def plot_fit_results( histograms, category, channel ):
global variable, b_tag_bin, output_folder
from tools.plotting import Histogram_properties, make_data_mc_comparison_plot
fit_variables = histograms.keys()
for variable_bin in variable_bins_ROOT[variable]:
path = output_folder + str( measurement_config.centre_of_mass_energy ) + 'TeV/' + variable + '/' + category + '/fit_results/'
make_folder_if_not_exists( path )
for fit_variable in fit_variables:
plotname = channel + '_' + fit_variable + '_bin_' + variable_bin
# check if template plots exist already
for output_format in output_formats:
if os.path.isfile( plotname + '.' + output_format ):
continue
# plot with matplotlib
h_data = histograms[fit_variable][variable_bin]['data']
h_signal = histograms[fit_variable][variable_bin]['signal']
h_background = histograms[fit_variable][variable_bin]['background']
histogram_properties = Histogram_properties()
histogram_properties.name = plotname
histogram_properties.x_axis_title = fit_variables_latex[fit_variable]
histogram_properties.y_axis_title = 'Events/(%s)' % get_unit_string(fit_variable)
label, _ = get_cms_labels( channel )
histogram_properties.title = label
histogram_properties.x_limits = measurement_config.fit_boundaries[fit_variable]
make_data_mc_comparison_plot( [h_data, h_background, h_signal],
['data', 'background', 'signal'],
['black', 'green', 'red'], histogram_properties,
save_folder = path, save_as = output_formats )
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 )
def compare_combine_before_after_unfolding(measurement='normalised_xsection',
add_before_unfolding=False):
file_template = 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += '{measurement}_{channel}_RooUnfold{method}.txt'
variables = [
'MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'
]
for variable in variables:
combineBefore = file_template.format(variable=variable,
method='Svd',
channel='combinedBeforeUnfolding',
measurement=measurement)
combineAfter = file_template.format(variable=variable,
method='Svd',
channel='combined',
measurement=measurement)
data = read_data_from_JSON(combineBefore)
before_unfolding = data['TTJet_measured']
combineBefore_data = data['TTJet_unfolded']
combineAfter_data = read_data_from_JSON(combineAfter)['TTJet_unfolded']
h_combineBefore = value_error_tuplelist_to_hist(
combineBefore_data, bin_edges_vis[variable])
h_combineAfter = value_error_tuplelist_to_hist(combineAfter_data,
bin_edges_vis[variable])
h_before_unfolding = value_error_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = '{0}_compare_combine_before_after_unfolding_{1}'.format(
measurement, variable)
properties.title = 'Comparison of combining before/after unfolding'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = True
properties.x_limits = (bin_edges_vis[variable][0],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
if 'xsection' in measurement:
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
else:
properties.y_axis_title = r'$t\bar{t}$ normalisation'
histograms = {
'Combine before unfolding': h_combineBefore,
'Combine after unfolding': h_combineAfter
}
if add_before_unfolding:
histograms['before unfolding'] = h_before_unfolding
properties.name += '_ext'
properties.has_ratio = False
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def compare_unfolding_methods(measurement='normalised_xsection',
add_before_unfolding=False,
channel='combined'):
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += '{measurement}_{channel}_RooUnfold{method}.txt'
variables = [
'MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'
]
for variable in variables:
svd = file_template.format(variable=variable,
method='Svd',
channel=channel,
measurement=measurement)
bayes = file_template.format(variable=variable,
method='Bayes',
channel=channel,
measurement=measurement)
data = read_data_from_JSON(svd)
before_unfolding = data['TTJet_measured_withoutFakes']
svd_data = data['TTJet_unfolded']
bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded']
h_svd = value_error_tuplelist_to_hist(svd_data,
bin_edges_vis[variable])
h_bayes = value_error_tuplelist_to_hist(bayes_data,
bin_edges_vis[variable])
h_before_unfolding = value_error_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = '{0}_compare_unfolding_methods_{1}_{2}'.format(
measurement, variable, channel)
properties.title = 'Comparison of unfolding methods'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = True
properties.x_limits = (bin_edges_vis[variable][0],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
if 'xsection' in measurement:
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
else:
properties.y_axis_title = r'$t\bar{t}$ normalisation'
histograms = {'SVD': h_svd, 'Bayes': h_bayes}
if add_before_unfolding:
histograms['before unfolding'] = h_before_unfolding
properties.name += '_ext'
properties.has_ratio = False
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
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'] )
def compare_combine_before_after_unfolding(measurement='normalised_xsection',
add_before_unfolding=False):
file_template = 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += '{measurement}_{channel}_RooUnfold{method}.txt'
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
for variable in variables:
combineBefore = file_template.format(
variable=variable,
method='Svd',
channel='combinedBeforeUnfolding',
measurement=measurement)
combineAfter = file_template.format(
variable=variable,
method='Svd',
channel='combined',
measurement=measurement)
data = read_data_from_JSON(combineBefore)
before_unfolding = data['TTJet_measured']
combineBefore_data = data['TTJet_unfolded']
combineAfter_data = read_data_from_JSON(combineAfter)['TTJet_unfolded']
h_combineBefore = value_error_tuplelist_to_hist(
combineBefore_data, bin_edges_vis[variable])
h_combineAfter = value_error_tuplelist_to_hist(
combineAfter_data, bin_edges_vis[variable])
h_before_unfolding = value_error_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = '{0}_compare_combine_before_after_unfolding_{1}'.format(
measurement, variable)
properties.title = 'Comparison of combining before/after unfolding'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
if 'xsection' in measurement:
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
else:
properties.y_axis_title = r'$t\bar{t}$ normalisation'
histograms = {'Combine before unfolding': h_combineBefore, 'Combine after unfolding': h_combineAfter}
if add_before_unfolding:
histograms['before unfolding'] = h_before_unfolding
properties.name += '_ext'
properties.has_ratio = False
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def compare_unfolding_methods(measurement='normalised_xsection',
add_before_unfolding=False, channel='combined'):
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += '{measurement}_{channel}_RooUnfold{method}.txt'
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
for variable in variables:
svd = file_template.format(
variable=variable,
method='Svd',
channel=channel,
measurement=measurement)
bayes = file_template.format(
variable=variable,
method='Bayes', channel=channel,
measurement=measurement)
data = read_data_from_JSON(svd)
before_unfolding = data['TTJet_measured_withoutFakes']
svd_data = data['TTJet_unfolded']
bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded']
h_svd = value_error_tuplelist_to_hist(
svd_data, bin_edges_vis[variable])
h_bayes = value_error_tuplelist_to_hist(
bayes_data, bin_edges_vis[variable])
h_before_unfolding = value_error_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = '{0}_compare_unfolding_methods_{1}_{2}'.format(
measurement, variable, channel)
properties.title = 'Comparison of unfolding methods'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
if 'xsection' in measurement:
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
else:
properties.y_axis_title = r'$t\bar{t}$ normalisation'
histograms = {'SVD': h_svd, 'Bayes': h_bayes}
if add_before_unfolding:
histograms['before unfolding'] = h_before_unfolding
properties.name += '_ext'
properties.has_ratio = False
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def plot_fit_results(fit_results, initial_values, channel):
global variable, output_folder
title = electron_histogram_title if channel == 'electron' else muon_histogram_title
histogram_properties = Histogram_properties()
histogram_properties.title = title
histogram_properties.x_axis_title = variable + ' [GeV]'
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
# we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets
for sample in ['TTJet', 'SingleTop', 'QCD', 'V+Jets']:
histograms = {}
# absolute eta measurement as baseline
h_absolute_eta = None
h_before = None
histogram_properties.y_axis_title = 'Fitted number of events for ' + samples_latex[
