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.

bincio/extract/metrics.py

@@ -14,6 +14,11 @@ 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]] = {
     "running": [0.4, 1.0, 1.609, 5.0, 10.0, 21.097, 42.195],
@@ -62,6 +67,8 @@ 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], ...]
 
 
 def compute(activity: ParsedActivity) -> ComputedMetrics:
@@ -81,6 +88,7 @@ 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)
 
     return ComputedMetrics(
         distance_m=distance_m,
@@ -102,6 +110,8 @@ def compute(activity: ParsedActivity) -> ComputedMetrics:
         mmp=mmp,
         best_efforts=best_efforts,
         best_climb_m=best_climb_m,
+        climbing_vam_mh=climbing_vam_mh,
+        vam_curve=vam_curve,
     )
 
 
@@ -161,6 +171,114 @@ def compute_mmp(pts: list[DataPoint], started_at: datetime) -> Optional[list[lis
     return results if results else None
 
 
+# ── VAM (Velocità Ascensionale Media) ────────────────────────────────────────
+
+def _rolling_mean_ele(data: list[float], win: int) -> list[float]:
+    """O(n) rolling mean via prefix sums."""
+    n = len(data)
+    prefix = [0.0] * (n + 1)
+    for i, v in enumerate(data):
+        prefix[i + 1] = prefix[i] + v
+    half = win // 2
+    result = []
+    for i in range(n):
+        lo = max(0, i - half)
+        hi = min(n, i + half + 1)
+        result.append((prefix[hi] - prefix[lo]) / (hi - lo))
+    return result
+
+
+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.
+
+    Returns (climbing_vam_mh, vam_curve).
+    climbing_vam_mh: VAM on ascending segments only (m/h), or None.
+    vam_curve: [[duration_s, vam_mh], ...] best VAM per standard duration, or None.
+    Only computed for cycling, running, hiking, walking.
+    """
+    if sport not in _VAM_SPORTS:
+        return None, None
+
+    # Build dense 1 Hz elevation array, forward-filling gaps
+    sparse: dict[int, Optional[float]] = {}
+    last_t = -1
+    for p in pts:
+        t = int((p.timestamp - started_at).total_seconds())
+        if t < 0 or t == last_t:
+            continue
+        sparse[t] = p.elevation_m
+        last_t = t
+
+    if not sparse:
+        return None, None
+
+    t_min = min(sparse)
+    t_max = max(sparse)
+    if t_max - t_min > 7 * 24 * 3600:
+        return None, None
+
+    ele_raw: list[Optional[float]] = []
+    last_known: Optional[float] = None
+    for t in range(t_min, t_max + 1):
+        v = sparse.get(t)
+        if v is not None:
+            last_known = v
+        ele_raw.append(last_known)
+
+    if sum(1 for e in ele_raw if e is not None) < 60:
+        return None, None
+
+    first_valid = next((e for e in ele_raw if e is not None), None)
+    if first_valid is None:
+        return None, None
+    ele_1hz: list[float] = [e if e is not None else first_valid for e in ele_raw]
+
+    n = len(ele_1hz)
+    ele_smooth = _rolling_mean_ele(ele_1hz, 30)
+
+    # VAM curve: sliding window per duration, only windows with net gain above threshold
+    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).
+    _LOOK = 30
+    _THRESH = 2.0
+    climbing_gain = 0.0
+    climbing_time = 0
+    for i in range(n - 1):
+        look = min(i + _LOOK, n - 1)
+        if ele_smooth[look] - ele_smooth[i] >= _THRESH:
+            inst = ele_smooth[i + 1] - ele_smooth[i]
+            if inst > 0:
+                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
+
+
 # ── best efforts & best climb ─────────────────────────────────────────────────
 
 def compute_best_efforts(
@@ -524,4 +642,5 @@ 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,
     )

