def plotHistograms(
		histogram_files, 
		var_to_plot,
		output_folder):
	'''
	'''
	global measurement_config

	weightBranchSignalRegion  = 'EventWeight * PUWeight * BJetWeight'
	weightBranchControlRegion = 'EventWeight'

	# Names of QCD regions to use
	qcd_data_region             = ''
	qcd_data_region_electron    = 'QCD non iso e+jets'
	qcd_data_region_muon        = 'QCD non iso mu+jets 1p5to3'

	sr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/AnalysisVariables'
	sr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/Ref selection/AnalysisVariables'
	cr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/{}/AnalysisVariables'.format(qcd_data_region_electron)
	cr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/{}/AnalysisVariables'.format(qcd_data_region_muon)
	
	print "Trees : "
	print "\t {}".format(sr_e_tree)
	print "\t {}".format(sr_mu_tree)
	print "\t {}".format(cr_e_tree)
	print "\t {}".format(cr_mu_tree)

	histogram_files_electron            = dict(histogram_files)
	histogram_files_electron['data']    = measurement_config.data_file_electron
	histogram_files_electron['QCD']     = measurement_config.electron_QCD_MC_trees

	histogram_files_muon                = dict(histogram_files)
	histogram_files_muon['data']        = measurement_config.data_file_muon
	histogram_files_muon['QCD']         = measurement_config.muon_QCD_MC_trees

	signal_region_hists = {}
	control_region_hists = {}

	for var in var_to_plot:
		selectionSignalRegion = '{} >= 0'.format(var)

		# Print all the weights applied to this plot 
		print "Variable : {}".format(var)
		print "Weight applied : {}".format(weightBranchSignalRegion)
		print "Selection applied : {}".format(selectionSignalRegion)

		histograms_electron = get_histograms_from_trees( 
			trees = [sr_e_tree], 
			branch = var, 
			weightBranch = weightBranchSignalRegion + ' * ElectronEfficiencyCorrection', 
			files = histogram_files_electron, 
			nBins = 20, 
			xMin = control_plots_bins[var][0], 
			xMax = control_plots_bins[var][-1], 
			selection = selectionSignalRegion 
		)
		histograms_muon = get_histograms_from_trees( 
			trees = [sr_mu_tree], 
			branch = var, 
			weightBranch = weightBranchSignalRegion + ' * MuonEfficiencyCorrection', 
			files = histogram_files_muon, 
			nBins = 20, 
			xMin = control_plots_bins[var][0], 
			xMax = control_plots_bins[var][-1], 
			selection = selectionSignalRegion 
		)
		histograms_electron_QCDControlRegion = get_histograms_from_trees( 
			trees = [cr_e_tree], 
			branch = var, 
			weightBranch = weightBranchControlRegion, 
			files = histogram_files_electron, 
			nBins = 20, 
			xMin = control_plots_bins[var][0], 
			xMax = control_plots_bins[var][-1], 
			selection = selectionSignalRegion 
		)
		histograms_muon_QCDControlRegion     = get_histograms_from_trees( 
			trees = [cr_mu_tree], 
			branch = var, 
			weightBranch = weightBranchControlRegion, 
			files = histogram_files_muon, 
			nBins = 20, 
			xMin = control_plots_bins[var][0], 
			xMax = control_plots_bins[var][-1], 
			selection = selectionSignalRegion 
		)

		# Combine the electron and muon histograms
		for sample in histograms_electron:
			h_electron = histograms_electron[sample][sr_e_tree]
			h_muon     = histograms_muon[sample][sr_mu_tree]
			h_qcd_electron = histograms_electron_QCDControlRegion[sample][cr_e_tree]
			h_qcd_muon     = histograms_muon_QCDControlRegion[sample][cr_mu_tree]

			signal_region_hists[sample] = h_electron + h_muon
			control_region_hists[sample] = h_qcd_electron + h_qcd_muon

		# NORMALISE TO LUMI
		prepare_histograms( 
			signal_region_hists, 
			scale_factor = measurement_config.luminosity_scale 
		)
		prepare_histograms( 
			control_region_hists, 
			scale_factor = measurement_config.luminosity_scale 
		)

