def do_pt_plot(pythia_dir,
herwig_dir,
selection,
hist_name,
output_name,
title=""):
h_pythia = grab_obj(
"%s/uhh2.AnalysisModuleRunner.MC.MC_%s_.root" %
(pythia_dir, selection), hist_name)
h_herwig = grab_obj(
"%s/uhh2.AnalysisModuleRunner.MC.MC_%s_.root" %
(herwig_dir, selection), hist_name)
c_pythia = Contribution(h_pythia,
label="MG+Pythia",
line_color=ROOT.kBlue,
marker_color=ROOT.kBlue,
fill_color=ROOT.kBlue,
normalise_hist=True)
# c_herwig = Contribution(h_herwig, label="Herwig", line_color=ROOT.kRed, normalise_hist=True)
c_herwig = Contribution(h_herwig,
label="Pythia only",
line_color=ROOT.kRed,
marker_color=ROOT.kRed,
fill_color=ROOT.kRed,
normalise_hist=True)
p = Plot([c_pythia, c_herwig],
what="hist",
legend=True,
subplot_type='ratio',
subplot=c_pythia,
title=title)
p.plot("NOSTACK HISTE")
p.main_pad.SetLogy()
p.container.SetMinimum(1E-12)
p.subplot_container.SetMaximum(1.5)
p.subplot_container.SetMinimum(0)
p.canvas.Update()
p.save(output_name)
def get_flavour_efficiencies(input_file, hist_name, bins):
"""Get dict of flav : [TEfficiency for specified bins] for a given input file & hist name."""
h2d_flav = grab_obj(input_file, hist_name)
flav_dict = {
'd': [],
'u': [],
's': [],
'c': [],
'b': [],
't': [],
'g': [],
'unknown': [],
'total': []
}
for (pt_min, pt_max) in bins:
h_flav = get_projection_plot(h2d_flav, pt_min, pt_max, cut_axis='y')
total_err = array('d', [-1.])
total2 = h_flav.IntegralAndError(1, h_flav.GetNbinsX() + 1, total_err)
total_err = total_err[0]
unknown_num = h_flav.GetBinContent(1)
d_num = h_flav.GetBinContent(2)
u_num = h_flav.GetBinContent(3)
s_num = h_flav.GetBinContent(4)
c_num = h_flav.GetBinContent(5)
b_num = h_flav.GetBinContent(6)
t_num = h_flav.GetBinContent(7)
g_num = h_flav.GetBinContent(22)
total = d_num + u_num + s_num + c_num + b_num + t_num + g_num + unknown_num
# print(total, total2)
if not cu.same_floats(total, total2):
# raise RuntimeError("totals dont match: %.9g vs %.9g" % (total, total2))
warnings.warn("totals dont match: %.9g vs %.9g" % (total, total2))
if total <= 0:
warnings.warn("Total = 0")
if total2 <= 0:
warnings.warn("Total2 = 0")
unknown_err = h_flav.GetBinError(1)
d_err = h_flav.GetBinError(2)
u_err = h_flav.GetBinError(3)
s_err = h_flav.GetBinError(4)
c_err = h_flav.GetBinError(5)
b_err = h_flav.GetBinError(6)
t_err = h_flav.GetBinError(7)
g_err = h_flav.GetBinError(22)
# print(pt_min, pt_max, b_num, b_err, total2, total_err)
# print(pt_min, pt_max, g_num, g_err, total2, total_err)
flav_dict['unknown'].append([unknown_num, unknown_err])
flav_dict['d'].append([d_num, d_err])
flav_dict['u'].append([u_num, u_err])
flav_dict['s'].append([s_num, s_err])
flav_dict['c'].append([c_num, c_err])
flav_dict['b'].append([b_num, b_err])
flav_dict['t'].append([t_num, t_err])
flav_dict['g'].append([g_num, g_err])
flav_dict['total'].append([total, total_err])
flav_eff_dict = {
'd': None,
'u': None,
's': None,
'c': None,
'b': None,
't': None,
'g': None,
'unknown': None,
}
bin_edges = [p[0] for p in bins]
bin_edges += bins[-1][1:]
bin_edges = array('d', bin_edges)
h_total = ROOT.TH1D("h_total", ";p_{T} [GeV];N", len(bins), bin_edges)
for ind, val in enumerate(flav_dict['total'], 1):
h_total.SetBinContent(ind, val[0])
h_total.SetBinError(ind, val[1])
for flav_key in [k for k in flav_dict.keys() if k != 'total']:
h_flav = ROOT.TH1D("h_%s" % flav_key, ";p_{T} [GeV];N", len(bins),
bin_edges)
for ind, val in enumerate(flav_dict[flav_key], 1):
h_flav.SetBinContent(ind, val[0])
h_flav.SetBinError(ind, val[1])
if (h_flav.GetNbinsX() != h_total.GetNbinsX()):
raise ValueError(
"flav %s has diff # bins to total (%d vs %d)" % flav_key,
h_flav.GetNbinsX(), h_total.GetNbinsX())
eff_flav = ROOT.TEfficiency(h_flav, h_total)
eff_flav.SetStatisticOption(1) # normal approx
flav_eff_dict[flav_key] = eff_flav
return flav_eff_dict
def do_projection_plots(root_dirs,
plot_dir="response_plots",
zpj_dirname="ZPlusJets_QG",
dj_dirname="Dijet_QG",
flav_matched=False,
do_fits=False):
"""Plot both/either q and g jet repsonse, for various genjet pt bins. Contributions from all root_dirs are shown on same plot.
flav_matched : If True, use hists with explicit gen-parton flavour matching
plot_dir : output dir for plots
do_fits : If Ture, try and do a Gaussian fit to each 1D hist
"""
pt_bins = [(20, 40), (40, 60), (60, 80), (80, 100), (100, 120), (120, 140),
(160, 200), (200, 240), (240, 300), (300, 400), (400, 500),
(500, 600), (600, 800), (800, 1000), (1000, 2000)]
print_pt_bins = [(20, 40), (40, 60), (60, 80), (100, 120), (160, 200),
(240, 300), (400, 500), (500, 600), (800, 1000),
(1000, 2000)]
print_pt_bins = pt_bins
zpj_fits = []
dj_fits = []
for (pt_min, pt_max) in pt_bins:
print pt_min, pt_max
if pt_min > pt_max:
raise RuntimeError("pt_min < pt_max!")
lw = 2 if len(root_dirs) == 1 else 1
plot_entries = []
do_plot = (pt_min, pt_max) in print_pt_bins
rebin = 1
for ind, root_dir in enumerate(root_dirs):
if zpj_dirname:
flav_str = "q" if flav_matched else ""
h2d_dyj = grab_obj(
os.path.join(root_dir, qgc.DY_FILENAME), "%s/%s" %
(zpj_dirname, "%sjet_response_vs_genjet_pt" % (flav_str)))
obj = qgg.get_projection_plot(h2d_dyj, pt_min, pt_max)
if obj.Integral() > 0:
obj.Rebin(rebin)
obj.Scale(1. / obj.Integral())
col = qgc.DY_COLOURS[ind]
dy_reco_kwargs = dict(line_color=col,
fill_color=col,
line_width=lw,
marker_color=col,
marker_style=qgc.DY_MARKER,
label=qgc.DY_ZpJ_QFLAV_LABEL if
flav_matched else qgc.DY_ZpJ_LABEL)
if len(root_dirs) > 1:
dy_reco_kwargs['label'] += " [" + root_dir + "]"
if do_fits:
do_gaus_fit(obj)
fit = obj.GetFunction("gausFit")
dy_reco_kwargs['label'] += "\n"
dy_reco_kwargs['label'] += fit_results_to_str(fit)
zpj_fits.append(fit)
if do_plot:
plot_entries.append((obj, dy_reco_kwargs))
if dj_dirname:
flav_str = "g" if flav_matched else ""
h2d_qcd = grab_obj(
os.path.join(root_dir, qgc.QCD_FILENAME), "%s/%s" %
(dj_dirname, "%sjet_response_vs_genjet_pt" % (flav_str)))
obj = qgg.get_projection_plot(h2d_qcd, pt_min, pt_max)
if obj.Integral() > 0:
obj.Rebin(rebin)
obj.Scale(1. / obj.Integral())
col = qgc.QCD_COLOURS[ind]
qcd_reco_kwargs = dict(
line_color=col,
fill_color=col,
line_width=lw,
marker_color=col,
marker_style=qgc.QCD_MARKER,
label=qgc.QCD_Dijet_GFLAV_LABEL
if flav_matched else qgc.QCD_Dijet_LABEL)
if len(root_dirs) > 1:
qcd_reco_kwargs['label'] += " [" + root_dir + "]"
if do_fits:
do_gaus_fit(obj)
fit = obj.GetFunction("gausFit")
qcd_reco_kwargs['label'] += "\n"
qcd_reco_kwargs['label'] += fit_results_to_str(fit)
dj_fits.append(fit)
if do_plot:
plot_entries.append((obj, qcd_reco_kwargs))
flav_str = "_flavMatched" if flav_matched else ""
if do_plot and len(plot_entries) > 0:
output_filename = os.path.join(
plot_dir, "jet_response_ptGen%dto%d%s.%s" %
(pt_min, pt_max, flav_str, OUTPUT_FMT))
plot = qgg.make_comparison_plot_ingredients(
plot_entries,
rebin=1,
normalise_hist=False,
title="%d < p_{T}^{GenJet} < %d GeV" % (pt_min, pt_max),
xtitle="Response (p_{T}^{Reco} / p_{T}^{Gen})",
xlim=(0.25, 1.75))
if plot.legend:
plot.legend.SetX1(0.65)
plot.legend.SetX2(0.95)
plot.legend.SetY1(0.5)
plot.legend.SetY2(0.89)
plot.plot("E NOSTACK")
max_y = plot.container.GetHistogram().GetMaximum()
line = ROOT.TLine(1, 0, 1, max_y)
line.SetLineWidth(2)
line.SetLineStyle(2)
line.SetLineColor(13)
line.Draw()
plot.save(output_filename)
do_response_graph(
pt_bins,
zpj_fits,
dj_fits,
title="Flav Matched" if flav_matched else "Not flav matched",
output_filename=os.path.join(
plot_dir, "gr_jet_response%s.%s" % (flav_str, OUTPUT_FMT)))
def do_all_2D_plots(root_dir,
plot_dir="plots_2d",
zpj_dirname="ZPlusJets_QG",
dj_dirname="Dijet_QG"):
"""Do 2D plots of response vs pt, with various normalisations, etc
plot_dir : output dir for plots
"""
line = ROOT.TLine(1, 0, 1, 2000)
line.SetLineStyle(2)
line.SetLineWidth(2)
line.SetLineColor(13)
for flav_matched in [True, False]:
for renorm, logz in product(['Y', None], [True, False]):
log_append = "_logZ" if logz else ""
if zpj_dirname:
flav_str = "q" if flav_matched else ""
h2d_dyj = grab_obj(
os.path.join(root_dir, qgc.DY_FILENAME), "%s/%s" %
(zpj_dirname, "%sjet_response_vs_genjet_pt" % (flav_str)))
flav_append = "_%sflavMatched" % flav_str if flav_matched else ""
output_filename = os.path.join(
plot_dir, "response_vs_genjet_pt_zpj%s_norm%s%s.%s" %
(flav_append, renorm, log_append, OUTPUT_FMT))
title = qgc.DY_ZpJ_LABEL
if flav_matched:
title += " (%s-matched)" % flav_str
zlim = [1E-5, 1] if logz and renorm else None
qgg.do_2D_plot(h2d_dyj,
output_filename,
renorm_axis=renorm,
title=title,
rebin=None,
recolour=True,
xlim=None,
ylim=None,
zlim=zlim,
logz=logz,
other_things_to_draw=[line])
# output_filename = os.path.join(plot_dir, "response_vs_genjet_pt_zpj%s_norm%s%s_box.%s" % (flav_append, renorm, log_append, OUTPUT_FMT))
# qgg.do_2D_plot(h2d_dyj, output_filename, draw_opt="CANDLEY1", renorm_axis=renorm, title=title, rebin=None, recolour=True, xlim=None, ylim=None, zlim=zlim, logz=logz, other_things_to_draw=[line])
if dj_dirname:
flav_str = "g" if flav_matched else ""
h2d_qcd = grab_obj(
os.path.join(root_dir, qgc.QCD_FILENAME), "%s/%s" %
(dj_dirname, "%sjet_response_vs_genjet_pt" % (flav_str)))
flav_append = "_%sflavMatched" % flav_str if flav_matched else ""
output_filename = os.path.join(
plot_dir, "response_vs_genjet_pt_dj%s_norm%s%s.%s" %
(flav_append, renorm, log_append, OUTPUT_FMT))
title = qgc.QCD_Dijet_LABEL
if flav_matched:
title += " (%s-matched)" % flav_str
zlim = [1E-5, 1] if logz and renorm else None
qgg.do_2D_plot(h2d_qcd,
output_filename,
renorm_axis=renorm,
title=title,
rebin=None,
recolour=True,
xlim=None,
ylim=None,
zlim=zlim,
logz=logz,
