def topna(cls,
d: Dict[Node, float],
pred: Pred = None,
k: Union[int, None] = 1) -> List[NodeA]:
'''Returns a list of the top k nodes in d, by activation, restricted to
nodes of 'type' and that pass 'filter'.'''
pred: Callable[[Any], bool] = as_pred(pred)
nas = [NodeA(node, a) for (node, a) in d.items() if pred(node)]
if k is None:
return sorted(nas, key=attrgetter('a'), reverse=True)
else:
return nlargest(k, nas, key=attrgetter('a'))
def test_as_pred_type(self) -> None:
pred = as_pred(Equation)
self.assertFalse(pred(After))
self.assertTrue(pred(Equation.make([5, 4], plus)))
self.assertFalse(pred(Equation))
def ldisable(*args: Pred) -> None:
'''Disables logging for args.'''
for arg in args:
enabled_for_logging.discard(as_pred(arg))
def lenable(*args: Pred) -> None:
'''Enables logging for args.'''
for arg in args:
enabled_for_logging.add(as_pred(arg))
def tss(self, pred: Pred = None) -> List[NodeTimeseries]:
'''pred selects Nodes.'''
pred: Callable[[Any], bool] = as_pred(pred)
return [ts for ts in self.tsd.values() if pred(ts.node)]
def tagmatch(tag: Any, pred: Pred) -> bool:
if isinstance(tag, PTag) and not isinstance(pred, PTag):
return as_pred(pred)(tag.tagpred)
else:
return as_pred(pred)(tag)