def do_mc_pt_comparison_plot(dirname_label_pairs, output_filename,
qcd_filename, **plot_kwargs):
# qcd_files = [cu.open_root_file(os.path.join(dl[0], qgc.QCD_FILENAME)) for dl in dirname_label_pairs]
qcd_files = [
cu.open_root_file(os.path.join(dl[0], qgc.QCD_PYTHIA_ONLY_FILENAME))
for dl in dirname_label_pairs
]
histname = "Dijet_tighter/pt_jet1"
qcd_hists = [cu.get_from_tfile(qf, histname) for qf in qcd_files]
N = len(dirname_label_pairs)
conts = [
Contribution(qcd_hists[i],
label=lab,
marker_color=cu.get_colour_seq(i, N),
line_color=cu.get_colour_seq(i, N),
line_style=(i % 3) + 1,
line_width=2,
rebin_hist=1,
subplot=qcd_hists[0] if i != 0 else None)
for i, (d, lab) in enumerate(dirname_label_pairs)
]
plot = Plot(conts,
what='hist',
ytitle="N",
subplot_limits=(0.5, 1.5),
subplot_type="ratio",
subplot_title="* / %s" % (dirname_label_pairs[0][1]),
**plot_kwargs)
plot.y_padding_max_log = 500
plot.legend.SetY1(0.7)
plot.plot("NOSTACK HIST E")
plot.set_logx(do_more_labels=False)
plot.set_logy(do_more_labels=False)
plot.save(output_filename)
def do_jet_pt_rel_error_with_var_cuts(histname, cuts, input_filename, output_filename):
ROOT.gStyle.SetPalette(palette_1D)
tf = cu.open_root_file(input_filename)
h3d = cu.get_from_tfile(tf, histname)
if h3d.GetEntries() == 0:
return
pt_hists = []
for cut in cuts:
max_bin = h3d.GetZaxis().FindFixBin(cut)
# print("cut:", cut, "bin:", max_bin)
h = h3d.ProjectionY("pt_var_lt_%g" % cut, 0, -1, 0, max_bin, "e")
h2 = h.Clone()
h2.Rebin(2)
if h.GetEntries() > 0:
h3 = qgp.hist_divide_bin_width(h2)
# convert bin contents to bin error/bin contents
for ibin in range(1, h2.GetNbinsX()+1):
if h3.GetBinContent(ibin) == 0:
continue
h3.SetBinContent(ibin, h3.GetBinError(ibin) / h3.GetBinContent(ibin))
h3.SetBinError(ibin, 0)
pt_hists.append(h3)
line_styles = [1, 2, 3]
n_line_styles = len(line_styles)
conts = [Contribution(h, label=" < %g" % cut,
line_color=cu.get_colour_seq(ind, len(cuts)),
line_style=line_styles[ind % n_line_styles],
line_width=2,
marker_color=cu.get_colour_seq(ind, len(cuts)),
subplot=pt_hists[-1])
for ind, (h, cut) in enumerate(zip(pt_hists, cuts))]
jet_str = pt_genjet_str if "_vs_pt_genjet_vs_" in histname else pt_str
weight_str = "(unweighted)" if "unweighted" in histname else "(weighted)"
ratio_lims = (0.98, 1.02) if "unweighted" in histname else None
plot = Plot(conts, what='hist',
title='%s for cuts on %s %s' % (jet_str, get_var_str(histname), weight_str),
xtitle=None,
ytitle='Relative error',
# xlim=None, ylim=None,
legend=True,
subplot_type='ratio',
subplot_title='* / var < %g' % cuts[-1],
subplot_limits=ratio_lims,
has_data=False)
plot.y_padding_max_log = 200
plot.subplot_maximum_ceil = 2
plot.subplot_maximum_floor = 1.02
plot.subplot_minimum_ceil = 0.98
plot.legend.SetY1(0.7)
plot.legend.SetY2(0.89)
plot.legend.SetX1(0.78)
plot.legend.SetX2(0.88)
plot.plot("NOSTACK HISTE", "NOSTACK HIST")
plot.set_logx(True, do_more_labels=True)
plot.set_logy(True, do_more_labels=False)
plot.save(output_filename)
def do_data_mc_plot(dirname, histname, output_filename, **plot_kwargs):
data_file = cu.open_root_file(os.path.join(dirname, qgc.JETHT_ZB_FILENAME))
qcd_file = cu.open_root_file(os.path.join(dirname, qgc.QCD_FILENAME))
qcd_py_file = cu.open_root_file(
os.path.join(dirname, qgc.QCD_PYTHIA_ONLY_FILENAME))
