## 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())
## Set the split parameters to their calculated value and make
## them constant.
st_mean = lambda st: 0.04921 + st * 1.220
st_sigma = lambda st: 0.012 + st * 0.165
pars = time_pdf.getParameters(RooArgSet())
for m, s in zip(means, split_cat):
for n, f in (('timeResSFMean', st_mean), ('timeResSFSigma', st_sigma)):
p = pars.find(n + '_' + s.GetName())