other_things_to_draw=[line])
# output_filename = os.path.join(plot_dir, "response_vs_genjet_pt_dj%s_norm%s%s_box.%s" % (flav_append, renorm, log_append, OUTPUT_FMT))
# qgg.do_2D_plot(h2d_qcd, output_filename, draw_opt="CANDLEY1", renorm_axis=renorm, title=title, rebin=None, recolour=True, xlim=None, ylim=None, zlim=zlim, logz=logz, other_things_to_draw=[line])
# Do box plot comparing them both
canvas = ROOT.TCanvas(ROOT.TUUID().AsString(), "", 800, 800)
canvas.SetTicks(1, 1)
canvas.SetLeftMargin(0.13)
canvas.SetBottomMargin(0.11)
h2d_qcd.SetTitle("Dijet & Z+Jets%s" %
(" (flav matched)" if flav_matched else ""))
h2d_qcd.SetLineColor(qgc.QCD_COLOUR)
h2d_qcd.SetFillStyle(0)
draw_opt = "CANDLEY(00000311)"
h2d_qcd.Draw(draw_opt)
h2d_qcd.GetXaxis().SetRangeUser(0.7, 2.5)
h2d_dyj.SetTitle("Dijet & Z+Jets%s" %
(" (flav matched)" if flav_matched else ""))
h2d_dyj.SetLineColor(qgc.DY_COLOUR)
h2d_dyj.SetFillStyle(0)
h2d_dyj.Draw(draw_opt + " SAME")
h2d_qcd.GetYaxis().SetTitleOffset(1.7)
h2d_qcd.GetXaxis().SetTitleOffset(1.2)
leg = ROOT.TLegend(0.5, 0.6, 0.88, 0.88)
leg.AddEntry(
h2d_dyj,
qgc.DY_ZpJ_QFLAV_LABEL if flav_matched else qgc.DY_ZpJ_LABEL, "L")
leg.AddEntry(
h2d_qcd,
qgc.QCD_Dijet_GFLAV_LABEL if flav_matched else qgc.QCD_Dijet_LABEL,
"L")
leg.Draw()
line.SetLineStyle(1)
line.Draw()
output_filename = os.path.join(
plot_dir, "response_vs_genjet_pt_both%s_box.%s" %
("_flavMatched" if flav_matched else "", OUTPUT_FMT))
canvas.SaveAs(output_filename)
def make_1d_plot(root_dir, histname, jet_str, gen_only=False):
entries = []
lw = 2
data_line_width = lw
msize = 1.1
dijet_cen_data_hist = None
if not gen_only:
# JETHT/ZEROBIAS DATA
dijet_cen_data_hist = grab_obj(
os.path.join(root_dir, qgc.JETHT_ZB_FILENAME),
histname) # use already merged jetht+zb
qcd_cen_kwargs_data = dict(line_color=qgc.JETHT_COLOUR,
line_width=data_line_width,
fill_color=qgc.JETHT_COLOUR,
marker_color=qgc.JETHT_COLOUR,
marker_style=cu.Marker.get(qgc.QCD_MARKER),
marker_size=msize,
label="Data")
entries.append((dijet_cen_data_hist, qcd_cen_kwargs_data))
# MG+PYTHIA QCD MC
dijet_mgpy_hist = grab_obj(os.path.join(root_dir, qgc.QCD_FILENAME),
histname)
qcd_cen_kwargs_mc = dict(line_color=qgc.QCD_COLOUR,
line_width=lw,
fill_color=qgc.QCD_COLOUR,
marker_color=qgc.QCD_COLOUR,
marker_style=cu.Marker.get(qgc.QCD_MARKER),
marker_size=0,
label="MG+PY8",
subplot=dijet_cen_data_hist)
entries.append((dijet_mgpy_hist, qcd_cen_kwargs_mc))
# HERWIG++ QCD
col2 = qgc.QCD_COLOURS[3]
dijet_hpp_hist = grab_obj(os.path.join(root_dir, qgc.QCD_HERWIG_FILENAME),
histname)
qcd_cen_kwargs_mc3 = dict(line_color=col2,
line_width=lw,
fill_color=col2,
marker_color=col2,
marker_style=cu.Marker.get(qgc.QCD_MARKER),
marker_size=0,
label="H++",
subplot=dijet_cen_data_hist)
dijet_hpp_hist.Scale(TOTAL_LUMI)
entries.append((dijet_hpp_hist, qcd_cen_kwargs_mc3))
# HERWIG++ QCD WITH PT REWEIGHT
col3 = ROOT.kRed
dijet_hpp_hist_reweight = grab_obj(
os.path.join(
root_dir,
"uhh2.AnalysisModuleRunner.MC.MC_HERWIG_QCD_PtReweight.root"),
histname)
qcd_cen_kwargs_mc4 = dict(line_color=col3,
line_width=lw,
fill_color=col3,
line_style=2,
marker_color=col3,
marker_style=cu.Marker.get(qgc.QCD_MARKER),
marker_size=0,
label="H++ (p_{T} reweighted)",
subplot=dijet_cen_data_hist)
dijet_hpp_hist_reweight.Scale(TOTAL_LUMI)
entries.append((dijet_hpp_hist_reweight, qcd_cen_kwargs_mc4))
# Must do un normed first, since it manipulates original objects
rebin = 1
p = qgp.make_comparison_plot_ingredients(entries,
rebin=rebin,
normalise_hist=False,
mean_rel_error=0.4,
title="%s\n%s" %
(jet_str, qgc.Dijet_LABEL),
xlim=[0, 4000],
ylim=[1E-1, 1E8],
subplot_type='ratio',
subplot_title="MC / Data",
subplot_limits=(0.5, 1.5))
output_filename = os.path.join(
root_dir, "plots_dijet_vs_qcd_mc_vs_data_pt_reweight",
"dj_%s.%s" % (histname.replace("/", "_"), OUTPUT_FMT))
draw_opt = "NOSTACK HISTE"
p.plot(draw_opt)
p.set_logx()
p.set_logy()
dirname = os.path.dirname(output_filename)
if not os.path.isdir(dirname):
os.makedirs(dirname)
p.save(output_filename)
p = qgp.make_comparison_plot_ingredients(entries,
rebin=rebin,
normalise_hist=True,
mean_rel_error=0.4,
title="%s\n%s" %
(jet_str, qgc.Dijet_LABEL),
xlim=[10, 4000],
ylim=[1E-10, 50],
subplot_type='ratio',
subplot_title="MC / Data",
subplot_limits=(0.5, 1.5))
output_filename = os.path.join(
root_dir, "plots_dijet_vs_qcd_mc_vs_data_pt_reweight",
"dj_%s_normed.%s" % (histname.replace("/", "_"), OUTPUT_FMT))
p.plot(draw_opt)
p.set_logx()
p.set_logy()
p.save(output_filename)
def do_plots(root_dir):
# QG variable plots
pt_bins = qgc.PT_BINS[:]
var_list = qgc.COMMON_VARS
var_prepend = ""
radius, pus = cu.get_jet_config_from_dirname(root_dir)
jet_str = "AK%s PF %s" % (radius.upper(), pus.upper())
for gr_append in ["", "_groomed"]:
if gr_append == "_groomed":
print("Doing groomed plots...")
else:
print("Doing ungroomed plots...")
if gr_append == "":
make_1d_plot(root_dir,
"Dijet_gen/pt_dijet_ave",
jet_str,
gen_only=True)
make_1d_plot(root_dir, "Dijet_tighter/pt_jet", jet_str)
dj_cen_dirname = "Dijet_QG_central_tighter%s" % (gr_append)
dj_fwd_dirname = "Dijet_QG_forward_tighter%s" % (gr_append)
for ang in var_list[:]:
print("...Doing", ang.name)
v = "%s%s_vs_pt" % (var_prepend, ang.var)
dijet_cen_2d_entries = []
dijet_fwd_2d_entries = []
dijet_cen_1d_entries = []
dijet_fwd_1d_entries = []
for pt_ind, (start_val, end_val) in enumerate(pt_bins):
entries = []
dijet_cen_entries = []
dijet_fwd_entries = []
# Get all plots
lw = 2
msize = 1.1
data_line_width = lw
####################
# DIJET CENTRAL REGION
####################
# JETHT/ZEROBIAS DATA
h2d_qcd_cen_data = grab_obj(
os.path.join(root_dir, qgc.JETHT_ZB_FILENAME), "%s/%s" %
(dj_cen_dirname, v)) # use already merged jetht+zb
qcd_cen_kwargs_data = dict(line_color=qgc.JETHT_COLOUR,
line_width=data_line_width,
fill_color=qgc.JETHT_COLOUR,
marker_color=qgc.JETHT_COLOUR,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=msize,
label="Data")
dijet_cen_data_hist = qgp.get_projection_plot(
h2d_qcd_cen_data, start_val, end_val)
entries.append((dijet_cen_data_hist, qcd_cen_kwargs_data))
dijet_cen_entries.append(
(dijet_cen_data_hist, qcd_cen_kwargs_data))
if pt_ind == 0:
dijet_cen_2d_entries.append(
(h2d_qcd_cen_data, qcd_cen_kwargs_data))
# MG+PYTHIA QCD MC
h2d_qcd_cen_mc = grab_obj(
os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/%s" % (dj_cen_dirname, v))
qcd_cen_kwargs_mc = dict(line_color=qgc.QCD_COLOUR,
line_width=lw,
fill_color=qgc.QCD_COLOUR,
marker_color=qgc.QCD_COLOUR,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=0,
label="MG+PY8",
subplot=dijet_cen_data_hist)
# h2d_qcd_cen_mc.Scale(35860)
dijet_mgpy_hist = qgp.get_projection_plot(
h2d_qcd_cen_mc, start_val, end_val)
entries.append((dijet_mgpy_hist, qcd_cen_kwargs_mc))
dijet_cen_entries.append((dijet_mgpy_hist, qcd_cen_kwargs_mc))
if pt_ind == 0:
dijet_cen_2d_entries.append(
(h2d_qcd_cen_mc, qcd_cen_kwargs_mc))
# PYTHIA ONLY
# col = qgc.QCD_COLOURS[2]
# h2d_qcd_cen_mc2 = grab_obj(os.path.join(root_dir, qgc.QCD_PYTHIA_ONLY_FILENAME), "%s/%s" % (dj_cen_dirname, v))
# qcd_cen_kwargs_mc2 = dict(line_color=col, line_width=lw, fill_color=col,
# marker_color=col, marker_style=cu.Marker.get(qgc.QCD_MARKER), marker_size=0,
# label="PY8",
# subplot=dijet_cen_data_hist)
# # h2d_qcd_cen_mc2.Scale(35860)
# entries.append((qgp.get_projection_plot(h2d_qcd_cen_mc2, start_val, end_val), qcd_cen_kwargs_mc2))
# dijet_cen_entries.append((qgp.get_projection_plot(h2d_qcd_cen_mc2, start_val, end_val), qcd_cen_kwargs_mc2))
# if pt_ind == 0:
# dijet_cen_2d_entries.append((h2d_qcd_cen_mc2, qcd_cen_kwargs_mc2))
# HERWIG++ QCD
col2 = qgc.QCD_COLOURS[3]
h2d_qcd_cen_mc3 = grab_obj(
os.path.join(root_dir, qgc.QCD_HERWIG_FILENAME),
"%s/%s" % (dj_cen_dirname, v))
qcd_cen_kwargs_mc3 = dict(line_color=col2,
line_width=lw,
fill_color=col2,
marker_color=col2,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=0,
label="H++",
subplot=dijet_cen_data_hist)
h2d_qcd_cen_mc3.Scale(TOTAL_LUMI)
dijet_hpp_hist = qgp.get_projection_plot(
h2d_qcd_cen_mc3, start_val, end_val)
entries.append((dijet_hpp_hist, qcd_cen_kwargs_mc3))
dijet_cen_entries.append((dijet_hpp_hist, qcd_cen_kwargs_mc3))
if pt_ind == 0:
dijet_cen_2d_entries.append(
(h2d_qcd_cen_mc3, qcd_cen_kwargs_mc3))
# HERWIG++ QCD WITH PT REWEIGHT
col3 = ROOT.kRed
h2d_qcd_cen_mc4 = grab_obj(
os.path.join(
root_dir,
"uhh2.AnalysisModuleRunner.MC.MC_HERWIG_QCD_PtReweight.root"
), "%s/%s" % (dj_cen_dirname, v))
qcd_cen_kwargs_mc4 = dict(line_color=col3,
line_width=lw,
fill_color=col3,
marker_color=col3,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=0,
label="H++ (p_{T} reweighted)",
subplot=dijet_cen_data_hist)
h2d_qcd_cen_mc4.Scale(TOTAL_LUMI)
dijet_hpp_hist_reweight = qgp.get_projection_plot(
h2d_qcd_cen_mc4, start_val, end_val)
entries.append((dijet_hpp_hist_reweight, qcd_cen_kwargs_mc4))
dijet_cen_entries.append(
(dijet_hpp_hist_reweight, qcd_cen_kwargs_mc4))
if pt_ind == 0:
dijet_cen_2d_entries.append(
(h2d_qcd_cen_mc4, qcd_cen_kwargs_mc4))
####################
# DIJET FORWARD REGION
####################
# JETHT/ZEROBIAS DATA
h2d_qcd_fwd_data = grab_obj(
os.path.join(root_dir, qgc.JETHT_ZB_FILENAME), "%s/%s" %
(dj_fwd_dirname, v)) # use already merged jetht+zb
# h2d_zb_data = grab_obj(os.path.join(root_dir, qgc.ZB_FILENAME), "%s/%s" % (dj_cen_dirname, v))
# h2d_zb_data.Scale(1235009.27580634)
# h2d_qcd_cen_data.Add(h2d_zb_data)
qcd_fwd_kwargs_data = dict(line_color=qgc.JETHT_COLOUR,
line_width=data_line_width,
fill_color=qgc.JETHT_COLOUR,
marker_color=qgc.JETHT_COLOUR,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=msize,
label="Data")
dijet_fwd_data_hist = qgp.get_projection_plot(