sample]
for fit_var_input in fit_results.keys():
latex_string = create_latex_string(fit_var_input)
fit_data = fit_results[fit_var_input][sample]
h = value_error_tuplelist_to_hist(fit_data, bin_edges[variable])
if fit_var_input == 'absolute_eta':
h_absolute_eta = h
elif fit_var_input == 'before':
h_before = h
else:
histograms[latex_string] = h
graphs = spread_x(histograms.values(), bin_edges[variable])
for key, graph in zip(histograms.keys(), graphs):
histograms[key] = graph
filename = sample.replace('+', '_') + '_fit_var_comparison_' + channel
histogram_properties.name = filename
histogram_properties.y_limits = 0, limit_range_y(
h_absolute_eta)[1] * 1.3
histogram_properties.x_limits = bin_edges[variable][0], bin_edges[
variable][-1]
h_initial_values = value_error_tuplelist_to_hist(
initial_values[sample], bin_edges[variable])
h_initial_values.Scale(closure_tests['simple'][sample])
compare_measurements(models={
fit_variables_latex['absolute_eta']: h_absolute_eta,
'initial values': h_initial_values,
'before': h_before
},
measurements=histograms,
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=output_folder,
save_as=['png', 'pdf'])
def compare_combine_before_after_unfolding_uncertainties():
file_template = 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'unfolded_normalisation_{channel}_RooUnfoldSvd.txt'
variables = [
'MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'
]
# variables = ['ST']
for variable in variables:
beforeUnfolding = file_template.format(
variable=variable, channel='combinedBeforeUnfolding')
afterUnfolding = file_template.format(variable=variable,
channel='combined')
data = read_data_from_JSON(beforeUnfolding)
before_unfolding = data['TTJet_measured']
beforeUnfolding_data = data['TTJet_unfolded']
afterUnfolding_data = read_data_from_JSON(
afterUnfolding)['TTJet_unfolded']
before_unfolding = [e / v * 100 for v, e in before_unfolding]
beforeUnfolding_data = [e / v * 100 for v, e in beforeUnfolding_data]
afterUnfolding_data = [e / v * 100 for v, e in afterUnfolding_data]
h_beforeUnfolding = value_tuplelist_to_hist(beforeUnfolding_data,
bin_edges_vis[variable])
h_afterUnfolding = value_tuplelist_to_hist(afterUnfolding_data,
bin_edges_vis[variable])
h_before_unfolding = value_tuplelist_to_hist(before_unfolding,
bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = 'compare_combine_before_after_unfolding_uncertainties_{0}'.format(
variable)
properties.title = 'Comparison of unfolding uncertainties'
properties.path = 'plots'
properties.has_ratio = False
properties.xerr = True
properties.x_limits = (bin_edges_vis[variable][0],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = 'relative uncertainty (\\%)'
properties.legend_location = (0.98, 0.95)
histograms = {
'Combine before unfolding': h_beforeUnfolding,
'Combine after unfolding': h_afterUnfolding,
# 'before unfolding': h_before_unfolding
}
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def plot_fit_results(fit_results, initial_values, channel):
global variable, output_folder
title = electron_histogram_title if channel == "electron" else muon_histogram_title
histogram_properties = Histogram_properties()
histogram_properties.title = title
histogram_properties.x_axis_title = variable + " [GeV]"
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = "upper right"
# we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets
for sample in ["TTJet", "SingleTop", "QCD", "V+Jets"]:
histograms = {}
# absolute eta measurement as baseline
h_absolute_eta = None
h_before = None
histogram_properties.y_axis_title = "Fitted number of events for " + samples_latex[sample]
for fit_var_input in fit_results.keys():
latex_string = create_latex_string(fit_var_input)
fit_data = fit_results[fit_var_input][sample]
h = value_error_tuplelist_to_hist(fit_data, bin_edges[variable])
if fit_var_input == "absolute_eta":
h_absolute_eta = h
elif fit_var_input == "before":
h_before = h
else:
histograms[latex_string] = h
graphs = spread_x(histograms.values(), bin_edges[variable])
for key, graph in zip(histograms.keys(), graphs):
histograms[key] = graph
filename = sample.replace("+", "_") + "_fit_var_comparison_" + channel
histogram_properties.name = filename
histogram_properties.y_limits = 0, limit_range_y(h_absolute_eta)[1] * 1.3
histogram_properties.x_limits = bin_edges[variable][0], bin_edges[variable][-1]
h_initial_values = value_error_tuplelist_to_hist(initial_values[sample], bin_edges[variable])
h_initial_values.Scale(closure_tests["simple"][sample])
compare_measurements(
models={
fit_variables_latex["absolute_eta"]: h_absolute_eta,
"initial values": h_initial_values,
"before": h_before,
},
measurements=histograms,
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=output_folder,
save_as=["png", "pdf"],
)
def plot_fit_results( fit_results, initial_values, channel ):
global variable, output_folder
title = electron_histogram_title if channel == 'electron' else muon_histogram_title
histogram_properties = Histogram_properties()
histogram_properties.title = title
histogram_properties.x_axis_title = variable + ' [GeV]'
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
# we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets
for sample in ['TTJet', 'SingleTop', 'QCD', 'V+Jets']:
histograms = {}
# absolute eta measurement as baseline
h_absolute_eta = None
h_before = None
histogram_properties.y_axis_title = 'Fitted number of events for ' + samples_latex[sample]
for fit_var_input in fit_results.keys():
latex_string = create_latex_string( fit_var_input )
fit_data = fit_results[fit_var_input][sample]
h = value_error_tuplelist_to_hist( fit_data,
bin_edges[variable] )
if fit_var_input == 'absolute_eta':
h_absolute_eta = h
elif fit_var_input == 'before':
h_before = h
else:
histograms[latex_string] = h
graphs = spread_x( histograms.values(), bin_edges[variable] )
for key, graph in zip( histograms.keys(), graphs ):
histograms[key] = graph
filename = sample.replace( '+', '_' ) + '_fit_var_comparison_' + channel
histogram_properties.name = filename
histogram_properties.y_limits = 0, limit_range_y( h_absolute_eta )[1] * 1.3
histogram_properties.x_limits = bin_edges[variable][0], bin_edges[variable][-1]
h_initial_values = value_error_tuplelist_to_hist( initial_values[sample],
bin_edges[variable] )
h_initial_values.Scale(closure_tests['simple'][sample])
compare_measurements( models = {fit_variables_latex['absolute_eta']:h_absolute_eta,
'initial values' : h_initial_values,
'before': h_before},
measurements = histograms,
show_measurement_errors = True,
histogram_properties = histogram_properties,
save_folder = output_folder,
save_as = ['png', 'pdf'] )
def compare_unfolding_uncertainties():
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'unfolded_normalisation_combined_RooUnfold{method}.txt'
variables = [
'MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'
]
# variables = ['ST']
for variable in variables:
svd = file_template.format(variable=variable, method='Svd')
bayes = file_template.format(variable=variable, method='Bayes')
data = read_data_from_JSON(svd)
before_unfolding = data['TTJet_measured_withoutFakes']
svd_data = data['TTJet_unfolded']
bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded']
before_unfolding = [e / v * 100 for v, e in before_unfolding]
svd_data = [e / v * 100 for v, e in svd_data]
bayes_data = [e / v * 100 for v, e in bayes_data]
h_svd = value_tuplelist_to_hist(svd_data, bin_edges_vis[variable])
h_bayes = value_tuplelist_to_hist(bayes_data, bin_edges_vis[variable])
h_before_unfolding = value_tuplelist_to_hist(before_unfolding,
bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = 'compare_unfolding_uncertainties_{0}'.format(
variable)
properties.title = 'Comparison of unfolding uncertainties'
properties.path = 'plots'
properties.has_ratio = False
properties.xerr = True
properties.x_limits = (bin_edges_vis[variable][0],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = 'relative uncertainty (\\%)'
properties.legend_location = (0.98, 0.95)
histograms = {
'SVD': h_svd,
'Bayes': h_bayes,
'before unfolding': h_before_unfolding
}
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def compare_combine_before_after_unfolding_uncertainties():
file_template = 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'unfolded_normalisation_{channel}_RooUnfoldSvd.txt'
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
# variables = ['ST']
for variable in variables:
beforeUnfolding = file_template.format(
variable=variable, channel='combinedBeforeUnfolding')
afterUnfolding = file_template.format(
variable=variable, channel='combined')
data = read_data_from_JSON(beforeUnfolding)
before_unfolding = data['TTJet_measured']
beforeUnfolding_data = data['TTJet_unfolded']
