Python Histogram_properties.additional_text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 additional_text [as 别名]
def compare_vjets_templates( variable = 'MET', met_type = 'patType1CorrectedPFMet',
                             title = 'Untitled', channel = 'electron' ):
    ''' Compares the V+jets templates in different bins
     of the current variable'''
    global fit_variable_properties, b_tag_bin, save_as
    variable_bins = variable_bins_ROOT[variable]
    histogram_template = get_histogram_template( variable )
    
    for fit_variable in electron_fit_variables:
        all_hists = {}
        inclusive_hist = None
        save_path = 'plots/%dTeV/fit_variables/%s/%s/' % ( measurement_config.centre_of_mass_energy, variable, fit_variable )
        make_folder_if_not_exists( save_path + '/vjets/' )
        
        max_bins = len( variable_bins )
        for bin_range in variable_bins[0:max_bins]:
            
            params = {'met_type': met_type, 'bin_range':bin_range, 'fit_variable':fit_variable, 'b_tag_bin':b_tag_bin, 'variable':variable}
            fit_variable_distribution = histogram_template % params
            # format: histograms['data'][qcd_fit_variable_distribution]
            histograms = get_histograms_from_files( [fit_variable_distribution], histogram_files )
            prepare_histograms( histograms, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )
            all_hists[bin_range] = histograms['V+Jets'][fit_variable_distribution]
    
        # create the inclusive distributions
        inclusive_hist = deepcopy( all_hists[variable_bins[0]] )
        for bin_range in variable_bins[1:max_bins]:
            inclusive_hist += all_hists[bin_range]
        for bin_range in variable_bins[0:max_bins]:
            if not all_hists[bin_range].Integral() == 0:
                all_hists[bin_range].Scale( 1 / all_hists[bin_range].Integral() )
        # normalise all histograms
        inclusive_hist.Scale( 1 / inclusive_hist.Integral() )
        # now compare inclusive to all bins
        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.y_axis_title = histogram_properties.y_axis_title.replace( 'Events', 'a.u.' )
        histogram_properties.x_limits = [fit_variable_properties[fit_variable]['min'], fit_variable_properties[fit_variable]['max']]
        histogram_properties.title = title
        histogram_properties.additional_text = channel_latex[channel] + ', ' + b_tag_bins_latex[b_tag_bin]
        histogram_properties.name = variable + '_' + fit_variable + '_' + b_tag_bin + '_VJets_template_comparison'
        histogram_properties.y_max_scale = 1.5
        measurements = {bin_range + ' GeV': histogram for bin_range, histogram in all_hists.iteritems()}
        measurements = OrderedDict( sorted( measurements.items() ) )
        fit_var = fit_variable.replace( 'electron_', '' )
        fit_var = fit_var.replace( 'muon_', '' )
        graphs = spread_x( measurements.values(), fit_variable_bin_edges[fit_var] )
        for key, graph in zip( sorted( measurements.keys() ), graphs ):
            measurements[key] = graph
        compare_measurements( models = {'inclusive' : inclusive_hist},
                             measurements = measurements,
                             show_measurement_errors = True,
                             histogram_properties = histogram_properties,
                             save_folder = save_path + '/vjets/',
                             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 additional_text [as 别名]
def compare_vjets_btag_regions( variable = 'MET', met_type = 'patType1CorrectedPFMet',
                                title = 'Untitled', channel = 'electron' ):
    ''' Compares the V+Jets template in different b-tag bins'''
    global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
    b_tag_bin_ctl = '0orMoreBtag'
    variable_bins = variable_bins_ROOT[variable]
    histogram_template = get_histogram_template( variable )
    
    for fit_variable in electron_fit_variables:
        if '_bl' in fit_variable:
                b_tag_bin_ctl = '1orMoreBtag'
        else:
            b_tag_bin_ctl = '0orMoreBtag'
        save_path = 'plots/%dTeV/fit_variables/%s/%s/' % ( measurement_config.centre_of_mass_energy, variable, fit_variable )
        make_folder_if_not_exists( save_path + '/vjets/' )
        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.y_axis_title = histogram_properties.y_axis_title.replace( 'Events', 'a.u.' )
        histogram_properties.x_limits = [fit_variable_properties[fit_variable]['min'], fit_variable_properties[fit_variable]['max']]
        histogram_properties.title = title
        histogram_properties.additional_text = channel_latex[channel] + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
        histogram_properties.y_max_scale = 1.5
        for bin_range in variable_bins:
            params = {'met_type': met_type, 'bin_range':bin_range, 'fit_variable':fit_variable, 'b_tag_bin':b_tag_bin, 'variable':variable}
            fit_variable_distribution = histogram_template % params
            fit_variable_distribution_ctl = fit_variable_distribution.replace( b_tag_bin, b_tag_bin_ctl )
            # format: histograms['data'][qcd_fit_variable_distribution]
            histograms = get_histograms_from_files( [fit_variable_distribution, fit_variable_distribution_ctl], {'V+Jets' : histogram_files['V+Jets']} )
            prepare_histograms( histograms, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )
            histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin_ctl + '_VJets_template_comparison'
            histograms['V+Jets'][fit_variable_distribution].Scale( 1 / histograms['V+Jets'][fit_variable_distribution].Integral() )
            histograms['V+Jets'][fit_variable_distribution_ctl].Scale( 1 / histograms['V+Jets'][fit_variable_distribution_ctl].Integral() )
            compare_measurements( models = {'no b-tag' : histograms['V+Jets'][fit_variable_distribution_ctl]},
                             measurements = {'$>=$ 2 b-tags': histograms['V+Jets'][fit_variable_distribution]},
                             show_measurement_errors = True,
                             histogram_properties = histogram_properties,
                             save_folder = save_path + '/vjets/',
                             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 additional_text [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 additional_text [as 别名]
def compare_qcd_control_regions( variable = 'MET', met_type = 'patType1CorrectedPFMet', title = 'Untitled', channel = 'electron' ):
    ''' Compares the templates from the control regions in different bins
     of the current variable'''
    global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
    variable_bins = variable_bins_ROOT[variable]
    histogram_template = get_histogram_template( variable )
    