		# BACKGROUND SUBTRACTION FOR QCD
		qcd_from_data = None
		qcd_from_data = clean_control_region( 
			control_region_hists,
			subtract = ['TTJet', 'V+Jets', 'SingleTop'] 
		)

		# DATA DRIVEN QCD
		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)

		dataDrivenQCDScale = n_qcd_predicted_mc_signal / n_qcd_predicted_mc_control
		qcd_from_data.Scale( dataDrivenQCDScale.nominal_value )
		signal_region_hists['QCD'] = qcd_from_data

		# PLOTTING
		histograms_to_draw = []
		histogram_lables   = []
		histogram_colors   = []

		histograms_to_draw = [
			# signal_region_hists['data'], 
			# qcd_from_data,
			# signal_region_hists['V+Jets'],
			signal_region_hists['SingleTop'],
			signal_region_hists['ST_s'],
			signal_region_hists['ST_t'],
			signal_region_hists['ST_tW'],
			signal_region_hists['STbar_t'],
			signal_region_hists['STbar_tW'],
			# signal_region_hists['TTJet'],
		]
		histogram_lables   = [
			'data',
			# 'QCD', 
			# 'V+Jets', 
			# 'Single-Top', 
			'ST-s', 
			'ST-t', 
			'ST-tW', 
			'STbar-t', 
			'STbar-tW', 
			# samples_latex['TTJet'],
		]
		histogram_colors   = [
			colours['data'], 
			# colours['QCD'], 
			# colours['V+Jets'], 
			# colours['Single-Top'],
			colours['ST_s'],
			colours['ST_t'],
			colours['ST_tW'],
			colours['STbar_t'],
			colours['STbar_tW'], 
			# colours['TTJet'],
		]

		# Find maximum y of samples
		maxData = max( list(signal_region_hists['SingleTop'].y()) )
		y_limits = [0, maxData * 1.5]
		log_y = False
		if log_y:
			y_limits = [0.1, maxData * 100 ]

		# Lumi title of plots
		title_template = '%.1f fb$^{-1}$ (%d TeV)'
		title = title_template % ( measurement_config.new_luminosity/1000., measurement_config.centre_of_mass_energy )
		x_axis_title = '$%s$ [GeV]' % variables_latex[var]
		y_axis_title = 'Events/(%i GeV)' % binWidth(control_plots_bins[var])

		# More histogram settings to look semi decent
		histogram_properties = Histogram_properties()
		histogram_properties.name                   = var + '_with_ratio'
		histogram_properties.title                  = title
		histogram_properties.x_axis_title           = x_axis_title
		histogram_properties.y_axis_title           = y_axis_title
		histogram_properties.x_limits               = control_plots_bins[var]
		histogram_properties.y_limits               = y_limits
		histogram_properties.y_max_scale            = 1.4
		histogram_properties.xerr                   = None
		histogram_properties.emptybins              = True
		histogram_properties.additional_text        = channel_latex['combined']
		histogram_properties.legend_location        = ( 0.9, 0.73 )
		histogram_properties.cms_logo_location      = 'left'
		histogram_properties.preliminary            = True
		histogram_properties.set_log_y              = log_y
		histogram_properties.legend_color           = False
		histogram_properties.ratio_y_limits     	= [0.1,1.9]
		if log_y: histogram_properties.name += '_logy'
		loc = histogram_properties.legend_location
		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, 
		)

		histogram_properties.name                   = var + '_ST_TTJet_Shape'
		if log_y: histogram_properties.name += '_logy'
		histogram_properties.y_axis_title           = 'Normalised Distribution'
		histogram_properties.y_limits               = [0,0.5]