qcd_hpp_file = cu.open_root_file(
os.path.join(dirname, qgc.QCD_HERWIG_FILENAME))
data_hist = cu.get_from_tfile(data_file, histname)
qcd_hist = cu.get_from_tfile(qcd_file, histname)
qcd_py_hist = cu.get_from_tfile(qcd_py_file, histname)
qcd_hpp_hist = cu.get_from_tfile(qcd_hpp_file, histname)
conts = [
Contribution(data_hist,
label="Data",
line_color=ROOT.kBlack,
marker_size=0,
marker_color=ROOT.kBlack),
Contribution(qcd_hist,
label="QCD MG+PYTHIA8 MC",
line_color=qgc.QCD_COLOUR,
subplot=data_hist,
marker_size=0,
marker_color=qgc.QCD_COLOUR),
Contribution(qcd_py_hist,
label="QCD PYTHIA8 MC",
line_color=qgc.QCD_COLOURS[2],
subplot=data_hist,
marker_size=0,
marker_color=qgc.QCD_COLOURS[2]),
# Contribution(qcd_hpp_hist, label="QCD HERWIG++ MC", line_color=qgc.HERWIGPP_QCD_COLOUR, subplot=data_hist, marker_size=0, marker_color=qgc.HERWIGPP_QCD_COLOUR),
]
plot = Plot(conts,
what='hist',
ytitle="N",
xtitle="p_{T}^{Leading jet} [GeV]",
subplot_type="ratio",
subplot_title="Simulation / data",
ylim=[1E3, None],
lumi=cu.get_lumi_str(do_dijet=True, do_zpj=False),
**plot_kwargs)
plot.y_padding_max_log = 500
plot.legend.SetX1(0.55)
plot.legend.SetX2(0.98)
plot.legend.SetY1(0.7)
# plot.legend.SetY2(0.88)
plot.plot("NOSTACK HIST E")
plot.set_logx(do_more_labels=True, do_exponent=False)
plot.set_logy(do_more_labels=False)
plot.save(output_filename)
def do_genht_plot(dirname, output_filename, **plot_kwargs):
qcd_file = cu.open_root_file(os.path.join(dirname, qgc.QCD_FILENAME))
histname = "Dijet_gen/gen_ht"
qcd_hist = cu.get_from_tfile(qcd_file, histname)
conts = [Contribution(qcd_hist, label="QCD MC", line_color=ROOT.kRed)]
plot = Plot(conts, what='hist', ytitle="N", **plot_kwargs)
plot.y_padding_max_log = 500
plot.legend.SetY1(0.7)
plot.plot("NOSTACK HIST E")
plot.set_logx(do_more_labels=False)
plot.set_logy(do_more_labels=False)
plot.save(output_filename)
def do_pthat_comparison_plot(dirname_label_pairs, output_filename,
**plot_kwargs):
qcd_files = [
cu.open_root_file(os.path.join(dl[0], qgc.QCD_PYTHIA_ONLY_FILENAME))
for dl in dirname_label_pairs
]
histname = "Dijet_gen/ptHat"
qcd_hists = [cu.get_from_tfile(qf, histname) for qf in qcd_files]
N = len(dirname_label_pairs)
pthat_rebin = array('d', [
15, 30, 50, 80, 120, 170, 300, 470, 600, 800, 1000, 1400, 1800, 2400,
3200, 5000
])
nbins = len(pthat_rebin) - 1
qcd_hists = [
h.Rebin(nbins, cu.get_unique_str(), pthat_rebin) for h in qcd_hists
]
conts = [
Contribution(qcd_hists[i],
label=lab,
marker_color=cu.get_colour_seq(i, N),
line_color=cu.get_colour_seq(i, N),
line_style=i + 1,
line_width=2,
subplot=qcd_hists[0] if i != 0 else None)
for i, (d, lab) in enumerate(dirname_label_pairs)
]
plot = Plot(conts,
what='hist',
ytitle="N",
subplot_limits=(0.75, 1.25),
subplot_type="ratio",
subplot_title="* / %s" % (dirname_label_pairs[0][1]),
**plot_kwargs)
plot.y_padding_max_log = 500
plot.legend.SetY1(0.7)
plot.plot("NOSTACK HIST E")
plot.set_logx(do_more_labels=False)
plot.set_logy(do_more_labels=False)
plot.save(output_filename)
def do_genht_comparison_plot(dirname_label_pairs, output_filename,
**plot_kwargs):
"""Like do_genht but for multiple samples"""
qcd_files = [
cu.open_root_file(os.path.join(dl[0], qgc.QCD_FILENAME))
for dl in dirname_label_pairs
]
histname = "Dijet_gen/gen_ht"
qcd_hists = [cu.get_from_tfile(qf, histname) for qf in qcd_files]
