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 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_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)
properties.x_axis_title = 'Mass'
properties.y_axis_title = 'Events'
make_data_mc_comparison_plot([h3, h1, h2],
['data', 'background', 'signal'],
['black', 'green', 'red'], properties)
properties.name += '_with_ratio'
make_data_mc_comparison_plot([h3, h1, h2],
['data', 'background', 'signal'],
['black', 'green', 'red'],
properties,
show_ratio=True)
properties.name = 'matplotlib_hist_comparison'
properties.y_limits = [0, 0.4]
make_control_region_comparison(h1, h2, 'background', 'signal', properties)
else:
fig = plt.figure(figsize=(14, 10), dpi=300) #, facecolor='white')
axes = plt.axes()
axes.xaxis.set_minor_locator(AutoMinorLocator())
axes.yaxis.set_minor_locator(AutoMinorLocator())
# axes.yaxis.set_major_locator(MultipleLocator(20))
axes.tick_params(which='major', labelsize=15, length=8)
axes.tick_params(which='minor', length=4)
rplt.errorbar(h3, xerr=False, emptybins=False, axes=axes, zorder=4)
rplt.hist(stack, stacked=True, axes=axes, zorder=1)
plt.xlabel('Mass', position=(1., 0.), ha='right')
plt.ylabel('Events', position=(0., 1.), va='bottom', ha='right')
plt.legend(numpoints=1)
plt.tight_layout()
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 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,
)
# plot with matplotlib
plot_with_plotting_script = True
if plot_with_plotting_script:
properties = Histogram_properties()
properties.name = 'matplotlib_hist'
properties.x_axis_title = 'Mass'
properties.y_axis_title = 'Events'
make_data_mc_comparison_plot( [h3, h1, h2], ['data', 'background', 'signal'], ['black', 'green', 'red'], properties )
properties.name += '_with_ratio'
make_data_mc_comparison_plot( [h3, h1, h2], ['data', 'background', 'signal'], ['black', 'green', 'red'], properties, show_ratio = True )
properties.name = 'matplotlib_hist_comparison'
properties.y_limits = [0, 0.4]
make_control_region_comparison( h1, h2, 'background', 'signal', properties )
else:
fig = plt.figure(figsize=(14, 10), dpi=300)#, facecolor='white')
axes = plt.axes()
axes.xaxis.set_minor_locator(AutoMinorLocator())
axes.yaxis.set_minor_locator(AutoMinorLocator())
# axes.yaxis.set_major_locator(MultipleLocator(20))
axes.tick_params(which='major', labelsize=15, length=8)
axes.tick_params(which='minor', length=4)
rplt.errorbar(h3, xerr=False, emptybins=False, axes=axes, zorder = 4)
rplt.hist(stack, stacked=True, axes=axes, zorder = 1)
plt.xlabel('Mass', position=(1., 0.), ha='right')
plt.ylabel('Events', position=(0., 1.), va='bottom', ha='right')
plt.legend(numpoints=1)
plt.tight_layout()
