def plot_bias_in_all_bins(biases, mean_bias, centre_of_mass, channel, variable,
tau_value, output_folder, output_formats, bin_edges):
h_bias = Hist(bin_edges, type='D')
n_bins = h_bias.nbins()
assert len(biases) == n_bins
for i, bias in enumerate(biases):
h_bias.SetBinContent(i+1, bias)
histogram_properties = Histogram_properties()
name_mpt = 'bias_{0}_{1}_{2}TeV'
histogram_properties.name = name_mpt.format(
variable,
channel,
centre_of_mass
)
histogram_properties.y_axis_title = 'Bias'
histogram_properties.x_axis_title = latex_labels.variables_latex[variable]
title = 'pull distribution mean \& sigma for {0}'.format(tau_value)
histogram_properties.title = title
histogram_properties.y_limits = [0, 10]
histogram_properties.xerr = True
compare_measurements(
models={
'Mean bias':make_line_hist(bin_edges, mean_bias)
},
measurements={
'Bias': h_bias
},
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=output_folder,
save_as=output_formats)
def plot_bias_in_all_bins(biases, mean_bias, centre_of_mass, channel, variable,
tau_value, output_folder, output_formats, bin_edges):
h_bias = Hist(bin_edges, type='D')
n_bins = h_bias.nbins()
assert len(biases) == n_bins
for i, bias in enumerate(biases):
h_bias.SetBinContent(i + 1, bias)
histogram_properties = Histogram_properties()
name_mpt = 'bias_{0}_{1}_{2}TeV'
histogram_properties.name = name_mpt.format(variable, channel,
centre_of_mass)
histogram_properties.y_axis_title = 'Bias'
histogram_properties.x_axis_title = latex_labels.variables_latex[variable]
title = 'pull distribution mean \& sigma for {0}'.format(tau_value)
histogram_properties.title = title
histogram_properties.y_limits = [0, 10]
histogram_properties.xerr = True
compare_measurements(
models={'Mean bias': make_line_hist(bin_edges, mean_bias)},
measurements={'Bias': h_bias},
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=output_folder,
save_as=output_formats)
def plot_fit_results(fit_results, centre_of_mass, channel, variable, k_value,
tau_value, output_folder, output_formats, bin_edges):
h_mean = Hist(bin_edges, type='D')
h_sigma = Hist(bin_edges, type='D')
n_bins = h_mean.nbins()
assert len(fit_results) == n_bins
mean_abs_pull = 0
for i, fr in enumerate(fit_results):
mean_abs_pull += abs(fr.mean)
h_mean.SetBinContent(i + 1, fr.mean)
h_mean.SetBinError(i + 1, fr.meanError)
h_sigma.SetBinContent(i + 1, fr.sigma)
h_sigma.SetBinError(i + 1, fr.sigmaError)
mean_abs_pull /= n_bins
histogram_properties = Histogram_properties()
name_mpt = 'pull_distribution_mean_and_sigma_{0}_{1}_{2}TeV'
histogram_properties.name = name_mpt.format(
variable,
channel,
centre_of_mass
)
histogram_properties.y_axis_title = r'$\mu_{\text{pull}}$ ($\sigma_{\text{pull}}$)'
histogram_properties.x_axis_title = latex_labels.variables_latex[variable]
histogram_properties.legend_location = (0.98, 0.48)
value = get_value_title(k_value, tau_value)
title = 'pull distribution mean \& sigma for {0}'.format(tau_value)
histogram_properties.title = title
histogram_properties.y_limits = [-2, 2]
histogram_properties.xerr = True
compare_measurements(
models={
# 'mean $|\mu|$':make_line_hist(bin_edges,mean_abs_pull),
'ideal $\mu$': make_line_hist(bin_edges, 0),
'ideal $\sigma$': make_line_hist(bin_edges, 1),
},
measurements={
r'$\mu_{\text{pull}}$': h_mean,
r'$\sigma_{\text{pull}}$': h_sigma
},
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=output_folder,
save_as=output_formats)
def plot_fit_results(fit_results, centre_of_mass, channel, variable, k_value,
tau_value, output_folder, output_formats, bin_edges):
h_mean = Hist(bin_edges, type='D')
h_sigma = Hist(bin_edges, type='D')
n_bins = h_mean.nbins()
assert len(fit_results) == n_bins
mean_abs_pull = 0
for i, fr in enumerate(fit_results):
mean_abs_pull += abs(fr.mean)
h_mean.SetBinContent(i + 1, fr.mean)
h_mean.SetBinError(i + 1, fr.meanError)
h_sigma.SetBinContent(i + 1, fr.sigma)
h_sigma.SetBinError(i + 1, fr.sigmaError)
mean_abs_pull /= n_bins
histogram_properties = Histogram_properties()
name_mpt = 'pull_distribution_mean_and_sigma_{0}_{1}_{2}TeV'
histogram_properties.name = name_mpt.format(variable, channel,
centre_of_mass)
histogram_properties.y_axis_title = r'$\mu_{\text{pull}}$ ($\sigma_{\text{pull}}$)'
histogram_properties.x_axis_title = latex_labels.variables_latex[variable]
histogram_properties.legend_location = (0.98, 0.48)
value = get_value_title(k_value, tau_value)
title = 'pull distribution mean \& sigma for {0}'.format(tau_value)
histogram_properties.title = title
histogram_properties.y_limits = [-2, 2]
histogram_properties.xerr = True
compare_measurements(
models={
# 'mean $|\mu|$':make_line_hist(bin_edges,mean_abs_pull),
'ideal $\mu$': make_line_hist(bin_edges, 0),
'ideal $\sigma$': make_line_hist(bin_edges, 1),
},
measurements={
r'$\mu_{\text{pull}}$': h_mean,
r'$\sigma_{\text{pull}}$': h_sigma
},
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=output_folder,
save_as=output_formats)
