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_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 do_pileup_plot(input_dir, trigger_names, output_filename):
# get histograms
hists = [
cu.grab_obj_from_file(
os.path.join(input_dir, 'MyDataPileupHistogram_%s.root' % t),
'pileup') for t in trigger_names
]
h_up = cu.grab_obj_from_file(
os.path.join(input_dir, 'MyDataPileupHistogram_PFJet500_72383.root'),
'pileup')
h_down = cu.grab_obj_from_file(
os.path.join(input_dir, 'MyDataPileupHistogram_PFJet500_66017.root'),
'pileup')
h_down3 = cu.grab_obj_from_file(
os.path.join(input_dir, 'MyDataPileupHistogram_PFJet500_59650.root'),
'pileup')
ratio_up = h_up.Clone()
ratio_up.Divide(hists[-1])
ratio_down = h_down.Clone()
ratio_down.Divide(hists[-1])
# hists.append(h_up)
# hists.append(h_down)
# trigger_names.append('72.383')
# trigger_names.append('66.017')
# Create contributions
mark = cu.Marker()
n_hists = len(hists)
conts = [
Contribution(h,
label=t,
line_width=2,
line_color=cu.get_colour_seq(ind, n_hists),
marker_color=cu.get_colour_seq(ind, n_hists),
marker_style=m,
normalise_hist=True)
for ind, (h, t, m) in enumerate(
zip(hists, trigger_names, mark.cycle(cycle_filling=True)))
]
conts.insert(
-1,
Contribution(
h_up,
label='72.383 (+4.6%)',
line_width=2,
line_color=ROOT.kRed,
marker_color=ROOT.kRed,
normalise_hist=True,
))
conts.insert(
-1,
Contribution(
h_down,
label='66.017 (-4.6%)',
line_width=2,
line_color=ROOT.kMagenta,
marker_color=ROOT.kMagenta,
normalise_hist=True,
))
conts.insert(
-1,
Contribution(
h_down3,
label='59.650 (-13.8%)',
line_width=2,
line_style=1,
line_color=ROOT.kMagenta + 3,
marker_color=ROOT.kMagenta + 3,
normalise_hist=True,
))
print([h.Integral() for h in hists])
plot = Plot(
conts,
what='hist',
xtitle='Pileup',
lumi=cu.get_lumi_str(do_dijet=True, do_zpj=True),
subplot_type='ratio',
subplot=conts[-1],
)
plot.subplot_maximum_ceil = 4.5
plot.plot("NOSTACK HISTE", "NOSTACK HIST")
plot.subplot_pad.cd()
# To shade region between up and down hists, need to create graph with
# the error bars as the up/down variations
x_low = np.array([
ratio_up.GetBinLowEdge(i) for i in range(1,
ratio_up.GetNbinsX() + 1)
],
dtype=float)
x_high = np.array([
ratio_up.GetBinLowEdge(i) for i in range(2,
ratio_up.GetNbinsX() + 2)
],
dtype=float)
x_mid = 0.5 * (x_low + x_high)
x_err = x_high - x_low
y_high = np.array([
max(ratio_up.GetBinContent(i), ratio_down.GetBinContent(i))
for i in range(1,
ratio_up.GetNbinsX() + 1)
])
y_high -= 1
y_low = np.array([
min(ratio_up.GetBinContent(i), ratio_down.GetBinContent(i))
for i in range(1,
ratio_up.GetNbinsX() + 1)
])
y_low = 1 - y_low
y_mid = np.array([1. for i in range(1, ratio_up.GetNbinsX() + 1)])
gr = ROOT.TGraphAsymmErrors(len(x_mid), x_mid, y_mid, x_err, x_err, y_low,
y_high)
gr.SetFillColor(ROOT.kGray + 2)
gr.SetFillStyle(3254)
gr.Draw('2 SAME')
plot.subplot_container.Draw("SAME NOSTACK HIST")
plot.subplot_line.Draw()
plot.save(output_filename)
