def make_mixer_class(input_dict):
gen_tmplt_input = tmpl_fit.rebin_chhadiso(gen_mixer_input(input_dict))
class HistoMixer(ppt.FSStackPlotter):
def configure(self):
self.result = []
for d in gen_tmplt_input:
self.result.append(d)
def make_mixed_histo(self, scale_factors):
assert(len(scale_factors) == len(self.result))
mix = op.sum((
op.prod([tmplt, wrp.FloatWrapper(float(scale))])
for scale,tmplt in itertools.izip(scale_factors, self.result)
))
mix.legend = "mix:"+(len(scale_factors)*" %d") % tuple(scale_factors)
mix.name = "pseudo data"
mix.draw_option = "E1X0"
mix.draw_option_legend = "p"
#mix.draw_option_legend = ""
mix.histo.SetMarkerStyle(24)
return [mix]
def set_up_stacking(self):
gen_inp = lambda w: tmpl_fit.cosmetica1([op.norm_to_integral(w)]) # clone histo and color..
self.stream_stack = (
gen_inp(w) for w in self.result
)
return HistoMixer
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
)
)
