def compare_vjets_templates( variable = 'MET', met_type = 'patType1CorrectedPFMet',
title = 'Untitled', channel = 'electron' ):
''' Compares the V+jets templates in different bins
of the current variable'''
global fit_variable_properties, b_tag_bin, save_as
variable_bins = variable_bins_ROOT[variable]
histogram_template = get_histogram_template( variable )
for fit_variable in electron_fit_variables:
all_hists = {}
inclusive_hist = None
save_path = 'plots/%dTeV/fit_variables/%s/%s/' % ( measurement_config.centre_of_mass_energy, variable, fit_variable )
make_folder_if_not_exists( save_path + '/vjets/' )
max_bins = len( variable_bins )
for bin_range in variable_bins[0:max_bins]:
params = {'met_type': met_type, 'bin_range':bin_range, 'fit_variable':fit_variable, 'b_tag_bin':b_tag_bin, 'variable':variable}
fit_variable_distribution = histogram_template % params
# format: histograms['data'][qcd_fit_variable_distribution]
histograms = get_histograms_from_files( [fit_variable_distribution], histogram_files )
prepare_histograms( histograms, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )
all_hists[bin_range] = histograms['V+Jets'][fit_variable_distribution]
# create the inclusive distributions
inclusive_hist = deepcopy( all_hists[variable_bins[0]] )
for bin_range in variable_bins[1:max_bins]:
inclusive_hist += all_hists[bin_range]
for bin_range in variable_bins[0:max_bins]:
if not all_hists[bin_range].Integral() == 0:
all_hists[bin_range].Scale( 1 / all_hists[bin_range].Integral() )
# normalise all histograms
inclusive_hist.Scale( 1 / inclusive_hist.Integral() )
# now compare inclusive to all bins
histogram_properties = Histogram_properties()
histogram_properties.x_axis_title = fit_variable_properties[fit_variable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[fit_variable]['y-title']
histogram_properties.y_axis_title = histogram_properties.y_axis_title.replace( 'Events', 'a.u.' )
histogram_properties.x_limits = [fit_variable_properties[fit_variable]['min'], fit_variable_properties[fit_variable]['max']]
histogram_properties.title = title
histogram_properties.additional_text = channel_latex[channel] + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.name = variable + '_' + fit_variable + '_' + b_tag_bin + '_VJets_template_comparison'
histogram_properties.y_max_scale = 1.5
measurements = {bin_range + ' GeV': histogram for bin_range, histogram in all_hists.iteritems()}
measurements = OrderedDict( sorted( measurements.items() ) )
fit_var = fit_variable.replace( 'electron_', '' )
fit_var = fit_var.replace( 'muon_', '' )
graphs = spread_x( measurements.values(), fit_variable_bin_edges[fit_var] )
for key, graph in zip( sorted( measurements.keys() ), graphs ):
measurements[key] = graph
compare_measurements( models = {'inclusive' : inclusive_hist},
measurements = measurements,
show_measurement_errors = True,
histogram_properties = histogram_properties,
save_folder = save_path + '/vjets/',
save_as = save_as )
def compare( 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_fit_results(fit_results, initial_values, channel):
global variable, output_folder
title = electron_histogram_title if channel == 'electron' else muon_histogram_title
histogram_properties = Histogram_properties()
histogram_properties.title = title
histogram_properties.x_axis_title = variable + ' [GeV]'
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
# we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets
for sample in ['TTJet', 'SingleTop', 'QCD', 'V+Jets']:
histograms = {}
# absolute eta measurement as baseline
h_absolute_eta = None
h_before = None
histogram_properties.y_axis_title = 'Fitted number of events for ' + samples_latex[
