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_plot(entries, output_file, hist_name=None, xlim=None, ylim=None, rebin=2, is_data=True, is_ak8=False):
components = []
do_unweighted = any(["unweighted" in e.get('hist_name', hist_name) for e in entries])
for ent in entries:
if 'tfile' not in ent:
ent['tfile'] = cu.open_root_file(ent['filename'])
ent['hist'] = cu.get_from_tfile(ent['tfile'], ent.get('hist_name', hist_name))
if not do_unweighted and 'scale' in ent:
ent['hist'].Scale(ent.get('scale', 1))
components.append(
Contribution(ent['hist'],
fill_color=ent['color'],
line_color=ent['color'],
marker_color=ent['color'],
marker_size=0,
line_width=2,
label=ent['label'],
rebin_hist=rebin
)
)
# print stats
print(ent['hist_name'], ent['label'], ent['hist'].Integral())
title = 'AK8 PUPPI' if is_ak8 else 'AK4 PUPPI'
plot = Plot(components,
what='hist',
has_data=is_data,
title=title,
xlim=xlim,
ylim=ylim,
xtitle="p_{T}^{jet 1} [GeV]",
ytitle="Unweighted N" if do_unweighted else 'N')
# plot.y_padding_min_log = 10 if 'unweighted' in hist_name else 10
plot.default_canvas_size = (700, 600)
plot.legend.SetNColumns(2)
plot.legend.SetX1(0.55)
plot.legend.SetY1(0.7)
plot.legend.SetY2(0.88)
plot.plot("HISTE")
plot.set_logx()
plot.set_logy(do_more_labels=False)
plot.save(output_file)
# do non-stacked version
stem, ext = os.path.splitext(output_file)
plot.plot("HISTE NOSTACK")
plot.set_logx()
plot.set_logy(do_more_labels=False)
plot.save(stem+"_nostack" + ext)
def do_response_graph(pt_bins, zpj_fits, dj_fits, title, output_filename):
"""Create and plot graph from fit results
Parameters
----------
zpj_fits : TF1
Description
dj_fits : TF1
Description
output_filename : str
Description
"""
gr_zpj = fits_to_graph(pt_bins, zpj_fits)
gr_qcd = fits_to_graph(pt_bins, dj_fits)
conts = [
Contribution(gr_zpj,
label=qgc.DY_ZpJ_LABEL,
line_color=qgc.DY_COLOUR,
marker_color=qgc.DY_COLOUR,
marker_style=22),
Contribution(gr_qcd,
label=qgc.QCD_Dijet_LABEL,
line_color=qgc.QCD_COLOUR,
marker_color=qgc.QCD_COLOUR,
marker_style=23)
]
xmin = pt_bins[0][0]
xmax = pt_bins[-1][1]
plot = Plot(conts,
what="graph",
legend=True,
xlim=(xmin, xmax),
title=title,
xtitle="p_{T}^{GenJet} [GeV]",
ytitle="Mean fitted response #pm fit error")
plot.plot("ALP")
plot.set_logx()
line_center = ROOT.TLine(xmin, 1, xmax, 1)
line_center.SetLineStyle(2)
line_center.Draw("SAME")
line_upper = ROOT.TLine(xmin, 1.1, xmax, 1.1)
line_upper.SetLineStyle(2)
line_upper.Draw("SAME")
line_lower = ROOT.TLine(xmin, 0.9, xmax, 0.9)
line_lower.SetLineStyle(2)
line_lower.Draw("SAME")
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 compare_flavour_fraction_hists_vs_pt_from_contribs(
contribs,
flav,
output_filename,
title="",
xtitle="p_{T}^{jet} [GeV]",
**plot_kwargs):
"""Plot a specified flavour fraction vs pT for several sources.