h2d_qcd_fwd_data, start_val, end_val)
entries.append((dijet_fwd_data_hist, qcd_fwd_kwargs_data))
dijet_fwd_entries.append(
(dijet_fwd_data_hist, qcd_fwd_kwargs_data))
if pt_ind == 0:
dijet_fwd_2d_entries.append(
(h2d_qcd_fwd_data, qcd_fwd_kwargs_data))
# MG+PYTHIA QCD MC
h2d_qcd_fwd_mc = grab_obj(
os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/%s" % (dj_fwd_dirname, v))
qcd_fwd_kwargs_mc = dict(line_color=qgc.QCD_COLOUR,
line_width=lw,
fill_color=qgc.QCD_COLOUR,
marker_color=qgc.QCD_COLOUR,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=0,
label="MG+PY8",
subplot=dijet_fwd_data_hist)
# h2d_qcd_fwd_mc.Scale(35860)
dijet_mgpy_hist = qgp.get_projection_plot(
h2d_qcd_fwd_mc, start_val, end_val)
entries.append((dijet_mgpy_hist, qcd_fwd_kwargs_mc))
dijet_fwd_entries.append((dijet_mgpy_hist, qcd_fwd_kwargs_mc))
if pt_ind == 0:
dijet_fwd_2d_entries.append(
(h2d_qcd_fwd_mc, qcd_fwd_kwargs_mc))
# PYTHIA ONLY
# col = qgc.QCD_COLOURS[2]
# h2d_qcd_fwd_mc2 = grab_obj(os.path.join(root_dir, qgc.QCD_PYTHIA_ONLY_FILENAME), "%s/%s" % (dj_fwd_dirname, v))
# qcd_fwd_kwargs_mc2 = dict(line_color=col, line_width=lw, fill_color=col,
# marker_color=col, marker_style=cu.Marker.get(qgc.QCD_MARKER), marker_size=0,
# label="PY8",
# subplot=dijet_fwd_data_hist)
# # h2d_qcd_fwd_mc2.Scale(35860)
# entries.append((qgp.get_projection_plot(h2d_qcd_fwd_mc2, start_val, end_val), qcd_fwd_kwargs_mc2))
# dijet_fwd_entries.append((qgp.get_projection_plot(h2d_qcd_fwd_mc2, start_val, end_val), qcd_fwd_kwargs_mc2))
# if pt_ind == 0:
# dijet_fwd_2d_entries.append((h2d_qcd_fwd_mc2, qcd_fwd_kwargs_mc2))
# HERWIG++ QCD
col2 = qgc.QCD_COLOURS[3]
h2d_qcd_fwd_mc3 = grab_obj(
os.path.join(root_dir, qgc.QCD_HERWIG_FILENAME),
"%s/%s" % (dj_fwd_dirname, v))
qcd_fwd_kwargs_mc3 = dict(line_color=col2,
line_width=lw,
fill_color=col2,
marker_color=col2,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=0,
label="H++",
subplot=dijet_fwd_data_hist)
h2d_qcd_fwd_mc3.Scale(TOTAL_LUMI)
dijet_hpp_hist = qgp.get_projection_plot(
h2d_qcd_fwd_mc3, start_val, end_val)
entries.append((dijet_hpp_hist, qcd_fwd_kwargs_mc3))
dijet_fwd_entries.append((dijet_hpp_hist, qcd_fwd_kwargs_mc3))
if pt_ind == 0:
dijet_fwd_2d_entries.append(
(h2d_qcd_fwd_mc3, qcd_fwd_kwargs_mc3))
col3 = ROOT.kRed
h2d_qcd_fwd_mc4 = grab_obj(
os.path.join(
root_dir,
"uhh2.AnalysisModuleRunner.MC.MC_HERWIG_QCD_PtReweight.root"
), "%s/%s" % (dj_fwd_dirname, v))
qcd_fwd_kwargs_mc4 = dict(line_color=col3,
line_width=lw,
fill_color=col3,
marker_color=col3,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=0,
label="H++ (p_{T} reweighted)",
subplot=dijet_fwd_data_hist)
h2d_qcd_fwd_mc4.Scale(TOTAL_LUMI)
dijet_hpp_hist_reweight = qgp.get_projection_plot(
h2d_qcd_fwd_mc4, start_val, end_val)
entries.append((dijet_hpp_hist_reweight, qcd_fwd_kwargs_mc4))
dijet_fwd_entries.append(
(dijet_hpp_hist_reweight, qcd_fwd_kwargs_mc4))
if pt_ind == 0:
dijet_fwd_2d_entries.append(
(h2d_qcd_fwd_mc4, qcd_fwd_kwargs_mc4))
dijet_cen_1d_entries.append(dijet_cen_entries)
dijet_fwd_1d_entries.append(dijet_fwd_entries)
#################
# SETUP PLOTTING
#################
rebin = 2
v_lower = v.lower()
if "multiplicity" in v_lower:
rebin = 2
elif "flavour" in v_lower or "thrust" in v_lower:
rebin = 1
elif 'ptd' in v_lower:
rebin = 2
xlim = None
if "width" in v_lower or "ptd" in v_lower:
xlim = (0, 1)
elif "thrust" in v_lower:
xlim = (0, 0.5)
elif "multiplicity" in v_lower and "ak4" in root_dir.lower():
if end_val <= 150:
xlim = (0, 50)
else:
xlim = (0, 80)
ylim = None
if "flavour" in v_lower:
ylim = (0, 1)
# elif "lha" in v_lower:
# ylim = (0, 5)
plot_dir = os.path.join(
root_dir, "plots_dijet_vs_qcd_mc_vs_data%s_pt_reweight" %
(gr_append))
subplot_title = "MC / Data"
subplot_limits = (0.5, 1.5)
xlabel = ang.name + " (" + ang.lambda_str + ")"
if gr_append is not "":
xlabel = "Groomed " + ang.name + " (" + ang.lambda_str + ")"
# dj+zpj
# qgp.do_comparison_plot(entries,
# "%s/ptBinned/%s_pt%dto%d.%s" % (plot_dir, v, start_val, end_val, OUTPUT_FMT),
# rebin=rebin,
# title="%d < p_{T}^{jet} < %d GeV\n%s" % (start_val, end_val, jet_str),
# xtitle=ang.name + " (" + ang.lambda_str + ")",
# xlim=xlim, ylim=ylim,
# subplot_type='ratio',
# subplot_title=subplot_title,
# subplot_limits=subplot_limits)
# dj central only
qgp.do_comparison_plot(
dijet_cen_entries,
"%s/ptBinned/%s_pt%dto%d_dijet_central.%s" %
(plot_dir, v, start_val, end_val, OUTPUT_FMT),
rebin=rebin,
title="%d < p_{T}^{jet} < %d GeV\n%s\n%s" %
(start_val, end_val, jet_str, qgc.Dijet_CEN_LABEL),
xtitle=xlabel,
xlim=xlim,
ylim=ylim,
subplot_type='ratio',
subplot_title=subplot_title,
subplot_limits=subplot_limits)
# dj forward only
qgp.do_comparison_plot(
dijet_fwd_entries,
"%s/ptBinned/%s_pt%dto%d_dijet_forward.%s" %
(plot_dir, v, start_val, end_val, OUTPUT_FMT),
rebin=rebin,
title="%d < p_{T}^{jet} < %d GeV\n%s\n%s" %
(start_val, end_val, jet_str, qgc.Dijet_FWD_LABEL),
xtitle=xlabel,
xlim=xlim,
ylim=ylim,
subplot_type='ratio',
subplot_title=subplot_title,
subplot_limits=subplot_limits)
# Do overall summary plots across all pt bins
# ------------------------------------------------------------------
ylim_mean = None
if "width" in v_lower or "ptd" in v_lower:
ylim_mean = (0, 0.4)
elif "thrust" in v_lower:
ylim_mean = (0, 0.5)
elif "multiplicity" in v_lower and "ak4" in root_dir.lower():
ylim_mean = (0, 100)
ylim_mean = (0, 80)
if end_val < 150:
ylim_mean = (0, 50)
ylim_mean = None
ylim_rms = None
# Setup variable names for MPL
marker = ""
if "_" in ang.name or "^" in ang.name:
marker = "$"
var_label = marker + ang.name + marker + " ($%s$)" % ang.lambda_str
if gr_append != "":
var_label = "Groomed " + marker + ang.name + marker + " ($%s$)" % ang.lambda_str
qgp.do_mean_rms_summary_plot(
dijet_cen_1d_entries[:],
pt_bins[:],
"%s/ptBinned/%s_box_dijet_cen_mpl.%s" %
(plot_dir, v, OUTPUT_FMT),
var_label=var_label,
xlim=(50, 2000),
ylim_mean=ylim_mean,
ylim_rms=ylim_rms,
region_title="%s jets in dijet (central)" % (jet_str))
qgp.do_mean_rms_summary_plot(
dijet_fwd_1d_entries[:],
pt_bins[:],
"%s/ptBinned/%s_box_dijet_fwd_mpl.%s" %
(plot_dir, v, OUTPUT_FMT),
var_label=var_label,
xlim=(50, 2000),
ylim_mean=ylim_mean,
ylim_rms=ylim_rms,
region_title="%s jets in dijet (forward)" % (jet_str))
def do_pt_min_delta_plots(sources,
plot_dir="deltas_ptmin",
zpj_dirname="ZPlusJets_QG",
dj_dirname="Dijet_QG",
var_list=None,
var_prepend="",
flavour_tag=False,
save_component_hists=False,
ofmt="pdf"):
"""Do plots comparing power of different ptMin cuts"""
var_list = var_list or COMMON_VARS
ptmin_bins = [50, 100, 200, 400, 800][:-1]
zpj_flav = "q" if flavour_tag else ""
dj_flav = "g" if flavour_tag else ""
output_append = "_flavMatched" if flavour_tag else ""
for ang in var_list:
v = "%s%s_vs_pt" % (var_prepend, ang.var)
graph_contribs, bin_labels = [], []
for source_ind, source in enumerate(sources):
deltas, components = [], []
# for the component comparison plot
colours = [
ROOT.kBlue, ROOT.kRed, ROOT.kGreen + 2, ROOT.kOrange - 3,
ROOT.kMagenta, ROOT.kAzure + 1
]
for pt_min, this_colour in zip(ptmin_bins, colours):
h2d_dyj = grab_obj(
os.path.join(source['root_dir'], qgc.DY_FILENAME),
"%s_ptMin_%d/%s%s" % (source.get(
'zpj_dirname', zpj_dirname), pt_min, zpj_flav, v))
h2d_qcd = grab_obj(
os.path.join(source['root_dir'],
qgc.QCD_FILENAME), "%s_ptMin_%d/%s%s" %
(source.get('dj_dirname', dj_dirname), pt_min, dj_flav, v))
start_val, end_val = 80, 2000
h_dy = get_projection_plot(h2d_dyj, start_val, end_val)
if (h_dy.Integral() > 0):
h_dy.Scale(1. / (h_dy.GetBinWidth(1) * h_dy.Integral()))
h_qcd = get_projection_plot(h2d_qcd, start_val, end_val)
if (h_qcd.Integral() > 0):
h_qcd.Scale(1. / (h_qcd.GetBinWidth(1) * h_qcd.Integral()))
ddelta_hist = get_ddelta_plot(h_dy, h_qcd)
c = Contribution(ddelta_hist,
line_width=1,
marker_color=this_colour,
line_color=this_colour,
fill_color=this_colour,
label="p_{T}^{Min} = %d GeV" % pt_min,
rebin_hist=1)
components.append(c)
deltas.append(calculate_delta(ddelta_hist))
if source_ind == 0:
bin_labels.append("%d" % pt_min)
if save_component_hists:
# need to clone here otherwise the colour will be set inside
# plot_delta, which in turn will nullify whatever colours we chose above.
plot_ddelta(
ddelta_hist.Clone(ROOT.TUUID().AsString()),
"%s/delta_ptmin_components/%s_ddelta_ptMin_%d%s.%s" %
(plot_dir, ang.var, pt_min, output_append, ofmt),
xtitle=ang.name + " (" + ang.lambda_str + ")",
ytitle="d#Delta/d" + ang.lambda_str)
if save_component_hists:
# plot all differential distributions for this pt bin on one plot
p = Plot(components,
what="hist",
xtitle=ang.name,
ytitle="p.d.f")
p.plot("NOSTACK HISTE")
p.save(
"%s/delta_ptmin_components/%s_ddelta_ptMin_comparison%s.%s"
% (plot_dir, ang.var, output_append, ofmt))
gr = construct_deltas_graph(deltas)
gr.SetName(source.get("label", ""))
if 'style' in source and 'line_width' not in source['style']:
source['style']['line_width'] = 2
c = Contribution(gr,
label=source.get("label", "").lstrip(", "),
marker_style=0,
**source.get("style", {}))
graph_contribs.append(c)
do_deltas_plot(graph_contribs,
"%s/ptMins_%s%s.%s" %
(plot_dir, ang.var, output_append, ofmt),
bin_labels=bin_labels,
title="%s [%s]" % (ang.name, ang.lambda_str),
xtitle="p_{T}^{min} [GeV]")
def do_plots(root_dir, title):
# QG variable plots
pt_bins = qgc.PT_BINS[:]
var_list = qgc.COMMON_VARS
var_prepend = ""
radius, pus = cu.get_jet_config_from_dirname(root_dir)
jet_str = "AK%s PF %s" % (radius.upper(), pus.upper())
for gr_append in ["", "_groomed"][:1]:
if gr_append == "_groomed":
print("Doing groomed plots...")
else:
print("Doing ungroomed plots...")