N = len(dirname_label_pairs)
conts = [
Contribution(qcd_hists[i],
label=lab,
marker_color=cu.get_colour_seq(i, N),
line_color=cu.get_colour_seq(i, N),
line_style=i + 1,
line_width=2,
subplot=qcd_hists[0] if i != 0 else None)
for i, (d, lab) in enumerate(dirname_label_pairs)
]
plot = Plot(
conts,
what='hist',
ytitle="N",
# subplot_limits=(0.75, 1.25),
subplot_type="ratio",
subplot_title="* / %s" % (dirname_label_pairs[0][1]),
ylim=[1E6, None],
**plot_kwargs)
plot.y_padding_max_log = 500
plot.legend.SetY1(0.7)
plot.subplot_maximum_ceil = 5
plot.plot("NOSTACK HIST E")
plot.set_logx(do_more_labels=False)
plot.set_logy(do_more_labels=False)
plot.save(output_filename)
def do_jet_pt_with_var_cuts(histname, cuts, input_filename, output_filename):
ROOT.gStyle.SetPalette(palette_1D)
total = len(cuts) - 1 + .1 # slight offset to not hit the maximum or minimum
# if len(cuts) <= 3:
# ROOT.gStyle.SetPalette(ROOT.kCool)
# num_colours = ROOT.TColor.GetPalette().fN - 1
# print('num_colours:', num_colours)
# for index in range(len(cuts)):
# print(num_colours, index, len(cuts), index / len(cuts), num_colours * index / total)
# print(index, ROOT.TColor.GetColorPalette(int(num_colours * 1. * index / total)))
tf = cu.open_root_file(input_filename)
h3d = cu.get_from_tfile(tf, histname)
if h3d.GetEntries() == 0:
return
pt_hists = []
for cut in cuts:
max_bin = h3d.GetZaxis().FindFixBin(cut)
# print("cut:", cut, "bin:", max_bin)
h = h3d.ProjectionY("pt_var_lt_%g" % cut, 0, -1, 0, max_bin, "e")
h2 = h.Clone()
h2.Rebin(2)
if h.GetEntries() > 0:
h3 = qgp.hist_divide_bin_width(h2)
pt_hists.append(h3)
line_styles = [1, 2, 3]
if len(cuts) <= 3:
line_styles = [1]
n_line_styles = len(line_styles)
ref_ind = 0
conts = [Contribution(h, label=" < %g" % cut,
line_color=cu.get_colour_seq(ind, total),
line_style=line_styles[ind % n_line_styles],
line_width=2,
marker_color=cu.get_colour_seq(ind, total),
subplot=pt_hists[ref_ind] if ind != ref_ind else None)
for ind, (h, cut) in enumerate(zip(pt_hists, cuts))]
jet_str = pt_genjet_str if "_vs_pt_genjet_vs_" in histname else pt_str
weight_str = "(unweighted)" if "unweighted" in histname else "(weighted)"
ratio_lims = (0.5, 2.5)
ratio_lims = (0.5, 1.1)
plot = Plot(conts, what='hist',
title='%s for cuts on %s %s' % (jet_str, get_var_str(histname), weight_str),
xtitle=None,
ytitle='N',
# xlim=None, ylim=None,
legend=True,
subplot_type='ratio',
subplot_title='* / var < %g' % cuts[ref_ind],
subplot_limits=ratio_lims,
has_data=False)
plot.y_padding_max_log = 200
plot.subplot_maximum_ceil = 4
plot.subplot_maximum_floor = 1.02
plot.subplot_minimum_ceil = 0.98
plot.legend.SetY1(0.7)
plot.legend.SetY2(0.89)
plot.legend.SetX1(0.78)
plot.legend.SetX2(0.88)
plot.plot("NOSTACK HISTE", "NOSTACK HIST")
plot.set_logx(True, do_more_labels=True)
plot.set_logy(True, do_more_labels=False)
plot.save(output_filename)
def do_zerobias_per_run_comparison_plot(dirname_label_pairs,
output_dir,
append="",
title="",
**plot_kwargs):
runs = [
(qgc.ZEROBIAS_RUNB_FILENAME, 'B'),
(qgc.ZEROBIAS_RUNC_FILENAME, 'C'),
(qgc.ZEROBIAS_RUND_FILENAME, 'D'),
(qgc.ZEROBIAS_RUNE_FILENAME, 'E'),
(qgc.ZEROBIAS_RUNF_FILENAME, 'F'),
(qgc.ZEROBIAS_RUNG_FILENAME, 'G'),
(qgc.ZEROBIAS_RUNH_FILENAME, 'H'),
]
zb_entry = {
'label': 'HLT_ZeroBias',
'color': ROOT.kMagenta - 9,
