Segments Phase 3: detection algorithm, CLI, ingest hook, and efforts API
- detect.py: ActivityTrack + detect_one/detect_all (bbox pre-filter →
start/end proximity 25m → path conformance 50m/30% → effort extraction
with avg speed/HR/power and Coggan NP)
- cli.py: `bincio segments detect` for retroactive detection over stored
timeseries JSONs, with optional --activity-id / --segment-id filters
- ingest.py: non-fatal hook at end of ingest_parsed runs detect_all
- server.py: GET /api/segments/{id}/efforts and POST /api/segments/{id}/detect
This commit is contained in:
Davide Scaini
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"""bincio segments — segment management CLI commands."""
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from __future__ import annotations
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import json
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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import click
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def _dt(s: str) -> datetime:
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return datetime.fromisoformat(s.replace("Z", "+00:00"))
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@click.group("segments")
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def segments_group() -> None:
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"""Manage segments and detect efforts."""
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@segments_group.command("detect")
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@click.option("--data-dir", required=True, type=click.Path(), help="BAS data directory (e.g. /var/bincio)")
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@click.option("--handle", required=True, help="User handle to run detection for")
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@click.option("--activity-id", default=None, help="Limit to a single activity ID (optional)")
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@click.option("--segment-id", default=None, help="Limit to a single segment ID (optional)")
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def detect_cmd(data_dir: str, handle: str, activity_id: str | None, segment_id: str | None) -> None:
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"""Retroactively detect segment efforts for stored activities.
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Walks every activity with GPS data, runs the detection algorithm against
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all (or a single) segment, and persists any new efforts found.
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"""
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from bincio.segments.detect import track_from_timeseries_json, detect_one, detect_all
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from bincio.segments import store as _store
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dd = Path(data_dir).expanduser().resolve()
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user_dir = dd / handle
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acts_dir = user_dir / "activities"
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if not acts_dir.exists():
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click.echo(f"No activities directory at {acts_dir}", err=True)
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sys.exit(1)
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# Choose which segments to check.
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if segment_id:
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seg = _store.load_segment(dd, segment_id)
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if seg is None:
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click.echo(f"Segment not found: {segment_id}", err=True)
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sys.exit(1)
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segments = [seg]
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else:
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segments = _store.list_segments(dd)
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if not segments:
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click.echo("No segments defined.", err=True)
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sys.exit(0)
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# Choose which activities to process.
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if activity_id:
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detail_files = [acts_dir / f"{activity_id}.json"]
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else:
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detail_files = sorted(acts_dir.glob("*.json"))
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# Exclude timeseries files.
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detail_files = [f for f in detail_files if ".timeseries." not in f.name]
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total_efforts = 0
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processed = 0
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for detail_path in detail_files:
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try:
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detail = json.loads(detail_path.read_text(encoding="utf-8"))
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except Exception:
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continue
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ts_url = detail.get("timeseries_url")
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if not ts_url:
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continue
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act_id = detail.get("id", detail_path.stem)
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sport = detail.get("sport", "other")
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started = detail.get("started_at")
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if not started:
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continue
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try:
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started_at = _dt(started)
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except Exception:
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continue
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ts_path = user_dir / ts_url
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if not ts_path.exists():
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continue
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try:
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ts = json.loads(ts_path.read_text(encoding="utf-8"))
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except Exception:
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continue
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track = track_from_timeseries_json(ts, act_id, sport, started_at)
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if track is None:
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continue
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processed += 1
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for seg in segments:
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from bincio.segments.detect import detect_one
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efforts = detect_one(track, seg)
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for effort in efforts:
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_store.add_effort(dd, handle, seg.id, effort)
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if efforts:
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click.echo(
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f" {act_id}: {len(efforts)} effort(s) on '{seg.name}' "
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f"({', '.join(str(e.elapsed_s) + 's' for e in efforts)})"
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)
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total_efforts += len(efforts)
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click.echo(f"\nProcessed {processed} activities, found {total_efforts} effort(s).")
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@@ -0,0 +1,278 @@
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"""Segment effort detection.
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Matches GPS tracks against stored segment polylines and produces SegmentEffort
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records. Works from either a live ParsedActivity (ingest path) or from a
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stored timeseries JSON (retroactive path).
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"""
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from __future__ import annotations
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import math
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from dataclasses import dataclass, field
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from typing import Optional
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from bincio.segments.models import Segment, SegmentEffort
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# ── tuning constants ──────────────────────────────────────────────────────────
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MATCH_RADIUS_M = 25 # max distance to segment start/end to open/close an effort
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CONFORMANCE_MAX_DEV_M = 50 # max allowed deviation for each interior segment point
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CONFORMANCE_MAX_FRAC = 0.30 # max fraction of interior points allowed to deviate
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# ── fast distance approximation ───────────────────────────────────────────────
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_R = 6_371_000.0 # Earth radius in metres
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def _dist(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
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"""Equirectangular approximation — fast, accurate to <0.1% within 100 km."""