zpj_dirname = "ZPlusJets_QG%s" % (gr_append)
dj_cen_dirname = "Dijet_QG_central_tighter%s" % (gr_append)
dj_fwd_dirname = "Dijet_QG_forward_tighter%s" % (gr_append)
for ang in var_list[:]:
print("...Doing", ang.name)
v = "%s%s_vs_pt" % (var_prepend, ang.var)
zpj_2d_entries = []
dijet_cen_2d_entries = []
dijet_fwd_2d_entries = []
zpj_1d_entries = []
dijet_cen_1d_entries = []
dijet_fwd_1d_entries = []
for pt_ind, (start_val, end_val) in enumerate(pt_bins):
entries = []
zpj_entries = []
dijet_cen_entries = []
dijet_fwd_entries = []
# Get all plots
lw = 2
msize = 1.1
data_line_width = lw
mc_msize = 1E-3 # small enough to not be seen but not 0 - otherwise the horizontal lines on error bars don't get drawn
mgpy_label = "MG5+Pythia8"
hpp_label = "Herwig++"
####################
# Z+JETS REGION
####################
# TODO: add these
# SINGLE MU DATA
# if os.path.isfile(os.path.join(root_dir, qgc.SINGLE_MU_FILENAME)):
# h2d_dyj_data = grab_obj(os.path.join(root_dir, qgc.SINGLE_MU_FILENAME), "%s/%s" % (zpj_dirname, v))
# dy_kwargs_data = dict(line_color=qgc.SINGLE_MU_COLOUR, line_width=data_line_width, fill_color=qgc.SINGLE_MU_COLOUR,
# marker_color=qgc.SINGLE_MU_COLOUR, marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=msize*0.7,
# label="Data")
# zpj_data_hist = qgp.get_projection_plot(h2d_dyj_data, start_val, end_val)
# entries.append((zpj_data_hist, dy_kwargs_data))
# zpj_entries.append((zpj_data_hist, dy_kwargs_data))
# if pt_ind == 0:
# zpj_2d_entries.append((h2d_dyj_data, dy_kwargs_data))
# # PYTHIA DY MC
# if os.path.isfile(os.path.join(root_dir, qgc.DY_FILENAME)):
# h2d_dyj_mc = grab_obj(os.path.join(root_dir, qgc.DY_FILENAME), "%s/%s" % (zpj_dirname, v))
# dy_kwargs_mc = dict(line_color=qgc.DY_COLOUR, line_width=lw, fill_color=qgc.DY_COLOUR,
# marker_color=qgc.DY_COLOUR, marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=mc_msize,
# label=mgpy_label,
# subplot=zpj_data_hist)
# entries.append((qgp.get_projection_plot(h2d_dyj_mc, start_val, end_val), dy_kwargs_mc))
# zpj_entries.append((qgp.get_projection_plot(h2d_dyj_mc, start_val, end_val), dy_kwargs_mc))
# if pt_ind == 0:
# zpj_2d_entries.append((h2d_dyj_mc, dy_kwargs_mc))
# # HERWIG++ DY
# if os.path.isfile(os.path.join(root_dir, qgc.DY_HERWIG_FILENAME)):
# h2d_dyj_mc_hpp = grab_obj(os.path.join(root_dir, qgc.DY_HERWIG_FILENAME), "%s/%s" % (zpj_dirname, v))
# col_hpp = qgc.DY_COLOURS[2]
# dy_kwargs_mc_hpp = dict(line_color=col_hpp, line_width=lw, fill_color=col_hpp,
# marker_color=col_hpp, marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=mc_msize,
# label=hpp_label,
# subplot=zpj_data_hist)
# entries.append((qgp.get_projection_plot(h2d_dyj_mc_hpp, start_val, end_val), dy_kwargs_mc_hpp))
# zpj_entries.append((qgp.get_projection_plot(h2d_dyj_mc_hpp, start_val, end_val), dy_kwargs_mc_hpp))
# if pt_ind == 0:
# zpj_2d_entries.append((h2d_dyj_mc_hpp, dy_kwargs_mc_hpp))
# MG+HERWIG++ DY
# if end_val < 151:
# h2d_dyj_mc3 = grab_obj(os.path.join(root_dir, qgc.DY_MG_HERWIG_FILENAME), "%s/%s" % (zpj_dirname, v))
# col4 = qgc.DY_COLOURS[3]
# dy_kwargs_mc3 = dict(line_color=col4, line_width=lw, fill_color=col4,
# marker_color=col4, marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=0,
# label="MG+Herwig++",
# subplot=zpj_data_hist)
# entries.append((qgp.get_projection_plot(h2d_dyj_mc3, start_val, end_val), dy_kwargs_mc3))
# zpj_entries.append((qgp.get_projection_plot(h2d_dyj_mc3, start_val, end_val), dy_kwargs_mc3))
# if pt_ind == 0:
# zpj_2d_entries.append((h2d_dyj_mc3, dy_kwargs_mc3))
# else:
# zpj_entries.append(None)
####################
# DIJET CENTRAL REGION
####################
# JETHT/ZEROBIAS DATA
if os.path.isfile(os.path.join(root_dir,
qgc.JETHT_ZB_FILENAME)):
h2d_qcd_cen_data = grab_obj(
os.path.join(root_dir,
qgc.JETHT_ZB_FILENAME), "%s/%s" %
(dj_cen_dirname, v)) # use already merged jetht+zb
qcd_cen_kwargs_data = dict(line_color=qgc.JETHT_COLOUR,
line_width=data_line_width,
fill_color=qgc.JETHT_COLOUR,
marker_color=qgc.JETHT_COLOUR,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=msize,
label="Data")
dijet_cen_data_hist = qgp.get_projection_plot(
h2d_qcd_cen_data, start_val, end_val)
entries.append((dijet_cen_data_hist, qcd_cen_kwargs_data))
dijet_cen_entries.append(
(dijet_cen_data_hist, qcd_cen_kwargs_data))
if pt_ind == 0:
dijet_cen_2d_entries.append(
(h2d_qcd_cen_data, qcd_cen_kwargs_data))
# MG+PYTHIA QCD MC
if os.path.isfile(os.path.join(root_dir, qgc.QCD_FILENAME)):
h2d_qcd_cen_mc = grab_obj(
os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/%s" % (dj_cen_dirname, v))
qcd_cen_kwargs_mc = dict(
line_color=qgc.QCD_COLOUR,
line_width=lw,
fill_color=qgc.QCD_COLOUR,
marker_color=qgc.QCD_COLOUR,
marker_style=cu.Marker.get(qgc.QCD_MARKER),
marker_size=mc_msize,
label=mgpy_label + "\n[NNPDF30_lo_as_0130, default]",
subplot=dijet_cen_data_hist)
dijet_mgpy_hist = qgp.get_projection_plot(
h2d_qcd_cen_mc, start_val, end_val)
entries.append((dijet_mgpy_hist, qcd_cen_kwargs_mc))
dijet_cen_entries.append(
(dijet_mgpy_hist, qcd_cen_kwargs_mc))
if pt_ind == 0:
dijet_cen_2d_entries.append(
(h2d_qcd_cen_mc, qcd_cen_kwargs_mc))
# Add NNPDF30_lo_as_0118
fname = "uhh2.AnalysisModuleRunner.MC.MC_QCD_NNPDF30_lo_as_0118.root"
if os.path.isfile(os.path.join(root_dir, fname)):
h2d_qcd_cen_mc_pdf2 = grab_obj(
os.path.join(root_dir, fname),
"%s/%s" % (dj_cen_dirname, v))
col = qgc.QCD_COLOURS[4]
qcd_cen_kwargs_mc2 = dict(
line_color=col,
line_width=lw,
fill_color=col,
marker_color=col,
marker_style=cu.Marker.get(qgc.QCD_MARKER),
marker_size=mc_msize,
label=mgpy_label + "\n[NNPDF30_lo_as_0118]",
subplot=dijet_cen_data_hist)
dijet_mgpy_hist2 = qgp.get_projection_plot(
h2d_qcd_cen_mc_pdf2, start_val, end_val)
entries.append((dijet_mgpy_hist2, qcd_cen_kwargs_mc2))
dijet_cen_entries.append(
(dijet_mgpy_hist2, qcd_cen_kwargs_mc2))
if pt_ind == 0:
dijet_cen_2d_entries.append(
(h2d_qcd_cen_mc_pdf2, qcd_cen_kwargs_mc2))
# Add HERAPDF20_LO_EIG
fname = "uhh2.AnalysisModuleRunner.MC.MC_QCD_HERAPDF20_LO_EIG.root"
if os.path.isfile(os.path.join(root_dir, fname)):
h2d_qcd_cen_mc_pdf3 = grab_obj(
os.path.join(root_dir, fname),
"%s/%s" % (dj_cen_dirname, v))
col = qgc.QCD_COLOURS[5]
qcd_cen_kwargs_mc3 = dict(
line_color=col,
line_width=lw,
fill_color=col,
marker_color=col,
marker_style=cu.Marker.get(qgc.QCD_MARKER),
marker_size=mc_msize,
label=mgpy_label + "\n[HERAPDF20_LO_EIG]",
subplot=dijet_cen_data_hist)
dijet_mgpy_hist3 = qgp.get_projection_plot(
h2d_qcd_cen_mc_pdf3, start_val, end_val)
entries.append((dijet_mgpy_hist3, qcd_cen_kwargs_mc3))
dijet_cen_entries.append(
(dijet_mgpy_hist3, qcd_cen_kwargs_mc3))
if pt_ind == 0:
dijet_cen_2d_entries.append(
(h2d_qcd_cen_mc_pdf3, qcd_cen_kwargs_mc3))
# Add CT14lo
fname = "uhh2.AnalysisModuleRunner.MC.MC_QCD_CT14lo.root"
if os.path.isfile(os.path.join(root_dir, fname)):
h2d_qcd_cen_mc_pdf4 = grab_obj(
os.path.join(root_dir, fname),
"%s/%s" % (dj_cen_dirname, v))
col = qgc.QCD_COLOURS[6]
qcd_cen_kwargs_mc4 = dict(line_color=col,
line_width=lw,
fill_color=col,
marker_color=col,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=mc_msize,
label=mgpy_label + "\n[CT14lo]",
subplot=dijet_cen_data_hist)
dijet_mgpy_hist4 = qgp.get_projection_plot(
h2d_qcd_cen_mc_pdf4, start_val, end_val)
entries.append((dijet_mgpy_hist4, qcd_cen_kwargs_mc4))
dijet_cen_entries.append(
(dijet_mgpy_hist4, qcd_cen_kwargs_mc4))
if pt_ind == 0:
dijet_cen_2d_entries.append(
(h2d_qcd_cen_mc_pdf4, qcd_cen_kwargs_mc4))
# PYTHIA ONLY
# if os.path.isfile(os.path.join(root_dir, qgc.QCD_PYTHIA_ONLY_FILENAME)):
# col = qgc.QCD_COLOURS[2]
# h2d_qcd_cen_mc2 = grab_obj(os.path.join(root_dir, qgc.QCD_PYTHIA_ONLY_FILENAME), "%s/%s" % (dj_cen_dirname, v))
# qcd_cen_kwargs_mc2 = dict(line_color=col, line_width=lw, fill_color=col,
# marker_color=col, marker_style=cu.Marker.get(qgc.QCD_MARKER), marker_size=mc_msize,
# label="Pythia8",
# subplot=dijet_cen_data_hist)
# entries.append((qgp.get_projection_plot(h2d_qcd_cen_mc2, start_val, end_val), qcd_cen_kwargs_mc2))
# dijet_cen_entries.append((qgp.get_projection_plot(h2d_qcd_cen_mc2, start_val, end_val), qcd_cen_kwargs_mc2))
# if pt_ind == 0:
# dijet_cen_2d_entries.append((h2d_qcd_cen_mc2, qcd_cen_kwargs_mc2))
# HERWIG++ QCD
if os.path.isfile(
os.path.join(root_dir, qgc.QCD_HERWIG_FILENAME)):
col_hpp = qgc.QCD_COLOURS[3]
h2d_qcd_cen_mc_hpp = grab_obj(
os.path.join(root_dir, qgc.QCD_HERWIG_FILENAME),
"%s/%s" % (dj_cen_dirname, v))
qcd_cen_kwargs_mc_hpp = dict(line_color=col_hpp,
line_width=lw,
fill_color=col_hpp,
marker_color=col_hpp,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=mc_msize,
label=hpp_label,
subplot=dijet_cen_data_hist)
dijet_hpp_hist = qgp.get_projection_plot(
h2d_qcd_cen_mc_hpp, start_val, end_val)
entries.append((dijet_hpp_hist, qcd_cen_kwargs_mc_hpp))
dijet_cen_entries.append(
(dijet_hpp_hist, qcd_cen_kwargs_mc_hpp))
if pt_ind == 0:
dijet_cen_2d_entries.append(
(h2d_qcd_cen_mc_hpp, qcd_cen_kwargs_mc_hpp))
####################
# DIJET FORWARD REGION
####################
# JETHT/ZEROBIAS DATA
if os.path.isfile(os.path.join(root_dir,
qgc.JETHT_ZB_FILENAME)):
h2d_qcd_fwd_data = grab_obj(
os.path.join(root_dir,
qgc.JETHT_ZB_FILENAME), "%s/%s" %
(dj_fwd_dirname, v)) # use already merged jetht+zb
qcd_fwd_kwargs_data = dict(
line_color=qgc.JETHT_COLOUR,
line_width=data_line_width,
fill_color=qgc.JETHT_COLOUR,
marker_color=qgc.JETHT_COLOUR,
marker_style=cu.Marker.get('triangleDown'),
marker_size=msize,
label="Data")
dijet_fwd_data_hist = qgp.get_projection_plot(
h2d_qcd_fwd_data, start_val, end_val)
entries.append((dijet_fwd_data_hist, qcd_fwd_kwargs_data))
dijet_fwd_entries.append(
(dijet_fwd_data_hist, qcd_fwd_kwargs_data))
if pt_ind == 0:
dijet_fwd_2d_entries.append(
(h2d_qcd_fwd_data, qcd_fwd_kwargs_data))
# MG+PYTHIA QCD MC
if os.path.isfile(os.path.join(root_dir, qgc.QCD_FILENAME)):
h2d_qcd_fwd_mc = grab_obj(
os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/%s" % (dj_fwd_dirname, v))
qcd_fwd_kwargs_mc = dict(
line_color=qgc.QCD_COLOUR,
line_width=lw,
fill_color=qgc.QCD_COLOUR,
marker_color=qgc.QCD_COLOUR,
marker_style=cu.Marker.get(qgc.QCD_MARKER),
marker_size=mc_msize,
label=mgpy_label + "\n[NNPDF30_lo_as_0130, default]",
subplot=dijet_fwd_data_hist)
dijet_mgpy_hist = qgp.get_projection_plot(
h2d_qcd_fwd_mc, start_val, end_val)
entries.append((dijet_mgpy_hist, qcd_fwd_kwargs_mc))
dijet_fwd_entries.append(
(dijet_mgpy_hist, qcd_fwd_kwargs_mc))
if pt_ind == 0:
dijet_fwd_2d_entries.append(
(h2d_qcd_fwd_mc, qcd_fwd_kwargs_mc))
