def plot_results(results):
'''
Takes results fo the form:
{centre-of-mass-energy: {
channel : {
variable : {
fit_variable : {
test : { sample : []},
}
}
}
}
}
'''
global options
output_base = 'plots/fit_checks/chi2'
for COMEnergy in results.keys():
tmp_result_1 = results[COMEnergy]
for channel in tmp_result_1.keys():
tmp_result_2 = tmp_result_1[channel]
for variable in tmp_result_2.keys():
tmp_result_3 = tmp_result_2[variable]
for fit_variable in tmp_result_3.keys():
tmp_result_4 = tmp_result_3[fit_variable]
# histograms should be {sample: {test : histogram}}
histograms = {}
for test, chi2 in tmp_result_4.iteritems():
for sample in chi2.keys():
if not histograms.has_key(sample):
histograms[sample] = {}
# reverse order of test and sample
histograms[sample][test] = value_tuplelist_to_hist(
chi2[sample], bin_edges_vis[variable])
for sample in histograms.keys():
hist_properties = Histogram_properties()
hist_properties.name = sample.replace('+',
'') + '_chi2'
hist_properties.title = '$\\chi^2$ distribution for fit output (' + sample + ')'
hist_properties.x_axis_title = '$' + latex_labels.variables_latex[
variable] + '$ [TeV]'
hist_properties.y_axis_title = '$\chi^2 = \\left({N_{fit}} - N_{{exp}}\\right)^2$'
hist_properties.set_log_y = True
hist_properties.y_limits = (1e-20, 1e20)
path = output_base + '/' + COMEnergy + 'TeV/' + channel + '/' + variable + '/' + fit_variable + '/'
if options.test:
path = output_base + '/test/'
measurements = {}
for test, histogram in histograms[sample].iteritems():
measurements[test.replace('_', ' ')] = histogram
compare_measurements(
{},
measurements,
show_measurement_errors=False,
histogram_properties=hist_properties,
save_folder=path,
save_as=['pdf'])
def draw_regularisation_histograms( h_truth, h_measured, h_response, h_fakes = None, h_data = None ):
global method, variable, output_folder, output_formats, test
k_max = h_measured.nbins()
unfolding = Unfolding( h_truth,
h_measured,
h_response,
h_fakes,
method = method,
k_value = k_max,
error_treatment = 4,
verbose = 1 )
RMSerror, MeanResiduals, RMSresiduals, Chi2 = unfolding.test_regularisation ( h_data, k_max )
histogram_properties = Histogram_properties()
histogram_properties.name = 'chi2_%s_channel_%s' % ( channel, variable )
histogram_properties.title = '$\chi^2$ for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test )
histogram_properties.x_axis_title = '$i$'
histogram_properties.y_axis_title = '$\chi^2$'
histogram_properties.set_log_y = True
make_plot(Chi2, 'chi2', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False)
histogram_properties = Histogram_properties()
histogram_properties.name = 'RMS_error_%s_channel_%s' % ( channel, variable )
histogram_properties.title = 'Mean error for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test )
histogram_properties.x_axis_title = '$i$'
histogram_properties.y_axis_title = 'Mean error'
make_plot(RMSerror, 'RMS', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False)
histogram_properties = Histogram_properties()
histogram_properties.name = 'RMS_residuals_%s_channel_%s' % ( channel, variable )
histogram_properties.title = 'RMS of residuals for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test )
histogram_properties.x_axis_title = '$i$'
histogram_properties.y_axis_title = 'RMS of residuals'
if test == 'closure':
histogram_properties.set_log_y = True
make_plot(RMSresiduals, 'RMSresiduals', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False)
histogram_properties = Histogram_properties()
histogram_properties.name = 'mean_residuals_%s_channel_%s' % ( channel, variable )
histogram_properties.title = 'Mean of residuals for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test )
histogram_properties.x_axis_title = '$i$'
histogram_properties.y_axis_title = 'Mean of residuals'
make_plot(MeanResiduals, 'MeanRes', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False)
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 plot_results ( results ):
'''
Takes results fo the form:
{centre-of-mass-energy: {
channel : {
variable : {
fit_variable : {