TODO: use this one more often - compare_flavour_fractions_vs_pt() is almost identical but has to deal with npartons
"""
flav_str = FLAV_STR_DICT[flav]
ytitle = "Fraction of %s %ss" % (flav_str.lower(), get_jet_str(''))
p = Plot(contribs,
what='graph',
xtitle=xtitle,
ytitle=ytitle,
title=title,
xlim=(50, 2000),
ylim=(0, 1),
has_data=False,
**plot_kwargs)
p.default_canvas_size = (600, 600)
try:
p.plot("AP")
p.main_pad.SetBottomMargin(0.16)
p.get_modifier().GetXaxis().SetTitleOffset(1.4)
p.get_modifier().GetXaxis().SetTitleSize(.045)
p.legend.SetX1(0.56)
p.legend.SetY1(0.65)
p.legend.SetY2(0.87)
if len(contribs) >= 4:
p.legend.SetY1(0.7)
p.legend.SetX1(0.5)
p.legend.SetNColumns(2)
p.set_logx(do_more_labels=True, do_exponent=False)
p.save(output_filename)
except ZeroContributions:
warnings.warn("No contributions for %s" % 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_pt_transfer_plot(tdir, plot_dir):
"""Plot ratio between pt bins of the spectrum. Check to make sure xfer factor << drop in pt"""
plot_dir = os.path.join(plot_dir, tdir.GetName())
cu.check_dir_exists_create(plot_dir)
hist_name = "pt_jet_response_binning"
h = cu.get_from_tfile(tdir, hist_name)
binning = [
h.GetXaxis().GetBinLowEdge(bin_ind)
for bin_ind in range(1,
h.GetNbinsX() + 1)
]
hist_factors = ROOT.TH1F(
"hist_factors" + cu.get_unique_str(),
";p_{T}^{Reco} [GeV];Fraction rel to previous bin",
len(binning) - 1, array('d', binning))
for bin_ind in range(2, h.GetNbinsX() + 1):
cont = h.GetBinContent(bin_ind)
cont_prev = h.GetBinContent(bin_ind - 1)
if cont == 0 or cont_prev == 0:
continue
factor = cont / cont_prev
hist_factors.SetBinContent(bin_ind, factor)
hist_factors.SetBinError(bin_ind, 0)
col_purity = ROOT.kBlack
conts = [
Contribution(hist_factors,
label="Factor relative to previous bin",
line_color=col_purity,
marker_color=col_purity),
# Contribution(hist_purity, label="Purity (gen in right bin)", line_color=col_purity, marker_color=col_purity),
]
xlim = [30, binning[-1]]
plot = Plot(conts, what='hist', xlim=xlim)
plot.plot()
plot.set_logx()
plot.save(os.path.join(plot_dir, 'pt_migration_factors.%s' % (OUTPUT_FMT)))
def compare_flavour_fractions_vs_pt(input_files,
dirnames,
pt_bins,
labels,
flav,
output_filename,
title="",
var_prepend="",
which_jet="both",
xtitle="p_{T}^{jet} [GeV]",
n_partons='all',
is_preliminary=True):
"""Plot a specified flavour fraction vs pT for several sources.
Each entry in input_files, dirnames, and labels corresponds to one line
n_partons can be a str, 'all', '1', etc, or a list of str to include
TODO: fix this - bit stupid input format
"""
bin_centers = [0.5 * (x[0] + x[1]) for x in pt_bins]
bin_widths = [0.5 * (x[1] - x[0]) for x in pt_bins]
if isinstance(n_partons, str):
n_partons = [n_partons]
contribs = []
for n_parton_ind, n_parton in enumerate(n_partons):
metric = 'pt'
if n_parton.lower() != 'all':
metric = 'pt_npartons_%s' % n_parton
info = [
get_flavour_efficiencies(
ifile,
bins=pt_bins,
hist_name=get_flavour_hist_name(
dirname=dname,
var_prepend=var_prepend,
which_jet=(which_jet if "Dijet" in dname else "both"),
metric=metric))
for ifile, dname in zip(input_files, dirnames)
]
N = len(bin_centers)
colours = [ROOT.kBlack, ROOT.kBlue, ROOT.kRed, ROOT.kGreen + 2]
for i, fdict in enumerate(info):
if flav in ['u', 'd', 's', 'c', 'b', 't', 'g']:
obj = fdict[flav].CreateGraph()
else:
raise RuntimeError("Robin broke 1-X functionality")
obj = ROOT.TGraphErrors(
N, np.array(bin_centers),
1. - np.array(fdict[flav.replace("1-", '')]),
np.array(bin_widths), np.zeros(N))
if obj.GetN() == 0:
