update example for new api

examples/partition_example/partition_example.py

@@ -33,7 +33,7 @@ def relativedelta(*args, **kwargs):
     metadata={"partition_expr": "LastModifiedDate"},
 )
 def salesforce_customers(context: AssetExecutionContext) -> pd.DataFrame:
-    start_date_str = context.asset_partition_key_for_output()
+    start_date_str = context.partition_info.key
 
     timezone = pytz.timezone("GMT")  # Replace 'Your_Timezone' with the desired timezone
     start_obj = datetime.datetime.strptime(start_date_str, "%Y-%m-%d").replace(tzinfo=timezone)
@@ -65,7 +65,7 @@ def realized_vol(context: AssetExecutionContext, orats_daily_prices: pd.DataFram
     The volatility is calculated using various methods such as close-to-close, Parkinson, Hodges-Tompkins, and Yang-Zhang.
     The function returns a DataFrame with the calculated volatilities.
     """
-    trade_date = context.asset_partition_key_for_output()
+    trade_date = context.partition_info.key
     ticker_id = 1
 
     df = all_realvols(orats_daily_prices, ticker_id, trade_date)
@@ -80,7 +80,7 @@ def realized_vol(context: AssetExecutionContext, orats_daily_prices: pd.DataFram
 
 @asset(io_manager_def="parquet_io_manager", partitions_def=hourly_partitions)
 def my_custom_df(context: AssetExecutionContext) -> pd.DataFrame:
-    start, end = context.asset_partitions_time_window_for_output()
+    start, end = context.partition_info.time_window
 
     df = pd.DataFrame({"timestamp": pd.date_range(start, end, freq="5T")})
     df["count"] = df["timestamp"].map(lambda a: random.randint(1, 1000))
@@ -93,7 +93,7 @@ def fetch_blog_posts_from_external_api(*args, **kwargs):
 
 @asset(partitions_def=HourlyPartitionsDefinition(start_date="2022-01-01-00:00"))
 def blog_posts(context: AssetExecutionContext) -> List[Dict]:
-    partition_datetime_str = context.asset_partition_key_for_output()
+    partition_datetime_str = context.partition_info.key
     hour = datetime.datetime.fromisoformat(partition_datetime_str)
     posts = fetch_blog_posts_from_external_api(hour_when_posted=hour)
     return posts
@@ -106,7 +106,7 @@ def blog_posts(context: AssetExecutionContext) -> List[Dict]:
     key_prefix=["snowflake", "eldermark_proxy"],
 )
 def resident(context: AssetExecutionContext) -> Output[pd.DataFrame]:
-    start, end = context.asset_partitions_time_window_for_output()
+    start, end = context.partition_info.time_window
     filter_str = f"LastMod_Stamp >= {start.timestamp()} AND LastMod_Stamp < {end.timestamp()}"
 
     records = context.resources.eldermark.fetch_obj(obj="Resident", filter=filter_str)