test : { sample : []},
}
}
}
}
}
'''
global options
output_base = 'plots/fit_checks/chi2'
for COMEnergy in results.keys():
tmp_result_1 = results[COMEnergy]
for channel in tmp_result_1.keys():
tmp_result_2 = tmp_result_1[channel]
for variable in tmp_result_2.keys():
tmp_result_3 = tmp_result_2[variable]
for fit_variable in tmp_result_3.keys():
tmp_result_4 = tmp_result_3[fit_variable]
# histograms should be {sample: {test : histogram}}
histograms = {}
for test, chi2 in tmp_result_4.iteritems():
for sample in chi2.keys():
if not histograms.has_key(sample):
histograms[sample] = {}
# reverse order of test and sample
histograms[sample][test] = value_tuplelist_to_hist(chi2[sample], bin_edges_vis[variable])
for sample in histograms.keys():
hist_properties = Histogram_properties()
hist_properties.name = sample.replace('+', '') + '_chi2'
hist_properties.title = '$\\chi^2$ distribution for fit output (' + sample + ')'
hist_properties.x_axis_title = '$' + latex_labels.variables_latex[variable] + '$ [TeV]'
hist_properties.y_axis_title = '$\chi^2 = \\left({N_{fit}} - N_{{exp}}\\right)^2$'
hist_properties.set_log_y = True
hist_properties.y_limits = (1e-20, 1e20)
path = output_base + '/' + COMEnergy + 'TeV/' + channel + '/' + variable + '/' + fit_variable + '/'
if options.test:
path = output_base + '/test/'
measurements = {}
for test, histogram in histograms[sample].iteritems():
measurements[test.replace('_',' ')] = histogram
compare_measurements({},
measurements,
show_measurement_errors = False,
histogram_properties = hist_properties,
save_folder = path,
save_as = ['pdf'])
def plotHistograms(
histogram_files,
var_to_plot,
output_folder):
'''
'''
global measurement_config
weightBranchSignalRegion = 'EventWeight * PUWeight * BJetWeight'
weightBranchControlRegion = 'EventWeight'
# Names of QCD regions to use
qcd_data_region = ''
qcd_data_region_electron = 'QCD non iso e+jets'
qcd_data_region_muon = 'QCD non iso mu+jets 1p5to3'
sr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/AnalysisVariables'
sr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/Ref selection/AnalysisVariables'
cr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/{}/AnalysisVariables'.format(qcd_data_region_electron)
cr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/{}/AnalysisVariables'.format(qcd_data_region_muon)
print "Trees : "
print "\t {}".format(sr_e_tree)
print "\t {}".format(sr_mu_tree)
print "\t {}".format(cr_e_tree)
print "\t {}".format(cr_mu_tree)
histogram_files_electron = dict(histogram_files)
histogram_files_electron['data'] = measurement_config.data_file_electron
histogram_files_electron['QCD'] = measurement_config.electron_QCD_MC_trees
histogram_files_muon = dict(histogram_files)
histogram_files_muon['data'] = measurement_config.data_file_muon
histogram_files_muon['QCD'] = measurement_config.muon_QCD_MC_trees
signal_region_hists = {}
control_region_hists = {}
for var in var_to_plot:
selectionSignalRegion = '{} >= 0'.format(var)
# Print all the weights applied to this plot
print "Variable : {}".format(var)
print "Weight applied : {}".format(weightBranchSignalRegion)
print "Selection applied : {}".format(selectionSignalRegion)
histograms_electron = get_histograms_from_trees(
trees = [sr_e_tree],
branch = var,
weightBranch = weightBranchSignalRegion + ' * ElectronEfficiencyCorrection',
files = histogram_files_electron,
nBins = 20,
xMin = control_plots_bins[var][0],
xMax = control_plots_bins[var][-1],
selection = selectionSignalRegion
)
histograms_muon = get_histograms_from_trees(
trees = [sr_mu_tree],
branch = var,
weightBranch = weightBranchSignalRegion + ' * MuonEfficiencyCorrection',
files = histogram_files_muon,
nBins = 20,
xMin = control_plots_bins[var][0],
xMax = control_plots_bins[var][-1],
selection = selectionSignalRegion
)
histograms_electron_QCDControlRegion = get_histograms_from_trees(
trees = [cr_e_tree],
branch = var,
weightBranch = weightBranchControlRegion,
files = histogram_files_electron,
nBins = 20,
xMin = control_plots_bins[var][0],
xMax = control_plots_bins[var][-1],