continue
n_parton_str = "" if n_parton == "all" else " (%s-parton)" % n_parton
c = Contribution(obj,
label="%s%s" % (labels[i], n_parton_str),
line_color=colours[i] + n_parton_ind,
line_width=1,
line_style=n_parton_ind + 1,
marker_style=20 + i,
marker_color=colours[i] + n_parton_ind,
marker_size=1,
leg_draw_opt="LP")
contribs.append(c)
flav_str = FLAV_STR_DICT[flav]
ytitle = "Fraction of %s %ss" % (flav_str.lower(), get_jet_str(''))
p = Plot(contribs,
what='graph',
xtitle=xtitle,
ytitle=ytitle,
title=title,
xlim=(pt_bins[0][0], pt_bins[-1][1]),
ylim=(0, 1),
has_data=False,
is_preliminary=is_preliminary)
p.default_canvas_size = (600, 600)
try:
p.plot("AP")
p.main_pad.SetBottomMargin(0.16)
p.get_modifier().GetXaxis().SetTitleOffset(1.4)
p.get_modifier().GetXaxis().SetTitleSize(.045)
p.legend.SetX1(0.56)
p.legend.SetY1(0.65)
p.legend.SetY2(0.87)
p.set_logx(do_more_labels=True, do_exponent=False)
p.save(output_filename)
except ZeroContributions:
pass
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_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_1D_plot(hists,
output_filename,
components_styles_dicts=None,
draw_opts="NOSTACK HISTE",
do_ratio=True,
logx=False,
logy=False,
normalise_hists=True,
title=""):
if (len(hists) != len(components_styles_dicts)):
raise RuntimeError("# hists != # components_styles_dicts (%d vs %d)" %
(len(hists), len(components_styles_dicts)))
hists = [h.Clone(h.GetName() + str(uuid1())) for h in hists]
contributions = [
Contribution(hist, normalise_hist=normalise_hists, **csd)
for hist, csd in zip(hists, components_styles_dicts)
]
if len(contributions) == 0:
return
# Ignore if all empty objs
total_entries = sum(c.obj.GetEntries() for c in contributions)
if total_entries == 0:
print("WARNING: all plots have 0 entries")
return
min_val = min([h.GetMinimum(0) for h in hists])
max_val = max([h.GetMaximum() for h in hists])
# print("Auto y limits:", min_val, max_val)
if logy:
ylim = [0.5 * min_val, 50 * max_val]
else:
# ylim = [0.5*min_val, 1.5*max_val]
ylim = [0, 1.5 * max_val]
# Auto calc x limits to avoid lots of empty bins
high_bin = max([find_largest_filled_bin(h)[0] for h in hists])
low_bin = min([find_first_filled_bin(h)[0] for h in hists])
xlim = [
hists[0].GetBinLowEdge(low_bin - 1),
hists[0].GetBinLowEdge(high_bin + 2)
]
p = Plot(
contributions,
what='hist',
ytitle="p.d.f." if normalise_hists else "N",
title=title,
xlim=xlim,
ylim=ylim,
subplot_type="ratio" if do_ratio else None,
subplot_title="Herwig / PY8",
subplot=contributions[0],
subplot_limits=(0.5, 1.5),
)
# p.legend.SetX1(0.55)
# # p.legend.SetX2(0.95)
# p.legend.SetY1(0.7)
# p.legend.SetY2(0.85)
p.legend.SetX1(0.5)
p.legend.SetX2(0.97)
if len(contributions) > 4:
p.legend.SetY1(0.6)
else:
p.legend.SetY1(0.7)
p.legend.SetY2(0.9)
p.plot(draw_opts)
if logy:
p.set_logy()
if logx:
p.set_logx()
p.save(output_filename)
def do_1D_plot(hists, output_filename, components_styles_dicts=None,
draw_opts="NOSTACK HISTE", do_ratio=True, logx=False, logy=False,
normalise_hists=True, title=""):
if (len(hists) != len(components_styles_dicts)):
raise RuntimeError("# hists != # components_styles_dicts (%d vs %d)" % (len(hists), len(components_styles_dicts)))
hists = [h.Clone(cu.get_unique_str()) for h in hists]
contributions = [Contribution(hist,
normalise_hist=normalise_hists,
label=csd.get('label', 'x'),
**csd.get('style', {}))
for hist, csd in zip(hists, components_styles_dicts)]
if len(contributions) == 0:
return
# Ignore if all empty objs
total_entries = sum(c.obj.GetEntries() for c in contributions)
if total_entries == 0:
