Add matched training signals, daily recovery and performance analysis

app/stats_presets.py

@@ -5,7 +5,7 @@ PRESETS = [
         "name": "HR in steady segments",
         "category": "Effort & recovery",
         "description": "Match five-minute moving segments by pace, speed consistency and HR coverage.",
-        "query": 'segments\n  during last 12 weeks\n  where pace between 5:20 and 5:40 /km\n  and pace_variability <= 5 %\n  and sensor_coverage >= 80 %\n  and duration >= 4 min\n  summarize weighted_mean(heart_rate) as "Steady-segment HR"\n  by week\n  compare none\n  baseline previous 90 days\n  flag above baseline 1.5 sd min 5 runs sustained 2 buckets\n  chart line',
+        "query": 'segments\n  during last 12 weeks\n  reduce by run min 3 min\n  where pace between 5:20 and 5:40 /km\n  and pace_variability <= 5 %\n  and sensor_coverage >= 80 %\n  and duration >= 4 min\n  summarize weighted_mean(heart_rate) as "Steady-segment HR"\n  by week\n  compare none\n  baseline previous 90 days\n  flag above baseline 1.5 sd min 5 runs sustained 2 buckets\n  chart line',
     },
     {
         "name": "Speed/HR decoupling",
@@ -92,3 +92,11 @@ PRESETS = [
         "query": "runs\n  during last 6 months\n  summarize median(distance), median(pace), count()\n  by weekday\n  having count() >= 3 runs\n  compare none\n  chart bar",
     },
 ]
+
+
+# Editable advanced examples. No account observations or inferred HR boundaries.
+from .training_signals import signal_queries
+
+PRESETS.extend({"name": d["name"], "category": "Deep training signals",
+                "description": d["notes"], "query": d["query"]}
+               for d in signal_queries())