sig_hslice = hslice[Plotter.signal_samples]
SM_hslice = hslice[Plotter.nonsignal_samples]
# plot signal
if sig_hslice.values():
for signal in sig_hslice.values().keys():
fig, ax = plt.subplots()
fig.subplots_adjust(hspace=.07)
sig_hist = sig_hslice[signal].integrate(
'process')
Plotter.plot_1D(
*sig_hist.values().values(),
sig_hist.axis(xaxis_name).edges(),
xlabel=new_xtitle,
xlimits=x_lims,
ax=ax,
label='%s, %s' %
(plt_tools.get_label(
signal[0], styles.styles),
signal[0].split('_')[-1]))
ax.legend(loc='upper right')
# add lepton/jet multiplicity label
ax.text(0.02,
0.85,
"%s, %s\n%s" %
(lep_cats[lepcat], jet_mults[jmult],
btag_cats[btagregion]),
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
hep.cms.label(
for ttbar_type in sorted(set([key[1] for key in histo.values().keys()])):
decay_label = "%s %s" % (plt_tools.get_label(dataset, styles), ttdecay_types[ttbar_type])
#decay_label = ttdecay_types[ttbar_type]
pltdir = os.path.join(outdir, dataset, ttbar_type) if isSignal(dataset) else os.path.join(outdir, "ttJets", ttbar_type)
if not os.path.isdir(pltdir):
os.makedirs(pltdir)
for genobj, (objlabel, mass_range) in objects[ttbar_type].items():
new_xtitle = xtitle.replace("obj", objlabel)
if hname == "mass":
x_lims = mass_range
tt_histo = histo[genobj, ttbar_type].integrate("objtype").integrate("ttdecay")
fig, ax = plt.subplots()
fig.subplots_adjust(hspace=.07)
Plotter.plot_1D(tt_histo.values()[()], tt_histo.axis(xaxis_name).edges(), xlabel=new_xtitle, xlimits=x_lims, ax=ax, histtype="step")
hep.cms.label(ax=ax, data=False, paper=False, year=args.year, lumi=round(lumi_to_use, 1))
#set_trace()
# add lepton/jet multiplicity label
if isSignal(dataset):
if "Int" in dataset:
sig_type = "Int, w $<$ 0" if "Int_neg" in dataset else "Int, w $>$ 0"
else:
sig_type = "Res"
sig_label = "%s\n%s" % (decay_label, sig_type)
ax.text(
0.95, 0.85, sig_label,
horizontalalignment="right", verticalalignment="bottom", transform=ax.transAxes
)
else:
plot.plot1d(
hslice,
overlay=hslice.axes()[0].name,
ax=ax,
clear=False,
line_opts={'linestyle': '-'},
)
# norm
#set_trace()
for corr in sorted(hslice.values().keys()):
Plotter.plot_1D(values=hslice.values()[corr] /
np.sum(hslice.values()[corr]),
bins=hslice.dense_axes()[0].edges(),
ax=ax_norm,
xlimits=x_lims,
xlabel=xtitle,
ylabel='Probability Density',
label=corr[0],
histtype='step')
## set legend and corresponding colors
handles, labels = ax.get_legend_handles_labels()
for idx, label in enumerate(labels):
if label == '4Jets':
labels[idx] = jet_mults[label]
handles[idx].set_color('b')
handles[idx].set_linewidth(2)
elif label == '5PJets':
labels[idx] = jet_mults[label]
handles[idx].set_color('g')
} # use SL+DL+Had events
fig, (ax, rax) = plt.subplots(
2,
1,
gridspec_kw={"height_ratios": (3, 1)},
sharex=True)
fig.subplots_adjust(hspace=.07)
for rewt, hslice in allTT_hslices.items():
vals, bins = hslice.values()[(
)], hslice.axis(xaxis_name).edges()
ax = Plotter.plot_1D(
vals,
bins,
xlimits=x_lims,
ax=ax,
histtype='step',
label=rewt_style_dict[rewt][0],
color=rewt_style_dict[rewt][1])
if (rewt != nominal_jobid) and (
nominal_jobid in rewt_style_dict.keys()):
ratio_vals, ratio_bins = Plotter.get_ratio_arrays(
num_vals=vals,
denom_vals=allTT_hslices[nominal_jobid].
values()[()],
input_bins=bins)
rax.step(ratio_bins,
ratio_vals,
where='post',
**{
#set_trace()
hslices = {
rewt:
histo['ttJets', jmult, btagregion,
lepcat].integrate('jmult').integrate('lepcat').
integrate('btag').integrate('process')
for rewt, histo in histos.items()
}
logy_min, logy_max = 1e-1, 0.
for rewt, hslice in hslices.items():
vals, bins = hslice.values()[(
)], hslice.axis(xaxis_name).edges()
ax = Plotter.plot_1D(vals,
bins,
xlimits=x_lims,
ytitle='$t\\bart$ Events',
ax=ax,
histtype='step',
label=rewt_style_dict[rewt][0],
color=rewt_style_dict[rewt][1])
if rewt != nominal_jobid:
ratio_vals, ratio_bins = Plotter.get_ratio_arrays(
num_vals=vals,
denom_vals=hslices[nominal_jobid].values()[()],
input_bins=bins)
rax.step(ratio_bins,
ratio_vals,
where='post',
**{
'linestyle': '-',
'color': rewt_style_dict[rewt][1]
print(', '.join([jmult, lepcat, btagregion, hname,
sample]))
fig, ax = plt.subplots()
fig.subplots_adjust(hspace=.07)
hslice = histo[sample, jmult, btagregion,
lepcat].integrate('jmult').integrate(
'lepcat').integrate('btag').integrate(
'dataset')
if hslice.values():
Plotter.plot_1D(
*hslice.values().values(),
histo.axis(xaxis_name).edges(),
xlimits=(-5., 5.)
