airflow dag with timezone aware

timezone.py

from datetime import datetime, timedelta

import pendulum

from airflow import DAG
from airflow.operators.python_operator import PythonOperator


default_args = {"owner": "airflow"}


def f1(tzinfo: datetime.tzinfo = None, **context):
    if tzinfo is not None:
        print(("local time:", tzinfo.convert(context["execution_date"])))
    for key in context:
        print((key, context[key]))


list_tz = [
    "Europe/Amsterdam",
    "America/New_York",
    "America/Los_Angeles",
    "Asia/Jakarta",
    "Asia/Kolkata",
    "Asia/Taipei",
]
for tz in list_tz:
    format_tz = tz.replace("/", "_")
    dag_id = "timezone_%s" % format_tz
    with DAG(
        dag_id,
        default_args=default_args,
        catchup=True,
        start_date=datetime(2018, 11, 1, tzinfo=pendulum.timezone(tz)),
        end_date=datetime(2019, 11, 1, tzinfo=pendulum.timezone(tz)),
        schedule_interval="@daily",
    ) as dag:

        # As of airflow 1.10.5, the marcos value is based on UTC.
        # For timezone aware task, I suggest to only use execution_date as a reference.
        # Don't use ds or prev_ds
        # See https://airflow.apache.org/timezone.html#templates
        task = PythonOperator(
            task_id="t1",
            provide_context=True,
            python_callable=f1,
            dag=dag,
            op_kwargs={"tzinfo": pendulum.timezone(tz)},
        )
        globals()[dag_id] = dag