# 'scale': 35918219492.947 / 29048.362
'scale': 1
}
for filename, run_period in runs:
zb_root_files = [
cu.open_root_file(os.path.join(dl[0], filename))
for dl in dirname_label_pairs
]
# PT JET 1
zb_hist_names = [
"Dijet_jet_hist_0/pt_1", "Dijet_jet_hist_unweighted_0/pt_1"
][1:]
N = len(dirname_label_pairs)
rebin = 2
for zb_name in zb_hist_names:
# add zeero bias ones
this_data_entries = [
Contribution(
cu.get_from_tfile(zb_root_files[i], zb_name),
label=zb_entry['label'] + " Run %s: " % run_period + l,
marker_color=zb_entry['color'],
line_color=zb_entry['color'],
line_style=1 + i,
rebin_hist=rebin,
) for i, (d, l) in enumerate(dirname_label_pairs)
]
for c in this_data_entries[1:]:
c.subplot = this_data_entries[0].obj
plot = Plot(
this_data_entries,
what='hist',
title=title,
xtitle="p_{T}^{jet 1} [GeV]",
ytitle="N",
xlim=[30, 1000],
ylim=[1E3, None],
# ylim=[10, 1E8] if 'unweighted' in ht_name else [1, 1E12],
subplot_type='ratio',
subplot_title='* / %s' % dirname_label_pairs[0][1],
**plot_kwargs)
plot.subplot_maximum_ceil = 10
plot.default_canvas_size = (800, 600)
plot.y_padding_max_log = 500
plot.legend.SetY1(0.7)
plot.legend.SetY2(0.88)
plot.legend.SetX1(0.5)
plot.legend.SetNColumns(2)
plot.plot("NOSTACK HISTE")
plot.set_logx(do_more_labels=False)
plot.set_logy(do_more_labels=False)
output_filename = "%s/DataJetHTZB-pt_jet1%s_Run%s%s.pdf" % (
output_dir, "_unweighted" if 'unweighted' in zb_name else "",
run_period, append)
plot.save(output_filename)
# ETA JET 1
zb_hist_names = ["Dijet_jet_hist_unweighted_0/eta_1"]
N = len(dirname_label_pairs)
rebin = 2
for zb_name in zb_hist_names:
# add zero bias ones
this_data_entries = [
Contribution(
cu.get_from_tfile(zb_root_files[i], zb_name),
label=zb_entry['label'] + " Run %s: " % run_period + l,
marker_color=zb_entry['color'],
line_color=zb_entry['color'],
line_style=1 + i,
rebin_hist=rebin,
) for i, (d, l) in enumerate(dirname_label_pairs)
]
for c in this_data_entries[1:]:
c.subplot = this_data_entries[0].obj
# plot zb
plot = Plot(
this_data_entries,
what='hist',
title=title,
xtitle="y^{jet 1}",
ytitle="N",
subplot_type='ratio',
subplot_title='* / %s' % dirname_label_pairs[0][1],
# subplot_limits=(0, 5),
**plot_kwargs)
plot.subplot_maximum_ceil = 5
plot.default_canvas_size = (800, 600)
plot.y_padding_max_log = 500
plot.legend.SetY1(0.7)
plot.legend.SetY2(0.88)
plot.legend.SetX1(0.5)
plot.legend.SetNColumns(2)
plot.plot("NOSTACK HISTE")
output_filename = "%s/DataZB-eta_jet1%s_Run%s%s.pdf" % (
output_dir, "_unweighted" if 'unweighted' in zb_name else "",
run_period, append)
plot.save(output_filename)
def do_jetht_trigger_comparison_plot(dirname_label_pairs,
output_dir,
append="",
title="",
**plot_kwargs):
# Unweighted pt, showing contributions from different triggers
# Have to add in ZB manually
zb_entry = {
'label': 'HLT_ZeroBias',
'color': ROOT.kMagenta - 9,
# 'scale': 35918219492.947 / 29048.362
'scale': 1
}
jet_ht_entries = [
{
'ind': '0',
'label': "PFJet40",
'color': ROOT.kRed,
},
{
'ind': '1',
'label': "PFJet60",
'color': ROOT.kBlue,
},
{
'ind': '2',
'label': "PFJet80",
'color': ROOT.kGreen + 2,
},
{
'ind': '3',
'label': "PFJet140",
'color': ROOT.kViolet + 5,
},
{
'ind': '4',
'label': "PFJet200",
'color': ROOT.kOrange,
},
{
'ind': '5',
'label': "PFJet260",
'color': ROOT.kTeal,
},
{
'ind': '6',
'label': "PFJet320",