sample]
for fit_var_input in fit_results.keys():
latex_string = create_latex_string(fit_var_input)
fit_data = fit_results[fit_var_input][sample]
h = value_error_tuplelist_to_hist(fit_data, bin_edges[variable])
if fit_var_input == 'absolute_eta':
h_absolute_eta = h
elif fit_var_input == 'before':
h_before = h
else:
histograms[latex_string] = h
graphs = spread_x(histograms.values(), bin_edges[variable])
for key, graph in zip(histograms.keys(), graphs):
histograms[key] = graph
filename = sample.replace('+', '_') + '_fit_var_comparison_' + channel
histogram_properties.name = filename
histogram_properties.y_limits = 0, limit_range_y(
h_absolute_eta)[1] * 1.3
histogram_properties.x_limits = bin_edges[variable][0], bin_edges[
variable][-1]
h_initial_values = value_error_tuplelist_to_hist(
initial_values[sample], bin_edges[variable])
h_initial_values.Scale(closure_tests['simple'][sample])
compare_measurements(models={
fit_variables_latex['absolute_eta']: h_absolute_eta,
'initial values': h_initial_values,
'before': h_before
},
measurements=histograms,
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=output_folder,
save_as=['png', 'pdf'])
def plot_fit_results(fit_results, initial_values, channel):
global variable, output_folder
title = electron_histogram_title if channel == "electron" else muon_histogram_title
histogram_properties = Histogram_properties()
histogram_properties.title = title
histogram_properties.x_axis_title = variable + " [GeV]"
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = "upper right"
# we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets
for sample in ["TTJet", "SingleTop", "QCD", "V+Jets"]:
histograms = {}
# absolute eta measurement as baseline
h_absolute_eta = None
h_before = None
histogram_properties.y_axis_title = "Fitted number of events for " + samples_latex[sample]
for fit_var_input in fit_results.keys():
latex_string = create_latex_string(fit_var_input)
fit_data = fit_results[fit_var_input][sample]
h = value_error_tuplelist_to_hist(fit_data, bin_edges[variable])
if fit_var_input == "absolute_eta":
h_absolute_eta = h
elif fit_var_input == "before":
h_before = h
else:
histograms[latex_string] = h
graphs = spread_x(histograms.values(), bin_edges[variable])
for key, graph in zip(histograms.keys(), graphs):
histograms[key] = graph
filename = sample.replace("+", "_") + "_fit_var_comparison_" + channel
histogram_properties.name = filename
histogram_properties.y_limits = 0, limit_range_y(h_absolute_eta)[1] * 1.3
histogram_properties.x_limits = bin_edges[variable][0], bin_edges[variable][-1]
h_initial_values = value_error_tuplelist_to_hist(initial_values[sample], bin_edges[variable])
h_initial_values.Scale(closure_tests["simple"][sample])
compare_measurements(
models={
fit_variables_latex["absolute_eta"]: h_absolute_eta,
"initial values": h_initial_values,
"before": h_before,
},
measurements=histograms,
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=output_folder,
save_as=["png", "pdf"],
)
def plot_fit_results( fit_results, initial_values, channel ):
global variable, output_folder
title = electron_histogram_title if channel == 'electron' else muon_histogram_title
histogram_properties = Histogram_properties()
histogram_properties.title = title
histogram_properties.x_axis_title = variable + ' [GeV]'
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
# we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets
for sample in ['TTJet', 'SingleTop', 'QCD', 'V+Jets']:
histograms = {}
# absolute eta measurement as baseline
h_absolute_eta = None
h_before = None
histogram_properties.y_axis_title = 'Fitted number of events for ' + samples_latex[sample]
for fit_var_input in fit_results.keys():