afterUnfolding_data = read_data_from_JSON(afterUnfolding)['TTJet_unfolded']
before_unfolding = [e / v * 100 for v, e in before_unfolding]
beforeUnfolding_data = [e / v * 100 for v, e in beforeUnfolding_data]
afterUnfolding_data = [e / v * 100 for v, e in afterUnfolding_data]
h_beforeUnfolding = value_tuplelist_to_hist(
beforeUnfolding_data, bin_edges_vis[variable])
h_afterUnfolding = value_tuplelist_to_hist(
afterUnfolding_data, bin_edges_vis[variable])
h_before_unfolding = value_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = 'compare_combine_before_after_unfolding_uncertainties_{0}'.format(
variable)
properties.title = 'Comparison of unfolding uncertainties'
properties.path = 'plots'
properties.has_ratio = False
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = 'relative uncertainty (\\%)'
properties.legend_location = (0.98, 0.95)
histograms = {'Combine before unfolding': h_beforeUnfolding, 'Combine after unfolding': h_afterUnfolding,
# 'before unfolding': h_before_unfolding
}
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def compare_unfolding_uncertainties():
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'unfolded_normalisation_combined_RooUnfold{method}.txt'
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
# variables = ['ST']
for variable in variables:
svd = file_template.format(
variable=variable, method='Svd')
bayes = file_template.format(
variable=variable, method='Bayes')
data = read_data_from_JSON(svd)
before_unfolding = data['TTJet_measured_withoutFakes']
svd_data = data['TTJet_unfolded']
bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded']
before_unfolding = [e / v * 100 for v, e in before_unfolding]
svd_data = [e / v * 100 for v, e in svd_data]
bayes_data = [e / v * 100 for v, e in bayes_data]
h_svd = value_tuplelist_to_hist(
svd_data, bin_edges_vis[variable])
h_bayes = value_tuplelist_to_hist(
bayes_data, bin_edges_vis[variable])
h_before_unfolding = value_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = 'compare_unfolding_uncertainties_{0}'.format(
variable)
properties.title = 'Comparison of unfolding uncertainties'
properties.path = 'plots'
properties.has_ratio = False
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = 'relative uncertainty (\\%)'
properties.legend_location = (0.98, 0.95)
histograms = {'SVD': h_svd, 'Bayes': h_bayes,
'before unfolding': h_before_unfolding}
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
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 )
def plot_fit_results( histograms, category, channel ):
global variable, b_tag_bin, output_folder, phase_space
from tools.plotting import Histogram_properties, make_data_mc_comparison_plot
fit_variables = histograms.keys()
variableBins = None
if phase_space == 'VisiblePS':
variableBins = variable_bins_visiblePS_ROOT
elif phase_space == 'FullPS':
variableBins = variable_bins_ROOT
for variable_bin in variableBins[variable]:
path = output_folder + str( measurement_config.centre_of_mass_energy ) + 'TeV/' + variable + '/' + category + '/fit_results/'
make_folder_if_not_exists( path )
for fit_variable in fit_variables:
plotname = channel + '_' + fit_variable + '_bin_' + variable_bin
# check if template plots exist already
for output_format in output_formats:
if os.path.isfile( plotname + '.' + output_format ):
continue
# plot with matplotlib
h_data = histograms[fit_variable][variable_bin]['data']
h_signal = histograms[fit_variable][variable_bin]['signal']
h_background = histograms[fit_variable][variable_bin]['background']
histogram_properties = Histogram_properties()
histogram_properties.name = plotname
histogram_properties.x_axis_title = fit_variables_latex[fit_variable]
histogram_properties.y_axis_title = 'Events/(%s)' % get_unit_string(fit_variable)
label, _ = get_cms_labels( channel )
histogram_properties.title = label
histogram_properties.x_limits = measurement_config.fit_boundaries[fit_variable]
make_data_mc_comparison_plot( [h_data, h_background, h_signal],
['data', 'background', 'signal'],
['black', 'green', 'red'], histogram_properties,
save_folder = path, save_as = output_formats )
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)
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,
)
def do_shape_check(channel,
control_region_1,
control_region_2,
variable,
normalisation,
title,
x_title,
y_title,
x_limits,
y_limits,
name_region_1='conversions',
name_region_2='non-isolated electrons',
name_region_3='fit results',
rebin=1):
global b_tag_bin
# QCD shape comparison
if channel == 'electron':
histograms = get_histograms_from_files(
[control_region_1, control_region_2], histogram_files)
region_1 = histograms[channel][control_region_1].Clone(
) - histograms['TTJet'][control_region_1].Clone(
) - histograms['V+Jets'][control_region_1].Clone(
) - histograms['SingleTop'][control_region_1].Clone()
region_2 = histograms[channel][control_region_2].Clone(
) - histograms['TTJet'][control_region_2].Clone(
) - histograms['V+Jets'][control_region_2].Clone(
) - histograms['SingleTop'][control_region_2].Clone()
region_1.Rebin(rebin)
region_2.Rebin(rebin)
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[0]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(
region_1,
region_2,
name_region_1=name_region_1,
name_region_2=name_region_2,
histogram_properties=histogram_properties,
save_folder=output_folder)
# QCD shape comparison to fit results
histograms = get_histograms_from_files([control_region_1],
histogram_files)
region_1_tmp = histograms[channel][control_region_1].Clone(
) - histograms['TTJet'][control_region_1].Clone(
) - histograms['V+Jets'][control_region_1].Clone(
) - histograms['SingleTop'][control_region_1].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD,
bin_edges[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_conversions_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(
region_1,
region_2,
name_region_1=name_region_1,
name_region_2=name_region_3,
histogram_properties=histogram_properties,
save_folder=output_folder)
histograms = get_histograms_from_files([control_region_2], histogram_files)
region_1_tmp = histograms[channel][control_region_2].Clone(
) - histograms['TTJet'][control_region_2].Clone(
) - histograms['V+Jets'][control_region_2].Clone(
) - histograms['SingleTop'][control_region_2].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD,
bin_edges[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_noniso_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1,
region_2,
name_region_1=name_region_2,
name_region_2=name_region_3,
histogram_properties=histogram_properties,
save_folder=output_folder)
'TTJet': path_to_files + 'TTJet_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(lumi), pfmuon),
'data' : path_to_files + '%s_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (data, str(lumi), pfmuon),
'WJets': path_to_files + 'WJetsToLNu_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(lumi), pfmuon),
'ZJets': path_to_files + 'DYJetsToLL_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(lumi), pfmuon),
'QCD': path_to_files + 'QCD_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(lumi), pfmuon),
'SingleTop': path_to_files + 'SingleTop_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(lumi), pfmuon),
}
b_tag_bin = '0btag'
control_region = 'topReconstruction/backgroundShape/mttbar_3jets_conversions_withMETAndAsymJets_' + b_tag_bin
histograms = get_histograms_from_files([control_region], histogram_files)
prepare_histograms(histograms, rebin=50)
histograms_to_draw = [histograms['data'][control_region], histograms['QCD'][control_region],
histograms['ZJets'][control_region], histograms['WJets'][control_region],
histograms['SingleTop'][control_region], histograms['TTJet'][control_region]]
histogram_lables = ['data', 'QCD', samples_latex['ZJets'], samples_latex['WJets'], 'Single-Top', samples_latex['TTJet']]
histogram_colors = ['black', 'yellow', 'blue', 'green', 'magenta', 'red']
histogram_properties = Histogram_properties()
histogram_properties.name = 'Mttbar'
histogram_properties.title = 'CMS Preliminary, $\mathcal{L}$ = 5.1 fb$^{-1}$ at $\sqrt{s}$ = 7 TeV \n e+jets, $\geq$4 jets, ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = '$m_{\mathrm{t}\\bar{\mathrm{t}}}$ [GeV]'
histogram_properties.y_axis_title = 'Events/(50 GeV)'
histogram_properties.x_limits=[300,1800]
histogram_properties.mc_error = 0.15
histogram_properties.mc_errors_label = '$\mathrm{t}\\bar{\mathrm{t}}$ uncertainty'
make_data_mc_comparison_plot(histograms_to_draw, histogram_lables, histogram_colors,
histogram_properties)
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 debug_last_bin():
'''
For debugging why the last bin in the problematic variables deviates a
lot in _one_ of the channels only.