'color': ROOT.kViolet,
},
{
'ind': '7',
'label': "PFJet400",
'color': ROOT.kOrange - 6
},
{
'ind': '8',
'label': "PFJet450",
'color': ROOT.kAzure + 1,
},
]
# PT JET 1
zb_hist_names = [
"Dijet_jet_hist_0/pt_1", "Dijet_jet_hist_unweighted_0/pt_1"
]
jet_ht_hist_names = [
"Dijet_jet_hist_{ind}/pt_1", "Dijet_jet_hist_unweighted_{ind}/pt_1"
]
zb_root_files = [
cu.open_root_file(os.path.join(dl[0], qgc.ZB_FILENAME))
for dl in dirname_label_pairs
]
jetht_root_files = [
cu.open_root_file(os.path.join(dl[0], qgc.JETHT_FILENAME))
for dl in dirname_label_pairs
]
N = len(dirname_label_pairs)
rebin = 2
for zb_name, ht_name in zip(zb_hist_names, jet_ht_hist_names):
# add zeero bias ones
this_data_entries = [
Contribution(
cu.get_from_tfile(zb_root_files[i], zb_name),
label=zb_entry['label'] + ": " + l,
marker_color=zb_entry['color'],
line_color=zb_entry['color'],
line_style=1 + i,
rebin_hist=rebin,
) for i, (d, l) in enumerate(dirname_label_pairs)
]
for c in this_data_entries[1:]:
c.subplot = this_data_entries[0].obj
# # add jet ht ones
for ent in jet_ht_entries:
histname = ht_name.format(ind=ent['ind'])
new_entries = [
Contribution(
cu.get_from_tfile(jetht_root_files[i], histname),
label=ent['label'] + ": " + l,
marker_color=ent['color'],
line_color=ent['color'],
line_style=1 + i,
rebin_hist=rebin,
) for i, (d, l) in enumerate(dirname_label_pairs)
]
for c in new_entries[1:]:
c.subplot = new_entries[0].obj
this_data_entries.extend(new_entries)
plot = Plot(
this_data_entries,
what='hist',
title=title,
ytitle="N",
xtitle="p_{T}^{jet 1} [GeV]",
xlim=[30, 1000],
ylim=[1E3, None],
# ylim=[10, 1E8] if 'unweighted' in ht_name else [1, 1E12],
subplot_type='ratio',
subplot_title='* / %s' % dirname_label_pairs[0][1],
**plot_kwargs)
plot.default_canvas_size = (800, 600)
plot.subplot_maximum_ceil = 10
plot.y_padding_max_log = 500
plot.legend.SetY1(0.7)
plot.legend.SetY2(0.88)
plot.legend.SetX1(0.5)
plot.legend.SetNColumns(2)
plot.plot("NOSTACK HISTE")
plot.set_logx(do_more_labels=False)
plot.set_logy(do_more_labels=False)
output_filename = "%s/DataJetHTZB-pt_jet1%s%s.pdf" % (
output_dir, "_unweighted" if 'unweighted' in zb_name else "",
append)
plot.save(output_filename)
# ETA JET 1
zb_hist_names = ["Dijet_jet_hist_unweighted_0/eta_1"]
jet_ht_hist_names = ["Dijet_jet_hist_unweighted_{ind}/eta_1"]
zb_root_files = [
cu.open_root_file(os.path.join(dl[0], qgc.ZB_FILENAME))
for dl in dirname_label_pairs
]
jetht_root_files = [
cu.open_root_file(os.path.join(dl[0], qgc.JETHT_FILENAME))
for dl in dirname_label_pairs
]
N = len(dirname_label_pairs)
rebin = 2
for zb_name, ht_name in zip(zb_hist_names, jet_ht_hist_names):
# add zero bias ones
this_data_entries = [
Contribution(
cu.get_from_tfile(zb_root_files[i], zb_name),
label=zb_entry['label'] + ": " + l,
marker_color=zb_entry['color'],
line_color=zb_entry['color'],
line_style=1 + i,
rebin_hist=rebin,
) for i, (d, l) in enumerate(dirname_label_pairs)
]
for c in this_data_entries[1:]:
c.subplot = this_data_entries[0].obj
# plot zb
plot = Plot(
this_data_entries,
what='hist',
title=title,
xtitle="y^{jet 1}",
ytitle="N",
subplot_type='ratio',
subplot_title='* / %s' % dirname_label_pairs[0][1],
# subplot_limits=(0, 5),
**plot_kwargs)
plot.subplot_maximum_ceil = 5
plot.default_canvas_size = (800, 600)
plot.y_padding_max_log = 500