selection = selectionSignalRegion
)
histograms_muon_QCDControlRegion = get_histograms_from_trees(
trees = [cr_mu_tree],
branch = var,
weightBranch = weightBranchControlRegion,
files = histogram_files_muon,
nBins = 20,
xMin = control_plots_bins[var][0],
xMax = control_plots_bins[var][-1],
selection = selectionSignalRegion
)
# Combine the electron and muon histograms
for sample in histograms_electron:
h_electron = histograms_electron[sample][sr_e_tree]
h_muon = histograms_muon[sample][sr_mu_tree]
h_qcd_electron = histograms_electron_QCDControlRegion[sample][cr_e_tree]
h_qcd_muon = histograms_muon_QCDControlRegion[sample][cr_mu_tree]
signal_region_hists[sample] = h_electron + h_muon
control_region_hists[sample] = h_qcd_electron + h_qcd_muon
# NORMALISE TO LUMI
prepare_histograms(
signal_region_hists,
scale_factor = measurement_config.luminosity_scale
)
prepare_histograms(
control_region_hists,
scale_factor = measurement_config.luminosity_scale
)
# BACKGROUND SUBTRACTION FOR QCD
qcd_from_data = None
qcd_from_data = clean_control_region(
control_region_hists,
subtract = ['TTJet', 'V+Jets', 'SingleTop']
)
# DATA DRIVEN QCD
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)
dataDrivenQCDScale = n_qcd_predicted_mc_signal / n_qcd_predicted_mc_control
qcd_from_data.Scale( dataDrivenQCDScale.nominal_value )
signal_region_hists['QCD'] = qcd_from_data
# PLOTTING
histograms_to_draw = []
histogram_lables = []
histogram_colors = []
histograms_to_draw = [
# signal_region_hists['data'],
# qcd_from_data,
# signal_region_hists['V+Jets'],
signal_region_hists['SingleTop'],
signal_region_hists['ST_s'],
signal_region_hists['ST_t'],
signal_region_hists['ST_tW'],
signal_region_hists['STbar_t'],
signal_region_hists['STbar_tW'],
# signal_region_hists['TTJet'],
]
histogram_lables = [
'data',
# 'QCD',
# 'V+Jets',
# 'Single-Top',
'ST-s',
'ST-t',
'ST-tW',
'STbar-t',
'STbar-tW',
# samples_latex['TTJet'],
]
histogram_colors = [
colours['data'],
# colours['QCD'],
# colours['V+Jets'],
# colours['Single-Top'],
colours['ST_s'],
colours['ST_t'],
colours['ST_tW'],
colours['STbar_t'],
colours['STbar_tW'],
# colours['TTJet'],
]
# Find maximum y of samples
maxData = max( list(signal_region_hists['SingleTop'].y()) )
y_limits = [0, maxData * 1.5]
log_y = False
if log_y:
y_limits = [0.1, maxData * 100 ]
# Lumi title of plots
title_template = '%.1f fb$^{-1}$ (%d TeV)'
title = title_template % ( measurement_config.new_luminosity/1000., measurement_config.centre_of_mass_energy )
x_axis_title = '$%s$ [GeV]' % variables_latex[var]
y_axis_title = 'Events/(%i GeV)' % binWidth(control_plots_bins[var])
# More histogram settings to look semi decent
histogram_properties = Histogram_properties()
histogram_properties.name = var + '_with_ratio'
histogram_properties.title = title
histogram_properties.x_axis_title = x_axis_title
histogram_properties.y_axis_title = y_axis_title
histogram_properties.x_limits = control_plots_bins[var]
histogram_properties.y_limits = y_limits
histogram_properties.y_max_scale = 1.4
histogram_properties.xerr = None
histogram_properties.emptybins = True
histogram_properties.additional_text = channel_latex['combined']
histogram_properties.legend_location = ( 0.9, 0.73 )
histogram_properties.cms_logo_location = 'left'
histogram_properties.preliminary = True
histogram_properties.set_log_y = log_y
histogram_properties.legend_color = False
histogram_properties.ratio_y_limits = [0.1,1.9]
if log_y: histogram_properties.name += '_logy'
loc = histogram_properties.legend_location
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,
)
histogram_properties.name = var + '_ST_TTJet_Shape'
if log_y: histogram_properties.name += '_logy'
histogram_properties.y_axis_title = 'Normalised Distribution'
histogram_properties.y_limits = [0,0.5]
make_shape_comparison_plot(
shapes = [
signal_region_hists['TTJet'],
signal_region_hists['ST_t'],
signal_region_hists['ST_tW'],
signal_region_hists['ST_s'],
signal_region_hists['STbar_t'],
signal_region_hists['STbar_tW'],
],
names = [
samples_latex['TTJet'],
'Single-Top t channel',
'Single-Top tW channel',