'''
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'normalised_xsection_{channel}_RooUnfoldSvd{suffix}.txt'
problematic_variables = ['HT', 'MET', 'NJets', 'lepton_pt']
for variable in problematic_variables:
results = {}
Result = namedtuple(
'Result', ['before_unfolding', 'after_unfolding', 'model'])
for channel in ['electron', 'muon', 'combined']:
input_file_data = file_template.format(
variable=variable,
channel=channel,
suffix='_with_errors',
)
input_file_model = file_template.format(
variable=variable,
channel=channel,
suffix='',
)
data = read_data_from_JSON(input_file_data)
data_model = read_data_from_JSON(input_file_model)
before_unfolding = data['TTJet_measured_withoutFakes']
after_unfolding = data['TTJet_unfolded']
model = data_model['powhegPythia8']
# only use the last bin
h_before_unfolding = value_errors_tuplelist_to_graph(
[before_unfolding[-1]], bin_edges_vis[variable][-2:])
h_after_unfolding = value_errors_tuplelist_to_graph(
[after_unfolding[-1]], bin_edges_vis[variable][-2:])
h_model = value_error_tuplelist_to_hist(
[model[-1]], bin_edges_vis[variable][-2:])
r = Result(before_unfolding, after_unfolding, model)
h = Result(h_before_unfolding, h_after_unfolding, h_model)
results[channel] = (r, h)
models = {'POWHEG+PYTHIA': results['combined'][1].model}
h_unfolded = [results[channel][1].after_unfolding for channel in [
'electron', 'muon', 'combined']]
tmp_hists = spread_x(h_unfolded, bin_edges_vis[variable][-2:])
measurements = {}
for channel, hist in zip(['electron', 'muon', 'combined'], tmp_hists):
value = results[channel][0].after_unfolding[-1][0]
error = results[channel][0].after_unfolding[-1][1]
label = '{c_label} ({value:1.2g} $\pm$ {error:1.2g})'.format(
c_label=channel,
value=value,
error=error,
)
measurements[label] = hist
properties = Histogram_properties()
properties.name = 'normalised_xsection_compare_channels_{0}_{1}_last_bin'.format(
variable, channel)
properties.title = 'Comparison of channels'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = False
properties.x_limits = (
bin_edges_vis[variable][-2], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
properties.legend_location = (0.95, 0.40)
if variable == 'NJets':
properties.legend_location = (0.97, 0.80)
properties.formats = ['png']
compare_measurements(models=models, measurements=measurements, show_measurement_errors=True,
histogram_properties=properties, save_folder='plots/', save_as=properties.formats)
prepare_histograms(histograms, rebin=20, scale_factor = measurement_config.luminosity_scale)
qcd_predicted_mc = histograms['QCD'][control_region]
histograms_to_draw = [histograms['data'][control_region], qcd_predicted_mc,
histograms['V+Jets'][control_region],
histograms['SingleTop'][control_region], histograms['TTJet'][control_region]]
histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']]
histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red']
histogram_properties = Histogram_properties()
histogram_properties.name = 'EPlusJets_BJets_invmass_' + b_tag_bin
histogram_properties.title = e_title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = '$M_{\mathrm{b}\\bar{\mathrm{b}}}$'
histogram_properties.y_axis_title = 'Normalised events/(20 GeV)'
histogram_properties.x_limits = [0, 800]
histogram_properties.mc_error = 0.15
make_data_mc_comparison_plot(histograms_to_draw, histogram_lables, histogram_colors,
histogram_properties, save_folder = output_folder, show_ratio = False)
histogram_properties.name += '_with_ratio'
make_data_mc_comparison_plot(histograms_to_draw, histogram_lables, histogram_colors,
histogram_properties, save_folder = output_folder, show_ratio = True)
#bjet invariant mass
b_tag_bin = '3btags'
control_region = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/bjet_invariant_mass_' + b_tag_bin
histograms = get_histograms_from_files([control_region], histogram_files)
prepare_histograms(histograms, rebin=10, scale_factor = measurement_config.luminosity_scale)
qcd_predicted_mc = histograms['QCD'][control_region]
histograms = get_histograms_from_trees( trees = [signalTree], 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'][signalTree],
histograms['QCD'][signalTree],
histograms['V+Jets'][signalTree],
histograms['SingleTop'][signalTree], histograms['TTJet'][signalTree]]
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 = '%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.x_limits = [xMin, xMax]
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 = True )
def make_correlation_plot_from_file( channel, variable, fit_variables, CoM, title, x_title, y_title, x_limits, y_limits, rebin = 1, save_folder = 'plots/fitchecks/', save_as = ['pdf', 'png'] ):
# global b_tag_bin
parameters = ["TTJet", "SingleTop", "V+Jets", "QCD"]
parameters_latex = []
for template in parameters:
parameters_latex.append(samples_latex[template])
input_file = open( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), "r" )
# cycle through the lines in the file
for line_number, line in enumerate( input_file ):
# for now, only make plots for the fits for the central measurement
if "central" in line:
# matrix we want begins 11 lines below the line with the measurement ("central")
line_number = line_number + 11
break
input_file.close()
#Note: For some reason, the fit outputs the correlation matrix with the templates in the following order:
#parameter1: QCD
#parameter2: SingleTop
#parameter3: TTJet
#parameter4: V+Jets
for variable_bin in variable_bins_ROOT[variable]:
weights = {}
if channel == 'electron':
#formula to calculate the number of lines below "central" to access in each loop
number_of_lines_down = (variable_bins_ROOT[variable].index( variable_bin ) * 12)
#Get QCD correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down )
weights["QCD_QCD"] = matrix_line.split()[2]
weights["QCD_SingleTop"] = matrix_line.split()[3]
weights["QCD_TTJet"] = matrix_line.split()[4]
weights["QCD_V+Jets"] = matrix_line.split()[5]
#Get SingleTop correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 1 )
weights["SingleTop_QCD"] = matrix_line.split()[2]
weights["SingleTop_SingleTop"] = matrix_line.split()[3]
weights["SingleTop_TTJet"] = matrix_line.split()[4]
weights["SingleTop_V+Jets"] = matrix_line.split()[5]
#Get TTJet correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 2 )
weights["TTJet_QCD"] = matrix_line.split()[2]
weights["TTJet_SingleTop"] = matrix_line.split()[3]
weights["TTJet_TTJet"] = matrix_line.split()[4]
weights["TTJet_V+Jets"] = matrix_line.split()[5]
#Get V+Jets correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 3 )
weights["V+Jets_QCD"] = matrix_line.split()[2]
weights["V+Jets_SingleTop"] = matrix_line.split()[3]
weights["V+Jets_TTJet"] = matrix_line.split()[4]
weights["V+Jets_V+Jets"] = matrix_line.split()[5]
if channel == 'muon':
#formula to calculate the number of lines below "central" to access in each bin loop
number_of_lines_down = ( len( variable_bins_ROOT [variable] ) * 12 ) + ( variable_bins_ROOT[variable].index( variable_bin ) * 12 )
#Get QCD correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down )
weights["QCD_QCD"] = matrix_line.split()[2]
weights["QCD_SingleTop"] = matrix_line.split()[3]
weights["QCD_TTJet"] = matrix_line.split()[4]
weights["QCD_V+Jets"] = matrix_line.split()[5]
#Get SingleTop correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 1 )
weights["SingleTop_QCD"] = matrix_line.split()[2]
weights["SingleTop_SingleTop"] = matrix_line.split()[3]
weights["SingleTop_TTJet"] = matrix_line.split()[4]
weights["SingleTop_V+Jets"] = matrix_line.split()[5]
#Get TTJet correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 2 )
weights["TTJet_QCD"] = matrix_line.split()[2]
weights["TTJet_SingleTop"] = matrix_line.split()[3]
weights["TTJet_TTJet"] = matrix_line.split()[4]
weights["TTJet_V+Jets"] = matrix_line.split()[5]
#Get V+Jets correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 3 )
weights["V+Jets_QCD"] = matrix_line.split()[2]
weights["V+Jets_SingleTop"] = matrix_line.split()[3]
weights["V+Jets_TTJet"] = matrix_line.split()[4]
weights["V+Jets_V+Jets"] = matrix_line.split()[5]
#Create histogram
histogram_properties = Histogram_properties()
histogram_properties.title = title
histogram_properties.name = 'Correlations_' + channel + '_' + variable + '_' + variable_bin
histogram_properties.y_axis_title = y_title
histogram_properties.x_axis_title = x_title
histogram_properties.y_limits = y_limits
histogram_properties.x_limits = x_limits
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
#initialise 2D histogram
a = Hist2D( 4, 0, 4, 4, 0, 4 )
#fill histogram
for i in range( len( parameters ) ):
for j in range( len( parameters ) ):
a.fill( float( i ), float( j ), float( weights["%s_%s" % ( parameters[i], parameters[j] )] ) )
# create figure
plt.figure( figsize = CMS.figsize, dpi = CMS.dpi, facecolor = CMS.facecolor )