plot.legend.SetY1(0.7)
plot.legend.SetY2(0.88)
plot.legend.SetX1(0.5)
plot.legend.SetNColumns(2)
plot.plot("NOSTACK HISTE")
output_filename = "%s/DataZB-eta_jet1%s%s.pdf" % (
output_dir, "_unweighted" if 'unweighted' in zb_name else "",
append)
plot.save(output_filename)
# add jet ht ones
for ent in jet_ht_entries:
histname = ht_name.format(ind=ent['ind'])
this_data_entries = [
Contribution(
cu.get_from_tfile(jetht_root_files[i], histname),
label=ent['label'] + ": " + l,
marker_color=ent['color'],
line_color=ent['color'],
line_style=1 + i,
rebin_hist=rebin,
) for i, (d, l) in enumerate(dirname_label_pairs)
]
for c in this_data_entries[1:]:
c.subplot = this_data_entries[0].obj
plot = Plot(
this_data_entries,
what='hist',
title=title,
xtitle="y^{jet 1}",
ytitle="N",
# xlim=[30, 1000],
# ylim=[10, 1E8] if 'unweighted' in ht_name else [1, 1E12],
subplot_type='ratio',
subplot_title='* / %s' % dirname_label_pairs[0][1],
**plot_kwargs)
plot.default_canvas_size = (800, 600)
plot.y_padding_max_log = 500
plot.legend.SetY1(0.7)
plot.legend.SetY2(0.88)
plot.legend.SetX1(0.5)
plot.legend.SetNColumns(2)
plot.plot("NOSTACK HISTE")
output_filename = "%s/DataJetHTZB-%s_eta_jet1%s%s.pdf" % (
output_dir, ent['label'],
"_unweighted" if 'unweighted' in zb_name else "", append)
plot.save(output_filename)
def plot_unfolded_with_yoda_normalised(self,
do_chi2=False,
do_zoomed=True):
data_total_errors_style = dict(
label="Data (total unc.)",
line_color=self.plot_styles['unfolded_total_colour'],
line_width=self.line_width,
line_style=1,
marker_color=self.plot_styles['unfolded_total_colour'],
marker_style=cu.Marker.get('circle'),
marker_size=self.plot_styles['unfolded_marker_size'],
leg_draw_opt="LEP")
data_stat_errors_style = dict(
label="Data (stat. unc.)",
line_color=self.plot_styles['unfolded_stat_colour'],
line_width=self.line_width,
line_style=1,
marker_color=self.plot_styles['unfolded_stat_colour'],
marker_style=cu.Marker.get('circle'),
marker_size=0.0001,
leg_draw_opt="LEP"
) # you need a non-0 marker to get the horizontal bars at the end of errors
mc_style = dict(label=self.region['mc_label'],
line_color=self.plot_styles['gen_colour'],
line_width=self.line_width,
marker_color=self.plot_styles['gen_colour'],
marker_size=self.plot_styles['gen_marker_size'],
marker_style=self.plot_styles['gen_marker'],
leg_draw_opt="LEP"
if self.plot_styles['gen_marker_size'] > 0 else "LE")
rivet_path, rivet_region, rivet_radius, rivet_lambda, rivet_pt_bins = get_matching_rivet_setup(
self.setup)
for ibin, (bin_edge_low, bin_edge_high) in enumerate(
zip(self.bins[:-1], self.bins[1:])):
hbc_args = dict(ind=ibin, binning_scheme='generator')
mc_gen_hist_bin = self.hist_bin_chopper.get_pt_bin_normed_div_bin_width(
'hist_truth', **hbc_args)
unfolded_hist_bin_stat_errors = self.hist_bin_chopper.get_pt_bin_normed_div_bin_width(
'unfolded_stat_err', **hbc_args)
unfolded_hist_bin_total_errors = self.hist_bin_chopper.get_pt_bin_normed_div_bin_width(
'unfolded', **hbc_args)
# Get RIVET hists, which are absolute counts, so need normalising
rivet_hist_name = '/%s/%s' % (
rivet_path,
rn.get_plot_name(rivet_radius, rivet_region, rivet_lambda,
rivet_pt_bins[ibin]))
rivet_hists = [
qgp.normalise_hist_divide_bin_width(
yoda.root.to_root(ent['yoda_dict'][rivet_hist_name]))
for ent in self.rivet_entries
]