if sample.startswith('QCD') else x_lims,
xlabel=xtitle,
ylabel='Events (Unweighted)',
ax=ax,
label='%s\n%s' %
(sample, format(lumi_correction[sample], '.3f')))
ax.legend(loc='upper right')
# add lepton/jet multiplicity label
ax.text(0.02,
0.88,
"%s, %s\n%s" %
(lep_cats[lepcat], jet_mults[jmult],
btag_cats[btagregion]),
fontsize=rcParams['font.size'] * 0.75,
horizontalalignment='left',
verticalalignment='bottom',
for sig in samples:
boson, mass, width, shape = sig.split(
'_')
opts = Plotter.styles.styles[width]
pos_histo = histo[
'%s_pos' % sig, jmult, btagregion,
lepcat].integrate(
'jmult'
).integrate('lepcat').integrate(
'btag').integrate('dataset')
Plotter.plot_1D(
pos_histo.values()[()],
pos_histo.dense_axes()[0].edges(),
ax=pos_ax,
xlimits=x_lims,
xlabel=xtitle,
color=opts['color'],
label=opts['name'],
histtype='step')
neg_histo = histo[
'%s_neg' % sig, jmult, btagregion,
lepcat].integrate(
'jmult'
).integrate('lepcat').integrate(
'btag').integrate('dataset')
Plotter.plot_1D(
neg_histo.values()[()],
neg_histo.dense_axes()[0].edges(),
ax=neg_ax,
xlimits=x_lims,
os.makedirs(pltdir)
print(', '.join([jmult, lepcat, btagregion, hname]))
hslice = histo[:, jmult, btagregion, lepcat].integrate('jmult').integrate('lepcat').integrate('btag')
if hname == 'Lep_iso':
if args.lepton == 'Muon':
x_lims = (0., 0.15) if lepcat == 'Tight' else (0.15, 1.)
if args.lepton == 'Electron':
x_lims = (0., 0.1) if lepcat == 'Tight' else (0., 0.5)
# plot original yields
fig, ax = plt.subplots()
fig.subplots_adjust(hspace=.07)
Plotter.plot_1D(hslice.values()[('Before',)], hslice.axis(xaxis_name).edges(), xlimits=x_lims, ax=ax, label='Runs $<$ 319077')
Plotter.plot_1D(hslice.values()[('After',)], hslice.axis(xaxis_name).edges(), xlimits=x_lims, xlabel=xtitle, color='r', ax=ax, label='Runs $\\geq$ 319077')
ax.legend(loc='upper right')
if hname == 'Jets_njets':
print(jmult)
#set_trace()
rows = [("Lumi: %s fb^-1" % format(data_lumi_year['%ss' % args.lepton]/1000., '.1f'), "Yield", "Error", "Frac")]
rows += [("Runs < 319077", format(sum(hslice.values(overflow='all')[('Before',)]), '.1f'), format(np.sqrt(sum(hslice.values(overflow='all', sumw2=True)[('Before',)][1])), '.1f'),
format(sum(hslice.values(overflow='all')[('Before',)])/sum(hslice.sum('hem').values(overflow='all')[()]), '.3f'))]
rows += [("Runs >= 319077", format(sum(hslice.values(overflow='all')[('After',)]), '.1f'), format(np.sqrt(sum(hslice.values(overflow='all', sumw2=True)[('After',)][1])), '.1f'),
format(sum(hslice.values(overflow='all')[('After',)])/sum(hslice.sum('hem').values(overflow='all')[()]), '.3f'))]
rows += [("Total", format(sum(hslice.sum('hem').values(overflow='all')[()]), '.1f'), format(np.sqrt(sum(hslice.sum('hem').values(overflow='all', sumw2=True)[()][1])), '.1f'), "")]
frac_name = '%s_yields_and_fracs.txt' % '_'.join([jmult, args.lepton, lepcat, btagregion])
plt_tools.print_table(rows, filename=os.path.join(pltdir, frac_name), print_output=True)
fig, ax = plt.subplots()
fig.subplots_adjust(hspace=.07)
ax.axhline(1, **{"linestyle": "--", "color": (0, 0, 0, 0.5), "linewidth": 1})
ax.text(
0.02, 0.90, "$t\\bart \\rightarrow e/\mu + jets$\nparton level",
fontsize=rcParams["font.size"], horizontalalignment="left", verticalalignment="bottom", transform=ax.transAxes
)
#set_trace()
for year in years_to_run:
histo = ratios[year][var] # get histogram
if histo._dimension == 1:
orig_bins, orig_vals = histo._axes, histo._values
# plot original distribution
Plotter.plot_1D(orig_vals, orig_bins, xlabel=var_opts[var]["xtitle"], ylabel=var_opts[var]["ytitle"], color=year_opts[year]["col"], ax=ax, label="%s Original" % year_opts[year]["leg_title"])
# get interpolated values from ROOT Interpolate
orig_ratio_hist = Hist(orig_bins, name="", title="")
for xbin in range(1, orig_bins.size):
orig_ratio_hist[xbin] = orig_vals[xbin-1]
output_bins = np.arange(min(orig_bins), max(orig_bins)+10, 10)
interped_array = np.zeros(output_bins.size-1)
for xbin in range(output_bins.size-1):
interped_array[xbin] = orig_ratio_hist.Interpolate(output_bins[xbin])
Plotter.plot_1D(interped_array, output_bins, xlabel=var_opts[var]["xtitle"], ylabel=var_opts[var]["ytitle"], color=year_opts[year]["col"], ax=ax, label="%s Interp" % (year_opts[year]["leg_title"]), linestyle="--")
if args.save_ratios:
lookup = dense_lookup(*(interped_array, output_bins))