print("WARNING: all plots have 0 entries")
return
min_val = min([h.GetMinimum(0) for h in hists])
max_val = max([h.GetMaximum() for h in hists])
# print("Auto y limits:", min_val, max_val)
if logy:
ylim = [0.5*min_val, 50*max_val]
else:
# ylim = [0.5*min_val, 1.5*max_val]
ylim = [0, 1.5*max_val]
# Auto calc x limits to avoid lots of empty bins
high_bin = max([find_largest_filled_bin(h)[0] for h in hists])
low_bin = min([find_first_filled_bin(h)[0] for h in hists])
xlim = [hists[0].GetBinLowEdge(low_bin-1), hists[0].GetBinLowEdge(high_bin+2)]
subplot_title = "* / %s" % contributions[0].label
if len(contributions[0].label) > 10:
# need padding to centralise it
padding = ' ' * int(len(contributions[0].label)/2)
subplot_title = "#splitline{%s* /%s}{%s}" % (padding, padding, contributions[0].label)
p = Plot(contributions, what='hist',
ytitle="#DeltaN/N" if normalise_hists else "N",
title=title,
xlim=xlim,
ylim=ylim,
subplot_type="ratio" if do_ratio else None,
subplot_title=subplot_title,
subplot=contributions[0],
# subplot_limits=(0.5, 1.5),
# subplot_limits=(0, 2) if logy else (0.5, 1.5),
subplot_limits=(0, 2.5) if logy else (0.5, 1.5),
)
# p.legend.SetX1(0.55)
# # p.legend.SetX2(0.95)
# p.legend.SetY1(0.7)
# p.legend.SetY2(0.85)
p.legend.SetX1(0.5)
p.legend.SetX2(0.97)
if len(contributions) > 4:
p.legend.SetY1(0.6)
else:
p.legend.SetY1(0.7)
p.legend.SetY2(0.9)
p.plot(draw_opts)
if logy:
p.set_logy(do_more_labels=False)
if logx:
p.set_logx(do_more_labels=False)
# p.save(os.path.join(output_dir, obj_name+".%s" % (OUTPUT_FMT)))
p.save(output_filename)
def draw_folded_hists_physical(hist_mc_folded,
hist_mc_reco,
hist_data_reco,
output_filename,
title="",
xtitle="",
logx=False,
logy=False):
entries = []
if hist_mc_folded:
entries.append(
Contribution(hist_mc_folded,
label="Folded MC [detector-level]",
line_color=ROOT.kGreen + 2,
line_width=1,
marker_color=ROOT.kGreen + 2,
marker_size=0,
normalise_hist=False,
subplot=hist_data_reco), )
if hist_mc_reco:
entries.append(
Contribution(hist_mc_reco,
label="Reco MC [detector-level]",
line_color=ROOT.kAzure + 2,
line_width=1,
line_style=2,
marker_color=ROOT.kAzure + 2,
marker_size=0,
normalise_hist=False,
subplot=hist_data_reco), )
if hist_data_reco:
entries.append(
Contribution(hist_data_reco,
label="Reco Data [detector-level]",
line_color=ROOT.kRed,
line_width=0,
marker_color=ROOT.kRed,
marker_size=0.6,
marker_style=20,
normalise_hist=False), )
plot = Plot(entries,
what='hist',
title=title,
xtitle=xtitle,
ytitle="N",
subplot_type='ratio',
subplot_title='MC/Data',
subplot_limits=(0.25, 1.75))
plot.default_canvas_size = (800, 600)
plot.plot("NOSTACK HISTE")
if logx:
plot.set_logx(True)
if logy:
plot.main_pad.SetLogy(1)
ymax = max(h.GetMaximum()
for h in [hist_mc_folded, hist_mc_reco, hist_data_reco] if h)
plot.container.SetMaximum(ymax * 100)
plot.container.SetMinimum(1)
plot.legend.SetY1NDC(0.77)
plot.legend.SetX2NDC(0.85)
plot.save(output_filename)
def do_flavour_fraction_vs_pt(input_file,
hist_name,
pt_bins,
output_filename,
title=""):
"""Plot all flavour fractions vs PT for one input file & hist_name in the ROOT file"""
info = get_flavour_efficiencies(input_file,
bins=pt_bins,
hist_name=hist_name)
leg_draw_opt = "LP"
plot_u = Contribution(info['u'].CreateGraph(),
label="Up",
line_color=ROOT.kRed,
marker_color=ROOT.kRed,
marker_style=20,
leg_draw_opt=leg_draw_opt)
plot_d = Contribution(info['d'].CreateGraph(),
label="Down",
line_color=ROOT.kBlue,
marker_color=ROOT.kBlue,
marker_style=21,
leg_draw_opt=leg_draw_opt)
plot_s = Contribution(info['s'].CreateGraph(),
