def count_samples(ns_run, **kwargs):
    'Number of samples in run.\n\n    Unlike most estimators this does not require log weights, but for\n    convenience will not throw an error if they are specified.\n\n    Parameters\n    ----------\n    ns_run: dict\n        Nested sampling run dict (see the data_processing module\n        docstring for more details).\n\n    Returns\n    -------\n    int\n    '
    kwargs.pop('logw', None)
    kwargs.pop('simulate', None)
    if kwargs:
        raise TypeError('Unexpected **kwargs: {0}'.format(kwargs))
    return ns_run['logl'].shape[0]
Number of samples in run.

Unlike most estimators this does not require log weights, but for
convenience will not throw an error if they are specified.

Parameters
----------
ns_run: dict
    Nested sampling run dict (see the data_processing module
    docstring for more details).

Returns
-------
int
def count_samples(ns_run, **kwargs):
    'Number of samples in run.\n\n    Unlike most estimators this does not require log weights, but for\n    convenience will not throw an error if they are specified.\n\n    Parameters\n    ----------\n    ns_run: dict\n        Nested sampling run dict (see the data_processing module\n        docstring for more details).\n\n    Returns\n    -------\n    int\n    '
    kwargs.pop('logw', None)
    kwargs.pop('simulate', None)
    if kwargs:
        raise TypeError('Unexpected **kwargs: {0}'.format(kwargs))
    return ns_run['logl'].shape[0]