Add VAM (climbing velocity) metric and per-duration curve
Extract pipeline now computes two VAM metrics per activity (cycling, running, hiking, walking): - climbing_vam_mh: VAM on ascending segments only, using 30 s forward lookahead to classify climbing vs. flat/descent (stored in detail JSON) - vam_curve: [[duration_s, vam_mh], ...] best VAM per standard duration (60 s – 1 h), sliding window on 30 s smoothed elevation, only windows with ≥ 10 m net gain count (stored in summary + detail) Athlete JSON aggregates vam_curve across all activities (all_time, last_365d, last_90d), same structure as power_curve. Frontend: - ActivityDetail shows "Climbing VAM" stat (grouped with elevation) - AthleteView adds a "VAM Curve" tab that appears only when the athlete has climbing data; renders VamChart (new component, mirrors MmpChart) vam_curve stripped from combined global feed; kept in user year shards for season-based on-the-fly aggregation in VamChart. Requires bincio reextract to backfill existing activities.
This commit is contained in:
Davide Scaini
@@ -14,6 +14,11 @@ from bincio.extract.models import DataPoint, ParsedActivity
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# Standard MMP durations (seconds). Log-spaced so the curve looks good on a log-x axis.
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MMP_DURATIONS_S = [1, 2, 5, 10, 15, 20, 30, 60, 120, 180, 300, 600, 1200, 1800, 3600]
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# VAM curve durations — start at 60 s (shorter windows are too noisy for elevation data).
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VAM_DURATIONS_S = [60, 120, 180, 300, 600, 1200, 1800, 3600]
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_VAM_SPORTS = frozenset({"cycling", "running", "hiking", "walking"})
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_MIN_CLIMB_GAIN_M = 10.0 # minimum net gain in a window for VAM to be meaningful
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# Standard best-effort distances (km) per sport.
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BEST_EFFORT_DISTANCES: dict[str, list[float]] = {
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"running": [0.4, 1.0, 1.609, 5.0, 10.0, 21.097, 42.195],
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@@ -62,6 +67,8 @@ class ComputedMetrics:
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# [[distance_km, time_s], ...] sorted by distance — None if sport has no distance targets
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best_efforts: Optional[list[list[float]]]
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best_climb_m: Optional[float] # max net elevation gain in one contiguous window (cycling only)
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climbing_vam_mh: Optional[int] # VAM on ascending segments only (m/h)
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vam_curve: Optional[list[list[int]]] # [[duration_s, vam_mh], ...]
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def compute(activity: ParsedActivity) -> ComputedMetrics:
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@@ -81,6 +88,7 @@ def compute(activity: ParsedActivity) -> ComputedMetrics:
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start_ll, end_ll = _endpoints(pts)
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mmp = compute_mmp(pts, activity.started_at)
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best_efforts, best_climb_m = compute_best_efforts(pts, activity.started_at, activity.sport)
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climbing_vam_mh, vam_curve = compute_vam(pts, activity.started_at, activity.sport)
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return ComputedMetrics(
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distance_m=distance_m,
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@@ -102,6 +110,8 @@ def compute(activity: ParsedActivity) -> ComputedMetrics:
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mmp=mmp,
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best_efforts=best_efforts,
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best_climb_m=best_climb_m,
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climbing_vam_mh=climbing_vam_mh,
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vam_curve=vam_curve,
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)
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@@ -161,6 +171,114 @@ def compute_mmp(pts: list[DataPoint], started_at: datetime) -> Optional[list[lis
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return results if results else None
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# ── VAM (Velocità Ascensionale Media) ────────────────────────────────────────
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def _rolling_mean_ele(data: list[float], win: int) -> list[float]:
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"""O(n) rolling mean via prefix sums."""