# Add NNPDF30_lo_as_0118
fname = "uhh2.AnalysisModuleRunner.MC.MC_QCD_NNPDF30_lo_as_0118.root"
if os.path.isfile(os.path.join(root_dir, fname)):
h2d_qcd_fwd_mc_pdf2 = grab_obj(
os.path.join(root_dir, fname),
"%s/%s" % (dj_fwd_dirname, v))
col = qgc.QCD_COLOURS[4]
qcd_fwd_kwargs_mc2 = dict(
line_color=col,
line_width=lw,
fill_color=col,
marker_color=col,
marker_style=cu.Marker.get(qgc.QCD_MARKER),
marker_size=mc_msize,
label=mgpy_label + "\n[NNPDF30_lo_as_0118]",
subplot=dijet_fwd_data_hist)
dijet_mgpy_hist2 = qgp.get_projection_plot(
h2d_qcd_fwd_mc_pdf2, start_val, end_val)
entries.append((dijet_mgpy_hist2, qcd_fwd_kwargs_mc2))
dijet_fwd_entries.append(
(dijet_mgpy_hist2, qcd_fwd_kwargs_mc2))
if pt_ind == 0:
dijet_fwd_2d_entries.append(
(h2d_qcd_fwd_mc_pdf2, qcd_fwd_kwargs_mc2))
# Add HERAPDF20_LO_EIG
fname = "uhh2.AnalysisModuleRunner.MC.MC_QCD_HERAPDF20_LO_EIG.root"
if os.path.isfile(os.path.join(root_dir, fname)):
h2d_qcd_fwd_mc_pdf3 = grab_obj(
os.path.join(root_dir, fname),
"%s/%s" % (dj_fwd_dirname, v))
col = qgc.QCD_COLOURS[5]
qcd_fwd_kwargs_mc3 = dict(
line_color=col,
line_width=lw,
fill_color=col,
marker_color=col,
marker_style=cu.Marker.get(qgc.QCD_MARKER),
marker_size=mc_msize,
label=mgpy_label + "\n[HERAPDF20_LO_EIG]",
subplot=dijet_fwd_data_hist)
dijet_mgpy_hist3 = qgp.get_projection_plot(
h2d_qcd_fwd_mc_pdf3, start_val, end_val)
entries.append((dijet_mgpy_hist3, qcd_fwd_kwargs_mc3))
dijet_fwd_entries.append(
(dijet_mgpy_hist3, qcd_fwd_kwargs_mc3))
if pt_ind == 0:
dijet_fwd_2d_entries.append(
(h2d_qcd_fwd_mc_pdf3, qcd_fwd_kwargs_mc3))
# Add CT14lo
fname = "uhh2.AnalysisModuleRunner.MC.MC_QCD_CT14lo.root"
if os.path.isfile(os.path.join(root_dir, fname)):
h2d_qcd_fwd_mc_pdf4 = grab_obj(
os.path.join(root_dir, fname),
"%s/%s" % (dj_fwd_dirname, v))
col = qgc.QCD_COLOURS[6]
qcd_fwd_kwargs_mc4 = dict(line_color=col,
line_width=lw,
fill_color=col,
marker_color=col,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=mc_msize,
label=mgpy_label + "\n[CT14lo]",
subplot=dijet_fwd_data_hist)
dijet_mgpy_hist4 = qgp.get_projection_plot(
h2d_qcd_fwd_mc_pdf4, start_val, end_val)
entries.append((dijet_mgpy_hist4, qcd_fwd_kwargs_mc4))
dijet_fwd_entries.append(
(dijet_mgpy_hist4, qcd_fwd_kwargs_mc4))
if pt_ind == 0:
dijet_fwd_2d_entries.append(
(h2d_qcd_fwd_mc_pdf4, qcd_fwd_kwargs_mc4))
# PYTHIA ONLY
# if os.path.isfile(os.path.join(root_dir, qgc.QCD_PYTHIA_ONLY_FILENAME)):
# col = qgc.QCD_COLOURS[2]
# h2d_qcd_fwd_mc2 = grab_obj(os.path.join(root_dir, qgc.QCD_PYTHIA_ONLY_FILENAME), "%s/%s" % (dj_fwd_dirname, v))
# qcd_fwd_kwargs_mc2 = dict(line_color=col, line_width=lw, fill_color=col,
# marker_color=col, marker_style=cu.Marker.get(qgc.QCD_MARKER), marker_size=mc_msize,
# label="Pythia8",
# subplot=dijet_fwd_data_hist)
# entries.append((qgp.get_projection_plot(h2d_qcd_fwd_mc2, start_val, end_val), qcd_fwd_kwargs_mc2))
# dijet_fwd_entries.append((qgp.get_projection_plot(h2d_qcd_fwd_mc2, start_val, end_val), qcd_fwd_kwargs_mc2))
# if pt_ind == 0:
# dijet_fwd_2d_entries.append((h2d_qcd_fwd_mc2, qcd_fwd_kwargs_mc2))
# HERWIG++ QCD
if os.path.isfile(
os.path.join(root_dir, qgc.QCD_HERWIG_FILENAME)):
col_hpp = qgc.QCD_COLOURS[3]
h2d_qcd_fwd_mc_hpp = grab_obj(
os.path.join(root_dir, qgc.QCD_HERWIG_FILENAME),
"%s/%s" % (dj_fwd_dirname, v))
qcd_fwd_kwargs_mc_hpp = dict(line_color=col_hpp,
line_width=lw,
fill_color=col_hpp,
marker_color=col_hpp,
marker_style=cu.Marker.get(
qgc.QCD_MARKER),
marker_size=mc_msize,
label=hpp_label,
subplot=dijet_fwd_data_hist)
dijet_hpp_hist = qgp.get_projection_plot(
h2d_qcd_fwd_mc_hpp, start_val, end_val)
entries.append((dijet_hpp_hist, qcd_fwd_kwargs_mc_hpp))
dijet_fwd_entries.append(
(dijet_hpp_hist, qcd_fwd_kwargs_mc_hpp))
if pt_ind == 0:
dijet_fwd_2d_entries.append(
(h2d_qcd_fwd_mc_hpp, qcd_fwd_kwargs_mc_hpp))
zpj_1d_entries.append(zpj_entries)
dijet_cen_1d_entries.append(dijet_cen_entries)
dijet_fwd_1d_entries.append(dijet_fwd_entries)
#################
# SETUP PLOTTING
#################
rebin = 2
v_lower = v.lower()
# if "multiplicity" in v_lower:
# rebin = 1
# elif "flavour" in v_lower or "thrust" in v_lower:
# rebin = 1
# elif 'ptd' in v_lower:
# rebin = 4
# elif 'lha_charged' in v_lower:
# rebin = 4
# rebin = 1
xlim = None
if "width" in v_lower or "ptd" in v_lower:
xlim = (0, 1)
elif "thrust" in v_lower:
xlim = (0, 0.5)
elif "multiplicity" in v_lower and "ak4" in root_dir.lower():
if end_val <= 150:
xlim = (0, 50)
else:
xlim = (0, 80)
auto_xlim = False
if "multiplicity" in v_lower or "thrust" in v_lower:
auto_xlim = True
ylim = [0, None]
if "flavour" in v_lower:
ylim = (0, 1)
# elif "lha" in v_lower:
# ylim = (0, 5)
plot_dir = os.path.join(
root_dir,
"plots_lambda_mc_vs_data%s_pdf_compare" % (gr_append))
subplot_title = "Simulation / Data"
subplot_limits = (0, 2)
xlabel = ang.name + " (" + ang.lambda_str + ")"
if gr_append is not "":
xlabel = "Groomed " + ang.name + " (" + ang.lambda_str + ")"
def _title(region_str, start_val, end_val):
pt_var_str = "p_{T}^{jet}"
s = (("{jet_algo}\n"
"{region_label}\n"
"{bin_edge_low:g} < {pt_str} < {bin_edge_high:g} GeV"
).format(jet_algo=jet_str,
region_label=region_str,
pt_str=pt_var_str,
bin_edge_low=start_val,
bin_edge_high=end_val))
if title is not None:
s = "%s\n%s" % (s, title)
return s
has_data = True
draw_opt = "NOSTACK HIST E1"
# dj central only
# dont' try to do multiple signal regions per plot, it looks rubbish
if len(dijet_cen_entries) > 0:
qgp.do_comparison_plot(
dijet_cen_entries,
"%s/ptBinned/%s_pt%dto%d_dijet_central.%s" %
(plot_dir, v, start_val, end_val, OUTPUT_FMT),
rebin=rebin,
draw_opt=draw_opt,
title=_title(qgc.Dijet_CEN_LABEL, start_val, end_val),
xtitle=xlabel,
xlim=qgp.calc_auto_xlim([
d[0] for d in dijet_cen_entries
]) if auto_xlim else xlim,
ylim=ylim,
data_first=has_data,
mean_rel_error=0.4,
subplot_type='ratio',
subplot_title=subplot_title,
subplot_limits=subplot_limits)
# dj forward only
if len(dijet_fwd_entries) > 0:
qgp.do_comparison_plot(
dijet_fwd_entries,
"%s/ptBinned/%s_pt%dto%d_dijet_forward.%s" %
(plot_dir, v, start_val, end_val, OUTPUT_FMT),
rebin=rebin,
draw_opt=draw_opt,
title=_title(qgc.Dijet_FWD_LABEL, start_val, end_val),
xtitle=xlabel,
xlim=qgp.calc_auto_xlim([
d[0] for d in dijet_fwd_entries
]) if auto_xlim else xlim,
ylim=ylim,
data_first=has_data,
mean_rel_error=0.4,
subplot_type='ratio',
subplot_title=subplot_title,
subplot_limits=subplot_limits)
# zpj only
if len(zpj_entries) > 0:
if start_val > 149:
# rebin *= 2
rebin += 1
# find nearest divisor
while (zpj_entries[0][0].GetNbinsX() % rebin != 0):
rebin += 1
if "multiplicity" in v_lower:
if start_val > 300:
rebin += 1
# find nearest divisor
while (zpj_entries[0][0].GetNbinsX() % rebin != 0):
rebin += 1
qgp.do_comparison_plot(
zpj_entries,
"%s/ptBinned/%s_pt%dto%d_zpj.%s" %
(plot_dir, v, start_val, end_val, OUTPUT_FMT),
rebin=rebin,
draw_opt=draw_opt,
title=_title(qgc.ZpJ_LABEL, start_val, end_val),
xtitle=xlabel,
xlim=qgp.calc_auto_xlim([d[0] for d in zpj_entries])
if auto_xlim else xlim,
ylim=ylim,
data_first=has_data,
mean_rel_error=0.4,
subplot_type='ratio',
subplot_title=subplot_title,
subplot_limits=subplot_limits)
# Do overall summary plots across all pt bins
# ------------------------------------------------------------------
ylim_mean = None
if "width" in v_lower or "ptd" in v_lower:
ylim_mean = (0, 0.4)
elif "thrust" in v_lower:
ylim_mean = (0, 0.5)
elif "multiplicity" in v_lower and "ak4" in root_dir.lower():
ylim_mean = (0, 100)
ylim_mean = (0, 80)
if end_val < 150:
ylim_mean = (0, 50)
ylim_mean = None
ylim_rms = None
# Setup variable names for MPL
marker = ""
if "_" in ang.name or "^" in ang.name:
marker = "$"
var_label = marker + ang.name + marker + " ($%s$)" % ang.lambda_str
if gr_append != "":
var_label = "Groomed " + marker + ang.name + marker + " ($%s$)" % ang.lambda_str
def do_angularity_delta_plots(sources,
plot_dir="delta_angularities",
zpj_dirname="ZPlusJets_QG",
dj_dirname="Dijet_QG",
var_list=None,
var_prepend="",
pt_bins=None,
flavour_tag=False,
save_component_hists=False,
ofmt="pdf"):
"""Do plots comparing power of different angularities"""
var_list = var_list or COMMON_VARS
pt_bins = pt_bins or PT_BINS
h2d_dyj = None
h2d_qcd = None
zpj_flav = "q" if flavour_tag else ""
dj_flav = "g" if flavour_tag else ""
output_append = "_flavMatched" if flavour_tag else ""
for (start_val, end_val) in pt_bins:
graph_contribs, bin_labels = [], []
for source_ind, source in enumerate(sources):
deltas, components = [], []
# construct a graph of angularities for this source
for ang in var_list:
v = "%s%s_vs_pt" % (var_prepend, ang.var)
h2d_dyj = grab_obj(
os.path.join(source['root_dir'],
qgc.DY_FILENAME), "%s/%s%s" %
(source.get('zpj_dirname', zpj_dirname), zpj_flav, v))
h2d_qcd = grab_obj(
os.path.join(source['root_dir'],
qgc.QCD_FILENAME), "%s/%s%s" %
(source.get('dj_dirname', dj_dirname), dj_flav, v))
h_dy = get_projection_plot(h2d_dyj, start_val, end_val)
if (h_dy.Integral() > 0):
h_dy.Scale(1. / (h_dy.GetBinWidth(1) * h_dy.Integral()))
h_qcd = get_projection_plot(h2d_qcd, start_val, end_val)
if (h_qcd.Integral() > 0):
h_qcd.Scale(1. / (h_qcd.GetBinWidth(1) * h_qcd.Integral()))
ddelta_hist = get_ddelta_plot(h_dy, h_qcd)
deltas.append(calculate_delta(ddelta_hist))
if source_ind == 0:
bin_labels.append("#splitline{%s}{%s}" %
(ang.name, ang.lambda_str))
if save_component_hists:
plot_ddelta(
ddelta_hist.Clone(ddelta_hist.GetName() + "x"),
"%s/delta_angularities_components/angularities_pt%dto%d_ddelta_%s_source%d%s.%s"
% (plot_dir, start_val, end_val, ang.var, source_ind,
output_append, ofmt),
xtitle=ang.name + " (" + ang.lambda_str + ")",
ytitle="d#Delta/d" + ang.lambda_str)
if ang.var != "jet_multiplicity":
c = Contribution(ddelta_hist,
line_width=1,
marker_color=ang.colour,
line_color=ang.colour,
fill_color=ang.colour,
label=ang.name + " (" + ang.lambda_str +
")",
rebin_hist=1)
components.append(c)
if save_component_hists:
# plot all differential distributions for this pt bin on one plot
p = Plot(components,
what="hist",
xtitle="#lambda^{#kappa}_{#beta}",
ytitle="d#Delta/d#lambda",
title="%d < p_{T}^{jet} < %d GeV" %
(start_val, end_val))
p.plot("NOSTACK HISTE")
prefix = "pt%dto%d" % (start_val, end_val)
p.save(
"%s/delta_angularities_components/%s_ddelta_angularity_comparison_source%d%s.%s"
% (plot_dir, prefix, source_ind, output_append, ofmt))
gr = construct_deltas_graph(deltas)
gr.SetName(source.get("label", ""))
if 'style' in source and 'line_width' not in source['style']:
source['style']['line_width'] = 2