		make_shape_comparison_plot( 
			shapes = [
				signal_region_hists['TTJet'],
				signal_region_hists['ST_t'],
				signal_region_hists['ST_tW'],
				signal_region_hists['ST_s'],
				signal_region_hists['STbar_t'],
				signal_region_hists['STbar_tW'], 
			],
			names = [
				samples_latex['TTJet'],
				'Single-Top t channel',
				'Single-Top tW channel',
				'Single-Top s channel',
				'Single-AntiTop t channel',
				'Single-AntiTop tW channel',
			],
			colours = [
				colours['TTJet'],
				colours['ST_t'],
				colours['ST_tW'],
				colours['ST_s'],
				colours['STbar_t'],
				colours['STbar_tW'],
			],
			histogram_properties = histogram_properties,
			save_folder = output_folder,
			fill_area = False,
			add_error_bars = False,
			save_as = ['pdf'],
			make_ratio = True, 
			alpha = 1,
		)
		print_output(signal_region_hists, output_folder, var, 'combined')
	return
def make_ttbarReco_plot(
    channel,
    x_axis_title,
    y_axis_title,
    signal_region_tree,
    control_region_tree,
    branchName,
    name_prefix,
    x_limits,
    nBins,
    use_qcd_data_region=False,
    y_limits=[],
    y_max_scale=1.2,
    rebin=1,
    legend_location=(0.98, 0.78),
    cms_logo_location='right',
    log_y=False,
    legend_color=False,
    ratio_y_limits=[0.3, 1.7],
    normalise=False,
):
    global output_folder, measurement_config, category, normalise_to_fit
    global preliminary, norm_variable, sum_bins, b_tag_bin, histogram_files

    # Input files, normalisations, tree/region names
    qcd_data_region = ''
    title = title_template % (measurement_config.new_luminosity / 1000.,
                              measurement_config.centre_of_mass_energy)
    normalisation = None
    if channel == 'electron':
        histogram_files['data'] = measurement_config.data_file_electron_trees
        histogram_files[
            'QCD'] = measurement_config.electron_QCD_MC_category_templates_trees[
                category]
        if normalise_to_fit:
            normalisation = normalisations_electron[norm_variable]
        if use_qcd_data_region:
            qcd_data_region = 'QCDConversions'
    if channel == 'muon':
        histogram_files['data'] = measurement_config.data_file_muon_trees
        histogram_files[
            'QCD'] = measurement_config.muon_QCD_MC_category_templates_trees[
                category]
        if normalise_to_fit:
            normalisation = normalisations_muon[norm_variable]
        if use_qcd_data_region:
            qcd_data_region = 'QCD non iso mu+jets ge3j'

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

    selection = 'SolutionCategory == 0'
    histogramsNoSolution = 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 == 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,
    )
示例#3
0
def plotHistograms(histogram_files, var_to_plot, output_folder):
    '''
	'''
    global measurement_config

    weightBranchSignalRegion = 'EventWeight * PUWeight * BJetWeight'
    weightBranchControlRegion = 'EventWeight'

    # Names of QCD regions to use
    qcd_data_region = ''
    qcd_data_region_electron = 'QCD non iso e+jets'
    qcd_data_region_muon = 'QCD non iso mu+jets 1p5to3'

    sr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/AnalysisVariables'
    sr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/Ref selection/AnalysisVariables'
    cr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/{}/AnalysisVariables'.format(
        qcd_data_region_electron)
    cr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/{}/AnalysisVariables'.format(
        qcd_data_region_muon)

    print "Trees : "
    print "\t {}".format(sr_e_tree)
    print "\t {}".format(sr_mu_tree)
    print "\t {}".format(cr_e_tree)
    print "\t {}".format(cr_mu_tree)

    histogram_files_electron = dict(histogram_files)
    histogram_files_electron['data'] = measurement_config.data_file_electron
    histogram_files_electron['QCD'] = measurement_config.electron_QCD_MC_trees

    histogram_files_muon = dict(histogram_files)
    histogram_files_muon['data'] = measurement_config.data_file_muon
    histogram_files_muon['QCD'] = measurement_config.muon_QCD_MC_trees

    signal_region_hists = {}
    control_region_hists = {}

    for var in var_to_plot:
        selectionSignalRegion = '{} >= 0'.format(var)

