feat(metrics): add timeframe/bucket math and series aggregation

backend/app/services/metrics_history.py

@@ -45,3 +45,49 @@ def aggregate_fsinfo(disks):
     used = sum(int(d.get("used", 0) or 0) for d in disks or [])
     total = sum(int(d.get("total", 0) or 0) for d in disks or [])
     return used, total
+
+
+_TIMEFRAME_SECONDS = {
+    "hour": 3600,
+    "day": 86400,
+    "week": 7 * 86400,
+    "month": 30 * 86400,
+}
+
+
+def timeframe_to_range(timeframe, now_ts):
+    """Convert timeframe string to (from_ts, to_ts) tuple."""
+    span = _TIMEFRAME_SECONDS.get(timeframe, 3600)
+    return now_ts - span, now_ts
+
+
+def pick_bucket_seconds(from_ts, to_ts, target_points=150, floor=15):
+    """Calculate bucket size in seconds for time-series aggregation."""
+    span = max(1, to_ts - from_ts)
+    ideal = span // max(1, target_points)
+    return max(floor, int(ideal))
+
+
+def aggregate_series(rows, from_ts, bucket, fields):
+    """Aggregate time-series data into buckets, averaging numeric fields."""
+    if not rows:
+        return []
+    buckets: dict[int, dict] = {}
+    order: list[int] = []
+    for r in rows:
+        b = from_ts + ((int(r["time"]) - from_ts) // bucket) * bucket
+        if b not in buckets:
+            buckets[b] = {f: [] for f in fields}
+            order.append(b)
+        for f in fields:
+            v = r.get(f)
+            if v is not None:
+                buckets[b][f].append(float(v))
+    out = []
+    for b in order:
+        point = {"time": b}
+        for f in fields:
+            vals = buckets[b][f]
+            point[f] = (sum(vals) / len(vals)) if vals else None
+        out.append(point)
+    return out