Resolves #367 time-weighted mean

apps/data-management/src/components/Orchestration/data-products/AggregationForm.vue

@@ -254,7 +254,7 @@ const taskName = ref('')
 const schedule = ref<TaskSchedule | null>(null)
 const inputDatastreamId = ref<string | null>(null)
 const outputDatastreamId = ref<string | null>(null)
-const aggregationMethod = ref<AggregationMethod>('mean')
+const aggregationMethod = ref<AggregationMethod>('time_weighted_mean')
 const outputInterval = ref<number | null>(1)
 const outputIntervalUnits = ref<IntervalUnit>('hours')
 const minValues = ref<number | null>(null)
@@ -264,12 +264,13 @@ const timezone = ref<string | null>(null)
 const selectedThingId = computed(() => props.initialThingId ?? null)
 
 const aggregationMethodOptions = [
-  { title: 'Mean', value: 'mean' },
-  { title: 'Sum', value: 'sum' },
-  { title: 'Min', value: 'min' },
-  { title: 'Max', value: 'max' },
+  { title: 'Arithmetic Mean', value: 'mean' },
   { title: 'First', value: 'first' },
   { title: 'Last', value: 'last' },
+  { title: 'Max', value: 'max' },
+  { title: 'Min', value: 'min' },
+  { title: 'Sum', value: 'sum' },
+  { title: 'Time-weighted mean', value: 'time_weighted_mean' },
 ]
 
 const intervalUnitOptions = [

django/contracts/openapi/data.openapi.json

@@ -651,7 +651,8 @@
               "min",
               "max",
               "first",
-              "last"
+              "last",
+              "time_weighted_mean"
             ],
             "title": "Aggregationmethod",
             "type": "string"
@@ -731,7 +732,8 @@
               "min",
               "max",
               "first",
-              "last"
+              "last",
+              "time_weighted_mean"
             ],
             "title": "Aggregationmethod",
             "type": "string"
@@ -823,7 +825,8 @@
               "min",
               "max",
               "first",
-              "last"
+              "last",
+              "time_weighted_mean"
             ],
             "title": "Aggregationmethod",
             "type": "string"
@@ -912,7 +915,8 @@
               "min",
               "max",
               "first",
-              "last"
+              "last",
+              "time_weighted_mean"
             ],
             "title": "Aggregationmethod",
             "type": "string"

django/interfaces/api/schemas/products/transformation.py

@@ -15,7 +15,7 @@
 from interfaces.api.schemas.products.rating_curve import RatingCurveSummaryResponse
 
 
-AggregationMethod = Literal["mean", "sum", "min", "max", "first", "last"]
+AggregationMethod = Literal["mean", "sum", "min", "max", "first", "last", "time_weighted_mean"]
 Period = Literal["minutes", "hours", "days", "weeks", "months"]
 TimezoneType = Literal["offset", "iana"]
 

django/processing/products/migrations/0002_alter_dataproducttransformation_aggregation_method.py

@@ -0,0 +1,18 @@
+# Generated by Django 5.2.2 on 2026-07-15 17:29
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('products', '0001_initial'),
+    ]
+
+    operations = [
+        migrations.AlterField(
+            model_name='dataproducttransformation',
+            name='aggregation_method',
+            field=models.CharField(blank=True, choices=[('mean', 'Mean'), ('sum', 'Sum'), ('min', 'Min'), ('max', 'Max'), ('first', 'First'), ('last', 'Last'), ('time_weighted_mean', 'Time Weighted Mean')], max_length=255, null=True),
+        ),
+    ]

django/processing/products/models/transformation.py

@@ -20,6 +20,7 @@ class AggregationMethod(models.TextChoices):
     MAX = "max"
     FIRST = "first"
     LAST = "last"
+    TIME_WEIGHTED_MEAN = "time_weighted_mean"
 
 
 class IntervalUnits(models.TextChoices):

django/processing/products/services/transformation.py

@@ -42,7 +42,7 @@
 rating_curve_service = RatingCurveService()
 
 TransformationType = Literal["rating_curve", "expression", "composite_expression", "aggregation"]
-AggregationMethod = Literal["mean", "sum", "min", "max", "first", "last"]
+AggregationMethod = Literal["mean", "sum", "min", "max", "first", "last", "time_weighted_mean"]
 IntervalUnits = Literal["minutes", "hours", "days", "weeks", "months"]
 
 

django/tests/products/services/test_transformation.py

@@ -320,6 +320,21 @@ def test_create_aggregation_transformation(get_principal, ds_free):
     assert result.aggregation_method == "mean"
 