# make subplot(?)
fig, ax = plt.subplots( nrows = 1, ncols = 1 )
rplt.hist2d( a )
plt.subplots_adjust( right = 0.8 )
#Set labels and formats for titles and axes
plt.ylabel( histogram_properties.y_axis_title )
plt.xlabel( histogram_properties.x_axis_title )
plt.title( histogram_properties.title )
x_limits = histogram_properties.x_limits
y_limits = histogram_properties.y_limits
ax.set_xticklabels( parameters_latex )
ax.set_yticklabels( parameters_latex )
ax.set_xticks( [0.5, 1.5, 2.5, 3.5] )
ax.set_yticks( [0.5, 1.5, 2.5, 3.5] )
plt.setp( ax.get_xticklabels(), visible = True )
plt.setp( ax.get_yticklabels(), visible = True )
#create and draw colour bar to the right of the main plot
im = rplt.imshow( a, axes = ax, vmin = -1.0, vmax = 1.0 )
#set location and dimensions (left, lower, width, height)
cbar_ax = fig.add_axes( [0.85, 0.10, 0.05, 0.8] )
fig.colorbar( im, cax = cbar_ax )
for xpoint in range( len( parameters ) ):
for ypoint in range( len( parameters ) ):
correlation_value = weights["%s_%s" % ( parameters[xpoint], parameters[ypoint] )]
ax.annotate( correlation_value, xy = ( xpoint + 0.5, ypoint + 0.5 ), ha = 'center', va = 'center', bbox = dict( fc = 'white', ec = 'none' ) )
for save in save_as:
plt.savefig( save_folder + histogram_properties.name + '.' + save )
plt.close(fig)
plt.close('all')
def compare_qcd_control_regions( variable = 'MET', met_type = 'patType1CorrectedPFMet', title = 'Untitled'):
''' 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/fit_variables/%dTeV/%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 + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
histogram_properties.name = variable + '_' + fit_variable + '_' + b_tag_bin_ctl + '_QCD_template_comparison'
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)
def do_shape_check(
channel,
control_region_1,
control_region_2,
variable,
normalisation,
title,
x_title,
y_title,
x_limits,
y_limits,
name_region_1="conversions",
name_region_2="non-isolated electrons",
name_region_3="fit results",
rebin=1,
):
global b_tag_bin
# QCD shape comparison
if channel == "electron":
histograms = get_histograms_from_files([control_region_1, control_region_2], histogram_files)
region_1 = (
histograms[channel][control_region_1].Clone()
- histograms["TTJet"][control_region_1].Clone()
- histograms["V+Jets"][control_region_1].Clone()
- histograms["SingleTop"][control_region_1].Clone()
)
region_2 = (
histograms[channel][control_region_2].Clone()
- histograms["TTJet"][control_region_2].Clone()
- histograms["V+Jets"][control_region_2].Clone()
- histograms["SingleTop"][control_region_2].Clone()
)
region_1.Rebin(rebin)
region_2.Rebin(rebin)
histogram_properties = Histogram_properties()
histogram_properties.name = "QCD_control_region_comparison_" + channel + "_" + variable + "_" + b_tag_bin
histogram_properties.title = title + ", " + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = "arbitrary units/(0.1)"
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[0]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = "upper right"
make_control_region_comparison(
region_1,
region_2,
name_region_1=name_region_1,
name_region_2=name_region_2,
histogram_properties=histogram_properties,
save_folder=output_folder,
)
# QCD shape comparison to fit results
histograms = get_histograms_from_files([control_region_1], histogram_files)
region_1_tmp = (
histograms[channel][control_region_1].Clone()
- histograms["TTJet"][control_region_1].Clone()
- histograms["V+Jets"][control_region_1].Clone()
- histograms["SingleTop"][control_region_1].Clone()
)
region_1 = rebin_asymmetric(region_1_tmp, bin_edges[variable])
fit_results_QCD = normalisation[variable]["QCD"]
region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = (
"QCD_control_region_comparison_" + channel + "_" + variable + "_fits_with_conversions_" + b_tag_bin
)
histogram_properties.title = title + ", " + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = "arbitrary units/(0.1)"
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = "upper right"
make_control_region_comparison(
region_1,
region_2,
name_region_1=name_region_1,
name_region_2=name_region_3,
histogram_properties=histogram_properties,
save_folder=output_folder,
)
histograms = get_histograms_from_files([control_region_2], histogram_files)
region_1_tmp = (
histograms[channel][control_region_2].Clone()
- histograms["TTJet"][control_region_2].Clone()
- histograms["V+Jets"][control_region_2].Clone()
- histograms["SingleTop"][control_region_2].Clone()
)
region_1 = rebin_asymmetric(region_1_tmp, bin_edges[variable])
fit_results_QCD = normalisation[variable]["QCD"]
region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = (
"QCD_control_region_comparison_" + channel + "_" + variable + "_fits_with_noniso_" + b_tag_bin
)
histogram_properties.title = title + ", " + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = "arbitrary units/(0.1)"
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = "upper right"
make_control_region_comparison(
region_1,
region_2,
name_region_1=name_region_2,
name_region_2=name_region_3,
histogram_properties=histogram_properties,
save_folder=output_folder,
)
def do_shape_check(channel, control_region_1, control_region_2, variable, normalisation, title, x_title, y_title, x_limits, y_limits,
name_region_1='conversions' , name_region_2='non-isolated electrons', name_region_3='fit results', rebin=1):
global b_tag_bin
# QCD shape comparison
if channel == 'electron':
histograms = get_histograms_from_files([control_region_1, control_region_2], histogram_files)
region_1 = histograms[channel][control_region_1].Clone() - histograms['TTJet'][control_region_1].Clone() - histograms['V+Jets'][control_region_1].Clone() - histograms['SingleTop'][control_region_1].Clone()
region_2 = histograms[channel][control_region_2].Clone() - histograms['TTJet'][control_region_2].Clone() - histograms['V+Jets'][control_region_2].Clone() - histograms['SingleTop'][control_region_2].Clone()
region_1.Rebin(rebin)
region_2.Rebin(rebin)
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[0]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1, region_2,
name_region_1=name_region_1, name_region_2=name_region_2,
histogram_properties=histogram_properties, save_folder=output_folder)
# QCD shape comparison to fit results
histograms = get_histograms_from_files([control_region_1], histogram_files)
region_1_tmp = histograms[channel][control_region_1].Clone() - histograms['TTJet'][control_region_1].Clone() - histograms['V+Jets'][control_region_1].Clone() - histograms['SingleTop'][control_region_1].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges_vis[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_conversions_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1, region_2,
