Python Histogram_properties.mc_error方法代码示例

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import mc_error [as 别名]
def plot_fit_results(fit_results, initial_values, channel):
    global variable, output_folder

    title = electron_histogram_title if channel == "electron" else muon_histogram_title

    histogram_properties = Histogram_properties()
    histogram_properties.title = title

    histogram_properties.x_axis_title = variable + " [GeV]"
    histogram_properties.mc_error = 0.0
    histogram_properties.legend_location = "upper right"
    # we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets
    for sample in ["TTJet", "SingleTop", "QCD", "V+Jets"]:
        histograms = {}
        # absolute eta measurement as baseline
        h_absolute_eta = None
        h_before = None
        histogram_properties.y_axis_title = "Fitted number of events for " + samples_latex[sample]

        for fit_var_input in fit_results.keys():
            latex_string = create_latex_string(fit_var_input)
            fit_data = fit_results[fit_var_input][sample]
            h = value_error_tuplelist_to_hist(fit_data, bin_edges[variable])
            if fit_var_input == "absolute_eta":
                h_absolute_eta = h
            elif fit_var_input == "before":
                h_before = h
            else:
                histograms[latex_string] = h
        graphs = spread_x(histograms.values(), bin_edges[variable])
        for key, graph in zip(histograms.keys(), graphs):
            histograms[key] = graph
        filename = sample.replace("+", "_") + "_fit_var_comparison_" + channel
        histogram_properties.name = filename
        histogram_properties.y_limits = 0, limit_range_y(h_absolute_eta)[1] * 1.3
        histogram_properties.x_limits = bin_edges[variable][0], bin_edges[variable][-1]

        h_initial_values = value_error_tuplelist_to_hist(initial_values[sample], bin_edges[variable])
        h_initial_values.Scale(closure_tests["simple"][sample])

        compare_measurements(
            models={
                fit_variables_latex["absolute_eta"]: h_absolute_eta,
                "initial values": h_initial_values,
                "before": h_before,
            },
            measurements=histograms,
            show_measurement_errors=True,
            histogram_properties=histogram_properties,
            save_folder=output_folder,
            save_as=["png", "pdf"],
        )

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import mc_error [as 别名]
def plot_fit_results( fit_results, initial_values, channel ):
    global variable, output_folder
    
    title = electron_histogram_title if channel == 'electron' else muon_histogram_title
    
    
    histogram_properties = Histogram_properties()
    histogram_properties.title = title
    
    histogram_properties.x_axis_title = variable + ' [GeV]'
    histogram_properties.mc_error = 0.0
    histogram_properties.legend_location = 'upper right'
    # we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets
    for sample in ['TTJet', 'SingleTop', 'QCD', 'V+Jets']:
        histograms = {}
        # absolute eta measurement as baseline
        h_absolute_eta = None
        h_before = None
        histogram_properties.y_axis_title = 'Fitted number of events for ' + samples_latex[sample]
        
        for fit_var_input in fit_results.keys():
            latex_string = create_latex_string( fit_var_input )
            fit_data = fit_results[fit_var_input][sample]
            h = value_error_tuplelist_to_hist( fit_data,
                                              bin_edges[variable] )
            if fit_var_input == 'absolute_eta':
                h_absolute_eta = h
            elif fit_var_input == 'before':
                h_before = h
            else:
                histograms[latex_string] = h
        graphs = spread_x( histograms.values(), bin_edges[variable] )
        for key, graph in zip( histograms.keys(), graphs ):
            histograms[key] = graph
        filename = sample.replace( '+', '_' ) + '_fit_var_comparison_' + channel
        histogram_properties.name = filename
        histogram_properties.y_limits = 0, limit_range_y( h_absolute_eta )[1] * 1.3
        histogram_properties.x_limits = bin_edges[variable][0], bin_edges[variable][-1]
        
        h_initial_values = value_error_tuplelist_to_hist( initial_values[sample],
                                                         bin_edges[variable] )
        h_initial_values.Scale(closure_tests['simple'][sample])
        
        compare_measurements( models = {fit_variables_latex['absolute_eta']:h_absolute_eta,
                                        'initial values' : h_initial_values,
                                        'before': h_before},
                             measurements = histograms,
                             show_measurement_errors = True,
                             histogram_properties = histogram_properties,
                             save_folder = output_folder,
                             save_as = ['png', 'pdf'] )