label="Strange",
line_color=ROOT.kBlack,
marker_color=ROOT.kBlack,
marker_style=22,
leg_draw_opt=leg_draw_opt)
plot_c = Contribution(info['c'].CreateGraph(),
label="Charm",
line_color=ROOT.kGreen + 2,
marker_color=ROOT.kGreen + 2,
marker_style=23,
leg_draw_opt=leg_draw_opt)
plot_b = Contribution(info['b'].CreateGraph(),
label="Bottom",
line_color=ROOT.kOrange - 3,
marker_color=ROOT.kOrange - 3,
marker_style=33,
leg_draw_opt=leg_draw_opt)
plot_g = Contribution(info['g'].CreateGraph(),
label="Gluon",
line_color=ROOT.kViolet,
marker_color=ROOT.kViolet,
marker_style=29,
leg_draw_opt=leg_draw_opt)
plot_unknown = Contribution(info['unknown'].CreateGraph(),
label="Unknown",
line_color=ROOT.kGray + 1,
marker_color=ROOT.kGray + 1,
marker_style=26,
leg_draw_opt=leg_draw_opt)
p_flav = Plot(
[plot_d, plot_u, plot_s, plot_c, plot_b, plot_g, plot_unknown],
what='graph',
xtitle="p_{T}^{%s} [GeV]" % get_jet_str(''),
ytitle="Fraction",
title=title,
xlim=(pt_bins[0][0], pt_bins[-1][1]),
ylim=[0, 1],
has_data=False)
p_flav.default_canvas_size = (600, 600)
p_flav.plot("AP")
p_flav.main_pad.SetBottomMargin(0.16)
p_flav.get_modifier().GetXaxis().SetTitleOffset(1.4)
p_flav.get_modifier().GetXaxis().SetTitleSize(.045)
p_flav.set_logx(do_more_labels=True, do_exponent=False)
p_flav.legend.SetX1(0.55)
p_flav.legend.SetY1(0.72)
p_flav.legend.SetY2(0.85)
p_flav.legend.SetNColumns(2)
p_flav.save(output_filename)
def compare_flavour_fraction_hists_vs_pt(input_files,
hist_names,
pt_bins,
labels,
flav,
output_filename,
title="",
xtitle="p_{T}^{jet} [GeV]",
is_preliminary=True):
"""Plot a specified flavour fraction vs pT for several sources.
Each entry in input_files, dirnames, and labels corresponds to one line
n_partons can be a str, 'all', '1', etc, or a list of str to include
TODO: use this one more often - compare_flavour_fractions_vs_pt() is almost identical but has to deal with npartons
"""
bin_centers = [0.5 * (x[0] + x[1]) for x in pt_bins]
bin_widths = [0.5 * (x[1] - x[0]) for x in pt_bins]
contribs = []
info = [
get_flavour_efficiencies(ifile, bins=pt_bins, hist_name=hname)
for ifile, hname in zip(input_files, hist_names)
]
N = len(bin_centers)
colours = [ROOT.kBlack, ROOT.kBlue, ROOT.kRed, ROOT.kGreen + 2]
for i, fdict in enumerate(info):
if flav in ['u', 'd', 's', 'c', 'b', 't', 'g']:
obj = fdict[flav].CreateGraph()
else:
raise RuntimeError("Robin broke 1-X functionality")
obj = ROOT.TGraphErrors(
N, np.array(bin_centers),
1. - np.array(fdict[flav.replace("1-", '')]),
np.array(bin_widths), np.zeros(N))
if obj.GetN() == 0:
continue
c = Contribution(obj,
label=labels[i],
line_color=colours[i],
line_width=1,
line_style=1,
marker_style=20 + i,
marker_color=colours[i],
marker_size=1,
leg_draw_opt="LP")
contribs.append(c)
flav_str = FLAV_STR_DICT[flav]
ytitle = "Fraction of %s %ss" % (flav_str.lower(), get_jet_str(''))
p = Plot(contribs,
what='graph',
xtitle=xtitle,
ytitle=ytitle,
title=title,
xlim=(50, 2000),
ylim=(0, 1),
has_data=False,
is_preliminary=is_preliminary)
p.default_canvas_size = (600, 600)
try:
p.plot("AP")
p.main_pad.SetBottomMargin(0.16)
p.get_modifier().GetXaxis().SetTitleOffset(1.4)
p.get_modifier().GetXaxis().SetTitleSize(.045)
p.legend.SetX1(0.56)
p.legend.SetY1(0.65)
p.legend.SetY2(0.87)
p.set_logx(do_more_labels=True, do_exponent=False)
p.save(output_filename)
except ZeroContributions:
pass
def make_resolution_plots(h2d,
xlabel,
output_filename,
do_fit=True,
do_rms=True,
quantiles=None,
log_var=False,
save_response_hists=False):
"""Make graph of resolution vs variables.
