())) for s in split_cat]
if options.sf_param or (options.mu_param and 'sigmat' not in options.mu_param):
assert(len(split_cats[0]) == 1)
## The idea is to create a simultaneous PDF, split using the
## placeholder which was put in place when the resolution
## model was constructed and then in each bin set the created
## split parameter to: (mean_in_bin - overall_mean.
## Set the value to the mean sigmat already here, even if it is changed
## later.
placeholder = sig_tres.sfPlaceHolder()
placeholder.setVal(split_obs_mean)
## Customize the PDF to be split using the placeholder and any other
## pars which need to be split.
from ROOT import RooCustomizer
customizer = RooCustomizer(time_pdf._target_(), split_cat, splitLeaves)
to_split = RooArgSet(*(split_pars[0]))
## CAREFULL: when do we need this?
## to_split.add(placeholder._target_())
customizer.splitArgs(to_split, split_cat)
states = dict([(s.GetName(), customizer.build(s.GetName())) for s in split_cat])
time_pdf = SimultaneousPdf(time_pdf.GetName() + '_simul',
SplitCategory = split_cat, ExternalConstraints = time_pdf.ExternalConstraints(),
States = states, ConditionalObservables = time_pdf.ConditionalObservables())