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import mc_error [as 别名]
def plot_fit_variable( histograms, fit_variable, variable, bin_range,
                      fit_variable_distribution, qcd_fit_variable_distribution,
                      title, save_path ):
    global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
    mc_uncertainty = 0.10
    prepare_histograms( histograms, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )
    
    histogram_properties = Histogram_properties()
    histogram_properties.x_axis_title = fit_variable_properties[fit_variable]['x-title']
    histogram_properties.y_axis_title = fit_variable_properties[fit_variable]['y-title']
    histogram_properties.x_limits = [fit_variable_properties[fit_variable]['min'], fit_variable_properties[fit_variable]['max']]

    histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']]
    histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red']
#     qcd_from_data = histograms['data'][qcd_fit_variable_distribution].Clone()
    # clean against other processes
    histograms_for_cleaning = {'data':histograms['data'][qcd_fit_variable_distribution],
                               'V+Jets':histograms['V+Jets'][qcd_fit_variable_distribution],
                               'SingleTop':histograms['SingleTop'][qcd_fit_variable_distribution],
                               'TTJet':histograms['TTJet'][qcd_fit_variable_distribution]}
    qcd_from_data = clean_control_region( histograms_for_cleaning, subtract = ['TTJet', 'V+Jets', 'SingleTop'] )
    
    
    histograms_to_draw = [histograms['data'][qcd_fit_variable_distribution],
                          histograms['QCD'][qcd_fit_variable_distribution],
                          histograms['V+Jets'][qcd_fit_variable_distribution],
                          histograms['SingleTop'][qcd_fit_variable_distribution],
                          histograms['TTJet'][qcd_fit_variable_distribution]]
    
    histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
    histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_%s_QCDConversions' % b_tag_bin_ctl
    make_data_mc_comparison_plot( histograms_to_draw, histogram_lables, histogram_colors,
                                 histogram_properties,
                                 save_folder = save_path + '/qcd/',
                                 show_ratio = False,
                                 save_as = save_as,
                                 )
    
    histograms_to_draw = [qcd_from_data,
                          histograms['QCD'][qcd_fit_variable_distribution],
                          ]
    
    histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_%s_QCDConversions_subtracted' % b_tag_bin_ctl
    make_data_mc_comparison_plot( histograms_to_draw,
                                  histogram_lables = ['data', 'QCD'],
                                  histogram_colors = ['black', 'yellow'],
                                  histogram_properties = histogram_properties,
                                  save_folder = save_path + '/qcd/',
                                  show_ratio = False,
                                  save_as = save_as,
                                  )
    
    # scale QCD to predicted
    n_qcd_predicted_mc = histograms['QCD'][fit_variable_distribution].Integral()
    n_qcd_fit_variable_distribution = qcd_from_data.Integral()
    if not n_qcd_fit_variable_distribution == 0:
        qcd_from_data.Scale( 1.0 / n_qcd_fit_variable_distribution * n_qcd_predicted_mc )
    
    histograms_to_draw = [histograms['data'][fit_variable_distribution], qcd_from_data,
                          histograms['V+Jets'][fit_variable_distribution],
                          histograms['SingleTop'][fit_variable_distribution], histograms['TTJet'][fit_variable_distribution]]
    
    histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
    histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin
    make_data_mc_comparison_plot( histograms_to_draw,
                                  histogram_lables,
                                  histogram_colors,
                                  histogram_properties,
                                  save_folder = save_path,
                                  show_ratio = False,
                                  save_as = save_as,
                                 )
    histogram_properties.mc_error = mc_uncertainty
    histogram_properties.mc_errors_label = '$\mathrm{t}\\bar{\mathrm{t}}$ uncertainty'
    histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin + '_templates'
    # change histogram order for better visibility
    histograms_to_draw = [histograms['TTJet'][fit_variable_distribution] + histograms['SingleTop'][fit_variable_distribution], 
                          histograms['TTJet'][fit_variable_distribution],
                          histograms['SingleTop'][fit_variable_distribution],
                          histograms['V+Jets'][fit_variable_distribution],
                          qcd_from_data]
    histogram_lables = ['QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet'], samples_latex['TTJet'] + ' + ' + 'Single-Top']
    histogram_lables.reverse()
    # change QCD color to orange for better visibility
    histogram_colors = ['orange', 'green', 'magenta', 'red', 'black']
    histogram_colors.reverse()
    make_shape_comparison_plot( shapes = histograms_to_draw,
                                names = histogram_lables,
                                colours = histogram_colors,
                                histogram_properties = histogram_properties,
                                fill_area = False,
                                alpha = 1,
                                save_folder = save_path,
                                save_as = save_as,
                                )

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import mc_error [as 别名]

#.........这里部分代码省略.........

    selection = 'SolutionCategory == 1'
    histogramsCorrect = get_histograms_from_trees( trees = [signal_region_tree], branch = branchName, weightBranch = '1', selection = selection, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1] )

    selection = 'SolutionCategory == 2'
    histogramsNotSL = get_histograms_from_trees( trees = [signal_region_tree], branch = branchName, weightBranch = '1', selection = selection, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1] )

    selection = 'SolutionCategory == 3'
    histogramsNotReco = get_histograms_from_trees( trees = [signal_region_tree], branch = branchName, weightBranch = '1', selection = selection, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1] )

    selection = 'SolutionCategory > 3'
    histogramsWrong = get_histograms_from_trees( trees = [signal_region_tree], branch = branchName, weightBranch = '1', selection = selection, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1] )

    # Split histograms up into signal/control (?)
    signal_region_hists = {}
    inclusive_control_region_hists = {}
    for sample in histograms.keys():
        signal_region_hists[sample] = histograms[sample][signal_region_tree]
        if use_qcd_data_region:
            inclusive_control_region_hists[sample] = histograms[sample][control_region_tree]

    prepare_histograms( histograms, rebin = 1, scale_factor = measurement_config.luminosity_scale )
    prepare_histograms( histogramsNoSolution, rebin = 1, scale_factor = measurement_config.luminosity_scale )
    prepare_histograms( histogramsCorrect, rebin = 1, scale_factor = measurement_config.luminosity_scale )
    prepare_histograms( histogramsNotSL, rebin = 1, scale_factor = measurement_config.luminosity_scale )
    prepare_histograms( histogramsNotReco, rebin = 1, scale_factor = measurement_config.luminosity_scale )
    prepare_histograms( histogramsWrong, rebin = 1, scale_factor = measurement_config.luminosity_scale )

    qcd_from_data = signal_region_hists['QCD']

    # Which histograms to draw, and properties
    histograms_to_draw = [signal_region_hists['data'], qcd_from_data,
                          signal_region_hists['V+Jets'],
                          signal_region_hists['SingleTop'],
                          histogramsNoSolution['TTJet'][signal_region_tree],
                          histogramsNotSL['TTJet'][signal_region_tree],
                          histogramsNotReco['TTJet'][signal_region_tree],
                          histogramsWrong['TTJet'][signal_region_tree],
                          histogramsCorrect['TTJet'][signal_region_tree]
                          ]
    histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', 
                        samples_latex['TTJet'] + ' - no solution',
                        samples_latex['TTJet'] + ' - not SL',
                        samples_latex['TTJet'] + ' - not reconstructible',
                        samples_latex['TTJet'] + ' - wrong reco',
                        samples_latex['TTJet'] + ' - correct',
                        ]
    histogram_colors = ['black', 'yellow', 'green', 'magenta',
                        'black',
                        'burlywood',
                        'chartreuse',
                        'blue',
                        'red'
                        ]