'Single-Top s channel',
'Single-AntiTop t channel',
'Single-AntiTop tW channel',
],
colours = [
colours['TTJet'],
colours['ST_t'],
colours['ST_tW'],
colours['ST_s'],
colours['STbar_t'],
colours['STbar_tW'],
],
histogram_properties = histogram_properties,
save_folder = output_folder,
fill_area = False,
add_error_bars = False,
save_as = ['pdf'],
make_ratio = True,
alpha = 1,
)
print_output(signal_region_hists, output_folder, var, 'combined')
return
def plotHistograms(histogram_files, var_to_plot, output_folder):
'''
'''
global measurement_config
weightBranchSignalRegion = 'EventWeight * PUWeight * BJetWeight'
weightBranchControlRegion = 'EventWeight'
# Names of QCD regions to use
qcd_data_region = ''
qcd_data_region_electron = 'QCD non iso e+jets'
qcd_data_region_muon = 'QCD non iso mu+jets 1p5to3'
sr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/AnalysisVariables'
sr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/Ref selection/AnalysisVariables'
cr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/{}/AnalysisVariables'.format(
qcd_data_region_electron)
cr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/{}/AnalysisVariables'.format(
qcd_data_region_muon)
print "Trees : "
print "\t {}".format(sr_e_tree)
print "\t {}".format(sr_mu_tree)
print "\t {}".format(cr_e_tree)
print "\t {}".format(cr_mu_tree)
histogram_files_electron = dict(histogram_files)
histogram_files_electron['data'] = measurement_config.data_file_electron
histogram_files_electron['QCD'] = measurement_config.electron_QCD_MC_trees
histogram_files_muon = dict(histogram_files)
histogram_files_muon['data'] = measurement_config.data_file_muon
histogram_files_muon['QCD'] = measurement_config.muon_QCD_MC_trees
signal_region_hists = {}
control_region_hists = {}
for var in var_to_plot:
selectionSignalRegion = '{} >= 0'.format(var)
# Print all the weights applied to this plot
print "Variable : {}".format(var)
print "Weight applied : {}".format(weightBranchSignalRegion)
print "Selection applied : {}".format(selectionSignalRegion)
histograms_electron = get_histograms_from_trees(
trees=[sr_e_tree],
branch=var,
weightBranch=weightBranchSignalRegion +
' * ElectronEfficiencyCorrection',
files=histogram_files_electron,
nBins=20,
xMin=control_plots_bins[var][0],
xMax=control_plots_bins[var][-1],
selection=selectionSignalRegion)
histograms_muon = get_histograms_from_trees(
trees=[sr_mu_tree],
branch=var,
weightBranch=weightBranchSignalRegion +
' * MuonEfficiencyCorrection',
files=histogram_files_muon,
nBins=20,
xMin=control_plots_bins[var][0],
xMax=control_plots_bins[var][-1],
selection=selectionSignalRegion)
histograms_electron_QCDControlRegion = get_histograms_from_trees(
trees=[cr_e_tree],
branch=var,
weightBranch=weightBranchControlRegion,
files=histogram_files_electron,
nBins=20,
xMin=control_plots_bins[var][0],
xMax=control_plots_bins[var][-1],
selection=selectionSignalRegion)
histograms_muon_QCDControlRegion = get_histograms_from_trees(
trees=[cr_mu_tree],
branch=var,
weightBranch=weightBranchControlRegion,
files=histogram_files_muon,
nBins=20,
xMin=control_plots_bins[var][0],
xMax=control_plots_bins[var][-1],
selection=selectionSignalRegion)
# Combine the electron and muon histograms
for sample in histograms_electron:
h_electron = histograms_electron[sample][sr_e_tree]
h_muon = histograms_muon[sample][sr_mu_tree]
h_qcd_electron = histograms_electron_QCDControlRegion[sample][
cr_e_tree]
h_qcd_muon = histograms_muon_QCDControlRegion[sample][cr_mu_tree]
signal_region_hists[sample] = h_electron + h_muon
control_region_hists[sample] = h_qcd_electron + h_qcd_muon
# NORMALISE TO LUMI
prepare_histograms(signal_region_hists,
scale_factor=measurement_config.luminosity_scale)
prepare_histograms(control_region_hists,
scale_factor=measurement_config.luminosity_scale)
# BACKGROUND SUBTRACTION FOR QCD
qcd_from_data = None
qcd_from_data = clean_control_region(
control_region_hists, subtract=['TTJet', 'V+Jets', 'SingleTop'])
# DATA DRIVEN QCD
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)
dataDrivenQCDScale = n_qcd_predicted_mc_signal / n_qcd_predicted_mc_control
