feat: implement forecast trending retrieval with fallback logic for completed simulations

src/aeros_simulation/service.py

@@ -1048,11 +1048,17 @@ async def get_forecast_trending(
     """
     Return EAF/EFOR/SOF/EDH for the last `limit` completed weekly or monthly
     forecast simulations, oldest first (for chronological chart rendering).
+
+    Strategy:
+    1. Try simulations named Simulation_weekly_* / Simulation_monthly_* (scheduled forecasts).
+    2. If fewer than `limit` found, fall back to any completed simulation, grouped
+       by week or month bucket (latest sim per bucket), so the chart is populated
+       even before automated forecast simulations exist.
     """
-    name_prefix = f"Simulation_{time_range}_"
     plant_node_name = "- TJB - Unit 3 -"
+    name_prefix = f"Simulation_{time_range}_"
 
-    query = (
+    base_join = (
         select(AerosSimulation, AerosSimulationCalcResult)
         .join(
             AerosSimulationCalcResult,
@@ -1060,24 +1066,56 @@ async def get_forecast_trending(
         )
         .join(AerosNode, AerosNode.id == AerosSimulationCalcResult.aeros_node_id)
         .where(AerosSimulation.status == "completed")
-        .where(AerosSimulation.simulation_name.like(f"{name_prefix}%"))
         .where(AerosNode.node_name == plant_node_name)
+    )
+
+    # First pass: named forecast simulations
+    named_q = (
+        base_join
+        .where(AerosSimulation.simulation_name.like(f"{name_prefix}%"))
         .order_by(AerosSimulation.completed_at.desc())
         .limit(limit)
     )
+    result = await db_session.execute(named_q)
+    named_rows = result.all()
+
+    def _format(rows):
+        return [
+            {
+                "date": sim.completed_at,
+                "simulation_id": str(sim.id),
+                "simulation_name": sim.simulation_name,
+                "eaf": calc.eaf,
+                "efor": calc.efor,
+                "sof": calc.sof,
+                "edh": calc.derating_hours,
+            }
+            for sim, calc in rows
+        ]
+
+    if len(named_rows) >= limit:
+        return _format(reversed(named_rows))
+
+    # Fallback: any completed sim, group by week/month bucket (latest per bucket)
+    fallback_q = (
+        base_join
+        .order_by(AerosSimulation.completed_at.desc())
+        .limit(limit * 20)
+    )
+    result = await db_session.execute(fallback_q)
+    all_rows = result.all()
 
-    result = await db_session.execute(query)
+    buckets: dict = {}
-    rows = result.all()
+    for sim, calc in all_rows:
-
+        ts = sim.completed_at
-    return [
+        if ts is None:
-        {
+            continue
-            "date": sim.completed_at,
+        if time_range == "weekly":
-            "simulation_id": str(sim.id),
+            bucket = ts.strftime("%G-W%V")   # ISO week
-            "simulation_name": sim.simulation_name,
+        else:
-            "eaf": calc.eaf,
+            bucket = ts.strftime("%Y-%m")
-            "efor": calc.efor,
+        if bucket not in buckets:
-            "sof": calc.sof,
+            buckets[bucket] = (sim, calc)
-            "edh": calc.derating_hours,
+
-        }
+    sorted_items = sorted(buckets.items(), key=lambda x: x[0])[-limit:]
-        for sim, calc in reversed(rows)  # oldest → newest for chart x-axis
+    return _format((sim, calc) for _, (sim, calc) in sorted_items)
-    ]