def get_legend_name(key):
if "Template" in key:
for tag in legend_tags:
if tag in key:
return settings.get_pretty_name(tag)
else:
return settings.get_pretty_name(key)
def write_uncert_tabular(self):
res = self.result
table = [
r"\begin{tabular}{l c c} \\",
r"\hline",
r"\hline",
r"Source & \multicolumn{2}{c}{Uncertainty (\%)} \\",
r"& $\Rvis$ & $\sigma_{\ttgam}$ \\",
r"\hline",
r"Statistical & %.1f & %.1f \\" % (
getattr(res, "R_fid_err_stat") / getattr(res, "R_fid") * 100.,
getattr(res, "xsec_err_stat") / getattr(res, "xsec") * 100.,
),
r"\hline",
r"Systematic & %.1f & %.1f \\" % (
getattr(res, "R_fid_err_sys") / getattr(res, "R_fid") * 100.,
getattr(res, "xsec_err_sys") / getattr(res, "xsec") * 100.,
),
r"\hline",
r"Individual contributions: & & \\"
]
# # sort by largest contribution
# for sys in sorted(summed_uncerts,
# key=lambda s: -getattr(res, s + "_" + self.xsec_calc + "_R")
# ):
for sys in summed_uncerts:
table.append(
"\;\;\;"
+ settings.get_pretty_name(sys)
+ r" & %.1f & %.1f \\" % (
getattr(res, sys+"_"+self.xsec_calc+"_R_fid") * 100.,
getattr(res, sys+"_"+self.xsec_calc+"_xsec") * 100.,
),
)
table += (
# r"\;\;\;top-quark mass & --- & --- \\",
r"\hline",
r"\textbf{Total} & %.1f & %.1f \\" % (
(
(getattr(res, "R_fid_err_stat") / getattr(res, "R_fid") * 100.)**2
+ (getattr(res, "R_fid_err_sys") / getattr(res, "R_fid") * 100.)**2
)**.5,
(
(getattr(res, "xsec_err_stat") / getattr(res, "xsec") * 100.)**2
+ (getattr(res, "xsec_err_sys") / getattr(res, "xsec") * 100.)**2
)**.5,
),
r"\hline",
r"\hline",
r"\end{tabular}",
)
with open(self.plot_output_dir + "sys_tabular.tex", "w") as f:
f.writelines(map(lambda l: l + "\n", table))
for n, l in enumerate(table):
setattr(self.result, "line%02d"%n, l)
def make_header(self):
self.table_lines += (
r"\begin{tabular}{l | "
+ len(self.cont.head_line)*"r "
+ "}",
17*" "
+ " & "
+ " & ".join(itertools.imap(
lambda s: "%17s" % settings.get_pretty_name(s + "_tex"),
self.cont.head_line
))
+ r" \\",
r"\hline",
r"\hline",
)
def combine_cutflow_histo(self, grp):
"""Adds histos with single bins to a combined cutflow histogram."""
grp = list(grp)
bin_labels = dict(
(
w.histo.GetMaximumBin(),
settings.get_pretty_name(w.histo.GetXaxis().GetTitle())
) for w in grp
)
lumi = grp[0].lumi
wrp = gen.op.sum(grp)
wrp.lumi = lumi
for bin, label in bin_labels.iteritems():
wrp.histo.GetXaxis().SetBinLabel(bin, label)
return wrp
def write_method_comparison_tabular(self):
res = self.results
l = len(res)
for r in res:
r.calc = r.name.split("_")[-1]
table = [
r"\begin{tabular}{l " + l*"c " + r"} \\",
r"\hline",
r"\hline",
"Method " + (l*"& %s" + r" \\") % tuple(
settings.get_pretty_name(r.calc)
for r in res
),
(r"$\Nsig / \epsilon_\gamma$ " + l*" & %.1f" + r" \\") % tuple(
getattr(r, "n_sig_ttgam") / r.eff_gamma
for r in res
),
r"\hline",
r"\hline",
r"Source & \multicolumn{" + str(l) + r"}{c}{Uncertainty} \\",
r"\hline",
(r"Internal (e.g. fit) " + l*" & %.1f" + r" \\") % tuple(
getattr(r, "n_sig_ttgam_err") / r.eff_gamma
for r in res
),
r"\hline",
(r"Systematic" + l*" & %.1f" + r" \\") % tuple(
getattr(r, "n_sig_ttgam_err_sys") / r.eff_gamma
for r in res
),
r"\hline",
r"Contributions: & & \\"
]
for sys in sorted(summed_uncerts,
key=lambda s: -getattr(res[0], s + "_" + res[0].calc + "_n_sig_ttgam")
):
table.append(
"\;\;\;"
+ settings.get_pretty_name(sys)
+ (l*" & %.1f" + r" \\") % tuple(
getattr(r, sys+"_"+r.calc+"_n_sig_ttgam") * getattr(r, "n_sig_ttgam") / r.eff_gamma
for r in res
),
)
table += (
r"\hline",
(r"\textbf{Total uncertainty}" + l*" & %.1f" + r" \\") % tuple(
(
getattr(r, "n_sig_ttgam_err")**2
+ getattr(r, "n_sig_ttgam_err_sys")**2
)**.5 / r.eff_gamma
for r in res
),
r"\hline",
r"\hline",
r"\end{tabular}",
)
with open(self.plot_output_dir + "method_tabular.tex", "w") as f:
f.writelines(map(lambda l: l + "\n", table))
for n, l in enumerate(table):
setattr(self.result, "line%02d"%n, l)