latex_string = create_latex_string( fit_var_input )
fit_data = fit_results[fit_var_input][sample]
h = value_error_tuplelist_to_hist( fit_data,
bin_edges[variable] )
if fit_var_input == 'absolute_eta':
h_absolute_eta = h
elif fit_var_input == 'before':
h_before = h
else:
histograms[latex_string] = h
graphs = spread_x( histograms.values(), bin_edges[variable] )
for key, graph in zip( histograms.keys(), graphs ):
histograms[key] = graph
filename = sample.replace( '+', '_' ) + '_fit_var_comparison_' + channel
histogram_properties.name = filename
histogram_properties.y_limits = 0, limit_range_y( h_absolute_eta )[1] * 1.3
histogram_properties.x_limits = bin_edges[variable][0], bin_edges[variable][-1]
h_initial_values = value_error_tuplelist_to_hist( initial_values[sample],
bin_edges[variable] )
h_initial_values.Scale(closure_tests['simple'][sample])
compare_measurements( models = {fit_variables_latex['absolute_eta']:h_absolute_eta,
'initial values' : h_initial_values,
'before': h_before},
measurements = histograms,
show_measurement_errors = True,
histogram_properties = histogram_properties,
save_folder = output_folder,
save_as = ['png', 'pdf'] )
def compare_vjets_templates(variable='MET',
met_type='patType1CorrectedPFMet',
title='Untitled',
channel='electron'):
''' Compares the V+jets templates in different bins
of the current variable'''
global fit_variable_properties, b_tag_bin, save_as
variable_bins = variable_bins_ROOT[variable]
histogram_template = get_histogram_template(variable)
for fit_variable in electron_fit_variables:
all_hists = {}
inclusive_hist = None
save_path = 'plots/%dTeV/fit_variables/%s/%s/' % (
measurement_config.centre_of_mass_energy, variable, fit_variable)
make_folder_if_not_exists(save_path + '/vjets/')
max_bins = len(variable_bins)
for bin_range in variable_bins[0:max_bins]:
params = {
'met_type': met_type,
'bin_range': bin_range,
'fit_variable': fit_variable,
'b_tag_bin': b_tag_bin,
'variable': variable
}
fit_variable_distribution = histogram_template % params
# format: histograms['data'][qcd_fit_variable_distribution]
histograms = get_histograms_from_files([fit_variable_distribution],
histogram_files)
prepare_histograms(
histograms,
rebin=fit_variable_properties[fit_variable]['rebin'],
scale_factor=measurement_config.luminosity_scale)
all_hists[bin_range] = histograms['V+Jets'][
fit_variable_distribution]
# create the inclusive distributions
inclusive_hist = deepcopy(all_hists[variable_bins[0]])
for bin_range in variable_bins[1:max_bins]:
inclusive_hist += all_hists[bin_range]
for bin_range in variable_bins[0:max_bins]:
if not all_hists[bin_range].Integral() == 0:
all_hists[bin_range].Scale(1 / all_hists[bin_range].Integral())
# normalise all histograms
inclusive_hist.Scale(1 / inclusive_hist.Integral())
# now compare inclusive to all bins
histogram_properties = Histogram_properties()
histogram_properties.x_axis_title = fit_variable_properties[
fit_variable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[
fit_variable]['y-title']
histogram_properties.y_axis_title = histogram_properties.y_axis_title.replace(
'Events', 'a.u.')
histogram_properties.x_limits = [
fit_variable_properties[fit_variable]['min'],
fit_variable_properties[fit_variable]['max']
]
histogram_properties.title = title
histogram_properties.additional_text = channel_latex[
channel] + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.name = variable + '_' + fit_variable + '_' + b_tag_bin + '_VJets_template_comparison'
histogram_properties.y_max_scale = 1.5
measurements = {
bin_range + ' GeV': histogram
for bin_range, histogram in all_hists.iteritems()
}
measurements = OrderedDict(sorted(measurements.items()))
