VAM: add --recompute-vam flag and compute_vam_from_timeseries helper
Refactors core VAM logic into _vam_from_ele_1hz() and _build_ele_1hz() so both the DataPoint-based extract path and the timeseries-based backfill path share the same implementation. render --recompute-vam reads stored *.timeseries.json files and updates climbing_vam_mh + vam_curve in activities/*.json and index.json in-place, without re-parsing the original FIT/GPX files.
This commit is contained in:
Davide Scaini
+71
-54
@@ -188,59 +188,14 @@ def _rolling_mean_ele(data: list[float], win: int) -> list[float]:
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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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def _vam_from_ele_1hz(ele_1hz: list[float]) -> tuple[Optional[int], Optional[list[list[int]]]]:
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"""Core VAM computation from a dense 1 Hz elevation array."""
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n = len(ele_1hz)
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if n < 60:
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return None, None
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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 curve: best VAM per standard duration, windows with net gain ≥ threshold only
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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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@@ -260,13 +215,11 @@ def compute_vam(
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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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look = min(i + 30, n - 1)
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if ele_smooth[look] - ele_smooth[i] >= 2.0:
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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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@@ -279,6 +232,70 @@ def compute_vam(
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return climbing_vam_mh, vam_curve
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def _build_ele_1hz(sparse: dict[int, Optional[float]]) -> Optional[list[float]]:
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"""Build a dense 1 Hz elevation array from a {t: ele} sparse dict, forward-filling gaps."""
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if not sparse:
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return 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
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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
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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
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return [e if e is not None else first_valid for e in ele_raw]
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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 from DataPoints.
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Returns (climbing_vam_mh, vam_curve).
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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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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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ele_1hz = _build_ele_1hz(sparse)
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if ele_1hz is None:
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return None, None
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return _vam_from_ele_1hz(ele_1hz)
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def compute_vam_from_timeseries(ts: dict, sport: str) -> tuple[Optional[int], Optional[list[list[int]]]]:
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"""Compute VAM from a stored timeseries dict (used for backfill without re-parsing files)."""
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if sport not in _VAM_SPORTS:
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return None, None
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t_vals = ts.get("t") or []
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ele_vals = ts.get("elevation_m") or []
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if not t_vals or not ele_vals:
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return None, None
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sparse: dict[int, Optional[float]] = {int(t): e for t, e in zip(t_vals, ele_vals)}
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ele_1hz = _build_ele_1hz(sparse)
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if ele_1hz is None:
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return None, None
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return _vam_from_ele_1hz(ele_1hz)
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# ── best efforts & best climb ─────────────────────────────────────────────────
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def compute_best_efforts(
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@@ -377,6 +377,58 @@ def _link_data(site: Path, data: Path) -> None:
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console.print(f"Linked data: [cyan]{target}[/cyan] → [cyan]{public_data}[/cyan]")
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def _recompute_vam(data: Path, handle: str | None = None) -> None:
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"""Recompute climbing_vam_mh and vam_curve for all activities from stored timeseries."""
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import json
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from bincio.extract.metrics import compute_vam_from_timeseries
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targets = [data / handle] if handle else _user_dirs(data)
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for user_dir in targets:
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acts_dir = user_dir / "activities"
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index_path = user_dir / "index.json"
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if not acts_dir.exists() or not index_path.exists():
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continue
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try:
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index_data = json.loads(index_path.read_text(encoding="utf-8"))
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except Exception:
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continue
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updated = 0
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for act_path in acts_dir.glob("*.json"):
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if act_path.stem.endswith((".timeseries", ".geojson")):
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continue
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ts_path = acts_dir / f"{act_path.stem}.timeseries.json"
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if not ts_path.exists():
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continue
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try:
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detail = json.loads(act_path.read_text(encoding="utf-8"))
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sport = detail.get("sport", "other")
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ts = json.loads(ts_path.read_text(encoding="utf-8"))
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new_vam, new_curve = compute_vam_from_timeseries(ts, sport)
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if (new_vam == detail.get("climbing_vam_mh")
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and new_curve == detail.get("vam_curve")):
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continue
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detail["climbing_vam_mh"] = new_vam
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detail["vam_curve"] = new_curve
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act_path.write_text(
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json.dumps(detail, indent=2, ensure_ascii=False), encoding="utf-8"
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)
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act_id = act_path.stem
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for s in index_data.get("activities", []):
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if s.get("id") == act_id:
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s["vam_curve"] = new_curve
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break
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updated += 1
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except Exception:
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pass
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if updated:
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index_path.write_text(
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json.dumps(index_data, indent=2, ensure_ascii=False), encoding="utf-8"
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)
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console.print(f" [cyan]{user_dir.name}[/cyan]: {updated} activity(ies) updated")
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@click.command()
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@click.option("--config", "config_path", default=None,
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help="Path to extract_config.yaml (reads output.dir from it).")
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@@ -400,6 +452,9 @@ def _link_data(site: Path, data: Path) -> None:
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@click.option("--recompute-elevation", "recompute_elevation", is_flag=True,
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help="Recompute elevation_gain_m/loss_m for all activities from stored timeseries "
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"(run once after upgrading the dropout-skip fix).")
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@click.option("--recompute-vam", "recompute_vam", is_flag=True,
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help="Recompute climbing_vam_mh and vam_curve for all activities from stored "
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"timeseries (run once after adding VAM support).")
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def render(
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config_path: Optional[str],
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data_dir: Optional[str],
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@@ -411,6 +466,7 @@ def render(
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no_build: bool,
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recompute_climbs: bool,
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recompute_elevation: bool,
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recompute_vam: bool,
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) -> None:
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"""Build (or serve) the BincioActivity static site from a BAS data store."""
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@@ -428,6 +484,10 @@ def render(
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console.print("Recomputing elevation gain/loss from timeseries…")
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_recompute_elevation(data, handle=handle)
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if recompute_vam:
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console.print("Recomputing VAM from timeseries…")
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_recompute_vam(data, handle=handle)
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_merge_edits(data, handle=handle)
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_rebuild_athlete_json(data, handle=handle)
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_bake_tracks(data, handle=handle)
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