Improve global snapshot robustness when target time missing

app.py

@@ -144,8 +144,20 @@ def fetch_wave_history(
         raise ValueError("Provide either coordinates or a grid definition.")
 
     if from_time and until_time:
-        if until_time <= from_time:
-            raise ValueError("until_time must be after from_time.")
+        def parse_iso(text: str) -> dt.datetime:
+            text = text.strip()
+            if text.endswith("Z"):
+                text = text[:-1] + "+00:00"
+            return dt.datetime.fromisoformat(text)
+
+        start_dt = parse_iso(from_time)
+        end_dt = parse_iso(until_time)
+        if end_dt < start_dt:
+            raise ValueError("until_time must not be before from_time.")
+        # If equal, nudge the end forward by a small epsilon to keep API happy.
+        if end_dt == start_dt:
+            end_dt += dt.timedelta(minutes=1)
+            until_time = end_dt.isoformat().replace("+00:00", "Z")
     elif not times_list:
         raise ValueError("Provide either a time range or an explicit times list.")
 
@@ -432,24 +444,34 @@ def run_global_download(
             alias_map[name] = alias
             variables_payload.append({"name": name, "level": DEFAULT_LEVEL, "alias": alias})
 
-        df = fetch_wave_history(
-            token=token,
-            model=model,
-            variables=variables_payload,
-            times_list=[valid_iso],
-            min_horizon=lower,
-            max_horizon=upper,
-            grid={
-                "minLatitude": -90,
-                "maxLatitude": 90,
-                "minLongitude": -180,
-                "maxLongitude": 180,
-                "step": float(grid_step),
-            },
-            members=members,
-            accept="text/csv",
-            timeout=240,
-        )
+        def request_payload(as_times_list: bool) -> pd.DataFrame:
+            kwargs = dict(
+                token=token,
+                model=model,
+                variables=variables_payload,
+                min_horizon=lower,
+                max_horizon=upper,
+                grid={
+                    "minLatitude": -90,
+                    "maxLatitude": 90,
+                    "minLongitude": -180,
+                    "maxLongitude": 180,
+                    "step": float(grid_step),
+                },
+                members=members,
+                accept="text/csv",
+                timeout=240,
+            )
+            if as_times_list:
+                kwargs["times_list"] = [valid_iso]
+            else:
+                kwargs["from_time"] = valid_iso
+                kwargs["until_time"] = valid_iso
+            return fetch_wave_history(**kwargs)
+
+        df = request_payload(as_times_list=True)
+        if df.empty:
+            df = request_payload(as_times_list=False)
 
         if df.empty:
             raise ValueError("No data returned for the requested global configuration.")
@@ -514,6 +536,10 @@ def default_time_window(hours_back: int = 6, hours_forward: int = 24) -> Tuple[s
 
 def default_valid_time(offset_hours: int = 0) -> str:
     now = dt.datetime.utcnow().replace(minute=0, second=0, microsecond=0)
+    # Align to the previous 3-hour boundary, which matches ECMWF wave output cadence.
+    hours_since_cycle = now.hour % 3
+    if hours_since_cycle != 0:
+        now -= dt.timedelta(hours=hours_since_cycle)
     return (now + dt.timedelta(hours=offset_hours)).isoformat() + "Z"