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dlat = math.radians(lat2 - lat1)
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dlon = math.radians(lon2 - lon1)
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mlat = math.radians((lat1 + lat2) / 2.0)
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return math.hypot(dlat * _R, dlon * _R * math.cos(mlat))
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# ── activity track representation ────────────────────────────────────────────
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@dataclass
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class ActivityTrack:
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"""Common internal representation for detection, independent of source format."""
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activity_id: str
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sport: str
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started_at: datetime
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# Parallel arrays — all same length, GPS-only points (lat/lon not None).
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lats: list[float]
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lons: list[float]
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times: list[int] # seconds from started_at
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speeds: list[Optional[float]]
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hrs: list[Optional[int]]
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powers: list[Optional[int]]
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bbox: list[float] = field(default_factory=list) # [lon_min, lat_min, lon_max, lat_max]
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def __post_init__(self) -> None:
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if self.lats and not self.bbox:
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self.bbox = [
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min(self.lons), min(self.lats),
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max(self.lons), max(self.lats),
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]
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def track_from_parsed(parsed: "ParsedActivity", activity_id: str) -> Optional[ActivityTrack]: # noqa: F821
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"""Build an ActivityTrack from a ParsedActivity (used during ingest)."""
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lats, lons, times, speeds, hrs, powers = [], [], [], [], [], []
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last_t = -1
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for p in parsed.points:
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if p.lat is None or p.lon is None:
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continue
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t = int((p.timestamp - parsed.started_at).total_seconds())
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if t < 0 or t == last_t:
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continue
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last_t = t
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lats.append(p.lat)
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lons.append(p.lon)
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times.append(t)
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speeds.append(p.speed_kmh)
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hrs.append(p.hr_bpm)
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powers.append(p.power_w)
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if len(lats) < 2:
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return None
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return ActivityTrack(
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activity_id=activity_id,
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sport=parsed.sport,
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started_at=parsed.started_at,
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lats=lats, lons=lons, times=times,
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speeds=speeds, hrs=hrs, powers=powers,
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)
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def track_from_timeseries_json(
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ts: dict,
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activity_id: str,
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sport: str,
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started_at: datetime,
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) -> Optional[ActivityTrack]:
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"""Build an ActivityTrack from a stored timeseries JSON dict."""
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raw_lats = ts.get("lat") or []
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raw_lons = ts.get("lon") or []
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raw_t = ts.get("t") or []
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raw_spd = ts.get("speed_kmh") or []
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raw_hr = ts.get("hr_bpm") or []
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raw_pwr = ts.get("power_w") or []
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n = len(raw_t)
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if n < 2 or not raw_lats or len(raw_lats) != n:
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return None
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def _pad(arr: list, length: int) -> list:
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return arr + [None] * (length - len(arr))
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raw_spd = _pad(raw_spd, n)
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raw_hr = _pad(raw_hr, n)
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raw_pwr = _pad(raw_pwr, n)
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lats, lons, times, speeds, hrs, powers = [], [], [], [], [], []
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for i in range(n):
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if raw_lats[i] is None or raw_lons[i] is None:
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continue
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lats.append(float(raw_lats[i]))
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lons.append(float(raw_lons[i]))
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times.append(int(raw_t[i]))
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speeds.append(raw_spd[i])
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hrs.append(raw_hr[i])
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powers.append(raw_pwr[i])
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if len(lats) < 2:
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return None
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return ActivityTrack(
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activity_id=activity_id,
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sport=sport,
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started_at=started_at,
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lats=lats, lons=lons, times=times,
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speeds=speeds, hrs=hrs, powers=powers,
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)
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# ── effort metric helpers ─────────────────────────────────────────────────────
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def _avg_nonnull(vals: list, lo: int, hi: int) -> Optional[float]:
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nums = [v for v in vals[lo:hi + 1] if v is not None]
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return sum(nums) / len(nums) if nums else None
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def _np_power(powers: list[Optional[int]], lo: int, hi: int) -> Optional[int]:
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"""Coggan NP from a slice of 1Hz power data (may have gaps/nulls)."""
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WIN = 30
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chunk = powers[lo:hi + 1]
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filled = [v if v is not None else 0 for v in chunk]
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n = len(filled)
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if n < WIN:
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# Too short for rolling average — just return avg power.
