def compare():
"""Make all da plots"""
# New
s2_new = '/users/jt15104/local_L1JEC_store/30June2016_QCDFlatFall15PU0to50NzshcalRaw_ak4_ref10to5000_809v70_noJEC_893ca_etaBinsSel16/runCalib_jetMetFitErr/'
f_PU0to10_new = os.path.join(s2_new, 'totalPairs_L1Ntuple_etaBinsSel16_PU0to10_maxPt1022.root')
f_PU15to25_new = os.path.join(s2_new, 'totalPairs_L1Ntuple_etaBinsSel16_PU15to25_maxPt1022.root')
f_PU30to40_new = os.path.join(s2_new, 'totalPairs_L1Ntuple_etaBinsSel16_PU30to40_maxPt1022.root')
f_PU45to55_new = os.path.join(s2_new, 'totalPairs_L1Ntuple_etaBinsSel16_PU45to55_maxPt1022.root')
pu_labels = ['PU0to10', 'PU15to25', 'PU30to40']
# common object name
corr_eta_graph_name = "l1corr_eta_{eta_min:g}_{eta_max:g}"
# --------------------------------------------------------------------
# New Stage 2 curves
# Plot different PU scenarios for given eta bin
# --------------------------------------------------------------------
graphs = [
Contribution(file_name=f_PU0to10_new, obj_name=corr_eta_graph_name,
label="PU: 0 - 10", line_color=colors[1], marker_color=colors[1]),
Contribution(file_name=f_PU15to25_new, obj_name=corr_eta_graph_name,
label="PU: 15 - 25", line_color=colors[2], marker_color=colors[2]),
Contribution(file_name=f_PU30to40_new, obj_name=corr_eta_graph_name,
label="PU: 30 - 40", line_color=colors[3], marker_color=colors[3]),
Contribution(file_name=f_PU45to55_new, obj_name=corr_eta_graph_name,
label="PU: 45 - 55", line_color=colors[4], marker_color=colors[4])
]
title = "Fall15 MC, Stage2, {eta_min:g} < |#eta^{{L1}}| < {eta_max:g}"
compare_PU_by_eta_bins(graphs, title, os.path.join(s2_new, 'comparePU'), lowpt_zoom=True)
def draw_folded_hists(hist_mc_folded,
hist_mc_reco,
hist_data_reco,
output_filename,
title=""):
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="Bin number",
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")
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_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 plot_cutflow_hist(hist, output_filename, title='', has_data=False):
"""Plot one cutflow histogram. Normalises so bins are fractions of the first"""
frac_hist = hist.Clone()
first_bin = hist.GetBinContent(1)
for i in range(1, frac_hist.GetNbinsX()+1):
frac_hist.SetBinContent(i, hist.GetBinContent(i) / first_bin)
frac_hist.SetBinError(i, hist.GetBinError(i) / first_bin)
col = ROOT.kBlue
entry = [Contribution(frac_hist, label='',
line_color=col, line_width=2, line_style=1,
marker_color=col, marker_size=0.75, marker_style=cu.Marker.get('circle'),
normalise_hist=False)]
hmax = frac_hist.GetMaximum()
hmin = frac_hist.GetMinimum(0)
hdiff = hmax-hmin
ymin = max(0, hmin - (hdiff*0.1))
ymax = hmax + (hdiff*0.25)
plot = Plot(entry, 'hist',
xtitle='',
ytitle='Fraction',
title=title,
ylim=(ymin, ymax),
legend=False,
has_data=has_data)
plot.default_canvas_size = (800, 600)
plot.plot("NOSTACK HISTE")
plot.save(output_filename)
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 make_comparison_plot_ingredients(entries,
rebin=1,
normalise_hist=True,
mean_rel_error=1.0,
**plot_kwargs):
"""Make the Plot object for a comparison plot.
User can then add other elements to the plot.