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n = len(data)
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prefix = [0.0] * (n + 1)
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for i, v in enumerate(data):
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prefix[i + 1] = prefix[i] + v
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half = win // 2
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result = []
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for i in range(n):
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lo = max(0, i - half)
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hi = min(n, i + half + 1)
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result.append((prefix[hi] - prefix[lo]) / (hi - lo))
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return result
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def compute_vam(
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pts: list[DataPoint],
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started_at: datetime,
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sport: str,
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) -> tuple[Optional[int], Optional[list[list[int]]]]:
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"""Compute climbing VAM and VAM duration curve.
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Returns (climbing_vam_mh, vam_curve).
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climbing_vam_mh: VAM on ascending segments only (m/h), or None.
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vam_curve: [[duration_s, vam_mh], ...] best VAM per standard duration, or None.
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Only computed for cycling, running, hiking, walking.
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"""
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if sport not in _VAM_SPORTS:
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return None, None
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# Build dense 1 Hz elevation array, forward-filling gaps
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sparse: dict[int, Optional[float]] = {}
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last_t = -1
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for p in pts:
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t = int((p.timestamp - started_at).total_seconds())
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if t < 0 or t == last_t:
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continue
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sparse[t] = p.elevation_m
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last_t = t
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if not sparse:
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return None, None
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t_min = min(sparse)
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t_max = max(sparse)
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if t_max - t_min > 7 * 24 * 3600:
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return None, None
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ele_raw: list[Optional[float]] = []
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last_known: Optional[float] = None
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for t in range(t_min, t_max + 1):
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v = sparse.get(t)
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if v is not None:
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last_known = v
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ele_raw.append(last_known)
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if sum(1 for e in ele_raw if e is not None) < 60:
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return None, None
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first_valid = next((e for e in ele_raw if e is not None), None)
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if first_valid is None:
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return None, None
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ele_1hz: list[float] = [e if e is not None else first_valid for e in ele_raw]
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n = len(ele_1hz)
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ele_smooth = _rolling_mean_ele(ele_1hz, 30)
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# VAM curve: sliding window per duration, only windows with net gain above threshold
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vam_results: list[list[int]] = []
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for d in VAM_DURATIONS_S:
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if d >= n:
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break
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best_vam: Optional[float] = None
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for i in range(n - d):
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net_gain = ele_smooth[i + d] - ele_smooth[i]
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if net_gain < _MIN_CLIMB_GAIN_M:
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continue
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vam = net_gain * 3600.0 / d
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if best_vam is None or vam > best_vam:
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best_vam = vam
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if best_vam is not None:
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vam_results.append([d, round(best_vam)])
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vam_curve: Optional[list[list[int]]] = vam_results if vam_results else None
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# Climbing VAM: accumulate gain and time only on ascending seconds.
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# A second is climbing if the 30 s forward elevation gain exceeds 2 m
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# (roughly 1 % gradient at 7 km/h).
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_LOOK = 30
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_THRESH = 2.0
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climbing_gain = 0.0
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climbing_time = 0
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for i in range(n - 1):
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look = min(i + _LOOK, n - 1)
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if ele_smooth[look] - ele_smooth[i] >= _THRESH:
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inst = ele_smooth[i + 1] - ele_smooth[i]
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if inst > 0:
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climbing_gain += inst
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climbing_time += 1
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climbing_vam_mh: Optional[int] = None
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if climbing_time >= 60 and climbing_gain >= 5.0:
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climbing_vam_mh = round(climbing_gain * 3600.0 / climbing_time)
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return climbing_vam_mh, vam_curve
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# ── best efforts & best climb ─────────────────────────────────────────────────
