def evidence(ns_run, logw=None, simulate=False):
    'Bayesian evidence :math:`\\log \\mathcal{Z}`.\n\n    Parameters\n    ----------\n    ns_run: dict\n        Nested sampling run dict (see the data_processing module\n        docstring for more details).\n    logw: None or 1d numpy array, optional\n        Log weights of samples.\n    simulate: bool, optional\n        Passed to ns_run_utils.get_logw if logw needs to be\n        calculated.\n\n    Returns\n    -------\n    float\n    '
    if (logw is None):
        logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate)
    return np.exp(scipy.special.logsumexp(logw))
Bayesian evidence :math:`\log \mathcal{Z}`.

Parameters
----------
ns_run: dict
    Nested sampling run dict (see the data_processing module
    docstring for more details).
logw: None or 1d numpy array, optional
    Log weights of samples.
simulate: bool, optional
    Passed to ns_run_utils.get_logw if logw needs to be
    calculated.

Returns
-------
float
def evidence(ns_run, logw=None, simulate=False):
    'Bayesian evidence :math:`\\log \\mathcal{Z}`.\n\n    Parameters\n    ----------\n    ns_run: dict\n        Nested sampling run dict (see the data_processing module\n        docstring for more details).\n    logw: None or 1d numpy array, optional\n        Log weights of samples.\n    simulate: bool, optional\n        Passed to ns_run_utils.get_logw if logw needs to be\n        calculated.\n\n    Returns\n    -------\n    float\n    '
    if (logw is None):
        logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate)
    return np.exp(scipy.special.logsumexp(logw))