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 plot_fit_results(histograms, category, channel):
global variable, b_tag_bin, output_folder
from tools.plotting import Histogram_properties, make_data_mc_comparison_plot
for variable_bin in variable_bins_ROOT[variable]:
path = output_folder + str(measurement_config.centre_of_mass) + 'TeV/' + variable + '/' + category + '/fit_results/'
make_folder_if_not_exists(path)
plotname = channel + '_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[variable_bin]['data']
h_signal = histograms[variable_bin]['signal']
h_background = histograms[variable_bin]['background']
histogram_properties = Histogram_properties()
histogram_properties.name = plotname
histogram_properties.x_axis_title = channel + ' $\left|\eta\\right|$'
histogram_properties.y_axis_title = 'events/0.2'
histogram_properties.title = get_cms_labels(channel)
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 plot( self, plot_options = {} ):
'''
Plots the stored histograms based on the plot options
'''
file_name = self.name
if plot_options.has_key('output_file'):
file_name = plot_options['output_file']
output_format = plot_options['output_format']
output_folder = plot_options['output_folder']
plot_type = plot_options['plot_type']
histogram_properties = Histogram_properties(plot_options)
histogram_properties.name = file_name
if plot_options.has_key('rebin') and plot_options['rebin'] > 1:
for hist in self.histograms:
hist.Rebin(plot_options['rebin'])
colours = ['green', 'yellow', 'magenta', 'red', 'black']
if plot_options.has_key('colours'):
colours = plot_options['colours']
if plot_type == 'shape_comparison':
make_shape_comparison_plot( shapes = self.histograms,
names = self.labels,
colours = colours,
histogram_properties = histogram_properties,
fill_area = True,
make_ratio = True,
alpha = 0.5,
save_folder = output_folder,
save_as = output_format )
elif plot_type == 'data_mc_comparison':
make_data_mc_comparison_plot( histograms = self.histograms,
histogram_lables = self.labels,
histogram_colors = colours,
histogram_properties = histogram_properties,
data_index = 0,
save_folder = output_folder,
save_as = output_format,
normalise = False,
show_ratio = True,
show_stat_errors_on_mc = True,
draw_vertical_line = 0 )
else:
print 'Plot type "%s" not known, exiting.' % plot_type
sys.exit()
def plot_fit_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 )
histogram_lables = [
'data', 'QCD', 'V+Jets', 'Single-Top', 'TTJet'
]
histogram_colors = [
'black', 'yellow', 'green', 'magenta', 'red'
]
histogram_properties = Histogram_properties()
histogram_properties.name = 'Closure_' + 'simple' + '_' + fitVariable + '_' + variable + '_' + str(
whichBin)
histogram_properties.x_axis_title = fit_variable_properties[
fitVariable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[
fitVariable]['y-title']
make_data_mc_comparison_plot(
histogramsToDraw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder='data/closure_test/' + test +
'/absolute_eta_M3_angle_bl/8TeV/',
show_ratio=False,
save_as=['pdf'],
)
pass
pass
pass
pass
def plot(self, plot_options={}):
'''
Plots the stored histograms based on the plot options
'''
# defaults
file_name = self.name
alpha = 0.5
fill_area = True
if plot_options.has_key('output_file'):
file_name = plot_options['output_file']
output_format = plot_options['output_format']
output_folder = plot_options['output_folder']
plot_type = plot_options['plot_type']
if plot_options.has_key('fill_area'):
fill_area = plot_options['fill_area']
if plot_options.has_key('alpha'):
alpha = plot_options['alpha']
histogram_properties = Histogram_properties(plot_options)
histogram_properties.name = file_name
if plot_options.has_key('rebin') and plot_options['rebin'] > 1:
rebin = plot_options['rebin']
is_list = isinstance(rebin, types.ListType)
for i, hist in enumerate(self.histograms):
if is_list:
self.histograms[i] = conditional_rebin(hist, rebin)
else:
hist.rebin(rebin)
colours = ['green', 'yellow', 'magenta', 'red', 'black']
if plot_options.has_key('colours'):
colours = plot_options['colours']
if plot_type == 'shape_comparison':
make_shape_comparison_plot(
shapes=self.histograms,
names=self.labels,
colours=colours,
histogram_properties=histogram_properties,
fill_area=fill_area,
make_ratio=True,
alpha=alpha,
save_folder=output_folder,
save_as=output_format)
elif plot_type == 'data_mc_comparison':
make_data_mc_comparison_plot(
histograms=self.histograms,
histogram_lables=self.labels,
histogram_colors=colours,
histogram_properties=histogram_properties,
data_index=0,
save_folder=output_folder,
save_as=output_format,
normalise=False,
show_ratio=True,
show_stat_errors_on_mc=True,
draw_vertical_line=0)
else:
print 'Plot type "%s" not known, exiting.' % plot_type
sys.exit()
def 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,
)
eventsPerBin = (xMax - xMin) / nBins
if fit_variables_units_latex[var] != '':
histogram_properties.x_axis_title = '%s [%s]' % (
fit_variables_latex[var],
fit_variables_units_latex[var])
histogram_properties.y_axis_title = 'Events/(%.2g %s)' % (
eventsPerBin, fit_variables_units_latex[var])
else:
histogram_properties.x_axis_title = '%s' % (
fit_variables_latex[var])
histogram_properties.y_axis_title = 'Events/(%.2g)' % eventsPerBin
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)
# Variables for diff xsec
for var in ['MET', 'HT', 'ST', 'WPT', 'MT']:
print var
controlTree = 'TTbar_plus_X_analysis/%s/%s/FitVariables' % (
channel, control_region)
bins = bin_edges[var]
xMin = bins[0]
xMax = bins[-1]
nBins = 40
histograms = get_histograms_from_trees(
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_to_draw = [histograms['data'][control_region], qcd_predicted_mc,
histograms['V+Jets'][control_region],
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,
)
h2.legendstyle = 'F'
stack = HistStack()
stack.Add(h1)
stack.Add(h2)
# 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')
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)
h2.linewidth = 0 h2.legendstyle = 'F' stack = HistStack() stack.Add(h1) stack.Add(h2) # 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)
elif template == 'data':
histograms[template].Scale( initial_values_[template][whichBin][0])
pass
histogramsToDraw = [ histograms['data'],
histograms['QCD'],
histograms['V+Jets'],
histograms['SingleTop'],
histograms['TTJet']
]
histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', 'TTJet']
histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red']
histogram_properties = Histogram_properties()
histogram_properties.name = 'Closure_'+'simple'+'_'+fitVariable+'_'+variable+'_'+str(whichBin)
histogram_properties.x_axis_title = fit_variable_properties[fitVariable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[fitVariable]['y-title']
make_data_mc_comparison_plot( histogramsToDraw, histogram_lables, histogram_colors,
histogram_properties,
save_folder = 'data/closure_test/'+test+'/absolute_eta_M3_angle_bl/8TeV/',
show_ratio = False,
save_as = ['pdf'],
)
pass
pass
pass
pass
'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 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,
)