        # Print all the weights applied to this plot
        print "Variable : {}".format(var)
        print "Weight applied : {}".format(weightBranchSignalRegion)
        print "Selection applied : {}".format(selectionSignalRegion)

        histograms_electron = get_histograms_from_trees(
            trees=[sr_e_tree],
            branch=var,
            weightBranch=weightBranchSignalRegion +
            ' * ElectronEfficiencyCorrection',
            files=histogram_files_electron,
            nBins=20,
            xMin=control_plots_bins[var][0],
            xMax=control_plots_bins[var][-1],
            selection=selectionSignalRegion)
        histograms_muon = get_histograms_from_trees(
            trees=[sr_mu_tree],
            branch=var,
            weightBranch=weightBranchSignalRegion +
            ' * MuonEfficiencyCorrection',
            files=histogram_files_muon,
            nBins=20,
            xMin=control_plots_bins[var][0],
            xMax=control_plots_bins[var][-1],
            selection=selectionSignalRegion)
        histograms_electron_QCDControlRegion = get_histograms_from_trees(
            trees=[cr_e_tree],
            branch=var,
            weightBranch=weightBranchControlRegion,
            files=histogram_files_electron,
            nBins=20,
            xMin=control_plots_bins[var][0],
            xMax=control_plots_bins[var][-1],
            selection=selectionSignalRegion)
        histograms_muon_QCDControlRegion = get_histograms_from_trees(
            trees=[cr_mu_tree],
            branch=var,
            weightBranch=weightBranchControlRegion,
            files=histogram_files_muon,
            nBins=20,
            xMin=control_plots_bins[var][0],
            xMax=control_plots_bins[var][-1],
            selection=selectionSignalRegion)

        # Combine the electron and muon histograms
        for sample in histograms_electron:
            h_electron = histograms_electron[sample][sr_e_tree]
            h_muon = histograms_muon[sample][sr_mu_tree]
            h_qcd_electron = histograms_electron_QCDControlRegion[sample][
                cr_e_tree]
            h_qcd_muon = histograms_muon_QCDControlRegion[sample][cr_mu_tree]

            signal_region_hists[sample] = h_electron + h_muon
            control_region_hists[sample] = h_qcd_electron + h_qcd_muon

        # NORMALISE TO LUMI
        prepare_histograms(signal_region_hists,
                           scale_factor=measurement_config.luminosity_scale)
        prepare_histograms(control_region_hists,
                           scale_factor=measurement_config.luminosity_scale)

        # BACKGROUND SUBTRACTION FOR QCD
        qcd_from_data = None
        qcd_from_data = clean_control_region(
            control_region_hists, subtract=['TTJet', 'V+Jets', 'SingleTop'])

        # DATA DRIVEN QCD
        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)

        dataDrivenQCDScale = n_qcd_predicted_mc_signal / n_qcd_predicted_mc_control
        qcd_from_data.Scale(dataDrivenQCDScale.nominal_value)
        signal_region_hists['QCD'] = qcd_from_data

        # PLOTTING
        histograms_to_draw = []
        histogram_lables = []
        histogram_colors = []

        histograms_to_draw = [
            # signal_region_hists['data'],
            # qcd_from_data,
            # signal_region_hists['V+Jets'],
            signal_region_hists['SingleTop'],
            signal_region_hists['ST_s'],
            signal_region_hists['ST_t'],
            signal_region_hists['ST_tW'],
            signal_region_hists['STbar_t'],
            signal_region_hists['STbar_tW'],
            # signal_region_hists['TTJet'],
        ]
        histogram_lables = [
            'data',
            # 'QCD',
            # 'V+Jets',
            # 'Single-Top',
            'ST-s',
            'ST-t',
            'ST-tW',
            'STbar-t',
            'STbar-tW',
            # samples_latex['TTJet'],
        ]
        histogram_colors = [
            colours['data'],
            # colours['QCD'],
            # colours['V+Jets'],
            # colours['Single-Top'],
            colours['ST_s'],
            colours['ST_t'],
            colours['ST_tW'],
            colours['STbar_t'],
            colours['STbar_tW'],
            # colours['TTJet'],
        ]