c = Contribution(gr,
label=source.get("label", "").lstrip(", "),
marker_style=0,
**source.get("style", {}))
graph_contribs.append(c)
do_deltas_plot(graph_contribs,
"%s/angularities_pt%dto%d%s.%s" %
(plot_dir, start_val, end_val, output_append, ofmt),
bin_labels=bin_labels,
title="%d < p_{T}^{jet} < %d GeV" %
(start_val, end_val),
xtitle="Angularity: (#kappa, #beta)")
def do_wrong_plots(root_dir,
var_prepend="",
plot_dir="wrong_flavs",
zpj_dirname="ZPlusJets_QG",
dj_dirname="Dijet_QG",
pt_bins=None,
title=""):
"""Plot all the sample/selection/flavour combinations to check distributions indep of sample"""
pt_bins = pt_bins or qgc.THEORY_PT_BINS
plot_vars = [
'jet_LHA', 'jet_pTD', 'jet_width', 'jet_thrust', 'jet_multiplicity'
]
if "puppi" in root_dir.lower():
plot_vars.append('jet_puppiMultiplicity')
for v in plot_vars:
v = "%s%s_vs_pt" % (var_prepend, v)
h2d_dyj_chs = grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/q%s" % (zpj_dirname, v))
h2d_dyj_wrong_chs = grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/g%s" % (zpj_dirname, v))
h2d_dyj_qcd_chs = grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/q%s" % (dj_dirname, v))
h2d_dyj_qcd_wrong_chs = grab_obj(
os.path.join(root_dir, qgc.DY_FILENAME),
"%s/g%s" % (dj_dirname, v))
h2d_qcd_chs = grab_obj(os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/g%s" % (dj_dirname, v))
h2d_qcd_wrong_chs = grab_obj(os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/q%s" % (dj_dirname, v))
lw = 1
dy_kwargs_chs = dict(line_color=qgc.DY_COLOUR,
fill_color=qgc.DY_COLOUR,
label=qgc.DY_ZpJ_QFLAV_LABEL,
line_width=lw,
marker_style=20,
marker_color=qgc.DY_COLOUR)
dy_kwargs_wrong_chs = dict(line_color=qgc.DY_COLOUR + 4,
fill_color=qgc.DY_COLOUR + 4,
label=qgc.DY_ZpJ_GFLAV_LABEL,
line_width=lw,
line_style=1,
marker_style=21,
marker_color=qgc.DY_COLOUR + 4)
dy_kwargs_qcd_chs = dict(line_color=ROOT.kGreen + 2,
fill_color=ROOT.kGreen + 2,
label=qgc.DY_Dijet_QFLAV_LABEL,
line_width=lw,
line_style=1,
marker_style=22,
marker_color=ROOT.kGreen + 2)
dy_kwargs_qcd_wrong_chs = dict(line_color=ROOT.kOrange - 1,
fill_color=ROOT.kOrange - 1,
label=qgc.DY_Dijet_GFLAV_LABEL,
line_width=lw,
line_style=1,
marker_style=23,
marker_color=ROOT.kOrange - 1)
qcd_kwargs_chs = dict(line_color=qgc.QCD_COLOUR,
fill_color=qgc.QCD_COLOUR,
label=qgc.QCD_Dijet_GFLAV_LABEL,
line_width=lw,
marker_style=25,
marker_color=qgc.QCD_COLOUR)
qcd_kwargs_wrong_chs = dict(line_color=ROOT.kRed,
fill_color=ROOT.kRed,
label=qgc.QCD_Dijet_QFLAV_LABEL,
line_width=lw,
line_style=1,
marker_style=24,
marker_color=ROOT.kRed)
rebin = 2
xlim = None
if "thrust" in v:
rebin = 1
xlim = (0, 0.5)
if "multiplicity" in v.lower():
rebin = 2
xlim = (0, 100)
for (start_val, end_val) in pt_bins:
entries = [
(qgg.get_projection_plot(h2d_dyj_chs, start_val,
end_val), dy_kwargs_chs),
(qgg.get_projection_plot(h2d_dyj_wrong_chs, start_val,
end_val), dy_kwargs_wrong_chs),
# (qgg.get_projection_plot(h2d_dyj_qcd_chs, start_val, end_val), dy_kwargs_qcd_chs),
# (qgg.get_projection_plot(h2d_dyj_qcd_wrong_chs, start_val, end_val), dy_kwargs_qcd_wrong_chs),
(qgg.get_projection_plot(h2d_qcd_wrong_chs, start_val,
end_val), qcd_kwargs_wrong_chs),
(qgg.get_projection_plot(h2d_qcd_chs, start_val,
end_val), qcd_kwargs_chs),
]
this_title = "%d < p_{T}^{jet} < %d GeV" % (start_val, end_val)
if title != "":
this_title += ", %s" % title
qgg.do_comparison_plot(
entries,
"%s/%s/%s_pt%dto%d_flavMatched.%s" %
(root_dir, plot_dir, v, start_val, end_val, OUTPUT_FMT),
rebin=rebin,
title=this_title,
xlim=xlim)
def do_component_plots(mgpythia_dir, pythia_only_dir, plot_dir):
# Do 2D plots for diff ptMin folders
jet_flavs = ["", "g", "q"]
ptmin_vals = [50, 100, 200, 400, 800]
ptmin_vals = [100, 800]
# first the pt vs ones:
plotnames = [
"genjet_pt_vs_constituent_pt", "genjet_pt_vs_constituent_zi",
"genjet_pt_vs_constituent_deta", "genjet_pt_vs_constituent_dphi",
"genjet_pt_vs_constituent_dr"
]
for plotname, ptmin, renorm, logz, flav in product(plotnames, ptmin_vals,
['Y', None],
[True, False],
jet_flavs):
rebin = [1, 1]
ylim = [ptmin, 2000]
dirname = "Dijet_genjet_ptMin_%d" % ptmin
this_hist = os.path.join(dirname, flav + plotname)
log_append = "_logZ" if logz else ""
qgg.do_2D_plot(
grab_obj(os.path.join(mgpythia_dir, qgc.QCD_FILENAME), this_hist),
output_filename=
"%s/constituent_plots/ptMin_%d/%s_mgpythia_norm%s%s.%s" %
(plot_dir, ptmin, flav + plotname, renorm, log_append, OUTPUT_FMT),
renorm_axis=renorm,
title="MG+Pythia",
rebin=rebin,
recolour=True,
ylim=ylim,
logz=logz)
qgg.do_2D_plot(
grab_obj(os.path.join(pythia_only_dir, qgc.QCD_FILENAME),
this_hist),
output_filename=
"%s/constituent_plots/ptMin_%d/%s_pythiaOnly_norm%s%s.%s" %
(plot_dir, ptmin, flav + plotname, renorm, log_append, OUTPUT_FMT),
renorm_axis=renorm,
title="Pythia only",
rebin=rebin,
recolour=True,
ylim=ylim,
logz=logz)
# Now LHA vs X ones
plotnames = ["genjet_LHA_vs_zi", "genjet_LHA_vs_thetai"]
for plotname, ptmin, renorm, logz, flav in product(plotnames, ptmin_vals,
['X', 'Y', None],
[True, False],
jet_flavs):
rebin = [4, 4]
xlim = [0, 0.5] if ("zi" in plotname and renorm != "X") else None
ylim = None
dirname = "Dijet_genjet_ptMin_%d" % ptmin
this_hist = os.path.join(dirname, flav + plotname)
log_append = "_logZ" if logz else ""
qgg.do_2D_plot(
grab_obj(os.path.join(mgpythia_dir, qgc.QCD_FILENAME), this_hist),
output_filename=
"%s/constituent_plots/ptMin_%d/%s_mgpythia_norm%s%s.%s" %
(plot_dir, ptmin, flav + plotname, renorm, log_append, OUTPUT_FMT),
renorm_axis=renorm,
title="MG+Pythia, %s-jet, ptMin = %d GeV" % (flav, ptmin),
rebin=rebin,
recolour=True,
xlim=xlim,
ylim=ylim,
logz=logz)
qgg.do_2D_plot(
grab_obj(os.path.join(pythia_only_dir, qgc.QCD_FILENAME),
this_hist),
output_filename=
"%s/constituent_plots/ptMin_%d/%s_pythiaOnly_norm%s%s.%s" %
(plot_dir, ptmin, flav + plotname, renorm, log_append, OUTPUT_FMT),
renorm_axis=renorm,
title="Pythia only, %s-jet, ptMin = %d GeV" % (flav, ptmin),
rebin=rebin,
recolour=True,
xlim=xlim,
ylim=ylim,
logz=logz)
def do_2D_plot_with_contours(obj,
contour_obj,
output_filename,
renorm_axis=None,
title=None,
rebin=None,
recolour=True,
xlim=None,
ylim=None,
logx=False,
logy=False,
logz=False):
"""Like normal 2D plotter but contour_obj will be plotted using contours"""
if rebin:
obj.Rebin2D(*rebin)
if renorm_axis:
obj_renorm = cu.make_normalised_TH2(obj, renorm_axis, recolour)
else:
obj_renorm = obj
if title:
obj_renorm.SetTitle(title)
canvas = ROOT.TCanvas(ROOT.TUUID().AsString(), "", 800, 800)
canvas.SetTicks(1, 1)
canvas.SetLeftMargin(0.13)
canvas.SetBottomMargin(0.11)
if logx:
canvas.SetLogx(1)
if logy:
canvas.SetLogy(1)
if logz:
canvas.SetLogz(1)
obj_renorm.Draw("COLZ")
if xlim is not None:
obj_renorm.GetXaxis().SetRangeUser(*xlim)
if ylim is not None:
obj_renorm.GetYaxis().SetRangeUser(*ylim)
obj_renorm.GetYaxis().SetTitleOffset(1.7)
obj_renorm.GetXaxis().SetTitleOffset(1.2)
if contour_obj:
contour_obj.Draw("CONT3 SAME")
output_filename = os.path.abspath(output_filename)
odir = os.path.dirname(output_filename)
if not os.path.isdir(odir):
os.makedirs(odir)
canvas.SaveAs(output_filename)
# Do 2D plots for premade jet pt cut objs
jet_pt_vals = [(100, 200), (800, 1000)]
for (jet_pt_min,
jet_pt_max), renorm, logz in product(jet_pt_vals, ['X', 'Y',
None][2:],
[True, False]):
rebin = [1, 1]
xlim = [0, 1]
ylim = [0, 0.05]
dirname = "Dijet_genjet"
jet_type = "g"
plotname = jet_type + "genjet_constituent_zi_vs_constituent_thetai_pt%dto%d" % (
jet_pt_min, jet_pt_max)
this_hist = os.path.join(dirname, plotname)
log_append = "_logZ" if logz else ""
contour_hist = grab_obj(os.path.join(mgpythia_dir, qgc.QCD_FILENAME),
this_hist).Clone("contours")
for xbin in range(1, contour_hist.GetNbinsX() + 1):
for ybin in range(1, contour_hist.GetNbinsY() + 1):
xval = 0.5 * (contour_hist.GetXaxis().GetBinCenter(xbin) +
contour_hist.GetXaxis().GetBinCenter(xbin + 1))
yval = 0.5 * (contour_hist.GetYaxis().GetBinCenter(ybin) +
contour_hist.GetYaxis().GetBinCenter(ybin + 1))
contour_hist.SetBinContent(xbin, ybin,
math.sqrt(xval / 0.4) * yval)
levels = array('d',
[0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5])
contour_hist.SetContour(len(levels), levels)
contour_hist.SetLineColor(ROOT.kRed)
do_2D_plot_with_contours(
grab_obj(os.path.join(mgpythia_dir, qgc.QCD_FILENAME), this_hist),
contour_hist,
output_filename="%s/constituent_plots/%s_mgpythia_norm%s%s.%s" %
(plot_dir, plotname, renorm, log_append, OUTPUT_FMT),
renorm_axis=renorm,
title="MG+Pythia, %d < p_{T}^{j} < %d GeV" %
(jet_pt_min, jet_pt_max),
rebin=rebin,
recolour=True,
xlim=xlim,
ylim=ylim,
logz=logz)
do_2D_plot_with_contours(
grab_obj(os.path.join(pythia_only_dir, qgc.QCD_FILENAME),
this_hist),
contour_hist,
output_filename="%s/constituent_plots/%s_pythiaOnly_norm%s%s.%s" %
(plot_dir, plotname, renorm, log_append, OUTPUT_FMT),
renorm_axis=renorm,
title="Pythia only, %d < p_{T}^{j} < %d GeV" %
(jet_pt_min, jet_pt_max),
rebin=rebin,
recolour=True,
xlim=xlim,
ylim=ylim,
logz=logz)
# Do 1D projection plots
for (jet_pt_min, jet_pt_max) in jet_pt_vals:
lw = 2
dirname = "Dijet_genjet"
jet_type = "g"
plotname = jet_type + "genjet_constituent_zi_vs_constituent_thetai_pt%dto%d" % (
jet_pt_min, jet_pt_max)
this_hist = os.path.join(dirname, plotname)
# Do projection plots for various zi bins
cut_zi_bins = [(0, 0.001), (0, 0.005),
(0, 0.01), (0, 0.05), (0.05, 0.1), (0, 0.1), (0.1, 0.2),
(0.3, 0.4), (0.4, 0.5), (0.8, 1.0), (0, 1)]
for i, (start_val, end_val) in enumerate(cut_zi_bins):
entries_normal = []
h2d_mgpythia = grab_obj(
os.path.join(mgpythia_dir, qgc.QCD_FILENAME), this_hist)
mgpythia_kwargs = dict(line_color=qgc.QCD_COLOUR,
fill_color=qgc.QCD_COLOUR,
label="MG+Pythia",
line_width=lw)
entries_normal.append(
(qgg.get_projection_plot(h2d_mgpythia, start_val, end_val,
'y'), mgpythia_kwargs))
h2d_pythiaonly = grab_obj(
os.path.join(pythia_only_dir, qgc.QCD_FILENAME), this_hist)
pythiaonly_kwargs = dict(line_color=ROOT.kRed,
fill_color=ROOT.kRed,
label="Pythia only",
line_width=lw)
entries_normal.append(
(qgg.get_projection_plot(h2d_pythiaonly, start_val, end_val,
'y'), pythiaonly_kwargs))
ofilename = jet_type + "genjet_constituent_thetai_binned_zi%gto%g_jetPt%dto%d" % (
start_val, end_val, jet_pt_min, jet_pt_max)
rebin = 5 if end_val <= 0.1 else 2
xlim = None if end_val <= 0.1 else [0, 0.6]
if end_val >= 1:
rebin = 4
xlim = [0, 1.2]
qgg.do_comparison_plot(
entries_normal,
"%s/constituent_plots/%s.%s" %
(plot_dir, ofilename, OUTPUT_FMT),
