madminer.limits module

class madminer.limits.AsymptoticLimits(filename=None, include_nuisance_parameters=False)[source]

Bases: madminer.analysis.DataAnalyzer

Functions to calculate observed and expected constraints, using asymptotic properties of the likelihood ratio as test statistics.

Parameters:
filename : str

Path to MadMiner file (for instance the output of madminer.delphes.DelphesProcessor.save()).

include_nuisance_parameters : bool, optional

If True, nuisance parameters are taken into account. Default value: False.

Methods

event_loader(self[, start, end, batch_size, …]) Yields batches of events in the MadMiner file.
weighted_events(self[, theta, nu, …]) Returns all events together with the benchmark weights (if theta is None) or weights for a given theta.
xsec_gradients(self, thetas[, nus, events, …]) Returns the gradient of total cross sections with respect to parameters.
xsecs(self[, thetas, nus, events, …]) Returns the total cross sections for benchmarks or parameter points.
asymptotic_p_value  
expected_limits  
observed_limits  
asymptotic_p_value(self, log_likelihood_ratio, dof=None)[source]
expected_limits(self, theta_true, theta_ranges, mode='ml', model_file=None, hist_vars=None, hist_bins=20, include_xsec=True, resolutions=25, luminosity=300000.0, n_toys_per_theta=10000, returns='pval', dof=None, histo_theta_batchsize=100)[source]
observed_limits(self, x_observed, theta_ranges, mode='ml', model_file=None, hist_vars=None, hist_bins=20, include_xsec=True, resolutions=25, luminosity=300000.0, n_toys_per_theta=10000, returns='pval', dof=None, n_observed=None, histo_theta_batchsize=100)[source]