def main():
global measurement_config, histogram_files
global electron_fit_variables, muon_fit_variables, fit_variable_properties
global b_tag_bin, category, histogram_files, variables
global b_tag_bin_ctl
title_template = 'CMS Preliminary, $\mathcal{L} = %.1f$ fb$^{-1}$ at $\sqrt{s}$ = %d TeV \n %s'
e_title = title_template % ( measurement_config.new_luminosity / 1000., measurement_config.centre_of_mass_energy, 'e+jets, $\geq$ 4 jets' )
met_type = 'patType1CorrectedPFMet'
for variable in variables:
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)
make_folder_if_not_exists(save_path + 'qcd/')
make_folder_if_not_exists(save_path + 'vjets/')
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
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 )
histograms = get_histograms_from_files( [fit_variable_distribution, qcd_fit_variable_distribution], histogram_files )
plot_fit_variable( histograms, fit_variable, variable, bin_range, fit_variable_distribution, qcd_fit_variable_distribution, e_title, save_path )
compare_qcd_control_regions(variable, met_type, e_title)
compare_vjets_btag_regions(variable, met_type, e_title)
def get_fit_inputs(template, variable, channel):
inputs = {}
for var_bin in variable_bins_ROOT[variable]:
print var_bin
histogram = template % var_bin
histograms = get_histograms_from_files([histogram], histogram_files)
for sample in [channel, "TTJet", "V+Jets", "SingleTop"]:
n_bins = histograms[sample][histogram].GetNbinsX()
error = Double(0)
integral = histograms[sample][histogram].IntegralAndError(1, n_bins, error)
if inputs.has_key(sample):
inputs[sample].append((integral, error))
else:
inputs[sample] = [(integral, error)]
inputs["QCD"] = []
for data, ttjet, vjets, singletop in zip(inputs[channel], inputs["TTJet"], inputs["V+Jets"], inputs["SingleTop"]):
qcd = ufloat(data) - ufloat(ttjet) - ufloat(vjets) - ufloat(singletop)
inputs["QCD"].append((qcd.nominal_value, qcd.std_dev))
print inputs
return inputs
def compare_vjets_btag_regions( variable = 'MET', met_type = 'patType1CorrectedPFMet', title = 'Untitled'):
''' 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/fit_variables/%dTeV/%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 + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
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)
path_to_files = '/storage/TopQuarkGroup/results/histogramfiles/AN-11-265_V1/'
lumi = 1959.75
data = 'data'
pfmuon = ''
histogram_files = {
'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),
}
control_region = 'QCDStudy/PFIsolation_controlRegion_0btag'
histograms = get_histograms_from_files([control_region], histogram_files)
prepare_histograms(histograms, rebin=rebin)
nonQCDMC = histograms['TTJet'][control_region] + histograms['WJets'][control_region] + histograms['ZJets'][control_region] + histograms['SingleTop'][control_region]
make_control_region_data_mc_comparision(histograms, control_region, 'PFIsolation_0btag',
x_label='relative isolation', x_min=0, x_max=1.6, y_label='Events/0.1')
make_control_region_comparison(histograms['data'][control_region],
histograms['QCD'][control_region],
'data',
'QCD MC',
'PFIsolation_0btag',
x_label='relative isolation', x_min=0, x_max=1.6, y_label='Events/0.1', y_max = 0.4)
make_control_region_comparison(histograms['data'][control_region] - nonQCDMC,
histograms['QCD'][control_region],
SingleTop_file = path_to_files + 'central/SingleTop_5050pb_PFElectron_PFMuon_PF2PATJets_PFMET.root'
W1Jet_file = path_to_files + 'central/W1Jet_5050pb_PFElectron_PFMuon_PF2PATJets_PFMET.root'
W2Jets_file = path_to_files + 'central/W2Jets_5050pb_PFElectron_PFMuon_PF2PATJets_PFMET.root'
W3Jets_file = path_to_files + 'central/W3Jets_5050pb_PFElectron_PFMuon_PF2PATJets_PFMET.root'
W4Jets_file = path_to_files + 'central/W4Jets_5050pb_PFElectron_PFMuon_PF2PATJets_PFMET.root'
ZJets_file = path_to_files + 'central/DYJetsToLL_5050pb_PFElectron_PFMuon_PF2PATJets_PFMET.root'
data_file_electron = path_to_files + 'central/ElectronHad_5050pb_PFElectron_PFMuon_PF2PATJets_PFMET.root'
data_file_muon = path_to_files + 'central/SingleMu_5050pb_PFElectron_PFMuon_PF2PATJets_PFMET.root'
# electron_QCD_file = File(path_to_files + 'QCD_data_electron.root')
# muon_QCD_file = File(path_to_files + 'QCD_data_muon.root')
histograms_of_interest = csm.get_histograms_of_interest_7TeV()
#get histograms
histograms = get_histograms_from_files(histograms_of_interest, {'data':data_file_electron})
#store normalisation of all samples
#for now OK.