bincio/extract/writer.py

@@ -101,6 +101,8 @@ def write_activity(
         "mmp": metrics.mmp,
         "best_efforts": metrics.best_efforts,
         "best_climb_m": metrics.best_climb_m,
+        "climbing_vam_mh": metrics.climbing_vam_mh,
+        "vam_curve": metrics.vam_curve,
         "laps": [_serialise_lap(lap) for lap in activity.laps],
         "timeseries_url": f"activities/{activity_id}.timeseries.json" if timeseries else None,
         "source": source,
@@ -257,6 +259,7 @@ def build_summary(
         "mmp": metrics.mmp,
         "best_efforts": metrics.best_efforts,
         "best_climb_m": metrics.best_climb_m,
+        "vam_curve": metrics.vam_curve,
         "source": _infer_source(activity),
         "privacy": privacy,
         "detail_url": f"activities/{activity_id}.json",
@@ -300,6 +303,25 @@ def write_athlete_json(summaries: list[dict], output_dir: Path, athlete_config:
     mmps_365 = [s["mmp"] for s in summaries if s.get("mmp") and _is_outdoor(s) and s["started_at"] >= cutoff_365]
     mmps_90  = [s["mmp"] for s in summaries if s.get("mmp") and _is_outdoor(s) and s["started_at"] >= cutoff_90]
 
+    # ── VAM curve aggregation ─────────────────────────────────────────────────
+
+    def _merge_vam_curves(vam_lists: list[list[list[int]]]) -> list[list[int]]:
+        best: dict[int, int] = {}
+        for vc in vam_lists:
+            for d, v in vc:
+                if d not in best or v > best[d]:
+                    best[d] = v
+        return [[d, v] for d, v in sorted(best.items())]
+
+    _VAM_SPORTS = {"cycling", "running", "hiking", "walking"}
+
+    def _has_vam(s: dict) -> bool:
+        return bool(s.get("vam_curve")) and s.get("sport") in _VAM_SPORTS and _is_outdoor(s)
+
+    all_vams = [s["vam_curve"] for s in summaries if _has_vam(s)]
+    vams_365  = [s["vam_curve"] for s in summaries if _has_vam(s) and s["started_at"] >= cutoff_365]
+    vams_90   = [s["vam_curve"] for s in summaries if _has_vam(s) and s["started_at"] >= cutoff_90]
+
     # ── Personal records aggregation ──────────────────────────────────────────
     # records[sport][distance_km] = {time_s, activity_id, started_at, title}
     # best_climb[activity_id] = {climb_m, started_at, title}
@@ -368,6 +390,11 @@ def write_athlete_json(summaries: list[dict], output_dir: Path, athlete_config:
             "last_365d": _merge_mmps(mmps_365) if mmps_365 else None,
             "last_90d":  _merge_mmps(mmps_90)  if mmps_90  else None,
         },
+        "vam_curve": {
+            "all_time":  _merge_vam_curves(all_vams) if all_vams else None,
+            "last_365d": _merge_vam_curves(vams_365)  if vams_365  else None,
+            "last_90d":  _merge_vam_curves(vams_90)   if vams_90   else None,
+        },
         "records": {
             sport: _serialise_sport_records(records[sport])
             for sport in SPORTS

bincio/render/cli.py

@@ -116,7 +116,7 @@ def _rebuild_athlete_json(data: Path, handle: str | None = None) -> None:
     from bincio.render.merge import parse_sidecar, _apply_sidecar_summary
 
     targets = [data / handle] if handle else _user_dirs(data)
-    _COMPUTED = {"bas_version", "generated_at", "power_curve", "records", "best_climbs"}
+    _COMPUTED = {"bas_version", "generated_at", "power_curve", "vam_curve", "records", "best_climbs"}
     for user_dir in targets:
         index_path = user_dir / "index.json"
         if not index_path.exists():

bincio/render/merge.py

@@ -421,7 +421,7 @@ FEED_PAGE_SIZE = 50
 # Extra fields stripped from the combined feed — preview_coords is the biggest
 # contributor (~24% of shard size) but the feed cards need it for thumbnails,
 # so we keep it.  mmp is never displayed in feed cards.
-_COMBINED_FEED_STRIP = _FEED_STRIP | {"mmp"}
+_COMBINED_FEED_STRIP = _FEED_STRIP | {"mmp", "vam_curve"}
 
 
 def write_combined_feed(data_dir: Path) -> int:

