Replace fixed DSL fields with parameterized metric expressions

app/training_sources.py

@@ -18,41 +18,41 @@ def daily(history, owner_id, zone, start, end, q, catalog):
         db.select(DailyHealth).where(DailyHealth.owner_id == owner_id,
                                      DailyHealth.day >= start, DailyHealth.day < end))}
     result = []
-    last_run = last_long = None
+    last_matching = {}
     streak = 0
     earlier = [r for r in history if r.day < start]
     if earlier:
-        last_run = max(r.day for r in earlier)
-        longs = [r.day for r in earlier if r.distance_m >= q["long_run"] * 1000]
-        last_long = max(longs) if longs else None
         day = start - timedelta(days=1)
         while by_day[day]:
             streak += 1
             day -= timedelta(days=1)
+    from .analysis_engine import matches
+
+    since_specs = [s for s in q["derived"] if "since" in s]
+    for spec in since_specs:
+        qualifying = [r.day for r in earlier if matches(r, spec["since"])]
+        last_matching[spec["name"]] = max(qualifying) if qualifying else None
     for index in range((end - start).days):
         day = start + timedelta(days=index)
         rows = by_day[day]
         if rows:
-            last_run = day
             streak += 1
         else:
             streak = 0
-        if any(r.distance_m >= q["long_run"] * 1000 for r in rows):
-            last_long = day
         values = {key: None for key in catalog}
         distance = sum(r.distance_m for r in rows)
         duration = sum(r.duration_s for r in rows)
         values.update(distance=distance / 1000, duration=duration / 3600,
-                      sessions=len(rows), count=1, run_day_streak=streak,
-                      days_since_run=(day - last_run).days if last_run else None,
-                      days_since_long_run=(day - last_long).days if last_long else None)
-        for days in (7, 28):
-            window = [r for d in range(days) for r in by_day[day - timedelta(days=d)]]
-            values[f"rolling_{days}d_distance"] = sum(r.distance_m for r in window) / 1000
-            values[f"rolling_{days}d_count"] = len(window)
-            if days == 7:
-                total = sum(r.distance_m for r in window)
-                values["long_run_share"] = max(r.distance_m for r in window) / total * 100 if total else None
+                      sessions=len(rows), count=1, run_day_streak=streak)
+        window = [r for d in range(7) for r in by_day[day - timedelta(days=d)]]
+        total = sum(r.distance_m for r in window)
+        values["long_run_share"] = max(r.distance_m for r in window) / total * 100 if total else None
+        for spec in since_specs:
+            name = spec["name"]
+            if any(matches(r, spec["since"]) for r in rows):
+                last_matching[name] = day
+            last = last_matching[name]
+            values[name] = (day - last).days if last else None
         for field in ("perceived_effort", "fatigue"):
             known = [r.values[field] for r in rows if valid(r.values.get(field))]
             values[field] = sum(known) / len(known) if known else None
@@ -69,6 +69,8 @@ def daily(history, owner_id, zone, start, end, q, catalog):
                               activity_id=None, activity_ids=[r.id for r in rows])
         result.append(row)
     for spec in q["derived"]:
+        if "since" in spec:
+            continue
         if "window" in spec:
             config = spec["window"]
             from .stats_analysis import statistic