qcd_from_data.Scale(dataDrivenQCDScale.nominal_value)
signal_region_hists['QCD'] = qcd_from_data
# PLOTTING
histograms_to_draw = []
histogram_lables = []
histogram_colors = []
histograms_to_draw = [
# signal_region_hists['data'],
# qcd_from_data,
# signal_region_hists['V+Jets'],
signal_region_hists['SingleTop'],
signal_region_hists['ST_s'],
signal_region_hists['ST_t'],
signal_region_hists['ST_tW'],
signal_region_hists['STbar_t'],
signal_region_hists['STbar_tW'],
# signal_region_hists['TTJet'],
]
histogram_lables = [
'data',
# 'QCD',
# 'V+Jets',
# 'Single-Top',
'ST-s',
'ST-t',
'ST-tW',
'STbar-t',
'STbar-tW',
# samples_latex['TTJet'],
]
histogram_colors = [
colours['data'],
# colours['QCD'],
# colours['V+Jets'],
# colours['Single-Top'],
colours['ST_s'],
colours['ST_t'],
colours['ST_tW'],
colours['STbar_t'],
colours['STbar_tW'],
# colours['TTJet'],
]
# Find maximum y of samples
maxData = max(list(signal_region_hists['SingleTop'].y()))
y_limits = [0, maxData * 1.5]
log_y = False
if log_y:
y_limits = [0.1, maxData * 100]
# Lumi title of plots
title_template = '%.1f fb$^{-1}$ (%d TeV)'
title = title_template % (measurement_config.new_luminosity / 1000.,
measurement_config.centre_of_mass_energy)
x_axis_title = '$%s$ [GeV]' % variables_latex[var]
y_axis_title = 'Events/(%i GeV)' % binWidth(control_plots_bins[var])
# More histogram settings to look semi decent
histogram_properties = Histogram_properties()
histogram_properties.name = var + '_with_ratio'
histogram_properties.title = title
histogram_properties.x_axis_title = x_axis_title
histogram_properties.y_axis_title = y_axis_title
histogram_properties.x_limits = control_plots_bins[var]
histogram_properties.y_limits = y_limits
histogram_properties.y_max_scale = 1.4
histogram_properties.xerr = None
histogram_properties.emptybins = True
histogram_properties.additional_text = channel_latex['combined']
histogram_properties.legend_location = (0.9, 0.73)
histogram_properties.cms_logo_location = 'left'
histogram_properties.preliminary = True
histogram_properties.set_log_y = log_y
histogram_properties.legend_color = False
histogram_properties.ratio_y_limits = [0.1, 1.9]
if log_y: histogram_properties.name += '_logy'
loc = histogram_properties.legend_location
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,
)
histogram_properties.name = var + '_ST_TTJet_Shape'
if log_y: histogram_properties.name += '_logy'
histogram_properties.y_axis_title = 'Normalised Distribution'
histogram_properties.y_limits = [0, 0.5]
make_shape_comparison_plot(
shapes=[
signal_region_hists['TTJet'],
signal_region_hists['ST_t'],
signal_region_hists['ST_tW'],
signal_region_hists['ST_s'],
signal_region_hists['STbar_t'],
signal_region_hists['STbar_tW'],
],
names=[
samples_latex['TTJet'],
'Single-Top t channel',
'Single-Top tW channel',
'Single-Top s channel',
'Single-AntiTop t channel',
'Single-AntiTop tW channel',
],
colours=[
colours['TTJet'],
colours['ST_t'],
colours['ST_tW'],
colours['ST_s'],
colours['STbar_t'],
colours['STbar_tW'],
],
histogram_properties=histogram_properties,
save_folder=output_folder,
fill_area=False,
add_error_bars=False,
save_as=['pdf'],
make_ratio=True,
alpha=1,
)
print_output(signal_region_hists, output_folder, var, 'combined')
return
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 drawHistograms( dictionaryOfHistograms, uncertaintyBand, config, channel, variable ) :
histograms_to_draw = [
dictionaryOfHistograms['Data'],
dictionaryOfHistograms['QCD'],
dictionaryOfHistograms['V+Jets'],
dictionaryOfHistograms['SingleTop'],
dictionaryOfHistograms['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'],
]
# Find maximum y of samples
maxData = max( list(histograms_to_draw[0].y()) )
y_limits = [0, maxData * 1.4]
# More histogram settings to look semi decent
histogram_properties = Histogram_properties()
histogram_properties.name = '{channel}_{variable}'.format(channel = channel, variable=variable)
histogram_properties.title = '$%.1f$ fb$^{-1}$ (%d TeV)' % ( config.new_luminosity/1000., config.centre_of_mass_energy )