site/src/components/ActivityDetail.svelte

@@ -183,6 +183,9 @@
     stat('Distance',    formatDistance(activity.distance_m)),
     stat('Moving time', formatDuration(activity.moving_time_s ?? activity.duration_s)),
     stat('Elevation ↑', formatElevation(activity.elevation_gain_m),                    'elevation'),
+    ...(detail?.climbing_vam_mh != null ? [
+      stat('Climbing VAM', `${detail.climbing_vam_mh.toLocaleString()} m/h`,           'elevation'),
+    ] : []),
     stat('Avg speed',   formatSpeed(activity.avg_speed_kmh),                           'speed'),
     stat('Max speed',   formatSpeed(activity.max_speed_kmh),                           'speed'),
     stat('Avg HR',      activity.avg_hr_bpm ? `${activity.avg_hr_bpm} bpm` : '—',     'heart_rate'),

site/src/components/AthleteView.svelte

@@ -2,6 +2,7 @@
   import { onMount } from 'svelte';
   import type { AthleteJson, BASIndex, ActivitySummary } from '../lib/types';
   import MmpChart from './MmpChart.svelte';
+  import VamChart from './VamChart.svelte';
   import RecordsView from './RecordsView.svelte';
   import AthleteDrawer from './AthleteDrawer.svelte';
   import Explore from './Explore.svelte';
@@ -19,12 +20,13 @@
 
   let athlete: AthleteJson | null = null;
   let activities:    ActivitySummary[] = [];
+  let vamActivities: ActivitySummary[] = [];
   let allActivities: ActivitySummary[] = [];
   let loading = true;
   let error: string | null = null;
   let drawerOpen = false;
 
-  type Tab = 'power' | 'records' | 'segments' | 'profile' | 'explore' | 'nerd';
+  type Tab = 'power' | 'vam' | 'records' | 'segments' | 'profile' | 'explore' | 'nerd';
   let activeTab: Tab = 'power';
   let mounted = false;
   let isOwner = false;
@@ -94,7 +96,7 @@
         isOwner = (e as CustomEvent<string>).detail === handle;
       }, { once: true });
     }
-    const TABS: Tab[] = ['power', 'records', 'segments', 'profile', 'explore', 'nerd'];
+    const TABS: Tab[] = ['power', 'vam', 'records', 'segments', 'profile', 'explore', 'nerd'];
     const rawTab = new URLSearchParams(window.location.search).get('tab');
     const resolved = TABS.includes(rawTab as Tab) ? (rawTab as Tab) : 'power';
     activeTab = (resolved === 'explore' && !isOwner) ? 'power' : resolved;
@@ -131,6 +133,7 @@
       athlete = resolvedAthlete;
       allActivities = index.activities.filter(a => !isUnlisted(a.privacy));
       activities    = allActivities.filter(a => a.mmp);
+      vamActivities = allActivities.filter(a => a.vam_curve);
     } catch (e: any) {
       error = e.message;
     } finally {
@@ -156,15 +159,19 @@
     return hi >= 900 ? `${lo}+ bpm` : `${lo}–${hi} bpm`;
   }
 
-  const ALL_TABS: { key: Tab; label: string; ownerOnly?: boolean }[] = [
+  const ALL_TABS: { key: Tab; label: string; ownerOnly?: boolean; requiresVam?: boolean }[] = [
     { key: 'power',    label: 'Power Curve' },
+    { key: 'vam',      label: 'VAM Curve',    requiresVam: true },
     { key: 'records',  label: 'Records'     },
     { key: 'segments', label: 'Segments'    },
     { key: 'profile',  label: 'Profile'     },
     { key: 'explore',  label: 'Explore',      ownerOnly: true },
     { key: 'nerd',     label: 'Nerd Corner',  ownerOnly: true },
   ];
-  $: TABS = ALL_TABS.filter(t => !t.ownerOnly || isOwner);
+  $: TABS = ALL_TABS.filter(t =>
+    (!t.ownerOnly || isOwner) &&
+    (!t.requiresVam || athlete?.vam_curve?.all_time != null)
+  );
 </script>
 
 {#if loading}
@@ -216,6 +223,16 @@
       <p class="text-zinc-500 text-sm">No power data found. Make sure your activities include power meter data.</p>
     {/if}
 
