Change resolution to resample_period

Signed-off-by: Flora <[email protected]>

Author: flora-hofmann-frequenz

README.md

@@ -50,8 +50,9 @@ client = ReportingApiClient(server_url=SERVER_URL, key=API_KEY)
 ```
 
 Besides the microgrid_id, component_ids, and metrics, start, and end time,
-you can also set the sampling period for resampling using the `resolution` parameter.
-For example, to resample data every 15 minutes, use a `resolution` of 900 seconds. The default is 1 second.
+you can also set the sampling period for resampling using the `resampling_period`
+parameter. For example, to resample data every 15 minutes, use a `resampling_period`
+of timedelta(minutes=15).
 
 ### Query metrics for a single microgrid and component:
 
@@ -64,7 +65,7 @@ data = [
         metrics=[Metric.AC_ACTIVE_POWER, Metric.AC_REACTIVE_POWER],
         start_dt=datetime.fromisoformat("2024-05-01T00:00:00"),
         end_dt=datetime.fromisoformat("2024-05-02T00:00:00"),
-        resolution=1,
+        resampling_period=timedelta(seconds=1),
     )
 ]
 ```
@@ -91,7 +92,7 @@ data = [
         metrics=[Metric.AC_ACTIVE_POWER, Metric.AC_REACTIVE_POWER],
         start_dt=datetime.fromisoformat("2024-05-01T00:00:00"),
         end_dt=datetime.fromisoformat("2024-05-02T00:00:00"),
-        resolution=1,
+        resampling_period=timedelta(seconds=1),
         states=False, # Set to True to include state data
         bounds=False, # Set to True to include metric bounds data
     )

RELEASE_NOTES.md

@@ -7,6 +7,7 @@
 ## Upgrading
 
 * Enforce keyword arguments in 'run' function of 'main' module
+* Change 'resolution' 'int' to 'resample_period' 'timedelta'
 
 ## New Features
 

src/frequenz/client/reporting/__main__.py

@@ -5,7 +5,7 @@
 
 import argparse
 import asyncio
-from datetime import datetime
+from datetime import datetime, timedelta
 from pprint import pprint
 from typing import AsyncIterator
 
@@ -56,7 +56,12 @@ def main() -> None:
         help="End datetime in YYYY-MM-DDTHH:MM:SS format",
         required=True,
     )
-    parser.add_argument("--resolution", type=int, help="Resolution", default=None)
+    parser.add_argument(
+        "--resampling_period",
+        type=int,
+        help="Resampling period in seconds (integer, rounded to avoid subsecond precision issues).",
+        default=None,
+    )
     parser.add_argument("--psize", type=int, help="Page size", default=1000)
     parser.add_argument(
         "--format", choices=["iter", "csv", "dict"], help="Output format", default="csv"
@@ -75,7 +80,7 @@ def main() -> None:
             metric_names=args.metrics,
             start_dt=args.start,
             end_dt=args.end,
-            resolution=args.resolution,
+            resampling_period=args.resampling_period,
             states=args.states,
             bounds=args.bounds,
             service_address=args.url,
@@ -93,7 +98,7 @@ async def run(
     metric_names: list[str],
     start_dt: datetime,
     end_dt: datetime,
-    resolution: int,
+    resampling_period: timedelta,
     states: bool,
     bounds: bool,
     service_address: str,
@@ -108,7 +113,7 @@ async def run(
         metric_names: list of metric names
         start_dt: start datetime
         end_dt: end datetime
-        resolution: resampling resolution in sec
+        resampling_period: The period for resampling the data.
         states: include states in the output
         bounds: include bounds in the output
         service_address: service address
@@ -136,7 +141,7 @@ def data_iter() -> AsyncIterator[MetricSample]:
             metrics=metrics,
             start_dt=start_dt,
             end_dt=end_dt,
-            resolution=resolution,
+            resampling_period=resampling_period,
             include_states=states,
             include_bounds=bounds,
         )