    histogram_properties = Histogram_properties()
    histogram_properties.name = name_prefix + b_tag_bin
    if category != 'central':
        histogram_properties.name += '_' + category
    histogram_properties.title = title
    histogram_properties.x_axis_title = x_axis_title
    histogram_properties.y_axis_title = y_axis_title
    histogram_properties.x_limits = x_limits
    histogram_properties.y_limits = y_limits
    histogram_properties.y_max_scale = y_max_scale
    histogram_properties.xerr = None
    # workaround for rootpy issue #638
    histogram_properties.emptybins = True
    if b_tag_bin:
        histogram_properties.additional_text = channel_latex[channel] + ', ' + b_tag_bins_latex[b_tag_bin]
    else:
        histogram_properties.additional_text = channel_latex[channel]
    histogram_properties.legend_location = legend_location
    histogram_properties.cms_logo_location = cms_logo_location
    histogram_properties.preliminary = preliminary
    histogram_properties.set_log_y = log_y
    histogram_properties.legend_color = legend_color
    if ratio_y_limits:
        histogram_properties.ratio_y_limits = ratio_y_limits

    if normalise_to_fit:
        histogram_properties.mc_error = get_normalisation_error( normalisation )
        histogram_properties.mc_errors_label = 'fit uncertainty'
    else:
        histogram_properties.mc_error = mc_uncertainty
        histogram_properties.mc_errors_label = 'MC unc.'

    # Actually draw histograms
    make_data_mc_comparison_plot( histograms_to_draw, histogram_lables, histogram_colors,
                                 histogram_properties, save_folder = output_folder,
                                 show_ratio = False, normalise = normalise,
                                 )
    histogram_properties.name += '_with_ratio'
    loc = histogram_properties.legend_location
    # adjust legend location as it is relative to canvas!
    histogram_properties.legend_location = ( loc[0], loc[1] + 0.05 )
    make_data_mc_comparison_plot( histograms_to_draw, histogram_lables, histogram_colors,
                                 histogram_properties, save_folder = output_folder,
                                 show_ratio = True, normalise = normalise,
                                 )

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import mc_error [as 别名]

#.........这里部分代码省略.........
                    signal_region_hists[sample] = histograms[sample][control_region_tree]
                elif sample is 'QCD' :
                    signal_region_hists[sample] = histograms[sample][signal_region_tree]
                else:
                    del signal_region_hists[sample]

            if use_qcd_data_region:
                control_region_hists[sample] = histograms_QCDControlRegion[sample][qcd_control_region]

        # Prepare histograms
        if normalise_to_fit:
            # only scale signal region to fit (results are invalid for control region)
            prepare_histograms( signal_region_hists, rebin = rebin,
                                scale_factor = measurement_config.luminosity_scale,
                                normalisation = normalisation )
        elif normalise_to_data:
            totalMC = 0
            for sample in signal_region_hists:
                if sample is 'data' : continue
                totalMC += signal_region_hists[sample].Integral()
            newScale = signal_region_hists['data'].Integral() / totalMC

            prepare_histograms( signal_region_hists, rebin = rebin,
                                scale_factor = newScale,
                               )
        else:
            print measurement_config.luminosity_scale
            prepare_histograms( signal_region_hists, rebin = rebin,
                                scale_factor = measurement_config.luminosity_scale )
            prepare_histograms( control_region_hists, rebin = rebin,
                                scale_factor = measurement_config.luminosity_scale )

        # Use qcd from data control region or not
        qcd_from_data = None
        if use_qcd_data_region:
            qcd_from_data = clean_control_region( control_region_hists,

                              subtract = ['TTJet', 'V+Jets', 'SingleTop'] )
            # Normalise control region correctly
            nBins = signal_region_hists['QCD'].GetNbinsX()
            n, error = signal_region_hists['QCD'].integral(0,nBins+1,error=True)
            n_qcd_predicted_mc_signal = ufloat( n, error)

            n, error = control_region_hists['QCD'].integral(0,nBins+1,error=True)
            n_qcd_predicted_mc_control = ufloat( n, error)

            n, error = qcd_from_data.integral(0,nBins+1,error=True)
            n_qcd_control_region = ufloat( n, error)

            if not n_qcd_control_region == 0:
                dataDrivenQCDScale = n_qcd_predicted_mc_signal / n_qcd_predicted_mc_control
                print 'Overall scale : ',dataDrivenQCDScale
                qcd_from_data.Scale( dataDrivenQCDScale.nominal_value )
                signalToControlScale = n_qcd_predicted_mc_signal / n_qcd_control_region
                dataToMCscale = n_qcd_control_region / n_qcd_predicted_mc_control
                print "Signal to control :",signalToControlScale
                print "QCD scale : ",dataToMCscale
        else:
            qcd_from_data = signal_region_hists['QCD']

