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)