        # Find maximum y of samples
        maxData = max(list(signal_region_hists['SingleTop'].y()))
        y_limits = [0, maxData * 1.5]
        log_y = False
        if log_y:
            y_limits = [0.1, maxData * 100]

        # Lumi title of plots
        title_template = '%.1f fb$^{-1}$ (%d TeV)'
        title = title_template % (measurement_config.new_luminosity / 1000.,
                                  measurement_config.centre_of_mass_energy)
        x_axis_title = '$%s$ [GeV]' % variables_latex[var]
        y_axis_title = 'Events/(%i GeV)' % binWidth(control_plots_bins[var])

        # More histogram settings to look semi decent
        histogram_properties = Histogram_properties()
        histogram_properties.name = var + '_with_ratio'
        histogram_properties.title = title
        histogram_properties.x_axis_title = x_axis_title
        histogram_properties.y_axis_title = y_axis_title
        histogram_properties.x_limits = control_plots_bins[var]
        histogram_properties.y_limits = y_limits
        histogram_properties.y_max_scale = 1.4
        histogram_properties.xerr = None
        histogram_properties.emptybins = True
        histogram_properties.additional_text = channel_latex['combined']
        histogram_properties.legend_location = (0.9, 0.73)
        histogram_properties.cms_logo_location = 'left'
        histogram_properties.preliminary = True
        histogram_properties.set_log_y = log_y
        histogram_properties.legend_color = False
        histogram_properties.ratio_y_limits = [0.1, 1.9]
        if log_y: histogram_properties.name += '_logy'
        loc = histogram_properties.legend_location
        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,
        )

        histogram_properties.name = var + '_ST_TTJet_Shape'
        if log_y: histogram_properties.name += '_logy'
        histogram_properties.y_axis_title = 'Normalised Distribution'
        histogram_properties.y_limits = [0, 0.5]

        make_shape_comparison_plot(
            shapes=[
                signal_region_hists['TTJet'],
                signal_region_hists['ST_t'],
                signal_region_hists['ST_tW'],
                signal_region_hists['ST_s'],
                signal_region_hists['STbar_t'],
                signal_region_hists['STbar_tW'],
            ],
            names=[
                samples_latex['TTJet'],
                'Single-Top t channel',
                'Single-Top tW channel',
                'Single-Top s channel',
                'Single-AntiTop t channel',
                'Single-AntiTop tW channel',
            ],
            colours=[
                colours['TTJet'],
                colours['ST_t'],
                colours['ST_tW'],
                colours['ST_s'],
                colours['STbar_t'],
                colours['STbar_tW'],
            ],
            histogram_properties=histogram_properties,
            save_folder=output_folder,
            fill_area=False,
            add_error_bars=False,
            save_as=['pdf'],
            make_ratio=True,
            alpha=1,
        )
        print_output(signal_region_hists, output_folder, var, 'combined')
    return
def make_plot( channel, x_axis_title, y_axis_title,
              signal_region_tree,
              control_region_tree,
              branchName,
              name_prefix, x_limits, nBins,
              use_qcd_data_region = False,
              compare_qcd_signal_with_data_control = False,
              y_limits = [],
              y_max_scale = 1.3,
              rebin = 1,
              legend_location = ( 0.98, 0.78 ), cms_logo_location = 'right',
              log_y = False,
              legend_color = False,
              ratio_y_limits = [0.3, 2.5],
              normalise = False,
              ):
    global output_folder, measurement_config, category, normalise_to_fit
    global preliminary, norm_variable, sum_bins, b_tag_bin, histogram_files

    controlToCompare = []
    if 'electron' in channel :
        controlToCompare =  ['QCDConversions', 'QCD non iso e+jets']
    elif 'muon' in channel :
        controlToCompare =  ['QCD iso > 0.3', 'QCD 0.12 < iso <= 0.3']