    for fit_variable in electron_fit_variables:
        all_hists = {}
        inclusive_hist = None
        if '_bl' in fit_variable:
                b_tag_bin_ctl = '1orMoreBtag'
        else:
            b_tag_bin_ctl = '0orMoreBtag'
        save_path = 'plots/%dTeV/fit_variables/%s/%s/' % ( measurement_config.centre_of_mass_energy, variable, fit_variable )
        make_folder_if_not_exists( save_path + '/qcd/' )
        
        max_bins = 3
        for bin_range in variable_bins[0:max_bins]:
            
            params = {'met_type': met_type, 'bin_range':bin_range, 'fit_variable':fit_variable, 'b_tag_bin':b_tag_bin, 'variable':variable}
            fit_variable_distribution = histogram_template % params
            qcd_fit_variable_distribution = fit_variable_distribution.replace( 'Ref selection', 'QCDConversions' )
            qcd_fit_variable_distribution = qcd_fit_variable_distribution.replace( b_tag_bin, b_tag_bin_ctl )
            # format: histograms['data'][qcd_fit_variable_distribution]
            histograms = get_histograms_from_files( [qcd_fit_variable_distribution], histogram_files )
            prepare_histograms( histograms, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )

            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'] )
            # clean
            all_hists[bin_range] = qcd_from_data
    
        # create the inclusive distributions
        inclusive_hist = deepcopy( all_hists[variable_bins[0]] )
        for bin_range in variable_bins[1:max_bins]:
            inclusive_hist += all_hists[bin_range]
        for bin_range in variable_bins[0:max_bins]:
            if not all_hists[bin_range].Integral() == 0:
                all_hists[bin_range].Scale( 1 / all_hists[bin_range].Integral() )
        # normalise all histograms
        inclusive_hist.Scale( 1 / inclusive_hist.Integral() )
        # now compare inclusive to all bins
        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.y_axis_title = histogram_properties.y_axis_title.replace( 'Events', 'a.u.' )
        histogram_properties.x_limits = [fit_variable_properties[fit_variable]['min'], fit_variable_properties[fit_variable]['max']]
#         histogram_properties.y_limits = [0, 0.5]
        histogram_properties.title = title
        histogram_properties.additional_text = channel_latex[channel] + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
        histogram_properties.name = variable + '_' + fit_variable + '_' + b_tag_bin_ctl + '_QCD_template_comparison'
        histogram_properties.y_max_scale = 1.5
        measurements = {bin_range + ' GeV': histogram for bin_range, histogram in all_hists.iteritems()}
        measurements = OrderedDict( sorted( measurements.items() ) )
        compare_measurements( models = {'inclusive' : inclusive_hist},
                             measurements = measurements,
                             show_measurement_errors = True,
                             histogram_properties = histogram_properties,
                             save_folder = save_path + '/qcd/',
                             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 additional_text [as 别名]
def plot_fit_variable( histograms, fit_variable, variable, bin_range,
                      fit_variable_distribution, qcd_fit_variable_distribution,
                      title, save_path, channel = 'electron' ):
    global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
    histograms_ = deepcopy( histograms )
    mc_uncertainty = 0.10
    prepare_histograms( histograms_, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )
    
    ######################################
    # plot the control regions as they are
    ######################################
    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_properties.y_max_scale = 2

    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
    histogram_properties.additional_text = channel_latex[channel] + ', ' + 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,
                                 )
    ######################################
    # plot QCD against data control region with TTJet, SingleTop and V+Jets removed
    ######################################
    histograms_to_draw = [qcd_from_data,
                          histograms_['QCD'][qcd_fit_variable_distribution],
                          ]
    histogram_properties.y_max_scale = 1.5
    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,
                                  )
    ######################################
    # plot signal region
    ######################################
    # 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.additional_text = channel_latex[channel] + ', ' + 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,
                                 )
    ######################################
    # plot templates
    ######################################
    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'
    histogram_properties.y_max_scale = 2
    # 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()
#.........这里部分代码省略.........