airflow/models/dag.py
def infer_automated_data_interval(self, logical_date: datetime) -> DataInterval:
        """Infer a data interval for a run against this DAG.

        This method is used to bridge runs created prior to AIP-39
        implementation, which do not have an explicit data interval. Therefore,
        this method only considers ``schedule_interval`` values valid prior to
        Airflow 2.2.

        DO NOT use this method is there is a known data interval.
        """
        timetable_type = type(self.timetable)
        if issubclass(timetable_type, (NullTimetable, OnceTimetable)):
            return DataInterval.exact(timezone.coerce_datetime(logical_date))
        start = timezone.coerce_datetime(logical_date)
        if issubclass(timetable_type, CronDataIntervalTimetable):
            end = cast(CronDataIntervalTimetable, self.timetable)._get_next(start)
        elif issubclass(timetable_type, DeltaDataIntervalTimetable):
            end = cast(DeltaDataIntervalTimetable, self.timetable)._get_next(start)
        else:
            raise ValueError(f"Not a valid timetable: {self.timetable!r}")
        return DataInterval(start, end)
Infer a data interval for a run against this DAG.

        This method is used to bridge runs created prior to AIP-39
        implementation, which do not have an explicit data interval. Therefore,
        this method only considers ``schedule_interval`` values valid prior to
        Airflow 2.2.

        DO NOT use this method is there is a known data interval.
        
Infer a data interval for a run against this DAG.
This method is used to bridge runs created prior to AIP-39
implementation, which do not have an explicit data interval. Therefore,
this method only considers ``schedule_interval`` values valid prior to
Airflow 2.2.

DO NOT use this method is there is a known data interval.
Infer a data interval for a run against this DAG . This method is used to bridge runs created prior to AIP - 39 implementation which do not have an explicit data interval . Therefore this method only considers ` ` schedule_interval ` ` values valid prior to Airflow 2 . 2 . DO NOT use this method is there is a known data interval .
def infer_automated_data_interval(self, logical_date: datetime) -> DataInterval:
        timetable_type = type(self.timetable)
        if issubclass(timetable_type, (NullTimetable, OnceTimetable)):
            return DataInterval.exact(timezone.coerce_datetime(logical_date))
        start = timezone.coerce_datetime(logical_date)
        if issubclass(timetable_type, CronDataIntervalTimetable):
            end = cast(CronDataIntervalTimetable, self.timetable)._get_next(start)
        elif issubclass(timetable_type, DeltaDataIntervalTimetable):
            end = cast(DeltaDataIntervalTimetable, self.timetable)._get_next(start)
        else:
            raise ValueError(f"Not a valid timetable: {self.timetable!r}")
        return DataInterval(start, end)
def infer_automated_data_interval ( self , logical_date : datetime ) -> DataInterval : timetable_type = type ( self . timetable ) if issubclass ( timetable_type , ( NullTimetable , OnceTimetable ) ) : return DataInterval . exact ( timezone . coerce_datetime ( logical_date ) ) start = timezone . coerce_datetime ( logical_date ) if issubclass ( timetable_type , CronDataIntervalTimetable ) : end = cast ( CronDataIntervalTimetable , self . timetable ) . _get_next ( start ) elif issubclass ( timetable_type , DeltaDataIntervalTimetable ) : end = cast ( DeltaDataIntervalTimetable , self . timetable ) . _get_next ( start ) else : raise ValueError ( f"Not a valid timetable: {self.timetable!r}" ) return DataInterval ( start , end )
Infer a data interval for a run against this DAG.
Infer a data interval for a run against this DAG .
"""Infer a data interval for a run against this DAG.

        This method is used to bridge runs created prior to AIP-39
        implementation, which do not have an explicit data interval. Therefore,
        this method only considers ``schedule_interval`` values valid prior to
        Airflow 2.2.

        DO NOT use this method is there is a known data interval.
        """
self
logical_date
datetime