name_region_1=name_region_1, name_region_2=name_region_3,
histogram_properties=histogram_properties, save_folder=output_folder)
histograms = get_histograms_from_files([control_region_2], histogram_files)
region_1_tmp = histograms[channel][control_region_2].Clone() - histograms['TTJet'][control_region_2].Clone() - histograms['V+Jets'][control_region_2].Clone() - histograms['SingleTop'][control_region_2].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_noniso_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1, region_2,
name_region_1=name_region_2, name_region_2=name_region_3,
histogram_properties=histogram_properties, save_folder=output_folder)
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)
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,
)
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)
b_tag_bin = '0btag'
control_region = 'topReconstruction/backgroundShape/mttbar_3jets_conversions_withMETAndAsymJets_' + b_tag_bin
histograms = get_histograms_from_files([control_region], histogram_files)
prepare_histograms(histograms, rebin=50)
histograms_to_draw = [
histograms['data'][control_region], histograms['QCD'][control_region],
histograms['ZJets'][control_region],
histograms['WJets'][control_region],
histograms['SingleTop'][control_region],
histograms['TTJet'][control_region]
]
histogram_lables = [
'data', 'QCD', samples_latex['ZJets'], samples_latex['WJets'],
'Single-Top', samples_latex['TTJet']
]
histogram_colors = ['black', 'yellow', 'blue', 'green', 'magenta', 'red']
histogram_properties = Histogram_properties()
histogram_properties.name = 'Mttbar'
histogram_properties.title = 'CMS Preliminary, $\mathcal{L}$ = 5.1 fb$^{-1}$ at $\sqrt{s}$ = 7 TeV \n e+jets, $\geq$4 jets, ' + b_tag_bins_latex[
b_tag_bin]
histogram_properties.x_axis_title = '$m_{\mathrm{t}\\bar{\mathrm{t}}}$ [GeV]'
histogram_properties.y_axis_title = 'Events/(50 GeV)'
histogram_properties.x_limits = [300, 1800]
histogram_properties.mc_error = 0.15
histogram_properties.mc_errors_label = '$\mathrm{t}\\bar{\mathrm{t}}$ uncertainty'
make_data_mc_comparison_plot(histograms_to_draw, histogram_lables,
histogram_colors, histogram_properties)
histograms['data'][control_region], qcd_predicted_mc,
histograms['V+Jets'][control_region],
histograms['SingleTop'][control_region],
histograms['TTJet'][control_region]
]
histogram_lables = [
'data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']
]
histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red']
histogram_properties = Histogram_properties()
histogram_properties.name = 'EPlusJets_BJets_invmass_' + b_tag_bin
histogram_properties.title = e_title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = '$M_{\mathrm{b}\\bar{\mathrm{b}}}$'
histogram_properties.y_axis_title = 'Normalised events/(20 GeV)'
histogram_properties.x_limits = [0, 800]
histogram_properties.mc_error = 0.15
make_data_mc_comparison_plot(histograms_to_draw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder=output_folder,
show_ratio=False)
histogram_properties.name += '_with_ratio'
make_data_mc_comparison_plot(histograms_to_draw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder=output_folder,
show_ratio=True)
def compare_QCD_control_regions_to_MC():
config = XSectionConfig(13)
ctrl_e1 = 'TTbar_plus_X_analysis/EPlusJets/QCDConversions/FitVariables'
ctrl_e2 = 'TTbar_plus_X_analysis/EPlusJets/QCD non iso e+jets/FitVariables'
mc_e = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/FitVariables'
data_file_e = config.data_file_electron_trees
ttbar_file = config.ttbar_category_templates_trees['central']
vjets_file = config.VJets_category_templates_trees['central']
singleTop_file = config.SingleTop_category_templates_trees['central']
qcd_file_e = config.electron_QCD_MC_tree_file
ctrl_mu1 = 'TTbar_plus_X_analysis/MuPlusJets/QCD iso > 0.3/FitVariables'
ctrl_mu2 = 'TTbar_plus_X_analysis/MuPlusJets/QCD 0.12 < iso <= 0.3/FitVariables'
mc_mu = 'TTbar_plus_X_analysis/MuPlusJets/Ref selection/FitVariables'
data_file_mu = config.data_file_muon_trees
qcd_file_mu = config.muon_QCD_MC_tree_file
weight_branches_electron = [
"EventWeight", "PUWeight", "BJetWeight", "ElectronEfficiencyCorrection"
]
weight_branches_mu = [
"EventWeight", "PUWeight", "BJetWeight", "MuonEfficiencyCorrection"
]
variables = [
'MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'
]
# variables = ['abs_lepton_eta']
for variable in variables:
branch = variable
selection = '{0} >= 0'.format(branch)
if variable == 'abs_lepton_eta':
branch = 'abs(lepton_eta)'
selection = 'lepton_eta >= -3'
for channel in ['electron', 'muon']:
data_file = data_file_e
qcd_file = qcd_file_e
ctrl1 = ctrl_e1
ctrl2 = ctrl_e2
mc = mc_e
weight_branches = weight_branches_electron
if channel == 'muon':
data_file = data_file_mu
qcd_file = qcd_file_mu
ctrl1 = ctrl_mu1
ctrl2 = ctrl_mu2
mc = mc_mu
weight_branches = weight_branches_mu
inputs = {
'branch': branch,
'weight_branches': weight_branches,
'tree': ctrl1,
'bin_edges': bin_edges_vis[variable],
'selection': selection,
}
hs_ctrl1 = {
'data':
get_histogram_from_tree(input_file=data_file, **inputs),
'TTJet':
get_histogram_from_tree(input_file=ttbar_file, **inputs),
'VJets':
get_histogram_from_tree(input_file=vjets_file, **inputs),
'SingleTop':
get_histogram_from_tree(input_file=singleTop_file, **inputs),
'QCD':
get_histogram_from_tree(input_file=qcd_file, **inputs),
}
inputs['tree'] = ctrl2
hs_ctrl2 = {
'data':
get_histogram_from_tree(input_file=data_file, **inputs),
'TTJet':
get_histogram_from_tree(input_file=ttbar_file, **inputs),
'VJets':
get_histogram_from_tree(input_file=vjets_file, **inputs),
'SingleTop':
get_histogram_from_tree(input_file=singleTop_file, **inputs),
'QCD':
get_histogram_from_tree(input_file=qcd_file, **inputs),
}
inputs['tree'] = mc
h_qcd = get_histogram_from_tree(input_file=qcd_file, **inputs)
h_ctrl1 = clean_control_region(
hs_ctrl1,
data_label='data',
subtract=['TTJet', 'VJets', 'SingleTop'],
fix_to_zero=True)
h_ctrl2 = clean_control_region(
hs_ctrl2,
data_label='data',
subtract=['TTJet', 'VJets', 'SingleTop'],
fix_to_zero=True)
n_qcd_ctrl1 = hs_ctrl1['QCD'].integral()
n_qcd_ctrl2 = hs_ctrl2['QCD'].integral()
n_data1 = h_ctrl1.integral()
n_data2 = h_ctrl2.integral()
n_qcd_sg = h_qcd.integral()
ratio_ctrl1 = n_data1 / n_qcd_ctrl1
ratio_ctrl2 = n_data2 / n_qcd_ctrl2
qcd_estimate_ctrl1 = n_qcd_sg * ratio_ctrl1
qcd_estimate_ctrl2 = n_qcd_sg * ratio_ctrl2