        # Which histograms to draw, and properties
        histograms_to_draw = []
        histogram_lables = []
        histogram_colors = []

        if compare_qcd_signal_with_data_control :
            histograms_to_draw = [signal_region_hists['data'], qcd_from_data ]
            histogram_lables = ['data', 'QCD']
            histogram_colors = ['black', 'yellow']
        else :
            histograms_to_draw = [signal_region_hists['data'], qcd_from_data,
                                  signal_region_hists['V+Jets'],
                                  signal_region_hists['SingleTop'],
                                  signal_region_hists['TTJet']]
            histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']]
            histogram_colors = [colours['data'], colours['QCD'], colours['V+Jets'], colours['Single-Top'], colours['TTJet'] ]

        
        print list(qcd_from_data.y())
        histogramsToCompare[qcd_data_region] = qcd_from_data

    print histogramsToCompare
    histogram_properties = Histogram_properties()
    histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + branchName
    histogram_properties.title = title
    histogram_properties.x_axis_title = x_axis_title
    histogram_properties.y_axis_title = y_axis_title
    histogram_properties.x_limits = x_limits
    histogram_properties.y_limits = y_limits
    histogram_properties.mc_error = 0.0
    histogram_properties.legend_location = ( 0.98, 0.78 )
    histogram_properties.ratio_y_limits = ratio_y_limits
    if 'electron' in channel:
        make_control_region_comparison(histogramsToCompare['QCDConversions'], histogramsToCompare['QCD non iso e+jets'],
                                       name_region_1='Conversions', name_region_2='Non Iso',
                                       histogram_properties=histogram_properties, save_folder=output_folder)
    elif 'muon' in channel:
        make_control_region_comparison(histogramsToCompare['QCD iso > 0.3'], histogramsToCompare['QCD 0.12 < iso <= 0.3'],
                                       name_region_1='QCD iso > 0.3', name_region_2='QCD 0.12 < iso <= 0.3',
                                       histogram_properties=histogram_properties, save_folder=output_folder)

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import mc_error [as 别名]
 
 qcd_predicted_mc = histograms['QCD'][control_region]
 
 histograms_to_draw = [histograms['data'][control_region], qcd_predicted_mc,
                       histograms['V+Jets'][control_region],
                       histograms['SingleTop'][control_region], histograms['TTJet'][control_region]]
 histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']]
 histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red']
 
 histogram_properties = Histogram_properties()
 histogram_properties.name = 'EPlusJets_BJets_invmass_' + b_tag_bin
 histogram_properties.title = e_title + ', ' + b_tag_bins_latex[b_tag_bin]
 histogram_properties.x_axis_title = '$M_{\mathrm{b}\\bar{\mathrm{b}}}$'
 histogram_properties.y_axis_title = 'Normalised events/(20 GeV)'
 histogram_properties.x_limits = [0, 800]
 histogram_properties.mc_error = 0.15
 make_data_mc_comparison_plot(histograms_to_draw, histogram_lables, histogram_colors,
                              histogram_properties, save_folder = output_folder, show_ratio = False)
 histogram_properties.name += '_with_ratio'
 make_data_mc_comparison_plot(histograms_to_draw, histogram_lables, histogram_colors,
                              histogram_properties, save_folder = output_folder, show_ratio = True)
 
 #bjet invariant mass
 b_tag_bin = '3btags'
 control_region = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/bjet_invariant_mass_' + b_tag_bin
 
 histograms = get_histograms_from_files([control_region], histogram_files)
 prepare_histograms(histograms, rebin=10, scale_factor = measurement_config.luminosity_scale)
 
 qcd_predicted_mc = histograms['QCD'][control_region]
 

