makePlots_datacardsZttEfficiency.py File Reference

Namespaces
	makePlots_datacardsZttEfficiency

Functions
def	makePlots_datacardsZttEfficiency.matching_process
def	makePlots_datacardsZttEfficiency.remove_procs_and_systs_with_zero_yield

Variables
tuple	makePlots_datacardsZttEfficiency.log = logging.getLogger(__name__)
dictionary	makePlots_datacardsZttEfficiency.models
tuple	makePlots_datacardsZttEfficiency.parser
string	makePlots_datacardsZttEfficiency.help = "Input directory."
tuple	makePlots_datacardsZttEfficiency.choices = models.keys()
list	makePlots_datacardsZttEfficiency.default = ["all"]
tuple	makePlots_datacardsZttEfficiency.args = parser.parse_args()
string	makePlots_datacardsZttEfficiency.weight_string = "(fabs(eta_2) < 1.460)"
tuple	makePlots_datacardsZttEfficiency.sample_settings = samples.Samples()
tuple	makePlots_datacardsZttEfficiency.systematics_factory = systematics.SystematicsFactory()
list	makePlots_datacardsZttEfficiency.plot_configs = []
list	makePlots_datacardsZttEfficiency.hadd_commands = []
tuple	makePlots_datacardsZttEfficiency.datacards = zttxsecdatacards.ZttLepTauFakeRateDatacards()
tuple	makePlots_datacardsZttEfficiency.model_settings = models.get(args.model, {})
tuple	makePlots_datacardsZttEfficiency.fit_settings = model_settings.get("fit", {"" : {}})
list	makePlots_datacardsZttEfficiency.excludecut_settings = model_settings['exclude_cuts']
string	makePlots_datacardsZttEfficiency.tmp_input_root_filename_template = "input/${ANALYSIS}_${CHANNEL}_${BIN}_${SYSTEMATIC}_${ERA}.root"
string	makePlots_datacardsZttEfficiency.input_root_filename_template = "input/${ANALYSIS}_${CHANNEL}_${BIN}_${ERA}.root"
string	makePlots_datacardsZttEfficiency.bkg_histogram_name_template = "${BIN}/${PROCESS}"
string	makePlots_datacardsZttEfficiency.sig_histogram_name_template = "${BIN}/${PROCESS}"
string	makePlots_datacardsZttEfficiency.bkg_syst_histogram_name_template = "${BIN}/${PROCESS}_${SYSTEMATIC}"
string	makePlots_datacardsZttEfficiency.sig_syst_histogram_name_template = "${BIN}/${PROCESS}_${SYSTEMATIC}"
list	makePlots_datacardsZttEfficiency.datacard_filename_templates
string	makePlots_datacardsZttEfficiency.output_root_filename_template = "datacards/common/${ANALYSIS}.input_${ERA}.root"
tuple	makePlots_datacardsZttEfficiency.categories = datacards.cb.cp()
tuple	makePlots_datacardsZttEfficiency.datacards_per_channel_category = zttxsecdatacards.ZttLepTauFakeRateDatacards(cb=datacards.cb.cp().channel([channel]).bin([category]))
tuple	makePlots_datacardsZttEfficiency.output_file
list	makePlots_datacardsZttEfficiency.tmp_output_files = []
tuple	makePlots_datacardsZttEfficiency.nominal = (shape_systematic == "nominal")
list	makePlots_datacardsZttEfficiency.list_of_samples = [datacards.configs.process2sample(process) for process in list_of_samples]
string	makePlots_datacardsZttEfficiency.systematic = "nominal"
string	makePlots_datacardsZttEfficiency.samples = "\", \""
	makePlots_datacardsZttEfficiency.channel = channel,
	makePlots_datacardsZttEfficiency.category = category,
float	makePlots_datacardsZttEfficiency.wj_sf_shift = 0.0
tuple	makePlots_datacardsZttEfficiency.config
int	makePlots_datacardsZttEfficiency.sub_conf_index = 0
tuple	makePlots_datacardsZttEfficiency.systematics_settings = systematics_factory.get(shape_systematic)
	makePlots_datacardsZttEfficiency.histogram_name_template = bkg_histogram_name_templateifnominalelsebkg_syst_histogram_name_template
tuple	makePlots_datacardsZttEfficiency.PROCESS = datacards.configs.sample2process(sample)
	makePlots_datacardsZttEfficiency.BIN = category,
	makePlots_datacardsZttEfficiency.SYSTEMATIC = systematic
tuple	makePlots_datacardsZttEfficiency.tmp_output_file
	makePlots_datacardsZttEfficiency.DST = output_file,
string	makePlots_datacardsZttEfficiency.SRC = " "
tuple	makePlots_datacardsZttEfficiency.output_files = list(set([os.path.join(config["output_dir"], config["filename"]+".root") for config in plot_configs[:args.n_plots[0]]]))
	makePlots_datacardsZttEfficiency.update_systematics = False
tuple	makePlots_datacardsZttEfficiency.processes = datacards.cb.cp()
float	makePlots_datacardsZttEfficiency.add_threshold = 0.1
dictionary	makePlots_datacardsZttEfficiency.datacards_cbs = {}
dictionary	makePlots_datacardsZttEfficiency.datacards_workspaces = {}
dictionary	makePlots_datacardsZttEfficiency.efficiency = {}
int	makePlots_datacardsZttEfficiency.nPassPre = 0
int	makePlots_datacardsZttEfficiency.nFailPre = 0
tuple	makePlots_datacardsZttEfficiency.sig_process = cb.cp()
list	makePlots_datacardsZttEfficiency.command
	makePlots_datacardsZttEfficiency.STABLE = datacards.stable_options
tuple	makePlots_datacardsZttEfficiency.datacards_postfit_shapes = datacards.postfit_shapes_fromworkspace(datacards_cbs, datacards_workspaces, True, args.n_processes, "--sampling" + (" --print" if args.n_processes <= 1 else ""))
dictionary	makePlots_datacardsZttEfficiency.plotting_args = {"ratio" : args.ratio, "args" : args.args, "lumi" : args.lumi, "x_expressions" : "m_vis", "era" : "2016"}
	makePlots_datacardsZttEfficiency.n_processes = args.n_processes,
	makePlots_datacardsZttEfficiency.signal_stacked_on_bkg = True
list	makePlots_datacardsZttEfficiency.bkg_plotting_order = ["ZL", "ZTT", "ZJ", "TT", "VV", "QCD"]
tuple	makePlots_datacardsZttEfficiency.postfit_shapes = datacards_postfit_shapes.get("fit_s", {})
int	makePlots_datacardsZttEfficiency.nPass = 0
int	makePlots_datacardsZttEfficiency.nFail = 0
tuple	makePlots_datacardsZttEfficiency.results_file = ROOT.TFile(os.path.join(os.path.dirname(datacard), "fitDiagnostics.root"))
tuple	makePlots_datacardsZttEfficiency.results_tree = results_file.Get("tree_fit_sb")
	makePlots_datacardsZttEfficiency.bestfit = results_tree.r
list	makePlots_datacardsZttEfficiency.bkg_process = datacards_cbs[datacard]
	makePlots_datacardsZttEfficiency.signal_scale = bestfit
list	makePlots_datacardsZttEfficiency.effnom = efficiency[category[:2]]
tuple	makePlots_datacardsZttEfficiency.processes_to_plot = list(processes)