h_ctrl1.Scale(qcd_estimate_ctrl1 / n_data1)
h_ctrl2.Scale(qcd_estimate_ctrl2 / n_data2)
properties = Histogram_properties()
properties.name = 'compare_qcd_control_regions_to_mc_{0}_{1}_channel'.format(
variable, channel)
properties.title = 'Comparison of QCD control regions ({0} channel)'.format(
channel)
properties.path = 'plots'
properties.has_ratio = False
properties.xerr = True
properties.x_limits = (bin_edges_vis[variable][0],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = 'number of QCD events'
histograms = {
'control region 1': h_ctrl1,
'control region 2': h_ctrl2,
'MC prediction': h_qcd
}
diff = absolute(h_ctrl1 - h_ctrl2)
lower = h_ctrl1 - diff
upper = h_ctrl1 + diff
err_e = ErrorBand('uncertainty', lower, upper)
plot_e = Plot(histograms, properties)
plot_e.draw_method = 'errorbar'
plot_e.add_error_band(err_e)
compare_histograms(plot_e)
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 make_correlation_plot_from_file(channel,
variable,
fit_variables,
CoM,
title,
x_title,
y_title,
x_limits,
y_limits,
rebin=1,
save_folder='plots/fitchecks/',
save_as=['pdf', 'png']):
# global b_tag_bin
parameters = ["TTJet", "SingleTop", "V+Jets", "QCD"]
parameters_latex = []
for template in parameters:
parameters_latex.append(samples_latex[template])
input_file = open(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), "r")
# cycle through the lines in the file
for line_number, line in enumerate(input_file):
# for now, only make plots for the fits for the central measurement
if "central" in line:
# matrix we want begins 11 lines below the line with the measurement ("central")
line_number = line_number + 11
break
input_file.close()
#Note: For some reason, the fit outputs the correlation matrix with the templates in the following order:
#parameter1: QCD
#parameter2: SingleTop
#parameter3: TTJet
#parameter4: V+Jets
for variable_bin in variable_bins_ROOT[variable]:
weights = {}
if channel == 'electron':
#formula to calculate the number of lines below "central" to access in each loop
number_of_lines_down = (
variable_bins_ROOT[variable].index(variable_bin) * 12)
#Get QCD correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down)
weights["QCD_QCD"] = matrix_line.split()[2]
weights["QCD_SingleTop"] = matrix_line.split()[3]
weights["QCD_TTJet"] = matrix_line.split()[4]
weights["QCD_V+Jets"] = matrix_line.split()[5]
#Get SingleTop correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 1)
weights["SingleTop_QCD"] = matrix_line.split()[2]
weights["SingleTop_SingleTop"] = matrix_line.split()[3]
weights["SingleTop_TTJet"] = matrix_line.split()[4]
weights["SingleTop_V+Jets"] = matrix_line.split()[5]
#Get TTJet correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 2)
weights["TTJet_QCD"] = matrix_line.split()[2]
weights["TTJet_SingleTop"] = matrix_line.split()[3]
weights["TTJet_TTJet"] = matrix_line.split()[4]
weights["TTJet_V+Jets"] = matrix_line.split()[5]
#Get V+Jets correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 3)
weights["V+Jets_QCD"] = matrix_line.split()[2]
weights["V+Jets_SingleTop"] = matrix_line.split()[3]
weights["V+Jets_TTJet"] = matrix_line.split()[4]
weights["V+Jets_V+Jets"] = matrix_line.split()[5]
if channel == 'muon':
#formula to calculate the number of lines below "central" to access in each bin loop
number_of_lines_down = (len(variable_bins_ROOT[variable]) * 12) + (
variable_bins_ROOT[variable].index(variable_bin) * 12)
#Get QCD correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down)
weights["QCD_QCD"] = matrix_line.split()[2]
weights["QCD_SingleTop"] = matrix_line.split()[3]
weights["QCD_TTJet"] = matrix_line.split()[4]
weights["QCD_V+Jets"] = matrix_line.split()[5]
#Get SingleTop correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 1)
weights["SingleTop_QCD"] = matrix_line.split()[2]
weights["SingleTop_SingleTop"] = matrix_line.split()[3]
weights["SingleTop_TTJet"] = matrix_line.split()[4]
weights["SingleTop_V+Jets"] = matrix_line.split()[5]
#Get TTJet correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 2)
weights["TTJet_QCD"] = matrix_line.split()[2]
weights["TTJet_SingleTop"] = matrix_line.split()[3]
weights["TTJet_TTJet"] = matrix_line.split()[4]
weights["TTJet_V+Jets"] = matrix_line.split()[5]
#Get V+Jets correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 3)
weights["V+Jets_QCD"] = matrix_line.split()[2]
weights["V+Jets_SingleTop"] = matrix_line.split()[3]
weights["V+Jets_TTJet"] = matrix_line.split()[4]
weights["V+Jets_V+Jets"] = matrix_line.split()[5]
#Create histogram
histogram_properties = Histogram_properties()
histogram_properties.title = title
histogram_properties.name = 'Correlations_' + channel + '_' + variable + '_' + variable_bin
histogram_properties.y_axis_title = y_title
histogram_properties.x_axis_title = x_title
histogram_properties.y_limits = y_limits
histogram_properties.x_limits = x_limits
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
#initialise 2D histogram
a = Hist2D(4, 0, 4, 4, 0, 4)
#fill histogram
for i in range(len(parameters)):
for j in range(len(parameters)):
a.fill(
float(i), float(j),
float(weights["%s_%s" % (parameters[i], parameters[j])]))
# create figure
plt.figure(figsize=CMS.figsize, dpi=CMS.dpi, facecolor=CMS.facecolor)
# make subplot(?)
fig, ax = plt.subplots(nrows=1, ncols=1)
rplt.hist2d(a)
plt.subplots_adjust(right=0.8)
#Set labels and formats for titles and axes
plt.ylabel(histogram_properties.y_axis_title)
plt.xlabel(histogram_properties.x_axis_title)
plt.title(histogram_properties.title)
x_limits = histogram_properties.x_limits
y_limits = histogram_properties.y_limits
ax.set_xticklabels(parameters_latex)
ax.set_yticklabels(parameters_latex)
ax.set_xticks([0.5, 1.5, 2.5, 3.5])
ax.set_yticks([0.5, 1.5, 2.5, 3.5])
plt.setp(ax.get_xticklabels(), visible=True)
plt.setp(ax.get_yticklabels(), visible=True)
#create and draw colour bar to the right of the main plot
im = rplt.imshow(a, axes=ax, vmin=-1.0, vmax=1.0)
#set location and dimensions (left, lower, width, height)
cbar_ax = fig.add_axes([0.85, 0.10, 0.05, 0.8])
fig.colorbar(im, cax=cbar_ax)
for xpoint in range(len(parameters)):
for ypoint in range(len(parameters)):
correlation_value = weights["%s_%s" % (parameters[xpoint],
parameters[ypoint])]
ax.annotate(correlation_value,
xy=(xpoint + 0.5, ypoint + 0.5),
ha='center',
va='center',
bbox=dict(fc='white', ec='none'))
for save in save_as:
plt.savefig(save_folder + histogram_properties.name + '.' + save)
plt.close(fig)
plt.close('all')
def debug_last_bin():
'''
For debugging why the last bin in the problematic variables deviates a
lot in _one_ of the channels only.