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import mc_error [as 别名]
def do_shape_check(channel, control_region_1, control_region_2, variable, normalisation, title, x_title, y_title, x_limits, y_limits,
                   name_region_1='conversions' , name_region_2='non-isolated electrons', name_region_3='fit results', rebin=1):
    global b_tag_bin
    # QCD shape comparison
    if channel == 'electron':
        histograms = get_histograms_from_files([control_region_1, control_region_2], histogram_files)
        
        region_1 = histograms[channel][control_region_1].Clone() - histograms['TTJet'][control_region_1].Clone() - histograms['V+Jets'][control_region_1].Clone() - histograms['SingleTop'][control_region_1].Clone()
        region_2 = histograms[channel][control_region_2].Clone() - histograms['TTJet'][control_region_2].Clone() - histograms['V+Jets'][control_region_2].Clone() - histograms['SingleTop'][control_region_2].Clone()
        
        region_1.Rebin(rebin)
        region_2.Rebin(rebin)
        
        histogram_properties = Histogram_properties()
        histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_' + b_tag_bin
        histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
        histogram_properties.x_axis_title = x_title
        histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
        histogram_properties.x_limits = x_limits
        histogram_properties.y_limits = y_limits[0]
        histogram_properties.mc_error = 0.0
        histogram_properties.legend_location = 'upper right'
        make_control_region_comparison(region_1, region_2,
                                       name_region_1=name_region_1, name_region_2=name_region_2,
                                       histogram_properties=histogram_properties, save_folder=output_folder)
        
        # QCD shape comparison to fit results
        histograms = get_histograms_from_files([control_region_1], histogram_files)
        
        region_1_tmp = histograms[channel][control_region_1].Clone() - histograms['TTJet'][control_region_1].Clone() - histograms['V+Jets'][control_region_1].Clone() - histograms['SingleTop'][control_region_1].Clone()
        region_1 = rebin_asymmetric(region_1_tmp, bin_edges[variable])
        
        fit_results_QCD = normalisation[variable]['QCD']
        region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges_vis[variable])
        
        histogram_properties = Histogram_properties()
        histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_conversions_' + b_tag_bin
        histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
        histogram_properties.x_axis_title = x_title
        histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
        histogram_properties.x_limits = x_limits
        histogram_properties.y_limits = y_limits[1]
        histogram_properties.mc_error = 0.0
        histogram_properties.legend_location = 'upper right'
        make_control_region_comparison(region_1, region_2,
                                       name_region_1=name_region_1, name_region_2=name_region_3,
                                       histogram_properties=histogram_properties, save_folder=output_folder)
    
    histograms = get_histograms_from_files([control_region_2], histogram_files)
    
    region_1_tmp = histograms[channel][control_region_2].Clone() - histograms['TTJet'][control_region_2].Clone() - histograms['V+Jets'][control_region_2].Clone() - histograms['SingleTop'][control_region_2].Clone()
    region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable])    
    
    fit_results_QCD = normalisation[variable]['QCD']
    region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges[variable])
    
    histogram_properties = Histogram_properties()
    histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_noniso_' + b_tag_bin
    histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
    histogram_properties.x_axis_title = x_title
    histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
    histogram_properties.x_limits = x_limits
    histogram_properties.y_limits = y_limits[1]
    histogram_properties.mc_error = 0.0
    histogram_properties.legend_location = 'upper right'
    make_control_region_comparison(region_1, region_2,
                                   name_region_1=name_region_2, name_region_2=name_region_3,
                                   histogram_properties=histogram_properties, save_folder=output_folder)

