Add saved analysis workspace and live dashboard statistics

app/effort_segments.py

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+"""Compact five-minute moving segments; gaps over ten seconds are excluded."""
+
+import math
+from itertools import pairwise
+
+from .analytics import numeric
+
+SEGMENT_SECONDS = 300
+
+
+def summary(intervals):
+    duration = sum(item["dt"] for item in intervals)
+    distance = sum(item["ds"] for item in intervals)
+    hr = [(item["hr"], item["dt"]) for item in intervals if numeric(item["hr"]) and item["hr"] > 0]
+    hr_time = sum(dt for _, dt in hr)
+    mean_speed = distance / duration if duration else None
+    variation = (
+        math.sqrt(
+            sum((item["ds"] / item["dt"] - mean_speed) ** 2 * item["dt"] for item in intervals)
+            / duration
+        )
+        / mean_speed
+        * 100
+        if duration and mean_speed
+        else None
+    )
+    drift = None
+    if duration >= 240 and hr_time >= duration * 0.8:
+        halves = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
+        elapsed = 0.0
+        for item in intervals:
+            for index, begin, end in ((0, 0, duration / 2), (1, duration / 2, duration)):
+                dt = max(0, min(elapsed + item["dt"], end) - max(elapsed, begin))
+                if dt and numeric(item["hr"]) and item["hr"] > 0:
+                    halves[index][0] += item["ds"] * dt / item["dt"]
+                    halves[index][1] += item["hr"] * dt
+                    halves[index][2] += dt
+            elapsed += item["dt"]
+        if all(part[2] >= duration * 0.4 and part[0] > 0 and part[1] > 0 for part in halves):
+            efficiency = [ds / hr_total for ds, hr_total, _ in halves]
+            drift = (efficiency[0] - efficiency[1]) / efficiency[0] * 100
+    return {
+        "distance": distance,
+        "duration": duration,
+        "heart_rate": sum(value * dt for value, dt in hr) / hr_time if hr_time else None,
+        "elevation": sum(item["ascent"] for item in intervals)
+        if duration
+        and sum(item["dt"] for item in intervals if item["has_elevation"]) >= duration * 0.8
+        else None,
+        "pace_variability": variation,
+        "heart_rate_drift": drift,
+        "sensor_coverage": hr_time / duration * 100 if duration else None,
+    }
+
+
+def analyze(samples):
+    intervals, segments, pending, elapsed, offset = [], [], [], 0.0, 0.0
+    for before, after in pairwise(samples):
+        if not all(
+            numeric(row.get(key)) for row in (before, after) for key in ("time", "distance")
+        ):
+            continue
+        dt, ds = after["time"] - before["time"], after["distance"] - before["distance"]
+        if not 0 < dt <= 10 or not 0.5 <= ds / dt <= 15:
+            continue
+        ascent = (
+            max(0, after["elevation"] - before["elevation"])
+            if all(numeric(row.get("elevation")) for row in (before, after))
+            else None
+        )
+        original = {
+            "dt": dt,
+            "ds": ds,
+            "hr": after.get("heart_rate"),
+            "ascent": ascent or 0,
+            "has_elevation": ascent is not None,
+        }
+        intervals.append(original)
+        remaining = dt
+        while remaining > 1e-9:
+            portion = min(remaining, SEGMENT_SECONDS - elapsed)
+            pending.append(
+                {
+                    **original,
+                    "dt": portion,
+                    "ds": ds * portion / dt,
+                    "ascent": (ascent or 0) * portion / dt,
+                }
+            )
+            elapsed += portion
+            remaining -= portion
+            if elapsed >= SEGMENT_SECONDS - 1e-9:
+                segments.append({**summary(pending), "offset": offset})
+                offset += elapsed
+                pending, elapsed = [], 0.0
+    if pending:
+        segments.append({**summary(pending), "offset": offset})
+    return summary(intervals), segments