def generate_toy_MC_from_distribution(distribution): initial_values = list(distribution) new_values = [rnd.poisson(value) for value in initial_values] #statistical errors new_errors = [sqrt(value) for value in new_values] toy_MC = value_error_tuplelist_to_hist(zip(new_values, new_errors), list(distribution.xedges())) return toy_MC
def generate_toy_MC_from_distribution( distribution ): initial_values = list( distribution.y() ) new_values, new_errors = generate_toy_MC_from_values( initial_values ) toy_MC = value_error_tuplelist_to_hist( zip( new_values, new_errors ), list( distribution.xedges() ) ) return toy_MC
def generate_toy_MC_from_distribution(distribution): initial_values = list(distribution.y()) new_values, new_errors = generate_toy_MC_from_values(initial_values) toy_MC = value_error_tuplelist_to_hist(zip(new_values, new_errors), list(distribution.xedges())) return toy_MC