# Create copy of data to go on top of stat unc,
# but remove vertical error bar so we can see the stat unc
# Note that you CAN'T set it to 0, otherwise vertical lines connecting
# bins start being drawn. Instead set it to some super small value.
unfolded_hist_bin_total_errors_marker_noerror = unfolded_hist_bin_total_errors.Clone(
) # clone to avoid restyling the original as well
for i in range(
1,
unfolded_hist_bin_total_errors_marker_noerror.GetNbinsX() +
1):
unfolded_hist_bin_total_errors_marker_noerror.SetBinError(
i, 1E-100)
data_entries = [
Contribution(unfolded_hist_bin_total_errors,
**data_total_errors_style),
Contribution(unfolded_hist_bin_stat_errors,
**data_stat_errors_style),
# do data with black marker to get it on top
Contribution(unfolded_hist_bin_total_errors_marker_noerror,
**data_total_errors_style),
]
# For subplot to ensure only MC errors drawn, not MC+data
data_no_errors = unfolded_hist_bin_total_errors_marker_noerror.Clone(
)
cu.remove_th1_errors(data_no_errors)
this_mc_style = deepcopy(mc_style)
rivet_styles = []
for ind, _ in enumerate(rivet_hists):
s_dict = self.rivet_entries[ind]['style_dict']
rivet_styles.append(
dict(label=s_dict['label'],
line_color=s_dict['color'],
line_width=self.line_width,
marker_color=s_dict['color'],
marker_size=s_dict.get(
'marker_size',
self.plot_styles['gen_marker_size']),
marker_style=s_dict['marker_style'],
leg_draw_opt="LEP"
if self.plot_styles['gen_marker_size'] > 0 else "LE"))
# Calculate chi2 between data and MCs if desired
if do_chi2:
# print("unfolded_alt_truth bin", ibin)
ematrix = self.hist_bin_chopper.get_pt_bin_normed_div_bin_width(
self.unfolder.total_ematrix_name, **hbc_args)
# stats are chi2, ndof, p
mc_stats = calc_chi2_stats(unfolded_hist_bin_total_errors,
mc_gen_hist_bin, ematrix)
# print(mc_stats)
# print(alt_mc_stats)
nbins = sum([
1 for i in range(
1,
unfolded_hist_bin_total_errors.GetNbinsX() + 1)
if unfolded_hist_bin_total_errors.GetBinContent(i) != 0
])
# reduced_chi2 = mc_stats[0] / nbins
# alt_reduced_chi2 = alt_mc_stats[0] / nbins
n_sig_fig = 2
chi2_template = "\n#lower[-0.1]{{(#chi^{{2}} / N_{{bins}} = {chi2:g} / {nbins:d})}}"
this_mc_style['label'] += chi2_template.format(chi2=cu.nsf(
mc_stats[0], n_sig_fig),
nbins=nbins)
for ind, h in enumerate(rivet_hists):
this_stats = calc_chi2_stats(
unfolded_hist_bin_total_errors, h, ematrix)
rivet_styles[ind]['label'] += chi2_template.format(
chi2=cu.nsf(this_stats[0], n_sig_fig), nbins=nbins)
mc_entries = [
Contribution(mc_gen_hist_bin,
subplot=data_no_errors,
**this_mc_style),
]
for h, s_dict in zip(rivet_hists, rivet_styles):
mc_entries.append(
Contribution(h, subplot=data_no_errors, **s_dict))
entries = [
# Draw MC
*mc_entries,
# Draw data after to put on top of MC
*data_entries
]
func_name = cu.get_current_func_name()
if not self.check_entries(entries, "%s bin %d" %
(func_name, ibin)):
return
ymin = 0
if np.any(
cu.th1_to_ndarray(unfolded_hist_bin_total_errors)[0] < 0):
ymin = None # let it do its thing and auto calc ymin
max_rel_err = 0.5 if "multiplicity" in self.setup.angle.var.lower(
) else -1
plot = Plot(
entries,
ytitle=self.setup.pt_bin_normalised_differential_label,
title=self.get_pt_bin_title(bin_edge_low, bin_edge_high),
legend=True,
xlim=qgp.calc_auto_xlim(
entries[2:3],
max_rel_err=0.5), # set x lim to where data is non-0
ylim=[ymin, None],
**self.pt_bin_plot_args)
plot.subplot_title = qgc.SIM_DATA_STR
self._modify_plot_paper(plot)
# disable adding objects to legend & drawing - we'll do it manually
plot.do_legend = False
