def do_mc_pt_comparison_plot(dirname_label_pairs, output_filename, qcd_filename, **plot_kwargs): # qcd_files = [cu.open_root_file(os.path.join(dl[0], qgc.QCD_FILENAME)) for dl in dirname_label_pairs] qcd_files = [ cu.open_root_file(os.path.join(dl[0], qgc.QCD_PYTHIA_ONLY_FILENAME)) for dl in dirname_label_pairs ] histname = "Dijet_tighter/pt_jet1" qcd_hists = [cu.get_from_tfile(qf, histname) for qf in qcd_files] N = len(dirname_label_pairs) conts = [ Contribution(qcd_hists[i], label=lab, marker_color=cu.get_colour_seq(i, N), line_color=cu.get_colour_seq(i, N), line_style=(i % 3) + 1, line_width=2, rebin_hist=1, subplot=qcd_hists[0] if i != 0 else None) for i, (d, lab) in enumerate(dirname_label_pairs) ] plot = Plot(conts, what='hist', ytitle="N", subplot_limits=(0.5, 1.5), subplot_type="ratio", subplot_title="* / %s" % (dirname_label_pairs[0][1]), **plot_kwargs) plot.y_padding_max_log = 500 plot.legend.SetY1(0.7) plot.plot("NOSTACK HIST E") plot.set_logx(do_more_labels=False) plot.set_logy(do_more_labels=False) plot.save(output_filename)
def do_jet_pt_rel_error_with_var_cuts(histname, cuts, input_filename, output_filename): ROOT.gStyle.SetPalette(palette_1D) tf = cu.open_root_file(input_filename) h3d = cu.get_from_tfile(tf, histname) if h3d.GetEntries() == 0: return pt_hists = [] for cut in cuts: max_bin = h3d.GetZaxis().FindFixBin(cut) # print("cut:", cut, "bin:", max_bin) h = h3d.ProjectionY("pt_var_lt_%g" % cut, 0, -1, 0, max_bin, "e") h2 = h.Clone() h2.Rebin(2) if h.GetEntries() > 0: h3 = qgp.hist_divide_bin_width(h2) # convert bin contents to bin error/bin contents for ibin in range(1, h2.GetNbinsX()+1): if h3.GetBinContent(ibin) == 0: continue h3.SetBinContent(ibin, h3.GetBinError(ibin) / h3.GetBinContent(ibin)) h3.SetBinError(ibin, 0) pt_hists.append(h3) line_styles = [1, 2, 3] n_line_styles = len(line_styles) conts = [Contribution(h, label=" < %g" % cut, line_color=cu.get_colour_seq(ind, len(cuts)), line_style=line_styles[ind % n_line_styles], line_width=2, marker_color=cu.get_colour_seq(ind, len(cuts)), subplot=pt_hists[-1]) for ind, (h, cut) in enumerate(zip(pt_hists, cuts))] jet_str = pt_genjet_str if "_vs_pt_genjet_vs_" in histname else pt_str weight_str = "(unweighted)" if "unweighted" in histname else "(weighted)" ratio_lims = (0.98, 1.02) if "unweighted" in histname else None plot = Plot(conts, what='hist', title='%s for cuts on %s %s' % (jet_str, get_var_str(histname), weight_str), xtitle=None, ytitle='Relative error', # xlim=None, ylim=None, legend=True, subplot_type='ratio', subplot_title='* / var < %g' % cuts[-1], subplot_limits=ratio_lims, has_data=False) plot.y_padding_max_log = 200 plot.subplot_maximum_ceil = 2 plot.subplot_maximum_floor = 1.02 plot.subplot_minimum_ceil = 0.98 plot.legend.SetY1(0.7) plot.legend.SetY2(0.89) plot.legend.SetX1(0.78) plot.legend.SetX2(0.88) plot.plot("NOSTACK HISTE", "NOSTACK HIST") plot.set_logx(True, do_more_labels=True) plot.set_logy(True, do_more_labels=False) plot.save(output_filename)
