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VAM: drop duration curve, show avg climbing VAM in Nerd Corner

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Remove the per-duration VAM curve everywhere (metrics, summaries, detail
JSON, athlete.json, VamChart.svelte, AthleteView VAM tab). Keep only
climbing_vam_mh per activity. Add it to activity summaries so NerdCorner
can plot average climbing VAM per week/month year-over-year alongside
distance/elevation/time. Add --backfill-vam-summary flag to copy the
field from existing detail JSONs into index.json without re-extracting.
This commit is contained in:
Davide Scaini
2026-05-16 22:03:40 +02:00
parent 7cd8a6b030
commit 003b540481
8 changed files with 77 additions and 346 deletions
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bincio/extract/metrics.py
+18 -59
View File
@@ -14,10 +14,7 @@ from bincio.extract.models import DataPoint, ParsedActivity
# Standard MMP durations (seconds). Log-spaced so the curve looks good on a log-x axis.
MMP_DURATIONS_S = [1, 2, 5, 10, 15, 20, 30, 60, 120, 180, 300, 600, 1200, 1800, 3600]
# VAM curve durations — start at 60 s (shorter windows are too noisy for elevation data).
VAM_DURATIONS_S = [60, 120, 180, 300, 600, 1200, 1800, 3600]
_VAM_SPORTS = frozenset({"cycling", "running", "hiking", "walking"})
_MIN_CLIMB_GAIN_M = 10.0 # minimum net gain in a window for VAM to be meaningful
# Standard best-effort distances (km) per sport.
BEST_EFFORT_DISTANCES: dict[str, list[float]] = {
@@ -67,8 +64,7 @@ class ComputedMetrics:
# [[distance_km, time_s], ...] sorted by distance — None if sport has no distance targets
best_efforts: Optional[list[list[float]]]
best_climb_m: Optional[float] # max net elevation gain in one contiguous window (cycling only)
climbing_vam_mh: Optional[int] # VAM on ascending segments only (m/h)
vam_curve: Optional[list[list[int]]] # [[duration_s, vam_mh], ...]
climbing_vam_mh: Optional[int] # average VAM on ascending segments only (m/h)
def compute(activity: ParsedActivity) -> ComputedMetrics:
@@ -88,7 +84,7 @@ def compute(activity: ParsedActivity) -> ComputedMetrics:
start_ll, end_ll = _endpoints(pts)
mmp = compute_mmp(pts, activity.started_at)
best_efforts, best_climb_m = compute_best_efforts(pts, activity.started_at, activity.sport)
climbing_vam_mh, vam_curve = compute_vam(pts, activity.started_at, activity.sport)
climbing_vam_mh = compute_vam(pts, activity.started_at, activity.sport)
return ComputedMetrics(
distance_m=distance_m,
@@ -111,7 +107,6 @@ def compute(activity: ParsedActivity) -> ComputedMetrics:
best_efforts=best_efforts,
best_climb_m=best_climb_m,
climbing_vam_mh=climbing_vam_mh,
vam_curve=vam_curve,
)
@@ -188,33 +183,18 @@ def _rolling_mean_ele(data: list[float], win: int) -> list[float]:
return result
def _vam_from_ele_1hz(ele_1hz: list[float]) -> tuple[Optional[int], Optional[list[list[int]]]]:
"""Core VAM computation from a dense 1 Hz elevation array."""
def _vam_from_ele_1hz(ele_1hz: list[float]) -> Optional[int]:
"""Climbing VAM from a dense 1 Hz elevation array.
Accumulates gain and time only on ascending seconds, identified by a 30 s
forward-lookahead on the smoothed elevation signal.
Returns climbing_vam_mh (m/h), or None when there is too little climbing data.
"""
n = len(ele_1hz)
if n < 60:
return None, None
return None
ele_smooth = _rolling_mean_ele(ele_1hz, 30)
# VAM curve: best VAM per standard duration, windows with net gain ≥ threshold only
vam_results: list[list[int]] = []
for d in VAM_DURATIONS_S:
if d >= n:
break
best_vam: Optional[float] = None
for i in range(n - d):
net_gain = ele_smooth[i + d] - ele_smooth[i]
if net_gain < _MIN_CLIMB_GAIN_M:
continue
vam = net_gain * 3600.0 / d
if best_vam is None or vam > best_vam:
best_vam = vam
if best_vam is not None:
vam_results.append([d, round(best_vam)])
vam_curve: Optional[list[list[int]]] = vam_results if vam_results else None
# Climbing VAM: accumulate gain and time only on ascending seconds.
# A second is climbing if the 30 s forward elevation gain exceeds 2 m
# (roughly 1 % gradient at 7 km/h).
climbing_gain = 0.0
climbing_time = 0
for i in range(n - 1):
@@ -225,11 +205,9 @@ def _vam_from_ele_1hz(ele_1hz: list[float]) -> tuple[Optional[int], Optional[lis
climbing_gain += inst
climbing_time += 1
climbing_vam_mh: Optional[int] = None
if climbing_time >= 60 and climbing_gain >= 5.0:
climbing_vam_mh = round(climbing_gain * 3600.0 / climbing_time)
return climbing_vam_mh, vam_curve
return round(climbing_gain * 3600.0 / climbing_time)
return None
def _build_ele_1hz(sparse: dict[int, Optional[float]]) -> Optional[list[float]]:
@@ -255,18 +233,14 @@ def _build_ele_1hz(sparse: dict[int, Optional[float]]) -> Optional[list[float]]:
return [e if e is not None else first_valid for e in ele_raw]
def compute_vam(
pts: list[DataPoint],
started_at: datetime,
sport: str,
) -> tuple[Optional[int], Optional[list[list[int]]]]:
"""Compute climbing VAM and VAM duration curve from DataPoints.
def compute_vam(pts: list[DataPoint], started_at: datetime, sport: str) -> Optional[int]:
"""Compute average climbing VAM (m/h) from DataPoints.
Returns (climbing_vam_mh, vam_curve).
Only computed for cycling, running, hiking, walking.
Returns None when the activity has insufficient climbing data.
"""
if sport not in _VAM_SPORTS:
return None, None
return None
sparse: dict[int, Optional[float]] = {}
last_t = -1
for p in pts:
@@ -277,22 +251,7 @@ def compute_vam(
last_t = t
ele_1hz = _build_ele_1hz(sparse)
if ele_1hz is None:
return None, None
return _vam_from_ele_1hz(ele_1hz)
def compute_vam_from_timeseries(ts: dict, sport: str) -> tuple[Optional[int], Optional[list[list[int]]]]:
"""Compute VAM from a stored timeseries dict (used for backfill without re-parsing files)."""
if sport not in _VAM_SPORTS:
return None, None
t_vals = ts.get("t") or []
ele_vals = ts.get("elevation_m") or []
if not t_vals or not ele_vals:
return None, None
sparse: dict[int, Optional[float]] = {int(t): e for t, e in zip(t_vals, ele_vals)}
ele_1hz = _build_ele_1hz(sparse)
if ele_1hz is None:
return None, None
return None
return _vam_from_ele_1hz(ele_1hz)
@@ -659,5 +618,5 @@ def _empty() -> ComputedMetrics:
avg_cadence_rpm=None, avg_power_w=None, np_power_w=None, max_power_w=None,
bbox=None, start_latlng=None, end_latlng=None,
mmp=None, best_efforts=None, best_climb_m=None,
climbing_vam_mh=None, vam_curve=None,
climbing_vam_mh=None,
)