def data(self, config, channel, category, weight, nick_suffix, exclude_cuts=None, cut_type="baseline", **kwargs):
if exclude_cuts is None:
exclude_cuts = []
scale_factor = 1.0
if not self.postfit_scales is None:
scale_factor *= self.postfit_scales.get("data_obs", 1.0)
data_weight = "(1.0)"
if kwargs.get("project_to_lumi", False):
data_weight = "({projection})*".format(projection=kwargs["project_to_lumi"]) + data_weight
Samples._add_input(
config,
self.files_data(channel),
self.root_file_folder(channel),
1.0,
make_multiplication([data_weight, weight, "eventWeight", self._cut_string(channel, exclude_cuts=exclude_cuts, cut_type=cut_type)]),
"data",
nick_suffix=nick_suffix
)
Samples._add_plot(config, "data", "E", "ELP", "data", nick_suffix)
return config
json_filenames = [os.path.join(args.json_dir, "8TeV" if args.run1 else "13TeV", channel_dir, quantity+".json") for channel_dir in [channel, "default"]]
for json_filename in json_filenames:
json_filename = os.path.expandvars(json_filename)
if os.path.exists(json_filename):
json_config = jsonTools.JsonDict(json_filename).doIncludes().doComments()
break
quantity = json_config.pop("x_expressions", [quantity])[0]
for lheweight_index, lheweight in enumerate(sorted(lheweights_names)):
config = sample_settings.get_config(
samples = list_of_samples,
channel = channel,
category = category_string,
higgs_masses = args.higgs_masses,
normalise_signal_to_one_pb = False,
ztt_from_mc = args.ztt_from_mc,
weight = lheweight + "*" + make_multiplication([clean_multiplication(json_config.pop("weights", ["1.0"])[0]), args.weight]),
lumi = args.lumi * 1000,
exclude_cuts = args.exclude_cuts + json_config.pop("exclude_cuts", []),
blind_expression = channel + "_" + quantity,
fakefactor_method = args.fakefactor_method,
stack_signal = args.stack_signal,
scale_signal = args.scale_signal,
project_to_lumi = args.project_to_lumi,
cut_mc_only = args.cut_mc_only,
scale_mc_only = args.scale_mc_only,
estimationMethod = background_method,
mssm = args.mssm,
controlregions = args.controlregions,
cut_type = global_cut_type,
nick_suffix = "_" + channel + "_" + category + "_" + lheweight,
)
json_config = jsonTools.JsonDict(
json_filename).doIncludes().doComments()
break
quantity = json_config.pop("x_expressions", [quantity])[0]
for lheweight_index, lheweight in enumerate(
sorted(lheweights_names)):
config = sample_settings.get_config(
samples=list_of_samples,
channel=channel,
category=category_string,
higgs_masses=args.higgs_masses,
normalise_signal_to_one_pb=False,
ztt_from_mc=args.ztt_from_mc,
weight=lheweight + "*" + make_multiplication([
clean_multiplication(
json_config.pop("weights", ["1.0"])[0]),
args.weight
]),
lumi=args.lumi * 1000,
exclude_cuts=args.exclude_cuts +
json_config.pop("exclude_cuts", []),
blind_expression=channel + "_" + quantity,
fakefactor_method=args.fakefactor_method,
stack_signal=args.stack_signal,
scale_signal=args.scale_signal,
project_to_lumi=args.project_to_lumi,
cut_mc_only=args.cut_mc_only,
scale_mc_only=args.scale_mc_only,
estimationMethod=background_method,
mssm=args.mssm,
controlregions=args.controlregions,