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import mc_error [as 别名]
def do_shape_check(
    channel,
    control_region_1,
    control_region_2,
    variable,
    normalisation,
    title,
    x_title,
    y_title,
    x_limits,
    y_limits,
    name_region_1="conversions",
    name_region_2="non-isolated electrons",
    name_region_3="fit results",
    rebin=1,
):
    global b_tag_bin
    # QCD shape comparison
    if channel == "electron":
        histograms = get_histograms_from_files([control_region_1, control_region_2], histogram_files)

        region_1 = (
            histograms[channel][control_region_1].Clone()
            - histograms["TTJet"][control_region_1].Clone()
            - histograms["V+Jets"][control_region_1].Clone()
            - histograms["SingleTop"][control_region_1].Clone()
        )
        region_2 = (
            histograms[channel][control_region_2].Clone()
            - histograms["TTJet"][control_region_2].Clone()
            - histograms["V+Jets"][control_region_2].Clone()
            - histograms["SingleTop"][control_region_2].Clone()
        )

        region_1.Rebin(rebin)
        region_2.Rebin(rebin)

        histogram_properties = Histogram_properties()
        histogram_properties.name = "QCD_control_region_comparison_" + channel + "_" + variable + "_" + b_tag_bin
        histogram_properties.title = title + ", " + b_tag_bins_latex[b_tag_bin]
        histogram_properties.x_axis_title = x_title
        histogram_properties.y_axis_title = "arbitrary units/(0.1)"
        histogram_properties.x_limits = x_limits
        histogram_properties.y_limits = y_limits[0]
        histogram_properties.mc_error = 0.0
        histogram_properties.legend_location = "upper right"
        make_control_region_comparison(
            region_1,
            region_2,
            name_region_1=name_region_1,
            name_region_2=name_region_2,
            histogram_properties=histogram_properties,
            save_folder=output_folder,
        )

        # QCD shape comparison to fit results
        histograms = get_histograms_from_files([control_region_1], histogram_files)

        region_1_tmp = (
            histograms[channel][control_region_1].Clone()
            - histograms["TTJet"][control_region_1].Clone()
            - histograms["V+Jets"][control_region_1].Clone()
            - histograms["SingleTop"][control_region_1].Clone()
        )
        region_1 = rebin_asymmetric(region_1_tmp, bin_edges[variable])

        fit_results_QCD = normalisation[variable]["QCD"]
        region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges[variable])

        histogram_properties = Histogram_properties()
        histogram_properties.name = (
            "QCD_control_region_comparison_" + channel + "_" + variable + "_fits_with_conversions_" + b_tag_bin
        )
        histogram_properties.title = title + ", " + b_tag_bins_latex[b_tag_bin]
        histogram_properties.x_axis_title = x_title
        histogram_properties.y_axis_title = "arbitrary units/(0.1)"
        histogram_properties.x_limits = x_limits
        histogram_properties.y_limits = y_limits[1]
        histogram_properties.mc_error = 0.0
        histogram_properties.legend_location = "upper right"
        make_control_region_comparison(
            region_1,
            region_2,
            name_region_1=name_region_1,
            name_region_2=name_region_3,
            histogram_properties=histogram_properties,
            save_folder=output_folder,
        )

    histograms = get_histograms_from_files([control_region_2], histogram_files)

    region_1_tmp = (
        histograms[channel][control_region_2].Clone()
        - histograms["TTJet"][control_region_2].Clone()
        - histograms["V+Jets"][control_region_2].Clone()
        - histograms["SingleTop"][control_region_2].Clone()
    )
    region_1 = rebin_asymmetric(region_1_tmp, bin_edges[variable])

    fit_results_QCD = normalisation[variable]["QCD"]
#.........这里部分代码省略.........

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import mc_error [as 别名]
def make_correlation_plot_from_file( channel, variable, fit_variables, CoM, title, x_title, y_title, x_limits, y_limits, rebin = 1, save_folder = 'plots/fitchecks/', save_as = ['pdf', 'png'] ):
# global b_tag_bin
    parameters = ["TTJet", "SingleTop", "V+Jets", "QCD"]
    parameters_latex = []
    for template in parameters:
        parameters_latex.append(samples_latex[template])
        
    input_file = open( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), "r" )
    # cycle through the lines in the file
    for line_number, line in enumerate( input_file ):
        # for now, only make plots for the fits for the central measurement
        if "central" in line:
            # matrix we want begins 11 lines below the line with the measurement ("central")
            line_number = line_number + 11
            break
    input_file.close()
    