fit_var = fit_variable.replace('electron_', '')
fit_var = fit_var.replace('muon_', '')
graphs = spread_x(measurements.values(),
fit_variable_bin_edges[fit_var])
for key, graph in zip(sorted(measurements.keys()), graphs):
measurements[key] = graph
compare_measurements(models={'inclusive': inclusive_hist},
measurements=measurements,
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=save_path + '/vjets/',
save_as=save_as)
def make_shape_comparison_plot( shapes = [],
names = [],
colours = [],
histogram_properties = Histogram_properties(),
fill_area = True,
make_ratio = False,
alpha = 0.5,
save_folder = 'plots/',
save_as = ['pdf', 'png'],
normalise_ratio_to_errors = False ):
save_folder = check_save_folder(save_folder)
# make copies in order not to mess with existing histograms
shapes_ = deepcopy(shapes)
# normalise as we are comparing shapes
for shape, colour, label in zip(shapes_, colours, names):
shape.SetTitle(label)
integral = shape.Integral()
if integral > 0:
shape.Sumw2()
shape.Scale( 1 / integral )
if fill_area:
shape.fillcolor = colour
shape.fillstyle = 'solid'
shape.legendstyle = 'F'
else:
shape.linecolor = colour
shape.legendstyle = 'F'
shape.linewidth = 5
if not histogram_properties.y_limits:
histogram_properties.y_limits = [0, get_best_max_y(shapes_, False)]
# plot with matplotlib
plt.figure( figsize = CMS.figsize, dpi = CMS.dpi, facecolor = CMS.facecolor )
gs = gridspec.GridSpec( 2, 1, height_ratios = [5, 1] )
axes = None
if make_ratio:
axes = plt.subplot( gs[0] )
else:
axes = plt.axes()
axes.minorticks_on()
for shape in shapes_:
rplt.hist( shape, axes = axes, alpha = alpha )
set_labels( plt, histogram_properties, show_x_label = not make_ratio, axes = axes )
handles, labels = axes.get_legend_handles_labels()
for i,name in enumerate(names):
labels.insert(i, name)
# always fill legends
if not fill_area:
for handle in handles:
handle.set_fill(True)
handle.set_facecolor(handle.get_edgecolor())
l1 = axes.legend( handles, labels, numpoints = 1,
frameon = histogram_properties.legend_color,
bbox_to_anchor = histogram_properties.legend_location,
bbox_transform=plt.gcf().transFigure,
prop = CMS.legend_properties,
ncol = histogram_properties.legend_columns )
l1.set_zorder(102)
#add error bars
graphs = spread_x(shapes_, list(shapes_[0].xedges()))
for graph in graphs:
rplt.errorbar( graph, axes = axes )
adjust_axis_limits(axes, histogram_properties, shapes_)
if make_ratio:
plt.setp( axes.get_xticklabels(), visible = False )
ratios = get_histogram_ratios(shapes_[0], shapes_[1:], normalise_ratio_to_errors)
ax1 = plt.subplot( gs[1] )
ax1.minorticks_on()
ax1.grid( True, 'major', linewidth = 1 )
set_labels( plt, histogram_properties, show_x_label = True, show_title = False )
if normalise_ratio_to_errors:
plt.ylabel( r'$\frac{1-2}{\sqrt{(\sigma_1)^2 + (\sigma_2)^2}}$', CMS.y_axis_title )
else:
plt.ylabel( '(1)/(2)', CMS.y_axis_title )
for ratio in ratios:
ratio.SetMarkerSize( 2 )
rplt.errorbar( ratio, xerr = True,
emptybins = histogram_properties.emptybins,
axes = ax1 )
if len( histogram_properties.x_limits ) == 2:
ax1.set_xlim( xmin = histogram_properties.x_limits[0],
xmax = histogram_properties.x_limits[1] )
if len( histogram_properties.ratio_y_limits ) == 2:
ax1.set_ylim( ymin = histogram_properties.ratio_y_limits[0],
ymax = histogram_properties.ratio_y_limits[1] )
# dynamic tick placement
adjust_ratio_ticks(ax1.yaxis, n_ticks = 3)
if CMS.tight_layout:
plt.tight_layout()
for save in save_as:
plt.savefig( save_folder + histogram_properties.name + '.' + save )
plt.close()
def debug_last_bin():
'''
For debugging why the last bin in the problematic variables deviates a
lot in _one_ of the channels only.
