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_pf_fraction_plot(hist_map, pt_bins, output_filename):
"""Plot PF particle type fractioin for matches, binned by GenParticle pT"""
entries = []
for pt_low, pt_high, mark in zip(pt_bins[:-1], pt_bins[1:],
cu.Marker().cycle()):
values = {}
for pf_ind, (pf_name, hist) in hist_map.items():
ax = hist.GetXaxis()
binx1 = ax.FindBin(pt_low)
binx2 = ax.FindBin(pt_high) - 1
if pt_high == ax.GetBinUpEdge(ax.GetLast()):
binx2 = ax.GetLast()
biny1 = 1
biny2 = hist.GetNbinsY()
binz1 = 1
binz2 = hist.GetNbinsZ()
values[pf_ind] = hist.Integral(
binx1, binx2, biny1, biny2, binz1,
binz2) # integral includes the last bin
sum_values = sum(values.values())
fracs = {k: (v / sum_values) for k, v in values.items()}
h = ROOT.TH1D("h_pt_bin_%gto%g" % (pt_low, pt_high), "", len(values),
0, len(values))
ax = h.GetXaxis()
for ind, k in enumerate(sorted(fracs.keys()), 1):
h.SetBinContent(ind, fracs[k])
h.SetBinError(ind, sqrt(values[k]) / sum_values)
ax.SetBinLabel(ind, hist_map[k][0])
c = Contribution(h,
label='%g < GenParticle p_{T} < %g GeV' %
(pt_low, pt_high),
line_width=1,
marker_size=0.75,
marker_style=mark,
normalise_hist=False)
entries.append(c)
ROOT.gStyle.SetPalette(55)
plot = Plot(entries,
'hist',
xtitle='PF particle type',
ytitle='Fraction matched as type',
ylim=(1E-3, 2),
has_data=False)
plot.default_canvas_size = (800, 600)
plot.plot("NOSTACK PMC PLC HISTE")
plot.set_logy(do_more_labels=False)
plot.save(output_filename)
ROOT.gStyle.SetPalette(ROOT.kViridis)
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 print_hist_comparison(entries, plots_kwargs, output_filename):
"""Print multiple hists on canvas, no rescaling, no stacking
entries: list[(object, kwargs for Contribution)]
"""
conts = [Contribution(e[0], **e[1]) for e in entries]
logy = plots_kwargs.get('logy', False)
if "logy" in plots_kwargs:
del plots_kwargs['logy']
plot = Plot(conts, what="hist", **plots_kwargs)
plot.plot("HISTE NOSTACK")
if logy:
plot.set_logy()
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_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_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 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 make_plot(entries,
output_filename,
plot_kwargs,
projection_axis='x',
start_val=None,
end_val=None,
is_pdgid_plot=False,
logy=False):
"""Make a plot from entries. Each one is a projection of a 2D plot.
Parameters
----------
entries : list[dict]
List of plot entries. Each is represented by a dict
output_filename : str
Filename for output plot
plot_kwargs : dict
kwargs for Plot constructor
projection_axis : str, optional
Axis to use for projection. If None, must be specified in entry.
start_val : None, optional
If set, use this to make 1D projection plot. Cuts on X axis.
Otherwise should be set per entry.
end_val : None, optional
If set, use this to make 1D projection plot. Cuts on X axis.
Otherwise should be set per entry.
is_pdgid_plot : bool, optional
True if for PDGIDs (sets special size & binning)
logy : bool, optional
Description
Raises
------
RuntimeError
Description
"""
conts = []
if not is_pdgid_plot:
for ent in entries:
h2d = cu.get_from_tfile(
ent['file'],
"ak4pfchsl1/" + ent['histname'] + "_JetEta0to0.783")
start_val = ent.get('start_val', start_val)
end_val = ent.get('end_val', end_val)
if start_val is None or end_val is None:
raise RuntimeError("Expected start_val and end_val")
hist = cu.get_projection_plot(
h2d, start_val, end_val,
ent.get('projection_axis', projection_axis))
if hist.GetEntries() == 0:
ent['used'] = False
continue
contrib = Contribution(hist,
label=ent['label'],
line_width=lw,
line_color=ent['colour'],
line_style=ent.get('line_style', 1),
marker_size=ent.get('marker_size', 0),
marker_color=ent['colour'],
marker_style=ent.get('marker_style', 1),
normalise_hist=True,
rebin_hist=ent.get('rebin', None))
conts.append(contrib)
ent['used'] = True
else:
# For PDGID plots, we want a different canvas aspect ratio, and only to include bins
# with non-0 contents. We also relabel bins.
custom_bins = []
for ent in entries:
h2d = cu.get_from_tfile(
ent['file'],
"ak4pfchsl1/" + ent['histname'] + "_JetEta0to0.783")
start_val = ent.get('start_val', start_val)
end_val = ent.get('end_val', end_val)
if not start_val or not end_val:
raise RuntimeError("Expected start_val and end_val")
hist = cu.get_projection_plot(
h2d, start_val, end_val,
ent.get('projection_axis', projection_axis))
ax = hist.GetXaxis()
bins = dict()
for i in range(1, hist.GetNbinsX() + 1):
value = ax.GetBinLowEdge(i)
cont = hist.GetBinContent(i)
err = hist.GetBinError(i)
if cont > 0:
bins[value] = [cont, err]
# print(custom_bins[-1])
custom_bins.append(bins)
nbins = max([len(d) for d in custom_bins])
first_keys = set(custom_bins[0].keys())
for cb in custom_bins[1:]:
first_keys = first_keys.union(set(cb.keys()))
all_keys = sorted(list(first_keys))
print(all_keys)
for cbin, ent in zip(custom_bins, entries):
h = ROOT.TH1D(cu.get_unique_str(), ";PDGID;N", nbins, 0, nbins)
for ind, k in enumerate(all_keys, 1):
content, error = cbin.get(k, [0, 0])
h.SetBinContent(ind, content)
h.SetBinError(ind, error)
ax = h.GetXaxis()
for i in range(1, nbins + 1):
# ax.SetBinLabel(i, str(int(all_keys[i-1])))
pdgid = int(all_keys[i - 1])
ax.SetBinLabel(i, PDGID_STR.get(pdgid, str(pdgid)))
h.LabelsOption("v")
contrib = Contribution(h,
label=ent['label'],
line_width=lw,
line_color=ent['colour'],
line_style=ent.get('line_style', 1),
marker_size=ent.get('marker_size', 0),
marker_color=ent['colour'],
marker_style=ent.get('marker_style', 1),
normalise_hist=True,
rebin_hist=ent.get('rebin', None))
conts.append(contrib)
ent['used'] = True
entries = [e for e in entries if e['used']]
for ind, ent in enumerate(entries):
if not ent['used']:
continue
if ent.get('subplot_ind', -1) >= 0:
conts[ind].subplot = conts[ent['subplot_ind']].obj
plot = Plot(conts,
what="hist",
ytitle="p.d.f.",
has_data=False,
**plot_kwargs)
if is_pdgid_plot and conts[0].obj.GetNbinsX() > 10:
plot.default_canvas_size = (800, 800)
else:
plot.default_canvas_size = (450, 600)
plot.legend.SetX1(0.5)
plot.legend.SetX2(0.97)
if len(entries) > 4:
plot.legend.SetY1(0.7)
plot.legend.SetNColumns(2)
else:
plot.legend.SetY1(0.75)
plot.legend.SetY2(0.9)
plot.plot("HISTE NOSTACK")
if logy:
plot.set_logy()
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 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))
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_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_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)