Also optionally save all input histograms to file.
"""
one_sigma = 0.682689
quantiles = quantiles or [0.5 * (1 - one_sigma), 1 - 0.5 * (1 - one_sigma)]
ax = h2d.GetXaxis()
bin_edges = [ax.GetBinLowEdge(i) for i in range(1, ax.GetNbins() + 1)]
bin_centers, sigmas, sigmas_unc = [], [], []
rel_sigmas, rel_sigmas_unc = [], []
# bin_centers = [ax.GetBinCenter(i) for i in range(1, ax.GetNbins()+1)]
for var_min, var_max in zip(bin_edges[:-1], bin_edges[1:]):
h_projection = qgg.get_projection_plot(h2d,
var_min,
var_max,
cut_axis='x')
if h_projection.GetEffectiveEntries() < 20:
continue
# h_projection.Rebin(rebin)
h_projection.Scale(1. / h_projection.Integral())
bin_centers.append(0.5 * (var_max + var_min))
if do_fit:
do_gaus_fit(h_projection)
fit = h_projection.GetFunction("gausFit")
# label += "\n"
# label += fit_results_to_str(fit)
# bin_centers.append(fit.GetParameter(1))
sigmas.append(fit.GetParameter(2))
rel_sigmas.append(fit.GetParameter(2) / bin_centers[-1])
sigmas_unc.append(fit.GetParError(2))
rel_sigmas_unc.append(fit.GetParError(2) / bin_centers[-1])
else:
if do_rms:
sigmas.append(sqrt(h_projection.GetRMS()))
rel_sigmas.append(
sqrt(h_projection.GetRMS()) / bin_centers[-1])
sigmas_unc.append(sqrt(h_projection.GetRMSError()))
rel_sigmas_unc.append(
sqrt(h_projection.GetRMSError()) / bin_centers[-1])
elif quantiles:
if len(quantiles) != 2:
raise RuntimeError("Need 2 quantiles")
q = array('d', quantiles)
results = array('d', [0.] * len(quantiles))
h_projection.GetQuantiles(len(quantiles), results, q)
sigmas.append(results[1] - results[0])
sigmas_unc.append(0)
rel_sigmas.append((results[1] - results[0]) / bin_centers[-1])
rel_sigmas_unc.append(0)
else:
raise RuntimeError(
"Need either do_fit, do_rms, or 2-tuple in quantiles")
if save_response_hists:
xlabel = h_projection.GetXaxis().GetTitle()
cont = Contribution(h_projection,
label="GEN: %g-%g" % (var_min, var_max))
p = Plot([cont], what='hist')
p.plot('HISTE')
rsp_filename = os.path.abspath(
output_filename.replace(
".%s" % OUTPUT_FMT,
"_hist%gto%g.%s" % (var_min, var_max, OUTPUT_FMT)))
rsp_dir = os.path.dirname(rsp_filename)
rsp_file = os.path.basename(rsp_filename)
p.save(os.path.join(rsp_dir, "responseHists", rsp_file))
gr = ROOT.TGraphErrors(len(bin_centers), array('d', bin_centers),
array('d', sigmas),
array('d', [0] * len(bin_centers)),
array('d', sigmas_unc))
gr_cont = Contribution(gr, label="")
ylabel = ""
if do_fit:
ylabel = "Fit #sigma"
elif do_rms:
ylabel = "#sqrt{RMS}"
elif quantiles:
ylabel = "Central %g" % one_sigma
plot = Plot([gr_cont],
what='graph',
xtitle=xlabel,
ytitle=ylabel,
xlim=[bin_edges[0], bin_edges[-1]],
ylim=[0, max(sigmas) * 1.2],
legend=False)
plot.plot()
if log_var:
plot.set_logx()
plot.save(output_filename)
gr_rel = ROOT.TGraphErrors(len(bin_centers), array('d', bin_centers),
array('d', rel_sigmas),
array('d', [0] * len(bin_centers)),
array('d', rel_sigmas_unc))
gr_rel_cont = Contribution(gr_rel, label="")
ylabel = "Relative %s" % ylabel
plot = Plot([gr_rel_cont],
what='graph',
xtitle=xlabel,
ytitle=ylabel,
xlim=[bin_edges[0], bin_edges[-1]],
