Python Histogram_properties.set_log_y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 set_log_y [as 别名]
def draw_regularisation_histograms( h_truth, h_measured, h_response, h_fakes = None, h_data = None ):
    global method, variable, output_folder, output_formats, test
    k_max = h_measured.nbins()
    unfolding = Unfolding( h_truth,
                           h_measured,
                           h_response,
                           h_fakes,
                           method = method,
                           k_value = k_max,
                           error_treatment = 4,
                           verbose = 1 )
    
    RMSerror, MeanResiduals, RMSresiduals, Chi2 = unfolding.test_regularisation ( h_data, k_max )

    histogram_properties = Histogram_properties()
    histogram_properties.name = 'chi2_%s_channel_%s' % ( channel, variable )
    histogram_properties.title = '$\chi^2$ for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test )
    histogram_properties.x_axis_title = '$i$'
    histogram_properties.y_axis_title = '$\chi^2$'
    histogram_properties.set_log_y = True
    make_plot(Chi2, 'chi2', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False)

    histogram_properties = Histogram_properties()
    histogram_properties.name = 'RMS_error_%s_channel_%s' % ( channel, variable )
    histogram_properties.title = 'Mean error for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test )
    histogram_properties.x_axis_title = '$i$'
    histogram_properties.y_axis_title = 'Mean error'
    make_plot(RMSerror, 'RMS', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False)

    histogram_properties = Histogram_properties()
    histogram_properties.name = 'RMS_residuals_%s_channel_%s' % ( channel, variable )
    histogram_properties.title = 'RMS of residuals for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test )
    histogram_properties.x_axis_title = '$i$'
    histogram_properties.y_axis_title = 'RMS of residuals'
    if test == 'closure':
        histogram_properties.set_log_y = True
    make_plot(RMSresiduals, 'RMSresiduals', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False)

    histogram_properties = Histogram_properties()
    histogram_properties.name = 'mean_residuals_%s_channel_%s' % ( channel, variable )
    histogram_properties.title = 'Mean of residuals for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test )
    histogram_properties.x_axis_title = '$i$'
    histogram_properties.y_axis_title = 'Mean of residuals'
    make_plot(MeanResiduals, 'MeanRes', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False)

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import set_log_y [as 别名]
def compare( central_mc, expected_result = None, measured_result = None, results = {}, variable = 'MET',
             channel = 'electron', bin_edges = [] ):
    global input_file, plot_location, ttbar_xsection, luminosity, centre_of_mass, method, test, log_plots

    channel_label = ''
    if channel == 'electron':
        channel_label = 'e+jets, $\geq$4 jets'
    elif channel == 'muon':
        channel_label = '$\mu$+jets, $\geq$4 jets'
    else:
        channel_label = '$e, \mu$ + jets combined, $\geq$4 jets'

    if test == 'data':
        title_template = 'CMS Preliminary, $\mathcal{L} = %.1f$ fb$^{-1}$  at $\sqrt{s}$ = %d TeV \n %s'
        title = title_template % ( luminosity / 1000., centre_of_mass, channel_label )
    else:
        title_template = 'CMS Simulation at $\sqrt{s}$ = %d TeV \n %s'
        title = title_template % ( centre_of_mass, channel_label )

    models = {latex_labels.measurements_latex['MADGRAPH'] : central_mc}
    if expected_result and test == 'data':
        models.update({'fitted data' : expected_result})
        # scale central MC to lumi
        nEvents = input_file.EventFilter.EventCounter.GetBinContent( 1 )  # number of processed events 
        lumiweight = ttbar_xsection * luminosity / nEvents
        central_mc.Scale( lumiweight )
    elif expected_result:
        models.update({'expected' : expected_result})
    if measured_result and test != 'data':
        models.update({'measured' : measured_result})
    
    measurements = collections.OrderedDict()
    for key, value in results['k_value_results'].iteritems():
        measurements['k = ' + str( key )] = value
    
    # get some spread in x    
    graphs = spread_x( measurements.values(), bin_edges )
    for key, graph in zip( measurements.keys(), graphs ):
        measurements[key] = graph

    histogram_properties = Histogram_properties()
    histogram_properties.name = channel + '_' + variable + '_' + method + '_' + test
    histogram_properties.title = title + ', ' + latex_labels.b_tag_bins_latex['2orMoreBtags']
    histogram_properties.x_axis_title = '$' + latex_labels.variables_latex[variable] + '$'
    histogram_properties.y_axis_title = r'Events'
#     histogram_properties.y_limits = [0, 0.03]
    histogram_properties.x_limits = [bin_edges[0], bin_edges[-1]]

    if log_plots:
        histogram_properties.set_log_y = True
        histogram_properties.name += '_log'

    compare_measurements( models, measurements, show_measurement_errors = True,
                          histogram_properties = histogram_properties,
                          save_folder = plot_location, save_as = ['pdf'] )

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import set_log_y [as 别名]
def plot_results ( results ):
    '''
    Takes results fo the form:
        {centre-of-mass-energy: {
            channel : {
                variable : {
                    fit_variable : {
                        test : { sample : []},
                        }
                    }
                }
            }
        }
    '''
    global options
    output_base = 'plots/fit_checks/chi2'
    for COMEnergy in results.keys():
        tmp_result_1 = results[COMEnergy]
        for channel in tmp_result_1.keys():
            tmp_result_2 = tmp_result_1[channel]
            for variable in tmp_result_2.keys():
                tmp_result_3 = tmp_result_2[variable]
                for fit_variable in tmp_result_3.keys():
                    tmp_result_4 = tmp_result_3[fit_variable]
                    # histograms should be {sample: {test : histogram}}
                    histograms = {}
                    for test, chi2 in tmp_result_4.iteritems():
                        for sample in chi2.keys():
                            if not histograms.has_key(sample):
                                histograms[sample] = {}
                            # reverse order of test and sample
                            histograms[sample][test] = value_tuplelist_to_hist(chi2[sample], bin_edges[variable])
                    for sample in histograms.keys():
                        hist_properties = Histogram_properties()
                        hist_properties.name = sample.replace('+', '') + '_chi2'
                        hist_properties.title = '$\\chi^2$ distribution for fit output (' + sample + ')'
                        hist_properties.x_axis_title = '$' + latex_labels.variables_latex[variable] + '$ [TeV]'
                        hist_properties.y_axis_title = '$\chi^2 = \\left({N_{fit}} - N_{{exp}}\\right)^2$'
                        hist_properties.set_log_y = True
                        hist_properties.y_limits = (1e-20, 1e20)
                        path = output_base + '/' + COMEnergy + 'TeV/' + channel + '/' + variable + '/' + fit_variable + '/'
                        if options.test:
                            path = output_base + '/test/'
                        
                        measurements = {}
                        for test, histogram in histograms[sample].iteritems():
                            measurements[test.replace('_',' ')] = histogram
                        compare_measurements({}, 
                                             measurements, 
                                             show_measurement_errors = False, 
                                             histogram_properties = hist_properties, 
                                             save_folder = path, 
                                             save_as = ['pdf'])

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import set_log_y [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 set_log_y [as 别名]
                print bins,
                print xMin, xMax, nBins
                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
                if channel == 'EPlusJets':
                    histogram_properties.title = e_title
                elif channel == 'MuPlusJets':
                    histogram_properties.title = mu_title

                eventsPerBin = (xMax - xMin) / nBins
                histogram_properties.x_axis_title = '%s [GeV]' % ( control_plots_latex[var] )
                histogram_properties.y_axis_title = 'Events/(%.2g GeV)' % (eventsPerBin)
              
                histogram_properties.set_log_y = True
                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 = False )