    histogramsToCompare = {}
    for qcd_data_region in controlToCompare:
        print 'Doing ',qcd_data_region
        # Input files, normalisations, tree/region names
        title = title_template % ( measurement_config.new_luminosity, measurement_config.centre_of_mass_energy )
        normalisation = None
        weightBranchSignalRegion = 'EventWeight'
        if 'electron' in channel:
            histogram_files['data'] = measurement_config.data_file_electron_trees
            histogram_files['QCD'] = measurement_config.electron_QCD_MC_category_templates_trees[category]
            if normalise_to_fit:
                normalisation = normalisations_electron[norm_variable]
            # if use_qcd_data_region:
            #     qcd_data_region = 'QCDConversions'
            #     # qcd_data_region = 'QCD non iso e+jets'
            if not 'QCD' in channel and not 'NPU' in branchName:
                weightBranchSignalRegion += ' * ElectronEfficiencyCorrection'
        if 'muon' in channel:
            histogram_files['data'] = measurement_config.data_file_muon_trees
            histogram_files['QCD'] = measurement_config.muon_QCD_MC_category_templates_trees[category]
            if normalise_to_fit:
                normalisation = normalisations_muon[norm_variable]
            # if use_qcd_data_region:
            #     qcd_data_region = 'QCD iso > 0.3'
            if not 'QCD' in channel and not 'NPU' in branchName:
                weightBranchSignalRegion += ' * MuonEfficiencyCorrection'

        if not "_NPUNoWeight" in name_prefix:
            weightBranchSignalRegion += ' * PUWeight'

        if not "_NBJetsNoWeight" in name_prefix:
            weightBranchSignalRegion += ' * BJetWeight'

        selection = '1'
        if branchName == 'abs(lepton_eta)' :
            selection = 'lepton_eta > -10'
        else:
            selection = '%s >= 0' % branchName
        # if 'QCDConversions' in signal_region_tree:
        #     selection += '&& isTightElectron'
        # print selection
        histograms = get_histograms_from_trees( trees = [signal_region_tree, control_region_tree], branch = branchName, weightBranch = weightBranchSignalRegion, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1], selection = selection )
        histograms_QCDControlRegion = None
        if use_qcd_data_region:
            qcd_control_region = signal_region_tree.replace( 'Ref selection', qcd_data_region )
            histograms_QCDControlRegion = get_histograms_from_trees( trees = [qcd_control_region], branch = branchName, weightBranch = 'EventWeight', files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1], selection = selection )

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

            if compare_qcd_signal_with_data_control:
                if sample is 'data':
                    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)
def get_histograms( channel, input_files, variable, met_systematic, met_type, variable_bin,
                   b_tag_bin, 
                   treePrefix, weightBranch,
                   rebin = 1, fit_variable = 'absolute_eta',
                   scale_factors = None ):
    global b_tag_bin_VJets, fit_variables
    global electron_control_region, muon_control_region

    boundaries = measurement_config.fit_boundaries[fit_variable]
    histograms = {}

    tree = measurement_config.tree_path_templates[channel]
    control_tree = measurement_config.tree_path_control_templates[channel]

    # Put together weight
    fullWeight = 'EventWeight'
    if weightBranch != '' :
        fullWeight += ' * %s' % weightBranch

    # Work out bin of variable
    variableForSelection = variable
    minVar = variable_bin.split('-')[0]
    maxVar = variable_bin.split('-')[-1]
    selection = ''
    if maxVar != 'inf' :
        selection = '%s >= %s && %s < %s' % ( variableForSelection, minVar, variableForSelection, maxVar)
    else :
        selection = '%s >= %s' % ( variableForSelection, minVar )

    bins = fit_variable_bin_edges[fit_variable]
    xMin = bins[0]
    xMax = bins[-1]
    nBins = len(bins) -1

    # Get data files here, without any systematic variations
    data_files = { sample : input_files[sample] for sample in ['data'] }
    histograms_data = get_histograms_from_trees( trees = [tree], branch = fit_variable, selection = selection, weightBranch = fullWeight, files = data_files, nBins = nBins, xMin = xMin, xMax = xMax )