+  <!-- VAM Curve tab -->
+  {:else if activeTab === 'vam'}
+    {#if athlete.vam_curve?.all_time}
+      <div class="bg-zinc-900 rounded-xl p-4 border border-zinc-800">
+        <VamChart {athlete} activities={vamActivities} />
+      </div>
+    {:else}
+      <p class="text-zinc-500 text-sm">No climbing data found.</p>
+    {/if}
+
   <!-- Records tab -->
   {:else if activeTab === 'records'}
     <RecordsView {athlete} {base} />

site/src/components/VamChart.svelte

@@ -0,0 +1,188 @@
+<script lang="ts">
+  import { onMount } from 'svelte';
+  import * as Plot from '@observablehq/plot';
+  import type { AthleteJson, MmpCurve, ActivitySummary } from '../lib/types';
+
+  export let athlete: AthleteJson;
+  export let activities: ActivitySummary[] = [];
+
+  type RangeKey = 'all_time' | 'last_365d' | 'last_90d' | string;
+
+  interface Season { name: string; start: string; end: string }
+  const seasons: Season[] = (athlete as any).seasons ?? [];
+
+  let selectedRanges: Set<RangeKey> = new Set(['all_time']);
+
+  const PRESET_LABELS: Record<string, string> = {
+    all_time:   'All time',
+    last_365d:  'Last 365 d',
+    last_90d:   'Last 90 d',
+  };
+
+  const PALETTE = [
+    '#34d399', // emerald-400
+    '#f97316', // orange-500
+    '#60a5fa', // blue-400
+    '#a78bfa', // violet-400
+    '#f43f5e', // rose-500
+    '#facc15', // yellow-400
+    '#22d3ee', // cyan-400
+  ];
+
+  function curveColor(key: RangeKey, index: number): string {
+    return PALETTE[index % PALETTE.length];
+  }
+
+  function mergeVams(curves: MmpCurve[]): MmpCurve {
+    const best = new Map<number, number>();
+    for (const curve of curves) {
+      for (const [d, v] of curve) {
+        const prev = best.get(d);
+        if (prev === undefined || v > prev) best.set(d, v);
+      }
+    }
+    return [...best.entries()].sort((a, b) => a[0] - b[0]) as MmpCurve;
+  }
+
+  function vamForRange(key: RangeKey): MmpCurve | null {
+    if (key in PRESET_LABELS) {
+      return athlete.vam_curve?.[key as keyof typeof athlete.vam_curve] ?? null;
+    }
+    const season = seasons.find(s => s.name === key);
+    if (!season) return null;
+    const curves = activities
+      .filter(a => a.vam_curve && a.started_at >= season.start && a.started_at <= season.end + 'T23:59:59')
+      .map(a => a.vam_curve!);
+    return curves.length ? mergeVams(curves) : null;
+  }
+
+  let chartEl: HTMLElement;
+
+  function formatDuration(s: number): string {
+    if (s < 60)   return `${s}s`;
+    if (s < 3600) return `${Math.round(s / 60)}min`;
+    return `${s / 3600}h`;
+  }
+
+  $: selectedKeys = [...selectedRanges];
+
+  $: plotData = selectedKeys.flatMap((key, i) => {
+    const curve = vamForRange(key);
+    if (!curve) return [];
+    return curve.map(([d, v]) => ({ d, v, label: key }));
+  });
+
+  $: colorMap = Object.fromEntries(selectedKeys.map((k, i) => [k, curveColor(k, i)]));
+
+  function getAxisColor() {
+    return document.documentElement.getAttribute('data-theme') === 'light' ? '#52525b' : '#a1a1aa';
+  }
+
+  function renderChart(data: typeof plotData, cmap: typeof colorMap) {
+    if (!chartEl) return;
+    chartEl.innerHTML = '';
+    if (!data.length) return;
+
+    const labelFn = (key: string) => PRESET_LABELS[key] ?? key;
+
+    const chart = Plot.plot({
+      width: chartEl.clientWidth || 700,
+      height: 320,
+      marginLeft: 60,
+      marginBottom: 40,
+      style: { background: 'transparent', color: getAxisColor() },
+      x: {
+        type: 'log',
+        label: 'Duration',
+        tickFormat: (d: number) => formatDuration(d),
+        grid: true,
+        domain: [data[0]?.d ?? 60, Math.max(3600, ...data.map(d => d.d))],
+      },
+      y: {
+        label: 'VAM (m/h)',
+        grid: true,
+        zero: true,
+      },
+      color: {
+        domain: selectedKeys,
+        range: selectedKeys.map((k, i) => curveColor(k, i)),
+        legend: selectedKeys.length > 1,
+      },
+      marks: [
+        Plot.line(data, {
+          x: 'd',
+          y: 'v',
+          stroke: 'label',
+          strokeWidth: 2,
+          curve: 'monotone-x',
+        }),
+        Plot.dot(data, {
+          x: 'd',
+          y: 'v',
+          fill: 'label',
+          r: 3,
+          tip: true,
+          title: (d: any) => `${labelFn(d.label)}\n${formatDuration(d.d)}: ${d.v.toLocaleString()} m/h`,
+        }),
+      ],
+    });
+
+    chartEl.appendChild(chart);
+  }
+
+  $: renderChart(plotData, colorMap);
+
+  let currentPlotData = plotData;
+  let currentColorMap = colorMap;
+  $: currentPlotData = plotData;
+  $: currentColorMap = colorMap;
+
+  onMount(() => {
+    const ro = new ResizeObserver(() => renderChart(currentPlotData, currentColorMap));
+    ro.observe(chartEl);
+    const mo = new MutationObserver(() => renderChart(currentPlotData, currentColorMap));
+    mo.observe(document.documentElement, { attributes: true, attributeFilter: ['data-theme'] });
+    return () => { ro.disconnect(); mo.disconnect(); };
+  });
+
+  function toggleRange(key: RangeKey) {
+    const next = new Set(selectedRanges);
+    if (next.has(key)) {
+      if (next.size > 1) next.delete(key);
+    } else {
+      next.add(key);
+    }
+    selectedRanges = next;
+  }
+
+  const allRangeKeys = [
+    ...Object.keys(PRESET_LABELS),
+    ...seasons.map(s => s.name),
+  ];
+</script>
+
+<style>
+  :global(.plot-tip text) { fill: #18181b !important; }
+</style>
+
+<div class="flex flex-wrap gap-2 mb-4">
+  {#each allRangeKeys as key, i}
+    {@const active = selectedRanges.has(key)}
+    {@const color = curveColor(key, i)}
+    <button
+      on:click={() => toggleRange(key)}
+      class="px-3 py-1 rounded-full text-sm font-medium border transition-colors"
+      style={active
+        ? `background:${color}22; border-color:${color}; color:${color}`
+        : 'background:transparent; border-color:#3f3f46; color:#71717a'}
+    >
+      {PRESET_LABELS[key] ?? key}
+    </button>
+  {/each}
+</div>
+
+<div bind:this={chartEl} class="w-full min-h-[320px]"></div>
+
+{#if !plotData.length}
+  <p class="text-zinc-500 text-sm mt-4">No VAM data for the selected range.</p>
+{/if}