Parameters
----------
entries : list[(object, dict)]
List of ROOT object & it's configuration dict, where the dict is a set of kwargs passed to the Contribution object
rebin : int, optional
Rebin factor
normalise_hist : bool, optional
Normalise each histogram's integral to unity
mean_rel_error : float, optional
Remove contributions that have a mean realtive error more than this value
mean realtive error = mean of all the error/bin contents
**plot_kwargs
Any other kwargs to be passed to the Plot object contructor
Returns
-------
Plot
Plot object to be modified, plotted, etc
Raises
------
RuntimeError
If there are 0 contributions
"""
conts = [
Contribution(ent[0],
normalise_hist=normalise_hist,
rebin_hist=rebin,
**ent[1]) for ent in entries
]
# if get_hist_mean_rel_error(ent[0]) < mean_rel_error and ent[0].Integral() > 0]
do_legend = len(conts) > 1
if len(conts) == 0:
raise RuntimeError("0 contributions for this plot")
do_subplot = any(c.subplot for c in conts)
if (len(conts) == 1 or not do_subplot) and "subplot_type" in plot_kwargs:
plot_kwargs['subplot_type'] = None
p = Plot(conts,
what="hist",
ytitle="p.d.f",
legend=do_legend,
**plot_kwargs)
if do_legend:
p.legend.SetX1(0.5)
p.legend.SetX2(0.95)
if len(entries) > 4:
p.legend.SetY1(0.67)
else:
p.legend.SetY1(0.78)
p.legend.SetY2(0.95)
return p
def plot_unfolded_normalised_pt_bin_offset(self, bin_offset_2=0):
for ibin, (bin_edge_low, bin_edge_high) in enumerate(zip(self.bins[:-1], self.bins[1:])):
# print(bin_edge_low, bin_edge_high)
ind2 = ibin+bin_offset_2
if ind2 < 0:
# print("...skipping")
continue
hbc1_args = dict(ind=ibin, binning_scheme='generator')
unfolded1_hist_bin_stat_errors = self.hist_bin_chopper1.get_pt_bin_normed_div_bin_width('unfolded_stat_err', **hbc1_args)
hbc2_args = dict(ind=ind2, binning_scheme='generator')
unfolded2_hist_bin_stat_errors = self.hist_bin_chopper2.get_pt_bin_normed_div_bin_width('unfolded_stat_err', **hbc2_args)
entries = [
Contribution(unfolded1_hist_bin_stat_errors,
label="Data (stat. unc.)\n%s" % self.setup1.label,
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=0.75),
Contribution(unfolded2_hist_bin_stat_errors,
label="Data (stat. unc.)\n%s" % self.setup2.label,
line_color=self.plot_styles['unfolded_unreg_colour'],
line_width=self.line_width,
line_style=1,
marker_color=self.plot_styles['unfolded_unreg_colour'],
marker_style=cu.Marker.get('square', filled=False),
marker_size=0.75,
subplot=unfolded1_hist_bin_stat_errors),
]
if not self.check_entries(entries, "plot_unfolded_normalised_pt_bin_offset %d" % (ibin)):
return
plot = Plot(entries,
ytitle=self.setup1.pt_bin_normalised_differential_label,
title=self.get_pt_bin_title(bin_edge_low, bin_edge_high),
xlim=qgp.calc_auto_xlim(entries),
subplot_limits=(0.8, 1.2),
**self.pt_bin_plot_args)
self._modify_plot(plot)
plot.subplot_title = "* / %s" % (self.region1['label'])
plot.plot("NOSTACK E1")
plot.save("%s/compare_unfolded_%s_bin_%d_divBinWidth.%s" % (self.setup1.output_dir, self.setup1.append, ibin, self.setup1.output_fmt))
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_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 plot_ddelta(ddelta_hist, output_filename, xtitle, ytitle, title=""):
cont = Contribution(ddelta_hist)
p = Plot([cont],
what="hist",
legend=None,
xtitle=xtitle,
ytitle=ytitle,
title=title)
p.plot("HISTE")
p.save(output_filename)
def do_pt_plot(pythia_dir,
herwig_dir,
selection,
hist_name,
output_name,
title=""):
h_pythia = grab_obj(
"%s/uhh2.AnalysisModuleRunner.MC.MC_%s_.root" %
(pythia_dir, selection), hist_name)
h_herwig = grab_obj(
"%s/uhh2.AnalysisModuleRunner.MC.MC_%s_.root" %