    # Now work out tree/variable for MC
    # Identical for data if central, different for systematics
    tree = tree + treePrefix

    if met_systematic:
        if variable == 'MET':
            variableForSelection = 'MET_METUncertainties[%i]' % met_systematics[met_systematic]
        elif variable == 'ST':
            variableForSelection = 'ST_METUncertainties[%i]' % met_systematics[met_systematic]

    # Work out selection again for MC, as variable (e.g. MET) could be different after systematic variation
    if maxVar != 'inf' :
        selection = '%s >= %s && %s < %s' % ( variableForSelection, minVar, variableForSelection, maxVar)
    else :
        selection = '%s >= %s' % ( variableForSelection, minVar )

    # Get exclusive templates for MC
    input_files_exclusive = { sample : input_files[sample] for sample in ['TTJet', 'SingleTop', 'V+Jets', 'QCD'] }
    # Get inclusive template for these (i.e. don't split up fit variable in bins of MET or whatever)
    input_files_inclusive = { sample : input_files[sample] for sample in ['V+Jets'] }
    # # Get control templates for QCD only, and inclusive
    # input_files_control = input_files

    # Get necessary histograms
    # Signal, binned by e.g. MET, HT etc.
    histograms_exclusive = get_histograms_from_trees( trees = [tree], branch = fit_variable, selection = selection, weightBranch = fullWeight, files = input_files_exclusive, nBins = nBins, xMin = xMin, xMax = xMax )
    # Signal, not binned.  For V+Jets template
    histograms_inclusive = get_histograms_from_trees( trees = [tree], branch = fit_variable, weightBranch = fullWeight, files = input_files_inclusive, nBins = nBins, xMin = xMin, xMax = xMax )
    # Control, not binned.  For QCD template
    histograms_control_inclusive = get_histograms_from_trees( trees = [control_tree], branch = fit_variable, weightBranch = fullWeight, files = input_files, nBins = nBins, xMin = xMin, xMax = xMax )

    # Currently needed, as get_histograms_from_trees returns the histograms as part of a more complicated structure
    for histogram in histograms_data:
        for d in histograms_data[histogram]:
            histograms_data[histogram] = histograms_data[histogram][d].Clone()

    for histogram in histograms_exclusive:
        for d in histograms_exclusive[histogram]:
            histograms_exclusive[histogram] = histograms_exclusive[histogram][d].Clone()

    for histogram in histograms_inclusive:
        for d in histograms_inclusive[histogram]:
            histograms_inclusive[histogram] = histograms_inclusive[histogram][d].Clone()

    for histogram in histograms_control_inclusive:
        for d in histograms_control_inclusive[histogram]:
            histograms_control_inclusive[histogram] = histograms_control_inclusive[histogram][d].Clone()

    # Put all histograms into one dictionary
    histograms = {}
    histograms.update(histograms_exclusive)

    # Always use central data sample
    histograms['data'] = histograms_data['data']

    # Get QCD distribution from data
    # histograms['QCD'] = get_data_derived_qcd(histograms_control_inclusive, histograms_exclusive['QCD'])
    # histograms['V+Jets'] = get_inclusive_histogram( histograms_inclusive['V+Jets'], histograms['V+Jets'] )
    
    # normalise histograms
    if not measurement_config.luminosity_scale == 1.0:
        for sample, histogram in histograms.iteritems():
            if sample == 'data':
                continue
            histogram.Scale( measurement_config.luminosity_scale )

