def set_up_stacking(self):
mc_tmplts = self.__dict__["input_func"]()
mc_tmplts = gen.gen_norm_to_integral(mc_tmplts)
mc_tmplts = cosmetica1(mc_tmplts)
mc_tmplts = rebin_chhadiso(mc_tmplts)
mc_tmplts = gen.apply_histo_linecolor(mc_tmplts, [409, 625, 618, 596, 430])
mc_tmplts = gen.group(mc_tmplts, key_func=lambda w: w.sample)
self.stream_stack = mc_tmplts
def set_up_stacking(self):
wrps = gen.pool_content()
wrps = gen.filter(
wrps,
{"analyzer": re.compile("CutFlow")}
)
wrps = gen.group(wrps)
stream_stack = gen.mc_stack_n_data_sum(
wrps,
use_all_data_lumi=True
)
def assign_name(wrp): wrp.name = "CombinedCutflow"
self.stream_stack = gen.pool_store_items(stream_stack, assign_name)
def set_up_stacking(self):
mc_tmplts = gen.fs_filter_sort_load({
"sample" : self.sample_name,
"analyzer" : ("TemplateChHadIsofake", "PlotSBID")
})
sb_wrp = get_merged_sbbkg_histo(self.sample_name)
mc_tmplts = [sb_wrp] + list(mc_tmplts)
mc_tmplts = gen.gen_norm_to_integral(mc_tmplts)
mc_tmplts = cosmetica1(mc_tmplts)
mc_tmplts = rebin_chhadiso(mc_tmplts)
mc_tmplts = gen.apply_histo_linecolor(mc_tmplts, [625, 618, 596])
mc_tmplts = apply_overlay_draw_mode(mc_tmplts)
mc_tmplts = gen.group(mc_tmplts, key_func=lambda w: w.sample)
self.stream_stack = mc_tmplts
def set_up_stacking(self):
key_func = lambda w: w.analyzer[:-4]
mc_tmplts = itertools.chain(*list(
gen.get_from_post_proc_dict(k)
for k in settings.post_proc_dict.keys()
if ("TemplateStacks" in k
)
))
mc_tmplts = histo_wrapperize(mc_tmplts)
mc_tmplts = gen.gen_norm_to_lumi(mc_tmplts)
mc_tmplts = cosmetica1(mc_tmplts)
mc_tmplts = sorted(mc_tmplts, key=key_func)
mc_tmplts = gen.group(mc_tmplts, key_func=key_func)
self.stream_stack = mc_tmplts
def test_gen_group(self):
from_fs = gen.fs_content()
filtered = gen.filter(from_fs, {"name": "histo"})
sorted = gen.sort(filtered)
grouped = gen.group(sorted)
group_list = []
for group in grouped:
group_list.append(list(group))
# length: 3 samples: 3 histos per group
self.assertEqual(len(group_list), 26)
for g in group_list:
self.assertEqual(len(g), 3)
self.assertEqual(g[0].analyzer, g[1].analyzer)
self.assertEqual(g[0].analyzer, g[2].analyzer)
def test_gen_group(self):
from_fs = gen.fs_content()
filtered = gen.filter(from_fs, {"name": "histo"})
sorted = gen.sort(filtered)
grouped = gen.group(sorted)
group_list = []
for group in grouped:
group_list.append(list(group))
# length: 3 samples: 3 histos per group
self.assertEqual(len(group_list), 26)
for g in group_list:
self.assertEqual(len(g), 3)
self.assertEqual(g[0].analyzer, g[1].analyzer)
self.assertEqual(g[0].analyzer, g[2].analyzer)
def run(self):
wrps = gen.fs_content()
wrps = gen.filter(
wrps,
{"analyzer": re.compile("CutFlow*")}
)
wrps = gen.filter_active_samples(wrps)
wrps = sorted(wrps, key = sample_key_func)
wrps = gen.load(wrps)
grps = gen.group(wrps, sample_key_func)
wrps = (self.combine_cutflow_histo(g) for g in grps)
wrps = gen.pool_store_items(wrps)
wrps = list(wrps)
self.result = wrps
count = len(wrps)
self.message("INFO: "+self.name+" stored "+str(count)+" histos in pool.")
def set_up_stacking(self):
key_func = lambda w: w.post_proc_key
mc_tmplts = itertools.chain(*list(
gen.get_from_post_proc_dict(k)
for k in settings.post_proc_dict.keys()
if ("_mc_tmplts" in k
)
))
mc_tmplts = self.norm_func(mc_tmplts)
mc_tmplts = cosmetica1(mc_tmplts)
def histo_namer(wrp):
wrp.analyzer = wrp.post_proc_key[:-10]
return wrp
mc_tmplts = (histo_namer(w) for w in mc_tmplts)
mc_tmplts = sorted(mc_tmplts, key=key_func)
mc_tmplts = gen.group(mc_tmplts, key_func=key_func)
self.stream_stack = mc_tmplts
def set_up_stacking(self):
mc_tmplts = gen.fs_filter_sort_load({
"sample" : ("whiz2to5", "TTGamRD1"),
"analyzer" : "TemplateChHadIsoreal",
})
mc_tmplts = gen.gen_norm_to_integral(mc_tmplts)
mc_tmplts = cosmetica1(mc_tmplts)
def leg(wrps):
for w in wrps:
w.legend = w.sample
w.draw_option = "E1"
w.histo.SetMarkerStyle(24)
yield w
mc_tmplts = leg(mc_tmplts)
mc_tmplts = rebin_chhadiso(mc_tmplts)
mc_tmplts = gen.apply_histo_linecolor(mc_tmplts, [409, 625, 618, 596, 430])
mc_tmplts = gen.group(mc_tmplts, key_func=lambda w: w.analyzer)
self.stream_stack = mc_tmplts
def set_up_stacking(self):
mc_tmplts = rebin_chhadiso(
gen.fs_filter_active_sort_load({
"analyzer" : all_analyzers,
})
)
mc_tmplts = gen.gen_norm_to_lumi(mc_tmplts)
mc_tmplts = gen.group(mc_tmplts)
def stack_with_purity_info(grps):
for grp in grps:
sub_tot_list = [0., 0.]
grp = list(com.count_ttgamma_photons(grp,sub_tot_list))
grp = gen.mc_stack((grp,))
grp = grp.next()
grp.sub_tot_list = sub_tot_list
yield grp
mc_tmplts = list(stack_with_purity_info(mc_tmplts))
mc_tmplts_plot = list(
gen.filter(mc_tmplts, {"analyzer": analyzers_mc})
)
self.result = mc_tmplts_plot
self.stream_stack = mc_tmplts_plot
def set_up_stacking(self):
def sum_up_two(wrps):
while True:
try:
h = gen.op.sum((next(wrps), next(wrps)))
except StopIteration:
return
yield h
mc_tmplts = list(sum_up_two(gen.fs_filter_sort_load({
"sample" : self.sample_name,
"analyzer" : re.compile("PlotLooseIDSliceSihihSB"),
})))
mc_tmplts = itertools.chain(gen.fs_filter_sort_load({
"sample" : self.sample_name,
"analyzer" : "TemplateChHadIsofake",
}),
mc_tmplts
)
mc_tmplts = gen.gen_norm_to_integral(mc_tmplts)
mc_tmplts = cosmetica1(mc_tmplts)
mc_tmplts = rebin_chhadiso(mc_tmplts)
mc_tmplts = gen.apply_histo_linecolor(mc_tmplts, [409, 625, 618, 596, 430])
mc_tmplts = gen.group(mc_tmplts, key_func=lambda w: w.sample)
self.stream_stack = mc_tmplts