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