    # # apply normalisation scale factors for rate-changing systematics
    if scale_factors:
        for source, factor in scale_factors.iteritems():
            for sample, histogram in histograms.iteritems():
                if 'luminosity' in source:
                    if sample is 'data':
                        # Skip data for luminosity systematic
                        continue
                    else:
                        histogram.Scale( factor )
                # For cross section systematics, only change normalisation of relevant sample
                elif sample in source :
                    histogram.Scale( factor )

    return histograms
示例#6
0
        for control_region in regions:
            # Fit variables (inclusive)
            for var in ['M3', 'angle_bl', 'M_bl', 'absolute_eta']:
                print channel, var
                controlTree = 'TTbar_plus_X_analysis/%s/%s/FitVariables' % (
                    channel, control_region)

                bins = fit_variable_bin_edges[var]
                xMin = bins[0]
                xMax = bins[-1]
                nBins = len(bins) - 1

                histograms = get_histograms_from_trees(
                    trees=[controlTree],
                    branch=var,
                    weightBranch='EventWeight',
                    files=histogram_files,
                    nBins=nBins,
                    xMin=xMin,
                    xMax=xMax)

                prepare_histograms(
                    histograms,
                    rebin=1,
                    scale_factor=measurement_config.luminosity_scale)

                histograms_to_draw = [
                    histograms['data'][controlTree],
                    histograms['QCD'][controlTree],
                    histograms['V+Jets'][controlTree],
                    histograms['SingleTop'][controlTree],
                    histograms['TTJet'][controlTree]
def make_ttbarReco_plot( channel, x_axis_title, y_axis_title,
              signal_region_tree,
              control_region_tree,
              branchName,
              name_prefix, x_limits, nBins,
              use_qcd_data_region = False,
              y_limits = [],
              y_max_scale = 1.2,
              rebin = 1,
              legend_location = ( 0.98, 0.78 ), cms_logo_location = 'right',
              log_y = False,
              legend_color = False,
              ratio_y_limits = [0.3, 1.7],
              normalise = False,
              ):
    global output_folder, measurement_config, category, normalise_to_fit
    global preliminary, norm_variable, sum_bins, b_tag_bin, histogram_files

    # Input files, normalisations, tree/region names
    qcd_data_region = ''
    title = title_template % ( measurement_config.new_luminosity / 1000., measurement_config.centre_of_mass_energy )
    normalisation = None
    if channel == 'electron':
        histogram_files['data'] = measurement_config.data_file_electron_trees
        histogram_files['QCD'] = measurement_config.electron_QCD_MC_category_templates_trees[category]
        if normalise_to_fit:
            normalisation = normalisations_electron[norm_variable]
        if use_qcd_data_region:
            qcd_data_region = 'QCDConversions'
    if channel == 'muon':
        histogram_files['data'] = measurement_config.data_file_muon_trees
        histogram_files['QCD'] = measurement_config.muon_QCD_MC_category_templates_trees[category]
        if normalise_to_fit:
            normalisation = normalisations_muon[norm_variable]
        if use_qcd_data_region:
            qcd_data_region = 'QCD non iso mu+jets ge3j'

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

    selection = 'SolutionCategory == 0'
    histogramsNoSolution = 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 == 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,
                                 )
            regions = ['QCD non iso mu+jets']
        elif channel == 'EPlusJets':
            regions = ['QCD non iso e+jets', 'QCDConversions']

        for control_region in regions :
            # Fit variables (inclusive)
            for var in ['M3', 'angle_bl', 'M_bl', 'absolute_eta']:
                print channel,var
                controlTree = 'TTbar_plus_X_analysis/%s/%s/FitVariables' % ( channel, control_region )

                bins = fit_variable_bin_edges[var]
                xMin = bins[0]
                xMax = bins[-1]
                nBins = len(bins) -1
                
                histograms = get_histograms_from_trees( trees = [controlTree], branch = var, weightBranch = 'EventWeight', files = histogram_files, nBins = nBins, xMin = xMin, xMax = xMax )

                prepare_histograms( histograms, rebin = 1, scale_factor = measurement_config.luminosity_scale )
                
                histograms_to_draw = [histograms['data'][controlTree], histograms['QCD'][controlTree],
                                      histograms['V+Jets'][controlTree],
                                      histograms['SingleTop'][controlTree], histograms['TTJet'][controlTree]]
                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 = 'QCD_nonIso_%s_%s' % (channel, var)
                if control_region == 'QCDConversions' :
                    histogram_properties.name = 'QCD_Conversions_%s_%s' % (channel, var)
                if category != 'central':
                    histogram_properties.name += '_' + category