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def compute_best_efforts(
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@@ -524,4 +642,5 @@ def _empty() -> ComputedMetrics:
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avg_cadence_rpm=None, avg_power_w=None, np_power_w=None, max_power_w=None,
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bbox=None, start_latlng=None, end_latlng=None,
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mmp=None, best_efforts=None, best_climb_m=None,
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climbing_vam_mh=None, vam_curve=None,
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)
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@@ -101,6 +101,8 @@ def write_activity(
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"mmp": metrics.mmp,
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"best_efforts": metrics.best_efforts,
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"best_climb_m": metrics.best_climb_m,
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"climbing_vam_mh": metrics.climbing_vam_mh,
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"vam_curve": metrics.vam_curve,
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"laps": [_serialise_lap(lap) for lap in activity.laps],
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"timeseries_url": f"activities/{activity_id}.timeseries.json" if timeseries else None,
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"source": source,
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@@ -257,6 +259,7 @@ def build_summary(
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"mmp": metrics.mmp,
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"best_efforts": metrics.best_efforts,
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"best_climb_m": metrics.best_climb_m,
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"vam_curve": metrics.vam_curve,
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"source": _infer_source(activity),
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"privacy": privacy,
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"detail_url": f"activities/{activity_id}.json",
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@@ -300,6 +303,25 @@ def write_athlete_json(summaries: list[dict], output_dir: Path, athlete_config:
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mmps_365 = [s["mmp"] for s in summaries if s.get("mmp") and _is_outdoor(s) and s["started_at"] >= cutoff_365]
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mmps_90 = [s["mmp"] for s in summaries if s.get("mmp") and _is_outdoor(s) and s["started_at"] >= cutoff_90]
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# ── VAM curve aggregation ─────────────────────────────────────────────────
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def _merge_vam_curves(vam_lists: list[list[list[int]]]) -> list[list[int]]:
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best: dict[int, int] = {}
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for vc in vam_lists:
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for d, v in vc:
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if d not in best or v > best[d]:
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best[d] = v
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return [[d, v] for d, v in sorted(best.items())]
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_VAM_SPORTS = {"cycling", "running", "hiking", "walking"}
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def _has_vam(s: dict) -> bool:
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return bool(s.get("vam_curve")) and s.get("sport") in _VAM_SPORTS and _is_outdoor(s)
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all_vams = [s["vam_curve"] for s in summaries if _has_vam(s)]
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vams_365 = [s["vam_curve"] for s in summaries if _has_vam(s) and s["started_at"] >= cutoff_365]
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vams_90 = [s["vam_curve"] for s in summaries if _has_vam(s) and s["started_at"] >= cutoff_90]
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# ── Personal records aggregation ──────────────────────────────────────────
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# records[sport][distance_km] = {time_s, activity_id, started_at, title}
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# best_climb[activity_id] = {climb_m, started_at, title}
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@@ -368,6 +390,11 @@ def write_athlete_json(summaries: list[dict], output_dir: Path, athlete_config:
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"last_365d": _merge_mmps(mmps_365) if mmps_365 else None,
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"last_90d": _merge_mmps(mmps_90) if mmps_90 else None,
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},
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"vam_curve": {
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"all_time": _merge_vam_curves(all_vams) if all_vams else None,
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"last_365d": _merge_vam_curves(vams_365) if vams_365 else None,
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"last_90d": _merge_vam_curves(vams_90) if vams_90 else None,
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},
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"records": {
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sport: _serialise_sport_records(records[sport])
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for sport in SPORTS
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@@ -116,7 +116,7 @@ def _rebuild_athlete_json(data: Path, handle: str | None = None) -> None:
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from bincio.render.merge import parse_sidecar, _apply_sidecar_summary
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targets = [data / handle] if handle else _user_dirs(data)
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_COMPUTED = {"bas_version", "generated_at", "power_curve", "records", "best_climbs"}
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_COMPUTED = {"bas_version", "generated_at", "power_curve", "vam_curve", "records", "best_climbs"}
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for user_dir in targets:
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index_path = user_dir / "index.json"
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if not index_path.exists():
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@@ -421,7 +421,7 @@ FEED_PAGE_SIZE = 50
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# Extra fields stripped from the combined feed — preview_coords is the biggest
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# contributor (~24% of shard size) but the feed cards need it for thumbnails,
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# so we keep it. mmp is never displayed in feed cards.
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_COMBINED_FEED_STRIP = _FEED_STRIP | {"mmp"}
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_COMBINED_FEED_STRIP = _FEED_STRIP | {"mmp", "vam_curve"}
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def write_combined_feed(data_dir: Path) -> int:
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