Add saved analysis workspace and live dashboard statistics

app/stats.py

@@ -61,6 +61,16 @@ DEFAULT = {
     "derived": [],
     "aggregates": {},
     "filter_groups": [],
+    "display": "separate",
+    "baseline": None,
+    "flag": None,
+    "color": None,
+    "size": None,
+    "labels": {},
+    "reference": None,
+    "source": "runs",
+    "cohorts": [],
+    "comparison": "period",
 }
 
 
@@ -121,7 +131,7 @@ def query_text(q):
         period = f"during last {q['days']} days"
     else:
         period = f"during last {q['weeks']} weeks"
-    lines = ["runs", "  " + period]
+    lines = [q["source"], "  " + period]
     for spec in q["derived"]:
         lines.append(f"  let {spec['name']} = {spec['expression']}")
     if q["end"] and not q["start"]:
@@ -166,7 +176,11 @@ def query_text(q):
             )
     for condition in q["filters"]:
         lines.append(clause(condition))
-    metrics = ", ".join(query_metrics(q))
+    metrics = ", ".join(
+        key
+        + (f" as {json.dumps(q['labels'][key], ensure_ascii=False)}" if key in q["labels"] else "")
+        for key in query_metrics(q)
+    )
     lines.extend(
         [
             f"  summarize {metrics}",
@@ -175,7 +189,14 @@ def query_text(q):
                 if q["bin"]
                 else f"  by {q['group']}"
             ),
-            "  compare " + ("previous period" if q["compare"] else "none"),
+            "  compare "
+            + (
+                "previous year"
+                if q["comparison"] == "year"
+                else "previous period"
+                if q["compare"]
+                else "none"
+            ),
         ]
     )
     if q["sort"] != ["date", "desc"]:
@@ -188,7 +209,7 @@ def query_text(q):
             "runs"
             if spec and spec["function"] == "count"
             else "sec/km"
-            if spec and field == "pace" and spec["function"] in {"sum", "stddev"}
+            if spec and field in {"pace", "gap"} and spec["function"] in {"sum", "stddev"}
             else next(iter(units[field]))
         )
         lines.append(f"  having {key} {condition['op']} {number_text(condition['value'])} {unit}")
@@ -203,6 +224,25 @@ def query_text(q):
         lines.append(f"  plot {q['plot'][0]} vs {q['plot'][1]}")
     if q["chart"] != "auto":
         lines.append(f"  chart {q['chart']}")
+    if q["display"] != "separate":
+        lines.append(f"  display {q['display']}")
+    if q["baseline"]:
+        lines.append(f"  baseline previous {q['baseline']} days")
+    if q["flag"]:
+        flag = q["flag"]
+        lines.append(
+            f"  flag {flag['direction']} baseline {number_text(flag['sd'])} sd min {flag['minimum']} runs sustained {flag['sustained']} buckets"
+        )
+    for key in ("color", "size"):
+        if q[key]:
+            lines.append(f"  {key} by {q[key]}")
+    if q["reference"]:
+        ref = q["reference"]
+        lines.append(f"  reference {ref['field']} = {ref['raw']} {ref['unit']}")
+    for cohort in q["cohorts"]:
+        lines.append(
+            f"  cohort {json.dumps(cohort['name'], ensure_ascii=False)} where {cohort['source']}"
+        )
     return "\n".join(lines)
 
 
@@ -254,16 +294,17 @@ def metric_value(row, key):
     if key == "heart_rate":
         return row.avg_hr
     if key in {"distance", "duration", "elevation"}:
-        return (
-            getattr(
-                row,
-                {"distance": "distance_m", "duration": "duration_s", "elevation": "elevation_m"}[
-                    key
-                ],
-            )
-            / {"distance": 1000, "duration": 3600, "elevation": 1}[key]
+        raw = getattr(
+            row,
+            {"distance": "distance_m", "duration": "duration_s", "elevation": "elevation_m"}[key],
         )
-    return (row.analysis_summary or {}).get(key)
+        return (
+            raw / {"distance": 1000, "duration": 3600, "elevation": 1}[key]
+            if isinstance(raw, (int, float)) and math.isfinite(raw)
+            else None
+        )
+    summary = row.analysis_summary or {}
+    return summary.get("summary", summary).get(key)
 
 
 def aggregate(rows, key):