def do_data_mc_plot(dirname, histname, output_filename, **plot_kwargs): data_file = cu.open_root_file(os.path.join(dirname, qgc.JETHT_ZB_FILENAME)) qcd_file = cu.open_root_file(os.path.join(dirname, qgc.QCD_FILENAME)) qcd_py_file = cu.open_root_file( os.path.join(dirname, qgc.QCD_PYTHIA_ONLY_FILENAME)) qcd_hpp_file = cu.open_root_file( os.path.join(dirname, qgc.QCD_HERWIG_FILENAME)) data_hist = cu.get_from_tfile(data_file, histname) qcd_hist = cu.get_from_tfile(qcd_file, histname) qcd_py_hist = cu.get_from_tfile(qcd_py_file, histname) qcd_hpp_hist = cu.get_from_tfile(qcd_hpp_file, histname) conts = [ Contribution(data_hist, label="Data", line_color=ROOT.kBlack, marker_size=0, marker_color=ROOT.kBlack), Contribution(qcd_hist, label="QCD MG+PYTHIA8 MC", line_color=qgc.QCD_COLOUR, subplot=data_hist, marker_size=0, marker_color=qgc.QCD_COLOUR), Contribution(qcd_py_hist, label="QCD PYTHIA8 MC", line_color=qgc.QCD_COLOURS[2], subplot=data_hist, marker_size=0, marker_color=qgc.QCD_COLOURS[2]), # Contribution(qcd_hpp_hist, label="QCD HERWIG++ MC", line_color=qgc.HERWIGPP_QCD_COLOUR, subplot=data_hist, marker_size=0, marker_color=qgc.HERWIGPP_QCD_COLOUR), ] plot = Plot(conts, what='hist', ytitle="N", xtitle="p_{T}^{Leading jet} [GeV]", subplot_type="ratio", subplot_title="Simulation / data", ylim=[1E3, None], lumi=cu.get_lumi_str(do_dijet=True, do_zpj=False), **plot_kwargs) plot.y_padding_max_log = 500 plot.legend.SetX1(0.55) plot.legend.SetX2(0.98) plot.legend.SetY1(0.7) # plot.legend.SetY2(0.88) plot.plot("NOSTACK HIST E") plot.set_logx(do_more_labels=True, do_exponent=False) plot.set_logy(do_more_labels=False) plot.save(output_filename)
def do_genht_plot(dirname, output_filename, **plot_kwargs): qcd_file = cu.open_root_file(os.path.join(dirname, qgc.QCD_FILENAME)) histname = "Dijet_gen/gen_ht" qcd_hist = cu.get_from_tfile(qcd_file, histname) conts = [Contribution(qcd_hist, label="QCD MC", line_color=ROOT.kRed)] plot = Plot(conts, what='hist', ytitle="N", **plot_kwargs) plot.y_padding_max_log = 500 plot.legend.SetY1(0.7) plot.plot("NOSTACK HIST E") plot.set_logx(do_more_labels=False) plot.set_logy(do_more_labels=False) plot.save(output_filename)
def do_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_jet_pt_with_var_cuts(histname, cuts, input_filename, output_filename): ROOT.gStyle.SetPalette(palette_1D) total = len(cuts) - 1 + .1 # slight offset to not hit the maximum or minimum # if len(cuts) <= 3: # ROOT.gStyle.SetPalette(ROOT.kCool) # num_colours = ROOT.TColor.GetPalette().fN - 1 # print('num_colours:', num_colours) # for index in range(len(cuts)): # print(num_colours, index, len(cuts), index / len(cuts), num_colours * index / total) # print(index, ROOT.TColor.GetColorPalette(int(num_colours * 1. * index / total))) tf = cu.open_root_file(input_filename) h3d = cu.get_from_tfile(tf, histname) if h3d.GetEntries() == 0: return pt_hists = [] for cut in cuts: max_bin = h3d.GetZaxis().FindFixBin(cut) # print("cut:", cut, "bin:", max_bin) h = h3d.ProjectionY("pt_var_lt_%g" % cut, 0, -1, 0, max_bin, "e") h2 = h.Clone() h2.Rebin(2) if h.GetEntries() > 0: h3 = qgp.hist_divide_bin_width(h2) pt_hists.append(h3) line_styles = [1, 2, 3] if len(cuts) <= 3: line_styles = [1] n_line_styles = len(line_styles) ref_ind = 0 conts = [Contribution(h, label=" < %g" % cut, line_color=cu.get_colour_seq(ind, total), line_style=line_styles[ind % n_line_styles], line_width=2, marker_color=cu.get_colour_seq(ind, total), subplot=pt_hists[ref_ind] if ind != ref_ind else None) for ind, (h, cut) in enumerate(zip(pt_hists, cuts))] jet_str = pt_genjet_str if "_vs_pt_genjet_vs_" in histname else pt_str weight_str = "(unweighted)" if "unweighted" in histname else "(weighted)" ratio_lims = (0.5, 2.5) ratio_lims = (0.5, 1.1) plot = Plot(conts, what='hist', title='%s for cuts on %s %s' % (jet_str, get_var_str(histname), weight_str), xtitle=None, ytitle='N', # xlim=None, ylim=None, legend=True, subplot_type='ratio', subplot_title='* / var < %g' % cuts[ref_ind], subplot_limits=ratio_lims, has_data=False) plot.y_padding_max_log = 200 plot.subplot_maximum_ceil = 4 plot.subplot_maximum_floor = 1.02 plot.subplot_minimum_ceil = 0.98 plot.legend.SetY1(0.7) plot.legend.SetY2(0.89) plot.legend.SetX1(0.78) plot.legend.SetX2(0.88) plot.plot("NOSTACK HISTE", "NOSTACK HIST") plot.set_logx(True, do_more_labels=True) plot.set_logy(True, do_more_labels=False) plot.save(output_filename)
def do_zerobias_per_run_comparison_plot(dirname_label_pairs, output_dir, append="", title="", **plot_kwargs): runs = [ (qgc.ZEROBIAS_RUNB_FILENAME, 'B'), (qgc.ZEROBIAS_RUNC_FILENAME, 'C'), (qgc.ZEROBIAS_RUND_FILENAME, 'D'), (qgc.ZEROBIAS_RUNE_FILENAME, 'E'), (qgc.ZEROBIAS_RUNF_FILENAME, 'F'), (qgc.ZEROBIAS_RUNG_FILENAME, 'G'), (qgc.ZEROBIAS_RUNH_FILENAME, 'H'), ] zb_entry = { 'label': 'HLT_ZeroBias', 'color': ROOT.kMagenta - 9, # 'scale': 35918219492.947 / 29048.362 'scale': 1 } for filename, run_period in runs: zb_root_files = [ cu.open_root_file(os.path.join(dl[0], filename)) for dl in dirname_label_pairs ] # PT JET 1 zb_hist_names = [ "Dijet_jet_hist_0/pt_1", "Dijet_jet_hist_unweighted_0/pt_1" ][1:] N = len(dirname_label_pairs) rebin = 2 for zb_name in zb_hist_names: # add zeero bias ones this_data_entries = [ Contribution( cu.get_from_tfile(zb_root_files[i], zb_name), label=zb_entry['label'] + " Run %s: " % run_period + l, marker_color=zb_entry['color'], line_color=zb_entry['color'], line_style=1 + i, rebin_hist=rebin, ) for i, (d, l) in enumerate(dirname_label_pairs) ] for c in this_data_entries[1:]: c.subplot = this_data_entries[0].obj plot = Plot( this_data_entries, what='hist', title=title, xtitle="p_{T}^{jet 1} [GeV]", ytitle="N", xlim=[30, 1000], ylim=[1E3, None], # ylim=[10, 1E8] if 'unweighted' in ht_name else [1, 1E12], subplot_type='ratio', subplot_title='* / %s' % dirname_label_pairs[0][1], **plot_kwargs) plot.subplot_maximum_ceil = 10 plot.default_canvas_size = (800, 600) plot.y_padding_max_log = 500 plot.legend.SetY1(0.7) plot.legend.SetY2(0.88) plot.legend.SetX1(0.5) plot.legend.SetNColumns(2) plot.plot("NOSTACK HISTE") plot.set_logx(do_more_labels=False) plot.set_logy(do_more_labels=False) output_filename = "%s/DataJetHTZB-pt_jet1%s_Run%s%s.pdf" % ( output_dir, "_unweighted" if 'unweighted' in zb_name else "", run_period, append) plot.save(output_filename) # ETA JET 1 zb_hist_names = ["Dijet_jet_hist_unweighted_0/eta_1"] N = len(dirname_label_pairs) rebin = 2 for zb_name in zb_hist_names: # add zero bias ones this_data_entries = [ Contribution( cu.get_from_tfile(zb_root_files[i], zb_name), label=zb_entry['label'] + " Run %s: " % run_period + l, marker_color=zb_entry['color'], line_color=zb_entry['color'], line_style=1 + i, rebin_hist=rebin, ) for i, (d, l) in enumerate(dirname_label_pairs) ] for c in this_data_entries[1:]: c.subplot = this_data_entries[0].obj # plot zb plot = Plot( this_data_entries, what='hist', title=title, xtitle="y^{jet 1}", ytitle="N", subplot_type='ratio', subplot_title='* / %s' % dirname_label_pairs[0][1], # subplot_limits=(0, 5), **plot_kwargs) plot.subplot_maximum_ceil = 5 plot.default_canvas_size = (800, 600) plot.y_padding_max_log = 500 plot.legend.SetY1(0.7) plot.legend.SetY2(0.88) plot.legend.SetX1(0.5) plot.legend.SetNColumns(2) plot.plot("NOSTACK HISTE") output_filename = "%s/DataZB-eta_jet1%s_Run%s%s.pdf" % ( output_dir, "_unweighted" if 'unweighted' in zb_name else "", run_period, append) plot.save(output_filename)
def do_jetht_trigger_comparison_plot(dirname_label_pairs, output_dir, append="", title="", **plot_kwargs): # Unweighted pt, showing contributions from different triggers # Have to add in ZB manually zb_entry = { 'label': 'HLT_ZeroBias', 'color': ROOT.kMagenta - 9, # 'scale': 35918219492.947 / 29048.362 'scale': 1 } jet_ht_entries = [ { 'ind': '0', 'label': "PFJet40", 'color': ROOT.kRed, }, { 'ind': '1', 'label': "PFJet60", 'color': ROOT.kBlue, }, { 'ind': '2', 'label': "PFJet80", 'color': ROOT.kGreen + 2, }, { 'ind': '3', 'label': "PFJet140", 'color': ROOT.kViolet + 5, }, { 'ind': '4', 'label': "PFJet200", 'color': ROOT.kOrange, }, { 'ind': '5', 'label': "PFJet260", 'color': ROOT.kTeal, }, { 'ind': '6', 'label': "PFJet320", 'color': ROOT.kViolet, }, { 'ind': '7', 'label': "PFJet400", 'color': ROOT.kOrange - 6 }, { 'ind': '8', 'label': "PFJet450", 'color': ROOT.kAzure + 1, }, ] # PT JET 1 zb_hist_names = [ "Dijet_jet_hist_0/pt_1", "Dijet_jet_hist_unweighted_0/pt_1" ] jet_ht_hist_names = [ "Dijet_jet_hist_{ind}/pt_1", "Dijet_jet_hist_unweighted_{ind}/pt_1" ] zb_root_files = [ cu.open_root_file(os.path.join(dl[0], qgc.ZB_FILENAME)) for dl in dirname_label_pairs ] jetht_root_files = [ cu.open_root_file(os.path.join(dl[0], qgc.JETHT_FILENAME)) for dl in dirname_label_pairs ] N = len(dirname_label_pairs) rebin = 2 for zb_name, ht_name in zip(zb_hist_names, jet_ht_hist_names): # add zeero bias ones this_data_entries = [ Contribution( cu.get_from_tfile(zb_root_files[i], zb_name), label=zb_entry['label'] + ": " + l, marker_color=zb_entry['color'], line_color=zb_entry['color'], line_style=1 + i, rebin_hist=rebin, ) for i, (d, l) in enumerate(dirname_label_pairs) ] for c in this_data_entries[1:]: c.subplot = this_data_entries[0].obj # # add jet ht ones for ent in jet_ht_entries: histname = ht_name.format(ind=ent['ind']) new_entries = [ Contribution( cu.get_from_tfile(jetht_root_files[i], histname), label=ent['label'] + ": " + l, marker_color=ent['color'], line_color=ent['color'], line_style=1 + i, rebin_hist=rebin, ) for i, (d, l) in enumerate(dirname_label_pairs) ] for c in new_entries[1:]: c.subplot = new_entries[0].obj this_data_entries.extend(new_entries) plot = Plot( this_data_entries, what='hist', title=title, ytitle="N", xtitle="p_{T}^{jet 1} [GeV]", xlim=[30, 1000], ylim=[1E3, None], # ylim=[10, 1E8] if 'unweighted' in ht_name else [1, 1E12], subplot_type='ratio', subplot_title='* / %s' % dirname_label_pairs[0][1], **plot_kwargs) plot.default_canvas_size = (800, 600) plot.subplot_maximum_ceil = 10 plot.y_padding_max_log = 500 plot.legend.SetY1(0.7) plot.legend.SetY2(0.88) plot.legend.SetX1(0.5) plot.legend.SetNColumns(2) plot.plot("NOSTACK HISTE") plot.set_logx(do_more_labels=False) plot.set_logy(do_more_labels=False) output_filename = "%s/DataJetHTZB-pt_jet1%s%s.pdf" % ( output_dir, "_unweighted" if 'unweighted' in zb_name else "", append) plot.save(output_filename) # ETA JET 1 zb_hist_names = ["Dijet_jet_hist_unweighted_0/eta_1"] jet_ht_hist_names = ["Dijet_jet_hist_unweighted_{ind}/eta_1"] zb_root_files = [ cu.open_root_file(os.path.join(dl[0], qgc.ZB_FILENAME)) for dl in dirname_label_pairs ] jetht_root_files = [ cu.open_root_file(os.path.join(dl[0], qgc.JETHT_FILENAME)) for dl in dirname_label_pairs ] N = len(dirname_label_pairs) rebin = 2 for zb_name, ht_name in zip(zb_hist_names, jet_ht_hist_names): # add zero bias ones this_data_entries = [ Contribution( cu.get_from_tfile(zb_root_files[i], zb_name), label=zb_entry['label'] + ": " + l, marker_color=zb_entry['color'], line_color=zb_entry['color'], line_style=1 + i, rebin_hist=rebin, ) for i, (d, l) in enumerate(dirname_label_pairs) ] for c in this_data_entries[1:]: c.subplot = this_data_entries[0].obj # plot zb plot = Plot( this_data_entries, what='hist', title=title, xtitle="y^{jet 1}", ytitle="N", subplot_type='ratio', subplot_title='* / %s' % dirname_label_pairs[0][1], # subplot_limits=(0, 5), **plot_kwargs) plot.subplot_maximum_ceil = 5 plot.default_canvas_size = (800, 600) plot.y_padding_max_log = 500 plot.legend.SetY1(0.7) plot.legend.SetY2(0.88) plot.legend.SetX1(0.5) plot.legend.SetNColumns(2) plot.plot("NOSTACK HISTE") output_filename = "%s/DataZB-eta_jet1%s%s.pdf" % ( output_dir, "_unweighted" if 'unweighted' in zb_name else "", append) plot.save(output_filename) # add jet ht ones for ent in jet_ht_entries: histname = ht_name.format(ind=ent['ind']) this_data_entries = [ Contribution( cu.get_from_tfile(jetht_root_files[i], histname), label=ent['label'] + ": " + l, marker_color=ent['color'], line_color=ent['color'], line_style=1 + i, rebin_hist=rebin, ) for i, (d, l) in enumerate(dirname_label_pairs) ] for c in this_data_entries[1:]: c.subplot = this_data_entries[0].obj plot = Plot( this_data_entries, what='hist', title=title, xtitle="y^{jet 1}", ytitle="N", # xlim=[30, 1000], # ylim=[10, 1E8] if 'unweighted' in ht_name else [1, 1E12], subplot_type='ratio', subplot_title='* / %s' % dirname_label_pairs[0][1], **plot_kwargs) plot.default_canvas_size = (800, 600) plot.y_padding_max_log = 500 plot.legend.SetY1(0.7) plot.legend.SetY2(0.88) plot.legend.SetX1(0.5) plot.legend.SetNColumns(2) plot.plot("NOSTACK HISTE") output_filename = "%s/DataJetHTZB-%s_eta_jet1%s%s.pdf" % ( output_dir, ent['label'], "_unweighted" if 'unweighted' in zb_name else "", append) plot.save(output_filename)
def 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))