def flag(self, diagnostic, thresh=None):
        '''
        Returns indices of diagnostic that satisfy (return True from) the
        threshold predicate. Will use class-level default threshold if
        None provided.

        Args:
            diagnostic (str): name of the diagnostic
            thresh (func): threshold function (boolean predicate) to apply to
            each element
        '''
        if thresh is None:
            thresh = self.defaults[diagnostic]

        result = self.results[diagnostic]
        if isinstance(result, pd.DataFrame):
            if diagnostic == 'CorrelationMatrix':
                result = result.copy()
                np.fill_diagonal(result.values, 0)
            return result.applymap(thresh).sum().nonzero()[0]
        else:
            return result.apply(thresh).nonzero()[0]
def flag ( self , diagnostic , thresh = None ) : if thresh is None : thresh = self . defaults [ diagnostic ] result = self . results [ diagnostic ] if isinstance ( result , pd . DataFrame ) : if diagnostic == 'CorrelationMatrix' : result = result . copy ( ) np . fill_diagonal ( result . values , 0 ) return result . applymap ( thresh ) . sum ( ) . nonzero ( ) [ 0 ] else : return result . apply ( thresh ) . nonzero ( ) [ 0 ]
Returns indices of diagnostic that satisfy (return True from) the
        threshold predicate. Will use class-level default threshold if
        None provided.

        Args:
            diagnostic (str): name of the diagnostic
            thresh (func): threshold function (boolean predicate) to apply to
            each element