'''
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'normalised_xsection_{channel}_RooUnfoldSvd{suffix}.txt'
problematic_variables = ['HT', 'MET', 'NJets', 'lepton_pt']
for variable in problematic_variables:
results = {}
Result = namedtuple('Result',
['before_unfolding', 'after_unfolding', 'model'])
for channel in ['electron', 'muon', 'combined']:
input_file_data = file_template.format(
variable=variable,
channel=channel,
suffix='_with_errors',
)
input_file_model = file_template.format(
variable=variable,
channel=channel,
suffix='',
)
data = read_data_from_JSON(input_file_data)
data_model = read_data_from_JSON(input_file_model)
before_unfolding = data['TTJet_measured_withoutFakes']
after_unfolding = data['TTJet_unfolded']
model = data_model['powhegPythia8']
# only use the last bin
h_before_unfolding = value_errors_tuplelist_to_graph(
[before_unfolding[-1]], bin_edges_vis[variable][-2:])
h_after_unfolding = value_errors_tuplelist_to_graph(
[after_unfolding[-1]], bin_edges_vis[variable][-2:])
h_model = value_error_tuplelist_to_hist(
[model[-1]], bin_edges_vis[variable][-2:])
r = Result(before_unfolding, after_unfolding, model)
h = Result(h_before_unfolding, h_after_unfolding, h_model)
results[channel] = (r, h)
models = {'POWHEG+PYTHIA': results['combined'][1].model}
h_unfolded = [
results[channel][1].after_unfolding
for channel in ['electron', 'muon', 'combined']
]
tmp_hists = spread_x(h_unfolded, bin_edges_vis[variable][-2:])
measurements = {}
for channel, hist in zip(['electron', 'muon', 'combined'], tmp_hists):
value = results[channel][0].after_unfolding[-1][0]
error = results[channel][0].after_unfolding[-1][1]
label = '{c_label} ({value:1.2g} $\pm$ {error:1.2g})'.format(
c_label=channel,
value=value,
error=error,
)
measurements[label] = hist
properties = Histogram_properties()
properties.name = 'normalised_xsection_compare_channels_{0}_{1}_last_bin'.format(
variable, channel)
properties.title = 'Comparison of channels'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = False
properties.x_limits = (bin_edges_vis[variable][-2],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
properties.legend_location = (0.95, 0.40)
if variable == 'NJets':
properties.legend_location = (0.97, 0.80)
properties.formats = ['png']
compare_measurements(models=models,
measurements=measurements,
show_measurement_errors=True,
histogram_properties=properties,
save_folder='plots/',
save_as=properties.formats)
def debug_last_bin():
'''
For debugging why the last bin in the problematic variables deviates a
lot in _one_ of the channels only.
'''
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'normalised_xsection_{channel}_RooUnfoldSvd{suffix}.txt'
problematic_variables = ['HT', 'MET', 'NJets', 'lepton_pt']
for variable in problematic_variables:
results = {}
Result = namedtuple(
'Result', ['before_unfolding', 'after_unfolding', 'model'])
for channel in ['electron', 'muon', 'combined']:
input_file_data = file_template.format(
variable=variable,
channel=channel,
suffix='_with_errors',
)
input_file_model = file_template.format(
variable=variable,
channel=channel,
suffix='',
)
data = read_data_from_JSON(input_file_data)
data_model = read_data_from_JSON(input_file_model)
before_unfolding = data['TTJet_measured_withoutFakes']
after_unfolding = data['TTJet_unfolded']
model = data_model['powhegPythia8']
# only use the last bin
h_before_unfolding = value_errors_tuplelist_to_graph(
[before_unfolding[-1]], bin_edges_vis[variable][-2:])
h_after_unfolding = value_errors_tuplelist_to_graph(
[after_unfolding[-1]], bin_edges_vis[variable][-2:])
h_model = value_error_tuplelist_to_hist(
[model[-1]], bin_edges_vis[variable][-2:])
r = Result(before_unfolding, after_unfolding, model)
h = Result(h_before_unfolding, h_after_unfolding, h_model)
results[channel] = (r, h)
models = {'POWHEG+PYTHIA': results['combined'][1].model}
h_unfolded = [results[channel][1].after_unfolding for channel in [
'electron', 'muon', 'combined']]
tmp_hists = spread_x(h_unfolded, bin_edges_vis[variable][-2:])