    #Note: For some reason, the fit outputs the correlation matrix with the templates in the following order:
    #parameter1: QCD
    #parameter2: SingleTop
    #parameter3: TTJet
    #parameter4: V+Jets
        
    for variable_bin in variable_bins_ROOT[variable]:
        weights = {}
        if channel == 'electron':
            #formula to calculate the number of lines below "central" to access in each loop
            number_of_lines_down = (variable_bins_ROOT[variable].index( variable_bin ) * 12)

            #Get QCD correlations
            matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down )
            weights["QCD_QCD"] = matrix_line.split()[2]
            weights["QCD_SingleTop"] = matrix_line.split()[3]
            weights["QCD_TTJet"] = matrix_line.split()[4]
            weights["QCD_V+Jets"] = matrix_line.split()[5]

            #Get SingleTop correlations
            matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 1 )
            weights["SingleTop_QCD"] = matrix_line.split()[2]
            weights["SingleTop_SingleTop"] = matrix_line.split()[3]
            weights["SingleTop_TTJet"] = matrix_line.split()[4]
            weights["SingleTop_V+Jets"] = matrix_line.split()[5]

            #Get TTJet correlations
            matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 2 )
            weights["TTJet_QCD"] = matrix_line.split()[2]
            weights["TTJet_SingleTop"] = matrix_line.split()[3]            
            weights["TTJet_TTJet"] = matrix_line.split()[4]
            weights["TTJet_V+Jets"] = matrix_line.split()[5]

            #Get V+Jets correlations
            matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 3 )
            weights["V+Jets_QCD"] = matrix_line.split()[2]
            weights["V+Jets_SingleTop"] = matrix_line.split()[3]
            weights["V+Jets_TTJet"] = matrix_line.split()[4]
            weights["V+Jets_V+Jets"] = matrix_line.split()[5]

        if channel == 'muon':
            #formula to calculate the number of lines below "central" to access in each bin loop
            number_of_lines_down =  ( len( variable_bins_ROOT [variable] ) * 12 ) + ( variable_bins_ROOT[variable].index( variable_bin ) * 12 )
            
            #Get QCD correlations
            matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down )
            weights["QCD_QCD"] = matrix_line.split()[2]
            weights["QCD_SingleTop"] = matrix_line.split()[3]
            weights["QCD_TTJet"] = matrix_line.split()[4]
            weights["QCD_V+Jets"] = matrix_line.split()[5]

            #Get SingleTop correlations
            matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 1 )
            weights["SingleTop_QCD"] = matrix_line.split()[2]
            weights["SingleTop_SingleTop"] = matrix_line.split()[3]
            weights["SingleTop_TTJet"] = matrix_line.split()[4]
            weights["SingleTop_V+Jets"] = matrix_line.split()[5]

            #Get TTJet correlations
            matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 2 )
            weights["TTJet_QCD"] = matrix_line.split()[2]
            weights["TTJet_SingleTop"] = matrix_line.split()[3]
            weights["TTJet_TTJet"] = matrix_line.split()[4]
            weights["TTJet_V+Jets"] = matrix_line.split()[5]
            
            #Get V+Jets correlations
            matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 3 )
            weights["V+Jets_QCD"] = matrix_line.split()[2]
            weights["V+Jets_SingleTop"] = matrix_line.split()[3]
            weights["V+Jets_TTJet"] = matrix_line.split()[4]
            weights["V+Jets_V+Jets"] = matrix_line.split()[5]

        #Create histogram
        histogram_properties = Histogram_properties()
        histogram_properties.title = title
        histogram_properties.name = 'Correlations_' + channel + '_' + variable + '_' + variable_bin
        histogram_properties.y_axis_title = y_title
        histogram_properties.x_axis_title = x_title
        histogram_properties.y_limits = y_limits
        histogram_properties.x_limits = x_limits
        histogram_properties.mc_error = 0.0
        histogram_properties.legend_location = 'upper right'

#.........这里部分代码省略.........