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non_null = [v for v in chunk if v is not None]
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return int(round(sum(non_null) / len(non_null))) if non_null else None
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half = WIN // 2
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window_sum = sum(filled[:WIN])
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fourth_powers = []
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for i in range(half, n - half):
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fourth_powers.append((window_sum / WIN) ** 4)
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if i + half + 1 < n:
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window_sum += filled[i + half + 1] - filled[i - half]
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if not fourth_powers:
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return None
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return int(round((sum(fourth_powers) / len(fourth_powers)) ** 0.25))
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# ── detection algorithm ───────────────────────────────────────────────────────
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def _bboxes_overlap(a: list[float], b: list[float]) -> bool:
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return not (a[2] < b[0] or b[2] < a[0] or a[3] < b[1] or b[3] < a[1])
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def _conformance_ok(
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track: ActivityTrack,
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seg: Segment,
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i: int,
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j: int,
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) -> bool:
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"""Check that the track slice [i..j] follows the segment polyline."""
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interior = seg.polyline[1:-1]
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if not interior:
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return True # trivial 2-point segment
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failing = 0
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for sp in interior:
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slat, slon = sp[0], sp[1]
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min_d = min(
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_dist(slat, slon, track.lats[k], track.lons[k])
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for k in range(i, j + 1)
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)
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if min_d > CONFORMANCE_MAX_DEV_M:
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failing += 1
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return (failing / len(interior)) <= CONFORMANCE_MAX_FRAC
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def _extract_effort(
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track: ActivityTrack,
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seg: Segment,
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i: int,
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j: int,
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) -> SegmentEffort:
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elapsed_s = track.times[j] - track.times[i]
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started_at = track.started_at + timedelta(seconds=track.times[i])
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avg_speed = _avg_nonnull(track.speeds, i, j)
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avg_hr_raw = _avg_nonnull(track.hrs, i, j)
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avg_hr = int(round(avg_hr_raw)) if avg_hr_raw is not None else None
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avg_pwr_raw = _avg_nonnull(track.powers, i, j)
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avg_pwr = int(round(avg_pwr_raw)) if avg_pwr_raw is not None else None
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np_pwr = _np_power(track.powers, i, j) if any(v is not None for v in track.powers[i:j + 1]) else None
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return SegmentEffort(
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activity_id=track.activity_id,
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started_at=started_at,
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elapsed_s=max(1, elapsed_s),
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avg_speed_kmh=round(avg_speed, 2) if avg_speed is not None else None,
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avg_hr_bpm=avg_hr,
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avg_power_w=avg_pwr,
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np_power_w=np_pwr,
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detected_at=datetime.now(timezone.utc),
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)
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def detect_one(track: ActivityTrack, seg: Segment) -> list[SegmentEffort]:
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"""Return all matching efforts for a single segment against a track."""
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if not track.bbox or not _bboxes_overlap(track.bbox, seg.bbox):
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return []
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if seg.sport and seg.sport != track.sport:
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return []
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seg_start_lat, seg_start_lon = seg.polyline[0][0], seg.polyline[0][1]
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seg_end_lat, seg_end_lon = seg.polyline[-1][0], seg.polyline[-1][1]
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n = len(track.lats)
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efforts: list[SegmentEffort] = []
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search_from = 0
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while search_from < n - 1:
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# Find next start candidate from search_from.
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start_idx = None
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for i in range(search_from, n):
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if _dist(seg_start_lat, seg_start_lon, track.lats[i], track.lons[i]) <= MATCH_RADIUS_M:
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start_idx = i
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break
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if start_idx is None:
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break
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# Scan forward from start_idx for an end candidate.
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end_idx = None
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for j in range(start_idx + 1, n):
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if _dist(seg_end_lat, seg_end_lon, track.lats[j], track.lons[j]) <= MATCH_RADIUS_M:
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end_idx = j
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break
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if end_idx is None:
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# No end found — no more efforts possible starting at or after start_idx.
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break
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if _conformance_ok(track, seg, start_idx, end_idx):
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efforts.append(_extract_effort(track, seg, start_idx, end_idx))
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search_from = end_idx + 1
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else:
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# Conformance failed; try next start candidate after start_idx.
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search_from = start_idx + 1
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return efforts
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def detect_all(
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track: ActivityTrack,
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handle: str,
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data_dir: Path,
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) -> int:
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"""Detect efforts for all segments and persist them. Returns effort count."""
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from bincio.segments import store as _store
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segments = _store.list_segments(data_dir)
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total = 0
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for seg in segments:
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efforts = detect_one(track, seg)
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for effort in efforts:
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_store.add_effort(data_dir, handle, seg.id, effort)
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total += len(efforts)
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return total
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Block a user