measurements = {}
for channel, hist in zip(['electron', 'muon', 'combined'], tmp_hists):
value = results[channel][0].after_unfolding[-1][0]
error = results[channel][0].after_unfolding[-1][1]
label = '{c_label} ({value:1.2g} $\pm$ {error:1.2g})'.format(
c_label=channel,
value=value,
error=error,
)
measurements[label] = hist
properties = Histogram_properties()
properties.name = 'normalised_xsection_compare_channels_{0}_{1}_last_bin'.format(
variable, channel)
properties.title = 'Comparison of channels'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = False
properties.x_limits = (
bin_edges_vis[variable][-2], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
properties.legend_location = (0.95, 0.40)
if variable == 'NJets':
properties.legend_location = (0.97, 0.80)
properties.formats = ['png']
compare_measurements(models=models, measurements=measurements, show_measurement_errors=True,
histogram_properties=properties, save_folder='plots/', save_as=properties.formats)
def make_shape_comparison_plot(shapes=[],
names=[],
colours=[],
histogram_properties=Histogram_properties(),
fill_area=True,
make_ratio=False,
alpha=0.5,
save_folder='plots/',
save_as=['pdf', 'png'],
normalise_ratio_to_errors=False):
save_folder = check_save_folder(save_folder)
# make copies in order not to mess with existing histograms
shapes_ = deepcopy(shapes)
# normalise as we are comparing shapes
for shape, colour, label in zip(shapes_, colours, names):
shape.SetTitle(label)
integral = shape.Integral()
if integral > 0:
shape.Sumw2()
shape.Scale(1 / integral)
if fill_area:
shape.fillcolor = colour
shape.fillstyle = 'solid'
shape.legendstyle = 'F'
else:
shape.linecolor = colour
shape.legendstyle = 'F'
shape.linewidth = 5
if not histogram_properties.y_limits:
histogram_properties.y_limits = [0, get_best_max_y(shapes_, False)]
# plot with matplotlib
plt.figure(figsize=CMS.figsize, dpi=CMS.dpi, facecolor=CMS.facecolor)
gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1])
axes = None
if make_ratio:
axes = plt.subplot(gs[0])
else:
axes = plt.axes()
axes.minorticks_on()
for shape in shapes_:
rplt.hist(shape, axes=axes, alpha=alpha)
set_labels(plt,
histogram_properties,
show_x_label=not make_ratio,
axes=axes)
handles, labels = axes.get_legend_handles_labels()
for i, name in enumerate(names):
labels.insert(i, name)
# always fill legends
if not fill_area:
for handle in handles:
handle.set_fill(True)
handle.set_facecolor(handle.get_edgecolor())
l1 = axes.legend(handles,
labels,
numpoints=1,
frameon=histogram_properties.legend_color,
bbox_to_anchor=histogram_properties.legend_location,
bbox_transform=plt.gcf().transFigure,
prop=CMS.legend_properties,
ncol=histogram_properties.legend_columns)
l1.set_zorder(102)
#add error bars
graphs = spread_x(shapes_, list(shapes_[0].xedges()))
for graph in graphs:
rplt.errorbar(graph, axes=axes)
adjust_axis_limits(axes, histogram_properties, shapes_)
if make_ratio:
plt.setp(axes.get_xticklabels(), visible=False)
ratios = get_histogram_ratios(shapes_[0], shapes_[1:],
normalise_ratio_to_errors)
ax1 = plt.subplot(gs[1])
ax1.minorticks_on()
ax1.grid(True, 'major', linewidth=1)
set_labels(plt,
histogram_properties,
show_x_label=True,
show_title=False)
if normalise_ratio_to_errors:
plt.ylabel(r'$\frac{1-2}{\sqrt{(\sigma_1)^2 + (\sigma_2)^2}}$',
CMS.y_axis_title)
else:
plt.ylabel('(1)/(2)', CMS.y_axis_title)
for ratio in ratios:
ratio.SetMarkerSize(2)
rplt.errorbar(ratio,
xerr=True,
emptybins=histogram_properties.emptybins,
axes=ax1)
if len(histogram_properties.x_limits) == 2:
ax1.set_xlim(xmin=histogram_properties.x_limits[0],
xmax=histogram_properties.x_limits[1])
if len(histogram_properties.ratio_y_limits) == 2:
ax1.set_ylim(ymin=histogram_properties.ratio_y_limits[0],
ymax=histogram_properties.ratio_y_limits[1])
# dynamic tick placement
adjust_ratio_ticks(ax1.yaxis, n_ticks=3)
if CMS.tight_layout:
plt.tight_layout()
for save in save_as:
plt.savefig(save_folder + histogram_properties.name + '.' + save)
plt.close()
