def flatten_bkg_templates(fnames_to_run):
"""
Function that writes linearized mtt vs costheta distributions to root file.
"""
if "3Jets" in njets_to_run:
histo_dict_3j = processor.dict_accumulator({
"Muon": {},
"Electron": {}
})
if "4PJets" in njets_to_run:
histo_dict_4pj = processor.dict_accumulator({
"Muon": {},
"Electron": {}
})
#set_trace()
for bkg_file in fnames_to_run:
hdict = load(bkg_file)
jmult = "3Jets" if "3Jets" in os.path.basename(bkg_file) else "4PJets"
for lep in hdict.keys():
for tname, orig_template in hdict[lep].items():
#set_trace()
proc = tname.split(
"_")[0] if not "data_obs" in tname else "data_obs"
sys = sorted(filter(None, tname.split(f"{proc}_")))[0]
#if sys == "nosys": continue
print(lep, jmult, sys, proc)
# perform flattening
flattened_histo = hdict[lep][f"{proc}_nosys"].copy(
) if sys == "nosys" else Plotter.flatten(
nosys=hdict[lep][f"{proc}_nosys"].copy(),
systematic=orig_template.copy())
## save template histos to coffea dict
if jmult == "3Jets":
histo_dict_3j[lep][tname] = flattened_histo.copy()
if jmult == "4PJets":
histo_dict_4pj[lep][tname] = flattened_histo.copy()
#set_trace()
if "3Jets" in njets_to_run:
coffea_out_3j = os.path.join(
input_dir,
f"test_flattened_templates_lj_3Jets_bkg_{args.year}_{jobid}.coffea"
)
save(histo_dict_3j, coffea_out_3j)
print(f"{coffea_out_3j} written")
if "4PJets" in njets_to_run:
coffea_out_4pj = os.path.join(
input_dir,
f"test_flattened_templates_lj_4PJets_bkg_{args.year}_{jobid}.coffea"
)
save(histo_dict_4pj, coffea_out_4pj)
print(f"{coffea_out_4pj} written")
def get_bkg_templates(fnames_to_run):
"""
Function that writes linearized mtt vs costheta distributions to root file.
"""
#set_trace()
for bkg_file in fnames_to_run:
hdict = load(bkg_file)
jmult = "3Jets" if "3Jets" in os.path.basename(bkg_file) else "4PJets"
for lep in hdict.keys():
for tname, orig_template in hdict[lep].items():
proc = tname.split(
"_")[0] if not "data_obs" in tname else "data_obs"
sys = sorted(filter(None, tname.split(f"{proc}_")))[0]
#if not ((sys == "ueDOWN") and (proc == "ttJets")): continue
if sys == "nosys": continue
print(lep, jmult, sys, proc)
nominal_hist = hdict[lep][f"{proc}_nosys"].copy()
x_lims = (0, nominal_hist.dense_axes()[0].centers().size)
# perform smoothing
smoothed_histos_list = [(Plotter.smoothing_mttbins(
nosys=nominal_hist,
systematic=orig_template,
mtt_centers=mtt_centers,
nbinsx=len(linearize_binning[0]) - 1,
nbinsy=len(linearize_binning[1]) - 1,
**{"frac": frac_val / 10.}), frac_val / 10.)
for frac_val in np.arange(2, 7, 2)]
#smoothed_histos_chi2 = {frac_val : find_chi2(h_fitted=smooth_histo, h_unc=orig_template) for smooth_histo, frac_val in smoothed_histos_list}
# perform flattening
flattened_histo = Plotter.flatten(nosys=nominal_hist,
systematic=orig_template)
#flat_chi2 = find_chi2(h_fitted=flattened_histo, h_unc=orig_template)
# plot relative deviation
fig, ax = plt.subplots()
fig.subplots_adjust(hspace=.07)
# plot original dist
orig_masked_vals, orig_masked_bins = Plotter.get_ratio_arrays(
num_vals=orig_template.values()[()] -
nominal_hist.values()[()],
denom_vals=nominal_hist.values()[()],
input_bins=nominal_hist.dense_axes()[0].edges())
ax.fill_between(orig_masked_bins,
orig_masked_vals,
facecolor="k",
step="post",
alpha=0.5,
label="Unsmoothed")
# plot smoothed versions
for smooth_histo, frac_val in smoothed_histos_list:
smooth_masked_vals, smooth_masked_bins = Plotter.get_ratio_arrays(
num_vals=smooth_histo.values()[()] -
nominal_hist.values()[()],
denom_vals=nominal_hist.values()[()],
input_bins=nominal_hist.dense_axes()[0].edges())
ax.step(smooth_masked_bins,
smooth_masked_vals,
where="post",
**{
"linestyle": "-",
"label": f"Frac={frac_val}",
"linewidth": 2
})
# plot flattened val
flat_masked_vals, flat_masked_bins = Plotter.get_ratio_arrays(
num_vals=flattened_histo.values()[()] -
nominal_hist.values()[()],
denom_vals=nominal_hist.values()[()],
input_bins=nominal_hist.dense_axes()[0].edges())
ax.step(flat_masked_bins,
flat_masked_vals,
where="post",
**{
"linestyle": "-",
"label": "Flat",
"linewidth": 2
})
ax.legend(loc="upper right", title=f"{sys}, {proc}")
ax.axhline(
0, **{
"linestyle": "--",
"color": (0, 0, 0, 0.5),
"linewidth": 1
})
ax.autoscale()
ax.set_xlim(x_lims)
ax.set_xlabel(
"$m_{t\\bar{t}}$ $\otimes$ |cos($\\theta^{*}_{t_{l}}$)|")
ax.set_ylabel("Rel. Deviaton from Nominal")
# add lepton/jet multiplicity label
ax.text(0.02,
0.94,
f"{leptypes[lep]}, {jet_mults[jmult]}",
fontsize=rcParams["font.size"] * 0.9,
horizontalalignment="left",
verticalalignment="bottom",
transform=ax.transAxes)
## draw vertical lines for distinguishing different ctstar bins
vlines = [x_lims[1] * ybin / 5 for ybin in range(1, 5)]
for vline in vlines:
ax.axvline(vline, color="k", linestyle="--")
hep.cms.label(ax=ax,
data=False,
paper=False,
year=args.year,
lumi=round(data_lumi_year[f"{lep}s"] / 1000., 1))
#set_trace()
pltdir = os.path.join(outdir, lep, jmult, sys)
if not os.path.isdir(pltdir):
os.makedirs(pltdir)
#figname = os.path.join(pltdir, "_".join([jmult, lep, sys, proc, "BinWidths_Comp"]))
#figname = os.path.join(pltdir, "_".join([jmult, lep, sys, proc, "SmoothValues_Comp"]))
#figname = os.path.join(pltdir, "_".join([jmult, lep, sys, proc, "MttBinWidths_SmoothValues_Comp"]))
figname = os.path.join(
pltdir,
"_".join([jmult, lep, sys, proc, "SmoothedFlatVals_Comp"]))
fig.savefig(figname)
print(f"{figname} written")
plt.close()