def hist_node(graph, cut, weights, variables):
"""
Creates a simple CutNode -> WeightNode(s) ->
HistNode(s) structure from the specified cut, weights and variables.
Args:
graph: a NX graph which is the parent of these nodes
cut: a (cutname, Cut) tuple with the cut to apply
weights: a list of (weightname, Weight) tuples to apply
variables: a list of (varname, variable, binning) tuples to project out.
Returns:
The top CutNode that was created.
"""
cut_name, cut = cut
#weight_name, weight = _weight
cutnode = CutNode(cut, graph, cut_name, [], [])
if not isinstance(weights, list):
weights = [weights]
from weights import reweight
for var_name, var, binning in variables:
hist_desc = {
"var": var,
"binning": binning
}
histnode = HistNode(hist_desc, graph, var_name, [cutnode], [])
reweight(histnode, [
WeightNode(w[1], graph, w[0], [], []) for w in weights]
)
return cutnode
def create_plots(graph, plot_nodes, filter_funcs=[]):
from weights import reweight, syst_weights
from tree import HistNode
#Add all the nodes for the final plots with all the possible reweighting combinations
final_plots = dict()
for t, descs in hdescs.items():
for name, func, binning in descs:
skip = False
for f in filter_funcs:
if f(name):
logger.info("Not creating a plot for %s" % name)
skip=True
break
if skip:
continue
hd = hdesc(name, func, binning)
#Make only the required plots per channel
lambdas = []
if t in final_plot_lambdas.keys():
lambdas.append(final_plot_lambdas[t])
final_plots[name] = HistNode(
hd,
graph, name, plot_nodes, [], filter_funcs=lambdas
)
final_plots[name] = reweight(
final_plots[name],
syst_weights(graph)
)
return final_plots
