Fix CU: use TB Air1 data in 7-day chunks (matches TB 282/310)

backend/api/routes/biology.py

@@ -48,7 +48,7 @@ async def biology_chill_units(
     season_start: str = Query("2025-11-01", description="Season start (YYYY-MM-DD)"),
     hub: DataHub = Depends(get_datahub),
 ):
-    """Accumulated chill units from IMS hourly temperature (Utah model).
+    """Accumulated chill units from ThingsBoard Air1 on-site temperature (Utah model).
 
     Model (Richardson et al. 1974):
       T <= 7°C   → +1.0 CU/hour
@@ -59,43 +59,52 @@ async def biology_chill_units(
     Season cumulative = running sum of daily CU from season_start.
 
     Two series:
-      - open_field: raw IMS temperature
+      - open_field: on-site Air1 temperature
       - under_panels: open_field × 1.1 (panels buffer nighttime → more chill)
-    Multiplier per Research/chill_hours/ANALYSIS_EXPLAINED.md.
+    Per Research/chill_hours/ANALYSIS_EXPLAINED.md.
     """
     import numpy as np
     import pandas as pd
 
-    PANEL_MULTIPLIER = 1.1  # under-panel chill ≈ 10% more than open field
+    PANEL_MULTIPLIER = 1.1
 
     try:
-        df = hub.weather._load_df()
-        if df.empty:
-            return {"error": "No IMS data available for chill computation"}
-
-        if "timestamp_utc" in df.columns:
-            df = df.set_index(pd.to_datetime(df["timestamp_utc"], utc=True))
+        from src.data.thingsboard_client import ThingsBoardClient
+        from zoneinfo import ZoneInfo
 
+        client = ThingsBoardClient()
+        tz = ZoneInfo("Asia/Jerusalem")
         start = pd.Timestamp(season_start, tz="UTC")
-        subset = df.loc[start:]
-        if subset.empty or "air_temperature_c" not in subset.columns:
-            return {"error": "No temperature data in season range"}
-
-        # Convert to Israel local time for correct day boundaries
-        try:
-            from zoneinfo import ZoneInfo
-            tz = ZoneInfo("Asia/Jerusalem")
-        except ImportError:
-            tz = None
-        if tz:
-            subset = subset.tz_convert(tz)
-
-        # Hourly mean temperature
-        hourly = subset["air_temperature_c"].resample("1h").mean().dropna()
+        end = pd.Timestamp.now(tz="UTC")
+
+        # Fetch Air1 temperature in 7-day chunks (TB rejects large ranges)
+        chunks = []
+        cursor = start
+        while cursor < end:
+            chunk_end = min(cursor + pd.Timedelta(days=7), end)
+            try:
+                df = client.get_timeseries(
+                    "Air1", ["airTemperature"],
+                    start=cursor.to_pydatetime(), end=chunk_end.to_pydatetime(),
+                    interval_ms=0, agg="NONE", limit=10000,
+                )
+                if not df.empty:
+                    chunks.append(df)
+            except Exception:
+                pass
+            cursor = chunk_end
+
+        if not chunks:
+            return {"error": "No Air1 temperature data available from ThingsBoard"}
+
+        full = pd.concat(chunks).sort_index()
+        full = full[~full.index.duplicated(keep="first")]
+        full = full.tz_convert(tz)
+
+        hourly = full["airTemperature"].resample("1h").mean().dropna()
         if hourly.empty:
-            return {"error": "No hourly temperature data"}
+            return {"error": "No hourly temperature after resampling"}
 
-        # Compute chill per hour using Utah model
         temps = hourly.values
         chill_hourly = np.select(
             [temps <= 7.0, (temps > 7.0) & (temps <= 10.0),
@@ -103,7 +112,6 @@ async def biology_chill_units(
             [1.0, 0.5, 0.0, -1.0],
         )
 
-        # Daily chill = sum of hourly, clipped at 0
         daily_chill = pd.Series(chill_hourly, index=hourly.index).resample("D").sum().clip(lower=0)
         cu_open = daily_chill.cumsum()
         cu_panels = (daily_chill * PANEL_MULTIPLIER).cumsum()