rebin=rebin,
title="%g < z_{i} < %g, %d < p_{T}^{jet} < %d GeV" %
(start_val, end_val, jet_pt_min, jet_pt_max),
xtitle="#theta_{i}",
xlim=xlim,
ylim=None,
subplot_type="ratio",
subplot_title="#splitline{Ratio wrt}{MG+Pythia}")
# Do projections hists for various theta_i bins
cut_thetai_bins = [(0, 0.01), (0.01, 0.02), (0.02, 0.05), (0.05, 0.10),
(0.1, 0.15), (0.15, 0.2), (0.2, 0.3), (0.3, 0.4),
(0.4, 0.6), (0.6, 0.8), (0.8, 1), (1, 1.5),
(0, 2)][-1:]
deltas = []
delta_components = []
colours = [
ROOT.kBlue, ROOT.kRed, ROOT.kGreen + 2, ROOT.kOrange - 3,
ROOT.kMagenta, ROOT.kAzure + 1
]
for i, (start_val, end_val) in enumerate(cut_thetai_bins[::]):
entries_normal = []
h2d_mgpythia = grab_obj(
os.path.join(mgpythia_dir, qgc.QCD_FILENAME), this_hist)
mgpythia_kwargs = dict(line_color=qgc.QCD_COLOUR,
fill_color=qgc.QCD_COLOUR,
label="MG+Pythia",
line_width=lw)
h_mgpythia = qgg.get_projection_plot(h2d_mgpythia, start_val,
end_val, 'x')
h_mgpythia.Rebin(4)
entries_normal.append(
(h_mgpythia.Clone(ROOT.TUUID().AsString()), mgpythia_kwargs))
h2d_pythiaonly = grab_obj(
os.path.join(pythia_only_dir, qgc.QCD_FILENAME), this_hist)
pythiaonly_kwargs = dict(line_color=ROOT.kRed,
fill_color=ROOT.kRed,
label="Pythia only",
line_width=lw)
h_pythiaonly = qgg.get_projection_plot(h2d_pythiaonly, start_val,
end_val, 'x')
h_pythiaonly.Rebin(4)
entries_normal.append((h_pythiaonly.Clone(ROOT.TUUID().AsString()),
pythiaonly_kwargs))
# rebin = 5 if end_val <= 0.1 else 2
rebin = None
xlim = [0, 0.5] if end_val <= 0.1 else [0, 0.2]
if end_val == 2:
xlim = [0, 0.2]
ofilename = jet_type + "genjet_constituent_zi_binned_thetai%gto%g_jetPt%dto%d" % (
start_val, end_val, jet_pt_min, jet_pt_max)
qgg.do_comparison_plot(
entries_normal,
"%s/constituent_plots/%s.%s" %
(plot_dir, ofilename, OUTPUT_FMT),
rebin=rebin,
title="%g < #theta_{i} < %g, %d < p_{T}^{jet} < %d GeV" %
(start_val, end_val, jet_pt_min, jet_pt_max),
xtitle="z_{i}",
xlim=xlim,
subplot_type="ratio",
subplot_title="#splitline{Ratio wrt}{MG+Pythia}")
ddelta_hist = qgd.get_ddelta_plot(h_mgpythia, h_pythiaonly)
this_colour = colours[i % len(colours)]
line_style = 1 + i // len(colours)
c = Contribution(ddelta_hist,
label="%g-%g" % (start_val, end_val),
line_width=1,
line_style=line_style,
marker_color=this_colour,
line_color=this_colour,
fill_color=this_colour)
delta_components.append(c)
deltas.append(qgd.calculate_delta(ddelta_hist))
p = Plot(delta_components,
what="hist",
ytitle="p.d.f",
xlim=[0, 0.2],
title="%d < p_{T}^{jet} < %d GeV" % (jet_pt_min, jet_pt_max))
p.plot("NOSTACK HISTE")
p.save("%s/constituent_plots/ddelta_thetai_jetPt%dto%d.%s" %
(plot_dir, jet_pt_min, jet_pt_max, OUTPUT_FMT))
gr = qgd.construct_deltas_graph(deltas[::])
c = Contribution(gr, label="BLAH", marker_style=0)
graph_contribs = []
graph_contribs.append(c)
bin_labels = ["{:g} - {:g}".format(*b) for b in cut_thetai_bins]
qgd.do_deltas_plot(graph_contribs,
"%s/constituent_plots/delta_thetai_jetPt%dto%d.%s" %
(plot_dir, jet_pt_min, jet_pt_max, OUTPUT_FMT),
bin_labels=bin_labels,
title="%d < p_{T}^{jet} < %d GeV" %
(jet_pt_min, jet_pt_max),
xtitle="#theta_{i}")
def make_1d_plot(root_dir, histname, jet_str, gen_only=False):
entries = []
lw = 2
data_line_width = lw
msize = 1.1
zpj_data_hist = None
if not gen_only:
zpj_data_hist = grab_obj(os.path.join(root_dir, qgc.SINGLE_MU_FILENAME), histname)
SINGLE_MU_COLOUR = ROOT.kBlack
dy_kwargs_data = dict(line_color=SINGLE_MU_COLOUR, line_width=2, fill_color=SINGLE_MU_COLOUR,
marker_color=SINGLE_MU_COLOUR, marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=msize,
label="Data")
entries.append((zpj_data_hist, dy_kwargs_data))
# PYTHIA DY MC WITH k FACTORS
h_dyj_mc = grab_obj(os.path.join(root_dir, qgc.DY_FILENAME), histname)
dy_kwargs_mc = dict(line_color=qgc.DY_COLOUR, line_width=lw, fill_color=qgc.DY_COLOUR,
marker_color=qgc.DY_COLOUR, marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=0,
label="MG+PY8 (with k-factor)",
subplot=zpj_data_hist)
entries.append((h_dyj_mc, dy_kwargs_mc))
# WITHOUT K FACTORS
h_dyj_mc_noWeight = grab_obj(os.path.join(root_dir, "uhh2.AnalysisModuleRunner.MC.MC_DYJetsToLL_noZReweight.root"), histname)
dy_kwargs_mc = dict(line_color=qgc.QCD_COLOURS[0], line_width=lw, fill_color=qgc.QCD_COLOURS[0],
marker_color=qgc.QCD_COLOURS[0], marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=0,
label="MG+PY8 (without k-factor)",
subplot=zpj_data_hist)
entries.append((h_dyj_mc_noWeight, dy_kwargs_mc))
# Must do un normed first, since it manipulates original objects
rebin = 10
p = qgp.make_comparison_plot_ingredients(entries, rebin=rebin, normalise_hist=False, mean_rel_error=0.4,
title="%s\n%s" % (jet_str, qgc.ZpJ_LABEL),
xlim=[30, 4000],
ylim=[1E-1, 1E8],
subplot_type='ratio',
subplot_title="MC / Data",
subplot_limits=(0.5, 1.5))
output_filename = os.path.join(root_dir, "plots_dy_vs_qcd_mc_vs_data_z_reweight", "zpj_%s.%s" % (histname.replace("/", "_"), OUTPUT_FMT))
draw_opt = "NOSTACK HISTE"
p.plot(draw_opt)
p.set_logx()
p.set_logy(do_more_labels=False)
dirname = os.path.dirname(output_filename)
if not os.path.isdir(dirname):
os.makedirs(dirname)
p.save(output_filename)
p = qgp.make_comparison_plot_ingredients(entries, rebin=1, normalise_hist=True, mean_rel_error=0.4,
title="%s\n%s" % (jet_str, qgc.ZpJ_LABEL),
xlim=[30, 4000],
ylim=[1E-6, 50],
subplot_type='ratio',
subplot_title="MC / Data",
subplot_limits=(0.5, 1.5))
output_filename = os.path.join(root_dir, "plots_dy_vs_qcd_mc_vs_data_z_reweight", "zpj_%s_normed.%s" % (histname.replace("/", "_"), OUTPUT_FMT))
p.plot(draw_opt)
p.set_logx()
p.set_logy(do_more_labels=False)
p.save(output_filename)
def do_plots(root_dir):
# QG variable plots
pt_bins = qgc.PT_BINS[:]
var_list = qgc.COMMON_VARS
var_prepend = ""
radius, pus = cu.get_jet_config_from_dirname(root_dir)
jet_str = "AK%s PF %s" % (radius.upper(), pus.upper())
for gr_append in ["", "_groomed"]:
if gr_append == "_groomed":
print("Doing groomed plots...")
else:
print("Doing ungroomed plots...")
# do a pt plot just for sanity
if gr_append == "":
make_1d_plot(root_dir, "ZPlusJets_gen/pt_jet1", jet_str, gen_only=True)
make_1d_plot(root_dir, "ZPlusJets_gen/pt_mumu", jet_str, gen_only=True)
make_1d_plot(root_dir, "ZPlusJets/pt_jet1", jet_str)
make_1d_plot(root_dir, "ZPlusJets/pt_mumu", jet_str)
continue
zpj_dirname = "ZPlusJets_QG%s" % (gr_append)
for ang in var_list[:]:
print("...Doing", ang.name)
v = "%s%s_vs_pt" % (var_prepend, ang.var)
zpj_2d_entries = []
zpj_1d_entries = []
for pt_ind, (start_val, end_val) in enumerate(pt_bins):
entries = []
zpj_entries = []
# Get all plots
lw = 2
msize = 1.1
data_line_width = 0
####################
# Z+JETS REGION
####################
# SINGLE MU DATA
h2d_dyj_data = grab_obj(os.path.join(root_dir, qgc.SINGLE_MU_FILENAME), "%s/%s" % (zpj_dirname, v))
dy_kwargs_data = dict(line_color=qgc.SINGLE_MU_COLOUR, line_width=data_line_width, fill_color=qgc.SINGLE_MU_COLOUR,
marker_color=qgc.SINGLE_MU_COLOUR, marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=msize,
label="Data")
zpj_data_hist = qgp.get_projection_plot(h2d_dyj_data, start_val, end_val)
entries.append((zpj_data_hist, dy_kwargs_data))
zpj_entries.append((zpj_data_hist, dy_kwargs_data))
if pt_ind == 0:
zpj_2d_entries.append((h2d_dyj_data, dy_kwargs_data))
# PYTHIA DY MC WITH k FACTORS
h2d_dyj_mc = grab_obj(os.path.join(root_dir, qgc.DY_FILENAME), "%s/%s" % (zpj_dirname, v))
dy_kwargs_mc = dict(line_color=qgc.DY_COLOUR, line_width=lw, fill_color=qgc.DY_COLOUR,
marker_color=qgc.DY_COLOUR, marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=0,
label="MG+PY8 (w/ k-factor)",
subplot=zpj_data_hist)
entries.append((qgp.get_projection_plot(h2d_dyj_mc, start_val, end_val), dy_kwargs_mc))
zpj_entries.append((qgp.get_projection_plot(h2d_dyj_mc, start_val, end_val), dy_kwargs_mc))
if pt_ind == 0:
zpj_2d_entries.append((h2d_dyj_mc, dy_kwargs_mc))
# WIHTOUT K FACTORS
h2d_dyj_mc_noWeight = grab_obj(os.path.join(root_dir, "uhh2.AnalysisModuleRunner.MC.MC_DYJetsToLL_noZReweight.root"), "%s/%s" % (zpj_dirname, v))
dy_kwargs_mc_noWeight = dict(line_color=qgc.DY_COLOURS[0], line_width=lw, fill_color=qgc.DY_COLOURS[0], line_style=2,
marker_color=qgc.DY_COLOURS[0], marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=0,
label="MG+PY8 (w/out k-factor)",
subplot=zpj_data_hist)
entries.append((qgp.get_projection_plot(h2d_dyj_mc_noWeight, start_val, end_val), dy_kwargs_mc_noWeight))
zpj_entries.append((qgp.get_projection_plot(h2d_dyj_mc_noWeight, start_val, end_val), dy_kwargs_mc_noWeight))
if pt_ind == 0:
zpj_2d_entries.append((h2d_dyj_mc_noWeight, dy_kwargs_mc_noWeight))
# HERWIG++ DY
# if end_val < 255:
# h2d_dyj_mc2 = grab_obj(os.path.join(root_dir, qgc.DY_HERWIG_FILENAME), "%s/%s" % (zpj_dirname, v))
# col3 = qgc.DY_COLOURS[2]
# dy_kwargs_mc2 = dict(line_color=col3, line_width=lw, fill_color=col3,
# marker_color=col3, marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=0,
# label="H++",
# subplot=zpj_data_hist)
# entries.append((qgp.get_projection_plot(h2d_dyj_mc2, start_val, end_val), dy_kwargs_mc2))
# zpj_entries.append((qgp.get_projection_plot(h2d_dyj_mc2, start_val, end_val), dy_kwargs_mc2))
# if pt_ind == 0:
# zpj_2d_entries.append((h2d_dyj_mc2, dy_kwargs_mc2))
# else:
# zpj_entries.append(None)
# MG+HERWIG++ DY
# if end_val < 151:
# h2d_dyj_mc3 = grab_obj(os.path.join(root_dir, qgc.DY_MG_HERWIG_FILENAME), "%s/%s" % (zpj_dirname, v))
# col4 = qgc.DY_COLOURS[3]
# dy_kwargs_mc3 = dict(line_color=col4, line_width=lw, fill_color=col4,
# marker_color=col4, marker_style=cu.Marker.get(qgc.DY_MARKER), marker_size=0,
# label="MG+H++",
# subplot=zpj_data_hist)
# entries.append((qgp.get_projection_plot(h2d_dyj_mc3, start_val, end_val), dy_kwargs_mc3))
# zpj_entries.append((qgp.get_projection_plot(h2d_dyj_mc3, start_val, end_val), dy_kwargs_mc3))
# if pt_ind == 0:
# zpj_2d_entries.append((h2d_dyj_mc3, dy_kwargs_mc3))
# else:
# zpj_entries.append(None)
zpj_1d_entries.append(zpj_entries)
#################
# SETUP PLOTTING
#################
rebin = 2
v_lower = v.lower()
if "multiplicity" in v_lower:
rebin = 2
elif "flavour" in v_lower or "thrust" in v_lower:
rebin = 1
elif 'ptd' in v_lower:
rebin = 2
xlim = None
if "width" in v_lower or "ptd" in v_lower:
xlim = (0, 1)
elif"thrust" in v_lower:
xlim = (0, 0.5)
elif "multiplicity" in v_lower and "ak4" in root_dir.lower():
if end_val <= 150:
xlim = (0, 50)
else:
xlim = (0, 80)
ylim = None
if "flavour" in v_lower:
ylim = (0, 1)