(herwig_dir, selection), hist_name)
c_pythia = Contribution(h_pythia,
label="MG+Pythia",
line_color=ROOT.kBlue,
marker_color=ROOT.kBlue,
fill_color=ROOT.kBlue,
normalise_hist=True)
# c_herwig = Contribution(h_herwig, label="Herwig", line_color=ROOT.kRed, normalise_hist=True)
c_herwig = Contribution(h_herwig,
label="Pythia only",
line_color=ROOT.kRed,
marker_color=ROOT.kRed,
fill_color=ROOT.kRed,
normalise_hist=True)
p = Plot([c_pythia, c_herwig],
what="hist",
legend=True,
subplot_type='ratio',
subplot=c_pythia,
title=title)
p.plot("NOSTACK HISTE")
p.main_pad.SetLogy()
p.container.SetMinimum(1E-12)
p.subplot_container.SetMaximum(1.5)
p.subplot_container.SetMinimum(0)
p.canvas.Update()
p.save(output_name)
def 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_roc_plot(hist_signal, hist_background, output_filename):
""""Make a single ROC plot"""
gr = make_roc_graph(hist_signal, hist_background)
cont = Contribution(gr, marker_style=21)
p = Plot([cont],
"graph",
xtitle="#epsilon_{ g}",
ytitle="#epsilon_{ q}",
xlim=[0, 1],
ylim=[0, 1],
legend=False)
p.plot("AL")
p.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_roc_plot(eff_dict, output_filename):
"""Turn dict of efficiencies into ROC plot
Parameters
----------
eff_dict : TYPE
Description
output_filename : TYPE
Description
"""
signal_effs = [0] * len(eff_dict.values()[0])
bkg_effs = [0] * len(eff_dict.values()[0])
for k, v in eff_dict.iteritems():
if k.replace("Dijet_Presel_",
"").lstrip("_unknown_").lstrip("_q").startswith("g"):
print(k)
for ind, eff in enumerate(v):
signal_effs[ind] += eff
else:
for ind, eff in enumerate(v):
bkg_effs[ind] += eff
# divide by totals
for ind, (s, b) in enumerate(zip(signal_effs, bkg_effs)):
total = s + b
signal_effs[ind] /= total
bkg_effs[ind] /= total
print(signal_effs)
print(bkg_effs)
gr = ROOT.TGraph(len(signal_effs), array('d', bkg_effs),
array('d', signal_effs))
cont = Contribution(gr, marker_style=21)
p = Plot([cont],
"graph",
xtitle="fraction jet2!=g",
ytitle="fraction jet2=g",
xlim=[0, 1],
ylim=[0, 1],
legend=False)
p.plot("AP")
p.save(output_filename)
def create_contibutions_compare_vs_pt(input_files, hist_names, pt_bins, labels,
flav, **contrib_kwargs):
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 = cycle([
ROOT.kBlack, ROOT.kBlue, ROOT.kRed, ROOT.kGreen + 2, ROOT.kOrange - 3,
ROOT.kViolet + 6
])
for i, (fdict, label, col) in enumerate(zip(info, labels, colours)):
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=label,
line_color=col,
line_width=1,
line_style=1,
marker_style=20 + i,
marker_color=col,
marker_size=1,
leg_draw_opt="LP",
**contrib_kwargs)
contribs.append(c)
return contribs
def plot_corrections(corrections_graph, xtitle, title, output_filename):
# print(x_values)
# print(corrections)
conts = [
Contribution(corrections_graph,
label="Correction",
line_color=ROOT.kRed,
marker_color=ROOT.kRed,
line_width=2,
marker_size=1,
marker_style=20,
fit_match_style=False)
]
# plot = Plot(conts, what='graph', xtitle=xtitle, ytitle="Correction", title=title, has_data=False, ylim=[0, 2.5])
plot = Plot(conts,
what='both',
xtitle=xtitle,
ytitle="Correction",
title=title,
has_data=False,
ylim=[0, 2.5])
plot.plot("ALP")
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 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_flavour_fraction_vs_eta(input_file,
dirname,
eta_bins,
output_filename,
title="",
var_prepend="",
which_jet="both",
append=""):
"""Plot all flavour fractions vs eta for one input file & dirname in the ROOT file"""
info = get_flavour_efficiencies(
input_file,
bins=eta_bins,
hist_name=get_flavour_hist_name(
