def test_compare_timecontext():
c1 = (Timestamp('20170101'), Timestamp('20170103'))
c2 = (Timestamp('20170101'), Timestamp('20170111'))
c3 = (Timestamp('20160101'), Timestamp('20160103'))
c4 = (Timestamp('20161215'), Timestamp('20170102'))
assert compare_timecontext(c1, c2) == TimeContextRelation.SUBSET
assert compare_timecontext(c2, c1) == TimeContextRelation.SUPERSET
assert compare_timecontext(c1, c4) == TimeContextRelation.OVERLAP
assert compare_timecontext(c1, c3) == TimeContextRelation.NONOVERLAP
def get_value(self,
op: Node,
timecontext: Optional[TimeContext] = None) -> Any:
""" Given a op and timecontext, get the result from scope
Parameters
----------
scope : collections.Mapping
a dictionary mapping :class:`~ibis.expr.operations.Node`
subclass instances to concrete data, and the time context associate
with it (if any).
op: ibis.expr.operations.Node
key in scope.
timecontext: Optional[TimeContext]
Returns
-------
result: the cached result, an object whose types may differ in
different backends.
"""
if op not in self:
return None
# for ops without timecontext
if timecontext is None:
return self._items[op].value
else:
# For op with timecontext, ther are some ops cannot use cached
# result with a different (larger) timecontext to get the
# correct result.
# For example, a groupby followed by count, if we use a larger or
# smaller dataset from cache, we will get an error in result.
# Such ops with global aggregation, ops whose result is
# depending on other rows in result Dataframe, cannot use cached
# result with different time context to optimize calculation.
# These are time context sensitive operations. Since these cases
# are rare in acutal use case, we just enable optimization for
# all nodes for now.
cached_timecontext = self._items[op].timecontext
if cached_timecontext:
relation = compare_timecontext(timecontext, cached_timecontext)
if relation == TimeContextRelation.SUBSET:
return self._items[op].value
else:
return self._items[op].value
return None