Add local time parsing function to handle ISO strings and epoch values in report_service. Update adapter to utilize this function for improved timestamp handling in DataFrame processing.

report_service/adapter.py

@@ -24,11 +24,11 @@ _LIGHTNING_COLUMN_ALIASES: dict[str, list[str]] = {
     "local_time": [
         "local_time",
         "localtime",
-        "time",
-        "timestamp",
         "captured",
         "datetime",
         "date_time",
+        "time",
+        "timestamp",
     ],
 }
 
@@ -64,6 +64,32 @@ def _apply_aliases(df: pd.DataFrame, aliases: dict[str, list[str]]) -> pd.DataFr
     return df.rename(columns=rename_map) if rename_map else df
 
 
+def _parse_local_time_series(series: pd.Series) -> pd.Series:
+    """
+    Parse strike timestamps from ISO strings or epoch values.
+
+    n8n may add a numeric ``timestamp`` field (epoch ms). Pandas treats bare
+    integers as nanoseconds by default, which collapses ms epochs to ~1970.
+    """
+    if series.empty:
+        return series
+
+    if not pd.api.types.is_numeric_dtype(series):
+        parsed = pd.to_datetime(series, errors="coerce", utc=True)
+        if parsed.notna().any():
+            return parsed
+
+    numeric = pd.to_numeric(series, errors="coerce")
+    if numeric.notna().any():
+        sample = float(numeric.dropna().iloc[0])
+        if sample >= 1e12:
+            return pd.to_datetime(numeric, unit="ms", errors="coerce", utc=True)
+        if sample >= 1e9:
+            return pd.to_datetime(numeric, unit="s", errors="coerce", utc=True)
+
+    return pd.to_datetime(series, errors="coerce", utc=True)
+
+
 def _build_turbine_df(turbines: list[dict[str, Any]]) -> pd.DataFrame:
     if not turbines:
         return pd.DataFrame(columns=["name", "lat", "lng"])
@@ -118,7 +144,7 @@ def _build_lightning_df(
     df["current"] = pd.to_numeric(df["current"], errors="coerce")
     df["p_type"] = df["p_type"].astype(str)
 
-    local_time = pd.to_datetime(df["local_time"], errors="coerce", utc=True)
+    local_time = _parse_local_time_series(df["local_time"])
     if timezone_name:
         try:
             local_time = local_time.dt.tz_convert(timezone_name)