'''
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'normalised_xsection_{channel}_RooUnfoldSvd{suffix}.txt'
problematic_variables = ['HT', 'MET', 'NJets', 'lepton_pt']
for variable in problematic_variables:
results = {}
Result = namedtuple('Result',
['before_unfolding', 'after_unfolding', 'model'])
for channel in ['electron', 'muon', 'combined']:
input_file_data = file_template.format(
variable=variable,
channel=channel,
suffix='_with_errors',
)
input_file_model = file_template.format(
variable=variable,
channel=channel,
suffix='',
)
data = read_data_from_JSON(input_file_data)
data_model = read_data_from_JSON(input_file_model)
before_unfolding = data['TTJet_measured_withoutFakes']
after_unfolding = data['TTJet_unfolded']
model = data_model['powhegPythia8']
# only use the last bin
h_before_unfolding = value_errors_tuplelist_to_graph(
[before_unfolding[-1]], bin_edges_vis[variable][-2:])
h_after_unfolding = value_errors_tuplelist_to_graph(
[after_unfolding[-1]], bin_edges_vis[variable][-2:])
h_model = value_error_tuplelist_to_hist(
[model[-1]], bin_edges_vis[variable][-2:])
r = Result(before_unfolding, after_unfolding, model)
h = Result(h_before_unfolding, h_after_unfolding, h_model)
results[channel] = (r, h)
models = {'POWHEG+PYTHIA': results['combined'][1].model}
h_unfolded = [
results[channel][1].after_unfolding
for channel in ['electron', 'muon', 'combined']
]
tmp_hists = spread_x(h_unfolded, bin_edges_vis[variable][-2:])
measurements = {}
for channel, hist in zip(['electron', 'muon', 'combined'], tmp_hists):
value = results[channel][0].after_unfolding[-1][0]
error = results[channel][0].after_unfolding[-1][1]
label = '{c_label} ({value:1.2g} $\pm$ {error:1.2g})'.format(
c_label=channel,
value=value,
error=error,
)
measurements[label] = hist
properties = Histogram_properties()
properties.name = 'normalised_xsection_compare_channels_{0}_{1}_last_bin'.format(
variable, channel)
properties.title = 'Comparison of channels'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = False
properties.x_limits = (bin_edges_vis[variable][-2],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
properties.legend_location = (0.95, 0.40)
if variable == 'NJets':
properties.legend_location = (0.97, 0.80)
properties.formats = ['png']
compare_measurements(models=models,
measurements=measurements,
show_measurement_errors=True,
histogram_properties=properties,
save_folder='plots/',
save_as=properties.formats)
def compare_QCD_control_regions_to_MC():
config = XSectionConfig(13)
ctrl_e1 = 'TTbar_plus_X_analysis/EPlusJets/QCDConversions/FitVariables'
ctrl_e2 = 'TTbar_plus_X_analysis/EPlusJets/QCD non iso e+jets/FitVariables'
mc_e = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/FitVariables'
data_file_e = config.data_file_electron_trees
ttbar_file = config.ttbar_category_templates_trees['central']
vjets_file = config.VJets_category_templates_trees['central']
singleTop_file = config.SingleTop_category_templates_trees['central']
qcd_file_e = config.electron_QCD_MC_tree_file
ctrl_mu1 = 'TTbar_plus_X_analysis/MuPlusJets/QCD iso > 0.3/FitVariables'
ctrl_mu2 = 'TTbar_plus_X_analysis/MuPlusJets/QCD 0.12 < iso <= 0.3/FitVariables'
mc_mu = 'TTbar_plus_X_analysis/MuPlusJets/Ref selection/FitVariables'
data_file_mu = config.data_file_muon_trees
qcd_file_mu = config.muon_QCD_MC_tree_file
weight_branches_electron = [
"EventWeight",
"PUWeight",
"BJetWeight",
"ElectronEfficiencyCorrection"
]
weight_branches_mu = [
"EventWeight",
"PUWeight",
"BJetWeight",
"MuonEfficiencyCorrection"
]
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
# variables = ['abs_lepton_eta']
for variable in variables:
branch = variable
selection = '{0} >= 0'.format(branch)
if variable == 'abs_lepton_eta':
branch = 'abs(lepton_eta)'
selection = 'lepton_eta >= -3'
for channel in ['electron', 'muon']:
data_file = data_file_e
qcd_file = qcd_file_e
ctrl1 = ctrl_e1
ctrl2 = ctrl_e2
mc = mc_e
weight_branches = weight_branches_electron
if channel == 'muon':
data_file = data_file_mu
qcd_file = qcd_file_mu
ctrl1 = ctrl_mu1
ctrl2 = ctrl_mu2
mc = mc_mu
weight_branches = weight_branches_mu
inputs = {
'branch': branch,
'weight_branches': weight_branches,
'tree': ctrl1,
'bin_edges': bin_edges_vis[variable],
'selection': selection,
}
hs_ctrl1 = {
'data': get_histogram_from_tree(input_file=data_file, **inputs),
'TTJet': get_histogram_from_tree(input_file=ttbar_file, **inputs),
'VJets': get_histogram_from_tree(input_file=vjets_file, **inputs),
'SingleTop': get_histogram_from_tree(input_file=singleTop_file, **inputs),
'QCD': get_histogram_from_tree(input_file=qcd_file, **inputs),
}
inputs['tree'] = ctrl2
hs_ctrl2 = {
'data': get_histogram_from_tree(input_file=data_file, **inputs),
'TTJet': get_histogram_from_tree(input_file=ttbar_file, **inputs),
'VJets': get_histogram_from_tree(input_file=vjets_file, **inputs),
'SingleTop': get_histogram_from_tree(input_file=singleTop_file, **inputs),
'QCD': get_histogram_from_tree(input_file=qcd_file, **inputs),
}
inputs['tree'] = mc
h_qcd = get_histogram_from_tree(input_file=qcd_file, **inputs)
h_ctrl1 = clean_control_region(
hs_ctrl1,
data_label='data',
subtract=['TTJet', 'VJets', 'SingleTop'],
fix_to_zero=True)
h_ctrl2 = clean_control_region(
hs_ctrl2,
data_label='data',
subtract=['TTJet', 'VJets', 'SingleTop'],
fix_to_zero=True)
n_qcd_ctrl1 = hs_ctrl1['QCD'].integral()
n_qcd_ctrl2 = hs_ctrl2['QCD'].integral()
n_data1 = h_ctrl1.integral()
n_data2 = h_ctrl2.integral()
n_qcd_sg = h_qcd.integral()
ratio_ctrl1 = n_data1 / n_qcd_ctrl1
ratio_ctrl2 = n_data2 / n_qcd_ctrl2
qcd_estimate_ctrl1 = n_qcd_sg * ratio_ctrl1
qcd_estimate_ctrl2 = n_qcd_sg * ratio_ctrl2
h_ctrl1.Scale(qcd_estimate_ctrl1 / n_data1)
h_ctrl2.Scale(qcd_estimate_ctrl2 / n_data2)
properties = Histogram_properties()
properties.name = 'compare_qcd_control_regions_to_mc_{0}_{1}_channel'.format(
variable, channel)
properties.title = 'Comparison of QCD control regions ({0} channel)'.format(
channel)
properties.path = 'plots'
properties.has_ratio = False
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = 'number of QCD events'
histograms = {'control region 1': h_ctrl1,
'control region 2': h_ctrl2,
'MC prediction': h_qcd}
diff = absolute(h_ctrl1 - h_ctrl2)
lower = h_ctrl1 - diff
upper = h_ctrl1 + diff
err_e = ErrorBand('uncertainty', lower, upper)
plot_e = Plot(histograms, properties)
plot_e.draw_method = 'errorbar'
plot_e.add_error_band(err_e)
compare_histograms(plot_e)
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 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,
)