# elif "lha" in v_lower:
# ylim = (0, 5)
plot_dir = os.path.join(root_dir, "plots_dy_vs_qcd_mc_vs_data%s_z_reweight" % (gr_append))
subplot_title = "MC / Data"
subplot_limits = (0.5, 1.5)
xlabel = ang.name + " (" + ang.lambda_str + ")"
if gr_append is not "":
xlabel = "Groomed " + ang.name + " (" + ang.lambda_str + ")"
# zpj only
qgp.do_comparison_plot(zpj_entries,
"%s/ptBinned/%s_pt%dto%d_zpj.%s" % (plot_dir, v, start_val, end_val, OUTPUT_FMT),
rebin=rebin,
title="%d < p_{T}^{jet} < %d GeV\n%s\n%s" % (start_val, end_val, jet_str, qgc.ZpJ_LABEL),
xtitle=xlabel,
xlim=xlim,
ylim=ylim,
subplot_type='ratio',
subplot_title=subplot_title,
subplot_limits=subplot_limits)
# Do overall summary plots across all pt bins
# ------------------------------------------------------------------
ylim_mean = None
if "width" in v_lower or "ptd" in v_lower:
ylim_mean = (0, 0.4)
elif"thrust" in v_lower:
ylim_mean = (0, 0.5)
elif "multiplicity" in v_lower and "ak4" in root_dir.lower():
ylim_mean = (0, 100)
ylim_mean = (0, 80)
if end_val < 150:
ylim_mean = (0, 50)
ylim_mean=None
ylim_rms=None
# Setup variable names for MPL
marker = ""
if "_" in ang.name or "^" in ang.name:
marker = "$"
var_label = marker + ang.name + marker + " ($%s$)" % ang.lambda_str
if gr_append != "":
var_label = "Groomed " + marker + ang.name + marker + " ($%s$)" % ang.lambda_str
# zpj_1d_entries[i][j] is the jth sample in the ith pt bin
qgp.do_mean_rms_summary_plot(zpj_1d_entries[:],
pt_bins[:],
"%s/ptBinned/%s_box_zpj_mpl.%s" % (plot_dir, v, OUTPUT_FMT),
var_label=var_label,
xlim=(50, 614),
ylim_mean=ylim_mean,
ylim_rms=ylim_rms,
region_title="%s jets in %s" % (jet_str, qgc.ZpJ_LABEL))
def do_angularity_roc_plots(sources,
plot_dir="roc_angularities_roc",
zpj_dirname="ZPlusJets_QG",
dj_dirname="Dijet_QG",
pt_bins=None,
var_list=None,
var_prepend="",
flavour_tag=False):
"""Do roc plots, with different angularities on the same plot"""
var_list = var_list or COMMON_VARS
pt_bins = pt_bins or PT_BINS
zpj_flav = "q" if flavour_tag else ""
dj_flav = "g" if flavour_tag else ""
output_append = "_flavMatched" if flavour_tag else ""
for (start_val, end_val) in pt_bins:
graph_contribs = []
for source_ind, source in enumerate(sources):
# construct a graph of angularities for this source
for ind, ang in enumerate(var_list, 1):
v = "%s%s_vs_pt" % (var_prepend, ang.var)
h2d_dyj = grab_obj(
os.path.join(source['root_dir'],
qgc.DY_FILENAME), "%s/%s%s" %
(source.get('zpj_dirname', zpj_dirname), zpj_flav, v))
h2d_qcd = grab_obj(
os.path.join(source['root_dir'],
qgc.QCD_FILENAME), "%s/%s%s" %
(source.get('dj_dirname', dj_dirname), dj_flav, v))
h_dy = get_projection_plot(h2d_dyj, start_val, end_val)
if (h_dy.Integral() > 0):
h_dy.Scale(1. / (h_dy.GetBinWidth(1) * h_dy.Integral()))
h_qcd = get_projection_plot(h2d_qcd, start_val, end_val)
if (h_qcd.Integral() > 0):
h_qcd.Scale(1. / (h_qcd.GetBinWidth(1) * h_qcd.Integral()))
roc_gr = make_roc_graph(h_dy, h_qcd)
ang_label = "%s [%s]" % (ang.name, ang.lambda_str)
roc_gr.SetName(source.get("label", ""))
# if 'line_color' not in source.get('style', {}):
source['style']['line_color'] = ang.colour
c = Contribution(roc_gr,
label=source.get("label", "") + " " +
ang_label,
marker_style=0,
marker_color=ang.colour,
**source.get("style", {}))
graph_contribs.append(c)
basic_gr = ROOT.TGraph(2, array('d', [0, 1]), array('d', [0, 1]))
graph_contribs.insert(
0,
Contribution(basic_gr,
line_color=ROOT.kBlack,
marker_color=ROOT.kBlack,
line_style=2,
label="y=x"))
p = Plot(graph_contribs,
what='graph',
xtitle="#epsilon_{ g}",
ytitle="#epsilon_{ q}",
title="%d < p_{T}^{jet} < %d GeV" % (start_val, end_val),
xlim=[1E-1, 1],
ylim=[0, 1],
legend=True)
p.legend.SetY1(0.15)
p.legend.SetY2(0.4)
p.plot("AL")
p.save("%s/angularities_roc_pt%dto%d%s.pdf" %
(plot_dir, start_val, end_val, output_append))
def do_all_2D_plots(root_dir,
plot_dir="plots_2d",
zpj_dirname="ZPlusJets_QG",
dj_dirname="Dijet_QG",
var_list=None,
var_prepend=""):
"""Do 2D distributions"""
var_list = var_list or qgc.COMMON_VARS_WITH_FLAV
for ang in var_list:
v = "%s%s_vs_pt" % (var_prepend, ang.var)
rebin = [2, 4]
xlim = None
if "multiplicity" in v:
rebin = [2, 4]
elif "thrust" in v or "width" in v:
rebin = [1, 4]
xlim = [0, 0.5]
elif "flavour" in v:
rebin = [1, 4]
recolour = False if "flavour" in v else True
for rn in ['Y', None]: # different renormalisation axes
qgg.do_2D_plot(grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/%s" % (zpj_dirname, v)),
output_filename="%s/%s/dy_zpj_%s_norm%s.%s" %
(root_dir, plot_dir, v, rn, OUTPUT_FMT),
renorm_axis=rn,
title=qgc.DY_ZpJ_LABEL,
rebin=rebin,
recolour=recolour,
xlim=xlim)
qgg.do_2D_plot(grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/%s" % (dj_dirname, v)),
output_filename="%s/%s/dy_dijet_%s_norm%s.%s" %
(root_dir, plot_dir, v, rn, OUTPUT_FMT),
renorm_axis=rn,
title=qgc.DY_Dijet_LABEL,
rebin=rebin,
recolour=recolour,
xlim=xlim)
qgg.do_2D_plot(grab_obj(os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/%s" % (dj_dirname, v)),
output_filename="%s/%s/qcd_dijet_%s_norm%s.%s" %
(root_dir, plot_dir, v, rn, OUTPUT_FMT),
renorm_axis=rn,
title=qgc.QCD_Dijet_LABEL,
rebin=rebin,
recolour=recolour,
xlim=xlim)
if "flavour" in v:
continue
# Flavour matched reco
qgg.do_2D_plot(
grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/q%s" % (zpj_dirname, v)),
output_filename="%s/%s/dy_zpj_%s_norm%s_qflavMatched.%s" %
(root_dir, plot_dir, v, rn, OUTPUT_FMT),
renorm_axis=rn,
title=qgc.DY_ZpJ_QFLAV_LABEL,
rebin=rebin,
xlim=xlim)
qgg.do_2D_plot(
grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/g%s" % (zpj_dirname, v)),
output_filename="%s/%s/dy_zpj_%s_norm%s_gflavMatched.%s" %
(root_dir, plot_dir, v, rn, OUTPUT_FMT),
renorm_axis=rn,
title=qgc.DY_ZpJ_GFLAV_LABEL,
rebin=rebin,
xlim=xlim)
qgg.do_2D_plot(
grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/g%s" % (dj_dirname, v)),
output_filename="%s/%s/dy_dijet_%s_norm%s_gflavMatched.%s" %
(root_dir, plot_dir, v, rn, OUTPUT_FMT),
renorm_axis=rn,
title=qgc.DY_Dijet_GFLAV_LABEL,
rebin=rebin,
xlim=xlim)
qgg.do_2D_plot(
grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/q%s" % (dj_dirname, v)),
output_filename="%s/%s/dy_dijet_%s_norm%s_qflavMatched.%s" %
(root_dir, plot_dir, v, rn, OUTPUT_FMT),
renorm_axis=rn,
title=qgc.DY_Dijet_QFLAV_LABEL,
rebin=rebin,
xlim=xlim)
qgg.do_2D_plot(
grab_obj(os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/g%s" % (dj_dirname, v)),
output_filename="%s/%s/qcd_dijet_%s_norm%s_gflavMatched.%s" %
(root_dir, plot_dir, v, rn, OUTPUT_FMT),
renorm_axis=rn,
title=qgc.QCD_Dijet_GFLAV_LABEL,
rebin=rebin,
xlim=xlim)
qgg.do_2D_plot(
grab_obj(os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/q%s" % (dj_dirname, v)),
output_filename="%s/%s/qcd_dijet_%s_norm%s_qflavMatched.%s" %
(root_dir, plot_dir, v, rn, OUTPUT_FMT),
renorm_axis=rn,
title=qgc.QCD_Dijet_QFLAV_LABEL,
rebin=rebin,
xlim=xlim)
def do_gen_reco_comparison_plots(root_dir,
var_list=None,
gen_var_prepend="gen",
reco_var_prepend="",
plot_dir="plot_reco_gen",
zpj_reco_dirname=qgc.ZPJ_RECOJET_RDIR,
dj_reco_dirname=qgc.DJ_RECOJET_RDIR,
zpj_gen_dirname=qgc.ZPJ_GENJET_RDIR,
dj_gen_dirname=qgc.DJ_GENJET_RDIR,
pt_bins=qgc.THEORY_PT_BINS,
subplot_type=None):
var_list = var_list or qgc.COMMON_VARS
for ang in var_list:
v_reco = "%s%s_vs_pt" % (reco_var_prepend, ang.var)
v_gen = "%s%s_vs_pt" % (gen_var_prepend, ang.var)
h2d_dyj_reco = grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/%s" % (zpj_reco_dirname, v_reco))
h2d_qcd_reco = grab_obj(os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/%s" % (dj_reco_dirname, v_reco))
h2d_dyj_gen = grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/%s" % (zpj_gen_dirname, v_gen))
h2d_qcd_gen = grab_obj(os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/%s" % (dj_gen_dirname, v_gen))
if "flavour" not in v_reco:
h2d_dyj_reco_q = grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/q%s" % (zpj_reco_dirname, v_reco))
h2d_qcd_reco_g = grab_obj(os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/g%s" % (dj_reco_dirname, v_reco))
h2d_dyj_gen_q = grab_obj(os.path.join(root_dir, qgc.DY_FILENAME),
"%s/q%s" % (zpj_gen_dirname, v_gen))
h2d_qcd_gen_g = grab_obj(os.path.join(root_dir, qgc.QCD_FILENAME),
"%s/g%s" % (dj_gen_dirname, v_gen))
for (start_val, end_val) in pt_bins:
lw = 2
dy_reco_kwargs = dict(line_color=qgc.DY_COLOUR,
fill_color=qgc.DY_COLOUR,
label=qgc.DY_ZpJ_LABEL + " [RecoJet]",
line_width=lw)
qcd_reco_kwargs = dict(line_color=qgc.QCD_COLOUR,
fill_color=qgc.QCD_COLOUR,
label=qgc.QCD_Dijet_LABEL + " [RecoJet]",
line_width=lw)
dy_gen_kwargs = dict(line_color=qgc.DY_COLOUR,
fill_color=qgc.DY_COLOUR,
label=qgc.DY_ZpJ_LABEL + " [GenJet]",
line_width=lw,
line_style=2)
qcd_gen_kwargs = dict(line_color=qgc.QCD_COLOUR,
fill_color=qgc.QCD_COLOUR,
label=qgc.QCD_Dijet_LABEL + " [GenJet]",
line_width=lw,
line_style=2)
entries = [(qgg.get_projection_plot(h2d_dyj_reco, start_val,
end_val), dy_reco_kwargs),
(qgg.get_projection_plot(h2d_qcd_reco, start_val,
end_val), qcd_reco_kwargs),
(qgg.get_projection_plot(h2d_dyj_gen, start_val,
end_val), dy_gen_kwargs),
(qgg.get_projection_plot(h2d_qcd_gen, start_val,
end_val), qcd_gen_kwargs)]
rebin = 2
xlim = None
ylim = None
if "flavour" in v_reco:
rebin = 1
ylim = (0, 1)
if "thrust" in v_reco:
xlim = (0, 0.5)
qgg.do_comparison_plot(
entries,
"%s/%s/%s_pt%dto%d.%s" %
(root_dir, plot_dir, ang.var, start_val, end_val, OUTPUT_FMT),
rebin=rebin,
title="%d < p_{T}^{jet} < %d GeV" % (start_val, end_val),
xtitle=ang.name + " (" + ang.lambda_str + ")",
xlim=xlim,
ylim=ylim,
subplot_type=subplot_type)
# Do flavour-tagged comparison
if "flavour" in v_reco:
continue
dy_reco_kwargs['label'] = qgc.DY_ZpJ_QFLAV_LABEL + " [RecoJet]"
qcd_reco_kwargs['label'] = qgc.QCD_Dijet_GFLAV_LABEL + " [RecoJet]"
dy_gen_kwargs['label'] = qgc.DY_ZpJ_QFLAV_LABEL + " [GenJet]"
qcd_gen_kwargs['label'] = qgc.QCD_Dijet_GFLAV_LABEL + " [GenJet]"
entries = [(qgg.get_projection_plot(h2d_dyj_reco_q, start_val,
end_val), dy_reco_kwargs),
(qgg.get_projection_plot(h2d_qcd_reco_g, start_val,
end_val), qcd_reco_kwargs),
(qgg.get_projection_plot(h2d_dyj_gen_q, start_val,
end_val), dy_gen_kwargs),
(qgg.get_projection_plot(h2d_qcd_gen_g, start_val,
end_val), qcd_gen_kwargs)]
qgg.do_comparison_plot(
entries,
"%s/%s/%s_flavMatched_pt%dto%d.%s" %
(root_dir, plot_dir, ang.var, start_val, end_val, OUTPUT_FMT),
rebin=rebin,
title="%d < p_{T}^{jet} < %d GeV" % (start_val, end_val),
xtitle=ang.name + " (" + ang.lambda_str + ")",
xlim=xlim,
ylim=ylim,
subplot_type=subplot_type)