site/src/lib/types.ts

@@ -36,10 +36,17 @@ export interface BestClimb {
   title: string;
 }
 
+export interface AthleteVamCurve {
+  all_time:  MmpCurve | null;
+  last_365d: MmpCurve | null;
+  last_90d:  MmpCurve | null;
+}
+
 export interface AthleteJson {
   bas_version: string;
   generated_at: string;
   power_curve: AthletePowerCurve;
+  vam_curve?: AthleteVamCurve | null;
   records?: Record<string, Record<string, EffortRecord | ValueRecord>>;
   best_climbs?: BestClimb[];
   max_hr?: number;
@@ -66,6 +73,7 @@ export interface ActivitySummary {
   avg_cadence_rpm: number | null;
   avg_power_w: number | null;
   mmp: MmpCurve | null;
+  vam_curve?: MmpCurve | null;
   source: string | null;
   privacy: Privacy;
   detail_url: string | null;
@@ -122,6 +130,7 @@ export interface ActivityDetail extends Omit<ActivitySummary, 'detail_url' | 'tr
   /** URL to fetch the timeseries — present for server-extracted activities. */
   timeseries_url?: string | null;
   mmp: MmpCurve | null;
+  climbing_vam_mh?: number | null;
   strava_id: string | null;
   duplicate_of: string | null;
   source_file?: string | null;