 
+def test_create_aggregation_transformation_time_weighted_mean(get_principal, ds_free):
+    result = transformation_service.create(
+        task=uuid.UUID(TASK1),
+        principal=get_principal("owner"),
+        transformation_type="aggregation",
+        output_datastream=ds_free,
+        aggregation_method="time_weighted_mean",
+        output_interval_units="hours",
+        output_interval=1,
+        input_datastreams=[TransformationInput(datastream=uuid.UUID(DS_IN_RC))],
+    )
+    assert result.transformation_type == "aggregation"
+    assert result.aggregation_method == "time_weighted_mean"
+
+
 def test_create_composite_expression_transformation(get_principal, ds_free):
     result = transformation_service.create(
         task=uuid.UUID(TASK1),
@@ -906,3 +921,52 @@ def test_run_aggregation(run_context):
     from core.sta.models.observation import Observation
     results = sorted(Observation.objects.filter(datastream=ds_out).values_list("result", flat=True))
     np.testing.assert_allclose(results, [2.0, 3.0])
+
+
+def test_run_aggregation_time_weighted_mean(run_context):
+    """
+    time_weighted_mean, 1-day UTC bucket [2025-02-10T00:00Z, 2025-02-11T00:00Z).
+
+    Observations at 06:00=1.0, 08:00=3.0, 10:00=2.0, 12:00=4.0. Boundary values are
+    flat-extrapolated (00:00 -> 1.0, 24:00 -> 4.0), then trapezoidal integration gives:
+      [00-06]: (1+1)/2*6 =  6
+      [06-08]: (1+3)/2*2 =  4
+      [08-10]: (3+2)/2*2 =  5
+      [10-12]: (2+4)/2*2 =  6
+      [12-24]: (4+4)/2*12= 48
+      total = 69 over 24h -> 2.875
+    This differs from the simple arithmetic mean of the same observations (2.5),
+    confirming the time-weighted method is actually wired through the run pipeline.
+    """
+    ctx = run_context
+    kwargs = {k: ctx[k] for k in ("thing", "sensor", "observed_property", "processing_level", "unit")}
+    ds_in = _make_datastream(**kwargs)
+    ds_out = _make_datastream(**kwargs)
+
+    for dt, val in [
+        (datetime(2025, 2, 10, 6, 0, tzinfo=dt_timezone.utc), 1.0),
+        (datetime(2025, 2, 10, 8, 0, tzinfo=dt_timezone.utc), 3.0),
+        (datetime(2025, 2, 10, 10, 0, tzinfo=dt_timezone.utc), 2.0),
+        (datetime(2025, 2, 10, 12, 0, tzinfo=dt_timezone.utc), 4.0),
+    ]:
+        _add_obs(ds_in, dt, val)
+    _set_end_time(ds_in, datetime(2025, 2, 11, 0, 0, tzinfo=dt_timezone.utc))
+
+    t = transformation_service.create(
+        task=uuid.UUID(TASK1),
+        principal=ctx["owner"],
+        transformation_type="aggregation",
+        output_datastream=ds_out.pk,
+        aggregation_method="time_weighted_mean",
+        output_interval_units="days",
+        output_interval=1,
+        input_datastreams=[TransformationInput(datastream=ds_in.pk)],
+    )
+    loaded = transformation_service.run(t)
+    assert loaded == 1
+
+    from core.sta.models.observation import Observation
+    out_obs = list(Observation.objects.filter(datastream=ds_out))
+    assert len(out_obs) == 1
+    assert out_obs[0].phenomenon_time == datetime(2025, 2, 10, 0, 0, tzinfo=dt_timezone.utc)
+    np.testing.assert_allclose(out_obs[0].result, 2.875)

packages/hydroserver-ts/src/generated/data.types.d.ts

@@ -2258,7 +2258,7 @@ export interface components {
              * Aggregationmethod
              * @enum {string}
              */
-            aggregationMethod?: "mean" | "sum" | "min" | "max" | "first" | "last";
+            aggregationMethod?: "mean" | "sum" | "min" | "max" | "first" | "last" | "time_weighted_mean";
             /**
              * Inputdatastreamid
              * Format: uuid
@@ -2289,7 +2289,7 @@ export interface components {
              * Aggregationmethod
              * @enum {string}
              */
-            aggregationMethod: "mean" | "sum" | "min" | "max" | "first" | "last";
+            aggregationMethod: "mean" | "sum" | "min" | "max" | "first" | "last" | "time_weighted_mean";
             /**
              * Id
              * Format: uuid
@@ -2325,7 +2325,7 @@ export interface components {
              * Aggregationmethod
              * @enum {string}
              */
-            aggregationMethod: "mean" | "sum" | "min" | "max" | "first" | "last";
+            aggregationMethod: "mean" | "sum" | "min" | "max" | "first" | "last" | "time_weighted_mean";
             /**
              * Id
              * Format: uuid
@@ -2353,7 +2353,7 @@ export interface components {
              * Aggregationmethod
              * @enum {string}
              */
-            aggregationMethod: "mean" | "sum" | "min" | "max" | "first" | "last";
+            aggregationMethod: "mean" | "sum" | "min" | "max" | "first" | "last" | "time_weighted_mean";
             /**
              * Id
              * Format: uuid

packages/hydroserverpy/src/hydroserverpy/api/models/products/transformation.py

@@ -3,7 +3,7 @@
 from pydantic import BaseModel, ConfigDict
 from pydantic.alias_generators import to_camel
 
-AggregationMethod = Literal["mean", "sum", "min", "max", "first", "last"]
+AggregationMethod = Literal["mean", "sum", "min", "max", "first", "last", "time_weighted_mean"]
 Period = Literal["minutes", "hours", "days", "weeks", "months"]