def configure(self): super(TemplateFitToolChHadIso, self).configure() self.fitter = Fitter() self.fitbox_bounds = 0.33, 0.62, settings.defaults_Legend["y_pos"] # here the stacked templates are taken for purity calculation # but they are replaced in fetch_mc_templates(..) self.mc_tmplts = gen.filter( settings.post_proc_dict["TemplateStacks"], { "analyzer" : ("TemplateChHadIsoreal", "TemplateChHadIsofake"), }) self.fitted = rebin_chhadiso( gen.fs_filter_active_sort_load({ "analyzer" : "TemplateChHadIso", "is_data" : True, }) ) ttbar_sample = next(( s.name for s in settings.mc_samples().values() if s.legend == "t#bar{t} inclusive" )) self.gen_bkg_tmplt = rebin_chhadiso( gen.gen_norm_to_data_lumi( gen.fs_filter_active_sort_load({ "analyzer" : "TemplateChHadIsofake", "sample" : ttbar_sample, }))) self.gen_sig_tmplt = rebin_chhadiso( gen.gen_norm_to_data_lumi( gen.fs_filter_active_sort_load({ "analyzer" : "TemplateChHadIsoreal", "sample" : re.compile("whiz2to5"), })))
def configure(self): super(TemplateFitToolChHadIso, self).configure() self.fitter = ThetaFitter() self.fitbox_bounds = 0.33, 0.62, 0.88 self.mc_tmplts = gen.filter( settings.post_proc_dict["TemplateStacks"], { "analyzer" : ("TemplateChHadIsoreal", "PlotSBIDfake"), } ) self.fitted = rebin_chhadiso( gen.fs_filter_active_sort_load({ "analyzer" : "TemplateChHadIso", "is_data" : True, }) ) self.gen_bkg_tmplt = iter( settings.post_proc_dict["TemplateFitToolChHadIsoSBIDInputBkg"] ) self.gen_sig_tmplt = rebin_chhadiso( gen.gen_norm_to_data_lumi( gen.fs_filter_active_sort_load({ "analyzer" : "TemplateChHadIsoreal", "sample" : re.compile("whiz2to5"), }) ) )
def run(self): wrp = tmpl_fit.get_merged_sbbkg_histo(sample) # multiply with weight if tmpl_fit.do_dist_reweighting: wrp = gen.op.prod(( settings.post_proc_dict["TemplateFitToolChHadIsoSbBkgInputBkgWeight"], wrp, )) wrps = gen.gen_norm_to_data_lumi((wrp,)) wrps = list(wrps) self.result = wrps gen.consume_n_count( gen.save( gen.canvas((wrps,)), lambda c: self.plot_output_dir + c.name ) )
def run(self): wrp = next(rebin_chhadiso( gen.gen_sum( [gen.fs_filter_active_sort_load({ "analyzer" : "TemplateRandConereal", "is_data" : True })] ) )) # normalize to mc expectation integral_real = next( gen.gen_integral( gen.gen_norm_to_data_lumi( gen.filter( settings.post_proc_dict["TemplateStacks"], {"analyzer": "TemplateRandConereal"} ) ) ) ) print integral_real wrp = gen.op.prod(( gen.op.norm_to_integral(wrp), integral_real )) # multiply with weight if do_dist_reweighting: wrp = gen.op.prod(( settings.post_proc_dict["TemplateFitToolRandConeIsoInputSigWeight"], wrp, )) wrp.lumi = settings.data_lumi_sum() self.result = [wrp] gen.consume_n_count( gen.save( gen.canvas((self.result,)), lambda c: self.plot_output_dir + c.name ) )
def run(self): wrps = tmpl_fit.rebin_chhadiso(gen.fs_filter_sort_load({ "analyzer": "PlotSBID", "sample": sample, })) wrp = gen.op.merge(wrps) # multiply with weight if tmpl_fit.do_dist_reweighting: wrp = gen.op.prod(( settings.post_proc_dict["TemplateFitToolChHadIsoSBIDInputBkgWeight"], wrp, )) wrps = gen.gen_norm_to_data_lumi((wrp,)) wrps = list(wrps) self.result = wrps gen.consume_n_count( gen.save( gen.canvas((wrps,)), lambda c: self.plot_output_dir + c.name ) )