histogram_properties.x_axis_title = variables_latex[variable]
histogram_properties.y_axis_title = 'Events'
if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']:
histogram_properties.y_axis_title = 'Events / {binWidth} GeV'.format( binWidth=binWidth )
histogram_properties.x_axis_title = '{variable} (GeV)'.format( variable = variables_latex[variable] )
histogram_properties.x_limits = [ reco_bin_edges[0], reco_bin_edges[-1] ]
histogram_properties.y_limits = y_limits
histogram_properties.y_max_scale = 1.3
histogram_properties.xerr = None
# workaround for rootpy issue #638
histogram_properties.emptybins = True
histogram_properties.additional_text = channel_latex[channel.lower()]
histogram_properties.legend_location = ( 0.9, 0.73 )
histogram_properties.cms_logo_location = 'left'
histogram_properties.preliminary = True
# histogram_properties.preliminary = False
histogram_properties.set_log_y = False
histogram_properties.legend_color = False
histogram_properties.ratio_y_limits = [0.5, 1.5]
# Draw histogram with ratio plot
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 = 'plots/control_plots_with_systematic/',
show_ratio = True,
normalise = False,
systematics_for_ratio = uncertaintyBand,
systematics_for_plot = uncertaintyBand,
)
histogram_properties.set_log_y = True
histogram_properties.y_limits = [0.1, y_limits[-1]*100 ]
histogram_properties.legend_location = ( 0.9, 0.9 )
histogram_properties.name += '_logY'
make_data_mc_comparison_plot(
histograms_to_draw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder = 'plots/control_plots_with_systematic/logY/',
show_ratio = True,
normalise = False,
systematics_for_ratio = uncertaintyBand,
systematics_for_plot = uncertaintyBand,
)
histogram_colors = [
'black', 'yellow', 'green', 'magenta', 'red'
]
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_nonIso_%s_%s' % (channel, var)
if control_region == 'QCDConversions':
histogram_properties.name = 'QCD_Conversions_%s_%s' % (
channel, var)
if category != 'central':
histogram_properties.name += '_' + category
if channel == 'EPlusJets':
histogram_properties.title = e_title
elif channel == 'MuPlusJets':
histogram_properties.title = mu_title
eventsPerBin = (xMax - xMin) / nBins
histogram_properties.x_axis_title = '%s [GeV]' % (
control_plots_latex[var])
histogram_properties.y_axis_title = 'Events/(%.2g GeV)' % (
eventsPerBin)
histogram_properties.set_log_y = True
histogram_properties.name += '_with_ratio'
make_data_mc_comparison_plot(histograms_to_draw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder=output_folder,
show_ratio=False)
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,
)
print bins,
print xMin, xMax, nBins
histograms = get_histograms_from_trees( trees = [controlTree], branch = var, weightBranch = 'EventWeight', files = histogram_files, nBins = nBins, xMin = xMin, xMax = xMax )
prepare_histograms( histograms, rebin = 1, scale_factor = measurement_config.luminosity_scale )
histograms_to_draw = [histograms['data'][controlTree], histograms['QCD'][controlTree],
histograms['V+Jets'][controlTree],
histograms['SingleTop'][controlTree], histograms['TTJet'][controlTree]]
histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']]
histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red']
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_nonIso_%s_%s' % (channel, var)
if control_region == 'QCDConversions' :
histogram_properties.name = 'QCD_Conversions_%s_%s' % (channel, var)
if category != 'central':
histogram_properties.name += '_' + category
if channel == 'EPlusJets':
histogram_properties.title = e_title
elif channel == 'MuPlusJets':
histogram_properties.title = mu_title
eventsPerBin = (xMax - xMin) / nBins
histogram_properties.x_axis_title = '%s [GeV]' % ( control_plots_latex[var] )
histogram_properties.y_axis_title = 'Events/(%.2g GeV)' % (eventsPerBin)
histogram_properties.set_log_y = True
histogram_properties.name += '_with_ratio'
make_data_mc_comparison_plot( histograms_to_draw, histogram_lables, histogram_colors,
histogram_properties, save_folder = output_folder, show_ratio = False )
