def plot( self, plot_options = {} ):
'''
Plots the stored histograms based on the plot options
'''
file_name = self.name
if plot_options.has_key('output_file'):
file_name = plot_options['output_file']
output_format = plot_options['output_format']
output_folder = plot_options['output_folder']
plot_type = plot_options['plot_type']
histogram_properties = Histogram_properties(plot_options)
histogram_properties.name = file_name
if plot_options.has_key('rebin') and plot_options['rebin'] > 1:
for hist in self.histograms:
hist.Rebin(plot_options['rebin'])
colours = ['green', 'yellow', 'magenta', 'red', 'black']
if plot_options.has_key('colours'):
colours = plot_options['colours']
if plot_type == 'shape_comparison':
make_shape_comparison_plot( shapes = self.histograms,
names = self.labels,
colours = colours,
histogram_properties = histogram_properties,
fill_area = True,
make_ratio = True,
alpha = 0.5,
save_folder = output_folder,
save_as = output_format )
elif plot_type == 'data_mc_comparison':
make_data_mc_comparison_plot( histograms = self.histograms,
histogram_lables = self.labels,
histogram_colors = colours,
histogram_properties = histogram_properties,
data_index = 0,
save_folder = output_folder,
save_as = output_format,
normalise = False,
show_ratio = True,
show_stat_errors_on_mc = True,
draw_vertical_line = 0 )
else:
print 'Plot type "%s" not known, exiting.' % plot_type
sys.exit()
def plot_fit_variable( histograms, fit_variable, variable, bin_range,
fit_variable_distribution, qcd_fit_variable_distribution,
title, save_path ):
global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
mc_uncertainty = 0.10
prepare_histograms( histograms, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )
histogram_properties = Histogram_properties()
histogram_properties.x_axis_title = fit_variable_properties[fit_variable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[fit_variable]['y-title']
histogram_properties.x_limits = [fit_variable_properties[fit_variable]['min'], fit_variable_properties[fit_variable]['max']]
histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']]
histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red']
# qcd_from_data = histograms['data'][qcd_fit_variable_distribution].Clone()
# clean against other processes
histograms_for_cleaning = {'data':histograms['data'][qcd_fit_variable_distribution],
'V+Jets':histograms['V+Jets'][qcd_fit_variable_distribution],
'SingleTop':histograms['SingleTop'][qcd_fit_variable_distribution],
'TTJet':histograms['TTJet'][qcd_fit_variable_distribution]}
qcd_from_data = clean_control_region( histograms_for_cleaning, subtract = ['TTJet', 'V+Jets', 'SingleTop'] )
histograms_to_draw = [histograms['data'][qcd_fit_variable_distribution],
histograms['QCD'][qcd_fit_variable_distribution],
histograms['V+Jets'][qcd_fit_variable_distribution],
histograms['SingleTop'][qcd_fit_variable_distribution],
histograms['TTJet'][qcd_fit_variable_distribution]]
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_%s_QCDConversions' % b_tag_bin_ctl
make_data_mc_comparison_plot( histograms_to_draw, histogram_lables, histogram_colors,
histogram_properties,
save_folder = save_path + '/qcd/',
show_ratio = False,
save_as = save_as,
)
histograms_to_draw = [qcd_from_data,
histograms['QCD'][qcd_fit_variable_distribution],
]
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_%s_QCDConversions_subtracted' % b_tag_bin_ctl
make_data_mc_comparison_plot( histograms_to_draw,
histogram_lables = ['data', 'QCD'],
histogram_colors = ['black', 'yellow'],
histogram_properties = histogram_properties,
save_folder = save_path + '/qcd/',
show_ratio = False,
save_as = save_as,
)
# scale QCD to predicted
n_qcd_predicted_mc = histograms['QCD'][fit_variable_distribution].Integral()
n_qcd_fit_variable_distribution = qcd_from_data.Integral()
if not n_qcd_fit_variable_distribution == 0:
qcd_from_data.Scale( 1.0 / n_qcd_fit_variable_distribution * n_qcd_predicted_mc )
histograms_to_draw = [histograms['data'][fit_variable_distribution], qcd_from_data,
histograms['V+Jets'][fit_variable_distribution],
histograms['SingleTop'][fit_variable_distribution], histograms['TTJet'][fit_variable_distribution]]
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin
make_data_mc_comparison_plot( histograms_to_draw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder = save_path,
show_ratio = False,
save_as = save_as,
)
histogram_properties.mc_error = mc_uncertainty
histogram_properties.mc_errors_label = '$\mathrm{t}\\bar{\mathrm{t}}$ uncertainty'
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin + '_templates'
# change histogram order for better visibility
histograms_to_draw = [histograms['TTJet'][fit_variable_distribution] + histograms['SingleTop'][fit_variable_distribution],
histograms['TTJet'][fit_variable_distribution],
histograms['SingleTop'][fit_variable_distribution],
histograms['V+Jets'][fit_variable_distribution],
qcd_from_data]
histogram_lables = ['QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet'], samples_latex['TTJet'] + ' + ' + 'Single-Top']
histogram_lables.reverse()
# change QCD color to orange for better visibility
histogram_colors = ['orange', 'green', 'magenta', 'red', 'black']
histogram_colors.reverse()
make_shape_comparison_plot( shapes = histograms_to_draw,
names = histogram_lables,
colours = histogram_colors,
histogram_properties = histogram_properties,
fill_area = False,
alpha = 1,
save_folder = save_path,
save_as = save_as,
)
def plot(self, plot_options={}):
'''
Plots the stored histograms based on the plot options
'''
# defaults
file_name = self.name
alpha = 0.5
fill_area = True
if plot_options.has_key('output_file'):
file_name = plot_options['output_file']
output_format = plot_options['output_format']
output_folder = plot_options['output_folder']
plot_type = plot_options['plot_type']
if plot_options.has_key('fill_area'):
fill_area = plot_options['fill_area']
if plot_options.has_key('alpha'):
alpha = plot_options['alpha']
histogram_properties = Histogram_properties(plot_options)
histogram_properties.name = file_name
if plot_options.has_key('rebin') and plot_options['rebin'] > 1:
rebin = plot_options['rebin']
is_list = isinstance(rebin, types.ListType)
for i, hist in enumerate(self.histograms):
if is_list:
self.histograms[i] = conditional_rebin(hist, rebin)
else:
hist.rebin(rebin)
colours = ['green', 'yellow', 'magenta', 'red', 'black']
if plot_options.has_key('colours'):
colours = plot_options['colours']
if plot_type == 'shape_comparison':
make_shape_comparison_plot(
shapes=self.histograms,
names=self.labels,
colours=colours,
histogram_properties=histogram_properties,
fill_area=fill_area,
make_ratio=True,
alpha=alpha,
save_folder=output_folder,
save_as=output_format)
elif plot_type == 'data_mc_comparison':
make_data_mc_comparison_plot(
histograms=self.histograms,
histogram_lables=self.labels,
histogram_colors=colours,
histogram_properties=histogram_properties,
data_index=0,
save_folder=output_folder,
save_as=output_format,
normalise=False,
show_ratio=True,
show_stat_errors_on_mc=True,
draw_vertical_line=0)
else:
print 'Plot type "%s" not known, exiting.' % plot_type
sys.exit()
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()
# plot template
make_shape_comparison_plot(
shapes=histograms_to_draw,
names=histogram_lables,
colours=histogram_colors,
histogram_properties=histogram_properties,
fill_area=False,
alpha=1,
save_folder=save_path,
save_as=save_as,
)