ylim=[min(rel_sigmas) / 1.2,
max(rel_sigmas) * 1.2],
legend=False)
plot.plot()
plot.set_logy()
if log_var:
plot.set_logx()
plot.save(
output_filename.replace(".%s" % OUTPUT_FMT,
"_relative.%s" % OUTPUT_FMT))
return gr, gr_rel
def do_projection_plots(in_file, plot_dir, do_fit=True, skip_dirs=None):
hist_name = "pt_jet_response"
tfile = cu.open_root_file(in_file)
dirs = cu.get_list_of_element_names(tfile)
for mydir in dirs:
if skip_dirs and mydir in skip_dirs:
continue
if hist_name not in cu.get_list_of_element_names(tfile.Get(mydir)):
continue
print("Doing", mydir)
h2d = cu.grab_obj_from_file(in_file, "%s/%s" % (mydir, hist_name))
ax = h2d.GetXaxis()
bin_edges = [ax.GetBinLowEdge(i) for i in range(1, ax.GetNbins() + 2)]
bin_centers, sigmas, sigmas_unc = [], [], []
for pt_min, pt_max in zip(bin_edges[:-1], bin_edges[1:]):
obj = qgg.get_projection_plot(h2d, pt_min, pt_max, cut_axis='x')
if obj.GetEffectiveEntries() < 20:
continue
# obj.Rebin(rebin)
obj.Scale(1. / obj.Integral())
label = "%s < p_{T}^{Gen} < %s GeV" % (str(pt_min), str(pt_max))
if do_fit:
do_gaus_fit(obj)
fit = obj.GetFunction("gausFit")
label += "\n"
label += fit_results_to_str(fit)
# bin_centers.append(fit.GetParameter(1))
bin_centers.append(0.5 * (pt_max + pt_min))
sigmas.append(fit.GetParameter(2))
sigmas_unc.append(fit.GetParError(2))
# output_filename = os.path.join(plot_dir, "%s_%s_ptGen%sto%s.%s" % (mydir, hist_name, str(pt_min), str(pt_max), OUTPUT_FMT))
# cont = Contribution(obj, label=label)
# delta = pt_max - pt_min
# # xlim = (pt_min - 10*delta, pt_max + 10*delta)
# xlim = (obj.GetMean()-3*obj.GetRMS(), obj.GetMean()+3*obj.GetRMS())
# ylim = (0, obj.GetMaximum()*1.1)
# plot = Plot([cont], what='hist',
# xtitle="p_{T}^{Reco} [GeV]", xlim=xlim, ylim=ylim)
# plot.plot() # don't use histe as it wont draw the fit
# plot.save(output_filename)
gr = ROOT.TGraphErrors(len(bin_centers), array('d', bin_centers),
array('d', sigmas),
array('d', [0] * len(bin_centers)),
array('d', sigmas_unc))
factor = 0.2
gr_ideal = ROOT.TGraphErrors(
len(bin_centers), array('d', bin_centers),
array('d', [factor * pt for pt in bin_centers]),
array('d', [0] * len(bin_centers)),
array('d', [0] * len(bin_centers)))
gr_cont = Contribution(gr, label='Measured')
gr_ideal_cont = Contribution(gr_ideal,
label=str(factor) + '*p_{T}',
line_color=ROOT.kBlue,
marker_color=ROOT.kBlue)
plot = Plot([gr_cont, gr_ideal_cont],
what='graph',
xtitle="p_{T}^{Reco}",
ytitle="#sigma [GeV]",
ylim=[0, 100],
xlim=[10, 4000])
plot.plot()
plot.set_logx()
output_filename = os.path.join(
plot_dir, "%s_%s_sigma_plot.%s" % (mydir, hist_name, OUTPUT_FMT))
plot.save(output_filename)
def do_comparison_graph(entries, output_filename, bin_title="", xtitle="", ytitle="",
other_elements=None, logx=False, logy=False,
do_line=True, xlimits=None, ylimits=None,
y_limit_protection=None, draw_fits=True,
do_ratio=True, ratio_limits=None):
"""Draw several graphs on one canvas and save to file
Parameters
----------
entries : [dict]
List of entries to plot. Each is represented by a dict, with the graph,
label, and various other style options to be applied.