dirname=dirname,
var_prepend=var_prepend,
which_jet="both", # since no jet/jet1/jet2 in hist name
metric='eta' + append))
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="y^{%s}" % get_jet_str(''),
ytitle="Fraction",
title=title,
xlim=(eta_bins[0][0], eta_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.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 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 do_pt_min_delta_plots(sources,
plot_dir="deltas_ptmin",
zpj_dirname="ZPlusJets_QG",
dj_dirname="Dijet_QG",
var_list=None,
var_prepend="",
flavour_tag=False,
save_component_hists=False,
ofmt="pdf"):
"""Do plots comparing power of different ptMin cuts"""
var_list = var_list or COMMON_VARS
ptmin_bins = [50, 100, 200, 400, 800][:-1]
zpj_flav = "q" if flavour_tag else ""
dj_flav = "g" if flavour_tag else ""
output_append = "_flavMatched" if flavour_tag else ""
for ang in var_list:
v = "%s%s_vs_pt" % (var_prepend, ang.var)
graph_contribs, bin_labels = [], []
for source_ind, source in enumerate(sources):
deltas, components = [], []
# for the component comparison plot
colours = [
ROOT.kBlue, ROOT.kRed, ROOT.kGreen + 2, ROOT.kOrange - 3,
ROOT.kMagenta, ROOT.kAzure + 1
]
for pt_min, this_colour in zip(ptmin_bins, colours):
h2d_dyj = grab_obj(
os.path.join(source['root_dir'], qgc.DY_FILENAME),
"%s_ptMin_%d/%s%s" % (source.get(
'zpj_dirname', zpj_dirname), pt_min, zpj_flav, v))
h2d_qcd = grab_obj(
os.path.join(source['root_dir'],
qgc.QCD_FILENAME), "%s_ptMin_%d/%s%s" %
(source.get('dj_dirname', dj_dirname), pt_min, dj_flav, v))
start_val, end_val = 80, 2000
h_dy = get_projection_plot(h2d_dyj, start_val, end_val)
if (h_dy.Integral() > 0):
h_dy.Scale(1. / (h_dy.GetBinWidth(1) * h_dy.Integral()))
h_qcd = get_projection_plot(h2d_qcd, start_val, end_val)
if (h_qcd.Integral() > 0):
h_qcd.Scale(1. / (h_qcd.GetBinWidth(1) * h_qcd.Integral()))
ddelta_hist = get_ddelta_plot(h_dy, h_qcd)
c = Contribution(ddelta_hist,
line_width=1,
marker_color=this_colour,
line_color=this_colour,
fill_color=this_colour,
label="p_{T}^{Min} = %d GeV" % pt_min,
rebin_hist=1)
components.append(c)
deltas.append(calculate_delta(ddelta_hist))
if source_ind == 0:
bin_labels.append("%d" % pt_min)
if save_component_hists:
# need to clone here otherwise the colour will be set inside
# plot_delta, which in turn will nullify whatever colours we chose above.
plot_ddelta(
ddelta_hist.Clone(ROOT.TUUID().AsString()),
"%s/delta_ptmin_components/%s_ddelta_ptMin_%d%s.%s" %
(plot_dir, ang.var, pt_min, output_append, ofmt),
xtitle=ang.name + " (" + ang.lambda_str + ")",
ytitle="d#Delta/d" + ang.lambda_str)
if save_component_hists:
# plot all differential distributions for this pt bin on one plot
p = Plot(components,
what="hist",
xtitle=ang.name,
ytitle="p.d.f")
p.plot("NOSTACK HISTE")
p.save(
"%s/delta_ptmin_components/%s_ddelta_ptMin_comparison%s.%s"
% (plot_dir, ang.var, output_append, ofmt))
gr = construct_deltas_graph(deltas)
gr.SetName(source.get("label", ""))
if 'style' in source and 'line_width' not in source['style']:
source['style']['line_width'] = 2
c = Contribution(gr,
label=source.get("label", "").lstrip(", "),
marker_style=0,
**source.get("style", {}))
graph_contribs.append(c)
do_deltas_plot(graph_contribs,
"%s/ptMins_%s%s.%s" %
(plot_dir, ang.var, output_append, ofmt),
bin_labels=bin_labels,
title="%s [%s]" % (ang.name, ang.lambda_str),
xtitle="p_{T}^{min} [GeV]")
def 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_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_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_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)