plot.legend.SetTextSize(0.03)
plot.legend.SetY1(0.6)
plot.legend.SetX1(0.57)
plot.legend.SetX2(0.93)
if len(entries) > 4:
# if lots of entries, try auto-expand
plot.legend.SetY1(0.6 - (0.02 * (len(entries) - 4)))
# plot.legend.SetEntrySeparation(0.005)
subplot_draw_opts = "NOSTACK E1"
plot.plot("NOSTACK E1", subplot_draw_opts)
dummy_graphs = qgp.do_fancy_legend(chain(data_entries[:2],
mc_entries),
plot,
use_splitline=False)
plot.canvas.cd()
plot.legend.Draw()
# Create hists for data with error region for ratio
# Easiest way to get errors right is to do data (with 0 errors)
# and divide by data (with errors), as if you had MC = data with 0 error
data_stat_ratio = data_no_errors.Clone()
data_stat_ratio.Divide(unfolded_hist_bin_stat_errors)
data_stat_ratio.SetFillStyle(3245)
data_stat_ratio.SetFillColor(
self.plot_styles['unfolded_stat_colour'])
data_stat_ratio.SetLineWidth(0)
data_stat_ratio.SetMarkerSize(0)
data_total_ratio = data_no_errors.Clone()
data_total_ratio.Divide(unfolded_hist_bin_total_errors)
data_total_ratio.SetFillStyle(3254)
data_total_ratio.SetFillColor(
self.plot_styles['unfolded_total_colour'])
data_total_ratio.SetLineWidth(0)
data_total_ratio.SetMarkerSize(0)
# now draw the data error shaded area
# this is a bit hacky - basically draw them on the ratio pad,
# then redraw the existing hists & line to get them ontop
# note that we use "same" for all - this is to keep the original axes
# (we may want to rethink this later?)
plot.subplot_pad.cd()
draw_opt = "E2 SAME"
data_stat_ratio.Draw(draw_opt)
data_total_ratio.Draw(draw_opt)
plot.subplot_line.Draw()
plot.subplot_container.Draw("SAME" + subplot_draw_opts)
# Add subplot legend
x_left = 0.25
y_bottom = 0.75
width = 0.67
height = 0.15
plot.subplot_legend = ROOT.TLegend(x_left, y_bottom,
x_left + width,
y_bottom + height)
plot.subplot_legend.AddEntry(data_total_ratio,
qgc.DATA_TOTAL_UNC_STR, "F")
plot.subplot_legend.AddEntry(data_stat_ratio,
qgc.DATA_STAT_UNC_STR, "F")
plot.subplot_legend.SetTextSize(0.085)
plot.subplot_legend.SetFillStyle(0)
plot.subplot_legend.SetNColumns(2)
plot.subplot_legend.Draw()
plot.canvas.cd()
stp = self.setup
fname = f"unfolded_{stp.append}_rivet_bin_{ibin:d}_divBinWidth{stp.paper_str}.{stp.output_fmt}"
self.save_plot(plot, os.path.join(stp.output_dir, fname))
# Do version with small x values only
if do_zoomed:
if self.setup.angle.var in [
"jet_thrust_charged", "jet_width_charged",
"jet_thrust", "jet_width"
]:
# plot.ylim = (1E-5)
plot.y_padding_max_log = 50
plot.y_padding_min_log = 0.5
plot.ylim = None
plot.set_logy(do_exponent=False, do_more_labels=False)
fname = f"unfolded_{stp.append}_alt_truth_bin_{ibin:d}_divBinWidth_logY.{stp.output_fmt}"
self.save_plot(plot, os.path.join(stp.output_dir, fname))
if self.setup.angle.var in [
"jet_LHA_charged", "jet_thrust_charged",
"jet_width_charged", "jet_thrust", "jet_width"
]:
bin_edges = cu.get_bin_edges(mc_gen_hist_bin, 'x')
# get the bin edge thats smallest between 0.2, and 5th bin
bin_lt_lim = [x for x in bin_edges if x < 0.2][-1]
upper_bin = min(bin_edges[5], bin_lt_lim)
plot2 = Plot(
entries,
ytitle=self.setup.pt_bin_normalised_differential_label,
title=self.get_pt_bin_title(bin_edge_low,
bin_edge_high),
xlim=(0, upper_bin),
**self.pt_bin_plot_args)
self._modify_plot(plot2)
plot2.subplot_title = "* / Generator"
plot2.plot("NOSTACK E1")
# plot2.set_logx(do_exponent=False)
fname = f"unfolded_{stp.append}_rivet_bin_{ibin:d}_divBinWidth_lowX.{stp.output_fmt}"
self.save_plot(plot2, os.path.join(stp.output_dir, fname))