output_filename : str
Name of output plot file
bin_title : str
Bin title e.g. x < pT < y
xtitle : str
X axis label
ytitle : str
y axis label
other_elements : [TObject], optional
Other elements to Draw on the canvas
logx : bool, optional
Log x axis
logy : bool, optional
Log y axis
do_line : bool, optional
Do horizontal line at 1
xlimits : (min, max), optional
Set hard x limits
ylimits : (min, max), optional
Set hard y limits
y_limit_protection : (y_min, y_max), optional
Set minimum and maximum y values in the event of a huge stat error or weird point
draw_fits : bool, optional
Draw fitted functions or not
do_ratio : bool, optional
Add ratio subplot
"""
plot = Plot(entries, what='graph',
title=None, xtitle=xtitle, ytitle=ytitle,
xlim=xlimits, ylim=ylimits,
legend=True,
subplot=entries[0] if do_ratio else None, subplot_type="ratio",
subplot_title="Ratio to All")
# replace legend with our own for now
delta = 0.12
middle = 0.77
plot.legend = ROOT.TLegend(middle-delta, 0.75, middle+delta, 0.88)
plot.legend.SetBorderSize(0)
plot.legend.SetFillStyle(0)
plot.legend.SetNColumns(2)
plot.legend.SetTextAlign(ROOT.kHAlignCenter + ROOT.kVAlignCenter)
plot.plot()
if logx:
plot.set_logx()
if logy:
plot.set_logy()
plot.main_pad.cd()
if not ylimits:
plot.container.GetHistogram().SetMaximum(plot.container.GetYaxis().GetXmax() * 1.05)
# Protection in case y limits are dominated by large stat error
if y_limit_protection and len(y_limit_protection) == 2:
y_min, y_max = plot.container.GetYaxis().GetXmin(), plot.container.GetYaxis().GetXmax()
y_lim_lower, y_lim_upper = y_limit_protection
if y_max > y_lim_upper:
plot.container.GetHistogram().SetMaximum(y_lim_upper)
if y_min < y_lim_lower:
plot.container.GetHistogram().SetMinimum(y_lim_lower)
# add protection for subplot
if not ratio_limits:
low_lim = 0.8
upper_lim = 1.2
y_min, y_max = plot.subplot_container.GetYaxis().GetXmin(), plot.subplot_container.GetYaxis().GetXmax()
plot.subplot_container.GetYaxis().SetRangeUser(max(low_lim, y_min), min(y_max, upper_lim)) # set limits doesn't work for y axis
elif len(ratio_limits) == 2:
plot.subplot_container.GetYaxis().SetRangeUser(*ratio_limits) # set limits doesn't work for y axis
plot.subplot_pad.Update()
plot.canvas.Update()
# if do_line:
# y_min, y_max = plot.container.GetYaxis().GetXmin(), plot.container.GetYaxis().GetXmax()
# if y_min < 1 and y_max > 1:
# x_min, x_max = plot.container.GetXaxis().GetXmin(), plot.container.GetXaxis().GetXmax()
# line = ROOT.TLine(x_min, 1, x_max, 1)
# line.SetLineStyle(2)
# line.SetLineColor(ROOT.kGray+2)
# line.Draw()
plot.canvas.cd()
cms_text = ROOT.TPaveText(0.17, 0.84, 0.2, 0.85, "NDC")
cms_text.AddText("CMS Simulation")
cms_text.SetTextFont(62)
cms_text.SetTextAlign(ROOT.kHAlignLeft + ROOT.kVAlignBottom)
cms_text.SetTextSize(FONT_SIZE)
cms_text.SetBorderSize(0)
cms_text.SetFillStyle(0)
cms_text.Draw()
bin_text = ROOT.TPaveText(0.17, 0.8, 0.2, 0.81, "NDC")
bin_text.AddText(bin_title)
bin_text.SetTextFont(42)
bin_text.SetTextSize(FONT_SIZE)
bin_text.SetTextAlign(ROOT.kHAlignLeft + ROOT.kVAlignBottom)
bin_text.SetBorderSize(0)
bin_text.SetFillStyle(0)
bin_text.Draw()
if other_elements:
for ele in other_elements:
ele.Draw()
plot.save(output_filename)
