def syst_weights(graph):
_syst_weights = []
for lepton, w in weights_lepton.items():
weights_var_by_one = variateOneWeight([x[1] for x in (weights_syst+w)])
# The unvariated weight is taken as the list of the 0th elements of the
# weight tuples
weights_var_by_one.append(
("nominal", [x[1][0] for x in (weights_syst+w)])
)
wtot = []
for wn, s in weights_var_by_one:
j = mul(s) #Multiply together the list of weights
wtot.append((wn, j))
for name, j in wtot:
filter_funcs=[
#Apply the weights separately for the lepton channels
lambda _x,_lepton=lepton: is_chan(_x, _lepton),
#Apply only in MC
is_mc
]
#Apply systematic weights only in case of nominal samples
if name != "nominal":
filter_funcs += [
#Check if the parent was a nominal sample
lambda _x: "/nominal/" in _x[0].name
]
syst = WeightNode(
j, graph, "weight__" + name + "__" + lepton,
[], [], filter_funcs=filter_funcs
)
logger.debug("Appending weight %s" % syst.name)
_syst_weights.append(syst)
#Always produce the unweighted plot
unw = WeightNode(
Weights.no_weight, graph, "weight__unweighted",
[], []
)
_syst_weights.append(unw)
return _syst_weights
("bdt_discr", Cuts.mva_vars['ele'], [nbins, -1, 1]),
("bdt_discr_zoom_loose", Cuts.mva_vars['ele'], [nbins, Cuts.mva_wps['bdt']['ele']['loose'], 1]),
("lep_iso", 'el_iso', [nbins, 0, 0.5]),
("lep_pt", 'el_pt', [nbins, 0, 200]),
("mtw", "mt_el", [nbins, 0, 300]),
("mtw_50_150", "mt_el", [nbins, 50, 150]),
]
#MC-only variables
hdescs['mc'] = [
("true_cos_theta", "true_cos_theta", [nbins, -1, 1]),
]
#define a LUT for type <-> filtering function
final_plot_lambdas = {
'mu': lambda x: is_chan(x, 'mu'),
'ele': lambda x: is_chan(x, 'ele'),
'mc': lambda x: is_mc(x)
}
def hdesc(name, func, binning):
hdesc = {
"name": name,
"var": func,
"binning": binning
}
return hdesc
def create_plots(graph, plot_nodes, filter_funcs=[]):
from weights import reweight, syst_weights
from tree import HistNode