output_files = {}
luminosity = 5050
for b_tag_bin in summations.all_b_tag_bins:
file_name = "TTbar_plus_X_analysis_%dpbinv_%s.root" %(luminosity, b_tag_bin)
output_files[b_tag_bin] = File(file_name, "recreate")
#sum samples up
#write them all up
for sample, histogram_list in histograms.iteritems():
for histogram_path, histogram in histogram_list.iteritems():
directory, histogram_name, b_tag_bin = get_histogram_info_tuple(histogram_path)
data = 'SingleElectron'
pfmuon = 'PFMuon_'
histogram_files = {
'data' : path_to_data + '%s_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (data, str(lumi), pfmuon),
'TTJet': path_to_files + 'TTJet_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix),
'WJets': path_to_files + 'WJets_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix),
'ZJets': path_to_files + 'DYJetsToLL_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix),
'QCD': path_to_files + 'QCD_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix),
'SingleTop': path_to_files + 'SingleTop_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix)
}
electron_selection = 'EventCount/TTbarEplusJetsRefSelection'
muon_selection = 'EventCount/TTbarMuPlusJetsRefSelection'
cuts = cuts_electrons
histograms = get_histograms_from_files([electron_selection], histogram_files)
print '='*50
printCutFlow(histograms, electron_selection)
data = 'SingleMu'
histogram_files['data'] = path_to_data + '%s_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (data, str(lumi), pfmuon)
histogram_files['QCD'] = path_to_files + 'QCD_Muon_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix)
histograms = get_histograms_from_files([muon_selection], histogram_files)
cuts = cuts_muons
print '='*50
printCutFlow(histograms, muon_selection)
histogram_files = {
'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': '/storage/TopQuarkGroup/results/histogramfiles/AN-11-265_V2/QCD_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(1959.75), ''),
'SingleTop': path_to_files + 'SingleTop_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(lumi), pfmuon),
}
control_region_1 = 'topReconstruction/backgroundShape/mttbar_3jets_conversions_withMETAndAsymJets_0btag'
control_region_2 = 'topReconstruction/backgroundShape/mttbar_3jets_antiIsolated_withMETAndAsymJets_0btag'
control_region_3 = 'topReconstruction/backgroundShape/mttbar_conversions_withMETAndAsymJets_0btag'
control_region_4 = 'topReconstruction/backgroundShape/mttbar_antiIsolated_withMETAndAsymJets_0btag'
histograms_to_read = [control_region_1, control_region_2, control_region_3, control_region_4]
histograms = get_histograms_from_files(histograms_to_read, histogram_files)
prepare_histograms(histograms, rebin=rebin)
for _,histogram in histograms['QCD'].iteritems():
histogram.Scale(5028./1959.75)
make_control_region_data_mc_comparision(histograms, control_region_1, 'mttbar_3jets_conversions_withMETAndAsymJets_0btag')
make_control_region_data_mc_comparision(histograms, control_region_2, 'mttbar_3jets_antiIsolated_withMETAndAsymJets_0btag')
make_control_region_data_mc_comparision(histograms, control_region_3, 'mttbar_conversions_withMETAndAsymJets_0btag')
make_control_region_data_mc_comparision(histograms, control_region_4, 'mttbar_antiIsolated_withMETAndAsymJets_0btag')
make_control_region_comparison(histograms['data'][control_region_1],
histograms['data'][control_region_2],
'conversions',
'non-isolated electrons',
'mttbar_3jets_withMETAndAsymJets_0btag')
make_control_region_comparison(histograms['data'][control_region_3],
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 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)