src/frequenz/client/reporting/_client.py

@@ -6,7 +6,7 @@
 from collections import namedtuple
 from collections.abc import AsyncIterator, Iterable, Iterator
 from dataclasses import dataclass
-from datetime import datetime
+from datetime import datetime, timedelta
 from typing import cast
 
 import grpc.aio as grpcaio
@@ -160,7 +160,7 @@ async def list_single_component_data(
         metrics: Metric | list[Metric],
         start_dt: datetime,
         end_dt: datetime,
-        resolution: int | None,
+        resampling_period: timedelta | None,
         include_states: bool = False,
         include_bounds: bool = False,
     ) -> AsyncIterator[MetricSample]:
@@ -172,7 +172,7 @@ async def list_single_component_data(
             metrics: The metric name or list of metric names.
             start_dt: The start date and time.
             end_dt: The end date and time.
-            resolution: The resampling resolution for the data, represented in seconds.
+            resampling_period: The period for resampling the data, specified as `timedelta` object.
             include_states: Whether to include the state data.
             include_bounds: Whether to include the bound data.
 
@@ -186,7 +186,7 @@ async def list_single_component_data(
             metrics=[metrics] if isinstance(metrics, Metric) else metrics,
             start_dt=start_dt,
             end_dt=end_dt,
-            resolution=resolution,
+            resampling_period=resampling_period,
             include_states=include_states,
             include_bounds=include_bounds,
         ):
@@ -201,7 +201,7 @@ async def list_microgrid_components_data(
         metrics: Metric | list[Metric],
         start_dt: datetime,
         end_dt: datetime,
-        resolution: int | None,
+        resampling_period: timedelta | None,
         include_states: bool = False,
         include_bounds: bool = False,
     ) -> AsyncIterator[MetricSample]:
@@ -213,7 +213,7 @@ async def list_microgrid_components_data(
             metrics: The metric name or list of metric names.
             start_dt: The start date and time.
             end_dt: The end date and time.
-            resolution: The resampling resolution for the data, represented in seconds.
+            resampling_period: The period for resampling the data, specified as `timedelta` object.
             include_states: Whether to include the state data.
             include_bounds: Whether to include the bound data.
 
@@ -230,7 +230,7 @@ async def list_microgrid_components_data(
             metrics=[metrics] if isinstance(metrics, Metric) else metrics,
             start_dt=start_dt,
             end_dt=end_dt,
-            resolution=resolution,
+            resampling_period=resampling_period,
             include_states=include_states,
             include_bounds=include_bounds,
         ):
@@ -246,7 +246,7 @@ async def _list_microgrid_components_data_batch(
         metrics: list[Metric],
         start_dt: datetime,
         end_dt: datetime,
-        resolution: int | None,
+        resampling_period: timedelta | None,
         include_states: bool = False,
         include_bounds: bool = False,
     ) -> AsyncIterator[ComponentsDataBatch]:
@@ -260,7 +260,7 @@ async def _list_microgrid_components_data_batch(
             metrics: A list of metrics.
             start_dt: The start date and time.
             end_dt: The end date and time.
-            resolution: The resampling resolution for the data, represented in seconds.
+            resampling_period: The period for resampling the data, specified as `timedelta` object.
             include_states: Whether to include the state data.
             include_bounds: Whether to include the bound data.
 
@@ -299,7 +299,13 @@ def dt2ts(dt: datetime) -> PBTimestamp:
 
         stream_filter = PBReceiveMicrogridComponentsDataStreamRequest.StreamFilter(
             time_filter=time_filter,
-            resampling_options=PBResamplingOptions(resolution=resolution),
+            resampling_options=PBResamplingOptions(
+                resolution=(
+                    round(resampling_period.total_seconds())
+                    if resampling_period is not None
+                    else None
+                )
+            ),
             include_options=include_options,
         )